From 5b0d9ab0a96511272312b696214dc683f1e6caf9 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Fri, 9 Jan 2026 21:19:24 +0000 Subject: [PATCH 01/18] feat: initial documentation with mkdocs Adds the following pages: - Getting started: installation, quickstart, first ODE fit, core concepts - Tutorials w/ notebooks - User guides for optimiser selection, calibration, parallelisation - Optimiser and samplers algorithms - Auto-generated from stubs for Python + Rust links - Visual gallery of example applications - Contributing guidelines, architecture, building instructions --- .github/workflows/docs-checks.yml | 202 +++++ .github/workflows/docs.yml | 58 ++ .gitignore | 3 + README.md | 10 + docs/algorithms/index.md | 126 +++ docs/algorithms/optimizers/adam.md | 29 + docs/algorithms/optimizers/cmaes.md | 31 + docs/algorithms/optimizers/nelder-mead.md | 30 + .../samplers/dynamic-nested-sampling.md | 29 + .../samplers/metropolis-hastings.md | 29 + docs/api-reference/index.md | 137 ++++ docs/api-reference/python/builders.md | 253 ++++++ docs/api-reference/python/cost-metrics.md | 214 +++++ docs/api-reference/python/optimizers.md | 290 +++++++ docs/api-reference/python/results.md | 310 +++++++ docs/api-reference/python/samplers.md | 212 +++++ docs/api-reference/rust/index.md | 223 +++++ docs/changelog.md | 1 + docs/development/architecture.md | 37 + docs/development/building.md | 48 ++ docs/development/contributing.md | 36 + docs/development/index.md | 254 ++++++ docs/examples/gallery.md | 170 ++++ docs/getting-started/concepts.md | 311 +++++++ docs/getting-started/first-ode-fit.md | 275 +++++++ docs/getting-started/index.md | 39 + docs/getting-started/installation.md | 159 ++++ docs/getting-started/quickstart.md | 157 ++++ docs/guides/choosing-optimizer.md | 32 + docs/guides/choosing-sampler.md | 30 + docs/guides/cost-metrics.md | 23 + docs/guides/custom-solvers.md | 38 + docs/guides/diffsol-backend.md | 27 + docs/guides/index.md | 136 +++ docs/guides/parallel-execution.md | 25 + docs/guides/troubleshooting.md | 36 + docs/guides/tuning-optimizers.md | 29 + docs/index.md | 159 ++++ docs/javascripts/mathjax.js | 16 + docs/tutorials/index.md | 260 ++++++ .../notebooks/01_optimization_basics.ipynb | 380 +++++++++ .../notebooks/02_ode_fitting_diffsol.ipynb | 487 +++++++++++ .../notebooks/03_parameter_uncertainty.ipynb | 592 ++++++++++++++ .../notebooks/04_model_comparison.ipynb | 94 +++ .../notebooks/05_advanced_predator_prey.ipynb | 168 ++++ .../06_custom_solver_integration.ipynb | 735 +++++++++++++++++ .../notebooks/07_parallel_optimization.ipynb | 650 +++++++++++++++ .../08_advanced_cost_functions.ipynb | 772 ++++++++++++++++++ docs/tutorials/notebooks/utils.py | 312 +++++++ mkdocs.yml | 200 +++++ pyproject.toml | 9 + tests/test_docs.py | 311 +++++++ 52 files changed, 9194 insertions(+) create mode 100644 .github/workflows/docs-checks.yml create mode 100644 .github/workflows/docs.yml create mode 100644 docs/algorithms/index.md create mode 100644 docs/algorithms/optimizers/adam.md create mode 100644 docs/algorithms/optimizers/cmaes.md create mode 100644 docs/algorithms/optimizers/nelder-mead.md create mode 100644 docs/algorithms/samplers/dynamic-nested-sampling.md create mode 100644 docs/algorithms/samplers/metropolis-hastings.md create mode 100644 docs/api-reference/index.md create mode 100644 docs/api-reference/python/builders.md create mode 100644 docs/api-reference/python/cost-metrics.md create mode 100644 docs/api-reference/python/optimizers.md create mode 100644 docs/api-reference/python/results.md create mode 100644 docs/api-reference/python/samplers.md create mode 100644 docs/api-reference/rust/index.md create mode 100644 docs/changelog.md create mode 100644 docs/development/architecture.md create mode 100644 docs/development/building.md create mode 100644 docs/development/contributing.md create mode 100644 docs/development/index.md create mode 100644 docs/examples/gallery.md create mode 100644 docs/getting-started/concepts.md create mode 100644 docs/getting-started/first-ode-fit.md create mode 100644 docs/getting-started/index.md create mode 100644 docs/getting-started/installation.md create mode 100644 docs/getting-started/quickstart.md create mode 100644 docs/guides/choosing-optimizer.md create mode 100644 docs/guides/choosing-sampler.md create mode 100644 docs/guides/cost-metrics.md create mode 100644 docs/guides/custom-solvers.md create mode 100644 docs/guides/diffsol-backend.md create mode 100644 docs/guides/index.md create mode 100644 docs/guides/parallel-execution.md create mode 100644 docs/guides/troubleshooting.md create mode 100644 docs/guides/tuning-optimizers.md create mode 100644 docs/index.md create mode 100644 docs/javascripts/mathjax.js create mode 100644 docs/tutorials/index.md create mode 100644 docs/tutorials/notebooks/01_optimization_basics.ipynb create mode 100644 docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb create mode 100644 docs/tutorials/notebooks/03_parameter_uncertainty.ipynb create mode 100644 docs/tutorials/notebooks/04_model_comparison.ipynb create mode 100644 docs/tutorials/notebooks/05_advanced_predator_prey.ipynb create mode 100644 docs/tutorials/notebooks/06_custom_solver_integration.ipynb create mode 100644 docs/tutorials/notebooks/07_parallel_optimization.ipynb create mode 100644 docs/tutorials/notebooks/08_advanced_cost_functions.ipynb create mode 100644 docs/tutorials/notebooks/utils.py create mode 100644 mkdocs.yml create mode 100644 tests/test_docs.py diff --git a/.github/workflows/docs-checks.yml b/.github/workflows/docs-checks.yml new file mode 100644 index 0000000..7c051a2 --- /dev/null +++ b/.github/workflows/docs-checks.yml @@ -0,0 +1,202 @@ +name: Documentation Checks + +on: + schedule: + # Run weekly on Monday at 00:00 UTC + - cron: '0 0 * * 1' + workflow_dispatch: # Allow manual triggering + pull_request: + paths: + - 'docs/**' + - 'mkdocs.yml' + - '.github/workflows/docs-checks.yml' + +permissions: + contents: read + +jobs: + link-check: + name: Check Links + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install mkdocs mkdocs-material mkdocstrings[python] \ + mkdocs-git-revision-date-localized-plugin \ + mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions + + - name: Build documentation + run: mkdocs build --strict + + - name: Link Checker + uses: lycheeverse/lychee-action@v2 + with: + # Check all markdown files + args: --verbose --no-progress 'docs/**/*.md' 'site/**/*.html' --exclude-path 'site/tutorials/notebooks' --max-concurrency 10 + # Don't fail on broken links in draft mode + fail: ${{ github.event_name != 'pull_request' }} + env: + GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + + - name: Create Issue on Failure + if: failure() && github.event_name == 'schedule' + uses: actions/github-script@v7 + with: + script: | + github.rest.issues.create({ + owner: context.repo.owner, + repo: context.repo.repo, + title: '📋 Documentation link check failed', + body: 'Scheduled link check found broken links in documentation.\n\nSee workflow run: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}', + labels: ['documentation', 'automated'] + }) + + test-docs: + name: Documentation Tests + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Install chronopt and dependencies + run: | + uv pip install --system pytest + uv pip install --system mkdocs mkdocs-material mkdocstrings[python] \ + mkdocs-git-revision-date-localized-plugin \ + mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions + # Install chronopt if possible (may fail if rust build needed) + uv pip install --system -e python/ || echo "⚠️ Could not install chronopt (rust build required)" + + - name: Run documentation tests + run: pytest tests/test_docs.py -v + continue-on-error: true # Don't fail if chronopt not installed + + notebook-validation: + name: Validate Notebooks + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install jupyter nbformat nbconvert + + - name: Validate notebook format + run: | + for notebook in docs/tutorials/notebooks/*.ipynb; do + echo "Validating $notebook" + python -m nbformat.validator "$notebook" || echo "⚠️ $notebook validation failed" + done + + - name: Check for execution errors + run: | + # Check notebooks don't have error outputs + for notebook in docs/tutorials/notebooks/*.ipynb; do + if grep -q '"output_type": "error"' "$notebook"; then + echo "❌ $notebook contains error outputs" + exit 1 + fi + done + echo "✅ No error outputs found in notebooks" + + spelling-check: + name: Spelling Check + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Check British English spelling + uses: reviewdog/action-misspell@v1 + with: + github_token: ${{ secrets.github_token }} + locale: "UK" + reporter: github-pr-review + level: warning + filter_mode: diff_context + # Exclude code blocks and technical terms + exclude: | + *.ipynb + *.py + *.rs + + build-preview: + name: Build Preview + runs-on: ubuntu-latest + if: github.event_name == 'pull_request' + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install mkdocs mkdocs-material mkdocstrings[python] \ + mkdocs-git-revision-date-localized-plugin \ + mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions + + - name: Build documentation + run: mkdocs build --strict + + - name: Upload preview artifact + uses: actions/upload-artifact@v4 + with: + name: docs-preview-pr-${{ github.event.pull_request.number }} + path: site/ + retention-days: 7 + + - name: Comment PR with preview info + uses: actions/github-script@v7 + with: + script: | + const comment = `## 📚 Documentation Preview + + Documentation built successfully for this PR! + + **Download Preview**: Check the artifacts section of [this workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}) + + **Build Details**: + - Commit: \`${{ github.sha }}\` + - Built at: ${new Date().toISOString()} + + To view locally: + 1. Download the \`docs-preview-pr-${{ github.event.pull_request.number }}\` artifact + 2. Extract and open \`index.html\` in a browser + + _This preview will be available for 7 days._ + `; + + github.rest.issues.createComment({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: context.issue.number, + body: comment + }) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..a7b8239 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,58 @@ +name: Documentation + +on: + push: + branches: + - main + pull_request: + branches: + - main + workflow_dispatch: + +permissions: + contents: write + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for git-revision-date plugin + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Install dependencies + run: | + uv pip install --system mkdocs + uv pip install --system mkdocs-material + uv pip install --system mkdocstrings[python] + uv pip install --system mkdocs-git-revision-date-localized-plugin + uv pip install --system mkdocs-minify-plugin + uv pip install --system mkdocs-jupyter + uv pip install --system pymdown-extensions + + - name: Build documentation + run: mkdocs build + + - name: Deploy to GitHub Pages + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + uses: peaceiris/actions-gh-pages@v4 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./site + cname: false # Set to your custom domain if you have one + + - name: Upload artifact for PR preview + if: github.event_name == 'pull_request' + uses: actions/upload-artifact@v4 + with: + name: docs-preview + path: ./site diff --git a/.gitignore b/.gitignore index 195e23d..ecb7737 100644 --- a/.gitignore +++ b/.gitignore @@ -65,6 +65,9 @@ coverage.xml # Sphinx documentation docs/_build/ +# MkDocs documentation +site/ + # PyCharm .idea/ diff --git a/README.md b/README.md index 9de9a41..d3a04e0 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,16 @@ - The Rust core provides a high-performance inference loop with fewer runtime errors. - Quickly integrated into Python workflows, and later use the rust crate directly for even higher performance. +## Documentation + +📚 **[Full Documentation](https://bradyplanden.github.io/chronopt/)** + +Visit the comprehensive documentation for: +- Getting started guides and tutorials +- Complete API reference +- User guides for choosing and tuning algorithms +- Examples gallery and Jupyter notebooks + ## Installation Chronopt targets Python >= 3.11. Windows builds are currently marked experimental. diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md new file mode 100644 index 0000000..62b29e5 --- /dev/null +++ b/docs/algorithms/index.md @@ -0,0 +1,126 @@ +# Algorithms + +Detailed documentation for each optimisation and sampling algorithm in Chronopt. + +## Optimisers + +Gradient-free and gradient-based algorithms for finding optimal parameters. + +
+ +- :material-triangle:{ .lg .middle } __Nelder-Mead__ + + --- + + Simplex-based gradient-free optimiser for local search. + + [:octicons-arrow-right-24: Details](optimizers/nelder-mead.md) + +- :material-chart-scatter-plot:{ .lg .middle } __CMA-ES__ + + --- + + Covariance Matrix Adaptation Evolution Strategy for global optimisation. + + [:octicons-arrow-right-24: Details](optimizers/cmaes.md) + +- :material-alpha-a:{ .lg .middle } __Adam__ + + --- + + Adaptive Moment Estimation for gradient-based optimisation. + + [:octicons-arrow-right-24: Details](optimizers/adam.md) + +
+ +## Samplers (Planned) + +MCMC and nested sampling for uncertainty quantification and model comparison. + +
+ +- :material-chart-timeline-variant:{ .lg .middle } __Metropolis-Hastings__ + + --- + + MCMC sampling for posterior exploration. + + [:octicons-arrow-right-24: Details](samplers/metropolis-hastings.md) + +- :material-layers-triple:{ .lg .middle } __Dynamic Nested Sampling__ + + --- + + Evidence calculation for Bayesian model comparison. + + [:octicons-arrow-right-24: Details](samplers/dynamic-nested-sampling.md) + +
+ +## Algorithm Comparison + +| Algorithm | Type | Gradients | Best For | Parallelisable | +|-----------|------|-----------|----------|----------------| +| Nelder-Mead | Local | No | < 10 params, noisy | No | +| CMA-ES | Global | No | 10-100+ params | Yes | +| Adam | Local | Yes | Smooth objectives | No | +| Metropolis-Hastings | MCMC | No | Uncertainty | No | +| Nested Sampling | Evidence | No | Model comparison | Yes | + +## Choosing an Algorithm + +```mermaid +graph TD + A[Start] --> B{Need uncertainty?} + B -->|No| C{Gradients available?} + B -->|Yes| D{Need evidence?} + C -->|Yes| E[Adam] + C -->|No| F{Problem size?} + D -->|Yes| G[Nested Sampling] + D -->|No| H[Metropolis-Hastings] + F -->|< 10 params| I[Nelder-Mead] + F -->|> 10 params| J[CMA-ES] +``` + +## Performance Characteristics + +### Convergence Speed + +Fast → Slow: +1. **Adam** (with gradients) +2. **Nelder-Mead** (small problems) +3. **CMA-ES** (large problems) +4. **MCMC samplers** (many evaluations) +5. **Nested sampling** (most evaluations) + +### Robustness to Local Minima + +Least → Most robust: +1. **Adam** (gradient descent) +2. **Nelder-Mead** (local search) +3. **CMA-ES** (global search) +4. **MCMC** (explores posterior) +5. **Nested sampling** (explores full space) + +## Implementation Details + +All algorithms are implemented in Rust for performance: + +- **Zero-copy**: Efficient memory usage +- **Parallel**: Where applicable (CMA-ES, nested sampling) +- **Numerically stable**: Careful floating-point handling +- **Well-tested**: Comprehensive test suite + +Source code: [rust/src/optimisers/](https://github.com/bradyplanden/chronopt/tree/main/rust/src/optimisers) + +## References + +Each algorithm page includes references to original papers and implementation details. + +## See Also + +- [Choosing an Optimiser](../guides/choosing-optimizer.md) +- [Tuning Optimisers](../guides/tuning-optimizers.md) +- [API Reference](../api-reference/index.md) +- [Tutorials](../tutorials/index.md) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md new file mode 100644 index 0000000..7a8ab4b --- /dev/null +++ b/docs/algorithms/optimizers/adam.md @@ -0,0 +1,29 @@ +# Adam Algorithm + +!!! info "Coming Soon" + Detailed algorithm documentation is being written. + +## Overview + +Adaptive Moment Estimation (Adam) is a gradient-based optimiser with adaptive learning rates. + +## When to Use + +**Best for:** +- Smooth, differentiable objectives +- Fast convergence needed +- Gradients available or cheap to compute + +**Avoid when:** +- Objective is non-smooth +- No gradient information +- Need global optimum (can get stuck in local minima) + +## API Reference + +See [Adam API](../../api-reference/python/optimizers.md#adam) for usage details. + +## See Also + +- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md new file mode 100644 index 0000000..3301247 --- /dev/null +++ b/docs/algorithms/optimizers/cmaes.md @@ -0,0 +1,31 @@ +# CMA-ES Algorithm + +!!! info "Coming Soon" + Detailed algorithm documentation is being written. + +## Overview + +Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic, derivative-free algorithm for global optimisation. + +## When to Use + +**Best for:** +- High-dimensional problems (10-100+ parameters) +- Global optimisation +- Parallel hardware +- Multi-modal landscapes + +**Avoid when:** +- Very low-dimensional (< 5 parameters) +- Limited computational budget +- Need deterministic results + +## API Reference + +See [CMAES API](../../api-reference/python/optimizers.md#cma-es) for usage details. + +## See Also + +- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Tuning Optimisers](../../guides/tuning-optimizers.md) +- [Parallel Execution](../../guides/parallel-execution.md) diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimizers/nelder-mead.md new file mode 100644 index 0000000..bc69ff2 --- /dev/null +++ b/docs/algorithms/optimizers/nelder-mead.md @@ -0,0 +1,30 @@ +# Nelder-Mead Algorithm + +!!! info "Coming Soon" + Detailed algorithm documentation is being written. + +## Overview + +The Nelder-Mead algorithm (also known as the downhill simplex method) is a gradient-free local optimisation algorithm. + +## When to Use + +**Best for:** +- Problems with < 10 parameters +- Noisy objective functions +- No gradient information available +- Quick exploration + +**Avoid when:** +- More than 10 parameters +- Need global optimum +- Very tight convergence required + +## API Reference + +See [NelderMead API](../../api-reference/python/optimizers.md#nelder-mead) for usage details. + +## See Also + +- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md new file mode 100644 index 0000000..a287151 --- /dev/null +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -0,0 +1,29 @@ +# Dynamic Nested Sampling Algorithm + +!!! info "Coming Soon" + Sampler documentation is being written. Python bindings are also in development. + +## Overview + +Dynamic Nested Sampling calculates model evidence (marginal likelihood) for Bayesian model comparison. + +## When to Use + +**Best for:** +- Model comparison +- Calculating Bayes factors +- Evidence calculation +- Multi-modal posteriors + +**Avoid when:** +- Only need posterior samples (use MCMC) +- Limited computational budget + +## API Reference + +See [Samplers API](../../api-reference/python/samplers.md) for usage details (coming soon). + +## See Also + +- [Choosing a Sampler](../../guides/choosing-sampler.md) +- [Cost Metrics](../../guides/cost-metrics.md) diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md new file mode 100644 index 0000000..0395077 --- /dev/null +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -0,0 +1,29 @@ +# Metropolis-Hastings Algorithm + +!!! info "Coming Soon" + Sampler documentation is being written. Python bindings are also in development. + +## Overview + +Metropolis-Hastings is an MCMC algorithm for sampling from posterior distributions. + +## When to Use + +**Best for:** +- Uncertainty quantification +- Confidence intervals +- Posterior exploration + +**Avoid when:** +- Only need point estimate +- Limited computational budget +- Need model comparison (use nested sampling) + +## API Reference + +See [Samplers API](../../api-reference/python/samplers.md) for usage details (coming soon). + +## See Also + +- [Choosing a Sampler](../../guides/choosing-sampler.md) +- [Cost Metrics](../../guides/cost-metrics.md) diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md new file mode 100644 index 0000000..d457655 --- /dev/null +++ b/docs/api-reference/index.md @@ -0,0 +1,137 @@ +# API Reference + +Complete API documentation for Chronopt's Python and Rust interfaces. + +## Python API + +Chronopt provides a comprehensive Python API with full type hints and automatic documentation. + +
+ +- :material-hammer-wrench:{ .lg .middle } __Builders__ + + --- + + Problem builders for different use cases: scalar functions, ODE fitting, and custom solvers. + + [:octicons-arrow-right-24: Builders](python/builders.md) + +- :material-tune:{ .lg .middle } __Optimisers__ + + --- + + Gradient-free (Nelder-Mead, CMA-ES) and gradient-based (Adam) optimisation algorithms. + + [:octicons-arrow-right-24: Optimisers](python/optimizers.md) + +- :material-chart-bell-curve:{ .lg .middle } __Samplers__ + + --- + + MCMC and nested sampling for posterior exploration and model comparison. + + [:octicons-arrow-right-24: Samplers](python/samplers.md) + +- :material-function-variant:{ .lg .middle } __Cost Metrics__ + + --- + + Metrics for comparing model predictions to observations (SSE, RMSE, GaussianNLL). + + [:octicons-arrow-right-24: Cost Metrics](python/cost-metrics.md) + +- :material-chart-line:{ .lg .middle } __Results__ + + --- + + Result objects returned by optimisers and samplers with diagnostics. + + [:octicons-arrow-right-24: Results](python/results.md) + +
+ +## Rust API + +The Rust core provides high-performance implementations of all algorithms. + +[:octicons-arrow-right-24: Rust Documentation on docs.rs](https://docs.rs/chronopt/latest/chronopt/) + +For developers building with the Rust crate directly, see the [Building from Source](../development/building.md) guide. + +## Module Structure + +``` +chronopt/ +├── ScalarBuilder # Direct function optimisation +├── DiffsolBuilder # ODE fitting with DiffSL/Diffsol +├── VectorBuilder # Custom solver integration +├── Problem # Built problem instance +├── Optimisers +│ ├── NelderMead # Simplex gradient-free optimiser +│ ├── CMAES # Covariance matrix adaptation +│ └── Adam # Adaptive moment estimation +├── Samplers +│ ├── MetropolisHastings # MCMC sampling (planned) +│ └── DynamicNestedSampling # Evidence calculation (planned) +├── CostMetric # Cost/likelihood metrics +└── OptimisationResults # Result container +``` + +## Type Hints + +All Python functions include comprehensive type hints: + +```python +from chronopt import ScalarBuilder, CMAES, OptimisationResults +import numpy.typing as npt + +def optimize_function( + func: Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]], + initial_params: dict[str, float] +) -> OptimisationResults: + builder = ScalarBuilder().with_callable(func) + for name, value in initial_params.items(): + builder = builder.with_parameter(name, value) + + problem = builder.build() + optimiser = CMAES().with_max_iter(1000) + return optimiser.run(problem, list(initial_params.values())) +``` + +Type stubs are automatically generated from the Rust implementation and are available in IDE completions. + +## Conventions + +### Parameter Order + +All builders maintain parameter order based on the sequence of `.with_parameter()` calls: + +```python +builder = ( + chron.ScalarBuilder() + .with_parameter("x", 1.0) # Index 0 + .with_parameter("y", 2.0) # Index 1 +) + +result = problem.optimise() +print(result.x) # [optimal_x, optimal_y] +``` + +### Return Types + +- **Optimisers** return `OptimisationResults` with `.x`, `.value`, `.success`, etc. +- **Samplers** return sampling-specific results with `.samples`, `.log_likelihood`, etc. +- All results include diagnostic information + +### Array Conventions + +- **Input**: Python callables accept NumPy arrays +- **Output**: Python callables must return NumPy arrays +- **Data**: Data arrays are `(n_timepoints, n_variables + 1)` with time in first column + +## Next Steps + +- [Builders API](python/builders.md) - Start building problems +- [Optimisers API](python/optimizers.md) - Configure optimisation algorithms +- [User Guides](../guides/index.md) - Learn when to use each component +- [Tutorials](../tutorials/index.md) - Interactive examples diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md new file mode 100644 index 0000000..3c3bd88 --- /dev/null +++ b/docs/api-reference/python/builders.md @@ -0,0 +1,253 @@ +# Builders + +Builders provide a fluent API for constructing optimisation problems. Choose the appropriate builder based on your problem type. + +## Overview + +| Builder | Use Case | Example | +|---------|----------|---------| +| `ScalarBuilder` | Direct function optimisation | Rosenbrock, Rastrigin | +| `DiffsolBuilder` | ODE fitting with DiffSL | Parameter identification | +| `VectorBuilder` | Custom solver integration | JAX, Julia, external simulators | + +## ScalarBuilder + +::: chronopt.ScalarBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_callable + - with_parameter + - with_cost_metric + - build + +### Example Usage + +```python +import numpy as np +import chronopt as chron + +def rosenbrock(x): + return np.asarray([(1 - x[0])**2 + 100*(x[1] - x[0]**2)**2]) + +builder = ( + chron.ScalarBuilder() + .with_callable(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5) +) +problem = builder.build() +result = problem.optimise() +``` + +### When to Use + +- You have a Python function to minimise directly +- No differential equations involved +- Simple parameter optimisation or test functions + +--- + +## DiffsolBuilder + +::: chronopt.DiffsolBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_diffsl + - with_data + - with_parameter + - with_backend + - with_cost_metric + - with_max_threads + - build + +### Example Usage + +```python +import numpy as np +import chronopt as chron + +dsl = """ +in = [r, k] +r { 1 } k { 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) + +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") +) +problem = builder.build() +optimiser = chron.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 0.5]) +``` + +### Backend Options + +- **`"dense"`** (default): For systems with < 100 variables, dense Jacobian +- **`"sparse"`**: For large systems (> 100 variables), sparse Jacobian + +### DiffSL Syntax + +DiffSL is a domain-specific language for ODEs: + +``` +in = [param1, param2] # Parameters to fit +param1 { default1 } # Default values +param2 { default2 } +u_i { state1 = init1 } # Initial conditions +F_i { derivative_expr } # dy/dt expressions +out_i { state1, state2 } # Optional: output variables +``` + +### When to Use + +- Fitting ODE parameters to time-series data +- Using Chronopt's built-in high-performance solver +- Models expressible in DiffSL syntax + +--- + +## VectorBuilder + +::: chronopt.VectorBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_callable + - with_data + - with_parameter + - with_cost_metric + - build + +### Example Usage + +```python +import numpy as np +import chronopt as chron + +def custom_solver(params): + """Your custom ODE solver (e.g., using JAX/Diffrax).""" + # Solve ODE with params + # Return predictions at observation times + return predictions # NumPy array + +# Observed data +t = np.linspace(0, 10, 100) +observations = ... # Your experimental data +data = np.column_stack((t, observations)) + +builder = ( + chron.VectorBuilder() + .with_callable(custom_solver) + .with_data(data) + .with_parameter("alpha", 1.0) + .with_parameter("beta", 0.5) +) +problem = builder.build() +result = problem.optimise() +``` + +### Callable Requirements + +Your callable must: + +1. Accept a NumPy array of parameters +2. Return a NumPy array of predictions +3. Predictions must match observation times in the data + +### When to Use + +- Need specific solver features (stiff solvers, event detection, etc.) +- Using JAX/Diffrax for automatic differentiation +- Using Julia/DifferentialEquations.jl via diffeqpy +- Custom forward models beyond ODEs (PDEs, agent-based models, etc.) + +See the [Custom Solvers Guide](../../guides/custom-solvers.md) for examples with Diffrax and DifferentialEquations.jl. + +--- + +## Problem + +::: chronopt.Problem + options: + show_root_heading: true + show_source: false + +The `Problem` class is created by calling `.build()` on a builder. It represents a fully configured optimisation problem. + +### Common Methods + +```python +# Optimise with default settings (Nelder-Mead) +result = problem.optimise() + +# Evaluate objective at specific parameters +value = problem.evaluate(params) +``` + +--- + +## Common Patterns + +### Chaining Methods + +Builders use a fluent interface - chain methods in any order: + +```python +builder = ( + chron.ScalarBuilder() + .with_callable(func) + .with_parameter("x", 1.0) + .with_parameter("y", 2.0) + .with_cost_metric(chron.RMSE()) +) +``` + +### Reusing Builders + +Builders are immutable - each method returns a new builder: + +```python +base = chron.ScalarBuilder().with_callable(func) + +problem1 = base.with_parameter("x", 1.0).build() +problem2 = base.with_parameter("x", 2.0).build() # Different initial guess +``` + +### Parameter Order Matters + +Parameters are indexed in the order they're added: + +```python +builder = ( + chron.ScalarBuilder() + .with_parameter("y", 2.0) # Index 0 + .with_parameter("x", 1.0) # Index 1 +) + +result = problem.optimise() +print(result.x) # [optimal_y, optimal_x] +``` + +## See Also + +- [Getting Started: Core Concepts](../../getting-started/concepts.md) +- [Cost Metrics](cost-metrics.md) +- [Optimisers](optimizers.md) +- [Custom Solvers Guide](../../guides/custom-solvers.md) diff --git a/docs/api-reference/python/cost-metrics.md b/docs/api-reference/python/cost-metrics.md new file mode 100644 index 0000000..ada62c7 --- /dev/null +++ b/docs/api-reference/python/cost-metrics.md @@ -0,0 +1,214 @@ +# Cost Metrics + +Cost metrics define how model predictions are compared to observations. They determine the objective function that optimisers minimise. + +## Overview + +| Metric | Formula | Use Case | +|--------|---------|----------| +| `SSE` | $\sum (y_i - \hat{y}_i)^2$ | Standard least squares | +| `RMSE` | $\sqrt{\frac{1}{n}\sum (y_i - \hat{y}_i)^2}$ | Normalised error | +| `GaussianNLL` | $-\log p(data \mid params)$ | Bayesian inference | + +## CostMetric + +::: chronopt.CostMetric + options: + show_root_heading: true + show_source: false + +--- + +## SSE (Sum of Squared Errors) + +The default cost metric. Minimises the sum of squared residuals. + +$$\text{SSE} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ + +### Example Usage + +```python +import chronopt as chron + +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + # SSE is used by default, but can be explicit: + # .with_cost_metric(chron.SSE()) +) +``` + +### When to Use + +**Advantages:** +- Standard least squares approach +- Well-understood statistical properties +- Efficient to compute + +**Limitations:** +- Not normalised (sensitive to number of data points) +- Sensitive to outliers (squared errors magnify them) +- Assumes Gaussian errors with constant variance + +**Typical Use Cases:** +- Standard parameter fitting +- When absolute error scale matters +- Comparing models with same number of points + +--- + +## RMSE (Root Mean Squared Error) + +Normalised version of SSE. Divides by number of points and takes square root. + +$$\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$ + +### Example Usage + +```python +import chronopt as chron + +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(chron.RMSE()) +) +``` + +### When to Use + +**Advantages:** +- Normalised (independent of data size) +- Same units as observations +- Better for comparing models with different data sizes + +**Limitations:** +- Still sensitive to outliers +- Assumes Gaussian errors + +**Typical Use Cases:** +- Comparing models with different numbers of observations +- Reporting error in interpretable units +- Cross-validation and model selection + +--- + +## GaussianNLL (Gaussian Negative Log-Likelihood) + +Probabilistic cost metric for Bayesian inference. Assumes Gaussian observation noise. + +$$\text{GaussianNLL} = -\log p(data \mid params) = \frac{n}{2}\log(2\pi\sigma^2) + \frac{1}{2\sigma^2}\sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ + +where $\sigma^2$ is estimated from residuals. + +### Example Usage + +```python +import chronopt as chron + +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(chron.GaussianNLL()) +) + +# Use with MCMC sampling for Bayesian inference +sampler = chron.MetropolisHastings().with_max_iter(10000) +result = sampler.run(problem, initial_guess) +``` + +### When to Use + +**Advantages:** +- Proper probabilistic interpretation +- Required for Bayesian inference (MCMC, nested sampling) +- Automatically accounts for noise level +- Enables uncertainty quantification + +**Limitations:** +- Assumes Gaussian observation noise +- More computationally expensive than SSE +- Requires understanding of likelihood + +**Typical Use Cases:** +- MCMC sampling for uncertainty quantification +- Model comparison with Bayes factors +- When probabilistic interpretation is needed +- Nested sampling for evidence calculation + +--- + +## Choosing a Cost Metric + +```mermaid +graph TD + A[Start] --> B{Using samplers?} + B -->|Yes| C[GaussianNLL] + B -->|No| D{Comparing models?} + D -->|Different data sizes| E[RMSE] + D -->|Same data size| F[SSE] + D -->|No comparison| F +``` + +### Decision Guide + +**Use SSE when:** +- Standard least squares fitting +- Single model, fixed data +- Speed is critical + +**Use RMSE when:** +- Comparing models with different data sizes +- Want interpretable error in original units +- Cross-validation or model selection + +**Use GaussianNLL when:** +- MCMC sampling (Metropolis-Hastings) +- Nested sampling (model evidence) +- Need confidence intervals +- Bayesian inference required + +For more details, see the [Cost Metrics Guide](../../guides/cost-metrics.md). + +--- + +## Custom Cost Metrics + +Currently, custom cost metrics must be implemented in Rust. Python-level custom metrics are planned for a future release. + +To implement a custom cost metric: + +1. Add implementation in `rust/src/cost/` +2. Expose through Python bindings +3. Rebuild the package + +See the [Development Guide](../../development/architecture.md) for details on extending Chronopt. + +--- + +## Mathematical Background + +### Relationship to Maximum Likelihood + +Minimising SSE is equivalent to maximum likelihood estimation under Gaussian noise with known variance: + +$$\hat{\theta} = \arg\min_\theta \sum (y_i - f(x_i; \theta))^2 = \arg\max_\theta p(data \mid \theta)$$ + +### Weighted Least Squares + +For heteroscedastic data (varying noise), weighted metrics can be important. Contact the developers if you need this feature. + +--- + +## See Also + +- [Cost Metrics Guide](../../guides/cost-metrics.md) +- [Builders](builders.md) +- [Samplers](samplers.md) (for Bayesian inference) +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/03_parameter_uncertainty.ipynb) diff --git a/docs/api-reference/python/optimizers.md b/docs/api-reference/python/optimizers.md new file mode 100644 index 0000000..34febc3 --- /dev/null +++ b/docs/api-reference/python/optimizers.md @@ -0,0 +1,290 @@ +# Optimisers + +Optimisation algorithms for finding parameter values that minimise the objective function. + +## Overview + +| Optimiser | Type | Best For | Parameters | +|-----------|------|----------|------------| +| `NelderMead` | Gradient-free | Local search, < 10 params, noisy | 2-10 | +| `CMAES` | Gradient-free | Global search, 10-100+ params | 1-100+ | +| `Adam` | Gradient-based | Smooth objectives, fast convergence | Any | + +## Nelder-Mead + +::: chronopt.NelderMead + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import chronopt as chron + +# Create optimiser with custom settings +optimiser = ( + chron.NelderMead() + .with_max_iter(5000) + .with_step_size(0.1) + .with_threshold(1e-6) +) + +# Run optimisation +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +### When to Use + +**Advantages:** +- No gradient computation required +- Robust to noisy objectives +- Simple and reliable for small problems +- Default choice for quick exploration + +**Limitations:** +- Slow convergence for > 10 parameters +- Can get stuck in local minima +- Performance degrades with dimensionality + +**Typical Use Cases:** +- Initial parameter exploration +- Noisy experimental data +- Small-scale problems (< 10 parameters) + +### Parameter Tuning + +- **`step_size`**: Initial simplex size (default: 1.0) + - Larger values explore more globally + - Smaller values for local refinement + - Start with 10-50% of parameter range + +- **`threshold`**: Convergence tolerance (default: 1e-6) + - Smaller for higher precision + - Larger for faster termination + +- **`max_iter`**: Maximum iterations (default: 1000) + - Rule of thumb: `100 * n_parameters` minimum + +See the [Nelder-Mead Algorithm Guide](../../algorithms/optimizers/nelder-mead.md) for more details. + +--- + +## CMA-ES + +::: chronopt.CMAES + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import chronopt as chron + +# Create CMA-ES optimiser +optimiser = ( + chron.CMAES() + .with_max_iter(1000) + .with_step_size(0.5) + .with_population_size(20) + .with_seed(42) # For reproducibility +) + +result = optimiser.run(problem, initial_guess=[0.5, 0.5]) +``` + +### When to Use + +**Advantages:** +- Global optimisation (avoids local minima) +- Scales to high dimensions (10-100+ parameters) +- Parallelisable (evaluates population in parallel) +- Self-adapting (no gradient tuning needed) + +**Limitations:** +- More function evaluations than gradient methods +- Requires population-sized memory +- Stochastic (results vary between runs) + +**Typical Use Cases:** +- Global parameter search +- High-dimensional problems (> 10 parameters) +- Multi-modal landscapes +- Parallel hardware available + +### Parameter Tuning + +- **`step_size`**: Initial search radius (default: 1.0) + - Start with ~1/3 of expected parameter range + - Too large: slow convergence + - Too small: premature convergence + +- **`population_size`**: Offspring per generation (default: `4 + floor(3*ln(n_params))`) + - Larger populations explore more but cost more + - Typical range: 10-100 + - Must match available parallelism + +- **`max_iter`**: Maximum generations (default: 1000) + - Each iteration evaluates `population_size` candidates + - Total evaluations = `max_iter * population_size` + +- **`threshold`**: Objective value threshold (default: 1e-6) + - Stop when best value < threshold + +- **`seed`**: Random seed for reproducibility + - Omit for non-deterministic runs + - Set for reproducible benchmarks + +See the [CMA-ES Algorithm Guide](../../algorithms/optimizers/cmaes.md) for more details. + +--- + +## Adam + +::: chronopt.Adam + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import chronopt as chron + +# Create Adam optimiser +optimiser = ( + chron.Adam() + .with_max_iter(5000) + .with_step_size(0.01) # Learning rate + .with_betas(0.9, 0.999) + .with_threshold(1e-6) +) + +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +### When to Use + +**Advantages:** +- Fast convergence on smooth objectives +- Adaptive learning rate +- Well-suited for large-scale problems +- Efficient (uses gradients) + +**Limitations:** +- Requires automatic differentiation +- Can get stuck in local minima +- Sensitive to learning rate tuning + +**Typical Use Cases:** +- Smooth, differentiable objectives +- Large-scale problems +- When gradients are available or cheap to compute + +### Parameter Tuning + +- **`step_size`**: Learning rate (default: 0.001) + - Most critical parameter + - Too large: oscillation or divergence + - Too small: slow convergence + - Try: 0.1, 0.01, 0.001, 0.0001 + +- **`betas`**: Momentum decay rates (default: (0.9, 0.999)) + - `beta1`: First moment (mean) decay + - `beta2`: Second moment (variance) decay + - Rarely need tuning, defaults work well + +- **`eps`**: Numerical stability constant (default: 1e-8) + - Prevents division by zero + - Almost never needs tuning + +- **`threshold`**: Gradient norm threshold (default: 1e-6) + - Stop when gradient is small + - Smaller for higher precision + +See the [Adam Algorithm Guide](../../algorithms/optimizers/adam.md) for more details. + +--- + +## Common Patterns + +### Running Optimisation + +All optimisers have a `.run()` method: + +```python +result = optimiser.run(problem, initial_guess) +``` + +For the default optimiser (Nelder-Mead): + +```python +result = problem.optimise() # Uses Nelder-Mead with defaults +``` + +### Configuring Stopping Criteria + +```python +optimiser = ( + chron.CMAES() + .with_max_iter(10000) # Maximum iterations + .with_threshold(1e-8) # Objective threshold + .with_patience(300.0) # Patience in seconds +) +``` + +The optimiser stops when: +1. `max_iter` iterations reached, OR +2. Objective value < `threshold`, OR +3. `patience` seconds elapsed without improvement + +### Reproducibility + +For stochastic optimisers (CMA-ES), set a seed: + +```python +optimiser = chron.CMAES().with_seed(42) +result1 = optimiser.run(problem, [0.0, 0.0]) +result2 = optimiser.run(problem, [0.0, 0.0]) +# result1 == result2 (same random sequence) +``` + +### Warm Starts + +Run multiple optimisations with different starting points: + +```python +import numpy as np + +initial_guesses = [ + [1.0, 1.0], + [-1.0, -1.0], + [0.0, 2.0], +] + +results = [optimiser.run(problem, guess) for guess in initial_guesses] +best_result = min(results, key=lambda r: r.value) +``` + +## Choosing an Optimiser + +```mermaid +graph TD + A[Start] --> B{Gradients available?} + B -->|Yes| C[Adam] + B -->|No| D{Problem size?} + D -->|< 10 params| E[Nelder-Mead] + D -->|> 10 params| F[CMA-ES] + D -->|Need global search| F +``` + +For detailed guidance, see: +- [Choosing an Optimiser Guide](../../guides/choosing-optimizer.md) +- [Tuning Optimisers Guide](../../guides/tuning-optimizers.md) + +## See Also + +- [Algorithm Guides](../../algorithms/index.md) +- [Results](results.md) +- [Tutorials](../../tutorials/index.md) diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md new file mode 100644 index 0000000..c24cb04 --- /dev/null +++ b/docs/api-reference/python/results.md @@ -0,0 +1,310 @@ +# Results + +Result objects returned by optimisers and samplers containing optimal parameters, diagnostics, and metadata. + +## OptimisationResults + +::: chronopt.OptimisationResults + options: + show_root_heading: true + show_source: false + +All optimisers return an `OptimisationResults` object with the following attributes: + +### Example Usage + +```python +import chronopt as chron + +problem = builder.build() +result = problem.optimise() + +# Access results +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +print(f"Evaluations: {result.evaluations}") +print(f"Message: {result.message}") +``` + +--- + +## Attributes + +### `x` + +**Type:** `numpy.ndarray` + +Optimal parameter values found by the optimiser. + +```python +result.x # e.g., array([1.0, 2.5, 0.8]) +``` + +Parameters are ordered according to `.with_parameter()` calls: + +```python +builder = ( + chron.ScalarBuilder() + .with_parameter("alpha", 1.0) # result.x[0] + .with_parameter("beta", 2.0) # result.x[1] +) +``` + +--- + +### `value` + +**Type:** `float` + +Objective function value at the optimum (`result.x`). + +```python +result.value # e.g., 1.234e-08 +``` + +For minimisation problems, lower values are better. + +--- + +### `success` + +**Type:** `bool` + +Indicates whether optimisation terminated successfully. + +```python +if result.success: + print("Optimisation converged") +else: + print(f"Failed: {result.message}") +``` + +**`True`** when: +- Convergence criteria met +- Threshold reached +- Normal termination + +**`False`** when: +- Maximum iterations exceeded without convergence +- Numerical errors occurred +- User-requested termination + +--- + +### `iterations` + +**Type:** `int` + +Number of iterations performed. + +```python +result.iterations # e.g., 157 +``` + +- **Nelder-Mead**: Number of simplex iterations +- **CMA-ES**: Number of generations +- **Adam**: Number of gradient steps + +--- + +### `evaluations` + +**Type:** `int` + +Total number of objective function evaluations. + +```python +result.evaluations # e.g., 314 +``` + +Note that `evaluations` can be much larger than `iterations`: +- **CMA-ES**: `evaluations ≈ iterations × population_size` +- **Nelder-Mead**: `evaluations ≈ iterations × (n_params + 1)` +- **Adam**: `evaluations ≈ iterations` (one per step) + +--- + +### `message` + +**Type:** `str` + +Human-readable termination message. + +```python +result.message +# e.g., "Converged successfully" +# e.g., "Maximum iterations reached" +# e.g., "Threshold achieved" +``` + +Use this for debugging and logging. + +--- + +## Common Patterns + +### Checking Success + +```python +result = problem.optimise() + +if result.success: + print(f"Found optimum: {result.x}") + print(f"Objective: {result.value:.3e}") +else: + print(f"Optimisation failed: {result.message}") + print(f"Best found: {result.x} (value: {result.value:.3e})") +``` + +Even when `success=False`, `result.x` contains the best parameters found. + +--- + +### Extracting Parameters by Name + +Results don't store parameter names. Track them manually if needed: + +```python +param_names = ["alpha", "beta", "gamma"] + +builder = chron.ScalarBuilder().with_callable(func) +for name in param_names: + builder = builder.with_parameter(name, 1.0) + +problem = builder.build() +result = problem.optimise() + +# Create a dictionary +params = dict(zip(param_names, result.x)) +print(params) # {'alpha': 1.05, 'beta': 2.31, 'gamma': 0.87} +``` + +--- + +### Comparing Multiple Runs + +```python +initial_guesses = [[1, 1], [-1, -1], [0, 2]] + +results = [ + optimiser.run(problem, guess) + for guess in initial_guesses +] + +# Find best result +best = min(results, key=lambda r: r.value) +print(f"Best from {len(results)} runs: {best.x}, value={best.value:.3e}") +``` + +--- + +### Assessing Convergence + +```python +result = optimiser.run(problem, initial_guess) + +# Check various indicators +converged = ( + result.success + and result.value < 1e-6 + and result.iterations < max_iters +) + +if not converged: + print(f"Warning: May not have converged") + print(f" Iterations: {result.iterations}") + print(f" Value: {result.value:.3e}") + print(f" Message: {result.message}") +``` + +--- + +## Sampler Results (Future) + +When samplers are fully implemented, they will return different result types: + +### MCMC Results + +```python +result = mcmc_sampler.run(problem, initial_guess) + +result.samples # (n_samples, n_params) array +result.log_likelihood # Log-likelihood values +result.acceptance_rate # For diagnostics +``` + +### Nested Sampling Results + +```python +result = nested_sampler.run(problem, initial_guess) + +result.log_evidence # Log marginal likelihood +result.evidence_error # Uncertainty in evidence +result.samples # Posterior samples +result.weights # Sample weights +``` + +--- + +## Diagnostics + +### Visualising Convergence + +```python +import matplotlib.pyplot as plt + +# Run optimisation with tracking (custom wrapper) +values = [] + +def tracked_func(x): + val = original_func(x) + values.append(val[0]) + return val + +# ... run optimisation ... + +plt.plot(values) +plt.xlabel('Evaluation') +plt.ylabel('Objective Value') +plt.yscale('log') +plt.title('Optimisation Convergence') +plt.show() +``` + +### Checking Parameter Bounds + +```python +result = problem.optimise() + +# Check if parameters are in reasonable range +for i, val in enumerate(result.x): + if abs(val) > 1e3: + print(f"Warning: Parameter {i} has large magnitude: {val}") +``` + +--- + +## Performance Metrics + +```python +import time + +start = time.time() +result = optimiser.run(problem, initial_guess) +elapsed = time.time() - start + +print(f"Total time: {elapsed:.2f}s") +print(f"Evaluations: {result.evaluations}") +print(f"Time per evaluation: {elapsed/result.evaluations*1000:.2f}ms") +``` + +--- + +## See Also + +- [Optimisers](optimizers.md) +- [Samplers](samplers.md) +- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Troubleshooting](../../guides/troubleshooting.md) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md new file mode 100644 index 0000000..990fed9 --- /dev/null +++ b/docs/api-reference/python/samplers.md @@ -0,0 +1,212 @@ +# Samplers + +MCMC and nested sampling algorithms for posterior exploration and model comparison. + +!!! info "Coming Soon" + Sampler Python bindings are currently in development. This page describes the planned API. + +## Overview + +| Sampler | Type | Use Case | +|---------|------|----------| +| `MetropolisHastings` | MCMC | Posterior exploration, uncertainty quantification | +| `DynamicNestedSampling` | Nested | Model evidence, Bayes factors | + +Samplers complement optimisers by providing full posterior distributions rather than point estimates. + +--- + +## Metropolis-Hastings (Planned) + +MCMC sampling for exploring parameter posterior distributions. + +### Planned API + +```python +import chronopt as chron + +# Will be available in a future release +sampler = ( + chron.MetropolisHastings() + .with_max_iter(10000) + .with_step_size(0.1) + .with_burn_in(1000) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess) + +# Access samples +print(result.samples.shape) # (n_samples, n_params) +print(result.acceptance_rate) # Target: 0.2-0.4 +``` + +### When to Use + +**Advantages:** +- Provides full posterior distribution +- Quantifies parameter uncertainty +- Captures correlations between parameters +- Enables credible intervals + +**Limitations:** +- Requires many function evaluations +- Need to assess convergence +- Requires likelihood (GaussianNLL cost metric) + +**Typical Use Cases:** +- Uncertainty quantification +- Confidence intervals +- Posterior predictive distributions +- Parameter correlations + +--- + +## Dynamic Nested Sampling (Planned) + +Nested sampling for calculating model evidence (marginal likelihood) for model comparison. + +### Planned API + +```python +import chronopt as chron + +# Will be available in a future release +sampler = ( + chron.DynamicNestedSampling() + .with_max_iter(5000) + .with_n_live_points(500) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess) + +# Access evidence +print(f"Log evidence: {result.log_evidence:.2f}") +print(f"Error: {result.evidence_error:.2f}") + +# Posterior samples also available +print(result.samples.shape) +``` + +### When to Use + +**Advantages:** +- Calculates marginal likelihood (evidence) +- Enables model comparison via Bayes factors +- Provides posterior samples as byproduct +- Efficient for multi-modal posteriors + +**Limitations:** +- More expensive than MCMC +- Requires careful tuning of live points +- Needs likelihood (GaussianNLL cost metric) + +**Typical Use Cases:** +- Model comparison +- Bayes factors +- Evidence calculation +- Multi-modal posteriors + +--- + +## Optimisers vs Samplers + +| | Optimisers | Samplers | +|---|------------|----------| +| **Output** | Single best parameters | Distribution of parameters | +| **Use case** | Point estimate | Uncertainty quantification | +| **Cost metric** | SSE, RMSE, GaussianNLL | GaussianNLL (required) | +| **Computational cost** | Lower | Higher | +| **Uncertainty** | None | Full posterior | + +### When to Use Which + +```mermaid +graph TD + A[Start] --> B{Need uncertainty?} + B -->|No| C[Use Optimiser] + B -->|Yes| D{Need model comparison?} + D -->|No| E[Use MCMC] + D -->|Yes| F[Use Nested Sampling] +``` + +--- + +## Cost Metric Requirement + +!!! warning + Samplers require a probabilistic cost metric (typically `GaussianNLL`): + +```python +# Required for samplers +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(chron.GaussianNLL()) # Required! +) +``` + +SSE and RMSE cannot be used with samplers as they lack probabilistic interpretation. + +--- + +## Workflow Example + +Typical workflow: optimise first, then sample for uncertainty: + +```python +import chronopt as chron + +# 1. Build problem with GaussianNLL +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(chron.GaussianNLL()) +) +problem = builder.build() + +# 2. Find MAP estimate with optimiser +optimiser = chron.CMAES().with_max_iter(1000) +opt_result = optimiser.run(problem, [1.0]) + +print(f"MAP estimate: {opt_result.x}") + +# 3. Sample around MAP for uncertainty (future API) +# sampler = chron.MetropolisHastings().with_max_iter(10000) +# sample_result = sampler.run(problem, opt_result.x) +# print(f"Posterior mean: {sample_result.samples.mean(axis=0)}") +# print(f"Posterior std: {sample_result.samples.std(axis=0)}") +``` + +--- + +## Future Tutorials + +Once samplers are available, see: + +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/03_parameter_uncertainty.ipynb) +- [Model Comparison Tutorial](../../tutorials/notebooks/04_model_comparison.ipynb) +- [Choosing a Sampler Guide](../../guides/choosing-sampler.md) + +--- + +## Implementation Status + +Track sampler implementation progress: + +- [GitHub Issue #XXX](https://github.com/bradyplanden/chronopt) - Metropolis-Hastings +- [GitHub Issue #XXX](https://github.com/bradyplanden/chronopt) - Dynamic Nested Sampling + +--- + +## See Also + +- [Optimisers](optimizers.md) - For point estimates +- [Cost Metrics](cost-metrics.md) - GaussianNLL required +- [Choosing a Sampler Guide](../../guides/choosing-sampler.md) (future) +- [Algorithm Guides](../../algorithms/index.md) diff --git a/docs/api-reference/rust/index.md b/docs/api-reference/rust/index.md new file mode 100644 index 0000000..d3b0333 --- /dev/null +++ b/docs/api-reference/rust/index.md @@ -0,0 +1,223 @@ +# Rust API Documentation + +Chronopt's Rust core provides high-performance implementations of all optimisation and sampling algorithms. + +## Official Documentation + +The complete Rust API documentation is hosted on docs.rs: + +**[:octicons-arrow-right-24: Chronopt Rust Documentation on docs.rs](https://docs.rs/chronopt/latest/chronopt/)** + +## When to Use the Rust API + +Consider using the Rust crate directly when: + +- **Maximum performance** is critical +- Building **Rust-native applications** +- Need **zero-copy** data handling +- Deploying to **embedded systems** or **constrained environments** +- Building **custom tooling** around Chronopt + +For most users, the Python API provides excellent performance with easier integration. + +## Crate Structure + +``` +chronopt/ +├── builders/ # Problem builders (ScalarBuilder, DiffsolBuilder, etc.) +├── optimisers/ # Optimisation algorithms +│ ├── nelder_mead/ # Nelder-Mead simplex +│ ├── cmaes/ # CMA-ES evolution strategy +│ └── adam/ # Adam gradient descent +├── sampler/ # MCMC and nested sampling +├── cost/ # Cost metrics (SSE, RMSE, GaussianNLL) +├── problem/ # Problem types and evaluation +└── common/ # Shared types and utilities +``` + +## Quick Example + +```rust +use chronopt::prelude::*; +use ndarray::array; + +// Define objective function +fn rosenbrock(x: &[f64]) -> f64 { + (1.0 - x[0]).powi(2) + 100.0 * (x[1] - x[0].powi(2)).powi(2) +} + +fn main() { + // Build problem + let builder = ScalarBuilder::new() + .with_callable(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5); + + let problem = builder.build(); + + // Run optimisation + let result = problem.optimise(); + + println!("Optimal parameters: {:?}", result.x); + println!("Objective value: {:.3e}", result.value); + println!("Success: {}", result.success); +} +``` + +## Adding Chronopt to Your Project + +Add to your `Cargo.toml`: + +```toml +[dependencies] +chronopt = "0.2" +ndarray = "0.15" +``` + +For ODE support with DiffSL: + +```toml +[dependencies] +chronopt = { version = "0.2", features = ["diffsol"] } +``` + +## Key Rust Features + +### Zero-Copy Performance + +The Rust API avoids unnecessary allocations and copies: + +```rust +// Efficient in-place evaluation +let mut output = vec![0.0; n]; +problem.evaluate_into(¶ms, &mut output)?; +``` + +### Type Safety + +Strong typing catches errors at compile time: + +```rust +// Compiler ensures correct types +let builder: ScalarBuilder = ScalarBuilder::new() + .with_callable(objective) + .with_parameter("x", 1.0); + +let problem: Problem = builder.build(); +``` + +### Parallel Execution + +Native Rayon parallelism: + +```rust +use rayon::prelude::*; + +let results: Vec<_> = initial_guesses + .par_iter() + .map(|guess| optimiser.run(&problem, guess)) + .collect(); +``` + +## Building from Source + +See the [Building from Source](../../development/building.md) guide for detailed instructions. + +```bash +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt/rust +cargo build --release +cargo test +``` + +## Documentation Generation + +Generate local documentation: + +```bash +cd rust +cargo doc --open --no-deps +``` + +This builds and opens the full API documentation in your browser. + +## Contributing to Rust Core + +See the [Contributing Guide](../../development/contributing.md) and [Architecture](../../development/architecture.md) docs for: + +- Code organization and patterns +- Adding new optimisers or samplers +- Implementing custom cost metrics +- Testing strategies +- PyO3 binding guidelines + +## Module Documentation + +Key modules (click through on docs.rs for full details): + +### `builders` + +Problem construction with fluent API. + +```rust +pub use chronopt::builders::{ScalarBuilder, DiffsolBuilder, VectorBuilder}; +``` + +### `optimisers` + +Optimisation algorithms. + +```rust +pub use chronopt::optimisers::{NelderMead, CMAES, Adam}; +``` + +### `cost` + +Cost metrics for objective functions. + +```rust +pub use chronopt::cost::{CostMetric, SSE, RMSE, GaussianNLL}; +``` + +### `problem` + +Problem types and evaluation. + +```rust +pub use chronopt::problem::{Problem, ScalarProblem, VectorProblem}; +``` + +## Performance Tips + +1. **Use release builds**: `cargo build --release` (10-100x faster than debug) +2. **Profile first**: Use `cargo flamegraph` to identify bottlenecks +3. **Parallel backends**: Enable Rayon for CMA-ES and Diffsol +4. **Sparse matrices**: Use sparse backend for large ODE systems +5. **Avoid allocations**: Reuse buffers in hot loops + +## Examples + +The repository includes Rust examples: + +```bash +cd rust +cargo run --example rosenbrock +cargo run --example ode_fitting +``` + +Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/chronopt/tree/main/rust/examples) + +## FFI and Bindings + +Chronopt's Python bindings use PyO3. For other languages: + +- **C/C++**: Use `extern "C"` FFI (planned) +- **Julia**: Use CxxWrap.jl (planned) +- **JavaScript**: Use wasm-bindgen for WASM (experimental) + +## See Also + +- [Python API Reference](../index.md) +- [Building from Source](../../development/building.md) +- [Architecture](../../development/architecture.md) +- [Contributing](../../development/contributing.md) diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..90cb31c --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1 @@ +--8<-- "CHANGELOG.md" \ No newline at end of file diff --git a/docs/development/architecture.md b/docs/development/architecture.md new file mode 100644 index 0000000..626d213 --- /dev/null +++ b/docs/development/architecture.md @@ -0,0 +1,37 @@ +# Architecture + +!!! info "Coming Soon" + Detailed architecture documentation is being written. + +## High-Level Overview + +``` +┌─────────────────────────────────────┐ +│ Python API Layer │ +│ (PyO3 bindings, type stubs) │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ Rust Core │ +│ (Optimisers, Samplers, Builders) │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ External Libraries │ +│ (Diffsol, ndarray, rayon) │ +└─────────────────────────────────────┘ +``` + +## Key Design Patterns + +- **Builder Pattern**: Fluent API for problem construction +- **Zero-Copy**: Efficient data handling between Python and Rust +- **Trait-Based**: Extensible algorithm interfaces + +## See Also + +- [Building from Source](building.md) +- [Contributing](contributing.md) +- [Rust API](../api-reference/rust/index.md) diff --git a/docs/development/building.md b/docs/development/building.md new file mode 100644 index 0000000..de8177b --- /dev/null +++ b/docs/development/building.md @@ -0,0 +1,48 @@ +# Building from Source + +!!! info "Coming Soon" + Detailed build instructions are being written. + +## Quick Build + +```bash +# Clone repository +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt + +# Set up environment +uv sync + +# Build +uv run maturin develop + +# Test +uv run pytest -v +cargo test +``` + +## Prerequisites + +- Rust >= 1.70 +- Python >= 3.11 +- uv (recommended) + +## Platform-Specific Notes + +### Linux + +No special requirements. + +### macOS + +Works on both Intel and Apple Silicon. + +### Windows + +Experimental support. May require Visual C++ Build Tools. + +## See Also + +- [Contributing](contributing.md) +- [Architecture](architecture.md) +- [Installation](../getting-started/installation.md) diff --git a/docs/development/contributing.md b/docs/development/contributing.md new file mode 100644 index 0000000..00c3f2c --- /dev/null +++ b/docs/development/contributing.md @@ -0,0 +1,36 @@ +# Contributing to Chronopt + +!!! info "Coming Soon" + Detailed contributing guidelines are being written. + +## Quick Start + +```bash +# Fork and clone +git clone https://github.com/YOUR_USERNAME/chronopt.git +cd chronopt + +# Set up environment +uv sync +uv run maturin develop + +# Run tests +uv run pytest -v +cargo test + +# Make changes and submit PR +``` + +## Areas to Contribute + +- Bug fixes +- New algorithms +- Documentation +- Examples +- Tests + +## See Also + +- [Building from Source](building.md) +- [Architecture](architecture.md) +- [GitHub Issues](https://github.com/bradyplanden/chronopt/issues) diff --git a/docs/development/index.md b/docs/development/index.md new file mode 100644 index 0000000..e61253a --- /dev/null +++ b/docs/development/index.md @@ -0,0 +1,254 @@ +# Development + +Resources for contributors and developers working with Chronopt. + +## Getting Started with Development + +
+ +- :material-source-pull:{ .lg .middle } __Contributing__ + + --- + + Guidelines for contributing code, documentation, and bug reports. + + [:octicons-arrow-right-24: Contributing Guide](contributing.md) + +- :material-file-tree:{ .lg .middle } __Architecture__ + + --- + + Understanding Chronopt's Rust core and PyO3 bindings design. + + [:octicons-arrow-right-24: Architecture Overview](architecture.md) + +- :material-hammer-wrench:{ .lg .middle } __Building from Source__ + + --- + + Setting up a development environment and building Chronopt. + + [:octicons-arrow-right-24: Build Guide](building.md) + +
+ +## Quick Setup + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt + +# Create Python environment +uv sync + +# Build Rust extension with Python bindings +uv run maturin develop + +# Run tests +uv run pytest -v # Python tests +cargo test # Rust tests +``` + +## Development Workflow + +### 1. Make Changes + +Edit Rust source in `rust/src/` or Python bindings in `python/`. + +### 2. Rebuild + +```bash +uv run maturin develop # For Python binding changes +``` + +### 3. Test + +```bash +# Python tests +uv run pytest -v + +# Rust tests +cargo test + +# Both +cargo test && uv run pytest -v +``` + +### 4. Update Stubs + +If you modified Python bindings: + +```bash +uv run cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +``` + +### 5. Format and Lint + +```bash +# Rust +cargo fmt +cargo clippy + +# Python +uv run ruff check . +uv run ruff format . +``` + +## Project Structure + +``` +chronopt/ +├── rust/ # Rust core implementation +│ ├── src/ +│ │ ├── builders/ # Problem builders +│ │ ├── optimisers/ # Optimisation algorithms +│ │ ├── sampler/ # MCMC and nested sampling +│ │ ├── cost/ # Cost metrics +│ │ └── problem/ # Problem types +│ ├── Cargo.toml +│ └── tests/ # Rust tests +├── python/ # Python bindings +│ ├── src/chronopt/ +│ │ ├── __init__.py +│ │ └── _chronopt.pyi # Generated type stubs +│ └── chronopt/ # PyO3 bindings source +├── examples/ # Example scripts +├── tests/ # Python tests +├── docs/ # Documentation (this site) +├── pyproject.toml # Python package config +└── README.md +``` + +## Key Technologies + +- **Rust**: Core algorithms, high performance +- **PyO3**: Python bindings with minimal overhead +- **Maturin**: Build system for Rust Python extensions +- **uv**: Fast Python package management +- **MkDocs Material**: Documentation site + +## Testing Strategy + +### Unit Tests + +Rust unit tests alongside code: + +```rust +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_feature() { + // ... + } +} +``` + +### Integration Tests + +Python integration tests in `tests/`: + +```python +def test_optimisation(): + builder = chron.ScalarBuilder().with_callable(func) + # ... + assert result.success +``` + +### Continuous Integration + +GitHub Actions runs: +- Rust tests and clippy +- Python tests on multiple versions +- Type checking with mypy +- Linting with ruff +- Documentation builds + +## Documentation + +### Rust Docs + +```bash +cargo doc --open --no-deps +``` + +### Python Docs + +Edit markdown files in `docs/`: + +```bash +mkdocs serve # Live preview +mkdocs build # Build static site +``` + +### Docstrings + +Use NumPy-style docstrings: + +```python +def example(x, y): + """ + Brief description. + + Parameters + ---------- + x : float + Description of x. + y : float + Description of y. + + Returns + ------- + float + Description of return value. + """ +``` + +## Code Style + +### Rust + +- Follow [Rust API Guidelines](https://rust-lang.github.io/api-guidelines/) +- Use `cargo fmt` (automatic formatting) +- Pass `cargo clippy` (linting) + +### Python + +- Follow PEP 8 +- Use type hints +- Format with `ruff format` + +## Performance Profiling + +### Rust + +```bash +cargo install flamegraph +cargo flamegraph --example your_example +``` + +### Python + +```bash +uv pip install py-spy +py-spy record --native -- python examples/your_example.py +``` + +## Release Process + +See the [Contributing Guide](contributing.md) for the release workflow. + +## Getting Help + +- **GitHub Issues**: [Report bugs or request features](https://github.com/bradyplanden/chronopt/issues) +- **Discussions**: [Ask questions](https://github.com/bradyplanden/chronopt/discussions) +- **Documentation**: You're reading it! + +## See Also + +- [Architecture](architecture.md) - System design +- [Contributing](contributing.md) - Contribution guidelines +- [Building](building.md) - Detailed build instructions +- [API Reference](../api-reference/index.md) - API documentation diff --git a/docs/examples/gallery.md b/docs/examples/gallery.md new file mode 100644 index 0000000..c5c41cc --- /dev/null +++ b/docs/examples/gallery.md @@ -0,0 +1,170 @@ +# Examples Gallery + +Visual gallery of Chronopt applications and use cases. + +!!! info "Gallery Under Construction" + This gallery is being populated with examples. Check the [examples directory](https://github.com/bradyplanden/chronopt/tree/main/examples) for current code. + +## Available Examples + +### Scalar Optimisation + +#### Rosenbrock Function +Classic 2D optimisation test problem. + +**Files:** +- [python_problem.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_problem.py) +- [python_contour.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_contour.py) + +**Topics:** ScalarBuilder, contour plots, optimiser comparison + +--- + +### ODE Parameter Fitting + +#### Logistic Growth +Single-variable ODE with DiffSL. + +**File:** [logistic_growth.py](https://github.com/bradyplanden/chronopt/blob/main/examples/logistic_growth.py) + +**Topics:** DiffsolBuilder, DiffSL syntax, data fitting + +--- + +#### Bouncy Ball +Physics-based model with event handling. + +**Files:** +- [bouncy_ball.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball.py) +- [bouncy_ball_sampling.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball_sampling.py) + +**Topics:** Event detection, parameter uncertainty, MCMC + +--- + +### Model Comparison + +#### Bicycle Model +Comparing different bicycle dynamics formulations. + +**Files:** +- [bicycle_model_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_diffsol.py) +- [bicycle_model_evidence.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_evidence.py) + +**Topics:** Model selection, evidence calculation, Bayes factors + +--- + +### Multi-Backend ODE Solving + +#### Predator-Prey Models +Lotka-Volterra equations with multiple solver backends. + +**Files:** +- [predator_prey_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffsol.py) +- [predator_prey_diffrax.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) + +**Topics:** VectorBuilder, JAX/Diffrax, Julia/DifferentialEquations.jl, performance comparison + +--- + +## Running Examples + +Clone the repository: + +```bash +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt +``` + +Install dependencies: + +```bash +pip install chronopt matplotlib +``` + +Run an example: + +```bash +python examples/python_problem.py +``` + +For ODE examples: + +```bash +python examples/logistic_growth.py +``` + +For multi-backend examples (requires additional dependencies): + +```bash +# For Diffrax (JAX) +pip install jax diffrax + +# For DifferentialEquations.jl (Julia) +pip install diffeqpy +# Then follow Julia setup instructions + +python examples/predator_prey/predator_prey_diffrax.py +``` + +## Example Categories + +### By Problem Type + +| Type | Examples | Builder | +|------|----------|---------| +| Scalar | Rosenbrock | ScalarBuilder | +| ODE (DiffSL) | Logistic, Bouncy Ball | DiffsolBuilder | +| ODE (Custom) | Predator-Prey | VectorBuilder | + +### By Algorithm + +| Algorithm | Examples | +|-----------|----------| +| Nelder-Mead | Most examples (default) | +| CMA-ES | Logistic growth, bicycle model | +| Adam | Coming soon | +| MCMC | Bouncy ball sampling | +| Nested Sampling | Bicycle evidence, model evidence | + +### By Difficulty + +**Beginner:** +- python_problem.py +- python_contour.py +- logistic_growth.py + +**Intermediate:** +- bouncy_ball.py +- bicycle_model_diffsol.py +- predator_prey_diffsol.py + +**Advanced:** +- bouncy_ball_sampling.py +- bicycle_model_evidence.py +- predator_prey_diffrax.py +- predator_prey_diffeqpy.py + +## Visual Gallery + +!!! info "Coming Soon" + Visual thumbnails and interactive demos will be added here. + +## Contributing Examples + +Have an interesting use case? We'd love to include it! + +1. Fork the repository +2. Add your example to `examples/` +3. Include a brief comment header explaining the example +4. Open a pull request + +See the [Contributing Guide](../development/contributing.md) for details. + +## See Also + +- [Tutorials](../tutorials/index.md) - Interactive Jupyter notebooks +- [Getting Started](../getting-started/index.md) - Core concepts +- [API Reference](../api-reference/index.md) - Complete API docs diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md new file mode 100644 index 0000000..b3c9757 --- /dev/null +++ b/docs/getting-started/concepts.md @@ -0,0 +1,311 @@ +# Core Concepts + +This guide explains the fundamental concepts and patterns in Chronopt. + +## The Builder Pattern + +Chronopt uses the **builder pattern** for constructing problems. This provides a fluent, chainable API for configuration: + +```python +builder = ( + chron.ScalarBuilder() + .with_callable(my_function) + .with_parameter("x", 1.0) + .with_parameter("y", 2.0) +) +problem = builder.build() +``` + +**Benefits:** + +- **Clear and readable**: Method names clearly describe what's being configured +- **Flexible**: Add components in any order +- **Type-safe**: Catch errors early with proper type hints +- **Chainable**: Fluent interface for concise code + +## Problem Types + +Chronopt provides different builders for different problem types: + +### ScalarBuilder + +For **direct function optimisation** where you have a Python callable: + +```python +def objective(x): + return np.asarray([x[0]**2 + x[1]**2]) + +problem = ( + chron.ScalarBuilder() + .with_callable(objective) + .with_parameter("x", 0.0) + .with_parameter("y", 0.0) + .build() +) +``` + +**Use when:** +- You have a direct Python function to minimise +- No differential equations involved +- Simple parameter optimisation + +### DiffsolBuilder + +For **ODE parameter fitting** using the built-in DiffSL/Diffsol solver: + +```python +dsl = """ +in = [r, k] +r { 1 } k { 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +problem = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") + .build() +) +``` + +**Use when:** +- Fitting ODE parameters to time-series data +- Using DiffSL for model definition +- Need high-performance multi-threaded solving + +### VectorBuilder + +For **custom ODE solvers** (Diffrax, DifferentialEquations.jl, etc.): + +```python +def solve_ode(params): + # Your custom ODE solver + # Returns predictions at observation times + return predictions + +problem = ( + chron.VectorBuilder() + .with_callable(solve_ode) + .with_data(data) + .with_parameter("alpha", 1.0) + .with_parameter("beta", 0.5) + .build() +) +``` + +**Use when:** +- Need a specific solver (JAX/Diffrax, Julia/DifferentialEquations.jl) +- Complex ODEs not supported by DiffSL +- Custom forward models beyond ODEs + +See the [Custom Solvers Guide](../guides/custom-solvers.md) for examples. + +## Parameters + +Parameters are the decision variables you want to optimise: + +```python +builder = ( + chron.ScalarBuilder() + .with_parameter("x", initial_value=1.0) # Name and initial guess + .with_parameter("y", initial_value=-1.0) +) +``` + +**Important:** + +- Parameters must have unique names +- Initial values are required +- Order matters: results will be returned in the same order + +## Optimisers vs Samplers + +Chronopt provides two types of algorithms: + +### Optimisers: Finding the Best Solution + +**Goal**: Find parameter values that minimise the objective function. + +**Algorithms**: + +- **Nelder-Mead**: Gradient-free, local search +- **CMA-ES**: Gradient-free, global search +- **Adam**: Gradient-based (automatic differentiation) + +**Usage:** + +```python +# Default optimiser (Nelder-Mead) +result = problem.optimise() + +# Specific optimiser +optimiser = chron.CMAES().with_max_iter(1000) +result = optimiser.run(problem, initial_guess) +``` + +**Returns:** A single best solution + +### Samplers: Exploring Uncertainty + +**Goal**: Sample from the posterior distribution to quantify parameter uncertainty. + +**Algorithms**: + +- **Metropolis-Hastings**: MCMC sampling for posterior exploration +- **Dynamic Nested Sampling**: Evidence calculation for model comparison + +**Usage:** + +```python +sampler = chron.MetropolisHastings().with_max_iter(10000) +result = sampler.run(problem, initial_guess) + +# Result contains samples, not a single optimum +print(result.samples.shape) # (n_samples, n_parameters) +``` + +**Returns:** A collection of samples from the posterior + +**When to use:** + +| Use Optimisers When | Use Samplers When | +|---------------------|-------------------| +| You want the single best fit | You want to quantify uncertainty | +| Point estimates are sufficient | You need confidence intervals | +| Computation budget is limited | You need full posterior distributions | +| | Comparing multiple models (Bayes factors) | + +See [Choosing an Optimiser](../guides/choosing-optimizer.md) and [Choosing a Sampler](../guides/choosing-sampler.md) for detailed guidance. + +## The Ask/Tell Pattern + +For advanced use cases, Chronopt supports the **ask/tell pattern** for manual control of the optimisation loop: + +```python +optimiser = chron.CMAES().with_max_iter(1000) + +# Ask for candidates +candidates = optimiser.ask(n_candidates=10) + +# Evaluate them (potentially in parallel or on remote machines) +evaluations = [problem.evaluate(c) for c in candidates] + +# Tell the optimiser the results +optimiser.tell(candidates, evaluations) + +# Repeat until convergence +while not optimiser.should_stop(): + candidates = optimiser.ask() + evaluations = [problem.evaluate(c) for c in candidates] + optimiser.tell(candidates, evaluations) + +result = optimiser.get_result() +``` + +**Use cases:** + +- Distributed optimisation across multiple machines +- Custom evaluation pipelines +- Hybrid optimisation strategies +- Integration with external simulators + +## Cost Metrics + +Cost metrics define how model predictions are compared to observations: + +```python +from chronopt import SSE, RMSE, GaussianNLL + +# Sum of squared errors (default) +builder = builder.with_cost_metric(SSE()) + +# Root mean squared error (normalised) +builder = builder.with_cost_metric(RMSE()) + +# Gaussian negative log-likelihood (for probabilistic inference) +builder = builder.with_cost_metric(GaussianNLL()) +``` + +**Common metrics:** + +- **SSE** (Sum of Squared Errors): Standard least squares, sensitive to outliers +- **RMSE** (Root Mean Squared Error): Normalised by number of points +- **GaussianNLL**: For Bayesian inference and sampling + +See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. + +## Results + +All optimisers and samplers return result objects with standard attributes: + +### Optimiser Results + +```python +result = problem.optimise() + +print(result.x) # Optimal parameters (NumPy array) +print(result.value) # Objective value at optimum (float) +print(result.success) # Whether optimisation succeeded (bool) +print(result.iterations) # Number of iterations (int) +print(result.evaluations) # Number of function evaluations (int) +print(result.message) # Termination message (str) +``` + +### Sampler Results + +```python +result = sampler.run(problem, initial_guess) + +print(result.samples) # MCMC samples (NumPy array, shape: (n_samples, n_params)) +print(result.log_likelihood) # Log-likelihood values +print(result.acceptance_rate) # Acceptance rate (for diagnostics) +``` + +For nested sampling: + +```python +result = dns_sampler.run(problem, initial_guess) + +print(result.log_evidence) # Log marginal likelihood +print(result.evidence_error) # Uncertainty in evidence +print(result.samples) # Posterior samples +``` + +## Parallelisation + +Chronopt automatically parallelises where possible: + +- **DiffsolBuilder**: Multi-threaded ODE solving +- **CMA-ES**: Parallel candidate evaluation +- **Dynamic Nested Sampling**: Parallel live point evaluation + +Control parallelism: + +```python +# Limit threads for ODE solving +builder = builder.with_max_threads(4) + +# Population size for CMA-ES (larger = more parallel work) +optimiser = chron.CMAES().with_population_size(20) +``` + +See the [Parallel Execution Guide](../guides/parallel-execution.md) for details. + +## Key Takeaways + +- **Builders** provide a fluent API for problem construction +- **ScalarBuilder** for direct functions, **DiffsolBuilder** for ODEs, **VectorBuilder** for custom solvers +- **Parameters** are decision variables with names and initial values +- **Optimisers** find the best solution; **samplers** explore uncertainty +- **Cost metrics** define how predictions are compared to observations +- **Ask/tell pattern** enables advanced control and distributed computation + +## Next Steps + +- **[Tutorials](../tutorials/index.md)**: Interactive Jupyter notebooks +- **[Choosing an Optimiser](../guides/choosing-optimizer.md)**: Learn when to use each algorithm +- **[API Reference](../api-reference/index.md)**: Browse complete API documentation +- **[Examples Gallery](../examples/gallery.md)**: Visual gallery of applications diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md new file mode 100644 index 0000000..f0a0eec --- /dev/null +++ b/docs/getting-started/first-ode-fit.md @@ -0,0 +1,275 @@ +# First ODE Fit + +This tutorial demonstrates how to fit ordinary differential equations (ODEs) to data using Chronopt's DiffSL integration with the Diffsol solver. + +## The Problem: Logistic Growth + +We'll fit a logistic growth model to exponentially decaying data. The logistic growth equation is: + +$$\frac{dy}{dt} = r \cdot y \cdot \left(1 - \frac{y}{k}\right)$$ + +where: +- $r$ is the growth rate +- $k$ is the carrying capacity +- $y$ is the population + +## Complete Example + +```python +import numpy as np +import chronopt as chron + +# Define the ODE model in DiffSL syntax +dsl = """ +in = [r, k] +r { 1 } k { 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +# Generate synthetic data +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) + +# Build the problem +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") +) +problem = builder.build() + +# Run optimisation with CMA-ES +optimiser = chron.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 0.5]) + +# Display results +print(f"Fitted parameters:") +print(f" r (growth rate) = {result.x[0]:.4f}") +print(f" k (capacity) = {result.x[1]:.4f}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +``` + +## Understanding DiffSL Syntax + +DiffSL is a domain-specific language for defining differential equations. Let's break down the syntax: + +``` +in = [r, k] # Input parameters to fit +r { 1 } k { 1 } # Default values for parameters +u_i { y = 0.1 } # Initial conditions +F_i { (r * y) * (1 - (y / k)) } # Right-hand side of dy/dt = ... +``` + +Key components: + +- `in = [...]`: Parameters to optimise +- `parameter { default_value }`: Default parameter values +- `u_i { var = initial_value }`: Initial conditions for state variables +- `F_i { expression }`: The derivative expression ($dy/dt$) + +## Data Format + +Chronopt expects data as a 2D NumPy array where: + +- **First column**: Time points +- **Remaining columns**: Observed values for each state variable + +```python +# Example: 51 time points, 1 state variable +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) # Shape: (51, 2) +``` + +For multi-variable systems: + +```python +# Example: 2 state variables +data = np.column_stack((t, obs_var1, obs_var2)) # Shape: (n, 3) +``` + +## Choosing the Backend + +Diffsol supports two backends: + +### Dense Backend (Default) + +```python +.with_backend("dense") +``` + +**Use when:** +- System has few state variables (< 100) +- The Jacobian matrix is mostly non-zero +- Simplicity is preferred + +### Sparse Backend + +```python +.with_backend("sparse") +``` + +**Use when:** +- System has many state variables (> 100) +- The Jacobian matrix is mostly zero +- Maximum performance is critical + +For more details, see the [DiffSL Backend Guide](../guides/diffsol-backend.md). + +## Visualising Results + +```python +import matplotlib.pyplot as plt + +# Get the optimised model predictions +predictions = problem.evaluate(result.x) # Returns predicted values at data time points + +# Plot +plt.figure(figsize=(10, 6)) +plt.plot(data[:, 0], data[:, 1], 'o', label='Observed data', alpha=0.6) +plt.plot(data[:, 0], predictions, '-', label='Fitted model', linewidth=2) +plt.xlabel('Time') +plt.ylabel('y') +plt.title('Logistic Growth Model Fit') +plt.legend() +plt.grid(True, alpha=0.3) +plt.show() +``` + +## Common ODE Patterns + +### Multiple State Variables + +```python +dsl = """ +in = [alpha, beta] +alpha { 1 } beta { 1 } +u_i { + x = 1.0 + y = 0.5 +} +F_i { + alpha * x - beta * x * y, + -beta * y + alpha * x * y +} +out_i { x, y } +""" +``` + +### With Algebraic Variables + +```python +dsl = """ +in = [k1, k2] +k1 { 1 } k2 { 1 } +u_i { A = 1.0 } +dudt_i { -k1 * A } +F_i { + -k1 * A, + k1 * A - k2 * B +} +out_i { A, B } +""" +``` + +### Time-Dependent Forcing + +```python +dsl = """ +in = [k] +k { 1 } +u_i { y = 0.0 } +F_i { k * sin(t) - y } +""" +``` + +## Cost Metrics + +By default, Chronopt uses sum of squared errors (SSE). You can specify different cost metrics: + +```python +from chronopt import GaussianNLL, RMSE + +# Use Gaussian negative log-likelihood +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(GaussianNLL()) # For probabilistic inference +) + +# Or use root mean squared error +builder = builder.with_cost_metric(RMSE()) # Normalised by number of points +``` + +See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. + +## Optimiser Selection + +Different optimisers work better for different problems: + +### Nelder-Mead (Default) + +```python +result = problem.optimise() # Uses Nelder-Mead +``` + +**Best for**: Small problems (< 10 parameters), noisy objectives + +### CMA-ES + +```python +optimiser = chron.CMAES().with_max_iter(1000).with_step_size(0.5) +result = optimiser.run(problem, initial_guess) +``` + +**Best for**: Global optimisation, 10-100 parameters, parallelisable + +### Adam + +```python +optimiser = chron.Adam().with_max_iter(1000).with_step_size(0.01) +result = optimiser.run(problem, initial_guess) +``` + +**Best for**: Smooth problems, fast convergence on well-behaved objectives + +For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimizer.md). + +## Troubleshooting + +### Poor Fit Quality + +1. **Check initial conditions**: Ensure they're physically reasonable +2. **Try different optimisers**: CMA-ES is often more robust than Nelder-Mead +3. **Increase iterations**: Use `.with_max_iter(10000)` +4. **Check data scale**: Normalise data if variables have very different magnitudes + +### Solver Errors + +1. **Stiff equations**: Try changing the solver tolerance +2. **Numerical instability**: Check for divide-by-zero or exp overflow in your ODE +3. **Backend mismatch**: Try switching between `dense` and `sparse` + +For more help, see the [Troubleshooting Guide](../guides/troubleshooting.md). + +## Key Takeaways + +- **DiffsolBuilder** is used for ODE fitting problems +- **DiffSL** provides a concise syntax for defining ODEs +- Data format is `[time, obs1, obs2, ...]` +- Choose between `dense` (default) and `sparse` backends +- CMA-ES often works well for ODE parameter fitting + +## Next Steps + +- **[Core Concepts](concepts.md)**: Understand builders, problems, and the ask/tell pattern +- **[ODE Fitting Tutorial](../tutorials/notebooks/02_ode_fitting_diffsol.ipynb)**: Interactive notebook with more examples +- **[Custom Solvers](../guides/custom-solvers.md)**: Integrate Diffrax or DifferentialEquations.jl +- **[Parameter Uncertainty](../tutorials/notebooks/03_parameter_uncertainty.ipynb)**: Use MCMC sampling to quantify uncertainty diff --git a/docs/getting-started/index.md b/docs/getting-started/index.md new file mode 100644 index 0000000..e7e8740 --- /dev/null +++ b/docs/getting-started/index.md @@ -0,0 +1,39 @@ +# Getting Started with Chronopt + +Welcome to Chronopt! This section will help you get up and running with time-series inference and optimisation. + +## Learning Path + +We recommend following this sequence: + +1. **[Installation](installation.md)** - Platform-specific installation instructions and troubleshooting +2. **[5-Minute Quickstart](quickstart.md)** - Simple scalar optimisation with the Rosenbrock function +3. **[First ODE Fit](first-ode-fit.md)** - Fitting differential equations to data with DiffSL +4. **[Core Concepts](concepts.md)** - Understanding builders, problems, and the ask/tell pattern + +## Prerequisites + +- Python >= 3.11 +- Basic understanding of Python programming +- Familiarity with NumPy arrays (helpful but not required) +- For ODE fitting: basic knowledge of differential equations + +## What You'll Learn + +By the end of this section, you will be able to: + +- Install Chronopt on your platform +- Create and solve scalar optimisation problems +- Fit differential equations to experimental data +- Understand the builder pattern and problem types +- Choose between optimisers and samplers for your use case + +## Need Help? + +If you encounter issues: + +1. Check the [Troubleshooting](../guides/troubleshooting.md) guide +2. Browse the [examples gallery](../examples/gallery.md) +3. Open an issue on [GitHub](https://github.com/bradyplanden/chronopt/issues) + +Ready to begin? Start with [Installation](installation.md). diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md new file mode 100644 index 0000000..2412c91 --- /dev/null +++ b/docs/getting-started/installation.md @@ -0,0 +1,159 @@ +# Installation + +Chronopt is available as a Python package with pre-built wheels for most platforms. + +## Requirements + +- **Python**: >= 3.11 +- **Operating Systems**: + - Linux (x86_64, aarch64) + - macOS (x86_64, Apple Silicon) + - Windows (experimental) + +## Installation Methods + +=== "pip" + + ```bash + pip install chronopt + ``` + +=== "uv (recommended)" + + [uv](https://docs.astral.sh/uv/) is a fast Python package installer and resolver. + + ```bash + uv pip install chronopt + ``` + +### Optional Dependencies + +Chronopt has optional plotting support via matplotlib: + +=== "pip" + + ```bash + pip install "chronopt[plotting]" + ``` + +=== "uv" + + ```bash + uv pip install "chronopt[plotting]" + ``` + +## Verifying Installation + +After installation, verify that Chronopt is working correctly: + +```python +import chronopt as chron +import numpy as np + +# Simple test +def test_func(x): + return np.asarray([(x[0] - 1.0) ** 2]) + +builder = chron.ScalarBuilder().with_callable(test_func).with_parameter("x", 0.0) +problem = builder.build() +result = problem.optimise() + +print(f"Success: {result.success}") +print(f"Optimal x: {result.x[0]:.3f}") +``` + +Expected output: +``` +Success: True +Optimal x: 1.000 +``` + +## Platform-Specific Notes + +### Linux + +Pre-built wheels are available for x86_64 and aarch64 architectures. No additional setup required. + +### macOS + +Pre-built wheels are available for both Intel (x86_64) and Apple Silicon (arm64) Macs. + +If you're using Apple Silicon and encounter issues, ensure you're using a native arm64 Python installation rather than running under Rosetta. + +### Windows (Experimental) + +Windows builds are currently experimental. Pre-built wheels are available but may have limitations. + +If you encounter issues: + +1. Ensure you have the latest [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) installed +2. Consider using [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) + +## Building from Source + +If you need to build from source (for development or if pre-built wheels aren't available): + +### Prerequisites + +- [Rust](https://rustup.rs/) >= 1.70 +- Python >= 3.11 +- [uv](https://docs.astral.sh/uv/) (recommended) + +### Build Steps + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt + +# Create Python environment +uv sync + +# Build Rust extension with Python bindings +uv run maturin develop + +# Run tests +uv run pytest -v +``` + +For more details, see [Building from Source](../development/building.md). + +## Troubleshooting + +### Import Error: No module named 'chronopt' + +**Cause**: Chronopt is not installed in your current Python environment. + +**Solution**: +1. Verify your Python environment is active +2. Re-run the installation command +3. Check that `pip list` shows chronopt + +### ImportError: DLL load failed (Windows) + +**Cause**: Missing Visual C++ runtime libraries. + +**Solution**: Install [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) + +### Wheel Not Available for Your Platform + +**Cause**: Pre-built wheels might not be available for your specific Python version or platform. + +**Solution**: [Build from source](#building-from-source) or open an issue on [GitHub](https://github.com/bradyplanden/chronopt/issues) + +### Installation Succeeds but Import Fails + +**Cause**: Binary incompatibility or corrupted installation. + +**Solution**: +```bash +pip uninstall chronopt +pip cache purge # Clear pip cache +pip install chronopt +``` + +For additional troubleshooting, see the [Troubleshooting Guide](../guides/troubleshooting.md). + +## Next Steps + +Now that Chronopt is installed, proceed to the [5-Minute Quickstart](quickstart.md) to run your first optimisation. diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md new file mode 100644 index 0000000..573a99c --- /dev/null +++ b/docs/getting-started/quickstart.md @@ -0,0 +1,157 @@ +# 5-Minute Quickstart + +This quickstart guide demonstrates scalar optimisation using the Rosenbrock function, a classic test problem in optimisation. + +## The Problem + +The Rosenbrock function is defined as: + +$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$ + +The global minimum is at $(x, y) = (1, 1)$ with $f(1, 1) = 0$. + +## Basic Example + +```python +import numpy as np +import chronopt as chron + + +def rosenbrock(x): + """The Rosenbrock function - a classic optimisation test problem.""" + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + + +# Build the problem +builder = ( + chron.ScalarBuilder() + .with_callable(rosenbrock) + .with_parameter("x", 1.5) # Initial guess + .with_parameter("y", -1.5) # Initial guess +) +problem = builder.build() + +# Run optimisation (uses Nelder-Mead by default) +result = problem.optimise() + +# Display results +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +``` + +**Output:** +``` +Optimal parameters: [1.0, 1.0] +Objective value: 0.000e+00 +Success: True +Iterations: 157 +``` + +## Understanding the Code + +1. **Define the objective function**: The function must accept a NumPy array and return a NumPy array (even for scalar values) + +2. **Create a builder**: `ScalarBuilder()` is used for scalar optimisation problems where you directly evaluate a function + +3. **Add parameters**: Use `with_parameter(name, initial_value)` to define decision variables + +4. **Build the problem**: Call `build()` to create an optimisable problem instance + +5. **Run optimisation**: Call `optimise()` to run the default optimiser (Nelder-Mead) + +## Using Different Optimisers + +You can specify which optimiser to use: + +### CMA-ES + +```python +# Use CMA-ES for global search +optimiser = chron.CMAES().with_max_iter(1000).with_step_size(0.5) +result = optimiser.run(problem, [1.5, -1.5]) + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +``` + +### Adam (Gradient-Based) + +```python +# Use Adam optimiser +optimiser = chron.Adam().with_max_iter(1000).with_step_size(0.01) +result = optimiser.run(problem, [1.5, -1.5]) + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +``` + +## Visualising the Optimisation + +If you installed the `plotting` extra, you can visualise the optimisation landscape: + +```python +import numpy as np +import matplotlib.pyplot as plt +import chronopt as chron + + +def rosenbrock(x): + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + + +# Create a grid for plotting +x = np.linspace(-2, 2, 200) +y = np.linspace(-1, 3, 200) +X, Y = np.meshgrid(x, y) +Z = np.zeros_like(X) + +for i in range(X.shape[0]): + for j in range(X.shape[1]): + Z[i, j] = rosenbrock([X[i, j], Y[i, j]])[0] + +# Plot contours +plt.figure(figsize=(10, 8)) +levels = np.logspace(-1, 3.5, 20) +plt.contour(X, Y, Z, levels=levels, cmap='viridis') +plt.colorbar(label='f(x, y)') + +# Mark the optimum +plt.plot(1.0, 1.0, 'r*', markersize=20, label='Global minimum') + +# Run optimisation and plot path +builder = ( + chron.ScalarBuilder() + .with_callable(rosenbrock) + .with_parameter("x", -1.5) + .with_parameter("y", -0.5) +) +problem = builder.build() +result = problem.optimise() + +plt.plot(result.x[0], result.x[1], 'go', markersize=10, label='Found optimum') +plt.xlabel('x') +plt.ylabel('y') +plt.title('Rosenbrock Function Optimisation') +plt.legend() +plt.grid(True, alpha=0.3) +plt.show() +``` + +## Key Takeaways + +- **ScalarBuilder** is used for direct function optimisation +- Parameters are defined with `with_parameter(name, initial_value)` +- The default optimiser is Nelder-Mead +- You can specify custom optimisers with different algorithms and parameters +- Results include optimal parameters, objective value, success status, and iteration count + +## Next Steps + +- **[First ODE Fit](first-ode-fit.md)**: Learn how to fit differential equations to data +- **[Core Concepts](concepts.md)**: Understand the builder pattern and problem types +- **[Choosing an Optimiser](../guides/choosing-optimizer.md)**: Learn when to use each optimiser +- **[Tutorials](../tutorials/index.md)**: Explore interactive Jupyter notebooks diff --git a/docs/guides/choosing-optimizer.md b/docs/guides/choosing-optimizer.md new file mode 100644 index 0000000..410d0bc --- /dev/null +++ b/docs/guides/choosing-optimizer.md @@ -0,0 +1,32 @@ +# Choosing an Optimiser + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed optimiser selection guidance. + +## Quick Reference + +| Problem Characteristics | Recommended Optimiser | +|-------------------------|----------------------| +| < 10 parameters, noisy | Nelder-Mead | +| 10-100+ parameters | CMA-ES | +| Smooth, gradients available | Adam | +| Need global search | CMA-ES | +| Fast local refinement | Nelder-Mead | + +## Decision Tree + +```mermaid +graph TD + A[Start] --> B{Gradients available?} + B -->|Yes| C[Adam] + B -->|No| D{Problem size?} + D -->|< 10 params| E[Nelder-Mead] + D -->|> 10 params| F[CMA-ES] + D -->|Need global| F +``` + +## See Also + +- [Optimisers API](../api-reference/python/optimizers.md) +- [Tuning Optimisers](tuning-optimizers.md) +- [Algorithm Details](../algorithms/index.md) diff --git a/docs/guides/choosing-sampler.md b/docs/guides/choosing-sampler.md new file mode 100644 index 0000000..c673a16 --- /dev/null +++ b/docs/guides/choosing-sampler.md @@ -0,0 +1,30 @@ +# Choosing a Sampler + +!!! info "Coming Soon" + This guide is being written. Sampler Python bindings are also in development. + +## Quick Reference + +| Use Case | Sampler | +|----------|---------| +| Uncertainty quantification | Metropolis-Hastings | +| Model comparison | Dynamic Nested Sampling | +| Just posterior samples | Metropolis-Hastings | +| Need evidence/Bayes factors | Dynamic Nested Sampling | + +## When to Sample vs Optimize + +Use **optimisers** when: +- Point estimate is sufficient +- Speed is critical +- Don't need uncertainty + +Use **samplers** when: +- Need confidence intervals +- Comparing models +- Want full posterior + +## See Also + +- [Samplers API](../api-reference/python/samplers.md) +- [Optimisers vs Samplers](../getting-started/concepts.md#optimisers-vs-samplers) diff --git a/docs/guides/cost-metrics.md b/docs/guides/cost-metrics.md new file mode 100644 index 0000000..69bc899 --- /dev/null +++ b/docs/guides/cost-metrics.md @@ -0,0 +1,23 @@ +# Cost Metrics Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed cost metric guidance. + +## Quick Reference + +| Metric | Use Case | +|--------|----------| +| SSE | Standard least squares | +| RMSE | Normalised error, model comparison | +| GaussianNLL | Bayesian inference, sampling | + +## Choosing a Metric + +- **SSE**: Default for most optimisation +- **RMSE**: When comparing models with different data sizes +- **GaussianNLL**: Required for samplers (MCMC, nested sampling) + +## See Also + +- [Cost Metrics API](../api-reference/python/cost-metrics.md) +- [Samplers](../api-reference/python/samplers.md) diff --git a/docs/guides/custom-solvers.md b/docs/guides/custom-solvers.md new file mode 100644 index 0000000..d1e1ed3 --- /dev/null +++ b/docs/guides/custom-solvers.md @@ -0,0 +1,38 @@ +# Custom Solvers Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for Diffrax and DifferentialEquations.jl integration examples. + +## Overview + +Use `VectorBuilder` to integrate custom ODE solvers: +- JAX/Diffrax +- Julia/DifferentialEquations.jl +- Custom Python solvers + +## Basic Pattern + +```python +def custom_solver(params): + # Your solver here + # Return predictions at observation times + return predictions + +builder = ( + chron.VectorBuilder() + .with_callable(custom_solver) + .with_data(data) + .with_parameter("alpha", 1.0) +) +``` + +## Examples + +See the predator-prey examples: +- [predator_prey_diffrax.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) + +## See Also + +- [VectorBuilder API](../api-reference/python/builders.md#vectorbuilder) +- [Examples Gallery](../examples/gallery.md) diff --git a/docs/guides/diffsol-backend.md b/docs/guides/diffsol-backend.md new file mode 100644 index 0000000..91925f9 --- /dev/null +++ b/docs/guides/diffsol-backend.md @@ -0,0 +1,27 @@ +# DiffSL Backend Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed backend selection guidance. + +## Quick Reference + +| Backend | Best For | +|---------|----------| +| `"dense"` | < 100 state variables | +| `"sparse"` | > 100 state variables, sparse Jacobian | + +## Usage + +```python +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_backend("dense") # or "sparse" +) +``` + +## See Also + +- [DiffsolBuilder API](../api-reference/python/builders.md#diffsolbuilder) +- [First ODE Fit](../getting-started/first-ode-fit.md) diff --git a/docs/guides/index.md b/docs/guides/index.md new file mode 100644 index 0000000..30b7132 --- /dev/null +++ b/docs/guides/index.md @@ -0,0 +1,136 @@ +# User Guides + +In-depth guides for making the most of Chronopt's optimisation and sampling capabilities. + +## Algorithm Selection + +
+ +- :material-tune:{ .lg .middle } __Choosing an Optimiser__ + + --- + + Decision trees and guidelines for selecting the right optimisation algorithm. + + [:octicons-arrow-right-24: Choosing an Optimiser](choosing-optimizer.md) + +- :material-speedometer:{ .lg .middle } __Tuning Optimisers__ + + --- + + Parameter tuning strategies and troubleshooting for each algorithm. + + [:octicons-arrow-right-24: Tuning Guide](tuning-optimizers.md) + +- :material-chart-bell-curve:{ .lg .middle } __Choosing a Sampler__ + + --- + + When to use MCMC vs nested sampling for uncertainty quantification. + + [:octicons-arrow-right-24: Choosing a Sampler](choosing-sampler.md) + +
+ +## Problem Configuration + +
+ +- :material-function-variant:{ .lg .middle } __Cost Metrics__ + + --- + + Understanding SSE, RMSE, and GaussianNLL for different use cases. + + [:octicons-arrow-right-24: Cost Metrics Guide](cost-metrics.md) + +- :material-script:{ .lg .middle } __DiffSL Backend__ + + --- + + Choosing between dense and sparse solvers for ODE systems. + + [:octicons-arrow-right-24: DiffSL Backend Guide](diffsol-backend.md) + +- :material-connection:{ .lg .middle } __Custom Solvers__ + + --- + + Integrating Diffrax, DifferentialEquations.jl, and other external solvers. + + [:octicons-arrow-right-24: Custom Solvers](custom-solvers.md) + +
+ +## Performance + +
+ +- :material-lightning-bolt:{ .lg .middle } __Parallel Execution__ + + --- + + Thread safety, parallelisation strategies, and performance optimisation. + + [:octicons-arrow-right-24: Parallel Execution](parallel-execution.md) + +
+ +## Troubleshooting + +
+ +- :material-help-circle:{ .lg .middle } __Troubleshooting__ + + --- + + Common errors, solutions, and debugging strategies. + + [:octicons-arrow-right-24: Troubleshooting Guide](troubleshooting.md) + +
+ +## Quick Reference + +### When to Use Each Component + +| Component | Use Case | +|-----------|----------| +| **ScalarBuilder** | Direct function optimisation | +| **DiffsolBuilder** | ODE fitting with DiffSL | +| **VectorBuilder** | Custom solvers (JAX, Julia) | +| **Nelder-Mead** | Local search, < 10 parameters | +| **CMA-ES** | Global search, 10-100+ parameters | +| **Adam** | Gradient-based, smooth objectives | +| **SSE** | Standard least squares | +| **RMSE** | Normalised error | +| **GaussianNLL** | Bayesian inference | + +### Typical Workflows + +**Simple Optimisation:** +``` +ScalarBuilder → optimise() → Result +``` + +**ODE Fitting:** +``` +DiffsolBuilder → CMAES → Result → Visualise +``` + +**Uncertainty Quantification:** +``` +DiffsolBuilder + GaussianNLL → Optimise → MCMC → Posterior +``` + +**Model Comparison:** +``` +Multiple models + GaussianNLL → Nested Sampling → Evidence → Bayes factors +``` + +## See Also + +- [Getting Started](../getting-started/index.md) - Core concepts and installation +- [Tutorials](../tutorials/index.md) - Interactive notebooks +- [API Reference](../api-reference/index.md) - Complete API documentation +- [Algorithms](../algorithms/index.md) - Algorithm-specific documentation diff --git a/docs/guides/parallel-execution.md b/docs/guides/parallel-execution.md new file mode 100644 index 0000000..8882c80 --- /dev/null +++ b/docs/guides/parallel-execution.md @@ -0,0 +1,25 @@ +# Parallel Execution Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for parallelisation strategies. + +## Quick Tips + +- **DiffsolBuilder**: Automatically multi-threaded +- **CMA-ES**: Parallel population evaluation +- **Dynamic Nested Sampling**: Parallel live point evaluation + +## Controlling Threads + +```python +# Limit threads for ODE solving +builder = builder.with_max_threads(4) + +# Population size for CMA-ES +optimiser = chron.CMAES().with_population_size(20) +``` + +## See Also + +- [DiffsolBuilder API](../api-reference/python/builders.md#diffsolbuilder) +- [CMA-ES API](../api-reference/python/optimizers.md#cma-es) diff --git a/docs/guides/troubleshooting.md b/docs/guides/troubleshooting.md new file mode 100644 index 0000000..8ad3ab6 --- /dev/null +++ b/docs/guides/troubleshooting.md @@ -0,0 +1,36 @@ +# Troubleshooting Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for common issues and solutions. + +## Quick Fixes + +### Import Error + +```python +ImportError: No module named 'chronopt' +``` + +**Solution**: Install Chronopt: `pip install chronopt` + +### Poor Fit Quality + +**Solutions:** +1. Try different optimisers (CMA-ES is often more robust) +2. Increase iterations: `.with_max_iter(10000)` +3. Check initial conditions +4. Normalise data if scales vary widely + +### Slow Performance + +**Solutions:** +1. Use CMA-ES for parallelisation +2. Reduce data points if possible +3. Use sparse backend for large ODE systems +4. Profile with `py-spy` to find bottlenecks + +## See Also + +- [Installation](../getting-started/installation.md) +- [Tuning Optimisers](tuning-optimizers.md) +- [GitHub Issues](https://github.com/bradyplanden/chronopt/issues) diff --git a/docs/guides/tuning-optimizers.md b/docs/guides/tuning-optimizers.md new file mode 100644 index 0000000..bdf4d53 --- /dev/null +++ b/docs/guides/tuning-optimizers.md @@ -0,0 +1,29 @@ +# Tuning Optimisers + +!!! info "Coming Soon" + This guide is being written. Check back soon for parameter tuning strategies. + +## Quick Tips + +### Nelder-Mead + +- **step_size**: Start with 10-50% of parameter range +- **threshold**: Use 1e-6 for standard precision +- **max_iter**: Use `100 * n_parameters` as minimum + +### CMA-ES + +- **step_size**: Start with ~1/3 of expected parameter range +- **population_size**: Default formula works well, increase for more exploration +- **max_iter**: Each iteration evaluates `population_size` candidates + +### Adam + +- **step_size**: Most critical - try 0.1, 0.01, 0.001, 0.0001 +- **betas**: Defaults (0.9, 0.999) usually work well + +## See Also + +- [Optimisers API](../api-reference/python/optimizers.md) +- [Choosing an Optimiser](choosing-optimizer.md) +- [Troubleshooting](troubleshooting.md) diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..3178056 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,159 @@ +# Chronopt + +**chron**os-**opt**imum is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. + +## Why Chronopt? + +Optimisation-based workflows run the forward simulation thousands of times. A performance improvement on the process can produce results hours or days earlier. The Rust core provides a high-performance inference loop with fewer runtime errors, whilst quickly integrating into Python workflows. + +## Project Goals + +- **Speed and numerical accuracy** through a Rust core +- **Modular components** with informative diagnostics +- **Batteries-included experience** spanning optimisation, sampling, and plotting + +## Core Capabilities + +- **Gradient-free** (Nelder-Mead, CMA-ES) and **gradient-based** (Adam) optimisers with configurable convergence criteria +- **Multi-threaded differential equation fitting** via [DiffSL](https://github.com/martinjrobins/diffsl) with dense or sparse [Diffsol](https://github.com/martinjrobins/diffsol) backends +- **Customisable likelihood/cost metrics** and Monte-Carlo sampling for posterior exploration +- **Flexible integration** with state-of-the-art differential solvers, such as [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) + +## Quick Links + +
+ +- :material-clock-fast:{ .lg .middle } __5-Minute Quickstart__ + + --- + + Get started with Chronopt in 5 minutes with a simple scalar optimisation example. + + [:octicons-arrow-right-24: Quickstart](getting-started/quickstart.md) + +- :material-function:{ .lg .middle } __First ODE Fit__ + + --- + + Learn how to fit differential equations to data using DiffSL and Diffsol. + + [:octicons-arrow-right-24: ODE Fitting Tutorial](getting-started/first-ode-fit.md) + +- :material-book-open-variant:{ .lg .middle } __Core Concepts__ + + --- + + Understand the builder pattern, problem types, and optimiser vs sampler workflows. + + [:octicons-arrow-right-24: Concepts Guide](getting-started/concepts.md) + +- :material-code-tags:{ .lg .middle } __API Reference__ + + --- + + Browse the complete Python and Rust API documentation. + + [:octicons-arrow-right-24: API Reference](api-reference/index.md) + +
+ +## Installation + +Chronopt targets Python >= 3.11. Windows builds are currently marked experimental. + +=== "pip" + + ```bash + pip install chronopt + + # Optional extras for plotting + pip install "chronopt[plotting]" + ``` + +=== "uv" + + ```bash + uv pip install chronopt + + # Optional extras for plotting + uv pip install "chronopt[plotting]" + ``` + +## Example: Scalar Optimisation + +```python +import numpy as np +import chronopt as chron + +def rosenbrock(x): + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + +builder = ( + chron.ScalarBuilder() + .with_callable(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5) +) +problem = builder.build() +result = problem.optimise() + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +``` + +## Example: ODE Fitting + +```python +import numpy as np +import chronopt as chron + +# Logistic growth model in DiffSL +dsl = """ +in = [r, k] +r { 1 } k { 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) + +builder = ( + chron.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") +) +problem = builder.build() + +optimiser = chron.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 0.5]) + +print(result.x) +``` + +## Next Steps + +
+ +- [:material-school:{ .lg .middle } __Tutorials__](tutorials/index.md) + + Interactive Jupyter notebooks for hands-on learning + +- [:material-book-multiple:{ .lg .middle } __User Guides__](guides/index.md) + + In-depth guides on choosing and tuning algorithms + +- [:material-flask:{ .lg .middle } __Examples Gallery__](examples/gallery.md) + + Visual gallery of example applications + +- [:material-code-braces:{ .lg .middle } __Development__](development/index.md) + + Contributing, architecture, and building from source + +
diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 0000000..06dbf38 --- /dev/null +++ b/docs/javascripts/mathjax.js @@ -0,0 +1,16 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"]], + displayMath: [["\\[", "\\]"]], + processEscapes: true, + processEnvironments: true + }, + options: { + ignoreHtmlClass: ".*|", + processHtmlClass: "arithmatex" + } +}; + +document$.subscribe(() => { + MathJax.typesetPromise() +}) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md new file mode 100644 index 0000000..9120487 --- /dev/null +++ b/docs/tutorials/index.md @@ -0,0 +1,260 @@ +# Tutorials + +Interactive Jupyter notebooks for hands-on learning with Chronopt. + +## Learning Paths + +Follow these progressive learning paths based on your experience level and goals. + +### 🎯 Beginner Track + +Perfect for those new to Chronopt or optimisation: + +
+ +- **[1. Optimisation Basics](notebooks/01_optimization_basics.ipynb)** + + --- + + Learn scalar optimisation with the Rosenbrock function. Compare Nelder-Mead, CMA-ES, and Adam optimisers. + + **Topics:** ScalarBuilder, contour plots, optimiser comparison + **Runtime:** ~5 minutes + +- **[2. ODE Fitting with DiffSL](notebooks/02_ode_fitting_diffsol.ipynb)** + + --- + + Fit a logistic growth model to data using DiffSL and Diffsol. + + **Topics:** DiffsolBuilder, DiffSL syntax, parameter fitting + **Runtime:** ~10 minutes + +
+ +### 🚀 Intermediate Track + +Building on the basics with real-world applications: + +
+ +- **[3. Parameter Uncertainty](notebooks/03_parameter_uncertainty.ipynb)** + + --- + + Go from optimisation to MCMC sampling. Quantify parameter uncertainty with confidence intervals. + + **Topics:** Metropolis-Hastings, posterior distributions, diagnostics + **Runtime:** ~15 minutes + +- **[4. Model Comparison](notebooks/04_model_comparison.ipynb)** ⚠️ *Coming Soon* + + --- + + Use Dynamic Nested Sampling to calculate model evidence and Bayes factors. + + **Topics:** Evidence calculation, Bayes factors, model selection + **Runtime:** ~20 minutes + +
+ +### 🔬 Advanced Track + +Complex problems and advanced techniques: + +
+ +- **[5. Multi-Backend ODE Solving](notebooks/05_advanced_predator_prey.ipynb)** + + --- + + Compare Diffsol, JAX/Diffrax, and Julia/DifferentialEquations.jl for predator-prey models. + + **Topics:** VectorBuilder, backend comparison, custom solvers + **Runtime:** ~20 minutes + +
+ +## Quick Reference + +| Tutorial | Difficulty | Key Concepts | Prerequisites | +|----------|------------|--------------|---------------| +| 1. Optimization Basics | ⭐ Beginner | ScalarBuilder, optimizers | None | +| 2. ODE Fitting | ⭐ Beginner | DiffsolBuilder, DiffSL | Tutorial 1 | +| 3. Parameter Uncertainty | ⭐⭐ Intermediate | MCMC, uncertainty | Tutorials 1-2 | +| 4. Model Comparison | ⭐⭐ Intermediate | Nested sampling, evidence | Tutorials 1-3 | +| 5. Multi-Backend | ⭐⭐⭐ Advanced | VectorBuilder, JAX, Julia | Tutorials 1-2 | + +## Prerequisites + +### All Tutorials +- Python >= 3.11 +- Basic Python programming +- NumPy fundamentals +- Jupyter notebook environment + +### Additional for Specific Tutorials +- **Tutorials 3-4**: Basic Bayesian statistics +- **Tutorial 5**: JAX or Julia knowledge (optional) + +## Running the Notebooks + +### Installation + +Install Chronopt with Jupyter and plotting support: + +=== "pip" + + ```bash + pip install chronopt jupyter matplotlib + ``` + +=== "uv" + + ```bash + uv pip install chronopt jupyter matplotlib + ``` + +### Clone and Run + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/chronopt.git +cd chronopt/docs/tutorials/notebooks + +# Launch Jupyter +jupyter notebook +``` + +### Google Colab + +You can also run these notebooks in Google Colab (coming soon with hosted versions). + +## Learning Outcomes + +By completing all tutorials, you will: + +✅ Understand Chronopt's builder pattern and API +✅ Optimize both scalar functions and ODE parameters +✅ Compare and tune different optimisation algorithms +✅ Quantify parameter uncertainty with MCMC +✅ Compare models using Bayesian evidence +✅ Integrate custom ODE solvers (JAX, Julia) +✅ Make informed decisions about algorithm selection + +## Notebook Structure + +Each tutorial follows a consistent structure: + +1. **Learning Objectives** - What you'll learn +2. **Prerequisites** - Required background +3. **Introduction** - Problem context and motivation +4. **Step-by-Step Code** - Fully explained examples +5. **Visualizations** - Plots and diagnostics +6. **Key Takeaways** - Summary of main points +7. **Exercises** - Practice problems +8. **Next Steps** - Links to related content + +## Alternative: Python Scripts + +Prefer scripts to notebooks? Check out the [examples directory](https://github.com/bradyplanden/chronopt/tree/main/examples): + +- `python_problem.py` - Basic scalar optimisation +- `logistic_growth.py` - ODE fitting +- `bouncy_ball.py` / `bouncy_ball_sampling.py` - Optimisation and MCMC +- `bicycle_model_evidence.py` - Model comparison +- `predator_prey/` - Multi-backend comparisons + +## Utilities + +The notebooks use shared utilities in `utils.py`: + +- `plot_contour_2d()` - 2D function contours +- `plot_ode_fit()` - ODE fits and data +- `plot_convergence()` - Optimisation history +- `plot_parameter_traces()` - MCMC traces +- `plot_parameter_distributions()` - Posterior histograms +- `compare_models()` - Multi-model plots + +Feel free to reuse these in your own projects! + +## Troubleshooting + +### Import Errors + +```python +ModuleNotFoundError: No module named 'chronopt' +``` + +**Solution**: Install Chronopt: `pip install chronopt` + +### Notebook Kernel Issues + +If the notebook doesn't recognize installed packages: + +1. Install packages in the correct environment +2. Restart the Jupyter kernel: *Kernel → Restart* +3. Check kernel selection: *Kernel → Change Kernel* + +### Missing Dependencies + +Some notebooks require optional dependencies: + +```bash +# For plotting +pip install matplotlib + +# For Tutorial 5 (optional backends) +pip install jax diffrax # JAX/Diffrax +pip install diffeqpy # Julia +``` + +### Performance Issues + +If MCMC sampling is slow: + +- Reduce number of chains or iterations +- Enable parallel execution: `.with_parallel(True)` +- Use fewer data points for testing + +## Getting Help + +- **Documentation**: Browse the [complete docs](../index.md) +- **Examples**: See the [examples gallery](../examples/gallery.md) +- **API Reference**: Check the [API docs](../api-reference/index.md) +- **Issues**: Report problems on [GitHub](https://github.com/bradyplanden/chronopt/issues) + +## Contributing + +Found an issue or want to improve a tutorial? + +1. Fork the [repository](https://github.com/bradyplanden/chronopt) +2. Edit notebooks in `docs/tutorials/notebooks/` +3. Test your changes locally +4. Submit a pull request + +See the [Contributing Guide](../development/contributing.md) for details. + +## Next Steps + +After completing the tutorials: + +
+ +- [:material-book-multiple:{ .lg .middle } __User Guides__](../guides/index.md) + + In-depth guides on choosing and tuning algorithms + +- [:material-code-tags:{ .lg .middle } __API Reference__](../api-reference/index.md) + + Complete API documentation + +- [:material-flask:{ .lg .middle } __Examples Gallery__](../examples/gallery.md) + + More applications and use cases + +- [:material-github:{ .lg .middle } __GitHub Repository__](https://github.com/bradyplanden/chronopt) + + Source code and development + +
diff --git a/docs/tutorials/notebooks/01_optimization_basics.ipynb b/docs/tutorials/notebooks/01_optimization_basics.ipynb new file mode 100644 index 0000000..cb76af8 --- /dev/null +++ b/docs/tutorials/notebooks/01_optimization_basics.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 1: Optimization Basics\n", + "\n", + "**Learning Objectives:**\n", + "- Understand the ScalarBuilder API\n", + "- Optimize the classic Rosenbrock function\n", + "- Visualize optimization landscapes with contour plots\n", + "- Compare different optimizers\n", + "\n", + "**Prerequisites:** Basic Python, NumPy\n", + "\n", + "**Runtime:** ~5 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The Rosenbrock function is a classic test problem in optimization. It's defined as:\n", + "\n", + "$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$\n", + "\n", + "The global minimum is at $(1, 1)$ with $f(1, 1) = 0$. Despite being simple to state, it's challenging for optimizers because of its narrow, curved valley." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import sys\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import plot_contour_2d, setup_plotting\n", + "\n", + "setup_plotting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the Objective Function\n", + "\n", + "In Chronopt, objective functions must:\n", + "1. Accept a NumPy array as input\n", + "2. Return a NumPy array as output (even for scalar values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def rosenbrock(x):\n", + " \"\"\"The Rosenbrock function - a classic optimization test problem.\"\"\"\n", + " value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n", + " return np.array([value], dtype=float)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build the Problem\n", + "\n", + "Use `ScalarBuilder` for direct function optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "builder = (\n", + " chron.ScalarBuilder()\n", + " .with_callable(rosenbrock)\n", + " .with_parameter(\"x\", 1.0) # Initial guess\n", + " .with_parameter(\"y\", 1.0) # Initial guess\n", + ")\n", + "problem = builder.build()\n", + "\n", + "print(\"Problem built successfully!\")\n", + "print(\"Number of parameters: 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimize with Default Settings\n", + "\n", + "The `optimise()` method uses Nelder-Mead by default:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = problem.optimise(initial=[10.0, 10.0])\n", + "\n", + "print(\"\\n\" + \"=\" * 50)\n", + "print(\"OPTIMIZATION RESULTS\")\n", + "print(\"=\" * 50)\n", + "print(f\"Success: {result.success}\")\n", + "print(f\"Optimal parameters: {result.x}\")\n", + "print(f\"Objective value: {result.value:.3e}\")\n", + "print(f\"Iterations: {result.iterations}\")\n", + "print(f\"Function evaluations: {result.evaluations}\")\n", + "print(f\"Message: {result.message}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Optimization Landscape\n", + "\n", + "Let's create a contour plot to see the function's shape:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create contour plot\n", + "fig, ax = plot_contour_2d(\n", + " rosenbrock,\n", + " xlim=(-2, 2),\n", + " ylim=(-1, 3),\n", + " levels=np.logspace(-1, 3.5, 20),\n", + " optimum=(1.0, 1.0),\n", + " found=(result.x[0], result.x[1]),\n", + " title=\"Rosenbrock Function Landscape\",\n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "print(f\"Distance from true optimum: {np.linalg.norm(result.x - [1.0, 1.0]):.3e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare Different Optimizers\n", + "\n", + "Let's compare Nelder-Mead, CMA-ES, and Adam:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define optimizers\n", + "optimizers = {\n", + " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", + " \"CMA-ES\": chron.CMAES().with_max_iter(300).with_step_size(0.5),\n", + " \"Adam\": chron.Adam().with_max_iter(1000).with_step_size(0.01),\n", + "}\n", + "\n", + "# Test starting point\n", + "initial_guess = [-1.5, -0.5]\n", + "\n", + "# Run all optimizers\n", + "results = {}\n", + "for name, optimizer in optimizers.items():\n", + " result = optimizer.run(problem, initial_guess)\n", + " results[name] = result\n", + "\n", + "# Display comparison\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"OPTIMIZER COMPARISON\")\n", + "print(\"=\" * 70)\n", + "print(\n", + " f\"{'Optimizer':<15} {'Success':<10} {'Final Value':<15} {'Iterations':<12} {'Evaluations'}\"\n", + ")\n", + "print(\"-\" * 70)\n", + "\n", + "for name, result in results.items():\n", + " print(\n", + " f\"{name:<15} {str(result.success):<10} {result.value:<15.3e} \"\n", + " f\"{result.iterations:<12} {result.evaluations}\"\n", + " )\n", + "\n", + "print(\"\\nFinal parameters:\")\n", + "for name, result in results.items():\n", + " print(f\"{name:<15} x = {result.x}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize All Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create contour plot with all optimizers' results\n", + "x = np.linspace(-2, 2, 200)\n", + "y = np.linspace(-1, 3, 200)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z = np.zeros_like(X)\n", + "\n", + "for i in range(X.shape[0]):\n", + " for j in range(X.shape[1]):\n", + " Z[i, j] = rosenbrock([X[i, j], Y[i, j]])[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 9))\n", + "\n", + "# Plot contours\n", + "levels = np.logspace(-1, 3.5, 20)\n", + "cs = ax.contour(X, Y, Z, levels=levels, cmap=\"viridis\", alpha=0.6)\n", + "ax.clabel(cs, inline=True, fontsize=8)\n", + "\n", + "# Plot true optimum\n", + "ax.plot(1.0, 1.0, \"r*\", markersize=20, label=\"True minimum\", zorder=5)\n", + "\n", + "# Plot starting point\n", + "ax.plot(\n", + " initial_guess[0],\n", + " initial_guess[1],\n", + " \"kx\",\n", + " markersize=15,\n", + " markeredgewidth=3,\n", + " label=\"Starting point\",\n", + " zorder=5,\n", + ")\n", + "\n", + "# Plot optimizer results\n", + "colors = {\"Nelder-Mead\": \"blue\", \"CMA-ES\": \"green\", \"Adam\": \"orange\"}\n", + "markers = {\"Nelder-Mead\": \"o\", \"CMA-ES\": \"s\", \"Adam\": \"^\"}\n", + "\n", + "for name, result in results.items():\n", + " ax.plot(\n", + " result.x[0],\n", + " result.x[1],\n", + " marker=markers[name],\n", + " color=colors[name],\n", + " markersize=12,\n", + " label=name,\n", + " zorder=5,\n", + " markeredgecolor=\"black\",\n", + " markeredgewidth=1.5,\n", + " )\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=12)\n", + "ax.set_ylabel(\"y\", fontsize=12)\n", + "ax.set_title(\"Optimizer Comparison on Rosenbrock Function\", fontsize=14)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"upper left\", fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of Starting Point\n", + "\n", + "Let's see how starting position affects convergence:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try multiple starting points\n", + "starting_points = [\n", + " [-1.5, -0.5],\n", + " [1.5, 1.5],\n", + " [0.0, 2.0],\n", + " [-1.0, 1.0],\n", + "]\n", + "\n", + "optimizer = chron.NelderMead().with_max_iter(1000)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"STARTING POINT SENSITIVITY\")\n", + "print(\"=\" * 60)\n", + "\n", + "for i, start in enumerate(starting_points, 1):\n", + " result = optimizer.run(problem, start)\n", + " error = np.linalg.norm(result.x - [1.0, 1.0])\n", + "\n", + " print(f\"\\nStart {i}: {start}\")\n", + " print(f\" Final: {result.x}\")\n", + " print(f\" Iterations: {result.iterations}\")\n", + " print(f\" Error: {error:.3e}\")\n", + " print(f\" Success: {result.success}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **ScalarBuilder** is used for direct function optimization\n", + "2. **Nelder-Mead** is the default optimizer - good for small problems\n", + "3. **CMA-ES** is more robust for global search but uses more evaluations\n", + "4. **Adam** can be fast on smooth problems but may struggle on complex landscapes\n", + "5. Starting point can significantly affect convergence speed\n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 2: ODE Fitting with DiffSL](02_ode_fitting_diffsol.ipynb) - Learn how to fit differential equations\n", + "- [Choosing an Optimizer](../../guides/choosing-optimizer.md) - Detailed optimizer selection guide\n", + "- [API Reference: Optimizers](../../api-reference/python/optimizers.md) - Complete API documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "Try these challenges:\n", + "\n", + "1. **Rastrigin Function**: Implement and optimize:\n", + " $$f(x, y) = 20 + x^2 + y^2 - 10(\\cos(2\\pi x) + \\cos(2\\pi y))$$\n", + " \n", + "2. **Parameter Tuning**: Experiment with CMA-ES `step_size` parameter (try 0.1, 0.5, 1.0, 2.0)\n", + "\n", + "3. **Constraint Handling**: How would you restrict the search to $x, y \\in [-2, 2]$?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb new file mode 100644 index 0000000..8128d1d --- /dev/null +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -0,0 +1,487 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 2: ODE Fitting with DiffSL\n", + "\n", + "**Learning Objectives:**\n", + "- Understand the DiffsolBuilder API\n", + "- Learn DiffSL syntax for defining ODEs\n", + "- Fit parameters of a logistic growth model\n", + "- Visualize model fits and residuals\n", + "\n", + "**Prerequisites:** Basic differential equations, NumPy\n", + "\n", + "**Runtime:** ~10 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The logistic growth model describes population dynamics with limited resources:\n", + "\n", + "$$\\frac{dy}{dt} = r \\cdot y \\cdot \\left(1 - \\frac{y}{k}\\right)$$\n", + "\n", + "where:\n", + "- $y$ is the population\n", + "- $r$ is the growth rate\n", + "- $k$ is the carrying capacity (maximum population)\n", + "\n", + "This tutorial demonstrates parameter fitting using experimental or simulated data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import sys\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import plot_ode_fit, setup_plotting\n", + "\n", + "setup_plotting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the ODE in DiffSL\n", + "\n", + "DiffSL (Differential Specification Language) is a concise syntax for ODEs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsl_model = \"\"\"\n", + "in = [r, k]\n", + "r { 1 } k { 1 }\n", + "u_i { y = 0.1 }\n", + "F_i { (r * y) * (1 - (y / k)) }\n", + "\"\"\"\n", + "\n", + "print(\"DiffSL Model Definition:\")\n", + "print(\"=\" * 50)\n", + "print(dsl_model)\n", + "print(\"\\nExplanation:\")\n", + "print(\" in = [r, k] → Parameters to fit\")\n", + "print(\" r { 1 } k { 1 } → Default parameter values\")\n", + "print(\" u_i { y = 0.1 } → Initial condition: y(0) = 0.1\")\n", + "print(\" F_i { ... } → Right-hand side: dy/dt = ...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Synthetic Data\n", + "\n", + "For this tutorial, we'll create synthetic data from the logistic function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Time points\n", + "t_span = np.linspace(0, 1, 100)\n", + "\n", + "# True logistic growth solution with known parameters\n", + "# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\n", + "y0 = 0.1\n", + "r_true = 1.0\n", + "k_true = 1.0\n", + "\n", + "# Generate clean data\n", + "exp_rt = np.exp(r_true * t_span)\n", + "y_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n", + "\n", + "# Add some noise\n", + "np.random.seed(42)\n", + "noise_level = 0.02\n", + "y_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n", + "\n", + "# Chronopt expects data as [time, observation] columns\n", + "data = np.column_stack((t_span, y_noisy))\n", + "\n", + "print(f\"Generated {len(t_span)} data points\")\n", + "print(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\n", + "print(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\n", + "print(f\"Noise level: {noise_level}\")\n", + "print(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plot_ode_fit(\n", + " t_span,\n", + " y_noisy,\n", + " t_span,\n", + " y_data,\n", + " title=\"Synthetic Logistic Growth Data\",\n", + " xlabel=\"Time\",\n", + " ylabel=\"Population\",\n", + ")\n", + "\n", + "# Update legend\n", + "ax.legend([\"Noisy observations\", \"True solution\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build the Optimization Problem\n", + "\n", + "Use `DiffsolBuilder` for ODE parameter fitting:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create builder\n", + "builder = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(dsl_model)\n", + " .with_data(data)\n", + " .with_tolerances(1e-6, 1e-8) # Relative and absolute tolerances\n", + " .with_parameter(\"r\", 100.0) # Initial guess (deliberately wrong)\n", + " .with_parameter(\"k\", 100.0) # Initial guess (deliberately wrong)\n", + " .with_parallel(True) # Enable parallel evaluation\n", + ")\n", + "\n", + "problem = builder.build()\n", + "\n", + "print(\"Problem built successfully!\")\n", + "print(\"\\nInitial parameter guesses: r = 100.0, k = 100.0\")\n", + "print(f\"(Far from true values: r = {r_true}, k = {k_true})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimize with CMA-ES\n", + "\n", + "CMA-ES works well for ODE parameter fitting:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create optimizer\n", + "optimizer = chron.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", + "\n", + "# Run optimization\n", + "result = optimizer.run(problem, [100.0, 100.0])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"OPTIMIZATION RESULTS\")\n", + "print(\"=\" * 60)\n", + "print(f\"Success: {result.success}\")\n", + "print(\"\\nFitted parameters:\")\n", + "print(f\" r = {result.x[0]:.6f} (true: {r_true})\")\n", + "print(f\" k = {result.x[1]:.6f} (true: {k_true})\")\n", + "print(\"\\nOptimization details:\")\n", + "print(f\" Final cost: {result.value:.3e}\")\n", + "print(f\" Iterations: {result.iterations}\")\n", + "print(f\" Function evaluations: {result.evaluations}\")\n", + "print(f\" Time: {result.time:.2f} seconds\")\n", + "print(f\" Message: {result.message}\")\n", + "\n", + "# Calculate parameter errors\n", + "r_error = abs(result.x[0] - r_true) / r_true * 100\n", + "k_error = abs(result.x[1] - k_true) / k_true * 100\n", + "print(\"\\nParameter errors:\")\n", + "print(f\" r: {r_error:.3f}%\")\n", + "print(f\" k: {k_error:.3f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Fit\n", + "\n", + "Let's plot the fitted model against the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate predictions with fitted parameters\n", + "r_fit, k_fit = result.x\n", + "exp_rt_fit = np.exp(r_fit * t_span)\n", + "y_fit = y0 * exp_rt_fit / (1 + y0 * (exp_rt_fit - 1) / k_fit)\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(2, 1, figsize=(10, 10))\n", + "\n", + "# Top: Data and fits\n", + "ax1 = axes[0]\n", + "ax1.plot(t_span, y_noisy, \"o\", label=\"Noisy data\", alpha=0.6, markersize=6)\n", + "ax1.plot(t_span, y_data, \"--\", label=\"True solution\", linewidth=2, alpha=0.7)\n", + "ax1.plot(t_span, y_fit, \"-\", label=\"Fitted model\", linewidth=2)\n", + "ax1.set_xlabel(\"Time\")\n", + "ax1.set_ylabel(\"Population\")\n", + "ax1.set_title(\"Logistic Growth Model Fit\")\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# Bottom: Residuals\n", + "ax2 = axes[1]\n", + "residuals = y_noisy - y_fit\n", + "ax2.plot(t_span, residuals, \"o-\", alpha=0.6)\n", + "ax2.axhline(0, color=\"r\", linestyle=\"--\", linewidth=2, alpha=0.7)\n", + "ax2.set_xlabel(\"Time\")\n", + "ax2.set_ylabel(\"Residuals\")\n", + "ax2.set_title(\"Fitting Residuals\")\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Add statistics\n", + "rmse = np.sqrt(np.mean(residuals**2))\n", + "ax2.text(\n", + " 0.02,\n", + " 0.98,\n", + " f\"RMSE = {rmse:.4f}\",\n", + " transform=ax2.transAxes,\n", + " verticalalignment=\"top\",\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\nFit quality:\")\n", + "print(f\" RMSE: {rmse:.6f}\")\n", + "print(f\" Max residual: {np.abs(residuals).max():.6f}\")\n", + "print(f\" Mean residual: {np.mean(residuals):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Different Initial Guesses\n", + "\n", + "Let's see how different starting points affect convergence:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try several initial guesses\n", + "initial_guesses = [\n", + " [0.5, 0.5],\n", + " [2.0, 2.0],\n", + " [10.0, 10.0],\n", + " [100.0, 100.0],\n", + "]\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"INITIAL GUESS SENSITIVITY\")\n", + "print(\"=\" * 70)\n", + "print(f\"{'Initial [r, k]':<20} {'Final [r, k]':<30} {'Iterations':<12} {'Success'}\")\n", + "print(\"-\" * 70)\n", + "\n", + "for guess in initial_guesses:\n", + " result = optimizer.run(problem, guess)\n", + " final_str = f\"[{result.x[0]:.4f}, {result.x[1]:.4f}]\"\n", + " guess_str = f\"{guess}\"\n", + " print(f\"{guess_str:<20} {final_str:<30} {result.iterations:<12} {result.success}\")\n", + "\n", + "print(f\"\\nTrue values: [r, k] = [{r_true}, {k_true}]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Optimizers\n", + "\n", + "Let's compare different optimization algorithms:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "optimizers = {\n", + " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", + " \"CMA-ES\": chron.CMAES().with_max_iter(500),\n", + " \"Adam\": chron.Adam().with_max_iter(1000).with_step_size(0.01),\n", + "}\n", + "\n", + "initial = [2.0, 2.0]\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"OPTIMIZER COMPARISON\")\n", + "print(\"=\" * 80)\n", + "print(\n", + " f\"{'Algorithm':<15} {'r (fitted)':<15} {'k (fitted)':<15} {'Cost':<15} {'Iters':<8} {'Time (s)'}\"\n", + ")\n", + "print(\"-\" * 80)\n", + "\n", + "for name, opt in optimizers.items():\n", + " result = opt.run(problem, initial)\n", + " print(\n", + " f\"{name:<15} {result.x[0]:<15.6f} {result.x[1]:<15.6f} \"\n", + " f\"{result.value:<15.3e} {result.iterations:<8} {result.time:.3f}\"\n", + " )\n", + "\n", + "print(f\"\\nTrue values: {r_true:<15.6f} {k_true:<15.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding DiffSL Syntax\n", + "\n", + "Let's explore more complex DiffSL examples:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example 1: Multiple state variables (Lotka-Volterra)\n", + "predator_prey = \"\"\"\n", + "in = [alpha, beta, gamma, delta]\n", + "alpha { 1 } beta { 0.1 } gamma { 1.5 } delta { 0.075 }\n", + "u_i {\n", + " prey = 10.0\n", + " predator = 5.0\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator\n", + "}\n", + "out_i { prey, predator }\n", + "\"\"\"\n", + "\n", + "print(\"Example: Predator-Prey Model (Lotka-Volterra)\")\n", + "print(\"=\" * 50)\n", + "print(predator_prey)\n", + "\n", + "# Example 2: With algebraic equations\n", + "with_algebra = \"\"\"\n", + "in = [k1, k2]\n", + "k1 { 0.5 } k2 { 0.3 }\n", + "u_i { A = 1.0 }\n", + "F_i {\n", + " -k1 * A,\n", + " k1 * A - k2 * B\n", + "}\n", + "out_i { A, B }\n", + "\"\"\"\n", + "\n", + "print(\"\\nExample: Sequential Reactions (A → B → C)\")\n", + "print(\"=\" * 50)\n", + "print(with_algebra)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **DiffsolBuilder** is used for ODE parameter fitting\n", + "2. **DiffSL** provides concise ODE syntax: `in`, `u_i`, `F_i`\n", + "3. Data format: `[time, observation(s)]` in columns\n", + "4. **CMA-ES** typically works well for ODE fitting\n", + "5. Parallel evaluation improves performance\n", + "6. Always check residuals to assess fit quality\n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 3: Parameter Uncertainty](03_parameter_uncertainty.ipynb) - Quantify parameter uncertainty with MCMC\n", + "- [DiffSL Backend Guide](../../guides/diffsol-backend.md) - Dense vs sparse solvers\n", + "- [Custom Solvers](../../guides/custom-solvers.md) - Using JAX/Diffrax or Julia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Add Noise**: Increase `noise_level` to 0.05 and see how it affects fitting\n", + "\n", + "2. **Different Model**: Implement exponential decay: $\\frac{dy}{dt} = -k \\cdot y$\n", + "\n", + "3. **Multiple Variables**: Try fitting the predator-prey model with synthetic data\n", + "\n", + "4. **Sparse Data**: Reduce the number of data points (e.g., 20 instead of 100) and observe the impact" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb new file mode 100644 index 0000000..cca4a3c --- /dev/null +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 3: Parameter Uncertainty\n", + "\n", + "**Learning Objectives:**\n", + "- Go from optimization to uncertainty quantification\n", + "- Use MCMC sampling to explore parameter distributions\n", + "- Interpret MCMC diagnostics and traces\n", + "- Calculate confidence intervals\n", + "\n", + "**Prerequisites:** Tutorials 1-2, basic Bayesian statistics\n", + "\n", + "**Runtime:** ~15 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Optimization gives us a **single best** parameter estimate. But how **certain** are we about these values?\n", + "\n", + "MCMC (Markov Chain Monte Carlo) sampling explores the full **posterior distribution**, letting us:\n", + "- Quantify parameter uncertainty\n", + "- Calculate confidence intervals\n", + "- Detect parameter correlations\n", + "- Make probabilistic predictions\n", + "\n", + "We'll use a bouncy ball physics model as our example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import sys\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import plot_parameter_distributions, plot_parameter_traces, setup_plotting\n", + "\n", + "setup_plotting()\n", + "np.random.seed(42) # For reproducibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Physics Problem: Falling Ball\n", + "\n", + "A ball falls from height $h$ with gravitational acceleration $g$:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= v \\\\\n", + "\\frac{dv}{dt} &= -g\n", + "\\end{aligned}$$\n", + "\n", + "where:\n", + "- $x$ is height\n", + "- $v$ is velocity \n", + "- $g$ is gravitational acceleration (Earth: ~9.81 m/s²)\n", + "- $h$ is initial height\n", + "\n", + "**Task**: Estimate $g$ and $h$ from noisy observations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ball_states(t, g, h):\n", + " \"\"\"Analytical solution for ball trajectory.\"\"\"\n", + " height = h - 0.5 * g * t**2\n", + " height = np.maximum(height, 0.0) # Can't go below ground\n", + " velocity = -g * t\n", + " return height, velocity\n", + "\n", + "\n", + "# True parameters\n", + "g_true = 9.81 # m/s²\n", + "h_true = 10.0 # meters\n", + "\n", + "# Time to hit ground: t = sqrt(2h/g)\n", + "t_stop = np.sqrt(2.0 * h_true / g_true)\n", + "t_final = 0.7 * t_stop # Stop before hitting ground\n", + "t_span = np.linspace(0.0, t_final, 61)\n", + "\n", + "# Generate clean data\n", + "height, velocity = ball_states(t_span, g_true, h_true)\n", + "\n", + "# Add measurement noise\n", + "noise_std = 0.1\n", + "height_noisy = height + np.random.normal(0, noise_std, len(t_span))\n", + "velocity_noisy = velocity + np.random.normal(0, noise_std, len(t_span))\n", + "\n", + "# Format for Chronopt: [time, height, velocity]\n", + "data = np.column_stack((t_span, height_noisy, velocity_noisy))\n", + "\n", + "print(f\"Generated {len(t_span)} observations\")\n", + "print(f\"Time span: [0, {t_final:.3f}] seconds\")\n", + "print(f\"Noise level: σ = {noise_std}\")\n", + "print(\"\\nTrue parameters:\")\n", + "print(f\" g = {g_true} m/s²\")\n", + "print(f\" h = {h_true} m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Height\n", + "axes[0].plot(t_span, height, \"--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "axes[0].plot(t_span, height_noisy, \"o\", label=\"Observed\", alpha=0.6)\n", + "axes[0].set_xlabel(\"Time (s)\")\n", + "axes[0].set_ylabel(\"Height (m)\")\n", + "axes[0].set_title(\"Ball Height\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Velocity\n", + "axes[1].plot(t_span, velocity, \"--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "axes[1].plot(t_span, velocity_noisy, \"o\", label=\"Observed\", alpha=0.6)\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Velocity (m/s)\")\n", + "axes[1].set_title(\"Ball Velocity\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the ODE Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DiffSL model for falling ball\n", + "dsl_model = \"\"\"\n", + "in = [g, h]\n", + "g { 1 } h { 1 }\n", + "u_i {x = h, v = 0}\n", + "F_i {v, -g}\n", + "stop {x}\n", + "\"\"\"\n", + "\n", + "print(\"DiffSL Model:\")\n", + "print(dsl_model)\n", + "print(\"\\nExplanation:\")\n", + "print(\" x = height, v = velocity\")\n", + "print(\" dx/dt = v (velocity determines height change)\")\n", + "print(\" dv/dt = -g (gravity accelerates downward)\")\n", + "print(\" stop {x} (terminate when height reaches zero)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Optimization\n", + "\n", + "First, find the maximum a posteriori (MAP) estimate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build problem\n", + "builder = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(dsl_model)\n", + " .with_data(data)\n", + " .with_parameter(\"g\", 5.0) # Initial guess\n", + " .with_parameter(\"h\", 5.0) # Initial guess\n", + " .with_cost(chron.RMSE(2.0)) # 2 observables (height + velocity)\n", + ")\n", + "\n", + "problem = builder.build()\n", + "\n", + "# Optimize\n", + "optimizer = chron.Adam().with_step_size(0.05).with_max_iter(1500)\n", + "opt_result = optimizer.run(problem, [5.0, 5.0])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"OPTIMIZATION RESULTS (MAP Estimate)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Success: {opt_result.success}\")\n", + "print(\"\\nFitted parameters:\")\n", + "print(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\n", + "print(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\n", + "print(f\"\\nCost: {opt_result.value:.6f}\")\n", + "print(f\"Iterations: {opt_result.iterations}\")\n", + "\n", + "g_map, h_map = opt_result.x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: MCMC Sampling\n", + "\n", + "Now explore the full posterior distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rebuild problem with GaussianNLL (required for sampling)\n", + "builder_sampling = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(dsl_model)\n", + " .with_data(data)\n", + " .with_parameter(\"g\", g_map) # Start from MAP\n", + " .with_parameter(\"h\", h_map)\n", + " .with_parallel(True)\n", + " .with_cost(chron.GaussianNLL(variance=noise_std**2))\n", + ")\n", + "\n", + "problem_sampling = builder_sampling.build()\n", + "\n", + "# Setup MCMC sampler\n", + "sampler = (\n", + " chron.MetropolisHastings()\n", + " .with_num_chains(100)\n", + " .with_iterations(1000)\n", + " .with_step_size(0.25)\n", + " .with_parallel(True)\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"MCMC SAMPLING\")\n", + "print(\"=\" * 60)\n", + "print(\"Chains: 100\")\n", + "print(\"Iterations per chain: 1000\")\n", + "print(\"Total samples: 100,000\")\n", + "print(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\n", + "print(\"\\nRunning MCMC... (this may take a minute)\")\n", + "\n", + "# Run sampling\n", + "mcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n", + "\n", + "print(\"\\nSampling complete!\")\n", + "print(f\"Samples shape: {mcmc_result.samples.shape}\")\n", + "print(f\"Acceptance rate: {mcmc_result.acceptance_rate:.3f}\")\n", + "print(\"Target acceptance: 0.20 - 0.40\")\n", + "\n", + "if mcmc_result.acceptance_rate < 0.15 or mcmc_result.acceptance_rate > 0.50:\n", + " print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", + " print(\" Consider adjusting step_size\")\n", + "else:\n", + " print(\"✓ Acceptance rate looks good!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze MCMC Results\n", + "\n", + "### Parameter Traces\n", + "\n", + "Trace plots show how parameters evolved during sampling:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot traces\n", + "fig, axes = plot_parameter_traces(\n", + " mcmc_result.samples, param_names=[\"g (m/s²)\", \"h (m)\"], true_values=[g_true, h_true]\n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "print(\"\\nWhat to look for in traces:\")\n", + "print(\" ✓ Good mixing (wiggly, no trends)\")\n", + "print(\" ✓ Stationary (mean stays constant)\")\n", + "print(\" ✓ No long excursions\")\n", + "print(\" ✓ Rapid exploration of parameter space\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Distributions\n", + "\n", + "Histograms show the posterior distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot distributions\n", + "fig, axes = plot_parameter_distributions(\n", + " mcmc_result.samples, param_names=[\"g (m/s²)\", \"h (m)\"], true_values=[g_true, h_true]\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Burn-in: discard first 20% of samples\n", + "burn_in = int(0.2 * len(mcmc_result.samples))\n", + "samples_burned = mcmc_result.samples[burn_in:]\n", + "\n", + "# Calculate statistics\n", + "g_samples = samples_burned[:, 0]\n", + "h_samples = samples_burned[:, 1]\n", + "\n", + "# Means and standard deviations\n", + "g_mean = np.mean(g_samples)\n", + "g_std = np.std(g_samples)\n", + "h_mean = np.mean(h_samples)\n", + "h_std = np.std(h_samples)\n", + "\n", + "# 95% credible intervals\n", + "g_ci = np.percentile(g_samples, [2.5, 97.5])\n", + "h_ci = np.percentile(h_samples, [2.5, 97.5])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"POSTERIOR STATISTICS (after burn-in)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Samples used: {len(samples_burned):,}\")\n", + "print(\"\\nGravitational acceleration (g):\")\n", + "print(f\" True value: {g_true:.4f} m/s²\")\n", + "print(f\" MAP: {g_map:.4f} m/s²\")\n", + "print(f\" Posterior: {g_mean:.4f} ± {g_std:.4f} m/s²\")\n", + "print(f\" 95% CI: [{g_ci[0]:.4f}, {g_ci[1]:.4f}]\")\n", + "print(\"\\nInitial height (h):\")\n", + "print(f\" True value: {h_true:.4f} m\")\n", + "print(f\" MAP: {h_map:.4f} m\")\n", + "print(f\" Posterior: {h_mean:.4f} ± {h_std:.4f} m\")\n", + "print(f\" 95% CI: [{h_ci[0]:.4f}, {h_ci[1]:.4f}]\")\n", + "\n", + "# Check if true values are in credible intervals\n", + "g_in_ci = g_ci[0] <= g_true <= g_ci[1]\n", + "h_in_ci = h_ci[0] <= h_true <= h_ci[1]\n", + "\n", + "print(\"\\nTrue values in 95% CI:\")\n", + "print(f\" g: {'✓' if g_in_ci else '✗'}\")\n", + "print(f\" h: {'✓' if h_in_ci else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter Correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scatter plot of joint distribution\n", + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "\n", + "# Hexbin plot for large sample sizes\n", + "hb = ax.hexbin(g_samples, h_samples, gridsize=50, cmap=\"Blues\", mincnt=1)\n", + "ax.plot(g_true, h_true, \"r*\", markersize=20, label=\"True values\", zorder=5)\n", + "ax.plot(g_map, h_map, \"go\", markersize=12, label=\"MAP estimate\", zorder=5)\n", + "ax.plot(g_mean, h_mean, \"mo\", markersize=12, label=\"Posterior mean\", zorder=5)\n", + "\n", + "ax.set_xlabel(\"g (m/s²)\", fontsize=12)\n", + "ax.set_ylabel(\"h (m)\", fontsize=12)\n", + "ax.set_title(\"Joint Posterior Distribution\", fontsize=14)\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.colorbar(hb, ax=ax, label=\"Sample density\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Calculate correlation\n", + "correlation = np.corrcoef(g_samples, h_samples)[0, 1]\n", + "print(f\"\\nParameter correlation: {correlation:.4f}\")\n", + "\n", + "if abs(correlation) > 0.7:\n", + " print(\" High correlation - parameters are not independently identifiable\")\n", + "elif abs(correlation) > 0.3:\n", + " print(\" Moderate correlation\")\n", + "else:\n", + " print(\" Low correlation - parameters are nearly independent\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior Predictive Distribution\n", + "\n", + "Use samples to make probabilistic predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Take random subset of samples for predictions\n", + "n_pred_samples = 200\n", + "sample_indices = np.random.choice(len(samples_burned), n_pred_samples, replace=False)\n", + "\n", + "# Generate predictions for each sample\n", + "predictions_height = []\n", + "predictions_velocity = []\n", + "\n", + "for idx in sample_indices:\n", + " g_sample, h_sample = samples_burned[idx]\n", + " h_pred, v_pred = ball_states(t_span, g_sample, h_sample)\n", + " predictions_height.append(h_pred)\n", + " predictions_velocity.append(v_pred)\n", + "\n", + "predictions_height = np.array(predictions_height)\n", + "predictions_velocity = np.array(predictions_velocity)\n", + "\n", + "# Calculate percentiles\n", + "h_median = np.median(predictions_height, axis=0)\n", + "h_lower = np.percentile(predictions_height, 2.5, axis=0)\n", + "h_upper = np.percentile(predictions_height, 97.5, axis=0)\n", + "\n", + "v_median = np.median(predictions_velocity, axis=0)\n", + "v_lower = np.percentile(predictions_velocity, 2.5, axis=0)\n", + "v_upper = np.percentile(predictions_velocity, 97.5, axis=0)\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Height\n", + "axes[0].fill_between(t_span, h_lower, h_upper, alpha=0.3, label=\"95% CI\")\n", + "axes[0].plot(t_span, h_median, \"-\", linewidth=2, label=\"Median prediction\")\n", + "axes[0].plot(t_span, height, \"--\", linewidth=2, label=\"True\", alpha=0.7)\n", + "axes[0].plot(t_span, height_noisy, \"o\", label=\"Observed\", alpha=0.4, markersize=4)\n", + "axes[0].set_xlabel(\"Time (s)\")\n", + "axes[0].set_ylabel(\"Height (m)\")\n", + "axes[0].set_title(\"Posterior Predictive: Height\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Velocity\n", + "axes[1].fill_between(t_span, v_lower, v_upper, alpha=0.3, label=\"95% CI\")\n", + "axes[1].plot(t_span, v_median, \"-\", linewidth=2, label=\"Median prediction\")\n", + "axes[1].plot(t_span, velocity, \"--\", linewidth=2, label=\"True\", alpha=0.7)\n", + "axes[1].plot(t_span, velocity_noisy, \"o\", label=\"Observed\", alpha=0.4, markersize=4)\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Velocity (m/s)\")\n", + "axes[1].set_title(\"Posterior Predictive: Velocity\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"The shaded region shows the 95% credible interval for predictions\")\n", + "print(\"This accounts for both parameter uncertainty and observation noise\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **Optimization** gives point estimates; **MCMC** quantifies uncertainty\n", + "2. **GaussianNLL** cost metric is required for sampling\n", + "3. **Acceptance rate** should be 20-40% for efficient exploration\n", + "4. **Burn-in** period discards initial non-stationary samples\n", + "5. **Credible intervals** provide uncertainty bounds\n", + "6. **Posterior predictive** distributions account for parameter uncertainty\n", + "\n", + "## MCMC Diagnostics Checklist\n", + "\n", + "✓ Acceptance rate in [0.2, 0.4] \n", + "✓ Trace plots show good mixing \n", + "✓ No trends in traces \n", + "✓ Distributions look reasonable \n", + "✓ True values in credible intervals \n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 4: Model Comparison](04_model_comparison.ipynb) - Use nested sampling for Bayes factors\n", + "- [Choosing a Sampler](../../guides/choosing-sampler.md) - MCMC vs Nested Sampling\n", + "- [API Reference: Samplers](../../api-reference/python/samplers.md)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Step Size**: Try different `step_size` values (0.1, 0.5, 1.0) and observe acceptance rates\n", + "\n", + "2. **More Data**: Increase the number of observations and see how uncertainty decreases\n", + "\n", + "3. **More Noise**: Increase `noise_std` to 0.5 and observe wider credible intervals\n", + "\n", + "4. **Longer Chains**: Run with 10,000 iterations per chain for better convergence" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/04_model_comparison.ipynb b/docs/tutorials/notebooks/04_model_comparison.ipynb new file mode 100644 index 0000000..b199346 --- /dev/null +++ b/docs/tutorials/notebooks/04_model_comparison.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 4: Model Comparison\n", + "\n", + "**Learning Objectives:**\n", + "- Use Dynamic Nested Sampling for model evidence\n", + "- Calculate Bayes factors for model comparison\n", + "- Interpret evidence values\n", + "- Compare bicycle dynamics models\n", + "\n", + "**Prerequisites:** Tutorials 1-3, Bayesian model comparison basics\n", + "\n", + "**Runtime:** ~20 minutes\n", + "\n", + "**Note:** This tutorial requires the Dynamic Nested Sampling feature. Coming soon!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "When you have multiple models, **which one is best?**\n", + "\n", + "**Dynamic Nested Sampling** calculates the **model evidence** (marginal likelihood), allowing rigorous Bayesian model comparison via **Bayes factors**.\n", + "\n", + "$$\\text{Bayes Factor} = \\frac{p(\\text{Data}|\\text{Model}_1)}{p(\\text{Data}|\\text{Model}_2)} = \\frac{Z_1}{Z_2}$$\n", + "\n", + "This tutorial will demonstrate model comparison using bicycle dynamics models.\n", + "\n", + "## The Problem: Bicycle Model\n", + "\n", + "A bicycle traveling at constant velocity $v$ with steer angle $\\delta$ follows curved motion determined by wheelbase $L$:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= v \\cos(\\theta) \\\\\n", + "\\frac{dy}{dt} &= v \\sin(\\theta) \\\\\n", + "\\frac{d\\theta}{dt} &= \\frac{v}{L} \\tan(\\delta)\n", + "\\end{aligned}$$\n", + "\n", + "**Task:** Estimate wheelbase $L$ and compare different model formulations.\n", + "\n", + "## Coming Soon\n", + "\n", + "This tutorial is under development. Check back soon for:\n", + "\n", + "- Setting up multiple model variants\n", + "- Running Dynamic Nested Sampling\n", + "- Calculating log evidence for each model\n", + "- Interpreting Bayes factors\n", + "- Making model selection decisions\n", + "\n", + "## Preview: Model Evidence Interpretation\n", + "\n", + "| $\\log(Z_1 / Z_2)$ | Bayes Factor | Evidence for Model 1 |\n", + "|-------------------|--------------|----------------------|\n", + "| < 0 | < 1 | Negative (prefer Model 2) |\n", + "| 0-1 | 1-3 | Barely worth mentioning |\n", + "| 1-2.5 | 3-12 | Positive |\n", + "| 2.5-5 | 12-150 | Strong |\n", + "| > 5 | > 150 | Very strong |\n", + "\n", + "## References\n", + "\n", + "- Skilling, J. (2006). Nested sampling for general Bayesian computation.\n", + "- Higson, E. et al. (2019). Dynamic nested sampling.\n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 5: Predator-Prey Models](05_advanced_predator_prey.ipynb) - Multi-backend comparison\n", + "- [Choosing a Sampler](../../guides/choosing-sampler.md)\n", + "- [API Reference: Samplers](../../api-reference/python/samplers.md)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb new file mode 100644 index 0000000..a1c40ab --- /dev/null +++ b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 5: Multi-Backend ODE Solving\n", + "\n", + "**Learning Objectives:**\n", + "- Use VectorBuilder for custom ODE solvers\n", + "- Compare Diffsol, Diffrax (JAX), and DifferentialEquations.jl\n", + "- Understand performance trade-offs\n", + "- Integrate external solvers with Chronopt\n", + "\n", + "**Prerequisites:** Tutorials 1-2, basic JAX or Julia knowledge (optional)\n", + "\n", + "**Runtime:** ~20 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Chronopt's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n", + "\n", + "- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n", + "- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n", + "- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n", + "\n", + "**VectorBuilder** lets you integrate any Python-callable forward model with Chronopt's optimizers.\n", + "\n", + "## The Lotka-Volterra Model\n", + "\n", + "The predator-prey equations:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n", + "\\frac{dy}{dt} &= \\delta x y - \\gamma y\n", + "\\end{aligned}$$\n", + "\n", + "where:\n", + "- $x$ is prey population\n", + "- $y$ is predator population\n", + "- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n", + "\n", + "This tutorial demonstrates parameter fitting with three different solver backends.\n", + "\n", + "## Coming Soon\n", + "\n", + "This advanced tutorial is under development. It will cover:\n", + "\n", + "### Backend 1: Diffsol (Built-in)\n", + "```python\n", + "builder = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(lotka_volterra_dsl)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + " # ... more parameters\n", + ")\n", + "```\n", + "\n", + "### Backend 2: JAX/Diffrax\n", + "```python\n", + "import jax\n", + "from diffrax import diffeqsolve, ODETerm, Tsit5\n", + "\n", + "def diffrax_solver(params):\n", + " # Your Diffrax integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " chron.VectorBuilder()\n", + " .with_callable(diffrax_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "### Backend 3: Julia/DifferentialEquations.jl\n", + "```python\n", + "from diffeqpy import de\n", + "\n", + "def diffeqpy_solver(params):\n", + " # Your Julia integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " chron.VectorBuilder()\n", + " .with_callable(diffeqpy_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "## Performance Comparison\n", + "\n", + "The tutorial will benchmark all three backends:\n", + "\n", + "- **Accuracy**: Parameter recovery quality\n", + "- **Speed**: Time per function evaluation\n", + "- **Ease of use**: Setup complexity\n", + "- **Special features**: Gradients, GPU, stiff solvers\n", + "\n", + "## When to Use Each Backend\n", + "\n", + "| Backend | Best For |\n", + "|---------|----------|\n", + "| **Diffsol** | General purpose, fast, built-in |\n", + "| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n", + "| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n", + "| **Custom** | Non-ODE models, complex physics |\n", + "\n", + "## Example Data\n", + "\n", + "The predator-prey examples directory contains:\n", + "- `generate_data_diffrax.py`: Creates synthetic data\n", + "- `predator_prey_diffsol.py`: Diffsol backend\n", + "- `predator_prey_diffrax.py`: JAX/Diffrax backend\n", + "- `predator_prey_diffeqpy.py`: Julia backend\n", + "\n", + "Run these scripts directly to see the backends in action!\n", + "\n", + "## Installation\n", + "\n", + "For JAX/Diffrax:\n", + "```bash\n", + "pip install jax diffrax\n", + "```\n", + "\n", + "For Julia/DifferentialEquations.jl:\n", + "```bash\n", + "pip install diffeqpy\n", + "python -c \"from diffeqpy import de; de.install()\"\n", + "```\n", + "\n", + "## Key Takeaways\n", + "\n", + "1. **VectorBuilder** integrates any Python callable\n", + "2. **Diffsol** is the default - fast and easy\n", + "3. **JAX/Diffrax** for gradients and GPU\n", + "4. **Julia/DiffEq** for specialized solvers\n", + "5. All backends work with Chronopt's optimizers\n", + "\n", + "## Next Steps\n", + "\n", + "- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n", + "- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n", + "- [Examples Gallery](../../examples/gallery.md) - More backend examples" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb new file mode 100644 index 0000000..d0b06c8 --- /dev/null +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 6: Custom Solver Integration\n", + "\n", + "**Learning Objectives:**\n", + "- Use VectorBuilder with custom ODE solvers\n", + "- Integrate JAX/Diffrax for GPU-accelerated solving\n", + "- Compare different solver backends (Diffsol, Diffrax, DifferentialEquations.jl)\n", + "- Understand performance trade-offs between solvers\n", + "\n", + "**Prerequisites:** Tutorials 1-2, familiarity with JAX (optional)\n", + "\n", + "**Runtime:** ~15 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "While `DiffsolBuilder` provides a convenient interface for ODE fitting using the DiffSL language, `VectorBuilder` offers maximum flexibility by allowing you to use **any** ODE solver. This enables:\n", + "\n", + "1. **GPU acceleration** via JAX/Diffrax\n", + "2. **Specialized solvers** from Julia's DifferentialEquations.jl\n", + "3. **Custom dynamics** that don't fit the DiffSL syntax\n", + "4. **Pre-existing code** integration\n", + "\n", + "In this tutorial, we'll solve the predator-prey model using different backends and compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import sys\n", + "import time\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import setup_plotting\n", + "\n", + "setup_plotting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Lotka-Volterra Model\n", + "\n", + "The predator-prey dynamics are described by:\n", + "\n", + "$$\\frac{dx}{dt} = \\alpha x - \\beta xy \\quad \\text{(prey growth and predation)}$$\n", + "\n", + "$$\\frac{dy}{dt} = \\delta xy - \\gamma y \\quad \\text{(predator growth and death)}$$\n", + "\n", + "where:\n", + "- $x$ = prey population\n", + "- $y$ = predator population \n", + "- $\\alpha$ = prey birth rate\n", + "- $\\beta$ = predation rate\n", + "- $\\delta$ = predator reproduction efficiency\n", + "- $\\gamma$ = predator death rate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 1: DiffsolBuilder (Baseline)\n", + "\n", + "First, let's solve it using the built-in DiffsolBuilder as a baseline:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate synthetic data\n", + "np.random.seed(42)\n", + "\n", + "# True parameters\n", + "true_params = {\n", + " \"alpha\": 1.1, # prey birth rate\n", + " \"beta\": 0.4, # predation rate\n", + " \"delta\": 0.1, # predator efficiency\n", + " \"gamma\": 0.4, # predator death rate\n", + "}\n", + "\n", + "# Time points\n", + "t_data = np.linspace(0, 15, 50)\n", + "\n", + "# DiffSL model\n", + "model_str = \"\"\"\n", + "in = [alpha, beta, delta, gamma]\n", + "x { 10.0 } = alpha * x - beta * x * y\n", + "y { 5.0 } = delta * x * y - gamma * y\n", + "out = [x, y]\n", + "\"\"\"\n", + "\n", + "# Generate \"true\" solution with noise\n", + "true_solution = (\n", + " chron.DiffsolBuilder()\n", + " .with_model(model_str)\n", + " .with_parameter(\"alpha\", true_params[\"alpha\"])\n", + " .with_parameter(\"beta\", true_params[\"beta\"])\n", + " .with_parameter(\"delta\", true_params[\"delta\"])\n", + " .with_parameter(\"gamma\", true_params[\"gamma\"])\n", + " .build()\n", + ")\n", + "\n", + "y_true = true_solution.predict(t_data)\n", + "y_observed = y_true + np.random.normal(0, 0.5, y_true.shape)\n", + "\n", + "print(f\"Data shape: {y_observed.shape}\")\n", + "print(f\"Time span: [{t_data[0]:.1f}, {t_data[-1]:.1f}]\")\n", + "print(f\"Number of observations: {len(t_data)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize data\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "ax.plot(t_data, y_observed[:, 0], \"o\", label=\"Prey (observed)\", alpha=0.6, markersize=6)\n", + "ax.plot(\n", + " t_data, y_observed[:, 1], \"s\", label=\"Predator (observed)\", alpha=0.6, markersize=6\n", + ")\n", + "ax.plot(t_data, y_true[:, 0], \"--\", label=\"Prey (true)\", linewidth=2, alpha=0.8)\n", + "ax.plot(t_data, y_true[:, 1], \"--\", label=\"Predator (true)\", linewidth=2, alpha=0.8)\n", + "\n", + "ax.set_xlabel(\"Time\", fontsize=12)\n", + "ax.set_ylabel(\"Population\", fontsize=12)\n", + "ax.set_title(\"Predator-Prey Dynamics: Synthetic Data\", fontsize=14)\n", + "ax.legend(fontsize=10)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit with DiffsolBuilder\n", + "start_time = time.time()\n", + "\n", + "result_diffsol = (\n", + " chron.DiffsolBuilder()\n", + " .with_model(model_str)\n", + " .with_times(t_data)\n", + " .with_data(y_observed)\n", + " .with_parameter(\"alpha\", 1.0) # initial guess\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.5)\n", + " .with_cost(chron.SSE())\n", + " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "diffsol_time = time.time() - start_time\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"DIFFSOL RESULTS\")\n", + "print(\"=\" * 60)\n", + "print(f\"True parameters: {list(true_params.values())}\")\n", + "print(f\"Estimated parameters: {result_diffsol.x}\")\n", + "print(f\"Final SSE: {result_diffsol.value:.6f}\")\n", + "print(f\"Iterations: {result_diffsol.iterations}\")\n", + "print(f\"Function evals: {result_diffsol.evaluations}\")\n", + "print(f\"Time: {diffsol_time:.3f}s\")\n", + "print(f\"Success: {result_diffsol.success}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 2: VectorBuilder with JAX/Diffrax\n", + "\n", + "`VectorBuilder` accepts any callable that maps parameters to predicted outputs. This enables using **JAX/Diffrax** for GPU-accelerated ODE solving with automatic differentiation.\n", + "\n", + "### Advantages:\n", + "- ⚡ GPU acceleration\n", + "- 🔥 JIT compilation\n", + "- 📐 Automatic differentiation (gradients for free)\n", + "- 🚀 Fast iteration for gradient-based optimisers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: install JAX/Diffrax if not already installed\n", + "# !pip install jax jaxlib diffrax\n", + "\n", + "try:\n", + " import diffrax as dfx\n", + " import jax\n", + " import jax.numpy as jnp\n", + " from jax import config, jit\n", + "\n", + " # Enable float64 precision\n", + " config.update(\"jax_enable_x64\", True)\n", + "\n", + " JAX_AVAILABLE = True\n", + "except ImportError:\n", + " JAX_AVAILABLE = False\n", + " print(\"⚠️ JAX/Diffrax not installed. Skipping GPU example.\")\n", + " print(\" Install with: pip install jax jaxlib diffrax\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if JAX_AVAILABLE:\n", + " # Define dynamics in JAX\n", + " def lotka_volterra_jax(t, state, params):\n", + " \"\"\"Lotka-Volterra dynamics in JAX.\"\"\"\n", + " x, y = state\n", + " alpha, beta, delta, gamma = params\n", + " return jnp.array(\n", + " [\n", + " alpha * x - beta * x * y, # prey\n", + " delta * x * y - gamma * y, # predator\n", + " ]\n", + " )\n", + "\n", + " # Configure ODE solver\n", + " solver = dfx.Tsit5() # Tsitouras 5(4) method\n", + " saveat = dfx.SaveAt(ts=t_data)\n", + " term = dfx.ODETerm(lotka_volterra_jax)\n", + "\n", + " # Extract bounds for JIT\n", + " t0, t1 = float(t_data[0]), float(t_data[-1])\n", + " dt0 = float(t_data[1] - t_data[0])\n", + " y0 = jnp.array([10.0, 5.0]) # initial state\n", + "\n", + " @jit\n", + " def simulate_jax(params):\n", + " \"\"\"JAX-native ODE integration (JIT-compiled).\"\"\"\n", + " sol = dfx.diffeqsolve(\n", + " term,\n", + " solver,\n", + " t0=t0,\n", + " t1=t1,\n", + " dt0=dt0,\n", + " y0=y0,\n", + " args=params,\n", + " saveat=saveat,\n", + " )\n", + " return sol.ys # Shape: (n_times, 2)\n", + "\n", + " def simulate_numpy(params):\n", + " \"\"\"NumPy wrapper for Chronopt compatibility.\"\"\"\n", + " return np.asarray(simulate_jax(jnp.asarray(params)))\n", + "\n", + " # Warm up JIT compiler\n", + " _ = simulate_numpy([1.0, 0.4, 0.1, 0.4])\n", + " print(\"✅ JAX/Diffrax solver ready (JIT compiled)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if JAX_AVAILABLE:\n", + " # Fit with VectorBuilder + JAX/Diffrax\n", + " start_time = time.time()\n", + "\n", + " result_diffrax = (\n", + " chron.VectorBuilder()\n", + " .with_objective(simulate_numpy)\n", + " .with_data(y_observed)\n", + " .with_parameter(\"alpha\", 1.0)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.5)\n", + " .with_cost(chron.SSE())\n", + " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", + " .build()\n", + " .optimise()\n", + " )\n", + "\n", + " diffrax_time = time.time() - start_time\n", + "\n", + " print(\"\\n\" + \"=\" * 60)\n", + " print(\"DIFFRAX (JAX) RESULTS\")\n", + " print(\"=\" * 60)\n", + " print(f\"True parameters: {list(true_params.values())}\")\n", + " print(f\"Estimated parameters: {result_diffrax.x}\")\n", + " print(f\"Final SSE: {result_diffrax.value:.6f}\")\n", + " print(f\"Iterations: {result_diffrax.iterations}\")\n", + " print(f\"Function evals: {result_diffrax.evaluations}\")\n", + " print(f\"Time: {diffrax_time:.3f}s\")\n", + " print(f\"Success: {result_diffrax.success}\")\n", + " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Method 3: VectorBuilder with Julia DifferentialEquations.jl\n", + "\n", + "Julia's **DifferentialEquations.jl** ecosystem offers:\n", + "- 🔬 Largest collection of ODE solvers\n", + "- 🎯 Specialised methods (stiff, stochastic, DAE, etc.)\n", + "- 📊 Advanced features (sensitivity analysis, callbacks)\n", + "\n", + "Integration via `diffeqpy` provides access to Julia's ecosystem from Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: install Julia backend\n", + "# !pip install diffeqpy\n", + "# Then in Python: from diffeqpy import install; install()\n", + "\n", + "try:\n", + " from diffeqpy import ode\n", + "\n", + " JULIA_AVAILABLE = True\n", + "except ImportError:\n", + " JULIA_AVAILABLE = False\n", + " print(\"⚠️ DifferentialEquations.jl not installed. Skipping Julia example.\")\n", + " print(\" Install with: pip install diffeqpy\")\n", + " print(\" Then run: from diffeqpy import install; install()\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if JULIA_AVAILABLE:\n", + " # Define dynamics for Julia\n", + " def lotka_volterra_julia(u, p, t):\n", + " \"\"\"Lotka-Volterra for Julia (u, p, t) signature.\"\"\"\n", + " x, y = u\n", + " alpha, beta, delta, gamma = p\n", + " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", + "\n", + " def simulate_julia(params):\n", + " \"\"\"Solve ODE using Julia's Tsit5 solver.\"\"\"\n", + " u0 = [10.0, 5.0]\n", + " tspan = (t_data[0], t_data[-1])\n", + "\n", + " prob = ode.ODEProblem(lotka_volterra_julia, u0, tspan, params)\n", + " sol = ode.solve(prob, ode.Tsit5(), saveat=t_data)\n", + "\n", + " # Convert to numpy array (shape: n_times x 2)\n", + " return np.array(sol.u).T\n", + "\n", + " # Test\n", + " test_output = simulate_julia([1.0, 0.4, 0.1, 0.4])\n", + " print(\"✅ Julia/DifferentialEquations.jl ready\")\n", + " print(f\" Output shape: {test_output.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if JULIA_AVAILABLE:\n", + " # Fit with VectorBuilder + Julia\n", + " start_time = time.time()\n", + "\n", + " result_julia = (\n", + " chron.VectorBuilder()\n", + " .with_objective(simulate_julia)\n", + " .with_data(y_observed)\n", + " .with_parameter(\"alpha\", 1.0)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.5)\n", + " .with_cost(chron.SSE())\n", + " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", + " .build()\n", + " .optimise()\n", + " )\n", + "\n", + " julia_time = time.time() - start_time\n", + "\n", + " print(\"\\n\" + \"=\" * 60)\n", + " print(\"JULIA DIFFERENTIALEQUATIONS.JL RESULTS\")\n", + " print(\"=\" * 60)\n", + " print(f\"True parameters: {list(true_params.values())}\")\n", + " print(f\"Estimated parameters: {result_julia.x}\")\n", + " print(f\"Final SSE: {result_julia.value:.6f}\")\n", + " print(f\"Iterations: {result_julia.iterations}\")\n", + " print(f\"Function evals: {result_julia.evaluations}\")\n", + " print(f\"Time: {julia_time:.3f}s\")\n", + " print(f\"Success: {result_julia.success}\")\n", + " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Comparison\n", + "\n", + "Let's compare all three backends:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collect results\n", + "results_summary = []\n", + "\n", + "results_summary.append(\n", + " {\n", + " \"Backend\": \"Diffsol\",\n", + " \"Time (s)\": diffsol_time,\n", + " \"SSE\": result_diffsol.value,\n", + " \"Iterations\": result_diffsol.iterations,\n", + " \"Evaluations\": result_diffsol.evaluations,\n", + " }\n", + ")\n", + "\n", + "if JAX_AVAILABLE:\n", + " results_summary.append(\n", + " {\n", + " \"Backend\": \"JAX/Diffrax\",\n", + " \"Time (s)\": diffrax_time,\n", + " \"SSE\": result_diffrax.value,\n", + " \"Iterations\": result_diffrax.iterations,\n", + " \"Evaluations\": result_diffrax.evaluations,\n", + " }\n", + " )\n", + "\n", + "if JULIA_AVAILABLE:\n", + " results_summary.append(\n", + " {\n", + " \"Backend\": \"Julia/DiffEq\",\n", + " \"Time (s)\": julia_time,\n", + " \"SSE\": result_julia.value,\n", + " \"Iterations\": result_julia.iterations,\n", + " \"Evaluations\": result_julia.evaluations,\n", + " }\n", + " )\n", + "\n", + "# Display table\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"BACKEND COMPARISON\")\n", + "print(\"=\" * 80)\n", + "print(f\"{'Backend':<20} {'Time (s)':<12} {'SSE':<15} {'Iters':<8} {'Evals'}\")\n", + "print(\"-\" * 80)\n", + "\n", + "for r in results_summary:\n", + " print(\n", + " f\"{r['Backend']:<20} {r['Time (s)']:<12.3f} {r['SSE']:<15.3e} \"\n", + " f\"{r['Iterations']:<8} {r['Evaluations']}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize timing comparison\n", + "if len(results_summary) > 1:\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " backends = [r[\"Backend\"] for r in results_summary]\n", + " times = [r[\"Time (s)\"] for r in results_summary]\n", + " sse_values = [r[\"SSE\"] for r in results_summary]\n", + "\n", + " # Time comparison\n", + " colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\"]\n", + " bars1 = ax1.bar(\n", + " backends, times, color=colors[: len(backends)], alpha=0.7, edgecolor=\"black\"\n", + " )\n", + " ax1.set_ylabel(\"Time (s)\", fontsize=12)\n", + " ax1.set_title(\"Optimization Time Comparison\", fontsize=14, fontweight=\"bold\")\n", + " ax1.grid(True, axis=\"y\", alpha=0.3)\n", + "\n", + " # Add value labels on bars\n", + " for bar in bars1:\n", + " height = bar.get_height()\n", + " ax1.text(\n", + " bar.get_x() + bar.get_width() / 2.0,\n", + " height,\n", + " f\"{height:.2f}s\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " )\n", + "\n", + " # SSE comparison\n", + " bars2 = ax2.bar(\n", + " backends,\n", + " sse_values,\n", + " color=colors[: len(backends)],\n", + " alpha=0.7,\n", + " edgecolor=\"black\",\n", + " )\n", + " ax2.set_ylabel(\"SSE\", fontsize=12)\n", + " ax2.set_title(\"Final Objective Value\", fontsize=14, fontweight=\"bold\")\n", + " ax2.grid(True, axis=\"y\", alpha=0.3)\n", + "\n", + " # Add value labels\n", + " for bar in bars2:\n", + " height = bar.get_height()\n", + " ax2.text(\n", + " bar.get_x() + bar.get_width() / 2.0,\n", + " height,\n", + " f\"{height:.1f}\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=10,\n", + " )\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Fitted Models\n", + "\n", + "Compare the fitted trajectories from each backend:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate predictions from each fitted model\n", + "t_fine = np.linspace(t_data[0], t_data[-1], 200)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "# Plot prey dynamics\n", + "ax1.plot(t_data, y_observed[:, 0], \"o\", label=\"Observed\", alpha=0.5, markersize=6)\n", + "ax1.plot(t_data, y_true[:, 0], \"k--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "\n", + "# Diffsol fit\n", + "problem_diffsol = (\n", + " chron.DiffsolBuilder()\n", + " .with_model(model_str)\n", + " .with_parameter(\"alpha\", result_diffsol.x[0])\n", + " .with_parameter(\"beta\", result_diffsol.x[1])\n", + " .with_parameter(\"delta\", result_diffsol.x[2])\n", + " .with_parameter(\"gamma\", result_diffsol.x[3])\n", + " .build()\n", + ")\n", + "y_pred_diffsol = problem_diffsol.predict(t_fine)\n", + "ax1.plot(t_fine, y_pred_diffsol[:, 0], label=\"Diffsol\", linewidth=2)\n", + "\n", + "if JAX_AVAILABLE:\n", + " y_pred_diffrax = simulate_numpy(result_diffrax.x)\n", + " ax1.plot(\n", + " t_data, y_pred_diffrax[:, 0], label=\"JAX/Diffrax\", linewidth=2, linestyle=\":\"\n", + " )\n", + "\n", + "if JULIA_AVAILABLE:\n", + " y_pred_julia = simulate_julia(result_julia.x)\n", + " ax1.plot(\n", + " t_data, y_pred_julia[:, 0], label=\"Julia/DiffEq\", linewidth=2, linestyle=\"-.\"\n", + " )\n", + "\n", + "ax1.set_xlabel(\"Time\", fontsize=12)\n", + "ax1.set_ylabel(\"Prey Population\", fontsize=12)\n", + "ax1.set_title(\"Prey Dynamics: Model Comparison\", fontsize=14, fontweight=\"bold\")\n", + "ax1.legend(fontsize=10)\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# Plot predator dynamics\n", + "ax2.plot(t_data, y_observed[:, 1], \"s\", label=\"Observed\", alpha=0.5, markersize=6)\n", + "ax2.plot(t_data, y_true[:, 1], \"k--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "ax2.plot(t_fine, y_pred_diffsol[:, 1], label=\"Diffsol\", linewidth=2)\n", + "\n", + "if JAX_AVAILABLE:\n", + " ax2.plot(\n", + " t_data, y_pred_diffrax[:, 1], label=\"JAX/Diffrax\", linewidth=2, linestyle=\":\"\n", + " )\n", + "\n", + "if JULIA_AVAILABLE:\n", + " ax2.plot(\n", + " t_data, y_pred_julia[:, 1], label=\"Julia/DiffEq\", linewidth=2, linestyle=\"-.\"\n", + " )\n", + "\n", + "ax2.set_xlabel(\"Time\", fontsize=12)\n", + "ax2.set_ylabel(\"Predator Population\", fontsize=12)\n", + "ax2.set_title(\"Predator Dynamics: Model Comparison\", fontsize=14, fontweight=\"bold\")\n", + "ax2.legend(fontsize=10)\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "### When to Use Each Backend\n", + "\n", + "**DiffsolBuilder (Diffsol):**\n", + "- ✅ Quick prototyping with DiffSL syntax\n", + "- ✅ Standard ODE problems\n", + "- ✅ No extra dependencies\n", + "- ❌ Limited to DiffSL expressiveness\n", + "\n", + "**VectorBuilder + JAX/Diffrax:**\n", + "- ✅ GPU acceleration for large problems\n", + "- ✅ Automatic differentiation (enables gradient-based optimisers)\n", + "- ✅ JIT compilation for speed\n", + "- ✅ Excellent for high-dimensional problems\n", + "- ❌ Requires JAX ecosystem\n", + "\n", + "**VectorBuilder + Julia/DifferentialEquations.jl:**\n", + "- ✅ Largest solver collection (stiff, stochastic, DAE, DDE, etc.)\n", + "- ✅ Advanced features (callbacks, sensitivity analysis)\n", + "- ✅ Best for specialized problems\n", + "- ❌ Requires Julia installation\n", + "\n", + "### Performance Insights\n", + "\n", + "1. **JAX/Diffrax** typically fastest after JIT warmup\n", + "2. **Diffsol** excellent balance of speed and simplicity\n", + "3. **Julia/DiffEq** best for problems requiring specialized solvers\n", + "4. All backends produce equivalent parameter estimates\n", + "\n", + "### VectorBuilder Flexibility\n", + "\n", + "The key advantage of `VectorBuilder` is **total control**:\n", + "- Any Python callable works\n", + "- Integrate pre-existing simulation code\n", + "- Mix solver backends in the same workflow\n", + "- Enable advanced features like GPU acceleration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps\n", + "\n", + "- [Tutorial 7: Parallel Optimisation](07_parallel_optimization.ipynb) - Scale optimisation with parallelism\n", + "- [Guide: Custom Solvers](../../guides/custom-solvers.md) - Detailed integration patterns\n", + "- [API Reference: VectorBuilder](../../api-reference/python/builders.md#vectorbuilder) - Complete API documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Add Gradient-Based Optimiser**: Modify the JAX/Diffrax example to use `Adam()` optimiser with gradients\n", + "\n", + "2. **Custom Dynamics**: Implement a different ODE system (e.g., Lorenz attractor, SIR model) using VectorBuilder\n", + "\n", + "3. **Benchmark Scaling**: Test how performance scales with:\n", + " - Number of time points (50, 100, 500, 1000)\n", + " - System size (2, 5, 10 state variables)\n", + " - Problem stiffness\n", + "\n", + "4. **Hybrid Approach**: Use VectorBuilder with a custom callable that switches solvers based on problem characteristics" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb new file mode 100644 index 0000000..cf53672 --- /dev/null +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 7: Parallel Optimisation\n", + "\n", + "**Learning Objectives:**\n", + "- Understand which optimisers support parallelism\n", + "- Configure population-based optimisers for parallel execution\n", + "- Benchmark scaling performance\n", + "- Identify performance bottlenecks\n", + "- Apply best practices for thread-safe optimisation\n", + "\n", + "**Prerequisites:** Tutorials 1-2, basic understanding of parallelism\n", + "\n", + "**Runtime:** ~10 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Modern CPUs have multiple cores, but not all optimisation algorithms can exploit them. Chronopt provides **automatic parallelisation** for population-based algorithms:\n", + "\n", + "### Parallel Optimisers\n", + "- **CMA-ES**: Population-based evolutionary strategy\n", + "- **Dynamic Nested Sampling**: Population of live points\n", + "\n", + "### Sequential Optimisers \n", + "- **Nelder-Mead**: Sequential simplex updates\n", + "- **Adam**: Sequential gradient steps\n", + "\n", + "This tutorial demonstrates how to configure parallelism and measure its impact." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import multiprocessing\n", + "\n", + "# Import plotting utilities\n", + "import sys\n", + "import time\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import setup_plotting\n", + "\n", + "setup_plotting()\n", + "\n", + "# Detect available cores\n", + "n_cores = multiprocessing.cpu_count()\n", + "print(f\"Available CPU cores: {n_cores}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Problem: Expensive Rosenbrock\n", + "\n", + "To see the benefits of parallelism, we need an **expensive** objective function. Let's add artificial computation time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def expensive_rosenbrock(x, delay_ms=10):\n", + " \"\"\"\n", + " Rosenbrock function with artificial delay to simulate expensive computation.\n", + "\n", + " In real applications, this might be:\n", + " - Complex ODE simulation\n", + " - Finite element analysis\n", + " - Machine learning model evaluation\n", + " - Database query\n", + " \"\"\"\n", + " # Simulate expensive computation\n", + " time.sleep(delay_ms / 1000.0)\n", + "\n", + " # Standard Rosenbrock\n", + " value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n", + " return np.array([value], dtype=float)\n", + "\n", + "\n", + "# Test single evaluation\n", + "start = time.time()\n", + "result = expensive_rosenbrock([0.5, 0.5], delay_ms=50)\n", + "elapsed = time.time() - start\n", + "\n", + "print(f\"Single evaluation time: {elapsed:.3f}s\")\n", + "print(f\"Objective value: {result[0]:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sequential Baseline: Nelder-Mead\n", + "\n", + "First, establish a baseline with sequential Nelder-Mead:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build problem\n", + "problem = (\n", + " chron.ScalarBuilder()\n", + " .with_callable(lambda x: expensive_rosenbrock(x, delay_ms=10))\n", + " .with_parameter(\"x\", 1.0)\n", + " .with_parameter(\"y\", 1.0)\n", + " .build()\n", + ")\n", + "\n", + "# Sequential Nelder-Mead\n", + "print(\"Running Nelder-Mead (sequential)...\")\n", + "start = time.time()\n", + "\n", + "result_nm = chron.NelderMead().with_max_iter(100).run(problem, [0.0, 0.0])\n", + "\n", + "time_nm = time.time() - start\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"NELDER-MEAD (Sequential)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Solution: {result_nm.x}\")\n", + "print(f\"Value: {result_nm.value:.3e}\")\n", + "print(f\"Evaluations: {result_nm.evaluations}\")\n", + "print(f\"Time: {time_nm:.2f}s\")\n", + "print(f\"Time per eval: {time_nm / result_nm.evaluations:.3f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parallel Optimisation: CMA-ES\n", + "\n", + "CMA-ES evaluates a **population** of candidate solutions each generation. Chronopt automatically parallelises these evaluations across available cores.\n", + "\n", + "### Key Parameters:\n", + "- `population_size`: Number of candidates per generation (default: automatic based on dimension)\n", + "- More candidates = more parallelism opportunity\n", + "- Trade-off: larger populations need more generations to converge" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CMA-ES with automatic population size\n", + "print(\"Running CMA-ES (parallel, default population)...\")\n", + "start = time.time()\n", + "\n", + "result_cmaes_default = (\n", + " chron.CMAES().with_max_iter(50).with_step_size(0.5).run(problem, [0.0, 0.0])\n", + ")\n", + "\n", + "time_cmaes_default = time.time() - start\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"CMA-ES (Parallel, Default Population)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Solution: {result_cmaes_default.x}\")\n", + "print(f\"Value: {result_cmaes_default.value:.3e}\")\n", + "print(f\"Evaluations: {result_cmaes_default.evaluations}\")\n", + "print(f\"Time: {time_cmaes_default:.2f}s\")\n", + "print(f\"Time per eval: {time_cmaes_default / result_cmaes_default.evaluations:.3f}s\")\n", + "print(f\"\\nSpeedup vs Nelder-Mead: {time_nm / time_cmaes_default:.2f}x\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CMA-ES with larger population for more parallelism\n", + "print(\"Running CMA-ES (parallel, large population)...\")\n", + "start = time.time()\n", + "\n", + "result_cmaes_large = (\n", + " chron.CMAES()\n", + " .with_max_iter(30)\n", + " .with_step_size(0.5)\n", + " .with_population_size(20) # Larger population = more parallel work\n", + " .run(problem, [0.0, 0.0])\n", + ")\n", + "\n", + "time_cmaes_large = time.time() - start\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"CMA-ES (Parallel, Large Population)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Solution: {result_cmaes_large.x}\")\n", + "print(f\"Value: {result_cmaes_large.value:.3e}\")\n", + "print(f\"Evaluations: {result_cmaes_large.evaluations}\")\n", + "print(f\"Time: {time_cmaes_large:.2f}s\")\n", + "print(f\"Time per eval: {time_cmaes_large / result_cmaes_large.evaluations:.3f}s\")\n", + "print(f\"\\nSpeedup vs Nelder-Mead: {time_nm / time_cmaes_large:.2f}x\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling Analysis\n", + "\n", + "Let's systematically test how performance scales with:\n", + "1. **Population size** (parallelism opportunity)\n", + "2. **Evaluation cost** (compute vs overhead trade-off)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test different population sizes\n", + "population_sizes = [4, 8, 12, 16, 20]\n", + "results_pop = []\n", + "\n", + "print(\"Testing different population sizes...\")\n", + "print(\"(This may take a few minutes)\\n\")\n", + "\n", + "for pop_size in population_sizes:\n", + " # Adjust iterations to keep total evaluations similar\n", + " n_iter = 200 // pop_size\n", + "\n", + " start = time.time()\n", + " result = (\n", + " chron.CMAES()\n", + " .with_max_iter(n_iter)\n", + " .with_step_size(0.5)\n", + " .with_population_size(pop_size)\n", + " .run(problem, [0.0, 0.0])\n", + " )\n", + " elapsed = time.time() - start\n", + "\n", + " results_pop.append(\n", + " {\n", + " \"population\": pop_size,\n", + " \"time\": elapsed,\n", + " \"evaluations\": result.evaluations,\n", + " \"time_per_eval\": elapsed / result.evaluations,\n", + " \"success\": result.success,\n", + " \"value\": result.value,\n", + " }\n", + " )\n", + "\n", + " print(\n", + " f\"Pop={pop_size:2d}: {elapsed:6.2f}s, {result.evaluations:4d} evals, \"\n", + " f\"{elapsed / result.evaluations:.3f}s/eval\"\n", + " )\n", + "\n", + "print(\"\\nDone!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize scaling\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "pops = [r[\"population\"] for r in results_pop]\n", + "times = [r[\"time\"] for r in results_pop]\n", + "time_per_eval = [r[\"time_per_eval\"] for r in results_pop]\n", + "\n", + "# Total time vs population size\n", + "ax1.plot(pops, times, \"o-\", linewidth=2, markersize=10, color=\"#1f77b4\")\n", + "ax1.set_xlabel(\"Population Size\", fontsize=12)\n", + "ax1.set_ylabel(\"Total Time (s)\", fontsize=12)\n", + "ax1.set_title(\"Optimisation Time vs Population Size\", fontsize=14, fontweight=\"bold\")\n", + "ax1.grid(True, alpha=0.3)\n", + "ax1.set_xticks(pops)\n", + "\n", + "# Time per evaluation (measures parallelism efficiency)\n", + "ax2.plot(pops, time_per_eval, \"s-\", linewidth=2, markersize=10, color=\"#ff7f0e\")\n", + "ax2.axhline(\n", + " y=0.01, color=\"red\", linestyle=\"--\", label=\"Sequential baseline (10ms)\", alpha=0.7\n", + ")\n", + "ax2.set_xlabel(\"Population Size\", fontsize=12)\n", + "ax2.set_ylabel(\"Time per Evaluation (s)\", fontsize=12)\n", + "ax2.set_title(\"Parallelism Efficiency\", fontsize=14, fontweight=\"bold\")\n", + "ax2.grid(True, alpha=0.3)\n", + "ax2.set_xticks(pops)\n", + "ax2.legend(fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n💡 Interpretation:\")\n", + "print(\" - Time per eval < sequential baseline = good parallelism\")\n", + "print(\" - Larger populations amortise overhead across more work\")\n", + "print(\" - Diminishing returns when population > number of cores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of Evaluation Cost\n", + "\n", + "Parallelism overhead (thread creation, synchronisation) matters less when evaluations are expensive:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test different evaluation costs\n", + "delays_ms = [1, 5, 10, 20, 50]\n", + "results_cost = []\n", + "\n", + "print(\"Testing different evaluation costs...\\n\")\n", + "\n", + "for delay in delays_ms:\n", + " # Create problem with specific delay\n", + " problem_delayed = (\n", + " chron.ScalarBuilder()\n", + " .with_callable(lambda x, d=delay: expensive_rosenbrock(x, delay_ms=d))\n", + " .with_parameter(\"x\", 1.0)\n", + " .with_parameter(\"y\", 1.0)\n", + " .build()\n", + " )\n", + "\n", + " # Sequential Nelder-Mead\n", + " start = time.time()\n", + " result_seq = chron.NelderMead().with_max_iter(50).run(problem_delayed, [0.0, 0.0])\n", + " time_seq = time.time() - start\n", + "\n", + " # Parallel CMA-ES\n", + " start = time.time()\n", + " result_par = (\n", + " chron.CMAES()\n", + " .with_max_iter(25)\n", + " .with_step_size(0.5)\n", + " .with_population_size(12)\n", + " .run(problem_delayed, [0.0, 0.0])\n", + " )\n", + " time_par = time.time() - start\n", + "\n", + " speedup = time_seq / time_par\n", + "\n", + " results_cost.append(\n", + " {\n", + " \"delay_ms\": delay,\n", + " \"time_seq\": time_seq,\n", + " \"time_par\": time_par,\n", + " \"speedup\": speedup,\n", + " }\n", + " )\n", + "\n", + " print(\n", + " f\"Delay={delay:3d}ms: Sequential={time_seq:6.2f}s, Parallel={time_par:6.2f}s, \"\n", + " f\"Speedup={speedup:.2f}x\"\n", + " )\n", + "\n", + "print(\"\\nDone!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize speedup vs evaluation cost\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "delays = [r[\"delay_ms\"] for r in results_cost]\n", + "speedups = [r[\"speedup\"] for r in results_cost]\n", + "\n", + "ax.plot(\n", + " delays,\n", + " speedups,\n", + " \"o-\",\n", + " linewidth=2.5,\n", + " markersize=12,\n", + " color=\"#2ca02c\",\n", + " markeredgecolor=\"black\",\n", + " markeredgewidth=1.5,\n", + ")\n", + "ax.axhline(\n", + " y=1.0, color=\"red\", linestyle=\"--\", label=\"No speedup\", alpha=0.7, linewidth=2\n", + ")\n", + "ax.axhline(\n", + " y=n_cores,\n", + " color=\"blue\",\n", + " linestyle=\":\",\n", + " label=f\"Ideal ({n_cores} cores)\",\n", + " alpha=0.7,\n", + " linewidth=2,\n", + ")\n", + "\n", + "ax.set_xlabel(\"Evaluation Cost (ms)\", fontsize=13)\n", + "ax.set_ylabel(\"Speedup (CMA-ES / Nelder-Mead)\", fontsize=13)\n", + "ax.set_title(\"Parallel Speedup vs Evaluation Cost\", fontsize=15, fontweight=\"bold\")\n", + "ax.set_xscale(\"log\")\n", + "ax.grid(True, alpha=0.3, which=\"both\")\n", + "ax.legend(fontsize=11)\n", + "\n", + "# Add value labels\n", + "for delay, speedup in zip(delays, speedups):\n", + " ax.annotate(\n", + " f\"{speedup:.1f}x\",\n", + " xy=(delay, speedup),\n", + " xytext=(0, 10),\n", + " textcoords=\"offset points\",\n", + " ha=\"center\",\n", + " fontsize=10,\n", + " fontweight=\"bold\",\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n💡 Key Insight:\")\n", + "print(\" Parallelism speedup increases with evaluation cost!\")\n", + "print(\" Expensive evaluations → parallelisation overhead becomes negligible\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thread Safety Considerations\n", + "\n", + "When using parallel optimisation, your **objective function must be thread-safe**:\n", + "\n", + "### ✅ Thread-Safe Patterns\n", + "```python\n", + "# Pure functions (no shared state)\n", + "def objective(x):\n", + " return np.sum(x**2)\n", + "\n", + "# Immutable data structures\n", + "DATA = np.array([...])\n", + "def objective(x):\n", + " return np.sum((x - DATA)**2)\n", + "\n", + "# Read-only global state\n", + "MODEL_PARAMS = {...}\n", + "def objective(x):\n", + " return simulate(x, MODEL_PARAMS)\n", + "```\n", + "\n", + "### ❌ Not Thread-Safe\n", + "```python\n", + "# Shared mutable state\n", + "counter = 0\n", + "def objective(x):\n", + " global counter\n", + " counter += 1 # Race condition!\n", + " return np.sum(x**2)\n", + "\n", + "# Writing to files without locks\n", + "def objective(x):\n", + " with open('log.txt', 'a') as f: # Multiple threads writing!\n", + " f.write(f'{x}\\n')\n", + " return np.sum(x**2)\n", + "```\n", + "\n", + "### Solutions for Non-Thread-Safe Code\n", + "\n", + "1. **Use locks** (but this reduces parallelism):\n", + "```python\n", + "from threading import Lock\n", + "lock = Lock()\n", + "\n", + "def objective(x):\n", + " with lock:\n", + " # Critical section\n", + " return result\n", + "```\n", + "\n", + "2. **Disable parallelism** for problematic code (future feature)\n", + "\n", + "3. **Refactor** to eliminate shared state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "### When to Use Parallel Optimisation\n", + "\n", + "**✅ Parallelism is beneficial when:**\n", + "- Objective function is expensive (>10ms per evaluation)\n", + "- Problem has >3 parameters (larger populations useful)\n", + "- Multiple cores available\n", + "- Function is thread-safe\n", + "- Global search needed (CMA-ES vs Nelder-Mead)\n", + "\n", + "**❌ Parallelism may not help when:**\n", + "- Function is very fast (<1ms)\n", + "- Low-dimensional problems (1-2 parameters)\n", + "- Limited cores available\n", + "- Thread-safety issues\n", + "- Sequential algorithms required (Nelder-Mead, Adam)\n", + "\n", + "### Tuning for Parallel Performance\n", + "\n", + "1. **Match population to cores**: `population_size ≈ 2 × n_cores` often works well\n", + "2. **Balance iterations**: Fewer iterations with larger population\n", + "3. **Profile first**: Measure single evaluation time\n", + "4. **Monitor efficiency**: Check if speedup scales with cores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Summary\n", + "\n", + "Let's create a summary comparing all approaches:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Summary comparison\n", + "summary_data = [\n", + " (\"Nelder-Mead (Sequential)\", time_nm, result_nm.evaluations, 1.0),\n", + " (\n", + " \"CMA-ES (Default Pop)\",\n", + " time_cmaes_default,\n", + " result_cmaes_default.evaluations,\n", + " time_nm / time_cmaes_default,\n", + " ),\n", + " (\n", + " \"CMA-ES (Large Pop)\",\n", + " time_cmaes_large,\n", + " result_cmaes_large.evaluations,\n", + " time_nm / time_cmaes_large,\n", + " ),\n", + "]\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"PERFORMANCE SUMMARY\")\n", + "print(\"=\" * 80)\n", + "print(f\"{'Method':<30} {'Time (s)':<12} {'Evals':<10} {'Speedup'}\")\n", + "print(\"-\" * 80)\n", + "\n", + "for method, t, evals, speedup in summary_data:\n", + " print(f\"{method:<30} {t:<12.2f} {evals:<10} {speedup:.2f}x\")\n", + "\n", + "print(\"\\n💡 Key Findings:\")\n", + "print(f\" - Best speedup: {max(s for _, _, _, s in summary_data):.2f}x\")\n", + "print(f\" - System cores: {n_cores}\")\n", + "print(\n", + " f\" - Parallel efficiency: {(max(s for _, _, _, s in summary_data) / n_cores * 100):.0f}%\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **CMA-ES automatically parallelises** population evaluations across CPU cores\n", + "2. **Speedup increases** with evaluation cost (overhead becomes negligible)\n", + "3. **Population size** controls parallel work per generation\n", + "4. **Thread safety** is critical - use pure functions or locks\n", + "5. **Best for expensive functions** (>10ms per evaluation)\n", + "6. **Monitor efficiency** - speedup should approach number of cores\n", + "7. **Trade-offs exist** - larger populations need more generations\n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n", + "- [Guide: Parallel Execution](../../guides/parallel-execution.md) - Detailed thread safety patterns\n", + "- [Guide: Tuning Optimisers](../../guides/tuning-optimizers.md) - Population size selection\n", + "- [API Reference: CMA-ES](../../api-reference/python/optimizers.md#cmaes) - Complete API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Optimal Population**: Find the population size that minimises total time for your hardware\n", + "\n", + "2. **Amdahl's Law**: Estimate the theoretical speedup limit based on your results\n", + "\n", + "3. **Real Problem**: Apply parallel CMA-ES to an ODE fitting problem from Tutorial 2\n", + "\n", + "4. **Scaling Study**: Measure how speedup changes with:\n", + " - Number of parameters (2, 5, 10, 20)\n", + " - Evaluation cost (1ms, 10ms, 100ms, 1s)\n", + " - System load (run with background tasks)\n", + "\n", + "5. **Thread Safety Bug**: Create a non-thread-safe objective function and observe the failure mode" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb new file mode 100644 index 0000000..00a1fba --- /dev/null +++ b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb @@ -0,0 +1,772 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 8: Advanced Cost Functions\n", + "\n", + "**Learning Objectives:**\n", + "- Understand built-in cost metrics (SSE, RMSE, GaussianNLL)\n", + "- Implement custom cost functions\n", + "- Apply weighted fitting for heteroscedastic data\n", + "- Use regularisation to prevent over-fitting\n", + "- Combine multiple objectives\n", + "\n", + "**Prerequisites:** Tutorials 1-2, basic statistics\n", + "\n", + "**Runtime:** ~15 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The **cost function** (or loss function) quantifies how well model predictions match observations. Chronopt provides built-in metrics, but real-world problems often require:\n", + "\n", + "- **Weighted fitting** when measurement errors vary\n", + "- **Custom metrics** for domain-specific requirements\n", + "- **Regularisation** to prevent over-fitting\n", + "- **Multi-objective** combinations\n", + "\n", + "This tutorial demonstrates advanced cost function techniques." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import sys\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "sys.path.append(\".\")\n", + "from utils import setup_plotting\n", + "\n", + "setup_plotting()\n", + "np.random.seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built-in Cost Metrics\n", + "\n", + "Chronopt provides three standard metrics:\n", + "\n", + "### 1. Sum of Squared Errors (SSE)\n", + "$$\\text{SSE} = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$\n", + "- Default metric\n", + "- Penalises large errors heavily\n", + "- Assumes constant variance\n", + "\n", + "### 2. Root Mean Squared Error (RMSE) \n", + "$$\\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}$$\n", + "- Normalised by sample size\n", + "- Same units as data\n", + "- Better for comparing across datasets\n", + "\n", + "### 3. Gaussian Negative Log-Likelihood (GaussianNLL)\n", + "$$\\text{NLL} = \\frac{n}{2}\\log(2\\pi\\sigma^2) + \\frac{1}{2\\sigma^2}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$\n", + "- Statistically principled\n", + "- Enables Bayesian inference\n", + "- Can estimate noise parameter $\\sigma$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate simple test data\n", + "x_data = np.linspace(0, 10, 50)\n", + "y_true = 2.5 * x_data + 1.0\n", + "y_observed = y_true + np.random.normal(0, 2.0, len(x_data))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_data, y_observed, \"o\", label=\"Observed\", alpha=0.6, markersize=6)\n", + "plt.plot(x_data, y_true, \"--\", label=\"True\", linewidth=2)\n", + "plt.xlabel(\"x\", fontsize=12)\n", + "plt.ylabel(\"y\", fontsize=12)\n", + "plt.title(\"Linear Model Test Data\", fontsize=14, fontweight=\"bold\")\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare built-in metrics\n", + "def linear_model(params):\n", + " \"\"\"Simple linear model: y = slope * x + intercept\"\"\"\n", + " slope, intercept = params\n", + " return slope * x_data + intercept\n", + "\n", + "\n", + "# Define problem\n", + "builder = (\n", + " chron.VectorBuilder()\n", + " .with_objective(linear_model)\n", + " .with_data(y_observed)\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + ")\n", + "\n", + "# Test each metric\n", + "metrics = {\"SSE\": chron.SSE(), \"RMSE\": chron.RMSE(), \"GaussianNLL\": chron.GaussianNLL()}\n", + "\n", + "results = {}\n", + "for name, metric in metrics.items():\n", + " result = builder.with_cost(metric).build().optimise()\n", + " results[name] = result\n", + "\n", + "# Display comparison\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"COST METRIC COMPARISON\")\n", + "print(\"=\" * 70)\n", + "print(\"True parameters: [2.5, 1.0]\")\n", + "print(\"-\" * 70)\n", + "print(f\"{'Metric':<15} {'Slope':<12} {'Intercept':<12} {'Cost Value'}\")\n", + "print(\"-\" * 70)\n", + "\n", + "for name, result in results.items():\n", + " print(f\"{name:<15} {result.x[0]:<12.4f} {result.x[1]:<12.4f} {result.value:<12.3e}\")\n", + "\n", + "print(\"\\n💡 Note: All metrics produce similar parameter estimates!\")\n", + "print(\" The cost values differ in scale, but optima are equivalent.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weighted Fitting: Heteroscedastic Data\n", + "\n", + "Real data often has **non-uniform measurement errors** (heteroscedasticity). Points with larger errors should contribute less to the cost function.\n", + "\n", + "### Weighted Least Squares\n", + "$$\\text{WSSE} = \\sum_{i=1}^{n} w_i (y_i - \\hat{y}_i)^2$$\n", + "\n", + "where $w_i = 1/\\sigma_i^2$ (inverse variance weighting)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate heteroscedastic data (error increases with x)\n", + "x_hetero = np.linspace(0, 10, 50)\n", + "y_true_hetero = 2.0 * x_hetero + 3.0\n", + "\n", + "# Error increases linearly with x\n", + "error_std = 0.5 + 0.3 * x_hetero\n", + "y_hetero = y_true_hetero + np.random.normal(0, error_std)\n", + "\n", + "# Visualize heteroscedastic data\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Error bars showing measurement uncertainty\n", + "ax.errorbar(\n", + " x_hetero,\n", + " y_hetero,\n", + " yerr=error_std,\n", + " fmt=\"o\",\n", + " alpha=0.6,\n", + " label=\"Observed (with error bars)\",\n", + " capsize=3,\n", + " markersize=6,\n", + ")\n", + "ax.plot(x_hetero, y_true_hetero, \"r--\", linewidth=2, label=\"True\")\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=12)\n", + "ax.set_ylabel(\"y\", fontsize=12)\n", + "ax.set_title(\n", + " \"Heteroscedastic Data (Error Increases with x)\", fontsize=14, fontweight=\"bold\"\n", + ")\n", + "ax.legend(fontsize=11)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Error range: [{error_std.min():.2f}, {error_std.max():.2f}]\")\n", + "print(\"Notice: Uncertainty increases from left to right!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Custom weighted SSE cost function\n", + "class WeightedSSE:\n", + " \"\"\"Weighted sum of squared errors.\"\"\"\n", + "\n", + " def __init__(self, weights):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " weights : array_like\n", + " Weight for each data point (typically 1/sigma^2)\n", + " \"\"\"\n", + " self.weights = np.asarray(weights)\n", + "\n", + " def __call__(self, predicted, observed):\n", + " \"\"\"Compute weighted SSE.\"\"\"\n", + " residuals = observed - predicted\n", + " return np.sum(self.weights * residuals**2)\n", + "\n", + "\n", + "# Weights: inverse variance (1/sigma^2)\n", + "weights = 1.0 / error_std**2\n", + "\n", + "\n", + "def linear_model_hetero(params):\n", + " slope, intercept = params\n", + " return slope * x_hetero + intercept\n", + "\n", + "\n", + "# Fit WITHOUT weights (standard SSE)\n", + "result_unweighted = (\n", + " chron.VectorBuilder()\n", + " .with_objective(linear_model_hetero)\n", + " .with_data(y_hetero)\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + " .with_cost(chron.SSE()) # Standard SSE\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "# Fit WITH weights\n", + "result_weighted = (\n", + " chron.VectorBuilder()\n", + " .with_objective(linear_model_hetero)\n", + " .with_data(y_hetero)\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + " .with_cost(WeightedSSE(weights)) # Custom weighted cost\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"WEIGHTED vs UNWEIGHTED FITTING\")\n", + "print(\"=\" * 70)\n", + "print(\"True parameters: [2.0, 3.0]\")\n", + "print(f\"Unweighted fit: {result_unweighted.x}\")\n", + "print(f\"Weighted fit: {result_weighted.x}\")\n", + "print(\"-\" * 70)\n", + "print(f\"Unweighted error: {np.linalg.norm(result_unweighted.x - [2.0, 3.0]):.4f}\")\n", + "print(f\"Weighted error: {np.linalg.norm(result_weighted.x - [2.0, 3.0]):.4f}\")\n", + "print(\"\\n💡 Weighted fit is more accurate!\")\n", + "print(\" It down-weights noisy (high-x) points appropriately.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize comparison\n", + "x_plot = np.linspace(0, 10, 200)\n", + "y_true_plot = 2.0 * x_plot + 3.0\n", + "y_unweighted = result_unweighted.x[0] * x_plot + result_unweighted.x[1]\n", + "y_weighted = result_weighted.x[0] * x_plot + result_weighted.x[1]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Data with error bars\n", + "ax.errorbar(\n", + " x_hetero,\n", + " y_hetero,\n", + " yerr=error_std,\n", + " fmt=\"o\",\n", + " alpha=0.5,\n", + " label=\"Data (with uncertainties)\",\n", + " capsize=3,\n", + " markersize=6,\n", + " color=\"gray\",\n", + ")\n", + "\n", + "# Fits\n", + "ax.plot(x_plot, y_true_plot, \"k--\", linewidth=3, label=\"True\", alpha=0.8)\n", + "ax.plot(x_plot, y_unweighted, linewidth=2.5, label=\"Unweighted fit\", color=\"#ff7f0e\")\n", + "ax.plot(x_plot, y_weighted, linewidth=2.5, label=\"Weighted fit\", color=\"#2ca02c\")\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=13)\n", + "ax.set_ylabel(\"y\", fontsize=13)\n", + "ax.set_title(\"Weighted vs Unweighted Fitting\", fontsize=15, fontweight=\"bold\")\n", + "ax.legend(fontsize=12, loc=\"upper left\")\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Highlight the difference in high-error region\n", + "ax.axvspan(7, 10, alpha=0.1, color=\"red\", label=\"High uncertainty region\")\n", + "ax.text(\n", + " 8.5,\n", + " 5,\n", + " \"High uncertainty\\nregion\",\n", + " ha=\"center\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularisation: Preventing Over-fitting\n", + "\n", + "When fitting complex models with many parameters, **regularisation** prevents over-fitting by penalising large parameter values.\n", + "\n", + "### L2 Regularisation (Ridge)\n", + "$$\\text{Cost} = \\text{Data Term} + \\lambda \\sum_{i=1}^{p} \\theta_i^2$$\n", + "\n", + "### L1 Regularisation (Lasso) \n", + "$$\\text{Cost} = \\text{Data Term} + \\lambda \\sum_{i=1}^{p} |\\theta_i|$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate data for polynomial fitting\n", + "np.random.seed(123)\n", + "x_poly = np.linspace(-1, 1, 20)\n", + "y_true_poly = 2 * x_poly - 3 * x_poly**2 # True: quadratic\n", + "y_poly = y_true_poly + np.random.normal(0, 0.3, len(x_poly))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_poly, y_poly, \"o\", label=\"Observed\", alpha=0.7, markersize=8)\n", + "plt.plot(x_poly, y_true_poly, \"r--\", linewidth=2, label=\"True (quadratic)\")\n", + "plt.xlabel(\"x\", fontsize=12)\n", + "plt.ylabel(\"y\", fontsize=12)\n", + "plt.title(\"Polynomial Fitting Test Data\", fontsize=14, fontweight=\"bold\")\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit high-degree polynomial (degree 8) - prone to over-fitting\n", + "degree = 8\n", + "\n", + "\n", + "def polynomial_model(params):\n", + " \"\"\"Polynomial model: sum of coefficients * x^i\"\"\"\n", + " y_pred = np.zeros_like(x_poly)\n", + " for i, coef in enumerate(params):\n", + " y_pred += coef * x_poly**i\n", + " return y_pred\n", + "\n", + "\n", + "# Custom cost with L2 regularisation\n", + "class RegularisedSSE:\n", + " \"\"\"SSE with L2 regularisation (Ridge).\"\"\"\n", + "\n", + " def __init__(self, lambda_reg=0.0):\n", + " self.lambda_reg = lambda_reg\n", + "\n", + " def __call__(self, predicted, observed, params=None):\n", + " # Data term\n", + " sse = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Regularisation term (note: params must be passed separately)\n", + " if params is not None and self.lambda_reg > 0:\n", + " reg_term = self.lambda_reg * np.sum(params**2)\n", + " return sse + reg_term\n", + "\n", + " return sse\n", + "\n", + "\n", + "# Note: Chronopt's built-in costs don't support parameter access yet,\n", + "# so we'll compare by manually adding regularisation to parameter update\n", + "\n", + "# Unregularised fit\n", + "initial_params = np.zeros(degree + 1)\n", + "initial_params[0] = np.mean(y_poly)\n", + "\n", + "builder_poly = chron.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", + "\n", + "for i in range(degree + 1):\n", + " builder_poly = builder_poly.with_parameter(f\"c{i}\", initial_params[i])\n", + "\n", + "result_unreg = (\n", + " builder_poly.with_cost(chron.SSE())\n", + " .with_optimiser(chron.NelderMead().with_max_iter(2000))\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"POLYNOMIAL FITTING (Degree 8)\")\n", + "print(\"=\" * 70)\n", + "print(\"Unregularised coefficients:\")\n", + "print(f\" {result_unreg.x}\")\n", + "print(f\" Max |coef|: {np.max(np.abs(result_unreg.x)):.2f}\")\n", + "print(f\" SSE: {result_unreg.value:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize fits\n", + "x_dense = np.linspace(-1, 1, 200)\n", + "\n", + "\n", + "def eval_poly(x, coeffs):\n", + " y = np.zeros_like(x)\n", + " for i, c in enumerate(coeffs):\n", + " y += c * x**i\n", + " return y\n", + "\n", + "\n", + "y_true_dense = 2 * x_dense - 3 * x_dense**2\n", + "y_unreg_dense = eval_poly(x_dense, result_unreg.x)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "# Left: Training data fit\n", + "ax1.plot(\n", + " x_poly, y_poly, \"o\", label=\"Training data\", alpha=0.7, markersize=8, color=\"gray\"\n", + ")\n", + "ax1.plot(\n", + " x_dense, y_true_dense, \"k--\", linewidth=2.5, label=\"True (quadratic)\", alpha=0.8\n", + ")\n", + "ax1.plot(x_dense, y_unreg_dense, linewidth=2.5, label=\"Degree-8 fit\", color=\"#d62728\")\n", + "ax1.set_xlabel(\"x\", fontsize=12)\n", + "ax1.set_ylabel(\"y\", fontsize=12)\n", + "ax1.set_title(\"Training Data: Over-fitting\", fontsize=14, fontweight=\"bold\")\n", + "ax1.legend(fontsize=11)\n", + "ax1.grid(True, alpha=0.3)\n", + "ax1.set_ylim(-4, 3)\n", + "\n", + "# Right: Extrapolation (shows over-fitting clearly)\n", + "x_extrap = np.linspace(-1.5, 1.5, 200)\n", + "y_true_extrap = 2 * x_extrap - 3 * x_extrap**2\n", + "y_unreg_extrap = eval_poly(x_extrap, result_unreg.x)\n", + "\n", + "ax2.plot(\n", + " x_poly, y_poly, \"o\", label=\"Training data\", alpha=0.7, markersize=8, color=\"gray\"\n", + ")\n", + "ax2.plot(x_extrap, y_true_extrap, \"k--\", linewidth=2.5, label=\"True\", alpha=0.8)\n", + "ax2.plot(x_extrap, y_unreg_extrap, linewidth=2.5, label=\"Degree-8 fit\", color=\"#d62728\")\n", + "ax2.axvline(x=-1, color=\"red\", linestyle=\":\", alpha=0.5)\n", + "ax2.axvline(x=1, color=\"red\", linestyle=\":\", alpha=0.5)\n", + "ax2.set_xlabel(\"x\", fontsize=12)\n", + "ax2.set_ylabel(\"y\", fontsize=12)\n", + "ax2.set_title(\"Extrapolation: Catastrophic Failure\", fontsize=14, fontweight=\"bold\")\n", + "ax2.legend(fontsize=11)\n", + "ax2.grid(True, alpha=0.3)\n", + "ax2.set_ylim(-10, 5)\n", + "ax2.text(-1.25, -8, \"Extrapolation\\nregion\", fontsize=10, ha=\"center\")\n", + "ax2.text(1.25, -8, \"Extrapolation\\nregion\", fontsize=10, ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n⚠️ Over-fitting Alert!\")\n", + "print(\" The high-degree polynomial fits training data well but\")\n", + "print(\" extrapolates poorly. Regularisation would help!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-Objective Cost Functions\n", + "\n", + "Sometimes we want to balance **multiple objectives** simultaneously:\n", + "\n", + "$$\\text{Cost} = w_1 \\cdot \\text{Fit Quality} + w_2 \\cdot \\text{Smoothness} + w_3 \\cdot \\text{Physical Constraints}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Fit with smoothness penalty\n", + "class MultiObjectiveCost:\n", + " \"\"\"Combined data fit + smoothness penalty.\"\"\"\n", + "\n", + " def __init__(self, weight_fit=1.0, weight_smooth=0.1):\n", + " self.weight_fit = weight_fit\n", + " self.weight_smooth = weight_smooth\n", + "\n", + " def __call__(self, predicted, observed):\n", + " # Data fit term (SSE)\n", + " fit_cost = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Smoothness term (penalise large second derivatives)\n", + " second_deriv = np.diff(predicted, n=2)\n", + " smooth_cost = np.sum(second_deriv**2)\n", + "\n", + " return self.weight_fit * fit_cost + self.weight_smooth * smooth_cost\n", + "\n", + "\n", + "# Fit with different smoothness weights\n", + "weights_smooth = [0.0, 0.1, 1.0, 10.0]\n", + "results_multi = {}\n", + "\n", + "for w_smooth in weights_smooth:\n", + " result = chron.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", + "\n", + " for i in range(degree + 1):\n", + " result = result.with_parameter(f\"c{i}\", initial_params[i])\n", + "\n", + " result = (\n", + " result.with_cost(MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth))\n", + " .with_optimiser(chron.NelderMead().with_max_iter(2000))\n", + " .build()\n", + " .optimise()\n", + " )\n", + "\n", + " results_multi[w_smooth] = result\n", + " print(\n", + " f\"Smoothness weight={w_smooth:5.1f}: SSE={np.sum((eval_poly(x_poly, result.x) - y_poly) ** 2):.3f}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize effect of smoothness weight\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "axes = axes.flatten()\n", + "\n", + "for idx, (w_smooth, result) in enumerate(results_multi.items()):\n", + " ax = axes[idx]\n", + "\n", + " y_fit = eval_poly(x_dense, result.x)\n", + "\n", + " ax.plot(x_poly, y_poly, \"o\", label=\"Data\", alpha=0.6, markersize=7, color=\"gray\")\n", + " ax.plot(x_dense, y_true_dense, \"k--\", linewidth=2, label=\"True\", alpha=0.7)\n", + " ax.plot(x_dense, y_fit, linewidth=2.5, label=f\"Fit (λ={w_smooth})\", color=\"#2ca02c\")\n", + "\n", + " ax.set_xlabel(\"x\", fontsize=11)\n", + " ax.set_ylabel(\"y\", fontsize=11)\n", + " ax.set_title(f\"Smoothness Weight λ = {w_smooth}\", fontsize=13, fontweight=\"bold\")\n", + " ax.legend(fontsize=10)\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_ylim(-4, 3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n💡 Smoothness Penalty Effect:\")\n", + "print(\" λ=0.0: No penalty → over-fitting\")\n", + "print(\" λ=0.1: Slight smoothing\")\n", + "print(\" λ=1.0: Balanced fit\")\n", + "print(\" λ=10.0: Too smooth → under-fitting\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Physical Constraints as Cost Penalties\n", + "\n", + "In scientific applications, we often have **physical constraints**:\n", + "- Parameters must be positive (e.g., rate constants)\n", + "- Conservation laws (e.g., mass balance)\n", + "- Monotonicity requirements\n", + "\n", + "These can be enforced via penalty terms:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ConstrainedCost:\n", + " \"\"\"Cost with soft constraints via penalties.\"\"\"\n", + "\n", + " def __init__(self, penalty_weight=1000.0):\n", + " self.penalty_weight = penalty_weight\n", + "\n", + " def __call__(self, predicted, observed, params=None):\n", + " # Data fit term\n", + " sse = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Example constraint: parameters should sum to a target value\n", + " if params is not None:\n", + " # Soft constraint: sum(params) ≈ 0\n", + " constraint_violation = (np.sum(params) - 0.0) ** 2\n", + "\n", + " # Positivity constraint: penalise negative parameters\n", + " negative_penalty = np.sum(np.minimum(0, params) ** 2)\n", + "\n", + " penalty = self.penalty_weight * (constraint_violation + negative_penalty)\n", + " return sse + penalty\n", + "\n", + " return sse\n", + "\n", + "\n", + "print(\"Example constraint penalties:\")\n", + "print(\" • Sum constraint: forces Σθᵢ ≈ target\")\n", + "print(\" • Positivity: penalises θᵢ < 0\")\n", + "print(\" • Bounds: penalises θᵢ outside [a, b]\")\n", + "print(\" • Monotonicity: penalises θᵢ₊₁ < θᵢ\")\n", + "print(\"\\n💡 Tip: Start with large penalty weights, then tune.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "### Choosing a Cost Function\n", + "\n", + "1. **Default (SSE)**: Good starting point for most problems\n", + "2. **RMSE**: When comparing across datasets with different sizes\n", + "3. **GaussianNLL**: For Bayesian inference and uncertainty quantification\n", + "4. **Weighted**: When measurement errors vary (heteroscedastic data)\n", + "5. **Custom**: For domain-specific requirements\n", + "\n", + "### Regularisation Guidelines\n", + "\n", + "- **When to use**: High-dimensional problems, limited data, polynomial fitting\n", + "- **L2 (Ridge)**: Shrinks all coefficients smoothly\n", + "- **L1 (Lasso)**: Promotes sparsity (some coefficients → 0)\n", + "- **Tuning λ**: Cross-validation or information criteria (AIC, BIC)\n", + "\n", + "### Multi-Objective Balancing\n", + "\n", + "- **Start simple**: Fit data first, add penalties incrementally\n", + "- **Scale matters**: Normalise objectives to similar magnitudes\n", + "- **Weight tuning**: Use logarithmic search (0.001, 0.01, 0.1, 1, 10, ...)\n", + "- **Validation**: Check if constraints are actually satisfied" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary: Cost Function Design Pattern\n", + "\n", + "```python\n", + "class CustomCost:\n", + " def __init__(self, **hyperparameters):\n", + " # Store configuration\n", + " pass\n", + " \n", + " def __call__(self, predicted, observed, params=None):\n", + " # 1. Data fidelity term\n", + " fit_cost = compute_fit(predicted, observed)\n", + " \n", + " # 2. Regularisation term (optional)\n", + " reg_cost = compute_regularisation(params)\n", + " \n", + " # 3. Physical constraints (optional)\n", + " constraint_cost = compute_penalties(params)\n", + " \n", + " # 4. Combine with weights\n", + " return w1*fit_cost + w2*reg_cost + w3*constraint_cost\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **Built-in metrics** (SSE, RMSE, GaussianNLL) cover most use cases\n", + "2. **Weighted fitting** essential for heteroscedastic data (varying errors)\n", + "3. **Regularisation** prevents over-fitting in high-dimensional problems\n", + "4. **Multi-objective costs** balance competing goals (fit vs smoothness)\n", + "5. **Constraint penalties** enforce physical requirements\n", + "6. **Custom costs** are easy to implement via `__call__` interface\n", + "7. **Scale and normalisation** critical for multi-term objectives\n", + "\n", + "## Next Steps\n", + "\n", + "- [Guide: Cost Metrics](../../guides/cost-metrics.md) - Detailed metric selection\n", + "- [Tutorial 3: Parameter Uncertainty](03_parameter_uncertainty.ipynb) - Bayesian inference with GaussianNLL\n", + "- [API Reference: Cost Metrics](../../api-reference/python/cost-metrics.md) - Complete API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Implement L1 Regularisation**: Create a `LassoSSE` class and compare with L2\n", + "\n", + "2. **Cross-Validation**: Implement k-fold cross-validation to tune regularisation strength\n", + "\n", + "3. **Robust Fitting**: Implement Huber loss (robust to outliers):\n", + " $$L_\\delta(r) = \\begin{cases} \\frac{1}{2}r^2 & \\text{for } |r| \\leq \\delta \\\\ \\delta(|r| - \\frac{1}{2}\\delta) & \\text{otherwise} \\end{cases}$$\n", + "\n", + "4. **Time-Series Smoothness**: Add a penalty on parameter changes over time for dynamic fitting\n", + "\n", + "5. **Information Criteria**: Compute AIC and BIC for model selection:\n", + " - AIC = $2k - 2\\ln(\\mathcal{L})$\n", + " - BIC = $\\ln(n)k - 2\\ln(\\mathcal{L})$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/notebooks/utils.py b/docs/tutorials/notebooks/utils.py new file mode 100644 index 0000000..a1faca2 --- /dev/null +++ b/docs/tutorials/notebooks/utils.py @@ -0,0 +1,312 @@ +"""Utility functions for Chronopt tutorial notebooks.""" + +from collections.abc import Callable + +import matplotlib.pyplot as plt +import numpy as np + + +def setup_plotting(): + """Configure matplotlib for nice-looking plots.""" + plt.style.use("default") + plt.rcParams["figure.figsize"] = (10, 6) + plt.rcParams["font.size"] = 11 + plt.rcParams["axes.labelsize"] = 12 + plt.rcParams["axes.titlesize"] = 14 + plt.rcParams["legend.fontsize"] = 10 + plt.rcParams["xtick.labelsize"] = 10 + plt.rcParams["ytick.labelsize"] = 10 + + +def plot_contour_2d( + func: Callable, + xlim: tuple = (-2, 2), + ylim: tuple = (-1, 3), + levels: np.ndarray | None = None, + optimum: tuple | None = None, + found: tuple | None = None, + title: str = "Optimisation Landscape", +): + """ + Plot 2D function contours with optional optimum markers. + + Parameters + ---------- + func : callable + Function that takes [x, y] and returns scalar value + xlim : tuple + x-axis limits (min, max) + ylim : tuple + y-axis limits (min, max) + levels : array-like, optional + Contour levels to plot + optimum : tuple, optional + True optimum location (x, y) + found : tuple, optional + Found optimum location (x, y) + title : str + Plot title + """ + setup_plotting() + + x = np.linspace(xlim[0], xlim[1], 200) + y = np.linspace(ylim[0], ylim[1], 200) + X, Y = np.meshgrid(x, y) + Z = np.zeros_like(X) + + for i in range(X.shape[0]): + for j in range(X.shape[1]): + Z[i, j] = func([X[i, j], Y[i, j]])[0] + + fig, ax = plt.subplots(figsize=(10, 8)) + + if levels is None: + levels = np.logspace(-1, 3.5, 20) + + cs = ax.contour(X, Y, Z, levels=levels, cmap="viridis") + ax.clabel(cs, inline=True, fontsize=8) + + if optimum: + ax.plot( + optimum[0], + optimum[1], + "r*", + markersize=20, + label="Global minimum", + zorder=5, + ) + + if found: + ax.plot( + found[0], found[1], "go", markersize=10, label="Found optimum", zorder=5 + ) + + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title(title) + ax.grid(True, alpha=0.3) + ax.legend() + + return fig, ax + + +def plot_ode_fit( + t_data: np.ndarray, + y_data: np.ndarray, + t_pred: np.ndarray | None = None, + y_pred: np.ndarray | None = None, + title: str = "ODE Fit", + xlabel: str = "Time", + ylabel: str = "State Variable", +): + """ + Plot ODE data and fitted model. + + Parameters + ---------- + t_data : array + Time points for observed data + y_data : array + Observed data values + t_pred : array, optional + Time points for predictions + y_pred : array, optional + Predicted values + title : str + Plot title + xlabel : str + x-axis label + ylabel : str + y-axis label + """ + setup_plotting() + + fig, ax = plt.subplots() + + ax.plot(t_data, y_data, "o", label="Observed data", alpha=0.6, markersize=8) + + if t_pred is not None and y_pred is not None: + ax.plot(t_pred, y_pred, "-", label="Fitted model", linewidth=2) + + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + return fig, ax + + +def plot_convergence( + values: np.ndarray, title: str = "Optimisation Convergence", log_scale: bool = True +): + """ + Plot optimisation convergence history. + + Parameters + ---------- + values : array + Objective function values over iterations + title : str + Plot title + log_scale : bool + Use log scale for y-axis + """ + setup_plotting() + + fig, ax = plt.subplots() + + ax.plot(values, linewidth=2) + ax.set_xlabel("Iteration") + ax.set_ylabel("Objective Value") + ax.set_title(title) + ax.grid(True, alpha=0.3) + + if log_scale: + ax.set_yscale("log") + + return fig, ax + + +def plot_parameter_traces( + samples: np.ndarray, param_names: list, true_values: list | None = None +): + """ + Plot MCMC parameter traces. + + Parameters + ---------- + samples : array + MCMC samples (n_samples, n_params) + param_names : list + Parameter names + true_values : list, optional + True parameter values to mark + """ + setup_plotting() + + n_params = samples.shape[1] + fig, axes = plt.subplots(n_params, 1, figsize=(10, 3 * n_params)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.plot(samples[:, i], alpha=0.7, linewidth=0.5) + ax.set_ylabel(name) + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axhline( + true_values[i], + color="r", + linestyle="--", + label=f"True value: {true_values[i]:.3f}", + ) + ax.legend() + + if i == n_params - 1: + ax.set_xlabel("Sample") + + fig.suptitle("MCMC Parameter Traces") + plt.tight_layout() + + return fig, axes + + +def plot_parameter_distributions( + samples: np.ndarray, param_names: list, true_values: list | None = None +): + """ + Plot posterior parameter distributions. + + Parameters + ---------- + samples : array + MCMC samples (n_samples, n_params) + param_names : list + Parameter names + true_values : list, optional + True parameter values to mark + """ + setup_plotting() + + n_params = samples.shape[1] + fig, axes = plt.subplots(1, n_params, figsize=(4 * n_params, 4)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.hist(samples[:, i], bins=30, density=True, alpha=0.7, edgecolor="black") + ax.set_xlabel(name) + ax.set_ylabel("Density") + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axvline( + true_values[i], + color="r", + linestyle="--", + linewidth=2, + label=f"True: {true_values[i]:.3f}", + ) + ax.legend() + + # Add mean and std + mean = np.mean(samples[:, i]) + std = np.std(samples[:, i]) + ax.axvline( + mean, + color="blue", + linestyle=":", + linewidth=2, + alpha=0.7, + label=f"Mean: {mean:.3f}", + ) + ax.set_title(f"{name}\n(σ = {std:.3f})") + + fig.suptitle("Parameter Posterior Distributions") + plt.tight_layout() + + return fig, axes + + +def compare_models( + t_data: np.ndarray, + y_data: np.ndarray, + predictions: dict, + title: str = "Model Comparison", +): + """ + Plot multiple model predictions against data. + + Parameters + ---------- + t_data : array + Time points for observed data + y_data : array + Observed data + predictions : dict + Dictionary of {model_name: (t_pred, y_pred)} + title : str + Plot title + """ + setup_plotting() + + fig, ax = plt.subplots() + + ax.plot( + t_data, y_data, "ko", label="Observed data", alpha=0.6, markersize=8, zorder=5 + ) + + for i, (name, (t_pred, y_pred)) in enumerate(predictions.items()): + ax.plot(t_pred, y_pred, "-", label=name, linewidth=2, alpha=0.8) + + ax.set_xlabel("Time") + ax.set_ylabel("State Variable") + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + return fig, ax diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..7b2f01d --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,200 @@ +site_name: Chronopt +site_description: High-performance time-series inference and optimisation toolkit with Rust core and ergonomic Python bindings +site_author: Brady Planden +site_url: https://bradyplanden.github.io/chronopt/ +repo_name: bradyplanden/chronopt +repo_url: https://github.com/bradyplanden/chronopt +edit_uri: edit/main/docs/ + +theme: + name: material + palette: + # Light mode + - media: "(prefers-color-scheme: light)" + scheme: default + primary: blue + accent: indigo + toggle: + icon: material/brightness-7 + name: Switch to dark mode + # Dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: blue + accent: indigo + toggle: + icon: material/brightness-4 + name: Switch to light mode + font: + text: Roboto + code: Roboto Mono + features: + - navigation.instant + - navigation.tracking + - navigation.tabs + - navigation.tabs.sticky + - navigation.sections + - navigation.expand + - navigation.path + - navigation.indexes + - navigation.top + - navigation.footer + - search.suggest + - search.highlight + - search.share + - toc.follow + - content.code.copy + - content.code.annotate + - content.tabs.link + +plugins: + - search: + separator: '[\s\-,:!=\[\]()"/]+|(?!\b)(?=[A-Z][a-z])|\.(?!\d)|&[lg]t;' + - git-revision-date-localized: + enable_creation_date: true + type: date + fallback_to_build_date: true + strict: false + - minify: + minify_html: true + - mkdocstrings: + handlers: + python: + paths: [python/src] + options: + docstring_style: numpy + docstring_section_style: table + members_order: source + show_source: false + show_root_heading: true + show_root_full_path: false + show_symbol_type_heading: true + show_symbol_type_toc: true + signature_crossrefs: true + separate_signature: true + line_length: 80 + merge_init_into_class: true + docstring_options: + ignore_init_summary: true + - mkdocs-jupyter: + include_source: true + execute: false + allow_errors: false + kernel_name: python3 + +markdown_extensions: + - abbr + - admonition + - attr_list + - def_list + - footnotes + - md_in_html + - toc: + permalink: true + toc_depth: 3 + - pymdownx.arithmatex: + generic: true + - pymdownx.betterem: + smart_enable: all + - pymdownx.caret + - pymdownx.details + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.keys + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true + - pymdownx.tasklist: + custom_checkbox: true + - pymdownx.tilde + - pymdownx.snippets + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + +extra: + social: + - icon: fontawesome/brands/github + link: https://github.com/bradyplanden/chronopt + - icon: fontawesome/brands/python + link: https://pypi.org/project/chronopt/ + version: + provider: mike + default: latest + +extra_javascript: + - javascripts/mathjax.js + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + +nav: + - Home: index.md + - Getting Started: + - getting-started/index.md + - Installation: getting-started/installation.md + - 5-Minute Quickstart: getting-started/quickstart.md + - First ODE Fit: getting-started/first-ode-fit.md + - Core Concepts: getting-started/concepts.md + - Tutorials: + - tutorials/index.md + - Notebooks: + - 1. Optimisation Basics: tutorials/notebooks/01_optimization_basics.ipynb + - 2. ODE Fitting with DiffSL: tutorials/notebooks/02_ode_fitting_diffsol.ipynb + - 3. Parameter Uncertainty: tutorials/notebooks/03_parameter_uncertainty.ipynb + - 4. Model Comparison: tutorials/notebooks/04_model_comparison.ipynb + - 5. Predator-Prey Models: tutorials/notebooks/05_advanced_predator_prey.ipynb + - 6. Custom Solver Integration: tutorials/notebooks/06_custom_solver_integration.ipynb + - 7. Parallel Optimisation: tutorials/notebooks/07_parallel_optimization.ipynb + - 8. Advanced Cost Functions: tutorials/notebooks/08_advanced_cost_functions.ipynb + - User Guides: + - guides/index.md + - Choosing an Optimiser: guides/choosing-optimizer.md + - Tuning Optimisers: guides/tuning-optimizers.md + - Choosing a Sampler: guides/choosing-sampler.md + - Cost Metrics: guides/cost-metrics.md + - DiffSL Backend: guides/diffsol-backend.md + - Parallel Execution: guides/parallel-execution.md + - Custom Solvers: guides/custom-solvers.md + - Troubleshooting: guides/troubleshooting.md + - Algorithms: + - algorithms/index.md + - Optimisers: + - Nelder-Mead: algorithms/optimizers/nelder-mead.md + - CMA-ES: algorithms/optimizers/cmaes.md + - Adam: algorithms/optimizers/adam.md + - Samplers: + - Metropolis-Hastings: algorithms/samplers/metropolis-hastings.md + - Dynamic Nested Sampling: algorithms/samplers/dynamic-nested-sampling.md + - API Reference: + - api-reference/index.md + - Python: + - Builders: api-reference/python/builders.md + - Optimisers: api-reference/python/optimizers.md + - Samplers: api-reference/python/samplers.md + - Cost Metrics: api-reference/python/cost-metrics.md + - Results: api-reference/python/results.md + - Rust: + - api-reference/rust/index.md + - Examples: + - examples/gallery.md + - Development: + - development/index.md + - Contributing: development/contributing.md + - Architecture: development/architecture.md + - Building from Source: development/building.md + - Changelog: changelog.md + +strict: true # Fail on warnings + +watch: + - python/src/chronopt + - docs diff --git a/pyproject.toml b/pyproject.toml index 1b68c77..59cd032 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,6 +41,15 @@ dev = [ "pytest>=8.3.5", "scipy>=1.16.2", ] +docs = [ + "mkdocs>=1.5.0", + "mkdocs-material>=9.5.0", + "mkdocstrings[python]>=0.24.0", + "mkdocs-git-revision-date-localized-plugin>=1.2.0", + "mkdocs-minify-plugin>=0.8.0", + "mkdocs-jupyter>=0.24.0", + "pymdown-extensions>=10.7", +] examples = [ # macOS x86_64 + Python < 3.14: diffrax with pinned jax/jaxlib "diffrax>=0.7.0; python_version < '3.14' and sys_platform == 'darwin' and platform_machine == 'x86_64'", diff --git a/tests/test_docs.py b/tests/test_docs.py new file mode 100644 index 0000000..2f30ef3 --- /dev/null +++ b/tests/test_docs.py @@ -0,0 +1,311 @@ +"""Documentation quality and coverage tests. + +This module validates that: +1. All public APIs are documented +2. Documentation builds without errors +3. All internal links resolve +4. Code examples in docs are valid +""" + +import inspect +import pathlib +import re +import subprocess + +import pytest + +# Project root +ROOT = pathlib.Path(__file__).parent.parent +DOCS_DIR = ROOT / "docs" +MKDOCS_YML = ROOT / "mkdocs.yml" + + +class TestDocumentationBuild: + """Test that documentation builds successfully.""" + + def test_mkdocs_config_exists(self): + """Check that mkdocs.yml exists.""" + assert MKDOCS_YML.exists(), "mkdocs.yml not found" + + def test_docs_directory_exists(self): + """Check that docs/ directory exists.""" + assert DOCS_DIR.exists(), "docs/ directory not found" + + def test_mkdocs_build_succeeds(self): + """Test that mkdocs builds without errors.""" + result = subprocess.run( + ["mkdocs", "build", "--strict"], + cwd=ROOT, + capture_output=True, + text=True, + ) + + # Check return code + assert result.returncode == 0, ( + f"mkdocs build failed with:\nSTDOUT:\n{result.stdout}\n\nSTDERR:\n{result.stderr}" + ) + + +class TestAPIDocumentation: + """Test that all public APIs are documented.""" + + def _get_public_apis(self, module) -> set[str]: + """Extract public API names from a module.""" + apis = set() + + for name, obj in inspect.getmembers(module): + # Skip private members + if name.startswith("_"): + continue + + # Include classes, functions, and constants + if ( + inspect.isclass(obj) + or inspect.isfunction(obj) + or isinstance(obj, (int, float, str)) + ): + apis.add(name) + + return apis + + def _find_documented_apis(self, doc_path: pathlib.Path) -> set[str]: + """Extract API names mentioned in documentation.""" + if not doc_path.exists(): + return set() + + content = doc_path.read_text() + + # Find patterns like `ClassName`, `function_name()`, etc. + # This is a simple heuristic - could be improved + patterns = [ + r"`(\w+)`", # Inline code + r"##\s+(\w+)", # Headers + r"class:\s+(\w+)", # Class references + ] + + documented = set() + for pattern in patterns: + matches = re.findall(pattern, content) + documented.update(matches) + + return documented + + @pytest.mark.skipif( + not (ROOT / "python" / "src" / "chronopt").exists(), + reason="chronopt package not installed", + ) + def test_core_apis_documented(self): + """Check that core chronopt APIs are documented.""" + try: + import chronopt as chron + except ImportError: + pytest.skip("chronopt not installed") + + # Get public APIs from chronopt + public_apis = self._get_public_apis(chron) + + # Find documentation files + api_docs = list((DOCS_DIR / "api-reference" / "python").rglob("*.md")) + + # Collect documented APIs + documented_apis = set() + for doc_file in api_docs: + documented_apis.update(self._find_documented_apis(doc_file)) + + # Core APIs that should be documented + expected_apis = { + "ScalarBuilder", + "DiffsolBuilder", + "VectorBuilder", + "NelderMead", + "CMAES", + "Adam", + "SSE", + "RMSE", + "GaussianNLL", + } + + # Check coverage + missing = expected_apis - documented_apis + + if missing: + print(f"\n⚠️ APIs missing from documentation: {missing}") + print(f"Documented APIs: {documented_apis}") + + # We expect at least 80% coverage + coverage = len(expected_apis - missing) / len(expected_apis) + assert coverage >= 0.8, ( + f"API documentation coverage too low: {coverage:.0%}\nMissing: {missing}" + ) + + +class TestNotebookQuality: + """Test Jupyter notebook quality.""" + + def test_all_notebooks_exist(self): + """Check that all referenced notebooks exist.""" + # Parse mkdocs.yml for notebook references + content = MKDOCS_YML.read_text() + + # Find .ipynb references + notebook_refs = re.findall(r"tutorials/notebooks/(\d+_\w+\.ipynb)", content) + + # Check each exists + for notebook_name in notebook_refs: + notebook_path = DOCS_DIR / "tutorials" / "notebooks" / notebook_name + assert notebook_path.exists(), f"Notebook not found: {notebook_path}" + + def test_notebooks_have_metadata(self): + """Check that notebooks have proper metadata.""" + import json + + notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("*.ipynb")) + + for notebook_path in notebooks: + if notebook_path.name == "utils.py": # Skip utility file + continue + + with open(notebook_path) as f: + nb_data = json.load(f) + + # Check structure + assert "cells" in nb_data, f"{notebook_path.name} missing cells" + assert "metadata" in nb_data, f"{notebook_path.name} missing metadata" + + # Check that it has markdown cells (documentation) + has_markdown = any( + cell.get("cell_type") == "markdown" for cell in nb_data["cells"] + ) + assert has_markdown, f"{notebook_path.name} has no markdown cells" + + def test_notebooks_have_learning_objectives(self): + """Check that notebooks start with learning objectives.""" + import json + + notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("[0-9]*.ipynb")) + + for notebook_path in notebooks: + with open(notebook_path) as f: + nb_data = json.load(f) + + # First cell should be markdown with learning objectives + first_cell = nb_data["cells"][0] + assert first_cell["cell_type"] == "markdown", ( + f"{notebook_path.name} first cell is not markdown" + ) + + # Check for learning objectives + content = "".join(first_cell["source"]) + has_objectives = ( + "Learning Objectives" in content or "learning objectives" in content + ) + assert has_objectives, ( + f"{notebook_path.name} missing learning objectives in first cell" + ) + + +class TestDocumentationStructure: + """Test documentation structure and organization.""" + + def test_required_sections_exist(self): + """Check that required documentation sections exist.""" + required = [ + "getting-started", + "tutorials", + "guides", + "algorithms", + "api-reference", + "examples", + "development", + ] + + for section in required: + section_path = DOCS_DIR / section + assert section_path.exists(), f"Required section missing: {section}" + assert section_path.is_dir(), f"Section is not a directory: {section}" + + def test_index_pages_exist(self): + """Check that all sections have index pages.""" + sections = [ + "getting-started", + "tutorials", + "guides", + "algorithms", + "api-reference", + "development", + ] + + for section in sections: + index_path = DOCS_DIR / section / "index.md" + assert index_path.exists(), f"Missing index page: {section}/index.md" + + def test_navigation_completeness(self): + """Check that mkdocs.yml nav includes all main sections.""" + content = MKDOCS_YML.read_text() + + required_nav = [ + "Getting Started", + "Tutorials", + "User Guides", + "Algorithms", + "API Reference", + "Examples", + "Development", + ] + + for section in required_nav: + assert section in content, f"Navigation missing section: {section}" + + +class TestInternalLinks: + """Test that internal links are valid.""" + + def _extract_md_links(self, content: str) -> list[str]: + """Extract markdown links from content.""" + # Match [text](path) but not [text](http://...) + pattern = r"\[([^\]]+)\]\((?!http)([^)]+)\)" + matches = re.findall(pattern, content) + return [path for _, path in matches] + + def test_getting_started_links(self): + """Test links in getting started guides.""" + getting_started = DOCS_DIR / "getting-started" + + for md_file in getting_started.glob("*.md"): + content = md_file.read_text() + links = self._extract_md_links(content) + + for link in links: + # Resolve relative link + if link.startswith("#"): # Anchor link + continue + + if link.startswith("../../"): + # Relative to docs root + target = DOCS_DIR / link.replace("../../", "") + elif link.startswith("../"): + # Relative to parent + target = getting_started.parent / link.replace("../", "") + else: + target = getting_started / link + + # Check if target exists (handle .md vs .html) + if not target.exists() and target.suffix == "": + target = target.with_suffix(".md") + + assert target.exists(), ( + f"Broken link in {md_file.name}: {link} -> {target}" + ) + + +def test_readme_not_in_docs(): + """Ensure README doesn't conflict with index.md.""" + readme = DOCS_DIR / "README.md" + assert not readme.exists(), ( + "docs/README.md conflicts with index.md (causes mkdocs warnings)" + ) + + +if __name__ == "__main__": + # Run tests with pytest + pytest.main([__file__, "-v"]) From e7b005ff8eb6ca3f7a0c32d18ae125ab2a143d40 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sat, 10 Jan 2026 16:04:36 +0000 Subject: [PATCH 02/18] fix: refactor utils.py into bindings plotting module, add `problem.initial_values` to bindings, --- ...ing-optimizer.md => choosing-optimiser.md} | 0 .../notebooks/01_optimization_basics.ipynb | 341 +++++++++--- .../notebooks/02_ode_fitting_diffsol.ipynb | 350 +++++++++--- .../notebooks/03_parameter_uncertainty.ipynb | 321 +++++++++-- .../notebooks/04_model_comparison.ipynb | 10 +- .../notebooks/05_advanced_predator_prey.ipynb | 10 +- .../06_custom_solver_integration.ipynb | 17 +- .../notebooks/07_parallel_optimization.ipynb | 385 +++++++++++-- .../08_advanced_cost_functions.ipynb | 17 +- docs/tutorials/notebooks/utils.py | 312 ----------- mkdocs.yml | 2 +- python/src/chronopt/plotting/__init__.py | 517 +++++++++++++++++- python/src/lib.rs | 13 + rust/src/problem/mod.rs | 11 + 14 files changed, 1725 insertions(+), 581 deletions(-) rename docs/guides/{choosing-optimizer.md => choosing-optimiser.md} (100%) delete mode 100644 docs/tutorials/notebooks/utils.py diff --git a/docs/guides/choosing-optimizer.md b/docs/guides/choosing-optimiser.md similarity index 100% rename from docs/guides/choosing-optimizer.md rename to docs/guides/choosing-optimiser.md diff --git a/docs/tutorials/notebooks/01_optimization_basics.ipynb b/docs/tutorials/notebooks/01_optimization_basics.ipynb index cb76af8..3ea4536 100644 --- a/docs/tutorials/notebooks/01_optimization_basics.ipynb +++ b/docs/tutorials/notebooks/01_optimization_basics.ipynb @@ -4,17 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tutorial 1: Optimization Basics\n", + "# Tutorial 1: Optimisation Basics\n", "\n", - "**Learning Objectives:**\n", + "**Objectives:**\n", "- Understand the ScalarBuilder API\n", - "- Optimize the classic Rosenbrock function\n", - "- Visualize optimization landscapes with contour plots\n", - "- Compare different optimizers\n", - "\n", - "**Prerequisites:** Basic Python, NumPy\n", - "\n", - "**Runtime:** ~5 minutes" + "- Optimise the classic Rosenbrock function\n", + "- Visualise optimisation landscapes with contour plots\n", + "- Compare different optimisers" ] }, { @@ -27,26 +23,31 @@ "\n", "$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$\n", "\n", - "The global minimum is at $(1, 1)$ with $f(1, 1) = 0$. Despite being simple to state, it's challenging for optimizers because of its narrow, curved valley." + "The global minimum is at $(1, 1)$ with $f(1, 1) = 0$. Despite being simple to state, it's challenging for optimisers because of its narrow, curved valley." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:05:31.222402Z", + "start_time": "2026-01-10T10:05:31.062542Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:11.314923Z", + "iopub.status.busy": "2026-01-09T21:37:11.314362Z", + "iopub.status.idle": "2026-01-09T21:37:13.871464Z", + "shell.execute_reply": "2026-01-09T21:37:13.870448Z" + } + }, "outputs": [], "source": [ "# Import plotting utilities\n", - "import sys\n", - "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "\n", - "sys.path.append(\".\")\n", - "from utils import plot_contour_2d, setup_plotting\n", - "\n", - "setup_plotting()" + "from chronopt.plotting import contour_2d" ] }, { @@ -62,12 +63,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:05:31.291766Z", + "start_time": "2026-01-10T10:05:31.226817Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:13.875012Z", + "iopub.status.busy": "2026-01-09T21:37:13.874668Z", + "iopub.status.idle": "2026-01-09T21:37:13.879298Z", + "shell.execute_reply": "2026-01-09T21:37:13.878617Z" + } + }, "outputs": [], "source": [ "def rosenbrock(x):\n", - " \"\"\"The Rosenbrock function - a classic optimization test problem.\"\"\"\n", + " \"\"\"The Rosenbrock function.\"\"\"\n", " value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n", " return np.array([value], dtype=float)" ] @@ -83,15 +95,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:05:31.345163Z", + "start_time": "2026-01-10T10:05:31.294438Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:13.882378Z", + "iopub.status.busy": "2026-01-09T21:37:13.882093Z", + "iopub.status.idle": "2026-01-09T21:37:13.886438Z", + "shell.execute_reply": "2026-01-09T21:37:13.885817Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem built successfully!\n", + "Number of parameters: 2\n" + ] + } + ], "source": [ "builder = (\n", " chron.ScalarBuilder()\n", " .with_callable(rosenbrock)\n", - " .with_parameter(\"x\", 1.0) # Initial guess\n", - " .with_parameter(\"y\", 1.0) # Initial guess\n", + " .with_parameter(\"x\", 10.0) # Initial guess\n", + " .with_parameter(\"y\", 10.0) # Initial guess\n", ")\n", "problem = builder.build()\n", "\n", @@ -103,18 +135,47 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Optimize with Default Settings\n", + "## Optimise with Default Settings\n", "\n", - "The `optimise()` method uses Nelder-Mead by default:" + "The problem class is constructed as the core object in chronopt, as such\n", + " an `optimise()` method if provided for fast optimisation. This uses Nelder-Mead by default:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:05:31.501585Z", + "start_time": "2026-01-10T10:05:31.370206Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:13.940616Z", + "iopub.status.busy": "2026-01-09T21:37:13.940316Z", + "iopub.status.idle": "2026-01-09T21:37:13.949194Z", + "shell.execute_reply": "2026-01-09T21:37:13.948410Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "OPTIMIZATION RESULTS\n", + "==================================================\n", + "Success: True\n", + "Optimal parameters: [1.00082688 1.0017059 ]\n", + "Objective value: 9.484e-07\n", + "Iterations: 159\n", + "Function evaluations: 342\n", + "Message: Function tolerance met\n" + ] + } + ], "source": [ - "result = problem.optimise(initial=[10.0, 10.0])\n", + "result = problem.optimise() # Uses initial guesses provided\n", "\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"OPTIMIZATION RESULTS\")\n", @@ -131,19 +192,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize the Optimization Landscape\n", + "## Visualise the Optimization Landscape\n", "\n", "Let's create a contour plot to see the function's shape:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:05:33.129997Z", + "start_time": "2026-01-10T10:05:31.550871Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:13.952288Z", + "iopub.status.busy": "2026-01-09T21:37:13.952008Z", + "iopub.status.idle": "2026-01-09T21:37:14.594434Z", + "shell.execute_reply": "2026-01-09T21:37:14.593679Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAK9CAYAAABLrq+bAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnQVYVHkXxs+3dmILJgiKBaigEoI0CnZ3u3auvbu6rmvrGmvXqmsnJkoYlIEo2Ch2YXe76/e85947DsPcYWjU+3seZJwZ7ty5+T//c877/u/z58+fSUFBQUFBQUFBQUFBQSHd+SH9P1JBQUFBQUFBQUFBQUEBKAGZgoKCgoKCgoKCgoJCBqEEZAoKCgoKCgoKCgoKChmEEpApKCgoKCgoKCgoKChkEEpApqCgoKCgoKCgoKCgkEEoAZmCgoKCgoKCgoKCgkIGoQRkCgoKCgoKCgoKCgoKGYQSkCkoKCgoKCgoKCgoKGQQSkCmoKCgoKCgoKCgoKCQQSgBmYKCgsJ3wP/+9z/q378/ZRacnZ2patWq9L2wcuVK3gfXr1+n7xl8f2wHbA8FBQUFBQElIFNQUFBIZBAt/WTNmpVKlixJXbp0oTt37ijbLYPA9lffL+o/+/bty9D9MmnSJPL19aXMhLGxMTVo0CCjV0NBQUFBQYasci8oKCgoKAj8/vvvZGJiQu/evaOjR49yoBYaGkpnz56lnDlzKpspA8iRIwctW7YswfNWVlYZHpC1aNGCmjRpEu/5jh07Ups2bXi9FRQUFBQU1FECMgUFBYVEqF+/PtnY2PDjHj16UJEiRWjq1Km0c+dOatWq1Te//V6/fk158uShzASylR06dKCvhSxZsvCPgoKCgoKCJkrJooKCgkIScXR05N9XrlyJ9/yBAwf4NQQvBQoUoMaNG9OFCxfivefly5c0ePBgLiNDtqRYsWLk4eFBJ0+ejPe+Y8eOUb169cjAwIBy585NdevWpbCwsHjv+e2337hMLzY2lsv48Jl4f9euXenNmzda133t2rVkbm7OmT1ra2sKDg7Wuszz589Tu3btqGDBglSnTh1+7dOnTzRhwgQyNTXldcd3GDNmDL1//z7B5/j5+fE658uXj/Lnz081a9akdevW6dyu/v7+/F3btm3Ln5VcDh06xN8BvxPrX8J2y5s3L5egIquFx0WLFqVhw4bRv//+G+/v//vvP5ozZw5ZWFjw9sP7sI9OnDjBr2PZCF5XrVqlKqHE8nX1kC1YsICqVKnC27NEiRLUr18/evbsmdZ+O+wTFxcX3kYonZ02bRqlFiEhIdSyZUsqU6YMr0vp0qVpyJAh9Pbt23jvS8r2wvfA+3FM4tjs3Llzgu8G4uLi+JgtVaoUf7aRkRGfO5rbKrFjKqnf4erVq+Tl5cXnK7Y9MuGfP39OsM9nz57N+wj7vHjx4tSrVy96+vRpira3goKCgjpKQKagoKCQRKSBIoIVicDAQB7cPXjwgIOaoUOHUnh4ODk4OMQbWPbu3ZsWLlxIzZs358E4BrK5cuWKF7ghsHNycqIXL17QuHHjuAwOA1lXV1c6fvx4gvVBlg6B3uTJk/kxBv/jx49P8L7Dhw9zMIjMEgafjx8/5oACpZeaYGCLoA6f3bNnT1V2cOzYsVSjRg2aNWsWD47xmSjFUwef7+PjQ0+ePKHRo0fTlClTqFq1ajr7u3bv3k2NGjXiz12zZg1nwBLj0aNH8X6eP39OyQGBBPZd4cKFacaMGfy9Zs6cSUuWLIn3vu7du/P2w0AfGdJRo0bxIB1lrOCff/7hQABBOR7jB4N3OXCcIABDMIDPwzGxePFi8vT0pI8fP8Z7LwIA7CuUZOK9FStWpJEjR3KQkhps3ryZ93efPn3or7/+4u2B3506dUrW9kJgg6AK2wDH2x9//EG3b9/moEwTfO/t27dzUIZzYuDAgXw837x5M0nHVFK/A7YnAiwEtpicwLmGH3Ww/4YPH87nMYJxrCMmNbBszX2koKCgkGw+KygoKCho5e+//8Z0+efAwMDPDx8+/Hzr1q3PW7Zs+Vy0aNHPOXLk4P9LVKtW7XOxYsU+P378WPVcdHT05x9++OFzp06dVM8ZGBh87tevn+wW/++//z6XL1/+s5eXFz+WePPmzWcTE5PPHh4equfGjRvH69etW7d4y2jatOnnwoULx3sO78PPiRMnVM/duHHjc86cOfn9msts27ZtvL+Piori53v06BHv+WHDhvHzBw4c4P8/e/bsc758+T7Xrl3789u3bxN8N4m6det+rlKlCj/eunXr52zZsn3u2bPn53///fdzYnTu3Fn1fdR/sExw8OBB/j9+q3Pt2jV+HvtVc1m///57vPdWr179s7W1ter/+H5438CBAxOsj/r3ypMnDy9T7ljCOoAHDx58zp49+2dPT89433nevHn8vhUrVsTbVnhu9erVqufev3//2dDQ8HPz5s0T3V5ly5b97OPjo/M9OL40mTx58uf//e9/fJwkdXv5+vry+6ZNm6Z67tOnT58dHR3j7YOnT5/y/6dPny67bvoeU0n9DgMGDIi3HGwj7BOc6yAkJITft3bt2njL3Ldvn9bnFRQUFJKLkiFTUFBQSAR3d3cuy0JmBIINKHFC/xhKrMC9e/coKiqKS6EKFSqk+jtLS0suR9y7d6/qOZRuoRzx7t27Wj8Ly7l8+TKXCyKDJWV/UArn5ubGJYYoo1IHWTd1kKHB3yLDpo6dnR1nAiRQ2oUsxv79+xOUm2kuU/oOyPyp89NPP/HvPXv28O+AgADObkjZI3VQsqfJ+vXrqXXr1pyJQHbohx/0uy1h2fgs9R9kaZKLtm2IkjaJrVu38vprZlDkvldiIKP64cMHzripf2dkI1GOJ21PCZTYqffMZc+enWrVqhVvHVMCsrQSONZwzNnb23Om69SpU0neXjhekOVEtkoCPXQDBgxI8Ln4LigvlSsD1PeYSup3ULeBkGwhsE+wb6SMG8otcQ6rZ2JxDmF/HDx4UOv6KigoKCQVRdRDQUFBIRHmz59PFSpU4JK4FStWcFCkrpZ348YN/o3eLE0qVarEAY8kjIHyKJRtIbjDwM7b25tLqsqVK8fvRzAGtJV2SWA91MslEVipI72GAS4G9xLly5dPsCx8L5R5PXz4kAwNDVXPQ1VSHXxHBA5mZmbxnsffIMiUtoHUV6ePx9i1a9c4yECZIkrLkgIG9wiUUwOpH0xzG6oHCPheKC1UD7hTgtwxg+AEx4L0ugSCf83AD+t4+vTpVFkflAeiHBUTDZqBkWYpqD7bC+uPXjAELupofl+cRyj/RGCP8kFbW1uW6Mc5IR2P+h5TSfkOOJalc079XABSiTHORfwd+jy1gfJkBQUFhdRACcgUFBQUEgGZCEllEUIGELlABismJibBgDMx0OOFbAJ6ZiBiMX36dB6Qbtu2jdUcpewXnkePjDY0P1NOvU9ToCApqGcbUpoNkgMDdvwgmwJhDGkbpxS5ddTMAkp8DeqHabGP1bcLskDoz0JfGvrTMHkA4Q5kfTUzsqm9vZAlbNiwIfu3YfLi119/5d5E9FJWr149Tb6DPuBvEIyhZ0wbmkGpgoKCQnJRAjIFBQWFJIDBKAaLULubN28el1GVLVuWX0OApsnFixdZJl9dNh5BSN++ffkHs+wQyZg4cSIHZFAwBMhspVYGSELKvqlz6dIlVu1LbHCJ74gBKpaBrJ/E/fv3WXBE2gbS+kMoRDObpgkyLRDzgFgJBBYgOgI1u5QiZQg1Ff00s05JAd8LwQIG/LqyZPoGrOrHjHqmBiVzyBym9r7XxZkzZ/g4gDqkugAGSgWTC75fUFAQvXr1Kt4EgrZzRNq+yJLhB8cYJiNQggqBF32OqaR+BxzLKLGUsmIAfw+gHiqtE8oXIeghN0GhoKCgkBooPWQKCgoKSQQy5MiaQQ4bZtEIsDCAxGBQPQjAABJZMJQlSrP4mqVTmIFHKZwkHY8yRgwEoV6HwawmKC1MLkeOHIknr3/r1i3asWMHq/ollvWQvgO+szp//vkn/4YCHsCyIEuOoBXbJrFsDnp0EOhI8v+aVgLJDQbwfTQl/aHgl1ygBIj116Zeqf69EHhrk3bXBAEXyhPnzp0b7++XL1/Ox4i0PdMDad+rrwceQ1UwueB4gXUBFEUlcPxrlqaiXFbzOMHxj2NIOif0OaaS8x0woaL+Xvw/W7Zs3KspZbOxzrB60ATfTZ/9rKCgoKAPSoZMQUFBIRlAChu9T5DjhsABSgyR4YJwBuTR4X2EwScCDsibAwgToBcIwiCQL0fmADPwERERKkEK9LYsW7aMl4VsEWS24TmF0iuICCBztmvXrmTtM/TgQK4bsuLo3ZECFG1BhiZYX/S1QdocA1FInUOCH0EoyjiRMQRYP0jiQyIfPlGSl1l0dDQPvvF+TZBBRCYDpaAIVEJDQ/k7Jxdsc6kvDRkrDPCRiUtJzw++X8eOHTmAQgYHGT1kWeB9hdckgQgE1NinCFQRaKMXr3bt2gmWh4wk5Nux7bEsSP4je4R9gu2W2qbX8KqD9LwmKAlEwINtBAsGHGfYhxAxSYnXFkoQkVlCBhk9WZUrV+ayXM0JCWSlEAAh+MF7IASCcl5kXiU7BX2OKZQoJuU7IDsLyXwc09g/sA+AkAp89aRsMY5xiM0gEITYDrYTAjbsfwh+INjDuaygoKCQYpKtz6igoKDwjSNJlUdERCR4DVLlpqam/AM5bwB5fAcHh8+5cuX6nD9//s8NGzb8fP78+XhS5cOHD/9sZWXFMt6QSMfjBQsWJFj+qVOnPjdr1ozl6yGxD+nyVq1afQ4KCkogUS/JdMtJrAP8H3L7a9asYVl9LBNS5ZrS8HLLBB8/fvw8fvx4lt+HTH3p0qU/jx49+vO7d+8SvHfnzp2f7e3tVduiVq1an9evX69V9l4iNjb2s5GR0edKlSpp/Xx12XJsO13g7yEJnzt37s8FCxb83KtXr89nz57VKnuvbVnSdlAH+xny7BUrVmR5dNgf1K9f/3NkZKTqPRcvXvzs5OTE3xt/L0nga9snksw9loftWbx48c99+vRhKXh1tG0rad1xXCQG3qPNJgA/3bt35/fgOHV3d/+cN2/ez0WKFGELAtg2pGR7wQKiY8eOvP9h94DHOK7Vl/no0SM+LrENsFy8D/L2mzZtSvIxldTvcOXKFbYdwDGCbY/voM12YcmSJSzpj8/FeWthYfF5xIgRn+/evZvotldQUFDQh//hn5SHdQoKCgoKCgoKmR+IfGzZskVrSbCCgoJCRqD0kCkoKCgoKCgoKCgoKGQQSkCmoKCgoKCgoKCgoKCQQSgBmYKCgoKCgoKCgoKCQgbx1QRkkM61tLRk5ST8QMkMqki6gAoSlJegpmRhYcHmowoKCgoKCgrfL1BGVfrHFBQUMhNfTUAGqegpU6ZQZGQknThxgo1EGzduTOfOndP6/vDwcGrbti3LT586dYplmfEDXyAFBQUFBQUFBQUFBYXMwFetslioUCH2/kHQpUnr1q3p9evX7D0jYWtry+atixYtSuc1VVBQUFBQUFBQUFBQ+EaMof/9918uR0TAhdJFbRw5coSGDh0a7zkYovr6+upc9vv37/lHAsafT548ocKFC7PBqIKCgoKCgoKCgoLC98nnz5/p5cuXVKJECfrhhx++v4DszJkzHIC9e/eO8ubNS9u3b6fKlStrfW9cXBwVL1483nP4P57XxeTJk2n8+PGput4KCgoKCgoKCgoKCt8Ot27d4paq7y4gMzc3p6ioKHr+/DmbOnbu3JkOHz4sG5Qlh9GjR8fLrOGzypQpQ9euXaMCBQro/Nth7hPp2tmb1HdWJ3Jr6yD7PmTdelUfSY/vPqMx6/qTjYeV1ui7d42R9ODGIxq6tBc5Nq+d4D1jm06nM4cvUOuRjajNyCayn3dwYxjN7b2UzG1MaUrAr6rnH999Sj9aDKX//vtMC05OJSOT+AGsOvdvPKLe1UdwlnDJ6elUpGShBO95cu8p9bQcTv/9+x/NCZ9AZSqWlF+nTUfor34rqGipQrTgxGTKkuUHndvr0aNHVKRIEZo3cCUd3nKM6jStRUMWJixV1ZfY6Bs0quF0+iFrFloQ8hsVKVmQ0oLDvpE0b+QGymuQi+YfGEO58+akzMCrF29pcKv59PzJG+owwJ0ad7TX+X71fZBas0EZzb///kcT/9hBkZHXycAgN02Z2ppKlNB9jmck3+I++JpQtn/Gk9n3Qdy9ZzRixEZ6/vwNWVmVprHjmlLWrFnoe9sHU4dtoBPBl6iqjTGNnd8x01QXrZ25l3yXHCSTyiVo6rbBabJeD28/ob6Ov9Hnf/+jaXtGUDmLMslaTsyJqzSmwVTKmiMbLY6cRAWK5E90Hxzdc5KmdVlIBYrlpyVRUylrtqxax5bDXMbT1dM3qfOEVtSkX714r//aeCqdDYmhFj/5UPufm/Nzx/acpCkd5vJyl52bRTcv3qGhjmMpa/astOT0DBrs8Au9ePyK6rSwpdAtR8m0ujFl+eEHuhR5lfrM7kKenZ1V693JrD+9fvqGnNvY06EN4WRW3ZiuRt/gcejcoxNplNcf9Ob5W5q4dwwVKG5A/axH0g9ZfqC/L82h/IXyUci24/Rnj0VU3KQoLTwxRbUPsd1/tBzG/19xYSYVKBb/Xn4u/BL92ng65SmQm/4+P1PrtgGXTl6j0d5TKGeenPT3uemUPWd2re979uwZmZiYUL58+Si1+KoCsuzZs5OZmRk/tra2poiICJozZw4tXrw4wXsNDQ3p/v378Z7D//G8LnLkyME/miAYSywgc25qR7fOxVF04EVq3scnkfc60I4F/hTtf5HcW9bV+p76Hd1o7cRtdHT7KWrY3SvB64171KMLwbEUtukE9ZrUWfbiYuthQwv+t5JunL5DuXLkohy5hO+H71PTw5oi/aMpeO0x6jWjk+z64r3V61rSmeALdGL3aa0BIN7j2NCWwnZEUMiGCOo3u4rs8rw7utGaX7fR0zsvKfbYDartXV32vTiJP3z4wMtvO7QphW09RRG7z9CnGf9pDQz1waZuAbJ2sqTokIt0YN0x+nFiG0oLGnRwpl1LQ+nO1QcUvDWK2gyKf/HLKLAte49qRjNHbybflcfIu4U9FTUqoNc+yIwDoeQycVJ7GjpkLV26FEeTJvrR3L86UaFCeSgz8q3ug68FZftnPJl5Hzx79oYmTd5Hr1//RxUrlqXJU9pTnjwJxxLf+j44fugiRYXdoBw5ctGQCW2oYMG0mexMKs8evaTAdRGU9Yfs1G1U8zRbr03T91GW/7JStbqVqIajZbKXc2D1Ecr6v+zk0dqRjM3K6LUPgtcfp6z/y0Y+XTyoSNEiWpd78Xgs3Txzj3LlzE1Ne3mTQYEvgd7ty/foYuhVyp4lO7Uc0Fg15g3dJCzXu7MHFS5SmDZv28X/d2xUm25E3aE3T95T8ZLFKC7mgfD5XT1p4eCV/NiluaNqObGnrtH7Zx85iInaf55fr1yzEl2PukOVbcvTu2cf6cOLT1TMqCjZetnQ5hnC59RwtaAy5UrzMiL3nObnPNo4x9uHB1aF8fNV61Qk4wrGCb736QMxwjo3tJXdNiA64CJv9zoNalMxw2KJ7qfUDOoz1xUtieCgVO/3UgeljUFBQfGeCwgIkO05Sw2QtQEnA8/Sq2evdb7XvnFN1YwGZuq14dquDv+ODDhNLx6/TPC6Q9NalCtvToq7/pDOH7kk+1mGJsWoYHED+vTxX7p88lq815oOqM+/ff/aS7cv3dW5zp6dhMDRb8VBnmXRhk8vd/4dvPkI/fvpX9ll5ciVnTw6OfHjXYsDSF/K1zChKvYVeNnH/KIoJbQcLHx3v5WH6eWTV5QWZMmahdoNFT5n66Igev3iLWUW3BpXpyrWxvTuzQdaMnUPfY/kypWdJk5qRUZGBejevWc0ZvQmevNG+zVFQUFBQRtv337ga8ed20+peHEDmjS51TcZjCXG+3cfaeHEnfy4SScHKmOa+IA2vdiyIIDvdeUty1BtT4s0+YxXz97weAI0H5hwEl1fkO0J2R7Bj5v09dTrb+5euU8n/E9zgODd3UX2fTsX+vPvui1tyUAt6wYCVgvrbu1pRcXKCEHL/RsP6fiek/zYq5srvX/7nvxXHeL/e3Z2oa2zBOG8Os1q0/Wztzib9fzBCx4jVrItT0VLFVYt/3Twef5tZFqcx8iGxkXp7hWhjciphR0d2SF8Z7tGNTnQDN1+TFh2U6FC7O2rdxSxTxj31W0ZfywftC5UeL5VwjE+xtjBW47yY0dxnK4NrHPYjhPCZza2ofTmqwnIUEoYHBxM169f514y/P/QoUPUvn17fr1Tp078nMSgQYNo3759NHPmTLp48SL99ttvLJffv3//NFtH48qlqEylkvTxwyc6sueUzvda1DGnfAXz0PNHL+lcWIzW96DkD+lcBB/SwaROztw5yKGJENgdWB8m+1k4QSvbVeDHmp9Vy7sG1fKuzsHagsF/ywZa0oGeO18uuhsbR9GHtNsNWHtY0qAFPWjZ2ZkcjOii4Y9C8BaxL5ruXY2fzdTFj1Pa0fwjE8inh6vef6N1Xd2qkknVUvT21XvatfwgpRV1m9hQ6fKGfLH2XZZ2n5NUcFz0+7URX0BD95+lyFD5oP5bBhkxlCsWKJCbLl+Oo3HjttHHj/KTCQoKCgoSnz79SxN+96WYmHuUL39Omjq1NRUunPe73ECblh6iuNtPqXDx/NS+rxtlFp48eE67/w7mxx1HNEizEso9Kw7yeMK4ckmycU9+0LdrSSCP+zBONKtmrN9nLxUSENaelmRUTnv7ycunr+jwpnB+3LCPZ4IER+CakHiT77zcJQFcTljdzYLHpIc3HaGXT19T8bJFKY9Bbs64ZcuRjfIXFkr3LOtWpqB1wnJ8fvSI9xmngs7w7/dvPvBvxxa2XHUFbBvUoCO7TqgSFg9uPqSLxy7z/+3EBMZxv1P04d1HKmFanEytyqqWeyvmLl06cZXHMs6tErZfYNyLFp28BXLz9pHj6pmbdO/aA8qeMxvV9Ep+dvObD8gePHjAQRf6yNzc3Lhccf/+/eThIezwmzdv0r1791Tvt7e3p3Xr1tGSJUvIysqKe86gsFi1atU0XU+nZkL0HbxViOzlQP2qXUNrfnxIPEG04dJG6EU7sCFM5+uJZaSquQoXh92L9sd7Hy5MfWZ1pWzZs/LMg3TCaCNXnpzkIvbG7VkSqPU9mNVo0MuD8hVM/IZUsrwR2XhachCIC5C+VKxpqvdFShf47i0HCdmrnYuD6P1b4SKR2qA/rsMwb368bVEQvXz2hjILJuZG1Ki9MKO0YMJO+vD+I32PlCpViCZOakk5c2ajk5HXafq03XwTUlBQUJAD14gZ0/fSsWNXKEeOrDRxYksqXeZLRuB74u6NR7R5mRD09BrdgHJlogzhprn+nL2raG1CNq6ppzmgDgIF30XCOKb5gHrJDvrevXlPe8UJ4qb99Wtx+PDuA+0Xs1bSRLc2MHGP9TSxKEOVapeP91r0ofP04OYjDrLsxezQxw8fyW/5AWG5vYUAbtfC/apgK/AfIaPm2rYOV3KBEqaGFHftAS+nrlpwdP5IDB3dHcmPpaxYQcOCPB4tW7kUZ/ge3XnCiYpqLlVo/0rh++BxkRJCa0roNjFj1qx2vO0rZcdqelWjAkXjZ/3AwY3CGNuhSS3KniOb7PaRsmM2HpbcQ5befDUB2fLlyzk7hhJFBGeBgYGqYAwgW7Zy5cp4f9OyZUuKiYnhv4EhtLe3MChOSxzFgOxU0NlEy9OcWwsHK5oU5YIpKdo/G3KRHt97muD1Gu4WlK9QXnr28AWdCRFmGrRRr5srGRTJx+WN4TuFg06iVHkj8uziogrsdIFgS1pnnDwppWFvYXn7/z7EF6L0xqlpTSpWujBvv6AN8oFxSqnToDoZVypBb16+46AsMwFRj0JF85FZ5RL0/u33GZCBihVL0G/jm3EAffjwRYqN1a3IqqCg8P2CicRFi4IoIOAs/fDD/+jXsU2oSpXUUVv7GrfFgj92cnVQDYfyVMczbSe+k8LDu09p7xphwN5xuE+aZccwfnh6/zkVKVGQnFskFGHTlwMbwrmFAuV8yBrpw6FNR1hUo2jpwlSrfjXZfSQFV/W6uSTYDvtWHFCNSyUhi6O7IunZg+dUuERBsmtkQzERsUJGLHtW8uxcl8J8j6uyYufDY/g8iNh3ShWwoYpLYsNUwXKqsn0F+vzfZ6pgY0qXTlzh5+wa2qg+36OTM2fcpPJJr66uqqDz2F5h2Y7Nasf7XofEgMtVi5gextah24X1dNZSzqhO+E4hYLRvJCRL0puvJiDLaPT1zy5bqSSVqmDEFybp4JGjuksVDpJQtnjqoPYSQNTxog4Xnx8izg5oZtrsG9mogiQ5cGLU7yHMnOyY55fgdan08eieSJ3fFZkpC8eKfJDvWiTUIqeEWvWrk6FJUU6B6yq7TCuw/Zr2FYLCrX/tl+3nSynIHHYY5kPWzpXItl76p8J1kSdvTlqwYxCNntWO8hXITd8zNWuWo1GjGtAfE1tShQpGGb06CgoKmZT164/Q1i1Cz8uIET5kZxc/4/A9IZS8X+aBet9fGmUaVUWwce5++vj+E1WtbUrVnSqmyWdg3LBl7j5+3LSfJ2+H5ICx186FQk99o94eOtWn1f/Gd76QtWrYy0O2VQT6AVeirnOw495B6N+XePHklWr86N3jS6lp0Foh44n3Y6wU+E+wqtTwxvnbPHbFGFbSHyhhZkgPbz3mwLDDr4JCI7gVc4eOiIkAfD6w8bSio2KJYlXHiqrX63V3pbOhF7mNBRoJ0EoAGE+jhww9aRVsyqmWDaGQO5fvsS4BgkZNog+fV61nNWf57Ojdq/fpmtgDp0tkLi1RAjI9uX7+tl7vw4WojhjchPoKF2s5cOJIcvZShK8Npxa2/FtbH5n6bAFmAVAHLAdqhnGwof/r+rlb8V6zcq5COfPk4DpbSJXqokn/+qqyxZSWuOGC00hMhe9eEqh34Jua1OvkRHkNctOdK/fpaCJBdEqwr29Ff6zvT+bVvtQ+ZxYMCmZOZcGMwNWtCgdmCgoKCtrYszuKli8TZvD79HEjjzQSifgaePP6PS2eLAg7tOpZl0oayyvYpTf3bz2m/evC07x3LHzXSR4/5C2Qh+p3jh/sJIUzoRc5KMiROzt56bmcC8cuU+wpIdCq302Ql9fG3mVBKvG5/IXit5QErQ2hj+8/kmk1YxZOAy+evGS5e+DWwYkn4Q9vFjNR7RxVY1aHprUpcI0QqEll/pDLz5U3l2r522YLomHQK7ggCtBhee9evyfjKqXp5sW7/P+KtczIpGoZVXYMQh9olQH+q8TyyHZ14ilLIjsoLRsBnCaHxXEzvrcuXYMju4TvaulYMcH2SS+UgExPju8T6mP1QQrIoHiTWBmes6gUgzJCZNW04dTcVlW2+PTB8wSvV3e3YLEN+IChsVGOYqWL8EGrXosrgRQ1BD7AgbVCQ6YcyKYhhY1Zh+PJCGDQq4VZDQnPTk58McFFJbFgUF9QOrBhuqD2lBg4iRv0EEo2N83am2ZBYWaaNVRQUFBQSDqHDl2g2bOFbEjbdnbUoqW8atv3wD9zA+jxgxdkVKYQteyh3cIno1g3y48Fy6zqVCBLe0HYLLXBeGHzHKHqqGFPFx6LJRffeULVkVu7Ohzc6YOUUXNpbZdANVHizcu3dEDss/LWEEPjHn6x2gmvSeMUvB/brpxVWQ6STgad4XEVxDuqOJhT8BYhEML/kRXLXzgv3RN7w6SxJPj3338pZKsQFFWxr8g9bMigQWEcNOrrRQdFERDPzs7ctya930MUF3n+6IVKXVFS55bWXUpUaKou8md/+pfCxb4wqZ1IjvBdQrminZ5lommBEpDpydEkSKybVStLxcsU4cDjZKC8SAbAgQ1JeijwnTpwVrZssbx1OT74pLSuOmhStPESzKU1+8M0sWsoBIvH9ggHnzqSsg7S1Dgp5MAsAy4YQJoZ0Rd8B2ToZvZcpOpBwwntJGYKdy/WX9xDF9imAf+E6J3ZbNzbnbLlyEoxkdfodKh21cvvnSMBF8lvk3xZ7PfE7dtP6ODB83Qg6ByrqykoKHz7HD0aS5Mm7uRMQIOG1al798wVgKQ3sefv0s41Qqak36+NKUdOecGE9Ob2lfsUuEmYeO48qlGafQ7GCzAThjIfxhHJJe76Azoiil7oK3WPSfEQUUBO6sfXRtCaEC73K21egquh1MF47NbFuzwx7dbeUfW8JG3vJeoLBIljPfSYQQb/9fM3VNy4qEqavoY7BNqIjKuWjid1f/FYLK8nRD6QNADlq5vQjXO3uMzQvJYZl1P+kOUHLoWM9D/NkviY9EcpIzgsitYhe1e20pc+TUzsQ0AEy5GSDZoZR3w2gkWruvLliqgMg3E0sBfF9jICJSDTk1uX7tGty/oNvDDDIDUFholNgrpK9iT/MunE0ob0Hqk5UROpjwyzBboyPLV9hOgfjZlP7z+L95qNVzUOZHAAH90tzF7IIdUgI6WtzSNNG1gvbBv0jeHkVe9BayD6l0ENB9Ks+iKVaGLZ0veWyigb/OhOu/QM8AoWMyCvDo6qLFlGG1hmFtSPpZImhWnP+mP0MC5hlvZ7AQOx0NBL1K/vKjp9+hadP3+HJk7cwSprCgoK3y5RUTdo/G/buV/Iza0yDRzo+V1XPWA7zBu/na+JTvUtybpO2mSgksvamXvpv3//o1oeVamStVCGlxZsEbNjnh3qUAGZDJU+7FgYyNuyhltV1iLQh4B/MHn+iQMVcxtTre/hDJjo9YpxluYxK/mSuXdwpDz5hR7yq6dv0OXIq5Q1WxYO0pBhCxPHnu4d69L+lYIKJEoMb8fc5Wze/374n0rpUB1JWbGGh6Uqq/ZOVLRGACaVRVp7WFKBogZ0aGOYqlwxSxahxDBorZDdUw8Y1dXMMaaUShvVkcbLEA3RVa6I9iL2TattpvJfywiUgCwJHNmtf3megygbip4kXXL0QMoOIWX66aP2skVJdCPqwFmtAQt8G9ADhuZGXSbRhY0Kkll14eKkmZHDASsp2qwet1HnekM2FR5pSGkHypQ4rp+ynX5pOJVO+EfT6xdv4l0IoIaDFLgEXNrLWZThdPZ+sVZYH6RaYiwbP7hwSLKm1V2r0PmjlxM16ZZoPsCLVYIig85SbPQNSm+QUd2zKoT+HPwPZTRSoItt+uzJK3p47xmVMStKLo2q0ZIpQr/A9wiyYnPn7Cc7OzMaNMiL+g/wpOHDfejvFcF0/drDjF49BQWFNODChTv0y89b6MOHT2RvX55GjGygl+DCtwyqJWJO32Z5+x9H+VBm4vrFu3TYVwgEOg5vkGafA9+qiIAzPG5o1i/5RtAYt+wTZd6b6Lkc3KP3LBH6wnx6ynu+QSDj2pmbnEWSSgAlnsQ9ozBR60CStZe8xwBEMlAGeXB9KLffIMOGcSYqvzA2kFpyvLo6q9pXajeIn2E6FSS0+6BPHyWPKFeU1BWRfZPKE+u2sufJdKkKTFIhR+YQY1p8nvQcQAClLoOvbftIMvaOTYXxsxyHxcBOsq3KKL7vK0oSCU/E7FkdBBhIk756+prOyBg/S1StU5EMiuZnqdMzIRe1vgdpWgRBmA2BMZ8mSDdL4h+Sf4McUsr6tBZz51bDG7GMPkQ/JIlUOep3F4I3v2VBWrNyWGcY+e1dGsSB2bY5e7gBFRLzMcevUMWaZqpsFk62hn08VGWLusRJ1Hl87xmN8plCy3/ZSENdJ9DMH5fS763nUCfzIbR1jh9dO3OLdouGiYlhZFJMdUJunp1QiTItUM/s4YLp2daOZ5r2rU1/xUmJ1y/f0f07X7KnF6Nv0dyx2/mxSwMr+vD+Ez3NRFm89CI29j5Nm7qbbGxMqHwFQxo08B+Ki3tGFhalqf8ADypZqhC9evUuo1dTQUEhFYFZ/KiRm+jt2w9Uo4Yxy9tn1THb/j3w9NErWjVbyKx0HeJFhYslPzOUFvwzbTffV2E3Y2ZROs0+Z/McoZfQsUlNKlGuWLKXs39VML158ZYDHn0Nic8Gx7CJMUoBXdokNEOW2CEqMEIMQ9MfFgbOyCJWtqvA40vw9tVblZpig16e8QI0KHVvnyNUENk1tlGZOiNoQwkjgi0Lx0qq5WMiHpk2ICUKUB2FcXEhwwJUuGRBDhZRrmjXyIZ9cBGcolyxouiThnJFYOVcmRMKElB5vH3pHita1vZJWK4YE3GFSxHR01fNVd6GAe85f+RyvEq0xIB91Lb5KVcZ10QJyJLARexgLV5g2kC2SZLODN2eiNpilh9UdashMiWJwKOjUCYol5FCQyRAwKZLTEQKyKIPJwzIcMJ2GteKH68au4FPKDnc2jlyEHH93G2tWTlk7eA50XdOF+o5pT0HR0tGrKEJrWfRmj+2comkukkfsma4uMAg8MT+aNKHwkYFyKWVHV980YwJn4mKtUxp8MLu5NnRiSbvGcnysfrSarCgIBniG0F3rz6gtARSs1JmTyoBwcWl7eB65L9Btx9cWnLs0AVaMGEHP0ZgbOtSiR7df0GL/9hHf47ZStlzZKWCRfLR9wYMo7t0ceLZ8ebNa1KtWuUoKOg8v1a1aim6evUBTZ2ym05EpI4wjYKCQsaCc3rE8A080VKlSkn6fUJzyp5MSfNvifXzg+nNq/dUwaIUebdJvudWWnAp6gaF+0Vz1iots2MYlEuZlZbiuCE54L4vydY37Y8qHf2G5YGrwlSiaNrK9QAmvyWvsEZ94velYcwkKRdKY0tJzANBUcnyRhzIxJy4wj1eEF5Dn5akG2DhWJmDSARW58KFpEMVe3PqbNZfVf10LiyGyzARSCGAwngRfmJSVuuo2NIDH7P8hfKpMl4wcJa2g6TmqCnaIdkkodRRKrVURxpLw5dNlxl06I4TQrliLTP2pNWH4O0RtG6qfqJxSUEJyPQE5XngqJ9+gQJwFKPtsB0RifpbSSWJEOWQyw65tHXgiwwOcqRxNYE/GMwEcTId09EDhhkMBAGYXXgSlzDAbNDbg0qYFueTWVdfm+DELpwkOxcIFxRNEJSum7SdZ2CGLu1F0wJ+pfY/N6OVMbP5tR0L9qvEPXBR8eoipNSlC5Q+eHR0pB4T21DLoT6cmm71UwOq4VqVipctwv1wcJ9H8KMPKJu0ca/KF5Ft8/Rfh6SyfvY+6mo7jhaP3UIHth6nezcecWCOMtG3r99TKdPi9O6NcOFKb1wbVucs2LGDF1QXRctaJmTtaErV7c3op8kt6dCeaNUM6ffCu/cf6T+1TPDLl+/owYMX/BgXdDOz4lTXuSKNGrWRrly5n4FrqqCgkFKuX39Ew4etpxcv3lLFikY0eUprypVLMMz9nokIjqGIQ5c5qzHgt6aZrnRz9dRd/NulWU0qU8EwzT4HvmPc8+Vahcyskm9lc2RXJAd3qKhya5fQ2Fgb964+oOgDF1S98nIE/HOY20rg2yW1qqhnkK6fvcViJFIpIPugiWO5Br08+P4v+daiAuvEviiWx4ep8/WzN/n5irXNuA8MY0p4fuG7TGwzi19DxgvkFOXonVs7qCbb0SMWKgaLCMA+fvhI4TsiVFL6wve8z+rbONbUyxIxRkYZpba+Mul1SQ5fqhyTQ7KnSkyFMd7fJKINkVwy15mUianlKaSRk+JThebMvAVy05O456oZBDmquVSh3PkhXf+MLhwV0qeaFClRiCzF7JY23zKcPM5thBP6oNgYqQ00YJpYCunp04eFGX51YADo1l6YMTmyU3d2r3G/evz78Oaj9OiuEFipgxrhyyevqgQ90CRaw81C5QT/6PZj2v7Xl/LAxn2ERmlYBty8cEfnZ6t/b3WO+UXRQMdx1K3qMApYE0JrJ++gP3stJX1pNUSoh9+/JkSrzUBqUK+9PRkUzsv198cCztKsIWtoSIMZNPuntTR7yBoqXd6QcubOuJt/p0EetHpuAB3YdYomDVlHZyKukZWdCTl6VaW547bT+oUHyH/bCYo6+v2IWTg5VaTgwxdpn99pWrs2nEU9nJ2F8gwcsxiYHD9+lUsY8+TJkdGrq6CgkExu3XxMw35aR8+evaHy5Q1p6rQ2yjmNSak3H2jBBCEz0LijPZlVLpGpjrHT4Zcp8tAFypL1B2o/LO362h7HPePxAWg9NGWfs2OBKDnf3ZVy5tbvvgGRDoyprD0suMxRG3jdb5nQduLd3U3Wl8yxua1KYh9ibxD0QCbLq6sLj38ObRDGkk0G1FeJciA5IPV+wUMM8PvjhFYHCL3hb/cuDVSVBQL4nKE1J1/BPKyufT48hpMMKBU8ulsQiEPGTVJElEQ5kEErUPRLWez58Esc+KFVx1aLKiIUEx/dfsJj6lr1BAVybSDpgB474NAkoam0Nh7cesyqmmkh6KMEZHpSUzR+jDp8gTNQ+oDyM5X6oY5ME0BKVXrvgQ3yJtEu4kyGlK6Ve/24XxTX9Mph7S4EmLtF9R1N7MWMXaR/tM7yxwrW5Vi6H5kdbZL1OLlmBI3jLJgUlOFEDRQvZvCgiDr4RVzEqFxxshWVIHctSZoEPmRdp3VfxD/Ivm17sIQm7hhOo1b24QvCSRlbAU0sHCpQRZty9PH9J/JdmDoy/JoULJqfnBpZcxnA6EXd6OelPajrmEZkYVeeJqzrT01/dGW1xcQyq2lFlRrG1K6PK92MfcB9i78v7sLHM0oW7918TIt3D6GB45vS6jnfT5bM2LgIjRjpQxcu3uW+ks5dHKl8+eKq13/5eTO9eP6GRo1qSIaGBTJ0XRUUFJLHrVuP6aef1tHTp6/J1LQYTZvehvJqMZz9HvlnXiA9uPuMChfPRx36yQtJZAQYX6yaIgSL9do7kFHZtFPL810QwOODSrVMybKOebKXc+X0DTodcpEzQA10CHOo8/b1O9q/Uig1bKxDAORMyAW6FXOXRTgQQMVbxqt3qkl9dV8yyZAZ2Si0ryALhfu/eU1TKlyikEpcI2funFyuiKAKKosIfLpNbKtaDt4DYTiMlUuYGdK71+9Ywfvh7Ucqn7J9K4Rg0bahDRUpWZj8lgsBomcXF5UiohSQaWavgkRPNayntiAWat38epOaqsl/bRzZfZKznKiAMyxblPQhXFSFNK9ZjlIbJSDTk1JmhlTStDgfnJFBCXuv5JBEIqD2ktjg2lVszIR0vZzCoWPz2pxlQiPkjQsJPbbQmFmmUklOKyMVLkeTgd68HHhQxEZ9MWmWMLUy5vJHKP9p8z5Tp2l/oX56z5JArSqRty7eobFNpqlmFCAxD8n725fvsV+FQeH8dDLwi/F2Q9HLA5KuuPjoy9XTN7lBdenJKdR+dBNV3TBO7ir2Fejh7YQZPG1gPVsN8ebHu5Yd0BnYpoTWAz3p4LYIunU5jrNlMK40r25MWxYEUCebX2jBmI00sccyeq3nBEBq4+BZldr3c6Ph01pTEUMDfq7TQA8+jiFbW9ulEpUqV5QCfXVbJHxLmJoWp8GDvei335pxiaI0UEMw9unf/2jIkPpU3NAgzczFFRQU0o6bNx/TT0PX0ePHr8jEpChNn9GW8udPvtHvt8Tlc3fId5UwEO442CVDKzi0ERF0js5HXOUSPPRhpxXwjN2z4qAqO5aSTMl2sS0CrRZFSxXS628gAS94gBUhG7FySxsYjwGXNg4JzKqD1gq+ZOgTk0Q4ILAmSc67ibZGB0TDZvcOdVUlgpiAl94HMTqpUqpg8S+TkLj/SeJyUgbPvkktlSBdzXrVVL1oPj96sDZD5H7B67deN8H37P6NhyptAugRSGCMKZlBQ3dAE4ydQ7YJgZy6KqM20E6kroquD2G7hPGOrY7MW3JRAjI9wUlnK4p0HPXTv2wRpYhS2aKk5CJHddeqZFAkH/c9nTqoPejDrAVUaoC2/i6sp6NYaysZ9mmjWOki5CD2uO3RkiXDcuA3Afb9rVtt0aFpTSpQzICePXiuVYwDtcvZc2XnIFIif6G8tGT4Gprbfzm9f/eBU9kSNdwtuIcNMzAHdWQLNTkReIZng5CVk0BmbsmodRTqe4KqO8sbA2pi612NylQsweuwa5lw8U1t0DPXqFtdevPqHdd5zxm2jgbWm8pB5Ry/EdRzXHMqWCwfrfjDlzKK65fv0+iuy3j9QOUaZcm0YgnavU64IPYf25jKV9XPM+VbAecGmvynT99D+/efpol/7KBPn/5lGXwEY9J7wMWLd+nKlQdsJK2goJB5uXHjEf00dK0QjJUrSjNmtiMDg4RiAd8jGOTO+XWbynPMyjbtfL2SA3qGpOwY7qmF07BCYffyg/Tm5TsyrlySaumpiKgNSM4f2igEKE376xdAsqfYImG85t65jqwACKqQpKBFm2E0xDqa9K9HLYZ8CSgx8Y5ywiIlC/EY7FbMHe4zQ/bOqZWdKnuGAA4T+ZjofnJPuK/V9hHKBp1E4Q0IxyEpAJPoK1HX+TmUHKKfH1m1H7L+wOWJUFO09rRkIZH//vtMle3NqaSZEb9fCtiwrmjXkYBdE/4Wy8OYWRP0sT1/+IJ78qq7xDfBVuf1i7cUdVBo2amjZ0D2/PFLlYF0LS8lIMtQ7LwFw7tjftGcKdMHlHlB/Q+EiAoycuAARwZMrkdMQnqPdMJpIkl3IjjSlWHCzIQ0W6KtDNOzi6DaeCrwDD24Ke+xxD1n7erwY3/xpNXEqbktbf5T8K+6dvYmB2g+P7pT3NUH1OqnhiybKoGLjNSoiouPvtkG55a2dOrAOT7po4Mv0KrxW2jR8DUs6PHHjmFJMvzDOki14dsX+Oss20wJDbo4cVZs26IgunfjIS0J/pWG/dWZipYoSEVLFqRW/T3p7rWH7M+WEZSvUpJKGRch39VfSmRRn1+8lCA/iyxkWbMvZXvfC8iMdetWlxYuDKILF+7SxEmtyMjoyyDg/v3nPNO+bOkhWrc2nH77bRudOJEwE62goJA5BDxwvj558prKlStGM2e2owIFlGBMYvuqMLpy4S7lNciV6TzHQPDOk3T13B3KnS8nteynv6pyUsF9eIcYELUYVF9vRURtwI4H40io+8GQWB+QMbp29hb3eNVpIe+thXJATKJCcKN8jYSldaXKG1G/OV1ZuEPCf5Uw8YyJeBgyY1wIbLys6PGdJ2yFhBaLW5fuqgKl18/fsrgbDKLBiJX9aPTaQarxkrWHFU+Ko6zwdLAQ/Hj3dKdD4kQ7tArwWYFrhHGju5iZU1eA9NTwTpOybBgHazN7PiyOizEO1mUGHbFfGMeXKm8o24enCfQJEDiaWpbRW5ExKSgZsiSAkwZR+avnbyg6WLtfmDZQxyqVLSbmrwXZdum9kkeXJkjf4kBDxum2eHKog2yTVG6oSz6+mktVTlkjdX10V8KyRCOT4pzhw8mxcZoggy6HRyfhRDq6K5KePxKU59Tx7uHGwWmXioPpt+YzqVQFI5YjnbR3NNUWe8bUgZQrSg+uRN+QFTnRBO72DXu506y+y2nugL95Fgup8VZDfci4cim9vc0knJvXIiPjopyx3LdamK1JbbBtn9x/TucirlDnkQ2puNpJ/vThC5o3cgOZVi3F2yKj6DnKhw7vPU07Vx+jFTP3UXjgeSpYOL6fyZblwbRlRdpso8xKhQqGNHNmezaL3b//i8k5vMlmzNjLvkWTJrdi36KBA71o6ZKDnDFTUFDIPEARFZkx9IyhDHnGzLZKZkyNuzcf05p5Qvlbj2H1qWCR+Nf+jAaBB3zHQPPe7lx9k1bs/wdCXy94MI7xQXLB2Gy3aOrcdID+5ZU7xWDQubUd5THIJZvNlLJoDUUfscR4cOsRKygCry7O9O+//6oyYgiapPJHZKTCxb4uZNIAsmlS4JMjVw7OoMUcj2WxjmziuAWBITzL8JxrOwc6vlco+4NR9Y3zt+hq9A1uoZGk7SGCB/sjiHbUUesfQwAlGVnDSFrbd1e9noi6IhTNgV1Da73LTiUvYnsxyZLaKAFZEmC/MHFHhCXSV6WptogD69Hdp3QpUvcseVUHcypkVIBrhE8FaRehwAUHgZK6ZKc6OLggI8qv68jKqZc3Htmtvd+s/S8t+DdOSPR8yYGes/LW5fiEQe+XJvj+A+d3p5Gr+tH0wF+p64Q2qpNAWwYMaW3UPqurEOlDi8He9MvaAbQ8ehp1+rUZeXZ0pDIVhZK6pM5m4SKDWTCw9a/9WvvjUgq2QaHiBvT0/gsuDQDIasIY+q/h66lgsfzUaWRDykgKFMpLvUb7UN78OSnu9lMaO68DVbWJX7Ji61qJDu6Ookf300aVMrOCpn94E33+78sx/Pv47XT71hOqWdOEjaSRLbO0LE2dOtehj2Lpp4KCQsZz8cJdzoxJaooQ8FDKFL+Ae/Nf47bT+3cfyap2OfJsrn+vTXrhvz6cq0jQh920l9B/lBbg/r95rqAI3WJQPa4MSi6Ba0N5ord4mSJUR091P5Q4Sm0qDXq56ww0Ht56zO0vCNz0wW9ZEGd+MK4sVaEERfqf5kojqCGi/DBILB8sUNSA32fjaaVS6IZ8vTonxF4w81pmFCyaOucQhTesPa0oJuIqB9HQOzCuUlqV8bL2tOJxH0AJo5TlUvdYizpwll49E0ylq9apmOB7nA6+QC8ev+JyRUunLwbVmiCDh2yXesIkMd69fk8nDwitRHZajKhTAyUgSyJ1GlmrImV9FfCg8iJJbyLzpXOH/PCDKkjSpcwolSXKBVySPxhUanAAywF3dBDhd4p9ILRl0WrWr84zD3//sl7nukO2VZJT1RZkIcCpVLs896+pIzc70aSvp8pYGxcjfUEKXfqdUmlSj3YOHBRBEOTgZt0lpymhfsc6tGTcVprcazn93GYenTlymao5mVPbIfW5LPDu9Ye0ZsYeyigQgLk2saIxs9pSRasyfOxLwjOc9jcpSm6NqtPfM/fR90bFiiXIp4FQznz48EXKly8Xrd/Qj7p2q0umZsVp9SqhPt7BoQJL4isoKGQ8Z87couHD17OfYOXKJZXMmBYCtkeytUn2HFlZVTctpL5TKsO/bpYQJEHIQ84gOTU4tOUYPbj5mKukvDok9L7SF1TqbPtrnyo7pqusTp29y4UyRHi6ltfwFFNn50JRRr+nu06FQfVAU5LAbyBm1PYuC1SVLyI4QgBTzrIsXT51lZ+vaFuePcJy5M7OpYiQypeIEAOyoqWK0N3YOMpbMA+XO/LyOjhR8GbJ6Nmex4mHNoXHy3h9+viJbZSAq9gKIxEsSu1D/0Cb/51kBu0gVpHJARVyBFioJDO30U8t8eTBc1yyiiDapEopSguUgCyJWDqas2cDZjfOJSLSoQ4OECkgS6wnyknsETuy64RsrxqMpJFRQdOltrJF1PQaVynFB5CufjTMYuACg4wcvB200WNye74QY7bj5kV5bzBIq0JU49bFu9z0mVJQegmzawz8g1brL+6RmqBUsKkYGG6atTfJZY/6Ur+DA/Wf0obMLMtQl9GNqGV/T/JsY6eS7i1hXJTC9kTR5egvwigZCS6GuOBh36AU9eWzN2RSwYhOhFxif7LvlWxZs1AFc6EpGWCgBwEQRXVRQSHzEBl5jUaN3Ehv3nwgq2plFGl7LTx5+JKWThUmATv0d6cSaSgjn1x2rjhEj+OeU7FShXhSM63ABOTGP/fy46Z9PbiHK7kc3xdNty/d4wljr85Cq0diYBy4WywbbKRFpEPiTmwcZ5EwXvPRU0YfZsxP7j1lYTbYHaF8UVLW9urq/MUYupUdt8lg3CmNAw1NitOuhf70a6Mp/H+Mj6JEM2ip1wzJg/uiZ5hl3Sp0MlB43amlLQdqUOFWt4iKDDjDoh2QyVcX7cBYI1xMaEhJC3Xw2dLrUsJCDinZgbG2vpMMR0QPYjufamk2MaEEZEkEaWoo8IFwmTI/bdT0suSDDieMZKQnB2ZAcDBCXhWKMdqAxKi1h6DwI3l6qYMDxrOzIMoRKDZnagMNldXdBY+1Uwe+9MGog5kRNHYmVgKZJ39uVVOm77zUyZRI6kMH1x7huuukgpMUPXIpwae7C9dr37p0j8JFydO0wKRySW5Ihvy9ccUSKn8N6Xs36+NGG/8SZHIzkpjTt+jS2du0Y004zfplK00eup66ek4n/+0nqIiRAX14n/qlnV8LJUoWpGNHYyk0JIYltFetDKFSpQppnc1TUFBIf0KCY+jnMZvp3buPVLNWOZo8uRXlSsEA+1tlwR876dWLd2z+3KxL2gU7yQWTgJvnCdmgjsMbqGxu0oKwnZF8/89rkJsaqPl2pUTqvn5X5wRy9HJAwO3JvWfcziKJumlDynShd764nr5avn8JARdE1rJlz0Y75+9jpWeULyIjCPl5TNp/EMchqHIKETNYkl4AyhsBMmUvn77miflrp29w8JZNzNLVbmDNY0wEVihVLG1eUjWeRLliHgPBnPqwWObo1MI2XpbrZJAQqKEUU1s5IrQGUEmFQFdq6dEGPNEQFPNn6NiWmgG59Dd2otp6WqCMEpKBg9hHFr77lN4z3zjxqrtVjZdWlQODN8kXQTLG04ZHRyH4gRqOtswNMlZoojwXhgbJONnlVHMRTa/VDJo1qdPMNlEpfQApVYAZlrjrD2Tfh5mWuf2WqRowdZVU4sLy6unrJEnggwj/09Sz2kha/ssGSgl58ueiRj8Ks03rZ+xO12wHPkuajbOvZ0WP7j2jqNAYykg2LT3MpYnIhBmXNyRzy1I0a0Mf6jW6Ac3e0JcatNXdTPutm0f/NMybduw8SdOn7WGRgEaN06YBWEFBIWn4+UXT779v515ORydz+v335pQjDQfyXyuh/mcpzP8sK+oOmdhC77K69GTTPH969fwtT166NNevDyg5sKjZn0KmsHFvdx4PJBcoJEYdPMfjMl2ZLs3Plya4oT6NiX25LJq/6P2F7Bj+/+Gd7klsZLpgIM3G1L09uTRx71IhE9d0kA8dFssJXdrVUfWvYYIY6+TYwpbU80R4Dr1nAAkFKTCM9BcCmTpNa1OYOH6ExgHeH7xFCL4cmgqBEYTskLEDzhqiHUFi4gGljdqOx0NiL5qtTw3ZbSSppOM7GJkUI7NqZUkfzoZfouePX3FPXRW78pRWKAFZMqjhWoVrZx/cekyxUV9qZ/U1iT68JfFeJEmUQ5ehNNQWEejFXX/IQZcm8G6QPMskCVNtSIHixWOx9PaVdhNi+8Y2fBG5HHlVp7hH2cqlqIabBffM6MqSoRYYSkDb/xLKAHQFpw3EYAgp+6QEQ9lyZKXbl+PIf3UIl5imhCZ9PHifXzl9kyICtGcSUwO+SO2MZIPLLQsDadXknbR47Bbq4zKR/p60gy6dukF+/wgNrxnF4D+aU/9xTWj+toHUorsTNeviSKXLFSODgnn4QphWZZ1fC+bmRjzQmzylFfXt507FxZuTgoJCxrFp4zGaMR1l55+pvrcV/fprE8quY+D2vYLM04IJgqpyy+51qVzFLyXYmYWHd5/SzuVC8NFlTKM0rUCAvynu+8j6ICBLCVLvGFpOiutZAnrh2GWKOXGV7626yhDDth+nZw9fsLcXlKuXDP+Hts7ew4qFUg8+9ATUVbADVgnbsFb96jxeZC+yp695EtzKpaoqODKuUoZLC7Nmz0qxp67xWLDL723ok1pLzdP7z2jbHCFwhdUQqOZqwX1kaP0oX8OYju4+qdI4uHTiCl0/K0jpY3wJTgac5vYZfIfK9hVIArZMUnLCXUv/HrJuh8SsnUsb3WbQkv2UY7NaepcehoqlkBDzSImYS2IoAVkyQMbCxs0iXl2pPsCPDCfVzQt3VE2OcljVrcTROAzu5PqxsB6SufNBmT4xyckcswdywQzk7dHciIMapnvagLoO0spg/9+6jZKbDfLm35itkZPur9/dlU/q6EPn6caF2zqX59m5LgdXsaeu08XjsaQvVk6VyKy6sSAxu1RI5ScXg8L5yKebUAK6fvquNMuS4QJx7fxdWvTLZjq2/zSe4ObT1gO9qLpjRZq2bTD3mmUk+QxyUUlj4WYiBV/YHtLFLSXeLN8KKIGCV5kmT568onXrwnlQqKCgkPbg2gTLicWLD/D/W7WuTT/9VF8pI5Zh8ZTd9PTRKyptWoza9kk71cKUsG7mXu6Pr1rblGq5JzQHTk02zBSCjPpd6qZIUh9BkVTl0ywJUveSyjQCjYLF5Cf3di7cr7IYwjGPQA6CHGsmbKU1f2zl9pd5A1bQpROCMAdL2/8jen2J7S1BawU1RbSeHNsdye0ehibFKPqQMC4sYWrIv53bOLB6NYK3+GWV6EXLT6+fveFkwZsXb/i1Gh6WdHjTUR5joiXHxKIse6UBlGDmLySoK0q9XegRUx9HoDcM4zjYNEmeZ+pg3IqxMsoZoWouBzKAx0UrKMem+mVVMcYJ2xWZJAPp5KKMnJKJXYPqSQ7IIAZi7SEEcoe36i5bRBQu1QrrKtVzaS3MBoRsEQ52bVk0VRB49laiGbntc+UzVvW6CbXT/qsOaf0sCZt61ahoqcJ8skrNoZpAaVFyd9+9WEiRy4GTzLZR9SRL4CNAaCEGhzsW+ieavk+M5gPqcWB44fgVOh2ivw9dUmk1wIN++6c3TfcdSp1GNqBBM9uTc1MbcvCpRlVtzThQzyxIF83Mpr6VGfn06V8aPWoTLV92mKZM2aVI4CsopMM5N3XqbtqwQZg979HDmXr1clWuVzJEBMdQ0I5TvH2GTGiWpn1ZyeUWql42COVp3X5pkqb7EqVqEG/DGKrFAK8ULQseYmwEXduMKtvqV/b29MFzValgI1FcTBvo3ToTcpFL+bx7uvH4sePYllTBxpQ6jmvBfVXLx6zjiXtMgGNiO8Ivih7ffUr5CuXl/q5nD59ThOhF5trekQLFYA2CdJJ8/bs3Qj++feOEohmbpgtZ1XKWxvwb6tyBogWSfcOa5Lc8SKX+iMDowHqh0serqzCu/PDug2q8qNknJ8ngI8GgbX8f3Pil70xXBguCIe/ffGClxPI15JUq1bkYcZWexD2n3PlzUbW68lL6qYESkCWT2p6WXHd77extundNvldKk7rigYbZhMSyLFJ2Cz1ncsEEyg0RsCBVrS27hRMRdbzqzZLaaDbYh09mKPTERl2T7efCZ2EWBOsvB8oH3MX+tv1/CylxbTQUa6hhQJiY8IZbJ6GpGBenp0nwunJqXouDw+cPX9KBJPagaVLYsAB5dXRU9ZKlFZDuLVTMgI8PKeBRz0QpfJ1kzZqFmreoyZnhoMBzNGbMJnr9+n1Gr5aCwjcJjNl/+WULBfif5XNu2HBvattOP1+m75HXL9/R3HHb+XHjjvZUqbp+/TXpzcrJO7nCwM7LkirpKVmeXKT7vEf7OlTYqGCylwMhid1LhYxQc3GSWB/8lh9kqXtkhSrUkP+uOxfsV5VCovQQ1PauzrL3KA3sPrEtVbatQFUczLnPK2hdKPmvFsZmbu0cOfA+tCGcxTwQxOXOl5MiA4R+MHjQYR0QkDy4IYh3VFErJ5SA91kho4IsAgJQdQVpfEwgFyxRgB9jPIoesHDf4/TmxVt+jyTAcWRnJJdUYrxWxeGLxxjGtpGBp7XK4AOMjcPE0kqXRHzXJN9ebCd9A3kIuvD2rGeV5hMUSkCWTDCrYCEelEf2CrMK+mCLssUc2bi36fq524maRBctXZgPXEnhRRPMBtQRJUClmRRNHJsLIgthvvJZuWJliqrKH/ev0F6SCAWexv0Fo+RV4zbqNEqG2zs44R+tUuDRxNrTkkqYGXLNsJQ6l6OcVWl2e1eXf9UHBJmN+3qo6rdTGtC0HFRfCFwPX6CLJ65QWqJ+wVAyUd8Gnp4WNHFSS8qZMxudjLxOQ4espcePhXp7BQWF1OHp09f009C1FHH8Kp9rE/5oQfXrCyX3CtpZNm0vPYp7TkZlClHnwfLZmIwEvdXhftEcYHce3ShNPysm8ipFBp3lifeWg/QvMdTG/lXB9PLJKzIqV4zsRS/bxMD4SiV130d+f6BfS1LabtzPK94EbpffW9GtmHscDMEvbOTKfjTZbwx593ChcDE4qdfdlcdFkveYR6e6tGdJIAdnKC88IOoPwJMW5M6fm/IUEKp02v/cnJoP9uFAT5Kbv3P5Ho+RINwGPLu4UKiomwChDqhHB60TlunWwUk1ttkv9rNhMl+9JxDjWqxLeetyVKp8wn7GE/6neYxcpFQhXl85MHZUmUHrWXqI7SKVKzo01G+/pQQlIEsBklt3uNioqA+oq5XKFoO36S5bxIFat6UQTOnyEpOkOxFwaSslrO1TnU8QBIC6BDnqdRXqxXGyyPV+NR/SgCVQcdLp6iUraWbIMx84oP3EWmFt3w/GiMD3r32JikE0ES82uEjJrZ82IC8LD4wb5+/QySB5JUl9QKrbtbWwT9ZnoFFzZi55eZSEDOb3SK1apjRrdnsqUDA3xcbepwH9V9P169onLRQUFJIG7CZwTsXExJGBQS6aObMd2dom7DtR+EJk2CXatyWCJwGHTmxBOTOhDQDGEssnCBk8jzZ2VFbN7zEtWDddyI65trZjRb7kgjGZJObRfKD+vYthvifo0Z0n3JPl1EJenh2lgOivEnxbK6nGVhhPGRTJTxVrmtJPruMpe67sKil8ZKOkfi5TK2OKiYjlAAriG/AHk5QWjauWYUENiLU9vvuEn0OmDAES6DKhDXX+vTWLvYEceQSrHvOapnRCNIhGgBWyTTR6bu/IpZEnxD4ut/ZCxdHje09VaoyeneJ7sx0WjaSdW2nPfh0Wq7UwDtbVv44eOkz+FzI0oEp6Xg+gihl34xFvFxsdvWmphRKQpQB7Uf4eNcZPkjAIlZoJdUnaa/aIHd1zUrasz8q5CuUvnI+VBLX5luUrmJffk5iPGBovi5QsxDM5x/eelA0o2/3cnB+vHr+ZPn6QD4zQXAr2rTgom01DMylO8Fsxd1WSqXLUaVqT1w8li4dFiVN9e/fqdanLj7fM1q3qqA+thnjzjQuzLVfFWSAFQQ5/bK+VNHfsdqW0MhEqVDCiv/7qRCVLFaT795/TwAGr2axWQUEh+URF3eBg7N69Z1SiRAGaM7cjVaxUQtmkOnj96h3N+XUbP27UwY6q2ujXW5PeHN1/mjNkOXJmow7DfNL0s6CqiPs7MnFtfkrZZ4XvjGQl7PyF85KH2PKgD77zRan7nu6ypXIIuvaKgmWN+3pxFgh2Q3heCk6Q8eoyvjV1nSCIgb1/+0XavkEvIfO2Z3EA/3ZqaUdRB86xAEkhwwJ0/4bQjgNBt5gIoSIIapPqE//oO4PACrJ/R8TSwbwF83JZqYVTJbp35T5nsIqVKUJV61Sk4M1HVaWR8CKTesT+++8zB4ilKnw5X7Eepw9f4Md1WyYMyBCIqpQbtbyuDhTLgW0Da72Fx8JE31kb96r8vdMaJSBLAcVKFyZzaxMefCbFMNjWG9KZWdggWnI8l8OsmjGVMCvOBzyCMm0g+4WaWF1li3VbCFkd/9WHZQfLMImWDurwnfL+YD69PNhnAr1k6DmTAyWQcH/HLE/Y9gjZAE/KzEEGXxcoz5T6zrbN9UvSoL9Jfy++uCJDduW0/lYF2ihd3oicxKA6LXvJNEEd9/1bjymzUtulIh/XyJIFbNffNP17pUSJghyUVbUoxb1kEPzYs0f/8mcFBYUvBPifoZEjNtCrV++ocuWS9Ne8zlS6dGFlEyXCsql76eG952RYuhB1GZwy4Yq0AgHA3xMF0YgmPV2oiFGBNP086b4Oq6JSZoKyYHLZOsdP5SGGcj19QMboxvnbfD/1EW1/tAHdgLtX7vOktnMbe1r922bqUfUnWjhkFfuLIbslKVVb1a3MpYvH/aK4zwuT+MiGQTgEPWXA50cPlTgHAjkEZ1J/GLCsW5m9x/79+CUgg1S9JI1/+9I97hOLu3Zf/FwXldcYxpYIhKT/O7cWNBKAJPDhLmbMJOB7i3Ee2lW0GV2jzw2JCrT2IBMoBxICUv9YnSb6KyWGi2NuO5/08RJVArIUItWihsqoCcplbKRGRqlhUA5kYpwS6RFTV6WB2AZmSTSp29qOLwa3Lt5lXwo57BoJgcbxPSdZFlUbmK2RjKJ1iXvgfQ16Cb4d23T4jcEhHhzbE8nBmy7gw5Ejdw66Ei2oCumLYdmi7DsBts4WLpApoc2wBvw7dEck3UgkqE4Nrl24Q71d/qCxHRboVLjMSMqaFadOA4WAefHk3fQwTildTAwDg9w0fXpbcnOrzH6Df870Y4luRRZfQUE/cK78vQLKpbvp06f/qG7dijRjZlsqUCC3sgkTITJUKFUEXKqYO/OVKoL964/Qrdj7lL9gHmrZP23721AhdFKcaJbu88nl/NHLdOF4LKs0NhLHQvoAAZG11+bRHztH6BQT2bNEyI55dHRiMbCTQWeo8++teAw4rct8GtdsBu1YsJ//j76wKZ3mka84FsO4K0euHJwd+/j+IwuHoF/uStR1HitiMh1jDeOqpemIOEHfbHADPt/UjZnxmeDBrUcqZW+oeuM7Q1DumBjUIPsGr7IzwedViogAqo+wNMqSNQsHiOpIY17pvZqEiG0/iXmKnTp4nl48fsXln9XqViZ9gFgfRPuwTSDokR4oAVkKqSM2aJ4OjaHnj/U3H64jZrSkqF0XTqIox/F9UbJlizBjRor5xeOXdFyLFH+e/FC3sVOVEMqB5kwEjCh/vHD0svw6iScI+tZ09XM17O3Jszznwy9xnbI24GeB1DZOdLl+Mwn4gLi3F5R2fOcLykL60nKIUHoAA0E5oRF9MalSiktWMXuzXqw1T0uKlihIL568ppuXIPkrHwRnNM26OpK5ZWl68+o9zfl1q1K6qAcwpx09phF1EpVEIdH927htrBKnoKAgD86R38dvpzVrhD6TNm1s6Zdfm1COTCjXntl49eItzfplq0pV0aJm5ixVfPv6Ha0R+7XbDqlPefLnSnPBtpWnp9HIZT+ScSWhpC6l2THXtvZcVZQUEGBhXCfH07jnKpl4BFfIhkFZ0bNTXRq8sCctiZ5B9o1s2Kaof+3RtGz0OnJubU+nD5/naqGGfbw4c7RL9C9rOsiHDm0I48f2TWrSkV3CstF/j+oc9KehYguKkcje3Ym9R/eu3ees2/9++B8HclgusnWS7H3M8VhVBsu8phm3zGCch+BPynhJ+gg2nlbc86b6fvdRrigEb3WaJuyhw7jzyG4hoeEoCtLJIfWZ4X3qwaQuJLE+iPelxH8uKSgBWQpBs6epZRmuiT2aBLVFmETj4MXMwH1RSlQO02plqYSpULYo+URogoNMkgQNXCN4P8gpH2LWAbW3cmWBUD8EpwKFmQ9tIICS+s2kumFtIEjERQBsmyOfJWvwo5BZQT10YhkgSXEofOcJepCEEj74TlRzrhyvyTYltBvRUCXOcjs2jtKSvAa5qe1gQQAFNyf4eGRG0LD80+QWPDsWGXqZ/DYnPuGgIGTCO3dxpFGjGlC2bFkoLOwSDRiwmuLinimbR0FBCw8evKDBg9ZQSEgMnzPDR/hQzx9d+L6qkDiLJu2ix/dfUIkyhTNtqSLYtiiInj54QYZli5C3OGmV1mAA7qKRrUkqd67cV/UtNRsoqFOnJgfWhPG4E31ZJlXLcPtH599a8YS6FEg1HVCf5h+bTC2GNKCs2bNym4m6VyyENNCnhWARbS+HNgoBWdU6lTgYQqB18ZgwMQ9RkCGOv/Jnzug2n2Z0W0BHxO9XWJTar+Vdg6IPnlOpe0uCHAiocI87KC5fUv7GhLZkx+QsjhNV3299mBC81TbTKqpyMvAM96YVMipAlXT4uiE7iD4+UFeHOIomR1TlioJ4X3qgBGSpgIOYJZPkMfUBSoWQtZd8xnSBA7mOaNys673uHYT6W6SIXz59pTX7Bd8HzKQc1bGu1V2FWZmTQfIiG+g38+oi9H7tFQ3/5GjSv54qvaxtvUCdZl/6zRBo6cKkammuh8aFYbfYjKovLYcKWbJ9Kw+z50VKMLMqS7XrV+OLxsY/015x0aezIxmWKcwCMtsX684kZiSlyxVTySYvm7qH4m7rLkNV+IKHpwXN/LMdFSyYh65dfUh9+6yk09GKcIyCgjrnz9+hfn1XskopShNnzGxH9eoJE4kKiXMk6DwbQCN4/WlKy0xbqvjkwXPaskAQoOg6plGmNKqWY+ucvRxw1KpnRcaVS6XqshFkHBSzwk36xZfkV88AoQweoE8M6oWS2bM0dgsUJe3Rz3U6+AILpsFr9sa5W/x8afMShFZ9iHpM2fcLrbu5iDbdW0arLs+jiXvG0Popguql5JNbzdWCrp+7xWV+CKRCxMwU5O1vX7pLZ4Iv8DHn3EboH7t+9ha30cAKyk7DDgB6B8CzoyDIpgkCNn3UFaEbIKgrFtDqn6YNeJ9BrA8oAdlXhkPDGqo61dcvhCZKfZCkTFFClxh1mgkljsf2npLNjkC+1MSiDJ+sULLRBAdtYlk0UMNduLGhZPHtK/nvA/8KBIto6tQlpw81HV6v9x/poJgS1wQXWu8ervFMDnXRWHSt91txUDbbpw1rdwsyrlKK0+h+K+RNq/Wl3XChxjxowxFWUkpLsI0k75XN8wPo6cMXlFlp0smBqlob09s3H+jPMVsStTRQ+EKVKqVowcIuZGZWnJ4/f0vDh6+nnTtOKuWfCgq41gZdpJ+GrqMnT16TSbmiNH9BZ6paNXUHvN8yz5++pr9+264qMa+cSQ2gwZrpqAb5QObVy5KjOM76GsCAPuCfkHiTwHIkxxsVVU7PH77k7JCDKDCmDUliH2rWlWqb8YQ3SjJtG9rQ6xdv2KBZCpikYA2+ttL4EBkyYC9qC6izbfYeevbgOZcjvnj0kvvOUM4oiX+EbjvOY1FI4JvbmJLf8gOq7Fyx0kXilRKiXDFP/i89n1dP36Cr0Te40qauRuYMIKlwREwqaAqBaBIs9aE1q6W/uuLOSJ5oL1/dmK2O0gslQ5YKlDEvQTVcq1CDHi4cdOgL6lkxk3D55DVOb+sCB7ShSVF69/q96kDUhpvYX3Vok3bfMte2dVSGzXIZIqNyxfkHpX26pPkNjYtRbVF9ZvVvG2Xfh6CtXjdhRmbP0iDZCxDqoLE9og6e4xNSF3YNral42SLc6ybNlOgD1kUqH0APmjYBlKRgbl2O9z2ydRtnpVxSPzGcGtWgCtXK0tvX71V19ZkR3AiGThZmXs9EXKMdq+V99BQSUqxYfpbsdnapxEIFc+bsp5kz/OhDCo9XBYWvFRz7s/7cRyuWH+FzwtHJnFVKDQ3TVnHvWwL33r9+86Wnj15RGdNi1HGA/kIT6c2NmHu0f51w3+gxtplO0YbkkpxgSB92LPDnFpMK1uXIok5FnabO+F5ylUNy67x9rp9qzIQ2E30qsqQgC95fmNw9uD6M17FMpZI8lpIENJBhQ0YJMvY3zt3m52xFiycJCL5J0vnlqwu9h7V8qnO/mlSSuP/vAyrlRowlpYCvfne3L+WK4jhVU7QjSFR9rOVdXWv/VviOEzwRD6NotKLoApPvyMrBtFpfQsRSTEc9DaRTCyUgSwVwQk3a/hP1mtyWD3x9wXvR06Qexev6DFcxzasrAJFk66MPnWfpVE1g8IcfNGlKDaHaqNdNyFZtnrFT50ULpoAAJ3dslLyPknsHJ05LY9ZD8rPQBLMmjqKipK5+M+miAd+N5Ejgu7S24/T1o7tP9cpOJkb7kULWKmBtaJJ62pIDZnh6jG3Kj/etDeebVmbFqHQh6jlSmB38e9Z+uhGre9JBIT45c2ajX35pTD+KfTH795+hCb/70YP7mTczqqCQFjx8+JKzYnv3RhPG5d26OdG4cU0pVyY0MM7MHNwdRWH+ZylL1h9o+NRWmboEcMVEX85S2Ne3oqppZOydFkEeskQ7FwvBSqufGsh+xspxm2hUvUnUz/ZnWj91BwchEMgAuipKULl06cQVypYjK0vpaxK4JoQzdOo8vP1YNd5r0FusLhJbTRAgBaw6zMkEmEtLfWQWjsLYFBmuYmXiS86jFBHLRLbtrrjOGL/djY1j2ftSFYxYAh9Zs7qt7FmeHr1qKIeUJvHPH7mkeo96ueJ///2nqqRya6c9+3VQDORc2tgnug/HbhhE66/Po8p28n1m6kCcDyJ96irq6YUSkGUwUpOhlLrVhWtbISA74X86wQmnnrWCuR4CFDlJ+rpis+phHcFIwz6ebIQH93acTHKYVTMhF3G9/v5lvez7MMshfa6uvq9mg7xVRoFQ2dFF/W4u3LgKxZ9IHQIkmuAm1EQsedwya0+KZ8mq2Janak6VOMjdlA5ZMgu78mRXz4qzciv+8KXMTP2WNcnGsQJnImeM3MTbSEF/cLNp3caWpkxtTfny5aSrVx9R374r6UTEVWUzKnwXnDx5nXr3WsF9Y3nz5qBhwz2obTu7NBlMf8s8vPeMFkzYyY/b9nYlsyopUxBMS6JCY+h4gBA4dv25caov/9mjF3RsfzTtXBJE545eZi+v1MqW7V8dQq+evuYMk71GX5QEgi/0bE3z/5m6TmhNWX74gUK3R9Cmmbu5j0tXad0O0TDarok1992rg7HQ1M7zqINJPy5JlPBbFsTBLUoJy1YqxZPnCOqggO3Srg7tXuzP74M4BuThYeIsKVGXLG/EfWEAv39uMIk2TBXGHXYNbbgPDBOGqPSSArxDG4SAybGlLYuN+K8SlL1d2tahbNmFSYB9fwvPObawjVeueDb0IvueQa2xthZBjeePXtDJQMGWwEVLOaNc8kMq30yMI3tO8dgKYn0lyiUUE0lLlIAsg4EsKbI9187coluXdGc7IA9vVt040VJCSd5eLiBzaiG8fjLwdLyTVp18BfOqGj/3r5SXyQedx7fmmyPk9jFrIkeDXoKSImY/cFJpA8EkmkExgN+7TLdYCGZivLoIDZ9bZyetfA/9agjmrp+7TRH7oymltBOzZPv/CaGH6SBi0e2XxnyzOh54lm9emRUcF0P+aE75DHJR7Pm7tG5h5hUjycxYW5twX5mxcSHuKxs1aiOtWhmiatpWUPjWwAByzT9hNGL4enr27A2ZmhbjfrFq1ZR+saRvy/9o5pgt9PrlOzK3LEVtegmKy5kRXNOWjt/Gj707OVIp0+Kp/hm/t59Hx/yiKcQ3gtZO3UnbFwZQiK9QBpcSWMFZLCdsNqCebBBwdPdJLtODIqKNhyV1n9SWekxuyy0pPzeaqjJ01gQT8VJpoVvnhIqT0pjJ2sNSFeRAHl4KuBr0Eiaid/zlp+oXgyURAlKsC3rCQHU3C4o6eJbv35gc72kxlIU7UF3l0cmZbsfc5fflEb3+zGqUU0nU1+/hSiFbhbGnZydnod9LzM7BbBpAB0HSOajXNf6xeGiToLqIEsPsORNmwGEVhe0MCX5k4lKbsF0n41lapSdKQJbBIHNU3VUwiQ7ZprtsUX1G4KDo3aANqezvXFgMPbqbMDjASVW6YgkOenSpLUonD2Tt5S4QoKSZEUuvgmBRwlQu2CpvXY7rltFLJkejPkIp4t5lBxIdcELWFbMzkQFnOLjSl3wF83CGDWBWKqVY1jEnS8eKvE03zkr73i7cpKC6CJaN356pRTMKFctPA34Tyiw3Lj5IF6IU1cDkYGhoQON+8yGfBtVY+Wr16lAaPWojD1YVFL4lnj9/Qz+P2UR//x3Mx3q9+pb017xOVKKEvEmugjw71xyh6KNXKEeubDRsSiu9vZgyggNbj9PVs7fZb6z9UKFiJjU5EXiWPv/3mQbO7kTT946iXpPb8Dhs74pDtHVe0rxNNQn1PcHiXvkL52VVQzkQbKD6SL2HvaSZIY1c2ZdL/2JOaK+AQFYJf4PetHJWZeK9hmAyQFQmRG+ZxOGN4Zx1g8y9Y/ParLgo9Wg1HehNm6bv4MfePd3oxD5hcvrNc+Geoh7wYNkI0LBf8FkQFJGCQ1ggIcNo5VyF7lyOY32CwiUKsj1S+I4IHvNhWeVrlFP1gGFMaWhSTDV2BAi0pGVqyuBLSJVdzmLiITVB71zUofMZUq4IlIAsncDMxtkjl7S+BvUXyc8qMaQesbOhMbLZKJx4UtkijPh0Lcd/lXACy6kj4iTCyReWiDQ/XNh1iYkAnMwIoMAOHYIamDlCbTJS5hF+CU2u1YE/BdLmvEw91BnVwQwWUvZnQmPovA4T7KT2kqFkIT2yZO2GePPF8crZWxS0OfFgPiNxrGdBrg0Fi4DpIzeyKIlC8kykBw/2olGjG3KPWWTkdfqx53I6dfK6sjkVvgmio2/Sjz1X0PHjV/l4Hzbcm4YP91HMnpMJendXzBTK3HqO8KFSJvH7gTITUFRcNWUXP24zyIsMCudNk0F3sdKFeRITY6SyFUtSxzFNqNMvTSl4ewRdPKG9xz0xsKwts4WWhYa93Lk3So5qLpV5sryD6QCWx5d4fPcpXT1zg5WgNcHk9B6x3aNBb6HaSB1URL18+prLDSFTL+E7T8iGNeqH8U5WXgb6xdAbhrYU+IxBzRBCbshcwbD5uDjuMlAridw8cxd/R9gqAQhqPLr9mAoaFqCbF26rJvGlHrS6Le3ZHkn6v0ubOqoy48B/glVWTerlmdGHz3OWLn/hfFTdtWqC74jAUsrEIbhMbY77n+ZxaRlzIyqdBtm3xFACsjQEB69Ul/zwzlNaPHqDrGKgVLaYmNoiLiTwL8Ny4eslh3SwHhRreeVMok8dOEtx1x9ofQ9OHpxEIMxX94AfQRQUEnFyS/XGcuWUENSAQaHk0K5JjlzZybOzkJ3buVBIteuiSX8hoxa4NjRJakVFSxUit3YOqZYls3KsqMqSbRIvzGkJbla4aQHcxDKrWbREn18aUVEjA7p38wktmZp5FSK/Bjw8qtL8BV2oTNnC9PjxK5bGX778sFLCqPDVggHn6lUhNOyndfTo0UsqXboQzZvfierX/zK4VEgauBdNG7GRf6OX17u1/kpzGcHWRYH0+N4zKl66MDXqljZlldZuVVl5b9WE7aoAAeOpyrXNqHrdShR16EKylhsdfIEuRV6l7DmzUSMtAZMUDGLMBYGziTtHUt8/O/M4qFHBrjShzWya3XcZ2frUoIIavWEALSHIvqG0UGpLUWfXIn9VO4ZUKnkl+rqqV6x+d1fOQEnvazrQh8sRJTVDqQ0mb8G8PAkP9cI4UbADQLADfx8ZIGTRsJ8ARDsgzoHvDcEOqTwRcvUYj8F8Wvo/QIYuMvC0SvFRnWCxzQYm1drUI8N2RPCkrrlNOa1m0SAlvYBSuaK9hqpkeqEEZKmMuhcVTnb8oIYXioexUTe4kVQTpMut6lbix2jsTAxJIlSXMiPSvSjlE5RshHpfTfGP6m5V+eCV0tzasGskZJ8g7CGZ/2mjkGFBDizBniUBOgU1GvUTggjpwqCNhr09edtF7IuiO7FxpAtLp0pUzqIMvX/znn3JkkKLIYIK4NE9p3R6qelL+xEN+fe+VcGs4pjW4KYFs+jHcc9p60JB2Smzkjd/Lvppckver/s2R7A5qULyMTYuQgsWdKH63lZc1rVubTgNHrSG4uJ0i+EoKGQ27t9/TsOHradVq0J5wAWT54WLupJpGvQPfU/881cgXb1wj/IXyM29vJlZCOVx3DPaPC9A1SONAX5qg/FO3gK5qf3IxnRsXxS1Nh3EQlxQADwTFkORQWfJrkFCIQl92DRDmNT16lxXVm1704xdNL7VLFo/2ZdL72rWs6IZQWOp3eimVNu7BvWb3YW6/dFG69/6ztunCrg0s28xJ66w+iIyXd49BFl5sF8UzrBrXJMMiuRn3QBMhkPtsE7z2nRgfajKe1YKnK6Iatntfm7OvmUSyLzhbxF84TC6c/keJxIwEQ9q1q8uete+4yqtirXMODiDkJdx1dIsJgLQXwbRjAo25TjLJvHp4ydVJZamDL6E1NajKzuGYzw5k9MIQk+I4nBKQPaNsHDUerpyWuiRCdwQTr+3/4t62vxMm+f4UZM+7lRS5gbjINaromFRH/8yHHQItuRk1ouUKEQ2XtX48X6ZskSvzsIM1P6Vh2V7kMyqm3AtMJpNow+d07le8JsACPDeigaB2qjfzZVPYpy8UAXSBuqpa9YT1n9XIlkybIsmAwS3+p0LA3gWJykecrY+1flCvXWOkNpPCciQVbErn25ZMty0uv3ShB9vnh9Ij8RZq8yKVW1Tat5VmBWb/es2evJAkXBPCZD9HjbMm375tTHlyZODlehQ7hXgf0Yxklb4KjgQdI569ljOpYo4nkePaUjDR/gokvYp5PTxq7RluVAaNvD3ZtzLm5lBlQcGxZVsTNLMBBqtIyjrM7c2oUVHJtCAPzvS4a3H6efms8h/bSg5Nq3JJYxJ5fKp66z0jHFNi8FCW4Y2rD0tuR0D0uposZg/eBX93HAqvXj8knvODI2LcoWQJjcu3OZgCpPsUMDWBC0gUutIweKCLx8SAUFrBXPqel0FGyPJiwyVT6hmgpohxNHirglVUqXNS/Bv9H5pBu+lzEuozJ0LGgq9nLUb1FC1lTg1t1Ut3wEeuz/8oAr4nJp/yehJVVsuoo2TRMS+aN4/UI6s5iLoKqiD16IPX1CNgTXB9h/m8QdN77GYdiwI4LG0pBSpD6cOnedxLiqnYAidESgZslSmUk1TGuz+B/Ws+TPtWX6QjCuXopHLfqTfNw5in7JSZoZa/86hkQ2fAEh5J3YQIUCqYl+BH4eJTuva8BRFOXBSagu4oLADadH7Nx6yAIg2sE6YuQHIVukCdcuoQ0Zafoc4m6MNlCzaNRCyabqUFBuLmbT9Kw/R25fyAR5wbWPPsz7YdvpkGdVpMVjIkgWsgdS+oDKUXLC9pF4yv5WH0yVLVqdBdapcsxzfzFZOFmSNMzMdB3lQuUpG9OLpa/rz5y2ZWpDka8HFpTItXtKNKlcuSa9fv6cpU3bThN996cULeTEeBYWM5NWrdzRp4g6aOHEnH7OVKpegJUu6kbt7wt4RhaTx8vlbmj5yE0/K1GtRkxw8Eg5wMxOxp29S4CYh+/Hj+LTJ5C0evZ4WjVpPfRzG0vrpu3jiFsIN80N/o9/WD6QBszpRx9HC5GZSkVoeYGOE6iM5LB0rUYvB3tw7NcVvNPeKYcyHDNc/E7ay+rS27y4ZQaNfXnP5yO4dEn27pDETQLYJgR7GiwgEUT4oaQq4d3SifSuE4Mq+sY0q0MqSTRB7sW9UM0H/fnHjorRNVLP+KFZLQY3x1sU7nJkrb2OqynBB+v7etfuqrJu7KHAC3QPI2vO2EnUHJALESi23dnW0liuinBGZNZRSYpypzvF9UbRy3GYuFS1bqSS34aCFZfUf2+jaWfkWGnXCdwvlinY+1TMsk6wEZKmMWxs7zlqMXtGLJm4bSp1+bsq1yZiFUE/NalKwuAFVdRCCrMNbEhdokFK2usoWbRtac8CFWRDpJFAHMzGSIqMus2kp0ybVDsuBGZGOY1vy441TfXX2c0HRRxIVkZOatfGyYjXINy/e0uENun3aII/aUKzb3jJrd5KyA9julWqZcaPrdnGmKSVUd64sZMnef0oXXzJcPH78vQU/hrjHpagblJlBo/6Iaa0pe46sFBl6mXb8Iy8Eo6A/RkYFaPacDtS1qxP3EBw+fJF6dF+meJYpZDpOnLjGWbGgoPM869+pUx2aM6cjlSipqCimFNz7/vptOz2Ke04ljYtQr9ENKLOv7+JxW/m3c1MbqljDJNU/46hfFAd9bYc1oEFzOtPZI5fpqVp1hmHZIsk2yb537YGq/wpG0IkBsbQNU3fwmAUZKau6lan1sIbsu4qyQk0QpEkiGE1Fn1Z10CKCihx4iFWq/cX8eNciYSzj3cOdxTXQLwa1w3KWZVnd8LAoL58jVw5WRYSA2w2x/x+9YMFbhNelksTnD18IyojlinGWEf5iGFuCam4WrLCNIBeCcviM/WL7SA0PSzIyEQIoSQ4fyoooa5TAWPHobkHx20NGnVJShkTApkn4rkhyaW1HTs1r8z4YNK8b9ZzclnLnzUl/9lpKzx+91LlPMCY/uldIONiLxtUZgRKQpTJFSxbi4Es6YNUDAzy3ebYfbZQZpEsynod0SMdrli2eC78Ur29NM+BCGhkErRUOZk2ktDFmH7QFiqCaa1W+ad68cCfRPivX9nXIuEppPsE3TRPkVOWyaUjP431y4h4I8JoPErJX+yFYkEgpIgIyNMtCMvasTMZPG9iOrX4SPmf3kiB6ncKsApYnzbQhS/ZQrQ47rTCvVpbcWghp/MVjt2T6crWyZsWp5wjh5gIFsKsXU96/p0AciHXo6MAS4RBFgODHyJEbaeaMvZyFUFDISN68eU+zZu2jkSM20IMHL6hECWESoXMXR72NWxV0E7A9kkL2nWGfyhHTW1PO3AlL4DIT4Xuj6ezRWJ7I7jom9U2gwZa5+zgYQ8VSDdeqVMjQQDVZinHPzqVByb5notUBfY82npbcy54YmPyF7P2s3ktpwdBV3Mdeq351Gjivu9b3I3uFSWt40KJfXlMIx2+5EPg06Se0bQAIq50JvsDBFCa/8d32LgtUZa+QjcIEtKmVsUoZ27JuFe5FNq1mzEqKGK8WMirIGgfgtNiyUtpcKOm0qWfFhtMAnrXSY7SuYKy27+8DqoBQs1xRs0cMweHHD5/IxKIMr5Mm0BG4cCyWx6Ha5O5RpqlZjYS2l75/duLg83giXrPRwRfpxZNXXGVlISZGMgLlCpgGjFs7QHViYnAuBRLwvqrmXJlrluWCLDRJQvwjMZNo+D5AOlUyWpbDrX0dVcClTWYetbrIziG1fVJsaNRmEm0tZsl2iBKqcmAmpusfbdVmbj7KvE+4UPAyF+yXvRgitY6T5NGtJ3REh2cagDKRR0ehP2nLn0lT8bNtUINnq1BuCf+zlGLlVJEs6pgLvmQz00dRsPPoRpQjZzY6H3GVQkS1oMyMT1tbqu1SiZt+pw7bQO/faT9WFJKOubkRLVrcjZo2E3pT9+6NVrJlChkKrBmQFdu9SyiFatLEmpYs7U5VtEh8KySPO9cf0cKJgmx8xwEeVKFq5t626HNa9rtgAt28txsVK1Uo1T8DFjQFiuQjI+OiX8YeXZ3p0inBKgQT5OeOXE5WmRr6mvxXC9mrlkP090zz6OhE2XJkJZOqZThDJjfZjIBr92IhkGrcr16CdURZIcoAIRMPkQ4JX9H4GaJsRUoWZr2Bq9E3eMIagm/SOK6yfQV69fQ1Z6tuXhT6+TEWXDxstfC6XQX+jvhYBGhFShVSmUIXKGpAL5+84on13AZC64ukAAkROEj4Y70kYTgEiRePx/IYVzOoknrN3DvEV1388rowxq3hbsEtL5pg20O7YUS9SXRUlOUHkNC/efEOV6npAqbgwEFUPM8olIAsDcBFZZDrBNr45x66e/VBvB1cvlpZKmJUUKsnGQKPGm5CrfdhUf5TF65thexW0Low2YDGwqkyH8DIRJ0MEKRG1cG6YbYmMWPq5mKflf/KQ/T6hW4jWjR6YmYFJ3Ckf8LPlMBMDWbFLkdele1hQ5avXnehIXW36MGhi+ZiSh8nZWLqjJrZOClLtnWun05FyaRmyfatDpYVX0lNipYoSC37C2Wbyyf4ypaCZhawjQb/0YwKFslHN688oKWKFH6qAp+y/v09aNas9pyJQEYC2bLp0/aw8a6CQnrw8uVbmj59Dw0btp7i4p6zwfmMme1owEBPRbgjFcHkHya24OVlUdOEWnSXNybOLPguPUhxNx9ToeIGqntXagORhkFzu/DEsoSpRRkqUqIgnT8eSxH+Z7i1JDnsWBjA99kKNUw4sNIX9Fx1ndCaMziMTDAIqXsEOhAC0RTBAHuWBKpsjKSSSwRUgf8cVhk/g21z9qhk5iEOgqoqKEHivcDSuQqdDbnIGTWUIULFEWM+KfiCFD6o6VWN7l29z+sPOX0pI3ZQLCeEBD5KIAPXCJ+PdZbWC1oAgGX9ReERgL7/MyEXRZulhN8RY9uDYhWVixazaIjWIegev2UImduY0vwhq6lRoe4cnC346R8W6EC2TA4Ew1L/mGOT9DeDVkcJyNIAnDwoWwxcH07Lx26mZb9u4sAM9bv4yVcoj+wAva6YyoUkamIp9DpNanFAgxmAKzJ9Q5gNktLDcqbNjmJZY5hvhGzZImRRS1csyTXEATrMpIXPzKJq2JRMAbWBC4LkQ7FVbBbVhk9PNz5ZTwWdTbRkElmuWvWr8bZLqlE0hEHg8wZhj/3irFdKsKxjTtWchAzQhnTKkjXv40FFMPi+/YS2LU55pi+tKVAoL/00Reh/27PhGIUH6lbyVEg6llZlOBOBbBnu+/v2naZuXZeyul1mL21V+HrBsXX40AXq2mUp7fMTJuYaNa5BS5d1p+rVy2b06n1zrJrjT5fP3aF8Brlo+LTWmb4E9MmD57RhjnCPRqlirjw5U/0zkGGSqpMgd8/PffqXx01VbM1ohPdUqmJbXlb9WheQd9+5SJgkbjWsYZIzbHny52YTZiC3r3aKY5h6XV0SqC8icIIyIZCqjaSJa6HE0YQsnSrTg1uPVGIeCNA2zRCEvxr29eKAD7wRJ9kxxgM1vavTpZNXOauFRIE0ufvmlSCuZl7LTJXtcmplF08sBGPEcFEtHP8HGFdKfXBeXV3ifY+D4hjRwqlSvL4yCYxtJZ8zCJBoMrvfCprRYzG/r/sfremfS7NpYcQk7jVrPbwhDVnUU+d+iA4RyxUL5+UxW0aSuc/YrxinJjXJtZUtDZjdkVXkxrWeQ3MGrWQZfMxgQd1HG/YNrXn24ebFu7KS8BII+jDbAA7oKFuUTATDd5zQmvlBXTJOOqSfow5qHxDjYoOUOfBbLq+MKOHSVjSU3n6cs3NyNBMzWuE7IujuFe0ZLVy0rNwqx7tA6aJJ/3oqOX+UIOoLlH2ggAS2zvZLkny+HB1GCzXx/mtC6H4SJFiTC/oFuv0sZOY2/bWfvV0yO9YOFah5NyEwn/XLVnqYyaX7v0YgJ45sGYQTypYtQs+evWF1u9GjN9HddFACVfi+uHfvGf3y8xb6/Xdfevr0NRuYz5nbkQYN8qLcGh5KCinnREgMbV0hSJwP/qM5FTVMaCycGWXuEdRUqFaWXFvUTJPPgLJi2K5IljOXkCqWKtcuT/9++o/qddZeJpcYe5Yd4HI/ZF/sGwmq0anJrZi7dMI/msdekmCZOgfWhPOkB/xkJT8vjDX3LhWyZs0G+fDf7vjLj9UJ0Z6C91+Jus5jTJQyYvtDhVFSVJS83/C90IMGUbi2o5vRh7cfeLL6qNg2UlAsG0Q54qWIK7wc9GpVsTdn5W8EcBAJMa8plAoe94vi0ke0x2DCXJ1DOrJf4JAoPoI+OwSx6kT4n+Y2IM/OTix3v/nPPRwQIsD26uREppZlE52YCN0hfCc7nxoZWq4IlIAsjbD1qUbH/U9TgSL56ceJbWj63pFUqaYZWTiYU99p7WQVfRBkWXtY8OPgbfKS9hIubexVQiBy8uGoA0bPGQ7UE1pKCHmWQ8xoSUaCWj+rrQO/9+rpG5yV0wVMAWEGCIM+XcbTEABBGhxNsZKXhjY8uzmp0t74Hrqwdrdg6VNcJPx0fB9teHV2ovyF87JyUkgS5fO1UdW+AlWrK2bJROPItAZKVfByQeD/96TML4MPOg/ypAoWpejV87c0bfjGVAmGFRJSpWoplsfv0tWRsmXLQhHHr1L3bsto9epQ+qClx1RBISngGFq7JowzsEePxlLWrD9Qx04OtHhxN6qayfuZvlaePHxJM0dv4cc+bWqTvXvmlrgHUDwMEJWTe09owS0DqU3ojhO0a8kBWvn7NprYeQErLSIwk8ZJBYvlp7Uxf1JpNXNifcHEtuRbilYHfbOR+GzI1OuDZAQNxUNNmXcEPIfWCIFM475fxDxOBZ1R9XI5tbTlHj0/Ud6++ZCGFCT6hNVuYE0HNwhlhgi6MD5BFdRNjSSAW3sn1UR5kdJF6P2b9yy8AQ9Z4N7BSaXG6NzKngNAqC3y/1s7qLKGkh+aa7s68YIeVDzFnrrOpZKOWsyeOcsuKjNqE/OAuqVbOwdq+KM7dRrbnE4EnGb7oqRkUKVyRbkkSXqiBGRpBE5yHGR3rtznkxDleQ16uFDroT5UvEwR1YBTW8kQpDtByLbjiZYU1fSy4hPq0e0nrLioDVzspINdTgDEW+zTQtkiZjK0kb9QPpYwBYdlyh8lcCI26iN4YuxaKC/aARqLGa2Af4Jle7eqOFZgY0JI4PvrCPCkz5Yyb77z9idpcJ8zT05q3EeYjdo8a0+qlHR1HCNkrPzXhnGgl9bg+/ea0FIlg3/x5DXK7GDGbuSMNpQrTw46G3md1i3M/OWWXysIxDp2rENLlwqlYxhEr1oZwoPoY0djM3r1FL5SIiKusmjHihXBfEzh2Fq6rAd16eLEVhcKqQ/GFjNHb6Znj1+RcQVD6jlS6IPOzOCeuuhXQQnYpVlNqmRTLk0+59yxWBq+pActjZhIplZlafHoDRyYnTp4np49esF+ZP9+TN7EHwb9CKww0a1Nhl2OY3tPUQfTATR/yCqd70NVkf8qoeeq6cCERtMH14fRq2dvqHjZImxvJCF5iyHwQS8XqyU+ecVZsBqelirxDPiHRR04y2OFWxfvsnohSgkR6KHl5pVoWQR5emTpwD0xMLPxtKJHtwVDaZQZSmWPmNSHfP2J/YJ8PMRDpO8iCbK5iy0qEofF4K2Gm4VWyX/4s92//pBy5smRILMGTgadpZqeVsJ3bmNPTfp50dbZe2lMo2l0PZEKMwDlRox38xrkZiG2jEYJyNIQ+JAhdSrN/sRG36Clv2yiwe4TedbmbPglrXXHKEPUt2wRXhYOjWvGS+1qAzMZUsClzR8M9cYVbMpxY7A0m6ENzIKo+0nowq2DE+XKm5NT70d2Cio2cn5jRUsX5gtHqExWENuwcV8vlUmiVBuuS/DEoGh+bhhFYJsUGvbyoBy5s/PMDU74lIIadWu3qhwYrp0mKGClhwy+R2uhN3DRL5u/CvPlEmUK08DxQvC6YdFBOn38akav0jdN6TKFafqMtvTr2CZUGFnhe89ozJjNNGbMJrp1M+1FaBS+DW7deszHzKiRG+n27SdUqFAe+vnnRnxslSmTsCdEIfXYsjyYToZdZnXdUTPb8O/MTvDOk3Tu+BXuier6c9rI3IOu45pzWSLo8mszWhg2nkwty9CSnzdSr9q/Ui2MO5Kh6oj7OErjAFocMFbTl21z/TgbBYVFXUDqHtk8VBlVd41vlI5AVlJRbNjHU5Wde/bwucqYuV43YYLdf+VBVaYr+uA5Vj5E9uz6uZuqnntQpU5Funb6hson7Uq08BiB3N3YOB5/obcevXhvXgp9ZA5Na7FQHII4ZPAwhhR0CP7l9S5buTS/D2M6SOyXqVSSJfXVv4dUrqhpEi0RLHryYkwMoRF13r1+R72mted2G2l8Y9egBi049gcZmRTjfYQMoS5CxXFp7fpWSdqPaYUSkKUhOIBw0B3Zc4p62IyhYfWn0M2Yu1S/sxMVLlGA5g9fw0GaJnny56Ia7sJJGCo2R+qibktbVfpWLlCBuzlOEpwYkiGgJlDqAboyULYNrHk25dqZm9wsqgv4sDXuL8zu/P3Levr3X+2zUSxD20NoSt2lQ0kRhoG4mOACIdUyy4GLPVzbweY/k2YUjRO8vth4umF66gRQncS+rgMbwhO1NEgtuoxuxBmnmFM36MCWlJdfpgfOPtXIs5kNl7BCMezZE3lzcYWUgwkhZ+dKtHLVj9SqVW0+F48dvULduy+jBfMDWSFPQUEbr169o8WLDrCdAo4ZHDvNW9Skv1f+SK5uVZIlI66gP+dOXqdVc4T7ZZ+fG7K3Y2YHLQySzH2r/p6sDJxWoC0Ehs8A4yJkWbqMbc7tI2h76CRWriQVtDKg0gWtDfW71tX77yCSEX3oPJfsqXuGaQIBDEw6g2YDvROcRyhLvHb2Fk8a1+v2RSBj34qDPKGOgKp8jXIsh398r1COh/fBhgjUaVZbZdqcJZtQPmjrY017xN4zSPGj5wxK2VLGrZBRAZWK4tHdQhDj1MJOtUyoI2I9pQDQudUXtURJTA5ZO/XvEnvqGl0/e4ul+B2aJuwhxJgteOsxrb5lUjVTnSbC30lJDwRmGCN6d3PhnjddZt9YfvhuIbtXR5Tmz2iUgCyNef74JUfhDbq7kO/dhTRh82Dy6uhITXp7sCP4/n+0Z6OkAy1U9EfQBWZQoCCEGQw5+XicCJ6d66pKA7WBml/MEsCv4kq04NGhCXwlKtoKxnlSqloXrUc05lkVqPUErZHPvNXv7soXqrOhFznY0waybQ3EIGvLrMT7sRr18eDA7PLJaxR16DwlheaD6lPWbFnodPAFOi/WS6cEc+tyZOst9Mr9M8mX0gNICbcdIgTEKyb6qhSSMjsYXJQ2Lca9ETNGbvoqsntfOxBa6NXblZav6EF2dmY8gNm6NYI6dlxEmzcfV/rLFFSgHHHrluPUocNC2rTpGH369B/Vrm1Ky5f3oL593Slv3tRXy1OIz4unr3nCCgNnl4bVyLN55hhQJsameQH06O4zKl66MDXv80UZMK3BZIE0KXtsXzS5tLTlLEpSwTI2zRTGHmhtQFCgL1tFI2pMoCNYkAMZJQRTBYoZqHxk1dm5wJ9/O7aqrZLyx2S3FBw1FFtFoAeA8YaFYyXKlS+Xqkrp04dPrPZtXtNUNdZCVgsVSsh0ISsGSpkbqcZskjCbiWVZzrJhsh0BG0TgMEHv08tDMKMOEcyovboKk/soBzx14Gy86ioJf1Gt26GxTTxLAolz4TFc4YRxH1pz9EEKzFCi2nmcoN4sR2w0kgqPObCt4Zo5+i6VgCyNCd4eweISTfp4JBhY/pD1ByqmReZTStEiQMFMCLJqukAQJZUt6vIvc2vnyCcLTAJvX0q4TDiyIwMGAnUET1C7Acf26s5SAcxWtBohzERt1RFEFTYqqJI03TFfaGbVRuN+XhwoIXCD7KouUJNcT7wwbJ6ZtEwXLpju4sVww/TUEcaQZuRwTFw9e4vSg8Y9nKmESVF6+uAFrZ8tv10zE1CKHPNnW8qeIytFhl6mzctSbkGgoB+lSxemPya2pKnT2pCJSVF6+eIdLVoYRJ07LSZ//zOJlgorfLtgcAerhK5dltCCBUF8bEA9cdKkljRpcisugVVIexAU/Pkz1GifU8myhan/uCZfRTby/q3HtGWBEDT0GNs0gYx7WiNto7rNalHvKW2TtYzIwDNczodBvFSBow8Q2pDGZi1ET1c5JLueBr3cuSVFnUd3ntCR3WI/VucvwRomx+OuPeDJb/RuoaxSUsOu38ON9iwO4OAdAm+h24WsUwVRARHqiOsnbVPJ4iNzBV4+fsXHWhUHcy6fhCT9ubCLqnJFf1Ewza5xTSpWuohK3dGuoTUrOIKQrcf4c1Ghpe4FhkyeZPbs2VkYo2kCf10An9ykHiv6VESFi9vRxs0i3Y9FOZSATE9eJdNItaJ1OYq78UgVvcOPDBLov7aYRUHrw2WN6BAcSWqLunrDJKSUbvCWo7JeYjCIthZFOaSTQROklQHKGuUyE1D9AVFBZxNVPAQ+P7oLmbfTN+jaGe1+aaDpgPqqDN7zR9qFRYqUKKRqFtVHAh/iHpjBgbrk9XOJN3mq0+qnBvy3x/ZG0RWxvjollLMoQ05ian7N5B0pXp4+IGX/4/jm/Nh3yQG6feU+fQ2gQb3vL4348eq5AXT2ROYXJvmWsLExYTXGYcO9qWjRfGwqPXXKburVawWFhsQo/mXfERjchIZeol4/rmCrBJg7o+dw6E/1admyHlTbVhjYKaQP21eF0rGDF/ieOnpWO8qd5+uwEVj62zb6+P4TWdWpQA4+CQUa0gtIuyPDkxw2TBMmZ9HSgGqhpPSOSdLzZtW/9FFpEht1nSebMRmPcZMme5cF8XIgtlGywpcAR5rsrt/djVtlID2P7BLaLyCGsWOeUAKJgAzBFayETh8SLI7QeoJSUgi2wc/swjGhIgi9/0D6ntaeVioFRe+e7hS0NlhlDP3xw0c6sC5EtQ4SQeJz0phNAjL7MKVGlk0SilMHvV8YywL3Dkm3JUhsggLXtBCx+syhkTCezQwoAZmeRB4SZgaSChzcS1cwop+b/Un9ncazL8bZI5dZCn1B2HgyNBaMAbXhIsp8ou8osYi/hltVKlAsPz1/9JIi9guqONpwFf3BEJBpWyZOXlyskDJHJk0bplbG7DGBk3jf8sTV8JCOruUtZNV0CYbgIgNhEaTTd4mGi9pAI6sUqMoFbhIoS7AX64O3ixclfcGMjqR4uTGVesnaj2rEFwtIrV6WMfNObWp7WFBNtypclrB4rCCP/DWAMhzXhtX4BjTlJ6WfLL1BmU/9+la0anUv6vmjC+XJk4OuXX1I48Zto969/qbwsMtKYPYNg/sDVDf79llJ48ZupatXH/Ax0LWrEx8TPj7VMr358LfGhVM3aMVModLhx1E+ZFpJEGXI7JwKvkhhe6O4Qqf3hJbpktFDZVFq3mPPhsXQmdAYrtCR/Er1ASJqfmLPFgykdSFVB0EVG5PP6mCiHQEZUPclu8y9aeeE3rSBwnptnyuURzbo5UmB/4TQS/illTdSeaFWsqvAgnHo3/rv039UwsyQJu0dwy0v6NHnz/vwiYU4LooBGj4fYwgEdY/vPOFlImtWw92Cju4+yWNPBFg2XkKwjeVjedjnbhrqilIFFsTXtF1DjvtF8fKhYgmfXF1gfKyPoqI6187dptuX41hcpXa9jJsc0ES5murJsYCE/l36MnxxD2ra14O6/daCSxfx493VmWcydJUAQTEGqfF7Vx9QzAndinM4GXFwJ1Zu6NCkJqdn71y+R5dOXNGu2ij2r0kKOJrgYgpPC7B97h69enyg8iMFZHIy9LzcwQ34MTzJ5CTwK9UuzylwCJRAjSgxmg0SMm9Ba0NlJf3lgNM7gFIjLAxSStmKJcm5pRDkrZ64ndILZMlwIzlx4Dwd9T9DXwM4HlCOU7pcUXr84AVNH7FJKZnLAHLkyEZt2tjSmrV9qH17ezaZjo29T7/+uoX69F5JIcExXM6m8G2AfYksKPYtVDcvXYqjnDmzUbv29nwMdOjowMeAQvry/OlrmjR0PZsZO9WzYM+xrwEM4hf+spkfN+ziRMYV0yeIXPrzRhpQdzztWCyU0qWUjTOESVmPjo5JUmf0W36Qs1ImVUuzR6ocmFw+sC5U1ZqhSfiOE9y/hd4ylAxKbP9rryoLhQApJiKWs2y43/v0cidfcSIa9kLH9wgiH4/uCCq6JcsLWbZqzlXoyb1n9GujKfz/fIWFni70n0GbIFe+nHQyUBgDQ6htvyjJ79GpLmXJkoUCVh9SqXlLPmP7xJJGtN+gJUUC/WhHpbJLmezXAbGCC2bRuiZ9Xr94S3/2Xka9rEfTjQu6vXHVkTx+a3pYsIheZkEJyPQkOiSGjXaTQ9ZsWcnG3YIbB6s7V6ZyVUtzoyLQdbDhPXAPB4dF+U9dSH4YR/ecotcyJZa8TNG34vBm7f1mUNKRghC5YAuKh/ChiLv+kFV/EgNGhPBiQw000ulyOLWoTcXKFKFnD57LBpYYqDcWVYrQ4CpXoikB93hzPTJvcmWGtepZ8SAFao2pQYdRjXnWCC7z54+lj+9TKdPi1LSXIIWLLBm2xdcAVCLHzGrHcs6Qd4YcvkLGkD9/LurWvS6tXdeH2ra140H65ctx9Ntv26h7t6W0f/9p+qQYen+1YN8FBp6lnj2WcRYU+xb7GOqba9f1pe7d6/IxoJD+4D48fcRGehT3nEoaF6GBE5p9FX1jYOfyQ3Trchy3YbQflj4+aeeOXqaIgDN8n62pIwjSFwiDHd8XzS0MaGXQF4xNfOfvV00M69pnu5cE8n25vHU5HrNosl2Uuvfp6aaSaMc4L1hsaWkk2gJtnS2MU5zbOLCHF3rLMO7DZ0OiHuOrsyEXedt8+iBMjldztSDfuXtVn//6qSDicVtUhLZwqMQBG8oXkbGKFL3J3Ds60YsnL7kEUQrQpO8tjd/gb6YOy+B/+ETGVUpxtZUmr5+/Yb82yVtMF3gfJuZLlTekMnoG+urlio5i8iGzoARkevLh3SeKTKJSnybJMRmWJO1RT5tYJsqsmjGVqViSD9BQXUGP6PkAUz5t61TdrSoHW0/uPaUzwRe0LgOmg67thNkNSRo1sV4m1B0DacZGLniVZoek9Lw2XNrY80wRSivlvMskcCFqMUS4iO5c6M8XpaTQZrjQyxS4JpRnqFIKvOk8RcGQv3/fmm5lX20H16PChgbc07h1kfy2zYz9ZMiUgbXzgzgwU8g4DAxyU4+ezjxI79DBnsvYbt58TNOm7qEO7RfSxg1HWRJd4esA+2rTxmPUscMimjxpF12//oj3KbKh2MdQ3yxQIHdGr+Z3DSaiIHCEiamf57SnPF+JkuWT+89pzUwhg9Pt58aUL52Oo9V/CNUnXh3qUAnTlNsBrBd7x5xb2VGJcvovD5PemIQuWNyApeHlQIAiqSc2H5RQ6v5S5FVVb5mkNA0Obgjj8UzZyqWokm0FLo+UxkMQ6JCCojrNa1OAaGckjSM9uzirxN0q1jJTyd5bOlXmCWiUMaIUEkGoJI+P7wC1Rkmoo7R5SQph3YJ/qZxVWTKuIniPReyL5kl1TMJrGjpjnXlbymyPsB0nBN+yiiV5mbqQPGYdm9XSe4ICQnmodhLKFfVTb9Tkwe0ntHe1fCVaclECsiRwxE++N0sfkjOjZeNpycHRo7tP6Vz45USXL5Utyol2SCqJmDFB0+cFLZLuCJ4koz5dQRGk6gHMCLWZTWvr/cKsDII8OLDLAdUdpNtjIq5wfbQ2UFop1VFvnb0n0aCmTtOaVNy4KNc5y8n+y1HFvgJVdTDni+aWOUnrQ5Oj/chGPMuFmvSTB4Tm2rQmV56c1P3Xpvx445x9fFH5WnBvUoPqtazJ+3nq8I308N6zjF6l7x4M0rt2q0vrN/TjHrOCBfPQw4cvacmSg9S61Tz6a64/GwUrZE6wb+A117bNfFq8+AALtxQomJu6dXOidev7cjZUCcQynlNHYmnNPOE+3G9sYzJRE3PI7Kz4w5dVpitUK0sebRJ6SaUFUYcvUHTIRb6/thVbDlLaixa+MzJeC4M+4F61dbYQjDbq46nTE+vQhjCeAIfkvDRhro7UE4aAUOotw/JVSord3Xj8d3B9GAcz5SzLcv9X8Gah7aRizfLcooKA7tHtJzy+MrEQgh2YNZ/YH8WZKRhFP777RCV+Auyb1laVKyIDdnCjWE7YRtIjEMosXdt+KT+Ugj/0jmGSXeLp/WcUdfCsqhxR67bYJKyzc2s7nWNmHFcnxFYiR7USzsQIFrNjKFdMrrhLyO6T9PfE1FHfVkcJyJLAscCzPBOQnuAktm8olC2GbEu8bBGZIwADwidx2get6CGThC4CZUQ2oLYjfKZ8sAXzQcyIIFCRaoJ1ASlUScURRtFyYFYF5oVSRksOBGRoSoX8PWaPdIELUbOBQi8ZSg/l+tjkaDtSyJLtWRaU5D40baAGvUEPIZW/csK2dMuSOTe1oaq1Ten9u4+05Let9DUBfzI0scODZ9KQdXzcKWQ8yKagxwyD+OHDfcikXFF69+4j+fpGslz+iOHruSdJkczPeLAPwsMv06iRG3jfwGvuzZsPVLZsEfppmDetX9+P2ndwULzEMgmYeILfGO4Pns1syKOp0G7wNXDu+BUK2nKcB9V9J7VSeUSlJdhOuJ+C+l3q6vT70pdNM3bxctHTb1y5lN5/B/8tqCZivNXgR3nPNSxbaodo0r+eqhxRAlVABzcIQUrTQV/ERK6cvE6xJ6/xGAilg1jO3mVClsuriwtLzkMYAyWKEfuFEkDjqqVVJYr7xGCubkt7ihQDLvT6S+WIdy7HqSqvkIVDxgwBI8od+e9a29Oju09UVVTS2DNej1hHQTtAAkbPyL7BA62EacKJhRePX9LJoLPxRO3kQGsOSiyNyhWjcpZldL5XfVuH7hACsjqizVJyCN+bsuSMHEpApif5C+WhV8/e0JlUMAlOrOdJE0cxOIFDfGKDGqgKIv2Mg15XACfV+mJGRZt4RgXrcpwuxmxLkNhoqg0pcArVI1gEnca14tkZnPSnDsj3njUW66Gxfi+eaA8ICxYvQJ6dhBN+kx5eYfAky184L4ukSKlufUEzLhQz37/5wBK2qUHroT6UM08OunzqOh3Zk7jJdmog3Bxbc6YybE8URR7SXpKaGcHkBMp18ubPSRejb9Gy6cKsoULmIHv2rFSvviUtXdqdpk1vw2bBmOCMjLzOPUnt2i6gv/8OprupUParkDTu3XtGK1cGc0npr79soYiIa7xvsI/gIQZDcG9vK96HCpnHgHvi4HX0/MlrKlfJiPr+KkwKfg1gwnPBmI382KutHZnrkHpPTWD6fDHiCouhtRmmf6+XHHE3HrI9EWg7snGS/lYykK7XzYU9UeVADz4MmjEW0CZ1v2dJIG9PC8eKZG5jqno+cKUoKd/GnpePssYrUdeFAK2Tk8ooGt6yR3dF8r3/0wehdxwq2fhMVEpB+EMKqiA3jyAO68Jlg5VKqnzJnFrYcQkkghooLcJ7DOMz/r+9ORUrUzRejxjKKGHQrLVcUSY7FuobweWQCAKhCqmLg2ImDYGbvtVnNy7eFdQVsydfXRFluOcjdIvsJRclINMTa5fK/PvIvuSrLeKkmvbjUmptOogey2Sv5CTtuacr7pmsFL06SGuDgxvl/cuquVblmRPMZoRuj0jwOg7w+t2EDM4+UbJVG47NhTIEpLzfvk68bwRO8PCtSMysGWaECAgxM7N/paDgo40WQxvwuh7dc1Llm6GrZK+JKAYizXrpCz6j3Wihj2nnokDZIDEpIBPYpLdwAV71x/Z0yyCYVC5JDbsKAfnCXzbxRfhrwah0IfppSit+vHPNETqwK30CWQVK0rlibW3CA/1/1vTh7JmBQS569OglrfknjHuVhg5dy0bTb5PYz6mgP9i2EOkYPmw9B2L/rA7jssR8+XKyUMfqf3rzPhIC569DIOJ7Ysnk3RRz+hZPQP0ypwP3j30t7FkdQlfP3aG8BXJTlzFJC2SSC3qjpOxYk94eVKi4QYqXufnPPRwgYAwGYTB9iT11nU4GCqIi6AnThe88QfTDq4szWwSpg8BGUpKWhMyk0r9j4r2vcV/heZg/S560KEuE5Dwqg2BKLfnH3jh/h3vCIgOEDA/GYuiLR2YKQaz/KmGsh/584NymjkqwAwbNuxYK6wo9AM7ILRU+06OjMJ4AQVIJY7s68a4r6FdjGfwf/icbkB0WvcckX105sL4nRLVoKTOnD2Fi6SkE9pKrrhi+T9h25a30y8olBSUg05NablX499F9p5NdXoaTA8bQr5+/pbBdiZf4SSCa/1K2mHhmR/LOQvCGdLfWdcnygypLFijTUwXRDnw2Zl0wm6INE4sy7PSO1HHUASHVnBiSVwayZE/itM+W40SWgiffv/xkS0VLVSihMqrWxyi6UR8PLiFAKcFpGcESOWx9qnNqHLXLukopk0LzAfUor0Fulmw9pIeSZmrRcbgPFSyaj+5ceUDbFycuypKZsHWpRG17C5MFc8dup6sXBSUohcyHkVEB7i/bsLE//fxzI7K2NubMTHTUTTaabtF8Lk2Y4EthYZc4I6CQcqXEo0djaeIfO3jbQqTj5Mnr/FqNGsa8DzZtHsBCHSVKfJGiVshcBPhG0p4Nwv1g+NTWPBH1tfD04QtaPVXIDnUe1ZAMRAn1tAb3z+vnblMeg1zUYkBC2fikAjGO/SuFXqg2I5KWndw4Q6jYqdvCVqfX7N0rcaryPvWASwL9+ZiIL2RYgOwbf1EEZHXpD5+4Gsq8phlL5ktGzQ16edDGab782LahtaoEERPAIHf+3HTr4l3+f4ufGtIFcZIf4zgEcnge6owAYyVMiuM1rAd0B/IVysviHqeDz7MKI7JsUivK43tPKfqg0BOvKWLiv1pYP/iUaXqsaf5t3Ra6LR24WuzTv2RWrSyVMdffRkEadzuI4+mUlCvW8qhKqY0SkOmJhX15Pjgf3n1KV84mzYROHccmNqoDKilgdgKE+p5INJMCMz2IUCQWwHmI9b2YLcHJoAlkalWGzjJliwicIBICju8VPC4So1R5I055Y+YJ3mByuLWvw+pEuEhIs0HaQN018F91mN68fKvzsyHbCsl+sGXWHkoK+K5txQvz9vn+iX6WPuQrmIdaiD5p/0zcnm59UZgd6jG2GT9eP8vvqxL4AO37u5N1nfLcCzdh4Bp6+Tzl+0Ih7UApnKtbFZo2vS0r98FcuGTJgtxrdujgBRr761YxgNjJvmZK5kx/3r//yAHtlMm7qHmzufTzmM104MB53rbYxp061WGrgukz2vI+UMoSMzex5+/SvN+EAXX7fm5Uy7kifU0sn+DL/lDlLctQ/Q7CQD2twX3zn4nCNms5sD4HDSll86y9vFyIelk66r8PYAWE/i2QmEQ+/FYxwV/TqxoLamiyU5WR+iJ1/+7Ne9q9SJgQRkAFdi8OUEnWw5wZIiHShDkms9E7JgV+qIpClmrGofHsD3bhqBCQvX4mWCUhwMM6mdcyowDRb6xRHy/aK6owQnQNomrS/xF4SeIYIVuP8t9WrG3G7TMSGLNKE//IBGrjwPowbrXB2BCVVLo4JEr9O2sRQJEDyZCrZ25x1tJWHNcmlZfP3tDpcGF71XL/jgOyyZMnU82aNSlfvnxUrFgxatKkCcXExOj8m5UrV/IgWv0nZ87kycUiGKtRV3AMPyKmLJODo9hIeDb8stYgSA6kzJH+x9+cSUTAQj3lq0ttsaSZIVW2r8AngWRIqInksI7X5WT3a3kLsw044fUVy3AXU9z+Kw/KZhxx0sPMEOxdeED2fXCKL12xBAdI/uIFRBfNBgh+IPCwuHlRfzNBANNseF68evqa9ixLncwSyhYLFsvPcvT7ViVNATIluDSvqRL4gDfZ1wQyvCOntyHDUgUp7tYTmjZ8g14G5QoZT/HiBmwuvGp1L1qwsAu1aFmLihTJR69fv6fAwHPsa9as6Rzuddq9+xSX2SnE5+HDF7RndxSbc2NbIaANCDjLEvZQu2zazIbmz+/M27hzF0cyNCygbMKvAAgW/TFwDX14/4lqOplTu76CkvHXwtmjsRS0+RjfX/tNaa3TZzU12b86hO5df8j30SZ9vsjCJxeYIe8VSwXbj26SpJLe7fP28ZgK9kFQMJQD4xXJPLmpKDimztXTN+iM6BfmI1oGSQqGKNkrWqYw2TepxS0HUnVQs0E+tOXP3fz5Nl5WqhaX0hVLshhZ3oJ5+P8VappR2UqCQMmFY5dVGUFUcb15JUxumlQtQ9fP3eIMWM361VUT7ihXhPeYFHR6iy0oGJ/t+/uQ1uwYqqdQrYUJaGTtNMHfBvwTotMsWgLrCXVqfTJp2rJjlnXMOdmQHCICz7Ixe1lzIyphIp/5/OYDssOHD1O/fv3o6NGjFBAQQB8/fiRPT096/VowsJMjf/78dO/ePdXPjRs3kr0OdvUEg8Gj+5PfRwbVn4o1TfkADNutX0ZJEjSQsmS6esPU+8hwcqHR86YOB3P39k7xpEs1QTlg7vy5+GSS61+r7lqVDIrkS9T0Od76tbannLlz8AkPrws5Gvb25AbTm+fvsGqRbHmjGLhtn+uXaAYRDa22DWokK0uGG0yrYcKs1NY5fkn2NNMGvl/bEcIy103fRe9ev6d0E/iYLAh8hPtF03FR3ehrAZ42v8ztQNlzZKUTIZdozV/CjJ3C1wGOP3NzI+rTx42l82fN7kDNW9QkQ0MDLl+EGuCsP/exLHu3rktp4cIgLsdD4Pa98ebNezp27AotXnSAfuy5nNq0nk9//ulH4WGXOROGIBfbbvacDrRxU3/q39+DKlYqofSGfUVgMnPy0PV0/85TMixdiIZPa50uyoSpBTIx80cLQh712tunm5AHMkbrRFEvyNzjfppSts7144wTxmqYDNcXZJ+knvsWg3WbYKM37M2Lt5wZs/a0TPD6tjmCaBXGfah6Ahg37l4sZMc8u9fl8QgmyyXJfLtGNnRgnRTY1KVTgUKflVSWWLSUoDpZw00cy+6OpBvnv1R82dSzovNiBujRnceqjNjRXSc4yKtapyL7gx3eGK6S14cAnGSerRIV6RBfXTFIVPPGuE+b/P/V0zd5LIgsYGJBVvC247wdkLmEDoK+SGbQDloCwqT2j9nXT55/WWJ8NWf7vn37qEuXLlSlShWysrLi7NfNmzcpMjIy0Zu+oaGh6qd48eSbBNb2sODBK5pV7914lOzlODYWDojQHfr3kam7loduP56oEAPqgGt6CQdNgGgOqA2nFrU5cEMTqrZsEU4eB7F2GSaHcpmsBr09+fHWWULteGKgeVXqYdP1N5jJkN4HtSE58B7Mvty9cp8VhRJDCqpQMplUs2e3tvZUvEwRnkWTZoRSSv3OdcmwrLBM34VCo2x6YFKpJDXpKfRjLfx5c6oEmOkJZPAHjhe81dYvOkhh/l9XUKkggBIaS8vS1LevO61Z24eWLO1GXbo6UuXKJfm1Gzce0ZbNx7kcr0njWdS3z0oOTlDe+PhxygV2MhtPnrxiqwB8xwH9V1OTxrNpzOhNtGnTMbpy5QH34VWqXIJLPxcu6solidh2Fhal0y0roZC6rPhzP0UdvUI5c2ensfM6Uj6D5IkOZBQ7lx+i6xfvUv6CeajL6PRThNy5OIiexD3nezKk7lMKBLt2LxEk4duOapykSQ38HXrMTaqWZg9ZXcG3FHA1H+yTIPCGaIdUtYTXJeAldjX6Bgc9dVoIE/S7FwnZsaYDfbhfDH5iRUsXplsxdzhwQfkfJssR1D289UhV1fT0wXOa2WMh/z9vASFzhrEg1s20WlmVJkD9Hm4UuCY4nniH9H8Ea9L22bfigCqAVM9AQewNY1agGahpCoHU9q6eQNhEk2Cx1x79efpy79oDunTyGt9LpLahpIKxUeTB8/zYLpmG0onx1WrcPn/+nH8XKqS72fXVq1dUtmxZLmeqUaMGTZo0iYM6Od6/f88/Ei9eCOUy+Pv8BXKThZ0ZRYdeotDdJ6l5n4QSpfoAgY6lv2yis2Ex9DjuKRUUFW0So4p9eZ4FQQBxfF8U2ScS6bt3qMPqg0HrQqjTby203qhRa23tYUHH/aI4S9ZpXMsE73FsUZvNlIO3HKFeMzponbVDQLZp2g7Oop0/EkMVa5dP9Ps0HlCfdi3y51ma6+dv8cyLNrx7utKuhf4UvuMEPbz9iApraQhFSan3j+60ceoO2vznLrJrpHvbVKptxjMs58Iv0ba//Kj7xDakLwjKW/7kQ/MGraKNM3eTV9e6Ok0f9SFL1h+o45gmNL3XMto8x4/qd3VK9MKUWrQdUo8O+0ZyyeSGuftZ8EMTHP+4uGfGskCXhtXo8rk7tOOfcJoxajMZlS1MxuWTP/GSWcnM+yC1MTEpyj/t29vTixdv6WTkdYo8eZ1OR9+ku3efUUzMPf6RKFYsP1WsaESmZsXJzKwYmZoWp0KF8qRqhigttj+W9+zZG4qNvU+xl+/T5dj7dPlSHMXFCfc3TYGUatXKULVqZcnaxpgMDHLHW056eRlmJN/qOXBoTzRt+1uYOB38RzMqa1Ys035Hbfvg0d2ntGaGUG3S9ZfG3F6RHuv/6vkb2iSaL3cY3Zjvoyn9XN/5+zmoKmdRhmp6Weq9PEyS+87fx4+bD/bWeU4e3nyEBTIMiuYn1/Z1EnwGRMPQv1apdnnux5Je3yP2bdVpVovyFMjNZY0xEYLhs0fnujTrx8X8unNrB5U6taQIjUnr9ZO3c9lihZqmNLn9HHr24DmVMCtOd2PvU9bsWSlKrJIpZ2lMV6JucD8ZAjRk0TgIbF6bAz2M8zAOghcZ1g1ZSklnwKurc7zvE74jgrcn/MLMa5km+K6oaJJ81lzaCsuT4/7NR3TheCxf1x0aWeu9bw5tFQJCS6eKbH2UnGPkhyz/o7F//0hRITFUrmpJVQxC33tAho05ePBgcnBwoKpV5dPJ5ubmtGLFCrK0tOSNN2PGDLK3t6dz585RqVKlZHvVxo8fn+D5hw8f0ocPH8jS0ZQDssM7TpBjc/kZEF38Lyfqc0vStbN3KGBjCDm31t9lvKa3Be1bFkz+aw6TWW3B5E8OE5tSXG746M5TCtl5hCo7aA+SangLARlOKK/eTgkGMaUtDSm3QS56cu8ZHdwaShZ1tTS4/kBUq1F1CtsSQTsW+VEhk8SDzBwFs1INTws66X+Glo1ZQ/0XddX6vtxFc1C5GmXo6smbtGn2Tmo+TLuMrEMra66fhrRq2N5jVN7GROfne3Z35IBs1+IAcu1syxc4fanmVZEKGhpwcLxtwV5yba9/c6kclZyMqbS5Id2KiaNVk7ZRq+EJVZfSitZD3WjBiK20ZX4AWTmXI8OyhROccziHcIPJjGU0DTtb06WzN+nCqdv0W99VNHZhG5aL/pbI7PsgLalcpTD/UEdrevToFV04H0eXLt2n2NiHdOvWM+4zw09w8Je+4rx5c1CJEgaqn+KG+alIkbxUtGheNrNOz+0P8+UHD17Sw4ev+Pe9u8/p7t3ndOfOM3r1KmEJJi7BpUoVpPIVilH58kWpUiVDKlo0n+r19++xnG8vM/g9ngM3Yx/SnF8FuXbvdjZkXr04PXjwgL6mfTB/1GZ6+/o9mVqWIkvncum2/ptn7md/2JLli1GVuiYp/ty3L9/RdlGGvt6PTjzu05eD647wGKmQUQGq7Gwquy7YbuunbufHrp3s6fnLZ0Qv42didoiBnVsXB9VyILohlf7ZNq9Bz549o11imX51z6oUdzfui7Ba1s/06PZjHre9fvaacuXLSbHRgneWlVtlun75hso7trKjOQdkhiZF6XbMPQ7QLp28wq/ZNbemPSuEip1q7lXozYfXqv9XdTKnTz/guvaAjvhGcvllkdKFyKhykXjffZ8opV+rYTWt2/M89BTuPuWxqnGNkjr34b5/BNVL89rl6FMW4bP12jdbhDaf6u4VU3SMGFUoSEYVbPl7KAGZCHrJzp49S6Gh8gp9wM7Ojn8kEIxVqlSJFi9eTBMmTND6N6NHj6ahQ4fGy5CVLl2aihYtSgUKFCDPlg60Zso+unLmDv3wKRsVSaZ0cN1mtena2W105vBlajVAfwPD+p1dOSCLOnCB8ucxSLReGhL4KKs7tf88OTeN32gp4dXehVYM30BxVx/Qy7tvyKx6wkDGo4MTKwKFbY4kt5ba084Ne3pxQHZ8dxQNXdw3geO8NnpO6Uh9A0bSsZ0nqeMvLal8jXJabwD1e7rQ/D6r6PC6o9R9QnutGSmIvbi3d+SZoYDloeTgrbsW2aNtEdo+y5+un71FR7ZGqXzG9KX1sIa0aNga2rvoEDXr653iLBnoPr4V/dZmLgWuOUJthjRM9vGVVLzbFaVjfucp8uAF2jgziP5Y3y9eYI59gP/jPMisA6Ff/+pEQ1ov5B6MFVODaPzCTjxz+K3wNeyD9ADneeXK5eL1V8XExHFmCeV8yDbdvv2EA51Llx7wjyYIyBCcQQBD+smXPyflzZOTX8MPehOzZcui+kEPxdOn/xJ9/o8+03/0/v0nFl94/+ET93G9evmOXr56x7+fP3/DpZSPn7yiJ49f6+x7w2lWsmQhKl++OP8gy2dewYjy5E15L8y3xrd2Djx7/Irmjd3Lx5GNYwXqPapxpi851dwHEUHn6ESQID4xZGYHMjRMn+oEZOUC/xEG2j3GtyJDI8MUL3PDql1CX1fFEuTd2U3vfYEs0r4lQrDQcmgDKlFKXoodcvHXz9zmqp62w5omMI1Gb9nLJ6+puHFR8u7irrqHbV27mz68/cDKiY4N7ejOrbsUvk3oi2raz5tO7Ihm5eqqjhXpiPi8SZUydC48hqzqVqFje4RgrePPrSg24hp9/u8zmViWobjLwvXxrTgxBAPoDVN8+XPrd3ajgbY/8/NenVx4n0fsjuL/e3Zy4WsxOL5T6K3y6uzMrUESyM6dPSyI0Pl081C9X531AYIfrWOzWlSytG4J+1P+QsmgW2sHrcvSxp0r9+nmhXt8fHq1rZtsQQ9NsmfPTvS9Z8j69+9Pu3fvpuDgYNkslxzZsmWj6tWrU2xsrOx7cuTIwT+a4MKDnyJGBamSjQk7dR/Zf4Yad9cu4ZkYdRrb0Mrft1HU4YvsS4b+J30wty7Hqd97Vx/Qsb1R5NJad2bGrV0dDshg/jxgblfu99Ikr0EeFu+Aag6c2CtYf3GDl4C3BQKyIztPsB+FNh8JmE1DchUNpif2RZFDk8Qzf2bVTMilrQPXS2+fu5dGrR6o9X029a24Bhq10CFbjqkk+zVpNbwRB2RYT/STQWJfDuzPNsMb0ZTO82nHAn++kOIiqS8+3V1o04zd9PD2EzqwPpy8RSPtlFC7nhVVsStP545cpvXTdtGguV0oveg7qTX1cZnIKfmQnafIuWn8WmvchKXzIDNSsHA+Gje/Iw1pu5BOhcfS33/upx9H6T/Z8TWQ2fdBRpA3by42osaPBAIkBGU3bz6mWzcf8+97957R/fvPuUQQARJ+btzQ7tOYFsAcG2WHhkYFqGSJgmRsXITKGhehUqUKUY5UmMz5XvhWzgGUpE0asp4e3ntGJcsWphHT21C2bFm/qn3w4d0nWvTLZn4OvcimVXVX7aQmuD8im1TZ1oxlzFNanoyyuu1/CZmpdiMaJWlfHN5+lMdkaAHx7uGq89jcIWbg3Ds6UcFi8dVPBdEOIQPVuJ8XZcsuXBf+/fdfbtsATfrXpyxZstDxXae4XwweYRh7SeWKplbGtGPePl6XONEU+uXTV7xsiH7g9bV/CKrKVR0qqnrzH995QtnVzMdreFhy5gzG0pj4r+1jTTfO3WaROC5fbFKLvyeUv08GnFb1iKl/93DfCBZ7gX8rVBu1nQMYmwLXNg46t9v9G48o5sRV3s91mtTU+/wPE7UaqtWtRAU0gt+UkBbXn6/j7BcP1AEDBtD27dvp0KFDZGKiuxxNGzioz5w5Q97eup3TE8Pe20oIyPyikx2QlTIzJOPKJen6+Tt0ZO8p8myvn18HDkbnlra0fupOCt56LNGAzMKxoiqQidgfrRLo0MSriwsHZBC56Dm1Q4LslnGV0lTFwZzLAeEn0WZkwmwSLhIure1p6+w9FLLtqF4BGWjcvz4HZKFbj9Hrv15THoOEwWnWbFmoQW8PWvnrRtoyazdLo2q7AKMPDb5ox/1OcQDZb7bugAaiJit+3cj13IFrQ8inhxvpC4LblkN9aPGItbRx+i7y7OhIWVN4Q8V36vZbC/rJazLtXxNKzQZ4UWkdQWVqUsK4KLUe4EX/TN9NS8ZtIRvXymxa/TVhYm5EP01uSZMGr6Ptq8KobHlD8mqevCZeha+XnDmzkRn3kyWcrYfH2f37L1g44+nT1/TkCX5e0etXQpD2+vU7evX6PX388C99+PiJf39kY3qhbwbXOZynOXJk5UAKvl74vHz5clLefDkpX96clD9/LipcJC8VLpSXChfJx9m43LmVjJfCl/HM/N930LnI65Q7bw4at6DTVyfiATbM2UdxNx9TkRIFqINMK0FacOvyPb4/gu7jW6ZKr+iuxYH04vErKmFanOq2tE3Svtw0Q8jyNOnnRbny5NQpLoGeKtB0QEKpe/RmQWANgRGyTRLon7939T5P3Lu2d+TP9F8mZOS8e7jT4U1HeIxXoJgBXTgqyNhDXj5g1WHKliMrj9uwjdr/3JyvYafEXrG4aw85q1a8bFEOvBxb2KpE0Zxb2dMu0QetbktBFdtvmSB2ggl8aZx2eFM4Vw9Usi2fYAJc6iuDuqI2TgWdoZdPXrHfrGXdyjq388FN4ao+MBhlJ1Vd0bGJ9rFvZiLr11SmuG7dOtqxYwd7kcXFxfHzBgYGlCuXcCHr1KkTlSxZkvvAwO+//062trZkZmbG9bbTp09n2fsePXqkaF3s61ejZeO305mjsZySTW4K1LFpTQ7IIOOpb0AmlSEiIIvwP80GjDD51RXF4+KydfZeNtOTC8jgWSEJhuCEdGyesNyvXhcXPrFhwNx6hHb1IYemtTkgO7b7JH36+EmvAAWNq2UqleSZFzR3IhunDZ8f3TmVDpWhE/7RbKaoDXh6ICDzX3mIuvzeivLklw8qsH7NBtanRcP+oa2z9lK9ri5JKhnx7u5CG2fsYjNI2BF4JOKhoQ9VbMuTrXc1Oro3ilaO30a/rulH6UWLfu50cFsE3b5yn1ZO3kn9p+gvdpJZcPSyYEPVtfODaN54XyplUoSq1EgfCWaFzE+uXNk5O4WfpICBDPoPUCrztWdnFDIWCBDt33qCVd9G/9mWSpfTr/wqM3Hz0j3aulDIrvSe0FJnIJLarPp9GwcSuE/ifplSkB3bIoqDoHUhKaXusOO5En2DcuTOQY37CmrTcvj+5Sd4hHlaUdnKpbS+Dlzb1qH8hYWeUQRfm2fsUImnITA6deAMZ6tQ0VO/hyv95DyOX/fo5ESbZ+zi4+qZyr9RGKf1nd2VRTogBAJ5/uy5slNkQLTKEw2Y25hR0JoQngBHgDXrx0X8PPxgoZbov1oQCvERvcfUvW5d28Ufw96JjaPTh8/zusipKx4Slbvhm5tFx7gL2+CAKPwhqY3rA6qkrpy5yeWK9g2SZwadnnw1d5WFCxdyE52zszMZGRmpfjZuFHwvAGTw4TUm8fTpU+rZsyf3jSErhn6w8PBwqlxZdySeGEZli5BJ5ZJ8QTgWIPg8JIe6zYQM0smD5+n5Y7WuzkSApCq8tOADARVFfTzJwNHdJ/nCow2cDJK8vJ8oX6qJU0tbvhjcirmrmoXRBEbT8CTDCX8m5IJe3weBXb1uQmZq3wphBkYbCHy9xQzWJtF3RBvWHpYc4OEis180XtRF/W4urAp1+/I9OqKHZL462B7NRVPHDdN2JuqBpi9dxjbnCxnMDM8fly+xTW3QB9d/qhCE7V0dShcir9HXCAxV63hV5XKJCQPWcF+ZgoKCQkYTGXaJlk4VFAm7D69PNo7m9LWBoGL+6E18fa3lUTXNfJm0ERN5jUJ3RvK4AffJ1GD30iB6/uglZ8dcE6k60kTyMq3X1VkVRGkD5YXoDwPNBifMJj66+4RCth1XBUAS6AHDeAtVS03ErNq22cJnQlnxbOhFnszOY5CbSwlBFYeKLIEPME6EYAYqjKQeNsBqg//+RxVsynGWCtm3c+FCv1fdVvacdcP+rWxXgXv7D64L5f66EmaGVMPdQhV0QeURYxVNGXrJ9Nra00rlf6YOlBmR+QOo+tLF1TM36cb5O7wNUK6oLzhOgJVjRTLQsW80eSMzTk5rvpqATJIQ1fyBN5kEShnhTyYxa9YszohBxh4ZtT179nAPWWogXYCO7Et+QIayRVPLMnxShIkHjj7gQoQsGUB2LTFg3IcLDeqtdQVwXp2FgAwnMvrENMmdLxc5iObUUipaE5Tz2DYQSsTQx6UvqKfGrAxObsjmy9F8iA/PXkUdPMem13LbRyoHYKPoTyg3kgdO9A3FrNzG6TuTLB/d4Ec3rte+fTlO1qstqRhXKknu7QQRluVjN6erpLWVQwVya1mbP/OvEev5ovy1gQzGT5Nask/Z8yevaXy/1fTmOzQUVlBQyDzciL3P5dQIaDyaWlPTzvpXxmQmQndG09mjsZyh6TuxVboZkOOetHzcZpUfKO6TKQWBgZQdazOiUZKyYzBCPuF/mgOSZgN0qyJDsh4T4siMIUOmCVosMFaB+bJZtS8VHTsXiIqL7R2pkGFBunHhNlsFYZs3GeBNOxcIZYUN+3jSoY1CFul/P/ygKkUE1d0sVNVKUkD27L4g216kZGFV4BQqjiebDfEhv+XC5HijvsL3kv7foJenqkJAMqGu5mpBBYt/KSPExHTAaqGkElVH2gjzjeBJc2giIOjThSSLX6t+NZVnmj6EiAEfNBuSwm+dFtH2xQfYHByklwXFVxOQZTbs6gmS9ycPnad3bz6kOEsWLDY26v13zYW/i/Q/rfKZkAMnrjR7IaWXtVGqQglOU+NmcXBDmKxICK/v5iOygY69ykj6CH38oNvAWgJebG7thbT2phny2a9ipYuo6rulJldtINsHc2yUEgZvSTxIajKgHtdtIyCMOiRcsPQFgWrzQUIAuHayb6plyTr93JTXCQIfx/cLs13pRY+xTXnG7Nr5O+S7LPEsY2YEBqsQ+ShYJC9di4mjqcM2pNq+UVBQUEgKz568ot/6rKI3r95TFWtj6v9bk3QLZFKTZ49e0qZZgvBExxE+VLx0wuxHWhERcIZOh1zkvqhOY5KmiizH3uUHubzP0LgoB3lJYdPM3fy7bks7NmDW5VEmGUG3GNogwX5HoLZ7UUACI+jnj15wbz1o2NeLf+8Qyxqt61lQjtzZVQbOJhZl6W5sHAtwnAk+x89lE0U6bH0EX9a7V+JYcA1gohXB0EWxAidLtqw8poNWwKsnr7mvHsEP2leunb3J72O/s07COA3v9VsujA28ujgn6A1D+wvGEOhl00bAGiGYgzK2rvPgv//+o0ObhW0AjQJ9gWfZ5VPXBc+yhjX0/rtrF+7Q1XO36eThC7R7pWCAnV4l6kpAlkzKVSlFxUoVovfvPtKpYP1K87QhuYbjIgPndH2ByIaJRWlRpSbxLJmb2KMGYY+n4syI9vcJPVCS34Um1d2qcqDz7OELOhmkPTtYs3417keD2qLkNq8PLYc1VCnzwHxQjoa9hTptBI1QD9IGZu4a9RMuYJtn7ko0w4SAsH53V368foovJZVGvT2ELNmle5zqTw0ged+kt2A+vuK3rekaTBQoko+DMrBm+h66fyv91OhSk6JGBWjsvI4sYX780EVaNk24KSooKCikFx8+fKI/Bq6luNtPybB0Ifr1rw4sBvM1gv751y/e8RioSY+UKwvrC+5/K34T1AEb/ehOxVIhEPzw7gNt/lMo/2s9vGGSRLkgsoFJZ9DqJ91qvhgHIUDBuEgaY6mzf+VBbvNAOSCUECXQB48xXnnrcqx+LRgwi4FMFyc6sDaExzYQxDiyS8gGFShuQBjuQHjjdsxdDkjwGJPjf7SZxcEfzKGBSdWyPE5D+WL0QSGwa9jbi9cHuLRxYPEyqfUDwVWBooLHLLxrH95+zGWaMKpWR8qOOePvtSjI4u8kYREItOni/JHL/H6UXdaqp39pbNguoRqsqn15HrPqS8iuU9S4hzN1Ht2Idq8KpuFN/qTL0TfjmaKnFUpAlkxwkNuJB0e4X/KzF0YmxahCDRPOSoXtTLwfTB23NkJJW9B6IZ2rC6gPVoLj+7//UdD6UJ39ZpgFuXzyGqfGNcEFC0o86mlkTSDV2nSgMMuzY54wm6MPZSuX5hMeB/yaCcKFVxuYwYEXB0owJTd6bTTu68U9XvguJwMTLy1tqVYOCTf4pABhlbTIkrUc7M2zVDcu3KEgHdnNtMCjtS1Z2JXn7bxgzKZ0LZtMTSpalWHlReC7Oox2r0+dslIFBQWFxMB1c+7YbayomCdfThq/sDMZ6Glzk9lA1gCiT0hoDJjWJl19HoM2hNP1c7dZ+bfN0NRRdPRbcYjbM9DjlFRBLmTHBIEOSzJVKzHUBIN4TAqDZoO8EyhYI9O0VexDaz7YWyVuwVL3i4QqoAaiiAYUDdHHBY8ycztTVZ8WlKVDxEqgR7eexCtFRNUTygnx+uXIqxyMSVoCNy8KY7zaPjas0ojgyqZeNQoTJ/k9uzjz+kmliVDjVm07sR/Os3PdeEEXyhBRjgjk7IkwdsR5gfJMjIF1cUgMeh0aWSfJlkhq50lqueKZI5fJqZE1mVmUpjl+I6m8VVnyXXqAhc6QKUvLrLYSkKVCH9lR/zMp6rORsmShYr2rviB4wsFxNiyG08uJIc1EQNpeDpgUQhQDyGV6pLRxmO9xHqxro143l/+zdxVQUW3fe/9e+ezu7hYDBKVTRBRUFLG769nd3d3dHYgBSimY2N3d3frq/1/fPveMgNMMgr751mIx4jBz5w5z7tl7f8ELD4qhqyfUa73Uodkwf1VH6frpW2rvw9zpToLXvG3mHo3USSwunsrUa90E4VKkDdnyZSE3ZZK4brzu+8eHTwd37jSxlsxEUzKM/AN6ieJ2xeht3CH7VsB57johgH757ReKCbtAR4MFDeJ7hEP1ctS0m5iszh29g45HXUnqQzLDDDP+A1g3P4JCt59kp7f+UwMoX+Hvz1ERgDRjZt91fNu1QWUqVj7/N3tu7DNWjN7Kt/171mA2iikec91EUSgF9K31VaGkDShe4DbNv9tPO3UyJvg0m25A2gCn6PiAsQWkFTBD84hldY8g59hW90CQUqDB3OxC1FV6eP0xT47uXn7I+6ACZfKyu3WuwjkobK0ooup0945j1lbJ3YL++esfntaB0YOm9acPn1TujqA04tzkLpqTXRnhIglWFY7PylM4W798/IqO7Dqh2uvFBoox/D6mfSUqF1F7TqR0Ru63NOGfv/+hA4qcB7RQfYHzeSnmBmv7DLG7BxMIAdn5i+fk506bIRXVauXImrw+tafS9kXhPLFMLJgLsgSgtHVhpna9e/WBTkcbv8GzVyr4M1GX6NUzaVWqG1nzZKKydsXj2IdqA3RkMM6AReuNM2IEqw4YU0tKoLqpCCZUEIuiU6Op8EBhZ1tHGI/sWiiscfVBkQoFyVFxhQycLcSs6uDa2J4LLmRnSKcedUDYMy6E4FlrKvDi3F+hHsCR8s4lzbRJdcCCCwt9YM140zku1mzjStnyZaZnD17SNsVm+FshT5HsFNBNFL9rJobQ25fv6XtFg3ZO5OZbkafEo7uvoZtXRHSGGWaYYUZiIDzoFK2YLjbRHQbWpEq22s0LkjNWT9lFj24/oyw5M1CdTsblrxqL7fP20bP7L5mm6NPu66LGGOxeEs7TMTymh6KL0hebpu3kjTkmPGXtSmi9r9SOQRKhLoInUMn68mrjxsVRfAv86q3Fz9HYho4LezjPli4UukwUXPZ1bSh0lSgOpeFFxhwZ6OPbT8wkggYMhR32QLGnO7C9lxlhRxWzN+yrItZHq36O+8vpmEO9qipKJzJbcR0tYV2E8peMa98frhRb8BtQN026efYOuyaiALaPR3WMD0h5oO9Do7uCs/7u6JHKdKycfQnKlF1QLHUBe13oIUeu6cj/ltPfHPmyUK8ZTan1kDrsOm1I4W4ozAVZAoDRspySRQWdNPpxICYtWj4/j78PKrxXfeGsZDKE6zGRQQFj7VVRq0siUNXHks0k0D1R52SIsW2NtsJ+PmiBEKKqg7SoR2EHUau+AIdZ/t7HdyIfIz4wuvZWFuYNkzU7I2LqJR0p5cKoi9pZRaFNSm65oVoyUAIQDbB/kxCiJhR4L5oPrsO3N0zbZVDRbgrU6+xO+YrloDcv3tPiUYbr65ILeOI3vDaVtSpIH99/piHtltEzLXpKM8wwwwxjceboDZoyQFDv67a0J+8A/cOGkxuunb1LW+YJl72OY+pTytTfLuQcpmXrFUpf04G+fD1MKMA0QX6oMdMxHM+uRWEqV0ZtgBnG8b3ChTG2lX3s/4dEAv/vHWt6huYxjDH49xQt/DZF/mFX14b1Zif3CQ3Wv3//ywyt0lWL02VFanHxsBgQNBlSj/droDoC5ZxK0akwId94dPMJf89TPJdqIpajYDaKCT6lasx//viZorYc+SpnbO8KYXYRO7wagA/C8X1nvrp/bMg8MUzb0mbUPuncrxia2PpYGqTv279FmarpKPhiQxaPGbOm4/1fbNYb/u3oW4kamYgqqwnmgiyBsK0hRriHg88kaCJiqwg5ZW6CvkAmAyp5TLz0mei4KlbqSD3XZOXJ9va+4g9ZU44XuMR4XuRj3Dp/V+19LJxK81gc+Rsn9ooPqT6AQBWLA3jOstuiDr6dhTPipSPX6HSEZjpdHcW1iIW1D3XnUUHcK41NICY1WEvWVZkojTOdlszJz5qKWOTnqeSaCeJC8q2Ai1WXCQF8e++6w3QqSnMsQXIHXsvgmU0ob+Fs9OzRaxrafjm9T6LMETPMMOPHxJ3rT2hkl5W8qUMeYsue2i3RkzNA3ZreczVPROy8K5C1h8ig+lZYO3EHvX/9kQqVzUvOCnvGFM6KLx69puz5smjUOWlC4NwQ+vT+MxUql5+sqmk3mdg0RbgwIi5InVYKsTzy/9E8ltgwUUgmHOpXpWz5svK+BQYeQO2uXrR+wnb6v3//jyp7VaBjSgGFPROmdr+n/p3fK0y47BSWkgyARng38tZA+cTvw24eOWeyANu1MJT/ZotZFmZNPxyqsQ/D0AAFH4BgaUy5cC2VbCYJmJzIbLM8RXN+9XpR2EhNmK6A57//+puiFC1a/Iwzbbh37ZEqDBq6M30Bi3vs4/v5TaeFw7bQzhUHKCbsPBfgKNaw381bNAclJswFWQJRrmoxDhWGFez5I9eNfhw7H/GHc3r/JQ7p0xcIS67kLhbISD0mMshxAOf42b0XdE7Lxlo6DmKShoT2+MiUIwPZeFeMI+6MD3RmHPzEBzZyo27jEQn88ddo46ZzAgehKkb3wNpxmjVfJa2LcmA1FhqZ2aENpWyKUTmHknz/LcqCaQh8OnjwlOzOpQd0QOnwJBQ4l61H1ufbOxdHcAL9t0Qpq0LkXE/8jc7ovUajdvB7QNr0KWnk/OZsh3/j0kMa3W31d5m1ZoYZZiQ/vHj6lqfv7958opLl81GvcfW/mW12YgCxJ5iQpUmfkjqMFuZI3woPbjyhHQvF/qL1iPoqw4uEANeujUqh1MDA6Rgma8gLk86K2gweuIhSmEj1eoomb2zA0l66WcPsQwIyDJkn5t/bR0Vf5CmYbXHKnDMjhSqW8RVcytKrJ68pfdZ0dEpxSfys6MHaTmzKx4djPh99OU4GGUKkAWTGSht829rWtEOhT8oAarlfAm1SvlZ5zHBujD/hgitk7Hik+Lh45Co9vvWUs19RTGrDidBz9Ob5O0qfNS1TD/WF1JxVcCrF+2ND/s5D1h4i+5oVmYF17cwdLspWTdpJz9Xk8iYGvt9VIpkAfN4q1YQJRvSukwkKiS5YJg93o6IMpC3KlPOIDYd0OuHBwtReCXcO05A1Blg4leJcDUxkDmgo9Kq3EpTEfasOaBQ6OihCzIPbj+mdSSbdfdgU5PgNVQdHHer3qsmdixP7zmjViPn94a3KLpMOQ9ogqQg7F4bywmkIsNjJKdnK0VtNNiUr71iSrNzL8t/IkuGb6VvDr4sLZc6RgR7eekarJn/f9vHZc2ekYXObUYqUv9LJg9do5rBt362LpBlmmJE8gPD5oe2X0eP7LylXvsw0dE5TSmECil1S4eHtZ7RyvCheoKHJlE0/PY6psGzEZi5EKrmWoYoupU0/HTPQWRGuzpgwweVQ5qFqAqZfOHbozNAUjo+g+Xt5KgNLezl94p/PC+EpE8KcoamHa6E086jXsxYXZ9gDlLIrRpdjxBCgcLn8bOyGDDJcxmDuAedI4FzUJd6fQb+G/VyeYjmZrogJ0psXb/n/YL7x4NpDNivJmD09OdavStdO3qRLR67yHlc26MGqkoYc8SmJ107dYhM33F+dtT8Qvl5MxyALia2XU4dQxT3cuZ5w/tYX+5WpmkNt/c08pIOoTxtnqtHMnpr1q0k9pzdliuLr5+9odv8NBslujIW5IDMhbRH29wnZ1Dkq4+VIA6cqNjUq0q8pfmV3P4gl9dWdIb8MY2F1QEevWnNHvr13pRCMxgfsXjEpe/P8LR1TuizxgZE4PuBYCM5E6h+4DFMQ18aCSrBurHBXUgeYi9gpBWbgHM1B0QirhusPTClk4rw2wGmyaMWCTE3YMkOzuYg2y32ZSxaudLtMgZbDwQn/HztyXjDQmj+hSJX2d+o4VkzpoCeQ2RzfK4qVyUP9Jwfw+QzZEkOrZn1bwxQzzDDjxwE235i2X7vwgNJnSk0jFzT/bu3tAexlZvZZy1mr5aoWJY8A09AF9cXFY9dp/1ZY7P+PWo0wzWQO06INigV9gz41DZqOoXCRQdAwC9NWJEDjheavpukYpnRwiAb8utdQTZ+w6Zf7Ex/FSRqyETxe3uK52JI+dLXQb9nXt6ZoRd9145y4FqNQBKrW+lKMyHyxf5W9acFywh3Twrk07VspHsuvR03atUg8L1hHsLGXZmygPYKNBJzdf5GLNjSdreNNuGQEEfZa2L+pO39y8qcr4PnD2490aIeQ77gECJmNPrh79SHdPHeP35uqNbRP4OIfG5oNd648jPPzIuXyUf/5rejl0zccFp3YMBdkJkAF+xIscn324BVdScAm1aluZZWzzAsDzAagW6qscJn3b9YdEm3hWIoyZEvH42CMhTXBvYkjLxQQnT66JQSgsYE/emflw7JvlfhgqyvsMBYHZDaFvmjQz5c3y0d3n6RbZ9Xr1IBaHYWdOegBWLjUAVQH/15i6rVpinBI0ga87ob9hZ0tKAqaAqi1vSfINQNWjd6qsfA1FAVL5yF3xSp24cD133yqY+NRlhx8KnEHb1rPVd891c/auSR1Gire5zVzwmjnOtNQTM0ww4z/DrAOTxu8mU5EX+Wp+/B5zShX/i+aoO8RIesO0cn9l1in3XViw0TNX1J3PnF9A9wb2VKhMnlN8rg7F4aJ6Vj+LAY7K0K+8fTuc25CeyrNam06MxQVBUrnUUk74lP7Xj19ww3l2DqsqM2HeQKH6ZaNtzAXk4WRT+fqdHLfWWFBnzUdvXz4ivcxWfJkolePX/PvPLoh9mlSOwZTtP2bhQP3x7cfeYL28LpwF86aOzPTHfF7mMRJnT+YT5CpyABq0BW/nL9QlQU92FYSKCQlldGzhfqwcOzj8Npw/tDM14aDO45z0ZqnaA4qVqkg6YuobTEqNpEh0QgozP06utHe9YdpQqdldPnkF7bV7csP6O2r91S8guasOVPBXJCZAFiwrFzFOD16Z8LcFktYFmK3xQMGFi8OSjGnD20RhZQUSYJuqAkQmZZXaAKa7uemTLFgEw/zDnWQQdLo7GCR0he5i+Qk5wBRfGydrFnLVda+JI/o0f2SQYlqj7WpA2XKmZGNOiQHWxswVi9YJi8f83Yt0zdN8OngxoXvwxtPKHSN6UKd4TSVItVvdPHodTqgLEDfEh1G1eNslBvn79OmOZo1ft8LvOpXpoYdBSVjzsjtFL33+81bM8MMM749lk0NVmWNDZzWiIqXNU0BkVSAZmbR8C18u0kfb8pd6Ntmp0VtP04Xjlzj61yzQbVN8piQKmxQtGMN+/kY5NoH2cH6CYGq8ObYxUh8/PnpT5VZh38fNJV/+uqxNk0Vx+HXo0acSdt2JeoHFvj4+YVDV9g0DZom0AD3LAlVGXCErRJ7CjRHgYru5biIQZGHAgtYMWwjPbj2iM8jUM6hFF07eYsLEJhzADU7VONiCvvGim5lWaoStfkIs5pwu7yz2ANCuiEdF+PnqR3ecZy9D7LkzsTHoQ57Vx5Q6ct0URDDFCdGsLkMaQQcUCKQZLavIchfIheNWNWRdWdDm8wlv+K9aEjjObRy4k6yrV7+mzQkzAWZiVBFsb8/tFt/N0F1cFQKK4zqDQG6MBBKIhDvwqGrOu/vrnSHorfHaJwqAW6NHFTTJ3WFXmGL/JS/VB766/NfGidgoP9h3I4PeMhyMdbWF40G1eWL3ImQs5zBoQ74oNRWRKhYCDUFRWMM76dMrTZMCtToMimBhbRBHx/VlMzQUGa4Hckp2Zpx2002JYOo10/RqC0ZuvGbcJtjA9l77Uf48e3VU3bTnR8gz6txZzfyrGfFzZDxvdbRuZibSX1IZphhxneAbSuiacNCQevvNqI2WTl80QN9j8B1fna/9fTu9UcqapGPardRP/FILGDqg+saULdzNb7emQKBc/dyplXOQtl0BhLHB/JW7197xFOXGm2Edl4TwtZEi8lT3szk5P81zfNI0HE25cJjVYs1TYLhBQowFEteynMEzhEFmpO/LX1484EOBcaoIoye3X3Bj/Hi4Sv65bdf6GSo2HvWaOvOReGrp69V2jNIWgA4BgJValmyRgyFUbWWziqGk0cz5zgyFQRVy4JSegVAylGsUqE4r0k27N0a26s1XkExhwmZvI82vHrymk6GiaaoIa6aOKc3zt7l/WJVNVNJTQDLJ3L7cRpQfwbNGbCeqje2pXXnxtO4jV2piqcFtR9Zj5r1/5p2mhgwF2QmAiZkEDPeu/6Y7l41foMqU8XPH7pKT++/0Pv3IJC0U7oC+7RYxUsUrVCQCpTOy4UUpmqaYFvbirszyNVSl0mGYgj2qjI3TB3wgfbtIlyEts7YpbMQio28xXOraJHaAqbxIc+QLT0LW2HVqgno7Ihk+wd0bI+wgtUGBz9rylEwK4/aJUfaEHi3ceEpGQrlfas1Z78Zinpdq1OmHOk5qDNwvm5NnKnhXNeK/+b//vNvpi6ayrgkqYC/485DfMjGpSRfdIZ1XEE3L8flk5thhhlmxEbYjpM0f6yYdjTt6k4edQzvzCc37A88QYeCz/B+5o+pjQ0yVDAFghaF0cNbT1l7Xq+baLQmFO/ffKSN04QRVZNBdQyajqFAXadMx+p0rc6xQNruu3ma+Huo3bW62ufZOkuZgrV2ZRt6iS3K7zk3tKNMOTJyptd+xSK+VqdqtGPeXm4YIk4oeJlgAhUuL2h0aHg/ufOcGUCvnrwi79SNaUGfVdxEzlsiN717+Z7pijDpwLVOuizCKfHRjcccHI3/r+prRU/uPuMQacBNiQTA65Ju2tLMTQLUS1WxpSFCAHtMFD5FKhRg1pE27N9yVFjnVypEuYvobzO/X2ELWdiXMMhdMWrnSdq9Kopqt3OlXAWy0pqpu/m8QT+G4uynn//3zVxSzQWZiZA6bUoqb1dcZe5hLLLkykilqwhHHkPpaNJqFAWJrqkJPpSSQx2ihPypA5x5QN0DpIVrfMiCDE6H+DBrmshhEcD4PCbYsPPjpXCYEW74/rX6aR4oBFJLhumXJtomFlMZWC1pA9qAixEEvACCog2dcmFKVl/5/TVjt+vUrun/uClUYdFrJ+1Qdb6+FfD302V8AE9lL8bcpMDFhheryQ14r/tNDqDSlQrQ+7efaGDrJfTgjmE5dGaYYcZ/A0cjL6mCn32aVKUG7b/tJCkxgPieuQM38G3/rtWoYMnc3/T5QXtbPV6YbjQdWJuvL6bANujAX7zjwsXJwCyzI7tOMm0QewcfZY+hCTEhp+nW+Xt83F7xChdVEHTYOdbG12zvHsfqXjaS63QTrJpdC/ZxEVPCuigXVbsXiYY0Gumwjk+XJQ09VOJvUFDJIOgt00XhGa5owMBgAqQxRxUfS35NcgImp1vQnaFAlGyosg4lKUeBbKrp3e0L97jxH99kI2LDQWYlIXssf0nxXPEhn0MfV8uIDeI8ONc3LEg9QjHDkywzfbF/+wmq3daF9fF1O7oz7XPTHHGu964/RLP6Ci3jt4C5IEsE2mL0TvWOg/pC2nUaqiNDdhbG++9efaCYkDN6FXAY74IKCDdATXBpaK/qcqibhCAAEDb56N7IBPv4SJkmJZuEaHNt1ARkb+QuloMdD2VCvDrU6uDBCwY40ppcHwHQG/G6sTCqm/rFh0dTR+7WYfoGYa+hqNHGhYWsj+88o+Dlhr12bXANsOWwTIRmrh4vOnjfEllzZ6TWg4UhxvKxgfTg5tfGL98bYFE9bE5TKlQiJ7189o4GtlpMz58YFntghhlm/Ng4f+IWjem+hv75+19y8ragtv2+OOV9z5g/ZBPbfBcokYsLsm+N1RMCWUIBMwyPxobRCjUBjyfzRBsP8DUoywyFydpx2/i2d1s3SpNBu2vmhoniOoymr5xCxcYWZUoHJ0JovSQ2Td7BUyFouApbFKDPHz/Ttlm7VfsVFGdg6UDTJeN9yjmX4kIOdEXsjRDm7NnSmXou7kg5CmbjYg6arsvHhNTj+YOX4vccStGLhy9ZB17BrSwd2KQENSt6/TClkHNr/MW4JETZt9jVqfzV65KNek1W9/evPqTLMTd4z+VUX7u74pO7z+n8oSv8WXKoK4xJ9MGti/e5EMZU11YZIOgDFJLYtxRWpnZpM6SiJr1rUFTQSf6/yydvU1VlX/8tYC7ITAi8ceh8XD1zh/M7jIVdrUr8BwnThqf39KctYqGR2Rgy70EbUGTIUGlthYZlNQv+8OJDLJ144qNmO9E5wlhb0xRJjrMPbjtK79+oNwBRB5wLt+biw7599m6NlEdYrdZoJ6Zpa7RY5cOsRNqubpyku5ABZVNqz9aO324wPQ9FYkDfWqopmalClfF+txnlz7eDFoWz5eu3RvUmdmRhV4ytkaf2WG0QHTW5Ik26lDRyQQvKmS8TPbr3kga2WkJvX+n/92qGGWb8uECY/LAOy3nNs7QvRj3HIIrk+99KgdkTsTWG9zDdpzY2yBLeFLh37RHtWCioeG1H+5skBBrYOiuYm9T5S+UmewM2+cCZ/Rfp4pFrrMGqo4M+icIHjtRgWtTp/iXoWeLl41cqJ0I/hTUj9VXS6t6/b21VAQQtFYo26L3A+gGgLUOuGPZEz+4K9sb/fhKNgLYTmzBFEi6Hlh6iiMiWLys7Q6bJmJp1ZSjUINcAbH0rU/TWo0w5zJwrI1V0K0dXT9ygm2fv8HsvzxV+Dxo6wL1pXHdJTOYuHr7KfzOaiq1QRUKDYhN7Tm3Yr0y5ytgW42JSX8ioqEpuZXmvqi/AJKvT3pXexTKkQ3EG7diCYZvp8olbZFNN7JG/Bb7/VSQZAWYHZasKuiEqbGOBKZekLUYpIk594aJkjB3eeYJ507rgpky/QteqN+0A8OGUmWA7Nei4wD3Ghw1Fm8yPiA90cEAZQBiidOvRF7Z+lZkygEmeNsojMj9wvEim12QCwvfrJUSaoAnIcb82oDsGAe2Da49Vi5Mh8GzhRNnyZqbnD19S0ALTab6QRm/tacHdnEWDhRD6WwIXhu6TG9HvqX6jc4evUdAyzRPM7wmZsqalMYtbUeZs6ej2tcc0uN0yDn01wwwz/ru4d/MpDWi1mN69+USlKuangdMbcVf+ewfyOWf1W8e363Z0o+LlRVbVt8TCQRv4OobrWUXF2S+hAJV/i5L31WRgHYOLvDXKdAzXb7BctGHd+O38HZS+bHm/jjyAUzOHMFsX4XxWCTSx0aSFM2IFlzJ8DjZNDlTlg4WvO8j7KjSSP38QzdxiVoXp0uHrfP3969NfXISUsS3B/4d93JFdJ/j2rfMihgmaNHFsdnRAoUY61K/Kmn6gVkdPLiS3K/o2OGOnzSh0WNjPYcoIkxLpuCghzUDKu5RVe35wLGGKtwAYPboQuUmhHSrO3PoAzxGpxD05+RlGVwRF06NBFZ4IA7LZ7u5vTRFbj/N5TaNm0plYMBdkJoa94u6SEPt7fhzFoEPaeOqLIuXzcxI7ih4UZbpQpVYl4c5486nWAsZLSWqHvT3EpvGBIqhacydVxoY6YPGQNvZRWw0ryHCMnsoxrBy+QWPxiGJWatq2KYuLOoAWgC4SaJbrJ2zX6/nrdhNdr9Vjtho8JYPDY6MBgt63ftIOtuA1FVqPrM+L6ZHdp+hE+Le3bM+RLwu1HCRe25JR2xM0HU5OyJEnE41e1JLSpk9Jl8/cpeEdV3BX3AwzzPjv4fH9l9S/5WJ6/eI9FS6Zi4bPbUa/p9Rsf/49Ye6gjfTyyRvKWyQ7Ne4p2CDfEicjLvD1C7Q2XM9MBei+4e5csGxesjXQCh3UuZOh5+LoyDXh9sV7Kpfp+r0FGyY2EJ0TqFja+/3hraK3/vPPPyonROSM4ecHtx9jx0A4KcIBUVrd1+pYTWXm8UnZP+QsnJ2/V/aqqDJfAUUQUzEUn3jtaATfuyKmYqA3vnn+lidloB5ePX6Dp38wO0PWavi6KFWBJhGqmJGxxCXWJBh7oN1LxPFUa6Y+lw2SEDSxwTKqWkv7+b939SHfH38Ddoq5nT64duo2PbjxhJ/DxrM8JQQ4Z9hb5iqYjWq3dSZHH/3pj6aAuSAzMapUL8cfKnBPnxhAN4wPyYO9cPiaQW6L7Hqo0Ba1uSfGptPJD4rMflCHgmXzUXGrwty90RQCDYEocHTPKXrx6JXa+8jAwuMhZwzKJJMLHY4XheMRLcWmr2KBDxMQTKQ0odHAOip6ALLJdAGC3jQZUvHI/4AyIjcEELTmKpydueCw4DUV8hbLSd6thaB8wYB1Gm3/ExM1mtlTuapFudM39Y9VPwR1EchfNDuNXNiCg9/PHL1Bo7quMpkxixlmmPF9ADrSfi0W0bNHrylv4Ww0alELpjb/CDi05zSFbznGtLOeM5pyruq3BDb2CwaK6RyuY7iemQLYg8j80ObDDKeVovEKuDex54xYbVg/fjtv5G19rNh0Iz7ALMIUEs1yuQcCsI+BDgzsG2RuyfvKLLInt58yJRDFFkw5UGjBJRoGG7/89rMq+xUTsL/+FM1C0CaBFKlS8PfiVkVYn1bIIr+KMlm3uzfr0gCHejYs99i7IpIb+YXK5VdN8FC8HdtzUq1G7HjIaT4eTJGgLVOH8PUH48QyaUOoktVq6a6b2hgbMiLKyqOcSUxgZLHcoJsnVWuoXfNmapgLMhMjU7b0VNq6cILNPWK7LcpxrL6QOrLj+87xB0oX5EIQufGQ1s28TGDfsyRc7YQKdER8kPHhlx/8+MBilatIDrbbP6ZYpeoLLEi1OonOzbIh6zRu+mGXWqpqMRa1wipWE8rYlWAnIWywN04Wzk7agI6SzDuDDa6uAO74wKIqp2Sw4NUUpG0MGvetxQvjrQv3ac/yb08bxMWu+5TG3KU6e+jqD0NdBBDyOmJeMzb8iDlwhcb3Xp8kRa8ZZpjx7fHqxTsa0HIxPbr7gnLkyUhjFrekDAbYaidngNI3s8/aL1TFCsJG/VsiZFUU3Tx3j6lhjfuJ3E9TANdoNAhLVi5C1tUNm5xcOnaNpRGY1gT01X5MaOaGKTqpgP7i+h4b2F9snrqTb9fvVSsObXK7YtwBK/kUKVOwfAKFjtSL7V4UqipoQleLa6osVixcy9Drp2946rVxciDN/WM5//zAZtGIR8MbdFq5B8TeC9Mz7BMQZxSxXhxzzfbVeC8TNF/slbzbe6iKEjTfsY+CvX78QlPa4EPOoi4oG14C8rzEd2aMD+zlQtfoT22UwHHLgsxRQ1H4PcFckCUC7Lwr8PfoXQlzW3RWeLRSsKgv8hXPRYXK5eNNY5QelMeKrmUofZa0HJp4UsmfUHs8DWy5e3bn4n2N7oTuinGHJpMQfNDtaosOkaZJm64pGTpE10/d0qolq9NV0AsD5wTTx/ea6YEB/XxViwtG9rrg06kaW84j5f7obsPfX5iJoHB9G4vbbgqgw9a4v7hwrBi91aTFnr7ImR/URR8VdfFHcF2UKGNZkIbMgmj6Z4oOOUeTB2z67rPXzDDDDO148/I9F2N3rj+hzNnT0dilrSmLAd3774Kq+PQt5S2aI0moitC5Lx+5hW8H9KlpUH6UNsAReddiQadrPtzPYAfMNWOEdgwB0nA21IatM3bzXqucYymeRsUHNFtwOITGKnZOF9wST+w7KyzwOwhTtO0KrRFGahmypqM9S8VrKFmlON8XTV1Y3gMPlLxbZIECO+YGs3YM95Ovt7RdCTodeYGf47nCtKre2o2O7jrJxSqKLDTRLx6+Qncv3Re29kp8EhdpyhSthhIVFNsMA1b4seUs8YH9EfaUKCAr66ASnj94lV2oU6X9nYtPfXHlxC3+vRSpfiMrD+PNNzC9TA4wF2SJAGmTeeHYDXqhRm+lL+x8K3GH5uqp23RfyZvQF05++rstwplHTtW0ZpKlT8XOPADG2+rg4GfDm1YUTBirq0P1Vi68YBwOOs7ca0OA0Xq15mJSt0vJ5VAH0AJAD0Thg4meJkBHBjombGN3LtRttoELRs127ionR4OnZD//RE2V/DBY8eozwdQXNVo6Md0DtsVrJurOWEsMeDd3IAvbYrzYT+n+/QdGx0ZF26I0cFpD+vmXnyh8xymaPnjLD0PNNMMMM+Li3ZuPnEV48/IjypglLY1b2pp1pT8KDgSdEK6KP/9EPac3+eZURWDtxB3s8pe7SHaq1fbr3C5jsWa8yPws71SKyjsZZhBy48wd1t9jj9Kgj/bpGBqfO5WipX5PYRQWH1tniikYiq7YzpWbpghWjkO9Kpz39fHdR9qzREydfDtXp30r9/Pjg1F0bLeQaKB4knb2D689jmNBDyfFOd2WiNsZxM+lCUhl70p09sBFvl29tSuFKPs3yEzwOoOVwg9mHsielQ6Tdy894Aa0SyO7rzTxK6/PovHBA3n/pA4y4se1kZ3OcPEwhdpo62vFRaG+kNMxaMcM+b3YwB6ljf0I6uQ2hh4rzpVJBXNBlgjImisjFa9YgDfrCQmJzpAlHVV0LsW3IxT3GX3h7F9F9aF6rIfJgjTkiN4eozVkWE7A4PyjTksDIaplNdENCVurfkqWp1guquojdGubpxheOIBbDRzecVyjRgyFDxyKgE1TgjRa8WMxgsgW2DZzt176oLrdvVgICztcydc2BHa1raiwRX768PYTbZhsusIJhXWb0cIGf/u8vSwMTgrq4h9TG7Pm6vzR67Rtofpcuu8VNi6lqO+kBryJ2bv1OM0cus1clJlhxg+G9+8+0aA2S+nahQeUPlNqGru0FeUpqF1H9D3h5dM3qsDb+p09koSqiOvTNkVL3XZMA5PZ7D+48ZiCFdp+0yF1Df79dYrJF3KwoPnShqAFe5kaiPBlKzW0yAuHr7DmHa8NxhmxaY7hCp3Pr6cwAVEVYIWzU6VqFqpCrnL1CnQ64gI/htzHwPUQaDSobpw9D85p+qzpeOKDxvhlxagte94sbGCGKR4f18HLPDVzaWRPnz58pgilIJLNbmCXomWDM6Ms0mIDxwOrfHWA8ZtkEHko+bOagD2XtLt3NZCuKBlg0gTPGJw/co2b2E8fvKLMOpw0ExvmgiyRYF+zApWtUpQyJ5De4ORnraItGjKNgUUqgqJjdx+0oUj5AizmhLZLmxkIsiQwesdkR3Kd40PyhZHOrmlCUvcPUSyFr40yKJMMwJgdYdGgCWBMrwkezb4EOsukeHVwDrDlHA7QCkK13E8Cr9+rlVi4Vo3eYvCUDEVLs6F+fHv73L30zADTFl2wci9LlVzLcBdt4eANlBTInjcztRkmLhTLx+2g25e/fT5aYsK+WlnqPb4+X9D2bDpGs0dsNxdlZpjxgwDxFkPaLmNnVTisjl3SivIX0U5b+56A69XM3mu58VqwVG5q2EN7vlZiAdcnXKdwvarsoX5jbwxWjdrKOnZLj3JUOpa9vD6AYVfkRmEL36DP126JsYF8LqkNQ66YOtOQjZPEFAw0wIzZ0scJglbRHC2FWZqcmMFt8dD2GKYQYgKG/DLAppYlG3yAjQhGTy5MFRVNfewiTdrfZ8mTmd9r6xoV6cS+M6qCK0zR91fysGBX6kOBMVxUIvMMmnoA8o0DW4R3QexCUl9gD4nXBD0/Qr61ISbkDL17+Z73VXLPaihdEUYgxkJGVCFvLKkjLMwFWSKhTjtXmrClOwfMJQRVvCpwJ+LO5Yds2GAI3BqKwghiSV1FAyZFcBMC9q7UTFvE6Nmxvpi+ScFmfCCFHpomFEKa3BBhqAEtFbozkXoUjPFRp5vgu++YG8Kp9hoDnRW7WmSEaKKX4fzWVYKf0R3Th2ZXv5fIOzt74BJztA1FZU8LvljA1Wj1ON22+/oC72O7sWKCc2jnSToR9u1t8AHPRlXJ0qUU/fX5b5rcdQVfeH8kONWwoJ7j6vH53rX+KM0ZGWguysww4weYjA1us5QunLxNadL9zlmEBYubxvUvuWDfxiN0KPgMbz57z2z2zQOgpc09rk+4TrUd42+wxksTbp2/q3KLhrOioViruCVW8a7ERhbaELw0gl4+fs35XPEdCKWNu7TCjx0E/ebFW9X0KaB/HVUeqrS692zpTKtGblIZbBwJEnso5I0Bv6cWToJ1ennxewejEOCfv/9lgw8YkgCgggKV3MtxoQlKKvJipTmIi8ygVf6N1yCLyvB10dychxdB0YoFDT6PsgEu95TaIAcATvVsDMqJi1CmatbVLIymK2JPGL3rdBzvh6SEuSBLJJhqgUGHRFb/hpp72NWuzB9CfBivnbyl8/6YbKHgunzsukb9l8yjAJCXoc48AoVQ9ZbOKlMNTecHzkLAboU3bQigZUNHB5O6vVp0bzXbe/A5hLvQsT2a6aM12rix+xDup09oddY8mam6MiVbOXKzwceP199ihMhbCV4WyTQLUwGmLrXaCqHtvH5rNNI1v0VgNLjsV8/cofUzTGdgklzgUrMC9RgjBOM71x2hWcPNkzIzzPghijGejLWmIqVz04+Ep/df0rxBG/l24141eEL2rYHJyfz+a1U29/lLmO4Ylg3bxAWVfW0rKmogDRMFkWwyNxpYW+t9cU3doDgzw2hMXVGL6RlPqLwqxnEoRCGHRjQYSSiWcJ9144XFfu2uXnQu6jKbhqVKm5L3Gbgv8sZiFEYSMkyxTyvnLKZJLcc25P0LkLtoDjbvQGGHrDJozTBFAqy9K9G5A5f4dQo/ACt69uCFyhwN9EWJPUsjVFIWQ/ey2DtePXGTj9FJad5rAl7LIaXgdNJx39hA01zuh53r6x8iHR8XY27Si8ev2UykvF1xSmqYC7LvANLOEwJGQ+hxqdOlpCqKY422jDEJjNStqllodUkEilkWpnwlc7NxQ5iG+3krxhfH956hhxrc9tyaOnKH7NKRq3T/mmG0NnzY63QXU601YzYzfUAdkEsBExFgyzRBL1AHLH4yv2zNGP3MGvyVhdjYKVlZu+JMq8AFatVosSCbCo37+VL6zGl4srpzifqg7sQG+NidxghN25qpe+jKqdv0o8HNtyL1HOvH9MXdG47S9MGGh4abYYYZSYv3bz/RwFZL6OKpO6IYW9zqhyvGcE2b3H0Fa5dLVCpIfh0Np6KZAjuXRjDbJ02G1NTEhDb30HRjc4+12BjtGEy6QHWsXL08U+20IWxNND26+YQyZEuvigOKDWjbg5eJ6269Xl/MPhAEHThbaMN8OnlysXMy9CzdOH2b9yo+nT1p10Khq6vWwlnlRJ02Yxp2UwQNEbBwKk0p04ocvJ9//pkux1zn21L+gKmdpEpKiqJzAzvaptjse7V2pZRpUnIWGfYfkIDkL5nnS0F1/AbvseQUDdDXwEruHbGXhAmbNhzeKdwecxbKRsUq6T+JOxd9mV48es1RCaC8GouDihN6ZbcybFSS1DAXZN8BrD0teNKFNPIbZ+8a9LsuSsZYxMbDem0UYfMqCzhNH0AsIjXaiOnWzkWhaotEWMVWdBWTPbkwqSsAKyj3idQjxDo+wG1GBwnhhJomcYBvZ08u/E5g4TujuShAQYbCDC5LhwKP63x+PHe1FsIMZc04YZNrKJorWrKwtQfplpappKHAZKrpIEGHWDl6G702oZujIXD0rUQOtSryhW5Sl+W8+P5ocPWpSL0UTVnIlhiaarbEN8OM7wZvX32g/i0XqTRjcFP80YoxIHBxJJ2OusJ5inBV1OV8lxiAbm3FKHGtbDaoNksbTAHsQZYM2aCiyeUzcOp2/9oj2rdaFBKNleumJmBftH6ikBlA6qCOLofpGCh/yEONrYuCEdmjWyIIWk6kguaHqBwPIWE4tOO4ylERzWpQS68eFwXXT8qwCvlj1xXW0+tnb+jayZt8+/2rD1wkSoYT8sEkrRIOzND9Y/8G7RmCpHcu2KtydZTYpxRwMBOB9b40OpN0Rm2FGd4HGQYtmVTaELFJ6PWc61cxaBInTe7sfCyNLqRwrCq6Yo2kpysC5oLsOwA6J1bu5eLYfOoLjMTx4UdqvT5THGRAIOcL+i9MfjTBrbEDOw3C3v6K0p2JDzmZgqWqpmLQsZ4YU0duMFxHhiDFJkPrqaZamvLGQG2EYxKwSRHharK0r9WpGt9eN2GbXtNIWN3iwnYy9Jwql8MQgJ9t52vFz7ViuOHUR23wbOZABcvkoXevP3BRlhTAIttprD9lyp6e7l57TEuS6DgSG87e5VXui6GBJ2lin/U/nG7ODDN+NLx6/o76Nl9IV8/dp3QZUtG4ZW2ocMlc9KPhzpVHtHSMKCJaD61DeQonjUkJMjJhPgGjB68W2t33DMGJ0HPsKA3GSuMB2umG6sAa83/+JStPCyqhJkssNo7sPMlZrNgnyQic2ICMQ4YsN+xXO06hsXXGLv4OiiEKOVAGpc6sRjt3ClkWwcdRqmpxzhSTzW1sRWAA8uTuc77GIMNsWI3JXBydUrJjIRUBMO3CNC1viVysBQP8/qipsue3qVmJbfajtx7lfSHMNGxrCxYWHk+ynmBXjz3jnO7LqHbmlqpCUZ15icSFQ1fY1Rt7VmsdRc7rZ29VdEkZu6QPUCBGKQ1zaXpnDK6fu8c292hQVFLon0kNc0GWRLh54T6tmKC/5bm09TywzTDaIhYoe4XyqM09UQIdFVm8hCkfZnUAR1k+ruQbxwcEpCgGMUbHWF4dsBCgoMHk6t6VB2Qo0FWSeWMRWo63rmJtj8UGxaYm1OlanQvNS0euqXI7tCFHgayqqeLqMcbRDpsNrcvTlejAGLbINRUgkG0/riHf3rU0gm6cvUNJARS6f0xppOrSHo/QfV6/RzhUL0cDpgRwTlnkrjM0uvtqDtA0wwwzkh+ePX5NvZssUOWMTVjRlgqV+LEMPOQGdmLnZTx9qeRUkmo00220kBi4ce4u7VLo8x3GBZhsQof90NKhQhcH5g4cpg0BJBWq6dhA7dMxQE7HvNu6xckBk9i1OJS1UbDCr+z1pSi5cvw6nY44zwVVzY6i8Rs0L4QLMGlytmOeYPpUrWWpKoww4ZIFDIBcMAk4Q+P5ALBPcE4xPQOKVijEmnhQQ5EjJumPNTuI55bTMcQI/fqbmDIhVBr7I7yuKjUr0fxeKzirtfHgunR872laNnQDDao5XkV91ERX1CdPLHLTYW5aFqlQgAqU0u7EGBsnws+zrT8ctCH7SChdsZJzKaNNQUwNc0GWRICBxNlDV+lwsLAj1Ye2iA4IaItX9TDoiA0pekTGmD4bRGlbf2DzEa33B8cZQBdGHRUNo2Qnf0GZ1GQ7ny5TWirvIjjAmvRo2oAFqEZb0aWSXSl1KFG5CD8P+NIbJgVqvF/G7BnIo6nIWtswUYh2daFBXx9eZJG7IR2ODAHoFW6NxUVy8aD1Btvoa4OFfQku5pFBMqfPGpM+tiGwdClNNVuI8zql+0qtWXffM2w9ytCQWU24EXI47CIN67CCPinhnGaYYUbywOP7L7kYu3fzKWXNmZ4mrmxL+Yv+ONb2sbF68i66dvYu7zmQEWkqwzFDgOvO3L5r+DqE65GFAfbmuhC1LYZNJFCoBPRRH86sDesnBvK+AJE+Ja2Lar0vmrTnoy/z+l67m9dX/4/H2TpDFCt1u9eIc67XK/lm0HVly5uF3aFRkEkzD+y3IL8A5fDaqVtcqEGvj4kipBS3z9/l1/hPLOYFKIgn9p7h/QdQzKoQnYu6JAqzZ6Iwc2/qyIUWHgfFKlhTD64/4gxVYa4mmEzStVr+zpWYGyrzD+xPdszby3sVFGrYG+1VwqUlYD4ivQpkXq02yCLYTQ9qY2zsV+z48XdkiCvjVxlmO0VBVrV6wpzQTQlzQZbI2Do/jJaNDaSnD74EGHPSes4MVKOpvd7B0RgB23iJ4MFwJSdDX5SuWpzzJt69+sAfTF0oa1+Sc7lwf4znNaG8c2mmA2JEL8fu8SELDYzHkXWhDh5NBXUBmWKaLOy1waO5Ey+QoE5Kcas6NOwvqAy7F4fxqF4T6vWqxRMrUAZu6jFVyl0kh6qIXW2kOUeTQXV4MnfmwCXO5TAl2ozy52L+3MErBgeMmxItB9WmvEWys6vRzL7rkqw4TGxUdixBIxc0p99T/UYnD12jgW2WsGmAGWaYkfS4c/0J9Ww0jx7dfUE58maiiSvbUe4Chk1VvhecO3KNNs4Sm+yuEwKSLPh2/5ZjdDbqMl+HcD0yFSCFWD58k4rdgmLGEIACGLJcFBaNdTgrAmsVrTgyTrPkyvTV/6uKqqzpmPInAdOyqM1i31a/t4+qAY2pF/ZQiAraPFU0gBErJKOA0mRMzd///CyaegH9a8dpfi8bIsK9pRbv0zuxf3Lws6GTCpURVMjgpWGqHDJQDvcoztaVPMpRtnwi8ByTscNBggpYs707RW09yhlmAByq4biIfQ60+32Xd6JjCt1QImrrMfrw5iPlKJiVLBy1F9x3Lj+gyzE3uJA0xCXxT2jslD2pNLszBshGvXv1EccE2FQzXQZeQmEuyBIZnz7+SRtmhtCcAetp6ejt9PD2M1X4HDpWT+7pHwrsrPBsIzYfNcjJDV0Eh7rijzd07UG97u+mCE6Dl2t26MMH212ZJiFlXh3QccpdNCd3TzQVbQ71qnDnBrkZ4Xq4QcZHhqzpyd5PnJut03dqLSBL2hTlD/VGxbJWU4FlV8c6zgKsC436+ypF3EmjpmTZ8mamWu2F6xXEyfo6Gun72P49hSPlosEbNBbGiQ0UKL1nNWdKH8IYkYfzo8LCujCNWdSSUqf9nS6cuE19my1kvYoZZpiRdLh89i71bjyfnj9+Q3kLZ+PJWPbcGX/It+T9m480sfNynkq51rNOspwlhBgvHCwKh3rdq/P1yFQA8waxPihI/LoZHnCN3FE0yC0cS6kClTXh6okbdGzPKb7Oo2kbH2gwSjpjzQ4eLP+QWM85qCKkuWCZfOy2uEG5L4Kgz0Re4LghmLdhAobHquhWjs5EiBzRv//8h/XmDn5frOEx3Xr56BVlzZeFaYrYV6KBjOODGyMmbGiuo5kPjTvg3syR9xZyv+bZUpizyfghHCP2SZiE2de1ZjdGACHUYAJJnNl/QeXkKBGiTMzQYNemMwNkvABcpg0pomP2nWWX0Cy5M1LJyoXJWOwPFPo8UHjhRp5cYC7IvkFAbpGyean75Mb04slr5nKP77iUdiyNpM1z95FHA/2zF2DvCT4wOMXoNhkCqXOCLSz4t7pQrZmYWkF0Ka1U1UEGIoJfrG7qhEXDVQmo3rsy7ohb4pdff1HxmgPn7DFqcuLXo6aKPvnwpvpMLxxLo4HCDnfXwlB6/+brDDUJdKKk2Yg+2jYUnfJcrFA6doaiQe+aLBSGy+N+E0+y/Lp4Us4CWen5w1e0bpL+2kVTo6hFPs6/AeYO2MANih8VJSvkp/HL21CGzGno+sUH1KvRPKZKmWGGGd8epw5fp/7NF9GbVx+oWNk8XIxlyW7YROV7AprAaPjmyJeZOow2PCTZVFg3OYie3X9J2fNloXpGFE2agEnRihHCCKth31pq9Vza8Pj2U9qzJJxvNxms2yZ/9agt/N25gS03beMDET/Ie4UeyUcxBwNePHpJe1dExAmCPrDpMN278pCb8l5tXGnFcOEQWb21K0+mANATUSymTCuCoL3befA+Q0Luk/KXFI6SmXNnUpl2HFUMQaCxD18bzfct61CSchbMzjq2p/ee816ySi3hTYAGP/LRAJlpBk0baI5AKZtilKtwDpUmEfvI2Hb/OJeSAumhg67IxiFK491VYRbpi/2KqZ1DbSudRZ9WuuIOcX4caooJYHKBuSBLZGTMmo4/dPeuP6ae05tSm2F1qbR1YTp35DrZVCtLVb0sDDPo8KlkFG2xsEV+KlgmL1ux7tcjYDpPsZwsNEXHZK8G/Rffr2hOnjrhftLRJz7ARxZ5G+fozqX7au8DHjMoe8i/uGzEhKloxUJUycOCu0IbJ2rWiCFjBBlqmBLB/VETipQvQDbelfh1rRmr35QMlAeM4EE5vHD4isGvAUYpft0FL335iM0mDXRG563t2AZ8e8vsELp37RElFep19qDSlQvTx/efaUKnZcy7/1EBx7ZJq9pxF/7+7efUs+E8unUl6c69GWb8FxG99zwNbruUPn74kyxsCtPYpa0pvUIH+xERsS2GwjYf42kJWAmplcyqbw2EEG+eKYwq2o1toHICNAW2zwmhZw9e8sTNu+2XSY++WDteTMcquJSOY02vDjAdi95+jPcxARpcHNeNF/uE6q1d4uRv7Zy/j58HNvaQj6AggSs0ULtbDZ5qQZeGazSMOF49eU3ps6ajE/uEdOGjQndHoYXnlzRC4H8//Y8uHhbuzq+eCM1YRTcLtr3Hfsq2thUFLxP7HLdGDnEa43C4lpbx0KKhSMOkEWZsMO+Y3nERda06iKa0nc8mJbKYgY4ua+5MXLBJyD1ieedSOk1VLhy6yk6MCGO2McBu/vPHP+nIHqH7sve1ogTRFa89ZrqitYeIXUouMBdk3wBlqxShsE2i61GyUkHybuZALQf6UPrMaXl0+kpxz9EHTgptMTrwuCofQh/ggyynZNoKrNjwbC6mZOBYa5taycmQdPGJD1isorgBIAxVByxg4E7L6ZUxkFMt8KMf3Xqi8TzUVgKgt0zfrbUYkHkkoasPsAhWF2BPK8WsxgY91+niSemzpuUL2Z5l6ieKxsKmenmydCvDF4d5fZPO4AOU2N6zmvGCfOn4TVozVb1j048C6FMmr2lPBYpmp+dP3lDvpgvo/AnDjHnMMMMM47Bz3REa0301r3tV3UrTiHnNKFUsp7ofDbDyntV3Hd9u0N2TSllpDzlObCMP7FPA7qliwqwnsHzWTxKyg6ZD/eLQA/V1VgxWrq9Nh4gsUG1YO3arSpslA5Rj4/zBy3Q64gLTBv16CEdnAKZo0jkR1EQANvUowkAlRPapdDtEcHPkRjE5gq4MVM9MOYXmr3D5AlykAQPWdGftF5CzYDbW8GfJk4k+f/hMOQpmU+1VEGEEt+i7l+6zKQj2V5h4YToHuMWaZO1RmtPQ/ENLhgkb9lNjdw1gWiSomnL/VKB0Xhq84Y8477M0bXNvrNvMI0zRx9n6WBrkbng0+Ax9fPeZC/ASlsb/TUMukRzpioC5IPsGqNnSkRr2qM4BlKsn76SmlQZRe6dRFLRsP10+cYtGt17Ijov6oEzVYpQpR3rOljqhiDb1hbN/Ve6YIdH+wQ31tL7YsKtdmVKkSsGhidoytpzqV+HpHcb1V47fUHsfiEQBcJc1hQNjvA4c2HzYKLvwcg6lOGgaF4BlQ8QFSdPEDqJbjNm1BVIXtypCVtXK89Rt/QTNU7fYCFAcF0H1vGiEhT0W6Ub9fFVFnexMmQJYTNuPb8gXjZh951Ti2KRA9ryZqcuEAL69btoeOnfYdHb/yRGZs6VjW+2S5fPRu9cfaUDLxdy1N8MMMxIHnO04PYRmDd/GTAfPelY0YGqA0UGy3wPQYIRuDPqxEpUKUsM/TEcRNBSHd5+iY3vP8vWmw4SGJnV33DA5iE3HwPpxaSCcnA3NHZPOiphaaQMKnP1KERPQX1yb42OtwqLB3gIOihIw7oDEJEvuTFzMATsUZ0Xc9//+/T/VHqRSNQsufHCebpy+pZJzAMh7ndpmPt9GcfXpvdhDwSBL6ugBPIdk/rg3dVLlnnm2dKHU6VPz/6HQK1AmL5VWNHNwPEZgNQDjDjTE0UAHS6hg2Xz8mPKY3758R384DuXPk8SFw1e5gQyKJSZy2gDWzwHFJRH7UUMQvlGcJ6e6lRP0tyQLMvsk0lRqg7kg+wZImToFuxttnR9KZw9fYz731utTaeLWP3hTWtWrPO1aGaW/QYfyRw9zD0OQOWcGqiAt5hVRpTbgg2+vPNfelZqnaphuQQAKyPDB+EBHB5ld6NBoKoIQfIiFC/eRHGhDgA9p63Ei7ypsdRQXXOoA2oRvF0++vX5SoNZJkVyAYfH6/KFu/Q8y0eQkctUo44KevVq7cOcLC/mWmXvIlMhTJAfVVV77vP5r2WwlqeDka0lu9a15cYe2Ek2GHxlpM6SiMUtakbVzSfrz8980uttq2rFadzagGWaYYRiw2Z4+eAutnSc2p407u1LX4bVNln2VXLFu+h46f/Q6N/b6zG6eZK8XTdd5fdfy7TqdPPi6YypA075ttpg6NR9ez2Drc+wLQlbs11s7tnFyEF+j0JwtbFHgq/+/ffEeOzJj/1G/9xezD+wrpHOibxcvLq5ACzy4XeigoJsPWR7BMpIiFQrSKSWrNU/xXEo+V0GeVqGJDoB6CBdp7I8uKpKIT+8+8cRIukFjK4OJGeiEOQtnVxV4mMTBSETmh9Xu4qUqauDoiCZ2IYv8/PpwfmDqIV+DM3Re//sfnQg9y7KTcvYl4zQ1wCCSDfyUqYXeTRNOhp2nN8/fUYZs6ai8UykyZCJ6LEScH2eFSWVsQDooi2gSJDe6ImAuyL7FSf7pJzodfYUuHLtBPac1pSqeFl8ZHRiyMXZQ7D4P7zqpcdqkCdJgA26L+lDWJAUvcuMh+vOT5ueCFSoQtjZKrYsfzgHEq8DOReqLtp9//plcAuxUj2MMilUqzHljeG0y40MdsBiio3Pj9G06ukvzpAg6ulJVi/GCtWWaZgfH2Ajo58tTMljFapssagKmjc2HCRrFxik7mVNuSgT08qaseTLRkzvPaf0U/V5TYqHD6PqUs0AWenL/Jc3ss/aHtcKX+D3lbzR4RiOqXr8yv9Y5owJp6RTjjGzMMMOMr/Hh/Wca1nEFBW+O4c1sl+G1qVEntyTJ3/rWFvdrpogNd+ex/pQzf9JZ+W+Ytose33nGbngBvQ3PBtOGVWO2slNy6SrFyLq6iAIyVDuGgh3aMTyGNqAJG7xMmF349/naWRHYNCVIFeYMTb0EiqFb50R2mNwfIdqH3Q8dSnLRFDgnWKXn2qs4H35QGpP//iPkFClj6f9CV+2nmOBT/BjQQAElbYrx60FBFaHo+GGtv1PJZYX2DHKKmD2n6NHNJ+xp4KLITIAQRVMm44e6zm5FxSsX4dv4zKCQ9G7jRrsXhdLmaTtVztoA9kWRigGZqx55YuHrRQPSoY61Qc2CKEWiU6B0HipYWv8Q6fiIVlhB5e2LUxoDTWC+BcwF2TdC9ryZ6PXzd5Q1lsXug1tPadOcvTSm7WJyMyCLoaRVYe6KgE8LG1BDULVmJUqR6jceMV85flPn/S2c5NTqAwcfawLsVbHAYBweoXCE4wMjelwgLxy8QveuPlR7H0dljH1s10n6+N44up6vwtXetShUY65ZukxpqGZ7D769cuQmjRtiFvH29VXp3/QJNIYDk7uSvyYzUgyFg58129yCsrh6rLDHNRVwgWg3Rhh8bJq+h+5f101fTSykSvM79Z3Tgq3woacMWfvjT4xwIeoyzJeadhU03g0LI2lC7/VG0XTNMMOML3j2+DXb2sccuEIpfv+VBs9sTF71jc8r+l4AOcSEjst4kuNS14pc/JLuNT+48YQ2TBVUubaj/HlaZyrAFExqv1qOrG9wkf3o1lPV7zcepHs6hmILEyw0ZcHgUVewhSr6qXq9vhSe2E+sGSMYMjXaurOj4cd3H1VN4jrdalDUliP08MZjNvOC4yKep1C5/PyYMPiA2zKMOT7EcoPG3m3F8I18++8//+ZJzw1lOobfxQQOcgy7utZcvAHSwTpI0apBGiK1W5isQWeGBrLMUgXjCQyd2KhcowLLXMDagTREAtKMty/esdYNdvnagEZ9lDIddPY3bMoVqRR9zn6CiWUsonaJPWxSRUDogrkg+0bIkS8LCwgXjdhC8wZtpEldlnMu2a1LD6n75EZkq4Q+69MpxyJk72MZxwZUX2BxrKIYbEjrUS1aPrwAAQAASURBVG3AZEtyfbXRHHFMni2d4whE4wNBipbVxOuMn/Ie2y0RwlRMDCWv2VCgIwRR7Jvnb9nyVROwgIK+iPwPbcUmXI3QfUJxtG2mfgYUDQcIegyCuM9GXTL4NeC8tx4tAjR3LQ5jEbIpYVurEgut0XVKSoMPoHiFAtS0j7iYzR24gWkFPzq40O/gQj3G+HExGrHzNPVvsZhe6VHwm2GGGV/jBq6l/nP4e8YsaWj8ijZk46I/Lep7Bdbu6b1W09MHLylXwazUaVyDJD0WNvL4/DdVcCqlkjyYCksGI6Pz/6iKd0UqY6td+6UOa8ZuFdMx1zJU1k577hhyUYMUE7JGA+qoLf6wH8A1tLRt8ThaNOR0wTkRbBe/nuLaBlt5UO9gXW9T01KVWeba2F7lfJgpp2jYy+txtRbOTEOUuHPxPpt0yIKqkEUB/jcs/5/eec4/82zlSkd3ingjOB5CJ4dCDT8DvNqKRiCwXZnQ2fpaUcbs6kPDcSygItbp6kW+nb/Y+QN7lOmhSwNbndTRA1uP0ecPf1KeojmopLWYwOmD5w9f0ukDYg/lqEhjjAEidm6cu8fFp41iipLcYC7IviEGLmrN3Qt8gEFTdPSpRP5dPMjKtTT/0R8Pv6B3x8ehjpVKOGuo8YMUwUZuOqyX5bgcRSP0WNuECPRGFCGwYb11/q7a+yDhHsB4Xl34MbtBKk49W7SEPGsDqI8+nYROatOUHcydVoeM2dJzgCOwdpxmV0QcU8N+wsFx64zdzNHWBXSYqrcUJiWwkDWm4CnvVJoquZVlPvmyYcZN2rS9Jgit0WGD8DpayeVIKvh1cqMKDiXo86e/aGz7xQZTcb9XuNeuRKMWKgHSJ2/THw3m0t0bpi2+zTDjR8fRyEuc8ycDn6eu60jFy+al/wJ2r4yi6J2neC3vN7clsw6SCod3naJjIWf4WDpOamRSmui56MucfwWWDaZjhgLmZFI7po+zIuh5aAwXrVSIrDy/pkZCyyVdo+vHC4qWtvYoqNCI/uvPv3gvAtT9w5udFhHxg4Yw9iugIMJk4+z+C3wfFLQw3ihuGTf8+Ojuk6pJGYCmM+DexJFOR4ocMO927hzyLM088PghyyK4kAVVUurDkMMq9V8+HeMWWrEh30O4aWOyF7tQwp6Qn6e52Otog3RihJOjIX8XB7bF8P4JQdA5EkDDjVamY3A9T585DSVHmAuyb5xJ5tfBjY08vJs78Ng0b9Ec/MeJr5WTdrLWTB8Uq1iQN/3oOBw00C2vomsZzpvA+PlUpFgAtKFQuXxUqGw+LiQ1ZY3x68uegay9xChY8qHjo0rNSqKbc/c5nVW6HvFRq2M17ixdOnLVqDwvAHo10ASQx7FfS2YbLGpx8UA3S5veCxQA5JdhEd6iOBfpoyUD5QCv81SE7vOsDq1G+fPfRsSGQ3T1hG6KqSGA0NpPCeqc33+tSR0djZkI9prZlDJkSUu3Lj6ghcPFBe2/gPI2hWnK2g6UI09GenT3BfUImEsnog3XHpphxn8NbJyw9AAN67BCZIxZF6Ipa9pz7t9/ATcv3qf5QwU1rvmAWtzoTSpAroDpGADjqLyx9FSmeJ8XDRDOyZ4tnFRFhSFYNWoLFz6W1Sx0asdeP3tD22cJQ63GA9VPx7bP3sPNWeiaYDEvcfPcHTqx9wwXjvX7+KjcpWGWkTF7epZurBi2XhUEHblBMJWg7UIjUlI8q7dypWLxCjLQFCGJeP3sLWvIoAkDvfHX34XJRkX3cpxNdjr8vIqeiHMXogRTe7ZwUT0WjM/wnuUtkUstHVMXQldH8fksZVOU90ba8Oj2Uzpz4BKfR1cDXTGr1KhALYbWpdodRfPcWBwIFE1nOxPGL5ga5oIsqU58rPEuBKqAcx0rOrT7tF6/jz9sF3+hOwvXYt2uMWBaMQYJXaPbbRGQdMTdi8O1Tnuw2PDjrj7A6e/xgbwQaf8qxbLqCjuXhkKDtdXIKRksXtGJAlaO2KhxSpY5Z0ZyVoxENk/V/FwYx0vO+ZZpu1QJ9tqQNU9mdkzkYxi52agpGQK95URz0cB1JqcWNuhZg7Lny0JP772gNRNEBy+pkClbeuo1oynf3rn8gMqe9r+AfEpXn23x33yiwe2W0fZV+hnvmGHGfxFoEE4btIUWTdjFnxPY2o9c0ILSJLNsocQCpjdj2y7m/YOlcymq3fbLZjspsGbiDnpy9znr22EcZUpEb4/hGBlMhlAgGQo0ZqXkAs6MugAmDBqURSoUoCq1hDwkNvB/m6eJxmxA/zrcUJTYpjRsbWtXppwFs/PeQ+aYwWwDFvZgEaGQQrQO6IRpMqamk2HCD0A2Ru3qWLPtfHykU0Kn02VNq9LdH9h0SGXMEbb6AH8eUGRBuoGMtAfXHnGhZ6/svdj0bKEwV/Nu+7XhzcK+q+jQjuMaGVT4/T1Lxf6tWgvd0zEpj7FwLKkzODo+sD/x71FD5S5uLF3xyqnbXCTb1jDcCOZbwVyQfWOApgfbTfkBeP/2I38wAWv3MnTtrHqqnzq4KPafJ8Iv0EsDnfg8FNOJqG3H1LoifvVcAbY87YGAVFPWmNRbQaT6/MFLVdK8puIO5h/gaatD7W5e/B35H88fvNDrNal7DHSdwLFGN0gT6nb3UuWfQWSrCQ71qnA3DMUYumP6wL9XTT5v56Iu0Smla2Uomg314yIaU7ZjwfoV7PoCXHRQF4Ets0Po1sX7lJSo5FyK6nUWHPdpPVfzQvpfQYbMaWjcstbk6lOBO4/zRu+gGUO3GhQAb4YZ/wW8ev6Os/xCtggnxfYDvNnWHuvkfwWz+6+nu9ceU+Yc6annjKZxioJvjTuXH9CWWUKPhOsJjKNMBWRXLR2ygW/X7ebF8T2GYtXoLVxEwAmxWEXtocKYem1TpmMNNWjHkMsFMwvowRC4LPHq6Wvap5hp1O4q6H1wcca+AqykGu3cKWi+MPZwamCrmo7lUNhOWfJk5n+jQMOUiyUcsQKc4Xh4+ahgT7x+LPZOZWxLsFkJYoqsa1rSTqXQAo0R2LtcFE4OflVUtvRXYq6zwzT2Jm7K/SQgN9kwaQcN95vMOjR1uHBIGLPhfXZUijxNwHmXjX8ZCfStcUCRZJStWpSZaskV5oLsG+PF4zc0b/BG6uUzhdo5jqRFw7bQ0KZzaXCj2XRozxm2xtc3JDp34exMXcTmDTxbQ1CicmHKUywnLwIQW+oCXAntfEWHIni5ekMOABdEJ8UEBGN6dShpXZR52XAVQraGOiAPA0JZvDbJczYUqdOlovp9hEMiKAKapmR4LksPC+ZYr58YqHVKFhBLS6aPCyQcKuWUbPmwjUZNPLLnz0I+yrh+4YB1eun+DIFN9fJk41WeH3dWj5VJPpWBwUdJy4Icbjqu/ZL/VEGCfJeeY+tRq97V+WK8Z+Mx3ni+fCa0AmaY8V/HrcuPqXv9OXTu+C1KlSYFDZ/XjHya2P7wtvaxsXfDYdq34QgXo8gbA9U7qYDrxayeq1jrDBv6KopswVTYvSSC7l19ROmzpiU/pXlqCG6eu0v7FZc+fXLHAucGc1EGGh7MLuIDbrhyOtagr08cM4vA2cE8scT+BpE5wLaZ4r7VW7rQ5w+fOfcLwJ7jcNBxJQj6dpznePfyPU1oPosb7V1nt2YXawBuhtin5C+dl6/XhcsXoHPRQvphX9eGToScZhojmuIo+BDkjHBqwF3R78dmJ4Ephb1dbEi5SeXqFdjzQB1ClPvY17HmQlAbILWAiyT0craKGd23xgGFruhQ8wu1NDnCXJB9Y2TJmYF1ZNUb21K7EX5UsHRuKlwmL1WtXp4e3XnG49TYdEZdcK4nXGcilAVHX3DnRelWSLGlLlRr7qiabGnLJJPZZUhkl6LT+M9dS7Gch4uROmqjeD5nlSOjsUVC7a7VuTOFDpK2vDEZAA3xK4InNQHdMGR6gMO9e7EQzupCgz61eDFCoj2ySYwBHgOvAy5L2gpiY9FhfEOmg5w7eIX2rdXtvpmYkOL0NBlSMc1gyaht9F8CPh9+LR1o2NymvOHExrOL3yy6dEb/6bkZZvyICN1+kkZ32UhPH72m3AWyMM3X0t5wt73vGWDYzO4nNEiNetagclW166ESG6HrD7E+CNc4XEdMCTTlVo4WeuImA+uwU7WhWDVKyAVQfEACoIsGCkkC0KCvr9qpI5ybXzx8SZlzZWSjCwk0aGXwsn8fX17Hb1+4y07LKJxrdfKk3YvCuMFY3KowxYQItkvekrm58QwTj2f3ntPPv37J59o0KZBSpknJU0JAsoVk78Haq6KK/YPp26apIhMNkT5gv+xZHMavCY8t7eqhU5P0zWrxzDj+idUAj503Fhv4/UhFl+/e5Mvr1wRk3gJwxtRVvCUGEC8F5hn21VUVN/PkCnNBlkS0LNd61lTRsSTVaulETft4c4HWYVR9GriwNZWuHFfIqQ3g1eKDf+HINS46DIFrgOgqYjGF6FIXkDMB/i8yyQ4Garakx2KDzg0mYCEa7O2dGlRlOiGOGVkWmiiCWORRhNw4FbeDpC9SpExBni1EYScXS3XAYoWOFhbLDZO0TMl++ZnqK3kjGyft0Cs7Cjq1mh0EDW+ZkVMynKtGStG4ctQW+mRkRps2nnbjvkKAvGjQer3y1hIT2fJkop7ThZ5s28JwlUPSfwmVHUvQtPUdKW+hrOwe17fpQooMOpfUh2WGGd8cmL7MG7ODpgzYxLcrOxan6Rs6sfbyv4RPH/6kse2ECy3Cbf27aXbH+xYAbW/hQFEcNuxTM0EueOqwcUoQvX76lq3SpWuxIbh++jY3hrHP0Sd3bNfCfSyjAIVQ5nLFl5xsnLxD1eyNTZFFcDLOBxq2dopGH0waoIqPFU+3EAwNOPnbstYL+KRoxv75SzBfUvwuHBTltO75wxecFcb3+ftfKmZZiAOn8ZpePHrJk7LyLmWUjFdhtV+rUzVmBElpBdwR5QT54PZjPAHEXg6/FxvYi0FuguYv5CfqAG3ZhzcfmblTzkFM7jQBxwY3b8DZQDMPUyFqh2jEW9gWS9JJsj4wF2R64u5Nw4odXYht+a7ptj7AZt/CvoRRmWQQ30JkqStjTALdItVUTQuNkK1XlWT6XQtD1RYg6N5IbvQexaJVHeWwqkIZOBJovMEDOlNYrOB8dP30LY33a6SIhXcu2EfPtOjWYN2P7hgmacFL1FMu1WnJIKq9dvIWXyCMgXdbV3bWfPHoNW2arl8emiGo3cmd8pfMzQHmi4eI8MmkhI1HWarT3pVvT/1j1X9KTyaRt1A2mrq+I1V1Ky3iDyaHsq7MHCJtxn8p7Llv84W0faXotNdqUpkGz2rMURH/NcwZsJ4nZBmzpaPes5rpzH5KbCwZtonZIvmK56Q68TKqEgoYhGxWrnMtR/qzfspQIHIGcKxnQwXLaI9BwCRp3XiRDdagjw83X+MD2agwCEmVLiXVaCP2OAAYQxuUXLF6vWqx1Txs4UMUSQYKIkg4YOCRKUcGunrqJq/nyF19cucZ/ZriF7p7+QH7CcTR9P+foBdiDwXmCJA9v2hCFLcuzBp7absvQ6cd6lehTDky0rHdp9jZEcWVixJdBIQoDBsYsMWfAAbNV8KjmzoyfV4dQhUKpEuAnU7dIly84eadLnMasnQvS/rgXwP3wLogzcGSaxh0bJgLMj1xNOKSaU98rD9kTbf1hWNd0Y2J3Gz4Rt+1oa1qrKzP5MZNGdHHhJyhF49eabyfc4AtCz6xyJw9cFHtfcCplh0XTeYejvVEV+XojpNG0xZzFMhGDvXF42ycrHn6Vcm9HOvWwAHH9EsT4BQJ7jiwdvw2vTbH6bOkU5mHQEumiaapDeh8tRgh8lc2Ttmp9fwbA1zwuk4TU6nglQeYvpjUaDHAR6Unk45i/zWkTvM7DZzekJp2c2eqSvCmGOoRMI8eKEGgZpjxo+LU4evUpe4sunDiNtN3B81oRLVbVklSA4ukQvDag7R3/WFuLvab24JdaZMS5w9fpd3LhZao67RmJjdUWT58E6/3CICuWquS4cd38DLnZIGqBmMsXUAjFsUDnAk91ORqYf+xdtw2VTQPYnUk9iwJ58kSnJWRPQZsmbaTGTelqhanUlWK0brx4nc9WjhTuEIzTJdF6LegCwOq+oq9nETuYjlpzSgRa4ACLk36VCqztOyFsvKkCl4AJayLspQE8FaCn6V5CKQfYAoBMBc5vlf8vkc8SiIKw6O7hNaqhtJQjw9o2qSxmD4GHWEKXdGhrrXeBfVPJvxso4l79cwdQVesbkHJHf+9Vc1IHI1QX1QkB9jWqsTdnOtn79DdKw8N+l0YdYAWCNHlleO6c67w4UfKOjjPYVoyyTDdQgdFTsnUAZauoDdirC2dieIDmSGYLGGxQ1aYsZDBjeB/P7z5WONkT07JdoF+8FIzbc+rtStlypmR89T0nZKhIEO3CkUqjsMYONStTCWsCnN+yIqRYqE2JUrbFCVPZaGe8ceKJDfUQFew/7xWlC5jal5Y5w81bUD29wJcpPzbOlGP8b6ULmMqun7xAXWpO5Oi9xrn3GmGGckZ6JKvmx9OA1stZkfFQiVy0sxNnamKq+F5ST8Cbl64T3MGiGlPkz7eSa4bQ3Ewq8cqvu3R2I7KmPh4rp26pXLmazuuocGGLSiepDMjtO9wQ9Q1HVs/QUy4Gg5Q79Z5MuwcXTp6jadYdRQXaAD7l42TxO/69/XlyRL2DnJiBS3a/o2H2Hoe13/snXB8kEicUfJJQVfEfuj107hu2R/efGB6KvLLgFxFcjDdMH/pPHQ1RuzXfDpV5/0EXkP+Unk4YBqTMamZh7ZMInBuCD+3VbXylKtwjjjPhTBpFIaQpuQtnkvteQKTCsdf3LKQxvuojv3tRzqwVQwJZHSPNrx9+Z4CF4ZykX9o50m6cuImN2L5/BjRwAailYzeclWLmpSuiHOYGOZn5oJMT9y4+IiePDDtRMJUgEtORWdxoYo0kA4HkSXCmoFwpcOiC1LIimBAbZDugge2HNGY26Uy7tDgyIjODrRkwBYjM8kAUAMqwUnxn39p+RDBeVcHOB8hCBsFDxYvbVOygH5iSrZm7FatJiexi9R6PbxVNrxSqGsIcGFqMzaAbwcvi2QHKVOj1XA/Sp8lLd259IA2zxSc96RE1twZmZ6D175rRRSFbTKO8vkjoIxVft6YlqqQnz68+0yjuq6iBeOC6M//kBOlGT824Cg6qM1SWj4thDeI7nUqcXB6LhPrk74XYFM6qvVCkTfmUorqd0lYQK4pELw0im5fvM97j9YjBWvDVMBGd2H/tfzdqZ4Nb/4NBYqnM/svsq17owF19NKOYTqWo0BWVZZq/GNaNXKTqhmbMdZ0MnrbUdbCw9kw9nQMBQmKLKvq5VW/C92ZdJ9GIxwNT2l0ge8nQ89xMYjrL/BIkcqg2AIe3niiYg49uPKIHwPmGyHLRVPYq7XIFIM5GY4ZxVUepRhFYRe8VNyvVryQZRQ8wUquGF6fOuDxkEOrzgxEHeDeDRdv6P8QHq0NYBkN9Z/OQ4HowBgKXnWAts/fR7uXRfI5Mpaaq6IrmjgM+tSh69TVbzaZGuaCzAAcCk2+3WgHRUQKt0VDK3dnf1HwQHypTycCiyQmFxDMIuRQE5Ayj9wuXEjkOF2dayEWINi+atJ3yYDng9uO0b0rD8hYtBojHKDgInTtpPppIBYzOU3bNmM3L2Ka4NXGjSkKrCVTFjNd8OnkQRmypaMH1x/T3pXG2fmDwmFf24o3Kwv6rTF5pwZdvLZjGvDtNRMC6YFyEUhKWLqUpgbdPfn2jN5r6NYl4/8OvndkyZGexi9vQ3VaKOHpy6OpZ8BcumdinasZZnxrnIi+Sp1qz6CTB69Rit9/pe6j6lKP0X58+78IrO1Te6yiBzefUjY0pmY2S3K65sNbT2n7bKH7bjvG/yvb9ITi6J7TnLmJYqqFEcUezhlkAVJ3Da28rmIAuVsAYm3UTceQIXr2wCX+P/8+ohErsVk6G3YQzobstjhTaN8aD/ajU2HnmRUD3VnOQjnYoREFF4pGgDVj/xMUS+w/ei/rrGKm4LVA+/bx7SdKnT4VT96Efl3Q1e3qWtOzBy/p8rHrzJJybmjH02VZoHm2/FJcRW44xFMoUDKtqsctUI6HnGZ9G679CLTWlD1259J9SpEqhV4GHbLJDidGXRNOxDaly5yGes1rTaM292AKrKVbWToRfp46OwynF48Ny9kFntx7QZdP3ubnruplWrpi9N5z9Oiucfm42mAuyAzAwX1ivGxK3Dh/j8Z3XEpzBorxurGo6l2RR+nI67hyQnORpA4QW+LDALMIfcKL0Qmy9RFmG7u00PXwQZBcbJl78dVjxXLzkcnv8YExfAX3Mrw4bVIcjoydkklx69LBazXeDzlqWLSga9OUkwaAmlC/jyje1k3Yrhe9D8GMEAwDq8cYb87QanQDvjicCD1n8rBowKW+DZV3LMnFdHLIJgMa9fSiCg4l6POnv7hjjFD1/yrQEGnTx4uGzmlK6TKkomsXHrA1/r5tQgNghhnfE7B2Lp2yhydjL5+9owJFs9P0jZ2oWt2kyS1KLtg8L5Sid57iz/uAha1NXvwYClwH5vRazdeFcvYlyNXEznmg/y0aIK7Nvp08KEf+rAY/BqJlLh65xtMj/97i+qwNaKZK/ZebBqt3sGCA6q1dOVtU4sKhy1yosLNhR2FqErrqADOCchWG26I1Bc0TLBOPpk4UvFQUshmyp2e2jopKqVxe209pRnlL5GJtmISk7aEQ4mNo5cbFlXzMEGXqhUkcJnenI87zxA4FnHR7BIIWCMMOrzauX02coIED3BrbazTz2K3cBw15sH204eHNJ3Q2ShSYrop0RRtwvBmyfMk8y5Q9PTnXs6ExW3tSBaeSdCxE6N4MAT43QGnrwiYNg0bBeyjU9LUAYC7IDMC5mJv0RkNyubH48PYTRWyNYRpWQvQ6yOeoooxlwzeKD6u+gNjS0U/kmYXoObWp3spZxSnWNkWCCQgEleBeo7uiDlgg+LmXR8R1GIr9fO0F/TF0zQGN99EHTYfV50UC/GpN0zZ0muop1vbrxm3XWjTBmAT8bghiNQVhxwe6dljU8TsQEhsDuC2qwqL7rzV5WDTOUZepTdn9CV2qiI2G5dwlBnARQQhqllwZ6P71JzS1+6pkUSgmJWycS9LsbV2pXOVCbIk9uf9GGt97Hb2LdUE3w4zkjLs3nlDPhvNow0KRN+nlX5mmbehE+Ytkp/8yTkdfoaVKBmO7kX5UvEKBpD4kith8hI6HnuMCsfPkxiYP4965KIyp8mgQN+gtrsGGbpaldgxRM3A01AbsuaTZBq756oqRyzHX6VTYOd7HyMgbibXjtqpkHHA2xPNLaQW0XY9vPaWD22P43yWrFOWG98+//ES3z9/jc/c+lpQD9EKYaUxpM0/1MxijYZ+A75isYW+SOVcG1pFlzp2RilQqyHp3oLoyDZOu1bDWj23mcfHwVf59GQMkgazYQzvEMcb/PwnsuSKVrFvPFrrpitKJsYJLacqa50sBqwk127hwEbty7DYVPVPi5eM39FGJBjAEkUo0k6ndFS+dvksvnr6llGnEuTUlzAWZnihQLAdTxI6Y2G2xVOVC3A1AF+Tk/oQ9totCPYzYdNTgDbp7Y9EZOhgYo1HvFRtYPLIXyMoLgzYb94zZM1Blz/JadWJwN4RZCLpCmu5TwqYI3wfarggtZiK6kLtITtVEbvsskdGhDtVbOnPRhDG+Jlt+AF24+koXDl00fXRh0J81HiR47WvGbjO6wAzoW4svXLiA7daTMmkIchfOTo2UCeD8Aevo7QvTNiOMAYS5Axa05g0Bssk2zzWuoP2RkCV7ehqzpBU17ebBm4aIoNPUwWc6O9SZYUZyBYqvneuOsIvi1fP3KU36lDRweiPqMqz2f5aiKPH0wUvOG8OeA5mlNZrqDuBNbCBja15fMb2q1dGZtUEmffyX7zljE2g6uG4cF0N9cWDzUZZSQI+FqBldgNYKBQ8MujRpp6TZB3LJwJyRuHHmNtvgo7CCmYfM8bp76T7TE6u1dGbqIv7OYU62b5VodhconY+/w5gDLJxflb91K88KNL3DQrp6/AaznQD5vWBZEWht6Wmh2o/Y17Pm48d+DXsjm5qVOK4HBiJymieBbLSlF6dSz4XtvipS4R0Ak5YiFQpQoXLqg7P3bz5Cnz98Zj0aHCO1Aa93n2LIoo8TI+6PyW/15o5MV/Uv1I16e42jrXNCaMXorXT70n1y1+NxYuPRnWd0+cQtdiS1r2naguzgPsEiq2SrXRdnDMwFmZ6o7Fgszpthsjfgp5/IXqng92/XHLasDyq5lKb0mdPwh/yk4t6jL4pVKvhF76WksOs67mrNhPh1jzIy1wQpksXioa5gwWP5dBLjfgQZqpt6YNHzVGzydy9W79qoL3w6VxfHszyC3r9+r9navp9YZNeO3abVtANdrQxZ09Gjm090Gp1I4NxhcUOGy6apxpmV4ILVeEBtvg3HRUltMCXqdvWkAqVy05vn72jd+F2UHFCyUkFqP7Ie3146ejudijLeffNHAaaHAe2dadKqdpQrX2Z69ug19W+xSBh+GEmLNcOMxMLzJ29oWIflNGv4NqYgV6hShOZu70Z2HnGDav+LwNRmTNvFnAdZqHRu6jyugcknUcZg4eANInOsRC7yaq2e2pcQrB2/na8z+UrmJi+FgWMI0ISW2jE4GiNqRtd5ljb2/n1qcXM1Pu5dfUhRSsNZNl7jT8cc6tlQnmLCcXDTFCGpqN7KFZWGqniq7FWRju0WFvyvn4mIn9dPxPf/xaLt7V0RSf/76X+UNrMw9cD5QFH25K7I4CxSviDrxXCszk1tWecO+PWoyfuooLkhXFwhvqe4ZeE4x4tjVGdYgn0Q4NFM8+RLZpchg1XX3+KFw1fZfARTPTiA64J8vEquZWhm5FCaGz2cKrmUoRPhFyhtxtTUc25rZoAZgv2BgrpftmpRk8ZDYG8qawBrJ5H/a0qYCzI9YekoTj7Exp+0UPSMgZ1SwR8OOZugzROoh9LcI1yPouorvZcS1LxvlX5FBcID8XtwM8JIXBOq1LLkrgxys6K3qQ+vxkLBuWWXHvDjabqPpD8mxNwDEzks+phMbZ+t2UWweisXlWmH5E9r0oVJiuOqUZv1op6COtB8uCgqNk/bpVqkDUWNNi7cqXz99C2tm6A5Y81YgBvfdbpwOIzedtLgQj+x4NXUjtwb2HAHeWy7JSzgNYOoZPl8NGtLF6Z9ScOPznVn0eUzpnfjNMMMYzY0YTtOUvta0+ho5GWedLftW4NGLWrBk14ziBYM3UyXjt8UE8NFbej3VF8XCt8apyIvUoiyL+gytQn9YuLMsQc3HtP2OSEqm3t1ocy6ELw8kgsoGGbU6SaartoA3RTs4bE3iR3yHBtrxmzlv1mbGhWpYBkx2QIgv4hUjMoC+gu2y4XDV+hk6Fk+9tpdvWjL9F28xyhQOi8dChT7nnKOpXg/ASkAMr3QVEUTHGYaN84I7X+Ntu70/P4LLswAuEPj39CEoRgDPFu50I2Tt/n48Xrdmjjw3nGnohNDELU+gLnZlZgb/Dl0biAyaeMDBmTnoi/ztEmv7LF14rzY+ljS76m1h7eD8XQk+DQN9ptK8/qtYYMSaO8a9PKmkRu7U612blS0vPqpnTbs3y4KMgcj8uu04dbVx/TwzgveF1nYxC14TQFzQaYnIDLOlisDd/NQlJkSpay+0BZPHUhYtx9CSOBg0ImvuLg6f9e/Chc8F49e0yvPDO5FFd1ER1ObYyD+eFHcAEEaNFOxc8t2K5zo+MiQLT0XU4CxOV4AiouGA+qqOlqaKIPgkzdQrO03TQnSSgOFwxK0ZJiSSYGsLkBwW6R8AeZHb5gknJqMKcKlDf7WmXtYTGtqlKpchLxbi47lzD9WGvx3lRjAe9hpjD8VLZeP3rx4R6NaLdSqZfwvIWXqFEz7Gj63GWXMkobuXn9CPQLm0pLJe8zTMjOS1M5+VNfVNLHPBnr3+iMVLZ2bZm3uQrWb2yW5c2ByCn8OWrZfuO3Nak65ChhuamFqYF2d3m0538Z1AFmVpsbiget5smPpUY6sPMoZdYyrFLpjQD9fnaYTuP/q0SLHM2BAbbXTsQfXH7EjM9B4sNgvSKwZs5kLNVtfKypsIbR9q0cJa3u3xg5cPEn3RRibwc4ehdo/CkNINm2LVy7C3ytXr0BXlGLr6vG4VHPY8QNWnuUpJvgU/22A5RO2QhTI1Vq4sFbswKbDzI6C1MI2XsC0JuyYJwo4mI+A5aMO+5RzUNGtbBxDE3XA65JaM9eG6gu82AhaHE67lkSQTfXy9Oz+S9qtTOKw14LjtzF29/dvPKHr5+7yXtbWS8hlTIWDSu5nRduifJ01NcyroJ7Ah0CGUpraYQUXI9sa4g/nwI6EuaSVrFyYsufLQh/ffWY+riFApwiOi8A+Pal3Hk3ECHzvqv0saNUE8LPRYYE4Fhaw6u/jouIrY5OtDs4NRNEWvi4qQYYOcFKEwxF48YFzNE/JkJOGDhQKrf2bDmudkiFQEsBCr09xgPddTsmQeQanJ2Ng7VWBxbNYDGHwkRhoNrgOZcqRnmkVq8YKTn1SAxfRgYtaq0KjZ/Vb9583+YiNyk4laF5gd3KuWZ4niRsXRVLnOjPp4snbSfaemfHfA9bpvduOUzvvqUz3QTe+aVd3zhbLX/S/bdwRG5dP3qJZ/URGZuNeNaiy0uxMaqweH8iNviy5MlKLoX4mf3y48UVtO8b7A9lcNBQ75u/lyVO2fFnIu536aVdsQPbAzop5M2vUjkGqACdEFELFrUThBEBXHqFMgQKUxi6MP2AUhteAAg/FGPT1Bcrk5SkWYOFcmq3zmaP4f0QlbYrSdSV+J2veLHz9TpMxNU/B0Gj9v3//j3IWykaXjggzDqk1gwYeLL+zkZd4X+qtBD8Hzg1WTdj0mTDi+MIU841aHTw068GUggwGbbpwfO8Z3lNhr1DeqbTO+0cFHue9RY1WzuTf05uiA49TzD4xZdy9NJLWTTZcziH30BXsi7OEx5Q4qERfVXVLnIB6c0FmAGRBdiT8osld7WRw3eHgMwlyW8QH1LGukkm22XBnPPmh40R2LQWWRFUfS+4GPb79TCw2GoCFEl0gYPfiMI25ZYUs8tNfn/+icA3GHVV9rZhTjaLu4uErZCzwgW80UCym68dvozcv3mrc9Pt0FvlX4JtrLTqRS5Y3My/0SL3XB1jsIZLljp1irWvMe95ufCO+GERvj6HTGiifCUGqtL9T02FiWrhlVjBdPWlYtEJiIXvezNRvfkt+7fs2HKHAxaY3N/megWK1zwR/GjKrCWXMkpbu3nhKPRvNp9kjttP7t4Y7V5lhhiF4dO8FDWy9hKb030RvX3+kQiVz0vQNnSiggwsXZmYIvHjymka2XEB///k3VfG0oAbdhaY6qXHt9G3aNEOYX3Wa1NhgLY8uYAoyr/dKvu3Z0pkKlMpj8GO8f/OB1it0/UYDa2u0bZcAtU/ev0FfH7X3hwQjZIWY1jQeFHc6htBn7P8snEqrdFpyOubSyJ4y58qkyiHz7+PLujDg03ux3srpHWQKmGghVwzOiwAKQEBOoqBll3b0yGEFfLtUZz0+UNG9HBt23Dx3hy4cvMz7GularQsotMB2gXdAGTv1eijs6RBODZMU7PV0Ya9Ca3WqV0XndAssLDx/oTJ5+d9Fy+enmm1ceWoGHNlzigqXE/9nTEFmX1OYt5kK7999ojcvP/Bew9q5JCUGzAWZAShTqQBn/rx59YHOxph2QyqyEtIyleOklsJGHzgpFvbHQs5yF8QQ2NSowA5BT+4+p/MHdRc8KFgclOeTLkKaIF1/sECpKzpRWEijEE0W8ljM5JRsw6SEaaZcGtlRwbL52KVovSLuVQffzp68IN08e4cOKVaqGimOfUXRsmFCoF70MLzmlqP8+fbuxeF0/9ojo14LwiPlhHFB39V6BXwbivLOJcixTmWetkztslQvR8lvgQr2Jaj1EMHjXzBsi9nkQ0Mzaf6O7uRepxJ3PYPWHlZNLMwww9QA/WzTkv2sFQPF/7cUv1CLHp40fX0nKlRCyV4yg4Fr4ejWi+j5o9eUt2gO6jmjSbKgcKLomNp5KRcJ9r6WqlgdUwKuytdO3eambrMhcQsffSE02G/ZaVDq4LUBRhuYpqHokUZh8bFu/HZ+3ZYeFnFcBRHMLHVa9XuLa/39aw/ZbRFoOKAOha+NUuWQYVoE92ho0S8cvMI0OuzJcN1/cO0x77VGbO9Ll48JGQzui3BpOVW7c1FEBeUrmYd/L0eBrFTOqbTKjbqaYlO/R2lyw2kR9vu6gGvAroX7VI1kTUYdOxX5iGN9Gz4ubUBxeXinKIY8NOS5xQaKR49Gdqwbk7CtWZF+/vln2r/lKD+elSJRMYSueOP8fS5wq1Y3bRh06jS/04qwvjRvR3dKn9FwB1B9kPSf+u8I+AOSU7LokHOmfeyff1LlJezfljC3xYKl87ATEhb66CDDKJAosOxri9BnaV2q71QNVENtFu6g1oEWyR9cZQGLD+cAOz7PMO64ffGe2vvUU9yO0DHSlG2mD/DBbzm6oSo0UdOxp82YhmopLpCgI2qjSiLHAx0v0BpkF0sXytmXpMrVy/MFcNlQ4wPCmwyqwxc2XOD0pZwainbjGrDz0Y2zd2nTdM2xAd8avm2dyaWuFV9Ex7ZdzLa3ZsRF2gypqMdoPxq7tDU7McLpbmSXVTS0w3J6eNdsimKG6fI6QY1dPHE3ff74F2fkzdnWjeq3cTRPxeIB15K5AzfQhWM3ePo0ZGlbSp3WtFMoY7F5ZjBdP3OHjSc6TGhk8seHZn7pUOGK2HhgbY0aJm3AXgIFGdB8WD2dVD00SVFsAXBRVjcdg3W8nGo1GigafRI75oaw5huNXLBbAGltb1W9AjsZSglEjXbuFDhHuUYq9Q4MPgDcH0yfMbsGUuHyBThcWiJHwWz8/5i0Scv8Y8Ei5NijuTOdiThPz+49p1TpU1KVmpX4Ne1dKY7XtZF+7pegRd44c4d+TfErZ6ipA/LJpMNkDT2mbjDzQCOmWMWC3CDWhVyFslGdTh6UOaew4f/7r7/5bwCymbEt51PlahZGT8fK2xXnfYqpgcI1b6FslFgwF2QGwlax5YWOTB9KnyFw9BUj4YN7zrDzjkloi0YE+ro2FBMo5Itps3uXKGNbnC3csVBFKAnyGjsiygQsWEOxgqR5SW3UFJqcv2Qedm7EYhU4O2FFQWWvCpS3eC7uTGkyEwH8/qjBLpBXT9ykQ4EiRFEdQDGInUumr2tmixH+/L5FbjxMV47fMOKVEC9myCYDEI6ZGDb4eI7240URu3pCoF7mL98COHddJzakohb5OLx9RIsFycJ8JDmivE1hmrO9G/m3c+JO4tGISzwtWz17H5sWmWGGMXj1/B0Hk/dusoBuX3vMdNnuo+rSuGWtKXeBLOaTqgYw8Ni9KprXrz5zmlOewslDU3f36kMO6QXajvFn0zFTAxKAV0/e8GSrVns3ox8D+w5kaMEkSxcgl3h69zk3TZE1qg4w8EIzG9bxsal8H999pC3TglTTMbxnkDpIa3s4K57Zf4GdC1FspU6fmu5deUgp0/zOz4mfYZomASv80lWL0+5FYbz/kJAa+w9vBbvJ0d+WtffshN3MiaOBgCq+lrzfwH4Ek7iUaVLSlZjrdDryAheVfMzvP9HLx6++eo3ymB3qWnMGmDqgqYvzAOOxYhUL6Ty3exWGlD7TMRRfkmEjp3O//CqcOy3dylD+krmovKPhtMADQScTJQz6W8FckBkIWF2mSpOCu8uXz6if4BiLkpYFKUuuDLzAHAtLGJXIRXFbPBV5gZ4/NMwsoqxdcR6xY0R+SPkD1wZ8oOr3rslhxxjxa4PMukDHBzb46lCzgxCpBi+NYH64Ovh0EroudIawUBoLUEPq/uHNtzdPC9JIw0OmCbjbAKZY2opxWOjylOzuc416ufgobJGfgyeBxQONN+bw7VSNchfJwc5MiWGDD7jUtyEr97L01+e/mdKSGPRIY4Dp7uDFbTk8+uaF+zSp6wqTN01+FCB4t3n3ajy5wJqGC++qWaFMMQONMSGGOWb8t4C/nc1LD1Arz0m0b5voUFevX5kW7vyDqtW1TBYZWskRyE+cN1hoj1oO8qHKrsnDxANr5rQuy3h9RzaUux5ueYbi/vXHtHVWsMrmXm7GDQF0XtIlsPWYAJ00TzTo1owWToyNBtRR6bPiT9x2zt+nuk9sBM4JYWokqIjSIn7HnBC2bof2HXsfGSKNiJ4NE8VtROwAWfNkoc8f/uTGLmDtVZGunrhBc7ovVT0HTD3AlMlfOi99fPuJaZW3z99VmYJAh4YCjOUdrURze4di5pE+W3pu6K4bt41WDNtIJ0LP0tw/ltOZyItfnYdwxZQEuj11wPq/Z6lomlfXIxMOYdw8cfvtF3JU9p7asHT4FprYbhEdDTlDr5TIn/9TrjnpM6el4eu7UVnb4mQIHtx8QjfO3WNaKHSY3yO+m4Js7NixZGVlRWnTpqVs2bKRr68vXb6s2yJ+48aNVKJECfr999+pbNmytGtXwsJtf/vtF3Yvi22BaSpgQZG5CTLYzljkLJiNStkUYb1PuIFTMhyHWyOx4IRo0HLFh2dzJ2o6xI+t8LUB0yi4C4FaJi1l4wMLm8wJC9YQOl3BtSzlKpKDO0tycTEWWDxhqY8CKlLLhK9ez5rM+YaW7IBi7aqpMJCOi8gx0deOvdmwekzpORl2nk6GGUeJxYLYdtwXG3xc+EwNXAy6TG1KKdOkoAtHrtGOhQkL6jYlsubOSIMWt+HzGL3zFK2enDzCrJMr8hXORmOXtKL+UwIoc7Z09OjuC6YxIlT6+kXjs/7M+PGBDRSmqx1qTaNFE3bRh3ef2coe7oldh9fmCZkZ6vHg1lMOf8Z1EFTruh2MmxAlBoIWhdP5Q1d5fe86rWmiFNSL+q9lelsl97JU2cjN87JhG1WPUdFVuENrA+iGaAJnz5+Vc7zUAdlhKFiKVirEVEEJTJo2ThIFVqNBfsz2+fzxM22bKa4v/r19eF/A4c8//Y+y5ctKD6494kaunHg9uCH04SjggFJVi9OIepPZxCyTQtv7qMgmYKIF2Ne1oaB5Ip/N7w9v2jhJBE+DIZSzSHYu6E5HnOfjaTuhEevdmo/0Z3OxlSM28f7p3tUHdPHIVVWzGTRE7K1AjUQumjpcOnadbp2/y1M9Z/+qek/HbLwrapy4xUbwiv1c8C8avIH6+0yi+QPWcdYdMKvnStoyW7xmQ3BghxgeWNgWM4m7YlI0Jb+bgiwyMpI6depEhw8fpr1799Jff/1FHh4e9P79e42/c/DgQQoICKBWrVrRyZMnuYjD17lzCdN/2bqLTlbU3nMmf9McfURBdiTkbIIpV67KB2mfEQWLHDsf33uWnpo4dLeaMiXbuTBU7QQDFwDfzmIaFTh3r9pzjKJRhjnuWZKwggCdMjn9gluSJgdNLDR1u4vAxbXjt2nXkrV0YWfJFw9farXVjw0Idmu0Fa9pySDjLdyh1avkVpY71wv6raHEAArvVsPr8+2lwzcnSv6ZsShduTB1mSCK0jVTdlPk9oRpMn904PPmUL0cLdjVgxq0c+ai/vSRG9Sl7iyaPngLswHMMCM2rp2/TwNaLmb94f3bzznv7o/RdWnaho4cTm6GZrx/+5GGNZ1Hb1++p+IV8lO3SY2SzRQRsSZLhompXYthfhyhY2qcCDtHB3cc50kG3IGNee3XT91SNWJbjdJtlY8CRGrH4JqINS4+4Hoo3REb9q8d57iC5n6ZjknNFSZpmKihwHOsX5VWKU6LtnWsVYYZ5V3KcNMYQdCwsc9fWrhIFqlQkEOkEaeTLW8WFWXxn7//ZfolwpolUMBBZ1bUsjCFrRGFT71eQpogj9ehng3Z17GhDNnSUUzIaWoy2I8qupbhnLNLR67RrkWhqilk8HIx+YJ8RNNUMUTJA7OvY80aQm1AoRemhGS7N9ZtjX/r4n3WiQ1Z3ZkWHBnF+whQV2d0W049qo2h6B3HyauFmP4ZgohtQkriUMs07opJ8Zn8bgqyPXv2UPPmzal06dJkYWFBy5Ytozt37tDx45o3W9OnTydPT0/q3bs3lSxZkkaOHEkVK1akWbNmJehYLO2KsWsUusm3rhjniqcJ0MDkyJ+FpyrHlMwDY+FQ24oXnlvn79GNc2LsrS9yFcrO1EXOoVCyKkwF5wBbdi28f/Uhndh3Vu19YPMq74MOkDq4NnbgbtTFw1fjcLONAQqydJnTcjdLk75N3g+UA1wQju0RQlt1gFi4yRA/Fc9dm9lJbDTs58uPfznmBmv4jF1I2k9sxF2zw0EnKGbvGUoMeLV0pHL2JZiGAYpLcqIHejSoQnXaCyHylO4r6fIpc/aWLqRKnYKadfeghbt6cIHGtJVNx6hVtUm0bGowvUsETaIZ3xce339JE/qspy5+s+jUYeQl/Ux+rRxo4e6e5FHHMlm4AyZngN49vsNSunv1EWXOkZ4GL2nLU4jkAHzep3VdxgVAmarFyFsPqpqhwOZ9bq9VfLtWOzfKr9D5DMVSxfwK0xsUMLoA3RVMKqBXc9egcdo4OYjlIih+Ylu8//XnXyxnAAL61+HrKiZma8dtVTkrCtbMYb72lncqwy6JaTOloWsnRGGFadAvv/1Cr56+VUkujuwSLKhcRXPwOZeGJFlyZ+amMCz1IzcKxk69nrUoZGk4N1mLWxXmSRheT6RSCPl28eLvzUf406vHr9nsLCbkDP0xvy2NDOzLPwfuXXnwRY/WVH3Rg72n9AKQmn9tOL7vHL1++paLQZljqw0FSubmyavcE0Ez1ndRW1p6ejyVrVqcMufMSPlLGPZ3cfvyA7p18QGvR7YmcAMd3HYphQWe5PcBn4tvtbf5blfP169FenmmTJqTww8dOkRubnGpANWqVeOfa8Lnz5/pzZs3cb4AvCHy67fff6HyVURQYPTec3H+L6FfePPta4o/KHT2E/JYqdOnJCsP8QEJW3/I4N+X3Y6QFfvpn3/+MdlrRMHh1kQ89vY5wWrvkyLVb+SicNeD5u9TfShif2XMnp4quAlb1FAlmNrYLwhvEegIrBy+gRdcdfdLkyGVaoolc8k0fbk2sqM8xXOx4HbbrN16HUf6rGmpbjexuC4dsp7+/PynUa8ntlB6Xp/VRj9O/L/N2P8Guk1vyu/V6QOXaOeSCJP9jZjiq/mAWmTlWpoNckY0n0dP7j1P8mMy9XuQGF9Zc6anvpP8aeLKtjztgNHH+gUR1NJjIm1cHEkfNHw2/gtf3+L8J8evZ49f0eyR26l19ckUvkM0ohA4vmDnH9SiRzVKmeo383ugx3lcOGwzN1qh4UQxljFbuiR/b+XX7mWRTBtDgdhtRrOv9j2m+BwEztvHVu7pMqehhgN8jXoMmFYc3X2KC5gmQ+rovP+71++52AIgJfjfT//76j7PH76gbbOEWUZTJfxa/h/Ck5EtmilnRnIKsBWvY04wvXrymml/bk0daPkwUSA6NaiqckRE0YQYGzgZAig+Xz95TVnyZOY81dPhgql1VskNxeYf19JTSgMalEYwbPC8VXytKGi+0Mt5t/fgY4hYfZALNOS3lqhchP7++29+72BGMtB7HFMh85fKo9or4Tv2W9LMDLRGdecrattR9g/A5K+sfXGd51cGRzvVs1F7bjXtAfGF29hbouj9999/6aef/0fOftYG/01EKM7kFZ1KUup0vyfoc3Dj8kM6c/QGRe89T1EhX1hw6u5rahiupEwGwIno3r072draUpkymoWwjx49ouzZ47oW4d/4uTat2vDhw7/6+dOnT+nPP79ogcpUzsP8+f17zpCbn+6ugCEobZufNs4iOrrvHN25dY9+T/W1+FRfVKxWkg4GnaSwDYfIq4OdQR3M4nYF+EPz4Ppjit59lIpZFjTqGPiDFu95bf0tWSR7ZOcJunjyMmXO/XV2hk3dilyMHdx+jDy7OfAHI/7jWNW0oOMhpyl4RQS5t3NI0JjZuk552jw1iJ7de0Grx20i707qef2Oja2543Yu6hJFbI2mUrZFNT6mdycXmtd1FXO/q/hVpJQKN1wbHBpZUeD8vXT/6iPaOGMHuTYxTlTt3saW9q2NoruXHtDaydupWkvddAJt7yGaIPHfg59Tg9vuQatHB9HiwRuoYIWclEXNe5lUaDGsBj249YTuX39KgxvPpv5Lmifo85SU0PQeJBay5ElJvaf40snoG7R50UF6cPsFLZm0hzYv2U81GlqRU80yaqk/Pyq+9flPDkDm5u51xyl062n6609B5S5VMS/Va2dHBYrB/vkvevLk29GVv+f3IHzjcdq+SLAvWo2oRelz/v5Nz502PH/wihYMXM+36/7hTr+m/Z/GYzP2PXjz/B2tHLmZb9fpUY0+/vWePj7RLDlRBzznvL4iSNqxgTX9kvYnnecwcEYIN0VzFMxKpZyLqL3/ujGB9BkhxeXzUUGrPKr74LWuHSeMQNxb2NOr1y85vHvzVKHl8u7sxvuXI0GiILDyKU8TG87h27cu3OHvcLL96zP+LUzgqrVxpMPBx7hR+FvK3+jPj39SplwZ6MWDV5S9QFa6c+E+FSibl85EXeD7OzepSvu3RfPULU3GVFTKqQg9eviI9i0XhVAFj9L08MFD1YSthEMhqtPLkyd0eB3Q2WFyBAmMpCI6BFTWeN52LhYSkCq+FejZM+3xMaBaHlKilSp4lkzw37OlVykeJBjyOPibCFfYROWd1L+/hiB4y2Fy8S1HhUvmoJnDt1H4zhPk18aWsuRA8wR/8/+LMxQyJb7Lqym0ZNCBRUWZPmupf//+1KNHD9W/MSHLmzcvZc2alTJkEMJLwK1Walo2KZTuXn9G//fXr5TdhJtQPFfOAlno4a1ndOv0Y3JQdGXGwM3PnpYO3EovHr2mp9dfG+xcg0wypK+f2H2B7LxEALQ+gN273KypK5JgzAJB6ZnIC3Q86Cw1Huyn9j7FKhViG/jzYVepYv/yX10Aqjd1pWX91tOTW8/o7qmHZFlNZIMYi2bD/Wlyq7m0d8l+ajKwvtoNJ46reitXFgkHTttLjr5VNRaCtdpWp6DZYXTv8gOKWhfzVa6JWmQTmWJz/lhB26fvJZ+2IpjaYGQjajmiPk3vtJS2Td9LNVu6M63AGODChNeIv83470FAj1p0KvQynT98lVaNCKLRW3okG00EzsGIVR2ph/dkunP5MS0bvosGLmrNuX/fG7S9B4kJzzrZyd3Hmikca+aE0ZMHr2jNrEgK3nCC6rV2JI+6ltzx/9GRVOc/KfDi6Vvatjyadq0/Qh8/iEYkpqVNurixK2dS4Xt9D05EXqRV48UEpkkfb6rRSOiokwOwoZ3efhXT5kpWLkwNe/pqXR+NfQ/WjgiiD28/saOwXxdvo9Zg0Pivn7jNjeLWoxpSpmxf9mTqgIDm3fMjVNf2nLm+DiV/8+Idha0QmavNhvnHaeJHbT1C96884nxP/561+TvMyF4+es2TK98ONWhxv9Uih8yzPF06cI21YjAlwyQQzBvQIJFD9s9f/5B1jYoU0LsurVQmaphiAZ/eCb+A5/dfqtyoF/RayTo7v661aE63JfxztyaOlCd/HqY7vnz4mgu6DeN20uvH71lHVsKqCB+jT1uhiUczPVdh8Zph8IECKmehbORSz17tewdjswtRIhPNp50n73W0IXh3JNMx85XMRVbOFRJ83c+m4/nU4fq5u/T4zgueDrr72VKqNLqb3tpw8+JT6jTEh/IWykoWlYvThgWRFLH9AgV0cKGM2b6Yhfz2m+kbu9/Piqagc+fOFBQUROHh4ZQnjxBIakKOHDno8eO4LnP4N36uCSlSpKB06dLF+QLwxxv7K2PmtFS6kuAuHwm/9NX/J+QLgcX23kKYGLXzVIIeC+nqtopzY8Smowb/PrRcwP7NR/mDp+/vLR20ngbVmkDLhmxk8w5ooiBYxfRX3ueLKUdEnJ/H/qrWQly4Dqw/yh/2+P+fKm0q8motHmfDxMAEn3sIdhFeDaoAOOGa7gfqAxaA8wcvsw5O0/1+/fUXdp8ENk/dyQuiPsfh3daN7eshdt00ZafRrwcGKkUrFuTnXT58U4LOjbrzj69ffvmFesxpyefjVMRF2rP8QILfB1N+5SqQjYYua8fCapjlLBu9PcmPydTvQWJ/4e+4Wl0rWrS7J3UZXptpjc+fvKV5Y4KohftE2rAwkl32kvr8/Kjn/1t9PXv0huaN3kEtPSaxlT2KsSKlctGI+c1p8pr2VKFq0SQ/xu/tPbh79TGNbbeEHRVd61lTQHfPJD+m2F8hq6LpZPgFXr97zm3Fn3VTvwfXT92m4GXCsbnj5CZ6PUf8L+wfQOOXuaBZcmXS+Ttbpu/moqxA6TxsVa/uPjLsGdb1VbwrxXmNa8dsVWnH02ZMwz/bOn0n/6xWx2pcZAUvE07QHs2dKHiJuP1SifTJkkeR1PwfbO8z07AtvSnF77/R2QOCpojiDbRHXJ8RDA6qIKiEYOkACH5GU1hmn3q2cOFjC1ZyxJB11mlGC0qdLhXN7LSEhtaeSFtn7Obp276VB2hMoxmq17NrkfgdrzZufM1Wdy6Cl0XyFMjCsRTvP3T+7awQUzq3hva8b9X3vUTxv3NxBL179SHBf78HFHdFyBPSpEuVoMdCs/Htqw+Uv0h2fm/yFsxGPk1s6f6tZ6wri9kvilV5//9sQYYOBIqxrVu3UlhYGBUsqJs+V6VKFQoNjevAB4dG/NwUqOIqLEPBNTU1pI4sJvQ865kSAmclF+LAthjmHBsCmDbAaUlkkum24pe8WvCh4epTs50bFxWg9/V2G0nzegm6AWBX24qNNJ7ee04xwafVH3sDW+Zg3734gK5qCEz261mTR/Iw/7gcc50Sgl9/+5VqKRlnGycFauQJ42JQs70H3142ZL1WR0TH+lX4goBzuEGxrdUFnLtWoxvw7c3Td2vMbNMFdCE7TGrMt/csi2Q728RA7sLZqfmQunx70eD19Oi2dqrDt0ZJy0LUc3pTvr1lfhjtVOgeZhgGbA686lemRXt6cRcRzIDXL97T8mkh1Mx1PC2ZvIeePjI9lcOMxHdNHN97HbWsNpF2rDnM1wlMxIbPbUYzNnUmK4fiyWfq/R3hxZPXNKTxHJ4Mwf2168SAZHUen9x9TgsHruPbzQbXoTxFNDerjQWuoXN6ruRrpFP9KlTGQJaOBJwCH1x7zHooFGS6AAfELdNE8dR0WH21EznknG6dLqzrG/aL66x4OOg4hzxjGlenm3g+xNFcPXGTo22827mzmyKKKcT5XDtxkw0xchbOzg6ayBSDXEDCxrsSX9fvXX3INvQSoFMCqdKn4u/e7TxYEw94tnSlPUvCmXYIZ8bCFgXoxaOXbNYlNWt2tStT+8lNacmFKeTa0J6NQLpWHURzeyxX6dGvnbpFl45e431Stebqp7PYs+1RDM28WquPBYgNGIdcOHyVp3hujezIEETvOEGze62iPjXGU0Lwf//3f3RAiYiSDuUJQbZcGWjIbLFPgB4OQENq7NLWZOdRhnauE036xMJP3xNNcdWqVbRmzRrOIoMODF8fP35x/mratClTDiW6devG7oyTJ0+mS5cu0bBhwygmJoYLO1Ogqltp/n7++C16+Uy455gKhcvmpVwFs7Kg/khIwmz6UVRhEUOn6HioYY+FPz7nBsI+P2ydGOtrg6xLqrd0YZtYWL+7N3HgYuTnX3+mA1uOMBda2s3D+AKQXab4QFcKCw6wTQmSjA90nmA7CwTOEbSQhKBmBw+mCN44c1vVmVIH/74+TEmARa22++Ecgi4BYPHXN6jb1teKxbrgtq9SAi2NQekqxVQL5uw/Ei8s2ae9G5WuUpQ+vvtMUzotSbTnMRZYsJv2ESHgcwZuoJgEhq//l4E8Ru8AG56Y9R5fnzuKmJBtXBRJLdwn8Ob+yjmhmTAj+Tr+HQm/SP2aL2TXxIig0zyFACVx3LLWPBFD5mZyKiC+J0CzA3v7J/de8LWcHRUVg4fkAGxmp3ZeysViKesi5NvBPVGeJ3TtQd64M81QaTIaChROq0aJa2DjQXX0ovCvHbuVJ1/IFMO1VB0wTULxlLdELrKr+0WSgWvX8qHrVY6IaBzLWBwAkgW8HjRtZczN1hmisMOkC5Abd2nqYe1dkaUcYwKm8vQFgPMi9kbYn4EuiIIJhifYO4GpgzDoLcpETkbzBC+N4OKpSKUC1H5SU85Q5cf69Rc2HJsRPYpt+/FY0qJ/jzJRq+pjRRmV+8fH0T2n6Nn9F5Q+S1qN5ys2YPYGVK5enjIrOWr6InzDYf7uUEfs7YzF1dN36NGd51wgW5kgWB1rXc68mVTvHz4jfypDDL/WjtSkq2jC03+9IJs7dy6L6JycnChnzpyqr/XrxYcGgA3+w4df7M+rVq3KBdyCBQvYKn/Tpk20bds2rUYghgDd4WJl8/CbdihUCDBNBfxhOPoK69XwrccS9FjoDDnWsVK5LRoK1wBR7MSEnGVnIV3PBYAn/ubFW+rpOoImt53P+SYo0sbs6MeLj4Ts1qCgQQ6IOtTuKhaiyA0HNRYzcqoVvjaa7WATgnSZ0pJPZ/F4q0Zu0jj9wsLm06maynFR25QMCxwCsXGRXj1KCJv1+RtoPUZkrIBucO+K8db+rUb5c6g19HhyITU18N73nNOKnaLOHLhEOxaKi0ByQoPunkwbAn0IwazXzhoWB2FGXOCi71KrAs3Z3pWGzm5CZa0K8qYem/tu9WZTz4bzKHT7Cd6ImJE88Oble9q0eD+19pxEwzqu4Mw5dLmdvC1o5qbONG5pa7KwLmwuxBJY7E7ouIw3jAjIho7VFGG1pgQoYycjLvBmFpTzxNDVothYrEzgEOmSVVL4DARo+yhS8hTNSdVb6rbjf3L3GVMRgVajA9RONdCg3jRFuC8ityv264/edoyjbVD41e/twz8DzRCad7AE6vfxod2Lw5i5gqbz3Uv3mSYIx8NrJ2/x/bEPgZ4LOjEYe0xrv4Dm91rOEzYZdSCNOPKXzsvfrb0rqbLFanX05P0M5BNoOrs0tGM3Qjg2A86NhdlX7OPG3x3w9P5zplACmNqFrhZ+C9U1BGIDktKIBrquxgHiC/atEQ36aopjtr7AHu5UpGJY4qe/L4E6HNghpmPW7mUSbNb19vVHOrDn7Fd7MDQfAXzHtCwx8d0UZNjsqvtCNplEREQE55PFRr169ejy5ctsZw8jEC8vMcI1FTDGBKKCEzbFUgcnpSA7Hn6BhaemCIk+tOsk83YNQb4SudlcA10Z+SHUJbztaD2AOcXouHSZ0YL+mNeGpzTZ8mXmyZgERvDIEcFIPlRD3hlyN4pZFeT7yEU2PkpaF2WtFBY/bTli+sKvR02efoGyELXliMb71enmxYsr6ACaMtXkB7vVmIaqhU/fIOVyDiU56BkFxJLBX5oPhgLdtsaKrf+SwRu4K5gYyFUoG7UaUU88z9BNdP96XA1nUgPvQ7dJDcnCrhh9fP+ZhjaZyx1sMxIGbHhsXErRhBVteVPvUrM8b0IunLxNk/ptpCZO42jRxF3MxTfj2wPXynMxN2ly/43UxHkcLZ60mx7de0lp0v1OdVrY09KQ3tR3YgMqUtq4XCgz4mLhsC10KPgM61aHLGtHuQsZblaQmHhw4wktGiKMJVoMq5soVEVg1eit9PLxa8pTNAfV7iKal4YCG/hN08T0qcVIf1XAsTasHbuNabcwDqvopt4Fe/O0nVwwwhreoV6VOJ+VdUrGWO2uXqrp2MoRG/m7R3NnnmKtH79NpSWTe5ccBcT7/NMvP8XRkKFJBV1Y4GzB8oEDIgD2C4y2bik5sdhr3b5wj8OYa3XyoE2TxQSu7h/eLKeIWHeQC7TMuTJS1dpfstIkZHEG7bg0EENxieIThaOmc4F9msxV1afgRcYZ3lccOyZkhiBi81HWqZWyKUI5C2ZLGF1xh9CP2ddMeBj03q0xdPbYTb796cOfdO74LRrbYy2tnRfOurJvge+mIEuusHUXtMXTR2+Y/E3LVywHFSqdmz/MB3er11jpiyLl83MOBow5DmzXTK/TBM/mIiAweHmk1kkQUKpKUfLvVYuGb+7JU7HfU/9OW6bvohaletBg34m0dMgGNsOQkF0bFFuaaG4eLcXzoyuF7oy6jbZXG0G5CFkervMYdQGLcJ3ugje+qP9q7kypQ8bsGVS5ZCu1TNMACGUrupXjwnaNARRE0eH7H0VtPUrnoi+RsfDp6E75SuTiTuOKEfpN6YwBAkXLO5bkztyk9otUXbvkAnQ4By9uSwVK5KIXj4XG493rb7Pg/heATX3vCf60IrQvNe3qzgYgsE/fvOQA51j1bDSPgjcdow/vhbOYGYmHF0/esOFKG68p1LvJAtq3DdPKv6lwyVzUfWQdWhnRn9r08WLthBmmwdb5YbR9kaDgQ7cK7VhyAtbjyR0Xs7FCWbviVKuta6I8z63zd2nbHNFA7TC5idF0zRUjNnHhAvo+dOe6gGYn9glAs2H11E56wcaByRbQdGi9OFOmk6Fn6UrMdZ4c+irsnFPh5/jnYAQE9K/NexUUMTDg+Puvf7kRXKBMXjq0Q7CZ/v37X0qVLhXdPn+PnRBjo2C5fJxrJu3TS9oUY+YRqIdnoy6qssYghbh7+QFP6aq3duW9xdaZojCt2aEa0x217V8krXPP0jBVyLMm/dPelchx/T/W90EPpwtw3gZc/KvqVSCroys6+wlvA2Nx5dRtenxX0BUtXcQ+PCE4ffi6yhdi05L9tH1lNOXKl5mOR12hLctM7+iuDuaCLIHIlT8LFSyegycYh8PFh8mUcFQS4xESnRBgUXJTtGCh60S6uyFA6B/+8O9cekAXj1zTel8krWPhRKbGhUNXqFnx7nQs5Aw1H16fus4U1Ag4/km4NXbgxQOUPE1TpkqeZYX74aNX3PFRe4z+VZmvjW4T6AYJBagKoFc+uPaIdivjfHXw7+MjHBejL/OirQ24QAAhKyI5NFIfwBDEU+laze+z2mhtFhbOTlOFYDVowT66fvo2JQaw6PeY3ZJSpf2dLh69TptnJFzXZ2qA5w8aUeYc6en25Yc0suUCM63OxMiULR1bBWP6AjqjpX0x3oRcOHGbpg3eQg3tR9P4Xut43TTUbMgMzXj/9hOFbImh/i0W8TRs6ZQ9PJkEpadaXUuasrYDzdzcmar5WdHv8TaLZiQMuE4vGCaaXa0G+5rEaMDU2Do7hM4fusrrMyjmiWFSgOJhdo+VvC+yrWVJlu7ljHqcW+fvsWYKaDu+oV40WsgM0PSs5F6OyjmIDXZ8bJ62i/VlhcsXILt4OqZ1yuQLRVCGrEJesXasaKBWb+3G0ylpFtJwYF3avWgf38Z90fCW068Pb0STr8vMVnEe/7HCEvhXad7KvQr2L+cOXGIaI6QQyDqVFvjYH2EvBWMz7HG82rjGKaZWj97MBWJ83L54j06GnuN117OFs8b3Co12wFNxtdYGUDEP7xRUQXcD6Yp3rzyka6dv82t0UGQ0xiJiqxgs2FQrm2C64uuX7+n6pYdUoKiIPDgUdoFa9apOzbp7UJ8J/nT5zF26d1N4HyQmzAWZCWDrrtAWE2i+oQ4OPmIUeyb6Cjs2JQTO9W14QTt38IrelDkJcKHtlYVLfni1AR+4/ZsOs/B1+eVpNCVsCDnWs6GCZfNR06F+bOzx8Iags2GxkXzn7cpIPz7QDfJUJmkysT4+MOavqhSwIcsTTlvEcWHBBTZODuRFXlMBKqdky4Zu0DolK1WlGOeV4EK1crigQOiDZkPrMYXy8rHrFKl0mIxBeafS5FDXmrths7ovTzTjjWx5M1P7cUL/tnLMNrqhUDKSE7LmzkgjV3fi83rm4FWa1CXxzsd/GVgLQGccuaAFLQ/rSy16eFIeGBZ9/Isidp6m4R1XcHE2bdBmOrb/skpEbYb+ePfmI2v1RnReSQF2o2nqwM106vB1QQ2qkJ+nYav3D6Duo+qye6LZqMP0OB19hSZ1XcG3fVo5Ud0O4pqQnIBw4uWKOUbbMQ0oR/4sifI8kRsP05n9F7lR2W5CI6MfZ9GANfw3bOtjRWVsS+jl/LdvpdBINx8hjLTi4+3Ld6pip/GgunEKUrgfoqmKNatez5r8s+unbynRNv+j+r1rsYQBztCYaKXJkIr3MqkzpKJzynRLFmKAWxMHqtbCmebEfHETxP8zDfL/iKdRT+4848eS2ncUZtgfyLDpmh0F1VNqx1wb2lH6LCKOCderVaM207IhG+jQjq+b9lLiAYfH7Pmzqj0fZ6MucV4ZroP2dXRruiI2Hmb5CHwCCpXNp/P+cX53k5B/VHQpTekVKqixU979iruilPYkBE8fvKJffvmZBrVdSt3951DqNL9TDoVumjl7Onr14h3lLpA4n5XYMBdkJqQtnjx4zeQ0nBz5slDxCvl5UYoKEnxZY5ElV0amkgHhygfDGNri/s1H6JMOK34UL8gfg0AUm/PYQKFWpHwBFTcbqNVBuNcc3XWCBbnqAG4zNhKnws/Tg+vqp0vuTcQxhq+Noj8/iUDThACLKY7z0c0nfNzapmSYIF48fFWr4yLQbHh9/h62NpqdHPUBJnX+vWvx7SVD1idomtN2XEMulOF8tU8R+yYG3BvZkY1XeZ5+TGy3MFlOoAqWyk2Dl7RhKgr46POHaKedmpEwZMmenuq3caQFO/+gqes6kG9TW8qUNS29e/OJgjfH0JB2yyjAdhSN67mW9u8+w4WGGerx+P5LClpziPNxUIRBqwdzKXze8hbORk27edDSvb3ZLRHTsFSpU5hPZSLhxvl7NKLFfPr7z7/JtkZ5ajO8brIrenkdbruIpziVq5Uz2IxBX3x4+5Hm913Dtxv0qUXZjSz6ToSepaO7T3FxJCNgdGHpoHW8V6pS05IpjuoA+QSs6kExlA1ciRXD1qtYO9nyiQJmw8Tt/B06MxQ1GyeL6Brv9u60frz4P7B3UKQgUwwSE0mN7zS9Jf8dyOu8NPGQph4y0gg6tOitR8XtTp48FcTrKO9cmnVlr56+pqjNh+MYmAFHd51kBg+a0W7x3k9MAPcqBl54fE0IURrsDn42XJTpgtwzuDUUpiL6AtfVMKWZ7JRAM49zh6+x3CBN+pRU0UnsaY0Fjgs0+2X7+tDoRS2ZyeHoZaH6f1yX8hfN/k0+z+aCzATAm5U7f2Ze8GL2f9FGmQoOSrCzzFtICFz8q6jcFg3deJauWoxFmLDJjQ7UTqHEwgN+9b0rIocDpiTomk1uM5/Wjt9O1l4VeeomgU4RFh8sQpL/HR8QpYKGAGgy7qjkYcHPiwwSU5h7pEz9u8pudt34rRrPGaZktZXMjyWD1mnVTRW3LMyLHx5rieJApQ/qdKvOdInHt57Sjnnqp4T6AE5X0uBj0YB1iWbwwQYa05uxje7Nc/do1RhBBUluqGBfgnrOEFTOwMWRtGGmeuMYM0z7t1HCIh+16+9NK8L7sc26l781ZcySlu3zI3edYUG1f9VRrDlbOzeMLp+9m+z0iN8Snz7+SSeir9LiibupnfdUau42gWaPDKSYA1d4M4jogYYdXWj21q40f0d3CmjvrOrympF4gI5lcCORNVbGpgj1mdU8URwLE4rV4wPp+tk7lC5TGuo+s0WibTBXjtrK0oJchbNTvT+MM1HD53yBUtTVbO9GeYrl1Pk7mG5BZ41JVksNBRymYyjIpLNi7OkYdO3IQ8XepdEgwYwBiydCifvx7+PLLovQl6GgylU4hyqnTDogQ58tUc6pNBdKwMmws6pGNRq8oBeiCQijD/x+ilQpWHONIhFOjVL3hRwyIGRZBH/G8X9FKxZSPcfWmXtUOnzsVWIjbE0UF8e5i+akChos4fH/aLAD1Zo56EU5vBwjXFmdFaM4fXH+8FVmZqVMk4JsvSuahK5oW6NCgqMkcF6D1h6mTrVncI5m485u5F7nC9X41uWHVMVFPfXV1Eh+q8Z3CCxsVRXaYnQi0Balg8z5ozfo2UPjAoIlbGtW5EnOvauP6MoJw3RWWLzcGos8K32s0zH1Ohl2ntpV7EuDfSbQwR0x3GHqNqcVeTT9+sNfo42byoVQEz1QaqnQ1VG3OYNOqn4vH1Vn659/1D+OIUAOCRbNG6dv8wRPEzDBSpsxNbsk7d+oPV4AblFY1I7sOkFn9usXmfB7qhRM9wTWjNnKFxdjAcerfCVz8wVkuQHUSUOBaAAUZcDG6XuYLpscAdpDuxHi3C4bG0gh6wyPhzDDOGDzCpv1LsN8aVVkP9Y4+bVyoLyFsjJ1B5qzFTP2Uvf6c6i+zQga2mE5bV56gHPOcDH9UQG2xclD12j17H1syFHPegQNbL2EBed3rj/h9aNMpQLUokc1WrirB83b0Z2adHGnQiVyJrvpzI+KV8/e0sAGs7hbD5OgocvaqaYfyQkXjl6jDVOE7qnrtKaUKVb0jMmNPBTZQccpTeM4KhsC2LRjqoSmbWPFLVAb0NxcPEAUcO5NHamAYiOvbToWXzsmc8eg2cpZKLtqD4EmMRq9CGZGU5bv09xJpSNDgYRcMTSCP779qDLxwG3sYz6++0jHg7+YssnPZnbFkRHPB0YP4NXajY6HnGYaI147jhGvDaHYQI22X7Li7l1+SKfChD5Mxu/EPh87FGmHdzs3jTpBsHRg7oKCt3RV3YHdct9n6V42TnyRPti3RngX2Pta8X7KWPz1598UvUs4Qjr6JlyjeSD4LOcxth9Qk99HmCD1aTKf9be4zmBNtfdU705papgLMhOhqpuooFkDYWJqFrQupawK8YcsobRF6KJAIwMitFDwNEEGDJ+OvEhP7j7XGUjce0l7ajUmgLrOakX1e9akWh09qISVeiqBbe3KlCFrOrZ1BVVBHarUslR1mDRRA6E1g7AWNMMjSqJ9QoDnq9VBLHhY9DUVeeiGSWfGNWO2aNUj5SmWS+UuuXjAWr2nlR5NHdmmF1OtNWONnzihcO08TRRKQQvCOBslsVDVuyJ5NLbj1zix3SJ6n0xpaL5tnKleZ3HBm95rDR0OPpPUh/SfAzYO0DhBUL1gZw+mkXQZXpuqupWm1Gl/5+nZ0YhLtGjCLs4586s8nHo3ns9TI1xYH9x+9l3qALHJuH7xAdNjZo/YTp3rzKR6lYfTgJaLadWsULasR/EJx0o334rUb3IArYseRBNXtaP6bZxYk2fGt8X7tx9pcMPZdP/GE8oGPeqaTpQmFusjuQBTkIltFnJh4dqgCtnFo+mZCljfZ3ZbrjLysPIwzsgDWZ3QYgMBfX3iSBs0Abqv0xEiIwyuieoA1ozKWXFIvThFyunI8yoXxYZKAfjo1hNVoDKcFZFDJidouQrn5OlYSjYjEyygFw9ERuqfH4VUAiYcmNjN77mCnzu2w2Pq9ClVGvrStiX4sXDsCHJeP2GbKmw6RcoUdDriPN2/+pDphM6KMRsQtlJM7kDPjK8PwxQPZiEwAEHBp+n9ClqgFHptXHU2cTh7bPUBvl2tmZCG6AtM//YrZmyusV6DMTh54BLvfzJmTUvlqhajhALXE486lpyh+ddf/9D54zdp8MwmfA2CDf7nz399swaXuSAzERAQnTlbOvr44U86deg6mRoOtSrGCcJLCFzq26jyIAylAOXIn5XK2ZcQfGBllK8NaTOmocqe5VkAii/QJTSBF6TGcZPl4+O3WA5DyBHRNEnyai3uI21iEwr/fr5ccN08e4dCV4lFSR18O3tyZwvuUNE6Ar2bDPHjaSUWbnDB9Z0mSJE0DFD0dWpUBwuHkuTsX4XfyxldlyUqHQwGHxCQP77zjOb1E53M5IgWA3zI3d9GBEe3W0xnkulE77+C7Lkzklf9yjR4ZmNaf2gwZ5y17uNFlR2Lc4bW509/cV4MpkZjuq+hVp6Tyc9qOAdSzxy2lbYuj+Im2aN7L5IF3RHHi+nWwX3naf2CCJrYZz0XX3Ush/F3GJuAPoPiDBto2NE7epXj6eHiPT1peWhf6jm2Hv8sbXpha23GtwdCgEc0n8/B8gh8Hr2+C2XJmTyjA+b3X0cPbz3lDNCOCTDY0EdbdC76MqVI9Ru1n2j888ABEQ1XaM98O+vOLsP1a+kQMd2q0c6dpQ3qsHbsVtZVFbMs9NV0DP8niyCZJbZ65CZugiC7C26NSwauUbF0EK0DIIMLeVzIm8P6Er8wOrHvDO1cKFwYATkZKm5VhK8xJW2K0r5VYurk0tCebe7P7r/IhaFfD29xPqaK8GoUaynTiM88XkfUJrG/qKno79WFPMPAS9Oe69Kx6zyFxEQXWn9dOLrnNL14JLLHkI1qCA7vOsWTSfgJlLVNWBG1f5uQy9jXqphgajDe3xuXHlJF26L875fP3lLbft68l3eqYUH/9++/dOXsPfpWMCxAwAyNQLcFGQa4mOJiW9lJtyOQIbDzrkDzh2ymC8duMGc9ezyjDENQ0aUMU+uwkGDSBccbQ+DW2J7OHLjEWRT+vWqatHuA3DJ0sWCrCh46xLLxgfH8psk76FzUJbp87BovbvEBEevGSYE80geNQhOFQV+ky5SWGvSrTYv6raJlQ9aRY/0q3L2KDxRttbtWp1UjN3MumW1tK410AejO8Fo2TNrBFxSr6uX1siC29LAgy2oW3K1b1H8tDd34h9Gvq83YhnRk9ym6cvwG7V4STt6x7HRNCUxme81vTX28xtPe1dFk7Vme7BRtZHIMjkZ4OoJdhzWbT+M3daOiFoa5SZlheuDiC/E1vuq2sOdJGOzcL52+SxdP3aFrF+7TrSuPuSmGQGp8xfn9X36irDnSc5GXLVdGvuhmyJKGMmVJSxkyp+ECB1M42IGnTPWb3nbg2IhB3/Xu9Ud6y18f6PWL9/Ti6Rt68fQtPX/yhp4+fE2P7r7g25qAArNwqdycEVaiXF4qUT4fH68ZyQugoY3vuJSdWTG1gFNrnsKC4pbccDDoBAWvPMDrWu95rePotk0JTCwWDljLtxv1r62xKNIFXPNxPQRajmygF+XxcNAJunTkGjdiA/r5qr0PXBGl42DLUQFx9iyXY64zTRAU4Pp9hNwBUy9E0wDNRwZQTPAp3m9g4oQs0Z0L9rHu6+6l+3wfGKUAr5/H/XyHrRHN28y5M9Hz+y+YHogC6Ok9YVFv4VSGQ6jx3JjMzeu5nH/u1sSRsuTOzLb1h4OO8/EiHFoiYv1B+vj2E+UqkuMrfRiKtXClWR7bHj8+di0U0zHo2bU1yiX2KJp8mHWheW4I9ilRS/AwSEjMwp+f/qKDe87E8VZICODoC80Y1n0UZ7gd203x5bN3VK7yF81eYsNckJkQth5luCCD0xU6mtJRxxTInCMDla1ShC8CyDqp3/nrroi+wIfJobYV7VwSQaHrDxpckNn5WtHsP5azkBWZZKVsRHfBFAAdD9bwmBrtWRpODfsL84nYyJIrEznWr0qhqw/wlKjPsq8LMjgkgd6IzDJM29pPFvS8hMC3iydtn72bnt59Tjvn71PRE+OjTjcv5qpjmhax/hC5BGh2I4KNbtD8fUwv2L/xMFve6oO24xqxFW/09mN0KuI829kbg8w5M1CzIXVpbq9VHNgNqomh3HB9UaZKMarX3YvWT9lJ07suoxKWhdj5M7kBn9t+81pyYDSsrAc1nE2Ttv1BeYvmSOpDMyMWcGHPWygbf7nXrqTaLN+79Yy7nneuPaG7N5/S/ZtP+We44D6695K/dAEboF9/+5nXSnyhYy0fH5oNTK8w7ULIsqE6tpSpU7AJVL7C2ShfkeyslStQLAflzJvJrP1K5kATYGqP1XRw92mOYoFmLLk2a54/fEnTuizj235dPamsrW6NkLFYMmQDvX76lvKVyEV1un5xATQUoCqioChuVZgcFSaPrmaInFz5dPZU28AF1o3fzrTgco6leOIVG2tGi9w45wBbyllQFNbrxm3jCRbs4ktaF6WezkNVung50SphXYROh5/nvwO4a6ZM+zsXSdBjSZMPFGDps6bjYkyigktZ1o6zll+hO+K5seYcVqzrsS8AtioGJHCDhMwh/gTMq7XLVwUOF2vvxHGUtVfvQPj+9Qe2r5d0RV14/vAVHdtzWhUwbQhA1zweKrwVXBVTOWMRE36BX1uWXBmopGVBSijgPOtQXVBrscbHLr7CAk+yi2OadN+OiWCmLJoQZS0LULqMqenNqw905qjpNTlOSkq9dJhJCKRlaVTgceaYGxqqi1E4sFsJbdQXcNnBNEYbvNsJHU/QvL3MW1YHKWKN2HAoDj87Nqq1EBotFG6aHscQYCLWSMkl2zwtSONjgqZZv1dNlVBY23MjT6Secl9MyfQ9ToRFy4V0QV/jw6KBmu3cqEiFAjwVWthfdDkTC437+1ARi/zcUZ3cYXGy1fugizlE2WzBIbS//0x6dEd9HIMZyauYhtugs3d5DvUcNL0RzQ3sTttOjmA3R2iueo+vz5bwNRvacBOtdKUClCtfZl67ZeEFGhSKLYQsv3r+jp49es1f6Jg+f/KWv0PPFrsY+y3FL2zhj+cvY1mQaYV1mtsxxRKar2kbOtK6g4No87GhNHNzF+o9wZ/82zqxPg7PbzbiSN7A38S8QRspdOMRnmj0n9eSLBJIv0osYF2d3HEJr12Fy+WjpoO+bmyaCpeOXqPdiwWFr8uM5gZPTySunbxFwcvEVKrD5CZ6TVIgH4A8AMwU/z6iiIkP0B93K6YYTQbHjSO4duomHdx+jH/WcIC4tj978IJCV4uiK2BAHdZ3wV0Ra0sVHyuVdv32eZGticIKQDEG7fqwrX3iPD8KOwB/M8DDm49V1vp4bsCvR00KnBPMf2Ngv+QtnpvevHhL+1aK81G725fmL3LRoBH7+def2cAkPqQBCKiVmtaU8A2H6POHz5S/ZG69Guqha6JEpqFNUcpX/EthqA8itxzlc1C0QgHKq4dbpjZIyY5DzYqJEmgee3KG11vDP2H2/IbCPCEzIfCBRSbZ7g1HWWBeoap68wpjYVejPM0ZsJ5uXrhPty8/oPwGfjBiA9OJ3EWy0/1rjylqewx5KNotfVG9uRPtXXmAIjcdpvYTG3ORpgvh6w/SuKazufs1I2qExvuBDrigzyqmGWDCZa8Uf3GOv3IR5oJfibnBE7AGfb+mKlSuXoGnPaBmQqNV1SdhyfAAFsBlQ9azCxI6UVhU1aFOtxq0bVYwBy7iYlWz/Rd3pPio270GbZ+1h/NEdN03NnBxgRsVLmRha6KZSmrs323XGS2om8MwCl0bTR7NHKi8Y+LYvOJi3XdRW+rsMJxORlygrbNDqG4X4zuqiYlUCh2pd+2pdPfqI+pfbwZN3NYj2WpFzNBOdwT9jymAlQpovF/sQgwdddXXX/+wnuDly5eUKXMm+vnnnynF779++Ur5G38348fF8rGBtGPpft7k9pzWhKpW/5JVlNwQOD+UToSd58YS1ltjiyRdwMR4Zrdl/LmB4Vc5DRMZXcDvz++7ir871a9CpWx0F7rIGV0+TDH/6O/LjVB1WD9BTMfK2JUgi3hMklUjN/F3R/+qnPcFbJuxixstuD+OY0LzWfxzh3o2FL5GuCFiWoXpltxfSPRZ1pnyl8zDmqx9qw7w63n74p1qepY1X2a6c/E+U11/SfELFyo4Jjye1M37dBIxO7sXhbEZRuHyBVjDJiGpl5WqlWUTtPjW/yiQQa2s1lw4UqvDniXhOos2CbyGkJWCeqnOHVsXwpXsMRjKJASfP/6pMtqCfiwx8dtvv7B50reGeUJmYth5CD4vaIumFpFD92Wp5CFEbNWeA6YL+BC6NxSOifuMsPguVaUo0xM+f/iTInVYvEuAVocuERYMdLU0AcYdNdoKC/wd80I0Hr90PkRumbpJCwoNWTBJEW5CAU577a4iW2X1qE0a7fmx4DYaWFu16MM5ShP4voNFd27liI16TyyxGDfoKzjvSwavV4VMGoPiloXIu62YuM3suixRQ5zRJWs7RuTELB2+ma6d1i8cOykAwf7YDV3YkOTRnedclL3UMJE14/sH1hUUVph0QWsG98KCxXNSsTJ5qGiZPFSgeHYqoui88H9Zc2bgyZq5GPuxgWzC9Uo+Ycex9cnFL64pRHLCjXN3afEQEWXSZpS/wRMNQ7B9TghdO3Wb0mRMTW2UNd0YQAfGLokpftU7BBpyBTRGs+TOxNE06oD/h95L3XQM+nOEMeNnjZXcMUylMKkC6vWqxU6LyPMC7OrYsIwCeP1UFGE4Xgno5qxriE28c0N7LmTkVCxDFlE4yamOd4dqFLle6Kqwn0BMzrtX7zlYGlpy7GeC5ou/N98uXqrjhkPj3pVieufe4usGLBq7AKQP8Ys1iRtn7jBLCWwAV2UPqA2Xj11nsxE0nSQzSl/cv/6YLim5ZY7xjFQMxdF95+jThz8pW55MVLyC5qba9wxzQWZigIMKcThoLuePG5bzpQ8cfYVlbeT2GIODndW5LeKDDoMOuN8ZAvye7JYEL9edSQagm2SjLFh7lmkvkEDHw4cYizRyvdQBHatU6VLyFAr3U4dqLUSXCDSD+9cErzuhQFA0rHjBE5eLtdrX0NadF1gIleVFQeN927ixQBfdto2ThahZH4Cvn71AVqZlbFJyZoxFi+H1KFOO9Py6Nin2wIkFrxaOVKVGBe5Ejm+9gLn2yRXQb47b2JWy5spI964/pgH+MxMtTNsMM8xIXti6IIyWjtnOt1sN9iVvPQJ0kwqYIoxrNZ8nQtbVy5N3a81TkoQCDJblI4T+qvXoBpQhm3HaYxwraPdA3W7Vv3IqVAcUL9IZsdnw+iraYHzAWAuPX965NFVwjasdk9ozODvnLyVMv5AtBo1SIYv8bCcPLRmarhXdy1HkhoN8G7ljWP+x90DBB+ogAHfJV09EoRazR8T2YAKGhiukGjAVenzrKRdCuQtnZ6lFppwZWae2a5HYH1Rv5crTd5iMILYHVMzYuvKgBfvY2AKZaMWtC8d5PS+fvKb9SpRR/Fyy2AhWzErwvJqKttjYu1rscWx9LPViQsVGuKJTq+BUijNJE4LwLYIq6lzb8oeld5sLMhMDHzYbVzHFikqEkGgbj7K8+Dy89YyunL6ToMeCBamFvXCDDF1v+JTMraEdT6FgnwonQ33g2UJkYoBqp20KkzVPZqriLYT6QUrAYXwgmd4lwC4Obzo+sNBW9qrAfGAsrqZyDKzf20c10frrT/WvAzQR6fq0YWKg1ikZ7tt6TEO+vWlKEC+u+k7s2owJEM8xaQdfJI0FHLjajRd2xWvGBXJ3K7GABbX7zOZsKnL3ykOaP2AdJWfA1XTsxq6UMVs6unXxAQ0MmEXvXn9I6sMywwwzEhFBy/fTgqGi6GjU04v8OupHJ08qLBy4nu5cElS6P2Y1T9SN67zeq7mRhrzRagkoUnF9R3wL7NQ16cDiA01LFEUwAVOnowIw1QlZLoqPFiMbxDkXZ/ZfYFMs7NeaDqvPP3v78h1tnSFMNBoPrsdNzhClcQzZBKZYbPaTQtA/ESIMyEe9e+kBjag3mV48ekl7Fe0X8KtCZc5dVOinYEh2YMsRvl2tuRM3nM9HX+YGtGwgyz0PXhvcIwEUloFzxNQMTs7x31swhXAfmI0Ut4xbrMWmeUJTD3g2123OgT2aNP9wa6x7mhYbGBiEKuwrZz0MWrTh7asPdCzsPN92qpNw6Ul8rJsfTtF7zycqM0gfmAuyRICdu6AtHtx73uSmBfhw2lQTnZ792xJu7uEaILovoesOGjxxw6KPLhwAC3x9YOlRjjLnykivn72lIzu1Z295K1oqFG/o/KkDXIYAUA80FTEB/UXQY+iq/XoXOrpQq1M1fv0Id5ScbnXAgoqOH6ZkmuiXEshGgb4OF7k1Y0T3Tx/gYlHGtjifo0WK9bCxcKxnQxVdy9Bfn/+imV2XJngKqw3pM6elXvNa84Vl97JINphJzshdKBvTF2ERfPX0HXZfRDisGWaY8eNh96pomt1P5FvB1RgFWXIGLO6DFHONXnNbqWhyiYETe8/TwcDj3JCFkYexBgug4K0cKQreZsPqUep0qfRyj9wyTRROzUf4a8yiWjp4nXBKrFGRnZtjY82YLSrjL+msiKYpsrIKlMlLtr5WtGLYBi5wyjqUpGO7xcTL2rsiO0ADmKSlzpAqjrEPzDYW9l3FujGJN8/e8jQNEy85mTqx94wq92zd+K2q6ziicJ7df85W94CUbsg9zouHL9lJEjr72IA8RtrY12qv2YF7/6Yj9Ob5O8qaNzNVctcd3I33+N3L9+yGbKiT8/lDV3kymDJNCrKrmTCL+uidJ9nJskDJXFSghGkpuK9evKOVM/bSqK6r2LApKWEuyBIB5asWYWtjZM5cPq3f5MgQyPyF/TtOJLjgs61ZkSduMPe4csJwiqV7E8FjhhmEPg6BWMDdFfOJnRqmWhKwpwXlDxatx4LEghgfoA+giMHCqakwKl21OJuA4D47NUzbDAWmc81HCK77yuEb6c3ztxonX5Kfvn78dq36MBQmLUeLaVfQvBBezPQBfq/DlKb8PXzdQTp/8LIRr+jLY+ECCzH4ybDzFKbkhyQWQGWo103w/6d1XkpP7ho/4fsWgJEOijLoOS+fuEWDG86mD++M1+6ZYYYZyQ971x+imX1Ec6tOe1dqPqBWsqZJIddqSqelKov7SvGyqUwJXMNWDt2qohgWLGN8xufy4ZvY3bewRX7yVKZDurB61GZmmyBUGYWTOsAZ8cDmI3GuqRKXjl5V5Y41UBgsKPK2Thc0/RajAsR0TZmOQcN1eIdofv/9pyi+YHUP/PkhbqM4bea0tE/ReMUOgi5VpThTDVHsHQwUchN57FJL1qCv0Jxvm7mbC0lY1ksqJRCoNHSRLRbfpAWv5/Htp3xdcqin2Txjh7L/qdHaVa9YJumiDat7Q0OY964RWWj2vlaq82AsIpThg7PiNG5KHNp3gRlURUrl4uiRpIS5IEskhxZrJRg6aq8Ys5oSls6lOLz02YNXHBSdUPodtDwAMskMRWVPC0qfNS1rn2JCRNdHF6q3cuGFEpSB+1c167rQdYMLEBC+RjOl0k8JTIQYV90kDc8ljTgC5+wx2Vi6WktnKlg2H/PZsYhqgntTB84EwVRw8zTt2qyKrmU5eBJdt+VDhYOUPihaoSBVUygI83qtTFChnqtQdmo0QFyo5vdZrbHYNBVgyVysYkGmAI5vs0CjUUpyQaHSeWjM+i6UJkMquhhzk4uyhBiqmGGGGcmrGJv6x2reNNdq5Uith9RO1sUYpiMT2i7g6xCsxZsNFoyQxMKqUVvpxcPXlKNAVtV1whjAXEJOddBQ1GfDf+/qQ9qp/E7rsQ01vi8rRwj3ROcGVfkaHRurldwxtyYOlKNANr69ecoO3jtgkgbt2Prx23iTDmfmy0euCsv3KsW48AEwrUEjG01eNI0l3r0Uk7HMucTGHmwX0CJfPnnF/65cvSJFro9W0SI3Tgrkx7byLM+6sPev36uYNH49RRwOcPPcHTq7/yIXkdCbx4fUqIORo0lPh6xT5MaiEJPSEW14cOMxnQo/z+fY08DsMbzuA9uEpb+7ErFkLF48fk1noq/ybUffhIdBx0f0XiEtslOYZ0kJc0GWSEC+DXBw33mT074wvajiKSx39weKXIaEAOnp/Fhbjhm8Gf7l11/IRRGd7lPEn7qQs2A2qqxQHXcqAYeaAI41FqErR2+wXaw6YNQPWiAKHk0mG/Z+NiygReEoc0QSCohvZS7Ztpm76P0b9ZoiLIDNFJ76psm69WGtFE0YuN5XT+hfcDcfXp8FxJdjbrANfkLg192Ls85wThcmsr4Lf0P9l7TjJgNoDqvHB1JyR5GyeWnMui4cHImmyJBGc8yTMjPM+M4RvPagqhir0cye2o+sl6yLMWDtxB10NvoKU8P6L26XaBb3ANz54KwIdJraVKVvMhQ4v3N7reBiBM59+trlL+6/hqdHiLSJbQUfG3BPxBQKAe6NFHZK7P9D+DL+L6BfbZWzotRsNRxYl7Vjch/h07k6BS0Q/wcaP447fZa0/G/Z/LX2+mKPDtdpmHs8f/BCJS6r4FKGrp+8xe8Lfo7XXKWWJbsyhiwXEyh/JbYHOjDQJvOVzM3UxvgFV9ValuwqGRvITTu8U+wDvVprDnmWjCQ7XyuNAdqxsUfJhIOEIXv+LGQIDu48QR/efmJ34tJVdOecacP+wBN83hEEDS23KfH29Uc6dfg630ZkVVLDXJAlEiztinFQ6KO7L+jm5Ucmf3wHH7EIRO04keCJQkXnUrzIwPXnRPh5o2mLh4JOaAxpjg8Zahy8PJKFppqQJVcmNuUAAjVQElHw1OooeNPbZu9RWwD/+htyOURXaM8S7VRJQ2BX15ryFs/FAuMdil2uJkdI5KYx3UPp3mlCsUqFyCVAdJXm916pd0GPRVaaiCwetI457gkpkrrObMG3Q1bspzP7L1JiAkV612nN+PbaiUF0OpGfzxRAaPSodV3Yeerckes8KTNrysww4/vEntXRNK2HKMZqtnCgTmP9k30xdibqMq1RGlidpzSlXIWFHioxgH3GtE5LuKCwqVWe9eDG4tCO4yqbe0y69MG5qEsUtfUoF1NtFPMpdVg2RDBLXBvZq7LFYuvK+P+agLWSS2UVL50VUeiBuojXWs6xFJ2PvsQFEhq+V0/c5L0GmpQSrcY0ZHv82JB0Rjnxe3LvBX93a+JIBzYLg4z6vWpx8QUao8wZw3NKpk3dP2qqdHmgZyLTDPBu97WpTPCyCC5SS9sWZ5MTdcDrC1t7UEV51AUcC3Jmgep6TNPiY5/yXPAoSGiAc+T243Ecxk2JI+EX6Z+//6UCRbNzjElSw1yQJRJ+T/UbVbIrlmhuixUdSnL+zcunb+lUlBCZJmTz7eQn8iVC9DTniI3C5fIz5Qw0u73KwqELGNGjQwTxK4Sm2uDbWWiM4JgEJyR1gDgWo/obp2/zwq3pPsDxkDP05M5TMtWUTJqGbJqyQyN1DYtS2/GN+TZoGrr0YeC942KFixYuXobY4OcomJWeP3hJ6ycmbNIE96wabcQ5m5HI2WQA/gY9mzrwhgjURX2L+6RE8fL5VfRFTMoGNjC7L5phxveGXSujaHovYYPu08qJOoyun+yLsdfP39L41vO5QHILqEquCtMlsbBl5h66flpkjjUaLFyGjQGuIwv6rVYxMUB91AVcExb0XaWSPBQorV63dvbARYoJOc2FU5MhfnH+73TkeaYcgkLYZEg9/tnHdx9VzoqYmEGvLqdRmI5JXRlYSQCkBxL4Wa1Onlysgf4o8f7VB/o99e+80cc06/b5u7w3SZs5DRdgxSwLUwmbYrRjrmjg+nYWjokHtx9jHRia466NvjgaRqw7yMcFamSFeNpA6PaD5im6MDVURonITYe5GZyrSHaycNQ9jTwWcoaNyHAsNt6GBSRDj3dKiSFyjWXZbwwe3XlGl47f5CLcvqZozCcGXVEy2pIa5oLsG4REJ0ZBhkXFwUeMtMO3CK5uQiB5vod3nYrjEKQvqrdyVo259ZnooHvk1Ups9nct1k5bRIZInhI56fOHzyob2/iA852LEnK4Q1mg4iNX4Rz8WJw8r+FxjAGeN2eh7EIjNiVI4/0snEqzPgzdpzWjhcuTJmCRr9td6N4WD1ij9xQUNvhtx4nu4cYpO9kFMiFoOaI+T97uX31EgTNNN1nUhPbjAzhw/MWj1zSx3SKTu5QmBoqVz09jN3RVGX2Yc8rMMOP7wfZF4SoDD5/WztRupF+yL8awLk5qt4ieP3xFeYvlpE6TRLMvsYAG4sqRW1SZY+mypDH6sbbN3EMPrj3m64q+NveYjF08fJUpkk2GimIqPnBdX9RfFNXQnuOaHPv/lgwQ/1e9tZvKWXHrjN2834ElPWQNm6cGceEC843rJ28y8yVb/ixs8gHpxJ2LXzJR8Tdy78qDOFRB+XcDC39AGmfU7OhJ4QoNEnp25JSxCUemNOSssGG2zdqtyi9NkTKF6rg3TxN7iprt3b+aNkVvO8YUSzg+x3dejA1M4/g4W7roNbEKUbJlXQNsDabAhm88IjR31kUoV6Ev+jpjsF+R5JStWpQyJTDHLD4+vv9MJxRtWnKgKwLmgiwRYe1ckgunu9ef0O2rps90cqotRrgH/5+9q4CKauvC+/0v7O7uwA4UVEopEUVRDOwAu7u7u7uwMBFMEJEQDOzu7u589a9vn3vHAWeGSQTffGuxHHWYuXNn5py79/5iz2m1tvDaomj5AlS4bD4WqUbqUeA5NLZmJx2ECp+P0W5i59rGnhe58zFX1OrD5EXOua2tgrao7iJdHudHBxxlO11VcGkrCsfdS0ONNvHBois7OUEMDE63OrQZIzaTfWsiWaCsCc0GNeAFG+cmdI124dsA3Jsq1irNnPfFA0RXUV8gmBJaAWDPknC6dc6w7LuEgA136KrO3H08EXaetsxWb5aS1DRlU7b2UljiD248h14rUVvMMMOMpIfN8/fR4hGCQu7V1Yk6jW2U5IsxYNvcEDoWeo7XyaGru7B22FRAUTC3xyq+xsB0xaW12Iv1AZgb66VIlw4TmrGpWEJAM3LlMFEwN+pbl63hVQHFCSzpMY1qOSKuduxY8GnF/8m6MrBtNk8Tgd+tRzXmwgwFGVC/W21mvAC5C+fkP3GO0WuWKYk4H6MbTuPbR3adVJyrVOlSssU97oeiC9cHhcvl54zQ9FnSsQsirj8A17Y1ufi6c/EenY28yJMgdyVa4onQs3T7wn1+blX6sKAFIQp7/D9SiClefCDnTDbzcFaa5KkDmClH9ohIIn3y5Q5sFgZsxpjYRgWJ82pf3/h0xeMHr9LXL39R7vxZqGBx8R7/aJgLMhMiTbqUCtpiVLB2DoS6ACLH7Hkzc6V/NPSc0aZkoWqMMTQBCyuKMiB4tRCqJgTkkVm5VdTqd6o3tOQsj4fXHzPlUJ32qlilQlxUhipZzyoDoYx4XnSV9kuJ9cYAHhc2vOB7rx6u3gSjlHVxFgGD8w27/ISCmr2HCE3YmjFbtC66cUHRdWYbXoBBdwSFwxBABFzdozJTMKAhgKuXKVGwVF7qOk1M+fzGb6fzhwyj5CYWCpXKQ1MDelGmbOno5oUHNKDBLHr28NWPPiwzzDAjHnDhvHbaLlo1QVyQI2Os/fAGyaIYu3j0Oq0aK5wCu0xpToVKq9YNGQv7N8TQybDzTKHvNb+9QecIOZnQM1lYFY1Dy9METHfQ6AV9rnE/4aissWjr487ac+X3ev14UXTX6+KqKOg2Tw1iKiBcGB2a1aCNkwP52IpVLky3L9xjeiH29PMxQgKB+4KKCIdFGc/uvWAq5I4FwYp/k6di0JYDmFzJ2jEURM/uPafYPSfj5IzJpiLW9Swpe75vBhpy3homfmiOKuPOeREojedTziuLD/nayqpORZ6kJQRE3eB8QoaijhqqDrcu3Keb5+7xIMLWQIv6+9ef0I3z9+jX3/5HNeoIEztjQmauVXcunWS+9+aCzMSwdRW0xYMhxqctYvRcU5qSRQQY7hxYs7E1f7mvnLhFd6+IUbwuqC2ZZhwMiOXFSxeqIwooFFLqgOkbsjA0URIBuYsEnZYq6iS6SE36C/77ZracNU5xgS9055lt+Tack7CgJzQlQ2bY9dOas9/qd3XlEEd017ZrsNaPD4h763cTRieL+6/TKiNOE7rObMUuiFdP3FJ05UwJ11a27P6JwnVS+8X0Ws3EMynmlE0L7EvZcmei+zeecFH28LZx9IpmmGGG4WBq25gA2jBTrKfthtWnlv3dk8xFWUK6sYntFvG6yJpbPSYYuuD10zccfQK0HNaA8hTVf5KAfMywDdF8nrvN1i5MGvRBNCMBTLbUBUeHrY9mWiEmUE0GxKVBYsqE6RimiY37Cyv5l49fKbRjbcc1o+f3XyiKKjge7pOKmGx5s3ABhmIU+O2P77O75nZdzo1Y2VURQco4DhSRcpF1VAp6dvNx5DxSUPpgVgZjEejOQ6XmcF2lUOe7lx/QsZDTPDXz7OH23fPu94tWuEwrF6DKQBNXbk5rY3UPyD4AslmbLjiwSUzHqriUY7aIITggMbUq2VsY/Fjx8eXznxQbeTlJ0RUBc0FmYljXKvWNtnjd+LRFe6kgOxZ+kXOcDEHGbOnJ0rlsHJccXVCyahHW/2ARgIhUG1RxLU+Zc2Vk/VVC5hWyaPXo7hM84VIF8LExrcPiHLtXdZi0m08t7jYhA+2oZBdrDGD6ZdPQijf8jVMELUNdmLVDk2p8v2UD12nU3EET1m6cCKD2n7Q9Qct8ZbQc1pAyZEvPC/uORYYFYqOr2HSI6E6uHrOVHt/WLrTaoIDqma1YHwGdxFTfZclCTwbkKZydpgf1pdyFstGTey+4KLtzRTM91QwzzDA92Cmw33oKWCI0NZ3HeVGT7t8ugpODbuz5g1eUp0gO6jm7tcmLyEX91zGVD5Q7GHAYcuwL+vjxbeRlgs2iDTZNDeKomtxFc6p0GATQyF07VhRt0KQpF23YW/1GCsZK3U4ulDmnmI5B662cO7Zlxk5+HOi8wcLBpCxvidxsn8/P8eVPntSg2IKMQBnQleH/6F9SZIDhcfE7hcsV4FBp2eoerJfQNaLYk83AQlaGcwMbmrfKzt+cK5GrCsD+XlkPB+D+h7aL6yWPLuo/v5FbDtPbF+/ZQA1Gagnh2qnbnA8H3RiuUXT9bu3fKK4bYTJjCP7991+FN0KtRlXJ2DgedYU+f/xK2XNnpBLl9A82NzbMBVki0BYrVS/Kt6NNMCUrZJGHCpTIxV2cmD2qCxBd4NSsuoIHrCs1DZsDdGFAiCQKTQiYyLm2Fr+zS3I3Ugdkc5S1s+DFbe9K1UYgWIxlW9ctM1S7DKZKm0oxSQuQBLPGgvcQkW0S7h+j0VCj/cTmvOidDDtHx0M0UwpB7QCNAva7CdEclYGis91YkX+2dtw2g10L7ZpWobK2JTlrZU73VUbP14sP8OaH+XXhTe7kgQvkP92475UpASrxtO19qCAMSp68oQGes+jSiVs/+rDMMOM/C2iGJ3VaSfv8D/PUoc+slmzikVywedZehW5s2JquWumvDAGyrSK2HOFz1XeRD7sx6wvE21w/dZsLknbjmmr1O0/vPqetkkkWHIrVmUsErwynx7efsUmIR1fXOP8HauDl2OusTW4mRcJAOyaHLzcf2pD/HiyZXmC6JlvPYyqD6yo5swuUfTTXVVnLp8kg6IQo8rBfPbzxWFFMRWwUmaBwdtw2aze7UZe1taAyNUoyc0W+Tmnc75vVPSaDsm68fre4rwmADf7XT1+pQOm8VMampMrzgv1Zzoyr29FJq+Bt2WCtRn1LnadSx8POc/GcIUtaqupqGMXw8olb9PjOc3YrtzZBYHNUsJD42NYum6Qm4+aCLBEgJ4Cbwm1R2dwjMlB7e3R1sHIrzxfy6MKdPajaPl4T4MqDIuvysRt0R4NRhzLq+MD55xdOhYcAVRPcpUIKvHJ1BWPDXnX4GGAZry5YuX4PNzYUwXNqohfqiuKVi5Cla3mmlKwfL3j+6nK36kt2/rDz1TT9wSLdaVorvg1LXl2OF9lrRSsU5I7aimGGBTzjOHrNb8cXBNAUaBtxYKierPtM8drXTwqiU5KdbnJA5hwZaMq2XlSiUkF26xrSeC4dP6B7zp8ZZphhGEALG9VqETctYbYwdJkPuTQzrU28MYFcxjXjhcshHBULlzFtVx/7xbyeq/l2o15urM3WF+9ff6CVwzfx7ZbDG1ImLd3yVg7fyDou5IFVV2PqgPd1/QSxz3oP9YwTVI09deVwoSuDPX2mHKKw2j5nj8gdK1eAqtapxDlkoBwihwyF3ctHryhD1vRMcwQwYZLRckRj+vzhS5xjQKH69sU7hdkH8sBgxIXiE0wSeTqGKZ9s5iEHQUduPsyFZ8bsGchFknwAYesOclEGHVpFx7LfFVqQZMjXQ+oKCoR4IzsNhaw2dEWck3CJcohrMl0Rul4UnpAaGBpOHi5Nx6q7ldc7fFwbuqKtCYo9Q2AuyBIB1jUteKR9++pjun/L+HoS2f7+TPQVg53doLGyk8SYsluOLoBotGpt0R3R9oIdFu9W7iLrYpe00KgD+NKgDEBMK/Oy4wO8b3vJYCRw/jexrTIgnK1WT5y3vVKCvbHQerToAIKaAPckdWg+1JMX7dvn7/HCrAnl7UuRjWdVXtyXDRKcfm2Arli3OW0VAZKXjgqbV30BDUGr4YJqAW0B6ISmBsxmoCnDa5/cYQk9T0ZGGegyTt7Skyo7WAhXrjaLKWK74XpPM8wwQztgTxzUaA6dPniFO+5j13WhGnUSpm8lFSDXaVJ7KW+seQ1yaamdGYYhWDbUn9dZBE2jiDIEfmO20ptnbzmkWRO9ThnXT92iA5K5GJqR6oqOgDl72LkRWWbxXQjD1h/kXFLssU0HCt04pmGyjXzzYY3o49uPFDBbZI016lOX1o8T5h9Z8ghqY95iuRT661/+9wu5+TqyMYgy/verODa5qfrwxhOFff1BSbrRbLAn69JQ9IDpgwBqZUt7ZJEpW93vWibYQnU7O3/32mH/j6YsGqOOLdXrvHYtFdc1dl7WLEdJCJiGMlWzWE4qZ6t66qYOb1++p6OSRMRZS7MWTdTHKMnuvmZDw4xB1LkrynTF4mVNa4ijK8wFWSIgXcbUVK6q4EzHhBq/Q567YDbOQsKCHb1L2JUaglpNRTETHXRciFV1hIskBt2/PlprMwlkbAAQoGJR0KSpUtANJVGuKjSQRLCgDqrTXSGPRDxnpFFDjy2sirGWDO+H7PykCukypWVHKGDt2K0JZo11mNScJ3+xe0/RmYgLOgU8u7QW4u95PVYZ7JIod0zfv0YX1fTURQCui0XK5met4cS2iww2KUlMoMM3yq8z2TcQTpVTuq6iwGXhP/qwzDDjp8ejO8+pn8cMjqJInykNTd7aiyrqeLH5I4F1DsUY6OaFyuSl7jNampxidTriAu1dKXROfRf7GDShQJD0zkWCNtdtdhutJyfLBm/gfaVmsxpq9WbY1zdPFXS/duO949i+Yz9fPWKjohiCyYasHQP1H9MxNHeRiYYJHookWNQjDDlbvqyKeJcHEvUQ+Peff2lU/SlMWWw54ltWHaZ4cID8569/WKsFy3tkUqIJD11aSatiVLJqUQqSTEPqdxNB0OejL9O1Eze5sELhJQOTORSS+HdVNvXy41h5VPzOeVEGXlOE1OSt2/F7u3xV2LsyXGHOputnLGLbUX6t2KMNnd6ePniF3rx4z9THCib4rkbvO6dgriUluiJgLsgSOSQ6xkS0RXsjhkSXti5GOQtkpU/vv9AhKV9DF1R1q0CZc2ZgPvHhndr9fiXHMpwij8XygMS5VgdwqkE3BCXxxpnbaosiLIJYJPaomYCBWohpGkTLhwJjyZjABgEa5qGgY2yLqw6gV2LiBxMSdVb9MtCtk+1tlwzUTHOMjw4TvblTCFdHOSRSX6AoxEbNtvq7TlLkFu0MXAwBePnQTaTJkIptn5eP2EzJCbgQGbigLdVrJzbYJSO30rIxAcnGqMQMM5Ibrp+9S/3qTaeHt55RjnxZaMaOflSiYkFKTlg5eivHfsDhdviabkanb8XH5w+faVaXFYoL+bJq9EnaAAXVgt6ruTFp18iKKtYS10AJAZlhJ/efZb1Wu/HC0EoVIAkArQ/6aod4mVchq8KZCoiIG8+eojn76slrCpgjpmEIl0bRImeNoYG7dbq4XaRCQS6+sC/jT9moA4AebajbBLJ0rRCnEfl7SlEMyk7Rnr3c+RiAhr3c2ekRJmKI7nGWmqPb54pjcW5lzxRJGbJurpa3DTdtlYGMU3m/dWmv3mETbpZfPn5h23oYjCSEW+fv0ZXjN/mcO+sxgd2/QZh5OLcQ0UmGIFxikNjWq8THY0ygUD8aLuiKNknIXVGGuSBLJFR3Kk0tujlSn4leJnl80BZR7V88dpMe331u0GPhcUCNAPat0z2TDJxqF8moY4/UddFGn1RXclHcs1xzwYAiCgu8JkoiIAt89y5XrTf79ddfFbztwPl7jTrpAT0DlAVgYe9Vai+8YUIiZ435jdqc4EQSgZcQc6OzBp65tgBvv81oYbe/auRm5rwbgsJl85P3IGEvvLDfWoMNQ7RB7sLZqf8iH74duGg/RW4zbhFtauAz3mVCE2o3VNBnAhaH0ZSuq406nTXDDDOITkRcooENZ9OrZ++ocOk8NHNnP8pbNK5TXVJHVEAsBcwX06V+izqws6KpsWrkFtZRYY9tr6X5hjqEbYih8zFXKEXqFNRxakutfgcskSUD1vJtaKyhtVaFR7ee0q4lgtbnO7lFHAt9rKf+E4XertkgTwUVECwUsG+KWxahGg2q0oYJAVyUFSyTj57cec7FXZHyBelMuGiao1H7v9/+913+J4qqUL9vuako3J7ff8kmVK8ev+aIHlAoMW2DIQgmcbskExGX1g5sKvb8wQsOsubXqWRpDzMQ+d9l9owydi0O5XMEI4+CZVVPonAds3dFuCJWSJspUPDqSEVWGfRsugARSVdP3uIGLaIYDMHXz3/Sob1n4ngjGBMnD13n3N6sOTNQifJJx11RhrkgSyRkzJKWWnZ3okImSgTPmisjlZdCqMONkEkG3Q6+yKcjL7Hbja5wa2fPvw/zByyeWj1nK1ueJECIelWNGYeM+t2EIQZ45uqKC3CnQR0AFeGEmnBk5H4gYwQBi2cjjWsY0WZsU55KgX4QHXBUY9YYdHSw8pezUTQVVhAvy6JniJq1Rb1OTlSoTD7eaFaP1t6tUR2aDfTgxwONcGHfNZQYqOZekZr0EfbLs3qsottaGsckFeA70aSHCw2Y14a7f1FBJ2i49wJ699qwyAozzDBDIHh9DI1suZAvvCrYlqCp2/uwwU5ywp3LD2hm91V8u0lvN6oh6Z1NCRRPQVI8Su+F7SlNev1dHD+8/UjLh2zg282HNKDs+bJo9Xu7l+1nYy9QDFsOb6T2fjLFv5JTue8mb3BMRG5n1jyZFY7LcDzeI+myOk5txdMzOXcMOWTy/+UrmYeLNhRVQIqU36ZjMjLmyMDHKQP29oBsTe/U0k7BdnFtV5OfG9b3gExNhCkHjL/gGl2oTP44mjgUVFVcK3wXyvz181c29QIa9BDXP+rMPG6eu8vXNY7NE552oYAN8xespNptRSNdF4RtFNRIRCZpo1XThGNhF/j8I8fTwlJ/Ixl1kBlqyB7TJgcvsZH0jsgMvVGrkRBAhm+LNXjakyN/Vqpgb8G3QyVxrS7IWTA7VXIqE4ebnBAwtodxhTZTMjgZgVqAjkqwRA2ID1ANXNo4aAyTRqBi7fbCUWjDRPWuiPoAr0fWsoFeoW5KJrLGRDdy4+SgBLPGGvZ04w4cCrgtEs1CG6CDhUBOeUNIqOhNCCie+y3xZfpo5NajFBOUOGYVbYZ78mcTblfjWsxnLVtyQy2vqjR2fVfuqp47fI361p1uDpA2wwwDgPV19cQgmtN/A1/sOuI7tq4rpTGxPbyx8eHtJxrXcgGvbxXsLKjNCMNMNbQBmBkzOy/n6wbXNnZkqZSHpQ/WjQ/gCRFMoBppmV+GaZXfKNEobDOmsVp9FAwtwtaJgie+hT5eh7yPQzuGvRXYNCVQFHDO5ThrbMv0HUwvrFCzNE+30KTMXiArHQ8RGnx2UvyF4hRnMu5feRj3OT98oRwFsircj8vYWNCpsHO8L9bxdaKNUwKZtmldrzIVKJWPdYF7lovCql7nbwYhMBwJWSUmb179ROanMiI2HWYmCnRq1T0sE7Sut21YVSvr+iO7TvLrz5o7U5wcNG2/c7L5mxyZZAgiAsU1BPTWxi6Y8H4fOXAxjoQoqcFckP1EqF6nAgtB711/QjfOGW7lLrs5hfkf0kvr4tZOZLyErotO0LBC8TsdaioWH03mHpg0yI5N6BqpOz5Zc4UA6PtX4y6kMuDAhMXz5P5zdOvcHTImGvZ2Z4rDzbN3KGqLeidFBFpz1ti7T7QmgekVNhmfyS349uZpO7jbpy3K2VlQzabVeYOY132lwQYfMPdo3FdQK+b2XMXTMlMDheXglZ246/rgxhOa1in5hEYrA+YCM4L6UtbcGen+jSfUx306XYi98aMPywwzkh3QmJvabTVtmiflS/V1o35zWxtsv53YwDo2o8sKun/tMWXNk4nXOax3pobf6K304PpjvijvOLm5QY8FQ4zt88T0qcvM1nHMNjRh3fhtzHaBaYa7JF9QhRVDNvD+VaNBFdaJK2O75LoIxombZP719O4zClktmrYth3txw3PvCqErbzygPhdrQLY8Wbi5pwh+lnra8a9dUIBhTwd+TyE+X0UrFVZMvMLWi2IRzWDoyGVpQYthjRTujzhG0BltGn4LPYYLMgpKUChV6e3k7LS6nZzUfiZQ1B3wP6STdf0+aZrn1NJWq6wyZZyLvkLP7r9kbbeV5K6tLz68+0Sx+8/H8UQwJs7G3qT3bz9TpqxpyaJiAUqKMBdkPxHQCbRyLmM0cw/Qw7DwgLJ4/pDudunW7hUpQ7Z03Ck7lkD4sbK9O5t7vPtEERoKGAAOTDg+2MyeClNtloIcD4QzovO3ZYbqYGEs3nBFBGQLXGMhfeZ05NW3Ht/2G7VJbWGqnDUG0w05WFIdQMfE4g9+O6iLuqDj1BZ83iDiNdTgA2g5zJMKlMpDr5++TTTqYsas6WnE2m68IR4NPkMbpmo/KUxKKFQqD83ZM5CKlcvP1sGDG881ynfXDDP+K3j59A0N8prDOZygAfed04paDaib5BzUtMGmGbvZSAuF5PA1XQ2mgGlLVdw+P4Rv917YQe1kShtgn53XazUXJzXqV6GqtbWLFwCtD3lgAPZBdQXHmciLHFiNBmqHiXELRxRzG6dsV9AQ5UJw4+RADmPGNAyBzNvn7OYCvkSVIvTk9jOmN2bIlp4uHLrC9//0/pPiMcvalaI/v/zFUynljxMCowH8H96jcwcv8d8r1irLpiQ4PoROw6ADe36FWmWoZNVi9PfffysKQNjs//6HOEY0RndIIc4NutX+7rN75fgNNhThTDGJ0aMugBtmHpASaGPIgmbuidBzcdyxdcG+9YI9ZedZlYcBhiBm92l+X/IVzUFF1OjjDIGcA1zNsbTOhWdiIWkelRl6Q85tiAg8YfD0A45O9lImmfzF0wVYPJwkDvNeSTSaELAQ1ekgFhwsUJqol6B7ga8N7FqimpIINB0gzCdC10Sy05IqNOrtruhevX6mmTKoz5QMnPj7Vx8pnJfUFaPgjmMBT2hKhvPUeXprxTFfOXZd6+PJkisTtR3ThG+jmEuIIpkQsPH1X9qRNyFkmRzcnjgFRbGKBanHLHEO1k0KoqPBIgcluQH6lqnbe1O12uV5o0en32/yzmQ59TPDjMTEjfP3qLfbNLp84halzZiaxvt3J+cmIrYluSF231laM0FcrHed3pJKWhZJVKoiolGquBo25di/7iDbucPIo8sM0WDUBtCboWgCZa6KmiIO6+GyQev4truvIzdblbFhwjZhaV++ANWSrjugHw9eKZqOLUc2prcv39GOhaL49OpXj/wnBSiasgAe86+v35qmLx68UBQu8qXIr7//yoVDqnQp+e+gIb59/o7yFs9FV48LhoNTKztKkzG1IuO0mRQEfSjoODsqo+iFfl3G8eDTXJTi32upyPGCmYfciFUXrI3zIxudwIlam4YErsswbQRzBvRSXcPDo4NEFqxrK8Oz8Q5IJl2g8xu7mYLP1iEpcsrGNWnSFQFzQZYEIBcdHz98ocUTdxqk/7KsVZo3ppdP3tD5I9pfpKuD7LbImWTxEuq1gSwSjd17midlWv1Ou5rcbUGGycUjmidzdSVK4qEdx1lTpQpwJLKwLsYcYnWFW6lqJdh9CfcJXaPZfl5XwEkR3TJg1XB/5sqrQ7vxTRW2tQjH1ATkszi2EF2tRf3W6PS5gcFH0QoFmaKxcphuEzZ1x9JE4r3P67UqUVwXAZcWNlTXR9Bcp/guo3vXHlFyBJofw1f4kFdX8XneOCeYxndYrpNpixlm/JeAjno/j5n07OErylskB83ePYDK10jY4jspAusWQu+xhtdp50BubdRbmhvbVVGmKnaaYhhVEXS5pYOFkUeLoZ48VdIGZ6MuUtTWI0zvk1kiqhCx8RBdOXaDG7HIAVPG03vPFYWW75RWCv3R6pEbFVqx8valadPkQC4kCpXNT/evPKJn915Q5lyZ6MZpsdfiXChDDnlWhsxy+fTuM0/Nrku/i8BoSCPkAixkZTgXvMg8g/kIsG2WYHLU6+LCbskydkhZba7tHChVGlHoycD1QrgUBaScVxYfmHTheGEkVlMLPRdeR4jUKHeXpCK6IDIglhk6+UvmphJqsuK0xfNHr+lszDWThUGfOXqD3r7+yOZ65aoY3yzEWDAXZD8IXz5/s7pGNwDdjdRpUtC9m88oNkLkJOgDTKVs3Cvy7QgjUJ9KWRWlXAWzcTEWo0cmGezfLaoWZQqD7OSTECBEBR0RkMf46oCFFdQ9PL4cmKgKnpK5Bsw91NmMy4HTe5fvN3rYsUc3V3ZwQqGybqz66VexSoWplrd47csGrdcqXwzmJXCJxKamLUAJ6T63rYLmcOHwVTIULYY2EK6Lz97RvJ6rEyUwGug0yZvKVC/O3dEx3vN4w02OwEVEhxGe1G9Oa/rtj9/ocPAZ6ltvhsExFmaY8TMBHf0Ns/bSeJ9lfEFYyd6CZu3uT3kKq7ZITw4mHmObz+f1q5R1Ueoy1bDCSFucPXiJAheIIqbPYh+DqIrA6lFb6M2zt7zna2vkARbPor5+fBuaL+znqoCiapUU9Nx0UH3WXykDNve4Dww7LF3ElA8ZpbJ+y2dKKy7ats/bKx5jYH3aNFVMI6FD+/uvf7jQw3VEfOodmp6dZrThNZnxL1FOyY4/b/HcvN9gb8eEix0S3SpS7qI5acdCcT0CYy9c4106eo33adBq63f/ZnWP3wPNEajb6fuCa59fBH/OoS0rXb2E2nMpT8fgVh2/qFOFo3tO0YtHr1hWAj2erpAjkdAUNXSiBTOPf//9l8pYFeHMQGMjau85hbtiYmgy9YW5IPsBCAs6SWeOfBPvHwm/RCFbJXeZOuUoRhqt6gs5vyF69ymDM47wRXP0Ft2W/Ru0K6jiw0Xq9gWvitD6It2ji1iYorYdTXCy5iXRDbEgqZsoIAsENrgIq46UXIHio6a3DTsqgVKArp0xAa5419nt+DYKR1Ap1AH8dxTWJ8POKRZqdUBeTBOJkrl88PrvMlM0oZR1cXKVctjm9ViltfGKRuriso684EUHHqOIzaYPjAZwrob5dWERPMTwU3yXGkzX/ZFwamJFUwN6U6bs6en2pYfUq/ZUOnVQ/yaNGWb8LIC2eEH/LbR+uogH8ehgT2PXdaG0GVJTcgQasTAlunf1EU+pEP6cGEYkOI8zOi7j227tHQx2VYRjr1wQ9JjXTuvXEOoXSddP3WZNc9ux6nPPdi8N5Xw0TLNkeYGMx7efKmiJrUcLKj6wfPA6vt5waFaDSlgWYQ03LOrL2ZfiIGQ0mWGkJeu/YCKGYgx0RGVAEoAIAFk3BshsHPnaBLKEfauFQ6Jnzzp0dNdJPl4YhMj0STmUumZzG5YNyNg6azcfJwrJvMVyxXlu7GPb5+5VxOOoK3ye3ntBR/ecjMMaSghyRqxLSxE3pGv22OXjN1mm4Ngsbii3PoAzuKmmYyjUD4WJa2o7N8M+56aGuSD7AXjz6gNF7T2r+Hv2XBlpx3pRJBQumZse3DasI17GuihlyZmB3r/5RCcjxGJjCBybii8cMsmeqaEFaoJDY2vuPuFi+UyUdsdTrGIhKmVdjIuEfX6a9WdWdStT7iI5eLS/f+1BtWHV8rhfHW0RFIJa3mLxTCgPTB9gwYW4F3xmObhSFRCGKYdaL+6/hm1yNaFx/3pcmGED2DJDN3OLDhOacVYbXCC3awjZ1hagQTYfLArE+X382E0qMQBe/aj1PXhDjQ05S2vGC3F3coVF5UI0Z69k9vHqAw1vNp+2LTL+5NYMM5IL2Im07gw6GX6FpxV9ZrWkLuObJOmOd0KAZuzIntNsTjRiXbdEy0tbNsSf9ws41fpOMmwih6JhbreVvDaBKgcttDbAZGnFMH++3XJEI7UGJsg0WzdOWNm3GtGI6d3KAC0Re2olp7JUzk489/noS3Q85AxPo9qNa0Z3Lt3n4k88l5ciqBnxPLB8/yOVsMdXXl7xu/JzBc4XRRGQKUcGLs4wCcRrADXz7z//4iIXOjLo4IIWiPu7dXDkYGpMog5KDJaGPd3jGJHIunLs4/FxeMdxfp+gQYcuTR0Q/YPJMc49jishoIA7vk9Mjdza605XDF0vmvNVXMqp1bRpiztXHtLNCw/4fNvWq0TGxukjN/haGO6KpSsXpKQMc0H2A+DgXoHu3fo2IUmTLiVfdM0bHUiLJuwgB3fDhLVwkLHzELah4dsNz4ZCgVC2RnFecPWZkolCR0zZdi/T3tUPwl1gz4pwphJoer0QscoLp7qLVrf2tXjzvnj4KtMZVAGdLnShYrbHcpFibLSROnhYQNHZU4cWwxtShqzp6O6lB7RLCoNUB9ATfKe0UDhK6WKDj03QR9qQ14zZqtPvagqMhh3++1cfaFZXIRhPLJOP3vMEDXPTzN0UsVV9GHdyAMIxpwX2Ieem1rzZrhwfRIsGB3DgrRlm/JdwaO8Z6uU2le5ff0KZsqejqQG9yMUInfkfichtsbRxunD+7T2vncE6HG0Bx2PsqUC/ZR0NCoCWY2cQRgztUqepLbX+vQ0TA+j10zdspNFAicIXH4h2QZwK7ucmGX7JwB59QDIcaz9R7IGyhT7g2rYm5S6Sk+mJ2IdAzbt+8hYXUqAVHtklro++SswSTNBSpxfTVhR50IChyXfzzB3FdErWR//yP/H3Oj5OFChRIZErBp0bInQwPYJWDIC5B5rLpaqXoKIVC8WxsgerpWjFgiqt7gOkxjDie+IXojLwuMHSdE5rq/s1kXw+9DHzwPOFbRLW+i4thLzCEERsF8YglrVKcXPY2IgOkcOgyyRZd0UZSfvoflJkzpaOMmRKQxuXhNOxqCu0fNoeatPbharYl6Ca7uWpXotq9OGdYWJ+efR7JOQs5zsYClfJEjVk7UG93N/cpYUC5hugDWoDOArhC4oi4WwCujosvJjCoYBBKKMqcO6HFDytLigajkl2jYVL1/oJxg2KlkMj0cnDorZu7Fa190uXKa2ieFs/bht33zTBoWl1hQ0+HKt0gWtbeypTowRTOBb0EXx+Q4BpJKiLv6f4nY6FnKW9UthlYqBWk2rk1bM2357ZbSVdOaHZGCWpA/pATAK6TW7KHcRj+y5yiPTdq5pjEcww42cA1skV47bTuPZL6eO7z1SqSmEatd6HSlRM2p3uhHDt9B1enwCsVzILxdRAtAZcFYEG3VypgpbTLHXA5GfliE2KkOb42i51gG5KpuLByEMdZQ7UwG2zBNUPNvfxp6Erhq7nwsK+STWmJQIXj1ylE/vOcEHUdHADlgfIRVujvnVp6yxRBMPqHrb1yvRBrLfKtvcy5Q3A8+Ax//3nXzYKu3PxPq/JqTOkYgdlTLFc29ekdeOERhwTLUzg/vz6J1MuAY8u34Kgv37+SoFS3EDjfh7f0RFh6nUu6hK/ZjlzVRVig09zuDUauNpowTDRDPETxmVu7YRkQRecirhILx+/Ya1/VQNdOXFOIyRXZgfJ0duYQFF9WKIr2tYuS0kd5oLsB6HjYHf+os8cupULNPs65al46TyUNkMqGtp+BXX2mG3QZKFouXyUr1hO5kPDjcpQ2NS3VGSSIQxQVxQpV4BKVinCX5BQSWibELA4OrcWY/oIf816JHTnnFvbx3EsUoV6Em2RLfDV2L03HyoCHKO3HaFHt753WTIUbcY2U4h1NTkpuvnUYgoEOnIJURGxmHeZ0Zr/hCOTnKmirZlErwUdeOE/vPMEHd4lOlaGoGCpvNR2tHDCWjJwPT28afzzqA7tRntxSCU++2Oaz6XniUSbNBXwntZtY0eTt/aijNnScTHWy22KOa/MjJ8acApGLt/WhYIh4NmxFk3a0pMyZJWCe5MpXjx+zesSmmdVnMvyepVYWNDbj3VP2Ffaj/umt9IXi/uvZTOSEpaFFYwWbbBkwFq+/oH7YNU6woRMFVYO8+fzVLpGCapeX2jjZZzcf5Zi95zifavtOG/+N1wzrRomGpIure0pV6EctG7cVi7s0Qi9EH2FXj56xbTDS4ev8tr6+vk3R2BMvVBwYRqHgo0f859/KWN26bZ0TSYHQyO/VM5PazKgPj2+9ZSPCY6R3kOEs/L+tVFcWEL/Zuv1LZIhYtNhnhBCbmDnJXJQlREkGa7I+nd1kBk0zq3stArhPh1+gSmLcOOuEe+caoNQiSXl4GVlsN7x8snb9PjuC0qZ+g+ycjZ+wXQm9ga9e/OJ3RWTOl0RMBdkPwh5C2WjBq1qkH/0MOoyTGhuBrdbToFrYqh24yqUM08mnp7pCyw0tRqJjsOBrUIwaXAmmZQqHyK56+iKOpK1KiYm2k7Z6kghiKfDLqq1tY9fbIF3jbBHVYCgt7hlYb5YlxfS+GCbWudyTBMLkqgIxgTMNGp61+DFfWHvVWoLb0ya2o8XGw0CJhMyN4FDo2zSAecqXSaZBUrlJa8+gtu+sI+fUezWPXvUprI2YvI23SfxjDZASxi4rCMVLJWHO3ljvOcy9SS5w8KyEI3296HyNsXp88evnFc2b6D/dyJ0M8xI7jgVdZm6O0/i6BYwH4Yt86GOYxrxRCI5A8XF2Obz6PmDV5SveC4avKJTotGoIjYf5pxITHkGLO/EDU9DcHzfGYrE4/3vF+q1wEfr1wGzqpjAY3wcXWe1UWtUgWzN0LVikiM3G1VlkoEWKJthoEhDwYFCodWoxvTg+iNufAINe9cl/8lCW5yrcA7+E26Jf//5zcwKexWcHifvG0HvX71X/Pvrp2/5+VGc4frg2F7R5Ibz4YNrj9i8w6OrC22eFsT/jsILx4RCcKMUBN24Xz1FwYQ9P1ByhcbvYa+PP8k84C+us2Q5hircv/aIjgWf4WPTtiCGqzJQs2l1nT8DoHoe3n2KbztJMhRDIE/HkMGZMrVhn0dViA4WdMXqTkk3DFoZSf8If2KsmhWiSA9PkfJ3/tBUqlGcnWBqelSkiN1njEJbPHvoGj29r7sZR3zI4X8xO07oZS1u72VFqdOl5KyMs1qae+S3yMN0OmjI9izXrD8rWDqfsMD/51/avUwEMsYHFq5mAxsoOlDqqICNeotMrb0rDrCo2NjwmdySF0M4PEVuFnxsVbBpWJVKWhXljWLNGM1h0UC78c1Yswceu2z5qy2aD21AOQpkpSd3ntOGCYabYmAB7L+8E7/nsNXfOlNQTxID0EWM3tiLMmRJy/Sg6Z1X/BRBy3g94zZ0o+Z9hZXynrXR1KfedNbWmGFGcgcuYFdP2kHDms2nV8/eUcGSuWlu8ECyqat+ipJcgPVnZldBowYVf/TGnszsSAygmTmvt6Cjew/yYLaKIQDdbkEv8XgNetRmDZS27++iPqv5Nmh4aASqAgqWJQNFweXU0pZKVCka5/+jA44yuwR7nZxJhvO7YugGRSZY9vzZaO2YLXztgCncsb2neJqHIgrFHvA4HgMG5mBTQkfS7fP32Aof+PU3cZmcMm2Kb1O0f/8lm0ZWCsfmhr3cWecWIWmrmkrXGIiieXj9MdMZoQOTAR37tRM3WZ8WXxcHhKwO50YbQq41Wd3vWCSokFXdKmilBQPb5tCOE3EyYnVB1PZjfFz5S+RizbYhwGchKuhkHGdwYwKPf2h/8qErAuaC7AciX6FsdOrQt/BmK4eSdPnMXb5t61qWjT4MAfIcylYrJni6gYabe0B0jBBAdPmi9Hi8lGlSstYJCJYCCbUBOkjAnhUHErTx9+gsiWhXHFBwv+OjeoMqlKdYLnZlhLhVFSxdyzNtAQXbgQ36TQQ1IXu+rNR0UAOFXT145qqAi+6OU1oqhMHqzEhkgMPvPdSTby8bvF6nwhnmIOhYAltm7aabZ8Vn0RDkLJCNOk8XYZ9rxm3jsO/EQs4CWWnE+u7cVUew+dqJolOZ3IFCt9WAulyYgcd/8/x96u4ymfZtPGx2YTQj2eLZg1c0sOFs2jQ3hD/HtVvUoFm7B3Do88+AdZOCOEwXFLvha7tRnkR6XShUZnRaxiZLMFtqPri+wY8ZNHc/Pbr1lKl0rUdqT7mEnOD2hfs8UcIESx2O7DrJ+ikULGgyfle0S5lkjfrUpQxZBZ0w3D+GixwUadgDUXTBsh6o4+vEBhpydhjnjqVLya6KytMp76GNeLoG5sq35/uH0mRMzUHQKKBvnL7NU0Fr98p069xdnuDW716bdiwI5uIP1EiwVfAZ3jJjhyKLLFXaVN/REeHqLB+/8vu1S9K443pG3QQRNv2gQyY0RVMGtGO4LipeuTA7IusKeAgAzkbIHjsReYleP3/HexiyBI2Ns8ducRh0+kxpqKxl0qcrAuaC7AfCrk45unL2Hr17LS6az5+4TUVL5eGiI12GVNS6lwt91iFXStOULNwIIdH4AjpLocWhehYptduIrkx04HF6p2XBWd2jMmXKmYFpA1FbNGvJanhWZa42eOJyt0rVBW3DXsLVCcJiVZMT6KrcOzorgqJNAeSH4VghOg7RYHxR1taCDU4w+QOdMCFtIXJaoBGAeYo2UzVlVKtbmY1PsLHM6rLMKDRDl1a2VMPDkvWDU9ov4u5qYqFMteLUc44oMv2n7aL9WoaTJwdUdrCgRQeGUgXbEtwkmdVnHU3puoqDZs0wIznh4M6T1NVpIl08hgvqlDR4cXvqNb25SWhMPwJhmw7ThqlCB9xrTmsqb1sy0Z47aGEonQw7z4yMgSs7f0eP0xW3L9yjPYsFW6Xb7DZcAGkDaLb9Rm3m2x0mePOFuCqg4Fo+ZD3fbtirDjcvlQEKIrJCYWIBkw4ADc2Vknas2WBPLnIW9/NTmGucCDnDe1r5mqUpVsrrQoGFuAHlWJkHVx/S5FZzmYaoDNnhUDYtsW9anWIChRQE2nUYWMlZaA16iFDs89GXFVMwOcYGeP7wJR3cdiROs1kZJ/adZRYRdGq1WghWkioc2n6CjW5Ajazk+L1DY3zgOgdNbaBuR+31fjJuX7zP2WNoKDg3N9xdUZbS1PS0NAkV+WCwMHer7lgq2URjmAuyH4gs2dNTiXL5aOH4HTSi4yra5X+EqtqXUPCMi5fJSykN5HmD6oHMFgTM3rr4wOBjrtXUmrtDF49cpwc3dKdJFa9ciAqVycf2suFqCqb4wAbi2FosAIFSZ0kd0N2CdgnYOnOn2uLFuZU9pc2YhukERyVO9Hf3wUL7x2907eQtunL8W5C3sYB8kmbSlGzDhG0aC5WOU1vywn428iLTIDQBx9xtjgihDpwfTLfO6zbp6jarDW8GoD1qMkjRpZDvOb8dC6nvXHxAK0eITTmx4NLChpr2Ffq42T1W07kY/bWZSQ3ILZqwsTu1G1qfNRmRgSeoq+NEpimbYUZSBxyAZ/RaQxM7rqD3rz9S8QoFaH7oELKvL2JbfgacP3yVZncXE5cmvd04iDexgIvoFcOFC6LvJG/KXyK3QY+Hi3pkjmFqVK1eZapRX3tnvFXDNzJjA1O62ipoejKCV4azWzJofvL+KOPLpy9MQwRgmpFGsqjH78CNGQ3Ohr3rsJYMBRH2TK++dZkCCKTPlJYpd7IpR/w4Hei9ju4+qaApAigWkKcJyiI0W4BjCzs6slNQ/+p3d2MWDRrM0KTJJiXbZokC3Lm1Q5wpWMDsPdychFMjJmnxAb24HNMD1ooq4LombI1oLtbr7MQN5IRwYv95zjSDmQfkI/pOx2CYpS4zTpfv/eFgkcVby0t4ExgTOL8xkhwoqYdBK8NckP1gdBlWj1waWVIZy0I0aWUH/lN58QuVMhr0RbqMqalKrdJ8O1wSUBoCWMRWlroxstuOrhfnMncZWSjaOkk6eFtzFwp5JwkVR5zZkSYFU+4g8FUF0Axk7vaOhaqLPGwIsivSVol6YGzgWEH7gAmJnGWiCjkKZKOmA+srnKfU0TGVQ6hhgYsNBwYfujh2ZsmdiXwmCjOR1SM3qzVI0QVYwPsu9uHb2+eH0In9qqMJTIU2IzzZKRQL9dgW8+n+9Z/HNh6bcZMeLjQ9qC/TNKEXHew1h+3CE6L4mmHGjwKmYd2dJtH+zUe5ydesV22asaMf5SoQdyKSnIF1Zoz3PF6va9SrTG1HCQffxAC++5PbLuTmZxXXcnpNReID2uyLR67x/tp1Zmutfw/7NoomoOvstmoNFlCwrZamaC2GeX6nsUOxgv0oW74siowvNDLR0AS8B3vSHyn/YGdFACwXaKlRhEFjHhMkroGgJQNQWKqanijT8X5PKRrkxSoWZlMPyBkQrYM9FeZfeYrlVBRfsLX/9ddfOWP0UNBxRbap8utDbhvQTNrP4+eqwfQE3wfPnuqz2S7EXKH7Vx5TitQp2F1RG8gafKcWtmozzdQBn98Dm4RezrW14Q2FQ3tO83uSr2gOKlY+PxkbZ47eYLpihsxpqFzVb9fUSR3mguwHA92XitWKUtOODpS7QFbFhTNnXvzvf7Rv23E6HHbRoOeo2UgIJiO3HzeKsYGTRFvEF1Sfx3NqYcP0iVvn79GFQ1e1+p30WdKSXSMrjRliyhlesgW+nHWizpURCy/coh7eUH2BDitbAOLdOxfvkbGBzaOd5KSI3LPXz9RntMGlCVMm0Bl2L0uYRsn5Lil+p1Nh5xWbg7ZAwGSpasWZp76wzxoyBqrWrkD1OomLgum+S1gEnVjAd2nAEh8qaVmYO5kjvGbTmxeJ9/yJAYvKhWjB/iFUu3l1Xj9gF44w3ZsX7v/oQzPDDAVwIYZmwYAGM9nyOnvezDQloDe1GVwv2bsoKgPry8jGs3m9KV6pEA1Y6qPVJMNY8Bu9lW6du8fUvr6LfQ3W/GBKtGKY0G55DazDRZE2wFq0oJdwE3ZsbkNlaqina66f8C0sGsYccZ7/0SvaKLkk+kxqwXsnsHPRPjYtwfHU6ehEp8PP83QMe59zGwcKktwM//frL9ygRMNRBthDoEjKEzMABRoad3LwM6iNsMAHVROo2cyGdkl6NJh/4doAEz0wbtx8xP62c6HQQVZ2KU/5S+ZRPDb2bejSYWZSxa3Cd68/YM4ehZ0+mrDqsFsqrmo2rcbPq817d2TPqThu17rgaPAZevPiPWXOmYEstaBHJoQD244ppmOGfi5VIXKvmL7ZuJRJNnRFwFyQJSGguJE/nFgQAFevKnTkgHaOhOpQ1bEMT4SePnhFF2NvGnyc1dwrUtoMqTnL4lS47sUiFpCazYS5x86lqt0QVcFd6vDBvvdtAhfTnj1Ed+nIrhP0QM00BNa36HYB6qh5RcoXZF0aFtd1440fFC1z3ItWLMRduzVSd1AV8B62GtlYERadkPsjXh/oGsDi/mt00m4pZ5Ohq4h8MmMAkzcYw8COflaX5YlqQoEmwCj/HpQjf1YWpI9uJrKAfiakTpuSes1oQaNWd2JHRlCVe9aeQutn7E5wqmqGGabGlVO32YAGzQJoYh0bW9HCsKFUxiqui15yh8hAnEcPbz7l9WbMpp46TyUMwemIC7RtjmhG9lnko3VgsyYs6OMnMseqFCEnSUKgDcLWR7OrIKZqPpNbqL0f6IDb54qCpPOM1t9lXMHIA27DJa2KUU1voa1CiLNcpLUc0Zh/Z+1YQWms4+NIISsP8HuBAgiMGRRp0JfL+OvrX3zNpeziiAINEypMw1CwARUcytC7l++56Lt09BrvG6Wql+AJmTyNg8EIrm0+vPlAe5aL65r63YR8AsD6KzeIsS/HL85fPXmtMBBrJEXQqMKb528pOiBW0TjVBiFro7gYLV29OBWw+FYgaot9kmOzU7PqBhc4zx+9pjPRV+N4HBgTf379iw5L7orJia4ImAuyHwxckKIQw4j1wzuRlfTkwSsFh7lUpQJ0+9pjgy9Ea9QpbzRzD/CyazWtxreD1wiXH11RTyquorfHshWrNrCwKkpFyhfgBTahcGl02KrUrsDnV3ZXUoUG3cWCuWdZGL1Tyh1RhmyrG7X5kEmCorEwd54hjCd2Lw2l66c1hEV3qKkIi94wISDBx/Ye3IA7ggis3DRNN9oltH5yNtn8XqsVNA9DgIuSwau78sZ5eNdJpq0mJjJlz0DjtvbmjfNS7A2akoj5aIkJa9dytDhiOOe7gJazbvoe6l1nKt04b/wprxlmJARcwK6aEER9606ne9ceU6bs6blp0H9ua46o+JmA/Xx65+Wss06TIRWN3dKL153EApqV0zos4b3Prb0DVatbyeDHRFMOezV0qr0WtOc/tQEoenJemPcQT40Bx8sHredGNPbtqm5xYw7uXn5A+yQdGPZKuXG9aUoQ74W5i+Yklzb23ICFOyMKrxoNrWjXEsGmAbUPgDlY/B5g3c4uNCZwIOUr+U1f96tkfIKCDbTJK8evK1wRUeQByAg9HHSM7l1+wDEGnr2EmceOhfvYwRmRPVbu3879wa1HREB0zoxUq7mNSjooigkL62KcVaopRwz3K1gmL7slavN5DJZMw9za19QryPz4fqHHcm6p3mREW8DxG5/N0lWLsBO4sXHq0HV6//YzZcqaLlmEQSvDXJD9YGBhAdd11cxgmtBrHXlVHcMmH0Pbr6AOrtNpx7pDdO38A4oJVa2F0ha1GgnhZNSOE0bRlbi2EjxihATqQ/2C5SoWEyzAoZJYNCEohx9i8UpoulJfcjbatzpCbTAwFv/C5fIzNU+dlqxohUKKoOhADRRIQ1DeoTTZN6nGz7FYg+YLBiedp7dW0BvuX32o8XExVZPv7z8pUC01Ux1aDm9IuYvmYMrDpomCJ28oUFS3G9uEby8ZuJ4328QEhO2jNnTnovDQrpO0ZIj/T2kXnzFrOhqx0pcGLWrH1r83LzxgCiNynn62yaAZSRcwmOnmNIk2z9/H6xsyhxaHD+Omwc+IFSO3cl4T6Jcj13WnAkqUNVMD69jsrivp+cNXlLdYTuo8Vf1ESlugqEJDDmjcty4HI2sLv9Gb6eXj19xElNkaqoA8ThR9mEyBah8fq0f482enmoelIpfr6d1nClt53ykt+bUvHbBG4c64aUogT7uwt149foOvH57eec7PIQNTrs4z2/D+du+ytJf+Qqy7k/VrYK+goZkxewZeN1EMwZCjnH0p2jRVRKl4dK3NBiO4zgiYvUthOqI8BdsuacShfZON22TA6VEuHht0V68dw+uRJRuObbSbUp6OuMjZong9tp66T6TCNx/h6ZpF1SKUTwrgNgTyUMAU0zHgYIjQp9vWLpMswqCVkbyO9idFxsxpqd+kxtSscy2atbELNWpnSzXrVaTuoxpQoeI5qbGPHeUtZJjQuVyN4pQlV0Z6/+YTxYaKbochKFIuPxWrUIALqrCNQuypK9x9RLdmz8pwrbVotbxrcJFx/+ojtYYdMixrV2DXI3D4IzaqdnTEIi2HOIJO8OnDZ41B0bC2NUVQNNBxWmvu7J2JuMCiYXVA1w0dRJz7RX0T1neh0KvkWJY3mfk9BJdfl+lq38Ud+Xb4hiN0JtIwPaMMzx6ubNWLDW5y20WJbj5RtkYJ1pQBO5aE0bZ5mt07kyvw+XZoYEmLI4dTjToVeFqGnCc4MZ6O/nncJs1Ienj/5iPN6b+BBjWaTQ9uPmVH0OErfGnQwnZqLc+TO7Yv3Efb5gnNUp8F7am8nfHzlTRh78pwitlxnIvBwX7dOPvTUKwY5s8FC4KH0aDTFjCoCJovzkX3ue2/K0JkcCElTdFgtBU/LBomXge3HeW1TNZb83EN3cBsGRRGNRpUZS0ZrgtQOFWoWZpO7DvDTbdUUqCzrE+UtWHAn1++MlVwmPtEpQMSf3x690nxOoDG/T0oZJWY0sH98WzURXYixp5dX5JIBK84wBM7XHfUbPatYALN8XLsdT4e5YBoGTHbj/E5hkbcVtLKqwKkAyiuoAusVl+7yac8HavVrJrOtFm8N7J5mzGs7u9cecS5mWCA2dYzftj7V9AVJc8F29rJr+FjLsiSCPIVzk4VrIvwn+WqFibXRpZUsXpR1pC161ubChQ1LEQSnQJHaUoWtvWoUY7ZVXL3gR2qPhMGh8bWLKaFScWZSO10csg8cZJsg7dLG5+m1wzjDiBoYbDaY7RvbM16K5hM7FdDweSg6JJ5WJAbqiZM2lAgb0VerJHDoqlI7TKzNW8wsXtPsU2vJmAj6zG/PW8Gx0JO66wHK2dnoeCqz+m6wijTFXQO+y/tyGYtCItelchW+IBdw6rkO15M6paP2EzhCcQJJGdkypaehi33oeHLfSlLzgz08NYzGtJ4LluOQ6xthhnGAtbZA9tiqaPtWApeLy7m6rSyoaVRI7gp8LMiclssLRkiTC/aj/YiR4nWn1i4c+kBLR4g8rvAQChW0XC61tmDl2iXpPPuvciHG3Tafgbm91rFUy3bhlZU2Vn9xTF0U5ePXmeNWevRTb57HHnq5djSlgqVEY58t87dUeitQGEEw0XWcrUd21RhjoHmpRxrg8kWwqDRlJJx7cQtmt1pCcffKNMwQYHEsecomI21Y/z3v/7m5ylUNj9VrVOJAmbv5vu6tHFgSiqmV4jakc3AlLVWsoOyQ7MaKumruD6RA6zja+eUIcf+oHCFdCQhIIs0RjL0qt3WgXTFtVO3+XOFvDY7T8Pt6SO2i2OxrFXaJE2ZUzHX6MO7zxwpVaqi8d0bTQ1zQZaEoFww4Lay46IxIOc9HAu7YJSLMAcvK14U8IW9evK2zr+P7l0tydxD7uJogwZSKv3RPae4mNOE2u1q8iZy/dRttRM1LJyKoOh56oOiYWkr67xMRXFrPqwRF50IlIzYqD5WIG/x3EzLANBdxGagCbi/HKK5uJ9uBh9AhwnNKFOO9Hy+141PWLumDaBt67fEl28HzAum2ODTlNho2N2V6ncWRfCMzivo5AHDqMFJGSjMa7hXoCWRI6huWzv+OyzHfWzG0G6/gz+lls6MxMXdq485cmFadz969ewd5S2Sg6YG9KYeU71/Oq2YMs5EXWLdGODR0ZEa91ZPOzMFsJ5Par2Am2WVncpSw561jfKYs6XXBO1yeftSWv9u+MZDrOXC3guDDnVAgbNs8HqFxiy++Qh0a8jexHVGu3HN4lAhAbvG1TjLC9MxFE6gRqJgOhZ8mgusJ3ef8V4NVg0/37vPFN/U70To2e8yyUBRBL58+KKYjmHfBxr2cmdb+8M7RHEha8dwrE/uPOPpFfRsMpCPFrnlSBzNujJunLlN5w5e5uOtq2J6JgOmJGejLvH93H21M/MI9otkJo1F1aIsE9EVe/1Eg9rGw5LzywwBrqtkuiJoy6akK9ZwKZ2ojqbGQvI74p8YyvafuC3/3Vi2oAVK5KKiZfNxhwhaMkOBLyiyVYDQ9aJbpSvkrg26OAk5JyobdmBihYU2KIHgYmSJubYT1Mgt09VroNDlwrTu3pWHdDzkjFo3RGwwt8/fY867KYDuWVMpDBN5Y5oKJ2xgeH2w3N27QgiNNaG5JKp+dPMJbZHCJ7UF+Oetx4sMHfwuplrGgLV7JarfRUwxp3dcSi8evabEBL5bnSY1IzvPKrxxjWs1n7uCPzNwYdxtUlOasaMvFS6dhwN55w/eSL3dptLlk+oNZcwwQ1PQK6zsuzmJUHJcQMPGfkHYECpbrdhPfeJunrtLY1rM5wkMsg47TfY2iZW3JiwbspFjZDJmT0/9l3U0ysXo+gnb2fkwc66M5Dupuda/BxbJ0oFr+bb3UE/Knl+93AJBzKDqgeInG0jJAI19mfQ4KIiy5xc28NdO3qSY7bF8jluPaswyAzknFLot/0nCdRFaM0zAQClE4Scj/nuj7KYIpMmYmouzIhUKMP0QFEiYdiBUOV3mtFTTuwYFzdursLUvYCEolrJ2DHb9KVJ9owZivxRatlIqTTi2zBC/h1gfTaYnOyRzMpsGVShb3oTNMNBg271MXBfUleJmdAHOWYTEGqndRrusM004f+QGPbn3glKnS0nVTKAf/fr1L4Ujua1rWUqOMBdk/zHIU7KwLcahLbpIrjuge4HPrStAqyhasSBvZvt1KOo8pU5TyOqIOIutKmChh5AXdD05SyQ+MJWSg6JlukN8wJnPqaWdIjPMVECQJBZmdNs0FZE4npYjRJHkN2ozOztpArqEED8D/hO3c5dPF1R2LUs2niJsekbHJSxENgZ8JjZjY5U3z97R1A6LjZKVpzN9cokPVbCzoE/vv9CIxrMTnLz+DLCwLExzgwdRlwmNuUi7fu4e9XGfTtN7+NGzh9+soc0wQx3wXd238TD51hjDVvZoalR1LkNLIodz0LM63dDPAtjaD2s4kx1oy1QvTgOX+ia6kcChnSdoh2T00H9ZJ6NY3INRsknae3rMbadV1pWMNWO2cJGVu0gONgFRB24MSsUIjDzkXDEZgXP38H3ApGgqhSijCAKdH4BTYYFS+VinJuu2YEsPp0Xs9/euCLMoFFQyQIsEFTE+3Q9uinJR9uH1R2bN/PO3YMF4dHWlzZJDMaz08V7LBhyNpNBnaMRg7Q+6Ie4vA01m6Mpk3Vl8IOQ6YpPQt3v1q6f2XGFvD5O0XB5dRSB2Qojde5ofnzNcG+pONzwYeIz3w9yQ0dgIIxVDIEtl7OpV0pr6qgtOH77OdMXM2dKxO3lyhLkg+48Bo2KMvK+cusNCa0NR3q4kZc+XhT68+cSOdfqgjmTFumvZAa0vxis7l2X3PyyOYf7qqX0A9GEQ/SYUFA1XRnTPQGFQ517YdHADXqxPhp6li0e0C7XWFRDe+k4VTlMbJm7jTUkdoJGDXS82pHXjEi4Skf8GwTOoLboafMjaNXQLsWHLm6mhwEY8ZE03SpH6D6aVbppunMfV6RhS/E4j1nenImXz87nERRbsfn924LPs0d6BlseMIuem1vxvYVtj+QIb2WXq3EnNMOP8ketcwM/qs47piXkKZ6fRazrTaL/OlFPDVORnAdaHYZ4z6NXTt1S4bD4a7d9TK12PMYEs0BmdlvHthj3dqIqL4ZMHNNrQcEPjzbZhVapRX3s3PBhgyHts19ntviuylAGqIoymKjqWoRoN4j4HImjkSReMPFKlFXTX2D0nee9F4dNmTFMueL7lkHmxQzFQ2saCXj99ywySZ/eeK6ZiyDEDE6asCrMVFGWy7suydnm6de4uPw+KPLg0oqGJognFGfZP5KFZugpNZMAcoSdz8K5BmXJ8K4iRb4o1FE1nVTq6wPnBiulZCcsias8VYn6+fPxCBUvno7I26oO1lbFHKgRdW9tpfB/UIXjNQYWjtqET388fv9LBnULLh+xBUyBqj6Ce1nApkyzpikDyPOqfHB/ef6ZDUrCdKcT9lezEFzpiu+GZZPjgO3kLHdg+PWmL0JFhkUSA8yktNTx43nqdBNUN/PGECgtZb7V/XRQHK6or3OT8E3WGIbkK5SBnycxEFhGbAnBoQuGEqeOyQYK2oc4Gv+ustorF/c7F+1oYfHRgQ5Cje04y710XZMmViTpPF8UitGS3L2h+Pl2s6LvPFllsa8ZuowuHTFPsagKmROO29aFcBbPRo9vPuCiDQ+d/AbDI7zu7Fc3eO5BKVSlMXz7/ydllPjZjKWTDoQQ1imb8t3RiY9oupgGes+jq6Tt8oeozypMWhQ8jK+eyiU7X+xEAzXd4o5m8TmC9GL+tr8EaG12B7+Tktgvp/asPVLxSIWo/Lq4hhr7YPF1Q0jFZ6T6nnda/h2bqnK7LpULOiqzqqHfROx99mZ0TMcnqopQrJmPj5ECeChUsk4+lAnKhuGSA2As9e9bh/XrDhG1sy1+4fAHWe6EgxDTv5aOXcXRh8vUBzCnG7xxCfZd1YbqhDLmQltc5nFPA1suatkzfoWCugN64c5Ew1gBdEseNSRwySvk+Pb/RLlG0yS6Tjft5fPcawezZvXR/HBdnVcCx75aMVep2ctLq+/X4zjM6FiIKFJn5owvuXnlIF49e5+a9kxHcFY+EnOXXi9yxUlUTzk7TFdiv5GtmB/dv72tyg7kgS2LAB6t1zck0rsc6unPdNLQpOf/hwLZjRjGncPIWX9hT4Re5Y6crsKE7txDUxx1LxAKlDVxaC00XuPPnYzRbeJeuUYKKVS7MBQ4yzNShkcRjh7097HBVwXtoQ16oju09pTHE2RBg0e06pz3/iY0LlAh1sHQpT9XqWfJmsqB3wlOv/CXzKCggC3uvTpDyGR9wuazqVoFppuikGssMwrmlLRfn2EQntVmotabQmIA198TAfpQ5ZwYuNkc2mc1d1f8KSlQoQNOD+tKQJe0pe97MrOmb3W89dak1kQ7tPfNT5rWZoR2eP3pNcwdsoC41x9ORkHO8BsI9ccWhUdSos2Z3uJ8JmHiM9p5Lt87f54DrCdv78bqR2FgzLoAbV9DkDFnbzSjnH2ve+gnCtKnLjNZsw64tQlZFMG0P+3mXWaK5pq5wWyhNslAswIRDGcgXC5wnZAMdJragX38VUyvQBBHCDNMM7MHQtwUtEAUPpmVrxgijD5tGVvTg2mM+HyjqoCGTUbFWWSpra8E0xstHvu2puC5IlU5M4UpULUoXYq5w07JElaKs0QYrpHG/erR15q7vpmP+k7czDdK6XmXOLJMRtu4gsy1yFMjGTs7fnS+/CC4m8xTLRVbu6otXGHkgqxN0S0ctiyOYpGGtrlirNOUportDd8ha0VzHxDWLESiwMl2xVqMqJpleHYu6Qp8+fqXsuTJSyfL5KLnCXJAlMaRI+TuVkdLFoyXHGGOjmls5LmRgfY0Op6EAx7i8bUleAELWaRfyHB91OwrRaeyeU1oXdekypWUKHrArgUIOhY2sO8PCrq7jj6lUSauivEAHqKE35i6Sk92dgO1zVevNjAFY/DpLbk2Ykmm6GAaVEBvPqbDzXMAlBBiCoMP4/MFLWjdeNz0czmXvhT481UQOixx4aSh4eje3LWsPwH0HFedHFAC5CmWnidv7caf1UuwNGttifqLnpP1I4H2w86hMyw6OJN9RDfli5N61xzSu/VLqW28GnYy8ZC7M/kN49ewtLRm5ldpXG0V718WIgF7XchzuDPdEsC7+K0ATanyrBXT+0FVKkyEVTQjoy/tfYuN46FnaOFVMbrAW5y5sWCwOgD0RDTa8Rky3kPmpLd6+fE/Lh2zg261GeWk0nUDhBhdh7B9txnw/1YNGDPsvAp1hWw98ev+J1kuMFBRfWJthhw/NYpXaFdgVEbE1uYrkoPOS4dafksYZtEgZiIn58OYDrRy6QZEnKgdF//O3uCb4LDUo3XwcFRE3nr3csTDSriX7FPsn1knosFF4AS2GCT03gH1ru1RUNuhRO44FPj/XP//QDsnCHv+vqUiRr0OcWthw+LQ272OI5I4oy0F0ASaRByRdW+1WImLI0PXjZOTlOB4GxkbUXjENtHMrl2zpikDyPfKfGHKgXeTecya58EmVJiVVk54DehFjwK2tvSKTTJ+JCaY2yLvCZr9XBwt8jPCBgwFHOXMjoYBkdNee3XuhNosLi6y3JL7dsTCEO1iqAOtbIHxDtNpJmjGAzQd0ClgIa8obQ3HVZIAH38bGmFABgYK8q9TF3DZrN925pBv1EKYjHae04NurR25muqkxAHOVYeu6c3F5ZPepBLPmTIWCpfLS+G29uSt5MvwCTemw9D9H28PnrmFnR1p1ZAwbNKBZdPnELRrWbD71rz+TTkVdNhdmPzFeP39HKycEUjurURS4LJz+/PIXlbEqQtMC+9DI1Z0oX7Gc9F8Cvv9TfJbS8f3nWe86dnNvKhxvupMYQBNtavvFfBv25/ZextHk4MIfDTY46vZa0EEn6umq4RuZ0VCwdF7ylEKSVQETK7gHy5Q/Zb0VcPHwFQr3j+Hn7jzzG5URujRMm5AHhkIJYdGwnUcx5TOlBW2dsVPhrIhw6D9gGvEvxXFPlLG47xraNuubThnXHJgEfvn4lQ1RQPvH2ofA6eunbrGmu3632mzOAc068kit6wp3adAZweio5FSWSlb95iZ6KuwcTxuxfyB6Jz6gU4ejM/Y7l9bfLPLjA/vqEUmbr8oyXxWO7j1NLx+/pgzZ0lE1yQVbFxwLPce6yIzZ0lMVF8PdCiMDT/A5KlGxAMdgGBufPnyh2AhR8NnXSX5h0MowF2Ra4nEiCvyta1nwuPzejad055ppaIuOUqcCQX3oiBmK6vUqcdDf8wev6Ph+/SZ7dX0dFeN2bR38ilcqTBZWRblTtlNymlIHCFuxmAOB89VPdUA9yG+Rhxff3ctUT94srIqRhXUxPney45KpwqLBlweW9PfTWGg1HeBBmXNlYhMQdU6RysCmgh9caMzvsVLni2u39jUVOrcZHZcazR0ReSmdpgib5eXDNtGlo9fpR6CkZREataEHU19idp6gGV1XJroDZFIAtHWwMF95ZAzV96nJOoyLx27S0KbzaECDWZxraKYy/lxmFUtHbaO2VUbQlvmhTNHCxdR4/+40dXsfKmNVlP5rwPd+ds/VFB10nNeDUet7UGnrxLfzl3VjmAbBmbbzVNEUMxQoDvxGb+Hbnaa11Gi/Hh+g08taKKFP/k2jAyMKKxhR1ZfyRJXP8aI+q/m2a1sHKlpB0P9Q6Mk6rtajmvDj+43axH93bGlHF6KvMKMiU86MikyxX5TdE3+PO50K8Qv/7rg+vP3Ef8p5ZfU6u9C+1aIxjNgcFKmycYdX33o8hXn97A1LG4Bmgz3jPJ5sbII4HVUOlUHzxXTMpa0DF2XqELQwhNfWKrXLc9NaG+xdKV6fS0tbvWiscoRRrabWGt9LbYGQeFNOx45GXGKpT+78WahIqdyUnGEuyLTEkSM3KLGQNn0qqmxTPE7QnbFR0d6Cee8wLYjdf94oLnWOkrlHiOTOoyuqe1TmHBV0d2DZqi0aSgXLrmX7Eww8hish6ANnIi7SlWOqL/Sx2DbuWy+OC5IqNOghnheBkcaygFcF0CPQwUPnT96YVIHF9RO9+faGCQH0/KEQNmtC19lteVoGd8PQNYLmoC3Qveyz2Je7gBBpB0kUDGMAFFbksuDcT2g5/4foyYCKDqVo6OourJc5sOkwzeu95j9bfGC96DzOiydm9Ts4cGF2IfYGjWy5kLq7TKbIoBPmcOlkDLjuzhvkT22tRtH2pQf4IqdY+fw0yq8zzdo9gCo7WPwnDDviA9/3xYP8KXR9DK8Dg1d2okq1Sv+QY4Fu7Fz0FV7rwSTQxz0vPsBomeG7hBtrcC92VQo1TgjY92Z3XsrnCNricnbqw6Nvnb+r2CNgRBX/Yn//2ii6HHudC5S248U+JptnYbJWuFwBcmhWnc5GXWT9NvZxGG2gyAPyFstFrx6/5qwwNBG+HePf9Otv3y51YcyBPUsGnu/rp6+Ut0RunkihgKvuWZXDpfF5x3PA/AoBz5ga4XUCuxaH8jmDNr1CzTLfXue5u3Rk90n+XVVTLTg4gzoJxC9KlQFK5T6Jetigm3bTsQc3nhhk5oFi+Wiw+H0XSddvCO5ff0LXztzl7w1o8KZA1F5xjWzrlvwNhZJVQRYVFUX16tWj3Llz84kPDAzUeP+IiAhFwLLyz+PHj5N0QQbY1Raj4oMh501yAYisFAgsgbDNxskkgz0qcDT4DL16qpk+qAro5rhIjkp7pC6PNkA2FqxpYXMbsflwghMnmRu/ScoWUYVazWuw8Bf0RnWaLJuGVnwfZK6AZmEqpMmQhjrPEPRCuEo9vKH+8+vY0pYndzDqWD5I5LUkpJcC5x+Ag5U6B0pNvy9TF1cM28hFozEgdGodKE/RnNz9nOaz5IdNp6q5V6RBHLb6C+31i6IlQ/z/s0UZAJF35/GNuTADpTFl6j/o5vn7NLnzSvKpMYbpbR91NIox48cAn+PzR6/T2HZLyddmLO1ZE81ThdJVi/BEbM7egWTtkvwvdPQF516N2EI7lobxOei/uAPV0IMGZgwcCzmjpBvrwAWIMQA2xcUj11jT1WdxR53ea/zuzbN32V6+0/TWGs/jor5+TF2z8azKRlTK+PLpC60esZFvNx/WiN18ARh3QDoA4PHRLF0h5ZCh4Di49Qi9fvqGM8guHBLGXvj8KgMF1q/xij8YNaFI4NtStIc8FbRvUp32SBM/sGXw2OvHb1WEPqMIBlNlx0JBp/fqUzfOOfOfLK5LbRpWpXwlvp/YIC4G58PavZLG9zB07UEO2c5bPBcXytpg19IwMVFzLaeXmUfohhhugsK1E7R9Q3EgQDh5o5kDJ19TOJIfixLvu71b4tEV7959QStWCH3hf7Yg+/DhA5UvX54WLFig0+9duXKFHj16pPjJnl13Ee7lSw/o5cv3lFiwqmXBBQpoi7evGkefEx9yHkRs2HnWCxiKghZ5qGSVIvyF3u8vRKG6onY7B/7z+L5zbN2qDdApq99FhCUGzA1O8GJZ1lpFB8Sq1T5h0cU0TTzmHrVTwUZ9hF3tuvFbTaoxqultQxUdy3JHbvngdWrvhw2r+1zhzhi2IZq7iQkB4ZZwusIUakl/9Rb76uDu68gcehzbdF/juS6CKjdsPbrAv1Ns8Jkfkk8mw75RVeo9X1hABy7aT6vGbPtPF2VyYQbTjzXHx1PLAe6UPlMaenznORtAtKo0jGlvj+8+/9GHaYYK4Lu6f/NR6l1nGtNODwcLB82qTmVoyrbe7LT5X52IycD58BsXQNskHWuPWa2oVhNh5pTYwHRmagehG6vXyYkcVLj26QM4CK4eJSZMiDNBpqi2eHz7Ga0dIwqVjlNb8vRIHQ4FHWfDKWiDO04TsSnKCJi9hxtv2fODov9Ng7Z80FreV6u4VaRKTuV4UgUnR7A6QPfbOlNox/Jb5OViD7+PZuQvklEH4DO5BU/A4kO2xMefFtWKK8xAqtapRAc2RCuyzaK2HOapF2iLnlJ8TsTGGNaso4iTDb4ANEsjJQv85kPi0hgBsH9koxDZ6VgV2PRjoTAQadDNVSujChSW+6THliOBdP28710tfr9OO+2npJpeQ9gW2V3RRHTFA5d4ApqvcDYqWDzxNK2RkZdoR5B+ubs/TUHm5uZG48ePJ0/P7z/omoACLGfOnIoffVxYcO116JB663FjI03alGRpK2iLEbvPmOQ5CpbMTUXL5qO///qHonaoNrnQFa4tbRQ8ZH0uWNHVgS4Jv7tnhfZTstrta/IijRyShCzwEa6IvDE8R6AGh0AUZCiKLx25ppbeWL97bTYKeXj9MUVIzkSmAC6Musxqq7DB1xRKXbxyYarjI+gKc7utSJBOCepI78W+/Niha6PoTORFnY+t72Jfpn5gs9QUvq0ripQrQN1mic7rmjFb6bSOx2ZMgMLRfYa4mNg8aw+tnah5Qv9fAVwYW/StQ37Hx1P3yc1YuP3x3WemvbW3Hk0jWizgHBpjFepm6I8n917Q6olB1KrycJrRaw277IJ6Wrt5dVoSOYLGrO1C5aonvjYqKWLD1J20cYbQDXWd1oLqSM3CxAZ0yoK2/Z6KVSpEHSV9raFAoTOtwyJ2IYRGSReqIgC9F4oAhCy7aPhd0AcX9xM291596zKrQhkvH78i/0nCar/9hOYKGuaJ0DMUE3iMJ1kdp7biKdrS/uJx0AgFfR+NhVLVitOpMEGzQ1En0xIB5JjBIVF2a5SRKp3QislTskzZM/CFPR7r4LYjfG2AQgs29uslF2I8J5yd//77b9o0Vaz9MPtQpl5unbWbTUKquFaIY4EvA9M+vJ9gsZTREPCMPFZMB7GnyhTJhBC55Qjn4+UsmI0s9QgIPxt9hR7efEqp0qYg+4aGF1DnDl+np/dfciyDbCJnbEQFC7qijWviTvGjD5omJzVZFWT6okKFCpQrVy5ydnammBjN1LIvX77Q27dv4/zIiD54hav+xPqRHWMi9+Bi5m+TPIcstEQnwxiPZ9vAkguju1ce0aXY63o9Rl1fUUzAbfHzx8/8b1ggNf0OQjllC3wIYRN6Ds9eogsXvCqc3r58p/I+GbKlV3S/QG9UdR+8VrbExQY+cRv99ddfJvs8FCiV95sN/sC1Gj8Tbcc34+OHYxR0cAk9dsmqRamOZKoyt9ty+vL563f30fQeZM2bmXylC4VVIzeza6OxXrdzK1u2/MVGN6nVAnr24IXJznFCP3Xa21PHiU3F+z11J62dFJioz5/Q9+BH/vyR8jdya1WDFkUMpTFrO1MlexGFcfzARRrTdgm1qzqS1kzdSQ9uPf3hx/oznn91P7iQjdh+jE1Y4Ji4ad4+tinPljsTtR5Ul1bHjqUe07wpb9HsP/xYk8p74D99l6Lh4juhCdX1qfnDXu+SQevp8rEblDZTGhq2rhtT8IzxuJum76Qrx28qXBV1Oa/Hg8+y+x/YKT3mtdf4u5umBfE0DXtEs0Ee3/3/quH+PNVC5pd902r8b9hHlw4UbA2Prq5ssrV52g7pcbJQVfdK3JjERXjOwtnZARS6MBRiys6KmABCCzZ6+wDKnOubo+Ond5/pV5zHv//hHLAju04oTDiiA8TjYjoG9+bbF+4xnRPNVxwbGq94XLwf7p2cFa/j5ZPXFLJKNJG9+tf97nV+fP+Jdi7ep8g71XR+5fs5tbTh16XN92CnFB4Nsy3UJrp+HuTpmEMjK3YRNfTzFbpJyEdsPSpx08fY34t3bz7SiWhRGNnWLmP0x1f38+DBS7p+/YkiLsGY+KmTHFGELV68mCwtLbnQWr58OTk4ONDRo0epUqW4HRMZkyZNojFjxqj8v5Mn79Dt2/cpdWrDhbTaoGCpzJQy1e/05MErOhJ5loqUMg5nXBmlbQrQ/379ha6evktnjl6kXIWyGvyYlV1K06GgUxS0fD9lLqA7b7hwlTyUOXdGevnwNe1atZ+qe1aiN2/e8AKkabpp08ySHRqjtx+jy2evsoWtOuQpk53yWeSie5ce0ZY5O8i9iyhG4sOxfXUKW3+Q6Y2nos9SHhVj8WqNK9HmaUG8SO9ds5+q1BGBkaaAe/daFL4xhk008FyWbupT6RsNcKOVAzfR2rFbqaxzCZ7kaUK9Xo4Ute0Ivw6/cZvIo8c32gMWooTeg0rupaisfQk6F3mFJrWZTyMCenyXv6IvmgxzoysnbtC9y49oTLPZNHhD5+/csxIL1RuVpzdv3tKmKXtp/eQd9PHjR6rfTXcBta7Q5j1IKshfJhv1mN2Yntx9SZEBJ+lg0Gl69vAV+c8K5p8SlQuQjUd5quxYklIpCeyTMpLT+ccx3jz/kI7sOUeH956nD2+EixxQqmohqtXUkirYFWezg6//fKKnT7/9/3/9Pdi1JIK2zhQXxF79XKmGVwV6+vQp/QgcDjpFOxcLPZPvtCb0S6p/jXIs9y4/VORPthhVn/75/S+tHxe6pjXDBVXRrXNNSpX1D7W/Cw32pilBfLvp0Hr09sNb/pFx+9w9ziXj/x/hQc+fC4rzoYDjdPPMHZ5kuXSypxuXb9FmydCqydB6tH6ieP7yjqUUGm/owlBkKWvIUHCNaTSNPHq60stHwikbxQrIOyh0Prz+SL/+8T9hXe9SlqJ3iMey8qhIKbP8TmvHirBpl/b29PHrB3r/+J0igNrV14E+fHlPH54KKUvAzL08sStULh/lssj63TkJWxPNU87sBbJQsWoF1J4zaNIPS1b31RpVVHm/+N+DW2fv0bWTt+i3P36lSnUsdP6MYLIWLbGkqtYrY/Bn7PPHrxS9SxiXVHYqbpLvT3TwRZ5q5i6QmVKl/yXRvqP7Qi7wn8WKZacII8vIfuqCrESJEvwjo3r16nTjxg2aNWsWrV2rWiszZMgQ6tu3r+LvmJDly5eP8uTJRE+efKJbt95SzZrqnYSMDWvHUhSx6wydO3yfqjmov/jWF5DTVbK34C722cibVN7K8Nfm4ePIBVns3nPUc0YbhZWsLqjX0Yn8Rm+lqE3HqUGn2tyxypYtm8ZNGNTUMjVKMGXxWNA5ajXiW1CjKjTuU49mdlxK4WsPUevhTdnoRNVj1mhQhakTYStjqP/KLiqeGF282rRx8nY6sDqG3NsKPZspgOOBiNh/0nbaMHo71Wxkq/b8evX0oKgNR+n66du0a3YY9VnaMYEHJ+oyozVNbbuQAmeFkFvrWtw9lDcAbd6Dgcu7UmfLIXTz9F2KWBNL3oPV8+R1xahNvamn7Wi6dvw27ZxzgDoZyfJZH7QZ3IhSp0pNq0Zvo+1z91O6dGmpWX+hJzQVtH0PkhLweS1rWZI6jmxMh/aeodBNR+hM9FW6cuIO/6ybtJeqOJUhu/qVyLJmKZ44J1Ukh/N/99pjigo6wXEmj24/j6P3c25qRU5NrSlXAcObbj/re4DJmFyMtR7WwOTfaU1AjtWqoaLwaDqgHrl4G4cyCcrc6P6z6a+vf5OVe0Xy7FJHJ7rXwkl+9OrRG8pRIBv5jm9BKdOo398XdPLjIgWZXvV8XOM8D97LiaPmcVHh0KwG1agjNO1wSg6YJnTbTfp7UJGShWj5oHUc2FykQkEqUaEoLey6mh/rr89/05+f/6Q0GVNzcfX3n9/ruDE92zlPvKcAirGMOTLQ6ydvKFX6VHT3wgN+rEa969Fw90l8n3Zjm9OjC8/ozvn7lCJ1CmoxxIuNS2ICY+nhtcc8VWwxyIv/5GP+8idfRwDNBjagHDniGmrgNYb5CXZWo151KWcu9Xon7G2Y9JW3L0UVbcpp9T3w3y701TYNqlJRi8KkK44EhnEhW7hMPqpaq6LB9D8wrr58+pNyFcxK1Z0rmYROeCpafEZq1q2oly+Evjh9WsQc2diUpGXLjfvYSXNXMSGqVq1K16+rzzVKkSIFpU+fPs4PYG0tslcOHbrOX4DE+rGvU15hf4+FxBTPIZt7hAcc5y+OoY9X3taCchfOzrSAqO3H9XoM8PUxAUFQ5Y0zd7Q+LtmIA1kc0MZpum+t5ra8yD6585xid59Sez/vwSIo+oB/DD2//1LlfWBfi2kQJlfo7JnyMwEXKvDE0X1cP26r2vv9/vtv1H1eewU18+rxmwk+tlNLO6rsXI51BXO6Lo9z3rV5D7BJd5MCp9eND6CbsLw10uvOVzw3DVjeiR87cME+itoWa9LznNBP0z7u1G6UKPrXTAhkCqMxvj+afkz9+Kb6QbgqhN2TNvdkilzrQfUoT+HsbK2OTupE3xXUosIwmtptNUXtOEmf3n/54cecHM4/jgcOl2un7qbO9uOpi8MEnkCiGENxW7NhFRq3viv5HR9HbQZ7UJ5C2X/4MSfV98B/2i5aO0HQFNuObETNB3r8sNeIvXN883kcVlzJsQy1GeVltMfeMGE7OyOCMdF3cUf69ddftf5d7Mc7JOv6Xot8KHW61Grve2zvaQWtseeCDt89D5yJLx25yg3FTtNaKf5958J99OTOM8qSOxN59fNgI4ygBcJYpd14b/IbJSZUZWxL8n6Lx0cxpg7QiaEoVMbXj8LkQ9az2TW2pmN7TgmHQreKVLhsAdo2e5ciEy1jtgz8uds0JVBBo4SeTD7mqM1we3xL2fJmITsv6+/OBQxNMJWEJsy1nYPac4Yp357lItusUe86Wn0PsFZGbDnCvwO5hz7fp33rhIlJ7TZ2On0e1P3gWhJwamxtlMf7X7yft68/0mnJ+bxm3Qom+Q6q+nn9+hNduHCfn7daNePnMf7nCrLTp08zlVFXWFkX4T9jj96gr0YIUtYWlWoUo7TpU9LLZ+/owonbJnkOa5dyTBuC4Bu5QoYCX3C3tkLrtEeiI+gK2MnbSsLSXUvFAqUNYKkLrjjoCZEJWODjgkXO6tgmhT6qArjtFR3LsAh6y0zVTn9Z82QhWy/hfBUwV/1jGQO4uO0+34dvb5u9m26du6P2vqWrlyDn1uK9mNt9RYLGCnjvei3y5edAVtveFdqfexmOLWzIpkEVPl9T2i1MMBtOF1SvV5maSl3rmZ2XMb//R6JpX3fqMKYx3143KYhd2f7r7osJIXvezOTduzYtix5J80IGkVdXJ9YzQUcSGXiCpnRZRc3KDKIRzRdQ0IoIzscyn9NvgJECTFLmDthArS2HUw/XKbRxTjDdu/6Em1hVHEvTgPltyP/cJBq4oC1Z1iqtcvpvhgA+W9CCypqx9qO9qFk/oQv+EcD0Y7rvUnYAhuvh4NVdjfb+wQxKjnvpOb8D51tqC5hDzeokMsdqNKrCjTt1wMRoYV/JgKN3HSpgkfe7z/CqYf58u/nQhrx/Aohd2TBRGHy0HefN+9C6sVu5oCpdowRTbE+GnuXP+aMbT/h+OQpmUzyubNKhDNARlSc0cO0F7RJNzVtn7/K/eXSrTcGrxF4HBgo00LF7TilyyIDT4ee5IMXvy7pxfvx//qGts8R1gUdXF5VhyoGSUyecIdOkF1M1VQjxi6QPbz6yHX5VN+2kDwc2xnAhl79kbmYI6YrrZ+7SzXP3WOdV0wjunaCmnz4ojNVqeYloJWMjOuQ8v6/FSuehPAUTb+J/+PA1HowUL56TsmVX7yqqL5LVKv3+/XsuqPAD3Lp1i2/fvXtXQTds3fpbFsbs2bMpKCiIJ2Lnz5+n3r1704EDB6hbt246P3exYjkpS5a09PHjVzp9Wv0FsLHxxx+/UTWn0gpzD1MAOUI2dSsaNZPMuUUNXjSvnbpNN6RFTx/aouwe9OGtdhoHuCJ+s8Dfm+CFHCZbOM5zUZfYIVAdmg4Q1LvglQc4pFIVPKWA6oiNh9g5ypSwqlOJc9CwKMniZ3XwndycqRXXTtyknYsSDm9G17DN2CZ8e9mg9dyh1AXYxNARRcg3TEVAPTUm0C2uWKs0d4/HNJ2j9v1ILDTu7UYdJzbj23BlWzFyi7mA0PJzUrRcfuowwpNWHxtLM3f1pybdXdilEdqA4+EXafHwLZxt1r7aaJo/eCMd3HnSKBEdyQlobFw6fpP8ZwfT4MZzqEmpgWySsnddDD1/+JpSpPydatSpwEXYxvNTaOy6rjyNTKWBSmaGAPaHVaO3shYUQHOlSR+xjv8obJ6xiw7tPMF72bD1PRLU/mqLTx8+09T2i9gcybG5jaLhqS3gIAj79/RZ0lKL0YI1og5bZuxk5+HMuTJRi+HfSwfgkAhHRDAq5IIHQJHGIdDlC5Bzazu6dvIm7VkmNHStRzelBT1X8u3yNcvQ8wcv2WwDz0NSvYX9EGZWKJriv8+y5lhuShYsk18xETsRcoaLGsS/IF5Gzh2rXt+S8hQVDfxtUtHl2q4WuzLKOLLzBJ8XTL/cpWsWZWAPPLpHBEVrCoLGccnB2Z49a/NEJiHg+Hcu2a9k5qE7NTBkrQifrl63EjvmGorwgGN8XGWsilDO/KYpliKla2F7d+PLeDQhRjIRqVFDOKD/pwuy48ePU8WKFfkHgNYLt0eOHMl/R8aYXJwBX79+pX79+lHZsmXJ3t6ezpw5Q/v37ydHR9UGDpoAR5Xqkh3woZjEs79XDryLCT1vsqwrpyaiMxK54yQLMg1Fxqzp+QsO7PXTT/lYqloxKlg6L9vmHgo4oVMuFqZf0E6djRLZIuoAioFjC2Eru2mqEB+rAnK2cCxYtPeuVD01srAqxra5oPtB42VqwAoYm8zxkDNsEawOmXJkpA4TvPn2quGb6MWjhItFzx5ubLGMzXFxvzU6Hxsyafos8lVs5GeljBdjABSVIX7dKEf+rPTwxhOa0n4xdyl/JBp2c2FrbGDr3GBaNGjDDz+m5ARcfFhULkTthtXnydnSqBHUfngDKm9TnD/jyDfb7XeQJnZcQd5lB1NHu3E0b6A/O3ndu/b4pzrXaD6diLhE66bvZmfExhYDqW+9GbRmyk7W30ETkyNfFqrXzo7piJsvTaPhK3y5CEN2nxnaAReNiwf70+bZIqYDTRU0V34kju07S6tHiWKg66zWnOtpLCwf4k8Prz9ht8NuswWtXFs8uvmEJ1UAcsTSZU6r9r6gG/pPFPtfp6ktuVBRBgqxzdJe6zulpcLm/srxG7RnuXAK7DGvAxcX83usUGjMrsRep/tXH7FZ1+NbT+NkiZHUd/3fb/+jMdsHsKY7PtDkAaAzQ8F3JkKYMzi3slMUW61HN+HoHNApgZYjBPvh7uUHdHS3KKqUC0gc23ppoufRTdAY40OeSKK40xQEHbv3FO9ncIzW1ur+bNRlunPxARuUuLTS7neUgWurCCkrzFXL59QEnI8DW2P5di0vIYUxNp49fqNgi9klYhj0x49f6ORJ8bw2NuaCjB0S8YbH/1m9ejWfJPwZEfGNIjdw4ECejn369IlevHhB4eHhVLNmTb3fkBrSmxATczVRM3XKWxWh9BlT05uXH+jM0ZsmeY4y1kUoZwERqnhoj5hAGgqZtnhg8xGmKOgKLH51JErhgQ0iG0QbQBcGq3QgYE7CmViN+9fj5zq04zhTFdQdizwBC5oforIwxn3ajBWTkt1LQhWZKKZCrsI5FBvP0gEiQFMd3Ds5sbU9qBorhmzQqujpvbgjNyLg6ngsRPfPRLV6lZl/j/dtWofF9OGtep6/rkDXeMTGXiI0eu9pWjfB9AVwQvDo6Eg9pMy0HUvCaHaP1ebsLT2Rr1hOatzNmSZv6UWbL02lUX6dyaODPRUqlYf/H0XYnrXRNLP3Oi7OmpYayMXLinHb6cC2WLpz5aFJg9qNAXwvXj59Q6cOXqati/bT5C4rycdmDHmV6E/DvefT+hl76FTUZV6TcZGGKVjXiU24WF11dAx1ndiU6YjxpwFmJAzs33N7+VGQ5GDYY2Yrbqr8SDy8+YQmt1nAn4s6HWpSnfb6X6vEx/HQs7RzsTAj6Le0E6XNqP0kBMeDPEtcvCMjNKFiYVFfP74vjDxqetf47v9XDF3P1wPI4ZJjZfAcSwes4T/RIC1jY8HuxmCtoNhoMayRIvsL0oAH1x6xvT0apMqmVv/89Q/1thlBb168pT9SffteyFb48pQsZ8Hs/L1CTtnp8At8vKWql6AaDarS6pEb+T72TUQOGbBlmiggq3nELapOhJ5lbTYawI2UCjXlQG85ZLrZIM1TxUBpOubWvpbW0+0d0nuKWBhd3lMZUduP0fs3Hyl7/ixU3t6CDMXNC/fpzpVHTH+0rScGJ8bGweCzYgJXuSBly6k95dZQpEjxO02e0pTatLGhAiaiSf7ULovGRoUKBShNmhT08uUHunTpIZUpE5cXbSpgEanhUob2bo6lg8HnWFdmig61UxMrWjdtN+3fclSRT2YIytuVpFwFs9Gj288oOug4OalYnBOCU/MatGL4Rnp47QldOHSVytlqt2hgwrNraRgd2X2Su3soXtQB/PbqHpYUE3SMAmbvoT5LVLsRYqNYOcyfO4CwwceCHR8Va5Xhjehs5EWmZXSd3Y5MCdBBQtdEcFcPoZNy0ajq/e02px31rD6cw5/r+DhqDKaUA6aRvYKg57ldV9D40AHsxKgLOk9vSacjLtCT289oUd811H95ZzIWilUsSD3nt6fpPkto/cRAKlqhIGvMfiTc2ztQilS/08yuK1kojU1/4LKOTD8yQz/g4sTapSz/AMjQOnfkOl08dpOunLxN187epfdvPnHxgh/ldROOgqBA5i2Wg3IVyEY58mVmDVv2PJkTpZBhe+oX7+npg1c85cPPozvP6f71J+yI+O6VarptzvxZyMKyMJWqWpgnhwUt8ph1YEYCtFAzuq6k8M1HuOHUe347Dn3/kcA6MbrxLLYfx1SsixRAbwy8ffGOpvss5tseXZypspP4HmmL0DVRdHzfGfo9xe/Ua6GPRloc8rzkMOfuc9p9d9+zURcpbN1B/vfOM9sq/h+/hz0Tz9F+gjdr0FaPEIVR86GNKGJjDGur8lvkpcM7jiveR/ncxcft8/coS+7MCp0ZHAThlvjl4xfKXTQHZ5PJerHZnZfybZ9JLejGmdtMQcTnos0YkTf58MZj2rdGsHyaxiuq5OkXqIpghcRHwNw93BhCIYuGqDogagbGH3hevEfa4MXD12yaAnhIZma6Qs4eq9PG3ijrS9gWMR2zci5LaSUHSmMjau/ZRJ+OATg/qAHwYyqYrxJ0wO+//0rW1YpS2P4LdPDglUQryOQPHwoy0Ba7jvAwyQWeU2NRkEGQ+ezBK8qWJ5NBj4ciwKWlDfmN304haw/qVZBB+1SzSTUKXh3JXGltC7J8JXJzWv3xfWe5iwTqhCYgqBEFGYqVduObqVxc0QVDgbJm9BZaN2Eb2XpZfcfzxgYDkTI2l73Lw6j5sIbs0GQqYBrYfmILmtNlKW9g6B5mzZ1Z5X2xIcDEBLQQGHwsOj45wZywduOa8QaIQM7Nk3bRgGVddTo+CJgHruxCA5zG0b41Uax901W7oAnOLWxYGxe0KJSncHOiRlP+kmKK8qOAzznE6JM7LKGDgce5izt8bbckbemenJA+c1qeFuFHpiLduviArp+7xx1a/ODvnz58YaML/JAK6ST0EhmzpqNM2dLx9z1t+lSsSUmTLiVT//5I8Tuvs8j2wfcEXdnXr19T+nQPWNgNsxpYOyNEHe/xu1cfudv87vVHevPiHb148oZePX3Lbq/qgAswNK0KlsxNRcvlo2Ll8lORsvn4uMwwPjAJmdh2ER0NPsPv6cClvmTfyHjrkb5F+zSfJUw9y5wzA43E5D+FcZoF+MzO7rKcTa6wJ/pMaq7T70M/vLifMOdoPcqL8hbPrZYejKnX/J6r+LZXH3fWYykDBdS8bsInvI6vE5WwFHRMFCzLBq3j22goZs+fjV0VMV2CBq1WCxvyLSOiiDDRitpymGmQYHug8FPQFpWf6+tfimIMezLOQ0qpIMuQNQNTN8s7lObsLqwfKJjK2lrQ+GYz+XfsmlSnfCXEPrJ+wjZ+jiq1K1Ap629UNejbTh84z8egajqGAnKv5JjYuF89jed5hxQEbV23MuvqtEH4hiOsB8Rkq4CF7nve7Yv36eLR6/w90IfuGB84j2AnALJzt7Hx6N5LunL2Pq+bNq5l6GeDuSDTEXa2JURBFnWFOneuZZJ8BVUoW6UQZcqajl49f0cnY66RVU3Dx8vxAV1C2WrF6NzhaxS29Sg16/U9D1ufCRfcq87FXKX71x9T3qLq8zfUwaOzExdk0YHH6dn9l5Qtr+qCIz4adHPlggxh0a1HemnMQ8O0qLhlYaYfwPii1UjBHVc1eds6cxd34NAJtG34/cJTyakcFbcsQleP36DAuXup7ThBYzQV6vg6UsjqcLp89Bot7ruahm/8lqMXHx0metPBgKMsQg6cH6xyI1EGzhkmhoNcJ9D+1QepdptaVN5Ot6y6sjYlqUn/erRx6g6a3XU5lbIuxpbGxkLHKc3p1vl7dPbgZRrlNYvmHhxjFHGyIbCpb0mj06SkcS3n07HQczSs4Uwa7d+TqWdmGBeYhBUrn59/ZOCiEYYXKMbu33jCE6nHd5/T0/sv+Qc6WUyn8AP6oymBPSJT9vQ8rQMtPFeBLJS7UHYqUCIXW/+bC/XEAS6QR3vP5b0I09Fha7qSlWvimgKoAtbFmKDjXPyP8O9l1LURk53owGP8HRm8phsXJbpgfs+V/B2BnjihogK6abBHsufPSi1Hen33/2BwwBUXdPP2E4WmGUCD8N7lB9xc9B7iyeYjstMiqIrbZ+/mKViB0vnoUNAx/ve//xZ0ZBRKcDWUp2UysL/JQDGWNlMadnDEfnYlVngANO7vQWMaTRfPM9yL7l99SFGSfXzzIZ6K6dh+yfSi1ShhdCVj8/Sd/GfNptX5NccHXheKxgKl8nIxpw6g8oeuPci3tZ2OIUsucqPQftXrpN90bI80HbN2q0CZdXDbVIfY/eeZDYAGV5VapUxGV5RlPLge/tlgLsh0hGWVwpQy5e/05MkbunbtCdtfJta41M6tLAWtPUQRe86YpCADQFtEQQZhZtOecYMc9UG2PJmZIgGx8r61B6m9ZBGuC9BpK2lVmC4fvUl7Vhxglz1tYOlanukJ6IaF+cdQXV/1Zi4iGLIuTWo5l7n2oCao6lKCp42ibP2EANo4JZBt9uOfI/y92WBPGus1nXYu3kfeQz0pRSrdNkJdgCkdrOq7VRnMVv+1258hSxfVFxrY9HwmNqdZnZfS6pGbuKBUtZnELzBrt6vJWWazOy2lJaemKoTY2qLVSC/WMVw/dZum+SymibsGaeUipQ2wIcONrEeNkSyKnthqPo0P7J/g9M/UsHQqQxO296VRTefQ+UNXaYD7ZBq/rS+H9JphWuCzxdTEvJmpskPctRIXaLjIfPXsLb169o5eP3vHt3Hx9OHtZ/rw7hN9fPeZaVMw0EDnV77g++uvv+iPFH/w44OamiLlH/wnLnTTZUzNF37pMqSm9JnTsPlAlhwZ+ALlR38W/+t4/fwtDW84i66fucNT0LGbelGZ6qYR5usCuCn6jdnGt2G0gWaVsYC1cGEfYcjUelRjKibpobRF9PZYOrjtKH92+y7rrPEzjGJmi1SgdJ7R+jsNFIqhtWO2KOzs02dOp6BTrhrur6DfY3+Fhuvlo1dsS29RrTgt7C2mbqnSpeTJV44CWTk7VEb8YkwBbMv/ftOWAflK5uFGaQ3PqnQh5jIbcMGIC9OyOZ2Fpb91vcpUqKygpcF8RJ6OwbRLBgrPqK2iePNSUahi6rd9ntCvo+mp6ToKDWMUnJhgQvKgDVDAv33xnrLkykjVJIdsXSfFBzYejqP1NxT7N4nzUbNRVZOtd5ESXdG2tm602+SCZOWymBSAYqxKFZGEHhMtshYSCw6SxefhsItGcUJUhRruFdhGGZ3lq2f0s6uPj9qtxTg8dEMMd3b0gVNrQXfcszKcL5S0AS6a5O4R7GQTMgWx87Li7uSrJ280Zph59nTjrjamaafCzqm8DxyVQD3AhiM7NpkSRSsUogbdhUPYkv5+ig6iKtTuUJMzXUCzmtdduFglBN+pLShjjvTscrVBctDSBej+Dl4taHsn95+jwPkJ2+/rAlDORm/pQylS/0Enw87T8qFCf/CjUaZacZq2exDn/dw6f5/6uUzkCyUzfhxwcQTaY4ESuamCTQly8LQkz461qEU/d+o4phH1mdmShi3zoTFrutDETT1oakBvmrmzP00P6ktDV7Xlv+Nn3Ppu7G44YH5b6jHVm9oOrU9eXZzItXl1qla7PJWoUICy5spoLsZ+MB7dekr9XCZxMYbpzNRdA5NEMYZp0dT2krarszO5tXMw2mOjIJjabiFf6Je1LUmN+4nsRm0Bd915PYTFfJMBHqzPVQfsH/O6r+S9HYULmpTxgTBn2c7ezUcYdQErh26gdy/fMxURYcuPbz9l7TXgO7UVLeqzil8LCqbLR67xdxd298qQs8eUJ4ugtMnFmJw7lqNgdrpxWrjkNezlTruWCEMMhE+/fvZWoRNr0l9E3MCNeJ+fMInzHtIwznNCV41CDfmkqs4N2DPP7r3gzxtyOdUBxeR2KaMMNE9tG+B7VoTzn7XbOajMPUsI0TtOKMw8KhlhmoUoktiw83zbuanhWWaqcP/WM7p56RFn0dVwFlFQpsSPyL40F2R6wMZWLOZRUvhdYqFEuXyUM19m1i0cCTeejbgy0qRLRdXcROEXtll0PAyFVe3yfEEKPcVhSYSqKyo6l+YF9/XTt3QwQPCUtYFrG3umKSALBPRFTcDChk0B2Dx9h1qufIas6RWB0hvVWOUjnb5+N0H53D5vT6J8uUETgR0x6JTBGgKdUaj2WdyRaSwwPZFF0pqAzmXrcSJPZuOUoDiUEG2R3yIPdZwqrOFXDPWnm3rm06lDkfIFqP9SYcgSMC+YQtYIqsmPRuGy+WlmyBDOd4OpQ1/XSZzPZ4YZZpgWKML6ukykBzee8MXnjJAhVLS86UT52gKNutGNZ3PBBA1Qp6m6absSgv/kILp45BprsAet0j1Yevng9TylylMsF7UcHrcYiQ80L0+GnWNDju5zvzfyuHX+Lu1eIjRSXWe1470RuHLsusLmvucCX95/4bSIEGhoujDFQT4ops9wWgQy58rImkwuuCTIeytyKWVAWyU7K8p294XL5efiDqZb5w5e4kIwd5Ec3DzdNCWQp2XQWctmV9vn7OYiE+6LygZYr56+od1LhTtn476qaZww8wDqdnLWyCY5uC2WtXLI7HRsrp3G/s6lB3Tu4GUuRF31nG4FS1FErq1sjcJUiQw8zu8LNLDQw5oCEbtFtE/FakUpfSJIEpQ/x4lVnJkLMj1gbV2Ufvvtf3T3zgu6d9e01ubxPyAOdUSxFLlHfe6UMcw9+DkCT+g90VIGFlp5SrZnlX6ZZCgeYAUMyEGI2gCFhJv0e1tn707w/h5dXFgwjKImds8ptffz6luXF0Q4IyHvTBVqd6jFG8nNM3e0KnoMBTJQZO3biqEbmCaiDuC1y1SLhX38mLefECzrlOfNC5vatPYL1VNFNKBuRyeyqlORP1eTWs3TKw5BE+waWVGLocIJa273lXQ+JnGbJuqAYmzmviFUpGx+7sYOcJ9Cx0JNE/RuhhlmEJ0Mv0AD6kzmRmDhsvloVugwvTTMxgbWzXHN5/HkDrS84et76DXlUIcLh6/SuglCg9VjbrsEKenxgeJq9zJRKPVZ7KuxoMDUCzb3QLNB9Sl3kbjnF03NuV2XcYFk09CKJ13yBe6S/pLNfUtbNtRAliYokii2fKe1ojWjNvF9nds6cAYY8OLhK/rlf7/w48n4V7qNY1EG6I18DH//QyWtiipyx9x9nRQ5aIipefX4Nevb5BwyXGdB1wW5AdB0YP04F+cBs3fzvlWiShGWRcQHir0LMVeYFVKvi/ooBbz2bXP2KK47tJUBwD0aqFDLgiUhugJafugocZ6N5S66f7PQszk2MY2Zx7///ksRu4QzZs266vV4xsKWLbH0SskBN7GKM3NBpgfSpk2psL6MlpK7Ewv2dYTV5/GDV+ndm08meY4KtiVYhP721Qc6FiYWMUNRu7Udf6hPR13ibqVej9HWnuk/l45epxtn72j9e57dkXr/CxdPN87cSbCAqytZyKqbfgGgI+LiHwiQFlVVBZJsQ79qhL9GGqGxgA2gcLkC3P1bLjlXqUPzoZ78OsCHXz9ebOCagPev+7z2bJgBdyrZ8lcX4DH6LevE+hp0+pYNTjgTTVe0HOZJNg2qcGd0rPccenznGSUFZMqegabuGUQVHUoxXXRU07kUnESmeGaY8TPhwObDNJInUF+ovG1Jpg0nBe0mLuYW9F1LZ6MuMXNjzNY+rOs1pnHJ5NbzuQhxbG5DtXR0Nga1b6bvEr5dr7OzooBSh+VDNrATI/RPKMjiY59fJJ2PvsxTri4zv4VRo0GJwgV0wg4TW3DhJjstenSrTdeO32RnX+wTN6WGp2xIIhdgynBuba+YogGY1innjmXKkZE+vv3E1MirJ27y6yxSoSA5NK1OGyYE8HQMRaGlawVFjijuj9cF50MZ7169px0LRaEGN2VVFEP/ySIvzaWNPWXJpd6gBRlrOBacAzQqtX1/Q9cJAxCn1tXTh86aAAEAAElEQVRJH0DLD0Dbr09BFx+3Lz9kh1uca4cGlmQKXL/4kB7cecFyGmtH0xiGyLhx4wlFR1+hjBlT8/f15s2ntGTJAQoOPkufPn01qZGfuSDTE3JSN964xETB4jmpYLEcfLF52EjFUnyg6HGUcshCN6nXUumCHPmzkqVz2TjZF7oCi3ON+uILv2vZAe2fu0A2hRui3JHShIa96nB3C10ubCZq7yc5FIb7RzPfXBXg5ATaCCZuERsPkamBTmvPhb58GyYc2PTUAcLrrrPb8m12jrxwL8HHxwYjZ6utG7tVL+oi9F79V4g8MhioHN51gowJUDAGLO9IRSsUoDfP3tFor1m8AScFwE597Jbe5NisGl80ITx67aTAH8JXN8OMnw34Hm2YtpOm+i7jPdLOswqN29aH1+CkgMAF+2jP8gN8UTd4dRcqWDqf0R6bA5y7r2TDi5yFslH3uWJt1wUosNCgw+TOd4rmqJhLR64pqHswlYo/4Xn78h0tG7iWb7ca1Zjt7OUJ4fLBovhq1KcuZcubhWmP0HfBcAWGWrCaB6q4VaILh66IEOiPX1g/pAqwx8+gFFWDAgsFLz4DuQpnV0zYoAWTp2HtJzRnuuDeFWLiBDdkvC/YKzZJzdgmAxvEofTBmRj/D6Mx67qVvjuO66duccYZGsDQ3mlCwFxh+oHCWVXMjiqAhg+aa36L3FRKjzxanPtQ/0MKuqIxELZFTMeqOJahDFnSkikQIU3HYGaXWqnwNgXCwi5S+XL5+bMQEXGJ1q2NoUcPX9PuXadpwAB/emfCawlzQaYnqtcoTiiUL19+RM+evaPEhL1k7iFzak0B56Yi9Dh2/wV2IDMG6rSzN9jco25H4ZR4wD+GPrzV/ovRqI+YVEVsOsQdvYSKDnTcgC0zhHOUKsB1CeYYWPRlhylVUzIUZcCGidvU6tKMidLVS5BbB3GeZnVaotEEBYHY1eoJGuKsTkvpbxWZLvEBkTJ+D697WodF/Lu6wtK5HDXsJUxIZnZc+p1Q21CkTJOSRm/py7k+sMSf3HahVq8tMYBiv/9iH2rWXwjt10/eQTO6rNDarMYMM8xQfbE5q/sqWjNemA559axNg1d2Mlqml6E4uvc0LR20nm/7TGxG1u7fX9AbAlinh286xFT6IWu6cwakLjgTeZF2LhLTnz5LO2mMicGaP7fbCr6NvbK8/fdTC7+Rm1grh6JTbl4CgfP20r0rD7kIQfH15dMXWjFEnJfG/Txo/5pINsSAXuzEPnEhLh8LdEqgLMqQM7tCVoXTk9sSE0L679+l4Pe0mdJy8wvuiZeOXFVMw2BAAu0Y9rFKTmWpnBTnguPDcectnoucW9kpngsFYaBkwOE9OG6hFt8K375J9e/om8pAIQinRDmeRxvg2gGZqkD9Li56TWoO7z7NpmVgQEHbbyiwpx7YJqIIHBubJs/v77//ocg9Z+MY25kSOKuQJAHRB6+SvYMFjR7TkObNb03582Whs2cTblzrC3NBpieyZElLFlIY36FDiUtblBPKzxy9Sa9fvDfJc+QvnpNKVi7EC5kc9mcoqrqUY5vWN8/f0eHd6vVZmlDOtiTlL5mbKV8IcdYWJasUZUthLL67pK5eQkHRwOGdJ+j+tUdq79dimBA8g2+urtBr0L02d/7uXnpAR4w8DVIH36kt2UgF+S6BksBYHbrPa8e6OVAokMGWELAR9Fzow/ROhDJvnbVLr2NsP74ZO1Th8zDFBAUT8upGbe7DlJCje06bhB6pL3AO245oSD1mteYLqP3+h2iY5wx6+9I032czzPiZ8f71RxrhNZv2rYvm6UT3Ga3IZ1wTo0VrGIqb5+7SpNYLWPsEZ7xGUjPKWID77fxewh4esTDKFu3aAMXGTF/h+Oju60iVHDXbiu9bEUU3z95hE6mOU7+fpN06d4d2SRqsbnPbKzRyzx+8oDWjN/PtDpNacNEIdgamcpiUOTSrQevHi+lYkfIFuVGHyRco+HKhpUxZzFM8F//5+umbb0/+L1GmnBno7fN3lD5ret6jADRGZbMrWOy/evKaQlZHKHLIAEy/ts4URVXLEY3jOKTivijUchXOQXZe3zsJ4jVEbhGMIuRuagJ08Li2gnlJ/ABtdTgReo4e3XzKWZa1mulHV9y9Urgzura248agoTh98Aq9fPKGZQxVnUwT1HzhxG168fQtpU2fkipLhnqmhHvdCnT9+lM6evQG5cmTifLm/UY7ffDwFWU04bQ9aaxWyRQ1FLTFxC3IcufPQsXK5OEvdPQ+YTVqCjhLAs3QjUeMQqnC4uYsiUiD/aL0vpCVwxMDF+7TaeKEwghAQZbQNCJ/yTzcwcTrVqcRA5D3ZWFdjJ2h4MyoCmkypKF6nYW4V6ZCmBqYzPlMFhslNjhsPuqQPV9W8pkkXL5WDtuo1bQKU8RO01vx7TWjt3AOja5A53rouh7M/Ud3VubeGxMlqxSh/ss68e3t80NopxbFeGLCvb0Djd3cmwt2CK37OE3QW2Nphhn/RSBGorfTeDoVcZHXktGbelFdH2HklBSARt3IhjOZalbBoRT1mNPGqDoU7GUTYZD04QuVdyiVIFVOFZBJifOIoighquLTey9o23SxJ/pObvEd3Q575sI+q7n4tG1kRRVqfrtQX9zPj88Dsr9c2jrQ84cvaeNkMdH0mdyCVo/w5+IQboeyCQemYuKBxR/yhAxTrVtqtOTvpEY1XBQBu8bV6HTYeX5shFwjWxPmHHIOGSZmwI4FwVz8wV0S+jLliSAKR7lZqypnK44VvobMN7hH7l0pCsP6XdWbfsRHkDS9dImnl9PFzON05CX+7Lm1+Tb5MwT7JSduhwaVjVLgqYJsYFfdqQz9YaLnUEbOnBmpsmVBmjsnhEJCztGkiTvp8OFrrCH78+vfVLpMXpM9t7kgMwC2UrV++tQdevPmIyUm7GW3RRPSFu0bWPJ04c6VR3TNWJlkEm8ZDlgQ7eoDFHXoEqFbFBus/eu38axCWfNkZuv8qC0JW/rDSRHYJ3XGVAGLW6sRoru2a3GoWmdDz17uvGBdPHSFOfGJAadWduwEha7fUonLrw7unZx4Y8Jmic1UG7i2daDKzuW4GJ3WfpFeEy7QQuAEBqwbt43Ox6jX7OkLey8rajNavEcISj0WYrrvjL4B0rDFhy03irFetcTFpRlmmKEZZ6IuUc9a4+n+tceUNU8mmhE8hJkYSQWfP3ymkY1m0rP7L3itG76hp1EdFYEVwzbS9VO3KX2WtHpZ3EMnHTB7j0ILpklvh2Jrfo+VbDEParyriuy08I0xdPrAeTbWQJaYjFMHzrFWDBPMHgt8eHoJp0UUkth70mdNp/j/nIVy8L4C3fj7V+8VWWPKEzJM0EC/YyjVtyhWwISBs+KV2Ov8b3AUDlog6IZNBzagj8ouioMa8D4O6uQ2ie0Bww7logsh2Y9vPWUDFux7qsw29krTN68+mjPfkHeGUOccBbPFMQzRhIc3n9CxkLNxZBu6Yq/kcF3FpSxr+g3Fu9cfKWbP6TgSF2Pjrz//puiQ83EM7UwNfP7q169My5Z3oO7dnalY8ZzktzqaXRf79a9j2uc26aP/5MiTJzMVLpydO0GHD4kvfmLBTkoqP3/iNj17pFkTZYgBQXUpk2y/JNw0FBALw2UOCJHcgvTRByFfDNixSHCqtQE2QtnNaPv84ASnfsgrgRMTOlry4q0KsL5F1w33QwC1uolSreaiGN02Wz+Kn67Ahtd9XgfebPavjeINUdN9ey304Y0P1sPaUCvxuH2XduLpDuiOmvR2mgCevlMLG/4egdZjCtqe90APcm5ly88xoeV8nVw6EwMFS+WlOWHDqaRlYbZvHtZwJgUt2W82+zDDDDXYvTKChnrO5O8Lvjdzw0dSkXLa0b8SA2hQTWq7iB1pERA8NqAfU7uMCeRIYjIDwL0WDUddgLgTRJhgL4QWDJEkmoCCCQYZv/3xK/VaJIoqZcCFcJHU0ENRk6uQmFDBYRjTMaBuZxcqWqEQ2+tHbIzhC2DQGlcMEZRyx5Z2FB0gGqayDACTJ2WAcRJnyvgvKUxF5DyyrHmy8Ouq5mFJR3ed4KZq7qI5yaaRFWu+4aKI+Be5KIIjJCJJoEur1fybHTxYOBsmiSmeR1cXhdujMjDxQuMTjwdtmqbPxDap+G3Y003r4nm3ZGJm6VKO8khTP12nqNDuA3WMFECO7LE/v/xFhUrloaLljGdOo4zTR67T29cfKWOWtFTeqjAlFvC5SZ06BdnalaBBg+rS4iXtqEkTKypSJLtJn9dckBkIO/sS/GfUQeN39jUhW66MVMaykMnNPeRMsojtx41mOgALfCBk7UG9sqwAj85OvCCf2H+O7l1Vr/GKD3ffWpQi1R+8SYImpwl4/CZSVlfAnL08PVJ3v6YD6itcmNRlesm6tOhtRzksMzFQsmox3gAB5MF8/fwtPDM+YJcvTwXn9Vip9vUqAxk3XWcJN681ozbr5boIIFA0d9EcLOaGyYexXQfxHvWa354pQ3hdIz1n0LP7xjUSMYot/u5BCgfGRQM30JyefkbJAjTDjJ8FyJda0G8dzeuzhqlkNRtb8/cmc44MlJSwdNAGOrLrJE+KRm3urdeFtCZg6jbdR+i+PHvUpmpaTluUgSKIqYr5slA3yXFXHVBsLegtii2Pni5cfHz3eIPXs54rv0UeajLwmw1+6JoozuPE9A1ZX2xzL7E26nVxZUMOOBRCy/zh3SeejMiTul8l63oZHSY258IqvnYZextYKNg7KtQqo3BWdG5lT1ulZiFo/GDIyJMwOCuiqETBuHXGDoXzo/IU81DQcT52NB7lGBtlYH2WJ4wNe9bRSEfFY+F8Q3sHLaE2QKMX7op8rvScjh3aeZIbnZgiV5Hcrg3Fvo1CL+fc1NpkVvCRkpmHjUsZlTRRY+LWrWd048ZT+vLlz+9eDwppXSfP+sBckBkIW1uR4H7yxG16r8UFrDFRs57oxIRLlqCmQAW7kpQlZwZ69+oDxYYaR69WvV4ldvl5+fgNu/7og5wFs5OVe0Wdp2QZsqZXpNtvnpHwpMq+STVe/NFd27Nc2OOqArpu4KuDfw7HJ1UoVLYA2XpZ84bhN3IjJRY6TPRm6geE38hc0YSWIxpxhxAuUKtGaHeMyFtBlxEbk76B0diIoSdDlsmhHccpSMp6MSawWY/w78mmMM8fvqIRDacz1SQpARRhODDCkACbAnLKBntMY+G0GWb814HvwdS2K3g6hu9Hm+GeNHAZLNeThpOijMAFIfwDDFjeiUpXM64ZAQrRSa3mM/WtaMWCvMbritPh5xWMjn7LOicYDbBs0Ldiq16373OzLh29RruXCY1u78Xf3C0/vPlAK4cKF8UWwxrxHhy2/qCiAGs2uAGtHCamYzU8q9Kh7bHsYI21+X+//kJ///nNxRcGHyWqFmWqo/LUTNaVoZDjv//yC+vDYJpxLOQ0FzXQidl4VqUNE7axlgza7xoNqiomf4pCqUMtxeNir14nmYygGEuf+Xtbd7BPUBzDet8pASv5bbN385/1Ojlx7Iw2iNx6hK8rsufLonH6pgly1JBrS1ujFDbIHoOMBTEENRtWIVPg65c/6dD+C3GM7EyJ0aMCqFfPtTRqVABt3nSU88jka/rtAcdp+TJhAGNKmAsyA1GwYFbKnz8L/fnn3+zKkpiwdSnDF7C3rjymO9dMYwSArkAtKZPswNZYo10YyxkYsuuPvlMyYP+GaK2mOTLQxQJN4njIGbZE1wQsXk0lkTREveqmFThP8gQMlAR1eqo2Y5ryZhETeIyunRTuT6YGTEVAXQQ2TgmkOxfVv2ZsEqCiALD41UbThdfTZ0lH3swwedw4RT/jkuKVCpPvlBZ8e+nAdRyaaWzAGXJ84ABhh3/uHo31npvkJlA4n7DsHrulF3dlLxy+Rt3tRtOl2MRdX8wwIynhyombrK+8euIOX8iP3tiDvAfUM2lQqz5AQ2nxAFGAtB/bhDWsxsbqUVvofMwVPg/D1vfU2dof9LoZPt9cFaEF1oSzURcVGik47CIXTBmYeM3vIdngt7FXmGTwsY7YxFovaOjq93CjT+8/KWzuvYd4Urh/DDcLM2bPQOeiRW4mijZ+3L/jMiVqt6tJm6eJ/UXZ/h66sqx5M3MBBd30qbBz/Llo3L8+a8CB9hObs2HVXqmx2m68N98Hxa3s/Niwl3ucQil2zynOR4PtPvJJ4wP7/EbJqKtxv7rfZbHFL1gvHrnG1z/1u2pndY/Xg+w6oK6vo15TGhSaZw5e5tdqrOyx0E2CUmrlXJYyZjVesLkyjkVeoY/vv1DWnBmodOUCZEogWwxW93PntSJr66J07NhNGjliGw0ftpW2bTtGe/eeoWLFjDvhVgVzQWYE2NoK2uLBg4kbEp0uY2qylIxFTElblEOiY8PO0+vnxslcc2trzwsEXH/g/qMPKtYsTXmL5WQueJi/4EdrA0yyqtcXXR1NDorK5hjofmExR2dPHVzaOLDoF+JfbDKqUMAiL9X0rsG314wRm0BiwLaRNXPpsfks7L1KIyWwimsFFi7jPjM7as4xk4EJXPe57RXmHDfO3NbrOJHJUqN+Fe50jm8+h/UhxkaOAllpbEB/Fn+fDr9As7osT5JarSrO5WjOgeE80cM0eVDdqRS23jiOp2aYkVyAz/ueVRHU320yvXj4inIVzkazw4aRlZ7TAlPiwuGrNKmN0GTV8alFTaSsQWMiNvg0bZom6HV9FvtSnqLq867UAQ0vmGpB091x2jfjDVXA+j+78zJF8VbWRrCClBG8MpyuHr/BDSRfyd0XwBRsx0JhptFjvg8XjnAaxvuYs1B2trlfN24r/38p6+L0+KYwzoCWS1WdDVo9Gqnx7e8B2NwDMAmR9+PogKO851VyLkdlapRkR0c04MraWSjcH/f5RdCDa49Y56ecl8Yh45J2rF5nZ5XTsYNbj9DD64+5Genu+/3UUBmQPQA1m1XnSBptcC76Mt04c4dlFm7t9dN+Ba89qDCPwpTNUGBvDpeikEBXNBUO7DytyB4zdXzF169/k4tLWY6zatCgMk2b7k2LFrcjd/fydOTIdXr06DXZ2X//uTc2zAWZEWAjFWSxR2/Q58+JG+7q4C42pYjdp012oVagRG4qVj4/28+GSyGAhgIuP3D7UXb/0RX4kmKDkHM9dHn9XlJQdNiG6ASDorGJeEnTL1jbq7Pah9gXXTJg7bitagOTW47w4gndkZ0n6PrpW5RY6DyjDXfnTu4/R1FbNbtMwtJe5Jg95PBMbYCNpkaDKvy6p7ZdoJfmEEV6v2UdKWehbPT41jOa4Wt8PRlQrGJBdjyDiUnYhhjyGyMuCpIa8hXLRbP3DyOb+iKIe+3YHTSr2yqm3Jhhxs8OfM5ndF1Jc3uvYQMB6zoVaOSWLtyIS2q4e/kBjfKaxe6A1u4Vqfus1kaf3oEaB0dbuUiwb6z7BfGx4NO0W4r/6Le8M0/ZNGHDxO0c5Iz9oIMUj6KM18/eKCZebUY3pUw5MvJt6LLmdF3GRkqwkIfV/MMbj2mLFA/TcWorWj1yI0/rYBN/KlyYTv2eQkzf4i/7sMLH3iVDWVsG0yyc9wJl8tGdi/e5gHHv5MzFFtBqZGN68eiVYjrWepSghP/59U9FQeg9pGGcc3E26hKbVUEDCF1ZfGBf2iRN6zx7umkM0oYE4OB2UcR49tA+g277PEEpdWxhw4WqrkDxGbo+WpE9ZgyciLhIr569owxZ0pJlrdJkCrx784liIwU7p5YkzTEloBtr2sya0ijFCaRPn4qcXcpSfY9KVKFCgUSZxJsLMiMAo8wcOTLQly9/0fHjiXeBDVjVLEkpU/9Bj++/ostnTJcg7uotMjlC/A8Z7QIZGUwA3H/0NQxxaWXLi+/tC/fprA7GKujGWVgV1Toouo6vI3f/UKAc26te91a/W21eONE1i9h0SOV98pXIQ3ZNxPmUxcaJgdxFcrLFL7Cw10qN0yfkmMlmHdiQsclpZZyxyJdzaW6evUt+IzfpTSsEDQd03JigY7R9nuiwGhtVXMpRr/nCct9/yg4u6pMimJbk14U6jG3MNB2ESPd2HK+TmY0ZZiQ3IAKij/ME2r9BOPG1G9WIhq/tqvHC90cB7IlhHtNY64PswyF+XY1uQgBt7sSW8+jN83dUtEJB6jRV0Lt1AbTQshFIgx5ucTLCVOH66du0UcqH7DanHe8L8QE9Fx4X0yuPbt+oeKAKXj56jdevTjPa8HUDCjQUThUdy1LqDKkpbN1B3jdgEPLp3WfWiGF6pmxzD6CR2G5Cc4oJ+iabUNaWodjCZ+SPPwR1E0ZW4f7R3ByEjgzTMc4d+/oXlapegso7lFbov1AsgeFRt7PIN5WxWZpCgi2C/48P0CIRN4BGbEIUROjcoXlDTlyR8trR7x7fecYumkADHfLKlBGz4wS9evqWsuTKSNaSY7ah2L9ZOG5DO4Y92hQ4FHqer80KFstBhUqI8G9T4dOnrzRl8i56/vwd/fbbr/T161/04MFLunf3Bf//h49fqJGXaXRy8WEuyIwALCg2EnUwOpFpiylT/UHVHEvFCdAzBewbVFZkkl09bRzLcEunsuz6A/efmJ0J26yru3h3aiksagN0vHCXO1XaBEWnSZ+a6viIaVx8dydl4GJB1pKtnxCgVkvWWHJvRGbL03vPKbEAzj64/JgKyl1NTYYmCMfGwgjKijYh3HAKhJ4M2DJjF2sP9EEJyyLUaZqgviwbvIH596ZA7bYO1Gq4J99e0GcNHQwwjk7SFGtMox6uNHB1B+5U3774gHo4jKUDm4XTlRlm/EyICojlz/et8/e5wTMpqD817etucuqSPkBja3iD6RyYjMkd7O0RzWJsrB65hS4cusqNweH+vTTqldRhXvcV9PLRK8pXMjf5qJh2KQPFzEzfxfynbUMrsm/8fdbU8X1nuKhhHfHSTgp3wg9vP9LKYf58u9WoxpQ1d2Y6uO0InQw9yxOnngt8FPuPc2s7/j/gzTOR46ls2IHibPjmvvRHit/o/avvm4hygW7lXpl12SgSannXUEzDmg32pLcv39EuKbrGe7CnQjvmL1ESG/f3oBSpvk1H4IIcu/eU0PNKzsPxsXm6aKbWbl9T4/QK+nY5CNqze23SFruW7OfpYsVapamARR7SB7JGv3YbO6Pk38Hc7cg+MaV0amJ8baSMcEmC41DX9NOxyIjLlDlzWsqaNR09fPCKVqyIpG5d/Wjq1N20dGk4ubqWo8qV1Qd9GxNJb3VL5jqyw4ev019qqGqmgoMUEh2196xe4bzaIG2G1GQj0SNDNqie/OgKdBBlkekePWmLsu4IOLr7FHdVdQqKziuCotVNs+IXcNgcoDvSZMhRv5srF4qgecDiXhWKVy7CnTtsPOjcJRawicMBC9i1JJTOHRQialXAZtRjQQfe8EDd2L9aUB8SAvR52KTQEZ3WbiFTUvSBRxcXsmtkxRvn+OZz1YZzG4oWQz1Z74HjndJuEZ1OIA7hR8LCqjDNixxJ5W1LslZiqu8ymt1jtZnCaMZPQ1Gc03M1TWy3mLXBpasVowUHR1F5u28mEUkJcO8DTRHmUDAKmrBjAGuRjI3Du07QZolN0W9pJ9ZB6wo0/yIQvPzr/2jQ6m7MLNEE6Kth0oS9rPs8oQ+O/9oRpQLU716bI1ZkrB+3lR0Z8xTLxf+HhufywaIAazqwPj/utRM3eW/BOoapGQpNIEXquMcFQ6rqHlUoZHVcAzB5OoOCB3pgHA/g3NqBghaG8GcJgdOgSsJdGPsQpnhVpaw17PmPbj7h9wv0RmXIbsRwZVSl0bt59g6dCD3LUzll3Zkq7F0ZzoUMYl2s3CuRNsCxy86I+k7Hbl96QOdirvL77SblthqKyKATHDuB7LEiZUyTPfbi6Vs6e/SmQj9maly//pgsSuXm21u2HKXff/uVtgX0os6da9HNG0/owoWE2UHGgrkgMxJKlcpDGTOlZpvM06cTJ2NKRqUaxShdhlT06vl7OnfMdJRJF4m2iC/lZyl80VDUbmXHi9q56Ct6m3vkL5GbqriW4wtqXYOi63cRi93WWXsSpGIic6tm0+oKGp+maRo45QA45uoeFw5QADp3oFwkFkDXqN1eWPtO77BQYzZZ9nxZyWey6KRumriTHZu0QecZrVkwDuH4or4iEFRXCPdGIVpHPtnkNgu0mtLp8zzdZ7ehGvUtmdIypvEspuokVSBvaWJQf2oxyENhjd/TYSzd1DMDzgwzkgLuXH7ALop7/cTEpVk/d5q6ayDrg5Ii0Cia2HqBcDtMn4rdWxHHYmygaJjaTujGGnR3JduGwmRLV0rl3G7CBbHFsIZUokpRjfd/cP0x+Y0SplMdp7ZUSdmDthjHli1vFnYslHHr3B2FvXuXmdAt/07b5+zh+8Icy6NbbVo2aK2ieQc9M+Q5KMBByZaDnYG0GVNzcw9aMBiHKOvHwNyQp2MwrDq5/ywXH7gd6ieKGVAln917TjsWCi1Wh0kteMqKfWTjFLGHN+xdN46zIqZjkVvExK7F8IYqz88WqTiGWVauQtk10ky3ScZhXn3qau2SiMIZ00Dsofpa3cMIB7B2q0BZcxvnOyQ3412afT8tNRai9pzla6ZSFQtQjjym/+5Xq16Mrl0V157p0qUih5oW/D6VLpOXUqVKQQ8eJN61mbkgMxLwBtao/mNoi+gU1XARXPBIE7otlq1WlHLky0If332mQxp0VLogW97MZOlsmLmH8pRs35pInSYymIxgUb994R7FavGakJkCRG+P1RjujCmZHEB96oDq/DYstOjgobO3buwWSkygYMqSOxNr3bZI1At1qNvJmcrZl6Kvn77SrE7amWygKO2/sqsoGFaGsxW0PsDjjNjYm8/l8X1nacMk7QxG9Pn+Dl7dhcrZWfDne1j9aXxRklSB4201tAFNDOzHnfm7Vx5RL8fxFLg41OzCaEayAtaTXcvDqYf9WLpz6QFnVE7Y3pfajmxk8jBYQ455VtcViuDnsdv6aq0N0gXYG8Y1m8OZXKWsi5HvZN11Yyg+YAQCaiUs4ZsPFRRtja+t0xJ+7oqOZbggio8nt54pNFadZ7ZRmGHgudjI4+9/OFMMNMJXT15z9pcc6owGJBpsCKM+EyFyprLmyfLNOVHJO8GplT1dPHSVZvqKglRZP4biDdMxXP9cPS4mKh5dXGn3UrEG2jayYq34mtFbOJMMe5hc3CB25vb5e3zcHvH0X2vHblX8fpHyBb977TAmOSC5KDfuL6QH6hCx+Qi/1ozZ07PeXRvguWU9s75W95g6hm0UxVPdDt+/f/rgxvl7dP3cPY48qNXIdJqqCEl6kxjTMaB8+fz0+++/UmOvuXTy1G0KPyAYMnBWvHfvBVWv/m3ya2qYCzIjwtZOsr+PvmIy6qA6yB/e6H3n9DbISAjoLDk3tYqTQ2EM1JES6/etj1bQDnRFJccyrI3CxXSoZPOqDSBSdu8otGGbEihMgIKl8zGfHvDXUBwgR0XeyDZNFRtXfKBYQccOQOg0FvrEArLJOk5rzbf9JwVw91LT+95vWSf6I9UfdDbyIu1dLvjwCaG8fSmFnm5WxyW8MeuDwuXyUw+JMrN27DbWLZiKzjl6c2++sAKNdXCdyawLScqo6FCKFsaMISu3CuxCt3iQP41sMsccJG1GssCrp29oVNM5NL/fWqatVapVmj/PlWqaxr3NGMAF84phG3mfAbtj6NpuKq3gjYH5vVbztB60umEberK5ha5A+DOmR2hqDfLrnqCWaM+yMDoTcZHv32dxx+/c5fD6/YaJIgd28pgSyQhZFU4XYq4whbDrbGGYtHzIem6SFrcsQmVsS9JmKbcLtH1M7jB9g3vktyf4dhO6tXFNZrDDc3xgesSPY1lEYVtfxa0CHd19kidl7Sc0p3tXHlDoGjEpwl7L2rG//6bVI/wVYc+gZMq4de4uHdx2lO/XaqSXyvODnE0UnCjuoHVWB5wnZJfy83SvrbXm79LR63T99B3W7Lu20c8ZMTIglj68+cTTuwoOxqH7hvgLvXL12uVURgAYAw/vPKer5+7z+2dbWzTqTQ0YeQwcVJf6D3CnAvmzUmDgCfJqNJf8/Q+To2NpSpuIJkLmgsyIqFixAKVLl5Jev/pIF84nHu8UKFulEAfovX/7mQP1TAWnJmLxPRN9lZ4ZaZQLt7vs+bMwzzpqu362+igaZK71Dh0t8Bv2cOMu2/noy3Qp9nqC928+TNAYIjcf0lhEwSoXCws2Q3WmFOXsRNcOC7wsME4s1GxWgyrUKsNF8OzOSzSeM2TGeA0UUQFLBq6Nu4FqQLtxTalgmXycKzO9w2K9pzcure0UOq9JrRcwFdIUSJMhNU3cMYDF+SjGhtSdwseelJExa3oa7d+Duk5rwZbRx/adpc7WIyg6SL+ppBlmJAaOBp+mLtVHUWwIjB5+o86TvWn8tj5sDJSUsXHaTtoyS9DQei/yoer1KpvkeaA9ClkdwUXfkLXdmRqoK8D8WDZIaLd8p7akfCWEVkYdntx5RksGCDphu/HNKFfh77Vq+9dF0bmISzwZRPakXLDB/n7ZQPG7rUc3Zbr7qQPn2G0R90GBNq/bctZHWVgXp+P7TitofapQyaksOy7CVTI+MNl6dPMp768Prj1WPGfgPJH15dq2JuUtnpv3VBhjWNerzNMy4MCGaLp76QFnh8WfcPlLjpI2DatSoTL5v3te7HuhayIV1E9NADMGNPIUqVOQe0fNGWXK2D5f0CsdmlTTy+oekPVnbm0gCTH8Mh/NkvCAYyanK0buOct/VrAuQhmzmKboUwcrqyLUt58bBQb1oVmzW1CLFtXJu7npXqsqmAsyI1fa8ngzKkp7C3ZjAF862dwjfJdx6ISqAMpiuerF+MI4bKtqwwpdgZF8nbZiSrZLy+mLKjg2r0Gp06Wk+1cf0alwQYXQBlnzZObiBNg2K2GDDVgOIzwZCz2cBNUB3SnnVqLDtXqk+hBo5JIBWOgf335KhmgadKFrYpPsvbgjd+KQ7yJntqiDawd7srAqxlx/uC5qU1yhKzh0XU/evOFatVNyutIHXWe1oRKWhdlaemyTWXpPUxNCxuwZaNKuQXwBhM/SMI+pTBlKysB76dHRkeZFjqIiZfOzc+n41gtpeuflSf7YzfhvAZ/Hmd1W0qimc7nZUbB0XpobPpIadHFOki6KyghaFEqrRwl6ue8kb6NlO8XHleM3aH7PVXy7zZgmVMlR92kBmDKTWs3jSVZVt4pskqQJWM9ndlzKNMDSNUqwLb6qieaSfmv4dssRjTjCRcbq4Ru5qVq4fAFq2KsOa5Nnd16qsKF/8fAlhzpjL8hXIhdrxdJnTUdvX7xnGpwy8pbITcM39aUD/qrZLqArAqAhvnn+lnIUyEZFKxXkjDUUsJAWgPURtl78fsvhXorib+0Y8f41GVCfmSIyQFFHkxVoPlR1sYWJF7RreN7S1QUjSh1k7VjttvZaT5RgdQ85BNCwh/aOjMpAEXj5uHCbdJEcqA3F4ZCz9P71R8qaOyNVsDPNNPjff/+liN2JS1dUdS0K+mK+fFk4yiqxkbRXv2QIOc07KuoKX7AnJmp5CH700YjLHKxnKjhJ6ezIozBWJlnt1rZMx7hy4hZdO6WfoQK6Zk4tbBUbpy7w6i2mP1gMNdH3ZDQdJAw50MHUFCyNTQsLI6Zk6izgS1Urwd1AFFTahjCrwgiPyeSVvQNv5toiT9Fc1GpUE769pJ+fRnMRdCP7LuvE7xOKK9gda4NCZfMrjEGWDljHAar6AAHd0JOBmgIaz9zuK02ml4KBy+Q9gyhDNjzXHRrRcAZ9/vCZkjoKWuSh2QeGsyHC/6TMsk7WIyh2n+g8mmHGj8TJAxeoc/WRtG9dNDcRGnZ3obkHRlCh0nmT/BuDgN2FfUUx0nxIA8WeYWygwIBuDAZD1epVpqYDNOuU1AE5kDfP3OH1EgHQCQXbYi/DPoUGXf/lnVVqlxb2XsUNsQKl85KXFN0CXD1xg2n3QPe5HVj7B/dgaJRh5NF6dGNaMXSDItMzXNI3vcX06xdi5z4ZmApN3DOUb4N+GB94bBT1mMBBBwa0GN5IoYWu6W3DmZvYS8E8sXQtrzAx2ecXyfs7mm5wf1QGNHG4ZkPxiqarqvcFdE7AW9KSqwMmcMeCz/A5b6CD1X3QwlA+BkgwCunpYghTHKCae0WOjDAGQjcKuqJzE2u9NG3a4OblR3T3xlO+vpCjnP5rMBdkRkalSgU57fvFi/d06aJ+F576AgF6CNJDBydmn2ojCWMA9vcIo35w8yldOKr9xb8mYOGwqW8Zxx1IH9TrLKgBsXtO0aNb2k+bUDRYupTjxTBwgaAMaALMHyysi3H3UXZwUgW4bsmOhuvHCytdVWg+rJFiU0QXUleAOonuII5n+eB1Ov0uclaKVizE3U3Zxlgd8lvkoVajRLdxQe/VWmeoYVOC/TCmWpNaztVb54hCaei6HlxshK6N0irUW1/kLZaLJu0cxDRG5P+MbjJboyNlUgE2NBgiTA8eQrkLZ6fnD1/RyMazaXqXFfwem2FGYgMX0HN6+dFQzxn07P5LZg9M2zuIOk5oxgVAUsfB7cd4egSAGt96hGa6mkHOja3mc1gx3GUHruyi19TwZNg5RU5W36WdVLokKgNarsXS5AvFE+h+8XEs5DRFbBQh3R1meCu0aNBkYRKG5lit5jZU1taCG3sbJor9zmdyC27eoTjjDMXzd3mfwroKxNe09VzoS7kK5WCqo7J2DLo0QK4rK7uUZ2t9aMlgrx8dILRfyB17dOuJgvHRfGgjxbndOHm7Yjqm7Kz4/OFLBRVRXbGFfR50y2KVClFl53Iaz2eARJ1EQa3KNl8VMJkMkdwhPbtrDppWBzBkZDMPt7bGsbp/9vAVnYy8HKcZbwoc2CmYXVUdSlJaKQIhsXDixC16+fI9/WiYCzIj448/fiPraqIbE5XIbotAzXoVTE5bxEJm30AUT8FGyiQD3NsL2mL41qN606xgga8orCQutrZoKFnVB6+KSPD5sfDL4c47Fu2jTxqmJ00HenBXD5vkxSNX1WrJSlYtyhtVkLSY65oxI+P0gfN0OVb7IGVsiANXd+NJ3qGgY/yjCU36e1BJq6J8jmZ30o66iIuKAau6cPfz+qnb3L3VFxVrlaH2E5rx7UV919CFw6rPqTEAg4/xgf35YuDUgQs0rvk87lwnB5SyKsoGCZ7dXPjzun9DDHWyHk6Hdn3fdTbDDFMBnzffqsMUuhZQaxfFjKEy1YSmJ6njyJ5TUuTGv2yy0GmaMIcwBVaN2Eynws7zejNycx9F0aILkNc4te0Cvg3tEqzlNQHrNyjoWM+xB3n1/X4i9+XTF5rffTnfrt/djQqV+6av2rlwH2eK4Vg7TRdGUSuHbeACA03Lyi7laN24rfzvVdwqsmEIKIp4PpxH5ekYHgO29Yd2HKOlkh5N2TkQv4eGMxqoR/eIdcyrrwct6S+KSdd2Ndl4C6HTWKfBPEGBCICOiOkY9qC6nePmjm2aEsT3B1WzjAqDFhRiMEcBsO9rev/BmJGNxRr2+p72qQ6h6w7yOYF+Gdcw+iBs42E2NsNjwPDJGDiwNZY/I2Wsi1JuyUjF2IAJXoR0zeroIXLiEgtfEXUzejs1aTyPbtzQXzJiDJgLMhPATgqJjoq8nOgW1PaSjgx5ZM8e6z5p0RauzUUeV/TOk/ThrXHokQgBzV8yt7Bs3SxG5PqgYU9BEQj2i2SrX22BrlcBizy8kUBQnRCw0eUumpMpHLsWh2qckjm1FFRKfzX5ZVjg0bUDAubs1nlKFrU17vla0GsVvX2pfZByobIFyKufh/S7K+nTe/XvKYrLASu7shYAXdPQNdpRF7PmzsxujQC6t6DH6IvGfetyHg8253HNZps0xw1202MD+nEnH9EIU9ou5G5rckDK1Cmo08RmNCNkCOUpmoNePn5DY1vMp7Et5tGzBy9/9OGZ8RPj5ZM3rGPE5w2fuzxFctDU3YPYfEaediR1nNh/jsZ7z+V1xqGxNfVa0MFkOjdkXymHP+tDWWMdmO8SNsPIVzI3x5skBKzfKG4wWe+/ootKShr2LWRQQm/deoyguMsmF6uG+ytcDDPnzMQGVphuATDy8BuxiQsNmDvFSPooPI58vKDCy6jlbUP3Lj+g8U1mMt0wvmZMPvcwmXr56BXvvylS/05Xj99gyUL7Cd78/JGbD/OeKjsJw45fNs1q2Ms9znQMLI/dEtOi9ajGqs+RXySbi2AaZ+eleUq0fV4wN1YtrIpq7b6J45NlFtD66fMZ4/iIFeK6pa5PTaM0DfCYoZuFo7azCadjCIJ++ewd5+la2iZuo+bUydv04cMXypw5LRUqZJqCU1uYCzIToErVwpQy5e/09OlbuiY5ACUWEKRXunJB/iJF7TWdbqRkpYJUAMLcz39SZKBx3NywgNRpJ8bs6KbqW8xWqlWGheIo7IKlrqy2zw8xMhAwd2+CtDpsXN6DGiiCItFFUweIjEH1OLL7JFvrqoJNQyu28EVB6C/RPbQFjDaAPks7c67a5aPXqGe1YWz7qy3Aw8dGB7rMiiGC768O+UvmodYSdXFhn9Vauy6iiJUdp6a0WaC3gyHeKxR3KKBfPnpN45rONlncA1DezoJGbe7NFy2gL033XZro0RYGT8uix1DTvu5cUB/adYo6WQ2noCX7k9XrMCPpA5+nHUvDyMdyKDt94oK7SZ86PK0tZ6PZCCEp4XTEBRrdeBZPThAaP2BFJ5PpZ26cuUPTfZcomk32jfW7+N21ZD/FBB1jtgPMlNCQ0QQUIwt6r1JQFQuU+l7Ld/30LdooaZtRYMmZY9if53ZbxjQ5TMKwrv/59U9FZphzG3umqMvaMuiVUZjBgOMx5ARSvSAXXqgf6vg60aapgYKFoFRPIJ8MxRAo4zD8OB4spimYyK2fIPZK7yGelClHRoWlPZ5fzhFDgQbHSQR4x9eObZwsng9GHWBfxAf+b6Nk1d+ob12N2Xh4fTuXiMKq6QAPrYuik2Hn2UAKx+espxHHuZirnOOHZoeztzApMxQXj92kBzeecgSCTV3TTa5kRpdt7bJ6RTsYAvg9ADY2xfka7UfCXJCZAClS/E5VqxaJ82YnJmSHGnkEbApgoZHtT+V8CmPAsWl1nkTcOn+fnYL0PTaZg71jUahO0wzHFjYcmAxOfdiG6ATv79TKljeYV0/eKDYedXokFFzAJmlxV3Xc6PABCM+EBbG2KFa5MP+Jad3s6PGstUI2S6/qw7TWeWHzRkEHBC0IpnMHL2m8P6gboLiwa5qvZtt8ZaBri40f1I4ZHRbpXXjjwmD0tn6UNmNqunjkGi3otdqkE2lL53I0dF133pAPbDxEMzstS1bFDDbVdqMa0fyoUWRRtQhTWxYN3EC9ao6ji1rEPZhhRkK4cuImf54WDljPTaJiFQvSvIiR1H60F3/+kgvORV+mkY1mst23tXtFGrIGlG7TXCjCLGK01wz68vELVXYuq6Bj64o7F+/T4n5+Ct0WdMHaTNPwPqGgkin4yoAr4fT2ghGAoGTlzLGju07SkZ0nuPjru0xo3VDcwGgDRiLtxnnTTN/FisnX0d0nFJRKcQBxn6vN2GaUMUcGLp5U/b9sngXHQpmO+Orxay7u2KSjhxudibzAjsE4plYjxbQLRaI8xQMLRTl3DHvj3hUHFNMxVQUUpn1oUsKcpE4HoQdXh93Lw/h8omFpXbcSaQt5OubSyk5R8OqK3dJ0rGZja72orqoQIklS7DwqUWoT5XGhqR8teR7UrCskN4mFv/76m2IOXY1jyPcjYS7ITARbaex6MOpKotMW0WX49bf/0fWLD+n+LdPkNQG1vKrywnf19B26dck4BibpMqUhu4ZV+fbOZfpb4NdsWp03hSd3n9OhHWIj0NbJr5HknrVp2o4EL7ixScsiYARAazJ9aDqwvkLvpc5pEMYXFWqW5g1n3Vhhz6sNSlgK3eKV49epcLkCND92MhWpUJBNHMCn1xawV67jI4Kyp3dYqFEbh8Kk/8ouTF1EWLPsQKVN4Td0vQg5xcQQGjx9AcH0kLXC5AM0051LTGfyASBzaIhfV+76718fTbO6LE9WRRkARztQGLvPaEVpMqSi62fuUF/niWxFntQz18xImsDnZnaP1dTbcQJ/nvC5wudrdthwKqKkN0oOOBd9hYY3mM627NDyDFvfw2RdexQ845vPpSd3nlPuIjnYsEifKRz2nYkwS/r8J1PvPSU9tCagEDkRKlwVQUFXNfmBturG6duc2dVjvs+35/v0lRb1Xa3I24Ru68H1RwpmR9c57dlyHkYeaHD+8++/TPtEwQUGCPYMZcAJERMuNCKVqYoyMmZPz6+taMWCdPHwVS6c2o7zVhiHeA/25H1F1ia7dXBkqQCAfQnaMRibNOztHudxUUDiuLDnlrcvpfL9kbPJoAXXFO4MhgaYNUBjdrnV7n18ePMJOzIC9TqJvVdXQOIQs1Nc59TtUJOMgQ/vPlHUDqHTq93COBM3VYiNuEyfPnyh7LkyUqlKBSgxcebMXXr39jNlzJiaypbVz9XSmDAXZCaClXVRzjO4f/8l3bmt3YTCWMiQKQ1VlIxF5FwHUyBj1nRk5SLyUUI3Cp6xMVDPR3ShQA3T9wIR3Vh36XGCdLzgd/dx5A3o4fUndEiLcF2Xtg6cWQVOuyY9VfHKhcm6bmUWh68dK0TO8YGNpt0EYREPF0Ftp2SlJIH82YgL7HqFcFVMu/B4CMI8uE3796fjtFb8erCZrkyAuljAIi+1G9+Uby/uv4azXLQBikafKS349pL+a9nGXl9UcS1P7ceLrvLCPn5MNTIloF2TizKIt2d3XcEagOQEXCxAZ7DixCRFVg2syDtUHkLb5ockG+MSM34s8DkJWLCPPzfBa6K4+ejYrBotPz6RP1+moviZcjI2vME0prvDenzkxl7cpDMVlgxYxyYXoJmP3tqP0mXSLwx36cB1Cov7Aau6JlgMIO9SdlVsO7apysBo0N3Xjxf7VLc57ZkOKGP7rGCeTGGfANUdwHQOnwe4H5Z3KKUozlxaO7A7I/D6yRvek6CxUnZYHrSmB9/euyJeU08aWL1++pabv2ml8wM3x5tnbvPkCgWfeycnOht5kVkdKJ69pRwxmJHIRVuL4V5xtGPQHQdLWnE5CzQ+DmyI4T0Y7pDuvprDnSM2HWL6fOZcGRW5ptogYG4wf2+quJZjJo0+CFkbzYVlySpFjNYAidx+nCmn+YrlJAtLzdNWY9AVHeqWT/QcQgxMgBo1iieJterHH8FPCljfV6pc8Me5LUqj3wM7T5l0QifTFsO2xhrtIq5E5UL8g8cLljI19AEKMnT90PG8cfaO1r+HzbFeJ7H4bpm5K8Hzhw3Aq19dvr15uuapGkI+5cVbnZaslHVxRS7Zluk7tDpmUE7AP4fwGBs8UMKyiGIqN6vTErb21QYIy+y7vIuCugiRtCZAJA3+PS5ipndYpHVx4tnDjazdK/HmPMF7NndO9UXjfnWplncN7q4iw0ebLDlDYNfIigatAk3nF9q3Jopmdk5+kzL5YqjvgvY0M3QoFS1fgD68+UTLhm2ijlWHc8c1saf7ZiQP4HNxZO9p6lJ9JC0dupE/N/j8TA8eTAOW+HJDKLnh7MFLNKy+KMYqO5Wl0Vv6mJRmuWfFAQpaKJqFg1Z3Zd2zPkB2puwACLfcLLkyabw/1udp7RfxegtHwfhTI/n9ndNlGe/BVWpX4AJIxs2zd2jvYilzbF4HSpU2FZs7gb6I/RY6M4REQ1sGTXSkZDiFYlF+bGXgMTJmy8DuwChoZLDZx7+Cmg7YNqrG90FBh6iW9eO3KWiImFytGycYJbU7OHKhCOxesp8bpaDwu0kNWhnbZu1WOCsixiY+sJ7L2jk8n6bPAl7TttkiCNqze22ti/i3L9/TPinPs5GkX9cVOE7ZwVR2qjYGZCmKa/NqJnMVRV7uMakoqpnIdEWct+hoQVe0tUsa2tb/s3cVUFGmXfjuurvq2l1rd7eoWISggiIGIhK2ohiIASIhKCGgYmAhqNigCIqFCAZ2d3d3r27xn+d+842ATDID6M9zDocRJ7+Z+d733vvEz9n9BH5kdJAkmsNtMavR1rA+Z4U9vveKrpzJeOOvCTTvVJeKlynCJ5ZjezSXfdZ9mHDyjAtPUtvRDk5OEGOLWjJVYDbKmGkVV47doAvJigvqrkP0hanazad0IEr2NAqBk6JL00rPjTKvhywVsWP4+qns4OnU1Ek9S2HRDBkXxlQLAI5YyE2Btszfdj53DJVBC6PG1Nm2o2CJPGKJ9P4yAjpLk8LsWUx84eAV2qKkbT9O8ujmYvGEoDl4lGCrrA5wX46Lh1HtFtX4tbr3CuINgTYB1zVspMRJWeDQJd+N+2J61GtVg4IT3chxwSDuBiPDz9t6IU028acrKgSN5+LHx9WTt2my6SzytJxHD64/4Y32uHkD+fPzvVjZp8fZ/ZelNEXouGDgo81i7ELyFVowVjDTsPPsS217COuUqsD0JmiooNOCBgyhxoqA8zOmSaD44byd0WRgV3ginU26yMdgzMKh0g052BfBI5dyPpiueSs2aYJGa5HjCmne5OePnzlPE4BWGEwLrI1oFqbf1xco+ju1MWtJt8/fJW+LoDT/J1IXcR5Hk1Q8n7fv05qLUBhJQb8NM5FjO07TmcSL3BwVm5BY62AQAlhN7UW//va1SMJ+ZZvEfAN0x4wKjkNbjtP9q49Yc2Y6Iq1NfkamHLcv3Oc1sFu6wk8e4kL38meuWqNK1ERNm/pTey+wNKNgkd+pg7n8iANlAQkKpCiYShr0EbTv2sCh+As82atSqyz/ZCUuXnxIr19/pIIF81GTJllLlZSF3IJMi9DVrcknu9u3n9O9e8q50GkKKMbaStLOxcA9bQAdMWjJgN2SNHdNoIN5KypSoiCHiB7brb5bJEI8gYR1hzhEUllgU9rZRrCqj5oTp/D6oEKYjxHcmzYExMidLIji4eQtx2VOyZroNaA6OjWZNx8lsUJWhME+/XlzBHG3mEuGRch59TheWNFdnNrNR+miAdRFZLagI7peYhksC+DrD59lw5eXT13Hz0EZ4P5dJEHP0ByI4ZzqAK/RI3ICU0bw+L42C7Q+tUJRBvqiaPThN3CR3OI1JwPnKmOb9rT8pA/1n2TK2hI4d0EXhOLs/rXH2f0Uc5GNeHjzKfkOXkzj9L2ZdfBr3l/IYnxXCjvlS13tOuQIyo+61vbTzAKkmjFPLRdjT+48p+kWc3kjikm7lUvGQcSKgPOMz4B5HO1Su2V1GiShbcsDzouhLoLBxfBZ1lS++rebYBQ6ojmIracFhzSLiJm/k5uU+Qrm40kYsHbmZraqx7Td0sWcgoYKRk2tujXjczog6tOwLKa2uQedEUUUgqU/vPma/Slev3AJgaKo27MVm4LgtqAOimsiXBaRTRY6ZTX/23xsNy7SRO0YjEDwb8gKUgPME0wIqzWuTK26fjuZwfNfLynm4MqoyGgjas42/t1lUCelaaeYzm1dvEeagaruFGrbcqH4NejfVmOf292S6ZhO54YsTdEWEiWSGtGILitxUMJca9NGkBflBHyfZ9DvBIUK5f9KW8yGKZm+mdAtg/29NjUhRpJ8ihN7L9FLiRNSZoHNoJEku2t7mHDCUQf12tTkqQlocbEqGj6IQdFHtp1ix0JFMBvdhbuOCD5GXpUsoGsIxypgrYxCBydnawk3P3r+Dnp2T7GWrHDxQtJQz/V+0VLqIByffHa4MqURnVGxM6gIoJGATsLP0yea7py7L/f6piMMebKGItLPVvkAZQR3wnIZmOewnLuS6gJTUc+oCfz5Obr9NIW5ridtA5uqaWvHcDdx/6ajNMNqvlYt+LUNbD7spvViHVDnAbpcLIO+CJv8OQ7h9ORu1mpic5G9wPsNw5dhLV1p36ZjfG4y7N+W9YeDp/fVmKNbdgCmQh4SN0VszD02jJNr3JBZYMrjbh5Ib5+/Y7aEU+gItTfiq6ZHscEFzuuua8cpNB5BATdr0EJeC1saN/kmHPmr8+IiISRapyYbdojAGiS6FVp59ORzLZp1Yr4X1grkcULLhkbbf//+y1OuP2qV42aomCUmTr4KFivA+jMUbXgdqYGmIT5X715+4OmYSJvvMcqY6f7QNtVrW5udi5ERBkt7TOFSa8c2BsRI2Sapp2Mo0qIl5hsDp/fL8Phj2nbtxC0ucDD1k4erJ27SyfjzXCyChq8sDmw6yjq24mWLcGNPHTy+85yOSQxBoNnUBLBuIwwaMLISJCnaAHJykT+WHQXZf/+l0P79wp68nSQ3OCcgtyDTMjpKrDT3ZUNB1kSnOhUrWYjevflEJyVcWW0Aos96LavxiXa3Bs09kEmGk+XJhIv09K56E0bmm0tcE+HAJy8rLD1QyOh0a8qL1MbZQgdMHrAIiYvcqukb5U7J4CgF7I88TA9kFHvoMEKbhQU03E25wqK7vREvZPcuP6RDMcelf2/UoR4N8RFMNFZ5bmSrZWXQqV9bpohggVwyPkJuocHZYMtH8sKIonTVdOVdIrFowukK+o2ZVsFy3SoVoU7LGhysKmoAd2di6qaK+yLnlOX9lQ5vO8UdcFU+azkRpSuWIKeQIRRyyItad2vCi9iuiAM0pJkLBY9dkVuY/eB4dv8lLXCKoKHNXdjwBef3lkaNODZh4uKh/Pn4nnFg8zHyspzHG9B2PVsKBh5aLMYwrcfUHsUDHP8Q2ZHaZEIVnNpzjh0CgQlLR1C5al+nWLKw3j+Giww4GU+QUQjCefHErrOC82L46DTOi8ibxDkNurNOVm359WAahrUB9MXqTatITUDMRhvz/aBIEfW8yBJLjREBtvw4yyZHZPh8xQIT0z80RDGB07dqT7vCBfdlMDKwTqz02CClJYqW9tHB2zm6plTFEt9Mx2DywQVdm1oZWtNj3Y7wFl6H8SA9flz5x1XQeetbtqVyVQVnR0XAY0QvEHR/0Kur6+K5fXkS31cz/fpUUU1DkPQ4uvs8vXv9kaUozTt+q63TFBK3nuHn3qB5Fc7PzUpcvvyQnj9/T/nz/0atWgmRQTkBuQWZltG2rUBbvHXrGTsuZiVwMtUzbax12iLQxVpXSlvUlOMcTm4tDIWgxqQNQsdGHUBHhlBJdNsS1gpUPmXRb5IwcYJ7Ik7wimAxqQdPybDwHZeEV2YEdEd1ujXjTS6shTMCFszhAbZ8OWH1Abp7Sf6ESjTk6G4vZLDFLU07CQPXHvbEyIGZ3jtQqSkOnsPYhUOpSKnC9ODKY4rwlF9klSxfnBwXD+fLeF2KssxE4DsyZZUDUy5hsyw6gKkLPcu2UirQXPtQFuxrG626NCGvzRMo7++/0Ynd55gG9VES2P09o0rdCuS5bizN3j2VmnaqxxuwHSv380YdhRl0k7n4cfDo1jO2sB/c1Jm2hSYKtuAd67Lxi3fk+O/Oxj4j7Fl7kHxAaf7nX9Lr14amRozWeiDt0ilreGqPIsRz0wS1C1pMVVDYYTOLiJKOfRVPMUAzFJ19HeYN5vN0erx4+JKWTBSoirCUR0NSxMHoo5QcfYz3FGMWDOF1YefyBLp24iY3AGGJv3RSBH9WYLt/YPNRqUYMerP0lvoo6lAoIXNMzBdLTWWEzT0clouVLcrNPWCwjxVtmrOV10xo1+q3rc2mHViXYdrRY7QwyXr36r3UjANZaKkNNkDHFONZBnnLno6d3y+4NVpOEfRosoDGJ4K4cT+WEu2aMkBu5rVTt7mBpyjbTBZQVO5aLVBCewxXzy4/I4jSE4O+reSGYGcG+OzujT3Nl/V7aC9wWhbEAUnbtjXotywOopaH3IJMyyhS5Hdq2rRyGovNrIToXHM08TJ9zISLnSK0N21GvxfKx13zs8mam8aZSDI1Dmw6mcYqVxXgpGIm0ZKhK6VKwdhAtw4vHlhoouYq1pLBXUyckq2esUnulMxqqjAlwwTnwbWMaXpwSgQtg7t2SuaSoasHICAT+SSpj4PrekemuKBQglmHMi56oC6OXyIUWVGzt9L1U/IDu0HHNB7Yie9bdPNSBtgkTFnpwIvb1sXxtHedasVzeth69GGLerx30/vOUdqSPzNopt+AfGInC8f44FVy7urLYvYfAfV0apBvzETOMENhhuPKhVmLqeQ7aDHnT+Xi+8Wt8/do1vBl/H7Cwh7vb+P2dch/22Tyi53Exi8/AuBsGDBkCW/sje060KTlI7W28RSxdUm8lCY3abk9T/HVAQpI6MZAAYQRxKi5AxXeBudfP9sFPOHsZNGG9PpnbMm+YMxyDjWu06oG9ZKwSgBo1OY7LJc2HKs0qETvX32k8GkCa2OglyVroQ/HnuDjiP+/IzG4eP/yA1O5U+uWK9f/g9wjndjiPD7iK3sBz0+kNYp6MtDZ8fgV61Sg2q1q0P5IgYFjN71fGtOOAa69pYXXxlkxTLms2rAS6Q/46g4pTgkxEQXzBDrtjIB1G+g69KtboyyAgQG06d6cKtX9WsAqguiuqW/ZRuEEThaOxp3j41SmUkmeXGsCkJycTLyUxkFbG7h15THdvfGUPxvIzc1K/PdfinQv3kGLE0B1kFuQZaXbooSzmpWoXq88VaxWiv768g8dir+oVRMRvV4t06S7awI40ZSqUJxPPPujFWeCyYKxXUcuGKFPAt9bFfSXdMnilu1ViuoHtyt0QS8fuU6nE2Q/FlMmTJrxQhTuJtAuZJmAAOgmgrOvCMgygd0w7heUyNTAhMxto5PgDLhqnzSjRRHa9mhJOj2E5zp72GKFxiD2c+xYTA26ypKJyk+7YLEsFqoQemdGT4YFHy5iovOim1kAu2tpGw10a9OsnVN52ocu6ESjmUpNV78X1G9dkwszWJzj+4kFbt/mY+TQYTpN7RlEx+PPfXe5bP+vQNPkxJ4L5GIWSKPaedLeDYcFamLnhlx4oxhDUfajvNbVPtEU4iRQ5MwdjGl8yBCtm5Gc2H2WFo6XTJ6mW1BHNfVCAGjg0AFDV+W2QTlbfpx/QflDcTE25KtjYnr0cerB+ZATlo2kPHm+FqiYfME6HlowMa8ratY2Pqei6IGd/GJJSLSBdXvaukig4uWRGCWgsBfzxIDBM6w40wwxLHgtqQFaIwq5fyR29Kf3CIZeeNzV3lH8HoIeiecZt3QPT9cwHets15Gvh/sUXX4Hzeif5nXgHLwjVJiO2bgJ+uz0OLvvEj8nZaZjmLYlrD2YhkmjLBX44BZhL2NmLzSKVQWOQ8LaI1Kre019hiE5wfm8fqvq9Ed1xTRYdSEytnT06lLBwvINUzSNq1cf0bNn75iu2LKl9vLV1EFuQZYF0G1Xi4Xx1649ocepcjayAjj56nUXRsLiiFhb6GLVln8nbz+rsY0vTjRdB3bgy1tDBe64OihQOD91kXDJRe62soDzVo2mVejLpy9K2edDH9BNEiK5TsLzlwU4Y+E92h91RGY4ctWGlamjhdCtWusjdO8UQb+/0BlMWPNtjhuMN0AxAVZ5bKCbZ5ULZbbx7s1C7Bunb7OzljwUKPw7TVw+ki/HLUtIo2dT+DgefTlYFJ1dL4vZmdJigT46ffNEXrSh1Zved3aWGG7UbFqFAuNdqWT5YkxrmaDvLVMr+L0CFuegsIUkTyc9i9Zc5J9KvEhufebSCB03ilueyJrAXOQ84LsVF5bEOWLTes+m00mX+P3r2KsVzU9yJ+8oRy68fxSgQbB44mqK8BbOWzZuvWjErAFaD6K9de4ezeg/j4tcQ+v21N9ZeVpbesDaHeZKwPjFw+mPWt+GOafH4a0n+fwLIGJEngMg2CCLTwfweiPiZPxZaViz49KRrLEDuyIxIllq5BEZuJXdG9GAenD1ERuklKlSij6++fTV5l5CxIADrg6akP/9xxO51BApizhnYL9UpX4lev/6I0+eipYpzHEy+LuthwV9fPdJaibS3+Wrpf0Gvy2p9GHN09w/MkIxHWvYvg417lQ/w2MgrmtgmSiajkUv2MnFJu6vro7y35UtC3dxQxPZZ9Ubq2e3jkbf3YuP2OnU2FYwP8ss8J6IzfQuA5QPtlYV0B4mScKg9btnbfYYsH/fV3fFvFoMfVcHuQVZFqBo0d+pXbva1KVro2+ErVkB0cHm3LFb9OqZcmYO6qBGo0pUrcEf3N1K3Kz8BlwRuth14NH2tZO36epJ+XQ5eTAb1VkwCdlznu5deaj07XAbCyehAxYTskupAgFBxaBvgI8uL1gZnT6xY7pawvHPCKBkAKBsyKI3pgZoKVi8MKXLiGKIXJUOfdtwN2zh2DClqIvQkYmui6tnRCkMjAYlBIGaQODQxdxRVLYIn7pmHEcPgPoi0mXUBQpk7y2TJFTNKxQ0TDmqZmYBDUbQXjcqX70M58SgKLuWic9vTkW1BhVpyrLhFHbaj3o5GPFxhkX+/AkRNKDuBAqZvIbuXFb++5YL7eHBjSe0xGUdWddzovmOq+jOpYc8kehpb8jvn0v4SG4m/EjAJjxw6FLaIqGJjQqyIeupGWdPaRIvH72maT1nsbMiNt/jF8meTinCs3svmHYonrv1ZdAOUwN0dbAZAJyHm+pnTNFLjdTP78+Pn2nO8CVSB2EYQ2Htw33i/NllsB6baIhGHh0tdNktERralxJGQPrTrJ1nP14X13hvYk1a6mIMRSu0Y4COaXPavVJwV7Z270tLJggsC5MRRrxmonACbRNTO+NBnaTaurhlgpOynZfQ6BSBSZqoHYN7cUbvw9XjN+hUwnl+foqmY6BEbl8mNIjFvYEywO12hCXy5b6O6gVBA2imAO17tqQiJTRjS396/1V6ev8lN6/bmWpP1wVnxVfP31OhIvmpZRYHMqekfHVXFA33chJyC7IsgoenOU2aZELls9hNBihXsTjVbVJJoBZtVz/TSxkY928jTXnX1KYXHGsdE4EjHbNEOKmqA2RltTYRTjTi4qwsoEUqW1UwBtm96tupU3qUrliSDCT8dVmmHSJAx+BcspjjMvVZ6Fq26dGCj6noKiUPxcsWo479hInlvNGhGVLIkOECygt3PNcJ1AtF0LPU5WIPiyeCprFoy8Pgmf2pZnOBMuhvp3wuGIooWDmjqNy9ch/tlCxi6qJqg4rkvmE8L7aJ6w/RCg/lHSAzg7KVS9GcvW48YYWWbHIXXw7y/BFRtnJJGj7TklZfCqSR/v3ZlOfj2z8pdkkCjWztRhOMfSh+bXLu1CyLgYkDjrtTF18a2nwqRYfE8/tSvlppGuGL9yuIRvpZ8fv3I04CYWufsC6ZN/2Tw0ZK9cTafly3ngH04sErLho8Ih3TmEuoWlB6W87hcyjOpaCDKwLWiTnDl7IxBmiFymSUpQcahAieBrtgiK8V/23FtHUc9IxJ1xA/awocvJCnRG26t6Cj20/ydUqUL85/E/VgIvA8jAfrsUEIXIhTA+sJ8sTeSBrG7168Z814s86N6MPrj0zVhzvkQK9+9OjmE4oOFvTcI4PspNOxyIAYvg3s8NMXn9CFYXJWt3VNamqQsWZJZLOg2BWzzGRh+/K9XGhXrluBWnZR3rJ9e1gifXr/mad+YN6oA3wO9ksa3qArago71whTT/3eLVmCoi0kSqZj0I5p20gnPa5ff0JPnrylfPl+pZY5yF1RRG5B9n8C0dxj71bt0hb1erXiMfrtSw/phoLcKlVgaC0Uevs3H+NFRl2I4c17Vh9UyWwBG/ne44SO1qa5cUoVFv0mm0kLLXnaL+SSwRUQWO0tm5IIITPuDzksV47Jn06JtsDQGlw5ev0bLZlYNILuIRZtyrg4AqA7gs4BXcKySRlbFovACXfqmrHchT+bdIk2SQI0lQFoJXZe/fjy/DHLZVI6lUUzg4Y0LmSIdPEVO5XaRtHSRVhT1kRCw3TrGUhJGzUXop4Tc8x6juxMy0/50MzNE0i3e3PeDF86coOC7JdT/1rjOdfqfPLVXK2ZloAGzLmDV9kF06rOBD7uFw9f5waHTpfGNGOTI4We9CHzUUZUsOj3myMmD5igTDb2YUYEpjZemyaQgRKTpcyCjTes5/P5CqyCmbGTlQ4LzgjQcF05eoOnUdCNKVPYbVuyhw5vPcHnX+dVDnwbMCvgfvjm+VejJ1m4dvImGzgBYEXkL5ifqerR87bz34YE9uc15fqp2/y8YLrx9M5zdlxEwYbP2Tc294G2TF1MT1VMvR4BoBtePITg8V95DYuQFG+2nv04WgYRMChSmxs15mgYMSNt6+LdaRqcIqBjFqdjQ2b2z3A6hiiC5C3Hpeu2PMBqf9Nc4Tj0djRRmvaKHLgtC4Xn2HtsF7XpsvHrDjEttGKdclSnpWaKijcv3tORXee0Tlf88vlvOrj7Qpo9aXbQFXV0quc4uiKQW5D9n6BDt0ZM+7tx6RHdva49m+pCRX8n3W7CF23XOs2Ze1Rt+AfVal6VO287VyqeUMlCo/Z1qGazqtwtg/OVKjCy68gZW49vPeNQR2Uoax36CHTEVQrs4pGhgoXiUOwJdqzKCNUbVyFDW0FPF+q8RuEEsmSFEtTXqQdfhnlHRlOyvpN6sIskqBTTTH3p9VPFGkdsLiaGj+bLWATP7ruo0GRk9NxBfBnOXKoUVqCOIB4AC9D0PkH0/nXmtInQEYp2+MGjl9MxOdEEmgRoIN5bJlJ785b8Gfa1C6HIOXFZQp3MLmDD0dygAbmtHk0RFwPIbpo5T2X+/PCFc60mdfOngY2m0LJpG1kT8SMfi6wAjt/Nc/cozDOK7BpOpskm/uyCCee8clVK0UC3XrTqYiBN3zCOWhg21Lp+Kjvx6NZTGq/nxZ8raJvQEGlp3DhL3oP5Y8PZ3h7sA6/NTkplhMlC4vpk2rJgJ1+evGK0UjlXKC4WOwkmIoNmWjLFD/pVr75BtH1ZPFmUHUaPb8veA/z9198UODiEp1bQLmP6hbVj3uhlzLLB36o2rkwrJNmY3UcZSSdWf/0p5EfieqlhMcmMmnduTCd2nWEqJ5C6MII2GZMvNHPfvhQapeZju3GW2eunb/kYwr0Yeuek9cIkZ5i/tfQ+wqat4zUCTTxopFMjwnsTF8mw45elHRMjAdqZt+IGqTzsXLGPKZDIODOwSuviKA8Ho4+zsQhomcgsUwdoBG9dKhSXBlY6GqPdIgga61LNxpWoWn35rz8zOJZ0hf78+IVKly9K9Zqpp5/TBF2xQw6kKwI/7hk5F2lQpFgBKV83QcvmHqJdalL0Cfr8Sf2A3/ToLkmijwtPUujyp0xQdOxi1YKiEeLZc7SxdMKijJOcjUcf7hZiSnb1xE2Z18Mi0K5XK768Ro5hxsDp/bhzeDbpotycMxFmY7rwxAJF3pFtAqUkNdA59dw8icrXKEtP7jwnb4vZSr0uTJtMJMYl0BQoOo7g+euaCcWIn818LoiVATaNk1eO5hy5J7ef0ayBIZmeqth59mWBPTYcM/oH0zUFNv6aAo61S4QDu7sBoVPXU8iECKVpnN8zSpQrRv0ndaflp3zZndHYpj27nsJxbNP8nTRWz5sGN3Xh4uzikev/F8dEE8B34cqJm3zcBjdxptHtPWnjnO30/MEr1vIZWbcj/62TaPlpX7KcaMomMz86kPE0vuN0bpzhvDF7rxvVaVk9Sx57nV8MbQ/dy+uM86rRKpk9pMfdyw9otkTDZenck63VFQHTm5lWwVycoDDpPd6E/46plMnwzuS7Yxpb1186dI1CxofTheQr3zRC1vlE83qBQlbUDO8KT2Q9MhgXyMbcFBDH1LkqDSoy5R0TKxQo+I2iKjXa92lNQ/0G8GVR4wXgcfP8ImxBoTsH2vRoSQ+vPeapG8KlNwbESKdev/z6C61wF4rATpa63KAUp3nI6QSGB9ikKVKgFU9YLTRwQXfMCCjyYKoF2LgLLpKygNe3MVCYHPab2F1pyh1e66Z5O6VB0OoGkB/ffY4e33nOU+02PTQzYcJz2ymaeUiM2bRNV8R0LKsbQrduPaOHD19z7hgmZDkRuQVZDoK2O8QGPb66LWpzw9O4XS3WIyAU98DWb4sAdSEIWAvyZuPozrPq3495SypTuSRTFuMlJ3Jl0dPBWChwLtynI9tOKbx+5bp/kJ7E8VCeaUdqsfE+poJkXCSUrlSKejoItMvwaesUfmYKFy9EPUYJBUBUkLCQpEeRkoVp5jYXXmyxuMYpOTkcNsuaSlYozhSVZZPlUxfxuhyXDmdtGBy5VLHCL1y8ILlHTuBCFEWlIudKReDnsngYNTVowPoa2OE/vv2MsgIwLBkZYE0j/K34ecQujmcXtsw4SX5PwGuGO6PjgkG07vpcmhYxmjqYt+RpAt4DFGdOxr40oPYEDidO3nqSp7e5+ApoV3BcQPscUMeJxhvM5OOGjRriNtqaNiXXlaNo3bU5NGHhYGrcoe4PPQ1LjQObj7FOE+d2MCHmJLrzhD4rgDzJFR4CvW70XDtuQKkLuAiCEYDzUxP9BmyXrwyWTVnDRkigSU9eMYrfd0zHUDjAmANA8XH5yDVmQ8wbtYz2bfzKZGEHXUkUCooxZFDCLEM8v9t6WrCubG+EoDluqt+QLhy4wp87Mdrj7y//fHVXZEt4gboP6mDqpiD+htBo3Bavs2qjSnTtuNC07DelJ+1akUTvXr5nDZ7BgPZsInVk60mmP9t5fj0ey13WSC33azVPu9GGBICDpHu04Hw1edMxBGxjmqhoYgmDFaxjyNpUKQj65C1ew0yHqhcEDcQsFgpaNLSUiTxQBldO3qb7159Q3ny/UseeLUhbePv6Ix2TBDJnC11xv0BXbNGyKlve50T8f5ylcyhwovj06Qs9fPiKrl17rHXXp5ad6lDBIvnp5bN37HSjLWAR6CrhIe9coznaIk7cOBEB25arr/+BHqyXREu2RcWgaND1ukuCn9f5xyhVRFtP68VTsiNxp3jBkwUsBqJ7Vpir0AnMCJbO5lw84b6OxikuCs0cwFf/iYstWQ6NsFBGbguwbMpq5uQrQoEiBchp+Si+HBuyizUL8oDCD9bLAIKflaF9iqjZrBqNXSB0a1e6b2QL6MwAGxSYfOCYgxLjauqfKW2iqug1titNjRjNC/ShrSdpUueZ9DKLIzGyG9hQtOvRnKausKf1N+bS1PCRpNe3NRUokp/fC4QTe1svpL5Vx7IhxdqArXTh8LUsiS3ISYD2BFPDNf6xNMnEn/pVG8fHBbRPfHbzF8zLdvU4jhtuBpP7mjHUvmcLjW3YvgfgPBw5O45mDJjPxg4wbwrcPZU3zlkBnI9mj1gmnZz0UDNfSnwtAYNC6P6VR9zwggZXmeBq0CRFeiPyF5H1BRQuUZD1vt79ZpOXRRCVq16Giy3XdY7Uc4xAC8RnDD8BgxYy+6R9bx3qaNFWYg6ymO3nUeB2GaJPQUMWsT6sXS8daa5XsbJF02jGxGWxqWFDqt2yBheY7j3901wn/XQMxd2TO8/YXbetWUva4L9Fqh1DERY2VSi8DG06SC3/wRQ5tec8yzEQUJ0amI5Bby1GqWQEaLuhHcPeS9F0DPuEDQFbpedvVb5fYtQOB0GXLkLq4N7VR0I8xc8/kamELaQJ7F4n6Jnb92jG1HptYd/2s1yA16xfgSrX1F7GmUy6oqQYFHOBcyJyC7JsAjLJAgPiaPLk9TTKfiWtCD9AS5cm0u3bijfC6gKj2g6SVPSEWMUb+czAsJ8On3AvHb9Fd66oH+6bHt0GdeKT56m9F+nhTfW1cEa2HViA/OD6Ezqm4rQNJ2MUh1eP32Rbe0XA4tFJ4ngoj44I2Hr25cUXYaJYbDIChM3i1Gu1d6RSWrIWXSS6vhWCXa6swg1hnDCemDNCOWt4psU4SqztB4dwMKei64ui6aBhi7nbqixgs2wy3JCfl6/1fHp44wllBshKg+Ae01J0kd3MZvFrzyp06K1D/tudpQHSY9t70M2zioO/f0SgwdChVyuaEjqc1t8IJp8tTmQ20pD+qFmWqaUwpFg1I5omdvGjPpUcaIrpLIrw3UIn9pxn6tSPBEwEEda8amY0OfcIoD6Vx/DUMMJnC50/eJVpU9ASwaoex2nDrXlsV49JI47j/xtwPIIdwihU0sQys+/MzZZ8BbLmWFw+doO8U2WNDZ6puqNhamyYFcNFAjeNIidQMSU28Jh2BQ5ZxJfNx3alVl2bpmFJzIybyrRhZI216ylQ4wEUMq+fvWE6IBgU7GZYvCCNWTiM11msF2j64blMXjmGVnls5OKuWLmiTE3EZB/6Lhh6iDliqTHc34YLvJmWc3hyJwIOjKCvQ0+FxnTDDnWZFglYTe1NYa5rmdbeqGM9XjsPbj7Kay3WXRt3objCOiBSGLsONWQX5dRAThmuAw1cjSYZRznAfRGA1luRduxQ7Em6f/UR7xtMhhuQssDkXzQMESUP6mCrxGZfp2sTKlNJM46onz58pn0xJ9NITbSFhBihiWpgpj1LfVnAvvrevZf06695qG3bnJuvmFuQZQOiIo/RlMnr6ebNZ1S8WEGa6dOXfHwtmM60erUgWNUW9CW0xeT4ixrVd6VH8dJFqLWxYOu6Y7VylurKAJqAlkZCURmz5CsfXVWAdthtiNBl2jxvh0q3RQev6xB96ZRMGYimHQejj7HoWhbKVy9L3YYJJ/uwaetlFkW9J3TnDh2KQmWmRV0GCc9394pE1hnImmw6hdrz1AZd0/VKUgMH+1hR9SZVmF6CbqqiQm6glwXbD2Pj6WszXyU94Ki5A/m2H958JM/egQpt9xWhRPliNHPrFO4iXz1xi7z6zeENXlahfttaNHefB1WsXZ4pPxMMvHmS+v8MbP6a6dUne38rCj3hQyvOzaIxc2ypnVkLLl6hjzl74Aqt8Yulab3n8ARtSDMX8h+2lCKDd9DJhAucwZTTge8JnueZfZeZcug3ZAkNae5CvSs5cFjz2llb+f9A5wJtF1OvMbNtWIcXdtqXrepxnNS1U/8RgHPO1O6zaEdYEp9fEfY8arYtr6VZAdCvp/WYRV8+fWEb8wlLhEJGXaARB+MjYHTwIKU0aJjcQFsLV8lqjSrRUIlFfWqgaBo1dxDV161DMQt3stvtjTO3KTIolpkRmCZFeEVKreRRBKLIWyIxB0GuFyaPMQuEtbLbSANKWi9Mn/C4/DzSySD0rdpRjaZVaduS+G/0zpiUobhCjAyQ7/d8vB7A1v+POuU4owwFHhx9cT5eOmmV1BxELLxwrC4cFOiSVq6CW3Bq/d3etcK+Y8C0tP8nvc6lB1KWhqzrSJ9vSopUO9ZjZGdu5imLTcE7uOhs3rkhVWtYidSlKe9ZJ+wNuw9Tn/KYHvu2nGSTjQrVSlOD1hlTOjWBezef0bXzD7hJ37Gb9s110mNf0mX+3apVdSpQIC/lVOQWZFmMR49e081bzygsfBgtWTqY+lnq0MoVgo5p4MD2dPfui28cijSJek0rU9mKxbkYO5J4ibQJkba4d9Nx3kRpCmYjBDOJPWuTWaem9v3YG/FJ/+y+yypPJvrC7jbPz9y1Uybsl007zIXOpKJCZ4BrL15kELIJOkZGwIIpTsnA71dU1LTu3pzpL3CHkpeLVrF2BbKfLeTcoEuZWl8g16xi9VjeSB/bflra6ZQFdGNdIsaw6cDF5KvsgqUs8FjoGoOKhI7rbA2EPMMN0ztmMlvzn4w/T7MGhWSpqUT5amVY69JErz5vvj37zqUNgdtyXQclgB4VeTvTVo1iauPSYzO4MNHv14Yq1BCoL5iWJ248QsvdI8m112zqX9OR+lUby9ln0FpFzt1BB2NO8CQSdMiscnTE47x79YGun7lLB7acYMMNWNFP7OrH1EM8T0zBYMqRFHWUHt4Qpv6YgBlYtuFCdNEhL1p/cy7rwkyG6FGF6mW0Tm//HoAiYmwHTzq3/zIbxEzfNEFKRc8KQEvkYurHE9raLauT2/rxfG5TF9BY+VgF8/qPhh/YAMoAhQKKEzTocB6WZxhRu0V1svGwoNhFu2i9XzSZjjCiqg0q0Sy7+YIRiHFj6mzbka8LnS8aXyiqeo3vRvMdkGeZQh36tqF96w7xZxvNSbAKMGlLDaw1Q/2suViMnic4MKYH8spQxNVrW4uO7xSaimMWDqWwqev4cg97Y6pSvyJtDdnFhlNonllI2BW435WS6RheQ8nyxdPcd7jreql2DK85I2CCBrTvpcPHQB7OH7xCV47d4GalqOFWBtAyirml2DOoi4T1h9mdFoyBpp3qkaawQ6IF7GKtq9VzCnwLgObtalHREupHQKgDfE6TkiRh0J1yLl0RyNpUtlxwIB3C6YoVK8BHo379P+jLl7/p+fN3VKpUYapVsyzdufOcqlVTbG+rDvCl0zNpTOsWJ1Li1jPUyUR74somHepQ6QrF6NnD15S8/Qzp9VJf5JwazfTrU6Xa5eje1ccUv/YgZx6pg1J/FGeaVFLkEdo8fydNCh2h9G0RlqnXry0lrD1IkUHbyHXtWIW36e/Sk3NgIAzuP9WcDT8yAqglWIyj5+2gCO8oambYMMOTpZVrb6aUoNMXt3SPtEDLCAjPROdzhuUcWucXTfoD2lGFGhmL3bvbG9ODa49pc3AczR25lOkkinjvletV5C5qqPNqWuS4gp8zDEhkAR3b8YuGkc+AebR25mZqqldfpiVxemDxRR7PRAMv1ghgM9RHQptUF3Vb1SD3jY7k3jOA9kUeYVqoQ/DALNv4IvR0ZsxEWjghgl3awtw20O0L98hx0dD/Ky2QIuD9qFS7PP+gOAFQ8Fw9eYtunLlLty7c559HN5/R25cf6O3LG5x/lh5oeBQvU4Q/10VLFaJipQozFQlNAugoQP1D8Y+w2l9/y8MbbWwCX796TYULP+EOPyhV4g82pdDZCD8fONfn5aM39OrpGzY5kPd6MPWv1qAi1WxSmalVNZpUoqIlC2v1OH7vAM181uDFPFUB5RgZY9i8ZxVQ1Dt38+XgZzR0QH3ODF0Uk35M/PH5gfmEw/zBSt0OTbvUEzVljgHMK/CDx4Rz8JqZm5hpAWdDMCRYErDnHCWsOcB6pfFLRrCLoeiyiLwwZJDhuwINIyj2oB+KwP0E7PXkrEoUWg+vf0stx/2gAMX38O/PwvcDheCzO8/p+slbzGAZ4NabC0I8PzGHE88XSFyXzM8Z9wMHytRAhhlcjfHcB/sIuuj0AN0d67C4LivCekkTE1IHFKHKAtE6OD/UaFqFcyjVLShEzTy0Y3h/NNFQun72Hl0/d4/PcZ0thHgebQDnTew1UxvLZSVu3XpODx68YrpimzbamwJqArkFWRajePGCPDJNSrpMFSsWp8jIY1Thj+L8d8BpYjc+kWgTet2bcEF24uB1evPqAxWVPLamAdqIUf82tDpwO6fAa6ogwwmpx3BDWuAUQbFLEqjHcAO1XcSgB0NBlrjhMNl59KHSFUsofVsLJ1MuyFBk4QRfoUZZhcYU6Nghawy5ZCgqZN73pB4c7okJ0um9F9hmPj2w8MH1CpbGKz02sMuUPCoFOpvNQvfw1A3GHZ6bJsm87vBAGzp/8DIvjsH2y8g9yokUoY+TKR2KOcabhFkDF5J/vBvlySNbjK5nqcvPZWd4IgepLj7lr5ReAkB22sjZtrRwbDg7i8GcI6NjpApadG5Ek8NHka/NAjYdwfEd5KWcu5kmgE3/2HkDeXMe4hTBn0loHD02jOfmQS4yBuh8LTs34h8RmDTCOezBjcd8DPHz9N5Lenb/Bb168panAU/uvuCfrEDRUoWpXNVSXHwhEwznisp1ylPFWuVyC24VgI3o1oUJtCloF18G5dd9/Tg+vlkF0MdAU0TTClbvPnHO3MBRF3gdmPTfOnePGwRgAChDQ0Wx4mstaNdwLoXGVhWguAFtcbWEqjh63mDWG//54U8Ktl/Kf+sxqgufe6Z2ncn/RqNQpC1iEoeMu/TsjD4TukudLaPmbEvzf9j8w8RDvE2nfrq0e2USfwcwuXM29hbuw6k7Ozwun7qWJ5BgmBjZCa6GoNwvlxh8wOBKNC8Rj+VyV2HCZjxIT2bTE86KmKBBa4d1WR6uHL9BJ3afY0aMxcTuSh9fFGKI1gH6ju+mdnPvwqFrdPfyQw4376zBYHPsyQDdbo3ZvVpbuHDiDj17/IZ+L5iXdPTqUnbSFX//PefSFYHcgiwbMHRoJ9q29TQtOXefGjepRBYWOly8/PPPv3Tp0iOK332eJjh11Vp3vmK10lSzQQW6fuEh7d9xjnoM0F72ROd+bWhN0A46d+g6PbrznMpXkT01UQWg84R7RdGjW8/Y4ANBp+qgdotq1KhDXaa9wAkJduTKomrDStSqaxM6tuMMd9CclimesCELBQUZrIeR9YL7kDUFMhlmwK5Z4W7rqal+gww/D6YjOvMCCbFxdPB2zmuRBdzefs4gGtZwAh3acpwpP+juZgQUUhOWjqQxrV3oUMxxdlFs07eZ3NeG20xa4UD2zSaxIQnybOQ9H2BU8EC6dOQa3bv8kPxsFpDvDheli2uzUcZskxwfsZ9mWM6lhUd9MhXCCnSyaMMTjvljwtleH3kvfSdkbvqmCvAeIaemYp3yNHPAfLp+6jaNaedOrqsdePOZC+UA+ikmTvhJD7g0vnj4mqcc0HC9ef6e3rx4x9OWT+8+84YbP5hs/Q33ub/+ZQc6tun+91/eLP+cJw/ly/8bb5KwmcTjFSpagCed+MEGvUT5olSibFHuqP8/67w0BUwhA4YtoeQtgptrt6H6NCrIRuksKE0Am2x380CmqUN36hvnrFITLyNsCIjlST8mTW7cfFF8fyg8goYuZiofznnjFg3NcH3A53h6n0CeLtVrnfb8IboqYrqla96KreWBpZMi6NHNp1xs2nn3I3+b+azVw1p1KuE8VfrynEblv0YhT2vT2zzF0jgn5iuYl4OigaPbT9Gp+HPS/0OjGcUYvjNfPv3FuuOrJ4TpNYyhTu4+yxEqKK77TDBlDduWedv5/wfPtJI6TcYs3EXP77/k49Q7HQ3wdMJ5Or//MlMLZbkmglEi6svslIgTWDNToDYaDminVDC3iIR1yUxZhAFHe0nGqDrYskgo6vQt2vAUXxPAdDQxWvgedbVWPtxaHYjTsXZGDdlaPyuRkpJC+/Z9H3RFRkou5OLt27c426S8fv1a40fqy5e/U169+vDN3+1Hhqfs3XtRq+9M9MqDKV3qOKeM6TM/RdtwtZyf0qXsqJRwnxi1bv/vv/+mPH78mH+nxmLntSnGhQeluPWdk6nnd2znmRSjfNYpZiWHprzL4P2Qh0tHrqV0/rV/inG+ASmPbj5R6jbe/eakGP5skeLZO1Du9V4+fp1iWsiGr7sv8rDM6yWuP5hi+FOflB5FbFLevnyn8PHdzPz4+kFDFym87qa52/i6XfNaphyJP/7Ne5ARdq9M4tsY5embcv7gZYXXv33hXopJAWt+nWt9N6eogi9/fkkZrePCtx3acELKx3efUjSBdf4x/L7iZ/vyvSnZgce3n6aMaOnCn80uBWxTooK3pzx69Eip9yAXmoes81AutI/71x6lDG/uzN+FroXsUraFJmT5Yf/7r79TppnN4nOCWfHBKVdP3sz0fR6JO5XSOU8/Pn/FhuxS+nbRC3bwbbrk7Z9y9YTs5xE4eCGfi21rOqT88/c/af5vtXcU/1+vkoNSXj19w387tuMU/w0/pxLOpWwP3SM9/4e7refLkXnrgzCXspZqS68r/qyZuYnv58qx6ykmv1ul+b/OP/cV1oVfLPj3YqeV/LtnMbuUB9cfpZgXt+N/bw6O4/uY7xDK/3bQcU7577//+G/vX3+QXm9HWNrzMq4zpq0rH5eF48NlHhMvi9l8HY9eAQqP8/VTt4X1Pa9Vyv2rj1KUBc4RQ5tM5s/rpuDtKeri8Z3nKV2LDuZ9zu1LDzR2Ltq5Npn3ZIPbeEiPrTbw5fNfKb1bevJe8+zRzH9fVMWNG09T9PV8UoyN/FM+fvycokmgJkBtgBpBU8g19cgmxG07Q3fuvJBqyf766x/au/cS+flu5Uyymze0G1TbybQxO95gSnb3uvr28crAWDKB273+cBqueWYhBiwe332eJ2XqAu5YVRtU5A5snCRXRVnABQvuSaCNrJ8Vq9Rt0LkTHRdvnLkj83owrujrJFAkQMNAR1MWFRGUPdBHZIU/p0a/yQJnfk/EPnr15LXc65qP7caGIHC6mjd8Ob19oTirC1oAZMWAEjLLbgF3aeUBugeHeYJmYoX7Rs5LUxagzXhumkjFyxWjOxcfkL/tApVy5WTBcnIPzhMC5tqHUtJGIaslKwE3sbmJ7qTXrw1/vpZOWUuLxq1lOl4ucvH/goNbjtMYXXf+fmPa6LJuJHUdpHworyYAk59ZgxZx1hd0T15bJlItBVQ3RQBDAZRDdPHBhuiuZHYZpnNLJwpBzcMDbKhW84yfR/KWY0wHx1oDbVjqLLPb5+/SmhlCIDLcF0EVx3kacSdAzzFd+byMaRnQz7knbV0kZGm1/e8h/9altHE2OAebj+vGlEd3M3+eJqa2ucfrhPYa5zLohXeFCzbu/V3M2egK+jlMzaCFhn526+LdUhdfcfqHSABcD8+ts22HNI8PJ0fo3DCxtpySsS4Mtv5gp+D+UodLy8I6SRYaNHcIqFYWoDjeu/KIdXaqBEhnZHWPdRRuqlXqZsxmUQdiPqxx/7Za1Ukf23eVPr7/TKXKFaEGLTKOHtAm9u8XpmMtW1XL8XRFILcgyyZ8+vMvWr/uMJ0+dYfc3KJo8KBltHnTcSpTpjAFB9uQZX/tiSwB6MZadRRGuHtitGuz3dqoERUtWYheP3tHR3dn7BqoDuA21sKwAZ/o4zIRFI0TUh/Hbnw5JiRe5eBZa4nlLqhzz+6/VHh98OE7WgiZHzC0kAcUZNAVgMqxe+W+DK8Dih8oKcCW+TsUFk3129amem1qcZEFMxBFx2ZS2GgqU7kUPbvzggIGLlRKUIwCC4svhNsh48IVXt94UCcysGrHi/VMq2CVbMvh6OURNUEIWY49wUWdJoA8IdPhBvx6/QeG8H1nNZClNCXcnuwDrXlDdXTrGRrfcTpv5nKRix8ZaEAtdV7LGV+f3n+mBrq1aX7ydKrZPGs3dmjwBI8SmjJwE4T5T6P2mdPCwIgGuYegyUIPC/2WMoBuzFsSzaFr1lKm4x/yIGcPWyxdQxp1+GooAQ0WCkHcB5ptsKcHwl3XMRWwbNXSXAQtmfTVZfHB1UdMW6xV4mcq/7cQYF+J3lOZlI/S+x0RYMO6tB2he9nNNzVAawTdDvcPgAqOwgqNxCZ6DaTOvLC5h1YLxlBYCxA+jcBoAGuCSGGEVX9qfTLO0aumC1o4FLayAsHF3LGOfVvLlAuIgAzgYLSQH2Y5RXB3VBab5gkB3V0GdlI7bBmNt10Sh0azkcrnninC3auP6MrJ29yQR16sNiG6K8I8Tl2d/48eBp0auQVZNsHAoB5X72Fh+6la1VLk4tKd/Pz70aDBHal+gz+oYBYEfIqON+D4atPmGxx/4/5CARK3SrD41xTETI5dqw9ySKW66NinNbsbYiFRdRqCAgc6NEz/0ouYZcFqqjn/hiGIvFwyuEj1dzaTLiayisU2PVrwwokpX4RkYZIHM4eu/BtdT0UTLOhhvGKnsCAbXUiRfy8PBYoUoCmrxkjCRRPpwKYjcq+P641bNIwq1a1ALx+95g2DKp9J6CMclwyX2hkr8xwVAc8JzmX6/XWFcFOreWwvndXA80CgqP9OZw5ShcDbQded9mjgNeYiFzkRyOSb0tWPM5yAPuO7kf8OZz5HZ/WmbtGEVbQzPIk1UM6rHKhVlyaZLjRRVKHJhkYXTDyU0cFJdWO3n7E5jNPykRlON1BABgxcwAUU1oSBM9IGVYe5rmP9Mhp9E5YJroqXDl/lfDJg/OLhdOXodXZWxP+hYIPGDVOu+q+uk3hWxu/W9Jgvjwi0JX2r9vT3X39TZGBapoj4HKGzxGtoatCADsUIzS24SS5zXs1/72Spy2vpkW0nWQuGY4IJoIj1vtG8xsNVF+tdauyPPMKui3gMWcYbmLp9zR3rrfB4R84WokdamzRjBo2ywAQTcTgoLHtK9HTqIGHDYfrw9hPr1loafTUsyix2rRX2NzqdG3JerLYAw7hjkoIoO9wV7955IQ2DzunuiiJyC7JsAlwV69QtT+a9WnARVrdeBWkRJk4gtJlHBrTsVIcKFclPL5+9ozMZWENrEmLOxen9V9jcQ1OAMx5yitDJQ1aHusDJ30xy8sQmQFVb2f4S690dyxOVmu4g90TMJVvrI4iGZQHGHRAwo7soa6KFYysuXqB63L0ku8gD2vfWYTE4bItFly15AEWkp6PQjQ0ZH07P7il+D9GV7SehjiAwGg538oDi033jBMr3e146nXCBVnsLlBpl0dmmA/WT5NQEDl1Ml49ep8wCZjuTlo/k9wodZc8+s+lM0kXKDtRvU4u8to1n+2SI4gOGLKE59qFpqEG5yMX3jqM7zpC9jitdSL7K+WLT1o6lYb79M5XxpQ6wBixzXksxIbsF2t+yEdShd+YnCiGOK+nM3gtcPHhtmaS0sywMnkBzx5Ru2vrxVKhYxs54m+bEsXst55KtGceRJyJO7z3PodAAaIx4bEzMME3D6+1s15HDo+eNWiZ1KoyaLVy/fPWypPPfA3r+O9GdosS/W9NDamrQkJ0VATTCMJ1LDdwvJmJotBUqXpDev/rIfwOt/ePbP/lYgN0wxMdKmIpKQqBh9FGuqmDS9ODaI2nBONC7f5pCFOdl0VkRxVhq18XUENkoWPsUxQM8f/CS9qwWmseWU3qQKtgYJBwvUM0RyaAOcHxilwryCbMR6rtIpwfcZRMij6aRkmgLSdvO0r///McGcpVrZs5sSx2IZh4tWlTNkgGHJpBbkGUjdNvWotu3hY1taqt7nGygKdO2/f1vv/0iTU3fGyOMlrWFspVKUjMJRXKXhL+sCWDDbDZSCNCMWRyfqXwOk6F6vEhCq3Aq4YJKt21m0IAdG7E5hguUMhC7dOg+omMpTyclTtTg5ihrAw5qh27Plkz1CHURbIFlAYu0SJMBzfHxbcU6QpNRhpyngs6rh3mAUhNJW8++3NEEPWWm5RyZOrjUdM7xi4fx5TUzNqs8kQLNsE33FvT3l7/5OSoqApUBqIIuEQ6k060pL2huPQPpQrJwss9qFClViGbETiKbaeZ8nti5Yh+Nbe8hd8qai1x8D8DGepnLOnLvFUTvXn6gGk0q04JD3tTeXDNxKaoA60i42waKmiOEGo9dOJgbPplFTMgu2rpIKPBcIsYwZU8ZIEpkiUQ3NmyWNdVumXHH/9rJmxQmsYQfOXtgGhddMCGChiziyybDO1Nr0+Z8ebnLWnYehD5vZKAd68Zg6Y8gZjjOvnr8mgpXLUAniyWSxdjnVHYyUdXxxL/7jX1OP1m9oTef3/C5fcMsQXMlAqwKAA0koLNNR7px+javsyjARPt6aJWhmQV1EY8Np0VLF2HNAxDTApZCq25NqYWRsGcRsT00ganxeP5iYZgeOD8iXxIYIJEYyAPedzBeQCdN704pD4i/OShxAbWYoH4Q9LkDV5gJgePU2UpzVvcH407Tu9cfqVT5YtRcgwHTGWHPFkEKY9hTvjuz9sKgL/PlDpJ95/eA3IIsG9HPsjUNGdJR+u+TJ29TeNg+1pNN94zmCv/Vq68cbW1AXzJKTt5zkf7UsllAV5t2UnMPLL6agpF1e+6kIigaFvjqArlTXeyE9yNSshArCyyw/SYJnbTYRbvp47tPCm+DENgOfVrzySNMEu4pC+hUgt6CxVEUO2eEIb4DmCpxZOtJOrtP/rFABgsCnPFehE8TOozywPqJqIm8WGJRDRysWE+Gws91vSPrB7CpUGbqBftl5N3gvv1s5jN9SVmgk+gc4UDVGlXi6R90GqBxZhaYoLqtH8cGLl8+fSHX7rPo4uFrlB1AEwK6Rd+4KbwJQQPBQdeDYjPZkMhFLrILD28+JScDb4qaK2iEQPWak+ShMNtRW4jw2iQ1aUJAvMnQzGt40FwKGb+CLw/x7U9tzZQrNMG4AMURBQnWC/MxAt08PdAg87Oex4UEtFc4h6YGCq2nd58z3XFEoMCmgI395mBhrXNaPopZBaJ5Bww6kqOP0asaTymm/xq60OUC3U43fMK/A+7NpT9m/0HDHcbQ/StfjT44MPqvf3g6hjWmXttadDL+rHT6dWLXWbpz4T6vuwh3/vLnF+n60H9qL2mmJiJUEL2CdW14gO039u1rJLowa7feMsO5V7ht4HMjmA7VG8vXIL58/JrilgnTKWvXr0WhMoiSsGsQh5OZoHKYeQCGlm01ZnUPbF8l0Ny7WuvyOqIt3L72hG5efsR7hk6Spn9Wh0Hfk9AVdXW/n7iY3IIsOw++ZAJ28eIDGjZ0OU2ZvJ4SEy9TZ6MGZG3Tlg4lX6OVKzWruUqPOo0rUoXKJejLn39Tcrx2qVjgLBcrXZjevHivUXMPiGaNJFkaWxbFZ+q+EBSNheT03ot09cQtlW7b1qwFVaxdnj68+UQxC2UXTelzybiA2naSLh66KrcgEDt7oF6gc5kRKtauQCbDhIU4dIrAzZdXRA7zt+HfieuS6fopxa+XNQ9RE/kY7dt4WEpnkQfQThyXCBlt63w204WDil0UR82xY8ct5LjA5EPRZC01fi+Un7y2TGaNxK2zdzXmvMiOjlFO1ESvPhd5rqb+2VaUAU316tOiozOppXEjnggudFxFnn3ncL5WLnLxPQDnp10r99EoHVc+32Lz7rFhHNkH2WRLdptgDhFFqyX0tpGBNtRDSfdDeYAJD7ISwV6AC63FROVocNDRQk+LphSyCZ1CM9aNAYsnrGQjCky2cL5NfT0UQnFL46WFV/6C+dkNcc4wYWLWfaQRNWxfVxoIbTa6C8Wv3Ecvqz+h0/0P0r95/iH6iSi9Lzf+nUIp9OmvTxRedglfX/rc//mXN/1YD4H6unV5EoecPjQYMfUCrKb2Yvpl9Lwd/DqRfwaaPoDz9pKJK6VTvfRBz7gNGm+g33cdknHRfOXYDUpGQffzTzTQWzC/koeNgVuZCYHcR+R/Kgs8j/iIA5mejj1/+IoOxQmMJVOJRl4TuHPlEV08dpP3G8ZW2qUrJkiM4mAcV1jiJJ5dYdAFCuR8d0URuQVZNgMn3BPHb1OPHk1pS4wjDR+hT0+fvKW6dSvQFOfudPr0Ha0abrBoVzIlEx1xtAV0S4z6tU6TEq8p9BhuyK/lePx57raqC3C+wf1OzQVXZTojOi5umhun0CwDQAFnJJnKrfKUr+UyGtiJqtT/g+l/sAmWBRuPvqzDwkJ0NE6+gyZE33r9BUrExnRibFnAwg2zCwCTNWUc/zpatJVa4ftYBTPtUVHx47bBkW2DLxy8QksnC4u3skDhOH3zRNYmYDEOdV5LmgB0GV7RE6lxp3r8/k418cvWogwTMq/NTjQywJqL9iNxp2lky6l0ZLt2v8u5yEVmgXMAHBRnjwxlRzkYIy065kNt0xk2ZGUxttIzUlqMDfOz4gZdZoEGybTu/uyoWF+3NlOylbUah0ET9LSgrnlEOnGzKSPs23iICy7c7+QVDmzE9PXx39KsgQv5cnd7Y3Y1BEKd13CwdJ0K+Wm4VX3aOtyPity7QTrlf6Jy7x7SfzdP0QWLw1xwKdoppvzEkUx03uIwVcz7jGr9/JZqpLymVmWJf/fRr0CHQ4Q1y87LkjbOipGGTWMSB82WaMM/0MuSz/8ATDiun7rNrxv099SAbjwyUIh5sfXoK9MYZYX7Bv5tYN3hm4IuPWDqJZ2OTeutkiU8GApojNVpWZ0dQdXF1qUJXLg3al9Ho1b3O1YL07E2xo2oeBntmXlgvyqGQYt7y+yiK3b6HsKgv9eCbP/+/dS9e3cqX748f1G2bJG9KRWRlJREzZo1o7x581KNGjVoxQqBMpBT8OHDZzpw8CoZGTdk4WG7drXo4sWHdP/eS+7oVKlcis6d064+RK+78KU5c+QmG3xoE0aSzsypfVfoqRIW8cqifLXS1MpYcCISxbDqoq+ku5Ucc4IeXBdcpJRFh76tOa8ERRPE4MoAky9MnEAfQfEhC+g2gncPRM/fSc/uZayPgqjZTGKHvNJDoGrIQ19Jt3Z/5GGlNVemI42oZZcmvACBuvjvv4rz5WBpjGODxXfWwAUKnxfoSlNWjJZ2QhPWqDYthrX/xOX2fDkyaCttW5K56akIFLveWyZxUYZJGRdlcqab2gYaAeYOxhR8wJMq1SnPnVqP3rNpzqjlSjUFcpGL7DDuGNFiKp9j0agbMqMf+W13ptIVS2TLm4FzEeIy1voKe4rhswZQ3wmmmb5fGGZ49ApgjROmOMhMVHbyh8yzNZLi0HHxcNbXZoRHN59ILe5B/Wtm2CidM+Miprrj9qLxE2iAsSECNdGn2EXK116X+q33okWUQDMeRlLvVR7UuPF++ufXf5XfJf5M9O+v/1KTxvtp4b/xfF9eDzby7xEJweT4YR/VbV2Tf0RzKuiYYdaydHIEF+X12tbmxp04YVvpLlD5oQ0rWiptEbE5eDsXZXhdYlMxPbCenow/x+sr8j8VYdPc7Twdq6tTg3XhygJ00W2SQg6OoOpme+EYbF8hWN2b2wtTQk3g86e/KCHyWBrpiLYAg7hXz9+zYVyrjuoXpurixo2n9PDha/ZIaNO2Jv3QBZmdnR0XRtmBjx8/UuPGjWnhQqHbowi3b98mExMT0tPTozNnztD48eNp6NChtGuXcqYLWYEiRX6nwoXz05Urwsb/3Nl7VLFiccqXX+gQ2dq1o+rVS2v1OZSrWJzqNavMJ29tT8nKVylFjdvV4seClkyTMBshUPXi1xykj+/U34hWqfcH6XRrws9R1DQoCxRNVhIxMqZkyuiXIGbuIgk6XanAsl7HpBk17FCXC6GVnhvlFlng00PrpcgCvkaTqhzUia5czHzBZloRsOCMXzKCJ1gI49yoRCg2upzTNgh5YZjcKRNiDZ2FSNWcM2KpXPOTjADLervpQgDo/DFhbNuvyaJMpC+6mPjR2X2XKDtRvZFggoCuPht+hCeRfStXOi+nyM9FLrISmBLNHrmMjTswjUCDZm6SB1k4mWpV0yIPgoZ3A63zj5HSFPuMV59ylsZ+flAIXTp0jXVS3rGTWX+rDJ7ceUb+tvOluVpiVlh6wGZ+huUcbrxg+ibmUYpAEwp6Ypxzp64dz+ctXDdwSIiUBph/3Gj6+6c8Ujt7AK2y+eoYSqYQzdMRbp/6vv6in2lXnmpsqY+MMdHmvnHH+qx1TlqfzA3oMfOHSB0F41ftYwompn29HNO+H6+fvpHS5VFoyfrsrPAQpmNYX2EfLw+Y2G2VNO0GTBVMk5RF/OoDbEQDfR6kC+oifm0yF5l4rq26aE57dWDbKd4TlalYgpp20G6RlCAxiOtk0lipOAdNY1+SsN7ptK5O+SX76O8FKp8B3759S4aGhlSzZk3y8fGhhw+zLqC0a9euNGPGDDI3V05ouXjxYqpatSoFBQVR3bp1ycHBgfr06UNz5syhnARTkya0Keo42dospnnzdlOVKiWpVCmBclCjRhku2LSNzj2bS51xtG0M0FWi99q19hALkDWFpnr1qGKtchwimrAhc06OFpLu6J41yTxxUAWgPGK6gxP0NgXByyLAo0enGBbA5w9clq/78hsgPLeI/bxgZYQiJQuTpbPwPQl1Xs3iZ3mAyBpA1xTCb2VQumJJsp89UEpdhA5OESCotp8j3AZOkMi/UQRQMJt3bsTukp69A3nBUgUo6KDbQMEJcTyKVE0AmxvQF2GMgs6ma3d/OhF/jrIToFSO8Lci/50uVKZSSaYkTew8kxaMX6kRc5Nc5EJdnEw4z1OxXSv383ms97iuFHJkBtVsVjXbDirWusWTVtOGAKGhBO2aJmiKQJjres60xHQGWWOK6HIicJ6b3ieIWRa1WlSjkUG2sh9j6jq6fvIW28mj4MJjiQC7Y+lEwUJ+qO8AqaPjwnFhnGVWulJJdmwMvZSHRqYY0JM8hek/SRHy8neim8VBRVTtNUNThtu9kmxZ/qOf6MlvxcieDKmo0xh6fOsZT61wnho+y0aiEVslLQ5BoQdQNIpGU1jHRIMPEdD54XwGB1/Y2GcENMjOJl3idRXrqyKAdYLzOJyEwf5QFpjkiXl5vcZ0UbuxgGMRs0TYL/S0N9Rog2LnakEi0mVAW60GNH/6+IUOSbwIsp+uWJe+N6j8zoAmiCLM3t6eNmzYQFWqVOFCKSoqiv7+O+PQ2uzC4cOHuXhMDWNjY/67LHz58oXevXuX5kf8smjrp5NeXRo6rCPZjzKgiZO6kbWNrlYfL6MfXaP6lDffr3Tv5jO6eu6+Vh+rtXFDKlqyEL188pYO7zqr1G3wRVPmOqZD9fj9il2SQP/884/az7Fu6xpUp1V1nkTFLNqt0m0RoNlvkmC/GzU3jj5/+qzwNiX/KE5GdsKUbIXHRqYAyrouFqHWps1YjxXhFSXzer3Gd6MyVUqxUBqcfXmPDzthaMNAvVgwZnmGj5/Re4Dcmm7DDPj/fKzmcvimoteK63fq15YLJJ8BwWxQIvd4/kQ0ZdVo1vc9uvmU/GwXqPTe4rmNWzT0qxlHdz/uQGvis/xbvl/JM8qRWnZpzFQXD/NAOrztpEbuW9ZrUeZ6DdvVpoVHZ1CXgYI+ceuSPTS8uQud3HNea8/t/+FH2eOf+/P1GLx98Y4Chi6hqaazmK5ctmopmrXLhYb6WNKveX/JtvcA55B5DmFMhwZGB9uR2Whjjdx37KJdtGGWMHFzXDqc6c3K3A7n3bkjl9KN03eoSMlCrKNFQZHRdY9uP0VRswWWwYTQkVSyQnHp/2Fy5m8zj8/n0Iz1cBBe177IQ7R7hRByPXnFaLp85Brnkt37qTAdnRFK8SlC0fY+k4OFtxIfhesN29OwvzrSm5IVqO+k7hTmKmh5e403YfORpA3JXFCCzWHt3kf6/OG2iAlq+RplqfsoozSv+86l+2x1DwzzF5qTGR2fCEm+JgxEsL7KO+7v33zgSALAcnIPlT5j+zYd5UKzcImC1NmmvdqfGejfH1x/wkwSA8u2Gvse3L78kC4dv8VmHoYWOlo9Nx3cdZ6+fP6bylcuwflj2nys/zL4uXbtMT1+/Iby5v2FWrasqvXH0zTUmieWKlWKJkyYwD+nTp2i8PBwsrGxoYIFC5K1tTWNGjWKJ2jZjSdPnlCZMmkD6fBvFFl//vkn5c//7eTJ19eXpk+f/s3fnz9/Tn/9pb0A1t9+I6paVZiKPXv2jLIDTdtVoyN7rlLs2oNk66g5d5+MoNujEcWFJdOW0L1Us0V5udfFBx+TWZyAFHV3GhnUpPwF8/KJLWFTMjXOBIfZcGBbunLsJsUu2UN6tjqU93flV6kGhrWoRIVi9PLha4qav40M7RTztjsPb0e7VyXRuX2XKCFyPzWS0+ExGWNAR7ad4gwzo+Ht6Y/a5TK8nsXU7jR/eBhtDIihFj0a8XOShQEzepGroR9Punas2kMtujZW6j3oO6073b50jy4nX6eZVnPIc5uTNH9GFvpP70kXDl2hp3eeU8CQBWS/0E4hRcRhyUDy6hnMdMdlrqvJXBJUrSzsQ2zI2zyYHlx5TM7dZpJb9DiNWQrbLxhA/zn8Ryd3nicvizlkP9+GWplo1u5Xle+BiP4eptRQvxaFu0Sx5nBq91nUvm9LsnQxoYLZ4H71PUOd4///DByn49vPUYTHFmYL4PttaNuW+kzqyiYV6qxzmnoP0AxaPmUjHdh4jJ/X4FkWpGPeWCNr7+n4C7RgbDhf7j2xGzU2rqP0/cavOMCBxNg8jwqxI8r3X4a3ffPsHetwAcOB7alG68pprrdl7k42dcpfKB/Z+felFy9e0PuXHyhYEvhs6tCZilQqRC56PvxvfRtd2r3pGN34qQXdL1uTur3FpEZ9pkz+Lz/RphZ9aMOjIvTlpzfUe4whxSzeQfcuP+TzTie7NvTowSNaPlUo0LrZG9Bf9IVfw5Nbz6RW/JZuZvTm7es0971o4kp+/5oZNaAytUtkeHwuHrjK07E8v+Yhw6G6Co9/7II97AZZrnppqtGmktLvFz6Ha3yj+TI+2+8+vqV3aiYVRc4T5BHtezejD3++4x9NfA+2LBeK1yYdatI/PwnHWFvYGSWETuvo1+Q9c1Zjx3bBxKxx4z/o/fs39F6+d1imgOOvaWSK4Pn48WOKj4/nnzx58lC3bt3o/PnzVK9ePZo1axY5OjrS9wYXFxcuNEWgeKtYsSIXoUWLZpwA/6PAtF9bLsiOJ92gcdP7aJX/23t4Z9oefoguHb1Nf38gqlCttNyTDxZNvAcKF+HSRF3sOlD0wnhKXHuMOvdtr/Zz7GKtT5sCd3H369SOy2SmosgWuWQh41fSzqX7qO+4HgqPZ+nSpanHKGPaPHc7RQfsJP0+7WW+3tL6paldr1Z0cPMxvq537JQMr2cyyIiSIg4zDXLnokRyXDpS7uNDPL3BfwtFB+6gLjYG0sdX9B64b5hIwxs50d0LD2jrnD00Qg7NRngwIte1juTUyYMObzlJzQ0aswOY3JsYlKaxC4ZQ0NDFtDlwBzVqW49DoJVGaSLf7a40vp0bPbz6hELsI2hmnLPG7LWnRzpRwODFHEAaMnoV/ZZnGBnZZj5MVq3vQSro9y5NbYxbULhHJG1dvIcORB6ns4lXaLhvf9Lv31ZtAfr/G9Q9/v+PAFV20cQ1dGyHoNmEZbtjyBA2S8ju9wCZWIFDFtOByGNc+EwMHcFaU00ARdDCUaso5b8UMh7YiYb5WCv9/cI5eo1HtDSnrJO5rkwXu0CrxfTuxXuq0qAijZ0/nCmAIi4mX6HoIGHq5zB/CNVrVkegZo6O4KIMtxnma8uxKC/uv2JH2gpVy9HeiGS+n43PitKhfw2p8qs9dK9oyjdW9/Lw039Eld78RN55TKlWcwN6fXIP37+RVSeybyasUYNm9KcqNSrzdO/Z3RdUvGxRsp1mQfkKCBliSxxW079//0stjJuQkZVemuOHLMtTu87z+2YfNJDXrPTAa90cEMyXTYcbUr2m8p32kBm6c9k+vgyX5LJllc++O7bzDD24+oQnfJZOPdnSXx2ggXzh4HV+rRbjTKl06VIa+R6A/nooTogY6j6wU4bHS1N49ug1XT79gC/3sGpHpUvLbv5qAykpKXTihGCAZ2TUSKuvFfgNUxQNQ+UzGmiJmzZtIlNTU6pcuTJFRkayWcajR49o5cqVtGfPHtq4cSN5eXlRdgNfrKdP01qg49+FCxfOcDoGwI0R/5/6B8CH/kf/adKmBpUoU5g+vPuTju27qtXHKle5FLXQryfVkim6Pk4+yt632YjOTMk4k3SZ7l5+pPZz/PXXX1jnAGyau4P+/ec/lW7fdZAeLzbP7r/kfBJlbgNDEDbjOHOHDm05Ife6cFyEZgCbnlN7zmd4HTRKhko0Z7tX7qNHN57Ivc/+zj1ZgI5OZvLmY0q/ByXLF6eJYaP4cdDdxJRN0WttoFuHhvpZSzN0sJlRdJsug/S4aAVm2YXQg2uPVXpPylYuRTO3OTMtBJPIoCGCO5kmPtO/5f2NnFc5UNfBekwnnT18KcUs3KWR+1bmPZD3Aw2Gwxw7mr3XjaMTsJkLHLaUpnYPoIc3nmr0Of7IP+oe//+XH2iCNwRuo5EtpvJ5CXS7AVN7slasfpta2f4egFaMCTaaJnhurmvGkuEAofGV2R807jx6BnBwfAujxmxvj/OvMrcFrXxGv7msR9Kz1KW+E7rLvO4aryh2ScQ64bbRifIXyCf9P5im+NnM5wmSwYD21NmmI/9975qDHPKM1zxl5Ri6euwGxSzYyec+i0k9aK2PUAj+XuR3LiYf5y1OBY424OwxlfAT8e1qW3en7RLXwbEhwyh82nqmi8P5FhljLx+9ptVeUdIC7fdCv/PzxBpwcPNRXr/hCJn6+OF9Fy3sERVTpV7FDI8P1s1rJ2/xFHbAtN4Kj31sSDx9eP2RI2j0+7dT6T2PnC1MtUyG6lOREoXU/uxsC03k+4GRR4VqZTT2PTi47TRP/kr/UZxaGjTQyOdc1s/eWKH50rh1dSr7RwmtPtbPGfzcvPlcSlds3aZmljympqHyPZYrV46GDRvGxdixY8foxIkTNHLkSGnhAsDVMCdMk9q0aUMJCWkt0DHNw99zMt6+/UTvs8GuGiJSA4kQE+Ye2kY3if1q/PojvFBqCmUrl6S23QWTkmglredlwdi2AxUvW4S1D3vXqZadhm5jv8mCpfxa32i2QFYEmHH0kRiKYPHBAi0Lf9Qsx5oHYMmkCJnXrdemNrU2bc6LtDxnRqBAkQLUa5zgaAUevyo86bY9WlJviTtZ4KCFSpmD4LW279OaN3IzLGaz3kQR7GfbstMkhN8w+UCHUxXAWAQiexSzoHwud9FMRpn4HRq/aCj1Ht+N/73IKYJWz9isdaMcZYFN8cLD3jTYy4L1b2cSL9LIFi4U6ro+1yI/F5nCqb0X2NVzhUckd+Ybd0Su2EyydeudLSHP6YFiZaqpPx3feZbPzcgpbN+rlUbuG1ljU018+TdMStw2QvulHMNEMCsK4ttWb1KFJixLG+qcGgh4XusjWOHD5bZSna85VTjHzBsdytTk8tXLcCGE+8F5eMHY5XwdGw8LqlCrHAUNEQKhjQfq0ZYFO3ntqNKgEr1+8obp5v/89S+VOluV8v79E/2s5BKA6+H6tZ81ZedEPB9Y2GOatW/jYS6y8JywkV3sJJgMweYe+Zri84cBFdDZthNVbVApzf2fTjjPNEQwTWzcMrawx+sIl1rlm1Kx0kUUfibghixOx1Qx0kD+5IXkq/x8YOahLuB+GC9xQjYbnnG4tbqIW3lAutfSposp3rt4yZ7RyFzYe2U1kiRmHjo6Nb47d0URKr9DcCjENAzW802aZOxEg2IMlvOaxocPH9i+Hj8AHgOX7927J6Ub2tp+pUqhULx16xZNnjyZrly5QiEhITy9y8lUyvCwfdS3z3zaujV7gl0Nezbj3ycOXKM3Lz9o9bFaGtSnkuWL0rvXH+nQjrMave9eo434d2LkEXqjxCZfFhBQKbpubZwdp3JINzpnEFu/ePCKdq4QaBGK0NvRhF2z4KCoqAi0duvN171z4T5rD2TBzkuwQ4a98JVj1+XeJ4I6YWd/5+J9Ohx7glTBEL8BbDoCh7Bp3X0VFkvYMExcPipNPpmiIhAbHbf146nUHyXo/pVH5DtgvsrvC1wbnUIF+ubGwK1SzYImgNc03H8A2Upyb1Z5RdHiiRFaEQGrAxw/mM4sOenLHVkUw5Gz42ho4ynsCpdTisdcfB94fPsZTbeYSy4m/jyxLlq6ME0JG0n+O1zSFAzZidfP3tJEQ292+INu1He7M7U01ozGE4XFtO5+zD6A7fmMrcIEXhngu4Y4DxhbwN7dc5MTu7dmhBePXpGf9Ty+DRwJMc1JDdjEs318np/JefU4fg4oUHytg+nTuz95OtVvshmFjA3jXDScP3/5LQ/dv/KQipQqTA+vC469//z1D0+6fvv8K63fnJeHZIqKMvw/rrd6wy/U3rA132fR0kXYSXHxBCH7teeYbtwMw3QPmZd4nuMkBRoAbfD5/ZfZot9WElUiAufO0KmC66LJcEN2iMwICWsO8pqAYyk2NuVhy8JdPEHC5xQZoqoA50zAwEqXDUrUxa6IA/Tnhy9UqXY5dorWFG6cu0dXT9/lqaixJP9VW7h48g49uf+K8hfIS20N61O2uCsmCgWZnv73566odkEG8458+QSub1YD07imTZvyDwCtFy67u7tLNW1icQbA8j4uLo6nYsgvg/19aGgoOy3mVJQuXYQ3l+KHK6tRsVppqtXwD56mJMVptkhKD0wojPu3TZMirynUbVWdajWrSn9/+Ye2hytXCMkrqgoW/Z03G4e3KrZ2T1/QwbUJWD8rhv76ongSCHqZxUTBpXH1jE1yp2SFihVkmiGwanqkzPtH1hgcEQG4KMorDkBZNBstdPxWuK+nf/7+h5TFr7/9Su5RE5mqiSJx0XjFQezYOIB6g4nNse2nKTJQcT4Zwq89Ngm3gdtYmKuwWKuCzjYdaIhPf768aMIqhXltqhZl1tN68TRPtFT2twth/UpOQflqZcg7eiJN3zSBc29ePn5NvnYh5GQwg6lDuciFokJkhWckDWvqTIe2nuQNds9RRrT87CzWZOUUbSKKD8dOnnTz7F0uFgPjpzFdWhPA9xkW9VeP3+QiwGf7VD73KQtMZxB4j2PntmE8Z1JmBKwBPv3nSqdoo+YK0SGpLe7nO4TyZWSR1dURTNXW+22hi8lXucHmHDGWkrcco53hifzeWE3rRTskboWYGGKtlCKFSKf4ZzK78pni1hDlR42WImjEUgP/xt/x/9vXEPW++Tc9Wy+cv0GpR0Pv7qUHfGwQX4J1B9MxAEWlaMWPdUv8u/nYbhypkhp7Ig5w0Yq1wkqSS5keWKewXgJgpqS3yk8PMCw2Sxw2rVx6qjRBun/tMR2JE5rmIhtCHeB93bJYyD4zH22s0e/M9ghhPdM1acKu1tpEfLQwHWtv3JDyqWB+pilcuvSQnj59y5MxHZ3q9L3iu1Imd+rUiSvh9D8rVgibPvxOSkr65janT59mO/ubN2/SwIFpT2Q5De3a1+ITw82bz+j+vZfZ8hwMzISCNyFG+7RFI8s2TGU4d+g6PbiZVu+XGeDEhiwPYFvo3kxthLEI9BgpGHpAH6HqBAGhlOigYUq2a0Xaz6csoCDCIoauK7p+8gBNFaZwoKpsWyybooksGizM2DzsXL5X7n3C3AOPj6JKDOBUFlhM3TZO4Pdg14pEOrBZcF6SByzMo4MH82VYI184qLghUbtFdXIKtefLGwNiKT5C9cB6dIzNJRPQWYNC6PguzQRHizB36EJTVozi5kPihkPk1jMgx1EDW3drSktP+ZKNWy/emIGKM67jdC7Ont59kd1PLxc5DNhEbluWQIMaTKR1/rEcDdJUvz7TE5HjhYZOTgHs48d39KRHN55yBMicRA8uaDQBFBeg/p2MP8dTrRlbp7AOSVngdssmCxQ9NG5gTy8LK9zWs+mH0LyawI0+EbC4R+QIMrQad6pP/aaY8d9vnr1Dq70F6/cxC4byZHC+g0BdxHV2hO5lrSvOvVg7UBRK8RNRtyKvKCXPL2R8k+jBbKK5O4mqvkn7vPBv/P1hEJHRTaL/fvqZWr2/TlUbVqJ2vXVopSSYGVoufC6QnYkcSKxDtp59pfezec42enj9MRez6QsuZGgulzTcrFzNZdIQ41ft5+IbkzmEaSvCtiV76P2rD5wZ2tFCNRlL1NztvA9A/EwlFd7z9EiOPUnP7r3kiAN9C9UmdPLw8f2flLj5OF82sVXf2EwZfP7zLzqw63wahlVWI1EywNDVrUl5cwA9+v+iIPt/QJEiv1Pz5sKCkSjhxGY1OnZrzGPuG5ce0Z1rT7T6WBCbttAXRtw7JB0dTaF9z5ZUrEwRevXkLR2MUY16lx5mo4x4swqxMAInVQEWTzguAutnxSpVHEKwbSHJMuPJlxz9GZ6XyKlf57tF5oa/eNliZOcpUBcRuimvMEAxNiJQmO6smRHF0xNV0KBdXS52gMDBC+nuJcH9SB66DjUgfat20gBnUHQUAZ34/i5CADaoP5ePyqdjpgeKRgSvQkiPjaZXn9ns5KVJGFi14wDpvL/nZfOVyUYzmUKVk4DPqPVUcwo7H8BZOjguoC8OaTyZlkxZS29faNE/OBffBbABPRJ3isOd549dQa+fvqVy1UqT+/px5LttClWpp1zwcVbhVMJ5cjLw4ueJomPuPk+qUDPjeBB1jsUix5WUsPagNPhZnEopgwfXHtEMy7lcEMGNUWQkZIRDMcdpvf8Wvuy03J4q1Ej7GkKnrJGGQztHjGEjDOSP+Q4IZjqybs+WfF6F5f2bZ2+pcr0/+DlfO3GT8hXMR7fO3+X7wXlXxAg/a2r06Dz99K+wVhX9TDT6KNHeeQXJyr8T6c7tyr8T5xXgvxf5Itzu55T/qBPdp2F+A2il+wbOEitXrQyZjjTi5ySGPQ9w7U1FSwmF1asnr2nNTGGyNdTf+pvJ1qY5cfTq8WsqW7U0T88yAiZsa2YK2jqsmzA6kQc8F2SEAv2dzVSajj1/8EoqD7BwFDTT6mLzQqGBivzU1E6ZmQWKsc+f/qKKNctSg9aZczZVhMMJl+jPj1+o7B/FqH5zYeKZlfj33/9o/74rfBmZvt8zcguyHIiOkvypfdlUkBUpVoBadhDyu0ShpjbRzVbgwu/ecIQFzpoCxLamQ4Sg6C2L4jOljSlaqrDUwjxyjuCspArgvIfu3/P7L2nPGtlar9TAIo3JF0TZsYvkm5MYDezIIZqgtGBaJAs9RhvzpgTXiwyUfT2gs21HqqNTkzuvMPhQFdABsPnGuz/JrYe/QsMOFAHjFw+nKvUr8gLsbTFbKbrkQC8LatujBXfqPXsFsmOZKoCGYVL4KGpp3IQXauhBEHCtSUCvEhDvyp1QFPXjO3gwBTanAZ+3iUuHs/EHTBlwTEHrGVhvAq32ic5x071cZA3OJF0kRz0v8ugzh7WtCMEdFWRDy077k65ZixxDTxQB7e20HrOYVolQ5qAENypRTnM23Nj8wwwDmBRmTy27ZKynzwjvX38gN7NZ9OHNR9Z1jV04RObxe3TziTRvDMVIhz5pJzlwsxX1r5PCR1PJCiWkrrWgCmLNGbd4BNOxodtCIdZzbFdaJzEGKVA4P9MTf/r56+PXaVWDercuTgW+CBpycdWMpyo0mgzp2eeSVOBDIf7tX38QxVPlNNcrRl+o9INrUhdHGHnA1GXtzE18boYNfs8xAisBWOG2gdcYrDWG1mljQlKvZ0Nm9pdpDrN10W5eJ8FE6T5SuekYstwwNVU18iBqThwXuo3a16H6bWuRurh8/CZdOXGL9yndh2ou9xX7HLG5DTMPbX83RSaVgVkzrTgPKsKFCw/o5csPVLBgPmrRohp9z8gtyHIg2rWrRb/88jPdufOC7tzO+nA9wKiX4JSzN/Y0n3y0CUzIylQsweLa/bGqabQUwWRwJ/o17y909eRtunQ0c7oYWOCDXnli9zm6eU7oKioLdL/6SLpp6/1jlCo0QIOx8xTEzbAlxgIuz6gBXUkgMmgrPbnzTOb1Bs8UdFNRQVvlTr7YnGKWDV8GxfHhddWmpdCTeURN5M4mqCRefYMUvu78BfOTZ/QkptdcOnSVQp3XKHwcLAJTVjlwvg46sh69AlQu7LEoukU6Ur22tdiQxLmLD2+GNIk6LWvQnCRPnirAInt8Rw+mB+ZEVG9cmU0ZZsRMohpNKtOn958pwnszDazvxGL2zx8/Z/dTzEUWAJ/PyV18aEpXP7p89AZrNi2cTGnFxSBmDWgzq1Ldzeha3y3kZ7eQ162OfVrTzK1TNBYAD2xZuJNWeghutaPnDWJ7eWWBKfwMy2BuxpSqWIJNPFLTD1MDrAg0peAECDdC2MCnBs7dYB8AcMYVMxkPbDpCcUvj+fw9JWIs/f35L6m+rO/E7mw3j8lcrRbV2X4ebBhY3YuwmtqLfooUqI6gIP79Ux7yp5YU9FML+vLTL9JpGory65efUtBPLWlnB1v6m36mf+knLsxuuMzi98LAuj1HAIAhITYA7ecMlL7mW+fu0q5wgT4/Msjum+IBhS+K6prNq8k03cC6KE7HsF7KMkURgabSBkmRZ+1qrrQbJoAJ445wQXZgOUVgvaiLaMl0TM+iNTd8NYVrZ+7SrYsPed9j0EczLqKy8PLZOzp9SNhX6UscurMaSYmXpPvmX3/NQ98zcguyHIjUlX7SvuyZkrVoX5uKlijITosnDlzV6mOBLiBOybaGq64Dkgec6PQl/PBN84WOnbqA8UE7c+EEt36WYuOJ9DAZbsBuVtiMI5dMGcCIA7lR4LqLi4gsgJrSRK8+RwgslzhSZYT2vVsLk69PX2i1l7DwykLD9nWprVlLXoA3BajuRAgbf+/Yr7lfoLEoAig5YqbZpjnbaN/GQwpvg/v32jKZqZbXTtyigEEhKrsaguYyI3YK6x8woZtiNIOdHzUJuEnO3T+dareoRu9efmD64v6oI5QTgc1RS6NGND/Zi1xWjWYr7bfP37NFvm3dCbmF2Q+Mcwcu05RufjRB35vO7rvMhZeZfWdacWk2DZnRT6MFjqaAZs/sEctohaRY6jPBhFxWO2jUch9ZjgvHhvNlW48+1FMO1TA9UKAsHBdOp/YImjOcr2BOJOu680aFst4KU/Vp69Pa6LN74oBgphJXa1yZ3W2BZ/ee0+xhQrYiKOONO9YjnwGCy2Ld1jU5sBtFGJpkdyQsABSu4oSsRIXitMAhlP4MEww2nuQpTPYpBrTnp69UNPG6P+fJw88T68m8o3+RPRnS57J/sNtis5eXqEjxglxk4TowksLjtOnRgnR7fi0Slk2B+2wKa7jqtxVYOandO0VN9FBfK5nTF6yLWB+xTiKfTBG2LNjFxw3aMWTQqVpEodlXq3k1aqYvW/OnDO3xoKT53HOkoHXXtJlHe9OmagdVK4vErWf4/avXrDKVryRMZ7Ocrrj/6g9BVwRyC7Icik6dBBeofRJubFYDXTOx4yE66Gjb3AP5J9fP3uMOjybRy0GgMByOO0OPbmU8OVIW/SVdsQObj7HTkqobftFxcY1PtFKOiyhWB3lb8uUt83bI1R5hAw3dF+t/NhyiqyduyrzeMH8hkHl7aALdlmgIZGGg5PFPxJ1lGoyqAAUR2gdgw6wYOr1XEADLQztzHbKYKByrwMEh3ElVpmCGlgOf3X2Rh2mVp/xiU5Zrpd9OV6Z/YvOCokzTei+I0gP2uFEb0+ZMCZxhNY83FTnVbh4boU59W9Oy037ktHQYT/jEwsymtiOtnrmZ3r3M1Zh978Dn70T8OXIynEGTjHw4ow4UN5gSQVs4arYtlSiX/fmiGQFTpGlmAWyaBBaDQ/BAGu43QKMUKpxTgoYK+V29xnUjaxlZWLIQs3AnbV0sTK6g9aohx1wEhQgMkfBapq4VIj5SA2YZYji06zpHLjrRgAoYtJAnRqAdIuoE2jPRZdFkmKFgi/8zAoV/4sYdfgOYkGHzPmu3G42fY0MvfitKN5rpkU9dW7r309fpDaYuuG7x8sV4WoTnhUIYuugShrr0z9FjlPBbdXpEBWiohznrxBD0jM8Spquj5g6S3hcMlE7sOsvn6yE+QkGZGgiTRhHXzKAh/2QEMCKwLgJYJ/F5VTZ3bAByxxRcP81t3/1JW5fs4cuWk7pnigq4FYYq//5HjdvXoWoN0+atZQbv33yifdGCXr6bls08cL7YvVl4LEOz7DHzOHv2Hr1584kKF85PTZtmvX5N08gtyHIo2rStyePXe3df0q1MFhHqorPEMedo0mV680q7mWSwZW1n0iRNh0dTqFynArXs3JBPIKLFrLrAyRPOSrgvRROrjGA63JB57nC12hmWqNRt0FXEAouJFmyM5QGLvKG1cCJeOnm1zE1+ow71qF0vHV4U4LwlrxhAQCemb7jOSknopqqA9qHrEAO+D3/b+UoFQA/2taJmnRvx6/Ywn0XvXine9KMjDB0aABoLLKVVBXQXs3ZPY0oR9DLOXWay7kOTQIfcPdKRaV/Actf1FDRsSY6yxU8PdOiNbDqwrblYmGHKFzEjmqxrjecQ7FxXxu8P+MztWXuQ7HVcybVHgDTs1nS4AYVfCCTHkCEyc59yAkCHHtfegw1zYJwDGmAPJVz2VMHR7afJz2Y+TwO6DtFnIyBVNuTHdpxmExBgqJ8Vn09l4ULyFZ6kCde1pmaGjdI9l1O0zjeaLzsuHSnNeosO3s6FD84tzqvH8rlLZECMCLKT6oARAP3o5lMukPB6xKnXtI1OVKnuH9Sydzuq8OwmRdY1o6sXhGwyEaI1PgKkge6jjNnaHoWd/Ww7WjtnJ/n93ZTmNbSjzqNM6MufX2jJxFV8XYtJZlJbfzQjQ8aF8eUeo7qw8Uf6Y5C4PlnSPPy2WBOB44D1AdM/rJPK5I6Bkg43TD1L1bK5tofu5YIOt23TXf0CBM93hyST1EzD07H49Yfpy+e/qVr9ClSvpXb1VFfO3qf7t55T3vy/UoduaT+jWR0G3b59bfpFheI6pyK3IMvBtMVWrYQ8hb17VXP10xSq1CpLNRtUoH//+Y9H09qGiZ1QSCRtPk4f3soPFFYV5pKg6N2rD/IJOTPoL5lyJaw7RE/uPldZS4bME2Ct3xa57okisCgNlAQ7o8OqiEaH6yJcExRB5HrJArj8WLxhp5y4Tn4RbONhwbbIyVuO09E49XR+9nMHUsU6FZgyM8NyDts1ywPcwlzXjWd6zZPbz2im5Rz691/Feka4lokOj0FDF3MYrKqA8HxWvBu7dN46e5dcTf00bmiB6efouQNp9Fw73tDsXrWfnLv6KFWsZifQVRYLM5eVo1hv9uXTX7QlZDdrzGYMmM/ao5w68cuFgHevPjDtdGDdCRQwZAndPn+f8hXISz1HG9OKS0E0JngglamccwsxkVo5RteN7l15yIY0sxPdqbWJZrv1p/dekOhf/6VO/drSuEXDVCrGYBA0s3+w1FGxr5PgnpsRoAvz7hvElEQ8Vp9018W5Hw0toLu9MbvDAtdP3aLlU9fy5eGBtjy58rOZJ6UJXj16nSf+oMyLbANMyEQM87dJM4XatjThm1xGcZqEhiKmZO376Ejp5MgUE6eA/ByC7Pj8jcgUmG3g+VhIzsnA5rlxrKPD+TW1/T0gZJUJRVyXwXpUo2nVDI8V7nfbEqHBOniGpcL35OO7T7Q5WDDkssZ0TAVnRRSQmyWSBwsnk0xNXuPXJPMepGzlkqTTVXkzGEXAcYtbJTQgTQd21LqZhzgdQ/ZYgYL5soWueFAip+koYZR978gtyHIwxMRxhERn1+amc0/B3GNPFmSS1W9VnarUKc8dnr2bjmn0vpt2qsccc7g57VJSvyULdVpW59wdTJei1HBc7DJIj6cvrx6/oR1KTsmaGTZkx0JQ3CK85DseopttPkbQNoRNWydTS4W8MEuJZXyoyxru3MkCdFVdhgmOlejeKlNIZkTZdNvgyDSbM3sv0Dz7ZQo/14WLF6Lp0ZO5cEQHXBkNGjB4piXr/dD9dzcPYJtpVfFHzXJMX4Sd9OUj12laD3/OxNE0zEYZk3fMZKYVnT9whca2c6c7F1WnhmY1sEHrZNGGHRl9tk5m/SK+E6DzQns0tr0nT1406Zyai8zjxpk7NMc+lAZUH8u00xePXlPxskVo4PS+FHFtLtkHWnNxk9Oxa2USm+9gSgtNz/xkb6opY/OuLtDMcTebxcVLa9PmNGXlaJU28qDUTevuz82cRh3ryS3mQP2b0W823wY07wnLRqa5rqgbg14KJhcjZ9vx3z++/cjmH1gbWndvTqYjOnNBg0YSNNQtOjdmajrfl+R8i4Zdahpi6hwu3N9Kj7RMCEzQ8PiYZKGhhsK9TOXSdPPMHaY6DnDrTYFDFvF1oDlu3rkxx5ZskNj1D/UbILWiR9EJx0WxECxQJK3OKXFdMmdlYp0Y5C00IjMCQqBRcDbRbyA3w01EbMhu6XRMlkGILCSsTeb3Bd8LvX6qTdbSFxHRIbulTWJVPkuKcHr/VXp0+zn9Xigf6fVSPC3MbPbYvu3n+HJnc2GPmNU4feoOvX37JxUpkp+aNPn+6YpAbkGWg9G6dQ3Kl+9Xevz4DV29kj0W2R27NWKO963Lj+mWlp8DFoyuNkLHb/uqgxotQnHf5vZCFy92aQIvHJkBOOTArpX7OOdGFYAOJGrJkEum7JQMXUB+zPBEhbbsmBBhg3/r3D0WostCnwmmXMDBjh8GGvLQc0IX7o6CIiTPWl8eqjasTK7rHXkitDM8kXaGyQ+oBpAh5LhspJSicihWCLxUxnmxjk4N3sBMNfFjC2VVgcdGUcbF0v7L5GGuuoOjsrb4c/dNp7JVS7Hpy7j27nQoNnPZeVkFfDabGzYk/+3OtPi4D3UZ2JE3fLD3x+TFqvpYWjRxNU8xcpE9gFMdzlXjO02n0W3caOeKfVxkVGtUiRwXDaGVV+bw5L9w8YI5/i3CuXvJpNUUNGyp1EkxELb25TVnaw9cOXaDJ+NoVMEp0G1jWmMNRcDt3HvOYno6okY8opzkulIunRTBBSDONR6bJrLjbGqAciiGQ4M5AN0Y1kjo2nBOLlulFE1e4cCara2LdvFtBvtY0TJnIXwa50KYWUCrjeItNQ0RtxcBF2AUuamBiRiKMpEl0K5vK6ZIAg4LhlLC6gOcawazlzELh/LfF44N488dDKT0+gumXcDyqWukf4cLY2rg3Bo2TSgGLZ17yjQ9wfq3e4XgdjhIwh6RBzxvhDkDA6aaq1QI4fO2IUAw8eo9tkumnEWPbD/NOnaEZBune+2ZRdwKwRDN0EJHodNkZnFoz0Vp9liDFpoJWlcVInOsQ4c6Gi1ssxM/xqv4QZE//2/Utm3NbKUtFi5WgHQk7jV7tmjWkj4j6PduRXnz/Up3rz6mi8cyNqVQF3p9W7Nj1bP7Lyk5k/b60CrBKQ+bmuiFwuKnCkBdKflHce427pTY6CpCA9061B66r/9SKNRZoKfIAtwGrd1682U4LoKukRHy5s9LQ3wFjv56v2gO6ZQFdCxF6+V1vptVDosWodOtGQ2aacWX4b4Fuo0i6PdvJ82uAWXn7mXFEyTRyUy03cfmSJ1iCt133+1TuSt8OuECTe8dpJQhi6rABBddfny2sGHx7DObVs/YrLJbZHaiaoOK5LhoKK2+PpfsPPtQmUol6cPrj6zdGNbUmbOstoclshYjF9oFNusoKoIdwqh/1TE0e2QoW9djstmxjw4F7ZlGIUdmUJeBnTTqRKhNoKAIsF5K0RL6mPW0XjR1zRiNb0BxTnLp6sMbeeSYecCeXoVjhEkINGeY9GDCPmPrFLnF7p7V+yl6nlAwTFk5hv6oVT7N/5+MP0trZghTpXGLh0vDoUHZO7DpKDdNp22YwG6KovEILO53r0xiVghof/gsAP/89U8aMwvodBt3rM+XMdWKmp3WQTjPL8I2EUXl2+fv6I/a5ejqsZtcqLTv05r1WzAZAeCqWLJ8cTq89QQXhngcxyUjpJM+GE3FSxqEMPhIT/2Lnr+DC1hQHGGcIgvLpqzhdbB9bx3OclOEzcE7uDEH1kPqaaAySNx4mB0fi5QqRN0kuabqYtOCXdIgaKwnmsLzh6/paLxglmUiyUvVJuI3C/snw57Zkz3211//0MGDQmSMvn49+lGQW5DlcOhJPmwQL4oC3KwGvnRA4razWs8kK1jkd+poLozb41ZmjlqYHhAxI5cM2BySOXMPLDD9JnaX8u3hwKTSc8n7K1lKOPXr/GOULhQG+/TnRQ4i8bP75BfpPR26CiHQz97KNQOBDoFNQz5+oYjp8p0JO1q05QUQhejGWTGkLiwm9aCWXZvy/Xj0nEWvnwoicXlAMQgbfmw63Hv4KeXsB0fDmducmVYD2qG/7QK1Chy8ZtwPNIBwB/PuO1srRRliAny3O0vNPlZ5RXFu0fcWyAyqlNUUMwq/FEQztkykNqZYuH+iS0euU/DoMLKs4kC+diF0dMeZHG1k8j3i4Y0nFDFjMw1uOInGdZxO25cncoGP2ILBXhZMS5wa4UANdGvnuEBneQA9bkxbN7p06DpvZt03jCdb9z4afw2gdE4xnikEN7etRd4xU1Qu+DDtgt4W05TpmydyISALV4/fkFrVIwMMlL/0Wikfq2AusGGMhOYUgGyvJU6CUQiaaqAxBg4J4WYHiqSipYvyxA3H6sXDl8KUS3KsRIYIiq1hfoLjLs6LixxXcMEmArphaMiROfbg6iP+t25PHbp/6RFPecYsGMpZkTiPN+5Un4wH6bGRR8h4wZSkt6MpswwAPP8lE4Xna2jTgerqCM3m1JpGcZ2CDlrWMT+TeIGO7zzD66CYqSkPuN8oibMiogpUmo79+5804qb3mK6UT0K7VAdXTtykS0duCEHQwzQXBA3sXJvM+8OGbWpSpVplSZt49ugNnT16SxoGnR04duwWffz4hUqVKkQNGlakHwW5BVkOR4sWValAgbycRH7h/P3seQ7tan3NJJN0JbSJ7gOFDs/BbafplYYtx02H6vMJ8crxm3TxyPVM3ReclsBHxwK4TWKHqwpgJw26IKZkMOtQBljYuw3Vl+q+5NE68TpHSCZam+bEcZcvI7CTlSQAevuyPXInVrguDD6AbUt2y52oyQO6alPXjOPjB6H69D6BCgscBE27RzkxNQcuYd79ZitFPYULmefmiXw8Dmw+yu6T6gDOlN6xU7iwPxJ3SmtFGWhRMPuYsHQ4P+eDW46TQ5tpdPfy90f5w+YHdEzPSEdafT2Yhs60pMr1KvAGLmnjYXLvFUSWlUdT0IhldHz3udziTE08uP6Y1gfEkoOuOxdiq2dG83ck7++/kUF/XZq1aypb1/eb1D3HWtfLw57VB2h8R0+enpSpUpLmHpguzYTUdDE2ubM3T1PQpPKJc2FmgCrYHBwnNY+YFD6Km0iygPOnZ68Aqf4LVvWpgfMLDEXQfEKQs8P8wdK/I18MjbzmRo2p13gTig3ZxdpcFDK2nhZSR9xSFUvSm2fvuDDDepEnVXju8ABbnp4JWWFhtD/ycIbPE0UZAJYC1gjAdroF3bv8gG+DZgsmXlgfNvjHsAkTplwiS0MMrAbtG+fPQTO+LaQwAcRaCr1yeiqjCBSNy5zX8GWT4YZyC10RkYFbuYmHwrBDHx1SBQejj7NbZcFiBch0ROYcEaMXCms8ptMlymruO4gm+c41h9IYo2kTCbGn+fPSqFU1KvtH9mhNEyWMsU6d6kqjG34E5BZkORy//fYLW3oCiZJE8uzMJNu9Sfu6lhqNKlHdFlWFE81qxaHAqqB4mSKk30+gLEQGC/kl6gJFhZhLFhW8nbvQqk7JQLkB1s+KUXoKgvwbLLpXjt7gLqw8wKK/qUED3ugud1krt9jQ66/LXbZg+6Vyp0jNOzeSTslWSUJY1QE6rNO3TGbdAbJy5o5colA3iFwbr1hnqTHIYkmHWBHw+r6GTccxNUYdNNVvkKYo8+ozWy2DE2UAKhm0MRCSw5FsXDt3OhIr2zUzpwMan74TTGjJCV+af3A69RxlxBEDH958YodJ5EhZVBxFvrYLuVjLpTXKBr6faCqt9NpEI1tOpSGNJlO4eyRdP3WbNygtjBrRlLCRtOHuQpocNpIad6j7XU3DRKDwmDcmjGYNXsTFB16XZ+x4qlLvD60XY9COFij8u8pZZYudIqT29qILoqzXNr1PEL14+IrdZ50jxn5D/1o8YSVrs0B7dI90ot/y/cZ/XzpRMO0ABX9S+Gi6d/khhU4RGk2DZvanFW7r+fyMIPr7Vx6yphMMCBRjuA2AAtB8rEAL3BGaINWdiUAzCCY9aICx3XudCvTl0xeB+lenHDsghkjs+eGyiILnyZ1nnH0GIBNT1MH9+eFPnr4BfSf2YEOp1AAFfcsCiUPjLGuZUyzQM6+duMXn/9TFnixwTplEUgAKtSr0OqxF6yQsEJyrChROq+lTBZBJHIgR9k69Rgladk0BVMVXT99SkRIFqW3XxqRN4JgkSKQrhubZMx3788+/6PDh62kYZD8Kcguy7wBiAjkSyTFCzw4Y9RKcdI4mXaFXz7UfAmsqmZJtX30w0wYc6dFnrOBAeHTHWXpw40mm7guBuaACQQQdF6rYoCI9Olu35y4fbi/qIhQBm9he44WFFNx9eZ8JbMJGSsKisVkAPUYWRgTasWAcugd5RiC4L1F3FrdsD53br36joGLtCoLJR56fWVsg2hjLA3LRJq904Mtb5u+QdmwVQb+/Lg3xETqzyATaH3VErecMe2gUZaAvIhPIs1eQ1ooy0HoWHp3JDobYUIU4RLChwfdM88PnB7o8+yAbWnMzmALjXan7CEN2+kMnOynyCNMZ+/4xiiYZzaR1s2K50PietHTaAMyDEjccpsDhS1kTNq6DJ6313UK3L9xn+haMVcYtHEzrbs+nmTGT+POu6nQnJwFUPSe96cw+wGcGzavp0ROpQFHViiRlAFbAZEOvtMVYEdUeB0HNoERj09rd3kgabJ8RcB00vi4dusqPA61r+uIPujIUSUKQ9FiO4gD2rjsotZdHMYb32KtPoHRahokOzuG432d3XwiPJ/nugC0Ad1+sWZPCRvN9wzl2RQb5kjjHFC1TRJot2H1kZ4pbKpxrbbz7UFTQNrbQR7EoTvZCnVfztA/nqw59v2q1Vntv4sITLo39Jc6+qY/F4gmruPhr070FG6hkBOwDVkoagDCjAh1dETYGbeXjgvdU1TiE47vOSqMgRAq5uoCRmDaCoFNLO4z6t8mU4YgyuHzmHj28+5Ly/f4bteus2NlSGzh86Dp9+fIPlS9flGppmZ6Z1cgtyL4DIIEcSeRIJD9zRsgQyWpUrlGGajeqyCeVvVu136VvZ9qUOz4vH7+hI7sFsaqmULFWOc7/wEIQvVCwoFUX2AiJjolwcVLVNAK3t3EXOn1Rc+KUDiBGlg0mTLBH36sg/BidSzEsOtRlrcwpVIlyxcjaXciEWe6yhu2P5U2cug014Mtzhi/OVEHS0rgJB6ACi8aHK2Xy0c5chwZ6Ca6T80aH8mZIGcB9EpslHAM/2wXsWpbZogyaMjezALmxAZkBNh6+cc5kIdEsonB30vfiDev3DnSsG7arQw5z7WjNzXk0N8mDLJxMmcqKDdi5A1dohUckU/H6VRpNXv2COQ/o+uk72dacyirg3Hcg+jgtnrSap2DQ3fkNDKH4iANMQYO9dfterWjisuG0/u4Cjh7oNliPiiqxUc3pOLztJI3ScaWrJ24Jphixk1kvpg03NZhdTO48gy3Rob1Spxi7ff4eefQK5CIGVMrRwQJ9TxaQzwWXQEwzYcaR3sTj9vm7NHfEEr48YFpvatVVYKggvgPnWwCFTatuzfjfKMJAEYS+bNtiYU2DmRGmcJiOgW0C6uQdiTsvmmAwfhIzwb5xCpY8dRSJOFd26NOaIoMELVW3YQZM4ROt6x3mDWbtK86D+zYK9EX7OV9fP9xVRQdf0BphJJUayMo8sfssM3FGBArU+YwQv2o/v048796OJgrfE5hOiVICW/feKk+I10ucFU2G6mfKfRTMl50r96fJQ9UU7l1/Qqf3X+FjbmKrfbririhhytfOqAHl16ApiSpITBTWbD29et/l1F8ecguy7wBIIG/fobY0kyy7YNRbMNvYvfmk1nPRQOfrYiXkfcSt0Ky5B9DbwZh/x69NprdKmEPIg0H/tuwmh0UNdtKqomPf1pw7A1rIJok1ryKgGOs3RTAFgU2womLAbroFd8/OJF6US3PsOaYLb4ZhBIKupjxAd1a8XDGm08kzDVEG6HgiwBQbGmgmXiuhHbRy7UWdLHV54w4NGnQ0ioATODZLumYtuZPr3jNAYYSAPPrizDgX7qCe2nNOK+HRIljAPqMfjQsdTAWL/s6bSPuWLt+NNb6yxVldnRo0ZEY/Cj3jT+EXA7lQgyEIig9MkZNjT9CSyWvIoa0b9Sk3gqZ09aUw9410aOtJ1mJ+r2HU+P7C8CQmZDcFDF3CAdtW1cbQDKt5FL1gF0/BAIRw9x7Xlfx3ONPGB4to2poxPGX/HuzqlQG+/0smryaPXkE8rcIkNeToTNYhagMwvZhiNENq4OG7Y6rKxRgaIy7dfPj83aBdHXKOGCO3cMRUfdlkgdY4cvbAbyZCeC6evYWJVwvjxmTt3of/jqbXzP5zeVIOAw1MpRAbkrThEJ8f4L6ICRVQX7cOm6CAeYDzXLnqZejXfL9wDBnOs7WaV+frIYR+vW90msdHYUQpRJXqVqCH1x+zIRLuH9EomHAN8bOm0AlruMiDAQks7TFlm2e/lG/fY3SXNEYeC8eF8TkaOW74SZ+9tniiEAINV8UKNcrK/H6s9BSmY5ZTzJSikm4M3Mq0zXqtazLNXhVcSL5KFw9d4zWz91jB3Vdd7Fx1gD68/UQVapShVsaqPQ9lre51OjekMhVLkDYBm/v9O4XsMWPJXjCr8eHDZzp+/NYPSVcEcguy7wR6nQTa4oEDV+lvLTsdykLHro3Ykv7+zWd09Zz2DUa62rTjzs/Z5Gv0+I5Am9AUGurWoppNq/AJOy5MOdt5eSYMfZ2Ejl3k7DiV6WTYiML9CdiyYBe7QimDXmO7Mo0FVBBRRC4LuB5skIElE1fJnOTBOAObBCBmwY40+TQZFYXoeAKRQbFKOSXKK5RAv8GCD0G4u5k/u3Upus3E5fZUu2V13rzhNvKmeiKwWXJZM5bq69bmzQ/sraF9UAewqMcmDtlB5/ZdIucugjubttDcqAEtODKTKTjQXsEaf5HTKq1RJrMT5auVYSojDEEiHy6i2XvdaLB3P2rVpTEf70/vP9OZpEucETTdYi7nnWGKhiINU6Ud4Ul0/uBVbpTklEIN7xPMWWDUssZ3C/nYLKARLVzIvPRwjgQIcYqgPWsOchYdPt9VG1Yk0+EG7Iq44d5Ctqkf7mdFTTrV1zo9KasB06EJnTylTSnzsV1pdpKHlKqnaZxKOJ/G2t5vh+qaMRQ0uA80AyrX+4O8tkziqbkswBnRp/9c/jyCYSBGeYgALdfPZh49uvGEDZ9AVcyTRzDhgF7rxunbrAFD0ff45lOphmvQDEuKDdnJn3XkjF06fFW4P8kUGYHZp+LP87RMZBagSMJ0LXUzD1ljKLTyFczHjTagj1MPSlyfzJcnrxhN+zYk0+1z97lwHRsiBF2v8Y6iJ3ee83NO7Xx4NO4UnYo/x59V+znCupIaccsS+HHYldVV0FPLmihincNnwWy00EyVBxhF4b4BGzWcONf6CdoxQ+t2mcq3Q8EpBkH3GdNFoxbxKJASooTmqukg7VvdH9h1nj5/+osqVC5B9ZtnT/bYwYPXeP9buXJJqlpVO+eF7MSPdUb/gdGocSUqXrwAvXr1kU6cuEVt2qS1jM0KFCiUj3Q716e9W89QfPRJqtNYs1zo9EDHp4VBfToWf4GSok5R41aa64jgBN1rtBH5D11KW5ftZV1ZZrJ4jG07sJ4DC0HihkNkZKPaCbJtj+bcVQQnH6YTg7wVh11C4I3Fz9d6Hk+oug7WkxmkKQZtgvaBhRNZMwNcMxZFt+zShLNpsJAiVNR9o5PM+wSVBQURNAtrfTbT6GDBBUwdFCpWkCdO43Sn0ZWjsKifT24bneQupqC/eMVMIYdWLixeRwfZe6uzdBMj+3a/kXfMZN4A3rlwn5y7+NCc/dOV0iVklA83K96NN2aw1p9o4MW0J2wytAGI7IP2ulO42wamuYLCeDbpErlEOPCm8EcEmh7129Tin34TTZmuePfSAza2wGcPv2FsgJwqFGn4SQ0UcOWqlqZSFUtQqQrFqXTFElS8XFF+j0C3wm9MHjHtVIcGg40X8tbQTMEkDyHkoByCNoXNOqYoKLJePJLtSgoNHRzv0CiCdrCeTg2VpzXfKxLWHqT5Y8NZQ4iJzMTQkdSme9ppiiaBQmF639k8PQJlGjlj8gqpjICp0LQe/gJdsGIJbszgHCYLcEp06+HPBWCjjvXIYcGQbz5ra2du5ucG0yDPzZP4synqyaDXZT3Z6nFUpFRhcu85jYupJvoNuJEG6h8KKky4YF3/W/7f6K8//2J6oaiXnbLSgdcZFIRzRy79hi3BNvf//cta4s8fPnPMycHNwm2NB+pRpbp/cJ4jACdH0NzvXLzPBRPgMH+I1MgD34mlk4Tpl/k4EypfPe30C9mYq72i+LKNRx+ZxTC+Qxsk5hrQAIvGJvKA/EY0WzGxbGagmtYJk+qTe84LcoRJQhNTXezbfJyeP3hFxUoXJgNLgfGjKRyOO89GYn9UL0NNO9QhbQPMKJEplV1UwaQkCV1RX7Zz6feM3ILsOwG6+rD43Lz5BO1NuJQtBRnQuVcLLsj2bT9Hw51NeWKmTcDGFQVZ8tazNHK6BeXPRA5IerTv2YKWu0fyJikp6igZDRDyXdQBFoleY7pSqOt67tgbWLVTSe+AzpmNWy+a3ncOa2TQBYR5hyJ06teGNs3dxs5Tq6ZH0biQoTKvi2MHB6uZVsG0zncLdbbt+I3bFYCT7YgAW7JvPpkORB1hig2KtIyA6w72GUBTOnuxdgH2y+WqlqHMmHxA4A6BPRy1UGimF4GnR/GyxcgzehJN6ODO+TTLJkVIp3zygM0TAp/Ht3dnao6riS8FJLir3CUHareoTkF7PWiy0QymCk3o5EH+u6axrkMbQMd5uP8AatKpHlPcbp2/R6Nbu/L7i6nSj8atTw98tyCOxw90UwCmvijKbl24T7fO3aP71x7Rw+tP2JQAG/2bZ+/yjzzguKF4g1HCr3l/4eOMiQM2ZzBG+OvLX/TzT3m4IITjHDbEoJBh86cscP9/1CjL9v+V6/7Bv/E60In/0d+39MA0GYVY4nrBTRfurZgGYtKiLWDa42+3kCdEbXu0INf141VuxqHY8LaYw063KCBRjMn7rv/919/kbRHEjANou+CYiCIqNZAtuUpCy8PkqWazanwZlOrgkUulDrugOILyeP3kLX5s6Ik9zQP4/6G3e/fiPRuffHzziadpaFIAyAhDgQVsDIhlumNq4LMOdke5aqW5eYAmRc3mVWnfxkPMhhjiN4DCp62TuiyKWtx5o5cJx9KsJZtyiIgK2srFKk+/pn57Dl/rE82NCzhBinrkjLDKM5K/Y3V0alCnfoqLGjzmrhUC62XITEuVv1OrfQQKZ2eb9lS2SmlSFzg2myRGXT2GG3CRrSngvvduPCHdI2n7vPHg9nO6ePIOM5ayK3vs7dtPdOrkHb6MvfCPiFzK4ncEfQNhQnTo0HW2/swONGpVlUqXL0of33+mwwnKGSlkBs071aMyFYvTx3efaV+M0KHRZNcdJ0pg84JdmaY1mQzT527kg+tP6MDmYyrfHpsDUNGw0VunpCYLhdxwMUMsNEGhjqqjRRtq2L4Ob15XuMu2rEcXtdc4gYa5cGyYXLMSGFw0M2zIVJcFY5Zn+jg2aFeXO60ANgCHYuRb+wPQRIDyCGyaG8fui8oAlvJ+O6fypgFOfm5ms1Q2ZhGB/JzZSZ68Mbt/5REXesro2jIDiP2XnPRjrQmKggVjw8ndPJC7yv9vwISjZrOqPK22D7Qmn9jJtPLybIp9FUpLTvqSd7QTjZk3kLO49C3bUlP9+lStUSVufKDgAvDZhRYI9ChsSu9decTFHT4bN87cpXuXH9OdSw940wcra0zDUhdjmLJhQ1unZXXSNWtBZvadmWY5Jdyegvd50Mb7IbT5yRKan+xFE5eN4BiAVl2a8Ofw/60YO3/wCusgUYxhMgPadlCCm1aLsa2LdpOv9XwuIGBJ77bRUeViDLTCgEEh3PzhSXvsFC6s5ToqjlzG+l12RIyZIp18ibh/9SH5WAlURpNhhjyNEqdI03sHcuEP1sIAt96c57UxMJb/f1TwIJo/OpQLKeSUoRjj2735xL+LShgTKO6g9xUndWtmCJOp1MB9oIDDZx/oP7U3rZHoiO2m96O7F+9LXXBtZ/bl7wz0a8gWw3EQ6evA49tPabV3lDTrrECRAmkeC0YfMBMBkJWJtTgj4Hpi4YjrKfMdQWMS8S1wVcREXRVg0n4y/jx/HvtPztx07HTSJbp1/j5nAZoOEd5PTeHi0Zv08OZzPu6GFqplq6kDMKKA5u1qUYnS2mF+KILoMl6jRhmqqGW9XHYhd0L2HaFOnfJUrlxRevz4DR0+fIP0s0HUiALAsGczWhuyl+KjT1Enk4wnJ5rshHezbU/hM2Noa9h+Mu7fVqMbl26DOtH6oG1059JDOh5/nloZqS+6Bc2j15gutMprE+tD4ICmypSMp00z+tFko5nMf4dGDJoqZXRMmGBhgwBbYNe14+Q+BhY2h9autCdiPwupazTJmA9u49GXkjYkc1d346wYMrbvKPN+sRjbN5vMtJmENQfI0DpznPZuwwzp5tk7HHbqax1MwckzpUJxWUD3F2G4Ya5rKWR8OJWuXJLa9mip8LHgcIYOt5P+dN5coPPtudlJ5iZBHmCIMveAF00xnsHaCMcOHnzfso6xJoCCYkbMJIpZuItCXdbR0e2naXjTKTR2/mA2jPl/B6bXyK2Sl12FjTAK8Y9v/+TMJEzUsEFFkwHTkH8lut23795S8eLFeWqGzRAojvjB9BnNGG24AP5o4GaQx0baHLyDjzsKWOeVo5mmqS1wppTfFgqfJti7Y7rjMG+QypoeMUB577pkLkjcIydQ/baC4ZYsbPDfQrtWJPJ0Ae6GiO1IPyUU9K+fWNc6WhL+LNIKMb0HJXLqmnFsYx84OIT/v7ejKevgQEEvU7kka8pSA02yU3vO8+TLaGAnOrLtJBeTGwNivmZmYilNEbRjKf+lcNGGiRpMQ/ZFHuLrwZ1R37odjWo2hW/Sbbgh1W1Tk5+vSEm0dDaXav3wvKFtw/uM+zFMR9/n/x+/gr9bKJrSG32kRpjrOi6u0KwENVwRwE7YJ6FnDpxuQapinb9AjTSw0s3UdExs8gLG1u15cqkNq/tOvVpQQS3TmlEEJcScTmPslp1h0Po/oJmHiNzV4zsCNtPih1H8cGYHDHsKJ9DTh27Q88fqGzkoCyPLNkwfunXxAV08dlOj942OdteBnfhypJIOh/KAAEncJ6hToNypCoj1xWkTCjtlIQqp0bG8dlK+bXztljW4O4yFcenkCJkTLRSYIvUPHP6nd2TbrFeuV1FqmR8yLkwpl0RFgAi8qUFDpqu49fBTauoDnRzoL3hNvgOCWQSvDKDdmZEqW4wpTWraqqPDP3vfdKrRtAq7VU5Eoaemvb6ywMbSfExXNvxA8Qda0cwB88jHer7SJjH/7+dWhK2XKFeUcwHh7ofuOgKVmxs05CkWQokbdqjNkzX8HVMwFHllK5fiyUJuMaYYV47foFGtprJxB76jxgM70aLjvlotxlCELJkUIS3GBrj2ojHzB6tlsADd5tbF8fx5gcGFaEcvC8h+XD51LV8eFTyYdLqlpXv9+++/5DMgmJs3KLo8oiZKqYyYRiWtFwq/aesdmeqK60KDhsINxwzZjSj0ipUrxpEpeX8XrMjL1yjLJkNA/XZ1KHbhTi7gcD6KnpdqnUshNvpAMYbgZ9j343tQvXEVzkjDYyLzMcxlHesgoV8d5i9ElMDREdM0ZJqJhlHA4dgTXPxByyaafqTGoZgTdFJi9DFytp3MY3fpyDXWuOH1pTYKkYeV04WpXCeLNjz9VgWgMx+JO82Pl1nt2J3LD+nEngv82nvaazYI+vXzd3Rox1m+nBVW96eSr9PLZ++ocNHfSaeT9rVqGeH583d07ty9H9ZdUURuQfadQSzIjh27Se+1ZLGtCOUqFqdGrarxgiqOsrWJwsULUOuugjB3m8TmVZMwH9WZF4/zydfoqoJiRhEgwsdkC1jrH6NWmC0oTgC6sCjslAE24dCtAbARVkQbHDzTkhfi0wkX6KAceiVMO0CVgfh9/QyheygLFpN6UHUUA68/UoREC5EZYELltnECVahZjp7de0Fu3X2Vcl4cs3AoP2cUcq6mvvTsnnJ5XegEe0RhMiZQcYLtl6lNv4Q5SGCCB9ND0UmeYjxTbtyAplC1QUUKPuhF1q69mHaTtPEwDWs8SS0KbS5yoUl3SRQz4zt4Mt0TU13vLZPIaelwbvxoC5hyzrJbyEZJwMggWxro1U8tlsU6v2haJ7GIH7NgCAdvy8OF5CtsTATATdFsdJdvrrPCbQMd33Ga9UXToydLTZlwWzS2ABQj9drUpmWTV7PZEfRctp79aPYwIY+sbutadOXIdcrzy89Md8fxxBQOTb2ff/mZG3AWk8zY4MLDfFYaii0mvTi3/5b/V6mjLnLPtkqyzEAdf/XkDcUtFaiKE8NG8/1fPXqDtktcDJ2Wj5Jmi+F9XjRhhdSdEUHU6d+PpVMEa/4+Tt1l2tzjvIsiGoDWWRmzIhT7R+JOcUGFzDpVgQB6oENvHW7KZAZbQoTj1cakKZWvlrlJW3rsXHOI39vqDStQ9QbaN3ES93h63Ztkm7Nr4t7LHNnQsGFFKp1NlMmsQG5B9p2hStVSVLVaKfrnn//YAjS7IOZQ7Np0Qq2iQ1UYWgrUs4PbTtOr9CGWmUTJ8sWok4TaFRksiHAzA+hGUJjdvfSQktXQvaE7D4oGFqUIBVlgqTHYpz9PeEC7UzSdAx2jnyTQetGElV8pLOmAjcvIIDte5E5sPyvtusoqoERrYziC3b38gDILNt7Y4coTCOh4QOFRVCThebhvnMCarlePX9PUbj5KB26D+glLabzeHcv30pKJsieIioDPAJ473ktsepCvJmowtAksmtDjBB+YzhsiWGF7W84lL4s5vLnKRS6yWis2soUL08Fgw67Xry0tPe1POt3kT5cyC0yS4GoIB0dMmTDR6j1ecaBwRtiycCeFuQoTtmH+A6j7SPlTDwQ4e/Scxd975H5lNAnC9Gy9n1DgOYXaS008Xjx8SV59AnnT3b5Pa+o7sQcba4iTLeSNLRy7nF8faNwXD0ks7v8TzlMNdGtT8pZj/JpnxDrTUN8BTBucaSmYkKQGJmMANG1wZtQxacbTfDxvMDWQMQaNGtDZriNTEBE2HTZJOBZgIzTq8HVigcIXsSXQRGZkY7910S629EdB3t+5p8zjh4bYpUPXeFqHAloZQDsGGFq3Z6MQVXDv6iM6GC00zCwl66K6ePnkDSVITGp6j1Fs0a8K8JmIWyXQFfX7aZ8++P7NJzqcIKz5nc2153qqCHsljDADiY/Cj4rcguw7BBLKU39IswO6Rg3YBv/Zozd0+nDak7w2UKl2Warfqhr9+89/tD3ioMbvv4/kxImAWeiQMgN0MM1GGfHltX5b1NrQ23kI2Sngw8NUQBnAMVGkjiybslphNpXllJ7s+AWr/tUzZBd+6LZ2HWbIlxHyCccwWWjcsT67bWHjFSrphGYW0NFN2zCBJz7QvW2aI1gsywNE5DPjpvLGAPbonr0ClM7q6ti3DTkuHSE1CFk1PVLt544CGToTbFywYQoeFcr3lxW5WHB+DDnuQ1YuPXlzhuwrTMt2rkjKMblcufhxAUMKOCg66XsxJQ+bcPcN4zmeoXCJQlp9bFCmJxl6MTUOm3pEXHRWMYpExM6wRFo4Nlw6PbKYKH/D/ub5W3I18WHzDJg0TV07/psYDsSbBA5ayJf7TOhO+lYC9QzFzvQ+QdxEQUNpUtgonigGDV3E/99vSk829QBzArEN7yWZh2j+oLgClfHYzjP8twnLRlLLLkLRC90b7PRTAzIAmJv8Ubs8hz7jPnTNWrLbI1gCmI7FLYln2jfWtGH+gnlUZEAsPbrxlIqVKUJDJfRF4Nn9F7TWR1hHhvpZf+OIDOr0aqlJiAUbnGQEaM+WOa+RvF4zPocrwunEi3Ri9zk+zw3IwNFRERBZg3Nia9NmzDLIDGIWxfMkELEV9Vtrlop7aMcZjtMoWqoQteys/eJk77YzXARWq1OOqtctT9mB+/de0vXrT5gS3qFj9lAmswq5Bdl3CD09wfLzzOm79Pq19kJo5QF293qmgqHH7k3apy0CpgOFBXXH6mQ+SWgS0IK07NyQN83RizI/xTB3MOYFB8XU0R3CAqkKsBh36CO4J0EAryxAG4R9NoTeMSGCqFhesTB6rjDRguPVwxtPZF4XXcpCJQpyZldUkPyCaKjfAF4YoSM4sVvgumcWTfQasBU/sHRSBHeMFQFuhyjKxNDmgMEhSk9zuwzSo1GSY4NNxHp/5VwvMwKOxfjFw8jaTch9i/CKotnDlrBZhLYBBzmI2xcc8mZNG+iks4cvpYmG3krTYXORC1WAje2+yCM0tNEk1lsByEgMPRdA7cxbaf1gooAZp+vGUSAo/GbtcZMZ26EI8RH7afbwJXwZ0zU7T0EnKwtwQ8RUDk09NLu8Yp25IEyN10/fsCaWnRMNG/L5UsRixxVSWiIojACaSWAwYDqFLL39kYe5YEIm2PN7LzhvDLRoWN2jkOPn6mhKRnaCNvrGmdtSymFq/P3lHy52QAcHbNz7UphEZ4epHJgGy12EwmigtyXTsEFrFGmbmPqJuWt4zzFJA00cRaG+hD6f3qAD5x+sbbDgl4XNwXFcIEJTB0t/RcBj475Fp2NljLDST8eSNgpGINZqFHOpgfdhW5hgud93fNrQb00gdvk+/t3VWlfr9EEc112S4OlsNfNIFAYPzZtXoSI/eC5jbkH2HaJ8+WJUu3Y5Lh7277+S7bTFQ3su8mhb22jTtTEVK1WIKYuHd2pmo58avRyEKdnuNQfZFCEzKFy8IJlKLPXF7puqsPPoy1Mh8OJB+1EG6EoOnC5QPNbOjFZo6ACKCnJtUOBiqiYL2NgM8BQoKKu9I6V6A1lZYj1GCcdyzvDF3CnXBMzHdeP7xbGENuNM4gWFtwGlx2PTJMGieX0yhUkE9ko93piuHEQKLJ+6jqdl6gLTTjtPC86JAx1yZ3giuZr6a+zYKAK0ffOTvWmYnxUL/88fAI3MmYt9dW3+c5GL9EAh4trdnw1lEIgNwwf/Xa7kuHgYFxnaxoWDV2hcOzdp3hccT9U1DAFtDo6GON/AlXFEoHzbdRh0+FkHc0EFVz2f7VO/CZrHBMyzdyAXQdDGwnVRjFyIj9jH2i0h/HksFxYLxoZx4D2abCjcFjut5Ou27NqUzuy9wOcShD/jacHqHnRARHjAIVfESo8N0stwU0wNNNlw+wbt63DzDKYfOGf2n9qL/O3mCy6LHeqS6cjOfByYIfHlb6rfrpY00wzA1E408nBcOvKb43T56HWp5gyGKrIMcJBLtt5P0CoPniFQ8BUB+tirJ26x2+mAqd/SJBVBXJ/bmDbjUPbMYPuKfezQWql2OdLp0pg0iZsX7rOpGbSCXa3Vz0xVFtcvPqTbV59w4affXbv0Yrl5a3t/fHdFEbkF2XcK0WkmO2mL1euV51E2NvMYbWsbODF0kZyItoZr3tyjSce6VK1hRfry6S/atjwx0/fXe2xXXlCwWJyIP6/y7TkwU5Jfssx5rdJFHbj+Ver/wXbKCFaWB7bBD7Thwg+mE2eTZGfLte3VgproN2BROMJA5T2fQTMseUOEjQeCmjUBPFfk7rTvrcOUEOgsHt5QnPOFnDTQd0S3SOgYlAVondBjAYudVrGWJDMwHdGZg6+xeTi15xzb4oPqkxXAxq/vBFMKPTuL9Tv43mIzMrTRRKYz5tIYc6EuMO1Z5RVFw5pMZuoYztXW03qxVqypXv0sObD7Nh7mYHY000AVnHdoBsdQqIOD0cfI12Y+Nz27DtFni3x5xRi+O4sdV/I5FGZJ+I6jMZX+OvNHLWP3QtADZ2x1psLFBeomIj7E8GfQIuHGuGf1ftq9IomLLjgdLnJcwRMoOMIe2y7QD/NIojlwKhaLLVtPC2m4PXRqR7Z+ZbCA1ohzD1ClQUW6fe4eMzkata9HJ3adYXMRlzXjKHbhLrqYfJX/D/mOoFwizgS0RxRdNjP7So/Hx7cfaeG4cCmlMn0mG9xq540O5dcPgw6YJ8kC2APQxiFPUN9KvmkK3/c//0rzNPuMN2EapdrTMdfMTcewJm2RsGv6jO2ilounPGyT7HnamTalEmVVe53qQJyO6RrVp0JFtGe8Iw83bz6j+/df0a+/5qG2uqplyn2PyC3IvlN06lSHu2IXzj+gpxo2uVAWOCEb9RKEnvGbs4a2iFE9iofzh6/T3auPNP56cCIFYpYkZHpygMVBnJLBnEOdDa/1tN68gF45doOSY04odRt0H8HhB7Ys2ElP7jyTe/0q9SuSyXBBIxYyYSUvcrKOz9iFQ3nDcWLXWbmBzfkL5qeJy0dJDT6uHLtOmgA2Bs4RY6mOTk2mv8Cw4+2LdwpvB/oONirAfIfltD/qsNKPae3Wh/q7CIs1tCSxKhR0sqaSCJCGpgY202PaTFMYVaBJIC/IK3oi63lAC3p69wUbfriY+HEQay5yoSxwTtsfdYSGNJxIq2ds5ulJU4MGtOSUPzvdIQMuK57DWt9omtF/Lj8+dFABCe7fTKeUBc5rMyznsg4WujPQjRVtrjcGxNKWBUIY/ZSVDhxunx5oBmEyLuaRIf9Q1JzBAATrDQLerd370PVTt2juCIEqae3Wlw5tOU6XDl9jh0OYfkBLjdw7vF7GT0KxValuBc5wBK6euEn+tvPSPAfQt1HU/V4oH2trgcEzraQhzQhxRhNxlecGab5kuaplWJOHbEd+Pu59qULNr+6Iqzwj2TwJDUSrDCZUO0IT6MbpOxId2ld6Zno8uP6Y4pbuEZ7HLGulCprdEfv5djB96u3YjVTFOr8Y/vy07d4801mRiZFHWN+F6AzRJExTAAMpcfPxNNINbeLzn39RUpzAQjLurTjLU1tIlAwcdHSqUwFJI+FHRm5B9p2iVKnC1KhxpWyfkkFHho7ZzcuP+EfbKFW+GLUxbpSGT61JdOzVispUKsnUid2rM28eYjHBRDolO75LdZolNu2gzgEIfVY2G6tV1yY8zcKCvXSyYnMNaCMKFStAt87elas9A81G5PUvmbhKrlEGNA/oiALooGrKjRObvOnRkzgTBxQdhKoqssMXCyuT4QL1xs96Hp3eq9zUEoXoIO9+rM8D5juE0dZFgi20uoCj2vzDM7lLjc3MhI4evLHNKuA1Qc8DXQ/czjDRQIjsiGbOXJTDkCAXuZAHbLIRYj/Dah5rfpC/N23dOPLb7qKyy526AAVw1sCF0owx87FdyS1ywje6LWVxKPYEefebw00pZDU6LbdXWBjEr9rHmVzAiEDbNFS+NHlkEj2W/dxB1NJY0LRBR+ptMZszvpAdhunUuxfvpQUaqInFyxelGMlkvlqTyvTm2TsurD68/vh1apci0MrdI514Eo7MRo+e/qwTS40/JVE5vxcWTECg9TqVcI4nnKAmgpo53yGUWRDIgBS1XijGMHkE9Tl15tidi/elhejoeUO+KcDB0ljhLhR3tp595RbJSyet5uOObDdohpV579fMFPRscAwWp4LKApppxIIAMD7KDLCmbJovrJs9R3Zm7a4mEb/+MH35/DdVq1+B6reqTtpG8u4L9OnDFyrLEUdVKTuQkpJCSUmX/2/oikBuQaYkvmSBAF9VGOgLVJC9ElvS7EDhYgWojcSKNCsyyYAeQwSxckLkUZ6SaBJYzHqPFfRPUfN2ypwWKYuipYtQ9xFCxzLCe7NaU7K+E0y4WLp7+SElrFGuSMRCbR9ky91YWOAr0lvB9niIj5U0GweBn/LCl6FpgE4jMnCr3Psd4juAaS/QVYDyoikUL1uMZsRN5eOCzrGv9TzWcCjOKBsipTx6mgew4F0Z4LZDfa3IQrIZmeewXKFpiiJgAzt3vxdvzrD5wkZwzUz1JqnqAprDQV4WtOxsALU2acaf9y0LdpFdHUeKnL2NNz25yEVqgIY8a1AIjW7tSmf3XWKam41bby7ukeGkTsZXZpwU96w+wKyJsQuG0Kg5A9UO6D689SQXR6DydurXlqasHK3wvuBIGDgkhC+jUQXHxIwCjmfZCXlk5mO7UU+Hr2YPyBaD4RAmX14xU7io8O43m51veeLk0kvq8Aj31wsHrjA1ETolnopJzhU4x/rudKXK9SqyC+703gGs4WP89DVzDFcHHRDnd2jNmhk24iBnrHvjQobRwc1H6fjOM9ygQdYa3ktow6C/xTGGPT/MPgDWlI1dzpNE3Z4tWYucHnDvffviPU/u5EUFQL92eOsJfgxMx5TB9tC9/FnEWtR9RGe1csdASUXzMrPasZMJF3h9zl8wL3UdKDQhNQU0MretFOiKpgM7Zsn3C3FGgJF5c41TL5XFpUsP6cmTt5Q//2+k07oG/T8gtyBTEieu3aechg4da9Mvv/xMt249o9u3lQu/1QbEfIq9saezZAPXsE0N7hShY7RrnWK3PVVhNKAdUyCe3ntB+zUQ5tvXsRvl/f03unbqtlpTMlA9+kmmMxHeUUofY4izTSQL1SKnVQqna12H6lPd1jVZyC1vqgY64vBZggUyCoj7V2XT3EqUKyalsYQ6r2F9gKYArcL0LVN485AcfYwWjglTWMyIlMdGHevxc3HpMpMzg5QuyvwGSIsyWElnxugDgJbEO3Yy9Ron0G2gh/CzmZ/lRhswXwCN0W+HC39u4BYG3SL0ZXvWHFR6MpuLHxeYdix3XU+D6jvxZwLfNWSKLT8fyAWZulMpdXDz7F0a09qVs6rwHfKJc+HpjrrAZAw5gVyMWbQh51UOUrMNWUD+F3SsKEgMrNunsYAXAbo4pl2YOLXu3pxGBAlOscCO5QnsKAhMWjGaz2cwHUKOJAo0GHsEDQ1hlgN0V0clujFMw9L85mw0a6rVXJicxCzYSZePpKKIpxAXOsgZg7YYNGnAdno/ZjmIroowIpk3ahn/22KyGWvg/vz4mSdmQB9HU2lWGrB7ZRKdSbzIBfmIoG9z1pBDuWX+Tmkgt1jIpQeaQNDnAmajjZUKgca5GzRVcbqljPlH+vclYW0yXx7gbEaZRZQkv7SLXUcqWFSzToAnEy/T4zsvqEDh/KTXS/tuh4/uvqDzx2/zemfYsxllFxL2CHr2du1qUb58mp045lTkFmRK4uBF5TrpWYlChfJTK53q2U5bbKZbk0qWLULv3/7JjovaBk4U4pQM5h6a3ixiY2E2UphqRc3dmemJBaZkPSRTsjU+6jku9hhlxPRF6H0QWKwsYHmOgg5UxPiV8ime6ITBAQvHN3F9MjuWyQICQ6F3wGZhzoglcumIvRxN2TEM1LxlkzVj8CECGxXQfPCc4VAmhqcqpjxOZvoNXMWmGHkrbawhFmWWU4RFHBuJ9bMEVzB1gY2f/Ww7Gr9oGF/euy6ZHDu4S+2osxIwQFl4dCZNDB0hxCfcfs7TEPsWLrxpzTX++P+DsPndQra1xtGGgFj+zjfqUJfmH/LmTDFoErMSR2NPsxmOSPODeUfzzgKNXR2AQeDVd/bXYixijMJi7Pb5uzTN1FdKK8TkKP0kAQ6qsLfHOQbnmqlrxknzyGCeFGwvFD+2HhbUzlyHDmw+ShsDY/lvE0LtKTIwlvPb8D3EuQDaL0zC8B1M7ZaIJlo3iQYYhcZqbyEgOTVQNGJChsIQrxPF4b7IQ0xDxMTMxqMPZ51hmoWGjJWrENGx2iuKHxvvsY1EgwuANrl0onAut5vej3VmqYHnOG/0ci62Wps2l1I0MwI0xtCzgXJp4y4YKClCZNA2fg4VapTlSAVVsdpnCx+T5oYN2QAmM7hy4iad2X+ZPzPm9qpP6hQhdrlgo9+5X+ssaXrERwuFf/N2NalUuaKUHfjnn3+ldEUDw6wxBVIFl+49pWkrMmfwlRFyCzIlcfDiXfonB3aJDQxE2uLFbNssgdYhmnvsjMz8REkZdOrZgumSzx68oqO7VXcwVITuQ/R4qnXz/D06LcnByAx6j+vGXbwrx2/SqQTFdu3pgRPxAIkLFMTzmGAoA7arnyZMqMLd1vP0Sx7QAYWrGLBgXLjMYheFybhFw/l5oaMrWhpnBPDpxaDlbUviNZZNJqJ979bSLJ8lTiuZYqMIKFJ9d05jWhA2HM5G3kyBUgZ47YNn9pduHpa7rOXJZWa/fzBWgUU4prPXT92m0a1c6Nz+S9nzfbbtSGEXg2jwDEvu+EIr4tlnNo1t5840rdzC7McHdEVRc+PIrvZ4jkf48OYTTy+mb3KigPhpHDyelcC5CDlZC+xX0BfO8GrEOsxKddK6GaoCND9gBiJqxpQpxh7dfELOXWbyxBCZW9Bt/fpb2g4+7m9m/7mc21i8XDHyjnVmZgEAEwpQCvkx++uyiQem9KmDol8/ecOukXguKMhQfGKChfM3GGvQfwHQbzstH8WFHu7Dsb3b17VBUrMh5gKA4yRiCXB/1RpVobOJF/n8jeDqXWGJdGz7aTZswmQO52xQLaOChAJx9LzBaYqBVdMi+fXXbF6NM8/SY/fKfUzFxG3gUCkLuI+VHpFSjZmYayYPoGJGzRUab0NmWsqcvMkCjr9I/bd1U90mPz02zBaei36/1lS6YgnSJO5ff0InEi+laUJr+zu2J+ZUGuZTduDkidv09u2fVLTY79SsWebopNrAnlPXaf8FzQ9pcgsyJfH242c6czPnOZC1bl2DObbg2l66mH3Pz6hXCz5pnD16i0fe2gaKG+MBgng6VhLEqElg8etqJ7gZRQYLouXMOi6aDBUKnYgZ6mnJ0AlEAYEu5kYF2q3UQHYXKGmvnryRdmDlAZb1oAHdPHOHdsqZxpWtUpoGzZDkdLmsYbcwWWiq35DMRgsOlsEjlyhlwqEKQLnpMlifNQEz+s2mC8mKc9sgMPePd2ctF8Jkp3YVNlnKAJ91W4++fKxEp7FQF+WjCeQZoSw85sshzjCWmdx5BlvtZ0cBBH2Z5eQetOpaMBt/YGN39fhNmmYWQGPautHR7bmF2Y8IbPo3Bm3lidjSyWv4fINJhMsqB1p80o/adG+eZToxEe9ffyB3s1m0wV+YRveZYEo+cbCNV7yBl4WdYYlMD8akxMiuI01RgqYIh0NM1DHtR8AxCq30Uwu2t3dYTsd3nOZ1CtowhNSLUzOYEEH7DKdYTNZQQOFvmERi4o8QazFvDLosBFz/mu9XaaHFpwLJ4bfxsGCqI5uD9JudVvubAm1wIS5ei0kcXUFdhK5346wt0kILlMOlEuYCdMRVG1RisyYUiDifgo7ZpvtXqhys749vO8P3hTiR9McM75WYaWnt3lvuBBUh0zAQQqFvKpnyKcIq7yh+TfXa1GLtmqoAS0XUjmV2Onb3ykM6HHeavw8W41V3eVSE2DCB1aJj1IDKVS5J2sap5Ov04slbKlgkP7XWlx1PoG0kSBhfnTrWVVsTqtVstDM3tHLfOeuV5nAknr1JOQ3g1urq1sx22mKZCsWoma5wctudReYeJrbt2bTi7MFrdO/aE43fv/koI150Tiddohtn72b6/vo4mvDid/noDbVyydAJREcQ2Dxvh1zjjdRAt1M07IgK2qaQngexN7qVADrS8mzlzcZ0YToOCpmwqevk3u8QXyvemDy581xhPpqqECZ2w5g+BBoR6ETI9lGE0hVLkv9uN6aV3jh9m6Z19+XJgLKwcjFnfYRofb1gbObdJLGBmbPfizv26KJD1O9vu4A+f5Q/3dQWME2E8ceqq3OpD1xDf8/LNv1uPQPY2AFOZZk1v8lF9gMbaVATbWqOpVCXdUwJK1u1FDkuGcaGHXqWbbNlc3TjzB0a3WoqT2Zx/hw534bt0xUVT/IAQ56gYYt5c2UyzICcQkcqfG04D6IYe3L7GTe4/HZNy3Cis8F/C8UtjZcGPIuTRJwX/GzmcdBzyQrF2SkWEymfAcHcEMK5EQWS74Bg/j4169yIju08zbfN8/NPXDhKkULU1qyl1PkV5kqgpaculH/L/xsX06Aqivb40POCpcC0RdPmZDSwE2eEgQ7ZoF0dMpdoWZEHhucEmvzo4MHS+0QjDXloQK9xJlSjybcOfGhOiUYevcebyDyej28/o+h5O6QaOGXeTzynXSuEImWYb3+VGwNC7pjgrGjrJtAyM4NIiXasrWkzqqhhZ9GP7/6kPZKMNLOhqtMy1cGOjcf4t0GPphp3ilQWf/75FyUfvJZj6Yq3nryiu89e06+ZOP/IQm5BpgL2nr2RI6k6oiVo0r7L2Sq+N+7TUppJlhUbtDIVS5COkaAd2CY5SWv0/iuVpA7mwmuKnJv5KRnyScQu4Mrp6lHc2vZowZ1BFB3INlMWcBbEgotiA85eitDD3oiqNarE3cslk2RfH1QZh/lD+PLOsL1y88ZA2Rk5206ayaNMqLOqBSvoQ3id6CajKINbmSIgDwgOZZgKIgwVdCJVzGmw6YD+C5uDWGz0hi7O9OcfXXeX1WNoeIA1NwUS1h6ksbpu9PjGU8ouYMo73G8Arbo2lwOmUZjB+tzHej4Nqj+BN7mqFLO5yBlAgwRRBwOqjWFq4ruXH3giBh1h2IUg6jpIL1PFT2awa0USjdOdxo6uiLmYvW866WbS2GCdXzQb8og2+WjkKHKSw2TLpetMundZKKYwWYfTa3pAe7t86lq+bD9nIGvDRKzy2MghzSjCPKMn8+3XeG/iSRoKTfdNE2nxhBU8GUc25MNrj5mamL9Qfvr86a8070G9trXJbeMEPufBVGnVdCEcObW+TIwkQZA0LPIRsZG/UD4OpoYWbczCobQ/8rA07Hn8khF8HJCBtjFAmETiOqmLTkwo8V4UK1eUqZbpcevcXT4HAqPnDpRLJ1w6KYLdbpFbh2mVMsDnE4UpHGHrt61N6k7HWps2y7Sz4vMHryhRUjBZOH51ztQUdq8/zO975drlqHEWhCK/evaOjiYJzJKufVtRduHw4Rv0+fPfVK5cUapbV71gd20iUTIda15Dfaq0LOQWZEoi76956Mmr93TlvvyQ3exA8xZVqXDh/PTm9Sc6fTrzkxx10VqvLhUpXoBePX9Pxw8IHQ5to/sggVa4Z+NR+qhBBz8RFpKwyf3Rx+nhzcxvhvs5mXDQMzRCsFlWFdj0D/Ozkm5WsEFQ9najgwfxRBEdQkXaJCyk2KjgdsjZkXf9Brp1yNCmA28GIAyH7bI8vRc6v+jYip1WTQKFDGhE6M5iguhs7C2XSikCnd6ZcVP59gi99rGayzQgVfRfsMlG8QT9BIJlM+s4imPfd0J3Ctjjxp3quxcfkLtJEJsQZCdA9cRncPWNYHbXK1yiIJt/LBwvbOrhxPfsvuJCOBfZB3xXLx6+xsX0wLqOHHWAKQkMHaasGMUTMegIVdXnaApoOM0ZsZQChyxiIwqdbs0o5IQfG1Bk5jUvc15DYa5CZtkA115spqNoyoImA8w5rp+8xRRAFGOga6cHokUCBi6QTo9gcS8CkR9wpAWgp8XU7MCmI9JCavziEbRvwyF2LcT6UKRUYdaNoYBCdhimXKmbPDiX473B1A0TLjgoAijaUMShwMIUDVliiBzBed9iYg8Kd10nnUjhdYuuipbO5kx9xGPMHraYi54OfdukKSihI13vJzgbDvAw56Iu/fENcVzBBU/7Xjqs8ZOFU3vO0cHoY3y+VOY9EIOuD2w+Js2FVBUIvd8XKRRQNhI9dmawOWQ3H69G7etQ7eZf3Sc1AbyvW8OFJjO0Y1lBEYZ2DO97vaaVqXLNtCYtWYm9CYIxnJ5+vSynRisDka7YvqHm89lyCzIl0ap2pRxLW/zllzzUoWOdNMnm2QHYj2PUDeyW5FhoG03a16ZKtcrSnx+/0O51AhVBk6jWoCK1Mm7Ei40mpmSgxpmNEuyZI2ZGq0Vvq9+mFus4sPCt8BQE0cqgRpMqXDgAC8evUDhNrde61tfrj13BNBdZGBlkx1RHiNjX+8qmI+IE6zBvMG8c0JlFR1kbFDuf7a5MAULBCmv7j28Va8PQcZ2+ZTJ3sGGjHzBoocJss9QwGNCe3DY48vcAm41p3f00YvPfqEM9WnTSnxq2r0OfP3zhYm/+mDC5odxZAWTXoSBbfXM+C/dBb4NrG5z4oD+a0T+Yzh24nCNZBf+vQKGza2USU00dO3pycwabsGaGDcl3uwstOu5DBlbtsm0iJm6cx7Rxpe2hCXy+GOhlQV4xk5QyfJAFnOtQ4IFWDGDyPNCrn8INH75jHuazBCv6wvnZCCgjExHkGcLeHhMfsBGGBwqxIMD5A5cpSJJVhqKos01HLi78bYVsMmhr0fyJmi3ogpvqNWAXRhRhf74XaMpiwSUWT7C4x9oxZ9hiOrP3q0kUCgSYUeFcXa5aaSljoeeYbqzXwt91zVuxK+OsgQtYy1a7ZXUaME2g720O3s7UbZxDcZ5Ofb94DXh9OibNqFV3YZ1PDeTBnU0SculGpHr96YHXGuK4UmpzD82aIuAcgkYPYDCgHVVtUJFUxVq/GL6ftt2b81qYGeC47ZS4FvcdJ2ijNQkYecDqHlou/d6q6+RUBY7LzijBkK1LX+0/niy8e/cnHT9+iy8b5MAw6Mev3tHl+8/o559+onb1NG82kluQKYkOkmo4KQcWZKlpiwcOXKW/Up28sxpGvQU6CUbfmJRpG1hQzSTuQ7CH1QZl09JJcJHasy6Zniup25KHPuO60u+F8tGtc/coOUY9vR3s7PHasfHH4q4s7FLZ4Msz7BAB0wo4NaI7Gh8uhFPK2pxD/wCs9dnEttCygHwbMZsMHVoI5TUN6LD84924SMQGw9XUlzN1FAFdXdAe2X5+7UGaO2KpSkVzO/NWNGMbHNXy0emECzS5s7dcDZ6ywITMf/c0Mh1twP8GLWhM22lKZ6hpE5gq9hjZmcIvzSGPSEdq3LEeb/L3bzpKEw28aUjDiRQ1J04jxyEX6gGGDgsdV5JVldEUNGwpU02xce4yqBMtOu5Lfttd2AI8uzvSmMaPbunCzxfNK98dU2mAa+9MhdOiqILjIeJCMClyWjaSJ8+KgMJhhuUcOhV/jqdWaPKkzuES8fj2U3Lt5sPNF+QbIudQtLcHvc+zV4BQqPVpTUP8BrCG172Hn9Qyv615S6kFvr5VOzoscYnlKVc6wGAD52Sck3Bu2hmemOb/f87zE335JDZqfuKCDkYhMGqB/gp0ywlLR1J08HYu5PDdxfPFtA3n+PBpkgnaLBsqVuar5fnmuXF05dgNpnWPDRGYE6mB77aYJWb9P/auAizK/Ove/5bd3d3d3aIotmCBoiIgAtLdqYSSIiGChdiJAaLYHdjd3d3ufM+5LzMLSMwMM8Du53ke1lkdJt783XvOPccpZyOPzQsT2eYebCOMkaQBDITAHqJZNkVKa/zs2DFN+1GUVyQsTqFP779wrlvHga1I0diSZuYxaEK3fLG6R+7Yo7svqViJItRrsOK/j7TYv/8Kff/+N9WrX4nq1svfOA1pICZk2jSoTuVKKTZvDvhVkEmJrk3r8GDttYfP6d6z11TY0KpVLapcuTR9+PCFDh/Kfo5H2ajTsAo1bVOLF2TJafapykZ/9S5szf347gulWOA379KQWvVozN3F9QsS8/x6KHBGGQldteVe6+ViydAhRKcQiLBeITULwayGi7rEBj83+3y4mOl4C06K6+dtZ6fG7NBnXHceNMd2mq+XczbZJIcx1LhjAzYDwWJEGSwKCr+5iU5cgGI2zGMcsoZyb1Zg2B3ZZli8YS4u1Chaps+HLC/IDLGf4Upo1sdFIZliKBLH248gz602vJhBUW3Q0ZY704UBMEXoMbITW6KHn5hLQ3T68UIWWUqRNitoUl0j8poUzOYMv0xA8sekI2FRMhn3cCL9Dra0acFO7uxXqVORZnhPpLhboWQeoUcN2tShggaKGd+pCzjzDhLBtv1bUsRp3zzliwG4vtmrzWGZL5hrx3gzUpUitwrH5xytYDq8+QQXrnBKzGpmCXJoMPC4LkLu6b7RmnMOgbcv35GDmjfP4eJaZx1rxFJtFGh4Pma6YAg0Z6Jg4gHmCecGgGIUElJI+sQAiynOO9sansTB0pkhLlzrt6nLxSAaOUN1B/JzUURZLzHieA8EUAOY6cUMLa6Lvtoh/PlQJIqjTwC8TqxzvEQJgaIuM6JsVvD3xHfSsMi+2EXhtsxNyEmb5oFIjRJS7QuE1ANjjFV5llBWLHVfx4qSbsPa55kdw/G5cWESP1afrarwJsaDm0/pxG7B6l5tqjCSoWyI2bF+w9pQ0eKyhWwrEnvSFF4D+hc+M4/0hEy/NsqJ/PhVkEmJMiWKUodGQoJ8ytnCx5Jh8ShmyXalaXALCoPTKPad6/InSBYXkCFaPTKEKCprlmx77F6WZeUVuLFAAnP7wv08sWRYLCDAGaG90gKGHcikgRNW3BxhJiAnYAGDBQXkcotsVmT7PNxAMARevFQxnlvIiYFDR9Y61pC7wMgNO7RJOfl1DdrUJc+tdmw/jeF5zLhJUwD30ehGljGGksBp2FjLciw36dSQ5u91ZdnkvcsPyaSnE3f9FQGErIaf9uVuPBZtPtqh5K0ZLLVlf34AhjBmC3Up/m4YmYTp8OwPWIK9a4+wbf6kekYUYb2c3Rp/SRoVB8wtHtx0nNzHBdCEWrMoaFY0NwVQzIO99d5qQ7GXA2mc5XBuGBQGIO9qZnsbSlq2j+9jYPHn7nDgYiIveP7wJZn3dWEZHa5JYK4hJ8wNkClDzgfDCxRxLuusOLYjqyLSQW0OPbj2iBkhr232VKJMCQkrByt7sXsipNC4BgXoR0hm0VzWWtI8nYUSEw/km8F8o2S5EhwmjWJM7KxYsWZ5cl5ryXlnkF8vdV0l+RziggD3EzTDkA12M81h1iRcT1J8jTEZyrEamBHDuYjCC8UaACkn5ppLlSvBRZ/4NQUL/0U8x4d5tMHTfi5mETGCeWbALFwvx7lDuDfiOlW/TR1STVf05YTtMSm8HbHNJtiMJHlcOvdvOM7fSTutGZkX7Fy2n/cZTL/6jlW8+cWmtDVMpwEtqLocxaesePfmEx1MPJ9B4VQQeP78HZ05Iyhr+vYrOMv97PD6/Sc6df0+P/5VkBUC9GvTMIPLSmHDQJWW/OexozdYi1tQ6D20NRUr/hc9uP2czp/I3XpcERg+tQ/fwFIPXqVbFxWfxwZZQr2WNXkBvCU6o0xEHuDGN8pwsIQlk0dqiRBKBE4DsKnGTVYa4Iap56vFjyFdyU32hq4rZoRwQ4Pb3/GdZ7J9bsXq5UnbTRi4XpRLNlmd5rU4PwxYMHsxfXqvnGO2RZojGY6PXcv20aK0jJzcgFkPy8WzhKJs4U4KM4mRqXjAkHzgAXfO2IHBCJgyzIYoAtjOvklOHE+A74VZPP121gUSJJ0TsBBWmzGAFhzxorCj3jTaWJUNC149eUPrAreRUTdH0m5qyh3wX8WZfID0DUXYXO0FNK7GTHLTCKADG4/z9QBM+ow5AhvmvMqUc60KS64PmA8Eqpv1dmEWBnmAfsnOpOU4Ns+fERI1057OLAuHO+i8PS7MXOf6mX784NlRyJVRxDquNqfOQ36el4JxEdxYr564wYUCLPBxTqa3twcrD4kfCjX8G661ycv38/nquMqcZccXD1/l55SpVIobN2CMUJThmoNiTFwYTfOYSCVKCxIpzIKhmSYGrkkoHD++/cSGO2+eCdLgYfoqtGvZXnaahU3/VM+JtC4gQeKyaBqux68PqeJyd2EWeVbQdKpQ7R/3SBiPHN9xhl9/9oIZP7FB2IdoVokbd3AAzg53Lt2nLeECszTTf4pU+xhSSxRxYhMWbCtZscRdMFPpo9FVrtmz9MA5Jc4lHWc6ROGmN+/ffKSkeGEWfpRu/ljdp2w9Q1+/fKe6jatS45YC6VAQ2L37ImfstWxZkx0WCxv2nb9JP/4WUaMaFalmReV8vsJxZf6XoG9rgaY8c/MhvXhbeLrRYtSrV4nq16/MGtx9+3IPxlUWipcoQn2HCTa221bnjyNcpRrlqMfQNkpjydIHP24K38WLIEWwZLjBgCXbm5aNIivGW4+gspVL04Prjykh6mcJS3aAPAZMC24wC0xiIHwj2wABAABJREFUcy00MPg9aLognwietShHe/ORRqos3wGTuMAkJsfXxTB5tfpVeNGA/BploYtaB7KMnsWP18zbQss9hJt8bhik3ZfMFxnw442h27lwlKUoQ84ZrLrFVvx2Q7wpZdUhUgSwoJnspE4B+9x4G0IWadnfnQOq8+rwqAwg7Npg3hRaeTuU3NZbUB/1rmydD4fGNfO3cnGm1XA2hZrE0Imks1I3GP4/Ah36xKX7yH18oKQI273yIC/KK1Qvx3lxmA2LOOVD4yyGc1FSmIDrlXk/Vz7nUXggcw8SRRjY5BUw0TDt6cQuhTUaVaOgg55s/S5NMeY/PUxSNDnEm1H3EZ2yZtC0Q+nUrnMsyYU7K2R/YiBWRCyRdNtgzaYVkCJGWC2VyP6e3XtBG4K38f+36w8Tj4v0Z5E/JEY9Yvt6/An2DLNjwP71RzkyJD3wWcXnSs2mNfg6UKlWBareqBp/DqgQbJebcP7ZYntB4aDnN4WvTfguYrMOSLVhTCQGJIihxkKxNd5mVIbvmH4eDEUvGowz5gjuv1kB34MzGn/8zdEt+M7SALOnaN6goERenKxA5uex7UKI9WTHvDsrJscfoucPXlH5qmVIJW1kQJHYEXeIre7rNq3OhmX5gcT1grpm8NiOBTpDmrxLaFYOLITZY+nliv3TiBll4FdBJgOqli9FLepU4Sq+MLotpg/S251csJ1ysVPPwcQL9O51znNKigLsYYE964/TWwXICjMDmWSQKaA7CdlCXoGbmHqaFHKp53qZbNbTMxDiIecVXus5L0ca4MI7K2gqLxpOJKbyrERuULdR4xs9covivLOXOqKzjCIGN8GU+IO8iMgORYoVkeSYrQ9KYPmSsqAypQ8vhoAlLqtoXcBWqX5PdVo/Moucydts04IdFGK4SKa5P8zh+ex0YMkYFj5ek4Jotf9mhUn14IYZfsqHBk/ty6+JBZthZzvOEyqMQFe527AO5BA3m9Y8DCfHlSbUe2wXLs6wUN28MIns1eaSRjV9chk7jxd9kIX9f5Y24tpw4dAVzh407eNK42sakP+McDb1AYsAdmmMyRAK2OtKK26GcF5cYZgNywycN8irm9nOmi4eusrXL5slRmS/YrZU80S5AUyxzSBPnpVr2qUhBe53p2r1fraozwwwPSiywKBzMbbSlK3bsywsjKK5qYJCB3JGyJPFAJMudksEuw6JIALqPcfP52Jk8NR+VK91bZqvu5Cf03d8d96HAI5/SAMl7/W3iK+lMEuCSQhs9b0nBvzzYdLWzmLTB+R5ndt3kT/XjLmaFCs26PCZTPVa1eaZOLHLotg9d33gNjbrgNxRHHMiRujsaC6GwPBPtPu5mAHrH+ssSCene03k+eTsgIYjTEQ42DstizI3YNYNBRkwzX0836tkxTLP9fznQM2eVLNR3oKboWIRs2NjDAfzd1EkcAxuSWsmj5yRP1b31y48oOsXH/Ix03+4dFlwysDtW8/o+vUn3GQUO4YXJnz6+o0OX7yjVLki8KsgkxH92zbMkEVQ2CCeI0tNvUtPnxacq1mjFjWoQbPqvABN3iwMKisbLTo3oPota9KXz99opxIs8HFzxBAvgAuzIjr4ow0Hs1zl0c2nlChnkQenNNxsUCiuCxC6rtIAvzPWXHCQXGi+JFfWD91gZMYAa/w3swQlOyBnZ5zVSImTYk4Oe5AEwV0MCxafycFKky4CY82G0VSPCfw43GIJJS6Rjk0dOmMAF5nimTJZ3Rcx6O+4yoxGGQ+RDMHDzESeIjwrYGFrGW1ALmstJPEDxt0caanbGoW9hzKAxSSKMRRlax9FkMdGKzYDwfwQZnSQ1RdqEkvTWljQ5EYm5DM1jI0qIEn7LxdoYEowm4MIAadRfjS2ih6Z9XXjggwyN3x3mBNo2o+mkEMetOxaEM30m8yRGHlxJFQmwNzYqXpzKDMbd/RrQZGpfjQwjf3JC7A94n028iwlrss9RnUiv13OfC5IsxD20Q75R6YYb0a91btl+Vw0crZGJPF1AO6EkICKcXTbKQmjhGtM/0m96NWT15xhhqIZRiUwM/IaH8CFERgp5HEBmOeDVBEsmRiYp4O5UNt+Lbm5gpk0cfQIigFkoWHeDOcJLO5hzQ/ozNGkNf5buLjrMKgNjZo9hKKslzFDVr5aOXZZxOdHoRjjIMyX6ftNoYo1KkjeG020PSsPcnFqFWMoMSpJv70RvQFGtmnnhjRkRvbsFb57hNUyfjzBdpRUBbK4wYjfbdyhPvVWz332LzOQs3dy1znep5NsZZ89y4zDW0/Rg+tPuHEwdFqfPL/eT6+/4yw9ffCKSpcrQf3G5I/1/PbVQjOg56CW/L4FBbHvQZcuDaiMHLJUZePQhdv0+dt3ql6hNDWuqby5vsJ55S7EGNC2Ef95/Mo9evsxdxvt/AacFlu3riXR5BYUcMEXs2Q46fNj8ZTeAn9r7D6lOLkNntyL5QrP7r9k+UJeAS3/BCthjmqF90a5pGZgHKZ7CnNbawIS6MWjV1L/7iT70Tx0DtZrtV9GKUxW6D6yI4e0YmGAhVVO+3WyiwbLbTCgnlsItHHoDGbfHt54QhGWws1bWYDlvnqa7TVMPvauOSw1U2YVa8gLJbiWQd4kyzGG7p9h4FSaFTiVj1VITB2H++TqdCkLwMJFnZvH5gU8o+O+lgy72Bdatiw9YHrQZWg7NgOJux1KoYc9OQC2Td/m3MHFgh4zjDCqmNHaiqV6KFaWe66n4ztT/7W2+ijswQDuiT9E4VbLyLyfG42upEvm/dw5ewmW31iYIoAbMk+YpIAFCzvmTdquGtz8KGi7+lxDbhcm0oxWFnQq+RzvZ8PgaRzjkJM9urRAAYaMsWj7lRLzCsyMSmMXjlkwr0mBXHxwMbbKjMPrswIY9RWewjwSzIvgKivG1ZM3yGtCADv54TqBawyKTmSYgfWFdBIui7DRh9wUkm78+fbFe5atQx4Ihuzbl3+aJ2ZRBmwuhH+DWyOOAQD5ZHB9xLwWijgw8Pg9XJMR5ozzAFEfKPLA0iHDDPOZAIor/D3uM3O1hOK124iOGQoquEOKA6PHW4/MwACKcWJbKh3ZcpLPS/Mo/RznwVBYgU2DrHp82r0uN8AWf2ukIMHHDKQ8x7eYHVOZ3EvqIjA7sPIgrdk5Qq8/N8AUjU2LhNn0oVN68TmibHz88IXnx4Ah4xRvTiItcM6IFV0DCqlcUUzAYP2vzGvtr4JMRtSpUo5MR/eiaItxVLKo8vMh5IH4oC7IkGig37C2VKTYn3T3xlO6fEYxDnO5oc/IDnyDenr/JR3akarw10dncqyxwJKtDtimkNyzYbr9qWL1cnzT2hEj3/wbOsLNujSkLx+/UKyL9LNYxUoUlRh8rPLdzBk5OQEXIyymsB2QC4NFZHb4q8ifvCBAAZO8Yr+kG5wV0HXEggVIiExidkBZwHfQ85tMqtP7MyvnPSlQ6qIMRh82y2Zz5xiZSd6aQbyokwWjjYeQ63pLXjCeTDpLpr2c6NGtp6QogBVAQDVkYKXKl2R7fKOuDhRtH6eQ2cf8AFgedMYn2owkv0RHWvc0irwTbGmS3Shq3bsZH39ghFGsLHVfSw7DfUij+kyaUGcW2anNoUjbFRyAjOMIsqvCwKahQIZ5Bbr2G0K2U4BBFJn2dqExlXSZAZwzJZTWB22n8wevsP04ZkN7jupEer6abIiy+kE4yzxhkoImyr8B9689IssB7hRsFM0FRfPujdkhdJShqkKYPBQrdkO8JBljaHaAxZfGMAIspLv6PNq/9gjL4ZzXWlDP0VkzMbgmgVEXs1/DZw6S/NuD6484hwzfD9b0cDZEETpHM4guHbnG1zbX9VZc5IgdFuGaCCdaLLzRkMH1BNduMTAzhsIOrxNmGsPHsBiQU75//ZEZPXznpl0bSQoeyJZXp82YmUbos5kI2K3A/R7kk+gkYfSWua1hMw/Y65tHCYyZGFFWy7iJBqmilvPPOWGQxS9zXi8p2HIKdoZzJDLMAOyXzExbdoiyi+Nrc7fhHahtX9kX6Wf2XqTTuy9wkT3RRroiMOfXu0TXTt/m/TVCX/ZZttxw4/w9On8Ubqi/kZp23hljabA3IZU+ffxKNepWpFadcp+xVBYuXLhPT568oeLF/6Ju3ZQ3nyUvvn3/QfvOCQ2N/m2VJ1cEFGsR8/8E2ioFZw0qDXr1akIhwYmsyb19+znVrVuxQD5HiVJFqbdqa0racJJ2rD1Bzdopf6YBF0xc0FYGbKcNEbup17D2Cn+PoVP70Kp5CfTw5lO20+0rh5wiPXCTmmAzgkJNllC87xYarN1H5g4Zbqj6flpk2tuVEpfspREGKtRIikF2oM+4bjzXAfv8RbZxvJDPCeg2glmLdV7NzFcHlVbZzg+guzpiliobYgTNiqLIVH+eG8sKkOZgEYKw00D9CFp4ypdtnpUBbC/TCD0eak9aspeLMizKkKOWG/pP7Mn7Bx1xWGNjIYUwaWkXGwAG2+eluJLTSF82dTHuak/Oa8wVYmog/n4wSoBMCu6QKasPU7zPJtq//hiZhevybMu/CWgcdFRpzT8AOvw3Uu+wpTt+MAcDo4iXj17zz8mkjHmEmJGpWqcSs7CVapbngqZc1bK8OEYBC9YATAUC2xE+K20XFIUeilxIt8B2gF14+/wdsx9g0V88fMlh8jCYeHL7ebaMKgpMzHwhXqJx+3rUvGsjqt6waqFmvnICmBcwSmBoIZ2D3Bl5hiNmDVaYpBJW6I4jfOjh9cfMWNjHmTDDKg0QEu862peNObDtXTdYs8lRVkAzSRzcjAJEHGovnnOyH+LF+xvGIbCmRxEA50FEeeBYguX95gU7ON4D79VlWAdKjE2h3//8ndkBQGxvL54t1vfX5kYPHB/B3omhpjuQmnZpRHqtLfj/Ow9tz6/L13//KWyjj9fEvGz6GTh8JoTeA1eOX6fVvhv5MebGylb6x/Alde8FSdg0XBjRVMsMGLG8evyGqjWowveBnBBptZyPBeTJdZXyXgxDHxhx4DPrzc3eKCSnczLGaTU/HqrTj8/7vCLef6tEIVM2h1k5ebE5LQi6h1o7qphPDoPi7DEomQryOrM7Ta7Ys1cTKpLF8VbQOHblHr3//JUqli5Orev9bGyjSPxPVBhah4UYb9++pTJlytCrV6+obNnCZ8WZHRwd1tDhw9dJU7M7TddRvN5ZWlw4dZssNSOYKVuxz55KlCwq82ugS/j06VOqXLmyVDfzl0/fkHYnZ5ZqzN9iQc061idFY4XvZlrmtZHqtqhJCw+65fmChgWmTisrenrvBen7arIDozxAlx2sVateTcl/l5PUnwuSFqMu9nwzQ6gxiqOc9gFusrM62fKsEpy5bJcK7FZWQEdVp7kpvXj4ime4xMYaWQELWp1mprzAQSda02EsKRNiZzUM8wuZQ5bsyCgNYP+PRR0WnJiHcdtoI7OUBe6SLqP9OAMIC5DZoTo0NBs3MVnPg/TA4hAsBfYBgLkdsISwyf6vALM0kDrdOn+PM9/uXX3EUsCnd1/IxJBBhgUpMaRhWDTj/2GqgO3PbKjof1xYIQIDP7K8NhboaGjUbFyNrx2Q9OKnZuOqCrfQLijA5TBo1iLeFwDs5s0i9XjmKa8QnwP3Uh+T9yQhew+yR4/NgpuhNECOl9MIH/6cKBQ9t9hl26DALJXYkGO4wWAyDtWRXFNxvFkPdOOGAMKKgw55Ufmq5Vj6HWWznJ8HCSQki2DX2KnXaiStSiuGEND95M5zSd4Yzn8cV2YR+iw99Bg/n04lneXfwzGG77nwtB83gk4mplKtptV59hj3uUkOY+nc/os8R4Zw5uDD3tzEyOo+M6uDNe+bfhN7kP0KU8m/YXZXv60Vs7go/MCwZQakz7hPoOjz3m5HnQa1zfH6aD90Dn+/yDN+zLjlBihODDraMXs3evYQMvCfTLICmZxu44OoSPG/KPbCvDzn2V08ep3MB3nzdSDmjA83dBSJNy/e05SOjnwfmbfZgpp3yn29kpd7AXDj0kMyGhPC32lZii2VLV+SCgLfv/8gDfUQjmny8Z1AHTsWHFOXHTxWJNH6g+dJo1drsp/4z7359evXVK5cOXrz5g2VLq2YIv2/cQf4hZ/Qf0ALLsgwRzZteu8C64A0b1eHajWoTPduPKW9287S0HzQKpevXIb6je5ISauO0MZFKUopyEboDqB1wTuY3TiWeJa6DP5nuFseoBMJOVbgrGiK99tMqlP7yKVT1/GaSAc3Hqdz+y+zIQKYGGkAedgw/YGcE4OFe8RpvxxdrfBvFlEzyaSHI3eQVSb35i5oVkB+jmm4Pg+3Q77SY1RnatUr6+DH0uVLkf48bfKZEkIrPNby4DvCnZUFLLRhhw+WA9Il1zF+3OXuNjz37YaOuvd2B3IaPpflmzaDPMgrwY6/g7QAUwNbfP/pwixbwMxIunX+Lun7T1boAh3MH4KkFzuspK0Ru2jX8v10NOEU6XhPoiEz+hdaIwhZgPOlWZdG/JNZmoaF65O7z7kAxgIZ7BUy8t48e8czN5A/orgCMIsDlz5ZgOtrqfIleN9j1guMMWRpkCJXqF6eKteuQNUbVKWKNcr9J7Z1VsB2BMMuZljAPEIOPXCy4u4/KEwSwnfTaq/NXBRAAum6zlLqxgI+IySOkA6CNfXe5sA5hVnh0ObjwlzYj79JRbsPGYVMl3wPHFOYD0MxBnYV1wEUYyjgFtkK1vJwE4RcEa8BjDFTo00LBJe+ynUqMWsqLsbwJ4oxNHYQmKzfxpKLEvF3BmaH6VKc5zouxmD+8eXjVy7GMAMmEv3NxRiYezg/ZlWMAXBeRDEGqaJRsOBum96uH8UYWGTdNBl7euDzBRpE8XbvOrI9dUhj3LICrqeYMQZGGg6WqhgDEA6O712ybHE2rJEVKOjEuWPI+cxrMQas9BNcMwdO7KHwYgzYtnQ/F2ONWtemZvlUkOxYI7Bj3Qc0L7BiDDhx4hYXY+XKlaB2+aCgkgcT+7WjimVKUPfmyluHiPGrIPuPonv3RqzJffToNZ0/f59atcpbIKK8wA0M+RaLfLfRjtXH8qUgE4cqoiA7sPU0PXvwinPKFAnISoZO60trg3fwLFleCzJARasnrZmfwNKr9cHbScvhH2mMrGHRK302cVh05yFtpV7YT/OcwLk1CChdF5hAE6xzdqaCu9YIQ1XaGLKdgg0XpckRs5btobCC5fPO2D3kP30BhZ/xz3bRAMZt39rDbMUPKeGC4z5SDejLC3Sm7VeY0Jz//Y8liO7q/hzcisIxN7Tp04L8kl14kYeZEMt+rhwSi8WZtMB3c1hpwp3tJS6raWPoDi7KMCuiSAYLi0OEuyJbDQurG2du85/bopPJMHBajqGu/2ZASooFYW6LQizmPr//zMzHp3efmQUGg/rj2w9Bavi//9Gbt6+pYsWKLKUFuwImDX+iG/9fLbRyA1w80ciBlA2MldiVVGfOJJ7nVRSwyJ+vG855awDkzcYLdLKU1WWF5w9fku0gDy5IhDBnp2zzyZAb5qExj/c7HGAtEOORtn/xd5gdhY079j+KsVpNarAUEC6xKKBGGqpyU8ewsy0XMDAA2bv6EBf9KHie3nnG80I/vv/NeWMoylr1bsYse6xTPD269STD56larzLPrKasEr57k06N6PyBS/xaIwwHk72qF/893hsSzqws3hFvggxGACwcCsn0/wb3WMBysSGVKPOz4x7uCVdP3GRpr6bLqBy3NRwvYdCETDxtt3EkDWCCsiQti3KS3Wi5jp29a45wkxSfUcNMjfIKzI0dTzrHBfN4cyGiRpEAY7klRpArjtbvny+N88+fvtKeNDMPVY2CM/NIL1fs269ZoQmsz4yG1SvyT36gcG6Bfzlev/9E5249KtDPULTonzxLBiQlnS/QzzJgZDumxpF5gZ/8QP0WNalNj8Z8o9sSK1zwFI3Rs1SYKbpw+BqdO3glz6+HwknbRcgUWxu4nWV78mCc1XAqU6k0D9TLEhZdqlxJicHHCo91uRp8AFPdx1HFGuX55gs3rZxgEKAtcVKMTusiZwXclLAAgkXz3UsPKDLNMlmZwLZHUdZ3Qg9mSDzGzacDG6QLNcec3LwUN+7GQipn1tuZHt+WzaQD31nLcSzb1mORh6BYZIldOaH4vEMU0guOepPB/CnMKmGRZdLTieZOCZXJofO/BiwIsJADa1m7WQ1hpqt9fWbcWvZoynNdDdrWoUbt61H91rU5rBaBy9hf/1+LsdO7z5NBBxsKM43lYqx+mzocVG4WoafQYgxsklkfFy7GsDiGeQfc/aQtxsD8mPVy4mIM16v5+zyyLcZYijzGT+JaCLMhMOnigidwZiQd3HBMCH7eaM0ulyigoABA0dh5aDua4qpBzqN86N3L9zwXiH9/fv8lFyhgaAEUY/yaf4v4uui11S7tfLzB7JcYWk7qPHsnLsZgPIJiDIYeFtGzyGt8IGejggXXdFKn5/df0O6VB/j6n54Z9NUO4ccwJEk/KwsJ5/wZQi4aCklITDMDr4VmEQD2vmwOQeN47iofwVjEYJ42KySkwbrAbSyprlK3Eo2Y9Y9pirRgV9k0Z0Xke6JpmlesTJsdw5x4Xp0as8LejSfo1bN3VLF6Weo1XPHz7lnhwM7z9OHdZ6pasxy16ap49ZC0+PTpKx08eI0fDxigmNnpfzv+f95FlABcqMXSggfP35DvGvnc8hSJgSrCHNC+vZfpqwIys+QFKHHkXADbVkm3yFUEEK4IbF9+kD6nu8EpChWqlSMVzZ78OM5X6DzmFb3GdOIFHxy7VqXdDGQFboBTnIXZK1iCy2KrDnkR5s/QrYSERZr3MgqeLlU2GbquKLSAzWE76eLh7ItYSL4QGCsOW8UCRNkAU2a71Jg74ri5e44PYKZOGmB+JWC/B8+SoEMNKeetc0KQpKy29SGHvXjGCAs3s94uEgmYor/rGBM1irkcyEwDCkJIT6c1NaUVXuvY9OAXfiEnQw3MPlqreDAjAbYF7GvY8blcvCoSYG8MOtqyzBByUNv4WSyDk5ZNuH7mFpn0cKDHt56yEyHO09pNa2RbjLmM8mWXSwQo2y2fzecKgPt7hOVS2rFYcHS0izOldv1bsfTVdrAnOyHCzt4q1ojcNebzfC2aNBWqlaWrx2+wRBISWQC/nx6QDxYtUZSWuq5m51UxUCBh7hYqBABGNCeTBPdgTUd1lnWjEIY8FDNfx7adoj2rDlLqnvNs/LFr+T5+bojRIv58mFfUnzclw3tDYgkpLxoMOnM1f9om+N4wNYGsDsYgg7RznkkPN18qMfLorZF1hEBmwCUy3nczP57uPl7qQjs9dq04wM0+sJ/I98wr7lx+QIe2nuLHymDHsF03RArX9hHT+3LTOj+wM83MY9CYjgXaSDqw/wp9/vyNatQsR02bKtcs49+CXwVZHvDwxT9WtLg54Of7j7/p9YfPdOHOY9p//hYVJNq0qU0VKpSkd+8+07FjBZtDJM65SElIpQ9peSrKRmeVVlS1dgXBInidEICoaIwzG8o37NMpF+nisbyHheMCOdVVYMm2ROziWRd5MGR6P17UYzZmhfcGqX8Px/CswGm8YIAzX+rei1Jlk3VVE7LJ5s0IzzEKoINKGxo0tS/fjAL0InLMXcPNf4iOMEQ7P5fnKgrYl+iIQzaJosxrYqCkM50bMCOExR6khy8fvSLzPi5yFZKQ1oUe8WaZJxaG2KbzdMOVYlmPBaPFopkUctiTmnVtxI0AuGdObWJK2xYlKyXL7xf+vYCrIOTJyBSDeQKuE2BvUNgPn6miUNkRjAuWe65jYwiBaapPoUe9qVn3jDOCOQGugRZ9XSTFktA0qZxt4ScuxsAgQTKcXu4d57WenSPFGWFwMUTjCgYhaMLAdAMzpBEWSyg15QKzXbiOYJYX8kRc2yHvhLGL2F0R0PObwnNgi+3jaJn7P5EleB7YIrjTIoMP7Bq2A85RzINiZhHGJKhLHeLN6OKhK1wY++1yIbPImdwoO7fvIksl964+zMyi9RKjDC63Z/dd5KBrwDzKIEsZOVx7MSMLObrpwhk5FsKYS0VANq6jhkHTpC6akbcHt9oW3RtT3/FZh3LnBNwbkOMJjLMYxqx1XoFRBKDH8A5UJ5sCPi9IPXiVbl18wNtVVbMH5QfuXH9C50/e5mNBZYx05lXKgli5paLS8l/rJqto/CrI8gCfVXvo6v1n/Hj78ctkGbWFRrnGUMS2I6TRqw3VqKB4e1RZgJvjgAGCe9SuApYtIueiZr1KzFSlbFV8Plh23x+dJ3HoojIMRavWqUgDJgg3kJUKYsk6DGxFLXs04YVB3FzhJiMrsJCY6SfIDzeE7OCOtrRo2LYuqekN5MehxtHc7cwJuJhilgMLEOTurA8Scmeyg77fFB4qx/D2Eqf4HJ+L4XLIwu5dfkArvXOWRCoKWEwgABrW0ZC8Ik9I3GnODRVrwKTDnVr2bMqdaxh9wOFQVkA657bBkucvsH13LN5Dpj2d6ckt4XqjaEB2GbjfnRziTHheBQUlwnb121mzScwvM97/3+DsKfc1NLWxCc+L4bxAwyDyrD8Zh0xXqDxRnC/mPNKPZXI49tR0B1DAXjeZgqQhObZT9eJIAsxnQVYMVUNWAOvkMspHUozBHTF95AYYqlhn4VplEDCVWWU0K3BtwOwo8v7m7HDg4gWOrVjwTnIcy3NXYsYf1wOc13gPMTQdx5KGxXBa4ryK567EwEwirn2r/TZTSvxBov8RNxZh4gEWHm6NEZaClFvXdwq74nYc3IZzH8XANRaycxR0AOz6G7Wvn2Gf+k0Nlcz8ZeU0Cdl8RJpkfLKLOjOM2QFGJwstlkrCuWs1kY71uHjkKge+4zoHGbU8i3OESMO0B/t3mF7ec8Ie33lOe9YcVRo7BmyI3M1/DprQlUqVlU7WmVdsXy00prv0bUoVc5CdKhvPn7+j06cFBYl4jfoLvwqyPKFlvWo0xW8ljXFfQiv3nKY6lcuRu/ZgCpk1iqw0+lL9agUf3jlQRTjYjxy5Tu/ziZnKCrjIDhnXKcNFIT8waGI37pbdvfqYTu5RTlD2BIth3CnG8C+GgBWxraa6CoGcO5fskzs0uPOQdmzqgYVDpE32M1tZYar7eJZ+QI4k7grnhMq1KvJsAbDEeTXPbGQHdHExVA5gyBxd3pzm2gyDBElknPd6pQZGpwdmRiyiDdLCWUXkqx3KUiVpgM8MY4+uwzuwzMdtrB/LLmUFOuqYK5u7w54lSTfP3iGnIf7s5KYM4P36ju9O0Rfm88IIi0zM3LiO9afZ3R1zDPb+hf8msMheG7CVpjScTUvd1rLhSaMO9ck/2Zk8NllTnWbSuefJgguHrtDMDjbMtCC3CwwuZ2LJkPMHgwqYcrD0cFQnmrvDkU1tssLxHaeZ5cK5inM2czEGyfACE8ExcIrLOJb6gr2brxcuZI1hlmyDNec4xjiu5OdpWI6gOK91XLhiZu3l49ds3pNePg5mUdttPLNUuLaJgfsVPi+YPAQ0A5BYgkHCv9muMKFQo0V8Xcf5qm4+jItWMF9ih1fEM6z238zvC1YNs32TMplELTSNpce3n3EDRs8/o4xRjHCLpcLvt65NY01zNslAziFiJsC6azpKZ0jFMlArQRoPKSRmNmXFh7efJHJHLcfRCjGAWhO4jfddu77NORdQ0Xhw8ykdS2uSj9ARmsbKxpfP3yh5kyDBHDq+gM08dl/k+2rLljWpenXFGq7lFTgm0zPY+YlfDFkeMKxLM/rrjz9ozvShtMB4DBmP7EntG9akMiWK0h+FxDGmfv3KHAz97dsP2rs3fxaz2WHAyPask0YGRn6Ze5QoXYxUJ3Xnx+sjpFtQy4rq9StT3zStfPy8nNkhadGqZxMOXOZBZQ/5mSE9H01mfI5uO00nd2UMy80JKJr0/YQCa7nHOp6/yA1DdPqzbTOkdYEzo3JkVdCFFksXEX6KDJzsgMH6AVq9+AYJ10VkleVXUWYWNZOH4PE5581YKJH35AYsjlzXWbHkEhd3yLwW2S7nhZysgHRz4UkfatGjCbv/eY4P5NdThoQRwPwGFp1LrwXTRDthgYPgZZvBXmQ10J3lWL8Ys/82cGwhzF27sQkzMWCswHg4xptS6BEvpQSL49yI991E5n1deX6yRqNqFHTQk5sisrxGtN0KCp4l2LPj/HNanX1o+9GEkxlkigh4T1+MYYY0QFcwvEBBouWszsc+jIYQ7oxGHOSCKFoQZg8M01ehpCUpzMyBUcJ81G9//MbyRjHA+uv6TqaDG4+Rg5p3hvPJKsaQylYpy9EfQK+xXen+VcGgwyhUh5s74kIKc2PicQkxvnz6Iljti0R0M/UOS+JgWJT+e6GQhOMtfg8S7ayMNyC1xFwpviMK4pzceu9efkDxaWoOGK5Ia+QBV8RLR6+zU+lUN6EJKSvWBW1jaT4k+oOn9Ka8AmMCO5ft58cTrYaTMrB5seAx0HlgS6rZIHvWUZHYt+MsvX/7marUKEfte0gv+1UGkncJ7ooDBxYuduzdpy98TmSe8cwvFI6q4V+KauVLU4mif9HHz1+pVJou++GLt3T8yj1atP0oJZ26Sp8QJFqAwME1cKBgqJGcZjFaUChTrkSBmXvgBDu97zLdvJC96UReIJY1HNxykm5fUkyxOS2NJdsdf4hupMpuECHurI4wUOHH4ZbLZJoJgsGHuMAKnR2T6yIcx5rJQl3uap9KPsfSnZwwK2AqVa5dkdk0sfwmu9edvUCXqjesygs1WF/nV0EA1sh4wQwaPVvYv0EGkblKMsVAIWwWqc9dcGCV7yaaOzlYrlk4dNl9kxxJbZYgx4FszLibgyR8VxkAozDdcwItvR5Mo4yHMBOAWRLLAe7seIeZm1+F2X8LMHMBIza5gREtMInlYgIOgMgcjDrrT300uinFCODVk9fkOMyHou3iuPHSf2IPCjs+h+XT0gLnFYoYsfQP5x3OP7EpR2agKIGbIiTZvcZ2IafV5hmKFljfQ5IoLuyQj4hrEQoPsWoALodwtPWaGMDPG6jVmxk+MFMwyYA7JPB3mqNiyTTnvwm2o2lb5C5yG+vPVvjiz4jiq9OQdsyqg02DQyPs9LFNEOSMIi95uVAk2S6bnWXhA/fG+q3r0qWjgoOdUYgO1Wn+T+wNTEgC0opHyCWzyoR89+o9u0kCMBXJnO33k+nHrEW8HbsMbU+91btKzb5GOwiM4jiL4dnKSXMCGMR1QYLhCVQl2e1rWbAqIIHnodEUbd0z64y6vACMXlL8kQzGY/mB7asEZZKqRqcCNfO4fesZXb/+hP744zfq3UexBkDy4tmb97Qs+STNCFjDP2duSD/ioUj8KsjyCJ8ZatSyblW68fA5ua9IIquoreQdn8y290cv3yXzcIFKL0j07y9Yip5NvUvP5LRSV7S5x96EVPqUFsSqbFSpVYF6DmuXQbetaGDoF8O/6YeB8wq4LYJ5ww1vsbNgOSwPNB3GSORn22Okd//kAitsBi/Ej+84Qyd35s6wIf9msrO6pADMybofrovimYeEyCQ6syf7OUfMpznGmzHDCsvpPSsPUH6BZxsCpvLiBVhoFkvL3NZIVYywnb2TOne9sVjYs/IgZyGBcZAV6FBPcBhBXgm2PB8Ci31Y42+LSlZqYVSuSlkyDJxKsVeCaLjBIDYbuHDwCtkN8eaicN/aIzkaufxC4QeORxhoTGlgzM0RmGCgWTI7VIdi4cQ5vZ9CFrtZAey9XltrdjhEMweW+bbLjPmclxYoIOxUPWl33AFhBjTGkM+77OaRYHThrjGPF959xnUj+7iMBh6YKRNb30MWaBKuy68FKeRihzh+jr7/FM7ucx6ZJncc1oELMZyXUBjAal4c+iyeq3qfFjhevHRxirQWmlClK5biRhmeB8YO7BYcGstWLs0ZZTD0QDNqsHZfCjdfwr8zY65WtoHW+MqYeeXCdlJPGpyJYcRcMAoZOC5quwvNoszAMYDvAlY0txyx5BUH6Ozei8ykG4VIb+SBQurJHcGsZKyZfHNamNtGQdu4Q33qOaoj5RWI/RCzY1o2I0gZ2L7iIK99ajeuSu16509BcuvqY7p05i6by8BdsSCxK40Y6Ny5AZUpkz+zc7lhzb6zdPnuU3KYOIDaNahO6w+eoy/fhNn5tx8/05NX+aPK+VWQ5RGt61WjV+8/kdOSnfT123eeHdvgMpWCZo0ix0kD6cOXb5R6s2CqbTGqVC3DwdBYs+1OVs4clSzmHjXqVKBPH7/Svu35N5OC0EUgZcMJevnkH3dMRWKChaCxT1l7lO13FYEpLuq8wDiReJbOSOF4mBUwcK+Vpulf6rZGJhv8mo2rk4alUIiscNmQQXaTHTDTgJkDLPJCjYXZi+wA22hIfABIAjGjkh0wkA6rZ2CBSQw7vuUXsMiAxGiKq7A4Weq2mi2wpS2EEMaMAFlYX2NmbnZ3B7p/Vb7rQsdBbSjitC9LGcFeBsyMJNcx/krfHuIFOhgzyLcghbpy/AZ5jA9gu/xNYTulOj5+ofAA86k4lzTrGrKBBhookMKZR+rTkqtCAS7L7JYsAEOywDSWHIfPFQqElrXYRREGE7IYOyD3anY3ey4KUMTB6RDnW3bATBikzyiCIIW2W26SoRhDYwhFlljGaLPUmOXLyPYKMVwkMciAgsB+qDfLFZt0bsjnNuYsixQvwkXRhzefeOYLhRHkdO1V2nBGGTBPJ4zfHyYfb5+/o9///J2fV7piaTqy9ST/Pox2rhy7zq9rHWtI/joL+XdQQKpbDKft0cncGMosg0YxBdUBn68LMroiQoIpdlyEcVFW+xbfYWdsSloe5Ew+z7MD7iXiwhJzY9k5WGYGbPbj5ghMpo7XxCzdHXPD03svJDmb09w1FOLUtzZ4B3378p1adGtErXspvlgCi7gxSrC6H2swMN/cBXesEdixrv2bU/lK/wSC5zfAIhdGueLBC7fJcER3Xs9P7NeOnr1+T1uPCuutmJ3HaVmyMHunbPwqyBSAmMTjNKBdI/KcOoTaNsjoLASnxRuPhCDIwmDukbSrYN0WcQEarC6YeyTko2yxaft61LxTfe54bo2VzjFPVjRqV5c6qbTiG+vKefJliGVGjQZVaKiO0OFc7LhKbiZkuP5AZq9eP31Ly3MJcM6MCbajWLaEYFPxnEBOwOIGeWO46e9dIywAcsIMHy1ePGBOLXR2dK6fBQPqKPYgH8xPyRyO3cnOGhKTEciWIJ+UVgaKwNWgg17skvYgbRF5erf0c33pgcH5OdvtSNdHk1lDWJDrtbHixZyyUbF6eZo5bwotuxnKLnFgX7EARPE9qbYBRVovlzkY+xfyDzhnICl2HuVH2o1m08bQHVxIN2xXl+xXzGZGDPOgOc0M5RXXz9wmo64OknwtSGIRVo48P1kAW3ucR5ixwjUKtvaI1sgOmItDMYSFIQo/MXMtBmIqnIbPZcYLxRPmw7AdwFzB2AfbDmYcUz0mcDYXjnsUsM27NmZ2Dtc8MD6YaYKzIizqcX7i8zkOm0PHtp3+qaD5q9hf9OPbD/4Toc5A3/E92UIe1xxIE5FPBvloraY12J7+2qmbFGIUzY2h9NdXyDChNgDw3aBCSF8EiefcxluPpMYdGvy0fXAcBBoIRSeK8exYODGWuK5mNhUFJ6SN0mKR3Uo2KQHDOGCSfJbvK+Zs5AKnde9m1K5f3hf3aEZsi9nLjydZDVdKsbR/8yl68eg1latcmvqO7phvZh67NwvHndhYraBw/vx9evr0LZUoUYS6dmtIhQH3n7+m73//TZXKCE6x5UsVp+mDO1N8yhn+/2sPn9OQjoqXrmaFXwWZAlD0rz/o/eeMneEr956S05IdTHcO6VjwOtm+fZvRn3/+TrduCvrdggTyL3CTunb+AV09r7wZmMwYpScUNglLDyjNEEHTVpA5JMcfpoc3FbMo1bQbxUPPV07c5EFreYBFxawAwUlrY+hOuivDnBu6l1iAA6v9ttCt83dz/R1ISCbZj+bHIcbRLN/JDpiDwKIDcxFJS/bm6CKI72G12FAiXdyyMJHyG1g8QmqJzwvnRY9x83iQXhpAJhR8ZA4vRN69+sC23JBByQPMAYyzHMGsAtgFsAxOI3159iMnplFRKFe5DE11G0crbi8go5DpbGCA7wTnTCz0wdqh2y6PkckvKB6wOcfsoV4bS7IZ5EmHt5zgAgMBvj6Jjhzq3G9CD6VJEwE0L+LmbCDjrvYs7YN7qOdmG5bEysrEgemC/BcMVdPODSn06BzOGssK+J4rPNfRgtkCY4/iAYYYYL7EAHNtN8SLi5IOg9qQy1pLNriBiY2b+jwJo2YYPF0iY0YeGNi4DcGCTL1eq9qcSYbrtfiahyYgADlmZuB5X9PuRSjKAEgM968Tiqzp3pPYmOPUrnP8XJe1Fvz3yEcEg4f8MjBmAJogMEgC1M2HsxW+GDgH8W84PzGXNtklawONWOdVXGSiuNXxnpjj9kdQ96bQHfwYmWOQtksDOOXuWXWIt6FhoDCXJyvuXHrAEQMAcjsVUTytD93J64ImHepR+/6KZ29wDK6LEBi94dP6yBV+LQ/27zgnMfNoV8BFkNjHoFevJlQkn75/bihdvCjNUO1Mz98KcmKgU5Na1KVpHXKM3cEeES3qVqX8wK+CTAFAwXXk0l2K3HaEXJclkm7AGpqzardA+Y/tQ8UKwYFXsmRR6tatUYZAvoJC2fIlqefglhkGTfMD3VXbUOWa5enty/e0Z73s2VDSoGnHBtRhQEtmyeIVxJLBkWu00WB+HOO6Vu55HUjdug3rwAuLhTLI7QBYR7cfLLg+Ip9Kms8AWQ8WKOgWLzQTZh+yAwbLx1mP4sfo4r58/Crb5zZsV490fQQHyHCLJXQjNe9RA7Ji8NR+5LjaguepkNOFwgo5Q9IWMn7JLpLwaTjChRgt4tBYedCgTV1mF8S21JDx6La2zDebehTsI9PCgWGFjgU+GIiDm46zMyOc+lZ4r+cO/y/kL3COw2QCcrcJNWayOyeiLLC4H2k4mCMO5u5wYPZW2fIpSHRhBhPjGM9FCq4pyDHrotZeptfBOQO5MJgu8QyY/x5XKl81a1MIFCMIaxbniMG6HvNf6b8vrOfth3rxPFL7ga3Idb0VF4hXTtyQyBdR/FhGoxHzmyTqA3NqKPTETagbZ26z/BCvA8ApEkAhByv89MD7i58HlhnfC1lip5PPSbYPQuKXuKzi58DYCOYcUBGg6EPRBBZMHDgNGSauQU27NKLpmYopMJF4XcgP7ZbPzmBekr4gXR8oFJYmC2bkOMOHz4f7AM5zzNjh3iINsC8WWggSR9XpfalRO/ks5TFTjffuMaIjtejWmPIKFKpbowUp4QTLYUo5F84eukY3z9/nfTBMuxflF7anyRWhTCpIM4+vX79Tyh4h4mZAIZIrli5elFTaN2aTPuBbmuJFrXNT2nb8EjWpJZ0MVxH4VZApAI1rVqK5OkPp5btPVPSvP0m9V2uyG9+f3KcM5iwy8cK3oB3JVNIcDqHhLeghfLUJghPTnoQz9OFd/uSjofM7QqePxNxDWfsjPUsGe2JFQN10KDt0gdnavfKg3K+j76fFncyTSedkYttwg9L2VJeEP0vD6uB9LKL0mUlC8GducroprhoSOWKAXkSO+2e0yVAeosdCCUPwcIfLb/Qa04XDYMVzYRZ9XXgoXBpgsYfZFEifgM1hO8l2sCc7oMkDvB5YTORDQUIFIwAUQ4EGUcyM5FcQO/YJFviLzs9jaRdmZCBFjXVaRZp1Z5G92hw+fn/NmikX2P9gonRampNpL2eeCcI2r92sBmfMxd8LJ6Pg6ezCqmyg0Fjlt4lDxnHtEM9Ege1Bc0IWoJnmrxVO69MCl1EQwZADMRNZAYUKmCFxQDNYLbBD6RfckD06DPUWijGV1uS+yYYNKu5cuk/2Q7yYbYbbrGAq9A8L9OD6I/IYN5+/HxpPV0/e5Nf9M+05iOuANBnAXJK4cBMDhh0AzlewfCjwYKSBfQcJoLrlCHZ55GBsPRUOqoccESoCXFMx+4bMQwBFLrYtzjeHlaYZCq47F+/RIlshhxKFKOaCMwPSSr/pC/m9Bk/tm2uRvCMqhZ1/UUjC5l5aJC3bR9dO3eJjAOy6PDh34DId2XqK5aHTPOR7jczYuDCJPr77THVb1KQuqtIVl7JifRo7NnBcFyqV5raZH2YeF0/dSTPzEEzHCgriLNxKlUpRmzaySZOVhaev39OSpBPs/yDGn3/8Tt9//E1Na1WmGapdaEin/FO4/SrIFASEQtuO78c/A9s34uIsYtsR0pm/muL2nKbHL9/l2wBndujcuT6VKVOMXr36QCdP3irQz9KifR2q07AKffn0jZI358/AJKA6qYckKPr4buXEADTv3JDa92shLETmJyjMgny8haDRX+K2Tm7JJayY4fYFICxaFgv28tXL0jRPoYCIcYiXqvjAYDpMPoBgw+gcDUWwiIB0EYUcijcsIrMDziXIBjGvce/KQ4qyFsJF8xtt+rSg+XvdmcVEcLNJdwdeyEkDfAdNh7HkttGaC11Io4w629H1M/Kfm8iHijzjx+wHkBC5i3RbWcotdZUXCAw2DplO8ffDyTpmFrXq1ZQ72nDrnKMVTOOr6zNrczLprExRDL+QPdDI2LYombPitOob8SL93uWH3JHHYj5gnxstOjePM+awcM8PQJZo3N2RFtnG8VwW2FPY56tM7i3z/fD2hXs8L3Z+32U2zoBNPazts+v6o0njPMqX4zeweLdeYsTfPT0Q3cDFWJpM0X2jNRd3MAqxHujO2xQSP7eNNhkklc8fvJDIJSHVxfcEylQsxa/VuFMDllGKsW9NxjlayBehosD1GA2LP4v8QXVa1GR5Il7DcZU5zZsexoUSzmlIgmF/L5ZcTvWYKLGrxzzgar9N/Bizu+mNNVCQ+miHcuMKdvrDZg7KcltF28dJjEAMArRz3A/4vBvmCbN/yKqUtqjGd4lxEtyCNe1Hy1yMAygYF9kLTOeQ6X2pVmOBgcwrO7ZhoTB3p2k9Qiks0v0bTyRB0KN0pc/VyysSVgr2+t0GNKcKlQUGqKCQlCh8/wEDWnDzrjBg27FLHFX1159/0Oev3zmqyn9tCu06fY2vT7OGC0Yf+YXCsVVkwIIFC6hu3bpUtGhR6tKlCx07lr3kLTY2VhKaKP7B7ykLWHCs3pdKqg6LyGjBBkpJvUHDuzanF28/kuvyRLr37DUVJP7443fq10+wwN9VwLJF7IuhEzpLZIv5xR4iKHqIljBEvH6h0LFSBibZCM6ESXEH6ZmCpFojZw2iSjUr8HD2xgXyz05NtB3JphBwgtwcJtvrDJs5kBca6BrnlB2WHnAmxMIDnzvMLDbH52KoX9tdKPoWmsbQwxuPs30uhuYRagogLBVW1QUBSAZh1gF5EnKHTHs4sgRKWnQf0YmCD3uxtbX492FbLS/QcAD7AbYMi0VsdyxMPScEcgc+PwGmAcXA/BQ3irkUwCYgYARw/KDgtlX1onHV9WmebjgXa/JktP1/Bpw1UYRhO2pU02MZGbLicD0FqwOXvFUPI7gobtmjab41BdEwinVZTbM62fKsERpK+CxzttszEyQr9q87wsXYoxtPqGKt8hSw3516qwuzU1kB81vWA9zo+PbTXJCi0FKZLKgjxMAMnfOIufxZwQiJi7FHt56QWS8nevnoFTNf3tvsM8j3EExvM8iD1Q+4Hr94KFzfkUcGYwhEUtw8c5sLF8yqocgB/kibsYLSAcUpJIe4BrPR1bT+fPyjcHRaY0GrfDcKRiU1K3DhifMFbBxMLCBlhLkRAEbdb2ooP1bTHcg5ZukBMxBsfzAy5lEzs9z/YPc3LdjJj6FoyCnUGccVnDHx+YX5uYzbNCes8NogyWcTN4xkxaHNJ+ny8RtUpPhfpJU2o5xXbAhL5Hw3sGM9Rsgmn5UWmxalBUGr5F8Q9McPXyRmHmJFUkHh9euPdPToDX6sMqgVFRbsTr1BI7sL8sngjfvZbfHVu08UtuUQecbtYqYsP/GvKshWrVpF5ubm5OLiQqdOnaI2bdrQ4MGD6enT7M0TSpcuTY8ePZL83LkjX8CuNHj08i2dvfmI5YpRZurUpUktalarMs0e1ZMaVq9AW44UrOU8oKIiyBYPHLhKHwvYorr/8HZUpOifdPvaE87IyC+MmoFMnd8o9eBVupaqnPdt2a0xB0tCaw8rXUUACwttV8H2Pd5vM89myQMsLsRSDzguSiuzA9A9nL1AhyUze+IPSjWnhM9tGW3AiwEMYmPmKieoWwyjlj2b8iIEcxHfcghXh/X7cAPh5j53cgg9T1sc5TdQ+AQd9GSzDsxxoHsOh0lpwbMhR+dQJ9W2vEBEwC3mfXL67lKxZal+NM5yuMTxUqeFObNmBWG0AakUZEpLrwVTwF43UtMbyKYOYCF2LN7Dckb1yjPIdaw//z/Cgn/h5wUxmNgVXuvIuLsDM40owsA0gnFp0LYus9jLb4aS3y5nUp3WL8cFtjIA1gnGIZDo4frXfURHlrDis8haEII9jbJexplhYrbIfZsVN0GyA5gekx6OdPnYdZbU+Sa7UBe1jHIt2L8jlBkFTs8xXchlnSUzYE/vPafZXe3ZIAfXScgh0fgRAw0D5JNBOl6+WjlCGxHFCRjHN8/eMlP+x5+Y6frBMuSRhqoUfTGQZ8G+f/3OhQSyyMCCwbUWQOj8zhhhfmm61yS6f+Uh5xXinLVfaSp8B+1QbtagmLFcbMjb8cePHyxpZPfFJtVJf35GZgvMmdgV12ShHrujZgauNfN0BedFZM3hepoTcO2GUyTm5IxDBXMTaYDttS5ImE8zmDdFLkMLHAsxLmv48RhjVW4q5hVgODeG75LkjimDHYPMNmmVwFSN1hPid/IDKVvPcLxQjboVqU2X+vn2vll+lj0XeUymceOqVLeu7A0ZZeDT129UtVxJJkwAhEEjtspr2hDa5DqVHr18R49f5W9u77+qIJs/fz7p6urStGnTqHnz5hQeHk7FixenxYuzzzrCBaNq1aqSnypVlNedQG5BuVLFqH/bhtS+YU1qVKMShScIJ2Lf1g3p4t2CdTcEmjStRjVqlqMvX75zUVaQKFm6GPUeKtwAtsXnnwV+pRrlqM9I4Qa9Lu1irAzAOhfYHruXXiiImeg/oTtnfKGjt9JHkKnIA0iGMISO14EFsSxAHhjmgwBYJEszvwV5jTjPDHNNObkuwvkMg+fo6iLnarn72hxfW89vMneysYjymRzMC5WCABZuvrucqcfozrzQ8xw/n1b5bpKa/cU8iMcWW2aRxKwf5tLAcOWFodL10WLTDwSNo1jE9jfp6UTXTxeMbBnXZBTcpgt1WdLot8uJhs9U4QUWFt1Y9IExA3Nm0NGGomxXcOGP3Kr/rywYLNV9p4XRxNoGPIsV67yaLh+9zscW9iuKMJiqhJ/0oUl2o6lKnUr5/jnBfnhrBnNgOJgfyImdVpmR63pLqlAta8ONnICCHEzUav/N/P8IZsfMZqkKwtxUVoCsDxl/mN0CMwUbfFjSp0fS0r08d4oFfr+JPXg2DHJpFGPmvZ0FlqtKGZYdwlZe3LCC/A/n9Ll9l3gGqkwlFFUvuHCDFBvXK7NIfXr+4J8GF7LL3Mb60Z2L97mI+fLxKzNoKISw7/pP6sXFISSFXYd3oKadG3Bws7g4A6u5dt4WtsCHgRDYM7CNQJzXesF9EfLNNRYZsrzQ5EBTB+8Be/8+47pnub2QPYdthX0F+WFOwHdEZh2gZjBA6vlDZtXMl/D2hqlUl7QsNlmxa8UBlqeXrlCSNMwySk/lBYoxMTvWfbhy2LGEJft5f9dvWZPa9Mi7AYm021wcKzR0fOcCH5cRG8kNTCMECgOK/fUnjezWkjYePE97z96gDo1rcqMZePPhMxMsNSvmvej/TxZkX79+pZMnT9LAgQMlf4duBv7/8OHsO9Hv37+nOnXqUK1atWjkyJF04ULOc0Nfvnyht2/fZvgB0FHO7ad9wxp0+d5Tyf/D8r5JrUr8uFGNCjSsczMeHpTmtZT1gxMVGl6xbLEgPwt+VDWEXIx9O87Rm5fvc/zcinxfcVD0/i2n6fG950r5bq17NaHmXRpyBxWzZIp4TVxXp6exW1sik1leI8/rAIZBguUwDDcgsZPm2BE/nuKmQRVrluduNKySpXnPyS7qPCeBwinUJCbH51asWYFMIvT5c8bP3UDnDlzK9rmYx0BeEOZKINda5bNRKftTmh8smvBZYI0PLLJdTvP1wunrl69S7t//scQTxgJYeGFQ36CDNR3fcfqnfSDLD8xSgg56kL7/ZF5AYjFv2NmOQmcvZglWQW0vGBtA+oQZmRV3FlDoES+a7DyWiwzg+unbtNpvMxuUjCo/nV36FtnH0ZGEk/Tmxdt8/7yKvg5l/kEzAWYRO5ekcFE6vYUZjaumR3Mmh3Ah8eLhK2acuw5rT6bhuhSHbXbUmybYjGT2pCD24ZfPX9m0Y2oTE2bNsagZZazKrFjPMZ3l2mYw2pjV0YbnKiHBdYg35bxCHC/Zvd7BTcfIop8rX1/AFAYe8GDmKP1zNoXtIN+poTxeAHt5q1gjfk1Yxlv2c2UWqmKN8hRy2JubI2BQECNx//ojmjs5mA5vPsHnOM+Nnb1LRUsWZSYf5y3mvjqqtqPGHf9hI5BfdmJnKs/MwNYe7pbYPjAQadGjCd1IvcUMF8xWYFDirjGf2bVe6l1prLkanUg8w/NdAMwzYOuP74Hw6uXuAltkHDaDGbj0x1DgzAiWXNZMY86y2l6n95yntfMFJ2CoHlBk5rRPkC+Iz4rjbLjxQKn35f4Nx+h08nnebnq+mnIdYx/ff6IlbkJjbpzFMCpWqmiej9t3r96zmQcwyUqYc1b0uYFm5aZoQa44duYAhV4/cnqty6l36ealRzyPPWBEO4V/L1l+7tx5RpcvP+Ljvm/fpgX6Wf7O9NOybhWqXqE0zVu3lzYfvkAmYRvpwPlbFLX9CM+O5fb7isb/RAVt/SclHj58SDVq1KBDhw5Rt27/aMetra1p7969dPTozwwLCrVr165R69at6c2bN+Tv70/79u3joqxmzZpZvo+rqyu5ubn99PeXL1+mMmVyH0J1jd9HqLE/fPlGD1++I6dxvahJjQpUmPD0yTsyM1vLN5GQ0HFUrlz+SlrSA4efy4w4unfzOU007E2D1H/unuHAx/7D9lekpMBPfzldPHaLBmt1pQkWKqQMXDx8nXynLub5Ad8kCypfVfZB5qy2me/kSLp48Dp1H92e9OfnnBeTExbbrKaUlUeoVrNq5J5gnm0GUVb74EzyBZo3JZIXNC6bzahBu6wzgNLj5pk75Do8gER/i8gkWoc6quYskYkwWUYH1hyjynUrkleSLS9ossO++CMUZb5CmMPYaEoNO8hnqawoJEbvpeUu6/i7tujZmIwjdahEWenPtad3nlPwjGi6c+E+n6vDjFWov043Kl+hfJ7Og1eP39AKtw10NG2+oFT5EjTWaij10+zO266wACHmFw5cpfP7rtCF/Vf4c2dGlboVqV6b2lS3dS2q16oWH8eQeCkDir4O4fWe3XlBdy89pNtn79Et/KTepfevfza+qdOyJrXs3YRa9W5CjTrVzzLTqiCQuvsiLXfZQI/TMhfrt6tDU701qF7rWnJvk60LdtE63wTB9KJhFZodPYNqNKqa4z5IWryXljkL51rrfs3IKGI6F3LpkbAwmeI9BAnfIJ0+pOk2hl8D0sE5GiF8vlWuU5Hs185mAyOcc3i/eZPD6d7lR/Tq0Wu+1lWpV4ke33hKRbi4Ivr07gu1G9SSxlqpccMJr7fUcQ09vf2cHlx9zNd+yBWBclXL8HGM1wDDdvXYTSpTuTTZrJxFoTNj6eG1x3w8O6wzoddP3pCLmj99eP2Remp0Jr1ALf5M7168J4dBPvx5eo7rTPqBGZmtA2uPUcTsZSzLd9lqQfVa/+xoB1bIfqAPvXjwivpO6kY6fsLcbna4ePAazRknzKrZrTakas0rSXUegBmyG+BDz++/opEmKjTWQmhUyYpNwUm0PiCRKtYoR3OSrRSS4bU5bDetD9pF1RtWJs8tyMFU/LVvz9qTtNRrG1WoWobmbhbyM/PjWhTtk0QHdlyk7ipNSddevnk9RWHtmlO0YUMqtW1bk6yslbPOyiuevf1IF+4+oyNX7tOLd59oROfG1KJ2JSpfMvvoB2z/pk2b8p8YjVIE/tMFWWZ8+/aNmjVrRhMnTiQPD49sGTL8iAGGDOzaixcvqGzZ3OlLUJ3JZ65TkT9/pzb1q1PNisICHJv58v1ndOnuUxrTo+BpWxOT5XTxwgPSn9mP1NUFc42CQkL8UQrz2Ew161Wk8C2mP9HrvHB59owqVaqk0Ivm8eQL5DolnG/cS0+455i7Ii+w322G+dH5Q1dJTacfGfprKuR1r566RSa9XHlbLTjszpI9eYA5tBmtrbgTPCtQm0bMzPqCmd0+8NVewAxbvZa1KPSYdwZb6OwQbb+SWQ9I1CLO+FLpCqVylMnMbGfFVtCqOv3JLI01y25bz9UKppRVh7iDHXbSRyn7VBZAaoQ5OHTEYWUNR8WsbKezA2R64RZLKSFC6OQ26dKAHOPN5TJGyIyTu85SuPlSSUg4jqGZ86dQ276FJyMm/b6FycHFQ1fo/EHhB1lMWQFzPAjKhtMjjFKwqK/RsBpVrl1BquMzO8h7HQKD8ujmU/68YL8eXHvMEjbkgX3JYo4XizbIglv2bMIsSovuTXI8RwoCcDyEcyKMKMTbfLrXRBo4uZfc12hIBf2mLqATO4XXRE6f8YIZGQqrzPuAMxXNl9CWMMGUAtcI41CdDPsZx84S51W0cs4G/v8JtqNpqsd4vnbCeMNaxZ2e3H7G1wzfZGfJuYWZFzBbcHBdO38L/x0aQjiX0biALBQKARxj2Le4nq28H8G/5zrKl46lGYpAIYHPUKFGeXrx4CWbeUCyizkxfDe8J2TZuFaAnQs+4s0zaTD3gXsjTJT8drvwjBu+v9sYf3ahBfuFudP02wffB4w6Ci5t9/GcBZkV/HXCmXGtVr8yLTzp81Pxmrmomtnehr8j5j6NQqZJfR7AyGOZxzo2J4k668vySlnx6skbmt7Kire7TawB9dXIu0EFLO6ntbFhh0XrKF3qq96FFA3sK/3envTw1jPSdR2jUHfFnK5F799+oin9fOjL52/kt1yPmkvRKFUWcNxPmRxBjx+/IXuH4RJTuYLGszcf6NDF25R06hr7PLSsW5Wa1a5MlctK38x7/fo1VahQQaEFmfx3p3xGxYoVebbkyZOMc1j4f8yGSYM///yT2rVrR9evX8/2OUWKFOGfzMBBL82Nplyp4pxDJgZcWv5I6zr/+FtEcbtPU5/W9alSGeV0caWFysCWXJAlJV6gceMK1oFnwMj2FDNvB92/9ZzOHb9Nbbs2+Ok5uHlKuw+kRacBLahWo6p079pjSoo/KpExKhqT7UeRzTBfSly2nyZYqFGlGj8PV8sTQN17bBfat+4oRTutJu/N1nK9DqyHp7ppUIhxDC1zW0v9x3fPdvGX1T6ARfLxnal06/w92hC8ncZbjcz1PbVdNXhBgUIgyGAROa8xz1bjjrkqOClaDXCjHdG7qf2A1tRvguCSmRVMw/VY5oeFUpBBFNmvMClQ/Xy34R15jgXhsigoTLo78rwHgnilQdHiRcl0oR4XSchmu3L0Bhl2tCWLaAN2Z8wLOg1qS+1Ot6StEUm01HUNL/5sVDypq1p7DpaF42VhAgos/AzRGSCZk0Gm0dWTN+jqiZuci4T9jgUcfiCTSg8cBygcILWtVKMCzwnBXAE/pcqXYgdWSNCwOMXCG4t6sAxgjcGMQEr29PEz+vLyOxcCmHfDz+f3X7hx8Pr5W3r7/B0XFq8ev2Zr9Kf3XvDiODuA6arTohY1aFOHmnRswBbrKCYVwQAoa54Nx8q2KBjDiHjbjJ49hLScxubJPOT07nM88wRJJraJUYgOqU7vn+W5K74OfXr3ibwmBnJRiL/TmaNJ46xGZPgdyPdCjaL5GBfPZU20Gy0Jqoa1PWY0UVQhrD19owPvsdp3k6QYw+cSBzlDUo1jDccTXBBhToPFPY4RuB6iGENhjeMEi1JIClGEo9BSndaflrmvkcgcT+xIlcyIoWGDYxOh1zgf8fou6634OgDApAPXTkjRkDeWfpvDAAjNHxxvzbs3oYm2o7O8Xx7ddpqLMbw/8uBy22+rfDZxMYY5M925mvya0tyP0URb7S9sO925k6h4DmxDToibu4m3O2ae+43rppDrOeT+2F81GlahPmO7KIUdO5p4jouxkmWKkapmD4W/R3b7YG/CWS7G6jaqQi3a1y3Q+9/58/e5GCtW7C/q0aNJgQZTp4dH3C6OqurevC5dvveEzux+SN9+/E19WtWnKQM78Jo9t+2mjO/yrynI/vrrL+rQoQMlJyfTqFGjJF0C/L+RkWB9nRtwcT537hwNHSrkMCkLi7YfpRoVy7C5R5G0Th0KM2hSOzetRbvPXKfxfdpSQaJvv2YUFraLbt58SjduPKEG+WTFmhWKlyjCRdnWlUf4J6uCTBnACQXXo2CrONoYtZuGT++jMElBerTp1ZQdF88duEKrA7aRob+WQl53mrsGHdp8gkOejyeepU6Dcpb/ZYehOv1pa8QuLqpiXdfQ7JDpMhlZ6PlqcaYUCjpYUVerl3OyPTq9tkuNyLibIx3YcIztz+HAlh3grIZOL5zlAvTCqVGH+lSzUdbZICXKlCDb5bN5HiQl/iA169yIxpgqZgBcXjRsW4872S6j/ejy0Wtkp+pJswKnsTGKtDfLvuN7UIN29chN3Y/unL9PLqN8aZi+CunP05ar6ywGio5RRkOo/8SebFMOB8YjCad40aYypTdpu46T2HYXNqBxgFwr/IiBAgnsDRazKICZlbrxmP8EUwHjCfyggMtPQEYJ2R2YOvxZu0kNnutDIVBYMnlyAsLFEca8dn4CM34A7NdnzNXM9lyUBigiwF6BMUfhgpkvFDj1WuXc1QcThHMA+xosFM75nqO7/PTacCcEY47zDGzb8LQcLoQloxjDsYD5Ld9dLj+59iUt20tRtkK+IYp3SAgBzIjh+EEhj9+r20KQZxYrVYwc1LzpzO7zkkKezRza1OF8MXyGEYaqtMJTmIUCgwVlwhKXVfz/YPYad2hAO2P30I6YPTxzA5dHsTsi5khjnYQMLqPQGXxdSQ8weTBBwrHmEGeSpfycTX1mRvJj5FHCNCQnoIiEMRFgGDSNC0ppZ2cWmC3h748Mwj5yslr3rj6ibdGCA6XunAkKKS4gCV4XIjCqWrYjlXb+rQ8XYnWGTO5JxXNgIBVu5pFmkDakEJl59OrdhIoWEpn1+09fOIIq1PCf2IRvP37QgfO3aUnScapUtgSN6FowKpF/TUEGwPJeW1ubOnbsSJ07d6bAwED68OEDuy4CU6ZMYVnjnDlz+P/d3d2pa9eu1LBhQ6YX/fz82PZ+xowZSv2cp64/oLCth2h4l+bs3KLaoQkHzwGoymGNX9AFWenSxahbt0a0b99l2rnzHM2aVXAFGaA2oQsXY4eTL9LzJ2+oYpW8z1pJgwHqnWmZ7xZ6+uAV7d14ggZoKF66ILbUtTngRzuW7KPx5mpUsbrsrmOZUb1+Fc4mWxe0nSJt46h9/xbZzoDlBPzOrABtslLxpITIZFKd2pe7kdICWTRYvKSmXKQA/Qjy2emY640Akqyp7uNYvhhmGsuZSekDTTNjsosGnd1/kR3OvCcGUNAhL3ZGywpYZOj7T6Ew0xiKsFpKzbo1pmZdGlFBonzVcjRvjyvN1w2n5BX72UntZuptMgzRkZoNqdGwKrlsMadtIXu4a4+uP8xY7FaY/LQ4k6e4mR2qQ6ONh1CMUzztX3eUIwr2xB9iB0SYRpSrkr+OU/IA7Bb2deb9zTMXz9+xK96z+y+ZvYJNOf4OP2C2MIAvMF7Cnz++/80MB2aZ8PPbH7/Tb7//j4tYNG7wXliE409IY5FDJWbcylYqw9I0/qlZIUdJWGEGJLNbwpMoznsDM5LiggSOfK17501+hPBl2LZfPXFDkqMFA4r0boFZ4eLBqxSqH8PFDFgbGOCgkEmPT+8/sVU+DDWwr2yWGnNTA0DwOmIpsN9hkjE30YmNQGBnD0Yd0sUDG46S//Qwfj6iKMTSTNjCw/ETkkXntZaSYgwOjCgQYUICcyEUidhetZpW52IMwHkEx0Qwi3A+bNe/JTeOxA6SYH4vHrnKzD4wxXU8te0njDfAARKB6lhwYzvh99Pj0ObjtCFYsJWHoqBy7axdNkONF7MxBzITIdvMCZBeztePYNav67AO1HO09KMNaOogjB73FuNg6e3xMwNB0jj3uqq1o9ZpQdh5xYaFifT+zUeq3bQ69R6jnHGNK2fu0PmjN7gwHzG9L+UXzp+4TXeuP6Eixf6kAUrKVJMWX79+p5Q9lzLELRUGPHn9nt0TH798R1XLC2qgP3//nfq1acA2+F4rk0mtczP6vSDYPNG/DCEhIaLatWuL/vrrL1Hnzp1FR44ckfxbnz59RNra2pL/NzU1lTy3SpUqoqFDh4pOnTol0/u9efMGM3aiV69eSf07Ry7dEekHrRVdvvdUpDN/tUgvcI1o4ZZDol2nrorMwjeJDpy/JSoMOHToqqh/P2/RmDGBou/ffxT0xxFZaoaLVJvaipaHJmX4+x8/fogePXrEfyoD8UE7RKpVZ4lm9vMU/f3330p5D7yuheoc0eDS00QLrJYr7HXfvnwvGlt9pmhQUS3RtsV78vRa3pNDRCp/ThQZdnX46XjIbR/cu/pQNLS4pmjgb+NECZG7pHo/vIdpb2f+HcuB7rnu32f3n4tGV5gqGvg/ddEiuxW5bm/PCfP5uZMbGIo+vP0oKgzA54r32ShS+U2DP9vs7vai5w9fSvW76ffBicQzonHVdfk1VP8aL4rzXi/6/u27wj7nxSNXReb9XHnf4GdYycm8zd+8eCf6/wplX4cKE758+iLaFLZDNLGOgeQYmNrURJSy+lCevz/Ogc1hO0TDSmjy8Tu6vLZo37ojUv3ehpBtokF/jOPfm9XJhq8JmfHyyWv+NzwH73Fs+z/3/MvHrolGldPmfzPoYCV68/yt6PaFuyL1ytP57xyHz+FzC+cU/t9W1VPy2LyvM/+Jn3WBWzN8rsCZEfz3aiU0ReNr6PHjSXX0RYP/FH43QD9CNL6GruQ9ntx9Jjl/Xcb48jZ9eu+5SKOqDv+d08i5Gbbzx/efRD5TQ/h7ffn8NcP3xfVjbKVp/HsLzWKy3X7JK/bzfhz05wTRhUNXct3eawO28vNHlNEWPbnzTOrz4PPHL6LJjWfzvSTSJufrdE64cPgq39dUi08W3b54X6So++XomrP4Prxvw3GRsuCtH81rCj+jWKW8fnb7wNssjtdQgU7rRAWNlD0XeX05YXxooVhfpkdEwmHRMKdoUeS2I6LbT/65/244eE40O2yjSBqgJkBtgBpBUSj8WolMgDwRLBeMN2Dk0aXLP4xGSkoKxcbGSv4/ICBA8tzHjx9TQkICz5ApGx0a1aR3H79QvSrlaJGZBo3q3pI+fPlKMYnH2WKzTX35JR6KRKdO9als2eL0+tVHOnE8fyU8WWHoBGFfbl9znDvT+fa+U3pRsRJF6Palh3Ryj3LCu9Eh1LQZIckle/ZAMQHGyL6ZlPa6S93X0WcpMsGyg76vFlsfXz15k7YtEuQW0qJmuo5rhNUy7ujmBkhFEBiNbjJkPgkROWfCVaxRgcwiZ/JjWNuDHcppeyMMVTx4L872KWjgc423HkleCXZsa3/x8FW29754+IpMr9NBpQ1FpvpT95GduIO92CGOzHo78VyMIgCGyT/ZmebucGBTgc8fv1C8zyaaXN+IFtnF/Qpt/o8CErP1wdtoSqPZFGK0mJ7de8FGE+aR+rTo3Dzqo9EtT7MTmNeyG+LFoec4piBHDj/jT73G5KxMQCBz0MxIWjB7MTMmMPyYv9eNrwnpAdMU0x4OzLqB9fXZ5UydVIV7/vkDl1imCNkeAtwhN0QeIuZTMfeHqIVRxkOZKcM5BWbs8rFr/BhB1Gf3CtebqR4TaIyJmuRzgfUWz6gVK1mEXjx8ydvs7csPfB/rpdGNbpy5xfNxmCWzWDyLZ99gS4//B6MFOa3LKB+efYS5ju2yjK5/YA2tY4zIH+Ye6Rh1ML/+0xcIbF+bOjR9TtamUbDzDzJcxI+1HMfw98+NvVzsIORTQpIui2wZc2OPbwnxAXgveQAmEKoPQGVyL6rTTLrMs9ywISxRkjvWQ0kM0qM7z+nAllP8WFlz6Vnh5bN3dDBNIjhsYsH6AgA7E89JsscKmyxbb2hXMh3diy7ffUqWkVtopGsMWUVtpV2nr9HwrgVnPFK4ttJ/BBgIrFC6OCWeEoKXh3RqSiajetFym0lkqd6XShaTf+ZDkfjjj9+pf3/h4EtMzDgAXxDoMagllS5Xgl48eUtHUy7n2/uWKluch26BtWHKC4pu26cZtezemL59+U7x/kL+iyIwTH8gVa1biech1gXvkPt1MEMxzV3IOFvsuIoH+GUBFinNujbiGZPAmVFSBSJDhjdj7iR+jJwbBH/mBCzcBk/tx68NudPrZ9l/RhQ8tsuMeRYDQ+yJS4Q8mMIALBJDjs7hBRkWZgiA3rRgh9Qh0uL5Pdf1VmQVY8iFNMxM4Ei5MWS7QjJSUDxiPiv4kCe5b7TmBR/2LWZKtOobcUisNIX3LxR+wJQk3mcjTW5gRAvNlnDxAKmlYfA0ir0SREN0+sslhxYDxzWkunqtLehkYiobZBgETOUg9dwcQ1HE4fxIiNrFx+QEx5FkFWtIRTLdR1E8wZkQodRV61WmoIOeklBoyAztVL34+EUROHenI18vLfu7cRGEY1vPfwp5TQxgAwm4IOJa9P7VB3ZFvZF6m18Hoe2aDmMlM+keGvNox+LdnA+JAhBRDVXqVuKiEdLXVr2bkejH33T52HVunrlusKJom+XsFop5LNcN1ixnxWwsDGogd4UEMzt32GKZjDFgOgJZJran3XKTLOXPuBb4TF0gmH10a5yt82L658+bEc5FIsyHhupmlEfmBDS/4n2FIO+ZflpyS3X3rj1Kl45epyLF/yJtZ3VSBCBxRRC0eIRAWQYTGyKSWZbaoW8zatBSvvgHebBzLRrZf7OrYoNm0jv5KgMvX36g48eEJv+gQiRXFKW7vw5o14g8p6lSwMwRZDm2DxMpc6YNoYHtCm684VdBpiSYjOpJnZv841ImdlrEiVqYoDJIcHs7ePAqvX8vP7uiCPz11x80aEwHfrx99bF8fW+Ye0DvnXrwKl1LvauU98BiQttRGCTduWw/Pb6jmMUsbsLiQmrN/ASeh5AXMIpo2LYuL9BQlMkCdMEsFs1kBzDMXGD+SBrA3AIzZOiYIwD3W1pmT3aYFTSNLeQxCwFntpyKj5Y9m5GWswY/xjD7lbR5lcIAsIrBh72pt0Y37sKDxfPRDuFZJlmOqUHafSnq7DxqN6AVMxwLTBaTeR9nundFsLPPK/Ae3YZ3oIUn5nJhhqIbi7WNoTtoSsPZNHdKKF07VfAM+y/IjhePXlGU7QqaVGcWz3OiOAGrbLpQl2KvBtEoQ1VmsPMCFFRwGUWwMtgpOEkuPOXHDZzcFsWYyZrVwZrNcNBg8dhsQ2qzBv40k3Ro03GexxIzXSjGxPESmAdzHDaHry8dB7chz6129Pj2My7y0AyBq+XM+drkrj6PF+0wDXr15DUbh2BbIDhdfG3Udvtn7irCYqngeFjkD6rZpAbPjIFJwndCsQdbep5HW3+U59gcV1tQ8vL9EsMOzH7iGrAhaBvtjjsgmUvLaZY2w7bZe4FiHAUWyzBoumSeLTPgfovZWziH2iwxzLWwxhzx+QOX+flmkXoyzX+FWy2nb1++Ubv+LajX2C5yzy2K7z0IgcacoCKwYWESF6X1Wtak7sOVw469fv6OElce5sfqhvmXuQUmdlvamgnz+AWN3ckXeK3brFl1qlW78OTwLt55nOL2nKbbT17S12/fqdhff1L18mWoV6v6pNalGW52MjVFFY1fBZmS0KB6xSwzDXAhLkxo1KgK1alTkb59+0F79+YfK5UdhowThmxPHrhGj+4pRtYnDSrVKEe9RwjF4PoI2eR6sqBVjybUrm9zXoDHpXUSFYHeYzuzEQfMCJa4r5P7dVBUGQZN5cdwP7x8PPuIiKwAW/KJ9kLRudAsVmIEkBOwgLFZYsTuYNdO3qRY55wLQXSPnddY8EIR3WFxvlB2QFcbQ+lYKLiN8StUcjt8F8d4MzYhwYIMCzaT7g4yF1MY4kfXf3ZabtOFg1dIv60Vxc/doDD5L/YTCrOgAx7kk+hIbfo259cG8zGrkx1ZDnDjhXF+yo1/QXZgwQHzCJhEaNUzZIdDMEeQccEGPfZKIOdN5dV6H+8De/wZLc0kVu1T3SdwsVS7aY1cfxfGNcgIQ5EFBivshA91GtLup+etC9jKMkM0IyAznJfixiY6AJhxj3HzucmDAsFtow09uPZIKN6evqEGbeuy+6LnuAAuxpp0asDnIbLiYMiCoGgYvkBGCEZPXJxsDtspMdGoUqcy3bv8gBmv3//8nVkiFGaQFSfGpvDvQIL46MZjWu4hOCzODtOlLkPb0+EtJyjCcgn/Ha5R0uZJ4hrmPSmIF70DJ/emIZlMPtK7JKLQBmb6T6HqDXKOCEKTa5HtCn48zXOC1MUhcHjrSTbywPabNV9bbiOPjQsS6cnd52x8pW46VOHsmKbNSKWxY1tj97HlfKM2talNj5xloYrEsb1X6PnjN6ww6qkqXaRKfrgrDkpr+BcGfP/xNy3YcpC2HLlIxgs2klnEFlqx+xRde/CM/90iYgslHL1UoM6Uvwqy/+fAwSd2wNmVdhIVJKrXrkDtezQSbuZp9q35hTEzhZvavs2n6Mm9F0p7n8kOQmzDrpWHWF6jCOAGo+8jSP92xKTQzXPys3wtujUmlcm9+XGw0WKZF9gTbEbx4g4LqdDZMVL9DuYNLKKE+TAsEE/vzvlYhC02FlLAUpdV3C3OadtAughLbXTrPcbPL1RFA85BdfPhnIME2Sgs2zFXtjtuv0yvg+853GAwLTo/nxemKECj7eNoVicbuiJjYZ3b54WUyT/ZhUKPelP/iT14EQaXTZcx/jx/BPkb5nN+ofAAzMOu5ftpdndHMunhRLtXHuTGEMKnPTZZU8QZPz7v8xKeLQYaCpb9Xdl1FaxE0y6NaOEpX26O5Pb6YNHc1P0pwnKpMC+m1YuCDnox25QeOIdDjKIp3GKJ4D6op0Iem20lsr71QQnkOzWUXwMyZ4eVZnTr7B2eGUOjCEwdJJPRdnFp/1+fSlcsRVeOXefiqsuQ9swsQQ6IzC/EdQCQBItnUnHdwtwmng+XzUc3nrDUE46Jm0K383M43LpUMQqeJbgnajmp82e9efYO54ahqAITh+sTchdzAxQBYBvFM2go7rJaRGJ/e00K4utAp8Ftc5UeYhsGzVrExTnmRkcaqpK0QCNwgalQWKqbqvHnkge4Z6xMa1TChTcvkR7psTZk5z/s2DDl+AiAgd2yeC8/1jBUydeF/bZVwlpp0OgOrDQqSNy48ZSuX39Cf/zxG8crFRacv/2IBrRtRCvtNCnKVIO6NatDe1JvkEXkFtIPWktXHzwj1Y5NCvQz/irIfoEGqrRgDfzZs/fo8eOCZw+GTxIGUneuO87dpvxCw1a1qG2vJnwD3xApZJ8oA807N6ROKq34feIUOEvWskcT6jWmM9/gI2zi8kS9z/CeyIuM66dv0+aFiTL9LjrhFosMeJG+J/4gW6hLA+QaoTMP+E0P44VZTsAia9DUvvx9fSaHSKRF2eWTiec1sMhCMGthA2zEw0768owL5ljAYGC25MsnIYxWFrbMK8GezQIwtwLbbeOu9ixlFGdIKQoIM7ZbPpuW3QjhUF7M0SAQFl35ibVnkY92KMvOFDHT9gvy4e7lBxRusZQm1DLg/YF5JoQQI64i7PgcCtznzuyMIlgDmFzg3NJvY8kmGFhQz5ynTYEHPKhO89znaSB9RTPi4IZjfB0xDJ5ONkuMf1qYQ1LtNGIubVm4kxe+en5TyGShLsvxcN2LtlvBDD0w1lSNzBfNpAuHrrCpB9gSyG59Ep2odPlSbHXfdXgHKlu5DB3ffoa3Ta+xXWlrpGDUYRisw58dr4vMMJxHeAwWDIxS6QolOdwbWXcIcp7kOFZyfZniOo5a927Gs2m4TuGahb9DAegy2pfPc8ycoRicMVfIpwR7CebszJ6sm1KYGzu16xxvE2S2ZRcTEGUbx80dhFZbLjbItUBAzIWYyTSP0pfJiGG553o+76vUqUiaabJ8ebDCeyMXTg3b1qEBE7uTIoBZ6I3hwr6c4pB1WLYikBh/mN6++kDV6lak7kPzL9YISiIoisTZYwUNcWO/a9eGHK9UWNCiblUyGtGD3n38zHb3WgPas+neZrfpVLVcKapduRyVlTO8XFH4VZD9AlWqVJrathWCOHclZc805Bc69WlKlauXpXdvPtG+7Wfz9b3VDYSCYMeKg/T25XulvY+W3Uj+c3f8IXqgIJYMmOE1nm+oZ/ZcoGM7UuV+HSwsdLwn8uNYlzXstiYL0GGFmyAARzV0PqUB8o2qN6jC7wfTiNxgFDyd58nAfPlNXZDjwh8yKdMIfX4c57Wejm0/TYUNFaqVI58kJ+6iYwEFt0sUUwiylZn5ntKHoi8FsSMdFpDo7M9oYUb71x1RuE4exgy6czUp7k4YO2dijgedeTAylgPcaWoTUw72xqLtF5SPd6/ec8i3WR8X0mlhTusCE7gQgQxP220crbi9gKwWz+I8QEUBhTdMZZa6rmaJIOSFUefn01izYfT77znPLeF4RHEFuS4kfzApCjjgyaHlmQuJJ7eekWlPR55TRVHiss6Sc7zwPOSB+euEMUMLTPOcyOHpMBKxHyKYemBede5OJ55JE7NcuGYe23aai7EJtqPYqAPQ853MmV+4rsDhUSw5hNQRxzLYs7otarNJBz6LppM6haU5Qapo9+H5TjYTefuJWvVqRibhuvxvKNAwowY27dnd59RugBBuvsxtDYc/7155gNzV/fmc+WluLC0cGrO02RW5CHbH+Q5YLp71U+h1ZsCcJyytgJ3iqkH1WkonnQRunb9H64OF9zIMnJprjlx2eHD9MSUsEra77pyJCiucVgdsoy8fv1KTDvWo6xDlFErIbNsQIXz2MfoD8tVVEPP2OH869GzECqOCBLZDcvKFDP4EhQV//v471alSjkoVF45PNEh+/P03jxGVK1mMBrXPP4lpdvhVkCkZ2OlHLt3hsLlPX/KP7ZEVgwcLJ8/OnWcLdKgRwMVMPJi6JU4YkM0vtE9zRsI8wpYYQX6gDDTpUJ86D27Nx4ciZ8mg+R9lNJgfR9mt5AWKvBiq04+ad23EcpSFFktl/n0tp7E8E4FiDNIiaQAGC3MsuEhiMY/5pJyfX4ylSFhIobu70jvnebL+E3uyXAjHOBZFirKJVySweIV5AGbC0LVHl9uwky3P48h6bparXIbnV+bscGTJFwpXBObaDvZg5kTRwFzf4Kl9acGxOezOiMUs5uSwyI51Xk2a9QzJvJ8rbYtK5qLhFxQHsFOHNp8gj/EBNL66PgUaRLE5A86lbsM7kudmG1p2I5S0HMfycaEo4JjCuQSJImapcMxC4ue11U6qGSQw225j/bhxg0Ku24iOzBSDfc2M08nnyEXNn+5eesCF1Ly9btRjlMAKwAwHrBPmtvCdzaNmsqPgvrVH2FQE13QwgWCPxS6GcEqcp7uQWXwUZbMCp9LGtNmwUcZDSMNyhFDkTQ9jF1SgSaeGdOPMbVaVoDBD/AauP5i5WmS9nL9Dj9GduZiDxT+2T62mNbhwRJg9JJZihgvxF1axRrQxZBs5Dp9DJ3el8vMc4kzJId6Mrp++xZ8ReP7wJXlNCOCCrv+knqQ6vX+282X+Ogsl36HL0JwlerimzNcVpKVgDlHcSgv8bsjsGJaPdh/RkbqqyW+WgfsVXqfT4NbUtm8LUgQQL7M1WlC7aDuOUZqM8NC2M/T47gue4Ro4rmu+nvNwVwSGji94M4/Tp27TixfvqVTpotSly8/nb2HCb7/9TxL+PLpnKxrTo+DdIH8VZEoGzn/PuF20dv9ZOnDhFhVW9OzVhIoV+4sePnxN58/fL+iPQ4PGduQb5LXzD+jqufz7PLhgaxgJ7kibo/eyLlxZ0LIVGKQ9q4/QHQUujidaj2D7ZNg2b4+R3+odHUqTBTosPTy0+SSl7pYtow2mANYxs/j3sSg6sEE658wW3ZtwMQcEG0bTwxuPc3x+w3b1eI4CWOq6Klupjxiw8oatNRYgMAP49F6xMj5Fof3A1jzX02FQG15MBhlEUbButFzMbcdBbSjy7Dye4cHiEQtC/TYWFGWzXOEyRvF5hCwzswg9in8QzscBTEDw95CMBsyM5KLBaYQPm8cok43+LwOLfzC9vtPCaFw1PXIZ7cfnGv4ezRBdH01acTuM3DdaURe19grt3H/7+o1W+22i6c1MKGXVIV7gYIZx8aVA6ju+h1SLXxQzM9ta0cGNx9mJEOY2bhusf5qlYoY3dDvZD/WmD68/MgMfemwuNe7QQCJLs+rvyiwXWCuX9VZUv3UdirJdzkUMZuX6ju9OzmstJLNgKHT8pi2gpCV7+Rql6zeZlrispnevPnBhouc3mYtFvCfMQf732/+Y5cI8Jp7fpm9LjprA+WQarktx3uv5foH5Tfs4U25O1GhUlVkwNFcQU7E+MEHCXMG6H7OwA7V6c1MJMsaJtqOpROni/O8oOD++/cjMJgoVzJuJc8rA9Ge1fbm40otkwxK4R+qmRYrkhJ0xKXQy6SxvN7CmssQboGmGor9I8SI0a/4Ukhen91ygw1tPCfthTu6fWVqsmp/AMTOIm2nXTzn5UmxAkxaXoza1NxUtnjdXUlmwb/s5evv6I1WuVpa6FIJ5rcQ0uWK/fs3pzz/lj8nIb9SpXI6Kp10XChK/CjIlAxdNca4BQucKK1CM9enTlB/v3CkE+hUkypYvSb3T3IIS8tnco6daW6oKd61XH2hnmoWtMtC4fT3qPqw9s2RLPQWJjSKA2S/NNKfDZZ7r6cNb+RfcuPmPmT1EeC3nDVwYyAIUS+mli9IuvNHZbtWrKRcL3prBuVrhq07rx/MZ2JZYuLx8/Crb56JLjdmL8tXKsQvZvBkLFcYKXz15g7wmBbIDmyIKPbjFeW+z5xkZLFhPbEslg3ZWdDJJdjkqOvJwuYu+EMAzM1ikYkGt3ciYg22VZXQCCRPkkzABgVQORQKOK+zTIwmnuJuvUVWXbAZ5cigxQml/IXuAcca8j/u4+aReeQY5DJvLBQPmqrCIx8zUwpM+FHnGj8ZZjuC/UyRwrkD2CvkrCnrMQTXv3oQWnPBhl09pjCnAOmEeC0UUGKQaaREQMLfJXGhghhIyRHEodA/1TuS324XlvQCY3tnd7OnKcSEMGuY4ONaNutrxvBU+LyzrbZfP5nMfwLGOyAy4mqIIQCEYP2eDxHkR1viPbj5luTBYOeRhtevfks7tv8TP7zmmCzd+UIRidi3OewOHM8MyH9cWNKPA3iMnMPCgJ0t6sc3AjgG6PlrUW72b5DuieAzY70Gdh/7DMC11W0OtejWnspXKsPMsGhlih9nsZIGY9xXPgdkvny0pPrMDWLdwS0H9ACkrjEWkBQrFKDshvFnLYbRM4dHpgX0Bu3xguN4AhYVAP733gnYs2SeZHVMWO3bmwBW6euYOF7Qjpveh/ETCyiP8p9rELgUevozYpAP7r2RQXP2CbPhVkOUDBqZpU/efv0Wfc1lYFiTEJ9HelEv0ScaFtzKglpY2jzmy92/yj8VAh1A8S7Y+PJkXrsrCFEfhRnFwy0m6clJxDKrajH5Us1FVevPsHcXNyVuxp+U4hrNgnt59Qav9tsj1+7Wb1eDubojhIqn3ge1SYzalwEILnevcYBSqw11hvA9Co8VSn+wKHSxs8D57Vx9m6+y8AgsuLOBS4g9SmGkMTaptQCFGizj7LC8FH5hKyIiwsKtavzIbCdgO9uTXlofBhXTRY5MtO9JhIYxFaJBBJOm3tVT6XB3YAhQJKBYiU/14XqV+69q80D6VfI5Diac1NSXtJia00HwJz8JAMvv/GViwwpAi1mU1GXdzYCYMpjeQ2aFhgfkgZPnNT3HlgnfmvCmcJagMgB0y6+3Eslc4xGLWFDNKAfvcqWHbelK9BhdQ3R14HgsNFBjzoIDMap7t6d1nZNbbWSJDnOGjRfpBkyWFBmaqTHs48DwWZk9hqd+wfT023qC0U65c1bJsCy+eY8N1AXl/e1Ye5PMfweo4/5EdhsLIN8mZJZEo8mDUgUKji1oHZpXxGXqrd6V9a4RGnc5cLbbnh5U+nBI9t/zj8gjgPVGMXTx8hU16cB0AiwgpZGbgueKiIVA/gv/ELOnRbac4vgKADFOcr5YZ18/c5mw0YMbcSbla6OOzBOpHcSEPUxEU8rIApiEIwsZs7hgT+e3pty3eQ7cv3OfYE9wrFIX4eQl8727buxm17qk897xVwTv5z8GTulPZiqUov3Dz0mO6ev4BN+qgKCpo7E25TF++fOcYpSZNqhX0x8kWm49cYHfFL3kY51AWfhVk+YAWdaqwqwtmyA5fvE2FFS1b1aJq1crSx49fOSi6oNG0TS1OnP/65Tsd2HkpX9974LgufHF9ev8l7d9ySmnvU7dZDeo/Xig8l+Uy/yQLYC2t76vJjzcu2MkD0/ICXVl9P8EBbJXfZmaVZAEWT2LpYsrqwyxvkgZYCJlF6kus8GEakBPQFUeRhUBTmJoszaWIgzQSgbAAOv0oCPICSHdQWMDyGYUOXCKRV2TU2ZbzlPIaSo0Fq0eiNQ2fJcwI4rUN2lvR5WPyMe+Yp4FFPkJlwSxgvzqoeZPVQDe6dFT5bD6MAyY7qVPEaT+KvRJEer5a1LZ/S15gYCG8PghzNXNpdIXpZN7XhRmDU7vOKkViWZgA9ghOiGvmbWFJ59jKM8i0lzOt8FzHf4+FNFgcyE9Dj3jRynsLyThkOsvplOUgh1lLzwnzyaiLHWfc4Vyb7KxBS66FMDMtzfvic2MOC0HPyBtEswWzZlaLDTMUMRlCoTva8HNxfGIOUmzeASQt20u2gzwkEsOgQ17sWOg3LYwLKmBW4DTqo96NQ+Exj4XPsGB2DBdjOM5wvYA5B36q1qvMhjrI0rNRcedCpVm3xmyPjwIM79tbo7vk+gWWbEt4It0+f4/Z9rmJTpL8s8xFJaTRMLkBMw3JdE5sDaSgOA/gXorZSzB5AIruPuOydh7E/BxyyXgGb3hHGj079wJp+6LdXOxBcimrVPHS4euUuFRgn8zCZzAjJw+wjZd5rOfHkx3HUOnyubOr0uDRrae0c5kwf6xlLyg0lIErZ+5Q6oGr9Psfv9HYtNic/MLuTYLhWZ8hrVlRVNCA/wAwaHCrAs3yygkw8QjacIDMIzbTiasFP5qTGb8KsnwADk7kHwDJZwqvbBHdP3EmWVLi+UKx3cTmHns2n81X62zo/8Xyg7ULZTdTkAUcVPn7b3Ri13m6cERxx0dn1bY8II0uYaStIC2RF7DTbztACLQOMFgk877AIPykNBllsFE0vXj0Ssr37UKq0/vx9p87JTTXbKtaTWqQmdhJ0Xs97V+fs9wVWTvIOUIh5aExT+ZA5vSo11pwKoWpAeZosIDsN7GH4Hq5+zwXZljUPrguvySPpVjB0/m1wVrev/qInemirJfJbI8vLtwx+I+FNeRi4s8KdsB1jC/dviCbu6O8qNGwKi+2/ZKcaN2zaHJZa0FDdPqz2x4YonP7L9My97VkM9iLRpefRvrtrTkzCS6UV0/e5OH2fyNwHqFZgkU+pGMoPFGAggmLtF7Okk4sWlG89NHoxll9cLIMP+lDU93G8XmlrCIMgJxwvm446bQwYyaZJfiTe1PMlSC2b8+qkMoKT+48I1tVT87vguy5vUprnmnErFlW2wQuiWhiQAYI2TNCoTFTKf53OBH6aofy9ai3RjeWKaLx4zhsDjPUKC5Q0KAwgXU+DEICZ0Zw4wVujoDNUmMufMBwARPtxvC/Q8L89fM36ji4DZ9vB9Yf5dfrN7Enpaw6yM/FPB6aA49vPuFrN5wbazb6mRXADBqMPcDag32Dxb6YqUMjBXNxmQFZJbYLiiuYlIgDq+EWmR3CTGN5ZhjyVMvombkuiFHoLUyTT073mkB1W+QeSSAGzrUYO8HWf5jeAG5syQtkjmEfQyoJVYeisNR7I1832vdvQS27Kc89b02oEAnTd1RHqlIr/xwO37z6QEd3C03zYWkxQQWJ+/df0oULD3gNOXCgYgxZlIEzNx7Sy3cfqXTxItS5qfTHfH6hYBPk/h9hYLuGnAq+9+xN+vrtO/2lgPBNZUBlUEtauvQAnTp1m549e0eVKuUfBZ8V+g1rS4v8ttHTB2/o9KEb1Kl3/gX3DZvam1aFJNLN8/fp1N7L1KGvcoZmq9evTIO1etL2JftoiecG8t1qrbDX1vPRpFPJF+hIwmk6uescdRgon7YbN3htr7F05ehNunj4KiVEJdNwfcH8RFpoOoyhowmn6NqpWxSgF0kem62l6qTNCpjKXXksOHymLCDPrTY5LkL7T+rFMkcEw/pNDaXaTatnaw+N9zePnMmBrvheYGRCjnhzPpGsgNW7OEtJbKSBnyfezyjWOZ7nVbCoPbD+GGd2QY6U24xHdsDrRp2bz3M1cKJc7b+ZDm0+zvlvLXvKfpzCAhxzNCjOYL2dtDSFjRYObTpBfcZ3ZzYE0qT8ABjZnqM784948Xg6+TydSblAl45cpce3n3G2Gn7EwIIZslgsLOu0qEl1m9fi/wdjgnmeggaKCLAwKKDBNt06d4+DgcGuZCU7RQGG/di6TzNq1bMpNWxfP19nRDBbhJkqFLxgdsSMKmzkUVjI8r0TIpK40IH0FHM2yNsaaaSa5TmMAgaFFq4TAGYPMaNVpJiQQ/b5w2cK0VvMs5QA5lOne09iQwwHtTl09cQNZshd1lnxOYJGDs5xk4V6HAi9xl9wtEWhhqIHLCQKHgAzbQhbxqJygt0YOrHzjOT18Dm2pOUx4nw4vv00mwLh33R9J7OtPqSQ6e390SABwyl2hPTYbCMpYGEzD+dHFGo4PofqChL5DOYcMxaywypkofg+2R3He9ccph2L9/D3tFliyGxibtbkMIHB7F/rPs1llhuu8t1Mj28+o3JVy9B0zwkkLx7ffkobQ4XieIb3BIUEkgO3LtynlDVCI26aizopC/dvPKFDacehuqFs98K8YteGU9yMaNC8OjVpXfCFRWKa70DHjvWoYj7KNmWF2Mehb+sGbINf2FA4q4L/IFrXq06VypSgZ28+0NEr96hXS+n09vmN6tXLUatWtejcuXucJzFhQsF2X+BYNHBUe9q8/DBtjTucrwUZFkWqmj1o06I93AlTVkEGTLQaTrtWHqKz+y9T6r5L1Ka3Yt6rdpPqNGLmQNoQupMibFbQwqNeMklT0gOMzFT3cbTQfClFO8RTj5Gdcs23SQ/ccGFpP6ujLUtlkpbu43BaaazwHeNNmTU4vvMMrQtIyNWaGQ5pN8/eZuniycSzOYbSoihCaLRxFzuWys3VCiaPLba5ZidlBqSKKGwgVdy1DN+tL/89ZksQbgsGCoG1yE9aOWcDL6RMw/WoXX/5imSYJ8DSHjImzIBhsW/ex4VZv6meEyRubbIAnxUzQRqWw9lIAOwAGId9qw9Rv0k9SctJI0smQJnAzBt+huoOkBQLFw9dZQno9VO36NrpW7yoxuIVP+mBRSqytzBfBAlsherlqWKNclShWnkqW7k0z61gAYtzXZ7zAovbT+8+sWwOs3hvnr3luZoXj1/R0zuCFA6Lb8w4gXXJCihSMO8DtgtMDtiQWk2rK5X5yg4ww1nls4mleOJCDFJIFD0tewimT9Li0a0nzK6BcQXgbIqGQXYzUNifYKnBpmGbIJAZLKm4aSMuYmA5DyYXToM4x8A4I2MMM21wl4UhR9POgiIFvyuWSordVxF7ASkj8Cqd+Q+KMbDbMCZZOXeDRCqJHD+Y9ADjbUYyW4tiDJJHFEp1W9SkTaHb+doBhl5ciPpOXcD5ZLgmgNFGYDuAAtx1tK+ENes74WeWEO8H1hTHJGSVmL3Mehs/pcCZUfwY+Wlt++Vu3Y3Xhrwa11WrxQYyHWe4xojniA38J0uy3ORBpO1KZgHb9m1OXRSYD7bEcz3v816jOlKjtHxVZQDz5XifziotqW5T6c1Q8gocW9tWC47FUBAVtDwQc6BJae6KkCsWVvz9t4iS0wqyAWlGe4UN/xMVdOhUIcfbt2+pTJky9OrVKypbVvrFZ1aYu2o3rdqbSiO6Nie3KcIcSGFEQsIZmj9vOw9nRi+eUeAn/L2bT0lPLYA/R/ROS6pWS7GuYTnh2YNXNK2rM/34/jfN32JBzToqLkg1MxZYLqctUbupWecGND/RXmHbHYvFaS0tedE6O2Qaqc3IOr8m1w7/06dUoUJFMuvtyjKxvuO6kf1yY5lfC3KkaPuV7AYJYwcMvUsDOAFCpoZFStABd17A5gRYYV85dp07+9Lg+plbLP/Dwhkuj2AD5Ppudit4YbjoQgA7pGUGZJQLZkfTi4fCYhABsjP9tXPtbIv3QeXKlX9aRKEIjLBYQjti9kiKZ8yGYc4lL8cRtsly9zXMlgFgD3qO7UrjrEZmmRNVEMAtDIUPirHbF+8z6wSpJYwWwAJICxQBRUsUZUc9SJZxnEGOBrtzcejwl49fSPS3iI+RT+8+y2SqgiKiesOqXNDWbl6TGrSuQ/Xb1OG/K2iHNERLrJ23hXbG7pEUji16NKEpruPZXVCWYwjbCU2TZW6rWZ6IbakzRzNbVgzHNZ6/2H4Fd/1RPDutschgEgJTEwQlw3ijVIWS5Lbeih0IIftzGj6XnSchb/Xe7iApisTAOTFPJ4wfw0DEMnoWM2o3Uu+wk+vuuAN0YMNR+vz+M02wHc2GGmBhS1coyU6IuO4A6hbD6cWDl7Qn/iAfF46rzKn7yI70/et3ZueQH4YmCQoeSD3BxqFoQwh1m7RcLXxXr4mBPJOGa8SC4z7cBEkPGICgsQLJHQrH7ObBIB2E4cnVEzd5hg6mLrmxTFAZzGxvzfvYdKEuqellZOZyO89sh3jT6d0XqHXfpuSz3UHmppUYp3afJzs1H96OYUc8qV5LxbA8l0/cINMBXnydijjqSbUaK6d59PLpG5ra2Zkt9f02mlHLLjnfixSJE/uvkJNeLBUvWYSWpdhScTmDuBWF06fvkKVFHJUoUYTWrptNf8k5T5gfcsVp81ZRyaJ/UbKPfp5Vaq9fv6Zy5crRmzdvqHTp0gr5jIVzy/1HodK+MRdku1NvkEMhli3C/j4kOJHu3HlOV68+LnDHnBp1K1LLTnXo/PE7zJLp2sjmBpUXVKpRjgZodKHElYcpPngnuS01UNp7TbAcRonLD9ClYzfoyLYz1E0t50BPaYHu/2SH0RRmsYyWuK+lPupd5O5sYuFoEqZDxt0c2aADDFdHFWG2Q1qA3cICHwYF6CL7JjlJ1aXF4gFOZyhovCYF08KTc3NkgRB+K20xBmABaBY5k4foMX8GxgvyR1kw1kyNdsft5+IAckLkC2U1F9d+QEta7LCSZVDIQTqWcIpmzp/K3Xh5CijsT4voWdxtD54VxWwBnPDw/Y1CdH5a9MmyTVzXW7OdP6SMsNTGYhI/mJsZbzOaOqi0LtCmDd4b3w8/6fc3FpBgrbAt8INFMorgFw9f8p+YXUGTAsUsgEUqFyMvZP8MMEYA4waGpWyl0swcg5ET/6BYqFK3coEXXpkBeS2aCAfWHeEOMoDFPYLJMbMl636FGUyAXriEqUQhAmOeGg2rZds0gawYrDGABoJltAGVKPPP9QkFERoYKNbAJM6O1qHm7ZvS0YST5Dk+gItiyIXBjB3ZcpIZb/HxjpmvAF0hJHm08RAySGPGYLLx/P4LjpOAKQl+YGnvNGIuz+xVa1CFWvdqJinGtN3H050L9ySsleMqM+o5uosgifztNy7mUXzBeCVxaQqzZQCuJ+JiDIi2XcHnDp6LojPzeQn2F+ctirE+47qxhDg7RFgu42IMLK/jStNcizEUynOnhPAxjn0rZpylReLSvVyM4btOdpc/ZBmfY6HlPzb3iirGgCUegjFW/wndlVaMAZsXpXAx1qxjPWrROX8bU1ALAT1Vm1HRYgWfnZWYKMgV+/ZrVmiLsfRyxd6t6hfatXfh/FT/UbRtUJ0qlilBzyFbvHyXerVSHtuSF5QsWZR69mxMe/Zcop07zhZ4QQYMHN2GC7LE9Sdo8myVfL0QaRiqUFL8ETqWdJ5uXXpA9RSUk5IZFaqWpVEGKhxmGeuxnjqrtlHYAk5Ntz9tiUzmDulyrw00M801UR40alePRhoOpg0hOyjIMJoiT/mw/EVaYEFjs9SIDDrYUGrKRVoXmEAa5jlLEAEsAMwi9Zidw1wRpDr2K2bnqRh49+o9sx7lqpTlz4WQVkiiMOiPfCw4rzXvJr1MFgP5kPyx/f2qQ1zQwfUsM7DgNA6dQQO0evMCFqwOCsGdMbs55Dpzl19adFARAqBXem+gVb4buYBCjtIkh7Hc4Zd3ngoBvLDJv3XuDs+rwaUOclD8IGpgjIkaF5PyzsQpAzgusF/xk5PxAGSH71+9ZzYNi3v8iWMCRi8oUsCIgdn4/c/f6d37t1SpciVmfTDrVrx0MSpWqlihmFOTFph1QuGyLnArZ1uJ0XloO2Y+W/cWArxlAWa/Yp1W0dbwRC5SwPZiJhGzV9m9FlxNfSYHM+uFRb5BwDRuuoifD7fBBcaLKSFKCN2FeQds3999fMvGHGEmMbx/YL4BtgqZY2ikgGGDCcjhLSe52MNzhugM4GIMr/3181eW7AGXj16jXmO78mdxHjGXGb3m3RtT/VZ1JMUYWKqbqbf5fOZCarUFdR/Zif8Nr4e/+6vYX1xIPb//UlKMwQVTLFsGdizezecOgOZJmz4ZzQ/wudzG+nOzAM0g8yiDbLcdmlJwWQVslxpJlQG23GOdUMCVKyGV8Ud6YB9FpGWFTXYeS1Xqypc5BmyNTOa5OjCEk53GkqJw9sAVOp1ykfeHls3PsQKKwvs3H2lLrOAwqT5LJV+bUQ/vPKcT+wUzj34jZWuEKgOIR9q3VziXBg36JVfMKwpXu+4/jt9/+03itphUiEOiAVVV4WTfvfsifS0E2WmtutSlqrXK0/u3nyllq+yhuHlBzQZVqOcwQeO+JlS4SSsL6rNVqWSZ4nTn0gPas0YIfVQE0D018BeKsE0Lk1jalRdou2rwIuDJ7WecjyQrINsySHMNi3GIp1vnM87+ZAdIguyWGwsW+qsOcRCqvDi77yKNrTid8648xs2T/L2urxYvuDBD4zLaj2daZC1e1M2G8WPkrn14+zHb5zbv2pgXj9O9JvGiFAWOfhtLdpGTJ2MMgAHCVI8JFH7Gn1r1bsaLzBjHlaTX2oJn8PKCeq3q8Dzc0ushLKVCIY5iErNCmnUM+H1k3V4FDTQ9ylQszWxFnWY1WYqJogTzOO0HtGIGsNPgttS2bwtq0rkBNe3ckBq0qctzbfi9f0sxBpke8ramNTFhhgjFGJoQ/Sf1pIgz/uS11Z6LBFkWmCjuEiKT+DVRJKEYgwsjXEZRjGT1Wig8ws1j2VoeC30UH5DuIbxZ/PznD16QZX83Lsbwdzg/HOPNuPBZ7ryOQo0Xc6EFZst1gxVFWS3jYgwYNLUf7Vt3lM1B8ByExpuE60peG00D313O7BI5yXEsbYvaxTNoOE86DG5L1epVkRRjJmG6zA5D9ggZnP1KM0kxBuD7omCHZBaxETBsAdR0BzLLmP5agzlPAAY5aPykB14naFYUF4gomNw32XDBnxVwfs3XFXLKYA7UeUjuSgpIPjG3yt9poW62M2nZAXPD719/pIbt6tKY2dmzdtIcg8u8hM+h7aLO31URwPbD7BigOqU3M9LKwtbYffTx3Weq06Qadc3nmaktcUf4u3bs1Ziq1vw5YiG/gSDoz5+/UY0a5ahFi/wxfZIH524/osev3lHxIn9SjxbKyWhUBH4VZPmMQR2EggzBdN++Ky9wOK9o174uOyy+e/eZDh0q+OIRN0OxBf6WuMNKtaHPChqGg/jPvZtO0uO7z5X2PrhBaZgKN7xlXht56FlRgMNijxEdufO/0GJZnrYhFgumYTr8GE5ZFw7Lnls3ZEZ/6qrWnr8jLO2ltS4H26HrI2SshZsvkSszC/I1MGGTHMaQ0ypz+rPoX2zJDUA+abvMmOd7IHlzHD5H5uyrya7jeMGO94m2i8uVVZtoN5oWnQ+gTkPa8fZY4bWOdFuaMcMlL1BczNvjxvbfkNBhpgoLT1hp378mv/U+AIMCMAdxd8NZ9oXiHHM8WBRPrm9I9mrenOcE6dUvFBxwjp8/eJnmTg6mCTX1KdxiCbPLuM5MsBlFy24uILvlJjI5J6afdZrdzYEzvrDQBlPqv9uVC3YUqtnNJCJbDKy4uHAJPTY3g+06zDfAnsMQA3OmkCLi/IDDotsYP9q5KIWfhyLNeIEOzdUK4cIN9wi4KcK4JUA3XAhhnjmIzKJm/jTrBCMdnPtLnFdRgH4EH6e9xnbh14BrKRo+MA05vec8G/Tg/22WzWa5cXqgyENRC3YY8lfIKiG7NA77Z/Ya5iaYfxNb9Gs5/+z8tzFkuyT82iHejKo3qJqt3M9bK5hltk27NKRpHv8UfdkB1y4f7QVcnA7U6sXxCbIATOPetUd4G5iH68ltCgUs9VhP7199oHqtapHqtH/Yw7zi2M5UunD4Gje1IP1XFtAk2xApzOmOnz04X413Pn34wgohYLhmwVvdAzt2nJO4cxe010BOSDp1VeKuWKSQyhWBXwVZPqNt/RosW3z/6QsdufSPbXNh7BqLKegdO4TAv4KGyqj2VKTon3Tz8iO6eDp/t12jNrWpXe+mXMzAXUmZGDlzIFsdP7n7nHYsEaQRioKebxoTk3KRDm4SLu7yArbSKpN788IH8kFZi0dBgqjP8iZYmCNnSlqMNVXjxREWOV4Tg1h6KA1ePXlNXpMCaY5WEKWsPsRD+gAKMGSQifPRYE8NiR4KGTBAczSDmA2QFsgvwoIOAHMAV8ncgALOa6sdmwOggw1jAcy04EfeAgrbGLLJxZeDeJthUYVCCcUerMhzYu+knV3DTODS66EsG2s3oBUfD7AFR+GnWdeATU7EzMEv5J9bIkwlwIqa9XLiIgOMLwKlUbSsuBvORhuyMiUAmgw4f0x6OLItPGSbBgFTaeFJ3wzzUumBc2eV7yZ2MgWThOub5xZbPkdwrgA4bjDPBuYMjRA0RBYcn8sMEMKVYWABO/w/i/5JDvGmNGymCtmperETKAxTHFdbcGEFAw+8FoKUjRfMyHLRDDkk3FTF7omajupsfoTjFnJU57WWnDOGeS+8tstaS/4cWeUUosCFXBJSV3x/NEDEBSByE9EEwbxiow71ySrG8KfPk7r3AhfKACz0ITvODsimg8MotjkyzaSxio+0WsafEU0TZBjKAhRzISYx/FjdVI0ZMnkBVUbCot38eKavpsLk+JAcx7it48cj9AZQxerKY452rDhEb1++p6p1KlLvEe0pP7F7yxn6+P4LVa9dgdr3KHiXwEePXtOZM3cIdVhhlyvuOiU0bQe2V14mnSLwqyDL7w3+2//+NbLFwarCSXbyxC3OJCtolCpbnPoMFW5WW1cqTs4nLcYZCywZDD5eP1fe9sACZaKV0OVb6b9FbulaVqhapxKpmwmmKAiLhnwoL9D306IylUqzxHK1v2CHLAtQ8JiG6/JjLGowWC9toWERPZOLGLGEJzfGD/8eYrSIrcnnbHcg13WWdHb/JVrusZYC9SJYdoSYBTHg/ui20ZoNG8BUYZ5FFlYRcjdY0ANY/EkTCI3vBbOA6IsBPM+DbjTeGwXUImQ5ycjUiQHzE5iGYL4MMzcoZFf7baJpTWazxTk673kBPic6775JzhR7NZjGWY7gQhvzMFhkT2tqQiY9Hbk4hZHDLygen95/YhMLFPATa82kSOtl7DaJAkN1en8KPTqHiyZIA4vJ4cz26cNnWua+hqY3NWFnQhyrkAPGXA7iGcLsioM7F++RaU8nWmS7nI87SP5wHHZR65BhBg0yShTvWEDBeTT4kBebgZw/cIkMO9kyow3TFPs1xmxrb97bma8XYNFgKw8GWMxyQ04LM5usuvY4/lDIiWfCMJeWmnKe7fkhwfVKsKO9qw9KzDdc1ltRu4GtyGHYHP4ekDCKASt+5JtBegmW0W2DlWSOEtsLuYawikcx5L7RWlJ8pne3dFefx40+SEfHpkmdswI+74ZgIUjaJtaQqtWrnOs+QwGLzEgAFvfYVrIgxnk1z8VVq1+ZZ8fkBa6bC8yW8veESgPyX0UhZe1Run3xAUv9x5srz/ALDcd14bskc+V5YQrl2X5bVghmHsM1uxVIJEZm7NwpNOrbt69LVar87CZcWHD21iN68vo9lSj6F3VvrrwYBEWg4Pfq/0MMTMtASCnkssUaNcpTy1Y1hQ7DLiFnoqAxPC2V/sDO8/RKiUVRVmjTozE1bluHvnz+RpvSZDPKwhDtPlS5dgV6+fgNbV0kSCQUhfEWatxFfHLnOa0L3pGn1ypdviTn0QBxczby4kNWgOmCWyOOszmTQ7gDKW2R4bASXeLf6cCGY5w1lBPAEPzx1x80c542z1nB7GG89SiWQuHvwYild3cDsPADe4aFHQqX+LkbZfpuev5T2LUOEiPX0X5SSx/B0On6aPHCtZNqW17IIsTWsocH7YzZwyYT8soYvbc5MDtRo1E1zkKCK6NOCzNezCtCCoxFNDr9K+9HcIZSF7X23IiCBC3YcBFNqK5L1iruPLsDBuEX5AeaNfvXHSH3cfNIo8oMtlRHAY+Fb/NujTnnbtXDSM7/QkyEPLIiFOswkNBuaERLXVfzeyJTLPTYHDanKF81a0YCbFX83A1k0N6aZ6NQDOBzuK63yhAHAUnlzHZWzNxyvli4HlktNuRzdHt0Mhc8kMOC3Qs+7MXnKtg5FJuId5i/151O7z7HxRyg6TCWGbusviuyzgw7wkzoAhdfcEFEYwImH/h87pttaO38rWxagwU3DDzgJopiEccvtqu48ATbDjYPDSGcS3N2OEiuH/juXhMCJDNhsOOvWCMjG/nhzQcuniH3BHuGz5Ld/rlz6T7N0w3nxxNsRmaYY8sOYPv9pgt2/wh/liajLD2wXzaHCTO6s0N1uLCXF3vXHKGz+y6xOgMqDUUWSUs9hZk0SP0VNZOWFfasP07PH76mcpVL00CNjNJVZePc8Vt05/oTSS5rQQP36sSd5zP4DRRW7DotyBX7tKpfqOWKwK+CrADQrmF1qlC6OL2DbPGydGYGBYXBg1tLktgLQ2RdwxY1qGmbWrxA3bZKCEfML+BmKWbJNi9OYbclZQELEy3bkfw4fl4CD1QrCshamu4pzB7E+26mZ/cF2Z686DehO3Uc1JoLnvn6kXIVC4ZB0ziP6dm9FxRkECX1sQYDBphwAOEWy3gRkR3Qua7dtCZF2wsLt9fP3tCmBdupUq2KLOPKLrAWWURY4AGLHeK4cJEWMHyA/Kl8tXK8gARDJwtqN60hFFBb7ahm42r09vk7NtCAfOvm2TtyH8dgJ6LOzSPD4OnMOsAZDot54652bPyhiHMd3x0Odp5b7CjuXgTp+U7m0GPczOH8iNmdcVVnkGV/V56hAdvwC7kD8tykZXvJdawfaVTWYXe//WuPsCkFHAaRpRV9MZCCDnpxCHLmJoO0wDEAN78ZLc35uEXxDtdRh5WmXATBvCY73Ei9TSY9HCjaPo4XzXBwjDo3n5k6cdGB6wRkjBZ9Xfi8x/kfeNCTPzMKnzDTGD7WxbNXAfs96O7F++Q5OpBDnDF3FnjAk7YvSma7eQAyTBjaZFXYoOCz6OPMkkucS2i04POJGSyfJGc2vkBBi8IBbFeHQa3JeZQvnUo6S0VLFGH2DEwYiim7IV78uzCDgUmIuDBlNmj2Yman8DoeW+y4EZJZwumtGcRug5CNwsQjM3smBpg2j3EBLIls278lTXXPfW5MCKYOY6kkClmdObIVQTiW5ulG8ncZPLUvzx/Li0/vP3MINDDBegSrNBSF7Uv2srS/fNUyLPVXFrA91y4QjF5G6/Xn/ZqfwNw80H94WypZOmuzl/xE6pk79OTJG84e69Gz8MoA//4XyRWBXwVZAbstipPDCyuQSVakyB909+4LunI5b0YAisLIyd35z4T4owo1vZAG3VRbs7sSXJa2xCh2viszBkzoTnWa1WB2ZXWgMOugKCCnpUW3xnyTh3QxL8Dix2SBDneczx+8QpvSuqqymoTAwh5d6X1rj9Cu5ful/l3kCyGkGl1pj/GBLB/KDlpOcPYqSc6jfGhGCzNq1rkRDdHJPSgbEijMYAHITrt4RHoTk4rVy5PTanNmiWAQgMW0rOgytD27Jk5wGsULQ3TrDTpYs1udvHNgMBMZZTSEHRPhOIf9d+X4DZ55Me3lRCeTUhXWhKlQrRxpWI6g0KNzaemNUF44Y6GIGybYigUmi9mlUb+tJUVZL2MbcmlNXv7rwGIQzA6MXsz7ODMTBgfBgxuOMVsFR7nx1iNZjhh7NYR0vCdxIZ+X90NQMo4vmFFACohsNUgA4Z7Yd3yPbJmcL5++MFMF4w4cS2CdEAOBojz9vBpm3CDng4wRxVe/iT3486PIA/MEBlU834VjEw6Lu1fs5+Loy8evzPb47XahWOd42hgqyPjw+WBUktX3QeEHlguFBhjn2Qt1OdaCC7uWtchnlzMtslkmKbzAaLXp15KcRvikK8bsOQID3xHuq2IJpU+SU4Zwe7wX2HRsI/s40ywjF2Id4+nYNmFeDbJoXCOylVkbRvPcHeTd9suNpZLKocGBGTi8PlxpZXUCXea+jh5cf8wMpL6vYKAkL1b6bma2DhJLDbOsQ67lAe5dK30FmfwkqxHZFrSKwOEdZ+ne9SdUonQxGjqlJ+Unnj95Q4d2XeTHwybJZsiiLIh9Bfr3b05FCrHL7Lnb/x65IkP0CznizZs3WJGIXr16pdAtdezyXVFbg/miPpZhoq/fvxfqveDltUnUv5+3KDBge4G8/48fP0SPHj3iP4GvX76JJvXyEqk2tRXt3nI63z/P7nXHRKpVZ4nGNbMSffrwWanvdXjbadHg0tNEwyvriZ7ef6HQ176eelukWnyyaFBRLdHJ5HMy7YOssCUiSaTy50TRsNLaovvXHsn1mZZ7rRMN/G2caERZbdGjW0+k/r2P7z6JdFqa8++a9XEWffv6LcfnXz15Q3Qm5bzo65evUr/H9+/fRY4j5ogG/k9dpF55uuje1YciWbDMfQ3/7rCSmqJ7Vx6IZIV4Hzy+81TkpuHPr4WfcdVmiLZHJ+e4b6TBy8evRAvNYkRDi02UvLZJTwfRse2nRH///bdIGXh487FozbzN/D4qv2lI3pe3UwlNkdVAN95u5/ZflGlfKQPSnAOKep8bqbdF6wK3ilzG+IrGVJyWYbvgZ0YrM1GM00rRtVM3FbZv8L771h0R6bW1kLzP8FJaohjHlaIPbz/m+vsnk1JFUxoZSX7XTd1P9Pzhy5+ed2jzcdHYSsJ3GlJ0omhrRKLkO1w4dFk0voYu/9uI0pNF+9cf4fMuwnKJ5HVdx/uK3r56J3IaOZf/f9Af40S7lu/L8jO9ef5W5DDMW/K7AXrhot0rD0iOcaOudqIHNx6JTHs5Sr7vuQOX+LpuOcD1n7/bf5Ff78unLyKbwR6Sz4ftnx4JUbsk77UuYGuWnyl5xT7Jc/BZcsKG0O18TRv0x3jRmT3nRdLg5tk7oiHFNPn3NoftFMmKKyduiAYXmcTX8kObT+TpPLh//bFIrfRUvscc2nJSpEjEz9vK98apra15TaAs4Ng0HjSX7/kx3ptE+Y0lgTt5rWOpGZ7v16Ks8P79Z9EQVV9eE168eF9UmOG/NoXX2XaLtyn8tVEToDZAjaAo/A//KeiisDDj7du3VKZMGXr16hWVLVtWYa/7/cffNMgukl69/0QLjEZT9+aFNxvh5MlbZG0Vz4HRa9Ya53saOzqcT58+pcqVK0uGWePCkmlZyC5q0roWBa6ala+fB0yMbi93enT7Oem5jqXR+rkzLPICp6fVUB86f+gqDZ7ci8xCpyn09WF/vzEskSU84ce9WSop7T7I6jk2qt4c9ty6dzPyTXSQefgYjlkW/VzpwsEr1KJHE5q320Xq4WmEXht1sec5LTj/6aVJGRVtnACJ1bVTt9hQJOigJ8+iSQPIlGxUPJgRwswJfjc7e3Bp9gGkhTAxEAfRYg7FMGh6jiHI0gDdbMz+JETuYhkq0LhjAw66Rci1suyNISEFG3EiMZVO7DzDErn0QLe/cacGnN0GpgKzedJue0VAmnNAHoDhBJt06cjVtJ9rPFeUmUFuN6AldRjUlk1ZkJWlyOvZ3tWHWK4HWa34/UYZD2GTCZiz5ASwXXAJxNwVAFZl9gLdn+acwCzB8U8caAwXRdtls6ley9p8nYP0ENJISBRrN6tBLuusqFLN8iztQ5A1MNlFg/pN604RhsskkkC4e2YVvo4QczBqj289ZWMeyKI/Qj5nuZT/HTJKnC8IY4b8F2ye9zZ7qteqNv8eTD6wHby22VPLHk15ls5N3Z8/C7No2xyoVa9mkvc7tPk42/KD9YVsFExlZsCEBHNnUHaA1ZwxN/trFOTXlv3def/gWoZrWm6ASZNRVwe6de4uR4q4b7KW6XzFdzTq6kg3z91l1QEYubycBy7q8+lIwmnqoNKKvDZZKezagTnjaW1t6MObT2QZMYMGThBUM8rAid0XyEkzjK8/scfcqWzFnM8HRQIqgSn9fejNyw9kHziJeqXlninrWiQNtm1LpXn+26h27Qq0OOaffL/CBpFIREOdounxy3c0X2849WvbUKGv//r1aypXrhy9efOGSpeW/j6eE34VZAVUkAFeK5Np7f6zNKp7S3LRUqHCCiySNSeFsdOik/Mo6tv3n5tQfiCriw8MPXChws0bBRkKs/zEjhUHKcgyjipUK0uLD7sqNRz24rHrZK7izZK3iKOeVKtxNYW99oc3H2l6ayt6/fQt6c6ZSOqmWUtKpL0BwF5Zr70tffn4hUzCdEhtxgCZP9OjW09pZjtrLqw0HcZINTMhBmZe3DXm82OXtRbUc3Rnmd4biziEH+cESKpgKoDvikLFf4+r1K51WLwad7VnR0cUnHAlFLuy5Yas9gFu2JtCttNyz7X08a1gGNJ3fHeeo4G5Rl7w/OFLWuu/mQszsdMn5mfGmKpRv4k9lXrM47veuXCPzu2/zLbgqXvO8zxMZmA2D9JHBDVjIQ2pXo3G1eRyEZTmM+VlEYTF7sMbT+j+lYe8YL6Reotlb/i7zMCCH4t9BFS37NWMw6ilsTiXBVi8ww4fLpjioh526qONh9IYMzUqXb5Urg2GbZG7eA4L1xFcn0bMUqWpHuN/mlu7cvw6+U4N5ZkpQN18OE3zmsjHEJocCEVOTpMpIw/McrEhvyZML7CNUFBZxxpSjzFdyE3Dj45uFooxzFYiUywzdq88QAF64Sxrw9yb0xpz2r3iAAdjA5Dq4jvCbRGSTMgB5+505HlSBzVvunj4Kkt44d6IBge+q8+UEC468b4oxtJb/EPCbD3AjSWRcJ40X2Tw00IVLqvIbUOx3WN0Zza8ye44gux6VidbdinlwijORKqFb4jxYi54IaWMTPWjcpVlc79b4b2BlriuodIVStKis/4sVZX3PDi+M5UcR/lzQy38hDfVbpL1jK48iHSIp/WhiVSvZU0K3eeqMAv9rBb1liPn08XjN3l2TM9NfqdJeZC08STNt1tLlaqVoZhEK0lzsiALMlOT5XTu3D2aMaMvTSwkEsqscO7WI5riF89h0Mk+M6mogomEXwXZf6wgO37lHukFraXSxYvQrrn69Gc+2qjKipjFe2n58kPUqVM9muszIV/fO7uLj7/NakrefJoHXa18pV+0KwJYCE/v6kIvHr8hE/9JpKrZQ6nv5zoxmI5sO0M9R3Ykx6WKZQR3Lt1H8/WjqHiporQo1Y+LzMyQ5QawLmgbRVgt58UdXq9ijaznI3LC7pUHaY5WMC9C5u50YAt5aRFusZSDZ7GgCj7kmSF0NqcbL2aX4LCGHLAeo3Iu5JCrhaIMiyt02t032kjN5MEGHL+LBSfMCmCSIM1NNad9gCJxscNKdmDEd8FnwWyclrMGz2/llblaN38ru1hiQB/Agm+EwWAaZjBI5kWfPMB3v3f5AS+U8QMmCYv77AQeMGmASUSV2pXYdKFynYq82MbCu3y1sjxHKGtnN7dzAIUFFtIvH71m44ind56xAx9MB1DwoIDHvFRWwGds1q0xNevSiN0RG7arp/ACLD0DunnBDkqITJIUuaXKl6SxpsNopJEqZ8vlBoQ3LzSP5fxAAI0Jk4W6Pxl94Dq53H0Nz1Xhu2P7W8YYUqfBbfnfb52/Sx7j5vO+RUbeNI8JNN5mFIe9g7nCjBeONcxZoSiFyQeOcTirum204WywzGwjWGPMagIwwbCMnkURlkto/7qj/HdwL+02oiPZDPJgMxFse8yBFS9dnOxUPbkAhFuf1zYH3h/Y78EGURw+jfMKnwUznWIgmwxZb9iWCHaHvX3mfQdbf5PuDmwCAmMb/z1u2c48gRGzHuRJZ/depDrNa1LIYS++lsnSjPJOsONZOVlw8+xdMurmwE1O65hZNFAz61kpae4FKEz1O9hxc22syRDSm6s4Z8XHd56Tbkd7Zhk915lRxzwYjuSGs4eukc3YQPqzyB8Uc8SNKlTNP0Ye1zbjsaF049JDmmauSuN0+0j+raAKsocPXtHkyeHcfIlbaUiVKuUfWygr5q/bR8uST9Lgjk1o7nTFzS6K8asg+48VZD/+/ptU7aPo+duPFDxrFPVqWY8KKx4+fEWTtcI5BHBF3Kx8zZ3I7uJz5dw9Mh0XxjfnpbttqFw+SgmADRG7KdJ1HVWvV4ki9zsrrUsnDtU06O7CF+ngPU7UuH09hW5f0z5udOXETRowqQdZR8/M8jnS3gDAqJr2dmEZVifVNuQpo2xGDNg871i8hxdx6PZKK+8DE2E3xJvO7LnAi/IFR71zXWQKw/OLeBgf0hQ4ujVqXz/H37l4+Apbcn/9/I3d45BnJO33hGzRdrAHL36Q2QWb+NwgzT64fuYWF2YIuAWw6IPsTMNyuNxue+kXldsX7aZNodu54AAgce07oQeNNBrCjpf5CRRAzDSduU3XT99ia3CwT1kxaZkBxgUsAAqzkuVK8PEBVgqhw38V+YtZECw6xEDd9/XLV3r76h399r/f6eunr2y2g58Prz9yYS4uVnMC3qNWk+pUu3lNatCmHjVoU4ele+lt4JUFGINsDttBe+IOSMyQKtWqwIwYQpYRtZAbENAcYbWMM7oAyPzAxg43GCQJQxYDMkAf7RBJ0YacLUgExRLIXcv3UdDMSGZfIXN0WGnGrGDS0r3MbuEzopniscWWi6YIy6XMcP3vt/+RQ7wZ9VHv9pOzI4o7MF7Yd5McxrJDoMsYP/4MuE/Aph/XBOcRc/k4wb6Au+Lvf/zG5zKKfDbqSHRiNhjnXKhRNF8X8Jow6egzrnuG7YFsMpwPkAzPA1ueaTvy9UjVk69HaBSEHPHONipAHP6MiAvIJUOOeEll0ALGXb+dNR+P46xGkO5c2Yw48Bln93Cm62duU7fhHch1rXm21zJprkNLPdbRCu+NHK+yKNVXqoJSWvjqRdHuVYepbZ9mNGeTpVIlc/bjQ+j0vss0bGpvMpyTv03f8ydvk5VWBBUp+ievb0qns/QvqIJM3Jjv2LEe+fjmb2NeFkA2rAa54qt3NE9vOPVXsFwR+FWQ/ccKMmDuqj20au8ZGt61OblPGUyFGebmKyj1zF2aNq03aU1WLiOUHjldfEzHh9GVs/doiskgmjizH+UnsJDQ7uhEb199IJuwadR39M9zDIqEn34UJccfpra9m9GczYq9EV0+foOLMhQm/kkO1Kpn0wz/LusNAK5gs7o48AySWYQuDZnWT67ta9jZjhdJmBFx2yD9d0a+lWFne2Youg7rwL+bayH5/Qc5Dp9DJ3amMqsXfNg7gzNcVji48Rg70eEGgMJH33+K1J8RcrG5k4P5sXHoDBoxK+fzX5Z9AJnfItsVnIMkZkDgQAcGBPlOeQEWb3D4WxuwVfL6AJid4QaDqeeYLkp1PMsNKI7uXn7AjNTTO8/TWKpn9OLBS2Zc3r36oLT3RsEFGSUKDBQRYOgq16nEwbpY/FeoXj5fZy4gS0SgMAoxNEjEaN69CbuGggmWhtmFzTtYrvWBCcx+oDhR0x9E2m7jfmqU4D1hQ4/n45wqU7EUmYTrc94gACkyZsXELFZ7ldZkt3w2H6OL7VdyLhiAGTSbpcZcmCCQGhlogF6gFo01Gi45B3BebArdwY6NaI6g6LFbYcLNAueRPsxaoshC/tnrp2/Ie1IgPw+sHiSPkFejYAJ7hfPeL9mFIzDSF2PYZ3CLHKTdN0PAtHlvJ/69Wk1r0Py9bj8V1nw91QmjxNgULkowN5qTJBrusj7aofzYaZUZ9VYXcjdza4ChmIS8FyxiwD43mdnV5V7raanbWt4HUWd8uQkm73XoIcvW7fja77jCmHqNkU02nhMw22bYS7hPhaQ4U6N2ypu9v3LmDpkO8eViPfqQK1WplfO9QNGYYxZH+3acI1X1TmTiMSbDvxVEQZZ+dMXRaST169ecCitSbz6kqf6rlCZXBH4VZP/BguzU9fukM38NlSxWhJLn6tFfhTi4LinxHM2du5WqVStLS5fNzNBBViZyuvjs3nya/GxWU4UqpSk2CVKR/JV9rgzYTkt9t1LtxlVp4R7ZTSxkAaRPMyDV+PKd3NeYUudBig1kDDJaTNui9/BAfdgRzwwGH/LcAFbP20KL7Fbygiri1FxeoMoKdGwxd4FuOcJJ0YmXhREw6+3CCwPMtWjaZ7ypZbfwnN3dgYtA2GEH7PPIlV3bEbOH5umESSy6JztrSP0Zl3uspSUuq3jBB+li+u57Zsi6D7BoQWB2rNNKyewOCoWJdmNYzijt7FpOgLQMjBlMIcD2AZCq9h3XnQZN7cdFWmEb+kbBgEX6u5fvuTj78PoD/wnWC8UG/h0LdqbF0gGypS/fvlC5iuWoaPGiVLJscSoJhi3tT0h9pWGZlA3sd0g6UQSkrD7E0lj+/H/9Qb3HdeM5LxijSANsC8wkxXmv5+0FgMVCfh3m9rIyrQC7BYMdoMeoTjQ7TFfCCGGWDAUR5uZw/9By0qBJjmN4/hFW/sgAAybZjyFt9/F8nIMtirQSjDhmztemHhM6Ss6Bx7efkv/0MGacAYSQW8ca0cmks3xOYn9ithCh74e3nKAwkxjePngeWDYYfqAYw6wWCjnkiWH2MnMxZhVjSCpT+mS4Tlj2d2NmFr+HPLSsmjexTvEcVwA5JrLG0ksdMwPbxqyPK1+vJtiOIh2viVLtI7x+rPNqLvjCT/lQ9QZVSV6pot1SI86VzAm5XYecxsyjY9vPUPsBLcl7i3zqiOzgpB5Ax5POUZ8xncku5mclhyLhPi2SDu9IpYHjupBF0BTKb6t77QG+Qh7fxtlUr0m1Ai/Ijh27QXa2q6lU6aK0enX+m7vJAr81KRS35zQN7dSUvKYNIWVAGQXZL9v7ArK9F+PHj79FA20j2JozJfW6qDDj06evomFq/mx3evr07Xx735wsXr98+Saa0MOTbWFTElJF+Y33bz6Kxja2YEvcfZsVa+ubFaIcV7HVr14XR9H374q1vH3z4p1Io5YBWxTH+23J8G/y2Ozi85n0dmH7ZGtVL7ntudcGbGULZ1g5Xz9zS6bf3bYomX9X5ffxoqPbTkn1O49vPxWNqy5Yb8P6Whq7dViUi62sN4RIb7GLbRI0K4p/T/Wv8aLTu7OPH5DX6hi24Ttj94g06xpIPuPE2vqihMikXOMBpMWLRy/Znl6r/qwM9uzTms4WrfBaJ3py56no346CtJqWBk/vPRet8t0o0mlhmmEfYL/Hea8XvXzyOk/HzPTmJqIDG45meR6/e/VeFGQQ+U8UQ3VdttAXA9tstf9mPsbx75PqzJRYyd86f1c0paGhxAYftvBibFv0j438cs+1kn2Az7cjZjdbz4ujJDaH7eDjGdEN4t+xV/MSvXn5ThRu8Y9tfoB+hOj7t++iS0evikaX1+a/wzZ7dv+55LOKz0lEMSQuScnwXWGJb9bHSYi/qKKTbfxFegt8PM4tcmJCrZl8rXIc4SP1MXZu/yXRoD8n8O8lLt0rkhXYXgad7fga7TJ2nlTX6JzOg8NbT/L9Y2gpbdHdK7LFguSG1P2X+d43tPwMttNXJm5ffsD39CHVDEV3rsgX4aIQq3utiEJzLXJzXc9rv5CQRFFhxo8ff4tU8mFNrQzb+1/B0AUMdAlV2gkh0UlpieKFFUWL/imhqcXBgAUNdGnUJghSmE3LBNvl/ASCIkfOEGQs8UE7FBakmx0mWAxjxubOpQe0K06x37d0+ZKk6y10ZePmbqSn94Q5IXmBmTqrRfo8k3U6+TztiEmR63VGzx7CnWV0jhH8LEsQMpggNd0BvF+8JgXxnFFuAJPnlWDHHWdYX2OgP7f9OsZETcKMoQu/J166fYPusWHwNOql3pU71AivhdGBIoHZHkitFl8OYmkkOvkwMwjQjyCd5qa0LWoXMyF5ARgQhG4vuRbCrpMq2n1YtgimJMZxJWnWnUWW/V1pa0QSO03+guLkmdsWJfO2RbB2lM1ylgvjnAOjA8YHQdwT7UZLZb6Czvu+tYdJr7UF+U1bwHJPyPgsFhlQZOo8ljimZzxwXoAdxXEENgkYojOAoi8ESCSKeA3rge7McuEYh4ti+Gk/atmzGUt+Z3ezZ8YM513gAQ/qP6kX/96hTccpUD+CH8MiXtNBcLh78eAVOY/0ZWYM8ke4lUac8WeDHLshXmzoA8B6HtJF/2kLaO18IUB4mudENh+BpTw+E5jRpl0a0fx97lSxRgV2UwzQDactC3dmyYzhPHEZ7Uvn9l1iJnjODgeq2ehnN1NENwQZRPJjnBdDc3CbhQTYY3wAPX/wkmo1rU62y4ykYj1eP3tLnhMDmUUZoNmLBmoJ200WrPbfStdPCyYmxiHT8sRmgY0Mt1rBj8fMHqJQN2AcZ9HOgmRVdUovqtFAcbEPWSE+SIhm6D6kDatf8hNfv36n7WuO8+MRWoXDxfDNm4908OBVfqyqqlhljjLkis/efGDVWbdm/4Iw6PRQWGn3H4WyGTLg1LX7XM33NAsVfVFQx1pZOH/+HndJhg7xE31QciiytN2gF0/fioa1cuCO0uXUu6L8xtuX70WjG5hxR+3wzrNKf7+1wdu5U6jZ1FzhwdTokJr1d+cup8ekYIV05NYEbOUO7MgK00VP7gpdaFmBgNeJdQy4E+w5IUAmtg2hoWZ9Xfh3pzQy5teSBkcSTooG/S6EFYNhkIXtQljtwY3HpP6MCJwVh9OCvXpy95nSuqJ4LzB66O6LO/gaVXWYgXjzQrptIw3ev/kg2r54t8iyv0sGxgasA4KgwZjIGq5dkCgsDBnCtBE6bN7XWXJ8in/MejuJtoQnit6/fi/Ta4Jx2rvmkGhmeyvJa42uMJUZt+yuMQ+uP2IGSvz8qU2MMzC8OB/Aso0o8w+LJQ6BxvvFOsdLftein4vo1dPXGUKmwZbh3/ynL+DfwXbfFLZDNKyUlsCmFZnA5yVe6/Lx6xI2D+8Ddg6MoX47y5+Yt5TVh/h3xQz4x3cfJWyR58QA4fz9/Wdm7Mvnr5Lvi/dAgHRWQKi3mLmbMzko12tVsFE0X5tGlNEW3b38QGr1gc1gT/69ac1NRR/ffRLJimunbolUi2nxtTlpWdbB2rKcBzEuq/m+Mam+sVyfJyekrD3K97yR1WaKXjyWnumVB/euPRYNrW7I9/Pr5/J/PZG04SSvZbT6zmEmtzBci9atO8brPn29xaLCjjnxu3k97bRkh1Lf5xdD9h9Fm/rVqVKZEvT+81c6fElwpCqsaN68BtWqVZ4+f/5GKSmXqTCgfKVS1Geo0LXZuDT/WTJ0F4dN6y2ZKVM2SzZcdwBVrlWBnj98RRsX7lLoa6NDahSozTMP+9cfo5PJ5/L8mqONh1Dzro14TsR/Rjh34WUFnNkcV5qyAUHK6sOcjSUteLh/tTlVrVuJO/EeEwLZbCA3gJWbFTSdHy92iKPEJSlSsV0DtHpx19pj3DzulEsDzHPBThvGD2CvbAd7st28MoD3AqO39HoIzZynzU57CGHGvItWXViEL6XnD/LGjgIlShfnTCa/ZFdacTuMg3Jh+Y3zA8HfYEymNZlN05uZMLMD1kKa/fL/DWBt4OiJvC+9NhY0pYERW87DFh1mMnBpxLZdfiuM5u91p2H6KlI7auK1weaCEYNDIWaiwAyD1Vl2I5TGWY38yaAFAc+xzvE0o4UZHdt2ms8vsMNgqdr2a8nPwbHrrjGPWTac9zARwb+r6anwLJr9EC+enxRngiEDTGyI8e7Ve2ahwIjD2MM4TJfnv8z7OLMT6uf3n9mOfuEpX5612rIwkUx7ODATV71BFTbjqVKnIhl1sWMHThh6+O92YeZtQ/A28hw/n2dSkQXmucWW5/6+ff1GnhMCKCX+IF9jMF+WnhnDcek9MYC/L9hHzy12HBadGZhpA0sH5q5V72ZkHvVzHll64LOLg7Jtlhjy+S8NENyOOTl8FlzbZHUxRByB7/SF/L16jupEA7KxuJcWMNFZM19gJmfNn6JQV0V81hg34VjRMBlC5ZXs8AylC86rboNbU4OW+Ztv+n/sXQVYVGvXXfe71+7u7u7uQuwGGxABkRSV7u6QUhFEUATsQkCxULEQu7u7vfad/9l7HK6BMAxT3N/1fPMxeJlzzpx817vXXksgEGSPYUZN7S52lIqskZIsHAeoqsouYkBazuVpWcJK3pCO4vXJKhN+SxaVRLY4uJDIFunhMvRryTolRTlki4SxX10f01NO4+mjV3Jf/3i9QWxPe+nETbbJlSXIknum7Th+vyYoKbvZXlpo2KYuRs8RBpWHmsYUWM7G0sXIOShWshhbP28KE0qb8gsyiND+KqkMN1vBlufiggJOKR+IXPBIhkjW0uJgjIEqJs0fxe/9ZofjyFc7+dzkgQujDLIliE7jfXD24EWx1kVBvBxOW7sS5zJRaC3ZWMsKNBAlZ8iYKyGwjDXmgT1Zt5O8a0ZDA/6+lLEkDVStW4UlZCGHPbHqZjibQnQY1IZNeEjWSM56lOU0vrIWD8Y3hmxneamsJzeUEfSd71y+z/JO18n+UK+hw7l1NAinc56eF237tYR+gCZir4ViSZYv79v8mOYQqaIcMp3WZmyyQaYvJIWeZjuBl6nh9HO4M+Fw0nHMbm3GLopEasghcfEJXzazEZnEkNRQp8187F9/mAeUJBMkB0IymyDCN7ezBY7vPM1Ez3yFIZ8L37oC/lX0r+zfi5YoyqTdYaw3k3i6h0x3ngA/khjWrsQkKtQ4iq81IlghRzxx/dRNPpfIUZNs88lmvkX3piydDTNdnn1d2yWaseMo7Qsij+QcSnEIThsWou83lvpEWn1mheLAxqNMPmni5NtQ6G+dXckghNZLRiJOG8xzDU/P2nUGISbC7dFynczkUxyQcUqM4xp+bxSiLVbO4o+IdVqLG2dvo3zVsjAO1S6QVJEdD01W8DHoNrw9eo7uBGli67LdnD1WsXo5TDCUrRP1veuPsHu9UC44ZZ5szCByw5ljNzh3jMYS5K6oDLhy5SG/ihT5EwMH/XzeKxOyrtzjGKkyJYqhe/O6KGz4TciUBCqdhGx+76mr+PhJmBGjrBgypDUPCs6cvoPbBewzkhYat6qFVp3q48vnf7AtXhgAKk+Ur1wmOxxapD+XJQZM6o4GrWvj7ct3iPMR9kdIEzPtxrP18d0rD5Dgu7XAy6vVpAZ0vYThoJHWq3lGVRJMmDeCQ2XJBY/6LmgmWlzQIMk82oDfrw9K4owzcTDbazoGz+grrHpN8mNnwdxAg1Cy8aZgVrLutxnhzgNRcYkLhdQSgaTP2Iz0wLu3eedbFQQ0+KUelMXHfeC2zZpn9mlwlRy1C7NamMJiqAv2bzgstepV1TqVuSrivcMeax9FsrskZVRRpZmqKTSgp0E2VWAmVtWG3RhPJiNk5Z+f411YQISASDu5CTqr+WFq3TlMQqgHaW9iBudlUd4X5b0RcV7zKBJ+u524ylm9ftV8rYv696i6Rf1mgXOWMhkmIkYEbOX1UGg6T84x748yvixVXWE70oOdCamvzH7NfHgm22bnZNHkgbdWCBNqspcnl9KQwx7smEg9USnRu2HyXSXLDUNm/FuFEqFEqeJcmSNQxYoytsiOnfL6lp3xx9DZ/XHtxA0mdpSHRtcbkVPbBDPEe27kQHm6P1BgO/WkkbOon3Y4O0USKDeNSCBNnpBbovVwdxzakskTXRz6PKLTd2SMJibSVqazasA20QydhrT7aZvpGrUd5ckW+OS66J5kzRl3v8K9qw/4/kX3lIFTemGK5Vixjh+RPo/pwVzBoZ4xylnLL+j+tcZfeE83DZvN95qCYFf8QZzad56rdVQdk6arIvX4xXkLn28zrMfxhJoskRCcyvu2y6BWaNJO/gN6UXVs0JgO3+WOKRIiv4CePZugbFnFu8jmhp1fq2MD2jVCESWpLuYHf5AWUtEb8f/Z9l4EugkMs12GRy/ewF9vNJ9Qygxr60QcPnQVkyd3h46ubPO/xLV4TU8+Dfd5cShXsRQHKeY2OykLPL73HLO6O/Bg1nu9Kdr0EFY9ZYXMtDOwGe/PVYalh115kCNN7Ft3GG7TQ3hWOPSQC4pV+KtANrt0q7EZ5YVjqafQrHNDBO5zkkiSQc3s+p0suAm+36QesFltkq9BwArHRKx0Wcfr9kq1Rbt+eeepUOM9ZRodTT7B8knKE6KsotxAZIxmzM/sv8ADSt9djhw2Kw5oALxggCMPcjsObsOW2TR7Ly+rYyIICd4beaAqekRQ5Y6y0lS1B8okyJgGv2QwkLXzFI6nneb9RrK1b0HHmWIZmndtwjlSDdvWZaJd0NBrcSANq2kauFM16srxa7h07CpHM9w4c/snskvXHMn8qBJDMsAW3ZvkO1vqW1w+fg3rg7YxwRHFE1BFjUjd0FkDWF6aEyhrK8o6DinLd/N5QPeasUbDMcNhEsdZiHBw81EOeaY4ATpGkxaMZtt6ugfTdUDkUpQ7RkSJiGVuhIX2E4U3f/zwGSN0BqOfWg+uZn3+/BnRjqux1nsb7zMiPyQvrNeiFtynBbGkkECZe5quk/Hu9Xs4TfTlqjhNIpIFP8kmv61oXT5+nQ06yBq/bd+W3x1vyhDbsWIvkzHrVSY5xlLQdhAJPbztOF/nFCpfr0XtX343MiUy6WXH5iuUG0ZySiIz4uRAWQ/3wPGdp1C7aQ2EHvH47hiIAzoWc7tYcwV28LTeMF8+FwW5DmhCcHZ7c7x49AqznNWgvlCoJpAWIu3XYE3Qdj6+YfsdZSrhe3j7KbR7OvKkrv+W+WjRuSHkiQd3nkF7qDDTcvEWU9Rr/Ovnubxs7z99+gK1ScF49eod3D3U0K1bI6WWK6paR3CFLNhgLHq3aiDT9f3OIfsPEzKC79o9WLUrC6qdm8Fj1nAoM/anX4SDw3pUrFgKq+MN8JcMb5Ti3nzowag5xAdPHrzEfI9JGDz215kvskKw+Wokxe5Hu95N4bnGRObrs53gj2M7z6D36E6wjRVWf6QFGoDZU6ZM8km069sC86K1UK1atQI9AIhE6XYwx5sXf0PTSQ1TrcSbGf4R5zIuwaw/PTy/wDB4FsbkEar84/dynxrEvWhEroIziMzm7aT17s07DmClgF3qR6OBFzmz5Qaagad+sAtHrvBMNDkQ1mspnsSI+oYsVFzw/u0HtB/YmqVUr96+kmv2DIUrk7SNBuREhEVkofeEbhipp8KZVLLKGaO+HiJo5w5exNmDF3Dh8BU8vpNzRZ4G5vVa1UGtxtU5R6pWk+qo3qAqy9qo4iLP+xD1vDy585T7FWn/3b/6gCtRJBF7eONxjjLMCtXKsSS3ebemTL5ooF7QAG/ajozNx7ApdDu7AopA6yGpam6h0EQaNgQlYY3fZq5aEogUzXKb+t218urZa5YBUgWJQERhfuTc7P6qu1fuw3miH66dusmkhqSN5PgoyflLx95rZnB25hi5NZou0eMMMacJvrh7+T5XuWj9A6f0xqNbj2EzwoP3O/U0UQWt67AO/Nkn957BUsWFSRFdlx7JtmjcocF3xzpQbym2R6YJyVicKU/+/Aj6O2/NEP7+tG7vnQ5o1bPZL78D3a/sx/qw9JkqdyGH3VG5ZkWxvn+M0xrEOq9lqeeiDFc0aJ3/Ck7ovBXYFJrC616a5ZUrKRbnOggzi8GWJWnc+xZ+xO277MqCglx+tTtRwPRnOCWaoNvQnyuT0kSIZTy2rUhH+z7N4JFoDHkjwjsJ65eno2OvJnBbJuxdVjQh27v3ApydNqBSpdI8zqP2A2XFsUt3oBO4huWKaV56Mq+Q/SZk/3FCdvr6fcz0iUcJThfXQ4mi8q3w5AefP3+BunoIXjz/G84uE9Crl+waKPNz80lYugfRASlo0qoWgtYYyD2UlmbZZvdy4llon43z0LpbY5mu78a5O5jby4Fn1fxSrNCqu3SrctSkrtPBkiVAegFTMFZXtcAPgJ0r07mhnGbbF+13QeMOPwfMigOa8Q83i+HlBKY7o1mXxvmyaJ4/wJHJFVVcFh1wZVlYXqCKAfWn0OCPPue3xynPahFVucwHO/FMPA286TN1mgllXnnhzP7zLKmi3q7WvZvDOEob9RrWlRshE4H6CMn8gezAaZ+JQAMxMksgyWONhrK1oiY8f/iCye2Fw5dx5cR17qkiE5TcQJK8yrUrsgS3bOWyKFepDMvy6HiXKFOcB+v0ogrkn0X+ZJJCL8E///B1RbIyGkjTMXj84DGKFSmO928+sPkE2c7Tz5ePX+HZ/Rd4eu8ZSwxzA5lMUGWvaefGXOWjanGVOpWldq+i/ZIStRtpcenZ/aX0fYhQUUUst+uEKiibQ5OR4L2JvxuhSccGMFik/R3RENndh8+L5qoYVZ8mzh8NDad/e8nS1x/mcGYKpqbzngjRtxWo/GBPwgEsMljG34f6ygyCtNhef0/CQfjPDuftJnMah3UL0axzI1zKvAq70V7cz0Xkw3WrFRq3FxKuO5fusfGGSHrptcM+W3Ypet6QeQj18NH3slxpggGThXL0n3qnKDw6PIVJm+P6hegxqvMvvwP/vVEUG3lQRcxvt4PY9ywyB6LqGC3DMsaQr7f84mjKSVYoEFw3m6OransU5Hn87OZrmA1w4W3y2m6J9jn01RUEvnOWYefqg2jTuxm8t0o3YPpHPH3wAlrdHZj8ea0zQdue8jWEePf2A2YM8MRbqugu1kDXfj8bxiiCkFlaJuDokWuYOq0ntLV/lhcrEzzidyFx30mM7t4STjNl22tI+E3I/uOEjG5sI+2jcO/pK3jPHqH0LjFLFu9CYuJh9OjRGK5uwgwmWSA/N5+Xz99i5gCh1MUnVhetO8u2bJ0TFi2Mw/aVB+Q20xZkHI3tK/aheeeGCNhpI/UH12rvzYh2WIOylUpj2UlvHtAW9Dx3VgvEgU1HeSAUethNLMlOjsuZ5I/9G46w/CrsmCdnqYkLmiU36m7DVTvq93LZbCHWDCD1wBApoxn7Jp0awifN4ZeSr28rCVRdu3byJg8QSb6Yl+RRhHOHLrG0iioVjTs1gHeqvUQz29ICDXa3LdmBXav3c/VOBMqDGqo5AH0mdGcSJC8QISLZHxmhEFG+e/UB/6RqFJEoRYDOZyKo1RtWRc2G1VGzcXU2X6jXqrZM5J4kv0tblc59WnSOiUCEgzLoRs5RYclpbtU0yqNb7b6eCZaIbFNFi/K9vr3vUv8TZdiRDJD/rnktLIiai5bdm2aTd+oBpHw0AkkvyUBD3ErQj98r2HAZ99IR6HrTCZyGVp2bI8oqLjtzjGS9VMUior13TQZ8NEN40oX62KgvkvoWCRePXuGeTiLNJPH2TLVDjQbVvnvWBOgu4f5Juo+S6cjg6UIH3R9BzqurPTbw31muNOaqXG6gbV08P4b/3n6NGXqP6yrWPqD7DEm0aZtH6A6GabgOJNmPeh0t+diOmasCg0BNSAraR/fu3ofr+FBcP3Mbg6b2gnnkHEgTl7NuwKi/M78P2mWHZp1k+xxfbLcWm5btRssuDeG7yUzuE7lbVx9CqPMm1KpXCUuTzPIc58iDkD18+BLTpoaBCvqxsXNQs1YFKCs+f/kHKlZL8fzNO4QajkPPlpJN8uYHvwnZf5yQEYI2pCN6xzEM6tAYvjrS1WNLGzdvPsEsrQieRYxPMOSytiyQ35tPkP16JK85ip6DW8EueDrkjW+16H6b5/NNXpZ49vAlZnWw5MGxzYq56DP217O0koDc1PS7WuP2xfsYPnsATIJzl1OIPUDoZMlVhTEGQ2EQoCHRcmj2fW4XS5aHkRzJZbN5vh5QlzKvwayfAw/eRumrwCh4llgPY3IfNOtrzzI+khPSoC+vnkX6zhTgS+SBqjUU2iuufJF6jSyHuuDN87dcVaFAWnJlVCRI1kbOdFSJOZF2mqtJBJItdR3REYOm9kG3ER2zKyaK2kaSDz6+84yNJl49ec3HgSpab1+/Ywt1Mgoh4vb542eubH/59Jmv3T/+9wdXPuj+RhWmYiWL4o+//oey5UujeOniKFuhNPcM0Yukr5VqVUTlmhVQqWZF/jdZD+qoz+pY8gkmxoe2HMvuDaP932NMF44cIBdEMq/4FajytyN2H2KdEtk8g0By3On2k5iIfCtppL8l23iKR6DrhSR65O5Ioc2iY3z/+kM2vqFqMH1/tYXCXrIiEqg9yNyFiB8dNzoOZA4yxXocLp+5iqXGK1m2TKBt0HRR5+t+pfNaxDglZveqUd+XqL8wc8dJDl6n+yQRO7pmvw3LFhl4cM/Y//7AAgqFzsF0hLA+kKrz0fzeJFyXowZyA/WX2Y/15mtE12c6JpmNEvveu2CQE84dvMSVSlIC5Pd6ookrl8lBPHFVkAmwb5/Hy50TkOiVxOf5shNeBTYG+XF7zUd44fSBSxig1h0WEbqQ9fOTqmOkAnGLN0THfi0gT9D+nDMqELevPcYcm1EYM72nWJ+RNSGLidmPFdHpaNe+Lvz9p0GZcej8TegHr0f5UsWR6qmLIrnc85SZkP0OhlaCYOhvce7mAw6162YcJHjz7oNA2WFosIIDA+PiDspsHfkNQbxx+QEHKw5rYSW4d+upQBEIMFvJwZK2U0Lksr4Y9w0cnKnV3oKDkKWNrN1nOPSTXqf3X5DKMo8kn+BQUnodTT0p8XIuH78mGF5yGoekrnBMzPfnKUR2yJ/q/HkKKxYXF49dEYz6GlLrOMHnlyGe3+LZwxcCnbZm/JkJVbQEV05cz9f6xlbU4M/qtpsvePZAPvckcfD47lPBao/1gtmt530XVDyy1DSB/VgvQdKynUq1vYU1GPr18zeCXav3C5wm+ghGlJz63b7W72wu2BiyXaxw7/d/v+cAaY2mRtmfV6upI9gcniL4+OHjT39/OOm4YFZLk+y/pUDle1cf/HQdjft6ftK5TeHOkoACokUBzfSic4rOfULGlmPZ18CY8jMF6esP8b9TeLXrZP/szyyev4IDo0VIi0sXqBZV5/9mPsRJ8PaVMAxaBPpbz5mLskPdaR//Ctsj07LXs8ptXZ7f59qpmxz6TPcXf90l+Qq1DzJYxp8bU0GTg7glwc5V6XyPpRDoi5lXBQXF3asPBCPLa/GzICVmr0Da2L/5GD/LRlXVFTy6Lfvnd7jtGn5Wm43yzdexkRaOpV/k8cq4Tg6CN2IGasv6XvTlyz+CqVNCeWy3Y8e/Ye/KCoeYFB43u6zaIbd1/g6G/n+A5nWqok6V8vjw6Qv2nb4GZYfqMGEmWfL2U0qTGUTuRJ16N+Ht2RQr/6BogpqRCs/qHtt9DhdPyD7se6KRKipULYt71x5hS8QuqS+/bd8W6KsmzEUJNIhiiVNB0WVoO4zWF84s++kswSsJ89SoGd8kTCjjoab3jC2Z+fp8n/HdoOc7g99TPplIHpUXmnZqxDbZVJGgzCVq7qdZ9txAM/IkV6QZepIgLRzoyBJAcb+n9Tpjrq6RScK8vvZsXKAMIDkaVSqWnvLjAGCyLaeeHurtoUqHv85iqNfUhWE3S86wou1XlvuFsuPR7SdcmaLq6oQqszg3LH3dYa5SUTWLcvJon4cd9eJ8rdwqp1QNpAgBsr0n50OSdlJ1T9d7BgeFj5qj8l01iype1iPcWeZH7pD0t/OWzuHIAlHPIFXqvDSC4TzRl23Km3drgrBMb3QcLHw2iAs6H6jaR9lo5AZJVSpySww95sXupJFWq2A32pOrxHT9hGd6o/e4btznatrblvvJqKJnFjEHer4zuTJIyyRZoce0IK4gUh+dyxar79wJyUGVzELIBVJk4JFTzxhhV1w6n8sEMkYhg5K85IY2Iz15v7fr35INiMStnKZE7+F+M/p7q1gjsYyHcjLGCDERVvKm2YxD044FU2vQ/gydR9mUn9C2b3MMmZ7/Xra8KoLkrEgYbzgUVWrnX+aa3+rY9pX7+f20+cPlLlUkrI8Wrn/ohM4oJcVA7YIgK+sGHjx4iVKliqFPn1+b1CgDPn3+gl0nrvB7MsQrzFBey5T/p+Dg5a+ZZKmZQkmGMmPAgBYoXrwI7tx5hrNnpRMiKw2M0xDq+VPXZ+KtAvpIatavggHjhQRmdcB2ma+PTAlm2ggHB3FemyUmN7lhsvVIJhQc5OtX8GwywmyPqezMRk5pwYZREg/SVTT6sSU7wUsjhN3t8oPxJsMx1kgYBOqtFZpn1pgIHQa2gf3aBTwQ3BW3H8Fzl+X5HWhQSwNactOjAaz5YGfuExMHdZrXhO8eJ+6Zo8G0aR87NilQpvsXDZ51vKZj1Y1wHjRTHxLJLGm/kCEI5WDptV+AmY0MOAuLen5ePH6p6E1XqlwyMnFYPH8FdNqYMXkiJ0NyFySDEerZIvIbdswLMVdDoeszM884BSIGyyxX8rIireN4MoDOIVG4NNnUf+vqSFJPkiYSOTq6PYuNcyaajcKKy8EYPntQ9sCV7NONe1hnkxkiKGRaI+rZEhdE/Chzj4gTyYApymDRIQ9oe0zD07vPePIh3msj/+0Qrb7w3+fMhJAs4A26WOLqiRssm6MMPzL7EEksyYqfer0IE0xHwGqVyXfSYtrX5NC4e/UBvoZt4+fl6KZIoIkFurfQeUzElUhfbgN42oeUTUb7ngyAKLdNXBfCKydusIkJYYbDRJb+5hdkk++tJTRVadalEaZYCLPdCoL09Uc4tuSvon/CKEhT6gRmW9RunlSkyUU1U9kHM68J3cHkktoKOvTN3UhDFrhx6QGOH7jMkw9jZuQtVZQXtied5J+DBrdCMTnHB+UXGedv4vW7D6hctiQ6NBbPLEtZ8ZuQKSFUOglZ/oFzN/Dm3b9N88qIkiWLod9XRyBRgKAygKxj6zSqyu5FqeuOKWQbJpsM5Rvt4dTTuHzylszXpzKjD4dFv3n5N1Z6bJL68smVTtdbGO4c77UZty/dL/AyycLZcoUBD+b2rj2EHbFC+2xJMMdvJlt60wDEaZI/VxDEBQ0s6PPdR3TkBzRZU5N5gTjoPrITLGONeBnbInZimcXKPEkZmV54ptixEQZtr8UQZ87eEgdk7U4DUjJdIIdBMhihnCllA+0PqurNsJ+E0COeiL+7lKsr3Ud14v6jBzces6W+q7o/JlWbzSRt0dwI7IjdywP9/y8VNDIlOZx0nImSWT97jKuoxSYu6wK2smU77UeKF9D310TM1RBEnQuEtvtUNOnYMNcBMe0/mljwmB6EGQ0N2DmRHD8ppJ3MKohckevij9WizWEp0GxihFVu6/gaoiw0qnwSARGZtdCyyUTEsKtldk8kVX7JFj8/+Y+0vkSfTdnEjwgLhVSHHvVkt0SqSM3psJBdNen+YxNvipluk/jvKEib9hO5QRLhp0pau36tsqt2lA1G1yPtI4OgWZjjr/ldPx1VrcgW/9DWr6HQGxayIU1OIOJH5ykR4iEz+8EwRDvXfU/fy0UtANdO3WKHSbetlmIbDtH5QGZFdB/qNrwjptmMhyRI8N7Mgc0Upkz32IJmeNHkUfiCWH4/Un8gT6RJExSFEuclDIGebjU23xlr+cWzRy85ooYw1Uwx1TFREHSPwa1QXcbVQHFBmWP79wsnCIcNk23UgDSQ+rVwMbhjU/wpZ/dhaeN3MLSSmXqIHnYTnGNw/eEzuGgMxchuklkFywunTt7CvHmrUKJEUSSuMWSSJk1I2sCalHgEwQ4bUL12BSxLXqCQDA1vg2jsXn8UXQe3hlOsvszXd2LveViO9mGCszjDGXWbiefkJ+4xqFKlCuzH+/MsKckYvZOtpPIgW+25EcvtE3nwEH7EnQeNkoBmo+d2tuRZ9iEz+mLh8rn52j4yd5jXz4Fn3MkZL2i/i9gN65RZJJIzUVWIiEje63vHRgPHd57mQSZZg/ccI6ys5nUdUFXJepibMNi2TAk4bljIFbvCABowk0tfVtppnNh9hu3rfwQZBpA0rUXXJiyDa96tsUwcCsWFNBrpyYGQvisRJcqZu3jkChvS/Ahy4uys0h6dh7Zj6R9VVcUFVX2oYrUpNPm7/dq2X0uW2VG15UejD3rmkOkDyQKp8kqga3C25zTOK/v2GiLCQPJcCg0n0KSCXeJ8VKqRPxe2MwcusIOiyBWSto8MMsh4gohjqElUdpg0rcNqpQnLYG9evYlY63Us2ySoaPZnybLI7IIknhTiTtcwES2qipG08VsQibMe7sYVW7p2XLZ8Hwr9LagyaTvSg+W3lH1ms3peruSG9qU/OzXu5gknX7K379xI7KqWw1gfJugkRyXnWEkcVen8mtffiQnkgmVzoDIzZ6fI/CBAfxmSo/dyxp/jFmPUrlNLqoYSy+wSsXZRMuo2q4Hwg84yDYHm9Tmvx7rwNDTrWB8BWxfInZC9ePoGMwd6sUzTd5UeWnUU3xlQlqYeGzccQ3DwDjRqVBVLloovsVUEPnz6jEEWS/D2/Ucsn6+G9o3kVyGThamH9FL8fkNqoAtApVNTLEk6hB2Zl5SekLVpWwe1a1dk2eKePRcwfLhyzKoMHNUe0f7JeHDnOQ7vPs+ui/LGVLNh2LvxGI7sPMO9ZM3a5y4tKija92uBHiM6IGNbFmvxnRJMpH5uGgVpQLejFc++7liZDpUZBX/Ykxvb8bQzOLn3HDw1QhGw1xF/Fcn/7Ymsva3jTGCp6sbucY07NsB44+H5kn7SbLZpbzvcu/IAdmO84LPTngdWeYGkUmRNT1KzGMdEflBOs52Qx/poMGjFfUHkWOg00RcWMUZ5WmgTiJz47HKE4zhvnNh9lsmZ+QqjX/a/KBMorJkym0S5TZQvRufT+UOXeDB5OfMaZ04d33GKXyJUrFGBM7watqmH+q3r8uCQiAMRFmUaOFCo9f1rj3Dn4j2W+F4/c5MJAvVh0SD5RxD5b9O7BWfNEfmgSIT8fp9bF+4iJWoXkpfvzs4QI0LST60nxhkP54rar3LLFputyA5cpgmIGQ5qGK4z6Kdr8Oa52+wWSNcGTSBMs5vILov5uVap+knEj/ouCWUqlIKurwaGavbn70yhyQG6izmOghQG02wn8nVEA/TLWdf4Gnl4/THLKKnqRVJl0b4iW3siY2TvTt+Dejxb9vi+r4QIG10rFApN5w05llI/aE44te9cNhnrMqwDZ5LlRRRWuq5jMkbbbrPaRGwyRljhkMhkjI4bWeNLQsao8uelGcbn2QD1nhgyo+B9Xif2nmMyRjANnZWvKqg4oEmJjeE7+L22i5rMydiLJ6+xNVqoxpg6b5hC7h1JCYeZjDVtUxstO8h2XJAfbN9+KtsfQJnuqTnh4LmbTMaqlS+Ntg2kM/msSPwmZEqKIV8J2cHzN/Hq7/coW1I5mj1zAl20RMKWLt2NpG0nlIaQFS9RFMPUuiIxYi82xR5UCCGr3agaBkzoirQ1h7HKdxucV86V+Tq1nSfhSMopHE4+yRUzImnSRPX6VTHdZhwibROwxCIOnYe0ZclSQUDVy4VR+tDvbImLx64h1mU9tJzVJFpWh4Gt2aQjfN4KLFkQy/lPHQeJXzmimX73JGuY9LbDhcNX4Dk9GHZrzMSqsFIFghvTrVZxrxRZp5Ndd26gwY1dghl8tcO4IuA5fREbF4h64nID5Z+5JdmwKcG+NRlM7CgMl7ajMKFCtfLcuyPq3yHTGKruXDp2FReOXMb5Q5c5Z4y+G72OpQh7HEQgKV2NRtVQtU4lVKldGZVrV+IqU7kqZXlgTj9p4F+8VPECDTLIGp+qr3+//Jvt2J8/fMk/qQeSBvqUUUf28fTKiXgRylUuwxK7Ft2b8qtpp4b5qoB9C6ok7Vt7CCnLd2XbwBOoP4x6IodqDfjloP7ulfuI99jA5hFU1SESMGn+aKiZj/lJLkb/nbK5iLjRgJ+WTxVZUdiyOKCKLtnSU+Ay9XcRYRmqNRBablO4N/Xty7e8fCKUIpK6cLkBWvdqzuunCnSIUSRL+arWrczV5BbdmmQvf9/aDL4O6L9T/pjLZku+V/1IKK1U3biSTucHyYbp/vCrUHYyMyEy1kmlHRzXLciTiGxZvAMxjkJTCoNFWixnFhdkJkQGJIR5S/R+SaDzwuIFsUxwaB8ZBWsVeFBNstUggyh+P0JnIFr3asbVGWkiwi6RTVc6DWqNrir5M4ORBOvCd/L3atK2LroMkv+44OPHz9i6WjghMU6jl9IQnytXHvLrr7/+h0EK2C/5RWrmxezxMt1PCjt+EzIlRaMaldCkZmVcvveEHWTG9mwNZcYQlTaIjNyL8+fv4dq1R2jY8PsHoaIwcmoPrFuejlNHruHK2bto3Er+TZ9T56mybPFo2llczLqBZh1kG1pYu3F1jNDuj81L0hBhk4BFe+2lLtecYDIMe9cdxpWsG+y6Zbe64AHYNJg2CdWG69RFiPfahE5D2qBtH8nI5DijYbxtO2L2wnVyIEKPeKBGA/HPSerPct6wEOYqrhxgHT4vGgZB4g1uyBlO8I+AzQSW265m+Sj9W26gGWEafFLlaMviVJZyUdWIpI95rZMGiTarTZkUbwzezhU6IgZ6fkKnucII+k5UWaAXGSgQiAhQT9X1UzfZpfHW+Tu4e/kBD66JmFBVjV65gfZliTLFmXAULVGUqzxFihVBkWL/Pgq5dU0gYFL46f0nHuDT4O3vV3/ze3FB8ls6j2o3q4l6LeqgcYf6aNS+PueUFWQARtuVsfkYdsbuxbGUE9n5Y3SeUfaWqtZAJgK/qjKQgQYZdpCToSg7rr96T8z2nM5E60fQeeg5YxHLakXSQgp6Flc+SqHoG4KSuCdOFNRN20nra9C6Lv9OfVzUP0jHkvYNVfSIqFFlmswxgg2Wcc8aod2glrCN+3f9JN8iohfrvObf/LE405/C2kkiStUu6oUiYxTPZBtUrfvz9xX9rfVwd84so9Bp6i/LK/8rff1hBBtG8nuq6I3Wz3tCRYSrJ2/AVzuc35Nr5mAJ3QvJdCN5+R5hqHWUvlQC2ld5bGSCRxNV2i7qkDay9pxjRQedv7ru6jInJ88fv8KWKGG1b7r5CIWQofTtp/D8yWtUqloWvVWUR2a+bdsJ/tm7dzOUK/f99aNseP/xM/Z+dSJX6Sg0wivs+E3IlBhDOzfD5c1PkHzsotITsooVS6FnryZI33cRSUknYWiYe1CmvFClejn0UW2DPVtPYv2K/TD3lv4DJS/UbFAVAyd0wc7Ew1jllySXKtk0i9FIiz+Iq6dvYceq/VCVQg/Bt2B76fDZMOrtgP0bj/Kr99ice5/yg74Tu0Ml+QRSY/bBWysc4UfdJZLt0EPWNHw2D9qpT8Rpoh+HqoojPRSB5GMWKwyY0G0KTeHm/Gk2uUsQRSC3ORoo0sCXqmU0e0dW8LmBJI5GobNRvmo5HlyudFnLAcYGwbPyJFb02bmBWizZjLBYyTbpZAdutdKYZZH/BRCJatm9Kb++BVUw7l99wCYhZHJCg/rHd57g+YMXXM16+fVFxIUqLSQrpVdBt6V81bIoX608O8JRhY8G99XqVebKRPUGVQtMvL4FVZRIUkhVUBr4iySJBKrwkNHE4Bl9UbH6r/u4nt5/jjW+m7E5NJmruCLyQuf0j7I+EcjYhowzaEBOFTRN58kYbzpCLEnZ80cvsdZvC7aEp2QTMQo31vGekd3r+OTeM3aQTF97iH+v2agaFkQZsIkJgaqj7lOD2DWVBuwaTmror9kju6JIZM1bI4QdEAlE5HKaiDi8LRMuakKjH3I3dd1i9cuqJPW22Qx3522m0HenjRbfOVDm+Jn9F+AxPZjPrxE6g6DhmHf/qAh0LB3H+worcUPaQttDaJyUX9D576+3lN+rLRjFPb4FxbXTt7A2IInfGwbOZGMVuq9JC9Qzt8Q6nt+P1B6Aes1lP2GaGJyKD+8/ce9Yl4HyrwLROSIy8xg5tTtLb5UB799/QtpOoWx5+AjlUDnlhv1nr+Pdh0+oXrEMWtfPfySEMuI3IVNiUB9ZyOYDOHrxNp6+eotKZQs+2yVLkFSRCNnOHWegqzsARcW0+JU1xmv2YUK2b/spaJmpMkmTN6aYqmLXuq9VMjn0kpWrVAbTLMZgqXU8op3Xo8+YzvwwlSYatauHSWYjEO+9matk7fu3lMqM7NwADZw5cJEHgf66EbBPNJVoYEsz2g5r57PJB/Xv+M4K556O/CyLJHTUjxJmGo1o+0TOeBqlL6zY5AUa6JJsjfrJiCR9+vA5z54y2jaqihEpI3kWVcuowkC9YXk9uOmzRPqIEHhrhnIVxayfA1w2W6ByrUr4r4JIdoM29fiV2yBIVOX6+/X77GrXpw+fWIZIxyYbf/x7/hAJEb6KonjpYnj38W/UbVQXRSTob8wvqCJIxidEJg5sPMJW9SKQ3E5Foz8GTeuDei1zltx9S6oSvTchdcWebCJGVR+qUOUmizuanMXnEUkyyWCCpLFkuJEX6Hxd47OZq7VEMggN29XDVOsJbIpBkwc0qN+2ZAeWWa1ickxki2zpZzqp8/GkPL81vlt4QoPIKFXuyJyDyJRILke9aA5jvbgvj6qcJuE6GKo54KftIYlksEEEVwOpD4yqe1SJ/qWBxygProyRu6TzJos8J3Gun7nFfXV0LvUa04UnVcS9x5Abo+uUQCZTZONP/a+SqBloOZ4zQ9ixtUW3xtBwnIiCgvZ74NxI/tlrTGf0HC3s95QmUlfux42zd1C6XElMtyq4Lb84uWMiZ8UZC0cqpDp2+uh1XDl3D0WL/YXhal2hLNi79zzevv2AGjXKo4OMVTzSQPKxC/xzaMemSiP5LCiUY8T8GzmCAqJb1auGszcfYmfWZaj3a6/Ue6pTpwaoWrUsHj16xbapAwcqhxlJk1a10KZLA74Rbl55ENoLZJ9vklOVjHLJqJcszi9JLo6Lo3QGImn5Hty5/ACrvDdD122y1NcxzWoMu7PROqinzCRkVoGXSdUHmzhjmPZxYLngprBUjDUQX/7zLahiZJc4DxYqrpx3Va9lLbHcD3+UP9JgmAKNg42i2P2PJF7igNZFg00aWFJPGQ3aNJzzluVQ/xj1GpFUjHpLiBQ6rFsg1jr7q/diUuYw1htXsq7DsJsVz/Lnx1zgvwba3zSwpldFCSdTRc5msnJrJdJIFd1DW4/jyPbjOHvgIg+GRaCKTu9xXdF3Ug+u3ORVNaUohATvjVx9EkkTyTCEiFEX1fa/PAfJkCTUKIot4wkks6Seytyqb6IKHJGsdYFbsyuQlH813W4SOzuK1kcmIiRPpL5A0d+YLtHL7kcjuS25OJ7ae45/pzBn08V6PNkjqs6QxNFHM5SlqkROHdcvRPOu//aTEehvl9uszs4vIzdG6s36lQFJ5o6TfM0Qce84pC2cNpjnScaocmep6s4ySIrcsFplnK/zgyZ6stLOsLzVcd18sa3xf0SM01qcOySMBqAQaUkMkX7E+kXJ3M9Ly5zrNwOymHCIcRP2zE21GC3xd89vdUyUO9bxa1yPvEEtFITBYzuhbAXlmWQnZRNh2LC2St+P9ebdB6Sfvs7vVbso5jjKAoXbtP//AVQ7f834OiZsXlRm0INoqGqb74IFlQXjNYWuddsTj3A2mSJAVTK60bHjYtYNma+P+mPmeE7h95sWp+HOFfFytfIDqhyYhApJWFLkbpzeL5y1KiiadGiA2Z5C6U6ExSpc/WqNLQnIzto4dHb2wEVkl50fkASJKmM0aKZwWMolEhdUKdP1Fg5oKNspyma1WBlb5I5HVYmSZUvg9L7zMOtrjyd3nom1TpKgLTrkjnota7PhhFlfu+wenN9QHpCRBfdPGSzjoOzZrc04wJnICJExMrYgcu6VaofE+xGc49ZpSLtcydj10zfhOMEHcztbMJkXVYb89zojMN0VXYd1+CUZo/NykX5ENhkjCWDgftdfkjGqgO1avZ8t5KfW0WOpLZExqohRdSn4kAf3s9H6yC6feiMNOlswGaOJF8oHCzroxmSM1p0SvRu6befz9yfH0/nL9NlmXlR5J4nbGs8tTJyIjBEJCj3q9RMZI1JJUkYRGaOq84LIub8kKlQNtBvtxWSMyKOLGJUxmiSxGOrGJjMUZE2V6GIlcu8z+9EAhF60b0hanFe496+QtfssEnyE+V3zFutwNbOgoOpjjMs6fq/nNRWVa0k/IyvBP4lNcai3lyYPZY2nD14gaaWwOjZ9gWJ6x25fe4Qjey4I+yQ1lMcN99atpzhz+o7QaEdV9qYqBcWeU1fx8fMX1KtaAc1qF/x8Vxb8rpAVAtmi//q9OHH1Hh48e816WWWGqmpbrIw9gOPHb+D+/Rdc/lYGdO3fHDXrVsK9W0+RuiETY6aLV+GQJmo1rIpBk7phR8IhxHhthVu8oczX2XlwG3Qd2pZdFyNsE+EUX3DzjR9BxhvDZvXH9qg98NOLwOKj7vnq1foVqCp2YtdZZGzNhPv0YIQccv2l1CgvDNMeyDbX6wK3sQyL7NLzMwCiB6jhIi28evKaK20O433hlWLLA0JxMGnBaPxZ5E82B4n33IBP7z9Cz08jz0EBuUMGpruwwQBJsxxH+sF9m/Uvbbq/RY0G1RB0wBUe0xfh8LbjXG27euI6tD2nFVqzj8IMIhwPrj9i50jqUzqdfp4zuL4l5yS9az+gFboO78jEqWYj8cp5JPHLTD2FLYtTcHjrcV4mnVsDp/ZmGau45zr1mJHLIQ3MHNYvRM/ROfeFklshBUiTKyhVOkSg62G86chsaaJo21KidnPwtaj3jSrMFDQtktKScUiQ/lIc2Hg0u5JnHm343fennjT3KYGcW0cYY6DK/WJFin7vfPjyySu2xqdJDOp1m7dUL0cpowjp6w6xOyn1GFIGoE38vDzdFIkMWg135woZSQ09tlvnq8JD8R6hJsv5/Sy3yRLLAalH0lsrjI/3sFkD0HfC93lrkoAqiwH6QjfLjoNaSyXD7EeQHH19SAq/13VT58lDWWNNyA6WJrfq2gjt++TcMylriHrHug1ojtoNlIdIiCbQu3VrhMqVlXuMSUg5JnSUVe3c7D8jVyT8JmRKjqrlS3PYXdaVu9iRdQkzBolvo6sIVK9eHh061sfxzBtISTkFTU3p38wlAQ0Oxs7shTDXzdgUewAjp3RXSFA05ZLtWncEx/eex5lDV9C6e2OZr1PHVR2ZaWdxePsJHN91Fh1l0Mis4z4FR1NOcfbScvs10PedXuBl0o3WbKkO5nS+xllO4WYxMFuiK/k2ek1jK/XjaadhP9YHIYfdxQ59Fp1D5isMWJ5EFTLrER7wTbNH4w7iWX+PNxGaIVBvGBHDt6/ewXSJbp7kiHqjFmW4swU3bf/8/g5cNRDHUrtUuVKcxbTCPoEttdf4bcHVUzdhE2cqsdX6b+QNGiBT7xYFP5Ns9MqJG7hy/BqfOz+CctTaD2jNlRmSIuZn0oEqbFvCU7Ft6Q7uQxKBZI0zHSbl2V/2Lai6Rf2OBP0Are/IGH0fkhKeO3iR7ecp904EMjAZPL0vm4rUalzjJ7fCEOOobPdLMiAxDNbm/izRcvckHORrgsga9UlqOKlj0sLR310XRF6JNFE2GbljmkXMwaCpfXLMYrMb5cEDfqosU2B1Z5VfGxRQX52fdhhXEWmfWcYa/UTwcqoK2o/zYTJNRj+eydb5CsUmsx0yGKEKKGUGUo6bpMSJyBhVwGs3rYE5UpIVblu2i3t4SUZJ6gdZDHiX2CRwPyPZ3HcfLvtWjCf3/62OTVswXCGD+BfP3mDnxuPZfe3Kgk+fviA1VeigOkxJIotyw4s373Do/M1s47v/En4TskIA1U7NmJAlH72o9ISMMGxYOyZkydtPYcaM3gohPjlhyLhOiAnegfu3nrFsoMcg+fe4Va9bGUOn9kRSzH7EeG+F93pTma+zTtMaGKUzABvDd2KpTTxC0x2lHrxJfQamYdqwHePDPV99x3dFq54Ft6ItV7kszJfP5T4NsnMm+eHgaXmHJucE+s428aYw6m7NAzaH8T7w2WGXp531t6CZc8f182E1zJ17fOin/14ntjcXBzSrT1IsGgRSrtP7t+85CDqvng/qhfPb4wS7sZ44m36RzQz0fDUwziTvwQUNbGe5TUWj9g3gqxXKQcskZyNzg2ZdZD8h8F8GEQoiEncu3ceNM7fYlp9eV7Ou50i+qBLQoG093u/t+rVE6z4t8jWYF+HJ3adsJU+GFaIKFcn6yOhjhN4Qscw3fuz/EpExAlV+FptF48vnf3Dr4l1cOX79O2dHqqD1GNMFo+eqckVPVA37VvIWbbeaJZMEIkdEtEh+KTrXaZ1ktiGqilGvGkU/NGr3r6EAVdfiPTYixjGBSRNFCBgs1kTHPj8PHGmSxHmSHxtbkGyPAtd/lTFG2BSazESQoKo1AKZL9fKcHKHIAXJEpOobfSfqrRO3ikmgbSNpJPWkkuOkWYSexOSAZIrHUk+xTNJutYnE6oFvQfl5kTYJ/H6Wsxqq5xCDUFAc23mGJwfpfkySenmQo/ig5H+rY70VM4hPij+Mjx8+o0nrWmjdWXlMMzIyLuPFi7/ZKZsqZMqOtBOX8fmff9C0dhU0qC59Ka0i8ZuQFQIM6tAYXom7ce7WQ9x+/ILNPpQZvXs3RZmyxfH48WscPXoN3eVQBRIHxUsWxbBJXbFm2V6sj96vEEJGmGyiitT4QzidcRkn9l+UywNimjnZ4Gfgxrm72Ba1B6N1B0l9HV1U2mLIjD7YEZsOf/1lCD/smi+y8yt0GNAK06zHYaXbeiwyjETTTg3yPeAUgWRF1Oth0ssO5w5egq/2YlitNMrXoIAGPm5bLLFgkBNnnVkMdUXgPmc20hAHNGgmUkYz/lQdoBl3CobOa18R6V2wUh+JLluwfVkaws2iuSJgGDxLrCZ+coykmXTnib5MSE1723IlhHrj/kuyD2mByAAFdNPgmZwGyU7/xsVb+PjqEx7ffYr7Vx9yFexXFvokP6TsMXIyJMkghUFTYHFeFZjcyN+FI1fYOCNt1b7s/DHqEyRJLMkA87Jn/xUow27AlF7YvVooqVoftO2nv6EBNPVKdRveEcN1B6NqnZ/P92cPnnMmWNKyNK4A0Xk1VLM/ZrlP5WgA0ffYHrkLSxfGMEGh5VKf5RTrcd+dx0TYSGZLTpMEsvY3WDQLr/9+9dN+2bo4lSWAtM6WPZuxyQeFTf9qPyZ4bWQJpahPbo6/xk+kMicnQ7fJgcjccYol2XQPaNxe/IE1fd5ZLYBdBSvWICOSBRJLu0nyuuJrAPXcQA0+LgUF7Zcgo+Vs99+yexOM1BsMaYP2wVLr1fx+tN4gniyUNR7ceoKUuIP8fqbFKIXc64RB0Iey+9mV6X4rkiuqqLTBX1KeqJUFUjOFcsWhnf4b2WPf4jchKwQgu/suzerg8IVbfDJqqyqPVWpOILt7urjXrT2KbVtPKA0hI4ya1gPro9Nx5th1XDx9G83aiC/pkRaq1KyA4TN6YXPkXsR6b0W7XrK3bSVnQE278Qg2i0WM6wb0Hd8F5SuLL9cTF3pe03jAQhWDle4beZZVGphmOx5nDl5kqZTrlEVYdCB/mWLfgsgcOS9aD/fA7vgD3E+m4Zi/7SRyRLPjZCtP33XhEBf47XZA5Zrizdj1mdAdjhvMmRwd2pIJq2FucN5ozhLD3ECSLrL3pu+wdGEsS9WIFNgmzGNL/rxA1YewY17wmRWGAxuOsMnC6f3n2X2OTBbkibMHL7IUjfqqSMb15M5TdqGkqgwN3Chcmwbr1HtH3/uvryHOIht6CnMWhjoLg53Jil709/yTKvN//EH/4+uLKiw0YKcoAvpJM+Yf33/kF5k5vHv9nnOtiGARUSAyJo75iqiCWa9VbdRvVZdJFw2Q6SUp+foWREySInayKcvdy/ez/52yukjuRoYdeZGJvED7xzLWmA1D7ly8991/q1qvCpNJ+j6/6q2i6tla/y1ctRNZ3ZMEU9t96ndxBHcu3UPgnKVsL09o0qkhm2382ONGRidURaYeKZLOGYfqMCEjmd63hIzMO0IMI5kAEigGgOSMv5rcoM8vXRDDkmHCVOvx0HSZnOf9l8xEvGaG4ODmY3y+OW1cyDmF4oLOoxCjKK7i0X3LdbN5joRWHNC+dp8RwufxwCm9oKrZH9JAyoq9XHGj7zcvXFsmyhaaDLx18T7KVSrNk4TyQFzAdp686NC3Odr2/N78RV7Yu+0knj95g8rVyylVEDQ5Yh879tWtcJjym3k8efkWxy7d4fdDO/235IqE34SsEJl7MCE7rvyEjDByZAcmZIcOXcHjx69QJR+9OrIEZZD1H9EOaZuysC4qHdYBkoVwFhRqhipIXnUQ545ew/G9F9Cpf8FDPPOCqmY/JEXvxdVTtxDttB6mwZpSX0eZCqVgGKgJ58lBWOO/DX3GdUUTKWSa0ODAcoUB9LtYsSQs1HQF5i+VvJ+MzDKMQ7URoLcUK13WoU7TmmyAkB/QDLxXqi3M+jni3pUHbK3vu8vhlzPzP4IqDe5JNrAf48WucvMHOMJju012JeFXoMHjRLNR3HtEVbastNMw7ErW9uZo0DrvmXIifQ5rF2BdwFYss1yFPfEH2GjCOs5Ubtb4VNkz62fPg0plB5FviiCgoOfSlUqidqOaqFKnMps51GhYlXuoJK1M5ZVvtSl4O3bE7mWDBQIN5nuN68rSv1+FOUsKInW5mV/8iixS+PPWJamc3UWgrDDKOCN5sQgf3n1AgtcmoaHNx8/8PYgIjTUa9p18+t3b91gyP4YnGghE1MhkI6eKOBmBkHkHSYfpmtD2mAa1haN/Sa6I5PtqhyFtpdBtlExF6DrKC0TiAvWWYk9iBk8MOKwx4/tHfkAEcFtEGm8bZY3llv+W17b4zl7Cbqu1m9SAcYh0erxIqrjEfBW/13CYILECITe8evYGse6b+P1M2/EoXV66uZg54e61R0hLFLrqzrQYCUWAyPiGFcL+tdHTeihNEDQheftJnqhq174u6tRR/qzKnVmX8Y9AwEHQtSrLP09W1vhNyAoJBrVvAo/Vu3DpzmNcu/8UDWso98VTt24ltGlTB6dP30ZysrCXTFkwYVZfJmQHUs/gwZ1nqF5b/jrkStXLY9j03ti0bDdifbZyJoqsq2REaub6TMP8oR5IiU3HiFn9pUKWfgSFiJLb1751h+EzezFCDrrk6VomrqzKKtYQFkPdkRK9h3twJO0nIwyfPYgrDom+W+A7ezGq1quM1r3yl2lCs9w+afaY39+RXRAtVd3gs9NebMc1Mjeg3jCqkFF4tWkfO7Y4r16/ap6f7TGqMxYddONQWur5Me5hzQHSfcbn7bQmInXNuzVhUkeE0rSXDbRcp2Di/FEFrrjkhUNbjjEZo4y2KZbjmNSQ5LNYyWI8YKFB+h//+4MrWTS7TeHNwgDnT9yHQS6V/PODMNyZql3034V//xlfPn3hgQZXuAT0E7w8rpz99T9eL1UCqP9GFABdokwJ7gsqVbYEvycSRsYnIhmdKIesatWqMts/1KN0JOk4NoVs/844g0jOKP2hnENWorR8K5k54dGtx4j32sR9kLT/RT1gMx3U0GN05+/uZYeTjiPUOIrPUULnoe1gHKbDLqDf4uLRK+wIKqoCTpg3kqWOOd076G9dJvmzjJQIM00mkCvlr0BVO1d1f3YbpWO/IGouhszol+f3pGO+aO6ybOdJsqenyl9+QFW1pQtX8nsy3ugxSvI+8DV+W3Fo23E+d21WGUmlqk3XSKBhFAemU6j0eGPZ5HRGO69nd8oGrWtDVUM+Zl+r/JL4PtB1SGs07yie+ZK0cfzAZVy/+IBbJlQnKc9kOlV9RdljI0cod8atCCmZF/+z1TGG4DdyxcuXL0mzInj+/LnC95RR6AZBe31/QejmA4LCgNSUU4KBA9wFUyaHCr58+Ufi5Xz58kVw//59/iktWGtHClSbWwrC3TYLFIWnD18IxtQ3EahWnyvISDklt/V6ai8RDC2rJZin4ib4559/ZHIMnj96KVCrO1egUny6YJlNvECaWOG0RjCkyBTByHKagmunbxVoWZ8/fxE4jPMRDP6fmmBc5VmCWxfuSrSc2xfvCibV0OHlGHSzErx58TZ/n790TzC9gb5g8B8TBWo1dQRXTlwX+xi8fPJKsHCwE3+WXsttVws+f/4s9rpfPXstcJrok/15cxVnwZN7zwSyRNDcCF5XpPUqQWGBLO5DBLoGj2w/LvDSDBaMKT8z+zio/DlJ4DTJV3By71mxr1NZ4961B4IA3cUC1aLq2dtp0ttGcDjp+E/beP/6Q4H9WK/sv1OvpSPYk3Dgp7/7+OGjINo+XqDylxr/3eTauoLMnTnfD+m8XuWzVjCs2GT+W63mxnzt5YYXj18KjHta89+PKDlVcGjrMbG+Kx3nQP0IvqaH/Kku2BG7T5BfnD98WTCi1HReRsCcpQU6jlm7zwiGFpvK975ty9IE0kJy9B6+T48opyW4dfGeTK6Di5nXBKrlZvFz59T+CwJ54ObF+4JhNQz4+Xr5VMGeEwWBpWaE1Mca0rgXZWRc5vHZ2DEBgg8fPgmUHfefvuLxb4e5/oKHz18renOYExA3II4gLSiH/d1viAXKXCAkH7sgdm+DItG3X3OULl0cDx++RGamUKesLJigJbSdTVl3DK9f5tyUL2tUrFoOY2YLJUIrPDfzbKw8MMtpIoqVLIpzh65gz9r8hySLA7KTNwnW4vdrA7bh3KHLUlv2NJvx6DCoNT78/QEu6oHf5SBJJIVcaYTmXRvj9bM3bGVPeUf5Re2mNVm+SBWVi0evckYR9SGJ/fkmNRCw35X7jyholkKgyZ5fHNA6Seo4wXREdvi07UgPzmMSB2UqlGZ7cOojo4oRuTBSOC/lrckKT+8JA65Jbimv814ZTUP2bzgMo+5WnDO3Y8VePmfI8IF6w2KvhcI+cT5L/xRpAkDbeXhbJuzGeEKziRGHRlPVktwVfXc5ImCfy3dh06+evcbi+Sswq7kJDm46ylXJSfNHIep8EIedf/tdrpy4DsNuVljpspYrpmRMsuSkb46SQJI9+s0Ox3LzeJY9knQz+JA7X3u/AvVXmvSyxbmMSyyn9kyxRbcReVeo6PkaZhrNLpa0vQuXz8Xg6fmzKqeKoChsuotqexgFSy4vJGkoZTFStWfIjL6cOSYNUIzAEguhuYmG/QSZmGzQ9R22cBXv0wFq3dGml3yqG6v8k3idPYe1Q2MF9IoTrpy7hxOHrnJVdpyG8qiECNTfT1AZ2ob7/pUdqV+rYxQDRXFQ/0X8JmSFCP3bNkLxIn/h9uOXOHdTKP9QZhQrVgSDBwvzZpK2CUvjyoIOPRujQbPqeP/3R2z/qjFXBCYaDGGJ1I3z97BvszCjRNaoUqsiJpsJB++R9mvYVUsWoLDTQVN68SCCAqNpYCINEImyijFE5doV2V47QC+iQBMU1M/ivMmce4LIYMJhnI9E20r9W14pNjzwO3/oMiyHueWLlJEhCA1u2/ZrySTTZrgbdq7cJ9Zn2ULaX5ONGYhUHUs5ydb2JO0SBzRQHK4zGKHHvFh6RsYBJPFymxLwnd25tNB3Yg9eJ7kGkiTs/xMpI2KxZXEqtFvOg9MEXybwZFxBkkSSr66+vZh7sKrWVWxwLPVoEbmf2cgQtqM82XyGruVOKu0QsM8ZPmmOLLkVkQwyR0n02QSNxkbcn0ikqcOgNlic5QNdn5nfyevIjINs9qn3kfK8aFLBNn4e5+vlZE5z98p9JlZEWkl6SlJG6oPMzQSHzn36DEkgq9WrwhMerXvn3atL95IlC2KxKTSFv9uCSH0mQfntl7IZ6cmmJOS0aZcwT+KoEZLheswIwYtHr9CgdR0YBWtJhaDT9wyYG8n3qGadG2K8iWykiuTue+HYNZQoXQyznSdBHrh29g72bcrk99MXCJ91igAZiBH6qrZBtVr5j7eQFcgBm/r7CcMLQfYYYfuxi98VJv6L+E3IChFKFi+Kvm0bfndyKjuGf9UmHzx4Cc9zyOVRFOiBNk5TOGO1eWUGDx4UgTLlS2K8ntCCfqXvNn74ygMTjFRRvV5lPLn3HHHeW2S2njm+01Gxejl2Iox1XS/VCpxtnDEPcvauPYTN4akFWh4ZcbhttcwmU14aIRKRBAqJ9t5hx66WFw5fYUt86psQF5Ql5ZFsy5UCqkJ4zQzmQGdxCSe5zFHVgAw/qFF/Xh87bA5LEfvz9VrU5s9Pt5vIs7pky6/TxowrJNIEbSdlTtF1SAYOQXOW/qdJGe3/M/vPI1BvCabU1sOiuRFMFOh8m2I1DisuB8M4dDZXw2Tdv5fXdlKYM9nNT607B9F28Xwe0XZST1fU+UB4Jtt+R2yogpYSvRuazYwRYbGSz3cy4yAXUuqH/DEL7MKRy5jbyYKDqOl+13t8Nyw748/Vs5ywN/Eg/z31WJarUhYW8QZcQcyNlJBL44IBjhxXQNdk0EE3PrfFrYyJXBhNF+tARSPvXrMf+wCdJvpxmH2VOpU4ZoNiLiRFjNNanNp3npdhF28qsbtsTgHQIlfFBREUUC/98+7tq3eIchDa809dOFqizD1JsMJT+EzrO6YTGrSUvkGJOHh8/wX2Jp36TpGjLEhJPsWTK9TnX6+eZG6f8sT1B89w4fYj/PW//2FIx/+e3b0IvwlZIYNq5+bZ5dsvhWAA06hRVTRrVgOfP/+DHanCPBllQf/h7VCxShk8ffQq+8apCIzVHYCyFUrh7tVH2LlGPtU6MjHQ9xY6TK4PTcWtH6yupQUytzAOnsXv1wUmSVW62LJ7U+h6Cb/DkoUrcf6IeNWgX4HCnR3WLWBTifR1hxFuFiNR5Y0GgD477bLli0TKXj9/I/bnycTAapVJtgNclE0c/HUWs1mFOCCb8dAjHizpookGsrZ3UfcXmxiSVTuF+C7KcEfdFrXw7MELrpB4aQSLLYMUB2Rjbr7CkM0SyLZ84SAnrnj+10BEkwKI5/W1Z7kfBUZTcLFB0CysuhnOod0Vqytu9pzO8eunbyLaPh7aLU25qkQW+zQhQGYi5tGGWH1nCeb4aaBOs1rffS+StZK81XdWGB7ffsoEhIh2WKYXuqj+K2MkvHvzDmGmy2Hcw4adUmlSxWa1KezXzM/RWZQqbkRcXScHcMWYbOZDj3qiVS65jfRdKEONnEvJyIOqeVR1FIcIsIGHQSQ2hiTzds9brMvGP/kBLcNHK4xdU6kiSFll4kZh5ISMLZlY7SV0Jpy3WIdzBKWBe9ceYqmlUKo4y0VNJq6KBJrse/7oFWo1roaxc4dAHiDn4iM7z/CE0kxzxTgrEjbGHmQZbrvujdC4lWJIYU4gIrZ9u1CxNGJE4aiOJR+7wD+7t6iHCkpgaiQrFDpCFhoaivr166N48eLo1q0bjhw5kuvfr1mzBs2bN+e/b9OmDZKSklCY0atlPZQpUQyPX75F1hXZDKKljeFfL/pt204oVe9bkaJ/YfR04aws2dIqattKli6OSUYq/D7Of7vcqnXdVNujm2o7nqUON4+T2ffvMbIjBk0VShd9Zi+RqkRyrKEq+ozvyoNH6id7/jD//V/fgpwbzaMN+P3G4O1I9N0s0XIo74tIGTn1XTp2DeZDXPJFZqhKQrbcNGgnwkJudjbDPfD2hXgSSJG1PS2Dqojpaw9Bv+NCnD8sPiEmC3zKLKPKCA1Qd8bu4wE7Ddalda4Mnt4XFl9lljSI1e+wkMmZMt0nCipP9NYM4Sol7cMhGv3gvdMe0ZeD2fJdUY6JIhK23HY1ZrUwgW67BVjluo6rOnQsVDT7M/lZdNCdifO3tv70Wep90+9ozrJWchelCpqO13REX1zEwed//vnnT39PEs0Ni4R9PYOm90HkuUD0V++VY6WLYhGMe9qwtJMw2XIc96tR3tuvQPexYMNIhM+L5nUM0x4E1y2WYjkREpEK0l+W3TM2f9kcDNfJHxmjdS6eH8NVZZrUsV9jVqDAZlIVeGmF8fsxBkPRX60HpAH6rn66Efjw90e07dMcYw2Ezx5p4/al+9gYLowvmOM5hZ+3sgYdg2gP4T1bRb07ajXM261WFnj75j2S1xzJDoJWJhw/fgMPHrxEqVLFuM9f2SEQCJD8/0CuWOgIWUJCAszMzODg4IDjx4+jXbt2GDp0KFsR54SDBw9iypQp0NbWRlZWFsaOHcuvM2eUq1KTHxQt8hcGtm/83ayBsmPgwJYoUaIo7tx5hhMnbkGZMFy9G9vRXrtwH5n7hQnwisBIjb6oULUsHt15hpTVB+W2Xn5QFvsLWXvO4cBm6crSvsVcvxk8mLp39SGWWq2W2nJp8GS2VJerW9Sg7jo1SOxK0q8wYHIv6PvP5PfLLOOwI2avRMsh2RZZ4FMl4ErWDSwY6MTVpvyABu3Om4SSpxO7z8BplB8bFYgDkbV94H4XtpV/cOMxSxjjvTaKLQ+kgThVRkjyRYPLl09es5zNZoQ7Ht58DGlg4JTeWHY2gE0iqKoRoLuY86Vk0bsmTxCpMOpuzblXNFtvEWME8+WG6DCwzXeERZ4DG6pMxTqtwezW85iExbmv54E/ydZ6junC25hwPwILowzQtFOjnz5/cPNR6Hcy5963a6duckzADPtJbECitnDMT4HMFPhN1Sr6e7Kop/PQfbsNLGOMuYKc0zaShHVuJ5hX1wYAAQAASURBVHOhRLFyGf57CpjOrQfr7cu3sB3lgS3hQuKr6z0D85bqZUcW5GX/HaC7lCcCaPKDJmSGShC2nOCzGRsWbef3VCXsNETyoF2atHKa5M8h5VQZ1POeBmlhY0gKzhy4yPeU+Ut1ZCKR5SBss1gmyTTp16UA+yI/yNp3AaczLnOI/FSz4VAUUtYexd9vPqBOo6ro3Ee5JHbbv1rdU38/9fkrO87ffoRbj16gWJE/0b+dfHIyFQZBIULXrl0FBgYG2b+T5WfNmjUFHh4eOf69mpqaYMSIEd/9W7du3QR6enqF0vZehIxzN9j+s/+CMMHHfNhbKxL+/tvZYtXZeYPS2E2LsMRjC9vSms9cKlAkNkXuZoveae2tBO/efpDbemPcNrAd8fSW8wXv3ryX2TEg22ayV6bX4e1ZAmni5vk7gjEVZ7EldJjZCqksc6l5LNtVDy06RXCkANtL26ZeW4+XpdHMRPDw1uN8L4Ns8CfX0WPr7vGVtQRZu07n6/NvXrwRuKj7ZVuQLxjokO/tIHvyla7/Wo6PLDVNsNpjveDD+48CaYDOrQTvjdmW6vQ9E302Cd7/nfM5KW/k5xp4++pv3n76HhOrzhJk7jgpkDdoO6+dvinYHJYscJ3sL1CrMTv7+NOLjiPZ0qet2id48/LXMQ20/7ct3SHQbmWa/dlRZaZzvMLLp69y/My7t+8Fsc5r2Gae/p6OaZRNHP97bvb0DuO8co1fyOkYUGSEVguTbFv79PWHxN5Hnz5+ErioB/C1qfKXuiBtVbpAEqSu2MPLoNfagK2CgoCs8V2nBvG9TL2uvuDpfemNPW5euCsYWV6L78GSWueLcx3sWpPBz5RRVXUF968/EsgDtN+MVb34GRpuu0agKHz6+Fkwc4Anjym2Jx6RyTokfR4/e/ZGoDLEk8dily8/EBQG+K/by+Nd84iCXVeFwfZe+b0uv+Ljx4/IzMyElZVV9r/RzM7gwYORkZGzPTP9O1XUvgVV1DZu3PjL9Xz48IFfIrx6JZQZ0YyysjSdd2xcCxXLlMCz1+9w+PxN9Gwp/XBfaWPE8HbYuiUL+9Mv4tmzNyhfvqTYn6X9TjNustr/Y2b0xOZVGTh15BounrqFJq3zbv6WBYZO6YF14WlcJdsctQcT5w6Wy3onGA/FjrgDeHT7KVb7bYWG7TiZHIO2fVuwPGZjaCoC9COx+Kgbm19IA9RbsTBqDpwmBWBDcDKadm6IAeo5mwSICy23yXh67znS4vbDWc0fnsk23FMjybb57nKA5VBXNnIw6+/Ibozk6iguqDoVdMAFtqM9cf3kLViouGBuoCZGzlERy3GNgo6pL63j4LZsWkChw3rtFsAoVJtlY+KAKhRkQEG9aWTCcWb/BURax3FoLlXRug7/dTCvuKBQ6vYDW7Opyq1zd7DUPBZrA7ZiqvU4qGoPkovs6VfIzzXwvz//YOdEqvKRY+fju0/x6dNnmRgnfOuKeDnzOptmkDEN/aQKy7egSliHga3RV60Heo7uwqHKIvz4vaiauyUshStWVBklkPxvlL4KHydRhevbz9H+IclepNUq7ikjkFEJnWd1v5pq5LT/jiRlIUBvCUc+kNxP03UKJswbwc/4b//+x2OQmXoS7lODuD+ycq2KcFy/EE06NRTrGFGPmtuUIBzaepzXSfEXFKqe33vcke1Z8NNZwu8nmo3EOONhBbpPrl+0HXvXHOLrjYyLylctK5VnHykHvLXC8fH9J3Qc1BpDNftJtNy8rgM65yJsEvi9mtlwVK1bSS5jp4yUU7h04iZLbicZDlbYeG1P0kk8uv8C5SuVRv+RbWWyHZI+j6l3jPr5qa+/YcMqSjOm/RX++UeAlK9yxSGdmijV9spiW/4gVoZCgHv37qFWrVosQ+zR418ttbm5Ofbu3YvDh382QyhatChWrFjBskURwsLC4OTkhIcPc7aNd3R05P/+Iy5cuIBy5cpBWRC2/Ri2HL2MQW3rY8FY6WjLZQ072y24du0JpkzpjJGjfs6Zye3Ef/nyJe9/WTmQLXVPQcaOC+jSvwnmOihO6nBgy0kss9+MkmWKw3urIVviywPHUs8gxCiOByauW0xQvUFlmRwDGgzYjwjA/WuP0X10e+gHSU+KQ1jjnYQtITtRtERROGwyRp3mv84oEgefP36Gv2YETu+9gFLlS8JmnZHEyyRJpadaKB7eeILy1crCPE4/X8uiY/D44ROsc0lCxkahvHTA9F6Y6TqRJTri4sG1Rwg3isG1rJv8e4+xnTDTbRJKV/i1hfiPoMfGgXVHEe+6CS8fCSetOgxpjWlO41GtfsHt2knqdGDtUWzw344nd4SZZZXrVIS69Wh0G91RIblc+b0GXjx8idC50biQITSbqd6wClR1B6LPpK58fkq8HV/+waObT3D7wj3cPn8Pt8/dxfWTt3ny4EdQ3mCjjvXRvHtjNO/RGI061GdDn9xw8+wdJEfsRsaGTHz59CV736to90O/yT1YppgTrhy/gVUO63Hla+ZkpVoVoG4zBt3H/Pp4kTQvzmkD9qwSyrRrNq4G/VAN1P9FbpToGJQtWxY7ovbxZwX/CNC4UwOYRM5m8iIOSBobOGsZzqZfQpHiRWASMQvtBrZEfnHx8FV4TQ3Hp/ef0HN8J+gFTS/Q/fHs/kvwmbGUj/F0p3FQkaI731rfZGwJTUOpciXgmjyf3W9lcR2s9kxCyvL9qFavEly2GLNJkTwG7o6Tl+L25UcYMasXJhoNhCJA90X72atw59pTTJjdEyOndZHJeiR5HtM+mm+2Do8evYaObi/0769cUsqccObWYyyM3omSxYpg9fxxKCphdIQsQPuf/ClE9yNp4DchE6NCVqdOHTx9+hTly//sBKUoZF29i9kBa1GqeFGkuuugeCEI9ktKOokA/2TUrFUBy5eTdv0P8Qeijx+jSpUqMiNk1y8+gOH4YN6mpUlmqFFHcmesgoD6GYyGeOLmxftQMxwCDavR8nuQTApCZtoZtO/fAm7rzb4bREnzGFw4ehXzB7rwA8IiWh/9J3WHNPef7ShvZO06gxoNqmLRAecCV+Hev30PS1V3rjpUrFEeAXuduBdG0oBX62Ee3MtDBIhc2Jp3E/aE5gXRMahcuTLW+W9FlPVqPm6tejWHTbxpviylabZ8tfsG7iGiwR+FEZuE66D7yLxDc7/F21d/I851PZs1EImiChblaU22GotylQv+kCKDm+TINMS5b+DqCYFynWgd/Sf3kpoFuDiQ5BqgfZLgvYmPl8jlkghRlTqVucJENu7UJ0XVNKqI0IsmRWgi4MP7j/j47iNnJb568hqvnr7Ci8ev8fLRyxyNf+h6JYJPFaIW3ZuyCykFjYuTf0XEKH3dIaQs382VTxFa9miK8aYjuL/sV8uhXrkYhwR2JiUUK1kMky3GYILZKK5W/Apk4kJBz5T9RxhnPBxablNy/Qwdgzs37yDOfiN2rz7A/0ZGKcZhOmIP/Mnx1H6MD4dF036nDEIy88kvrmRdx8LBLlwR6jaiI+zXzBOrZ+1XoP1g1MueA+oHT+uN+cv0pDbxcDbjEsxV3Pmeax1rwEZIsrgObp6/C4M+TnxPcV5jis6DW0Me2LXuKPyMY3gCM/KgA5vMKAJH912Eo34MSpQqhuidC1FaRhOqktyLjh27DivLRDbzWB0/l/v6lR2eCbuxJv0URnZrAacZsjGfkRQvXrxApUqV/n8SMpIslixZEmvXrmVjDhE0NDR4x2zaJLSG/RZ169ZlyaKpqWn2v5EhCEkWT54UL6iYCBnNQjx//lypCBndWEfYR+LBs9fwnj2iUGQzvHv3EWqTgvH33x/h6zcVHTrUE/vmQ8YtVatWlWlGj61OFDL3X8bo6T2gbyMfIpQTMpJPwllrKQ9Mog45omJV+VRm7117BL3utvj04TOsl89B328e2tI+Biuc1yHOYyNKly+J8CPuqFrn1+5p+QXJxAx72uLB9cfoMKg13LdYSBzK+u0gbv4AJ1w/fQs1G1dH4D6nHK26xdq+Z2/YRp4IHg8IN5qzjCwv/HgMDicdh/vUQB4QVqxeHnZr5qN1r/y5ZpGszVsjhN31CINn9IV+gGaO4by54eb5O2xpfnzHqWxp26QFo1l2Jg0nQaporPHdjASvjdmh3ZTZNkx7IMYYDuPgX1mjINcAWb4nR+3G+sCtbK5SUBCpq9eqDktZKZC8aedGHLcgjqPgt9/n9L7zSFmxm104378VTkSSAUnfST0wwXQEmnf9tUT30a3HiHVei9To3fw8IvJADouarpNztXonC/tllqvYgINQtW5lNsFoPyDva+D2pXtwGOfFlUHaTj2fmRhnMlxs4kJVaqvh7rhx5jafP5Q9SKQzv7h75QFM+9hzzlmbvi3gkWSdK5EU5/w27evIRikU0uy3y/4ngxRJQWRbv6sN7l9/xG635pFzZHId0FDScpQPTqZfQM+RHWC/ygjyAE1O6PR2xsPbT6FpNRrqxkOhKFhoRHDrw3itPtAxH65U9yJHx/VI33cRY8d2gpGxcpGbnPD5yz9QsVqK52/eIcRgHHq1Uq7WHOIdFSpU+P9JyAhkc9+1a1cEBwdnn5REugwNDWFpafnT36urq+Pvv//Gli3/Bt/27NkTbdu2xeLFiws1ISMEbUxHdOoxDGjXCP56iiMQ+UFAQDL3kg0Y0AK2dv8Sa2UgZFkHr8BaOxLFShRBTJoFZ4MpAnRJzhvpi4vHb2C0dj/ou6rJbd2rvDYj1n0jy1kijrpnSyalfQyoQmM20AUXj13j3jKv7ZZSPbbXTt2CSV8HfPj7AyaYDoee9/QCL/PJvWeY18eeB9SN2gtt7ctUKC3xIMlxvC+Op53mvh6rr/0ruSGnY3Dn0j12sKOKG5FOsrknZ8b8zKyTNfsK+wSs9d/K516FauVgFKqT5/b8CPrssdSTiLKO4+oBgRwmp9lOxAi9wZxvVlBQdABVccie/P41oeycqtrU1zbOeAQ70slKziiNa4BClKkS8uz+C/4uLx+/4v4sqoTRNUHxDSQTJAkqDfBJ2khVQKrycjWtSlnep5VrV5TYqfH+9YfYGbMPqTF7sqtTBAoTJ0JFNve52ctTrxpVVrct2ZFdqaMKmqbLZCaHueFoygkOx6awaQLlfOn6zkSpsnn3FFM4ucf0RXj78m8+R20TzLg/TVzQtUKVbnIHpYqw53ZriWzpidTN6+fA+47uA367HL7rxcsv6LrxnBmK3QkHWXIZesgt1/2fXwQZLUfSsl086bWY7ukF2NbcroNdiRnw1ongyYKlR9xQXU6Bw5si92Cx7RpUrFYOkQcd2TVZEbh4+jZM1cLw51//w/Id5qgioSRUFvci6tufrB7KCpKIZdpoqKA4gPzg4LkbMAjZgPKlSyDVQwdFFOBMK29CVqhcFuPj4wXFihUTREdHC86dOyfQ1dUVlC9fXvDggdAtZsaMGQJLS8vsvz9w4IDgr7/+Evj6+grOnz8vcHBwEBQpUkRw+vTpQu2yKMKlO4/ZfaaLUZDg5dt3gsKAixfvs8PPUBUvwfPnv3b2kqfL4rcuTXPHBrE7UlyYZA5U0kJW+gV2ixpZx0jw4NYTua33w7uPAq32FuyQtdhqtUyPwZ3L9wWjK2mz41ein/QdlPauPcROZfTaEbtPKsskR7eJ1XXYTc2whzW76UkKcid0GO/Dyxryp7pgU1hyrn//q2Pw95t3AtcpAdnudG5TAyTarrMHL2S71dHLcYKP4PHdp/leDm3fnoQDgplNDLOXNb3hXMG2iJ3s1CgN0DoythwTLBzs9J1zoE5bM0GMU6Lg+plbfD1LE/K6D0kbtB9unLstWO25QWDcy+a7/TW63AyBv0644MyBC3nur6f3nwmWLoxhd03R5+cPcODzJi88e/hC4KUR/N35kLnzlFjb//nTZ8Eyy5XZn53TeWG+HUIvZV4VTKw2m6+1mU2NBfevP8zX57/9HlotTYXLaWIkePag4OOCNf5b+R6lWmK64OS+cwJpImPb8Wxn26w9Z2V2Hbx69kag3siEnxurfbYI5AW696m3Mudn5bYV0rnHSwo301U8dvCxSJD5uvJ7L1q58gCPuwwNpONALA/YLN/O41uPeMWOxeTpsliocsio4uXr6wt7e3u0b98eJ06cQHJyMqpVE7qV3bp1C/fv3/+uGhYXF4elS5dyZhnJHUmu2Lq1fHTNskaTWpXRuGYlfPr8BWlZwsZxZUfTptX59enTF6SmnoYygWbWJ8wSNlFvWnkQ779KoxSB9r2boX2fZjxjvtJ3m9zWS7Obc32E1aRNi3fi+tk7MltXrcbVMefruqId1+DqKaHJhLTQd0I3TLEUVmED5y7DpcxrBV5m7SY14J1qyxWLC4evwGGcD7u1SQLqebFLNMMI3cE8Sx5sGIVo+4R8hyKXKFUc1qtMWGpIMi7qrTHoYoGrJ2/kazktezTD4uPe7KRIy9m/nsJ8TbExZDtXdsQFzdj2U+uJyLMB3NtD1QyqJlC+mEYTI2wKTeaqXEFA66B+N+8d9lh6yo8rLXTukqQ0xjEROm3MoNXcBMssV3IYtjK5c8kDHz98QuaOkxySrNnUCLNbzWPnw3MHL/J9ruOQtrBaaYyEexGYt3QOWvVs9svKIuXeBekvxfQGBhyUTvK65t2awGuHPXzSHPi8+RXovCFp4qzmJpznR+ugXjE6Zh0H5W3sRNllCwY6cnYeYdTcobBZZ8yOiuLiaPIJdjZ98fgV9x8Gpjujev2qEsmWLVXd2EylSp1K8Eq1lVi2/K1D4zKrOH6v5zMdbfu0gLTw9P4L+OtF8PvxRqpoL0GfnLiIdl7H+7dusxqYYKwKeWHj0t14+fQNajaoApUpBXPVLQju3XqKA6nCfNsJs/pCmUByYlH22IiR7VEY8O7jJ+w+KRzTqnZW/vBqaaFQSRYVAWWWLBIik48gZPMBdG1WB0tMJqIwYOvWLDb3qF27IqJX6OYpMZKXZFHUhK+t6oeHd59jru1ojJqmOAfLi1k3YDrch/dPaJoVGrSoJbd1u84Ixf7NmWjZvTF8twvlwLI4BnT7cVQLZOvp+q1qY1G6U4F6MXI6dxzG++FwUhYq1ayA4AMu+RrM/QoXj17hhn6SHnYf0REO6+ZL3NBP+2Cly1rEOK3l36kvyiRs9k99b+JcB2cOXID7lEAeyJIU0iBIC8N1BudbxkdkjqRlF44IH4pkFGG6WPensGBx8O7te5a3UQ+YKBibSNqk+aMxcs4QqfSYEV49e42Dm47hwIbDbIf+rfEFHfMeo7uwtK5d/5YSySfleR+SBESc6HuTbPT4zlPZPWEEMluhOIHuIzujx+jOYkniLh+/hjV+m7E34SAP6ggU+zDNZgK6Ds/b6fLisasINojAxaNX+XfqcSOC3qKbeNERR5Oz4DUzmCWd1BtntkwffSZ0y9cx2B65C4H6EWwyQf2kDmvniyWP/BF0nVsMdeW+Tzp3/fc68eRMQXDj7B2Y9nXgnrphswbANHy21OS2dK7ajvFF5s7TaNi2LoL2OUrN7fDH6+Bi5jWYDnLj+5j3Ngu07f1rgi5NUC+uVnfaf+9hEa6F/mM7Q1EIc9mELXGHOATaZamWzNeXn3sRmXlYmMezmUfiGiMUz8NlVRmQmnkRFpFJqFmpLLY6z1KIq25e+H/fQ6YIKDshu/vkJUbaR4HO12Q3HVQtL51cJ3mZe/j4TkHHjvWVaiC0ZVUGwlw3o1qtCohMnl9gQ4iCwE1nGfZvzUKXQa3gvHKu3Nb7+M4z6HS14UGdSZAGhs7sI7NjQI3xel2s8eLRK4zSGwzDQA2pLp96TmjgQw5gTTo24IZ5abjzndx7DtbD3dnKv9+kHtwHVpBzhfqigg0jefDbRbU9bONNvzNpEPc6IFMTb80QHN52nH/vp9YDpov12MQgP6DqRtLSnZw1RvuQerUo90zDST07gyo/oEoiGVskeG/M7iEiNzQijOSaKE1jDnJ/PLo9Cwc2HuH9QANqEci2vcOgNmjRrSmad23MZFMcMwxlImSfPn7iauClY9dw/vAldiz8th+MQH1SXYd1YPLUWaWtWMSXjvnhrcexYdE2zqoTocuwDphsMRZt+rTIc3BEPWbLbeORHLWLB+m0v2e5TWXyLU7fG1X3ltusxlr/LdlEzjZhHmo1riH2MaD1kuEIvQiDp/eBWcQciXLsyETGdrQXTuw6w+er325HiXrPvgX1Dxr3tsf9a4+4h9YjyUqqGXuUZbbEIo4nt4IPOKOeFCfzvj0GFDlgPMAFV0/dwuApPbFg8WzIC5GuG7E2dAcatKyFkB3S7UHOD148fQONQV74+OEzPJbPRvvu+Z+0yi/ycy9yctyAffsuYMyYjjA2UZzhSX4wb/Fm7Dl1FdpDu8JwjHg5mfLGb0KmACg7ISNo+MTj1PX7WDCxH6YN7IjCgKCgFGzedBx9+zaHg+PPQcSKHAiRVFFzsDdePnsLcx91DFBgmf8uOR/2c8GXz//Ae70p2vTIfzCxpFgfmoql1vE8kF96xAUfBe9ldgyOpp6C7Rgffu+0zgzdpRAy/C3IYcy4lx3PtvdX6wGrWEOpzLqR5IhkiyQtHTilF8xXGBYo/PfgpqNwn7aIB4FkGOC62SK7opef64D+lqzWiUxR1Zec7CxjjXlAnV88e/AcSxbEYFfcfv6dBqUzHNQ4IFiSqiCZV+xcmY4Erw24c0koMSeyR5Wb0XNVmSxJc0aUBvhZO0/xvs3YcgzPH7787r/Tuuq2qIXGHRugUdv6vN/p9aNtv6IIGVX+iHxdP3WLXfioenn91M2frO/JLr9lz2Yc/t1teEf+DuLux+ePXiIlaheHQItcIEm2OmByLw6Bbty+gVj7eeOiJKxyXcdVH8Kg6X2g6z0DFauLF8lAFvoe04KyTWGIqM/xm5ntOCjOMaD9EjhnKVJX7OXfKVRc01ldonOKrkP7sT5ccSxRuji8d9gxiS8I6Py3Gu7JEzrVG1Thqr00IiJEuHb6Fox7O/B+MFqkiZE6gyBNfHsMtkTsQrh5HEqXK4llme5sOCMPPL73HLN7OfFkmGPMHHQbIn6uqbQRE5SK1Yt3o2mb2ghMmCuXao6496KnT99gymShmcfSCG00aqT8Zh6v/n6PQRZL2GVxje0MNK4pH3OY/OI3IVMACgMhi99zAl6Ju9G6fnXEmv8bgq3MuHr1EXR1InnwujreAJUq/bqyp4iB0OrwXYhZtAMNm9dAyHojhZbMgy1WIylmP5p3agD/LfPlti00kDfu74Krp29hoHoPzHQeJdNjsMR8FdYHJ3MuE1nhV6oh3evtVPp5WAx15++l5ayW3V9WUBzYeBQu6gG8XJqJXxA1t0CkjGSCdmO8uXJI0jLXLRZo2LaeRNcB2drTAPfe1YdMeqZYjcd0+4kSEamsXae5J4nIAaFOs5rQ9ZnJGUySnJNUjTm0JZN7yrLS/u0npeUSMRsysy9KlZOu0yntQ5LQnd53DheOXsHFI1eyq3U/ggaXdZrX4u2hn1XrVUaRMn+iefumKF+lnFTlZSTlfHTzMR7efMIugHcv3WOL97uX7nNvTk4gYty0S2M07dSQs+io0pIf+SdVkU6nn8fWJalse0+TCrzciqW5J2/03KGoWreKWMvZt/YQ96iJnC+p6jg3UEvsGAZaRlLETj6/iARRBXZ+pD56jv4+WDeva4BkbE4T/bhiSITScNEsjJozBJKAKrqO4/3YGZLiKdy3WUk0ofEtuFfUKApbl6YxwQtKd2aptrRA22zU24HlkN1HdIDjmnlSf16IjsGfX4pCr7sdx24Y+s3AyNkDIC/4z4vFjvhDaN2tEbw3SP87iou3b95Dc5AX3rx6D9tF09FrSCu5rFfcZ8HK2ANYvnwfWrWqhUXBM1EYsP7Aabis2skeCYk2M6Cs+E3IFIDCQMievnoLFasI/CMQYLOTFupUUc7t/BHGRjE4e/YutLT6YvqMXkpFyF6/+BszB3lxKKtrhBY69VZcztuzhy8xq4cjD1TsonTRc1g7ua372/4A82ht9B/TQ2bHgGbYTTiL5xY6DmoNt80Lpb6ubcvSEDQ3kt/bxZsWKCD1W1AoruuUQO5VGarZH2YRegXadqro2Yz0YAMBktNZx5mgi2o7ia4DqlaEmkQhNXoP/96sSyOYrzBC3ea1JCJRyZG7EG0Xn00UqDdptsc0NOsieeWAcsw2hyZjZ+y+7OoKDYL7q/XEoOl9OetJUpt3cSqAJP27euIGrp68jqsnb+LelQe5foakYOWrlsu2oqd4iCLFi6BosaIoUuyvbMIratEmK3u6fmnATKYYJAEly3si3VS1pfMmN1SvXwUN2tZDwzb1+CeRMAonl2QgSoQvbVU6dsTsya5QEqjyM0JPBf3Ve4ot6T178CKWmseyWYhIJjnLbQpb54t7jlJYOhm+iCS2HQe3wcJowxyzzHJ7Fty5fB+2o7xw9/J9vmZI8kvSX0nvRU4T/LgCTvvCdaulROHRP2JdUBKWLFzJx81xrRl6jMpfEHteCDGNxpYlaWyfv+SoO5+j0oboGEQsXIcDmzPRjCYKd9gUaBIqP7hx4R4MBglDrgO2LUDzjnlXb2WFtVH7EOmzHbUbVMGSraZyG5+IMyaiqtj0aeF49OgVLK1GYciQwmFmpxu4Fkcv3Ybx2N7QUvl+QkaZ8JuQKQCFgZAR5gavR8b5m5g7qid0huUvP0hRIJdFL8+tqFq1LFau0v/lDV1RUqGlnluxYcUBtOveCJ7L5aeNzwnRHpuRsCgFdZtWR9gu+T38CKELVrI0pXr9ylhyyAXFShS8/yo3yZJhT3sevOp6TsUEk2FSX0fovBXYFJrCrny+aXZoXgAi8S32rslguSENrsmYw3SxToHOV3J1o9n+k3vOcXVLx2s6ek3pxK6ykix3b+JBlnK9efGWv7u2+zSMNR4m0bLevnyLOPcN2LAoCZ8+fOJ/6z2+G2dR1Wsh+Yw/kTEiZZvDknHz3L8OnyTbJOfGAVN6MxmR9Yw4BTkTWbl94S6fk5Rh9fDGY9y/8QgvH+VcsSoIqJpD1VCSllIlrlajGqhNlblmNVGrSfUCG59QXxdVsegcoKqYCEx61Xux/DQ/hi0km4y2j+cKJy+nZDEOAp+0YFS+tpW2J2huBF4/e8MmNETmxpuO+OU5+atnAcn/6Fqh5VAvostm8zwz0XIjYy5qATi0NZOJN4VHt+tf8MpHxtZMOE7wZ5Ku6z0NE01HQJo4sOkYnCcH8XuazOo8pC1kAToGKfF7EaQfy+dtyD4HNGxdB/KCw8xwHNlxBr1HdoBNhOKeyx8/fobWYG88e/wapq4TMHSC/ExFxBkTHTx4GXa2a1G2bAkkJBqiqBR7FGWFRy/eQNUmAjSPtc1Fm009lBW/CZkCUFgI2aaMs3CMTUWD6hWxzm6mUrrS/IgPHz5BXS0Er1+/h7v7JHTr3lipCNmjey8wa6gP928FrTFA09bSk5bkF29fvWNHqdfP38IscDqGqMvP/fHNi7+h08Uazx+9wnTrMZhuMUam69u2bBcWGS3nnpiA3fY8AJcmSFroONGfnRfJMW3RfmepGUpQn5WXRgjP3qpo9GMjgYKQZ+oDIaMPcosj9J/aAwsi5qLY156a/OLJ3afw11nMVuCEtv1aYkHkXNRoKIwOkaTassIxAWmx+/g7E3GkitYM+0kSL/NbOV3ayn1MJIhEikDLpSoOEQkyV5DXvU50HypftjwHO1OFUBTs/Perv/Hpw2eugFFfy5dPn4Xb9ccfbLhEZi8U8lzs66tk2ZJcxRBV2SpULSd18yCq/JHr5L61GTi5+0y2UyJtFzlNDpreD/0mdc8XgSJyGuOYgL2JGfw7He+hWgMx03ESKtcSP8z4xeOXCDWOwp6Eg/w7me1Q1bZ+q9wH9jk9C7aEpyLUNJqva6ryOW9cKLEdPR0/p4n+XBmjSQuXTebck1dQUNV1Xn9HNkkaPnsgTEK1pXrekuxWv7st3jx/i0nzhmO2u+xaF8gwhwyfnt1/CTXTYZjlNAnywplDV7BwXAATwSV7bVG7keT3mIIiZe1RBNqtR6VqZbE8daFUTVnygjhjImurRBw+fBVqat2gN2cgCgNWph2H37q9aN+oJpbPV4cy4zchUwAKCyF7/e4DBlsswcfPX7Daahqa11H+5k1CWOhOrFt3FD17NoGL60SlczfztUhE2uYs1oaTRlyRWBu+E5HOG1C5ZnlEpDugeEnp2cPnhV2JGfDWicBfRf9C+AEn1GlaMMvnvAbjLlMW8YxvjQZVEZrhglLl8m9VnVclxqy/EzfA0yAwYI+D1Naxa/UBISn78g8GTeuDhcsL1lNG+4MqUUsWxPKgmnqFKL9M0gZ6Ub/O4vkreIBI1Y1Z7lMx2mCoxLLAG2dvI9puNffTEYhcqM4ayJlmBSW7VLEgx8Q9CQe4IkOSPxFqNq6O3mO7sgtgq17NJLKzFxfK5LL4q+NKVUUi2wc3HcHZAxe/y7QjskJVRnqJY3v/LW6eu43VnhuwO25/NrHrP7kXZjpMQp1m4ktfaXuoKhZiFMlElgbWU63HY5rtBLH6Gr89BnR9hZpEszspgQxI5i+bI3FsBlXlyaAnc8cpXoYzkTExstLyAvUHGvWyw+PbT9F+QCu4b7WQOCIjJxARNVf1wJkDF3nyyn+XnUzJwRLr1dgQugPV6lbGksMuUnGsFffcmT/aD+ePXcfwGb1h5K24fnk6D/VGBuLO9ceYbT4cE7T6yH39ud2LHjx4wXJFuvxXxOhxxFBhwHSvOJy9+RCW6gOg3k+5M9N+EzIFoLAQMoL5sq3Ycfwypg/qiPkT+qEw4Natp9DSXMozrXGr56JKDoNMRQ6Ebl5+iDmjA3k2kzTidRoqjujSzLtOH2c8uvMMWtZjoGakIrd1U++Q1VgfnNp3Ca17NoX3NnOZHguqBBp0t8XDW0/YFdEyWl/qlZBHt5/CuLcdVzvIFtxl40KpVSlIvugxPZgHS+y+GG1Q4GUfTjrOfWrv33zgYFrHdQsKVD0kAwZ/nfBse3PKmJq/TB/1WtYpUD4bSdmOpQiDSGnAPXBqb6gtHCOxhOzHTLPDWzOZnB3ZfiJbLimS37Xp2xIdBrbhzCkyQpHmOaqMhOzBjUc4uecsG65k7TydnfP2LQnrNa4bRx/UaJD/agKZwsR7bsgm2gRyw9R0nsz7Nz94cu8ZFs2NQMbmY/w7VTfnR85Fs86N8n0Miv5RHG5Tgti8g+4Ls9ynQH3haInvEUTy7cd6IyvtDJ9Hrluk0zNGcQsLBrvg8vHrnFsWtN8JZSpIN5pmpdt6xLpuQMkyxRF6yBU1C1CZzgtXTt6EcX9nJuVOiSboNlR+/cwHt5+Ey6ylKFa8CKIOOaFiNen3x4m9LTvPwsVoJUqVKY6Y3ZYoWUo+pFTce1Fk5F7ErTqIjp3qw8encBi93Xz0HGMdo/Hn//5AqocuKpaR7iSstPGbkCkAhYmQ7T55FWZLNqNyuVJIdpuNP5Vk0JAX5pmuxKlTtzFzZm9oaPZRuoGQs2EsMtLOYci4TjBzV2z49q51R+BjuIIfvlEZTiiXizulNEHH4FzWRdiMDMKHvz/CZJEmhmn0lek6zx26jPmDXXkm3GyJDobOlP76Lh2/hvkDnHl2fJj2AJiGSS+cNX39YR40EinrO7E7LGONCjRzTccga/8pBOsux93LD1hSRWHNQ2b0LdAykyLSEGEey1VDkomqW4zlqoXIalwSkNww1nnNd+6J3Ud1gvrCMewIKI19TH1eR5KycGhbJo4ln/jJjZDcAlv3bo42vVuwKQjlWRWkMqHo+xCtn3razh68hFP7zuL0vvM/OURSZad1nxZse99rXFdUrVNZokrEsZQTSPDexGSPQMeLlkcVz/yGg9NkztbFOxBlHZd9jpHb5xTrcfmuaNI+OJR6FCFzVnDFicw7KP+v+0jJzTFom4iMUa8mOR+SmyKdNwUFXfcObAxygp1jA/c5oVbj6pC6c6yqBxMki6g5PPkjK5BJxLzBbrh0/Dq6DmsDxzgTuV0HtC/1B7jh9pWHUDceCk2r0VAU6PqYNzkcF0/dhrpuf2jOk3+2V273os+fv7DV/bNnbzlSiKKFCgMWb83AkqRD6NWqPkIMco9CUgb8JmQKQGEiZJ/IdttyCV79/QGLjSegW/OCz0jLA7vSzsLNbTNb31OV7K8fKgmKHgjRjddUPQx//vU/RKUsRNWaijsPaF8YD/XG1TO3MWb2AMxxkQ9BFB2DA2tPIMImkXNnlh51k/ksZbzPZiy3X4NiJYsi5KAL6jarKfV1HNx8DM5qATyo0XCchGnW0nsYUGXBdXIAW4rTINkucZ7EkirRMShZtBS8NcK4YkYYZzwMut7TC0Q2Ht95ikUGEdkmDbWb1oBxmA5XmwoCqphREPT+9Uey5XPNuzXBxHkjeYAvLekW7Ruy4ycCSNUiIivfhkETiMA27tiQTVyInNVvXYe/p7g9VPK8D5FMk6zvSYJI34ss+i8cvswW49+CKpBNOzdiaR3lt7Xs0VRiIk39U9QbtsZvc3a0AVV1qcJJJF0SsxYy/yAjGdp2UcXObJm+xNXSbRE7EWIchc8fv/CxoypxvZa1C2ScYz3CAxcOX2Fy57bNUmyr/txA5/oiwyhsi0jj885nhy1adJNuhiQ5dOp3s+Wq6OBpvbFwmR5kiXUhKYiwSWBHUdetJmjWprHcnsfbVqQjxDIeZSuU4uoYbYOicOLQVVhpLUPRYn8heqc5KlQuI/dtyO1eRCHQFAZdsWIpjhT6cTyljKDrZYzjctx+/BLuWsMwrIvyk8jfhEwBKEyEjOAatxPr9p/G6O4t4TSzcKSyk1vRlCmhePH87xxndBRNyAh0A6Yb8ejpPaBvo7jZOUJW+gVYqwXzTPOSfXaoWV86hhS5QXQMKlWsBLMh7rh84ib6je8Kq+VzZL5eq5HeLKtr0KYOgvY6SkxocsPmxTsQYryc35tH6XOemLRAOUZkoU1VOLKId96wkGfi84tvrwNCjOMarHJbz+9b9mwKu/h52SHSkj4U09cdQqjJcjy7/5z/jXrgdLxnoFIN8YJ9f4XbF+9ije8W7Fy5L1tmSH1MI3SHQFV7YIGXn1P4LoULEzE7vf88zqSfZxlsTiD5J+WMkdSrWv2q3LdYrX4VVKpZkU1fRKRRmvchqhq9ePQKT+4+w5M7T/Hg+iPcu/qAZaSUGXf/6oPsXq1vQf06TTo3RNs+LbnqRwSsoA6MtN6ti1OREr0Hr56+5n+j83O4zmB2PJSkykYunHR+bgzZzhVuIjvUpzhyzhCJ+hSJLNL1KTK3IdmkRbRBgfo+KQzbUtUN107e5GqqR5JVgaIbvkWCz2ZE2sRzZdEuwRS9x0rXvpvORdsxvsjceZpDzYPTHVG8VP7vKeKCwsL1etixOsI4cCY6Dmsut+fx32/eQ7uHI148eQ19t0kYPas/FAnRWGDUtB6Ya6uYsUBu9yLzhauRmXkDU6f1hLZ24WhdOXX9PjR84lGiWBGkeerxT2XHb0KmABQ2QpZ5+Q5mB6xB6eJFsdNLD8Wk2DwsS2RrnjvWh4/vFKUjZFkZV2A9K5L16yt2WaBcBekG1uYXtlNCkLnnPPqP6wyLMC2Zr+/bY3D11G2YDBD2EbisNUUXGdkri/D0/gvM7W7DA1hVrf6YF6Ytk/Uss1qNRL8tTHTdt1mivRSsrkU4te8cZyRR1YbIk9sWS5Qun79zKKfr4OCmo/DWCuNcKzL5IPlWQV3haDAdZbOa3euIpNFgerr9JK7EFbSiRfbrFAS9belOnuEXVWF6jumMkXoqTFhlcY3TvqNssfOHL+PS0au4euoGbp+/+8vQZRFoQE1ys/LVynHvT5ESf6FitQooVbYkTwyQVTvlj9F+EfzzDzfR07r++fwP3r99z71JNIglZzoiO8LXG/7ueWWPEdmo16oO58VRFYx6/KiyJI0+x08fP3EvV9KyNGSmCvv9ROR01JyhTJwk6XWi706xBREWK7OPb5+J3TE3QDNfLozf4u6VBxy8TjlxdDwmmg/HLOep+Osvyc9F6mczH+LCOX9Eur1SbLmnTRrYHX8QHjND+L2+/0yMM1SFtJHgswVR9ol8Di7a74T6BagS5gW6B9iM98fxXWfRpnczeGyajydPnsjteRzjvRWrA7ajJmV97bXj+7OicOHkLZYrKlot86sx0e3bT6GpsZSdXSlKqHp15R+zEjwTdiNh7wkM79IcblrSj7qRBX4TMgWgsBEyGiSPsIvEg+ev4aMzEoM7SFcmISs8fPAS06eH8/ZHR+uiTt1KSkXI6KFkohaKy2fuYurcgZhhNASKBEkWjVS8hNKYZAs0aSdbeeqPx2CpTTzWh6Siap1KWJzhzIN2WSJr91lYjRB+3wXL9DBkWm+ZfEePGSHYu+YQD4b90uzRsK309uuFI1dgNcydLdwbtqsHjyRrVMzHA/NX1wFVOJwm+fNMPw1YyYKcen0KmlV38dhVdsMTyc1oJn5uoBY6DWknFUnevjUZ2Lokld0Av7W0H6o5AINn9JVaFEFuIIJ0++I97s2iKtWDm4/4J2WOkRQsL9JUEJCRUYXq5bmqWbVeFa7Q1WxUHTUaVeOKHVUNpW1kQ7b1Kct3I3XFnmzCROvoPLQdRs5RYVmtpISP5KlhpstxLuMS/04ZanODZqGzSrsCBa77zg5nqSYRY4sYQ9RpV71AzwIieJZDXbnqQwTUO9UWtZtKRwpNFSu7Md4sUR5npAp9v5mQNqiHcIGKm7C3dvFsDNWQbRUkLSEDProRHHgeftAZNRtWldvz+OmDF9Du6cTqAsoco+wxRUJZ+sl/9SwQuVb36NEYrm7yiyMoCD59+YKhVhF4/uYd945RD1lhwG9CpgAUNkJGCNqQjugdxzCwXWP46Y1CYYGtzRpkZFzBhAldMNdgsFIRMkJ6ymm4m8ahdLkSiEmzQAk5Oyv9CB/DaOxadxRtezaB51oTmeYx/XgMyC59Tg87PLj5BKN0BsLAV/aRACvdNyDWZT33kwXvd0a9FuLbbedHGmU53BNn9l9ApZoVELjXUarEgHpqiJQ9f/gSNRtVg2eKLUvkxEFu1wENWEKMopC8fDf/TpUmixUGqFyzYHbHtM7UFXsRabkyu5rURbU9h1Q3aJM/l71f4frpm2xdTpUVMlgQgSR5A9R7ofeE7pzTJW/Qdydr9ucPXjA5o36j+7cf4H9f/od3r9+z6ynJL/n16TP+98f/eGaa/o9IDbn18atkMc4dK1upNEvjylYqw1UZysqSdvbYr5wYKe+LnCmpyiQCTQaoaA7A8NmDCpQZd//6Q66o7ok/wL/Td6YcunEmwyWOISDCHmGxChuDt/PvFGlgE2fC12RBngUkY7Ua7sFklCITvFJsUL2+dJxzr2TdwPxBzlwFp3w3q1hDqT+v6Hw06GHHPZ8D1HvAYrn03We/W9/T19DtYoOXT99A0248Ji8YKdfncdCCOCSvOoAWnRvAb/N8hearKpPjck7H4P17Ya7rmzfv4eGphq5d82e+oyjsP3sdRqEbUaF0CXZX/KuAE4nywm9CpgAURkJ26c5jqLuvRJG//kSapy7KlJSdtlyaoBBDCjMsXbo4EtcYothXHbGyEDJymdIbGYC7N54oJHvkR5D9Pdng08DQIVoP3YfKTjqY0zHI2nMOVmN8+QHls90CrXs0haz3v81ob2TtOot6LWthUbqTTDJwaOA9f6ALZ2uRcQBllJWrLFnm1y9n6FXduBJTsUYFeCZbi2VyIM51sCNmLxYZRjJhJgkjZaB1HVbwWWWq6sU4JmJzWAo7nlF1R0WjPzSc1SWWov0Ikvelrz2ElOjdbGcuMgGhdRHB7DepJ3qM6aIQcqZM9yFxQL1o1A9IL6rMikAEkKphlBFHzoQFkaBSdTHOfT02hyZzgDndB6iyOcttSoHOCariuU9blE0e1RaMgpbrZN7WghwDkg1T9YqqbY071Gc3RUkDpH/E/euPMK+fIxN3yhpz3WyOolLug6H7n91YYd8YuTWGHJS9MsFHLwJp8Rmo37IWQvY5FPgY5Ac3L97H3IFurJrx3WSGVgomGL6WiUjbpByZpDkdg6Skk/DzTUKNGuUREzuH75uFAbbR27HtyAVM7t8eFmoDUFggC0Km3E+V35AITWpVRqMaldh1Me3Evw9jZUeXLg1RvXo5nuHZvfs8lA0kAZs0WygPWReVzrO4ikTV2hUxTkd4A1vmsoFlMvJEh/4toTK9Nw+cA42imRjKev9bROmjYvVyuHnuLkJMV3wXfCstUO+M2xZzljPduXSfB0GUfyUt0GAqYJ8Tu/yRecb8/o44c+CCVJY9ZGY/hB31ZEkkVbRsRnpi8YKYAp+r1O9GcsXIcwHoO6kHD5KoGqfRxIj7hV49E5pBFARErmn7fXc5YuWNMOh6z0CzLo14Xcd3nkaA3hJMrqkDs/72WB+4jaWav/HvAI0kphQ1oN/JHDMbG/JxITJGRIkI7bwleki8HwHXLVboPa6bxGSM4gZWua3DjEYGWBewlclYx8FtEJbpBfNoQ4nJGF3LZNph0MWKyRhJFF02mXM1tqC9i+SkSpVpImNUefVNc5AaGXv5hK4zLyZjDdvUhUPiPKmTMUKcx0YmY9Q3ZrfaWOZk7HDKSSZjdP5QzIk0w6zFQZTbRr72e6i2UzgZe3j3OfZsE/ZaTtJRTqOMLZuFrrujRnUoNGTs3YdP2HXyKr8fVgicFWWN34TsPwi6gYpO7u1HpTPQkwfoJjLyq0ZcdHNRNgwc1R5Va5TH8yevkbpOGHCqSEwyUuEssrtXHyEpNl3u69dxVWf51Z3LDxDns0Xm66N1WUTP5XNlR2w6tkcJJXrSBjkAemy1RJkKpXhQ6zZ1ETv3SQskJfTb7cgueeT+Z6Hiiv0bjkhl2dS7E3zQFWMMhC6r6wK2wai7Na6fEVqZFwS1GteAXYIZgg66cVYTkfBEn02Y0dCAK2hkCCINkLPfpAWjEXLYEzFXQqDtPpWNLWiARs6J4WbRTAaJFATqLeEgbhoY/38BkReSCiZH7YLH9CCoVZ8Nw66WfAxIlkd2+GSDbxw6G/F3l8BnpwO7JpJcUlJ8ePeBCdjMRoaItotnckPE3z3JGp4pdmjcvoHEy3717A1cJwfCX3cJV0op2HvJCZ8C5YuJQHJYpwm+fK72GNWZezcL4s74LcisxWaUN+5cvo+qdSvDbYuF1Jb9LYiIrXLfyO+NQ7TQoLXk4e3i4O2rdwieF8PvxxkMQYsujeTuJHxkxxk2z5hlMwaKxrrl6fjy+R+0794IzdrIdt9LggsX7uHSpQcoUuRPqA6TrcmWNLHn1FUmZbUrl0Ob+tLN6CuM+EMgiynm/xAKo2SRcO/pS4ywi+Kehu2us1GtgvyzMiTB8+dvMVk9BJ8//4PFS7TQpEl1pZMKbVmVgTDXzeywFJm8QKGuT4StK/Yh1DIBZSuWRlSGo0wyWnI7Bvs3Z8J1RijLoYL32PEssawhchmjoGX/XXZo2qmhTNZzNuMSLFXduUdrgHpPmEfPLbBZxregwafb5EAc2nacJ1IMg7UwWj/nuApJroOMLZnw11nM1TJyBJztMQVjjYZJ5TqiR8eRpOOIsl3NhiKiStp4kxHcP5RfF0lx8OjWY852O7DxCPf5kXzyW9RvVQdt+rTgcGQijESspdV3osj7EK371vm7bJhx5sB5nNx99qdQaKqYEJHpNqITeo7pIjWZLZ2jZItPxJt6HwnU/6jhPBn91XsWeF8cTT4B39mLuVpM9xBNF3WWKea03PwcAzo/VzgkZkdDqM4aAJOw2VKr9FC/KUmoKVCaqnl+u+zZEVPaeHznGQx62HL/2HDtATAJmQVZY5FpDJKW72EDj7AD30vDZX0dkDTTeKgnrp29i9Ha/aDvqgZF4tnj19Ac7M2VYPcobXToIZ1ohILgx2Pg470NycmnMHhwK1hZKzaWJz8wCduIfWeuY/awbjAY1ROFCb97yBSAwkrICLP8E5F15S5MxvaGpop0c1BkCReXjdiz+zyGD2+H+QuGKx0h+/D+E7SGeOP5kzfstESOS4oESRX1B7jhztWHUDdSgaa19GcU8zoGztNCcHDrcTRuVw+BaTYyl7fQ9jipB+HQ1uOoVrcyQjJcmJDKAke2Z8Fhgj8P/kfqDoJR8CypNpfTchcZRLIFOWGy5VjMcp380zokvQ7Iat5v9pLsIGnqcZm/bI7UzAxou/avP8zVGQoyJlCVYJzxcM6xksQ+XRyQAcjpfedYznh85ynu+fsR1KPXoltjNO/ahCtsJBMlqZokx09e9yEiEmTEQbK9y5nXcCnzKgcXUx/ftyDy0qxrY7Tr1xKdh7bnaqs0rzvavxRPQERM5MhIBjdTrcdDRbN/gddFMmAy7qB4BUKd5jVhscIQzTo3KvAxoGp2gN5SNqQhzHSYiOl2E6V23dI16zI5iKWQlNlGwc+ymBQiEmCh6sETQ43o3rrHXuLgb3FxMv0CLEZ683vvbRZo27uZXK+DlNUHEWi2is2zIg86yuy+Li4ifbZjbdQ+tGhfF35xcxRqLJLTMXj79gObeXz48BmLFs1Aq9ayi0CQJp69/pvdFT//8w/W2c1EwxrS6UWWF34TMgWgMBOy9ftPwyVuJxrXrIREmxlKcSMRB6dO3cY805UoVuwvJCQaolSpYkpFyAhrI/ch0nc7atWvjCVb50m1aiIJDqWcgpPmEhQtXgTL9jugSi3pBu3m9RAme2K9bnY8aNSwHYcpC2Xv7knrMuxpzw31XVXbwWmdmczOjz2JGWyJT4PlKRZjoOWiLtXl03JpJp9m9AkDJvfCgsg53w2+CjIQouVvXbITSxfGcsWDBpHUmzNSb7D0BqlfvrApxyrXddnkiBz3VLUGcsWMLN1liRePX3LVjEKgT6WfZ0KTk209OR1SJa1Wkxq8TeS2RxUfkpyRpO9X+0PawdBP7z3nKtejm49x9/ID3L50l3sW71y8x059P4KqFM27NUbLHs3Qtl9LtOzZDCVkEAb85O5TdjckqR/l2xGq16+CKdYTMGRmX4mdE3802PDTWcLZcASq2s72mJpn6Ls4x4C22VktgEk6STdNw3UwTHsgpAW6lvz1IjhIm6rO1G8qzczCbxFmFoNN4TtQsmwJhBx0Qa1GkrthigO6N8zt5YB71x5hxKz+MAr42bZfloSMSPrsXs549vAlZjuMw4Q5/7otKwKvnr+FxmBvvP/7I5wWa6BrP+Xoc/r2GGzYkMl29w0aVkFEhHahGefF7zkBr8TdaFG3KuIsp6GwQRaErHCkBv+GRBjSsQk8E3fjyr2nuHT3CZrVln2ujzTQpk1tvrlcv/YYKcmnMX5CZygbhk/uhoSIPey4eHDnWfQZ2kah29NNpQ3a9GiC0xmXsdx9E8xDNeW6/krVy0Pfeyrn1cR5b0GPkR1RXwa29N+CJHHU3G7a3wlHkk8i3nszplqOlcm6+qv14H6RoLmRWO21CWUqlcZE0xFSWz49RKfbTuDeskD9COyOP8DW1o7r5ktFekbLHzVnCJsv+GqHM3FZZLAM6esPwWypnlSqZX/++Sf6q/di0w+qmBExu3bqJjaGbOcw6F7jumKC6Qi06tVcJoOG8lXKsVkFvUSDyyvHr+H84Su4cOQyE7T7Vx/g9bM3OJ1+nl8/ggbYlAtGr3JVyvLsPJE0ehUrVQwfv3xA1RpVULxkcR7sUy8j/aRBOlWqP3/8zC9aNxED6rOin+RISJVKkvzRz2f3X/wkt/xuO4r+hbota6NJx4Zo3KEBV78atq0nU5v8y8evYcOiJOxevT/bIIhcRtXNx7J7ojSqb0Q0l1nFsVsngfbzgkh9dJJSuDxVFimAnSq1RGBt403RbURHSAt0nBcviGUyRsfeeqWhzMhY2uoDTMYI5pFzZE7GCLFuG5mMVa5ZAbOc5J9jtX5xGpOx6nUrYZSW4s0zNsYeZDLWsEUNdOn7faVQGUA9tZs3/WvmUVjIGCHpiPD+O6JrC0VvitLgdw/Zf7hCRpi/dAt2nbgCjcGdYDq+LwoLtmzJQmBAMmrVqoCo5Tp48uSxUlXICLHBOxAXtguNWtRE8DpDhd8ML5+8BZNh3sIZ3K0L0KKT5E32ksyK0nodJy/C4eSTaNqxAQJ2WMslZyklZh/PWNP+d15vhq6q7WW2LiJjy+0S+L1xqDZG6gyS+jqOp52G8yR/HshT5cZtqyUH10prZpp6NDaFbEeUTTz3xlEVi2zFxxioSrXSS+dDVtpprPXfwn1CIjTp2ABjjYaj/+ReMnGjy8uYgoKgb569w9WZe9ce4N7Vh0zURP1R8gJdG+TkSTJAygGjY0yGLLWa1mAnTnm42hEppH48ImJE0kUgJ8JJ80czmZHWPTdzxymWET68+Zh/p/wzXe/p+TLByO0aOHfoEhzG+bK8kvLKyKGRCK00Qdc+3QMIC5bNgcpM2TxTr566iXn9nfn6nGo5BhoOsg8hPnf4CuYP9eDr1inBGN1+cR+VVYWMiNisHo78na2XaqPPKOkRaUnw9s17aA7ywptX72EdOFXhk645HYPbt9/C0iIRJUsWZTVRSRnEwMgCNx8+x1inaPz5vz+Q4q6DSmWl328sa/yWLCoAhZ2QERkjUlalXClsd5uNP5WI0OSGd+8+si6a9NHu7pNQr35ppSNkJGeYOcgLH959gvMSTaWYQfOfF4sd8YfQvFMD+G+RXpCmuA/hJ/eeQ6+7Ld6+fAdt50mYZDIM8sAi4+XYFrGLB3fBB5xlNptMg5VI63gk+gkdJRdGzsGQGdIflNEMv+0oTzy48ZidHm0T5nHvlzQHQuQMR6525FpIIDmc2RI9NJCBKQtJGMmhb1dcenY8AhkhDJnZH6raA1GvheL7Hj59/MQywid3n+Hp3WdsokCVLXJvpGy692/e49Xz1/jnk4ANHYjYCv4RsCySzguqrlFl66+if6FoiaJ8LpYqU4J/lqkoDIMu/zUQmggDvaiqqKicMnJoTF2xh7+viCD2U+vBpizNukjPuIAqgovnx2DX6gPZ8sd5S3TRcXBbqd2HqKJMlV86txq1r89kjAxdZDUZY7hIC6PnDIHMpNi97HH/2iN0GtIGLhsWyFwSTySIpIp3rz7E4Km9sCBc+5d/KytCFmC2EqmrM6T+7JIUiRF7sdw/GXUaVsHiLaZKNfYQHYOQ4HRkZFzBuHGdYGikgsKC8K0HsTTpMHq2rI9Qw3EojHghA8ni7wrZf5yQffz0GYMtl+L1uw9YYjIBXZvJ3gFPWggJ2YEN64+hR4/GMDTqo3SEjBDhnYT1y9PRvF0d+K/WV/hDhGYZtXs6sszCIlwL/cdKR+6Zn4dwSmw6AgyXcz8bOXTVbix7O1tqfl+o4sbyNLKEpub34jLoryHQ4Jt7O0JThLKlVcboO0Eok5MmaCBrP86HDR1IFqfvr4FuE9qhWrVqUrsO6LgmRaQhwnIVy+toUD7BdDim20+USX8SkZvty9JYskaSTBFIkjdUayATglJlpW8bLg0om7lQfkEyygMbjiB5+S6c2HUm+9+JGI/UU8FIfRWWzEpzf21ftoslikQy6FoZbaDKhjXUwyiNY0C/R9snYrXHBv7v3Ud0hHWcicTL/xU2haUg1HQFv9fxnIpJZiMhC9D3cZwUgMNJJ1CtXmXuG5OHqcVSm3isD0llqeLiDBeULl9SrtfBlVO3YKwqVHf4bZ6Pll1k45qbH+MuclZ88fQN5ntMwuCxiq3W5XQMzp69BrN5a1m2uDxaF3XrFg5TDDrGox2W486Tl3DTVMXwQipZfPE7GPo38ouiRf7iXjJC0pHCk0lGGD1aeBM8fPgqHj9+A2XEBK0+KFrsL1w4eRsnMoQBh4pExWrloGYonClb7rqRZz7lDQqL7jigFc9W+86J5EqCrEGVCbs4Y65CXD9zG4EGUTIJjSYQ6db3m4Ghmv35Yeg5MwSHk7Kkvh6qpPjtcsCgaX24ChNqshzRlolSzUOjAdVIvSGIPOOPXmO7sIQt0XcLtFuZsb28tPch9cNNthyH2GuhcNpojh6jOzPZJDv3AN3FnKflNjWQHSFz67H6DfEHbif3nIXvrDDet54zFjEZo3O4k0o72MbPQ9ztJdB0mSxVMnYp8xpM+9hzPySRMZKpLspwg0GgptTIErlAOk3wyyZjagtHw3HDQqmTsW3L0rLJ2HSb8TIjY4QY53VMxmgyi+5n8iBjJFXcECrsVTMO0siVjMkCdI9ZYr+Wf/Yf11nhZIyQvPYokzGKtuk/oh2UEWlpF/j507Fj/UJDxginbzxgMlaiWBEMaKf4CAFlQuGb5vuNfEM0A5GWdRkfpDiYkzXoJtOhQz2+6exKuwhlRMUqZTBMrSu/jwsT2pYrGuP0BqFyzfJ4dPc5NkbIJjg5N9BgzzRYk53BLhy9irVB2+WyXpKA2aw05AH+7oQMbAgRGgfIAkRkTMNns9kHGSCQq9vRlJNSXw+5LFqsMMBsz6m8X3etPAgrVXc8/2pDLi2QuYLjugUs9SJJ2ePbT+E4wZcNEu5cugdpgypxPUd3gfNGC8TdWozZntO5f4pI/J74A7Ad6YHJtXQ59PlochbLCX9DPBCRPbH7DIINl2Fq3TlYMNARKdG72VCDetVmOqgxIfZMtkU/tZ5S7eOjCiiRMMNu1jh/6DKTI/0ADQRnuOVqZ59fUN+fSW87tp0nqah5tAF0PKdJXdqXHL2HjXwIE+eNwAz7CZAV0jccxWqvzfzeNEybSaysQRN2fnMjmQyRVLGrivxDhfdvzcKZw1dRrHgRzLKRjSlTfvDx42esWSaMS5g0u5/Cc0Z/tY17dl/i92PGKFf1Tlwzj4HtGjMp+41/8ZuQ/T9Ah0a1UL1iGbx5/xH7Tl9DYcLorzebPXsu8U1IGTFRmxzI/sSZzBs4ffS6ojcHxUsWhZaVMIssITgVzx+/kvs2VK1TCXM8p/D7lR6bcOP8Xbmst03v5tD1nMrvIyzjkJl2WmbrosGf+XJ99B7bhSWTjhP9kblT+usjIqa+cAycNi5A8dLFcGrfeRh0scKFI1ekvq7uIzsh4rQfplqP43OaMthmt1nAPUA/5mBJC5VqVIC6+RhEngtEyGEPjDUcxjI6CrPeFrET1sPdMbGqNtynBWLX6v3cz/Ub34McQNPXHYLPrFCo19TBwkFOLAulnjiaGBmmPQgB+5yx4nIwZjhMYiMRaYKqtuSmqdXclLPLaIBP0Q1R5wMw3ni4VM19Tu46B6PuNrhx5jYqVi8Pv90OMunj3BG7DwF6Efx+nJEqSxVlJUmnHktfnSX8frzxMAya0gvyQIzbRty98hAVq5eDnvtkyBs0ARPpspHfTzQYIvW4FkmwY30mnj58hUrVykJFCR2eCenpF/H69QdUqVIGPXoKFVCFAZ++fEFKppBIDu+qHBECyoTfhOz/AUi7r9pZaDix7evsRGFBz55NUKlSabx69R7704UXsrKhcrVyUBkvvHGvXrwLyoD+4zujSbu6PCu+wlNoQCFvDKEZ16Ftmaz4zVkmValdbhhroIIhM/pwZdV9Rig3qssK5IRnvcoIPUd3xqcPn+Aw3hdZ3/TnSBPdhneE07b5qN2sJvdfmfVzwPZI6Z9vZBeu5TIZS0/68jqp4rIucBs0m5lgy+IdMpMS0mCXzCQMFs1C/N2l8Ei2ZTklDbqpv2336gPwmBbE5Mysnz3ivTayVTvJ8v6/gQgPRQqs8dsCCxVnTKwyC86T/JAavYfNSMimX3XWQLhutcKah5Ewi5iD1r1bSJ1Q0HYc3nYcuu0XItQkGq+fv0XDtnWZJFFvpbR70iirz2/mUp4caNG9CUKPeKBFN+kPSHfHH+ScNPp+FBcxx1d2OZ60zxzVAvH+7Qc27pntJt2Mw1/hzMFLWP9VRWASpMHmQfLGpsg9eHj7KSpVL4eJcxWbOUagZ1Xi0j38Xk2nH4oWVc5kKJHV/YgR7RWegZofHDp3Ey/evEOlsiULlZ+BvPDb1OM/buohwtV7TzDRNRZ//e9/SPXURYXSJVBYsGJFOmJW7EeLFjUREqoBZcTDu8+hreqLL5//QUC8Ppq3U/zN5vyxazAb5ccDiaDt5kzQJIWkjdxP7z8XBka//FtugdGEjx8+wVzFHeePXEHd5jURuNcRpcqWkOmD3EU9EIe2HedwW5dNC6WeTyQ6BqWKl4bfrMU4sOko//sInUGYG6j5XYi0NEGW9eHzV+D2BaF0sU7zmpjlOoV7zuRhYkPfm+RvGZuPcm8ZVUa+BZEPCkpu27clm4OQy54sLOMVaepBJJgIGPXandl/Hid2n2V7929BmWHdRnRCj1Gd0bp3c5lHThAZXma1mgOYCVTV1HBUw3CdQVJfNxEwb80wZGw5xr+P0B0sPOdlIHkiN0hvrTCe0BmmPQAmodoyO950XO0n+ONY6ilUq1uZHWJpP8oa1H9HrooPbj7hnl+z0Flyvw5ePHnNBlR/v36P+UEzMVhN+sZI+UXymqMIsl+PCpXLYPmOhSyjVDZcufIQerpR+PPPPxC3ei4qSyGnUl6wikpC8rGLmDqgAxZO6o/CjBe/XRblj/8KISNM9ViF87cfwVJ9ANT7yS6rSdp4+vQ1pkwOY3OI8MWaaNq0BpQR/tZrsWNDJroNaAHHsJlQBngbRGP3+qPcKO27yUziAXRBHsJpCRkcGE0SuEW77dBQBrbqOeHp/Rcw6mXPpLD7yI5wSDCR6UCaSCCRMjL4IFJGoc6dBksvu+bbY0BY7bkRK+wTeRaf+k3sEuZxj5AsQNXNrUt2INZ5HVvBE6hCoe0+Fe36tYQ8QeG/VJU5lnoCp/ac48HltyBDhKadG6F51ya8Xxp3bIhaTaoX2GJeXoSM1kM9UhRqfTnzGi5lXmV5KlVQfqxktunXEp0Gt+W8MMoxkwcoxy3aPgH71h7KNtQZZzwMU63H5ytTLD8GIS7qAXhw/RH3i2m4T8Qk49EyOQY7V6bDd/ZiJmNk2jNv8WyZHusl5quwPjiZ7xf+u+zQuH19yAOLTGOQtHwPqtathPADzvmarJLWdRC0IA7Jqw6gcZs6CEo2V7hzKZHj2cP98eD2M+hYDMd4zT5QRvj5JiEp6SS6da8PV1d1he83cfHm3QcMtlyCD5++YKX5FLSqL3v3ZVniNyFTAP5LhGzVruPwXbsXbepXR4y5sL+nMICtgB3W4MCBaxg6tA3MLWTnclUQ3Ln+GLojAniAHLLeiAOjFY3H955Dp7cwYNRy8Sz0G9NJouUU5CFM+8N5WggytmWhfqvaTMrkFQhMpiILhrixnHCKxWhoOk6S6foon8plchCTMho8OiSaouuwDlJZdk7H4GjKCXjNDGGZGg2GF0bN5cqVrEBh1Ym+m7E+MIlt1AkdB7WBhrMaWnZvCnmDiOKlY1e5YnT24AWupL1+9nOPGZGX+m3qct5ZvZa1Ubdlba4oVa1bGUWKFlEIIaNtp5BkIl+3zt3hPqKb5+h1hyWaP4J6wagC2LJHM7Tr3wrNuzWRa7A2betK13VIXbGXXT9pcmfglF7QcFKTyUQA3Te2Lt6BcLMVXIEmsxmb1SYoX7eMTEhxasxe+Oks5fUOnz0QxiGzZDrYJcOQAH2hYYjtKiP0GS80h5I1ju08A9sJ/vzea8tCtOubP9txaVwH39rc+2ych9bdFO+2t2tzFnwsElG2Qims2GnOvdjKhlev3mGyegg+fPgMe/vh6NO3TaEhZBsPnoHTyh1oUK0i1tnPVHhEUEHxm5ApAP8lQvb01VsMtY7Al38E2OCggfrVpKfvlyXoAXDgwBk4OmxDkSJ/ciJ9ORnMxEoDXgvisWfbSfQc3Ap2wdOhDIjz345Yn62oUrMClqbbS/SgKehDmAwa9LrbMXGYQA3yrmqQF3as2g/f2cKGeYuoOTyIlCVo8Og2dRE7wFFV0C7eFD1GSUaExTkG1E/mOiUQ5w4KeywnmI7ALPcpMh2sU9Vxles6JC3bld1TRv1mMxwmStVNL7+gAR65Qp49eAmXM6/iStZ1XDt5M5s85tRfW6VOZVStV5mdJitULY8K1ctzfEKZiqVRunwpfhEZKlLsL7x8/RI169REseJFeV2iWAAiKB/ffWSTApr8oN7NN8/fsgEJ/SR54bMHL/DswXM8u/+Cyc2jW0/4czmByHyjdvXQpGNDrvBRNZKIpCIGX/evP0KC10akRO9hR1GR+QvlickiRFxkUhI4JwJ7Eg7y79SjuSBKnycdZFGl3L58NwLnLOPjOVJvMAyDNGW6r0/vvwjL4R68P2fYjcd0a/mE47558Tfm9LTDk7vPMVp3EOb6TJP7s4D28YIx/jh39Brb3FuEaUHRIPWN/uhA3L72GJrzhkJdVznldAnxh7B06W40alQVTs7DpZpJKWvMDliDzMt3YDi6F7RV5TP5IEv8JmQKwH+JkBGMQjdi/9nrmD2sGwxG9URhAD0AHj58CCenZFy+9ACzdfpjypQeUEbcuvoIc0YFCsODNxqjQTPFyytpgKjbx5lt8GcsHImpZsPyvQxpzIpmJGXBaUowz4z5JFmgdU/5VVSi7BKQ4LuVB7q+qdZo3lW2M7JU/fDSCMPetYe4n8Yq1rDA4dG5HQNaX6T1aqz138q/k1SPTBVkLWOjwToRsx0xe1nmReg0pC2mWI1D277SN5GQBF++fMGdS/dxkytQd7KrUPevPlRITt+P8kqqLtVpXosJV/1WdVCvVR2OAJBFH1x+pYkJ3pvYbVBEHKkaOtNxElr1FJpEyQIkz3Sftgj3rz3kCAuKfJg4bySfS7KQjW4ISUa4WQy/H60/hLPSZHnePrz5BEa97XlyiqpiFNUhr+vEWzcCuxIyULNhVYTtd0LxUsXyvYyCHoM9G47Ba+5ylmlG7LfniUJFY9/2U/AwW43SZYsjOs0CpaScZSct0jhzxmI8ePASZvNV0alTjUITUn/v6SuMsIsEnebbXLRRo2Lh6XuTJyFTTguZ35AZRnRrwYSMsiD0R/TgGeLCAHpgjR7dAX6+27FlcxbU1LoppbtQ3UZV0XdYG+xNOoVVYbtgG5T/GUhpgx58WrZj4aW/HIkhqRgyubtCHoI9hnfgBvLUlfvhO2cZwg44oWQZ+ZjLaDpNwq0L95Cx9Tic1AMRtM+JrfllBRpMW8YYfM1EO8gDzPdvdaEys5/M1qfnM4ONLXy1w3H5+HXod7aE4aJZUNHoJ7MBX40GVbEgUh+TLcawAx4ZImTuOMUvkteRnT317yly0EC9YyxVbFH7u3+nSZPnD1/g/rVHeHjjEVewntPrazXr7Yu3LNF88/UnVb/Evd5ooFu6Qml2ritdoRQHYpNbZIXqFVCpRnlUrl0JNRtVQ8UaFZRuQHXx6BXEe236LhicSPZ02wlsFiIr0ECfQsmj7RK46krW/DSpQOeRrEB9mMvtE/n9eJNh0POeLlNy9PbVO9hP8GMy1rh9PSyI0JUbGdu3/giTMXrmL1g8WyIyVlC8//sjIl2FQd7qxkOVgoyxe2eoMEN0zIxeSknGCIcPXWUyVqZscQwc2BIvXz5HYcH2oxf4Z+cmdf4TZExW+E3IxMSbTx9Q+OtjQL+2DVGqeFGesThx7S46Nv5+kKLMGDCgBZfrHz58yTennr2UM39jypyB2Lf9NA6knsH1Sw/QoKnim1epd2zr8n04e+QqIl02wDJcfFctaULPYwpO7D3P7l4RtolstywP0KDXYrk+5g10xvXTt+GkFgC/nbYoXkp2D1+qjJlHz0WxkkWRvHwPyyZJyjZm7lCZrZOkkUuyvOGlEcJ9VUTOjqWcgFHobJStWFpm66VKnMUKQ8x0VMMa3838fckR0GGcD1d7xhkNw+CZfVFChvs7v6CBcMXqFfglTsWHKm337txD+XIV8OXTl+yBNP38439/MBGjCqwyVAUlmX0/vDUTG4K383kjAjk2TrYcI/P+wEe3n/C5mpUmjIygwHWTcB2Wi8oCRDSX2yUg3lsYxDzddjxm2E2Q6bEjkuk+IwQ3zt7h3C+HBFPubZQHntx7jkXzYvn95AUj0VJBPVtrw3bgyb0XqFanEsbrDYQyYH/KGVa2lCpTHGNnyif/TRJs2Ch0GB02rB2KFaJAZe4FPXwuuyDwG7+Gck3NKTEO3L+B/wJKFC2CQe2FN+OkI8JZi8ICugnRzYiwaVMmlBX1mlRD76Gt+X18uHLkktFAQ99tEs+O7t2YidMZlxWyHeTmNT9MSAa3R+/F4eQTclt3idLF4bTGDOWqlMGVEzfhqRnOA1FZgqq48xbrsAsdIdR0BVZ7bZLpOqkXyjPFFlquk7lCtycxA7rtFnDVStagiplx6GzEXg3m6hj1/JD0bZFhJKbVm4sIi5UsRSuMoGuICFepsiVRvko5rnrRi2z3y1QozbEDhY2MvXr2hk1aNJsac4YekTGaSBgysx+WnfaD88aFMiVjNFhLW5UO3XYLmYwRQZkfMQfWcSYyI2NUEQkzi8kmYxT4PNN+osyPHTkqkr09EXentWZsKCMP0Pf1nxvFld4mHepjqrl8okd+xKM7z7A2dAe/17Yfy/tB0aB9ExcmfEaP0+iF0jKMRikIbt16iuOZN1jyN3p0RxQmnLv5EDcePkexIn9mjz0LO55/+BuHHt6U+nJ/EzIxseveVfxXIJqlSM28hI9yCuuVFuhmRDelY8eu4/btp1BWTJkzgH+mf519UwY0al0Hw2b05vfhtmtkFvCbF8jVa5yBCr/3N1jOEjF5oVq9yjwzTQNrki9GWMbJfJ000JvjMx3Tvjbu08w8rVckB5MVEZxqNQ5B+13YTfDpveewVHVDiHHULw0upIlKNSpgtsdUxN0Mg36ABvdJUQAuSdI0mprAeoQHDm3NlDkh/o2fQecdVS8pa2tKnTmIsFiFBzces4mJ2sLRiLm8CObL53JPm6zJoNuUIHjODGFJKPV1hh3zhOqsATIjR2S446UZhk2hwkBko2AtTDKTvWvvliU7sSlcSEbMo+agaaeGMl+nCFsjd+P47rNMgMwjdBTWmxjhtB4f3n9C6+6N0XuEdJxnC4oDqWdx88pDro6RXFFZsfnrBHT37o1Ro0bh0mptO3Kef/Zv1xilS8hfJisLJN+6BL1966W+3N+ETEyk37uOD18KF3n5FTo1qY2q5Uvj9bsPSD9zHYUJdDPq9lVusXGD8lbJyMyDnBZp8BO/ZDeUBTPNR6J0+ZK4fu4ukmL3K2w7tOwnsAU+9VLQ7K0sycmPaNWjKfduEDaEpGQPlGQJGmBqOE7CbA9h3MQa/63w01kic1JMg9zwTC+M/iqTpIHonI4WOLNfPtVx6hEcbzwcyy8EwmnDQnQZ2p6PNYVN243xxoyGhlhuF487l+/LZXv+P4OcTjeGbMecjuYw6W3HRizUF9ewbV2YLdVj8qzjOU0ulRtyINVpMx9712RwFZeMQgLTnVneKiu8e/seDhP8sDv+IFcBLVcYYJTeEMgamTtPI2y+UC44y1kNvWUYS/Ejbl++j0i7Nfxe23kS6jRRjMlU1r4L2L81i4/1XDc1pagkC6tjot6xnkpbHXv37iNSU4VS3jFjC+7WK098+vKFg6AJI7r+d+SKKbeF30na+E3IxMTbLx9x8MF/Q7b45//+h2GdhT0TWw8LZy8KE8ZP6Mw/U1JO482b91BWTNEXauT3bjvJGWXKAOoj0rAQSlZivLfyLLWi3OUsl+mylfjRHaexdZl8SWv/Sd2h5SzMJFu8IBZH5CSdVJs/CvMjdHlgkhqzD85qgTJ3+iMZmFHwLLgnWaFSzQq4e/k+zPo7ItQ0mgep8gBV7Mi6nLYh+mIQJs0fxRUZsuyPc98AreamMOljhy3hqUwcfkM6oHOLrONtR3lBvZYeQk2ice3ULb7+SJYYdMAFi497Y5j2QLn0M7188ooNbqi3kIxT6jSviUUHXDDDbiKTJFmB4geshnviWMpJoWRw/XyZx18Qrp68CZcpi9ilcvC03lBbIL8MTQqq95y1hM+BDv1bYuRsoWpD3iBrf1JkEEZq9kGDlrWgDMjYeQ43Lj9EydLFMHamUDmijNiRegZv335ArdoV0KlTAxQmHDp/C8/fvEOF0iXQvYVsIjLkjVcf38uMC/wmZPlAym1hzs9/AaO6t+Sf5LhIF0xhQseO9VG3XiWeOSJSpqxo3LImug9swXbgq77OxCkDhk3vhfotanImTazPNoVtR/2WtaHtJCRFy+wSeTZXnlBfMApDNfry8XGfEcqDJ3lgqEZ/OCTO40FxxtZMWI/05P4OWYOqU9QXpKo1gKtUG4O3Q6+9OY6nyfcaqtW4OnS9pyP+djhs4kzQRbU99zZSjhr1mhFxsB7uwQHENJD+jfyHkx/cdBQe0xdhUnUduE2loPLjTAqadm6IuYGaiL+zmGWJ1B8mj2oFnW97EzMwu/V87I4/wMeb5JGLM73QrIts+0oe3X4Ks/5OLNMkdYBnsjW6qraX6TpF67Ub58dGPu36tYBpmLZcK0MrXDbg6qlbPAm3YLG2wtw8t0Ttxe3LD1CuUmmOXVEG0PkY97W/e8z0nihTTjmrY7SdGzYIzTzGjOlUaFyxf5QrqnZuhiJ/ym7CRZ7Yc/cqPv3zDxqUkb5D6G9Clg/svHMZX/75b/Q8NKpZGS3qVsXnL/8g5WtJubCAHmrjxgqrZJs2ZmbnHykjphkM5p97t51irboygGai9V2FRCgpJh1XTt9W2LaM1hvEs7c0i+utE8E9HvI8j4wWaaF9/5Y8aLId68sZQfIAuSG6b7PkwOHT6Rcwr78TBwXLGmSUMH/ZHLhvs0KVOpXYYMNCxZX7iah6IU+QCUZ/9Z68LatuhjFJo/w0Ig5HU07AZ1YYJlXX5d43qpw9uftMrttXmEDElYgOk7AaumzQQREEdF6TffxU63GIPOuP0MMe7HhJJiTyAoVg24325vByqn7Wb10Hiw66sjySzgFZ4vrpWzDt64Cb5+9yddgvzZ4ly7IG9cTZjfPlAPV6LWvBPt4ERYrKr3eLesbWBSfz+3khWtzTqQg8f/wKK/2Ek35a1mNQulxJKAMy0s7h2oX7KFGyqFI7K2Yeu86GHiVLFoWqalsUJlBLzJ6TV/5z7oopXwszA2tKfyLpNyETE2X+Koan7//G8Sd38V/ByG7CKtmWQ0JL0sKEISqtUapUMdy9+xxHj1xV6iqZqJdM5OakDGjbsyn6je3EZDbMOoH19IoAzdrOD9dmonA56wZivmbUyAs0SLJbbcz9bCShsh3rw+YT8kDbPi3gv8uBB4oUVGzS10FuVTqqSkWc8uXeMiKm1E+k3cqMf8qzn0+EyjUrsowx7Kgnlp8P4J4iOibUY0fukFQ5m1JXH3odFiLCchVO7D4jV/KubCAzFMoLW+25AWYDHDGxmg5LAYmE/f3q3f+1dxZgUW1dGP6u0t0CClKKASh2t6JY2N2N3d3d3d1dmNhY2IUKFiAhiDRII/+z9ggX/RVFYeacufv1OQ8HHIYzJ/faa63vAyltthnRlAU+pHjZa3ZHmJeQbqkYHbsjy0+zrBhl6BQUC6LbtLZYe29+vmfFiCfXXmBk3ZkskC9asjDzHrS0z/+yKTJpn9151Vd5ex3MOT4m3xQjfwSVoS8dtJWtN+1dB1VlKKCxfd5JJMQloXjZosz/UgjQs27PmktsvUXXatDSld6xyS3HvmbHnBo7sPGOmLj46DWSU9NhZaKHUuaFIA8kp6fh2leBv3pFeEAmM2qZSlSRLshR2SKlkRUKFMDLgI94FyJcxcIfoaqqhMZNJDNGx08IV9yD6DK4Pvt647wX3r8RRpaM6DutNVTVleH9wA+XDt2V2XYYmOpi5JqebP3wynN4dOVfHyRpQIOlOSfGsu0g8+hZHVaw/gtpQIIKNFAkRTtSQhxdbxYeX5E0cOc3JN9OvWUkpkBZCxJYoUzZmPqz4P9CdllT8jSjnqLNz5ay4KzPvE4oWaUYCxyp/+nQYjeMbTAbrfV7Y7zTXBaUUDkaDYTlFRpEUrbn5Dp3zGq/jJUiDqkyGdsmH4DXdW+WVaQAlsoA6Xju9V+LgUu7o2RlyX6TNiQa41pxAjaO2c1UPe1rlmC9at2nt4OSFDyUKDCd3GwhC07pby+7Nl0qYiVMYn7gFjy+8oIZL88+Plpq8vYETaasHLYTESHRMCtugn5zO0BWeD/0w8UDd9i6xHJFGPP/ty6+gN+rUPbsa9OrJoRKcHAk7t2VDP5dRCbmQZz+OtHfvHIpQYi45AU3Q/yQkJYKEzVNlNbN+yBTGFeICKhf2DorIJPFDHJ+oKephup2Fmz9jAjFPVxalmcS+Pfv+QpaAt+qhAmqN/yaJROILxlhYKKDzqOd2fq2OScQF50gs22p3rw8m80lFg/cInVhB8Mieph9YgzUNFXw7IYPlvbfLLWsIQ3Yll2dBodaJZEQl4hJzRbi/I5rkBalqhbH+gcL0HtuRyZ48MzjJQY4jsOGMbvwOVZ250RmcNZxvAtW3ZqDQyGbMHH3UDTsVgu6hbTZQP/RpWcsKCHVQBfdXixbtGXiPtw8fo+ZDYv1Xk3nPylR7p17FFNbLERbo77oX3Ys1gzdhhtH7yIuMp6Vu5JQypDVvbHr7WoWwFIZIJlcy2rwS1nmRT3XYmTt6Sx41tRVZwqOS65Mz3cZfYKO9565x7Cgx1qWQa3RqhLmn5kgtRLNbVMP4fK+W0y0Z/KeIbApK3m+Sovzu27g1qmHLBs5bnN/qRlP/yg7umbCAbbesEMVlCgnDDEKuqfvXXM5y3dMU0cYJZQ/4uSJR6DbV+XK1ihSRA9iIvBTNB6/+4AC//wDZzlSV7zwNSHT0Cx/em9lY0ghQqoaF4VyQQUExEfDJ/oTSuoaQV7KFj2e+bLmy8EtqjEFRrFgWlgXlSpb4+6ddzh58hGGDMl/CeM/pbNrfTYzR1myzoPqMfNoIeDSty4u7PdkTde7F59mksSygmZzvW6/Zlmqpa7bMOvQcKnOrFnZm2PK/mGY6rKUmSnrFdJG/4WdpbINNGCknrIlfTawv72s/yYEvwlFr9ntpTK4Jm+iThNaoV6nGlg/aidunbiPo8vPMInw3nM6omH3WjKf4dYx1EK9zjXYQgMrKvN86vEST6++wLPr3oiNiGPZIloyoYDA0qEorByKMhEZ8mQrYmvKSsmEMGtLvV8f3obi/ctglpVky/NApj75PZRxoWCLMj6O9exhW9E6X5UJcwNllE+uPY89s4+yrBTtW1JupCCfzLOltQ0rBm7Gpb0SO4+2o5qi77xOUjtvyULj8DJJv9TI9X2kIhySHX/vYKwft5et95jSGsXKFoWsOL3zBnyfBzERld5TXCAUbpx/nuU71qqHcJUVSbDs/PlnbN2llfiyY5kT/JVLmDOLJXkg/csXXA6S9MQ1KpI/fag8IPtN1BSUUNPEkgl7XAx8LTcBWS07S2iqKiMsOh4PXgexC0hMtGpVgQVkF9y90Lt3LajJaEbwd7NkFJRRlmziMokflayhmVTXee0xsd0qnNlxHU26VJeZLDHN5k7cNhDD6s7C/QvPcGrzFbToLyn3lBbl69tj9KZ+WNR7A46tPs8yMe1HS0cZjEq5JuwazFQI9847joOL3RD8NhTjtg+S2kw3CUDMODoG9849ZrL4FCws6bOeDbapBM6hlqTvVNbQINvSzpwtLoMbswAt8NUHeHu+wcs7r1l/FQU51A9IGT9askPZJWNLIxQyN2Rm4UZFDaBvrAsdIy3oFtJhX9V11P+4vI4yBNTLExsexwJFyhxRLxMFWhHBkQjxC8OHdx9ZputnUPBYrJwVC7zsqtvCxtFSMAFY9owUBe+bx+9hn4cgJcehq/swDzxpQfuYSo0pMKfs1JBVvdCsn/TuHdeP3sXGr8EQ2Wk06lYL0oREkRb03sB85crVK402wyS+g7IS8ti96DRb7zGhOXQMNCGUvstM3zHKjgnVd4y4cMGLSd1TZqxCBemZiOfVPeH03ZffqHnLAw8+BSEiOQHaSiqoVMgMn2Pj8vxv8IAsFzQyK84CMkpbDnMQ7uxKblBSVECj8sVx9KYXy5KJLSAjXw4zMz0EBkayoMyllUR9Uai9ZJlZso4D68KyuDGEQNkatqjZ3BE3Tj3G2okHsfjESJllDyxLF2EGphvG78fmKQdhV7UYy1xJk/qdqiM6LBabJuzD1ikH2eBcWgMsCjTIQJqCsmUDNrHSu0+B4ZhxZDR0jbUhLSo1cUTZenY4ueY89sw5ijeP/DC67kxUd6mIPvM656uB75/ut6Ili7Clce+6WRmTAO9g+D59D1+v92ydPNhC/cJYFof9/BciKiT6QsGbqqYq88zDP4CqmgoKKhZExpcMFghS/xZ5LSV9Tv66JLGvv4ueiS7MS5iykj6L0mZssXQwZz1+QoaC3k3j92YFu5R1JPGQRj1rSzWbSplSMnymgJCMyKccGI4KDaWnSPfE4yWbwKGBaPMBDZidhrShe6X/y2CWRR6zoa9Ms9lU/v45NhE29mZo0lU446Sb7s8R8C4MGloqgvYdY5YkX/viqXdMbFL3T959QHBELNSUFVGnjKTVRx64mKmuWNgGigXyZ2KMB2S5oH5hG1YT+yLqIwLjo2GmoQN5oFnlkiwgu/T4DSZ2qAdVKTRd5xV0s6IgbPWqC0yRqIWAvTosbU1Qs7E9C8j2rL6Eqau7Qij0m94G9y69wIt773Dp0B007FBVZtvSckADPL76EnfPP8XcnuuxxmM6VDVUpLoNbYY3QdTHaBxefhbLB22Flr4mqjhLT62sQdeaLGszs91yvHrgi8FVJzPvMt2i0pttpuwQKR826FYLu2YcxtnNl1g2xPPUQzj1rMMU8wyL6EOo0PZTD8/3fTwUqIW8+4hQ/0/MaiAs4BOzO4gKi0H0x2hEfYxBbEQ8GxhRHxKJndDyJ1DJpJaBFgvqDQvrwaCIPutXNDI3ZEG3sZURVNWle27/Le+9g7Bj6kE2WUCQnx6dJx3GtZT6dXr37GPM77aG9V4aWxpi5tExsLQzk9rff/3QFzPaLmfnSfWWFTBoaTepT2bdPv0Ip7dcZetjN/ZlpdayQvL8kAhEDV7QgRnCCy87VoOVLAqVR4/8EfA+ggmXNXKyh9jIzI41LFccqkriGUvmBD0LLgS9yUrM5Bc8IMsFeipqqGxkDs+P7+Ee8Bp9S1WCPFDGyhRmhtoI/BSDK0/eis4zwsnJHtu2eiA4KAr37r1DlSrSK5X5kywZzdTdvvQCb14Eo1hp2ZQHfo9hYV10Ge3MZje3zDqBKo0c2GBSFtCAhqTwXWtMR/Dbj1g7di/GrO8j9e3oM7cjE1e4uOcm5nVdg7lu42Bfw1Zqf59k8Vffms08pchHaUyDOei9oB1aDZIIsUgLXSNtDF/XFy0HO2HrpP24c/ohzm29gkt7bqCFayM2EKfXiAUK1CgblZPIBA3gkuKTmKgJeUolxiUhJTkFn8LCoaGmgYz0DPxToAArj6MJoIIKBaCirsL6vGhR01Jj14/QSgz/hlD/MNYjRtYIZJdB1ylNHPSc1UGqSoKZA6RDS09h66QDbJ0EcaYeGC61fjUi8HUI8y7MNH6esGOQ1AMQMp9ePmQ7W28ztDHK17eDrKAyXbJQIZw6VxOMkAdx/dyzrOxYy27C9R0jjh39KnXvZC86qfvk1DRceCQJXJrKkZiHd1QYS8KoFFRALdP8O6+FMX0hIpzMJdGxe6C4zJRzgh6sTTM9yb7ObogJmklydi7D1o8dvQ8hU9SmEOo0k2zr7lUXISRa9a+HorYmrPeF/GNkiZaeBiZsGcAGu5f23cKlA7dlcl2MWNcHlZ3Lsh6Naa2XsNI9aWJqXQgrbsxElablkJqcio0j92HzxH1s8CNtqJRu9slxTFadhCVoe0j4o7v1UKZsKG1j6fyEBtbq2mowMjNgfWqkREn9c/a1SqCyczlUa1mRmXtXdnZknm7lGjiw15B4iKm1MSsdk5dgjIydlw/YhJ62I+C+4xoLxqq3rIhNTxdj3I7BUg/GkhOSsaD7WmyZuJ8FY0371cf8sxOlGoxRIDSx6UKWOS1ezhIzDo/Md5Pr7yGbh/m9N7A+SRLw6DmtNWSJ2zYP+L4Ihoa2KnpObAGhQOXEVJFCtOldS9DZMVKLvnPnLVOPbtVauO0XP+O6ly/iE5NhrKeJ8sXyX1VVWpz/Ot6vbWrF9CTyCx6Q5ZJMdZWHn4IQlvDzhmyx0fxrVuzeqwCERIpvYEVKRDR4f/jQH/5+nyBkurjWZzPr96+/gveTAAgFEvgYsqAjWz+35xZ8pBx8fI9dteLoMrElW18zajeC3oZKfRtIfXDynqFwqFmCGZxOarEIAT7SNYenXqIZR0eh43jJvji6/CwmNl0gdWuATEjpb+nVGZh7egITcCD5+YOLTqKr1RAm7hAREiWT7eLkLSQoIwnEhuPslstsEoACz5W3ZmPGsTEsQJc21Cc2y2UVPA7fYQHvkJU9MWxNb9brJy3oupvcYhETaCHhFYldhvQFInbOPg7ve+9Yj+OkHYOkug++JzwkOkvIo9dkF8EIeRCXTz7Ch4AIaOupo2XXahAyx49JsmOVK9uITuqeOPXVe8y5YgnBto78Ce4BkoDMySx/K2R4QJZLTNS1UNbAFORuczFIfkyiTfW1UaFYEeZ7IUZPMmNjHVSvXlwURtGFLQzQoGU5tr5njWTmTijYVbFBg/aV2fraCQdZ6ZYs6Ti6GcrULMFEEub1XM8yVdKGvLlmHBnFZsKpt2his0Ws50iaUJN+z5ntMGRdd1YS9+TqC7hWngSf+xIZXllkD0n4Y82deZh1YhyKlbNkx+jQklPoZj0UKwZtZgN6jvh488gXczquQO+SI1ggRhkGx/p2WH59Jha6T0apKvnXQ5ET992fYlj1qQj0CWHqp4suTEaLQY2k2rMVH/0Zk5rTpMwHGBTWw/xT41g2VNrcv/gMh1eeY+uj1vSCiaVsVZ83TjuCxM/JKFHeEo27CCfood6+TO/P9v1qMzNooRIfnwR3dy+23rqN+LJj4TGfcfulv9ypK76LicDrmHAo/FMA9b76EecXPCD7A5y+NvW5f1VdkRcyLyIqWxSjoWpmiv/iBS/ExSVCyHQaVJf1nTy69QYvHkluYkKhz9RWUNdSxVuvQJzddUPmpWPjNveDtoEmfL0CsWH8PplsB+2POSfHMnVBki+f0HQBIkKipb4dlZqVZSWMRYqZIDwoEqPrzsKZLZdldr3SYJhK99bem8/KGUtVK85KGc9susQG9LPaL8OL269EeT/5L0FqkWR1ML7RHLhWnAiPw56sNJGC7uUeM7HowlTYVS8hk22jSaFdM49gSotFiI9OgLWjOVbfng37GtLdHuoVm9JyCd49fc9EWhacnSD1ck2CMtBLBmxh68371UONlrIdvD+48gI3Tz9mVR9DF3aUuV9hdtyPPkDYh2joGWqiaccqEDJnzz5FUlIqLC0NUa6cdA3F84Kz932Q/iUDDpYmsCgkvuzer8ygqxkXhbZy/mbChXPliIhMlRXP0PeISRb2wD831HcsBhUlBQSEReOZXwjEhoODGaytjZCcnIazZ55CyBgX0UPDr4aPu7/WtwsFKjch/xhix3w3RH6Mken26JvoYvyW/mzwf27ndVw+6CmT7aCgcP7p8cy3ikqnJsiobJAMjmlASoEQzQCvdN2KhT3XsQGjrKBjU6VZeay8MZsN4KnPigb0N47exYia0zCkyiQmAkLbyxEOdM6cXOeOvnajMbnZAjy67MUG1vU718DGx4tYWaqdlAOf7JDy5aRmC7Bn7jEW1Dv3rYdJh4aw7JQ0ocz89LbL4H3vLTR01dl9wKy4CaQNlY0u6LsJMRHxsHYwR785HSBLaL+sm3yIrbfsUwdWpYXTN0Rqqgc2StQnOwyoA2UV4Sr+0aTDya+VPeStKgTT+txA1+apOy/Yeouq8pMdy94/5mSe/4JePCD7A6y09FFc2wBpGV9wOVg2JUP5gbqKEuqXLfZNLbCYoJtYZpbs5MlHMi+3+xUdB9RlfVtP77zDY09hnUfO3WuieNmirG+KylFkTbm6pdFlgqRRfNWInQh49UEm20GS5QvPTYSBqS7ztaJeEipjkjYkODH98EimBEkD6Cv7b2FI1Snwex4IWUMD+DmnxmPTk8Vo3KsuFJUV8fqBLxb2WIPO5oOYAEiIr8REmCMbfJ+9x6ohW9GxyECsGbqNmWpTL1KbEU2x49VKTNg9lAmUyBKvmz4YVHEiHl9+DmU1ZYzb7irpFyM/OCkP7Od0XoWnHt5Q01TBPLdxUvdGzGTn7GPwuvkKqhrKrG+MLAdkyaE1FxDiHw59Ex10HdsUQuLcoXuI+BgLA2NtNG4nbEVsT8+3CA2NgaaWCuo3KA2x4R0YhrcfIqCsWBCNysmmpDm/2FCrNWZUbIiGX/Uj8hMekP0hmdFyZjpT3soWLzx8zSRMxUa9eqWgra2Kjx9jcPOmsI9NocK6aNJe8qDYueKCoMq6qFRw2OJObLB/3e0R7l+WzH7Jkk5jm6Ns7ZJITkhh/WQkJiELqF9j/tkJrGzp7ZP3rIyJfJCkDZUGdRjbAksuTWEZAxpUU4/Nue1XBXEuWdqbY/SWgdj3fh16zu4AfVNdllEkAZDuxYZhYpN5uH7kDhvwcqSTDSOVxOE1pmKA4zicWn+BnbckTDF4VS/sD1iPgUu7y7wfiSbSKCM2tsFsRIZEo2jJwlhzezYadJG+mS8pGZLlxb3zT1kv6axjY2BbwQqywPPsYxxaIekbG7mmNwpbF4IsCXr7EYfWSJSCB8xsAzUpe9DlRFJiCg5u8mDrnQbWhZIMBU9yI+bRrKkjVAScyfsZbp6SCfw6ZWygqSac8yCvdCN62FaAoWr+2wDxgOwv+8g8PvgiIU36QgP5RcXiZjDW1URcYjI8nr2D2FBWVkTz5hID36NHJMalQs+SUSnFq2eBuHNFWGIq1nZmcOlXl62vm3RIJoIa3weJVLpIDf3+L4OxdswemQUe5ramrGxJU0+DlTFNbbVUZiWD1Nuz7t48VGjkwI7R8gGbMa/Laplk7n4EiR50mdQae/3WYsbRMajQSGL78ODCU8zusBwdCw/AqsFb4H33jSACSXmCgptHl56x7GR7k/5Y0mc9Xnq+ZgqFtdpWwaKLU7H1xTK4DG4sE6XA7yHlwvFOc1nPGJW8UhC26vbsHD3j8lVWvvs6eJ5+xLK80w+NkKoPYXZC/MKwdOBWtu4yqAFqtaoIWULX6ZoJB5CWkoYKdUuhRjPJM1couO25jajwODbpmdkaIFTevQvDkyfvmSph8xbC2o+/Q2paOtwf+LD15l/tkzh/Bg/I/pBSuoVQWF0bSelpuPFBtvLgeQndFDIN/U7dEVaA8Lu0dCkPBYUCePEiGD7esilt+12o2bhlN4kq1e7VF1lzvZDoOsYZRoV1ERYUCbfNshX4IMiAeMKW/uw8vbj3Fs7vvC6zbaGypXluY1n54PNbrzC1lcQkVlZBzxy3cayEkQbbHkfuYGCFiXh+S/KgFAK0XdVdKmL+uUnY+XolOk5wYVkz8lE6teEihlWbgh7Fh2PrpH14+8SfB2d/CN1Dnt/0wdrh21mJKAU41L9HGeXCxUzQa05HlrWcenAkHOvZCUaE4bbbA3bOPrvuzZREx24dyMoUVdVVZNKrtbjPRtw8cZ/JyU8/OBzlG9hDFqQkpWJuj/WIj0lAiYrW6DOrPWTNTben8PJ8yyYTBy/oIKiep/jYRBzeIsmOdR3SQKZ2AL9D5sRxzZq2KFRIG2LjxnM/RH9OgoG2OqqUlE0pr7wgjDuxCKEbkLyqLTarIgnIPL39mZSp2NDT00DdepKZmiMiyJK17V0LahrK8HsVihvnn0NI0GBo0FzJAMB99x28fyV7sZcytUqix1SJCeq6sXvx6qHsJkSKl7fCvFPjWP8N9XZQUJb0WTZBWWYJ44rrM5ihdFhAOMbUn40d0w+x2X4hQebJfeZ2wl7/dVhwfjLqd6kJFTVl1lt2YOFJDCo/Hn1Kj2LBGWXOhDZRITTo+D6+8hxrR+xAV8shGFl7Ok6sOY/I0Gho6qqj+cCGzD9su/dydJ7YCnrGOhAKNImxfOBmzGi7DHGR8bBxtMD6e/PQsFstmWUVl/bfjGuH77Ae3yn7h6GikySrKws2TTqAt0/fQ0tPA5Nl7DdGxETE4eByiRAV9Y0Zy0BpMieObruB+NgkmFsboW7zshAykZHxuHJFUu7XVuB9bj/j9F3J9jetWAIFBTK5I1b43suDPjIS9khJT4e8QJKl9pYmTML07D1xZsnatpXc3Dw8fBAWJmyja00dNbTuWTMrS0azs0KiipMDqjjZIz3tC9aMPyCIwXH7kc6o2tSRqfbN7bEOsZGyM2mnWWsqX/w3KFsqs6CMsK1gzUoYG3Stycq+9s0/geE1p+P9yyAIDSpDLd/QARN2DcGh0E2Ysn8EarSqxErEqCeOgjPKnHUyd2UmxaTaGBclu2MtJEj+/OLu65jXZRXaGffHuIazcWL1OVb2R+ciBTRzTk3AwQ+bMGxtX+YfJqRMBvHyzmsm3HFu21W2bW1HNcXKG7NYJk8WSDJjG3B5/y3WPztp9xBUcZZdGdml/bdweqtEKXDspr5MVEjWbJl5HJ9jEmFZqjBc+tWDkIiOiMeJ3bfYevfhjdj9RciQ+FhqajpKlSrMFrERFZ/IMmTy5j0mK4R9tgqc8gaFYaCijtiUJHh+fA95osXXi8vtjjg9yWxsCqFMWXM2IHU7+QhCx6VHdWjpqCHYPxxXTj2B0Bg4ux1U1JTw8r4vzn194MkSGryNWd8HplZGCAuMwKJ+m2QaKLKg7Gum7NkNH0xxkY3QRybUDzRu2yBM3juMZUjePPKDa+XJOLryrCAC6p9lY2u3r4rpR0bjyMfNmLhnGOq0r8o+S2RIFDMpJl+ztkZ9Maz6FJb5I5n2RBkGv9KEAtE7px9iw5hd6F92LFNIXNRzLa4euMX6BalslVQtZx4fi8MhmzBux2BUdnaUeUblR5CQy7YpBzCqzkxmIUF+XmT03H9BF5ltLwVji3pvwNWDnqy8dvKeIaguQ48vyoqtGrGLrZPCbMWGDpA1T26+wpWj90Fx/ZCFHVkGUUgc2nwNSQkpKGZXGNUaCDtASE5OxSk3ydikbVvZ9gT+Kefv+yAt/QtKmhnB2lRYmdI/5UtGBltkAQ/I/gJKz2aWLZ4PkHgVyAtOFWyZhOm7kAi8fC9Oieo2bSQ3udOnHzPDRSGjrqGCdn1rs/W9ay8jRWB+TYaFddF6sETgY9vcEwiXgSny91Dv1pTdg5n62YNLz7F3oZtMt6dEJRsmiZ2ZKZvcYjE+xyTIdJtqt6uCTY8XoYJTGWbYvHHsHoxpMAfBb2RfepoTFITV61Qdk/ePwOGPm1nfmcvQJjAvWZhNsnjfeYO9c44yI+NWer2ZcuDmCXtx49hdliESOzQJFvw2FFf23WS9YAPKjUMbw76Y2nIRji4/Az+vADYpUay8FTqOb4nl12fiQPBGpmpZrUUFKKkoQai8fuiLwZUn48AiN4lwR9ea2PhwAcrUlt0Amgl49Fj3b5nivqGo4SK7QTKVbs7ptpb1j1Vs5IAu4yWWH7KEBIOoQoKo264CSgjMvPhTaAxO77/L1nuMaCS4bPD3XL78EjExiaxvrEZN2YjF/C00YS8v2bGU9HSEJcSjwD//sEUWCG/qTIRli3vfPMbFwNeYXckJCnJSQ6upqox6ZYvh3H0fnPR8gdIWxhAbVarYwMREByEh0bh48XmW+qJQada5Co7vvImPwVE4d/AuWnarDiFRv0MFPLz0Cq8e+zPVxWnb+8t6k2BlZ4ahy7thycCt2LvADTZliqKqDEuMSla2wYIzEzCp+UK8vPMGE5ouZD1mlKWSFSScMddtHM5suYJN4/YwwQcST+gxox1aDWsi+LIeJWVFpsyYqc5IAdfDC8/w5NoLPLv+Ep8CI5hyIC3ZP3MxR0tY2Jsz8RWS4KcyOCFmi2igG+AdBD+vQPi/CGQeYZTRjI2I+7/XkkS9fc2STIzDsb49y4qJhZSkFOyZcwyHlp7Gl/QvzDZi2OrerERVllDZ84Ie65iABwVjU/cPQ5Wm5WS2PZTBXtR/M0Lfh8PEwhDjNvUThPDK/uXnEOwbBr1CWmgzRDI5JyQObLjKjqVdeQuUqybxUxXyhMuxo/fZukur8oK/B/+IV0Gf4BMYBkWFgmhSUXbm8XlBUHwMdrx6gN2vHsJB3wQ763dAZFIiCv7zD5O9lxb/ZIixHk2KxMbGQltbG1FRUdDR+f9G6NQv6ah0ZBWiU5Kwv2EXVCkkPyozd3zeY9CqYyw4u7igP5QVFWT2gAoLC4ORkVGuH0ykYLRu3WWYm+tj6zZ6sAl71uzswbtYPeMEtPXUse3CWKipK0MIZB6DhIg0DGu8kPWTTd3WH9WayK7ZPTsk7uG26TLLTq26OhVFbGQ7gfDu6XtMaLoAsRHxsClbFPNOjYe2gabMroNMQv0/YfnATXh85UVWVm/khn6wtDODGKHH18f3n/DMw5v1I/nce8uyRzTg/x7qCSpU1BCFbYxRuJgxClkYwsjMAIZm+uwrBQgKOdzj/nT/0yCRSi4pcKTy2k9B4QjxDWMZMMpUhgdF/rAsnIJHa0cLlKhow6wN7GuVFJQYR25NnlcM2sL6AgkqRR28oge0DbSkfg18HwzP7rQS992fsf1NAh6y7Bkjds8/wSaXyPR5xaXJMjOhzo7viyAMdVrIrqspW/vCupxJnh2DvODD+3D0b7acPZcW7eoP+4qWEDKPHvlj7Jj9zHPs4KEh0Milh1teXwd/wuLD17Dv6mM0cCyGxf2aQcwsfuKB5LRUjHWsgyl3z0NNUQm3QvxZ6WJrKzsMsf//yfHo6Gjo6uoiJiYGWlp5E7QJb7pQZCgWKIgGRYrhiK8Xzgf4yFVAlulJFhoVxzzJGpUXX1q9cZMy2LHjBgICInD/3jtUrmIDIdOodQWmEvUhIAIndt5EZ9f6EBIWJU3RdlADHFx9AesmH0KZGsWhLgD/ov7zOuCdVwBeeL7BrC5r2EBGlr5K1mWKYrH7JIx3XsDMo8c0mosFp8ezzI0sMbYwxIJzk3B++zVsHLeHBTCulSah3aim6DK5NSv/FBNUlmRsYcSWRj0kJb/UU/b2kR/ePXsPf68A+D0PhP/zQNbTRyqOtJAH2o/Q0FGHtqEWtA01oa6pBlVNFXYekQx7QcWCSE5Jgpa2FgvuaHBKA0AqdyM/poS4JCTFJ7G/Q6WqMeFxiAmPRULsr3sJtfQ1YWlvBovSZrC0M2dKg1ZlLASZ0csN1Nu2ZdJ+nN1yhX1PHoJDKSsmw3LATOg4TW+7nMns03k//fAIlK8vG2n7TDzPPGbBGDFsRXdBBGOkOrli9F52vldvWhZVG5dhwYCQ2LWKxLC+oELN4oIPxoijRyTZsSZNHHIdjAnFe+zsfYmlSouqpSF27n0MwMyKjaBcUAFvYyNQ19Qal1r0x40QP+x9/QjBn2OYzVV+I+67vUBobG7LAjKSv59WoaHM6k/zo0euWeWS2HL+Hk56vhRlQKaurgznpmVx5PA9HD58T/ABGZXMdBvWEAvHHMCRbTfQtFMVaMuw3O1HdBrZBNdPPUKIfzh2zHPD4PkdZL1JLLMxeacrhtaeiQCfD1jmug2Td7nKtI+ABtcUlE1wXogA72CMqj8bC89NYMGDLKF90qR3XWYkvXbETub/RP085F1Gg+UKAhAP+FtxECrroyUTykCRBDxlpIJeh7DsFNkCUPkjfY34EMUGnBRA0JLXPXYUWBkU0YdBET0YFdFn2bnCNiYsU0ellJQ9FXrPS26g/X3tkCfrWaT9TjTpUxd953WCpq6GrDePed9NdVnCTN3VNFUw69gYmZk+Z/LeJxiL+m9i6y0G1EeDTsIoWXfbcg1vngZAXUsVrl8tUITE25cf4HH2GVvvOdIJQicwIAJ3775lwigurWQnGvM3XPfyRXR8IvMeq1qyKMRerqhYoCBK6RVC2pcvMFLVgKudxBu2poklVjy9gcikBB6QZScyMhJDhw7FqVOnWIq2TZs2WLlyJTQ0fn5zr1OnDjw8JAaBmQwYMAAbNmzI0wNaw8QSGopKCE2Iw7OIEJQ1MIW80KxKKRaQ3fF+j7DoeBjpyP5hmltat67A6rUfP36Pt28/MgVGIVOriT0Ob/WAr3cIM7jsO9YZQoJmk4cu6oRJ7Vfj9I7rqN2yPOwEEOjqFdJmQdg454W46fYQB5edRcfRTWW6TeYlCmPZlSmsl4zK1EbVm4MFZ8ezn8sawyL6mHFkFG6dvM8CM9q+SU0XoFabyui/sAtTvpMXKNjRN9Fli0OtUj/MAsRHxSP6UyxiaAmPY5ktyrYlxiUiMT6ZZcLiYuKgrKTMxChIia+gQgH2lQIuFQ3KpqmwgFBNS41l2agkjzJuGjpqginvym/8XwRh7cgdeHrtZVbf24j1feGQLUCWJRQgTm65GL7PAqCpp8HM3clPUJbERydgVuc17DxzqFkC/efKfpKL+BgYgZ0LT7H1PlNbsXus0FRad65wZ1/rNC0D65LCH3sdOXofVKFctaoNigjAxuBvxDyaVSoJBRH2v2VHT0UVYx0l1RXEuLJ1srQgSOTjCzJgry8dGw7RZMi6dOmCkJAQXLx4EampqejVqxf69++Pffv25fh7/fr1w6xZs7K+V1NTy/NtozQnpThPvfdmaovyFJAVNdJFWWtTPHn3gXmS9Wwk+1KT3EIqRrVrl8DVq97MKHrChOYQMjRw6zG8EaYP3IlTez3h0r06DArlf7o8NzjWLAGnztXgvu82K2dZe2miIMrdSlWywaBFXbB65C7snH0MFqUKo0oT2ZqDUkZs6aWpmNhsAd6/DMaYhnMx9+RYFCsnjNKa6i0rMpGInTMO4+Rad1w/ehd3zz1B5wkt0WZkUyaqIe9QUz0LnqinqaRw+zaEzOfYBCbacWKNO5OQpx6ojuNaov2YZoJRfQzxC8PEZpLJESqfJP9AWfdPSkQ8NiGY5P/N9DFpx8AcexmlmeUkVUXqs6MJN6fOVSE0nt3zxYMbr9nECFWWCJ3o6ARccPdi6+3bV4YYiYj9jFsv5Md7TE1BCY4GkglSCsSstfWz/m+L9z2U0pXeBL4onire3t44f/48tmzZgsqVK6NGjRpYvXo1Dhw4gA8fJE3CP4MCMGNj46wlr5rvfmYS7R74SpS+XTnRXOSeZNmNoq9eeYnw8P9XLxMaFWvZolS5okhJTsO+dZL+C6HRd5pkxpSUt/YuOwuh0LR3HbbQubqw70b4ewfLepOgb6KDJRcmo3g5S5Z9Ges0D4+vSoQ1hAD1SQ1a2h1r786DXY0SSE5IxvZph9C/7DimPifW656T/1B28ezWK+hVahSOrjjLgjGS3t/8dDG6TmktmGCMeglH1ZvFgjHqpVx2ZarMgzFi97wTuOf+jAWwU/cMgU4uhU7yi0uH7+LB1ZdQVFbAsEWdBDcJQfekHcsl2bHGbSvC1PzfgbRQcXN7xCxtbG2NYe8g+3PvT6DesfQvGbC3MIaVifD3eU6QaAdVtW15eQ8RSQn/J4PvoG+M7rblIS1kPw3zG3h6ejKFwwoV/q23bdCgAbtB3L17F61atfrp7+7duxd79uxhwVjz5s0xderUHLNkycnJbMmuspg5i5VTqr6WsSWUChSEf1wUvCM/ooSubPtE8pL6ZW2w6NBV+IVGwssvBHZSlsCn/U43378plShuaww7uyJ4/jwIJ048RO/etSB0yEtlfPfNcD/6AC7dq6GIpaHMtuVHx4DKs1znt8ec3ptxdP1l1GjmCBt7YTxkBizoiIBXH+B16zVmdV6N5ZcnQ1NHtr14GrrqmHdmPGZ3XIWnHi+ZefS4rQNQs3UlqV0Hv4KEJRZfnIxrBz2xeeI+Zto7q/1y2FW3Rb+FnWFbwRr/VaSx/8XGo8vPsWn8XiaaQlBP3MAl3VDRSaK+mtf76k+PwQvP15jRdjkrDbQoXYRlqPVMdGR+LD2O3cP+xafZ+tDl3WHtYCbzbSIiP8Zg47QjbL3zqCYobG2UtV1CuQ7uXvWG95MAKFMmdmAdmW/Pr6BA7OSJh2y9TduKbB/+6USXrI4B/U03T8lEYtPKJQW/z3/FnteP8OBTEJ5HfsTdsACMsK/BhDyKaRugbmFrOJtL5Px/9Dnz47OLIiALDQ1lZSLZUVBQgJ6eHvu/n9G5c2cULVoUpqamePbsGcaPH49Xr17h2LFjP/2d+fPnY+bMmf/380+fPiElJSXH7aykZ4qb4YE45vMYfa2F7XmVW6qVKIKrXu9x8OpDGDWVbtkinfgkLUo3g7+ZpWvQoBgLyE65PULDhjZQVhb26W9kpoYyVS3x1NMPmxedxuCZsuuH+tkxsHY0RsVGpXD/wkssHbEL03b3YeUjQqDfkraY2XYdPviGYXa3NRi5sTvr95E1Qzd2w8ZR+3H/7DPM774OQX4fUL+bpIlYGtfB71C6ng3mXx6HMxuu4vyma3h+6xWG15iOqi7l0GZ0ExgVFffM6J8gzf0vdN6/CMahBafh5fGKfa+urYqWwxuhQffqUFBSyDcVvj85Bg/cvbBh+D6kJqehWAULjNzSC2kFU2SuFOjnFcTEh4jGvWvCvp61zLcpkzVjDuNzTCIsSpqgZmuHb7ZLCNcBCfBsW3aOrddvXQZpGUkIC0uCkLl27TUrWdQ3UEeJEnp/daxldQzehkTi7YcIKBYsgHLmf/cZhMBp3xfoYeGAScWqoP/9M1j92AP/4B9senEHTibWGF7855OltP/zGpmOSCdMmICFCxf+slzxT6Ees0zs7e1hYmKC+vXr4927d7C2/vFM78SJEzFq1KhvMmRmZmYwNDT8oQ9Zdprb2LOA7GZUMCYZCV/tJzd0qFueBWQeLwIwqUsjqCpJr6+Ebj7UlE/H4G9uPo2bGODAgUcIDY3B06dhaNZMtr1Fv0P/8c0xtPVqPLj+FpEhSShRRjYyyDkdg+GLumDgvbkI8AnFjWPP0H5IIwgBmsSZsX8YxjRegOc33+DUGg/0n9cRQmD6gZHYMGYPTm+6jF3TjiM5Lg09prfJUWkvr66D38YIcF3UA+2GNcPO6Ydxae9NeJ54hPtnnqJJn3roNKGlaH2x/gSp738BQuV+u2YdwdUDt9n3NMHRfGADdJnUiglkCO0Y0PW1btRuNnCt7FwWE3a6QkVN9t6OESHRWDNUEiRWbOSAwYu6CcYc+Obpx3h4xYdNrI1e2R0mpsaCuw4uHn+EIN8IaGipovuQxtDUlr31Sk7Q+XfxgkQcpU2bSjAx+bsqI1kdg+3XJNmxOmWsYV20CMTMx4Q4JGSko0kJSTY/Oi0Zqyq1grmGDitfnHzvPNI1VGCi9uMSYiUlJfkKyEaPHo2ePXvm+BorKytWbvh9JJ6WlsaUF+n/fhfqPyPevn3704BMWVmZLd9DJ/2vTvxGZsUx+e55vIr+BP/4KFhpyc8scsXi5iisr4XgiFhcffoOzSpLt5mTbj6/cwxygn63dZuKWLf2EvMBadbMUfBG0Va2JqjvUg4Xjz3EjuUXsHBnP5nJY//sGOgX0kH/mW2wbPhu7F16DlWdyqCorXRUiX5FsTIWGLO+L+b2WIcT6y/B3NYUzr3qyHqz2D4csqIHExbYPfsYDi4+xYyDR23sl6P3VF5cB7mlkLkhxm13RathTbBtygE8vOiFUxsu4sKu63AZ7IS2I51zbfArVmSx/4UAWQPsX3gS7juuIS01nf2sbodq6DGjHUytCwnuGNAAePv0w+y6Ipz71sOQ5cLIkJNIxpxua1lQZl7CFBO2DoCiAEQ8Mu0A1k8+zNbbD3WCzU980GR5HSQnpWLPmktsvcOAOoKzhfkR9+/7wt8/HKqqSmjatGye7DdpH4Pk1DScuy/JiLeqZi/6e+Cb2AjUNLVkn8MvNhKtrexhoSVRvVQsWBCB8TEorPHzCcf8+Pwy3aMU3ZcoUSLHhaLQqlWrMlfshw8l9bfElStX2CxBZpD1Ozx58oR9pUxZfqCjrIpqxhJPBlJblCcocGn+1QDQzVMieSpGyIiRvMmCgiLh6fkGYqDbkAZskO513w8Pb76GEGnQrjIq1i/NDHKXj9zNGvuFQk2XCug22YWtrx2zF0+v/3nWPS+hB2rXSa0wakNfZjR85cBtTG6xiPlgCZFijpaYf2YiFrpPRolKNkz44+BiN3QrNhxbJx9gJsgc+QvEVg3Zip4lR+LM5sssGCvf0B5r787FxN1DpB6M/Q4pyalY3GdjVjDWfVobDFvVUxDBGAWKK4buwKuHfsyEnDL45O8lFKhvLDo8DmbFjNFxuDCrfEh5ODw0BoYm2mjRRXjKjz/i6JF7ojaCJmgiPi4xGcZ6mqhUQhi94n9DLVMrDLarxq5JSy09DLL791y6EPgadnrSv7eJIsQtWbIkGjduzCTs7927h1u3bmHIkCHo2LEj6w8jgoODWQBH/09QWeLs2bNZEOfv7w83Nzd0794dtWrVgoND/hmfNvnaBHhOzgIyonnlUszM8P7rQASFS8w+xYaamjKaN5f09x06dBdiwNBEB82/Pni2L3MXZCMtBRfDFndmg4tXj9/j+CZhKUN2HtsctdtUYoHi7G7rmMS0UHDqURuzj4+GqoYKnnp4MwPpj+/DIVQc65bGyhszMfPoaNiUtUDS538Ds80T9jGTZY64IfPsZQM2sUCMyv4oEHOsVxpLr0xjQTkF50IkNiIOE5suxOX9t9gkB012dJnoIhjT7b0L3XD18B0WHE7eOQimVsIR/7pzwQuXj9xjk68jl3URpN1FXEwiDm66ytZJ5l6I2/g9vr5huH/fj+3XVq3FaQRNZIp5tKhSCgVFnh3LVFjUUFTOujeQfRWRmJYKN/+XaGhWHNJGNHuV1BIp4KIeMGdnZyZ9v2mTxNWeIG8yEuxISJBIV1Jm7dKlS2jUqBH7PSqPJDNpMpbOT+ggFvjnHzyPDEVAnHwNTEz1tVDZVlLCcOqrMaAYoZuigkIBPPcKwsuXspdE/x069KsNNQ1l+PqEwOPsMwgRAxMd9JvRmq3vWnQaQW+FE/TQTXfU2t4oUcGKZaCmd1jJynOEQoWGDlh6aQrrySKvsuG1Z8Dn3lsIFdqfVZuXZ5kSFpg5SgKzw8tOo1uxYVjafxMCBGA3wMkdrx68w6wOK9DHfgzOb5eUJ5apUwpLLk/FwvOTYV9DMuEoRILfhmJ47ZlMgEZNSxVz3cayyQ6hQIHYnvkn2fqQpV3hWEc4Hk50L1w1VuLp2npgfZSsIFuj7J9xaPM1xMcmwaJYIdT7OrEqdA4fliQJatSwhampLsRISGQs7vi8Z+stqkgqpcTMy6iPmHT3HGqdWI+bIX5ZapfxqclQVVDEvMqNWQuStBFNQEaKimQCHRcXx9RNtm3bBg2Nf5uILSws2E6tU0fSH0JCHB4eHoiIiEBSUhLevHmDRYsW5ZsPWSb6KmqobCQJWs4HCrO87G9o8bVs8dQdb3z5Ik5vIgMDTdSvL/kchw9JbpZCR0tXHW37SAYXO1dcYBK6QqRRx6ooX6cka1ZfPmoP8ygSCmRcPW3fUBgU1kXQm1DWx5EqoP1oXaYoVl6fASsHc0R9jGFeZR5HhJ3FzQrM7szFrONjmDw+DeKp16hvmbGY6rIYjy57cR8zAUPX6M3j9zCq7kwMrTaVrdOztErTcljuMQOLL0yBQ82fuGULhGc3vFkwRjYNhcwNsPzqNJSrZweh8PLeWywbLFFUbDO0MZr0FE6gmFmqGBUWCzObQug2thmEyKfQGLjtkYjJ9BzpJBgRlJz49CkOVy5LMksdOorTCDpzAp5ilorFzVDYQBtiZ97DK8zwuXvx8rgU9AazHlyC85mtWPLEA6EJcTDX1JXJM0v4Z7QIafLVJPp8gA/kjbplbKChqsxmTKh0Uay0ay+5Od68+QofRFJi1apHdegbaeFjcBRO7/OEEMksXVRVV8bL+75w23INQoKMrGcdHA5VDWU8veGDlcN3CipYMDLTx7LLU5kiXEpSKuZ1W4O9808Iaht/dtxpAL/s6nQ2iCdjYPrZ3bOPMaHJfPR3HI8zWy4jKeFfj0eObImNjMfRlWfRq9RIlhWjzBKV0jXoWhObHi9iAXbpqtKfJc4tZ7ddxQTnhYiLjIdtBSs2qWFRSjgKcB8DwjGr8xo2SVW1qSN6z2wLIfFNqeKKbsygWojsXXMJKclpKF3eApXqCDdTm53jxx8gLe0LHBzMUKKEpL1GbNDEe2ZFVMuvE/JiJjYlCZHJCczwuW+pSjj87hnKGJhgTJnaTNxjwwtPZgotizJnHpDlA05mtqBD+Tj8A0I+y1eju4qSApzKF/+mpliMWFoaomIlK3azOXb0PsSAiqoSq5sn9m+4yurphYhRET30nS4pXdyxwE1QpYuElb05Ju0YxHpMLu27hf1LJMasQoF6yaYfGonWw5qw73fNOoq5XdewkkAxQIP4GUdGYavXErR0bQQVdWW8fxmEla5b0anoYKwZvh3vnkrKXzjShQL7F7dfYVGvdexYbBy7B6F+n5hkfafxLbHn7SqM2zaImScLHeoHXT96N1YO3sbW67SrgkXuk5hyqZBKAae2XY7oT7GwtjfHuE39BJXZiYtOwOpx+/8tVSwvzN5A/9ehuHhcIurWe3RjwfQE5sTnz8k4feoxW2/fQbzZsUdvgxAUHgMNFSXUc7SB2PGNjUQRdck94mrwW5hp6MDF0g71ithgQ+02uPsxkGklyALh3BnkCCM1DZQ3lDzQ3OWwbNGlmqQU5PKTN0x1R6y0aycx/Tt37hni4oQZ3HxPA5dysChujPiYRBzaJKzsU3aadK3OShcpy7Nk+C5BqS4SFRs6YPDiLmx915zjuHrkDoQEDdoGLOyM4Wt7Q0GxIG4cu4fR9efgU1AkxEKR4iYYvKIn9vmtQf9FXWBiZYTPMQlwW38RgypOxNDqU3F682XERcXLelPlnsjQaNbf17/sOIysM5N5yqUmp7Iy2WFr+2Cv72r0mt0B+iLpcaFs2BSXJTix7gL7njz8hOIxll3tcXa3tQh4FQIDU13MODiMTbYIiY1TDyPyYwyKWBdC1zFNIVS2LjnHJk+rN7JDKUeJkrXQOXf2KQvKzM31UbmyeAOZk18n3huVLy5V/9n8oqSuEQvobfYuwDHf57DOZk91NfgdU1xULCAbRVYekOUTjbPKFuVPbbF00UKwNtFHcmo6zt8Xb1lmuXIWsLIyQlJSKk6fklgiCB0aqPcaJZEjPrnnNitfFCJ0wxu+tItEdfGRP46uvwyh0bRPXbT+amK9zHUbnt8W3uSJc++6WHhuInSMtODrFYCZLVfh2Q1xXXMk7912RFNsf7kMC85NRK02lVmQ+er+O6wavBUdzQdjTqeVrLwxLVU4PX1iJ/FzEq4d8mR9fJ0thzAFzPfewayXksQuVt2ahXX35qFZv/qCCmR+RYD3BwyrOQOPLj9n2ddpB4aj8wThKClmZiKXD9nOrlU1TRXMOjQchoUlHkdC4daZJ1mliqNWdGXnhRB57PkWD268ZkbVvUYKU4r/R32ZR79W3rRtW0nwfqc/Iz4xGZcev/lGP0DsKBdUwMbabbCnfidMLl8feiqqaOu+C+M9z2L/28doWKSYzLaNB2T5WLZI3P8UiE+JwlFzywvowZeZJTt++znE/Dnata+UVestVKGM76lYyxZlKlsxQYrdqy5CqBia6mLAbEm/xO7Fp+EnQNW9PrPao1ozR9bfMaPjKgS8+gChQUIZq2/Ogk3ZooiL/MxkvY+tOif4vrIfGWmWq2+PKfuHYy9lzRZ2gaWdGcvUXD96lwUOHYoMwtJ+G3Hv/BNBCa6IBerRo2zq3M6r0L7wIMzrupoFul/Sv6BU1eIYsb4vDgSsw+jNA1Cioo2ggpjfgQLM2a3XINT/E4wtDLHsylRUbyk8KfFdc4/j6qFMeXtXViYtJMhrbNXXUsW2rg0Fq6pIFi9bF59j6806VUFhCwOIAQ8PH4SFxUJHVw0NGwlHXCa3XHj4GkkpabAopAsHy/zx75W21H0mVYyLwlhNE7MrNUZbKwekZaRjcOlqaGEhO/VTHpDlE0U0tOGgZ8xOgItyWLbYtHJJKBQsAO+AMLwK+gSxUrduKRgaaiIiIh6XLomjJ44GUb3HSPqLrpx6gnfewgsishtGV25kz5T3lg7bzb4KLeM4fssAlKxkjfiYBExpsxwRIcLLOpLYx+KLk1G1pSMbXG8cvw/zu69FYnwSxIiukTbajmyKDQ8XYN3deWg1tDHLAlLPjftOD0xpsQgdigzEgh5r4XH4Dit15PwY6k9y33kN09ssRTuTAZjdcQU8jtxhxt3GloasN2zb86VY4TEDzn3qQV1bTXS7kjKnG8ftxcJeG1gZdPkG9lh9axasHYRXvnZuhwf2L5b0pQ5b0R3l6wtrQE4TOdQ3RqIuFiVN0WWMM4TKtTNP2fONLF86DaoHMUD79/BXj9OWLctDSUnibyVGTnydcKcJeLFN3vyI9S88sd3nPt7FRDC/MYLG6B2LlcX0Cg3hYGDCbKtkBQ/I8pEmRSVKQGflUG1RV0MVdRysv7loxYiiYkG0blORrR86eEc0Uv7F7YqgVhMHdvPfsuisYLMlTHVxUSdo6Kjh3fNA7F8hme0UElSqM+PAMBS2KYSwwAhMa7cSCQLsKaSysgHLO2HQ0q5s5p0k8YfVmoHA1yEQK6yW39ECg5Z2x/7367D44hQ0H9iQCTPERyfgyv5bmNtlFdqZDsD4JvNYH5TvswDBnu/SKofyvvcWe+Ycw4ha09HRjLKKm+B56iGSE1NgZG6AtqOaYo3nHOz0WcF6w6ifT6yEB0dinNN8HFt9nn3f3LUeZh4bBS29f21vhILn2cdYPXIXW+80thmcutWE0Lh67D5un3vKSgDHrOouWHNl6sEjixeifb860NZVhxh48vg9Xr8OhbKyAlq0KAex8vZDOLz8Q6FQoACaVRa27cXvkPblC5Y+8cDRd17offUQBl0/xoIzn6gw9v8DPY4xxUVZBp7iDd1FQBPzElj4+BrufHyPyKQE6KmIb2YyJ2jWhOqLz97zxohWNaGsKM7TqWnTstiz+xYCAyPh6fkG1asLX+qZoF6y25de4Mmdd7jv8UqwUsAkNT94fgcsHLQdB1a6o2L90ihRTlhqXtr6mphzZCRGNpyHd14BmNNtHWYeGg5Fgc1u0sOixcCGKOZoiTmdVzPz5aHVp2H4mt6o26EqxAxlK8vULsUW1+U94H3nNe6cfgzP0w8R+OoDHl9+zpbN2MeyaY517WBXwxZ21WxRtHQRVhIpj5AgDqlSkteW1w0fPL/p83+m5iTOQX5w1VtUgFWZonIxm008uvIcC3quQ8ynOGb2PGpDXxSrai4opcLsXmPze21gk3qNutZA98mtIDTCQ6KxfvIhtt55pDOs7cwgVNz2eiLsQzT0C2mhZbdqEAsHD0oEopo0KQMdHfGO+TIn2mvZW0FfSxzBcE48Cf8AJ3NbrK/Vmqmfnw98hQuBr7HN+z7MNXXgHfURa2q6QJYIa7QhZxTV1EVp3UJ4EfWRHXhKi8oTVUqao5COBj5Gx+Pa03dwqiDpmxMb6urKbCZr/35PHDp4VzQBmXERPbh0q44j265jy5JzKF+jGMucCJE6LhVw94IXrh1/gCXDdmHNhYlQURNWE7mJpRFrvh/XbBEeXX3BhD7GbuoryIE+Scuv9ZyN+T3W4dl1bzZopQH7wMXCbc7PDTTgtqtegi1953dC0OsQ1ldGJtP0eaPDYnH14G22EJq66ihZpRiKl7eCbQVrFK9gxcoixQZl/iI+RMHn/jsmeuJz/y1eP/D9v9JUKjt0rGeHik5lUL6hAytplbcs4P4FJ7BnrsSDj8zSp+wbBhNLQ4SFSWa0hQRlqae3X8nKKSs5OWD4yh6CC4qpH2vZiN1MobdYGXO0HyoRNBIiMVGfcWDDVbbefVhDZvkiBt69+4j79/2YiEebtpLKGzGSkpqGM3e92bpLNfkQ8yhjYIKxZWszHzITdS30KlGRLVSyOOHOWSSkpco8acIDMimULVJAdi7AR+4CsoIFCqB51dLYcu4uk0YVa0BGtGpdAUeO3MPz50F48TwIpe2E78NDdBhQBxeOPUDguzC4H30AZwH7nbjObQ8vzzcIfheGbXOOw3VeBwiN4uUsMWWXK6Z3WIWrh+9AS18DAxd0EtzgitAz1sGCM+OxZ94J7F9wEme3XmWlbJP3DIWZiEvUfgSV3NFC3mwk9uF95w2eerzE81s+eHnnLcsY3Tv3hC2ZGBTWg4WdGSxLmzHxEPOShWFqXYipPsoaCjJiI+IQ9CYUQa8/wO95ICvF9PMKQEx43P+9ngIwEnexr1kC9jVLsvNUqJMvfwtZOyzqvT5LTbRxrzpwXdqNTTRQUCE0qOd0cptl7By0LW+JSdsHCfLYnNrmgcfXfZjx85hVPZjSqVDZt+4yPsclwaqECeq3FE/Z38EDkuxY7dolYCoSC4kf4eHli+jPSTDUVkfVUhaQBxQLFIRVNol7CsToPkzjWF1lVTQrKvuyTB6Q5TNNzG2x5IkHboe+R1RyAnSVxZvC/hEtq5ZiAdkdn/cIiYyFiZ4WxIi+vgYaNLRj3iEHD97FLJEEZBpaqug8uD42zD2F3asvoU6zslBTF6aENWUxRi7vhimd1uDU9uuo3MiBeZUJjQoN7DF6fW8s6rcZJzdcgq6hFjqOaQYhQgO/HtPawL6GLRb2Wg8/r0AMrjoFg5Z0Q+OetQUZSP4tVEbqUKskWzIFH6ikz/suZZPe4dUDX5ZRo94jWh64P/3m9ynINrU2RiFzAxgU0WPeW4aF9VkZpJa+Jvt/+von5ar0gCfVSBqcU5ld9KcYls2jQfunoAh8CoxkX0N8P/5f2WEmNLtuUdoMthWtUaKSDcv4UUmmEMv08hrP04+wbOBmxEbEM0n7oat6okHnGhCyH9qkVssQFhCBwtaFWJkzbbfQCHgdim1zT7L1PlNdYF7cGEIlyO8TzhyQiGL0HecsmvP+Y2gMrl6VZJXad6gCMXPitkTgrEWV0ky8TR4pQM/Gr8/HTsXKwkhV9j2pPCDLZygiL6FjBJ/oMFwKeot21g6QJ4oY6KBicTPcfx2IU3deor9zFVEbRVNAdvv2awQGRsBMJGVATTtUxqk9txH8PgJHtnig+3DhlqJQANa8Vy0WkC0ftQfrr0yGpgDr7Ou1r8oGhRsm7MeO2cfYYL1x91oQKuXq2WHdnbkss/Dk2kuscN2KBxeeMWNpIYof5CUKigosaKElk8+xCfB/HsgyT/4vJF8pSIv6GMOOa2zEW/jce5vj+1JApqKhwgbYlJ2hjMKXjC9QUlZiZaykGJqemob0tC9MgCAxLhEJcUm5MkE3NNNnmT+LUkVYWZ6lvTmKliwiF2WnuYHESLZOPoCT6yU2HiT0MmnXYBS2EW7gkPQ5GVPbr2TebvomOph7fDR0DIQ3IUkZ5cVDdkjUKdn9tzaEDJlA0zVFPdGOVcVjqEwVNtQ/SP6mxQUc8P6K0Mg4eHr7y5X32K+w0BSGRyAPyKSUJaOA7Nx7H7kLyIiWVUuzgMzN8wX6Nq4sWhPEokUNULWqDTw93+Lw4XsYNUoiLS90aKDYa3QTzBm2B8d23ESTDpVhaCzc/pneU1rh0XUfVrq4etw+TNzYR5CZHJdBDRH5MQaHlp/FquE7oaGthhoC9DzKhAaF88+Mx9EV57B9+mHcPHGf9SKN3ToAZWvLzltFFqhrqaF0NVu2ZIfUM0PefUTwu49ZGTTKWNFXKhWkMkLyemOZrpQ0pEbGsyxIbqF7oJaBJrQNtFgwr1dIB4ZF9GFopgcjMwMUKmoIU5tCojJkzi/ePPZnEwkBPhL7DipL7TWrnWDV/wg6N+Z0X8uuLyqBpWDMuKgwPbL2Lz+Ht16BTOl25PKugrzXZvLsni/uXPFGgYIF0HesOJ6/RFxcIs6elWTi2wu4beB3OHX3JUjEtnyxIjA30pH15vyn4AGZFHAuWgLLn93AzVA/1lCopaQCeaKeow00DikjOCIW914HoEoJ4XnD/C5UakAB2QV3L/ToUZOVMoqBag1KoXR5C7x46I+dy90xZmF7CBUS8xi7pidGN1+CG6ceo2L9O2goUIXAXtPbsIzK+V3XsaDPRsxQV0GFBsLyFcoOZW7ajWqKsnVKMaEP6lEa33g+XAY7offs9v+5zMv3qGmqwrqsBVtyEpQg37Ok+CQmpkFmy5QNodLIyMgoaGlqARlAQcWCrGSUJkQUlRWgpqEKVU0VpgaoqqEiSDEYIUH7mWwMds8+xrKNesbaGLWhHxMqEfp2Lxm4BQ8uPYeymhJmHR4Oi5KFIUS8H/rh4Cp3tj50YUfoGwt3gE39gZsXnmHrzu0rwczKCGLh1KnHSEpKhZWVESpUEJaCcG6gDB9NrGdOtMsjsx9cQjFtAzSzKAkNRWFNiPEnhhSw0TZAcW0DpH75gstBOZfJiBFVJUU4V5RIrh+/JV5PMsLevghKly6M1NR0HDt6H2KBZj37jZcYfF52e4xXXoEQMrZli6LrmKZsff3kwwh5Hw6h7tehK7qjVquKbNA4u9savLwr/Gu4WDlLpsLo3Kcu+/7EWne4VpnCZvQ5OUM9K1TmSX5eRUsVYaWQJMVPqob2tWxZwFDZ2REVGjrAsW5p2NcogRIVbZhoCGXBKDvHg7GcCXoTgjEN5mD7tMPsuiK5/g335ws+GGOmyiN2wePoPRaMT909GKUqCbOs7nNcIha5bmeD7DqtKqBWi/IQMldPPcHblx+gqq6MLoPr//0bPn4MODkBT/4V+skPkpNTcfTrWKF9+0qCzkD+iodvghAUHgN1FSU0cCwGeSP4cwy2+dzHpLvnsoyhhQQPyKREY3NJ6QypLcojratLsgZXn75DVLzwTHV/F7qZduxYNWvWK/47uWkhY2tvhvotHNn6pgVnBG+e225II5SuZI3Ez8lYMnRnrnpvpD1AH7upHyo2tEdyQgqmtlsOX68ACB0VdRXmTzbnxBimyEg9VCPrzsK2aYeQkpQi683j/Aeh7NLRlecwqNJkvLzzBmqaKhizuT+mHhgGbQNNCBm6n26adIBly6kkdfzmfkwASKisn3QIoQERMCqihyELOkLIJCWkYPsySSav44A60MmLypRDh4ALFyRf8xH3816IjkpAoULaqFtP3KXhx255sa+NK9hCVcAlw3+Ke8Ar9rWikRkMBSDi8T28ZFGKJtGrvG7B44Mv4lOTBZcq/VtszYxQ0twI3gFhzL+ia33xSNV+T5WqNqyf7P37cJxye4xOnYVZTvcjeox0ws0Lz/Hy0XtcP+eF2s7C7Vlkgc6aHhhUbx5e3vfFodUX0GmkMPsGSOBh8i5XTG69DC8832Ciy1IsPjse5ramEDqUddj4cD7WjtyFa4c8cXDxKdx2e4iR6/swPzMOR1pZsaX9N7NAjChX3w4j1/VhmUgxsHveCRxfJxEdGbGmF2q1rgShcu3EA1w+co8FjuPW9oS6liqEzKEtHogIi0Whwrpw6V4d6enpSE39uwyG0tGjLOPw5ehRpEybhvyaYLh48SkMDdXQuXMlpKWlskUa5Z20f5KSkvIsGx/7OQnPfYNhrK0Klyol2HvLG3eCfWGqpAaXIr/+fIqKiihYULrWEP9kCH0aXcbExsZCW1sbUVFR0NH58/pr2s19rx1GSd1C6FOyotzJ3xNHbjzD3P2XYWWihyNTuudZ6p5uPmQGamRkJLVSIHf3Z1i08Az09NSxd58rlP5AAltW7F17CXvWXGYPt01nRuZJc3x+HoPLh+8ys2hq5F5ychRKlhduDT71Fo1vvhhvn75nPS9Lzk5g3lbSIC+Owa2TD7BmxA5Ehsaw67PloIboObMd63ni5P/+/y9CAhiHl5/BvvknmSUAZcX6LeiMJr3q5PoZIatjcHjlOWyddpituy7ughb986CkLp/4GBiBwQ3m43NsIrqMds4qDc8r8voYfAqJRj/nZUhOSsWk5Z1gU8YQ0dHRf/WeikFBsGn0r9rw24sXkVo47/v8qG8sJiaBSagbGGpKrVyRxpN0HGj/59Xf/JyUwoIyBYWCzH9M3kj/8gWhCRKPR2M1TeY/9itozG9sbPzDfUznqK6uLmJiYqCllTfqquIZZYocOqBb6wpXaCEvIGPopUc84BsSCS//UDhYitectl690ti+7To+fYrDxQvP0bSZeEy92/SqhXOH7uNjcBRO7r6Ndn2FLXNcr20l3L/yAh4nHrKehzUXJwp2RpcMeuedGI1xzRbB/0UQxrdYjMVnJwhWYe17qreswMyFN03Yh4u7b+DEugu45fYQrsu6oVpzYfeYcMQHecOtGLyVXStE+Yb2GLGmt2iyYsSJ9RezgjES+RFyMMYER4btYsFYifKW6DSiMYTO9uXuLBizK28BawcDNtClYE9NTe2Pg42C584hg4KVL1+Q8c8/KOrlhfQaNfI8KPrwIQoqKmnQ0VGHrq70ghj622lpaVBQUMiTgIzeL/BTNFS00mGgow4ddWE+f/8G8gFOT9CEqoIizDV1f7k/EhIS2MQDYWIinbEsD8g4eYamqjIali/O/MiO3fQSdUCmqFgQbdtWwvr1l3Hw0B00buIgGoNKUjHsOdIJSycexoGNV9GwVfm8qcnPT+GMhZ3g89Cf9TysmXCAldkItTmaBB/mU1DWdBECX4dgQvNFWHxuAgwLC8PL5He2f8ym/qjbvipWDd2OUP9PmNl+Bao2KwfXZd1hJBL/PY5wiY2Mx44Zh3F2y1U2uNE21MTARV1Rt0NVwV7XP8Jt82XmRUh0GtsMHUblbbYprzm40h3P77xlwhh0DyXhESHj8zSAiXkwUaoJzizbQMGYvv5f3oPOns1apfdWPHsWiiNHIi/5/DkJaWn/QEFBCUZGulIdH+R1QJaQnIK0jAIoqFgAhjraohnr5IaE5M/4R0kRumpaUFH5dUWIqqokKM3MBkujfFH+9jpHprhUk0ilXnj0mqXAxYxz0zLQ0FBBcFAUbt+S9D2IhXotyqJY6cJIiE/GzpUXIHQoIzZ+fS9Wtnjt+ANcPnwPQkbXSBvzT46BiaURQt+HY0LzxQj/EAUxUb6BPest6zCmGRu4eZ5+hH6O43FwyWlmdMzh5BYqozq3/Rr6OIzFmc1X2MCxYbea2PJ4Iep1rCaqYOzs9mtYN2YvW28/ogm6T24FIfP87lvsXSqRjXed1wEmAs/aM5GUBZLtbeBSDuY2hmydMmN/RWws4OFBJ6Pke/p67RqZhSEviYz8zL5qa6uJPoCJ/ipepqWmIvrP8iNS09ORkCYZj2or/b5+Q+a5+Lf9jL+L/O15jkxxtC6Moka6SExOxYWHr0V9NNTUlNGypUScZP/+24JXLcwO1Zb3n9iMrbsfecDkhIUO9Y5l9jusm3QQH/wk5QJCxcBUFwvcxsDIXJ8ZDY9vtkh0QRkZE/ee3QHr7sxmAh/kt7Vt6kEMrDAJ990lRqcczu/w6oEvRtaZhRWuW5l3n0XpIlh8YRLLxmrpC1tB8Xsu7LmBVSN2sfXWQxqh14y2gg4m46ITsMh1B5O4r9emIhq0F745scfZZ/B+EsAqOnqM+Lff66/3MykrpqV9+zP6nn6eRyQmpiAhIQW0qdIsVcyv3qqYBElApqMhf6WKRExqEllHQk1BEUoFf78wUNrXPA/IBAIN9oPiY3A1WPgeR786gTOzZMdvi9uTjGjdpiKUlRXw6lUoHj70h5igmvw6Tcuwc2vDvFOiCCjbD20Euyo2TAp/oesOJgggZAqZG2DR6fGiDsoIi9JmWHJpMsZsGQDdQtoIfhuKKS5LML3tMqaOx+H8jE9BkVjUZwOG1ZzOfO5ItGPAoi7MB8+hZknR7TgKxpYP2cHWWwyoj35zOgg6GKP7+srRe/HpQxRMLQ0xWOAS95ky91uXnGPr7frUgr5R3ogiME6dAhS+G3TT9/TzPCLqa3ZMU1OVtTeImdiEZGR8yYCSYkGoyaHUPRGTLAk4tZWEHXDygEzGfPk6SKYb/ue0FCx+4pH1M7HSvEopKBQoAC+/ELwJFqbh7++io6MG56YSQY/9+zwhNnqPbgxlFUW8eOiPG+clHiNChsolxq3pCQ0dNbx+8h475p2E0CFBj++DsogQ8QVllFVt2KUGtj5bjDbDm7AyxjtnHqN/uYlMMj/6U6ysN5EjIJI+J2HP3GOsPPHyvlvsZw261MCWp4vRemhjKCiKr0X93M7rWDZ4OwtymvWti0ELOws6GCPO7b6JW2efQEGxICas7w01ESimksx9eGgMjEx10KZ3rd/7peBg4NGjnJeHDwE3tx9nyE6elPz/r96D/s4vjKDjvpb4kQpzbqHz6cSJE7/9+p49e8LFxQV/g7+/P/u7T35gkh391TeWhDxye67v2LEj1+rjderUwYgRIyCbckUVCBkekMmA93H/DtZILpWITErA84gQ+ESF4YrIs2T6Wuqo5WD1jdGgmGnXrhILFJ48eY+XL3O+WQsNQxMdtOsreeBtXXyOqVkJHcPCuhi9ohtbP7bxCu5c8BJdUDbWeSHLHIgR6ufrv6AzNjyYhypNHZlht9uGi+hlN4Z5mCUlJMt6EzkyJC01Dac3X2bnw+45x5GcmMLKXVfdmImxWwZA3+TP7WFkyZlt17By2L+ZscFLugo+GPP3+YBN04+y9Z6TWqBYGXMIHVL/PbrtOlvvN74pmzD8LTp1AsqXz3mpUAGIifnx79PP6f9/9R6dO/9W7xj1lytnyyiFhoZi+PDhsLGxYaIRhQoVQvXq1bF+/Xqm2CdEklJSWXsJ/gG01XMfrHTo0AGvX+euNeXYsWOYPXs2ZFOuWBBChgdkMmD2g0t4FiEpA9r3+jG6Xd6PFud24KT/S4wsUwsldIwgdtrUsGdfySQ6MUX4QUBOFCqkjQYNSos2S0YzkIYm2ggLicax7TcgBqo4OcClX122vmzEbnwKjhJNUEZljB98wzCmyQKmYChWyPR65pFRWHhuAmzKFkVCbCK2TTuEXqVH49TGS4IvJ+XkvWDHjWP3WMZ09TCJlx2J2kzeMwRLL0+BbQXJJJwYObX5ClaPlPSMtXJtKIrMGE2MzOu3hU2yla9TEq3614MY2LL4LFKS01CmshWqN5Q8V3+Lvn0BUsf71XH5WYXRryqP6H3p/fv0+elLUlPTEBeb+H/ZMV9fXzg6OuLChQuYN28eHj9+DE9PT4wbNw6nT5/GpUuXIEQys2OkkK34B4qcpERICoS5QU9PD5qa0uspjRFJuSLBAzIZUKmQOdqc34UGbpvg5v8S1Y0tsKlOG6yr1QqupauiiIY2xE6VEkVhqq+FuMRkXHokLoXCH9GxE8k1A7dvv4G/n7gG2SqqSug9uglbP7j5GjPiFAO9p7iwGd+4qM9Y6LqdZWrEEJQtOTcBplZG+BgQjjHOC1jGTMyUrVMaq2/NwtitA1CoqAEbiK8ZsZOVql3cc0MUx4Xz51D53m23BxhcdSrmdFnN+gtJxp4sEjY/WYhabSoLPnjJiaOrz2PtmD1ZAh7953UUxedZO/EgAt9+hL6xNsas7iEKs/Jn93xx0/05ChT4BwMmNc/dfu7eXVJyWKwY1Vfn7YbR+xUvLnl/+js5ZMdYtkVNCaqqSlk/d3V1ZRL0Dx48QPv27VGyZElYWVmhZcuWOHPmDJo3b/7T9/Ty8kK9evVYcENy//3790d8fPz/vW7mzJkwNDRkJsQDBw5ESsq/Ktbnz59HjRo1WPkgvUezZs3w7t27HD8yCcBEf5YEK7oaqrCwsMCcOXPQvXt3aGhooGjRonBzc8OnT5/Y56CfOTg4sM/4s5LFGTNmoGzZsti9ezd7P21tbXTs2BFx2RQuvy9Z/JO/O+Pr38nOihUr2HtlL/Vs0bIlVi5egrolHVDMpDBmzZrF7ALGjh3LAsMiRYpg+/btEArCv4LlkFaWdlBXVMLqmi7YWb8D+peqglK6haChqPxb7uFigG64LtXs2PpxOShbNDfXR40atmz9wME7EBu1nR1QqlxRJCemYstiSTO10FFUUsCEDb2hqqGCF/feYe/Sf71lhIxhET3mS2Zua4Lw4CiMdV6AgFfCV7nMCRrsNehM/UGL2EBcz1gbH9+HY0m/TehTZhzcd11npWwc+QrEyAphSLWpmNlhJXyfBTDBjq6TW2HHi6VoOaghu0bF/Pn2LDiJzVMOse/bj3QWvIBHJhcP3cGlQ3fZc3b8ul7QMRC+iiWZVm+cf5qtO3eoDMvixrl/k1KlJH1eOQRNf0SPHpL3pff/CWlp6YiJkWSU9LP5ekZERLDM2ODBg6Gu/uOesp+dU58/f4aTkxN0dXVx//59HD58mGXThgwZ8s3rLl++DG9vb1y7dg379+/H8ePHvyn7o/cZNWoUC1rotXS/btWqFctq/4zYhCQWlCkqFIC6iiS4XL58OSuzpAxf06ZN0a1bNxYode3aFY8ePYK1tTX7PieBMAoEqUeOMoO0eHh4YMGCBT99fV793R9x9epVhIWGYv/ZU1i2bBmmT5/OglXa33fv3mWB7YABAxAUJDGulzXyMfoXGYaq6tBXUUPI51goF1TI6iMLT/qMITdOYPTtU/D6WtIoZlpWLc0+2+N3H+AbEgGx07FTFfb1yuWX+Bj6kzp1gUIPBNcpLdgD/Pq5Z2ymUgyYWhhi2OJObP3ASnc8vOYNMaBvrINFZ8Yz6W/KKI1tsgBvHotLpfNHKCkrsoH49hdL0WdOB2gbaCLENwzLBmxGH4dxOLv1ClJE7j/4X4cynlcO3MagSpMxo91yvH3ynk2KdBzXAjt9lqPblNZQ0xR++U9O0MBuy9TD2DNfIhrUY0or9Ba4tH0mAa9DsXbCQbZONiH2VYtBDLgfuQ9fnxBoaKmg69AGf/5GFPRQVmPHDkmJ4Z/2BZHyIv3+zp3Atm2U9srx5VFRCey8UVVR/CY79vbtW/ZzW1vJhG0mBgYGLLtDy/jx43/4nvv27UNSUhJ27doFOzs7lilbs2YNyzB9/PhvZYWSkhK2bduG0qVLs4CFsmVr167NCrjatGmD1q1bs/41yhzRaynz9vLly59+niwxD41/xTycnZ1ZgFKsWDFMmzYNsbGxqFixItq1a4fixYuzz0GBYfZt+x7aJsqc0eepWbMmC64oSMyJvPi7P0JbRwcT5s+BY2l79O7dmx0j6uebNGkS+1sTJ05k+/bmzZsQAjwgkxHrarVGNeOiOOX/EuM8z2Duw8s4H/AKsSlJqGRkjrGeZ5D6RdylQEY6GqhhZyk3EvglSpiiXDkLNtN3UIRZMuuSpmjSvhJbXz/3lGhKzeq4VIBztxrsobdo8A6Ei6TkUsdQC4tOj0PxcpaIiYjH+BaL8fy2uL35svuXtR/dDLt8lqHf/E7QMdJi/XIrh2xHN9uR2LfwJGIj/7/shiNs1UTqDextPxYLe62H3/NAFoiRcfhO72XoNbMdtPT+zQyIFbp/rx29h5UqEgPmd0SnsT8vKROaXPz8AVuZkIpjrRJoP8wJYiA26jN2LHdn612HNoR2Xnh3UVaLSgytrXNfwkivp9/7zWwbnTPR0RIxD109jd8K3O/du8dUDSmISk7+sRASBRllypT5JrNGmSIKal69epX1M3pNdsPsqlWrsrLGwMBA9v2bN2/QqVMnViZJJY2ZpXsBAQE//LvJqWlIIDGPr+qKmVBpYCYkSkLY29v/38/Cwn7uEUp/O3uPmImJSY6vz6u/+z2kVm5lW5xlCzPNoOl9sr9vwYIFWYlnbt43P+EBmYyw1THEg09BmHH/AoxUNVBG3wQrnt7AEPvq6GBTBhaaunAPFP/grXV1Sdni6bveSBVJAJATnTtXZV/Pnn2KSBEOOHsMbwRNbVX4vw7F2YP3IBYGzGoLazszNshfMHAb0lLFcS7RAHb+yTGwr2HLRDEmtVqK+xefQV5QUVdB2xHObMBO3lOGRfQRHRaLnTOOoGux4UwuP/C1+LP98kx4cCQTa+lSbATrDaTAmjKfPaa3we7XK5hxOH0vD5AQzaK+m3B661U2qB62ojtauf5rSixkaEJqzYQDTFlR11ATY1Z3Z+q/YmDnyouIi0mERXFjNOuYh6bVmSWMrVrl7vfo9fR7JX/PJy86OoGV9ykrKUBDQzK4z4SyUnQuZQ+gCAqO6P+oNyy/oR61yMhIbN68mZXi0UJk7zPLTtTX7JjGd2Ieior/qkZmBp0/+llOpZDZX5/5Ozm9/k/+boECBf6vfDE19VvxOEpokPVGdjNoep8/2T5pIY6rWU456fcCE8vVw5iytdHMohRaWpZmGTOivGERvIwUtxgAUb20JQy11Vl6/NqznJtMxUBZx6IoVaowUlPTcfiweAKaTDR11NB9uGQAsmv1RcRESWb9hI6SiiImbe7Delion2znwrwz+ZSGjPycIyNRyckBKUmpmNlpNa4dkTww5QXKmJH31I6XSzB+20BYOZgjOSGFyeX3LTMOk1suxn33p4J58P3XocHMi9uvsaDnOnQvMYrZGcRHfYaxhSFcl3bDrlfL0XmCCzTzIpMhEJI+J2NGx1XwOHZP4tm1dQCce9WBWHDfdxuXD0v6xqi3Vs9IHOJfb18E49whybOSyubJ3zBPoeySqen/m0H/DHpd4cK/LFHMhO5ZUV+fk7p66v+XHaMMS8OGDVmpIfVy5QYS/3j69Ok3v3fr1i0WcGQvgaTXJCZKgijizp07rBTSzMyM9bBRMDhlyhTUr1+fvWdU1M9ViSmwjPkakJGYhxgxNDRkNgPZg7LvPdZS0tNF4T2WHR6QyZAyBqa4H/ZvM2ExbQO8jQlH6/M7sdrrFuoVtoHYUShYgPWSEUdvil/cg27GXbpWY+tuJx8hJkaY/iI5QWWLlrbGiI9JxK6VFyAWqJ9s5LKubP3I2oui8CfLRFlVCVP3DEHtNpVYdm9Bn404seEi5A2akazXqTrW3ZmD+WfGo7JzWXbNPLjwDFNclqC33VjsX+SGCJGUncobn2MSWJA8sOIkjKo/G1cPerLSZbvqtph2YDi2PV+Clq6NWIAtT8RFxmNCyyV4ePk5lNWUMOPAcHYtioW3XoFYN1kiPtJjQgs4VCsOMUADZiqPp691mpWBfUVJC0OeQpM8Bw/+vxn0z6DXHTgg+b3fgIQ8qGRRUbEgtLR+HMCsW7eOqfdVqFABBw8eZKWIFCTt2bMHPj4+rDTuR3Tp0oV5lvXo0QPPnz9nIhRDhw5lfVeZZXqZma4+ffqwnrCzZ88ylcFBgwaxwI0EKigo3LRpE+tnu3LlChP4+BmkfJ3+JYONzTSy9cKJiTp16jAVxkWLFjEREeqnO3fu3Ddm0GkZkuPLAzLOb9HEvASeRnzADp8HmPXgIhY9uYZZFZ3Qp2QlnHbuhQpGReRiT7aqbsck4+/6BCAgTPwDscqVrWFjUwhJSak4fvxfKVaxQGUugya3YOvnDt3HmxfiMbuu0cwRLftIZrWXDtuJkPfhEAukSDd+S3+06F+ffb9h/H7smHU018pRYoCCsHL17DDr6Ghs9VqMVkOcoK6thhC/MOyYfpiVM05vtxy3Tz3kfmb5DM3wP7rynGXDOloMYWWk/i+CWGDi1KMW1tyahaWXpqB6ywqiKYHLDWGBERjdeD587r+Dho46FriNRYUGklJ6MfA5NhHz+m9BanIaKjW0Q9vBfyGIIWWunnqCl4/fQ0VNCX3GSKxX8pzbt6m5KHe/Q6/3/LWnKGWTMlsT9HLoHSMVQFIIbNCgAROKoJ4vCs5Wr16NMWPG/NQImfrC3N3dWbkhiVi0bduWZbko25Yd+hmJUNSqVYuZMVOJIolfEBSUHThwAA8fPmRCGiNHjsTixYtzJeYhNkqWLMmCYArEaF9Tvx7t50xiUiRy/gX/KZBVrigG/smQx9FAHkJqL+SlQCng7H4LecWDsCBs9bkHfWU1NCtaElWMi2b9X0JaCnyiwlDOUPyB2eA1x3H7pT96NqyA4a1q5npAQU2XZEAoFK8Vj2vemDXrBDQ0VLD/gCvURDijvHDMAVw78xS2DmZYtn9gjvtWSMeA+kDGtV4Bn4d+sLIrgmVuo1kGSizQLffg0jPYMfsY+96pW03Wy/KrUh4hHYM/NbIlY+Fz267hhee//bFa+hqo3bYKGnSuDtuK1oIdJIhp/9M5RkHXtcN3cGX/LRaUZFK0VGE07Vsf9TtVYwGKmMjtMfB9HoipbZezjKyBqS7mHB0Ji1LieZ7ScZzTZzNun3sKoyJ6WHNhgszLSH/3GCR8Tka/JksR+SkOvUY5oX2/nMtDSW3Qz88PlpaWLGv02wwbBqxf/22GjMoSaRk0SPJ/1F/0tYQt6/9dXYGVK3/ZO/bxYwwUFArC0tKQlYsK5bygjBx5n+XmfpmSmoa3HyT3ApvC+lD63TJPkfEuJgKf01JgqqYFA9U/v15yOiejo6NZdjImJoYJqeQFwn6q/AegLNj6Wq0xp3Ljb4Ix4nNqKuY/uorkdPH7+7StIVG2Oen5gt0UxE6NmrYwM9NDfHwS3NweQ4z0HecMVXVlvHoWiIvHHkJMmaZJm/pAW18Dvs+DmEGqmOaV6AHacUwzDF/Vkz3g3XffYL0tifGSWT15hcrgGnatiWVXpmLTowVoM7wJ8zOLjYhn6n7Da89Ej5KjsHnifvjck0hJc34f2l/vvYOxZ95x9C83gZUlHljkxoIxDR01NB9QH6tuzMTGB/OZdYHYgrHc8vS6N8Y0WcCCsaIlC2P5xcmiCsaIw2svsmCMet4mbe4r82AsN+xdc4kFYybmenDpUSN//siPyhUzFRRJgXHZsh8rMf5G2SJdT/9mx9QFE4z9DZliHuqqSnIbjKWmp7NgTGzligQPyARAYHw0PiX+q9jnHRXGvie/MkNVDVwMegOxU9Peiol7RMmJuAeV9nT6qrh45PA9JH+VkBUT+kZa6DpEUj63bZk74qLF0w9naKrLGtvpIXnx4B3W8C42mvSohal7h7Ds3v2LXhjrvBARoeIv6f0daIDcf0Fn7HmzEnPdxqJuh6qshI7Mpo+sOMuCs27FRzLVvwcXnyFFhNeXNKDeFso2bpm0n/nAUSC2e/YxBPh8YBMXVZuVw6TdQ7DfbzWGrOgJ2wpWgs1A5iVXj9zBlDbLmbKpXbXiWHJuAjNsFxOPb/hg53w3tj5wdjvYlv12wlbIkIrvid2SezKVxyvll4H4j8oVvzd5/pmZ9C/KFuPikph4Fz3rtbXFKX7xffll9Gdxi3n8DpnliuoKSlD8U486GcEDMgFAHmSXg95mfX8/LBB7Xj9i6w2LFMtSXhQz1EDqUs1ObsQ9iPr1S8PISIspMJ07+xRipEWXaihqU4j5xOxaJS6RibI1bFmDO0EN72+e/thzRchUdXbEojPjmKz426fvMbLBXPh7i6en72+hMs0KDR0wYYcrDgWuw9T9w1CnXRXmf/UpKIJlzia3WIz2RVwxq8NKnN58GR98P/6ns2eU8bm45wbmd1+LDuaDMarebBxefhbBb0NZEFapcRmM2TIABwPWYsbhkajdtjKUVMRT0vs30Hmxb/EpLOyziZU212hRHvOOjxZVZokIC4rEgoHb2SC6YYcqcO6eTxmmfDoGa2e74Uv6F1RrUBoVa31rmJynHJIInTBz6JxMnn9mJp35+z/4DJERkklyXV3Kjol/qMzEPNIlYh6aquJrsfhdolMSRZkdI+QzZyky6pha40nEB3QsVpZ9X0q3EHpfPYQWFqURlhiP0rr/qu2IGQrItpy/i3uvAvE+LApFjXQhZqiuvGOnKli18gIOHLgD56Zl828mMJ+gUhjXqS0wvsdmnD14F05tK8KmlCnEAjW4ez/0xR13L8zpswkrz4+Hjsg8k2zLW2HFpcmY0nY5gt9+xGineZi8wxXl6knUSf8rUEljDZeKbCHj28dXX+Du2ce4e/YJIkKicMvtAVuIQuYGKFu3FEpXs2UZEFPrQnKb+Yn4EIVnN3zgdcsHXjd8WPYrO1SOWKlxWVRrXh7lG9pDTVN+Z79zggKwlcN34tK+W+z7NkOc0HtWO9EJlVA2eG6/Lcxz0cbeDIPndxDVuU1CHs8f+EFZRRH9JzTNvz+UWa5I2NgAx479mxX7GZQ9q1hR4kP2+rWkbHH58v8zlo6PT0ZyShqrwNDR+T15fKETFZ8gejGPX5GSnoaEtFTQp+MBGeePcC5aAjtePUB8ajI0FJXhEx2GjjZlMcbzNALjorG9Xnu52LOm+lqoXsoSN1/44cSt57kW9xAiTZqUwd49t/HpUxwuXPBCs2aOEBsOlaxQ29kBHmefYd3sk1iyd4BoZgRpO0ev7I4RzosR7BvGTKPnHhiS9143+YyJpRHrcZnZeTVeeL5hwZnroi5o1rcu/otQGWcVZ0e2kIjAuyfvcf/CUzy6/Bzed9/iY0A43HdeZwuhY6SFUpWLoVh5SxQvZ4lijpaiNDMmRT3fZwF49eAd66Pzue/LMoXfU6ycJSo2cmDZxRKVrEV3vueHrP3s7utY4FqgYAFRXzsbphzG6yfvWVZvytZ+ohIs+hyfhC2LJfLjnQbVQ6HC+TjpSr5c1BvWtClAqoS/6SuWVcI4ZAhAZs70PpRB+1F2TEdddAH9j0hOTUNCUmpWQCavxKQks69qIixXJLjKooxVFjNZ+sQDTyNCmDqMgYoallZvDhM1Lagriudm/DtcffoOoza6sRpm93n9vnGJF6u62dEj97Bu3WUYG2tj564BLHMmNsI/xqC/8zIkJqRgxOzWLFMmpmPw/lUIRjZdjMTPyWjVvx76z2wDMUKz4yuH7cDlA5LehpYDG6D/3A5swC30YyAtSPzk+a1XbPBN5savH/r+UDrfsIg+LO2KoGipIrAoXQRmxU1hYmUELT2NP/q7ebn/E+ISEfwmFEFsCYGfVyDePXuPUP9P//dams22LmMO+xol4FCzBPMM09IXX7CZF/zoGAS+DsH0DivxwTeMGcdP2jEIFRpIRKTExtldN7B6/AF2zGftGYQKAsyS53QdbFpwGsd33kLhovpY5zYiVxUjf6SySFmyv7kWf/D7JNQVHBzFsmOkrCjE53luVRY/RsUhIjaB+Y6Zi7wyKSfIx5cyZKbqWjBQ+fsyZWmrLIqrvkqOGVWmFh6FByP1yxcU1zaAnopa1oUXn5qC3a8fMn8yZRF5KvyImnaWMNBWR3jMZybu0bCcOAwuc6JpM0fs2++J0NAYXLr4Ao2bOEBsGBTSRrehDbFp4RlsXXIOVeqXgraI+i6K2pqwTNmcvptxfNMV2DiYoZ6IjF8zUVJWxJgNfWFWzITJ4p/ccIn1Bk3YOhBqWuKric8PqL+solMZthApSSl488if+Uy9fuSHN4/82D6jzBIt984//b8SPxOrQjAqog99U10mh65fWBfaBlosWCMZfvqqoqGSq9lxMlhOiEtiQhLx0Z8R/SlWsoTFIvxDFD4FhuNjgGSboj7G/PR9DArrsSxfiYrWsK1kzdb/q2WIv+LBJS/M770Bn2MSYWSujxn7h8HKzgxi5MW9d1g/5TBb7zmxhSCDsZzwex2Kk3skE0mDpuSjkEd2fhKM0bgpIjEC8Snx0FDSgL6q/o8Dl+9+n/3e1+wYlSoKMRjLLf8VMY+U7OWKiuJ8Vop7dC9H0M2ifDa/sS8ZGSjw9QaiqaTM/MjOvvdBKyvxGFr+VNyjamlsOX8PR248k4uATEVFEe3aVcbmTVexb/9tNGxkJ8oyhxZdq+LiiYfwexWK7UvPY8QccWWZqjcti47DG+PAyvNYNWYfC9KsRTg4y5TFL2xjjCUDt+DBpecYXn82ppEio474Bwh5DQlWlK5WnC2ZUEDk9zwI772DmB/X+5dBLBsVGRqN+OgEFrTR8itIJENZXRnKKkooqFAAXzK+QEFRMiudnpqOtNQ0pKaks6AwOUEitfy7aBtqokgxE5gVN2Gqk1YO5rCyN//PZr9yAw2cj6+7gM2TD7IBZ6kqNpi2Zwh0DPNmplrakLoq9Y2lpaajZnNHtBvSEGKCsmZrZpxgQh7VG9mhfA3ZPNejk6Kx88lOrL63Gu+i/lVztta1xtBKQ9GjbA/oqPy80ikhIRlJSaksO0ZiHvJAdjEPDTkW84j5qq4o1nJFggdkAsLjgy/iUpOZQTQFY4/Dg+EXG4nWVvZobF4Cp997iz4gI1pVt8NW93sScY+PUShaSPwp9BYtHHHggCeCg6Lg4eGDevV+0VwsQKgsbsh0F4zuvAHuRx+gUZsKKOUoHqllouvYpnjrFYAHV15iVs+NohT5yKSmSwUYFzXArK5rmNjHyAbz0H9xOzTqYCTrTRM85LFlX8OWLd+bU4f6hbHytvDgSJa5ItEM+kp9SOSJFhsZlxVcUSkkLfH4/Nt/W0lFEeraaqyvjQIEWnQL6aCQuT6MzPRhaGYAYwtD0Sn/CYWUpFQsc92WVdbbqGsNDFnWjWWXxVqmPKfvFkSFxcKipClGregmOtGFC0cf4uXj91BVU8LASc1ksg3ub93R5lAbJKT+v32Lb5QvRrqPxOQrk3G0/VE42Tj932uyZ8e0teUjO0ZE/wfEPLIHZDrK4syOETwgExAfE+KY5xgFZIShijom3jnHArI6ha2w9vltJKWlQkVBnA+eTEz1tVGjtCVuPPfDkZvPMLpNbYgdNTVltGlTETu238C+vbdRp05JURpJUgDm1KYCC8jWzDyB1UfEJZBBmclxa3uxfjIS+ZjbdzPmHRrGMh1ipJijBVZfm4Y53dfh+e3XWDloNyIDY1kG7b/cR/Y3So4Wpc3Y8qtBclJ8EpISUlgQRwEa9WxEhkeynuJ/8A8UlBSgoFSQnVuKyopQ11KFqqaqaM81MUAln/M6b4L/i2Am3tFvTnu4DGoo2oEmk4ifeBA+D/1YKe3Ubf3ZOSomoiPjsXWpRMij27CGrPxdFsFY031N2f6kf9+T+bPE1ET2ujOdz/xfUJaYmILExFR2LpERtDxw4dJlODVsAM+nr2BT2ECqf9vf35/1Xj1+/Bhly0oUxPOL5GzliloilLvPhD/RBaa2SEFZ+lf3+CIaOghP/Mx8yvpcPYyaJpai7yHLpF1NSZ/VqTsvkfSDhnwx0qpVBairK8PP7xNu3XwNsdJrdGNoaquy0kW3vT83zhQqmjpqmLZ9AGvwf373HTZMlfRliBXKsMw/OQZNe9dhA45dc09gVpc1rPSOkz9QtoVKBymjZW5rygJjsiewdjRHqSrFWHkkmSxbOxSFeYnCTCWTXs+DsfzjiYc3htebzYIx6vMjf7FWro1EG4wRJzZfxYX9nmzybsK6XjC1MITY2LbkPOJjEmFVwgQtulSVSZkiZcbo3vgFkrHTz6D/p9fR6+n3svNvdkw119mxnj17svPw++Xt23/9ZWVBfIJEdVBdVQlK+TixSp/fxcXlm5+ZmZkhJCQEdnZ20jODVlSCYgHxTCB/Dw/IBARJ3huraWLnqwesXHHK3fPoXNwRNtoGKKyuje625UX98MlOtdIWMNbTRMznJFx6LN7gJTsaGipo1ao8W9+16ybrbRAjJObRa1Rjtr571UV8Cvn2wSUGzIsbY/y6Xux6ObvrJs7svAExQwP9wUu7otecVlBUVsCds08wtM4s+HqJzwybw8ltf9LBZWcwyWUJKym1KF0Yq65ORdnakkoSsfLgygtsmXmMrfed1hrl64qvzJ38xi4ef8jWqdxdFtUU1DNGZYq/CsYyodfR63c93fVNdiwhIQU0vPrT7Fjjxo1ZAJJ9oQyRrKDxR3xSssyk7gsWLAhjY2OmBJnfRCdLREt0lMQtWsIDMoExtmwdhCTEod+1I0xxsZNNWXSwKYMl1ZoxKU95oWCBAmhdXSJNTOIe8kKbtpWgpqYEX98wUWfJnNpWQMmy5kwGf/3cUxAjlRrYMbUyYv2UQ3h2W7zHI5Pa7Spi8bkJTFEuxC8MIxvOw6X9EiNcDkfeoL6+mZ1WY/vMo2yA2aBTNUza159lLsVM4JtQLBi0nX2mRp2qwqW/+DzTSIBk7ayTbL1xu4rseSFtKNtFAh5/wqq7q9jvExHhceyrlpYaFBX/LIBQVlZmAUj2hYISwsPDA5UqVWKvMTExwYQJE1gJdCYWFhZYsWLFN+9HZX4zZszI+p4mF7ds2YJWrVpBXV0dpUqVgpub2ze/c/bsWRQvXhyqqqqoXacOAgMkE3YaKj+3TwoICEDLli2hoaHB5Nvbt2+Pjx8/Zv0/bQNty8aNG1nWS01Njb2G5N4z/3/nzp04efJkVmbw2rVrrGSR1p88ecJeRz+j793d3eHo6Mi2sV69esxC4dy5cyhZsiT7+507d0ZCQsJv75uktDTY6hrhyI7d6NamHds+ei9PT0+WoaxTpw7bX9WqVcO7d/8KvQgRHpAJjGI6BhjvWBcP2g7H7EpOMPkahJHqIi3yhEu10lAoUABPfUPwJjgc8oCWliorXST27LmVdcMXG9SfNGxmK6Ys53n5JTyvvIQYIbWy2i7lkZ72hTXOf/ALg9gp7miBNR7TUb6+HZITU7Bk4FYsG7yN9TpxOPLCq4d+GFx7Ju6ef8qywsNX9cTItb2YaIqYiYtOwMyeG5kJeKmKVhg8v4MoK1+O7bgJ/zcfoaWjhl4j/18kQxqQtD2pKf6obywn6PX0e5GJkSw79vkvs2M5ERwcDGdnZ1SsWBFPnz7F+vXrsXXrVsyZMyfX7zVz5kwWDNH7UEaua9euiIyMZP8XGBiI1q1bo3nz5iwIat2hM5YvnMv+72fnF2WfKRij96Cg8eLFi/D19UWHDh2+eR0FNocOHcKpU6dw/vx51hfm6urK/m/MmDFsm7JnCCn4+RkUSK1Zswa3b99m20y/SwHXvn37cObMGVy4cAGrV/9+kB2TIsmObV62Aj169GCfvUSJEiywGzBgACZOnIgHDx6wsdgQMgMXMDwgEyAUpBBK2aQ7SXUxUwZfXjDU1kAtByu2fvSmPGXJKkJVVQlv336E523Z1pD/DRbFjdGmV022vnHuaSQl5k7WWwjQg2jksq6wdSyKuKjPmN5tAxsQiR3yyZp1eAS6TmzJPuOFPTcxrO5svPcJlvWmcTh/BQ0Sj65xx2ineQgLiGD9ecsvTkaTHrVEGbh8n1WaP2ArExwyKqzLRDzEqA4ZFhyN/euvsPV+45tCS0aKoeQz9jfEpcRl9Y5RduxvvNNOnz7NskyZS7t27djP161bxzJLFIRQoEC9VhRYLV26lJ3rue3V6tSpE2xsbDB79mzEx8fj3r177P8o0LO2tmbvW9TSCk7NXODS7tvA6nsuX74MLy8vFgyVL18elStXxq5du1hwdv/+/W8MkunnlJmqVasWC5gOHDiA0NBQ9lkp25U9Q6ik9POMHAWi1atXZ1myPn36sL9F2+7o6IiaNWuibdu2uHr16m/tDwqyMvvHunTvzoI7yhCOHz+eZei6dOkCJycnljEbPnw4y9IJGR6QcQQh7nHmrjcSk1Pl4miQZK5Ltl4ysWbJiE6D6sG4iC4+hcbgxPY7ECPKqkqYtmMADE11EfTuIxsQ0cBI7JCiZNcJLbHAbQx0C2kjwOcDhtWZDffdN0R9znH+u0SHx2J6h1XMX4yu0erNy2ONxzTYlBGX/caPoGty3aSDeHzdBypqknuSGC056HPsXH4FKclpKFvFGvVbOspsW8j0+W9Q+KKMz5+T8yQ7VrduXZadyVxWrVrFfu7t7Y2qVat+M5lAAQkFU0FBQbn6Gw4OkvESQWV4VOJHJX+Zf4cCKiIqTpI1qlolZ5EV+h0KFmnJhEohdXR02P9lYm5ujsKFC2d9T5+HgslXr17lavu//wyFChViJYZWVpKJ+cyfZX6m31FXTEqXlH5WLOv4zXsQ9vb23/yMAsvY2FgIFR6QcWRKJVtzmBlqIz4pBe4Pc39xC5V27Soxw+g3b0Jx966w65ZzQkVVCa5TW7L1i0ef4J33B4gRPSNtzNg1kA2EaEC0cdoRyAtlapXEupszUK5uaVbCuHzIdszruZ5lBDkcMakoulafjvsXnrESxSFLu2HKblfm6SYPnNh0Fef23GIDcxIcEqNpPXHtzFO8fBjIhIZIyEOWWUt9VX1m+kw2FLmBXk+/l/FZKavV4G+yY5kBEmWuMhfqFctNi8D3k2ipqf8/Qa2o+G02lfb991k2UumOSZBkjTRUf56pkhXZPwNt/68+U4Ec9k301+wYQRm67O/xo79F5DYrKU14QMaR7QlY4J8scY/D1+WnbJGyZC1dJFmy3SLPklWsZYsaTnasAX3NjBNITxfuDS0nrEoXyVJePL3jOpOclhd0jbQx59hI9J7Rlimd3TjxAIOqT8Ozm/IzycGRT8jzjTJiE1suQWRoDMxtTbDq6jQ061tX9CWKmdy96IXNXxUV+01vjSpO/2YJxERcTCK2LDrL1jsOrIPCFtL1tvoeOj+GVhr6R787qJyrRFmRJuz0/i7TlhOZAhPZxwC3bt2CpqYmihQpwr43NDRkvVeZUBbHz88v13+HyhdjPyexZ7WSYkE8fvjgl79DfVy0ZPLy5UtER0ezTFl24Y8PH/6djL1z5w4LlGxtbdn3VKKYnp4/VSeGP9k3knJFSSZQXuABmYBJSU/H5aA3uBos3j6k36FlNTvmkfEy4COe+4dCnrJkysoK8PEJwT0RZ8mIARObQlVdCa+fB8Nt922IFRoI9Z4iyfhtmn4UnuefQl6gB2T7kc5YdmESClsXQnhwFMY3W4St0w4jJUk+yoE58oXv80DW+0g9YzTAatKzNlZdmwbL0pKBqjzg+yIICwdtl3y+rtVFqaiYyfZl5xEd8RmmRfWy+otlTY+yPaCmqIYCvzmcLfBPAfb6xiat8iw7lhMkfkEBz9ChQ+Hj48PUCKdPn45Ro0axezZBaoO7d+/GjRs3WE8XiVNkKjT+LgMHDsSbN28wbtw4+L17i8vnTjH1w5xo0KABK+ujXqtHjx6xgK579+6oXbs2KlSQiJMRKioqbJtITIS2cdiwYaxfi/rFMpUQnz17xkoYw8PDf5jd+1Pq/WTfpGV8QXJ6OgrkMjsqZHhAJmAOv3uKvteOYMVTcXso/QpdDVU0LFeMrR++Lj8DZF1ddbRoUY6t79wp7r4ePUMttB9Qg63vXHUBH4OjIFbaDGoA52412PFY6LoDr5+8hzxhW94Sa65Ph1O3muwzHl55jnmWvZGzz8kRL5Rlp/NyeF0yeg5i5uczDgzD8JU9oKL2b+mR2Pn0IQrTuq1H4udklKlRHK7zxKmoSHjd98O5QxIBie4j6wnGBF1HRQdH2x9l+/VXQRn9P5UrHmh1EAppKpLsmH7+ZccI6r0iOXoKdsqUKcMCJxKzmDJlStZrSAmQgqBmzZqhadOmTPiDBDpyA/V57TtwABfOn0Vr5/rYs2M75s2bl+Pv0D6jAFFXV5eJdVCARv1cBw8e/OZ1VIJJCo6kFtmoUSPWB0ZiJZn069ePZcsoiKOMFmUA84qJP9k3mb1jmkryc7/4J0PMo0QpQOlRbW1tREVFsUZHaRKe9BmVj65mcvdXWw6AhaYe5JWnvh/Qc8lBKCsWhPu8/tBWV8n6P6r5pSZPIyOjrBklsRAV9Rldu6xHUlIq5sxti6pVJYGn2KBjEBr6EcvGueHFQ39UqFkcszb2FO3gIj0tHTO6b8CDqy+ha6SFFWfGwqiIsK+vP7kObp9+hFUjdiH6UywrZew8rjk6jHKGwh967fyXEfN9SEgEvgnBMtdt8L4nqRqo3KQsRq7uyYIyeToGJGs/xmUZ/L0/MKP6pW6joSHSfjgqK3V1WYVg/3DmUdlxcPU8PwYkuEClaGSmTBmZ3OL+1h1tDrVhps9Edin8zB4zyowd63AMpZQrMDEPyo6ZmEh3XJcX0LCdfMzIdDn7Mzg4PAYxn5PY+KmwgXae/C2SqT9x4kSWn5hQPv+r6E9I+ZKOoho60FbOH0PonM5JKuukQJb82EhcJS8Q9h3tP46BijqqGUvUpU75/6t4I484WJqgeBFDJKem49QdcXpe/SxLltlLtmO7uLNk1O83dIYLFBQL4sGN1/A4K95sJgUnEzb2hkVJU0SFxWJ6t/VsACVvVGtWDhvuzEKNFuTFlo7d805geL05ePuUZ8s40s+KHV19HoNrzGDBmJqmCvMWm7F/6G8FY2KCFCLn9tvCgjGa8Jm1x1W0wRixf/1VFozpGWqi9+jGECJONk4IGhWEFY1XwEr3X9U+gr6nnwePCkZN0zoSZUUSBcnn7Jg0SUv/gtivYh56muI9136HhLRUFowV/OcfucqQ8YBM4LSwKM2+nvaXnyDlR9AsT6YE/pEbz0QduHxPhw6VmeKi2H3JCDMrQ3QcIOmB2Dj/jKg9vdQ1VTFz1yA2YPL3+YDZvTexmWB5Q8dAC5N3uWL8lv7Q0FHHu2cBrG9n24wjTJWRw8lv/F4EYXSjedg85RDrZyxXrzQ2eM6WC2+x76Fn16px+7Lk7WftHoRCZvoQK36vQnB4qwdbJ8VdDa38yUbkVfnisMrD8GboG4SPDYffcD/2lb6nn2uraP/rO6adv71j0iY6PhE0bFJRUmCLPBPzVcxDS0mF9QTKC/LzSeQUJ7PiUCxQAK9jwlmKVp5xrlgC6ipKeB8WhXuv/lX9kQfFxVatKshFLxnRvl9tmFsbIToiHpsXSxS3xAqVKdLstaq6Mp7eeo3lI/cIWhb3T6FBb912VbD5/hzUdKmAL+lfcGj5WbjWmM6VGDn5BgVfO2YdxZBaM+HzwDcrKzb32CgYiThIyYl9y87i4gFSofsHEzf2gY2DOcSc1Vw57RjS076gWoPSqN5QMkEshvudvpo+LHQs2NfMoD8xMSWb75j8ZMdoTBEVLwlSdDXV8nSSg0oWhVauGJ0syQRqK+W+tFXI8IBM4NAMQG1T6/9ElkxNRQlNK5Vk64dviLcc7ke0a18JqqpKLEt2+/YbiBlq5h4+uzW76V889hAPb72GmLGxN8OUrf1QUKEArh1/gO1zT0JeIXn8yTtdMXXPYGYmHfz2I8Y1XYglg7YyU14OJy99xQZVm4YDS8+wctlqzRyx6d5cucyKZUI+Y3uWSCapSMCjUgM7iJnT+zzx6lkQ1DSU4Tq1BcROeHgc+6qlpSZX2bHPSSlITUtnkwDaciSK8yM+p6UwhcWC/xSAhqJ8fVYekImAZkUlQcrp996iz678ina1JGWL156+Q1i0pLRAXrJkLq0kvWS7dorbl4wo5VgUzbtUYeurph1nKmJiplztkhi5rCtbP7LuEk5uvQZ5pnrz8th0dw6a9q7DBseX9t1CvwqTcW7ndbnMEHKkR/iHKMzvtQETWixG8LuP0DPWxpTdgzFt71AYmOrK7aG4c8ELa8bvZ+udRjZB0x7CkIX/U0KDIrFjxQW23mtUY+gbSafPL7+ejeQ5xnzH/qHeMXXIE1FxkuyYjrqK4MVu/pbobNmxAvk8sSPtcZp8Hzk5oUGRYlApqAD/uCi8iPwIecbG1ABlrU2R/iUDx28/hzxBvmSZWbJbN8WdVSJ6jnCCkakOwj5EY+dKyYNbzNRvVxk9J0pmgTdOPYLrbg8hz2jqqmPo8u5YdnESrOzNEBf1GSuH7cDIBnPx6qGvrDePIzLSUtNwZNV59Ks4CR7H7rHZ+ub96mHT3blMVEae8X7ohwUDtjJD3kadqqLb2KYQfR/ctONISkiBXQVLOHeolO9/U1FRkX1NSMifvuSIiH+zY4pypDJLmbG4RMmEqK6GfIt5fMnIQGyKJCDTkUK5Yua5mHlu5jfyc1bKMeqKSqhX2AZnA3xYlsxOX2LGJ6+0r1UGT959wNEbz9DbqSJT0pGXLFmbNhWwZ89tbN9+HVWrFUPBguKdE6G+q2GzWmFK3+1w2+OJWk0cWOZMzLQf2gjhIdE4veM6Fg/ZyZTRKHsmz5SsaI3V16bh5MbL2DP/BF499GNKjI261kDPaW2gVyhv5JM58gkN3u9deIbNkw8i6E0o+1nJStYYvKQrbMqI+37wOwS+CcWMbuuRnJSKivVLY9iiTqIvybxw9AEee76FkrICRsxuLZWsC5n9krUQ2RoQamp51wtFvWPx8Z/Zurp6QSZnLmayy95HxiXgS1oqVJUVkPElDUlJEn8ueeRzSjJSk5NZuWLB9C/5dhxp/1IwRucinZO5Nen+U3hAJhKaWZTMCsjGO0rKjOSVBo7FsPSoBz7FfGali/XL2kBeaNuuEo4ffwh//3B4XPNGvfriaJL+GeWrF0cDl3K4dOIRVkw9hjXHhoq6Np+uq4Fz2iE2Mh7X3R4x5cUFR0fAtqx8DyzJBqD14Eao07Yyts88got7b+HCnpu4efIB2o9silauDaGsqiTrzeQIDH/vYGyadACPrrxg32sbaKLPzLZo0Lm63JdOEWFBkZjUcTVioz7D1rEoJm3qw64lMRP+MQabFp5h692GNURhCwOp/W1jY8lkc2ZQlldERsYjNTWdVagEBUkCMzFDAQOVltM1Rq0dlJnV0VRFSlwU5Jmo5EQkpKawJIV/lCTjmZ9QMJZ5TkoD8Y6c/mPUNbWGuoISgj/H4FF4MMobFoG8oqhQEK2q22PLubs45PFUrgIyTU1VJvBBnmQ7d95E7TolRZ0lI/qPb8p8yQLfheHA+ivoPrwRxAwdj9GrurNB1pMbrzCtyzosPTkKRWwKQd6hbNjodX3g3KsO1o/bh9eP/JhK3umtV9BzamvU61D1PzHQ5uRMREgU9ixwg/vuG0yxk7wJWw5siM5jm0FdxH5buSE6PA6TO65B+IdoFLEuhJm7XaEickEFGuivnXUSCfHJKG5fBK26V5f6hJiJiQkznU5NzRsLkufPg7B61W12ji5a1FEu1BUpGIuIiIB3SCyWn3oEPU1VbBjeFopSyuTIgpT0NAx3381EPVZUbwFLfZN8/XtUpiitzFgmPCATCSoKimhkVhzH/Z7Dzf+lXAdkRJvq9th2/h4evAmCb0gENOToPtO6dUUcPXofQUGRuHTpOZycJEImYkVTR40pcM0bsQ8HN3sweWSb0oUhZpSUFTF1W39MaLMSb54FYHLH1VhycjQMC8uvKMH3ZYwrLk/GtSN3sWPWMYQFRmDJwK04vvYiekxrjYoN7eU6S8/5MZ9jEnB45TkcX3cxy8OO1BP7zmoPU2v5n7DIJCE+iU3UBL37CENTXcw7OBTacmAyfP2cF+5c8WbBy8g5bWSW7aOBcF4MhinA3L7tJj59SkCbNhVhaiq9bF9+B2QUMOy77oXQmES41CgDTXX5Eir5Ho+AV3iTEA0TNU2UN7XId0EPWSCaqc65c+eiWrVqrK6Y0oi/ezFOmzaNzbioqqqiQYMGePNGvJLjLSxKsa9n3nsjTc6V0Iz1NFHHQSL3f/jGM8gT6urK6NBBolC4e9ctpKWlQ+zUdLJHzcb2bLZ86aQjSE0Rfx27moYKZu11RWErI4QFR2FSh9VsVvy/AmXC6rWvii0P5qHPzHZQ01LFO68ATGu3AqMbz8fTGz6y3kSOlEiMT2K+db3KTmAy9hSMlapsgyXnJzD1xP9SMEbearN6bWQTNVp6Gph7YIhcTNSQr+S6OW5ZXpMWxcXfq37nzlv4+IRARUURHTtVhTzhGxqFp74hUChQAK1r2EPecftq+9SsaCm5DMZEFZClpKSgXbt2GDRo0G//zqJFi7Bq1Sps2LABd+/ehbq6OpycnETb0FndxAK6yqqISEqA58f3kHfa1y7Dvp6554OE5LwpXxAKLi7loaOrhpCQaFy4IB9qkpQl09JVh//rUBzcJB+y8ToGmmz2m2bBaTZ8cqc1iI/JHxUwoaKkooh2I5pgx9OFaDO0Mfv+5Z23GN9sEZM297r1StabyMknkhKSmXJiT4dx2DbjCOutNCtugml7h2Cp+0TYVS3+n9r3aanpmDdgK57efM1EjWbvdYVZMfEHLjR5vWbWSVambWlrjI4D6kIePhO1BmQ+b/X05CuDdOqBJLlQr6wNDLXFn53NibiUZFwOfvtNYkIeEU1ANnPmTIwcORL29va/fTGuWLECU6ZMQcuWLeHg4IBdu3bhw4cPOHHiBMSIYoGCcDYvwdZPyblJNFHJ1gwWhXSZ6eFVL3/IE9Rc3LGjZMZu966bSJGDjJKOngZcp0hk4w9svApfnxDIA0ZF9DDv0FAWnPk+D8L0buvZQPW/BmUD+s1pj+1PFqBZ37qsrInMf8c6L8TYpgtZvxlHPqDnJ5Um9nQYjy1TDyEmIh4mlkYYs6EPNnjOQrVm5f5zJavp6V+weOhO3L3gxSYlpu8ciOJyIvZDpYq3LjxHQYUCGDWvHRRFLMyUyc0br5nFDD1r23eoDHkiLiE5a0yUOXEtz1wMeo3k9DRYauqhtJ78ZuPFf9X9BD8/P4SGhrIyxUy0tbVRuXJleHp6omPHjj/8veTkZLZkEhMTw75GR0dDCNTTK4JL/zyHIRQFs035ibOjJU56JuKf9FT2eeVJUKBWLUvs31cQ1ta6CA35BC1tVQi5Zj02NhZKSko5HgOHqmYoV8sKQX6fEB0dheho4X6m3KChr4xxm7phRvcN+EfpC6JjYqCSoiTIY5DfFFT9B12nNkejntVwYt1FXD7oicc3vBD+qT6MosVfuiX0/S8tntx+gfCwcBiZ6aPN8Mao07oS6ymKi4/7Tx4DCshQMB0ZBdMxfHlXFC1tJDfP4NS0RKhpFUSjNuVhYKqW4+cSy3WQkpoAIyNV1KhRDBkZKYiOlvQ8ygMfIqJhX1gb4XHJsDJQl5vz8KdQibSqDmoamGeNyWVN5j7PS/PofzKkbUX9l+zYsQMjRoz45Ql4+/ZtVK9enWXEqIcsk/bt27OZvYMHD/7w92bMmMGycRwOh8PhcDgcDofzI969ewcrKyuIPkM2YcIELFy4MMfXeHt7o0QJSZmeNJg4cSJGjRqV9T0FfkWLFkVAQADLsHGkD83GmZmZITAwEFpaWvwQ8GPwn4RfB3z//9fh14Ds4cdA9vBjIHsoU2dubg49Pb08e0+ZBmSjR49Gz549c3zNn0aemWZuHz9+/CZDRt+XLVv2p7+nrKzMlu+hYIwHA7KF9j8/BvwY/Nfh1wHf//91+DUge/gxkD38GMievCzblWlAZmhoyJb8wNLSkgVlly9fzgrAaFaB1BZzo9TI4XA4HA6Hw+FwOPmFcDsyv4NKBp88ecK+pqens3Va4uPjs15DpY3Hjx9n69QnRr1mc+bMgZubG7y8vNC9e3eYmprCxcVFhp+Ew+FwOBwOh8PhcESmskgGzzt37sz63tHRkX29evUq6tSpw9ZfvXr1jQLLuHHj8PnzZ/Tv35/1gtWoUQPnz5+HiorKb/9dKl+cPn36D8sYOdKBHwPZw4+B7OHHgO///zr8GpA9/BjIHn4M5PMYiE5lkcPhcDgcDofD4XDkBdGULHI4HA6Hw+FwOByOvMEDMg6Hw+FwOBwOh8ORETwg43A4HA6Hw+FwOBwZwQMyDofD4XA4HA6Hw5ERPCD7Dn9/f/Tp04f5mKmqqsLa2popqaSkpOS4I5OSkjB48GDo6+tDQ0MDbdq0YSbUnNwzd+5cVKtWDWpqatDR0fmt3yGDcbI6yL40btyY734pHgPSByI1VDJip2unQYMGePPmDT8Gf0hkZCS6dOnCzD/pGNB9KbvNx48gxdnvr4OBAwfyY/CbrF27FhYWFkyJt3Llyrh3716Orz98+DCzW6HX29vb4+zZs3xfS/EY7Nix4//O99yoKHP+n+vXr6N58+bMIoj254kTJ365m65du4Zy5coxxTkbGxt2XDjSOwa0/7+/DmgJDQ3lh+EPmD9/PipWrAhNTU0YGRkxqyxScf8Vf/s84AHZd/j4+ODLly/YuHEjXrx4geXLl2PDhg2YNGlSjjty5MiROHXqFDsgHh4e+PDhA1q3bp2rg8GRQMFvu3btcm3gTQFYSEhI1rJ//36+S6V4DBYtWoRVq1ax64UM2NXV1eHk5MQmKzi5h4IxugddvHgRp0+fZg9psvD4Ff369fvmOqDjwvk1Bw8exKhRo9gE3KNHj1CmTBl2/oaFhf3w9bdv30anTp1YoPz48WP20Kbl+fPnfHdL6RgQNGGR/Xx///493/9/AVkF0X6nwPh38PPzQ9OmTVG3bl3mDUv+r3379oW7uzs/DlI6BplQ0JD9WqBggpN7aAxPCZY7d+6w529qaioaNWrEjsvPyJPnAcnec3Jm0aJFGZaWlj/9/+jo6AxFRcWMw4cPZ/3M29ub7AQyPD09+e79Q7Zv356hra39W6/t0aNHRsuWLfm+ltEx+PLlS4axsXHG4sWLv7kulJWVM/bv38+PSy55+fIlu3/cv38/62fnzp3L+OeffzKCg4N/+nu1a9fOGD58ON/ff0ClSpUyBg8enPV9enp6hqmpacb8+fN/+Pr27dtnNG3a9JufVa5cOWPAgAF8/0vpGOTmGcHJPXQPOn78eI6vGTduXEbp0qW/+VmHDh0ynJyc+C6X0jG4evUqe11UVBTf5/lAWFgY278eHh4/fU1ePA94huw3ILNpPT29n/7/w4cPWQRNJVqZUNrS3Nwcnp6evx8dc/4KStvTjJCtrS3L7ERERPA9KiVolpTKI7JfA9ra2qzkiF8DuYf2GZUpVqhQIetntG8LFCjAso85sXfvXhgYGMDOzg4TJ05EQkLCH2zBfy8jTPfx7Ocv7Wv6/mfnL/08++sJyubw8116x4CgMt6iRYvCzMwMLVu2ZFlljvTg14FwKFu2LGsZaNiwIW7duiXrzZGrGIDIKQ7Ii+tA4bdf+R/l7du3WL16NZYsWfLT19BAVElJ6f96bQoVKsRreKUElStSiSj1/r17946VmDZp0oRdDAULFpTWZvxnyaxVp3M+O/wa+PP9+X25iYKCAnsg5NQX0LlzZzY4pd6DZ8+eYfz48ayM5dixY3+4Jf8NwsPDkZ6e/sPzl8rYfwQdB36+y/YY0OTbtm3b4ODgwAZN9Jym3lcKyooUKZKHW8f5GT+7DmJjY5GYmMj6iTn5CwVh1CpAE3jJycnYsmUL6yemyTvq7eP8OdTCRGW41atXZ5OcPyMvngf/mQzZhAkTftj0mH35/qYfHBzMBvrUS0N9GRzp7v/c0LFjR7Ro0YI1UlLdLvXc3L9/n2XNONI5BhzZHwPqMaNZOboOqAdt165dOH78OJuk4HDkjapVq6J79+4sM1C7dm028WBoaMh6wDmc/wo0MTFgwACUL1+eTUjQJAV9JQ0Ezt9BvWTUB3bgwAHkN/+ZDNno0aOZEl9OWFlZZa2TKAc1qdJJvWnTphx/z9jYmJVbREdHf5MlI5VF+j9O7vf/30LvRWVblOGsX78+PwT5fAwyz3M652m2LhP6ngZLnNwdA9qf3wsZpKWlMeXF3NxTqGSUoOuAFGM5P4buFZRJ/14ZN6d7OP08N6/n5P0x+B5FRUU4Ojqy850jHX52HZDYCs+OyY5KlSrh5s2bMtwC8TNkyJAsQa1fZdzz4nnwnwnIaNaMlt+BMmMUjNFsw/bt21kde07Q6+hBcPnyZSZ3T1CZUEBAAJvB4+Ru/+cFQUFBrIcse3DwXyc/jwGVitKNh66BzACMSlaoZCK3apnyzO8eA7pv0AQP9dTQ/YW4cuUKK5/IDLJ+B1I9I/h1kDNUck77mc5fyrATtK/pe3oo/+wY0f9TOUsmpMjF7/nSOwbfQyWPXl5ecHZ2/sOt4OQWOt+/l/fm14HsoXs/v+//GaSlMnToUFZdQlVWNL75FXnyPPgr6RE5JCgoKMPGxiajfv36bD0kJCRryf4aW1vbjLt372b9bODAgRnm5uYZV65cyXjw4EFG1apV2cLJPe/fv894/PhxxsyZMzM0NDTYOi1xcXFZr6H9f+zYMbZOPx8zZgxTtPTz88u4dOlSRrly5TKKFSuWkZSUxA+BFI4BsWDBggwdHZ2MkydPZjx79oypXpI6aWJiIj8Gf0Djxo0zHB0d2X3m5s2b7Hzu1KnTT+9Db9++zZg1axa7/9B1QMfBysoqo1atWnz//wYHDhxgqqA7duxgKpf9+/dn53NoaCj7/27dumVMmDAh6/W3bt3KUFBQyFiyZAlT1Z0+fTpT2/Xy8uL7W0rHgO5P7u7uGe/evct4+PBhRseOHTNUVFQyXrx4wY/BH0L3+Mz7PQ0Rly1bxtbpmUDQ/qfjkImvr2+GmppaxtixY9l1sHbt2oyCBQtmnD9/nh8DKR2D5cuXZ5w4cSLjzZs37P5DSrsFChRgYyFO7hk0aBBTb7127do3MUBCQkLWa/LjecADsh/I6NIF8KMlExrs0PckNZoJDTpdXV0zdHV12c2pVatW3wRxnN+HJOx/tP+z72/6no4VQRdJo0aNMgwNDdkFULRo0Yx+/fplPcQ5+X8MMqXvp06dmlGoUCE2qKJJjVevXvHd/4dERESwAIwCYi0trYxevXp9ExB/fx8KCAhgwZeenh7b/zSxRIOkmJgYfgx+k9WrV7OJNSUlJSbBfufOnW8sBei6yM6hQ4cyihcvzl5P0t9nzpzh+1qKx2DEiBFZr6X7jrOzc8ajR4/4MfgLMiXUv18y9zt9pePw/e+ULVuWHQeaBMr+XODk/zFYuHBhhrW1NZuMoPt/nTp1WHKA82f8LAbIfl7nx/Pgn69/nMPhcDgcDofD4XA4UuY/o7LI4XA4HA6Hw+FwOEKDB2QcDofD4XA4HA6HIyN4QMbhcDgcDofD4XA4MoIHZBwOh8PhcDgcDocjI3hAxuFwOBwOh8PhcDgyggdkHA6Hw+FwOBwOhyMjeEDG4XA4HA6Hw+FwODKCB2QcDofD4XA4HA6HIyN4QMbhcDgcDofD4XA4MoIHZBwOh8PhcDgcDocjI3hAxuFwOBwOh8PhcDgyggdkHA6Hw+H8hE+fPsHY2Bjz5s3L+tnt27ehpKSEy5cv8/3G4XA4nL/mn4yMjIy/fxsOh8PhcOSTs2fPwsXFhQVitra2KFu2LFq2bIlly5bJetM4HA6HIwfwgIzD4XA4nF8wePBgXLp0CRUqVICXlxfu378PZWVlvt84HA6H89fwgIzD4XA4nF+QmJgIOzs7BAYG4uHDh7C3t+f7jMPhcDh5Au8h43A4HA7nF7x79w4fPnzAly9f4O/vz/cXh8PhcPIMniHjcDgcDicHUlJSUKlSJdY7Rj1kK1asYGWLRkZGfL9xOBwO56/hARmHw+FwODkwduxYHDlyBE+fPoWGhgZq164NbW1tnD59mu83DofD4fw1vGSRw+FwOJyfcO3aNZYR2717N7S0tFCgQAG2fuPGDaxfv57vNw6Hw+H8NTxDxuFwOBwOh8PhcDgygmfIOBwOh8PhcDgcDkdG8ICMw+FwOBwOh8PhcGQED8g4HA6Hw+FwOBwOR0bwgIzD4XA4HA6Hw+FwZAQPyDgcDofD4XA4HA5HRvCAjMPhcDgcDofD4XBkBA/IOBwOh8PhcDgcDkdG8ICMw+FwOBwOh8PhcGTcs1OcAAAAMklEQVQED8g4HA6Hw+FwOBwOR0bwgIzD4XA4HA6Hw+FwZAQPyDgcDofD4XA4HA4HsuF/GeYT1QbtaCcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance from true optimum: 1.896e-03\n" + ] + } + ], "source": [ "# Create contour plot\n", - "fig, ax = plot_contour_2d(\n", + "fig, ax = contour_2d(\n", " rosenbrock,\n", " xlim=(-2, 2),\n", " ylim=(-1, 3),\n", @@ -151,6 +244,7 @@ " optimum=(1.0, 1.0),\n", " found=(result.x[0], result.x[1]),\n", " title=\"Rosenbrock Function Landscape\",\n", + " show=False,\n", ")\n", "\n", "plt.show()\n", @@ -162,39 +256,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Compare Different Optimizers\n", + "## Compare Different Optimisers\n", "\n", "Let's compare Nelder-Mead, CMA-ES, and Adam:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 58, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:16:32.406534Z", + "start_time": "2026-01-10T10:16:32.325837Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:14.602032Z", + "iopub.status.busy": "2026-01-09T21:37:14.601684Z", + "iopub.status.idle": "2026-01-09T21:37:14.628019Z", + "shell.execute_reply": "2026-01-09T21:37:14.627121Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "OPTIMIZER COMPARISON\n", + "======================================================================\n", + "Optimizer Success Final Value Iterations Evaluations\n", + "----------------------------------------------------------------------\n", + "Nelder-Mead True 2.866e-07 130 290\n", + "CMA-ES True 1.304e-09 78 469\n", + "Adam False 6.927e-09 2000 2001\n", + "\n", + "Final parameters:\n", + "Nelder-Mead x = [1.0001253 1.00019857]\n", + "CMA-ES x = [0.99997614 0.999955 ]\n", + "Adam x = [1.00008317 1.00016666]\n" + ] + } + ], "source": [ - "# Define optimizers\n", - "optimizers = {\n", + "# Define optimisers\n", + "optimisers = {\n", " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", " \"CMA-ES\": chron.CMAES().with_max_iter(300).with_step_size(0.5),\n", - " \"Adam\": chron.Adam().with_max_iter(1000).with_step_size(0.01),\n", + " \"Adam\": chron.Adam().with_max_iter(2000).with_step_size(0.25).with_threshold(1e-12),\n", "}\n", "\n", "# Test starting point\n", - "initial_guess = [-1.5, -0.5]\n", + "initial_guess = [-1.0, -3.0]\n", "\n", - "# Run all optimizers\n", + "# Run all optimisers\n", "results = {}\n", - "for name, optimizer in optimizers.items():\n", - " result = optimizer.run(problem, initial_guess)\n", + "for name, optimiser in optimisers.items():\n", + " result = optimiser.run(problem, initial_guess)\n", " results[name] = result\n", "\n", "# Display comparison\n", "print(\"\\n\" + \"=\" * 70)\n", - "print(\"OPTIMIZER COMPARISON\")\n", + "print(\"OPTIMISER COMPARISON\")\n", "print(\"=\" * 70)\n", "print(\n", - " f\"{'Optimizer':<15} {'Success':<10} {'Final Value':<15} {'Iterations':<12} {'Evaluations'}\"\n", + " f\"{'Optimiser':<15} {'Success':<10} {'Final Value':<15} {'Iterations':<12} {'Evaluations'}\"\n", ")\n", "print(\"-\" * 70)\n", "\n", @@ -212,19 +338,42 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Visualize All Results" - ] + "source": "## Visualise All Results" }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:16:36.906135Z", + "start_time": "2026-01-10T10:16:36.471917Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:14.631493Z", + "iopub.status.busy": "2026-01-09T21:37:14.631193Z", + "iopub.status.idle": "2026-01-09T21:37:15.176960Z", + "shell.execute_reply": "2026-01-09T21:37:15.176063Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Create contour plot with all optimizers' results\n", + "# Create contour plot with all optimisers' results\n", "x = np.linspace(-2, 2, 200)\n", - "y = np.linspace(-1, 3, 200)\n", + "y = np.linspace(-5, 3, 200)\n", "X, Y = np.meshgrid(x, y)\n", "Z = np.zeros_like(X)\n", "\n", @@ -253,7 +402,7 @@ " zorder=5,\n", ")\n", "\n", - "# Plot optimizer results\n", + "# Plot optimiser results\n", "colors = {\"Nelder-Mead\": \"blue\", \"CMA-ES\": \"green\", \"Adam\": \"orange\"}\n", "markers = {\"Nelder-Mead\": \"o\", \"CMA-ES\": \"s\", \"Adam\": \"^\"}\n", "\n", @@ -272,7 +421,7 @@ "\n", "ax.set_xlabel(\"x\", fontsize=12)\n", "ax.set_ylabel(\"y\", fontsize=12)\n", - "ax.set_title(\"Optimizer Comparison on Rosenbrock Function\", fontsize=14)\n", + "ax.set_title(\"Optimiser Comparison on Rosenbrock Function\", fontsize=14)\n", "ax.grid(True, alpha=0.3)\n", "ax.legend(loc=\"upper left\", fontsize=10)\n", "\n", @@ -291,9 +440,55 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 60, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T10:16:46.824367Z", + "start_time": "2026-01-10T10:16:46.665402Z" + }, + "execution": { + "iopub.execute_input": "2026-01-09T21:37:15.185691Z", + "iopub.status.busy": "2026-01-09T21:37:15.185395Z", + "iopub.status.idle": "2026-01-09T21:37:15.195011Z", + "shell.execute_reply": "2026-01-09T21:37:15.194279Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "STARTING POINT SENSITIVITY\n", + "============================================================\n", + "\n", + "Start 1: [-1.5, -0.5]\n", + " Final: [0.99938601 0.99871559]\n", + " Iterations: 60\n", + " Error: 1.424e-03\n", + " Success: True\n", + "\n", + "Start 2: [1.5, 1.5]\n", + " Final: [1.00023483 1.00043031]\n", + " Iterations: 79\n", + " Error: 4.902e-04\n", + " Success: True\n", + "\n", + "Start 3: [0.0, 2.0]\n", + " Final: [1.00004402 1.00004508]\n", + " Iterations: 65\n", + " Error: 6.301e-05\n", + " Success: True\n", + "\n", + "Start 4: [-1.0, 1.0]\n", + " Final: [1.00040626 1.0008615 ]\n", + " Iterations: 105\n", + " Error: 9.525e-04\n", + " Success: True\n" + ] + } + ], "source": [ "# Try multiple starting points\n", "starting_points = [\n", @@ -303,14 +498,14 @@ " [-1.0, 1.0],\n", "]\n", "\n", - "optimizer = chron.NelderMead().with_max_iter(1000)\n", + "optimiser = chron.NelderMead().with_max_iter(1000)\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"STARTING POINT SENSITIVITY\")\n", "print(\"=\" * 60)\n", "\n", "for i, start in enumerate(starting_points, 1):\n", - " result = optimizer.run(problem, start)\n", + " result = optimiser.run(problem, start)\n", " error = np.linalg.norm(result.x - [1.0, 1.0])\n", "\n", " print(f\"\\nStart {i}: {start}\")\n", @@ -326,17 +521,17 @@ "source": [ "## Key Takeaways\n", "\n", - "1. **ScalarBuilder** is used for direct function optimization\n", - "2. **Nelder-Mead** is the default optimizer - good for small problems\n", - "3. **CMA-ES** is more robust for global search but uses more evaluations\n", + "1. **ScalarBuilder** is used for direct function optimisation\n", + "2. **Nelder-Mead** is provided as the default optimiser - good for small problems\n", + "3. **CMA-ES** is more robust for global search but requires more evaluations\n", "4. **Adam** can be fast on smooth problems but may struggle on complex landscapes\n", "5. Starting point can significantly affect convergence speed\n", "\n", "## Next Steps\n", "\n", "- [Tutorial 2: ODE Fitting with DiffSL](02_ode_fitting_diffsol.ipynb) - Learn how to fit differential equations\n", - "- [Choosing an Optimizer](../../guides/choosing-optimizer.md) - Detailed optimizer selection guide\n", - "- [API Reference: Optimizers](../../api-reference/python/optimizers.md) - Complete API documentation" + "- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - Detailed optimiser selection guide\n", + "- [API Reference: Optimisers](../../api-reference/python/optimisers.md) - Complete API documentation" ] }, { @@ -347,7 +542,7 @@ "\n", "Try these challenges:\n", "\n", - "1. **Rastrigin Function**: Implement and optimize:\n", + "1. **Rastrigin Function**: Implement and optimise:\n", " $$f(x, y) = 20 + x^2 + y^2 - 10(\\cos(2\\pi x) + \\cos(2\\pi y))$$\n", " \n", "2. **Parameter Tuning**: Experiment with CMA-ES `step_size` parameter (try 0.1, 0.5, 1.0, 2.0)\n", @@ -372,7 +567,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb index 8128d1d..c12eb17 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -6,15 +6,11 @@ "source": [ "# Tutorial 2: ODE Fitting with DiffSL\n", "\n", - "**Learning Objectives:**\n", + "**Objectives:**\n", "- Understand the DiffsolBuilder API\n", "- Learn DiffSL syntax for defining ODEs\n", "- Fit parameters of a logistic growth model\n", - "- Visualize model fits and residuals\n", - "\n", - "**Prerequisites:** Basic differential equations, NumPy\n", - "\n", - "**Runtime:** ~10 minutes" + "- Visualise model fits and residuals" ] }, { @@ -37,21 +33,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:50.671292Z", + "start_time": "2026-01-10T11:10:50.561207Z" + } + }, "outputs": [], "source": [ "# Import plotting utilities\n", - "import sys\n", - "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "\n", - "sys.path.append(\".\")\n", - "from utils import plot_ode_fit, setup_plotting\n", - "\n", - "setup_plotting()" + "from chronopt.plotting import ode_fit" ] }, { @@ -65,9 +60,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:50.943354Z", + "start_time": "2026-01-10T11:10:50.699179Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DiffSL Model Definition:\n", + "==================================================\n", + "\n", + "in = [r, k]\n", + "r { 1 } k { 1 }\n", + "u_i { y = 0.1 }\n", + "F_i { (r * y) * (1 - (y / k)) }\n", + "\n", + "\n", + "Explanation:\n", + " in = [r, k] → Parameters to fit\n", + " r { 1 } k { 1 } → Default parameter values\n", + " u_i { y = 0.1 } → Initial condition: y(0) = 0.1\n", + " F_i { ... } → Right-hand side: dy/dt = ...\n" + ] + } + ], "source": [ "dsl_model = \"\"\"\n", "in = [r, k]\n", @@ -97,12 +118,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:51.051629Z", + "start_time": "2026-01-10T11:10:50.964816Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 100 data points\n", + "Time range: [0.00, 4.00]\n", + "Value range: [0.102, 0.856]\n", + "Noise level: 0.01\n", + "\n", + "True parameters: r = 1.0, k = 1.0\n" + ] + } + ], "source": [ "# Time points\n", - "t_span = np.linspace(0, 1, 100)\n", + "t_span = np.linspace(0, 4, 100)\n", "\n", "# True logistic growth solution with known parameters\n", "# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\n", @@ -116,7 +155,7 @@ "\n", "# Add some noise\n", "np.random.seed(42)\n", - "noise_level = 0.02\n", + "noise_level = 0.01\n", "y_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n", "\n", "# Chronopt expects data as [time, observation] columns\n", @@ -132,17 +171,34 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Visualize the Data" - ] + "source": "## Visualise the Data" }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:51.273527Z", + "start_time": "2026-01-10T11:10:51.052619Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "fig, ax = plot_ode_fit(\n", + "fig, ax = ode_fit(\n", " t_span,\n", " y_noisy,\n", " t_span,\n", @@ -150,6 +206,7 @@ " title=\"Synthetic Logistic Growth Data\",\n", " xlabel=\"Time\",\n", " ylabel=\"Population\",\n", + " show=False,\n", ")\n", "\n", "# Update legend\n", @@ -168,9 +225,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:51.334523Z", + "start_time": "2026-01-10T11:10:51.274634Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Problem built successfully!\n", + "\n", + "Initial parameter guesses: r = 50.0, k = 50.0\n", + "(Far from true values: r = 1.0, k = 1.0)\n" + ] + } + ], "source": [ "# Create builder\n", "builder = (\n", @@ -178,15 +251,15 @@ " .with_diffsl(dsl_model)\n", " .with_data(data)\n", " .with_tolerances(1e-6, 1e-8) # Relative and absolute tolerances\n", - " .with_parameter(\"r\", 100.0) # Initial guess (deliberately wrong)\n", - " .with_parameter(\"k\", 100.0) # Initial guess (deliberately wrong)\n", + " .with_parameter(\"r\", 10.0) # Initial guess (deliberately wrong)\n", + " .with_parameter(\"k\", 10.0) # Initial guess (deliberately wrong)\n", " .with_parallel(True) # Enable parallel evaluation\n", ")\n", "\n", "problem = builder.build()\n", "\n", "print(\"Problem built successfully!\")\n", - "print(\"\\nInitial parameter guesses: r = 100.0, k = 100.0\")\n", + "print(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\n", "print(f\"(Far from true values: r = {r_true}, k = {k_true})\")" ] }, @@ -194,22 +267,54 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Optimize with CMA-ES\n", + "## Optimise with CMA-ES\n", "\n", "CMA-ES works well for ODE parameter fitting:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:52.883046Z", + "start_time": "2026-01-10T11:10:51.336076Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "OPTIMIZATION RESULTS\n", + "============================================================\n", + "Success: True\n", + "\n", + "Fitted parameters:\n", + " r = 0.996728 (true: 1.0)\n", + " k = 1.002875 (true: 1.0)\n", + "\n", + "Optimization details:\n", + " Final cost: 8.220e-03\n", + " Iterations: 121\n", + " Function evaluations: 727\n", + " Time: 517.299 milliseconds\n", + " Message: Function tolerance met\n", + "\n", + "Parameter errors:\n", + " r: 0.327%\n", + " k: 0.287%\n" + ] + } + ], "source": [ - "# Create optimizer\n", - "optimizer = chron.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", + "# Create optimiser\n", + "optimiser = chron.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", "\n", "# Run optimization\n", - "result = optimizer.run(problem, [100.0, 100.0])\n", + "result = optimiser.run(problem, problem.initial_values())\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"OPTIMIZATION RESULTS\")\n", @@ -222,7 +327,7 @@ "print(f\" Final cost: {result.value:.3e}\")\n", "print(f\" Iterations: {result.iterations}\")\n", "print(f\" Function evaluations: {result.evaluations}\")\n", - "print(f\" Time: {result.time:.2f} seconds\")\n", + "print(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\n", "print(f\" Message: {result.message}\")\n", "\n", "# Calculate parameter errors\n", @@ -237,16 +342,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize the Fit\n", + "## Visualise the Fit\n", "\n", "Let's plot the fitted model against the data:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:10:53.404042Z", + "start_time": "2026-01-10T11:10:52.987366Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fit quality:\n", + " RMSE: 0.009066\n", + " Max residual: 0.025379\n", + " Mean residual: -0.000468\n" + ] + } + ], "source": [ "# Generate predictions with fitted parameters\n", "r_fit, k_fit = result.x\n", @@ -259,8 +394,8 @@ "# Top: Data and fits\n", "ax1 = axes[0]\n", "ax1.plot(t_span, y_noisy, \"o\", label=\"Noisy data\", alpha=0.6, markersize=6)\n", - "ax1.plot(t_span, y_data, \"--\", label=\"True solution\", linewidth=2, alpha=0.7)\n", "ax1.plot(t_span, y_fit, \"-\", label=\"Fitted model\", linewidth=2)\n", + "ax1.plot(t_span, y_data, \"--\", label=\"True solution\", linewidth=2, alpha=0.7)\n", "ax1.set_xlabel(\"Time\")\n", "ax1.set_ylabel(\"Population\")\n", "ax1.set_title(\"Logistic Growth Model Fit\")\n", @@ -308,9 +443,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:11:00.907523Z", + "start_time": "2026-01-10T11:10:53.408496Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "INITIAL GUESS SENSITIVITY\n", + "======================================================================\n", + "Initial [r, k] Final [r, k] Iterations Success\n", + "----------------------------------------------------------------------\n", + "[0.5, 0.5] [0.9967, 1.0029] 78 True\n", + "[2.0, 2.0] [0.9967, 1.0029] 86 True\n", + "[10.0, 10.0] [0.9967, 1.0029] 244 True\n", + "[100.0, 100.0] [0.5947, 23.9276] 321 True\n", + "\n", + "True values: [r, k] = [1.0, 1.0]\n" + ] + } + ], "source": [ "# Try several initial guesses\n", "initial_guesses = [\n", @@ -327,7 +486,7 @@ "print(\"-\" * 70)\n", "\n", "for guess in initial_guesses:\n", - " result = optimizer.run(problem, guess)\n", + " result = optimiser.run(problem, guess)\n", " final_str = f\"[{result.x[0]:.4f}, {result.x[1]:.4f}]\"\n", " guess_str = f\"{guess}\"\n", " print(f\"{guess_str:<20} {final_str:<30} {result.iterations:<12} {result.success}\")\n", @@ -339,18 +498,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Comparing Optimizers\n", + "## Comparing Optimisers\n", "\n", "Let's compare different optimization algorithms:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:11:05.018231Z", + "start_time": "2026-01-10T11:11:00.922897Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "OPTIMIZER COMPARISON\n", + "================================================================================\n", + "Algorithm r (fitted) k (fitted) Cost Iters Time (s)\n", + "--------------------------------------------------------------------------------\n", + "Nelder-Mead 2.527654 0.604107 2.331e+00 37 0.210\n", + "CMA-ES 0.996730 1.002923 8.220e-03 60 0.542\n", + "Adam 0.953633 1.070344 1.648e-02 1000 0.313\n", + "\n", + "True values: 1.000000 1.000000 \n" + ] + } + ], "source": [ - "optimizers = {\n", + "optimisers = {\n", " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", " \"CMA-ES\": chron.CMAES().with_max_iter(500),\n", " \"Adam\": chron.Adam().with_max_iter(1000).with_step_size(0.01),\n", @@ -359,18 +541,18 @@ "initial = [2.0, 2.0]\n", "\n", "print(\"\\n\" + \"=\" * 80)\n", - "print(\"OPTIMIZER COMPARISON\")\n", + "print(\"OPTIMISER COMPARISON\")\n", "print(\"=\" * 80)\n", "print(\n", " f\"{'Algorithm':<15} {'r (fitted)':<15} {'k (fitted)':<15} {'Cost':<15} {'Iters':<8} {'Time (s)'}\"\n", ")\n", "print(\"-\" * 80)\n", "\n", - "for name, opt in optimizers.items():\n", + "for name, opt in optimisers.items():\n", " result = opt.run(problem, initial)\n", " print(\n", " f\"{name:<15} {result.x[0]:<15.6f} {result.x[1]:<15.6f} \"\n", - " f\"{result.value:<15.3e} {result.iterations:<8} {result.time:.3f}\"\n", + " f\"{result.value:<15.3e} {result.iterations:<8} {result.time.microseconds / 1e6:.3f}\"\n", " )\n", "\n", "print(f\"\\nTrue values: {r_true:<15.6f} {k_true:<15.6f}\")" @@ -387,9 +569,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:11:05.059961Z", + "start_time": "2026-01-10T11:11:05.033320Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example: Predator-Prey Model (Lotka-Volterra)\n", + "==================================================\n", + "\n", + "in = [alpha, beta, gamma, delta]\n", + "alpha { 1 } beta { 0.1 } gamma { 1.5 } delta { 0.075 }\n", + "u_i {\n", + " prey = 10.0\n", + " predator = 5.0\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator\n", + "}\n", + "out_i { prey, predator }\n", + "\n", + "\n", + "Example: Sequential Reactions (A → B → C)\n", + "==================================================\n", + "\n", + "in = [k1, k2]\n", + "k1 { 0.5 } k2 { 0.3 }\n", + "u_i { A = 1.0 }\n", + "F_i {\n", + " -k1 * A,\n", + " k1 * A - k2 * B\n", + "}\n", + "out_i { A, B }\n", + "\n" + ] + } + ], "source": [ "# Example 1: Multiple state variables (Lotka-Volterra)\n", "predator_prey = \"\"\"\n", diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index cca4a3c..07a1a0b 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -6,15 +6,11 @@ "source": [ "# Tutorial 3: Parameter Uncertainty\n", "\n", - "**Learning Objectives:**\n", - "- Go from optimization to uncertainty quantification\n", + "**Objectives:**\n", + "- Go from optimisation to uncertainty quantification\n", "- Use MCMC sampling to explore parameter distributions\n", "- Interpret MCMC diagnostics and traces\n", - "- Calculate confidence intervals\n", - "\n", - "**Prerequisites:** Tutorials 1-2, basic Bayesian statistics\n", - "\n", - "**Runtime:** ~15 minutes" + "- Calculate confidence intervals" ] }, { @@ -23,7 +19,7 @@ "source": [ "## Introduction\n", "\n", - "Optimization gives us a **single best** parameter estimate. But how **certain** are we about these values?\n", + "Optimisation gives us a **single best** parameter estimate. But how **certain** are we about these values?\n", "\n", "MCMC (Markov Chain Monte Carlo) sampling explores the full **posterior distribution**, letting us:\n", "- Quantify parameter uncertainty\n", @@ -36,21 +32,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:20:18.324983Z", + "start_time": "2026-01-10T11:20:18.185476Z" + } + }, "outputs": [], "source": [ "# Import plotting utilities\n", - "import sys\n", - "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "from chronopt.plotting import parameter_distributions, parameter_traces\n", "\n", - "sys.path.append(\".\")\n", - "from utils import plot_parameter_distributions, plot_parameter_traces, setup_plotting\n", - "\n", - "setup_plotting()\n", "np.random.seed(42) # For reproducibility" ] }, @@ -85,9 +81,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:20:18.460807Z", + "start_time": "2026-01-10T11:20:18.385369Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 61 observations\n", + "Time span: [0, 0.999] seconds\n", + "Noise level: σ = 0.1\n", + "\n", + "True parameters:\n", + " g = 9.81 m/s²\n", + " h = 10.0 m\n" + ] + } + ], "source": [ "def ball_states(t, g, h):\n", " \"\"\"Analytical solution for ball trajectory.\"\"\"\n", @@ -134,9 +149,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:20:18.818246Z", + "start_time": "2026-01-10T11:20:18.462229Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", @@ -171,9 +205,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:20:18.855080Z", + "start_time": "2026-01-10T11:20:18.821847Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DiffSL Model:\n", + "\n", + "in = [g, h]\n", + "g { 1 } h { 1 }\n", + "u_i {x = h, v = 0}\n", + "F_i {v, -g}\n", + "stop {x}\n", + "\n", + "\n", + "Explanation:\n", + " x = height, v = velocity\n", + " dx/dt = v (velocity determines height change)\n", + " dv/dt = -g (gravity accelerates downward)\n", + " stop {x} (terminate when height reaches zero)\n" + ] + } + ], "source": [ "# DiffSL model for falling ball\n", "dsl_model = \"\"\"\n", @@ -204,9 +264,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T11:20:19.373109Z", + "start_time": "2026-01-10T11:20:18.858953Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "OPTIMIZATION RESULTS (MAP Estimate)\n", + "============================================================\n", + "Success: True\n", + "\n", + "Fitted parameters:\n", + " g = 9.8035 m/s² (true: 9.81)\n", + " h = 9.9841 m (true: 10.0)\n", + "\n", + "Cost: 0.183349\n", + "Iterations: 197\n" + ] + } + ], "source": [ "# Build problem\n", "builder = (\n", @@ -248,9 +332,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T13:16:54.156325Z", + "start_time": "2026-01-10T13:16:49.674678Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "MCMC SAMPLING\n", + "============================================================\n", + "Chains: 100\n", + "Iterations per chain: 1000\n", + "Total samples: 3000\n", + "\n", + "Starting from MAP estimate: g = 9.8035, h = 9.9841\n", + "\n", + "Running MCMC... (this may take a minute)\n", + "\n", + "Sampling complete!\n", + "Samples shape: (3, 1001)\n" + ] + } + ], "source": [ "# Rebuild problem with GaussianNLL (required for sampling)\n", "builder_sampling = (\n", @@ -259,7 +369,7 @@ " .with_data(data)\n", " .with_parameter(\"g\", g_map) # Start from MAP\n", " .with_parameter(\"h\", h_map)\n", - " .with_parallel(True)\n", + " # .with_parallel(True)\n", " .with_cost(chron.GaussianNLL(variance=noise_std**2))\n", ")\n", "\n", @@ -268,10 +378,9 @@ "# Setup MCMC sampler\n", "sampler = (\n", " chron.MetropolisHastings()\n", - " .with_num_chains(100)\n", + " .with_num_chains(3)\n", " .with_iterations(1000)\n", " .with_step_size(0.25)\n", - " .with_parallel(True)\n", ")\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", @@ -279,7 +388,7 @@ "print(\"=\" * 60)\n", "print(\"Chains: 100\")\n", "print(\"Iterations per chain: 1000\")\n", - "print(\"Total samples: 100,000\")\n", + "print(f\"Total samples: {mcmc_result.draws}\")\n", "print(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\n", "print(\"\\nRunning MCMC... (this may take a minute)\")\n", "\n", @@ -287,15 +396,40 @@ "mcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n", "\n", "print(\"\\nSampling complete!\")\n", - "print(f\"Samples shape: {mcmc_result.samples.shape}\")\n", - "print(f\"Acceptance rate: {mcmc_result.acceptance_rate:.3f}\")\n", - "print(\"Target acceptance: 0.20 - 0.40\")\n", - "\n", - "if mcmc_result.acceptance_rate < 0.15 or mcmc_result.acceptance_rate > 0.50:\n", - " print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", - " print(\" Consider adjusting step_size\")\n", - "else:\n", - " print(\"✓ Acceptance rate looks good!\")" + "print(f\"Samples shape: {len(mcmc_result), len(mcmc_result.chains[0])}\")\n", + "# print(f\"Acceptance rate: {mcmc_result.acceptance_rate:.3f}\")\n", + "# print(\"Target acceptance: 0.20 - 0.40\")\n", + "\n", + "# if mcmc_result.acceptance_rate < 0.15 or mcmc_result.acceptance_rate > 0.50:\n", + "# print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", + "# print(\" Consider adjusting step_size\")\n", + "# else:\n", + "# print(\"✓ Acceptance rate looks good!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T13:18:13.636686Z", + "start_time": "2026-01-10T13:18:13.507439Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 1001, 2)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.asarray(mcmc_result.chains).shape" ] }, { @@ -311,13 +445,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 48, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T13:18:54.687398Z", + "start_time": "2026-01-10T13:18:54.067005Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "What to look for in traces:\n", + " ✓ Good mixing (wiggly, no trends)\n", + " ✓ Stationary (mean stays constant)\n", + " ✓ No long excursions\n", + " ✓ Rapid exploration of parameter space\n" + ] + } + ], "source": [ "# Plot traces\n", - "fig, axes = plot_parameter_traces(\n", - " mcmc_result.samples, param_names=[\"g (m/s²)\", \"h (m)\"], true_values=[g_true, h_true]\n", + "fig, axes = parameter_traces(\n", + " np.asarray(mcmc_result.chains[0]),\n", + " param_names=[\"g (m/s²)\", \"h (m)\"],\n", + " true_values=[g_true, h_true],\n", + " show=False,\n", ")\n", "\n", "plt.show()\n", @@ -340,13 +508,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T13:19:35.922334Z", + "start_time": "2026-01-10T13:19:34.620912Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot distributions\n", - "fig, axes = plot_parameter_distributions(\n", - " mcmc_result.samples, param_names=[\"g (m/s²)\", \"h (m)\"], true_values=[g_true, h_true]\n", + "fig, axes = parameter_distributions(\n", + " np.asarray(mcmc_result.chains).reshape(3003, 2),\n", + " param_names=[\"g (m/s²)\", \"h (m)\"],\n", + " true_values=[g_true, h_true],\n", + " show=False,\n", ")\n", "\n", "plt.show()" @@ -361,9 +551,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-10T13:19:42.924922Z", + "start_time": "2026-01-10T13:19:42.798628Z" + } + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'chronopt.sampler.Samples' object has no attribute 'samples'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[50]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Burn-in: discard first 20% of samples\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m burn_in = \u001b[38;5;28mint\u001b[39m(\u001b[32m0.2\u001b[39m * \u001b[38;5;28mlen\u001b[39m(\u001b[43mmcmc_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43msamples\u001b[49m))\n\u001b[32m 3\u001b[39m samples_burned = mcmc_result.samples[burn_in:]\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# Calculate statistics\u001b[39;00m\n", + "\u001b[31mAttributeError\u001b[39m: 'chronopt.sampler.Samples' object has no attribute 'samples'" + ] + } + ], "source": [ "# Burn-in: discard first 20% of samples\n", "burn_in = int(0.2 * len(mcmc_result.samples))\n", diff --git a/docs/tutorials/notebooks/04_model_comparison.ipynb b/docs/tutorials/notebooks/04_model_comparison.ipynb index b199346..6ebe716 100644 --- a/docs/tutorials/notebooks/04_model_comparison.ipynb +++ b/docs/tutorials/notebooks/04_model_comparison.ipynb @@ -85,8 +85,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.11.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb index a1c40ab..fc2f1aa 100644 --- a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb +++ b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb @@ -159,8 +159,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.11.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index d0b06c8..0dc3248 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -38,20 +38,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Import plotting utilities\n", - "import sys\n", - "import time\n", - "\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "sys.path.append(\".\")\n", - "from utils import setup_plotting\n", - "\n", - "setup_plotting()" - ] + "source": "# Import plotting utilities\nimport time\n\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np" }, { "cell_type": "markdown", @@ -732,4 +719,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb index cf53672..ed93750 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -39,25 +39,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:26.262506Z", + "iopub.status.busy": "2026-01-09T21:37:26.262254Z", + "iopub.status.idle": "2026-01-09T21:37:26.748292Z", + "shell.execute_reply": "2026-01-09T21:37:26.747510Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available CPU cores: 8\n" + ] + } + ], "source": [ "import multiprocessing\n", "\n", "# Import plotting utilities\n", - "import sys\n", "import time\n", "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", - "sys.path.append(\".\")\n", - "from utils import setup_plotting\n", - "\n", - "setup_plotting()\n", - "\n", "# Detect available cores\n", "n_cores = multiprocessing.cpu_count()\n", "print(f\"Available CPU cores: {n_cores}\")" @@ -74,9 +83,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:26.750985Z", + "iopub.status.busy": "2026-01-09T21:37:26.750736Z", + "iopub.status.idle": "2026-01-09T21:37:26.815774Z", + "shell.execute_reply": "2026-01-09T21:37:26.814367Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single evaluation time: 0.055s\n", + "Objective value: 6.500\n" + ] + } + ], "source": [ "def expensive_rosenbrock(x, delay_ms=10):\n", " \"\"\"\n", @@ -116,9 +141,39 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:26.820002Z", + "iopub.status.busy": "2026-01-09T21:37:26.819656Z", + "iopub.status.idle": "2026-01-09T21:37:28.259563Z", + "shell.execute_reply": "2026-01-09T21:37:28.258618Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Nelder-Mead (sequential)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "NELDER-MEAD (Sequential)\n", + "============================================================\n", + "Solution: [1.0007473 1.00148331]\n", + "Value: 5.725e-07\n", + "Evaluations: 127\n", + "Time: 1.43s\n", + "Time per eval: 0.011s\n" + ] + } + ], "source": [ "# Build problem\n", "problem = (\n", @@ -163,9 +218,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:28.263471Z", + "iopub.status.busy": "2026-01-09T21:37:28.263125Z", + "iopub.status.idle": "2026-01-09T21:37:31.606800Z", + "shell.execute_reply": "2026-01-09T21:37:31.605399Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running CMA-ES (parallel, default population)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "CMA-ES (Parallel, Default Population)\n", + "============================================================\n", + "Solution: [0.95664499 0.91893292]\n", + "Value: 3.296e-03\n", + "Evaluations: 301\n", + "Time: 3.33s\n", + "Time per eval: 0.011s\n", + "\n", + "Speedup vs Nelder-Mead: 0.43x\n" + ] + } + ], "source": [ "# CMA-ES with automatic population size\n", "print(\"Running CMA-ES (parallel, default population)...\")\n", @@ -190,9 +277,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:31.610740Z", + "iopub.status.busy": "2026-01-09T21:37:31.610400Z", + "iopub.status.idle": "2026-01-09T21:37:38.477716Z", + "shell.execute_reply": "2026-01-09T21:37:38.476133Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running CMA-ES (parallel, large population)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "CMA-ES (Parallel, Large Population)\n", + "============================================================\n", + "Solution: [1.0111185 1.02443279]\n", + "Value: 5.530e-04\n", + "Evaluations: 601\n", + "Time: 6.86s\n", + "Time per eval: 0.011s\n", + "\n", + "Speedup vs Nelder-Mead: 0.21x\n" + ] + } + ], "source": [ "# CMA-ES with larger population for more parallelism\n", "print(\"Running CMA-ES (parallel, large population)...\")\n", @@ -232,9 +351,63 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:38.481560Z", + "iopub.status.busy": "2026-01-09T21:37:38.481180Z", + "iopub.status.idle": "2026-01-09T21:37:49.815581Z", + "shell.execute_reply": "2026-01-09T21:37:49.813834Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing different population sizes...\n", + "(This may take a few minutes)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pop= 4: 2.28s, 201 evals, 0.011s/eval\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pop= 8: 2.30s, 201 evals, 0.011s/eval\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pop=12: 2.20s, 193 evals, 0.011s/eval\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pop=16: 2.21s, 193 evals, 0.011s/eval\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pop=20: 2.34s, 201 evals, 0.012s/eval\n", + "\n", + "Done!\n" + ] + } + ], "source": [ "# Test different population sizes\n", "population_sizes = [4, 8, 12, 16, 20]\n", @@ -278,9 +451,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:49.820794Z", + "iopub.status.busy": "2026-01-09T21:37:49.820388Z", + "iopub.status.idle": "2026-01-09T21:37:50.099719Z", + "shell.execute_reply": "2026-01-09T21:37:50.099009Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 Interpretation:\n", + " - Time per eval < sequential baseline = good parallelism\n", + " - Larger populations amortise overhead across more work\n", + " - Diminishing returns when population > number of cores\n" + ] + } + ], "source": [ "# Visualize scaling\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", @@ -329,9 +531,62 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:37:50.102826Z", + "iopub.status.busy": "2026-01-09T21:37:50.102547Z", + "iopub.status.idle": "2026-01-09T21:38:27.951748Z", + "shell.execute_reply": "2026-01-09T21:38:27.950628Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing different evaluation costs...\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delay= 1ms: Sequential= 0.14s, Parallel= 0.41s, Speedup=0.34x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delay= 5ms: Sequential= 0.61s, Parallel= 1.81s, Speedup=0.34x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delay= 10ms: Sequential= 1.15s, Parallel= 3.43s, Speedup=0.34x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delay= 20ms: Sequential= 2.30s, Parallel= 6.86s, Speedup=0.33x\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Delay= 50ms: Sequential= 5.34s, Parallel= 15.80s, Speedup=0.34x\n", + "\n", + "Done!\n" + ] + } + ], "source": [ "# Test different evaluation costs\n", "delays_ms = [1, 5, 10, 20, 50]\n", @@ -386,9 +641,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:38:27.956140Z", + "iopub.status.busy": "2026-01-09T21:38:27.955811Z", + "iopub.status.idle": "2026-01-09T21:38:28.501363Z", + "shell.execute_reply": "2026-01-09T21:38:28.500676Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 Key Insight:\n", + " Parallelism speedup increases with evaluation cost!\n", + " Expensive evaluations → parallelisation overhead becomes negligible\n" + ] + } + ], "source": [ "# Visualize speedup vs evaluation cost\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", @@ -545,9 +828,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-09T21:38:28.503993Z", + "iopub.status.busy": "2026-01-09T21:38:28.503779Z", + "iopub.status.idle": "2026-01-09T21:38:28.510678Z", + "shell.execute_reply": "2026-01-09T21:38:28.510048Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "PERFORMANCE SUMMARY\n", + "================================================================================\n", + "Method Time (s) Evals Speedup\n", + "--------------------------------------------------------------------------------\n", + "Nelder-Mead (Sequential) 1.43 127 1.00x\n", + "CMA-ES (Default Pop) 3.33 301 0.43x\n", + "CMA-ES (Large Pop) 6.86 601 0.21x\n", + "\n", + "💡 Key Findings:\n", + " - Best speedup: 1.00x\n", + " - System cores: 8\n", + " - Parallel efficiency: 12%\n" + ] + } + ], "source": [ "# Summary comparison\n", "summary_data = [\n", @@ -642,7 +953,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb index 00a1fba..2fba81d 100644 --- a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb +++ b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb @@ -39,20 +39,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Import plotting utilities\n", - "import sys\n", - "\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "sys.path.append(\".\")\n", - "from utils import setup_plotting\n", - "\n", - "setup_plotting()\n", - "np.random.seed(42)" - ] + "source": "# Import plotting utilities\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(42)" }, { "cell_type": "markdown", @@ -769,4 +756,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/utils.py b/docs/tutorials/notebooks/utils.py deleted file mode 100644 index a1faca2..0000000 --- a/docs/tutorials/notebooks/utils.py +++ /dev/null @@ -1,312 +0,0 @@ -"""Utility functions for Chronopt tutorial notebooks.""" - -from collections.abc import Callable - -import matplotlib.pyplot as plt -import numpy as np - - -def setup_plotting(): - """Configure matplotlib for nice-looking plots.""" - plt.style.use("default") - plt.rcParams["figure.figsize"] = (10, 6) - plt.rcParams["font.size"] = 11 - plt.rcParams["axes.labelsize"] = 12 - plt.rcParams["axes.titlesize"] = 14 - plt.rcParams["legend.fontsize"] = 10 - plt.rcParams["xtick.labelsize"] = 10 - plt.rcParams["ytick.labelsize"] = 10 - - -def plot_contour_2d( - func: Callable, - xlim: tuple = (-2, 2), - ylim: tuple = (-1, 3), - levels: np.ndarray | None = None, - optimum: tuple | None = None, - found: tuple | None = None, - title: str = "Optimisation Landscape", -): - """ - Plot 2D function contours with optional optimum markers. - - Parameters - ---------- - func : callable - Function that takes [x, y] and returns scalar value - xlim : tuple - x-axis limits (min, max) - ylim : tuple - y-axis limits (min, max) - levels : array-like, optional - Contour levels to plot - optimum : tuple, optional - True optimum location (x, y) - found : tuple, optional - Found optimum location (x, y) - title : str - Plot title - """ - setup_plotting() - - x = np.linspace(xlim[0], xlim[1], 200) - y = np.linspace(ylim[0], ylim[1], 200) - X, Y = np.meshgrid(x, y) - Z = np.zeros_like(X) - - for i in range(X.shape[0]): - for j in range(X.shape[1]): - Z[i, j] = func([X[i, j], Y[i, j]])[0] - - fig, ax = plt.subplots(figsize=(10, 8)) - - if levels is None: - levels = np.logspace(-1, 3.5, 20) - - cs = ax.contour(X, Y, Z, levels=levels, cmap="viridis") - ax.clabel(cs, inline=True, fontsize=8) - - if optimum: - ax.plot( - optimum[0], - optimum[1], - "r*", - markersize=20, - label="Global minimum", - zorder=5, - ) - - if found: - ax.plot( - found[0], found[1], "go", markersize=10, label="Found optimum", zorder=5 - ) - - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_title(title) - ax.grid(True, alpha=0.3) - ax.legend() - - return fig, ax - - -def plot_ode_fit( - t_data: np.ndarray, - y_data: np.ndarray, - t_pred: np.ndarray | None = None, - y_pred: np.ndarray | None = None, - title: str = "ODE Fit", - xlabel: str = "Time", - ylabel: str = "State Variable", -): - """ - Plot ODE data and fitted model. - - Parameters - ---------- - t_data : array - Time points for observed data - y_data : array - Observed data values - t_pred : array, optional - Time points for predictions - y_pred : array, optional - Predicted values - title : str - Plot title - xlabel : str - x-axis label - ylabel : str - y-axis label - """ - setup_plotting() - - fig, ax = plt.subplots() - - ax.plot(t_data, y_data, "o", label="Observed data", alpha=0.6, markersize=8) - - if t_pred is not None and y_pred is not None: - ax.plot(t_pred, y_pred, "-", label="Fitted model", linewidth=2) - - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title) - ax.legend() - ax.grid(True, alpha=0.3) - - return fig, ax - - -def plot_convergence( - values: np.ndarray, title: str = "Optimisation Convergence", log_scale: bool = True -): - """ - Plot optimisation convergence history. - - Parameters - ---------- - values : array - Objective function values over iterations - title : str - Plot title - log_scale : bool - Use log scale for y-axis - """ - setup_plotting() - - fig, ax = plt.subplots() - - ax.plot(values, linewidth=2) - ax.set_xlabel("Iteration") - ax.set_ylabel("Objective Value") - ax.set_title(title) - ax.grid(True, alpha=0.3) - - if log_scale: - ax.set_yscale("log") - - return fig, ax - - -def plot_parameter_traces( - samples: np.ndarray, param_names: list, true_values: list | None = None -): - """ - Plot MCMC parameter traces. - - Parameters - ---------- - samples : array - MCMC samples (n_samples, n_params) - param_names : list - Parameter names - true_values : list, optional - True parameter values to mark - """ - setup_plotting() - - n_params = samples.shape[1] - fig, axes = plt.subplots(n_params, 1, figsize=(10, 3 * n_params)) - - if n_params == 1: - axes = [axes] - - for i, (ax, name) in enumerate(zip(axes, param_names)): - ax.plot(samples[:, i], alpha=0.7, linewidth=0.5) - ax.set_ylabel(name) - ax.grid(True, alpha=0.3) - - if true_values and i < len(true_values): - ax.axhline( - true_values[i], - color="r", - linestyle="--", - label=f"True value: {true_values[i]:.3f}", - ) - ax.legend() - - if i == n_params - 1: - ax.set_xlabel("Sample") - - fig.suptitle("MCMC Parameter Traces") - plt.tight_layout() - - return fig, axes - - -def plot_parameter_distributions( - samples: np.ndarray, param_names: list, true_values: list | None = None -): - """ - Plot posterior parameter distributions. - - Parameters - ---------- - samples : array - MCMC samples (n_samples, n_params) - param_names : list - Parameter names - true_values : list, optional - True parameter values to mark - """ - setup_plotting() - - n_params = samples.shape[1] - fig, axes = plt.subplots(1, n_params, figsize=(4 * n_params, 4)) - - if n_params == 1: - axes = [axes] - - for i, (ax, name) in enumerate(zip(axes, param_names)): - ax.hist(samples[:, i], bins=30, density=True, alpha=0.7, edgecolor="black") - ax.set_xlabel(name) - ax.set_ylabel("Density") - ax.grid(True, alpha=0.3) - - if true_values and i < len(true_values): - ax.axvline( - true_values[i], - color="r", - linestyle="--", - linewidth=2, - label=f"True: {true_values[i]:.3f}", - ) - ax.legend() - - # Add mean and std - mean = np.mean(samples[:, i]) - std = np.std(samples[:, i]) - ax.axvline( - mean, - color="blue", - linestyle=":", - linewidth=2, - alpha=0.7, - label=f"Mean: {mean:.3f}", - ) - ax.set_title(f"{name}\n(σ = {std:.3f})") - - fig.suptitle("Parameter Posterior Distributions") - plt.tight_layout() - - return fig, axes - - -def compare_models( - t_data: np.ndarray, - y_data: np.ndarray, - predictions: dict, - title: str = "Model Comparison", -): - """ - Plot multiple model predictions against data. - - Parameters - ---------- - t_data : array - Time points for observed data - y_data : array - Observed data - predictions : dict - Dictionary of {model_name: (t_pred, y_pred)} - title : str - Plot title - """ - setup_plotting() - - fig, ax = plt.subplots() - - ax.plot( - t_data, y_data, "ko", label="Observed data", alpha=0.6, markersize=8, zorder=5 - ) - - for i, (name, (t_pred, y_pred)) in enumerate(predictions.items()): - ax.plot(t_pred, y_pred, "-", label=name, linewidth=2, alpha=0.8) - - ax.set_xlabel("Time") - ax.set_ylabel("State Variable") - ax.set_title(title) - ax.legend() - ax.grid(True, alpha=0.3) - - return fig, ax diff --git a/mkdocs.yml b/mkdocs.yml index 7b2f01d..1f51b68 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -157,7 +157,7 @@ nav: - 8. Advanced Cost Functions: tutorials/notebooks/08_advanced_cost_functions.ipynb - User Guides: - guides/index.md - - Choosing an Optimiser: guides/choosing-optimizer.md + - Choosing an Optimiser: guides/choosing-optimiser.md - Tuning Optimisers: guides/tuning-optimizers.md - Choosing a Sampler: guides/choosing-sampler.md - Cost Metrics: guides/cost-metrics.md diff --git a/python/src/chronopt/plotting/__init__.py b/python/src/chronopt/plotting/__init__.py index 4d450b1..2c73e1a 100644 --- a/python/src/chronopt/plotting/__init__.py +++ b/python/src/chronopt/plotting/__init__.py @@ -1,9 +1,9 @@ """Plotting utilities for Chronopt. -This module currently provides a convenience helper to visualise two-dimensional -objective functions via contour plots. The implementation only depends on -``numpy`` at import time and lazily imports ``matplotlib`` when required so that -plotting remains an optional dependency of the project. +This module provides convenience helpers for visualising optimisation and sampling +results. The implementation only depends on ``numpy`` at import time and lazily +imports ``matplotlib`` when required so that plotting remains an optional +dependency of the project. """ from __future__ import annotations @@ -16,12 +16,36 @@ if TYPE_CHECKING: # pragma: no cover - type checking only from chronopt import Problem -__all__ = ["contour"] +__all__ = [ + "contour", + "contour_2d", + "ode_fit", + "convergence", + "parameter_traces", + "parameter_distributions", + "compare_models", +] ObjectiveLike = Union[Callable[[Sequence[float]], float], Callable[[np.ndarray], float]] Bounds = tuple[float, float] +def _setup_plotting() -> None: + """Configure matplotlib for nice-looking plots.""" + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError: + return + + plt.rcParams.setdefault("figure.figsize", (10, 6)) + plt.rcParams.setdefault("font.size", 11) + plt.rcParams.setdefault("axes.labelsize", 12) + plt.rcParams.setdefault("axes.titlesize", 14) + plt.rcParams.setdefault("legend.fontsize", 10) + plt.rcParams.setdefault("xtick.labelsize", 10) + plt.rcParams.setdefault("ytick.labelsize", 10) + + def _evaluate(objective: ObjectiveLike | Problem, point: np.ndarray) -> float: if hasattr(objective, "evaluate"): return float(objective.evaluate(point.tolist())) @@ -118,3 +142,486 @@ def contour( plt.show() return contour_set + + +def contour_2d( + func: Callable, + xlim: tuple[float, float] = (-2, 2), + ylim: tuple[float, float] = (-1, 3), + *, + levels: np.ndarray | None = None, + optimum: tuple[float, float] | None = None, + found: tuple[float, float] | None = None, + title: str = "Optimisation Landscape", + show: bool = True, +) -> tuple[Any, Any]: + """Plot 2D function contours with optional optimum markers. + + This is a convenience wrapper for notebook usage that provides a simplified + interface for plotting objective functions with marked optima. + + Parameters + ---------- + func: + Function that takes [x, y] and returns scalar value. + xlim: + x-axis limits (min, max). + ylim: + y-axis limits (min, max). + levels: + Contour levels to plot (optional). + optimum: + True optimum location (x, y) to mark (optional). + found: + Found optimum location (x, y) to mark (optional). + title: + Plot title. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + x = np.linspace(xlim[0], xlim[1], 200) + y = np.linspace(ylim[0], ylim[1], 200) + X, Y = np.meshgrid(x, y) + Z = np.zeros_like(X) + + for i in range(X.shape[0]): + for j in range(X.shape[1]): + result = func([X[i, j], Y[i, j]]) + # Handle both scalar and array-like returns + Z[i, j] = result[0] if hasattr(result, "__getitem__") else result + + fig, ax = plt.subplots(figsize=(10, 8)) + + if levels is None: + levels = np.logspace(-1, 3.5, 20) + + cs = ax.contour(X, Y, Z, levels=levels, cmap="viridis") + ax.clabel(cs, inline=True, fontsize=8) + + if optimum: + ax.plot( + optimum[0], + optimum[1], + "r*", + markersize=20, + label="Global minimum", + zorder=5, + ) + + if found: + ax.plot( + found[0], found[1], "go", markersize=10, label="Found optimum", zorder=5 + ) + + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title(title) + ax.grid(True, alpha=0.3) + ax.legend() + + if show: + plt.show() + + return fig, ax + + +def ode_fit( + t_data: np.ndarray, + y_data: np.ndarray, + t_pred: np.ndarray | None = None, + y_pred: np.ndarray | None = None, + *, + title: str = "ODE Fit", + xlabel: str = "Time", + ylabel: str = "State Variable", + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot ODE data and fitted model. + + Parameters + ---------- + t_data: + Time points for observed data. + y_data: + Observed data values. + t_pred: + Time points for predictions (optional). + y_pred: + Predicted values (optional). + title: + Plot title. + xlabel: + x-axis label. + ylabel: + y-axis label. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot(t_data, y_data, "o", label="Observed data", alpha=0.6, markersize=8) + + if t_pred is not None and y_pred is not None: + ax.plot(t_pred, y_pred, "-", label="Fitted model", linewidth=2) + + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + if show: + plt.show() + + return fig, ax + + +def convergence( + values: np.ndarray, + *, + title: str = "Optimisation Convergence", + log_scale: bool = True, + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot optimisation convergence history. + + Parameters + ---------- + values: + Objective function values over iterations. + title: + Plot title. + log_scale: + Use log scale for y-axis. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot(values, linewidth=2) + ax.set_xlabel("Iteration") + ax.set_ylabel("Objective Value") + ax.set_title(title) + ax.grid(True, alpha=0.3) + + if log_scale: + ax.set_yscale("log") + + if show: + plt.show() + + return fig, ax + + +def parameter_traces( + samples: np.ndarray, + param_names: Sequence[str], + true_values: Sequence[float] | None = None, + *, + show: bool = True, +) -> tuple[Any, Any]: + """Plot MCMC parameter traces. + + Parameters + ---------- + samples: + MCMC samples with shape (n_samples, n_params). + param_names: + Parameter names. + true_values: + True parameter values to mark (optional). + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, list[Axes]] + The matplotlib figure and list of axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + ValueError + If the number of parameter names doesn't match sample dimensions. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + n_params = samples.shape[1] + if len(param_names) != n_params: + raise ValueError( + f"Number of parameter names ({len(param_names)}) must match " + f"sample dimensions ({n_params})" + ) + + fig, axes = plt.subplots(n_params, 1, figsize=(10, 3 * n_params)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.plot(samples[:, i], alpha=0.7, linewidth=0.5) + ax.set_ylabel(name) + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axhline( + true_values[i], + color="r", + linestyle="--", + label=f"True value: {true_values[i]:.3f}", + ) + ax.legend() + + if i == n_params - 1: + ax.set_xlabel("Sample") + + fig.suptitle("MCMC Parameter Traces") + plt.tight_layout() + + if show: + plt.show() + + return fig, axes + + +def parameter_distributions( + samples: np.ndarray, + param_names: Sequence[str], + true_values: Sequence[float] | None = None, + *, + bins: int = 30, + show: bool = True, +) -> tuple[Any, Any]: + """Plot posterior parameter distributions. + + Parameters + ---------- + samples: + MCMC samples with shape (n_samples, n_params). + param_names: + Parameter names. + true_values: + True parameter values to mark (optional). + bins: + Number of histogram bins. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, list[Axes]] + The matplotlib figure and list of axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + ValueError + If the number of parameter names doesn't match sample dimensions. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + n_params = samples.shape[1] + if len(param_names) != n_params: + raise ValueError( + f"Number of parameter names ({len(param_names)}) must match " + f"sample dimensions ({n_params})" + ) + + fig, axes = plt.subplots(1, n_params, figsize=(4 * n_params, 4)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.hist(samples[:, i], bins=bins, density=True, alpha=0.7, edgecolor="black") + ax.set_xlabel(name) + ax.set_ylabel("Density") + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axvline( + true_values[i], + color="r", + linestyle="--", + linewidth=2, + label=f"True: {true_values[i]:.3f}", + ) + + # Add mean and std + mean = np.mean(samples[:, i]) + std = np.std(samples[:, i]) + ax.axvline( + mean, + color="blue", + linestyle=":", + linewidth=2, + alpha=0.7, + label=f"Mean: {mean:.3f}", + ) + ax.set_title(f"{name}\n(σ = {std:.3f})") + ax.legend() + + fig.suptitle("Parameter Posterior Distributions") + plt.tight_layout() + + if show: + plt.show() + + return fig, axes + + +def compare_models( + t_data: np.ndarray, + y_data: np.ndarray, + predictions: dict[str, tuple[np.ndarray, np.ndarray]], + *, + title: str = "Model Comparison", + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot multiple model predictions against data. + + Parameters + ---------- + t_data: + Time points for observed data. + y_data: + Observed data. + predictions: + Dictionary mapping model names to (t_pred, y_pred) tuples. + title: + Plot title. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot( + t_data, y_data, "ko", label="Observed data", alpha=0.6, markersize=8, zorder=5 + ) + + for name, (t_pred, y_pred) in predictions.items(): + ax.plot(t_pred, y_pred, "-", label=name, linewidth=2, alpha=0.8) + + ax.set_xlabel("Time") + ax.set_ylabel("State Variable") + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + if show: + plt.show() + + return fig, ax diff --git a/python/src/lib.rs b/python/src/lib.rs index 05d9775..828d30c 100644 --- a/python/src/lib.rs +++ b/python/src/lib.rs @@ -93,6 +93,15 @@ impl DynProblem { } } + fn initial_values(&self) -> Vec { + match self { + Self::Scalar(p) => p.initial_values(), + Self::ScalarWithGradient(p) => p.initial_values(), + Self::Vector(p) => p.initial_values(), + Self::Diffsol(p) => p.initial_values(), + } + } + fn optimise( &self, initial: Option>, @@ -312,6 +321,10 @@ impl PyProblem { self.parameter_specs.clone() } + fn initial_values(&self) -> Vec { + self.inner.initial_values() + } + /// Return the default parameter vector implied by the builder. #[pyo3(name = "default_parameters")] fn default_parameters_py(&self) -> Vec { diff --git a/rust/src/problem/mod.rs b/rust/src/problem/mod.rs index 7f42eae..2719077 100644 --- a/rust/src/problem/mod.rs +++ b/rust/src/problem/mod.rs @@ -139,6 +139,13 @@ impl ParameterSet { .collect(), } } + + pub fn initial_values(&self) -> Vec { + if self.0.is_empty() { + return vec![]; + } + self.0.iter().map(|spec| spec.initial_value).collect() + } } #[derive(Debug, Clone, PartialEq)] @@ -363,6 +370,10 @@ impl Problem { self.parameters.bounds() } + pub fn initial_values(&self) -> Vec { + self.parameters.initial_values() + } + /// A convenience function for optimisation of the problem pub fn optimise( &self, From a085bc499ba4f270724727d6231ea786aa78c6d4 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 11 Jan 2026 10:16:25 +0000 Subject: [PATCH 03/18] docs: fixes for tutorial notebooks --- docs/javascripts/mathjax.js | 8 +- .../notebooks/01_optimization_basics.ipynb | 124 ++--- .../notebooks/02_ode_fitting_diffsol.ipynb | 116 +++-- .../notebooks/03_parameter_uncertainty.ipynb | 272 ++++++----- .../notebooks/05_advanced_predator_prey.ipynb | 4 +- .../06_custom_solver_integration.ipynb | 78 +-- .../notebooks/07_parallel_optimization.ipynb | 124 ++--- .../08_advanced_cost_functions.ipynb | 462 ++++++++++++++++-- 8 files changed, 787 insertions(+), 401 deletions(-) diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js index 06dbf38..084e0b8 100644 --- a/docs/javascripts/mathjax.js +++ b/docs/javascripts/mathjax.js @@ -1,13 +1,9 @@ window.MathJax = { tex: { - inlineMath: [["\\(", "\\)"]], - displayMath: [["\\[", "\\]"]], + inlineMath: [["\\(", "\\)"], ["$", "$"]], + displayMath: [["\\[", "\\]"], ["$$", "$$"]], processEscapes: true, processEnvironments: true - }, - options: { - ignoreHtmlClass: ".*|", - processHtmlClass: "arithmatex" } }; diff --git a/docs/tutorials/notebooks/01_optimization_basics.ipynb b/docs/tutorials/notebooks/01_optimization_basics.ipynb index 3ea4536..d916426 100644 --- a/docs/tutorials/notebooks/01_optimization_basics.ipynb +++ b/docs/tutorials/notebooks/01_optimization_basics.ipynb @@ -30,15 +30,11 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:05:31.222402Z", - "start_time": "2026-01-10T10:05:31.062542Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:11.314923Z", - "iopub.status.busy": "2026-01-09T21:37:11.314362Z", - "iopub.status.idle": "2026-01-09T21:37:13.871464Z", - "shell.execute_reply": "2026-01-09T21:37:13.870448Z" + "iopub.execute_input": "2026-01-10T22:21:26.832609Z", + "iopub.status.busy": "2026-01-10T22:21:26.832243Z", + "iopub.status.idle": "2026-01-10T22:21:27.524510Z", + "shell.execute_reply": "2026-01-10T22:21:27.523489Z" } }, "outputs": [], @@ -65,15 +61,11 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:05:31.291766Z", - "start_time": "2026-01-10T10:05:31.226817Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:13.875012Z", - "iopub.status.busy": "2026-01-09T21:37:13.874668Z", - "iopub.status.idle": "2026-01-09T21:37:13.879298Z", - "shell.execute_reply": "2026-01-09T21:37:13.878617Z" + "iopub.execute_input": "2026-01-10T22:21:27.527692Z", + "iopub.status.busy": "2026-01-10T22:21:27.527458Z", + "iopub.status.idle": "2026-01-10T22:21:27.531149Z", + "shell.execute_reply": "2026-01-10T22:21:27.530296Z" } }, "outputs": [], @@ -97,15 +89,11 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:05:31.345163Z", - "start_time": "2026-01-10T10:05:31.294438Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:13.882378Z", - "iopub.status.busy": "2026-01-09T21:37:13.882093Z", - "iopub.status.idle": "2026-01-09T21:37:13.886438Z", - "shell.execute_reply": "2026-01-09T21:37:13.885817Z" + "iopub.execute_input": "2026-01-10T22:21:27.533691Z", + "iopub.status.busy": "2026-01-10T22:21:27.533430Z", + "iopub.status.idle": "2026-01-10T22:21:27.538107Z", + "shell.execute_reply": "2026-01-10T22:21:27.537575Z" } }, "outputs": [ @@ -121,7 +109,7 @@ "source": [ "builder = (\n", " chron.ScalarBuilder()\n", - " .with_callable(rosenbrock)\n", + " .with_objective(rosenbrock)\n", " .with_parameter(\"x\", 10.0) # Initial guess\n", " .with_parameter(\"y\", 10.0) # Initial guess\n", ")\n", @@ -145,15 +133,11 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:05:31.501585Z", - "start_time": "2026-01-10T10:05:31.370206Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:13.940616Z", - "iopub.status.busy": "2026-01-09T21:37:13.940316Z", - "iopub.status.idle": "2026-01-09T21:37:13.949194Z", - "shell.execute_reply": "2026-01-09T21:37:13.948410Z" + "iopub.execute_input": "2026-01-10T22:21:27.574708Z", + "iopub.status.busy": "2026-01-10T22:21:27.574501Z", + "iopub.status.idle": "2026-01-10T22:21:27.583798Z", + "shell.execute_reply": "2026-01-10T22:21:27.583240Z" } }, "outputs": [ @@ -201,15 +185,11 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:05:33.129997Z", - "start_time": "2026-01-10T10:05:31.550871Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:13.952288Z", - "iopub.status.busy": "2026-01-09T21:37:13.952008Z", - "iopub.status.idle": "2026-01-09T21:37:14.594434Z", - "shell.execute_reply": "2026-01-09T21:37:14.593679Z" + "iopub.execute_input": "2026-01-10T22:21:27.586141Z", + "iopub.status.busy": "2026-01-10T22:21:27.585957Z", + "iopub.status.idle": "2026-01-10T22:21:27.968383Z", + "shell.execute_reply": "2026-01-10T22:21:27.967837Z" } }, "outputs": [ @@ -220,9 +200,6 @@ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" }, @@ -263,17 +240,13 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:16:32.406534Z", - "start_time": "2026-01-10T10:16:32.325837Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:14.602032Z", - "iopub.status.busy": "2026-01-09T21:37:14.601684Z", - "iopub.status.idle": "2026-01-09T21:37:14.628019Z", - "shell.execute_reply": "2026-01-09T21:37:14.627121Z" + "iopub.execute_input": "2026-01-10T22:21:27.973637Z", + "iopub.status.busy": "2026-01-10T22:21:27.973424Z", + "iopub.status.idle": "2026-01-10T22:21:28.085544Z", + "shell.execute_reply": "2026-01-10T22:21:28.084924Z" } }, "outputs": [ @@ -283,17 +256,17 @@ "text": [ "\n", "======================================================================\n", - "OPTIMIZER COMPARISON\n", + "OPTIMISER COMPARISON\n", "======================================================================\n", - "Optimizer Success Final Value Iterations Evaluations\n", + "Optimiser Success Final Value Iterations Evaluations\n", "----------------------------------------------------------------------\n", "Nelder-Mead True 2.866e-07 130 290\n", - "CMA-ES True 1.304e-09 78 469\n", + "CMA-ES True 3.681e-08 95 571\n", "Adam False 6.927e-09 2000 2001\n", "\n", "Final parameters:\n", "Nelder-Mead x = [1.0001253 1.00019857]\n", - "CMA-ES x = [0.99997614 0.999955 ]\n", + "CMA-ES x = [1.00017279 1.00035395]\n", "Adam x = [1.00008317 1.00016666]\n" ] } @@ -338,34 +311,29 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Visualise All Results" + "source": [ + "## Visualise All Results" + ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 7, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:16:36.906135Z", - "start_time": "2026-01-10T10:16:36.471917Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:14.631493Z", - "iopub.status.busy": "2026-01-09T21:37:14.631193Z", - "iopub.status.idle": "2026-01-09T21:37:15.176960Z", - "shell.execute_reply": "2026-01-09T21:37:15.176063Z" + "iopub.execute_input": "2026-01-10T22:21:28.088177Z", + "iopub.status.busy": "2026-01-10T22:21:28.087996Z", + "iopub.status.idle": "2026-01-10T22:21:28.441449Z", + "shell.execute_reply": "2026-01-10T22:21:28.440265Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -440,17 +408,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 8, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T10:16:46.824367Z", - "start_time": "2026-01-10T10:16:46.665402Z" - }, "execution": { - "iopub.execute_input": "2026-01-09T21:37:15.185691Z", - "iopub.status.busy": "2026-01-09T21:37:15.185395Z", - "iopub.status.idle": "2026-01-09T21:37:15.195011Z", - "shell.execute_reply": "2026-01-09T21:37:15.194279Z" + "iopub.execute_input": "2026-01-10T22:21:28.447521Z", + "iopub.status.busy": "2026-01-10T22:21:28.447189Z", + "iopub.status.idle": "2026-01-10T22:21:28.461367Z", + "shell.execute_reply": "2026-01-10T22:21:28.460670Z" } }, "outputs": [ diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb index c12eb17..fe9a522 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -35,9 +35,11 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:50.671292Z", - "start_time": "2026-01-10T11:10:50.561207Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:29.582370Z", + "iopub.status.busy": "2026-01-10T22:21:29.582083Z", + "iopub.status.idle": "2026-01-10T22:21:30.043052Z", + "shell.execute_reply": "2026-01-10T22:21:30.042273Z" } }, "outputs": [], @@ -62,9 +64,11 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:50.943354Z", - "start_time": "2026-01-10T11:10:50.699179Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.045734Z", + "iopub.status.busy": "2026-01-10T22:21:30.045475Z", + "iopub.status.idle": "2026-01-10T22:21:30.050602Z", + "shell.execute_reply": "2026-01-10T22:21:30.049845Z" } }, "outputs": [ @@ -120,9 +124,11 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:51.051629Z", - "start_time": "2026-01-10T11:10:50.964816Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.052995Z", + "iopub.status.busy": "2026-01-10T22:21:30.052808Z", + "iopub.status.idle": "2026-01-10T22:21:30.079769Z", + "shell.execute_reply": "2026-01-10T22:21:30.079161Z" } }, "outputs": [ @@ -171,15 +177,19 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Visualise the Data" + "source": [ + "## Visualise the Data" + ] }, { "cell_type": "code", "execution_count": 4, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:51.273527Z", - "start_time": "2026-01-10T11:10:51.052619Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.082238Z", + "iopub.status.busy": "2026-01-10T22:21:30.081984Z", + "iopub.status.idle": "2026-01-10T22:21:30.230233Z", + "shell.execute_reply": "2026-01-10T22:21:30.229694Z" } }, "outputs": [ @@ -190,9 +200,6 @@ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -227,9 +234,11 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:51.334523Z", - "start_time": "2026-01-10T11:10:51.274634Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.232622Z", + "iopub.status.busy": "2026-01-10T22:21:30.232409Z", + "iopub.status.idle": "2026-01-10T22:21:30.236659Z", + "shell.execute_reply": "2026-01-10T22:21:30.236189Z" } }, "outputs": [ @@ -276,9 +285,11 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:52.883046Z", - "start_time": "2026-01-10T11:10:51.336076Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.238643Z", + "iopub.status.busy": "2026-01-10T22:21:30.238402Z", + "iopub.status.idle": "2026-01-10T22:21:34.759904Z", + "shell.execute_reply": "2026-01-10T22:21:34.759178Z" } }, "outputs": [ @@ -298,9 +309,9 @@ "\n", "Optimization details:\n", " Final cost: 8.220e-03\n", - " Iterations: 121\n", - " Function evaluations: 727\n", - " Time: 517.299 milliseconds\n", + " Iterations: 545\n", + " Function evaluations: 3271\n", + " Time: 514.311 milliseconds\n", " Message: Function tolerance met\n", "\n", "Parameter errors:\n", @@ -351,22 +362,21 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:10:53.404042Z", - "start_time": "2026-01-10T11:10:52.987366Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:34.764024Z", + "iopub.status.busy": "2026-01-10T22:21:34.763803Z", + "iopub.status.idle": "2026-01-10T22:21:35.052762Z", + "shell.execute_reply": "2026-01-10T22:21:35.052223Z" } }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAPeCAYAAAARWnkoAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Qd4VGXWB/D/9Jlk0gsJSahBekcQsCCgiBXRtawodl3Xtq6uumt3V9zVtRfUz+7axQpioalIk957SyC9l+n3e84bZkwnlJCb5P97njHMnXvv3Jl3kJw573uOQdM0DURERERERER01BmP/imJiIiIiIiIiEE3ERERERERUTNippuIiIiIiIiomTDoJiIiIiIiImomDLqJiIiIiIiImgmDbiIiIiIiIqJmwqCbiIiIiIiIqJkw6CYiIiIiIiJqJgy6iYiIiIiIiJoJg24iIqJGjBkzRt2Oli5duuDKK6/ke37AW2+9BYPBgN9++61VvSe7du1S1y3Xf6jmz5+vjpWfLXUNRER07DDoJiKiVqE1BWe//vorHnroIRQVFTX7cwUCAbzzzjs47bTTEB8fD4vFgsTERJx++ul49dVX4Xa7oQcvvfRSswSH8j7L58JoNGLv3r11Hi8pKYHD4VD73HzzzWiNn/n6bvfcc0+9x8yaNUu9J0REpB/mlr4AIiIiPfv+++8PK+h++OGHVUY7Ojq6xmObN29WAeLRUFlZifPPPx/fffcdRo0ahTvvvBMdOnRAQUEBFixYgJtuuglLlizB66+/Dj0E3fKlQHNl+W02Gz744AP87W9/q7F9xowZaO0eeeQRdO3atca2fv36oXPnzuozIF+0VA+6X3zxRQbeREQ6wqCbiIioEVar9agHh0fLX/7yFxVwP/PMM7jttttqPPbXv/4VW7duxQ8//NDoOXw+n8qWH+3XeaydeeaZ9Qbd77//Ps466yx89tlnaK0mTpyIYcOG1fuY3W4/5tdDRESHhtPLiYioTVm5cqUKUiIjI+F0OjFu3DgsXry4zn5r1qzBKaecoqYep6am4p///CfefPNNNXVX1so2tqb7+eefR9++fREWFoaYmBgVEElwJ2Rq71133aX+LNnJ4HTg4DnrW9Mt09AlgJbHJCiX67niiiuQl5fX4OuUqdT/93//hzPOOKNOwB3Uo0cPle2uvQb4ySefVIF69+7d1fNt2LBBPT537lycdNJJCA8PVxn68847Dxs3bqzxnsnxX331VWjb8uXL1bYhQ4bUeG4ZgxEjRoRe8/r161X2Pfh+1H5PZRr8HXfcgYSEBPX8ksHPzc1FU/3xj3/EqlWrsGnTptC2rKws9Zrksfrk5OTgmmuuUbMDJHgdOHAg3n777Tr7yfjImEVFRan3ZerUqQ0uHZDnv/DCCxEbG6vOKZ+N6u/X0VR7Tbdco2S5RfWp6ERE1LKY6SYiojZDAjsJGiXgloynTLt95ZVXVIAnAV8wCMzMzMSpp56qApJ7771XBXkSwDYlC/3aa6/h1ltvVYGVBLsul0sFozKNW4K7yZMnY8uWLSrr+vTTT6sp1UKCyfqUlZWpa5bg9uqrr1bBqwTbEqhlZGSEjq/t22+/hd/vx5QpUw75fZIvF+S6r7/+evWaJUD88ccfVaDcrVs39cWBTFuWLxdGjx6NFStWqMBZpjRL0PnTTz/h3HPPVef6+eef1XT51atXq/XT8t5L5lym2Mv5hQT4t9xyi/oS5B//+IfaJoFudfK4fIHx4IMPqmBSjpE12B999FGTXtPJJ5+svqyQLz9kOraQY+U5JdNdm7w++Vxs27ZNPY98QfLJJ5+owFUC6uAXGZqmqS8ffvnlF9x4443o3bs3Pv/8cxV41/f5k/crJSVFrbmWz9XHH3+MSZMmqUy7fJFwOIqLi+t8AVPf5+KGG27Avn371OyGd99997Cei4iImoFGRETUCrz55pua/LO1bNmyBveZNGmSZrVate3bt4e27du3T4uIiNBOPvnk0LZbbrlFMxgM2sqVK0Pb8vPztdjYWPUcO3fuDG0/5ZRT1C3ovPPO0/r27dvotT7xxBN1zhPUuXNnberUqaH7DzzwgNp3xowZdfYNBAINPsdf/vIXddyqVatqbHe73Vpubm7olpeXF3pMrkeOiYyM1HJycmocN2jQIC0xMVG9D0GrV6/WjEajdsUVV4S2nXXWWdrw4cND9ydPnqxuJpNJ+/bbb9W2FStWqOf58ssvQ/vJe1b9faw9ruPHj6/xeuX1yTmLioq0xjz44IPqeHmtd955p5aenh567Pjjj9euuuoq9WfZ589//nPosWeeeUZte++990LbPB6PNnLkSM3pdGolJSVq2xdffKH2+89//hPaz+fzaSeddJLaLtcfNG7cOK1///6ay+UKbZPXNGrUKK1Hjx6hbfPmzVPHys/GBN+b+m7Vx7P6Nchr5K93RET6wunlRETUJkjWV4qeSVZRsrVBycnJKgMtmUrJxIrZs2dj5MiRGDRoUGg/yfZedtllB30eyfRKBnrZsmVH5bolAyrTmuvLgjY2NTj4WiSTW50U0pKsevAmxbZqu+CCC2pk3vfv36+mZkuWV96HoAEDBqiq6HLOIMnKS+a7vLxc3Zf3VdZTy3spWW8hP+XaTzzxxCa/D5IVr/565XlkTHfv3t3kc8g4S+Zaxib4s6Gp5fKakpKScOmll4a2ycwImcUgsw9kZkRwP7PZjD/96U+h/Uwmk8rMVyfF62Qq+0UXXYTS0lKVmZZbfn4+JkyYoNbXywyLwyFTxiV7Xf1GREStB6eXExFRmyDrfysqKtCzZ886j8mUYJnyLOugZS22BHISdNeWnp5+0Oe5++671VTs4cOHq/2lNZcEdjKt+HBs375dBcGHKiIiQv2UALE6uY5gUPbEE09g4cKFdY6tXQk7GNg29N5JsTYJsmW6tATDUnxt0aJFSEtLU+uiZZtMra4edPfp06dGAH8wnTp1qnFfppqLwsLCJp9j8ODB6NWrl5piLl+OSFA9duzYeveV1yxr3mtXkpfXG3w8+FO+uKn95Ubt90qCfEmo33///epWH3mvZOr5oZLPWkOF1IiISP8YdBMRER0CCcqk7dc333yjMuaSqZZ2WA888IBqE3asSHAp1q1bpzLlQZLBHj9+vPrze++9V++xUjzucEnwJwXCZF23BMrSE/y4445Tgbe8D1IQTYLuQ12/LNnj+lTNDG86+QLk5ZdfVl9KXHzxxUetPdvByJc6Qtq2SWa7Pk35UoeIiNoeBt1ERNQmSLAp1cQlIK6vorQEX5KZFTLlWjKTtdW3rT6S8ZWATm4ej0cVT/vXv/6lirJJQHooFaOlgrgEzodKip5JoPq///2vSdPiGxOcgt7QeydFu+Q1C2ktJplXCawl6JZgW8hPCbjlerKzs1Vhs+qOVRVtCbrlCxCZMt9YMTF5zVIAT4Ll6oF5sPp58D2Rn3PmzFEzCqpnu2u/V8ElDTJFPfilR0tgtXIiIv3hmm4iImoTJACVqd5ffvlljZZfEgDKdGNZXyyVtYVkImV6tKxjrr4mVwLGg5E1utVJECpTqSUj6/V61bZggNpQW6nqZGq5VP6WitiHkuWVgFeqnUsV8xdeeOGIssQyfVrWZEu7rOrXLF8GyDp5WbNdnQTYUq193rx5oaBbAnOZBfDvf/87tE918p405f04UvIlhlQ+nzZtmvpyoCHymqSlWPXq6DJtXiq2S3At7eSC+8l2yZ4HyVpz2a86yfhLNXSpli8Bf22H0v7sSBzKZ4+IiI4NZrqJiKhVeeONN9S07tqkxZP02pb1zBJgS39qKYAlQZBkYP/zn/+E9pV2YjL1WoqESUGsYMswCWQl+G4sWyiBvawVlrXT0vZKWn1J0CttqYLrrIcOHap+SnusSy65RGU/zznnnFBAVJ309P7000/xhz/8QQXRcqxcg7QMmz59eo2p47VJcLlz5071Gj788EP1HBL8SQEvWcv99ddf17tOuz6y/luy57LWXXpXB1uGSW9qaSFWnQTUktmXNfLVg2vJbsv7Le3FpH1XdfK6JHCVMZJp1nKdDa23PlIN9S2vXbhNrlWKx0mvcblmGQd53+R9DY6lvKcy1tICTL7MkS9YZsyYodp41VfwTD57/fv3x3XXXaey3/Klj3zBI8X35MuV5hb87ElBOPlySb6Mks8gERG1oJYun05ERNQUjbVPktvevXtD7aomTJig2j6FhYVpp556qvbrr7/WOZ+0C5O2TzabTUtNTdWmTZumPffcc+pcWVlZDbYMe+WVV1T7sbi4OHVs9+7dtbvuuksrLi6ucf5HH31US0lJUS23qrcPq90yTEibrptvvlntLy3P5Hpkn+rtvhoi7avkvRk7dqxqeWY2m7X4+HjVvmr69OlaZWVlaN9giylpaVafH3/8URs9erTmcDhUW7FzzjlH27BhQ539pJ2WtPOSVmzy/EHSfkvOf/nll9c5Rt5TaTcmx8g+wfe0oVZwTW2rVb1lWGNqtwwT2dnZqqWYvF/yvku7r+rtt6qPj7wmeU+ioqLUn+XzU7tdl5B2ddJiLSkpSbNYLGpMzz77bO3TTz895Nd2sDZ59bUMk/GQlngJCQmqLR5/1SMiankG+U9LBv1ERER6cfvtt6vsp6zfbaiwFxEREdGh4JpuIiJql2T6dO212lJ4S6YHM+AmIiKio4VruomIqF2StctS+EqKf8m629dffx0lJSUN9lgmIiIiOhwMuomIqF2SqtRSOOvVV19VhdOGDBmiAu/ara6IiIiIjgTXdBMRERERERE1E67pJiIiIiIiImomDLqJiIiIiIiImgnXdB8FgUAA+/btQ0REhFoXSERERERERG2bdN8uLS1Fx44dYTQ2nM9m0H0USMCdlpZ2NE5FRERERERErcjevXuRmpra4OMMuo8CyXAH3+zIyEjoMROfm5uLhISERr+BoWOPY6NPHBd94rjoE8dFnzgu+sRx0SeOiz4FWkEMI61GJfkajAcbwqD7KAhOKZeAW69Bt8vlUtem1w9se8Wx0SeOiz5xXPSJ46JPHBd94rjoE8dFnwKtKIY52BJjfV89ERERERERUSvGoJuIiIiIiIiomTDoJiIiIiIiImomXNN9DNckeDyeFntur9er1kTofT1EeyNj4/P51E+ODRERERFR28Og+xiQYHvnzp0qsGqp/nHy3NJDjn3E9SU4NmVlZejWrRusVmtLXxIRERERER1FDLqPQVC1f/9+mEwmVU6+JbKZcg2STTWbzQy6dUbGRmYhZGdnq89Jp06dOEZERERERG0Ig+5mJsFuRUUFOnbsiLCwMLQEBt36JWMjX4YkJiZi37596vNisVha+rKIiIiIiOgo4QLfZub3+9VPThumxgQD7eDnhYiIiIiI2gYG3ccI11ITPx9ERERERO0Pg24iIiIiIiKiZsKgm5pdly5d8Mwzzxyzd3r+/PlqZkFRUdExe04iIiIiIqL6MOhuJQIBDTtyy7B6b5H6Kfeb05VXXqkC18cff7zG9i+++OKQp8ovW7YM119/PfRszJgxuP3221v6MoiIiIiIqI1h9fJWYF1mMT5bkYFtOWVwewOwWYxIT3TigiGp6JcS1WzPa7fb8e9//xs33HADYmJiDvs8CQkJR/W6iIiIiIiojScc88qxJ6scZcZydIt3wmg8tMSfnjDT3QoC7ufmbMXajGJEO6zoEh+ufsp92S6PN5fx48cjKSkJ06ZNa3S/zz77DH379oXNZlNTyf/73/82OL1cWmQ99NBDqh+17C+t1G699Vb12COPPIJ+/frVOf+gQYNw//33N/j8s2bNwnHHHQeHw4FTTz0Vu3btqvF4fn4+Lr30UqSkpKi2bf3798cHH3xQI6u/YMECPPvssyqLLzc5h1QSv+aaa9C1a1d17p49e6p9iIiIiIioeazLLMajMzfg4a824LmfM9RPud+ccU9zY9Ct8294JMNdUO5RmW2n3QyT0aB+yn3ZPmNFZrNNNTeZTHjsscfw/PPPIyMjo959li9fjosuugiXXHIJ1q5dqwJqCZDfeuutBgP0p59+Gq+88gq2bt2qpqtLECyuvvpqbNy4UU1HD1q5ciXWrFmDq666qt7z7d27F5MnT8Y555yDVatW4dprr8U999xTYx+Xy4WhQ4di5syZWLdunZrqfvnll2Pp0qXqcQmkR44cieuuuw779+9Xt7S0NAQCAaSmpuKTTz7Bhg0b8MADD+Dvf/87Pv7448N+T4mIiIiI2rtAA0tnQwnHvUXoZ9iO6/0fo7s595gkHJsTp5fr2K78cjWlPDnKUWcdtdyX7VtzStV+3RKczXIN559/vso0P/jgg3j99dfrPP7UU09h3LhxoUy0ZJwlQH3iiSdUBrm2PXv2qOy5ZNGlN7VkvIcPH64ekwB3woQJePPNN3H88cerbfLnU045Bd26dav3+l5++WV07949lF2XbLQE/zItPkgy3HfeeWfo/i233ILvvvtOBc/y3FFRUaqPumTB5dqqf+nw8MMPh+5LxnvRokXqOPmigYiIiIiI6pIgWmKUUpcPEXYzusSFh6aHN7R0dnL/OCz76RMMyP8OMG5FXsCDM4oKoNmj4Eq8RO0vCcc+yZGtbqo5g24dkw+pfBAdUaZ6H3dYTcguCaj9mpMEsGPHjq0RuAZJZvq8886rsW306NFqOrlMz5bAtbo//OEP6jEJos844wyceeaZKkttNld9FCXbLBlvCeaNRiPef/99lRlviDz/iBEjamyTrHV1ch2SsZdgOTMzEx6PB263WwXZB/Piiy/ijTfeUF8WVFZWqmPlSwgiIiIiIjq0elRCMtYyY1cSiIlhhehetBA91v4Mz7p1WBnlgGYPBtQm7LKY0bt0IX5JvPSYJBybC4NuHZNvheRDWunxqynltcl2eVz2a04nn3yyykDfe++99WavD4VM2968eTN+/PFH/PDDD7jppptUVlzWVEvmWwJwWev9+eefq+yz1+vFhRdeeETPKeeXKeQS7MtU9vDwcFWpXALoxnz44YfqiwbJoksgHxERoc61ZMmSI7oeIiIiIqK2msWuHlRL8rDS41fTwzMKKuCwmOAo3ogzLT9jYOYqDC3fHDpvidEAzVCVFAvAgIAxBjNiT4M35sJjmnBsDgy6dUw+wPKtkHxI023OGlPMpSDZ/uJKDEiNVvs1N2kdJhlemb5dXe/evbFw4cIa2+S+TDOvneUOkqJkElzL7c9//jN69eqlpoQPGTJEZbynTp2qppVL0C1rxWX/hsjzf/XVVzW2LV68uM71SDZ+ypQp6r6s1d6yZQv69OkT2keeSzLitY8bNWqU+mIgaPv27Y28S0RERERE7TOLLdO+q9ejCsYuETYDjgvbAG/+LMCwEZlWN1ZoRjiNlRha7dwaomGrTIU7vC+sMafBbuyCDJcbDovjmCYcm0Pru+J2RL4xkg9wZmFlaG23fMMjHzgJuGPDrZg8JOWYrGmQDPFll12G5557rsb2v/71r2r99aOPPoqLL75YrXl+4YUX8NJLL9V7HimwJsGtTAmX6d3vvfeeCqo7d+4c2keKoUkwLWoH9LXdeOONKhN91113qeOksFvtIm49evTAp59+il9//VW1PpOp69nZ2TWCbqmwLhlsqVrudDoRGxurjnvnnXfU+m9Zz/3uu++qIm/yZyIiIiKi9qSxLLbEKxK3BGMWq+ZGcskCmCrmocS/FVvMfmgOQ41a3mtsVpzo7YZNkSdhY+Ro7LYeh1+25iPWaMXA+GhUj3COdcLxaGP1cp2TPty3juuB/qlRKKr0YFdeufopHzjZ3px9umuTll6SJa5OstOyVlqmYku7L6nwLfs1NA09Ojoar732mlr3PWDAADXN/Ouvv0ZcXFxoHwl2JcMsGfDa67Vrk0JsUhFdqqAPHDgQ06dPV+u3q7vvvvvUdcoU+TFjxqhiaZMmTaqxj0wjl8y8BOLSV1zWcEt/cqmMLl8myHVI67HqWW8iIiIiovagKV2V5qzcguNLfsT43Htg3X8J1rumY41xM3ZZAtAOZL2lRrkPkQi3Hg9X7D14rsdb+CHpOmSE9UGlD+gY7UCs06qC9zKXD/6Apn7K/WOZcDzaDJp8bUBHpKSkRFXALi4uRmRkZJ12VTt37lTZUbvd3ixrJw5Ghtjn86mp27WroOuRXK8E3hLg3nHHHWjLgmMjN8myH+nnhI4O+XIpJycHiYmJqqAf6QPHRZ84LvrEcdEnjos+cVwOTtp6PfjVekQ7rDVqTYV78tCz9Gf0LfwJPSpWwgIfNlsseD42+vf3F0aYjfGwGvpiRfEwOOy90L9jTJ2lsxJYS2Lx/MEdMWNlJrZll6G00oUIhx09OkSogPtYJhyPNA6sjtPLWwkJsFtblb7DkZubq7LmWVlZDfbmJiIiIiKiY6d6V6UIdyZiSr+B170UmcY8JJeVo0+lK7RvutcLq2aGwdwRVutQeMLGw2xMwvbccvRPdaDC62906awE1n07RmFHXhn27MtBp46J6BbvbJUZ7iAG3aQrklmMj4/Hq6++qtZfExERERFRy3JW7sLx3v+Daf8q7DMWYp/MBFSRpBGrbTacWOlCnikR5V3PwMflg7HH3xnJzliYrCZ4PH7sLi5XQfV1J3dT5wsWY5Nq5FIcTTLc1TPZKuEYHw5nIByJ8U2f4atXDLpJV7jagYiIiIio5VVmr8f6VW9i1d4F2OApgMdcsxCaCBgc2BfWF3cZz0dEl+Nx39l9MHF/CSoOElT3SY487KWzrRGDbiIiIiIiIgLytgHrP1e3RWU7MCPiwPLWauuvAwhDhDEdCBuHMuNwbC7xIjbaiqlDU1XgLIH1wYJqYztZOhvEoJuIiIiIiKidqF2gOdG/GxvWvIlOO35FUtb60H4DjcZQ0O20RWFAhyGITTwLi/Z3w47cSrjLJYsdqJPFbo9B9cEw6CYiIiIiImonvbZV669965HomgWjYRWyzKXwGwwYV16B86vtG9dxKM5N648uvSahe9qJMBlNavvpR9BVqb1i0E1ERERERKQDR9Im+GDWbFqL2XOfh8W3DDCXYL81eN6qnyvtNkyK6glD38lAn/OA6E44vZ7zMIt96Bh0ExERERER6SQLLQXIpD2XFCBLT3TigiGph9SfunrgHoUSBHZ/inlbP8fayn3wydpsi+z1eyBvNjhh8vWAKfE8BC74o+qfrY7fW8RM9lHCoJuIiIiIiKiFA+7n5mxFQbmnqn91VFX/6rUZxcgsrMSt43o0KfCW83y9bDNi9vyA4WXzkOpZgY1WE1bGRNUohmZBOKLMveENnwi/bTDK3X5sK/Xgx43ZWLyz4IgDf6rp93rvRE00ZswY3H777cfs/XrrrbcQHR19zJ6vMbt27VLf/q1atUq37xcRERERtR6SmVbrrMs9KsB12s0wGQ3qp9yX7TNWZKr9GqJ53Vgw70V88/l5OH3DJNxY8B8M8SyDGX708njgCARg1GyINw5EdOTdsCW+A3fcPxCwD1G/2zqsJhSUefDGwl0q0I92WNElPlz9lPvyhYAE9HR4mOmmel155ZV4++2362zfunUrZsyYAYtFzUtRunTpooLK6oGlBMpyv6ioiO8wEREREVEDZCq3ZJYlwy0BcHVyX7ZvzSlV+1WvCK75/cjY8jV+W/8+lueuQRF8gAn4LcyEoZ6qfYosiVgdNQ4l3t5YU9kLwzolwOmwVJtcXqXC7UNhpQcGI9CvY1ToOlTgb3Oq65PAX1qBsWjaoWPQTQ0644wz8Oabb9bYlpCQAJOpqnIhEREREREdGVl7LVO5ZUp5fSQLnV0SUPtJtnvturnYtPFtbC5cjhzNXWf/zdZw/Bx3KtZHT8CesH7QDEZYKr0w7C3EzoLyGkG10LSqNeCia2z4IQX+1DScXk4NstlsSEpKqnGTgLv6dGn58+7du/GXv/xF/YWU2/z583HVVVehuLg4tO2hhx5S+7vdbtx5551ISUlBeHg4RowYofavTrLknTp1QlhYGM4//3zk5+c3acr3xx9/jJNOOgkOhwPHH388tmzZgmXLlmHYsGFwOp2YOHEicnNzQ8cFAgE88sgjSE1NVa910KBBmD17do1zL126FIMHD4bdblfnWblyZZ3nX7dunTq3PEeHDh1w+eWXIy8vj58sIiIiIjooqVIua6dlDXd9ZHuisQh5Sx7Ho9MH47WFN+Dngl9rBNwGGBHh74hE+x9hTXwbs1Luwu7wASrgFmE2M2IcVoRbzSprXebywR/Q1E+5Hy6Ph1nhsJkbDPzliwEJ/OnQMeimIyJTzSVoleB1//796jZq1Cg888wziIyMDG2TQFvcfPPNWLRoET788EOsWbMGf/jDH1RGXaatiyVLluCaa65R+8m66VNPPRX//Oc/m3QtDz74IO677z6sWLECZrMZf/zjH/G3v/0Nzz77LH7++Wds27YNDzzwQGh/2f7f//4XTz75pLqWCRMm4Nxzzw1dS1lZGc4++2z06dMHy5cvV18cBF9HkEyfHzt2rArMf/vtNxW0Z2dn46KLLuIni4iIiIgOStqCydrt/cWVKuscZAm4MKDwe1y96048t++P6LvxVeQaykKPy55OfxyiDWdiwvDPkOF4Arm282EwhtUbuMc6rbh6dBf0T41CUaUHu/LK1c8BqdG4enRXxIZbGw385YsB+YKADh3ftZbwyilAWc4xfUqz/LV0dgBuWNDkY7755huVvQ2SbO4nn3xSY5/Y2FiV/Y6IiFCZ8KCoqKppK9W37dmzR01Xl58dO3ZU2ySIlUBVtj/22GMqEJYgXIJlcdxxx+HXX3+tk4Guj5xLAmdx22234dJLL8WcOXMwevRotU2CecmiB0mwfffdd+OSSy5R9//9739j3rx56guDF198Ee+//77Khr/++usq0923b19kZGTgT3/6U+gcL7zwggq45dqD3njjDaSlpalMu1w/EREREVFjfbilOrhUKd+WXYz+1oWwen/E0NItOL3i9+JlSX4gzetFkTkWEdahqIg4Hz5zGjbllMGSaUR6ghNrM4vVGuza08cloJfg+rQ+SepW+xrE4p35qmhaY8cH96VDw6C7JUjAXbrvmD1d8K+MVqdkQuMky/zyyy+H7st08COxdu1a+P3+OoGoTDmPi4tTf964caOaUl7dyJEjmxR0DxgwIPRnmeYt+vfvX2NbTk7Vlx0lJSXYt29fKCAPkvurV68OXYucUwLu6tdSnewrgXr1LyeCtm/fzqCbiIiIqB1pKKg+WB/uDtiCMY4XsaZsKXK9VVXQfA4jTquo+l0+19QBy6NOgxY3HnCko/TA88ljst56W24ZpozohMyiylBRNpkSLhlqCZgliz15SEroWupblx0K/JtwPB0aBt0twZl4TJ+uapKKdsjPK0F2enr6UbsOma4tWXGZql27GFt9Qeuhql5RPfjtXO1tkrk+muQ1nXPOOSpLXltycvJRfS4iIiIi0q/GgmpRuw+3tyIHrp2v46M9y1FoPJDRrhbTFlpsKBh4CQpTJuMfy53onBChWok1VGgtKcqh+nkHr0G2yTVIhloC5oP12ZbHj+R4ahiD7pZwCFO8jwpNg8/nU+ucm4PValUZ7INtk2nYsk2yzVLwrD69e/dW67qrW7x48VG/ZllvLlPcFy5ciFNOOSW0Xe4PHz48dC3vvvsuXC5XKNtd+1qGDBmCzz77TLVNa673l4iIiIj0H3DXDqolSyzTtTMKKuCwmtVjPRIcSC1fiMLiz5CjZaDUUrP3toTUvcNTMbzHORgw+FpY7VEozi2Dde16dT5p4dXYemvJYEtbr4ay7QcjgfWRHE/1YyE1OmIScP7000/IzMwMVe2WbZIFljXVsq2iokJNtb7ssstwxRVXqAJsO3fuVNXBp02bhpkzZ6rjbr31VjWVXNZbS0EzWTPdlKnlh+Ouu+5SGeqPPvoImzdvxj333KOKt8l6cCGF2CQ7ft1112HDhg2YNWuWuq7q/vznP6OgoECtH5dK6TKl/LvvvlPV22t/6UBEREREbXNKuWSHJaiWzLYExpKRVj2uVYE0F/bt3IBrvO/j7s0X4epd9yFf24OA4feAO8LvwLiOZ+CfF83CTVfMx7CRf1UBd2OF1qqvt+6RGBFaby0BsgTfA9Oi1c9DDZiP9HhqJ0G3FMGSoE+yk9KSSgK7xkjhrJ49e6pWU1IAS9pfSXaTmkYql0vbru7du6s+3kIqmN944424+OKL1bb//Oc/arsUTJOg+69//at6zydNmqSCVWkRJk444QS89tprqqDawIED8f3336uK5M1BAvw77rhDXYus/Zbg/quvvkKPHj1CU96//vprtRZdsvT/+Mc/6kwjD2bLJcA+/fTT1XmknVp0dDSMxjb514uIiIiIqpGscHAddPUCZAZ/MaKK38AF/rvwqecmTCx4F9HeHDg0DYNcbtg1E1INvZAUfie2maaj25B/IiqubhHeYKE1WVddX7svrrfWP4NW++uSVk6ylhLUTZ8+XQXcElBLxW3JZCYm1l3TLBWqr776alVxWgJFqTh95ZVXqorWTz31VJOeU4pySbVu6Ust05ark+BdMrpdu3atUZDrWNKqTS+v3eyeWlZwbOQmX1y05OeEfidr/2UZhPw/g1+e6AfHRZ84LvrEcdEnjkvbHJfVe4vwr5kb0SU+HEZoCK+cD5TPQm5gJwKGAKyahmm5ebBpQABGbIk4AQtjx2O78yRoJocKnqV118Pn9q23wFlja8Ylw91W11sHWsHvY43FgdW1uUWoEijLdGCZ3isk+JapyxJUy/Th2qQdlVSslqnEQjLkMlW49rpiIiIiIiKi2mTdc6QxG+H5X6A8sALZqKh64ECuy2MwYK6tI7KNpyGz0ySUWatmhh5qOy6ut2692lTQ7fF4VGXse++9N7RNvhUZP348Fi1aVO8xkt1+77331BR0KaC1Y8cOtXb38ssvb/B5pMWV3Kp/wxH8NqZ2dWy5L3+ZgreWEnzuNjaxoU2oPjb1fYbo2Av+veVY6AvHRZ84LvrEcdEnjktLv/8adhVUoNTlRYTdgi6xYWrqdnBcfD4/9hSV13m84fP5sHbNe1i08SPY/DuQe6BnUJBNM6ADuiHTPw6zUsei0q+hqNCD5Ejv7+24SqracZ0/WLreyO8eB/9dvUtcWLV7TTumNQq0gt/HmnptbSroloJdsrY22KM5SO5v2rSp3mMkwy3HnXjiiaGpvrIW+e9//3uDzyOFvx5++OE623Nzc+usBfd6vWowglOIW4K8rmBRL04v15fg2AQ/J/n5+TXanFHLkLGQaUIyPnqdztQecVz0ieOiTxwXfeK4tJzNORWYtSEfOwtc8PgDsJqM6Bprx5l94tAj3o7lO3Lw84K92FXgrvN4z8SqIDegacgocsNTlIXO+75B0u4v8IW1HPkmU6jTl0HTkBCIgtU0GnusZ2NhhQ3RDjOuHhCrHq+6hvLQc6TH2TGxdxwSzG41lZpa19+X0tJgx/R2FHQfjvnz5+Oxxx7DSy+9pNaAb9u2TVWvfvTRR3H//ffXe4xk0qUAV/VMtxRgk4Jh9a3plsGQ9dQt3VKKwZy+yf9M4uLiuKZbJ/+Tly+o5O+0Xv8n3x5xXPSJ46JPHBd94ri0jPX7ivHeqr0oVO28wkNZ5u2FlXhvVT7O7JuEzzeUocJvqPfxW8fGwOdzYe5Pr2PMvl8w0rUEJlRlOEeGh+EbZzhiYEbfmCHIwLlYXdYVbp8fNpgwpIsTk4d0RN+OVWuuR/fpXG+2nVrn35em1mJqU0F3fHw8TCYTsrOza2yX+0lJSfUeI4G1TCW/9tpr1X2pPl1eXo7rr79eVauub4BtNpu61Sb71t5f7suHJXhrCfLtUPC5menWl9pjU99niFoGx0OfOC76xHHRJ46LPnFcji2Zej1j5T4UlnuRnhgR+r3L6TAi3W7G1uxSvPbLTlgMAfTuGAPDgd/Dgo/vy1qFT775D4q09QjAg8u9BaGAW8RqPXFBj3Nwyik3wWhxVE1hb6THtZxeroPaxt+Xpl5Xmwq6rVYrhg4dqnpDSyuq4Dckcv/mm2+u9xjpH137zZLAXXD9MxERERFR22vnJeR+pMOK7bmF6NPh98cDARfCyr6C2zUXEcZsFFRbMv2rw45TXQ78Fns2fouZiGVFkRhQGY1TTPYaPa6J2mzQLWTa99SpUzFs2DBVGE1ahknmOljNXNqJpaSkqHXZ4pxzzlEVz6UPc3B6uWS/ZXsw+CYiIiIiotZHMs7SXssRVf/v9SYj4AtoMBkNMHi2wlr2MQp9a1EOj3o8GKabNQ2pWjJ2xZ2J/0RORMBQFUYlR/mwNadUBfcMtqndBN0XX3yxKmj2wAMPICsrC4MGDcLs2bNDxdX27NlTI7N93333qW+15GdmZqZaMyAB97/+9a8WfBVERERERHSkZIq39LOWNdpOe93Qx+/XcIJhA46r/AK7/ZmhIDso1meGu6IPkDgF+VE96hwv67+zSwIquCdqN0G3kKnkDU0nl8Jp1UlxswcffFDdiIiIiIio7ZA11emJTqzNKEa6zRmaQm73l2JwwWwMzpmBNOzFigob3rBGhrLanbQkGMPOxJySUcjXAhhqjUF9k8YlmJegXoJ7oobw00FtlnzBcuqpp6KwsBDR0dGHfZ5du3aha9euWLlypZo5QUREREStg6yxvmBIKjILK7E1pwRdbYth8X6L8ws2YYC7LLTfALcbnTwGWM1DUBp9GXYa0rC/uBJJ0RZ0TTRjX1GlKqxWfV241H+SfQakRqvgnqghDLqpjoNVOJdZAQ899FCbfOeuvPJKFBUV4Ysvvghtk3Zw+/fvV9XxiYiIiKh1SYv24OS4T7G84ju43KVwAVhsN2KAu+pxrdNIbOlwDjzekVib74W7NACbxaOC6clDUtQ+z83ZGirIFmwpJgF3bLhV7cO2X9QYBt1UhwSYQR999JFaH7958+bQNqfTWeMbPr/f3+I9yJuTFNRrqOUcEREREemP/I66PeNX/LT8JazKXoFAwFvj8f1WOzzD/gDr8ddCS+iF2Jwc3BOfgD2FlfW2+7p1XA98tiJDBd6yhlumlAeD8n4pVT24iRqiz4Zn1KIkwAzeoqKiVOY7eH/Tpk2IiIjAt99+q9qzSb/yX375RWWIg23agm6//XaMGTMmdF/at0nVeJmq7XA4MHDgQHz66aeNXstLL72EHj16qMbzUgzvwgsvDD3mdrtx6623IjExUT1+4oknYtmyZQ2eS7LztaeHS3X7Ll26hB5/++238eWXX4b6qssUdZleLn9etWpV6LgFCxao6vjy+pOTk3HPPffA5/u9gIa8brm2v/3tb4iNjVXvXVudHUBERESkB9Ije9P+PHwy5yk88s4YPP3NVKzYvyQUcBs0Df3N0fhTv2vwwDUrYD37KaBDn9DxwXZfA9Oi1c/q2WsJrO8/qw8ePrcv/nFWb/XzvrN6M+CmJmm76UlqVhJkPvnkk+jWrRtiYmKadIwE3O+99x6mT5+uAumffvoJU6ZMURXjTznllDr7//bbbypwfffddzFq1CgUFBTg559/Dj0uAe1nn32mAuXOnTvjP//5DyZMmKDavkmge6juvPNObNy4ESUlJXjzzTfVNjnPvn37auwnVe7PPPNM9UXDO++8o76IuO6661TgXz2wluuSFnZLlizBokWL1P6jR4/GaaeddsjXRkRERNTaA2Jpq1VfFvlo2LBrH3bOfR0JOR9jQVSl2hY8e7imYVRMH5w47M+IS58gaykP6znYg5sOF4PuFjJnzxzM3TP3oPulRaThxoE31tg2ffV07C3de9Bjx3Yai3GdxqE5PPLII4cUPEpW+rHHHsOPP/6IkSNHqm0SsEuW/JVXXqk36Jb2buHh4Tj77LNVdl0Ca+mnLqT3+ssvv4y33noLEydOVNtee+01/PDDD3j99ddx1113HfJrkmnzkoGXa21sOrlk32Wd9wsvvKAy4L169VKB+d13362m4gdb0g0YMCBUFV++ZJD958yZw6CbiIiI2pV1mcWhqdnSM1umZktFcSlwdiRTs2UKeUn2Gnh/eQddNnyAPoFytX1BWDR2WyxI8prgNA3H+PH3ol/P3zPaRMcag+4W4vK5UOwuPuh+Mba6WeQyT1mTjpXnaC7Dhg07pP0l+1xRUVEn4PR4PKFAujbZVwJtCc7POOMMdTv//PMRFhaG7du3w+v1qsxxkMViUVO+JVvdnOT88sVB9YJzch1lZWXIyMhAp06dQkF3dTINPScnp1mvjYiIiEhvAbcUISso91QVIYuqKkImLbykorislT7UwLvCU44lq17Hgk2fwlq0B/cWFNborz3Cl4ow5wRkJpyNVbkulGwB+vTQWOyMWgyD7hZiN9sRZTv4/2CcVme925pyrDxHc5EMdHWS3ZVvG6uToDhIAlIxc+ZMpKRUVYEMknXR9ZHs9ooVK9S66u+//15lkWX6dmPrthtzsGs82uRLgOokSJd17URERETtZUq5ZLgl4JbMdjBh4bSbVc9syXzPWJGJPsmRTQqIs4p2YsGSp7Ek4yd4PAfafVnM2GaxoLPPgNXRp+PX+AuQ5egROiY5yoCtOaVqarus0yZqCQy6W4hM+z7cqd+1p5vrgazLXrduXY1tUngsGHj26dNHBdcyZby+qeQNkaro48ePVzeZqi39tufOnavWblutVixcuFBlw4MBtATkUsCtoWvMyspSgXfwf/rVi6MJOadUY29M79691Vry6ueR65AvCVJTU5v82oiIiIjaMgl0g222areklfuy/WABsfy+tWH3PMxb/jI25q2FIfB74VrRXbPi27DzUZB8OdzWujNEpb2XVBuXteRELYVBNx0VY8eOxRNPPKEKi8nUaymYJkF4cOq4BKRSqOwvf/mLyvZKpfHi4mIVrEZGRmLq1Kl1zvnNN99gx44dOPnkk1WxtlmzZqlje/bsqTLtf/rTn9TabSl2JlO6pZCaTGG/5ppr6r1GqSiem5ur9pMq6LNnz1ZV2OX5g6SS+XfffadapMXFxanq7bXddNNNqur5LbfcgptvvlntK18ISNG04HpuIiIiovZOAl1Zwy1TyuvTaECsaVi4+k3MWf8uskv2qunjwbDdqmnoHojHiL5XIrbPpXjwm82IDlhRX9guU9llDbkUbyNqKYwQ6KiQzPP999+vKooff/zxKC0txRVXXFFjn0cffVTtI1XMJVssa7Rlurm0EKuPZLVnzJihAnrZX6qef/DBB+jbt696/PHHH8cFF1yAyy+/HEOGDFHrxiVgbqiaupxDiqC9+OKLql3Z0qVL1RcB1UkVcgnqZc26ZMblS4HaZHq8fAEgx8t5brzxRhXo33fffUfwDhIRERG1LRLoSsArgS8OEhDLVPQduWVYvTsX2Qvfg/baOGya9wByDgTcItqvoX/gOMTaH8Y809N4de/xKPNCTV3fX1xZZxmh3JftPRIjVLV0opZi0Gp/OumQSYspyYhK5rZ61lS4XC7s3LlTBZbSUqolyBBLD2mZql17ag+1rODYyE36gbfk54R+JzMqpOid9IDn7AX94LjoE8dFnzgu+tTexkUC6UdnblBF06qv6Q7+DiRTzwekRuP8wR3xweJ56L93Pk4v/QZxgTy1zw6LGU/FxiDVa0Sc9URkR1+Ocmt8vcc/P3fb78XarFXF2iTgjg23HrRYW3sbl9Yi0ArGpbE4sDrOsyAiIiIioqNOiqNJWzCpUh5c2109II4JM6OD4ze89s3b8Gp70d1dhLiA+/fjkYaOrgnITpiM4rCwBteEh9vMKrAOtiWTKeuSQZeAfPKQlCNqS0Z0NDDoJiIiIiKiI85qS0E0WZ8t08VlOrcE3RLw1g6IzWYPekbOg9/7I9ZszVLTx2XV99ywMAx1ebAp8kQsjPsDPi/ogoJKH06x2Q+6JnxgWrSqgl7fNRC1NAbdRERERER0RL24g0G1FE6TLLNMJ5cstwTdcpOAeFXGLixb8xI2Zc9FcUmxOjYYEif5NCSah+Kp465Ggb1T1baAC/tLC5Fb5kZSlOOgRdIkwGZbMNIjBt1ERERERHTYAfdzc7b+vp46qmr6uKzjlmnlkuXuEF6Irxb+C6v2LYbmq6xxfDePEVG2U5GReDm2WmpOA493WmEzGZFV7EKHSHudNeEyRV2mkLNIGukdg24iIiIiIjqsKeWS4ZaAu3qhNKfdjHSbE3n796Dw63fQoeQrrHKaoB143KJpGG6JRf/0y/Hk9iGICAuH01I3LHF5A+gY7UCYzVTvmnApkiZrtjmFnPSOQfcxwiLxxM8HERERtSWyfjoYDAcD7oDmQ5hrHSbmf4fBhbNh0bxq+yBLBLZarTglIh0nnfAXOHucgYAGdC07UN3cVre6uQTWgzvFqOrkM1ZmskgatVoMupuZxWJR/wPJzc1VfZ9bomUXW4bpl4yN1+tFUVGR+mzI54WIiIioNZCCZbKGW6aU+wIVCFR8hQrXjzB7cjCkoCAUaASMFvyh80Q4Rt4CS/KA0PFS46yx6ubBTLasCe/bMYpF0qjVYtDdzEwmE1JTU5GRkaH6MLdUYCd97qS/Hft060twbKSHunxO5PNCRERE1BpIATOTuQSuonfh9i+CJVCqqpAXm0xYbrdhgMeMueFnot/ku9G5a496z1FfdfP62n2xSBq1Zgy6jwGn04kePXqojGZLkKAuPz8fcXFxum0s314FxyYpKYlZbiIiImo17cD2l+zGTyueQqR/HgKBSgTn6hk0Db18FmyJuRQv+M5Gj04pmNg5vdHzB6ubs90XtVUMuo8RyWC2VBZTAjuZtmy32xl060xwbJjhJiIiotbQDsxi3okOxhlw+TbC6HeHgm0pjtbHEw5D+AVYFzURGSU+xEY0vdAZM9nUljHoJiIiIiKiRtuB9Qh3Ybzrc2RVzMQihxHBuZNhgQBOsCSj2H4p5nmGwuUDbC6tzvRwovaMQTcREREREdWZUv7J8t1AyS78RZuF43d+A6vmRr7RiMX2WMQEAuiJrph85sNwdDtJ7X9KrSnobOVFVIVBNxERERERhbj9bny17H/Yt+8NXFixA6MrykOPRQcMOM0/AKudUzA7kIqREX3RjdPDiRrFoJuIiIiIiFDuLcdPa9/BvPXvo7xsP2wAFoTZMbaiHJrBjqVx5+KX+ItRbO0Af0CDO68qs01EjWPQTURERETUzqqPV5/6XewqwtyVr+LnLZ/DU5GrtgUfTfYb8G3iZVgdfykqzNGhY6SXtrT2kvMRUeP4t4SIiIiIqJ1UH5dAOT3RiQuGpCI5xosfl7+IRdu/ga+yMHSMtP0aqlnhsE7A14GzkdkhEQbD70G6pmnYX1ypiqVJAE9EjWPQTURERETUxquPJ0c54IgyqQz1ur2FiM2YjZ2W/8HjLQntb9Y0jEQYxg2+DvHDrse6HDfmzNmqAnZ1vLXqeAm4Y8Ob3g6MqL1j0E1ERERE1AanlEuGWwJuyWxLptqg+TG6ch7GFL+LJPcOfO4Mx5zwMFg1DScZozB2+K2IGjQFMFWFCP1S7Lh1XI9Qpjy7pCpTznZgRIeGQTcRERERURsja7iDGerKQAYMpR/gr9krkObOCO0ztqISRmcqxp94J8L7XggYg923fyd9tvskRza6JpyIGsegm4iIiIiojZEAucy7C5GYAXfJOtVje525HGnuqsd3O/riffvFOPPMKxDeKabRc0mA3S3BeWwunKgNYtBNRERERNRGqpM7bSZ4tZ2YtewJJGA5DB4PrAf2W22zoQd6Yl6HK7HGNABFLi8udlgOWt2ciI4Mg24iIiIiolZenXxrTinKvVsRa/gUFm0rzNrvwXac348+gU7Ii74OrzsHVlUfzylTa7PL3T48OnNDvdXNZWo5ER05Bt1ERERERDrVWBY6WJ28sHwPYsxvw4ktMAe8oWMTfD6k+7tgtfFy/BI/pKr6uMsXqj4+MC0Kz8/dVqe6+dqMYmQWVqoiagy8iY4cg24iIiIiIp0G1Q312JbiZl/+tg2jcj7ERPeneCLaCNeBQmhJPh86ebrC1fHPGHDSeGxbmaky29Wrj08a3BGfr8ysUd1cOO1mpNuc6jlnrMhUz8Op5kRHhkE3EREREVELaCyoFvX12F6TUYSM/J34a+Ra3LxuOqICRWrfMRVhWCNrtg09kRlzPZYZu6GowoPxNjPuP6tPncC+enXzYMAdJPdlu0xZl/1YRI3oyDDoJiIiIiI6xoJTw+ub2p1RUAGH1VwjCy3rsDXTZsRb34e1YgtSd2fBqWnqXAEY0NE8AlvjrsQKR3e1zRHQVGZbAu36qo/Ldgn05XnrI1PRg8cT0ZGp24yvDXjxxRfRpUsX2O12jBgxAkuXLm1w3zFjxqj/kdW+nXXWWcf0momIiIio/Uwplwx3MKiWKd0mo6FqaneiE/uLXfhtdwGSI+1q/1LvehQW3Q9f4cOweDZAM/gwNzxMBdsrnKfiuePexiedH0XOgYBbSAAvmXPJbNdHtsvjsl99DnY8ETVdm/tb9NFHH+GOO+7A9OnTVcD9zDPPYMKECdi8eTMSExPr7D9jxgx4PJ7Q/fz8fAwcOBB/+MMfjvGVExEREVF7sKugotGp3TFhVuwuKEeJbzM8RR/C4N0Ci/Z7gbRUrw9l6IfHO9+AFa5ktQa7+llUdfLiSrV2W6aS10e2S4AvmXV1fLXraMrxRNSOM91PPfUUrrvuOlx11VXo06ePCr7DwsLwxhtv1Lt/bGwskpKSQrcffvhB7c+gm4iIiIiakrXekVuG1XuL1E+5fzClLm/V1G5r3andEvCarDvRM/Z52CoegcWzHuYDAXea14tx3nT4I57EtzGPYujxo1QVcgngy1w++AOa+in3ZfvkISkNFkGT7bJ2/HCPJ6J2mumWjPXy5ctx7733hrYZjUaMHz8eixYtatI5Xn/9dVxyySUID2/4Wz23261uQSUlJepnIBBQN72Ra5L/gevx2to7jo0+cVz0ieOiTxwXfeK4HBvr9xWrCt/bcsrh9vlhM5uQnhiugtW+HaMaHBen1QSb2YhKt09NKQ8yBbyIKn4TBb65SLJ4amS2e6IndsXegCX27tiWW4b+KU6M65WIjtH20DVkl1RdQ/+UKEwe0hF9kiMa/f1PHr9lbPfDPr6t4N8XfQq0ghimqdfWpoLuvLw8+P1+dOjQocZ2ub9p06aDHi9rv9etW6cC78ZMmzYNDz/8cJ3tubm5cLlc0OOHobi4WH1o5UsI0g+OjT5xXPSJ46JPHBd94rg0v805FXh98T4Uu3xIdFoRHWaEy+fHil152J5VhGtO6IieiWH1jktEZAAdnUZsyilBlxg7TAhgWOmPmFDwHsL9OXgwPhblRiM6en3o7O6CrRHXYG5YD7g8AeQUFCLaYcaYLg7k5eUiwQxcd3wcMoqcKHP74bSZkBptg9HgRk5OzkFfx5Ee3xbw74s+BVpBDFNaWtr+gu4jJcF2//79MXz48Eb3k0y6rBuvnulOS0tDQkICIiMjoccPrKzTkevT6we2veLY6BPHRZ84LvrEcdEnjktzv78aXlm6ERV+A3p3jPm9xzWAuEhNZaLn76rE6D6da0zPrj4uU04Mw7/nzIel6Ef8rWQeEr2Zof0uKC2DJ2EIEk+4Fx/sjcN2yaRXVGWhh3RJUFno2pn0pJo5p0N2pMe3Zvz7ok+BVhDDSOHudhd0x8fHw2QyITs7u8Z2uS/rtRtTXl6ODz/8EI888shBn8dms6lbbfJh0OsHQj6wer6+9oxjo08cF33iuOgTx0WfOC7NZ1d+GbblliM5KgyGWr9bVfW4DlOB957CyjqtuuTxvaV78PPGZxHhnQ+/qQw2f0Ho8W2RIxB36t/RffAYdf/+gVqdHttcZ3308e+LPhl0HsM09braVNBttVoxdOhQzJkzB5MmTQp9QyL3b7755kaP/eSTT9Q67SlTphyjqyUiIiKi1uhwe1xnlmZgxvInsCV7IQzuEsjRXoMBPznsGBM5ECUn/A3d+p9aI6iur8c2EbUubSroFjLte+rUqRg2bJiaJi4twySLLdXMxRVXXIGUlBS1Lrv21HIJ1OPi4lroyomIiIioNaje47p6IbSGelxnlWdh1m8vYMWObwFXcai9V4zfj4mONIyY/DBM3U9FxDF+HUR0bLS5oPviiy9WBc0eeOABZGVlYdCgQZg9e3aouNqePXvqTAOQHt6//PILvv/++xa6aiIiIiJqLZra4zoizIV3f30WS7d+Ca3i9ynkUX4/Jlg7YOSpD8LS8wyZQ9tCr4SIjoU2F3QLmUre0HTy+fPn19nWs2dP9T9IIiIiIqKDCfa4ziysVD2tk6Mcakq5ZLgl4JYe15d2KcXaL+7CkpLNoeOcgQDGIQonn3IfbP0mM9gmaifaZNBNRERERNSUKuSHW6SsX0oUbh3XA5+tyFCBt6zhlinlpySU4XLX+4j+7At0gYa58bFwGwwYb4jAyaPuQnGHU2FJSmbATdSOMOgmIiIionZnXWZxKGCWomgSMMuUcclgS0DdFLJfn+RIbM7Jxy/bv4RzzyxcsmE+DIGqAmoWANd57Ug48Q6EDbkSAYMJaCe9r4nodwy6iYiIiKjdBdzPzdmKgnJP1dTwqKqp4bJGW6aMSwa7KYG31+/Fzzu+xfcrXkZZ4XYYA36MNwSQIA86YoGT/orOx18DWBxVBwQCzf7aiEh/GHQTERERUbuaUi4Zbgm4JbMdLIImVcilKJpkvmesyFQZbFHf9HN/wI+lexdg1m/PoTBvE3Agsy0VgraGRSFh6I3ACTcB9qpzEFH7xqCbiIiIiNoNCaKDxc+qVx0Xcl+2b80pxQ8bsrB4Z0GN6efdE8IxsGse1u94FVnZqwC/J3TsULcPZx13ARJP+TsQzha0RPQ7Bt1ERERE1G5I1lqCaJlSXh+pQr4j14M3Fu6CP6CFpp/nu3Zh8763kblvE2yaO7R/X7cXZ3c+HWljHwKiUo7hKyGi1oJBNxERERG1GzJNXLLWsoZbppTXVuH2obDSA4MR6NcxCpIL71m6GLGFL+F7e1lov24eL87tMALp4/8FxPc4xq+CiFoTBt1ERERE1G7IumxZyy1F02QNd/Up5ppW1UJM7RcThk4V63FG1nR0K18FWbW90hwDiwb08aRi6BmPI63fiYfUlizcZkKYJiu/iag9YdBNRERERO2GFEKTtmBSpTy4tlumlEvme39xJew2N+LNs9G5ZBluyllV45fmCyo7YGHi9Xjf1Qddo3oj7VDbkpmN6Og0YsqJdvRPjWn210pE+sCgm4iIiIjaFWkHJm3BggFxdkkAFosXHWKWwO/+BqaKTOzRNGSaTUjx+ZFrTcN3yTdgfeQpKHP7YfN71DT1Q25L5vZhU04Jnpu7DbeNO67J/cCJqHVj0E1EREREbVL1qd3VW34JCXilLdi23GIs3PUd1mx/E56iHTBrVb20LQC222KxpMOVWB57FgIGs5p+LtnwAanR6lyH05asS4wdmWWeUFuy4PUQUdvFoJuIiIiI2pw6U7stRhUAy9RyCbglgF6+/1d8vfQZFOSsAwJeVTTNpGkY5dEQMJyFV8LPR2x4DBxaVZZaAu7YcCsmD0lpNFg+aFuyyKq2ZLJftwTnMXg3iKglMegmIiIiojal3qndHr8qniZruc8fbsTqHS8iI3Mp4KsMHTdM2n/1uADxY/6BdUVmbKk2/VyCdslwS8B9sGnhTWlLll3qVvsRUdvHoJuIiIiI2ozGpnZLtXJ7xi/At68jw14cOqaX24PzUk9F2rhHgJjOalu/cKjp3w1NTz+StmSyXR4/2LpwImob+DediIiIiNqM+qZ2y1TyJNcOTMx6WfXclqZdq01V1cMnRfdBr0mPA8kD6pxLAuzDmf59sLZk+0tlXXhMo+vCiajtYNBNRERERG1G9andPs2FQtf36FI6H7dmLYNRhdtQa7evQhISJ/4Thh7jj11bMrcPewpd6BAdftB14UTUdjDoJiIiIqI2Q6ZsWy0BZFX8BIN3BsI9e5EFDbstJnT1+lBgTsTHkVfi9EtugSEx8pi2JZM+3b07hOOy0elsF0bUjjDoJiIiIqI2QaZu53vWwWL8N6zlW2BBVaEyi6Zhl9WJDXF/xHuBCeidlogu8RHNfj3BtmTBdeHhNhPC/GVI6sD+3ETtCYNuIiIiImr1thZswefLnsaevQsR7q1Q2wyahiFuHzpYxmJ20pXYXmZBrPPgLb+OpurrwgOBAHJyyo/J8xKRfjDoJiIiIiJdViFvSuXw7PJsfLHiJazdPguoLAxt7+n2oLdpIGaFXYNvtQ6weaTlV0STWn4RER1NDLqJiIiISHd9toNroaUomrTXkmrgUpysesAcyN+JHd/eibWlq0Pbkn0+TArvhj5n/xtayjB0O4yWX0RERxODbiIiIiLSTSZbAu7n5mxVfbZV1e8ok+prLe23pBq4FCfrF6sh99vHELP2DZygebEoJhr5JiNO9oTjuOH3o+voiwCDQVUpP5yWX0RERxODbiIiIiLSRSb7/MEp+Hxlpgq45X6wv7XTbkZ3azjW5y3Dh989h4eyf0KC+/ep5BeWAQujL8Ub9jMQtSMMt3Yt4RRyItINBt1EREREdMw0lsneklWKCo8fqTFhoYBblPj2oLziAyRjFYrcLmzTytALgNdgxcL4P2B+4uVwm5zoqmkqkJ+xIlNVDedUciLSAwbdRERERHTMppRLhru+THa6zYnVe4tQUOFFj8SqKeGuQCGKKj8DKn+G018SOs86mxXltpPwY8qfUGRNDm2X80kgvzWnVE1d59RyItIDBt1EREREdExIICyZaAmMq2eyhdxPinJgf4kLWWVFsFh/gbdyJiK8OWpttujo9WFQZQLmWu7B0k7DYaqnKJrDakJ2SUCtFSci0gMG3URERER0TEggLGu4ZUp5fWLDTbCHrUalexacrkw4EFDbI/0BnOK2Y43xWszpcgZySt2I9vhVhrw2maoua8SlOBsRkR4YW/oCiIiIiKh9kEBYAmIJjGszaAEkF36MAeFvooO2F0YEYNE0jK3wY6DhQrzpeAlb48Zj6uguSO/gxP7iSmiaVuMccl+290iMUNXQiYj0gF8BEhEREdFRa/nVGNlH1nJL0TRZwx2cYt65fDXO2vcC0io34qXoKGywWTGs0oNI7UR845gKry0afRIjMHlIiqpKbjQYVPuw4FR1mVIugbwE3LHhVrUfi6gRkV4w6CYiIiKio9Ly64IhqY226pJAWPaRgHlzTjbiwpbj5vxFGFQyP7TPBaVlOCv+ZKRd8G/sMqSgez1BvTyH9OsOXoOs4ZZrGJAaHQrMiYj0gkE3ERERER2Vll8STEsw3FjQ2zMpDCN6bcFP616DtSIDBndR6DFXbC90OGsa0H2sut+tkeuQ55C2YIeabSciOtYYdBMRERHRUWn5Vb1HtqgeEHeODcOqnOX4ctnTKMhahQi/R+3zXXgY+hgjYRp7H+xDrwCM9RdZq48E2GwLRkR6x6CbiIiIiI5Ky69gj+wfNmRh8c6C0PRzWHIRbv0SdvdymLxlVftrGk50e3FmnymwnHIPYK8K1ImI2hoG3URERER0VFp+SUGzHbkevLFwF/wBDfGRPhitsxGo/BHhZQWh/Xq6PbggaRQ6nj4NiO3Kd5+I2jQG3URERER0yC2/6uuRXeH2obDSA4MR6JfoQmbZY4j0ZMCAqtZeiT4fTvbE4aTzn4Gp20l814moXWCfbiIiIiJqkmDLr4Z6ZMv0c+m3falpAf6x9XqcUrJNBdxhgQDOLAe6mq/BG1HPY3fEYL7jRNRuMNNNRERERE3qwV295VftHtl7SjIx2LAPU33TkZ6zXZ3n/FIjnAEDouxnYnHnq1BpcMCVV3VeIqL2gkE3ERERETW5B3ftHtn7SkoB8xxEm+fhnKIdSPe7Q+faF3Ey9iTfhEJrR3W/0uVT55NAnoiovWiT08tffPFFdOnSBXa7HSNGjMDSpUsb3b+oqAh//vOfkZycDJvNhuOOOw6zZs06ZtdLREREpKce3NJzO9phRZf4cPVT7st2eVxI4H3PxB6YMGQPkiKfRJLvM0R48/ClMxxuA7DL3BWvdn0O73f+ZyjglunnMi29R2KEypwTEbUXbe5rxo8++gh33HEHpk+frgLuZ555BhMmTMDmzZuRmJhYZ3+Px4PTTjtNPfbpp58iJSUFu3fvRnR0dItcPxEREZGee3D3TorA+vy1mLH0KeTtXw6Dz6X2cwQCGOs3I3vUvzBt3/HIr/Aj2eILTT+XgDs23IrJQ1LUNHUiovaizQXdTz31FK677jpcddVV6r4E3zNnzsQbb7yBe+65p87+sr2goAC//vorLBaL2iZZciIiIqL2pCk9uNfn7Ma/57yNfZlzAFdR1WOahlEuD87ufSkixvwdcETjlmpT1LNLqqaoD0iNVgG3ZMmJiNqTNhV0S9Z6+fLluPfee0PbjEYjxo8fj0WLFtV7zFdffYWRI0eq6eVffvklEhIS8Mc//hF33303TKb6e1ASERERtace3DI1vET7Fkbf18jcnotgSN7D48GFcYORctGTQHyP0P4SWPdJjqy3GBsRUXvTpoLuvLw8+P1+dOjQocZ2ub9p06Z6j9mxYwfmzp2Lyy67TK3j3rZtG2666SZ4vV48+OCD9R7jdrvVLaikpET9DAQC6qY3ck3yj6Uer62949joE8dFnzgu+sRxaTvjEm4zwWY2otLtq9GD2xTwYmT+Z8iq/BAr7VXlgGL9fpxvjMGACY/BcNzpUM9Sz3N1iQurdk+up2absfaGf1/0ieOiT4FWEMM09draVNB9uG+UrOd+9dVXVWZ76NChyMzMxBNPPNFg0D1t2jQ8/PDDdbbn5ubC5apa16S311hcXKw+tJL5J/3g2OgTx0WfOC76xHFpO+MSpmno6DRiU04JusTY1bY+Fctwft50JHozUGw0Yps1Bid5NIzofQ28/a9ArskK5OQ086tpO/j3RZ84LvoUaAUxTGlpafsLuuPj41XgnJ2dXWO73E9KSqr3GKlYLmu5q08l7927N7KystR0davVWucYmb4uxdqqZ7rT0tLU1PTIyEjo8QMra7Hk+vT6gW2vODb6xHHRJ46LPnFc2ta4TDnRjv/OWYFdFR/g4spV+GPRqtBjEQENt8eMQ8I5/wTCE5rpyts2/n3RJ46LPgVaQQwj3bLaXdAtAbJkqufMmYNJkyaFBkvu33zzzfUeM3r0aLz//vtqv+BgbtmyRQXj9QXcQtqKya02OV6vHwj5wOr5+tozjo0+cVz0ieOiTxyXtjEuXr8XeyvmIsz8MsIDu7Ha4sckg0FlwPeED4DntMeQPuikZr/uto5/X/SJ46JPBp3HME29rjYVdAvJQE+dOhXDhg3D8OHDVcuw8vLyUDXzK664QrUFkyni4k9/+hNeeOEF3HbbbbjllluwdetWPPbYY7j11ltb+JUQERERNT+Zurkqe4VqAVaYtRJGv0dtl5Jne50dEH3CfUg94TIYTfr8pZeISO/aXNB98cUXq7XVDzzwgJoiPmjQIMyePTtUXG3Pnj01vpGQaeHfffcd/vKXv2DAgAEqIJcAXKqXExEREbVlmWWZ+HT5C9i643vAVay2GTUNp7i8mNj/KoSd8jfAGt7Sl0lE1Kq1uaBbyFTyhqaTz58/v842aRm2ePHiY3BlRERERC2v3FuOmRs/ws9r34FWkhHa3svtwYWJw5F0xn+A2G4teo1ERG1Fmwy6iYiIiKgBfi9mz7kbP+3+EQj41KZ4vx+TjbHof9a/Yegxnm8dEdFRxKCbiIiIqL3YsQD49m+YkLcZS+Jj4TMYMMGtYeyIv8Ay4k+AydLSV0hE1OYw6CYiIiJqw4rdxdi3fwV6L30T2PCl2uYEcHVxCaydzoLppIdgSusCGKV0GhERHW0MuomIiIjaIH/Ajx93zMLslS/DlLcFD+TkIuLAYxlhffCB80ZsLDsOth9zkJ5YgQuGpKJfSlQLXzURUdvDoJuIiIiojdmQtx4fLP0PinJXweCrVNtmO8Nwvs+B95xXYaZhDJKiw9HFakKlx4+1GcXILKzEreN6MPAmIjrKGHQTERERtRF5lXn4bNWrWLPlSxgq8lWvbYOmYbTLg4m9/ognPRfit6wA0hOdMBiqppM77Wak25zYllOGGSsy0Sc5EkZONSciOmoYdBMRERG1MoGAhl355Sh1+RBhN6NjtAU/7vgaP658Fb7CnTBAU/t183hxTngvdL/gGewypmH9V+uRHOUIBdxBcl+2b80pVeftliCrvomI6Ghg0E1ERETUiqzLLMZnKzJUZtrtDQDmPNgsbyLCvQlGv0ftE+X3Y0y5Battf8IjGIP0pQH0TylS+zuiTPWe12E1IbskoAJ5IiI6ehh0ExEREbWigPu5OVtRUO5Rmemutp04fd9TeC98H8qMRhg1DSdXeuAJnIEv4i5FeFQcor0BtWZ7S1YpvP6AWsMtU8prk+02i1FlzomI6Ojh/1WJiIiIWsmUcslw55e7MSAOOC37eZyQ/zmMCOC8gA3L7XZ0c/XAN+E3ITI5HRaXCyajIbRme2t2Kdy+APYXV6r71aeYa5qmtg9IjUaXuPCWfJlERG0Og24iIiKiFlyPLUFuUwqX7cwrw6qcZXBavsC1W1cixVsUeiw9kIBF1hvx95KeGNwhGlH1rNnuGB2GjMIKlc2WqemSKZcp5ZLhloA7NtyKyUNSWESNiOgoY9BNRERE1ELrsSUAlkriB+uRnVmWibd++y+ivXMQrpXjR4cfU72Ax2DH3A5T8Uv8xcgqD8CXXQBzrYA7SAJsi8mIC4ekYk1msboGWcMt1yAZbgm42aebiOjoY9BNRERE1ALrsaWg2cF6ZFd4KzBz08f4ae1bCBRnIDjx22U0YGXUqfgu+WYUWzuobf6AC2aTAT6tqnJ5Q2u2B6ZF47xBKYeVbSciokPHoJuIiIjoGK3HloD7YD2yg1PJF2f+it+2vQJPwQYYAj7Vczve58fJ5U5sT/4HPo4YVmNNdkmlBx2jHCqQ7hBRM/CuvWZbAmy2BSMiOjYYdBMRERE1M8kqB9dRN9Yj+4cNWfhx2wZsz3sX0b4VCNMqVLBt1TRMcAXQp9ef8FjuycitCCDZ4quxJjvOacNZA5Ixc81+bMstQ4zNAKtNQ6WXa7aJiFoSg24iIiKiZibZ54P1yN6R68EnPy2HZpiGjsgOPTbY5UKnwFCknfsE0nochz9XWxde35rs7glOfLZ8L9ZnFKLYUw6bxcQ120RELYhBNxEREVEzk3XTEhw31CPb5XJjbOkXuMH/ATbZfHgrKhJJPh/GuGOwLvFBvFLeHQM2+XBfd00F1jINvaE12fJ4rw5OrNi6F5bwSEQ5rFyzTUTUghh0ExERETUzCYplLbcUTaveI7sssB9dy3fhuj0voZt/p9o21AW4DR7kxEzFtx0nI2AwI9nsU9PPJdCWtdgHW5Mtj3eKsSMxMRpGo5HjS0TUghh0ExERETUzCYKlLZhUKZdp4QmRQDl+QKBiNgaUZaCbvyS074qYiZif9CeUWWJrTD+XqeSS2SYiotaFQTcRERHRUa5UXt/Ub5n2ffPY7pi+5GvkFfwPcb5dMMGP1XYbNldYYEMnzEj6C3JjBjXY7kvOR0RErQv/z01ERER0FHtxB4ucSeE0CZRlWrlkuZ3OQsze/CwMxfOQ6CtT+9sCAZzlAbqO+ycezxmFNZllSNe0GhXOa7f7IiKi1kU3QXd2djbuvPNOzJkzBzk5OeofmOr8fn+LXRsRERFRUwLu5+ZsVb24pQWYVCqXDPWqjCysy/sIsaZ5sJTvVy3AxPGVLkzqeiaiTvsX4EzA5MxiZBRtDbUWq94OLDbcqqqTB4ulERFR66GboPvKK6/Enj17cP/99yM5OblOD0siIiIiPU8plwy3BNyS2Q7+HuM1b0KY7R1EuHbAjKoEQrLPh4usHdHjD88AacND55Dp57eO69FoOzAiImp9dBN0//LLL/j5558xaFDddUxEREREeiZruIMZ6mDA3bl8NU7LeRIzwn6fSj7RA4wZ/XeYh10DGOv27D5YOzAiImp9dBN0p6Wl1ZlSTkRERNQaSIAsa7hlSrnTW4CJWS9hSOFsyG82m02RCAtoiPUNR9KkJ2A+rnuj5zpYOzAiImpddNO48ZlnnsE999yDXbt2tfSlEBERER2ScJsRHsta+Aqm4Y7Nl6qAW0h++ixXB+RFPY6P4+5GWEwHvrNERO2MbjLdF198MSoqKtC9e3eEhYXBYrHUeLygoKDFro2IiIioIbtLduOztc8gwTcXnkA5tlq8GOAGKk1OfJd0A5bEnIOtuVJ9PILVx4mI2iGznjLdRERERK2lD3dOWQmW7/8cG3d+DJRkwnbg8R0WC8pt4/F9xxuRF4jE/lxWHycias90E3RPnTq1pS+BiIiI6KBtwT5dvherchfD5P8UCf6doarkST4fJhk7YEnKP/FZRVe4i6T6uIfVx4mI2jndBN3BXtxffPEFNm7cqO737dsX5557LkymutU9iYiIiI51wP3vH39BifsTxGsr4QiUq+1WTcNp5T70G/AXpJ12C/rAiFNZfZyIiPQWdG/btg1nnnkmMjMz0bNnT7Vt2rRpqqr5zJkz1VpvIiIioqMxNfxQ23HJcZ8s/gle12NIC+SGtg91udDNeALecVyFZZ6uuA9GVh8nIiJ9Bt233nqrCqwXL16M2NhYtS0/Px9TpkxRj0ngTURERHQkmerPVmSoftrS3stmMSI90YkLhqSq/tgNCviRv+BV3LX+X/ggwoDVdpuaSn6qOwZrOzyE2eEDEebyYWtOqQro2e6LiIh0GXQvWLCgRsAt4uLi8Pjjj2P06NEtem1ERETU+gPu5+ZsRUG5B8lRDtVPu9Ljx9qMYmQWVuLWcT3qBN7Z5dlILNwLw6w7kbBvpdp2QakRqT4T3FFTMKvjBQgYqn6VclhNyC4JqAw6ERGRLoNum82G0tLSOtvLyspgtVpb5JqIiIio9ZOp4ZLhloBbMtsGQ9V0cqfdjHSbU2W+Z6zIRJ/kSDU1vMJbgZmbP8ZPa97GVbvXY4jbHTrXtvBxWJn6Z5Ra4ms8hwTwkjmXKetERETV6eZfhrPPPhvXX389Xn/9dQwfPlxtW7JkCW688UZVTI2IiIjocMiUbwmsJcMdDLiD5L5sl6nhO/PKkOtdj48WPQlP/noYAl58FhGOPh4PbPG98FbMzZhZ3A3pZieqn0XTNOwvlj7c0ezDTURE+g26n3vuOdU2bOTIkbBYLGqbz+dTAfezzz7b0pdHRERErbRQmvxZ1nDLlPL6yNTwjNJcvPDLfSjJWwCHv0wF1VKVfJQrgNzj/460CX/B8VkVWDRnayiAl+Mkwy0Bd2y4FZOHpDSpKBsREbUvugm6o6Oj8eWXX2Lr1q3YtGmT2ta7d2+kp6e39KURERFRKy6UdkLXOPVnCZBlSnl1fs2DvRU/wIqv4cnOhuPA9kEuN7prg/G+/Tp8mZeMW7Mq1JpvWfsdfA5Zwy3nlQy3BNyNFmMjIqJ2SzdBd1CPHj3UjYiIiOhoFErLKKhATJgV+4oq1Rru4BTzfN9GlJf/D5GuzbCgqgBavM+P8W4nNibeh++dQxGnaTXWfEtgLT8Pp+0YERG1Ty0adN9xxx149NFHER4erv7cmKeeeuqYXRcRERG1rUJpYVYzYsKtoanhXQM7EVf4BHaZC9W+Fk3DqRVeGJyXYHbyJfAbLXXWfAfbgUmAzbZgRETUKoLulStXwuv1hv58tLz44ot44oknkJWVhYEDB+L5558PFWer7a233sJVV11Vp5K6y+U6atdDRERELVsoraDCgykjOmHFtr0YvO1ZnFb2JcqMwCNxMUj3etHN3Re/pf0VZfakOudnOzAiImq1Qfe8efPq/fOR+Oijj1TWfPr06RgxYgSeeeYZTJgwAZs3b0ZiYmK9x0RGRqrHg2r/g01ERET6dbBCaXaLEYWlW6Bl/Ih7tr0NQ1m22h4VAO4NRMF70qP426oERMMKZz3Hsx0YEREdCSN04uqrr663T3d5ebl6rKlkGvp1112nstd9+vRRwXdYWBjeeOONBo+RIDspKSl069Chw2G/DiIiIjq2ZF11sFBaba5AIfaW/x/i8DTmb38dleU5VQ+Y7cDY+xD/p8XoMPgsNS1dqpBL+y/U0w6sR2IE24EREVHrLqT29ttv4/HHH0dERESN7ZWVlXjnnXcaDZqDPB4Pli9fjnvvvTe0zWg0Yvz48Vi0aFGDx5WVlaFz584IBAIYMmQIHnvsMfTt27fB/d1ut7oFlZSUqJ9yvNz0Rq5JfmnQ47W1dxwbfeK46BPHRZ+O9biolmAFFSh1eRFht6BLbBg6xTiQnhCOtZnFSLdWrekOaD7keH6Cp+JzxHkzVQuwMqMRv9ltOKnTWGgTpgHRnQ6cVcPkwR2RUViBbTmlSI6s1g6spKod2PmDk9V+8vytAf++6BPHRZ84LvoUaAUxTFOvrcWDbglY5c2Um2S67XZ76DG/349Zs2Y1OC28try8PHVM7Uy13A+2IautZ8+eKqAfMGAAiouL8eSTT2LUqFFYv349UlNT6z1m2rRpePjhh+tsz83N1eVacPkwyGuT91i+hCD94NjoE8dFnzgu+nQsx2VzTgVmbcjHzgIXPP4ArCYjusbacWafOIzpEobtWUXYuK8QYeEZ0LRPEeXbjHCtqnZMtN+PSf4wdDvlUWR3ORXwAMg5kPUGkGAGpgyKO3D+8tD50+PsmNg7DglmN3Kq7a93/PuiTxwXfeK46FOgFcQw9c3U1mXQLf255RtpuR133HF1Hpft9QW4R8vIkSPVLUgCbukP/sorr6jK6vWRTHr1auvyxUFaWhoSEhLU+nA9fmDlfZTr0+sHtr3i2OgTx0WfOC7te1zW7yvGe6v2olC1BAsPZaK3F1bivVX5uHVsOq4fp+H1xS/CXzYfkYGqWWhGTcNYlwdnDLwe1hPvACzBTtx1yXf8o/t0rpNJb43twPj3RZ84LvrEcdGnQCuIYaonjHUddEsBNfn2YuzYsfjss88QGxsbesxqtapp3x07dmzSueLj42EymZCdXVUgJUjuy1rtprBYLBg8eDC2bdvW4D5S3VxutcmHQa8fCPnA6vn62jOOjT5xXPSJ49I+x0WmdM9YuQ+F5V6kJ0b83hLMYUS63awql7+1+Af4/W8grGQ7DKiaAn6cx4M/xA5B8iVPAbHdmvRc8hLkOdoC/n3RJ46LPnFc9Mmg8ximqdfV4kH3Kaecon7u3LlTZYuP5A2VIH3o0KGYM2cOJk2aFPqGRO7ffPPNTTqHTE9fu3YtzjzzzMO+DiIiIjpGLcEATDD9htM2PY8XY/zwGY2I8vtxvhaOoac9DUPvs+W3Ng4HERG1mBYPuoMkoy0qKiqwZ88eVRStOllz3RQy7Xvq1KkYNmyY6s0tLcOkAnqwF/cVV1yBlJQUtS5bPPLIIzjhhBOQnp6OoqIi1d979+7duPbaa4/6ayQiIqKj0xIsoPkR78nCufueRs/SxWrbeWV25FisOHPA1bCffDdgDePbTURELU43QbcUIZPA+Ntvv20wA90UF198sTrXAw88gKysLAwaNAizZ88OFVeTgL56Nr2wsFC1GJN9Y2JiVKb8119/Ve3GiIiISF8twcJsBuR6F8FQ9jnu2bcKMYHfu4kMiR+OsPOeBuLTW/R6iYiIdBl033777SrTvGTJEowZMwaff/65Wov9z3/+E//9738P6Vwylbyh6eTz58+vcf/pp59WNyIiItKnLnHhqo/2ssx1iLLPQLhrHayaG7OcFlxW4ka+MQ5zu9yBCy67CTDpc90fERG1X7oJuufOnYsvv/xSTQuXTLRMNz/ttNNUNXCZCn7WWWe19CUSERFRCyj2FMEW9gVijV8gsrJQbTNoGiyaAZ+HXYC5iVfhhnEDYGTATUREOqSboFvWXQf7ccs0b5kiLi3E+vfvjxUrVrT05REREdEx5gv4sGDXD5i5/AV48rciUguo7V09XgxypeD7qNsRntIPNwxJQb+UKI4PERHpkm6C7p49e2Lz5s3o0qULBg4cqPpky5+nT5+O5OTklr48IiIiOoa2FG7Bx0ueQlbGr4C3Qm0LDwQwyWdF16EPIrvT2bjBYVFTz1tjH20iImo/dBN033bbbdi/f7/684MPPogzzjgD//vf/1QbsLfeequlL4+IiIiamfTjlvZgpdlb8e7yW+CqyApNJT/R5cHZvf+I8FP/AdgjkcTRICKiVkI3QfeUKVNCf5YK4tK2a9OmTejUqRPi4+Nb9NqIiIioea3LLMYXv+3AcTvexnnF72Oy3YD3oyLQ2evFRRHHofMFzwId2FmEiIhaH90E3bWFhYVhyJAhLX0ZRERE1My+3bwSa+YtwNW5r6KjP1NtG+kCfAYntjhvQOlpNwEdojkORETUKrVo0H3HHXc0ed+nnnqqWa+FiIiIjq1idzE+X/sW5q18H2MrMtHRX6q2B2DEorjJmNPhGqzPB/at3Ic+HaO4dpuIiFqlFg26V65c2aT9DAYWSCEiImora7aLK93YXPQrlm15Fa68zYjQAljmsOOUikrA2htfptyBLEcPdUxylA9bc0rVcd0SnC39EoiIiFpX0D1v3ryWfHoiIiJqjkJoLh8i7OYalcVlzfZnKzKwLmcLfP6PERdYC7vmgjwaFghgfIUBS5P+hlUxZ0AzGEPndFhNyC4JqHMSERG1Rrpd001EREStx/p9xZixch+25ZTB7Q3AZjEiPdGJC4akqsef+nE1cl0zEW2Yjyh/fui4EZVuOHwn4cfkG2EKj6lz3kqPX51LgngiIqLWSDf/gp166qmNTiOfO3fuMb0eIiIiaprNORV4b9VeFJZ7kRzlgCPKpILltRnFyCiogMe0ARWud5AW2AsjAuqYVK8Po71J+MJ8EzaauiKszIT0MK3G7wKapmF/cSUGpEarrDkREVFrpJuge9CgQTXue71erFq1CuvWrcPUqVNb7LqIiIio8Snlszbko7Dcg/TEiFDQ7LSbkW5zwrRrAU7wvYzvIj1qu+PAVPLimBvxbcxEeNwB2AorVDZbsuQqaLdWBe0ScMeGWzF5SAqLqBERUaulm6D76aefrnf7Qw89hLKysmN+PURERHRwuwoqsLPAheSo8BpZ6khPDs7a/zwGlM6DBmCHPQqxfg0dLafil843wGWOVPs5rAZYTEZcOCQVazKLVeAta7glCJcMtwTc/VKiOBRERNRq6SbobsiUKVMwfPhwPPnkky19KURERFRLqcsLjz+gstOaFkCedzmSSr/DHfsXwBaoVPtIKD6hPBnfd/4bNjiOq3fN9sC0aJw3KKXBQmxEREStle6D7kWLFsFut7f0ZRAREVE9IuwWWE1G5Ln3wuP/CA7XKhQFKrHT7EMvD1BqisazhilY4jwdfe0xKgBvaM22BNhsC0ZERG2NboLuyZMn17iv/iHevx+//fYb7r///ha7LiIiImpYnNMHu3Mm3OVzEBMoDG3fZrGiIOIcvGK8BNGxiUjy+rlmm4iI2iXdBN1RUTXXaxmNRvTs2ROPPPIITj/99Ba7LiIiIqrLH/Dj5z1z8fVvzyGiYjMMWrWq5J4k/Bp3B5a5U1UhtOtO7qYekz7dXLNNRETtjW6C7jfffLOlL4GIiIiaYFvhNny89L/Yt3ch4K1QU8alKvnplQbssF6NF8LHwaqZMSA1okYhtD7JkVyzTURE7Y5ugu4gmU6+ceNG9ec+ffpg6NChLX1JRERE7aL1V1OKmO3e+yuemX8nUJYT2nZCpRvnHnchnGMfQPdyC05o4Bxcs01ERO2RboLujIwMXHrppVi4cCGio6PVtqKiIowaNQoffvghUlNTW/oSiYiI2mRQvS6zODT12+2tateVnujEBUNSf2/X5XUBvz6HTj//Fz2dNmy2WZHm9eIP4emIPOl+OPucrJaGdQtr2ddJRESkN7oJuq+99lp4vV6V5Za13GLz5s246qqr1GOzZ89u6UskIiJqlRoLqsVzc7aioNyD5CgHHFEm1cZrbUYxMgsrcfHIMIwL7AZm3wMU7lJTyS8q9WKLIQGjT3kU6H8RcnJzW/olEhER6ZZugu4FCxbg119/DQXcQv78/PPP46STTmrRayMiImrNAXdDQXVGQQUcVrN6TIJwg6Eq8+20m9HJqmFb/v/w7vxf0CF3F/p5PFUnNJjQ4fgb0GHMPYA9CoFAVQE1IiIi0nnQnZaWpjLdtfn9fnTs2LFFromIiKi1TymXDHd9QXW6zakC8oIKD4amxYQeC2h+5Hl/haf8U6QG9sIY0PBppBM98wpg6XIScOYTQGLvFn5lRERErYcROvHEE0/glltuUYXUguTPt912G5588skWvTYiIqLWSNZwy5RyyXAHg+oguR8TZkWZywefpqltxb6d2FfyL5iLXkCUZw+M0BAeCGCszwbzBa8DU79mwE1ERNRaM91XXnklKioqMGLECJjNVZfl8/nUn6+++mp1CyooKGjBKyUiImodpGiarOGWKeX1kYy3KHTlo1KbDUPlAkT5i9U2g6ZhVKUHFsPpSLv4MRhSOlQVY8srq1GMjYiIiFpJ0P3MM8+09CUQERG1KRIYS9E0WcMdDLBrT3eLjF4Kzfcdwr1Zqkia6ObxYpi/Gz6x/gkJXfphcnJig8XYJg/uiATd/DZBRESkP7r5Z3Lq1KktfQlERERtimSiJTCWommyhrvGFPOAH71yZqKn6T1s0aqKoUX6AxjrsmFHxC14zj8MsU4bJg9JwYb9JQ0XYyuswJRBcUhMbLnXSUREpGe6CbqDRdO++OIL1TZM9O3bF+eeey5MpvqnxREREVHDpA+3tAWT1l/Btd0OqwlxJRsxaf/T6OndiDyTEY/FxWJkpRd+w1l4M/xiGM0ODOgYoQLuPsmReHTmhgaLsW3LKcW3G/Mxuk9nGHVTKYaIiEg/dBN0b9u2DWeeeSYyMzNDbcOmTZumqprPnDkT3bt3b+lLJCIianX6pUTh1nE98PHyHViXMwtnZC3FtSULVJE0Ee8P4J8xx8N+yePY5Y9H72rrtSVo35Fb1mgxtuRIB3bkl2NXQQXSEyNa6FUSERHpl26C7ltvvVUF1osXL0ZsbKzalp+fjylTpqjHJPAmIiKiQ6NpGioNG+Hz/xfJ/rXYY3XDa9Bgk5g7rgcw8d8ISx+n9u12GMXYJHPu8QdQ6qrb9pOIiIh0FHQvWLCgRsAt4uLi8Pjjj2P06NEtem1ERESt0d7Svfhk6dPYsXse4C5VhdJKjUbsDItCr1F3AiNuBMzWIyrGJtutJiMi7JZmfCVEREStl26CbpvNhtLS0jrby8rKYLU2/gsBERER/a7cW46v172Lhev/B610f2h7f7cbk1PGImHK40Bk8hEXY5Ms+v6SSqTH2dElNoxDQEREpOeg++yzz8b111+P119/HcOHD1fblixZghtvvFEVUyMiIqKGSQ/tHXml+HXvPKzY+hq8RVth0PzqsUSfDxdYk9F38tNApxOOSjE2yXDvL65EbLgVE3vHqf2IiIhIx0H3c889hyuvvBKjRo2C2Vx1WT6fTwXczz77bEtfHhERkW6pHtrL9yJ738vwYj5smltNJbdqGiZ6DDh19P0wD70SMJqOqBhbsE93dklVn+4BqdE4f3AyEszuo/6aiIiI2ooWD7oDgQCeeOIJfPXVV/B4PJg0aZLq2S3T13r37o309PSWvkQiIiJdB9wffTsXk3NeQFffCjwSHwuPwYBhlW7EG05C6rnTYO7e+YifRwJvaR+2K79cFVcLVjgHNOTk5ByV10JERNQWtXjQ/a9//QsPPfQQxo8fD4fDgVmzZiEqKgpvvPFGS18aERGRbnn9Xuwv2IaSr57Fg9kfw4yqqeQXl5TCZT0OKxPvwDelSRiwoRz3ddWOyvRvOUe3BGedae1ERESk46D7nXfewUsvvYQbbrhB3f/xxx9x1lln4f/+7/9gNBpb+vKIiIh0RYqXrclZjRlLnoB73wo8kLMf5gM9t4ssidie+GesjRorTbSRbPRha06pyk7XDpaJiIionQTde/bswZlnnhm6LxlvmVq+b98+pKamtui1ERER6UlWeRY++e05bN7xHeAqVttmh4fhnHIvfkq4DAsSL4PXaA/tLwXPZP21TAcnIiKiltHiqWQplma3//4LgrBYLPB6vYd9zhdffBFdunRR5x0xYgSWLl3apOM+/PBDFfDLunIiIiK9qPBW4NM1b+CRGRdh84aPQwH3cR4P7OiPf3Z5Gz8mXVMj4BZSYVwKnsn6ayIiImoZZj1Mk5Oq5dKnO8jlcqlWYeHhUqClyowZM5p0vo8++gh33HEHpk+frgLuZ555BhMmTMDmzZuRmJjY4HG7du3CnXfeiZNOOukIXxEREdHREdAC+HXvAnzy61PwFm6B8cC67Ri/H2PdkUg78Rn8L6cr1ksPbadWt4d2caWqMF5V8IyIiIjaZdAtlcprmzJlymGf76mnnsJ1112Hq666St2X4HvmzJmqMNs999xT7zF+vx+XXXYZHn74Yfz8888oKio67OcnIiI6GrLK9uOtXx7Gnr0LYfRVqqlpFk3DyRUBuByX4mXHRERvduCsAdGN9tCePCSFPbSJiIjac9D95ptvHrVzScux5cuX49577w1tk2Jssk580aJFDR73yCOPqCz4Nddco4Lug3G73eoWVFJSEmp/Jje9kWuSjIcer62949joE8dFn9rVuORuQtjsu5Fftj5UVHSwy41O5pOwOO1GVJij0U3TsC23DKv2FOHPY7rji1WZ2JZTjuwSP2xmE/qnRGHykI7okxzRrO9ZuxqXVoTjok8cF33iuOhToBX8+9LUa2vxoPtoysvLU1nrDh061Ngu9zdt2lTvMb/88gtef/11rFq1qsnPM23aNJUVry03N1dNjdfjh6G4uFh9aFkRXl84NvrEcdGntj4u6nV5SuBc9jzC1r+PSM2PiWEOLLXbMdTdEStib8FaWzdASp54K9UxMTYD1mcUYGKPcFx3fBwyipwoc/vhtJmQGm2D0eBu9h7abX1cWiuOiz5xXPSJ46JPgVbw70tpaWn7C7oP5026/PLL8dprryE+Pr7Jx0kmXdaNV890p6WlISEhAZGRkdDjB1bW+cn16fUD215xbPSJ46JPbXVc5JeJldm/4fvfnsMtm35FeEVB6LETjLFYEZiKHzpPgMlkhKPWsVabhmJPOSzhkUjqEI2kmt85HxNtdVxaO46LPnFc9Injok+BVvDvS+2C4O0i6JbA2WQyITs7u8Z2uZ+UlFRn/+3bt6sCauecc06dKQJms1kVX+vevXud46ToW/XCb0HyYdDrB0I+sHq+vvaMY6NPHBd9amvjsrdkLz5Z+l/s2D0f8JRhlsmDi+UBSxhw4h3IPu5KrJ61A9E+DU7z70XSgiq9Up3chCiHtUXfk7Y2Lm0Fx0WfOC76xHHRJ4PO/31p6nW1qaDbarVi6NChmDNnTqjtlwTRcv/mm2+us3+vXr2wdu3aGtvuu+8+lQF/9tlnVfaaiIjoSAUCGnbll6t+2dK+KzbCj5nr3sLiDR9BK/99+neByYhAvwthPO0RICoFnQMa0hNzsFaqk9ucrE5ORETUCrWpoFvItG+piD5s2DAMHz5ctQwrLy8PVTO/4oorkJKSotZly3SAfv361Tg+Ojpa/ay9nYiI6HCsyyzGZysyVHVxl9cDv3kZIgxfw+nJgAGa2ifR58NZlhREjH4Yu7qcgC4R4apaudFowAVDUlmdnIiIqBVrc0H3xRdfrAqaPfDAA8jKysKgQYMwe/bsUHG1PXv26HZ6AhERtb2A+7k5W5Ff7oYzIgNhhvfh8GyBWZNqaIA9EMDpPgtKoq7Hs76T4FoO2NasR3qiUwXb/VKi1O3WcT1CgXt2SQA2i1H135Z2YPI4ERER6VebC7qFTCWvbzq5mD9/fqPHvvXWW810VURE1N6mlEugXFDuwdiwTdhc9hwCWrl6zKBpGFHphdMyEV9FTUFWpQnJUXYkHeixLdPJpfe2BNvBwLtPcmSNKepd4sLZf5uIiKgVaJNBNxERUUuu2ZaAWO6X7NuC+8rfwKDsn/CLw44PIyOQ7vGgH/rj57ib8EOWHbEGoF/H39drO+1mtX5bstozVmSqYFummcutW4KTA0tERNTKMOgmIiI6Smu23d4ArBYNUZFrcHXlWjy+/yNYVGNtYGSlCz5zCjbF3445EYNRVOFBmSsP3ePDaxRIE3I/OcqBrTmlKnhnsE1ERNR6MegmIiI6wjXbMoU8KdIOb/gOVJS9A1/eFmyoLMPQAwF3iTkO3yddhxUxE6EZTGpbmcunfjrtlnrP7bCa1PptyZ4TERFR68Wgm4iI6DCmj4vgmu2O8RUoq3gdxrJliApUqMeW2m04vcyDXxyTsLrz1fCaq44RmqahsMKjppKba2W5g2RttxRMk+cjIiKi1ov/khMRER3C9HEJhKW6+Ald47ApJwsOxw8oKZwDp78wdEwPjwc9/f1xf/Q1KA/rCHd+AMlRPpW9lmB6f3GlKpwm08b3FVUi3W5mD24iIqI2ikE3ERG1a/VlsaVoWe3p47LG2hFVFTSvycjHyv3fwBH4GpGeLARz1fF+P0Z74rAt/jbMDRuIorxyXDokFWsyi+tt9yXk/PKYOn+1oDw23Kr2CV4LERERtU4MuomIqN1qKIstPbKlanhw+rhsCxY7i7JqCFiegt+7EWb4Q/22T3aZUR59LeYly7ptIypdPnW+gWnROG9QSoOBPXtwExERtW0MuomIqF1qKIsd7JEtgXcwA60Cbk1Dn5KfcOb+l7DDmIe3oyIP9Nv2IdJxLn7rfDm8RkdozbZkqyWjHQywG6pAzh7cREREbRuDbiIiapdTyuvLYlfvkf3Nmv0q+22MLENs+XZckPUmupWvUvvFAthtrkTAPxRzI65HniEWyR4LHFbtsKaHswc3ERFR28Wgm4iI2h2Z6l0ji11Pj+yM4iJohnkoLPwB/csz0K28JLTPzvBB2B57E9ZpXTBlRCcs3llQ75ptyWITERFR+8agm4iI2h1ZWy1ZbJlSXltA88OFpXAYPkC0lgmjX8Mquw3bK8yIMiTh2+SbsD7iRGzLLceA1Aic1idJ3Rpas01ERETtG4NuIiJqdyQwloy0TAWXKeXBddgl/i2oLHsXNs8WxGpetd2saRhZoWFJ9LVYm3ARynxG7M8trzN9vKE120RERNS+MegmIqJ21xJMbrKWW4qmyRruikAOKsrfg8G9HOGBytCxQ91enJhyHmaYL8W6AiPcBR5OHyciIqJDwqCbiIjaZUswuUmV8r0578Ni+h5O/+9rtrt5vDgtehj6X/wkENsVdzXSy5uIiIioMQy6iYioXbYE++uIMPzb+CL2FM3Ea9GR6ph4nx+jfInodvK/kD50fOhcrC5OREREh4tBNxERtZuWYGE2A3rEVuD43W+i+4dfwKx5EA1gkMuNTuYo9BtyN5JOuAxGk7GlXwIRERG1EQy6iYiozbcEU0XSfBtRWf4e+pdtxuSK7NC+Bns0rj3hb8Dx1wJmW4teNxEREbU9DLqJiKhNtwQr82eiovxdmFyrEa65sMMCbLBa0MMDFPa/Coln/h1wxLT0JRMREVEbxaCbiIjaHCl2ZrAUI7fkXRh9v8LhLws9lu7xYJdjFF6Luwa3nDwOiQ62+iIiIqLmw6CbiIjalBJPCZbuehNhgf8h3FcQ2p7o8+F4Xwq2xt2KN8rTMKBjtKpCTkRERNScGHQTEVGbsXjbLHy89El4ivcgGE5H+gMY4XYiN/ZGzLKegP0lLsSGWzF5SArbfhEREVGzY9BNREStn6sEWPgsYn6bDk+kQ22yahrG+qxwRVyOz+xj4HIbYAt4MSA1WgXc/VKiWvqqiYiIqB1g0E1ERK2SVCSvcBUifPVHwE9PABX56Amgn82IGIMVEwdeg8hRtyNgdmBIfrkqriZrvWVKufTdJiIiIjoWGHQTEVGrszV/M75c8iSwdzH+un8PQiG00YIbjvsjDKf8DQiPq9oEoFsCi6URERFRy2DQTURErUZWxX7MmPcENuyeC7hL1bbVNisGuT0o7HYuSkfdg9RufWBgJpuIiIh0gkE3ERHpXoGrAN8sfxmLN38Oo7sotD3J50NeWF/cF30ttlR0h21BGdI3bsAFQ1K5ZpuIiIh0gUE3ERG1mEBAw65G1luXecrw/bp38NP69+Ery1JTxUW034/TTB3wm/UqTA/0R3KUA12sJlR6/FibUYzMwkrcOq4HA28iIiJqcQy6iYioRazLLMZnKzKwLacMbm8ANosR6YnOUJb6t+3f4oMlT8BdvCd0TFgggNMNETjp5Pvw+K7eWJtZoo4xGKoCdafdjHSbU51zxopM9EmOZNE0IiIialEMuomIqEUC7ufmbEVBuUdlqR1Rv2epi/Oy8PfIb5Gw5X9wR1d127ZoGk7xWzCi19XoMOY27CryYtuy9erYYMAdJPdl+9acUpVFZxE1IiIiakkMuomI6JhPKZcMtwTcwSy1pgUQbi7ADdo3GL3nA4RplYgHMNRlgt1ox8QBVyFy5G3IKSoHTBaUuipVdlyC9fo4rCZklwTUtHUiIiKilsSgm4iIjinJPsv0b8lGixLvJnjK30NqxSaMz8sKtf8KmOy4su9VMJz4FyAsFoFAAEC5ekzWf8t0dMmOy5Ty2mS7PC77EREREbUk/jZCRETNVgitPrKvZKn9zt0oKH4fBs862DQPck3Ab3Ybhrh8mOuYgI7nPYi+vXrVew55HsmSy3R0WcNdfYq5pmnYX1yJAanRaj8iIiKilsSgm4iImqUQWkNKfBmwG5+Ht3ANbJortL2z14tcx3D8K+kGbPUn4uG41BqBfXGlB95yF+LjNZjNRvU8UqU8mDWXKeWS4ZaAOzbcislDUlhEjYiIiFocg24iIjqqhdAaate1rzQT3yx7Fqt3/YBIf2mNXtsDA12xM/Ym/BTWUwXRA1IjVJa6ZmDvBwJ+9N1ahguGpqnzy/MEH5c13BL4S4ZbAu7GAn8iIiKiY4VBNxERHXEhtIO161qw/GV8uuZ1aK6i0JrtOL8ffV2JyIj5E36OGlKVpc4pC2WpN+wvqRnYRxpRWFqOtZnFyCxyhQJ7eZ5DneJOREREdKww6CYiosMuhHbQdl3uzcC8f6LnrgVAXIzsgCi/H2fYktBx0N/wXm4PbMsthzuvvEaWWgLpR2duqBnYaxrCrSbERdrVMdUDe7YFIyIiIr1i0E1ERIckWAitoXZdZosLEYXLEPf1f4A9P6ptSQDGVFQiypmEk8f8A9a+k1UAfn8Dhdh25JY1PbBPcHIEiYiISLcYdBMR0SFpqF2XT6tEWeW3cFV8i85aNsL3FIQecztTcdKIvyJ+5BUwmn8/pqEs9cECe/bhJiIiotbCiDboxRdfRJcuXWC32zFixAgsXbq0wX1nzJiBYcOGITo6GuHh4Rg0aBDefffdY3q9REStSbBdl1QJl/Zcfs2NkopvUFBwO4yl7yLcn4c8kwmLHXaUWBLwZsytuDL8Zfx1S188+u1mVRztUAL7+rAPNxEREbUWbS7T/dFHH+GOO+7A9OnTVcD9zDPPYMKECdi8eTMSExPr7B8bG4t//OMf6NWrF6xWK7755htcddVVal85joioPfXQbgo5h7Tr2ltQgm25XyHSOBsOfxYcBx43aRpGeE3YGjYFL5rOQXx0FNKsB69uXh37cBMREVFb0eaC7qeeegrXXXedCpyFBN8zZ87EG2+8gXvuuafO/mPGjKlx/7bbbsPbb7+NX375hUE3EbW7HtpN4fF7kF0yGxHGV2DT9sLg19R2o6ZhqBsYkX45ZvjPxor9Ta9u3lBgX6MPt8WIco8fGaVS4dzGPtxERETUKrSp6eUejwfLly/H+PHjQ9uMRqO6v2jRooMeL9Mk58yZo7LiJ598cjNfLRFR8/bQlqxytMOKLvHh6qfcl+1Nmd7doJJ9+PKLy/H5gvvgLdsDAzQYNA3DPBpuSbsYl9+4AtYT7sTGfH+TiqA1JtiHu39qFIoqPWr/Ypcf/VOiD5opJyIiItKLNpXpzsvLg9/vR4cOHWpsl/ubNm1q8Lji4mKkpKTA7XbDZDLhpZdewmmnndbg/rKf3IJKSkrUz0AgoG56I9ckXyjo8draO46NPrXmcVE9tJfvRUG5G+kJ1bLMNhPSE8KxLbcMny3PQK8OzkObal6aBcPCZ4Dlb+FUePFzXCwkvz3YB0zseTE6nHgn4IhWuxbnFsHt9au+2tLmqzbJWGeX+FFc6Tnoe9wnOQK9JvbCroIKlFR44K0oweD0VJjNplY5Pm1Ra/770pZxXPSJ46JPHBd9CrSCf1+aem1tKug+XBEREVi1ahXKyspUplvWhHfr1q3O1POgadOm4eGHH66zPTc3Fy6XC3r8MMgXC/Khlcw/6QfHRp9a87jsKXRhfUYhYuymev9/FGMzYH1GAVZs3YtOMfZGz+UL+LBy3zxE7fgBJ22eDYO/6svGeAB/qPAiufskRAz+EzR7DHJKPUBpjnrcW+4CAn4Ulparvtq1yRRxedxbXoKcHE+TXpfUNw+zBlBc6UJeXm6rG5e2rDX/fWnLOC76xHHRJ46LPgVawb8vpaWl7S/ojo+PV5nq7OzsGtvlflKSdImtnwxienq6+rNUL9+4caMKrBsKuu+9914VmFfPdKelpSEhIQGRkZHQ4wdWsl1yfXr9wLZXHBt9as3jst9TBBhNiIkIh6meTLbVpqHYUw5LeCTi46NUBrnU5UWE3YIusWEq++0NeLFkx2x8v3I6Cgu3I9rnxUi/GxZZhmMJA46/FqNH3gKES/hdV3y8hr5by7A2sxhxkfYaU8zlH05Zk90/NRZDeqQdUra9NY9LW8Zx0SeOiz5xXPSJ46JPgVbw7750y2p3QbdUHx86dKjKVk+aNCk0WHL/5ptvbvJ55Jjq08drs9ls6labfBj0+oGQD6yer68949joU2sdlyiHFTaLCZXeQI0e2kGVXr96PKfEjX+t3lSj0Fq3BDt6Jm3Ahp3voKhwO6AFICFxscmEdQ4nuh73R0SOuxPGyA5qe0PkLbtgaBoyi1zYllteVQTtQPVyaTMmRdAuGFo1Rby9jEtbx3HRJ46LPnFc9Injok8Gnf+739TralNBt5AM9NSpU1Xv7eHDh6uWYeXl5aFq5ldccYVavy2ZbCE/Zd/u3burQHvWrFmqT/fLL7/cwq+EiOjQNaXVVkq0A5+uyERhuUcFxLZIDUUVv2D3vs+Qm7kPRrVau0pvtw8R2ki87LwMlQUJSF+QhwuG2A9axCxYBC1YQT27pCqwH5AaraqOswgaERERtRdtLui++OKL1drqBx54AFlZWWq6+OzZs0PF1fbs2VPjGwkJyG+66SZkZGTA4XCoft3vvfeeOg8RUWtTb6utalnmmDCLCqkl4JbgvNy9ECVFH8Pqy4C9WrDdx+NHFEZhnvlSOGJTEG01wXYIfbaFPC5twZqjVzgRERFRa2HQJPVBR0TWdEdFRamF/npd052Tk4PExETdTs1orzg2+tQWxqW+Pt09EiMwomss3luyG13NhZhY8iH2eObi5zBr6Lhebr/KbO9IvBbrS+w1+mwL+SdDzikZ6/vO6n1MA+i2MC5tEcdFnzgu+sRx0SeOiz4FWsG/+02NA9tcppuIiOrPMidHm7F183pMzXsaY1w/wKz50MdoxCJHLNI9ASRZTsaGpKvwXa4ZpiID0mIO3me7W4LUFSciIiKihjDoJiJqoyQLLUGxy+fCTxs/wSs/vImxmZswobI8tI8dNpymnYqVHa9ApjkGZS4fTMZy1e9bpqXXR7bLGm0J5omIiIiocQy6iYjaqApvBX7a8AHmrnsXFSUZatvcMDvGVJTDhzAsib8QCxMuQoU5ukahte4JEcgpcal14PVWQPdIBXSjyp4TERERUeP4GxMRURtT6inF/DVvY8HGD+EqywptN2gaegWM2DvoZjxfcBoyXVYk+xxwGLVq7bysmDqqMz5fmdloBXRZ0y1F0YiIiIiocQy6iYjaiGJ3MX5c+SoWbp4BT0VujWB7mN+ECf2mIGnk7YA9EtdVK7RWXzsvo8HQYAV0CcxlP1YhJyIiIjo4Bt1ERK2dpsG3/UdM++U+lFXmhzabNA0jAhacNuAqJIz4M2BzNrmdF/tsExERER0dDLqJiFqpgN+PnOVfImLZswjPXYWRznD8EB4Gi6ZhFMIwfvD1iBl2HWCxN1porSHss01ERER05Bh0ExG1MnuKtmPGgv/ivE0L0bVyV2j72PIK+BwJOG3ErYgcfAVgshzxcx0sMCciIiKixjHoJiLSKWnbFZz+7bSZ4NN24vtlz2DjvqUw+l1YbyxH1wP7Zlq740PHRdjiPBU9k3uh31EIuImIiIjoyDHoJiLSoXUHCp1tzSmFy7seMZgBE3bArHlhPLDPKrsNfbVu+CnxcmyKGAUNQH5OGWasyFTrtVnojIiIiKjlMegmItJhwP3sj5tQVLEYcaaZiNB2wwR/6PE4vx/H+dJQHHM1Xu04BDjQ0kv+K5XGJVCXDDmnhRMRERG1PAbdREQ6m1L+5S/fwF8xHQnIgtEv+esqST4/klzdMN99ESp7ngCTKZjz/p209pIWYDIlnYiIiIhaHoNuIqJmXo9dux1XgzKXo2Luk7hjx7d4MD4WFcaqoLqz148002DsjbsGC92JWLe3EJYyN5KiHHVOIb20pee2PCcRERERtTz+VkZE1EzrsbfllMHtDaggOD3RiQuGpKo2XNWD8oziHLgzZ2L8jtkw7P4FwTrhJ1dUYqfVgQTridiROBWrrYlqe7wlAJvJiKxiFzpE2mE4MLVcaJqG/cWVGJAarYJ8IiIiImp5DLqJiI5ywP3cnK0oKPeo9dWOKJPKPq/NKEZmYSVuHddD7ffu0qUozH4HAayCM1CB4Xn5qArHgSJjLHyOC1DQYTL2mSJqnN/lDaBjtANhNpMK6tVzWKueQwLu2HArJg9JYRE1IiIiIp1g0E1EdJRI9loy3BJwS2Y7mIV22s1ItzlVgbPn586EseJj+ANbYIVHPe4zGPBTmAPHu2PhG3Ez3iofgVX7XEg3OlVxtNqZ7MGdYnD+4I6YsTJTBd6yhluy6ZLhloA7mE0nIiIiopbHoJuI6CiR6eLB7HP1ad8BzQ+XexESjTOgle1RlchNBx4LCwTQzx+L7NibcKvrePSrjMP5QztiT/G2RjPZElj37Rh16OvGiYiIiOiYYtBNRHSUSPAra7hlSnmQv/w7lFZ+AaM/KxRoi3ifHz3QFcVRV2BjeFXbrw4un8qGh9vMahp6cF14Q5lsCbDZFoyIiIhI3xh0ExEdJZJtluC40u3DEN9ynJj7Efb71+LjyN/XZad5/eiAgciPuxrrHF0bbPc1MC0afZIjmckmIiIiauUYdBMRHS2GDEwwzsQpOz5Hmn+P2tQZwCxnOFJ8RmjekZjtPQ/dO3WD02E5aLsvZrKJiIiIWj8G3URERyCgBbBmzwLMXf4yduStwwllJUjzl4YeL7GmoJ9hEr61jEdYdATSrWbsK6pEut3Mdl9ERERE7QCDbiKiegQ0DTvyylHu9tdbpMztd2PRho8xf907yCveDWgBtX2Z3YZzyspQFDYAn9smYZF5OKxWC3omRqj12EJairHdFxEREVH7wKCbiKiW9fuK8d4vGdhXFoDbV1XETFqAXTAkFcnRHvy0cjp+3T4LlRW5NY5L8vkxNnEows65CxEpw3FufjlOraeyeFOKpBERERFR28Cgm4iomnWZxXhu7jbkFJWjU1wkHDazWmu9ac9uvLLvYfiNG2H0VdZ4z3r6DRjX9Qz0Hv03GKLTQtsbqiwugTWLpBERERG1Dwy6iYgOCAQ0lYEuLPegS4wdYXYzYrz7MbLgMwwtmIknYyzIMVf9b9OsaRiCcPTtfCFiB12PzskdYDiEHtkskkZERETUPjDoJiI6YFd+uZryHe/0w+H6Fpflrkafkl9gRNV67TEVdswOD8dgSyrKHBfiR88wfLPXAFvWbqQn5qvp55weTkRERETVMegmIjpgW85GhLufgeZdhzKtEjEVBaGA22uwwmwfi0LPGfjUchz85RqSoxxIsprU9PO1GcXILKxU67UZeBMRERFREINuImrXfAEfVm7/FgtWvY6dBZtgNvgAreqx+WEOnFNhw6K4yVgWdy6yvE6s3luIWIsP/TpGhVp+Oe1mpNucKks+Y0WmWq9dvdI5EREREbVfDLqJqF2u3V63fx9WrH8LG/bNQnllNiREDobJYYEAjvPGwBV1Ff7TaSL8Rgs0TcOurGL1eNfY8Bo9toXcl8z31pxSNU29oSJqRERERNS+MOgmonZlw869mDnvEWxzL4IFLrUtGD539AcwLGoIfnWdg6/9ndHJHgkHzKh0+bC/uBLhNrNKgktF8/o4rCbVAqzU5TuGr4iIiIiI9IxBNxG1D9nrUTDvRXTb/BnOtATwckxVP2yDpqGHxwyb6WScdMbd6NMjHckZhaj8ZRv2lXmRXeoO9dEe0TUW7y3ZrdZwy5Ty2mS77Ct9uYmIiIiIBH8zJKJjPrVbpl9LNliC0y5x4c22/jmnJBM/r3gRvXYsQd89vyH2wPY+HqCL14toQzIC4ZOwK/F0bMl1IXuTB/d119C3YxRuOyUVFSYnyt3+0HWKxTvzVdE0WcNdfYq5TD+XbLgE58F9iYiIiIgYdBPRMbMus1j1wZaCY25vQGWF0xOdh9xqq7HA3R/wY93uufh51evYlLsG8Huwz+1B3wPHug12rIw9A6aYSdgelh46Z3KUIbQeu0tcGIwGA7rFy3mNNZ5brlWqlMtrkDXcMqVcMtwScMeGWzF5SAqLqBERERFRCINuIjpmAfdzc7aioNxTFaxGHV6rrYYC9wn9nCjY/xEWbv8axWX7axyzw2pBXnQ3fOk/A7tSz4XP4jzs9dhyjXKtwWuQY4LTzyXgZrswIiIiIqqOQTcRNTvJTEuQKgG3BMiH22qrduBujzSivGI1cvbMwJuZW2HR3DX2j/MHcFJsH4wcdguyo0dh/tcbEO23wmnBEa3HlsBarvVYTZMnIiIiotaLQTcRNTsJToPTsQ+31VaNwD0hHF0r1uC43E/xqXUDDEYNlgO9taUwWj/YcGL3s9DnhNthiEpR27sENBXwN2099oGTNUICbLYFIyIiIqKDYdBNRM1OssEyFVymlNen9tTu+tZs78wrw979W/DHwFKcuHUmEt271b5rY6KwxWpFpD+ANF8yxh5/A3qMuBS7Ct1YU+JDhKcslIVu6npseX4iIiIioqOBQTcRNTsJnGXqdlNabdVes202u9DVPg9ez3wkezJxfl5eqK+2OKXSBKd1BLKiL8W8sjgkOdLw4bdbGizWxvXYRERERHQsMegmomYnmeamTO0ud/vw/NxtyC93IylsF8K1L1Hi24DiUpfat8JkwCarBb09XuwIH4SlsediXdQY+I1WlLl88Por8OmKDBVsN1asjeuxiYiIiOhYqdkLp4148cUX0aVLF9jtdowYMQJLly5tcN/XXnsNJ510EmJiYtRt/Pjxje5PRIcuOLVbpnBLBloCZH9AUz/lvmyfNLgjPvxtEwyl7yPV+Deg4iFUeFfArFUF3KKTx4BfbOPx5HH/w2vdX8DqmNNVwC2B+76iCrh9ARVwS4AvGXWT0VBVrC3RqdaCS7E2mToeXI89MC1a/WQBNCIiIiJqLm0u6P7oo49wxx134MEHH8SKFSswcOBATJgwATk5OfXuP3/+fFx66aWYN28eFi1ahLS0NJx++unIzMw85tdO1JYFp3b3T41CUaUHu/LK1U/JcP91RBgWLb4Z+fuvhsX4FfyBbBgOFDMLCwTQ35eA4wyXY5npNcyPuh2Li2PrBO52iwk2s7FJxdqIiIiIiI6VNje9/KmnnsJ1112Hq666St2fPn06Zs6ciTfeeAP33HNPnf3/97//1bj/f//3f/jss88wZ84cXHHFFcfsuonag+pTu8vKK9Bh/1wkbvsYhg/mYkVEOMxhjtC+XbxGxFiGIj/mEuyyd1MBtiGvHBcOScWazOI6PbL7pUTiw6V7VXG0+jS1DzcRERER0dHUpoJuj8eD5cuX49577w1tMxqNasq4ZLGboqKiAl6vF7GxsQ3u43a71S2opKRE/QwEAuqmN3JNMv1Wj9fW3rW3sfH6vVi95Uss2/Ahrty1CuHl+aHHRlW6sNZmQ1qgIwzOc5ARPx75xgMNtTUNlW6fymRLpvycAcnYVVCBUpcXEXYLusSGqfufr8hU+9VbrO3A8eE200Hf7/Y2Lq0Fx0WfOC76xHHRJ46LPnFc9CnQCn4fa+q1tamgOy8vD36/Hx06dKixXe5v2rSpSee4++670bFjRxWoN2TatGl4+OGH62zPzc2Fy/X7+lM9fRiKi4vVh1a+hCD9aA9jU7XeejPWbP4f1uX9Bre3VG1fHijHyQf28TtTEN1zMsIKR2B2UQS6WOwwuCUj7QudY0+hC707hCPMX4a8vHJIN2+nVd5Ej7ofpmno6DRiU04JusTY6xRrq358Tk7jU8zbw7i0RhwXfeK46BPHRZ84LvrEcdGnQCv4fay0tOr32nYVdB+pxx9/HB9++KFa5y1F2BoimXRZN1490y1rwRMSEhAZGQk9fmAlAJHr0+sHtr1qy2NT6irCb2vexuLtX2NfaQYMWs1vAjfZHTip20Rog6+AoevJiDAYcd6+Yuyeuw2ZZR4kR1bro11aiQ7R4bhsdDqSOkQ1+JxTTrTjuSM4vj2MS2vGcdEnjos+cVz0ieOiTxwXfQq0gt/HGosZ22zQHR8fD5PJhOzs7Brb5X5SUlKjxz755JMq6P7xxx8xYMCARve12WzqVpt8GPT6gZAPrJ6vrz1ra2OzdecczFv1f1iXtxYBX9XMj2DO2appGGyNwwnHnY/0odfD8P/snQeYXGXZ9+8pO7Ozve9m00MSkhBCCCWAKAgIKCKoCGIBEbGiYP1EEUT0RSxYAEVfRXz1VZBXRBQEkWKhQ2ghJCQhPdnep7fvup+Z58yZs1POmTkze2b2/7uuzWZnzsycOeV5nrv97/r2tJ7bh85rpctOXq706e6fDCZrtlvpXevmiprwXBT7+mo+L9UCzos1wXmxJjgv1gTnxZrgvFgTm8XXY3r3q6qMbpfLRUcccYQQQTv77LMVDwn/femll2Z93Xe+8x361re+RQ888AAdeeSRZdxjAKwHt9RioTMWHGusdYoe23lbavlHiTbeRfTi72nvyEZ6qZGTv1MsiTvomLlvpHVHfpJq567L+VbF9tFGH24AAAAAAGAlqsroZjjt+8ILLxTG89FHH00//OEPyev1KmrmrEg+d+5cUZfNXH/99XTVVVfR7373O9Hbu6+vTzze0NAgfgCYTWzcN65EibnfNUeJucc199jWRonHfUP07Iu/ohW7nqW5Wx8miobE40fabPSnhnpqjBMd3byUjln9AepefR6Rkwuw9SH7aBdKsa8HAAAAAADALKrO6D7vvPOEoBkb0mxAr127lu6//35FXG337t1paQA//elPher5Oeeck/Y+3Of761//etn3H4CZNLh//NBWGvGGRE9rT3OiHvrlveO0b9Qvemwv766ll7b8iZ5+9U56deRVikdDdILPR+9JGtxMY+cq+sKqt9HcdR8mR1MvTigAAAAAAJjVVJ3RzXAqebZ0chZJU7Nz584y7RUA1k4p5wg3G9wc2ZbK39x66yBXPe0feILuuP875Iu/RsHwVNprn62tpXdFPeRYcy7RYedTrPtQigx7aeN4hBqDU4ZSwwEAAAAAAKg2qtLoBgAYg+unOaWcI9xKq63IPnJO/pEmwhuo2TZOo6lgtqApGqeu6HzytL6DXn3HBbR6fkciPf3eTbrS0wEAAAAAAJgNwOgGAAjBMjaSGxqjtHL8CVo79neKB56i/26pTzs67liMeiNtVON6Ew21v5O2xxvowLifnntkJ52xJkj3vnQgZ3o6DG8AAAAAADDbgNENwCzHF5yk3Tv+h97uf4jO2fws1cUS6eNRrs+OeWjKZqOFEQ9NBg6l3a6zqGb+qlT6OREtdTfQ1v5J+tk/X6c6l2Naejo/z5HvuzbsE6rkSDUHAAAAAACzCRjdAMxCwpEQbXztbnrm1TvpleFNFI0GaYUtpBjcjN/RQkfQetrVcDo9MrqYRmJhOqKzNZV+noT/bvK4aPvQKB0+vyXj8xz53jowKdLYoSoOAAAAAABmEzC6AZglRGIR2vL6g7Rh0x304sDzFAh7057f4qqhIbuHXnEdS5vaT6NdLUeTN2wT6eP1tTaK22zkcWceMhx2okg0Tk6NwS3xuBzUPxETaewAAAAAAADMJmB0A1DlDPe9QH9/9mZ6vv858oUmpj3fFIvTEc1L6cgV76a+jjPp0ZdHEkJow0EhhLZmXgutX9xGv31ql6jR5pRxLdEYkdNho0g8nnEf+HX8Xo0ZXgsAAAAAAEA1gxUwAFXW+otTuP3De6lnz33UtvNeogMb6LHO9rTtamNxOqR2Dq1f/g5ase7DZK/vUJ5btaBHvAdHpdlI5pZfzJM7hoUoGtdoq1PI4/E4TfhD1NvMdd8R6m6KT3ueo+VsvMv3AgAAAAAAYLYAoxuAKoAN20df/if969lf06LxV+iD46+RnRJRZza3l4TCtKfGSQviLRSKHUHb7G+lh6mXdo810LvHami1yhZmobNMddfc9otVyGVrMU4Z5wg2G9TtDW46Y80coV6e6fm2ehe9a91ciKgBAAAAAIBZB4xuACrY0N7Tt4E2vPRbenrvYzQeGiGOL0dromRLGtzMHtdBdHTPW6hvaj09FuqhOa0eanUZb+fFz/N2f9ywVxjWXKMt08/ZoObnD+psyPk8AAAAAAAAsw0Y3QBUmKG9t+952vDyb+j5/U/QkH9IeU4mdI/a7bTJs4AONJ5MLzafRI+Pt5O/L2ZKOy82nHk7bfq5fF2+5wEAAAAAAJhtwOgGoMz11tpaaV2M7aH+l35PP9n+RxoODE972haP09yohxpda8lb/3b6bfcq7tUlnmsKBUxt55Ut/Vzv8wAAAAAAAMwmYHQDUAY27htX0q6D4UTaNUed33V4L3VmuAtj8Rj5BjZRw9Z/EL16D9G+50Rtto8F0ex2xdBe7mykxa1H04N9x9JIzzoa595dGtDOCwAAAAAAgJkDRjcAZTC4f/zQVhrxhhICY82peuq9oz76wNp26uriPtdh2rbjH/Tiq3+kFwc20ILJQfr42ETazbo2GKSRhk5aO/cNtHbNhdQ47yh6fchLd9zzCrWEY9SQwehGOy8AAAAAAABmDhjdAJQ4pZwj3GxwZ6qn3to/RA9t+Aft2PYSbRp+hXwRn/LaV10u8tts5OHe192HEq16B71vxZlk616Z9hmcps7vjXZeAAAAAAAAWA8Y3QCUEK6Tli20lHrqqJdqffdTJPAEzbHtphF/mEb8mhszHqeV7g7ynfAR8hx6LlH7QeJxW5YaarTzAgAAAAAAwJrA6AaghLBoGtdwtzQEaOXYU7Rq4j/U7n2Srm+rnbatOx6n1Z5eOmzhm2nVYRdQbftSXWJsbHSjnRcAAAAAAADWBEY3AHkM2kJe3+B2UE3oVdr8wu/oDP8z9P6+zeSkiLJtb9hJ+2ucVBcjaot00RtWnkHHHPsRqmnsNizGxlFuNrrRzgsAAAAAAADrAaMbAB0GrZ7X3/nsTho+8DDVR/5NQft2iti84rkuZyTN4PbbG2iVbTV53EfSs/7jyN7TSseetI6cTkdBYmz7Rv30mZOXif1EOy8AAAAAAACsBYxuMCui0LnQa9BmYso3SA898Wt6assDNEF7iWxh8mps5wGnk7a5uug1xzG0vf0E2te8jrxhGx0Y91NbYw29dWW78l2y9fLOJcbGjoK7NuyjVXOaTDsmAAAAAAAAAHOA0Q2qPgpdjLq42qBl2CD2D++jrr5HaWLf3+gHk5uI4rFpCmeOeJx6Yo0Uja6khu4zyf/2t9IzL+xPfIfhoPgOa+a10DsPn0OdzmDO73nM4vbpYmxJ+G9+fOvApNi3JZ0NRR0PAAAAAAAAgLnA6AaWRm8UutBIeEZ1cY1B+1r/EN35j18Q7Xyajh7cQIeEt4nnW4iopquDwsnXeWJE3dRNNa51NNnwVppwz6epQIS2eEP0jtoa+toZqzJEseM0MDBAr+wfpxsf3p7xe27aP0FTwYh4PBMel4P6J2LifQEAAAAAAADWAkY3sCx6o9DxeJzuen5fQZFwqS7ORq4aW3gv1Xn/TjXBFyge30//3h6ltYEgLQ5PKNvUENG6gJ122ReRp+lNNN5wCg07G7IaxJnqrfk7xnj/N+zL+j3Z8TDqC5E/GKEGD39qOmyc83dmQx4AAAAAAABgLbBKB5ZFTxT6+d2jtKV/QhjORuuxGTZU2WD1BQLUQc+S0/9vmoy8RlM0Top5nfzoV101Qg6tv3Y5bW56A73aeCw9MNJDw/4IHdHZSg3OwgzivWNB2jbgzfo9OSI+4gvRjhEvre5tTtuGHQ5cG86p6rL+GwAAAAAAAGAdYHQDy5ItCi2prbHT/jE/tdW76LD5LbrqsdPSz/0jtOjAw3RW4Df0z+BrNGRLpWerTd/6qI3aaA65PUfSt1e8jbyuVFuvhfYwDe8ZLcogngpGKRiJiqh4JurcTmr1uKje5VScELwtG/RCjK3eRe9aNxciagAAAAAAAFgQGN3AssriMgrNxiUb0lqGpkIUjMaop7k2p8DYg5v66MkdI7S1f5haAo/TUeFt5IpsorneTWSnOB3vcNCjHW2p18bj1BGrI0d0CU3Yj6VXY0fRirmd5MjwncwwiLmnt9vpyPo9+fG2Bhd9YP0C8T34czhlXYqx8fsXKygHAAAAAAAAKA0wuoFllcXZeOf34lRxjlxro8h9435hrHY2uLNGwiMDG+nhR/+bbPQqtdqHKO6IUrvfS/O8PmW77miUFkSJ7I455I+uon10Am3zHETLuhrp5MVtdOCpXSU1iOe1uGlpVz29vG8i4/eU0fK3rOoRP6VqnQYAAAAAAAAwHxjdYEb6W+uBjUk23vm9skWRa2sc5A/HqMFhF6+xRYapzvcQxYLP0Vh0N7W7A+TVvO8mt4ve7vXRLuci2tf+Bjrm1HPpSwuPpZjdlbFH9pM7hrMa/mYYxHabTRjn+8YCuqLlaAsGAAAAAABA5QCjG5S8v3UxkVg22tl4l9F0dRT57MN76e4Nuyiw41k6seYV6os8Tk87h2hCfpzmYxtidmq39ZKzfh1d13UG7Y+10Zg/RD1Nh9ASp5vsWQzafIa/GQbxIb3ZvyfSxwEAAAAAAKhcYHSDkimLcz01b1dsZJYNbzbe+b3GfUEKjT1DYwN30uJ/7aav7nicHOFJsd0Lbhc93ZKKrDviRK2RZmp0r6JI3RtpzHMUjdhTl7wnFtfV3zqX4W+mQaz+nkgfBwAAAAAAoDqA0Q1Koiyu7k9djBgbp3AP9L9IW167h17b/zS9Nr6DfLGgeE37+AQdGU78n1kWClNr1E2e+EKqbz6OVqw+h377/Di1eFxZ67H19rcul0GcqZc3AAAAAAAAoHKB0Q1KoixuxKDVirE12/fRMvfT1OjYTHv9O2k8khI9U7PFVUNH2huJlpxAscUn0HDL0XSOa056Pfa+TXnrsfX2t4ZBDAAAAAAAADAKjG5QUBQ6n7K42qDN1VKMDe5b//4szRnbQBfbNtEK//N0e90IbYu6Mu6Lh2x0cP08Wj7nSFq5/Eyi+cezNSzqsRckfwqtxwYAAAAAAAAAs4HRDQpuCabHoN10YGLa65e3jtMRjS/Q+MSztLV/C31/YGea5tnBzjra5koY3TVxooM8XXRw11pavvgUmr/srWSv8eg+a+WqxwYAAAAAAACATMDoBkW1BMtl0DL8+sDkDlpa8yw5aCNNBvdQf7+P7utPfoiN6IDTQb2RqPgzRnbqsM2nRfaF5HMcQTvjR9Pb3n50UXXOECgDAAAAAAAAzBQwukFRLcGuPGMlrTpjVXr6uHOUgjv/Sj98+jfUHNlLLnuABqOZ23jZ40Qv1S2hba4j6fWGw2ln/RoKOhIGdjQWp6mhxPsWC+qxAQAAAAAAADMBjO5ZYDy/PuSl3X1emrJ7aUlHg64aZr0twXYMTpIn+BLZ9z5Dh/VvIdr1GNHYbqolorHOdvLbudo63chujzeSy7aIhqOrqc9+LN3bM6doMTYAAAAAAAAAsCKwZmZDPXb/FE36A9ToGaKl3Yl6bHUtcyahs2wtwWxRP9UHnqYm/7PUHNxKP/3TMAXiETooFKLPjo6ntku28HrJ7aZ2aqY6xyKKu9fSVN2bKOBsI28sTrsGp6inuVbUgJuhLg4AAAAAAAAAVgNG92yox26qpRZXnMhRk1aPzYZ3NqG0Yxa3i/9HfP3UGn+KbMGXyB/ZSaM0ShO2eOrqSf53V00NhVn4zFlLNO8oGu04kvbuaiFH4zoK1LdSIEMUu9bloLevmUN/3LAP6uIAAAAAAACAqqQqje6bb76Zvvvd71JfXx8ddthhdOONN9LRRx+dcdtXXnmFrrrqKnruuedo165d9IMf/IAuv/xyqqp6bDZy/WHycD12baoem6PJNz68TRFKq220UYNvF7VtfYE8r22m64ObqN+2n37ZkoyKZ8hKr7c56aD6Xjqo81CKnf5uogXriZxuao7FyXbvJtrDLcXq4lmj2G9Z1UNzW+ugLg4AAAAAAACoSqrO6L7jjjvoc5/7HN1yyy20fv16+uEPf0innXYabdmyhbq6uqZt7/P5aMmSJfSe97yHPvvZz1I1MK0eOx6fVo/9Wv8E/eZfQ1Q3/i9a4t5CofEdNBYfojMmRujIQFDZvl5Tk90Qc1FtrIsczhV0ytFn0zFr30I2R3oKOsN143p7ZENdHAAAAAAAAFCtVJ3RfcMNN9All1xCF110kfibje97772Xbr31Vvryl788bfujjjpK/DCZnq9EMtVjx2Mx8gReJnfwWWoMbab6yD6aCvnEFbBXpSy+o8apGN1RshM1r6Tl9i4aiB5EffGjaLx2MS3raqR36uhxbaRHNtTFAQAAAAAAANVIVRndoVBIpIlfccUVymN2u51OOeUUeuKJJ2i2wGJobNxyVLnD6aPz9nyDHnRupy0u1UbTg9PkjjtowL2UHqhfTztqV9NjvgX0hTMPp0vnNk8TWtOjgM4gig0AAAAAAACYzVSV0T00NETRaJS6u7vTHue/N2/ebNrnBINB8SOZmJgQv2OxmPiZaRa0emhpZz29vG+cGjrqaKF3Iy2qi9EWl0oFPE7UFKujetscctaupJD7SPK5D6G9dgftJaIp7o3tClO9m63zOC1qr1N9QlzUjRuh2NdXK3y9cI27Fa4bkALnxZrgvFgTnBdrgvNiTXBerAnOizWJVcA6We++VZXRXS6uu+46uuaaa6Y9Pjg4SIGAVqd7ZjhxUR1t7xujTQfGabvrYJob3k4d0U4KR+dTpGY1nXrk2+jvr8fp6QEvLXLVko1zy4Mh8Vq+uHePBmhldz3VRadoYMA701+nauEbdXx8XBxzzsoA1gDnxZrgvFgTnBdrgvNiTXBerAnOizWJVcA6eXJycvYZ3R0dHeRwOKi/vz/tcf67p6fHtM/h9HUWa1NHuufPn0+dnZ3U1NREVoA149raWoVK+Y/6vkqDfgc11NaKeuz3ruulQ3qbqat3nH788DbaN8VtxVRCZ5N+6m6pp/e/YSn1dOeu2wbFDyYsbsfXjlUHk9kIzos1wXmxJjgv1gTnxZrgvFgTnBdrEquAdXJtbe3sM7pdLhcdccQR9NBDD9HZZ5+tnCz++9JLLzXtc9xut/jRwheDlS6IQ+e10iG9LfT60ELavX+AFvR20ZKOBqUem5+/7OTlKaGzyWBS6Kx1mtAZKB08mFjt2gE4L1YF94s1wXmxJjgv1gTnxZrgvFgTm8XXyXr3q6qMboYj0BdeeCEdeeSRojc3twzzer2KmvkFF1xAc+fOFSniUnxt06ZNyv/37dtHL7zwAjU0NNDSpUup0hGq4B311BCrp66O6QJoEDoDAAAAAAAAgNJRdUb3eeedJ2qrr7rqKurr66O1a9fS/fffr4ir7d69O80jsX//fjr88MOVv7/3ve+JnxNOOIEeffRRmg2gXRcAAAAAAAAAlIaqM7oZTiXPlk6uNaQXLVokivMBAAAAAAAAAACzsWZyPAAAAAAAAAAAUAXA6AYAAAAAAAAAAEoEjG4AAAAAAAAAAKBEwOgGAAAAAAAAAABKBIxuAAAAAAAAAACgRMDoBgAAAAAAAAAASgSMbgAAAAAAAAAAoETA6AYAAAAAAAAAAEoEjG4AAAAAAAAAAKBEwOgGAAAAAAAAAABKBIxuAAAAAAAAAACgRDhL9caziXg8Ln5PTEyQFYnFYjQ5OUm1tbVkt8PPYiVwbqwJzos1wXmxJjgv1gTnxZrgvFgTnBdrEqsAG0baf9IezAaMbhPgi4GZP3++GW8HAAAAAAAAAKCC7MHm5uasz9vi+cxyoMsLs3//fmpsbCSbzWZJDww7BPbs2UNNTU0zvTtABc6NNcF5sSY4L9YE58Wa4LxYE5wXa4LzYk0mKsCGYVOaDe7e3t6c0XhEuk2AD/C8efPI6vDFatULdraDc2NNcF6sCc6LNcF5sSY4L9YE58Wa4LxYkyaL2zC5ItwSaybHAwAAAAAAAAAAVQCMbgAAAAAAAAAAoETA6J4FuN1uuvrqq8VvYC1wbqwJzos1wXmxJjgv1gTnxZrgvFgTnBdr4q4iGwZCagAAAAAAAAAAQIlApBsAAAAAAAAAACgRMLoBAAAAAAAAAIASAaMbAAAAAAAAAAAoETC6AQAAAAAAAACAEgGju0q4+eabadGiRVRbW0vr16+np59+Ouf2d955J61YsUJsf+ihh9J9991Xtn2dbRg5N7fddhvZbLa0H34dMI9//etfdOaZZ1Jvb684vnfffXfe1zz66KO0bt06oZ65dOlScZ7AzJ4XPifae4V/+vr6cGpM5LrrrqOjjjqKGhsbqauri84++2zasmVL3tdhjrHeecH8Unp++tOf0po1a6ipqUn8HHvssfS3v/0t52twr1jvvOBemRm+/e1vi3n88ssvr8p7BkZ3FXDHHXfQ5z73OSGpv2HDBjrssMPotNNOo4GBgYzbP/7443T++efTxRdfTM8//7yYrPln48aNZd/3asfouWF4Qjhw4IDys2vXrrLuc7Xj9XrFeWBniB527NhBZ5xxBr35zW+mF154QUwGH/nIR+iBBx4o+b7OJoyeFwkbGur7hQ0QYB7//Oc/6VOf+hQ9+eST9OCDD1I4HKZTTz1VnK9sYI6x5nlhML+Ulnnz5gnD4bnnnqNnn32WTjrpJDrrrLPolVdeybg97hVrnhcG90p5eeaZZ+hnP/uZcI7koqLvmTioeI4++uj4pz71KeXvaDQa7+3tjV933XUZtz/33HPjZ5xxRtpj69evj3/sYx8r+b7ONoyem1/96lfx5ubmMu7h7IaHwD/96U85t/nSl74UP+SQQ9IeO++88+KnnXZaifdu9qLnvDzyyCNiu9HR0bLtF4jHBwYGxHH/5z//mfVwYI6x5nnB/DIztLa2xn/xi19kfA73ijXPC+6V8jI5ORlftmxZ/MEHH4yfcMIJ8csuuyzrtpV8zyDSXeGEQiHhuTvllFOUx+x2u/j7iSeeyPgafly9PcPR12zbg/KdG2ZqaooWLlxI8+fPz+uJBaUH94u1Wbt2Lc2ZM4fe8pa30GOPPTbTu1P1jI+Pi99tbW1Zt8E9Y83zwmB+KR/RaJRuv/12kX3A6cyZwL1izfPC4F4pH5/61KdERqHWNqm2ewZGd4UzNDQkBpDu7u60x/nvbLWN/LiR7UH5zs3BBx9Mt956K/35z3+m3/72txSLxei4446jvXv34jTMENnul4mJCfL7/TgvMwQb2rfccgv98Y9/FD/spDrxxBNFGQcoDTwecXnFG97wBlq9enXW7TDHWPO8YH4pDy+//DI1NDQIDZCPf/zj9Kc//YlWrVqVcVvcK9Y8L7hXysftt98u5m3WqdBDJd8zzpneAQBACva6qj2vbHCvXLlS1Llce+21OFQAqBZF/KO+V7Zv304/+MEP6De/+Q2OU4miEVw395///AfHtwLPC+aX8sDjEut/cPbB//3f/9GFF14oavCzGXjAeucF90p52LNnD1122WVCl2I2iAbD6K5wOjo6yOFwUH9/f9rj/HdPT0/G1/DjRrYH5Ts3Wmpqaujwww+nbdu24TTMENnuFxZZ8Xg8OC8W4uijj4ZBWCIuvfRS+utf/ypU5lmUKBeYY6x5XrRgfikNLpdLdLlgjjjiCCEQ9aMf/Ug4z7XgXrHmedGCe6U0PPfcc0JYmLvDSDhDlMezm266iYLBoFhHV8s9g/TyKhhEePB46KGH0lLN+O9stSr8uHp7hr1MuWpbQHnOjRYefDglilNpwcyA+6Vy4CgG7hVzYV07Nuw4FfPhhx+mxYsX530N7hlrnhctmF/KA8/7bDxkAveKNc+LFtwrpeHkk08Wa1yeu+XPkUceSe9///vF/7UGd8XfMzOt5AaK5/bbb4+73e74bbfdFt+0aVP8ox/9aLylpSXe19cnnv/gBz8Y//KXv6xs/9hjj8WdTmf8e9/7XvzVV1+NX3311fGampr4yy+/jNMxw+fmmmuuiT/wwAPx7du3x5977rn4e9/73nhtbW38lVdewbkxUSXz+eefFz88BN5www3i/7t27RLP8/ng8yJ5/fXX43V1dfEvfvGL4n65+eab4w6HI37//ffjnMzgefnBD34Qv/vuu+Nbt24VYxerndrt9vg//vEPnBcT+cQnPiE6Kjz66KPxAwcOKD8+n0/ZBnNMZZwXzC+lh483K8jv2LEj/tJLL4m/bTZb/O9//3vGc4L1mDXPC+6VmeMEjXp5Nd0zMLqrhBtvvDG+YMGCuMvlEm2qnnzyybQL+MILL0zb/g9/+EN8+fLlYntuh3TvvffOwF7PDoycm8svv1zZtru7O/62t70tvmHDhhna8+pEtprS/sjzwL/5vGhfs3btWnFelixZItqJgJk9L9dff338oIMOEk6ptra2+Iknnhh/+OGHcVpMJtM54R/1PYA5pjLOC+aX0vPhD384vnDhQjFXdHZ2xk8++WTFsMt0Thisx6x3XnCvWMfoPqGK7hkb/zPT0XYAAAAAAAAAAKAaQU03AAAAAAAAAABQImB0AwAAAAAAAAAAJQJGNwAAAAAAAAAAUCJgdAMAAAAAAAAAACUCRjcAAAAAAAAAAFAiYHQDAAAAAAAAAAAwugEAAAAAAAAAgMoCkW4AAAAA5ORDH/oQnX322ThKAAAAQAE4C3kRAAAAAKoDm82W8/mrr76afvSjH1E8Hi/bPgEAAADVBIxuAAAAYBZz4MAB5f933HEHXXXVVbRlyxblsYaGBvEDAAAAgMJAejkAAAAwi+np6VF+mpubReRb/Rgb3Nr08hNPPJE+/elP0+WXX06tra3U3d1N//3f/01er5cuuugiamxspKVLl9Lf/va3tM/auHEjvfWtbxXvya/54Ac/SENDQzPwrQEAAIDyAaMbAAAAAIb59a9/TR0dHfT0008LA/wTn/gEvec976HjjjuONmzYQKeeeqowqn0+n9h+bGyMTjrpJDr88MPp2Wefpfvvv5/6+/vp3HPPxdEHAABQ1cDoBgAAAIBhDjvsMLryyitp2bJldMUVV1Btba0wwi+55BLxGKepDw8P00svvSS2v+mmm4TB/V//9V+0YsUK8f9bb72VHnnkEXrttddwBgAAAFQtqOkGAAAAgGHWrFmj/N/hcFB7ezsdeuihymOcPs4MDAyI3y+++KIwsDPVh2/fvp2WL1+OswAAAKAqgdENAAAAAMPU1NSk/c214OrHpCp6LBYTv6empujMM8+k66+/ftp7zZkzB2cAAABA1QKjGwAAAAAlZ926dfTHP/6RFi1aRE4nlh8AAABmD6jpBgAAAEDJ+dSnPkUjIyN0/vnn0zPPPCNSyh944AGhdh6NRnEGAAAAVC0wugEAAABQcnp7e+mxxx4TBjYrm3P9N7cca2lpIbsdyxEAAADViy0ej8dneicAAAAAAAAAAIBqBK5lAAAAAAAAAACgRMDoBgAAAAAAAAAASgSMbgAAAAAAAAAAoETA6AYAAAAAAAAAAEoEjG4AAAAAAAAAAKBEwOgGAAAAAAAAAABKBIxuAAAAAAAAAACgRMDoBgAAAAAAAAAASgSMbgAAAAAAAAAAoETA6AYAAAAAAAAAAEoEjG4AAAAAAAAAAKBEwOgGAAAAAAAAAABKBIxuAAAAAAAAAACgRMDoBgAAAAAAAAAASgSMbgAAAAAAAAAAoETA6AYAAAAAAAAAAEoEjG4AAAAAAAAAAKBEwOgGAAAASsDOnTvJZrPRbbfdVtT7fP3rXxfvM5s48cQTxU8+Hn30UXFs+Hcp4c/g8wAAAAAUAoxuAAAAoADYmGZjLNPPl7/85Yyvue+++zIabz6fTzxeauPRKB/60IfSvpfb7ably5fTVVddRYFAYKZ3DwAAAKgInDO9AwAAAEAl841vfIMWL16c9tjq1atp4cKF5Pf7qaamJs3ovvnmm6cZ3mx0X3PNNeL/2gjvlVdemdWILwdsaP/iF78Q/x8fH6c///nPdO2119L27dvpf//3f0vymX//+99L8r4AAADATACjGwAAACiCt771rXTkkUdmfK62trboY+t0OsXPTMGf/YEPfED5+5Of/CQdd9xx9Pvf/55uuOEG6u7uNv0zXS6X6e8JAAAAzBRILwcAAADKUNPNqdoc5WbUKdu8XWdnp3ico93ycRkNz1TTzX9feumldPfdd4uoOkejDznkELr//vun7QenrLNTgB0ABx10EP3sZz8rqk6cX3f88cdTPB6n119/Pe25v/3tb/TGN76R6uvrqbGxkc444wx65ZVX0rbp6+ujiy66iObNmyf2e86cOXTWWWeJ45Crpnvv3r109tlni/fu6uqiz372sxQMBqft36JFi8Sx1qJ9z1AoJNLkjzjiCGpubhbvy/v+yCOP5D0Gk5OTdPnll4vP4u/A+/OWt7yFNmzYkPe1AAAAZh+IdAMAAABFwCnXQ0NDaY91dHRM2+5jH/sY7d+/nx588EH6zW9+ozzOBvdPf/pT+sQnPkHvfOc76V3vepd4fM2aNTk/9z//+Q/dddddIvLMBu6Pf/xjeve73027d++m9vZ2sc3zzz9Pp59+ujBs2aCPRqMiHV4a+YUiDeTW1lblMf5OF154IZ122ml0/fXXi5R5/l5soPN+sIHK8D6yIf7pT39aPDYwMCCOCe+33EYLp+mffPLJYpvPfOYz1NvbKz7v4YcfLvg7TExMiLT5888/ny655BJhSP/yl78U+//000/T2rVrs7724x//OP3f//2fcHysWrWKhoeHxfl49dVXad26dQXvEwAAgColDgAAAADD/OpXv4rzNJrph9mxY4f4P28n+dSnPqU8r2ZwcFA8fvXVV097jh/Tvob/drlc8W3btimPvfjii+LxG2+8UXnszDPPjNfV1cX37dunPLZ169a40+nMuB9aLrzwwnh9fb3YP/7hz/ve974Xt9ls8dWrV8djsZjYbnJyMt7S0hK/5JJL0l7f19cXb25uVh4fHR0Vn/vd73435+eecMIJ4kfywx/+ULzuD3/4g/KY1+uNL126VDz+yCOPKI8vXLhQ7He+94xEIvFgMJi2De9fd3d3/MMf/nDa49pzw9+JzyUAAACgB0S6AQAAgCLglHFW9C43p5xyikgXl3BkvKmpSUn55qj2P/7xDxE958iwZOnSpaIO/S9/+Yuuz/F6vdMi4xy9/vWvf62kqHOkemxsTESN1VF/h8NB69evV1K2PR6PqNfmlPeLL744LVKeCxag42j9OeecozxWV1dHH/3oR+lLX/oSFQLvG/8wsVhM7D//5lT8fGniLS0t9NRTT4nMBfWxBQAAADIBoxsAAAAogqOPPjqrkFopWbBgwbTH2IgdHR0V/+e0bU7LZiNbS6bHssG14NJA57rq73znO+K92YCWbN26Vfw+6aSTMr4HOwMYrn/m1PPPf/7zQoDtmGOOobe//e10wQUXUE9PT9Z92LVrl9hnbR36wQcfTMXAjoPvf//7tHnzZgqHw8rjWjV6LXwMOJV+/vz5oib8bW97m/gOS5YsKWp/AAAAVCcwugEAAIAKREZptSSyoc39HI6qS7jmecWKFaJG/Z577hGPcYSY4TrrTMazWn2dBcjOPPNMIQL3wAMP0Ne+9jW67rrrRH324YcfXvT+ZhOI48i/+pj99re/FYJrLM72xS9+UYih8fO8L9wOLRfnnnuuEF3705/+JNqbffe73xXOBK6x5ywCAAAAQA2MbgAAAKBMZDMIC1USzwUbkRyl3rZt27TnMj2mF07zZuVwFmZ78sknRbRaprnzZ6oN9Gzw9hzt5h+OkrNoGUec2RDOBPc837hxo3AoqI/Vli1bMkb7OVU8U7RcHYlmITT+mw1l9XteffXVuo8Di9jxD0f+WUDtW9/6FoxuAAAA00DLMAAAAKBMcFsqRmsUcn1ypsfNiFBzRJlrj9UGN7f2KgZWHud9/va3v61EvzmF/L/+67/S0rQlg4OD4jcrmgcCgWkGOKuvZ2r/JeH0bf4ObChL+L1+/vOfT9uW34+dAdwSTPLXv/6V9uzZk7adjHqrMwO4TvuJJ57I+d05Ys6K9WrY2cC13bm+AwAAgNkLIt0AAABAmeD6X4bbXrGhyobfe9/7XlEfza2n7rjjDiHK1tbWJvpv808xcD9uTn9+wxveIFqSscF40003ifd94YUXCn5fbknGvbZ/8pOfiDZZK1euFO3BPvjBD4qIL38nFl/jFl/33nuv+Hz+3Ndee020/uL0bP6+nHbOKdr9/f3iNdngll78eq6bfu6550SUmVPZpbNCzUc+8hFhnHOrNP4cThXnCLpadI7hWnKOcrPQHPcT37FjB91yyy1iv6amprLuC7cW4x7jLOp22GGHUUNDgxCse+aZZ0S0HgAAANCCSDcAAABQJrgHN0eJ77//fmGgstq3hHtGz507V6Ru8+PqqG4xRj5HtTnlmmunuQ819+lmw5dTz4vhc5/7HNntdlHLzLzvfe+jhx56SHwHrnG+7LLL6Pbbbxep42ygMyw8xt+N1cuvuOIK8cP9sv/whz+I/t3ZYOOa3/vUU0+lG2+8kb75zW8KBXUWNNPCzgw2ftnA5/pxjlxzpJsNZTVcz82R+RdffFE4Qbi+nI3zfKJ4vC+cUs5OC05F5/PFae7sgOBjAgAAAGixcd+waY8CAAAAoGph8bBXXnlFUR0HAAAAQOlApBsAAACoYrhtmBo2tLnv9Yknnjhj+wQAAADMJhDpBgAAAKoYrn/mVGpW6mYFb669ZsGv559/npYtWzbTuwcAAABUPRBSAwAAAKoYFhT7/e9/T319feR2u+nYY48VtcwwuAEAAIDygEg3AAAAAAAAAABQIlDTDQAAAAAAAAAAlAgY3QAAAAAAAAAAQImA0Q0AAAAAAAAAAJQICKmZQCwWo/3791NjYyPZbDYz3hIAAAAAAAAAgIWJx+M0OTlJvb29ZLdnj2fD6DYBNrjnz59vxlsBAAAAAAAAAKgg9uzZQ/Pmzcv6PIxuE+AItzzYTU1NZMVI/ODgIHV2dub0wIDyg3NjTXBerAnOizXBebEmOC/WBOfFmuC8WJNYBdgwExMTIvgq7cFswOg2AZlSzga3VY3uQCAg9s2qF+xsBefGmuC8WBOcF2uC82JNcF6sCc6LNcF5sSaxCrJh8pUYw+ieYfhC2rRpE23ZsoWmJicpHo+VptZgapIaG1BzbjZ19Q20fPlyOuSQQ6i+vt709wcAAAAAAABUNjC6ZxCv10u33XYrDezfRfN7O6i1hb04JRBiixM1uWrIXctumLj57z9Licc5peQA3XfPC/Tvf3XRRR/+CLW1tc30bgEAAAAAAAAsBIzuGeQvf/kLTY0eoI9+6N3U2VE6Y40j3T6vl+rq66GuXgImJqbot3fcQ3f+4Q/0sY9/vBQfAQAAAAAAAKhQrJ0cX8UEg0HaumUTHXPkoSU1uEHpaWpqoBOPP4r2791JIyMjOOQAAAAAAACAyjW6b775Zlq0aBHV1tbS+vXr6emnn865/Z133kkrVqwQ2x966KF03333Kc+Fw2H6f//v/4nHuR6X+6tdcMEFogVYqRkaGqJwKECLFmaXlgeVw8IFcykei1B/f/9M7woAAAAAAADAQlSU0X3HHXfQ5z73Obr66qtpw4YNdNhhh9Fpp51GAwMDGbd//PHH6fzzz6eLL76Ynn/+eTr77LPFz8aNG8XzPp9PvM/XvvY18fuuu+4SgmbveMc7Sv5dIpGIKLauqakp+WeB0uNy8XmMC0cOAAAAAAAAAFSk0X3DDTfQJZdcQhdddBGtWrWKbrnlFqqrq6Nbb7014/Y/+tGP6PTTT6cvfvGLtHLlSrr22mtp3bp1dNNNN4nnm5ub6cEHH6Rzzz2XDj74YDrmmGPEc8899xzt3r27LN8pk7r8Rz71ZXK3rRA/9V2rafnak+mKq79LgUAwbTu5zVPPvJD2eDAYojkHrRfP/fM/TymP/+uxp+m0sy6kniXrqWXuWlp15Gl08Sf/H4VCIfE8byvfU/vT1z9YqkNAu/fup7PO+5jYp3nLj6MvX/WdpFMiOyOjY3ThR79AHQuOoK5FR9HHPv1Vmprypm3z8itb6KS3vZ+a5qyhg1afSN/78S/SnmcD+VvfuZlWrHuL2ObIN55FD/zj32nb/PvxZ+id53+cFq16ozgOf773HwW1CQAAAAAAAADMTirG6GbDkI3hU045RXmM+7Xx30888UTG1/Dj6u0Zjoxn254ZHx8XBlRLSwvNJKee/Eba9eq/afOGB+m737qCfnHbHfSNb984bbv5c+fQr393V9pjf/7rg9SgaV+1+bXX6R3nfpTWrV1ND/31N/Tcf+6hH3z7SnLVuCgaTW9T9vLTfxOfrf7p6mwvyfeMRqN09nkfo1AoTP+8//f0i5u/Tb/5/Z/omut+nPN1F370i7Rp8za6765b6U+330L/fuJZ+uRnr0oTNzvj3RfTgvm99OTDf6TrrvkiffP6m8RxlFz9rR/RL359B/3g+ivphSfupUsuei+de8Gl9MJLm5RtvF4/rVm9gn70ndR7AwAAAAAAAEDVqZdzDTQbaN3d3WmP89+bN2/O+Jq+vr6M2/Pj2Xpmc403p6RzE/ZcImj8I5mYmFAauPOPHng7VhWXP2r4T7fLRd1dHeLveXN76KQTjqWHHn2M4vHPp237/veeRT/5+W/pe9+6gjwe7glGdNv//lE8ft33firei9//kX89Kd7vv77+BeW1SxbNp1NPPj75mbwficdZ2K2lefr31+6nGTz48H/o1S3bhfHM+8cG7tVXfIa+es336covfYpcLte012zesp3+/tC/6bF/3ElHHL5aPPaDb39VRMuvu+ZL1Duni35/5z3CkP/Zj78p3mPliqX04suv0o9+chtdfOG54jW/u+PP9P8+9zE6/ZQ3ib8/etF76eFHH6cf3PQruu1n3xGPnXbKG8WP6iBkPA7yPBq5BtTXgZHXgNKD82JNcF6sCc6LNcF5sSY4L9YE58WaxCpgnax33yrG6C41nGrMaeZ8Yn/605/m3Pa6666ja665Ztrjg4ODwnDX60Tgbf0+H/nc6YZlNBKhaDQi2nwxm7Zsoyee2kDz5s1RHpMcsmIpzZ83h+74v7/Que96G+3d10f/eeJZ+vY1XxBGdzDgF6/hHuCcIv6Ph/9Nx61fl3GfeFvG7/WRy+kgvcxfoTJKM/Ced76VbrjuKxmf4/TtVSuWUmO9R/luxx+zjiYmp2jD8y8LI1zLvx57ipqbGmnl8sXKa445co3IfPjP40/T209/Mz32xLN07NFrKRIOix/mjccdKVLM9+87QC0tTRQIhkR6v/qY1tQ46bEnn512nJVjFAxmfI7T4fk5Vi/PpjGQ7Ubl7Aq+7nj/gTXAebEmOC/WBOfFmuC8WBOcF2uC82JNYhWwTp6cnKwuo7ujo4McDsc0dWj+u6enJ+Nr+HE920uDe9euXfTwww/njHIzV1xxhRB0U0e658+fT52dnXlfK/H7/UJR3VNXJ/pnq3E4nfTAQ/+h+SvflDTmQuJC++F3rpq2rdvtpos+eA79/v/upQ998D1055/vF5Hb+fPnJp6v9YjXnHP2W+mJZ16gt7/no9TT3UFHH3EYvflNx9L7zztLtLyS2zKrjzkj7TMWzJtDzz/+16zf5el//innd21sbJi235KR0XHq6e5Me37hwvni9/hkore4ltGxSZHurn2urbWZxsYnxeNDI2NCGV69zYLkMZmY8lHv3Dn0lpOPp1t++Xs6+cQ30JLFC+jhfz5Bf73/EZFRkW1/+Xhneo7PEz/X1tZGXV1dZGQw4XIGvnasOpjMRnBerAnOizXBebEmOC/WBOfFmuC8WJNYBayT2Z6rKqObU4SPOOIIeuihh4QCuTwR/Pell16a8TXHHnuseP7yyy9XHmPhNH5ca3Bv3bqVHnnkEWpvz1+7zMYV/2jhi0HvBcHb8UUkf9Twnyccv55u/P7V5PP56cc/vY2cDie966zTpr+RzUbvO/cddOU3bqAdu/bSb35/N93w7a8q78m/+P9Op4P++6br6JqvfpYe/feT9PRzL9J3fvgz+v6Pf0H/+ccfaE5PlyLq9vC9v6WGhvq06G8uobClBy2iYlG/f+r/04+N/E6U/F6Znsz8Xqr/J7e54bqv0icu/xqtOeYM8feSxfPpgve9i379v3/M/n0znC/53vxj5BpQv7aQ14HSUg3nJRaL085hL00GItRY66RF7fVkt1e26F81nJdqBOfFmuC8WBOcF2uC82JNbBaf9/XuV8UY3QxHly+88EI68sgj6eijj6Yf/vCH5PV6hZo5wz22586dK9K/mcsuu4xOOOEE+v73v09nnHEG3X777fTss8/Sz3/+c8XgPuecc0S7sL/+9a8iwinrvTlimameuFzU13to6ZKF4v8/v/G/hLL2r37zfyKqraW9rZXeduqJ9PHPfFWkOHOke1Kj5C2Z29stotv88/WvXEarjzqd/vtXt9NVV3xG2YYjxJlqurPRNj9zurrk/PecSTffMD0dn+np6qRnN7yc9lj/4HDiue5ETbuW7q5OGhwcmRZpFlHzZB08R88Hku+Tet+htPfl2vX/++3NQhV+eGRM1IJzLfniZKQdgEpm475x+uOGvbRtYIqC4Ri5a+y0tKuB3r1uHq2e2zzTuwcAAAAAMGuoKKP7vPPOE3XTV111lTCO165dS/fff78ilsZtvtTehuOOO45+97vf0ZVXXklf+cpXaNmyZXT33XfT6tUJ8a19+/bRPffcI/7P76WGo94nnngiWQH+Tiz49aUrr6f3nvN2RTBNzYXvfzeddd5H6QuXXSLS8PXQ2tIsjFOvL1HLXSj50subGhPp65lYf9Ra+vYNtwgDWSqkP/TIY+I1Kw9emvU1Y+MTtOGFjUKNnWGhOM58OOqINco2V3/zh8KxInuhP/To47R82WLxvdXU1rqFM4K3/dNf/k7nnH26wSMAgPUM7h8/tJVGvCGa0+whT7OD/KEovbx3nPaN+ukzJy+D4Q0AAAAAUCYqyuhmOJU8Wzr5o48+Ou2x97znPeInE4sWLSqJIncpePdZp9MVV32XfvqL/6XPffriac+zwva+rU9QU2PmWuTbfvtHevW11+mst79F1C8HA0H67R1/Fm23uGWWGjaAtT3B29taFONVi4zIF8JbTnoDrTz4ILro418Sbb1Y7O3r//Uj+vhH3kfupMDcM8+9RB/+5P+j+/90mzCOeXtuqfaJy6+im77/dQqHI3T5/7tWCMn1zkk4YNg5wT24P/aZK+kLn/kIvfLqVrrpZ7+h737zy8pnP/3si7T/QD+tOXSl+H3t9TcJw/3zn/mIsg33/t6+I9WzfeeuvUIFvbW1mRbM6y34ewNQypRyjnCzwc2R7WA4SmO+ELU3uGipu0FEvu/asI9WzWmq+FRzAAAAAIBKoOKM7tmK0+mkT1zyfrrhxl/Sxz58PtXX102rd+hob836+nVrD6Fnnt9Il37+63Sgb4Aa6uuEavidv72J3vSGo9O2PfTot057/b8euF1Ej82Go/LcZ/vTn7+G3nTae6m+zkMfeO/Zom2YxOf302tbd1A4klAhZ3798+/S5V+6lk5/54fIbrPTO888VdSyS1jd/N4//pIu++I36JiT3k0dba301S9+kj7yofOUbQLBoOjVvWPXHnE8Tn/LCfSrn16fllr/3Asb6dR3XKj8/aUrvy1+f/D8s0VPcQCsBtdws2HNEW4eF14f8tFUMEK1NQ6qdzvF41sHJsV2SzqzZ6EAAAAAAABzgNFtQbIZc1+8/KPiRxIcydyfnGHDUT7P0XxuvfWrW76TUxCNxdtyvWepWDh/Lt3zh0Sdvd79amttof/57+/nfN9DDzmYHr7vf7M+z86GF5+8N+d7zNQxAaBQWDSNa7g5pZwJR2Npvz0uB/VPxMR2AAAAAACg9FhTBg4AAEBBsEo5i6ZxDTcTS5bQRGOJ3/w4P8/bAQAAAACA0gOjGwAAqghuC8a13AfG/SLLRW1089/8+LKuRrEdAAAAAAAoPQh1zBAJhXGbEAEDlY88j3qV4wEoFSyOxm3BWKVctAuLxMhus4m67nF/mNrqXfSudXMhogYAAAAAUCYQ6Z4huA+4w1lD+/Yn+oKDymYvn0e7kzo6MvcWB6CccB9ubgt2yNwmUd89FYiIGu4181rQLgwAAAAAoMwg0j1D1NXV0aIly+ipZ1+mFcsPmqZGDiqHUChEjz3xHHV0zaGurq6Z3h0AFMN7UXsdDU2GhIja0Yvb6MNvWIwINwAAAABAmYHRPYOcccbb6Ve3/oJu+eUdtGzpAmprbRbtr8wmTnEK+P1U6/GQjdCX18zjOjY+Qa9t201RWy198IJ351SHB6DcsGB5k6dG/L+lzgWDGwAAAABgBoDRPYNwKvIlH/04Pffcc7R58ybavmcnxaIJxWEzYRkln9dLdfX1MLlNxGa3U11dPR125Jto3bp1iHIDyxFKtgljAmHzxxYAAAAAAJAfGN0zTEtLC5188snip1TEYjEaGBgQRqHdjjJ+AGYLsjc3w4JqAAAAAACg/MACAwCAWWF0I9INAAAAADATwOgGAIDZYHSHEekGAAAAAJgJYHQDAECVEoqwokMCpJcDAAAAAMwMMLoBAKBKQXo5AAAAAMDMA6MbAABmgXo5R7rj8VTkGwAAAAAAlAcY3QAAUKWEVYrlbG+rjXAAAAAAAFAeYHQDAECVojWyUdcNAAAAAFB+YHQDAECVEo6mp5NDwRwAAAAAoPw4Z+AzAQAAlIGQKr2cQa9uAAAwn1gsTjuHvTQZiFBjrZMWtdeT3W7DoQYAKMDoBgCAWaBeziC9HAAAzGXjvnH644a9tG1gSmQTuWvstLSrgd69bh6tntuMww0AEMDoBgCA2VLTHYaQGgAAmBWlZoP7xw9tpRFviOY0e8jT7CB/KEov7x2nfaN++szJy2B4AwAEMLoBAGDWRLqjM7YvAABQTVFqNtb5tWxw82tstoSh3lDrpKXuBvGed23YR6vmNCHVHAAAITUAAJgNLcOYACLdAACQFqXmqHSLx0WLOurFb/6bH+fnc8HRcTasOcLNopWv7BunoamAeI4NcH5868Ck2A4AAKBeDioC9ii/PjhFL+4ZE7/5bwCAPvXyGkciAoM+3QAAMD1K7XHZKRKLJaLUXQ3icY5S51prcDo6R8c9LgdN+EPkDUVpaCqkPM+P8/O8HQAAIL0cWB6IlABQGNLI5jrFEW+YAmGklwMAgDpKzVHprf2Twjg+bF4zuZyOtCj1ks6GjAeMx1VOR+cabplUFFfZ6Pw4P8/bAQAAIt2gqtO/AJjNyJruBneN+A31cgAASI9SM75QVBjM0jGpJ0rNgmscFT8w7qdINPG6eNLq5t/8+LKuRrEdAADA6AYVk/7FaV8Ou81Q+hcAsxlpdMtISxCRbgAASItSs7UdSZbiRJLrCT1RalY4Z8G1tnoX7RsLiPE2GovTVCAiouj8+LvWzYWIGgBAAKMbVEz619BUkEa8QfEcREoAyE8omfPY4E4a3RphNQAAmI2oo9RqrQs2mo1EqVnhnNuC9bbUivF21BeiMX+I1sxrQbswAEAaKDQB1k//anZQNBajHUMJBVBOL2cPM6d/9U9ApASAfEJqSqQbRjcAAChRau6lzc59jlJzJh2vO8Z8YUNRaja837Kqm5o9o1TvdtDHTzjIUK9vAMDsAJFuYFllcXX6V8L7nBApCSVrpyBSAkB2OFojIzipSDeE1AAAQB2lPqizQUSpOS2cje5CotTs0Gzy1FBHg1sIr8Hgzgw60YDZDCLdwLLK4jL9i0XT5rbUpk1ubmci/YsnR4iUADAd6ahiWAdB3Dvo0w0AAAq8TrnguIViXcHR7qMWtdLFxy8xbDQHkmMrj7sgM+hEA2Y7MLpB0criLGjGddecBs7RZzaSOWXLqKc4V/rX60M+Jf1rzBumgYkgREoq2NPN9focUeBsBqThlQZ1nWIj1MsBACAjrFzOUWoxVtbWFBSllqrnUogNlHe9CEAlAKMbmKIs7g9HhchZZ6OblrobROSblcVXzWkqKs1Kpn/98j876Intw8KLPFYTpmOWtIt6KwzSlQU83eUjHEks/vj2q3Mn2uKgTzcAAKTjDabKbgodI3kNxCDSnX+9yA7hWDye6ERj4noRAKsDoxuYoiy+c8hLU8EoeWoc1FBbIx7fOjAptuP6pmJgw/rC4xaKmiuOdq+Z10yfPHEpBucKA57umYl01zjs5Hbalce41pvvWQAAAGx0p3px+wsoweExVYpUwujOvV5kY3vj3nFyOu102LyWaZ1oil0vAmBlIKQGilMWdyUjaMkJJ5RUS+bH+XneziwVZk7/am9wk9Nhh8Fd4Z5uj8tOE/4w1bkc6Lle4h7dLicb3Yn7NCFEiLZhAABzqAZhrCmV0V1IpJvruaV+RjSeaDkGMq8X2TnBy0ShLxIvzXoRAKuCSDcoWlmcDadI0tiOxWIlURZXC0BxOw9Q2ZkRfWN+2jcWoAVtddTTXAtPdymNboedahw24uA2r3ESQoQJIxwAAGZ7uZAvpDK6C+jwoDbUZZcVJBNlXi/G4qqe6PE4OWw2dKIBswZEukFRyuKsIK5uQ8QBb/by8uPLuhpNUxZXT2oTgTBSuCo8M0JGW0PJawee7tIZ3Zxezo6O2qShDQVzAIBZ5UIshNXicdGijnrxm//mx/n5SoFbhUkCoQKMbo2hDjG1XOtFldFdovUiAFYFRjcoSlm8rd4lvNy8wOdanalgWPzNj7PQmVmiGOqBmr3I435EuyvV083I7Du5OEHPdfMJJYXUapw2Jc2cQa9uAIB55ULSUEoKY3U1iMdZGKtSUs29KkObS+QiBktw5LwmQV139vXi7hGfsl6cDIRKsl4EwKrA6AZFK4svbK8TImfsLeaIJvfONrv9g7bOaswXMu29QXk93ezZ5glXXf8GT3dp08uZ2hp7Wj9ZAAAotlyI5/xNByZo17BPPKcVxrI6bCBzyzA1UqNGL9oxlec1kHm9OK/Fo6wXR33hkqwXAbAqqOkGRcED5TvXzeWpVizy1y1opY++aYnpHkut+BMP1qByUPdc58VaIBwRhjfX0sHTXXohNUbWcfOCBwAAii4XanbQgfFQWsssWS7UP1EZwliynptrsNlByVl17ORvcDsLTi+PJjVuwPT14mmre8jjcor56X3rF9CblnUiwg1mDYh0g6LhiVUqizd6nCUZQGWk25l8b0S6K9fTfei8ZvKFEp5u7o8KT3dpkMY113Qzsm1YIUJBAACQqVxIplarU6orqVxI9uiuq3EomiPadPF8aOvAEenODmcVyPVid1MtDG4wq7D+iAgsz7gq6lwqkSZZ093V6Kb94wEomFew4b1qThM57Ztp17CXuprcdOUZKzHxlrhPN8OLYAZCagAAM8qFWDRN9riW+hyyXIidqZUgjCXbhdW7neSw22iMwoZ1L9RRfiaS7OICcvdEV2v1ADAbQKQbFM2YStSsVIOojHR3N9eK36Oo6a5YOBOiqdYpPN2eGgcM7hLBve0ZbhemTi+HkBoAwIxyoWZPDQ1OBUWqMP9MBUojpFoOI5DTyaXuhT9UZE13hQjIzQSTKqMbpU5gtoFINyiaCb+q3YbG42sW0pjvaUoY3VAvr456Y4h6lU9ITaaXI7oAADAja+m8o+bTnlGfWAP4Q3GhWs4Rbja4K0UYy6uKdEeTEWpt5Dof2nUPjO7McBZEeqQbpU5gdgGjGxQFD5rqCapU9aIyJbYnGeke84XFAM5KqaDyCCcjAZySyMahTIEGJh5jbXo5jG4AgIk01tYI8VTWdeHx5hMnHkSHzWupiAj39PRyBwUjtoKCB1ojHUZ3ZtjJrtbERaQbzDZgdAPT6rlLVS/KxrU05jsb3EJllI01TlNqqq0x/fNA6VH3QeUFDoxu85ER7RqpXl6TTC8vUTYKAGB20TfhF45vFsZiOhvdFWVwM95QKr3cbkuMjYh0l4bJoGa9iJpuMMtAeAkUxUQgMYg2uGW9aEwYyWbXpsq3rHM7FEN7zIu2YZVeb8wgxbzUkW5Z0430cgCAeRwYD6T9XarysrKol7u4pttR0PfQGo+IdOc+1qnjVnnXCwDFAKMbFAWneTPc+qFU3ks5MMs+mq11SaPbn+gPCirXIKzUhVolEE7eh6jpBgCYTSwWp/6k0S1bg3E7qEpDLaTGwp4FpZdrvrdUcgfpcJtQNUgvB7MNGN2gKKSgWXuDS+mhbXaKuYyEcqSOU9la61zi71FNajuonMWaek0Cb3dpswlcMr08qV4OJwcAoFhGfCEKReMik2Zeq6eg/tbWElJzqNTLC6vplkY7It256+clSC8Hsw0Y3cCUSDenfMv0VbPF1KRRJo2GZhnpRtuwiiSs6WGK9PLy9OmWC0pEFwAAxdKXjHJzlhunZjO+CsxamkqmPCdahiUdkway9YTmTPJ7yzI7GN25jW7pnMBcBGYbMLqBKTXdLXU15E4u6s2OpKkj3YyMdEuDH1RuPTeDSHepjnO60S0j3qXqMAAAmH313NzGU0nLrrBINxvHMkpdrzK6jUS62bkpM7f4PcT7mqxrUy3IrALOjGQQ6QazDRjdoCik4dvsqaHaZCS6VDXdckJkA58ZRaS74pXLGUS6y9OnW94/pegwAACYXfSN+8XvOc0c6XZUZE23VC5nvRj+Dp7k9zDimJTzF1fXydcj0p0Z7jjDtNXD6AazExjdwJSabm4ZUqjyZz6kES8j3dLolr26QWVHulFjXNrjXONMVy/nyAzuGwBAMfRNJCPdzbWKsWm01ZZl6rldDqEX4ynAMSnnL17/OJK6NhHNHAfSj3fK6K6s6wWAYoHRDQqGJxtpEHOkO5VebnKkW6aXJ9+/xZPykiJKWnlEUNNdFmS9nEwvl5oI7KeS9d4AAFDI3D+SbNkpjO4C0rKtJaLmTNO94LWF3mi1NLr5GDg4ZM5ioQgG5FQvb08a3ajpBrMNGN2g6Cg3Tza8oE+ll5tc0y3Ty5Pvz7WpUrAEKeaVRziCSHep4Ui2VkiNVYaTa0I4qwAABdOfjHKzs51F1OoqNtKdrOdOCsHJNYaRDCzp+OdovxLpRsuwnEJq6vRyZF2B2QSMblC00c0TrzoSHSxxpJtpgZha9aiXI8XMdDhKI4Mtsqab0yflohIRBgBAoewfkyJqbvFblpZVWk23NAIbkn3G7XabUoaj14Egt+PXOR3JSDeM7owaIzIzsr0+cd3wHKUtNwOgmoHRDUwwup3pfYBL1DJM7YVO1XWHTP0sUHq09W4oETAf9UKGI9wSRcG8wiJSAADrRbrntCT6c8tId6WNKzK9XO4/Y1SbRqbU8+vsyVQiRLqzp5Y77TZqSq4ZGZQ6gdkEjG5QMONJ5XIZdS51Tbc0GNR13aNoG1axqtqSSluoVQJyIcPZjs5kpFtbswgAAMW2C2OkkBqPK9ruFJWgXs49uiVK+zO96eXJoAC/zmlPjK9RTTYXSGUVcP08Z13JjIIg5n8wi4DRDQpmTJNeXqqabm3LMKZVRrr9iHRXqtEtbUFMuqUXUZPIbBSoxgIACoFrcJVId3OtMvdLvYhKquuekjXdaqPbZSx4EExTL088VkF+h7Ib3Y3JVH4ZREGkG8wmYHQDU9qFqaNopke6NS3DGNR0Vy4y9a7BnbhuAoi6lq5Ht+qeYZToAo75rIRrTV8fnKIX94yJ36g9BUYZ8YbE+MFpwh0NbqUWWjrdK0nBXKaXqyPd0rlvtKab1z+OZKRb26EDpLdnY1KRbhwrMHtIjTQAFCuk5ixtn251pBs13ZVvELLHm68hpJeX7hir67lLKXYIrM/GfeP0xw17advAlDj/fC0s7Wqgd6+bR6vnNlM1wc6EncNemgxExDizqL1eGIbAvNTy7iZ32jHlCDEboJUU6da2DGOMOg8U9fIah7JWQcuw6UwqonU1aQKfiHSD2QSMblBwitmENr28RPWi0ihTR7pbk3XknB7GqbTaiB6wvpCaTDNj0S9W25btVoCJkW5HylHFIL189hrcP35oq4hSzmn2kKfZIYyKl/eO075RP33m5GVVY3jPJufCTNAn67mbEyJqEm4dxr27K0nBPNUyTCWk5ipQSM3loHAyi0srFgrUWQXJSDccwGAWAksFFAR7d6VxnTK6k/WiJYp0q1uGsYEvjXDUdVemQVif7I3KINptLqFkL/QapybSjfTyWQdHfdkIZYObjc/Eojcu2iTx3/z4XRv2VUWquXQusDOBxTYXddSL3/w3P87Pg+Lo09RzF5qWPdOw4JvcV9kyjKk12DJMCqlxhJxT7hl2IoPM6uWyrExGuqEvAmYTMLpBUanl3GpDRplLtaCXBpm6ZRirX6ZSzBP7AiqrppudKPKagdFdqkg3arpnO5xmzVFfjnDzuLlt0EvP7x6jUCQq/ubHtw5Miu2qyrlQ6xTZM9XoXJhJDoz7xe8ejdEt225VSk23N7mfbCdLxfI0JXadJTgBpWWYXWkZFuUG1CCLermMdDvSRD8BmA3A6Aam1HOnRbojMdMWNvw+suewOtKtTjEf9ULBvBINQm6vohjdmHhLcoynqZcbbIcDKh+ua2YDQhoTvPjl4Vkugvlxfp63qybnAqdBD4iobLyqnAszCY8bnEKubhcmkYarUaN7psT91PXcfH1M+x66I93Jmm6Xg5xJDQ1EunWolyuRbhjdYPaAmm5QlNEto83ammseSOUirxjUA7I2aqdEupP7AioDWe/GIl/CCAxEYASajBSn0WodyNRJRBdmD7zIZYclG0Mc9ZU9hKUAFD/Oz8vFcMU7F5odYjzZPeJTrvV5bR4xH/VPWMe5UIlibwMTQfG7yeNMEx9j5HzvM+DQm8n6e1+yR7eM0BesXi4j3U5uGQajW69onVLTDaMbzCIqe5YFM8aYLzQt0u102EVNE6cP86LHDKNbRuTYQOP3V5NqG4ZIdyUahHw+FfE9qGmXpU+3NMIR6Z49sDHHhgzXNS911Ss9hLmWkgUxOV14zbwWsV21OBcC4ZRhvZ+Fv2xELZ4ayzgXKlXsTaaWz9FEudXGq0y3trq4n+zRrW4Xpja69YyRnFEky6VEn26ZXo4ShmkOJpnOL483arrBbATp5cCUHt0SsxXMM/XolrQmI92jqOmuKOSChB0psk5fitEAc5AlGdqWYeoSEDA74OgpG3Nt9S56bWBKGAqxZPcJNvr48Xetm2v5KKte5wIbhhOBsNLGimEj7tW+SVrW1TjjzoVKFnuTImraem51WraMIBupv+d1AzuByll/n6ldmHoNIzNBciENc7a1E326E/eQNMRB8liHIsRl7nyc6pMCqqjpBrMRGN3AtJru9EW9OUaUfB91j24JL1QYCKlVbr2xkagCME9IDenlswuOGHLk8OCeRnHuWUl4IhAREe5qaRemdi7sHvGLe2Bus4c6G9zCCOd04Xmtnhl1LlS62Jvs0c2RaS0ys82vw1jV1t9vHZiiV/ZPiAyFctXfp4S9NGnyBuYkaZjzuMr7jfTyPMfa5VDuP9R0g9kIjG5QELJHN6fsqUmpUZsTSVNPalpa6hOfzQsqpHNVXhSWSxGQXj5DQmrILJh1sGH98RMOoiMWttLhC1rosHkt9P9OP7gqDG4Jf5ePvHGxSCFn58KwNyTa5vF3Xt3bLIy7RzYPzJh4l9bY5Lrzl/aOiTp7q4u9cSlCqkd39ki3X0ekWyvux2nlHAmVKd/lEPfT9o2epl6uQxBWGubyu6NlWO5jrXZwyJpuOIDBbGLmi5tAxcGT71jW9HJzI5cy0u1WtQuTNLqdSg05R945wgGsDyLdZazp1jirlLZ+qKGftfeeeszmcbMrQxZRJVPnctK6Ba3CEDr78LmKSNm/tw3R/Rv76PZn9tDvnt4lHLrlrqdWi72xqvrQZFDMX2yUNHlclhN7U8NlXGyI8pzb0eDOeNz1CpCp6++5hZRMx5brhnKI+ymGYHK/JerWpOyclN8rZzvT5D2ESHdm5PWsrp9HpBvMRhDpBobhSVVGK7Xp5Wb36lYi3Zp2YdN7dUNMrfJqulNCaoi8lkasTlvTraSXR2PCeQZmFz6NyNXQVPWNm7uGfWJuWMvR/PkttKSzQaS0nrC8kw7uaRA109ynPByJlb2eWm1schcHaWzK31ZWkpcial2NbsW4zFzTnRDo01t/r450shErxf1KXX8vo+ra9HK+VuQ4ma/9mXQwyHkMRndmvBlE61L6PygtA7MHGN2g4HpuTssqdR9gOSGrvc9qpNEPMbXKQZ5Tp1pIDZFXU2GDInNNd+J485pYGuZg9qC9z7iGuNqQqdkLNQYbpwqzQc4q2021NTQ4FaLByUBZ66nVxqZacIwN8HIZm4WSK7VcnZbNhy+f011df781TdwvUjZxP3n8tUa3kbZh8n6SDgcIqWVmKphcM6qcSXIuQno5mE1UnNF9880306JFi6i2tpbWr19PTz/9dM7t77zzTlqxYoXY/tBDD6X77rsv7fm77rqLTj31VGpvbxfe8RdeeKHE36DykcJlvHDRYnZ6uXyfTJFuphVtwyqOSLJPcI3drpxXCKmVSr08/b7hyLdcx8LRMfvQGhHD3kTf5WqBo2b7xxIR2UXtdRnrqVf0NNH8toQQGAuu8dhTrnpqtbG5fdCrGJucfmt1JXmpXJ5JRE1p65nc73wRYrW435LOepW4X5jWzEs8XupUfynupW0ZZkTBPLU+STe6+ZyC3FkFsn0lOmmA2URFGd133HEHfe5zn6Orr76aNmzYQIcddhiddtppNDAwkHH7xx9/nM4//3y6+OKL6fnnn6ezzz5b/GzcuFHZxuv10vHHH0/XX399Gb9JdUS6ZWp3KdPLc7UMY1qTYmpQMK8cOKojI93S2w0D0FxkFFsubCRsXCDCMHuRxpAcT6st0r1nxC8irZwB1ZJ0yGYS7+ptqVP6Ssv01nKId6mNzd7mWsXYnBTGprWV5PNFunlsSSmY63O683d9//oFirgf1+J/6s1LS34MItGYMudwTbkWvQrmqfRyTaQ7OceBBFPJFn6swyNBJw0wG7Fe4VAObrjhBrrkkkvooosuEn/fcsstdO+999Ktt95KX/7yl6dt/6Mf/YhOP/10+uIXvyj+vvbaa+nBBx+km266SbyW+eAHPyh+79y5s6zfpRp7dJc20p05vVwurEZR012RUVi0DCuvejkjakrDUWQXzELkeMqtszjSWm1G9+4Rb8Yot7aemtNctUrT5aynZqPyTQd3ksflFPfquoWt9NE3LrFkhFs6JlgJPpfRLY1VdlpotQPy1fuq1xL8Oe0ZhNrMxJvcPz7c0sBWo3demq5enhhvEenOfLwzRbpZz4DvwUw6AQBUGxUT6Q6FQvTcc8/RKaecojxmt9vF30888UTG1/Dj6u0Zjoxn2x4U16N7JiLdsmWZ3CdQOQahumUYhNRKc4y1Nd2luEdB5SAjc3NbEinCbHRbtSd0Iewc8mWs59bWU3P9tFr0aibqqUemQsLYZAOTDX2rGtzMwERQ6EA01TozpmNLZKTbiNN9MpnmLWFF93K2sOIIfdb2ZwaNbkfyvWQJFcivXs6grns6M9XWEJSWiol0Dw0NUTQape7u7rTH+e/NmzdnfE1fX1/G7fnxYggGg+JHMjExIX7HYtzX0XqDLe8TLyrM2rcx9njH42IC1r6nm9WS43HRq9OMzwuEI+L9XA5bxvdr9jjF86PekLg+Mk2gVsbsc2N1+Ltyah/Dcy6fVz5/gVDUUseg0s+LWMQIw2L6d0gdc3Pu0XJS6edlpvGxsRGPU0eDi/gyYINzxBssut2iFc5LQijNK77fgrbajPvyrsN7ae+oj7YNTIp7JBaP0aQ/Irpf8DF45+FzRCuvUi9w+bgPTQX5oxQjsBTHzqzzsm/UJ45rd5M753vVskMvHhfpxHo/c9KfWE+wM5AdgQOTgZJfR/Iz62rsGT/L5UyMkXy/5NoXUa7B6xNnYn1is8XF39Eo5VyPWOF+KRf8PUV6OR9vV+p4s49JjkH+UJjcfMxnGKucl1f2jwtRx20DXpFlwiVhS7vqhd7DIb3WLD+ZDeclF3r3rWKMbitx3XXX0TXXXDPt8cHBQQoEEnVPVrsYxsfHxUXL2QHFcmB4jPz+CEV8EzQwkJ6eODXhI7/fT8Nj0ay19kYYHpsgvz9I3olxGhiYHs3mATsQ4MgF0Y69fUJRvZIw+9xYHa514+uDGR0eEqll/Dc/0tffT3aLOE0q/byMT3qFUTE+OkKOYHpGSjjA92iA+gaHqMNpvfGqms/LTDM0Ok5+f4gCU+PkpjAN+8O0dc8BWpwUFqvk89I/GRLXPaet2gITNBCcnLZNp5PoA2vb6b5Nw/Tsngmhll1ji9O6eY301pXt1OkMmjJv5WPYGyafLzEOMkOj5syXpTovr+0ZEeN0bbwm535GAl6xXd/gMA3U66uN7xPriSD1tHtox6SfdhwYpoE5pV2a7ulL7GfcE8/4fUK+KfH8wPAoDQxkn5MGxf0UJN9kYn3CdeJyfjvQP6CUMFjxfikXAdYt8CYyUPzjoxSeSh2TSCggjtn+/kEKJfV5ygGn/+8dCwqBN14zzmtxi7WHFc7LlgEf/fLJ/TQeiFBXg4ta6uwiE3DDziHa3jdGFx/TSwd3TS+fqWZiFjgv+ZicnD7fVLTR3dHRQQ6Hg/r7+9Me5797enoyvoYfN7K9Xq644goh6KaOdM+fP586OzupqamJrEbCA2sT+1fsBSsilfZB8nhqaMm8nmkREq/dSx7PFNW4XdTV1VXknhM53ePk8dipt6uDuroaMm7T1TpGE/4wOeubqautsgYjM89NJcBtWjyeQfH/uT3dYvLzeIbF382tHUp64kxTyeeF71Gna4AcNUS93V3TtBfaWwLU57NRXWMzdXW1UyVRyefFCjjcY+TxOKi3u5PmT9jI1zdJNncjdXW1Vfx52T45TB6Ph5Z1N1CPJsNNDU9Lb1i1kH771C56fNswrV/cRhcet6is6d3DBybEvrKPkR3GDleNKfOl2eeFI/47R3y0fXyQwjYnLZ/fRV1drVm37+6L0vaxGLnqGvV/Hydfk3Zas7ib+nz9FKDSHAs1rokh8ni81NPenPGzusdt5OkLUY2nPue+yPXJ3OT6hB2dHs+QeK69o0MRrbTi/VIuBieD4phwJkPvnPT7srVpVIjgNja3lm3tliuKvLKncUbPC99vP3v6VfJFbbSyt1U4LLiEoaPZRe1Ncdo2OEWP7vSL8cvK5ShmE6uA+4U7ZFWV0e1yueiII46ghx56SCiQyxPBf1966aUZX3PssceK5y+//HLlMRZS48eLwe12ix8tfDFY9YLgC9aM/eM2G6IU1GYTImba96vjmh2bjYJRczxSIRbdstmo1uXM+n5s+E+IdiMRyx7/cpybSiAat4nzyfOFM7kgqXHahbgan+t6Cx2DSj0vnL4fJz7OLJo2/b7he4nPAR/vSvtulXxerIBQbLbZqKG2hjoa3bSlf4pGfGFTjuVMn5fdo37x3RZ3NOTdB356YUcDben3UnO9WxmLygVHunlf57Z6aO+on/zhWMmOW6HnZeO+cfrjhr20rX+KXhuYJBvZqMG9j2prnFnVxetr5fyv//tMcYq2zUZLOhuIbANiHg/HON28dOfEr7oPMu1nnbtG1xjJRhFv53EnxlmRlp7M1uIxONdrZ/p+KRe5jrUQyLUlznc5jgNf0zc+vF1oWXDrO3byc4nAy/smaN9YgD590kHU6Zy587JzeIq2DXppTnMd2ex22j44KUQJ18xzCnE/fpwNbx7rxP0yi7BZ/H7Ru1/W3PsscHT5v//7v+nXv/41vfrqq/SJT3xCtPySauYXXHCBiEJLLrvsMrr//vvp+9//vqj7/vrXv07PPvtsmpE+MjIienNv2rRJ/L1lyxbxd7F139UKR5QZFn5xZhRpMle9nFu4qNVEc/XqHk32DweVpaqtKMUmW/eAYo9xPK13rhYpXmeWkBoEXyonA0JpceR0KFlKUpW60r+b7K+9MINyeSb4GKhbhpU7+idV5BP7ELOUUBIbJz9+aCu9vHec6twOqncllN+3D06Jx/n5TMixXK96eVjVuqursVZp4zY8Vdprktu0MdlE4RQhtTzfQ65P5PZsb8sKKamKP9vHUaUfeoauAHK9WA5RTz6e7ERig5sFFdlBwuMG7xf/zY/ftWH/jCrPq9sasttGjtehMrc1BKWjYiLdzHnnnSfqpq+66iphFK9du1YY1VIsbffu3WnehuOOO45+97vf0ZVXXklf+cpXaNmyZXT33XfT6tWrlW3uuecexWhn3vve94rf3AucjXSQjuyHnUm5XL2g54V/sW0geECUhhhP+NloTvYLZzEcYG3kQkRtDHLaGVfDoFe3uT26hVBNhvtPqsaasdBRomEDU2IxwPcpL2DevW6eZfsNz+brQq7ja112aq93Kyra1TAvcX02X9rzWnUa3bJzQtJwKidCRI2I5rfV0ZOvj4j/8wJb3VJpptAaJ9wZhKNMTe6EccL3OqfnrprTNC3FtY6zaHQYq1rjV3ay6Gx0065hn3BK9CYV9ktV5pTYX0fB1wYfJzmGSmcDHydWMI+wmJoBo7max1FpdGe6tmXbsHIY3eyU4+PLEW42rNmh5HTa6bB5zeK88eP8/N6xeurJXp1SUtRtDfm3tP+TU3pZ2xqC0lBxZ46j1NnSyR999NFpj73nPe8RP9n40Ic+JH5A8T26GXVKGEcQ5CRc6CJRDjrZWoapI93SIQCsi2wNos6SQK/u0mUTZFLPNet4y2iYkqrXnEzV2ztO+0b99JmTl1X8grGaCITUXQPsSqSb1cvZwVlpnR/UyCj33JY6ZSGfD/cMRrql0d3dVKuodlvF6FYbJ3xNyKg1G6jSONk6MCm206a46o0QZ4qC8nt3NCSMbnl8SgULaDHZjnetjpZh6swsdSYeOzpZIJR/9FDt46h0rDRmONZyXVeOlmFKFLnZISLHnBAWDccoFEmoz3MUuX8iqlwbM4Fsa8jnfk5zqkY4mlTv5raGa+a1lK2tITCfikovB9bu0S0nHBnFlKlXhSK9n7wWzNRvWNKSjHSPItJteeRCpEYVIZETr1klCbMduYDJZnyYsdDRRsN40cz3fnqq3r6qSZGsBqQBwYYRGzitdTVibOW6VW2v5EqDDTUjqeVpZRZljnSzQSUX9ty6TYpH6jVUy5vimnJKSMMyV4qrjBzn62+t/ix1mjdHutXp96Xu050tvVyPY1KeLx5P1RlF8v96xr7ZMI56Q9aIdKujyGqHiGhLK6PITseMdsDhzBHObmCHKJdysAOdo/KTgahwhPHjLPg2m0TUqg0Y3aCgmu6WLEa3mTW6cjHEk1quKIw0ujnSzd5AYF1kj+7MkW7r9mCszEh35ntGiNcU6eTQRsP2j/mpfyLRfkwbDQPWM7rlPSjH8UpPMTdaz512H5Q50i0NyiaPUyzyleiwRZyOauNEndoq21/lSnE1WtMtI93yvdgJwZQ+0p3dEGTkOclVay9E1DKUvsnjpCfSzcrw6nG0b9xPW/omhKFVLeOo1rEyU5FuGUXmaLGcI6XQm4wi8/PcPmwm4awGzm5Y0FYnjgtnCkwEwiLCXelZD6AC08vBzDLmD+WMdDO1JtXoSg97PhXTFo9LmSD5M63SdgpkF/mCkFp5xeoyLXSKiS6oU/XYkcIKzOwX6+ZIlU2m6kHwxUookTlVKixHTliAksXUFnVUZsoi1+f2TySMtIUG0i6V+6DMzr7BpEHZ2eBOjw5bJNKtTnFd6m4Qqa0MR9fypbjK78Jjix5Nl6lgOD3SnTwmQ1OhkpU88Hglx758kW7plMlUJifPlxTkk8jvrKemezIQVsZRpn8ymMwiCFOzx1UV42iurILUXBQtWxSZU/Y5M4bnST5Xo96gCCYlosi9ZLeV1uGjBzasz1jTSw47d3aJ0folbXTRcYsR4a4CEOkGpqaXqxd1xQ6k0miXaYDZ4BQlmRKEFPPKMAhlNCA9zdMai85Kh2vU9KSXF2N0Z0rV4ySTaDLTpNIEX6pVOViNjOiqxaO4hpYZLnFksRyp5Z0NrqxGVC7DqtzK4TKKK1OpjUaHy5niylFYjsBz5JXH53wprjJCrDdyr42C8nvz2/I54dZhpcCbTO3nz8m2tmBjLFX2lHmclJlCWie/EaO7sbYmLatAXofSEVRp42jB6uVlcnzJKDJnxMgo8qg/FUU+pNc6UWS+N1g7qb3BLRwwSCmvDir3TgZlhz3PrBCb1+jOM1mZHelmuGf4VNAvUsxLqXoKiiMSm576LCMFSC8317GRTQfBDKdYdsEXXsxWluBLNSsHZ4zMaSLdDNeOViq7lNRyY9caZ2SpRTtr7Y6yppdLh4c0VK2kaSGNE74vHn51QDgEOGJ52PxWYXBnuy/YMFCE4ULRvE4QrUHGJQ98TXKkm49TrnVGoUwla4x533JF0nkckAJ3mVBq3TXOTYeB9PJFbXVpWQWyOo4/t1qEs5RznKOmW3bcKAd87Z59+FxyJqPI7Pj46ttWkMNhp1hyfWKlAJfVxgZQHDC6gaHBkycSnqeyqZenRRCKHChS7TjyJ2RwXTenuKJtWGWkl0O93ALp5UU4xdSpejuGvUqq3mQgRCPecMUIvlS7cnDGyFwGo7uSe3XLSPeiDv313HIM4owbntP4XlA7I8oZ6ZaZB1aJdEv4uue2YBwRZAP4wmMX0bEHtee9p/n7SKNbv7J1aj3Bzgg2uvk4sUFaqnTnfErx7AyeoEjW7+FPdgPQXjfcMozR0+9ZPY4Kp18kKhwB4/6QuF8rZRzV04c9Z3p5mY1Kvt/Va1hvOEpNOcR6Z7KUk4HRXT1Y6yoDlkZ63rj1Q65aLSXSXaQ4hhxo9LSAkXXdXJ8IrEskaXSro7CpnqjWWnRWKiGdRneiJV+86GjYQZ0NqVQ9X+UIvswG5WA1MmKnNhLaGyo70s2Lena2FhLpTp+ryjP28LU0nBStk5HuWoOK3+WEjT02ojnF9aCuBl3GnxFhuEypx/K4lEpMLZ+ImkSmjWebl7KmlyezuORcp3ccPXReszBQhXCWP1Ix42gufMlUfp6KMgVPZBZjOSPdma7NgaQIqFXgcUJdx18OdXdQHhDpBqb16DZTHTkt0q0jvZzb32i9g8C6UVi10yaldo+JxZxjnLumWx5vtrf5HismwscLwguOXSgWmHxu37d+Ab1pWWdFRGa0Cuzcr3rHkJeWdTWKMS5XP+JKREZS1UaCjHTzcxzRqzQRSlbN50g1a3q0J7+LETiF2BuKls3hx5ojvL8cYZfK8XUWUy/XIpWlpYMiH/IaYoG7QpStS902TBqC9XmudZk2nu3ayOTEUuuV6Il0q8fRFd2NNDARFOMoOwRkynMlM5kUyqvPksrvmiExQzkW8i7xaeLjvrSrkawCq5WrLx8EJKqHyr6jgeVE1NSTVbHeOaVlmI70cl4kswLlqwcmqlYIqapSn1ULuJnql1uthCO5W4bxolDaxGZ40NngloIv3U21FWFwZ+pHzBFIvjyHvcG8/YgrkWCG9HKONEmRJvm9K4mdSn/u+oKUrqVDt1yRJE6blhkG8j5J9em23nXG8yj3cdebcZb2ffI4EdiYl8ddLRRW6rZhimJ6rd5Id24hNa3Rbbfpr+lWE6XUOMrvGSxz9LeUonWcHZkv66qcyHM3pymhR9I/aa1It7qem4HRXT0g0g2M9+hORpWzUWtapDtzS45MdZn/88ROem7XqFLjV41CSNWAXIjUqAwzmWJW7n651Uooj5AaGyd8zHlRnLjHihMrUhsslZQGp1Zg5wW4jH7IWs1qUA7O1adbHe1mxwKnmM9rNVYXbRURtUKFpsyaq/Qio7cympuWjp287qyE2hjSG+nW2wJNpnmzc1D93vLYcKkKO2mzlckUawjWZ2gDlunayOY8UIxuzXGRkW7Zak0vWrVzvh8ztSqrJKSDI1sqvxLpLvO8IbMwFnbU0/7xgIh0WwkWBJYZnHwfQGS2ekCkGxgeCPJFus0Sx9AT6ZZCSJwmygM4T/hNtTVCCIkf5+eBtUW+yr3wne1Caup7yozsAvV5k6molYBUYGeF4EiUHRBJYzscoXg8Jh7nVPNKVg5WI406jyv9uqhUMTXWI9g5JCPdhTkLlPugbJHudOVyI5HhmUAeF7Yj8/XclnhqnLq+jxRR06qI89+8huD02lJoDXhDOoXU8hrdsTwtwworC5JUqs6Cmqmkg6MhX6S77EZ3Yr8WJceN/olgUfompYp0c+aYDFZwf3lQ+cDoBuanl6v6n5ayZZhaCGl5d4NIU+bJ2+W0VaUQUjUgFxZSbCYtvTzZJgUUh1zAqFP4S9GrW/k81WKgkoxudT/izX1TwlnBdZi8mOa/K105WG8NqqyFHkmmPlcKA5NB8Z1cDlvBbSKV9PIylbbkinRb0emYqud26E7fT9V064t0a9O8+XNKWdedElLLnUGX77xku58cdntae8xiIt2Vjtqxki/SXa65n9eD0mGysI3LUhLnUl4XVmAsudbuUo0T0LypDmB0A8sa3XJgzNYyLF0IyZ7q+RiJiYlbLYQErIH01qarl6cLewFzHBvZarrVjqxienVL1AaLGe9XTqRy8NxWj6LAzr85AlLpysF6WoYxXENaaTXdvHB+YvswDU8FRXZToW4RGekul8GrtAvLEOnmsc9q0Sx5P+ut51anl+c7ppOBVDcULfL4DJagrlu2DMvXQzxfBla2+0kRUjN4KrXnnkX3Kh3lWGcp0VEHVMo196szF7h0SDodOdpttbV2S50r1WHBgk45YJzKLhgBZYO9kKyoqG7PlQ2zBol8kW5FCKnZoaTT+immGB28mOmfqB4hpGognPTmy4WJ/D/b4LzmKGe/3GolJBfKOdLL1dkFxaKuxa+kSLeEDeu3rOoS/XVlDemZa3uryuDmKJo819p02PYKSzB/g3MAAPZLSURBVC/nkiHOcPrP1iFR8sQtwwKRTQVpeJSzZRjPhxPJuUidXs7RdqmizAZBo4UUq40ql6uN0EIj3UwpI91KTXe+lmF5je7M5W8yM8ZopFsrvCZby1Uyk3nas7FjWF77nDFVjrlfGt18TfO54mgyCxwOTAZoSUed5fSThKhehPudw+iuBqwzugPLD55sFPEAmU9YyDwhtdwTvloIialR0rriVSmEVA1Ib75TtbDkrASZ5gkxNTMj3dmHdzNbtagN7UrNVBiYCAnl4EPnNovfgxaKepiBeizWClPKmm7uDSz1AKyK1PBgzQ6+ynls725yFazhIdtbliO9XEa5ub2Z2vHBC395TqxW1y3vZyOR7pQau06j2z09c65Uvbr5+pbfqT6PSJnUPsgkYsVBCDlXZYt0a9PF86Hdvhoi3TK9PJt6uSgHTM5T5SrxkNelzMjoStZNW0lMbSx57jmrVDrIIaZWHcDoBroYT4qo8SInX42jWfWiitGdJb1cLYTEk6AzmU7LEyv/XW1CSNUl8pV+DUFMzTxkjXUuo9tMJ4f6Pq/ESDePFf0TiZYxh81vsVyqYSmiO2p48SnHbCvXkao1PBa01YnoGH+XzsbagjU8yunsy1TPrTXw8hmq5Saf4zsTihp73vTy7GneHY3JtmGTIVNrfWWPbh4as5WtSWSGXabvkahBpswtwwo0umWwQB5rzuIw+h5WQ49onVzflattmE9rdCfvR450WyUwIQXoZKSbQaS7OoDRDYzVmORJLWfkIMERt2Jq1FItORx5hZC4tlsKIbF3lf+uNiGkao7CpkoSKs9os6pjI1d0SlnomJFerlqUVlpNt0yr5kU0O4IO6W0Sjw1OBSp+watGGnPa1HIZbZK9ka2c0qrW8FAWzjUOIVxVqIaHmSr+ent0q1PLjRqqlZBerm4ZlstglpHuTJlofIykwJXXREfEpKqFVT5huFwReznmcVRbO5cVGumWa6XWOpcYi/jl2n7NlQQ7v3KVEEjcSqQ7WtZ2YXKdKhXCraJgLs85XwM8LsiWdJU4t4LpwOgGhgaCJk/+VG31BF1otJsnLGmg5WoZJoWQDp3XLAw2Nrg5FX7NvJaqE0KqBiJZRL7gzS2B0Z2rZZiM8Jmw0Kn09PK+8YCy+GJHHY9ffAjNTm2dSbKJPkna6t2Wj3QrGh4uR+oaV80N/Dg/b0TDo5yR7kztwiSeZKqz9SLduXVVchmrHLnNFb3MpWzNhmxLUrDVzLpu6axp0NH/Wi0Iq82eSCmXTx9jlZZh8cIi3Zyxx4Y3M1JB4oZafGF2uiT+X5/jeMsSj3JFuuW5kz3QpYOHrw0rKJinAlw1idI7JdJdeXMrmA6MbpAXnnBe658USrG8qMmXvseR5WJTzNVevXwTPhvWXztjFX3yxIPo8AUtdMqKLrryjJUwuC2IFJdxJuvvJam6JWstOis6m8CZS73cPCG1tPRyi9cE5zO6eZEjIx/y8WrAn9folmJq1l3kqzU85DhSo8piKkTDo5yR7pzp5RaPdBup6WZnnzwtgWRv+Ezki4KWoq5bfmZdHhE1RkYYM42TSo/uDPcTCzIWU9PNDun2ZObJiDdc8crlnPmQq8f7TNd087XdlnRylEK4r9B2YawtwiAgUV1AYQroUor999YhUde9f8xPe0Z9eZVieVFfjOKiHIB5Aso1YKsN/WXdjaL9DXtOkVJubYNQ1t9rvd3oRWlin+6ckW5zjG4hKKROL69Ab3xfsp57TnPC2O5pdtPuEZ9S510NyIVmtjpWmV5u5Ui31PBg0TTuza123kkND85wMqLhoSxoSxzp5v3LHemunppudlyxQcN1qb5whJqpJqNTXb53ttZdHY1u2jowRUMmGkKpdmH5I/cs9snXWSgaF84QdWmGPE9y3lLjSF6bMqtLLzJ7g8slqiHSnatmfyZruhUHpOp8djW5RZkRp5g3NJJl2oUxEFKrLhDpBrqUYtn45QhCa70+pdhi24bJRZCRyb4+OZHKmh1QOanP8Oaat7hPidXZdag2F7fI55RIdUCnkiPdPUmjO1XjV0VGt7LQdOaMdFvZ6FZreOwfD4jrnIOKxWh4KM6nEjuLeCHNDkfeNXmsqzXSnfZ9sjgRZNsuXldkm+NL0atbGt352oXlm5eyKZenRbrjhUW6uSY8dT9WfqQ7n9Fd7ki3LDFQn7uuxqSCuQUi3VK0mJXL0wISFhsbQGHA6AZ5lWI5ukDE/RRtQkhNj1KssqgvMJImJ3sjfRtljQ4PqlYQxAD6WoapU/kwsRQHL9zkLZm7ptvcDgOVql7O15vsT92TNLaV9PIqMroVUcoske52VU23EfXvciM1PPgc8bXG0eMxf6hgDQ913W4p5wwZ5eae6Jkyt6QzJF9v60qo6dbzfSYDqW4o2QTNOhUFczPTy/X16NZeH1pnSOp+mn5cChVSU2eBpYzumTcCC0WPiFpaiUeZhMK06eUy0m0VBXMZ6ZZGN9qpVhdILwd5lWJtGkVkrVLskk42ysnUyKWsmTIU6U4OojzX8SQpjXBgoSisSiwm4+K3AtOTrYRcuGUSqyuJ0a25vytNYVX2ZmWBSLkQl0Y3R5n4+xg1OCqxppu/PxsLnLnANYWZorFWgQ3rNy7roI6GSXrLqm5aM69ZpJQXUlKkFf004uQ1wkCOem71eQlYVb08T3stLZ48Gh2p1OPpqeeSzobEfchOMTZg9ZSZ5UNmweWLvk4zujXOA0WYMFkWkFFILTa7I93S6M7n4JDja7kcttIRpL7X5ZifuE+nr2dnJr1c1nSjs0s1gUg3yKsUyzVNHARgh7SspcunFFtsC6hCPOwcPZWfK9PXgHXgRYUMJtVME1Irn4pwNSPTu3ndl2uRmspEKe54S6NdGvj88cW0CSw3XAesjnLLBbkU45JGeaWjCD9laBnGsCO1kqJrvHBmoaF1C1qF07dQDQ82cGRCSCkdfrnahamjbtaLdOfvhJAv66zQKCg7gni9wbaoWWUPcr1Sr9MhL50H2nHSnxSIy9TOtFCjOxybXtPNzjKr1fnrRarTN+pNL4+UW73ckVbKIBTMg+a2qCsE7s+eFum2qEMOFAaMbpBXKTaUnHBEXVcyFSyfUmwqba+4SHe2dMhsoK7busiWKJlbhiG93AzU9dy5+tCaVcsqF0rqcaCS6rq1ImqSaqvrzhTd0dJeAb26JUqfbh2CWLkQLXnK0DYsl3J5roiqZYTUjEa6ZY/rLIaCHoMs0T8+WddtUop54ZFurXp59vtJGt3q+U4PUVU7TV5ryTHVyh0FcuEN6Yx0SyG1chndyf1SZ0Ly8W5NRpaHZzC7gNfL8p7RGt3l6mMOSguMbpBTKZYjQXKCkQt1qRS7rKsxq1KsNKKKbRlmNLVTDqSIdFvXILRliMIivdzcY5xP+EidXl5MLau8T/m+k7WMlVQioG4XpqY7WeNXLXXd+fp0p7cNs7bRzZkUcl5RR6sKpRxtw3Ipl6u/h9WiWYVGuvMJqSmR7jwGGSuYm9k2TK4L9DprpPMgm5BaLqPbqDaCbIMnXy/vx9EKTTHXq16einSX/trnuU4RUtOMHXIOGJwKz3hqOc/P8tpSAhIVppcCMgOjG+RVit057BOLee6JqlcpVhrLxbYMM+phlwN8IQrmPEm+PjhFL+4ZE7+tLChUiShCMfaEKJ8aiIWY3S4sd7qterFYTFqfvE954SQN+UqJdCechwmjurfFk/ZcT5X16q4mo9uX/C48hOT6Pnop9djD96RMGZVGpBb5PayWXp6q6Tbq/M4d6Z7UKbLVaWKkm79LvjZleoXUcrXgKzTSLbeXzkvZO3rEZ+37sVj18mJFd43AnyFPi3bs6EremzMZ6ZbK5bKeO218sphDDhQGlKZAVqRS7PX3b6at/VNi4cADJCvFssGdSylWiXQXXNNtXEhNPdkbrcuR/cjZocD7zMY+R/rz9SMH+onkaGUFsRBz0NMuTC7seG3HC5BiBKTU6accXef7rlIUzHk84/3nQ6WNQFZberk0ErLVdKcpmFs8vVxRH65x5Cyh0It07JZqUSvTg3mRL8U+tcjzwoaXcHAbjCyXChl9NBrprs1Toy7Ty8sZ6ZZGII99etcV2YxuOe5lup9k7/hYwS3DEq+vJI2FTA5Nverl5azplmMHXwNax3SXjHTPpNGtUS5XX4MctOA1lLbzC6gsYHSDnLDB+aZlHdRUW0NvWt5BRy1q06UUKyPdhdd0J9PLDRoDsn5ITrBG+pGzWAursnuaHWJw5n7k+0b9BbWiAdPJplyu7UXJE7YZi+nZSCgi6wJzT8x8fPke5cVk4h7NriKcC3l/szdeprRXioK5jHJzj1ZtuQO3kBE9oINRsXjUGxmzInw/SaNBT003L/KtfA/Ksb3OpHOibhtW6nrubMeUjUDpBGNDtdkz8wtrvgYKVS/Ply4/FUy1DMtFR/KaNMXolrW8bv3OGk+WelqlpjujkFrid0TVScJIJpgjOT+2VrCCOd9L8vtInR0r1HSrRdS014AVIt1jGSLdagcRp5g3wOiuaGZ+ZAeWZ9wfEUqxRy9u160UW2zkstBIt1GjW9uPnL2yvADn33r6kYMCIt0a5XL19cKHuVLSk60c6dZz35hRy6qklzs5vbx8aYJmIKPYPRoRNYa/i0zvrPQUc76fsqVUqmnx1AhHA3erkOm/lhZRM6GeW204laqmO1XP7cot6GYxlWKOusvrptCa7kxlXiIKqjfSncxAYedXsSJzsp67wUAr0WzrmFyZI6w+zkSTNdp6kdvXVEGkW66/eB7Kp8tTzkh3tnputcghbyOj9MVQSLlipkg3r7dT3YCsMTaAwqlc9z0oCzxQjPkT6YZS3VEPxS4glAia0Ui3wdYr6n7knN61pX9C1JFxqpGefuSggJruDJFunnhlpIcXONXQG9nK6eVm9epWO8dkpLtS0stlpFvdLkwrpsb1zQMTAeGAq1QCSeVlviRy1fpz2iIb3qO+sEgx5+wmKy/os6VqWy29PJ9yuYSdCDxvWaWuWz0uGHV+K+rlGtVv+b7s2NGTeszzP7cOm/BHxHFc0F5Hpe4bnVEQTnVtpGWOZIp0JyOo0XhhNd1aITWOfvI6rNC2eDNB6ljnv0fLGemW93gmhx2vOTi7wO/3i37dTZ7sTrJSlSuOZTC6xb7V2MV9A6O78kGku8oR3rYhL23q84rfRiO2rEDJ63ge740swopd0EvPsvGa7sSEqtdTqe5Hzl5G9obzgCvJ148cmGMQynRnBq0xCidkyOguPrKmdo6ZYcSXk75kj25tuzBtXXelK5hLA4ENiHxpte3JyKKV2xRJITV1y59iKPV1m69Ht1Xbhimp5c7c7QczIc8NX3vaNceUgShomphakSnmeoW98gUP2Hksv1KtK4OQmqPAlmHJ7aVjrKk20RGCH5bGWOUpl+dfM6pLEYvppGEo0p0lmCNTzAeLGPNluSKXJ7Z4XLSoo1785r/5cX4+f6Q73eBPialVxtwKsoNIdxWjeNv6p2jSH6BGzxAt7TYmDiaj3FxjYsTTalak22jE02ifbnU/cmkUyt96+pED/cgat2zRNk7l40UaJpbiswnyqZfL480Uk86vtBSqsEg37+NQUqU7U3q5+vH+CesaoEaN7ny017tom8V7dfuSacJ6omh6UOtJmA0bETLSLRf0hSp+z5iImkHHt/ZaY1V4tYNEyVTQef7YWbF90Ft0XXfqc40b3epzIv/Py6FMafdSfdxogEOp6U6+nh0dHHnl64fL3GTkuxJIOTh0RLqT1xcfLnZU6Jm7CkWuCz1ZHHbyHu0vUC1fW67I9z9XC4hyRXeDiHxzueKqOU3T1tO87USWSLfVSk9A4SDSXaWke9tqaH6zW/zW421Tw4MH05qsbzRcJzdDkW69fbrV/cilsSA81PG4rn7kQD/hZM2aVGfVokwsFSLEZUXCKiM4H3KboiLdikCXqmVYBRjdXM/NQRVeFDZmyeCRaeeJbStX00Fpb6QjHTtVR2pdo9ubZ+FsFKVesgTjzgRnUkViolY+n9HksdjCWh3pNgobjvJ12nR5GQXNdt9pkWn5xbYNkx1N9PboVqfJ8zmUY0BqzMucOWK3FRrpnj4/tiVL+qx8P2ZCr3I5o3ZclHru8OeLdDclrjV1tqMR1OWKfN0/t3uM9owkMqq05YqZ1rxyvTzd6K4skVKQHRjdVUgmcTC+4T1uh2FxsJSaojGjW9bp8MQjBbSMIAdfwzXdyQmVBy89n6vuR75vzC+i3JzmNeIL6+pHDsyNdItzhxSqgpFZGnqEj4p1jKlfyxkplZRenhJRS+/PrU215sPI30eOg9Ue6a6EXt0+k2u6FfXyEow7MjrLonz5Wv1IA89qNd1GRdTypcunUo/1OU34muQIIAcK9ApSaeHX7ByaouGpIE36EzXSur5Dckxje1sGAlKdADIfFxnplunixWietCVT6yvV6K7X4RjjtZVcE5R67lCrl+dMLy/Q6FaXK/Ix4OtGfe5ylSvKrFLeN63TPBXptv7cCnKDnNkqRO1tY2N719AU7R/z0aIOojktdYbEwUZ9xkXUtBO10TYH7FGWUQejrUp4YSkFubw6W6/IfuTf+MsrtHvEnzC6vUFd/ciBeSJfiHSXuabbDPVyVWRdloKEotYwGooRUZPROm4nxttyXbds4VNpBAwY3a31NcLA2RKMCANHT3vISq/pNsP5lAk27F7aMyYMvfb6mrxiWJlEu2aSQtuFSdh44BpV7feRBpmeki02tP/3qV303K5R0feaW3gaLZGTZXaPbB4QGXBcG/78njFd78GOEjYI2SDm+4iNJmn4ZLuf7AUa3ak+3SqjOxnskOuwaox0M+ywDUejJY9051IvV2dVsLo+p6IbHWMylSsK4cBITMyRucoVZT03Z6RmV9G3xtgACgdGdxWieNuaEwOL8LCrPLU84PRP6BMHYyXbQiLdss2BVFw0Il7ChoPM5jSa2sZOBp7sucUID5raNJ1s8OR7zJJ26mz0isHyPUfOo5NXdFtuwVnJyEkoW8QnJRaCiaVQ5KKlRk/LMJWATaFk7NNdAd542QYsWz23WsFcGt0r5zRRJZKrvZHWOPnDs3uEgcMGABuLy3saDRk4SlRx2CvmF15cmm24m1/Tbf6CVhp6T2wfFvXxu0d8wgmc61jK8xMIWaymu8BIt+JE0Hwf2aM735pAlshxpJCNDk7b5tRwLpFj45sd5fmuS/V7cNp2Y62NWpOiVnrfg53B4WhEOA9a1T26sxjdSk13PHEv6L32ZWaerOmulHIPM0TrxNwRjJY8fTpfejnPic0eJ4WSWh6LO4yZSLJcka8v9W0zFQgLpy2XK3IwJ1O54nhyrd2cIcAFIbXqAUZ3FaL2trGnUXrJpMfZiDjYWIGRbnWbA6MRBLk9l0YVMuGzd5KNbr113RJ+Dfcjl+qRMLjLm16eWvxa32izgvFRrJCaKS3DpPZCjUpIzeJ91hNaDYGcyuXpCubj1F/BvbpT6bAOXcaJTL3k7Y0YJ8wr+8fpruf3G26VY2a0aqZrutMNvcR8zBHLfMcyV2/rmUApHTFY4pUvXV5Pj25tiRyfG24/xu24+O9cglTZ3mPDrlGKk02IwnLtrp73kPcBj+HS2M53P6mNZm4bZiedRreiXm6veKNbbx/2dAdweMbTyxnOSjngJdEqcnGHMS0fWa7I9/mrfRNkI5u4Hji7gkt2cpUrZmsXxkBIrXpATXcVohYH4wWmOm3XiDgYb5tKLzeeWllo5FIu5Pn1RluVqAd6I4sXjsKq0+AmA5Vbw2lVInqF1Kog0s0L72vv3URX3/MKfeveV8Vv/luvgGE5arrN7NPNn6e8n8WdJtzzl+91XvfkU5SWbcMqWcE8X0231jhhxyOPu1xbakQDZMuAj3788LaCWuXohfdBfp96s9LLTazp1h7LGC+7bTZqa3DlPZYppexYVdR0S8NGO55P6kg91pbIpdYwsbyCVJneg9cySX+kuK71vkemtH9DRreBFHNtn25Z7iEdF1ZpJZcPvrb3jQVEpgxneeipny+XCKceh11H8pgXqmAuyxU76t3i+7ADgo8DR7hzOS/HcxjdSkACQmoVDyLdVYja28aTDnt1uR6KB+3X+idFCw494mATqh7detO0zVjMyEm6kFYlanVSvb26GW2qPfpym08og1BMNRrd6kgXL+y4zIPvPaNRw6LSy3XVdBfXF53TIeVCkc9dpUS6Zd9trt/LJ24la74HpwJiAa1eEFcK+dJhtQYOL4AnkxknWuMkmwYIL6zv2zRMo8LYbFScpXpa5RRSz81vr6dG3dA8lVSoLsTRm+lYagVB8x1LpWWYRSLdxdZ0pyL3mSPdjTl6OGtL5GQkVO6TnhI59XvIzhl86cl7WG+ZnbaeVo6X2a4/jsYXZHTL+VF1f/D35g4LnIXHARCPK7vwo1Xmvv97bg89tm1IfPcf/OM1OlhHiYpSmlRio1Kew1y12gmjOywi3YXC3/XIRa20fywgHOH8/b5w6nKqyxH5V9LLc0a6rT23gvwg0l2lSG/bofOaadIfER5inrCWdNbrXvTL1HIeBApZKBWatic97NnUQfNRnxxQZe2fHrSRbUS6zUfWrGWLnEilWL5WKxVtpIsdDCxM1VBrvHNAyYXUiox0q41rfi95Xq2uXs6ZPvlE1CScisrfjb9qsX2CZ4rUQtORV3GXkY4IeY3mUtyV7Bzx0Y6RAM1pYsOdaHPfBG0fmBLPGYkq6lUuF4KZJjlA1LohxV676mPJx521SfhwynKPXMdSHn+rCKkVG+nO9H3YqaFHZEtdIpfYB1vamKOnRC5N1CrNGWkzVGaXauUWS/udbX3C16W8NI20DVNahmmOd1t9ZSiYS2fzi3vGhZHZ5HFSe72+TJeUvkispOsP+f65HHYy0l1o2zA5drKGA2cN9bZ4RHs87o6TC0VILUNWKfRuqgcY3VUMG9ZfO2MVXf2OVXTSslY6YmErnXvkfN1RtkJ7dBcrUCO9nXIgNopcXMp+rnrQLoI4yg/MJaIz0l1o5NUKaKOGO4a8tHVgSugLmGl85E0vdxqo6S7weMsMFjYohHCiEjGMVoWIGsPnTKaYy9dVGjLSmM1I0Bo48vbkelS9xgk7KdkgYkMrFImLFH6uYZSGhB7D3VCvZZPquWVkUdo5xaaYq49lyjhz6DL06mpkWVSiDMw6kW5HkUJqqXPORo/UnchV76stkZNORB7f9JbIqd9DmwFkpMxO2/pMj0aCjKYbca6GM6SXM21JI9DKRrfa2Ty/zSOOMztrGmprdDmbU5Hu0hnd8ryxUzBXQIdruhkeqwrVV+C1J9/C/FkrehrFYzuHfVm35+sxV3p5qp2qtedWkB8Y3VUOL4aXdNTTIT311FRbI2pL9CJ70xbaKqfQVixFR7qVmm4jke5I2oCH9HLzCeus6bZ6pDQX2qih/C5K2yaTjI/8Nd35F8rK8S4wHVxp65dcMMmIWKnr8sxKL5cpwPnoaXan9fauNNKNv/wGjrrlkV7jhCM5fP7ZMIkl73P1ZxsR79SjimxWuzAmkVJvjsNIfSzlgl0an/mOZa0rmWEQt0aJRrHq5fIcpWulRFKZMTnKx2SJHAtPsROTxzUukfMGIuLvXIJUmd5jx3CiKwl/lSkD75GplWW+cg214aw30s3GqPSzaEUwZdDDyka32tmsFYTT42wuR023Wrk8VwkJ74tUEB8oUMtD3YteirHtyuFoZ2ciHzferaYMY6Ra0wBUNjC6ZwltyUGEVRT1UmiP7mIjl3JSKzrSbaimO+FgmNtSq/xthWhDNZFK8cuiXi7TyyvYm6uNGspInzSGzTI+8qqX64h0F9viS4mEJe9TpaY7WRtrRfg8DCbTBvWkl6eLqVWq0Z27BlVr4ITCCQOHjUa9xsmitjpa3FZLByb8aYYGG25Goor5UETUTGoXNr1ut7hFrfpYcpswUc/psOsy9Hg7ad8GQrHKr+lOOhHUzm8jPbrVJXL8HnwMp0KRvIJUmd5jfmud+D4cTRzz5xe1Sv8e6ZHufH2609uGxQ05pDNFutsbrG90q53Ncp5XZ7TlczaXI9LtM5Al050U2Cx0zFdKKNxOISjJ7BnxZa3xl6WcvH0mnZFq0bsBEFKbNSRSZvw0ZKBOpdAe3cXWjCqtSgoUUmsoItLNHtlNByZFtIG9j0b6i1cLpWp1laklSrVNLOo+nQe56kUtsBSRk8ZHtj6d5RZSK1ZASnufyt98mvlc62lbVm7Y4Ob94wUz1xzqQUkvr0Cjmxd5Sh1jjsWmNE44RZRbK7GBw2fvqEXtwkjMZ5zw+PC2Ve302xeGVVFFG416wyK7Sm9UcSYi3amsrLAp6sDyWF59z0baNxqgEV5Q20jc97mOJd9/fF2yaJYvHKFmKszZbZ2a7umRbimiJrPR8sHHisX3NvdN0g//8ZoY1/7f6QcbSnnn9zhtdY8Y79YtbKE3H9xlaE5TtEamqZfnjtQbiXSrjbEaTSaYjHTLIIjVnc2+8PTxJp+zuSyRbh0ZCmpHx8SuMXpm54gQ3DS6BpJBHP6+3CGD72v+fJ7/57XWTds+V2p5Yp9leUW8YgU9QYLZZ1HMUtrrEqea6+xYUCKfam+xPbqLMaKCBgbHTEiFyEIi3SycVO9yCIObH5ttRjeLnfDCuxR9dsN5a7orv0+3unMA13JL42PSH6Zt4ahpxkcm2HAOFyCkJhfYRu83JSMled7Ui3N+Pz37UG7U/bn1Ohmk0T3i5T6y0YIzcGYCtcEjy33yGTgPbuoXYwBHaL58+grd1+rBXXX0mZNa6Rf/2UlPvT4iFofseDl+aacuw13X9ylBTbf6Gjar3d0hvU20Zm4L9TQFxXfnFFM9C3dpdFuhPVRKeb3A9HJNLTQzGUwZI3rhY7ZyTqO4D9mI5bnZaJ05rwVY1OqweS1ZFfizIY3HlJBa/vWJjHRHZZ8ynXMjD0naa6RdJaTGDvFSzB1mOptldpc8/3qczeVQL9cb6ebWh/dvHKUNu8fo5X3j9NSOEcNrIKkJxOtHnmcWttcJx9GuYV9hRrdq7ObrT6/TClgP662KQEngthM8sLFDVU+aEg+USk13gZFuRfyhzJFuNppTYhZxQ5Furk3kH/VjswWpPlqqPrtSvTxfpJsXVtJ4rERkpItbpcg+nbzYNJLSWAhs5MiAiZ7oFC8M5fqtkLQ+bXo5LwYVlWGL1p4ZEVGT8MJJGgmF1vjNFNLg4bFUz2JdaIB01lN7g5sa3U7DC/xDepvpQ8ctEqKdhy9IRBWvPGOladd8KYTU0rM+zFn0c3op119ybeiblncKQ0/PsZTRYSNZWqWv6S5QSC15jtiglOO5jHQbdWaz4SLvwUI6i6jn92KDB/nKNdRtw6QYod5It7pdmIS/Nz/Om0jjzGqoyyrYsZlok+XQXT8v55By1XRn45X94/TLJ/fTnlGfWC97auwFrYGmNNcbG91Mtpp22S6Mgz6ZEEKlVVB+B2B0zxp40upI1qnoqetmTx0bP4X26FYPpMYj3cXVksnUQ57n9RoTk6pas2Im92ppdcVOGo7Qij67JrW6kguvTAsLhicVGXys9ImFjYyPn3CQYnwcd1CHqcZHrmgJoye1O01AqoDjnck5Vq5+q8WLqOk3uiu5rjtfu7BMFNtvna9Djiqy4c5p1WZG5qQ4mdnp5akFrTmL/qGkYGmLp8ZQxgcv8hP7Ea34mm7h6EmeeulEMFLTraUYZ7g63bdgFfZwVDiO5TibW0gtccxk1DcfkRwio3z/yGxDUapgUXhu++gJS8S9z9cOj5V66+eLbV+pB0XYMMtYyGsbXuOMByK0ortR3Le8O6xNYHQNpL3OFyYj/BzpzhQIyhfpTusGZFGHNtAHchRmEZ0NLto/FlCEhErZozu9ZZjRSHdxQmq8aOSIG9fRclpZvrRZHkTVg6QcKGdT2zC1+ihPfOzx7W6spXltddPUR42m52lbomRbhPLncISWP5+vmUZjdpHlYCcDL0AYdmAYLJk2jDSS+HbVW/PF2Si8mCxksaNNLxf/5+hGMGooYlEqDQEtvNjpS/bolka0Xlh0je+PSqvrNlLHaJaokfrcc+sw/juXUrURuPVeSdLLC3QQZ0POsVwPWqzid6XWdPN4ztcdG9x8XHktkRKYMu7IT83L4YJ6Jqvfo9B1jNrgyZWJJ0uo9Pqt8rXT5A4yg1MhGuUsxU6yLJwKv25Bq5iDzjtqge7xvJw13dkcdok1kJe6Glyi/JLHLN4ffygmgg9G1kBaJ8+8Vo8INvAcx8a7cEiqGNNhdHOK+QRFLOGQA4UDo3sWIRcAeozu0SJTy9NbhhlVLy+ulozhmpeQLywm/PY8206peirWu5yzMr1cUR9tdgjBFl4s8EQwL/k8e4f7J4prdZUvvZzhRVrC6K78iUWdIsrRkULqpo2grufWW69cTGRam16ufj+9i6dSaghoF97sSOJIA+9jp2bRk4/uJndF9urWkwqrpdjWb6Fo+rXE44lRJ0c2ZM9ns2saCy2FysZQMpusw+B1Vpt0Jsx0ejnfLzKiW2ikWzpH+LvI76NupWSUVAaasTmIs9h4fme7r5DPlfcOi+zJaGm+cg17cvyVEexi0ssZTs+WmjxWhqPbPPdwadVh81t0v86sln3F6EGINVAkSi11KWFQHgMT45nT0BpIe53znNzb4hEdDXaN+KYZ3TLSzans2agGoVmA9PJZhVwAyNS3XEilzGw1JnqonaFIt3pRxnXdRgZInkibZmF6uVp9VC4A1AsGM1pd5RNSS1/8Vv7Eor3uJ0pcj6f06DYQVUxN5DGT08tjM64hoP6ca+/dRF+/5xV6fvcYvbR3jK5/YLOh92ejkSPlW/om6cU9Y/T64FRRpRblQi40pTFnNL28kNZvWmOd1cvNQkYspW6HWUhhrhmPdFtkYa2+fwuNdKvHF78J6eVNBTrD5TzO0UqjHRrU34FvBTmG5+oEkCakpnOMiOg0ukWk28LI8hvppNSLUWdtMZHubOdOrIGcDiWbQet8NLIGyqQhsChZ163t183zyISeSLcFhWZ533kurKQ5caZBpHsWIaM7vCDI1yIopVzuMiVlz0hLIjnhFxPpNtKrWxG9SBrqpYp0Z0qjtaL6qKwrTKS8casrMqXVlRKJzVC3ZoYRaDW0KaJcrtDVVLrPC0dk+r7+hWUxtXRKernKOab3/bQaAnJsEBoC7gYR+eb6OVbTLibVXBr2/DkcbeD7jh1yfJ2zwrxeYTuOXG7YPSrSpV8f8orxpRQR+ZItNAtIL+f7ng1vo85P7bkf9pojPsfXTL6Fc6GYXVNaaKRb2xN6ppCGBhuBejqd5JuH/eGEqGmhQmpMoVorch6XRrtReNzg48CGscwAzNcJwG7U6JZ6J1mOtVyHVUKkmzGa2VKemu7cY2FiDVRPG3YOUXtTPJXynnQ+6l0DccBAfg+1gS7qurcO0c4h37RMDL5M+JLJZdArYo8WiXSXK0ut2oDRPYvg3oO8tuWFC3uccyl5cnscplX09y4MmZZmtG+vXMwXUwdYn6zbkTWARupvSiGklm2AetfhvdTptFarK95HPl9cF8xpT6zYXGyrK14wy/VHjTNHpNviQlxG0C6cjdYiFlrTbUS4qZi0Pvl56vRTvRELtYYAG9y8kBiYDIrFGr+HGRoCWsN+x5BXfBZfyyykptew53v3p49uF4t3mZrOx9io4V5J6eU8TwijO1K40c3jPWe36OmWoQeet2Tg3fQ+3Saql7MBJb+z0Ui33I+ZTi9XlMuLrMVXjO5QQqdDRnTZuWaUQp3hqahj4dcMO0P4fWSkmcW1ShHpzqbFwWs3q0e62TDtT3Z3MGp0y+uMx4tStUXLl17On8lrnO19Y7RtcIpqHA6KxRNR6G3h/ArsErnm5PFPnQUmFcx5nuMyBTmGSeVyvr5zvbcS6bbA2kjtzOa5mssS+fhWwpw406Bl2CyCF4pSBTNfirk5ke7C1KiVSHcR6eV1brl40Z9eLid1de1YIemVhtJoH94m+kJaqdUVt1OSra54UDWj1VVYlaqeSaG1GiPd2mu+1BoB0tA1ZnSbEelOF1JLvF9Un4ZAcgHEAmXcakZGCPlxfr6YY6Y17NWLLq04oB7Dvbe5VhxbdjaYqepfSlKRYf3XhBQ0LDTdU75GLrz1lDPpQRqivPjUKxQ4E+rlop9yPPGeslTJeGR4po3u4tp2TnciRJTUcn5PI2NU8ZHuwpXLtc5g2UY1nzaHw6DRLbfLFpiQ6zAur5jp0oNscMSW71Fe8xl1Nqmvs1JFu/NFumXLw4uP6aVD5zYL41a0+wxEDK2B1NebOruTM6zkcWFtkWn13HlKOeV6eKbXRtM63dQ6Te90U83A6J7FKeal7NGtXbzpHSiiJgm41BuIdE9Mi3TXpIlfmT1A8TgsByj2XP/t1WHLDFA8qZy0oktpdfXpk5aZ0upKbzurahILkQtn2bu6bDXdRoxuWUNfSE13uHAhNbWGgFq9V0Z8zNAQ0Br20vEjF/x6DHu14V6XTImVjjy9hvtMwhFGxqiAXzFtw+SYKduyjZiUXi6Pe73JUW6zxx2OZDG8wDZaQ6yIdlnE6C4+0p1SY5fGiFFHhESOBWx4GpkvFad6AYrpEqmJILVuzDa65dgtW41N+/wah6JjIPfBagwkU8s76l2GnSp8vKQfrRR13bymlRHifKUpB3fV0VfftpK+dPoKsQY6enGboTVQSiNo+vW2sG16XbeedmFWWhtNy1KLRGnfqE84KHi4086JqPtOxwKJraCcdDbW0pb+qZxGtxk9urVq1HrT9tTbmSGkVkikmxcaHE1hRwEb5MUoTmsHKI7kcZrr0s4G0QZkTpOHXh/20s4RHy3taiQrwAaPbHXFaW1mpHopNWuidZaOFKoqinR3NdXS3lF/ySPdhQipmZJenhbp1mesqTUEuIZbivbFYsbq5/Qa9uzk0ioE6zHs1ar+4Whiu0RdKr+XzRRVf6vVdMvzOFlkpJvHPKJR4cDlY19sdFo6UM2u5za7pjRVz23cYS2/20ynl2fqTFAISo/rUKKkrdDUcqbelXBYc/IZR1X1rk3MiHTL76HUdOs0uqUTsVj1cobXC96QXwgTJu4ta9E3nkwtTzrbjMBrAr7WEu0r+dovbt2phdcTMmlRz1jIax6OdkuVcU4zt5Ot6HKGRR119OyuUdqpinRzL3OmJc/1XGgLXrNRz4nMgTE/DUyGaN9YQDiGeM2YcLJFUPedAUS6ZxlyITA4GShpj+5CjSgZPeNoaDGLNEVITcfiJdMgaZaYmjbaxvU7PPjLBQg/zgaKlZTSfSpPqh4hOrOUy0uhIjyTyIWzTCmzZk23CenlGWq680XOpYYA18mxU0pEr+Jxccz472I1BNSGPRvwbMxLP4DDYVMM+2VdjTkNe7Xh3uRxCkekPxxTzq0ZEflSIs+R4Ui3o/DrQhpsPNfwOM72hBnRuVSk23yj28wokkynNyqipt6PRP16vOJruhVhONaRCRTX7o3HAil2amS+5CACk0vDRu95kWO4TDfPhiPpWI7qPId65sd2qWBu0Ui3IqLWWFh7QCXrqgSRbnWrN73CgDzOyKw8eQ0V6+QRYmpEou5ZOsmVSHe+9HKLrI20WWphlWOJ5/Htg17aP+anv7y0n777wJaSdyepNGB0z9Ze3UlvfKl6dGdSMNeDTAEqtp+x0jJMh9GYSntLDXoyBa7YlGDtACW/n/Rs8+O8wC1mQVBKATA96fl6kJHMfAahUrdkAbGQYpEqo7K2tdSOFblwK0i9vICJPFX3OV29XE9astQQOHReM/mCEbEoZ2eUGRoCWsP+tYEpschhwz4Q1G/Yqw137r0rF0YjUyHdhvtMIsfdbOJBpWjhI/t083UhWx2ZIaYmHR0yzd9MtErFM9EuTH2eeBdKqeSsP9JdpNGtEobj6DRTzFxXSK9uU4TUVG3DdLUMS47BUVVZlRmRbsYsYUKzYV0OhjVhCqEYR59+PQiHoei7zKaQgSg95GqLx46TBrdDZECwYZp4b53p5RZZG6nnRB4r5bXLLdHmt9YmNE9cDvrXlkF6Zf84RWMxMffyLdGAum8Y3bMNuRBgw1p62krRo3t632Vjke5iJ3sZDclnNPKgkTnSbXxy1zNAye/Hi3+xaJ/w05L2WlqUrPWZaTgNXH2u9KTnG2lnlWtRUW3p5TK1t0tGuv3mCPNlI1xAHWZKtdnY8Y6ptRcypJfrNeLZsP7aGavo6MXton7utEN6TNEQUL8/G/AHdzcKQ4Kv57FAWLdhr43I8+Kb710WfTMrIl9K9IgHZcJIv/VsBhu/h4zOmdGruxw13cUau3x/F2N0s1NSOs1msm2YeTXdKae7tjVnIRjNQEvM79Od6kbRti/16K3p1hvpjuWu6WbaLWx0q6/7LoM9urWR7lLUdPsLdD5KQ1hGo4ut6WZDXka7ZYq5/ppua6yNtHMizzE8J7JxPRWM0pq5zXT++oXi2ufg10QgSlsHpmj3aOL7VoIWSilBpHuWwf0x+ebluSDb4G2GcnmhrVjMUk2V0RAebHOJrmRrY2JWenlatK1/MrE/Io02oiza37qy3TKLdm06Pg+iZY10WySFiuHzxD2ZX9wzRq8PThkS7+FFiJzo5eKbr7NSqhIXIqRWqHGl3l59r7ocjoIEuISB1uDO2zalENiw/sSJBwlxwDcc1EHXvOMQQ4a9OiLPl4BUYl7cUW/p1ihCPEgpAShfpFttsMmaSDN6dUsHqtGFsx7YGShvm0JEBdXjp7zH2+vdxdVBz+AYaFakW54rnwk13YUomCfKVnihX9znaiOkeWu6ZXq53ppumV6eY+zjLBvOvNt8YMLwfFRqOIjD9z3vf0eB130q0h0te7uwbCiRbgNGd65INyOzolhMjYMccvuWPOttK/XpTstSC8kstajizF63oFWMf4fPb1EENbn1bDS5DjSjO0mlYs1CNFAy2MvERsCeEb/wTGbqpzhqQo/u6ZEvnZHu5IBbrIBLXQ23BUpELryh7D3J5eTNCx21QWhmr245QP368Z20f2xITMQ8tR61qJ3eefgc6nSao+5rBtroimmRbp2pz1aZWDgt6rf/2Uv7pxKRf9lXnR0oeowsNjql3cnXEk/2IsUykOrPaYWa7kKPt7xPeZGlrpErRA1dnS5Xqv7sfA5ZHJAXAIX0/eZzzv282TP/pw37hDPmjcs7LGtwy+8s1+VGF5ty/DXqPFFnQLDRbW56eaRkRrcUcuJ7tBghp6FktI+zxAqNEvOClGtIZ1JMzbSWYaqabjmXsuO/UIw6w+VncuZbMRox2si2NvJdrJCa3C5bTTfXv/7uqV303K5R4QzePxagpd365yP1/cljGB8/npfYADTDySnruXltWej7ScdgKSLdSsZPgUa3kTJDbTccLbJfN7cN4/uc16g8j+bTqrBSQEI9J7LBzdH6jxy/hI5c1CrOPzuFeC3A08f8No8I5LEWyqg3RB2NtZbXQikls+8bA9E2TBrd5Yp0667pTi7W801q+eAbnydKHmz5J5vRnRJZSb8VzIp0qweo961fIPaFI5KcYnTFW1cIFeSBgQGyCloj26yabhmFzSdioqRQzWA9o+ir/vA2Ghjz0oL2JvK4nWKSYAEQFkDRE92U1zGvPxI1+86k0R3O6OgytU+3IfXy4iLdWsNC6e9swFhTGxfZSl7MExSzFzWmsMH+tjVz6H+e2EWv7Jugtx/aa5kslWzfWThGDO5jShDP2P2vPn98bUnhTnPSy6NFCXHlg68N/oxi0jelcrlszVkIVmgbpq7LLwZ1LbQUmCvO6DbmDNd2JilbpDt5v+mNRstMsEz3qZiPHtoqHFdsqHAUnTP5jMxH8n24fSln2LFT1KgjWVc9dxFzm7uENd3yXjJaZiOjz3rTyzm7KFVGkfma623xiOADjzVb+ycV4z5fe0E5d4WicVO6QZiBWGe7HGJdt6y7QZkLtd1JOmSgbyoksp/M6E5SqSC9fBbCNwCTyejmQcNcITWDNd0mRbrT67ojhpUmzYx0q2vlOdrGgw4vPKy4WJcLW7lr5qmXZ19UFCO8Zzayrzp7ZBe11oqURJ7cjAqAqCd5nkxlPeG4P2JNITWjRncW51ghacnq7IpSRDnUn2F00ZWJZV0N4n3YYbfDwjVpSrswV+IaNEIhzhP1dcS3Od/rbclUU75vitUzkKUvpYh0F9s+TyLnVDnHFoIV2obJ+7vYmm51jbq8NoqJbsnX6lWTNqNdWKYIaelahtkzzkd8//D8w+sGvpf5mBqZj6ThXioladmju9B67lLXdPuKTS9Pron1ljMw9W5H1mtjfmsi2v3S3sRx19P+Tr0mLlVGmFE4PV6uO9T3hLbum/c9TnFxvW7um7C8FkopgdE9C5Fe+EwK5qwwalaP7kIi3YqQWpGRbnVdd67Fi/SEa0VWjE7uelBHe2QkwWrIYyVFWzg13wzkoiLfIk4acTyQ662HMxOlr3qTh/hK5DTi8WQfTSMCIIqRl5zkZd/zUrYNK6SmW6b08YLYiFGUzTlWiBGvrl0tmdFdYOusTLBXf/XcJvH/l/aOkVWR12C+9kaZkOfRaOaBOgOC7xfuPctzCc8pLCRYDH4lvbx0kW6mmEj3oAmRbnXbsJlCOluKTS/PZLAWk6nQZDADzYx2YWrlaOXvPOOINJ51R7qThgu3M8w4HzV7kiUQqbpnvfOR1nCPUULrwUwl6f6JZI/uYiLdRYg36s3g8xgcO6SYsN5It9IWLxn9zYZMMZdO23ztwqSx7kpeHzMtppZP2yVT3Tdf46FITNR6W1kLpdTA6J6FSDVl9sprF9pjyXpuM3p0py9kjLUMMyXSraNtmKI0qfGEy8mdBxWzDAF1XWOpjAuzJicp/iWE5kxI+ZWLOK0nP9fiJtc1wwsErhsqRORMb191jkpzuijXz0n0CoBojTyz1PBzES6iT7fRxU7W9PICWi+lRbqj8ZIIBCmZByZFSTk1jtm4b8KU+6MUyIWZ0YWmkX7r2e5z+XqeQ2RddzFianwtKZHuLBGkcot+5qrp7mwsPEtMRuOkk6GSI93a7BKtdopRZGr6VDCsa3wxo11YpoyefI4sOc3pr+lOjt2a+VE9HyX2I5kFkSz70jMfqQ13dmZv6ZsUoq5mKUnzeC0zPIpJL0/NHSUQUiswvVwGnlJaD7nh65LJJ9rHmQZ8/Y77wjQ8FRTzk555z2p13Wrth0z2guxOwuKll528VIiZsqOHa8FnK6jpnoXwIojvD75h2BOsjmib2S4sPWUvVvZIt0wvzx3pzpx+xoMIp3DxJMXbSBXeYhiuAKNbGkCcFmq3TSbUmsNRaipioaT25GcTipHwwM3Hnq8XnlgyRUVKWZum7qseTLY5U0f79AqAaI1u6cQptu+72UJqnALMYwGfZz6WeiPBcgGiXXzKhROvh3l/9DjPtPcnv67W7rDEoisbSzrqxTXAi93tg146uKeRrIY/HCm4jl3tPClI9Vp1DfJ8w/W8PP4t6aSCHQjSxmKRzFIgHVCFRpE4M0eO8Z0NhRsfHktFuos/1uqUXu5RXAxy3OXd4+OTL+vBrPRyKQjHcLQxnzaJdC5LteZ8SONcW6erno/YkOO/OaosI/h65iPFcG920KiPAy2JTDI2+tjoZsO9f6JwJekhb1DsP98/xawblbViCaK4haqX83wo1yMc7e5qdJiSWcER8Q27R0X2D48bXBO/Y8ibdw0jMtMCEcsY3Up3jBxOKKmFMr+tjl7ZPymO0etDU7S0y3pzZjlApHsWwhOGjD5o67pHFKO7+HruQvp0pyLdJqSXJydk2ZLBiNAKT0ZmRifZSFG/D0f0StmzuVDU0SQ5QZlR152KwubPnnDnEFMrdW2a0ld9wq9MKNJhIPqqj/tpWVdjXgEQrXBLOSLdqf7I+jNU+DovJMKXco6lL0I4tV2WD+t1LGkXEKUQU1OiviYZbLyQkIujFy2aYl7Md1ZquiMFGt2qz1Qi3UWIqckyF54X8hk8M1XTzZF8tp14H5s8hRt5MqrpD82cY1YK6JkS6VYZxsWmefO5l/OSnrE0W/mYUdT3kB7HpLxEdbcMS26nnR+V+WjcL+Yf+T0SgqxRXfOR2nBXl3iwCjpTrJI0t4KS9dxGtSPMcPTpQTp2Cykvko4EPQ5zZT2Zo4SC1yi3/HO7WJfyd+bj3lpXo2sNY5Ve3RIjLSk5GHDY/MScuWGXNefMcgCje5Yi04e1RrdULm8zyeg2KowlF21m1F7KVLRcba+4hp3JNOGYqWAuU8vVk2opJpdikSmN9S5ucyXT84v3qkpPvp4obG2Wa0Zbm8ZvxQsPM2vTpABIa72L+qZCwgDk8zTpDyt91fUIgKRquu2aWsTS13QbTd8spJZOSS/XfBYvulL9VvW9nzbSXZqavuSiy0QRrsPmJRYQm/ZPlEx1vRi0ugJGKLZ/u/q66DChV7dMpy2ViJoZC9qhyZCih1GM8SENPLPaNRZC0MyabtVcXkyv7EJETs2KdKs7AOgzuu2G0sulGJU20q0VpOJr0+3kDLyYGHf0zEdqw11dm8yn2IgjORt944nyq+7G2rK2lzWCzBopZPwwIqamKJdnud7Ua5iFbXVirhbaF3UuXWsYZW1kESE1o60F1y1oFb837h+3TLS+3MDonqVIoRfZ4qQUPbrTopY6B1K5nSmR7mQqWy6jMdekbKaCuYzysNCI0UhgOVErBOtxWpitXp7eOzqWtTaNFzO86Hj1wKTIZTajNk3CEcxPv3mp8FbzOeKJlNNGuY5XrwCINsooI18spFaqDAd1f2S98OTOBg3XlW0fnNTtsFDSyzMsQOW9q/f69pcl0m1uejmzoK1ORCh44cF1klajGPE4o+cwW003o/TqNiHSXap2YWZ0TlBE1IpQLmeks3MmF6Vm1nSrDR0zzp90hucTOeVx1qyWYTxk8/ngcZIdePnGSTnPyWhyPmQaeibNE7Ug1Zg/pOjMdDXV6pqPpOHO8zmv93h85f2aDIQMOZKz0T+ZNLqLbIVZqkg3XweFppcbFVNLrSczX2/qNYx6Gx5v9axhrFbTnWrFqe+4zmv1CE0pXqu8XGRWYqWCmu5ZSr5It1np5eqUPVlDVL6WYbmF1HjykcZRpp6Keid3I5FujoLwMeeJk39k3blV8KmiY9JpkSs9Xy8yRVtXpFtJL49mrU3jxQ+/ZTQap0g8Tk4TatPUcP3RId11FLHXiAnikjcuESIgehcmWoNHOjB4PcHHuBTGg9KnW2ekW9bGP7p1gLyBqLgu//nakK7a+FwebqNRUq1gVCmcUUYXB3rgsWzNvGZxzDjF3GpqrMXUsRca6U6VOKSuC9kJgR1XeuYAM1v+FOIgLjTTQs6lxRrdM13TzefIVPVy1fVXbMRZ/R75xnme26UjspjPlePkUzuGRco/G00sfJZrnLQnr3Gjke5smif8OSw+xcbYC3vG6KFXB2hpV73uMYe3e9uaObR/3K/UEXNrWHYks8FdzNjVn4x09zQXd92narrNve75WpbnwVPqSHdyrZQto0O9huExkpcTPF/LDId8axilXNMy6eWZW4dmw2az0bqFrXT/xj56btcoHbWojWYbMLpnu9GtinSb3aNbfTPGdAorGb2Jc6HUJGcRUpMDG6d8Z/o8MyPd0uhmrzIPtryws2ZKqmzL41CcFjK105RIty6jO7M3V12bplaCZ8Ob14bF1qZpVYiV/to2G7U3uAxFArQGD39vFhGaCkZFtNtso5vvXSPp5bI2no8jO37sZBNOFq4r2zfqzxtByRUJKzbSXYr0cjP7dKvhRSsb3Rzp5uvVTKN+JhXbC+m3rt5enV7OpRpsg/B55bFYOqCMILNtSpteXlxNt8wak+n0Be9HsiRlpmq6EyJbZFqkm8dkrocNJ8t0OEpcTGeUJp3zsnye5/ZCFdPV4yTXpjvtMXH95hsnpfHMc5OxPt22vIJUHOF+ZucoDUyGhJGn937i487pvbzu4XPx/vUL6I3LOos6F/w+Q8m5mPerGBRHn8nrokDyPuJLwEg7Ta3RrS/SnTS6s5wTrTDeqt6mpIMmcQ7yrWGsFukuJEi2dn4LPfBKH+0a9okxs9jxstJAevksRV7o7L2TCyXZo9tmUo9urbCSnsW0qZHuPOnR6tTyTNGX1ORefORUqtqy8VaoSFGpYcMtFVFyplqumZBenqrpthVc2yVr0/aO+dL6XfN7m1GbpkZbdpFLAT8TgQz1tGZqBGRatMmgSr6FhbY2vs5Vk+wB69BdG58rvdyowTZNvTxSgvTCEqSXM3Oaa6mzwSUMlVcPTJCVKMbR4HY4Cmrfo0RIVU5MNnikpkGhKebqcalUFKteblakW35HvmZL0T4vH/Le5imxECNFa7T+/qndIqr1/O4xuu3xnXTtvZuKErzUO45OFCmiph0n2TnJ4yTPi/nGSRnpjupML5dtB7U13Zlgg667KXGN7Rzy6h4DXx/yJuuHa0Q3FjaSi20Lm2g7m3CG5RIPm8mabl+yiwPfV4Vk2TR7XIaNbrl21KIVxuN9knOonjVMtizAmUKu6Y0EyZo9NbS8q0H8f8OuUZptwOiepfDEIaMG0sBQ9+jWM/jrVkfW2QqCBx0lbdXESDcviDNFL/LVe5lpJHE6NNNe7y44dbPU8P7I9UMi0p2/Jt54TXfhkW5Zm8bXJk+AZtemqeEWR2qMKrhnqqc1M3MiW3qiHseGuq6M70+10q7e2vic6eWKkFrU0LGS7YTMrunj95PXtYwimptinujZ/dLe8aqp6ZZjFJ8KI33IFdVrjbGWSjEvTEytHJHuYtTLeXyQjgF2rBaD2kkyE4trdbZCMYJwMkrMrZBSKs3Fd5rQO44WK6KmHSfl3MXR6HzjpIxY61UvNyI0ynDEm9k+OKVre85o5HUM79e81jrTnJv9E7Keuzjl8rQMqSi3B4yb7nwsNAtJHenOtV+8HpFjbrY1pVYYj/Vi+Brh33rWMCmRWWusHZWyLYNBsiMWJtLKN+wemxHH4kxScUb3zTffTIsWLaLa2lpav349Pf300zm3v/POO2nFihVi+0MPPZTuu+++tOf5Jrrqqqtozpw55PF46JRTTqGtW7fSbKzrlj26WRzITFJiarkXEInBNvkaM9LauL1McvDKlCI9kWdSNstI4kXrWNJL2saR7gJTN0uNXDRyH1Ke/PNlChRiFOqJdOfy5nIq39p5LWJykiJno76IIZEzPUhHlFxHcK/yYoW7Ur26zY90S0OVL/d8DjOlrixpwMjFpLrujZ/P5WxSVKozpZcnv7Pu9PLkdScXN2ZHOmR6od2EyF0m1iTboLzWPzmjitNa/Caklxt1gmQSUlMbooW2DZOOv1JGuotRL5fjBV/DxWZp8f0r5z95b1hZkThflHhxR71hleZc6HWGF9suTDtOynWMvE5yjZOOAo1uvcGOJR2JSCg7NPTw+mBiu4XtdVRvonMzZXQXl1quHjN4DWim47VYPQhlXoqkNIByKZfzmjNX5FcrjMfZCvxbzxrGbbX0cqV1qLGxYsWcRrE2YkcG9+yeTZi2AhkbK33ftTvuuIM+97nP0dVXX00bNmygww47jE477TQaGBjIuP3jjz9O559/Pl188cX0/PPP09lnny1+Nm7cqGzzne98h3784x/TLbfcQk899RTV19eL9wwEEoPJbFIwHzVZRE2iRLrzeO3lZG9GWlvifRJ1qtlSpPPX3yQGW65DNBLt0cJ18jyRsDHLKVhKernFarplNFf2Va03VUhNf71xLm8u7wsbwFyb9tbVPXT4gha68NiFdOUZK00VspLlAL0tnoLq2jP1BZVOHHVqvFmo67nzRRzUdWWJ19jS3kNPbbyMaGZML5eRbh3XN3+mdMjIxY3ZWgfq1PJiozGZ6Gqspd7mWrHA/vsr/fTinjF6fXBqxj34SolDAREeXvxLh6UR52AmIbU0BXOVFoMRpDNDjkmloJh6SbNSy7X7MhNiaiETjG51lFg95vNYU2ynCb1CaqlMtsIcNdpxkueCVXMaldK8XOOkYaNbCqnpNLrZkcH0TwR1zc/S6F7SWW9qeRt/vmlGt6oU0cyARDHtwsR+OVO94XOlmKtF1PLNM7xW+doZq+iadxxCXz1jpfitZw1jtT7dhZaD1szint0FjarXX3+9MIAl5557LrW3t9PcuXPpxRdfpFJxww030CWXXEIXXXQRrVq1ShjKdXV1dOutt2bc/kc/+hGdfvrp9MUvfpFWrlxJ1157La1bt45uuukmJcr9wx/+kK688ko666yzaM2aNfQ///M/tH//frr77rtptkW6pTqjWT26jbYNk14zNrrMWhzXSzGwDBGDfJ5wITCV3I1iUqxTImqJFCwlvdwiA6d2cpJp5TKqZLSeOZcnP5s6q15v7iv7xoUDg9XFWYSEa9NYqMmMlHJlX6Mx5ZxxL02jde1sbEkHkjrK2OQxTw1fizRU9QgfaevK5KKY30NvbXyuaJiiAq3j+pbXFp8+eXzMzgApJuKrl+a6Gtqwe1SkzX7r3lfp6nteKbp2tdhrOJRcyBdax15IRk42gy3Vq9v6Nd2FpLemRNTMmTvlIn8mI93FiKipo8Q8HjR7nNTZmOpfriebJhvSSZ7oAKKnHWhhkW7tOMl12g1JYc1846Q0nvWqlystw3QGGzgLrSdp6O5IGtTZ4H3dkYwmLuloUKn0Ry0V6RZrI6U0KWaZ9HKmRYeYmnSm6xW2k8J4h81vEb/1rGGsJqRWjPDxulnas7ugUZWN3fnz54v/P/jgg+Lnb3/7G731rW8VBm4pCIVC9Nxzz4n0b4ndbhd/P/HEExlfw4+rt2c4ii2337FjB/X19aVt09zcLNLWs73nrEgvN6lHt6RWqWHOfWPJG8+Mem6JIgaWwRucr+aLJ4FU27DCo5OyjpFTy9WLmbBlI92Oaceu2BoruRjXV9Od3UkjezuyR1jup9kD9oiPWxsR1Tht1N2cWEz4DET71Wnx8tpPi3TrEGQxSjiiP31fW1cma+O9OuvK9NZ068nkULfyUhs9ZmLGoisXbFg/vHlAOGpYOGluq4daPMXXrhZDQLVoLTRiqQgbGTG6lVZTjiyR7kJrukvfMiwlaGR80W92pHsm24aZIWaaHiW20cE9TbS4I1GHzBTTaUI9VuQy2ouNdBdTf2svcXq5jFoz+dJzByY5Gh4VmXbcJ9klRRKLNGx57JbdbqSwW7EY7XxRrrGDnarqlrq50suziaiZgWJ0W0RIrZhWnPOSPbv5XLOauVUyxEpNQVcHG6rS6P7rX/8qIt2nnnqqqLVmg7UUDA0NUTQape7u7rTH+e/Nmzdn3c9M2/Pj8nn5WLZtMhEMBsWPZGIioVob/9jHKO7K7emOH3QQ0ZVXpj/4zW+Sbfv2nK8Trz3rLKKzz0494PeT7ZOfzPs6XkXYP/5xinV0pB575hla9KOb6JwDEyLKFPufFjpq/wSticRoWVcDxeXAUVtL8Z/+NP39br2VbP/+d/6PPeoook9+MjGQsjI2G2+XX040mlmxsDkQoXMGpoTRFXN/huiEE1JP7ttHNu1xy/a53/8+UVtCqKGuxk7LN/yHen/7T4prFkTrD0zSmnCUDuqspzh7MufOpfg3v5m2zRv/fBvVbnmVGm5PbpPtM089lej889Mes110kfg9f9RP50wGqavJTfEWD60f8dFBUyGhfExNbnJ86EPp5+bll8l2ww36vuuvfpX+wO9/T7a//z3/61avJvr859Me67juG3TO5tdFTX/8F/XUEI/TOXsSBkPkrmbFex8/7zyi009PvXBkhGya99Jyyv4JMbi6l32XYh1LU0/8859ku+22tG3nq66D+K+aEg+2ttLkdd9JCMfEiQ6Z00iD1/+A5jz2pIguxZMR6Yzf9Y1vJPrwh9Mes33iE0RZSkhq/GHx+TX2ONU3XE4U7xSpYzGORmzbRrZvfSvnd7VHYnTO/gm69+NfS9xbyShG10P30Tm33i4iy/HbmkwdI1qSx2zfCadS7C3L844Rh3DGki9Mu0a8IsOFv9+vT/oArX7TEfSudb0ijTL21FNk+8lPpu8fEZ25O5ES1vinZorXedLGCF7cHfGPu2jNrRsp3l6X+1itXEO07C3kqUn2Ko3Hadm3v0ZxZxbHRDxOTTz2ut0U4/tLxxjR5Q3ROcM+sfiO39YwbYwQ3H8/2VTZW1nRjBG8QNh/5bV04eZN4v+8eGZnlTCa4nGa4I4Qv3ZRjNMGT8s+RuQj/rnPER16qO4xwhGOibFd+D/O/lP6QkbnGHGMu5v+dtoHKBBKXvu8v3x89+3Lel7eOBqiI8Mxag5cQLH3vlN5utU/QefcmDg3kdubcxoX4vjOnZv6+9FH6bTv/kj8v/2uJopniwa2tlJce0x+8hOyPfNM3u/KY4T9oovIRol2Wfyd3Zd+MusYkfbaT36SBiaaxTHoqHdR7LXX8o4Rymv5/vIkSlgEd99Ntj//md486KV1/jDNafNQPENLHd1jhOp+iSejzPnWEXOS9wuXe8R/mR7FjX/1q0RLVeP3M89kHCMWxYk+v29MGGVNbidFXG66+2NfS+4SR4l99MFXH6ZF//wJxW361hFq3n7bd8g2OkauPzRQPEtk8dj9ExR/45lUf/wi5fo1uo5YNaeNPn3SQaL+3PPwP+jop/5ODnbGe2pEFlTLQzViPExj7lyyf+Uqcew540R89ve/TzZVWWPaOEY2eteexHjqnjyHYh98v64x4nhfmHqHvIl5ck5T9jFiMkjnjPrF+Oe4q4HWjgeoZzwgMg/iSVG1QtYRkWCE3ujqoRfee7FwLucdI7THN8M64swffEU4vBpynNdMY0SmdYRk5YiPOpPrrfiiObnHiAz3C3PiqI/q5q2m0eUfTV1LmnXE/PEAnTMeyLge4TGC+DqW6FhHZBojWAd01VMP0epnHqH47S0zbmu8ef+EOF9zVOdL7xjBnDnoTRjacaKBulr63gVXiP7z7Mw6pLc5ZWtkOS+5xggb3w9ZbI20137oQ6bYGurrwnSju7W1lfbs2SMM7/vvv5++mVyE8GDKhnG1c91119E111wz7fFgfz8FnbkPabSujiY1NeiNBw6Q48CBvJ8b6OujgPq1Ph+16HgdX7CTo6MUGRgQ2QFMTV8f1Q32Ue14INEqyuYj+2iA3Gyg230UTEbM4rW1NK7Z37q+PnLp+NzwgQPkHRiggG+K/H4/DQyPUuDAAbJluRHCwSi5J4IivXisv59Cqs+19/dTk57vyilA/f0UjyS8jpGAl2JTkxThczOVbjTbh/3kForNXgrypGGz0YTmu9ZMjpJ7bJjCkUkKenLUuPb1kV/zWnluIvyd+LtFXBT0O8npDZPbF6ZYsIZCXidNjY5SWHVunP391KDzu45pPtPT10duHa+NtLfTlOa1wf4+8V1rgk4KhhOOJM+EXyzYA3avkobs52Opeq1teJia83xmzYifbLE4TY4M08BAyuB09fdTnea1cTbOxwJiYR6kROpcPBCgxzftIr/PTz1NLop4x4jGhsX+xv0OCgaze9pDfX3k03xX3l9blgV10BchN/dldRKFxobJTw00SGGhHeHo7xf3ay6Cyf2PhwNpehPxkSGxv6JnsW0qpdJmwhgh7x2aHE/XuMgxRvA0vsIeJ68rSru9QTq4pYYuOqKVXI6geA8eI+ozvJaj4u4xf2JfnT6KejxpY4R3cpLsk+NEQwMUDOWOgEy1dpN/np+iNVHyT9nFOBEfGqRgPPH+0+A5JhKhkNNJPp1jRMQfIfdUiFxuBwWjk9PGCMbd308eHfeNdozYPRqgif4hWuafEFEtzs5w+IjcyahKW5woMhWn8dgk2XOMEfmY6u+niMo5nG+MCIUT1yDfswMFjhHONrc4H32DQ9RIPvFY0/79ZM/02uR5cUxGyB2NU0Dc56oxYmSY6iZGxDHy2rw5o+8T/f0Uq0mN1bYD/eK+YWJOHwWz3Dc8RmjnKr5+a3R8VzFGDA5SLBwU53Bv34A4RtnGCDWTBw7QXm8iQh4PTNCwjjFCwvMc1aUW6LV9fVR74AC5JkPkDkQoEq6h4GRN4WOE6n6R402+dUQ0eb/UBJwUjKS3wZvs76doU2r8zjZGMPMjUYp7AxSejJPD46Epr09kYAxMhajF46RDPGEKbda/jlBTOzFKNDZCodgkBbMYZ/YRP0W9UxScHKOBmLfgdUSnk+iSo9ppalcdtT2fmJc89hDRuI+C45nHiNHhYXHv8CHvT14PTvXnqs4LG+1yPPUO9E+b07ONEbx2qRWp70S+2JSYlzONEf7k+qOxvkaMfw5fmNzcqYbPb8BV8DrCF4iQo72W6m2JuVGSdYzQkGkd4R4fJgrHKBKfomCOyLR2jMi0jpDEk9/fEXFRwE25x4gM94t4f1+Y7M3jtLd/mAY6bBnXEdGpELn9EarhezaYfs96+/rEGk+iZx2RaYzgqH18coJqRocpyBNNjjLMctgajpHEGjqeXEMbGSO8wShNjAaoNhgVwYlaWz3V2qO0YecQbe8bo4uP6aXDpa2R5bzkGiPEudFhdOtdR2RCvY6YnEysLUpidL/rXe+i973vfbRs2TIaHh4WaeUMi5UtVXs4TKSjo4McDocYwNTw3z09PRlfw4/n2l7+5sdYvVy9zdq1a7PuyxVXXCEE3dSRbnZAuLu7yZ0v0s0q6V1d6Q/OmUM2X2JRkwtXTw81qV/Lg7pqv7N+ZjxOja2t1NbVpRh21NNDtt5eItuEqK0KtNaRP5bYh7q5LanruraW3Nr95dfq+FzXnDlU39VFXQMx8oxEyV3XQLX8utrM9T9T3hAF7T5y1zqphScP9eeGw7o+k+nk1ya9Tz0jRHsbGinS1kFulfeRJypvOOFd9vQ2JQzK3l6q1XzXwc5OCra0U6S5ltzJVOOM37Wnhxo1r5X766XEMXZ3NYjvZhsPUHA8QFFWMm/1UENrK7Wqz013t+7v2lXEuanTvHa8tYOCoz6yN9WSuyXxXaPxRISaOhvInVzcuLTnxunM+5mB6LhYcC/o6qR29WszfVeu1YtPUNhG5J6T9Oa2ttJen110GFi/vJu6ujop1NVFgy3t5OJUxmTfx4zftaeHGjKdm2xG94iPgs4g1dc5qKu3hzyjHrI57YljPTGR97uGuJ4xPkWtLY1p5yc2by7tb2kX/7f3NFNNlmhfIWOE15e8d1rb0q8JHWMEj1i7HGPU0tJIDc1tKSHFLNcSpxDLxbi7t4VsmjGiJ1BDOxqbKdzakfO8MPbWDnFOO9saqKu9iTyeAEVa2sldG806lsWCQXK53dOvwyxjRIzvN2eAGhtcyhigHiMM3XOaMeJAaIz8ngYKt7SJxfNksmaZs2JERkM8nsiS6Gim5hxjRD6mfdc8+xsMhIXDyl7jKHiMsNe3i3PT0NRCXV2J+1DMGRlKTeR5CdiDItrf3d2Vfp87nTTKY2kwStH2enLn6JDRofmuE20dYgwWC7PeHNGd1tbpcxXfN3rGw+QY0do0KjI/GppbE3OVDqPb29xOtVGP0KtYOn8O2UI+/eM3f1d1pDt5bmy1fgpOBinS5CZ3Uswx0xjBDtGdIz5RKlXT3E6tPb60Nan6fpH11PnWEbGJxP3SoLpfsl6HOa4ldrfZuxLZNMMRB/X7YiJlfd2iTpFNs+T+zWTr26V7HaFmrLOTvFEbRVs95M6Q0s+OQX94nBz1DbRkXo+iE1LoOkJw0DyyLUpkeOakt5e6uzvJ40k4ijo7u8jO53Q48bf2vLD0QjCSsN5beDsDY4TNPilSfIOt9dSWYYzg63+M515PnBq7E3O4fTJIwVE/hetqyJ0UZCtkjOD3iDa20EG9HWmvzzZGaMm0jhjje531czqMjRG5xsOQY4qC3C6tvY5q53TnHCMy3S9MjTdEscZmijk96d9VNUYEB70UdIepps1Dbk12Ct9zafurYx2RaYwQbT0bm8R46OhJZR/OlK3hj4yJU+3ubVLKyvSMEfE40eZ9YzRe76Z4XTzR1reujjqaG6i9KU7bBqfo0Z1+Or67R9w72c5LrjHClsPWSHutSbYGd8jSgy1eQLFmOBwWImUc7f7Qhz5Ehx9+uHj8Bz/4ATU2NtJHPvIRKgWcun700UfTjTfeqITzFyxYQJdeeil9+ctfnrb9eeedRz6fj/7yl78ojx133HFCMI3r0vmr9/b20he+8AX6fDI9lg1ovqluu+02eu9736trv/g1XAs+Pj5OTSoPj1Xg48SeyC61YZfkN0/uok37J2jt/GZ6Yc+4SCn78ltXmPr5j2weoL9v6qejF7fSOw+fl3W7x7cP0V9ePECHzm2m961fYMpnP7F9mO55cT8d0ttEHzhmofI4C2J8+2+bxQLlm2etzlq/+tCr/fSPVwfy7ns2eFHEokq8EP3iaQeLOrB/bx2k+17uo8Pnt9A5R8zNem7Kze1P76YX947TGYfOoeOXJdLdb35kG+0d9dMHj1kohMsK5Wt3bxTH4EunHSyEz3LBRgqLUTHfOjtxbriu/L/ue1WkIX3h1OVCQO3VAxP0P0/sErVBn3qzec6+n/9ruxCnOXlJPR27cgF9875E+cq1Zx2iS+iGa3j/96ndtKi9jj52wkFpz/F34FrDT5+0VFFGN4Nnd47QHzfso4O7G+hDb1hs+PV8L/A98ckTDxIidbkYmAjQD/6xVaRQX3Xmqqzfn9vTfFzz/bX8Z+sQ3fvyATpsXjMd3NNIf3h2rxAvuvj4xYbHsmz89aX99Ni2YTpheQedvlrfZKoXTo3j+5truFm1ltu/cA0li+ms6m2kqUBUtINhdVrZW7cccH3c7c/sEa2FLnnTkoLe4zdP7KRNBybpnYfPpaMXq4yPHOfllqeGKBiN0+dPXa6Ip0n+8Mween7PGJ12SDedeLBmMZiDPSM++smj20syN2nhGvwD4wH68BsW0bLuRl2vkeMQK9h/+uRlps6ZRy5spXcfkXne4fuM23JxfbFw6tbYxb3DdchSBbmQ++X+jQfon68N0fFLO+iMNcXfL8IxMOwV4x6nOLPwWLHCl/nu6eGpIH3v76+JUpevv+OQknQtyFcX//V7Non/872vFaVTn5fJYFRZi/zXO1Xp4QaOw/rFbXT24ap06yT7x/x048PbRGbJ196+SkTpWfDxzmf3ijLCD2cZZ/Xwi3+/TtsHvWINI/suF8uvH99Jm/sm6d3r5tKRi8x5zxsf2kr7xwN00RsW0fI893S2+4Vbs/38X69Te72LvnDawRlfK9dKHzhmQSI1ukRc9eeNwkiV68mZgksnvvbnV8T/rz5zlaG67tdV8yY78XkMczltoh0s3whcH6+eNwsZx8qNXjuwoEh3TU2NMFS1fPazn6VSwtHlCy+8kI488khhfLPyuNfrFWrmzAUXXCAU1Dn9m7nsssvohBNOoO9///t0xhln0O23307PPvss/fznPxfP80B8+eWXi/R4jtovXryYvva1rwlDnFuLzQZk27Ct/QkxjjaTRdQYmUqYV73chFYlWqSSpLZ/riKixp7fHAsAvT1Bs8ECbGxs8kdIBcyUQqe1SjG4NRoj26zlOn5GYOeWFIqp0XFu1eJjfE2wYNor+yeEwc0LWza4xX4m08/M7o08lOwl3FbnFPVyoi6bMyNCUWr22ItSy+brja+lQq+n/P2RCxOL4cUwG9169ku5T7MIHhoRw5HnTqgcJ/fddPXyEgqpSYVjFk1b6m4QjhRWsmbH0ag3JK4l7r+aSwm+FCh94osQDzJ6Pvg+T7SJS/WZViMXiEZ7dUshJNlVoZRIEUcjQmpSRK3DJBG1xH7kFlJjg5sdBCzex+23PM0OcZ3zdbhv1J+332+p1cszqTSbSUrgNJJHRK2m7AY3w3XfknxiavJ5ve3C1LAaORvdr2fp1y1bhbEDWOoomNWylJ2LsmWiWRQi3mikZaQZ6uU8zmW6pmTLsEL7whsZG8LRyIwrfhcj1jkpuxs0O6je5hBZQqFInMb8YZFpx/NW/0Rh3Q2sjm6j+5577tH9pu94xzuoFHDkenBwkK666iohdMYp4FxTLoXQdu/eneYF4aj27373O9ES7Ctf+YowrLkV2GoWkUrypS99SRjuH/3oR0Wv8eOPP168p95UgUpHqq1Kg8vsHt3pLcPy9OkuQgkxG0qfbk3LLzlA5lM21dsTNBuyRQ4vOKVxr7TiSbb0sQr+pAGkVvmU/y+mV7e6bYqehQVHk1mFm725fM3wAKyols9rnq7wGzJvgubPk+e6rS6xYONjwOqvbCDKXtK6jLwMBrC4nsbN79Ute13rUS/PdZ3rOc8pdeNsRrdDt1NJvSBiT3cpjG7FAC2B0S0VjtnQkX2JWZF1z6ifXto7LgyffErwpYB72Rf7nVm9nwnp1GkR9zlfhrbMBlt7Q2G9uqVjpk5nK55ikNeukQWtolyeQfCsFC3DOHLMEW4+juzw4fuMBeA404IdP3wdsvDXqjmFZSaVwvltNvnm5WKVy4tFLRTIHQ1yEZHtwgqI4HG/brb/+BrkOUVr8Ellc7XTQzqWihlneZ6Qx5gFYs2ikDaFep12xTgguZ0lH2ce43itrG0LJsqIksdDb8uwQuGgBFcPz7jRLbsNObm/uq3g7gYNtU6RFdU3HhDXMdsgxXQ3sDq6v5HeyC8f/FKKqXEqOf9k4tFHH5322Hve8x7xk2t/v/GNb4if2Yh2odBaCqNbWYSXP9Jd78rcMmxS5wCptHkq0EhK9eh2lXRiMbW1Ro0zQ6S78Hta3RpNirHp9uZGouLccToSw6UHyjayZRgL9mTxPhtF9ttlBWq5OOHewGx06+3VLg1JuX9qlF7dJrcNCxcZnUotYvPvl8xYydZSyMj1LY0KPsZKKxuT549S9+lmw5oji0qqbyQmFtJ8TE9Z1V1wxLEYzHBgGm0tpHYiymiamvb6wnp1lzPSXUikTY4ZZrULU1+rmSLdnKotHTx8v27umxJK1NySi8dAfnzrwKTYjiOcRgmZHOkuBU15xquJInt0FwufB74FeOqL5nGuR5LPc7SvkOtkTlOtSJ/mkiju+ax2znBatLq9mPq+LibTjkuMZGZkMa3linHY6oGzCOS9XEzLMHaicJYaZ1Zw2zDtupHnRBlcYCOylEh9gnyZo6UmX8abkQyxrsaE0c2Rbp67Doz7ZyRDrBzovjr0yqGDyoIXCmywsBHKxhFHO3mwNjMyo/QWzBvpjpke6a5PRro58qP+XpM6J2V1enkhx4VryxiZEp2+sIta0+hWpZfL6FIxkW4ZheVDp7cPacqbG6O9o6nUcnWdqIziiRY/nKpkwqJcpr7yIjbtGprUn8aeK7IqIxFmp01Jx4Zep4aWRneN7vOc6sVsL97oVgxiuzJ5mx/pjpUs0i1hw5oji7J2dUv/BG3YxX1HveLcFHpeCkUe12IWmkYNUHneskU+2pKRbk7RNHJMpMO0VE6TQuaqTEa3menlufp0q1Mzdw4ljB+1Q7DY1MzKiHTnHkdT8/vMRco4ch2NxfJGuotJL5dRbDa6OaqtNrr3j/vF2MfO495mj6lO/76k0d3TZG5GqMvk9HL1/ZMp88wI7DBno5vHr3mtma83vm9LPdZbpVe30qO7gONqz5Ah1uB2iBaDnNXIGRwzkSFWDqw7qoKywH2PN+4fp+d2jdLzu8fo10/somvv3SRqxspd0y0HETM97BxBUwwz1SClN/2MvZu8fhRq5wXUDsuoDgtwaD3N6gjwTMOiGJk8wjK6xD3Wi3lvxshklPLmJuoUtanl8v1kOrVZqVZyAS2jcuprSHekW6azZTDyjESUC6rpLnDCl975bDWSGdPK8tV0R+Pp/aFzpf7VOJXrw2yj21fCmu5Mtau88GXRRU6T4wXa/2/vT6Ajucq7f/xKLbXU2qWZ0ezjsfG+b2CWvAFswGAOgR/Ogg/kmLC94ZidA4ET/vjlTcAhhxcfFhMSCCSQEAjYkMVgcGxjErCxPeN1vI2X2RdJo31vtfp/vrf7qa4uVXdXVVd13e7+fjhCnlZLKtVy77N+n3ueLagW1wr5m4NkIYIawBJcK7V+Yy2RewP97v4z3dE7UFLd4jWLhEAcqmAA5vOGnul2qTCS0kwEI5B1cz4z1ZZmFoIn0Qc5giJ/G+4N2V/syDoWp9Pdmg88Sfl4KVaqdrpz2UBkut36ueHA2J2XMJzuEYyn1KXl4TrdYfd0W5oh7YmqHbiBvJo61vQ477fCGhWv011tkuzcfIXYedv6tWhaejWr70n8PAjNxlEhVgsC3yHog7777rt1H/XycvEG+oEPfCCMYyMRI2IsGJGChRiLPgyHMMRY7MhDWSmzW3iIw3O6kVnFz4MRhSyeOFCFRbJ8phsLNYxFGFY59VV/5Wrjs/VRXi4RYdgJdmcRZdb2nv8gBDEq5J6ZmF/WgSFnabmzDB3H7wg+B8ISRdIGdLa4WsJvpjvZWrq8PPRMt3ehOjekXE760rxlwsqXl0swoLM1UfFcIdBj1zoIq13A/jtqkSkVEEB49dnD6ke7DqtfPjWqXrhzqKa/P4zsvt91SsrLSwV+cD0RfERWDsFIrwa7WwVOVPgtbx2bya3vfam2UJ1UCXziWUMm1F4hJKWZv33uhF5bcV7xGWOy8K7i0kz/uiHyt5tcXo77Wsq3sa87tWjsQmpxocvF05WF1CRo4GUyhhu4zrqve3ZZO4SiOyL7JsTW7NjX2aBVjccjznSHZRst5vVe3PZiv8h5nZpf63R71QgKA8ksi70cF5LEqqYi5lxbhRgCiN+776C2E4M+C/VAoDsE87ivuuoqPY4LzvfQ0JAaGxtTXV1dWtKdTrf52MVYtg10qtHZtF640dOND7sYS7URQrsibDljuiDQFK5xBYdiMb2s5pGRyE+MEOfCyyKJjVucbj/gb3XNdEeg0FkthYxjouj6dJfoifeDbKB+FlK5Z1B9AZtls6O0XMDx4rpU03NuR65X7nctFme6Pf6OhTIR4Go1AiqXlwd7Vgtl72lfZcRuYNMUxXfc4+Ui4XaRG7uzBmc9jHUA65w8Z1Fnup1ctH1Q/ffeMXV8eknd/fSoeu25m2r2u8MINPh3uiv3AqPEXDvdPhTMJdjVXYNMt1QGeF2bR2cXQxdRc5ZsIqBo7yGV0kw43VhHsD7BKUf1APZzBHilNLNSpUmQ59sEsEf1dLRbExfWOt3pot7vODPdFZ3u/Ne9tl45wTOO1qvDk4u6h/vC7QP6d+4/Mb+mn9t5XSsFRZ3k+sRn1WOH0fKVVettbVhGZrrTIg5b/X2A8VYAfcdOaiWiFrQFJtLy8ir31VbbdINj00t6z7x/37g6K6AQpOkEWlUxGuwNb3iDmpiYUKlUSt17771q//796pJLLlFf+MIXwj9KEjp2MZZUst3KzmAzc4qxVIsYzyjRLjemomAch7vZF5ymFZdNuXIkPGhJMJw0/E06mOHidCM7GcQoioJSYkWSXarGqRWjAjNT/RqdmHtZKsttdyrC2IAQJCnOdOfozt8/Xkvsy40ocWoEhIUlflRleTki9jgPXlVL3cD64cVhw++xi5whYCDxntAyHbaMZZQ93aWMiSvPyTnav3l2zLUsMSrKtTh4xe9oISvTXcZZK4ip5Z4zX5numgip+VtPRvOZ7jBF1OTekefLrcT8tI096uQNPdrBRsANRj8cbmS4q61Qq4ee7koBTCMy3XknOsqRYYI4LXCIASoVcR3xzCBg7Twua5310eKGyki0Hn7qJ3vUvc+d0C2J3/jVc6G2Ioad6Q6ztahceblXjaBQy8tj7umOYp14YX42O2a1u1UUNAKBztZDDz2kPvrRj+rxXIlEQi0tLant27erv/7rv9ajuYj5WGIsyYQWMHA6XHgdXw9D8AnGtOwn5XrlCsZ8uMaVlAdLT64Ix/nJdAO/50JKy+HY2/uZ7Y6RKX3dBbGi4vMhkVsssEGPNUj5HMYVQeEbQnT4fPaWPt+CQ36BwylBEns7gDV2zmPgoVwE2K4RMBvifPFqhdTkOiMQ5HnKQBlDxkuZbq7yRRVVWFiOXshGFwyDoJmkajhzU686aV2XPq93Pnm8Zr83jNm0fkcLpT0YYUHGhs3VdGSY955uBM2eODat1yiscWEHUCXI4BYAeOrYjN5XXnXWsHrjBVvURTsG1NtftlN96vVnVd0SVg893cUK5sXrKK6FPPdx9nQnPDrdsnZXU1KLvm17H/ezeec7N1KseN0Lss5KKyJaDzvaW/R5xflHxhuvh+V4h61eXpiOkQitvBztmHGOqOs0Rb08ghG/G3o71Cnru7VdgGx3IxLoKW9vb7fmYaOcHH3doL+/Xx08eDDcIySRUDwnr12XkZ9sK0MKc04eFnmrr7uMcxRVhN2Z6YZB6me8Q9CSYMnm2EvLgT2jt2SI011K7TjnrOT+W5fnV9Fv7HUkCjbwf3/wiCXuh39//e5nXTf2wqzu6jfpsXyQZLCrvcgA6vab6S6TZUQGC4532Army1U63cgwyHNX6bi8lJ96yViIYYznQY670G8YUqY7AsPA79r3unxZ+f3Pj6sH9o2rhw9C1Xw2sioXewWB29g6r/gdLeQt0+3P6cbfIutOVw2uoVf9Ecn6/Xj3Yb1G/fNvD4QuQCrrh1uVERR+wfnbBnWWE9MxEByuthUMDqJVmWR8pts9GC79tVhSalEdUa3THUamW+Z1Yw9DhlCcb2dpueBnUoRzLnwL/tfSovq7kvrfeB2tiGGsZ5YIZ1jq5SE63aLHgqy282+dyd9zUY8LM0lITZz+sCtTX3RyLtv9wP4JYypBwyTQHXLRRRep+++/X5122mnq5S9/ufr0pz+te7q/+93vqnPPPTf8oySh45yTZ18sYOiEPScPiymMh1JZNESnxTkL20AuKHBnijZpr+Mdgo55kr5Fe9bUHmnGuTBFTM3eW+s81u5kbj4lghb9+RKrQFnYfKDOS0T9wMS8Tdyvo6S4nzgVYWS6C6XlxaWismF7yXTjbxWjtVQ/rYweQQZ/60BhlEs1pFfEUA5uuCFzATEeGBXlymUrlZfbv1Yua+42P7sjPyoutPJyy+mOz4E4aV23Gki1qTueHFH37ZtQQ11JbfRi/UVvbtgqrc4KglqVespzXi5DKuXlMNS9iDgt256nWgipeVEvlzUKfwMcHQRl8byELUBaalY3AgLIdIPzt/WrRw5N6v/GelIt9mttvtPt3vYl+zR6vsMSY6zG6Zb7txRi91RTiQObCXsJ2rHQFrg/3xb4gnzZuZOOhPfeaXsrIs5nYSRm65pWRClzN6enu/qKHwHBctEqwT1mt4VqqSFQaIGJe053NJWp52zp0zYXyvifOj7TcL3dgVbVz33uc2rz5s36vz/72c+qwcFB9d73vleNjo6qv/u7vwv7GEkEiBgLHEIsqOgJQ8QVn/FvuxhLtcC4QsQRZXjPjMy4Rq+wkEs58eHJ+VAjXFKWKJluv6VAhc3dZ3m5iKi5jJIxTcFcsrjdLoIjhZFZwTKzMjKlksiXPaK+YzBlaQxsHUyVjKhL9iuMqK/MVHc63aLg7kW93K4CX8op9XM/4W9FVrRSdnQ5I1nj4Iay1zaKSurlua9Vvr8XbONchLDHhi2Iem1MmW5x0h4/OqPvX/xdcNAgygMnLczSTEGeBQSsqrkfAquXl3HWoPKN48Jt7CZI5EQCpfieoHoFYZa3FgmQDqb0LGY4S9gvw876lRob9uTRGe2oQXcC/bqFiQjVO93yd+N8x9GOEcZ6JecB91qc+M10BxXBFHJluVn17w8fUcemFnUiY70j4B/k2ba3ItqDCLK2hNmKKM9feOrl4U2ugC1sKZgvlA70NIuQWlQaTG2JVnXJSblZNI1YYh5oVbr00kut/0Z5+W233RbmMZEaIXPyYETA0T4+vaozMMhww+EOI1oPgxI//5dPj6jZxYzOJv5q71hRhgfv+d5v9+tyYihifuY/Hg81CyQ96+JYFkQv2kJXdrZTUC5fmzW0MnqZ1eBz+0KknFiR1RO/XG15eavniHomW5gZLpuMW0S93DzboDO6S2W68XfAGCjnVMgmDyG4UlkWq12hgtMhzw7OCYyactlRa2RYFY6JXUzNU3l5mc3WS5m4OMT2ey5sZX+3bHotEScNAasXbOhWY7NpdWRyQZ25uVdXGIU5JSLsv9nvaCEvs+LxTMBBHZlZ0kEuZxVQ6X7u0s9TmFSatGFfo4pV8XMtQ2Fm/eS5WMgrMDtLy7EG4PhkfwpDqK8elMu9ZrrjFFHzI6SWzgelEx4qwcqBX7P7wISaXsglUJCJ/cufPuG6XxTW2YzPVsQ26++RoEKYrYhyXHDsETSodnSUBMrDajOA0z0xn9ZzpXeorjUaAjUtL49ZSC3K1q0X7hzSKuYiqNbbaba+hB9MsPdJjNjn5OXmULfpkvIwDEB7GV53sk33AmHxs5fhAbwHcx+x4GJBkSxQWKV6zpFPsil7US53Zib9zA8el55ut0y3LaPXZUBCQcqw3J3uKjPdlkPY4i2i3o8ZrAm1c12XlcURJwKBIXtEXTagUMrL8+0AGxxjUGCAwoCCIYBNPNmWrDwfucxcUCuIs5T29OzoCQP9CW3clHouLPXyKoxlryr9XuZzyv1dbpaoGET2LHRHyD3dVm9zTJluu5OG0v/xuSndWgAjDetSmE6aIAGoav9mv6OFJPBTyWHDeqid7rllldsBKv8t3TUYF+actOE27s6+Rh2fTq+5f93WqKAU9CpWiwxde2k5kOwbnK1qkUCC6aXl5SqGCvu7IZnuCtMgMhKUrsLmwn5xy+5DemwcxFDxs4bLtGX5EVJztiLane6wWxHt991yGE63VV4ezr3glukWgV5cPuf0lygo6CPFPKc7HV2AbkNeUO25sTmd7b78zA2qUQh0J5588sllHY/nnnuummMiNcY+Jy8snOIbEPbA/GIsqigXfnZ0Vv3g/gPauBmdXVKb+jv1LGxk6hAtDDML1O0oj/ZbXi7RSzhdMOK9zHyEcYS/B7hlc9rt5V3xBuSLSn3d/jarpzmg022ps1aI5Dsj6sN9xaNO3CLqsplWO6cb96sESZyZbqx1yLTBqEXgZiAX4A6sGl2pjHuNcE1+rS33XFSrXq5/vkeBN9nsy5aXi1BPvuy9rEFku+fCLi8v9B/G43QXB5IQdGzLqeSnMc4nXCctTOVy+2ghGfVYyYn3Ul5uXw+9iKlJoLRWglgyaaPUjHn7GmWJNNl6zcPM+snvtgcUkfnBPrShJ6k25ddHp8BTNXull9YRU5B1FM+T/e/2W8kWd3m5lGt7FRottV/AEYQdNZ8P0mzs79QOkdt+IZMnvKyz0ooI5x0/C/c4KhKxtqJyMcxWRJwzCXDLGlkNlqhpSOuH29gwCZ5jf65NNY60wOQmJoRVIWVKT7ddUE073fvH1StOX68ahUCr0oc+9KGif6fTafXggw/qMvOPfexjYR0bqWOc4huyAR0YX9AfcBJ+NnYMVXnaEJUovbwvTIEO56xpvzMV4QjAgIXxA+PYi9MtBiVK292M1aKMngFOt0Rr3TYnccaCOraWQ1jBGHdG1O0bWKmIelhzusfnl5W+FIlc35ZzVnV3/h6tpGDuJbMqvYalysuLyuxXs+rZ0RmtToxggNtzoccV5Q+3mr7XXg/l5TgvkoUuW16eqGzUWf12tnMVttZBGPOqq8EZSJJqD9E5CNNJE+xCR9XgV/CxIKRW/vcOdiX1vQ+dAoxUK1dZVSgPrY0DJZM2tOgnzqOt0sa5RhUClZIdDzfrJ3+z/B7waF40TUrL7WMIV/NjCL1WcJUzpOsi0237u9GG4Axmxl1ebgmp5YNRpZC1IGim275fTM4vq/nlRZVM4D7GNXS3o6xKJI8VRfZWxNsfP64dYtgMYbYiClg/VpYzoVQ7halebg9w2ceG+a2crJZO27OJtTmu1qmo1MvtgmqoHEB5+Z1Pjqi2lXk12zqnTlnfE1ugIQwC7WQf/OAHXV+/6aab1AMPPFDtMZEGwJ7hEUcDGW3xZbAh6QhvSyGjgufIPl4rrCxQd7J41rQleuHD0IVRnHO602qjIwNbzukecunnNlFITZxF6d92M/4q9fqWwhJeqbBQOiPquqw6mSurhjHrFlEPa2SYKM2j/x7GrNPp9qpg7iWyLpszyozdKC5hXVRTcPaXMzlRnJaWNc+FlPVWK8bjRUgNhpCcmmrVy910BMIeGWM5oDEZJs5AkmSzcM2imBJRJB4Xwt+MgIBXp3tJK+i3lHXYUAb7o12HtH7HQwcn1ONHp8vqd4iQmtu6FBXlJm3IGnVwfF47MVgbkemBAGmpNSoo1tqWv4dxLz99PDd/GfeM/ZiwPyEoCAO1Oqe7fnq68XfDKEdFWa41rt2sTHc+KIKssLeRYcHOuX2/aG1NqmPTi2q9nj7RUtKO8jMyzNmKCIcTVW9/+vJT1IXbB0N3gHBs2GerLZ/W4wZDbi9C6+OaTLclolab+w0l99jnsYdgTYhjb8O5taaYRBTQbku0quHeDvWrvaPqvufHVV9Hi+pNjalTN0Yz9aNWhHqXvO51r1Of/OQn1be//e0wfyypQ5wZHjif0g8DECWFwqZqyaot/V2uDnBYWSBE4qRkEEZckE0Z70UfYilHqZQol3NGd1RltNUuoFI63uXS+yQGrxf17rLl5R6ysH7F/eyGqZ9++5Iiao5+7jUK5hUCD5a4SJmSK3tG2a08zP7sTMznggHYYPH+ns72Nc+FZATwY6pRHPaS6RajHKe5XFbdiyCaW1WA1WsYek93PE6EM5AEOxhG+OxiWj2ztBKqkxZFH7uVEfOc6U6UdLpFp2BkJqffAcNxINVeVr9DhNRqWamQO2/pkiJTOMZrXrRDHRif1+smjj1sAVK7LoQEp544Op0rLe/tUBv7ioO5cLThdFerYB6GNkQtgaMtTrdgXKa7Unm5jAwLGDB12lqX7szNOS5nR/l5rt2eD3ycvik88ce1VVLpsq1Jfkcnhiak5lJePrtU+yAPzn86sxKbmBruabmtowrQPXZ4Sv362RM6gYXrt7GnUyWT5feLeiDUu+RHP/qRGhoqfuBJc+JWKizqnHCOkMETIRiosXotJw6CnjXd0aY3Yxhx4jj7WST9zuouZLrdnbhiwah4S2V0b5BsTh3lhNSqVS9vCV3crzNvmJbqwaxWuVyQTbtSib04POU2+e6kbd7n0kpRMMr+7Dx0YFJn0OS5GJ9P62vhfC7s/dzV9JRJpL5UMKC4n7v87/I2MmztuQq9vDxkIZ0g2ANJ9z8/rq8pqnv+12kbQi/NDLOn226cyz3m5Tl3C8bYdQrO2Nirdh2Y1AZxsi2h7/VS+h3yvMkaVAvk3i03BxcO3cU7BnVQ9TXnbApVgLRUT7eMljvfVlouYA3BjOZqxdTqKdMNcN6PThWy27jPsKbK1+JE9rtK4+OskZoBM91B2rKCrrP2YKi9zDlMJAtf7RxqWTsQ3KtG68QOgoQAdomoq9c60y3nfibGWd2LHkajhjX1Y9tgSp/jycWMOqWvTZ3aGc3Uj1oR6C656KKL1jzYx44d03O6v/a1r4V5fKRO8VIqfPUl2/R7D096LycOCgx7/eDOI4ORW6j8lOF5VXZ2lisPuSiXr3VK4hWtqbQ5dVc7p9vDKKGg4n74mfix+BVwCIM63RCEKe90F896r0Y5Gn8bMhI6M7WQXuN0y7PzyCEoXaf1Zo719vjUou4zdT4X4hBVm53qxoSBvHBWqd5Qr+IpfjLdqQidbmuEW0yZbmcg6aePHVX//tARPT7sY1eeGYnBUPibwykv93o9tEHe4n4f2vtOW1tb9foHgxH9ysmuZEn9jnKjDOOcg4v9Cc8krusF2wul3mEiaw6up720/Lx8sNqt17TasWH1mOkG4vhgfcb6hXWsp0Y6AKVobfGY6XaM4PL9ewK0ZQVdZyXoiqBhtcripQir2smajhHi2oF1SEq7kbzBuY2jsqIj5lndduXyKMTj9tn2i+UVVLLMqhNzaXUyqhlbWyOZ+lErAq1Kb3rTm4r+jU10w4YN6hWveIU688wzwzo2Uud4LRWOelY46NYb8JLOsAMsnH4idF76Xd1mdK8v0dNdXF4et9NdfnOyysvTmUBqmWmrZy38xRkLPjJ6KDGcT6+o/oCqdGMyLqyE093tM9NdyeGRctBS9xPu+4u2D+ggD+4VBAVgxF1y0oD645fsLHou0rqXtrp+bisYkK8IQTbW3en2lgkTp7ycUTfvJqTmU+DH9DndzvOLLC9E8aDYHlWEXkoOw3BUC2WolY07GKItbe4BGafGBwJNi+klLWDY35Usqd8h7RxxZLrLBYyOojVKKbV5IBXZcchzAVV4VIPBOUOPo5umiDU2rMry8qgVicNGguHyd9uzjnFnwKRHO5PPZFfq6a5m/fbbllUI+mcCrS1RBjEl0FdtT7c4pG4tc9XYG3jWYC9AtK7Y6a5tebkXpxv2WhTjgKNeJ2Zs+wXsz7bxFrW0lFWTC2k12N0RydSPWhHoLrn++uvDPxLSkHgpFY5yVrggRhtERgB+h58InZ9MNzKPYgSUznQnQnUuqsEq4SyRGZCMCzIIcGL8GsCS6Y4qMi5Od1AxNWwgkiEq1dPd5XFWuVfhrr4K9xMCITjXKGH9oxduVz/fc0wdHF9Ql5+1cY0BJRmBMErooAiMZ7DUZmaVl1cwugqZ7oyvcxVmphsVWGGWWoeBPDtBRQn9PM+h9HTLOlXheuBc6/aONvcs6VqNj6Q6Pr2kJubSaue6bEn9DhEurH1Pd2mDVlf2idPd3xnhcSCLlFt3ISQEpCWr1HpSaiKCV5brsLwcyHplOUA1DNKUQpbjSlu8tGVUo8fh146ygqI+7Q9LPCvCoEx4mW7ZX8K9l8XpFpshnp7uyiX4aEeRIAz2bayv5UQrTVIu73XsF1v6U2ppack6x1FM/agVno94enra8w/t6+sLejykAfFSKhzFrHC3bK0YS35LgZybezkm5pa1oQTDRTKkJquXVyrhhDEgI9NQvuff6Q4nE1uKzuTaebZ+kP57/P2lRhN5zXR7nQst5aClhPmeODqje763DKTURTsG9Wv/+sAhLaZ05Tmbit4bxoxuQQsaThUMiaAR7ko93cjuiCMXVU83jFmx28JSr62Wgj7CSlXCf+UyG0cnF9SJ2SWtU1DtHFev1wPXs5yqvbPvFE6RlGlOzS+r0dllV/0OqcIxKdONKiZ8DZU7pSpjwgDnE8YyAnNwprEHnVfCWLbWkyqd7nrr6XZqrZiiXA5Ew0Z6tkshmfCg6uWB2rLagmWTa3F/SOm0HtlXBYVJIuHeC1JVgqwr1vA4ystFqLWUkJqIVsK20e0G/bl2g7BEyCzB2Ij21Z2O/WJTX6daWMjqZySqqR+1wvPdODAw4NlAyFSpOkhI2IgzJYJZfjdlP+XlUloOkZ1Sz4xZTnfl3icELbTTDTG1Xn8/XyLWYRgVbqSq7G8am8m3ApQxoK1Md6Webo9Ot1UWWcJIxiglgMwFOBNKsS1KZwdRag4FY8Hq6Q4j011hnJn38vLyjos9QGJXepfv8yLcVYmFiMVegiDBGwRUkC0Is+xdMht3PDmijWnoV9z99GhVmQ2v65Q9K+V2H7r1nUKU6MjUonri2Iw6fWPvmr5T/E7JAprU0y2B2039nZGVMMu1vO/5E/o+QeATvw8TNIZdysvtYwirCebUX093ccWQKcrl9naqSiPDwsp01yKbHLWzFWqm2yovD/dYrVaOhZw+kFy/Wo417CwTmLCLVsJxlbUAAXU4sGGIkEUdfGl17hdY81Yx9WNFHZ1ejGTqR63wfMbuuusudeedd+qPb33rW2p4eFh9/OMfVz/+8Y/1B/5748aN+muEmIYsiKJpEjTTjcWmUn+jpVxeorQ8itFIYUSEu8tEhC0hsQBlsZLpTrZFs0AWZnWvVqlcXvp6WZnupdxoslLIMVQquypkaNKuhu/e49AmVersLTmnG84ZNlCw58jUmveHVUlgKZhX6XRXctasQE97oljgR4+L8dZDXAl71UEUYi9BQIuF3BthlphLZgOZARj7WK8Gu3PjVfC6KF/7pXjKQmnkvoBicylDSPpOIQQ2ubCsjWLcH3Cor3vlC9YEBmRdwlJZy6CJ1VNa4t49MrmgPyP7EgX2a4lgH64lniesvaWuZV/Kvj+tNlFPd7v1LGFdnjYo0y3Pgex/UfZ0+yWwkFr+/fXQ0+02HSMMBroKs7olyIP1qZbPTLnycrsIGUyVI5PzekQlwD5oFyGrdp2IMvhybtF+kVYHp5b0Z2S463VcGPC8Mr385S+3/vv//t//q774xS+qa665xnrt937v99R5552n/u7v/k5de+214R8pIVXQ7XAo/fZ8YXHBwopNBwttR0/CU6a7FCZluq2+yTKbU481q9u/MyTldZFluq3S72BOzKg1o7tMpjt//0DMqNRoMhh9Uu5VKYNZLqOMDRHR88Gu9qKe0XO29GsF4z1HptUrzhhe29MdgmMivaGlHEKJrEsJYKX7G+cLRqUzi1Mo/WuN7LnwWupfaxDYWEwvayfKXrEQFHtmA6roE/NpKyMz2JWsKrPhtQzVa4bU3neKTNG/3H9AtShMTVh7jaSqBM9eLYMmYjyXzHTndUE2D4TvdDuzVE8fn1FTmax2yM7Z0qsOTSy6Xkscs7QA4bwGNYbrNdON9RIOiImZbnGqK2me1DTT7UEsMK6ebq+BPs/aFiE73VZ5+Tyc7niCPOWqcewiZEgoYM1oaVnUtsTWgVQoImSyH0Q9FeTc/H7x3NisOnBkRO3YMqxOWd9TlxluIdAZu+eee9Sll1665nW8dt9994VxXISEirP0J8gi6bWvG/2UYKiEcrnXOca1AmN7QHdEmW6/c7oDl5evVJfpLtefCSNFMhGlAg8wYCQJXlG9PJ+Zcst0w6kWJ9vubJy1uVeXSmMeL5RTvcxHDtTTXUbgzXOm23Ysbvd4YZ55m6sxKH9TOEI6ZjndYYupFY1XyRuquE9gxFeb2ejwWJFjOWse7kHpO71wx6CeVY5jdMveShCtlqXlXlojjkyKiFr4yuX2a4nzIoFKnINUsr3stZQ1pRoF83rr6YaOhRj+uvfdpEx3fu3OZL2NDIsqKF3JsS1XuRVUSDOMY6u6pzui8vKBrsJ4PlnDa+90S6Y7U1aETMTechnvRd22BttBRMgQ5HtudFY9fHBSf640U76WwZei/WJ9tzp7U7f+XM8ONwj05Gzfvl194xvfWPP6N7/5Tf01QkzDadwHiYR7VTC3ysu9ZLoNKC/34pxI0KJST7MbYQp9uSHHLfOJ/QCDQ3q615UpL/cSeFioMO/cvSwyU5QJwab35NHi0nL795w01KX/+/G8Yw7SIWanKmkXeDXKUUYtmR43p7uUwrY9A+PHGIyr/zAI0qoQdO59ycxGEpmNZVvFQu784/WcINdKdD3dAe/B87fmZlzDGHT28VeaqhBHFgmBADFko1Aut1/L3LG0FrW+lLuW0rJSzaxuS7085rn2frBXDcl5cRt3WGskyFwp0y1fjyoo7YY8p1hi/QQ4pZIr2kx3MGX1UsmEVESZbqxPUtVY68oKqxrHZV0WETKIjU0t5I5v60Cn3o/RNvbQwUm9P6Dk/C9ufVxd/+971GdvfUJ/xr+9tCLVos2gUQm0m914443q6quvVj/72c/UZZddpl9Dhnvv3r3q5ptvDvsYCama7jVOd5BMd2UxNThN4nSv99LTXQfq5fbsHHqa/bIStdPdHry8HH+7RMTXlalMEGcJBm2pTLfXGd3ys+CTwt7CRtifj54/NzaXG8uWTFgOth1kv/edmNfZ8Jeeuj589fL8dS7tdHs3umDYrSxn8t9TbJQslrjnijLkmdWqjLvC9TDLMLCepYAj7kpmNpZWCvPmbWXr1YxX8Suk5jdDun0opY1YPFdPHZsp6tPToo1axDCeTLdbv6SIqKH1I4pgjnNUDhz73lS71fZR7loWBJ6qKBv1UbFgCjg3EJdEMFy0KEzIdEu5eCWn26oEi0FIDWB99hosq0VZcVBl9SB2TRDsrYaoOrPvmyYEBkWEbO/xWbXvBKYetGv9Cai4P35kSrehjc+l1Z/d8qi2nbYOdPlWN69lprvRCPTkXHXVVerpp59Wb3jDG9T4+Lj+wH/jNXyNENPoCrG8vNxYFgg9YI/FBlou2m71vGayFdVNo8ZLGadkef2WxCJbmY44ki8b0EKATVpKy1EyVsnwqKRg7qeHGKWjhQxNeo1q+Vkl+m/PyWe/nz8xZ12LUOd0VxAM9FNeWK5v0KqucJwrVAlIRX21ASm5HrUuT/bqdM+ElOmWzAbuCZRkYu0RsR8Zr3LacG+g8Spes06Ws+bT6cZzILOnnRmWuMrLLWVgl/v/aMTzue1ZKly71tbW/D7SUvFaytiwoJlu/HwreGJYdYiXNQvK7lKqbYTT3eI10y0ihLULdOC5C9LiVptMdzhVgNYkkQgqZaTE/NDEfFFblilzuuEwX3HWsK62RPAHgXqsp686a6N69/86WY3P5aagYB/OqpzmilY3H+7RSSPoRpQrNS9UkdVPcM4UAt8pKCP/3Oc+F+7REBLhBoiFAiWdMAxTATYNGMtwuFHae8amnOFjd4ywSD1yaEL3dG+sYJTZjdMw+lerwcooldmcJJLrN5tsn9/bHrGQWpA53QXl8sqCVnYFczdKlUyXAj2YMJDxsT1v9ErZuLO0XBjsTupSscOTi+rJo9Pq0p1DhZ7uEMrLYfDI/GTcF07jyk/PZ7ne2IJBlFhjDCILg++p1um2lOQNi8Zbz1JITrdkNu57blwHcHYMdelnDkEZOGnVjFcpZJ0yofV0O8Hs6f/eO6aePDajf478ThF4LLcuRYEYktLiYNdVKDjd4fdzlxqthmcEWahK11Ky4UF7unP9varuMt0SvBRVeQRpaunAVsp0SyCgFPL1WgqpATxnep314dwu1jLTXfX6H01Pt1SVYHynVJXIs2fKWEOpgLt4x6C67OQhraGBQBRsVuhB3PrIUZ0cwj6PdRd7xsa+zjUaIKVmvhfsALP21nrA853yyCOPqHPPPVdHXvHf5Tj//PPDODZCQp15ev++cZ2pgzPw2Z894Wt+rf4Zuw6pXfsn1KOHJ9VDhyZ1VFB+hvwOfP3o5KJ2qNAfU+p3IBsFW85vT1XYwCmWBbR8pjth9SD7wW5wRC6kFsjprtwK4DXTLZmxlEeDJJfBWrBKuQ9Pogcrre9PGQ/mBkrM4XTjnss53eGNDMtl4Nt0+RnKNZ26BH7Ly0sKqZXIdAc1BsuWlxuW6S48S+GNDMP9snNDt0qv5mY67xub09UIGK8CJ63aOd1LEWW6wbbBlC7Xhuo61LrlWL0IPEaB3NtYm50tDkdlXFhEmW77qBzsJ3C8oTTs5Vpame68er1f5DnFvlTL8VXVIlltcbpNyHJ7LS9HoF6+XOtzjn0G6iF+gpu1GCknP7saITV874nZZb03YtoAnOQwBbgk0y3Uuqe7UI2zqu8h59+G63RgfF7v5797+ga1zpZUgL2BiREXbOtXhyYX1OjMsjo4Pq829uE9LZ7UzWsRfGlUPK9OF154oTp27Jiez43/xsV0E7rB65lMOL1qhIQ18xQlM8hUJxOruqfFa++K/WdgU4dRifFZA6mk9TNef/5mHTnE70CGAJv+QKq97O+wMnqrmVjF1CRzDUOrXFl0oQ/Vn6MgDiF+flQ9a4U53bkZ2n7GC6HEynemu8Q5kMyqV+EWpzCfZLlP29hTtlQcJea/ePy4enZ0Tgca5ByHlZ2CAZFzutf+nX4y3XI8bmW6BfXyRGR6B1Y23bBSWbnuUmESBo8entJBnNees0m98cItOjgmmY1qjE2v10LWsCBOt5SY3/30mHr40KS1VsbV0w3nR/QWYFyKEwBtCpQwgy0D0WS63Uar4Tn0ci2tnu6AmW57P7cpc+29YBeltP+7HkaGIUgm1DzTba3PZmW6cd5QUYh97ZmRGd8jomCv/ct9B3QCBK17n7v1CXXqxkKCJAzkWRNq3tNtW2dx/Zw2x76xeYUleai7vcjhtutGLK1k9ZoCpxu3KBIkOPdeNEBqEXxpVDzfKc8//7zasGGD9d+EmI5z5une4zNqMpPVCyT+7WV+rXMG7mNHZnQfNsYuYON5dnRGfeHn03qR2tzXqSYW0tpg6e9KquHejrK/Q4txaKcpvky3ZBxRgltuYxP1chwrFlyviy3OVSGzH5HTnd9wsGng+JJtLZGUlxfUy6sXUgN9jlnd9lFh5Rju69RCWQgYQHxKHKKwhOrKian56emWvlA/6uXFY8Oq7Okuk02PE+s+CjhX3g0YmACVDy8Y7g3t59qrFcoFtCzV64BGGIxhON24n2V9KReYib7fNff77SKAo7O5nmGs28jMR42MVvOKZLrhfCJA4LfEuh6Vy4HTOainTLf9a1G1X1UrkujmbEU1EQIO8w93HdTrGc7N9f+2R52+qdezwywJkuPTi/rvg40GfQs/SZYgTnet7zmZDoL1CIF3p9MNuxO4VcyJbgTOCT7Lz1lZWVWJ9hbdxoKqmnIaIIXgi1l7az3g+U456aSTXP+bEFNZM/M0b4TkxJq89a7YfwaMEdic8COP5Hv75pdXdRkTypOPTOUcOCkxrvQ7TBgbZo3lqZBNQlRcFmf0NHs1rsVxinIGKYxgyUzBUPaSbUMw5fmxWW3kY55qpXFh9nNUMtPtM7Nqn9UNBxpZNPwdZ2ys7DQh2/3Lp0bVY0emQhVSKzcaD06Xnwh3uRnPZTPdZdSj/WAJ2xlWXi5VI9CX8FuZ4Qbunf0nUEqo1IU7ciO4wkIqGiQTUqoEVu6LoLoCWwdSal13Uo/gwci8C7YPWCPVumtcXg5grGun23YP2kXUTMwEoxJH1mgEzKD/4AerisWAfmg/OB2eWvfXVuN0S1Aat1Ot5w8H6Z22gq4RzHG3VyXC1oIOT09n+YrBUgkStKwg+AS7RQuEdXhLsnilP1V4tnDtumNao/A3iridm9P9Ahe71qkbgT0IFQEQAZ6ZqKwBUmQH1FmAzgQCnbF//Md/VLfeeqv1749//ONqYGBAvfSlL1X79+8P8/gICXHmae5zqr3N8/xa+89ItLaqUzf06H6+Tf0d+gOGItYmGDjoiUF2Gwu+ZDHL/Q4p74oz0+1lRjeAkWk5Cz4ydKJc3u4j++wXHJsXYRH75o5++z//yWPqgX0TaveBCXXTnc9UnE9ZKdMtmVWv0V9LvXxhxVItxybpxUkUFXOMBZFqBT8Z/vLH5a5UD2Ne7EdP5eVlRr+Um6FqlTSH1dNtWAmctClIkKhacP+C04d7Qp9PbG9ZKFd5UMh0twZ+hs/Lq5ijVN7P2lQrBXMZF7Y54tLyoOAcSiAvSIl5wZA263mphPOeN6W83IuQmnytluPCgma64dRagZmQ7xFnVSLWEdzPCMp6VdS2J0jEpILNBpwJkDD2SJTAQzRXRqLWmoLNU/z7ESxHH3spp9uuG4E1FzYaRu1BUwMZ7krBDezLfuwAUkygMwbV8lQqt/Hcc8896qtf/ar667/+a7V+/Xr14Q9/OMiPJCTSmacAzvKZm3vzghHe5tc6fwacayg97hjq1h8beju1UBOc7ZPWdaud67tz/X75TEi531HIdMfpdOfH8nicLe23F9Wa0R1x6ZxkTOU6VYqmI3qeyl+X3o42bejj9XKOt9dMt9dy2EJ5eU4Rv5xquVtmECVuMIJEDC688nIpUy3+O+0BDT/q5U7nGZFya4ZqPgDmWl4eVk+3YZluVNxIT2S1fd0wQh88MKn/+5KThlTYINsh2e1yc3PDmO8so8NQfYIst/zM7hiySG6zukWoK6pxYWEga0qQsWHVKNDHiUxcMLW8vNxIUMmCR1kJVu04QMGeEbf3FEdRlSjOMs6PV4fZniCRALj9vvCSZPECbISv3fWMLoHH2vvb58Z1EL9S0D5sSiUaoPUCtvR3WokSN+BY//9ef7b6/y7aqi7aMaDe9uId6lOvP6ti+b3cB7i9622tMIFAZ+zgwYPq1FNP1f/9k5/8RP3+7/++es973qNuuOEG9d///d9hHyMh4cw8RSags90SAfQyv9b5M+zg39MLy2pLf0ov5G5fL/c7wnIunEb4c6Oz6uGDk/pzucgwsJwfD0IgldS73ZAsflTK5WtndWc8R9NbWnLRdPRCeommS6Zbz7Z0MaT89nRDkA/RciiHYsPGz8R8bi/guM/d2pe/B3MRd5S/Vrre/srLV0qKqHkpry2VSbFHyjuTrZGMjMF5MFlhVfrmq1Uwf3Z0VjtYaGk4a3N4vdx+Kw+qzXSDTdAq6Enq7J/0qMOwi+P6Fcbd5Z5pPGdWpttkp1vE1PKjjPxQyGKa97x4mbhgbKa7TFDdar+KQS3e6zhAQZ4FZOXDHsnmrEqUCSASPPLiMNsTJOPzy2t6r70kWSohQXvor+CY8LN6OhM6iF8paB9dYDDjuZ/bLah68vpuLbaGYLuXsnv5fQjamNhmYzqB7r6enh514sQJtWPHDvWLX/xCfeQjH9Gvd3Z2qoWFXDSYkLipZuap15+BxUrUy/3+jrAz3TK2DMehR6O150ZPlRMhsTLdHrKBcBL19/jIzhXGWbXG7nQ7o+libOB7ndH0neu6ypYFw6FzZlAXfTjdMoIOpcEwymCgbR3s1A54v0ehFzhD+H4Y2MgI3Hj70zpTXq1KaykhNb9CS9boF0fPmVQjwHhzi5R3hCCkZnfYTRNSA8hAoEJB+paDIs7pBdv7I5tNjHUKM7PLlaFWo15eHEjqV3c9Nap++/wJa12Kw7Bzlm5C7BDnAIeCebam0oyZblmzoNiMNWN8bkmdNNRV8x5pJ5K99pbpjsHp9tnGI2tqFEEwu8OMHmzYTVMLKzoQjspBLw6zJEiQfUa5NK6/6BpIAqSSQFg5nEH75SPTek1AkAc/M8ye8aDl5fg7xenGFBQv4Hz7CQAvGRzMrgcCnbVXv/rV6l3vepf+ePrpp9VVV12lX9+zZ4/auXNn2MdISGDsvSuTC8t6fi0+e+ld8foz3njh1kC/o9DTXX2m2142jXFmKHOXsWblIrCWkJqHEs7uANk5ifJHPYPUS3m5M5ous4fFyasUTYdTI+91y/Z7LWe2rtXhKX3eYUjAWYFh7zVanlN5PaRn8uJ79Zi6vEprtRF3Mdxnl9JFGX0rwu3RKC+V6bafJzeHKoyRYfI7cN9F5YyGJaYWFNzrogVwyUmDKiq8VB6E5bBBQA33HBwoVG/IHNpa4yzdlCz3hp6OyAOI1VDN2LB6FUfCWvffe8esct8v3v50LOW+TiAEBlZsY8FM6um22n88rrP2DGfYOCsK4Szj9ME+gf6Hl6pESZDgvOP+z43+a9EOOBxRL0mWcjiD9rIuYj0Iu2fc1xplC2ojkIuAG+4ntDt6Ae11YNbjmhG1gn2jEyjTfdNNN6lPfepTusz85ptvVuvWrdOv79q1S11zzTVhHyMhNZ956vdnBPkdYWW615ZNtyDk6Um1s9zoJifdVnm1HyG16NXLnbO6vUbTLUchfx28RNPR1w1HQGf7e/yPqHJeqyeOzuggBgyEczb3qUMTC57H2E3MLauT13ep0dncZtmfatOjjKqNuEvvOmISuD/EQfQrolMo0S026iyBrBI/J4zyclP7uZ1VI9WMDUPQBu0b0KhAj3/kvZ8enO5qHbbRmUX15LEZ7eQiC4jSTThQYc7YDXLvHpkyv58bWEJqVWW6zXxmygUwcb9g3YCY5FBXR+gjooKQyAeasY6WmlIgmidxBAb9CqlF2a7jVlGINXJ8Lq1LuVEC7cVhxrVG8mNmKed4IgGCNQkJEHx/NfeCFbTvT1itHFD8FqcVe83x6ep7xr0i18HeHiBZ7pPWdXkODvrNdMt9QBG1GjrdUCqHeJqTz3zmMwEPgxCzZp4G+Rl+f0ehjLY6p9sZgd1/Yk6P3jl3S7/eWMuNLRMHutLIMHs2ec7ATLeX8vKi+ZQdPUVO99rys9J93TAEnM4SjCcJnpRzutdGy1uUWsr1sKWSbb7H2GWyq5bTjai+11F45YABiGsN5xgbsTjdfvt2SznPEhgp5RC3h6Bebp8/byKiD4CRL9WWliPLHWUJduE6ZiIVUss5UM/o9QW/E0Yz1qU4HCinernpyuXhZLrry5h2jog6PLmoBb6iGBFVTaYbIIDk1rctmW7p/64lpYQuS+FnXGQQpKLQapFbWdV7Dsaxen32keXF9128Y1Bd86Id+rUgSRYvQfuNeQ2K1nxCIYyecT/I3mYvL39mND8qzEM/95p2Mq/l5dZ9UB/rhGkEPmsQTHvb296mx4QdPnxYv/bd735X/c///E+Yx0dIwxLWnG5n2TSir3B2J/NiIuXKpi0hNQ8ZwcLIMP893VFH8uVvL+d0SzQdZWbPjMzo96LfDhu71/Iz6et2ZvsXbY5luc1orWBM7rPMCfc7xg6l4NjkB7shEBieSqvbrO5CeaHXnu7y5eWl7jm/ZY/1NKPbaegELS8fmVlUB8bntdDYhdvDnc0dJCPmrBqpxoE6Z3OvVbKJNcfryKAo1cuP1oFyedFEhIW1wp6VsAIndWJM24OPUn3Tnj/2OMp9ndgd6UyJaxFrT3eZkY5u1EKYUhS1P/N756jr33COeuHOQXX6xl49ecYLMgUEpdVoVcEHAs9hBF3cRHXF4fYqzBtlC4yI6AKMtvVKr0wrcREDLn8fmLm3mk6gpwcl5VdeeaUeG7Z79261tLSkX5+amtLjxAghlZEyvmrLy51jzaRkTbKx5SKwBafbe0/3vA9HoVZCaimPc7olmo6NHMYGnB5sNl57/C0Fd0eG0sqstreW3eCd1wpG/Okbe7QCfpAxdjAuoXiOzV6pymPqqhFTK2TCEqFkujtrUV5uqGFQbU/37v25MWFnbOqNXK1ZevhLBQfhOIjzENRhsztQqY52lcqr2mPUYBwOVIetdBOZnbG55bpwuuWZRwbVT3C0HjPd9uAj2mo29CatdTTMEVFBsTvS8nyU7OmOs7zckEy3s2LwxaesUxfuyFXxoJXGC9LHj8keURxXIWg/q+0GXNewesb9IsEPsXkOTy7kBF7bE77ajaTKEbdiuaRFEMFYspZAT/pf/uVfqq9//evqG9/4hmpvL2z4L3vZy7QTTgipjDUyrMpMtz0Cm1ld1T1k9shlqQhsbl6yd/VyyfL6KYmtVXm5l55uAY71u/7XKbos93dPX68+88ZzPM2nBN0l+tqtzGqFjWjNGLvWVi2ApkIcYxdGxF0yZq5Ot8dMR6kMaaXqCgnQVPNcWCXs7Wb3dPsdGYZsBqo0fr7nqO7bjTrL7SXTbX89aHm5swJka39KOy39Xe2xOFDiWKCCZWR6CRIZ2qE1ZRxVKeC8yb3lt6+72mqFWmMPPmKu88nre6yRaXGU+zpB5ZFUH1V0umNUL/ca3LR6eWsotHdefk9+7FBlpxtr6fP5oNw5W/qNFeYNX0httaif+wXD/krpsWbIPgmbsdGCc6YRaDV66qmn1O/+7u+ueb2/v19NTuYi8IQQr053NjQRkr3HZ7WzgtI2qGE/fXxGre/pcI3AIsItfo2XMlzJ8uqy7NWsp4V9pVZCavnMmJdILYDxDgPtlPXdvvqeu0QAy5np9uh012KMXRgRdzdxFb+9XPaRYXYhoUoBCongh1Fe3mloeblVNeIjGykjAR8+OKkOji+o9rYWdcvuQ7qXP0pjr1K5v7yO+y1oxm7NyKCeDv0h1Lxf0lY5cyRfWo454vUAAmYIjKKve1Nf4RyakskMC6dGh13XIIwRUdWC40Ff90q2UAniRKrS4ujp9iukFodq9Tlb+tS/PXRYHZlaVGOzS9qWKVdajuDY1oFOvQeaLMwbRabbms8dQMcFay5sGNiMwxWKBKheXh2BdshNmzapZ555Zs3r6Oc+5ZRTqjwkQpqDMEYjOSOwen7kyqqOWOIzeptKRWBl3napeclOuvQ8a+VLdVlK591EZGotpGZH5tiK8JBXcA7cMt2VSqZrPcauWidMysvtkW/p/fNqlIuzBnvTbnTOexVSC6G8HMJKJiLtHCgv99JHZx8JiOoRGHpwAh87PF31iLhKVCr3X8rkznWyime8FtUbQdXLj00v1kVpuSDVARgn2MiZbtPKfd0QZ1oy2k7k9agrwSoFRU1VrUZwUoLilUrMZQ08pwbZZimBD7NnPHA1TnpVX8P943O+RdSEXh/tTnFUPDQSgcLG7373u9UHP/hB9a1vfUtH844cOaLuuece9dGPflR9+tOfDv8oCWnkTHdI4kBwtN7xOydrpwbZbjgvV523qaQDNi+CVh3u85KdYGNBdhI/Hx9eSi0lkl/t/F6vToyX8nIRmwP2ckQvlBKT85tZrcUYu2qQjKJdBdl3ebntmqOqQrKgErCoNDKsGoFBuR5etAriQFo1pI+u3HHaRcYwIu6hg1P6ed0+1KXPYdQqzV7Ly6txHGpRvRFIvTy9qo5M1ody+RoxNZ+l+PVYNupUvMbIprBGRIXpdJcSAJT2K5TH1xr7c11qpJkJGU6UmOPaosT8lWcMu74H68SzeRExTG1pBuzVOPtPzOuqRWgbrAuQ5fczNizKee3NQCCL5BOf+IRaXV1VV1xxhZqfn9el5h0dHepjH/uYete73hX+URLSgIQ1MswOnGG7I4m5z6VYkH7u9jZfTid+h1cBqJUaZbrFgcO5hKNfqcx1OmimW9TLHX+/BDD89BDXYoxdUHpde7r9lZfj2OCI4ZrAeUHruheH2Mtc6ErYhe1MRPro4HDD0CnndNtFxuBIwX5HO0XuXqx+RJzn3s8SQZDCfOfWhnGgJLC0uJJRx+st0y1jwwL2dNdbBsuUcl832ipkuqHBYn9fLbFXIuH4KgXN4prPfLatxPzE7JJa51Ji/vjRaf13bOzrUBt6vbdU1DNWG1ZmVbcVghdsKG6z8C+cmvYcnDN1b21IpxsX9c///M+1k40y89nZWXX22Werv/3bv1Unn3yyOnbsWPhHSkiDEYZKsxNxhod7O9TIzJLu/SwVxZa+ZC8iagJEekZn1vY0lyJdo55ubAD4E1GZCkemt4IDELS8vFSmGyrHJgt3Bc10F/d0+ysvz723VaUzmaKstVVeXuJcifGH7/GSgXFjoQ7GmkA1FveqfpYgPl9JZKw/oaanc/dtt3bSc+cFGWE4qFGJjFXKdBdmdLc0jAMlM3CxnuDvg1O0oUw/qUn0pdZWqXjBCqrlp2rUE1EFH8PKdFdWL49PSE2e7UoTRuJSrYZDiGuLQBxKzF/hku3ec2SqSHitGZDrgDVK/n606AShkOn2rl7OTHcwfFnCGA32yU9+Ul166aVaqfynP/2pdrb37NmjzjjjDPWlL31JffjDHw54KIQ0F7LplYqCV+N0o68HzguM+tHZ3Eg/J5V6a8v2onrs6U6HUHrqBThmYih76eu2nO58/6NXJEAhYnJrhNTygm6N4nTjHpEWAenp9hPhdpsFWzhX5cvLYUwELTGv9DvqaWyYXWTMrdogapExz+XlIWXATOiXxHpl/7WYE2xC5tRPebmscV7AMy6PWr1luk2motMtlWAx3FtSieS1qijO9gNLxdylrxtOoGR6424nqCUIksh9M5HXbwjSz23v6Z71EKirxbz2RsbXWUO/9t/8zd+onTt3queff179wR/8gXrPe96jbrzxRvX//t//06/92Z/9WXRHS0gDIcYsNt5SPV9+kcxkX2eb2jaY60E8OD7v+l7prZU5jV6Q93ouL7eEYqJfoMXhXVxerWhgynnyX16e25wkoy4sLJufWfUDstByyaSqQRw+mS/vbxZ97ntxn8umXdLptt0rQVsvvI5wqwen2y4yZmUZrCxH9CJjBVEx92CWBEbCyHSbAoJ49kxOvSiXF5eXe698sAe3otbfaCa8CqlFXQlWcTKBh+BmnKrVKDHHqTw8uai1Lew8dWxGn8cNPUld4dcs5PbSjC65RyvJ5r4Oq0w8aKbbS7UU1curw9eT/sMf/lB95zvfUT/60Y/UL37xC5XJZNTKyop6+OGH1Vve8haVqMOyJELiwq4SW41olB0x4LH4bh/s0v+NEnM3CmW+bQEy3R7Lyy2hmOgNcq+ZbmwscJoRJRZBK6/g7xBHzp7t9zoyrJ6cjp4OEWRKBxJSs79XHG30yAqlzlXO4alOwdz0Od1+ZnXbVZqPTS9pkUTcu7VSabYCJxWF1FobzKBdsQzajf31Y8yLpgfWJM/joPLPp87w10lGvx7AyDCwWmJCgWTAEzEFrNwqkUpRqHSq/ZoKe+bk9d2uKuaP2lTLg7Qi1SPI+P/FrY+re58/oR48MKl27Z9QDx2cDDzFwppWUqGCEUHeehRcNAlfZ+3QoUPqkksu0f997rnnavE0lJM3y41OSJjAcJZHJ4yxYXZnGFk0qBuDAyUy3eKY+OvpzpcdexZSq90YGsmcOsd5lVIuR0YoyNol2X4ZuRZnv1st+7qtCLePnm5rLF7+PpBADzbscoGYgt6B9znWApxSySAZXV7uI4CFssn3X36q6u9s02sFxL3CHBHnJXCC81peSK2loQza/3mmYND+ePfhSMeyhQmeLTGIvfZ1y/NJQzpcxJmWMvJSmiftMWW6nZVI5YJQcTtbbiXm2B+ePj7TVKXl9vGRSIJgn8Z+CQ2foOMje/MBdgRyy42wxD0gX24kW6eW+KpFQGY7mSzI0be1tameHvPEKwipB+DwwSmZD1FMTRwkOMeS8cCcWWxOTuGLuQDl5eKgexktYTfUa9GzJg5WpUx3UOXy4mz/clGmux7KmYM63agMsPd8+gmgODMpXgM90muYXvFfXi7XAvEUk50Ir+XlwrbBLnXRjkF9Dv/kZTv1810LkTFLvbykkJq0HZh7rv0atChhxbqIdQsBnH1jc/r1qAMcYe0raC8anV3Wa50XC02ez3qZ0V0vyL5XMtNdw0qwasRc7V+Pa01FJvvfHj6iJ7Lg+USFD3q5UU031N2uttTJdIFqsI+PRMvRU8dn1HQmqzraWtTZm3vVc2PzgcZHig2IWDUC47I3lboPsNTHoUPQdE43IiBvf/vbdYYbLC4uqj/90z9V3d3FvWS33HJLuEdJSIOStI19qBY8n2LAY9GEU4kPCOpg/q1T3TVIebksxvK9RvV02+ZWes10B8HKdNvOQRBRuvpxutOBjS5nz6DXMvxcgChdMQNTdlxYm7f583HR49Ppnphf1n/PjnVd6sIdg6rWhjmMWxh9TmNuqUZiibU2aPcen1HLK1ndBnDaxt7I56GHCQIy2uleXFFeRNflOaMicbi0tnjs6Y65vLxSpZ3sqXjGK43jjFTFfH23enZ0TpeUv/z0DVZWF7O5TV7rw8I+PhJ/rwRrcG7Q3ht0fCSuKQLhsGOQUCnpdNuUy5vhfEeBr6fn2muvVcPDw6q/v19/vO1tb1Nbtmyx/i0fhBBvdEj5bQiZbjg20kMtjuGOMiXm4pz4E1LzqV4ume4aGBXiyImoWSlE1dc+z9wPVl973llCsKMRy8ulpxubsH0slB+Hw2nUWb3WFTPdwcfpFYTazM7ayXM368PpBoMy8NwQ7YlGKS9fa9Dm/u6ujpyBaTdoTafP56xueWaY6Q4XyQbKPG4nKzHO6QZetTPiLi13KzGHbfHksVxp+TlbmsPvsMZH5vdPqS6SKSx4HV8PMj6yMKu79PdSubzGme5vf/vbIfxKQojTuQjD6RaVaUSjZTGG042osJuCeZDsrAiPweH0MkNZggC1yHR3eiwvDzqj23kO5PzBIJFERiOVl9s3YWtUlc+/z6l87fWe85qBcUOuv5/e83ooLz8xm3O6UVZZS+AQwCfAPY573RlYEke83oXU7PPQ7SPQuvNBtqjnoUc2NmygspnHnu6oR4apskJqcamXey8vNyOojBLznzx0WD1+ZFr9y30H1OjMkp7Ssn0oN6ml0bGPj4Ti+NaBlLZHhvLlLNWMj8R+j77wckHgILoupJhoBnsSQnyJFIVRXm4vLRdnWDajgxMLRU4ySinFOZHMrR9HAYfrZoCXynTXQiimq71GPd0OZ0n6IeFz1HuJbamebvkb/WY6pFzVynSnVzz1dFejXl4PM7qdrRpeAliT+VmsgzV2urX2RFurznK4XQ95DX2FjWTQoke0s61VretJ1mQeeiRjw3SAoPLxsqc7GqRaQjLacU73qGadlQxn3JluaCs8OzKrDowvqPv3jevyfezDe45MG6+1EAYyPhIiaqd29Oiy8PW9nUXjIyGuGWR8pCWc6iHT7WeCCSmGZ46QGEmGmOm2i6gJWwZS2hmE4yRGO5i3OabirHoB2SzZeCtl6ODYSwa4JuXleSdrcdljpjtfklVtpns+70giy91IfU4F9fJCT7dfo2tteXm+9LvCPedUPQ/U02141UF3PtiFZ6RSoAiMW+Xlwe7barCuo8v1KPR017c5YZ+HDgMWBu1wX6d2nGoxDz1M+lJtRWud90y32c9M3QqpVch0xxWs9brOmtA+JSKHCJpjPYKdg88n5pYDq3bXG/bxkWiFgYOMeyiM8ZE9tv3e9IqHeqa+d0lC6pxqymhLZrptGT4YwuhFdPZ1y1gtOD9+F2jJUko5eylkHEqtnG7ZCMo5MFDhlvLQ6tTLC33tVg9xg21EvfkS1aLy8rag5eWrxfddheqKap6LelGSt898L5ddECbm4unprqQ90Sg93VEatHGVl3sdGSYCSezpDhe5V0plumWkZtzq5ab3dNtFDpHRxnEjwA1b5JzNvfp1iBzifY0O/n5MUThvW78eG4nsfxjjI7v99HQzOBcY8+ukCGlgymWQgma6ncqTmNeNMRtwui/YPuAQtPK/ieLnT8ynK4qpSelcrcYJiQNTTlk9V26Zy0DYgxPVzOm2MquGlzP7RSomcB2nF1YClZU5jTqvDnGyCcrLAZSxcbyVZnWjTUPu3Vr3dNt7+cs53fWe6bYbtDDw4Wijhxv3PAxaONz1UsIqQmowoEuNq7LDnu6ohdQqqJfH1NPtbP8phazbfjU9ohA5bG9L6Cos7EkIQLa0tgZW7a5XsA5higL+XjzjOB/Vjo/EmEFQrqe7cB/U/1ofF3S6CWmQ8nLJPNvLy0VM7Z5nTxRluuW9fvq515ZXl3e6ZQYpDI9alF1b5eVlMt12EbWgx+TMdHsdg1VvwPFFZgNZjrHZpep6uvNBJQmIVOrpDqO8vB6uh9YHmF2u2KohyuU4/5XOXbSzutc+W3KNknXe0x2lQVtrejvacuJ3q5UrkgB7uqOhtYLTnTFkZJjbc21Sptspcgib5ujUom6fqzeRwzDvrTADDNa0kjLnMO77oBGg001Io5WXuzjdAD2JyJghI+VV0Kq8kFj5jXq5xqVz4mRhY4Ax4/Z7qxVRs2e6UWqF32NCv1tUIPqNeb8FpzsRzKjLnyNLWdxjpjvIyDCvv8MEJEBWaWzYxFxeRA2ZnRh0A8qtU4Xy8sYxxMI2aOM4frSHTM0vqxkPTjcz3fFkutNWpjteIbVK62zce5xT5BCB7xfYns96Ejk0lR5fmW7z91ZTaZxdkpAmLy+XzKvT6YbwEspY8SuOTi7m3ps3xLoDZbq9jTqSPrZa9QnaM5ulst2TITjd6GcSvwfZ/nrKrAbdiMfy46r8Z7rzRl3+/l7wmukOo6e7DsrLJYBT6VlCzyIY6q69iFq56wGBsYKQWv1kgpsBEVPz4nRLUIwZrIhGhpUo8beqwRKmjwzL9/LGtMc5RQ7t1JvIYT0EgJ3nWFjkyLCqodNNSKOol+fLgpzl5ciMSbZbSsyDzOgWOpOtOmP8xLEZ9dzobEnxkvRKbaP4yO6I0VhKTE3Ky6XnMejvEcV3nEdrI2pAp1vE1OS8Be3plvJVr6X4zSCkBrqT3jLdk6JcHkM/d1F5uSM4aDfWRWyNmCWmNusr023+M1NPJFoq9XTXh5CaleGMqay4kUQOTUXsRtyqpXRxrNGh7OkODGsxCImRasponcxame61htO2oS71+NEZdXAi53QHLS/HWI6bdx1Su/dPqseOTKkHD0zoCDQ2RKfIkBgUtRRYQhAB51IyquV6uqsBJfYQv0KGspoAhuk4AzhBy8th1MOwE3G9VJQ93VYpZGvdnN9y4n/F48KSRhnn1vVpyc2pJwbO6q4Q0AHs6Y6GRL76Y8UmKipA4E4nFFvMHxlmQi9vo4gcmgoCP7AHsRchCOysmCyqeGBwLjB0uglpgPJylAOJmrbTUQKlMt1+hNRkTuahyXl93Oj3HUgl1aOHptThiYU14yrEwaplvxqym5MqXTLTHUZPt4jJjebP41IdZVaDlpcLQcvLYVzK+CLcDpV+Tkc16uUeZ4GbgBg2lUaGxTkurFxwUK4PstyNNKO+EZBqHn+ZbkZOIsl0u5Tr2h3xuDLdXtfZuHu6G0nk0GRgO8Kmwbnd2FfuPuA6ERSeOUIaoLwcxrCMH3GLUG4dSOk+5Mn5tM72ioPuNdNtn5N5yrpunb3Gr4NThky325xMiLaB9hoacuJoVcp0D3RVn+kGyHQ3qnq5fYxI4Ey3Lf2Je0/OUyUHLWh5OYJPVrl/HVQeyLNaafzeeF5ILY5xYWUz3SKiRiPM2GfXU0+3XEc63TUTUrM74qaPDFsyxOm2ixxi/Ck+0+EODxGiK9XuJHsrhdSCQ6ebkBipJqNnRxZJ/Dy3cm5slpv6OvV/HxyftzLdbqXoXuZk2p1qOFD2OZmCfL2WmW5xtNwy3SuZwkiRanq6i8emZQqZ1QAzz+ulp1vw28uFe0PucQl4eAn0BK0AgfMgtmw9BEG8CKkhuyD382BMQmql1imr7JS15cYh1TxexihZFQssG41GSG21dKYb8ce4ErWyziJgj/3R5PJyUkMxtRJrhrShdPI+CAyfIEIaoLy8MC6stKOxfSg30/LQxLyaz/d0pzyWl1tzMpMJq/8Mh5zJ923rXup08ZxMMTRq2tPdXtrpns4fG4IA4jQHxT6rW35XIxqsa3u6WwPf4yIG5iUDHbQCRCoccI1red9Ve36hD1BKMVaUy3HPxnWPFZzujHumm0aYcUhgEZnuUveWU4Ge17F2TrdUl2Otiqs1w95LLu1gJqqXk1qPDcsFyO2gitEKvvA+CIz5VgkhDUxY5eXWCDCX0nK3vm6rvNzj4mmfk5lobbWMM8mYu83JlEBCWw1FYsTpXnQpL7eLqFVr5EhwA+exnkZU+cU59zSI0ycOm5SXe7nn7MGocg6Dk4U6uxYSvMGfWEpMTZzuuJTLQTKRcA0O0uk2P9ONjOpiPkPlRpECPYMnkTjd0vplRxzxuErL9e9OtFqVaKVsELuzxV7exkaCwG7VMfa1n5nu4NDpJiRGqhmN5FZe7iaiJmwfzDndB8cXLCPAq3PinJNpL68uNSdTyudqmXGU0mW3THdYyuW53yP9kiuWQVIP5cx+6U62WTPJA2e689dfzr+Xe06eCzij5TIwpZXLE3VjlMt9U6rEXIIVcfVzl1cvz6zp3SdmgHVXqkpExNANWb/ge9WyFaiZnO7Vck53zPPtCyKJJcZE2Z55PufN29MtpeVYI+KaK98I8MwRYsCGhw241LxrX+XlZcrFN/R26Ei1ONxYN706Uc45mbAlMPLkxOxSyTmZhZFhNezpbq+V0537Pfj7hUZ0unE97YGcIPM5rfJyy+mu3NJgN+78tF5IeXk9XYuefNVEKfGawriwePq5yzndLDesDzE1Wfsq9XNTgb52mW55Le5AR6WxpVLJhX2czlZj09ORn3jgkum2BEopmlkVdLoJiZGgzoUTUT8uV14Og0qy3ZKt9WNkyZzM87b1a4MBC/PEfFrPyXSOCyseGVbbOd2l1MstpzsE56U77ziKI4ngRaOqqBY53VWUl0/lnUcv5eW4L5P5YI2fKhCr1L+ODAN5ZkuVl8c9Lsx+DZcca5TVC8zMh9mzusuIqUmGk/3c4SN7HwLUxjrdFWZ1U0St+Xq6UcFXam9tRO2aWsI53YTEHAkXvxebW9CyWIlMlisvB9sGU2rX/gmtLA6nBtl1P86izMl89MiUuunOZ7Sh9vErz3AVx7JGhsXQ013O6e5zKHIHoSufnRRbql7KmYNmYjHfHIbj/hNzvueiJmUsTcZfSwPuLZQv+3O6C8J+9eZ0l1KZlp5uE8rLpcRQYE+32WA/wJzuhw9OqvU9Ha7PbiHTXT+BqnpBYlH2mdzOkWFxZ48rtbiZMqOb1FDYc2lFtw3akzLs6w+Hulllx8fH1Vvf+lbV19enBgYG1Dvf+U41Oztb9nsWFxfVddddp9atW6d6enrU1VdfrY4fP170ng984APqkksuUR0dHerCCy+M+K8gxC2jV31fN9SPK6mXP3Z4Sv300WPa6X7wwKT61d5R9Re3Pq5f9wOMtvO39qud67v1SKlRW4m1nTh6usuql4dYXt7tKJGup3JmP+DeuOupUeueuf7f9/i+Z5wZND9Ot9/not56uu2GzrzLrG4YPqL6HquQWglhOzrd5oJn9Od7jqmHj86qf7nvQMlnl8rl0QHRUSBTPtz2RylBN3VsKZXLmwfZi1CEITalwEx3kzndcLj37Nmjbr/9dvWf//mf6le/+pV6z3veU/Z7PvzhD6v/+I//UD/84Q/V3XffrY4cOaLe/OY3r3nfO97xDvVHf/RHER49IaWRTHBV5eXWyDD3TDcMrS/fsVcdGJ/TBjQEM7DAPnoo97pfxxvBgi39ubnfhycXXN9jzemuodPdmZ+VXa6neyCE8nL0NdltpUac0S33zNHJBX3PDHS3q4FU0vc948ygeQ1QFBSz3cuuG8XpFn0At55uvIYKASQcBkIIFgVFAoPwt+39qZbTzfJyI5/dQxMLKtnaogONpZ5dZrqjI5HPFEpWu9TIsDixWkcqZLpZCdH4IABk7UeOyiupImNPd3XUhaX4xBNPqNtuu01985vfVJdddpn6nd/5HfWVr3xFff/739eOtBtTU1Pq7//+79UXv/hFdfnll+ts9re//W31m9/8Rt17773W+7785S/rbPgpp5xSw7+IkHAVzMXpdisvRwn5zbsP6TLV0zf2anEdOM1w0KFIjtdv2X3Yt5DbloHc3O8jJZxuMc7bW2MYGZZeLfp7VjKFGeIyv7Ya5Pw5f2+jYL9ntg916WqF9tZW3fPl955xZrplU/f6faWMQTdkVFw9XQ/po5Oxf3Ym5gotEXGWodqdavv1kEAhDXJzsD+7J6/v1s8u1uJSzy57dqMjkSgjpCaaJzEHrKRHt1J5OZ/xZlMwT7tqP3BGdxM43ffcc48uKb/00kut1171qlep1tZW9dvf/tb1e3bt2qXS6bR+n3DmmWeqHTt26J9HiHGZ7oBON8o9Z8tkuvedmNMK45v7U0XOIowx/Buv7x2Z0e8L0+mOI9Ntd7ZEbdMuJISsgow7qxa789hoG5H9npG/Te5Tv/dMMmimu4ry8rrq6c63KriNDJvIl5YPdceX5ZaWEjdhu6X8+aYIl6HPri1wBU0Gt2fXrl5OwqWt3MiwrCFCalbrSPmRYY22xxF/s7ol083gSxMIqR07dkwNDw8XvdbW1qaGhob010p9TzKZ1M66nY0bN5b8Hq8sLS3pD2F6elp/Xl1Fdq26ectRgGOCY2bisTU7uCbIBOP6LKZXAl0j9ILKpp5qa1nzM6YWlrVxnOpr1fWhm/s7dbnq+p6k/jeUno9PZ/T7/Pz+zf0d+vuPTi2q5fTKGud6GU5vNquwp9fq3oP9gvMJh39+aUV15g2KibklfSzIGOJc2/tSgz4zWoU7/3NSba0N9XzZ75mulla1bbAz1wsvf6+PewblrZbinN60196jbmgB8mxWH4e8v9J10X3R2azq0CKB9XE9uvCHZrNqZjG95pjHZhf111BaHvffgyDd8sqKWsR6k2orZD+yWX2tuMeY9+xmFYQylcpksuqpYzPq9OGeNc/uQjr3zMDnjvseaziw7mWzej+yn1v8N6qv8Mzo8Zsxnne9RWazWnzU7TgW6nBNDQptZQSBc3aNFk+1Xe9FuQ9isHVW68CH8XpssTrdn/jEJ9TnP//5iqXlpnHDDTeoz3zmM2teHx0d1eJtJt4MKLfXC3wNxzeRyujNd3lRLSwqdXz0hNrY7i5KVo4Tc2m1sLCgF8PxE2Nrvp6eW1RqNaMmZuasLO+mrlaVTS8rtDlrwYzVjErPTauRkVxmzQvaeV1ZUnPpVfX480fUpr5ioafJqRm1sLCkpqcm1UiH959bLavpRbWwlFEHjx5XK30d+rV9x+b0OWrtzKqRkZFQnpnM0rz+mWBxfkaNjDROJsB5zwzi0mZwn+VKzvzcM3Mzs9Z5AjOT42rBQ3ZnaSF3zY6PjauR7hVP12VsYlof4/zMlBoZ8f8sxcHC7LL+O8cyy2vuzf1HT+Tu23TS030bJemlBbWwsKKOjoyq1qXcczWun/FlNTM9qTralrnHGPbsdiVb1bbehDo0nVGTsxn16PKy2tSbxINkPbujJyb0PbY41x77PdZoTC2s6HObXm4pOrd6HZueUQuLaTU/l4j1vC/M4RleUGPjk2pkZO2aOmLdH7MNf3/QVlZqNW/XHB45oUb6C47kyPhk7j6YxbpR2+qM1TrwYWZmZsx3uj/60Y+qt7/97WXfg17rTZs2rXnYV1ZWtKI5vuYGXl9eXlaTk5NF2W6ol5f6Hq988pOfVB/5yEeKMt3bt29XGzZs0OrqpoEbFmVlOD5Tb9hmBdemv+eEmlxdVd29/Wp4eJ3vnzE3NqdSqQm1rie5piIErF+fVefsnVWPHp5S6/qQ5S4smFjEDs3MqvO2DamLT9vue9b0CzbPq+dG59RSW5caHh4q+lpH14xKLbWq4fXr1PBwr6oV6wamVGZ6SXX3Darh4R792pOToyqVmlNbN/S7nqMgz8zG9Wl1YCaXwd20fkgND69XjUKY98ymdIdKpeatbOmWTRs9HcO6I2mVmlxVXb191jWrdF0SHeMqtdqmtm7aoIbz7Q+mk+pLq1RqUmVRfbJ+Q9H5zDyNZzujTt6yQQ0PD8Z6nIN9U2pZLane/sJzleyYVKlUQm3esF51Z+e5xxj27A71dqpB1aL6+9rU0yOzulT4ydFFdfmZG6xntxPPWSqtNqwb9LQ2Eu90LuDcjuvKMvu5xTrWuX9apTqX1NBAYX2Lg+GpFpU6uqw6u3tcj6Pj4LJKpVbUxvW4PzaoRoa2slJbJ1vUY6NplejsLrofks8v6r1oo7Z1/NupjX5dOjtzwsJGO904gfioxEte8hLtPKNPG4Jo4M4779QXAsJqbuB97e3t6o477tCjwsBTTz2lDhw4oH9eNWC8GD6c4GYw9YbADWvy8TUz6KlqacmqtJ6Z7f/6LKDXpqVF9XS2u34/Xrr6ku3q8OSiemZ0Tvf0oecV5WRHpxbUUHeHuvqSbaotQE8fRLaeG5tXx6aX1vxuLRSDkWhtiZred7pnvWVZLa0Uzuc0RKpaWtRgd4fnY6n0zPR0tOufCbo63M99vRLmPdOJnuX8eUp1eL8XOttz34f7yP495a6L7jvTugX1cz16O5PW+VnMZFWP7ZxOorKgpUUN9XTG/vd06OuxrNXU5ViW0dbS0qKvccsy9xjTnt1nx+bUYEeLGuztVCcNdalHDk1rcS+sjRMLK2pdd1IdnVxSJ+aW1eQ8qlhwDePtMW4k2vEst7SoXGi2+NzmtscW1Z6o7f5Yap1NO9ZZAc87vp5KtsW+BtWCZreVe9FG1tKihT3t50DuA6z1cZybFsOvi9fjqoue7rPOOku99rWvVe9+97vV17/+dS2Q9r73vU+95S1vUVu2bNHvOXz4sLriiivUd77zHfWiF71I9ff361neyEij9xsZ6Pe///3a4X7xi19s/exnnnlGz/tGnzdKJx566CH9+tlnn617wgmJGjeBokDjwsqIR527tV994IrTtKotRHaOT6+qjvZWdf62AfXmi7fqrwdBxNTcxoatrMYzTkjGRdnHhsm4MPR0hwWcUPQ9oV/vxOyS7qtvJIM1rHvGLrzSBQPPI9b8eo+j9NAjCcOx3tTLcc9AlG9+OaN1CKxZqauY0Z27b4e64t+L7LO6haW8uA6+tlq7DhLi9dnddVDtOTShppbntBDWFWcNq8xqTlDtcz99XFee7No/oaYXVtSx6UX1+NFpdfXF2wLvB6QY+wxuCKe1Ktu/8zosbfn9Py4qTYmw1MvraE0lwenN20jOEZaLdTiO00TqwukG//zP/6wdbTjWiCgge41xXwIccWSy5+dzZYzgxhtvtN4L4bMrr7xSfe1rXyv6ue9617v0DG/hoosu0p+ff/55tXPnzpr8baS5qXZO9xwELmyjh0oBQ+rszX1atRbKlBgNsXNdd1WOojjdx6YW1zidhZEoLbE73XCOw5rRDTDn9gf3H9QGK4wnjOG557kTDWewhnHP2JWtvY4LCzIybNH2vnpTWO3OO90wdIZtgSLY5VA37ssLl8WJnFMJDqLNQNYsXCvz1EyaGzy7Z27sUbv3HlTt3X2qP5XUz+58OqP+6qdPqPuen9ABQwQi8Vz3d7bpOd6HJxa0w95I65gRTvdqVtn9FRkjZox6eYl1liPlmgsJ+jqdbplUQae7OuLfyT2CbPX3vve9kl+Hg+xUJEaN/U033aQ/SvHLX/4y1OMkxC9WRi9gpns2P9+3Oz96qBxwlk7ZkOvHDAOUJ8IYx8Y8OrukNvYV+lrSMTnd4thp1VVHplsrcIfgcH/5jr16VBoMFhhNcOYb1WCt9p6xjyLq9OF0Iwvn57lA6bv+He2tdVdxgIDZ6Oxy0azu8fy4MNxb9p56U4xzOA2y5XYk6HSbCJ6DHYOdanh4wCp/xNQFvI5bCgY2LmFLvj1p62C7rmrBHG8E2+rtOTKNRMvazHbh37nPbXG3jVRyuulsNRUyUhZOtz2RwuBLONRXOoCQBqQ9rPJylxndUQNjbctAp2uJuczpbq+xUSGlxTJXEmXHMnOyWqcbmxDKrZHZPnl9tzXrHFmkU4d79OswWN3msjYrRZluH6VpKGW330eVkPK3eiotF7qSa7MLE3M5p3vQgNJyN+PcXoHAOd31A6pWnh+bUxdsG1CD3cmirKzbHG8SHDgsEreQzLbTCUePfZwkE4myc7qlggjBTNL4IBCHWBECqqiKEWS9Z6a7OvgUERIzVkYvaHl53lCXsqBaIyXmyPwKqDqxyudqbFSkHOXl03mHG8ENP+XNbsAQRSYIhqnd0cAlpMHqTltLiy7vR9/7tJ5FnfVVAeK1vFyudz0aBfLsyrMMEMABQzbHKE6c10OcbzjjJmTiiTcQgEQvPoK0pw33qI19Hbq8XDRBoFWBr0ugkoRTYu5c99DjHUdQulRws1Km216xRBr7fpXg+Gx+DcC9W3C66TY2RXk5IY1KtUJqkh3r7kgY43Tbo/oSVKgVUsKMHllnaXm1zoEYrKn+hM5gwOGAzSSGFQxWCI7RYC2U4t+865DV+45qiAPj85563yWokfZZXl6PmW6pUhF9BiAiavZsZJxY1yMfHJT1ilnu+gIONhwtPC9oazhpXXfR1/E6vo73kerB3oBWK2emWzRP7H3fcVAuuAlnS6tW09lqKrAuzGmNEexBnUX3BoMv1cGQBSGGlJd7zeiZVF4OtlpO96Klq2AvCa61010oL8+ErlxuN1jhwJ+3rV+duwXOY+4a0mBd2/uOecGIjufEmnK973gdX/dUzpzxmemuspohDiRgZi8vl57uwZDE/6pFjC1xti2nu8bPN6kOiKmhFQaj/5w6OPg3Xj9tuFe/j1SPCKWtZt0z3aaolyMw4MzGL64UyovpbDUPUnklyQOxpWCrxh0kqne4WxJSx0Jq2CQRkYzT6d7Q06EXYwQNxmaXi0TUsD7XepEuCKk5Mt0hOC9Og7W1JdcHCWiwuve+43yhAgDnqTfV5rn3vT1geXk9ZrrFyJm3CamZ1tNdUJPPFPWAMtNdf33GqDRB2wJaZVBCiioUfMa/8TpGAlJELbzzDUpluk1RL3cLcMpIQDpbzYVUuUgQWIIv9di6ZRp0ugmp45FhELqQAHq3B/XyqIwK9DjbS8whXhZHltttZFiYyuU0WP33vsPZFhVfGJhee98rjbJxsrjcOEJqqBQRLQJjerodI9xEqJBOd/3O8UalzuTCsto3Nqc/n79toOGmL8SNONWZvJO9JtMdc083jk/8fmeAk85Wc9LTkZ/Vnd+DJPhSb6M4TYRNO4TEjF/nws583khHdjfOsh8omKNXF073BdsHYp1Bane6kX2eypfphlFebjdYkcmFY4kebpScw2BFhogGa3HvO9g80Kl7lDGWyGvvu728HNexUj++GIipZGvdC6lN5O9ZnINqxf9Cr8iRnu78Zxpi9QnWKYwFQ+ALzyGyW6jkYYY7XCTgKE62aT3dWFdROo790mmD0Nlq3p5uMJPfj6hcHh50ugmpYyE1S0QtZsNc+rplbJj8LW0xZLrFSYGNg81CMoaYdxwWNFj9iTWt7+nUH4KX3nepksB1RLtCsq28cSrCefVYAic93ahcQcn9xFxeRK0raYwyuDM4SCG1+gcO9ikbeuI+jIYmkc9kZ1aL93dJfEulW9zPtna6HdV2zHQ3JxIEnnX0dDPAWj10ugmJGdl0kR1Gb52fyPdcvgdUIpNxscUhpiaZ7jgMCjhryLDjGODcTeazhmGUl9uhwVq59x2iaad29BQ5jtL7jsqAcmJNdoEuGIOVypil3Lkey8u78+XlMhu1MC7MDBG1cnO6KaRGSGlEKM3ZPSZ7ZNyZ7nLVdsx0NydrerrF6a7DvdU06q8Oj5AGw9737DfbLYui9ITGxXBvh55VjWj5xHw61p5uKV+W8zObD0yE7XSTaHvf8TWvVSDIDqO1AbPAx2aXPM8CNwX8rVKhgRJzCRSZMi7M7nQ753Szp5uQ0kBsE6w4M91WC1arcQE1gc5Wk2e6HeXlzHRXDzPdhMSMFjJpbVHYg7HpicPoBekBlUUyLlBGvqmvUx2eXNTOjxgacY1DQbYTfYrHpxetjLspvbHNQhi973DooJJtH0HnOgt89yH1X48f19luZIl/8+wJT7PATQLTB1Aij2e6MC7MHKe7VHk5DTFCPAipOdXL8e/W+EeGFc/qLkxPyP2bz3gzIpWTcLoRwJbgSz22bpkGnW5CDDFo4TD4VTCfW453RredrYMp7XSjrxsOOGiPKYovgYtjeacbWW5TemObiWp737Wjt5SxyhxLzQKHo43AT2+iVc+1Rln74YmFulJi7ulIqNGZnKFj2rgwu9MtbTAyMozzewkpTWsJpxv/boHTbUB5OYKh5TLddLaai55km4K5hHYn2JiL+fuik+rlVRN/XQshpGSk2bOQWl6IKU622MaGSSldnJlucGxqMVTlchK89x2q9vjsRx1Z2hPEwSs9C7xb90YisNKXSnqeBW4SEjiDTsN4XkjNlHFha3rsV1atQAjLywkJmOmOSWy0tP3hFFKjs9WMYI8WcV7YmEvs6Q6N+J92QkjgsWGmlJcXi6ktaLXpWHu68063lJf3h6hcTmqHZFGXV7JlZ4HDfpWJPH5mgZtEd16X4cTckjVjftAgITUENSRegnVKqnLodBNS/rkplem2f91MITUKaDUrhSAwM91hQqebEAOQTU+cVa+ISJgJ5eWb+ju1UY5jOjG7HOs4lE4rSksRtUZ4LtwqQKxZ4MmESueNRcR4pKMBr+Pr5WaBm4QEzg6OL1jl5iaVbss8X7CUKcz0pXo5If6cblTfZA0bGQaY6SbO/QgjVyX4wjaD6qHTTYgBdAQsLzcp042s9nBvrpf7wPh8rMqszrFRVC6vT0S93C0YZZ8FvpAvdc4ZBS2eZ4GbRFe+RQSVImDAoH5ut4yYNTKMfX6EVHS6pZwcpG1K5kZkuq02HvdMN52t5kNa8jBxxBLUy/f+k+DwDBJSp+XliJZD7diUTDfYMpBzug9PzhvR0y3Q6a738vLVkrPAMfN7Pi8oKNddZoGfNtxbdha4SUjgTIxzk/q53dYpqpcTUplEXsAzI6ltR9bbiJFh+XVzTXk5na2mxV5ebgVfDKq8qlfif9oJIQVj1od6uSiXY0/vMmSUw9Z8X7f8GXGVnqaSxb+XTnd90t7WUrICxD4LHJUVGCuG+83PLHCTcAbOTFIud5vVzUw3IZWRwHPGVq1jZb1bMMdbmTsyTHq66Ww1HVIhhvYsEdRjprt66HQTYgCl1EPLAZVjAIfbFMdCxNSEuJRZU+3FDgyd7vokmcgFk0rN6ZZZ4HCwkaXBfOvJhWU9C7yexoW5tYgYmelOrM10s7ycEA893bZM90reARfRR1Mr7Sz1cpYVN+2s7hkIqTHTHRpm1KQS0uQEKS8vjAsz5zGGmJrMd4y1vDwvpCZCNV22f5P6wZ5ZLcU5W/rU+dv6tVr571+yTZ20rsvXLHBTcD7HQwYplwuS6UAQxJrTnQ+MEELKlJfbSsozBo0Ls6+zJed0M9PdvEJqC2lLU4W9/dVjjrVOSBNTUC9frUsRNQGL8vrupBoV9XIDhNSQ5TYhm0D80+4hGKXniK5k9Vi43zltfWxj6qqlM9GqZhbT+m/F39CXajc20w0tCauFhEJqhPgSUlvJC6nJDG8Tg/4IDIizxbLi5i0vH5/L2XL24AwJjjnWOiFNjFVenldh9uN0m5Tplmz3s6NzOoAwOrOoBd9qnXXE5pCL0K6qgVRbLMdAolPVtTM6s6Q/D3Ul69bhfuzwlLp59yG1+8CESq9ktaH+tbueUb9/yXajSuTFOLePYcu95m/UISHN5nRjD3IrLzcq021bZ+393ezpbj6cwp6YJEIbqnrMstYJaVKCCKkVyssTRjkPv3xqVD1yaEpHyo9PL6pHDk9pwataOQ84hn994KDatX9CH8PzY3O6N62Wx0Bq13YhTvf6HvN6oL3er1++Y6/OKPQk29RKW1ZXZjx2eFodmdxrVG+6XI/ZpbTlNMCpsDsUhJBKmW6znG63Od2L+QQA2rNMGGtGakt3sq2oVZCl5eFQn2kBQhqMID3dol5uSnm5OA+YM4y/B+VJfak29eih3Ov4eq2O4fEj09aMZpQd1/IYSPS9hnZGZ3NO94b8jPh6As4qMtxwuDH+rKsDhk6Lvm/xb7x+y+7Dxji1UnkgmW6WlhNSHhkJturS022KM+s2mlEy3XS2mhNktbttWjgsLQ8HOt2EGKYK7Fe93ITycrvzcNbmXl3mC+ehp6O9Zs6D04FBXzeOob+zdsdA4st0b+jtqLvTv+/EnB5vBhE43KvIKomBg3/j9b0jM/p9JiDzfMXppiFGSHlE1sSe6RbtFlOE1OyVdtl8alMy3Z3s421aYL85135SHWY88YQ0OdWUl5uQ6bY7D22JhDViBJH8WjkPTgdGSveShjowJJye7rHZ+nW64bxCx0HU9iXjJGr7eB1ft/dQm3A9ZO1hppsQb5nuTF48LfffhpWX559r+Nuy1kqmm85W8yJjwwArHsKBTjchhvZU1ZOQmtN52LmuS23p77QCArVwHpzHsLGvU4uo9eU3DtMcGFJ9phsZo4n5dN063SgjRxvEwnLOwN080KlO2dClhvN/C16XNgmTrgfUy+3/JoS4I8ls+8iwFcPKy1FhIwM+ZK2VTDerWZqXXpttyfsgHLhjEmIAHVWVlyeMcx76Ukm1bajLGtVVC+fBeQzDfZ3q9E19qjWfaTDNgSHVB6OQ5UZ2Bq0E9v6zegHzxNH6cHRqQZd1Iiu2vqdT37P4N14/bbhXv88EnIaXZMgIIe4k8vuPm5CaKdMWsE87W9yWZEY3y4qbFma6w8eMJ56QJsevkNpKZlUt5DdFE8rLnc6DnVo5DyYcA4m+19DO2MyyleWux1nsEKuBqv5Qd1K3RswuruiMGD7j33j9zRdvNWZUizOzzdJTQsojJeSrtvUL+7dJmW57QE0CnJj4YX+dNB9221JaBkl18CwSYpRzUZiNWY65fDYXezayfHFjgvNgwjGQ6HoN7ZkiYXR2sW5LywWMA8NYsPO29avJhWW1b2xOfz5/24BR48LcMttSoUMIcae1xfyRYW6Bf8l0M7DWvNgz3ZzVHg7xp8gIIZYxiwA4ouCVVE3t/dymZPjEeYCCOJzc49OrupwbzgOc3Vo4DyYcA4nGyYMxmHJE2yXTXa8zugXcl2dv7tMif9AcQAsEKjJMCxA5M93s6SakPG35iQSZjD3TnS36momilZLppnp584KWremFtNZOGZ9b0pNfTNuT6g063YQYgIwKAulMVuVFjCs73UmzHmETnAcTjoGEA64Zng08E25Od2FGd/1muu1/6ykbepTJrCkvZ+kpIZ4y3RlbebkomRtVXp5fWwtCauzpbmYeOzylvnvPPrVr/4SuGDw2vaiePDajqwmZvAiOWRY7IU0KMtsINCPIrJ2LCqJQMrLHBBE1E50HE46BhJeBSWcya8aGoce7nmd01yNrhNTodBNSFikhd1Mvb5ch3gZlumVUmPR2M7DWnA73l+/Yq4PaWONxD/en2tSjh6bU4YkF49qe6glznnhCmpxkIudAL3no6xblchNE1AiJQ2RwGiPiVla1rsFQV32Xl9cLLC8nxB8JF6db/tukTHcyX14nzjbVy5sTlJCjPW98blmdMdyj13y0MPZ2tGuhWrx+y+7D+n3EP3S6CalDBfO5ZXNmdBMSx9gwyXKv605W1EAgIV0Lx3nmyDBC/DvdaJdxtpUZJ6QmmW6qVjcVaMuDHs7m/pRqac1lueU+hvON1/eOzOj3Ef/QUiGkDp1uKHIDZrpJsz4X4nSvZ2l5zYDRZS83ZXk5IeURp2XFNdPdauw6a/V0VxKYIQ0FdHCW0oUWx039nbq0XBI8eB1fx/uIf8x54glpcsSYdfauujHPTDdpEpyqumtE1HrYz13T62FzujlGhpDytLr2dK8ap17utD+Y6W5OIDyL6oaF/FhaZLbP2NRnTcnB6/g63kf8Q6ebENOcCy+Z7nxPd1cFwTVC6p2OCpluiqjVFntJOTPdhAQQUpORYUb1dOfbeNLMdDczmPSC3u2jUwtarNQO/o3XTxvu1e8j/qHTTUg9lpcvpfVnRhtJsz4XYw00Lqx+M900IQjx1NOdzVpOzIqBQmr2TPdKZtXqO2dPd/NVZmAs2FB3Uvd2o5URASN8xr/x+psv3soRrAHhjklIPQqp5TPdFFIjTfNc2FT9MdZmcj4XeKLTHc/1cP43IWQt4ljD35bEoVVebqLTvbJa1MrDnu7mA+PAMBbsvG39anJhWe0bm9Ofz982wHFhVcKifEJMm5NZoac7nVm1+q26k3yESXOM0rMHo07MLuvP3cmE6uIzUFOKhNSoGk9IWezZbGS4k60tVqm5SVMX7OvsYr7EPJloYUaziR3vszf3aZVyiKahqhIl5aJRQIJBi50QQ5AyrkqZ7vl8lhv7dSfHeZAmHBkmImpULo/vegCWnhJSnkRegAqIsy2l2yZlugsVRXC6czZGRzs1Y5oZONinbOiJ+zAaCnPCbIQ0Oe0ehdRm8v3cKC0XRUlCmqntYkxE1KhcHq+QmkGZOkJMz3Sjr1t/NrCnuyCklrECnJ1sHyEkVLhjElJnPd3z+VEOPSyrJU06Mmw0X17Ofu741in4CyY5DYSYCALjEpvK5DPcECozrbxc2kbQ3sZMNyHRYM4TT0iT43VO9+zSiv7c1cHuENLkmW4ql9ec9kSLml5Iq+mFFfX82JxatY1CIoSspa21tSjTLerlRpaXrxQ0YzidgJBwodVOSJ3N6cboBtBLp5s04ZxujN2Rnm463bXlscNT6pbdh9Vjh6cVOluu//c9eqbrmy/aojbQmiDEldZ8G5iolmcMdrrhcEumu5M93YSECrdJQuquvDzndHNcGGnG52J6KaNWMlmVSLSooa5kzEfXXA73l+/Yqw6Mz+tr0tORUAOppHr00JQ6NDGv3nbhOjU8HPdREmIebYkWpdI2ITUTne580B/JeAns0+kmJFxYXk6IIXS0JTyWl+ei0F0dVBYlTSQwmH8uxudyQoLrujs4vqRGoIT85t2H1Pjcsto+mNLXBP2oPZ1tOtM9MbesfvbECZaaE1Im0y1Ot/R2m9TTbRdFFLFWlpcTEi7mPPGENDnolXSORnIzfvefmFMnZpfUzEKaRi5pOq2DE/M5g5Cl5bUDs1qfGZlVm/vhcCeKsnQQitrcl1LPnVhU+8bna3hUhNQH8qyI0y1l5iZlujEeCnO5AfQaAJ1uQsKF5eWE1El5Oco7kW2668kRNbeU0X2tDx6cVFdfvE2du7W/xkdLSK1H2UimO2cQrue4sJoxs7iiz3+qP6G6WxJq22BKDXS1W19PJRM6KDKzmAuIEEIKJGxONwLneT21XNm5YWvtciajhRIBy8sJCRdmugkxrbzcxemWfkr0T0IJtbezTQ3m+ynxOr5OSKOXl0NEjZnu2oP1pqO9VS0sZ3Rme8tASnXZRhbidZSn9nYWHHFCyFqnO53PcttfNy3AOZ0PntHpJiRc6HQTYtw84lzPtls/JfonsU/D8EWmCf/G61AU5uge0ohIiSOyQxi1cyLf0z3McWE1Y+e6br3WHJ1a0IEPO/j30ekFdcq6TrVzqKt2B0VInSDONdYvKTG3jxIzLfAvujEsLyckXMx64glpYiTKjNbVFZuYmr2fEht2xlaapvsp+1Nq78iMfh8hjUaRwM/iimUQsry8tv2eaGMZ6k7qtQjqxliL8Bn/xuuvO2sdhe0IqZDplhnd0FYzLNFt2SACM92EhAudbkIM3PDsCuZWP2UyoY5NLerXujsSKpGPkuN1fB3vI6QRHT4RGTwyuaA/QzUb9z2pHdCN+MAVp6nztvWryYVltW9sTn8+f9uAev/lp6ozhpnlJqSSkBrGHYojjqC5STgz28x0ExIuFFIjxBCwCWNzRiQ8vZJVKlncT4k+q2PTOad760CqqJ8SX8f7CGnUbHc6k1FH8kGn9T2czx2X43325j5dVYMgH9YclJ4rlVUjIyOxHBMhdZPpzmaNVC4XmOkmJFpopRNi2Ka3spxRSysooW0v6qe8+6lR/W+IFYlysO6nnFrQ2aac8UtIYz4Xc8sZdWgiN5JqA5XLY608OGVDT9Fr1JMgxFt5ufR0myai5mzlAcx0ExIuLC8nxMTxSDYFcxi5V569SaUzqzrbPdTdrvu+7f2Ub754K/spScM/F0cm85luiqgRQupNSC1TKC9nppuQ5oOZbkIMjDTDwbYDdeCzt/Sr8bklvWmjnxIl5chww+HmnG7SDGPD0EoBmOkmhNQLiXzv9qouLzc3082ebkKihU43IYZnuk/MLqkH9k3ojPafvfYM/Zq9nxKZcEIaGacxuIGZbkJIXY4My+3teW1II0eG5f67lbYFISFDp5sQA52LZZvTfceTIwrB8dM39qzppSSk2ZxuGLADqZymASGEmA7Ge4r2QbpOysvZz01I+LCnmxADNz0pLx+ZXlQPHZzU//3qszfGemyExF1eDga72piBIYTUDa0t9ky3ueXldLoJiRY63YQY2NMt5eXIcmezSp29pU9tG+QcXNKcQL9AWJdX7ieEkHqgrTW3fqG0XALqRjrdtuBmR3uh1JwQEg50ugkxMNKM8nKMAnvk0JRCkPzVZzHLTZoXGKjTC2mtbwAxIo6oIoTUC+LLwt+WTLfp5eWddLoJCR32dBNiotOdWVX/9fhx/d/nb+1Xm/o7Yz4yQuLhscNT6t8ePKL2HJnWmaJD421qbOkJdfUl26naTwgxnkQ+072iM93mlpfb+7jZ001I+NDpJsQg2vIZvV8/M6ZOzC6rvlSbuoJZbtLEDveX79irDkzM64AUyjSHutrUo4en1OHJRfWBK06j400IMRrJaqNKp156upnpJiR86HQTYpCDcfPuQ+rRQ8joZfWmfNrGHnV8epEjkkjTgRJyPA/jc8tq20BKHRhfUEpl1WBXu+pOpdQzo3Pqlt2H1dmb+yisRggxFhnruZLBnO7VuigvZ6abkPBhTzchBrDnSC6jt/9ELqOHGdwQj8I8brwOh5yQZmLfiTn1zMis2tyfskbuQMU80dKiWlpa9Ot7R2b0+wghxFTEwUYwHY43sGmWGUNHoiCexkw3IeFj4GNPSHOBkjNk7JDR2zGU0o4FnIot/Sl15qZe/Tq+TvEo0kwg4LSUXlWpZEIl88Zgd0fBKMTr+DreRwghpo8My2SR6TZXSK3NJlg5PrdEm4OQkGF5OSExc2hyST0zMqczdxBQA9iPtwyk1mT0TtnQE/fhElITpNpjYTmjtQ3O2NSjUu0JlVle0l/H6/g63kcIIaYilTrIdJva063b23YdUrv2T+hjPDa9qJ48NqOuvngbdTMICQlmugmJmdmljFpayejMXX+qTTsRO4a6rP4qZvRIM7JzXbc6dbhHj87DrPr+VNKaI5vNZvXrpw336vcRQoipJOzl5Qb2dItgJQQqO/OBzP7ONvXoodzrbG8jJBzodBMSMz0dCdXRltCZO4wWOWtznxruK4wIY0aPNKv4ELIsQ91J3ds9u7iijda55Yx6ZnRWv/7mi7dSRI0QYjTQoVjT051/zSTBSgQ50cuNCrveznb9b7a3ERIedLoJiZltAx3q1OHufEYvtyELzOiRZubcrf16LNh52/rV5MKybrGYWsyo87YOcFwYIaSuMt0rBpaX2wUr4WzLcSEBQMFKQsKFzXCEGCCygowd5g7L5oeScmS44Ygzo0ea3fHGWDDtcC8sq/TctLr4tO2qra0gqkYIIfVQXp42rLzcEqzsz62n2wZT+jVU4AHYIsenKVhJSFNlusfHx9Vb3/pW1dfXpwYGBtQ73/lONTs7W/Z7FhcX1XXXXafWrVunenp61NVXX62OHz9uff3hhx9W11xzjdq+fbtKpVLqrLPOUl/60pdq8NcQUsw5WxwZvbE5/fn8bczoEYJSc4gIXrBtQO0Y7GRJOSGkLkeGmZbptgtWgqHuDnUSdDLy5e9sbyOkCTPdcLiPHj2qbr/9dpVOp9Wf/MmfqPe85z3qe9/7Xsnv+fCHP6xuvfVW9cMf/lD19/er973vferNb36z+vWvf62/vmvXLjU8PKz+6Z/+STvev/nNb/TPTCQS+r2ExJXRQ6QZmyFEouBwEEIIIaTOM935nu68TqoxgpUQTTu1o0eXlDvb2xD8p2AlIU3idD/xxBPqtttuU/fff7+69NJL9Wtf+cpX1FVXXaW+8IUvqC1btqz5nqmpKfX3f//32im//PLL9Wvf/va3dTb73nvvVS9+8YvVO97xjqLvOeWUU9Q999yjbrnlFjrdJNaMHiGEEEIay+nOGFZeLoKVhycW2N5GSMQYEmsrDxxhlJSLww1e9apXqdbWVvXb3/7W9XuQxUZGHO8TzjzzTLVjxw7980oBZ31oaCjkv4AQQgghhDSzkJpkuk0pL3cVrGR7GyHNm+k+duyYLgO309bWpp1jfK3U9ySTSe2s29m4cWPJ70F5+Q9+8ANdkl6OpaUl/SFMT0/rz6urq/rDNHBMKBMy8diaHV4bM+F1MRNeFzPhdTETXhczaFFZ1GrrLPdKJmePwec2ySY7e3OvOvN1Z6p94/NqZjGtR4btHOrSmXCTjjNK+LyYyWod+DBejy1Wp/sTn/iE+vznP1+xtLwWPPbYY+qNb3yjuv7669VrXvOasu+94YYb1Gc+85k1r4+OjmrxNhNvBmTw9ULfWhfFDU0Dr42Z8LqYCa+LmfC6mAmvixlMzqXVwsKCyq60qtXlhFpYXFbzs0qNjIwYZ5Ohua0niZtnWY2Nzalmgs+LmazWgQ8zMzNjvtP90Y9+VL397W8v+x70WW/atEkvTnZWVla0ojm+5gZeX15eVpOTk0XZbqiXO7/n8ccfV1dccYUWUfvUpz5V8bg/+clPqo985CNFmW4IsW3YsEGrq5t4w0IcA8dn6g3brPDamAmvi5nwupgJr4uZ8LqYQdvcskqlJlSyrVUlkwmVyiTU4EC/ruCkTWYOfF7MZLUOfJjOzk7znW6cQHxU4iUveYl2ntGnfckll+jX7rzzTn0hLrvsMtfvwfva29vVHXfcoUeFgaeeekodOHBA/zxhz549Wmjt2muvVZ/97Gc9HXdHR4f+cIKbwdQbAjesycfXzPDamAmvi5nwupgJr4uZ8LrET3sioUdwZVAim81dk/ZEzh6jTWYWfF7MpMVwH8brcZl59A6gOP7a175Wvfvd71b33XefHvmFkV5vectbLOXyw4cPa6E0fB1gRBhmeSMjfdddd2mHHWPG4HBDuVxKyl/5ylfqcnK8D73e+ECZOCGEEEIIIdWQSIh6uTJSSI0QUhvqQkgN/PM//7N2tFEGjogCstdf/vKXra9DqRyZ7Pn5eeu1G2+80XovhM+uvPJK9bWvfc36+o9+9CPtYGNONz6Ek046Se3bt6+Gfx0hhBBCCGk0ErbZ10srGaNGhhFCakfdON1QKsfM7VLs3LlTN9k7a+xvuukm/eHG//k//0d/EEIIIYQQEjb2rDbKy/Vr9LkJaTrqorycEEIIIYSQesMtq83yckKaDzrdhBBCCCGERACqy20V5hqWlxPSfNDpJoQQQgghJCLlZXtfN2Cmm5Dmg043IYQQQgghEVHkZLcoRR01QpoPOt2EEEIIIYTUwOlGaTmy34SQ5oJONyGEEEIIIRFh7+FuS9D0JqQZ4ZNPCCGEEEJIjTLdhJDmg043IYQQQgghEUGnmxBCp5sQQgghhJCIoNNNCKHTTQghhBBCSETYR4ZxXBghzQmdbkIIIYQQQiIikaCQGiHNDp1uQgghhBBCaqFeTiE1QpoSOt2EEEIIIYREZWyzvJyQpodONyGEEEIIIRHBTDchhE43IYQQQgghtVAvT9D0JqQZ4ZNPCCGEEEJIRCRaC+Y2e7oJaU7odBNCCCGEEFKL8nKbkjkhpHmg000IIYQQQkhUxrbN6eacbkKaEzrdhBBCCCGE1CDT3W4rNSeENA988gkhhBBCCInK2Gamm5Cmh043IYQQQgghtch0s6ebkKaETjchhBBCCCERYe/jtiuZE0KaBz75hBBCCCGERESihUJqhDQ7dLoJIYQQQgiJiIStpJzl5YQ0J3S6CSGEEEIIiQhmugkhdLoJIYQQQgipgZCa/b8JIc0DnW5CCCGEEEJqIKTWlqDpTUgzwiefEEIIIYSQWjjdzHQT0pTQ6SaEEEIIIaQmI8NYXk5IM0KnmxBCCCGEkIhgeTkhhE43IYQQQgghEcHyckIInW5CCCGEEEIioq21YG6zp5uQ5oRONyGEEEIIIRFhFyyn001Ic0KnmxBCCCGEkIhI2DPdHBlGSFPSFvcBEEIIIYQQ0qhAr3x6Ia3SmVV1eGJebWjPxn1IhJAaQ6ebEEIIIYSQCHjs8JT6zj371K79EyqzmlV/ddtTakdfm3rb73Sq87YN8pwT0iSwvJwQQgghhJAIHO4v37FXPXVsRiXbWlVvZ5sa7E6qJ0fm1JfvfEZ/nRDSHNDpJoQQQgghJERWV7Pq5t2H1Pjcsjp5fbdqT7SqlpYW1dvRpnYOdqqJuWV1y+7D+n2EkMaHTjchhBBCCCEhsu/EnHpmZFZt7k9Zc7pbWuSjRW3uS6m9IzP6fYSQxodONyGEEEIIISEys7iiltKrKpVM6Cx3Ktmq+jvbVIuWVVP6dXwd7yOEND4UUiOEEEIIISRE0L/d0d6qFpYzqqezTZ23tT/3hXw1OV7H1/E+Qkjjw0w3IYQQQgghIbJzXbc6dbhHHZ1aUNksPG1kuHNZbvz76PSCOm24V7+PENL40OkmhBBCCCEkTAO7tUVdffE2NdSd1L3ds4sremQYPu+bWNSvv/nirfp9hJDGh043IYQQQgghIXPu1n71gStOU+dt61eTC8tq39icmlxIq7M2dqv3X36q/johpDlgIwkhhBBCCCERAMf67M19WqUcomndHQnVlZlVmzbS4SakmaDTTQghhBBCSESghPyUDT36v1dXV9XICMeEEdJssLycEEIIIYQQQgiJCDrdhBBCCCGEEEJIRNDpJoQQQgghhBBCIoJONyGEEEIIIYQQEhF0ugkhhBBCCCGEkIig000IIYQQQgghhEQEnW5CCCGEEEIIISQi6HQTQgghhBBCCCERQaebEEIIIYQQQgiJCDrdhBBCCCGEEEJIRNDpJoQQQgghhBBCIqItqh/cTGSzWf15enpamcjq6qqamZlRnZ2dqrWVcRaT4LUxE14XM+F1MRNeFzPhdTETXhcz4XUxk9U68GHE/xN/sBR0ukMANwPYvn17GD+OEEIIIYQQQkgd+YP9/f0lv96SreSWE09RmCNHjqje3l7V0tJiZAQGAYGDBw+qvr6+uA+H2OC1MRNeFzPhdTETXhcz4XUxE14XM+F1MZPpOvBh4ErD4d6yZUvZbDwz3SGAE7xt2zZlOrhZTb1hmx1eGzPhdTETXhcz4XUxE14XM+F1MRNeFzPpM9yHKZfhFswsjieEEEIIIYQQQhoAOt2EEEIIIYQQQkhE0OluAjo6OtT111+vPxOz4LUxE14XM+F1MRNeFzPhdTETXhcz4XUxk44G8mEopEYIIYQQQgghhEQEM92EEEIIIYQQQkhE0OkmhBBCCCGEEEIigk43IYQQQgghhBASEXS6G4SbbrpJ7dy5U3V2dqrLLrtM3XfffWXf/8Mf/lCdeeaZ+v3nnXee+ulPf1qzY202/Fybf/iHf1AtLS1FH/g+Eh6/+tWv1Bve8Aa1ZcsWfX5/8pOfVPyeX/7yl+riiy/WQh6nnnqqvk4k3uuCa+J8VvBx7NgxXpoQueGGG9QLX/hC1dvbq4aHh9Wb3vQm9dRTT1X8Pu4x5l0X7i/R8zd/8zfq/PPPt2YKv+QlL1E/+9nPyn4PnxXzrguflXj4q7/6K72Pf+hDH2rIZ4ZOdwPwgx/8QH3kIx/R6n67d+9WF1xwgbryyivVyMiI6/t/85vfqGuuuUa9853vVA8++KDerPHx2GOP1fzYGx2/1wZgQzh69Kj1sX///poec6MzNzenrwOCIV54/vnn1etf/3r1yle+Uj300EN6M3jXu96lfv7zn0d+rM2E3+siwNGwPy9wQEh43H333eq6665T9957r7r99ttVOp1Wr3nNa/T1KgX3GDOvC+D+Ei3btm3TjsOuXbvUAw88oC6//HL1xje+Ue3Zs8f1/XxWzLwugM9Kbbn//vvV3/7t3+rgSDnq+pnJkrrnRS96Ufa6666z/p3JZLJbtmzJ3nDDDa7v/8M//MPs61//+qLXLrvssuz//t//O/JjbTb8Xptvf/vb2f7+/hoeYXODJfDHP/5x2fd8/OMfz55zzjlFr/3RH/1R9sorr4z46JoXL9flrrvu0u+bmJio2XGRbHZkZESf97vvvrvk6eAeY+Z14f4SD4ODg9lvfvObrl/js2LmdeGzUltmZmayp512Wvb222/PvvzlL89+8IMfLPneen5mmOmuc5aXl3Xk7lWvepX1Wmtrq/73Pffc4/o9eN3+foDsa6n3k9pdGzA7O6tOOukktX379oqRWBI9fF7M5sILL1SbN29Wr371q9Wvf/3ruA+n4ZmamtKfh4aGSr6Hz4yZ1wVwf6kdmUxGff/739fVByhndoPPipnXBfBZqR3XXXedrih0+iaN9szQ6a5zxsbG9AKycePGotfx71K9jXjdz/tJ7a7NGWecob71rW+pf/u3f1P/9E//pFZXV9VLX/pSdejQIV6GmCj1vExPT6uFhQVel5iAo/31r39d3XzzzfoDQapXvOIVuo2DRAPWI7RXvOxlL1PnnntuyfdxjzHzunB/qQ2PPvqo6unp0Rogf/qnf6p+/OMfq7PPPtv1vXxWzLwufFZqx/e//329b0Onwgv1/My0xX0AhJACiLraI69wuM866yzd5/IXf/EXPFWE2IwifNiflWeffVbdeOON6rvf/S7PU0TZCPTN/c///A/Pbx1eF+4vtQHrEvQ/UH3wox/9SF177bW6B7+Ug0fMuy58VmrDwYMH1Qc/+EGtS9EMosF0uuuc9evXq0QioY4fP170Ov69adMm1+/B637eT2p3bZy0t7eriy66SD3zzDO8DDFR6nmByEoqleJ1MYgXvehFdAgj4n3ve5/6z//8T60yD1GicnCPMfO6OOH+Eg3JZFJPuQCXXHKJFoj60pe+pIPnTvismHldnPBZiYZdu3ZpYWFMhxFQIYr17Ktf/apaWlrSdnSjPDMsL2+ARQSLxx133FFUaoZ/l+pVwev29wNEmcr1tpDaXBsnWHxQEoVSWhIPfF7qB2Qx+KyEC3Tt4NihFPPOO+9UJ598csXv4TNj5nVxwv2lNmDfh/PgBp8VM6+LEz4r0XDFFVdoGxd7t3xceuml6q1vfav+b6fDXffPTNxKbqR6vv/972c7Ojqy//AP/5B9/PHHs+95z3uyAwMD2WPHjumv//Ef/3H2E5/4hPX+X//619m2trbsF77whewTTzyRvf7667Pt7e3ZRx99lJcj5mvzmc98Jvvzn/88++yzz2Z37dqVfctb3pLt7OzM7tmzh9cmRJXMBx98UH9gCfziF7+o/3v//v3667geuC7Cc889l+3q6sp+7GMf08/LTTfdlE0kEtnbbruN1yTG63LjjTdmf/KTn2T37t2r1y6onba2tmb/67/+i9clRN773vfqiQq//OUvs0ePHrU+5ufnrfdwj6mP68L9JXpwvqEg//zzz2cfeeQR/e+WlpbsL37xC9drQnvMzOvCZyU+Xu5QL2+kZ4ZOd4Pwla98Jbtjx45sMpnUY6ruvffeohv42muvLXr/v/7rv2ZPP/10/X6MQ7r11ltjOOrmwM+1+dCHPmS9d+PGjdmrrroqu3v37piOvDGRUVPOD7kO+Izr4vyeCy+8UF+XU045RY8TIfFel89//vPZF7zgBTooNTQ0lH3FK16RvfPOO3lZQsbtmuDD/gxwj6mP68L9JXre8Y53ZE866SS9V2zYsCF7xRVXWI6d2zUBtMfMuy58Vsxxul/eQM9MC/4v7mw7IYQQQgghhBDSiLCnmxBCCCGEEEIIiQg63YQQQgghhBBCSETQ6SaEEEIIIYQQQiKCTjchhBBCCCGEEBIRdLoJIYQQQgghhJCIoNNNCCGEEEIIIYREBJ1uQgghhBBCCCEkIuh0E0IIIYQQQgghEUGnmxBCCCFlefvb367e9KY38SwRQgghAWgL8k2EEEIIaQxaWlrKfv36669XX/rSl1Q2m63ZMRFCCCGNBJ1uQgghpIk5evSo9d8/+MEP1Kc//Wn11FNPWa/19PToD0IIIYQEg+XlhBBCSBOzadMm66O/v19nvu2vweF2lpe/4hWvUO9///vVhz70ITU4OKg2btyovvGNb6i5uTn1J3/yJ6q3t1edeuqp6mc/+1nR73rsscfU6173Ov0z8T1//Md/rMbGxmL4qwkhhJDaQaebEEIIIb75x3/8R7V+/Xp13333aQf8ve99r/qDP/gD9dKXvlTt3r1bveY1r9FO9fz8vH7/5OSkuvzyy9VFF12kHnjgAXXbbbep48ePqz/8wz/k2SeEENLQ0OkmhBBCiG8uuOAC9alPfUqddtpp6pOf/KTq7OzUTvi73/1u/RrK1E+cOKEeeeQR/f6vfvWr2uH+3Oc+p84880z939/61rfUXXfdpZ5++mleAUIIIQ0Le7oJIYQQ4pvzzz/f+u9EIqHWrVunzjvvPOs1lI+DkZER/fnhhx/WDrZbf/izzz6rTj/9dF4FQgghDQmdbkIIIYT4pr29vejf6AW3vyaq6Kurq/rz7OysesMb3qA+//nPr/lZmzdv5hUghBDSsNDpJoQQQkjkXHzxxermm29WO3fuVG1tND8IIYQ0D+zpJoQQQkjkXHfddWp8fFxdc8016v7779cl5T//+c+12nkmk+EVIIQQ0rDQ6SaEEEJI5GzZskX9+te/1g42lM3R/42RYwMDA6q1leYIIYSQxqUlm81m4z4IQgghhBBCCCGkEWFomRBCCCGEEEIIiQg63YQQQgghhBBCSETQ6SaEEEIIIYQQQiKCTjchhBBCCCGEEBIRdLoJIYQQQgghhJCIoNNNCCGEEEIIIYREBJ1uQgghhBBCCCEkIuh0E0IIIYQQQgghEUGnmxBCCCGEEEIIiQg63YQQQgghhBBCSETQ6SaEEEIIIYQQQiKCTjchhBBCCCGEEKKi4f8PtyGOCm0TfTQAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" }, @@ -445,9 +455,11 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:11:00.907523Z", - "start_time": "2026-01-10T11:10:53.408496Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:35.056134Z", + "iopub.status.busy": "2026-01-10T22:21:35.055933Z", + "iopub.status.idle": "2026-01-10T22:21:42.610049Z", + "shell.execute_reply": "2026-01-10T22:21:42.609184Z" } }, "outputs": [ @@ -461,10 +473,10 @@ "======================================================================\n", "Initial [r, k] Final [r, k] Iterations Success\n", "----------------------------------------------------------------------\n", - "[0.5, 0.5] [0.9967, 1.0029] 78 True\n", - "[2.0, 2.0] [0.9967, 1.0029] 86 True\n", - "[10.0, 10.0] [0.9967, 1.0029] 244 True\n", - "[100.0, 100.0] [0.5947, 23.9276] 321 True\n", + "[0.5, 0.5] [0.9967, 1.0029] 73 True\n", + "[2.0, 2.0] [0.9967, 1.0029] 77 True\n", + "[10.0, 10.0] [0.9967, 1.0029] 183 True\n", + "[100.0, 100.0] [0.5947, 23.7961] 410 True\n", "\n", "True values: [r, k] = [1.0, 1.0]\n" ] @@ -507,9 +519,11 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:11:05.018231Z", - "start_time": "2026-01-10T11:11:00.922897Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:42.612961Z", + "iopub.status.busy": "2026-01-10T22:21:42.612739Z", + "iopub.status.idle": "2026-01-10T22:21:46.725883Z", + "shell.execute_reply": "2026-01-10T22:21:46.725244Z" } }, "outputs": [ @@ -519,13 +533,13 @@ "text": [ "\n", "================================================================================\n", - "OPTIMIZER COMPARISON\n", + "OPTIMISER COMPARISON\n", "================================================================================\n", "Algorithm r (fitted) k (fitted) Cost Iters Time (s)\n", "--------------------------------------------------------------------------------\n", - "Nelder-Mead 2.527654 0.604107 2.331e+00 37 0.210\n", - "CMA-ES 0.996730 1.002923 8.220e-03 60 0.542\n", - "Adam 0.953633 1.070344 1.648e-02 1000 0.313\n", + "Nelder-Mead 2.527654 0.604107 2.331e+00 37 0.204\n", + "CMA-ES 0.996760 1.002815 8.220e-03 57 0.529\n", + "Adam 0.953633 1.070344 1.648e-02 1000 0.374\n", "\n", "True values: 1.000000 1.000000 \n" ] @@ -571,9 +585,11 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:11:05.059961Z", - "start_time": "2026-01-10T11:11:05.033320Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:46.728570Z", + "iopub.status.busy": "2026-01-10T22:21:46.728356Z", + "iopub.status.idle": "2026-01-10T22:21:46.732842Z", + "shell.execute_reply": "2026-01-10T22:21:46.732181Z" } }, "outputs": [ @@ -701,7 +717,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index 07a1a0b..ad01e3a 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -34,9 +34,11 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:20:18.324983Z", - "start_time": "2026-01-10T11:20:18.185476Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.226019Z", + "iopub.status.busy": "2026-01-10T22:21:48.225767Z", + "iopub.status.idle": "2026-01-10T22:21:48.858444Z", + "shell.execute_reply": "2026-01-10T22:21:48.857896Z" } }, "outputs": [], @@ -83,9 +85,11 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:20:18.460807Z", - "start_time": "2026-01-10T11:20:18.385369Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.861095Z", + "iopub.status.busy": "2026-01-10T22:21:48.860829Z", + "iopub.status.idle": "2026-01-10T22:21:48.866723Z", + "shell.execute_reply": "2026-01-10T22:21:48.866205Z" } }, "outputs": [ @@ -151,9 +155,11 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:20:18.818246Z", - "start_time": "2026-01-10T11:20:18.462229Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.869022Z", + "iopub.status.busy": "2026-01-10T22:21:48.868803Z", + "iopub.status.idle": "2026-01-10T22:21:49.144073Z", + "shell.execute_reply": "2026-01-10T22:21:49.143541Z" } }, "outputs": [ @@ -164,9 +170,6 @@ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -207,9 +210,11 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:20:18.855080Z", - "start_time": "2026-01-10T11:20:18.821847Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:49.146902Z", + "iopub.status.busy": "2026-01-10T22:21:49.146594Z", + "iopub.status.idle": "2026-01-10T22:21:49.150528Z", + "shell.execute_reply": "2026-01-10T22:21:49.149899Z" } }, "outputs": [ @@ -266,9 +271,11 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T11:20:19.373109Z", - "start_time": "2026-01-10T11:20:18.858953Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:49.152830Z", + "iopub.status.busy": "2026-01-10T22:21:49.152632Z", + "iopub.status.idle": "2026-01-10T22:21:49.635464Z", + "shell.execute_reply": "2026-01-10T22:21:49.634461Z" } }, "outputs": [ @@ -332,11 +339,13 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 6, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T13:16:54.156325Z", - "start_time": "2026-01-10T13:16:49.674678Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:49.638310Z", + "iopub.status.busy": "2026-01-10T22:21:49.638105Z", + "iopub.status.idle": "2026-01-10T22:21:58.286731Z", + "shell.execute_reply": "2026-01-10T22:21:58.286103Z" } }, "outputs": [ @@ -348,16 +357,16 @@ "============================================================\n", "MCMC SAMPLING\n", "============================================================\n", - "Chains: 100\n", - "Iterations per chain: 1000\n", - "Total samples: 3000\n", "\n", "Starting from MAP estimate: g = 9.8035, h = 9.9841\n", "\n", "Running MCMC... (this may take a minute)\n", "\n", "Sampling complete!\n", - "Samples shape: (3, 1001)\n" + "Samples shape: (10010, 2)\n", + "Acceptance rate: [0.284 0.295 0.258 0.24 0.248 0.267 0.284 0.24 0.262 0.277]\n", + "Target acceptance: 0.20 - 0.40\n", + "✓ Acceptance rate looks good!\n" ] } ], @@ -378,17 +387,14 @@ "# Setup MCMC sampler\n", "sampler = (\n", " chron.MetropolisHastings()\n", - " .with_num_chains(3)\n", + " .with_num_chains(10)\n", " .with_iterations(1000)\n", - " .with_step_size(0.25)\n", + " .with_step_size(0.035)\n", ")\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"MCMC SAMPLING\")\n", "print(\"=\" * 60)\n", - "print(\"Chains: 100\")\n", - "print(\"Iterations per chain: 1000\")\n", - "print(f\"Total samples: {mcmc_result.draws}\")\n", "print(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\n", "print(\"\\nRunning MCMC... (this may take a minute)\")\n", "\n", @@ -396,40 +402,17 @@ "mcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n", "\n", "print(\"\\nSampling complete!\")\n", - "print(f\"Samples shape: {len(mcmc_result), len(mcmc_result.chains[0])}\")\n", - "# print(f\"Acceptance rate: {mcmc_result.acceptance_rate:.3f}\")\n", - "# print(\"Target acceptance: 0.20 - 0.40\")\n", - "\n", - "# if mcmc_result.acceptance_rate < 0.15 or mcmc_result.acceptance_rate > 0.50:\n", - "# print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", - "# print(\" Consider adjusting step_size\")\n", - "# else:\n", - "# print(\"✓ Acceptance rate looks good!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T13:18:13.636686Z", - "start_time": "2026-01-10T13:18:13.507439Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 1001, 2)" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.asarray(mcmc_result.chains).shape" + "print(f\"Samples shape: {mcmc_result.samples.shape}\")\n", + "print(f\"Acceptance rate: {mcmc_result.acceptance_rate}\")\n", + "print(\"Target acceptance: 0.20 - 0.40\")\n", + "\n", + "if np.any(mcmc_result.acceptance_rate < 0.15) or np.any(\n", + " mcmc_result.acceptance_rate > 0.50\n", + "):\n", + " print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", + " print(\" Consider adjusting step_size\")\n", + "else:\n", + " print(\"✓ Acceptance rate looks good!\")" ] }, { @@ -445,24 +428,23 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 7, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T13:18:54.687398Z", - "start_time": "2026-01-10T13:18:54.067005Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:58.289229Z", + "iopub.status.busy": "2026-01-10T22:21:58.289039Z", + "iopub.status.idle": "2026-01-10T22:21:58.887409Z", + "shell.execute_reply": "2026-01-10T22:21:58.886603Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" }, @@ -472,17 +454,17 @@ "text": [ "\n", "What to look for in traces:\n", - " ✓ Good mixing (wiggly, no trends)\n", - " ✓ Stationary (mean stays constant)\n", - " ✓ No long excursions\n", - " ✓ Rapid exploration of parameter space\n" + " - Good mixing (wiggly, no trends)\n", + " - Stationary (mean stays constant)\n", + " - No long excursions\n", + " - Rapid exploration of parameter space\n" ] } ], "source": [ "# Plot traces\n", "fig, axes = parameter_traces(\n", - " np.asarray(mcmc_result.chains[0]),\n", + " mcmc_result.samples,\n", " param_names=[\"g (m/s²)\", \"h (m)\"],\n", " true_values=[g_true, h_true],\n", " show=False,\n", @@ -491,10 +473,10 @@ "plt.show()\n", "\n", "print(\"\\nWhat to look for in traces:\")\n", - "print(\" ✓ Good mixing (wiggly, no trends)\")\n", - "print(\" ✓ Stationary (mean stays constant)\")\n", - "print(\" ✓ No long excursions\")\n", - "print(\" ✓ Rapid exploration of parameter space\")" + "print(\" - Good mixing (wiggly, no trends)\")\n", + "print(\" - Stationary (mean stays constant)\")\n", + "print(\" - No long excursions\")\n", + "print(\" - Rapid exploration of parameter space\")" ] }, { @@ -508,24 +490,23 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 8, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T13:19:35.922334Z", - "start_time": "2026-01-10T13:19:34.620912Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:58.893041Z", + "iopub.status.busy": "2026-01-10T22:21:58.892726Z", + "iopub.status.idle": "2026-01-10T22:21:59.407317Z", + "shell.execute_reply": "2026-01-10T22:21:59.406395Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -533,7 +514,7 @@ "source": [ "# Plot distributions\n", "fig, axes = parameter_distributions(\n", - " np.asarray(mcmc_result.chains).reshape(3003, 2),\n", + " mcmc_result.samples,\n", " param_names=[\"g (m/s²)\", \"h (m)\"],\n", " true_values=[g_true, h_true],\n", " show=False,\n", @@ -551,23 +532,41 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 9, "metadata": { - "ExecuteTime": { - "end_time": "2026-01-10T13:19:42.924922Z", - "start_time": "2026-01-10T13:19:42.798628Z" + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.411973Z", + "iopub.status.busy": "2026-01-10T22:21:59.411102Z", + "iopub.status.idle": "2026-01-10T22:21:59.453567Z", + "shell.execute_reply": "2026-01-10T22:21:59.452263Z" } }, "outputs": [ { - "ename": "AttributeError", - "evalue": "'chronopt.sampler.Samples' object has no attribute 'samples'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[50]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Burn-in: discard first 20% of samples\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m burn_in = \u001b[38;5;28mint\u001b[39m(\u001b[32m0.2\u001b[39m * \u001b[38;5;28mlen\u001b[39m(\u001b[43mmcmc_result\u001b[49m\u001b[43m.\u001b[49m\u001b[43msamples\u001b[49m))\n\u001b[32m 3\u001b[39m samples_burned = mcmc_result.samples[burn_in:]\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# Calculate statistics\u001b[39;00m\n", - "\u001b[31mAttributeError\u001b[39m: 'chronopt.sampler.Samples' object has no attribute 'samples'" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "POSTERIOR STATISTICS (after burn-in)\n", + "============================================================\n", + "Samples used: 8,008\n", + "\n", + "Gravitational acceleration (g):\n", + " True value: 9.8100 m/s²\n", + " MAP: 9.8035 m/s²\n", + " Posterior: 9.8061 ± 0.0210 m/s²\n", + " 95% CI: [9.7679, 9.8473]\n", + "\n", + "Initial height (h):\n", + " True value: 10.0000 m\n", + " MAP: 9.9841 m\n", + " Posterior: 9.9834 ± 0.0134 m\n", + " 95% CI: [9.9569, 10.0096]\n", + "\n", + "True values in 95% CI:\n", + " g: ✓\n", + " h: ✓\n" ] } ], @@ -623,9 +622,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.468223Z", + "iopub.status.busy": "2026-01-10T22:21:59.467853Z", + "iopub.status.idle": "2026-01-10T22:21:59.813369Z", + "shell.execute_reply": "2026-01-10T22:21:59.812640Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Parameter correlation: 0.2667\n", + " Low correlation - parameters are nearly independent\n" + ] + } + ], "source": [ "# Scatter plot of joint distribution\n", "fig, ax = plt.subplots(figsize=(8, 8))\n", @@ -669,9 +695,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.816483Z", + "iopub.status.busy": "2026-01-10T22:21:59.816233Z", + "iopub.status.idle": "2026-01-10T22:22:00.197985Z", + "shell.execute_reply": "2026-01-10T22:22:00.197208Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shaded region shows the 95% credible interval for predictions\n", + "This accounts for both parameter uncertainty and observation noise\n" + ] + } + ], "source": [ "# Take random subset of samples for predictions\n", "n_pred_samples = 200\n", @@ -791,7 +843,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb index fc2f1aa..8a7b5e9 100644 --- a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb +++ b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb @@ -73,7 +73,7 @@ "\n", "builder = (\n", " chron.VectorBuilder()\n", - " .with_callable(diffrax_solver)\n", + " .with_objective(diffrax_solver)\n", " .with_data(data)\n", " .with_parameter(\"alpha\", 1.0)\n", ")\n", @@ -89,7 +89,7 @@ "\n", "builder = (\n", " chron.VectorBuilder()\n", - " .with_callable(diffeqpy_solver)\n", + " .with_objective(diffeqpy_solver)\n", " .with_data(data)\n", " .with_parameter(\"alpha\", 1.0)\n", ")\n", diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index 0dc3248..7bbfa2f 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -35,10 +35,10 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, + "source": "# Import plotting utilities\nimport time\n\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np", "outputs": [], - "source": "# Import plotting utilities\nimport time\n\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np" + "execution_count": null }, { "cell_type": "markdown", @@ -72,9 +72,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Generate synthetic data\n", "np.random.seed(42)\n", @@ -115,13 +113,13 @@ "print(f\"Data shape: {y_observed.shape}\")\n", "print(f\"Time span: [{t_data[0]:.1f}, {t_data[-1]:.1f}]\")\n", "print(f\"Number of observations: {len(t_data)}\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Visualize data\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", @@ -141,13 +139,13 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Fit with DiffsolBuilder\n", "start_time = time.time()\n", @@ -179,7 +177,9 @@ "print(f\"Function evals: {result_diffsol.evaluations}\")\n", "print(f\"Time: {diffsol_time:.3f}s\")\n", "print(f\"Success: {result_diffsol.success}\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -198,9 +198,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Optional: install JAX/Diffrax if not already installed\n", "# !pip install jax jaxlib diffrax\n", @@ -219,13 +217,13 @@ " JAX_AVAILABLE = False\n", " print(\"⚠️ JAX/Diffrax not installed. Skipping GPU example.\")\n", " print(\" Install with: pip install jax jaxlib diffrax\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "if JAX_AVAILABLE:\n", " # Define dynamics in JAX\n", @@ -272,13 +270,13 @@ " # Warm up JIT compiler\n", " _ = simulate_numpy([1.0, 0.4, 0.1, 0.4])\n", " print(\"✅ JAX/Diffrax solver ready (JIT compiled)\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "if JAX_AVAILABLE:\n", " # Fit with VectorBuilder + JAX/Diffrax\n", @@ -311,7 +309,9 @@ " print(f\"Time: {diffrax_time:.3f}s\")\n", " print(f\"Success: {result_diffrax.success}\")\n", " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -329,9 +329,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Optional: install Julia backend\n", "# !pip install diffeqpy\n", @@ -346,13 +344,13 @@ " print(\"⚠️ DifferentialEquations.jl not installed. Skipping Julia example.\")\n", " print(\" Install with: pip install diffeqpy\")\n", " print(\" Then run: from diffeqpy import install; install()\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "if JULIA_AVAILABLE:\n", " # Define dynamics for Julia\n", @@ -377,13 +375,13 @@ " test_output = simulate_julia([1.0, 0.4, 0.1, 0.4])\n", " print(\"✅ Julia/DifferentialEquations.jl ready\")\n", " print(f\" Output shape: {test_output.shape}\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "if JULIA_AVAILABLE:\n", " # Fit with VectorBuilder + Julia\n", @@ -416,7 +414,9 @@ " print(f\"Time: {julia_time:.3f}s\")\n", " print(f\"Success: {result_julia.success}\")\n", " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -429,9 +429,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Collect results\n", "results_summary = []\n", @@ -480,13 +478,13 @@ " f\"{r['Backend']:<20} {r['Time (s)']:<12.3f} {r['SSE']:<15.3e} \"\n", " f\"{r['Iterations']:<8} {r['Evaluations']}\"\n", " )" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Visualize timing comparison\n", "if len(results_summary) > 1:\n", @@ -543,7 +541,9 @@ "\n", " plt.tight_layout()\n", " plt.show()" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -556,9 +556,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Generate predictions from each fitted model\n", "t_fine = np.linspace(t_data[0], t_data[-1], 200)\n", @@ -623,7 +621,9 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -719,4 +719,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb index ed93750..1e8590a 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -42,10 +42,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:26.262506Z", - "iopub.status.busy": "2026-01-09T21:37:26.262254Z", - "iopub.status.idle": "2026-01-09T21:37:26.748292Z", - "shell.execute_reply": "2026-01-09T21:37:26.747510Z" + "iopub.execute_input": "2026-01-10T22:22:06.718164Z", + "iopub.status.busy": "2026-01-10T22:22:06.717720Z", + "iopub.status.idle": "2026-01-10T22:22:07.287201Z", + "shell.execute_reply": "2026-01-10T22:22:07.286361Z" } }, "outputs": [ @@ -86,10 +86,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:26.750985Z", - "iopub.status.busy": "2026-01-09T21:37:26.750736Z", - "iopub.status.idle": "2026-01-09T21:37:26.815774Z", - "shell.execute_reply": "2026-01-09T21:37:26.814367Z" + "iopub.execute_input": "2026-01-10T22:22:07.289542Z", + "iopub.status.busy": "2026-01-10T22:22:07.289285Z", + "iopub.status.idle": "2026-01-10T22:22:07.350768Z", + "shell.execute_reply": "2026-01-10T22:22:07.349658Z" } }, "outputs": [ @@ -144,10 +144,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:26.820002Z", - "iopub.status.busy": "2026-01-09T21:37:26.819656Z", - "iopub.status.idle": "2026-01-09T21:37:28.259563Z", - "shell.execute_reply": "2026-01-09T21:37:28.258618Z" + "iopub.execute_input": "2026-01-10T22:22:07.355771Z", + "iopub.status.busy": "2026-01-10T22:22:07.355407Z", + "iopub.status.idle": "2026-01-10T22:22:08.805007Z", + "shell.execute_reply": "2026-01-10T22:22:08.804066Z" } }, "outputs": [ @@ -169,7 +169,7 @@ "Solution: [1.0007473 1.00148331]\n", "Value: 5.725e-07\n", "Evaluations: 127\n", - "Time: 1.43s\n", + "Time: 1.44s\n", "Time per eval: 0.011s\n" ] } @@ -178,7 +178,7 @@ "# Build problem\n", "problem = (\n", " chron.ScalarBuilder()\n", - " .with_callable(lambda x: expensive_rosenbrock(x, delay_ms=10))\n", + " .with_objective(lambda x: expensive_rosenbrock(x, delay_ms=10))\n", " .with_parameter(\"x\", 1.0)\n", " .with_parameter(\"y\", 1.0)\n", " .build()\n", @@ -221,10 +221,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:28.263471Z", - "iopub.status.busy": "2026-01-09T21:37:28.263125Z", - "iopub.status.idle": "2026-01-09T21:37:31.606800Z", - "shell.execute_reply": "2026-01-09T21:37:31.605399Z" + "iopub.execute_input": "2026-01-10T22:22:08.808619Z", + "iopub.status.busy": "2026-01-10T22:22:08.808319Z", + "iopub.status.idle": "2026-01-10T22:22:12.220604Z", + "shell.execute_reply": "2026-01-10T22:22:12.217981Z" } }, "outputs": [ @@ -243,13 +243,13 @@ "============================================================\n", "CMA-ES (Parallel, Default Population)\n", "============================================================\n", - "Solution: [0.95664499 0.91893292]\n", - "Value: 3.296e-03\n", + "Solution: [0.98713938 0.97199636]\n", + "Value: 7.646e-04\n", "Evaluations: 301\n", - "Time: 3.33s\n", + "Time: 3.40s\n", "Time per eval: 0.011s\n", "\n", - "Speedup vs Nelder-Mead: 0.43x\n" + "Speedup vs Nelder-Mead: 0.42x\n" ] } ], @@ -280,10 +280,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:31.610740Z", - "iopub.status.busy": "2026-01-09T21:37:31.610400Z", - "iopub.status.idle": "2026-01-09T21:37:38.477716Z", - "shell.execute_reply": "2026-01-09T21:37:38.476133Z" + "iopub.execute_input": "2026-01-10T22:22:12.225212Z", + "iopub.status.busy": "2026-01-10T22:22:12.224831Z", + "iopub.status.idle": "2026-01-10T22:22:19.045346Z", + "shell.execute_reply": "2026-01-10T22:22:19.043950Z" } }, "outputs": [ @@ -302,10 +302,10 @@ "============================================================\n", "CMA-ES (Parallel, Large Population)\n", "============================================================\n", - "Solution: [1.0111185 1.02443279]\n", - "Value: 5.530e-04\n", + "Solution: [0.98069027 0.96068513]\n", + "Value: 4.870e-04\n", "Evaluations: 601\n", - "Time: 6.86s\n", + "Time: 6.81s\n", "Time per eval: 0.011s\n", "\n", "Speedup vs Nelder-Mead: 0.21x\n" @@ -354,10 +354,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:38.481560Z", - "iopub.status.busy": "2026-01-09T21:37:38.481180Z", - "iopub.status.idle": "2026-01-09T21:37:49.815581Z", - "shell.execute_reply": "2026-01-09T21:37:49.813834Z" + "iopub.execute_input": "2026-01-10T22:22:19.049873Z", + "iopub.status.busy": "2026-01-10T22:22:19.049477Z", + "iopub.status.idle": "2026-01-10T22:22:30.396419Z", + "shell.execute_reply": "2026-01-10T22:22:30.395476Z" } }, "outputs": [ @@ -381,7 +381,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pop= 8: 2.30s, 201 evals, 0.011s/eval\n" + "Pop= 8: 2.32s, 201 evals, 0.012s/eval\n" ] }, { @@ -402,7 +402,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pop=20: 2.34s, 201 evals, 0.012s/eval\n", + "Pop=20: 2.33s, 201 evals, 0.012s/eval\n", "\n", "Done!\n" ] @@ -454,16 +454,16 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:49.820794Z", - "iopub.status.busy": "2026-01-09T21:37:49.820388Z", - "iopub.status.idle": "2026-01-09T21:37:50.099719Z", - "shell.execute_reply": "2026-01-09T21:37:50.099009Z" + "iopub.execute_input": "2026-01-10T22:22:30.400184Z", + "iopub.status.busy": "2026-01-10T22:22:30.399842Z", + "iopub.status.idle": "2026-01-10T22:22:30.693763Z", + "shell.execute_reply": "2026-01-10T22:22:30.693093Z" } }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAHqCAYAAAB/bWzAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA6HJJREFUeJzs3Qd4U3X3B/DTQSdQ2kIZHey9954i0wEoS5n66h8nuAWVoa/zVUFERVBBRSgiQ2XvJSB77w1ltFAopaU7/+f7CzekaVrSNm3W9/M8eXqT3CQ3N7fJ75577jluOp1OJ0RERERERERERERkF9xtvQBEREREREREREREdA+DtkRERERERERERER2hEFbIiIiIiIiIiIiIjvCoC0RERERERERERGRHWHQloiIiIiIiIiIiMiOMGhLREREREREREREZEcYtCUiIiIiIiIiIiKyIwzaEhEREREREREREdkRBm2JiIiIiIiIiIiI7AiDtkRWNH78eHFzc1OXChUqFOi6HTZsmOG1OnToIPZIWz5cZs6cKc4Cn632vvCZExXm/74G/1PG/2POxBG+34iIiOx1TLp+/fpMY4SzZ8/m63Vyej5X+83+7bffpFmzZlK0aFHD+27QoIHh/itXrsjw4cMlNDRUPD09DfMsWrTIqp+Ls+5nEVFmDNqSw9q3b588//zzUrduXSlRooR4eXlJ6dKlpVOnTvL5559LXFycwwdl7JmjDxQwqDR+D5ZcHPF9OsI6x4A2JCREunTpIr/88ovodDpxdc4UkL1z54589tln0qJFC/VdXaRIESlZsqRUr15devbsKe+8844cOnTI1otJREQuxDR4ZnxBMK5WrVry0ksvyenTp229qFQI46zsLqbJGStWrJBBgwbJjh07JCEhIcvzYgz7+OOPq+e/dOmSpKen8/MjonzxzN/DiQpfWlqavPbaazJ58uQs90VHR6vLunXr5NNPP1VHQhEIKix4LQz0ICAgoEBfa8CAAVKnTh01HR4eLvbof//7n2G6adOm4iwQZNIOCrRq1crWi+PwMKCNiYmRVatWqcvvv/8uCxcuVME9yh7+p4z/x+zRzZs3pV27dnLgwIFMt1+/fl1djh8/LkuXLpVSpUpJ7dq1Her7jYiInBOCcUeOHFGXn376Sf7880/p3LmzrRfLbrnSb3ZkZKRhOigoSF588UUpVqyYSj6A8+fPyz///GOY56GHHpK2bduKu7u7Wkfe3t6Zxm54jrxy1v0sIsqMQVtyODjqPXXqVMP1cuXKSb9+/VTmFgIDf/zxhwoCXbt2TR5++GFZu3attG7dulCWDQG8wgridevWTV3s2euvvy726rnnnlMDKWNvvPGGYbpJkybSv3//TPdrA6JnnnmmkJbSeQUGBsqYMWPU9NWrV+XXX39Vf2HJkiXy7bffysiRI228lPYNQU7jQKc9+uSTTzIFbB999FF1CiEC8tix2bZtm+zfv98hv9+IiMh5YMyHsV9KSops3bpVFi9erG5PTEyUwYMHq9PYEXCzNuyzJCcni5+fnzgqZ/jNHjFihFSuXDnL7ab7defOnTNM9+jRQyZMmJDt/TBp0qQsz2ut/SN73s8iIivSETmQf/75B+dNGy6NGjXSxcXFZZpnzZo1Ond3d8M8tWvX1qWnpxvub9++veG+oUOH6o4cOaLr06ePLjAwUOfr66tr3bq1btWqVYb5161bl+k1zV1mzJih5h03bpzhtvLly2daLlzX7sN8S5cu1bVo0UK9ZmhoqO6dd97RpaSkqHm/+eYbXY0aNXTe3t66ihUr6j788ENdRkZGpufDsmvPh/dkbP/+/bonn3xSvaaXl5fOx8dHFx4eruvYsaPu7bff1l28eNEw7+nTp3UjR47UtWnTRhcWFqbz8/NTjylXrpzuoYce0v3111+Zntt4/Zm7GL9vc+vI2OrVq3WPPfaYev94zWLFiukaNmyoGzt2rO769etZ5jddhzt37tT17NlTFxAQoNYj3sOmTZt0eWW8vFi/2TFdjuy2laNHj6r3EhERoZavadOmumXLlql5o6OjdU899ZSuZMmS6vPBdrdx40azr3flyhXd6NGjdfXr19cVLVpUbReVK1fWPf/887pz585Z9N7wf4LPNqfPo1+/fob7O3fubLgdy9WrVy+1TRQpUkTn7++v1kG3bt3U+79586ZFy2C87Zj+fxw/flzn5uZmuL9t27aZ7k9MTNR9+eWXulatWulKlCihliMkJETXvXt33dy5c7O8lulncerUKd3EiRN1NWvWVOsP7+WVV17R3bp1K9PjcvofPnPmTKbnxGtY8rgFCxboBg0apKtbt65aZm0dYlleeOEF9bzZvYa5i7bN4TM0vt2UNdYZvouw3FhnpUqV0j399NO62NhYnaXw/6w937Bhw8zOc/bsWd3Bgwct+n4zXs/ZXUy/D5OSknRff/212qbwPY/1UKZMGd3jjz+u27Jli8XvhYiInIfpb57puAjjaOP7sX+hPQ7jN/y+4bcE41eM8TAuw+8cxuCmTH/TMHbDuAC/yRj7LFy4UM33448/6vr27av2AYKDg3Wenp5qbIzx35tvvqmLiYnJ85jUeKwB2Df65ZdfdA8++KD6fcdvI8akPXr00C1ZsuS+68v4+ay1T2I6lrp06ZJuyJAhal1gPWC/5NixY2reXbt26bp27arGxRjj4Df9/PnzOkuZjqGMx3Tm3G/8Ycn4xJLPBft78+bN0z388MNqrIp1hrFLgwYN1Lg1OTnZ4v0sjN/79++v1re2n4V9zylTphj2OY2ZPt/KlSt1HTp0UGNWrGeM+03Ha5oLFy6obRTLidfBuBGv++ijj6rnAXyW2vO3bNkyy3MsXrzYcL+Hh4cuKioqx8+EyFUwaEsOxXhQgAuCfuYMHDgw03zr1683Gzhq3Lixrnjx4ll+VBH0/f333wssaIuBnnGAyjhQ+NJLL5l9jffeey/bdWE8QDp06FCm4Jy5ixY4hL///vu+72/ChAlm119+gravvvpqjs+DQK7pwMB4HTZr1kwNME0fh0HC4cOHdXlh+llkx9IBMrYvc9tWZGSkCsZbsuwIKmEQnd16QsA6u2CvqcGDBxse16VLl0z3xcfHq50O7f7Zs2er2/E/hoFTTp8VDnzkN2gLxu+zatWqhtsvX76sDr7ktAwI/qempmb7WXTq1Mns4xBIv3PnToEGbbFsOS07voO0nTxrBW2tsc5wEMTc49q1a6ezFAK+2uPw+ZseZMuOtYK2ODiCHYjs5sX/46RJkyx+P0RE5BpBWwS2jO//7bff1O2vvfZajr9BCI4ZJ3+Y/qZhfINgr/FjtKCtuXGj6djYNJCVl6AtDuri4HxOr4Vxen6DtrndJzH+jQ8KCtJVqFAhy/wIMGN9Ycxseh/WrfGYztGCtlh2JKPk9Bw3btwwzJ/T9jtmzJgcnwcHsm/fvp3pMcb3I5nE3L4qAugYWxlDkB+B2uxeC8lBsGPHjky3Y/swZhzUxcEDItJjeQRyKJs2bcp0evUDDzyQ7SlOc+bMyfS49u3bZ5lv165dqrwCTpWPj4+XH3/8UZ2ilJGRIc8++6yqUYtTWlAzaOXKlarepump3XmpI7Rnzx51WnOfPn1k+fLlqpg9/Pzzz+pvw4YN1an7qJt04sQJddtXX30l7777rmq4lhM8B07lgrCwMFUs39/fXy5evCgHDx5UpyMbQxMonK6MU8JQV7J48eKqlhfqMaE2MHzwwQfy9NNPqy6oWlkB41IC2illltbyxanwX375peE61kXv3r1VwX4sP04Vi4qKUusHDYqwjKa2b9+u3t+TTz4pFy5ckNmzZ6vb8flhXRmX0LAVbF9YN5UqVZIpU6aobQzbFmp/AU63Q1mPr7/+WtVqNl32W7duSa9evVSpDyhfvrx6Pl9fX1UGBOsGtXUfe+wxtZ3cb92jky3WPaxZs0bVf9ZqcKGjLRpGAZpF4fOAadOmGZoo1KhRQ/r27as+D5zavnfvXtm9e7dV1hVqm6LGqaZMmTKGaXzGxo2q0OABDULw/4hTGGH+/Pny0UcfydixY80+P8qk4NT8+vXry7Jlywz/c/iLJlnZPc4asD7xXVKzZk313YH/YZSCQN1erEd8zm+99Zaq7YraZvi+2blzp8ydO9ds3TJLSrBYY51t3rxZfcfi9bB9aGUONm7cqL5H0Fjsfho1amR43IYNG9Tn2rx5c2ncuLHqvIzGkfgfyEvdcA2WBe9Fo9XV0/7HsJ0Cas498cQT6nsD32/47sX/4yuvvKK+vwqrjA4REdk/7bfSdFyCMTX2KdAIGb/ZGJNh/ILSTqiBi/IKL7/8shw+fNjs82rjeoxxMSbB6fTa+A1jMpR2w74HntvDw0ONhzEewGtg+r///a8qIZUf+N1bvXq1msaYBOPSqlWrqt/refPmqWZaGKfjtxq/m3mV230SY7GxsWpcilJZ2C/54Ycf1O3ogYAxKsYCqCeL9YcxsbZuMV7Rxtm5gXWMsZcp7A9i30gbf3z33XeG5nTGpdQwVsL9p06dyrQPgv1FjP0sgZ4t2I40qA+M94rtA2M6rWTH/WD/EeM7TdeuXdUYB2NPfCa3b99W+8bYDjDONwfjJIz7sZ1iHIUxKmA7xP7y22+/ra5j/WPfQPuc0bztkUceUfuW+Kww/tZgfWHsqH3u+Ey1/UH836B2tPE+CxHddTd4S+QQjDMBkT2VnT179mQ6kofTyM1l+yFT0/gIJ46iGz9u+vTpFmXSWTKP8ZFwHKXUMs5wmo/xa+JUKe3I5/LlyzPdZ3zKVXZHtV9++WXD7R9//HGWZcSpzeZOb8ZyIAMUpxF//vnnuv/973+Zjo7jFCpjOR3dvd88OM1Lux1H0XHEX/Ptt9+azT4wXYc4Vcc42wCn7xuXzcgLa2fa/uc//zHch/IGxvfhtHjNgAEDzC77V199Zbgdp0YZl4zANoJsA+1+zHs/OOXKOMMXn7UGR7S125977jnD7Y888ojh9jlz5mR5TmR0JiQk6Cxh/L+H94NtDJc33ngjS9YJShmY+1/GqVeatLQ0dXqVcVaGVgrF9LN45plnDI/DKWHGWagoC1KQmbbaayIjGqc+4r3hfQ8fPtzwGGSMGJ+qdr/SBznNY6111rt3b0NZFmx7xhnXkydPtugzR+kDnLaYXfYFTv3EKaLXrl3L9LicTrU0duDAgUzPj1MltfW4b9++TK+1du3aTI813ubxXomIyHWY/ubhNHL8NqMkGU5NN76vdOnSmTI48bv577//6mbOnKnO1sDjTM8gMz5V3/RMwZzO8MCYCmc5TZs2TZU4wnPjFHPtsZUqVcpXpi1+z/Hbq93+008/ZXo+7DMZnxl4v+ez5j6JabbqrFmzDPcZj11wQQkBwDgFZQSyyxDOjukYKruLafkC0zJ7pu5X/iC7+7EejD8XrHucBWcM25TxWDG7/Szj0lTIXjWGM0mNx2DG+xbGz4fSBsYlxIyfE2UFNabbvZaRbvy/kt2+Ns6w08o9GJ/5if1k4zIQRK6Ombbk0tDNs0KFCobrOFo6bNgwSU1NNWRK/uc//7H66+IoOo7agvHrQ8+ePdVRaDAtXH/jxg2L3tPkyZPVNDJz//rrL3WktHr16irDDffjyL0GjRWQlbdly5YcnxdHxa0BR2KNGw/h6CyyFDRDhgyR559/PlOmA7JNTSFrElnSGry/3KynwoCMAo3p54zmeRrjz9l42Y27z+L24ODgbF8Lnx8yO3KCo9/YvseNG6euIxsdWQo4aq5lkZse3cb2gm0I8Njvv/9eqlWrptY3jtojWxLPm1t4P8bZ2saQEfDCCy+YzXQZOnSoYRrbMdaxNg+yMo4dO6YyWk0h41KDJlhY/9p6wLaN7IPSpUtLQfjtt99k1KhRhoxpc5BljfvLli2b79ez1jpDVr322SLjBxmxWrM4S//HkB2O71Gs6wULFhgyMTTIMJ81a5bKHkL2d262JWR3oPHJzZs3DWc8IOMWn6/p/w8gqzc79/v+IyIi54ZMS+MzXDQ+Pj4qOxF/AeMl7BvgTJmcYGyBTElTyLrUxjimkHWI30tkQub0vPnx77//qt9ezVNPPaUu5iDDEr/beW2Sltt9EmM4q8u4ITDG0drYBb/z2hlhGDdUrFhRna1nT/sAuYXsU+PPBZmspmcWmdueTOHz0s4wgl9++UVdzMHr4cxFc03kMG7GGUoajP1xpqjpOsZZWRqMJU0zs93d3TPtA2G/DxnFV65cUeNenHWGzxkZ3hrsl97vzFIiV+Ju6wUgyg3jgEZOgyXTzp3ZBUK0U8M1GDgYB8a0YIC1GQcbTX+UjO8zLQuAU3nvB6dBo5soOtzitHYMcGbMmKF+/Dt27KgChManTSMgaknAAkEla8APvf5grp5poAwBa+NBSnaDL9MgqHFHX0vWU2HIy+dsvOwIqFkKpyBZAoFXDKAA2waC9hgoaQcqcGq5cbkPBBsxcMP/BraB9evXq1OpMODCKU716tWTy5cvS37guREQxKn4P/30kzoFSwu8ma4D0+3F9Hp224vp/7rp48z9rxtvp3n9H0D5CByIyClgm5/nN8da68xa/2MoD4KyHHgdfNdMnDhRHZzStkNAKRZtZ8ASWJ8I7iPYq+1MYLvRDngV1P8PERE5PyQTILiIJAKUDMDvDSAwiHHz/QK2Of2mYxxuruwXTuvH2CqngK12Gnl+5Oa3EeMg49JVuZXbfRLTcZvxejIeR+M+42BvduPo3MA45G6/n0wX07FQQTH9XBCItsZ+Vl7HP5aOAY2X25Jlxvh+xIgRhusokWBaGiG7gwhEroqZtuRQcERWqyOEHwnUyTGXPfX7779neZw5qOlpDAMK48EJalEWBC0gZY65gVxuof4ljmgjQHL06FFVLxRHtzHYREAbg1DUl0SG3b59+wyPw9FR1PdEQBFHrjEosnYwAxkGeG5tQKFl7mlQt8p4wJpdHSjTdZiXbM+Clt/PGdmNxgceXn311WznteToO0RERKj/GdQyw2eAuleo8ZpdDSksJ47Qf/HFF2p7wjaDC46MY2CImmQYfGv1mC2FDEwEjHOzDrTtxfjAiun2k932gv9142xs08dp/+vGgUStxq9pHbrcQEBcG9xiG0XtZWTaI7iIICOCl9ZmrXVm7f8x7HC1bNlSXXAwAIFcBLSN1y9q4N4PviOw3rAdav8bK1asyFIb13Q9vP/++5my+omIiDQIJuLAdk7+/vvvTGeMYGyEng+oOYoatujRcD/GBxeNGWf5InkBZ6dg/wUZvqhhm112bm6Z/jairqlxIoEpS3pVWGOfpLD3leyN6edy5syZXPdMMbfvivqy2e0HQ3bjLkvHgMbLjWW2xP/93//Jhx9+qBJGcJYVzuJDjw6trwtqPRPRPc73jUdODcXgjYNDaN6DwK3x6RvIBDQe+KD5TnY/VijCjsCRdjQRj9MyDgEF+M39eJme4mtP8IOJIAx+tLt3764ugAL6KCYPWvMo06PnOCKOZmPaeswpYIsBk3YaT27WB06xwo+xduoOgloTJkwwBFNMT+GxpOmSs8J71w5A4LPAZ4jMVmMIvGLAY1pKIyc4gq01oECzBC1jBNu4cRkBQGAMAWE0qUNJCg0ycrUgsrWakZlj+vnj///TTz81HGTBqfXGA0fjwKwxBAi17wH8jxsf2ME2r2WfGg92sc7RUALrFhkzn3/+ea6X3/h/DDs+KMugBYZNDy7lNFjOzemJ1lpn1oAdNWRk49Q7050s09P+LDlIhs8OjfdwOp+2TtFQzFwmjOl6QFAXJR9MIcvHUU+nJCKiwmM6bsaBbi2omdNvem6fG2eoPPjgg2oaB361RlvWgLIEyFLVmsxivIFsWFPYP8IYUCvnVtD7JK4OYyXjfSuM29D42Xjsh0A3xuM5BbRxUABNwLT9LGxXaOZm+hgESZG0YcmBhpy0adPGMCZDIz4kgxg3gsN+ChpGI2nEuKkfyiQgkQH3v/nmm4b7mGVLlBWDtuRQsBOOo3M4Igfo8on6OQiEYIccpzBhYKMNRJDZhVO5jbPnTAMAqMuJQFV8fLzqhqnBIAw/KBotmKkFczBQQ0AYRx5x9NteMrgQeEY9rA4dOqhOsMhCQ2Ya6peaBkeqVKmi1o2WCYgfdfzI4wceGQc5wfrQylAg0wCPwTrAEVKc5p4TnP6lBQcxKMSRZNSmwmDEOCiPU54LIhPRUSDjA12CcSo4BnHYVrFN4nNDEBGDaQTXkTmJ07osPZUK6xrbAEoCGJcSwbrGYNAYTmVHwBOfKZ4fwU1kuRsH1wsqIx0Q4MdrIzANyARHtj0GmStXrsxUvxXbb3b/69OnT1f/twh6Y5BqfDreM888Y5g2zWrAOkeXaOxUnDx5MtfLbxwQxfrGOsb3GGqAYfmzY/x9o2XB43F4f/jfyan+rrXWmTXgfSKbApm+WI/4vsYOBXbkMLDXYKfQkgM0yNBFVq0GWeN4T8brEgcZUB8N6wE7vVq9ZtRvxmePg3F4z9j2kfmDnQx8Z2LHg4iIKDumBznxm45AJHo15DewiufWfq/wfAMHDlS/mfjdQr1Ta8HBWgTGMC7SxgjYn8JvMLJ6UXYIr4eSRaiJr5WGKOh9ElvDsmI9mNLGFAUNwW0kJyGrGjDuxH4mynFgHSFDGWe5oSTZ/dYZekagLqxW3x9jX5zlhdfA/ho+W4zP8HkYB1jzAv00vvvuO8PZaRivYl0icIwD4thPwec/adKkTI976aWXVNAWkpKSDCUYTGviEhGDtuSApkyZoo4W4i9gcIHAkikECfBjgKBLTkc18SOoZaFpsEOPDETjU4KQKYajnVpW6cyZMzMF1+wlaAuoDWQayDCmHdFE+QMMEPBeAUdCcQoxIOiD05i0mpGmcIRcW+8ICI0dO1ZNI4B9v6AtGiFhwICGC4AAmmlNK5yqhVPDnPEUKEth+0ONJ2S4InCLshH3C6ZbAoNy7AxgkGUsu6Pb2OZxSqA5+F9BEL4gITMU2xROPQTsGJnuHCH7csyYMdk+B3asUC8OF2MI4Bkf4cep+8jIRRY+ICCuZc/06NFDlTTIDRzcwXauNchAVigugJ2h7MpKYDkwmNbqBWM70Op9YfB7v6Zp1lhn1oSdBPw/Z7cNffPNN5nOmMiO6fcEdmBwMYbgsLaDhfWAHU4cjMLBKWzH2W3LREREOcGp5nXr1lVJIoCDoNqB0Jx+0y2Bg6h4PJJIQDuwiXEwAnBoamotCKDh4Kl21hXOWsTFlvsktqbtC5kyHlMUNCTBIJlFG2vi4PJXX32V6+dB4BPlyz7++GN1HftzuBQElDvD+BLBX2y7yJw1HW9j3GpuH7xJkyaZAuX4/zItE0FEbERGDgiDl6+//loF/XCqK45CYmcftyNLED8MOGqM05px+s39jmrjlA6UBcDRRwRecaQZP5amRx5xKgd29hEEzq4elT3AEVkEUDt37qxOGUagGesGASAErlBHCkc3NViXCNTiRxfBcJy+giO0eK85BUyRPYcBZlhYWLadX+83MEFGAQJHCNDitXG6NI7MvvfeeyrLIL+n7DgDbI8IVGGdIMCIjESsbxxlx3VkD2I9tmvXLlfPa1q7FkFA7bQ1Y6jVhjIkeH5kGyDgiwx2TCPrF3XIsM0VJPzv7dixQ20zCGYimK39v+NgCnZsMGDMaXvFdo4DPfi+wJF8/D9g+8VOiukBF/yPoDM0nh/zIkMBjRK0A0W5gcEnshlwkAOfHV4L2bwIYOZUOw+vi+8hfIfl5dREa6wza0BGNtYddiCQ+Yr1jv91rAdk3WAdYDlxIKcg4MAUOmXjAAWycnFGBv5/8B2OJjN4XewI4zuPiIgoJ/j9wrgBv11IDsFvNcpF4ay+8ePH52vl4SyqjRs3qt99jN0xJkbAEGfNYExvTXh+nLWC5BYckMYYEOMB/DajJBT2i/CetOSKwtoncXUYYy9evFglC6A0AsZy2OYwDsTBAoxbLS2V9dFHH6ksW4xzcKYctlU8F87kwjaG+7UzsvIL2xD2VTCWwpgZ2y5eC/t3+Jxxf3ZZusZYGoHIPDddbtoLEjkBBHW1gvc4Km6cMUtEzgGnY6EzsQYZJYXVAZiIiIiIiLKHMhxILDAuu5eXRCAiZ+e65x0TEREREREREVGBQ/1aBGtR7xZnbWpw9iwDtkTmMWhLREREREREREQF5sqVK5nOhINKlSqp0g9EZF7BtYwmIiIiIiIiIiIygj4LaPKGOtGog0tE5rGmLREREREREREREZEdYaYtERERERERERERkR1h0JaIiIiIiIiIiIjIjrARmQUyMjLk0qVLUqxYMXFzcyv4T4WIiIjIBel0OomPj5dy5cqJuztzCwobx7xERERE9jPmZdDWAgjYhoeHW/PzISIiIqJsXLhwQcLCwrh+ChnHvERERET2M+Zl0NYCyLDVVmbx4sWlMLIcYmJiVEdFZpkQtxHi9wjx94ZspbDHJLdu3VIHyrWxFxUujnnJ3nC/iLidEL9LyBl/cywd8zJoawGtJAICtoUVtE1KSlKvxaAtcRshfo8Qf2/IVmw1JmE5KtvgmJfsDfeLiNsJ8buEnPk3535jXhYLIyIiIiIiIiIiIrIjDNoSERERERERERER2REGbYmIiIiIiIiIiIjsCGvaEhERERGRxdLT0yU1NdUqtePwPKgfxz4OxG2k4Hh5efF/jIjIATFoS0RERERE96XT6eTKlSty8+ZNqz0fArfx8fFsPkfcRgoQDopUrFhRBW+JiMhxMGhLRERERET3pQVsQ0JCxM/PL9+BVgRt09LSxNPTk0Fb4jZSQHBg5NKlS3L58mWJiIjg/xoRkQNh0JaIiIiIiO5bEkEL2AYHB1tlbTFoS9xGCkepUqVU4BYHSYoUKcINj4jIQdhVI7KPP/5YmjZtKsWKFVMDwl69esmxY8dyfMyCBQukSZMmUqJECfH395cGDRrIr7/+mu38I0aMUEcXJ02aVADvgIiIiMg5IKAWm5Ail+KS1V9cJ9el1bBFhi0RORatLAIOvhARubybF0Qu7c16ubxPPGMOqb9m78fjXDnTdsOGDfLCCy+owC2OAo4ZM0a6dOkihw8fVgFZc4KCguSdd96RGjVqqB+jxYsXy/Dhw1XQt2vXrpnmXbhwoWzbtk3KlStXSO+IiIiIyLHE3UmV+bsuys9bzsq52MS7tx6U8kF+MrRVBXmscZgE+DJTy1XltyQCERU+/t8SEd2FwOuUxiJpyWIuq7Wk5MDTW+TFXSIlwsUlg7bLly/PdH3mzJkq+Lpr1y5p166d2cd06NAh0/WRI0fKzz//LJs3b84UtI2KipKXXnpJVqxYIT179iygd0BERETkuDYcj5HnZu2SOylZs7HOxybKB4sPy+crj8l3gxpL+2qlbLKMRERERER5knjdbMDWIngcHl+IQVu7Ko9gKi4uzpBNawmctrdmzRpVUsE4yIvi64MHD5Y33nhDateuXWDLS0REROTIAdvhM7bLndR0QSEE02II2m24H/NhfiKyD2fPnlXZlHv37rX4McOGDVPl6LKDBBqUoLOl9evXq7MpUU+5sJcJ+5RlypSR+Ph4sWfXrl1TiU4XL1609aIQEZEzZ9oaQ6B11KhR0rp1a6lTp859g7uhoaGSnJwsHh4e8u2338qDDz5ouP/TTz9VXWlffvlli14bz4OL5tatW4ZlwqWg4TUQgC6M1yLHxG2EuI0Qv0vImm7dSVUZtiowe5/Step+N1Hzb3mroxS3YqkEjn2oIMTExMjYsWNlyZIlcvXqVQkMDJT69eur27Cv4WgQbEUQc9GiRYbbwsPD5fLly1KyZI4ndjq8/v37S48ePQrltUaPHq3O1ES/FUhKSlL9UXAW6JEjR+Shhx7K9BkYB5pfffVVOXTokPpc3n33XfWZFRR85kOGDJFx48bJjz/+WGCvQ0REhc9ug7aobXvw4EFV5uB+8EOKo8q3b99Wmbb4kaxUqZIqnYAf1a+++kp2795tcS0fNESbMGGC2QEffqwLGnZYEIhG4Nbd3a6ToclGuI0QtxHidwlZ09w9V1VJBEtbjSFwi/l/3nhM+jcMsdpy2HtGGzmmxx57TFJSUlQJNewjIHCLfYbr16+Ls0DiCrJCnZ2vr6+6FLTz58+rXilff/214TY08cJrIxFo/vz5Zh935swZVYoPwd3ffvtNbWf/+c9/pGzZsln6rVgTero0btxY/ve//1l8lioREdk/uwzavvjii+pHcuPGjRIWFnbf+RHYrFKlippu0KCBOvKJwCuCtps2bZLo6GiJiIjI9IP72muvyaRJk9SpROaOqiLwa5xpi6OkpUqVkuLFi0thBOQQYMbrMWhL3EaI3yPE3xsqSDhIPP/AkTw9dv6B6/Jil9pWa3Lj4+Njlech0iAjFfsDyH5s3769uq18+fLSrFmzLPO9/vrr8ueff6oz7po0aSITJ05UGbmaTz75RN2WmJgo/fr1U2N19OTQShJg3wP7ItjH0KD8AE7nx2n9gOdGE+U5c+ao18QZhTgrUOvTgflwtuHcuXPV3wsXLkibNm1kxowZKvA3fvx4FXwG7f9u3bp1UqFCBalYsaLs2bNHLQP2d5599llZu3atXLlyRe0LPf/886r/R24hmxRl5rAsWIc//PCD2jeCU6dOqf0mNHtOSEiQmjVrqv2wzp07Gx6PsyCx3vD4gIAAadu2rfzxxx+G/R68/2nTpqnlrFatmrz33nvy+OOPm10Wbf1o5RKwPrB82LfD427cuCHdu3eX6dOnGzJkc/sa8Pvvv6vPHmdzatAY+7vvvlPT//zzj2EZjE2dOlV9Dl988YW6jvWBJCS8fy1oi8+6bt26KtCOzxLlH/773//KE088ofaDsW5Kly6tAsZ4L4D3hftWrlypEpWwj4ym3QjWAkoAotk2Gm8//fTTufyEiYjIXnna204DTkHBjw0GVvjBywv8MGvlDVDL1njQAPjBxO3aj5wpb29vdTGFAGphBVExCCvM1yPHw22EuI0Qv0vIGmITUlSTsdxCVi4edyspXQL9vayyLBz3OKiczkTDWNbLy/y8SNlOSxPx9MTAJud5jeUiuF+0aFF1QWCvRYsWZsf40LdvX5VFuWzZMhVY/P777+WBBx6Q48ePq8xFBPEQIPzmm29UEPXXX3+VyZMnq8zd3EDg7fDhwxIZGWkIsnXr1k0OHDggVatWVfMgKPz555+r18D/xKBBg1RAGZmb+IsEFSSVIJALWL5Lly5l2R9CYG/evHkSHBwsW7ZsUUFcBH4RcLYUluXDDz+UX375RQUXEfgdMGCACloCAogoV4B5sG4x38MPP6zqwSJQvHPnTpWZivfSqlUriY2NVUF0DQK8s2bNUsFOvH8k7eD9IiCuBdnvB4FjfL5I+kFwE+8PAXYsU15fA8uIwH1ubd261ey+JwLNxhCsffPNN2X79u0qQP/cc8+pbaF3794qGIsgL/ZXkfHr5+engszYbrB9ohzCyZMn5c6dO5meEwcisNwM2hIROQ9PeyuJMHv2bHWEG0dGcSQUMHDSToNBvR4c8cSPL+AvflArV66sArVLly5VgwLtKCgGKbgYK1KkiDp9qHr16oX+HomIiIjsSUJyWr4efzs5zWpBW3JQfftmfx8CX+PG3bs+aBDSTQ1XPdDDQUtSQB+Lu2N8BRmDd3tLZPL33xYvGvpaIDvzmWeeUUG7Ro0aqUAdAo/16tVT8yATEsEznJ2nBXURNEUgEFmPCHYiexbBMC0ghszI1atX56p0GgJwCLTiLwK2gCAssnVx+0cffaRuS01NVcuK/Rst0Pv++++raQSgsV+E/Z6cyiFgf8e43BuSYRBQRPA5N0FbLMuUKVOkefPmhmAjskexvhAkRDaqcTbyBx98oIKPf/31l1puvFdkqKL+K/bvkOXcsGFDNS/eA94z1mPLli3VbQiC4/NA0NzSoC0C1PiMtcxaBDtRlgBB27y+xrlz5/IUtMX+K7JkjeE6guwIsmr7tFhnqHWrneWJIDOCsdhOAfWWsT+7f/9+dbAB6xHrTVsmZFabwjaFTGsiInIedhW01QKt2ulBGgxitOLt+MEyzsLAaTg44otumfgRrFGjhjqSiiL1RERERJQzf+/8DQeL5vPxRIVR0xZ1RpGFiNP4ka342WefqdP8sY+xb98+lTFqmuiBIBuyOAHZrahTagxBQJQmsBSyaVG2AKfnG0Ng0fi1kVmpBWwB2bEIKOcWsoJ/+ukntf+E94K6viidkBsIejdt2tRwHftaKPeA9YGgLdYbMpDR5A2N0NLS0tRr4TUBzaERqEWgFBnFuCCbFO8R2aLI5DVuIA1YTi2wawkEMLWAren6yutr4D0UZLkW7YABoEwCPn+UTNBogV/tfSATF9sx+rR06dJFld1A5rIx7AvjvRIRkRmpd0ROrhHZ+ZM4ErsaZaM8wv2gbIIxHOXGJTfM1bElIiIickWBfkWkfJCfKnVgaSMyQDXNiCA/KeFXpACXjhzCvHnZ32da6mvWrHvTOp2kp6WpwKChPIKxH3+02iIiAIfAHS441RzNocaNG6eCtgg8ItBnup8BCFBaCoklpvszyFTV4HUQoEOjZPw1hgxa4yxZ05JYluwnGUP5BWTxorYqgssIaqJJ1b///ivWhNdYtWqVykxGjxEEDlErFkFRwOsi0Ih1i3qsyCBFkHfHjh1qfQACvsa1YyG7MhbmmFtfyL6FvL4Gsl5RaiG3kP2MRnfGcB19UYwbqJlbZuPbtHrF2vtAbVtk/+KsUqxvlO7AWapY7xqUnkDJByIiMg7UrhY5tEjk+HKRFP1vgiOxq6AtERERERUuBAeGtqogHyw+nOvHDmtdwWpNyMiB5SYj0Xhe05q2+XneXKpVq5YqfwAomYDT2hE8NnfaOaAkAAKeKNWmQdauMQTMkG2qQVbtwYMHpWPHjuo6MjtxG7In0Ywrr1BbFs+TE9ScRSYmzkjUaFnDuYHMWdSl1Rq3oVYtGnBhfWivg8A3sme1IKlpggzWK+q84oJAOQLhaJCGADoCp8jKtbQUQl4+57y8Bj4r1JDNLQTIEVg1hiCrVpohP7B9DR06VF2w/aA5nHHQFtua6RmrREQuGag9sUrkMAK1KxwyUGuMXa6IiIiIXNxjjcPE18vDbNzMHHc3UfP3aRQmrginnSO4h+xN1PpEfc+coBkUTivH/DgF2jSos2DBAnXKM06RRhB87969WZ4Dne8RkEHGHuYx17leyyjEMiGrLzAwUJ1G7cquX78unTp1UuXTUB/0zJkz6vNAeYRHH31UzYNgIoJqWFfIBkXQEY273nnnHRWwhJEjR6pSAyjbhuZkCD4eOnQo02vhdbD+cTl69Kg6pd34c0JZhCeffFIFfvGZY1mw7aBHBx5jKWx7eC8IoF67di1TNq8GDbew7CtWrFDLi+xiZLfmFrI/0SgaAWtkCCNAixqrWhAXr4P3gm0WZSaeeOIJQ3YooDkYGrbhfmSKolEZ7kdvEWThIlP3lVdeUbVyEVRGVu7XX3+trltDXl8DzcNQA9g0OI5ALt4Lslrj4uLUtPH/K0ponD59WjUZwzbw7bffqjrCeP38QIYy+r6g3AO2O6xXLXAOKIuAzwffI0RELhmoPfyXyLzhIp9VFvl9sMjB+ZkDtj4lRBoMEun+mTgSZtoSERERubgA3yLy3aDGMnyGPviY04nYWmB36qDG6nGuBp3eX331VdUoCsFRNKhCgAcBtJCQkCzzI/g3cOBAFZhDMyY03UVwEIGjOmi8dbdHQ5s2bVSDKK0RkSkEZbSaoGhcZM78+fPV49F4CQFEZEki+86VoewAPqeJEyeqgB0CnOHh4Wo9jRkzRs2DIDgC6QjSDh8+XGJiYtRp7u3atTPUFkW/DDwewTg0H0N9UQRlERTVPPXUUypwiaAssksRqNOybDUI+qK022uvvSZRUVHqNHwEQbFtWArLjnIDaEqFzFbU1TXNEP6///s/1ZQKy433h20QWbeo55sbqD371ltvqWAslhcZnj8ala348ssv1ftGVi/eC+ZF0y0NsmoR1EVJBKw3BHnnzJkjtWvXNjQuQwYp/j8Q7MT8yHzWPhtryMtroBwBPkM0MMP/t6ZHjx4q+KzR6uJq5SvQ8A0BeHz2X331lYSFhanaycbPkdfsavzf44ACDsjgc0AJDA0CuhEREfnK4CYicigpiSInV90tfbBCJDUh6zwI1NZ8SKRWb5GK7UQ8vUQuZT0wbs/cdLktkOSCMPAICAhQR1OR3VDQcPQZp01h4G/cdI2I2wjxe4T4e0MFacPxGBW4zTAzOtSScJFhi4Btu2qlHH7MlRcIAKIx05QpUwzjNgQBkY349ttvZ5kfQTMEZZEZp0GQDg2hEPg1hoAMgj4ItmXXMArBOgQCUW/TuN4qArQI3E2YMEGefvppq69/BNyQGYrls1aDJuyGYLkRHHPEMhsIRKLEgrnMaBKH30aQUf/XX39lCszbK3ynvPzyyyq4bk5B/P/aE+4/E7cRVwvULhQ5vtJ8oNY3UKRGT5HaCNS2F/EwSTBA0HZaPkryPLtBpFzumnrmZ8zLTFsiIiIiUiqV9DcEbD3c3STdKHqLpmOoYYtSCsV9XC/DFtBcCacgG2e64gA7Tq/HqdTm4HZk5hpD1p1WT9VakLmLTEgsD7L/UKMVgV80n9Iyek0lJyeri0bLkEQAxPgUd+02BNC0i7Voz+WIeSSOvOyOxFbr+dlnn1UHR/B/gTIL9golMlBTeMCAAdmuI+3/1tz/tjPQvp+c8b2RdXAbcWCpiapGrdvhP0VOrBA3XDehU4Hah0RX81F9Rq1xoNb0e8E3UNw8vcUt7d74x1I6T2/9a1nhu8bS7ysGbYmIiIhI+X3nBcOaGPVAFXmieYSci7oq5UNLS5C/t0NmQ1o7OIIal9op8xpcR/1KcxA8NTc/brcmnPatZX/ilHVk3X7xxReqDi5qmgYFBWV5DE4XR2auKZQHQGaeMZQVwA4Gsh5xsQYEWbSaoY64bWlBAGutD7K/bQTlHsCeP2Nk3OPAUE7N6bD82F5R4xl1ip0N3huy1bC98ExV4jbi+BCY9Tq/QXxOLRfv8xvEPe1OlnkyvEtIUsUHJalyN0kp1/xeoPb6jfs8u7e4918u7klZ59PpMuR2/G0pWqyouLllPes9wydQMpK9RaKjJb/i4+Mtmo9BWyIiIiKStPQMQ9AWWbb9mkZIoJ+XpAZ4q7+OGFRzJVrGBuqyot6qVj8VNTXReAs1Tk0hY9g4CxgZhSj1gPqf5sojYAcDp6njYk2OGkR6//331YUKnqNuI/YC/7MIZqLZobOWR8BvFL67GLQlbiMOKiVBlT5wQ41a/DWbURukz6it9ahIhbbi41FE8vSNZqYHAaiD0zExElgI3yWWfhczaEtEREREsv5YjFy9pT9VrGP1ECld3IenmppAoyUPDw+5evVqpttxHY2rzMHtuZk/r8qWLav+1qpVy3Cbt7e3VKpUSc6fP2/2MbgfF1PYUTHdWcF1BEW0izUgK057Lh4UIG4jBUf7vzX3v+0snP39Uf5xG7HTQC2aiB1GM7GVImYyagWB2poPi9TuJW4V2qqMWjcn2E4sfX5+oxERERGRRO64VxphYLNwrpFsOrg3btxY1qxZkykrA9dbtmxpdp3hduP5YdWqVdnOn1dYLgRgjx07lqmkAZqblS9f3qqvRURERJTnQO3BBSK/DxH5rLLIH8NFUK/WOGDrFyzSaKjI4EUir58QeWSySOVOWZuKuQBm2hIRERG5uKu3kmTdMX19rjLFfaR9tVK2XiS7hXICQ4cOlSZNmkizZs1k0qRJkpCQIMOHD1f3DxkyREJDQ1W9WBg5cqS0b99e1Zft2bOnREZGys6dO2XatGmG54yNjVXZsJcuXVLXtcArsnG1jFzUwMXl5MmT6vqBAwdUc6SIiAhVrxblDEaMGCHjxo1TJQ4QqEUTMujbt6/V3j8b/RA5HjbLIyKbSr6tmogJSh+cWGU+oxaBWmTU1uqlSh+IB8OVwLVARERE5OLm7bwg6Rn6ruP9moSJpwdPxspO//79VaOusWPHqiBqgwYNZPny5YZmYwi+Gp/y1qpVK5k9e7a8++67MmbMGKlataosWrRI6tSpY5jnr7/+MgR9AV3gAQFYNBaDqVOnZmoa1q5dO0Pd2mHDhqlpBGlRu3Lw4MFy584dad68uaxdu1YCAwOtkmWM94XAMupG4np+SxpoTbywzCyPQNxGCgb+z/Cdhf8x1gYmokIN1B5fri99cGJ1NoHakobSB1K+DQO1ZrjpeNjtvtCUISAgQHWkNG3KUBCQwRAdHS0hISGsyUPcRojfI8TfGyrgcYdO2n++Ti7E3hHE4Da92VHCAv1sMiYp7DEX5W79p6SkyOXLlyUxMWtzkLzAbgi2Ma1eLhG3kYKB/y80JSxatKhTbmTcfyZuI/YYqEVGbVIOgdreIuVb21WgNqMQx72WjnntZ+0QERERUaH759Q1FbCFtlVLGQK2RKaQXYtyDMiOTU9Pt8rO0fXr11VHezYPIm4jBQcZtmiiSERkdcnx+mZihxaKnFxtPlDrX+pe6QM7C9TaO64pIiIiIhcWud2oAVlTNiCjnGmnWFvjNGsEbfE8Pj4+DNoStxEiImcM1GoZte48cJQXDNoSERERuajrt5Nl5eErajrY30seqKmvy0pERERElClQe8yo9EF6cjaB2kfu1qhloNYaGLQlIiIiclHzd1+U1HR9A7LHG4eJlycbkBERERGRiCTd0teoPbRIn1FrNlAbIlLrkbulD1oxo9bKGLQlIiIickFoAhW5415phP4sjUBERETk2nITqEXpg4iWDNQWIAZtiYiIiFzQjrM35HRMgppuXjFIKpVyzq7iRERERHSfQO2xZfrSByfXmA/UFi19r/QBA7WFhkFbIiIiIhcUuf28YXpgswibLgsRERERFaKkuHs1alVGbUoOgVpk1LZgRq0NMGhLRERE5GLiElNlyYHLajrAt4h0q1PG1otERERERAUeqF2mL31wak02gdoy92rUMlBrcwzaEhEREbmYRXujJDktQ033bhgqPkU8bL1IRERERFRggdqFIqfW5hCofVRf+iAcGbVsTGsvGLQlIiIicrEGZHOMSiMMaBZu0+UhIiIiIiu6c/NejdrsArXFyt4rfRDenIFaO8WgLREREZEL2XcxTo5eiVfTDcJLSI0yxW29SERERESU70Dt0rulD9aKZKSaD9QioxalDxiodQgM2hIRERG5bAMyZtkSEREROX2gFhm1Yc2YUetgGLQlIiIichG3k9Pkr32X1LS/l4c8VK+crReJiIiIiCx154bI0aV3Sx+syyZQW+5ejVoGah0ag7ZERERELmLxvkuSmJKuph9pECr+3hwKEhERETlEoBbNxE6vNx+oLR56r/RBWFNm1DoJjtSJiIiIXMScHRcM0yyNQERERGTPgdol+tIH9wvUovRBaBMGap0Qg7ZERERELuDI5Vuy78JNNV2rbHGpGxpg60UiItK7eUEk8XrWtaHTiWdsrEj6ZRE3t6z3+wWLlGBtbiJyEomx+kDtYS1Qm5Z1nuJh90ofMFDr9Bi0JSIiInLBBmRu5gIgRES2CNhOaSySlpzlLncRKZnTYz29RV7cxcAtEblIoBYZtY2ZUetCGLQlIiIicnJJqemycE+UmvYp4i6PNgy19SIREekhw9ZMwNYieBwez2xbInK4QO1ifemDMxvMB2oDwu/VqGWg1mUxaEtERETk5JYeuCy3kvQ7BD3rlpPiPkVsvUhEREREriM3gVoto5ZnRbk8Bm2JiIiInFzk9nsNyAY0Y/1HIiIiokIJ1B75+27pgw0iuvSs8wREiNR6RKR2H5HQRgzUUiYM2hIRERE5sZPRt2X72Vg1XSWkqDQpH2jrRSIiIiJyTgnXRY7+fTejdmP2gdraKH2AjFoGail7DNoSERERObG5O+41IBvQlA3IiIiIiGwTqO2lv5RjoJYsw6AtERERkZNKScuQ+bv1Dci8PNylT6MwWy8SEZF1XTsmUqyMiH8pEXcPrl0iKhwJ1+6VPjizyXygtgRKHzBQS3nHoC0RERGRk1p1+KrEJqSo6S61S0uQv5etF4mI6J7k2yIXd+ZvjSx4Vv/XzUOkaGl9ALdYWZHiZe9NGy5lRHwDWTOSyFndvCCSeD3r7TqdeMbGiqRfNv//7xcsUiLcSoHa8vpsWgRryzXk9w3lC4O2RERERE4q0qg0wsBmETZdFiIiuXVZ5PxWkQv/6v9eOWg+6JEXeJ74S/pLTjx9zAdzi5fLfLuXHz8wIkcL2E5pLJKWnOUudxEpmdNjPb1FXtxlPnCrArV/6UsfnN2cc6C2dm+Rsg0YqCWrYdCWiIiIyAldiE2UTSeuqemIID9pWSnY1otERK4kI0Mk5mjmIO3NeweSrKbmIyLpqSLxl0Xir4jcvooIbvbzpyWJ3Dirv+TEO+BuMNcosFvMKLCL25HZ61HE6m+JiPIAGbZmArYWwePweC1oeztGH6g9rAVqM7I+JrDCvdIHDNRSAWHQloiIiMgJzd1xwTDdv2m4uLubOR2QiMhaUu+IRO0WubBN5Pw2faA2KS6HB7iJhNQSCa6sD47kVdvXRMo1uHc9PU0kIVofxEVmrxbMjTeavnVJJOlmzs+bHKe/oGZuTu/Bv2TOGbu44NRrd+T6EZHdunNDZMeP9w/UIpsWwdqy9ZlRSwWOQVsiIiIiJ5OWniHzdumDth7ubtK3MRuQEZGV4ZRhFZy9G6S9tFckIzXnsgShTUQimotEtBQJayriW0L/uPwEbU15eOoDp7iE3ifIbC6Yq27D5ZI+6Jt2J4cn0YkkxOgvV/ZnP5u7p0hRLWs3h9IM3sUZBCKylV97m8/SD6x4r0YtA7VUyBi0JSIiInIy647FyNVb+lMEO9UIkZDiPrZeJCJyZDqdyPVTd0sd3A3SXj+Z82P8SopEtNBfwlvogx2edtQMsYivSFBF/SWn9518yyRjVwvsapm8KMlwRSQjLfvnwX23LuovOS6Tv1FQN4fSDEX4nU5kfUYB26BK90oflKnHgylkMwzaEhERETmZyO3GDcgs6IZMRGQsLUXk8j6jerTbRBL1NbKzFVz1XhYtgrQoe2CuS7sjwfL7BOgvITVyrt+L9ZMlY1cL9t69HRm5OUlNEIk9pb/kxDfQTDDXpDSDf4g+69jZGk2h7qgpnU48Y2NF0i+b3+ZQnsJcgykiY8VDRer115c/KFPX8b+/yCnY1bf4xx9/LAsWLJCjR4+Kr6+vtGrVSj799FOpXr16to/B/B999JGcPHlSUlNTpWrVqvLaa6/J4MGD1f247d1335WlS5fK6dOnJSAgQDp37iyffPKJlCtXrhDfHREREVHBuxKXJOuORavpsgE+0r5aCFc7Ed2/luOFHfeCtFG79A27suNeRKRcQ6MgbXN9bde8QEANndvz0kAIj8PjbQ31aouG6C/IKM4pGI5GaWYzdo1q7yK7936fFy7Rh7Ofx81dH7jNrs6udjsCwI4QnELAdkpjs9sJqgWXvN928uIuBm7tEbLZ8V2TkmByuS2SmnhvWv1NNJpO0B/kMH5MYmz+lmXAb/rvNSI7YldB2w0bNsgLL7wgTZs2lbS0NBkzZox06dJFDh8+LP7+/mYfExQUJO+8847UqFFDvLy8ZPHixTJ8+HAJCQmRrl27SmJiouzevVvee+89qV+/vty4cUNGjhwpjzzyiOzcubPQ3yMRERFRQZq384Jk3D3Dr2+TcFXTlogoU5Dk5vnM9Wijj5iv5ahBpimyZ7UgLQIbKC9gDciAREDNTAZlhk4nsbGxap/P3RkyKFEeAst7v2VOvq0P7maXsavdnp5DoBtNlFC2AZfLe7Ofz8Mra/kFc2UZvIuKTWH7yEtgH/A4PN6RthV7/N5IT8kaXM0UOEVA1STQqgKv2QVd71431/DLJjheIvtjV0Hb5cuXZ7o+c+ZMFXzdtWuXtGvXzuxjOnTokOk6ArI///yzbN68WQVtkVm7atWqTPNMmTJFmjVrJufPn5eIiIgCeCdEREREhS8jQydzd+obkCG+0a8JG5ARubz0NJGrBzMHaRH8y0mJ8vrgrBakLVldn01aULILZGZkSJpHtEhISMG+vr1BgBQXlJjIKYiGbNtMwVwztXcR/M0pKIZAHIL4uOTEq9j9G6mh2Zo91S12VempWQOjWTJZc8pmzSbomlPdZltDo0McgLhfljqRg7GroK2puLg49RdHVi2h0+lk7dq1cuzYMVVWIafndXNzkxIlSpi9Pzk5WV00t27p//EzMjLUpaDhNfBeCuO1yDFxGyFuI8TvEjJn04kYuXhD3+m8XdWSUi7AJ1/jicL+vXGUsc8333wj//vf/+TKlSvqTK6vv/5aJQRkZ968eeqsr7Nnz6pSXhin9ujRI1O5r6lTp6pEBWQV7tmzRxo0aJDpOaZNmyazZ89WZ5DFx8ers8dyGss2b95c9u3bZ/a5yMklx4tc3HkvSItpBF2y4+ahr99o3DQMwTmybzgy5xekv5Sunf18Gekit6NzbqSG6Tv3ObU8JV7kGi7H79+AzmwjNaNAL0ppuHvk7X07E3w2hsCopWUAsstmNbqOQLy9cvcU8Sp69+In4uV/d9pfpIjJdXW/No377/41zON373bUb760V2Rae1u/QyLXCNpi0D5q1Chp3bq11KlTJ8d5EYQNDQ1VA1QPDw/59ttv5cEHHzQ7b1JSkrz11lsycOBAKV68eLa1dSdMmJDl9piYGPX4wnjveE/YSXJ3pSPKZDFuI8RthKyB3yXO5+fN95rXdKtWXKKj9bVtHWUbQTDS3s2dO1deffVVFWRFYHTSpEnq7C4kDeAMMVNbtmxR406MLx966CEVeO3Vq5cKvmpj3ISEBGnTpo3069dPnnnmGbOvi5Jf3bp1U5fRo0fnuIxvvvmm6t2AoC25AJwqjwCtFqS9ciDnzEoEO8Ka3gvShjax/anvVHAQHEXw9H6B+NQkfSkFs43UjIK8CCDmBA3ZcLl6IOcDBVrzNOOMXdPSDD4l7KPeLg4opt3JXX3VHDNa707jOe0VaiIbB0xzCppq1y0JujITm8g5graobXvw4EFV5uB+ihUrJnv37pXbt2/LmjVr1EC6UqVKWUonoCkZBsPY8fjuu++yfT4MhPEcxpm24eHhUqpUqWwDvdaEHSRkAuP1GLQlbiPE7xHi7w1Z4trtZNl0Wn+WUrC/l/RuVlW8PN0dakzi4+Mj9u7LL79UgVX0UAAEb5csWSI//fSTvP3221nm/+qrr1Sg9Y033lDXP/jgA1W6C+W68FjQGugiEzc7SGaA9evX57h8y5Ytk5UrV8r8+fPVNDkZBI9ijt5rGIa/9zutHTVJtQAtLiG19VlpRMaK+IgEVtBfcirJgEzu+zVSw9+M1ByeJ13kVpT+khNP33vBXCxffpzdrM8SzkvQ9X6BaptyyyGwanoxDayay3S9O43mbfYQMCdycXb5a/3iiy+qhmIbN26UsLD712LDTkSVKlXUNE7/OnLkiMpmMA7aagHbc+fOqRIKOQVfvb291cXc6xRWEBU7SIX5euR4uI0QtxHidwkZW7jnkqSm6xsJPd4kTHy8PB3u98bexz0pKSmqhIFxpiuWuXPnzrJ161azj8HtxskAgMzcRYsWWX35rl69qgLKeG4/Pz+rPz/ZQOodkajd94K0uCTpD86Y5yYSUitzkDYgnMEXsg4E8XyK6y+lquV8cAHlFu7XSC0hJucGeMhEvXFGf8mvle+IzSEoet9sVJPrOQZdMb8v/7+JnJhdBW2RAfvSSy/JwoULVRZBxYoV85wVYlyTVgvYnjhxQtatWyfBwcFWXGoiIiIi24+h5u7QNyCDAU3ZaLUgXLt2TdLT06V06dKZbsf1o0ePmn0M6t6amx+3W3sbGDZsmIwYMUKaNGmSY9auhn0c7FDCNRWYdVOlDv4VubxX3HLIWNSh+U5oY5Hw5qJDLdrwZiI+ASYz6fQXB8Q+Dg7MN0h/KV0n54ZZaJSmGqbpg7luhunLd8s1XBa3HA9UWJ/OwzuH7NW7QdW7t+nuV5dVu46/BVHH14H/v60OpZzy8fAMrEcHqa1Pjv+bY+lreNpbSQTU+frzzz9VyQNtMBsQECC+vr5qesiQIap+LTJpAX8xMK1cubIaeC5dulR+/fVXQ/kDBGwff/xxVTcM2bsYaGvPiwZnXl7sbklERESObfuZWDl9TX/6ZotKQVKxpL+tF4kKGZqhoSbw/erdGmMfBxvT6cQj7qx4XdklRa7sFq/Lu8QzLudge7pPkKSWbSwpZRpJapnGklqypr5juuZWssit/NWytiesve4KvES8IkSCcDE/h1tqonhd3CKBK17I86sk1H5S0ktUFF0RX9F5+klGEX/RFfHTX8dfz3vXVbMsa0i7e7mD2rV2XL/WSbgn6qSUh5e45aERm87DS64l6iQjn70AyLFlFGIvB0v7ONhV0FYLtJrWop0xY4bKHIDz589nWnlo3PD888/LxYsXVWC3Ro0aMmvWLOnfv7+6PyoqSv766y81bdo5F1m3pq9FRERE5GgijbJsBzZjlm1BKVmypGp6izIExnC9TJkyZh+D23Mzf16h/BdKMZiW+EJyw5NPPik///xzlsewj0MhQyDh8j7VMMztbqkDNzRsyoEuuKoqcaALby4S3kLcgiqJl5sbwlwugb0+yMA9f9m2vq2eESlbnyvUmYWEiO7FnaJLjDX7XXLjxg0JDAw0H4zzC5KSKCVDLi2jEHs5WNrHwa6Ctohm349p84X//ve/6pKdChUqWPS8RERERI4oLjFVlh64rKZL+BWRrrWtGwyke3CGVuPGjVXj2169ehkG+LiOngzmtGzZUt2vNRIDNCLD7dY0efLkTGPiS5cuqdq5c+fOlebNm5t9DPs4FLA7N0Qu7LhXjzZql0haUvbzuxcRKdfwXi3a8Obi5l9S3eXK7YDYx4Hubgj5WhHueLyd100nKwgsr7+YysiQ9CLR4h4SYvf188k1fnPcLXx+uwraEhEREVHuLNxzUZLT9HWxejcMFZ8iBVAzjwzQVGzo0KEqg7VZs2YyadIkdebX8OHDzZbyGjlypLRv316++OIL6dmzp0RGRsrOnTtl2rRphueMjY1VZ5Mh0ArHjh1Tf5GNq2XkorwXLidPnlTXDxw4oMqJRUREqJJf+GusaNGi6i9KiFnS2JfyCUkiN8+JnP/3XpA2+nDOj/EpoQKzhiAtArY4NZuIiIiIQVsiIiIix4WziVgaoXChBFdMTIyMHTtWBVFRfmv58uWGZmOmpbxatWqleja8++67MmbMGKlataosWrRI6tS515wHpby0oC8MGDBA/R03bpyMHz9eTU+dOlUmTJhgmKddu3ZZyohRIUpPE7l6UJU6MARp4/UZ79kKrKBKHBiCtCWrM/OPiIiIssVMWyIiIiIHtffCTTl6Rd/IoFFECalWupitF8kloBRCduUQTEt5Qd++fdUlOwi63i/wiuCtFsC1BEuEWVlyvMjFnfog7QVcdoik6pv/meXmIVK2XuYgbTGWLiEiIiLLMWhLRERE5KAit99rQDagKRuQEVnNrUt3s2jvBmmvHBDR6cuQmOVVVCSsqUhES32ANrSxiLe+RAURWYFfsIint0hacu4fi8fh8UREDoZBWyIiIiIHdDs5Tf7er6+BWtTbUx6qX9bWi0TkmDIyRGKOZA7S3jyf82OKlbubQYsgbXORkNoiHty1IiowJcJFXtwlkng9y10ZOp2qDY763qrhmCkEbPF4IiIHw5EFERERkQP6a+8lSUxJV9OPNCgnfl4c1hFZJPWOSNSue0Hai9tFkuJyeICbSOnad5uG3Q3SBoTnu5s9EeUSAq/mgq8ZGZLmES0SEsI60UTkVDi6JyIiInJAc3fcywQcyNIIZM9uXjCbHSc6nXjGxoqkXzYfALVWdlzCtXsNw/D38j6RjNTs5/f0FQlrci9Ii2nfEvlfDiIiIqJcYNCWiIiIyMEcvnRL9l3UZwbWLldc6oYF2HqRiLIP2E5pbLYOpbuIlLxfHUqcDp2bwK1OJ3L9ZOZSB7ieE/9S+lIHqmlYS5EydUU8vfiJEhERkU0xaEtERETkYCKNsmwHNGMDMrJjyLDNS+MgwOPw+JyCtmkpIpf3GgVp/xVJvJbz85asZlTqoIVIUCWWOiAiIiK7w6AtERERkQO5k5IuC/dEqWnfIh7yaINytl4kosJz54bIhR36UgcI0KI2bVpS9vN7eImUa3gvSIu//uwiT0RERPaPQVsiIiIiB7L0wGWJT0pT0z3rlZXiPkVsvUhEBSf+ssi+Y/eCtNGHc57fp8TdUgd3g7QI2Bbx4SdEREREDodBWyIiIiJHLY3Q1ApNmojs2ZwBOd8fWOFeBi3+ovSBO6rlEhERETk2Bm2JiIiIHMTJ6HjZcfaGmq4SUlQalw+09SIRFR43D5Gy9YyCtC1EipXhJ0BEREROiUFbIiIiIgcRuf1CpixbNzc3my4PUYELaypS5UF9gDa0sYh3Ua50IiIicgkM2hIRERE5gOS0dFlwtwGZl4e79GkUZutFIip4PT4XKdeAa5qIiIhcDgs+ERERETmAVYevSmxCipruWqeMBPl72XqRiIiIiIiogDBoS0RERORgpREGsgEZEREREZFTY9CWiIiIyM6dv54om09eU9Plg/2kRaVgWy8SEREREREVIAZtiYiIiOzc3J3nDdP9m4aLuzsbkBEREREROTMGbYmIiIjsWFp6hszbeVFNe7i7yeNsQEZERERE5PQYtCUiIiKyY2uPRkt0fLKafqBGiIQU97H1IhERERERUQFj0JaIiIjIjkXuMGpA1izCpstClGt+wSKe3nlbcXgcHk9ERETkgjxtvQBEREREZN7luDuy/li0mi4X4CPtqpXiqiLHUiJc5MVdIonXs9yVodNJbGysBAUFibubmTrNCNji8UREREQuiEFbIiIiIjuFWrYZOv103ybhqqYtkcNB4NVc8DUjQ9I8okVCQkTceQIgERERkTGOjoiIiIjsUEaGTubeLY2AJMR+TZlxSERERETkKhi0JSIiIrJDm05ek6ibd9R0+2qlJLSEr60XiYiIiIiICgmDtkQORof6bwkpcikuWf3FdSIicj6R288bpgc0ZQMyIiIiIiJXwpq2RA4i7k6qzN91UX7eclbOxSbevfWglA/yk6GtKshjjcMkwLeIjZeSiIisISY+WVYdvqqmSxb1lgdqhnDFEhERERG5EAZtiRzAhuMx8tysXXInJT3LfedjE+WDxYfl85XH5LtBjdUptERE5Njm774oaXc7kD3eOEyKePDkKCIiIiIiV8I9ACIHCNgOn7Fd7qSmC3bfTYshaLfhfsyH+YmIyHGh7I3WgAwGsAEZEREREZHLYdCWyM5LIiDDVgVm71O6FvdjFsyPxxERkWPadjpWzlxLUNMtKwVLhZL+tl4kIiIiIiIqZAzaEtkx1LBFSQRLe41hPsy/YPfFgl40IiIqIHN3GDUgaxbO9UxERERE5IJY05bIjk+PRdOxvJj5z1kZ1qqCuLm5WX25iIio4NxMTJGlB6+o6RJ+RaRr7TJc3XmQmJgoq1atkn/++UcOHz4s165dU7+JJUuWlJo1a0rr1q2lc+fO4u/PLGYiIiIisk/MtCWyUzcSU+VcbGKWGrb3g/nxuJuJLJFARORoFu6JkpS0DDXdp2GY+BTxsPUiOZQDBw7IsGHDpEyZMtK7d2/55ptv5OTJkypgi4Ohx48flylTpqj7MA/mxWOIiIiIiOwNM22J7FRCclq+Hn87OU0C/b2stjxERFSwEFSM3H6vAdlAlkbIlf79+8v8+fOlSZMmMn78eHnwwQelVq1a4uGROfCdnp6usm9Xrlwpf/zxhzRs2FD69u0rc+bMsdZHSURERESUbwzaEtkpf+/8/XsWzefjiYiocO25cFOOXY1X043LB0rV0sX4EeSCu7u77Ny5Uxo0aJDjfAji1q1bV11ee+012bt3r3z66adc10RERERkV1gegchOpWdk5Cnwiiq25YP8VC1EIiJyHJHb7zUg69+UDchyC5my9wvYmoPH5DbLFmUXKlSoID4+PtK8eXPZvn17jvPPmzdPatSooeZHsHjp0qWZ7l+wYIF06dJFgoODVSkHBJJNTZs2TTp06CDFixdX89y8eTPT/WfPnpWnn35aKlasKL6+vlK5cmUZN26cpKSk5Oq9EREREZF9YNCWyM7cSUmXb9adlI6fb1AlDvJiWGs2ISMiciTxSany977LarqYt6c8VK+srReJsjF37lx59dVXVUB09+7dUr9+fenatatER0ebnX/Lli0ycOBAFVDds2eP9OrVS10OHjxomCchIUHatGmTY8Yvmqt169ZNxowZY/b+o0ePSkZGhnz//fdy6NAhmThxokydOjXb+YmIiIjIvvH8aSI7kZ6hkwW7L8oXK4/LlVtJeXoOdzdRTWv6NAqz+vIREVHB+WvfJbmTmq6mH2lQTvy8OETLL2SrHjlyRAVMNStWrJAPP/xQkpOT5YknnpCRI0fm+nm//PJLeeaZZ2T48OHqOgKjS5YskZ9++knefvvtLPN/9dVXKtj6xhtvqOsffPCBrFq1SjVEw2Nh8ODBhmzZ7IwaNUr9Xb9+vdn78Rq4aCpVqiTHjh2T7777Tj7//PNcv08iIiIisi1m2hLZgU0nYuShrzfLG3/sNwRsPdzd5MnmETJ5QEMVjHVD3YMcaHdPHdRYAnxZGoGIyJHM3WHcgCzCpsviLN58802VFas5c+aM9O7dW/0FZMui5EBuoNTArl27pHPnzplq6eL61q1bzT4GtxvPD8jMzW5+a4qLi5OgoKACfx0iIiIisj6mcRDZ0NErt+TjpUdlw/GYTLd3rhkib3evIVVC9E1oAvyKyHOzdqnSCaAz81yeHm7y49Cm0q5aqUJZdiIiso5Dl+Jk/8U4NV0ntLjUCQ3gqrWCffv2GbJb4ZdfflFNyFCioGTJktK/f3+V6frss89a/JzXrl2T9PR0KV26dKbbcR3lCcy5cuWK2flxe0E6efKkfP311zlm2SLjGBfNrVu31F+UWcCloOE1dDpdobwWOSZuI8TthPhdQs74m2Ppa9hV0Pbjjz9WjRgw6EUDhVatWqnaXtWrV8/2MZj/o48+UgPT1NRUqVq1quoErJ1mBljpqDs2ffp01bShdevW6lQxzEtkC1dvJcmXK4/LvF0XJMMoAls3NEDG9KgpLSsHZ5q/fbVSsnX0A6p8wsx/zsq52MQsz+nl4S71wrijT0TkaCK338uyHdCUWbbWzDJFYy8Nmn89+OCDKmALmF62bJk4o6ioKFUqoW/fvqqUQ05j7wkTJmS5PSYmRpKS8laqKbc7LPicMFZHxjIRtxHidwnx94ZsJaMQxyXx8fGOF7TdsGGDvPDCC9K0aVNJS0tTjRPQSffw4cPi7+9v9jE45eudd95RHXm9vLxk8eLFqsZYSEiIOvUMPvvsM5k8ebL8/PPPqqPue++9p+7D86KLL1FhQWOxaRtOyfRNZwy1CyG0hK+82a26PFyvnLijFoIZKHkwvHVFGdaqgsQmJMu5qKtSPrS0/G/FcYnccUESUtLlh01n5PWu2R/kICIi+4IzKBbtjVLTvkU85NEG5Wy9SE6jbNmyqqYtXL58WZU10OrQwu3bt3M9IEfAF9m6V69ezXQ7rpcpU8bsY3B7bubPr0uXLknHjh1V8sP9yj+MHj1alYkwzrQNDw+XUqVKSfHixaUwdo7c3NzU6zFoS9xGiN8lxN8bsqWMQhyXWBqLtKug7fLlyzNdnzlzpgq+YpDdrl07s4/p0KFDputoKIHg7ObNm1VgFhHySZMmybvvviuPPvqo4fQ4nJa2aNEiGTBgQAG+IyK9tPQMmbvzgkxcdUKu3b53GmIxH095sWMVGdqqgmogZgl8iQT6eUlqgLf6+9IDVWX+7ouSmq6TGf+ckafbVJRAfy+ueiIiB7DkwGWJT0pT0w/VKyvFfFiT3Fow7kN5AGSM/vvvv+Lt7a1q2hqXT0CzrtxAgkDjxo1lzZo10qtXL8MAH9dffPFFs49p2bKlul9rJAZoRIbbCyLDFgFbLOOMGTPuu8OBdYKLKTyusIKoGNcU5uuR4+E2QtxOiN8l5Gy/OZY+v10FbU0hLRksbaCAAO3atWtVp1yUVQA0m0DNMOMGEAEBAdK8eXPVAIJBWypIaps8Gi0fLzsqJ6NvG273dHeTwS3Ly8udquY7wIos3X5NwuW3f8+rbNvpm07Lm91qWGHpiYiooEVuP2+YHsAGZFb13//+V53m/+uvv0qJEiVUMoBWWxYZpX/88Yc6wyu3kJk6dOhQadKkiTRr1kwlByQkJBiyeIcMGSKhoaGq9ICWUNC+fXv54osvpGfPnhIZGSk7d+7MlAUbGxsr58+fV1mygLEsIBtXy8jFeBYXlASDAwcOSLFixSQiIkKNlRGwRTJD+fLlVR1bvHdNQWX1EhEREVHBsdugLbIWkJGA+rN16tS5b3AXg2M0UsApa99++62qUwZak4fcNIBgUwayhoNRcSpYu/V0bKbbu9cpI290rSYVgvUlP/JS5Nq0QPaI9pXk950XVLbtz1vOylOtK0gQs21dGht3ELcT+3fiarzsPHdDTVcNKSoNworbXUOmwv4usebrFC1aVH777bds77t48aL4+fnl+nnRwAwB0bFjx6qxZIMGDdTZYtpYE8FX4+wJlCmYPXu2OusLpb/QUwFnexmPb//6669MpRu0pAL0ZBg/fryaRtM04/qz2lloyKgdNmyYyt5FQBeXsLCwTMuMz5CIiIiIHIubzk5Hcc8995xqDoEyB6YDT3MD/NOnT6vaZDj97IMPPlCDYWQbbNmyRQV+kbmA2maafv36qbTnuXPnZnk+DI7NNWU4fvy4ymgorOLHyAjmqWKO5/KtZJm65ZKsOJo5WFu3rL+81DZM6pUrWiDbyGdrz8uC/fqsmsFNSssLbXL+vyHnxu8R4nZi/yZtuCCRe6LV9Kh2YTKgUeYDzK74XYKmDNWqVVOvWRg1VSkzZCDjsy6s9Y/tKzo6WpVD45iXuI0Qv0uIvzdkSxmFOC6xdMxll5m2qAmGhmIbN268b8AWsDKrVKmippHtgKYTOCUNQVvtdDA0fDAO2uI65jWHTRkoL27dSZVvN5ySmVvOSUravUyh8kF+qslYt9ql1YGCgiqQ/Vr3YrL40AZJSdfJ/P3X5OUutSW4aNY6deQa2NyFuJ3Yt+S0dFlxbL+a9vJwk8Htqqs65a7+XZKfBrEY+7300ksqiza3g+ZvvvlGjf+IiIiIiOyFXQVtkfSLwfbChQtl/fr1UrFixTzvYKDEAeA5ELhFBq4WpMXgHA0pkM1rDpsyUG4gQPvbv+dk8poTciMx1XB7oF8RefmBqvJk8/Li5ele4AWyQwP9VT3EX7aek8SUdPnhn7MyuntNq78uOQ427iBuJ/Zr9ZErht+MbnXKSnDRvAcrnem7JD+vgRIEn332mQwcOFCdUdW2bVtVNsuc1NRU2bBhg/z+++/qgrqwDNoSERERkT2xq6AtmkFgwP3nn3+qMgRazVmkDPv6+ppt7oC/aARRuXJlFahdunSpajjx3XffGXY0UBsXzShQQwxB3Pfee0/KlStn6PpLlNeDDMsPXpFPlx+Vs9cTDbcjQDu8dQV5vkMVCfAt3C7geM3I7RckJT1DftlyTp5pW0lKMtuWiMjuRO4wbkAWbtNlcRb79+9X40g04UL9VxyER91YjP0CAwPV7/aNGzdUk9qDBw+qwG3dunVlypQp8uSTT9p68YmIiIiI7DdoqwVaUdbAmNZgwVxzB3Trff7551UzCQR2a9SoIbNmzVJNIjRvvvmmmu/ZZ5+VmzdvSps2bVTDiPycgkeubde5G/LR0iPqr7FeDcrJ612rS1hg7hubWEOZAB8Z2Cxcft56Tu6kpsu0jadlTA9m2xIR2ZNz1xPkn5PX1XSFYD9pWSnY1ovkFHCgHsFXXPbs2aP6G2zdulW2bdsm16/r13dwcLAaK7711lvy6KOPSqNGjWy92ERERERE9h+0taQnGsomGEMGLS73G8S///776kKUH2evJchnK47K0gP6LHBNi0pBKjhaL6yEzVfw8x2ryJwdF1TZhl+2nlXZtqWKsbYtEZG9mLvjgmG6f9MIq9U7p3saNmyoLkREREREjsqugrZE9upGQopMXntCZm07J6np9w4uVC7lr4K1nWqE2M1Od+niPvJEswiZueWsJKVmyLSNp+SdnrVsvVhERIRaqukZMm/XRbUuPN3d5LHGoVwvRERERESUBYO2RDlISk2Xn7eclSnrTkp8Uprh9pJFveSVB6tJ/ybh4ulR8I1Zcuv5DpVlzvbzkpyWIb9uOyfPtKskIcVYDoSIyNbWHo2WmHh9s9QHaobwu5mIiIiIiMxi0JbIjIwMnfy9/5J8tvyYRN28Y7jdp4i7PNu2kjzbvrIU9bbff5+Q4j7yZPPy8tM/Z1S27fcbTst7DzHblojI1iK3Gzcgi7DpshARERERkf2y36gTkY1sPXVdNRk7EBVnuA2VD/o2DpNXH6yumn05ghHtK8lv/55T2bYo6/B/yLYt7hjLTkTkjC7dvCMbjseo6dASvtKuailbLxIREREREdkpBm2J7joZHS+fLDsqq49EZ1on7aqVktHda0jNssUdal0hQDuoRXn5cfMZFbj9bsMpGfdwbVsvFhGRy5q386Jk3C2L3rdJmHi420ctdCIiIiIisj8M2pLLQ23BSauPS+SOC5Ku7U2LSI0yxeSdnjWlrQNnQo1oX1ll26JEwux/z8tz7Ssz25aIyAbw+/L7zgtqGrHafk3C+TkQEREREVG2GLQll3UnJV1+2HRapm44JQkp6YbbyxT3kde6VJM+jRw/C6pUMW8Z3KK8TN+kz7b9dv0pGf8Is22JiArbphMxhhrp7auVknIlfPkhEBERERFRtuyv7T1RIWU7dfh8nXyx6rghYOvv5SGvd6km617vIH2bhDt8wFbzf+0rqwZqMHv7ebkSl2TrRSIicjmR2/VZtsAGZIVDp9PJ999/L82aNZOSJUuKh4dHlounJ/MXiIiIiMg+caRKLmXj8RjVZOzolXjDbQjODmwWLiMfqKYyU51NyaLeMqRlBZm28bSkoLbt+pMy4dE6tl4sIiKXKsOz+shVNY3fmU41Qmy9SC7hzTfflC+//FIaNGgggwYNksDAQFsvEhERERGRxRi0JZdw5PItFazddOJapts71ywtb3evIVVCiooze7ZdJfl16zm5k5ouc7ZfkBEdKkvZAJ6aS0RUGP7YdVHS7tZMf7xxmBTx4IlOheHnn3+Wxx57TH7//fdCeT0iIiIiImviXgM5NZQCeGPePukxeVOmgG39sACJfLaF/DC0idMHbA3Ztq3Kq+mUdGTbnrL1IhERucwp+nN3nDdcH9CUDcgKy507d6Rz586F9npERERERNbEoC05pdvJafLFymOqbu28XRdFp09wktASvvLVgAay8PnW0qJSsLiS/2tXWfy8PAy1FS/dbYhDREQFZ+vp63L2eqKablU5WMoH+3N1F5IHHnhAduzYwfVNRERERA6JQVtyKmnpGTJr2znp8L918vXak5KUmqFuL+7jKWN61JA1r7WXRxuEiruTNBnLjSB/LxnaqoIh2/bb9SdtvUhERE6PDchs59tvv5Vt27bJRx99JNevX7fhkhARERER5R5r2pLTnH66+ki0fLLsiJyKSTDcXsTDTTXherFjFQn09xJX90zbSvLLlrOSkJIuc3dckOc6VFHZx0REZH03ElJk+cErajrQr4h0rV2aq7kQVa9eXTIyMuS9995TFx8fH/Hw0J9xonFzc5O4uDh+LkRERERkdxi0JYe3/+JN+XDJEfn3TGym23vWKytvdq3OU1HNZNt+u/6UpKbr5Jt1J+Wj3nUL+yMjInIJC/dEqTMboE+jMPH2zBwwpIKFJmQIyhIREREROSIGbclhXYhNlM9XHpM/917KdHuT8oEypmdNaRQRaLNls/ts263nVN3feTsvyPMdKktYoJ+tF4uIyOnOAIk0akA2sBkbkBW2mTNnFvprEhERERFZC2vaksOJu5MqHy89Ig98sSFTwLZCsJ9MHdRI5o1oyYBtDlAmYtjd2rb6bNtTBf+hERG5mN3nb8rxq7cNBxOrhBSz9SIREREREZEDYdCWHEZKWob8tPmMtP/fOvl+42nDKaeoEzj+4Vqy8pX20q1OWZ4KaYH/tK0oxbz1ifbItkXWMhERWU/k9ntZtgOaRXDV2sitW7dkwoQJ0qxZMyldurS6YPr9999X9xERERER2SsGbckhTjFdsv+yPDhxg7y/+LDcTExVt3t5usuI9pVlw5sdZVjriuo6WaaEn5cMb63Ptk3L0Ne2JSIi64hPSpXF+y+raRwg61G3DFetDVy6dEkaNmyogra3b9+W1q1bq0tCQoKMHz9eGjVqJJcv6z8nIiIiIiJ7w5q2ZNd2nYtVTcZwmqmxPg1D5bWu1SW0hK/Nls3RPd2mksz456zEJ6fJH7suygsdq0h4EGvbEhHlF0r33ElNV9OPNiwnfl4cbtnCW2+9JVeuXJHFixdLjx49Mt23bNky6du3r7z99tvy888/22T5iIiIiIhykue9CGQsHD16VK5du6ZORy9ZsqRUq1ZNihVjzTbKv7PXEuTT5Udl2cErmW5vVTlYxvSoKXVCA7ia8ynAr4gMb1NRJq85obJtp6w9KZ8+Xo/rlYgon4wbkA1oytIItrJ8+XIZNWpUloAtdO/eXV5++WWZPn26TZaNiIiIiMiqQdszZ86obIQ///xTDh48KBkZ+pqiGnd3d6ldu7b06tVLhgwZIpUqVcrN0xNJbEKKCiLO2nZOBRI1VUOKqmBth+qlWLPWip5uU1Fm/HNG4pPS5I/d+mzbiGBm2xIR5dXBqDg5GKWvlVo3NIAHGW0IZRBQwzY7ZcqUUfMQERERETls0Pbw4cMyduxYWbhwoZQoUUI6dOigTilDUDYwMFDVHL1x44YK6u7atUumTJkiH3zwgfTu3Vv9rVmzZsG/E3JoSanpMnPLWflm7Ul1ur6mZFFvea1LNenbOEw8PViz1toCfIuowO2k1SckPUMnX689If/rW9/qr0NE5JJZts3Cbbosrq5WrVoyZ84cGTFihHh5eWW6LzU1Vd2HeYiIiIiIHDZoW79+fenZs6csWbJEOnfuLJ6eOT8sLS1NVq9eLVOnTlWPTUlJsdbykpPJyNDJn/ui5PMVxyXq5h3D7b5FPOTZdpXUxd+btQAL0vDWFeWnzWfkVlKaLNgTpbJtK5T0L9DXJCJyRokpafLnnkuG37FH6pez9SKJq9e07d+/vzRr1kyef/55VcYLjh07psao+/fvl7lz59p6MYmIiIiIzLIoGoZBbW6yZRHU7datm7qg7i2ROVtOXZOPlh4xnEYK7m4i/ZqEyysPVpPSxX244got27aSTFx9/G627Un5oh+zbYmIcmvJ/suGs0Uerl9WivkU4Uq0IZwVhvIHaDaGbFv0YACcIRYSEiI//fSTPP744/yMiIiIiMhxg7b5KW9Qo0aNPD+WnNOJq/HyybKjsuZodKbbUa92dPeaUr0Mm9kVtuFtKsiPm0+rbNtFe6PkpU7MtiUiyq3IHRcM0wOasQGZPRg2bJgMGjRIdu7cKefOnVO3lS9fXpo0aXLfM8eIiIiIiGzJaqNVZC2sW7dOkpOTpU2bNlKsGANvlFl0fJJMXHVC5u44L0Y9xqRW2eKqyVibqiW5ymykuE8ReaZtJflilT7bdvLaE/Jlvwb8PIiILHT8arzsOndDTVcrXVQahpfgurMTCM62aNFCXYiIiIiInDpo+84778iWLVtUkFYL2Hbp0kXWrl2rpiMiImTNmjVSuXJlay8vOWiNv+kbz8j3G09JYkq64fayAT7yepfq0rthqLijLgLZ1LDWFeTHf87IzcRUWbQnSl7sWEUqlSrKT4WIyAKR242ybJtGGE7Fp8KzceNG9bddu3aZrt+PNj8RERERkcMHbefPny+PPvqo4foff/yhgrQffvihajz2f//3fzJ+/Hj59ddfrbms5GCQsfnHrgvyxcrjEh2fbLi9qLenPNehsjzdpqL4FPGw6TLSPcXuZtv+b8UxlQmN2rYT+zPblojofpJS02XBnotq2svTXfo0CuVKs4EOHTqoYPmdO3fEy8vLcD07SDTA/enp9w4oExERERHZC/e8PCgqKkqqVKliuL5gwQKpVauWjB49Wnr06CHPPfecrF+/3prLSQ4EO0Hrj0VLz8mb5K35BwwBWw93NxnSsrysf6ODvNCxCgO2dgifTwk/feOcP/dGyamY27ZeJCIiu7fi0BV1lgJ0r1NGSvh52XqRXBLOAMNZXwjYGl/P7qLdnxfffPONVKhQQXx8fKR58+ayffv2HOefN2+e6vOA+evWrStLly7NdD/G0jhrLTg4WAWS9+7dm+U5pk2bpgLRxYsXV/PcvHkzyzyxsbHy5JNPqnlKlCghTz/9tNy+zd9yIiIiIpfJtEVtMNSu1QJ0yLIdMmSI4f7SpUvLtWvXrLeU5DAOXYqTj5celc0nM3/+XWqVlre615DKPN3esbJt15yQSQMa2nqxiIjs2lzjBmRN2YDMVtq3b5/jdWuZO3euvPrqqzJ16lQVsJ00aZJ07dpVjh07JiEhIVnmR0mxgQMHyscffywPPfSQzJ49W3r16iW7d++WOnXqqHkSEhJUT4h+/frJM888Y/Z1ExMTpVu3buqCRAlzELC9fPmyrFq1SlJTU2X48OHy7LPPqtckIiIiIhfItMUAc9asWXLjxg2ZMWOGXL9+XXr27Gm4H915S5ZkUylXcjnujrz2+z556OvNmQK29cNLyO//11KmDWnCgK2DGNqqggTezbb9a98lORnNDB0iouycu54gW05dV9MVS/pLi0pBXFl2olOnTiqxIDvItMU8ufXll1+qwCoCojjTDMFbPz8/+emnn8zO/9VXX6lA6xtvvCE1a9aUDz74QBo1aiRTpkwxzDN48GAZO3asdO7cOdvXHTVqlLz99tvZNlQ7cuSILF++XH744QcVTEYQ+Ouvv5bIyEi5dOlSrt8nERERETlgpi0GlQ8//LAhMNu6dWvp2LGj4f4lS5ZI06ZNrbeUZLfik1Jl6oZT8sOmM5KclmG4PSzQV97qVkMeqleWzVgcDGoOP9uusny6/KjKtp285oRMHshsWyIicyKNsmz7Nw3nb54dQamu//znP9neHx0dLRs2bMjVc6akpMiuXbsyZbq6u7urYOvWrVvNPga3IzPXGDJzFy1aJNaE10FJhCZNmhhuw3Jh+f7991/p3bt3lsfgzDnt7Dm4deuW+puRkaEuBQ2vgbP2CuO1yDFxGyFuJ8TvEnLG3xxLXyNPQdsHH3xQndKFU68wOOzfv7/hPmTfoguvcaMycj6p6RkSuf28TFp9Qq4npBhuL+7jKS8/UFUGtywv3p5sMubItW2nbzotsQkp8vf+S/JSpypStXQxWy8WEZHd/RbO26lvQObp7iaPNQqz9SKRiZwakZ08eVKKFcvdbxvKf6FxGUqBGcP1o0ePmn3MlStXzM6P260Jz2dangElzYKCgrJ9LZRsmDBhQpbbY2JiJCkpSQpjhyUuLk7tICG4TMRthPhdQvy9IVvJKMRxSXx8fMEFbQGng+FiKjAwUCZOnJjXpyU7h4131eGr8snyo3I6JsFwu5eHuwr0vdipChuwOAF/lW1bST5ZdlR0OpGv1pyQKU80svViERHZlTVHouXabX2WYueapaVUMW9bL5LL+/nnn9VF89///lemT5+eZb2gidf+/ftVA11Xhoxh4yxgZNqGh4dLqVKlVDOzwtg5QmAdr8egLXEbIX6XEH9vyJYyCnFcgua0VgvaovEBanXlRX4eS/Zl74Wb8tGSI7L9bGym21EC4c2uNSQimJ+z02XbbjytMqmXHLgsL1+Nl2rMtiUiMojccd4wPaBZONeMHcC4E1mixlkMpoNuDMb9/f1lxIgRquRXbqA0mIeHh1y9ejXT7bhepkwZs4/B7bmZP6/wfCj5YCwtLU1iY2OzfS1vb291MYV1VlhBVHwehfl65Hi4jRC3E+J3CTnbb46lz2/RXDji/v7776tutJaKiopSA+GICHZRdnQXYhPlpTl7pNc3/2QK2DatECgLn2+lMjAZsHU+fl6e8n/tK6lpLduWiIj0om7ekQ3H9cHB0BK+0rZqKa4aO/Dcc8/JgQMH1KV8+fKq2Zd2Xbsgwxb1X3GfaTmB+/Hy8pLGjRtnanCGrAxcb9mypdnH4HbThmgoMZbd/HmF50MGMWruatauXauWD43JiIiIiMixWJRp+91338n48eNV4BZNx9DUAF1vK1asqMoh4JR51LI9c+aM7Ny5U1avXi3btm2TqlWryrffflvw74IKRFxiqkxZd0J+3nJOUtLvFUmuVNJf3upeQ7rUKs2GK05uUIvyMm3jabl2O0WWHrgsx67ES/UyrG1LRPT7jgvqgBb0axIuHu7Z104l28C4tCCgnMDQoUNVw69mzZrJpEmTJCEhQYYPH67uHzJkiISGhqp6sTBy5Ehp3769fPHFF9KzZ0+JjIxU4+Vp06YZnhPZsOfPn5dLly6p68eOHVN/kSGrZcmiLi0uqMULCECjJi8SJFC3tmbNmtKtWzd55plnZOrUqZKamiovvviiDBgwQMqVK1cg64KIiIiIbBy07devnzz++OPy119/ycyZM+XDDz9U3XNNmzsgeIsMhC5dusgff/whjzzyCE91ckDJaeny69Zz8vXakxJ3J9Vwe5C/l4zqXFUGNouQIh48hc1Vsm1HtK8s/11y5G627XH59snGtl4sIiKbSs/QybydF9Q0YrX9mrIBmb1DmQQ0ljDXqTe3Z4WhAS9KMOCMMgRRGzRoIMuXLzc0G0Pw1fiUt1atWsns2bPl3XfflTFjxqikhkWLFkmdOnUM82CMrQV9AYFWGDdunEqcAARijZuGofEvzJgxQ4YNG6amf/vtNxWofeCBB9QyPPbYYzJ58uRcvT8iIiIisg9uOkRacyk5OVmdeoUuudevX1e3BQcHS40aNdQpY+ZqYzkyNGUICAhQg/3CasqAmmQ4Za8w63thU0Dt0k+XH5ULsXcMt3t7usvTbSrKiA6VpbhPkUJbHrKPbeROSrq0/WydodnOspFtpWbZgv8/IMf8HiHHwu0kb9Ydi5bhM3ao6U41QuSnYU3FWRX2NmLtMRfOFvvyyy/l9OnT2c6Tnp6e79dxFq4y5iXHwW2EuJ0Qv0vIGX9zLB1zWZRpawpBWWQN4ELOYcfZWPlwyRHVbEyDROreDUPl9S7VpVwJX5suH9mOr5eHjGhfSWXbwuQ1J+S7Qcy2JSLXFbndqAFZUzYgs1fITH3hhReka9eu8tRTT8k777wjr7zyiurWizPHkBn78ssv23oxiYiIiIjM4iFtF3c65rb83687pe/UrZkCtq2rBMvfL7aRL/s1YMCWVG3bUsX0GfTLDl6Rw5duca0QkUuKjk+SNUei1XRIMW+VaUv26euvv1YB22XLlsmzzz6rbkNNWZT5Onz4sCqZoJ0xRkRERERkb+wqaIuGDU2bNlVNFZCO3KtXL0MjhuxMnz5d2rZtqxqi4YImadu3b880z+3bt1V9r7CwMPH19ZVatWqp7AtXdv12soz786B0mbhRVhy6ari9WumiMmN4U5n1dHOpExpg02Uk++FTxEOea1/ZcB21bYmIXNEfuy5KWoa+slTfJmHiyRrvduvUqVPy8MMPq+kiRfTlndCTAXA62n/+8x82zCUiIiIiu2VXQdsNGzao09i2bdsmq1atUl1v0dQMHXmzs379ehk4cKCsW7dOtm7dKuHh4eoxUVFRmbr8okHErFmz5MiRIzJq1CgVxEXTB1eTlJou364/KR3+t15+3nrOsOOJLMpP+tSVpS+3lY7VQ7I0mSN6onmEyioDBPoPXYrjSiEil5KRoZO5O/QNyKBfE5ZGsGcIzKalpalp1Arz8/OTCxfufX5IEkAjMSIiIiIie5SnmrYFBYFVY6g3hoxbND3TOuSaQpdcYz/88IPMnz9f1qxZI0OGDFG3bdmyRYYOHSodOnRQ13GK3Pfff68ych955BFxlR3NRXuj5PMVx+RSXJLhdj8vD3m2XSV5pm0l8fe2q82B7DHbtkNlmfD3YXV90uoTMn1IE1svFhFRodl2+rqcu55oKCNUPtifa9+O1alTR/bt22e43qJFC9WYrEePHqrRBMaC1apVs+kyEhERERFlx66jdOiiBkFBQRY/JjExUWXoGj8GDdOQVYsmFOXKlVPZucePH5eJEyeafY7k5GR1Me7qBhjg41LQ8Bo6nc5qr/XPyWvyyfJjcsioDqm7m0j/JuEy8oEqElLcx/C65BisvY1YakCTMJm64ZRcvZUsqw5flQMXb0jtciyjYY9stY2QY+F2kjuzjRqQ4TfUFf6/CnsbsebrDBo0SJXDwpgOTXQnTJigymhFREQYSibgQD8RERERkT2y26AtBu0oY9C6dWuVKWGpt956SwVmMSg3bkSB7FrUtPX09BR3d3dVCze77F3U1sXA3lRMTIwkJd3LUi3I946ANXaSsKx5dfr6HZmy6aJsOZu5aVSrCsXlxbZhUinYVyTplkQnsamUo7HWNpIXgxqFyBfr9aeXfrb0kPzvkSqF+vpk/9sIOQ5uJ5aLu5MmKw7pT6UP8PGQBiXdJDpa35DMmRX2NoLmYNYyfPhwddFgTHno0CH5+++/xcPDQ5XTYqYtERERETld0Pb8+fPy0UcfqVqyCGYuWrRIBUGvXbsm77//vhokN2zYMM8Lhtq2Bw8elM2bN1v8mE8++UQiIyNVJq2Pjz57VAvaok4usm3Lly8vGzduVM9vGtzVjB49WtXBNc60Ra3cUqVKqZpohbGDhJqyeL287CBF30qSSWtOyu87L8jdkrVK7XLFZXT3GtKqcrB1F5gKXX63kfz4T8dg+W13tFy5lSybTsfJ1VRvqcumdXbHltsIOQ5uJ5Zb/M8ZSU3X/6g+3iRcwsqVEVdQ2NuI8fitIFSqVElGjhxZoK9BRERERGSzoO3hw4elbdu2aiDfvHlzOXnypKHRQ8mSJVWgFc3DfvzxxzwtFJqELV68WAVXkR1ric8//1wFbVevXi316tUz3H7nzh0ZM2aMLFy4UHr27Kluw/179+5VjzEXtMUpdLiYws5KYQU/sIOU29dLSE6T6ZtOy7SNpyUxJd1we7kAH3m9a3Xp1SBU3FEXgZxCXrYRa/D1dpcXOlaR9/48pK5PXnNSfhzWtFCXgex7GyHHwu3k/pBlOnfHRcP1gc0iXOr/qjC3EVdar0REREREVg/avvnmm1KiRAmVvYqBPJqFGUNwdO7cuXnaKXrppZdUgBXZshUrVrTocZ999pl8+OGHsmLFCmnSJHNjJNS3xcV0JwCnxTlLLbr0DJ3M23lBvlh1XGLi79XiLebtKc91rCxPta6omkgRWUu/puHy7fpTcjkuSdYcjZZ9F25K/fASXMFE5JR2n78hJ6Jvq+mmFQKlSkgxWy8SWQBjP4xT7yc9/d6BbiIiIiIihw7aIgN27Nix6lS569evZ7kfDR6ioqJy/bwoWTB79mz5888/pVixYnLlyt3acQEB4uvrq6aHDBkioaGhqu4sfPrpp2pZ8LgKFSoYHlO0aFF1QTmD9u3byxtvvKGeA+URNmzYIL/88ot8+eWXYm8QuI5NSJFLccni6Z8iwUW9s93hwLzrj8XIx8uOyPGr+p1J8HR3kyebR8jLD1RVjyeyNm9PD3ke2baLDqrrk1YflxnDm3FFE5FTmrNdX8cbBjTVN7Ei+4fxoekYCgHas2fPqrJe1atXl4ceeshmy0dEREREZPWgLTJU/fz8sr0fNW7NlRe4n++++0797dChQ6bbZ8yYIcOGDTPU0jXOmsVjUlJS5PHHH8/0mHHjxsn48ePVNOrcok7tk08+KbGxsSpwi8zcESNGiL2Iu5Mq83ddlJ+3nJVzsYl3bz0o5YP8ZGirCvJY4zAJ8C1imP9gVJwK1v5zMnPQvFvtMvJmt+pSqVTRQn4H5Gr6NQmT79adlEtxSbLuWIzsvXBTGjDbloiczK2kVFm8/5KaLubjKT3qlrX1IpGFtHGgOZcvX5YWLVqwERkREREROVfQtlGjRrJkyRJ5/vnns9yH2rYIkmIgnFvIHL0flE0whmyJ+ylTpowK/NqrDcdj5LlZu+SOUR1azfnYRPlg8WH5fOUx+W5QY6kaUlRNL9wTJcarC8Gyd3rWlKYVggp34cmls21f6FRF3ll4L9t2JrNticjJ/Ln3kiSl6sspoTa8rxfLDTmDsmXLqoP3H3zwgQwcONDWi0NEREREZJ2gLbJWcTrZc889JwMGDFC3Xb16VTUB++ijj+TIkSMyZcqUvDy1y0HAdviM7YL4q7mQtXYbArrDftquSh+kZtybMyLIT97qVkN61C1jUd02Imvq2zhcvl13SqJu3lGlOlD3sVFEIFcyETmNyO3nDdMDmoXbdFnIuvz9/eXMmTNcrURERERkl/LUord79+4yc+ZM1WysU6dO6rZBgwZJly5dZPfu3apebLt27ay9rE4HJRGQYasCtvdJMtaCulrAFqUS3u1ZU1a92k561ivLgC3ZhJenu7zQsYrh+qTVJ/hJEJHTOHAxTg5duqWm64UFSO1yAbZeJLKSgwcPyuTJk1kegYiIiIicK9MWBg8eLH369JGVK1fKyZMnVZ3bypUrS9euXVUTMbo/1LBFBu39i0Jk1qpysHz3ZGMJ8LtX45bIVh5vHCbfrDupsm03Ho+RXeduSOPyzLYlIscXucMoy5YNyBxOxYoVzR7UvnnzpsTFxan+DGhIRkRERETkVEFb7bSy3r17W29pXAjq96LpWG5h1yPqxh0p7puvj47Iqtm2L3WqIm8vOGCobfvr0825honIoSWmpKl6tuDn5SGPNChn60WiXGrfvn2WoC2uBwYGqkQDlPgKCmIvACIiIiKyT/mK/KWmpkpUVJTcuHHDbBMxNCwj824kpsq52MRcrx6sZTzuZmKqBPp7cfWSXXgM2bbrT8qF2Duy6cQ12XUuVhqX544wETmuxfsvy+3kNDX9cL1yUtSbB0sdDUp5ERERERE5qjztgeC0stdff11+++03SUlJyXI/ArjIZEhPT7fGMjqlhLs7gnmFHUkGbcleFPFwl5c6VpU35+9X1yeuOiGz/sNsWyJyXGxARkREREREDhe0HTZsmPz999/qtLLmzZtLQAAbc+SWfz4zdpjxQ/amd6NQmbLupJyPTZTNJ6/JjrOx0rQCs22JyPEcvxovu8/fVNPVSxeTBuElbL1IZIH3338/1+sJSQbvvfce1y8RERER2Z08RQ7RfOzll1+WiRMnWn+JXESgXxEpH+SnAly5aUSGymwRQX5Sgk3IyA6zbV/sVEXe/EPLtj0us59pYevFIiLKtTnbjRqQNQs328yK7M/48eNz/RgGbYmIiIjIXrnn5UHBwcFSpUoV6y+NC8FOwtBWFfL02GGtK3AHkuxSn4ahUj7YT01vOXVd/j193daLRESUK0mp6bJwT5Sh0WLvhqFcgw4iIyMj1xeW8iIiIiIipwraPvvssxIZGakGu5S/5k2+Xh5iaQKPu5uo+fs0CuNqJ7vkidq2naoark9afcKmy0NElFsrDl1RzT6hR50yUsKPTT+JiIiIiMhByiOg9ldycrI0adJEBg8eLGFhYeLh4ZFlvj59+lhjGZ1WgG8R+W5QYxk+Y7uqe6DLoU6CFtidOqixehyRverVoJx8s+6knLmWIFtPX5dtp69Li0rBtl4sIiKLRG6/YJge0CyCa42IiIiIiBwnaBsVFSVr166VvXv3qkt2p//zlLP7a1+tlMwY3kyem7VL7qSkq9uMY7daEq5vEQ8VsG1XrVRePjKiQs62rSKv/r7PUNt27v+15CdARHbv7N2DTVCppL80r8hmio5u//798vXXX8vu3bslLi4uy1liGK+eOnXKZstHRERERGTVoO1TTz2lBr+jR4+W5s2bS0BAQF6ehowCt1tHPyALdl+Umf+clXOxiYZ1g6ZjqGGLUgrFfZhhS47hkfrl5Ou1+mzbf8/EypZT16RV5ZK2XiwiohxF7riXZdu/KRuQObr169dLt27dJDAwUJ0dtmfPHunUqZMkJSXJ1q1bpXbt2tK4cWNbLyYRERERkfWCtps3b5a33npLJkyYkJeHkxkoeTC8dUUZ1qqCxCYky7moq1I+tLQE+Xuz6Rg5ZLbtyw9UkVfm7jPUtm1ZKZjbMhHZrdT0DPlj10U1XcTDTR0sJcc2duxYqVSpkmzbtk1SUlIkJCRExowZowK3//77r3Tv3l0+/fRTWy8mEREREZH1GpGVKVNGgoJ4ymBBwGl6gX5eUi7AW/3FdSJH9Ej9UKlUyl9Nbz8TK1tP6U85JiKyR2uOXJVrt5PV9IO1SkvJot62XiTKJ5wV9vTTT0vx4sUNvRe00l04U+z//u//VJ8GIiIiIiKnCdq+9tpr8sMPP8jt27etv0RE5BQ83N1k5ANVDdcnrj4uupy67RER2dAcowZk/ZuyAZkz8PT0lGLFiqnpEiVKSJEiRSQ6OtpwP7JwDx8+bMMlJCIiIiKycnkE1ALDwLdKlSrSr18/CQ8PN2QwaJAh+sorr+Tl6YnISTxUr5xMXnNCTsUkyI6zN+Sfk9elTVXWtiUi+3LxRqJsPBGjpkNL+ErbKvyecgYYp544ccIwLq1Ro4YsXLhQnnzySXXbkiVL1NljREREREROE7R9/fXXDdNTpkwxOw+DtkSEbNuXH6gqIyP3GrJtW1dhbVsisi+/77wo2okAaEDm7s7SRM6gR48e8tNPP8nHH3+ssm5fffVVGT58uFStqj8L5NSpU+o+IiIiIiKnCdqeOXPG+ktCRE6bbfv12pNyMvq27Dp3QzafvCZtq5ay9WIRESnpGTqZt1NfGgGx2r5N2IDMWaBe7ciRIw1ngw0dOlRNz58/X/195513ZNiwYbZeTCIiIiIi6wVty5cvn5eHEZEL17Z9ac4edX3iquPSpkpJNtkjIruw8XiMXI5LUtMdq4dI2QBfWy8SWQlKeQUHB2e6bdCgQepCREREROSUjciIiHKjZ92yUq10UTW9+/xN2XjiGlcgEdmFOdvPG6YHNGMDMmfy5ptvyp49+gOG1vbNN99IhQoVxMfHR5o3by7bt2/Pcf558+apmrqYv27durJ06dJM9y9YsEC6dOmigswoMbZ3r76skGlPiRdeeEHNU7RoUXnsscfk6tWrmebZsWOHPPDAA6rxWmBgoHTt2lX27dtnpXdNRERERHYXtK1YsaJUrlxZUlNTDdfRcTenC+YnIlJfNCrbtpphZSDbVqcVkCQispHoW0my5mi0mi5d3Fs6VmfpFmfy9ddfS5MmTVQNW5RKOHDggFWed+7cuao+7rhx42T37t1Sv359FRyNjtZvS6a2bNkiAwcOlKeffloFkXv16qUuBw8eNMyTkJAgbdq0kU8//TTb10WD37///lsFgDds2CCXLl2SPn36GO6/ffu2dOvWTSIiIuTff/+VzZs3S7FixdSyaWN4IiIiInKy8gjt27dXR/3d3d0zXScislT3OmWkeulicuxqvOy9cFPWH49RpyITEdnKvF0XVU1b6Ns4XDw9eAKSM0EQdeHChSrI+tlnn8lHH32ksl0HDBgg/fr1k+rVq+fpeb/88kt55plnVFMzmDp1qixZskQ1PXv77bezzP/VV1+pYOobb7yhrn/wwQeyatUq1cwXj4XBgwerv2fPnjX7mnFxcfLjjz/K7NmzpVOnTuq2GTNmSM2aNWXbtm3SokULOXr0qMTGxsr7778v4eHhah4EluvVqyfnzp2TKlWq5On9EhEREZEdB21nzpwpGzduVAPBUqVKqetERLnOtu1cVZ7/bbe6Pmn1CelQrRQPABGRTWRk6GTuDn0DMujXRB/kIueBLNMhQ4aoy82bN1UDst9//10FTcePH6/KFCCAay7Qmp2UlBTZtWuXjB492nAbkho6d+4sW7duNfsY3I7MXGPIfl20aJHFr4vXRLYsXkeDADSyavH8CNoiCI3SCQjujhkzRtLT09U0Arso5UBERERETtqIrGPHjvLrr7/KE088UbBLREROq1vtMlKjTDE5eiVe9iHb9liMdKzBbFsiKnxbT1+X87GJahrNESOC/fgxODHUeEV5AlyuX7+uxrTIQn3nnXdyFbS9du2aCoaWLl060+24jkxXc65cuWJ2ftxuKczr5eWl3kd2z4Mg9fr161XpBQSmAaUhVqxYIZ6e5of8ycnJ6qK5deuW+puRkaEuBQ2vgXJJhfFa5Ji4jRC3E+J3CTnjb46lr2Fx0Jb1J4nIGtm2ozpXlRGz9Nm2E1cflw7VmW1LRLZuQMYsW1eATNVly5apcgmoDYsasFoZAWdw584dFZRu3bq1zJkzRwWXP//8c+nZs6dqUObr65vlMR9//LFMmDAhy+0xMTGq8Vlh7LCg9AP2M7QybETcRojfJcTfG7KFjEIcl8THx1s3aEtEZA1dapWRmmWLy5HLt2T/xThZezRaHqiZOQOJiKggxSakyMpDV9V0kL+XPFiL30HOKi0tTVauXKkCtX/++afKJC1btqyqR9u/f39p1apVrp6vZMmS4uHhIVev6rcfDa6XKVPG7GNwe27mz+45UJoBZR6Ms22Nnwf1blETF+UStB0N3BYYGKjeO0pBmEKZB+PSDVg/CGSjHFrx4sWlMHaO0CcDr8egLXEbIX6XEH9vyJYyCnFc4uPjY/2gLZuPEZFVats+gGzbXYbatp1qhPD7hYgKzYLdFyUlXX9K0mONQsXb04Nr3wkh6xR1Y2/cuKGCrQMHDlSBy3bt2uX5NwclCho3bixr1qxRZQi0AT6uv/jii2Yf07JlS3X/qFGjDLehERlutxRes0iRIup5HnvsMXXbsWPH5Pz584bnSUxMVDsYxu9Nu57dKXje3t7qYgqPK6wgqtbsmEFb4jZC/C4h/t6QrbkV0rjE0ufP1VIMGjRIZRdYcsmudhYRUdfapaVWWX0Gz4GoOFlzJJorhYgKBU53ijRqQNa/aQTXvJNCwLZ3796qpuvly5flu+++k/bt2+f7ICEyU6dPny4///yzHDlyRJ577jlJSEhQ2buAxmfGjcpGjhwpy5cvly+++ELVvUUTtJ07d2YK8qLZ7969e+Xw4cOGgCyua/VqAwICVBAar71u3TrVmAyvh4AtmpDBgw8+qALUL7zwglquQ4cOqXkwJkdvCiIiIiJyLLmKrKJjbbVq1QpuaYjIJWCHGbVtn/31brbtmuPyQE1m2xJRwdt17oacjL6tpptVCJIqIUW52p0USgcURBIByiqg5uvYsWNVULVBgwYqKKs1G0P2q3H2BEowoEzBu+++K2PGjFHNwRBQrlOnjmGev/76yxD0Ba2UAZqlIcgLEydOVM+LTFs0D+vatat8++23hsfUqFFD1epFjVoEczFvw4YN1bKhJAQRERERORY3nYUdxjDwmzVrljzxxBPialDfCxkOKEhcWPW9oqOjJSQkhKeKkdNuI/jqeXjKZjkYpe9UPW1wY+lS2/L6fuT82wgVPFfcTl77fZ/M331RTX/Zr770aRRm60Wya4W9jeR3zLV9+3apUqWKBAUF3XfeM2fOyKZNm1RmLFln/eeWK34HUe5wGyFuJ2QN/C4he9tOLB1zcXRERLbLtn3gXuY+attaeAyJiChP4u6kypIDl9R0MR9P6VGX2YfOBhmmyCw1Ljvg5+cnGzZsyDLvli1bMmW3EhERERHZEwZtichmUBKhbmiAmj58+ZasuNvNnYioIPy1N0qSUvUNmXo3DBWfImxA5mxMD/7helJSkqSnp9tsmYiIiIiI8oJBWyKyeW1bzVdrTkhGBrNticj6ELybs/1eA7IBbEBGRERERER2zDM3tR2IiKytU40QqR8WIPsuxsmRy7dk5eEr0q0OT1kmIus6EBWnMvoB3zm1yhV8vU4iIiIiIqK8YqYtEdlBtm3m2rbMtiUia4vcYZRl2yyCK5iIiIiIiJwj05aIqKB0qF5K6oeXkH0XbsrRK/Gy/NAVNggiIqtJSE6Tv/bqG5D5eXnIw/XLce06sbNnz8ru3bvVNDrywokTJ6REiRKZ5jtz5oxNlo+IiIiIyBIM2hKR3dS2HT5jh7r+1eoT0q12GXF3d7P1ohGRE1iy/7LcTk5T04/ULydFvTn8cWbvvfeeuhh7/vnnzdY5xu8PEREREZE94l4LEdmFDtVKSYPwErL3wk05djVelh28Ij3rsbYtEeXfnB3nDdMsjeDcZsyYYetFICIiIiKyCgZticguINvplQerydCftqvrX605Lt3rMNuWiPLn2JV42XP+ppquUaaYakJGzmvo0KG2XgQiIiIiosIL2m7cuDFPT96uXbs8PY6IXFO7qiWlUUQJ2X3+phy/eluWHLjM2pNElC9zthtl2TYN5+nwRERERETkPEHbDh065GonR6sRlp6enquF+fjjj2XBggVy9OhR8fX1lVatWsmnn34q1atXz/Yx06dPl19++UUOHjyorjdu3Fg++ugjadasWab5jhw5Im+99ZZs2LBB0tLSpFatWjJ//nyJiGAHaSJ7y7Yd/KOWbXtCNSTzYG1bIsqDpNR0WbgnSk17e7pL74ZhXI9EREREROQ8Qdt169YV/JKIqIDqCy+8IE2bNlWB1TFjxkiXLl3k8OHD4u/vb/Yx69evl4EDB6oAr4+Pjwry4jGHDh2S0NBQNc+pU6ekTZs28vTTT8uECROkePHi6n7MT0T2pU2VktK4fKDsOndDTkbflsX7L8mjDfT/y0REubH84BWJu5OqpnEAKMCvCFcgERERERE5T9C2ffv2hbNztXx5puszZ86UkJAQ2bVrV7alFn777bdM13/44QeVQbtmzRoZMmSIuu2dd96RHj16yGeffWaYr3LlygXyHojICtm2navJoB//VdcnrzkhD9Urx2xbIsp3aQQiIiIiIiJH4S52LC4uTv0NCgqy+DGJiYmSmppqeExGRoYsWbJEqlWrJl27dlVB4ObNm8uiRYsKbLmJKH9aVwmWphUC1fSpmASVbUtElBunY27Lv2di1XSlUv7SrKLlYwkiIiIiIiKHyLQ1JykpSWW07t69WwVXERw1zZb78ccf87xgeL5Ro0ZJ69atpU6dOhY/DnVry5UrJ507d1bXo6Oj5fbt2/LJJ5/If//7X1U+ARm9ffr0UWUfzGURJycnq4vm1q1bhmUyfZ8FAa+BusCF8VrkmFxhGxn5QFUZpNW2XX1CetQpw2zbXHCFbYTyz5m3k8gd97Js+zcJU+8TF7LvbcRar4OD+IMGDZLHHntMnnzySas8JxERERGR3Qdtz507Jx07dpSzZ89KiRIlVNAWma03b95UzcdKliwpRYsWzdeCobYtmott3rzZ4scgMBsZGanq3Gr1arXB/6OPPiqvvPKKmm7QoIFs2bJFpk6dajZoi4ZoqH1rKiYmRgWrCxqWGesUO0nu7nadDE024grbSOWiOmkYWlT2RN2W09cSZNamo9K9ZrCtF8thuMI2QvnnrNtJanqGzNt5QU17urtJu3BvdRCX7H8biY+Pt8rz+Pn5yerVq6V79+5WeT4iIiIiIocI2r7xxhtqAL9t2zapVKmSKjkwd+5clRU7efJkmTJliqxYsSLPC/Xiiy/K4sWLZePGjRIWZlmn588//1wFbTFAr1evnuF2BJA9PT2lVq1ameavWbNmtgHh0aNHy6uvvpop0zY8PFxKlSqlmpgVxg4SMpXxes60E03W4yrbyOvdPOXJu9m2P++MlifbVBdPD+d9v9bkKtsI5Y+zbifLDl6RG4lparpLrdJSvQKbGTrKNmLNJrFoQrt161Z55plnrPacRERERER2HbRdu3atPP/889KsWTOJjdXXi0MGhre3twroHjlyRJU2QC3Z3MBzvPTSS7Jw4UKVLVuxYkWLHocGYx9++KEKFDdp0iTTfV5eXtK0aVM5duxYptuPHz8u5cuXN/t8eB+4mMLOSmHt1GIHqTBfjxyPK2wjrauWkuYVg1RdyrPXE2XxgSvSp5FlB3LINbYRyj9n3E7m7rxomB7QLMKp3puzbyPWfA0kEaCfwbvvvisjRoywOBGAiIiIiMgeuOe1TliFChXUNDJPMZjXmoZBy5Ytc1XWwLgkwqxZs2T27NlSrFgxuXLlirrcuXPHMM+QIUNUJqwGNWrfe+89+emnn9QyaY9BHVsNAsnIBJ4+fbqcPHlSDeL//vtvFXgmIvv2yoPVDNOT15yQtHTnq71JRNZzITZRNp2IUdNhgb7SpkpJrl4XVb9+fbl48aIqe4UD9Tggj3Gr8SUgIMDWi0lEREREZL1M24iICDUIVk/g6SmhoaGqVAKae8Hhw4fzdHrbd999p/526NAh0+0zZsyQYcOGqenz589nysLAY1JSUuTxxx/P9Jhx48bJ+PHj1XTv3r1V/VoM2l9++WWpXr26aqKG0+aIyL61qBQsLSsFy9bT11W27aK9l+TxxsyWIiLzUMtW6zfWv0m4uLu7cVW5KDQhQ2IBEREREZHLBG07deokf/75pwqMAgKqCIjeuHFD1T779ddfVUZsblnS1RllE4yhGZolnnrqKXUhIsczqnNV2Trtupr+eu0J6dWgHGvbElEWyMT//W5pBMRq+zYJ51pyYTNnzrT1IhARERERFW7Q9u2335YdO3ZIcnKyOtVszJgxcunSJfnjjz/Ew8NDnnjiCfniiy/yvlREREaaVwqWVpWDZcup63LueqIs2BMl/RiMISITG0/EyJVbSWq6U40QKRNgvaZWREREREREdl/TFuURcMqZ1qwLpRB++OEHlWl77do1ldnAGmFEVFC1baesPSmprG1LRCbmbL9gmB7QNILrh1RZLTQhQ2mswMBA2bhxo1orGK+iZNaePXu4loiIiIjIeYK2KDPw77//Znv/9u3bWYqAiKyqaYUgQ0Oh87GJsnB3FNcwERlE30qStUej1XTp4t7SoXoprh0Xhx4LDRs2VM1oK1asKLdu3ZK0tDR1X8mSJVXTXDSnJSIiIiJymqAtMmlPnTqV7f1nzpyRn3/+OT/LRUSUxSsPVjVMf73uBLNtichg3q6Lkp6hr42P8imeHnka4pATefPNN6VEiRJy/PhxmTVrVpbeCT179pRNmzbZbPmIiIiIiHJSIHs0qG/r6+tbEE9NRC6scfkgaVtVn217IfaOzN+lbzhERK4tI0MnkTvOq2k3N33QlgilEJ577jkpVaqUuGHDMFPuKyqKZ20QERERkYM3Ivvzzz/VRTNt2jRZvXp1lvlu3rypbm/atKn1lpKI6K5RnavJphPX1PTXa09Kn0Zh4uXJjDoiV4YmhTiQAyijEh7kZ+tFIjuQkZEhfn7ZbwsxMTGG/gxERERERA4btEVdsHnz5qlpZCugpu2uXbsyzYPb/f39pV27dvLll19af2mJyOU1Lh8o7aqVko3HYyTq5h2Zv/uiDGzGhkNErmzO3SxbYAMy0jRq1EiWLFkizz//fJaVgtq2kZGR0qJFC64wIiIiIrJLFqenjR49WuLj49UFNcF+/PFHw3XtggYPly9flsWLF0u1avc6vRMRWdMrne/Vtp2y9qSkpGVwBRO5qOu3k2XloStqOtjfSx6sVdrWi0R2AmPX5cuXqxIJBw8eVLddvXpVnRHWpUsXOXLkiLz99tu2XkwiIiIiovxl2pqebkZEZCsNIwJVZ/j1x/TZtvN2XZAnm5fnB0LkghbsjpLUdH2Dqccas1wK3dO9e3fVPHfkyJGqrBcMGjRIJR8UL15cfvnlF3V2GBERERGR0wRtNWfOnJFly5bJuXPn1PXy5curAXLFihWttXxERNnWtkXQFr5Ze1Iebxwm3p4eXFtELgTBN60BGfRvygZklNngwYOlT58+snLlSjl58qRKPKhcubJ07dpVihUrxtVFRERERM4XtH3ttdfkq6++ypJ16+7uLqNGjZLPP//cGstHRGRWg/AS0qlGiKw9Gi2X4pLk950XZXALZtsSuZKd527IqZgENd2sYpBULlXU1otEdgj9Fnr37m3rxSAiIiIiypU8tVz/4osvZOLEiSpzYevWrXLz5k11wfTjjz+u7sOFiKggjXzgXm3bb9edlOS0dK5wIhcyZ/u9LNuBzZhlS+ah1wKakfXo0UNdMI3biIiIiIicLtN2+vTp8sgjj8jvv/+e6fbmzZurTrxJSUny/fffyyuvvGKt5SQiyqJ+eAl5oEaIrDkaLZeRbbvjggxuWYFrisgFxN1JlaUHLqvp4j6e0r1OWVsvEtkZJBQgw3bjxo3i4eEhZcvqtxE0IsM4tW3btrJo0SIpUaKErReViIiIiMg6mbZnz55VtcCyg/swDxFRYdS21Xyz7pQkpTLblsgV/Lk3SpJS9SWaejcMFZ8irGlNmaEB2aZNm+TTTz+VGzduqB4MuGD6k08+kc2bN6t58uKbb76RChUqiI+Pj0pa2L59e47zz5s3T2rUqKHmr1u3rixdujTT/QsWLJAuXbpIcHCwuLm5yd69e7M8B5IiXnjhBTVP0aJF5bHHHpOrV69mmQ/N1+rVq6deKyQkRD2GiIiIiFwkaIsB4L59+7K9H/eVKlUqP8tFRGSRumEB0rlmaTV95VaSzN1xgWuOyAUakM3Zfu9/fUCzCJsuD9knZNGiFMLrr7+u6tpqMP3GG2/Ic889p+bJrblz58qrr74q48aNk927d0v9+vVVwkJ0dLTZ+bds2SIDBw6Up59+Wvbs2SO9evVSl4MHDxrmSUhIkDZt2qgAc3ZwBtvff/+tAsAbNmyQS5cuqVJlxr788kt555135O2335ZDhw6prOKcEi2IiIiIyAmCtji1LCZG36m9b9++8sMPP6gsBQwyNZjGYBP39e/fv2CWmIjIxKjORrVt159kti2Rk9t/MU6OXL5lKJNSs2xxWy8S2aEiRYpI9erVs70fma+YJ7cQGH3mmWdk+PDhUqtWLZk6dar4+fnJTz/9ZHZ+NO7t1q2bChTXrFlTPvjgA2nUqJFMmTLFMM/gwYNl7Nix0rlzZ7PPERcXJz/++KN67U6dOknjxo1lxowZKiC8bds2NQ8yiN9991355Zdf5IknnpDKlSurjFuUNCMiIiIiJ65p27FjR/n111/VIBCDTZy2NWbMGDXALFeunJoHR/zT0tLUvO+//35BLjcRkUGd0AB5sFZpWXX4qly9lSyR28/LsNYVuYaInFTkDqMGZE3ZgIzMQ/kAZKWOGDFC1bQ1hvEqejMgESE3UlJSZNeuXTJ69GjDbe7u7irYioa85uB2ZOYaQ/ZrbrJ88ZqpqamZgroIOkdERKjnb9GihaxatUoyMjIkKipKBYfj4+OlVatWqoFweLj5/5Pk5GR10dy6pT8YgufBpaDhNZA5XxivRY6J2whxOyF+l5Az/uZY+hoWB22x4BpkE6xZs0b+/PNPWbZsmaoPBsgiQFfehx9+WNXjIiIqzGxbBG3h2/Wn1OnSrHFJ5HwSktPkr72X1LS/l4c8XF9/4JjI1KBBg+TFF19Ugctnn31WqlSpom4/ceKETJs2TQVgn3zySVXiwBiyYLNz7do1SU9Pl9Kl9WV5NLh+9OhRs4+5cuWK2flxu6Uwr5eXV5amacbPc/r0abUD8NFHH6ns3oCAAJV5++CDD8r+/fvV4019/PHHMmHChCy34+w61NAtaFheZBFjPwPBbyJuI8TvEuLvDdlKRiGOS3Bw3apBW3MeffRRdSEisrXa5QKka+3SsuLQVYmOT5bZ/56Xp9ow25bI2Szef0kSUvQNBx9pUE78vfM1lCEn1r59e8P0jh07DAkFxokIxvPgdsyDoKyj7mggG3fy5MmqqRnMmTNHypQpI+vWrTNb2xYZw8ZZwMi0RVYuelMUL168UJYZ6xyvx6AtcRshfpcQf2/IljIKcVyChrGWyNWeDrNniciejepcTQVt4bsNp+SJ5sy2JXI2mRqQNWUDMsoear5aW8mSJVWphatX9b81GlxHcNQc3J6b+bN7DmQG37x5M1O2rfHzlC1bVv1FnV0NdjqwzOfP3yspYszb21tdTGFHpbCCqNi/KMzXI8fDbYS4nRC/S8jZfnMsfX7P3J5mhoulbxT1woiICguaEXWvU0aWHbwiMfHJMmvbOflP20r8AIicxNErt2TvhZuG//d6YQG2XiSyY0OHDrX6c6LEAJqAoUxYr169DFkZuI5SDOa0bNlS3T9q1CjDbag/i9sthddE0zQ8D2r1wrFjx1QwVnue1q1bG24PCwtT07GxsaqkQ/ny5fPxromIiIjIFnIVtEXzg2rVqhXc0hAR5dPLD1RVQVuYuuG0PNm8vPh6ZW5AQ0SOKTJTlm04zwAim0A5AQSEmzRpIs2aNZNJkyZJQkKCDB8+XN0/ZMgQCQ0NVfViYeTIkaoMAxqC9ezZUyIjI2Xnzp2qrq4GwVUEYNHUVwu8ArJocUF92qefflq9dlBQkCpd8NJLL6mALZqQAcboKFuG18NzYx6UP0DDMjQJJiIiIiInDtpigPrEE08U3NIQEeUTsu961C0jSw9ckWu3k+W3f5ltS+QMklLTZcHui2ra29NdejUItfUikYvq37+/atQ1duxY1QSsQYMGsnz5ckOzMQRfjU95QyO02bNnq6ZgY8aMkapVq8qiRYukTp06hnn++usvQ9AXBgwYoP6OGzdOxo8fr6YnTpyonheZtsnJyapG7bfffptp2X755Rd55ZVXVHAY8yJYjGVDli4RERERORY3nXE3hhxg4Ddr1iyXDNqiKQMyHNBFrrCaMkRHR0tISAjrexG3kTw4diVeun21UfDtVrKol2x8s6P4eblWsyJ+j5CzbScL91yUV+buU9N9GobKl/0b2HqRXEJhbyOFPeYi265/R/oOItvgNkLcTojfJeSMvzmWjrk4OiIip1O9TDHpUVffkOXa7RRV25aInKgBWTM2ICMiIiIiIufGoC0ROaWRD1QVNzf99PcbTktiChsjEjmq0zG3ZfuZWDVduZS/NK0QaOtFIiIiIiIiso+gLdKEXbE0AhE5pmqli0nPu9m21xNS5JetzLYlclRzdxg3IItgAzIiIiIiInJ6zLQlIpfItp228bQkJDPblsjRpKRlyB+79A3Iini4SZ9GbEBGlkNTsBEjRkj16tUlKChINm7cqG6/du2avPzyy7Jnzx6uTiIiIiKySwzaEpHTqlq6mDxcr5yajmW2LZFDWn3kqsqWhy61y0hwUW9bLxI5iMOHD0vDhg1l7ty5UrFiRdXoIS1Nf/CuZMmSsnnzZpkyZYqtF5OIiIiIyCwGbYnIqb38QFVxN2TbnpLbzLYlcihztp83TA9sygZkZLk333xTSpQoIcePH5dZs2aJTqfLdH/Pnj1l06ZNXKVEREREZJcYtCUip1YlpKg8Ul+fbXsjMVV+3nLW1otERBa6EJsom09eU9PhQb7SqnIw1x1ZDKUQnnvuOSlVqpTZOsgRERESFRXFNUpEREREdolBWyJyei8ZZdtO33Ra4pNSbb1IRGSB33deEC05sn+TcHHX/pGJLGyi6+fnl+39MTEx4u3NchtEREREZJ8YtCUip1e5VFF5tIG+edHNxFT5Zes5Wy8SEd1HWnqGCtqCh7ub9G0SznVGudKoUSNZsmSJ+e0rLU0iIyOlRYsWXKtEREREZJcYtCUil/BSpypGtW2ZbUtk79Yfi5Grt5LVdMfqIVK6uI+tF4kczOjRo2X58uWqRMLBgwfVbVevXpXVq1dLly5d5MiRI/L222/bejGJiIiIiMxi0JaIXEKlUkWlV0N9tm3cnVSZ+Q9r2xLZs8gd+ixbGNiMWbaUe927d5eZM2fK3LlzpVOnTuq2QYMGqYDt7t275ZdffpF27dpx1RIRERGRXfK09QIQERWWlztVlT/3XpL0DJ2qbTu0dQUp7lOEHwCRnbl6K0nWHYtW02WK+0j7aqVsvUjkoAYPHix9+vSRlStXysmTJ1Wd28qVK0vXrl2lWLFitl48IiIiIqJsMWhLRC6jQkl/6dUgVObvvii3ktJkxuazMrJzVVsvFhGZmLfzgjq4Av2ahImnB08Morzz9/eX3r17cxUSERERkUNh0JaIXMrLD1SRRXujVEDox82nZVjrChLgy2xbInuRkaGTuXcbkLm5CRuQUb6lpqZKVFSU3LhxQ3Q6/cEA04ZlRERERET2hkFbInIp5YP9pU/DUJm362627T9nZFTnarZeLCK6659T1+RC7B013aZKSQkP8uO6oTy5efOmvP766/Lbb79JSkpKlvsRwHVzc5P09HSuYSIiIiKyOwzaEpHLealTVVm4J0rSVLbtGRneuiKzbYnsROR24wZkETZdFnJsw4YNk7///lsGDBggzZs3l4CAAFsvEhERERGRxRi0JSKXExHsJ481ClOnYMcnpanA7asPMtuWyNau306WlYevqOlgfy/pXLO0rReJHBiaj7388ssyceJEWy8KEREREVGusbMHEbmkFztVEU93NzU9Y/MZiUtMtfUiEbk8NAlMTdfXHH28cZh4eXKYQnkXHBwsVapU4SokIiIiIodkV3tDH3/8sTRt2lSKFSsmISEh0qtXLzl27FiOj5k+fbq0bdtWAgMD1aVz586yffv2bOcfMWKEql82adKkAngHROQoUCcTQSGIT0a27WlbLxKRS0N90cgd90oj9G8abtPlIcf37LPPSmRkpGRkZNh6UYiIiIiIHLs8woYNG+SFF15Qgdu0tDQZM2aMdOnSRQ4fPiz+/v5mH7N+/XoZOHCgtGrVSnx8fOTTTz9Vjzl06JCEhoZmmnfhwoWybds2KVeuXCG9IyKyZy90rCJ/7Lqoatv+9M9ZeapNRSnh52XrxSJySTvO3pDTMQlqunnFIKlUqqitF4kc3HvvvSfJycnSpEkTGTx4sISFhYmHh0eW+fr06WOT5SMiIiIicpig7fLlyzNdnzlzpsq43bVrl7Rr187sY9AR2NgPP/wg8+fPlzVr1siQIUMMt0dFRclLL70kK1askJ49exbQOyAiR8u27dskXOZsPy+3k9Pkh01n5PWu1W29WEQuKXL7ecM0G5CRNWDst3btWtm7d6+6mIOzr9LT07nCiYiIiMju2FXQ1lRcXJz6GxQUZPFjEhMTJTU1NdNjcFocMizeeOMNqV279n2fA1kZuGhu3bpleJ7COMUOr4HTRHk6H3EbKXjPt68kf+y6oOpozvjnjAxvXV4CnSDblt8j5EjbSdydVFly4LKaDvAtIl1rhdh8mcg224g1X+epp56S3bt3y+jRo6V58+YSEBBgtecmIiIiInLZoC0G7aNGjZLWrVtLnTp1LH7cW2+9pcofoLatBiUTPD09VQdhS2vrTpgwIcvtMTExkpSUJIXx3hGwxk6Su7tdlR0mO8FtxHqKiMhDtYJl4YFrkpCSLpNXHJLnWmcureKIuI2QI20n8/ZGS3KaPljXtXqgxN24brNlIdtuI/Hx8VZ7rs2bN6txobkxHRERERGRvbPboC1q2x48eFANuC31ySefqIYTqHOL+raA0gpfffWVyrTAKXCWQEbGq6++minTNjw8XEqVKiXFixeXwthBwrLi9Ri0JW4jBe/V7sVk8eENKtv2j30x8lKX2hLk79jZtvweIUfZThAMXHL0uOH6sHbVJCSkmE2WhWy/jWjjN2soU6ZMrs7WIiIiIiKyJ3YZtH3xxRdl8eLFsnHjRtU0whKff/65CtquXr1a6tWrZ7h906ZNEh0dLREREYbbULvstddek0mTJsnZs2ezPJe3t7e6mMLOSmHt1GIHqTBfjxwPtxHrCQ/yV53qZ207r7Jtf/znrLzVrYY4Om4j5Ajbyd4LN+XoFX12ZYPwElKrHE9hd+VtxJqvgbHed999J08//bQULcrGdkRERETkWOwqaItsGzQLW7hwocqWrVixokWP++yzz+TDDz9UTcbQIdgYatkal0qArl27qtuHDx9u1eUnIsf1Qscq8vuOi5KSniE/bzkr/2lTUYKLZj14Q0QF2YAsnKuXrAYlrYoUKSJVqlSRfv36qbOmPDw8sgSkX3nlFa51IiIiIrI7nvZWEmH27Nny559/SrFixeTKlSvqdjSO8PX1VdNDhgyR0NBQVXdWq1c7duxY9bgKFSoYHoOMClyCg4PVxRgG8Dhlrnp1doknIr2yAb4yoFm4/LL1nCSmpMu0TadldPeaXD1EBeh2cpr8te+Smvb38pCH6pXj+iaref311w3TU6ZMMTsPg7ZEREREZK/sKmiLU9igQ4cOmW6fMWOGDBs2TE2fP38+06lzeExKSoo8/vjjmR4zbtw4GT9+/P+3dyfgUZVXA8dP9j1hDXvYFAFZZRUQVCi0BZWCZdEKItrWKsWl7gVEaxGLip+o1A2UpUEUqOBWdlSw7BRkEWUJEEjAQBISss/3nDeZYRIyyQSS2fL/Pc+Qe2feu8zN5c47J+ee1yX7DcA33H9jS4nffMxk23648ajcd0MLqUO2LVBlVuxKNH8kUbd2aiQRIR7VLYGXO3z4sLt3AQAAALhsHlceoTxaNsFeaTVpy3M5ywCoHtm2env2B5uOyoXcfHlnwyF56tdk2wJV5V9bjtmmKY2Ayta0aVMOKgAAALwWo1wBgJ0/3XSVBAcWXhq1VMKZ89kcH6AK7DuZJruOnTPTbRtES/tGDEAGAAAAAB6ZaQsA7lYvOlTu6B4nczceMdm2/1z/kzwzuK27dwvwOSUHINPaosCV0AFstYTW/v37zfgFOl/eeaWv//TTTxx4AAAAeByCtgBQwp9ubCn/2pwg2XkFMu+7o3Jf3xYSGxXKcQIqSVZuvizdccJMhwb5m3q2wJXq16+fCcJaxz6wzgMAAADeiKAtAJQQq9m2PeJkzrdHJCu3QP65/pBMGkK2LVBZPt99UtKy8sz0r9s3kJiwIA4urtjcuXNlw4YNkpKSInXr1jXzAAAAgLeipi0AlOL+fi0lpKi27fzvjkpyehbHCagk8ZvtByCL47ii0tx0002ycuXKKj+ib7zxhjRr1kxCQ0OlR48esnnz5jLbL168WFq3bm3at2/fXj7//PNiry9ZskQGDhwotWvXNtnBO3fuvGQdWVlZ8sADD5g2kZGRMnz4cElKSip1ez///LM0btzYrOvcucLa0QAAAPAuBG0BwEG27e96Fo48rmUSZq87xHECKsGPyedl85EUM31VbKR0bVqT44pKY7FYqvxoLlq0SB555BGZMmWKbN++XTp27CiDBg2S5OTkUttv3LhRRo8eLePHj5cdO3bI0KFDzWPPnj22NhkZGdKnTx+ZPn26w+0+/PDDsnz5chMAXr9+vSQmJsqwYcNKbavb6tChQyW8WwAAALgLQVsAcOCP/VqaeptqwX+PSnIa2bbAlfpo68Us21HdGIAM3ueVV16R++67T8aNGydt27aV2bNnS3h4uLz//vultn/ttdfkl7/8pTz22GPSpk0bef755+W6666TWbNm2drcddddMnnyZBkwYECp60hNTZX33nvPbPvmm2+WLl26yJw5c0xA+LvvvivW9q233jLZtX/5y18q+Z0DAADAlQjaAoADdaNC5C67bNs31zHCOHAlcvIK5JNtx810cIC/DLuuMQcUla4qBx/LycmRbdu2FQuu6sBnOr9p06ZSl9HnSwZjNTPXUfvS6DZzc3OLrUfLLcTFxRVbz969e+W5556TDz/80DYgGwAAALwTA5EBQBl+37elzPvuqBmQbOHmBJN9Wz8mlGMGXIaVe5Pk54wcMz3w2npSKyKY44hK97vf/c48nA3w5uUVDornjDNnzkh+fr7Uq1ev2PM6v3///lKXOXXqVKnt9Xlnadvg4GCpUaOGw/VkZ2ebMgz/+Mc/TDD30KHyy/roMvqwSktLMz8LCgrMo6rpNrSkhSu2Be/EOQLOE3AtgS9+5ji7DYK2AFBOtu2Y65vJ2xsOmSzB2et/kmdvvZZjBlyG+C0JtmkGIENV0WzUVq1aVbsD/NRTT5nyC84GrNW0adNk6tSplzx/+vRpM/CZK76waOkH/YJEZjA4R8C1BHzewJ0KXNgvSU9Pd6odQVsAKMfv+7aQeZuOyoXcfLJtgct0LCVTvj54xkw3qRUm17eozbFElRg7dqzccccdVbLuOnXqSEBAgCQlJRV7Xufr169f6jL6fEXaO1qHlmbQWrX22bb261mzZo3s3r1bPv7442KDsuk+P/PMM6UGZzXQq4Oq2WfaNmnSROrWrSvR0dHiii9Hmu2s2yNoC84RcC0BnzdwpwIX9ktCQ527e5egLQCUo05kiIzp1VT+ub4w2/bNdT/Kc7e147gBFbBoi/0AZHHi7191dUeBqqIlCnQQsNWrV8vQoUNtHXydf/DBB0td5vrrrzevP/TQQ7bnVq5caZ53lm4zKCjIrGf48OHmuQMHDkhCQoJtPZ988olcuHDBtsyWLVvknnvuka+//lpatmxZ6npDQkLMoyT9ouKqIKp+OXLl9uB9OEfAeQKuJfC1zxxn10/QFgCc8AetbbvpqGTm5Ev85mOmtm3DGmEcO8AJefkFsnhbYdA2wN9PftuFAcjgvTQzVbN5u3btKt27d5eZM2dKRkaGjBs3zrw+ZswYadSokSk9oCZOnCj9+vWTl19+WQYPHizx8fGydetWefvtt23rTElJMQHYxMREW0BWaRatPmJiYmT8+PFm27Vq1TJZsBMmTDAB2549e5q2JQOzWn9XacmEkrVwAQAA4Pn4kzYAOEEHTBrbq5mZzskvzLYF4Jy1B05LUlrhYEc3t46V2GgG84P3GjlypMyYMUMmT54snTp1kp07d8qXX35pG2xMg68nT560te/Vq5csXLjQBGk7duxoyhcsW7ZM2rW7eMfGp59+Kp07dzZBXTVq1CgzP3v2bFubV199VYYMGWIybfv27WuCuUuWLHHpewcAAIDr+FmsBa/gkNb30gwHLUjsqvpeycnJEhsby61i4BzxICkZOXLD9DWSkZMvQQF+sv6xmzw225brCDzpPLn3gy2yal+ymX7/7q5yc+vC4BY8n6uvJa7uc8G9x5/PKnCOgGsJXIHPG3jaeeJsn4tMWwC4jGzb3HyLvLGWbFugPKdSs2TN/sKAbYOYUOnXKpaDBgAAAADlIGgLABVw3w0tJDKksBz4R1uPyfGzmRw/oAyLtx6TgqJ7en7btYmpaQsAAAAAKBtBWwCogJoRwXJ3sWzbnzh+gAMFBRZZtLVwADI/P5ERXRmADAAAAACcQdAWACro3hua27JtNYvwWArZtkBpvvnxjBw/e8FM9726rjSuGc6BAgAAAAAnELQFgAqqER4s43oXZtvmFVjkzXXUtgVKE78lwTY9qlsTDhIAAAAAOImgLQBchnv7tJAoW7btcbJtgRLOnM+WlXuTzHSdyGDp36YexwgAAAAAnETQFgAuQ0x4kIzr09yWbTtrDdm2gL1Pth03dZ/V8C6NJTiQLgcAAAAAOItvUABwmcb3aS5RoYXZth9vPy4JP1PbFlAWi0UWbSkcgEyN6hbHgQEAAACACiBoCwCXKSYsyARuVX6BRV5fc5BjCYjI5sMpcuhMhjkWPVvUkuZ1IjguAAAAAFABBG0B4AqM630x23bJjhNy9OfCQBVQncXbZdmO7k6WLQAAAABUFEFbALjCbFsdlOxiti21bVG9pWbmyue7T5rpGuFBMuja+u7eJQAAAADwOgRtAeAKjevTTKKLsm2X7jghR4puCweqo6U7jkt2XoGZ/k3nRhIaFODuXQIAAAAAr0PQFgCuUHRokNx3w8Vs2/+jti2q8QBk9qURGIAMAAAAAC4PQVsAqAR3925mSiWoZTtOyKHT5zmuqHZ2Hjsn+0+lm+nOcTXkmvpR7t4lAAAAAPBKBG0BoBJEmWzb5ma6wCIyi9q2qIbiN9sNQNaNAcgAAAAA4HIRtAWASjK2VzMz8JJatvOE/ES2LaqR89l5svx/iWY6MiRQhnRs4O5dAgAAAACvRdAWACo127aFLdv29dUHObaoNpbvSpTMnHwzfWunhhIeXDg4HwAAAACg4gjaAkAlZ9vWLMq2/XRXovyYTG1bVA/xmxNs05RGAAAAAIArQ9AWACqR3hb++74tbdm2/0e2LaqBvYlpsut4qpm+tmG0tG8c4+5dAgAAAACvRtAWACrZmOubSq2IYDOtNT5/TE7nGMOnxW+5mGU7qjsDkAEAAADAlSJoCwCVLMJk2xbWtrVYRF5b/SPHGD7rQk6+LN1xwkyHBvnLbZ0aunuXAAAAAMDrEbQFgCrKtq1dlG274n+J8kMS2bbwTZ/vPinpWXlmenD7hhIdWljTGQAAAABw+QjaAkAVCA8OlD/0s8+2Pchxhs+XRhjdvYlb9wUAAAAAfAVBWwCoIr/r2VTqRAbbshEPnCLbFr5F6zVvOXLWTF8VGyldmtZ09y4BAAAAgE8gaAsAVZlt27elLdv2/8i2hY+J33zMNj2qWxPx8/Nz6/4AAAAAgK8gaAsAVZ5tG2KmP9t9UvafSuN4wydk5+XLkqIByIID/GXYdY3dvUsAAAAA4DM8Kmg7bdo06datm0RFRUlsbKwMHTpUDhw4UOYy77zzjtxwww1Ss2ZN8xgwYIBs3rzZ9npubq488cQT0r59e4mIiJCGDRvKmDFjJDEx0QXvCEB1FxYcIH8sqm2rXltFbVv4hpV7kyQlI8dMD2pXX2oVDbwHAAAAAPCxoO369evlgQcekO+++05WrlxpAq4DBw6UjIwMh8usW7dORo8eLWvXrpVNmzZJkyZNzDInThRm/2RmZsr27dtl0qRJ5ueSJUtMIPjWW2914TsDUN2zbetGFWbbfrHnlOxNJNsWvlUaYXQ3BiADAAAAgMoUKB7kyy+/LDY/d+5ck3G7bds26du3b6nLLFiwoNj8u+++K5988omsXr3aZNTGxMSYALC9WbNmSffu3SUhIUHi4uKq4J0AwEWhQQFyf7+W8tyKvWb+tdU/yD/v6sohgtdK+DlTvvnxjJluWjtcerao7e5dAgAAAACf4lGZtiWlpqaan7Vq1XJ6Gc2s1QzdspbR9epgKTVq1KiU/QSA8tzRI05ii7Jtv/o+Sb5PLLy+Ad5o0dYE2/SIrk3E358ByAAAAADAZzNt7RUUFMhDDz0kvXv3lnbt2jm9nNav1bq1Wtu2NFlZWaaNllSIjo4utU12drZ5WKWlpdn2SR9VTbdhsVhcsi14J84R7xMc4Gdq2z63Yp+ttu3s311XZdvjHEFVnSd5+QWyeOtxMx3g7yfDOzfk88qHufpaQt8HAAAA8PCgrda23bNnj3zzzTdOL/Piiy9KfHy8qXMbGhp6yeuagTtixAjz5eOtt94qc0C0qVOnXvL86dOnTdDXFV9YNBtY99Pf36OToeEmnCPeqX+zUHkrIkhOZ+TKf/Ymydd7jsg1seFVsi3OEVTVebLhp3OSnF74h80+zWNEstIkOYs6zb7K1deS9PT0Kt8GAAAA4A08Mmj74IMPyooVK2TDhg3SuHFjp5aZMWOGCdquWrVKOnTo4DBge/ToUVmzZo3DLFv11FNPySOPPFIs01YHOKtbt26Zy1XmFyQt36DbI2gLzhHf8sDN2fLs8sLatvN2/Cxv39WsSrbDdQRVdZ588cXF0ghj+rQ0tefhu1x9LSntj+4AAABAdeRRQVvN4pgwYYIsXbrUZMs2b97cqeVeeukleeGFF+Srr76Srl27OgzYHjx4UNauXSu1a5c9YEpISIh5lKRfVlwVRNUvSK7cHrwP54h3GtU9TmavPySn0rJk1b5k+T4xXdo3jqmSbXGOoLLPk5OpF2T9D6fNdMOYULnxmnrUs60GXHktod8DAAAAFPL3tJII8+fPl4ULF0pUVJScOnXKPC5cuGBrM2bMGJMJazV9+nSZNGmSvP/++9KsWTPbMufPn7cFbG+//XbZunWrLFiwQPLz821tcnJy3PI+AVRfoUEB8qebWtrmZ676wa37A1SE1rItsBRO/7ZrE1PTFqiO3njjDdPv1MzgHj16yObNm8tsv3jxYmndurVp3759e/n888+Lvb5kyRIZOHCgSSzQIPnOnTsvWYeW6NK+sraJjIyU4cOHS1JSku31Xbt2mTEb9O6wsLAwadOmjbz22muV+K4BAABQbYO2WmdW66bdeOON0qBBA9tj0aJFtjYJCQly8uTJYsto8FUDs/bLaLkEdeLECfn000/l+PHj0qlTp2JtNm7c6Jb3CaB6G9mtiTSIKbwFePX+ZPnf8XPu3iWgXAUFFlm05ZiZ9vMTGdGtCUcN1ZL2S7WM1pQpU2T79u3SsWNHGTRokCQnJ5faXvubGkwdP3687NixQ4YOHWoeOnaDVUZGhvTp08ckIzjy8MMPy/Lly00AeP369ZKYmCjDhg2zvb5t2zZTrkQTIL7//nt55plnTKLDrFmzKvkIAAAAwBX8LFqTAGXSmrYxMTEmoOyqmrba8deON7cJgnPEN8377qhMWlb4hf3m1rHy/t3dKnX9XEdQ2eeJlkUY+35hNuGN19SVueO6c5CrAVdfS1zd57ocmlnbrVs3WzBUj5Fmt2qJryeffPKS9iNHjjRBWR2vwapnz54mmWD27NnF2h45csSUB9Pgrr5upcdD6wrr3WiaqKD2799vsmk3bdpk1lcazczdt2+fGc/BGfR54Wnoz4DzBFxL4IufOc72uTyqpi0AVBcjujaWt9b+KImpWbJmf7LsPHZOOjWp4e7dAhyK33xxALJRZNmimtK7uzSj1b5Ul3bqBwwYYIKnpdHn7Qe4VZqZu2zZMqe3q9vUkl+6HSsttxAXF1dm0Fa/CNSqVcvherOzs83D/guE9UuLPqqabkPzR1yxLXgnzhFwnoBrCXzxM8fZbRC0BQA3CAkMkAduvkqeWbrHVtuWzEV4qtPp2bJyb2HtzDqRIdK/TT137xLgFmfOnDHjI9SrV/z/gM5r5mtpdByF0trr887StsHBwVKjRg2n16NlGbSUw2effeZwvdOmTZOpU6de8vzp06dNDV1XfGHRwLJ+QeLuMnCOgGsJ+LyBOxW4sF+Snp7uVDuCtgDgJr/t0kTeXPuTnDh3QdYdOC3bE87KdXE1+X3A43yy/bjkFY1AdnuXxhIU4FEl8QGUoPVyb7vtNlN3Vwc4c0Qzhu2zgDXTVks9aCkGV5UE04HXdHsEbcE5Aq4l4PMG7lTgwn6JDk7rDIK2AOAmwYH+8sBNV8nTS3eb+ddWHZQP7qFOKDyL/qXZOgCZojQCqrM6depIQECAJCUVZp5b6Xz9+vVLXUafr0h7R+vQ0gznzp0rlm1b2nr27t0r/fv3l9///vfy17/+tcz1hoSEmEdJ+kXFVUFU/XLkyu3B+3COgPMEXEvga585zq6f3hEAuJFmLTaqEWYb6Gnb0bP8PuBRvjuUIofPZJjp61vUlmZ1Ity9S4DbaImCLl26yOrVq4tlZej89ddfX+oy+rx9e7Vy5UqH7Uuj2wwKCiq2ngMHDkhCQkKx9Xz//fdy0003ydixY+WFF16o4LsDAACAJyHTFgDcnG074ear5Mklu221beeN78HvBB5j0Ra7Aci6N3HrvgCeQMsJaFC0a9eu0r17d5k5c6ZkZGTIuHHjzOtjxoyRRo0amXqxauLEidKvXz95+eWXZfDgwRIfHy9bt26Vt99+27bOlJQUE4BNTEy0BWSVZtHqQ0cXHj9+vNm2DiympQsmTJhgArbWQci0JMLNN99sBjnTdtZat5oZrLf5AQAAwLuQaQsAbja8S2NpXLMw2/brg2dk29EUd+8SYJzLzJHP9xQGfmqEB8mga52/nRvwVSNHjpQZM2bI5MmTpVOnTrJz50758ssvbYONafD15MmTtva9evWShQsXmiBtx44d5eOPP5Zly5ZJu3btbG0+/fRT6dy5swnqqlGjRpn52bNn29q8+uqrMmTIEBk+fLj07dvXBHOXLFlie13XqwOIzZ8/Xxo0aGB7dOvWzUVHBgAAAJXJz6LF6lAmHZRBMxx0FDlXDcqQnJwssbGx1PcC50g1ymZ84pPCbNs+V9WR+fdeWbYt1xFUxnky59vDMnX5XjN9T+/mMvmWthzYasbV1xJX97ng3uPPZxU4R8C1BK7A5w087Txxts9Fpi0AeIBh1zWWuFrhZvqbH8/IliNk28K99G+68ZvtBiCjNAIAAAAAuAxBWwDwAEEB/vLgzVfZ5rW2LeBOO46dkwNJ6Wb6urga0qpeFL8QAAAAAHARgrYA4CGGdW4kTWsXZtt+++PPsvkw2bZwn/jN9gOQxfGrAAAAAAAXImgLAB4iMMBfJtx8tW3+1ZVk28I90rNyZfmuwoGUokICZUiHBvwqAAAAAMCFCNoCgAcZ2qmhNCvKtt106Gf57tDP7t4lVEOf7kqUC7n5ZvrWTg0lPDjQ3bsEAAAAANUKQVsA8OBsW2rbwh0Wbbk4ANloSiMAAAAAgMsRtAUAD3Nbp4bSvE6Emf7uUIps+olsW7jO94mp8r/jqWa6XaNoadcohsMPAAAAAC5G0BYAPDDb9s/9r7LNv7rqB7FYLG7dJ1Qf8ZsvZtmO6sYAZAAAAADgDgRtAcAD3dqxkbSoW5htu/kw2bZwjQs5+bJs5wkzHRYUYLK+AQAAAACuR9AWADxQgL+fTOx/sbYt2bZwhc92n5T0rDwzPbhDA4kKDeLAAwAAAIAbELQFAA81pENDaVmUbbvlyFnZSG1bVLH4zQm26dHdm3C8AQAAAMBNCNoCgAdn2/7ZPtt2JbVtUXUOJqXL1qNnzfTVsZFyXVxNDjcAAAAAuAlBWwDw8Gzbq2IjzbQG1L758Yy7dwk+Kn6L3QBk3ePEz8/PrfsDAAAAANUZQVsA8KbatmTbogpk5+XLku3HzXRwgL8M69yI4wwAAAAAbkTQFgA83OD2Dczt6mp7wjnZcJBsW1SulXuT5Wxmrpn+Zbv6UjMimEMMAAAAAG5E0BYAPJy/ZtsOuJhtO3MVtW1x5SwWi6Rk5EhiarbM++6o7flRDEAGAAAAAG4X6O4dAACU79ftGsg19X6UA0npsiPhnKz/4bTceE0shw4VlnohVz7Zdlw+2HhEjqZkFnutVniQXNsgmqMKAAAAAG5Gpi0AeGG27aurDppMSaAiNNh//bTV8vyKvZJQImCrUjJz5foX15h2AAAAAAD3IWgLAF7il9fWl9b1o8z0rmPnZN0BAmtwngZix83ZLBdy80XD/Y5C/vq6tiNwCwAAAADuQ9AWALwo2/ahYtm21LaF8yUR7p+/rTBYW06Ctr6uTbS9LgcAAAAAcD2CtgDgRQa2vZht+7/jqbL2QLK7dwleQGvYXsjJLzdga6XttP2S7ceretcAAAAAAKUgaAsAXpdt28o2P5PatiiH1j7WQccux9xvj1A7GQAAAADcINAdGwUAXL5B19aTtg2iZe/JNJNtu3pfsgxoW49DCkMzZE+lZcmp1CxJSsuSw2fOy9FSBh0rjybl6nLnMnOlZkQwRxcAAAAAXIigLQB4GT+/wtq2v5+3zczPXP2D9G8Ta56H78ovsMjP57OLBWST0grnk4qe0+n0rLxK3e757DyCtgAAAADgYgRtAcAL/aJtPbm2YbR8n5gme06kycq9STLw2vru3i1cQWDUGog1P9OzJKkoCHsqLVuS07IkOT3bBG5dLTKErgIAAAAAuBrfxADAa7NtW8l9H2611bbVQC7Ztp4lL79ATmt2bMnMWFtANkuS07JN0PZKhQT6S73oUKkfHSr1YvRniJmPjQqRFz7fZ7ZTkZCv5m3H1QqXGuFBV7xvAAAAAICKIWgLAF5qQJtYad8oRnafSDX1bf+zN0kGkW3rssG90rLyLmbGWksUmPls2/yZ89lSGcmxdSKDSwRkQ6VeUVC2ftF8TFiQw6D9mfM58vyKvRXe7t29m/GHAAAAAABwA4K2AODltW3Hf2CXbdumnvj7U9v2SuTkFUhyul1mrLVsQVGAVssU6M8LuflX/DsMCwowQVcNwFoDsvWiCgOx1oBs3cgQCQ70v6LtDO/SWGb854DZZ4sTQWQ9hUKDAmTYdY2vaLsAAAAAgMtD0BYAvNjNrWOlY+MY2XU8VfaZbNtT8st2Ddy9Wx6bHXsuM9c2cFdhluzFgbwuZsfmXPG2NOhZJzLkYvC1KACrpQqsmbEaoI0KCXRJJqtm4b71uy4ybs5mU/egrMCtdXdm/66LWQ4AAAAA4HoEbQHAB2rbjpu7xZZtO7Bt9RuQLCs339Rs1QG87Af0staMtQZms/MKKmVgLpMZaxeQ1Z/2pQq0nEFgwJVlx1a2fq3qypxx3eX++dvkQk5hlrB97NbPLvtXA7Z9W9V1y34CAAAAAAjaAoDXu/GautKxSQ3Zdeyc7D+VLl/uOSndm9eSxNRsCYzIkdqRIV5bl7SgwCIpmTlFZQnsMmM1MGsXoD2bmXvF2wrw9zOZsMUyY4vKFthKF0SHmqCtt9LA7aan+suS7cdl7rdH5GhKpu01HXRMa9hqKYXoUDJsAQAAAMCdvPebJwCgWG3bcXMKs20nLtopufnWHMo90rRWuIztVRiM86Tb3TXbs3ipgsLMWPtashqovfheLl90aOClmbFFWbGFAdkQqR0RYgK3vk7PgXG9m8vdvZpJSka2HD2RJE0b1ZNaEd4b3AcAAAAAX0PQFgB8gIbaNN5YYJFLgpwJKZny/Iq9ZiAqrWuq2ZZVKb/AIj+fzy4MvBYN4KWZsSUDtGlZeVe8raAAzY69WJagsEzBpaULwoIDKuW9+RIN0NYMD5bcmBDzk4AtAAAAAHgOgrYA4OXW/3Ba7pm7xeHgUtanL+Tmm4GotK7p5QZuz2fnFQZei4KwtlIFdnVjk9OzTeD2StUMDypWJzbWVrbgYgkDDTb6V4PsWACe5Y033pB//OMfcurUKenYsaO8/vrr0r17d4ftFy9eLJMmTZIjR47I1VdfLdOnT5df//rXtteXLFkis2fPlm3btklKSors2LFDOnXqVGwdWVlZ8uijj0p8fLxkZ2fLoEGD5M0335R69erZ2iQkJMj9998va9eulcjISBk7dqxMmzZNAgPp8gMAAHgbj+rBaadSO6379++XsLAw6dWrl+nUXnPNNQ6Xeeedd+TDDz+UPXv2mPkuXbrI3//+92IdZx0xfMqUKabtuXPnpHfv3vLWW2+ZTjMAeLPUC7lmYCkNkZYXJjVBXT8x7bWuqX2phLz8AjlzPqcwEGsdyKtEZqwGZjVoe6WCA/2L14mNssuMLQrQ1o0KkdAgsmMBeJ5FixbJI488YoKsPXr0kJkzZ5oA6oEDByQ2NvaS9hs3bpTRo0ebfu6QIUNk4cKFMnToUNm+fbu0a9fOtMnIyJA+ffrIiBEj5L777it1uw8//LB89tlnJgAcExMjDz74oAwbNky+/fZb83p+fr4MHjxY6tevb7Z58uRJGTNmjAQFBZm+MQAAALyLn0Ujmh7il7/8pYwaNUq6desmeXl58vTTT5tg7N69eyUiIqLUZe68804ThNUAb2hoqAnyLl26VL7//ntp1KiRaaPPaUf5gw8+kObNm5tMh927d5v16jLlSUtLM53j1NRUiY6OlqpWUFAgycnJpuPv7+9Zo4/DM3COwOr9bw6b0gcVvZB3bVpTakYE2wKyZ85nm9IKV6pOZLAtC/aSzFgToA2VGuFB3IrvIbiWwNPOEVf3uS6HBmq1rzpr1izbMWrSpIlMmDBBnnzyyUvajxw50gRlV6xYYXuuZ8+eJpNWA7/2NBNX+6olM231eNStW9cEfG+//XbznCY5tGnTRjZt2mTW98UXX5igcGJioi37Vtf/xBNPyOnTpyU4OLjc90afF56GzylwnoBrCXzxM8fZPpdHZdp++eWXxebnzp1rDpbeKta3b99Sl1mwYEGx+XfffVc++eQTWb16tcku0Ji0ZkD89a9/ldtuu8200cxc7cwuW7bMBIkBwBvp9e2DjUcua9mtR89WqH1YUEBRNuzF0gS2QGxRHVmtLatZtADgq3Jycky/9KmnnrI9p536AQMGmOBpafR5zcy1p5m52g91lm4zNzfXbMeqdevWEhcXZwva6s/27dsXK5eg29FyCZrM0LlzZ+ffaFaWSGlBXv0CY/+8tnPEmbYFBYXP5+SI2CdSZGcX3R5SCh0wMSTk8trqdnSbjtjvg7va6v5aB4XMzdUU6spvm5dX+KiMtvo7tn6xrcy2QUEiAUV33Gg7PU8cfYG2b6vHQI+FI1oqxFoupCJt9Xemv7vKbqvnrp7DldFWj4Eei8puW9n/7yujbcn/99ZriT70vXGNqH7XiPLaWvdXj4Gz/z+5RvjONcLZfoT9tUTXWZX9CCd5VNC2JI04q1q1ajm9TGZmpunUWpc5fPiwqTdm38nVaLZmSWjntrSgrdYJ04d9BNwadddHVdNtaDDGFduCd+IcgUrJyJGjKZlXdDD0c6huZGEg9mJA1jpfNKhXdKhEhQY6lR3Ldcu7cC2Bp50jnn4NOXPmjClDYB8YVTqvma+l0X5oae31eWdpW82UrVGjhsP1ONqO9bXSOOrzWu66SyzWgI4dS9euIpMn2+b97rzT8Re5du3EYleWwe+ee3QDlzSroctfe60UvPLKxbb33y+SnFz6eps0Ecsbb1xs+9BDIseOld42NlYs7757se0TT4gcPFh62+hoscyff7Gtvs+i8muXCAkRy+LFF+f//nfx27q19LZ63D799OLMyy+LX1FJi1LbfvTRxS99s2aJ3+rVjtvOm6dfbApn3n5b/L74wnFbPQ7W8h1z54pfGX80sGgWeVxc4Ux8vPjFxztu+/LLItaSc8uWid/cuY7bvvCCSPv2hTNffCF+//yn47aTJol062auCUEbN2rWjcO7iiyPPy7Sp0/hzLffit9LLzle78SJIv37F85s3Sp+zz/vuO0f/iAyeHDhzO7d4vfMM47b3n23yLBhhTMHD4rfo486bqvfP++4o3AmIUH8HnzQcduhQ0X0/45KTha/e+913PZXvxLR/zsqNVX87rrLcVs9BnosVFaW+I0Y4bht794i+n+niF9Rtn+pbavgGmFcfXXhuVbGNcJcS/T/JteIanWNMNauFb/XXnPc9vHHpaBXr8L+jF4jZsxw3JZrhM9eI2zKuUbYriVV3I8oeO898eqgrX5AP/TQQ6b0gbXelzP0FrCGDRvagrTWTmpFOstaSmHq1KmXPK+3lukgEK547xqw1osK5RHAOQJHElPL+IunE94ZcY20qR8hgQ4H8tLgyQXJStcHvwdfxOcNPO0cSU/nYuNKjvq82bm5kl1KhkpuRoZk2H0JMl9sHGQs5WVkyHm7tjH6pa9kW4tFcvPyJC8zs1jb6Kws8Xew3oILFyTNvu2FC47bZmUVaxuVmSkBDtpasrIk1a5tZEaGBJaRjXXOrm1ERoYEOdv2/Pny2xYFbcPT0yW4jLapp0+LpejLbtj58xJSRtu0M2fMp7ppm55eftuifQhNS5PQMtqm//yz5BcFhUJSUyWsjLbnU1Ikr+hYlNv27FnTVq9BuefPS4S+Twd/PM44e1Zyi9YbdPasRJSx3sxz5ySnqG3g2bMSWUbbC6mpkm1tm5LidNuAn3+WqDLaZqWlSVZRW/8zZyS6jLbZ6elywdm258/b2vrpbbdltM1JT5dM63mZlSU1ymirx7/Y//uy2lb2NaJIfmampJd1jSi6liiuEdXrGqGCz52T8DLa6jVC/39qfybo3Lky/y9zjfDRa4SdMq8RdtcS/cypyn6Exhe9rqatPb2VS2tzffPNN9K4cWOnlnnxxRflpZdeknXr1kmHDh3MczoQgwZ+tb5XgwYNbG11oAfNGtPBJJzJOtBaZWfPnnVZTVv9BWrtMoK24BxBWZm2XV9wnIFTnm1/7S81w8uvcQjfxecNPO0c0T5XzZo1PbamrZZHCA8Pl48//tgMJmY1duxYM9jtv//970uW0RIGWh5BkxGsdIBcLY+wa9cup2rarlmzRvr372/6ovbZtk2bNjXr1UHKJk+eLJ9++qns3LnT9rrecdaiRQsz6Flp5REc9nlPniz9+FfybY2286tePfGnPEIhyiMUu/XZnCMnT0rdmjUdX4MojyDVvTxCsc8qyiNcRHmEQkFBUuDnV3iO1Kol/mWViaA8gk9eI5wtj3BJv7cKyyOk5eQ41ef1yExbHQ1XB2vYsGGD0wHbGTNmmKDtqlWrbAFbpSPoqqSkpGJBW5237wzbCwkJMY+S9JfmqiCqBpRduT14H84R1I4Mkaa1wiUhJbNCA5FpnkpcrXCpFRHCgGDgWgKP+rzx9H6Plijo0qWLGTvBGrTVDr7Oa/+1NNdff7153T5ou3LlSvO8s3SbQUFBZj3Dhw83zx04cEASEhJs69GfL7zwgm0ADet29ItA27ZtK9bnDQ83j3I506astgUF4hcWZgK2xX73YWHOr7cibZ0YgNij2pbyu6mUtvrF1omB6TylrV9QUOE56cz1QduUUtqjUtpa61xWZtvK+H9UHduW/H9vvZaUdp5wjfD5a4RTbfUc0f6MXk+cPRZcI3znGuFs27KuJZX8ee9fVh1m+3biQTTpVzu8S5cuNRkFmmngDM2uff75581AZl21RoYdXYcGbrWTa59F8N///rdCnWUA8DTa8Rjbq9llLXt372YEbAHgMmjW7DvvvCMffPCB7Nu3z9wdlpGRIePGjTOv60C49gOVTZw40fRRX375ZVP39tlnn5WtW7cWC/KmpKSYDNm9e/faArI6by3lpeMxjB8/3mx77dq1ZmAy3Z72ZXUQMjVw4EATnL3rrrtMBu9XX31lBuJ94IEHSg3MAgAAwLN5VKatdioXLlxobi2Liooq1lENK4p+a0e4UaNGpgaXmj59urkdTJdr1qyZbZnIyEjz0KCGZjb87W9/k6uvvtoEcSdNmmTq3trf1gYA3mh4l8Yy4z8H5EJuvsM7QOxp+drQoAAZdp1zdzEAAIobOXKkuXVO+5/a79Q7tzQoax0/QbNf7bMzevXqZfqpGkB9+umnTX9USyPYj9mgZQ2sQV9lHShXyyhokFe9+uqrZr2aaaslDQYNGiRvvvmmbZmAgABzp5oGkTWYGxERYco2PPfcc/wKAQAAvJBH1bR1NDL5nDlz5G4dkVNEbrzxRhOcnVs06qBOHz169JJl7Du5+hZ1/u233zb1xvr06WM6ua1atXJqvzQzVwPHrqqvprfZWW9t8/TbBOEenCOwt/6H0zJuzmZTIqGsK7peYvUqO3dcd+nbqi4HEVxL4HGfN67uc8G9x5/+DDhHwLUErsDnDTztPHG2z+VRmbbOxI91kLGSAzY4EwzWLAMyDQD4on6t6sqccd3l/vnb5EJOYWF9+6up9c9hYUEBMvt3XQjYAgAAAADg4TwqaAsAuPzA7aan+suS7cdl7rdH5GhKpu01HXRMa9hqKYXoUCcH3AAAAAAAAG5D0BYAfERMWJCM691c7u7VTFIysuXoiSRp2qie1IoIYdAxAAAAAAC8CEFbAPAxWhKmZniw5MaEmJ+O6oUDAAAAAADPxChXAAAAAAAAAOBBCNoCAAAAAAAAgAchaAsAAAAAAAAAHoSgLQAAAAAAAAB4EIK2AAAAAAAAAOBBCNoCAAAAAAAAgAchaAsAAAAAAAAAHiTQ3TvgDSwWi/mZlpbmku0VFBRIenq6hIaGir8/cXVwjoDrCPi8gXu4uk9i7WtZ+15wLfq88DR8LwLnCbiWwBc/c5zt8xK0dYL+0lSTJk0q43cDAACAcvpeMTExHCMXo88LAADgOX1ePwupDE5F2xMTEyUqKkr8/PykqmnEXQPEx44dk+jo6CrfHrwP5wg4R8C1BL74eaPdUu28NmzYkLuN3IA+LzwNfV5wnoBrCXzxM8fZPi+Ztk7QA9i4cWNxNT1JCNqCcwRcR8DnDdzNlX0SMmzdhz4vPBXfi8B5Aq4l8LXPHGf6vBRMBQAAAAAAAAAPQtAWAAAAAAAAADwIQVsPFBISIlOmTDE/Ac4RcB0BnzdwF/ok4PyCO3ENAucJuJagOn/mMBAZAAAAAAAAAHgQMm0BAAAAAAAAwIMQtAUAAAAAAAAAD0LQFgAAAAAAAAA8CEFbD/Xiiy+Kn5+fPPTQQ+7eFXiI/Px8mTRpkjRv3lzCwsKkZcuW8vzzz4vFYnH3rsGNNmzYILfccos0bNjQXDOWLVtmey03N1eeeOIJad++vURERJg2Y8aMkcTERH5n1UhZ54jVvn375NZbb5WYmBhzrnTr1k0SEhLcsr9wvWnTppnfeVRUlMTGxsrQoUPlwIEDxdpkZWXJAw88ILVr15bIyEgZPny4JCUl8evCFaPPi9LQ70VJ9HnhDPq98LV+L0FbD7Rlyxb55z//KR06dHD3rsCDTJ8+Xd566y2ZNWuWCbDo/EsvvSSvv/66u3cNbpSRkSEdO3aUN95445LXMjMzZfv27SbYrz+XLFliPpA0OIfqo6xzRP3000/Sp08fad26taxbt07+97//mXMmNDTU5fsK91i/fr3pmH733XeycuVK8wefgQMHmnPH6uGHH5bly5fL4sWLTXv948+wYcP4leGK0OeFI/R7URJ9XjiDfi98rd/rZyFNz6OcP39errvuOnnzzTflb3/7m3Tq1Elmzpzp7t2CBxgyZIjUq1dP3nvvPdtz+hcfzbqdP3++W/cNnkGzKJcuXWr+WljWF+Tu3bvL0aNHJS4uzqX7B888R0aNGiVBQUEyb948t+4bPMfp06dN5oF2Uvv27SupqalSt25dWbhwodx+++2mzf79+6VNmzayadMm6dmzp7t3GV6IPi/KQr8XZaHPC2fQ74Uv9HvJtPUwGvEfPHiwDBgwwN27Ag/Tq1cvWb16tfzwww9mfteuXfLNN9/Ir371K3fvGryIfghpB6ZGjRru3hV4gIKCAvnss8+kVatWMmjQINNh6dGjR6klFFC9rhOqVq1a5ue2bdtMFoJ930Qzs/UPP9p5BS4HfV6UhX4vrhR9XpREvxfe2O8NdPkW4VB8fLy5hVkz4YCSnnzySUlLSzMXjICAAFPr64UXXpA777yTgwWnaG0erXE7evRoiY6O5qhBkpOTTbab1pTUuzv0dtQvv/zS3P6zdu1a6devH0epGn6h0Xr6vXv3lnbt2pnnTp06JcHBwZf8sUfv/tDXgIqiz4vy0O/FlaDPi9LQ74U39nsJ2nqIY8eOycSJE01NDeoIojQfffSRLFiwwKTpX3vttbJz505zgdHBhcaOHctBQ5n0r4UjRowwA9dpbWTA2lFRt912m6ndpLQsz8aNG2X27NkEbatp9uOePXvMnRxAVaDPC2fQ78Xlos8LR+j3whv7vQRtPYSmYOtffrSerZVmUurohzrwVHZ2tsmuRPX12GOPmawDrT+p2rdvb+qS6uiHBG3hTOdVz5c1a9aQZQubOnXqSGBgoLRt27bYUdGaTZ7ceUHVePDBB2XFihWm79G4cWPb8/Xr15ecnBw5d+5csawDHUVXXwMqgj4vnEG/F5eDPi/KQr8X3tjvpaath+jfv7/s3r3bZE9aH127djW3vus0AVtkZmaKv3/x/7J6Xlj/YgiU1Xk9ePCgrFq1SmrXrs2Bgo3e+tOtWzc5cOBAsaOitbObNm3KkaomNANfO646SJ3+Yad58+bFXu/SpYsZrE7rqlvpOZOQkCDXX3+9G/YY3ow+L5xBvxcVRZ8X5aHfC2/s95Jp6yGioqJsNTSsIiIiTICl5POonm655RZTw1YLYGt5hB07dsgrr7wi99xzj7t3DW6k9Uh//PFH2/zhw4fNH3q0kHqDBg3MiJdaK1v/iqjZ+9Y6PPq6dlxQvc8RvZ5oNtPIkSPNaKk33XSTqWm7fPlyWbdunVv3G669NUxL7/z73/82/RHrdSImJkbCwsLMz/Hjx8sjjzxizhutiT1hwgTTcXX1CLrwfvR54Qz6vSiJPi+cQb8XPtfvtcBj9evXzzJx4kR37wY8RFpamjkf4uLiLKGhoZYWLVpYnnnmGUt2dra7dw1utHbtWoteyks+xo4dazl8+HCpr+lDl0P1UNY5YvXee+9ZrrrqKnNt6dixo2XZsmVu3We4lqPrxJw5c2xtLly4YPnTn/5kqVmzpiU8PNzym9/8xnLy5El+VagU9HlREv1elESfF86g3wtf6/f6Fe00AAAAAAAAAMADUNMWAAAAAAAAADwIQVsAAAAAAAAA8CAEbQEAAAAAAADAgxC0BQAAAAAAAAAPQtAWAAAAAAAAADwIQVsAAAAAAAAA8CAEbQEAAAAAAADAgxC0BQAAAAAAAAAPQtAWADzAjTfeaB6V6dlnnxU/Pz/xZN6wjwAAAKgc9HkBwHkEbQH4vLlz55rAoPURGhoqrVq1kgcffFCSkpLEm2VmZprA57p168STnD9/XqZMmSLt2rWTiIgIqV27tnTq1EkmTpwoiYmJ7t49AAAAn0Of1/Xo8wKoSn4Wi8VSpVsAAA/owI4bN06ee+45ad68uWRlZck333wj8+bNk6ZNm8qePXskPDzcrftozbKtaPD1zJkzUrduXRMg1eCtvby8PPPQILUr5ebmSo8ePWT//v0yduxYE6zVDu33338vy5cvl8WLF9ver7v2EQAAwNfQ56XPC8C3BLp7BwDAVX71q19J165dzfS9995rsj9feeUV+fe//y2jR4/2uV9EYGCgebjasmXLZMeOHbJgwQK54447ir2mAfOcnBy37yMAAICvos/rGvR5AVQ1yiMAqLZuvvlm8/Pw4cPmp2Z8Pv/889KyZUsJCQmRZs2aydNPPy3Z2dnFltPnhwwZIv/5z39MFqlmibZt21aWLFniVL1W661rR44ccbhvGticPHmydOnSRWJiYkyJgRtuuEHWrl1ra6PLa5atmjp1qq38gzXjtrTtV/Q9akZy9+7dzXts0aKFfPjhh+Ue159++sn87N279yWv6Xqio6MdHqO77767WCkL+4d9JrHur2YXX3XVVeZ9NGnSRB5//PFL3gcAAEB1R5+XPi8A70TQFkC1ZQ0uasatNftWA6XXXXedvPrqq9KvXz+ZNm2ajBo16pJlDx48KCNHjjSZDNpGs0V/+9vfysqVKytl39LS0uTdd981ZQSmT59uApanT5+WQYMGyc6dO00bDdi+9dZbZvo3v/mNKfegj2HDhjlcb0Xe448//ii33367/OIXv5CXX35ZatasaYKqWuagLFpyQmmAt6IVeP7whz/Y3of1ceedd5rXYmNjzc+CggK59dZbZcaMGXLLLbfI66+/LkOHDjXvR38nAAAAuIg+L31eAF5Ka9oCgC+bM2eORg4tq1atspw+fdpy7NgxS3x8vKV27dqWsLAwy/Hjxy07d+40be69995iy/7lL38xz69Zs8b2XNOmTc1zn3zyie251NRUS4MGDSydO3e2PTdlyhTTztH+HD582PZcv379zMMqLy/Pkp2dXWy5s2fPWurVq2e55557bM/p+9F16bZKKrn9y3mPGzZssD2XnJxsCQkJsTz66KOWsmRmZlquueYas7yu5+6777a89957lqSkpHL3saSDBw9aYmJiLL/4xS/MMVHz5s2z+Pv7W77++utibWfPnm3W9e2335a5fwAAAL6IPm8h+rwAfAWZtgCqjQEDBpjsVL2VXjNLIyMjZenSpdKoUSP5/PPPTZtHHnmk2DKPPvqo+fnZZ58Ve75hw4Ymu9VKb/kfM2aMqeV66tSpK97XgIAACQ4OtmWWpqSkmNIGWpN3+/btl7XOir5HLfmgJRms9Nhdc801cujQoTK3ExYWJv/973/lscces5WDGD9+vDRo0EAmTJjgdAmDjIwMc4w1w/df//qXOSZKBzJr06aNtG7d2gzEZn1Yb/2zLyEBAABQ3dDnpc8LwDcw+guAauONN96QVq1amVIG9erVMwFIf//Cv10dPXrUTGuNVHv169eXGjVqmNftabuS9WJ13dZas7rclfrggw9MWYL9+/dLbm6u7fnmzZtf1voq+h7j4uIuWYcGUM+ePVvutrQO70svvWQeut7Vq1ebcgazZs0yr/3tb38rdx333XefuZ1v48aNthIW1tIU+/bts9XzLSk5ObncdQMAAPgq+rz0eQH4BoK2AKoNHVBLM1XLUtrAYZfL0bry8/PLXXb+/PmmfqzWatWMVa3nqpmmWn/WWpessverJGtma0kVrVOrNW7vuecekzWrg5ktWLCg3KDta6+9ZrJr9TjoYG/2NPO4ffv28sorr5S6rGZSAwAAVFf0eQvR5wXg7QjaAkBRYFGDgZrFqbfeWyUlJcm5c+dsg2vZD9KlwUv7zuAPP/xgfjZr1syWlap0ec1ktSqZ0Vqajz/+2AQ4lyxZUmwbU6ZMuewgc0XfY2XT49GyZUvZs2dPme2+/vpr+ctf/iIPPfSQbRAye7qOXbt2Sf/+/Ss1yA4AAODr6PPS5wXgPahpCwAi8utf/9och5kzZxY7HtZszsGDBxd7PjEx0dTDtUpLS5MPP/zQZIVaSyNocFFt2LChWJ1WLXvgbJarfVar1ondtGlTsXbh4eHmpwZdK/s9Xi4NqGqN2ZI0WL13715TlsKRkydPyogRI6RPnz7yj3/8o9Q2+vqJEyfknXfeueS1CxcumGMMAACAS9Hnpc8LwHuQaQsAItKxY0cZO3asvP322yYA2q9fP9m8ebMJsGqJgptuuumS+rU6uNaWLVtMfdz333/fZKzOmTPH1mbgwIGmLqy20xIHGojVdlqLNSEhoczjPmTIEJNlqyUFNJh6+PBhmT17thkc7Pz588UG/dLnFi1aZPapVq1a0q5dO/O40vd4uVauXGkygm+99Vbp2bOnGfBNBy/T966DkD377LMOl/3zn/8sp0+flscff1zi4+OLvdahQwfzuOuuu+Sjjz6SP/7xj2bQsd69e5uSE1r7V5//6quvyi2DAQAAUB3R56XPC8CLWADAx82ZM0fTVS1btmwps11ubq5l6tSplubNm1uCgoIsTZo0sTz11FOWrKysYu2aNm1qGTx4sOWrr76ydOjQwRISEmJp3bq1ZfHixZesc9u2bZYePXpYgoODLXFxcZZXXnnFtj+HDx+2tevXr595WBUUFFj+/ve/m23p+jt37mxZsWKFZezYseY5exs3brR06dLFbEPXO2XKFPO8/ix5ma/oeyyp5H6W5tChQ5bJkydbevbsaYmNjbUEBgZa6tata9a3Zs2aYm1L7qOuW+dLe1jfl8rJybFMnz7dcu2115rjU7NmTXMM9L2lpqaWuX8AAAC+iD7vRfR5AfgCP/3H3YFjAPAmWrNWM1lXrFjh7l0BAAAAqgR9XgBwL2raAgAAAAAAAIAHIWgLAAAAAAAAAB6EoC0AAAAAAAAAeBBq2gIAAAAAAACAByHTFgAAAAAAAAA8CEFbAAAAAAAAAPAgBG0BAAAAAAAAwIMQtAUAAAAAAAAAD0LQFgAAAAAAAAA8CEFbAAAAAAAAAPAgBG0BAAAAAAAAwIMQtAUAAAAAAAAAD0LQFgAAAAAAAADEc/w/zow4dXmCrEkAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -534,10 +534,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:37:50.102826Z", - "iopub.status.busy": "2026-01-09T21:37:50.102547Z", - "iopub.status.idle": "2026-01-09T21:38:27.951748Z", - "shell.execute_reply": "2026-01-09T21:38:27.950628Z" + "iopub.execute_input": "2026-01-10T22:22:30.696868Z", + "iopub.status.busy": "2026-01-10T22:22:30.696562Z", + "iopub.status.idle": "2026-01-10T22:23:09.262187Z", + "shell.execute_reply": "2026-01-10T22:23:09.260026Z" } }, "outputs": [ @@ -553,35 +553,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Delay= 1ms: Sequential= 0.14s, Parallel= 0.41s, Speedup=0.34x\n" + "Delay= 1ms: Sequential= 0.14s, Parallel= 0.44s, Speedup=0.33x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Delay= 5ms: Sequential= 0.61s, Parallel= 1.81s, Speedup=0.34x\n" + "Delay= 5ms: Sequential= 0.60s, Parallel= 1.81s, Speedup=0.33x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Delay= 10ms: Sequential= 1.15s, Parallel= 3.43s, Speedup=0.34x\n" + "Delay= 10ms: Sequential= 1.14s, Parallel= 3.42s, Speedup=0.33x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Delay= 20ms: Sequential= 2.30s, Parallel= 6.86s, Speedup=0.33x\n" + "Delay= 20ms: Sequential= 2.37s, Parallel= 7.03s, Speedup=0.34x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Delay= 50ms: Sequential= 5.34s, Parallel= 15.80s, Speedup=0.34x\n", + "Delay= 50ms: Sequential= 5.41s, Parallel= 16.19s, Speedup=0.33x\n", "\n", "Done!\n" ] @@ -598,7 +598,7 @@ " # Create problem with specific delay\n", " problem_delayed = (\n", " chron.ScalarBuilder()\n", - " .with_callable(lambda x, d=delay: expensive_rosenbrock(x, delay_ms=d))\n", + " .with_objective(lambda x, d=delay: expensive_rosenbrock(x, delay_ms=d))\n", " .with_parameter(\"x\", 1.0)\n", " .with_parameter(\"y\", 1.0)\n", " .build()\n", @@ -644,16 +644,16 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:38:27.956140Z", - "iopub.status.busy": "2026-01-09T21:38:27.955811Z", - "iopub.status.idle": "2026-01-09T21:38:28.501363Z", - "shell.execute_reply": "2026-01-09T21:38:28.500676Z" + "iopub.execute_input": "2026-01-10T22:23:09.267174Z", + "iopub.status.busy": "2026-01-10T22:23:09.266754Z", + "iopub.status.idle": "2026-01-10T22:23:09.644278Z", + "shell.execute_reply": "2026-01-10T22:23:09.643629Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAiQBJREFUeJzt3QmcTeX/wPHvjMEMZuxj35IIFUIbUZFUlpIWRCkVlUKbX9lK0WJJJS2ypH2h8iuKUH5RShQlaypmscQY+8zc/+v7zP9c9965M3Nn5p652+f9el1mzvqcc55z5n7Ps0U5HA6HAAAAAAAAv4v2/yYBAAAAAABBNwAAAAAANqKkGwAAAAAAmxB0AwAAAABgE4JuAAAAAABsQtANAAAAAIBNCLoBAAAAALAJQTcAAAAAADYh6AYAAAAAwCYE3QBC2p9//ilRUVFePzExMVK1alW59NJLZcaMGXLy5EkJxWMaO3as23zXebfccotf9qnbcd1uQe3YsUOGDh0qzZo1k3Llyknp0qWlWrVq0rRpU7n22mvliSeekA0bNkg4s+O6hLvly5fnev96fhYsWBCwdOo96JoWvUdDQaimW23fvl1GjhwpF1xwgSQmJkrJkiUlPj5ezjrrLBk0aJB8+eWX4nA4Ap1MAPAJQTeAsJWZmSl79+6VZcuWyeDBg82Xt/379wc6WWHnq6++Ml+EX3jhBfntt9/k8OHDcuLECUlNTZXff/9d5s+fL6NHj5YPP/ww0EkFwkJ+L+ZCWUZGhjz44INyxhlnyMSJE2X16tWyZ88eMz09Pd28vHv99delS5cu8tlnnwUkjbNnz3Y7//ryCADyEpPnXAAIMVWqVJEOHTqYn5OTk80XNg2+1U8//WRKSD766KMApzJ86Jfgvn37mkDbcvbZZ0vdunXl+PHj8scff8hff/0V0DQidNSrV09at27tdV6tWrWKPT2hTmua9OrVy/l72bJlJZhpyfUNN9wgH3/8sdt0fZ40b97cPMv1RZ71TMnKygpQSgGgYAi6AYQVrd7sWqK6dOlS6dy5s7Maon6Z27VrF1/g/WTRokWmFMqiLzS0OrlnqZxek8qVK/trtwhTHTt2NKWI8I/rr7/efELFlClT3AJubaryxhtvSO/evd2W+/HHH2X8+PGFagoDAIFA9XIAYe2yyy6T9u3bu03TEm+lpbAPP/ywdOrUSRo2bCgVKlQw7QYrVaokF154oUyYMEEOHTrkUzvJefPmyXnnnWe+JFpfBDXQf+aZZ0zJjb4M0DbOpUqVMss0adJEbr/9dlm/fr3fj1nbrusX1csvv9y0hdR9asCrx6np9Gfp0NatW3Ocb0/169eXBx54QG699dY82/NqsLVu3Trp2bOnSW+ZMmVMk4C82vL+888/8tBDD0mLFi0kISHBtCXX/em+8mpDriXzU6dOlYsvvtjsS8+RXp9u3brJf//731zX27lzp9x8882mrwBN37nnnutTkJhXe++CtuHXavtDhgyR2rVrS2xsrMlLzz77rKl+62vtBCuf6kePx9PBgwfNtq1lNK9atmzZInfeeac0btzYnAM9dzVq1DAl1HfddZd88MEHUpxVe/XFmiftw8F1mZUrVxbpni9Ku+m8+ksoTHp0Gw0aNHCbNm7cOK/VnX1p0633yR133GGqc2tJeFxcnEmLpluDW28886Q247n//vtNTQW9BzV92qTE1zypjh49ao7X1VtvvZUj4Faa1/S5cMUVV+SY9/nnn5vSfb0/NC36XDjnnHPMc0JfuHqjfxP69esnp512mjl+zfu6vj5/7rvvPtN+3PWZ5fksu+SSS4rUJwaACOAAgBC2Y8cOLcJ2fjp06JBjmd69e7st89Zbb5npr732mtt0b59GjRo5UlNT3bY3ZswYt2VuuummHOupkydP5rv9kiVLOt5///08j0n358p13oABA9zmJScnO9q0aZPnPq+66irHsWPH3NbT7Xim3xfPPfec23pXXHGFY+nSpY6jR4/mu+6yZcvc1r3hhhscpUqV8prmKVOm5Fj/k08+ccTHx+d5bt94440c6/3xxx/muuZ1jm6//XZHVlZWjvWqVq3qdfl77703z+uS17yCXO9LLrnEUbt2ba9puOaaa3KkOTf9+/d3rqfn0PN6zZ49223b3377rZn+66+/5nnO9dOwYUOHrzzzgOe58ebQoUOOsmXLul0rTx07dnTOP/30053T/XXP6zXzZV5+91Zh0pPf8vrR8+pL2p5//nlHiRIlct1OVFSUY/z48TnOr+syF110Ua55cuDAgQ5fff75527rtm7d2lEQ+rz19ix2/SQkJDi++OILt/W++uor86zIa73LLrvMa37N7QMAnqheDiCs6ffDX3/91W1a9erVnT9rqUSjRo1MKWfFihVNO2TXNoNaqjdq1ChTcpabd955x5ROtWzZUsqXLy9r1qxxm6+lolryoyWq2qP67t27TYmutk/UUmktHbzyyiuL3N5Sj1VLeFz3r6Wgp59+ujkOLVVTWpI7bNgwmT59uhSVlsZ5VjfXjx6ntsFs166dKbnWHuTzKwF67733TMnpRRddZKqsu5ZUa8dKWhqo21R6TbUGwbFjx8zvev61VErXX7VqlSmp1XOrbfi1RNZKp5am6bnetm2bc9taSq7thXV/WpKttKMmzRdaOmbp37+/W1V67T9Ar/mmTZtMJ3LFQTsF1PPYtm1bc8zff/+9szRRO6x79dVXTSl0frR0cu7cueZnLUnVPOHa9levhUXzj15H9fzzz7uVvLZp08aUcqekpJhSVP2/KLQk8brrrssxXe+NOXPmmJ+1lF7TaqVfqyNrXtbzoZKSkuSbb75xrjtgwAC3bfnjnvengqZHj/3IkSPyxRdfOLdx5plnmvbbrs+c/CxcuNCU4lqio6PN9dR718pX+kx57LHHTB7Q+82b//3vf+Z/LU3W3sX1d6s5z6xZs8z6niXz3ug+XWlNnYLQns71WWzRfKL3idYMsZ4laWlpJn9pDSMtzVdPP/20c2SLEiVKyPnnn29qGuhzWkdlcO18U8+rnn99TrjWAtAaM76ccwARLEcYDgBhUtKtpb7333+/2/xy5co5Dh8+bObv2rXLkZKSkmObWlp44403OtfR0k1XnqVHlStXdvz000/O+VYpsm5n/fr1XksftbTFdRsLFy4sckn3Z5995jZv4sSJbuvdfffdznlauvXXX38VuaRbde/ePd+Sn1atWjk2b97stp5nqZFem40bNzrnP/TQQ27z77jjDue8Xr16OadXqFDBsWnTJue8vXv3OurWreuc37lzZ+e8F154wW2b7777rnNeRkaG27GUL1/eceTIETPvf//7n9t6Z555ptmPOn78uCnhz+265HXNCnq99TN37lznvP/+979u88444wyfrpnmyXr16jnX09ogrufPteTv8ccfd85zPc5Bgwbl2O4vv/zieP311x2+8rXkUK+Fq6+//tptvp4H19Jb15LanTt3Ouf56573V0l3YdOTX57xJW0tW7Z0m6c1RyxLliwx5861tN2V5/WZNGmSc56WjLvOmzVrlsMXgwcPdlvv5ZdfdvhKawK41pLR87V161bn/FGjRrlt2zXvNmnSxDn9ySefzHEdVq9e7XjnnXfcpusxeatZAAC5oU03gLCyYsUKZ7s6LdHWdruuHn30UVMaqmrWrClr1641JTha6qHTdT0t8Xn33Xed62jp5r///pvrPrW9cqtWrZy/aztCpdvSkistLdW2v/qzliLp9K5du7ptY/PmzUU+dm3L6EpLnLRUx/po6bpFS9mtdopFpW149RjzKqnX86ztL7UULzfaC7praZ2WkFnn0irlVdomffHixc7p2v5Sr6t1nFrS69puXUtPtYTb8xxpqZam3VpP88Hff//tnK+l5d999535+euvv3ZL6/Dhw50dw2mbZm2/Why01N61DbaW2rvWNtB8pCV0+dE8qCX3Fi3ptnqg15Jjq+RPl3MtKdYST4uWtE6ePNnkIy2V1VhMh4677bbbpDg6XNP2wxbX+9W1lF7b2mrP1xZ/3fP+Eqj06MgOP//8s/N3zUPdu3d365tBO6C0aGm7jpvtjZ5fbc9t8Xy2ac0Du+n9qcMUWrSGi1WSrf7zn/+Ytt0WrY3jLU9rnxdaa0KfNZpuvRbaV8eNN95o+zEACG9ULwcQETTY1eqHjzzyiHOadjKkHRD5QqslatDsjWdHbRYNcjU40ODNl+0XlWcnSfmNYWtVpS4qDTq1iqYGyTpmt774+Pbbb83xW9VMlX5p10BNq5t74xpwK62qql/o9Qu/sgJi7bRJOwNzDSDyGgZOA0j9Aq2dJLmeI33xkN/wcdY50g7b8kqrdpRXHDz3a+3bejlgnScN5vKjwbT2AK3XSKsrf/LJJ9KnT588g1atjqwdmen513MyYsQI5zx9CaEvVvQes5oBFJSmydeO6fSlwRNPPGF+17RrUwMNTrV5gcWz0zp/3fP+Eqj0eA7j5y3/6jTXF3N6L+g95EmrletLAos2sXGV14s2V9rpoyvXF2D58XyWeR6PvpjTINx60aB5V+9/ffGm+VWfS9ZwZHfffbdzPb2PtHNFDdpd7wMAKCiCbgBhO063BtraG7CWvl1zzTVugYj2YqsBhyttn6ulZ/pF7LfffjNfwCyuwaMnbdPqjX6Zcw24dTltP6ylWZ5tMvPavl00Df6kQbIOF2YNGaYBrvYIbLX5VFa78lA51tzWK0oPxda48ZaitoMuLA1CtK22viBRWrKq7eatnq+9tYfWUkENXKZNmyZLliwxJevW8ezbt8/0Nq2l5tpm1u4gRdNmBd0akGotBm2Da91L2qbXdfg6f97z/ri+xZUeu2n7Z1ea9sLQEmVXGvA/+eSTYjftQ0L7wXjppZdMCbdrHtJaI6+88oqpWaN52rW0HAAKgqAbQFiP051Xpz2uX451yCWtJm7Rzs1cv/DmxbWUx5Vr6aN+mV69erUpFVb6s2vQ7Q+u1W2t0h+7Ax8tWbSqzXvSobu01Mg16Pa2nEWDDFdamupa2lWnTh1niapWZbeqQ2tprGf177zOkXVd9eWHBopaCpYfHT7IM63acZslv7yinXxZVbY9qwm7lszmx/Mcedu3dZ58DVytoFsDi9dee815X1gdlnnSwFuDbqXHpEGKDtt17733mnUPHDggb7/9tlutEjtfGljDgWkJvWsVaB1qyrXJgz/veVfWPW1xvb5ayqvVx70pSnqKOiyV53PBW77auHFjnuv4m9YK0ntb70mlHZV9+umnbtXePen51SYo+R2P1oJw7TxRO050fTmgz2ftPNFaVodC1GYWY8aMcb5A1Jc6VjVzhgUDUFC06QYQkawAyOL65VyrRWuJnT/3oV8Mrd6VtfTU+jLnT55tKbWdpWs1bOsLpVY7v/rqq/2yTy3V1B7SNQhz7dlbaUChX5pd6bK50XPu+mX5qaeecvZObn0pV/pl2bVnY63Orm0xvZUkTpkyxVka6nmO9DpokOPaFlRp79zaC7KW0ls0sHel27V6Ndb1XffhjWuP+RrkWm34tRf2iRMniq+0psCbb77p/F0DZSvoVNoLti9Vyy3XX3+9s48Dz+PwDFqVBiKaf6wqw5qndXxnrZau4xv7u+lCflyrj2sVc9cepT2rltt1z7teWzVz5kxn/tde97X3bG+Kkh7Xc618acfvmWatdWPRF2Pam7lFS3y1qYjrixbXNtJ20GPSatye/Tx4e4mq50drL1lts3V0BNeXH9qLv+sLGG3+4tqEx3V8b+1hXV/aWS9A9CWcNo/QfbtyzdNFPf8AIlCuXawBQJiM0+2N9mzr2juv9uatYyDr+tpzs+u8gvZWbGnfvr3bco0bN3ZceeWVjurVq+fYvmvvw4XtvVx72j3vvPPc5mvP3npc3bp1M+N3x8bGeu1FubC9l7v24qvHpL16aw/XXbt2ddSsWdNtm/q763jQ3nquLlOmjOPSSy91nHXWWW7TY2JiTM/Ylp9//jnHmN7aw7KOQd6lSxczVrR1jl3PkfZcX79+fbf1EhMTTQ/nV199teOcc85x9tytvXu78hz/vEqVKo7LL7/crad0b9dF3XzzzTmOp06dOjnyQX7X2zrPep3btWtntuM6b/r06Y6C6tevX4596Gf58uU5lrV6wNdxstu2bWvylV5rz/HLdfx2X3jmAT3n2jO9t4+3XrDT0tJMnvFM+2mnnZZj1AC77nndruf+9Xxob/zezqs/0qPHpj26u66r942eJx2D3Zd0a2/lrvN0G+eff77JV57jVs+bN8/tXOaV133tWd0bPS4db97znOk9pve2Pltc79/58+c71x02bJjbOnr+dXxtz2eJ5hfXkRR0u9azUscc79Gjh7mvdUxv1/U+/PBD5zo6WoXrPL0Wuh09/08//bTPxwsgchB0A4jIoFvpEFTevhTXrl3bceeddxbqC7irFStW5AiKrKBJh2Hyd9Ctdu/e7Tj33HO9HpfrJzo62i9B9+zZs/Pdl37i4+NzBHGeAddtt93miIuL87q+tyDuo48+yjWwcf0MHDjQbb3ffvvNBOX5raeBm6vff//dBNrelvUMXD2viw6FltuxeebDvK63Brinn3661+3ocGeZmZmOgvrqq6+8Hru3oe5ch53L7aMvlw4ePOjXIcP0c99993ndRt++fXMsO3bs2GK95zXQ9bZdzWca+OV2bxU2PXldC9fh1fJLtw71pcF2budcn1Xegma7gm518uRJx4gRI/JMl7dhzk6cOOG4/vrr830OuQ7P6Bp05/XRF6iaLoveG82bN/e6rAbtAOCJ6uUAItbLL79sqvZq1UmtJqtVLrVKqnaq41lltDAuvvhiU01Tq0VrFV5tI6s9nWvbQNdhn/xJO2vTNsLaA7RWpdbj0GPTKpPaxlqrleswT569FxeWVsH+5ptvTM/lOsSQ7kOrXmo7d+10SNtKahVbbR9qdXCXG22fq+deq41q50y6He1cSXsYd+0l26KdZGmbV+2VvnXr1qbXZK16rvs9++yzZeDAgaZqql5nV2eeeabpFEmrxGu1ce18T9ua6zXSvKDtmGfMmCE//PBDjqrxmj6tSq1tT/Wc6n60A6a5c+fm2+u4dlCmHZVpNWL9aF7Q6vmafl9pD8+arnvuuce0S9UqtVqlXPOxHmtu/QvkRavmerYD157BvbVbHTJkiOncSqvnanVjPdd6zrVdv14r7RhM01ecHU55ViP3HOasOO55bQ+s94Dmf92u9gGgPb1rdXfP/gD8lZ7nnnvODNXXoEGDPPtKyIsOfffTTz/J7bffbtKgedp6VugzSp8l2sN6cdJj0WPTphQPP/ywtG3b1nmP6jNU++3Q9GrVcu1Z3KLnT9v1a5MWHSFBm1noNL3XtDNNfYboc+iqq65y258O9zdq1CjzLNDj1n3ovnSf+gx//vnnTVV713Osecxq4633ZGHuOwCRJUoj70AnAgAQeTQIdW0rrW0rPQMouHfa5OuQWgAAIHjwag4AAAAAAJsQdAMAAAAAYBOCbgAAAAAAbEKbbgAAAAAAbEJJNwAAAAAANiHoBgAAAADAJoUb2DFIZWVlye7duyU+Pt7r+KIAAAAAAPiDjr596NAhqVmzpkRHR0dG0K0Bd506dQKdDAAAAABAhPj777+ldu3akRF0awm3ddAJCQl+KTnfs2ePVK1aNc83F8GyXUQ28hXnljwVWvdEMKQvEGkojn3auY9guG4IL+Qpzi/5KrTuC9e0paenm0JfKw6NiKDbqlKuAbe/gu5jx46Zbfk76LZju4hs5CvOLXkqtO6JYEhfINJQHPu0cx/BcN0QXshTnF/yVWjdF97Sll/T5uA6AgAAAAAAwghBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAACIh6M7MzJRRo0ZJgwYNJC4uTho2bChPPPGEOByOQCcNAAAAAIACi5Eg8vTTT8vLL78sc+bMkWbNmsmPP/4ot956q5QvX16GDh0a6OQBAAAAABC6Qfd3330nPXr0kKuuusr8Xr9+fXnnnXfkhx9+CHTSAAAAAAAI7erlF154oSxdulQ2b95sfl+/fr2sXLlSunbtGuikAQAAAAAQ2iXdjzzyiKSlpUmTJk2kRIkSpo33k08+KX379vW6/PHjx83HouuqO+90SKlSDhk+3CFnnXVq+ZQU3UeU+fn88x1y553u2xs/XmTbtuz5s2Y5JCsry7Qn1/+XLhWZNy973qBBDrnwwlPrHT0qMmRI9rzmzR0yYoT7dp9/XmTduuz506Y5pGzZU9tds0Zk+vTseTfc4JArrnBfd9CgKMnIEKlXT2TsWPe27W+8IfLtt9nrjh/vkFq1Ts3btEmr62fPu/JKh/Tu7b7d4cOj5N9/RSpWFJk82X27H3wg8vnn2es+/LBDmjQ5NW/XLpHHHsue1769QwYOdN/u2LFRsnOnSEyMyGuvuW930SKR997LXnfIEIe0aXNq3qFDIkOHZs9r0cIh993nvt1Jk0Q2bMieP326Q+LiTs377jvdV/a8fv0cctll7uveemv2vIYNHfLYY+7zXnlFZPXq7PkTJzqkWrVT8379Vc9N9rwePRzSs6f7uppeTXdiop5r92N95x2RL7/MXvfRRx1y+umn5v35p8i4cdnzLr3UITff7L5dPb96nmNjRV5+2X27CxeKfPRR9rpDhzqkZctT8/buzZL77kuQ0qVF2rbNkiFD3Lf79NOaL7LXffVVh5QseWreihUis2dnz7vlFod06HBq3smTInfckT2vSROHPPyw+3anTxdZsyZ7/qRJDqlU6dS8n3/WPJ89r1cvh1x9tfu6gwdHybFjYvKu5mFXb74p8vXX2euOGeOQ+vVPzdu6VeTJJ7PnXX65Q266yX27Dz8cJampIvHx2fecqwULRD75JHtdX58R1rPgiSccsn27w/mMcOXPZ4Sm2xLIZ8T+/ZoPy8n06Vkh+YywrtvKlVkyc2bwPSMWL9a/YwkyblyWnHGG/c8IvZ4jRmTPa9PGYZ4Rrn/niusZUaHCqX3a9YzYvDlLxozJfh526ZLl12eEHp9etw4dHHLXXVl5fo8I92cE3yP884yoWlXvzex7IpDfI7w9I8Lhe4R2DXXPPdFSpUpWwL5HBOIZYT3fv/8+S2bMCL1nhMOhf+9jZejQrKCLNS655NTfMdf7NmSC7vfff1/eeustefvtt02b7nXr1sn9998vNWvWlAEDBuRYfsKECTJu3Lgc01NSjktMzHFJSUmXatUynNNTU6MlKSnh/5c5IampR9zWS0qKl6SkEv+/7AFzEg8ePGhOakpKnCQlZZ/9lJQjkpp6wrnekSO6bgXzc+XKGZKamu6x3bKSlJT9ZEpJOSjlymU6t5ucXNrMz553VFJTT71EULt3V5DMTJGSJTMlNfWQ27zk5DKSlFTq/9dNk5IlT130lJQYSUoq9/8/H5PU1GMeaSov//6rDyqHpKYe9Dh/sZKUFPv/P6dLpUqnzmFKyqlzmJyc8xzu3p19DkuUyD6H7tst7TyHycmHJTX1pHNeWlqUSZOqVu2kpKYe9khvOUlKys6uKSkHpEwZ1+2WkqSkMrmeQ+valCmT8xympJw6h6mpaRIV5f0cJifnPIe7d5eX9PQoyczMMuu6Sk7W/FL6/7dzSBISMp3zUlNLmLyWvdxxSU096rHdBElKipbYWG/X5tQ5TElxP4d79+q2y0pMzAlJSsp5DnfvPnUO9dq4/rF0P4fu+Vv/WFrnMCEh//ydkXHqAZiSUjLP/K3X/NixKHPe8zqHet30+p3abt7nUPOo3u+HDuU8h8nJ7vnbl2eE9SzIfkacOoe5X5uiPSOOHj11DpOTSwbsGbF/f5SUK6fpTZXo6OiQe0ZY1y07H5YLymdERobuc78JRO1+Ruzbd+ocWs8I179z2du1/xlx4sSpv4HZ6fX/M0Lv5dTUOPM8tOMZodctOTndy3bdv0eE+zOC7xH+ekZkyoEDB8w9YT1rA/E9wtszIhy+R2jw9u+/aZKamuE8v8X9PSIQzwhf/wYG6zPC4RCpXVu3e8TtO0hwxBpHnX/HDh92305IBN0PPvigKe2+8cYbze9nnXWW7Ny50wTX3oLukSNHyvDhw91KuuvUqSPVqpWWUqVKS7VqpUwJg0UvXo0a2W8psudlZxRLjRqaqbPnJyYmmswaFRUlVatWlWrVoj3WdX/7ZM2rUUPnlcmx3ZQUa92qpqTb2m716rlvV9Wsmf32qWZNTZPLKxcRqV7d9XiquK2rbyvdt5vgkaYo8wZU3z4lJmY/1C36lja3NOmD05pXvXrOc6jp1WX07ZOew9y2m73uqXn6Nsn9HJbNcQ71j0H2dhLd3j7llV7rWLP/d+Q4h67rJia6n8Oc6U3IcaxWKVZiYvaDw/u1cU+TPjjdtxufY7uaV/X6FOTaREdnSWLiCSldupTXc6h5SB842cea6PbH0tdrXrOm9/x9at2qbm+ofbk2+oY6O3/nfg4TE93X1UoteZ1DnacPY33T63kO87o2uT0jrGdBjRpxcvRo9oM/r/xd1GeEaylWXum1+xlRurRDYmMzzLG6/sELlWeEdd2qVasgNWqUCMJnhJaY6n1bSRITo21/Rug59zyHrn/nataMLpZnhL5g8OVva1GeEQcOnHoe+v8ZkX3dqlcv42W77t8jwv0ZwfcI/zwjqlbVGiAVzD1hPWsD8T3C2zMiHL5HaGBUsWKCedZa57e4v0cE4hnh69/AYH1GOBwOqVIl1pxD1+8gwRFrxDv/jqWnu78AyU2UI4jG46pcubKMHz9eBg8e7JymAfesWbOc7bzzokG39nSubx4SEtwvfGFoZtUSHs8vnMG6XUQ28hXnljwVWvdEMKQvEGkojn3auY9guG4IL+Qpzi/5KrTuC9e0adDtS/wZVCXd3bp1M22469ata6qX//zzzzJ58mQZ6NkoEAAAAACAEBBUQfcLL7wgo0aNkiFDhpi3B9qW+84775TRo0cHOmkAAAAAAIR20B0fHy9Tp041HwAAAAAAQl1wVZAHAAAAACCMEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAABEQtBdv359iYqKyvG5++67A500AAAAAAAKLEaCyJo1ayQzM9P5+4YNG6Rz587Su3fvgKYLAAAAAICQD7qrVq3q9vvEiROlYcOG0qFDh4ClCQAAAACAsAi6XZ04cULmzZsnw4cPN1XMvTl+/Lj5WNLS0sz/WVlZ5lNUug2Hw+GXbRXHdhHZyFecW/JUaN0TwZC+QKShOPZp5z6C4bohvJCnOL/kq9C6L1zT5mv6gjboXrBggRw4cEBuueWWXJeZMGGCjBs3Lsf0PXv2yLFjx4qcBj2JBw8eNCc1Otp/zd/t2i4iG/mKc0ueCq17IhjSF4g0FMc+7dxHMFw3hBfyFOeXfBVa94Vr2g4fPhzaQffMmTOla9euUrNmzVyXGTlypCkJdy3prlOnjqmmnpCQ4JcTqqXsuj1/B912bBeRjXzFuSVPhdY9EQzpC0QaimOfdu4jGK4bwgt5ivNLvgqt+8I1benp6aEbdO/cuVOWLFkiH3/8cZ7LlS5d2nw86YXx18XRE+rP7dm9XUQ28hXnljwVWvdEMKQvEGkojn3auY9guG4IL+Qpzi/5KrTui4KmLfiOQERmzZoliYmJctVVVwU6KQAAAAAAFFrQBd1aXK9B94ABAyQmJigL4gEAAAAACM2gW6uV//XXXzJw4MBAJwUAAAAAgCIJuqLkyy+/3PQEBwAAAABAqAu6km4AAAAAAMIFQTcAAAAAADYh6AYAAAAAwCYE3QAAAAAA2ISgGwAAAAAAmxB0AwAAAABgE4JuAAAAAABsQtANAAAAAIBNCLoBAAAAALAJQTcAAAAAADYh6AYAAAAAwCYxhVnpyJEjsnLlStm4caOkpqZKVFSUVK1aVZo3by4XXXSRlClTxv8pBQAAAAAgnIPuL774QmbMmCGLFi2SjIwMcTgcbvM1+I6JiZGuXbvKXXfdJVdccYW/0wsAAAAAQHgF3d9++62MGDFCfvzxR6lfv74MHDhQLrjgAmnYsKFUrlzZBN/79++XrVu3yqpVq2Tx4sVy5ZVXSuvWrWXy5MnSrl07+48EAAAAAIBQDLo7duwoPXv2lEmTJkn79u1zXU6rlg8YMMD8vGLFCpk6dapZV0vFAQAAAACIND4F3WvXrpVzzjmnQBvu0KGD+axbt66waQMAAAAAIPx7Ly9owO2qRYsWhV4XAAAAAIBQxpBhAAAAAAAEsnr5448/XuANa0/mo0aNKkyaAAAAAACInKB77NixXoNq5W3YMJ1G0A0AAAAAiHQ+Bd07duxw+z09PV369+9vxuQeNmyYNG3a1EzfuHGjTJkyRbKysmTu3Ln2pBgAAAAAgHAKuuvVq+f2+9ChQ6V06dLyzTffmMDbcvbZZ8t1110nF198scyYMUOmTZvm/xQDAAAAABDOHam9//77cuONN7oF3JaSJUuaeR988IE/0gcAAAAAQGQF3WlpaXLw4MFc5x84cCDP+QAAAAAARIJCBd0tW7aUF198UbZt25Zj3tatW+Wll16SVq1a+SN9AAAAAACEd5tuT08//bR07txZmjVrJj179pTGjRub6Zs2bZJPPvnE9Fw+ceJEf6cVAAAAAIDwD7rbtWsny5cvNz2Xa/tuV+eff75MnjzZ/A8AAAAAQCQrVNCtzjvvPPnuu+9kz549sn37djOtQYMGkpiY6M/0AQAAAAAQeUG3pWrVquYDAAAAAAD8HHSnp6eb3sqzsrJyzKtbt25RNw8AAAAAQOQF3e+++66MHz9efv/991yXyczMLOzmAQAAAACIzCHDFixYIH369JGMjAy58847xeFwyE033SS9e/eWkiVLyrnnniujR4/2f2oBAAAAAAj3oPu5556TM888U9atWyePP/64mTZw4EBT+v3jjz/KH3/8IS1atPB3WgEAAAAACP+g+5dffpEBAwZIbGysREdHu1Ulb968udxxxx0yYcIE/6YUAAAAAIBICLo1wK5cubL5OS4uzvx/8OBB5/zGjRvLhg0b/JVGAAAAAAAiJ+iuXbu27Ny50xl069jcP/30k3O+Vi8vW7as/1IJAAAAAECk9F5+4YUXypIlS5ztubt37y5Tp041AbgOHfbSSy9Jt27d/J1WAAAAAADCP+geMmSIzJ8/X44ePWoC7SeffFJ++OEHGTt2rJnfrFkz09kaAAAAAACRrFBBd5s2bczHUrVqVdOTuXawVqJECdOzudXBGgAAAAAAkapQQXduzj77bH9uDgAAAACAkFak4uhvvvlGHnvsMRk0aJBs2rTJTEtPTzfTDxw44K80AgAAAAAQWUOG3XDDDXLJJZfIU089JW+88Ybs3r3bzIuJiZGePXvK9OnT/Z1WAAAAAADCP+h++umn5aOPPpLJkyfL77//Lg6HwzkvNjZWrrnmGvn888/9mU4AAAAAACIj6J47d670799f7rvvPqlSpUqO+dqR2rZt2/yRPgAAAAAAIivo/vPPP+WCCy7IdX6FChXk33//LUq6AAAAAACIzKA7Pj5e9u/fn+v8rVu3mmHEAAAAAACIZIUKutu1ayfz5s1za8tt0RJu7VhNO1kDAAAAACCSFSrofvTRR2XLli1y6aWXysKFC8209evXyyuvvCKtWrWSw4cPyyOPPFKoBO3atUv69esnlStXlri4ODnrrLPkxx9/LNS2AAAAAAAIpJjCrNS6dWvTe/ntt98ut956q5n2wAMPmJLvxMREmT9/vjRt2rTA29VS8osuusiUkn/xxRemiroG9xUrVixMMgEAAAAACL2gW1111VWmQ7WvvvrKOWxYo0aNpEuXLlKmTBkp7FBkderUkVmzZjmnNWjQoLBJBAAAAAAgNINuVbp0abn66qvNxx8+/fRTE7T37t1bVqxYIbVq1ZIhQ4bIoEGD/LJ9AAAAAABCJuj2t+3bt8vLL78sw4cPl//85z+yZs0aGTp0qJQqVUoGDBiQY/njx4+bjyUtLc38n5WVZT5FpdvQEnx/bKs4tovIRr7i3JKnQuueCIb0BSINxbFPO/cRDNcN4YU8xfklX4XWfeGaNl/T53PQrZ2mFURUVJQsXbq0QOtoorW9+FNPPWV+b9mypWzYsEFmzJjhNeieMGGCjBs3Lsf0PXv2yLFjxwq079zSc/DgQXNSo6ML1edcsW4XkY18xbklT4XWPREM6QtEGopjn3buIxiuG8ILeYrzS74KrfvCNW3agbhfg+7ly5dLyZIlTamzr0F3QdWoUSNHB2xnnnmm6bTNm5EjR5pScdeSbm0Trh2wJSQkiD9OqB6Hbs/fQbcd20VkI19xbslToXVPBEP6ApGG4tinnfsIhuuG8EKe4vySr0LrvnBNW3p6un+D7piYGBPNd+rUyfRYru24/X0CtOfyP/74w23a5s2bpV69erm2KdePJ02Xv9KmJ9Sf27N7u4hs5CvOLXkqtO6JYEhfINJQHPu0cx/BcN0QXshTnF/yVWjdFwVNW3RBxs/W6txbt26Va665xnRy9vDDD+cIkoti2LBhsnr1alO9XPfz9ttvy6uvvip333233/YBAAAAAEBx8Tno1uLzESNGyK+//iqrVq2SHj16mIBYq4NfcMEF8vrrr/tcvJ6bNm3amDG+33nnHWnevLk88cQTMnXqVOnbt2+RtgsAAAAAQCAUqqy+bdu2pnOzpKQkmTt3rpQtW1buvPNO0yZ73rx5RUqQVlvXwF47QtPxvxkuDAAAAAAQkUOGxcbGmlLo+vXrm/rsS5YsMcN+AQAAAACAIgTdWso9Z84cmT17tmzZskVq1qxpehPXTtYAAAAAAEABg+6TJ0/KJ598IrNmzZIvv/xSSpQoId27d5cpU6ZIly5dgrJnOQAAAAAAgj7oHjp0qOlN/N9//5WzzjpLJk2aJP369ZNKlSrZm0IAAAAAAMI96H7xxRclLi5ObrrpJmnVqpVkZGSYquV5jV2mQ4ABAAAAABCpClS9/OjRo6a0Wz/5IegGAAAAAEQ6n4PuZcuW2ZsSAAAAAAAiNeju0KGDvSkBAAAAACDM+KW78aysLPnrr7/kxIkT/tgcAAAAAABhwS9B9549e6RBgwaycuVKf2wOAAAAAICw4LeBtR0Oh782BQAAAABAWPBb0A0AAAAAANwRdAMAAAAAECxB9+HDh+Xxxx+XxYsXO6fFxcXJgAEDpGbNmv5OHwAAAAAAkRN0ly1bVp566in5+++/ndMSEhJk1qxZ0qRJE3+nDwAAAACAyKpe3rBhQ0lOTvZ/agAAAAAAiPSge8iQIfLaa6/Jvn37/J8iAAAAAADCRExhVoqPj5dKlSpJ48aNTVvuRo0aSZkyZXIs179/f3+kEQAAAACAyAm6b7nlFufPU6ZM8bpMVFQUQTcAAAAAIKIVKuhetmyZ/1MCAAAAAECYKVTQ3aFDB/+nBAAAAACAMFOojtRcHT9+XHbt2iUnTpzwT4oAAAAAAIj0oHvt2rVy6aWXmk7V6tatKytXrjTTU1NT5bLLLpMlS5b4M50AAAAAAERG0L1u3Tpp3769bNu2LUdnaYmJiXL06FGZM2eOv9IIAAAAAEDkBN2jR4+WmjVrysaNG2XixInicDjc5mtJ9w8//OCvNAIAAAAAEDlB97fffiuDBg2ScuXKmaHBPGl18927d/sjfQAAAAAARFbQfezYMSlfvnyu89PS0oqSJgAAAAAAwkKhgu6GDRvKTz/9lOv8r7/+Wpo2bVqUdAEAAAAAEJlBd58+feTNN99066HcqmY+adIkWbRokdx8883+SyUAAAAAACEopjArPfDAA/LVV19Jly5dpEmTJibgHjZsmOzZs0eSk5Olc+fOMmTIEP+nFgAAAACAcC/pLlWqlAm6n3vuOYmLi5PY2FjZvHmzVKlSRZ555hlZuHChREcXeghwAAAAAAAit6TbrBgTY0q39QMAAAAAAHKiOBoAAAAAgECWdM+dO7dQG+/fv3+h1gMAAAAAIGKC7ltuucV0luZwOHL0Vq6s6a7TFEE3AAAAACCS+RR0L1u2zO33kydPysMPPyz79u2Tu+66yzkm98aNG+WVV14xHao9/fTT9qQYAAAAAIBwCro7dOjg9vvo0aPl2LFj8uuvv0p8fLxzevfu3eXuu++W888/X7799lu57LLL/J9iAAAAAADCuSO12bNny6233uoWcFsSEhLMvFmzZvkjfQAAAAAARFbQvWfPHsnMzMx1vs5LTU0tSroAAAAAAIjMoLtJkyby2muvyb///ptj3v79+828M8880x/pAwAAAAAgvNt0exo7dqxce+210rhxYxk4cKD5X23atMlUK9fA+8MPP/R3WgEAAAAACP+gu0ePHiaovu++++SZZ55xm1e7dm157733pGfPnv5KIwAAAAAAkRN0q2uuucYE3z/99JNs377dTDvttNPk3HPPlejoQtVaBwAAAAAgrBQ66FYaXLdp08Z8AAAAAACAO4qkAQAAAAAIZEm3VhsvqKioKNm2bVth0gQAAAAAQOQE3XXr1jVBNAAAAAAA8HPQvXz58gJsEgAAAAAABF2bbh3/W0vUXT9NmjQJdLIAAAAAACj+3sv//PNPWbJkiaSkpEjfvn2lfv36cuLECUlOTpbq1atLqVKlCrzNZs2amW06ExhTpCQCAAAAABAwhY5oH374YZk8ebJkZmaaEukLLrjABN3Hjh2Tpk2byvjx4+X+++8veIJiYkzADgAAAABARAbdr7zyijz77LMydOhQufrqq+Xyyy93zktISJDu3bvLZ599Vqige8uWLVKzZk2JjY01gfyECRNMR27eHD9+3HwsaWlp5v+srCzzKSrdhsPh8Mu2imO7iGzkK84teSq07olgSF8g0lAc+7RzH8Fw3RBeyFOcX/JVaN0XrmnzNX2FCrqnT58u11xzjUydOlX27duXY/7ZZ58tL774YoG3e95558ns2bOlcePGkpSUJOPGjZP27dvLhg0bJD4+PsfyGpDrMp727NljStyLSk/iwYMHzUmNjvZf83e7tovIRr7i3JKnQuueCIb0BSINxbFPO/cRDNcN4YU8xfklX4XWfeGatsOHD9sXdG/evFkGDx6c6/yqVavK3r17C7zdrl27ugXuGoTXq1dP3n//fbnttttyLD9y5EgZPny4W0l3nTp1zP61xN0fJ1Srzuv2/B1027FdRDbyFeeWPBVa90QwpC8QaSiOfdq5j2C4bggv5CnOL/kqtO4L17Slp6fbF3Rr1e+8ovqdO3dKhQoVpKh0G2eccYZs3brV6/zSpUubjye9MP66OHpC/bk9u7eLyEa+4tySp0LrngiG9AUiDcWxTzv3EQzXDeGFPMX5JV+F1n1R0LQV6gjatm0r8+fP9zpPq3W/+eabctFFF0lR6ZuDbdu2SY0aNYq8LQAAAAAAiluhgu4HH3xQVq1aJTfffLP88ssvZpoOE7Z48WLp2LGj/PPPP/LAAw8UeLu6zooVK8xQZN99951pN16iRAm56aabCpNMAAAAAAACqlDVyzt16iQvv/yy3HffffL222+baRqAKx2b+7XXXjM9jxeUBusaYGvnbFpHvl27drJ69WrzMwAAAAAAETNO9x133GGGBvvggw9k06ZNpve2Ro0ayfXXXy+1atUq1DbffffdwiYHAAAAAIDwCbpV9erV5d577/VfagAAAAAACCPB1xUcAAAAAACRVtI9dOjQAm982rRpBV4HAAAAAICIC7pffPFFn8cssxB0AwAAAAAimc9B944dO/JdZsuWLfLoo4/KmjVrpEyZMkVNGwAAAAAAkRF016tXL9d5qampMm7cOHn99dclKytLbrvtNvM7AAAAAACRrEi9lx8+fFieffZZmTx5sqSnp0uPHj1kwoQJ0qRJE/+lEAAAAACASAq6MzIyZMaMGTJ+/HhTyn3RRRfJ008/LRdeeKH/UwgAAAAAQKQMGfbee+/JmWeeaXozr1y5sixYsEC+/fZbAm4AAAAAAAobdC9dulTatGkjffr0kWPHjslrr70mv/76q3Tv3t3XTQAAAAAAEFF8rl7euXNnMxxY69atTSl3XFycKeXOy7XXXuuPNAIAAAAAEP5tuh0OhxkOrH///vkupwF6ZmZmUdMHAAAAAED4B92zZs2yNyUAAAAAAERq0D1gwAB7UwIAAAAAQKT3Xg4AAAAAAHxD0A0AAAAAQDB0pAYAAAAAdtOOmbVT5oyMjCJtJysrS06ePGmGPI6OjpzyxlA/7qwgSH/JkiWlRIkSftkWQTcAAACAoAm2Dxw4IHv27PHLSEi6PQ3gDh06ZEZXihShftyOIEl/hQoVpHr16kVOA0E3AAAAgKCQnJxsgu6EhATziYmJKVLAo8GblpYXdTuhJtSP2xHg9Ov+jxw5Iqmpqeb3GjVqFGl7BN0AAAAAAk5Ltg8ePChVq1aVKlWqhEXwFiihftyOIEh/XFyc+V8D78TExCJVNfe5gnydOnXk3nvvlaVLl/qlqgcAAAAAWLQNrwZbZcuW5aQgKJQpU8aZN4vC56C7R48esmDBAuncubOJ9G+++WaZP3++KXYHAAAAAH8IxZJZhKcoP+VFn4PuF198Uf7++29ZvXq13HHHHfLjjz9Kr169TPUPDchnz54t+/bt80uiAAAAAAAIBwXuf71t27YyYcIE+f333+W3336Txx57zHR4cNttt5me3S655BKZNm2a/PXXX/akGAAAAACC2NixY00p6cUXX5xj3v333y/169eXSNKzZ0/p2LGjRKoiDXrWpEkTGTlypHz//fcmyJ4yZYppYP7AAw9IgwYNpFWrVrJo0SL/pRYAAAAAQsS3334ry5cvD3QyEGB+G2m8Vq1acs8998iSJUskJSVFZs2aZd7gbNiwwV+7AAAAAICQoB3CaS3hJ554ItBJQbgE3a4qVqwo/fv3l48//tiUegMAAABApBk1apR8/fXX8t133+W53M6dO+W6666T8uXLm2C9S5cu8uuvv+a7/TfeeEOaNWtmhreqXLmytGvXTtasWeOcHx0dLRMnTpSHHnrI9MUVHx8vt9xyixw6dMhtOzo2+pAhQ8x41KVLl5Zzzz1Xvvzyyxz7++9//yvnnXee2Z9ub/DgwXL48GG3ZbQZcocOHSQ2NlYaNmwoc+bMybEdTUPz5s1zpEGr5GtfYRatPa0Fu88++6wp5NXexLU/saSkJAkljNMNAAAAADa4+uqrpWXLljJu3DhZvHix12U0ANb2zhogz5gxwwSrTz75pGkP/ssvv5ihm7355ptvTL9aWsh55ZVXmlGlfvjhBxO8unrhhRdMs18Nfnfs2CGPPPKIHDt2TN59910z/8SJE2aEKq2trPvV4HbevHly1VVXydq1a+Wss84yy3344Ydyww03yK233mqORwNf3da///7r3JZu9/LLLzcvDt58800zbfTo0ZKWliaNGjUq1DnUEbPq1asnL7/8stnXww8/LNdee62sWrVKQgVBNwAAAADYRDue1lGfNCDW6uaetFmulnRv3LhRzjzzTDNNS4rr1q0rU6dOlUmTJnndrm6vUqVKphTYooGy0vHOLVpyrUM/a99bSkupb7/9dtPZm/bR9dZbb8m6detk/fr10rRpU7OMlrRv2bLFVI1///33zfY0uNeg+/XXX3duW0vGNeDXEn0tcddS6t27d8umTZucQXbLli2lcePGhQ669aXEF198YWoBKH0Jcdlll5mXGJrOUEDQDQAAACD4LViQ/clPw4Zar9t9mrar3r49/3V79sz+WI4eFdESatdpBXTNNdeYqtSPP/64LFy40GtnazrfCriVBtNa+rxy5cpct6ul1/v37zdVtfv27SsXXXSRqX7tqVu3bs6AW2k1di0h16Bdg26tRq6l2WeccYZkZGQ4l9P9a4m32rx5s3kxoC8BXJfRlwNaQq/DSWvQrR1s67G4Btinn366nHPOOVJYOjqWFXCrSy+91Jwf3RdBNwAAAAD4y5EjIvv25b9clSo5px086Nu6ug9XWmLsOa2AtJ3yo48+KjfddJOpru1Jq0xXq1Ytx3Sdllen1Bp8ahXu559/3gSfWi1dA2oNjLWPLUtiYqLbegkJCWZZq1303r175eeff5aSJUvm2IcVrOsy1gsEb/7++2/zv27Tc3/WsRzVFxiF4G17Oi2U2nVT0g0AAAAg+GkpbuXK+S/nUirqNs2XdT1LiqOick4rhOuvv95U59bq2to+2ZWW2v7xxx851tE21jovL/369TMfDYo/+eQTGTZsmAmeXauAp6amuq2j7au17bVWDbf2f/bZZ8vMmTNz3Y+VjhdffNF0pOapZs2a5n/dprcXCykpKSbYt2jQr23JPV8+eOOZfmualf6ICrq17v6uXbtMVYIKFSr4a7MAAAAAkLPqd0FodXMNoAsqLq5IVcstWgVbS7sHDBhgOk1zpT2OaydlGnhr22crANWhmO+44w6ftl+lShVTZfzzzz83vYe7+uyzz2Ty5MnOUmvdl5a+t2nTxvzeqVMns54Gzlbw7EmrodeuXVu2b98ud999d67p0Dbrc+fOla1bt5pq5Wrr1q2mvXj79u2dy+m2/vnnH0lPT5dy5cqZad56S1fLli2TgwcPOquYa2/wWq3eW/Af8kOGaeN6vVj7PKpl6FuVrl27mgbt559/vqk6oO0VAAAAAADZ+vTpI6eddpoJIl1pb+Ba+q2doGkv4NrpmfYAHhMTI/fff3+up2/MmDFmOC0NorUn85deekkWLVpkOhlzdfz4cenZs6fpjGz69Oly3333mWroVhtyHepZg319GfDqq6/K8uXLTRp0+yNHjjTLaJCuseC0adPkrrvuMoG8Br/aCZxuS9t8K21fXr16ddNr+wcffGA+3bp1M9Ncae/jWto+cOBA+eqrr0yVeO1l3Rsd5kzjzU8//dQE9Np+XYP7UGnPXaCSbu2+Xt+ADB8+3G269nynPcdpBmrRooVp7K9dyGsVBb24AAAAABDptKRZg1iNnzyDSg10Nc7Sku3MzEzTKZoG0rkNF6a0pFqDVe1dXKuMa+nxgw8+aHpLd3XvvffKnj17TDV0rdKt7bK1mrhr7+YaQGv1dx0yTNtKa8m59jquY3dbevfubWo06zJWB2v169eXK664wtkmXXtG1xJrHb9b96fDj40aNcpUfXcdykx7SdchzLSwVsfd1tJ+7UVd40lPml49Ng32tQaAdvCmsWkoiXK49iefB+1xTkuyX3nlFec07cFOByzXeatXrzYXTC+oDqauPeDp4OnFSTObVjvQ6geubQYKKysry7QX0Ib6WiXEX+zaLiIb+YpzS54KrXsiGNIXiDQUxz7t3EcwXDeEF/LUKVryqeNIa3yhbX79QUMd7W1bS421tDZSWMddqlQpM6SYDvcViulv1KiRKTV3fUkQ6Dzpes9q9Xhf4s/ogrTZ1m7kXekbEaVvQDTgVlWrVjVvNbw1oAcAAAAAIJL4HHRrFO/ZQZqO7aZvjHTsNFcNGzY0jdsBAAAAAIhkPrfp1nr02vOcq++++84E4lbPdBatCmD1QgcAAAAAKH5aFTqUq9Xv2LEjpNNf4JLu1q1bm97irEHIV61aJb/++qvpYt7Tb7/9lmt38wAAAAAARAqfg+5HHnnENBjXMdq0i3YNtrUDEe1y3tPChQtDatw0AAAAAAACGnRrD+Xz58+XunXrmhJu7cHtvffekwsvvNBtOR0+TINzHUsNAAAAAIBI5nObbqXdtesnLzpI+aFDh4qaLgAAAAAAQh4DTAIAAAAAEOig+4477jBDhFlOnjwpH3/8sezduzfHsl999ZVcfPHF/kslAAAAAADhHHS//vrrbkOGpaWlSe/eveWXX37Jsay26f7f//7nv1QCAAAAABBp1csdDof/UgIAAAAAQJgJ2jbdEydONAOh33///YFOCgAAAAD4bOzYsVKuXLl8l9NYp379+rac2ccff1zi4+N9WvbBBx80tZhdrVy5Ui655BKpWLGiVKlSxYxOtW7dOgkHgwYNMp+IDrrXrFkjr7zyipx99tmBTgoAAAAAhK3du3fLSy+9JI888ohz2h9//CGXX365lC1bVt555x2ZOXOm7N+/Xy677DJJTk6WUPfwww/L3LlzZcuWLZEZdKenp0vfvn3ltddeM29VAAAAAAD20MLORo0aybnnnuucNn/+fNOU+IMPPpArrrhCevToIe+++64JvLXT7EA4evSo37Z1+umny0UXXWReNgRd0H348GFzoq2P0jG5XafpRwPnwrr77rvlqquukk6dOhV6GwAAAAAQbCXK3bt3lzJlykitWrXkmWee8brcP//8I/369TNVuuPi4syoUD/99JPbMlpK265dO6lUqZIpqOzYsaPbSFMFodu67rrr3KbpSFWlS5eW2NhY57Ty5cv71K9XVlaWTJ48Wc4880yzjerVq5uq6wcPHnQu880338iFF15ojk+Pc+DAgc74Uv35559SqlQpmT17tqkGXrlyZWnbtq2Zd/z4cfnPf/4j9erVM9vX/bz99ttuadi4caNceeWVZj09340bN85xvjVNb731lmRkZIjdYgqy8F133WU+rq699lq/JUbfnqxdu9ZUL/eFnnD9uPaobl1o/RSVbkMzlT+2VRzbRWQjX3FuyVOhdU8EQ/oCkYbi2Ked+wiG64bwQp7KeS6sj79Y2yrOTqC97VNLizWgnj59ulSoUEGefvpp+fvvvyUmJsa53L///muCaW0PPm3aNBPovvjii3LppZfK5s2bJTEx0Sy3Y8cOufnmm6Vhw4Zy4sQJE0dpcL5+/Xo544wzvKbFGx2dSgNcDYBdl7vhhhtM+h599FEZPny4M9CtU6eOeXGQ1zbvueceefXVV0179c6dO5tC2v/+97/m/4SEBPMCQafri4L3339fUlJSZOTIkSZQ1hGwSpQo4dyW7lODZw2qrfxx/fXXm/bmo0ePNgH3559/bl5S6DnVdueqW7duUq1aNTMCl55DPU49967pvuCCC8zw1z///LO0bt061+toPfOt577rPevr3wKfg+4BAwaInTTD3Xfffaa6gusblbxMmDBBxo0bl2P6nj175NixY0VOk55EfSOjJzU62n818e3aLiIb+YpzS54KrXsiGNIXiDQUxz7t3EcwXDeEF/KUe+mqng8tefRW+vjJJ1Hy6afZ993992fKWWedmpeSogFadrB2/vkOGTQoOxjSezUzM1Oeeipatm/PXnfmzEy37X79dZS89Vb2vNtvz5ILLjgVmGmN5i+/jJIePQoWsFvBmHUcixcvlh9//NH8r52TKQ2uTzvtNFNabS2nJcQHDhwwwacVYHfo0EGaNWtmSmq1s2krGHXdl27z+++/lzfeeEPGjx/v9nIwr5Lc1atXm/+bNm3qtlyDBg1MWnv16mViLqUdvn3xxRemnXdu29QXAzNmzDCduGm7adcXDkrX0/Rp6bdWYS9ZsqSZXrNmTVPb+bPPPpOrr77a5AWlfXzp9ixLliyRTz/91ATxGrgrPXatRTBmzBgzTQNpfSkxadIksy3Vvn175/4tWvqtAf6qVaukRYsW4o0ur+dx3759zrS63rNaE9yvQfesWbPETvrGQ8f3btWqlXOa3iBa9UDf7ujbFde3HkrfiOibF9eSbn37UrVqVfMWpaj0hGoP6ro9fwfddmwXkY18xbklT4XWPREM6QtEGopjn3buIxiuG8ILeeoULTTT0lAt+dWPJ63g+u+/2T87HLrMqXl6O1rzjhyJkpgY9/vz0KESzvme29b4zpqXkVHCbbsafuh+vSQnT9bzwdqXBtxa4moFikqrPmuTWq3pay23dOlSE0RaAbfSKtQaeLsu9/vvv5tS6O+++87EUJZt27Y5l/FMgze6ri6npcL6bHMNnrW0WztT0xJ1vTYaxGopt74Q0OW90dhNg1GtEp7bfv/3v//JjTfeaKqWW7SEWkuqNQDu2bOnM8DVoNl1O3p+9CWF63lUms7BgwebY9C0adXzUaNGmeBYO3+rXbt2jnTodnWfWtKeW1p1up4fvVZWwbDrPetrs+oCZh/76Mn49ddf3abdeuut0qRJE/OWxDPgtjKgfjzpifHXH0I9of7cnt3bRWQjX3FuyVOhdU8EQ/oCkYbi2Ked+wiG64bwQp7KpveUngvr46lsWQ1Us38uVUrP26l5GipY83SULGueBoC6LW2OXLly9kTPTWvsZ62rcZXrfL3Ndb9ekpPvNXX9X3v81iDN87is4NWarqW0Wvqs7Zk9aVVyXU5fTHTp0sVsT0vGNcDUgPD22283wbEu41qN2tu5tGjBpga4ns8zDei1NFrbe1v0ZUDdunVNtfennnrK6/a0XbYGqrkF5VYVet22t3Oh81yne74M0BJn3Ye382OdZw2wv/zyS3MMWtVdS6O1kzg9V1oF35XGktY588bKi57P/ILes34JuvXkaNUDffvRsmXLQm1Dx5Br3ry52zStuqBvFTynAwAAAIgsPXtmf7zRGG/27NzXHTUq98D5ssuyP95oQJ7bPguiRo0apgmsJy1ldaWluNpb+BNPPJFjWauwUUuDtX3ywoUL5ZxzznHO11JdbyW6edH9aeCtgadrE9/ffvvNtHl2pe3MtddvLU3PjcZuWiVbS9BdS+s99+laOu96LnSeK89gWOfrywZtx+2NtU9t1649r2s1da0NoNXxtZ33rl273MZP16r8mma7+eUVrTbeX758uQm+AQAAAACnaM/bGhR//fXXzmn6u7ZRdqXVzTXg1Q7CtHMv189Z/9+I3Ro6y7W0VwNL7RCtoLRds9I20K609Fw7GHMtMdemvDqutbbtzo12+KaBcl5Nk9u1aycLFixwa1+t/XppAKzz8qLnR19e6LF7nh/9eJaAaym+Vs3XMcg1/dr226LbOXLkiPMc2Cloqpd7o4E8AAAAAIQyLb3Wvqv69u1regXXtsTaQZlnP1TaX5UOY6WBonYyrdW5NTjUTtK0s7Fhw4bJ+eefb0prdahlDSa19FY7EdNhyArzMkCrg2v/WhroW3TEKm1brent37+/s023loprNfbcaAmzrvvYY4+ZauDahPjIkSOm47OxY8eaNGq1b+0tXdtr33vvvaaEW49D06I9ledF23JribWez4ceesh0tKbVx7Xnc+2hXHsr/+WXX2TEiBGmTbpWydeXG3qu9WWB/m7RdvYqv0DfH/zWGCmvtgIAAAAAEKk0Vvrkk09M2+I777zTBKbaKZnn+Nha1VnbdGtv2tqvlXYQpoG2lmKfd955znbOWnVaq2hrr+BTp06VV155xVT9LihtzqudmGmv5K50uzqclwayGrxqx2ja8dmyZcukUaNGeW5TO8HWNt/aO7kG1oMHDzbt0LU5sdJzoG2uteRZmyg/+OCDpudyTYO3frw8ffjhh+b86dBrmvbbbrvNbE9fVChtL64fDbR1vp5v7Wxbl3Hdvu5PezXPq/25v0Q5/DBgnb6d0HYKWj1CqxQEil447RVQ32b4q/dyqz2Cv3svt2O7iGzkK84teSq07olgSF8g0lAc+7RzH8Fw3RBeyFOnaGmqVnPW4ap8HUI4PxrqaDVmLc2NpELCghy3DtPVp08fE9OVKVNGgoHD5uum29ZaBDoEm5bkFyRPut6z2nu5L/GnX/5a6NsB3blrwK3VHAAAAAAAwUtLo7VauFbNjhRvv/22qaKvLxuKQ7S/3xh89NFHpi5+Xg3sAQAAAACBpyXJM2bMCJpS7uKgNZXeeOONPMcw9ye/7GXDhg0m0fPmzTNjp2nitdE8AAAAACC4tWnTxnwiRb9+/Yp1f4UOurUxvBbLa7Dt2vObNmTXTgG0Rz4AAAAAACJZgauXr1ixwjQ2147TtCc6HXBcu2vXxu7arb3OI+AGAAAAUBh+6OcZCKq86HPQ/eSTT5ru4S+55BLT3bp2vb5u3TpZu3ZtnmO1AQAAAEB+SpYsadoX67jLQDDQMcatvFks1ctHjRplxn779NNPzXhnvoyhBgAAAAC+0PhCh1/as2ePHD9+3AzBVNQhoxgyLDSHSnMEeKg33b8G3Do0mNbiLmrs63PQrdXJdXD04cOHy/r16001ch1kHAAAAAD8oXr16hIXF2eCnbS0NL8ETzq0sfZWHYrBZ6QetyNI0q8Bt+bJovI56P7777/l888/l5kzZ8q4ceNkzJgxcvHFF8stt9wiLVq0KHJCAAAAAEQ2DbA00NES78zMTFPaWRQauOnoSpUrVzYBXKQI9ePOCoL0a5Vyf9Xu9jno1oPVgdP1o2+e5syZI7NmzTJBd6lSpcwNoiXh1hsJAAAAACgMjS20anFRx1HW2ESDp9jY2IiKUUL9uLNCPP2eCnUEiYmJ8uCDD8pvv/0mK1eulL59+0rZsmVl5MiRUq1aNdOxmpaKAwAAAAAQyYr82uDCCy80Vc6TkpLktddekzPOOMOM3d2tWzf/pBAAAAAAgBDlt7J6LekeOHCg/O9//zMl4CNGjPDXpgEAAAAACEm2VJBv0qSJPPPMM3ZsGgAAAACAkOFzzwRz584tcOcHN998c2HSBAAAAABAZAXd2ku5NUaajpuWH4JuAAAAAECkK1Af/Npl+7XXXmuGDStq9/0AAAAAAIQ7nyPnSZMmyezZs+Wtt96Sr776ylQdv/XWW6Vp06b2phAAAAAAgHDvSG3YsGGyfv16+f77701ptw4TdtZZZ8n5558vr776qqSlpdmbUgAAAAAAwr338jZt2sj06dPNuNxvvvmmxMfHy5AhQ6RGjRrSv39/2bBhgz0pBQAAAAAgUoYMK126tPTp08dUNd++fbu0a9fOVD3/+OOP/ZtCAAAAAABCVJF6Q9u9e7fMmTPHtPXesmWL1K5dW1q1auW/1AEAAAAAEElB98mTJ2XBggXyxhtvyJIlS0wv5t27d5dp06bJ5Zdf7hxWDAAAAACASOdz0L127VqZNWuWvPPOO7J//35p0aKFTJkyRfr27SsVK1a0N5UAAAAAAIRz0N26dWuJi4uTa665xgwV1rJlSzPd4XCYINybSpUq+S+lAAAAAACEc/Xyo0ePmpJu/eRHq5lnZGQUJW0AAAAAAERG0D1gwAB7UwIAAAAAQKQG3dqeGwAAAAAAFMM43QAAAAAAwE9Bt7bnvvHGG2XixIl5Lqfz+/TpI8ePH/d10wAAAAAARHbQPXPmTPn444+ld+/eeS533XXXyYcffiizZ8/2R/oAAAAAAAj/oFsD7quvvloaNmyY53Knn366dO/eXT744AN/pA8AAAAAgPAPun/55Re5+OKLfVq2Xbt2sn79+qKkCwAAAACAyAm6Dx06JBUqVPBpWV0uLS2tKOkCAAAAACBygu7y5ctLUlKST8smJyeb5QEAAAAAiGQ+B90tWrSQzz77zKdlP/30U7M8AAAAAACRzOegW4cL+/7772X69Ol5Lvfyyy+b5W666SZ/pA8AAAAAgPAPugcMGCBt27aVe++9V/r27SvLly+XAwcOSFZWlhw8eND83q9fP7nnnnvkvPPOk/79+9ubcgAAAAAAglyMrwuWKFHCVC/v1auXvPPOO/Luu+/mWMbhcJgeznWcbl0eAAAAAIBI5nPQrapUqSIrVqwwwfdHH30kGzZsML2UJyQkSPPmzU1A3q1bN/tSCwAAAABAuAbdFg2sCa4BAAAAAPBTm24AAAAAAGBD0D1z5kzTYVpBZWZmyuuvv17g9QAAAAAAiJige8SIEdKkSRN58cUXZe/evfkun5KSIlOmTJHGjRvLgw8+6I90AgAAAAAQnm26t2zZIo8++qgMGzbMBOCtW7c2w4c1bNhQKlWqZHot379/v1lu9erVsm7dOrPebbfdJo8//rjdxwAAAAAAQOgG3VWrVpVXX31VxowZIzNmzDBDgj3//PNel23WrJk89thjMmjQIKlRo4a/0wsAAAAAQHj2Xl6rVi154oknzCc1NVV+++032bNnj0RFRZnAXANuHVassF5++WXz+fPPP83vur3Ro0dL165dC71NAAAAAABCasgwlZiYaD7+VLt2bZk4caI0atTIVFmfM2eO9OjRQ37++WcTgAMAAAAAEBFBtx08x/5+8sknTcm3thMn6AYAAAAAhJqgCro9hxv74IMP5PDhw3LBBRd4Xeb48ePmY0lLSzP/6/BmhRnizJNuQ0vc/bGt4tguIhv5inNLngqteyIY0heINBTHPu3cRzBcN4QX8hTnl3wVWveFa9p8TV/QBd2//vqrCbKPHTsm5cqVk/nz50vTpk29LjthwgQZN25cjunazlzXLyo9iQcPHjQnNTrap9HVArpdRDbyFeeWPBVa90QwpC8QaSiOfdq5j2C4bggv5CnOL/kqtO4L17RpAXFIBt06trcOOaYHor2kDxgwQFasWOE18B45cqQMHz7craS7Tp06plO3hIQEv5xQq5M4fwfddmwXkY18xbklT4XWPREM6QtEGopjn3buIxiuG8ILeYrzS74KrfvCNW3p6emhGXSXKlVKTj/9dPPzueeeK2vWrDHDk73yyis5li1durT5eNIL46+LoyfUn9uze7uIbOQrzi15KrTuiWBIXyDSUBz7tHMfwXDdEF7IU5xf8lVo3RcFTVvwHYGXNwmu7bYBAAAAAAgVRS7p/uOPP2T79u3m59NOO81UDy8srS6uY3LXrVtXDh06JG+//bYsX75cFi9eXNRkAgAAAAAQOkH3119/Lffee69s2rTJbXqTJk1k2rRpctlllxV4m6mpqdK/f39JSkqS8uXLy9lnn20C7s6dOxc2mQAAAAAAhFbQrQH3FVdcYdpTDxo0yNnJ2caNG+Wdd94xpdWLFi2SSy+9tEDbnTlzZmGSAwAAAABA+ATd//nPf6RatWqyevVqqVWrltu8UaNGyfnnny+PPvqorFq1yl/pBAAAAAAg5BSqI7VffvlF7rzzzhwBt6pdu7aZt379en+kDwAAAACAyAq6tb11fHx8rvN1jOwKFSoUJV0AAAAAAERm0N27d2/TdjsjIyPHvJMnT5p5ugwAAAAAAJGsUG2677rrLvnuu+/k4osvlmHDhpkey9Xvv/8uU6ZMkczMTLPMX3/95baeDgUGAAAAAECkKFTQ3bx5c4mKihKHwyE33nij2zydZi3jSYNxAAAAAAAiRaGC7tGjR5ugGwAAAAAA+DnoHjt2bGFWAwAAAAAgohSqIzUAAAAAAGBTSfc333zj03La0RoAAAAAAJGqUEF3x44dfWrTTcdpAAAAAIBIVqige9asWTmm6Zjd27Ztk9mzZ0v9+vXlzjvv9Ef6AAAAAACIrKB7wIABuc578MEHpVWrVkVJEwAAAAAAYcHvHalVrFhRbr/9dnnmmWf8vWkAAAAAAEKKLb2Xa+C9fft2OzYNAAAAAEDkBt3Hjh2TN998U6pXr+7vTQMAAAAAEP5tugcOHOh1+v79+2XVqlWyZ88eefbZZ4uaNgAAAAAAIi/o1h7KvalUqZKcccYZMmXKFOnTp09R0wYAAAAAQOQF3VlZWf5PCQAAAAAAYcaWjtQAAAAAAABBNwAAAAAAga1eftpppxV4w1FRUbJt27bCpAkAAAAAgMgJuuvWrWuCaFf//POPCaoTEhKcQbmOzZ2WliYNGzaU2rVr25NiAAAAAADCKehevny52+9r166VTp06ydSpU+Wuu+6SUqVKmeknTpyQ6dOnyxNPPCHvvfeePSkGAAAAACCcO1J74IEH5Prrr5ehQ4c6A26lP99///1y3XXXyYMPPujPdAIAAAAAEBlB9w8//CAtWrTIdX7Lli3NMgAAAAAARLJCBd1xcXHy/fff5zp/1apVEhsbW5R0AQAAAAAQmUF3z549Ze7cufL4449Lenq6c7r+PG7cOJk3b55ZBgAAAACASOZTR2qenn32WVm/fr2MHTtWxo8fLzVq1DDTk5KSJCMjQ1q1amWWAQAAAAAgkhWqpLtChQry3XffyYwZM6Rz585SpkwZ89GfdZpWL9dlAAAAAACIZDGFXjEmRu644w7zAQAAAAAAfirpdnX8+HHZtWuXGaMbAAAAAAD4Ieheu3atXHrppRIfHy9169aVlStXmumpqaly2WWXyZIlSwq7aQAAAAAAIjfoXrdunbRv3162bdsm/fv3d5uXmJgoR48elTlz5vgrjQAAAAAARE7QPXr0aKlZs6Zs3LhRJk6cKA6Hw22+lnT/8MMP/kojAAAAAACRE3R/++23MmjQIClXrpxERUXlmK/VzXfv3u2P9AEAAAAAEFlB97Fjx6R8+fK5zk9LSytKmgAAAAAAiNygu2HDhvLTTz/lOv/rr7+Wpk2bFiVdAAAAAABEZtDdp08fefPNN916KLeqmU+aNEkWLVokN998s/9SCQAAAABACIopzEoPPPCAfPXVV9KlSxdp0qSJCbiHDRsme/bskeTkZOncubMMGTLE/6kFAAAAACDcS7pLlSplgu7nnntO4uLiJDY2VjZv3ixVqlSRZ555RhYuXCjR0YUeAhwAAAAAgMgt6TYrxsSY0m39AAAAAACAnCiOBgAAAAAg2ILuv//+WwYOHCi1a9c21c21x3Kl7bp1+po1a/yZTgAAAAAAIiPo3rFjh7Ru3Vo++ugjadasmWRmZjrnVa1aVX788Ud5/fXX/ZlOAAAAAAAio033o48+ajpK27Bhg+lILTEx0W3+lVdeKZ999pm/0ggAAAAAQOSUdOv43DokWJ06dZzjc7uqV6+e/PPPP/5IHwAAAAAAkRV0p6WlSY0aNXKdf+LECcnIyCjwdidMmCBt2rSR+Ph4U3res2dP+eOPPwqTRAAAAAAAQjPo1hLujRs35jp/9erVcvrppxd4uytWrJC7777brK/jgJ88eVIuv/xyOXz4cGGSCQAAAABA6LXpvvbaa2XGjBly2223OUu8rWrm2rnaBx98IOPGjSvwdhctWuT2++zZs02J908//SQXX3xxYZIKAAAAAEBolXRrR2o6VNh5550n/fr1MwH3xIkT5YILLpDrr79ezjnnHBkxYkSRE3fw4EHzf6VKlYq8LQAAAAAAQqKkOyEhQVatWiWjRo2St99+WxwOh6kOXqFCBdPB2pNPPimxsbFFSlhWVpbcf//9ctFFF0nz5s29LnP8+HHzcW1rbq2rn6LSbeix+WNbxbFdRDbyFeeWPBVa90QwpC8QaSiOfdq5j2C4bggv5CnOL/kqtO4L17T5mr5CBd1W4P3888+bz549e8yOdYxub72ZF4a27dYhyVauXJlnx2veqrFreo4dO1bkNOhJ1NJ2PTYdIs1f7NouIhv5inNLngqteyIY0heINBTHPu3cRzBcN4QX8hTnl3wVWveFa9p87Xus0EG3Kw22/emee+6RhQsXyjfffGOqsedm5MiRMnz4cLeSbu3kTdOjLwX8cUL1JYJuz99Btx3bRWQjX3FuyVOhdU8EQ/oCkYbi2Ked+wiG64bwQp7i/JKvQuu+cE1benq6/UH3+++/L/Pnz5ft27eb30877TS55pprTLvuwtC3Bffee6/Z5vLly6VBgwZ5Ll+6dGnz8aQXxl8XR0+oP7dn93YR2chXnFvyVGjdE8GQvkCkoTj2aec+guG6IbyQpzi/5KvQui8KmrZCBd1ajK5jaH/99dcmUNa23GrNmjUmEH/llVfk008/lbJlyxa4Srm2Ef/kk0/MWN3Jyclmevny5SUuLq4wSQUAAAAAIPR6L1+6dKkpld69e7fs37/ffPRnnbZs2TKzTEG9/PLLpn58x44dzVBk1ue9994rTDIBAAAAAAioQpV0axDcu3dvmTp1qtv06tWrm2m7du0yy3jOz4+WmgMAAAAAEC4KVdKtHZZdcskluc6/9NJLncN3AQAAAAAQqQoVdJ999tmyZcuWXOfrvLPOOqso6QIAAAAAIDKD7vHjx8trr70mn332WY552gna66+/Lk899ZQ/0gcAAAAAQGS16X7rrbfMcF7ag3njxo3lzDPPNNN///13+eOPP0wp97x588zHtVv1mTNn+i/lAAAAAACEY9A9e/Zs58+bNm0yH1e//PKL+bgi6AYAAAAARJpCBd1ZWVn+TwkAAAAAAGGmUG26AQAAAACATSXdnjIyMuSHH34w43M3bdpUmjVr5o/NAgAAAAAQGSXdy5cvl6FDh0pqaqrb9B07dsi5554r7du3lxtvvNEMJzZw4EA70goAAAAAQHgG3dp52uLFiyUxMdFt+i233CK//vqrXHjhhTJs2DBT0j1nzhzzAQAAAAAgkvkcdGv18csvv9xtmvZa/u2338rFF19s/n/uuefMco0aNZK5c+fakV4AAAAAAMIv6E5OTjbBtGeVcx0K7Pbbb3dOi4uLkz59+uQYMgwAAAAAgEjjc9B9/PhxE1C7WrNmjfm/Q4cObtPr1KkjBw8e9FcaAQAAAAAI76C7bt26snHjRrdpK1euNG28Nch2deTIEalQoYL/UgkAAAAAQDgH3do7ubbT3rBhg/l9/vz5smXLFunatWuOZbVjtVq1avk3pQAAAAAAhGvQPXLkSFPF/JxzzjGl29ddd52UKlVKRowY4bZcZmamfPrpp9KuXTs70gsAAAAAQPgF3Q0aNJAVK1bIlVdeKZUrVzYl3NqRWrNmzdyWW7ZsmZnfo0cPO9ILAAAAAEDIiCnIwq1bt5bPPvssz2U6depkqpcDAAAAABDpfC7pBgAAAAAABUPQDQAAAACATQi6AQAAAACwCUE3AAAAAAA2IegGAAAAAMAmBN0AAAAAANiEoBsAAAAAAJsQdAMAAAAAYBOCbgAAAAAAbELQDQAAAACATQi6AQAAAACwCUE3AAAAAAA2IegGAAAAAMAmBN0AAAAAANiEoBsAAAAAAJsQdAMAAAAAYBOCbgAAAAAAbELQDQAAAACATQi6AQAAAACwCUE3AAAAAAA2IegGAAAAAMAmBN0AAAAAANiEoBsAAAAAAJsQdAMAAAAAYBOCbgAAAAAAbELQDQAAAACATQi6AQAAAACwCUE3AAAAAAA2IegGAAAAAMAmBN0AAAAAAERC0P3NN99It27dpGbNmhIVFSULFiwIdJIAAAAAAAiPoPvw4cNyzjnnyEsvvRTopAAAAAAAUGQxEkS6du1qPgAAAAAAhIOgCroL6vjx4+ZjSUtLM/877rxTHKVK5bmuo2FDkccec584frxEbdvmspBDEnT7pUuLIyrq1OQePUR69jy13NGjEjVkiE9pdjz6qGSddpo4HA7JysoSWbNGoqZPz3/F2FhxvPyy+7Q33pCob7/Nf59t2oh4pC9q+HCRf//Nf91bbhHp0OHUhF27JMrzvOW27qRJIpUqnZqwaJFEvfde/ivWqiWO8ePdp02aJFEbNuS/z8svF7npJrdpUbfe6lt69ZycddapCb/+KlGTJ/u27qxZ7hPeeUeivvwy//WaNxcZMcI9vXp+d+3Kf90bbhC54grn71l790rCffflyK9e19XzW6vWqQkrVkjU7Nn57lMqVhSH5zmZPl2i1qzJP73t24sMHOg2LWrwYJFjx/JfV/Ov5mPL1q0S9eST+adX19X7Ky7u1IQFCyTqk0/yX8/lGaH3qt6zjieeEMf27fmvW8RnhJx++qkJgXxG7N/v9RkYMs8IL8/wYHpGyOLF+Z9fPz4j9HpGeWwr179zdj4jctunH58RWZs3S8KYMb49Dwv6jPj/9DuaNpWsUaPy/h4R7s8Ivkf45xmhf1+efjr7e2EAv0d4fUaEw/cIh0NibrpJsjp3Dtj3iEA8I6zvLlnffy9RM2aE3jPC4f1vRTDEGlk33OCM5dzu23ANuidMmCDjxo3LMf14Soocj8n70DLLlJFDqalu0+KTkqREUtKpCQ6HZGZkyAndlsvFPpacLMdc1z1yRCq4rpeHQykpcrJcOTl48KC5WKWTk6WsD+s6YmPloEd6yyQnSykf1j2ZlCSHPdYtn5QkUT7cCEdSUuSEy7rRKSmS4OOxHkxJEUdGhvP30ikpEufDullRUZLmkd5ySUkS48O6x5OT5ajHur5em/SUFMmoVs35e0xKitmvLw547DMuOVlK+7BuRuXKku6xbsLu3RLtw7pHNZ+7rrt3r5RNTc2RX71JS0mRrJIlnb+XSkmRMr7kw2PHcuRDzb8lfVj3RHKyHPGWD334Qn04OVlOuqxbIiXF3K++OJCSIlKmjPP32ORkifVhXddnhD5Q9Z6N9zEfFvUZkZmQ4Py9ZCCfEfv3e30GhswzwsszPNieEfmdX38+I6L27TPX1U0uf+dsfUbksk9/PiOiU1MlzsfnYYGfEf+f/qMVK+a4Njm+R4T7M4LvEf55RjgccuDAAfO9MDo6OmDfI7w+I8Lhe4TDIen79pm/Vdb5Le7vEYF4RljfXUr6mA+D7hnh8P63IhhiDT1WK5bT5tFhH3SPHDlShutbFJeS7jp16kjpatWkdH4l3TVqSFxiovvEGjUk6siRU8voG4zjx6VU6dKmYzdLqerVJcF1XX37VKOGT2kuVa2aZCUmmu1VrVpVoqtX923d2Fgp7ZleH9ctVaOGlPVY16wXG+tTesV13ZMnfT7Wqrqu69unatV8W7dmTYn1dm327cs/vdWrS7y3Y/VBjmP1Nb2iqxX+2pTxTG/NmuZBU9D0ZkVHy4nExBz51ZsqhT3WihVz5kO9Nr4ca/XqUs7btfHhC7Wu65betDTfr40eq+sbah+vjeszQv9w6TmNq1FDoo8e9Sm9RXlGuB1rAJ8RjtKlvT4DQ+UZ4e0ZHkzPCE1zvufXj88IiYnJkd7c/s7Z+YzI62+rv54RWQcO+Pw8LOgzwkp/aS/XxvN7RLg/I/gekc+18fG+0TxVoUKF7O+F/x8UBuJ7hLdnRDh8j9DzW65yZamQmHjq/Bbz94hAPCOs7y4VqlWTEiH4jHDk8rciGGKNsi6xXHp6ev770/Ph0CMKQnog8+fPl56uVSvyoUF3+fLlzZuHBJe3wIWlmTU1NdV8WXLepH5g13YR2chXnFvyVGjdE8GQvkCkoTj2aec+guG6IbyQpzi/5KvQui9c06ZBty/xZ3AdAQAAAAAAYSSoqpfrm4KtW7c6f9+xY4esW7dOKlWqJHXr1g1o2gAAAAAACOmg+8cff5RLLrnE+bvVXnvAgAEy25ceEQEAAAAACCJBFXR37NjRNJoHAAAAACAc0KYbAAAAAACbEHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBNwAAAAAANiHoBgAAAADAJgTdAAAAAADYhKAbABD03n33XWnVqpXExcVJpUqV5LrrrpNt27bluc5//vMfad++vVSoUEFiY2OlXr16MnDgQNm5c2expRvBjXwF8hTAs7Y4EHQXEX+wAcBeM2fOlJtuukl+/vlnqVGjhmRmZspHH30kF154oSQnJ+e63pdffilHjhyRRo0aSZ06deSvv/6SWbNmSZcuXbhkIF+BZxVQDPgb/v8cYeTgwYMOPST93x8yMzMdSUlJ5n9vXn/9dbM//TRo0MCRkJBgfk5MTDTr5aZly5aOmjVrOlq1auU4/fTTndto3LixX9KNyJRffgXnNhTz1PHjxx1VqlQxz8hevXqZabt27XLEx8ebaffee2+u6x4+fNgt/f369XM+b/fu3es4efKko02bNub3Sy+91CyTkZHhaNu2rZnWsWNHW489GM5vINJQHPvMbx/hnK8QGOSp4BUMz9pIPe5wvS8yXc6tr/EnQbePJzRSMhFCVzA8XMNVpJ7bYDjulStXOp+Pb7/9tnN6586dzbRGjRrluq6me8KECebZ6fqCs2nTpo6srCyzzB9//OEoU6aMmf7qq686nnnmGfNzhQoVHDt37gz78xupQXc45ysEBnkqeAXDszZSjztc74vMQgTdMVaJNwpmzZo1snfvXvNzr169zP81a9aU888/X7766itZtGhRrutq28Jp06bJ/PnzZf/+/bJ161YzvWnTpqatYlRUlMybN09atmwpX3/9tbz22mty4MAB+eGHH0zbxDlz5kh0NC0DAIS/v//+2/lzYmKi8+dq1aqZ/7XKeF527dplnp0Wfa4uXLjQPGfVGWecIZMmTZLBgwfLAw88ICdPnjTTX3rpJalbt67fjwfBgXwF8hTAs7Y4EbkF+A+2FXDrF0EN1j2/CCr9IjhmzBjzM18EAcA0jfLpNDz66KNy4sQJ2bRpk1xyySWmXXjfvn1Nu3DLXXfdJV27dpW0tDQ5evSo3HDDDdKnTx9OcwQiX4E8BfCstQNBt5/xBxsA/Ec7QLOkpqbm+NmX0ugSJUpI48aN5f777ze/L1++XJYuXeqcr0F5UlKS8/c///zTLShH+CFfgTwF8KwtTgTdhcQfbACwX5s2baRy5crmZ+2xXO3evVtWr15tfr7iiivM/02aNDGfF1980fy+ZcsW+fTTTyUrK8v8rv+7Nvs5fPiw8+dRo0bJunXrpHbt2lKlShX5/vvv5cknn+TyhjHyFchTAM/aYuUII8XdkVrlypXz7UhNeyTXzwsvvGB+37x5s2P+/PlmWd2ufgYPHuzsHODjjz927uOhhx4y02rXru3stG3cuHF+OTaEn2DoMCNcReq5DZbjfuWVV7yOFKHPRX2WKmv+mDFjzO/Lli0zv5ctW9Zx9tlnO6pVq+ZcRp+p1t+JFStWOKKjo830L774wvHuu++an2NiYhxr1qwJ+/MbqR2phXO+QuCQp4JTMDxrI/m4w/G+yKT38uIdMiwcMxFCV7A8XMNRpJ7bYDruefPmOVq0aOEoXbq0o3z58o5rr73WvMS0eD5rt23b5ujRo4cZnlHXKVWqlKNhw4aOO++80/H333+bZfR5W69ePbPewIEDndvq3bu3cxjHI0eOhPX5jeSgO1zzFQKLPBV8guFZG+nHPS/MnrWFCbqj/v9Aw4J2glO+fHk5ePCgJCQkFHl7Wh1R2w1qR2m59Rb+1ltvyXPPPSe///676ZX8sssuk4kTJ0qjRo3MfKtjNO0IbezYsbJ9+3YZPny46f183759pg24VlXv1KmTPPbYY6Z6ox7H2WefLTt37pSBAweaQeXV9ddfLx988IFpm6idAcXFxRX5GBE+fMmv4NxGUp4K9vQHQ/oCkYbi2Ked+wiG64bwQp7i/JKvQuu+cE1benq6T/EnQ4YVkfaCq5/ceL7TOO200+Tjjz/OMxPpBdOOfDy9//77RU0uAAAAAKAYBddrAwAAAAAAwghBNwAAAAAANiHoBgAAAADAJrTpBgCEjczMTDMe9xtvvCHbtm0zHZtoBycNGzY0HVPquN4lSpQIdDIRYshXIE8BPGuLgqC7mPAHGwDsfcZOmzbNfLx1RLl+/XrTiWX9+vVl6NChct999wVdb6gIPuQrkKcAnrX+QNBtM/5gA4C9jh49Kv369TNBtSpRtoRUaF9ByjUtJyXiSkjm0UxJ35guB1YeMAG5Dtu4cuVKmTdvHkMvgnyFYsOzCojc+4Jxum0cH64gmSjzcKZZ5tprrw25TITgEMzjGYa6SD23oXDc+mKzd+/eMn/+fImKiZLqN1aXihdXlOhSOdObdTxL/v32X0l+N1kcGQ7zvNWhGANV3TwYzi/jdIdfvkJwIk8FTjA8awMhFI47M0SftYUZpzs4r0AY0Eyk43drwK2ZqEa/GtJ4SmOpcWMNiT87Xso0KmP+r3FTDWk8ubGZr8vp8hqo6/oAgLxpdXLrj3W9EfWkcqfKXv9Yq+jS0Wa+Lmc9b1944QVOMchXsB3PKiCy7wuCbptEUiYCgEA231H6drzcmeV8Wk+X0+WVrs9LTpCvYCeeVUBOkXZfEHTbINIyEQAEgvZSru27tOlOxfYVC7SuLq/r7dixQxYvXmxbGhF6yFcgTwE8a/2NjtSC8A926vxU80Xwo88+ksuvuNxMd4jDuYzrz+Z3R+7zfF3OL9twTaPLcjmWdfuxcNvIkUZH0bfhuawv6cor/TmTWPBt+JomlZmVKQf+PSAVpIJERUXluo3CXN8C5ZdCXF9f02TLtfDh2HSaaatzJEGioqPyP04/X19fz0Ght5FL+vX/tENpEn8gXqKjogt9P9n1XHphanaNIO0rQ2sMFYQuX6FdBdm3eJ+MmjxKdtfZnWfaPNOQ333lS/7TbRw+fFjKJJXJ9Z4tyHOqMGnUZY4cPiJl/ikjURLl834Lm69VliNLjhw5ImV2lskz/b7sN7dldTndR9z2OOe59TWN7z/7vl/y1fBnhsv/Ev5XoOMpyPUr7HPN67YKksZC/m32ui0/31MBT2Mu+Wr5U8v9kqcGPzlYOkZ19Lqc6zPE/O5yP+c333PZnL8Wftt+3VYht63X6cTxE1K6dGm37ee3b7+l0yOtRd2Wr+nU4z5+7LjExsY61ynovu1Kp3rnuXf8cl/MnDlTrrzySgl2BN020PFh/ZGJBo0fJHUP1rUjiQAQ8rb+sdX8r51TFka5ZuXMs/a3Lb/J82uf93PqEKp2bN/hl3y1c8dO+XTbp35OHUJR6t+pfslTqf+kytrUtX5OHRAY27dt98t9sX179naCHdXLbbBt27YiZyJ1Ys8Jv6YLAMKJ9mSqdDSIwoiOy/4TmHUsezsA+Qp24FkF2HdfHDp0SEIBJd020K7jFV8EAcA+Vk0iHX6xMLKOZv/Bj471/f2zW5VMf1SD1NqoUXlXyytolb2CplGre2vzAX9WgcwvjY4sh3MIG1uqaTqym9zElIgpcBp3ltkpJ+REkfNVbNlYqZ9QP8d8f57HglQZLq5qpEWpquzXNNpUnTrfNHrZVlK5JNmXuq/IeSo+Pl7a1WpX8CryNlW3z3fbBWgOk9+2cvxegGPWeSdPnpSSJUv6vG+/pTO/bftzW16WzcjMcD4HbW12UtBtO0T+jPvTL89avS9CAUG3DcqVyy6pLmomqlm5poy+YHSB/5j6+kfKlm34+ocpry9NPm7D1y9i/voym1u6CtveqLDnMbfl9AF34MABqVihoml37I8vMoXOc4W4voX9cuVzuvK6FvlsQ8dj3Ldvn1SpUuVUcOKxzQLls0Jc3zzPgT+24SX9etx79uyRxKqJzrbsfrkWfnou3fDJDbLg7wWS/lu6GYKxoNI3Zr8gvbL1lfJh/w9z7C8SxlBlnO6cer3dSz7+8+Mi56tOLTvJR9d85IerhFCX2iJVPt5e9DzV4ZwO8nKnl21IYXgLhmdtIAT7cfd6r5d8vLPo98Vpp50moYCg2wYNGzaU9evXFzkTtTqzlfQ+o7cNKUTYPlyjgvfhGurnNvZYrCTGR9a51eM+Xuq4JJROCMrjvv2222XB/AVy4NsDUu2aagXqQ0Ortf377b/m59tuu61Yg20Et4EDB5qhO/2RrwDyFMCzVgXft6gw+YOt9A+21V7BV/zBBgDfXHHFFVK/fn3JPJzpDHR8pctnHcmSBg0aSJcuXTjlIF/BNjyrgJwi7b4g6LZBpGUiAAiEEiVKyNChQ83Pye8mS/rv2bWE8qO1kHR5pevrdgDyFezCswrIKdLuC4JuG0RaJgKAQNFn5bXXXiuODIfsnLRT9i3Zl2sNI52u83dO3mmW79Wrl/NZDZCvwLMKKF5DI+hveJQjv64FQ0haWpqUL19eDh48KAkJCQHtgCAzM1Ouv/560y4sKiZKqt9YXSq2r+i1bZhVpVwDbisTvf/++0HZhhLBK9g7zAhlkXpuQ+W4jx49Kv369TPPW1WibAmp0K6CGX5RhxTRzim1rwyrJpHS5+ybb74pcXFxEX1+6Ugt/PIVghd5KnKftYEQKsd9NASfta7nVket8iX+JOi2MbOGYiZC6AqVh2soitRzG0rHrS86X3jhBXn++eflzz//zHU5bbqjb8b1E+hjCobzS9AdfvkKwY08FZnP2kAIpePODLFnLUF3EJV0h2omQugKpYdrqInUcxuKx63P3MWLF8vMmTNl+/btZhi9ChUqmCFFtDdp7SsjWJruBMP5JegOv3yF0ECeiqxnbSCE4nFnhsizlqA7CIPuUMtECF2h+HANFZF6bkP9uIM9/cGQPoLu0DhnCG/kKc4v+Sq07ovCBN2M011MNKC+8sorzSeYMxEAAAAAwH+I+AAAAAAAsAlBNwAAAAAANiHoBgAAAAAgkoLul156SerXry+xsbFy3nnnyQ8//BDoJAEAAAAAEPpB93vvvSfDhw+XMWPGyNq1a+Wcc84xPXtrx2MAAAAAAISSoAu6J0+eLIMGDZJbb71VmjZtKjNmzJAyZcrIG2+8EeikAQAAAAAQukH3iRMn5KeffpJOnTo5p+mQWvr7qlWrApo2AAAAAAAKKqjG6d67d69kZmZKtWrV3Kbr75s2bcqx/PHjx83HkpaWZv7XcbD1U1S6DYfD4ZdtFcd2EdnIV5xb8lRo3RPBkL5ApKE49mnnPoLhuiG8kKc4v+Sr0LovXNPma/qCKuguqAkTJsi4ceNyTN+zZ48cO3asyNvXk3jw4EFzUrXE3V/s2i4iG/mKc0ueCq17IhjSF4g0FMc+7dxHMFw3hBfyFOeXfBVa94Vr2g4fPhx6QXeVKlWkRIkSkpKS4jZdf69evXqO5UeOHGk6XXMt6a5Tp45UrVpVEhIS/HJCo6KizPb8HXTbsV1ENvIV55Y8FVr3RDCkLxBpKI592rmPYLhuCC/kKc4v+Sq07gvXtKWnp4de0F2qVCk599xzZenSpdKzZ0/nQenv99xzT47lS5cubT4Wfdug9OD9cXF037qtuLg4vwfddmwXkY18xbklT4XWPREM6QtEGopjn3buIxiuG8ILeYrzS74KrfvCNW1W0G3FoSERdCstuR4wYIC0bt1a2rZtK1OnTjXF9tqbeX4OHTpk/tfSbgAAAAAA7KZxaPny5UMn6L7hhhtMm+zRo0dLcnKytGjRQhYtWpSjczVvatasKX///bfEx8ebIn9/aNOmjaxZs0b8yaoGr2n1RzV4wM78isg+t6F+3MGe/mBIXyDSUBz7tGsf/A1HuD4Lwlmknt9QP+5gTr+VNi3h1oBb49C8BF3QrbQqubfq5PnRqge1a9f2a1q0jbldgbFul6AboZJfI12knttQP+5gT38wpC8QaSiOfdq9D/6GI9yeBeEsUs9vqB93iSBOv2va8irhtgRXBfkgdPfddwc6CYDPyK/2idRzG+rHHezpD4b0BSINxbHPYDi3gK/Ir/aK1PMb6sd9dxCnv6Bpi3Lk1+obtlRN0zci2tV8sL69AQAAOfE3HABQUJR0B4D2uD5mzBi3ntcBAEDw4284AKCgKOkGAAAAAMAmlHQDAAAAAGATgm4AAAAAAGxC0A0AAAAAgE0IugEAAAAAsAlBdxBauHChNG7cWBo1aiSvv/56oJMDAAB8cM0110jFihXluuuu43wBAJzovTzIZGRkSNOmTWXZsmVmLO9zzz1XvvvuO6lcuXKgkwYAAPKwfPlyOXTokMyZM0c+/PBDzhUAwKCkO8j88MMP0qxZM6lVq5aUK1dOunbtKl9++WWgkwUAAPLRsWNHiY+P5zwBANwQdPvZN998I926dZOaNWtKVFSULFiwIMcyL730ktSvX19iY2PlvPPOM4G2Zffu3SbgtujPu3bt8ncyAQCAH/9+AwCQG4JuPzt8+LCcc8455g+zN++9954MHz5cxowZI2vXrjXLdunSRVJTU/2dFAAA4CP+fgMA7ELQ7WdaHXz8+PGmMxVvJk+eLIMGDZJbb73VtN2eMWOGlClTRt544w0zX9+wu5Zs6886DQAABO/fbwAAckPQXYxOnDghP/30k3Tq1OnUBYiONr+vWrXK/N62bVvZsGGDCbbT09Pliy++MCXhAAAgeP9+AwCQm5hc58Dv9u7dK5mZmVKtWjW36fr7pk2bsi9ITIxMmjRJLrnkEsnKypKHHnqInssBAAjyv99Kg/D169ebquq1a9eWDz74QC644IIApBgAEEwIuoNQ9+7dzQcAAISOJUuWBDoJAIAgRPXyYlSlShUpUaKEpKSkuE3X36tXr16cSQEAAD7i7zcAoCgIuotRqVKl5Nxzz5WlS5c6p2kVcv2d6mcAAAQn/n4DAIqC6uV+pp2fbd261fn7jh07ZN26dVKpUiWpW7euGS5swIAB0rp1a9Np2tSpU03bL+0NFQAABAZ/vwEAdolyOBwO27YegZYvX246QfOkgfbs2bPNzy+++KI8++yzkpycLC1atJBp06bJeeedF4DUAgAAxd9vAIBdCLoBAAAAALAJbboBAAAAALAJQTcAAAAAADYh6AYAAAAAwCYE3QAAAAAA2ISgGwAAAAAAmxB0AwAAAABgE4JuAAAAAABsQtANAAAAAIBNCLoBAAAAALAJQTcAAF4sX75coqKiZPbs2QE/P7fccotJCwrvhhtukIsuuiggp/Do0aNSs2ZNGTduXED2DwAILIJuAEDQBry5fWJiYiTcaHA/depUCXb//vuvPP7449KmTRupUKGClCpVSmrXri29evWSjz/+WBwOh237XrBggYwdO7bA6/3vf/+T999/X8aPHy+BEBcXJ4888og8++yzkpSUFJA0AAACJ8ph519HAAAKGXRfcsklctNNN8mVV16ZY350dLT06dOnWNIwa9YsU9Jst44dO8qff/5pPp5OnjwpmZmZEhsbK4H0ww8/SI8ePSQ1NVW6d+8uHTp0kISEBNm1a5d8/vnnsnr1annppZdkyJAhtuxfr8OcOXMKHNh36dLFpPnnn3+WQDl8+LBUr15d7rrrLhN8AwAiR/gVFQAAwkarVq2kX79+EulKlixpPoGUnJws3bp1k2PHjsmKFSukXbt2bvNHjRolixcvNiXhwWTr1q3y1VdfyaRJkwKajrJly8q1115rajRoiXvp0qUDmh4AQPGhejkAIGQdOHDAlP5qMOPNyJEjTXX0devWmd93794tI0aMkBYtWkjFihXNuk2bNpWnn37alCTnRwMm3Z6Wgnsrqa5fv77btC+//NK0JT7ttNNMFWOtjn355ZeboNWVrqfTdu7c6VaN3tpPbm26f/nlF7nmmmukcuXKzmN55plnchyLtf7Bgwdl8ODBkpiYaJbXNs7ff/+9+EJLZ7W0WM+VZ8DtWqJ84403uk17/fXXzcsTPf7y5cub41+5cmWOdf/73/+akvMqVaqYZevWrWuu6+bNm53nV0u5les5yq/N/YcffmhKxr3VmNDzrttdv369dOrUScqVK2fOjeaRjIwM84LhgQcekFq1apnzdfHFF8vvv//utg1dRqu8N27cWMqUKWOu8VlnnSUPPvhgjv117dpV9u7dK8uWLcszzQCA8EJJNwAgaB05csQEKZ60HbFWa9YAR6s5f/LJJ7J//36pVKmSc5msrCx566235OyzzzZBthWkartjDVQbNmxoqm0vWrTItLfdvn27vPLKK35NvwaEmq7+/fubds9aDVuD0Msuu8wEXu3btzfLaVtufUGgxzplyhTn+meeeWau2/7xxx9NkKol4HfffbepuvzZZ5/Jww8/bIJIPXZvQXHVqlVl9OjRsm/fPpk8ebJcddVVsmPHDomPj8/zWD766CNz3gcMGODz8Wta9CVA27Zt5amnnpJDhw7Jq6++aqrt6zWzAmF94aDXsXnz5uY86HXVFyRLliwxJdVnnHGGPProo+aafvvtt/Lmm28693HhhRfmmQbdtm5Pt+HNP//8I507dzYvR6677jrzokTPi/YbsHHjRtMJmuYPvTbPPfec9OzZ0wTe2sRB6bl/4403zDUePny4Cda3bNkiX3/9dY59XXDBBeZ/fZlyxRVX+HweAQAhTtt0AwAQTJYtW6aNdnP9XHXVVc5lFy5caKa99NJLbttYsmSJmT5p0iTntCNHjjiysrJy7K9fv36O6Ohox+7du3OkYdasWc5p+rNO03meOnTo4KhXr57btPT09BzLJScnOypXruzo2rVrvutbBgwYYPbr6sILL3SUKFHCsX79euc0PbbevXubZfX4PdcfPHiw2zbef/99M33GjBmOvKSlpZnlzjrrLIevNm3a5IiKinJcdNFFjuPHjzun79q1y1G+fHlzrBkZGWbasGHDzPZTUlLy3Ka385CfunXrOlq2bOl1nqZBt6fnwVWrVq1M2rt37+6WX55//nmz/KJFi5zTKlasmONa5iUmJsZx9dVXF+gYAAChjerlAICgdccdd5j2uJ6fJ5980q30tlq1ajJ37ly3dfV3La3s27evc5pWW7aqaZ84ccKUQmsJpm5DS1G19Njf7Xgt6enppnS5RIkSct555/lcrdsbreb93XffmdJhLcm36LFpibCaP39+jvWGDRvm9vull15q/teS2bykpaWZ/7V2ga+0JFurdT/00EOmhNyiQ2fdeuutpiq91bGZVju3StO1pNif9uzZ41YDwpNWHe/du7fbNK0+r2m/99573ar1WzUTXM+Xpl1LxDds2OBTejQtev0AAJGDoBsAELQaNWpk2tp6fs455xznMlZgrUGs1f5Xe4rWauTaflgDcosGdNqJlVY11ja62hZaq1vffPPNZr6/OwHbtm2baeOs7ce1+ra2V9b9aU/fRdmXVgdXzZo1yzFPq6Rr1WetLu9J25a70uNX+jIgL1awrdXD/ZFGa5qVxnvuuUdatmxpej3XoFSrnU+bNs0EzEWlQXNevZ03aNAgxzS9Xt7mWdNdz5c2DdBrqe24tcnC7bffbl446EscbzQtjLkOAJGFoBsAEPK0Pa2ySrs14NaSZc/2x9rmVnvZ1o69dCgwDX615Fw7B1O5BUqWvIIlzxJa3b92vKVtxu+77z7ToZf27q370xLmQIzYqaXs3uSXFn1hUK9ePdm0aZNp4+xvGvyvWbPGtHPX0mUN7rVUXl+OrFq1qkjb1pccWqOhoOfE1/OlQ6jpMG/azlyv69KlS027b+2gTWtTeNIAXdMEAIgcBN0AgJCnJd/6mTdvngmINPi2OllzpYGRBsLvvvuuCci1N2ktOfe12rRVTdlbEGeV7Fo0+NLOwLRjNO3dulevXqbkXfenJfGeClL6aZXAarVmTxoY68sDz1LtotKexDWIdO3ELC/W/r2l8bfffnNbxgpwNVDVpgPaWZpWPdcXF1ozwVKYEmLtnE1rHOT3QqUoNF/o0HavvfaaKb3XKvV6DFri7UqDc305o2kCAEQOgm4AQFjQIFrbCb/99tum52jtjVqrkLvSwM6zVFcDYNcew/Ni9YCtvWq7euedd0yA7bkv5bk/7R3bW3tuHa5KS0F9KQHXYa20127trdy1LbGuO2HCBPOz9tDuTxpIagmt/p9b6bMem77QUPrCQ4NkHWpMe4m3JCUlmVoGWnKuVcqVtx7qmzRpYtrgu77g0HOk8iq59qSBvJacW4G+P+nQbDpsnSs9Zuu4PNO5evVq87/2Og8AiBwMGQYACFpr1641pdfeaBVeKwhT2q5bA0JtF6ylmt6GttIhoXRYMA3ItcQ5JSXFDPdktW3Oj47FrOvpNjTA1aHIdAxw7bTs9NNPdwsutTMuHcZLx3zWEk4dMkyX1ZJibf/766+/um37/PPPl4ULF5r2zRpQa9Cu1ZU1wPbm+eefN8Gbdu5lDRmm62sV9j59+phhyfzJ2r5Wp9Zj0/OvtQa0loC+cNBq9Dr+9ssvv+w8VzpWtQ4ZpsvpObeGDNMSbB3SzHoxMWjQIDN0l9YE0GBcq7C/9957Znmr6YB1jl588UVzjXWoMx0uTTul89Yu26I1DHToMm1K4O8SZk1fjRo1zAsGDbT1WmmNBz0H2v67W7dubstrGrRdvw6ZBgCIIIHuPh0AgIIOGaafLVu25FhPh2LSeY0aNfJ6Ug8fPux44IEHzDBSpUuXdpx++umOCRMmOIcXcx0ezNuQYSopKclx3XXXOeLj4x1ly5Z1XHHFFY7ffvvN65BfOpxXly5dHBUqVHCUK1fOLPPNN994HfpK0zZw4EBHYmKiGb7MdWiy3IbKWrdunaNHjx5m2KpSpUo5mjRp4nj66aedQ3H5MtSWTtf5vtq3b59j7NixjnPPPdeRkJDgKFmypKNWrVqOXr16OT755JMcy7/66quOFi1amPOt56xTp07mHLj66KOPHN26dTPb0eOoUqWK4+KLL3Z8+OGHbstlZmY6RowYYZazzpHn9fFGh/Rq3rx5jul6vfSaeBozZozZ9o4dO9ym6+86XecrHQrtkUcecbRp08ZRqVIlk3bd5q233urYvHlzjuHjNL9o/gMARJYo/SfQgT8AAIBdtDq81h7QTuy0pkIgaM0EHc5NhxvT0nEAQOQg6AYAAGFPh27766+/zPjmxU2ry2uncXfddZeMGTOm2PcPAAgsgm4AAAAAAGxC7+UAAAAAANiEoBsAAAAAAJsQdAMAAAAAYBOCbgAAAAAAbELQDQAAAACATQi6AQAAAACwCUE3AAAAAAA2IegGAAAAAMAmBN0AAAAAANiEoBsAAAAAAJsQdAMAAAAAIPb4PzLkIwfUkgKhAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -831,10 +831,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2026-01-09T21:38:28.503993Z", - "iopub.status.busy": "2026-01-09T21:38:28.503779Z", - "iopub.status.idle": "2026-01-09T21:38:28.510678Z", - "shell.execute_reply": "2026-01-09T21:38:28.510048Z" + "iopub.execute_input": "2026-01-10T22:23:09.647536Z", + "iopub.status.busy": "2026-01-10T22:23:09.647312Z", + "iopub.status.idle": "2026-01-10T22:23:09.653024Z", + "shell.execute_reply": "2026-01-10T22:23:09.652554Z" } }, "outputs": [ @@ -848,9 +848,9 @@ "================================================================================\n", "Method Time (s) Evals Speedup\n", "--------------------------------------------------------------------------------\n", - "Nelder-Mead (Sequential) 1.43 127 1.00x\n", - "CMA-ES (Default Pop) 3.33 301 0.43x\n", - "CMA-ES (Large Pop) 6.86 601 0.21x\n", + "Nelder-Mead (Sequential) 1.44 127 1.00x\n", + "CMA-ES (Default Pop) 3.40 301 0.42x\n", + "CMA-ES (Large Pop) 6.81 601 0.21x\n", "\n", "💡 Key Findings:\n", " - Best speedup: 1.00x\n", diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb index 2fba81d..46cb832 100644 --- a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb +++ b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb @@ -36,10 +36,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:24.274746Z", + "start_time": "2026-01-11T08:53:23.724643Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:11.244941Z", + "iopub.status.busy": "2026-01-10T22:23:11.244567Z", + "iopub.status.idle": "2026-01-10T22:23:11.951631Z", + "shell.execute_reply": "2026-01-10T22:23:11.951005Z" + } + }, "outputs": [], - "source": "# Import plotting utilities\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(42)" + "source": [ + "# Import plotting utilities\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "np.random.seed(42)" + ] }, { "cell_type": "markdown", @@ -50,19 +68,29 @@ "Chronopt provides three standard metrics:\n", "\n", "### 1. Sum of Squared Errors (SSE)\n", - "$$\\text{SSE} = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$\n", + "\n", + "$$\n", + "\\text{SSE} = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2\n", + "$$\n", + "\n", "- Default metric\n", "- Penalises large errors heavily\n", "- Assumes constant variance\n", "\n", - "### 2. Root Mean Squared Error (RMSE) \n", - "$$\\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}$$\n", + "### 2. Root Mean Squared Error (RMSE)\n", + "\n", + "$$\n", + "\\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum_{i=1}^{n}(y_i - \\hat{y}_i)^2}\n", + "$$\n", "- Normalised by sample size\n", "- Same units as data\n", "- Better for comparing across datasets\n", "\n", "### 3. Gaussian Negative Log-Likelihood (GaussianNLL)\n", - "$$\\text{NLL} = \\frac{n}{2}\\log(2\\pi\\sigma^2) + \\frac{1}{2\\sigma^2}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$\n", + "\n", + "$$\n", + "\\text{NLL} = \\frac{n}{2}\\log(2\\pi\\sigma^2) + \\frac{1}{2\\sigma^2}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2\n", + "$$\n", "- Statistically principled\n", "- Enables Bayesian inference\n", "- Can estimate noise parameter $\\sigma$" @@ -70,9 +98,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:25.025601Z", + "start_time": "2026-01-11T08:53:24.381272Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:11.954892Z", + "iopub.status.busy": "2026-01-10T22:23:11.954600Z", + "iopub.status.idle": "2026-01-10T22:23:12.139229Z", + "shell.execute_reply": "2026-01-10T22:23:12.138722Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Generate simple test data\n", "x_data = np.linspace(0, 10, 50)\n", @@ -93,9 +146,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:54:37.374413Z", + "start_time": "2026-01-11T08:54:37.268493Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.142024Z", + "iopub.status.busy": "2026-01-10T22:23:12.141801Z", + "iopub.status.idle": "2026-01-10T22:23:12.156991Z", + "shell.execute_reply": "2026-01-10T22:23:12.156108Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "COST METRIC COMPARISON\n", + "======================================================================\n", + "True parameters: [2.5, 1.0]\n", + "----------------------------------------------------------------------\n", + "Metric Slope Intercept Cost Value\n", + "----------------------------------------------------------------------\n", + "SSE 2.3840 1.1288 1.650e+02 \n", + "RMSE 2.3840 1.1288 1.668e+02 \n", + "GaussianNLL 2.3840 1.1288 2.953e+02 \n", + "\n", + "💡 Note: All metrics produce similar parameter estimates!\n", + " The cost values differ in scale, but optima are equivalent.\n" + ] + } + ], "source": [ "# Compare built-in metrics\n", "def linear_model(params):\n", @@ -153,9 +238,42 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:25.672816Z", + "start_time": "2026-01-11T08:53:25.291845Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.159555Z", + "iopub.status.busy": "2026-01-10T22:23:12.159356Z", + "iopub.status.idle": "2026-01-10T22:23:12.308578Z", + "shell.execute_reply": "2026-01-10T22:23:12.307933Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error range: [0.50, 3.50]\n", + "Notice: Uncertainty increases from left to right!\n" + ] + } + ], "source": [ "# Generate heteroscedastic data (error increases with x)\n", "x_hetero = np.linspace(0, 10, 50)\n", @@ -198,9 +316,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:25.744253Z", + "start_time": "2026-01-11T08:53:25.678148Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.311675Z", + "iopub.status.busy": "2026-01-10T22:23:12.311373Z", + "iopub.status.idle": "2026-01-10T22:23:12.323897Z", + "shell.execute_reply": "2026-01-10T22:23:12.323183Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "WEIGHTED vs UNWEIGHTED FITTING\n", + "======================================================================\n", + "True parameters: [2.0, 3.0]\n", + "Unweighted fit: [1.93530391 3.27981493]\n", + "Weighted fit: [1.97635486 3.10714606]\n", + "----------------------------------------------------------------------\n", + "Unweighted error: 0.2872\n", + "Weighted error: 0.1097\n", + "\n", + "💡 Weighted fit is more accurate!\n", + " It down-weights noisy (high-x) points appropriately.\n" + ] + } + ], "source": [ "# Custom weighted SSE cost function\n", "class WeightedSSE:\n", @@ -230,6 +379,9 @@ " return slope * x_hetero + intercept\n", "\n", "\n", + "# Construct custom cost\n", + "weighted_sse = WeightedSSE(weights)\n", + "\n", "# Fit WITHOUT weights (standard SSE)\n", "result_unweighted = (\n", " chron.VectorBuilder()\n", @@ -242,14 +394,12 @@ " .optimise()\n", ")\n", "\n", - "# Fit WITH weights\n", + "# Fit with weights\n", "result_weighted = (\n", - " chron.VectorBuilder()\n", - " .with_objective(linear_model_hetero)\n", - " .with_data(y_hetero)\n", + " chron.ScalarBuilder()\n", + " .with_objective(lambda x: weighted_sse(linear_model_hetero(x), y_hetero))\n", " .with_parameter(\"slope\", 1.0)\n", " .with_parameter(\"intercept\", 0.0)\n", - " .with_cost(WeightedSSE(weights)) # Custom weighted cost\n", " .build()\n", " .optimise()\n", ")\n", @@ -269,9 +419,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:26.131942Z", + "start_time": "2026-01-11T08:53:25.753702Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.326233Z", + "iopub.status.busy": "2026-01-10T22:23:12.326024Z", + "iopub.status.idle": "2026-01-10T22:23:12.499606Z", + "shell.execute_reply": "2026-01-10T22:23:12.499014Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAKyCAYAAAAEvm1SAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Qd4VGXaBuBnSnoljSRACD1AaAEMiAIigl1ARaXZXbe4u+o2y+7quuvq/lvc4rqu66o0O1UUC9IUQUWxEEJNqAGSkDKp085/vV8ys5PJCYSQMuW5veZKMufMmXPOzETOk/d7P4OmaRqIiIiIiIiIiIg6kbEzn4yIiIiIiIiIiEgwlCIiIiIiIiIiok7HUIqIiIiIiIiIiDodQykiIiIiIiIiIup0DKWIiIiIiIiIiKjTMZQiIiIiIiIiIqJOx1CKiIiIiIiIiIg6HUMpIiIiIiIiIiLqdAyliIiIiIiIiIio0zGUIiIiv7Nu3ToYDAb37ZFHHmm2zmOPPdZknYcffrjZOr///e+brCPbPReTJ092byszM/OcttUZ2+0Msr+ufZfj8Dee74+Wzr3n6yO3wsJCBJKOeg39+b3h+XrfcsstrX6c/K7yfOzpbuXl5e7Hvfjii02WbdiwoYOODGrbns8lz01ERNRRGEoREZHfOe+882Aymdw/b9mypdk6n3zyyWl/9n6cbC83NxeBxvsiONACEwpOEgR5vq+DXWsDPn8OAomIKDCZu3oHiIiIzlZMTAyys7Px1VdfqZ+3bdsGp9MJo7Hhby2apmHr1q1NHvPpp5/C4XA0CbM8gyrZXnR09Dm9GJMmTUJSUpL6PiUl5Zy2ReQrLr/8cpw8eVJ9P3To0K7enYAyZswY9O7dW3dZaGhokzDp2muvdf+cnJzcYfsk2/Z8Ln+rziQiIv/CUIqIiPzS+eef7w6lKisr8e2332L48OHq5/z8fJSVlanvpSJAQqqqqip88803GDlypLp/9+7dKC0tbbK9c/Xoo4+e8zaIfM0///nPrt6FgPX973+/VcP/pKqpsyqbJHh84403OuW5iIiIOHyPiIj8kneI5DkUz/N7qfI40zpi/PjxTX6urq7GU089hYkTJyIxMVFVLXTv3h1XXXUV1qxZ06beT6dOncI999yDHj16IDw8HIMHD8aTTz4Jm812VsNqpCrs6aefxogRIxAREaEqG26++WacOHGiWQ8a76CsT58+Le6j7Md///tfTJs2TVV6yTHLsU+dOhWLFy9Wz6tHwj25uHYd15AhQ/DnP/9ZVaa1hed5zMjIUKGit3HjxrnXGTBggPt+6cPz61//GqNHj0ZcXBxCQkJU9Zrs05w5c/C3v/0NdXV16Cx6/XkkQJ09e7baLzlfEpQuXbq0yeM++OCDJo97//333csOHTrUZFleXl6T97XnshUrVjR7raTfmgxV7datm3qN5XW74YYb8PHHH+sew5nem4cPH1bvP3kfyvtx1KhR+Pe//61et7PpvVRfX4/f/va3GDRokDovsl8//OEPYbFYmg1Hfemll5o81vN5vPexLZ9lcfDgQcyfP18dV2RkpHpPdVV/pZZ6Srk+K7KvLhs3bmzWc6+1652pp5QM//V+jGzz9ttvR1paGsLCwpCVlYW//vWvpz0WeY/Ie0V+z8ybNw8FBQUcakxEFKw0IiIiP7Rv3z5JKty3BQsWuJfdfvvt7vvXr1/v/n7evHnude68884mj5ftuezevVsbMGBAk+XetzvuuENzOp1N9mnSpEnu5b17926yrLi4WBs0aJDutqZPn66lp6e7f5bttLRdWe/qq6/W3c6QIUO02tpa9ZgXXnjhtPvvvY/Hjx/Xxo4de9r1r7jiCq2urq7Jvp04caLFczVz5kytZ8+eLR5XS/773/822c5HH33UZHlBQUGT5Y899pi6v7q6Wp2DMx334cOHtdZq6Xy19PrITfbPxfP95zonoaGhuvv10ksvuR8nx+K53q9+9Sv3siVLljR53LPPPute9sQTT7jvNxgMWmlpqXvZli1btO7du7d4XmT93/zmN82OT467pddw7969Wmpqqu72brvttiY/33zzzS1ud9SoUVpubq7udi6++GL3Z+3Xv/71GV9fz31s62dZHpecnKy7/j333HPa4zod7/2Xz2lreH+e5X2l997Tu8lztnY9vfes5z56f/YuvfRSLS4uTnd7eu+l+++/X3fd+Ph49dlo6XNERESBi8P3iIjIL/Xr109VO7iqg/SqoOQv91IhIF+LioparJSSv9bL9kRtba2qrtq/f797uVSySNWGVLi4Kg3+85//qAqdn/3sZ63aX6n4kCGDLtK/SqpVZHvvvvtuq4/72LFjWLVqlaogkueX3llSCSKkYuaVV15RFSmuHjRy365du9yPv+yyy1TVh+u4hWQvsu5nn33mXk+qHfr374+9e/e691uqSu69994mw7mk8kvW8ez3Jccl1TzLly9HW1x//fVqu67jkmOaMGGCe/lrr73m/l6qNRYsWKC+l+fzrBqS/ZdqtIqKClXNI9UYXU32USp1LrzwQpSUlDR5baTCy3Us8hqNHTvWXb3kWcXkXdH00Ucf4a677nJ/7yLDWRMSEtT3x48fV5VBriGrct7kdZLl27dvV58jeR/86le/UpVKUsnVGlIhJdt2ke1JRZEcl1TdtdaXX36pvg4cOBDp6enqGKVyT8ismFLVI59lqXiT9+rnn3/epOrHsweSq+/VuXyW5XUoLi52/yxVbVLdI0OD//73v6O9SMXjW2+9pTuZQ2t+t7j62L3zzjuoqalx76vc7yLnTF7b1qx3ttauXat6+cl7yW63q/eSi1SByu8LV6++9957D3/605+aPD4nJ0e91+X3WFt/XxARkZ/r6lSMiIiorWbMmNHkL+snT57UysrKVMWH/Dxr1iy13nXXXedeRyp7ysvL3evI7ZprrnFv8+9//3uTbb7yyivuZXa7vUmVklQI1NTUnLFSSipzjEaje1liYqKqMBEOh0NVebVU6eG9XddxWa1WtUyqiE5XteFdmaFXfbB69eom60i1jafvf//77mUmk0k7dOiQul++eh5XSkqKe/tSeeJZsaZ3XKfjeU6kEkfOk4tU1biWTZkyRbdKSKrSPB8jjhw5oqqK5PVvrfaulJLqp08++UQtk/2TKrmWHvvQQw+574+OjlbvPzF8+HD3ayFf+/Tp4z7nCQkJ7sf86Ec/0q1Qkcd9/PHH7mXyHh4zZox7+cCBA1tVKSWVV577LhVJ8vkSUrEnFU6ne296blduP/zhD93LFi9erFvF4yLb8lyup62fZTk3no8bPHiwVlJSopbV19er6qDTHdfptKbSy/t30ukqpc70GnlrzXpnUykltzfffNO9XKrOWtrPadOmNVn2l7/8xb3svffea/I7uaXfVUREFHjYU4qIiAKqr5TMqOfqQeRa7rme9zre/aTefvtt9/cyU9/rr7+O6667Tt2k745U3LhIBY53byo9UuXh2Y/pzjvvVFU8QqoMHn/88bM67j/84Q+qV5KQCiKpTnKRirCz5XnMQqpUXMcstx07driXSZ8oqXho6bhcfaqkEkd6F7WVVOC4SCWOq4eOVGW5qmqEZ58i1zkVUhUlVT/SU0mqW6SKQypkpKJIek11FalAkn5Yrtde+nd58nz9PHsjSaN+eR1cTf1dFWWuY5UKOqkSk75leo/3fI2joqJUzy/X6yt9k2S7Lnv27GlSXdSSDz/8sMnP999/v7v6TnpCnU3jf6mW8Xy/SEWfp3N9X5/NZ9n7uO677z7Vi0pIlZu8r6jBBRdcgFmzZp3xdZPP36ZNm9z3u/qFuVxyySW4+OKLeVqJiIIQh+8REZHf8m5OLheV0mjXe7lnKCWBlOc63sulka9nAPPmm2+edh88hxC15MiRI01+HjZsWJOf5QItPj5eNek+EwmgXEMNXSRkcTWDlmbRZ8vzmMXq1atbdczex+U9/EeGTUozbddMiGfjoosuQu/evd3PJUP4pkyZgldffbXJufActnXllVeq5u8yK6PVasXvfvc79zJpqixD5mRYoKzXWhIcuYI3vYbrwrsBvAQgLZEhYJ68AzLP10/elxKCyLG4hubJkDLX8/385z9X58W1zPM8Sygojb31XmMJoFrzvvZ+n53t+zo7OxutJc8VGxvbqvPSWm39LJ/pfe0aHtgeXnjhhVbNvuerWvt+lmGjnhMMyDmVz5b3+0Ua/BMRUXBhpRQREfmtMWPGqIt2z8DJVe0gwZP0tnH1LZHKDc9KKRepOJLttJWrP8vZkMCgrVw9globgnSElo75XI5Lb1uu/kpi2bJlqseQZyglFS+u/liu11yqt2QGN+lxI0GUi/QXkgov6askPblay/Miu6XQ0Pt+CRhb+/qd7rVz9ZXyrGBz9ZOSwE76I0n/J1co1VI/KX98X3f2e7qz3teBpq2vG88pERG5MJQiIiK/JUGT51/qpfnxp59+6g6iXBVREjy5Lu5lnW3btrkfIxf2nuGFXOx7hgISZkiFTEu3H/zgB2fcT2lK7kmGk3k6evRoq6qk2qI1F3+ex+yqGDndMf/xj39U6/Xs2bPJ4zybjLuG3bWlSkpvCJ9UWsg0866ha0KvwkRCpIceesjdAF6GaEkDZc999WzUfibSeNuzwsj7GOV182xgL9VhnsMpz5XnEDwJpFzBk6vxu+urdyjl+Tjv17hv376nfX3l1ppqsjO9rz1fq65+X5/NZ/lM72vP5vS+orUhT1eFQdJU3fP3rHxmvCsPO/L9QkREvouhFBER+TXPoXdy0ekaxuY9tM+1ngwhca2jt55nTxSpnPjJT37iHj7lIo9/+eWXMW/evFbto8xw5VlB8Nxzz7n72chQrAcffBAdxfNCUEjvIW/efWB+/OMfqx5GnuS8ybA+z7BCjstzCI4cl8y6J+SCU2aTOxcypEt61rg8/PDDTYIVGY7n6YsvvlAzvrn6KskFuAQM11xzjVr/bIZcukyfPr3Jz7fddpsKJeR1k+3Ie8Dz/eHdI+pceYZL8tp5h1Ku8yNDFj2Hq3mHUp6v8YEDB/DEE080G3YowZ+8hj/60Y9atW8ynNLTX/7yF3e4Ku+XRx55BL7yvj6bz7IMHfU+Ltd7Sh5/Lr3SOuN8nK7/VmvXa2/y+89zpj/57HjOzvj++++rWRaJiCj4sKcUERH5NQmV5KLxTE3QvX9u6f477rhDbc91gS9TtkuDZOlVJJVXEiZJ5YQMJfOuMGqJTHEvjZWXLl3qvhiU/jtSvSXPs2/fPnQUz+bfQpoSy9A2qR6TgEmqjWRIm9znqiCTyqJevXqpKjSZzl0qnr755psmPWGErCM9neT8iBMnTqjjkunsJZyShtnnSvbPFcR49hWSoX3eVR8Sttx+++34zne+g6ysLFXJYzabVYgkDdJbOien873vfQ//+Mc/3KGEnCPphyMX2dKnyJO8P37605+iPXn3lZKG0XqhlGfA5N1PSkggI/2LXKHRAw88gGeeecbd20eapUv1imzHMzw4HXnPyP65hszKe0Qqy+R9I58R795M7cn7NZTzIVWP8rrceuutuOKKK9r8WZZtyWfzs88+Uz/v3LlTDZOU6kupBnMFr75Ezoergks+dzJ0WSYdkPeCVDa6JiBo7XodQRrGr127tsnECM8++6x6LaSysaWebUREFNhYKUVERH6tpbCppUqpM60nw3xk1i7PJs8nT55Uf8l/6623VEWKXMSebd8bGXrm6v/jmu1LmvpKICWhkARXLp59ss6VVPp0797d/bMER9JTSZo+u2bVkwtSCaJcPbiEhBfr169X1VFyce4KpLybE//9739vcq5kiJscl1zwSiWNDGc7FzLDnGffKNf+eg7t8ybBjQwFktdRjtUzkJKQ7WwquOTcybny7p3jHUhJBcrChQvbtQm2Xl8pIQ3BXU3FBwwY4J7x7nT9pOT9Ja+l57oSrkhIIOfJVf11tu/rl156qcn7SxqxS+8uCaTuvvvuJuu25/ta+ol5Vv1I8CQzLcpr5Xq9z+WzLK+lDDlzKSkpUccl56y1FZKdybP/mqtqUPqwyfnwHBrc2vU6gsywJzM0ukgIJb9bJHSWYbfymnbU+4WIiHwXQykiIvJrMnOdd28bqXrwDHmEXGB69gdq6bFi8ODB6oL1b3/7mxrKI4+Vihu5yJVKA6kO+te//uXuX9Uasg2pKJG+NbJvUh0gIdWTTz6pZlCTi2WX1NRUtJeoqCgVEknwJTPhtdRTRsIjaQD/4osvqmFPsg9STSV9u6R6Qqqq/vznPzerEpFAQqocJICQx8iFpJxnGeL0zjvvnPOFpQQwM2fObHKfVPLoVXTI/VLVJFVp8homJiaqsEHOgYRFcu4liDvbxvYyFE4qan71q1+pKjA5j/J+kAtpqc6RChCpppk9e/Y5Hevpnt/TuHHjmoSDrqqpltZ3kaoqCZ9kVkIJaeU45PxIUCfna+7cuSqMWblyZav3TT4PEizMnz9fvcfl/SKVSDIM0LtqrD3f1/K5fffdd1Xw6TlrX3t9lqXSTo5rzpw56n0kxyVhn1RbyTnyNRLoyDmXc++a1OFc1usoUo0lw/bkcyO/A5OTk9U53r59e5OQUd6XnqEgEREFLoPGWlkiIqIOJ8OvZAiY3oW5zBb3y1/+0v2zXLTJECQiXyc9maQyTQIuT/LPSxlGKQGIy4cfftisXxMFFwm19f4QIGGpBL6uXnYy/FRm0iQiosDHUIqIiKgTSF8mqcySKpfs7GxVYSQz00l1klQJuMhQI6m6cc0cSOTLpEpOAgRpOi+Vf1LdIkPdZOin52x8UqW1efPmLt1X6nqXXnqpqjqU94z8PhT79+9XQ0tdfdOkmlOq4GS4HxERBT42OiciIuok0rNHhvC5GkN7k/5Aa9asYSBFfkX6MkkVlNz0SAWM9C0iEtJgfsmSJbonQ8J46b/HQIqIKHiwUoqIiKgTyMxxMhOYq4JEGkJLSCX9aqS/yzXXXKOaEHtPdU/ky0pLS1Wz+w0bNqiKF3lfS6WLNFSX2eqkz5bczqZ5OgUuaTz/6quvqh5eMumCDP+UnmbS30uGdsqQT8/G9EREFPgYShERERERERERUafj7HtERERERERERNTpGEoREREREREREVGnC7pG59K/49ixY4iJiVE9D4iIiIiIiIiIqP1omqZ6B6anp8NobLkeKuhCKQmkevXq1dW7QUREREREREQU8LOu9uzZs8XlQRdKSYWU68TExsbC36u+ZJab5OTk0yaPRMGOnxUifl6I+P+WIGGzAQcPAvJvY3PQXer4LKemodhiQXJMDIwcrULU8mfFZkNxZSWShwyBMSzMr89UZWWlKghyZTAtCbrf1K4hexJIBUIoVVdXp46DoRQRPytE/H8LEf8dFvQklIqOBuRiLiQk6E+HL4VSdSYTYuPiGEoRne6zYrWizulsuMb381DK5Uxtk1heQ0REREREREREnY6hFBERERERERERdTqGUkRERERERERE1OkYShERERERERERUadjKEVERERERERERJ0u6GbfOxuapsHhcMBut8NXZ9+z2WxqBj7OvkfUeZ8Vs9kMk8l0xpkkiIiIiIiIqGUMpVoIo8rLy1FcXKxCKV/eT7nYtlgsvDgm6uTPioRSKSkpiIuL4+ePiIiIiIioDRhK6Th+/LgKpWJjY9VNqiJ8sSJCLrSlistX948oED8rrm1VVlaiqKgItbW1SEtLa7d9JSIiIiIiChYMpbxIZVRFRQWSk5ORlJQEX8ZQiqjrPisxMTEICwtDSUmJqpiSyikiIiIiIiJqPTY69yJ9Z+QCNioq6ixOIxEFI/k9Ib8v5PcGERERERERnR2GUi3gcDgiOhP+niAiIiIiImo7hlJERERERERERNTpGEoREREREREREVGnYygVZEONWnPbsGFDV+8qEREREREREQU4zr4XRBYtWtTk54ULF+L9999vdv/gwYM7ec+IiIiIiIiIKNgwlAoi8+bNa/Lz1q1bVSjlfb+3mpoaREZGdvDeEREREREREVEw4fA9amLy5MnIzs7G9u3bMXHiRBVGPfjgg2qZDO175JFHmp2xzMxM3HLLLU3uKy8vx49//GP06tULYWFh6N+/P5588kk4nU6ecSIiIiIiIiJipRQ1V1paissuuww33nijqqLq3r37WZ0mqayaNGkSjh49iu985zvIyMjAli1b8MADD6CoqAhPPfUUTzsRERERERFRkOPwvbNUVlbW5pMtVUdSNaRHKos0TWvVdrp164aOdPz4cfzrX/9SgVJb/PnPf8b+/fvx5ZdfYsCAAeo+2VZ6ejr+7//+D/fff7+qoCIiIiIiIiKi4MVQ6ixdcsklbT7ZP/vZzzB79mzdZdddd50Kplrj888/R0eS4OzWW29t8+Nff/11XHjhhSo8Kykpcd8/depUPPHEE9i0aRPmzp3bTntLRERERERERP6IoRQ106NHD4SGhrb5zOzduxdff/01kpOTdZefPHmSZ52IiIiIiIgoyDGUomYiIiLO6qw4HI4mP0szc6kok8owPQMHDuRZJyIiIiIiIgpyDKWo1WQ4nvcQQ6vVqpqXe+rXrx+qqqrUcD0iIiIiIiIiIj0Mpc7S+++/j3NpdN6SN954o9WNzruKhE3SD8rTv//972aVUtI365FHHsG7776L6dOnN1kmoVZ0dDTMZr71iIiIiIiIiIIZk4Gz1FEz38XHx8PX3XHHHbj77rtx7bXXquF5X331lQqekpKSmqz305/+FKtWrcKVV16JW265BaNHj0Z1dTW++eYbFb4VFhY2ewwRERERERFRe6q3WmG121tcHmo2I+wc+inTuWMoRa125513oqCgAM8//zzWrl2rZtiTyrGLL764WUXYxo0b8fjjj6uZ+BYuXIjY2FjVS+rRRx9FXFwczzoRERERERF1qKKSEhQWFam+x4eOH1f3ZaSmwmg0qu8z09KQmZ7OV6ELGTRfHzPWziorK1UoUlFRoYISb3V1dSp46dOnD8LDw+HL5KWz2+1qKJzBYOjq3SEKus+KP/2+IGot+UebzJKakpLi/gcbEfGz4jdsNqCgAAgLA0JCunpvqJFT03CyogIpcXEw8rqFuqBSSlrObNu5U92XO3QoTCaTT1ZKOa1WnCwtRUp2NozyeyyAsxcXVkoRERERERERUcCRwEluEkqFNQbVMZGR7lCKuh7/BEpERERERERERJ2OoRQREREREREREXU6hlJERERERERERNTpGEoREREREREREVGnY6NzIiIiIiIiIvKJmfJa4msz5VH7YChFRERERERERF2qqKQEhUVFcDqdOHT8uLovIzUVRmPDAK/MtDRkpqd36T4yOGt/DKWIiIiIiIiIqEulJSUhMT4eDocDNodD3ZeTlQWTyeSulOpq/hCc+Zuuf1WJiIiIiIiIKKjJ0Dy5SSgVFhKi7ouJjHSHUr7AH4Izf8MzRkREREREREQUAMGZv+Hse0RERERERERE1OkYShERERERERERUadjKEV+Y/LkyerW1sdmZ2ejKz3yyCMwGAytWnfRokXIyspCSEgI4uPjz/n4iYiIiIiIiHwNQ6kg4wpGSkpKdJdLcBPMwcexY8fUOdqxY0eX7UN+fj5uueUW9OvXD8899xz+/e9/++y+EhEREREREbUVG52T33jvvfc6/Dkk6Hn00UeRmZmJkSNHoits2LBBTTH617/+Ff3792/x+H1hX4mIiIiI/EW91Qqr3d7icpk5TZpYE3XV+9JptaK6rg6WqioYrdaG96U0Vw8LC9gXhaEU+Q35MAaDkydPqq+uYXvBdvxERERERB2hqKQEhUVF6g/Ah44fV/dlpKbCaGwYQJSZlobM9HSefOqy9+XBoiLYbDb0q6mBqXF2PylCkFug4vA9OmPVjgz3e+211/C73/0OPXv2RHh4OC6++GLs27fPvd7f/vY3NQ1meXm5+74//elP6rH33Xef+z6ZOjMmJgY///nP3ffJh++pp57C0KFD1ba7d++O73znOygrK2uyL3o9lQ4ePIirr74aUVFRSElJwb333ot3331XPa/su7e8vDxcdNFFiIyMRI8ePfCHP/yhybGOHTtWfX/rrbeqbcjtxRdfdK+zbds2XHrppYiLi1PbmDRpEj7++ONmz/PRRx+pbcnxyDC8Z599tlXvNPll8+tf/1p9n5ycrJ5fhuh5H39r9pWIiIiIiP4nLSkJowcPRk5WFtKSk9VNvpf75CbLibr0fZmYiOS4OOSMGIHRo0erW1paWkC/KKyUolZ54okn1F8QfvKTn6CiokKFOXPnzlUhjbjwwgtVuCRhzJVXXqnu27x5s3qMfHX58ssvUVVVhYkTJ7rvkwBKwhQJV374wx+ioKAA//jHP9S6EvhIs2891dXVmDJlCoqKivCjH/0IqampWLp0KdavX6+7voRcEijNmjULs2fPxhtvvKHCsWHDhuGyyy7D4MGD8Zvf/Aa/+tWvcNddd6ljEueff776+uGHH6r15BeDBEdybC+88ILaBznG8847T633zTffYNq0aSpUkkDJbrer9SVsOxMJ5xYuXIjly5fjmWeeQXR0NIYPH95svTPtKxERERERNSVD8+QmfygPa7zGiImMVH9cJ/KV96XdZlPXgSGRkUHxojCUaq26CuBEHnxKymDAHNUpT1VXV6caaruGkHXr1k0FQd9++61qjj5ixAjExsaqcEZCKU3TVEB17bXXYtmyZSqIkg+WK6iaMGGC2o6s85///AdLlizBnDlz3M8n1UwSIL3++utN7vck1UcHDhzAihUrcM0117gDrlGjRumuLz2YJPCZP3+++vn2229H79698fzzz6uwSUIj+SpBz/jx4zFv3jz3Y+V47r77brVf77zzjnsWPXk+qfB6+OGH3T2f5PGyvhxrRkaGuk/Og4RfZzJjxgx1niWUuu6665DUwl9rTrevRERERERERP6AoVRrSSD1wqXwKbe+A6SP7ZynuvXWJj2NXJU5EgpJKCVBk1TpbNq0Sd2/a9culJaW4he/+AXefPNNfPLJJ7jkkktUUCPru/olSegkQ+FkmeeMgFKNJCGWVD21FEqtXbtWDcGT4XsuMlzuzjvvxP33399sfdmeZ3gjxyPVTXIMZyJB0d69e1X4JMflSYYyLlq0SFWKSRglwwclXHIFUq7KpunTp+Ptt98+43MRERERERERBQOGUtSMqwrIk2fA4qqUEp59nySokuFqtbW1KnySsa85OTmqikp+luBJKqNk6JyLBD0yHFD6QZ2u6bce6Scl/Zq899dzxjpP0g/Le105jq+//rrF5/DcT3HzzTe3uI4cR319vTr+AQMGNFs+aNAghlJEREREREREjRhKBRmpJBISnOipqalxr+OppXHWUhnkcsEFF6iZAqQqSkIoVzWVfJWf8/PzUVxc7L5fSHWRBFIyfE+P9GVqL605hpbIfor/+7//w8iRI3XXkUosCaWIiIiIiIiI6MwYSrVW9yHArWvhcz2lzpL0UBK7d+9Gr169mgVShw8fVk2620KGwsmQOAmg5PbTn/5U3S9NzZ977jmsW7fO/bOLVDp98MEHqsdURETEWR+LzKYnoZJnBZTnrIDtUSXm2k8hfbOmTp3a4uMlRJPjcFVWeZJz3p5a2lciIiIiIiIif8BQqrXC44De4+FTpMLHbj+rh0j/IwmOZGY3mTVOekG5/Pvf/1YzxUkD7baQCquxY8fi5ZdfxqFDh5pUSkll1t/+9jcV7nhOaSlD+f75z3/isccew+OPP95ke7Iv0iDd1X/Km/Roev/997Fq1Sp3o3NpyC4BWFtFRTU0ji8vL29yv/S4kn3/4x//qHpcSVWUJ6kAk0BKqrFkv6T5upwD17BH6bElvabaU0v7SkREREREROQPGEoFGRkqJzO2ScNuqViSJuGRkZHYsmWLCpOkSuqqq65q8/YlgHriiSdU83LXbHPynNJPSSqFbrnllibrT5o0Sc1g9/vf/141E5fnDwkJUZVG0gT9r3/9q5qFTo887h//+AduuukmNROghF0yDNA1/LAtlUQSPEkI9q9//QsxMTEq+MnNzUWfPn3ULIES2Mlse9L4XZqsHz16VDVjlwqq1atXq208+uijqgm7nIvvfe97Klz7+9//rh7Xmv5V7bGvRERERERERL7uf2UyFDQeeughLF68GA6HA7/5zW/wk5/8BF9++aUKU6TqyLN66my5qqNkJj7P7XhWTXmTUEWqtKSp+YMPPogHHngAH374oZopT4b1tUSqlWQ9qfiS8Oq3v/2t2v4vf/lLtVyvN9aZSCD20ksvqYqnu+++WwVeGzduVMsmT56s+mWNGTNGhWH33HMPXnzxRaSmpuLee+91b2P48OGqKkoqpyQA/O9//6vO7cyZM896f9q6r0RERERERES+zqC1pstzJ5FqmWXLlqmG2NKXR4KNJ598UlXZuEgw4H3hLRUzEmy0RmVlparikZnSpLrFmwz/KigoUNUmbQk1OpO8dFKFYzab2V/Iw1NPPaVCoiNHjqhqJqKO+qz40+8LotaSiR3kjwRS5Xouf6QgCnT8rPgomw0oKADCwuQveF29N9TIqWk4KTNux8XB6AN9UeWP85t37FDfXzhyZIsTIlHgvD7tvc2O2sdN27ejuqYG02fMQEhkJPzZmbIXF5/616aETd///vexdetW1StIZnKT4VzV1dVN1rvzzjtRVFTkvv3hD3/osn2mruU9i6CEBM8++ywGDBjAQIqIiIiIiIjIh/lUTynpw+NJhkbJX2q3b9/eZMY26YEkQ6aIZs2apZqJjxw5UiWwMixRKu2ktxQRERERERER+S6fCqW8ScggEhISmtwvgYOEDxJMSVNu6SEkQZWe+vp6dfMsIXOVXMvNm9wnQ31cN1/n2kd/2NeOIJV0zz//vHpPSLnjkCFDVMP2G264IWjPCXXeZ8X1e6Kl3ydE/sj1/0G+p4n4WfFL8v9j+X+960Y+M3xP/b/FR14T1/64vjf4yH5Rx70+7b3NjtxH7TR5hT9p7f6bffkAfvzjH6tG19nZ2e7758yZg969eyM9PV3NZPbzn/9czeomvaha6lMlTaa9FRcXq6Fe3mTIoDy39J+Rmy+TN6wEMaI9++T4kx/84Afq5s3XXzsKjM+KvM/k90VpaalqPE8UCOQ9LX8Uks8Ne0oR8bPid+TfgFVV8pdpwOyzlzpBRy62K6qr1cW2T/SUcjpR3Vi4cLKyEib2UPQpHfH6tPc2O3If6+x2nCwpQYif96y1WCytWs9nf1NLb6lvv/0WH330UZP777rrLvf3w4YNQ1paGi6++GLs378f/fr1a7Ydmcntvvvua1Ip1atXLzUzWkuNzuXkSUNkufkDXgwTdc1nRX5HyEV7YmIiG51TQIVSEt7K/ycZShHxs+KXjc6lHy0bnfsUVUkCINmHGp1HyXsEQEpsLBud+5iOeH3ae5sduo9OJ1KSkvy+0XlrJ4LyydRFKl/eeustbNq0CT179jzturm5uerrvn37dEOpsLAwdfMm/9DW+8e23Cf/GHfdfJn8Fdu1j76+r0SB+Flx/Z5o6fcJkb/i+5qInxW/Jf8/lv/Xu27kW/9vabx1Nc3jWs9X9ok69vVp72125D4aTpNX+JPW7r/Z1y4c77nnHixfvhwbNmxQ06yfyY7GaRilYoqIiIiIiIiIiPyD2deG7C1duhQrV65ETEwMjh8/ru6Pi4tDRESEGqInyy+//HI1XEZ6St17771qZr7hw4d39e4TEREREREREZE/hlLPPPOM+jp58uQm97/wwgu45ZZbEBoaig8++ABPPfUUqqurVW+oa6+9Fg8//HAX7TEREREREREREfl9KHWmqdolhNq4cWOn7Q8REREREREREXUM/+6cRUREREREREREfomhFHUIGW6ZmZnZ5sdGR0ejK7344otq5oPCwsIzrrt27VqMHDlSTXkpjykvLz+n4yciIiIiIiIKBgylgshrr72mQhOZ3dDbiBEj1LL169c3W5aRkYHzzz8fvqampgaPPPKImqmxq5SWlmL27NmqEf/TTz+NRYsWISoqyif3lYiIiIiIiMiX+FRPKepYF1xwgfr60UcfYebMme77Kysr8e2338JsNuPjjz/GRRdd5F52+PBhdbvxxhvP6rmee+45OJ1OdCQJeh599FHd5vid5bPPPoPFYsFjjz2GqVOntnj8vrCvRERERERERL6EoVQQSU9PR58+fVQo5emTTz5RTeavv/76ZstcP7sCrdYKCQlBMDh58qT6Gh8fH5THT0RERERERNRWHL4XZCRc+vLLL1FbW+u+T6qjhg4dissuuwxbt25tUuEjy2RY34QJE9z3LV68GKNHj1ZD1hISElQVlVRTedLrqSRD3ebPn4/Y2FgV4tx888346quv1Palh5O3o0ePYsaMGaq/VHJyMn7yk5/A4XCoZdLrSe4TUoEk25CbDJFzyc/Px3XXXaf2Ufo9jRkzBqtWrWr2PDt37sSUKVPU8fTs2RO//e1vW1XlJRVPcgxi7Nix6vnluL2PvzX7SkRERERERBRsWCkVhKGU9D3atm2bexiZBE/SM0puFRUVaijf8OHD3cuysrKQmJiofv7d736HX/7yl6qP0h133IHi4mL8/e9/x8SJE1XY5V0x5CIhz1VXXYVPP/0U3/3ud9U2V65c6Q51vEn4NH36dOTm5uKPf/wjPvjgA/zpT39Cv3791OMl5HnmmWfU9zIUcdasWepxrv2WoEmCtB49euAXv/iF6vMkPbUk5HrzzTfdwxePHz+uhiva7Xb3ev/+979VQHUmDz30EAYNGqTW/81vfqOq0GT/vJ1pX4mIiIiIiIiCEUOpVrJYLdhbthe+pH98f0QYzxyetNRXSkIpCWMkoJJwSAKV7t27q2USmEivpG+++Qa33XabeszBgwfx61//WlUSPfjgg+5tSsgyatQo/POf/2xyv6cVK1aoYYJPPfUUfvSjH6n7JKS55JJLdNevq6vDDTfcoAIwcffddyMnJwfPP/+8epyER1IFJd/Lvs6bN6/J4+U5pEG79HwKCwtT933ve99Tx//zn//cHUo9+eSTKliTc3Deeeep++RcDBgw4IznUvZdqrkklJIqM6nE0nOmfSUiIiIiIiIKRgylWkkCqZvX6lf1dJUXp7+I4YlnV20zePBgVfXk6hUlw+eqq6vds+vJV6mOkgBHQiSpWHIFWcuWLVMVT1IlVVJS4t5mamqqCnFk5r6WQqm1a9eqPkt33nmn+z6j0Yjvf//7+PDDD3UfI0GUpwsvvFBVeZ3JqVOn1DalekmCNbm5SPWVBGsSJkkV1dtvv41x48a5AylXZdPcuXNVyEZEREREREREHYOhVJCRXkYSPG3atEkFTBJApaSkoH///mq5LPvHP/6hvpdlwhVK7d27VzVEb6mK6HTNvaXKKi0tDZGRkU3udz2vN+kB5erD5NKtWzeUlZWd8Rj37dun9lOqrFyVVt6kQbmEUrJfMkTQmwzLIyIiIiIiIqKOw1AqCEnItHr1ajU0z9VPykW+/+lPf6oqiaSaSmbs69u3r1omIZaEWu+88w5MJlOz7UpD8vait/3WcjUpl8boUhmlp6UwjIiIiIiIiIg6B0OpVhrQbQBeuvQl+FpPqbbw7CslodSPf/xj9zKZVU96MG3YsEH1Wbr88svdy6TnlFQgSUPvgQMHntVz9u7dWw3vq6mpaVItJVVNbSUBmR5XiCaVW1OnTj3jfkkFmLfdu3ejPbW0r0RERERERETBiqFUK8WExiCnew58iQRE0qj8bElDbhket2TJElUR5VkpJYGUNBR/+umnVa8pV4Dlamj+wAMP4NFHH8XixYubBC2yL9LLyTVLnzepWHruuefUzdXoXCqa5HnayhVulZeXN7lfhiNKE/dnn30W99xzjxo26Ekam7uGBkroJs3XZVZAV18pWS7npj21tK9EREREREREwYqhVBAKDQ3F2LFjsXnzZhVCSXWUJwmp/vSnP6nvPUMpqZSSmfckmCosLMSMGTMQExODgoICLF++HHfddZcaMqdH1pXQ5/7771fVUVlZWVi1apUKstpaSRQREYEhQ4bg1VdfVZVbCQkJyM7OVjcJu2Tfhw0bppqrS/XUiRMnVPP2I0eOqAbv4mc/+5lqnn7ppZeqsExmypPZ9KSC6uuvvz7rfWrLvhIREREREREFI2NX7wB1DVfY5Bqu52nChAnqqwROI0aMaLLsF7/4Bd588001c55UTEkIJeHStGnTcPXVV5+2R9SaNWtwww034KWXXsJDDz2k+lW5KqWkcqst/vOf/6iG5ffeey9uuukmvPHGG+p+CYA+//xzXHHFFXjxxRfVLH//+te/1H7/6le/cj9eqqhkWOHw4cPxxBNPqKqpBQsWuKu52lNL+0pEREREREQUjAyajLsKIpWVlYiLi0NFRQViY2ObLa+rq1OVP9I3qa1BSWcP3zObzX7bs2jFihWYOXOm6m/lCsOI/OWz4k+/L4haS4ZWywylMhRagnwi4mfFr9hsQEGB9KSQBqNdvTfUyKlpOFlRgZS4OBh94LrF4XBg844d6vsLR448p0mWyD9en/beZkft46bt21FdU4PpM2YgxGvm+kDLXlz4r03qNLW1tc0+dH//+9/VG1T6WBERERERERFR8GBPKeo00nRcgqnx48ejvr4ey5Ytw5YtW/D444+rnktEREREREREFDwYSlGnmTJlimqg/tZbb6lhT/3791eVUj/4wQ/4KhAREREREREFGYZS1GnmzJmjbkRERERERERE7ClFRERERERERESdjqEUERERERERERF1OoZSRERERERERETU6RhKERERERERERFRp2MoRUREREREREREnY6hFBERERERERERdTpz5z9l8Kmvr4fVam1xeWhoKMLCwjp1n4iIiIiIiIiIuhIrpTpBUVERtm/fjs8++wxvvvmmusn3cp/cZDmd2fe+9z1ccskl53yqHnnkERgMhrNat6Sk5JyfNxhNnjxZ3dpTYWGhek1efPFFdIRf/OIXyM3N7ZBtExERERER0f8wlOoEaWlpGD16NHJyctT3cpPv5T65yc+dRS7k5YLedQsPD0d6ejqmT5+Ov/3tb7BYLG3e9pYtW1SIU15ejvZWUFCA//znP3jwwQfRER5//HGsWLGiQ7btrzry9WyNpUuX4qmnnur05/3xj3+Mr776CqtWrer05yYiIiIiIgomDKU6gQzNi4mJUTf53vNn132d7Te/+Q0WLVqEZ555Bvfcc4/7YnzYsGH4+uuv2xxiPProox0SYvz1r39Fnz59cNFFF53zth5++GHU1tY2uY+hVMe8nu+99566tWco1bt3b/X6zZ8/Hx0hNTUV11xzDf74xz92yPaJiIiIiIioAXtKBanLLrsMY8aMcf/8wAMP4MMPP8SVV16Jq6++Grt27UJERAR8gc1mw5IlS3D33Xe3y/bMZrO6kb7q6mpERUW1y+mRfmntzVXh15Fmz56N66+/HgcOHEDfvn079LmIiIiIiIiCFSulOpGmaairq0NVVZWqPpGffcmUKVPwy1/+EgcPHsTixYvd90vl1C233KIuziUMkEqS2267DaWlpe51ZJjXT3/6U/W9VDS5hgdK/x/xwgsvqO2npKSoyrAhQ4aoKq3W+Oijj1RPp6lTp7rvk3OXlJSE++67z32f0+lEfHw8TCZTk+qeJ598UoVQct71ekrJ9xLEvPTSS+79luP1JNuT+2T7cXFxuPXWW1FTU3PGfc/MzGy2Lb1eSxs2bFDP+9prr+F3v/sdevbsqc71xRdfjH379jV7/LZt23D55ZejW7duKkAaPny4qibzlJ+fj+uuuw4JCQlqWxJCeg9Jcw3n3Lhxo+rZJa+PPHd7vZ5tPU55zJo1a9R70fXcci5P11OqNccrAadUfw0YMECtk5iYiAsuuADvv/9+k/Vc77WVK1c2OyYiIiIiIiJqHywX6STFxcXIy8tTvZEkPJHZ+JKTk5GVlaW++goZEiV9m2TI1Z133qnukwt2qRiRIEYCqZ07d+Lf//63+rp161YVEMyaNQt79uzByy+/jL/85S8qMBKuY5PAYujQoaoKSwKi1atXqxBEzsX3v//9Mw4jk+cYNWqU+z75ecKECdi0aVOT8KyiogJGoxEff/wxrrjiCnX/5s2b1WOjo6N1ty/DGO+44w6cd955uOuuu9R9/fr1a1Y5I+HM73//e3zxxReqv5UEMhJ4tacnnnhC7f9PfvITdSx/+MMfMHfuXBVCucjrIRVt0ovsRz/6kXpNpLLtrbfeUj8LeW3k/PTo0UM17pbgSoKgGTNmqEb7M2fObPK88lrIa/WrX/1KBXRSSddRr2drjvOhhx5S9x85ckQ9v2jp9WvN8cpXIWGbPLfr9a6srMTnn3+uXlPPJvoSPMp7QN5H995771m9hkRERETBqN5qhdVu113m1DRYbbZO3yci8n0MpTopkJKLbbnYDwkJUZU8MjROZt2TC2+Z6ctXgimpXJEL8v3797vvk7Dh/vvvb7LeuHHjcNNNN6kqpgsvvFBV6kjzdgkxJABwVbW4SCWO53DAH/zgB7j00kvx5z//+YwhhlTASPVLbGxsk/vleSWAkObs0ptLwifpN9S9e3f1vYRSEpJIsCCBWkvmzZunhgZKJZh8r0dCreeff979s1SJyc/tHUpJJd2OHTvcw96kEkqCpm+//RbZ2dlwOBz4zne+owIpWU8qt1w8K+/kMRkZGWqWR1fPMnkdpSro5z//ebNQSs7vunXr1HvTpaNez9YcpwREEjCVlZW1+Jp4OtPxukKpt99+W1WYSah6JvJ+kCCZiIiIiM6sqKQEhUVF6t/fh44fV/dlpKaqP0TKv1PjYmPRs/EPnURdMmqpvh52pxPlFgsS4uJaPSM7dSwO3+uEN7+EKjLUS6pNpKpE3vxy4SxBlNwvy31pKJ9UpHjOwucZPkiYIEPpJJQSUmHSGp7bkCBOtjFp0iRVgSU/n44EQBJaeJNQSkIaqaQSEkTJfXKT74WEHDL0Tu47F979rGR7sl9SadOeJDzz7MPk2m85T+LLL79U1XbSlN4zkBKuX6qnTp1S/cGkukteRznXcpP9lVkW9+7di6NHjzZ5rFTFeQZSHfl6tuY4z8bZHK+cM6mqkvvORN5zsh0iIiIiOrO0pCSMHjwYOVlZSEtOVjf5Xt03eDCSdP49T9QZisvK8PHXX6Pg2DEcLCrCxi++wEc7dqj7qesxlOpgrgv2OJ0kVn6W6h9Z3poL+c4ivZek8sjzol8qUaQCScIICdNkKJto7X5LtZL06ZFhVRIMyDZkmGBrt6EX2kklT2RkpDuAcoVSEydOVEOyJEBzLZOKmXMhVTieXCGZVPK0pzM9j6uCTaqJWiK9meR8SX8wOc+et1//+tdqnZMnTzZ5jOv1bK1zfT3b83yezfG6ZhMcOHCgmmlS+ma1NNukbJN/PSEiIiJqnbDQUMRERqpbWEiIurl+lltoSAhPJXU6CZ627dyJ4yUlCDGbERkWhoiwMBSVlqr7GUx1PQ7f62D1UiJot6the3qkWkSqO2Q9XyA9fCRU6N+/v/s+qUCRaiS5gB85cqSqpJKyXBmuJV/PRIIUaWQt/bNkeFevXr3UcctQKukXdKZtSDNqvbBCzqkMfZS+UhJMHD9+XIVSEp5JQ2sZMimhVHv07WqpiuhMFW4thRpS4aW3zbY+jyfX+ZR+TVIppMfz9RVnM9Piub6e7XWcbTleCSxl/6WBufRNk95gss//+te/VJ8pT/Kec/XSIiIiIiIiPxy1VFiImro6JMXHo7bxmlsC1PCwMBSXlyP/4EG1jH+M7joMpTqYDNOTIXsSkugFU9LwXJa7+uB0NWn6LVwX93JhLr2GpMJEmmC76A1/aumDLE2wJXSTmdA8K2TWr1/fqn2S8GPJkiUqLJOKM08SQklfpw8++EAFCLKu7Ic04ZZASm7SFPxMOuqXkFQAec4E6CKzyknPorPlasAuwxI9ZyP05NquvN9aWqc1Our1PNfnb8vxyv+QJBx29c+S4YNyk6pACaqkAbp3KCXDJEeMGHHOx0FERERERJ2voqoKJeXliIuK0h+1FBWFkrIytV68x0ihrmCz2fD2hx+qvmz9vPr5BjoO3+tgEqJIWCKBincViPwsPYlkuXfY0hWkL89jjz2mhnLJTGieFS3e+/7UU081e7wM5RLeIYzeNuR8vPDCC63ar/Hjx6vHbt++vdkyCaUkIJH9kSF6rl82cr8EbMeOHWtVPynZd73w6FxJiCQzFEr46CKz5B0+fLhN25Mhi/L6yPF676/r/MqsgJMnT8azzz6rmunrNd5vjY56PVtLnr81QwHP5nilz5QnqfqTKirvSkV5XqmoOv/888/pGIiIiIiIqGvU22ywOxxq2J6eULNZLZf1ukqlxYIXXn0VV91yCx77y1+w8p13fKrfdGdgpVQHk5BEqndcvaWkWkMu6uUiWIbtSU8kV3VPZ3rnnXdUg3XZnxMnTqhA6v3331ez10kFTHh4uFpPel5JJckf/vAHld7KjGgy7EmqSLyNHj1afX3ooYdw4403qsqVq666CtOmTVPDu+R7mTlOqlOee+45FSbohQjeJGySIXxSDTVlypRmgZVUmu3evRt33XWX+37Z52eeeUZ935pQSvZdti/D0dLT01XwI0MDz5VU37zxxhtqqKMMg5SgY/Hixe6Kp7Mls5fIccm5lKGUUu0jM/HJaykNvN9991213tNPP63Om/RNkibmUk0kr/Mnn3yihmh+9dVXZ3yujno9W0ue/9VXX8V9992HsWPHqgBJnlPPmY5XZvoTUkEnAZZsWyqmpPeYvD4ye6AneS/I/wyuueaadjseIiIiIiLqPNLXzGwywSbtdHSCKavdrpbLep3tyLFjWLpiBVa9956aFdDlaFER8vbtwTTtzC1RAgVDqU4g/Ywk4JDp5aVpuARStbW1Kkxoj35HbeEaiifhglycy8W8VN9IyOHZ5FwsXboU99xzj7rwlwt1CSUk1JLwxpMEB1JpJf151q5dq3r9SHg1aNAgdeH/8MMPq74/qamp+O53v6uO+7bbbjvjvso+SuXW66+/jscff7xZNc2oUaPw2WefNWlm7gqipN+RBG1nImGUhFqyj/La3Hzzze0SSskwyD/96U9q+zJj3pgxY1Sl1P33339O25ShcjKkUrYt51lCLgljXIYMGaICF1nnxRdfVBVCEhrJufIchnk6HfV6ttb3vvc9FSZJBZb0fZLXsaVQqrXHK+9jGX4owap8DmWbv/3tb1W/NE/yXpP3U1vDQyIiIiIiX1FvtaoApiVSMSR9lnyFXHNKUGN3OlFusSBBZ9Kw1oiLjlb9oqSpeWJsbLPnqKyuVrNGynqd5eu8PCxetgwbPvmkWS9eLVyDMceMl1NWYHTR+ZjaX79fbqAxaEFWGybD5WSonFQuSRWQN5mxTS68pVLGVS3UXqQqSSow5KtU+EgYdC4VUq4+OVIpFOiN2Q4cOKACPAnDpMk2UUd9VqRhvnz+X3nllTNWSnXk7wuiriL/QJIZKyXYlepMIuJnxa/IMByp6Jd+rV1Q/eBvF/8dQSb02dxYpX7hyJFqlIhT03CyogIpcXEw+sB1i94+BrLCY8dUryL5f/yh48fVfRmpqe7/z2empSHTq+Cgq8hseHkFBcg7cEC9b9ISE5HcrRuyMjPV17ZsT2bZq66pgaWmBiajEamJibDU1iIyPBy5Q4e2abtn+x766NNP8d9XXsHXu3Y1W+bspsGRo8E5RBrlNtw3JjkHL1z+EgI5e3FhpVQnkoth14VrPDv8nxUZjnX77bfjiSeeYChFHUoqBqVykEP3iIiI6GwVlZT4zcV/MGuvShx/IdVAifHxKkixORzqvpysLHeQImGpL/AMkGS4nQRIEWFhqtKporq6TQGSrC+Pk6Dr1IEDqNc0NQufnJOs3r3bFEi1xRfffNMkkNLkv16AY7QGp878V58Xf4FdpbswOHEwAp1vvPsCnAwTkkbX8kvA1VBZ+km5fwmEhvrM7Hu+zNUjiqgjSfBJREREFMgX/8HMVYlTcOyYqsSR67RzqcTxB1KdJzd5X7r6J8VERvpUhZgEhfmFhaipq1ND7iQ4ErLf4WFhKC4vR/7Bg2rZ2QaI8rpOiIlBbV2dCiLHZ2efcxB5tsHmjddcgyXLl8MOO5yDGsIorYUuPt0QjztG3oaM2P/NdB7I+FuxE0jz58LCQvW9K3xyNV4WmZmZ6kZERERERP7LHy7+g1lHVOJQ+6ioqkJJeTnioqKahTvyc2xUFErKytR68V49kFs9aqnxWlwefy6BVEvBZrjJhP4ZGegWH9/sMSGxIeg9uxd2xR4AGiY5b2ZU3FDk2IajrzUDlw2YhZCQSAQDhlKdQBqay+xxLZFKKSIiIiIiIvK/Shw6d/U2G+wOh+4sea4qQ4uMPJK+cT4UbMrQ3LLycnz21Vc4fvIkRg8ahB/ccot7/QNVh7Do0HKsLvoA9WnWZtszGYyY1n0iFmTMwuDo/vhk23oknVoLlI8GIgN/6J5gKNUJpDqKw/OIiIiIiIgCsxKnPQVjw3ypLDSbTLDZ7brBlJwPWe6qQOzqYLNbTAx2HziAA4WFsErAaTAgOiYGn+floaamBl/V5mPhwWX4qPQz3W3FmKNwXc/LMafX1UgNTwFKd8O5+ZcYv/ctmDQbHNtTgfQ/IhgwlCIiIiIiIqKA5i+VOMHaMD8uOlpVqclQykSvmdokDKqsrlY922S9riKB5bHiYhw+ehTv7tmjfvbsFVdXV4OQlDBct/4HOBx6VHcbvSLSMDdjJmamT0OkMRQ4tB74ZhFQ9Ck85zw2fvMKMO3XQFjXBqSdgaEUERERERER+ZT2rhbyh0qcYG6YL9Vq0mxeentJRZsEiNLzS94HltpaRIaHq9nyumpo5aGjR7Fo+XIckf5jFRWSlP1vYQjgTNVgTXEg1GBEBSzNHp8Tn40Fva/F5ORcmGw1QN6rwM4lgOWI/hNGdANOFQBpwxHoAu/d3E4kjSUi4u8JIiIiIv+vFvKHSpxgb5gvzcKl2bw0ET914ADqNU31/pLXRQKpzm5CL++LL7/9FovffBObP/0U4RERGDR4MMxmM+xSURcJGHoY4UiR0ibA7DSp96vd0BCmmg0md7+ooXEDgYpCYMvvgT3LAQmmdJRFD8a+2EkYOedhhEQHfpWUYCjlJSQkRKWv1dXViIiI6JpXhYj8gvyekN8X8nuDiIiIiHy3WsjXK3GogQRPE2JiUFtXB7vTifHZ2UiIi+v01+X9TZuw6I03kLd3r/u+2poaVFksiE2PQ0U3G0zxHu9BDYhwRKA8tBymcCNu6zUbN0m/qLAk4OgW4JM/A4c26j+ZKRTofyUcQ+ZiR0GVaqIOY2AHkJ4YSnmRX3JxcXEoLi5GfX09YmNjVRLqi7+cJLm1S5mpj+4fUSB+VlzbqqysVLf4+PiA/6sVERERUSBUC/laJQ7pk3+vy4yIQprOd8W17tvr1jUJpDSTBmcWcHjUUfQzRiDGGYNaZy0cBgdMmkkFUqGhZlyZPRmzB1wmRVTAnlXAzkVA2X79J4lMBobMAQbPBiISAAlfC7Yj2DCU0pGamqqqpE6ePKkuOn2VXBxLeaCUsDKUIurcz4r8gygtLU2F2ERERETkH3ylEod827xrr1VD9rQIDY4RgGOEBkQBlbBgn3Uf0mvSEWuLhVEzIj4sFsN6DsSlQyage6gV2P5PIP91oL5Cf+PJw4Fh84E+0xqqpIIcQykd8gtJqh/kYlOSeamK8EVykV0qY6ITE91jq4mo4z8rUnEloRT/8UJERETkf3yhEoe6Xv6+fRjQp49u9V1snxhEXRuNUz0qm6UmllAL9ofuwxjDKIwzj8CMEVOQUFcAwxe/BQreB7SG4aZNGEwNIdSwBUD3kS3+Ib3OalXVe+UVFUiKiAiK9yZDqdOQN4BcfMrNVy+0pZdNeHg4QykiflaIiIiIiOgM1wWbtm3DyytW4ItvvsHjv/gFpk2a5A6FtpRux8JDy9RX9G7++FhzNK7veQVm97gCe3cWIqVsKxLW3wlDyU79JwyLAwbfAAy5CYhObXG/isvK1LBSae5vtdmw6aOPkJKaiqysLCQnJwf0a+qbaQsRERERERERUTuQftGbtmzBBxs3oqaqSipQ1P0ys96FE3Lx9vH1WHRoGfZXH9J9fO/IHpiXMRNXp1+CSGs1nDtfQeI3ixBmb2GIXrf+QPZ8YMBVgPn0E6gVl5Vh286dqsG5FMQYDQbVTqioqAgVFRXIzc0N6GCKoRQRERERERERBZxTZWV4/a238Nrq1Thy4oS6LyaiISTSIjV8nbQbF2+YA4tWrfv487qNwPzeszAx6TwYS3cDm38D7F8Do8OKhgGgngxAxuSGflHp49zB1+lomob8wkLU1NUhKT5efbU5nQgLC0NEVJSagC0/Px9JSUkBO5SPoRQRERERUZBXEFit1haXh8osaI39d4iI/MGBgwexdPlyvP3hh2o4HDTNvcyZqMExpmE2PUlEvAMps8GMy1InYX7GLAyO7gMc/BDYegtQ9Jnuc2khUTAMmgUMnQvE6Yz5O42KqiqUlJcjLiqqWegkP8fGxqKkpERVTEnf60DEUIqIiIiIKIjJEJHCwkLVa+XQoYahKxkZGe6epZmZmepGROTLpOrosx07sHjZMmz5/POmy6DB0M8IU64Jtr76j48LicHsnlfgxp5XI8UQAuxeBuy8G7Ac1V2/NiwFR5Knoe9FP4Apom0zctfbbLA7HAhpoY+1/FHAYrGoPx4EKoZSRERERERBLC0tTc1QK7NO26SiAEBOTo57Riq5KCIi8lUSqK9dv16FUXsOHGiyTDNrcA4GHDlASKL+77LMyJ6YnzETV6VPRYSlCPj8n8CeFYC9Rv8J08fBMWQutp2KAwxG9A2NbvO+h4WEwGwywWa36wZTUsUqfaYCuVqVoRQRERERURCTix25SSjluvCJiYnRnSadiMgX/XvJEhwpKnL/LP2iHCM1OIYDiNR/TG7CSBVGXZg4FsZjW4H3fgQc3qS/simsoWm5NC9PGAg4HEDZjnPe77joaNVLqqi0FImxsc0qvyorK9UfDuLi2laJ5Q8YShERERERERGRX5KhxnNmzMAfnnkGziQNjpz/9YvyZoIJl6dehAWZs5AVkQ7sWQWs/y1Qvl9/45EpwNA5wODZQHi3dt93g8GArMxMVFRXq95SMpRPwigZrldVVobIyEhkZWUFbJNzwVCKiIiIiIiIiHy+eXnf3s0biTs1JxJGd4NztgG2nk7dx8aHxCLXNBoXhufiyowBMOW9AuS/AdRX6D9ZynAgewHQdxpgDEFHSu7WDblDhyKvoACnKipUY/ba2lpVISWBVHJyMgIZQykiIiIiIiIi8sl+URs/+UT1i/p61y68/PTTGNC3oVN5raMOq4vWYdHBZSisOQL0bP74PlG9MC9jJi5PnoS9W1ehR8GLMO7YDmiO5isbzEDf6cCw+UDKCHSm5G7dMCEmBjU1NaiqrsbECy5AUmpqQFdIuTCUIiIiIiIiIiKfIcPXtnz2Gf7yj3/g6PHj7vuXLF+O7//gFrx8eDVeP7IG5bbKFvtFLci4Fhd0Gw7jgXehrbkZo0ry9J8sLB4YfAMw9CYgqju6isFgQHhoKBx2O+Lj4oIikBIMpYiIiIiIiIioy5WcOoWXV6zAC6+9huraWsREREhao5ZJv6iV5vexcvM62HUqnUIMIbg8bbJqXj7IFAfsehVYez9QWwLdeKfbAGDYAqD/lYA5vOMPjnQxlCIiIiIiIvJR9VYrrHZ7i8tDZbr4UP2p7on8xd4DB1QV1Nr162G321UgJTRocPYBHKM1aBmNK3sFUtIv6oaeV+LGXlchyXIc2P4fYN8awGlr9jyaxFMZk2GQMCo91x14UddhKEVEREREROSjikpKUFhUpHrrHGocxpSRmqpmHBOZaWnITE/v4r0kOnsyy9zW7dtVv6htX37ZdKEZMA43wTYO0BI03cf3jcpQVVFXdp+M8CMfAe/9CCj6XP+5QqJwtNsFOJIyDWNzp8NkMvEl8xEMpYiIiIiIiHxUWlISEuPj4XA4YHM0VIjkZGW5L6qlUorIn1itVqzdsAFLli3D/oMHmyzTojQ4RmgIGR4GQ6QBenHU+IQcLOg9C+dHD4Bx9zJg01VA1TH9J4vNALLnwdnvauzL29cxB0TnhL/BiIiIiIiIfJQMzZObhFJhIQ1T08dERrLSg/xWbV0dnnz6aTU01cWZrKkhes5BAEyAwasLlPSLujJtippJb6Bks98uAvbcDdhr9J+kxzggewHQayJgNAGNgS75HoZSRERERERERNQp4mJjcfW0aXjtrdVw9m3sF9VLf91uIXGqV9TsHlcgqTgP+Oj3wOHN+iubwoABV6vKKCQM7NBjoPbDUIqIiIiIiIiI2rVfVNGJE0hPTW22rMZRh9iJsbAla9C66T8+1ZSCi8Im4Ecjr0PUwfeBVfOB8gP6K0d1B4bMAQZfD4S3sEHyWQyliIiIiIiIiOicyTDTdR99hMVvvonCI0ewZuFCxERHq2Un6krw8uFVeP3IGlTaqwCd/Oj8xNGY13MGTHtPoWfJB4h8/QrAWqn/ZN1HAtnzgT6XAMaGoa3kfxhKEREREREREVGb1dTWYsXatXh5xQoUnTzpvn/5O+9gzPSRWHhwGd49sRF2rXlvp1BjCK5MvRjzMmZgQPUpaDteAgo/gG6bc4MZ6HdpQxiVMpyvWABgKEVEREREREREZ+1EcTFeWbkSy955B9U1/2s6rhk01S/qb3UvoX7bf3Qfm6D6RV2N2enTkHh4C/D+T4CSPK8W541kWN7g2Q3D9KJS+EoFEIZSRERERERERNRq+fv2qSF672/erIbsuWghGpxDAceohn5RdtiaPbZ/VG8s6H0tLo/LRlj+cmDLtUBtie7zaAkDYZBZ9PpfAZjD+QoFIIZSRERERERERHRaTqcTH3/2mQqjtn/zTZNlWrQGx0gNDhlR10J2NCFxDBb0noXxzjAYvl0M7P8p4GweWmkwoDQuB0e6T8ewC26CyczYIpDx1SUiIiIiIiIiXfX19Vizbh2WLl+umpd7cnbX4MjR4BwIwATdflFXpU3F/J5Xo1/JPuDjvwDHt+s/UUg0kHUtnFk34tsDpQ33GXQH81EAYShFRERERERERLoOHDqEx//+92b9ohyjNWg99R+TEBqPG3tehdkpE5F44H3grTuAqiL9lWMzGhqXD5wJhEbJFH4AGkMpCngMpYiIiIiIiIhI1+ABA5AzbBi253+t+kXZczQgXn/dAdF9MD9jJi6PyERY3ivAxj8D9lr9lXuMB4YtAHpNBAxGnv0gxVCKiIiIiIiIKIhpmoay8nIkdOvWbNnxupOIujwa1gu1FvtFXZA4FgsyZmJcdRUMXy0Gjnykv6IpDBhwDZA9D0gY0M5HQf6IoRQRERERERFRELLb7Xh/0yYsXrYMdfX1eP3ZZ2E0NlQtfVuxGwsPLsN7JzfBoTmbBVJhxtCGflHpl6Lv0e3Aul8CFQX6TxSVCgydA2RdB4Q3D74oeDGUIiIiIiIiIgoilqoqrHrvPbyyahVOlpS479/86TbY+zqx8OCb+KJ8p+5jE0O74aZeV2N2fA667VkFfDofsFr0n6j7qIZ+UX2mAsaQjjoc8mMMpYiIiIiIiIjaoN5qhdVub3F5qNmMsNBQnzm3JaWl+HDzZvzy229RW1fnvl8L0eDIBu4/8TjqLVbdxw6M7oMFGbNwGWIQuvNl4ODvAKmg8ibhU99LG8KolGEdeTgUABhKEREREREREbVBUUkJCouK4HQ6cej4cXVfRmqqewhcZloaMtPTu/zcfpufj4VvvIHVH3wAp6YhJiICMBigxWhwjGoIpGR4ngPNA6mJSedhfs+rkXvqCAxb/wWU7tJ/kvAEYMgNwJAbgciUjj8oCggMpYiIiIiIiIjaIC0pCYnx8XA4HLA5HOq+nKwsmEwmd6VUV5GgbOMnn6h+UV/l5Uk3cxVIqWWpGhyjNTgHSmVT88eGG8NwdfpUzE2ehL6Fm4C19wK1pfpPlJjVUBXV7wrAHNbBR0WBhqEUERERERERURvI0Dy5SSgVFtLQMykmMtIdSnWF2tparH7/fby8ciUOHzvmvl8zaDBkGWHKNcPWS/+xSaEJuKnXVbg+oh+65S8DPp4POG06axqAzClA9gIgbayquiJqC4ZSRERERERERAFi/ZYt+MMzz7h/1kI1OIYCjhwgJE6/v1VWTD/M73UNLq2zI/SbpcCJL/Q3HhLdMIPe0LlAbM+OOgQKIgyliIiIiIiIiALEJRMn4u8vvICT1pKGflHSa7yFUXWTknKxIG0axhbtgmHjk0BVkf6Kcb2BofOBgTOA0KgO3X8KLgyliIiIiIiIiPyIpmmorqlBdFTzgCiveh8ib4yGNaRYt19UCEIwo8c0zO82Gn32vQ+8/UPAXqv/RD0nNPSL6nUhYNDZGNE5YihFRERERERE5AesVive3bABS5YvR6/0dPzfL3+p7rc7HVhX/DEWHVyGryp26VZGJYcmYJxxNK6wRuL8gu0wfPS/IX5NmMKBgdcA2fOAbv07+Igo2DGUIiIiIiIiIvJhlRYLlq9di1dXrUJpWZm6b//Bg9h9eD+2Ob/CkkMrcKzuhO5jB8f0x/weV2BaWTEcX76IyPrj+k8SlQYMvQnIuh4Ij+/IwyFyYyhFRERERERE5INOFhdj3aZNeGjnTtRbre77tVgN9lFO3LTzh7CZ7M0eZ4ABk5JzsSDpQow59DkM6x4BrBb9J+meAwybD2ROBYyMCKhz8R1HRERERERE5EP9or7Oy8PCN9/EmnXroAGIiYgADAY40zQ4Rmtwyqg6I+BA00AqwhiGa9KnYV54P/Te+zbw2Y8Azdn8OYxmGPpeBgxbACRnd+LRETXFUIqIiIiIiIjaLVCpq6+H3elEucWChLg4ntlWcjgcWP/xx1i8bBm+3b1bTqYKpGAAHAM1OMZo0NL0H5sSloibelyB6+uNiMt7DSh9Wnc9qzkWx5IvRq/JP4QpJpWvDXU5hlJERERERER0zorLypBXUICCY8fg1DTVlDu5WzcM7N0bMHLmtpbU1NZi5bvv4uUVK3DsxP/6QmmhGozDTTCdZ4a9hWxP+kUtSJ2K6cUHEfLxP4G6U/orJg6Gc+g8bLWkwWkMRa/I5HN+vYnaA0MpIiIiIiIiOudAatvOnaiuqUGI2QyT0YiIsDAUlZaivKoK/Xv3RgqrpnS9+Oqr+O+rrzbpF+XI0eDIBsyhIbr9oiYnj8OCuFEYfeAjGD74NeC0Nd+wwQj0vrihX1TqGGhOJ5w7dvCdTj6FoRQRERERERGd05C9/MJC1NTVISk+HrX19er+sNBQhIeFqcCq8OhRDOzZU/VFoqauu/JKvPjG67B3d6gwytUvypv0i5qRfgnmGZKQsXs1cOJ/QVYToTFA1nXA0LlATA+ebvJpDKWIiIiIiIiozSqqqlBSXo64qCgYvEIn+TkmKgpllZVqvYTY2KA8006nE3a7HaGhoU3utznt2G7/FuF3RKIsqlL3sfGGWNyccQWur6pG3BdvAtVF+k8SlwlkzwcGXgOERHXEYRC1O4ZSREREREREHdzs2zusCST1NhvsDocatqcn1GyGw+lU6wUb6av19ocfYsmyZbj4ggtw94IF6v5KWxXePPoOlh5eieN1xYBOhjQkZgAuqMvENaf2o9fmv8LgqNN/kp4XNIRRvS5oGLJH5EcYShEREREREXVws++szEz1NRCFhYTAbDLBZrfrBlNWu131mJL1gkVZeTneWLMGr7/1Fk6Vl6v75PupV12IN06sxbJja1GrEzJJv6gpyeOxILwvRu77EMZjz+s/gTkCGHB1QxjVrV9HHw5Rh2EoRURERERE1MHNviuqq5E7dGhABlNx0dGql5QcZ6LX8DypGrNUV6NbbKxaL9AdP3kSv//HP/D2unWwNlaGafJfD6AkpwIzP7tbkqdmIkzhmJV6MeZaQ9Fr9wqg4hX9J4hOa+gVJT2jwlqYko/IjzCUIiIiIiIi6uhm3+XlyD94UC0LtKF8cjxSCSbBm/SWkqF8EsjVW62w1NYiIjwcmT16BNxxe77227/+Gk8//zy+3rkTMRERqqG7ZtTgHAA4RmvQUvUf2z0sCXNTJuPaU8cRu20RYKvSf47uOTAMWwBkXgwYeRlPgcOn3s2///3vsWzZMuTn5yMiIgLnn38+nnzySQwaNMi9Tl1dHe6//3688sorqK+vx/Tp0/HPf/4T3bt379J9JyIiIiKi4HWmZt+xUVEoKStT68XHxCDQSAWYVILJ0MVTBw6gXtNUMJeWlISBGRnQjIHX60gal3+weTMWyzXs3r0qgBNamAbHcMAxSgNaeKmzYwdiQewITD30JUI2/UXVU3nTjGaciB+HIynTMWrCTJhMpo4+JKLgDqU2btyI73//+xg7dqz6gD/44IOYNm0a8vLyEBXV0Pnt3nvvxRoZm/v664iLi8MPfvADzJo1Cx9//HFX7z4REREREQWp1jT7tjgcAd3sW4KpCTExqK2rU03ex2dnqybvErecrKhAoKiqrsbyd97BKytX4kRJyf8WdDPAdJ4J1hHygjcPmYwwYkpyLhYYkjFy73swnPpA/wkiEoHBN8I56Hrk7znSgUdC1PV8KpRau3Ztk59ffPFFpKSkYPv27Zg4cSIqKirw/PPPY+nSpZgyZYpa54UXXsDgwYOxdetWjBs3rov2nIiIiIiIgllrmn3L8kBv9i1VYTJcUUhFmPwsw9sCyW//+ldVIeXZL0qG6IX0C9UdohhpisCslImYU1WPXl+vBurK9DecOBiQIXr9LgdMoYDDAYChFAU2nwqlvEkIJRISEtRXCadsNhumTp3qXicrKwsZGRn45JNPGEoREREREZFPNvuurK5WQ9mCodl3oLv+yivx/seb4BwIOHL+1y9KZs7zlBaegrndzsOsEwWI+eQFQLM335jBCGRObZhFL3W06kVFFEx8NpRyOp348Y9/jAkTJiA7O1vdd/z4cYSGhiI+Pr7JutJPSpbpkb5TcnOprKx0b19u/kz2X/4H5+/HQdTR+Fkh4ueFiP9vaf3/L13f+2VTavl3sRyD69bJBvbujfKqKtXU3NbY7LvOakVVTY1q9q16KzWGVGfLqWn/e300DQYfrT7S20/XffK1PbbXEfvYbJ3GayyjVy+sCpsFX0TnQfuOCfYIu36/qJiBWBDeD1MLtyJk1z9119FCY4Gsa6ENmQPE9PBYoHXYsfvze8jXttmR+6gFUF7h16GU9Jb69ttv8dFHH51z8/RHH3202f3FxcWqabq/v8hSTSZvXO9flkTEzwoR/99CxH+HnQ2Hw4Hq6mr1/cmTJ/2zqbLdDlRVyV+mgRZ6O3UooxH9e/fGgSNHUFxWpi4yw8LCkBAbq2afk2bfbe2t5HA6Ud34x/aTlZUq8PJFevsp50Fm5pOLbeNZhp0dcdyn22ZdfT0+2LABy95+Gwtmz8bkCRPU/UfqirD8xDt4r2QD6pz1QETTbRphQLahP2bXmHDF8R0w1+r3i7LH9Eb1gNmoy7wMmjkCkOv2Ft4T7X3s/vwe8rVtduQ+1tntOFlSgpDwcPgzi8Xiv6GUNC9/6623sGnTJvTs2dN9f2pqKqxWK8rLy5tUS504cUIt0/PAAw/gvvvua1Ip1atXLyQnJyPWq6zW37j+giXHwlCKiJ8VIv6/hYj/DjvXUMo1uZD0dfXLUEqaiEuwJj2Nuqh3U0pcHPqlpWGdpqmLzNzGZt/nWnmmXp/GXk0psbE++/ro7aeqJJFG6HFxZx9KdcBx623zVFkZXl29GsvWrEFF48X0W++uRZ9xPbD40HJsLNmm+kd5izJFYmbiebjp1Cn02PceTJpV9zm1nhdAy54PY88JiDEYW5qU74z7eS5kMjHZgrwvQw0GFZb6YkVkZ73mvrS9Jtt0OpGSlISQyEj4s/BWhmo+FUpJxc8999yD5cuXY8OGDejTp0+T5aNHj0ZISAjWrVuHa6+9Vt23e/duHDp0COPHj9fdpvxlQm7eJMQJhCBHfokEyrEQdSR+Voj4eSHi/1vO/AdPaXshF67yh1zp6+qLF6ynJf8mln123bqIVE3IcD0hF/7tUUWhGQzu10OCnbMNdzzVW62q8XpLZKbAsNDQdt1P9W+xNux3ex633jYLDx3CyytX4p0PP1RN6tVyowbnIODrnD2444uf624jPTwFc2KyMevoLsRsW6j/RFIJNXAGkD0Phvi+Xh2nOvfYpXIvr6AAhUVFKiS02mxqtsSszEz11R/eQ+eivbfZkftoCJC8orX7b/a1IXsys97KlSsRExPj7hMVFxeHiIgI9fX2229XlU/yP0mpdJIQSwIpzrxHRERERNQ20toiLy8PBQUFKpyS0QlSjS+TCslXCixFJSUN4YTTiUON11wZqanui8jMtDRkpqcjUEkxxK49e/D+hg04dPCgO8DUwjU4hgGOURrQQj/6EbGDMN+Ygov3b4a58kX97UenwzB0ruoZhbA4dDUJpLbt3Inqmho1M6QKTcPCVFN+GVaZO3ToWQdTwf4eovbjU6HUM888o75Onjy5yf0vvPACbrnlFvX9X/7yF/VGl0op+UvO9OnT8c9/6jePIyIiIiKiMwdS27ZtU/2kZFSCDEORPwgXFRWp/qW5ubkMpgKMzAKYGB+vhgtJQ3aRk5XlHoIkVS6+FCBJnye704lyi+WchkLKTO7vbdyIxcuW4YudO9V9MRERcMZrahY951AAOqM+jTBiakIO5tc4MDJvHWCr0t1+efQgHEm5FIMn3QpTSPPROl11/vILC1FTV6dmh6xt7IMkVUzhYWGqKX/+wYNq2dmcV396D5Fv86l3SmtmoZBxiU8//bS6ERERERHRuf37Oz8/HzU1NUhKSkJtba26X9pfyL+7JbCS5bLM74byUYskkJCbBAphjb23YiIjfa5PlWvIWcGxYw1DzqSCrw1DziotFtW4XHpGFZeWume4M/Q2wna+Bmc//cdFmyIxK34k5hQfQ4/ty+UT03wlYwjQ/wo4Bs/FjiONs74bfecyu6KqCiXl5YiLimr2GZafY6OiUFJWptaLj2lNpyv/eg+R7/OdTwsREREREXUqqYQqKSlRbTJ0L1hjY9VyWc9zoiEifxpy9t0HHsDu/fs9+kVpMI8KhTHVqCa/89YjPAVzw/ti5sEdiN73iv5GI5KAITcCg28AIpOkSzVwZAd8rUqs3maD3eFQ51CPVDRZHA61XiBy9b5yeByjpaamSUVXW3tfUftgKEVEREREFKRcjc1l2J6e0NBQNa23rEfkr0POZkyfjif++zQcwzQ4RkkJVMOQPG/Do/vjFmcMLtq/Cea6z/U3ljQEyF4A9LsMMIX6fJWYVDGZTSbVxF0vmJLARpa7qp0Cjav3lXAd4449e9zL2fuq6zGUIiIiIiIKUjJMz2w2q147esGUXAjLcr3ZrIl8aciZNNzWm+2roPowdmbug/UuTffqV8KpMcbemF9RhUmFG2HQGvojNX1SI5B5CTBsPtA9p8NndmzPKrG46GgV3sljE2Njm4V/ldXVqj+UrBeIXL2vWsLeV12PoRQRERERUZCSYXvSL0qamicmJja/YK2sRFpamlqPqLOczZCzmtparH7vPSxdsQL/9/DDGNivn3rvflr2FRYefBObSj5teJDXpqJMEZgZMRBzjx9Az+KN+jsSGgtkXQcMnQPE9IA/VonJOlJdJWGWBH1yXiXkkmFtltpaRIaHI6t374DtGefqfUW+i6EUEREREVGQUhesWVnu3lIylE96rchwPRm2FxkZqZYH6gUr+abWDDmT9+orK1Zgxdtvw1Jdre5fuOJNjL1plAqj9lQV6G5b+kXNM6ViZuFniKparb8D8f2A7HnAgKuBkEj4e2NyqaqS6ioZDnjqwAHUa5oKu6SKSAKpsxkOSNTeGEoREREREQWx5ORk5ObmIi8vD6dOnVKBlMzCJxVSEkjJcqLOdLohZyWlpfhm717s2bcPe/Pz1X1auAbHCGBVv3VYuXOd7jZzovtifp0RF+35GCZHC/2iel0IDLsZ6HF+hw/R6+zG5BI8TYiJQW1dnWqcPj47u02N0zsSm5IHJ4ZSRERERERBToKnCRMmqDBKKlDGjx+PhIQEn7pgpeDhPeTM5nCowDQvPx/lVVWwWa0oOnoUzgQNjhwNziH6V7YmgxHTogdhfmkxhn2zQf/JzJHAwBkNlVHxfdDVOrIxuZxXGQIopMrK1z7fbEoenBhKERERERFRwwVreLg6E/Gt7FdD1FGksidn4ECsXr8eO/LzVRWN0WCAxVKJIhSh4uJKOPvqPzbGFInrwntjzpFdSD3wbgsr9QCGzgUGXQuENa3G6krB3JicTcmDE0MpIiIiIiIi8hnlFRV48+238eqqVThVVgan0QhzWAhCBhlhOa8GWor+43qGJWOeFocZBdsQZW0Y2tdM2lggez7QewpgNMHXBHNjcjYlD04MpYiIiIiIiMgnWK1WXHfXXSivrFQ/axEaaofUwTTGDkO0fhCTE9kbCyz1mJz/GUzQmq9gDAH6X9kQRiUNhq9jY3IKJgyliIiIiIiIyCeEhobikokT8eqW1Q39ogYDZp3+SWaDCdMiMrHgxCEMLdisv7GIJGDoTcDgG4CIRPgTf2hMTtQeGEoRERERERH5OOknVFdfrwKKcovF7wMKOR7v/Zf7Pjn1BXaNOgDbQK3FflHXm1Nx08GvkFq7Xn/jydkNVVF9LwVMofBXvt6YnKg9MJQiIiIiIiLyYcVlZcgrKEDBsWNwapoa4iaVNNJ7SL76k+qaGqxYuxYr330Xz//pT4iJjka9w4q3j6/HwkPLsK+qUPdx3RGLWxGJmQe+RKRTp1+UwQT0uaQhjOo+ShKdjj8YCjrS20tmQHQ4HKi32dR9lpoamEwN/clCzWbVG4taj6EUERERERGRDwdS23buVGFOiNmsml5HhIWp2dmkGXbu0KF+EUwVl5Tg5ddfV4GUHItYvHYZMMqIV46sxilrue7jRoel4/oTJ3Bp2bfQbUseFgdkXd8wTC86vWMPgoJeUUkJCouKGt56jcNKd+zZ4z4vmWlpyEzn+/BsMJQiIiIiIiLyQTKcLb+wEDV1dUiKj0dtfb26XyoxZFhXcXk58g8eVMt8dWjXrr17sejNN/HO+vUqUBPORE31i/pn9BLgAHT7RU0PScf8on0YWrlVf8Px/RqqogZcBYREdvBREDVIS0pCYnx8i6dDKqXo7PCMERERERER+aCKqiqUlJcjLiqqWegkP8dGRaGkrEytJz2HfIXT6cQ3eXlY8vLL+HLnTnWfQ3pI9dZgH61By9R/XKz0i9JicdPhb9DdWqC/Uq+JwLAFQI/zOUSPOp0Ewhye174YShEREREREfkg6VljdzjUsL2WqjIsHr1tulp9fT1Wv/8+/vbiizhRXIyYiAhoZqgZ9LRRgC1Zv3l5Rkg85tc4cXXhTkRqzddxGMNQlDgRaZN+BFNCv044EiLqLAyliIiIiIiIfJD0rDGbTLDZ7brBlDRcluWu3jZd5VRZGV5/6y11K6+ogKW2FogC7LkaHCMBqNF1zYcXjg1NwYKSE5hY+jUaBvZ5iekB55C52FLfHw5TJNLiWiixIiK/xVCKiIiIiMhPSCWKzLzWklAZWtI4hTz5v7joaNUvSpqaJ8bGNus3VVldrXrcyHpd5WRJCWbcdhusjdVa0i/KNNIMY7YJDp2rTbPBiMuMSZh/bDcG1xzS32jaecCw+UDGRZDCKceOHR18FETUVRhKERERERH5iaKiIhQWFqqePYcONVzQZ2RkwNjYQDozM1PdKDBI36iszEw1y570lpKhfNIsXKall2qkyPBwZPXu3aVNzlOSkpCdNQifWb5RzculX5RJ5zIzxhiOG+zhuOnITqQ4CptvyBQK9LuyIYxKzPrf/Q5HBx8BEXUlhlJERERERH4iLS0NiYmJcDgcsDVWpuTk5MBkMrkrpSiwJHfrhtyhQ5FXUIBTBw6gXtPULHxSISWBlCzvKnWOerxVtA4F04tgd+r3i8o0x2FuZRWuPrlXt18UIpOBITcBg28AIhI6fqeJyKcwlCIiIiIi8hMyNE9uEkq5hunFxMS4QykKTBI8TYiJQW1dHexOJ8ZnZyMhLq5TKqQsVVVY/s472PrFF/jH736nqvJK6k/hlcOr8dqRNSizVeg+brgjGneWn8DEykP6/aKShwHZ84G+0xuqpIgoKDGUIiIiIiIi8nESQIU3BpHxMTEdHkgdO34cS1eswMp331VhmHh162rsjNuLt4s2wKY1n/HPbDDhMi0a84/uwWBr835RmsEEQ59LgGELgJSRclAdegxE5PsYShEREREREZHyza5dWLxsGdZv2aJ6l2nQ4OwD1S/q8ep/AtXNT1ScMRyz6zTcdHw/kh3OZsu1sDhU97kakaNugSEmnWeayEO91apm0pQK2HqbTc22WVVVBXNjP7VAn8CCoRQREREREVEQk/BJQqily5fj61271H2aWYNzaEMYpSXqPy7TGI355adw1anDiNDpF6XF94Nh2AJo/a9EVbUVkdFxHX0oRH6nqKQEhUVF6vuwkBDYbTbs+PprGMzmoJjAgqEUEREREREFbXVCS0LNZoQFeON4GZa3/qOPsG7jRtTV1KjhdFqUBscIuQGI0H9criEGC04cxAXV+v2iSuJG4WjKdGRfOBcmubBWgZW1ow+HyC+lJSUhMT5efe+0WlFSVoakrCwYG3//BPoEFgyliIiIiIgoaKsTpEro0PHj6r6M1FTVyFtkpqUhMz0wh5qVnDqF11atwutr1uDYyZPqvqiMcDhGa3BmAdDpm2+GEVfYwzD/xAEM0ukXhZBIYOAsOAbfhG8LyhruY88oojMKk+F5jcGT02xGbW0tYqKjYQzgIXueGEoREREREVHQVidIHxdbY++WnKws90yGUikViL7Oy8NdP/sZ7A4HNM0JQ38jTLkm2Prorx9vCMXs6nrcWHJIt18UYnoC2fOAQbOA0BhAncvGUIqI6AwC8zctERERERFRK6oTJJSSPi4iJjLSHUoFqsEDBiA+KR4nupfAkQOEJOgPDepjiMD8UydxVUU5wnX6RSE9F8ieD2RMBoyBfc6IqOMwlCIiIiIiIgoCJ+tK8cqRVSi9qRJ2o07QBGC8Fo75Jw5jQm1t835RplCg/1UNYVTioM7YZSIKcAyliIiIiIiIAkRFZSXefPttnCguxgP33KPu21W5D4sOLcM7xzfCrtnhnTaZNQOutALzi49hoM3WfKORKcCQm4DBs4GIhE46EiIKBgyliIiIiIiI/Nzho0exdMUKrHrvPTWzIAzAgEv64u2qDfis7Gvdx3QzhOC68krMKS9FklOnX1TycGDYAqDPJQ1VUkRE7YyhFBERERERkR/SNA1f7dyJxcuWYePWrepnzazBOQJw5Gh49PDfdB/XF6FYUHICV1RVNe8XZTADfac1DNHrPrJzDoSIghZDKSIiIiIiIj8izdk//PhjLH7zTezcs0fdp0VrcIyQG4Bw/cedbzdhfkkRJtTWSSFVE1pYPAwyPE+G6UWndvxBEBExlCIiIiIiIvIPdfX1+GjrVvzxr3/F8eJidZ8zRVNVUU7pO64zCV4ojLiyxop5p4oxQKdfVHV4TxzpPh39J38XprCozjgMIiI3VkoRERERERH5sJMlJVi6fDlefOMN1NbVIToiHM5+BjhGa9B66T8mAWbcWF6G2RUVSGzWL8oAZEyGY+hcfHYiDDAY0N/cQnkVtYoMnZTQ0O50otxiQUJcHAwG73o0IvLGUIqIiIiIiMhHvbdxI375f/8Hh92OWkcdjKNNsI0DtG5evaAa9XeaML/0JK6orkaY9yohUcCgWcDQuUBcbxkHCJzc0SnHEciKy8qQV1CAgmPH4NQ0WK1WJHfrhqzMTPWViFrGUIqIiIiIiMhHjcrOVv2i7MM0hAwLgyHCAL04aoJVw4JTxRiv0y8KsRkNQZQEUqHRnbPjQRRIbdu5E9U1NQgxm2EyGhERFoai0lJUVFcjd+hQBlNEp8FQioiIiIiIyAftrNiDhceWoe42BzSDDLprGjeFwoCrqmoxr7wM/XX6RSF9XMMsehmTAKNOwyk65yF7+YWFqKmrQ1J8PGrr69X9YaGhCA8LQ3F5OfIPHlTLOJSPSB9DKSIiIiIioi5yqqwMb6xZg8iICMy79lo4NAc2FG/DwoNv4ovybxtW8ip9StCMuLH8FGZXVjXvF2UKAwZc1RBGJQzsvAMJQhVVVSgpL0dcVFSz0El+jo2KQklZmVovPiamy/aTyJcxlCIiIiIiIupkhYcPY8myZVizbh2sNhti4qNgH+nEa0VrcLi2SPcx/Wwabi4/hcv1+kVFpgBD5wCDZwPh7GPUGeptNtgdDjVsT0+o2QyLw6HWIyJ9DKWIiIiIiIg6abjX9q+/xuI338RHn33WcF+0Bsc4DSXDLPjTgf/oPu6COhsWlJ3CuLr65v2iUoYD2QuAvtMAY0jHHwS5hYWEwGwywWa36wZTVrtdLZf1iEgfQykiIiIiIqIOZLfb8f6mTVi8bBl279+v7nN21+AYrcEpI+yMzR8TJv2iLFWYX1GBvjZ7k2WawQxD3+nAsPlAygi+dl0kLjpa9YuSpuaJsbFNXyNNQ2V1NdKSktR6RKSPoRQREREREVEHqKmpwaI33sBrb72FkyUl0AwanP0BR44Graf+YxKdBtxUUYbrK6uQ4NUvymaKxrHki9Fz8g9hik3na9bFpG9UVmammmVPekvJUD6Zfa/eaoWlthaR4eHI6t2bTc6JToOhFBERERERUTs6dvw4XluxAh9v3YoQkwlaKOAYJTcNiNd/zECbE/PLy3B5VTVCvRcmDIRzyFx8Ut0LTmMoekZ15+vlI5K7dUPu0KHIKyjAqQMHUK9pahY+qZCSQEqWE1HLGEoRERERERG1E+kX9dfnn1dDtxALGMaHwzEMQLj++hfW1GFBRQVym/WLMgC9LwKGLQDSzoPmdMK5YwdfJx8kwdOEmBjU1tXB7nRifHY2EuLiWCFF1AoMpYiIiIiIqF3V19fDarW2uDw0NBRhYWEBedaHDBwIR4oDppEhMA42wqHXL0oDrrZYMK/S0qxfFEKigEHXAtlzgdiMTttvOvehfOGN7+n4mBgGUkStxFCKiIiIiIjaVVFREQoLC+F0OnHo0CF1X0ZGBozGhoQmMzNT3QKJQ3Pgw5NbsKh2GWxzARNMzdZJcmi4qaIC11uq0M2rX5QKoLLnAQNnAqFsjE1EwYGhFBERERERtau0tDQkJibC4XDAZrOp+3JycmAymdyVUv6q9NQpvLpqFQYPGICLJkxAlb0ay4++iyWHV+Jo7XHdxwyy2tUQvUv1+kX1GAdkLwAyJgEGnbIqIqIAxlCKiIiIiIjalQzNk5uEUq5hejExMe5Qyh/tLyzE4mXLsHb9etjsdvQd1hufJ+3EsmPvoMpeo/uYSTW1WFBRibHe/aJMYcCAqxsqoxIGdtYhEBH5HIZSREREREREOjRNw7YvvlBh1NYvvlD3OdM0OHI07BpQgF2HCpo9JlwDrrFYMLfSgj7e/aJk1rwhc4DB1wPhgTUrW73VCqvdroLI+sbqOEtNjQoinZoGa+N9RESeGEoRERERERF5kCbt727YgCXLl2NfYSE0gwbnAMAxWoOWrn+qkh1OzKmoxHWWKsR79YtyJA2HNesmGPtegrDwqIA810UlJSgsKlLfh4WEqK879uxxh3txsbHomZTUpftIRL6HoRQRERERERGASosFy9euxWurV6Pk1ClooVIVBThGaUCc/inKqrdiQWUlLq2qQUMU08AJE4oTcnEkZTosUf2AWiDzVAUy0wMzlEpLSkJifLzuMqmUqqjRH+JIRMGNoRQREREREfm8+vp6VcHUEmmeHtY4u9/ZKi4pwQcbN+KhnTvVMDQtVoNjkgZHtpT9NF/foEm/qBosqLRgjFe/KGdYPGwDroVt4PWIjEyGZ8eoUHPgXn6FyflvoYG9hFK1HL5HRDoC97ciEREREREFjKKiIhQWFsLpdOLQoUPqvoyMDBgbg6jMzExk9uhx1tv9y3PP4T8vvwxNWj71DYdjDODsD0An3wp3arimqgrzKizItHv1i0oYBGTPh7H/lQgzh+llWURE5IWhFBEREVFbqzIaZxUjoo6XlpaGxMRE1Ujb1lh1k5OT457RTz6TbdEjrTsMg40w5Zph66m/TordgZsqLbjeUoW4Jv2iDEDmFCB7AZA2FjA0mWOPiIjOgKEUERERUVurMjIzee4oYAJYNWtafb26z2KxNAl7fCGAlX2Qm+yna39iYmLc+6mcxRAxi60ay46+g8Xxy2G+Vj/QGiz9oioqMb26ab8ohEQDWdcCQ+cCsb3afExERMGOoRQRERFRJ1ZlEPlqACtcYc+OHTvcy/09gD1ZUoJXVq7EZRddhAF9++JI7XEsObQcy46+ixpHbbP1DZqGyTW1ql/UaK9+UYjNUEP0MHAmEOr/DctlVry6+nrYnU6UWyxIiIuDgdVeRNSJGEoRERERtbUqgyiAAtiW+GsAu3vfPixetgzvbdoEu8OOPfUFCJ0QhnUnt8AJzyF4DcKdTsy0VGNupQW9vftF9TgfGDYf6DURMLStmbqvKS4rQ15BAQqOHVONyKVaLrlbN2RlZqqvRESdgaEUEREREVEQcwWwgUCG2275/HMsXrMGn3/7LTSDBucAwDFaw8a0z4CTzR+TYrdjTqUF16l+UdLuvJEpHBhwNZA9D0gYgEAigdS2nTtRXVODELMZJqMREWFhKCotRUV1NXKHDmUwRUSdgqEUEREREVEHYcP8ziFVPmvWrMHSxYtRsHs3tAgDHKMNcIzSgFj9xwypr8eCCgumefeLikoFhs4Bsq4DwgOvYkiG7OUXFqKmrg5J8fGobewjFhYaivCwMBSXlyP/4EG1jEP5iKijMZQiIiIiIuogbJjfscrKyrBs2TK8/vrr6nstxgnHFMAxTMYdelQ9efSLukj6RVVYkFPftF9URdQAxJz3HRj7TQOMTWKqgFJRVYWS8nLERUU1C53k59ioKJSUlan14mNiumw/iSg4MJQiIiIiIuogbJjfMY4fP44PPvgAO3fuhNVmhZamwXG5A86+TklWmq0f0dgval6lBb08+0UZQ+DsMw1fhpwHS1Q/XNh3JGAM7L5x9TYb7A6HGranJ9RshkVmYjyLmQyJiNqKoRQRERGRH+PwMN/GhvntP/TsoYcewvLlywEjEDkyEo5RDmjdm1dFie52u2pcfq2lCrGe/aLCE4AhNwBDboQWlgiLx2yDgS4sJARmkwk2u103mLLa7Wq5rEdE1NEYShERERH5MQ4Po2Aiw8siukXAOM4I0xgT7LFes+Q1Glpfj5srLJjq3S8qMQvIng/0uwIwNzZ3dzgQTOKio1W/KGlqnhgb2yz0q6yuRlpSklqPiKijMZQiIiIi8mPBOjyMFWLB51DlISzetRhvpb8Fc6pZt1/UxTW1mF9hwSjPflEGI9B7SkMYlTZWd3hfsAV7WZmZapY96S0lQ/lk9r16qxWW2lpEhocjq3dvNjknok7BUIqIiIjIjwXr8DBWiAX2a/vaa6/h1ltvVe/lL05+gYU7F2L94fXQ0HyYXqTTiVmWKsxR/aI8qp5CY4BB1wJD5wKxPTv3IHxccrduyB06FHkFBTh14ADqNU3NwicVUhJIyXKi1pDqurr6etidTpRbLEiIi2OgSWeFoRQRERER+Z1grRALZHl5eVi8eLFqYO6AA0XxRShILEBeaZ7u+qnSL6rCgllVXv2iYjKAofOAwbOAkKjOOwA/I8HThJgY1NbVqUBhfHY2AwU6K8VlZSrYLDh2DE5Ng9VqVe8rqcRjsEmtxVCKiIiIiPxOsFaINalOkDDBbkd5eTkSEhL8sjrB6XRi8+bNKoz68ssvoYVpcIx0wDHCgTVYA5Q2f8ywunosqGzoF9XkYqbfFGDMnYCxLxAeAbBR9xnJeya88fMTHxPjl+8h6rpAatvOnaiuqVEN82UIaERYmOpVJkNDpRKPwRS1BkMpIiIiIiI/UlxcrKqKCgoKVKijqhOSk5GVlaW++gMJ1N566y0sXboUhw4dgjPOCcckB5yDnWjambyBsbFf1IKKSoyot7r7RWnmCBhG3Ajk3g2kZAFSNVdQ0NmHQx1AelzJTIASPNc3VkNaamr+Vw1pNiOMFZFdFornFxaipq5ONc2XoZ9CXg8JOYvLy5F/8KBaxqCTzoShFBERERGRHwVS27ZtQ3V1NUJCQtQFekREhOrDVFFRgdzcXJ8OpkpLS1W/qDfeeAPlFeXQ0jU4rnDA2ceJ/3Umb94vam6lBT09+kXVhSXhaI8rkDnrVzBFJ3XuQVCnKCopQWFRkfo+rLHqbceePe7lmWlpyExP56vRBSqqqlST/LioqGahk/wcGxWFkrIytZ5U4BGdDkMpIiIiIiJ/qU7Iz0dNTQ2SkpJQW1ur7pfhi+Hh4SqwkuWyzNeqE44dO4bHHnsM7777LqwOK5wDnHBc6oCW0rxxuUhz9YuyVCFG81in1zg4zrsL24rjoRlNyIxgQ+5AJU3XE+PjW1wulVLUNaRyTWZtlGF7Lb02Fo8KN6LT4SeZiIiIiMgPSCVUSUkJ4nRmt1LVCbGxarmsF3+ai/nOJEML//a3v2Hnzp2IToqGY7hD3RCtv/5w6RdVUamG6rkvVIwhQPa1wLi7gfRRgMMBbfPmTjwK6goyFIzD83xzKKRUrplNJtjsdt1gSp5Llrsq3IhOh6EUEREREZEfqJdp1+UisIULPZlx0GKxqPV8hexTbXgtTNNNsA63ttgvSpqWz6+0YGS99X8LopKBMbc13GJSO3W/iQJJew+FjIuOVv2ipKl5Ymxss4rOyupqVekm6xGdCUMpIiIiIiI/IMP0zGYzbDabbjAlVUmy3DUbYVeSC9PPjn+GhTsX4si0IzAZms+KGOV04lpLFeZUWtDDo18UUocB474HDJ0FhIR37o4TBaD2HgoplZlZmZlqlj3pLSVD+WT2PanIstTWIjI8HFm9e/vcMGLyTQyliIiIiIj8gAzbk35R0tQ8MTGxeXVCZSXS0tLUep3p6NGjWLNmDe644w44NAfWFq7FwryFyD+V37CC13VpD5tdNS6faalCtKtflMEIZF0B5H4X6H2+XPV26jEQBfRwuw4YCpncrRtyhw5FXkEBTh04gHpNU7PwSQAmgZQsJ2oNhlJERERERH5AVSdkZbl7S8lQPrlIleF6MmwvMjJSLe+s6oSvv/4aixcvxoYNG+AIdaAwtRBb67aiuLZYd/2RdfWYX1GJKZ79osLigJz5wHl3Ad16d8p+E/k6f5l5UIKnCTExqK2rg93pxPjsbCTo9LwjOh2GUkREREREfiI5ORm5ubnIy8vDqVOnVCAls/BJhZQEUrK8IzmdTqxfv16FUd988w2c8U44JjrgzHJiddnqZuubPPpFjfDsF5U4AMj9DjDiJiCMfWeI/HXmQQmgwhuHDMfHxDCQorPmO+9mIiIiIiI6IwmeJkyYoMIoqZYaP348EhISOvRisKamBqtWrcLSpUtx9NhRaD01OK50wNnHqbt+tEe/qHTPflH9Lm7oF9VvCmA0dtj+BpL2HspFvo8zD1IwYShFRERERORnVHVCeEMT8Pj4+A4LpE6ePIlXX30Vy5YtQ2V1JZwDnXBMckBLauwFheb9ouY19ouKauwX5TCG4XjqFKRe/SuYUod0yH4GMn8ZykVE1BYMpYiIiIiIqIk9e/aoIXrvvfcebGYbHNkOOIY7gCj9EzWqrg4LKiy4qKYW7nn24nrBOeZ2fFI3APaQaKQmD+JZDvChXEREZ4u/wYiIiIiISCkuLsavf/1rfPrpp3B2c8JxgQPOwU7dqwbpFzVN+kVVWDDM6tEvKmM8MO67wKAroMEA++bNPn12ZebCuvp61ai53GLxuUbNHMpFRIGMoRQRERF1OmnObPW8iPUSKtNXNzZOJaLOI0MB82vzYbvKBmemfr+oGIcT11mqcFOlBWmOxn5RplAg+1og924gfeT/VnYt91HFZWVqSvuCY8fg1DT1e0lmFMvKzOSU9kREnYChFBEREXW6oqIiFBYWqpm8Dh06pO7LyMiAsbHxcWZmproRUeewOqxYc2ANFu1ahOLJxbrr9LTZVL+oGZZqd78oRCUDY24HxtwGxHT3q5dLAqltO3eiuqYGIWYzTEYjIsLCUFRaiorqauQOHcpgioiogzGUIiIiok4n09cnJiaq2aRsjbNJ5eTk/G82Kc4kRdRhJAj+/PPPMWvWLJyqO4XXdr+GV/JfQWldqe76OY39oiZ79otKHd4wi172LMDsf1WNMmQvv7AQNXV1SIqPR219vXuonExvX1xejvyDB9UyXxrKR0QUaBhKERERUaeToXlyk1DKNUwvJibGHUoRUfuHMF9++aVqXr5582bVL2pL9BZsOLkB9Y6GQEavX5SEUdmuobYGI5B1ZUO/KOkb5cdhTUVVFUrKyxEXFdUsdJKfY6OiUFJWptaLj4npsv0kIgp0DKWIiIiIiAKUBL/r1q1TYdTOvJ3QemmwX2WH1lvDu0Xvttgvak6lBamuflDhcUDOAuC8u4D4DASCepsNdodDDdtraUY7i8Oh1iMioo7DUIqIiIiImmAjev9XXV2NFStW4OWXX0ZRcRGcA51w3OSAltTYC8pLL+kXVWHBjKpqRLr6RSUNBHK/A4y4CQiNQiAJCwmB2WSCzW7XDaasdrtaLusREVHHYShFRERERE2wEb3/On78OF555RUsX74cVc4qOIY54LjCAUTqrz+6tg4LKi2Y5Nkvqv9UIPe7QL8pQOPkA4EmLjpa9YuSpuaJsbHNhjpWVlcjLSlJrUdERB2HoRQRERERNcFG9P5n165daoje+++/D3u8HY5cB5yDnLr/2jdrGqZX12B+RSWGWhuHp4VEAiPnAOd9B0geiEAnfaOyMjPVLHvSW0qG8snse/VWKyy1tYgMD0dW795sck5E1MEYShERERFRE2xE7z+OHTuGu+++G198+QW0DA32Kxv6RemRflGzLRbcWFn1v35RcRnAeXcCOfOBiG4IJsnduiF36FDkFRTg1IEDqNc0NQufVEhJICXLiYioYzGUIiIiIiLyUyERIfjC9gVsc2zQEvXDqIzGflHXePaL6j0ByL0bGHQ5YAreSwIJnibExKC2rg52pxPjs7OREBfHCikiok4SvP8HIiIiIiLyU6W1pXi7/G1srtoM60VW3XXG1tZhfmO/KNUZyhQKZF8HjLsbSBvR2bvs00P5wsPC1PfxMTEMpIiIOhFDKSIiIiIiH1ZQUIBDhw5h0qRJ2Fu2F4vyFmHNgTWwOq26/aIubewXNcTVLyoqBRh7BzDmViA6pfMPgIiIqAUMpYiIiIhOQ2biqpOhPXY7ysvLkZCQwEoK6pT33eeff66al3/08UeIyIpAVm0Wtp3Yprt+rMOB2ZYq1S+qu6tfVNpIYNx3gaEzAXNDJRAREZEvYShFRERE1ILi4mLk5eWpShWn0wmr1Yrk5GRkZWWpr9Q69fX16ty1JDQ0VDVXJ6jw87333lNh1O79u+HMcsIx1wFrglU3kOpts2F+hQVXufpFGYzAkGuAcd8DeuXK2DSeViIi8lkMpYiIiIhaCKS2bduG6upqhISEwGQyISIiAkVFRaioqEBubi6DqVaSc1ZYWKiCPRmGJjIyMmA0qk5HyMzMVLdgZrFYsHz5crzyyis4UXUCjmEOOG51ABH6659XW4cFFZW4sLauoV9UeDww+mZg7J1AfK9O3nsiIqK2YShFREREpDN0Kj8/HzU1NUhKSkJtba26X6p5wsPDVWAly2WZNEmm00tLS0NiYiIcDgdstoY+Rzk5OSroc1VKBatjx47h5ZdfxsqVK1EVUQXHSAecg5xAw6lp1i/q8qpq1bw8q7FfVHVkT0RMvhfGkTcBoVGdfwBERETngKEUERERkRephCopKUGcztTw8nNsbKxaLuvFx8fz/J2BhHlyk1DKNUwvJibGHUoFo2+//VYN0Vv34TrYe9nhuMQBLUPTXTfOo19USmO/KK3/VHwdeQHKuo3EhaMnAkF8LomIyH8xlCIiIiLS6YEkvX1k2J4eqeyR4VayHtHZ2L9/PxYuXIivdn7V0C/qJge0BP0wKtNqU1VR0i8qQvpFhUQBOXOA3O/A2a0vyjZv9vmTz4kCiIjodBhKEREREXmRah6z2ayGmukFU9K0W5azOTedrUpHJb6I+OK0/aJyG/tFXeDqFxWfAZx3FzBqPhDRWJnnmmHPh3GiACIiOhOGUkREREReZNie9IuSBt3SC8m78qOyslL1SZL1iFpjd9luLCpZhO1h2+E4z9Fiv6gFlRYMauwXhd4XAOPuBgZdDhj9a3geJwogIqLWYChFRERE5EX6RmVlZbl7S8lQPul/JMP1ZNheZGSkWs4m56Rn3759qkl+9rBsfHT0IyzcuRDbjm/TXTde+kVVVuFGiwXJDmlwHgqMnAvk3g2kDffLE8yJAoiIyC9DqU2bNuH//u//sH37dvWXSZkWd8aMGe7lt9xyC1566aUmj5k+fTrWrl3bBXtLREREgSw5ORm5ubnIy8vDqVOnVCAls/BJhZQEUrKcyDOI2bp1q2pevnX7ViRPTkZIbggKKwt1T1If1S+qEldW1TT0i4ruDoy9Axh9KxDt3+8tThRARER+GUpVV1djxIgRuO222zBr1izddS699FK88MIL7p/Zy4GIiIg6igRPEyZMUGGUVEuNHz8eCQkJrJCiJv3F3nvvPSxZsgT7ju+DY5gDjlscOBpxFKhsfqLG1dZiQYUFE1z9otJHAbnfBYbOBMyhAXFmOVEAERH5ZSh12WWXqdvpSAiVmpraaftEREREwU2G6IWHh6vv4+PjGUiRuxro5ZdfxjvvvIMSYwkcIx1wTpfhd81PUIim4YqqasyrsGCQzQbNYIJBQigJo3qdJ2+ygDqrnCiAiIj8MpRqjQ0bNiAlJQXdunXDlClT8Nvf/rZZA1IiIiKiYBw+VldXpyq6ysvLWdHVQQ4fPoylS5di1epVsCRbgImA1kvTXbebw4EbKqtwg8WCJIcTNnM0DvW6Cj1mPgJTQm8EKk4UQEREARlKydA9GdbXp08f7N+/Hw8++KCqrPrkk09U89GWyofl5iKz5Qin06lu/kz2X/4B6u/HQdTR+Fkh8t3Pi+v5XN/7YuNwf9hHmels165dKCgoUPso//aRoYeDBg06p95X7X3sHXEuO+P1ke1/9dVXaojexi0b4RjkgP06O9BNf/2+Hv2iwjUNWnIW7GPuxBZLTzhNYUiLSYfhHD9jvv6+HDhwoApH5b1ps9nUv9UlNK2qqkJERIRaLvvvOoZ2O3Y5r7LMdWvN9jz2Q743tGGf/FVnHbvreeRroArW91GwHndHcbo+K/K7zM+v81v7b0m/CqVuvPFG9/fDhg3D8OHD0a9fP1U9dfHFF+s+5ve//z0effTRZvfL/yDlf4z+/iJL6bi8aY1G1ZWAiPhZIfKr/7c4HA7VU1KcPHmyxT8ydSVf38eysjJ88803qu+VMJvN6vUrLCzE8ePH1b+ZpMLcF469I85lR78+n3/+ORYuXIj8o/nQRmjALQAaRnM2M76xX9T5jf2i6jIm49SwBbD2PB8OpxOWL78EYPfZc9ne+vfvjwMHDqh/d8vvFhnWJz3ZMjMz1XtU9rstpBpQJh+QbcofqqUyyx1M2e1AVZX8ZVo+DK3anrw21Y1/xD5ZWQlTEP27urOOXS60K6qrIXGF0ccC1PYSrO+jYD3ujuK02VBRVwetpATGkBD4M5mtOOBCKW99+/ZFUlKSmna3pVDqgQcewH333dekUqpXr17qr4axsbHwZ66/DMmxMJQi4meFyB//3yIX1lFRUep7GZ7vixfWvryPcmG/Z88e9X2PHj3U0DIhIZRc/JeUlKhAQKpS2lJJ097H3hHnsqNfnwPVB7C7325ol2ot9ou6srFf1EDpFxUSBYy9E87z7kJoYn+EegQpsm+yv6Ghoec8vNKX35cusl/yB+R169ap/ZXZLM/1uOX9LP/2l0BLfl/t3LmzaVWgzSazJ0ljK6CVF3TqXMr6ss+xsT55LjtKZx27qqCRySPi4gIulKq3WmG12+F0OGBu/P9mpNkMY+O5DDWbERYaGJMY6Anmz09HcFqtMNhsSE5KgrHxvPorVz/OgA6ljhw5gtLSUjU1c0vkLzJ6M/TJP7QDIciR/6kHyrEQdSR+Voh88/MioYrrAtVX/3/my/sow6Pk30JSKSL75dpP12sof4CT5fLXSmnS3tXH3hHnsiO26dSc2Hh4IxbmLcTndZ8DWc3XSWjsFzW70oIkGaIQnwHk3g3DqHlAeJy6APcMUvLy8lT1mgQpMmOfBChZWVltHl7py+9LT3KBKsP1hARS53LBKufxs88+UxViISEhaluRkZGqIlD+8CyhV7K8z+W8uG6toBkM/zuX8tkJsNDEV45d/V4KwPN7orQUhUVF6vvwxvDpq7173csz09KQmZ6OQBXMn58OYWg4n778e721Wrv/PhVKyRhz+cuHi/RF2LFjh/ofmNxkGN61116rZt+TUt2f/exnqix4+vTpXbrfRERERF1BekdJBY5coOuRihwJpDz7awaa9mjwvnv3bhVuJKYmYuX+lVictxiHLId01+1ntaoheldUVyNM2hb1vgAY911g0GWA0aQbpGzbtq1JkCIhTVFRkRoqq4KUc+j7FSzkdc7Pz0dNTY0aKeEarip/fJa/xst5luVJublNAkGijpaWlITE04T+UilFRC3zqU+IjNm/6KKL3D+7ht3dfPPNeOaZZ/D111/jpZdeUv/gSE9Px7Rp0/DYY4/pVkIRERERBTr5N5D0kJJG0nrBlFTkyPJA/beSqwLJ1eD9bCqQZP0tW7Zg8eLF+GzXZ+g9szdOdD+BSmvDpDjeJtTUYkGlBeNr62AwhUEbMRcl/WcjYchEGFr4a3Crg5SkJJ9rVO5rJMCT4ahN+kc1kp+lKlCWy3pnXxNI1HYyNC+Qh+cRBVUoNXny5NPOwPHuu+926v4QERER+TK5QJdAQ6puEhMTmyyTf1PJkCZpcyDrBZq2ViBJ1djbb7+tZtKTflGOkQ44b3Zir2kvYG26bqhTw1XVDf2i+kuvouhU4KI7gDG3QotIgP0MjbrPKkhpw/DKYNLqqkCr14tIREQ+zadCKSIiIiJqPQk2pCrIFX64mmnLBbxcoMuQNFkeaFU4balAktna3njjDbz2xmsojS+FI8cBrYf+H0OlX9SNlRbMrqxCovSLSs9pGKI3ZAZgbqyIaMVU1xxe2QVVgVKx0jgrIRERBWgoJTO43H777WpYnfR3IiIiIqKuIdVAUhUkw9gkeJEgREIaqZA6l0bavuxsKpCk7YNURb317luo618H+xV2tDS+q39jv6jLpV+UTLUnIZSEUT3HtrpptqdgH17ZZVWBZWVdtp9ERNQJoZT8T/WBBx7AL3/5S1x++eW444471Fd/7w5PRERE5I8keJowYYIKo6Raavz48W1q+O0vzlSBJPcfPHgQv/nNb7Dxi41wDHfAMdcBtDA7teoXVWHB+Lo6GCK6ARPuAsbeAcT1PKf9DObhle0tWKsCiYgCXZtCqZ07d2Lr1q14/vnn8dprr2H16tWqYuqWW27Bbbfdhn79+rX/nhIRERFRi+RiXIauCelPFMgX56erQDpw4ICaHMdityAvNg/Wm62Azt9Nw5xOXFlVg/mVlehnswPJg4FL7gaGzQZCI9tlPxmkdEFVoPT+IiIiv9Hm0qZx48bhueeeU3/5+c9//oM+ffrg97//vRraN2XKFCxdujSgpx8mIiIioq7hqkCSqhnPSXJUr6kT+ahKqELFgApUDaxq9q/dRLsD3y8rx3uHj+GR0jL06zMVmL8C+N4nwOhb2i2Q8g5S5A+4EqJJHyxXkNJSM3Y6c1WgXHv07t0bkyZNwgUXXMDzSEQUrI3OpVT21ltvVbc9e/bg0Ucfxcsvv4yNGzfinnvuwfz583HfffchIyOjffaYiIiIAoaECHV1dWoojvT+CeQhZ9R+vCuQ6mx1OO44jq15W2E322ENs+JY5DHA4600wNUvqqoaoaHRwNi7gPPuAhI7vsI/2IZXdrRgqgokIgp07TL7nsPhwKpVq9RwvrVr16r/MVx00UWqtPof//iHqqiSyqlrrrmmPZ6OiIiIAoDMkCbDcAoKCuB0OlXTZ7l4D9Tm3NR+pJVEr1690HdYX6zYsgIFFQUq4HQanKgMrVSBlCXUota9UPWLqkRuXT0M3TKB6Q8Do+YC4Z3bx4lBChERUTuHUjLVrgRRixYtwsmTJ5GSkoKf/OQnuPPOO919pfbt24fZs2fjZz/7GUMpIiIicgdS27ZtQ3V1teoJJA2LIyIiVFsAqX7hsCbyJsHlpk2bsHjxYnxR9AX6Xt8X+4z74HA6EBkfCbNmht1gR425BmGaE9dXVmNepQV9pV9U5oUNs+gNvBQwmnhyiYiI/DmUkiDqv//9r2p2LqZOnYq77rpLhU7SdNJT//798cMf/lDN0EdERESk+v7k56veOtIXSIY0CamwliE5EljJclnGYTkkwztlUp0lS5fgYOhBOEY6oF2oYTd2A9JOygDUhNSoE5Vkd+D2Mguut1ShmyEEGH4TkHs3kJrNE0lERBQooZRUQkmzxl/84hfq+8zMzNOuP2TIENVbioiIiMjVB0iaVXuHTvJzbGysWi7rSb8YCk6lpaV49dVX8drK11DesxyOixxACyPuBtVbMb/SgsukX1RMGjD5h8DoW4GopM7ebSIiIuroUGrZsmW46qqrVKl9a5x33nnqRkRERCSz80qzZxm2pyc0NBQWi4Wz+Aap/fv3Y8mSJVizeQ3qh9TDca0DCNNfd1JNLeZXVOK8unpYYgbAPPNnwNAZgDm0s3ebiIiIOiuUmjFjRlseRkRERKSG6clwf5vNphtMScNzWS7rUfAM6fz0009Vv6iPD3wMxygHnHOcgLH5uuFOJ66uaugX1ccBOAdfjS9Dx6EybhAuzL4QaOUfTYmIiChAZt8jIiIiai0Ztif9oqSpeWJiYrNworKyEmlpaWo9Cmzyeq9ZswaLly7GbsduFUZpY6RRVHPJdjtuqqxS/aLiw+KB3B8CY++AFp2Kys2bO33fifxZvdUKq92uZlGvt9nUfZaaGvdImFD5w0AoKw6JqOMxlCIiIqJOJX2jsrKy3L2lZCifXAjJsD4ZthcZGamWs8l54Ku2VePZ7c+icHwhEKu/Tla9FQsqK3FpVQ1CUoYAE+8Ghs8GQiIaVnA4EEzkcyLVhCpMqK9X98nnxh0mhIayypDOqKikBIVFRer7sMaK1R179riXZ6alITM9nWeSiDocQykiIiLqdMnJycjNzUVeXh5OnTqlLq5lFj6pkJJASpZT4Cq1l+KPn/8Ry/cvR3Vmte46k6trsKDSgjF1VhgGXgqM+y7QZ6KkmghmUmFYWFiovncNcd2xY4d7uUxAdKZJiIjSkpKQeJqJJKRSioioM/C3DREREXUJCZ4mTJigwiiplho/fjwSEhJYIRWAQ/R27typZmP+qvgrPF/8PL6q+QoaNN1+UddIv6gKCzKNEcCo24Dz7gQS+3XJvvsiCW69h716kkopojORoXkcnkdEvoChFBEREXUZGaIXHh6uvo+Pj2cg5YOBUl1dnQoNy8vLzyo0lOFl69evx6Ili/B17dfoNaMXCuoLdNdN8egXFRfXG5j6c2DkXCC8hTF9QUyqozgJABERBQqGUkRERETUTHFxsRpeWVBQAKfTqfoYSXXbmYZX1tTUYOXKlVjy+hIcSTgCR45D9YvSC6QG11sxv6ISl1bXIKTPJODy7wIDpgFGzqBHREQUDBhKEREREVGzQGrbtm2orq5GSEiIaqIdERGh+hlJg3rpB+YdTJ08eRKvvPIKXn/vdVj6W+C4zAHojCQzaBom1dQ29IuyG2CQpuW5dwPdh/JVICIiCjIMpYiIiIioyZC9/Px8VfGUlJSken4JGTImQy0lsJLlskyG8u3ZsweLFi/CO1+9A9twG5zXOgFj8xMa4dEvKtUYi5AJP4VhzG1AVMv9kYiIiCiwMZQiIiIiIjephCopKUFcXFyz/lHyc2xsrAqm1q1bhzfefAPbyrfBMdIBbVbzxuWuflFzKi24zlKN2LQc7EqZhK3J5+OCCy4CTBymR0REFMwYShEREVFAqK+vV32PTjcrGRtEt+48SmNzGbbnTXpLSY+pPfv2YHvNdpQNLQNi9LczpL4eCyosuKTWitAhM4Bx34UjbRRObt58Ni8rERERBTCGUkRERBQQpN9RYWGhCk4OHTqk7svIyIDR2DCWLDMzU93o9CS4M5vNsNlszYKpyvpKfFr0KQzdDahPqAdCmveLukj6RVVYkGOMgmHM94CxtwOx6Q0rOBw8/UREROTGUIqIiIgCQlpaGhITE+FwOFSgInJyclSTblelFJ2ZDNuTflES8sn5lB5TZY4y5B/MR0F5AWJiYlAeWo4ac02TflEzLdWYW2lBRsIgYPqvgWHXAyERPOVERETUIoZSREREFDAVPnKTUMo1TE8CFFcoRa0XFRWF8IhwfHvwW+RX5qNMK4NJMyHGEQOryYpjkccAA9DdbldB1CxLNeIGXApc9V0g80JpPsXTTURERGfEUIqIiIiIVB8paV7+4isvIj8kH+kT0hFhiUCsPRbRWjScBqeqkJJAKkMrwYKTFky1mRCaMx84704goS/PIhEREZ0VhlJEREREQayqqgrLly/HotWLcLLnSTgmOFSvqEM4BMQBkfZImDUzHAYbxtWX4sESC0ZFpcMw8X5g5BwgrIVO5zpkKGBdXZ0KwMrLy5GQkNBshj8iIiIKHgyliIiI6LQ4q11gkp5RS19eijc+eQM1g2vgvNyphuQ1IT+bqnBVY7+oXhkXAFO/CwyYBjQ2kG+t4uJi5OXlqdn7pBm9zJSYnJyMrKws9ZWIiIiCD0MpIiIiOq1gndXOFcZJjyr5XlgsliaN0129q/yJBEMLFy/Eewffg32EHdqVmu56qdIvqsKCa6qtqE2ZhO63/wpIG9am55RAatu2baiurlYz+sk5jIiIUO+tiooK5ObmMpgiIiIKQgyliOj/27sP8CjrbI/jvynpmSQkobeAgoBKU0QEK4oFsSsgRV23r+ta9u7q7tUtruted+/2K/YCKHZdC+7aFQsBhNBD7x1Cep+Z+5w3TCQkwQApM8n34zOPybxvJu/MMJmZ35xz/gBwWG11VbtQGGdC4VNWVlb19kgK4yxQnDNnjp6e9bQWBRYpMMiv4Al173tyaZmm5hfoPHeKvMNu1xdlfVQZlaSOHQYc1e+2lr3s7GwVFxc7q/qVlJRU36axsbFOYGXbbRutfAAAtC2EUgAA4LDa6qp2oTCuPpEQxlmF11tvvaWnXn9KWzpsUfDUgIJRtfdzB4MaXVyiqXn5GtR+kFwX/0Dqf5n8cqtyzpxjOgarhNq7d6+Sk5NrhU72fVJSkrPd9ktJSTmm3wUAACILoRQAAMBhwrhIZRVKczbM0X1L75N/dKUlQLX2iQ8EdFVBoa4vKFH3fpdJl/1A6nbK1zv4/Y0SjNlgc2vbqy/cs7bIUIsk0FzKystVXllZ1aJ7oAq0oLj46ypQr1cxERA+Ay2Bxw8aC6EUAABAK1IZrNTb69/WzOwZWpmTLfWyc2sGUp0PzIu6ojJKyad+Rzr1Zimpc5McjwV7Xq/Xaf2sK5iyuV22PZIDQESmHXv3auOOHc7XMQf+bWatXl29PaNzZ2V06dJixweEMx4/aCyEUgAAABHMKqJs/lVsSqzezXtXn+Z/rLzNBXXuO7C0TFPyCzQ6IUNR5/xCOvlaKSq2SY/P2vZsXpQd46HtkHbs+fn5Tquk7Qc0p87p6Uo7TMuoVUoB4PGDpsVfWgAAgAhklUfvvvuunnztSW1uv1kaEFBpoPww86IKNKjX+XKN+YGUMarOdr6mYHOj+vXrVz1bylr5rD3K2vWsbS8+Pt7ZzpBzNDdrzaM9D+Dxg5ZFKAUAABBBrLLolVdf0fSPpmt/r33yjwpUBUyBmvslHJgXNalU6jpwknTad6RUp5ev2bVv317Dhw/XihUrlJOT4wRStgqfVUhZIGXbAQBA20MoBQAAEAG2bdumGc/N0GsrXlPZScXynxfaUrPiqUtFpSblF+gqb7oSh/9CGny9FONTS7PgaeTIkU4YZdVSI0aMUGpqKhVSAAC0YYRSAAAAYWzJkiV6ctaT+iT3EwUHVsp/Tt37DSot09S8fA2O6au0S/4sT98LJbdb4cRa9GJjq2ZYpaSkEEgBANDGEUoBANBA1nJkK4XVx5a2ZwUxNIZAIKCPP/5Yj73ymFbGrVCwv18Br6vOeVEXFBVrUkGpBva/Rl91O1UrEnrozD5nhl0gBQAAcChCKQAAGshWD9u4caMTGGzevNk5r0ePHnIfePOfkZHhnICjVVxcrDfeeENPvv+k9nTfqcrTgge21AykEgMBXV1QqGvLotVzxI+loTfIH5Os4jlzuPEBAEDEIJQCAKCBbCizLWnv9/udlc/M0KFDnZXEQpVSwNEq95fr8S8f19PbH1PFmXXv0/XAvKixcb2VOvp3Ur9xkufAyzm/nxsfAABEFEIpAAAayFrz7GShVKhNz+fzVYdSwNHYX7pfL656Uc+vmK695flSWu19hpSWalJekc7uOUaxF98qdR3KjQ0AACIeoRQAAEAzCgaD2r9/v3I9uZq57Bm9se4NlQUra+3nCQY1pqhY44sCGjL0ZrmHf0fydap31pmFpfa1KSgoqFHBx6wzAAAQjgilAAAAmoEFR7Nnz9Zj/3lMBcfv0/70ojr3s3lR1+QX6spgqnqd/Vu5TrpGiqpase5ws85MKHzKysqq3s6sMwAAEK4IpQAAAJpQbm6unn/5ec1cMFNlJ+Sq9PTaq+iF5kVNyS/UFZ1HKuHcW6SeIyVX3fvWNeusPsw6AwAA4YpQCgAAoAnYCo1PzHpCs7e+pcBJZSofYQFT7ZBpaGmpphb7dU7/CfIM/67ULuOoZp0BAABEGkIpAACARpwXtWjRIj380sNaFJyrir4B+TvVDqNC86ImK0kDh/1MGjRRiknkfgAAAG0KoRQAAMAxsiHj77//vqa9M007269TUb/QlpphlM8f0DUFhbrE01N9L/iT3H3GSG43tz9aHQbwAwAaglAKAADgKBUVFenl117WU3OfVGmffSoaUvcMqO4VFZqYV6TRHc5Vl4m/kDpUp1YIwyClPqxk2HAM4AcANAShFAAALaQtvwFuDVUUOaU5+s2//ltfFH+q0mF1z4s6paRU1xT4debJNyr5+luk+NQWOVYcWZASCAScmWCmR48ech+oZmMlw4ZjAD8AoCEIpQAAaCFt+Q1wJFdRrN2/VjOzpunNze+rXAEppmYY5T0wL2pcabKGnXe/YgZeJXl4yRVJQYqFpRUVFc55Q4cOrRGWomEYwA8AaAheIQEAWlxbrRhqy2+AI6mKwkJDa9Nbmr9E07/6uz7fv6LO/Wxe1NUFhbog+kSdePl98nQ7pdmPFY0TpNhjMvQ3x+fzVT8mAQBA4yKUAgC0uLZaMdSW3wBHQhWFhaWvv/W6nvzsEVUOyNPu6LqD0x4VFbo2r1TndBmrjGvvlXwdm/1Y26JIaQGNlOMEAKAlEEoBAFpcW64YQvjJycnRMy89ozfWPqeiPsUqGVz36ninlpRqqjtNZw27RZ6Tr5W8BAvNKVJaQCPlOAEAaAmEUgCAFteWK4YQPtavX6+HXnpIXxW9q9xeAVWeZLOi3LXmRV1UVKwpqYM14Ow7pZ5nSK66V9xD04qUFtBIOU4AAFoCoRQAAGizgsGg5s2bp3+++Tdt9S3W3k6hEKpm0JTk9+vK/BJN7nuVOo24VWrXs0WOF5HVAhpJxwkAQEsglAIAAG2OtYnOfne2nvj0H8rtvkP7+1gYVbtNr2dFhS7fX6FRPa5Rv0n/LVeMr0WOFwAAoDUilAIAAG2GDZie8eoMvZn9tHJ6F6l4QN1h1GklpTo/L05nnn63up05RTowdB9AmA+N57EKABGFUAoAALQJq/et0u9e/KGWxu5SZb+650VdUFCsM8t76szL/qCUPsNb7FgBHOXQ+K5duekAIIIQSgEAgFY3J6q0tFSVlZXK2Z+jlfkLNeOrv2lu8RYpvva8qGS/X2NzSzQifpSGT35Qce06t9ShAzgMhsYDQOtDKAUAAFqNPXv2aPny5Vqzbo1yy3fr3VVvaLs3R9vjc6VDFjnLKK/QmJygzug1SUOm3iW3l1XQgIgfGl9R0VyHAwBoBIRSAACgVdi6dauef/l5bdu/RvuTClXirpTX5VVKeYriK+O1NmmtCqILdFpJmUbu9+nMUb9SnzOvaenDBgAAaLMIpQAAwBG3xeXm5io1NVUuV81WuJawd+9eTXvhb9qSs1KKSVFeSkF1h16lq1IFrgIlV/g0Oq+TJp18pvqNvFNK7NDShw0AANDmEUoBAIAGtcWtWLFCGzZsUCAQcFbAat++vfr16+f8vyWsWr1K0177vVZHzde+JJ9OjDtRZe6SGiOjYoMBDQhGq2+HEyRfL3Uafr6UmNIixwsAAICaCKUAAMA3BlKZmZkqKipSVFSUs/x6XFycsxJWXl6ehg8f3mzBlFVrffLFJ5r54f1an75NezrZUvAeJZV75Q66ncook+L3q0exS8NOvETR7TIUCAa1b9++6mXkAQAA0PIIpQAAwGFDoOzsbBUXFys9PV0lJSXO+TZsODY21gmsbLtta8pWPqvMeuntWXpn2T+1rkuxCnu4nTAqxMKogCugruUudSy10Owk+bp1kzelp+RyqbysTF6v95uHJAMAAKDZEEoBAIB6WSWUzWxKTk6uFTrZ90lJSc522y8lpfHb4uxyn3jpz5q77xWt6ShV9rBjsEDqa1HBoEbk71VGqUcpXS5Q954Z2rJlS41gLT8/31lO3q4HAAAAwgOhFAAAqJe1u9lgc2vbq0t0dLQKCgoavS0uEAzo02XP6p9z/qRVCQGpU+0qrHZ+v0btqtCgxAs15sbfqNIfcNoMLSSzY7Y2QzsuO774+Hhn/lU4DGYHAABAFUIpAABQL2t3s7a3ioqKOoMpa6trzLa44rJCvTn3Qc3c+JY2qkJKqL1Pr7IKnbLDo1P7fE/n3/H9Gr/b5lvZQPacnBwnkLJ2Q6uQasmB7JHIbju7b/1+f3XgaOGeBX2hMJJWSAAAcKwIpQAAOELWDlZaWupU4+Tm5io1NbXVVuBYu5vNi7Kh5mlpaTW2NVZbnAUfe/I26oXPf6uX9i5UXs3uvGpDC0vVf3e6zjz7Ho349kVyu2vvaMHTyJEjnTDK7p8RI0a06vunqdj9vXHjRufrUPiUlZVVvT0jI8M5AQAAHAtCKQAAjoAN9rZKnA0bNigQCDjVJBaEtNZKHAtz7LqFZks1ZlucDU9/5sUH9PneV7U83aVKu4xDciabF3VBqVdd9vTShVffp34DTmrQMdsQdmNzrgikjpwFjYeGkAezSikAAIBjRSgFAMARBFI2s6ioqMhpZbNwJi4uzqkqsdDGWsdaYzBl16kx2+J27NiuJ1/6qb5yLdSapCipfe2Kp1S/X+PjMnTdiLuUnnFWI14bNIRVR9GeBwAAmhqhFAAADWCtatnZ2U51j7WzWShj7I27VeVYYGXbbVtrrMxpjLa4xYvnatZ7d2th0k7tSLaXILVnVB1XGdCUDiM09qxfKTa5eyNfCwAAAIQTQikAABog1L5ms5MODWLs+6SkJGe77WctY63R0bTFWYvjf96bodlL/qav0spU4FRF1X75MSi/TKcHB+pHP5gpV1TV7wAAAEDrRigFAEADWMuaVQjVtQJdaMaOzVgKrVTW1tnt8PyLv9Yne/+lRe3cquxgAVbNNr3oQFBDdpepe+FAXXblLzV4yJBWWWUGAACAuhFKAQDQANam5/V6VVFRUWcwZQPPbXtbn8OzZ/d2PfvS7fpCi7UyMUpK9dTaJ6XSrxM3+3V8/KW6buJP1KNHjxY5VgAAALQsQikAABrA2vZsXpQNNT90VTKbN5Wfn+8M/rb9WrpCyQKy+lhFV1MEZ8V5W/T6nF/rmR1faHti3fOiepRWqOf6aJ3a5we6+o6pLX5bAQAAoGURSgEA0ADWVmYrzYVmS1krn62+ZyGQte3Fx8c721u6/cxCs40bNzqznDZv3uycZ5VIbndV61xGRoZzaiw7N83Rc18+oJdLNqnAfkd07ZcWA3LL1WFzB51/5p26+KZLnGAMAAAAIJQCAOAIVqAbPny4VqxYoZycHCeQstXorELKAinb3tLsWKySy+/3O62GZujQoU6AZhojEAr4K7V84eOaueIpvRcsVKUFcQdCr4PnRZ24s0Idcgfq6ivu0Om3nt7igR0AAADCC6EUACBsWBtcaWmpU4WUm5ur1NTUsAsyLHgaOXKkE0bZcY4YMSKsjtNa8+xkoVSoTc/n81WHUscib+9WZX75Gy1K3q7lCQfCrUOud2pAOqO4vWL2Ddbk63+o448//ph/LyJfqK3U/l2GFgOwCsODw9K2Po8NAIC2iFAKABAWM4v27NnjVCBt2LDBaT2z32EBULhUIB3MAqjY2Fjn65SUlLAJpJrKhuUf64X3f6mP43O0rYu9dKhdbdUnGKWpvS/TJSN+ruiouBY5ToSvUFupCf19yMrKqt7e2G2lAAAgMhBKAQBafGaRBVKZmZkqKipyVraz6om4uDjnd9kMJ2uZC7dgqtULBjX/vX/oteWP6uOkgApS3HW+bDjT205TBn1fp584sdWHczj2ttL6MGcMAIC2iVAKANCiM4usZS87O1vFxcXO6nbWFheqprBqJAusbLtta62hRzi1LQbKijTnrbv1SckX+swXJX87O47a86KO31api/p8TzeNv71FjhORJdRWCgAAcDBCKQBAi84sCq1ml5ycXCuIse+TkpKc7baftcq1NuHStli4a41efu0nek/rtCQuWoqqHS4mV/jVYWWFovcM1F133KPBgwc32/EBAACg9SGUAgC0+IwqqxCytr26WOWVDUQODUduTcKhbXH74jf1wif36T/xBdoWV/e8qM5FlUpcHqNhPSbq+NP7OMd08sknN+lxAQAAoPUjlAIAtCirtvJ6vU4rYF3BlFUO2fbW1vrTom2L/gptX/SMnl38qF51F6kwue55Ub33VMqVnaYrzvy2rnrgKiUkJGjOnDmNeywAAABoswilAAAtytr2LHix6qBDByFbcJOfn+/MsbL9WpMWaVss2qcln/+Ppm98W+9Hu+T31j0vqtvGSsVuP0E3XPk9XfCTC6rDQmvZBAAAABoLoRQAoEVZAGPzk0IhjbXyWRubtetZ2158fLyz/UirheznrcqqPtYW2JLVV83ZtlixY4k++PIPmr5/sRbHREkxNYMok1QeUOrKoLoFz9LlF16hCy644KjnhAEAAAANQSgFAGhxNqPI5ifZwO+cnBwniLF2NquQOtqB31Z5tXHjRmd4+ObNm53zevToIbe7KpDJyMhwTq22bTHgl3vLB1qw61X9YW1A26K8kgVSh+jn9SllQ3t101BNuWWKc5vs3r271a50CAAAgPBBKAUACAsWPI0cOdIJo6yCaMSIEUpNTT3qcMQCLWsHtJYzC37M0KFDq6t/rBKpVbYtluZr5Qd/0MvrXtbbvigVtbcQrnZl1NkJPTV12J0a1uMc5/vQ7WwhHgAAANAcCKUAAGHDghEb8m1sjtKxVOtYhZGdLJQKVRv5fL6waUlr9LbFfev0ydt36/WChfowIVaB5NoVVrFBly7vOFyTTr9Lvdod1/hXCgAAADgChFIAAERq22IwqLI17+mN93+j1z17tCQ2RkqMq7Wbryyg+GVu3TPpHzp7+NlNd4UAAACAI0AoBQBApLUtlhdr37wn9NLCaXotPqjtCfZ0XrsyKi23UkVfBnVmr8t1w/dv0IABA5r2ygAAAABHgFAKAIBIaVvM26Y1HzygF7e+ozd9sSpKrt2K6AoG1XFHQCWLfDqxw6kafd1oXX755WHTtggAAACEEEoBABDOgkEFt8zT4i/+pGdyFunDeJsXFV9rt2h/UMlrpKgNXTR13A0a96NxWrhwYYscMgAAANAQhFIAAISjynJVLn9N78//m2b491bNi0qoPS8qoTQg7xKPepScrBsn3KjRvxvtVEXZgHcAAAAgnBFKAQAQTor2Kn/B43p15Uw9FyvtiPJKUbXnRbXLDah0fpRO7zhaU2+eqiFDhhzTaoUAvhYMBlVaWurMecvNzf3mOW8AAOCoEEoBABAGEgo3KrDpBT24JFuv++JVnFj7KdreEp+TerLWvZWrkRmjNPn3k9WjR48WOV6gtdqzZ4+zIuaGDRsUCARUXl7uLEjQoBUxAQDAESGUAgCgpQT8CmbP1rw5D+pF/3Z9mBCngCux1m5xLo+u6HmhJg/5kXok9VBgbEBut7tFDhlo7YFUZmamioqKFBUV5bTCxsXFaceOHcrLy9Pw4cMJpgAAaESEUgAANLfSPJV/9bTe+ephPR/r1zKbF6Xaw8s7ehN0/YCpunrAJCXHJFefTyAFNE3LXnZ2toqLi5Wenq6SkhLn/JiYGGd1TAusbLtto5UPAIDGQSgFAEBz2bdOuV/8Xa+se0OzfLHaleyt86k4KcfmRUXr6T+9rG5dunH/AM3AKqH27t2r5OTkWqGTfZ+UlORst/1SUlK4TwAAaASEUgAANKVgUFr/kTZ89mc9n7dMr/kSVNIusc794jf45c9K0OjBl2jyrycTSAHNqKyszBlsbm17dYmOjlZBQYGzHwAAaByEUgAANIXyYgUXP695mf/Uc+48fRQfp2Cyr/YTcWVQUSvdKl/g0emDRuuuh+9S586duU+AZmZtel6vVxUVFXUGUzbw3LbbfgAAoHEQSgEA0Jjytqoi8xG9u3KWZsS5tdxX97youOKgKhd51XlfV02+ZrLand/OmVvToUMH7g+gBVjbns2LsqHmaWlpteZN5efnO4Gx7QcAABpHWC3d8+mnn2rcuHHq0qWL07v/+uuv13pBcO+99zovCGwllPPPP19r1qxpseMFAKC6RW9zpvJemKQnnjxDF215WXelxGl5HRUV8XuD8v7bqwELTtWfJvxZb770pq677jonkALQcuy1Z79+/RQfH+/MjrJWPnvtae16NuTczrftDDkHAKCVVkrZ8ruDBg3St771LV111VW1tj/44IP6+9//rmeeeUa9evXSPffcowsvvFArVqzgxTwAoNm5AhVyLXlRmxY8pJmlW/SvxASVpNauonAFg4rd4JJ/UbTO6neeptw9RQMHDqze7vf7m/nIAdSlffv2Gj58uPPaMicnxwmkbBU++0DUAinbDgAAWmkodfHFFzunutgnVX/961/13//937r88sud86ZPn66OHTs6FVUTJkxo5qMFALRZRXvUY+Pz2rHnP7otwa2PbV5UdO15UfHuKF3Sfaw++sscjR01VhMfmqhu3SJvNT17Y27zdCw8Cw15toHPHo+negB0OMzZiZTjRHiz4GnkyJFOGGXVUiNGjFBqaioVUgAAtPZQ6nA2bNignTt3Oi17IdbTb59mffnll/WGUvai9OBVUmwegAkEAs4pktnxW1gX6dcDaGo8VpqGvfG3N232f6soaIw3baH7KvR1Y7TJNOpl7lyqyrkP6T8bZmuGL14rO9Sxip6kzjHtNPHEm3RVn6vki/bp56eXO4FI6Bia9BiP8TLrerxs27ZNmzZtcr4OXY9FixZVb+/Zs6cyMjLU0iLlOBH+7DFgAaadkpKSnO9Dj6cQnlvClP3tsvsqdEJYCBx4DNn/ATTgsRIIVP09i2ANzSkiJpSyQMpYZdTB7PvQtro88MAD+s1vflPrfJsNUFpaqki/k/Py8px/tG53WI0HA8IKj5XGt3//fq1fv945hW7fdu3aOa3V9v+jZQGXtXKb3bt3V1e4HItjvsyAXzGbPlTFkqf0RvEazUpK1O70lDp37ZeQoWuOn6xRHUbJ4/aoJLdE9l+TH2MjXmZdjxdbcaxHjx71/oxtt9/R0iLlOBH+GvL44bklTFVWSoWF9sm0Pehb+mhw0BvtvKIiWSTlboQPXoDWKlBRobzSUgX37pW7jpVgI4lVqzdEq/9Lfffdd+uOO+6oUSnVvXt3pzTbPvmKZKFPvu26EEoBPFaai4X6a9eudd6w2XBue7NmQVRhYaFz/mmnnXbUc1fsjWBCQoLzta1C11jhzFFdZmmetGimNi14VI+7C/SGzYuKTalzXpRWBeReHK37/vxH9e3bt/mOsQkuk+cWoGGPHx4rYaqiwgbVStaqG+Fv6FpbKGVRVPvkZEIp4HCPlfJyuSoq1D49Xe4IHznQ0EV8IiaU6tSpk/P/Xbt2OcMmQ+z7wYMH1/tzodLrQ1mI0xqCHAulWst1AZoSj5XGYdUzq1evdtr2LHgKVZzak46timqBlW23N3FH04Jmlx/6ucb623bEl7l3rYJzp2l+9kuaER+lT5LjFHTVnhflqQhKS93yLovRhadfqPF/GO9Uih3NMTfF9bY3zNa+bjNx7AOZI2mv5PGCtq6hj0keK2HI7iu770InhA3n8XLgBKDeB4pay3NLQ48/YkIpe6FvwdQHH3xQHULZi+zMzEz94Ac/aOnDA4A2wdq6bKl0m+l3aMBh31sFqm23/VJS6m5xC0tW7bTuQ1XMnaZ/7/xc05OSlN2+9ip6JqpQCizyqN3Wdrru8ut03b3XKS0tTeHEwkFbPczmMVo4ZcO/LURk9TAAAACEk7AKpUKtHyH2YjorK8v5dNdmRNx222363e9+pz59+jgh1T333KMuXbroiiuuaNHjBoC2IlR5E1VPS4QNl7b+8YMXmAhr5cXSkueVO3eaXqrY6cyL2tM+vc5dvTa+cKFXPSszNGniJF166aUNLktu7kDKPrCx9kq7n6ztyKrYduzY4YSFtkAIy9oDAAAgHIRVKLVgwQKde+651d+HZkHdcMMNevrpp/Wzn/3MeZH93e9+V7m5uRo1apT+/e9/h+WbAgBojawd2oZFV1RU1BlMWUWOba+rbTqs5G6R5j+mDVkzNDM26MyLKnXXUdkVDMqz1iP3Io9O6XKKJv9wss4888ywLae2lqPs7GwVFxcrPT3dabM0dn/Yc6UFVrbdtjXGCn8AAABAqwmlzjnnnFrL7R7MXkD/9re/dU4AgOZnbXsWaFjVzaEta/b329qqbe6f7Rd27Plly1wFMx/R/I3vanpSoj7pkFjnru7yoFzLvfIu9WrM8DGa/MfJGjBggMJdq22vBAAAQKsUVqEUACC8WbBhc4lC4Ye18ll7mLXrWdtefHy8sz2sqnD85eq48yN13PqG3v5ql6Yn+bSqU92rA3aN76hz212gtx54S1ddepUm/HxCjcU1wl2ra68EAABAq0YoBQA4IjaPyOYS2SDtnJwcJ+CwNjELb452kLZdhrX+2TLsocDEwpPQMuwWphxxS2DhHmnBk8pd8IQ+cnk0OzFFe7zdVewtrrXr4PSBmnrSjTq3+7nyur368Rk/dgK2SNNq2itbsdC/9foc1b91AACACEUoBQA4YhY8jRw50gmjrDJnxIgRzqIUR1shZe2AGzdudL4OvSG3hS5CMjIynFPDLmyxNPdhrc9+TTPiOmql92TFVyarU4FbHVwB5Ufla3v8dhVFF2pMxoWaMmCKBrYfWOMiIjGQivj2yjYi9G/dVkXcvHmzc54t5hKaU3ZE/9YBAAAiHKEUAOCoWAAVWmjC5hMdS8ueBSWHhiiHVo8cVsAvZb+tYOY0Ze76StOTk5SV3lvH5x+vxEC0SjwlqnRVyhv0KqU0RQk7E1S5r1IPTn0wvFoN22J7ZRsT+rduVYFW0WaGDh1aoyoQAACgrSCUAgC0OKuOOqqWpZJcaeF0lc97TLP9+zQjyafVnTtKQemEvC6KDkSrwFsgWQZTGpR/l19l+8vUp3cfDRk3RK1RU7RXovH/rVsoFfo37/P5qkMpAACAtoRQCgAQefaukTIfVs6S5/VinFvPJ/m0z/t1pVV8ZbySKpKcCikVSv6tfkUXRmvokKE6ccyJTqWQBTWtdRW6xm6vBAAAAJoCoRQAIDIEg9K6D6S507R+08dOVdSbnVJUdmAWz8G8fq88+R4FdgaU5k1Tp66d1LVrV/Xq1cuZ3WPzfFr7KnSN2V4JAAAANAVCKQBAeCsvkhbPUjDzYX1ZuEUzkn36rFuXOnd1lUnuZR65VruUNjhNI04foZ49e2rLli01L5JV6AAAAIAWRygFAAhPuZuleY+pbOEzmu2tdMKoNb4Ode7qypU8i72KWhWli0ZfpOv/er327NnjrHR2qHBahc4qtSwgs/lCoaotq+A6eOj1Uc3aAgAAACIAoRQAILxa9DbPleY+pJzVs/WCL17Pd/App54h0EM7DFW/wn7699P/1tVXXq0Jf5igDh2qgitb4SzcV6Gz0Gzjxo3O16HwKSsrq3p7RkaGcwIAAABaI0IpAEDLqyyTlr0qZU7T2r0rNDPZpze7dVa5u3Zo5HV5NCbjQk0dMFUnpp/oVBr9ZOxPnKAp0lahs2Ox8Kw+VikFAAAAtFaEUgCAllO4W1rwpILzn9CXgXxNT/Lp826d69w1wROv8f0naGK/ieqU0KlGcFNfeBPuq9BZdRTteQAAAGirCKUAAM1ve5aU+bDKlr2it+OiNCPFp7XRh58XdVbns3X75NuP+FexCh0AAAAQngilAADNw18prXpbmvuw9m2dqxeSfHqha/t650W5trrkyfIofme8Lh93ua6//nruKQAAAKAVIZQCADStkv3SwhnSvEe1pninMy/qre5d65wXJb/kXuN2wqj2gfYaP368rrnmGiUlJXEvAQAAAK0MoRQAoGnsWe206AUXz9Ln3oBmJPn0RWrd86JUKnmWeeRZ4lGfzn006YeTdOGFFzLoGwAAAGjFCKUAAI0nEJDWfSDNnabS9R/qrcR4zeyQpHX1DCJ35Va16LlXujXi1BGa/D+TnRXzwmUQOQAAAICmQygFADh25YXS0helzEe0d/+6qnlR3bto/+HmRS3yKHprtC65+BJN+uUkHX/88dwTAAAAQBtCKAUAOGqxJbvUddtsub+cotWBEs1I9untHl1VUUelk9fl1cW9LpZroUtffvClMyvquuuuU3p6OvcAAAAA0AYRSgEAjkwwKG36Qu65D2lY9mx9ERet37bzaW5cSp27J8ck67q+12lCvwnqEN9B+YPyFfXdKMXFxXHLAwAAAG0YoRQAoGEqy6Rlr1TNi9q1VG/avKiuHbU+OqrO3bsndNeNJ9+occeNU5z36wCKlfQAAAAAEEoBCFtlZWUqLy+vd3t0dLRiYmKa9ZjarIJd0oInpAVPam/pPs3y+fTS4eZFbakaXn7lRVfquhOua/bDBQAAABAZqJQCEJZ27NihjRs3KhAIaPPmzc55PXr0kNvtdr7OyMhwTmhC2xdJcx92qqNWeaXpST69077ueVHyS+7VbieMSixO1BVXXKGxl4zl7gEAAABQL0IpAGGpc+fOSktLk9/vV0VFhXPe0KFD5TlQnWOVUmgC/kop+y2nRS+wZa4+i4vV9A7tlBkXW+fuweKgAosCisuOU8fEjppw/QRdeeWV8vl83D0AAAAADotQCkBYstY8O1koFWrTs6AjFEqhkRXnSAunS/MfV0n+Vr2ZmKAZXTtrYz3zolz7XfIs8qh4QbG6d+6u2352my666CJ5vTytAAAAAGgY3j0AQFu2Z5WU+bC0+HntCZRqVlLVvKjcw82LWuSRe5Nbo0aO0oBbB6hv374666yzCAwBAAAAHBFCKQBoawIBad0H0tyHpHUfKjs6SjOSfZqdmKbK+uZFrXLLs9ij2PxYjR07Vtf/6XpnxtecOXNa4hoAAAAAaAUIpQCgrSgrlBbPciqjAvvWao7Ni+rUQfPqmReVEpOi8SeM15LpS7R+6Xpdd911uuaaa5SQkOCsjFhQUOCskmjs64PnfbEyIgAAAIBvQigFAEfJAhkLZ+oTNuHM/k3SvEelhTNUXJ7vzIuaeZh5Ub2Te2vKgCm6tPelivXGaleXXUpJSam+LrYqop1M6LysrKzqn2dlRAAAAAANQSgFAEdpx44dTjgTCAS0efNm5zxraXO73S0fzgSD0qbPnVX0tGq2drtdmpWUqJc6dlFePfOiTu98uqYOmKqRXUfK7aq6DqZjx451roxYH1ZGBAAAANAQhFIAcJRC4YytEFhRUeGcN3To0BptbM2uolRa9kpVGLVrqVbYvKj0dvp3Qvzh50VleTTx5xN1ZrczG7wyIgAAAAAcC0IpADhKoXDGQqlQSOPz+VpmFbqCndL8J6QFTypQvFefxsU586Lm1zMvSiWSZ6lHniUeJboTdfXVV+ukk05q7qMGAAAA0IYRSgFAJNu20BlcrmWvqjhYqTdsXlS3ztoUVfe8KNc+l1MVZdVRXTp00cQfTNQVV1yh+Pj4Zj90AAAAAG0boRQARBp/pbTyjaowakumdnk8mpWcqJd8icqvp0rLtcklb5ZXrs0unXTiSZr8u8k677zzWqaqCwAAAAAIpQAgghTnSAufkeY9LuVv1XKbF9U+Tf+pb15U5dfzojz7PTr77LM1+d7JGjRokFx17Q8AAAAAzYhKKQAId7uzpcxp0uIX5K8s0SfxVfOivqpvXlTxgXlRSz2KDcTqsssu08SJE52VAQEAAAAgXBBKAUA4CgSkte9VraK3/iMVu1x63eZFJXfWlnrmRR2fcrwm9Zukp372lCpLKzX+pvHOAPPk5ORmP3wAAAAA+CaEUgAQTsoKpKxZVfOictZpp8ej59ql6GVfogo87jp/ZGTXkZraf6pGdBnhtOUN/ctQdevWTdHR0VUXWVam8vLyen+l7RdaPRCRL3R/26qQ9rUpKCionh/G/Q3w+AEAIFwQSgFAONi/Ucp8VFo0QyrL1/LoaE1vn6Z365kX5Q64deUJV2py/8k6vt3xNbb17t27xvc7duzQxo0bFQgEtHnzZuc8a+Vzu6tCroyMDOeE1iF0f5tQ2JiVlVW9nfsb4PEDAEC4IJQCgJYSDEobP6uqilo1W/5gQB/bvKjOHbQw9jDzopZ45Fnm0Q3Tb1Cvdr2+8dd07txZaWlpTuVMRUWFc97QoUNrVM60Rm21Yih0f9entd7fQGPg8QMAQPMilAKA5lZRKi17WZr7sLRrqTMv6jVfgmYm+bS1nnlRrr0uZxU992q3khOSdfWEhs+KsuDFThbOhEIYn89XHc60Vm21Yih0fwPg8QMAQLgjlAIQ1oLBoEpLS1VZWanc3FylpqY6c5MiUv4OacET0oKnpOK9DZoX5d7odsIo1xaXunfrruvvvF7jxo1TXFxcsx9+pKHiAQAAAAhvhFIAwtaePXu0YsUKbdiwwZmHZK1Y7du3V79+/Zz/R4ytX0mZ06Tlr0mBSi2NjtaMA/Oi/HUFbJWSO7sqjHLvd2vQoEGafOtknX322dVzoPDNqBhCOGtVgTsAAMBRIpQCELaBVGZmpoqKihQVFeW0mll1kLVk5eXlafjw4eEdTPkrpJVvVLXobZ0nv6SPbF5UcqoW1TcvqqhqXpRrqUvuUrcTQo0fP14DBgyo2lxU1GrnIAFtSasJ3AEAAI4RoRSAsKwgyM7OVnFxsdLT01VSUuKcb2FMbGys84bOttu2sKssKM6RsmZI8x+X8repyOZFJfmceVHborz1z4taVDUvyh10a9iwYTrnnHOc679y5UonjGKlPKB1iPjAHQAAoBERSgEIO/bGbO/evc4g70NDJ/s+KSnJ2W77paSkKBzEF21Wt61vyv3ZHKmyRDs8Hj2bmqJXfIkqrKfl7qxuZ+mKblfo3pvuVbuUdppwywRdcskl1UPJ7Y1rW1kpD2gLIjpwBwAAaAKEUgDCTllZmTNnxaoI6mLBTEFBgbNfQ1l7jP1MfTOZjqotLhCQ1rwr99yHdNqGT5yzlsREa0a7NL1Xz7yoWE+sLjvuMk0aMEm9k3s753V6pJPTtnPw9W1rK+UBbUEkBu4AAABNiVAKwDGzcMhCn/ocaeBj+3q9XlVUVNQZTNnvsu1Hcpn2Rm/dunVOpcLmzZud83r06HF0bXFlBdKiZ6V5j0g56515UR/Gx2lGcpKyYus+prhAnL59yrd1bd9r1S62XY1tJ598coOvB4DI1RSBOwAAQCQjlAJwzGwWysaNG52Bvccc+EhOFYG1r9jlpqWl1dhmoVJ+fr46d+7s7NdQdnnHHXec8/MWdh1VW1zOBmneo9KimVJZvgpdLr2a5NNzh5sXtcflrKIXtz1Ok8dPVnxsfIOPGUDr0hSBOwAAQCQjlAJwzCwgsvDIWs6OOvA5pI3F2tlCrS5WWWCXZdUDVkUQHx/vbD+SmSt2DNYGZ6HUEbXFBYPSxjlVq+itmm1naJvXo+cOzIsqqqcd0L3B7Qwvd21zOfOirh1/rRPaAWi7miJwBwAAiGSEUgCOmYU8oeHcjTUHyVafslWobNn0nJwcJ5CyocD2hq1Zlk2vKJGWviRlPiLtWuactTgmWtOTfHo/IV6BgwOxoBRfGS9PhUfBdUGVZZXJnetWz549NekXkzR27FgqHwA0SeAOAAAQyQilAIQtC55GjhzphFH25m3EiBFKTU1t2jds+Tuk+Y9LXz0lFe9TpaQP4uM0PTlJS+qYF+Ur96lLbhcl7kqUO88td9CtpIFJGjNmjHOqb7A6gLapxQN3AACAMEIoBSCsWQBlS6UbW42qyQKprV9Jcx+SVrwuBSpVcNC8qO31zItK2pmkPjv7KLo4WgX5BerQoYNOO+00Z1aMhWj79u3jDSaA8AjcAQAAwhChFIC2y18hrXhNynxY2jrfOWur16NnU1L0Wn3zooIH5kVledQ9rrviU+PVrWc3tWvXTnFxcU61g72x3LNnj7Kzs535MbzRBNBigTsAAEAYI5QC0OZ4K/LVZfu7cn/1falgh+VMVfOikpOcVr0a86IOiPPG6bLjLtNpntP0i3/+wlldcNiwYerbt68SEhKqVx009uYyKSnJmRljs2PsDScAAAAAoCZCKQBtx+4VcmU+rBGLX5AnUO7Mi7Kh5TOSfHXOizId4jpoYv+JurbvtUqOqVoRK/HvierVq5c+++wzJ5Cqb7U/G1xs82IAAAAAALURSgFo3QIBadU7avfZP+Te9qVzVr77wLyoZJ92eOv+M9jF00U/PuPHurDnhYryRNXYdsYZZyg3N1der1cVFRXODKlDlZeXO9tDqxECAAAAAGoilALQOpXmS1nPSpmPyL1/gywa2uL1OIPLX/Ulqvhw86IWeZQSn6Kxk8bWO+clOTnZmRe1Y8cOpaWl1byYYFD5+fnOfCnbDwAAAABQG6EUgNYlZ72U+ai0aKZUXuDMi8o6MC/qw3rmRalCcq9wy7vYK1eey1kFa8zlY5xVseqqgjIWVtny7TYzymZH2b4ej8dp17O2vfj4eGc7w4sBAAAAoG6EUgAiXzAobfhUmjtNWv1vp+Sp4sC8qOnJPi2rr4WuUPIs9siz3CNXmUu9e/fW5J9M1kUXXeTMhGrIsu7Dhw/XihUrlJOT4wRStsS7VUhZIGXbAQAAAAB1I5QCELkqSqQlL0qZDztDzEPzol7x+fRskk+76pkXFdgRUCAzoPht8XIFXDrttNM0efJkjRgx4ogrmyx4GjlypBNGWbWUXYZVWlEhBQAAAACHRygFIPLkb5fmPy4teEoqyXHO2uL1amaST6/5ElRS37yo9W55sjwqWlXktNpdcvUlmjJlivr27XtMh2MBVGxsrPN1SkoKgRQAAAAANAChFIDIsXW+NP9RacW/pEClMy9qYUyM06L3UXycgnVUObkqXXIvdyuwIKCo4iglJibqzIvO1Lnnnqtx48Y54VRbYQPYS0tLnYouWz2Qii4AAAAALYlQCkB481eow65P1W3rm/J8vNo5y+ZFvXdgXtTyeuZFdYzvqEn9JyluTZz+9OmfnJXybvjBDbr00kv11Vdfqa3Zs2ePM/tqw4YNCgQCKi8vd1oPmX0FAAAAoKUQSgEIT0X7pK+elHv+ExpQsMM5K8+ZF5Wo5w4zL+qktJM09cSpOr/n+YpyR6m8b7naJ7VX//791alTJ6daqC0GUpmZmSoqKnJWE7TqsLi4OO3YscNZPdCGtTOUHQAAAEBzI5QCEF52La9aRW/pS1Jlqawhb/OBeVGv1zcvKiCdlnaabjn9Fg1uP7jGTCdbRc9a9Xbv3q22yEK47OxsFRcXO9ViNpDdxMTEOHOwLLCy7baN4ewAAAAAmhOhFICWn1kU8Eur/yNlTpM2fFp1WZK+io3R9CSfPq5nXpTKJc8KjzyLPep4ekcNuWwI9+YhrBJq7969Sk5OrnVf2PdJSUnOdtvPhrQDAAAAQHMhlALQcjOLSvOlRTOleY9I+zdWz4v6jzMvKkkrY6Lr/rkCOUGUZ7lHrnKXU+Vj7Xmtadh3Yx1nWVmZcxnWtlcXqyQrKChw9gMAAACA5kQoBaD5ZxbtWyfNe1Ra9KxUXuCcled26yVfomYlJWp3PfOiXDtd8mR55F7rlivoUp8+fTR58mSNGTOm3tDl0OO0VrVwH/bdmEPJrU3P6/WqoqKiztvILtu2234AAAAA0JwIpQA0z8yitDS5NnwiZT5c1arnNOhJG21eVLJPbyTWPy/Kv8ovf6ZfcblxTrXQsNOGOWHUyJEjG1w9tH//fq1du9Y5znAe9t3YQ8mtbc/uF/v5tLS0Wvddfn6+Onfu7OwHAAAAAM2JUApoRNYCZZUn9bFWqdZWkfKNM4sS47V31VzlLbpXKTkLnfMtjlpwYF7UJ/XNiyqT/Iv98s/3S3lyqnmGnDZEF1xwgbp06aJu3bo1OJCy8MWqjiwwC+dh300xlNz2swqr0P1krXwWdNm/VWvbi4+Pd7aHYwsjAAAAgNaNUApoRFaNsnHjRqflavPmzc55PXr0kPtABVBGRoZzak3qnVlUViDtWKTobYtV4I9TmTY786L+nRivGUn1z4tyF7jlynLJvdytqLIoZxD35RMv15VXXlmj0scCvoayQKa+uUzhNOy7qYaSW2WVVVhZS2BOTo5zn1ngZRVS4da6CAAAAKDtIJQCGpG9ybfgxO/3OzN8zNChQ53KlCMNUiKlmuvQmUUxpXuVlL9ark1brPRH5YpWwOXXa4nRejGli/bUMy9qUPtBmjJgipa9tkyzsmY5Yd6kSZM0duxYp0roWK+z3SfhPuy7KYeSW/Bk7Y4WRtnvGDFiRNgOeQcAAADQNhBKAY3IAho7WQASCnZ8Pl91KNUaq7mcmUWpKdqxepHS85aqc+GO6m37XW4tiUrVyvgCLU/xSofkH26XW+f3ON8JowZ3GOycN/j6wRp2yjCNGjWq+piOld0Xdh9YcFZXMBguw76beii5BVChgM8qrQikAAAAALQkQimgjWnUaq6ivXIteEr9Ml9UXnF37VWcfIrWPk9AS6MTtM/tU7mnXJsTdtQMpMql8f3H66bBN6lrYtcaF9mxY0fn1JgsOLMQxoZ6h/Owb4aSAwAAAGhLCKWANqZRqrl2LpMyp0lLXpL8ZbKJRKdor96M7q8vPO1V6I5SwBVQflSutsdvV0F0QdXP5UmexR55VnjU6eZO6jqqZiDVVKwiqFevXs7qe+E87Juh5AAAIJJYFfeXX33lnPIKCpwP++pi5xeXlSk+JqbFX2+hYex+8iUkaPiQIRo5bNgxj9MA6kMoBaBhAn5p1TtS5sPSxjnVZ+e63XrRl6hZSXHa69mi+Mp98ga9qnRVqthb7FRIuba75MnyyL3eLVfQpQ4dOtSqWGpq7dq102mnneasXhfOw74ZSg4AACLB3pwcfe/nP9em7dudURCdOnWqN3CyqCrg8ajA7z90mgPClAWJ67Zv16v/+Y+6dOighx94QF06dWrpw0IrRCgF4PBK86RFM6XMR6TcTdVnr4/yamaST28mJqj0oNlPxVHFVV8EJPcad1UYtatqu4U/kydP1vnnn+/MRmqJwMfCsHAf9s1QcgAAEO5+9rvfqaSyUs9On64TTjjhsPtaKFXp98vr8RBKRZj169fr9v/6L93+61/r+WnTwu51MyIfoRSAuu1bVxVEZT0rlRdWv6DIjI3RjOQkfRofV+ePBUuDCmQFFLcyTq7CqietM8880wmjbHZVSz+RRcqw70g5TgAA0PZs27FDS7Kz9fv77//GQAqRrXfv3vr5T3+qW2+7TWvWr1ff445r6UNCK0MoBeBrNgdg/UfS3IelNe8eiKGcueSanZigGUk+rY6pZxD6gXlRJfNKnB+ITo3WuKvG6frrrz+i1fwAAAAQ3pasXCm53TpjxIiWPhQ0g2Gnniq3x6Ol2dmEUmh0hFJos2ymkA1nrI+tQhcaBN7qVRRLi16uqozas7L67P02LyopUbN8Pu3z1j0I/dB5Ub4Yn84ec7buvvtupaenN+OVAAAAQHMoLStz/p+QkHBUP//09Om67ac/Ve7u3Q3+mRu//W3l5ubq9ZdfVlvnionRay++qCsuv7xZfl9UVJTiYmNVXFLSLL8PbQuhFNqsHTt2aOPGjQoEAtq8ebNzng1pdB+Yj2TVPa29wiemdI+6bpstd+aNUsn+GvOiZhyYF1V20LyoEI/LozE9x2jKgCl69LeP6st1Xzq31cSJE52V/CzQs8HibSXYtJUM7Wtjq/mFVjJsU8EmAABoU2y0wKHjBeoLjj7+5BOdN2aMcnbtUruUFI2/9lpdctFFasvsfUivE07QonnzNHjQoCP62R2bNh3Ra+2jCQEPxSgJNBVCKbRZtuqaDb22QKGiosI5z2YeHRwotNoWvS3z5Jr7kE5f8YZcNpH8QKPel7GxmpHs02f1zIvyRfl0Td9rNLHfRHVO7Oyc94Mf/EDjx4/XGWec4azSMWfO1yvztZVg04TCp6ysrOrtbSHYBAAAOFJxcXHOqa06XLdGQ9hKh0BrUbsEAmgjLESwqh472dcHfx86r1WpLJcWvyA9dq705Bi5V7zuBFL2lPhaYoKu7tpJ3+vcoc5Aylvo1c+H/VzvXfue7jj1jupAygwYMECjRo2qrjBra8HmKaecUu/JtgMAAKB25U5Khw41zvvdAw+oQ7du8qWl6dvf/77u+uUvNXjYsFo33Z/+/Gd17tlTaZ0760e33lr94XJdrHLrimuuqXHebXfeqXMuuKD6e/v61ttv18/uvlupnTqpU48e+vV999X4Gav++t4Pf6iO3bsrNilJJw0Zorfefrt6+2eff64zzztPccnJ6n7ccc7lFRUVVW/P6NtX9/3+95r6rW8pKT1d3/3hD50qKTPktNOcdrzQMc1fsEAXXHyx0rt0UXL79jr7/PO1cNGiGsdj+7/+r385X9sHpPb9q6+/rnPHjFF8SooGnXqqvpw7t7pK7abvfEd5eXnOfnay6/fb++93rseh7Da/59e/rvc2BRpb23sXCTQDqxgqLS1VYWGh8yRm37eYwj3SJw9Kfz1Jeu270vaqJ7Uct1vTUpI0pntX3ds+TWvqqAxzbXPJ+7ZX7qfd6rKzixKijm5uQGt1aJB56KnVBZsAAABN4NlZs3T/H/6g/7n/fn315Zfq0b27pj36aK39PvrkE61bv14f/ec/eubxx/X0jBlOwHWsnpk505mPlTlnjh78/e+dwOa99993ttmoj4svu0yff/mlZj71lFZkZekPv/tddXfFunXrdNG4cbr6iiu0ZMECvTBzpj774gvdctttNX7Hn/7yFw06+WQtyszUPXffrXmff+6c//477zjteK++8EL1KIgbpkzRZx9+qLmffqo+xx+vSy6/3Dn/cH5577366W23KWvePPXt00cTp05VZWWlM4z+r3/6k5KSkpzfY6ef3n67vnXDDVqZne2EYCGLsrK0ZOlS3TR16jHfpkBD0b4HNLI9e/ZoxYoV2rBhg/MkZuW57du3V79+/Zz/N5sdS6TMh6WlL0v+qnlHZp3Ni0pO0psJCSp315wD4AhI7jXuquHlu6ty6y5durRssAYAAICI8Nbs2UpMTa1xno3LOJx/PPSQbr7xRt10ww3O9/f+8pd69/33nQ94D2ZzlP75t785gZC9th578cX64KOP9J2bbz6mYx548sn61X//t/N1nz599M9p05zLveD88/X+Bx9o3vz5Wrl4sfr27evs07t37+qffeCPf9SkCRN02623Vv/83//8Z6fCado//qHY2Fjn/PPOOUd33n579c95DoyAsHEiB7fjnXfuuTWO7dGHHnKqyj759FNdOnZsvdfBgqaxl1zifP2be+/ViYMHa+3atc7tlJyc7MyEOvj3JCYm6sILLtBTzzzjrK5n7OuzzzqrxvUDmhqhFNDIgVRmZqZTrmurVNgTpvXL2+whK5kdPnx40wZTAb+0arY092Fp02fVZzvzouJiNT3Jp8/rmRelUsmz3CPPEo9chVVh1UknnaTJkyfr3HPPrf40CAAAAKjPuWef7YQxB5s7f76m3HhjvT+zavVq/fB736tx3mmnnqoPP/64xnkn9u9f4zVp506dtHT58mO+MwaedFKN7+1yd+/Z43ydtXixunXrVh1IHWrxkiVOddGzzz9ffZ59mGsfTtuH1P3793fOO/WUUxp0LLt27dJ///rXTtudHYMFesXFxdq8Zcvhr8PJJ9c4fmM/b6FUfSzM+9Z3v6s///GPziiO5154QX/54x8bdJxAYyGUAhqJPflkZ2c7Txrp6ekqObBkqrVw2SckFljZdtvW6KtXlORKi2ZK8x6RcqtWEjRlLunthARnePnaega3B3OCiloaJfdKt1wVVauonHPuOU4YNXDgQFbaAAAAQINZG9zxxx9f47wt27Y1yi1oH/oezF63WvhTHwtaDq32r2sG1eEu95sGsls11/e+/W3d+qMf1dpmK3uHJMTHqyFuuPlm7cvJ0d/+93/Vs2dPxURHa8TZZ3/jcPSDr0PovcbhbhszbuxY573Ka//6l7PIk90211x1VYOOE2gshFJAI7FKqL1791aXxx7Mvrc+bttu+6WkpDTOL927tqpFL+s5qeLrYYr73G69kOTTC0mJyqmnwsnmRZV/Ua7g2qBiE2Od4OyyKy/TxIkT1b1798Y5PgAAAOAbnNC3rzPbaOrkydXnzf/qq2O+3dqnp2vZIZVUWUuW1AqhvqkCaevWrVq9enWd1VJDhwzRipUrawVx3yS00vehrY02u+qhv/9dl1x8sfP9li1bnPcQx8J+V10tlF6vVzdMnuy07dk+E669tk2vioiWQSgFNJKysjJnmGB9T3L2h94GFNp+x8Q+7Vn3YVUYtebdGpvWRkU5VVFvJSaovI5qLLetbZCtqnlRe9zyBrxK6ZKi6667zgmjLFADAAAAmtOPf/hDfecHP3Ba3M44/XS98PLLTktc7169julybY7TH//8Z02fOVMjhg/XzFmznJBqyODBDb4Mm7F01pln6uoJE/TnBx/U8ccdp+xVq5wPnS+68EL9/Kc/1elnnqlbfvITfftb33Iqoiykeu+DD5z5V/Xp0KGDEwD9+9131a1rV+cDYnstboPNZzz7rE4dOlT5BQX6r7vvPuagKKNnT6ei64MPP9SggQMVHx/vnMy3b7pJ/QcNcr7+/JB2SaA5sPoe0Eis9NU+bahvWVorubXtR70iW3mxtOBJ6f+GSzOvqg6krCD587hYfa9je13ZrbNe9SXWCqSSopN080k3652r3lHP5T2VVJKkYcOG6YorrtCVV17pPCkuWbLEaTEEAAAAmtOkiRN1989+pp/edZeGnn66NmzcqBunTKkeEn60LhwzRvf84hf62S9+oWEjRzofEE+dNOmIL+eV5593hoHbinYDBg92Li9UeWSVVJ+8/75Wr1mjM887T0OGD9e9v/2ts1DQ4dj7AhuI/sjjj6tLRoYuv+Ya5/wnHnlE+3Nzndthyk03OW2B9lr9WNgKfN//znc0fvJkte/aVQ/+7/9Wb7PB7La93wknaPhppx3T7wGOhivYxpbUys/PdxJoa6GydqpIZj3Cu3fvdv5IWb80jo49ocyZM8f5+swzzzzqgd72UPrss8+coea2ioaV2oZ6ye2TFAt8OnfurFGjRh3ZnKbcLdL8x6SvnpFKc6vPLnW59HZCvFMZta6eeVE9fD00ZcAUXXbcZYqPqvo0ZPbs2c4x2oob9sRs19dW4rCv7ROTYxnG3li3ZVM+Vux+CsdjbI7bMlzvH4QXnluallXL2ocU9ni0hTGM/d0NPR6tqvaoP7xAs+KxEqbsw8ENG+zTQhuy09JH02q99s47+v1DD2n+l182aH97w1np98vr8aihr4IvuPhi5zXqjKeeOqZjxTfcN8Gg+gwY4Ayav+O22+rd75zRo3XztddqyoHwDE0jUF6u3fv2qcNJJ8kd4a8HGpq90L4HNBILmmx1i9BsKWvlszcZ9gYkFPjY9gYFUpYVb8mU5k6TVr4pBb/uAd970Lyo/fWECikFKbrv8vt0Vrez5Ha5azzp+Hw+p3zXgrNmHcYOAHA+FNh4YBnwUPiUlZVVfctkZGQ4JwBoS2yhoIcffdSpbPK43Zr14ot6/8MP9d7s2S19aK2avfZ//sUXtXPXLt10ww0tfThoowil0OZZUFNaWuqESLm5uUpNTT3qQMYqjOwT7xUrVignJ8cJpCz4sQopC6S+sQKpslxa/mpVGLXj6zcpZvWBeVFvJyaooq7j80vuNW5nXlTJnhL1uKRHjUCqKYexH/zJf2hmVqgKy/DJPwBUsecD+1DgmwbfAkBbYq9DZ//737r/f/7HeV1ug89feeEFnT96dEsfWqvWoVs358PoRx96SO3atWvpw0EbRSgFtfVPByxA2rBhg1OGb8GKBUcNCpDqYT83cuRIJ4yyoGvEiBHfHHQV7qmaF7XgCalwV/XZgQPzoqYn+zS3vgGHpZJnmUeeJR65iqp+R7du3ZxQrHfv3s0yjJ1P/gGgYaw6ivY8AKjJBnm//+9/c7M0s+CxLsAENAJCKbTpQMrmeRQVFTkhjVX12BOiBSxWKXQss5UsgAoNZrSKo3oDqR2LpbkPS8telvzlNeZFvZkYr5lJSVofXc88hP1Vq+h5sj1yVVZd/uDBgzV58mSdddZZdc4ZO3gYe13B1NEOY+eTfwAAAADAkSKUQptt2bPZSda/biWrzTpbKeCXst+WMh+WNn1eY9Nej1vP+3x6Mcmn/Z66h9e7tricMMq90S2XXE74NPqC0U4YdeKJJx72V1vbnl2n0DD2Q28TG0ZnAZPt19o++bfAzarA7Hq2tRZD2isBAAAaV0bfvrrtllt02623ctMCx4BQCm1SU81WOqySXGnhdGneY1Le5hqbVh2YFzU7MVEVdWVgfsm/3K/AvIASSxKds2xw+hVXXKEJEyZ845KzTTKMPcLY9V23bp1z3dracGHaKwEAABrX/M8/V0JCAjcrcIwIpdAmNdVspTrtXSMteEzKmiVVFNWYF/WZMy8qSZlxVa1+h4oNxqpiQYUzL6pwV6FzXvve7TVx4kRdeeWVzkp6zT6MPUJZhdhxxx1XZ1tjax8uTHslAADA19XzjfG6r7W+ZgaaW93vzoAwE6rkqe90pOHRwbOV6nK0s5WqBYNql7NQJy/5jTzThkvzH68OpEpcLr3oS9Tl3broR5061BlIZSRl6J7T79G/LvmX4hfGy1XsUvfu3fWtb31Lr7/+uqZOnXpUgdShw9h79eqlnj176uyzz9aoUaNa9ZOrvfiw26y+U2tt3TN23drqdQcAAG3bORdcoFt+8hPdduedSu/SRRdeeqmWLV+ui8eNU2Jqqjp2764pN93kVNWH2PuLSTfcoIR27dS5Z0/95W9/cy7HLuPg9r2//v3v1d9v3rxZl199tXOZSenpuu7667Vr19cLGP36vvs0eNgwzXj2Wednk9u314TJk53fBbRlVEohIoTaj2yFPPuDb3r06FFd9XKkrVdNNVtJ5UXS4llyz31Yg/atqbFpj8etWUk+vZSUpFx33e1xwzsP19QBUzWq6yi5XVXX7fbbb3euq82/stYzC8saQ4OHsaNZMf8JAACgcT0zc6Z+8N3v6vOPPlJuXp7Ou/BCffumm/SXP/5RJaWl+vkvfqHrJk3Sh//5j7P/HT/7mT7/8ku98cor6tixo+79zW+0cNEiDR44sM7Lt/col19zjRITE/XJ++87HRk/+slPNH7yZH383nvV+61bv16vv/GG3nrtNe3fv9/5nX/44x91/29/y12ONotQChHVfuT3+6urm4YOHVpjSPWRaPTZSrmbq2ZFLXxGKs3TwT+VHR2lGUlV86IOLJJXk1/qU9lHD1zzgE5IPaHW5muvvda53nPmzDmi64jIxPwnAACAxtXn+OP14AMPOF//7oEHNGTQIP3+vvuqtz/56KPqftxxWr16tfO+45kZM/Tc9Okafd55zvanHntMXQ7zAfgHH36opcuWacOqVU53g5n+5JM6cfBgzV+wQMNOPbU6vHr68cerOx6mXH+9PvjoI93PHY42jFAKESG0upuFM6FWI/tjHgqljsYxz1YKBqXNc6W5D0nZb0lBmxJVxb6ac2Be1Lx65kWpRPIs9Tinfa596jap21FfF7QezH8CAABoXKcMHVr99eIlS/TRJ584bXaHskomq5yyD8FPOxAkGeueOKFv33ovf2V2trp361YdSJkB/fs73Qi2LRRKZfTsWWMER+dOnbR79+5GuY5ApIqoUOrXv/61fvOb39Q474QTTlB2dnaLHRMiW2i2koVRVi01YsQIpaamHr5CqrJMWvaqlDlN2rG4xiabF/VGYoJmpKRok7fukW2uHJc8WR65s91y+at+T3rPqlbC448/vnGvICI2gAUAAEDjSIiPr/66sLBQ48aO1f/cf3+dHw6uXbeuyW72QxdZsvccAfugG2jDIiqUMieeeKLef//96u8ba74O2q4Gz1Yq3C0teFKa/4RUVPMTjd0ej55PStSLySnKc9X9xOLafCCM2uSW60CD3ymnnKLJkyc7wVh9q8IBAAAAaBxDhwzRK6+95syjreu9ZO9evZzwaP5XXzlzXY2N/Fi9Zo3OGjWqzsvs36+ftmzdqi1btlRXS61YuVK5ublOxRSA+kVcomN/ODp16tTSh4G2ZHuWlPmwtOwVyV9eY9NKZ15Ukt7xJapSFkbVDKSClUF5Vnuqwqh9VaGThU9jxoxxwihrEwQAAADQPH70/e/rsSef1MQpU/SzO+9Uart2TnXU8y+9pMcffthpr7thyhT91113Ods6dOigX/32t85r+Po+vD5/9GidfNJJmnTjjfrrn/7kdGD88NZbdfZZZ+nUU07hrgVaUyi1Zs0adenSxalssVarBx54oDrBrovNCbJTiK2qFhoyZ6dIZsdvK8VF+vU4musc+roxVoyr8zIDldKq2XJlPizX5i9r7i/p07g4TW+XovkxoRLcQ6qjSiT/Ar/8C/2KccU4l5mQkKArr7xS48ePd1bxCP2+sLrerVRbfKwAR4vHC8BjJaLZc729vgmd0DQ384Hb9khu4dC+LXGvHPzRcecuXfTZRx/prl/+UmPGjnXeK/bs0UMXjhkjl9vt7Pe/Dz6oH9xyiy698kolJSXpv+64w6mEiomNrXH81Zfrcun1l1/WrbffrrNGj3YCrIvGjNHf//KXWtf70J8/9Lxwv99pN2z62zgYet8S4e9dGvreyxUMvSuNAO+8847TA2xzpGz+js2X2rZtm5YtW1ZjYNw3zaEytrJCfT8TSXeylZLa4L220vplg84XLVrkfD1kyJBjGnRe12UOHdBbiatfU8KymfIUbq+xX3FoXlS7VG2u59fGFcepZE6JtFIqKShxzrPQdNy4cTr//POd++pIVwpsjuvdWJcZrtriYwU4WjxeAB4rEa2yUtq1y4b3WItFSx9NqzX7/ff1f089pblffNGg/e0Npz8QkMeqjRR5ioqKlHFgBb+bbrxRbdHoCy7QtZdeqmsvu6ylD6VVC1RUKK+gQMk9e8p9yAyySGOr2vft29d5H2bhbn0i6i/1xRdfXP31wIEDnZXTevbsqRdffFE333xznT9z991364477qhRKWV9vjbg+nA3TCQIVbfYdWkrb7QtSLGKI2OltI0VzrR37Ve3bW+p8+JP5aoorrF9l8ejWUmJeimlnfKdOqnaRnQeoSn9p6hgaYFu/+vtznnHHXecE0SFAp+9e/c6x96tW7ewud6NfZnhqi0+VoCjxeMF4LES0SoqLEGwlUOqgik0ieT4eOc1lfcIXz9GRcjrzUVZWcpetcpZgS8vP1/3HRiKfuXllx/xdW4t3C6XkuLi1CE5uaUPpVULlJfLVVGh9unpckf44kehuc3fJKJCqUPZUGpL3tauXXvEK1nZH9HW8ObU3mi3luvSEFbYF2ozO+brbeWE6z6Ua+5DGr7ug1qbV0RHaXpKO/0nIe7AvKiagVS0O1qXHnepJvefrD7t+jjnlXUo03e/+12dfvrpOvnkk2u1xFmV1NEcs71JtNJi60+3YPUbVwhs7tsyArS1xwpwLHi8ADxWIpY9z9vrm9AJTXMzH7htG3oLBw/aNxLuFTvG//3LX7Rq9Wrn9fspQ4ZozocfOkFBW7/fQ/c9mojL1WpehzX0+CM6lLJWvnXr1mnKlCktfSiIJGWF0uJZUuYj0r41NZ4Y/ZI+iY/TjNR0LYgKbTmkw7VYOi/1PN1z2T1Kj6v5xGQB6J133tmoh7tnzx6tWLFCGzZscMKp8vJyp+LHhqTb/wEAAIDmfrMZmnsT6W+c6zJk8GB9NXduSx9GWLEui9bcWYGWE1Gh1E9/+lNnNo+17G3fvl2/+tWvnAfGxIkTW/rQ0Ezsya+0tNSpGLIlVo+oYmj/Jmneo9LCGVJZXq15Ua8nJujZtPba7LJoqjbXPlfVKnqr3NqZsVNp16WpqVkglZmZ6fSx29K09u89Li7OmalmvbnWwkowBQAAgOaUmpLiDJLfunXrYRedQuuwb98+lZSWKq1du5Y+FLRCERVK2R89C6DsQWFvxEeNGqW5c+fypryNOKqKIZvjv+kLKXOalP22FKzZgrfT5kUlJ+vFpCQVumxb7UDKtckl7yKvXFtcsv+qLjaonJwcpaU1XTBlvyM7O1vFxcVKT09XSUlJdTWW9efa7WHbbVtrXjkPAAAA4eXUgQMVGx2t1994Q7fecktLHw6a2L/efFMel0sjTjmF2xptO5R6/vnnW/oQ0EKOuGKoskxa9oo0d5q0c0mty1seHa3pqel6Ny6qznlRqpRTEeVURuV8XZJsv2fy5MnOzKimDoLsetlwdFsx7tDfZd/boH7bbvvZfDUAAACgOdjr8ClXXaXHZsxQYVGRLrzgAnVo377e9i57tV3p9ztDwvkoNTJYEcCevXv1wYcf6oUXX9TEceOUFOGr1yM8RVQohbbpiCqGCndLC56QFjwpFe2pcTlWA/VxfJymp3fSQo+/7nlRRZJ7iVvupW65S91O+OP1enXRRRdp0qRJ6tOnaqB5cwgNNrcQri42dNGW2bT9AAAAgOb03cmTFRMdrefffFOvvvrqN+7vDwTkaYXzp1q71ORk3Xzddc79DTQFQimEvQZVDG1erbwXZill9YtSoKLWvKjXfImamdZRW1VeZ4teYHdAgXkBBZYHqjdba54FUdddd12LtIha6GaBWEVFRZ3BlLUv2va6Vpc8HAux7GdtWGEo0LJwK/TJloVdR3qZAAAAaFvsdfiN48dr6rXXau2GDcorKHA+TK5LIBjU/sJCtUtMZPW2COG8z0pMVJ/evVvlMHuED0IphL16K4ZsPtTu1YreskAFBcUqU6akihrzop5LTdfLvgQVBCstxql12fG74lX2eZm8W7xV86Jipc6dOztB1OWXX96ibXEWwln1l7UoHjq7yp7w8/PznWO1/Y6EXd7GjRudr0PhU1ZWVvX2jIwM5wQAAAB8Ewss+h533GH3sVBqd16eOiQnE0oBqIFQCmHv0Ioht79MiYXr5doxWyrLV7mi5VWsYg6ETstsXlSHLno3Kii/tec5gdRBl+eJ0bjjxmlK/yla+slS3ffifZJbGjhwoDMv6pxzzgmLTwPs0wkb4h6qFLNgzqqZLKSzyqb4+Hhn+5HOtrIg63AD2q1SCgAAAACApkYohbBXXTG0cbXSi9ao266lcgereuysQDhfPnXSLs2Pq9DMTn21UKW1B5dbO15smib0m6DrTrhOqbGpznndLu6mxYsX68orr9TJJ5+scGNtgzZc3VYdtNX+LJCymVoWLB121cFvCPlozwMAAAAAtDRCKYS3QECudR+o3+onlLelXHsVpyR5FK2AyhWlHJdPu6IDmtUhoLVeC2gskKrJtdel7w/7vr498tuK9kTXqgq69957Fc4seBo5cqQTRlm11IgRI5Samtrkq/8BAAAAANCUCKUQnsoKpcWzpMyHpX1rZXHTcKUqW8dpr1K12+XT5phorYgv1sa4LSrwFtS6CPdGtzyLPHJtdWnrvq2KPity29IsgLKVBo3NuSKQAgAAAABEOkIphJf9m6R5j0oLZ0hleTU2tVeOtkcX6tWUvloQ5VK5q0LF3mLZfPJqlZJ7pVuexR659389F2rr1q1OlZHNpgIAAAAAAC2Pd+hoebZ07KbPpbnTpFWzq1bVO4hNj/ow0afpHXsoK2AVUfm1L6NI8izxyLPMI1fp1ymVtb3Z8PJTTz2V6iIAAAAAAMIIoRRaTkWptOwVKXOatHNprc2FLpdeS+uoZ1PaaZu/SHICqZpce1zyZHnkXu2WK+CqnhN1ySWX6Prrr1fv3r2b5aoAAAAAAIAjQyiF5lewU5r/hLTgSal4b63N270ePdupt16NkQoDZZIFUocIrAkoZlmMMy/K/gvNWrr22mudkw0CBwAAAAAA4YtQCs1n28KqweXLXpUCFbU2L46N1fSuffV+IE8BlUk1u/gU44lRcFlQhZ8USjlSnC/OOb9Hjx5Oi97YsWMVExPTXNcGAAAAAAAcA0IpNC1/pbTyjaowaktmrc2Vkj5ISdP0Dt20pGK/FMittU/7uPaa2G+iru17rZ7Y9YQezXnUOX/IkCGaMmWKRo0aJbf766HmAAAAAAAg/BFKoWkU50gLn5HmPS7lb621ucDl0qudMvScL17bKwokC6QO0S+1n6YOmKqLMi5SlCfKOW/ChAnKysrS6NGjnUDK4/FwDwIAAAAAEIEIpdC4dmdXDS5f/IJUWVJr8zabF9XjRL3qLlGRv0yyQOpgQcmzyaM/X/9nnXv8ubVWzOvUqZNuvvlm7jUAAAAAACIcoRSOXSAgrX1PmjtNWv9Rnbtkxfv0dNe++qhyrwLBXMl/yA4VknulW57FHrlz3VrXZZ3O63Me9w4AAAAAAK0UoRSOXlmBlDWral5Uzro650W95eugp1JStd5bLFXuqX0ZhZJniUeeZR65yr6uilq+fHnNX1VWpvLycvn9fudrU1BQUN2+Fx0dzZBzAAAAAAAiCKEUjtz+jVLmo9KiGVJZfp3zol7qNkCz4lzaWWHbi2vt49rtkifLI/cat1yBqjDKWvXOOussTZo0yRlifrAdO3Zo48aNztehFfZstlRIRkaGcwIAAAAAAJGBUAoNEwxKGz+rqopaNVsKBmrtsiU6Ts/1GqRX/Tkq9tvw8kMvQ3JvcMuzyCPXdpfsv1DING7cOF1//fXq0aNHnb++c+fOSktLq/fwrFIKAAAAAABEDkIpHF5FqbTsZWnuw9KupbU2B61iKaWjpnc9Xh8Wb1OgfHvtfcqD8qz0VM2LynNXn5+amqrx48fr6quvVkpKymEPw4KrUIVUW0LbIgAAAACgtSKUQt3yd0gLnpAWPCUV76212Yqg3u/aX9NTkrSseIdUvKXWPqnRqdr9790KZAUUGxVbfX7v3r01efJkXXTRRVQ4fQPaFgEAAAAArRWhFKqrcYx7Z5aiFz4h76o35QrYqPKa8j0evdJ7mJ7zlGhn2X6puKjWPgPSBmjqgKka3X20rn/pei0vXS5FSaeddpoTRo0YMcKZH4VvRtsiAAAAAKC1IpSCdmzbrKIFL6jb1jfky19V5y2yJaGdns0YpFfLtqukcnvV0noHsflQ53Y/V1NPnKqhHYY6oZOtlDdmzBj5fD7ddddd6t+/P7f2EWqrbYsAAAAAgNaPUCqCq5oCgYCKiopUUFAgt9tdPfC7wSFGcY701dPqOe8xuQrqmAUlaUH73prRqYc+LtygYPH62pdRLiVtTtKzdz6rjJTaq9/169fPOfXt2/eIrysAAAAAAGi9CKUidMaQBVKbNm1SRUWFjjvuOHk8Hmd7RkaGczqs3SuludOkJS9KlSUH1sCrOS/q3d6naUZClJYXbpEK6wijCuQMLves8DhB2bpz1ilj9Df8XgAAAAAAgAMIpSJ0xpC1xlnFVElJiYYOHSqv11tdKVWnQEBa866UOU1a/3Gdu+RFJ+qpjv31anSx9gd2SoW193HtcsmzyCP3Ordcga/jrHnz5mn06NE19g0GgyotLVVlZaVyc3Od1faYJQUAAAAAAAyhVITOGLJQyv5vgU9iYqKioqLq/oGyAmnRs9K8R6ScOiqeJG1u10Mze56o1wvXq8S/SwocskNQTgjlyfLItcOmR1WFUdYyeM455zjDywcOHFjjR/bs2aMVK1Zow4YNTlWXBWjt27d3Wvns/wAAAAAAoG0jlGqtcjZI8x6VFs2UyvLrnBf1Vc9TND01XR/nZiuYt7z2ZZTLac+zNj1X/tdVUXFxcbrssst0/fXXq2vXrrV+zAKpzMxMZ96VhWXWWmg/Y62HeXl5Gj58OMEUAAAAAABtHKFUaxIMShvnSHMfllbNPhA91VThidZ/TjhbM6LKtSJ/g5S7p/bF5AXlXeqVZ7lHrvKvwyircBo/fryuuuoqJSUl1XMIQWVnZ6u4uFjp6elOe6Gxqq7Y2FgnsLLtto1WPgAAAAAA2i5CqdagokRaOkvKfETatazOXfJ8HfXS8adrVtkW7S5ZJVVlRTV0j+quDS9uUGBVQLEJsdXn9+nTx2nRGzNmTP1tgqHfk5envXv3Kjk5uVboZN9bmGXbbb+UlJSjvcYAAAAAACDCEUpFKKtI8ufvVMctH6jw73crpXRzrVX0zKauAzWzy/H61/5lKsldVGu72+XW6B6jNXXAVPVL6qexT4xVga/AqWwaMmSIE0YNGzaswVVNthKfzbmqL7yyQewFBQXOfgAAAAAAoO0ilIpAe1Yv0MoPZip/907tD7q1Vf2Vro7qp3VqrxwFXR4t6HuOpifF65O9SxTcu6DWZcR743VVn6s0qf8kdfN1q54DddZZZzmzoE488UT17t1bvXr1OqI2OwuzbCXAioqKOoMpG3hu220/AAAAAADQdhFKRRgnPFq0VMW79ihZxYpShSoUpR3qqP2edJX2TdLLMbu1Mm+NtLeOC8iXjss7TjN+NkO+aF+tweQ2uNwGk3fo0OGoBpNb257Ni7KfTUtLq1XdlZ+fr86dOzv7AQAAAACAtotQKoJUDxF3JSg9MUbuwn1V58cnaktKZ20oLNHOnbu0KnmdDu3lc+1wyZPlkXudW1uDW7Xn+j3y9fY1+mBy26dfv37Vs6Wslc9CLmvXs7a9+Ph4ZztDzgEAAAAAaNsIpSJIjSHi3U/Vzk2ZWhQTr62uPFUWb5FXXiVVJCm+Ml7FUcVSQE4I5YRRO901LmvOnDlOe15TDCa3qiqrrlqxYoVycnKcQMrCLquQskCqoVVXAAAAAACg9SKUiiChIeL5lfmaX7RRm6JLpaCdqrb7XX65g255y73yLPPIs9gjV8HXQZPb7db555/vDC8fMGBAkw4mt+Bp5MiRThhllz9ixAilpqZSIQUAAAAAAByEUhEkNES8pKJEm/I31druKfMomBOU6x2XvPlf37XWMnfllVdqwoQJTrVScw0mt0orawE0VmVFyx4AAAAAAAghlIogBw8R7xzfWTuKd1RtKJTTnpdQkaD8vHyV5pc6Z3fs2FETJ07UFVdcocTExG+8TAaTAwAAAACA5kIoFUEOHiLep7iPivYVaf+q/YoPxDsVSVbttHPnTvXv399p0Rs9erRT5dTQy2QwOQAAAAAAaC6EUhEmNETct8Inf75fiz2L5fK6lJ+fr27duunHP/6xhgwZckStcgwmBwAAAAAAzY1QKgKFhogXFxdr5cqVzpyoqVOnKiMj45gvk8HkAAAAAACgORBKRajQEPGxY8fqwgsvrHf1vKO5TMNgcgAAAAAA0JTcTXrpAAAAAAAAQB2olEKjKysrU3l5ufx+v/O1KSgokMfjcb6Ojo5WTEwMtzwAAAAAAG0YoRQa3Y4dO7Rx40bn61D4lJWVVb3dZl8dy/wrAAAAAAAQ+Qil0Ohs8HpaWlq9261SCgAAAAAAtG2EUhHeGldRUaHCwkJ5vd6waY2z39/SxwAAAAAAAMIboVSEt8ZVVlY6rXG2ct7RtsYxAwoAAAAAADQ3QqkIbo0LBALau3ev0tPT5Xa7j7o1jhlQAAAAAACguRFKRZiDW+MslCopKZHP56sOpY4GM6AAAAAAAEBzI5QCM6AAAAAAAECzO/ryGgAAAAAAAOAoEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2RFKAQAAAAAAoNkRSgEAAAAAAKDZEUoBAAAAAACg2XnVxgSDQef/+fn5inSBQEAFBQWKjY2V202+CPBYAXhuAXgd1sZVVEiFhVJxseRtc291wlYgGKx63+L3y+1ytfThAGErUFGhgsJCxebnyx0To0gWylxCGUx92txfavtjaLp3797ShwIAAAAAANCqM5jk5OR6t7uC3xRbtTJWXbR9+3b5fD65Ijylt+TRwrUtW7YoKSmppQ8HCFs8VgAeLwDPLUDL4bUY0PYeK8EDFZJdunQ5bGdXm6uUshujW7duak3sH2uk/4MFmgOPFYDHC8BzC9ByeC0GtK3HSvJhKqRCGEQEAAAAAACAZkcoBQAAAAAAgGZHKBXBYmJi9Ktf/cr5PwAeKwDPLQCvw4BwxPsWgMdKfdrcoHMAAAAAAAC0PCqlAAAAAAAA0OwIpQAAAAAAANDsCKUAAAAAAADQ7AilItT//d//KSMjQ7GxsRo+fLjmzZvX0ocEhJ0HHnhAw4YNk8/nU4cOHXTFFVdo1apVLX1YQNj7wx/+IJfLpdtuu62lDwUIS9u2bdPkyZOVlpamuLg4nXzyyVqwYEFLHxYQVvx+v+655x716tXLeZwcd9xxuu+++3AIrnMAAAezSURBVMRIY0D69NNPNW7cOHXp0sV5zfX666/XuFnscXLvvfeqc+fOzuPn/PPP15o1a1rlTUcoFYFeeOEF3XHHHc7KewsXLtSgQYN04YUXavfu3S19aEBY+eSTT/SjH/1Ic+fO1XvvvaeKigqNGTNGRUVFLX1oQNiaP3++HnnkEQ0cOLClDwUIS/v379fIkSMVFRWld955RytWrND//u//ql27di19aEBY+Z//+R9NmzZN//znP7Vy5Urn+wcffFD/+Mc/WvrQgBZn70fsffz//d//1bndHit///vf9fDDDyszM1MJCQnOe/7S0lK1Nqy+F4GsMsqqP+wPvAkEAurevbt+/OMf66677mrpwwPC1p49e5yKKQurzjrrrJY+HCDsFBYWaujQoXrooYf0u9/9ToMHD9Zf//rXlj4sIKzYa63PP/9cc+bMaelDAcLapZdeqo4dO+qJJ56oPu/qq692qj5mzpzZoscGhBOXy6XXXnvN6eoIVUlZBdWdd96pn/70p855eXl5zuPp6aef1oQJE9SaUCkVYcrLy/XVV1855Xshbrfb+f7LL79s0WMDwp39MTepqaktfShAWLLKwrFjx9Z4jgFQ0xtvvKFTTz1V1157rfNBx5AhQ/TYY49xMwGHOOOMM/TBBx9o9erVzveLFy/WZ599posvvpjbCjiMDRs2aOfOnTVejyUnJzvFKa3xPb+3pQ8AR2bv3r1Of7alpAez77Ozs7k5gXpYRaHNx7GWi5NOOonbCTjE888/77SEW/segPqtX7/eaUmyUQq/+MUvnMfMrbfequjoaN1www3cdMBBVYX5+fnq16+fPB6P8x7m/vvv16RJk7iNgMPYuXNn9Xv8Q9/zh7a1JoRSANpMBciyZcucT+gA1LRlyxb95Cc/cWav2QIaAA7/IYdVSv3+9793vrdKKXt+sbkfhFLA11588UU9++yzeu6553TiiScqKyvL+YDQ2pJ4rAAIoX0vwqSnpzufNOzatavG+fZ9p06dWuy4gHB2yy236K233tJHH32kbt26tfThAGHH2sJtsQybJ+X1ep2TzV6zAZv2tX26DaCKrYQ0YMCAGjdH//79tXnzZm4i4CD/9V//5VRL2fwbW6FyypQpuv32253VkQHUr9OB9/Vt5T0/oVSEsdLwU045xenPPvgTO/t+xIgRLXpsQLixIYEWSNngwA8//NBZkhhAbaNHj9bSpUudT7FDJ6sEsRYL+9o+DAFQxdrAV61aVePmsJk5PXv25CYCDlJcXOzMvj2YPZ/YexcA9evVq5cTPh38nt9aYW0Vvtb4np/2vQhkMwys5NXeMJx22mnOyki2pORNN93U0ocGhF3LnpWM/+tf/5LP56vuwbZBgbbyC4Aq9vg4dNaaLT2clpbGDDbgEFbpYQOcrX3vuuuu07x58/Too486JwBfGzdunDNDqkePHk773qJFi/TnP/9Z3/rWt7iZ0ObZisdr166tMdzcPgi0BZnsMWOtrrYScp8+fZyQ6p577nFaX0Mr9LUmrqCVEiDi/POf/9Qf//hH5022LdltLRY2jR9AzeVV6/LUU0/pxhtv5KYCDuOcc85xnl/sgw8ANVlL+N133601a9Y4bxbsA8PvfOc73EzAQQoKCpw30laxbi3i9oZ64sSJuvfee53uD6At+/jjj3XuuefWOt+KT55++mmn4+NXv/qV84FHbm6uRo0apYceekh9+/ZVa0MoBQAAAAAAgGbHTCkAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAIAwU1lZqZEjRyohIUHZ2dk1tj366KNyuVy69957W+z4AAAAGoMrGAwGG+WSAAAA0Gg2bdqkwYMHq2fPnsrMzFRMTIyWL1+uYcOG6ZRTTtHHH38sj8fDLQ4AACIWlVIAAABhyMKoJ554QosXL9add96pkpISjR8/XrGxsXr22WcJpAAAQMSjUgoAACCM/fCHP9S0adN0xhln6IsvvtArr7yiq666qqUPCwAA4JgRSgEAAISx0tJSnXTSSVq3bp2+853vODOlAAAAWgPa9wAAAMKYte9t3rzZ+XrZsmXOEHQAAIDWgFAKAAAgTOXn52vixIlKT0/X/fffry+//FK/+tWvWvqwAAAAGoW3cS4GAAAAje273/2uswrfe++9p/POO0+LFi3SH/7wB51//vk699xzucEBAEBEY6YUAABAGLKV97797W/rF7/4hVMlZXJzczV48GBVVFRoyZIlSktLa+nDBAAAOGqEUgAAAGEmOztbp5xyihNAffLJJ/J6vy5utxa+s846SxdffLHeeOONFj1OAACAY0EoBQAAAAAAgGbHoHMAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAAA0O0IpAAAAAAAANDtCKQAAAAAAADQ7QikAAAAAAACouf0/AP2M032mCYYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Visualize comparison\n", "x_plot = np.linspace(0, 10, 200)\n", @@ -337,9 +512,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:26.463057Z", + "start_time": "2026-01-11T08:53:26.135719Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.502354Z", + "iopub.status.busy": "2026-01-10T22:23:12.502153Z", + "iopub.status.idle": "2026-01-10T22:23:12.657074Z", + "shell.execute_reply": "2026-01-10T22:23:12.656367Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Generate data for polynomial fitting\n", "np.random.seed(123)\n", @@ -361,9 +561,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:26.716639Z", + "start_time": "2026-01-11T08:53:26.472033Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.659574Z", + "iopub.status.busy": "2026-01-10T22:23:12.659370Z", + "iopub.status.idle": "2026-01-10T22:23:12.739142Z", + "shell.execute_reply": "2026-01-10T22:23:12.738443Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "POLYNOMIAL FITTING (Degree 8)\n", + "======================================================================\n", + "Unregularised coefficients:\n", + " [ 1.76916563e-03 1.96793262e+00 -4.26606389e+00 1.70512990e+00\n", + " 3.98781374e+00 -2.59931773e+00 -3.98125430e-01 1.08590028e+00\n", + " -2.43719499e+00]\n", + " Max |coef|: 4.27\n", + " SSE: 1.8069\n" + ] + } + ], "source": [ "# Fit high-degree polynomial (degree 8) - prone to over-fitting\n", "degree = 8\n", @@ -425,10 +653,55 @@ ] }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "source": [ + "### Visualise Overfitting\n", + "\n", + "To visualise model overfitting, an extrapolation region is plotted.\n", + "This provides insight into whether the model accurately captures the system dynamics and not the underlying noise model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:28.476762Z", + "start_time": "2026-01-11T08:53:26.738978Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.741769Z", + "iopub.status.busy": "2026-01-10T22:23:12.741459Z", + "iopub.status.idle": "2026-01-10T22:23:13.202027Z", + "shell.execute_reply": "2026-01-10T22:23:13.198370Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "⚠️ Over-fitting Alert!\n", + " The high-degree polynomial fits training data well but\n", + " extrapolates poorly. Regularisation would help!\n" + ] + } + ], "source": [ "# Visualize fits\n", "x_dense = np.linspace(-1, 1, 200)\n", @@ -503,9 +776,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:29.626967Z", + "start_time": "2026-01-11T08:53:28.719534Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:13.206075Z", + "iopub.status.busy": "2026-01-10T22:23:13.205389Z", + "iopub.status.idle": "2026-01-10T22:23:13.701531Z", + "shell.execute_reply": "2026-01-10T22:23:13.700970Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smoothness weight= 0.0: SSE=1.807\n", + "Smoothness weight= 0.1: SSE=1.816\n", + "Smoothness weight= 1.0: SSE=1.976\n", + "Smoothness weight= 10.0: SSE=2.267\n" + ] + } + ], "source": [ "# Example: Fit with smoothness penalty\n", "class MultiObjectiveCost:\n", @@ -531,16 +826,17 @@ "results_multi = {}\n", "\n", "for w_smooth in weights_smooth:\n", - " result = chron.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", + " # Construct custom cost\n", + " multi_cost = MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth)\n", + " result = chron.ScalarBuilder().with_objective(\n", + " lambda x: multi_cost(polynomial_model(x), y_poly)\n", + " )\n", "\n", " for i in range(degree + 1):\n", " result = result.with_parameter(f\"c{i}\", initial_params[i])\n", "\n", " result = (\n", - " result.with_cost(MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth))\n", - " .with_optimiser(chron.NelderMead().with_max_iter(2000))\n", - " .build()\n", - " .optimise()\n", + " result.with_optimiser(chron.NelderMead().with_max_iter(2000)).build().optimise()\n", " )\n", "\n", " results_multi[w_smooth] = result\n", @@ -551,9 +847,46 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:30.777552Z", + "start_time": "2026-01-11T08:53:29.664947Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:13.703941Z", + "iopub.status.busy": "2026-01-10T22:23:13.703698Z", + "iopub.status.idle": "2026-01-10T22:23:14.285027Z", + "shell.execute_reply": "2026-01-10T22:23:14.284354Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 Smoothness Penalty Effect:\n", + " λ=0.0: No penalty → over-fitting\n", + " λ=0.1: Slight smoothing\n", + " λ=1.0: Balanced fit\n", + " λ=10.0: Too smooth → under-fitting\n" + ] + } + ], "source": [ "# Visualize effect of smoothness weight\n", "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", @@ -601,9 +934,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T08:53:30.977736Z", + "start_time": "2026-01-11T08:53:30.881434Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:14.288666Z", + "iopub.status.busy": "2026-01-10T22:23:14.288443Z", + "iopub.status.idle": "2026-01-10T22:23:14.293720Z", + "shell.execute_reply": "2026-01-10T22:23:14.293151Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example constraint penalties:\n", + " • Sum constraint: forces Σθᵢ ≈ target\n", + " • Positivity: penalises θᵢ < 0\n", + " • Bounds: penalises θᵢ outside [a, b]\n", + " • Monotonicity: penalises θᵢ₊₁ < θᵢ\n", + "\n", + "💡 Tip: Start with large penalty weights, then tune.\n" + ] + } + ], "source": [ "class ConstrainedCost:\n", " \"\"\"Cost with soft constraints via penalties.\"\"\"\n", @@ -751,9 +1109,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From ff5038bd214a30d7bcdcfbeee623cc37fdaca7d3 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 11 Jan 2026 10:50:41 +0000 Subject: [PATCH 04/18] refactor: ScalarProblemBuilder.with_callable renamed .with_objective, optimizer -> optimiser --- README.md | 2 +- docs/algorithms/index.md | 4 +- docs/algorithms/optimizers/adam.md | 2 +- docs/algorithms/optimizers/cmaes.md | 2 +- docs/algorithms/optimizers/nelder-mead.md | 2 +- .../samplers/dynamic-nested-sampling.md | 2 +- .../samplers/metropolis-hastings.md | 2 +- docs/api-reference/index.md | 6 +- docs/api-reference/python/builders.md | 12 ++-- docs/api-reference/python/optimizers.md | 2 +- docs/api-reference/python/results.md | 4 +- docs/api-reference/rust/index.md | 12 +--- docs/development/index.md | 2 +- docs/getting-started/concepts.md | 10 +-- docs/getting-started/first-ode-fit.md | 2 +- docs/getting-started/installation.md | 2 +- docs/getting-started/quickstart.md | 6 +- docs/guides/custom-solvers.md | 2 +- docs/guides/index.md | 2 +- docs/guides/tuning-optimizers.md | 2 +- docs/index.md | 2 +- .../06_custom_solver_integration.ipynb | 1 - .../notebooks/07_parallel_optimization.ipynb | 2 +- .../08_advanced_cost_functions.ipynb | 62 +++++++++++-------- examples/model_evidence.py | 2 +- examples/python_contour.py | 2 +- examples/python_problem.py | 2 +- python/src/builders.rs | 2 +- .../test_diffsol_dynamic_nested_parallel.py | 2 +- tests/integration/test_mathematical_suite.py | 2 +- tests/test_docs.py | 2 +- tests/unit/optimisers/test_adam.py | 6 +- tests/unit/optimisers/test_cmaes.py | 4 +- tests/unit/test_dynamic_nested_sampler.py | 30 ++++----- tests/unit/test_optimisation.py | 6 +- tests/unit/test_optimisation_api.py | 4 +- tests/unit/test_python_autodiff.py | 2 +- tests/unit/test_samplers.py | 6 +- 38 files changed, 109 insertions(+), 110 deletions(-) diff --git a/README.md b/README.md index d3a04e0..a5fa851 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ def rosenbrock(x): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) ) diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md index 62b29e5..83bc06c 100644 --- a/docs/algorithms/index.md +++ b/docs/algorithms/index.md @@ -34,7 +34,7 @@ Gradient-free and gradient-based algorithms for finding optimal parameters. -## Samplers (Planned) +## Samplers MCMC and nested sampling for uncertainty quantification and model comparison. @@ -120,7 +120,7 @@ Each algorithm page includes references to original papers and implementation de ## See Also -- [Choosing an Optimiser](../guides/choosing-optimizer.md) +- [Choosing an Optimiser](../guides/choosing-optimiser.md) - [Tuning Optimisers](../guides/tuning-optimizers.md) - [API Reference](../api-reference/index.md) - [Tutorials](../tutorials/index.md) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md index 7a8ab4b..04a8e70 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimizers/adam.md @@ -25,5 +25,5 @@ See [Adam API](../../api-reference/python/optimizers.md#adam) for usage details. ## See Also -- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md index 3301247..2765c7d 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimizers/cmaes.md @@ -26,6 +26,6 @@ See [CMAES API](../../api-reference/python/optimizers.md#cma-es) for usage detai ## See Also -- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Tuning Optimisers](../../guides/tuning-optimizers.md) - [Parallel Execution](../../guides/parallel-execution.md) diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimizers/nelder-mead.md index bc69ff2..3b460e0 100644 --- a/docs/algorithms/optimizers/nelder-mead.md +++ b/docs/algorithms/optimizers/nelder-mead.md @@ -26,5 +26,5 @@ See [NelderMead API](../../api-reference/python/optimizers.md#nelder-mead) for u ## See Also -- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md index a287151..71cfe41 100644 --- a/docs/algorithms/samplers/dynamic-nested-sampling.md +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -1,7 +1,7 @@ # Dynamic Nested Sampling Algorithm !!! info "Coming Soon" - Sampler documentation is being written. Python bindings are also in development. + Sampler documentation is being written. ## Overview diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md index 0395077..b35029b 100644 --- a/docs/algorithms/samplers/metropolis-hastings.md +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -1,7 +1,7 @@ # Metropolis-Hastings Algorithm !!! info "Coming Soon" - Sampler documentation is being written. Python bindings are also in development. + Sampler documentation is being written. ## Overview diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md index d457655..8164fbe 100644 --- a/docs/api-reference/index.md +++ b/docs/api-reference/index.md @@ -71,8 +71,8 @@ chronopt/ │ ├── CMAES # Covariance matrix adaptation │ └── Adam # Adaptive moment estimation ├── Samplers -│ ├── MetropolisHastings # MCMC sampling (planned) -│ └── DynamicNestedSampling # Evidence calculation (planned) +│ ├── MetropolisHastings # MCMC sampling +│ └── DynamicNestedSampling # Evidence calculation ├── CostMetric # Cost/likelihood metrics └── OptimisationResults # Result container ``` @@ -89,7 +89,7 @@ def optimize_function( func: Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]], initial_params: dict[str, float] ) -> OptimisationResults: - builder = ScalarBuilder().with_callable(func) + builder = ScalarBuilder().with_objective(func) for name, value in initial_params.items(): builder = builder.with_parameter(name, value) diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md index 3c3bd88..3134ce3 100644 --- a/docs/api-reference/python/builders.md +++ b/docs/api-reference/python/builders.md @@ -18,7 +18,7 @@ Builders provide a fluent API for constructing optimisation problems. Choose the show_source: false members: - __new__ - - with_callable + - with_objective - with_parameter - with_cost_metric - build @@ -34,7 +34,7 @@ def rosenbrock(x): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) ) @@ -129,7 +129,7 @@ out_i { state1, state2 } # Optional: output variables show_source: false members: - __new__ - - with_callable + - with_objective - with_data - with_parameter - with_cost_metric @@ -154,7 +154,7 @@ data = np.column_stack((t, observations)) builder = ( chron.VectorBuilder() - .with_callable(custom_solver) + .with_objective(custom_solver) .with_data(data) .with_parameter("alpha", 1.0) .with_parameter("beta", 0.5) @@ -212,7 +212,7 @@ Builders use a fluent interface - chain methods in any order: ```python builder = ( chron.ScalarBuilder() - .with_callable(func) + .with_objective(func) .with_parameter("x", 1.0) .with_parameter("y", 2.0) .with_cost_metric(chron.RMSE()) @@ -224,7 +224,7 @@ builder = ( Builders are immutable - each method returns a new builder: ```python -base = chron.ScalarBuilder().with_callable(func) +base = chron.ScalarBuilder().with_objective(func) problem1 = base.with_parameter("x", 1.0).build() problem2 = base.with_parameter("x", 2.0).build() # Different initial guess diff --git a/docs/api-reference/python/optimizers.md b/docs/api-reference/python/optimizers.md index 34febc3..bf5b02a 100644 --- a/docs/api-reference/python/optimizers.md +++ b/docs/api-reference/python/optimizers.md @@ -280,7 +280,7 @@ graph TD ``` For detailed guidance, see: -- [Choosing an Optimiser Guide](../../guides/choosing-optimizer.md) +- [Choosing an Optimiser Guide](../../guides/choosing-optimiser.md) - [Tuning Optimisers Guide](../../guides/tuning-optimizers.md) ## See Also diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md index c24cb04..ceb28ab 100644 --- a/docs/api-reference/python/results.md +++ b/docs/api-reference/python/results.md @@ -169,7 +169,7 @@ Results don't store parameter names. Track them manually if needed: ```python param_names = ["alpha", "beta", "gamma"] -builder = chron.ScalarBuilder().with_callable(func) +builder = chron.ScalarBuilder().with_objective(func) for name in param_names: builder = builder.with_parameter(name, 1.0) @@ -306,5 +306,5 @@ print(f"Time per evaluation: {elapsed/result.evaluations*1000:.2f}ms") - [Optimisers](optimizers.md) - [Samplers](samplers.md) -- [Choosing an Optimiser](../../guides/choosing-optimizer.md) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Troubleshooting](../../guides/troubleshooting.md) diff --git a/docs/api-reference/rust/index.md b/docs/api-reference/rust/index.md index d3b0333..274352a 100644 --- a/docs/api-reference/rust/index.md +++ b/docs/api-reference/rust/index.md @@ -49,7 +49,7 @@ fn rosenbrock(x: &[f64]) -> f64 { fn main() { // Build problem let builder = ScalarBuilder::new() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5); @@ -100,7 +100,7 @@ Strong typing catches errors at compile time: ```rust // Compiler ensures correct types let builder: ScalarBuilder = ScalarBuilder::new() - .with_callable(objective) + .with_objective(objective) .with_parameter("x", 1.0); let problem: Problem = builder.build(); @@ -207,14 +207,6 @@ cargo run --example ode_fitting Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/chronopt/tree/main/rust/examples) -## FFI and Bindings - -Chronopt's Python bindings use PyO3. For other languages: - -- **C/C++**: Use `extern "C"` FFI (planned) -- **Julia**: Use CxxWrap.jl (planned) -- **JavaScript**: Use wasm-bindgen for WASM (experimental) - ## See Also - [Python API Reference](../index.md) diff --git a/docs/development/index.md b/docs/development/index.md index e61253a..694602d 100644 --- a/docs/development/index.md +++ b/docs/development/index.md @@ -152,7 +152,7 @@ Python integration tests in `tests/`: ```python def test_optimisation(): - builder = chron.ScalarBuilder().with_callable(func) + builder = chron.ScalarBuilder().with_objective(func) # ... assert result.success ``` diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md index b3c9757..dff3866 100644 --- a/docs/getting-started/concepts.md +++ b/docs/getting-started/concepts.md @@ -9,7 +9,7 @@ Chronopt uses the **builder pattern** for constructing problems. This provides a ```python builder = ( chron.ScalarBuilder() - .with_callable(my_function) + .with_objective(my_function) .with_parameter("x", 1.0) .with_parameter("y", 2.0) ) @@ -37,7 +37,7 @@ def objective(x): problem = ( chron.ScalarBuilder() - .with_callable(objective) + .with_objective(objective) .with_parameter("x", 0.0) .with_parameter("y", 0.0) .build() @@ -88,7 +88,7 @@ def solve_ode(params): problem = ( chron.VectorBuilder() - .with_callable(solve_ode) + .with_objective(solve_ode) .with_data(data) .with_parameter("alpha", 1.0) .with_parameter("beta", 0.5) @@ -178,7 +178,7 @@ print(result.samples.shape) # (n_samples, n_parameters) | Computation budget is limited | You need full posterior distributions | | | Comparing multiple models (Bayes factors) | -See [Choosing an Optimiser](../guides/choosing-optimizer.md) and [Choosing a Sampler](../guides/choosing-sampler.md) for detailed guidance. +See [Choosing an Optimiser](../guides/choosing-optimiser.md) and [Choosing a Sampler](../guides/choosing-sampler.md) for detailed guidance. ## The Ask/Tell Pattern @@ -306,6 +306,6 @@ See the [Parallel Execution Guide](../guides/parallel-execution.md) for details. ## Next Steps - **[Tutorials](../tutorials/index.md)**: Interactive Jupyter notebooks -- **[Choosing an Optimiser](../guides/choosing-optimizer.md)**: Learn when to use each algorithm +- **[Choosing an Optimiser](../guides/choosing-optimiser.md)**: Learn when to use each algorithm - **[API Reference](../api-reference/index.md)**: Browse complete API documentation - **[Examples Gallery](../examples/gallery.md)**: Visual gallery of applications diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md index f0a0eec..441b672 100644 --- a/docs/getting-started/first-ode-fit.md +++ b/docs/getting-started/first-ode-fit.md @@ -240,7 +240,7 @@ result = optimiser.run(problem, initial_guess) **Best for**: Smooth problems, fast convergence on well-behaved objectives -For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimizer.md). +For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimiser.md). ## Troubleshooting diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 2412c91..1c7846c 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -54,7 +54,7 @@ import numpy as np def test_func(x): return np.asarray([(x[0] - 1.0) ** 2]) -builder = chron.ScalarBuilder().with_callable(test_func).with_parameter("x", 0.0) +builder = chron.ScalarBuilder().with_objective(test_func).with_parameter("x", 0.0) problem = builder.build() result = problem.optimise() diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md index 573a99c..392c9aa 100644 --- a/docs/getting-started/quickstart.md +++ b/docs/getting-started/quickstart.md @@ -26,7 +26,7 @@ def rosenbrock(x): # Build the problem builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.5) # Initial guess .with_parameter("y", -1.5) # Initial guess ) @@ -125,7 +125,7 @@ plt.plot(1.0, 1.0, 'r*', markersize=20, label='Global minimum') # Run optimisation and plot path builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", -1.5) .with_parameter("y", -0.5) ) @@ -153,5 +153,5 @@ plt.show() - **[First ODE Fit](first-ode-fit.md)**: Learn how to fit differential equations to data - **[Core Concepts](concepts.md)**: Understand the builder pattern and problem types -- **[Choosing an Optimiser](../guides/choosing-optimizer.md)**: Learn when to use each optimiser +- **[Choosing an Optimiser](../guides/choosing-optimiser.md)**: Learn when to use each optimiser - **[Tutorials](../tutorials/index.md)**: Explore interactive Jupyter notebooks diff --git a/docs/guides/custom-solvers.md b/docs/guides/custom-solvers.md index d1e1ed3..40e840d 100644 --- a/docs/guides/custom-solvers.md +++ b/docs/guides/custom-solvers.md @@ -20,7 +20,7 @@ def custom_solver(params): builder = ( chron.VectorBuilder() - .with_callable(custom_solver) + .with_objective(custom_solver) .with_data(data) .with_parameter("alpha", 1.0) ) diff --git a/docs/guides/index.md b/docs/guides/index.md index 30b7132..b371d3a 100644 --- a/docs/guides/index.md +++ b/docs/guides/index.md @@ -12,7 +12,7 @@ In-depth guides for making the most of Chronopt's optimisation and sampling capa Decision trees and guidelines for selecting the right optimisation algorithm. - [:octicons-arrow-right-24: Choosing an Optimiser](choosing-optimizer.md) + [:octicons-arrow-right-24: Choosing an Optimiser](choosing-optimiser.md) - :material-speedometer:{ .lg .middle } __Tuning Optimisers__ diff --git a/docs/guides/tuning-optimizers.md b/docs/guides/tuning-optimizers.md index bdf4d53..654294c 100644 --- a/docs/guides/tuning-optimizers.md +++ b/docs/guides/tuning-optimizers.md @@ -25,5 +25,5 @@ ## See Also - [Optimisers API](../api-reference/python/optimizers.md) -- [Choosing an Optimiser](choosing-optimizer.md) +- [Choosing an Optimiser](choosing-optimiser.md) - [Troubleshooting](troubleshooting.md) diff --git a/docs/index.md b/docs/index.md index 3178056..2f3efc9 100644 --- a/docs/index.md +++ b/docs/index.md @@ -91,7 +91,7 @@ def rosenbrock(x): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) ) diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index 7bbfa2f..13954af 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -205,7 +205,6 @@ "\n", "try:\n", " import diffrax as dfx\n", - " import jax\n", " import jax.numpy as jnp\n", " from jax import config, jit\n", "\n", diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb index 1e8590a..d9e3b81 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -709,7 +709,7 @@ "ax.legend(fontsize=11)\n", "\n", "# Add value labels\n", - "for delay, speedup in zip(delays, speedups):\n", + "for delay, speedup in zip(delays, speedups, strict=False):\n", " ax.annotate(\n", " f\"{speedup:.1f}x\",\n", " xy=(delay, speedup),\n", diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb index 46cb832..5257a88 100644 --- a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb +++ b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb @@ -39,8 +39,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:24.274746Z", - "start_time": "2026-01-11T08:53:23.724643Z" + "end_time": "2026-01-11T10:46:04.282907Z", + "start_time": "2026-01-11T10:46:04.000312Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:11.244941Z", @@ -52,6 +52,8 @@ "outputs": [], "source": [ "# Import plotting utilities\n", + "from functools import partial\n", + "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -101,8 +103,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:25.025601Z", - "start_time": "2026-01-11T08:53:24.381272Z" + "end_time": "2026-01-11T10:46:05.280410Z", + "start_time": "2026-01-11T10:46:04.327254Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:11.954892Z", @@ -146,11 +148,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:54:37.374413Z", - "start_time": "2026-01-11T08:54:37.268493Z" + "end_time": "2026-01-11T10:46:05.876850Z", + "start_time": "2026-01-11T10:46:05.688696Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.142024Z", @@ -241,8 +243,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:25.672816Z", - "start_time": "2026-01-11T08:53:25.291845Z" + "end_time": "2026-01-11T10:46:06.607936Z", + "start_time": "2026-01-11T10:46:05.889635Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.159555Z", @@ -319,8 +321,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:25.744253Z", - "start_time": "2026-01-11T08:53:25.678148Z" + "end_time": "2026-01-11T10:46:06.775918Z", + "start_time": "2026-01-11T10:46:06.667958Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.311675Z", @@ -422,8 +424,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:26.131942Z", - "start_time": "2026-01-11T08:53:25.753702Z" + "end_time": "2026-01-11T10:46:07.700277Z", + "start_time": "2026-01-11T10:46:06.788131Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.326233Z", @@ -515,8 +517,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:26.463057Z", - "start_time": "2026-01-11T08:53:26.135719Z" + "end_time": "2026-01-11T10:46:08.500949Z", + "start_time": "2026-01-11T10:46:07.742793Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.502354Z", @@ -564,8 +566,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:26.716639Z", - "start_time": "2026-01-11T08:53:26.472033Z" + "end_time": "2026-01-11T10:46:09.033038Z", + "start_time": "2026-01-11T10:46:08.563801Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.659574Z", @@ -653,8 +655,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "### Visualise Overfitting\n", "\n", @@ -667,8 +669,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:28.476762Z", - "start_time": "2026-01-11T08:53:26.738978Z" + "end_time": "2026-01-11T10:46:11.447302Z", + "start_time": "2026-01-11T10:46:09.035951Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:12.741769Z", @@ -779,8 +781,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:29.626967Z", - "start_time": "2026-01-11T08:53:28.719534Z" + "end_time": "2026-01-11T10:46:14.310435Z", + "start_time": "2026-01-11T10:46:11.575479Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:13.206075Z", @@ -825,11 +827,17 @@ "weights_smooth = [0.0, 0.1, 1.0, 10.0]\n", "results_multi = {}\n", "\n", + "\n", + "# Cost wrapper\n", + "def wrapper(x, y_poly, cost_func):\n", + " return cost_func(polynomial_model(x), y_poly)\n", + "\n", + "\n", "for w_smooth in weights_smooth:\n", " # Construct custom cost\n", " multi_cost = MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth)\n", " result = chron.ScalarBuilder().with_objective(\n", - " lambda x: multi_cost(polynomial_model(x), y_poly)\n", + " partial(wrapper, y_poly=y_poly, cost_func=multi_cost)\n", " )\n", "\n", " for i in range(degree + 1):\n", @@ -850,8 +858,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:30.777552Z", - "start_time": "2026-01-11T08:53:29.664947Z" + "end_time": "2026-01-11T10:46:17.318731Z", + "start_time": "2026-01-11T10:46:14.462850Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:13.703941Z", @@ -937,8 +945,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2026-01-11T08:53:30.977736Z", - "start_time": "2026-01-11T08:53:30.881434Z" + "end_time": "2026-01-11T10:46:17.544721Z", + "start_time": "2026-01-11T10:46:17.432337Z" }, "execution": { "iopub.execute_input": "2026-01-10T22:23:14.288666Z", diff --git a/examples/model_evidence.py b/examples/model_evidence.py index 10fb988..69a95a9 100644 --- a/examples/model_evidence.py +++ b/examples/model_evidence.py @@ -12,7 +12,7 @@ def rosenbrock(x: list[float]) -> float: builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", initial_value=1.2) .with_parameter("y", initial_value=1.4) .with_optimiser(chron.NelderMead().with_max_iter(2000)) diff --git a/examples/python_contour.py b/examples/python_contour.py index 896d6bb..6c5503e 100644 --- a/examples/python_contour.py +++ b/examples/python_contour.py @@ -16,7 +16,7 @@ def rosenbrock(x: np.ndarray) -> float: # Setup builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) diff --git a/examples/python_problem.py b/examples/python_problem.py index 257560b..65552e2 100644 --- a/examples/python_problem.py +++ b/examples/python_problem.py @@ -11,7 +11,7 @@ def rosenbrock(x): # Simple API builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) .with_optimiser(chron.NelderMead().with_max_iter(1000)) diff --git a/python/src/builders.rs b/python/src/builders.rs index 116d06d..52229a3 100644 --- a/python/src/builders.rs +++ b/python/src/builders.rs @@ -245,7 +245,7 @@ impl PyScalarBuilder { } /// Attach the objective function callable executed during optimisation. - fn with_callable(mut slf: PyRefMut<'_, Self>, obj: Py) -> PyResult> { + fn with_objective(mut slf: PyRefMut<'_, Self>, obj: Py) -> PyResult> { Python::attach(|py| { if !obj.bind(py).is_callable() { return Err(PyTypeError::new_err("Object must be callable")); diff --git a/tests/integration/test_diffsol_dynamic_nested_parallel.py b/tests/integration/test_diffsol_dynamic_nested_parallel.py index 32b10fa..de3589f 100644 --- a/tests/integration/test_diffsol_dynamic_nested_parallel.py +++ b/tests/integration/test_diffsol_dynamic_nested_parallel.py @@ -69,7 +69,7 @@ def test_dynamic_nested_sampler_parallel_fallback_for_non_parallel_problems(): # Scalar problem does not support parallel evaluation; sampler should still work problem = ( chron.ScalarBuilder() - .with_callable(lambda x: 0.5 * (x[0] - 0.5) ** 2) + .with_objective(lambda x: 0.5 * (x[0] - 0.5) ** 2) .with_parameter("x", 0.5, bounds=(-5.0, 5.0)) .build() ) diff --git a/tests/integration/test_mathematical_suite.py b/tests/integration/test_mathematical_suite.py index 5016bd3..3e128d4 100644 --- a/tests/integration/test_mathematical_suite.py +++ b/tests/integration/test_mathematical_suite.py @@ -161,7 +161,7 @@ def test_python_objectives_converge( ): """Ensure optimisation reaches known minima for several analytic functions.""" - builder = chron.ScalarBuilder().with_callable(objective) + builder = chron.ScalarBuilder().with_objective(objective) for idx in range(dimension): builder = builder.with_parameter(f"x{idx}", 1.0) diff --git a/tests/test_docs.py b/tests/test_docs.py index 2f30ef3..2e7583c 100644 --- a/tests/test_docs.py +++ b/tests/test_docs.py @@ -102,7 +102,7 @@ def test_core_apis_documented(self): pytest.skip("chronopt not installed") # Get public APIs from chronopt - public_apis = self._get_public_apis(chron) + self._get_public_apis(chron) # Find documentation files api_docs = list((DOCS_DIR / "api-reference" / "python").rglob("*.md")) diff --git a/tests/unit/optimisers/test_adam.py b/tests/unit/optimisers/test_adam.py index f62754b..dabe27a 100644 --- a/tests/unit/optimisers/test_adam.py +++ b/tests/unit/optimisers/test_adam.py @@ -17,7 +17,7 @@ def quadratic_grad(x): def build_quadratic_problem_with_gradient(): return ( chron.ScalarBuilder() - .with_callable(quadratic) + .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -40,7 +40,7 @@ def test_adam_direct_run_minimises_quadratic(): def test_python_builder_optimise_with_adam_default(): builder = ( chron.ScalarBuilder() - .with_callable(quadratic) + .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -61,7 +61,7 @@ def test_python_builder_optimise_with_adam_default(): def test_adam_falls_back_with_numerical_grad(): builder = ( chron.ScalarBuilder() - .with_callable(quadratic) + .with_objective(quadratic) .with_parameter("x", 0.0) .with_parameter("y", 0.0) ) diff --git a/tests/unit/optimisers/test_cmaes.py b/tests/unit/optimisers/test_cmaes.py index 1d972e3..0fe839a 100644 --- a/tests/unit/optimisers/test_cmaes.py +++ b/tests/unit/optimisers/test_cmaes.py @@ -11,7 +11,7 @@ def rosenbrock(x): def build_rosenbrock_problem(): return ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) .build() @@ -39,7 +39,7 @@ def test_cmaes_direct_run_minimises_rosenbrock(): def test_python_builder_optimise_with_cmaes_default(): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) diff --git a/tests/unit/test_dynamic_nested_sampler.py b/tests/unit/test_dynamic_nested_sampler.py index de58757..e79739e 100644 --- a/tests/unit/test_dynamic_nested_sampler.py +++ b/tests/unit/test_dynamic_nested_sampler.py @@ -37,7 +37,7 @@ def gaussian_nll(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(gaussian_nll) + .with_objective(gaussian_nll) .with_parameter("x", mu, bounds=(prior_lower, prior_upper)) .build() ) @@ -85,7 +85,7 @@ def exponential_nll(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(exponential_nll) + .with_objective(exponential_nll) .with_parameter("x", 1.0, bounds=(0.0, x_max)) .build() ) @@ -124,7 +124,7 @@ def bimodal_nll(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(bimodal_nll) + .with_objective(bimodal_nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() ) @@ -164,7 +164,7 @@ def pathological_nll(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(pathological_nll) + .with_objective(pathological_nll) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -195,7 +195,7 @@ def high_dim_quadratic(x: list[float]) -> float: """Simple quadratic in high dimensions.""" return 0.5 * sum(xi**2 for xi in x) - problem = chron.ScalarBuilder().with_callable(high_dim_quadratic) + problem = chron.ScalarBuilder().with_objective(high_dim_quadratic) for i in range(dimension): problem = problem.with_parameter(f"x{i}", 0.0, bounds=(-3.0, 3.0)) @@ -228,7 +228,7 @@ def sharp_peak(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(sharp_peak) + .with_objective(sharp_peak) .with_parameter("x", 0.0, bounds=(-1.0, 1.0)) .build() ) @@ -258,7 +258,7 @@ def large_offset(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(large_offset) + .with_objective(large_offset) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -286,7 +286,7 @@ def simple_quadratic(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(simple_quadratic) + .with_objective(simple_quadratic) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -321,7 +321,7 @@ def quadratic(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(quadratic) + .with_objective(quadratic) .with_parameter("x", 1.0, bounds=(-3.0, 5.0)) .build() ) @@ -360,7 +360,7 @@ def narrow_gaussian(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(narrow_gaussian) + .with_objective(narrow_gaussian) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -389,7 +389,7 @@ def simple_problem(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(simple_problem) + .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -431,7 +431,7 @@ def multimodal(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(multimodal) + .with_objective(multimodal) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -459,7 +459,7 @@ def bounded_quadratic(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(bounded_quadratic) + .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(lower, upper)) .build() ) @@ -487,7 +487,7 @@ def simple_problem(x: list[float]) -> float: problem = ( chron.ScalarBuilder() - .with_callable(simple_problem) + .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) @@ -514,7 +514,7 @@ def nll(x: list[float]) -> float: return ( chron.ScalarBuilder() - .with_callable(nll) + .with_objective(nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() ) diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 302cf3b..fe4ec1f 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -5,7 +5,7 @@ def test_builder_exposes_config_and_parameters(): builder = ( chron.ScalarBuilder() - .with_callable(lambda x: np.asarray([float(x[0]) ** 2])) + .with_objective(lambda x: np.asarray([float(x[0]) ** 2])) .with_parameter("x", 3.5, bounds=(0.0, 10.0)) ) @@ -31,7 +31,7 @@ def bounded_quadratic(x): def test_python_builder_rosenbrock(): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.2, None) .with_parameter("y", -1.2, None) ) @@ -52,7 +52,7 @@ def test_python_builder_rosenbrock(): def test_python_builder_bounds_respected(): builder = ( chron.ScalarBuilder() - .with_callable(bounded_quadratic) + .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(0.0, 1.0)) .with_parameter("y", 0.0, bounds=(0.0, 2.0)) ) diff --git a/tests/unit/test_optimisation_api.py b/tests/unit/test_optimisation_api.py index 6fbf309..4e0c91e 100644 --- a/tests/unit/test_optimisation_api.py +++ b/tests/unit/test_optimisation_api.py @@ -73,7 +73,7 @@ def rosenbrock(x): builder = ( chron.ScalarBuilder() - .with_callable(rosenbrock) + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) @@ -112,7 +112,7 @@ def test_all_builders_support_copy(): import copy # ScalarBuilder - scalar_builder = chron.ScalarBuilder().with_callable(lambda x: x[0] ** 2) + scalar_builder = chron.ScalarBuilder().with_objective(lambda x: x[0] ** 2) scalar_copy = copy.copy(scalar_builder) copy.deepcopy(scalar_builder) # Test deepcopy diff --git a/tests/unit/test_python_autodiff.py b/tests/unit/test_python_autodiff.py index ad5a64f..9b6e129 100644 --- a/tests/unit/test_python_autodiff.py +++ b/tests/unit/test_python_autodiff.py @@ -27,7 +27,7 @@ def quadratic_problem(): """Creates a 3D quadratic optimization problem using ScalarBuilder""" return ( chron.ScalarBuilder() - .with_callable(_quadratic_objective) + .with_objective(_quadratic_objective) .with_gradient(_quadratic_gradient) .with_parameter("x1", 1.0) .with_parameter("x2", 2.0) diff --git a/tests/unit/test_samplers.py b/tests/unit/test_samplers.py index 414261d..43f846c 100644 --- a/tests/unit/test_samplers.py +++ b/tests/unit/test_samplers.py @@ -13,7 +13,7 @@ def quadratic_potential(x: np.ndarray) -> np.ndarray: def test_metropolis_hastings_runs_and_returns_samples(): problem = ( chron.ScalarBuilder() - .with_callable(quadratic_potential) + .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) @@ -45,7 +45,7 @@ def test_metropolis_hastings_runs_and_returns_samples(): def test_dynamic_nested_sampler_runs_on_scalar_problem(): problem = ( chron.ScalarBuilder() - .with_callable(quadratic_potential) + .with_objective(quadratic_potential) .with_parameter("x", 0.5) .build() ) @@ -68,7 +68,7 @@ def test_dynamic_nested_sampler_runs_on_scalar_problem(): def test_dynamic_nested_invalid_live_points_are_clamped(): problem = ( chron.ScalarBuilder() - .with_callable(quadratic_potential) + .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) From 78d907e94f782fe43f158b0733eda014fac4dfa1 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 11 Jan 2026 10:55:48 +0000 Subject: [PATCH 05/18] feat: adds acceptance_ratio to Samples struct, SamplerResults bindings return NDArray type, adds aggregated samples method, stubgen fixes --- python/src/samplers.rs | 44 ++++++++++++++++++++-- rust/src/sampler/dynamic_nested/results.rs | 12 +++++- rust/src/sampler/metropolis_hastings.rs | 19 +++++++++- rust/src/sampler/mod.rs | 31 ++++++++++++++- 4 files changed, 100 insertions(+), 6 deletions(-) diff --git a/python/src/samplers.rs b/python/src/samplers.rs index a2034e8..a51a2e9 100644 --- a/python/src/samplers.rs +++ b/python/src/samplers.rs @@ -1,4 +1,4 @@ -use numpy::{PyArray1, ToPyArray}; +use numpy::{PyArray1, PyArray2, PyArray3, ToPyArray}; use pyo3::exceptions::PyTypeError; use pyo3::prelude::*; use std::time::Duration; @@ -79,8 +79,36 @@ pub struct PySamples { #[pymethods] impl PySamples { #[getter] - fn chains(&self) -> Vec>> { - self.inner.chains().to_vec() + fn chains<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray3> { + use numpy::ndarray::Array3; + + let rust_chains = self.inner.chains(); + + if rust_chains.is_empty() { + let empty_array = Array3::::zeros((0, 0, 0)); + return empty_array.to_pyarray(py); + } + + let n_chains = rust_chains.len(); + let n_iterations = rust_chains[0].len(); + let n_params = rust_chains[0][0].len(); + + // Create ndarray from nested vec + let mut array = Array3::::zeros((n_chains, n_iterations, n_params)); + for (i, chain) in rust_chains.iter().enumerate() { + for (j, sample) in chain.iter().enumerate() { + for (k, &value) in sample.iter().enumerate() { + array[[i, j, k]] = value; + } + } + } + + array.to_pyarray(py) + } + + #[getter] + fn samples<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray2> { + PyArray2::from_vec2(py, &self.inner.samples()).expect("Valid Array2") } #[getter] @@ -88,6 +116,12 @@ impl PySamples { self.inner.mean_x().to_pyarray(py) } + #[getter] + fn acceptance_rate<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray1> { + let rates = self.inner.acceptance_rates(); + rates.to_pyarray(py) + } + #[getter] fn draws(&self) -> usize { self.inner.draws() @@ -164,12 +198,14 @@ impl PySamples { } /// Iterator for Samples chains +#[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass] struct SamplesIterator { chains: Vec>>, index: usize, } +#[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl SamplesIterator { fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> { @@ -331,12 +367,14 @@ impl PyNestedSamples { } /// Iterator for NestedSamples posterior +#[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass] struct NestedSamplesIterator { samples: Vec<(Vec, f64, f64)>, index: usize, } +#[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl NestedSamplesIterator { fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> { diff --git a/rust/src/sampler/dynamic_nested/results.rs b/rust/src/sampler/dynamic_nested/results.rs index 9b54484..87f6620 100644 --- a/rust/src/sampler/dynamic_nested/results.rs +++ b/rust/src/sampler/dynamic_nested/results.rs @@ -151,7 +151,17 @@ impl NestedSamples { .iter() .map(|sample| sample.position.clone()) .collect::>()]; - Samples::new(chains, self.mean.clone(), self.draws, self.time) + + // DynamicNested doesn't use MCMC so no acceptance data + let acceptance_data = vec![Vec::new(); chains.len()]; + + Samples::new( + chains, + self.mean.clone(), + self.draws, + self.time, + acceptance_data, + ) } } diff --git a/rust/src/sampler/metropolis_hastings.rs b/rust/src/sampler/metropolis_hastings.rs index ef801e0..1132338 100644 --- a/rust/src/sampler/metropolis_hastings.rs +++ b/rust/src/sampler/metropolis_hastings.rs @@ -104,6 +104,7 @@ struct ChainState { proposal: Vec, samples: Vec>, rng: StdRng, + acceptances: Vec, // Track accept/reject per iteration } /// Phase tracking for MCMC sampling @@ -167,6 +168,7 @@ impl MetropolisHastings { proposal, samples: vec![initial_point.clone()], rng, + acceptances: Vec::new(), } }) .collect(); @@ -278,6 +280,8 @@ impl MetropolisHastingsState { chain.current_log_likelihood = proposal_log_likelihood; } + // Track acceptance for diagnostics + chain.acceptances.push(accept); chain.samples.push(chain.current.clone()); // Generate next proposal @@ -341,6 +345,19 @@ impl MetropolisHastingsState { } } - Samples::new(chains, mean_x, total_samples, self.start_time.elapsed()) + // Extract acceptance data from all chains + let acceptance_data: Vec> = self + .chains + .iter() + .map(|chain| chain.acceptances.clone()) + .collect(); + + Samples::new( + chains, + mean_x, + total_samples, + self.start_time.elapsed(), + acceptance_data, + ) } } diff --git a/rust/src/sampler/mod.rs b/rust/src/sampler/mod.rs index d84cda1..530d4f7 100644 --- a/rust/src/sampler/mod.rs +++ b/rust/src/sampler/mod.rs @@ -239,15 +239,23 @@ pub struct Samples { mean_x: Vec, draws: usize, time: Duration, + acceptance_data: Vec>, // Per-chain acceptance history } impl Samples { - pub fn new(chains: Vec>>, mean_x: Vec, draws: usize, time: Duration) -> Self { + pub fn new( + chains: Vec>>, + mean_x: Vec, + draws: usize, + time: Duration, + acceptance_data: Vec>, + ) -> Self { Self { chains, mean_x, draws, time, + acceptance_data, } } @@ -255,6 +263,10 @@ impl Samples { &self.chains } + pub fn samples(&self) -> Vec> { + self.chains.iter().flatten().cloned().collect() + } + pub fn mean_x(&self) -> &[f64] { &self.mean_x } @@ -266,6 +278,23 @@ impl Samples { pub fn time(&self) -> Duration { self.time } + + /// Get the acceptance rate for each chain + /// + /// Returns a vector of acceptance rates (proportion of accepted proposals) + /// for each MCMC chain. Each value is between 0.0 and 1.0. + pub fn acceptance_rates(&self) -> Vec { + self.acceptance_data + .iter() + .map(|chain_accepts| { + if chain_accepts.is_empty() { + 0.0 + } else { + chain_accepts.iter().filter(|&&a| a).count() as f64 / chain_accepts.len() as f64 + } + }) + .collect() + } } /// Unified result type for all samplers From efc529cc99ab095ad96c5f1d61c5439e1178a1c4 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 11 Jan 2026 22:27:39 +0000 Subject: [PATCH 06/18] feat: population-based optimisers & sampler support parallel capable objectives via enum based polymorphism Problem.optimise & Problem.sample default to batch based evaluation, automating parallelisation when selected. Gracefully fallback to sequential otherwise. --- rust/src/optimisers/cmaes.rs | 19 +++++----- rust/src/optimisers/mod.rs | 46 +++++++++++++++++++++++++ rust/src/problem/diffsol_problem.rs | 8 +++++ rust/src/problem/mod.rs | 30 ++++++++++++++-- rust/src/sampler/dynamic_nested/mod.rs | 42 ++++++++++++++++++++++ rust/src/sampler/metropolis_hastings.rs | 42 ++++++++++++++++++++++ rust/src/sampler/mod.rs | 36 +++++++++++++++++-- 7 files changed, 208 insertions(+), 15 deletions(-) diff --git a/rust/src/optimisers/cmaes.rs b/rust/src/optimisers/cmaes.rs index 06c0c58..2530ab6 100644 --- a/rust/src/optimisers/cmaes.rs +++ b/rust/src/optimisers/cmaes.rs @@ -311,6 +311,8 @@ impl CMAESState { .collect(); // Take ownership of current phase + // Placeholder MaxIters is set, will be replaced + // in handle methods below let phase = std::mem::replace( &mut self.phase, CMAESPhase::Terminated(TerminationReason::MaxIterationsReached), @@ -700,23 +702,20 @@ impl CMAES { } } - /// Run optimization with parallel evaluation - #[allow(dead_code)] - #[cfg(feature = "rayon")] - pub fn run_parallel( + pub fn run_batch( &self, + objective: F, initial: Point, bounds: Bounds, - objective: F, ) -> OptimisationResults where - F: Fn(&[f64]) -> Result + Sync, + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, { - use rayon::prelude::*; - let (mut state, first_point) = self.init(initial, bounds); - let mut results = vec![objective(&first_point)]; + let mut results = objective(&vec![first_point]); loop { if state.tell(results).is_err() { @@ -725,7 +724,7 @@ impl CMAES { match state.ask() { AskResult::Evaluate(points) => { - results = points.par_iter().map(|p| objective(p)).collect(); + results = objective(&points); } AskResult::Done(opt_results) => { return opt_results; diff --git a/rust/src/optimisers/mod.rs b/rust/src/optimisers/mod.rs index d8268d0..d712461 100644 --- a/rust/src/optimisers/mod.rs +++ b/rust/src/optimisers/mod.rs @@ -73,6 +73,52 @@ impl ScalarOptimiser { ScalarOptimiser::CMAES(cm) => cm.run(objective, initial, bounds), } } + + /// Run the optimiser with batched candidates + /// + /// # Arguments + /// * `objective` - Function that batch evaluates the objective + /// * `initial` - Initial point (will be auto-expanded if needed) + /// * `bounds` - Optional parameter bounds + /// + /// # Returns + /// Optimization results including best point, value, and diagnostics + /// + /// # Example + /// ``` + /// use chronopt::common::Bounds; + /// use chronopt::optimisers::{ScalarOptimiser, NelderMead}; + /// + /// let optimiser = ScalarOptimiser::from(NelderMead::new()); + /// let result = optimiser.run_batch( + /// |xs| xs.iter().map(|x| x[0].powi(2) + x[1].powi(2)).collect(), + /// vec![1.0, 2.0], + /// Bounds::unbounded(2) + /// ); + /// ``` + pub fn run_batch( + &self, + objective: F, + initial: Point, + bounds: Bounds, + ) -> OptimisationResults + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + match self { + ScalarOptimiser::CMAES(cm) => cm.run_batch(objective, initial, bounds), + ScalarOptimiser::NelderMead(nm) => nm.run( + |x| { + let result = objective(&vec![x.to_vec()]); + result.into_iter().next().unwrap() // ToDO: This needs proper error integration + }, + initial, + bounds, + ), + } + } } impl GradientOptimiser { diff --git a/rust/src/problem/diffsol_problem.rs b/rust/src/problem/diffsol_problem.rs index 7ff7847..377fbe5 100644 --- a/rust/src/problem/diffsol_problem.rs +++ b/rust/src/problem/diffsol_problem.rs @@ -270,6 +270,8 @@ impl Objective for DiffsolObjective { }) } + /// Population based evaluation supporting + /// both sequential and parallel configurations. fn evaluate_population(&self, params: &[Vec]) -> Vec> { if self.config.parallel { params.par_iter().map(|x| self.evaluate(x)).collect() @@ -278,6 +280,12 @@ impl Objective for DiffsolObjective { } } + /// Support parallel evaluation if config.parallel + /// is set by user. + fn supports_parallel_evaluation(&self) -> bool { + self.config.parallel + } + fn has_gradient(&self) -> bool { // Check the backend config matches!(self.config.backend, DiffsolBackend::Dense) diff --git a/rust/src/problem/mod.rs b/rust/src/problem/mod.rs index 2719077..2e74c8b 100644 --- a/rust/src/problem/mod.rs +++ b/rust/src/problem/mod.rs @@ -216,9 +216,18 @@ pub trait Objective: Send + Sync { Ok((self.evaluate(x)?, self.gradient(x))) } + /// Evaluates a batch of candidates provided as `xs` + /// Default is sequential evaluation, certain objectives + /// support parallel evaluation. fn evaluate_population(&self, xs: &[Vec]) -> Vec> { xs.iter().map(|x| self.evaluate(x)).collect() } + + /// Boolean flag to denote whether the objective can support + /// parallel evaluations. + fn supports_parallel_evaluation(&self) -> bool { + false + } } pub struct ScalarObjective { @@ -392,8 +401,15 @@ impl Problem { match opt { Optimiser::Scalar(scalar_opt) => { - // Only need objective values - scalar_opt.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + if self.objective.supports_parallel_evaluation() { + scalar_opt.run_batch( + |xs| self.evaluate_population(xs), + x0, + self.parameters.bounds(), + ) + } else { + scalar_opt.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + } } Optimiser::Gradient(grad_opt) => { // Needs objective + gradient values @@ -433,7 +449,15 @@ impl Problem { match sampler { Sampler::Scalar(scalar_sampler) => { - scalar_sampler.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + if self.objective.supports_parallel_evaluation() { + scalar_sampler.run_batch( + |xs| self.evaluate_population(xs), + x0, + self.parameters.bounds(), + ) + } else { + scalar_sampler.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + } } Sampler::Gradient(_grad_sampler) => { unimplemented!("Gradient samplers not yet implemented") diff --git a/rust/src/sampler/dynamic_nested/mod.rs b/rust/src/sampler/dynamic_nested/mod.rs index f9bea96..d8ff260 100644 --- a/rust/src/sampler/dynamic_nested/mod.rs +++ b/rust/src/sampler/dynamic_nested/mod.rs @@ -590,6 +590,48 @@ impl DynamicNestedSampler { } } } + + /// Run Dynamic Nested Sampling with automatic evaluation loop + /// + /// Internally uses the ask/tell interface. For external control, + /// use `init()`, `ask()`, and `tell()` directly. + /// + /// # Arguments + /// * `objective` - Function to evaluate. **Must return negative log-likelihood** (-log L). + /// The sampler will negate this internally to obtain log-likelihood for + /// nested sampling calculations. + /// * `initial` - Initial point (currently unused, reserved for future) + /// * `bounds` - Parameter bounds + pub fn run_batch(&self, objective: F, initial: Point, bounds: Bounds) -> NestedSamples + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + let (mut state, first_batch) = self.init(initial, bounds); + let mut results = objective(&first_batch); + + loop { + // Call ask and break if an error is encountered + if state.tell(results).is_err() { + break; + } + + match state.ask() { + AskResult::Evaluate(points) => { + results = objective(&points); + } + AskResult::Done(SamplingResults::Nested(samples)) => return samples, + _ => unreachable!("DynamicNestedSampler always returns Nested results"), + } + } + + // Final ask call for if tell returned an error + match state.ask() { + AskResult::Done(SamplingResults::Nested(samples)) => samples, + _ => panic!("Unexpected state after tell error"), + } + } } impl Default for DynamicNestedSampler { diff --git a/rust/src/sampler/metropolis_hastings.rs b/rust/src/sampler/metropolis_hastings.rs index 1132338..eda9acb 100644 --- a/rust/src/sampler/metropolis_hastings.rs +++ b/rust/src/sampler/metropolis_hastings.rs @@ -84,6 +84,48 @@ impl MetropolisHastings { } } } + + /// Run Metropolis-Hastings MCMC with batch sampling + /// + /// This method evaluates the problem's objective function to sample + /// from the posterior distribution using a random walk Metropolis algorithm. + /// + /// Internally uses the ask/tell interface for consistency. For external + /// control of the evaluation loop, use `init()`, `ask()`, and `tell()` directly. + pub fn run_batch(&self, objective: F, initial: Point, bounds: Bounds) -> Samples + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + let initial_point = initial; + let mut state = self.init(initial_point.clone(), bounds); // ToDo: performance improvement, remove clone + + let mut results = objective(&vec![initial_point]); + + loop { + // Call ask and break if an error is encountered + if state.tell(results).is_err() { + break; + } + + match state.ask() { + AskResult::Evaluate(points) => { + results = objective(&points); + } + AskResult::Done(SamplingResults::MCMC(samples)) => { + return samples; + } + _ => unreachable!("MetropolisHastings always returns MCMC results"), + } + } + + // Final ask call for if tell returned an error + match state.ask() { + AskResult::Done(SamplingResults::MCMC(samples)) => samples, + _ => panic!("Unexpected state after tell error"), + } + } } /// State for ask/tell interface of Metropolis-Hastings sampler diff --git a/rust/src/sampler/mod.rs b/rust/src/sampler/mod.rs index 530d4f7..b9e40b2 100644 --- a/rust/src/sampler/mod.rs +++ b/rust/src/sampler/mod.rs @@ -42,12 +42,12 @@ impl ScalarSampler { /// Run the sampler with a scalar objective function /// /// # Arguments - /// * `problem` - Problem defining objective and parameter specs + /// * `objective` - Function that evaluates the objective at a single point /// * `initial` - Initial point for sampling /// * `bounds` - Parameter bounds /// /// # Returns - /// Sampling results (type depends on sampler algorithm) + /// Sampling results pub fn run(&self, objective: F, initial: Point, bounds: Bounds) -> SamplingResults where F: FnMut(&[f64]) -> R, @@ -65,6 +65,38 @@ impl ScalarSampler { } } } + + /// Run the sampler with a batched scalar objective function + /// + /// # Arguments + /// * `objective` - Function that evaluates the objective with a batch of parameter candidates + /// * `initial` - Initial point for sampling + /// * `bounds` - Parameter bounds + /// + /// # Returns + /// Sampling results + pub fn run_batch( + &self, + objective: F, + initial: Point, + bounds: Bounds, + ) -> SamplingResults + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + match self { + ScalarSampler::MetropolisHastings(mh) => { + // Delegates to individual sampler's run() method + SamplingResults::MCMC(mh.run_batch(objective, initial, bounds)) + } + ScalarSampler::DynamicNested(dns) => { + // Delegates to individual sampler's run() method + SamplingResults::Nested(dns.run_batch(objective, initial, bounds)) + } + } + } } impl GradientSampler { From cf1cdc855b7e048e00b68b2a0d71d190702723bb Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sat, 17 Jan 2026 10:45:55 +0000 Subject: [PATCH 07/18] chore: increment diffsol/diffsl dependency --- Cargo.lock | 381 ++--- README.md | 3 +- docs/api-reference/python/builders.md | 7 +- docs/getting-started/concepts.md | 3 +- docs/getting-started/first-ode-fit.md | 18 +- docs/index.md | 3 +- .../notebooks/02_ode_fitting_diffsol.ipynb | 24 +- .../notebooks/03_parameter_uncertainty.ipynb | 6 +- .../06_custom_solver_integration.ipynb | 2 +- examples/bicycle_model_diffsol.py | 4 +- examples/bicycle_model_evidence.py | 4 +- examples/bouncy_ball.py | 3 +- examples/bouncy_ball_sampling.py | 3 +- examples/logistic_growth.py | 3 +- examples/model_evidence_diffsol.py | 3 +- .../predator_prey/predator_prey_diffsol.py | 4 +- rust/Cargo.toml | 2 +- rust/benches/diffsol_benches.rs | 2 +- rust/src/builders/mod.rs | 3 +- rust/src/problem/diffsol_problem.rs | 4 +- rust/tests/diffsol_optimisation.rs | 3 +- rust/tests/dynamic_nested.rs | 4 +- .../test_diffsol_dynamic_nested_parallel.py | 4 +- tests/integration/test_diffsol_sampling.py | 6 +- tests/integration/test_mathematical_suite.py | 3 +- tests/unit/test_diffsol.py | 16 +- tests/unit/test_optimisation_api.py | 8 +- uv.lock | 1296 ++++++++++++++++- 28 files changed, 1497 insertions(+), 325 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index a20b4fc..426e78a 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -153,7 +153,7 @@ dependencies = [ "regex", "rustc-hash 2.1.1", "shlex", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -179,9 +179,9 @@ dependencies = [ [[package]] name = "bumpalo" -version = "3.19.0" +version = "3.19.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "46c5e41b57b8bba42a04676d81cb89e9ee8e859a1a66f80a5a72e1cb76b34d43" +checksum = "5dd9dc738b7a8311c7ade152424974d8115f2cdad61e8dab8dac9f2362298510" dependencies = [ "allocator-api2", ] @@ -203,7 +203,7 @@ checksum = "f9abbd1bc6865053c427f7198e6af43bfdedc55ab791faed4fbd361d789575ff" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -226,9 +226,9 @@ checksum = "37b2a672a2cb129a2e41c10b1224bb368f9f37a2b16b612598138befd7b37eb5" [[package]] name = "cc" -version = "1.2.48" +version = "1.2.53" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c481bdbf0ed3b892f6f806287d72acd515b352a4ec27a208489b8c1bc839633a" +checksum = "755d2fce177175ffca841e9a06afdb2c4ab0f593d53b4dee48147dfaade85932" dependencies = [ "find-msvc-tools", "shlex", @@ -257,15 +257,15 @@ checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724" [[package]] name = "chrono" -version = "0.4.42" +version = "0.4.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "145052bdd345b87320e369255277e3fb5152762ad123a901ef5c262dd38fe8d2" +checksum = "fac4744fb15ae8337dc853fee7fb3f4e48c0fbaa23d0afe49c447b4fab126118" dependencies = [ "iana-time-zone", "js-sys", "num-traits", "wasm-bindgen", - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -286,7 +286,7 @@ name = "chronopt-py" version = "0.2.0" dependencies = [ "chronopt", - "clap 4.5.53", + "clap 4.5.54", "nalgebra", "numpy", "pyo3", @@ -319,9 +319,9 @@ dependencies = [ [[package]] name = "clap" -version = "4.5.53" +version = "4.5.54" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c9e340e012a1bf4935f5282ed1436d1489548e8f72308207ea5df0e23d2d03f8" +checksum = "c6e6ff9dcd79cff5cd969a17a545d79e84ab086e444102a591e288a8aa3ce394" dependencies = [ "clap_builder", "clap_derive", @@ -329,9 +329,9 @@ dependencies = [ [[package]] name = "clap_builder" -version = "4.5.53" +version = "4.5.54" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d76b5d13eaa18c901fd2f7fca939fefe3a0727a953561fefdf3b2922b8569d00" +checksum = "fa42cf4d2b7a41bc8f663a7cab4031ebafa1bf3875705bfaf8466dc60ab52c00" dependencies = [ "anstream", "anstyle", @@ -348,20 +348,20 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "clap_lex" -version = "0.7.6" +version = "0.7.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a1d728cc89cf3aee9ff92b05e62b19ee65a02b5702cff7d5a377e32c6ae29d8d" +checksum = "c3e64b0cc0439b12df2fa678eae89a1c56a529fd067a9115f7827f1fffd22b32" [[package]] name = "cmake" -version = "0.1.54" +version = "0.1.57" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e7caa3f9de89ddbe2c607f4101924c5abec803763ae9534e4f4d7d8f84aa81f0" +checksum = "75443c44cd6b379beb8c5b45d85d0773baf31cce901fe7bb252f4eff3008ef7d" dependencies = [ "cc", ] @@ -730,9 +730,9 @@ checksum = "930c7171c8df9fb1782bdf9b918ed9ed2d33d1d22300abb754f9085bc48bf8e8" [[package]] name = "diffsl" -version = "0.6.1" +version = "0.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8734b13a8f6961387db25fcfaa25ebfff86916397a10447c9ed65855bb7760d" +checksum = "72660d2c9a38eff333fa73aaf4ff2db9e564e7e193be3b5bdbd0abdd0da265d2" dependencies = [ "aliasable", "anyhow", @@ -749,6 +749,7 @@ dependencies = [ "lazy_static", "libc", "llvm-sys", + "log", "mmap-rs", "ndarray", "num-traits", @@ -764,18 +765,20 @@ dependencies = [ [[package]] name = "diffsol" -version = "0.8.1" +version = "0.10.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c9a4db7fb66c4839af9d320584a6b48621d192cd52dbbacc5a5f819bd7c4601f" +checksum = "3a2ba5b9cf0c9a7badb8f44cdad0058e286f8a69f9b56e899d79ecccb8917b48" dependencies = [ "diffsl", "faer", "faer-traits", + "log", "nalgebra", "nalgebra-sparse", "num-traits", "petgraph", "serde", + "serde_json", "thiserror 2.0.17", ] @@ -820,7 +823,7 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -849,7 +852,7 @@ checksum = "3bf679796c0322556351f287a51b49e48f7c4986e727b5dd78c972d30e2e16cc" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -860,7 +863,7 @@ checksum = "44f23cf4b44bfce11a86ace86f8a73ffdec849c9fd00a386a53d278bd9e81fb3" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -904,7 +907,7 @@ checksum = "2cc4b8cd876795d3b19ddfd59b03faa303c0b8adb9af6e188e81fc647c485bb9" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -933,9 +936,9 @@ checksum = "2acce4a10f12dc2fb14a218589d4f1f62ef011b2d0cc4b3cb1bba8e94da14649" [[package]] name = "find-msvc-tools" -version = "0.1.5" +version = "0.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3a3076410a55c90011c298b04d0cfa770b00fa04e1e3c97d3f6c9de105a03844" +checksum = "8591b0bcc8a98a64310a2fae1bb3e9b8564dd10e381e6e28010fde8e8e8568db" [[package]] name = "fixedbitset" @@ -945,9 +948,9 @@ checksum = "1d674e81391d1e1ab681a28d99df07927c6d4aa5b027d7da16ba32d1d21ecd99" [[package]] name = "flate2" -version = "1.1.5" +version = "1.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bfe33edd8e85a12a67454e37f8c75e730830d83e313556ab9ebf9ee7fbeb3bfb" +checksum = "b375d6465b98090a5f25b1c7703f3859783755aa9a80433b36e0379a3ec2f369" dependencies = [ "crc32fast", "miniz_oxide", @@ -1086,16 +1089,17 @@ checksum = "5881e4c3c2433fe4905bb19cfd2b5d49d4248274862b68c27c33d9ba4e13f9ec" [[package]] name = "generator" -version = "0.8.7" +version = "0.8.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "605183a538e3e2a9c1038635cc5c2d194e2ee8fd0d1b66b8349fad7dbacce5a2" +checksum = "52f04ae4152da20c76fe800fa48659201d5cf627c5149ca0b707b69d7eef6cf9" dependencies = [ "cc", "cfg-if", "libc", "log", "rustversion", - "windows 0.61.3", + "windows-link", + "windows-result", ] [[package]] @@ -1119,9 +1123,9 @@ dependencies = [ [[package]] name = "getrandom" -version = "0.2.16" +version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "335ff9f135e4384c8150d6f27c6daed433577f86b4750418338c01a1a2528592" +checksum = "ff2abc00be7fca6ebc474524697ae276ad847ad0a6b3faa4bcb027e9a4614ad0" dependencies = [ "cfg-if", "libc", @@ -1243,9 +1247,9 @@ checksum = "8babf46d4c1c9d92deac9f7be466f76dfc4482b6452fc5024b5e8daf6ffeb3ee" [[package]] name = "glam" -version = "0.30.9" +version = "0.30.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bd47b05dddf0005d850e5644cae7f2b14ac3df487979dbfff3b56f20b1a6ae46" +checksum = "19fc433e8437a212d1b6f1e68c7824af3aed907da60afa994e7f542d18d12aa9" [[package]] name = "glob" @@ -1320,7 +1324,7 @@ dependencies = [ "js-sys", "log", "wasm-bindgen", - "windows-core 0.62.2", + "windows-core", ] [[package]] @@ -1334,9 +1338,9 @@ dependencies = [ [[package]] name = "indexmap" -version = "2.12.1" +version = "2.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0ad4bb2b565bca0645f4d68c5c9af97fba094e9791da685bf83cb5f3ce74acf2" +checksum = "7714e70437a7dc3ac8eb7e6f8df75fd8eb422675fc7678aff7364301092b1017" dependencies = [ "equivalent", "hashbrown 0.16.1", @@ -1372,7 +1376,7 @@ checksum = "ad9a7dd586b00f2b20e0b9ae3c6faa351fbfd56d15d63bbce35b13bece682eda" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1404,7 +1408,7 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1451,15 +1455,15 @@ dependencies = [ [[package]] name = "itoa" -version = "1.0.15" +version = "1.0.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4a5f13b858c8d314ee3e8f639011f7ccefe71f97f96e50151fb991f267928e2c" +checksum = "92ecc6618181def0457392ccd0ee51198e065e016d1d527a7ac1b6dc7c1f09d2" [[package]] name = "js-sys" -version = "0.3.83" +version = "0.3.85" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "464a3709c7f55f1f721e5389aa6ea4e3bc6aba669353300af094b29ffbdde1d8" +checksum = "8c942ebf8e95485ca0d52d97da7c5a2c387d0e7f0ba4c35e93bfcaee045955b3" dependencies = [ "once_cell", "wasm-bindgen", @@ -1479,9 +1483,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe" [[package]] name = "libc" -version = "0.2.178" +version = "0.2.180" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "37c93d8daa9d8a012fd8ab92f088405fb202ea0b6ab73ee2482ae66af4f42091" +checksum = "bcc35a38544a891a5f7c865aca548a982ccb3b8650a5b06d0fd33a10283c56fc" [[package]] name = "libloading" @@ -1490,7 +1494,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d7c4b02199fee7c5d21a5ae7d8cfa79a6ef5bb2fc834d6e9058e89c825efdc55" dependencies = [ "cfg-if", - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -1611,7 +1615,7 @@ dependencies = [ "sysctl", "thiserror 2.0.17", "widestring", - "windows 0.48.0", + "windows", ] [[package]] @@ -1636,7 +1640,7 @@ dependencies = [ "glam 0.27.0", "glam 0.28.0", "glam 0.29.3", - "glam 0.30.9", + "glam 0.30.10", "matrixmultiply", "nalgebra-macros", "num-complex", @@ -1654,7 +1658,7 @@ checksum = "973e7178a678cfd059ccec50887658d482ce16b0aa9da3888ddeab5cd5eb4889" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1741,9 +1745,9 @@ dependencies = [ [[package]] name = "ndarray" -version = "0.17.1" +version = "0.17.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c7c9125e8f6f10c9da3aad044cc918cf8784fa34de857b1aa68038eb05a50a9" +checksum = "520080814a7a6b4a6e9070823bb24b4531daac8c4627e08ba5de8c5ef2f2752d" dependencies = [ "matrixmultiply", "num-complex", @@ -1922,9 +1926,9 @@ checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" [[package]] name = "pest" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cbcfd20a6d4eeba40179f05735784ad32bdaef05ce8e8af05f180d45bb3e7e22" +checksum = "2c9eb05c21a464ea704b53158d358a31e6425db2f63a1a7312268b05fe2b75f7" dependencies = [ "memchr", "ucd-trie", @@ -1932,9 +1936,9 @@ dependencies = [ [[package]] name = "pest_derive" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51f72981ade67b1ca6adc26ec221be9f463f2b5839c7508998daa17c23d94d7f" +checksum = "68f9dbced329c441fa79d80472764b1a2c7e57123553b8519b36663a2fb234ed" dependencies = [ "pest", "pest_generator", @@ -1942,22 +1946,22 @@ dependencies = [ [[package]] name = "pest_generator" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dee9efd8cdb50d719a80088b76f81aec7c41ed6d522ee750178f83883d271625" +checksum = "3bb96d5051a78f44f43c8f712d8e810adb0ebf923fc9ed2655a7f66f63ba8ee5" dependencies = [ "pest", "pest_meta", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "pest_meta" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bf1d70880e76bdc13ba52eafa6239ce793d85c8e43896507e43dd8984ff05b82" +checksum = "602113b5b5e8621770cfd490cfd90b9f84ab29bd2b0e49ad83eb6d186cef2365" dependencies = [ "pest", "sha2", @@ -2049,9 +2053,9 @@ dependencies = [ [[package]] name = "portable-atomic" -version = "1.11.1" +version = "1.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f84267b20a16ea918e43c6a88433c2d54fa145c92a811b5b047ccbe153674483" +checksum = "f89776e4d69bb58bc6993e99ffa1d11f228b839984854c7daeb5d37f87cbe950" [[package]] name = "portable-atomic-util" @@ -2078,7 +2082,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b" dependencies = [ "proc-macro2", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2099,9 +2103,9 @@ dependencies = [ [[package]] name = "proc-macro2" -version = "1.0.103" +version = "1.0.105" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ee95bc4ef87b8d5ba32e8b7714ccc834865276eab0aed5c9958d00ec45f49e8" +checksum = "535d180e0ecab6268a3e718bb9fd44db66bbbc256257165fc699dadf70d16fe7" dependencies = [ "unicode-ident", ] @@ -2193,7 +2197,7 @@ dependencies = [ "proc-macro2", "pyo3-macros-backend", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2206,7 +2210,7 @@ dependencies = [ "proc-macro2", "pyo3-build-config", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2243,7 +2247,7 @@ dependencies = [ "proc-macro2", "quote", "rustpython-parser", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2260,9 +2264,9 @@ dependencies = [ [[package]] name = "quote" -version = "1.0.42" +version = "1.0.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a338cc41d27e6cc6dce6cefc13a0729dfbb81c262b1f519331575dd80ef3067f" +checksum = "dc74d9a594b72ae6656596548f56f667211f8a97b3d4c3d467150794690dc40a" dependencies = [ "proc-macro2", ] @@ -2291,7 +2295,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1" dependencies = [ "rand_chacha 0.9.0", - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2311,7 +2315,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" dependencies = [ "ppv-lite86", - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2320,14 +2324,14 @@ version = "0.6.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c" dependencies = [ - "getrandom 0.2.16", + "getrandom 0.2.17", ] [[package]] name = "rand_core" -version = "0.9.3" +version = "0.9.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38" +checksum = "76afc826de14238e6e8c374ddcc1fa19e374fd8dd986b0d2af0d02377261d83c" dependencies = [ "getrandom 0.3.4", ] @@ -2348,7 +2352,7 @@ version = "0.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "513962919efc330f829edb2535844d1b912b0fbe2ca165d613e4e8788bb05a5a" dependencies = [ - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2394,9 +2398,9 @@ checksum = "03251193000f4bd3b042892be858ee50e8b3719f2b08e5833ac4353724632430" [[package]] name = "regalloc2" -version = "0.13.3" +version = "0.13.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4e249c660440317032a71ddac302f25f1d5dff387667bcc3978d1f77aa31ac34" +checksum = "08effbc1fa53aaebff69521a5c05640523fab037b34a4a2c109506bc938246fa" dependencies = [ "allocator-api2", "bumpalo", @@ -2539,9 +2543,9 @@ dependencies = [ [[package]] name = "ryu" -version = "1.0.20" +version = "1.0.22" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "28d3b2b1366ec20994f1fd18c3c594f05c5dd4bc44d8bb0c1c632c8d6829481f" +checksum = "a50f4cf475b65d88e057964e0e9bb1f0aa9bbb2036dc65c64596b42932536984" [[package]] name = "safe_arch" @@ -2616,27 +2620,27 @@ checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "serde_json" -version = "1.0.145" +version = "1.0.149" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "402a6f66d8c709116cf22f558eab210f5a50187f702eb4d7e5ef38d9a7f1c79c" +checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86" dependencies = [ "itoa", "memchr", - "ryu", "serde", "serde_core", + "zmij", ] [[package]] name = "serde_spanned" -version = "1.0.3" +version = "1.0.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e24345aa0fe688594e73770a5f6d1b216508b4f93484c0026d521acd30134392" +checksum = "f8bbf91e5a4d6315eee45e704372590b30e260ee83af6639d64557f51b067776" dependencies = [ "serde_core", ] @@ -2682,9 +2686,9 @@ dependencies = [ [[package]] name = "simd-adler32" -version = "0.3.7" +version = "0.3.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d66dc143e6b11c1eddc06d5c423cfc97062865baf299914ab64caa38182078fe" +checksum = "e320a6c5ad31d271ad523dcf3ad13e2767ad8b1cb8f047f75a8aeaf8da139da2" [[package]] name = "siphasher" @@ -2700,9 +2704,9 @@ checksum = "67b1b7a3b5fe4f1376887184045fcf45c69e92af734b7aaddc05fb777b6fbd03" [[package]] name = "spindle" -version = "0.2.5" +version = "0.2.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f794dedb367e82477aa6bbf83ea9bbce9bc074b3caacaa82fc4ba398ec9b701d" +checksum = "673aaca3d8aa5387a6eba861fbf984af5348d9df5d940c25c6366b19556fdf64" dependencies = [ "atomic-wait", "crossbeam", @@ -2742,9 +2746,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.111" +version = "2.0.114" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "390cc9a294ab71bdb1aa2e99d13be9c753cd2d7bd6560c77118597410c4d2e87" +checksum = "d4d107df263a3013ef9b1879b0df87d706ff80f65a86ea879bd9c31f9b307c2a" dependencies = [ "proc-macro2", "quote", @@ -2767,9 +2771,9 @@ dependencies = [ [[package]] name = "target-lexicon" -version = "0.13.3" +version = "0.13.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df7f62577c25e07834649fc3b39fafdc597c0a3527dc1c60129201ccfcbaa50c" +checksum = "b1dd07eb858a2067e2f3c7155d54e929265c264e6f37efe3ee7a8d1b5a1dd0ba" [[package]] name = "textwrap" @@ -2806,7 +2810,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2817,7 +2821,7 @@ checksum = "3ff15c8ecd7de3849db632e14d18d2571fa09dfc5ed93479bc4485c7a517c913" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2850,9 +2854,9 @@ dependencies = [ [[package]] name = "toml" -version = "0.9.8" +version = "0.9.11+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0dc8b1fb61449e27716ec0e1bdf0f6b8f3e8f6b05391e8497b8b6d7804ea6d8" +checksum = "f3afc9a848309fe1aaffaed6e1546a7a14de1f935dc9d89d32afd9a44bab7c46" dependencies = [ "indexmap", "serde_core", @@ -2865,33 +2869,33 @@ dependencies = [ [[package]] name = "toml_datetime" -version = "0.7.3" +version = "0.7.5+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f2cdb639ebbc97961c51720f858597f7f24c4fc295327923af55b74c3c724533" +checksum = "92e1cfed4a3038bc5a127e35a2d360f145e1f4b971b551a2ba5fd7aedf7e1347" dependencies = [ "serde_core", ] [[package]] name = "toml_parser" -version = "1.0.4" +version = "1.0.6+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0cbe268d35bdb4bb5a56a2de88d0ad0eb70af5384a99d648cd4b3d04039800e" +checksum = "a3198b4b0a8e11f09dd03e133c0280504d0801269e9afa46362ffde1cbeebf44" dependencies = [ "winnow", ] [[package]] name = "toml_writer" -version = "1.0.4" +version = "1.0.6+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df8b2b54733674ad286d16267dcfc7a71ed5c776e4ac7aa3c3e2561f7c637bf2" +checksum = "ab16f14aed21ee8bfd8ec22513f7287cd4a91aa92e44edfe2c17ddd004e92607" [[package]] name = "tracing" -version = "0.1.43" +version = "0.1.44" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2d15d90a0b5c19378952d479dc858407149d7bb45a14de0142f6c534b16fc647" +checksum = "63e71662fa4b2a2c3a26f570f037eb95bb1f85397f3cd8076caed2f026a6d100" dependencies = [ "pin-project-lite", "tracing-core", @@ -2899,9 +2903,9 @@ dependencies = [ [[package]] name = "tracing-core" -version = "0.1.35" +version = "0.1.36" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a04e24fab5c89c6a36eb8558c9656f30d81de51dfa4d3b45f26b21d61fa0a6c" +checksum = "db97caf9d906fbde555dd62fa95ddba9eecfd14cb388e4f491a66d74cd5fb79a" dependencies = [ "once_cell", "valuable", @@ -3100,18 +3104,18 @@ checksum = "ccf3ec651a847eb01de73ccad15eb7d99f80485de043efb2f370cd654f4ea44b" [[package]] name = "wasip2" -version = "1.0.1+wasi-0.2.4" +version = "1.0.2+wasi-0.2.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0562428422c63773dad2c345a1882263bbf4d65cf3f42e90921f787ef5ad58e7" +checksum = "9517f9239f02c069db75e65f174b3da828fe5f5b945c4dd26bd25d89c03ebcf5" dependencies = [ "wit-bindgen", ] [[package]] name = "wasm-bindgen" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0d759f433fa64a2d763d1340820e46e111a7a5ab75f993d1852d70b03dbb80fd" +checksum = "64024a30ec1e37399cf85a7ffefebdb72205ca1c972291c51512360d90bd8566" dependencies = [ "cfg-if", "once_cell", @@ -3122,9 +3126,9 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "48cb0d2638f8baedbc542ed444afc0644a29166f1595371af4fecf8ce1e7eeb3" +checksum = "008b239d9c740232e71bd39e8ef6429d27097518b6b30bdf9086833bd5b6d608" dependencies = [ "quote", "wasm-bindgen-macro-support", @@ -3132,22 +3136,22 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro-support" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cefb59d5cd5f92d9dcf80e4683949f15ca4b511f4ac0a6e14d4e1ac60c6ecd40" +checksum = "5256bae2d58f54820e6490f9839c49780dff84c65aeab9e772f15d5f0e913a55" dependencies = [ "bumpalo", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", "wasm-bindgen-shared", ] [[package]] name = "wasm-bindgen-shared" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cbc538057e648b67f72a982e708d485b2efa771e1ac05fec311f9f63e5800db4" +checksum = "1f01b580c9ac74c8d8f0c0e4afb04eeef2acf145458e52c03845ee9cd23e3d12" dependencies = [ "unicode-ident", ] @@ -3175,9 +3179,9 @@ dependencies = [ [[package]] name = "web-sys" -version = "0.3.83" +version = "0.3.85" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9b32828d774c412041098d182a8b38b16ea816958e07cf40eec2bc080ae137ac" +checksum = "312e32e551d92129218ea9a2452120f4aabc03529ef03e4d0d82fb2780608598" dependencies = [ "js-sys", "wasm-bindgen", @@ -3239,41 +3243,6 @@ dependencies = [ "windows-targets 0.48.5", ] -[[package]] -name = "windows" -version = "0.61.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9babd3a767a4c1aef6900409f85f5d53ce2544ccdfaa86dad48c91782c6d6893" -dependencies = [ - "windows-collections", - "windows-core 0.61.2", - "windows-future", - "windows-link 0.1.3", - "windows-numerics", -] - -[[package]] -name = "windows-collections" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3beeceb5e5cfd9eb1d76b381630e82c4241ccd0d27f1a39ed41b2760b255c5e8" -dependencies = [ - "windows-core 0.61.2", -] - -[[package]] -name = "windows-core" -version = "0.61.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0fdd3ddb90610c7638aa2b3a3ab2904fb9e5cdbecc643ddb3647212781c4ae3" -dependencies = [ - "windows-implement", - "windows-interface", - "windows-link 0.1.3", - "windows-result 0.3.4", - "windows-strings 0.4.2", -] - [[package]] name = "windows-core" version = "0.62.2" @@ -3282,20 +3251,9 @@ checksum = "b8e83a14d34d0623b51dce9581199302a221863196a1dde71a7663a4c2be9deb" dependencies = [ "windows-implement", "windows-interface", - "windows-link 0.2.1", - "windows-result 0.4.1", - "windows-strings 0.5.1", -] - -[[package]] -name = "windows-future" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fc6a41e98427b19fe4b73c550f060b59fa592d7d686537eebf9385621bfbad8e" -dependencies = [ - "windows-core 0.61.2", - "windows-link 0.1.3", - "windows-threading", + "windows-link", + "windows-result", + "windows-strings", ] [[package]] @@ -3306,7 +3264,7 @@ checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -3317,56 +3275,22 @@ checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] -[[package]] -name = "windows-link" -version = "0.1.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5e6ad25900d524eaabdbbb96d20b4311e1e7ae1699af4fb28c17ae66c80d798a" - [[package]] name = "windows-link" version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5" -[[package]] -name = "windows-numerics" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9150af68066c4c5c07ddc0ce30421554771e528bde427614c61038bc2c92c2b1" -dependencies = [ - "windows-core 0.61.2", - "windows-link 0.1.3", -] - -[[package]] -name = "windows-result" -version = "0.3.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "56f42bd332cc6c8eac5af113fc0c1fd6a8fd2aa08a0119358686e5160d0586c6" -dependencies = [ - "windows-link 0.1.3", -] - [[package]] name = "windows-result" version = "0.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7781fa89eaf60850ac3d2da7af8e5242a5ea78d1a11c49bf2910bb5a73853eb5" dependencies = [ - "windows-link 0.2.1", -] - -[[package]] -name = "windows-strings" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "56e6c93f3a0c3b36176cb1327a4958a0353d5d166c2a35cb268ace15e91d3b57" -dependencies = [ - "windows-link 0.1.3", + "windows-link", ] [[package]] @@ -3375,7 +3299,7 @@ version = "0.5.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7837d08f69c77cf6b07689544538e017c1bfcf57e34b4c0ff58e6c2cd3b37091" dependencies = [ - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -3417,7 +3341,7 @@ version = "0.61.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc" dependencies = [ - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -3457,7 +3381,7 @@ version = "0.53.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "4945f9f551b88e0d65f3db0bc25c33b8acea4d9e41163edf90dcd0b19f9069f3" dependencies = [ - "windows-link 0.2.1", + "windows-link", "windows_aarch64_gnullvm 0.53.1", "windows_aarch64_msvc 0.53.1", "windows_i686_gnu 0.53.1", @@ -3468,15 +3392,6 @@ dependencies = [ "windows_x86_64_msvc 0.53.1", ] -[[package]] -name = "windows-threading" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b66463ad2e0ea3bbf808b7f1d371311c80e115c0b71d60efc142cafbcfb057a6" -dependencies = [ - "windows-link 0.1.3", -] - [[package]] name = "windows_aarch64_gnullvm" version = "0.42.2" @@ -3665,26 +3580,32 @@ checksum = "5a5364e9d77fcdeeaa6062ced926ee3381faa2ee02d3eb83a5c27a8825540829" [[package]] name = "wit-bindgen" -version = "0.46.0" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f17a85883d4e6d00e8a97c586de764dabcc06133f7f1d55dce5cdc070ad7fe59" +checksum = "d7249219f66ced02969388cf2bb044a09756a083d0fab1e566056b04d9fbcaa5" [[package]] name = "zerocopy" -version = "0.8.31" +version = "0.8.33" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fd74ec98b9250adb3ca554bdde269adf631549f51d8a8f8f0a10b50f1cb298c3" +checksum = "668f5168d10b9ee831de31933dc111a459c97ec93225beb307aed970d1372dfd" dependencies = [ "zerocopy-derive", ] [[package]] name = "zerocopy-derive" -version = "0.8.31" +version = "0.8.33" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8a8d209fdf45cf5138cbb5a506f6b52522a25afccc534d1475dad8e31105c6a" +checksum = "2c7962b26b0a8685668b671ee4b54d007a67d4eaf05fda79ac0ecf41e32270f1" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] + +[[package]] +name = "zmij" +version = "1.0.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bd8f3f50b848df28f887acb68e41201b5aea6bc8a8dacc00fb40635ff9a72fea" diff --git a/README.md b/README.md index a5fa851..baecdea 100644 --- a/README.md +++ b/README.md @@ -86,8 +86,7 @@ import chronopt as chron # Example diffsol ODE (logistic growth) dsl = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md index 3134ce3..c0a7833 100644 --- a/docs/api-reference/python/builders.md +++ b/docs/api-reference/python/builders.md @@ -73,8 +73,7 @@ import numpy as np import chronopt as chron dsl = """ -in = [r, k] -r { 1 } k { 1 } +in { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -105,9 +104,7 @@ result = optimiser.run(problem, [0.5, 0.5]) DiffSL is a domain-specific language for ODEs: ``` -in = [param1, param2] # Parameters to fit -param1 { default1 } # Default values -param2 { default2 } +in_i { param1 = default1, param2 = default 2 } # Parameters to fit with defaults u_i { state1 = init1 } # Initial conditions F_i { derivative_expr } # dy/dt expressions out_i { state1, state2 } # Optional: output variables diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md index dff3866..9fa8cb2 100644 --- a/docs/getting-started/concepts.md +++ b/docs/getting-started/concepts.md @@ -55,8 +55,7 @@ For **ODE parameter fitting** using the built-in DiffSL/Diffsol solver: ```python dsl = """ -in = [r, k] -r { 1 } k { 1 } +in_i {r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md index 441b672..7e2ff07 100644 --- a/docs/getting-started/first-ode-fit.md +++ b/docs/getting-started/first-ode-fit.md @@ -21,8 +21,7 @@ import chronopt as chron # Define the ODE model in DiffSL syntax dsl = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -59,16 +58,14 @@ print(f"Success: {result.success}") DiffSL is a domain-specific language for defining differential equations. Let's break down the syntax: ``` -in = [r, k] # Input parameters to fit -r { 1 } k { 1 } # Default values for parameters +in_i { r = 1, k = 1 } # Input parameters to fit with default values u_i { y = 0.1 } # Initial conditions F_i { (r * y) * (1 - (y / k)) } # Right-hand side of dy/dt = ... ``` Key components: -- `in = [...]`: Parameters to optimise -- `parameter { default_value }`: Default parameter values +- `in_i = {...}`: Parameters to optimise with default values - `u_i { var = initial_value }`: Initial conditions for state variables - `F_i { expression }`: The derivative expression ($dy/dt$) @@ -147,8 +144,7 @@ plt.show() ```python dsl = """ -in = [alpha, beta] -alpha { 1 } beta { 1 } +in_i { alpha = 1, beta = 1 } u_i { x = 1.0 y = 0.5 @@ -165,8 +161,7 @@ out_i { x, y } ```python dsl = """ -in = [k1, k2] -k1 { 1 } k2 { 1 } +in_i {k1 = 1, k2 = 1 } u_i { A = 1.0 } dudt_i { -k1 * A } F_i { @@ -181,8 +176,7 @@ out_i { A, B } ```python dsl = """ -in = [k] -k { 1 } +in_i { k = 1 } u_i { y = 0.0 } F_i { k * sin(t) - y } """ diff --git a/docs/index.md b/docs/index.md index 2f3efc9..2310320 100644 --- a/docs/index.md +++ b/docs/index.md @@ -111,8 +111,7 @@ import chronopt as chron # Logistic growth model in DiffSL dsl = """ -in = [r, k] -r { 1 } k { 1 } +in_i {r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb index fe9a522..a59ba93 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -79,15 +79,13 @@ "DiffSL Model Definition:\n", "==================================================\n", "\n", - "in = [r, k]\n", - "r { 1 } k { 1 }\n", + "in_i {r = 1, k = 1 }\n", "u_i { y = 0.1 }\n", "F_i { (r * y) * (1 - (y / k)) }\n", "\n", "\n", "Explanation:\n", - " in = [r, k] → Parameters to fit\n", - " r { 1 } k { 1 } → Default parameter values\n", + " in_i {r = 1, k = 1 } → Parameters to fit\n", " u_i { y = 0.1 } → Initial condition: y(0) = 0.1\n", " F_i { ... } → Right-hand side: dy/dt = ...\n" ] @@ -95,8 +93,7 @@ ], "source": [ "dsl_model = \"\"\"\n", - "in = [r, k]\n", - "r { 1 } k { 1 }\n", + "in_i {r = 1, k = 1 }\n", "u_i { y = 0.1 }\n", "F_i { (r * y) * (1 - (y / k)) }\n", "\"\"\"\n", @@ -105,8 +102,7 @@ "print(\"=\" * 50)\n", "print(dsl_model)\n", "print(\"\\nExplanation:\")\n", - "print(\" in = [r, k] → Parameters to fit\")\n", - "print(\" r { 1 } k { 1 } → Default parameter values\")\n", + "print(\" in_i {r = 1, k = 1 } → Parameters to fit\")\n", "print(\" u_i { y = 0.1 } → Initial condition: y(0) = 0.1\")\n", "print(\" F_i { ... } → Right-hand side: dy/dt = ...\")" ] @@ -600,8 +596,7 @@ "Example: Predator-Prey Model (Lotka-Volterra)\n", "==================================================\n", "\n", - "in = [alpha, beta, gamma, delta]\n", - "alpha { 1 } beta { 0.1 } gamma { 1.5 } delta { 0.075 }\n", + "in_i { alpha = 1, beta = 0.1, gamma = 1.5, delta = 0.075 }\n", "u_i {\n", " prey = 10.0\n", " predator = 5.0\n", @@ -616,8 +611,7 @@ "Example: Sequential Reactions (A → B → C)\n", "==================================================\n", "\n", - "in = [k1, k2]\n", - "k1 { 0.5 } k2 { 0.3 }\n", + "in_i { k1 = 0.5, k2 = 0.3 }\n", "u_i { A = 1.0 }\n", "F_i {\n", " -k1 * A,\n", @@ -631,8 +625,7 @@ "source": [ "# Example 1: Multiple state variables (Lotka-Volterra)\n", "predator_prey = \"\"\"\n", - "in = [alpha, beta, gamma, delta]\n", - "alpha { 1 } beta { 0.1 } gamma { 1.5 } delta { 0.075 }\n", + "in_i { alpha = 1, beta = 0.1, gamma = 1.5, delta = 0.075 }\n", "u_i {\n", " prey = 10.0\n", " predator = 5.0\n", @@ -650,8 +643,7 @@ "\n", "# Example 2: With algebraic equations\n", "with_algebra = \"\"\"\n", - "in = [k1, k2]\n", - "k1 { 0.5 } k2 { 0.3 }\n", + "in_i { k1 = 0.5, k2 = 0.3 }\n", "u_i { A = 1.0 }\n", "F_i {\n", " -k1 * A,\n", diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index ad01e3a..0d40d36 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -224,8 +224,7 @@ "text": [ "DiffSL Model:\n", "\n", - "in = [g, h]\n", - "g { 1 } h { 1 }\n", + "in_i { g = 1, h = 1 }\n", "u_i {x = h, v = 0}\n", "F_i {v, -g}\n", "stop {x}\n", @@ -242,8 +241,7 @@ "source": [ "# DiffSL model for falling ball\n", "dsl_model = \"\"\"\n", - "in = [g, h]\n", - "g { 1 } h { 1 }\n", + "in_i {g = 1, h = 1 }\n", "u_i {x = h, v = 0}\n", "F_i {v, -g}\n", "stop {x}\n", diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index 13954af..fb2d3d3 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -90,7 +90,7 @@ "\n", "# DiffSL model\n", "model_str = \"\"\"\n", - "in = [alpha, beta, delta, gamma]\n", + "in_i { alpha, beta, delta, gamma }\n", "x { 10.0 } = alpha * x - beta * x * y\n", "y { 5.0 } = delta * x * y - gamma * y\n", "out = [x, y]\n", diff --git a/examples/bicycle_model_diffsol.py b/examples/bicycle_model_diffsol.py index 94329e8..1d8808b 100644 --- a/examples/bicycle_model_diffsol.py +++ b/examples/bicycle_model_diffsol.py @@ -8,8 +8,8 @@ DELTA = 0.05 # steer angle dsl = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { x = 0.0, y = 0.0, diff --git a/examples/bicycle_model_evidence.py b/examples/bicycle_model_evidence.py index 5e6ce10..33769ba 100644 --- a/examples/bicycle_model_evidence.py +++ b/examples/bicycle_model_evidence.py @@ -8,8 +8,8 @@ DELTA = 0.05 # steer angle dsl = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { x = 0.0, y = 0.0, diff --git a/examples/bouncy_ball.py b/examples/bouncy_ball.py index 5eaa9d7..2965422 100644 --- a/examples/bouncy_ball.py +++ b/examples/bouncy_ball.py @@ -11,8 +11,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} diff --git a/examples/bouncy_ball_sampling.py b/examples/bouncy_ball_sampling.py index 475300e..70294d4 100644 --- a/examples/bouncy_ball_sampling.py +++ b/examples/bouncy_ball_sampling.py @@ -11,8 +11,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} diff --git a/examples/logistic_growth.py b/examples/logistic_growth.py index dafcb7d..c55ed85 100644 --- a/examples/logistic_growth.py +++ b/examples/logistic_growth.py @@ -3,8 +3,7 @@ # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/examples/model_evidence_diffsol.py b/examples/model_evidence_diffsol.py index c143520..8919b3a 100644 --- a/examples/model_evidence_diffsol.py +++ b/examples/model_evidence_diffsol.py @@ -7,8 +7,7 @@ # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/examples/predator_prey/predator_prey_diffsol.py b/examples/predator_prey/predator_prey_diffsol.py index 5dd0d2c..4dc015e 100644 --- a/examples/predator_prey/predator_prey_diffsol.py +++ b/examples/predator_prey/predator_prey_diffsol.py @@ -6,8 +6,8 @@ # Example diffsol ODE (logistic growth) ode = """ -in = [a, b, c, d ] -a { 2.0/3.0 } b { 4.0/3.0 } c { 1.0 } d { 1.0 } x0 { 10.0 } y0 { 5.0 } +in_i { a = 2.0/3.0, b = 4.0/3.0, c = 1.0, d = 1.0 } +x0 { 10.0 } y0 { 5.0 } u_i { y1 = x0, y2 = y0, diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 7d4c18d..9b890ec 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -13,7 +13,7 @@ categories = ["algorithms", "science", "simulation"] include = ["src/**", "benches/**", "Cargo.toml", "../README.md", "../LICENSE"] [dependencies] -diffsol = { version = "0.8.0" } +diffsol = { version = "0.10.1" } nalgebra.workspace = true rand = { version = "0.9.2", features = ["std"] } rand_distr = "0.5.1" diff --git a/rust/benches/diffsol_benches.rs b/rust/benches/diffsol_benches.rs index ea76050..bb1a20d 100644 --- a/rust/benches/diffsol_benches.rs +++ b/rust/benches/diffsol_benches.rs @@ -7,7 +7,7 @@ use std::time::Duration; macro_rules! build_logistic_problem { ($backend:expr, $parallel:expr) => {{ let dsl = r#" -in = [r, k] +in_i { r = 1, k = 1 } r { 1 } k { 1 } u_i { y = 0.1 } diff --git a/rust/src/builders/mod.rs b/rust/src/builders/mod.rs index 878e4d6..b80faab 100644 --- a/rust/src/builders/mod.rs +++ b/rust/src/builders/mod.rs @@ -57,8 +57,7 @@ mod tests { #[test] fn test_diffsol_builder() { let dsl = r#" -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1} u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/rust/src/problem/diffsol_problem.rs b/rust/src/problem/diffsol_problem.rs index 377fbe5..8339c43 100644 --- a/rust/src/problem/diffsol_problem.rs +++ b/rust/src/problem/diffsol_problem.rs @@ -308,9 +308,7 @@ mod tests { #[allow(dead_code)] fn build_logistic_problem(backend: DiffsolBackend) -> DiffsolObjective { let dsl = r#" -in = [r, k] -r { 1 } -k { 1 } +in_i {r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/rust/tests/diffsol_optimisation.rs b/rust/tests/diffsol_optimisation.rs index ce4e965..300ed33 100644 --- a/rust/tests/diffsol_optimisation.rs +++ b/rust/tests/diffsol_optimisation.rs @@ -4,8 +4,7 @@ use nalgebra::DMatrix; #[test] fn diffsol_builder_supports_end_to_end_optimisation() { let dsl = r#" -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.1 } F_i { a * y } "#; diff --git a/rust/tests/dynamic_nested.rs b/rust/tests/dynamic_nested.rs index 535391e..0b977f5 100644 --- a/rust/tests/dynamic_nested.rs +++ b/rust/tests/dynamic_nested.rs @@ -48,9 +48,7 @@ fn build_logistic_objective( parallel: bool, ) -> (impl Fn(&[f64]) -> f64, Bounds) { let dsl = r#" -in = [r, k] -r { 1 } -k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/tests/integration/test_diffsol_dynamic_nested_parallel.py b/tests/integration/test_diffsol_dynamic_nested_parallel.py index de3589f..56a7543 100644 --- a/tests/integration/test_diffsol_dynamic_nested_parallel.py +++ b/tests/integration/test_diffsol_dynamic_nested_parallel.py @@ -4,9 +4,7 @@ def _logistic_dsl() -> str: return """ -in = [r, k] -r { 1 } -k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/tests/integration/test_diffsol_sampling.py b/tests/integration/test_diffsol_sampling.py index 256dd66..84c550c 100644 --- a/tests/integration/test_diffsol_sampling.py +++ b/tests/integration/test_diffsol_sampling.py @@ -12,8 +12,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr def test_diffsol_sampling_tracks_bouncy_ball_parameters(): # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} @@ -70,8 +69,7 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): def test_diffsol_dynamic_nested_sampler_produces_evidence(): dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} diff --git a/tests/integration/test_mathematical_suite.py b/tests/integration/test_mathematical_suite.py index 3e128d4..a61b21e 100644 --- a/tests/integration/test_mathematical_suite.py +++ b/tests/integration/test_mathematical_suite.py @@ -176,8 +176,7 @@ def test_python_objectives_converge( _LOGISTIC_DSL = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ diff --git a/tests/unit/test_diffsol.py b/tests/unit/test_diffsol.py index 348b2f6..5ed2c83 100644 --- a/tests/unit/test_diffsol.py +++ b/tests/unit/test_diffsol.py @@ -9,8 +9,7 @@ def test_diffsol_builder(): """Test basic Diffsol builder functionality""" # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -54,8 +53,7 @@ def test_diffsol_builder(): def test_diffsol_builder_remove_methods(): ds = """ -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.0 } F_i { a * y } """ @@ -111,8 +109,7 @@ def test_diffsol_builder_remove_methods(): def test_problem_optimise_defaults_to_builder_params(): ds = """ -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.1 } F_i { a * y } """ @@ -146,8 +143,7 @@ def test_diffsol_cost_metrics(variance: float) -> None: """Ensure selectable cost metrics produce consistent values.""" ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -198,8 +194,8 @@ def build_problem(cost_metric=None): def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: ds = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { y = 0.0, psi = 0.0, diff --git a/tests/unit/test_optimisation_api.py b/tests/unit/test_optimisation_api.py index 4e0c91e..eb9e279 100644 --- a/tests/unit/test_optimisation_api.py +++ b/tests/unit/test_optimisation_api.py @@ -6,8 +6,7 @@ def _test_optimisation_api(): """Test basic Diffsol builder functionality""" # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -38,8 +37,7 @@ def _test_optimisation_api(): def test_diffsol_builder_allows_multiple_builds(): ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -117,7 +115,7 @@ def test_all_builders_support_copy(): copy.deepcopy(scalar_builder) # Test deepcopy # DiffsolBuilder - diffsol_builder = chron.DiffsolBuilder().with_diffsl("in = [a]") + diffsol_builder = chron.DiffsolBuilder().with_diffsl("in { a }") diffsol_copy = copy.copy(diffsol_builder) copy.deepcopy(diffsol_builder) # Test deepcopy diff --git a/uv.lock b/uv.lock index e1c6de7..d423aa4 100644 --- a/uv.lock +++ b/uv.lock @@ -27,6 +27,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" }, ] +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, +] + +[[package]] +name = "asttokens" +version = "3.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" }, +] + [[package]] name = "attrs" version = "25.4.0" @@ -36,6 +54,59 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" }, ] +[[package]] +name = "babel" +version = "2.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852, upload-time = "2025-02-01T15:17:41.026Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" }, +] + +[[package]] +name = "backrefs" +version = "6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/86/e3/bb3a439d5cb255c4774724810ad8073830fac9c9dee123555820c1bcc806/backrefs-6.1.tar.gz", hash = "sha256:3bba1749aafe1db9b915f00e0dd166cba613b6f788ffd63060ac3485dc9be231", size = 7011962, upload-time = "2025-11-15T14:52:08.323Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/ee/c216d52f58ea75b5e1841022bbae24438b19834a29b163cb32aa3a2a7c6e/backrefs-6.1-py310-none-any.whl", hash = "sha256:2a2ccb96302337ce61ee4717ceacfbf26ba4efb1d55af86564b8bbaeda39cac1", size = 381059, upload-time = "2025-11-15T14:51:59.758Z" }, + { url = "https://files.pythonhosted.org/packages/e6/9a/8da246d988ded941da96c7ed945d63e94a445637eaad985a0ed88787cb89/backrefs-6.1-py311-none-any.whl", hash = "sha256:e82bba3875ee4430f4de4b6db19429a27275d95a5f3773c57e9e18abc23fd2b7", size = 392854, upload-time = "2025-11-15T14:52:01.194Z" }, + { url = "https://files.pythonhosted.org/packages/37/c9/fd117a6f9300c62bbc33bc337fd2b3c6bfe28b6e9701de336b52d7a797ad/backrefs-6.1-py312-none-any.whl", hash = "sha256:c64698c8d2269343d88947c0735cb4b78745bd3ba590e10313fbf3f78c34da5a", size = 398770, upload-time = "2025-11-15T14:52:02.584Z" }, + { url = "https://files.pythonhosted.org/packages/eb/95/7118e935b0b0bd3f94dfec2d852fd4e4f4f9757bdb49850519acd245cd3a/backrefs-6.1-py313-none-any.whl", hash = "sha256:4c9d3dc1e2e558965202c012304f33d4e0e477e1c103663fd2c3cc9bb18b0d05", size = 400726, upload-time = "2025-11-15T14:52:04.093Z" }, + { url = "https://files.pythonhosted.org/packages/1d/72/6296bad135bfafd3254ae3648cd152980a424bd6fed64a101af00cc7ba31/backrefs-6.1-py314-none-any.whl", hash = "sha256:13eafbc9ccd5222e9c1f0bec563e6d2a6d21514962f11e7fc79872fd56cbc853", size = 412584, upload-time = "2025-11-15T14:52:05.233Z" }, + { url = "https://files.pythonhosted.org/packages/02/e3/a4fa1946722c4c7b063cc25043a12d9ce9b4323777f89643be74cef2993c/backrefs-6.1-py39-none-any.whl", hash = "sha256:a9e99b8a4867852cad177a6430e31b0f6e495d65f8c6c134b68c14c3c95bf4b0", size = 381058, upload-time = "2025-11-15T14:52:06.698Z" }, +] + +[[package]] +name = "beautifulsoup4" +version = "4.14.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "soupsieve" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" }, +] + +[[package]] +name = "bleach" +version = "6.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533, upload-time = "2025-10-27T17:57:39.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437, upload-time = "2025-10-27T17:57:37.538Z" }, +] + +[package.optional-dependencies] +css = [ + { name = "tinycss2" }, +] + [[package]] name = "certifi" version = "2025.10.5" @@ -45,6 +116,149 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de", size = 163286, upload-time = "2025-10-05T04:12:14.03Z" }, ] +[[package]] +name = "cffi" +version = "2.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pycparser", marker = "implementation_name != 'PyPy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/12/4a/3dfd5f7850cbf0d06dc84ba9aa00db766b52ca38d8b86e3a38314d52498c/cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe", size = 184344, upload-time = "2025-09-08T23:22:26.456Z" }, + { url = "https://files.pythonhosted.org/packages/4f/8b/f0e4c441227ba756aafbe78f117485b25bb26b1c059d01f137fa6d14896b/cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c", size = 180560, upload-time = "2025-09-08T23:22:28.197Z" }, + { url = "https://files.pythonhosted.org/packages/b1/b7/1200d354378ef52ec227395d95c2576330fd22a869f7a70e88e1447eb234/cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92", size = 209613, upload-time = "2025-09-08T23:22:29.475Z" }, + { url = "https://files.pythonhosted.org/packages/b8/56/6033f5e86e8cc9bb629f0077ba71679508bdf54a9a5e112a3c0b91870332/cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93", size = 216476, upload-time = "2025-09-08T23:22:31.063Z" }, + { url = "https://files.pythonhosted.org/packages/dc/7f/55fecd70f7ece178db2f26128ec41430d8720f2d12ca97bf8f0a628207d5/cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5", size = 203374, upload-time = "2025-09-08T23:22:32.507Z" }, + { url = "https://files.pythonhosted.org/packages/84/ef/a7b77c8bdc0f77adc3b46888f1ad54be8f3b7821697a7b89126e829e676a/cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664", size = 202597, upload-time = "2025-09-08T23:22:34.132Z" }, + { url = "https://files.pythonhosted.org/packages/d7/91/500d892b2bf36529a75b77958edfcd5ad8e2ce4064ce2ecfeab2125d72d1/cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26", size = 215574, upload-time = "2025-09-08T23:22:35.443Z" }, + { url = "https://files.pythonhosted.org/packages/44/64/58f6255b62b101093d5df22dcb752596066c7e89dd725e0afaed242a61be/cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9", size = 218971, upload-time = "2025-09-08T23:22:36.805Z" }, + { url = "https://files.pythonhosted.org/packages/ab/49/fa72cebe2fd8a55fbe14956f9970fe8eb1ac59e5df042f603ef7c8ba0adc/cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414", size = 211972, upload-time = "2025-09-08T23:22:38.436Z" }, + { url = "https://files.pythonhosted.org/packages/0b/28/dd0967a76aab36731b6ebfe64dec4e981aff7e0608f60c2d46b46982607d/cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743", size = 217078, upload-time = "2025-09-08T23:22:39.776Z" }, + { url = "https://files.pythonhosted.org/packages/2b/c0/015b25184413d7ab0a410775fdb4a50fca20f5589b5dab1dbbfa3baad8ce/cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5", size = 172076, upload-time = "2025-09-08T23:22:40.95Z" }, + { url = "https://files.pythonhosted.org/packages/ae/8f/dc5531155e7070361eb1b7e4c1a9d896d0cb21c49f807a6c03fd63fc877e/cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5", size = 182820, upload-time = "2025-09-08T23:22:42.463Z" }, + { url = "https://files.pythonhosted.org/packages/95/5c/1b493356429f9aecfd56bc171285a4c4ac8697f76e9bbbbb105e537853a1/cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d", size = 177635, upload-time = "2025-09-08T23:22:43.623Z" }, + { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" }, + { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" }, + { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529, upload-time = "2025-09-08T23:22:47.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" }, + { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" }, + { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519, upload-time = "2025-09-08T23:22:51.364Z" }, + { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572, upload-time = "2025-09-08T23:22:52.902Z" }, + { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963, upload-time = "2025-09-08T23:22:54.518Z" }, + { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361, upload-time = "2025-09-08T23:22:55.867Z" }, + { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" }, + { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" }, + { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762, upload-time = "2025-09-08T23:22:59.668Z" }, + { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230, upload-time = "2025-09-08T23:23:00.879Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043, upload-time = "2025-09-08T23:23:02.231Z" }, + { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446, upload-time = "2025-09-08T23:23:03.472Z" }, + { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101, upload-time = "2025-09-08T23:23:04.792Z" }, + { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948, upload-time = "2025-09-08T23:23:06.127Z" }, + { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422, upload-time = "2025-09-08T23:23:07.753Z" }, + { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499, upload-time = "2025-09-08T23:23:09.648Z" }, + { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928, upload-time = "2025-09-08T23:23:10.928Z" }, + { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302, upload-time = "2025-09-08T23:23:12.42Z" }, + { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909, upload-time = "2025-09-08T23:23:14.32Z" }, + { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402, upload-time = "2025-09-08T23:23:15.535Z" }, + { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" }, + { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320, upload-time = "2025-09-08T23:23:18.087Z" }, + { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487, upload-time = "2025-09-08T23:23:19.622Z" }, + { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049, upload-time = "2025-09-08T23:23:20.853Z" }, + { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793, upload-time = "2025-09-08T23:23:22.08Z" }, + { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300, upload-time = "2025-09-08T23:23:23.314Z" }, + { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244, upload-time = "2025-09-08T23:23:24.541Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828, upload-time = "2025-09-08T23:23:26.143Z" }, + { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926, upload-time = "2025-09-08T23:23:27.873Z" }, + { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328, upload-time = "2025-09-08T23:23:44.61Z" }, + { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650, upload-time = "2025-09-08T23:23:45.848Z" }, + { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687, upload-time = "2025-09-08T23:23:47.105Z" }, + { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773, upload-time = "2025-09-08T23:23:29.347Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013, upload-time = "2025-09-08T23:23:30.63Z" }, + { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593, upload-time = "2025-09-08T23:23:31.91Z" }, + { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354, upload-time = "2025-09-08T23:23:33.214Z" }, + { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480, upload-time = "2025-09-08T23:23:34.495Z" }, + { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584, upload-time = "2025-09-08T23:23:36.096Z" }, + { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443, upload-time = "2025-09-08T23:23:37.328Z" }, + { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437, upload-time = "2025-09-08T23:23:38.945Z" }, + { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487, upload-time = "2025-09-08T23:23:40.423Z" }, + { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726, upload-time = "2025-09-08T23:23:41.742Z" }, + { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" }, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/27/c6491ff4954e58a10f69ad90aca8a1b6fe9c5d3c6f380907af3c37435b59/charset_normalizer-3.4.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6e1fcf0720908f200cd21aa4e6750a48ff6ce4afe7ff5a79a90d5ed8a08296f8", size = 206988, upload-time = "2025-10-14T04:40:33.79Z" }, + { url = "https://files.pythonhosted.org/packages/94/59/2e87300fe67ab820b5428580a53cad894272dbb97f38a7a814a2a1ac1011/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f819d5fe9234f9f82d75bdfa9aef3a3d72c4d24a6e57aeaebba32a704553aa0", size = 147324, upload-time = "2025-10-14T04:40:34.961Z" }, + { url = "https://files.pythonhosted.org/packages/07/fb/0cf61dc84b2b088391830f6274cb57c82e4da8bbc2efeac8c025edb88772/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a59cb51917aa591b1c4e6a43c132f0cdc3c76dbad6155df4e28ee626cc77a0a3", size = 142742, upload-time = "2025-10-14T04:40:36.105Z" }, + { url = "https://files.pythonhosted.org/packages/62/8b/171935adf2312cd745d290ed93cf16cf0dfe320863ab7cbeeae1dcd6535f/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8ef3c867360f88ac904fd3f5e1f902f13307af9052646963ee08ff4f131adafc", size = 160863, upload-time = "2025-10-14T04:40:37.188Z" }, + { url = "https://files.pythonhosted.org/packages/09/73/ad875b192bda14f2173bfc1bc9a55e009808484a4b256748d931b6948442/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d9e45d7faa48ee908174d8fe84854479ef838fc6a705c9315372eacbc2f02897", size = 157837, upload-time = "2025-10-14T04:40:38.435Z" }, + { url = "https://files.pythonhosted.org/packages/6d/fc/de9cce525b2c5b94b47c70a4b4fb19f871b24995c728e957ee68ab1671ea/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:840c25fb618a231545cbab0564a799f101b63b9901f2569faecd6b222ac72381", size = 151550, upload-time = "2025-10-14T04:40:40.053Z" }, + { url = "https://files.pythonhosted.org/packages/55/c2/43edd615fdfba8c6f2dfbd459b25a6b3b551f24ea21981e23fb768503ce1/charset_normalizer-3.4.4-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ca5862d5b3928c4940729dacc329aa9102900382fea192fc5e52eb69d6093815", size = 149162, upload-time = "2025-10-14T04:40:41.163Z" }, + { url = "https://files.pythonhosted.org/packages/03/86/bde4ad8b4d0e9429a4e82c1e8f5c659993a9a863ad62c7df05cf7b678d75/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d9c7f57c3d666a53421049053eaacdd14bbd0a528e2186fcb2e672effd053bb0", size = 150019, upload-time = "2025-10-14T04:40:42.276Z" }, + { url = "https://files.pythonhosted.org/packages/1f/86/a151eb2af293a7e7bac3a739b81072585ce36ccfb4493039f49f1d3cae8c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:277e970e750505ed74c832b4bf75dac7476262ee2a013f5574dd49075879e161", size = 143310, upload-time = "2025-10-14T04:40:43.439Z" }, + { url = "https://files.pythonhosted.org/packages/b5/fe/43dae6144a7e07b87478fdfc4dbe9efd5defb0e7ec29f5f58a55aeef7bf7/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:31fd66405eaf47bb62e8cd575dc621c56c668f27d46a61d975a249930dd5e2a4", size = 162022, upload-time = "2025-10-14T04:40:44.547Z" }, + { url = "https://files.pythonhosted.org/packages/80/e6/7aab83774f5d2bca81f42ac58d04caf44f0cc2b65fc6db2b3b2e8a05f3b3/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:0d3d8f15c07f86e9ff82319b3d9ef6f4bf907608f53fe9d92b28ea9ae3d1fd89", size = 149383, upload-time = "2025-10-14T04:40:46.018Z" }, + { url = "https://files.pythonhosted.org/packages/4f/e8/b289173b4edae05c0dde07f69f8db476a0b511eac556dfe0d6bda3c43384/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:9f7fcd74d410a36883701fafa2482a6af2ff5ba96b9a620e9e0721e28ead5569", size = 159098, upload-time = "2025-10-14T04:40:47.081Z" }, + { url = "https://files.pythonhosted.org/packages/d8/df/fe699727754cae3f8478493c7f45f777b17c3ef0600e28abfec8619eb49c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ebf3e58c7ec8a8bed6d66a75d7fb37b55e5015b03ceae72a8e7c74495551e224", size = 152991, upload-time = "2025-10-14T04:40:48.246Z" }, + { url = "https://files.pythonhosted.org/packages/1a/86/584869fe4ddb6ffa3bd9f491b87a01568797fb9bd8933f557dba9771beaf/charset_normalizer-3.4.4-cp311-cp311-win32.whl", hash = "sha256:eecbc200c7fd5ddb9a7f16c7decb07b566c29fa2161a16cf67b8d068bd21690a", size = 99456, upload-time = "2025-10-14T04:40:49.376Z" }, + { url = "https://files.pythonhosted.org/packages/65/f6/62fdd5feb60530f50f7e38b4f6a1d5203f4d16ff4f9f0952962c044e919a/charset_normalizer-3.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:5ae497466c7901d54b639cf42d5b8c1b6a4fead55215500d2f486d34db48d016", size = 106978, upload-time = "2025-10-14T04:40:50.844Z" }, + { url = "https://files.pythonhosted.org/packages/7a/9d/0710916e6c82948b3be62d9d398cb4fcf4e97b56d6a6aeccd66c4b2f2bd5/charset_normalizer-3.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:65e2befcd84bc6f37095f5961e68a6f077bf44946771354a28ad434c2cce0ae1", size = 99969, upload-time = "2025-10-14T04:40:52.272Z" }, + { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425, upload-time = "2025-10-14T04:40:53.353Z" }, + { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162, upload-time = "2025-10-14T04:40:54.558Z" }, + { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558, upload-time = "2025-10-14T04:40:55.677Z" }, + { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497, upload-time = "2025-10-14T04:40:57.217Z" }, + { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240, upload-time = "2025-10-14T04:40:58.358Z" }, + { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471, upload-time = "2025-10-14T04:40:59.468Z" }, + { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864, upload-time = "2025-10-14T04:41:00.623Z" }, + { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647, upload-time = "2025-10-14T04:41:01.754Z" }, + { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110, upload-time = "2025-10-14T04:41:03.231Z" }, + { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839, upload-time = "2025-10-14T04:41:04.715Z" }, + { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667, upload-time = "2025-10-14T04:41:05.827Z" }, + { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535, upload-time = "2025-10-14T04:41:06.938Z" }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816, upload-time = "2025-10-14T04:41:08.101Z" }, + { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694, upload-time = "2025-10-14T04:41:09.23Z" }, + { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131, upload-time = "2025-10-14T04:41:10.467Z" }, + { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390, upload-time = "2025-10-14T04:41:11.915Z" }, + { url = "https://files.pythonhosted.org/packages/97/45/4b3a1239bbacd321068ea6e7ac28875b03ab8bc0aa0966452db17cd36714/charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794", size = 208091, upload-time = "2025-10-14T04:41:13.346Z" }, + { url = "https://files.pythonhosted.org/packages/7d/62/73a6d7450829655a35bb88a88fca7d736f9882a27eacdca2c6d505b57e2e/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed", size = 147936, upload-time = "2025-10-14T04:41:14.461Z" }, + { url = "https://files.pythonhosted.org/packages/89/c5/adb8c8b3d6625bef6d88b251bbb0d95f8205831b987631ab0c8bb5d937c2/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72", size = 144180, upload-time = "2025-10-14T04:41:15.588Z" }, + { url = "https://files.pythonhosted.org/packages/91/ed/9706e4070682d1cc219050b6048bfd293ccf67b3d4f5a4f39207453d4b99/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328", size = 161346, upload-time = "2025-10-14T04:41:16.738Z" }, + { url = "https://files.pythonhosted.org/packages/d5/0d/031f0d95e4972901a2f6f09ef055751805ff541511dc1252ba3ca1f80cf5/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede", size = 158874, upload-time = "2025-10-14T04:41:17.923Z" }, + { url = "https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894", size = 153076, upload-time = "2025-10-14T04:41:19.106Z" }, + { url = "https://files.pythonhosted.org/packages/75/1e/5ff781ddf5260e387d6419959ee89ef13878229732732ee73cdae01800f2/charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1", size = 150601, upload-time = "2025-10-14T04:41:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/d7/57/71be810965493d3510a6ca79b90c19e48696fb1ff964da319334b12677f0/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490", size = 150376, upload-time = "2025-10-14T04:41:21.398Z" }, + { url = "https://files.pythonhosted.org/packages/e5/d5/c3d057a78c181d007014feb7e9f2e65905a6c4ef182c0ddf0de2924edd65/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44", size = 144825, upload-time = "2025-10-14T04:41:22.583Z" }, + { url = "https://files.pythonhosted.org/packages/e6/8c/d0406294828d4976f275ffbe66f00266c4b3136b7506941d87c00cab5272/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133", size = 162583, upload-time = "2025-10-14T04:41:23.754Z" }, + { url = "https://files.pythonhosted.org/packages/d7/24/e2aa1f18c8f15c4c0e932d9287b8609dd30ad56dbe41d926bd846e22fb8d/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3", size = 150366, upload-time = "2025-10-14T04:41:25.27Z" }, + { url = "https://files.pythonhosted.org/packages/e4/5b/1e6160c7739aad1e2df054300cc618b06bf784a7a164b0f238360721ab86/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e", size = 160300, upload-time = "2025-10-14T04:41:26.725Z" }, + { url = "https://files.pythonhosted.org/packages/7a/10/f882167cd207fbdd743e55534d5d9620e095089d176d55cb22d5322f2afd/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc", size = 154465, upload-time = "2025-10-14T04:41:28.322Z" }, + { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404, upload-time = "2025-10-14T04:41:29.95Z" }, + { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092, upload-time = "2025-10-14T04:41:31.188Z" }, + { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408, upload-time = "2025-10-14T04:41:32.624Z" }, + { url = "https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd", size = 207746, upload-time = "2025-10-14T04:41:33.773Z" }, + { url = "https://files.pythonhosted.org/packages/10/9a/97c8d48ef10d6cd4fcead2415523221624bf58bcf68a802721a6bc807c8f/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb", size = 147889, upload-time = "2025-10-14T04:41:34.897Z" }, + { url = "https://files.pythonhosted.org/packages/10/bf/979224a919a1b606c82bd2c5fa49b5c6d5727aa47b4312bb27b1734f53cd/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e", size = 143641, upload-time = "2025-10-14T04:41:36.116Z" }, + { url = "https://files.pythonhosted.org/packages/ba/33/0ad65587441fc730dc7bd90e9716b30b4702dc7b617e6ba4997dc8651495/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14", size = 160779, upload-time = "2025-10-14T04:41:37.229Z" }, + { url = "https://files.pythonhosted.org/packages/67/ed/331d6b249259ee71ddea93f6f2f0a56cfebd46938bde6fcc6f7b9a3d0e09/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191", size = 159035, upload-time = "2025-10-14T04:41:38.368Z" }, + { url = "https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838", size = 152542, upload-time = "2025-10-14T04:41:39.862Z" }, + { url = "https://files.pythonhosted.org/packages/16/85/276033dcbcc369eb176594de22728541a925b2632f9716428c851b149e83/charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6", size = 149524, upload-time = "2025-10-14T04:41:41.319Z" }, + { url = "https://files.pythonhosted.org/packages/9e/f2/6a2a1f722b6aba37050e626530a46a68f74e63683947a8acff92569f979a/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e", size = 150395, upload-time = "2025-10-14T04:41:42.539Z" }, + { url = "https://files.pythonhosted.org/packages/60/bb/2186cb2f2bbaea6338cad15ce23a67f9b0672929744381e28b0592676824/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c", size = 143680, upload-time = "2025-10-14T04:41:43.661Z" }, + { url = "https://files.pythonhosted.org/packages/7d/a5/bf6f13b772fbb2a90360eb620d52ed8f796f3c5caee8398c3b2eb7b1c60d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090", size = 162045, upload-time = "2025-10-14T04:41:44.821Z" }, + { url = "https://files.pythonhosted.org/packages/df/c5/d1be898bf0dc3ef9030c3825e5d3b83f2c528d207d246cbabe245966808d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152", size = 149687, upload-time = "2025-10-14T04:41:46.442Z" }, + { url = "https://files.pythonhosted.org/packages/a5/42/90c1f7b9341eef50c8a1cb3f098ac43b0508413f33affd762855f67a410e/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828", size = 160014, upload-time = "2025-10-14T04:41:47.631Z" }, + { url = "https://files.pythonhosted.org/packages/76/be/4d3ee471e8145d12795ab655ece37baed0929462a86e72372fd25859047c/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec", size = 154044, upload-time = "2025-10-14T04:41:48.81Z" }, + { url = "https://files.pythonhosted.org/packages/b0/6f/8f7af07237c34a1defe7defc565a9bc1807762f672c0fde711a4b22bf9c0/charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9", size = 99940, upload-time = "2025-10-14T04:41:49.946Z" }, + { url = "https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c", size = 107104, upload-time = "2025-10-14T04:41:51.051Z" }, + { url = "https://files.pythonhosted.org/packages/da/5f/6b8f83a55bb8278772c5ae54a577f3099025f9ade59d0136ac24a0df4bde/charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2", size = 100743, upload-time = "2025-10-14T04:41:52.122Z" }, + { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, +] + [[package]] name = "chronopt" source = { editable = "." } @@ -70,6 +284,15 @@ dev = [ { name = "pytest" }, { name = "scipy" }, ] +docs = [ + { name = "mkdocs" }, + { name = "mkdocs-git-revision-date-localized-plugin" }, + { name = "mkdocs-jupyter" }, + { name = "mkdocs-material" }, + { name = "mkdocs-minify-plugin" }, + { name = "mkdocstrings", extra = ["python"] }, + { name = "pymdown-extensions" }, +] examples = [ { name = "diffeqpy" }, { name = "diffrax", marker = "(python_full_version < '3.15' and platform_machine != 'x86_64') or (python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.15' and sys_platform != 'darwin')" }, @@ -87,13 +310,22 @@ requires-dist = [ { name = "matplotlib", marker = "extra == 'plotting'", specifier = ">=3.8" }, { name = "numpy", specifier = ">=1.24.4" }, ] -provides-extras = ["jax", "plotting", "diffeqpy"] +provides-extras = ["diffeqpy", "jax", "plotting"] [package.metadata.requires-dev] dev = [ { name = "pytest", specifier = ">=8.3.5" }, { name = "scipy", specifier = ">=1.16.2" }, ] +docs = [ + { name = "mkdocs", specifier = ">=1.5.0" }, + { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.2.0" }, + { name = "mkdocs-jupyter", specifier = ">=0.24.0" }, + { name = "mkdocs-material", specifier = ">=9.5.0" }, + { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, + { name = "mkdocstrings", extras = ["python"], specifier = ">=0.24.0" }, + { name = "pymdown-extensions", specifier = ">=10.7" }, +] examples = [ { name = "diffeqpy" }, { name = "diffrax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = ">=0.7.0" }, @@ -124,6 +356,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "comm" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" }, +] + [[package]] name = "contourpy" version = "1.3.3" @@ -206,6 +447,12 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" }, ] +[[package]] +name = "csscompressor" +version = "0.9.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f1/2a/8c3ac3d8bc94e6de8d7ae270bb5bc437b210bb9d6d9e46630c98f4abd20c/csscompressor-0.9.5.tar.gz", hash = "sha256:afa22badbcf3120a4f392e4d22f9fff485c044a1feda4a950ecc5eba9dd31a05", size = 237808, upload-time = "2017-11-26T21:13:08.238Z" } + [[package]] name = "cycler" version = "0.12.1" @@ -215,6 +462,49 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, ] +[[package]] +name = "debugpy" +version = "1.8.19" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/73/75/9e12d4d42349b817cd545b89247696c67917aab907012ae5b64bbfea3199/debugpy-1.8.19.tar.gz", hash = "sha256:eea7e5987445ab0b5ed258093722d5ecb8bb72217c5c9b1e21f64efe23ddebdb", size = 1644590, upload-time = "2025-12-15T21:53:28.044Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/80/e2/48531a609b5a2aa94c6b6853afdfec8da05630ab9aaa96f1349e772119e9/debugpy-1.8.19-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:c5dcfa21de1f735a4f7ced4556339a109aa0f618d366ede9da0a3600f2516d8b", size = 2207620, upload-time = "2025-12-15T21:53:37.1Z" }, + { url = "https://files.pythonhosted.org/packages/1b/d4/97775c01d56071969f57d93928899e5616a4cfbbf4c8cc75390d3a51c4a4/debugpy-1.8.19-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:806d6800246244004625d5222d7765874ab2d22f3ba5f615416cf1342d61c488", size = 3170796, upload-time = "2025-12-15T21:53:38.513Z" }, + { url = "https://files.pythonhosted.org/packages/8d/7e/8c7681bdb05be9ec972bbb1245eb7c4c7b0679bb6a9e6408d808bc876d3d/debugpy-1.8.19-cp311-cp311-win32.whl", hash = "sha256:783a519e6dfb1f3cd773a9bda592f4887a65040cb0c7bd38dde410f4e53c40d4", size = 5164287, upload-time = "2025-12-15T21:53:40.857Z" }, + { url = "https://files.pythonhosted.org/packages/f2/a8/aaac7ff12ddf5d68a39e13a423a8490426f5f661384f5ad8d9062761bd8e/debugpy-1.8.19-cp311-cp311-win_amd64.whl", hash = "sha256:14035cbdbb1fe4b642babcdcb5935c2da3b1067ac211c5c5a8fdc0bb31adbcaa", size = 5188269, upload-time = "2025-12-15T21:53:42.359Z" }, + { url = "https://files.pythonhosted.org/packages/4a/15/d762e5263d9e25b763b78be72dc084c7a32113a0bac119e2f7acae7700ed/debugpy-1.8.19-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:bccb1540a49cde77edc7ce7d9d075c1dbeb2414751bc0048c7a11e1b597a4c2e", size = 2549995, upload-time = "2025-12-15T21:53:43.773Z" }, + { url = "https://files.pythonhosted.org/packages/a7/88/f7d25c68b18873b7c53d7c156ca7a7ffd8e77073aa0eac170a9b679cf786/debugpy-1.8.19-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:e9c68d9a382ec754dc05ed1d1b4ed5bd824b9f7c1a8cd1083adb84b3c93501de", size = 4309891, upload-time = "2025-12-15T21:53:45.26Z" }, + { url = "https://files.pythonhosted.org/packages/c5/4f/a65e973aba3865794da65f71971dca01ae66666132c7b2647182d5be0c5f/debugpy-1.8.19-cp312-cp312-win32.whl", hash = "sha256:6599cab8a783d1496ae9984c52cb13b7c4a3bd06a8e6c33446832a5d97ce0bee", size = 5286355, upload-time = "2025-12-15T21:53:46.763Z" }, + { url = "https://files.pythonhosted.org/packages/d8/3a/d3d8b48fec96e3d824e404bf428276fb8419dfa766f78f10b08da1cb2986/debugpy-1.8.19-cp312-cp312-win_amd64.whl", hash = "sha256:66e3d2fd8f2035a8f111eb127fa508469dfa40928a89b460b41fd988684dc83d", size = 5328239, upload-time = "2025-12-15T21:53:48.868Z" }, + { url = "https://files.pythonhosted.org/packages/71/3d/388035a31a59c26f1ecc8d86af607d0c42e20ef80074147cd07b180c4349/debugpy-1.8.19-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:91e35db2672a0abaf325f4868fcac9c1674a0d9ad9bb8a8c849c03a5ebba3e6d", size = 2538859, upload-time = "2025-12-15T21:53:50.478Z" }, + { url = "https://files.pythonhosted.org/packages/4a/19/c93a0772d0962294f083dbdb113af1a7427bb632d36e5314297068f55db7/debugpy-1.8.19-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:85016a73ab84dea1c1f1dcd88ec692993bcbe4532d1b49ecb5f3c688ae50c606", size = 4292575, upload-time = "2025-12-15T21:53:51.821Z" }, + { url = "https://files.pythonhosted.org/packages/5c/56/09e48ab796b0a77e3d7dc250f95251832b8bf6838c9632f6100c98bdf426/debugpy-1.8.19-cp313-cp313-win32.whl", hash = "sha256:b605f17e89ba0ecee994391194285fada89cee111cfcd29d6f2ee11cbdc40976", size = 5286209, upload-time = "2025-12-15T21:53:53.602Z" }, + { url = "https://files.pythonhosted.org/packages/fb/4e/931480b9552c7d0feebe40c73725dd7703dcc578ba9efc14fe0e6d31cfd1/debugpy-1.8.19-cp313-cp313-win_amd64.whl", hash = "sha256:c30639998a9f9cd9699b4b621942c0179a6527f083c72351f95c6ab1728d5b73", size = 5328206, upload-time = "2025-12-15T21:53:55.433Z" }, + { url = "https://files.pythonhosted.org/packages/f6/b9/cbec520c3a00508327476c7fce26fbafef98f412707e511eb9d19a2ef467/debugpy-1.8.19-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:1e8c4d1bd230067bf1bbcdbd6032e5a57068638eb28b9153d008ecde288152af", size = 2537372, upload-time = "2025-12-15T21:53:57.318Z" }, + { url = "https://files.pythonhosted.org/packages/88/5e/cf4e4dc712a141e10d58405c58c8268554aec3c35c09cdcda7535ff13f76/debugpy-1.8.19-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:d40c016c1f538dbf1762936e3aeb43a89b965069d9f60f9e39d35d9d25e6b809", size = 4268729, upload-time = "2025-12-15T21:53:58.712Z" }, + { url = "https://files.pythonhosted.org/packages/82/a3/c91a087ab21f1047db328c1d3eb5d1ff0e52de9e74f9f6f6fa14cdd93d58/debugpy-1.8.19-cp314-cp314-win32.whl", hash = "sha256:0601708223fe1cd0e27c6cce67a899d92c7d68e73690211e6788a4b0e1903f5b", size = 5286388, upload-time = "2025-12-15T21:54:00.687Z" }, + { url = "https://files.pythonhosted.org/packages/17/b8/bfdc30b6e94f1eff09f2dc9cc1f9cd1c6cde3d996bcbd36ce2d9a4956e99/debugpy-1.8.19-cp314-cp314-win_amd64.whl", hash = "sha256:8e19a725f5d486f20e53a1dde2ab8bb2c9607c40c00a42ab646def962b41125f", size = 5327741, upload-time = "2025-12-15T21:54:02.148Z" }, + { url = "https://files.pythonhosted.org/packages/25/3e/e27078370414ef35fafad2c06d182110073daaeb5d3bf734b0b1eeefe452/debugpy-1.8.19-py2.py3-none-any.whl", hash = "sha256:360ffd231a780abbc414ba0f005dad409e71c78637efe8f2bd75837132a41d38", size = 5292321, upload-time = "2025-12-15T21:54:16.024Z" }, +] + +[[package]] +name = "decorator" +version = "5.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" }, +] + +[[package]] +name = "defusedxml" +version = "0.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" }, +] + [[package]] name = "diffeqpy" version = "2.5.4" @@ -261,6 +551,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c6/ce/c3bc53bdda6edb050859939a8e91e206123f96f50906e7ba7c027c6c535f/equinox-0.13.2-py3-none-any.whl", hash = "sha256:bc1ee687e4841945d8b776664403839639a05e2f2c02c1da353ff3386e0e43b0", size = 179235, upload-time = "2025-10-09T10:15:37.991Z" }, ] +[[package]] +name = "executing" +version = "2.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488, upload-time = "2025-09-01T09:48:10.866Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" }, +] + +[[package]] +name = "fastjsonschema" +version = "2.21.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" }, +] + [[package]] name = "filelock" version = "3.20.0" @@ -319,6 +627,54 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175, upload-time = "2025-09-29T21:13:24.134Z" }, ] +[[package]] +name = "ghp-import" +version = "2.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "python-dateutil" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d9/29/d40217cbe2f6b1359e00c6c307bb3fc876ba74068cbab3dde77f03ca0dc4/ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343", size = 10943, upload-time = "2022-05-02T15:47:16.11Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619", size = 11034, upload-time = "2022-05-02T15:47:14.552Z" }, +] + +[[package]] +name = "gitdb" +version = "4.0.12" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "smmap" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/61/5c78b91c3143ed5c14207f463aecfc8f9dbb5092fb2869baf37c273b2705/gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf", size = 62794, upload-time = "2025-01-02T07:20:43.624Z" }, +] + +[[package]] +name = "gitpython" +version = "3.1.46" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "gitdb" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/b5/59d16470a1f0dfe8c793f9ef56fd3826093fc52b3bd96d6b9d6c26c7e27b/gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f", size = 215371, upload-time = "2026-01-01T15:37:32.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/09/e21df6aef1e1ffc0c816f0522ddc3f6dcded766c3261813131c78a704470/gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058", size = 208620, upload-time = "2026-01-01T15:37:30.574Z" }, +] + +[[package]] +name = "griffe" +version = "1.15.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0d/0c/3a471b6e31951dce2360477420d0a8d1e00dea6cf33b70f3e8c3ab6e28e1/griffe-1.15.0.tar.gz", hash = "sha256:7726e3afd6f298fbc3696e67958803e7ac843c1cfe59734b6251a40cdbfb5eea", size = 424112, upload-time = "2025-11-10T15:03:15.52Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9c/83/3b1d03d36f224edded98e9affd0467630fc09d766c0e56fb1498cbb04a9b/griffe-1.15.0-py3-none-any.whl", hash = "sha256:6f6762661949411031f5fcda9593f586e6ce8340f0ba88921a0f2ef7a81eb9a3", size = 150705, upload-time = "2025-11-10T15:03:13.549Z" }, +] + [[package]] name = "h11" version = "0.16.0" @@ -328,6 +684,14 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, ] +[[package]] +name = "htmlmin2" +version = "0.1.13" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/be/31/a76f4bfa885f93b8167cb4c85cf32b54d1f64384d0b897d45bc6d19b7b45/htmlmin2-0.1.13-py3-none-any.whl", hash = "sha256:75609f2a42e64f7ce57dbff28a39890363bde9e7e5885db633317efbdf8c79a2", size = 34486, upload-time = "2023-03-14T21:28:30.388Z" }, +] + [[package]] name = "httpcore" version = "1.0.9" @@ -375,6 +739,64 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, ] +[[package]] +name = "ipykernel" +version = "6.31.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "appnope", marker = "sys_platform == 'darwin'" }, + { name = "comm" }, + { name = "debugpy" }, + { name = "ipython" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "matplotlib-inline" }, + { name = "nest-asyncio" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a5/1d/d5ba6edbfe6fae4c3105bca3a9c889563cc752c7f2de45e333164c7f4846/ipykernel-6.31.0.tar.gz", hash = "sha256:2372ce8bc1ff4f34e58cafed3a0feb2194b91fc7cad0fc72e79e47b45ee9e8f6", size = 167493, upload-time = "2025-10-20T11:42:39.948Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f6/d8/502954a4ec0efcf264f99b65b41c3c54e65a647d9f0d6f62cd02227d242c/ipykernel-6.31.0-py3-none-any.whl", hash = "sha256:abe5386f6ced727a70e0eb0cf1da801fa7c5fa6ff82147747d5a0406cd8c94af", size = 117003, upload-time = "2025-10-20T11:42:37.502Z" }, +] + +[[package]] +name = "ipython" +version = "9.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "decorator" }, + { name = "ipython-pygments-lexers" }, + { name = "jedi" }, + { name = "matplotlib-inline" }, + { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "prompt-toolkit" }, + { name = "pygments" }, + { name = "stack-data" }, + { name = "traitlets" }, + { name = "typing-extensions", marker = "python_full_version < '3.12'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/46/dd/fb08d22ec0c27e73c8bc8f71810709870d51cadaf27b7ddd3f011236c100/ipython-9.9.0.tar.gz", hash = "sha256:48fbed1b2de5e2c7177eefa144aba7fcb82dac514f09b57e2ac9da34ddb54220", size = 4425043, upload-time = "2026-01-05T12:36:46.233Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/86/92/162cfaee4ccf370465c5af1ce36a9eacec1becb552f2033bb3584e6f640a/ipython-9.9.0-py3-none-any.whl", hash = "sha256:b457fe9165df2b84e8ec909a97abcf2ed88f565970efba16b1f7229c283d252b", size = 621431, upload-time = "2026-01-05T12:36:44.669Z" }, +] + +[[package]] +name = "ipython-pygments-lexers" +version = "1.1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, +] + [[package]] name = "jax" version = "0.4.38" @@ -430,6 +852,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b8/97/88264b1af140f66ba7ca6eb2f3a108be233ee278bb3f1d5c750243e7458a/jaxtyping-0.3.3-py3-none-any.whl", hash = "sha256:a1c2f0f4351a8deda84b0e3b5c5a50894a1cdae2b82d841279fce4393aff4a7c", size = 55926, upload-time = "2025-10-01T13:46:50.621Z" }, ] +[[package]] +name = "jedi" +version = "0.19.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "parso" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" }, +] + [[package]] name = "jill" version = "0.12.0" @@ -446,6 +880,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e7/3e/c347d5aab834b1300b7e79ab664e38425f746e515ccc2abfb7029f9d51f1/jill-0.12.0-py3-none-any.whl", hash = "sha256:f3e5f3127382db1f6a9bf7de13005eb2ace6d4bd208ec25ed319d85355b5e81a", size = 40252, upload-time = "2025-06-07T17:57:08.613Z" }, ] +[[package]] +name = "jinja2" +version = "3.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, +] + +[[package]] +name = "jsmin" +version = "3.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5e/73/e01e4c5e11ad0494f4407a3f623ad4d87714909f50b17a06ed121034ff6e/jsmin-3.0.1.tar.gz", hash = "sha256:c0959a121ef94542e807a674142606f7e90214a2b3d1eb17300244bbb5cc2bfc", size = 13925, upload-time = "2022-01-16T20:35:59.13Z" } + [[package]] name = "jsonschema" version = "4.25.1" @@ -500,6 +952,60 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/39/6e/361d176cc94ccb8aac22a39677e146a91406005f8f6bb098b15606c523f6/juliapkg-0.1.22-py3-none-any.whl", hash = "sha256:fc197c21087345e296036190c769b888f7701bf1fb47d7036a20d01221ec550a", size = 21860, upload-time = "2025-10-08T20:04:36.389Z" }, ] +[[package]] +name = "jupyter-client" +version = "8.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-core" }, + { name = "python-dateutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/05/e4/ba649102a3bc3fbca54e7239fb924fd434c766f855693d86de0b1f2bec81/jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e", size = 348020, upload-time = "2026-01-08T13:55:47.938Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2d/0b/ceb7694d864abc0a047649aec263878acb9f792e1fec3e676f22dc9015e3/jupyter_client-8.8.0-py3-none-any.whl", hash = "sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a", size = 107371, upload-time = "2026-01-08T13:55:45.562Z" }, +] + +[[package]] +name = "jupyter-core" +version = "5.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "platformdirs" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" }, +] + +[[package]] +name = "jupyterlab-pygments" +version = "0.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900, upload-time = "2023-11-23T09:26:37.44Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884, upload-time = "2023-11-23T09:26:34.325Z" }, +] + +[[package]] +name = "jupytext" +version = "1.18.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown-it-py" }, + { name = "mdit-py-plugins" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "pyyaml" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/9b/5d/82a614a49493fa84b2019a3e03020a8b9927208ae177b81f7e0b30330c82/jupytext-1.18.1.tar.gz", hash = "sha256:5c0962ca8d222db45cbe1848b4805dbbe3ddb957603fc96651b6cd7fd403fafb", size = 4270997, upload-time = "2025-10-19T15:06:30.992Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bd/0d/2d240e7098e0cafba4d25e9530e7596b1bb1bd4476e41b10346bcaaa36d6/jupytext-1.18.1-py3-none-any.whl", hash = "sha256:24f999400726a1c658beae55e15fdd2a6255ab1a418697864cd779874e6011ab", size = 167143, upload-time = "2025-10-19T15:06:28.975Z" }, +] + [[package]] name = "kiwisolver" version = "1.4.9" @@ -605,6 +1111,101 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1b/a9/2d8d6002eb124e79f7c9ee2311b62f35289fc7e75ee0bbfbe5a4c7295bb9/lineax-0.0.8-py3-none-any.whl", hash = "sha256:1bd21d6c41afda233769d1c1096329ee75181825c9136be65c92b41f6daa1ddb", size = 68045, upload-time = "2025-03-27T17:38:53.394Z" }, ] +[[package]] +name = "markdown" +version = "3.10" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7d/ab/7dd27d9d863b3376fcf23a5a13cb5d024aed1db46f963f1b5735ae43b3be/markdown-3.10.tar.gz", hash = "sha256:37062d4f2aa4b2b6b32aefb80faa300f82cc790cb949a35b8caede34f2b68c0e", size = 364931, upload-time = "2025-11-03T19:51:15.007Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/81/54e3ce63502cd085a0c556652a4e1b919c45a446bd1e5300e10c44c8c521/markdown-3.10-py3-none-any.whl", hash = "sha256:b5b99d6951e2e4948d939255596523444c0e677c669700b1d17aa4a8a464cb7c", size = 107678, upload-time = "2025-11-03T19:51:13.887Z" }, +] + +[[package]] +name = "markdown-it-py" +version = "4.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mdurl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" }, +] + +[[package]] +name = "markupsafe" +version = "3.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631, upload-time = "2025-09-27T18:36:18.185Z" }, + { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058, upload-time = "2025-09-27T18:36:19.444Z" }, + { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287, upload-time = "2025-09-27T18:36:20.768Z" }, + { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940, upload-time = "2025-09-27T18:36:22.249Z" }, + { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887, upload-time = "2025-09-27T18:36:23.535Z" }, + { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692, upload-time = "2025-09-27T18:36:24.823Z" }, + { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471, upload-time = "2025-09-27T18:36:25.95Z" }, + { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923, upload-time = "2025-09-27T18:36:27.109Z" }, + { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572, upload-time = "2025-09-27T18:36:28.045Z" }, + { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077, upload-time = "2025-09-27T18:36:29.025Z" }, + { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876, upload-time = "2025-09-27T18:36:29.954Z" }, + { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" }, + { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" }, + { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" }, + { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" }, + { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" }, + { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" }, + { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" }, + { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" }, + { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" }, + { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" }, + { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" }, + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, +] + [[package]] name = "matplotlib" version = "3.10.7" @@ -669,6 +1270,223 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/9a/cc/3fe688ff1355010937713164caacf9ed443675ac48a997bab6ed23b3f7c0/matplotlib-3.10.7-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3886e47f64611046bc1db523a09dd0a0a6bed6081e6f90e13806dd1d1d1b5e91", size = 8693919, upload-time = "2025-10-09T00:27:58.41Z" }, ] +[[package]] +name = "matplotlib-inline" +version = "0.2.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110, upload-time = "2025-10-23T09:00:22.126Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516, upload-time = "2025-10-23T09:00:20.675Z" }, +] + +[[package]] +name = "mdit-py-plugins" +version = "0.5.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown-it-py" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b2/fd/a756d36c0bfba5f6e39a1cdbdbfdd448dc02692467d83816dff4592a1ebc/mdit_py_plugins-0.5.0.tar.gz", hash = "sha256:f4918cb50119f50446560513a8e311d574ff6aaed72606ddae6d35716fe809c6", size = 44655, upload-time = "2025-08-11T07:25:49.083Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/86/dd6e5db36df29e76c7a7699123569a4a18c1623ce68d826ed96c62643cae/mdit_py_plugins-0.5.0-py3-none-any.whl", hash = "sha256:07a08422fc1936a5d26d146759e9155ea466e842f5ab2f7d2266dd084c8dab1f", size = 57205, upload-time = "2025-08-11T07:25:47.597Z" }, +] + +[[package]] +name = "mdurl" +version = "0.1.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729, upload-time = "2022-08-14T12:40:10.846Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" }, +] + +[[package]] +name = "mergedeep" +version = "1.3.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/3a/41/580bb4006e3ed0361b8151a01d324fb03f420815446c7def45d02f74c270/mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8", size = 4661, upload-time = "2021-02-05T18:55:30.623Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2c/19/04f9b178c2d8a15b076c8b5140708fa6ffc5601fb6f1e975537072df5b2a/mergedeep-1.3.4-py3-none-any.whl", hash = "sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307", size = 6354, upload-time = "2021-02-05T18:55:29.583Z" }, +] + +[[package]] +name = "mistune" +version = "3.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9d/55/d01f0c4b45ade6536c51170b9043db8b2ec6ddf4a35c7ea3f5f559ac935b/mistune-3.2.0.tar.gz", hash = "sha256:708487c8a8cdd99c9d90eb3ed4c3ed961246ff78ac82f03418f5183ab70e398a", size = 95467, upload-time = "2025-12-23T11:36:34.994Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9b/f7/4a5e785ec9fbd65146a27b6b70b6cdc161a66f2024e4b04ac06a67f5578b/mistune-3.2.0-py3-none-any.whl", hash = "sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1", size = 53598, upload-time = "2025-12-23T11:36:33.211Z" }, +] + +[[package]] +name = "mkdocs" +version = "1.6.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "ghp-import" }, + { name = "jinja2" }, + { name = "markdown" }, + { name = "markupsafe" }, + { name = "mergedeep" }, + { name = "mkdocs-get-deps" }, + { name = "packaging" }, + { name = "pathspec" }, + { name = "pyyaml" }, + { name = "pyyaml-env-tag" }, + { name = "watchdog" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/bc/c6/bbd4f061bd16b378247f12953ffcb04786a618ce5e904b8c5a01a0309061/mkdocs-1.6.1.tar.gz", hash = "sha256:7b432f01d928c084353ab39c57282f29f92136665bdd6abf7c1ec8d822ef86f2", size = 3889159, upload-time = "2024-08-30T12:24:06.899Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/22/5b/dbc6a8cddc9cfa9c4971d59fb12bb8d42e161b7e7f8cc89e49137c5b279c/mkdocs-1.6.1-py3-none-any.whl", hash = "sha256:db91759624d1647f3f34aa0c3f327dd2601beae39a366d6e064c03468d35c20e", size = 3864451, upload-time = "2024-08-30T12:24:05.054Z" }, +] + +[[package]] +name = "mkdocs-autorefs" +version = "1.4.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown" }, + { name = "markupsafe" }, + { name = "mkdocs" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/51/fa/9124cd63d822e2bcbea1450ae68cdc3faf3655c69b455f3a7ed36ce6c628/mkdocs_autorefs-1.4.3.tar.gz", hash = "sha256:beee715b254455c4aa93b6ef3c67579c399ca092259cc41b7d9342573ff1fc75", size = 55425, upload-time = "2025-08-26T14:23:17.223Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9f/4d/7123b6fa2278000688ebd338e2a06d16870aaf9eceae6ba047ea05f92df1/mkdocs_autorefs-1.4.3-py3-none-any.whl", hash = "sha256:469d85eb3114801d08e9cc55d102b3ba65917a869b893403b8987b601cf55dc9", size = 25034, upload-time = "2025-08-26T14:23:15.906Z" }, +] + +[[package]] +name = "mkdocs-get-deps" +version = "0.2.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mergedeep" }, + { name = "platformdirs" }, + { name = "pyyaml" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/98/f5/ed29cd50067784976f25ed0ed6fcd3c2ce9eb90650aa3b2796ddf7b6870b/mkdocs_get_deps-0.2.0.tar.gz", hash = "sha256:162b3d129c7fad9b19abfdcb9c1458a651628e4b1dea628ac68790fb3061c60c", size = 10239, upload-time = "2023-11-20T17:51:09.981Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9f/d4/029f984e8d3f3b6b726bd33cafc473b75e9e44c0f7e80a5b29abc466bdea/mkdocs_get_deps-0.2.0-py3-none-any.whl", hash = "sha256:2bf11d0b133e77a0dd036abeeb06dec8775e46efa526dc70667d8863eefc6134", size = 9521, upload-time = "2023-11-20T17:51:08.587Z" }, +] + +[[package]] +name = "mkdocs-git-revision-date-localized-plugin" +version = "1.5.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "babel" }, + { name = "gitpython" }, + { name = "mkdocs" }, + { name = "tzdata", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0f/c5/1d3c4e6ddae6230b89d09105cb79de711655e3ebd6745f7a92efea0f5160/mkdocs_git_revision_date_localized_plugin-1.5.0.tar.gz", hash = "sha256:17345ccfdf69a1905dc96fb1070dce82d03a1eb6b0d48f958081a7589ce3c248", size = 460697, upload-time = "2025-10-31T16:11:34.44Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bc/51/fe0e3fdb16f6eed65c9459d12bae6a4e1f0bb4e2228cb037e7907b002678/mkdocs_git_revision_date_localized_plugin-1.5.0-py3-none-any.whl", hash = "sha256:933f9e35a8c135b113f21bb57610d82e9b7bcc71dd34fb06a029053c97e99656", size = 26153, upload-time = "2025-10-31T16:11:32.987Z" }, +] + +[[package]] +name = "mkdocs-jupyter" +version = "0.25.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ipykernel" }, + { name = "jupytext" }, + { name = "mkdocs" }, + { name = "mkdocs-material" }, + { name = "nbconvert" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6c/23/6ffb8d2fd2117aa860a04c6fe2510b21bc3c3c085907ffdd851caba53152/mkdocs_jupyter-0.25.1.tar.gz", hash = "sha256:0e9272ff4947e0ec683c92423a4bfb42a26477c103ab1a6ab8277e2dcc8f7afe", size = 1626747, upload-time = "2024-10-15T14:56:32.373Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/37/5f1fd5c3f6954b3256f8126275e62af493b96fb6aef6c0dbc4ee326032ad/mkdocs_jupyter-0.25.1-py3-none-any.whl", hash = "sha256:3f679a857609885d322880e72533ef5255561bbfdb13cfee2a1e92ef4d4ad8d8", size = 1456197, upload-time = "2024-10-15T14:56:29.854Z" }, +] + +[[package]] +name = "mkdocs-material" +version = "9.7.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "babel" }, + { name = "backrefs" }, + { name = "colorama" }, + { name = "jinja2" }, + { name = "markdown" }, + { name = "mkdocs" }, + { name = "mkdocs-material-extensions" }, + { name = "paginate" }, + { name = "pygments" }, + { name = "pymdown-extensions" }, + { name = "requests" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/27/e2/2ffc356cd72f1473d07c7719d82a8f2cbd261666828614ecb95b12169f41/mkdocs_material-9.7.1.tar.gz", hash = "sha256:89601b8f2c3e6c6ee0a918cc3566cb201d40bf37c3cd3c2067e26fadb8cce2b8", size = 4094392, upload-time = "2025-12-18T09:49:00.308Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3e/32/ed071cb721aca8c227718cffcf7bd539620e9799bbf2619e90c757bfd030/mkdocs_material-9.7.1-py3-none-any.whl", hash = "sha256:3f6100937d7d731f87f1e3e3b021c97f7239666b9ba1151ab476cabb96c60d5c", size = 9297166, upload-time = "2025-12-18T09:48:56.664Z" }, +] + +[[package]] +name = "mkdocs-material-extensions" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/79/9b/9b4c96d6593b2a541e1cb8b34899a6d021d208bb357042823d4d2cabdbe7/mkdocs_material_extensions-1.3.1.tar.gz", hash = "sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443", size = 11847, upload-time = "2023-11-22T19:09:45.208Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5b/54/662a4743aa81d9582ee9339d4ffa3c8fd40a4965e033d77b9da9774d3960/mkdocs_material_extensions-1.3.1-py3-none-any.whl", hash = "sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31", size = 8728, upload-time = "2023-11-22T19:09:43.465Z" }, +] + +[[package]] +name = "mkdocs-minify-plugin" +version = "0.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "csscompressor" }, + { name = "htmlmin2" }, + { name = "jsmin" }, + { name = "mkdocs" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/52/67/fe4b77e7a8ae7628392e28b14122588beaf6078b53eb91c7ed000fd158ac/mkdocs-minify-plugin-0.8.0.tar.gz", hash = "sha256:bc11b78b8120d79e817308e2b11539d790d21445eb63df831e393f76e52e753d", size = 8366, upload-time = "2024-01-29T16:11:32.982Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1b/cd/2e8d0d92421916e2ea4ff97f10a544a9bd5588eb747556701c983581df13/mkdocs_minify_plugin-0.8.0-py3-none-any.whl", hash = "sha256:5fba1a3f7bd9a2142c9954a6559a57e946587b21f133165ece30ea145c66aee6", size = 6723, upload-time = "2024-01-29T16:11:31.851Z" }, +] + +[[package]] +name = "mkdocstrings" +version = "1.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jinja2" }, + { name = "markdown" }, + { name = "markupsafe" }, + { name = "mkdocs" }, + { name = "mkdocs-autorefs" }, + { name = "pymdown-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/e5/13/10bbf9d56565fd91b91e6f5a8cd9b9d8a2b101c4e8ad6eeafa35a706301d/mkdocstrings-1.0.0.tar.gz", hash = "sha256:351a006dbb27aefce241ade110d3cd040c1145b7a3eb5fd5ac23f03ed67f401a", size = 101086, upload-time = "2025-11-27T15:39:40.534Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/fc/80aa31b79133634721cf7855d37b76ea49773599214896f2ff10be03de2a/mkdocstrings-1.0.0-py3-none-any.whl", hash = "sha256:4c50eb960bff6e05dfc631f6bc00dfabffbcb29c5ff25f676d64daae05ed82fa", size = 35135, upload-time = "2025-11-27T15:39:39.301Z" }, +] + +[package.optional-dependencies] +python = [ + { name = "mkdocstrings-python" }, +] + +[[package]] +name = "mkdocstrings-python" +version = "2.0.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "griffe" }, + { name = "mkdocs-autorefs" }, + { name = "mkdocstrings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/24/75/d30af27a2906f00eb90143470272376d728521997800f5dce5b340ba35bc/mkdocstrings_python-2.0.1.tar.gz", hash = "sha256:843a562221e6a471fefdd4b45cc6c22d2607ccbad632879234fa9692e9cf7732", size = 199345, upload-time = "2025-12-03T14:26:11.755Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/06/c5f8deba7d2cbdfa7967a716ae801aa9ca5f734b8f54fd473ef77a088dbe/mkdocstrings_python-2.0.1-py3-none-any.whl", hash = "sha256:66ecff45c5f8b71bf174e11d49afc845c2dfc7fc0ab17a86b6b337e0f24d8d90", size = 105055, upload-time = "2025-12-03T14:26:10.184Z" }, +] + [[package]] name = "ml-dtypes" version = "0.5.3" @@ -706,6 +1524,70 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7f/3c/541c4b30815ab90ebfbb51df15d0b4254f2f9f1e2b4907ab229300d5e6f2/ml_dtypes-0.5.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ab039ffb40f3dc0aeeeba84fd6c3452781b5e15bef72e2d10bcb33e4bbffc39", size = 5285284, upload-time = "2025-07-29T18:39:11.532Z" }, ] +[[package]] +name = "nbclient" +version = "0.10.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "nbformat" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/56/91/1c1d5a4b9a9ebba2b4e32b8c852c2975c872aec1fe42ab5e516b2cecd193/nbclient-0.10.4.tar.gz", hash = "sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9", size = 62554, upload-time = "2025-12-23T07:45:46.369Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/a0/5b0c2f11142ed1dddec842457d3f65eaf71a0080894eb6f018755b319c3a/nbclient-0.10.4-py3-none-any.whl", hash = "sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440", size = 25465, upload-time = "2025-12-23T07:45:44.51Z" }, +] + +[[package]] +name = "nbconvert" +version = "7.16.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "beautifulsoup4" }, + { name = "bleach", extra = ["css"] }, + { name = "defusedxml" }, + { name = "jinja2" }, + { name = "jupyter-core" }, + { name = "jupyterlab-pygments" }, + { name = "markupsafe" }, + { name = "mistune" }, + { name = "nbclient" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "pandocfilters" }, + { name = "pygments" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715, upload-time = "2025-01-28T09:29:14.724Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525, upload-time = "2025-01-28T09:29:12.551Z" }, +] + +[[package]] +name = "nbformat" +version = "5.10.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "fastjsonschema" }, + { name = "jsonschema" }, + { name = "jupyter-core" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" }, +] + +[[package]] +name = "nest-asyncio" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" }, +] + [[package]] name = "numpy" version = "2.3.4" @@ -821,6 +1703,54 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" }, ] +[[package]] +name = "paginate" +version = "0.5.7" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ec/46/68dde5b6bc00c1296ec6466ab27dddede6aec9af1b99090e1107091b3b84/paginate-0.5.7.tar.gz", hash = "sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945", size = 19252, upload-time = "2024-08-25T14:17:24.139Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/90/96/04b8e52da071d28f5e21a805b19cb9390aa17a47462ac87f5e2696b9566d/paginate-0.5.7-py2.py3-none-any.whl", hash = "sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591", size = 13746, upload-time = "2024-08-25T14:17:22.55Z" }, +] + +[[package]] +name = "pandocfilters" +version = "1.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454, upload-time = "2024-01-18T20:08:13.726Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663, upload-time = "2024-01-18T20:08:11.28Z" }, +] + +[[package]] +name = "parso" +version = "0.8.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d4/de/53e0bcf53d13e005bd8c92e7855142494f41171b34c2536b86187474184d/parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a", size = 401205, upload-time = "2025-08-23T15:15:28.028Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/16/32/f8e3c85d1d5250232a5d3477a2a28cc291968ff175caeadaf3cc19ce0e4a/parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887", size = 106668, upload-time = "2025-08-23T15:15:25.663Z" }, +] + +[[package]] +name = "pathspec" +version = "1.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/b2/bb8e495d5262bfec41ab5cb18f522f1012933347fb5d9e62452d446baca2/pathspec-1.0.3.tar.gz", hash = "sha256:bac5cf97ae2c2876e2d25ebb15078eb04d76e4b98921ee31c6f85ade8b59444d", size = 130841, upload-time = "2026-01-09T15:46:46.009Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/2b/121e912bd60eebd623f873fd090de0e84f322972ab25a7f9044c056804ed/pathspec-1.0.3-py3-none-any.whl", hash = "sha256:e80767021c1cc524aa3fb14bedda9c34406591343cc42797b386ce7b9354fb6c", size = 55021, upload-time = "2026-01-09T15:46:44.652Z" }, +] + +[[package]] +name = "pexpect" +version = "4.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ptyprocess" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, +] + [[package]] name = "pillow" version = "12.0.0" @@ -908,6 +1838,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/95/7e/f896623c3c635a90537ac093c6a618ebe1a90d87206e42309cb5d98a1b9e/pillow-12.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:b290fd8aa38422444d4b50d579de197557f182ef1068b75f5aa8558638b8d0a5", size = 6997850, upload-time = "2025-10-15T18:24:11.495Z" }, ] +[[package]] +name = "platformdirs" +version = "4.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cf/86/0248f086a84f01b37aaec0fa567b397df1a119f73c16f6c7a9aac73ea309/platformdirs-4.5.1.tar.gz", hash = "sha256:61d5cdcc6065745cdd94f0f878977f8de9437be93de97c1c12f853c9c0cdcbda", size = 21715, upload-time = "2025-12-05T13:52:58.638Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/28/3bfe2fa5a7b9c46fe7e13c97bda14c895fb10fa2ebf1d0abb90e0cea7ee1/platformdirs-4.5.1-py3-none-any.whl", hash = "sha256:d03afa3963c806a9bed9d5125c8f4cb2fdaf74a55ab60e5d59b3fde758104d31", size = 18731, upload-time = "2025-12-05T13:52:56.823Z" }, +] + [[package]] name = "pluggy" version = "1.6.0" @@ -917,6 +1856,73 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, ] +[[package]] +name = "prompt-toolkit" +version = "3.0.52" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "wcwidth" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" }, +] + +[[package]] +name = "psutil" +version = "7.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/73/cb/09e5184fb5fc0358d110fc3ca7f6b1d033800734d34cac10f4136cfac10e/psutil-7.2.1.tar.gz", hash = "sha256:f7583aec590485b43ca601dd9cea0dcd65bd7bb21d30ef4ddbf4ea6b5ed1bdd3", size = 490253, upload-time = "2025-12-29T08:26:00.169Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/77/8e/f0c242053a368c2aa89584ecd1b054a18683f13d6e5a318fc9ec36582c94/psutil-7.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:ba9f33bb525b14c3ea563b2fd521a84d2fa214ec59e3e6a2858f78d0844dd60d", size = 129624, upload-time = "2025-12-29T08:26:04.255Z" }, + { url = "https://files.pythonhosted.org/packages/26/97/a58a4968f8990617decee234258a2b4fc7cd9e35668387646c1963e69f26/psutil-7.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:81442dac7abfc2f4f4385ea9e12ddf5a796721c0f6133260687fec5c3780fa49", size = 130132, upload-time = "2025-12-29T08:26:06.228Z" }, + { url = "https://files.pythonhosted.org/packages/db/6d/ed44901e830739af5f72a85fa7ec5ff1edea7f81bfbf4875e409007149bd/psutil-7.2.1-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ea46c0d060491051d39f0d2cff4f98d5c72b288289f57a21556cc7d504db37fc", size = 180612, upload-time = "2025-12-29T08:26:08.276Z" }, + { url = "https://files.pythonhosted.org/packages/c7/65/b628f8459bca4efbfae50d4bf3feaab803de9a160b9d5f3bd9295a33f0c2/psutil-7.2.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:35630d5af80d5d0d49cfc4d64c1c13838baf6717a13effb35869a5919b854cdf", size = 183201, upload-time = "2025-12-29T08:26:10.622Z" }, + { url = "https://files.pythonhosted.org/packages/fb/23/851cadc9764edcc18f0effe7d0bf69f727d4cf2442deb4a9f78d4e4f30f2/psutil-7.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:923f8653416604e356073e6e0bccbe7c09990acef442def2f5640dd0faa9689f", size = 139081, upload-time = "2025-12-29T08:26:12.483Z" }, + { url = "https://files.pythonhosted.org/packages/59/82/d63e8494ec5758029f31c6cb06d7d161175d8281e91d011a4a441c8a43b5/psutil-7.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:cfbe6b40ca48019a51827f20d830887b3107a74a79b01ceb8cc8de4ccb17b672", size = 134767, upload-time = "2025-12-29T08:26:14.528Z" }, + { url = "https://files.pythonhosted.org/packages/05/c2/5fb764bd61e40e1fe756a44bd4c21827228394c17414ade348e28f83cd79/psutil-7.2.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:494c513ccc53225ae23eec7fe6e1482f1b8a44674241b54561f755a898650679", size = 129716, upload-time = "2025-12-29T08:26:16.017Z" }, + { url = "https://files.pythonhosted.org/packages/c9/d2/935039c20e06f615d9ca6ca0ab756cf8408a19d298ffaa08666bc18dc805/psutil-7.2.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3fce5f92c22b00cdefd1645aa58ab4877a01679e901555067b1bd77039aa589f", size = 130133, upload-time = "2025-12-29T08:26:18.009Z" }, + { url = "https://files.pythonhosted.org/packages/77/69/19f1eb0e01d24c2b3eacbc2f78d3b5add8a89bf0bb69465bc8d563cc33de/psutil-7.2.1-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93f3f7b0bb07711b49626e7940d6fe52aa9940ad86e8f7e74842e73189712129", size = 181518, upload-time = "2025-12-29T08:26:20.241Z" }, + { url = "https://files.pythonhosted.org/packages/e1/6d/7e18b1b4fa13ad370787626c95887b027656ad4829c156bb6569d02f3262/psutil-7.2.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d34d2ca888208eea2b5c68186841336a7f5e0b990edec929be909353a202768a", size = 184348, upload-time = "2025-12-29T08:26:22.215Z" }, + { url = "https://files.pythonhosted.org/packages/98/60/1672114392dd879586d60dd97896325df47d9a130ac7401318005aab28ec/psutil-7.2.1-cp314-cp314t-win_amd64.whl", hash = "sha256:2ceae842a78d1603753561132d5ad1b2f8a7979cb0c283f5b52fb4e6e14b1a79", size = 140400, upload-time = "2025-12-29T08:26:23.993Z" }, + { url = "https://files.pythonhosted.org/packages/fb/7b/d0e9d4513c46e46897b46bcfc410d51fc65735837ea57a25170f298326e6/psutil-7.2.1-cp314-cp314t-win_arm64.whl", hash = "sha256:08a2f175e48a898c8eb8eace45ce01777f4785bc744c90aa2cc7f2fa5462a266", size = 135430, upload-time = "2025-12-29T08:26:25.999Z" }, + { url = "https://files.pythonhosted.org/packages/c5/cf/5180eb8c8bdf6a503c6919f1da28328bd1e6b3b1b5b9d5b01ae64f019616/psutil-7.2.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b2e953fcfaedcfbc952b44744f22d16575d3aa78eb4f51ae74165b4e96e55f42", size = 128137, upload-time = "2025-12-29T08:26:27.759Z" }, + { url = "https://files.pythonhosted.org/packages/c5/2c/78e4a789306a92ade5000da4f5de3255202c534acdadc3aac7b5458fadef/psutil-7.2.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:05cc68dbb8c174828624062e73078e7e35406f4ca2d0866c272c2410d8ef06d1", size = 128947, upload-time = "2025-12-29T08:26:29.548Z" }, + { url = "https://files.pythonhosted.org/packages/29/f8/40e01c350ad9a2b3cb4e6adbcc8a83b17ee50dd5792102b6142385937db5/psutil-7.2.1-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5e38404ca2bb30ed7267a46c02f06ff842e92da3bb8c5bfdadbd35a5722314d8", size = 154694, upload-time = "2025-12-29T08:26:32.147Z" }, + { url = "https://files.pythonhosted.org/packages/06/e4/b751cdf839c011a9714a783f120e6a86b7494eb70044d7d81a25a5cd295f/psutil-7.2.1-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ab2b98c9fc19f13f59628d94df5cc4cc4844bc572467d113a8b517d634e362c6", size = 156136, upload-time = "2025-12-29T08:26:34.079Z" }, + { url = "https://files.pythonhosted.org/packages/44/ad/bbf6595a8134ee1e94a4487af3f132cef7fce43aef4a93b49912a48c3af7/psutil-7.2.1-cp36-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:f78baafb38436d5a128f837fab2d92c276dfb48af01a240b861ae02b2413ada8", size = 148108, upload-time = "2025-12-29T08:26:36.225Z" }, + { url = "https://files.pythonhosted.org/packages/1c/15/dd6fd869753ce82ff64dcbc18356093471a5a5adf4f77ed1f805d473d859/psutil-7.2.1-cp36-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:99a4cd17a5fdd1f3d014396502daa70b5ec21bf4ffe38393e152f8e449757d67", size = 147402, upload-time = "2025-12-29T08:26:39.21Z" }, + { url = "https://files.pythonhosted.org/packages/34/68/d9317542e3f2b180c4306e3f45d3c922d7e86d8ce39f941bb9e2e9d8599e/psutil-7.2.1-cp37-abi3-win_amd64.whl", hash = "sha256:b1b0671619343aa71c20ff9767eced0483e4fc9e1f489d50923738caf6a03c17", size = 136938, upload-time = "2025-12-29T08:26:41.036Z" }, + { url = "https://files.pythonhosted.org/packages/3e/73/2ce007f4198c80fcf2cb24c169884f833fe93fbc03d55d302627b094ee91/psutil-7.2.1-cp37-abi3-win_arm64.whl", hash = "sha256:0d67c1822c355aa6f7314d92018fb4268a76668a536f133599b91edd48759442", size = 133836, upload-time = "2025-12-29T08:26:43.086Z" }, +] + +[[package]] +name = "ptyprocess" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, +] + +[[package]] +name = "pure-eval" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, +] + +[[package]] +name = "pycparser" +version = "2.23" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/fe/cf/d2d3b9f5699fb1e4615c8e32ff220203e43b248e1dfcc6736ad9057731ca/pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2", size = 173734, upload-time = "2025-09-09T13:23:47.91Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/e3/59cd50310fc9b59512193629e1984c1f95e5c8ae6e5d8c69532ccc65a7fe/pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934", size = 118140, upload-time = "2025-09-09T13:23:46.651Z" }, +] + [[package]] name = "pygments" version = "2.19.2" @@ -926,6 +1932,19 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, ] +[[package]] +name = "pymdown-extensions" +version = "10.20" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown" }, + { name = "pyyaml" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/3e/35/e3814a5b7df295df69d035cfb8aab78b2967cdf11fcfae7faed726b66664/pymdown_extensions-10.20.tar.gz", hash = "sha256:5c73566ab0cf38c6ba084cb7c5ea64a119ae0500cce754ccb682761dfea13a52", size = 852774, upload-time = "2025-12-31T19:59:42.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ea/10/47caf89cbb52e5bb764696fd52a8c591a2f0e851a93270c05a17f36000b5/pymdown_extensions-10.20-py3-none-any.whl", hash = "sha256:ea9e62add865da80a271d00bfa1c0fa085b20d133fb3fc97afdc88e682f60b2f", size = 268733, upload-time = "2025-12-31T19:59:40.652Z" }, +] + [[package]] name = "pyparsing" version = "3.2.5" @@ -972,6 +1991,131 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/aa/19/c147f78cc18c8788f54d4a16a22f6c05deba85ead5672d3ddf6dcba5a5fe/python_gnupg-0.5.5-py2.py3-none-any.whl", hash = "sha256:51fa7b8831ff0914bc73d74c59b99c613de7247b91294323c39733bb85ac3fc1", size = 21916, upload-time = "2025-08-04T19:26:54.307Z" }, ] +[[package]] +name = "pyyaml" +version = "6.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826, upload-time = "2025-09-25T21:31:58.655Z" }, + { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577, upload-time = "2025-09-25T21:32:00.088Z" }, + { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556, upload-time = "2025-09-25T21:32:01.31Z" }, + { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114, upload-time = "2025-09-25T21:32:03.376Z" }, + { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638, upload-time = "2025-09-25T21:32:04.553Z" }, + { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463, upload-time = "2025-09-25T21:32:06.152Z" }, + { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986, upload-time = "2025-09-25T21:32:07.367Z" }, + { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543, upload-time = "2025-09-25T21:32:08.95Z" }, + { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763, upload-time = "2025-09-25T21:32:09.96Z" }, + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, +] + +[[package]] +name = "pyyaml-env-tag" +version = "1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pyyaml" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/2e/79c822141bfd05a853236b504869ebc6b70159afc570e1d5a20641782eaa/pyyaml_env_tag-1.1.tar.gz", hash = "sha256:2eb38b75a2d21ee0475d6d97ec19c63287a7e140231e4214969d0eac923cd7ff", size = 5737, upload-time = "2025-05-13T15:24:01.64Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/04/11/432f32f8097b03e3cd5fe57e88efb685d964e2e5178a48ed61e841f7fdce/pyyaml_env_tag-1.1-py3-none-any.whl", hash = "sha256:17109e1a528561e32f026364712fee1264bc2ea6715120891174ed1b980d2e04", size = 4722, upload-time = "2025-05-13T15:23:59.629Z" }, +] + +[[package]] +name = "pyzmq" +version = "27.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi", marker = "implementation_name == 'pypy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/5d/305323ba86b284e6fcb0d842d6adaa2999035f70f8c38a9b6d21ad28c3d4/pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86", size = 1333328, upload-time = "2025-09-08T23:07:45.946Z" }, + { url = "https://files.pythonhosted.org/packages/bd/a0/fc7e78a23748ad5443ac3275943457e8452da67fda347e05260261108cbc/pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581", size = 908803, upload-time = "2025-09-08T23:07:47.551Z" }, + { url = "https://files.pythonhosted.org/packages/7e/22/37d15eb05f3bdfa4abea6f6d96eb3bb58585fbd3e4e0ded4e743bc650c97/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f", size = 668836, upload-time = "2025-09-08T23:07:49.436Z" }, + { url = "https://files.pythonhosted.org/packages/b1/c4/2a6fe5111a01005fc7af3878259ce17684fabb8852815eda6225620f3c59/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e", size = 857038, upload-time = "2025-09-08T23:07:51.234Z" }, + { url = "https://files.pythonhosted.org/packages/cb/eb/bfdcb41d0db9cd233d6fb22dc131583774135505ada800ebf14dfb0a7c40/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e", size = 1657531, upload-time = "2025-09-08T23:07:52.795Z" }, + { url = "https://files.pythonhosted.org/packages/ab/21/e3180ca269ed4a0de5c34417dfe71a8ae80421198be83ee619a8a485b0c7/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2", size = 2034786, upload-time = "2025-09-08T23:07:55.047Z" }, + { url = "https://files.pythonhosted.org/packages/3b/b1/5e21d0b517434b7f33588ff76c177c5a167858cc38ef740608898cd329f2/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394", size = 1894220, upload-time = "2025-09-08T23:07:57.172Z" }, + { url = "https://files.pythonhosted.org/packages/03/f2/44913a6ff6941905efc24a1acf3d3cb6146b636c546c7406c38c49c403d4/pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f", size = 567155, upload-time = "2025-09-08T23:07:59.05Z" }, + { url = "https://files.pythonhosted.org/packages/23/6d/d8d92a0eb270a925c9b4dd039c0b4dc10abc2fcbc48331788824ef113935/pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97", size = 633428, upload-time = "2025-09-08T23:08:00.663Z" }, + { url = "https://files.pythonhosted.org/packages/ae/14/01afebc96c5abbbd713ecfc7469cfb1bc801c819a74ed5c9fad9a48801cb/pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07", size = 559497, upload-time = "2025-09-08T23:08:02.15Z" }, + { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" }, + { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" }, + { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" }, + { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" }, + { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" }, + { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" }, + { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" }, + { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184, upload-time = "2025-09-08T23:08:15.163Z" }, + { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480, upload-time = "2025-09-08T23:08:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993, upload-time = "2025-09-08T23:08:18.926Z" }, + { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436, upload-time = "2025-09-08T23:08:20.801Z" }, + { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301, upload-time = "2025-09-08T23:08:22.47Z" }, + { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197, upload-time = "2025-09-08T23:08:24.286Z" }, + { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275, upload-time = "2025-09-08T23:08:26.063Z" }, + { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469, upload-time = "2025-09-08T23:08:27.623Z" }, + { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961, upload-time = "2025-09-08T23:08:29.672Z" }, + { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282, upload-time = "2025-09-08T23:08:31.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468, upload-time = "2025-09-08T23:08:33.543Z" }, + { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394, upload-time = "2025-09-08T23:08:35.51Z" }, + { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964, upload-time = "2025-09-08T23:08:37.178Z" }, + { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029, upload-time = "2025-09-08T23:08:40.595Z" }, + { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541, upload-time = "2025-09-08T23:08:42.668Z" }, + { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197, upload-time = "2025-09-08T23:08:44.973Z" }, + { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175, upload-time = "2025-09-08T23:08:46.601Z" }, + { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427, upload-time = "2025-09-08T23:08:48.187Z" }, + { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929, upload-time = "2025-09-08T23:08:49.76Z" }, + { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193, upload-time = "2025-09-08T23:08:51.7Z" }, + { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388, upload-time = "2025-09-08T23:08:53.393Z" }, + { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316, upload-time = "2025-09-08T23:08:55.702Z" }, + { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472, upload-time = "2025-09-08T23:08:58.18Z" }, + { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401, upload-time = "2025-09-08T23:08:59.802Z" }, + { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170, upload-time = "2025-09-08T23:09:01.418Z" }, + { url = "https://files.pythonhosted.org/packages/4c/c6/c4dcdecdbaa70969ee1fdced6d7b8f60cfabe64d25361f27ac4665a70620/pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066", size = 836265, upload-time = "2025-09-08T23:09:49.376Z" }, + { url = "https://files.pythonhosted.org/packages/3e/79/f38c92eeaeb03a2ccc2ba9866f0439593bb08c5e3b714ac1d553e5c96e25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604", size = 800208, upload-time = "2025-09-08T23:09:51.073Z" }, + { url = "https://files.pythonhosted.org/packages/49/0e/3f0d0d335c6b3abb9b7b723776d0b21fa7f3a6c819a0db6097059aada160/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c", size = 567747, upload-time = "2025-09-08T23:09:52.698Z" }, + { url = "https://files.pythonhosted.org/packages/a1/cf/f2b3784d536250ffd4be70e049f3b60981235d70c6e8ce7e3ef21e1adb25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271", size = 747371, upload-time = "2025-09-08T23:09:54.563Z" }, + { url = "https://files.pythonhosted.org/packages/01/1b/5dbe84eefc86f48473947e2f41711aded97eecef1231f4558f1f02713c12/pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355", size = 544862, upload-time = "2025-09-08T23:09:56.509Z" }, +] + [[package]] name = "referencing" version = "0.37.0" @@ -986,6 +2130,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766, upload-time = "2025-10-13T15:30:47.625Z" }, ] +[[package]] +name = "requests" +version = "2.32.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "charset-normalizer" }, + { name = "idna" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" }, +] + [[package]] name = "rpds-py" version = "0.28.0" @@ -1192,6 +2351,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, ] +[[package]] +name = "smmap" +version = "5.0.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/44/cd/a040c4b3119bbe532e5b0732286f805445375489fceaec1f48306068ee3b/smmap-5.0.2.tar.gz", hash = "sha256:26ea65a03958fa0c8a1c7e8c7a58fdc77221b8910f6be2131affade476898ad5", size = 22329, upload-time = "2025-01-02T07:14:40.909Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/04/be/d09147ad1ec7934636ad912901c5fd7667e1c858e19d355237db0d0cd5e4/smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e", size = 24303, upload-time = "2025-01-02T07:14:38.724Z" }, +] + [[package]] name = "sniffio" version = "1.3.1" @@ -1201,6 +2369,41 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235, upload-time = "2024-02-25T23:20:01.196Z" }, ] +[[package]] +name = "soupsieve" +version = "2.8.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/89/23/adf3796d740536d63a6fbda113d07e60c734b6ed5d3058d1e47fc0495e47/soupsieve-2.8.1.tar.gz", hash = "sha256:4cf733bc50fa805f5df4b8ef4740fc0e0fa6218cf3006269afd3f9d6d80fd350", size = 117856, upload-time = "2025-12-18T13:50:34.655Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/48/f3/b67d6ea49ca9154453b6d70b34ea22f3996b9fa55da105a79d8732227adc/soupsieve-2.8.1-py3-none-any.whl", hash = "sha256:a11fe2a6f3d76ab3cf2de04eb339c1be5b506a8a47f2ceb6d139803177f85434", size = 36710, upload-time = "2025-12-18T13:50:33.267Z" }, +] + +[[package]] +name = "stack-data" +version = "0.6.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "asttokens" }, + { name = "executing" }, + { name = "pure-eval" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, +] + +[[package]] +name = "tinycss2" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" }, +] + [[package]] name = "tomli" version = "2.3.0" @@ -1259,6 +2462,34 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/bd/75/8539d011f6be8e29f339c42e633aae3cb73bffa95dd0f9adec09b9c58e85/tomlkit-0.13.3-py3-none-any.whl", hash = "sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0", size = 38901, upload-time = "2025-06-05T07:13:43.546Z" }, ] +[[package]] +name = "tornado" +version = "6.5.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/37/1d/0a336abf618272d53f62ebe274f712e213f5a03c0b2339575430b8362ef2/tornado-6.5.4.tar.gz", hash = "sha256:a22fa9047405d03260b483980635f0b041989d8bcc9a313f8fe18b411d84b1d7", size = 513632, upload-time = "2025-12-15T19:21:03.836Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ab/a9/e94a9d5224107d7ce3cc1fab8d5dc97f5ea351ccc6322ee4fb661da94e35/tornado-6.5.4-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:d6241c1a16b1c9e4cc28148b1cda97dd1c6cb4fb7068ac1bedc610768dff0ba9", size = 443909, upload-time = "2025-12-15T19:20:48.382Z" }, + { url = "https://files.pythonhosted.org/packages/db/7e/f7b8d8c4453f305a51f80dbb49014257bb7d28ccb4bbb8dd328ea995ecad/tornado-6.5.4-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2d50f63dda1d2cac3ae1fa23d254e16b5e38153758470e9956cbc3d813d40843", size = 442163, upload-time = "2025-12-15T19:20:49.791Z" }, + { url = "https://files.pythonhosted.org/packages/ba/b5/206f82d51e1bfa940ba366a8d2f83904b15942c45a78dd978b599870ab44/tornado-6.5.4-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d1cf66105dc6acb5af613c054955b8137e34a03698aa53272dbda4afe252be17", size = 445746, upload-time = "2025-12-15T19:20:51.491Z" }, + { url = "https://files.pythonhosted.org/packages/8e/9d/1a3338e0bd30ada6ad4356c13a0a6c35fbc859063fa7eddb309183364ac1/tornado-6.5.4-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:50ff0a58b0dc97939d29da29cd624da010e7f804746621c78d14b80238669335", size = 445083, upload-time = "2025-12-15T19:20:52.778Z" }, + { url = "https://files.pythonhosted.org/packages/50/d4/e51d52047e7eb9a582da59f32125d17c0482d065afd5d3bc435ff2120dc5/tornado-6.5.4-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e5fb5e04efa54cf0baabdd10061eb4148e0be137166146fff835745f59ab9f7f", size = 445315, upload-time = "2025-12-15T19:20:53.996Z" }, + { url = "https://files.pythonhosted.org/packages/27/07/2273972f69ca63dbc139694a3fc4684edec3ea3f9efabf77ed32483b875c/tornado-6.5.4-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:9c86b1643b33a4cd415f8d0fe53045f913bf07b4a3ef646b735a6a86047dda84", size = 446003, upload-time = "2025-12-15T19:20:56.101Z" }, + { url = "https://files.pythonhosted.org/packages/d1/83/41c52e47502bf7260044413b6770d1a48dda2f0246f95ee1384a3cd9c44a/tornado-6.5.4-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:6eb82872335a53dd063a4f10917b3efd28270b56a33db69009606a0312660a6f", size = 445412, upload-time = "2025-12-15T19:20:57.398Z" }, + { url = "https://files.pythonhosted.org/packages/10/c7/bc96917f06cbee182d44735d4ecde9c432e25b84f4c2086143013e7b9e52/tornado-6.5.4-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:6076d5dda368c9328ff41ab5d9dd3608e695e8225d1cd0fd1e006f05da3635a8", size = 445392, upload-time = "2025-12-15T19:20:58.692Z" }, + { url = "https://files.pythonhosted.org/packages/0c/1a/d7592328d037d36f2d2462f4bc1fbb383eec9278bc786c1b111cbbd44cfa/tornado-6.5.4-cp39-abi3-win32.whl", hash = "sha256:1768110f2411d5cd281bac0a090f707223ce77fd110424361092859e089b38d1", size = 446481, upload-time = "2025-12-15T19:21:00.008Z" }, + { url = "https://files.pythonhosted.org/packages/d6/6d/c69be695a0a64fd37a97db12355a035a6d90f79067a3cf936ec2b1dc38cd/tornado-6.5.4-cp39-abi3-win_amd64.whl", hash = "sha256:fa07d31e0cd85c60713f2b995da613588aa03e1303d75705dca6af8babc18ddc", size = 446886, upload-time = "2025-12-15T19:21:01.287Z" }, + { url = "https://files.pythonhosted.org/packages/50/49/8dc3fd90902f70084bd2cd059d576ddb4f8bb44c2c7c0e33a11422acb17e/tornado-6.5.4-cp39-abi3-win_arm64.whl", hash = "sha256:053e6e16701eb6cbe641f308f4c1a9541f91b6261991160391bfc342e8a551a1", size = 445910, upload-time = "2025-12-15T19:21:02.571Z" }, +] + +[[package]] +name = "traitlets" +version = "5.14.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" }, +] + [[package]] name = "typing-extensions" version = "4.15.0" @@ -1268,6 +2499,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, ] +[[package]] +name = "tzdata" +version = "2025.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5e/a7/c202b344c5ca7daf398f3b8a477eeb205cf3b6f32e7ec3a6bac0629ca975/tzdata-2025.3.tar.gz", hash = "sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7", size = 196772, upload-time = "2025-12-13T17:45:35.667Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/b0/003792df09decd6849a5e39c28b513c06e84436a54440380862b5aeff25d/tzdata-2025.3-py2.py3-none-any.whl", hash = "sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1", size = 348521, upload-time = "2025-12-13T17:45:33.889Z" }, +] + +[[package]] +name = "urllib3" +version = "2.6.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" }, +] + [[package]] name = "wadler-lindig" version = "0.1.7" @@ -1276,3 +2525,48 @@ sdist = { url = "https://files.pythonhosted.org/packages/1e/67/cbae4bf7683a64755 wheels = [ { url = "https://files.pythonhosted.org/packages/8d/96/04e7b441807b26b794da5b11e59ed7f83b2cf8af202bd7eba8ad2fa6046e/wadler_lindig-0.1.7-py3-none-any.whl", hash = "sha256:e3ec83835570fd0a9509f969162aeb9c65618f998b1f42918cfc8d45122fe953", size = 20516, upload-time = "2025-06-18T07:00:41.684Z" }, ] + +[[package]] +name = "watchdog" +version = "6.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220, upload-time = "2024-11-01T14:07:13.037Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e0/24/d9be5cd6642a6aa68352ded4b4b10fb0d7889cb7f45814fb92cecd35f101/watchdog-6.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6eb11feb5a0d452ee41f824e271ca311a09e250441c262ca2fd7ebcf2461a06c", size = 96393, upload-time = "2024-11-01T14:06:31.756Z" }, + { url = "https://files.pythonhosted.org/packages/63/7a/6013b0d8dbc56adca7fdd4f0beed381c59f6752341b12fa0886fa7afc78b/watchdog-6.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ef810fbf7b781a5a593894e4f439773830bdecb885e6880d957d5b9382a960d2", size = 88392, upload-time = "2024-11-01T14:06:32.99Z" }, + { url = "https://files.pythonhosted.org/packages/d1/40/b75381494851556de56281e053700e46bff5b37bf4c7267e858640af5a7f/watchdog-6.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:afd0fe1b2270917c5e23c2a65ce50c2a4abb63daafb0d419fde368e272a76b7c", size = 89019, upload-time = "2024-11-01T14:06:34.963Z" }, + { url = "https://files.pythonhosted.org/packages/39/ea/3930d07dafc9e286ed356a679aa02d777c06e9bfd1164fa7c19c288a5483/watchdog-6.0.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bdd4e6f14b8b18c334febb9c4425a878a2ac20efd1e0b231978e7b150f92a948", size = 96471, upload-time = "2024-11-01T14:06:37.745Z" }, + { url = "https://files.pythonhosted.org/packages/12/87/48361531f70b1f87928b045df868a9fd4e253d9ae087fa4cf3f7113be363/watchdog-6.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c7c15dda13c4eb00d6fb6fc508b3c0ed88b9d5d374056b239c4ad1611125c860", size = 88449, upload-time = "2024-11-01T14:06:39.748Z" }, + { url = "https://files.pythonhosted.org/packages/5b/7e/8f322f5e600812e6f9a31b75d242631068ca8f4ef0582dd3ae6e72daecc8/watchdog-6.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6f10cb2d5902447c7d0da897e2c6768bca89174d0c6e1e30abec5421af97a5b0", size = 89054, upload-time = "2024-11-01T14:06:41.009Z" }, + { url = "https://files.pythonhosted.org/packages/68/98/b0345cabdce2041a01293ba483333582891a3bd5769b08eceb0d406056ef/watchdog-6.0.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c", size = 96480, upload-time = "2024-11-01T14:06:42.952Z" }, + { url = "https://files.pythonhosted.org/packages/85/83/cdf13902c626b28eedef7ec4f10745c52aad8a8fe7eb04ed7b1f111ca20e/watchdog-6.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134", size = 88451, upload-time = "2024-11-01T14:06:45.084Z" }, + { url = "https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b", size = 89057, upload-time = "2024-11-01T14:06:47.324Z" }, + { url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079, upload-time = "2024-11-01T14:06:59.472Z" }, + { url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078, upload-time = "2024-11-01T14:07:01.431Z" }, + { url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076, upload-time = "2024-11-01T14:07:02.568Z" }, + { url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077, upload-time = "2024-11-01T14:07:03.893Z" }, + { url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078, upload-time = "2024-11-01T14:07:05.189Z" }, + { url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077, upload-time = "2024-11-01T14:07:06.376Z" }, + { url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078, upload-time = "2024-11-01T14:07:07.547Z" }, + { url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065, upload-time = "2024-11-01T14:07:09.525Z" }, + { url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070, upload-time = "2024-11-01T14:07:10.686Z" }, + { url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" }, +] + +[[package]] +name = "wcwidth" +version = "0.2.14" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293, upload-time = "2025-09-22T16:29:53.023Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" }, +] + +[[package]] +name = "webencodings" +version = "0.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" }, +] From ff5d7fdb6fbb3452b2ca2760c8b5141fcbfaaa3f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Jan 2026 19:19:47 +0000 Subject: [PATCH 08/18] docs: general cleanup, adds architecture diagram, updates to tutorials --- docs/algorithms/index.md | 2 + docs/algorithms/optimizers/adam.md | 2 + docs/algorithms/optimizers/cmaes.md | 2 + docs/algorithms/optimizers/nelder-mead.md | 2 + .../samplers/dynamic-nested-sampling.md | 2 + .../samplers/metropolis-hastings.md | 2 + docs/api-reference/index.md | 2 - docs/api-reference/python/cost-metrics.md | 12 + docs/api-reference/python/optimizers.md | 9 + docs/api-reference/python/results.md | 3 + docs/api-reference/python/samplers.md | 6 + docs/api-reference/rust/index.md | 12 - docs/development/architecture.md | 25 +- docs/development/building.md | 48 - docs/development/contributing.md | 1 - docs/development/index.md | 19 - docs/examples/gallery.md | 12 +- docs/getting-started/concepts.md | 12 +- docs/getting-started/first-ode-fit.md | 94 +- docs/getting-started/installation.md | 75 +- docs/getting-started/quickstart.md | 12 +- docs/guides/troubleshooting.md | 16 +- docs/index.md | 13 +- .../notebooks/03_parameter_uncertainty.ipynb | 2 +- .../06_custom_solver_integration.ipynb | 165 +-- .../notebooks/07_parallel_optimization.ipynb | 980 ++++++++---------- .../predator_prey/predator_prey_diffeqpy.py | 3 + mkdocs.yml | 1 - pyproject.toml | 2 + tests/test_docs.py | 14 +- uv.lock | 4 + 31 files changed, 663 insertions(+), 891 deletions(-) delete mode 100644 docs/development/building.md diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md index 83bc06c..bb25b39 100644 --- a/docs/algorithms/index.md +++ b/docs/algorithms/index.md @@ -88,6 +88,7 @@ graph TD ### Convergence Speed Fast → Slow: + 1. **Adam** (with gradients) 2. **Nelder-Mead** (small problems) 3. **CMA-ES** (large problems) @@ -97,6 +98,7 @@ Fast → Slow: ### Robustness to Local Minima Least → Most robust: + 1. **Adam** (gradient descent) 2. **Nelder-Mead** (local search) 3. **CMA-ES** (global search) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md index 04a8e70..527d8b4 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimizers/adam.md @@ -10,11 +10,13 @@ Adaptive Moment Estimation (Adam) is a gradient-based optimiser with adaptive le ## When to Use **Best for:** + - Smooth, differentiable objectives - Fast convergence needed - Gradients available or cheap to compute **Avoid when:** + - Objective is non-smooth - No gradient information - Need global optimum (can get stuck in local minima) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md index 2765c7d..c80d488 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimizers/cmaes.md @@ -10,12 +10,14 @@ Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic, deriva ## When to Use **Best for:** + - High-dimensional problems (10-100+ parameters) - Global optimisation - Parallel hardware - Multi-modal landscapes **Avoid when:** + - Very low-dimensional (< 5 parameters) - Limited computational budget - Need deterministic results diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimizers/nelder-mead.md index 3b460e0..9ae3895 100644 --- a/docs/algorithms/optimizers/nelder-mead.md +++ b/docs/algorithms/optimizers/nelder-mead.md @@ -10,12 +10,14 @@ The Nelder-Mead algorithm (also known as the downhill simplex method) is a gradi ## When to Use **Best for:** + - Problems with < 10 parameters - Noisy objective functions - No gradient information available - Quick exploration **Avoid when:** + - More than 10 parameters - Need global optimum - Very tight convergence required diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md index 71cfe41..7ca0527 100644 --- a/docs/algorithms/samplers/dynamic-nested-sampling.md +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -10,12 +10,14 @@ Dynamic Nested Sampling calculates model evidence (marginal likelihood) for Baye ## When to Use **Best for:** + - Model comparison - Calculating Bayes factors - Evidence calculation - Multi-modal posteriors **Avoid when:** + - Only need posterior samples (use MCMC) - Limited computational budget diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md index b35029b..1fc3508 100644 --- a/docs/algorithms/samplers/metropolis-hastings.md +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -10,11 +10,13 @@ Metropolis-Hastings is an MCMC algorithm for sampling from posterior distributio ## When to Use **Best for:** + - Uncertainty quantification - Confidence intervals - Posterior exploration **Avoid when:** + - Only need point estimate - Limited computational budget - Need model comparison (use nested sampling) diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md index 8164fbe..5383f0a 100644 --- a/docs/api-reference/index.md +++ b/docs/api-reference/index.md @@ -56,8 +56,6 @@ The Rust core provides high-performance implementations of all algorithms. [:octicons-arrow-right-24: Rust Documentation on docs.rs](https://docs.rs/chronopt/latest/chronopt/) -For developers building with the Rust crate directly, see the [Building from Source](../development/building.md) guide. - ## Module Structure ``` diff --git a/docs/api-reference/python/cost-metrics.md b/docs/api-reference/python/cost-metrics.md index ada62c7..fdebf87 100644 --- a/docs/api-reference/python/cost-metrics.md +++ b/docs/api-reference/python/cost-metrics.md @@ -43,16 +43,19 @@ builder = ( ### When to Use **Advantages:** + - Standard least squares approach - Well-understood statistical properties - Efficient to compute **Limitations:** + - Not normalised (sensitive to number of data points) - Sensitive to outliers (squared errors magnify them) - Assumes Gaussian errors with constant variance **Typical Use Cases:** + - Standard parameter fitting - When absolute error scale matters - Comparing models with same number of points @@ -82,15 +85,18 @@ builder = ( ### When to Use **Advantages:** + - Normalised (independent of data size) - Same units as observations - Better for comparing models with different data sizes **Limitations:** + - Still sensitive to outliers - Assumes Gaussian errors **Typical Use Cases:** + - Comparing models with different numbers of observations - Reporting error in interpretable units - Cross-validation and model selection @@ -126,17 +132,20 @@ result = sampler.run(problem, initial_guess) ### When to Use **Advantages:** + - Proper probabilistic interpretation - Required for Bayesian inference (MCMC, nested sampling) - Automatically accounts for noise level - Enables uncertainty quantification **Limitations:** + - Assumes Gaussian observation noise - More computationally expensive than SSE - Requires understanding of likelihood **Typical Use Cases:** + - MCMC sampling for uncertainty quantification - Model comparison with Bayes factors - When probabilistic interpretation is needed @@ -159,16 +168,19 @@ graph TD ### Decision Guide **Use SSE when:** + - Standard least squares fitting - Single model, fixed data - Speed is critical **Use RMSE when:** + - Comparing models with different data sizes - Want interpretable error in original units - Cross-validation or model selection **Use GaussianNLL when:** + - MCMC sampling (Metropolis-Hastings) - Nested sampling (model evidence) - Need confidence intervals diff --git a/docs/api-reference/python/optimizers.md b/docs/api-reference/python/optimizers.md index bf5b02a..a12b22f 100644 --- a/docs/api-reference/python/optimizers.md +++ b/docs/api-reference/python/optimizers.md @@ -37,17 +37,20 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) ### When to Use **Advantages:** + - No gradient computation required - Robust to noisy objectives - Simple and reliable for small problems - Default choice for quick exploration **Limitations:** + - Slow convergence for > 10 parameters - Can get stuck in local minima - Performance degrades with dimensionality **Typical Use Cases:** + - Initial parameter exploration - Noisy experimental data - Small-scale problems (< 10 parameters) @@ -97,17 +100,20 @@ result = optimiser.run(problem, initial_guess=[0.5, 0.5]) ### When to Use **Advantages:** + - Global optimisation (avoids local minima) - Scales to high dimensions (10-100+ parameters) - Parallelisable (evaluates population in parallel) - Self-adapting (no gradient tuning needed) **Limitations:** + - More function evaluations than gradient methods - Requires population-sized memory - Stochastic (results vary between runs) **Typical Use Cases:** + - Global parameter search - High-dimensional problems (> 10 parameters) - Multi-modal landscapes @@ -167,17 +173,20 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) ### When to Use **Advantages:** + - Fast convergence on smooth objectives - Adaptive learning rate - Well-suited for large-scale problems - Efficient (uses gradients) **Limitations:** + - Requires automatic differentiation - Can get stuck in local minima - Sensitive to learning rate tuning **Typical Use Cases:** + - Smooth, differentiable objectives - Large-scale problems - When gradients are available or cheap to compute diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md index ceb28ab..557a05c 100644 --- a/docs/api-reference/python/results.md +++ b/docs/api-reference/python/results.md @@ -82,11 +82,13 @@ else: ``` **`True`** when: + - Convergence criteria met - Threshold reached - Normal termination **`False`** when: + - Maximum iterations exceeded without convergence - Numerical errors occurred - User-requested termination @@ -120,6 +122,7 @@ result.evaluations # e.g., 314 ``` Note that `evaluations` can be much larger than `iterations`: + - **CMA-ES**: `evaluations ≈ iterations × population_size` - **Nelder-Mead**: `evaluations ≈ iterations × (n_params + 1)` - **Adam**: `evaluations ≈ iterations` (one per step) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md index 990fed9..bd0b20a 100644 --- a/docs/api-reference/python/samplers.md +++ b/docs/api-reference/python/samplers.md @@ -44,17 +44,20 @@ print(result.acceptance_rate) # Target: 0.2-0.4 ### When to Use **Advantages:** + - Provides full posterior distribution - Quantifies parameter uncertainty - Captures correlations between parameters - Enables credible intervals **Limitations:** + - Requires many function evaluations - Need to assess convergence - Requires likelihood (GaussianNLL cost metric) **Typical Use Cases:** + - Uncertainty quantification - Confidence intervals - Posterior predictive distributions @@ -92,17 +95,20 @@ print(result.samples.shape) ### When to Use **Advantages:** + - Calculates marginal likelihood (evidence) - Enables model comparison via Bayes factors - Provides posterior samples as byproduct - Efficient for multi-modal posteriors **Limitations:** + - More expensive than MCMC - Requires careful tuning of live points - Needs likelihood (GaussianNLL cost metric) **Typical Use Cases:** + - Model comparison - Bayes factors - Evidence calculation diff --git a/docs/api-reference/rust/index.md b/docs/api-reference/rust/index.md index 274352a..f3d5a7a 100644 --- a/docs/api-reference/rust/index.md +++ b/docs/api-reference/rust/index.md @@ -119,17 +119,6 @@ let results: Vec<_> = initial_guesses .collect(); ``` -## Building from Source - -See the [Building from Source](../../development/building.md) guide for detailed instructions. - -```bash -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt/rust -cargo build --release -cargo test -``` - ## Documentation Generation Generate local documentation: @@ -210,6 +199,5 @@ Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/chro ## See Also - [Python API Reference](../index.md) -- [Building from Source](../../development/building.md) - [Architecture](../../development/architecture.md) - [Contributing](../../development/contributing.md) diff --git a/docs/development/architecture.md b/docs/development/architecture.md index 626d213..9576393 100644 --- a/docs/development/architecture.md +++ b/docs/development/architecture.md @@ -1,28 +1,10 @@ # Architecture -!!! info "Coming Soon" - Detailed architecture documentation is being written. - ## High-Level Overview -``` -┌─────────────────────────────────────┐ -│ Python API Layer │ -│ (PyO3 bindings, type stubs) │ -└─────────────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────┐ -│ Rust Core │ -│ (Optimisers, Samplers, Builders) │ -└─────────────────────────────────────┘ - │ - ▼ -┌─────────────────────────────────────┐ -│ External Libraries │ -│ (Diffsol, ndarray, rayon) │ -└─────────────────────────────────────┘ -``` +
+ ![Paradigm Comparison](../chronopt.drawio.svg){ width="100%" } +
## Key Design Patterns @@ -32,6 +14,5 @@ ## See Also -- [Building from Source](building.md) - [Contributing](contributing.md) - [Rust API](../api-reference/rust/index.md) diff --git a/docs/development/building.md b/docs/development/building.md deleted file mode 100644 index de8177b..0000000 --- a/docs/development/building.md +++ /dev/null @@ -1,48 +0,0 @@ -# Building from Source - -!!! info "Coming Soon" - Detailed build instructions are being written. - -## Quick Build - -```bash -# Clone repository -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt - -# Set up environment -uv sync - -# Build -uv run maturin develop - -# Test -uv run pytest -v -cargo test -``` - -## Prerequisites - -- Rust >= 1.70 -- Python >= 3.11 -- uv (recommended) - -## Platform-Specific Notes - -### Linux - -No special requirements. - -### macOS - -Works on both Intel and Apple Silicon. - -### Windows - -Experimental support. May require Visual C++ Build Tools. - -## See Also - -- [Contributing](contributing.md) -- [Architecture](architecture.md) -- [Installation](../getting-started/installation.md) diff --git a/docs/development/contributing.md b/docs/development/contributing.md index 00c3f2c..1fe3fce 100644 --- a/docs/development/contributing.md +++ b/docs/development/contributing.md @@ -31,6 +31,5 @@ cargo test ## See Also -- [Building from Source](building.md) - [Architecture](architecture.md) - [GitHub Issues](https://github.com/bradyplanden/chronopt/issues) diff --git a/docs/development/index.md b/docs/development/index.md index 694602d..67ed534 100644 --- a/docs/development/index.md +++ b/docs/development/index.md @@ -22,14 +22,6 @@ Resources for contributors and developers working with Chronopt. [:octicons-arrow-right-24: Architecture Overview](architecture.md) -- :material-hammer-wrench:{ .lg .middle } __Building from Source__ - - --- - - Setting up a development environment and building Chronopt. - - [:octicons-arrow-right-24: Build Guide](building.md) - ## Quick Setup @@ -236,19 +228,8 @@ uv pip install py-spy py-spy record --native -- python examples/your_example.py ``` -## Release Process - -See the [Contributing Guide](contributing.md) for the release workflow. - -## Getting Help - -- **GitHub Issues**: [Report bugs or request features](https://github.com/bradyplanden/chronopt/issues) -- **Discussions**: [Ask questions](https://github.com/bradyplanden/chronopt/discussions) -- **Documentation**: You're reading it! - ## See Also - [Architecture](architecture.md) - System design - [Contributing](contributing.md) - Contribution guidelines -- [Building](building.md) - Detailed build instructions - [API Reference](../api-reference/index.md) - API documentation diff --git a/docs/examples/gallery.md b/docs/examples/gallery.md index c5c41cc..42dc673 100644 --- a/docs/examples/gallery.md +++ b/docs/examples/gallery.md @@ -13,6 +13,7 @@ Visual gallery of Chronopt applications and use cases. Classic 2D optimisation test problem. **Files:** + - [python_problem.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_problem.py) - [python_contour.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_contour.py) @@ -35,6 +36,7 @@ Single-variable ODE with DiffSL. Physics-based model with event handling. **Files:** + - [bouncy_ball.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball.py) - [bouncy_ball_sampling.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball_sampling.py) @@ -48,6 +50,7 @@ Physics-based model with event handling. Comparing different bicycle dynamics formulations. **Files:** + - [bicycle_model_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_diffsol.py) - [bicycle_model_evidence.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_evidence.py) @@ -61,6 +64,7 @@ Comparing different bicycle dynamics formulations. Lotka-Volterra equations with multiple solver backends. **Files:** + - [predator_prey_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffsol.py) - [predator_prey_diffrax.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffrax.py) - [predator_prey_diffeqpy.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) @@ -132,26 +136,24 @@ python examples/predator_prey/predator_prey_diffrax.py ### By Difficulty **Beginner:** + - python_problem.py - python_contour.py - logistic_growth.py **Intermediate:** + - bouncy_ball.py - bicycle_model_diffsol.py - predator_prey_diffsol.py **Advanced:** + - bouncy_ball_sampling.py - bicycle_model_evidence.py - predator_prey_diffrax.py - predator_prey_diffeqpy.py -## Visual Gallery - -!!! info "Coming Soon" - Visual thumbnails and interactive demos will be added here. - ## Contributing Examples Have an interesting use case? We'd love to include it! diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md index 9fa8cb2..9e559e2 100644 --- a/docs/getting-started/concepts.md +++ b/docs/getting-started/concepts.md @@ -45,6 +45,7 @@ problem = ( ``` **Use when:** + - You have a direct Python function to minimise - No differential equations involved - Simple parameter optimisation @@ -71,6 +72,7 @@ problem = ( ``` **Use when:** + - Fitting ODE parameters to time-series data - Using DiffSL for model definition - Need high-performance multi-threaded solving @@ -96,6 +98,7 @@ problem = ( ``` **Use when:** + - Need a specific solver (JAX/Diffrax, Julia/DifferentialEquations.jl) - Complex ODEs not supported by DiffSL - Custom forward models beyond ODEs @@ -293,15 +296,6 @@ optimiser = chron.CMAES().with_population_size(20) See the [Parallel Execution Guide](../guides/parallel-execution.md) for details. -## Key Takeaways - -- **Builders** provide a fluent API for problem construction -- **ScalarBuilder** for direct functions, **DiffsolBuilder** for ODEs, **VectorBuilder** for custom solvers -- **Parameters** are decision variables with names and initial values -- **Optimisers** find the best solution; **samplers** explore uncertainty -- **Cost metrics** define how predictions are compared to observations -- **Ask/tell pattern** enables advanced control and distributed computation - ## Next Steps - **[Tutorials](../tutorials/index.md)**: Interactive Jupyter notebooks diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md index 7e2ff07..3fffa32 100644 --- a/docs/getting-started/first-ode-fit.md +++ b/docs/getting-started/first-ode-fit.md @@ -4,14 +4,11 @@ This tutorial demonstrates how to fit ordinary differential equations (ODEs) to ## The Problem: Logistic Growth -We'll fit a logistic growth model to exponentially decaying data. The logistic growth equation is: +We'll fit a logistic growth model to synthetic population data. The logistic growth equation is: $$\frac{dy}{dt} = r \cdot y \cdot \left(1 - \frac{y}{k}\right)$$ -where: -- $r$ is the growth rate -- $k$ is the carrying capacity -- $y$ is the population +where: $r$ is the growth rate, $k$ is the carrying capacity, and $y$ is the population. ## Complete Example @@ -26,9 +23,16 @@ u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ -# Generate synthetic data -t = np.linspace(0.0, 5.0, 51) -observations = np.exp(-1.3 * t) +# Generate synthetic data from the logistic model with noise +from scipy.integrate import odeint + +def logistic(y, t, r, k): + return r * y * (1 - y / k) + +t = np.linspace(0.0, 10.0, 51) +y_true = odeint(logistic, 0.1, t, args=(0.8, 2.0)).flatten() # True: r=0.8, k=2.0 +np.random.seed(42) +observations = y_true + 0.05 * np.random.randn(len(t)) data = np.column_stack((t, observations)) # Build the problem @@ -36,14 +40,15 @@ builder = ( chron.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) - .with_parameter("k", 1.0) + .with_parameter("r", 0.5) # Initial guess for growth rate + .with_parameter("k", 1.0) # Initial guess for carrying capacity .with_backend("dense") ) problem = builder.build() # Run optimisation with CMA-ES optimiser = chron.CMAES().with_max_iter(1000) -result = optimiser.run(problem, [0.5, 0.5]) +result = optimiser.run(problem, [0.5, 1.0]) # Display results print(f"Fitted parameters:") @@ -58,28 +63,22 @@ print(f"Success: {result.success}") DiffSL is a domain-specific language for defining differential equations. Let's break down the syntax: ``` -in_i { r = 1, k = 1 } # Input parameters to fit with default values -u_i { y = 0.1 } # Initial conditions +in_i { r = 1, k = 1 } # Input parameter tensor with defaults +u_i { y = 0.1 } # Initial conditions F_i { (r * y) * (1 - (y / k)) } # Right-hand side of dy/dt = ... ``` -Key components: - -- `in_i = {...}`: Parameters to optimise with default values -- `u_i { var = initial_value }`: Initial conditions for state variables -- `F_i { expression }`: The derivative expression ($dy/dt$) - ## Data Format Chronopt expects data as a 2D NumPy array where: - **First column**: Time points -- **Remaining columns**: Observed values for each state variable +- **Remaining columns**: Observed values for each variable ```python # Example: 51 time points, 1 state variable -t = np.linspace(0.0, 5.0, 51) -observations = np.exp(-1.3 * t) +t = np.linspace(0.0, 10.0, 51) +observations = ... # Your observed data data = np.column_stack((t, observations)) # Shape: (51, 2) ``` @@ -90,46 +89,19 @@ For multi-variable systems: data = np.column_stack((t, obs_var1, obs_var2)) # Shape: (n, 3) ``` -## Choosing the Backend - -Diffsol supports two backends: - -### Dense Backend (Default) - -```python -.with_backend("dense") -``` - -**Use when:** -- System has few state variables (< 100) -- The Jacobian matrix is mostly non-zero -- Simplicity is preferred - -### Sparse Backend - -```python -.with_backend("sparse") -``` - -**Use when:** -- System has many state variables (> 100) -- The Jacobian matrix is mostly zero -- Maximum performance is critical - -For more details, see the [DiffSL Backend Guide](../guides/diffsol-backend.md). - ## Visualising Results ```python import matplotlib.pyplot as plt -# Get the optimised model predictions -predictions = problem.evaluate(result.x) # Returns predicted values at data time points +# Generate fitted curve using optimised parameters +t_dense = np.linspace(0.0, 10.0, 200) +y_fitted = odeint(logistic, 0.1, t_dense, args=(result.x[0], result.x[1])).flatten() # Plot plt.figure(figsize=(10, 6)) plt.plot(data[:, 0], data[:, 1], 'o', label='Observed data', alpha=0.6) -plt.plot(data[:, 0], predictions, '-', label='Fitted model', linewidth=2) +plt.plot(t_dense, y_fitted, '-', label='Fitted model', linewidth=2) plt.xlabel('Time') plt.ylabel('y') plt.title('Logistic Growth Model Fit') @@ -138,6 +110,8 @@ plt.grid(True, alpha=0.3) plt.show() ``` +![Logistic growth model fit](logistic_fit.png) + ## Common ODE Patterns ### Multiple State Variables @@ -184,7 +158,7 @@ F_i { k * sin(t) - y } ## Cost Metrics -By default, Chronopt uses sum of squared errors (SSE). You can specify different cost metrics: +By default, Chronopt uses sum of squared errors (SSE). You can specify different cost metrics as shown below. See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. ```python from chronopt import GaussianNLL, RMSE @@ -202,11 +176,9 @@ builder = ( builder = builder.with_cost_metric(RMSE()) # Normalised by number of points ``` -See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. - ## Optimiser Selection -Different optimisers work better for different problems: +Different optimisers work better for different problems. For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimiser.md). ### Nelder-Mead (Default) @@ -234,8 +206,6 @@ result = optimiser.run(problem, initial_guess) **Best for**: Smooth problems, fast convergence on well-behaved objectives -For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimiser.md). - ## Troubleshooting ### Poor Fit Quality @@ -253,14 +223,6 @@ For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimi For more help, see the [Troubleshooting Guide](../guides/troubleshooting.md). -## Key Takeaways - -- **DiffsolBuilder** is used for ODE fitting problems -- **DiffSL** provides a concise syntax for defining ODEs -- Data format is `[time, obs1, obs2, ...]` -- Choose between `dense` (default) and `sparse` backends -- CMA-ES often works well for ODE parameter fitting - ## Next Steps - **[Core Concepts](concepts.md)**: Understand builders, problems, and the ask/tell pattern diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 1c7846c..4a645be 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -2,44 +2,36 @@ Chronopt is available as a Python package with pre-built wheels for most platforms. -## Requirements - -- **Python**: >= 3.11 -- **Operating Systems**: - - Linux (x86_64, aarch64) - - macOS (x86_64, Apple Silicon) - - Windows (experimental) - ## Installation Methods -=== "pip" +=== "uv" + + [uv](https://docs.astral.sh/uv/) is a fast Python package installer and resolver. ```bash - pip install chronopt + uv pip install chronopt ``` -=== "uv (recommended)" - - [uv](https://docs.astral.sh/uv/) is a fast Python package installer and resolver. +=== "pip" ```bash - uv pip install chronopt + pip install chronopt ``` ### Optional Dependencies Chronopt has optional plotting support via matplotlib: -=== "pip" +=== "uv" ```bash - pip install "chronopt[plotting]" + uv pip install "chronopt[plotting]" ``` -=== "uv" +=== "pip" ```bash - uv pip install "chronopt[plotting]" + pip install "chronopt[plotting]" ``` ## Verifying Installation @@ -80,14 +72,11 @@ Pre-built wheels are available for both Intel (x86_64) and Apple Silicon (arm64) If you're using Apple Silicon and encounter issues, ensure you're using a native arm64 Python installation rather than running under Rosetta. -### Windows (Experimental) - -Windows builds are currently experimental. Pre-built wheels are available but may have limitations. +### Windows -If you encounter issues: +Windows builds are marked experimental. Pre-built wheels are available but don't currently support diffsol gradients due to LLVM integration issues. -1. Ensure you have the latest [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) installed -2. Consider using [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) +If you encounter issues consider using [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) ## Building from Source @@ -115,43 +104,7 @@ uv run maturin develop # Run tests uv run pytest -v ``` - -For more details, see [Building from Source](../development/building.md). - -## Troubleshooting - -### Import Error: No module named 'chronopt' - -**Cause**: Chronopt is not installed in your current Python environment. - -**Solution**: -1. Verify your Python environment is active -2. Re-run the installation command -3. Check that `pip list` shows chronopt - -### ImportError: DLL load failed (Windows) - -**Cause**: Missing Visual C++ runtime libraries. - -**Solution**: Install [Microsoft Visual C++ Redistributable](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) - -### Wheel Not Available for Your Platform - -**Cause**: Pre-built wheels might not be available for your specific Python version or platform. - -**Solution**: [Build from source](#building-from-source) or open an issue on [GitHub](https://github.com/bradyplanden/chronopt/issues) - -### Installation Succeeds but Import Fails - -**Cause**: Binary incompatibility or corrupted installation. - -**Solution**: -```bash -pip uninstall chronopt -pip cache purge # Clear pip cache -pip install chronopt -``` - + For additional troubleshooting, see the [Troubleshooting Guide](../guides/troubleshooting.md). ## Next Steps diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md index 392c9aa..97fd9b2 100644 --- a/docs/getting-started/quickstart.md +++ b/docs/getting-started/quickstart.md @@ -1,6 +1,6 @@ # 5-Minute Quickstart -This quickstart guide demonstrates scalar optimisation using the Rosenbrock function, a classic test problem in optimisation. +This quickstart guide demonstrates scalar optimisation using the classic Rosenbrock function. ## The Problem @@ -52,7 +52,7 @@ Iterations: 157 ## Understanding the Code -1. **Define the objective function**: The function must accept a NumPy array and return a NumPy array (even for scalar values) +1. **Define the objective function**: The function must accept a NumPy array and return a NumPy array 2. **Create a builder**: `ScalarBuilder()` is used for scalar optimisation problems where you directly evaluate a function @@ -141,13 +141,7 @@ plt.grid(True, alpha=0.3) plt.show() ``` -## Key Takeaways - -- **ScalarBuilder** is used for direct function optimisation -- Parameters are defined with `with_parameter(name, initial_value)` -- The default optimiser is Nelder-Mead -- You can specify custom optimisers with different algorithms and parameters -- Results include optimal parameters, objective value, success status, and iteration count +![Rosenbrock contour plot](rosenbrock_contour.png) ## Next Steps diff --git a/docs/guides/troubleshooting.md b/docs/guides/troubleshooting.md index 8ad3ab6..6b1d8da 100644 --- a/docs/guides/troubleshooting.md +++ b/docs/guides/troubleshooting.md @@ -1,10 +1,5 @@ # Troubleshooting Guide -!!! info "Coming Soon" - This guide is being written. Check back soon for common issues and solutions. - -## Quick Fixes - ### Import Error ```python @@ -16,18 +11,19 @@ ImportError: No module named 'chronopt' ### Poor Fit Quality **Solutions:** -1. Try different optimisers (CMA-ES is often more robust) -2. Increase iterations: `.with_max_iter(10000)` -3. Check initial conditions + +1. Try different optimisers +2. Increase iterations, i.e. `.with_max_iter(10000)` +3. Try different initial conditions 4. Normalise data if scales vary widely ### Slow Performance **Solutions:** + 1. Use CMA-ES for parallelisation -2. Reduce data points if possible +2. Reduce the number of data points 3. Use sparse backend for large ODE systems -4. Profile with `py-spy` to find bottlenecks ## See Also diff --git a/docs/index.md b/docs/index.md index 2310320..ad25c0a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,13 +4,16 @@ ## Why Chronopt? -Optimisation-based workflows run the forward simulation thousands of times. A performance improvement on the process can produce results hours or days earlier. The Rust core provides a high-performance inference loop with fewer runtime errors, whilst quickly integrating into Python workflows. +Chronopt offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. This package instead introduces an alternative, where the Python layer acts purely as a declarative configuration interface, +while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below, -## Project Goals +
+ +
+ ![Paradigm Comparison](chronopt.drawio.svg){ width="100%" } +
Conventional approach vs Chronopt: the optimisation loop moves from Python to Rust
+
-- **Speed and numerical accuracy** through a Rust core -- **Modular components** with informative diagnostics -- **Batteries-included experience** spanning optimisation, sampling, and plotting ## Core Capabilities diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index 0d40d36..b7c1c67 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -6,7 +6,7 @@ "source": [ "# Tutorial 3: Parameter Uncertainty\n", "\n", - "**Objectives:**\n", + "**Learning Objectives:**\n", "- Go from optimisation to uncertainty quantification\n", "- Use MCMC sampling to explore parameter distributions\n", "- Interpret MCMC diagnostics and traces\n", diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index fb2d3d3..ba17ab5 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -35,10 +35,17 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": "# Import plotting utilities\nimport time\n\nimport chronopt as chron\nimport matplotlib.pyplot as plt\nimport numpy as np", "outputs": [], - "execution_count": null + "source": [ + "# Import plotting utilities\n", + "import time\n", + "\n", + "import chronopt as chron\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] }, { "cell_type": "markdown", @@ -72,9 +79,13 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "# Generate synthetic data\n", + "# Generate synthetic data using scipy\n", + "from scipy.integrate import solve_ivp\n", + "\n", "np.random.seed(42)\n", "\n", "# True parameters\n", @@ -88,38 +99,56 @@ "# Time points\n", "t_data = np.linspace(0, 15, 50)\n", "\n", - "# DiffSL model\n", + "# DiffSL model for fitting\n", "model_str = \"\"\"\n", - "in_i { alpha, beta, delta, gamma }\n", - "x { 10.0 } = alpha * x - beta * x * y\n", - "y { 5.0 } = delta * x * y - gamma * y\n", - "out = [x, y]\n", + "in_i { alpha = 2.0/3.0, beta = 4.0/3.0, delta = 1.0, gamma = 1.0 }\n", + "x0 { 10.0 } y0 { 5.0 }\n", + "u_i {\n", + " y1 = x0,\n", + " y2 = y0,\n", + "}\n", + "F_i {\n", + " alpha * y1 - beta * y1 * y2,\n", + " delta * y1 * y2 - gamma * y2,\n", + "}\n", "\"\"\"\n", "\n", - "# Generate \"true\" solution with noise\n", - "true_solution = (\n", - " chron.DiffsolBuilder()\n", - " .with_model(model_str)\n", - " .with_parameter(\"alpha\", true_params[\"alpha\"])\n", - " .with_parameter(\"beta\", true_params[\"beta\"])\n", - " .with_parameter(\"delta\", true_params[\"delta\"])\n", - " .with_parameter(\"gamma\", true_params[\"gamma\"])\n", - " .build()\n", + "\n", + "# Define ODE for scipy\n", + "def lotka_volterra(t, state, alpha, beta, delta, gamma):\n", + " x, y = state\n", + " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", + "\n", + "\n", + "# Generate \"true\" solution with noise using scipy\n", + "y0 = [10.0, 5.0] # initial conditions\n", + "sol = solve_ivp(\n", + " lotka_volterra,\n", + " [t_data[0], t_data[-1]],\n", + " y0,\n", + " args=(\n", + " true_params[\"alpha\"],\n", + " true_params[\"beta\"],\n", + " true_params[\"delta\"],\n", + " true_params[\"gamma\"],\n", + " ),\n", + " t_eval=t_data,\n", + " method=\"RK45\",\n", ")\n", "\n", - "y_true = true_solution.predict(t_data)\n", + "y_true = sol.y.T # Shape: (n_times, 2)\n", "y_observed = y_true + np.random.normal(0, 0.5, y_true.shape)\n", "\n", "print(f\"Data shape: {y_observed.shape}\")\n", "print(f\"Time span: [{t_data[0]:.1f}, {t_data[-1]:.1f}]\")\n", "print(f\"Number of observations: {len(t_data)}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Visualize data\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", @@ -139,22 +168,25 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Fit with DiffsolBuilder\n", "start_time = time.time()\n", "\n", + "# Combine times and observations for DiffsolBuilder\n", + "# Data format: first column is time, remaining columns are observations\n", + "data = np.column_stack((t_data, y_observed))\n", + "\n", "result_diffsol = (\n", " chron.DiffsolBuilder()\n", - " .with_model(model_str)\n", - " .with_times(t_data)\n", - " .with_data(y_observed)\n", + " .with_diffsl(model_str)\n", + " .with_data(data)\n", " .with_parameter(\"alpha\", 1.0) # initial guess\n", " .with_parameter(\"beta\", 0.3)\n", " .with_parameter(\"delta\", 0.05)\n", @@ -177,9 +209,7 @@ "print(f\"Function evals: {result_diffsol.evaluations}\")\n", "print(f\"Time: {diffsol_time:.3f}s\")\n", "print(f\"Success: {result_diffsol.success}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -198,7 +228,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Optional: install JAX/Diffrax if not already installed\n", "# !pip install jax jaxlib diffrax\n", @@ -216,13 +248,13 @@ " JAX_AVAILABLE = False\n", " print(\"⚠️ JAX/Diffrax not installed. Skipping GPU example.\")\n", " print(\" Install with: pip install jax jaxlib diffrax\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "if JAX_AVAILABLE:\n", " # Define dynamics in JAX\n", @@ -269,13 +301,13 @@ " # Warm up JIT compiler\n", " _ = simulate_numpy([1.0, 0.4, 0.1, 0.4])\n", " print(\"✅ JAX/Diffrax solver ready (JIT compiled)\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "if JAX_AVAILABLE:\n", " # Fit with VectorBuilder + JAX/Diffrax\n", @@ -308,9 +340,7 @@ " print(f\"Time: {diffrax_time:.3f}s\")\n", " print(f\"Success: {result_diffrax.success}\")\n", " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -328,7 +358,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Optional: install Julia backend\n", "# !pip install diffeqpy\n", @@ -343,13 +375,13 @@ " print(\"⚠️ DifferentialEquations.jl not installed. Skipping Julia example.\")\n", " print(\" Install with: pip install diffeqpy\")\n", " print(\" Then run: from diffeqpy import install; install()\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "if JULIA_AVAILABLE:\n", " # Define dynamics for Julia\n", @@ -374,13 +406,13 @@ " test_output = simulate_julia([1.0, 0.4, 0.1, 0.4])\n", " print(\"✅ Julia/DifferentialEquations.jl ready\")\n", " print(f\" Output shape: {test_output.shape}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "if JULIA_AVAILABLE:\n", " # Fit with VectorBuilder + Julia\n", @@ -413,9 +445,7 @@ " print(f\"Time: {julia_time:.3f}s\")\n", " print(f\"Success: {result_julia.success}\")\n", " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -428,7 +458,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Collect results\n", "results_summary = []\n", @@ -477,13 +509,13 @@ " f\"{r['Backend']:<20} {r['Time (s)']:<12.3f} {r['SSE']:<15.3e} \"\n", " f\"{r['Iterations']:<8} {r['Evaluations']}\"\n", " )" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Visualize timing comparison\n", "if len(results_summary) > 1:\n", @@ -540,9 +572,7 @@ "\n", " plt.tight_layout()\n", " plt.show()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -555,7 +585,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Generate predictions from each fitted model\n", "t_fine = np.linspace(t_data[0], t_data[-1], 200)\n", @@ -566,17 +598,16 @@ "ax1.plot(t_data, y_observed[:, 0], \"o\", label=\"Observed\", alpha=0.5, markersize=6)\n", "ax1.plot(t_data, y_true[:, 0], \"k--\", label=\"True\", linewidth=2, alpha=0.7)\n", "\n", - "# Diffsol fit\n", - "problem_diffsol = (\n", - " chron.DiffsolBuilder()\n", - " .with_model(model_str)\n", - " .with_parameter(\"alpha\", result_diffsol.x[0])\n", - " .with_parameter(\"beta\", result_diffsol.x[1])\n", - " .with_parameter(\"delta\", result_diffsol.x[2])\n", - " .with_parameter(\"gamma\", result_diffsol.x[3])\n", - " .build()\n", + "# Diffsol fit - use scipy to generate predictions with fitted parameters\n", + "sol_fitted = solve_ivp(\n", + " lotka_volterra,\n", + " [t_fine[0], t_fine[-1]],\n", + " [10.0, 5.0], # initial conditions\n", + " args=tuple(result_diffsol.x),\n", + " t_eval=t_fine,\n", + " method=\"RK45\",\n", ")\n", - "y_pred_diffsol = problem_diffsol.predict(t_fine)\n", + "y_pred_diffsol = sol_fitted.y.T\n", "ax1.plot(t_fine, y_pred_diffsol[:, 0], label=\"Diffsol\", linewidth=2)\n", "\n", "if JAX_AVAILABLE:\n", @@ -620,9 +651,7 @@ "\n", "plt.tight_layout()\n", "plt.show()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -718,4 +747,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb index d9e3b81..a542a35 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -21,26 +21,16 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Modern CPUs have multiple cores, but not all optimisation algorithms can exploit them. Chronopt provides **automatic parallelisation** for population-based algorithms:\n", - "\n", - "### Parallel Optimisers\n", - "- **CMA-ES**: Population-based evolutionary strategy\n", - "- **Dynamic Nested Sampling**: Population of live points\n", - "\n", - "### Sequential Optimisers \n", - "- **Nelder-Mead**: Sequential simplex updates\n", - "- **Adam**: Sequential gradient steps\n", - "\n", - "This tutorial demonstrates how to configure parallelism and measure its impact." - ] + "source": "## Introduction\n\nChronopt supports **parallel evaluation** for population-based optimisers (CMA-ES, Dynamic Nested Sampling). However, the effectiveness depends on the evaluation cost and backend used.\n\n### Key Insights\n\n1. **DiffsolBuilder** with `.with_parallel(True)` uses Rust's rayon for parallel ODE integration\n2. **Fast evaluations** (< 1ms) may see limited speedup due to efficient solver caching\n3. **Python callables** cannot be parallelised with threads (GIL), but **multiprocessing** works\n\nThis tutorial covers:\n- Parallel ODE fitting with `DiffsolBuilder`\n- Using `multiprocessing` for expensive Python callables\n- Understanding when parallelism helps" }, { "cell_type": "code", "execution_count": 1, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:18.792193Z", + "start_time": "2026-01-22T19:00:17.788924Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:06.718164Z", "iopub.status.busy": "2026-01-10T22:22:06.717720Z", @@ -59,13 +49,12 @@ ], "source": [ "import multiprocessing\n", - "\n", - "# Import plotting utilities\n", "import time\n", "\n", "import chronopt as chron\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "from scipy.integrate import solve_ivp\n", "\n", "# Detect available cores\n", "n_cores = multiprocessing.cpu_count()\n", @@ -75,16 +64,16 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Test Problem: Expensive Rosenbrock\n", - "\n", - "To see the benefits of parallelism, we need an **expensive** objective function. Let's add artificial computation time:" - ] + "source": "## Test Problem: Lotka-Volterra ODE Fitting\n\nWe'll use the predator-prey model from Tutorial 6. ODE integration is computationally expensive, making it ideal for demonstrating parallel speedup.\n\n$$\\frac{dx}{dt} = \\alpha x - \\beta xy \\quad \\text{(prey)}$$\n$$\\frac{dy}{dt} = \\delta xy - \\gamma y \\quad \\text{(predator)}$$\n\nThe DiffSL model definition and synthetic data generation:" }, { "cell_type": "code", "execution_count": 2, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:18.910392Z", + "start_time": "2026-01-22T19:00:18.812730Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:07.289542Z", "iopub.status.busy": "2026-01-10T22:22:07.289285Z", @@ -97,52 +86,81 @@ "name": "stdout", "output_type": "stream", "text": [ - "Single evaluation time: 0.055s\n", - "Objective value: 6.500\n" + "Data points: 500\n", + "Time span: [0, 100]\n", + "Parameters to fit: 4\n", + "True parameters: [1.1, 0.4, 0.1, 0.4]\n" ] } ], "source": [ - "def expensive_rosenbrock(x, delay_ms=10):\n", - " \"\"\"\n", - " Rosenbrock function with artificial delay to simulate expensive computation.\n", - "\n", - " In real applications, this might be:\n", - " - Complex ODE simulation\n", - " - Finite element analysis\n", - " - Machine learning model evaluation\n", - " - Database query\n", - " \"\"\"\n", - " # Simulate expensive computation\n", - " time.sleep(delay_ms / 1000.0)\n", - "\n", - " # Standard Rosenbrock\n", - " value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n", - " return np.array([value], dtype=float)\n", + "# DiffSL model for Lotka-Volterra\n", + "model_str = \"\"\"\n", + "in_i { alpha = 1.0, beta = 0.5, delta = 0.1, gamma = 0.5 }\n", + "x0 { 10.0 } y0 { 5.0 }\n", + "u_i {\n", + " prey = x0,\n", + " predator = y0,\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator,\n", + "}\n", + "\"\"\"\n", + "\n", + "# True parameters for data generation\n", + "true_params = {\"alpha\": 1.1, \"beta\": 0.4, \"delta\": 0.1, \"gamma\": 0.4}\n", + "\n", + "# Generate synthetic data using scipy\n", + "# Longer time span = more expensive integration\n", + "np.random.seed(42)\n", + "t_data = np.linspace(0, 100, 500) # Long integration with many points\n", + "\n", + "\n", + "def lotka_volterra(t, state, alpha, beta, delta, gamma):\n", + " x, y = state\n", + " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", + "\n", + "\n", + "sol = solve_ivp(\n", + " lotka_volterra,\n", + " [t_data[0], t_data[-1]],\n", + " [10.0, 5.0],\n", + " args=(\n", + " true_params[\"alpha\"],\n", + " true_params[\"beta\"],\n", + " true_params[\"delta\"],\n", + " true_params[\"gamma\"],\n", + " ),\n", + " t_eval=t_data,\n", + " method=\"RK45\",\n", + ")\n", "\n", + "y_true = sol.y.T\n", + "y_observed = y_true + np.random.normal(0, 0.3, y_true.shape)\n", "\n", - "# Test single evaluation\n", - "start = time.time()\n", - "result = expensive_rosenbrock([0.5, 0.5], delay_ms=50)\n", - "elapsed = time.time() - start\n", + "# Combine for DiffsolBuilder (time in first column)\n", + "data = np.column_stack((t_data, y_observed))\n", "\n", - "print(f\"Single evaluation time: {elapsed:.3f}s\")\n", - "print(f\"Objective value: {result[0]:.3f}\")" + "print(f\"Data points: {len(t_data)}\")\n", + "print(\"Time span: [0, 100]\")\n", + "print(\"Parameters to fit: 4\")\n", + "print(f\"True parameters: {list(true_params.values())}\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Sequential Baseline: Nelder-Mead\n", - "\n", - "First, establish a baseline with sequential Nelder-Mead:" - ] + "source": "## Sequential vs Parallel: DiffsolBuilder\n\nThe key difference is the `.with_parallel()` method on `DiffsolBuilder`. When enabled, population-based optimisers evaluate multiple parameter sets concurrently.\n\nLet's compare sequential and parallel CMA-ES on our ODE fitting problem:" }, { "cell_type": "code", "execution_count": 3, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.020454Z", + "start_time": "2026-01-22T19:00:18.953488Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:07.355771Z", "iopub.status.busy": "2026-01-10T22:22:07.355407Z", @@ -155,71 +173,67 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running Nelder-Mead (sequential)...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Running CMA-ES (sequential)...\n", "\n", "============================================================\n", - "NELDER-MEAD (Sequential)\n", + "CMA-ES (Sequential)\n", "============================================================\n", - "Solution: [1.0007473 1.00148331]\n", - "Value: 5.725e-07\n", - "Evaluations: 127\n", - "Time: 1.44s\n", - "Time per eval: 0.011s\n" + "Solution: [1.14387427 0.51852859 0.48038414 0.75238014]\n", + "True params: [1.1, 0.4, 0.1, 0.4]\n", + "Final SSE: 11886.345\n", + "Evaluations: 801\n", + "Time: 0.98s\n", + "Time per eval: 1.22ms\n" ] } ], "source": [ - "# Build problem\n", - "problem = (\n", - " chron.ScalarBuilder()\n", - " .with_objective(lambda x: expensive_rosenbrock(x, delay_ms=10))\n", - " .with_parameter(\"x\", 1.0)\n", - " .with_parameter(\"y\", 1.0)\n", - " .build()\n", - ")\n", + "# Sequential execution (parallel=False)\n", + "print(\"Running CMA-ES (sequential)...\")\n", "\n", - "# Sequential Nelder-Mead\n", - "print(\"Running Nelder-Mead (sequential)...\")\n", "start = time.time()\n", "\n", - "result_nm = chron.NelderMead().with_max_iter(100).run(problem, [0.0, 0.0])\n", + "result_seq = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(model_str)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 0.8)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.3)\n", + " .with_cost(chron.SSE())\n", + " .with_parallel(False) # Sequential evaluation\n", + " .with_optimiser(chron.CMAES().with_max_iter(100).with_step_size(0.3))\n", + " .build()\n", + " .optimise()\n", + ")\n", "\n", - "time_nm = time.time() - start\n", + "time_seq = time.time() - start\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", - "print(\"NELDER-MEAD (Sequential)\")\n", + "print(\"CMA-ES (Sequential)\")\n", "print(\"=\" * 60)\n", - "print(f\"Solution: {result_nm.x}\")\n", - "print(f\"Value: {result_nm.value:.3e}\")\n", - "print(f\"Evaluations: {result_nm.evaluations}\")\n", - "print(f\"Time: {time_nm:.2f}s\")\n", - "print(f\"Time per eval: {time_nm / result_nm.evaluations:.3f}s\")" + "print(f\"Solution: {result_seq.x}\")\n", + "print(f\"True params: {list(true_params.values())}\")\n", + "print(f\"Final SSE: {result_seq.value:.3f}\")\n", + "print(f\"Evaluations: {result_seq.evaluations}\")\n", + "print(f\"Time: {time_seq:.2f}s\")\n", + "print(f\"Time per eval: {time_seq / result_seq.evaluations * 1000:.2f}ms\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Parallel Optimisation: CMA-ES\n", - "\n", - "CMA-ES evaluates a **population** of candidate solutions each generation. Chronopt automatically parallelises these evaluations across available cores.\n", - "\n", - "### Key Parameters:\n", - "- `population_size`: Number of candidates per generation (default: automatic based on dimension)\n", - "- More candidates = more parallelism opportunity\n", - "- Trade-off: larger populations need more generations to converge" - ] + "source": "## Parallel Execution\n\nNow let's enable parallelism with `.with_parallel(True)`:" }, { "cell_type": "code", "execution_count": 4, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.380944Z", + "start_time": "2026-01-22T19:00:20.205935Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:08.808619Z", "iopub.status.busy": "2026-01-10T22:22:08.808319Z", @@ -232,53 +246,67 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running CMA-ES (parallel, default population)...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Running CMA-ES (parallel)...\n", "\n", "============================================================\n", - "CMA-ES (Parallel, Default Population)\n", + "CMA-ES (Parallel)\n", "============================================================\n", - "Solution: [0.98713938 0.97199636]\n", - "Value: 7.646e-04\n", - "Evaluations: 301\n", - "Time: 3.40s\n", - "Time per eval: 0.011s\n", + "Solution: [0.8 0.3 0.05 0.3 ]\n", + "True params: [1.1, 0.4, 0.1, 0.4]\n", + "Final SSE: 29190.956\n", + "Evaluations: 9\n", + "Time: 0.14s\n", + "Time per eval: 15.72ms\n", "\n", - "Speedup vs Nelder-Mead: 0.42x\n" + "🚀 Speedup: 6.90x (with 8 cores)\n" ] } ], "source": [ - "# CMA-ES with automatic population size\n", - "print(\"Running CMA-ES (parallel, default population)...\")\n", + "# Parallel execution (parallel=True)\n", + "print(\"Running CMA-ES (parallel)...\")\n", + "\n", "start = time.time()\n", "\n", - "result_cmaes_default = (\n", - " chron.CMAES().with_max_iter(50).with_step_size(0.5).run(problem, [0.0, 0.0])\n", + "result_par = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(model_str)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 0.8)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.3)\n", + " .with_cost(chron.SSE())\n", + " .with_parallel(True) # Parallel evaluation!\n", + " .with_optimiser(chron.CMAES().with_max_iter(100).with_step_size(0.3))\n", + " .build()\n", + " .optimise()\n", ")\n", "\n", - "time_cmaes_default = time.time() - start\n", + "time_par = time.time() - start\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", - "print(\"CMA-ES (Parallel, Default Population)\")\n", + "print(\"CMA-ES (Parallel)\")\n", "print(\"=\" * 60)\n", - "print(f\"Solution: {result_cmaes_default.x}\")\n", - "print(f\"Value: {result_cmaes_default.value:.3e}\")\n", - "print(f\"Evaluations: {result_cmaes_default.evaluations}\")\n", - "print(f\"Time: {time_cmaes_default:.2f}s\")\n", - "print(f\"Time per eval: {time_cmaes_default / result_cmaes_default.evaluations:.3f}s\")\n", - "print(f\"\\nSpeedup vs Nelder-Mead: {time_nm / time_cmaes_default:.2f}x\")" + "print(f\"Solution: {result_par.x}\")\n", + "print(f\"True params: {list(true_params.values())}\")\n", + "print(f\"Final SSE: {result_par.value:.3f}\")\n", + "print(f\"Evaluations: {result_par.evaluations}\")\n", + "print(f\"Time: {time_par:.2f}s\")\n", + "print(f\"Time per eval: {time_par / result_par.evaluations * 1000:.2f}ms\")\n", + "\n", + "speedup = time_seq / time_par\n", + "print(f\"\\n🚀 Speedup: {speedup:.2f}x (with {n_cores} cores)\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.695642Z", + "start_time": "2026-01-22T19:00:20.397778Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:12.225212Z", "iopub.status.busy": "2026-01-10T22:22:12.224831Z", @@ -291,68 +319,77 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running CMA-ES (parallel, large population)...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Running CMA-ES (parallel, large population)...\n", "\n", "============================================================\n", - "CMA-ES (Parallel, Large Population)\n", + "CMA-ES (Parallel, Population=16)\n", "============================================================\n", - "Solution: [0.98069027 0.96068513]\n", - "Value: 4.870e-04\n", - "Evaluations: 601\n", - "Time: 6.81s\n", - "Time per eval: 0.011s\n", + "Solution: [1.18267573 0.602622 0.37072684 0.67821184]\n", + "True params: [1.1, 0.4, 0.1, 0.4]\n", + "Final SSE: 8171.282\n", + "Evaluations: 801\n", + "Time: 0.26s\n", + "Time per eval: 0.32ms\n", "\n", - "Speedup vs Nelder-Mead: 0.21x\n" + "🚀 Speedup: 3.81x\n" ] } ], "source": [ - "# CMA-ES with larger population for more parallelism\n", + "# Parallel with larger population (more parallel work per generation)\n", "print(\"Running CMA-ES (parallel, large population)...\")\n", + "\n", "start = time.time()\n", "\n", - "result_cmaes_large = (\n", - " chron.CMAES()\n", - " .with_max_iter(30)\n", - " .with_step_size(0.5)\n", - " .with_population_size(20) # Larger population = more parallel work\n", - " .run(problem, [0.0, 0.0])\n", + "result_par_large = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(model_str)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 0.8)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.3)\n", + " .with_cost(chron.SSE())\n", + " .with_parallel(True)\n", + " .with_optimiser(\n", + " chron.CMAES()\n", + " .with_max_iter(50)\n", + " .with_step_size(0.3)\n", + " .with_population_size(2 * n_cores) # Match population to available cores\n", + " )\n", + " .build()\n", + " .optimise()\n", ")\n", "\n", - "time_cmaes_large = time.time() - start\n", + "time_par_large = time.time() - start\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", - "print(\"CMA-ES (Parallel, Large Population)\")\n", + "print(f\"CMA-ES (Parallel, Population={2 * n_cores})\")\n", "print(\"=\" * 60)\n", - "print(f\"Solution: {result_cmaes_large.x}\")\n", - "print(f\"Value: {result_cmaes_large.value:.3e}\")\n", - "print(f\"Evaluations: {result_cmaes_large.evaluations}\")\n", - "print(f\"Time: {time_cmaes_large:.2f}s\")\n", - "print(f\"Time per eval: {time_cmaes_large / result_cmaes_large.evaluations:.3f}s\")\n", - "print(f\"\\nSpeedup vs Nelder-Mead: {time_nm / time_cmaes_large:.2f}x\")" + "print(f\"Solution: {result_par_large.x}\")\n", + "print(f\"True params: {list(true_params.values())}\")\n", + "print(f\"Final SSE: {result_par_large.value:.3f}\")\n", + "print(f\"Evaluations: {result_par_large.evaluations}\")\n", + "print(f\"Time: {time_par_large:.2f}s\")\n", + "print(f\"Time per eval: {time_par_large / result_par_large.evaluations * 1000:.2f}ms\")\n", + "\n", + "speedup_large = time_seq / time_par_large\n", + "print(f\"\\n🚀 Speedup: {speedup_large:.2f}x\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Scaling Analysis\n", - "\n", - "Let's systematically test how performance scales with:\n", - "1. **Population size** (parallelism opportunity)\n", - "2. **Evaluation cost** (compute vs overhead trade-off)" - ] + "source": "## Parallelising Python Callables with Multiprocessing\n\nPython's GIL prevents thread-based parallelism for Python code. However, you can use **multiprocessing** to parallelise expensive Python functions by wrapping them appropriately.\n\nHere's how to create a parallel-capable objective function:" }, { "cell_type": "code", "execution_count": 6, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.944100Z", + "start_time": "2026-01-22T19:00:20.709478Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:19.049873Z", "iopub.status.busy": "2026-01-10T22:22:19.049477Z", @@ -365,94 +402,86 @@ "name": "stdout", "output_type": "stream", "text": [ - "Testing different population sizes...\n", - "(This may take a few minutes)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pop= 4: 2.28s, 201 evals, 0.011s/eval\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pop= 8: 2.32s, 201 evals, 0.012s/eval\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pop=12: 2.20s, 193 evals, 0.011s/eval\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pop=16: 2.21s, 193 evals, 0.011s/eval\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pop=20: 2.33s, 201 evals, 0.012s/eval\n", + "Testing 20 sequential evaluations...\n", + "Sequential: 0.20s (9.8ms/eval)\n", + "\n", + "💡 To parallelise in a script, use multiprocessing:\n", + "\n", + "from concurrent.futures import ProcessPoolExecutor\n", "\n", - "Done!\n" + "def evaluate_batch_parallel(params_list, n_workers=8):\n", + " with ProcessPoolExecutor(max_workers=n_workers) as executor:\n", + " return list(executor.map(expensive_objective, params_list))\n", + "\n", + "# This provides near-linear speedup for expensive functions\n", + "\n" ] } ], "source": [ - "# Test different population sizes\n", - "population_sizes = [4, 8, 12, 16, 20]\n", - "results_pop = []\n", + "# Define an expensive objective function\n", + "def expensive_objective(params):\n", + " \"\"\"\n", + " An expensive objective function that simulates computation.\n", + " In practice, this could be a complex simulation, ML model, etc.\n", + " \"\"\"\n", + " alpha, beta, delta, gamma = params\n", + "\n", + " # Simulate expensive computation (ODE integration with scipy)\n", + " sol = solve_ivp(\n", + " lotka_volterra,\n", + " [0, 100],\n", + " [10.0, 5.0],\n", + " args=(alpha, beta, delta, gamma),\n", + " t_eval=t_data,\n", + " method=\"RK45\",\n", + " )\n", "\n", - "print(\"Testing different population sizes...\")\n", - "print(\"(This may take a few minutes)\\n\")\n", + " if not sol.success:\n", + " return 1e10 # Return large value for failed integrations\n", "\n", - "for pop_size in population_sizes:\n", - " # Adjust iterations to keep total evaluations similar\n", - " n_iter = 200 // pop_size\n", + " # Compute SSE\n", + " y_pred = sol.y.T\n", + " sse = np.sum((y_pred - y_observed) ** 2)\n", + " return sse\n", "\n", - " start = time.time()\n", - " result = (\n", - " chron.CMAES()\n", - " .with_max_iter(n_iter)\n", - " .with_step_size(0.5)\n", - " .with_population_size(pop_size)\n", - " .run(problem, [0.0, 0.0])\n", - " )\n", - " elapsed = time.time() - start\n", "\n", - " results_pop.append(\n", - " {\n", - " \"population\": pop_size,\n", - " \"time\": elapsed,\n", - " \"evaluations\": result.evaluations,\n", - " \"time_per_eval\": elapsed / result.evaluations,\n", - " \"success\": result.success,\n", - " \"value\": result.value,\n", - " }\n", - " )\n", + "# Test single evaluation time\n", + "n_evals = 20\n", + "test_params = [[0.8 + i * 0.02, 0.3, 0.05, 0.3] for i in range(n_evals)]\n", "\n", - " print(\n", - " f\"Pop={pop_size:2d}: {elapsed:6.2f}s, {result.evaluations:4d} evals, \"\n", - " f\"{elapsed / result.evaluations:.3f}s/eval\"\n", - " )\n", + "print(f\"Testing {n_evals} sequential evaluations...\")\n", + "\n", + "start = time.time()\n", + "seq_results = [expensive_objective(p) for p in test_params]\n", + "time_seq_python = time.time() - start\n", + "print(\n", + " f\"Sequential: {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n", + ")\n", + "\n", + "# Note: Multiprocessing in notebooks has pickling limitations\n", + "# In a regular Python script, you would use:\n", + "print(\"\"\"\n", + "💡 To parallelise in a script, use multiprocessing:\n", + "\n", + "from concurrent.futures import ProcessPoolExecutor\n", "\n", - "print(\"\\nDone!\")" + "def evaluate_batch_parallel(params_list, n_workers=8):\n", + " with ProcessPoolExecutor(max_workers=n_workers) as executor:\n", + " return list(executor.map(expensive_objective, params_list))\n", + "\n", + "# This provides near-linear speedup for expensive functions\n", + "\"\"\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:21.289092Z", + "start_time": "2026-01-22T19:00:20.946379Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:30.400184Z", "iopub.status.busy": "2026-01-10T22:22:30.399842Z", @@ -463,11 +492,14 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, + "jetTransient": { + "display_id": null + }, "metadata": {}, "output_type": "display_data" }, @@ -476,63 +508,77 @@ "output_type": "stream", "text": [ "\n", - "💡 Interpretation:\n", - " - Time per eval < sequential baseline = good parallelism\n", - " - Larger populations amortise overhead across more work\n", - " - Diminishing returns when population > number of cores\n" + "💡 Single Python evaluation: 9.8ms\n", + " With multiprocessing, you could achieve near-linear speedup.\n" ] } ], "source": [ - "# Visualize scaling\n", + "# Visualize DiffsolBuilder parallel performance\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", - "pops = [r[\"population\"] for r in results_pop]\n", - "times = [r[\"time\"] for r in results_pop]\n", - "time_per_eval = [r[\"time_per_eval\"] for r in results_pop]\n", - "\n", - "# Total time vs population size\n", - "ax1.plot(pops, times, \"o-\", linewidth=2, markersize=10, color=\"#1f77b4\")\n", - "ax1.set_xlabel(\"Population Size\", fontsize=12)\n", - "ax1.set_ylabel(\"Total Time (s)\", fontsize=12)\n", - "ax1.set_title(\"Optimisation Time vs Population Size\", fontsize=14, fontweight=\"bold\")\n", - "ax1.grid(True, alpha=0.3)\n", - "ax1.set_xticks(pops)\n", - "\n", - "# Time per evaluation (measures parallelism efficiency)\n", - "ax2.plot(pops, time_per_eval, \"s-\", linewidth=2, markersize=10, color=\"#ff7f0e\")\n", - "ax2.axhline(\n", - " y=0.01, color=\"red\", linestyle=\"--\", label=\"Sequential baseline (10ms)\", alpha=0.7\n", + "# Time comparison for DiffsolBuilder\n", + "methods = [\"Sequential\", \"Parallel\", f\"Parallel\\n(pop={2 * n_cores})\"]\n", + "times = [time_seq, time_par, time_par_large]\n", + "colors = [\"#d62728\", \"#2ca02c\", \"#1f77b4\"]\n", + "\n", + "bars = ax1.bar(methods, times, color=colors, alpha=0.8, edgecolor=\"black\")\n", + "ax1.set_ylabel(\"Time (s)\", fontsize=12)\n", + "ax1.set_title(\"DiffsolBuilder: Optimisation Time\", fontsize=14, fontweight=\"bold\")\n", + "ax1.grid(True, axis=\"y\", alpha=0.3)\n", + "\n", + "for bar, t in zip(bars, times, strict=False):\n", + " ax1.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " bar.get_height(),\n", + " f\"{t:.2f}s\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=11,\n", + " fontweight=\"bold\",\n", + " )\n", + "\n", + "# Single evaluation time for Python callable\n", + "ax2.bar(\n", + " [\"Sequential\\nPython\"],\n", + " [time_seq_python],\n", + " color=\"#9467bd\",\n", + " alpha=0.8,\n", + " edgecolor=\"black\",\n", + ")\n", + "ax2.set_ylabel(\"Time (s)\", fontsize=12)\n", + "ax2.set_title(f\"Python Callable: {n_evals} evaluations\", fontsize=14, fontweight=\"bold\")\n", + "ax2.grid(True, axis=\"y\", alpha=0.3)\n", + "ax2.text(\n", + " 0,\n", + " time_seq_python,\n", + " f\"{time_seq_python:.2f}s\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=11,\n", + " fontweight=\"bold\",\n", ")\n", - "ax2.set_xlabel(\"Population Size\", fontsize=12)\n", - "ax2.set_ylabel(\"Time per Evaluation (s)\", fontsize=12)\n", - "ax2.set_title(\"Parallelism Efficiency\", fontsize=14, fontweight=\"bold\")\n", - "ax2.grid(True, alpha=0.3)\n", - "ax2.set_xticks(pops)\n", - "ax2.legend(fontsize=10)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", - "print(\"\\n💡 Interpretation:\")\n", - "print(\" - Time per eval < sequential baseline = good parallelism\")\n", - "print(\" - Larger populations amortise overhead across more work\")\n", - "print(\" - Diminishing returns when population > number of cores\")" + "print(f\"\\n💡 Single Python evaluation: {time_seq_python / n_evals * 1000:.1f}ms\")\n", + "print(\" With multiprocessing, you could achieve near-linear speedup.\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Effect of Evaluation Cost\n", - "\n", - "Parallelism overhead (thread creation, synchronisation) matters less when evaluations are expensive:" - ] + "source": "## Understanding Parallel Performance\n\n### Why DiffsolBuilder Speedups May Be Modest\n\nDiffsolBuilder uses **solver caching** - each thread maintains cached ODE solver state that gets reused across evaluations. This makes sequential evaluation very fast, reducing the relative benefit of parallelism.\n\nParallelism helps more when:\n- Evaluations are expensive (>10ms each)\n- Large populations are used\n- Cache reuse is limited (e.g., parameters vary widely)\n\n### Multiprocessing vs Threading\n\n| Approach | Use Case | Overhead |\n|----------|----------|----------|\n| `.with_parallel(True)` | DiffsolBuilder only | Low (shared memory) |\n| `multiprocessing` | Any Python callable | Higher (process creation) |\n| `threading` | I/O-bound tasks | GIL blocks CPU work |\n\nFor expensive Python simulations, multiprocessing provides the best parallelism." }, { "cell_type": "code", "execution_count": 8, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.060489Z", + "start_time": "2026-01-22T19:00:21.315395Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:22:30.696868Z", "iopub.status.busy": "2026-01-10T22:22:30.696562Z", @@ -545,104 +591,98 @@ "name": "stdout", "output_type": "stream", "text": [ - "Testing different evaluation costs...\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Delay= 1ms: Sequential= 0.14s, Parallel= 0.44s, Speedup=0.33x\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Delay= 5ms: Sequential= 0.60s, Parallel= 1.81s, Speedup=0.33x\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Delay= 10ms: Sequential= 1.14s, Parallel= 3.42s, Speedup=0.33x\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Delay= 20ms: Sequential= 2.37s, Parallel= 7.03s, Speedup=0.34x\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Delay= 50ms: Sequential= 5.41s, Parallel= 16.19s, Speedup=0.33x\n", + "Testing DiffsolBuilder scaling with data size...\n", "\n", - "Done!\n" + "N=100: Sequential=0.11s, Parallel=0.25s, Speedup=0.46x\n", + "N=200: Sequential=0.67s, Parallel=0.22s, Speedup=3.10x\n", + "N=500: Sequential=0.17s, Parallel=0.22s, Speedup=0.76x\n" ] } ], "source": [ - "# Test different evaluation costs\n", - "delays_ms = [1, 5, 10, 20, 50]\n", - "results_cost = []\n", - "\n", - "print(\"Testing different evaluation costs...\\n\")\n", - "\n", - "for delay in delays_ms:\n", - " # Create problem with specific delay\n", - " problem_delayed = (\n", - " chron.ScalarBuilder()\n", - " .with_objective(lambda x, d=delay: expensive_rosenbrock(x, delay_ms=d))\n", - " .with_parameter(\"x\", 1.0)\n", - " .with_parameter(\"y\", 1.0)\n", - " .build()\n", + "# Test DiffsolBuilder scaling with data size\n", + "data_sizes = [100, 200, 500]\n", + "scaling_results = []\n", + "\n", + "print(\"Testing DiffsolBuilder scaling with data size...\\n\")\n", + "\n", + "for n_points in data_sizes:\n", + " # Generate data with different sizes\n", + " t_test = np.linspace(0, 100, n_points)\n", + " sol_test = solve_ivp(\n", + " lotka_volterra,\n", + " [t_test[0], t_test[-1]],\n", + " [10.0, 5.0],\n", + " args=(\n", + " true_params[\"alpha\"],\n", + " true_params[\"beta\"],\n", + " true_params[\"delta\"],\n", + " true_params[\"gamma\"],\n", + " ),\n", + " t_eval=t_test,\n", + " method=\"RK45\",\n", " )\n", + " y_test = sol_test.y.T + np.random.normal(0, 0.3, (n_points, 2))\n", + " data_test = np.column_stack((t_test, y_test))\n", "\n", - " # Sequential Nelder-Mead\n", + " # Sequential\n", " start = time.time()\n", - " result_seq = chron.NelderMead().with_max_iter(50).run(problem_delayed, [0.0, 0.0])\n", - " time_seq = time.time() - start\n", + " _ = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(model_str)\n", + " .with_data(data_test)\n", + " .with_parameter(\"alpha\", 0.8)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.3)\n", + " .with_cost(chron.SSE())\n", + " .with_parallel(False)\n", + " .with_optimiser(chron.CMAES().with_max_iter(30).with_step_size(0.3))\n", + " .build()\n", + " .optimise()\n", + " )\n", + " t_seq_scale = time.time() - start\n", "\n", - " # Parallel CMA-ES\n", + " # Parallel\n", " start = time.time()\n", - " result_par = (\n", - " chron.CMAES()\n", - " .with_max_iter(25)\n", - " .with_step_size(0.5)\n", - " .with_population_size(12)\n", - " .run(problem_delayed, [0.0, 0.0])\n", + " _ = (\n", + " chron.DiffsolBuilder()\n", + " .with_diffsl(model_str)\n", + " .with_data(data_test)\n", + " .with_parameter(\"alpha\", 0.8)\n", + " .with_parameter(\"beta\", 0.3)\n", + " .with_parameter(\"delta\", 0.05)\n", + " .with_parameter(\"gamma\", 0.3)\n", + " .with_cost(chron.SSE())\n", + " .with_parallel(True)\n", + " .with_optimiser(chron.CMAES().with_max_iter(30).with_step_size(0.3))\n", + " .build()\n", + " .optimise()\n", " )\n", - " time_par = time.time() - start\n", - "\n", - " speedup = time_seq / time_par\n", + " t_par_scale = time.time() - start\n", "\n", - " results_cost.append(\n", + " sp = t_seq_scale / t_par_scale\n", + " scaling_results.append(\n", " {\n", - " \"delay_ms\": delay,\n", - " \"time_seq\": time_seq,\n", - " \"time_par\": time_par,\n", - " \"speedup\": speedup,\n", + " \"n_points\": n_points,\n", + " \"time_seq\": t_seq_scale,\n", + " \"time_par\": t_par_scale,\n", + " \"speedup\": sp,\n", " }\n", " )\n", - "\n", " print(\n", - " f\"Delay={delay:3d}ms: Sequential={time_seq:6.2f}s, Parallel={time_par:6.2f}s, \"\n", - " f\"Speedup={speedup:.2f}x\"\n", - " )\n", - "\n", - "print(\"\\nDone!\")" + " f\"N={n_points:3d}: Sequential={t_seq_scale:.2f}s, Parallel={t_par_scale:.2f}s, Speedup={sp:.2f}x\"\n", + " )" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.316526Z", + "start_time": "2026-01-22T19:00:23.100926Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:23:09.267174Z", "iopub.status.busy": "2026-01-10T22:23:09.266754Z", @@ -653,11 +693,14 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, + "jetTransient": { + "display_id": null + }, "metadata": {}, "output_type": "display_data" }, @@ -666,170 +709,66 @@ "output_type": "stream", "text": [ "\n", - "💡 Key Insight:\n", - " Parallelism speedup increases with evaluation cost!\n", - " Expensive evaluations → parallelisation overhead becomes negligible\n" + "💡 DiffsolBuilder speedup is modest due to efficient solver caching.\n", + " For more parallelism, use multiprocessing with Python callables.\n" ] } ], "source": [ - "# Visualize speedup vs evaluation cost\n", + "# Visualize DiffsolBuilder scaling\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", - "delays = [r[\"delay_ms\"] for r in results_cost]\n", - "speedups = [r[\"speedup\"] for r in results_cost]\n", - "\n", - "ax.plot(\n", - " delays,\n", - " speedups,\n", - " \"o-\",\n", - " linewidth=2.5,\n", - " markersize=12,\n", - " color=\"#2ca02c\",\n", - " markeredgecolor=\"black\",\n", - " markeredgewidth=1.5,\n", - ")\n", - "ax.axhline(\n", - " y=1.0, color=\"red\", linestyle=\"--\", label=\"No speedup\", alpha=0.7, linewidth=2\n", - ")\n", + "n_pts = [r[\"n_points\"] for r in scaling_results]\n", + "speedups_scale = [r[\"speedup\"] for r in scaling_results]\n", + "\n", + "ax.bar(n_pts, speedups_scale, color=\"#2ca02c\", alpha=0.8, edgecolor=\"black\", width=30)\n", + "ax.axhline(y=1.0, color=\"gray\", linestyle=\"--\", alpha=0.7, label=\"No speedup\")\n", "ax.axhline(\n", - " y=n_cores,\n", - " color=\"blue\",\n", - " linestyle=\":\",\n", - " label=f\"Ideal ({n_cores} cores)\",\n", - " alpha=0.7,\n", - " linewidth=2,\n", + " y=n_cores, color=\"blue\", linestyle=\":\", alpha=0.7, label=f\"Ideal ({n_cores}x)\"\n", ")\n", "\n", - "ax.set_xlabel(\"Evaluation Cost (ms)\", fontsize=13)\n", - "ax.set_ylabel(\"Speedup (CMA-ES / Nelder-Mead)\", fontsize=13)\n", - "ax.set_title(\"Parallel Speedup vs Evaluation Cost\", fontsize=15, fontweight=\"bold\")\n", - "ax.set_xscale(\"log\")\n", - "ax.grid(True, alpha=0.3, which=\"both\")\n", + "ax.set_xlabel(\"Number of Data Points\", fontsize=12)\n", + "ax.set_ylabel(\"Speedup\", fontsize=12)\n", + "ax.set_title(\n", + " \"DiffsolBuilder Parallel Speedup vs Data Size\", fontsize=14, fontweight=\"bold\"\n", + ")\n", + "ax.set_xticks(n_pts)\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", "ax.legend(fontsize=11)\n", "\n", - "# Add value labels\n", - "for delay, speedup in zip(delays, speedups, strict=False):\n", - " ax.annotate(\n", - " f\"{speedup:.1f}x\",\n", - " xy=(delay, speedup),\n", - " xytext=(0, 10),\n", - " textcoords=\"offset points\",\n", - " ha=\"center\",\n", - " fontsize=10,\n", - " fontweight=\"bold\",\n", - " )\n", + "for x, y in zip(n_pts, speedups_scale, strict=False):\n", + " ax.text(x, y + 0.1, f\"{y:.2f}x\", ha=\"center\", fontsize=11, fontweight=\"bold\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", - "print(\"\\n💡 Key Insight:\")\n", - "print(\" Parallelism speedup increases with evaluation cost!\")\n", - "print(\" Expensive evaluations → parallelisation overhead becomes negligible\")" + "print(\"\\n💡 DiffsolBuilder speedup is modest due to efficient solver caching.\")\n", + "print(\" For more parallelism, use multiprocessing with Python callables.\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Thread Safety Considerations\n", - "\n", - "When using parallel optimisation, your **objective function must be thread-safe**:\n", - "\n", - "### ✅ Thread-Safe Patterns\n", - "```python\n", - "# Pure functions (no shared state)\n", - "def objective(x):\n", - " return np.sum(x**2)\n", - "\n", - "# Immutable data structures\n", - "DATA = np.array([...])\n", - "def objective(x):\n", - " return np.sum((x - DATA)**2)\n", - "\n", - "# Read-only global state\n", - "MODEL_PARAMS = {...}\n", - "def objective(x):\n", - " return simulate(x, MODEL_PARAMS)\n", - "```\n", - "\n", - "### ❌ Not Thread-Safe\n", - "```python\n", - "# Shared mutable state\n", - "counter = 0\n", - "def objective(x):\n", - " global counter\n", - " counter += 1 # Race condition!\n", - " return np.sum(x**2)\n", - "\n", - "# Writing to files without locks\n", - "def objective(x):\n", - " with open('log.txt', 'a') as f: # Multiple threads writing!\n", - " f.write(f'{x}\\n')\n", - " return np.sum(x**2)\n", - "```\n", - "\n", - "### Solutions for Non-Thread-Safe Code\n", - "\n", - "1. **Use locks** (but this reduces parallelism):\n", - "```python\n", - "from threading import Lock\n", - "lock = Lock()\n", - "\n", - "def objective(x):\n", - " with lock:\n", - " # Critical section\n", - " return result\n", - "```\n", - "\n", - "2. **Disable parallelism** for problematic code (future feature)\n", - "\n", - "3. **Refactor** to eliminate shared state" - ] + "source": "## When to Use Each Approach\n\n### DiffsolBuilder with `.with_parallel(True)`\n\nBest for:\n- ODE fitting problems where evaluations are moderately expensive\n- When you want automatic parallelism without code changes\n- Lower overhead than multiprocessing\n\n```python\nproblem = (\n chron.DiffsolBuilder()\n .with_diffsl(model)\n .with_data(data)\n .with_parallel(True) # Enable rayon parallelism\n .build()\n)\n```\n\n### Multiprocessing for Python Callables\n\nBest for:\n- Expensive Python simulations (>10ms per evaluation)\n- Complex custom objective functions\n- When DiffsolBuilder isn't applicable\n\n```python\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_parallel(params_list):\n with ProcessPoolExecutor(max_workers=n_cores) as executor:\n return list(executor.map(expensive_objective, params_list))\n```" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Best Practices\n", - "\n", - "### When to Use Parallel Optimisation\n", - "\n", - "**✅ Parallelism is beneficial when:**\n", - "- Objective function is expensive (>10ms per evaluation)\n", - "- Problem has >3 parameters (larger populations useful)\n", - "- Multiple cores available\n", - "- Function is thread-safe\n", - "- Global search needed (CMA-ES vs Nelder-Mead)\n", - "\n", - "**❌ Parallelism may not help when:**\n", - "- Function is very fast (<1ms)\n", - "- Low-dimensional problems (1-2 parameters)\n", - "- Limited cores available\n", - "- Thread-safety issues\n", - "- Sequential algorithms required (Nelder-Mead, Adam)\n", - "\n", - "### Tuning for Parallel Performance\n", - "\n", - "1. **Match population to cores**: `population_size ≈ 2 × n_cores` often works well\n", - "2. **Balance iterations**: Fewer iterations with larger population\n", - "3. **Profile first**: Measure single evaluation time\n", - "4. **Monitor efficiency**: Check if speedup scales with cores" - ] + "source": "## Best Practices Summary\n\n| Scenario | Recommended Approach | Expected Speedup |\n|----------|---------------------|------------------|\n| DiffsolBuilder ODE fitting | `.with_parallel(True)` | 1-2x (limited by caching) |\n| Expensive Python callable | `multiprocessing` | Near-linear with cores |\n| Fast Python callable (<1ms) | Sequential | N/A (overhead dominates) |\n| I/O-bound operations | `threading` | Varies |\n\n### Tips for Maximum Speedup\n\n1. **Profile first**: Measure single evaluation time to assess parallel benefit\n2. **Use multiprocessing for Python**: Bypass the GIL for CPU-bound work\n3. **Match workers to cores**: `n_workers = multiprocessing.cpu_count()`\n4. **Batch evaluations**: Amortise process creation overhead\n5. **Avoid shared state**: Ensure objective functions are stateless" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Performance Summary\n", - "\n", - "Let's create a summary comparing all approaches:" - ] + "source": "## Performance Summary" }, { "cell_type": "code", "execution_count": 10, "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.357601Z", + "start_time": "2026-01-22T19:00:23.331285Z" + }, "execution": { "iopub.execute_input": "2026-01-10T22:23:09.647536Z", "iopub.status.busy": "2026-01-10T22:23:09.647312Z", @@ -842,99 +781,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "================================================================================\n", + "======================================================================\n", "PERFORMANCE SUMMARY\n", - "================================================================================\n", - "Method Time (s) Evals Speedup\n", - "--------------------------------------------------------------------------------\n", - "Nelder-Mead (Sequential) 1.44 127 1.00x\n", - "CMA-ES (Default Pop) 3.40 301 0.42x\n", - "CMA-ES (Large Pop) 6.81 601 0.21x\n", + "======================================================================\n", + "\n", + "📊 DiffsolBuilder (rayon parallelism):\n", + " Sequential: 0.98s\n", + " Parallel: 0.14s (6.90x speedup)\n", + " Note: Limited speedup due to efficient solver caching\n", "\n", - "💡 Key Findings:\n", - " - Best speedup: 1.00x\n", - " - System cores: 8\n", - " - Parallel efficiency: 12%\n" + "📊 Python Callables:\n", + " Sequential (20 evals): 0.20s (9.8ms/eval)\n", + " For parallelism, use multiprocessing in scripts\n", + "\n", + "🔧 System: 8 CPU cores\n" ] } ], "source": [ - "# Summary comparison\n", - "summary_data = [\n", - " (\"Nelder-Mead (Sequential)\", time_nm, result_nm.evaluations, 1.0),\n", - " (\n", - " \"CMA-ES (Default Pop)\",\n", - " time_cmaes_default,\n", - " result_cmaes_default.evaluations,\n", - " time_nm / time_cmaes_default,\n", - " ),\n", - " (\n", - " \"CMA-ES (Large Pop)\",\n", - " time_cmaes_large,\n", - " result_cmaes_large.evaluations,\n", - " time_nm / time_cmaes_large,\n", - " ),\n", - "]\n", - "\n", - "print(\"\\n\" + \"=\" * 80)\n", + "# Final summary\n", + "print(\"=\" * 70)\n", "print(\"PERFORMANCE SUMMARY\")\n", - "print(\"=\" * 80)\n", - "print(f\"{'Method':<30} {'Time (s)':<12} {'Evals':<10} {'Speedup'}\")\n", - "print(\"-\" * 80)\n", + "print(\"=\" * 70)\n", "\n", - "for method, t, evals, speedup in summary_data:\n", - " print(f\"{method:<30} {t:<12.2f} {evals:<10} {speedup:.2f}x\")\n", + "print(\"\\n📊 DiffsolBuilder (rayon parallelism):\")\n", + "print(f\" Sequential: {time_seq:.2f}s\")\n", + "print(f\" Parallel: {time_par:.2f}s ({time_seq / time_par:.2f}x speedup)\")\n", + "print(\" Note: Limited speedup due to efficient solver caching\")\n", "\n", - "print(\"\\n💡 Key Findings:\")\n", - "print(f\" - Best speedup: {max(s for _, _, _, s in summary_data):.2f}x\")\n", - "print(f\" - System cores: {n_cores}\")\n", + "print(\"\\n📊 Python Callables:\")\n", "print(\n", - " f\" - Parallel efficiency: {(max(s for _, _, _, s in summary_data) / n_cores * 100):.0f}%\"\n", - ")" + " f\" Sequential ({n_evals} evals): {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n", + ")\n", + "print(\" For parallelism, use multiprocessing in scripts\")\n", + "\n", + "print(f\"\\n🔧 System: {n_cores} CPU cores\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **CMA-ES automatically parallelises** population evaluations across CPU cores\n", - "2. **Speedup increases** with evaluation cost (overhead becomes negligible)\n", - "3. **Population size** controls parallel work per generation\n", - "4. **Thread safety** is critical - use pure functions or locks\n", - "5. **Best for expensive functions** (>10ms per evaluation)\n", - "6. **Monitor efficiency** - speedup should approach number of cores\n", - "7. **Trade-offs exist** - larger populations need more generations\n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n", - "- [Guide: Parallel Execution](../../guides/parallel-execution.md) - Detailed thread safety patterns\n", - "- [Guide: Tuning Optimisers](../../guides/tuning-optimizers.md) - Population size selection\n", - "- [API Reference: CMA-ES](../../api-reference/python/optimizers.md#cmaes) - Complete API" - ] + "source": "## Key Takeaways\n\n1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n\n2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n\n3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n\n4. **Profile first** - measure single evaluation time to determine if parallelism will help\n\n5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n\n## Next Steps\n\n- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n- [API Reference: CMA-ES](../../api-reference/python/optimizers.md#cmaes) - Population configuration" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Exercises\n", - "\n", - "1. **Optimal Population**: Find the population size that minimises total time for your hardware\n", - "\n", - "2. **Amdahl's Law**: Estimate the theoretical speedup limit based on your results\n", - "\n", - "3. **Real Problem**: Apply parallel CMA-ES to an ODE fitting problem from Tutorial 2\n", - "\n", - "4. **Scaling Study**: Measure how speedup changes with:\n", - " - Number of parameters (2, 5, 10, 20)\n", - " - Evaluation cost (1ms, 10ms, 100ms, 1s)\n", - " - System load (run with background tasks)\n", - "\n", - "5. **Thread Safety Bug**: Create a non-thread-safe objective function and observe the failure mode" - ] + "source": "## Exercises\n\n1. **Multiprocessing Integration**: Modify the `expensive_objective` function to use a different ODE system and measure the parallel speedup\n\n2. **Process Pool Reuse**: Create a persistent `ProcessPoolExecutor` and measure the overhead reduction from reusing it across multiple batches\n\n3. **Hybrid Approach**: Combine DiffsolBuilder's parallel evaluation with multiprocessing by running multiple independent optimisation problems in parallel\n\n4. **Scaling Study**: Measure how multiprocessing speedup changes with:\n - Number of workers (1, 2, 4, 8, ...)\n - Evaluation cost (1ms, 10ms, 100ms, 1s)\n - Batch size (10, 50, 100, 500)" } ], "metadata": { diff --git a/examples/predator_prey/predator_prey_diffeqpy.py b/examples/predator_prey/predator_prey_diffeqpy.py index 70c7cc4..1d67721 100644 --- a/examples/predator_prey/predator_prey_diffeqpy.py +++ b/examples/predator_prey/predator_prey_diffeqpy.py @@ -89,3 +89,6 @@ def simulate(params): print( f"Relative error: {np.linalg.norm(result.x - TRUE_PARAMS) / np.linalg.norm(TRUE_PARAMS):.2%}" ) +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +print(f"Time: {result.time}s") diff --git a/mkdocs.yml b/mkdocs.yml index 1f51b68..6b7ab6e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -190,7 +190,6 @@ nav: - development/index.md - Contributing: development/contributing.md - Architecture: development/architecture.md - - Building from Source: development/building.md - Changelog: changelog.md strict: true # Fail on warnings diff --git a/pyproject.toml b/pyproject.toml index 59cd032..24d034c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,8 @@ docs = [ "mkdocs-minify-plugin>=0.8.0", "mkdocs-jupyter>=0.24.0", "pymdown-extensions>=10.7", + "matplotlib>=3.8", + "nbconvert>=7.0", ] examples = [ # macOS x86_64 + Python < 3.14: diffrax with pinned jax/jaxlib diff --git a/tests/test_docs.py b/tests/test_docs.py index 2e7583c..9c770e0 100644 --- a/tests/test_docs.py +++ b/tests/test_docs.py @@ -178,8 +178,8 @@ def test_notebooks_have_metadata(self): ) assert has_markdown, f"{notebook_path.name} has no markdown cells" - def test_notebooks_have_learning_objectives(self): - """Check that notebooks start with learning objectives.""" + def test_notebooks_have_objectives(self): + """Check that notebooks start with objectives.""" import json notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("[0-9]*.ipynb")) @@ -188,19 +188,17 @@ def test_notebooks_have_learning_objectives(self): with open(notebook_path) as f: nb_data = json.load(f) - # First cell should be markdown with learning objectives + # First cell should be markdown with objectives first_cell = nb_data["cells"][0] assert first_cell["cell_type"] == "markdown", ( f"{notebook_path.name} first cell is not markdown" ) - # Check for learning objectives + # Check for objectives content = "".join(first_cell["source"]) - has_objectives = ( - "Learning Objectives" in content or "learning objectives" in content - ) + has_objectives = "Objectives" in content or "objectives" in content assert has_objectives, ( - f"{notebook_path.name} missing learning objectives in first cell" + f"{notebook_path.name} missing objectives in first cell" ) diff --git a/uv.lock b/uv.lock index d423aa4..5b9b837 100644 --- a/uv.lock +++ b/uv.lock @@ -285,12 +285,14 @@ dev = [ { name = "scipy" }, ] docs = [ + { name = "matplotlib" }, { name = "mkdocs" }, { name = "mkdocs-git-revision-date-localized-plugin" }, { name = "mkdocs-jupyter" }, { name = "mkdocs-material" }, { name = "mkdocs-minify-plugin" }, { name = "mkdocstrings", extra = ["python"] }, + { name = "nbconvert" }, { name = "pymdown-extensions" }, ] examples = [ @@ -318,12 +320,14 @@ dev = [ { name = "scipy", specifier = ">=1.16.2" }, ] docs = [ + { name = "matplotlib", specifier = ">=3.8" }, { name = "mkdocs", specifier = ">=1.5.0" }, { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.2.0" }, { name = "mkdocs-jupyter", specifier = ">=0.24.0" }, { name = "mkdocs-material", specifier = ">=9.5.0" }, { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, { name = "mkdocstrings", extras = ["python"], specifier = ">=0.24.0" }, + { name = "nbconvert", specifier = ">=7.0" }, { name = "pymdown-extensions", specifier = ">=10.7" }, ] examples = [ From 90081867781b3616c3b88b1eaf8f408d7dc08628 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Thu, 22 Jan 2026 21:10:51 +0000 Subject: [PATCH 09/18] restores optimiser and sampler docs --- docs/algorithms/optimizers/adam.md | 139 +++++++++++++-- docs/algorithms/optimizers/cmaes.md | 139 +++++++++++++-- docs/algorithms/optimizers/nelder-mead.md | 110 ++++++++++-- .../samplers/dynamic-nested-sampling.md | 165 ++++++++++++++++-- .../samplers/metropolis-hastings.md | 140 +++++++++++++-- 5 files changed, 620 insertions(+), 73 deletions(-) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md index 527d8b4..c5e4218 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimizers/adam.md @@ -1,31 +1,142 @@ # Adam Algorithm -!!! info "Coming Soon" - Detailed algorithm documentation is being written. +Adaptive Moment Estimation (Adam) is a first-order gradient-based optimiser that maintains adaptive learning rates for each parameter using estimates of first and second moments of the gradients. -## Overview +## Algorithm Overview -Adaptive Moment Estimation (Adam) is a gradient-based optimiser with adaptive learning rates. +Adam combines the benefits of AdaGrad (adapting to sparse gradients) and RMSprop (adapting to non-stationary objectives) by tracking exponential moving averages of both the gradient and squared gradient. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Local, gradient-based | +| Parallelisable | No | +| Function evaluations | 1 per iteration | +| Best for | Smooth, differentiable objectives | + +## Mathematical Foundation + +At each iteration $t$, Adam updates parameters $\theta$ using: + +**Gradient computation:** + +$$ +g_t = \nabla_\theta f(\theta_{t-1}) +$$ + +**Biased moment estimates:** +$$ +m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t +$$ +$$ +v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2 +$$ + +**Bias correction:** +$$ +\hat{m}_t = \frac{m_t}{1 - \beta_1^t} +$$ +$$ +\hat{v}_t = \frac{v_t}{1 - \beta_2^t} +$$ + +**Parameter update:** +$$ +\theta_t = \theta_{t-1} - \alpha \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon} +$$ + +Where $\alpha$ is the learning rate (step size). + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum iterations | +| `step_size` | 0.01 | Learning rate $\alpha$ | +| `beta1` | 0.9 | First moment decay rate $\beta_1$ | +| `beta2` | 0.999 | Second moment decay rate $\beta_2$ | +| `eps` | 1e-8 | Numerical stability constant $\epsilon$ | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `gradient_threshold` | None | Gradient norm convergence tolerance | +| `patience` | None | Timeout in seconds | + +## Convergence Criteria + +The algorithm terminates when any condition is met: + +1. **Iteration limit**: `iteration >= max_iter` +2. **Gradient norm**: $\|g_t\| <$ `gradient_threshold` +3. **Objective change**: Change in objective below `threshold` +4. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Learning rate** (`step_size`) is the most critical parameter: + +- Start with 0.001 (the original paper's recommendation) +- Try orders of magnitude: 0.1, 0.01, 0.001, 0.0001 +- Too large: oscillation, divergence, or overshooting +- Too small: slow convergence + +**Beta parameters** rarely need adjustment: + +- $\beta_1 = 0.9$: controls momentum (gradient smoothing) +- $\beta_2 = 0.999$: controls adaptive scaling (variance smoothing) +- Lower $\beta_1$ for less momentum, more responsiveness +- Lower $\beta_2$ for faster adaptation to gradient scale changes + +**Epsilon** almost never needs tuning: + +- Prevents division by zero when gradients are near zero +- Default 1e-8 works for most cases ## When to Use -**Best for:** +**Strengths:** + +- Fast convergence on smooth objectives +- Per-parameter adaptive learning rates +- Handles sparse gradients well +- Works with noisy gradients (e.g., mini-batches) +- Low memory overhead + +**Limitations:** + +- Requires gradient computation (automatic differentiation or numerical) +- Converges to local minima (no global search) +- Sensitive to learning rate choice +- May oscillate near optima + +## Example + +```python +import chronopt as chron + +optimiser = ( + chron.Adam() + .with_max_iter(5000) + .with_step_size(0.001) + .with_betas(0.9, 0.999) + .with_threshold(1e-8) +) -- Smooth, differentiable objectives -- Fast convergence needed -- Gradients available or cheap to compute +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` -**Avoid when:** +## Implementation Notes -- Objective is non-smooth -- No gradient information -- Need global optimum (can get stuck in local minima) +- Supports automatic numerical gradient computation via central differences +- Bias correction is essential for early iterations +- Tracks both current position and best-found position -## API Reference +## References -See [Adam API](../../api-reference/python/optimizers.md#adam) for usage details. +1. Kingma, D.P. and Ba, J. (2015). "Adam: A Method for Stochastic Optimization". *ICLR 2015*. arXiv:1412.6980. +2. Reddi, S.J. et al. (2018). "On the Convergence of Adam and Beyond". *ICLR 2018*. ## See Also +- [API Reference](../../api-reference/python/optimizers.md#adam) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md index c80d488..b8d7b8f 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimizers/cmaes.md @@ -1,33 +1,140 @@ # CMA-ES Algorithm -!!! info "Coming Soon" - Detailed algorithm documentation is being written. +Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic, derivative-free algorithm for global optimisation. It maintains a multivariate Gaussian distribution that adapts its covariance structure to the objective landscape. -## Overview +## Algorithm Overview -Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic, derivative-free algorithm for global optimisation. +CMA-ES samples a population of candidate solutions from a multivariate normal distribution, ranks them by fitness, and updates the distribution parameters to bias future samples towards better regions. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Global, gradient-free | +| Parallelisable | Yes (population evaluation) | +| Function evaluations | $\lambda$ per generation | +| Best dimensions | 10-100+ parameters | + +## Mathematical Foundation + +The algorithm samples offspring from: + +$$ +x_k \sim m + \sigma \cdot \mathcal{N}(0, C) +$$ + +Where: + +- $m$ is the distribution mean (current best estimate) +- $\sigma$ is the global step size +- $C$ is the covariance matrix + +### Strategy Parameters + +The algorithm automatically computes these from dimension $n$: + +| Parameter | Formula | Purpose | +|-----------|---------|---------| +| $\lambda$ | $\max(4, \lfloor 4 + 3\ln(n) \rfloor)$ | Population size | +| $\mu$ | $\lfloor \lambda / 2 \rfloor$ | Parent count | +| $\mu_\text{eff}$ | $1 / \sum w_i^2$ | Effective parent count | +| $c_\sigma$ | $(μ_\text{eff} + 2) / (n + μ_\text{eff} + 5)$ | Step size learning rate | +| $c_c$ | $(4 + μ_\text{eff}/n) / (n + 4 + 2μ_\text{eff}/n)$ | Covariance path learning rate | + +## Algorithm Steps + +1. **Sample**: Generate $\lambda$ offspring from $\mathcal{N}(m, \sigma^2 C)$ +2. **Evaluate**: Compute fitness for all offspring (parallelisable) +3. **Select**: Rank offspring; select best $\mu$ as parents +4. **Update mean**: $m \leftarrow \sum_{i=1}^{\mu} w_i x_{i:\lambda}$ +5. **Update evolution paths**: + - Conjugate path: $p_\sigma \leftarrow (1-c_\sigma) p_\sigma + \sqrt{c_\sigma(2-c_\sigma)\mu_\text{eff}} \cdot C^{-1/2}(m - m_\text{old})/\sigma$ + - Covariance path: $p_c \leftarrow (1-c_c) p_c + \sqrt{c_c(2-c_c)\mu_\text{eff}} \cdot (m - m_\text{old})/\sigma$ +6. **Update covariance**: Rank-one + rank-$\mu$ update +7. **Update step size**: Based on evolution path length vs expected length +8. **Repeat**: Until convergence + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum generations | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `step_size` | 0.5 | Initial search radius $\sigma$ | +| `population_size` | Auto | Offspring per generation $\lambda$ | +| `seed` | None | Random seed for reproducibility | +| `patience` | None | Timeout in seconds | + +## Convergence Criteria + +The algorithm terminates when any condition is met: + +1. **Iteration limit**: `generation >= max_iter` +2. **Function tolerance**: Best objective value below `threshold` +3. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Step size** ($\sigma$) is the most important parameter: + +- Set to ~1/3 of the expected distance to the optimum +- Too large: slow convergence, overshooting +- Too small: premature convergence, stuck in local optima + +**Population size** affects exploration vs exploitation: + +- Default formula works well for most problems +- Increase for highly multi-modal landscapes +- Decrease for faster convergence on simpler problems +- Match to available parallel compute resources ## When to Use -**Best for:** +**Strengths:** + +- Global search capability +- Scales well to high dimensions (10-100+ parameters) +- Self-adapting covariance learns problem structure +- Parallelisable population evaluation +- Robust to local minima + +**Limitations:** + +- More evaluations than gradient methods +- Stochastic results (use seed for reproducibility) +- Memory scales as $O(n^2)$ for covariance matrix +- Overkill for simple, low-dimensional problems + +## Example + +```python +import chronopt as chron + +optimiser = ( + chron.CMAES() + .with_max_iter(500) + .with_step_size(0.3) + .with_population_size(20) + .with_seed(42) +) -- High-dimensional problems (10-100+ parameters) -- Global optimisation -- Parallel hardware -- Multi-modal landscapes +result = optimiser.run(problem, initial_guess=[0.5] * n_params) +``` -**Avoid when:** +## Implementation Notes -- Very low-dimensional (< 5 parameters) -- Limited computational budget -- Need deterministic results +- Full covariance matrix (not diagonal approximation) +- Lazy eigendecomposition for efficient sampling +- Bounds enforced via clamping after each sample -## API Reference +## References -See [CMAES API](../../api-reference/python/optimizers.md#cma-es) for usage details. +1. Hansen, N. (2016). "The CMA Evolution Strategy: A Tutorial". arXiv:1604.00772. +2. Hansen, N. and Ostermeier, A. (2001). "Completely Derandomized Self-Adaptation in Evolution Strategies". *Evolutionary Computation*, 9(2), 159-195. +3. Hansen, N. et al. (2003). "Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)". *Evolutionary Computation*, 11(1), 1-18. ## See Also +- [API Reference](../../api-reference/python/optimizers.md#cma-es) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) -- [Tuning Optimisers](../../guides/tuning-optimizers.md) - [Parallel Execution](../../guides/parallel-execution.md) diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimizers/nelder-mead.md index 9ae3895..51badaa 100644 --- a/docs/algorithms/optimizers/nelder-mead.md +++ b/docs/algorithms/optimizers/nelder-mead.md @@ -1,32 +1,112 @@ # Nelder-Mead Algorithm -!!! info "Coming Soon" - Detailed algorithm documentation is being written. +The Nelder-Mead algorithm (downhill simplex method) is a gradient-free local optimisation algorithm that uses a geometric simplex to navigate the parameter space. -## Overview +## Algorithm Overview -The Nelder-Mead algorithm (also known as the downhill simplex method) is a gradient-free local optimisation algorithm. +Nelder-Mead maintains a **simplex** of $n+1$ vertices in $n$-dimensional space. At each iteration, it transforms the simplex through reflection, expansion, contraction, or shrinking operations to move towards lower objective values. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Local, gradient-free | +| Parallelisable | No | +| Function evaluations | Sequential | +| Best dimensions | 2-10 parameters | + +## Algorithm Steps + +1. **Initialise**: Create simplex from initial point using step size +2. **Order**: Sort vertices by objective value: $f(x_1) \leq f(x_2) \leq \cdots \leq f(x_{n+1})$ +3. **Reflect**: Compute reflection point $x_r = \bar{x} + \alpha(\bar{x} - x_{n+1})$ +4. **Transform**: Based on $f(x_r)$: + - If $f(x_1) \leq f(x_r) < f(x_n)$: accept reflection + - If $f(x_r) < f(x_1)$: try expansion $x_e = \bar{x} + \gamma(x_r - \bar{x})$ + - If $f(x_r) \geq f(x_n)$: try contraction $x_c = \bar{x} + \rho(x_{n+1} - \bar{x})$ +5. **Shrink**: If contraction fails, shrink towards best vertex +6. **Repeat**: Until convergence criteria met + +Where $\bar{x}$ is the centroid of all vertices except the worst. + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum iterations | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `step_size` | 0.1 | Initial simplex size | +| `position_tolerance` | 1e-6 | Parameter space convergence tolerance | +| `patience` | None | Timeout in seconds | + +### Simplex Coefficients + +| Coefficient | Symbol | Default | Purpose | +|-------------|--------|---------|---------| +| Reflection | $\alpha$ | 1.0 | Scale of reflection step | +| Expansion | $\gamma$ | 2.0 | Scale of expansion step | +| Contraction | $\rho$ | 0.5 | Scale of contraction step | +| Shrinking | $\sigma$ | 0.5 | Scale of shrink operation | + +## Convergence Criteria + +The algorithm terminates when any of the following conditions is met: + +1. **Iteration limit**: `iteration >= max_iter` +2. **Function tolerance**: Change in objective value below `threshold` +3. **Position tolerance**: Simplex diameter below `position_tolerance` +4. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Step size** controls the initial simplex scale: + +- Set to ~10-50% of the expected parameter range +- Larger values explore more broadly +- Smaller values for local refinement near a known solution + +**Thresholds** control precision vs speed: + +- Tighter tolerances (1e-8) for high precision +- Looser tolerances (1e-4) for quick estimates ## When to Use -**Best for:** +**Strengths:** + +- No gradient computation required +- Robust to moderate noise +- Simple and reliable for small problems +- Low memory footprint + +**Limitations:** + +- Convergence slows significantly beyond 10 parameters +- Can converge to local minima +- Not parallelisable + +## Example -- Problems with < 10 parameters -- Noisy objective functions -- No gradient information available -- Quick exploration +```python +import chronopt as chron -**Avoid when:** +optimiser = ( + chron.NelderMead() + .with_max_iter(5000) + .with_step_size(0.1) + .with_threshold(1e-8) +) -- More than 10 parameters -- Need global optimum -- Very tight convergence required +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` -## API Reference +## References -See [NelderMead API](../../api-reference/python/optimizers.md#nelder-mead) for usage details. +1. Nelder, J.A. and Mead, R. (1965). "A Simplex Method for Function Minimization". *The Computer Journal*, 7(4), 308-313. +2. Lagarias, J.C. et al. (1998). "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions". *SIAM Journal on Optimization*, 9(1), 112-147. ## See Also +- [API Reference](../../api-reference/python/optimizers.md#nelder-mead) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Tuning Optimisers](../../guides/tuning-optimizers.md) diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md index 7ca0527..f3db770 100644 --- a/docs/algorithms/samplers/dynamic-nested-sampling.md +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -1,31 +1,168 @@ # Dynamic Nested Sampling Algorithm -!!! info "Coming Soon" - Sampler documentation is being written. +Dynamic Nested Sampling is a Bayesian inference algorithm that computes the model evidence (marginal likelihood) while simultaneously generating posterior samples. It is particularly suited for model comparison via Bayes factors. -## Overview +## Algorithm Overview -Dynamic Nested Sampling calculates model evidence (marginal likelihood) for Bayesian model comparison. +Nested sampling transforms the multi-dimensional evidence integral into a one-dimensional integral over prior volume. It maintains a set of "live points" that progressively shrink the prior volume while tracking the likelihood threshold. The "dynamic" aspect allows the algorithm to allocate more live points in regions that contribute most to either the evidence or posterior, improving efficiency. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Evidence sampler | +| Parallelisable | Yes (batch proposals) | +| Output | Evidence + posterior samples | +| Best for | Model comparison | + +## Mathematical Foundation + +The model evidence (marginal likelihood) is: + +$$ +\mathcal{Z} = \int \mathcal{L}(\theta) \pi(\theta) \, d\theta +$$ + +Nested sampling transforms this by defining the prior volume: + +$$ +X(\lambda) = \int_{\mathcal{L}(\theta) > \lambda} \pi(\theta) \, d\theta +$$ + +The evidence becomes a one-dimensional integral: + +$$ +\mathcal{Z} = \int_0^1 \mathcal{L}(X) \, dX +$$ + +This is approximated by iteratively shrinking the prior volume: + +$$ +\mathcal{Z} \approx \sum_{i=1}^{N} \mathcal{L}_i \, \Delta X_i +$$ + +Where $\Delta X_i = X_{i-1} - X_i$ and the shrinkage ratio is estimated statistically. + +## Algorithm Steps + +1. **Initialise**: Sample $K$ live points uniformly from the prior +2. **Iterate**: + - Find lowest-likelihood live point $\mathcal{L}^*$ + - Record point as "dead point" with prior volume estimate + - Replace with new point sampled uniformly from prior with $\mathcal{L} > \mathcal{L}^*$ +3. **Terminate**: When remaining evidence contribution is negligible +4. **Compute**: Sum contributions to estimate $\mathcal{Z}$ and uncertainty +5. **Return**: Log-evidence, evidence error, and posterior samples + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `live_points` | 64 | Number of live points $K$ | +| `expansion_factor` | 0.5 | Live set expansion aggressiveness | +| `termination_tol` | 1e-3 | Evidence convergence tolerance | +| `mcmc_batch_size` | 8 | Proposals generated per iteration | +| `mcmc_step_size` | 0.01 | MCMC proposal step size | +| `seed` | None | Random seed for reproducibility | + +## Tuning Guidance + +**Live points** control accuracy vs cost: + +- More live points = better evidence estimate but more evaluations +- Minimum recommended: $25 × n_{\text{params}}$ +- For accurate posteriors: $>50 × n_{\text{params}}$ + +**Termination tolerance** affects precision: + +- Smaller values = more accurate evidence but more iterations +- Default 1e-3 sufficient for most model comparisons + +**MCMC parameters** affect replacement efficiency: + +- `mcmc_batch_size`: larger batches for parallel evaluation +- `mcmc_step_size`: tune for ~20-50% acceptance in constrained sampling + +## Evidence Interpretation + +The log-evidence can be used for model comparison via Bayes factors: + +$$ +\ln B_{12} = \ln \mathcal{Z}_1 - \ln \mathcal{Z}_2 +$$ + +| $\ln B_{12}$ | $B_{12}$ | Evidence strength | +|--------------|----------|-------------------| +| < 1 | < 3 | Inconclusive | +| 1-2.5 | 3-12 | Positive | +| 2.5-5 | 12-150 | Strong | +| > 5 | > 150 | Decisive | ## When to Use -**Best for:** +**Strengths:** + +- Computes model evidence directly +- Handles multi-modal posteriors well +- Provides posterior samples as byproduct +- Robust to complex likelihood landscapes +- Parallelisable + +**Limitations:** + +- More expensive than MCMC for posterior-only inference +- Constrained sampling can be challenging in high dimensions +- Evidence accuracy depends on live point count + +## Cost Metric Requirement + +Nested sampling requires a **negative log-likelihood** cost metric: + +```python +problem = ( + chron.ScalarProblemBuilder() + .with_objective(model_fn) + .with_cost_metric(chron.CostMetric.GaussianNLL) # Required + .build() +) +``` + +The objective function should return negative log-likelihood; the algorithm internally negates to work with log-likelihood. + +## Example + +```python +import chronopt as chron + +sampler = ( + chron.DynamicNestedSampling() + .with_live_points(128) + .with_termination_tol(1e-3) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess=[1.0, 2.0]) -- Model comparison -- Calculating Bayes factors -- Evidence calculation -- Multi-modal posteriors +# Access results +log_evidence = result.log_evidence +evidence_error = result.evidence_error +samples = result.samples +``` -**Avoid when:** +## Implementation Notes -- Only need posterior samples (use MCMC) -- Limited computational budget +- Uses MCMC for constrained prior sampling +- Batch evaluation for parallel efficiency +- Automatic termination based on remaining evidence contribution -## API Reference +## References -See [Samplers API](../../api-reference/python/samplers.md) for usage details (coming soon). +1. Skilling, J. (2006). "Nested Sampling for General Bayesian Computation". *Bayesian Analysis*, 1(4), 833-860. +2. Higson, E. et al. (2019). "Dynamic Nested Sampling: An Improved Algorithm for Parameter Estimation and Evidence Calculation". *Statistics and Computing*, 29, 891-913. +3. Speagle, J.S. (2020). "dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences". *Monthly Notices of the Royal Astronomical Society*, 493(3), 3132-3158. ## See Also +- [API Reference](../../api-reference/python/samplers.md) - [Choosing a Sampler](../../guides/choosing-sampler.md) - [Cost Metrics](../../guides/cost-metrics.md) diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md index 1fc3508..6e961df 100644 --- a/docs/algorithms/samplers/metropolis-hastings.md +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -1,31 +1,143 @@ # Metropolis-Hastings Algorithm -!!! info "Coming Soon" - Sampler documentation is being written. +Metropolis-Hastings is a Markov Chain Monte Carlo (MCMC) algorithm for sampling from posterior distributions. It enables uncertainty quantification by generating samples that characterise the probability distribution over parameters. -## Overview +## Algorithm Overview -Metropolis-Hastings is an MCMC algorithm for sampling from posterior distributions. +The algorithm constructs a Markov chain whose stationary distribution is the target posterior. By proposing random moves and accepting/rejecting them based on the likelihood ratio, it explores the parameter space in proportion to posterior probability. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | MCMC sampler | +| Parallelisable | Yes (across chains) | +| Output | Posterior samples | +| Best for | Uncertainty quantification | + +## Mathematical Foundation + +Given a target distribution $\pi(\theta)$ (the posterior), the algorithm: + +1. Proposes a new state from proposal distribution $q(\theta' | \theta)$ +2. Computes acceptance probability: + +$$ +\alpha = \min\left(1, \frac{\pi(\theta') q(\theta | \theta')}{\pi(\theta) q(\theta' | \theta)}\right) +$$ + +For the symmetric random walk proposal used here ($q(\theta' | \theta) = q(\theta | \theta')$): + +$$ +\alpha = \min\left(1, \frac{\pi(\theta')}{\pi(\theta)}\right) = \min\left(1, \exp(\log\pi(\theta') - \log\pi(\theta))\right) +$$ + +## Algorithm Steps + +1. **Initialise**: Start at initial point $\theta_0$ +2. **Propose**: Generate candidate $\theta' = \theta_t + \sigma \cdot \mathcal{N}(0, I)$ +3. **Evaluate**: Compute log-likelihood $\log\mathcal{L}(\theta')$ +4. **Accept/Reject**: + - Draw $u \sim \text{Uniform}(0, 1)$ + - If $u < \exp(\log\mathcal{L}(\theta') - \log\mathcal{L}(\theta_t))$: accept $\theta_{t+1} = \theta'$ + - Else: reject $\theta_{t+1} = \theta_t$ +5. **Repeat**: For specified number of iterations +6. **Return**: Chain of samples $\{\theta_0, \theta_1, \ldots, \theta_T\}$ + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `iterations` | 1000 | MCMC iterations per chain | +| `num_chains` | 1 | Number of independent chains | +| `step_size` | 0.1 | Proposal distribution width $\sigma$ | +| `seed` | None | Random seed for reproducibility | + +## Tuning Guidance + +**Step size** controls exploration efficiency: + +- Target acceptance rate: 20-50% (optimal ~23% for high dimensions) +- Too large: low acceptance rate, chain gets stuck +- Too small: high acceptance rate but slow mixing +- Tune by monitoring acceptance ratio + +**Number of chains** aids convergence diagnostics: + +- Multiple chains from different starting points detect convergence issues +- Enables Gelman-Rubin $\hat{R}$ diagnostic +- More chains = more robust inference but higher cost + +**Iterations** should be sufficient for: + +- Burn-in period (discard initial samples) +- Effective sample size for reliable estimates + +## Convergence Diagnostics + +After sampling, assess convergence: + +- **Trace plots**: Visual inspection for stationarity +- **Acceptance rate**: Should be 20-50% +- **Autocorrelation**: Lower is better; high autocorrelation means inefficient sampling +- **$\hat{R}$ statistic**: Should be < 1.1 for all parameters (requires multiple chains) ## When to Use -**Best for:** +**Strengths:** + +- Full posterior characterisation +- Uncertainty quantification (credible intervals) +- Parameter correlation analysis +- Simple and robust + +**Limitations:** + +- Many function evaluations required +- Can be slow for high-dimensional problems +- Requires careful tuning for efficiency +- Does not compute model evidence (use nested sampling) + +## Cost Metric Requirement + +Metropolis-Hastings requires a **negative log-likelihood** cost metric: + +```python +problem = ( + chron.ScalarProblemBuilder() + .with_objective(model_fn) + .with_cost_metric(chron.CostMetric.GaussianNLL) # Required + .build() +) +``` + +## Example + +```python +import chronopt as chron -- Uncertainty quantification -- Confidence intervals -- Posterior exploration +sampler = ( + chron.MetropolisHastings() + .with_iterations(5000) + .with_num_chains(4) + .with_step_size(0.05) + .with_seed(42) +) -**Avoid when:** +result = sampler.run(problem, initial_guess=[1.0, 2.0]) -- Only need point estimate -- Limited computational budget -- Need model comparison (use nested sampling) +# Access samples +samples = result.samples # Shape: (n_chains * iterations, n_params) +``` -## API Reference +## References -See [Samplers API](../../api-reference/python/samplers.md) for usage details (coming soon). +1. Metropolis, N. et al. (1953). "Equation of State Calculations by Fast Computing Machines". *Journal of Chemical Physics*, 21(6), 1087-1092. +2. Hastings, W.K. (1970). "Monte Carlo Sampling Methods Using Markov Chains and Their Applications". *Biometrika*, 57(1), 97-109. +3. Gelman, A. et al. (2013). *Bayesian Data Analysis*. 3rd ed. Chapman & Hall/CRC. ## See Also +- [API Reference](../../api-reference/python/samplers.md) - [Choosing a Sampler](../../guides/choosing-sampler.md) - [Cost Metrics](../../guides/cost-metrics.md) From e75f2db6b592f3360e73511f6a0a0cbd596f6291 Mon Sep 17 00:00:00 2001 From: Brady Planden <55357039+BradyPlanden@users.noreply.github.com> Date: Sun, 25 Jan 2026 12:12:08 +0000 Subject: [PATCH 10/18] Changes package name to `Diffid` (#13) * feat: renames package to 'Diffid' * fix: CI packaging, missing diagram file * fix: move diagram assets to docs/assets folder * fix: add PR write permissions to docs-checks workflow * infra: removes scheduled docs workflow * docs: group dependency in CI, simplify notebook/docs testing suite --- .github/workflows/CI.yml | 6 +- .github/workflows/docs-checks.yml | 202 ---- .github/workflows/docs.yml | 45 +- .github/workflows/release.yml | 6 +- Cargo.lock | 54 +- README.md | 36 +- docs/algorithms/index.md | 4 +- docs/algorithms/optimizers/adam.md | 4 +- docs/algorithms/optimizers/cmaes.md | 4 +- docs/algorithms/optimizers/nelder-mead.md | 4 +- .../samplers/dynamic-nested-sampling.md | 8 +- .../samplers/metropolis-hastings.md | 8 +- docs/api-reference/index.md | 12 +- docs/api-reference/python/builders.md | 32 +- docs/api-reference/python/cost-metrics.md | 24 +- docs/api-reference/python/optimizers.md | 22 +- docs/api-reference/python/results.md | 8 +- docs/api-reference/python/samplers.md | 26 +- docs/api-reference/rust/index.md | 26 +- docs/assets/diffid.drawio | 188 ++++ docs/assets/diffid.svg | 4 + docs/development/architecture.md | 2 +- docs/development/contributing.md | 8 +- docs/development/index.md | 20 +- docs/examples/gallery.md | 30 +- docs/getting-started/concepts.md | 32 +- docs/getting-started/first-ode-fit.md | 20 +- docs/getting-started/index.md | 8 +- docs/getting-started/installation.md | 26 +- docs/getting-started/logistic_fit.png | Bin 0 -> 76253 bytes docs/getting-started/quickstart.md | 12 +- docs/getting-started/rosenbrock_contour.png | Bin 0 -> 561973 bytes docs/guides/custom-solvers.md | 6 +- docs/guides/diffsol-backend.md | 2 +- docs/guides/index.md | 2 +- docs/guides/parallel-execution.md | 2 +- docs/guides/troubleshooting.md | 6 +- docs/index.md | 34 +- docs/tutorials/index.md | 40 +- .../notebooks/01_optimization_basics.ipynb | 113 +-- .../notebooks/02_ode_fitting_diffsol.ipynb | 201 +--- .../notebooks/03_parameter_uncertainty.ipynb | 192 +--- .../notebooks/05_advanced_predator_prey.ipynb | 135 +-- .../06_custom_solver_integration.ipynb | 184 +--- .../notebooks/07_parallel_optimization.ipynb | 387 +------ .../08_advanced_cost_functions.ipynb | 34 +- examples/bicycle_model_diffsol.py | 6 +- examples/bicycle_model_evidence.py | 10 +- examples/bouncy_ball.py | 10 +- examples/bouncy_ball_sampling.py | 8 +- examples/logistic_growth.py | 6 +- examples/model_evidence.py | 8 +- examples/model_evidence_diffsol.py | 6 +- examples/predator_prey/README.md | 12 +- .../predator_prey/predator_prey_diffeqpy.py | 14 +- .../predator_prey/predator_prey_diffrax.py | 16 +- .../predator_prey/predator_prey_diffsol.py | 6 +- examples/python_contour.png | Bin 0 -> 386289 bytes examples/python_contour.py | 8 +- examples/python_problem.py | 6 +- mkdocs.yml | 14 +- pyproject.toml | 4 +- python/Cargo.toml | 10 +- python/src/bin/generate_stubs.rs | 4 +- python/src/builders.rs | 8 +- python/src/chronopt/_chronopt.pyi | 441 -------- python/src/chronopt/sampler.pyi | 83 -- python/src/{chronopt => diffid}/__init__.py | 24 +- python/src/diffid/_diffid.pyi | 956 ++++++++++++++++++ python/src/{chronopt => diffid}/errors.py | 24 +- .../{chronopt => diffid}/plotting/__init__.py | 20 +- .../plotting/__init__.pyi | 0 python/src/{chronopt => diffid}/py.typed | 0 python/src/diffid/sampler.pyi | 353 +++++++ python/src/errors.rs | 6 +- python/src/lib.rs | 26 +- python/src/optimisers.rs | 44 +- python/src/results.rs | 6 +- python/src/samplers.rs | 36 +- rust/Cargo.toml | 8 +- rust/benches/diffsol_benches.rs | 4 +- rust/benches/optimisers_benches.rs | 4 +- rust/benches/samplers_benches.rs | 2 +- rust/src/common.rs | 14 +- rust/src/lib.rs | 2 +- rust/src/optimisers/mod.rs | 12 +- rust/tests/diffsol_optimisation.rs | 2 +- rust/tests/dynamic_nested.rs | 4 +- rust/tests/dynamic_nested_advanced.rs | 4 +- .../test_diffsol_dynamic_nested_parallel.py | 12 +- tests/integration/test_diffsol_sampling.py | 14 +- tests/integration/test_mathematical_suite.py | 16 +- tests/test_docs.py | 285 +----- tests/unit/optimisers/test_adam.py | 18 +- tests/unit/optimisers/test_cmaes.py | 14 +- tests/unit/test_diffsol.py | 34 +- tests/unit/test_dynamic_nested_sampler.py | 64 +- tests/unit/test_optimisation.py | 12 +- tests/unit/test_optimisation_api.py | 16 +- tests/unit/test_python_autodiff.py | 4 +- tests/unit/test_samplers.py | 16 +- tests/unit/test_vector.py | 48 +- uv.lock | 160 +-- 103 files changed, 2330 insertions(+), 2833 deletions(-) delete mode 100644 .github/workflows/docs-checks.yml create mode 100644 docs/assets/diffid.drawio create mode 100644 docs/assets/diffid.svg create mode 100644 docs/getting-started/logistic_fit.png create mode 100644 docs/getting-started/rosenbrock_contour.png create mode 100644 examples/python_contour.png delete mode 100644 python/src/chronopt/_chronopt.pyi delete mode 100644 python/src/chronopt/sampler.pyi rename python/src/{chronopt => diffid}/__init__.py (87%) create mode 100644 python/src/diffid/_diffid.pyi rename python/src/{chronopt => diffid}/errors.py (90%) rename python/src/{chronopt => diffid}/plotting/__init__.py (97%) rename python/src/{chronopt => diffid}/plotting/__init__.pyi (100%) rename python/src/{chronopt => diffid}/py.typed (100%) create mode 100644 python/src/diffid/sampler.pyi diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 1ef20fd..e065f28 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -49,7 +49,7 @@ jobs: uv venv .test-venv source .test-venv/bin/activate uv pip install dist/*.whl --reinstall - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples pytest -v - uses: actions/upload-artifact@v5 @@ -104,7 +104,7 @@ jobs: uv pip install dist/*.whl --reinstall # Install dev dependencies from pyproject.toml using pip interface - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples # Run tests directly pytest -v @@ -132,7 +132,7 @@ jobs: sudo apt-get install -y llvm-18-dev libclang-18-dev libzstd-dev libpolly-18-dev patchelf - name: Generate stubs - run: cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs + run: cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs env: LLVM_SYS_181_PREFIX: /usr/lib/llvm-18 LIBCLANG_PATH: /usr/lib/llvm-18/lib diff --git a/.github/workflows/docs-checks.yml b/.github/workflows/docs-checks.yml deleted file mode 100644 index 7c051a2..0000000 --- a/.github/workflows/docs-checks.yml +++ /dev/null @@ -1,202 +0,0 @@ -name: Documentation Checks - -on: - schedule: - # Run weekly on Monday at 00:00 UTC - - cron: '0 0 * * 1' - workflow_dispatch: # Allow manual triggering - pull_request: - paths: - - 'docs/**' - - 'mkdocs.yml' - - '.github/workflows/docs-checks.yml' - -permissions: - contents: read - -jobs: - link-check: - name: Check Links - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install dependencies - run: | - pip install mkdocs mkdocs-material mkdocstrings[python] \ - mkdocs-git-revision-date-localized-plugin \ - mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions - - - name: Build documentation - run: mkdocs build --strict - - - name: Link Checker - uses: lycheeverse/lychee-action@v2 - with: - # Check all markdown files - args: --verbose --no-progress 'docs/**/*.md' 'site/**/*.html' --exclude-path 'site/tutorials/notebooks' --max-concurrency 10 - # Don't fail on broken links in draft mode - fail: ${{ github.event_name != 'pull_request' }} - env: - GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} - - - name: Create Issue on Failure - if: failure() && github.event_name == 'schedule' - uses: actions/github-script@v7 - with: - script: | - github.rest.issues.create({ - owner: context.repo.owner, - repo: context.repo.repo, - title: '📋 Documentation link check failed', - body: 'Scheduled link check found broken links in documentation.\n\nSee workflow run: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}', - labels: ['documentation', 'automated'] - }) - - test-docs: - name: Documentation Tests - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install uv - uses: astral-sh/setup-uv@v5 - - - name: Install chronopt and dependencies - run: | - uv pip install --system pytest - uv pip install --system mkdocs mkdocs-material mkdocstrings[python] \ - mkdocs-git-revision-date-localized-plugin \ - mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions - # Install chronopt if possible (may fail if rust build needed) - uv pip install --system -e python/ || echo "⚠️ Could not install chronopt (rust build required)" - - - name: Run documentation tests - run: pytest tests/test_docs.py -v - continue-on-error: true # Don't fail if chronopt not installed - - notebook-validation: - name: Validate Notebooks - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install dependencies - run: | - pip install jupyter nbformat nbconvert - - - name: Validate notebook format - run: | - for notebook in docs/tutorials/notebooks/*.ipynb; do - echo "Validating $notebook" - python -m nbformat.validator "$notebook" || echo "⚠️ $notebook validation failed" - done - - - name: Check for execution errors - run: | - # Check notebooks don't have error outputs - for notebook in docs/tutorials/notebooks/*.ipynb; do - if grep -q '"output_type": "error"' "$notebook"; then - echo "❌ $notebook contains error outputs" - exit 1 - fi - done - echo "✅ No error outputs found in notebooks" - - spelling-check: - name: Spelling Check - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Check British English spelling - uses: reviewdog/action-misspell@v1 - with: - github_token: ${{ secrets.github_token }} - locale: "UK" - reporter: github-pr-review - level: warning - filter_mode: diff_context - # Exclude code blocks and technical terms - exclude: | - *.ipynb - *.py - *.rs - - build-preview: - name: Build Preview - runs-on: ubuntu-latest - if: github.event_name == 'pull_request' - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install dependencies - run: | - pip install mkdocs mkdocs-material mkdocstrings[python] \ - mkdocs-git-revision-date-localized-plugin \ - mkdocs-minify-plugin mkdocs-jupyter pymdown-extensions - - - name: Build documentation - run: mkdocs build --strict - - - name: Upload preview artifact - uses: actions/upload-artifact@v4 - with: - name: docs-preview-pr-${{ github.event.pull_request.number }} - path: site/ - retention-days: 7 - - - name: Comment PR with preview info - uses: actions/github-script@v7 - with: - script: | - const comment = `## 📚 Documentation Preview - - Documentation built successfully for this PR! - - **Download Preview**: Check the artifacts section of [this workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}) - - **Build Details**: - - Commit: \`${{ github.sha }}\` - - Built at: ${new Date().toISOString()} - - To view locally: - 1. Download the \`docs-preview-pr-${{ github.event.pull_request.number }}\` artifact - 2. Extract and open \`index.html\` in a browser - - _This preview will be available for 7 days._ - `; - - github.rest.issues.createComment({ - owner: context.repo.owner, - repo: context.repo.repo, - issue_number: context.issue.number, - body: comment - }) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index a7b8239..d2399a7 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -2,19 +2,26 @@ name: Documentation on: push: - branches: - - main + branches: ["main"] + tags: + - '*' pull_request: - branches: - - main workflow_dispatch: +# Prevent concurrent deployments +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +# Default permissions: read-only permissions: - contents: write + contents: read jobs: build-and-deploy: runs-on: ubuntu-latest + permissions: + contents: write steps: - name: Checkout repository uses: actions/checkout@v4 @@ -29,30 +36,24 @@ jobs: - name: Install uv uses: astral-sh/setup-uv@v5 + - name: Cache uv dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/uv + key: ${{ runner.os }}-uv-docs-deploy-${{ hashFiles('.github/workflows/docs.yml') }} + restore-keys: | + ${{ runner.os }}-uv-docs-deploy- + - name: Install dependencies - run: | - uv pip install --system mkdocs - uv pip install --system mkdocs-material - uv pip install --system mkdocstrings[python] - uv pip install --system mkdocs-git-revision-date-localized-plugin - uv pip install --system mkdocs-minify-plugin - uv pip install --system mkdocs-jupyter - uv pip install --system pymdown-extensions + run: uv pip install --system --group docs - name: Build documentation - run: mkdocs build + run: mkdocs build --strict - name: Deploy to GitHub Pages - if: github.event_name == 'push' && github.ref == 'refs/heads/main' + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/') uses: peaceiris/actions-gh-pages@v4 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: ./site cname: false # Set to your custom domain if you have one - - - name: Upload artifact for PR preview - if: github.event_name == 'pull_request' - uses: actions/upload-artifact@v4 - with: - name: docs-preview - path: ./site diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f91c182..2bad492 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -50,7 +50,7 @@ jobs: uv venv .test-venv source .test-venv/bin/activate uv pip install dist/*.whl --reinstall - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples pytest -v - uses: actions/upload-artifact@v5 @@ -106,7 +106,7 @@ jobs: uv pip install dist/*.whl --reinstall # Install dev dependencies from pyproject.toml using pip interface - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples # Run tests directly pytest -v @@ -134,7 +134,7 @@ jobs: sudo apt-get install -y llvm-18-dev libclang-18-dev libzstd-dev libpolly-18-dev patchelf - name: Generate stubs - run: cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs + run: cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs env: LLVM_SYS_181_PREFIX: /usr/lib/llvm-18 LIBCLANG_PATH: /usr/lib/llvm-18/lib diff --git a/Cargo.lock b/Cargo.lock index 426e78a..142e7d1 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -268,33 +268,6 @@ dependencies = [ "windows-link", ] -[[package]] -name = "chronopt" -version = "0.2.0" -dependencies = [ - "criterion", - "diffsol", - "nalgebra", - "proptest", - "rand 0.9.2", - "rand_distr", - "rayon", -] - -[[package]] -name = "chronopt-py" -version = "0.2.0" -dependencies = [ - "chronopt", - "clap 4.5.54", - "nalgebra", - "numpy", - "pyo3", - "pyo3-build-config", - "pyo3-stub-gen", - "pyo3-stub-gen-derive", -] - [[package]] name = "clang-sys" version = "1.8.1" @@ -728,6 +701,33 @@ version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "930c7171c8df9fb1782bdf9b918ed9ed2d33d1d22300abb754f9085bc48bf8e8" +[[package]] +name = "diffid" +version = "0.2.0" +dependencies = [ + "criterion", + "diffsol", + "nalgebra", + "proptest", + "rand 0.9.2", + "rand_distr", + "rayon", +] + +[[package]] +name = "diffid-py" +version = "0.2.0" +dependencies = [ + "clap 4.5.54", + "diffid", + "nalgebra", + "numpy", + "pyo3", + "pyo3-build-config", + "pyo3-stub-gen", + "pyo3-stub-gen-derive", +] + [[package]] name = "diffsl" version = "0.8.3" diff --git a/README.md b/README.md index baecdea..759e859 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@
-# chronopt -[![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fbradyplanden%2Fchronopt%2Fmain%2Fpyproject.toml&label=Python)](https://pypi.org/project/chronopt/) -[![License](https://img.shields.io/github/license/bradyplanden/chronopt?color=blue)](https://github.com/bradyplanden/chronopt/blob/main/LICENSE) -[![Releases](https://img.shields.io/github/v/release/bradyplanden/chronopt?color=gold)](https://github.com/bradyplanden/chronopt/releases) +# diffid +[![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fbradyplanden%2Fdiffid%2Fmain%2Fpyproject.toml&label=Python)](https://pypi.org/project/diffid/) +[![License](https://img.shields.io/github/license/bradyplanden/diffid?color=blue)](https://github.com/bradyplanden/diffid/blob/main/LICENSE) +[![Releases](https://img.shields.io/github/v/release/bradyplanden/diffid?color=gold)](https://github.com/bradyplanden/diffid/releases) -**chron**os-**opt**imum is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. +**diff**erential **id**entification is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems.
@@ -21,14 +21,14 @@ - Flexible integration with state-of-the-art differential solvers, such as [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) -## Why Chronopt? +## Why Diffid? - Optimisation based workflow run the forward simulation thousands of times, a performance improvement on the process can produce results hour or days earlier - The Rust core provides a high-performance inference loop with fewer runtime errors. - Quickly integrated into Python workflows, and later use the rust crate directly for even higher performance. ## Documentation -📚 **[Full Documentation](https://bradyplanden.github.io/chronopt/)** +**[Full Documentation](https://bradyplanden.github.io/diffid/)** Visit the comprehensive documentation for: - Getting started guides and tutorials @@ -38,16 +38,16 @@ Visit the comprehensive documentation for: ## Installation -Chronopt targets Python >= 3.11. Windows builds are currently marked experimental. +Diffid targets Python >= 3.11. Windows builds are currently marked experimental. ```bash -pip install chronopt +pip install diffid # Or with uv -uv pip install chronopt +uv pip install diffid # Optional extras -pip install "chronopt[plotting]" +pip install "diffid[plotting]" ``` ## Quickstart @@ -55,7 +55,7 @@ pip install "chronopt[plotting]" ### ScalarProblem ```python import numpy as np -import chronopt as chron +import diffid def rosenbrock(x): @@ -64,7 +64,7 @@ def rosenbrock(x): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) @@ -81,7 +81,7 @@ print(f"Success: {result.success}") ```python import numpy as np -import chronopt as chron +import diffid # Example diffsol ODE (logistic growth) @@ -96,7 +96,7 @@ observations = np.exp(-1.3 * t) data = np.column_stack((t, observations)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) @@ -104,7 +104,7 @@ builder = ( ) problem = builder.build() -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, [0.5,0.5]) print(result.x) @@ -127,13 +127,13 @@ uv run maturin develop Regenerate `.pyi` stubs after changing the bindings: ```bash -uv run cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +uv run cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs ``` Without `uv`, invoke the generator directly: ```bash -cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs ``` ### Pre-commit hooks diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md index bb25b39..9510652 100644 --- a/docs/algorithms/index.md +++ b/docs/algorithms/index.md @@ -1,6 +1,6 @@ # Algorithms -Detailed documentation for each optimisation and sampling algorithm in Chronopt. +Detailed documentation for each optimisation and sampling algorithm in Diffid. ## Optimisers @@ -114,7 +114,7 @@ All algorithms are implemented in Rust for performance: - **Numerically stable**: Careful floating-point handling - **Well-tested**: Comprehensive test suite -Source code: [rust/src/optimisers/](https://github.com/bradyplanden/chronopt/tree/main/rust/src/optimisers) +Source code: [rust/src/optimisers/](https://github.com/bradyplanden/diffid/tree/main/rust/src/optimisers) ## References diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md index c5e4218..3b79cf0 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimizers/adam.md @@ -111,10 +111,10 @@ The algorithm terminates when any condition is met: ## Example ```python -import chronopt as chron +import diffid as chron optimiser = ( - chron.Adam() + diffid.Adam() .with_max_iter(5000) .with_step_size(0.001) .with_betas(0.9, 0.999) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md index b8d7b8f..5d868ec 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimizers/cmaes.md @@ -108,10 +108,10 @@ The algorithm terminates when any condition is met: ## Example ```python -import chronopt as chron +import diffid as chron optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(500) .with_step_size(0.3) .with_population_size(20) diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimizers/nelder-mead.md index 51badaa..c2623fa 100644 --- a/docs/algorithms/optimizers/nelder-mead.md +++ b/docs/algorithms/optimizers/nelder-mead.md @@ -88,10 +88,10 @@ The algorithm terminates when any of the following conditions is met: ## Example ```python -import chronopt as chron +import diffid as chron optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(5000) .with_step_size(0.1) .with_threshold(1e-8) diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md index f3db770..95e171d 100644 --- a/docs/algorithms/samplers/dynamic-nested-sampling.md +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -120,9 +120,9 @@ Nested sampling requires a **negative log-likelihood** cost metric: ```python problem = ( - chron.ScalarProblemBuilder() + diffid.ScalarProblemBuilder() .with_objective(model_fn) - .with_cost_metric(chron.CostMetric.GaussianNLL) # Required + .with_cost_metric(diffid.CostMetric.GaussianNLL) # Required .build() ) ``` @@ -132,10 +132,10 @@ The objective function should return negative log-likelihood; the algorithm inte ## Example ```python -import chronopt as chron +import diffid as chron sampler = ( - chron.DynamicNestedSampling() + diffid.DynamicNestedSampling() .with_live_points(128) .with_termination_tol(1e-3) .with_seed(42) diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md index 6e961df..594cd18 100644 --- a/docs/algorithms/samplers/metropolis-hastings.md +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -104,9 +104,9 @@ Metropolis-Hastings requires a **negative log-likelihood** cost metric: ```python problem = ( - chron.ScalarProblemBuilder() + diffid.ScalarProblemBuilder() .with_objective(model_fn) - .with_cost_metric(chron.CostMetric.GaussianNLL) # Required + .with_cost_metric(diffid.CostMetric.GaussianNLL) # Required .build() ) ``` @@ -114,10 +114,10 @@ problem = ( ## Example ```python -import chronopt as chron +import diffid as chron sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_iterations(5000) .with_num_chains(4) .with_step_size(0.05) diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md index 5383f0a..99997c2 100644 --- a/docs/api-reference/index.md +++ b/docs/api-reference/index.md @@ -1,10 +1,10 @@ # API Reference -Complete API documentation for Chronopt's Python and Rust interfaces. +Complete API documentation for Diffid's Python and Rust interfaces. ## Python API -Chronopt provides a comprehensive Python API with full type hints and automatic documentation. +Diffid provides a comprehensive Python API with full type hints and automatic documentation.
@@ -54,12 +54,12 @@ Chronopt provides a comprehensive Python API with full type hints and automatic The Rust core provides high-performance implementations of all algorithms. -[:octicons-arrow-right-24: Rust Documentation on docs.rs](https://docs.rs/chronopt/latest/chronopt/) +[:octicons-arrow-right-24: Rust Documentation on docs.rs](https://docs.rs/diffid/latest/diffid/) ## Module Structure ``` -chronopt/ +diffid/ ├── ScalarBuilder # Direct function optimisation ├── DiffsolBuilder # ODE fitting with DiffSL/Diffsol ├── VectorBuilder # Custom solver integration @@ -80,7 +80,7 @@ chronopt/ All Python functions include comprehensive type hints: ```python -from chronopt import ScalarBuilder, CMAES, OptimisationResults +from diffid import ScalarBuilder, CMAES, OptimisationResults import numpy.typing as npt def optimize_function( @@ -106,7 +106,7 @@ All builders maintain parameter order based on the sequence of `.with_parameter( ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_parameter("x", 1.0) # Index 0 .with_parameter("y", 2.0) # Index 1 ) diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md index c0a7833..80a78db 100644 --- a/docs/api-reference/python/builders.md +++ b/docs/api-reference/python/builders.md @@ -12,7 +12,7 @@ Builders provide a fluent API for constructing optimisation problems. Choose the ## ScalarBuilder -::: chronopt.ScalarBuilder +::: diffid.ScalarBuilder options: show_root_heading: true show_source: false @@ -27,13 +27,13 @@ Builders provide a fluent API for constructing optimisation problems. Choose the ```python import numpy as np -import chronopt as chron +import diffid as chron def rosenbrock(x): return np.asarray([(1 - x[0])**2 + 100*(x[1] - x[0]**2)**2]) builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) @@ -52,7 +52,7 @@ result = problem.optimise() ## DiffsolBuilder -::: chronopt.DiffsolBuilder +::: diffid.DiffsolBuilder options: show_root_heading: true show_source: false @@ -70,7 +70,7 @@ result = problem.optimise() ```python import numpy as np -import chronopt as chron +import diffid as chron dsl = """ in { r = 1, k = 1 } @@ -83,14 +83,14 @@ observations = np.exp(-1.3 * t) data = np.column_stack((t, observations)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) .with_backend("dense") ) problem = builder.build() -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, [0.5, 0.5]) ``` @@ -113,14 +113,14 @@ out_i { state1, state2 } # Optional: output variables ### When to Use - Fitting ODE parameters to time-series data -- Using Chronopt's built-in high-performance solver +- Using Diffid's built-in high-performance solver - Models expressible in DiffSL syntax --- ## VectorBuilder -::: chronopt.VectorBuilder +::: diffid.VectorBuilder options: show_root_heading: true show_source: false @@ -136,7 +136,7 @@ out_i { state1, state2 } # Optional: output variables ```python import numpy as np -import chronopt as chron +import diffid as chron def custom_solver(params): """Your custom ODE solver (e.g., using JAX/Diffrax).""" @@ -150,7 +150,7 @@ observations = ... # Your experimental data data = np.column_stack((t, observations)) builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(custom_solver) .with_data(data) .with_parameter("alpha", 1.0) @@ -181,7 +181,7 @@ See the [Custom Solvers Guide](../../guides/custom-solvers.md) for examples with ## Problem -::: chronopt.Problem +::: diffid.Problem options: show_root_heading: true show_source: false @@ -208,11 +208,11 @@ Builders use a fluent interface - chain methods in any order: ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(func) .with_parameter("x", 1.0) .with_parameter("y", 2.0) - .with_cost_metric(chron.RMSE()) + .with_cost_metric(diffid.RMSE()) ) ``` @@ -221,7 +221,7 @@ builder = ( Builders are immutable - each method returns a new builder: ```python -base = chron.ScalarBuilder().with_objective(func) +base = diffid.ScalarBuilder().with_objective(func) problem1 = base.with_parameter("x", 1.0).build() problem2 = base.with_parameter("x", 2.0).build() # Different initial guess @@ -233,7 +233,7 @@ Parameters are indexed in the order they're added: ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_parameter("y", 2.0) # Index 0 .with_parameter("x", 1.0) # Index 1 ) diff --git a/docs/api-reference/python/cost-metrics.md b/docs/api-reference/python/cost-metrics.md index fdebf87..bda1cae 100644 --- a/docs/api-reference/python/cost-metrics.md +++ b/docs/api-reference/python/cost-metrics.md @@ -12,7 +12,7 @@ Cost metrics define how model predictions are compared to observations. They det ## CostMetric -::: chronopt.CostMetric +::: diffid.CostMetric options: show_root_heading: true show_source: false @@ -28,15 +28,15 @@ $$\text{SSE} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ ### Example Usage ```python -import chronopt as chron +import diffid as chron builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) # SSE is used by default, but can be explicit: - # .with_cost_metric(chron.SSE()) + # .with_cost_metric(diffid.SSE()) ) ``` @@ -71,14 +71,14 @@ $$\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$ ### Example Usage ```python -import chronopt as chron +import diffid as chron builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) - .with_cost_metric(chron.RMSE()) + .with_cost_metric(diffid.RMSE()) ) ``` @@ -114,18 +114,18 @@ where $\sigma^2$ is estimated from residuals. ### Example Usage ```python -import chronopt as chron +import diffid as chron builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) - .with_cost_metric(chron.GaussianNLL()) + .with_cost_metric(diffid.GaussianNLL()) ) # Use with MCMC sampling for Bayesian inference -sampler = chron.MetropolisHastings().with_max_iter(10000) +sampler = diffid.MetropolisHastings().with_max_iter(10000) result = sampler.run(problem, initial_guess) ``` @@ -200,7 +200,7 @@ To implement a custom cost metric: 2. Expose through Python bindings 3. Rebuild the package -See the [Development Guide](../../development/architecture.md) for details on extending Chronopt. +See the [Development Guide](../../development/architecture.md) for details on extending Diffid. --- diff --git a/docs/api-reference/python/optimizers.md b/docs/api-reference/python/optimizers.md index a12b22f..65e73ae 100644 --- a/docs/api-reference/python/optimizers.md +++ b/docs/api-reference/python/optimizers.md @@ -12,7 +12,7 @@ Optimisation algorithms for finding parameter values that minimise the objective ## Nelder-Mead -::: chronopt.NelderMead +::: diffid.NelderMead options: show_root_heading: true show_source: false @@ -20,11 +20,11 @@ Optimisation algorithms for finding parameter values that minimise the objective ### Example Usage ```python -import chronopt as chron +import diffid as chron # Create optimiser with custom settings optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(5000) .with_step_size(0.1) .with_threshold(1e-6) @@ -75,7 +75,7 @@ See the [Nelder-Mead Algorithm Guide](../../algorithms/optimizers/nelder-mead.md ## CMA-ES -::: chronopt.CMAES +::: diffid.CMAES options: show_root_heading: true show_source: false @@ -83,11 +83,11 @@ See the [Nelder-Mead Algorithm Guide](../../algorithms/optimizers/nelder-mead.md ### Example Usage ```python -import chronopt as chron +import diffid as chron # Create CMA-ES optimiser optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(1000) .with_step_size(0.5) .with_population_size(20) @@ -148,7 +148,7 @@ See the [CMA-ES Algorithm Guide](../../algorithms/optimizers/cmaes.md) for more ## Adam -::: chronopt.Adam +::: diffid.Adam options: show_root_heading: true show_source: false @@ -156,11 +156,11 @@ See the [CMA-ES Algorithm Guide](../../algorithms/optimizers/cmaes.md) for more ### Example Usage ```python -import chronopt as chron +import diffid as chron # Create Adam optimiser optimiser = ( - chron.Adam() + diffid.Adam() .with_max_iter(5000) .with_step_size(0.01) # Learning rate .with_betas(0.9, 0.999) @@ -236,7 +236,7 @@ result = problem.optimise() # Uses Nelder-Mead with defaults ```python optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(10000) # Maximum iterations .with_threshold(1e-8) # Objective threshold .with_patience(300.0) # Patience in seconds @@ -253,7 +253,7 @@ The optimiser stops when: For stochastic optimisers (CMA-ES), set a seed: ```python -optimiser = chron.CMAES().with_seed(42) +optimiser = diffid.CMAES().with_seed(42) result1 = optimiser.run(problem, [0.0, 0.0]) result2 = optimiser.run(problem, [0.0, 0.0]) # result1 == result2 (same random sequence) diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md index 557a05c..926bc60 100644 --- a/docs/api-reference/python/results.md +++ b/docs/api-reference/python/results.md @@ -4,7 +4,7 @@ Result objects returned by optimisers and samplers containing optimal parameters ## OptimisationResults -::: chronopt.OptimisationResults +::: diffid.OptimisationResults options: show_root_heading: true show_source: false @@ -14,7 +14,7 @@ All optimisers return an `OptimisationResults` object with the following attribu ### Example Usage ```python -import chronopt as chron +import diffid as chron problem = builder.build() result = problem.optimise() @@ -46,7 +46,7 @@ Parameters are ordered according to `.with_parameter()` calls: ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_parameter("alpha", 1.0) # result.x[0] .with_parameter("beta", 2.0) # result.x[1] ) @@ -172,7 +172,7 @@ Results don't store parameter names. Track them manually if needed: ```python param_names = ["alpha", "beta", "gamma"] -builder = chron.ScalarBuilder().with_objective(func) +builder = diffid.ScalarBuilder().with_objective(func) for name in param_names: builder = builder.with_parameter(name, 1.0) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md index bd0b20a..9c271e2 100644 --- a/docs/api-reference/python/samplers.md +++ b/docs/api-reference/python/samplers.md @@ -23,11 +23,11 @@ MCMC sampling for exploring parameter posterior distributions. ### Planned API ```python -import chronopt as chron +import diffid as chron # Will be available in a future release sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_max_iter(10000) .with_step_size(0.1) .with_burn_in(1000) @@ -72,11 +72,11 @@ Nested sampling for calculating model evidence (marginal likelihood) for model c ### Planned API ```python -import chronopt as chron +import diffid as chron # Will be available in a future release sampler = ( - chron.DynamicNestedSampling() + diffid.DynamicNestedSampling() .with_max_iter(5000) .with_n_live_points(500) .with_seed(42) @@ -147,11 +147,11 @@ graph TD ```python # Required for samplers builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) - .with_cost_metric(chron.GaussianNLL()) # Required! + .with_cost_metric(diffid.GaussianNLL()) # Required! ) ``` @@ -164,26 +164,26 @@ SSE and RMSE cannot be used with samplers as they lack probabilistic interpretat Typical workflow: optimise first, then sample for uncertainty: ```python -import chronopt as chron +import diffid as chron # 1. Build problem with GaussianNLL builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) - .with_cost_metric(chron.GaussianNLL()) + .with_cost_metric(diffid.GaussianNLL()) ) problem = builder.build() # 2. Find MAP estimate with optimiser -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) opt_result = optimiser.run(problem, [1.0]) print(f"MAP estimate: {opt_result.x}") # 3. Sample around MAP for uncertainty (future API) -# sampler = chron.MetropolisHastings().with_max_iter(10000) +# sampler = diffid.MetropolisHastings().with_max_iter(10000) # sample_result = sampler.run(problem, opt_result.x) # print(f"Posterior mean: {sample_result.samples.mean(axis=0)}") # print(f"Posterior std: {sample_result.samples.std(axis=0)}") @@ -205,8 +205,8 @@ Once samplers are available, see: Track sampler implementation progress: -- [GitHub Issue #XXX](https://github.com/bradyplanden/chronopt) - Metropolis-Hastings -- [GitHub Issue #XXX](https://github.com/bradyplanden/chronopt) - Dynamic Nested Sampling +- [GitHub Issue #XXX](https://github.com/bradyplanden/diffid) - Metropolis-Hastings +- [GitHub Issue #XXX](https://github.com/bradyplanden/diffid) - Dynamic Nested Sampling --- diff --git a/docs/api-reference/rust/index.md b/docs/api-reference/rust/index.md index f3d5a7a..09d9e1a 100644 --- a/docs/api-reference/rust/index.md +++ b/docs/api-reference/rust/index.md @@ -1,12 +1,12 @@ # Rust API Documentation -Chronopt's Rust core provides high-performance implementations of all optimisation and sampling algorithms. +Diffid's Rust core provides high-performance implementations of all optimisation and sampling algorithms. ## Official Documentation The complete Rust API documentation is hosted on docs.rs: -**[:octicons-arrow-right-24: Chronopt Rust Documentation on docs.rs](https://docs.rs/chronopt/latest/chronopt/)** +**[:octicons-arrow-right-24: Diffid Rust Documentation on docs.rs](https://docs.rs/diffid/latest/diffid/)** ## When to Use the Rust API @@ -16,14 +16,14 @@ Consider using the Rust crate directly when: - Building **Rust-native applications** - Need **zero-copy** data handling - Deploying to **embedded systems** or **constrained environments** -- Building **custom tooling** around Chronopt +- Building **custom tooling** around Diffid For most users, the Python API provides excellent performance with easier integration. ## Crate Structure ``` -chronopt/ +diffid/ ├── builders/ # Problem builders (ScalarBuilder, DiffsolBuilder, etc.) ├── optimisers/ # Optimisation algorithms │ ├── nelder_mead/ # Nelder-Mead simplex @@ -38,7 +38,7 @@ chronopt/ ## Quick Example ```rust -use chronopt::prelude::*; +use diffid::prelude::*; use ndarray::array; // Define objective function @@ -64,13 +64,13 @@ fn main() { } ``` -## Adding Chronopt to Your Project +## Adding Diffid to Your Project Add to your `Cargo.toml`: ```toml [dependencies] -chronopt = "0.2" +diffid = "0.2" ndarray = "0.15" ``` @@ -78,7 +78,7 @@ For ODE support with DiffSL: ```toml [dependencies] -chronopt = { version = "0.2", features = ["diffsol"] } +diffid = { version = "0.2", features = ["diffsol"] } ``` ## Key Rust Features @@ -149,7 +149,7 @@ Key modules (click through on docs.rs for full details): Problem construction with fluent API. ```rust -pub use chronopt::builders::{ScalarBuilder, DiffsolBuilder, VectorBuilder}; +pub use diffid::builders::{ScalarBuilder, DiffsolBuilder, VectorBuilder}; ``` ### `optimisers` @@ -157,7 +157,7 @@ pub use chronopt::builders::{ScalarBuilder, DiffsolBuilder, VectorBuilder}; Optimisation algorithms. ```rust -pub use chronopt::optimisers::{NelderMead, CMAES, Adam}; +pub use diffid::optimisers::{NelderMead, CMAES, Adam}; ``` ### `cost` @@ -165,7 +165,7 @@ pub use chronopt::optimisers::{NelderMead, CMAES, Adam}; Cost metrics for objective functions. ```rust -pub use chronopt::cost::{CostMetric, SSE, RMSE, GaussianNLL}; +pub use diffid::cost::{CostMetric, SSE, RMSE, GaussianNLL}; ``` ### `problem` @@ -173,7 +173,7 @@ pub use chronopt::cost::{CostMetric, SSE, RMSE, GaussianNLL}; Problem types and evaluation. ```rust -pub use chronopt::problem::{Problem, ScalarProblem, VectorProblem}; +pub use diffid::problem::{Problem, ScalarProblem, VectorProblem}; ``` ## Performance Tips @@ -194,7 +194,7 @@ cargo run --example rosenbrock cargo run --example ode_fitting ``` -Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/chronopt/tree/main/rust/examples) +Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/diffid/tree/main/rust/examples) ## See Also diff --git a/docs/assets/diffid.drawio b/docs/assets/diffid.drawio new file mode 100644 index 0000000..3882c4f --- /dev/null +++ b/docs/assets/diffid.drawio @@ -0,0 +1,188 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/assets/diffid.svg b/docs/assets/diffid.svg new file mode 100644 index 0000000..ef51290 --- /dev/null +++ b/docs/assets/diffid.svg @@ -0,0 +1,4 @@ + + + +
Conventional Approach
Python (interpreted)
Problem Definition
Optimisation Loop
(for i in range(n):)
Sampler Loop
(MCMC iterations)
repeated FFI calls (N iterations)
C / Fortran Bindings (fast)
Forward Model
(single evaluation)
FFI Boundary
⚠ Bottleneck: Python loop overhead per iteration
Configuration Approach
Python (configuration only)
builder = DiffsolBuilder()
  .with_diffsl(model_code)
  .with_data(observations)
  .with_parameter("k", ..)
problem = builder.build()
Problem definition
(declarative)
result = problem.optimise()
Single call,
returns results
One-time handoff
FFI Boundary (crossed once)
Rust Core (compiled, fast)
Optimisation / Sampling Loop
Optimisers
Samplers
Cost
Metrics
Objective
+ Gradient
Forward Model
(DiffSL/Diffsol)
Parallel Batch
updated parameters
 \ No newline at end of file diff --git a/docs/development/architecture.md b/docs/development/architecture.md index 9576393..6c9f9df 100644 --- a/docs/development/architecture.md +++ b/docs/development/architecture.md @@ -3,7 +3,7 @@ ## High-Level Overview
- ![Paradigm Comparison](../chronopt.drawio.svg){ width="100%" } + ![Paradigm Comparison](../assets/diffid.svg){ width="100%" }
## Key Design Patterns diff --git a/docs/development/contributing.md b/docs/development/contributing.md index 1fe3fce..c28c44e 100644 --- a/docs/development/contributing.md +++ b/docs/development/contributing.md @@ -1,4 +1,4 @@ -# Contributing to Chronopt +# Contributing to Diffid !!! info "Coming Soon" Detailed contributing guidelines are being written. @@ -7,8 +7,8 @@ ```bash # Fork and clone -git clone https://github.com/YOUR_USERNAME/chronopt.git -cd chronopt +git clone https://github.com/YOUR_USERNAME/diffid.git +cd diffid # Set up environment uv sync @@ -32,4 +32,4 @@ cargo test ## See Also - [Architecture](architecture.md) -- [GitHub Issues](https://github.com/bradyplanden/chronopt/issues) +- [GitHub Issues](https://github.com/bradyplanden/diffid/issues) diff --git a/docs/development/index.md b/docs/development/index.md index 67ed534..72422d6 100644 --- a/docs/development/index.md +++ b/docs/development/index.md @@ -1,6 +1,6 @@ # Development -Resources for contributors and developers working with Chronopt. +Resources for contributors and developers working with Diffid. ## Getting Started with Development @@ -18,7 +18,7 @@ Resources for contributors and developers working with Chronopt. --- - Understanding Chronopt's Rust core and PyO3 bindings design. + Understanding Diffid's Rust core and PyO3 bindings design. [:octicons-arrow-right-24: Architecture Overview](architecture.md) @@ -28,8 +28,8 @@ Resources for contributors and developers working with Chronopt. ```bash # Clone the repository -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt +git clone https://github.com/bradyplanden/diffid.git +cd diffid # Create Python environment uv sync @@ -72,7 +72,7 @@ cargo test && uv run pytest -v If you modified Python bindings: ```bash -uv run cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +uv run cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs ``` ### 5. Format and Lint @@ -90,7 +90,7 @@ uv run ruff format . ## Project Structure ``` -chronopt/ +diffid/ ├── rust/ # Rust core implementation │ ├── src/ │ │ ├── builders/ # Problem builders @@ -101,10 +101,10 @@ chronopt/ │ ├── Cargo.toml │ └── tests/ # Rust tests ├── python/ # Python bindings -│ ├── src/chronopt/ +│ ├── src/diffid/ │ │ ├── __init__.py -│ │ └── _chronopt.pyi # Generated type stubs -│ └── chronopt/ # PyO3 bindings source +│ │ └── _diffid.pyi # Generated type stubs +│ └── diffid/ # PyO3 bindings source ├── examples/ # Example scripts ├── tests/ # Python tests ├── docs/ # Documentation (this site) @@ -144,7 +144,7 @@ Python integration tests in `tests/`: ```python def test_optimisation(): - builder = chron.ScalarBuilder().with_objective(func) + builder = diffid.ScalarBuilder().with_objective(func) # ... assert result.success ``` diff --git a/docs/examples/gallery.md b/docs/examples/gallery.md index 42dc673..f59d7ec 100644 --- a/docs/examples/gallery.md +++ b/docs/examples/gallery.md @@ -1,9 +1,9 @@ # Examples Gallery -Visual gallery of Chronopt applications and use cases. +Visual gallery of Diffid applications and use cases. !!! info "Gallery Under Construction" - This gallery is being populated with examples. Check the [examples directory](https://github.com/bradyplanden/chronopt/tree/main/examples) for current code. + This gallery is being populated with examples. Check the [examples directory](https://github.com/bradyplanden/diffid/tree/main/examples) for current code. ## Available Examples @@ -14,8 +14,8 @@ Classic 2D optimisation test problem. **Files:** -- [python_problem.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_problem.py) -- [python_contour.py](https://github.com/bradyplanden/chronopt/blob/main/examples/python_contour.py) +- [python_problem.py](https://github.com/bradyplanden/diffid/blob/main/examples/python_problem.py) +- [python_contour.py](https://github.com/bradyplanden/diffid/blob/main/examples/python_contour.py) **Topics:** ScalarBuilder, contour plots, optimiser comparison @@ -26,7 +26,7 @@ Classic 2D optimisation test problem. #### Logistic Growth Single-variable ODE with DiffSL. -**File:** [logistic_growth.py](https://github.com/bradyplanden/chronopt/blob/main/examples/logistic_growth.py) +**File:** [logistic_growth.py](https://github.com/bradyplanden/diffid/blob/main/examples/logistic_growth.py) **Topics:** DiffsolBuilder, DiffSL syntax, data fitting @@ -37,8 +37,8 @@ Physics-based model with event handling. **Files:** -- [bouncy_ball.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball.py) -- [bouncy_ball_sampling.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bouncy_ball_sampling.py) +- [bouncy_ball.py](https://github.com/bradyplanden/diffid/blob/main/examples/bouncy_ball.py) +- [bouncy_ball_sampling.py](https://github.com/bradyplanden/diffid/blob/main/examples/bouncy_ball_sampling.py) **Topics:** Event detection, parameter uncertainty, MCMC @@ -51,8 +51,8 @@ Comparing different bicycle dynamics formulations. **Files:** -- [bicycle_model_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_diffsol.py) -- [bicycle_model_evidence.py](https://github.com/bradyplanden/chronopt/blob/main/examples/bicycle_model_evidence.py) +- [bicycle_model_diffsol.py](https://github.com/bradyplanden/diffid/blob/main/examples/bicycle_model_diffsol.py) +- [bicycle_model_evidence.py](https://github.com/bradyplanden/diffid/blob/main/examples/bicycle_model_evidence.py) **Topics:** Model selection, evidence calculation, Bayes factors @@ -65,9 +65,9 @@ Lotka-Volterra equations with multiple solver backends. **Files:** -- [predator_prey_diffsol.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffsol.py) -- [predator_prey_diffrax.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffrax.py) -- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) +- [predator_prey_diffsol.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffsol.py) +- [predator_prey_diffrax.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) **Topics:** VectorBuilder, JAX/Diffrax, Julia/DifferentialEquations.jl, performance comparison @@ -78,14 +78,14 @@ Lotka-Volterra equations with multiple solver backends. Clone the repository: ```bash -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt +git clone https://github.com/bradyplanden/diffid.git +cd diffid ``` Install dependencies: ```bash -pip install chronopt matplotlib +pip install diffid matplotlib ``` Run an example: diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md index 9e559e2..78624ee 100644 --- a/docs/getting-started/concepts.md +++ b/docs/getting-started/concepts.md @@ -1,14 +1,14 @@ # Core Concepts -This guide explains the fundamental concepts and patterns in Chronopt. +This guide explains the fundamental concepts and patterns in Diffid. ## The Builder Pattern -Chronopt uses the **builder pattern** for constructing problems. This provides a fluent, chainable API for configuration: +Diffid uses the **builder pattern** for constructing problems. This provides a fluent, chainable API for configuration: ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(my_function) .with_parameter("x", 1.0) .with_parameter("y", 2.0) @@ -25,7 +25,7 @@ problem = builder.build() ## Problem Types -Chronopt provides different builders for different problem types: +Diffid provides different builders for different problem types: ### ScalarBuilder @@ -36,7 +36,7 @@ def objective(x): return np.asarray([x[0]**2 + x[1]**2]) problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(objective) .with_parameter("x", 0.0) .with_parameter("y", 0.0) @@ -62,7 +62,7 @@ F_i { (r * y) * (1 - (y / k)) } """ problem = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) @@ -88,7 +88,7 @@ def solve_ode(params): return predictions problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(solve_ode) .with_data(data) .with_parameter("alpha", 1.0) @@ -111,7 +111,7 @@ Parameters are the decision variables you want to optimise: ```python builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_parameter("x", initial_value=1.0) # Name and initial guess .with_parameter("y", initial_value=-1.0) ) @@ -125,7 +125,7 @@ builder = ( ## Optimisers vs Samplers -Chronopt provides two types of algorithms: +Diffid provides two types of algorithms: ### Optimisers: Finding the Best Solution @@ -144,7 +144,7 @@ Chronopt provides two types of algorithms: result = problem.optimise() # Specific optimiser -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, initial_guess) ``` @@ -162,7 +162,7 @@ result = optimiser.run(problem, initial_guess) **Usage:** ```python -sampler = chron.MetropolisHastings().with_max_iter(10000) +sampler = diffid.MetropolisHastings().with_max_iter(10000) result = sampler.run(problem, initial_guess) # Result contains samples, not a single optimum @@ -184,10 +184,10 @@ See [Choosing an Optimiser](../guides/choosing-optimiser.md) and [Choosing a Sam ## The Ask/Tell Pattern -For advanced use cases, Chronopt supports the **ask/tell pattern** for manual control of the optimisation loop: +For advanced use cases, Diffid supports the **ask/tell pattern** for manual control of the optimisation loop: ```python -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) # Ask for candidates candidates = optimiser.ask(n_candidates=10) @@ -219,7 +219,7 @@ result = optimiser.get_result() Cost metrics define how model predictions are compared to observations: ```python -from chronopt import SSE, RMSE, GaussianNLL +from diffid import SSE, RMSE, GaussianNLL # Sum of squared errors (default) builder = builder.with_cost_metric(SSE()) @@ -278,7 +278,7 @@ print(result.samples) # Posterior samples ## Parallelisation -Chronopt automatically parallelises where possible: +Diffid automatically parallelises where possible: - **DiffsolBuilder**: Multi-threaded ODE solving - **CMA-ES**: Parallel candidate evaluation @@ -291,7 +291,7 @@ Control parallelism: builder = builder.with_max_threads(4) # Population size for CMA-ES (larger = more parallel work) -optimiser = chron.CMAES().with_population_size(20) +optimiser = diffid.CMAES().with_population_size(20) ``` See the [Parallel Execution Guide](../guides/parallel-execution.md) for details. diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md index 3fffa32..ce2fd7c 100644 --- a/docs/getting-started/first-ode-fit.md +++ b/docs/getting-started/first-ode-fit.md @@ -1,6 +1,6 @@ # First ODE Fit -This tutorial demonstrates how to fit ordinary differential equations (ODEs) to data using Chronopt's DiffSL integration with the Diffsol solver. +This tutorial demonstrates how to fit ordinary differential equations (ODEs) to data using Diffid's DiffSL integration with the Diffsol solver. ## The Problem: Logistic Growth @@ -14,7 +14,7 @@ where: $r$ is the growth rate, $k$ is the carrying capacity, and $y$ is the popu ```python import numpy as np -import chronopt as chron +import diffid as chron # Define the ODE model in DiffSL syntax dsl = """ @@ -37,7 +37,7 @@ data = np.column_stack((t, observations)) # Build the problem builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("r", 0.5) # Initial guess for growth rate @@ -47,7 +47,7 @@ builder = ( problem = builder.build() # Run optimisation with CMA-ES -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, [0.5, 1.0]) # Display results @@ -70,7 +70,7 @@ F_i { (r * y) * (1 - (y / k)) } # Right-hand side of dy/dt = ... ## Data Format -Chronopt expects data as a 2D NumPy array where: +Diffid expects data as a 2D NumPy array where: - **First column**: Time points - **Remaining columns**: Observed values for each variable @@ -158,14 +158,14 @@ F_i { k * sin(t) - y } ## Cost Metrics -By default, Chronopt uses sum of squared errors (SSE). You can specify different cost metrics as shown below. See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. +By default, Diffid uses sum of squared errors (SSE). You can specify different cost metrics as shown below. See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. ```python -from chronopt import GaussianNLL, RMSE +from diffid import GaussianNLL, RMSE # Use Gaussian negative log-likelihood builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) @@ -191,7 +191,7 @@ result = problem.optimise() # Uses Nelder-Mead ### CMA-ES ```python -optimiser = chron.CMAES().with_max_iter(1000).with_step_size(0.5) +optimiser = diffid.CMAES().with_max_iter(1000).with_step_size(0.5) result = optimiser.run(problem, initial_guess) ``` @@ -200,7 +200,7 @@ result = optimiser.run(problem, initial_guess) ### Adam ```python -optimiser = chron.Adam().with_max_iter(1000).with_step_size(0.01) +optimiser = diffid.Adam().with_max_iter(1000).with_step_size(0.01) result = optimiser.run(problem, initial_guess) ``` diff --git a/docs/getting-started/index.md b/docs/getting-started/index.md index e7e8740..e3280eb 100644 --- a/docs/getting-started/index.md +++ b/docs/getting-started/index.md @@ -1,6 +1,6 @@ -# Getting Started with Chronopt +# Getting Started with Diffid -Welcome to Chronopt! This section will help you get up and running with time-series inference and optimisation. +Welcome to Diffid! This section will help you get up and running with time-series inference and optimisation. ## Learning Path @@ -22,7 +22,7 @@ We recommend following this sequence: By the end of this section, you will be able to: -- Install Chronopt on your platform +- Install Diffid on your platform - Create and solve scalar optimisation problems - Fit differential equations to experimental data - Understand the builder pattern and problem types @@ -34,6 +34,6 @@ If you encounter issues: 1. Check the [Troubleshooting](../guides/troubleshooting.md) guide 2. Browse the [examples gallery](../examples/gallery.md) -3. Open an issue on [GitHub](https://github.com/bradyplanden/chronopt/issues) +3. Open an issue on [GitHub](https://github.com/bradyplanden/diffid/issues) Ready to begin? Start with [Installation](installation.md). diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 4a645be..59e8ff3 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,6 +1,6 @@ # Installation -Chronopt is available as a Python package with pre-built wheels for most platforms. +Diffid is available as a Python package with pre-built wheels for most platforms. ## Installation Methods @@ -9,44 +9,44 @@ Chronopt is available as a Python package with pre-built wheels for most platfor [uv](https://docs.astral.sh/uv/) is a fast Python package installer and resolver. ```bash - uv pip install chronopt + uv pip install diffid ``` === "pip" ```bash - pip install chronopt + pip install diffid ``` ### Optional Dependencies -Chronopt has optional plotting support via matplotlib: +Diffid has optional plotting support via matplotlib: === "uv" ```bash - uv pip install "chronopt[plotting]" + uv pip install "diffid[plotting]" ``` === "pip" ```bash - pip install "chronopt[plotting]" + pip install "diffid[plotting]" ``` ## Verifying Installation -After installation, verify that Chronopt is working correctly: +After installation, verify that Diffid is working correctly: ```python -import chronopt as chron +import diffid import numpy as np # Simple test def test_func(x): return np.asarray([(x[0] - 1.0) ** 2]) -builder = chron.ScalarBuilder().with_objective(test_func).with_parameter("x", 0.0) +builder = diffid.ScalarBuilder().with_objective(test_func).with_parameter("x", 0.0) problem = builder.build() result = problem.optimise() @@ -92,8 +92,8 @@ If you need to build from source (for development or if pre-built wheels aren't ```bash # Clone the repository -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt +git clone https://github.com/bradyplanden/diffid.git +cd diffid # Create Python environment uv sync @@ -104,9 +104,9 @@ uv run maturin develop # Run tests uv run pytest -v ``` - + For additional troubleshooting, see the [Troubleshooting Guide](../guides/troubleshooting.md). ## Next Steps -Now that Chronopt is installed, proceed to the [5-Minute Quickstart](quickstart.md) to run your first optimisation. +Now that Diffid is installed, proceed to the [5-Minute Quickstart](quickstart.md) to run your first optimisation. diff --git a/docs/getting-started/logistic_fit.png b/docs/getting-started/logistic_fit.png new file mode 100644 index 0000000000000000000000000000000000000000..bc18c80d79ca3f92093ca358ee80465162776b50 GIT binary patch literal 76253 zcmd?RXH-*N*EWg;y#)~!0R;gS1?jgaMY@85NRbj+C?XS9pL4D z=M>D<&)?k}dQtwuzw#=wr|t&?`1q?SC_w&mhrG9+n}SiXxFs;k0UrZfe+~}8qwIgX z+%me%Id*Yy+|avX8Js#dv_BX;F;Kq%elu~J?0ZsO-S=o?q>jyvQ#!|81$H|pb;eeM zVf&XK2^j9*;Cc))%;YJ(#pF7Cf8f&NxWnekCtmlz|0WLeYSB$xcIddEXyqj-S|Zo9 zH5@qo2GI`xG$t<6P~P#88W>eKQUt7J`_f4&tHfRY``am>{W~8|UC;ZU&-?xv1Xj6y zJ=?WAXy+rxod;h;c0NBi{$F4H|K{yFgZ}0A?X)(P;kja}J=O!%y+rDJrtnce7Ur0; zUo`jwJ}BEXRc8N5p9f22eNGY86~a_e4i1C6d*^{oK)m9-7k8^rL2OUuclhfXpOLE$ zArlG7YJtcn2Tx@6^jNyOxp`L7aNQRgPJSdqD~(as7%RmP=73vTU;RAX;_Pi)O2b$u z3G0bht0^or2?+@?Ciiphx}>($dNC!Yg$Sixz6Wc+-tNS~@gVqZ7g?RX^Ik2F5AdbJ zGdcw09|hL&s9cIdLZZSPG5F~a+H88MbKSnQK%(thA7Qh%v~_n=_{Lg(X66+c>%su2 zKefHrG<=aSEp#ToVr!F?c3ZX6_Q&Q5VavfS)%S_N*=fa+jcyCE+l zGuydgcnOLJ@7aII%O7!fh}f>lJt$69$OIPiOn-YHp23~MS6ySZslP_*;1eN)wHcYV zcNcVL`!GSZ*SRUC+8Y76%|p_I#({xY3Swm_$7k*9ezBhF6-vsnhw!RmZbgAbyV+zk z=REA|$JOA{OMc^xFp(6B+6wTvcOE7x0zY(8^U&zt0^852Mb!~oo1q(wK1LE@{TgfT zv#CWtRPo})z+YeY&lXnB`|UR39%uQz{4ifS9L~CYdacB@WtmC##;e_HOI8Y8`bfYE)0h$`PMi=@fu_n=Or1dhlYxhb$dU*K z%E|qy2+Rj3eCht{7}e2#s+lBDa_U}xPrfTqQt1uC`h-FXvnkSjHb^UE@vcMgNR)Bx za8**kw}<;h`|t5iGmQrCqpyobto!CNo`WWGva>HkE-?FDcsJIm+I_hh;mDZZvrb#c z4lP#eDk+i)tR*OgY%}bG^21Z1?J=Mw$BmnbvYqa^X*oy4ud6035l!4$l$EtNrcIUH zyUt4fz^5QKK4)wg4Tmimzoa@;1s5;%OG!z!v+mp!wD-Bvy+rs`%PmLLUThR^uZWC{ z%-g_beDB{#)T|*V|5I91;~23%CWdX1uT*8F#m9f7Bj|85xe&FM^gfD88>%0Fmu?GJ zNDUr+-oE60Q$LoGNDm^zSTwwKXlTWgLn1R8m}*g)Gi$XYVzc}5%06Dvxl{#ay5{l^ z-L$^5Cl8_U*ueRLRd=e_w?uku3L!99Xj5EU zZhPGQ3T2@|Z1@g2T3AKx$^bPZVm{29%(;74^>Wuulk=g#1_jN4iV(#!^sKC`9MMhz zT;kies$VB0Gr~qob5y53JW6HY+OO6d+Msa(~DL3u+P1Ii%SyL!U@(s>1i zpqkjn^~}(LHu=38D}(-Kf*de1M1@MlLcCZXW3|*~E}+#2t9=*U+Pwl1XLYcBUmb9pe{R*NHMGMQj?1Ox(^@UOAx1bP)_uC{KnErn4p?5(=1n#LM%%Rm^ryIisVahX_ZN-Lo{GfNE;iW>6;CpQB#j6a-8*0bzhKh{E)QN5Ww6`qEXbQ(+J^R%7XMo3csfoir; zEVP%zq_TV^%*5=8QnguJH!~mvE%I$xrEK9)sP{9|n>-dbm zaOKM5IBGgvjcN<_VvWV9w}+%V1Py^Yw7050SJPf<5)fHmv;yyZtwyMci~iLEUUdTS zE<%MLvJE`0N&1I%q5ZVwVeqvI?8l-ny*9$JNlxJd2dd?!Hix%1hVvkDmaj^gMb9?Ne7~OXn8@DkcRTDA$mB~vRGe&2Mg!EfzEC9;p*=i+VZN~366V%(X{vSLHkUL>?FW3v9T(R9OjJ~vRT>0y z?J;K~>~vkEf92~1*6^0rjIZ2dsNAjCFz;Q(4j)#3kHv!Q>`0{)`8PHOkYxNn(+A?s2%#Yt+7Ce5hAuYhL3B^LJU;Sx4#6XqqJ zVOiAI*M7lMp+`WyV{Y_8_5OmzVJ+XEORLGqIf&>3L8q|J5L5ChRPgnkHhEQ>%3K^2 zwu_g8<5cI7KcXxOpSwV%NIV_{jHF#bM|(T$s+$YsPO|keed$QK*Lbeq0uiq(hAIHb zR%b}t+~jkKf5rS{(C8s3Iw!#0wr6_Ro_(d4+e5qY0#=l;r8WrEMUx;dD4zV8%LnyV z8RlCn>UNX3G(oHOg!xoh&x5*2992@U9| z1goZ@#L0x7wt-xMg5-*74BFvX5#$Vv65=*NrQ%QI>F-`4hE_CWRSen4B;fibd5p&0 z=D0kc1}rv7s%v|GY95l;vyG}YR`p0&2Y2_nh~z_K!;l}8An4C9MSAs$qlIt|8uXrA zwMU{$QY&$8^o0lk2aV&t4@ms;SKA&K?0Pzr6L!xtGYdq$e#u+^NPbw%;|z4Mz(s*U z*jo@3%p06?uqX6Q#1?Bf=&Cw*B*fvY#;bGOS81z8)~L@Zo|Hp(J$Dr|kWf)`oSRsI zN|0wV!6YpNLfieqcyvHRZ)trFM$q>R#@hOtgwsZY z_U4Ga&5ONs+WWUyRD;@LARG^oSHwIl7F__N6k|`K+Dn6X18d$^)ldU)FU`SOvl89h zF9F3iU6dQd5`~DP-|Px(;IF{Y@^kYMhq}WeL9CEF`B<1@af#~((ehYXywz2rTD^RD z|F9WfJtWIm!;puCe>q{Ld$x9JwYD*+#-nJwKr}mPK!`&UzU&qYzsYRc3wATP zJn_~_O!8>@FaZDT2;tRB&mS3-4Td4kzV@#1y6J#`RFHpueWV0udN~C4g4%PX0pNib zy-bXQ0Em~C10Vie{O&HUQja3Gur3OS5MtZY=!o5e*yCU? z_u2_M1jJcDJo%(a-#!udlO&=qWrQt0KcqhQFWSDm>p*$LQp&KNX2Yu!7XunkKUHzImo!ZWUE z$=z|%X7pGXR}awmj=&tROPq(c@kadCr7qAk0X)^9Ms;6OHU+Sd38MU*c#2Gb`zKTIGjVac>Z!vIDZiHzJ$W$BBCeEQCeqfvs# zqOFqjMI}{K`UWtKib*Z}>6&oZ%LX_Q`Fv!44=*(Pcs*(bK#n;VfN;-C$i>M!zpPAE z!=?UJ9XNJj3n^f&BcYhY9W=-0|iQ1OZcc(Q<(6PTXyg?+$ zYpJ-oxw)1dC<>)yWYDXWVQ_Z%Qa`YQ+uUT)y_bsHRmTpxy&4MY0e&6Mhlo>?Xif#O zSP^QRxc>Dt|F{N2V`H9)&qpQ+6(Ay5_!z2&Lb{s>Ztmy^ zvV&^?QIAPrvGE~KP%SeUkfEm*nS%pXmtj?#3{1r31Qu?K8O#hLRy1h2-nw<`0}wTk z@=N5UrMos(hUu1ZK}2Am=$1FYDQ`t!{a;s?BON-5Fc806V7G=cIjbWO*4?>4>;X@F z59urt-PG3B8?m`0mKz;sjP#&$!y$84U@%IrFTn-Uu*$~rg;k4&R6a>u8bZ3ZT^*H( zU-^b372EIzc}ls+dCVz&_V3r4aBf)pq08G#kJy3(0g>gxoN2^b{1W^=H7y8MgZKYJ z5p#GSd;-8*R`!V%4Rf7a*(A0BNFx z$cT?0TRwxQbRJ1^49$lUHeilCzzrM+UT(vNp>`yR4KS(!c+>v)m9PrbQdjrUJ|3Y@ z)7`nVfNSf!vR~wz3}E&+PGxQzc=ne$4)0n1e@}Lq-&r^{rK$Vmpcz1EaB*?5_hb9I zEo^x6rV@m7NfWU2FO;MQ`BI~34|Q}9#{V7?_pbnz7DOlA!1G6T{wp zk%lXwmz)i7_g)F-r5;pJI25{f8|eWzK6nfoKHMeF{c@SPcr?MP@A6mpzsC|AXkC%~ z=aMV$rrmqh(br_b{Wke(9dCfbe;CsP!nls`!1n@Z>FyatLYKKlGb3bzQO5tz6H9YY zE_Omm8k66*_QKA=_gC=Mpa7Zw`C^kaIjyL=2V)kZ<(jLHk7?>Kv~Y#!L}iBqK0%Ow zdU^UI+}*oV@Ye}8iOs$aS-|5JIBE%Y>N{-icrO`vA^o{de6udzd=2ue zYr@ob<4l=A`K$fD#oR}yl0Oyrtgtph0ctf6a0vHnA0IF@HMPEXF9&eIf*SxCY6WmD zPsRMZbBrtIN?I;?^qkKIxVIG`t}btGX(10v96#v6oEsp8P}36P;xZrZ*+26s-lU5i zZGO)<%zie&uao`49BJzEPMmwfq-7cEnW3Iuz^(F{)1L^0&xYC|>*8%8 z@N7r0RSw?DtLtS(K~N2TVk3vCf2GBmyEp^@z0m3Gq~-Za zy2qCXyUfS{bDIXxa-oy&-FgTS5h5YuaqVLo0P;vy@lFS@1CMcvG8!Bn{^wafD5$#H zJyiB846KotU6PUej<$C~Uc7>2c;S^5t-d?Frgp#W+!L_a6Zy{asRAGlMl~KAKlw7% z2Cps;VC1{AK(2PQlajF|7Mg~>m1pt#=Rkq&lAO(+GuWCeE9;XQj`>nIY*9nBH;O(h zq19?z>q}`|0RVGM#5mFS4g(yfFVXhjVNqpbEfb(gOky>e@664Gc*)sGj!1QSm4&rq zNVaJ3&xd)C$=3H{k7;A|(cM7UtQ7p~w%e+OsV1Ajy#j#BWQ%Fh5ZONu#UbL7SrkAO-;-apF!b4i9Hbq(A+#%h))F~4j z-K5`frY}z;qdPperlxoW;o*VwPd$^d_g|QXX@*us(u&%J^4bv}8^klvN5JDyOu)xc zp?H-ZqK$#}op1k09#@c-_TXwac5q0$9&<9kq(l)7#>*q`u)$Hl?m+G#^qHD++t2r= z%c<%9$e~#6&EN7H8u1dwe)H(j8iit0?sx0!>l6w<$A$X8?!h;FPt(gK;pd+!ksV;v zp$kLO*(jYTO=DjB6W?X77I zo;gC=m@wXAt@*g3s?yScl(8)~_@Fu8YkhZwA1BOBi&dLBpKHx}sZi5j8aC0KVA2mo zkkMJ;H?2mX=@FZ&$Jh0C_E+{~`sI*!g=ztjn=6eXX>e8zD%008@Ha0ra3O{@i@;>ZvJcZQmH4s#- z1aOOS{sh*1CB8zdWPL1F8(Q3EqD~vvFoomTo*TFhI~Ax6ZX35x{5RQ(ry#wgj)oMF zv>a`|6KJsvoHy^bO$o5i@H_?OOG~cf)gH{hH7wvM?oxm@@;uYR&COj|YHY1^=~5}Y zpkXYoWnyNTi9>5%_h(9RR5E(B`g6H(gZpP=?zWIyXfSoWoETo*ArUwCRg9PQ`WeP8 zh^DJpm0sNcR1dC6E5{9L39w$vVk8OTLJR6=E=1_}m&xI2(};i9V}2is&@bIcH+(X@ zs=2Cu#_tfxzkX~uYCY!ux8WnjaMSN~s{ZOQ(@l`b>5dR`#$FsI*#1Fl@B%T*{nR{ zM6RB#9J`J7i~RcfvjaTV&3ns9+=`xWGiC9x5{pFHz z(b^g?FC!xZYzv&l1(`-+YPx_xg|XP8fM*pK)P}e4g)EGIr)kBZz1M&!QxIEFR@U#H z-5r-9>38ys*2c2M>P9r*MX#5%Er8w?!?uPlMyyUG(J1>@sXow6(eFB1+YjWfw zXWj0okIbU6q6tn}^+PZXQk~;|xMuQafK88OCad~ptubZ=v8{IM*cFY>OTAhNUG{;2 zflot2qEB4B&W=SE@{LA4t$ACObtiV1nEH_==xZ( z;Zd=HnDv;(_V$?MOXOebqw4GzMt_#RCE`rQBWitGv?Jkb^nb$j!=AEVdXQe*!B>t8w(dp|<7_*351feEGNrLqJwVu@|z zCb+WxcK*aysa2Oze=WuKlzEjQra>G3LysZ zeIoX7jvw03Z_mni3=au3pJb;H01Ry&G<8wVRrcA&2yeuc{lF@a!RtSBp7W35JeZ8k zEGoD)IJEdE84VcgV+QOkm4i^u(bIVS!qbl9C?Ub8a(@zD1~a;LVd(149(#-173W0GXVy z6*H&1wYdS-xDKR1pK5IsO)zuBSw$1Ruhvh}&~KkrrNcCEVWwE3z$Yb`d;o)5^x}qk zI#>$|$3&7?-YtltjC>SbNp+6&1_(WDkI!R2nQWC^# z;2FQvv#5ZsvM~6qn4dx4f_|%K9f{CC+c$iK{kFH1gcc&A+qaxPDjyCAHNy;BQujV> zdBmSQE|hm-Y@f2RP80nZJw_o!Z*kK}T(eCfw$jnB9E`B{Mq1Cmy;t2iTW7cs{5G9f zvvSRJ{K(jo9CwgyHFDOo_b}}82;3hUtDm1At*r|{IUZ;CTLhzMVIixEzWBEh`ekU2Rr2H`_@6(9}ed)BG}G3&>C*w^)QN&*DvXbQTVz z>}Frc6#f8U!;CDPje-Ea!egcI;?Ik&C48^bIy*XhgLj4gqpo@(b~oGBZ%KwNq_Nl>2oDB697gH z0%)STjG^TuraK2&|3&lrEGR|+6!P9Z`GS^{q^aC%S6DS7d{O<*EXEDoC{Ef+4_$~4 zTw|%DEbTxT+J3Cn_<{p((C09h?0xq_1M4XtM{}N5hWr}3TD?52hL%U0U2%L;(pAmX z)2BXLC4j5c0GzqajP;dt(~FY4z3$!DVeVpAeSdiegIR~C!@J^$nzV!-{&^Vx1dz=D zV1ZcBpl=1ehNTw9IOF$=%vCSmB59gFct?1a?|v_pcrhGI4gh&>+0Hc4!0P!G{$h5O zS@$sz>xdYp<1WZejXB*jHR(fG-lkun^>@UM(imQ|MW=;i;V!A zOU(=ivW{w&((7u>uPSCOefR+aos}bHQ+Qc~% zS5}^eQwpdGH%JsrTvdv&eSVLorZFFR?G(}Ez10Vv6wfpat=e1jhd&~JF?5h?uPzWJ z#Uc;k6K@WkKFK9zZ=HJ!g7z0HM1#)zIk~iW@FUSIW{uSd$-9Ast})512o*60qc+x+ zq~*~)lP?U*XJ!)w4k1T=(mne=w~vR#Jp6QUP);=h2oF**=mtFJ1iE~6G9@MS0l4F9 z_RS=ZjWvc`{Se^eLxG1`?y)d+AN}?`S<&@H_i%$g-XP zt`L{QqWm!`ye;CVDDy3!~KwbErsf}QVw;+e1yMjG4gU4C*u5Ew-+gmG5#_kZ&{HN)*Jdra77baz8}$jf(YYoXNe7OQFj9QiEr>71&~7W^*t1sCl? z!Bxst7D?yUv}3Hq!6xD>-#z)I*DSdCB~M`rp;aIdVg;b5BHMvfbstE-W(x}Ed~|X^ zd0BgPzYtJBgP8nnG*fS+Bqg&`>GdOA00#CvIIBPOC$JX*2oFDr``GjaFm4ivl=S?Q z3j8I~O11ip2H%y{^6+q3S*cwK_F1|IN=%&n?CQyATRWPMG(5pj5*p{apQn;{EFvK? z0mL=%`(9>w6s><>(#^uGIa0$%E4;{Iau~(-P&alMxvb)*i_o zkvuRZLK@gOu9picYG#woYsX7BfFv3gzYN|CMaKPbt4 z|7f3qcVD~#-~1nDCD&Uy6HebZ`E}7R3Se}9Xp{}?UT&_3gRRSLGng^5eE^XJ2+zO z<*k#nG81p^fBx>w4xKnPcJGzQlw~5g-@#V6f96|0uBPzxr>fRVxf?h2ZCoq)a7k-Q z(j9%HA+q1_4GOZ5gPxE#&E2`N^W&!1$U=OD81I8Ea+mPnkn+fh;cy4D`9bxEq1`3* zDV#G+g;K{l8eg}#jRfE8UWqbjJNvnPNCk1 z*p`J5EWIo&x1_2l`rkpl+Fy}CjLXapA*?|1f9+2N;{5>%aZDHto#k#>PbL)TeTIop zM$+ds2XHyLp`GMQuZXc4HZ?cCmMAK{O^_a%_S}=tDg3yS#$I)SNF>AvRE&#e-n| zzbu#JG2E{KQkjiwc~qk-2y5~zb%sZs<_K&@TERg)mk)O~V2Wu%>~%m?{QbhPXm^<& zQ~#(0JKM}(_2yA1>C#Z&wDgFIE8YItMVzzQ=eQEzA~Q>S4s`M#k~w1Y@|YC)7iYp2`<-EIirS zw^LsOtcCfwJLSS~%JR(LH)}TQ^F2CfNxc&d_>IzE6zMi{9h;piR3Q@|>z>Yay;)Zu z9gTluF1PMezDTh9D6X8~?o(v9;UPVNqR2WK&muZxj)@K0+AfZdU!ts08#icuJnskW zK=jUv+el=0m^mR_$njr2iy%?-=A)%?6Eah;0=!lloKFm%Ntn1faIc?|z%8p&^_TE7 zIIP*5GFa<|Qlxm=xG05&hleMv49Ro#hueXZ{+P_!M4XblR5K5;*{w{xZX@u z9ySe6^5d!tE%`<c<AJ9?VNX$b^=@J1^F`s0gp>=cZx4mi7!!sxixRT|r363J%G$@L_VT^I z*YZhyrW4Zxq_>Iahn%|DKGCtaZ!#oJ9mB^Eg`m8ZBpECMLF1d==;0Hzc@V6(BgcoS z6(3bXO#kZm9n#f7TzHI5NK`GVZ|YVMmU&v5(AHaxF_LF5_MUJFfox0 zL;;Lu!<+8b6F>xLIQ@LZ0|0J&0?DJ65n;qu{k$=M3R zU7(^3gN>0MDcYgX_)L(YX?4cj?v^KQ*!Di4fazk;Rf+vnEFHomZP?~D8@kUDblm=G!?(+x`ge9^gE;Z}vZQx5d7{7l z@*_R_@;((5T(Ak+&AiHa^JXvFzOWuAulw|R)U?SLt)3dunPrP01X;4WoUZbCc>>k4 z+=eWvN6s`?yt4vH>Z0DJhiThBgT|xnwt_rwxPTS~2FWhAy#$A6&vIS%1n~b=iqzRc zWr@rg4`71qwX2o<(mx99G;Np;E6ax8)7@^@m_Lnu9rW8LM8ALI-)yy&x-^}k1X2x5 zvf2!kHib41{U{d`HACF0&IO;`r5XL#7`6vyN8eCA36t)cty!~U$Pl1aTQf4i9QjWe zvz3qx&GK^e(`lHt*So>#=^^Ieki+(9Z!+Z$9mf1EW*@bOlirnGJsqN4XuVYnB2{J@ zRbmQ$U1?q{VJ7WN7s`|T0sC#`bdDDe5dB=1|{18NgV5#ruOr#(`V3$^me zaw|W-HXpxuonj_{j5YW8dOkZw+9xtFNRnK0HeL^s@Ma3)`yhBbOUhvpM^-1~+~2C;q+G9}1W~^qb)| z;}uKraqd3P)Z5h;ZU@yeIrt(I@3`QnmG`K*DWrN)Zf0<-e4gYM2=>^6L&(;nIeW(lJj|4{Xn zHuw8W^Do$JO^Nr~HEuRZa)GR1o4cwVvtxyGmv6)id8^O=EPd?jcK6%eh|VBjwfJQw z{pvS?zpFieV)x~g{$BQ=DLZf@&*$H~itL*cTRzV{Hd#)RcXLjkU;6uj&3&{{WGjmipXy$em1a`*1NYcphA_6m&XN^uN|w ze!^wH$NZbX_HTtm&v4I#wZCCO+i{hm?`vOFgh5LqqxD=&UTsTSk`t_ACA9hV>E?GD zwkIn4A%fZTvX6sWPPwAoQwOEfS&25A@_O_g*K=yuIo(r9V@n>xzNqZFvMI|FQ)Xy1p#HU{x23Xm}k6pq-dsQ-Mp7x z^>HSMk`S?QC(2V8wLq3}{rM%wXGjHtG}o8B{>4P*>^C9)4xQ1RRpP1Vg|;)dP@^SEfi;%6VN^uq=*PxaRUjE24&N z38apDtuO)8x|(BTHn}|AJP5Dok}!JRlJ<`F@P5=@j(`6OhBNI}fyh(i^?^Suh>?8X z#mE#D6;QD?z;Hep9c6mY`AGo^U&GVCVpR{V1elAQyE-ywIUejF;%_{&noz!Q(%yWU zGdp^GI*+=8*;?}i)ofNE0k%x(rkd6Fd6CQJ?^$!`)@is5KpHMC(=!4T_|dO9IF9^h zA$*9?|E{IboqH@Sl*;@!0aIOw>~pe4I6GJ31A#Jz_|$uC?=lNE?y2&nNvPYM-o`RGV;vRQ5JXL{wjF$LaPxMxsF2+YgblcOz!yI9poOEx)H@hr><2 zK%FZA!8WZvS$cIhhu&X7sDD+wM=7XD4K0T;bA{YS@#T`%DxSPvRP4UwGWbPwsN?RD z-}gQuDZ#o4-c|Bjm2@e4r{O}HJ&@`E&9T~UO}?s^h9$LR*SxSY;CS%Q9}lcE*!YK< zNa*W96|pN_YOHjFxXBO%o42p0%1C~s-4^YfdHKR}b+LuCz9@s-_&o*_OMiRi(OX9K zo`k?JyP5&YRm=K&jne;GZnIQ3SE;i z;&TO-neq+Q6&P#e;zWe!62Zx8NYJSiURnB_F9C9zwQ|UO?UM*AfS*NV%iG}{r~XVP zUkb`D*U_xVGgwMW$SbWd0a#2y|mL!=_)&Kr&*V>-o{E}d2y z=YFE3PtF0^wzY|u-r6Xc0Vr&T22xr=uj+#$P+mh(MR3(UAbyASJWz$m#(EwD8^;-8WV(N5rs#@>+z5EWk|0T|+zV={j zQEdt}d2I?+A1Yj1NZ4oq#jlC@<(2TN8`Vg`8z&Y5SmDfb>{oDn2V5Q1(RI&PCM+vJ z(p5~AZYGv`ro26^n<~u=e=3smY6uqJVhrT_^?-T))~WeQ+kmp0tsMiU2T;RsQ?7mZ z@@ODTa1{&9TN8ohbxE4W*fv7>`npUB2)4o^1b5u4?^#PHz+h0iCIu(+XQEY(6=H@b zm8Bl-M-3m*SH&-j=$O?tqb3>5`=oZU%)#ytvDy77F#^(p51LT_79$$c84aU+p?KgWaLJFMAMcW72F zzFTp!+cYil0{@buWuAzfOM$Tw;bW{hL6J&z%QrMTyDOc$C34?XuTSS%5=K#wn3Y3fg{dwvX6+X~zg6@56dLdF zpvdJ75Y>}~cA|QIl{XW$yEsl=*fC9Osq=Hwklwk?6+tAx;Dc71Ww`e528+-aS9|D1 zb*UeQRIqRfZQ4N;6VH7ZW<=WN%;eM0wy+m-XU8!uUmgM$lUZuX-=T$CY{14BKXk|2TP>@KiE36fZ^2Xl4?{>5&rORQiW-mDLO@D9nAT45IJ35SkqcKZ zH;8^rwQV>b_|510MWBQY9aumbme=uW`D2Vu zMy8H)rI`IN-JO*1qj{ z$SDi8p>FubJrSGA>nWn)dI=avWwK`A9J5(-`s~C>L@B_x$#jnYNv?G_Q0!()8SW1Z zX43%tc(G|NF=O`>g-n)^-UJ!H_Fh0wT$IGz;fSeT)r$v1V>c?JjhHa_c}zZ!1X!Etiiu1C3%#{zdtre zA{wj31ZE5usJ!jm@WT!R2>94iTG3E_!^gX9htiE`cE5asGT$ooA5kJ6p7w7%(Xef9WyiP3A1?p6R} zK8yG}W;QmRLa7jq4uWH&;E(MziYcdP}7 z#)fW|Ajm@k?GaYZ*2!#kZ|%a#WxG6Re0_Ur!Q;0Fx79lKkGc4fdx<{nQ0bRY0_m?k5k}SEbdC?nbSj+W z;28bWYRK`y^uW_PHHmCJai>b_N7}aMSFA|&JvsO8)0Q6v{_IdNpM<%PHcs+yKIpz) zFHI&Vf9eKIgg4?ly7xKA$-R2q^1qVON7?_@tzMMi*(;ZdZe$O$ye8fNnO49ZX=(y}BVCv34S1V}s6B z;5al{R@8!KsLyBi?r*i+3};O(FFaCnfIv6CLd@IkojfY**)?V4&Y8U&55DmeJ{Jdc zj(&d`*lnyb4W#nApB)iT9T)|tTnGoV%L#KJco+-l@;ZVYdheEA-n#(QX`3F5uw%y` zUlMl?N#Gv+Xk~e(GFgU?f+c` z3sjf;o`XcOqE#q)<^ELIs&9Ra2c`$detv#-X=)k4Vth{hm^^KGLfJCJEj2TB%=ERS%*C9iibJz;4K(vp z$31qcX91g0_D0z!1E>#`FikJWx5!n)cYf4I6&4oO#kyPx1v{^0Y^}RV`V0eU+fpF6 zi&dKh8f1vO_MiMQLftM83U~!T_k=+>206UeVbzdrn*0ekq9Pk`(h}B&E9>~+|HhBm z5dZZ*CJV^kKbLXevjPlH9ojs}tGWEze~aC0HMD8Z-pU7w0QNx&R(&;>CqN zD>hi}gHn#0{XwZiqd> zJkD>zuzIOo9q0_{1sbw@$d%ib0|-OaW9GfH7}R(=A&=Q@NugR z#L}E%I#7s)2~<^%BTzF95Ik6+3ZPSW7T}J7suRLZ#Mmwl9Uz9L-w9;J>5JwM4h||e zXl^Wb1E(Y89y_>C325RJ0h*$LBC|Vg83OFbS}XO@eZ&Se2^5wfK*@DZs+yX__?fDM z%5&3^Z`Z>ZPXO-we%+)Ub+(pW?LFAc$X4U~WY+-$iVzcM0yxtXnmRze%T&S!P=H>n z`;*c}yE|XC)GmOAvX2^Im-QM>=QuuO&-U7@mcfwDP`A!r*#JGv%2IB~J}6pH<=<`v zc!Aess)e_qvF|K^C}umaU1R6%fP_0T@4)%vT;v33>t9EROpPtF4XV#(E7X z@*My7OTLeH8&DH8X*9T2O-GoDUw`%E36C(4Ay=3td1872Ii;0my$9NhDl`0nE(5gu z79cpO2b^yL`HUo>H3vY>98(gh!#azj>-*kV2c-{>knDOjwjw=5L-aDEg@N}Ws%2jz zmY%a*cgW(r638{2a2K!Y^Q}y_?fCn`6)VvFYtM)CJ@6hY#j9F0B{ltlX(^JuBavKr zp!7uq&>_*A)Iq=f@A=q*z6GWYltViAT5uMPHX)x=u`mo$v9QgYk=B~ zS$X#18$y@;?z+LY73z&$SGEgUoMWq>m6K2@O+38`6H`wPquutV6V}}4$B2JFI zUw53j(NA8R>EK!B{LePXw$p(sjb<^p(YX~$?rnGXl(8;JV{=NBAREn%`*pwIoPw^i zc+O&Kg2Ukz`4vNl%YuHiC=d#O${7##Q6UsKeJKBt7IaXR3Um-M(n!E@BODxWj2lBs zwHF>6Zuwz^a9wH=F3qCNgr8Sk=PP3Yb~AQf9I#$EV62j~R(8u;AQ9*$O=Y#Vwwh?O zJ2E-WKJq#BVKFzutVwev&spZ$jrSMto!^RFnn+R1)?Wd-r$31QGpPOni8KVx2%(w( zbhNh4Wp`vcffcroewlg+r2CP<^C1a?2>fnOV(Ddhwz~;#I@jDELKPj-22{TNL+23Q z>uC3LKFOJ6&qDX5+iYc8WNiQfRh>+#Eu91&wC548*3{$KHhw|DS)j#|IMa(_A2xA6 z1Hqi`dAYsT=h7z+B1c!L3`X2V?4}^${zb12AXvYyxmd(CU!7D0oIi8+)hDzMUV2g3 zyQ+Aa#jcN_)Gj^)PX91rH_I&r-nj8RQQo1Vw`L{|rbtef4Xk^@qXsM{5NOA9Z+?znT>eNPZdB4TS}1uW}b(^_Ae#JRvOC#rJ9gaaL&UV(GJ7sdjCI@npDOuQR7 zi6X13%beYC(88{@mVDOH)<*kjy4`~EJb!H{2)i%|wgk?6fW$QQ2U_z~&6%$l&Qmcn)x-A#jb`3m5DFe}ZPr>oTs{bm(Eu=& z^bYEnW8cu&A(Fye1ZuYA>~JeGHgEaSL}IIHzLNFtEydqG0Wd!Yt@bc zy)~}+(oPY&k_5)PCs0cNOxZrXe$>U7A6E#g<%;_zeIElxK4S+6-^aUUgx`#xue2az zHnZ9;h#3#w*P{3c!P&N*oxlvv>V%1P^@^kzo zJ>re>p%ds<(gUvl6iXBWtwQ=h3)orBo&8E-8*Uc0PP&{0jgpp`fV#G>UEvIMsXt(+ z0`1Fj9hi2^&MpJlIZ%xX)chC!jRB6hF$mq+KLNJuqy$?CZ~fX2@V2f6ZQQ1WWToEKMC^;%3NkFn>1(6JrB@AFhK|qO;k(`m7K?OuW z$uQ&~nUOqz3=H#C_uxL^eSg>Y`j6M8J*T@*pHrvmuDkB)kQrZprDv0DlP8V~&+v`( z4zo$?`qFsjE-aJn-Pl3JBAm1HQ7xRe@JmEgI;Nb>9W&ia+SOn8N#7(C=Nk=S>TFW0`d@Ns81GS>GylCJ5%Xio}UV9lMS)H|IoUC?=u91z-Vt*McyY zh5$!CVLxShUR}Gh^<696ygh?An+qvi0mcu`eBP*g>QqSXf5h3DcN&eXf2`$dlz2#I z21f8cc=xNmQ!?>ubowtvSHyPeWLk{Xdwz|6p)z2E$J1SHYQEu+F$as0xQFr;Wsht2 zEj&S}iQqaMtjq2ne6;jYwI)Ar0&vwbRt(;gk)T&W6zhgPIELbmmyRe zmUrA%<3cwkgs4_Ll=!f^oK|2pf{sw|3Z{quV4(ZQQVcV6S+YH~4sblssg|_H8wZGX zq+4tpJ>6q8M8UAeou4Y5tkUgEw@EJ5SH%IhK*vpcJyE{hiHW1)UA9aDB?dSC8&e#6MuMI`34PnW zdRn+DV{7qASNURMae4PV)kd%3ff46K)nA=GjMNTyn<~+#R6XN+w!OKz3A8+z#^bxW zDyD~yxia)63J-68-t=4|<~l7kliS+X3`>MPYrXHd-CYdk8*3vuK4AIi48JPe1x)967sH-Q zpN@911lGip3@Q!xZck=i<-Fbqkm-Jj*=Ko`VY&67X5NEjHm%sRscKQuz>=;V^X{8J07-wQ=EtDY4Tzq zaV3@0n~*@ej?g{I>*(L7y$`zPr0g<3njSlk(O-CUYkY3)8yS zo+h%)z94y}X{3`*W*uo9Kguq_o}EYW3zZe^Hhy@*3Szpn`;LrbM7PkEh{T`@@^_D_ z3(DjjI>)tEK0@31E&$0C1k^M5kExgA#&2XJ-&Vl zUse0e{PPhUyXB{s27TaCU@lXFOK(9E{R2j9JphB7D5X@JOJT{)>~?e=3#6SuWKs3zrweXJ_uKevp9 z8^156oJEpnmbi72oHV?_<&5U1gtHRay#o`U6`;^yw@ zI1-w1WMAmQni<4ckOjOc^yly!@rIRzdkKO0bVgu7VGE$ zqsRXhz;qFG3>0k$I(fQq8Xt&nYaI#E9XmBn<>TM!TaeSX$+D>2i;OHu8ClXkqvijv zj#Xs)41RXb|&Tb+Zg597Rau zS%&z>Vu3e%=NGz=XJGVfQ$;*p$vCak2ief5ZI?QvOUG~B@yt*^w<+AvCXZ{v9v_1Z zY~Sh(RPCLgC7{2prda)%)t?o&obH(=pHMm=ACi@G(lShZ=FiD;owM=JB(JaKvZtR} zk0hfHzWs9=75@mz-Vaq7P>8&pRcK8gsJP>18f}f!OUqHJFKN;28zB$RIr}kxZF^k7 zkof4%UJ(+2N0{e^l+yXO>8}vQLlFnqqx5|KcirP?F>xHvLd9sM^DJ++@N$x7tuvUB zrYhIsDF$pce}(Gq5Vn-fK&(6yr?nN(tn~pCN2Nfr{`DK=1P4n09A6C$9@7v!VSja9 zgC#Z%k`6jn!DX%DbeCLmo_!Im>}-YI>|IBZ3c$RADuq4Py?XdFe*cQPgTr+soGh1( zG^Y2viYrpW?we9}c|D|Ui&WFCFi1_&5rX3`Zuq1&PCB~~h2YIz3GS|0RSRETq=@xj zrKCui_=lUl5tUuUbP-O2Ptzh-9+0*cErwb>b0E<38xQBd^XS>gwoJ1BXRd4faRbyy;dJQOSQ{uIX&Qh|9-DZqgKR*BM6Ez&h4x3b;Z9e;&x-6twU z23_SjhAUc#G`y&;(r%;=iV~JHiZxpDHe3z=Zi}-IcH=C^zY17( zO3+)Cb>O?>*>k(RFSWjEp8O^V_K;vld6l+j#E)0AtG)pzX2;dfY54c^m$!R#LwF)$ z|Aw2I)rBCMjhHrHU)R;bmC|wDBLfB05_-n5u8~OGhEbs?jC$X@%Ew2Xa;Z_2A!(q5A zJ0ThT3Jm6&&gW>tW6j!iNFB7lVWDTSLIsFa&?qNf`7jQLkCGV9Qlu8|HHvAT?rbw8 zsR|X`TWdlN1;)JHtyHjQ#3QjN-0*GW1qGcfg{Z%;)S9+x_B$K9nT)_qUOhl{3IR0D z@LUwj7F{nzZb%b=n`xlGfZR~h=jQGW5BXeWBjHEXJqD~*FP?T_rXeSd`|Usjog}HqZmkc9jO_2Hn|bm#g53Bc-K)9FY(H0q!oqiG6L^@ zUtgW9=6)})BU`nKkTf2lTsJ}S*BEC*C04We^}MP+Iytqo>v0{aBpjdAzyDCrCZ!a< zq9l&;svggUEY|M`jAUhDH)cbTLrrppVj*6I?7`ziIPY3WY$ny0 z6B>j0z7_YafN#2cQ;JW;IQxT^+xg=czJHHOv;OQXom=f%Qv_coZXHqcXr$z1j zZ1=WGrEjmCwk^{z*%%oy(r;>eOJ|3RX?k-xT?>7S-IS#Flj9uRxPJCrBnm6MH66t( zU29vT$m;4^l@HDu2nh_ztNPJ$_kb*45>ovR00e9_dz%;@H^NNHylw>+u!C$HS^yzI z(n8~G7E*;GvAb9!C?rBkX5#vl#?*yvuYJvJ8^PQ!Ux@FRm|q> zgSM5+*T2Sy2&4qMe)`k2uw0adCAB{P=EWo$Wl@z@(bbp4jm6yafugFQG2jL|ZhW@G zqE*YEPDk=v>)b##w~kjW;8k<8r|=yv!`kN;Dy4?c zNeOzue?LDgH&9z8fGAsmWMB1skNmx#h3*Euh)fM54}x|iIJ8*9q((RuUKb-U-a-E3 zn|z06sf&-q&T=4V&E$rJg{8;^GtUBA=)`<=r~|OViNVFfPNfyMD$X9LC0uABfxgw? zUUy`!2+{v8X;-JH%eVI`uVo{Ne>+-(UbG;yK zF}L&^)ez`Z2!r-*Vi+xUEAA0U!4?t)Ur#=L`t&Te3ah8Tr4jI-503gb$l0w%%KtZib5K$*I_a98U~r z`GAN|9*<>?7>;&v@SqM zv0#O#67l35!{j5Ozxe<8Ciy#|N4NYpSw*KQ0XB|R%f@n@m||NcwXN{m8bv+&PN5bc zz*q0w=W{0Ej#sH1^^QxT_|UDlN22!91&^$_J|l!vs0GdV>~40$Ehi%3Q%!5T!%yL& zf6(ijEA63=r=2Z9|JSA3pvDv1xq?WRd4kX~STAjHi>|aBQN+z|!%yX2bapFFO)eey zZQ#6B6)@!a^D+(rEB~XKvW5ox9rxkQY0Dq;CF!aLsRG*|m2-V^v)_Xrijw@AkfNlz z+4IP35tv}MKqx>>-O#xJQ7PVt^CYJ4c^Kw3;RSX5Eu@LY;0huEu9VEpf1$wH+p?Bi zY*~pC^1phKGecQOl1|NG($p(%e1we}9Jo2JpkT@#{S<@~F!(!u?jEI2%rZru3%`SM zT06+##YaZceT@>xt#|%c1?%K2;(;1~=PpbWUR+{W4!78QswVqmXY0*(g_kX=fC}MX z?iE0UWWbH9Sa_g#mfuUg0Ha>8zq)FfQ^q`psrvEF4^gt#JyQx|Ah%dzA0$BV-y*i5 zmz;8)yzB*9)xsMRTYasCcH^H?CP7HM5OlMI{LhMnz0ic)zgqg~{G)O^2fWtH*S8xu zsc<-%Y=t2k5r5BZ9-zCukV~^2fXgvrtmZ6=MNL0pb6fJ85d@kK17$QPVGx9l$%XVz zw8h#~Rqt2G6`{qFq@HYzml_3L@R&-vZ=OB-_3Zukzm(6WMCpi-Ct(#~IYV3+^#I1b!i z%#1z)=L}xJ7Zz7u4=sAg@1B*qZ_a6^z{Rp5mu~u!MGpA|i^XT|+~8*XynUK2|0X`h zZuiOb?%-cMdID38KrO^P=RpL$>^xa=5gj0Ik4h!!UGQ+|(sq;+ zVZ6j*;#sb)mB`XcbEwOQO<+h^i+jSxjT2pdEpDKjqq0{=+|%I{5vdS%ec(0Z(ZSW! z*h^%}e13#7^4`Ww?zoZQavn9sepFx$BQ_plz8`E#4~Sqc6rL!#WS73Cx~Nrtg+Q~$0Z=J~oxz9-J0!>218MxcOm+1iwo?Mt`V($e!>WKn&}!2J_^x7hif>nSQEeL16f*(ckFePM9z3G%al0GP?cjlwSA@Es64IktaICENHM!C z#lLw%{5OQ^m9X)kWdCRQ)bU!B*y;I;`xle^y(+aSzS~swmcS=0{Oe z+=GPr^1)7}&QUVysh*9vAQI2*k_WS?VCLsFw9@CgaXMM2AM_(zEo=MK)Q#O= zQmTw(jsBG>VHFw596D8#@YulhU43sL%lC95!_GCb6teIzj#R-ZuxK!H8M{JtPz{}$ zY}V=$YsqJ2Sh%_qDYm9gn#S9wP9kx=cK`)q3+h5>L#^vz-)M>#H>Qjum*ZG;U+R`l zzjnhvF9?l{{Pq$g?2seQ@eKMEUL`?p%rT?;KvL?=sFy^%`C?74(U)zKQk&ONK{@-f z5WIpDNY}v+Fv0=!3-Fg@5P-2KZOqyl$(yOp+C&4twOo4p@|}u?4~ir1Vm1i97g^|- zvlJ7_l>VE`t?5eF8fWdzJ6$WUnzKQ2EmnNQJT7e#&59|^8YojQqL$(ex)!~ePGscb zJjsBzZBlN9q$r4PkQOOSV1p_q{%+0To(Ib>(Jw~Gx}LJshA zR|l90#vSx9+YuQo+d2isDXLE2tTn~dEd@vYc; zX?#kO6V~tenOyp3Py8#nglVH-`;vPsFLG+M_f4mvJ3h8j^dz_eghS&|VcKcpkZb)K zJ$P6IQA{NKf(G@HkEpB$(-I0=G)Y>9(wxeF3f|oB7apRyJjdV_?$Q=d0Vy#46nu*5YOg2pPm5ltI&|$15J?-g^-`0!vB{l3 zjSRR*F%yUAiUT7r$-wlwTO;5W=2{{Or5#H>h`5U0brdzoy0-xLv7!I6k5b1~XWVN; zlhlErk{H>Gl$kkm4mDlJX(_vVx##*+lx+fJ{@CE6SAS42zi*g3$o_ftgECEmM+Q1w z22~QLzEEr!0<&P|DSNBVyw~9GY1=3B@K2I^C=iXC94V5IO?3PQn$BaCqr65H<2#!e z->iIW?X$E=^@WGwA{wPF?9)$=CTup>XzWh<0Olq`T}Fm&Rc6?Oi!a-?i5r*@E#b zl=;wA`M-tz$4S3*Kq#mg=k*Z4Jtkt{(pxEwrHumH$TU{V{44^w1e3Jpjk@TP}3LO<7ub-Sg_2@sz>%wk69@f=!_M0Q_+!oQM?=h+Q zV%8NrJcrhh&sWDFZ;ZU4<`*cREOe1W$|r&P?si(Mx=e?itNL2^;ZxVLHzp?1Mt6kd zPm2^Qy!C7^d0Sl+jr{tU7iIlw_SPkzvO*+Mojg`L#Du5n-9tC;IGR*c7)tm@uL>k9 zzd06sbL7)NT?YIL^Nkukm)d;$CEFrOyWG-k)dZ7hhJW}t8*bhqFbqUQu>HVe0ZC+K zep`)1tLPH?Aw}&1KaM{M3NNBI;g{@YHJ?J*6nda&fOhd3r}L%--Ng=j5|!$qty?V zjfNaM6@?c{$1j5-CU|VqJt~uvlaM?0ae^i~W?$a$0FhI}*|~Tfc((}Kk!qg~xfKUo z+&)5d_7K2$v43obz<@JcLgKmWXTx(J{UQ#ja@{8jYQtV~Ta_fou6!T_j|q@f`VO2q zR&Y}Sr4dU&6_OG~r?OnPWBq;&;Fx?a3xiw|EzsR7Yh-7=L`k+9qHp0V?sj&0r#NoK zKRrx4*T@pr1!?4k~K48 zW?-g0QA0}$H`dVjpFj(;Lllv`0p~Do<;WcZdAj!u1ZdU%#Ps05ONf9~x z7^~rX?X1R;MVSK~zVhJ;%RL55?Ub4$L0VMMm$J4DOr>LU92-%$|6ZH{&S<13~e-H8Fh z@-!?;-08g~OK^m*KvI?yu6c`V&@BK_ACpK|godxu8%;Oz=A~Y$P7P!$OvP?76O1rkAsO z)-FG-D+z|xN{`5n&w*(FC7!u35YG}Y{nmr@%%bBldctqj8xNA@aySF`?1kzr8|a6D zXbjJLRs(EYRK5*joA&=3B+3r$D4tC=Yj`#$Lyf*lb0;XRu>#q(qoV`v&KS$|ZSzPx z76Ie_og@48r9#Z#0JQ6a2(+g+gzXk%(Id@0Edt)C9+G#GD1fKAW=6 zPNDDcznK1Oqr)xU+wW?RIxt@p!)Fx;U+$X?o12@v40)1iJkragGZ5y-yO*lF%WUl_ zZTiA0H{u5j8Ju6N(2C{oy?_j3b@t)bztLh}2upg_7$P)P=n6p-GAA*Eyhz^3g-(3>sjofd<-s?Zj@FD@M3Oc{ zw_T=eOwT2?lpF1kaw@VJ66Q3hycHT6iY#jAUAJugE0U9#@q~&GORpUVjn+7ZqQOgl z8}8p|1tjLS0pc5Cup;+MELJu~ylU!Krkbs8xNl`2czgxvnPMGaNnZ+mWegDYZ6G|0 zrSeu67OI<@o42gMhHg6a%^^JqUT4M$#FkIz)LaSWCb0v~&myzmixj1Ln97afvNF+Y z_3nf}M4&jP=^cQ^)eMc|Wd8=HSXC7}#^~khX<371XU~DS52tObwN1RDXo?0WSIxU1 z9yiA6gWv0YjmCCbz`J)0p|Bf@*RVJlr^urxEWu;I1zu*bmj>51R2TStLKwR}ID>auByer433 zu8T&?#M2EES3H`OHZrQKhqc2aBQv(ME<<%}H1)@kYN7?$Y%cN_WHN4}p4%}sq+Yyx z>fh@Vi=IRXx!wxTn29Q2Bi=WEp@b#eOV+aS)d^p#S~Q=rwFzk+x>SWAkr={}ZED0Q z@|a+|vWpaQxQq115e5BuBW>w89XxKDJX67Pqp>7C0*Os>`(iVBO8-PX@2o0 zUrr%Au)N6G9965XxTVqVPB-WDU9H4?=~F`5q*G!s?sLUrn21=_P%DBfVdi(1aC zk>9$x_aX&P^$EViC#fu^1|=*f=C~mdXgY1VpUfF#mg5sug)5P_e|O}c62hk@JD(aA z)rLUAIEnaRa(Pm*iX$eYsy4|BKvGAJQy@p;)7B@amb{sC?>Y;sHtfe1O zQ~RIKSJri+1Xg?{y}o)9(8myjB0obXbxRZNFKQm z$xrrl{n%bLXc*}k9ecLCLalGOkIfAR!pvMmC*P6%C-*g3cw~Bdx?#dj$}qdllX@`e zW~~c$@GHB>*(BHCqng#+MGF7%TC#$`=|?bRwnA5jt`)w{qw13{Xy=|^w!M)-nOi9J zfH{73lTKbi!?C5$;v!doPHYi*NBBq1fYbAEZzS}PTpV>Mg1=?|_>jStExzcnpUN*G z%=CM8#j_E9T`n{T$5c^(!$arPwNm*chI*?n90HytNPvoCFGfva4Be3vj zM;Xj}in5DwzKf+&lYtWSMPFhf(qC`vNT(;tZ1j5~tLJ?f<(d+HG2$C}vWIdPZ`2=7 zb#XgMFXe*WtZU#BOgtZDkt&4d*5n+i*>NIVEzzyDDbtQ{pYPMHKzE)s<)aQRb89CQ zX&GF2dR3*_XL}`l&SmCpRk0y5%)8xT*b(3!JVxZNOPI7Pu>clz>+BMZo1Q`r)BjcD+P`J9MNK|fk|1H$mT;ptw=8=-JEj8c@-(|^Q z`YGo_71x=AX7iKbnd_^qfM7t;-tQ$}!yr|)J4fr32hRmeUdL;Q8>t6TX|6giV=-a4 z@LJ?j!S0Oj8i*>S0z_l^dx1)`XjvIs>7qgM#Pse|O3I+XeLT*}ji3@byc(Wn0va0r zmD8>AXT{2K>j-L(5#D{Ogq0#(;TTTfI=5p5pT|0M#WSHbIEh?w$Op|_h_%$*ebHm! z8hV0Cnv-EA(~x!R{a#mTr+}#yrrc~kW|g~nVONA#AHfSnW?4hOY>Z2J@;=)y*A!BvGW) zbd>h--khSwrb-#rvq?SCd9ln=h9TpxFZ8%2Qxt$ zvaoS-c0;pVUC7#N`Y?Ew#zOE49ViGBTwto=AJ0|q=vZ=<%c8`yO$p*_pjW*fM+zO{ zTjxUM)Q|>uV#Jw5oeJk{o9fMbif{h@aA-_fc$Ufk51y+CC=kYNIDEPSp3OV^ARyV@ z)3K?yrKP)8fSRba6X;c!hqxtbrIWNJ$iM2Pqv>hbHvtS>RX&4cX)Gs9`a3KNQ+VWJJ$E0 zM~Ev@AgS1#)j@*#=e9#E(ecpo!5ljCR+R_BrC9CKY6DB|Ds?AA4_I#4=@>p_pQ)Eg?u6$o@uxl0o%5citj+4Rj>iA_6c%7-t}tmN>)a z17;@}IG6dN4A1Eyh=v~RK@5}qmIp+Xa97MRWw*)`2wkoXcsZ#D7(8y8n3%M9jjj*7 z+akTWTdrk5(4lCYsvMUCM1d>r^F`sqZusojVGa1TEL*ixF~7uyM_1m`Z-2x;+FH<% zH=u7o6|UXR-yeYvhHKD$NBEq3jVH;mU7A+t$`2fp0BZ{a1Cev<8Zs(VpY4NWhK@&5dD@5Qn04(Bj#0+S-9N zGAa^f=qe3JP)WN_-FW*_UkRJ>bc~QX@`i0u4=Xg%7xNnE&kncfH1{I8MX_4)n2*jz zaGcKGjkHPR{6JP{GjchmO3d8C6g|8NS>@2{V{X^`c7=tKwZjtqz8N_`F%xRPmInvHCS}odeOIUyr5HT6#^Rhqv~dvwRrQrE zlJInY7w*5KxANaNY&XP_bI0Dfv zJy9a(=~_<^^Mwo2@P|(H!TnBQ_e$swnUR%s17O5XXp0ruGY`@ZO87tiX{jVcyNBb6 zM75vB-TzlRCC9ki8lhBPKO{1?R+`f$wGW($|J`MX6+{j^f4oMJf`5v*cY%P&!jlVm zE4f~zW^?LGNDIw(L$|uO;Xs)rSa>bh(?*5mBF#|;vTZAhg8DkZ=8X0}NBq13+^^Dley|+Dl?JvI zNWY^QuCbf4_?-=%%@Hg&WD(ULULRgHEyIc*MN%^uXzjPff9v$0#?~e}G};EYZuKTx z!#|fREJFvg%6b;OsH-cuapK8VJj+NT029CZKWNL1gfXUUk|nM}{I+hzqCpEq=Ip z3yKZ@yNs2J!57^^>x}nDo&oY&Uq)W4rPkGF&=z-HnK(7z0jMd}zCFMQ9vFlZn(-96 z6E>&W3#BDK?Xbio0aeI?>K`o_Cq%4_ev(*8X#w#+z!}lE=;tilJ1IYKA95tDhDcjx;%k(S{P&K4kC$p7UG_Sz4I>hwn+c z;39=HD@CVKc#TKS3ah6^+xyPp@lLQ)GT**^3q&eNav)XzJ`&q+p*!b^qM=YNgTBit zt~beqPGvkKemZ;ejQOCU^?K!X7bmH^y&5;Uz8M*Y2(=Qdl>f;zfn&Xs_s6ax7FX%M?KwAT>8D&(1Xa^b?_c)n zSQ%TJ#Xk6UXb1?)P}%Ut;KA5_ETHhhJ3q$PJ{({$YZcpgx!DT53S!DPUEA)d>9|0h zsmA8c&o#rTA)Gr(+U0LM(luLw8;j3F(JN%uFuK!JWC{0;UxwBDcI>v5I>j#PTQzYB z8eH3dBKh0sQ-bp{Ts!TC)@L?S{#g(!j!| z^<2Wh?7Eu-GFOuGsHd^##%8%w{_F~S-CB*@*pO;YnNEM4r%v{U;$qZaay-AQ8q9_y z@VaLY%()FIl_)QUcU;^;)BPXNruU5Ay(PBcS$_DhP#lYlu~dKQF0E+uwm21G1p6@7>P&aM{UXYx-v6Kw){| zA-jrO9H(mNO~1FPHx6sut8V>LB6rQ~Iny;Cg*SY41S=druTEcV-c4?EUgG}IrTBTL{vN=e;q`h3~dT`ufyR%2*o zkUc9$kzegBFHI72!}aer=YzwnJ~|H}ffnYb06Lmx%cNsdkFG7vK&ym12lZmRRC&$P zqbJv-ySI4wt}zH?ffQqx25-;Q=eh5Hu@;Ch@qV#jmhDEw4w1d#O+=kOGthtEDg90) zs0EtzBr(nIblfp^`48@V$@)P3AwAcoErR0r(he@*S5n=T{#M_$tY~l z8ip(_4Cz{-$Pb3E{S_S1@uB@|_2K!X4+q3(b+S9=(^)Q9kV!fgCCD%saWoz;yjpxg z;VxcL;6|C$CoN_k`-rgeqLE3yPkrK z+!SF}1k+$9{e7TBp{G9lxdVB3&D~mA`Lsyl~ zAsLm|R^>BB>4U{zjW-Reer;Uct8{A1P^6B|=(RN7-eh4VJ191c!}wW}{DX_MoQNAK zX^DE|ZB&BnYzt8`;DwS>z2Xxg@>k5CTCsBk zv!3rfIE2h}{7KYRH@m}?s#dI6rD0XG{Vh*agiBgna9n3L;?+zZuneyHHhm9@Fuf#E zQ^ln4=?K0rYYQQ7VbWgU1Ce>}4QQYb*I%7>=U{*NGPtC*(jcq*qjO2==f7uPpWxKq zFca{a(K=@-uI4)D`EvNbc6U;MKzsXfk={_mwYp;R+@VTt&lLQX9|Pjy1Bg zhQBNlQmjlxe^QPX&Bi}0%+Pzh(A_7kde24n?i&rFi*GQ&ll3^mNzG}J3wHUbmDkd_ zK$DhZnU#)T>Q$rrwf-XY`aGGrU%*gKo%rjDCOTybC{UJ`>MNgGStO|7sTXCL(ag8x zET|7=;&Xyi8L!Kxrkt`u7>|@HR_UIFZvM66;RnIbno~;TzfzPm>%G-WBVBVJ7y@tL$R4yQU)UB<_ znWlKUFG-b})VWyLP6FC3PmGvNwz^#E2>tID%eU6rdzXB)c z8=}w4h3i@Abj%pf7i#<{k|>X{$=yKg38s(&u1TEi|DNLiQnPqN)|Wpx%q_9m39DJH z$lEg!8Fr~j<<>=QT79O_ff9Es2KO&K9kT%ZW}v{SI%XVSWl{_i63+`GGo8p~>7t}m~lM6Jt z)^`i@#rRlte%-inIk*4~#&l5CZtu9J??>$f^_hRH``Aq17fdF*UrIJ>$HkHl;@hsM zn(oMC%@5#8$#4kY-#v}}$0T+aCbiGG9ocqbaD1db-=}mM zZ82`9kJ^Xrq$?Y+ID>TcY%;#!d2iQm*f#~0!NGawPRD$o>4khRubNmjsWp|o6`#`{ zRw|hldE512zNKd<}6uoqd2;H+T8=;tYzm98@g~fwxWb`DdOljWEOK4mx($2DRvK z`=TblDaVBzlzG}fI?kdPQfD_V*n>RVu{x9^n06rMizA#bLYv-PyzeaCJDaOYD?12I zCEve&w1;t>dJT^eRFNOQ*alp;R#Et-x_Fu;aaZ2 z87*WGGh_4%)7{3=8aAz)cZwp1lM|8VIA|T8QA4kIj9xL)w{ueKd1eBvvz2Zwxvfhrqqk7YcN(pz7-iWiw$7hT@T=EW zpUA5u2~Eqcn1tJ%p^Snaa7$8?EiplS=K1P2BMW(=>u-#cI%8a(*Bgx?b>SGv(TEr^8@K44M!{=XZm2UVvi}Vk3xnGEBD=o%d_dcit?K0iC&#c`vK!NJ z!LL5c9%V$Xc_04~Qcv#>gm<#)+EysadwyQJMc-dmkW@_wx;0QT-HVO{p%yr8OCKlg z8v|tML-pgntlqNm4-D@LzbAhyRq2cXGL$#!%3!!xOuq(g_Z95UvmdwYwK(xJXow(5 zsU+?`;X%mz{(!*K`tJ6Z#LgkTp`vaBL?Gd99?fm*3sMTYe3KH zQ5wW+d{6ET#SO2H4M_F@>{Ax?`of3tZ(V~PTIsPB=}qG2*;AH+Li?aOa{ z{HS9!f{ccFh-MNB<+v*J`3)ViL$Ytp=7}a48qV%)I~+fLvfBuyJ!1~uK=1n1PADdS zt!E&K>3prRtJ!o*2X^X4Ua{@S&&%{E4`)}Vmp?uxa~D_XO5e<|`xb<~@oMmmkGi_wn{Y z^TOw~V|kUeoB|5ljVwPMw?~JOF%DXoWNJ0TJke;mmNl#-`7tYWJUPTk!4bVj(VXQY zO=-=ryNahI3mUS#dj;t3movFLuuC4e6pTL2rrSo??!}jFfsfgmjtwm6r`NRWG$*HiN z;4`S~Gf{}}^`&wJt=q2~m+pP(r76xtK$l;Sp1C;UMXut|FL%t1onFHk3Y5_5-Tz>) z01nBLOy690kkj&oZ~5~%H(e~fT7>eyJ;@8wQsw%0U-3^%2f-i6ZM?7l9`bN2LE7&| zMYuSY_!($xw~R?ZAU3y|{u+fFo$h^i=kYc`O2_Hxxj?LQ-C&*@yb|5bXGf4zOz`kw zu$`o>eY1|!XY<|M^CB4e5Z|#j9UM~d8w8P(RrDsDCYo8d?$}TR-jj%B;WVrE1-he9LZ~nmC6V()w!oHmsi4 zv10D>HbuTQw^KPEWA?GM*s4zs7Aa8Wl*^y#{6a$A>`foIQ2!)itEi|Zk@M;5>4OLczXW=+kF<2fYtA0^xs|P`=5aDaY%nUa zqFmp-#Pa?EP?h!0hl8L8^f^#YNJzLL5&94?w|&* zKQ=z=exScUUkQKIS3EI@tnsnFzP@`akDZ-;63D{KnK3VO<=2oV!owi;4sCR8?}AD3 z99je@e`)gpn~;!pe?l>_{9&>8YK65!Sa@>^-u6|K1etJ)zNe~!@aB@tM`7nQI|h>9 zeS)Wnk%LuemS_|~mMXaP^EdbH{%GTiq zp=(Ci(I(A&IA3TpBzjcFtRWzX*u(+5dZ71hdQes77mv6Ih*#T=$vr znM0}!w=1w@qR?|_?6jS1)}cepR@56j=gM^-m_5JoX9P21!(Q^0A)EebvNOb8 z_a1dk^d3ZlO2E8mit+2nuEbmpi~ntqT4MYDi@-nu8sDAu0b5?PHpalU{ub#NkCn!? z*q2U_Lvue_8Xg|ryuG>lb>qNU$D5Do;heq=`8%eqg$fg#X&jt|FZ_bL!VQC-4_A@g z&KzlPID!X3dZ$nT8uEoJWdHlDvM7#3t?O;u{U+MsO7d<{njeWMarlaf4RnK^$~kte zoDh)V_zdW4rD>Uxk?`&Jm)TUU^JxKV1Lp`;Y> z^3O}3ZiHoM{18S2l$?F$ML?mSYIs2G;=;WbzCjDz)r_yV7IYVDlim{39_mjJ$BE4V zep=eBQS!JLNwq^wR4z_HXK#k8_=(gwPu{TyZf@mw@7-&Hyxd=`tgO720IkeBI5?O< zM!slW!=)rR-7>%H`**891#F`Afd9|?VCnX%2N<7FXay~=qVn1i-=l~yg!683_!BIC z!5p+IYZYVJEHHKFD0uQz4un)+o8^FjDE!&?9CnM=>({UI0k19tIU6QnVIA?EAI3DL z%0N~i+{ei&3QeacaE4xdAIQMB zFk$=dbQ)G_kbryfg!pRK^xJnjh`+=XmiYm>CFYm9ZylDUzrTY8AmOkD2dJ!Wf)-QS z#Kc6{@JWpSOW1XNo9hEX*bTAR!!0MsVJSSHmnnULC9gcu|~qR`V)~M zKv*Wh?F^eSTg-8Ky26-6^!cu!g|U~Rw=MPq9jn1R$(9K6qbKTk3xUP=G?R}PYA-m| zYwm(Szn7!axQ8m)7!FZwGZXg@9~URSw;CMFY@W?q2$xb0>TZZRFLpff0(&cSjm?+v zlJL9BE$Dsrpqykg`=|UBcRfi}FJlCxuErbK4E0|3u)ELh`#!(V=beA{v%AARcbxm2bA9U)z3})a z6l9!-8CZ@>#=*D}q!_$(`wlZYMF)V{1=yFuXa8Z+&RZ#!Q(eaQh;`UPBfy=WbDkw` z?px0Ta|XF!6;&xl|KuJe!HHxC|3FIYHp9l9Eht*_nIBK2GB)ipUR9~O@htDn_f7G8 zfJcr#ElX)8^m)L&hefxCgo*R}Efrd@Sd211BVYk$v#NeMp8tV|M*R)IBlr{P8h5Tx z(N4O;@PLmNJ?TfL8wD+cnvb)+O-}e?u)(Ce9;_&-e?h^*G<$v6EGy`D#?_f&_5ddm z*njdWX+NHv*;}ER$8Xu%vZ_(V?!e@1UTes{5F=RV1N*8_bzv5E*4rJMssFxs{xz$9 zW{a(ZpPGNJk&4{B?QpY>Pj_4!n)4$c4AKKguS}iHBFfdb=ldZ$dD*-=4o>xp;@X^L z!#P2{Iqjn7R{{q-J!f{}&rZZ-{HISV_s5l{zw!BKa-~9(@}l7O#3YQGo{)RDU%q^1P~RQ76g&Np>=WbT41_In4!qCZ|q$GIk&8`bt;Xz`lGIe)b03 zHopp!K+e1ObTBU>KYEZ7)eX;@LMbwuQ$o?2<8 zDwjeyl=UFY#jzrG%%pkWiX?W+_>i3QfR{}nbg6o030CsdCPGf7?Ad^edxs&xC55(g z*uwI>ZN=6c(E{12LDBwl$BCn)QeOgb2w8z=OUl)@jpP46@A$y z$r9as@gI0X@>H{KhWk3snPTf-InDnJCXIoFw{4lQ{Ue);Jl_ohXqiSh14+sgr;#%t z>n4#Fl%&f|i4yJ4W^V||v@&#A8-GK(ieIFNg5G3aaeZX#IFY69y7Te^%RQy3=5SaD zZ;-lRknI^i-#1p=Y>0ZA7>d@#Rho!|;Yq*HMVi;7j1&S46-pj(>}=F6GaZW+nLp95 ztSlt6_Z6h1^t%4D%H7vc{^NNX`s>fHP~tl7De1>v8W}7r>?H{>$xce$)^dR{A<>j; zVj|m9@=Ty+&3Z7U&H_zEvhk8VphQ%NZTI_NQEFbKuB|frKzhQR_gO#NO`Rd-vcarG zYQ=2z;QJ(3h=4~$Z?WZ~2IEk&uh}K)o?L@Jff2G7Z^L(bG z-#^tRfsB)a3pA0DAwt#_D%OLOP*dA5%_N558H5Y|I^$_%z?oiZ#MtyQ6xl38j!{QP zN?nW)RaDHtpH;lXRRo<%pt!7Esm04vBEeB?18CT_M_wsEBf0)tcat4Fj?7!$Iy0H7 zX?cUe5mUK0ZHL`c>*my%`7b2?i5z%6La+>plleeP8p@YEhRv7dlg)!#=$mN1Ev9K<#4KDQa8Q4Y>! z4Msk<5~PyP{qf`P%lwR7=270)%TrC0S*SA+gMj)uj$iA*m(uU_&Dk{dIzivnl>1$Hs)oW#z<#@+a5irke=Kl=krW9um&&m1g z8nE7N;X^P;bJlHl`IK!T3F*hut7m7W>OLt$;r{d^Y5bZ#L7vmWtAJJrDT=Z<^HgXU z=ytF~NHrE>PVSAGHc~3f(O)_4b$2MGcVg5nKFgM_X-NZwcv#f>@h9DXF^wGCBHyK5 zs!#g`CUGIsIWjU*tm;WGofE!>!|uhWE7m>y9T;q4IsC?c0Ab0=5xdkgdS&C86>2;z zH2T-zh`k?_TJL%$8V4q{Vcyq-**a|DLD~z_`6dV>PkH})z-o|M{pT7AL74}$BrQ7YOvw{aMj{XFM~n@7k32Lp;V;@S{M3{H6fhqZ8#Eq zGLf0_yw{DlvWCm5>TmJY;;tyz8#TejZZ#y+t(0i>S$|tYYKgbuB~qbQZbHb zz8Sr-foIX=wbp3$sXj?MmiI}_ODL7jb2t$~Kl@QzyuybsJe^rvQKmT2f85Hk^ZtF# z{>s;Q>oOgT$wWW1fjr7gh%^b2?T|9K_kD?r<%*orKX1gT?gBe%zhDBNiOj&YI{x3e zeE);&;UN9$Z&IJ60^14t>-9hETEpIY?ks9p%S#1y3E44E1|_<#{)RGKuF&PSeTjZQ z?(h1QY)jk)nGoKF?JW8t+~~7$pcyIg1DZwz0=H>vOMVYj-_B`$1;#1 zileW*v3VWv9lxU#!N8b8$i~|LM=~H^1gHvp4ssxOitg5*8)I=)3nB}kt`Ldg<}024 zlR;IcLEUnAo-uXE7%=CeL3pt|79RW8$;a?N6dri`QsYWEt9!hCeA;`t53skD{t*?0 zz94k{i4#376PolUks&Kv+vWcHu7oW}dwMZn#5o?*}+<2OOW5<^+ zFxqz8A;hiyUd!5v88LtQ9xx2eViP#rNoS8G^;7PZ{r6k*Kr5?eaJw~3E!ZMQH!_|l z8Z5<)YS~L}UxH4~*stq`JYDPKS{gFV5Ho*tmR2C=UK~mB;N^wD{a^Gh7xc-~(`H+) zpD8MzvDHOyw-*{UNlPpAId>a9X>DHW{YiD|f^PhCozVaxpLIkz4x$SLZ~mfJuMuHD zjKwi0SHUJ6IMRD4eJ<^ISGC*6kKgRk9ve&TUv8CMxpibIj&%Aby^y)O_c%ORwzKkY zPOMvO-m&eecaE<4Aw*c# zeBvMstHvMyIX|fH_7K2{UzMcqpZ7vQ?wqs#70y%!H?jC`ImM;yE_nE>w%42pvxX)+ zJ-r%3dqZ!(&fo<2zrUn|ZiZQmL(UP)QGqJH5o_CwLAy}&_xsbaeB!B++EYVH1R?9? zBc7CX`QVg~M-i0aVdS8JZbs#8g^g)ip{IR<0)(@C9^UrOKXKD6cLauJAfQpC6u9nc$kK;md8?Iwz%}b?xX!p7n6(zDLUnPQT?Q8*6SBFXi!12 zZVPhJrdILYE3qO@;n3i#>4!f(01$e?V21CyGCx@ERLN+!^?Ok-qOPtEqcLeOJn=dI zXGowJQ3_jZ!-GJUml*20!9*bP=}(ugTAI8#|lxMqqcgUHE!LutCf;2hHd_ z9xVE}TzKp~-*~99LNH`G-ktmzYXQaQL2mVXuPw`Q*6dr;$#l3U_s?C>W%9iJIrp0D z_Dz~F1!s$A;aerw)t6)%E-hw=V(kFKtI)!i0~Vh# z$Lt_nw}RDqhl2k4$5{t_KPuZWYI9>BSxe2HreaROQt^fp2m!7ox9@EWD#EmlCc^hO zaG`>F-#m~G@V{DI`hCK9qk=}e#j#A3bO%g5ZfTBGJ^6mj&b95<5of^vs%$43T|@~0 z-*SK!H1O`-yY&#V!ImED7!?T--^=sWyE{~8&h(hJQi9lsr6fqU=si(soG2ph`!d3Z>%!@Uxdfp0MeW4sk&HKI9_(5%K^=~F} z`l|`;@gV+_f7{5B>K^K8FkUu@%9F9%o6XxQyz{<^UccTmfVCW|@d4w29sSv}#pB7a4nckV$mzQP~P1 z`*|L_Zo=G1;JG<$__+M-U))6}NzC)7K9ej;9r%$W=bn0%>Vo?Kvr3paaOA`QZVHP4 z%=&AYt-CO{y&JT)>6kh~#vhy4ZDBYTY2t^+=mUuNbj-hFCa(NDm6}uc#?bUI`%gKK zvd+nJCs);fY};Bm0m=qSM`v_wtmRb_5FFb6tMk>>iqP>rzMTv!@rw~SD|J2R)Qz3DoT%PTK3&95kq0dhX(4*yoZG@z z*Kl!&eP-p42WYv@z#W*jB_L;do}X}k4*{r6a!G>DONPras>n1rxt11f9l`)SfacDK zFH?_1CeBJrO9yM_8#`OZM}w@cJ8SKUI)t8wLt1p*iZ({=npR7d$F@u7nKWp@hIm(; zk)V&8yTIDYYwWg5GDV)>{b&$yt#uK){K=}QgjPTR2o)V&fiDx;{*e67az%q4z90$h zgtde6KbHsNobP`Vwl`hdrW4^;&2tKioabA%qB=!)k-U9`%9!GsXn!FhY)fao*YqpS zkJq@KPUmOE60P*U@1oXK+wf}tS?=+U<&AW=3~Zy+o!d24^6 z1sep=^5P#s|2Vc9U&@F`K9i^i`UmW7s57fTM!xiWEKoLrTX(!uQ9MjFORa&{B<2FL z@}IM*umh($iu+9fR!|QFMD85m27k%bILqh`>WL|jx+TzwNY%B=+^toey zGfe0;6d2jj+Q}IVA@G}0x#8!Q;J+1<-Eu*hqWxxzG)qV4TQ+fMHb{=sn0Np^omtx_HA}vc8(m>ESL5cCo&2It~siO z$V!Gk@Pq{j^$GNBmkz^*iTte0ZH-s2!bz)V_+Q%D{jMbUG}(RzObh&!(#Y0E-eSx( zDorj+C#~D#(ZN$qCdu5vjX6j2uW<9qxiH_8-IHRO$3JwULPksO#;+>^0oaX)YTU5v3+s?3@<(JR?Me4B(SAav&uvveDN;9Ie{*zO zvObCWhudsDcKv3-WiI8*UuHIePxdPRXc+db_B#DwXVq{;hq@+S&>g97;zgXdVjC&C#N7QBd z!OZH{P2QDP_WzKpR`1DG#VYlW*^rQBvJ6DJF7btZYn;08X-}V0E+L){N5v)=)TPNI zYpN7d`+t?+wUXU~dt3%tg0NO2U_U;{Jf-L=acsyH{i7`C<9}vuT16Fzj64sVFz`*2 zSwrzIyBk-@Uj()V_!H7A`R`du$~?c4R9%vt)Isk2zC^d0!^rbuhcypozO_EdGEpJj zZ>}pDx!gDT@BX)j)8X+-ch^ES*E!#^)bVZT_KvH+!>Mhe3j!?3+fC$iivD=K^Z!~_Xe1DBhOB(=Knw7SK#7H03g4yr++>)#O6$wn}@|mH;-DOrPdd9(!|Vj zw0z8&Ur9-g@Fv?KZ}RihFg&YeYrb?~imBhb6-$N>=jbooGjc?@srX@Fg~ohVS2j(< zxAHWK2JAoFD6&E4T5CU1cgFpGXXv2vHq?eI)SK7!<()lf#A_0n&N33)jA7sufaocX}G3%c!Z>;jO>*gl?DDxLx{~+`st$|o|gy29* z_J==JLefz)5}OGpeLIaNO8RNs-a4@= zNii1Q*4&urO)!nAKQh|H-n_D6?14D)U+V=(xgC^W(AaJ)H8R zxTF2JzwUt60c6Zm0NmWLr`zU+_1%Fn5tJ4Au|tYNoE*&SGwCez6PkK#_x)j*Z!G25 z88%_}`wcdc5-))iD@*|7BEe^+iaq2s^@WKr1$K@s_YftLl741eDP?ZH+6Z>N(nGR5 zn4|r7o*1Np8%R3Bmoc6J|Kc6cb#NnV6E9?ku`r(0CF-n9|FL= zIL8@xs@k1d&b*P&`KfL#QFQ(H8QgMKh`%rSF*$VT`2uo)e6mk*8Ih6c`TrX;yha^i z&f{P;pF}f7=Z$Q)-h8yB-D$>$be%O-Scgl z(LO7SKC2(T)X_l~cnSy?d*|Pe{pR8)oidotGG0u)a3=Uzgk*+ziAmCX+K|R_66!{# zg+z%Srr6UI)vvLm#R>%>ni3Op%D<8T(o)KWa8v;2EN~jW@ zDh1e?dN53-euZ4TgwYX)^vb@Vo& zLDSQReTlw~eY&UOV`7>`_Qohl43MAzv(a0zU7fu7c-DZettd}9Vf&z;ooUjR#HV|N zCz)wJG8+}<&G%kFRG;66-Nqc;5$>jwq4=GESlp$(4IP#rIfd5DwQrQ-A$Ur0LNttvIW448!K5?G(Aor-v z-WqU&{VW~|iK&Y}ncku{Rs7>$renQC|8Bg8W z)i(tq6NWQ495P?AGC!>)9g(a8W<4MpVdt}9H!1bAv-M3DGW)g#Fr;>hI>~|@zNj7v zP=L=tBN101@2-z#s%QEQ>G_C@0QggVE)aM934=)7HQ-InLj!k>V(Sh-o0uot?r0nCk`6=gH*@?41ACv6_4`HgvoSmjs7kflNy*wz*8e;xEVW+OX^Rx~bMA**Hb=-K2z{Zb@odt;in zy?4N)jD{ZiCCzLZM zInCeqv?d+NtH|dI1IWg6x$9J-C13gc{-b};9(+Lk7P23~xKU6B2RV1|UI^c|bl*mg z_C;=W|J9yX2WOrx%TtNF6aceu-&OC=BV8#m-ha?b*p$h^3J=@?0h8N-w|A}7S5|E| z*+#$Bi#M@>#M(Y&L$p)-!4PzpfLD*T-n`@Dd0ihH@51qTukMw&l=KbGDd|JFIdZ5` zN^o{!HVcb5h_i?e1y6bNG7BxO9>#Yae<^mpN;~NYrS*c{-i`T!Y%Q&13_o_uX=S>1 z?eSFAlNm`&0uN?e9_n0l1_;#IPLC!R^EssW?p>*+i3r(_*VMICgkiD0v7E32Z*Kwn zpKFP6hY~(nFJf;YK?|t1&zmvv*~B51hEL56i)He1E@k#*%O}X^hbr{~_ef4JB9O9e zX$a_E+Em;a?ybaUW@g2uZ}0A$svPp!d)dF;ZXS_N}Y<11$A(up%O z&l{yz%=VyuQ&!Sb{u6{hCd0|h*SemPMpuL*f_Me7&Hc{6y5;aRq)Jhmwj4GYt(5SEb0U?7X~=f!W2w zCw06hwoXt|MwFD?kd~35%Ie-F?2`xij6`&D_Yj=~QqTRlDn%BkY-U(koXx&2xbL}^ z?aRy>`tr9`;}PFU@1K7D4;*pp_vsDm@r}Hy`ST3eFvC;JbOV9tmBIjkQ}xiuL~fe> z*cBoYLc{RfNw+%9DMo0#(oS8Oasmf9E&pY9_Ta9rt}~vx$gO9lXczEe{O<;xG#F6X zx&_z@F)PC;NC*TOvZd$BLw4fUadhiLMmlcVJCY5VH43LCO`rR((k)r^qFd1ijuW-k zCqZ`(EBXr`4!Rw=% zXgZC0TrF08;>xrIvJmf&EDC-|R|Mom@e1Lf==!6>FX za58?j*mK>3`MR8t;+a7dytR9OdiZU4j8qsj+AtmAaM`}|pyFvF>4oXd`HH)KVR13U zwxt^(`IdSjENU0096RHFFqOrs^;q;GQtE@e+r|0X=hY4OlhLVnSChkuwfTghh`Nq9 zm)`V-UsRrzH5@_`CT@BFuN>vPs&Y3CXZNnqJqV#lkO9$ ztL-Z`Sp67zYjH%_93qa@KBWg7u=D4;mdR6ARnbxE>fPhP?!P9C+zxPNQ&Y5(CD59d z#O)I4ms+3I$%0)&uV$@{J2tf`IZ&q6X?T&gDC=L`n(=K18~Z=;KT%dwj#x3+Sq72hRiGJ8(A%=zm2{O*gS< z+!|-as5o3Bp`xkDzP-5~k$A_qsD8P)c5xFBv=%#PpI6?p7GpXHW`xb2OUr$aXz z+$S|V@BfGn>eT>62bFZmul?kH8Rhrh{}0H|7_m*F{$cP81rIO%ssl30P2HpMbzW}Ry@P`T4PBnJ>6ntf_)%Pw3+*tAUUcuxIovyqTJiQA%w+t)Q|1$pml`n ze5JZI%*=tm<^iJ&%{8cNgb;5QwJcR6tM|;H{=xXACEMjb`WsIZHm(#Mq^O`21v!F( zf_2Xe3en+}Eti?pi1EB8Zi}L+`#3}-$Xx!y$=SA{oP zOmsX_Y>pi}HVzr@{K+cVSA~Qz^HA$+tAx=W0AqCR7g>oO0#-D z$%yUw76GWdBnp}|Qny)X)9UMaZaWD5{`FE`BnqWlPHa|Mc{cQXjDmX7X|A{3kO#=y z2uL?-1RZ#V^)3V@Gyw8#MQp;Yl_?5KJ<9Jh$8)0l-k&&W%Gz1w?y3p=hi3Z5dmg0g zTHjQ94MoOwMpeLs~b?QQ;>v}liyeP6B)SAbnbPp2GLEy}%!eSR#yJ7(2pA=f{#kn0= zo@`m(C_(b8&biLzx{C2(FX?eM3glrrvcwiN=v+vmP4tKxTydD5GMH!#E367MrS~rSR{8mJ^Va@dxCN

MxRLNGx#;UHF_y$8bS!aH8f}s! zTdl03AxSLU9m|;yDTdx5xd_D~=XqfpLe<9JOLh!rGfl0avYV=H=(hPiO4Q_1Uy0>Htev zMLu9)V6ZMXOT?k88zG>};4sCC5P!*M0$2_B9}|GtvEHvlAzG;9`NeA=Yc$*l&lKo2 zAV5fexlbm(*!-V)@j(S7q8rp#SH+=C;AE))e~-Zq{7xgZ4`hYA9`^qI zADxZ&R>2alvv?KQcVz3p*Lu^gb!_^Yoh$G<<1Z=2uC909qINopetAmxR7RCGL+xT6 zyb;Y}({{M?{yb4FGdnnmnrn6E48A#S>zHX|$G-qG0qTb<`lS>k2=PKgiDlYOIpl(` z&v$|gamW6?rEW&L`FQu^N(!=WO=fAQ<=v+jS5ru}}5>5T*F4_TZtGz%f#0z5F3I&9OgeH%hN>9)W zmR;BPe2Ht_w^_0?C;kP8DH{uLqWKv=Z!3JRLem4WIFY!P8`bSPU?0PQwu4`v=x)Gy z`O{PRxXM}qdn^W(AE!Sa^}F#&ygrHiIc`aV3b%*Qr|3UM3o&|GcG~5GgkzMHg8gXl zZM539NZV_gnr0w*P_E$-p`j%|0vIfM{24`ES^*SMQonom6(=|M&qC;#NY}auOy~0} zuH(Uy%?9T{7l8s|7O$zNr$>n5Hw)2pT5b_^9jL2R1Xl+N;^|R8EK1=Y%|uvhihl^Z zTa{VESYw3uf6F$13k)64ATx7Lv&sU)jqziPGwnfws9eKEVii7=PcMONrgJRUxcTGb za9qTs#7^53FoW0Dk*89(JdJY_9|E>=;u$Ct)~f{q@;mC<@WAC~(BbTz0~tl#2u(2c z;2M8|jyQ;{IeZ}c(K7Nii(QsC9RO* zr+eYT1ypaZ1_thOg^9DKW+bfpKY7=MKnPMHrr14}Uf1G&1r!0IMVQt_kkk}RnQY75 zm6VKLmlBWe4)iyEp_bJR60UGM^t0|v)k$Z_*E*`6kdcvbRo`lHzNSk4L_DCu}>L7IA8(eh@;Pcz_PfUe1LM$>#T4l$4c4!`E?`*7pZF7-A2#IjR^M zHT3jbfACbGV<{KgR*~eku%yO*`e0wRD>fs#tBm9;Jo%(1fGEflRvSPc=28T8a94&U zS^;pnvdW*Ixo^6YX5<0C-u2nu?bo-``L$@kpoo3diL#!S}qAG#V zn!@8!(Ty}Pz&5C)stcM%GYW3YnHH80NutkX$e@q4zjdp;{Y`tKdUJMPr3{&s_5)T4 z7c;+|kam5d3$N-_RcKn#&V!vBzq}4KTiqdQ?K^st1{}OS+G$|g6MJ|}{P%<1ZVdx^ zi`(AzF)r^`HkOW?c}_+?A*aCp)P*M_(FRzNA*&QggXc=Z0}dUx*)A4H=~83T;?L%~ zq2hajLGH%qk)*XwXp~#oTG!=)qa!dm_CD6@_cVQ)>0S%nYRt3>Rh*zt&hI~LR^d#z~D0cQY@2FCj*>rXu(<3mtTU=geqP7vm4vK9p z-07rj8hQFrpAqB;d6Lyv$g=e}h!i2~qsNtQ%qv^noOU2>?_-`PG6&G}^z?o-9+reV zOy-IvLH8_=)plp8k$9PTOvr$0IYDL!Yg$-(xL;=Tu{tJMk`le%NzSU;oulOX%Hrwg z*nKOq^I2ijWphxG6iW_Uufi085lRm`yxg6YlI6sZAyd-}ChdlUbA$>>*FfCeB)T8! z*~gsbeGjL1*L2PDoAz+ntmCqACBL&01J_|2MEF#(mYR{_aX_4I$m03uTOL+{pDGEs zQ4|XErSy6g$M3=Q&MekUCQkjj+RB0cS5>NJ)sF7ftA(=_2Z&+e;l>2$6-gwt=xnegs9RKGEB?rsL9Ve_Ng5v_${u&==);Ll66<=k!o~0`vaq zaW0Jmv&)F3MXeYyq_UJpF zC>|^EY-)0Kv@OOmbgzhZ1d+D3Z^z?;;WzmtZzgSOf&-M)e3btn* zp0gdNL~YzL-kR>4A%z0>jKY?df85rLu=oH0NHd(hf1(f> z6oq=0w^?{PUgA|sQV>y1stX7Us=dqp^hUwy!QPYbRGi6 z&(l|S3s@__9M=rQ(?up-r3L_AHl4+^x zs2i2qidVVDubAl9sdK)CU9-jp96(MXxw2ir4P)>WqAXnpS}U38SS{-3T0Kg#LJON- z6uUol5^rlw6OZ5LB+H;5I3@PeWnd6@mRrYED|;H(uG1jjdT7F$C0X3nRnDfVKui0G z9=~Oe-b#0|Lp2H;P>IgEQeic}b57M=SJl}SU8q>bNXgLE2aAOTY(N5C#oc?MsbAVAA^P z6}}bwi)VfD*kOhA&I&X!ik~eTH~-RFz5iFk(9uRlg1o$|lrGp`C&!V^2*!`fejE0V zPh31_n3xy@dh{Q;=u`AN*!q^SI*`rEW^oQ_hI2W2R_P7tgmVplZitx2;Q5Pfc5F7v zoAS3&J*E~MnCuVV`sagU$9_m}2oAeY_9rJX&sHom&r~|Gti9!wBgvl6-#9lUC{1PX zEj6jb>{z6EG7^{N@q9Q+wXa25TgR0Cj8%5xhY!R&msNAV3mKENS~LXza*j%l?9V+{ zwdd0xJ6PG<%4V0SL4EIA%5d{}&L^)Px!U2lU#la8WYn#4pH8inj0peTbE$TN!1!|16~OQ#dSa)EFxI)n;|= z9GIKqK8+wMF=T$#yFk43N1VAR-DMg9m43u{11nko$E(yDt0&mdFw z>7FxJwJLhcoooW9Fkkn(-^}JmRVfncU@FC!1}FP;6B-m&pJfh9u1jirn-=C({h;MD z?)q4(`cgHP{sB{`JB!!P@9B>-*FIswxvjAQ<4~|&AI6#F7P;GZu1g%yu`*kN0(PQ511V)#lk~ z)!SRz?WSKd%IRNOBYo-#eJGT{e46^1X)y?l?!v7RWJWX5SsXU=tE%d{MghT9iduu{ z1A>jZk?8rtDn(Us*`YYeBg_R#9$gs^8L5kAf3=3N$TqKQUDkH5Ug1k+;2z*gE&MKL zRrgJFA@P97&bj;^=C(BqX86eVxf$n#UB*wi`8&$0#YZ6jiqQ&N;hc}?LaSIa7Var3 zFtl_$l-Op;VDq?R+FjnEneyTXV%*ZqyQY7AI$(yv#O}vQIXLnKbl2B(mx}tz!0P;!$(SL5lxdHh%5ccssFx{I7Gyo|1M*Wv?{6O+`o&AmXoYSMY} z{_;r*n^p7e%*PY3QNFCM^XxVG?Lwl~OI?b!LZU{5nbd3={<(v={UeDQJ*)3o7t(#w zwVcs3KeSkM1)i=)RNd!#(>1iHHGw)5UNza@xuC+P*c~p2b<*1`wFpSrL~SO#C?CQw zlvtPwx~^FYO@1^ON0k$J4~2qawz^Njb<*N;7T#?ubu-QV>*`|RQDyXvwl5mP@r4js z1r703lJNnIve~_ux7Joo$uCeHaT|13W_QeOmSzinDNmj0B9~arPpDj2m3c#NQ_=pp z^P++~1!0INtdXRCboyF5ezo#*D^5mHQE)S*G#c$pgEN?JO9ZdudSq*UkyQ^S8?8RY z*@ik5g*N-oykfPB=#Wd;Yercst`u1OucqMB9L`HKRhM~g4r@lq8{n9Ut|G|P7u&MRinpdHe2@3JZ%x!YeP$)sJIP-QteSs{m(-BF4>fg4>}s}@;X zT~*y$FHm-T)9y}qtPxSiVnMGk)<>-jclQIGx4G&XG={h=zVkbo-MTABpR*)O;bvy* zrxzuMYogGmZ_tUwLy|8aQM_1nrq7%`_=&EnoNf2BLIm!dt1x+5QS}A@Lvn9WU-Q)h zhV5EK1+Yugj>0c84x@-#g@To8?dDAOPV+=!o0rZ@JknpyUa9Q`6~jQA!DN=`TSh^O zI0q(&-NF!^tck*JLzsiH;MPd^q=v~lUBLD3@HSCLhO@HJp&9zn83$A=xMkJdK4Qq< zYa{hN|HPS+tV#N&?BXHwHLKb;r_$(j#Z8E%9h|Hye45+G4%wUlpqX!R27oHd!%iz4 zC)}$A&UUrNc${!=FQpAT!L3^PBtd3$>mw!${?6D~r& z0nMtE;}E@Lrob)QH=E@7aivzT&^%1&>&jUbcjMcr%!-PE_g`kDVs@Tnd628Smuffu zHW#A)Jn*5`wqe#f9yP#`?=kRbXJ0S+&R}*(58*QJu2js8iir1g8g?hVAB|t(3iGBE z3*FMpuLZ8G6@MFB_@Q=jjCs-fcr6yCn@_mpIQ9>?CWh+(|5atJPyM}IMwy)#Q~w3e z|2NxefAyi4;sbaY@5G6NQQ^jV!oQLtVlU) z5tPKx@)-`c*MaV7ex0_+*KzthgLTD2qQ|Cxiq5k`dn`9~rbTeaYTar?zXBEmgWQV6 z^2qER&eql0V=Zk39HMPAVU%>gAcWel&<9V~Y~kryIcw07BHdCJXd3IC=Ud((&1`?z zsuD&|V_{(Enq^0m{K``FYy`X6J)gL{-J6-X31OGMpz9qNSxL9p8`#z+d6Sx+2El^W z_iH||(bM!_wPnInq`H5$KZEm8YdFwtmiii^hD8tkNt!=08ZMFty=!>vd?gPz6ZPe+ z1-(jUvn6ITr=D>cy;Uiva4Va#r!qR5vqi#Cq%`Bo`9*#OPYw^jyXtSMh z3tN;voATMT-23`d%#JdaQg%~y=KT+>Bs&Bm$w?(rbN?1`epxrZWDr$^!jI&5xbwe`4`;O=JJ)AzLUUh5e4$a3iac&UTMY{?LKz*kvvTI?=g z{x)YE?O`MwMKQLLd9QCd79}wfZfTF4C}5d!S2lzg;k?uY6-f1kRI$yosVE+B;DI+a za+)nj=-BbpU)$>}yQ_jJd0HZuDb=zHL>BLM3bW>U?oJNeL(Um-%^xI4oP4tvSDl(r za2w~VL9ZC%6;&ECRcZ0|KSwNdn+?jwByt_UmY*oyrgel zsSgPYA1rLfxGvs#X>-^#<+tr zpwoq&I%#Z>kx?SIx#LgGeZs-hCih;uZOldUKHc%PZ456gwAMby$klWtAjr|+N8`tb zccQGA{`^aQL@rYDwnaPT*PxTplM;B}r)`B&)ODz-YDGCp(K)_=MAZmYa& zXqIz*@9$1-A%Ax?qUHjN_|9dKVK>>gp1*I^PArvXcRmSTbwAu-bA;g%y(^yQ33;&8 zDZk5Hyp_9*dlfZ9)*sOU(CsKZ(s*}y8H|akg`L~*kMx7@Ae+yn z;;gHI%}^b}o!-8E+Yn&gju7xHv0b|VKFAS|jpPyK2CJmaN^6w|1GKmmAV3B%Vq*&i z9z)~FlPAn#VtNP+yaC}(^fNR?b5k0D6Nt3zl4L+A;5dSfCiBn=DTw4K8XaZ6eq9Y{ zu8VvoKToT3=^h6s3lCz3Oabx)*tEw1-@(qwURqkJS1i8TrJQO|7XXaRl^1V8qadFK zuMYdx!U6W*&rvtrwk|px_wx^1J;HDSoqq1h_gG~Xq3cOM>xxBUAhQ~xunSMEldJ*z zF~1kbD!G{;lP-I`^E?4Id=Y>%J*PMVwwCjDlg+_k1idcv=zVyu`=+BaKs^)9e669# zPK#^p`b}sS!E4lbnw)|{;6={iT%@Jddpd!}SHI}$33!ts24Rbw=(sq&OO}{N>)>?} z_`0|-QuBSrz7Y^~W|Np_J_8OIUE>Frg3Ri5p+kvjpi`ewM8k2Oit{5QBfOTqSNWt8 z>u>KS!5KFJFKP&pUWlMTz03hO`79ox6aMgq zoWe`SPcNSYL9nUH&k(9U2y<5gucuO(rDkluQM)7 z&<$lmZ;Fo+YA=6NE8Mt6T!a1t@f+UW`w_OX1#)aXJ^a7u*EllhXyrV5fw%l>u=G`GjHsFJwl9QAejKaS_DFHeGNofHhjzxy>Q_c(&w7`O%|{*k#*f) zZ;0x_Kp3=RqUeM4JsrEO3uTPTsoe{LgZ3Y8LU%G=aQSUO{~^&hT*Au}yn(BroPc0A zolC&4P?>_>IpJ1;6EU5$xN%HBJKQXO=K@dl4Rw#wEf>uX9)t0PAc*$WjF^(9yxm>~kGkuw(JBhafGNceVt`nv3h?m=XR6Niot(K5-H(MT1@gp|bsb_cgX`<15YS7$S4&&bRG(kNe!de1 zx&cvEQc_YDgVL41X9Ze{J_2MeUJEN>5VpUHTNTKL4n#Nu^WQkaa(1@liUpKbtX^Gh zz0r_^*Z^(69|gP+s~TDd$Qy09i*uc$qmrxX**PZbI+qo2R#t&Y`76W(ry7EX~n3oqV;^GfjfcK=fg+w0Jw zh`&>{Cxz`kSWoI?U@Db0uxlc=E#FwBdA<&<#k^^V4m@qq2So6whz;CYZFFOd~H&tzsnM5nSfyB?n9Gir}^5}I|Fkiv~W)$Fh zXWDLh6ZMq<-Mz>#VxMj+YO$|4`sbu2&4qs7c=T*-AQVq5(ir<3}j-=hT@hE}bf zeyg?0hFvTsek?Z!=BGL^pV8ZpQWKJ!Hu>e%nk6_-v=+Z06OPy9l&$dhhYwB$`3^xo zu|}q<*Y&d$f`wS!I~o)zg=pJuY30K8{GzlHg!1}@`kGVxMJ+2`;A)Q}3(})LZl|B_ zfrV@Ve) z&)uv1sM#8!`JgH5AqREKct@<6-g@zcI@AUJimvTX*&qs;vQRBw)5Of;W;69t3Fu7f z#j+YlipLHe@;3HV^0Ct@ch-G;2DPrC>gHkjbMoK!5)>S{TG_@D_u`X@ufCO4Tf62{ zR+&yzoUojE-UDtB3tos}#{G*j zT0LaiL=9a929RH2YDLjGqJN25@+saPZXl?!%N&?`R%8D2k71n^N@dtk%Z{)u8)CLL zM(bN6%6=3$70sWoro4IELs~?|1N|oG1s>aU>cqRQV!i~QX(CpVa+U)Edkq~NgG2im zsA!D^kQ`A*Y(4Tq0^_z;=!>_0c+!-(T_AOvnwlPB{thLn-f2xssLbgGIo-syzEncs z;TKw|@Nno-%Ai#qCUk;|ic+K|!O%pchN2P(5P`EE^nJhc z&iDOu&YXWvX7$pHZ}C&4u+;7d_#c~I7Mv#U%zY%MBK>F8JO#QD7}gD=N_ zqRVc*3F+#i((&A8DE3Ab-ON=^7}=TH z?Sin~`cl$Amu6dv(6edgcTH*yO{Z>b#hLEFfbKEzbXqfFRZ~mH<75^!ZW$cRLc*4DC%xBb?Ht+3P&(y9GNRMkf!&Nwp-Pbbm-lK5rT?302=W?^<&Og zg)btFEX$l5nPu88vx=r$dW5_u1ka@8l+KBorWo?_hIp};J(}ZWtehC!W1K-Ic{Em# zp)!K#Zce{2XwRGUMF$S;3IvHq)0KO3)5N~u5RcKpaZO_ogXmoiroN8NXKmcP{rS^&{F7f^UXCj3xaUaD(!1;> zhJA)(Q>{>{oZj7OJA=01*Jd zjYy9!9~r)l&Kz$?jlHc^`74c(#Tpk_^6o&hRPI6~gIKWeu?O$=0rES|as^S8iQf?} zaZWuq){S+s1c*v(4F%E<{yTu|1T zR9FxbZ-jpmRL5?ZjLP0vAPvl1I^b^FoKqg)#*A^DXg0-n`sfd-lq1tpQ^%$~$}YN{ z%RO*`uErqjcs+g~hY=uxT!2J}uSS#qh~{!cKlBV_2Cp>>!-%mMl6ow1b%ifbX+AA^ z5`D~@mA@$}e(O?H`0i)uS)n|x`ZT9q#|f6FfxPf3M$v_W4+U&#y0#TL+V&PI;2$46 zCY3}a5i@0XUTbvWisf(x)Hy=dO@*cUMuRSzZ(86W5g>UpE1{K z?jAcYrhE}d`A@%)kE-H!IjJUnTkXLmCQ2O0Y?BnlJy6(cGDT^awIrq$8+hj8Uy^?+ z%E&YmckfaO%$bv*f7I&5clP#}HprYmYZo9}C|+dt(=2PpY1h5EX@zDv(gD{BcjQbl z$OT$gCMJJhI#2pir>0eRMW+&2Sgb>LPEpUF9f1|OkEeyo9bl8~%G^I~sQNeIz5D2{ z4Ir9rN~Xz2XlqO(VE*S&^knN;L#SL~SM1+^%+4nZ7}x z!*R+Rwvq?RAP+Zxskfnfr{J`TAeXw!XyK0Qt{Wy11CR&C0@qKm`PqLB*bdc~Xaa`u z6ID~&A@z(dSbB$qmHLsBaJN!2ogLM}>iiI$`c0aEcppI}YODnS^qqx-$VS(-6$))Z(4=%T(%q=Zhso07Pt_1bILV zoXLnrkv3;t-jz+on8lSHP;(NLXC6q`z?%KgVAO{)NG{p?D#yJ{QC>ydI~Io?2pqsL zw`Zjg7#Ph70pEfk5dU0-dbzzeEUW_LAvL9)ZO_jY%n6<0Rq=hH)xQZRoe?8k)xD9t z9)|PB>$~v61?@8>l@&fCiL_He>*}DIy6mt7vXi4gx>7`~ciDo+d1S`W7B`OV!M5ez zb@}moFT4>>^@;b01uCNC!y??*&hT*R5L5YL+s$>ujVbpGO)u&_d>e5}>+AB8$33+P z+2UVB``6F@!8)`XO*k963M~Nj(7(dl-M&40WBVcbN=SS z{m4jng#}RRnw;}@4Nm2m<_Fcn-gZ{>+Ltw#Yl=+s1U0eeS&;d2#;#CHfqjQi? z-2i7{k2jd!RXwliAQXrMYe;dFhmFWZo1t=+Jp{*IWx|C>(5B7=k-pm0oxu&Hnbw$& z{ZN^BQKu*7o+Rw^;gPtLJQZZMF9~Jz&PNXP)&_6>CBST{o`jQ1Ru(3K5aT|s8NR-y zUNpF{;^x-^B1NA27@}UR@J>Ap(omll_4-niM~)su?){e-WoV5VL}FI` zJj(M$7WuW}~55@!@u{3=B}WF2a%U0}R-lQ6V9qdvvN;PX_T&+TS~KMWbC$ z%!3;C12PILrPDLh($?pr@|a9TD!WVZEgTYYhwI4!^GJhGMlB&Nt#bV|Y|ZPph`Qxx zU5F4Q( zE<=(@VoMwR(w^M3DgoN4bMax3gZY(B_+$vlAqWA7)qHqN0sx4W zD1C-iJ~pBvF8;cD7{uE&=V||}+ASpcP!HaxsQd4ze>KOq+V>n^7{6<5mA(UW&?Cc{ zUC%zin^{ zcvCCk|90DURN6wQYTr&pm#aVe%vkBOt*tCX9Y(EFxx0471uK^H$_FM+5$#`d6fuMmHWW7= zH}Qn>fGwknbNzR+n@sXxJHHp+BACwESm1Zl$it4Dk*aw=%=!T25J2gv>91b3|@2Fc2YfUPdoQRgBG( z^*zcg04qx{_e&i+0yzlQ=QGSH^FpO%O3<@E{!^;Zeb3*3Sfgq80mrBfYQtv@#6el~Gv_4jD*Z>Nc=3=|kF=Hal*v&1m3 zkfqER1}$gZCPA%{`>ic`rF%A-fJf#aYFp9i*`KrB`kdI?{();OO>NW){o~>v;C^gB z6zUfDErgwfz>zt{Kni&T_Uyz*YTjQoi#@d^R)5j=0m#l#u+_w};412-?)SW)*-9Ds z-LRQ*e-~|Q?Sz6nv|R|k032qt>r5ec7{A>Q`(<>$mV-v(YOGhDdA=9h#+lkb8L39Q zJpuV%_utvOwBuKw%HM&YJWY-n2v$z=_~%^HuWtz31?{)-75`@-;;>&y8|{@P>M&$) zW;(X*>xilOniBSlLX_Bd#y*_?xY}oj3lAT?_>}a_*Y?kdqZ4?vC^dqcvR!bpKElbd zc1$oaF-h=)Rzy}XuX&3LA6|c-^JXx+Zfp@<2Su71W)_h$K z0w>R%Nl#Do4g2%6Cq&i;kX!+y@7QnfWbe1)hO0 z)Pcz7)+^I)#TTKQ2v5OdNles5-3-%J_9R&5G&l6LGp&i_|+00 zwK{Ts6Kl9qUAC2Hm|B5h9gwfki(zAbtB@lEyIzcRazJbuW%Qpxna*Ap$2@AaKelK* zm>jXq^rapKg!2k-8A^Xw72jt7TYXrX+wp+Pcp>uV`)keL3csS`Rri3eb^^6l`_gX_ z?xT;EWZUY}7j1_F`TJ~QW80;aFTe4j>CDp<+x?g-e0i+eEmfa$+%=<>YK0{v&!&uJV}qRG;-Hfi&wm|X7lI!)qtbXekNC4yS_BCyf5f5uUo-}ic6 zD2VG*K99stS#8u=<@UbKL%fxTAmlf@zfu{2u0vsOZ}qdFEAD7)N$s+{T2~d#A?5k* z2X51yx{FDkPY!GrlLY9)LGiXW{%d}}-!2#8y+@J%d!Z6O^&dWJS;$v2F1E|ayK>Bi zV(r!#j{va*Nn)zTmAYn8_+~oW$VNh`xJFO7Pgje7qJ0B#`=9F}``trQCv_bH>42eW zqO2NV6|n(3wJFUhTB=61zo2l4*F#sI)hz03g`O z{dQHlM@llz<{weTWQKT;$I1_mI}cH{JHAyt9{IQ!%By3-xpq8le0(BZGVBj3{yhxg zm<8bJ5^mk?31_iqgBqYfiVTxZ8@Wl5BD{u*#dYo~*i~dGQR~*NL$qiOkN}=#PDCTo zO;0ost86oD(lbxRr<8F|CL zth*QN`2_TT_gOBS&x*E4*zdAnDZkevgT*L5ZS3#1{XyfQh1rl0XE@>HRi)KUeUiAS6bp=)BvtrO5NaNc;CA$)!WDl~}8xfNA;o@iC0R zK7v(c)e8xx8sl_k8#=JJ(hr%FeQa)yU1Im!0$FbeiqTkPW{KEWXLfD-vErlbKhy$_jGg4Iv% z^ga|Xf1nVG(3_A9F=C%dE-`tc9{eyI{f2M{rHjhXfZK(A#1=ed4~0**ZyrT-**{-# zZ<$|8*deJWYsUdMXw8V9nVDGwlt?nrr+s`1oRW&6=40xops4hQBhLru>FmF8`<2KV zi4u~9edtmin$4%LEw=_1sV(Xk_py7;0ml+dV_Q|2etmggFIX__zlMDLLBD|qskk=j zP34uTeq$XuQKa>Pe3QA_}iB7JCSaW)1Dy^Eh<$`%Nn;a>thdyb*q@&{FY zNB2g-s4;m69B9MjFgOMN1f}Y;!(leVG&0uw*0rt*-g7*L1zWo90oYu}Q7+i50lY}Cx#5Z0&Qx2$C)b{P$ z^`eHscUpfczS#*@R@Zk#vpQF4zR97&8{fXXzBQB_JoVV73Z}e4w5^6#*D;zoOW9!e zJOFV)l^~0J%XFx+&6t`2&z=Kn8WX%ZfFpv(SE6l+rJq_`i9n5Xda7<~VUgfF-De{# zFimhGufQmu+#5v{ow^_zm}yx=&||5cV6~pr|EHLOo=hX)i8XDm1C2+knpkzT!3>-V zVgN&5J{vTP&0-^CU__8iG!=UZv3S`dbM0BW*y@(W&uwfiwkaogi-<}mX~k0|Nl~O?@ozBPRBmZ2vQa$=qt78Y;um?PgrAD)-eVggp7Y%M{(>9!*>*fpp=lWoVW&z9 zmMx(*;4eQ8Q&@HaM^j@5KYkjU(UK86h@7DV-NMF3>}_QZk}Xc1naIu_xzDKdE9hrG zU8$w8R;?-Be`QTSE{*>Ty^p--M1wl~lG}Pl53dP)#kc*Yj-(~2XkYa&UHF{%gjG8} z%Y6VgjUAJP zM5U$lnCNJ|ghD%W{2R)+!mm?cpVQYA6cWPT^y2R&UwFCx8_fMG_=t*cXD?`Jh4bn8 z{nX+=ub7KascTo3yKkoh;{P!6=*{NtMCJXF+amKHa6;S$WIaB4?(i^J~lSSAO;-L-Zob!+ksqnpN|sp z33~zQJ2#7lQF>rs;LLDfwIM$~8QPV7!WTr&wI>fSwx6%FBfOwJLfyjIf?SxXm_Xk< ze|v{P*Gq$_d!^j26Ku3b%G8ZLVPjXGib~_v7DHmB4C97NkO5Y~Ez~lq*#4gi@HYwB zEW`M+p&?cpE6mGo5o9m}MP;owU;sZrTyWL(r?8W;9ZX_XNf@Vj$X7*ZNUm{GfPOY* zWk_Xa;e1hA2dGgsenl{!ILli*pDO$CTMr7xJhkiDk7HsSC|o@MKGd8L7NEoT=t5xy zUsHx8$apPvz_iBK{{-agyCZZW1V*--!ge-DLHd-;UNEFK#iPnS(=2nMY^nRBPT9wa z{m*7i&aD5?&`<@C#+&e>FzynZo`F`tH;_DWl2brFIkpHp2=>$`cvc3$Ga4`p3!#?( zm(U0RhoYyM>Y=5Gqv6yGC(sEmF}9rA(38v7;L{I|Lj>Tv#Kb&kV^~%^=LaH_p+}0So}yiK zcCHEqr@Wd*_oP4lf*77Oa(p10Ed&OJz~|5b>SJQ*#BTSh=E0?Fu$wjggvo1_n^HGg ztp(1iPH_Qa?eEAT3*J$Fi(QKp>YSyykdm8hBMbi{TYMUi6FB*8t4|83G_Kj$;>Bjw zXfylw?R$fWD>}O<{6;n!TZNLyd6fvAF zyQ?sby9owMEn&+JLF)9`AaeRCDXDssJ^h|_{E9Rz)!P&y%vY;NmdYR~3%!5l;2KW( zsS*-;!KoB7^}tLv#plyW#W?2M$1q7F>UKXgN!nn_wzctXJ8vw8N2s>q6VfY zYv1S-x3+R72=7Cu$@x|-e0aSuMhoVjnq_p`hz2OsUEMN>-TnSbI$}&vd$6%qxRaUY z2S%w|kV$=ii;=Z;^~iBBf=pckeF2)zq)G?<^Tsx8NrG4OS}Fog`x2&bPE5Hl=b>n) z>3;;ixUL3~lO7{yyX&-Lz$_=#eUC4FkgH;u(oSh#hPFG}Zq?en+P6yW)auUaETj87 zw6?aI&zsspn(T2I1BE7*QeN8nBu3;p?lZW6^Z`qpLhEHl&|6zZ=sBwp%(HF$%wB?> z83A?GeKxQL9Gqy{dwLN6L-JLGa^Z+-?+IcOoGPm5q;NWvi!qDeX>(e^@zVVMy`*<~ z;7}f$otk`^!Ob(v*F&a8&F!a)jtJi6Fk#_cO>~t%eq28)bh2VfKo3tQxB1IZTaIVd zz|@m&c4q2T4W%W6n^*9I>t7+jht|F9(!@sJ!r)>vq3?V6;1X!8nt}@kSdfkC6!bYb z<@UlO#tX27VbVwpb<1(t0K5gJ^BWd0ZP`OgW=6P=}KsU0;Sb@~R|cwFA)oJ4vN z{zI8Zzx!IpNj0<>)VT%4dQyCTTMm|2)CDA!zoc+_{;Id)J?^{K2pHb|gOk#&0b7Jk zlJ35wv%66Zey2{v|QAFE0%b&(f zr+z{;7ur=BT+g=^EO&jCBc5v}hUIMQEfTL!E1iX9Q<8^z&lEgLLOxW@v)vza<1jLv zTNy>?&wAF_Yg+NS2ni+aHJ9q8emv!;`Hud>J))&!Ys73H(~(5xzhshidKp>L7)Qnq zyq(%wRSChNT_M!6tcOHt9VNG}D@Bx6?Le|fE0|{_W@8f*4>!giQ;w+)0y`~M^SzpE zn)$I+rX8}vY&B8%^21DS;z1dDm{C$^+jrO%>eeeaD>OG*lBX-Hde6^ZYOy(Q9m_d^ z)fK_z6_=5`t*pQr+M=P(d092)zo-cDkAbi-V+(D;o2($gKd~-kLp)1& z+1lS}Iu-GwkGYT~6A!=h)-)qj48sw`EoHRi`E0^i{T(npGcI$DBo&Ge+|)Dcx)HZ4 ztBheV3=Wm(aKwzQSBuAK3`GPlyt>h6rH|<%=C>ScO(awL{~Y#%Pv4TB+%-8JU*=h) z&SvlK;=J5Kkn;@Stwf!-PAZMabBAMBwz0XCZ&OQ4KXZicYL^vLNP8#pHc-gA&985+ zOb!Nv&txs6?~^xLo$dG-Bc(j%C z=(9bmRZ>Jj$4CEOpF;|)K!w77c%t;KErAv6W1qkYs$$q#hZ?q^786e=SeY~Z7e&W1 zOOb<9%&2P%-a`gO+GxWo{A;OQNsb|yqoTH~^5ccTTg;{;*qe3FCi%8&51VyQnc$)_ zN&HcBcjyIsLTtn{Zn-jqZ2S8)+ZID#{ubJ%o`3k4+?yU(>A3uXTrTLCK+f!xKlE1@U6fV6h&r*)pepC6O2W#)u^*!{{{ZU8h!zVxnSb!8dQm+c2c+NDClL0&y;i+jmYYy z6jP+6#Q#(A0DWN_tAcdDKSg>yDWwKI_7nW7K}YDxGcby99)o^D-%D$ax_H~ril^mB z^0!j`3l+Q=>v&#NTdSFc%xd;I@&LWxWo~VUQpUVMAQy@EQMk|Euh;EKxK1~3&0~@q zYzi)sMWW`O)42Q~1VwpL9k@zfwUM6o_UU<-XnZ?!^q$&F&YT)~?$h)(mr>#X87aY% z^v+T>04PMmq3fxR9br)ocIc)9&jsHo`^^&+3L_&U!Hf4cy3$a8GVPA2{4IWz4Wa%* ze+Gz4Ut8lm;b9CmapepGyeGO&Kvi!y7-|7r#CSFc!*9zrVeaUXNGZrLQFhtWe|DSz{{ zjkHU9v4lH(@)phg7(q@jo^3;iOewYoUNG(fBk-6v}QB@{Ci6JEui zbc8HU+Q>lyaqY-vcFZ4bbMnB0$>qyu0^3i(9lSGkxux1|4)ZWnxCnxfylncF&yxs+ zXPSvXn2MYbSE~L+!gQGctyAlksmPPD&b*k4ex%f>7xsC`XR7$EC_k+Q7=wn-TGB$- zFaMe`d|6Y9ZSz-Lp4&_3yX42EEnPr%t}L}~TXMh8MiURVxrX){65`l*!oM)WM@St8 zG3z$%PIyr(cQznERV<+8+9cofWP$qPEYn%ifSQWdFlS`ghIxSeMr62}Dt)!OZNwu| z`hwuIcU0rci|Iz?Qju^l)?jpQ^NWh3Dz4$b{;F<)pJz2AdUcRObReXfphP|k2B-20 z;n=CwK)r5|v4Y1m=*8CRAx{yMy4;utI|)3reCF`xZeVnw z96m!KTAG8qUq#-GS_vkaAI^lV>iW8s4rwI?1$qH$tv*I~`u9JEtW?r{vpP*&QU;z> z<;Qe7-0&>M;7lIDlH?o;V5=qyW&_cbf}~?uM!mW=O`dE=#*>l+D(xiWQy!rk7#B}3 znOn_b$U%sXb~-orU(BpR*Fw;^zh9dB5k@!iTJzez_!^73jw^Ms|8{<`j7=e*r!Yx_A1q!4R(lqZAVB=sLwab$0G&&)$1<{mKsDQC_=N zp65n?VY8w|BE3>iT>S{C&25@lXYU9M>q;y1nkHgGz|t_Rbn@iWx=lP!ubilr*{xep z&?@z>D^Z{rF7x_ruULxug68*uU+#iUhgXwQ;b;BV^#bnAwtp3CFx}mNu#11cFg$=> z{PR*kc*%cWf-qYC*Z)WCCCQ2M$Vf>`>rVJrO+W>W8^?K`5FVzvl1o`dTo}jy%>%x* z8p&9v#o*T+TIXTMQ<_3rK4i_rTbDGDjOE7)?~+=3)W|7RNe(2T02q!#EP zIu1nPpjBTa8R(eGS0TN)GC#N_^7vNA|M=^_X%AH{t7)&R@AUut<34B{@>YD${jY1$ z8fP@{-|w~myKA>r$HgVDCvYSwIxbEU5Kz#Y+w=5rRVaS2Z_)sY3ztKt6sMbr4tFP$49&P)PgkbdcFKwi!eEcsur(Qmzb=b_DW7v#-I zmlkoq$3Z{gnFb5K#V79`?1883zGYukz;s-nKG!TLe~*0x!?Pm=DdQYrqmM_nZjDhm zV#;56#kH`^nbz0j-TPfXLH0BnKWjQ8Hmi-3aCr^Pf3nzeTls81=f>B7$i9UNo9NbSZVf8 zCfhN@$b^u)vI2H!sx?17UVodpc*ll200t$c9}zbK94O)uR^&4e%E#h$Yo9MW;JEc0Hhw0Q%ucKI-` zScvC?NvZ4av%J1ZLVf($;yWfC~X+?hIW$EMgIZsFuzA*_}`_|OZ6&ihQU|k3AZp|-S(re(7IrYUtBm6~H058n>UwegNb{)C?WudCboM6)2 zmA@~b0KNsD+q#6RL&vYYFM_KG^_kp?B7bey!{;!&?D zF&RO%+5a^;lfTOgCSyr?zF}ZvxVpOFKj0F)IHJ{IUq&r3r(>A8GFN?H$bjPr0hdO~ zZ9TvWp`TGs+HWLfL5S=jgq~qmsI~4ZDJf|pl*LfSkwiDA-eX+)x>8spL?_fAtHg!c zlhbhsx~fVwqDWgZu1|z3;+)iZQ^s6dccxoeUr<3cV&xHqclyqioER9V1=){4@l5_( zfsX#f=&+H}r&b672v+~}=GJg;<(hQ;6}4KQ5rgAjMEC5WHwFJ?kqdXhL69(&shqw= z++jPWy=CtSq^U8iF^=jD1&RQfHN}NVg)6TWcf@iZly?W?letfyTd5k%-CHJ0#%8)c#p@2u|wA~)HWLD8y%yp#kMp;=7zC2kplUpKR4<&YL*?vx5*4vc{XT?i z8jGVD`W!wkO$aUDu%WoPc$nt&GSJ4>*x25^B6Hn_5abvq<4E#02!1{8Hp;cO15PdC z(8700H{p7{FzqvhYd2xb6CN$q&P^RVmtPXaJ?}9?wZ3@XNR~uZxVO_gYtzb=)YFM~ z6C{SwPr~*oet)A`^|i!kvtQeY)Y{&F4-`!p zFr{11kdAk77G8NfCHpwx{p0+cC?h9pZMhVO!1lMWF*aE(_&xX@eZD7~m1dX7L)rc@ z%c(t|jj&?cFRx=93ea`*z~zLiD#9d9+#?J?hGy*e_Z{~IQ~i_Ff|bDR63>y#+%it2 zEV^dp@brYU#MP)l&y_v;Ob3xPwgsN<1UZMFlVD*y2P|{7{!9CHrmH%#MqI9P$cyah zl(M|0&6lj&vEG$uBxG(b^v7T&IJ^l@n)@_dX6*bDK!gcOXi!;R?lVYK$Xol8+ueLIRXy~`j`?%^ z(kz`YW!T)hgV_6h+Q`;6CHVLO*!HU3YkMYX8+!8il2rWe1EnOM`8sY~OM+N4hY~*F zxmVc{VRarpq5EZe9=byrd3lFmy!@Bu zy20YbmD3b8AnS}W$k>AVte~kpuN{{-kzyL6P<(ix2SQ;N(=p^N=LfIt!{OoKU*aJ^ zn5lT%1hbFFL^OCYN9+}- z-&`&DKE2oTPFNIc%`aFf8eRjnEEFKPnG_u`3P%9n$0R7|6|Sq~s%|yVk&0n-dLnE& z#Qg~zYZ45zv!qRKjL!j)`#nIamwzVAGU+2RBXspCIO%^NGRMYew(ZyYc11nOA_w=U za=yf+_`94=MN*lN*TL(67Drwi{~TDZul-YT|3COEUsf*J*!0TaXQ%r$G~B^-yRZyu z3hAZ&Q1DY146~%*_mXPib6P9U!D?F%QH2OErmd^%8kGWfP3a&s{!*2wWD<#F5(Ekt z&7tP!CyysSd*%Q>tzwYYn?)g&+8~H209o~F6uy+ImTi!m{&W`{6*QkIDkxkl<>LCX zz7sivFebDVdvyK!ph%ChD0Q>!fjbYL7O^d-zH7pMO$q6NiqT=BtGEqtIHQmQ2>S8k zUT}Mutq@o>W<<0I*u9(TbEe~UdXaR(KoCus2uZ^gz5WJ}!vx{{oRZ^+9=j=7O?8?H zb+m$_;=8-s_S?fQR1DIXYruH;3h%Kvm^~%Ti2ns`E;+BSZ@WNR09M2O8rkr*|A3j? zJE#*L)t9+_-X73n#}A)}isVIe?fhCbO=(P99H_gK0d0`LBkKl1t#I)Oy86UXTZ%B0 z$jzsNe!`Pn4`K)Jmo%6OD%@d70JOF#3du*c2c&Hq(DhF(!xZl=99|sY6kQDV!31TG z{ZidEp)!n*YoB%hRUV?0zaH&+DUK5OXeom6MQ@(ZPT`mT6 zbU75X-X{}{yOJ|CS75RlX^^BcSR3wXvYv;ao18}4$cmD+q;)ZT0zk59U zK@Qa5kQH9&5*wV)>|@YjcTLaAlK7OY#srGGCiPMs+|CEzHVY<{l3$NO@%Xc}xL7V+ zKx8q#z7NhgWwK|vxaOVtkS{I29i|f1B5Fatg(z}Jw*XDXy|0O)O z){XGK<(uLB`)T&B*zt3K&z zn6?PQf@o%ivVy`}SU|rbrS}caw~sM{(19vsnXKVT)k8j~Nz&rt;ym1wgHV}9!5Y>D zhpdU-DrN8&?*gsL132KQz+QqLb6Lyv%5)qBuOiXkCujFqLAcEDy2SP85WMqWO^9`W oPSKic{{8U(fBsjzILn(}cm4C?j5a0~`OMnq4bByvxpMP=0Vo=CrT_o{ literal 0 HcmV?d00001 diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md index 97fd9b2..155330b 100644 --- a/docs/getting-started/quickstart.md +++ b/docs/getting-started/quickstart.md @@ -14,7 +14,7 @@ The global minimum is at $(x, y) = (1, 1)$ with $f(1, 1) = 0$. ```python import numpy as np -import chronopt as chron +import diffid def rosenbrock(x): @@ -25,7 +25,7 @@ def rosenbrock(x): # Build the problem builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.5) # Initial guess .with_parameter("y", -1.5) # Initial guess @@ -70,7 +70,7 @@ You can specify which optimiser to use: ```python # Use CMA-ES for global search -optimiser = chron.CMAES().with_max_iter(1000).with_step_size(0.5) +optimiser = diffid.CMAES().with_max_iter(1000).with_step_size(0.5) result = optimiser.run(problem, [1.5, -1.5]) print(f"Optimal parameters: {result.x}") @@ -81,7 +81,7 @@ print(f"Objective value: {result.value:.3e}") ```python # Use Adam optimiser -optimiser = chron.Adam().with_max_iter(1000).with_step_size(0.01) +optimiser = diffid.Adam().with_max_iter(1000).with_step_size(0.01) result = optimiser.run(problem, [1.5, -1.5]) print(f"Optimal parameters: {result.x}") @@ -95,7 +95,7 @@ If you installed the `plotting` extra, you can visualise the optimisation landsc ```python import numpy as np import matplotlib.pyplot as plt -import chronopt as chron +import diffid def rosenbrock(x): @@ -124,7 +124,7 @@ plt.plot(1.0, 1.0, 'r*', markersize=20, label='Global minimum') # Run optimisation and plot path builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", -1.5) .with_parameter("y", -0.5) diff --git a/docs/getting-started/rosenbrock_contour.png b/docs/getting-started/rosenbrock_contour.png new file mode 100644 index 0000000000000000000000000000000000000000..85f218ffe4800104f7e19fa21f3bdbcb618b7931 GIT binary patch literal 561973 zcmeFYWmFtp*ER^j-CYxsKyV4xSnvQrgG-|U5}d}}U4lb!2$0|stZ{c5g1fsk65OWp z-1qyfdES}d^J`XD!|GbqRj1G1XYcE}_CDb%O0qbZl$Zzz2sm=@rPL4*P!gVCs$*~PY8;}PWIMzPS&4I=v_WJLO$Es^00Gpvh%ajTRJ(} zLqHrHHvi`eb~{H4jt^A~>cCwv?BDA^5D>@+|Gp6|vVW@~AR-{hNr|hwWgKRrxDjiS zg(%Y>%LkyLBgZ~Re9_QE!seV`ptNM*EC0p@cpV>8q6Z;A<3h(@3<$>8qJM%M9}h3i zl@8kqXQVLV2{mm9HF@qdtvtl&$jQlJ;3JFw_o4cFKhee^fFAw7kN5VB|LaVcV*vH> z|Kr=ZUssj>`-bB9TL>8c|AqgV6xIKar-!L?VS#FRc-W{T0Ht<6$^hyVPDA#%W*RW+ zY?EsN%Q}QU{+Uo_@m{zV@n7hLM}L!$k_x&uc=xXb3nc=C0QWfU4|tJCUw-1Nsi`@Z zBaTGQX@oGGEk+j+8Cl|?6;2sJ4`RM4*)tLI$vUYhqzfe+%@jg|w_MRW9WPP{3k$dZ z{!Q52+?*pDOM5bBkuCn^%a>qV--p|aL;a1XAY*QB?(PF3I--AkNlMxToNM*h*}^CT zii(P^1%!mCY!@3k9$k(XF?=6y81GhkdJ5#P;(p9EG&M;sL7kmj%i*=(5HLpqN_107 z<~lo{d3k&H^zlZc;nB7?!CSFv%tr$5uHcdI^!$8=#YPC4?GhA4RaN!m;cBJk{B&B+ zPNUx^XY6ss_o?I8=Lw_D{y5N%)$i{ZLMv`Z8hlDhO4bWFRD40jsM0QLw_fYvjO}xC zItN~Lb#?0(a{oP9;t1^{>HME|)&`SUPPS4EI}LpwP^zk`I8A%8wSQR>WU_Rc#n6iA z=CAWIKO;6&&KAYtjSI!2>rCY`HR=k%vG!PP_aEMhDR&`Wij0UbT50t@x!K9)8Gksc zUol@{sIh>RS9^mS?2VIbJSTss9#*^_FHlIK^zQo-;Crj*^HA=NX}8(AVV;c`wx2;FFTRj``58O%atF5fv5GN9UU%a&=JArY1}C zZ&(mE`FBBkydz@?IGzF?|0Q$h{^tB-J(5FWD4DIJw>P9m0T~sQyU0pYGkMh6u*n4n z?EBzsIa5wEJ2w|TcPcI}e(3HbbzlNqK5P>iW2}$6VXJa~2h9FlD;B}MR8eoxFPnvAn)H?yk%buy zqSx);c&$?Pbl%+D+#Jr@)13;fIk|N7ijcnZn8Nh98uUA=qZv>O~6T62ZohIJxjG@2pE+9{)_svMOde&Rc# ziy#mipZC*?V6(Ydm=Eum2h4CS^457@y}|7?Prz4Km$LbMROpZ=a)+4bAlrSLf;8V$ zeq7y) z$1z`1NwHm{(LbiNTJ450r@-{rE5l?Hl|^s9Bl}`rJW%^wExE1>17U&f;x>YWMZUDOwA-DUn%Zn4nu?!Y zPDzQqR^Q9ZD?BnXQos;!i@9HEX=&Gsz!H^bOG!!5;&wUIcA&k7I5{P39_)V|1N;Q6Cj}rx%$X`ED9oKX10Hg7 z2XCuo=NLYpG4xGx^YEzdo1UIN%s7R2)LPBR&)3^=jM^C+tFCWuzFg=%oU3tewE#@U zmM)u0SHHHu&+K`;+~T=@PsRZz&hG0Ic7-pq?Jc(md6vl~yna=rl*!B$;`{WVwF7nb zhc}g98A4jFvv&1-ZvX793qo96l3QTF)X!c zG7Y)*S*HW{39#J*!<8Fvlu^nMQ2qTwm0`}4QJ?NQG`no7M9XT)!Z-^E2Fcgg*I`2o z^Yeo-rhPFquX+P!5-VfE!qBCM?ypvS*SW7na*vwO#1j7ca_lKj)J{yB5*jwSikgZF zJ-h4f*mD=9;49Rp9T@N9n(()5_rt>|30e(}LuI}^UI4fMUdysU)$x)Zao6wPzs)LWgj}M+JFY!qX+`7-iHPRT z@o0sa<{BIrh)PasN!fH0f%^|7A59eDd9rbGR&JiGb-lu)6E%a8k&-5V|NcGk{(2+! z6_5uTb!s1Qr!VoGy}XFxqJ zz6?|vcS$*rUWuo?3msYqX_o8jsOjqugS(Vaj31Z=*Bi%!$Ocs^S86QqgtYq8cr0e0 zuHY*id-Js=4VggHtAPaM?D9M9s+>N|LY=LC+6-q3X)TTdLzGq}9~c>7n`?wv44B4> zye2|-4sX{g*MIYQq7cWE7G>aIrXq!0K?C#nnl_g+g63uC={oQIFb^#3E(q@bnh71# zgb6&uM2`t4Y9$kRIIMvsX9&Bi)n!yF5wkhxD%-;b2MMK@LU5>(t>>zx7uh4e&c?kA ztC^RQ1OKrGlEnbv;&W%$b0EjfuvxjFoe$a-YPVN#SaOdNTEVIy+2{rqM{~|alPrFe zq0h}${6f85=SrfcuHFqI2pWyT6TNQym(Kxor*(8Hm8JLcYs?fi#{VDa?s9sa`UEy(R8=X%~B39b78OX#X{LS^Ri z^tKz8H0B?rBe z&2D~dw~}zF)6Erh-tmE0wFVMI7Z5Np?Kt4qn?B3Kmc~*C2 zdiCsEowAh-;;FJTR}BBIUY;Cm*S{nK!N|S4GyChSD2g5Q?RPN1-(9i*OlSl|u0yWv z8HF=^ z?zm(|C|b>G)hrWBFJ)EnB9XFxmCfiqqoq>;A4C?C{uRE2P8!u? zrE?DAu!B17@R<8G z<=eMGDH(#XjWxY z3hnCRU`C+~>k4aNaPaqD;`Y*SD6*Fm=D{;*x*$09nAFPYS;gs2m~8r;HiW`^p|Y-y zG_&iT&vvR=4bmpx^KJmevYb|ZF041v_Za3OR*RiRZN#C zg9dwl5!;z^gWw}Q#cmi3Pbph;B+rqv9F-yH?EHLgHzNh?5iYpOYpU^=sa#UKfbW54 zez5QRbiCNu1&TOPChwq+5`FQPMgf?$*bn(Xk!lPPI?827-IJ~bc9$k~Z*Tf_(L;t_ zk(+wFzpuN0@%1Y{=}-HURdLe4C;&B?H(KKNM`e*7>Vszzww2{SQU@i{@4yRIw;6{} z{N!<0rE!0Df#@5X#J-yw0C=zF$BLT31u=cw9xkcW`2vPC49lC!@6W2$G-k+s_<~}{ z0o#47;P^=Jg>==KjDR55lH#zED9F%&>%*<6!u>&#dubBNc9Lmykl53G0>&OwhAAT% z7z#Rr9sX=4C?^l9_Q5TTrNrG$53gFbx&QIs?tGMgl$u;LR?9F6 zBS%N>|KP=Qe}8`yuZ}DcPbYSc&7b-CM3L_MNcn1&sRi_`xvLQp&E}Uk+au{Ee?+=E zzlDIiAc0&2y)QOH4up!B+)Sim&Fh>1eAd%!Wg0R5C4d22^x@CP*U7E-XG14If_(RB zeSN+2QFdIFjEjqlq}pbUI!|wQcG13r#xA0Xyk{ncMu_=QcRq3jYv!VUK?oF)HT0$mOhQ-knbWbkF-# zL?qP8K7hUjP7kjI1tF8B`F#K&xEF1Y$#BF>aCCIEW-fX@*;gV)rAloIZ=(<{Qu0!C zmD%6hXVO&%McFcEd;3l?^>oMyEC`*V~m>?>*UCiHMqqmCMG6QXc zaqxwVNz5ZaIR|LlF6XUSE!wKvM=3UU$MbciuRazjg=cba;Y^cQE;kE$M(vEHTA{;Q zxO#Y;Tdqp9D$HjZUTebYve18n*h%@ zr@@4_!%1vbEih!Et6`yBroD*=jpg!{JQ8&CtxqRz!MHT{;3;IW^V?;jHLo;}QTv5X zI0EP_SQqUZFW(982;B3ZRlYtR_|F>QZ1;q>^dYAxq_B4Y5SmRiHh>j64P^B`Wb@|` zauohy1X)%5V0z*sRlf~WG|qgQ6j`#O)oB?3srOJj2nh+HO}xCI+b@NExfox(=K6O5|UQ#h~L$Ihb}_xE#$ViO`Dq2(ubM z)ekoT^83P*rpHYR-|*&vFiWq>1SN`+7ju1Mqi3e-qZuGZZ~_D|xxxmW_@wpWQt$G5 zgnxy#wk5)3w$j+h&JGqs1?%{W(9Z>;<0Ju~^l3&$+7C`6_Lveh_COO`(l+T8cSY;)4{YN^^t!znsE>wDPQj`S!b<( zCeJl^H4NJz8|(0%C?fL_;JMXs{eoryI9ZEmuab!c0y@~bRVXfv2`o1?R>^IR{NJD_ zn@ITAv~E&2K)e`xHu8wg1xy#8A;8<@F)=Z_cYvG@Hu?PdO?lt=cvbiOylw^{(>cgF zI#yd7{3XJMeQiMIPHuog2d4vLwaRogzKCIBVJTz*0>s=IKn7p2v$MYf=xm&b@6kN( zT=m-8+M&C!t>g~!&eV5+)g0CWsiLuaW+p|XuDY7p!O@Yh{cE~G`L0=_p}WSK+04;M4l5Mxu*u2GJs#TL)2U#)c4+6i>N_o`kG zB^9mz@^LuSnalUl%Y{Ao6Ad+W_W^@|K!X_|t{if;GJgAz&KKPh@ywFRCK$nY%oLql(t&I_IS^0IhM0I z`SWA-mWipU!r|ee+0fd~GrEntD*H8wmcBndWQP68J#h1kr>Z_$qMeS7$*XjH0)ppv ztz8dmR~T(YxYPnhlkVJ?BL9&N1L%vzHvdulQP3Swc9qCXknp1@1l-SFLMrnUXbiT= z5!c{wyln4F3W#Cd=LwlQb|+oH{BeSlfq-TNhy$$bNGVneb!?Dd>yCg3!B$I0u6PQl zD_pLxw&geY#wj9(OyfjZ|3oyN?!4#UXQPYpxlO-Y5zW*_TF#$j70vv9Y_uy{ahW7k&<`Ufn^@ z$CgB`rddxxoKr1!*veuLZy_6|Rf^AF7ipAe4m-5@K0RVW8nA>PuQ!GTqa8qQitDxE zr>_MZpPl!UvFWPsLuF=!L}P>fY;0_FVnj&|r?kw}h@5OUb})GUobm)M^c-MSoRfjw z{96i-AgVg6kE_n%$3E0@gv7+%`mkO3zC~N7Px(Z&vMqnLw}|QRw5{FS-t!PjexKgc z*(#TIK=0@|bQ^)K0_0R18_x$^8OEMvbw8nHT@UheW+r9V@0U3+@*;%ZJ3Il-nF9n= zRui<99e~3PC3iuhSA+!iyOx$R1s5GCGJ4^X_qLpyDt(X$_iF)ej0y!|!})b%C;$8? zPGr{oju~YWn}EFaLFR5Rvfz^};0tZE`Dsm6eWBk~&vcSDT=8Ri*;E zZ~QppJs>Nb?3YwjJEI?T2ABanx3D$zA3XAZ{z~!sMHEuDFD=nHLLj+-JB?KuOI|Jx zBrraI^$LZRmDT8IzLpbEO{5ux*w}D@u!FiYni&W{5=lUOn}wcK09LoZJ~6g;c4oV4 zlK)q-|DXG-gI+dJtpXt#pO_dK5dNxnfNx_>6e*)AD=Rm+98spGr&9^feJuRo56D2A zefNM^+5O3UI2Ez&@s`TstDNrHO^MS8I$aZ&+X;Qetp?JRuV^vk8iJ^jgOQn*By6jl+CN8d|CzdCVX|gSQ|~ z1ZKpX>5fN?5j@ZF@L=8N)VpE8rot!ELuID9JnupXD&&`+zVF2>NcYpiPC_pH3l#j( zwepuHNfSx`K!0$Qp3Wf`ZH?BsN5nK+*%Rpd9aTX;$n+WtO#F;DJqVxTlG`Y3w<*3z(1>xk`3-_d_R!|3Lf(%KMopZq=Xg%2*-!Kj6kb%S7cbAccg|3<#OP$CZ;bLDfpy1U*p=LK+ z!PtlE`;vO?N}LByW_xG#_R9P+G=B6r0pHU?&tA<9_K`qchN`vUM063F@mk5#qbbFM zE=iwmy`>cDU2&A9O!ax@cP@Rsitx&4mJ%vlnr88^W557v!7<1Yu%LAuF@fVHJSPIa zY|-y;E`!Rjl@51IECeojE@{=%9c5TQ=l}Q{)t*+E4tiWP@ewW#uG_yuF&u)i8}&u6 zR$<;*?3f`itd7o%fgVYwYv^{viUnO1(DcpV5Rb^>iKM}Tk?yu(#2gOU2YV(2C& zIv$8YfmOmwr#1~Pno~UYy^OF-4L`rO9qvH!^pv>uzCr?zLv{ybjZ zDf{MBVGMq1pLx!H(84qzC!Kk?KteA@@z z-y6gOx6=iqwZ1dAjN!`Pe6+U-yqU`;8>G|uZ?}u@Q}0LZf5`iITToaVxP29|ovXqaUVg-#L|yiF6n!aGLdoD` zkls~~mBstTeFX47MW;W4*Q*a2>wi+YGS>?Bysa+Y(Hr#(C|uQIEt9LJd#^2D)E{*uk;;EC zeXfEnsO(9mvX4f>Yb}&*YX$rWubHa+vw(A0rR>LlPkIq7H`CX<;@X*^MHbdu^`r#j zwhZc*hxwmgWCx--tLI(B9*7~fozxX>D%U-y zxUR3hpotycB#QVn!HV$Pl$~LsYtgD>7LH893u`#j6roQ0+h|ongtHs$fk_Pc?ze~Q&}J@q%l^VQfFZeD_8 zg{^o>)N;V1L*LNFjJw;4QJfP|FFQqv&|dAUxLq#D}A&Xq5qWJIIP05zbl?Z zvI`^iSzg~bKTVNuIUeuF(M9DWDhlrPR|^P5j)*m_qbAFofWG6GR1w>oWtiz^T23xV zLP5q$sv~Q>L{AJBC4vr3YpAO{;v!lfUZzhFXF0!23wG{%lV)_;hF&)*lOi;&cI?{S}3mUTbr^XQ4L>UgShe zuPcwc!MzBQt)rkax>`Rg0U4C*pJ z4m|~{@AhoF0#;Y&$>$#SPM74?otkJ{*IQ>nv+p2cjTm?{DIQ7@9~zz3i?16L?OFIt zs5%HX{Jn+VEz*8oLiAR@%5+$~oOih*>2Z<5e~eQ?Qir18Ei+^ZJIQ49#-JDoyPc@G z9NunEy|k?`W5zz50G~Prp*rigmx8U!Zjd1EJ4E7>#DAd;&{SdZRBt)nx!R0`h8qNO z-hXFi_Hwb+TklfW*!VeQV1u&}sCGzdtdTMEauZ%=b2Cw&8!?EJn}o%^dDF*Eu3yN) zs7|*OM~|C5DKk+Qt?d=@Ca*!+?`2_j~u=J|r^G7IRA*{~RL`Nnb~JeG7XojKVayzP>Xm2Gw!cRa;w%xTyExUSfSG`VV^ zR0E(lOcu$S#oqd$h`F7;Saq;a`AZSV6 z7H(+_DP3sP@!*ROzef8ojz#)h6T)w_lng^lEYy1Tj=v<#jvt+#sCfJC9Im0a8hEX) zg5>P&HuVg>mdgCGh)Xt%zSb@2B{^Cbb;}pxqLF!ShmhG@?J|q@Dj_u&ZCqiXxNTl% zN$m;NqH{jTKK5S>%&Pk0#g3>Z(Jm zj(m)xZx&scsi5w_t}*bnuYfA;;lSj%kff(~@Hb>1CASnU{YgOq5-p*{yD0(|7Od;l zE&`*Uqm(24{egCEb1y;Ef8AmpHqC}n>D%^FQ{>c){Fl|t5BaN~d=TbGOsf_24<2%q zOIitYb({Dl6+v!q?fu!kOQx5aBAXcBRnZvCY$+n|*xIe+law%i!VKl>jv4#Ojf;B;)f-)<%ZF`THpv~Ju8umt z1USN^)O-7xE}$T2477o*?(Gdw?4=qUa@LdA^?1bbtqK`vW*R} zo&3pLph2K#@x*Gn)Hv@x2!kj`G8C87^U~UKuKLwIAiS$2HFyY0^4Nwgo{IdA%_4l} zA&1iy^uS0G{>MvcIqoRFL^6d+kKVzTdvm8aQjNloE&+>D<|I$f~(Ec}I)XXWJVX)N^t4EXyCWP9#+sX8Ni} z1BzdoSt1BPi!oJ6NQt|SNj?e1PN6*6vgJg#C9=q|UqztM9CJ8iQuoCg25b!_`DXn! z{I{WkNLQc*$6(#*-Mf#eoE>jd*mI)_AXj0rRV{%xM@g993N0Qo@(A0ZJYsL7|~tfMAH%+S>XxH5I)?v&^L26=;ca z+e*>`ilZ34v2=Fo+jl@C#BBAhlVQCrjhVSQ_bF|5--RZxmnP-YMhS9TR=k@JH*+w_ z^;@YO@yB&jYLBAWx+Puzviv~ajf=tU7;%ENe*BVn$7BPiEiKA^Ck{2 z*^-w+Zf|fX7W2}_m4d$yYU0<3lOi7%w6ESAq zuhhOLk&Z;h!;0UkBHJdB$9<5o)uq{P9(tT6*%Pe+f5dapacVN1-xC!r@WmDbN;cbHWpW%Rxg}B z0gWCg0B6nx;dejuo5ToVF35NEB0?*IC4`DhzprIMce&pWinXOW1xwHEb}n{O2V6^( zpdx!@ZR@X1VqwfP@ZBomn0Db4I3bkY8t1QHnD%<*YhFYZxxyUO3fE(LrWiD38dMcp+|br>cVl>q<&cAa zJzI&1{srv_J436;tzV|Z&dO4~=1v)Gx~dkrWw1^1$^c>2(nX*|pB7EBFI zLh5^>Q!dw+QFeX3__LA`HKlJg%M)oUbT!yZ7MoZ7tdR>N(acG;aNOvctoEKeK;crQ zUFwhV9m`wsJk3Mzd>tl#R#EHwN)cx)Q8j)cDd8RXFcff&KF~1rF|>(R_Tlw$N**{M z6m_5YDBaZ>Q&E@SEEcacIjirz`dd@u?@?psPUS^kI9>>Jev38gV09lkk4E_zyhZRF zPQxG*Z1OYUYs|WU-TnUT$5;A&M~ci_9ESrt&+TdpgmT zJkZF5T6EyTWC`UG$xvLv>5kq0sbs*%fBXcBT>b5NuLIDd;^>NE$w^00IETu-!#2-^ z>l6k(CZzL|FFy6C;504@;cZf%F02!ix%4di_!EVG&rqbrQowSvF5Gln0r~QOUuiC~ ze3*6XM2+VP5CXxQaFapQFEp88%JskM5_@LJiRAL5voffV&#!on&BUe0d>zBC$+3#S zV!l|#gs@)QvBrwcoZ04gj@grLDR+@-!vk>-7WBc?GbEU#eRn7M1SSrv3-vXteROq{ z8eZQ3jqL{owSYY(pH2KQu4%7JNO{L#(5ue3l{A4z6o}L#MI%cwZB4t$WnbI!UNGs@ zE6q>O*B&-jm!ALLQx|amBb=%?{mEA{3%#u2LF**N@&1LrnD58M>{cRZ${pWX^*Oz{ zUPeRoGLhm+Z!V5($KqhdLvXCt5{z6IRTgcAtD-CU=gcIHuIt4(Xl0z{)JStCYGh}+ zJ&i zhpSxdMXT+G5u0dl+m4oBHXkRb7^uBR5t?Tpf58X3p)8zQ;(QcKwJUu(S4W5!y9Vcg{pE(b2T2+=; zXGS8I#6lDXX6F6`)rWKv(M5+8zjT4iU0a^%Uy}Fy_RSMBmGDwTgI$zt_y*Q6s_#Ip zD)>(J7W)*TqUc+!dM@PKPp@#ed_T$fk_)O8i>W5${*R2{H#zBf(s}+QQ4ZqH(_rDa z{&cBc<;SPW)y9$C`U~IW^5ntU zQg&dW&BukZ?Tf8hxM`o6Eh>_tQQoe}3~Qp)S1bn)|FXWEonOYJtY>|C=m;p_p9&T| zm@GqL1HCntC}BS?sW}@N*~;c}QZxJj#SW-P#In&{pkN;hcVHLX|E4iY8>3PY-ZUEU zb8fHr3U!0O>iWk!Rq;4h^PVPeA{Vg`+douaBnfD#rU{$8&X|eGienH_j+%a6t+wJz zZ>^!HeaR=1Ie2?8t3DkUbmYE>+v|gQK)mi_-=>3He0!ZMHiAuYK>eWcfek`;=!l zVKnL#Kh(MY3dA*7@B`%Y$ug^kWW?#-t{bH+byEI2rd{#(HXyp%(^P<9IW`a#pdT)Q zw_W%`*pXhMfk*=hf~>rac%46cGIl@Xn{WTJH6kP*9kkJe)3i&_mUWaPM&bae?dhG?>@HAI>$X;*&D0PZa>Y93)iDZY1 zlp0Dds_D23*Hh(^rmI36AOp|W2uFlJXvi7C_{MV5LT7(0LM!+4Z3j7q3>+Ib6!#kw zm`^i*2+(@6k_QBQM=ufcSwVX9MoozksG_JsI+`yzL=8D2j`;YJPdY*w-dk4~RX)53 zI|68uTNMohNY%dLeHD$vVwL1LM84w=g;C4Xa`AWM1FtnZ22=xafzB1p@=!1Pf!9)# z>n>20i795oaDor#BH{==;wLm{rnii$+EX~{O8rIM1{aji-Z+HqzROeL=p3=Z0~CI4 zTnEC#TmjSq6JKp)fY!TM*J-YwHz=DMp1kLlN+Gdy zGgDR)uG)`FsC+H1d*b9H;aBtRGCC?xG4}5b-VujNpH&Hlyzt`m^gg(6DLyI-3uyf) z(V^}kGL19+2+b4`br(Rf1-BxB_i=;HzRz46UGGdx4QD#+=z?BS3*aE?cPtH}3Q=(m zJygJ^tM`r+@{P{Y1A9UsiiK9N>9IUTBmrOMBt?z~Zw@bEi!T~*%`#M3x#qiNd$;4c zwzcIJeI|AvglykB+{f#4IZ`3Xzl2Kv@!qtel;~ol9#x#d<^_prIqO>Wf?l0f|2+RQ zreP9dVoveti3uX0qRadeLVdh~R^xpqFLKmuM<>7rj=HqU*Gh{^#&K2C$XBScEOI?S z{m@^=Tn*pq92xqIU|xI~<$55q1x8M7{GG<5zm;mcd((ny`L%weBC8P* zr3WSN<5GU^8bR@{wyYMvT9K`R<+5&56D?fsBW#5je)_H6mf6#;JOhdwoU`u+Id??V z;vh4plniwZzn!Igdtox_lq*9a6ENR2D{&i*%{BU~;k~o9+h}@SVvr}|uq}*TCDyO9 zE~qo$g@Zv|%=A|Wy{(vf*J{&I$QmtmWFDW@L{RuD84*Y1-Q-wPt6S@M0c@w9-)bso z>T2lQq?9fb8Jj>2K^b zfdTu50nJ;SB!2^o<#a9=grEMJA=B-hVUr(~2W}h@k#SQ2)p-eLU;L z2&5^qlXC%g=%%AsGb6?5tCDWlymfo$+CV^B8g*oeqvGcV(9Rp!UvU1qMr@t9)Q}_k zHMOWPg2zQMsH;7H(>8W_sBxi4;n)S))C`|^Au}UG`w$)Tc&I4#(`{L1}b&?*ngHu`RLq`+@(UVN5EI&G_ zj8V%g>Y}35niYpVu<-TsCWF~lz7uFC!7_APl0Y9JG7z);kMOa(kn?Ec*P@u5oS01H z^+)upe)F8#V=W~Q_Q*u>6R>K(3|aLo)_DQ?D=JT`{-I%xT392+qNw29;As7})wi5G zS|L(o&Tleg#U>5EFE6&5tva(A^qkG2Puzjvo<#i;JrCF)=*jj{hykzep}y^dm=dd| zjA8~s^OMF6T|f?FyhCW?0TMO z+MdN2tA$GD5d;YRPKPlWdi1Z}`SqBALgb!_Y36BR;+|tuQL!q?mTR<@KJU9PNSIlb zh-ZctHSdA?m80Ht_L_VLsTDC(+YWK?Wv{&M2i_0bR6KJPsn3<+iUQTCQeMWO!J0{gqODXzFQtz4TZSAje~oiBm$6d5W-++2uLJ8l zja=05o6WgFce&IYwdnL+yMKg8&yxR~{k;BYmYi+DjHBlkU{|!g{%83U3DH%6QX+T* zs#&6)MtVdgxiI>>$_{Lg@TCOU3u3qFSb<$BdH8S@8knA5Yzg$T;W+eGs1OMhj zbvvIYZ@dhVp`ALskPr|i8{rn#3b@1N!wfiHyg)5H3}K9rks+XBO&C+W>TM_yPv(?r zmIlhQYmS_J`}oMR*-oRlYtt<)8j87-SNgMW&Tg*!y+urj3=rhQ3~MKs-zVqBPLW10 ziICS9OMk!M19iBIQ}=j*4|`4Wn_049fitg_^66tb+YmVV^G`-1AGI^oC(78R7KMqW ze0UlUsDNzic+8o&7^rKZi`u<2X!EeOadwai@EVpS1ou2;Otts7&B&;NCQqFl< z{Ccmys1FyRl#;^sP)c;r_uSZ7>3|^nGqCC_1$Ihgus8S z#)2fgHI{r5rt93J=;n=%$u6P-W{t>vdg)>Auq~pM9=4;)-0=@%6 z#5(Tn9ynQOS0y0RHE9eq1`M;bS}H zmd;^AMQ;A94pB$Nw~4`k8fg-EcpU91Lo&jPO7~PaR_e{*;IU)U<$gt?*gu9~f2z_L z6@dls8r>pEF*TgaVJZF86tey&Ua+ae$(QyO=$6;NC<~LU204`r*cI z(2OMZ^h+_rc4`m{?4`Qj{F<4xH8bESg&9&$6_LQjt%(|JxIDJD3FfkOG+mWFGYR37 z8BF9KP8!`x$!A$mcSD1!xq5;%vxwnpMNS{0gq5~ii5c@rD^MH-$HDgTRz#KK*u6X1 zkp#S7LT|g!UUB=kPCjni$2fReX^3mG<$R6p>!~or?>kMs@g`L1Svg^U_`UONg=K(- z!lT!Z+;WOzv4Ks*S|KkJFw`JjzjM{tf#p9NP%xKE*#gPgTU|Z z>yLN$c)LqV$#gz&&5%*|s^_zHO%(!}4TkQa2y|qDxUL3$Q{F+u=Z;Q#)Wjt6O3z*e ztDwZ^kM?t4I}r>@cTsE8*yxvHr)*^5$6-^rR=2BXh*0N#^!<5m3i};F(0RIT(oX#I z$L8^;&_Sn`WHdJeoQy?DQkIXZxKG8Sj$+kM*co!ox&?QCm`-PdjCSX1g9RS0?$N^{ zOK6;*{dIS$#>U{20Y=s3UvO@1wQ4ZR|2z4aV^>p0 z2Ev6AjtN+sTU#B##?V}#VMwLT$Gft+`WYG;nkCQyDSX;ZRO$2Jp_A2|;Njcc(lWG_ z!J%F0SGJRnCmJ8#tvQ|i8tclkju8KggtU}Y<tNjS*rZ0%!36LIK&V)!X~LHBg=5?D$lvlxSqH7OCgaMwYCVW1kr{Z@ zg=o@zS4D{&*aCkx`aUcK$3;BCk6UDhVu1f zNi5El>Q5D5XSM1_dH@=C@(ZuO_z;WaaO(67?h;MBQ*1>z89U^lMX$Ra%ac7qYeBCW zV(9Cr9$}(YpI?IX_FzmZLzLj>1GUbawz!I~n1cuW_j?sgT#iGKz`Kl?c}s1CikWUe z<2)rOx~c$KJCCtBjhE7Z{%<6#ZW$omEs+@7so@yQHNg1f)Z{q(!=1*hHND^Q`=gbjcN z|C2FX;!pI<9x)~1#3@ykVA_j2$ylXXvbAuY-W^_#f88=aC+r`}bs81wL_%qO!#;44 zz8~!4cYa%n+x&owNhPTN?euaZ1f`BK>k$I~ba&E>WM`nKbrJ~YhcevfD(_$(7}kKq z&33k2H%kMywfti{ux1^+SJoBzc{-N)`heNRIP{^}sQ_#d{6Za)c1@l8T+ z9a)}=zt(bbF5|DSo+y;qgo}LKu^*)1$I_bk<|jUXp6{B*e(Pb!qQt(dZ!Xopb)-jS zm_B8csif3_9q_0Gg9HAs+~0GaF^xXBR`XL3Y-7cXvBlogGG$A5eqB6SFgze&n&sz8 zw^t(yXs|e%bI~IdY$;YqhX+6%V4r5x1pse2adB~lyBC+I!NAHTqA;Kwy#q+K$0p## zV>|uZGXMSXv9L#k3cr9|Nrp&hi;^f4|{9A04l{)G8Ir-M*v%zwv7FkS^FrFZRe&FiaWNk0oSU5 zrJ5XVwI0yvNH9n5* zeYxL#tx2ZWXa^i}ywP917P}x1kMP{w&$nAS%_tk-BiP^izsM?TM?m=+XGFQ0( zPiLor!0uvZ)pfxxIyeqzxsGvq@dw=wd+ZnAzh5IS2gNx(Lw!l7hppA0=S8#LGeU5} zLi z>=yCUXv(#V#Llbi5C1Z7!UFa^Y*8HXgbJV#kaO-7F-`wuDuPExQKW>B#2wxWw=s(4re&BFa2R6d&duboTg68Pry z41T5Y>WgAz#8_Aiu9XPJHJsD;G+2+dTa%mq5}YNE{cg5a9dEsv{8>ZU+52XgMCUB` zmy+>K$|P@1DZio)G8l?xLyOSQ$7>p~hVIL{?wAX_8Gq+OS_Q z+eAn*8kD|1zYP-j5Z%~|8L3cE0-Igou8-lYnHmDh$RWfK1_&Qy(8V-kX-doi!k^X8 zR$Ix63YDnU_>e^gd=%jTys!Yc9B%LLdjKFVeg73{k~B7cpDD`P&q*zP$E12S+`!E7 z{C&!4)BG*-`ckPn13d(XLXhAUUR7K6x<4p30i-AEhll70$e4iuwF&lkdiUrERX#l~ zS7!E*Do1oq-j5&mu#bZRzzPLG=b&M=z59GKzCulVLFs#bH>jX8K<+DSNYLL3I)I~u zQr6sbQIhamzuDf~8-ZeQZXz`s(FrVgr9KW{jcg3AOte`_y_s^r4=WcUx_v$#ML~EG1voqe~^Sr+2^`g~=R1X@-%eFRuvI}|( z=%hQ;fnY|}t<%l03|4eRey+-X|H`>tLgz8Yvz3FO1}&0X1FMR|tZ)93H3hgA(IgHLpZL?-A$ri~B1(hU>@zvNvYdKi5~P zYWJf)bKXhhLpSR}vZ1WlI(;TS87~WHU(rZE?3BqmijIe`kmeH;o8i}>#|IOlu26%f!}l&q)C&;xh?{^om~zPQWt_Eb1p z_U;lczh|Mt9M2J(xQ;gJw473A7Gl;3`WH4r)bpb=r8{3NrR&&nuWJK=b5fv$J5$6b zB?})BM%Yj8)uy~ddGXL;)B80tMi@0%NmZu6f7To&>zsb;2z6WJ}|m)@;Q=_X^< z%nf!dg5vq-I;5!RpZ%9^qy+EL9~Rz6YdJml40sT(;2!Wn#i!wBYlJ_1y0Jx%)t%)l zJuRb#6XQJp1nJP_#12Mg70&YmeJpU@+bmK{JsHMJE4LgfGz)?0h7%Lo&d{<7ce>@>}CAUi~H& zxu0SS&)N?+)H-B^W~sY`u4Uy5Cx(z*JmH1fdWgXZAPXv6I)XyWO62UJ&M~%LH%Uzk zYbZu-2veNlu^#}Cz)I;DIO6sK4j2oG)220lj8_zAVL_Yz2S!m5jk|wyWhKRHj?UPD zO(1=0C5~+$u&t@Ffnq9ZYZF5kATh$byZ*3PoQ2Dj%5XE$+Li2HLe$yhyJx_I6cSNS z5OPSoj{?BLmJhRlP{{NAjG-UmE{`GadDSq#u+{E!9p%iW9W&cAo&VE0mfiZkhkHWu zGs>(V+v?Gd6+zd`*}sKPM1Q1jecdTzCO3?HHkyg^444l@m&-ed6*501`lXA?@RY zH&LexXl{4s7GDHhzd5UZ^Xrpk>{NbD?$~{CsxG{ZkndNT=rc5iI;x``&WnHJ*ZeN8 z*R|M-_Y1U6evTJTan2apTFTvCJ7vuH(6-F+@f*E|Y_RE6jb;J?vnv$*ypV-gjrlgB zWu-M4%_?r6?ay$C#s+@iZl-Bkx$Oc2T( z^{oCo-yF}@lwc4+)9q|V&|M?;?@TmkeW9by-B6R^R9E~a?eqEBNP?Tx%@>?})BWi} zB;h;rG=9I!ed*gh*pfOopq|MWbUmDLzqOdd)-gHKG9puX9=DNTmy=}^|N6sxIK3yf zr<|j#>m-}g0Ufg9_2%%pI&|w_FQAh`VVD08<>S=ZjhuPE6YKEr)SX`<% z7R3gMN$*6eA3O2a8A1`s)-Z`)dNn_~AV6#~oU$Pfx@7Ns?15bTvuZ*WvSEP=9kE7Mk;L~!yHGagRY7e>bsPxsO9jZ2JXJgLye(Bhj zkriL=84kAii{XZK$QG^It-iG)W#QkP=v|y7=p;_NEjhk*iXLnmy|!^c36X4|n4>oC&({t+(bm?kAt6-^op)Sm zDHT+-v@{*#HtW7{K17Y3{D7K&|GIzZAFM9b{y-jqK|jcdAK5UK7g4=GJwU}9!^DaF zvU(B|^}CzO(D1iti-;lYao>s9A!fR&_=xq-@D@H}vyVC^DwORiGuPmYZBo9ww_?Jk z8g`{n{+BPmyFUV^lc&)aw1O6n&+1Q+Occ<0fF{k zk@d`a$!}bv7zL}V|3_yd9V9K2_~(-}Dkm!nOe$seq#f%_hWG$?PCpVUTk8kdE^54=n6#U`SS!}*lp3JNb|DWLuJO@H z&ULfz7()0L1x7-hDtg224pCdK@2-reG&dR^+u#>3eDPEW61_k|o6v3g_Nor$rh3h0 zC_ptWI$s3y#W#oPlRM5q^my6Pp*{Xy-kx&#EiL=?r|&G=F)nO_9sEQO`xDlo&`rbP zLN>d>kr8>@r#Bl{`82cs+RHk%c7E#o&iv9b(y!N-xYw0NH|3GXitf%9r-JRo|Yf zw#njk(sa2J2-el}x0|WX4i3NX1tzsrgHXlrndpY{nh!9SPS1NHa1e$x^90*5LOh*2*sZ0HWHH;B(?r{lr}NN-L8%u5_ugC?yhr$bGx25^<4&iS#c~ z!TZLhe=UGtfqJRugvytLKKrLajqS~b5C@_LCX@-ANQsGy+qKK?TZlIWMlcLjWY5xx zGtd0hJ~Dr+Fh1iZ*{cKOW0BEvf%sF!8w$r>{5ikNF3kVF58kZ(!A}}WoIX`L9wIH@ zPg7#~=DO~xkf-4_eypLSI6 zGgqm921jhiOJ5l3W-L>!v3DoN;|YWV3%T6#BL3=fS4sTBe#Ztp*0FiN_{7ep73-^<6|p3PKavtricK+vwmH?L_fN(ZM#6t;KVe@g$?$I?{rdAC z9Z^%a(i?*7tBt{(shpUvoQ#tgK!iseN9oII>v3+rb~?6+8p~w2yKEPUw<~aG87|>! zP9eMZ6h9hQdmPU$w7@^T;xx7?skrVM8(GQ{Nd6vB!v4vj)Fo=&3vyY_WLDd_zFf(PZ<CyLhOHx z`0#-Xj+}e`F6|v9kj$vOvAl7xjEo=SpXZCZyVLi@{4qjkE8QdvB_9!_6BWdP; zNAQfqxiYQh&Yqc+gwU*`mVsYjT~$1cv6v#^hw3>|^@mpO0z9I|!Q(Hs(}a(x+WxwP z|Hmm6#mMh^Y+7n!KhR|rl@r~HNZQ0r$Q~V=be(w{V4h(jPVTe4>@58$-husOa{XYG zP)Wr3^bgBFr8UMtHvOPE){EcBoxHDr!KOekcDTBX)X`-&<4zJ4ulSQWD;{v<^3FB%Cz4^;TGOUe7@@n}@+```d(WtUttlkX%o6KUVeCD9 zs8A|Z_@n+mG5J}%RYi}hM~X;@gTAwQzOVJdB%$c73RAxKg(bc39TK7H2h?CjWnTNJ zBtp3pv0Q{05>hFKX7sM>fkFb!6jxwr#nWuyPfLnXtxv3oxo_>UjyM^Gh~W>Y4v?$# zo8i}dFJg~*R#)Ow<0Q5t-%3bo4x|Babh0-Vt%S-t@$S}^c>-VulaL9gW5i%F>d1Ve zdP}py3`o7=pW?AWZN>O9vSt~+BXMI<*E|vUzwIgP2`P%3lf$eB-ZE+s`w_a#;3=A+ zuQr_r^P@ku-Lml>&q(mDlBHT#AY|>d{WHX^*BsGKEgKw&-nwQh!_I$$pvs|!p0HI@{ysy#X;g{mQ;dfB;q96H=^)H!UjvA|{zN!COWl}X z6zc!a$cWwZ=_*Krt^vKvV<6w?gLt)k^|uNUwVmTt1OKBX<|lul=0)bXMMh?DmUJ{x ztwa%(-zDB?rT{({H+Oo;h|mPpX8@Zo@Ff5=$zoW%yu1o@YjqaUx5DU?u+A6J5eFP} z^k5~ptgo{b5-3+>j%{!N*D=U)z8^%H+ne# z`vZoMhwt96~#*06akC^Jsuz5A>L1NH0+q7d%o7h~Fox}iX!S``bBK>UQ z?L|?v`tlo?f8vZ1alV-+Ux(!sbllm0QH6TKd@3(caE3x`IU_1S`s|M0o zP3*I(Dmabbn;>{gTX-~=rbG)|xrReKvTQhSxBg|1I4cX9dskpSPog;WD{ozs?gj2p z?C;^TaZk>|h;AW{`s0LJDXHB2~n9e~>;(h(o)PJrj zHjGto{|Q(fFk+Mff!2EE`R^2W4i49+i+g6JUu+0#t<1>vJAX%E_gxR}7q>_zCuLwb zl|1I-SH=wn>-dYN1(D)InDGfZfGNyal-BcUFAhL5p_$~FoGuAfcU|)3MAWLwd@lE3 zrKX{Q*KKmZ0wj7|;Bk7zUtNHXW@xDkkTjbqT?2DbaX`bEjvKeE-R8+@?hN(7aF==9 z9!fF+ToeilIsDEvm&c3v;h3a7G>(=N*<+Ancy7zlH-O>j8w*c`BV*gyb2-P|A{vLs zxwYp;7%?W4!L=Wwrz$<2Wlf*zmot~JCwa=@ErsIQJ6O`)gGMHwSOyu^$Vv#Bd;2Gh zF!aR(R9GEf)2Gx*J$3y7M&cL;Y3kOrX!K1aP|UB7>oR-i!9LZ7?P>e|&_@B1^M%iz zcPFXF!xu^K=~zPBt(GP~?Vuv5C%^Atw}?rS<9GT>v<74_R4J&tpYWoR<%F%hH#F#K znlwIL-exDspn+rk$O5g@OW&{88ic#9`@y$$PmPJ&h4#UO{+pC`s92A~PMDm^Ka z6@#*T8YMMGssFN%h5)zm`nF5J@?*Vb*b+ZI2#9y6xwzf}I%jl1H)1NScyV#D0qi?M z0aHm(b#?WqbbJwE-{2q$HlY0i?6jf4PZvN@zH7BF&>7Y<74Xzwg9Om zP8I(j@HBI{Pj6M2SqEq!n(vs`+RDr~@5-{Tw}hH;A);TkB~i&Dr+pQE6~oQE8T~qJ z>hqzD0(ivDl!E>hYnrYvg5^)YPE0JC+1XWeJeDuiJFwh1So9#mQu_}&@jD}ASd(#{ z9xd+IYIdqcOb2dJR?lo3gJt-1Sx6pC7;oa_qhH)Ktwa?#xESU!_3$`uWVIVvjqYd9%L3aR|}Z z9@6GUR@CSzotaJi0&F>R+e+q_pE^TBQIjQoXJW)TpazlH%t-Yy8yk*=xUv5MS#UMwH2aT>P`mG5X{}>!Uvp49h#WB@9oEE^D_{Rrv!~$D zc!p@r)_WlEq?&5ITKj#RpWQF#Rmzui9&?JVdj0?&SBB-T`dj-6d z_-EFiv2S#-pA#0#eI{!+5(tt_&s$DW8gw28HZRXa2KtBH4%bw5JwQM5?HokE?JhVx zRo%)|9)|cxYB>uU=8d%5-+Ws?EPPv2lV`%nR0M>5)Bc;0*{S8)x7(;c#&Hz2bRuPk zc~@wccRkPEQ{3L0T5#}nuy8VJdq#jDTILlB#T^t~imXhVxI-c%1-XJ8=jn!316_;0h#a3~G<#{WdQHIkR*g3UqU8zWnA~pb{vV7ckH{>3CZ43XUeDKL;@+~GAv~4RuMZ!lLdBPZY6z$!Ot>EwHD;!H*gPr1;i9JU@KmH~1rwpkNk2~Mp zyH^Cw8W`a17?0}>s+3MzWd>;zNg_Jcayk3N>ZJx$du%3%lG~)c^;uo#3VQktIh}Fk zOW)E97#F7UG2?GUE(Mgrp)pV=HWrLZuP6{3b6S8^@#GR44f-N%kF_V9<{I6gM)wjkuTC zO8L=hxh3|H*U0IjTf=`W@TA0RXK1OvB^a*q1WH60v_ z?d8^ou^?0$)wX+tH>#Q|D{koYcX=t5=&#NmeAC3QyYpY0Z8f5h6i!;cbJz0`kOpSJ6T_O%kR*6iC}>q1aO;uf7NBai!SW{H{YIYI@qGZPmpy4?~JT} z)wa$iRmw>1nWn+Wc<_cYwT%Q*`65m+Pc8#UyR{fKGOjA6cEgz#O9Dg@zt>+UY(DmvuIo;~g)9E3YEHhOd7sMuD5W7t;=|$mW9E#A!Vi1*g-{#@B!WUI zfBdNGQ5JI`EN*XK*#N$Gqh%2D&%3YtzCv#>%#I);Sc78U(0y*+We@hiug@F7y)xWm z*_GZ^!gB^e#(IRZF*GG)Lg2$VjE@-m&qSd{1ok|x!?EIQndRt7@8FTVMLSmvdmE~3 zpY|R0=Hmyuga*Pq30Xvki6x~OniarJb%79;=4At?A%G#I9Z@1?T<`PFjF<=>eDwJ5 zu7w5u%kDR=93y`t9wb-)04M+Bwn-_d_WVos8MaR&YZ#}JClvi6YWPE^$VQ;s%P1KJ zkS8f4qXUb<|1ak&&rsdnzMM{9UG9ftq{=ZMGuzCsnbNEV|KOI;Kg>uSs5hX(snKbu z7V*Y%aizsq_RJYA7T@lVzQT+_CN7zIfe{6b5=80Jwh1_Tt7qC5n0enWh zYSn9I(rVGO_LK|^WVf3mwZ>Xl`684SbB|<0ON6M6+yi0^=_F;DE3H?Zs}-c5H3+&! z*vmC+AANQRXI*dK{Zdsh03jv0^XXXzay6Bt!B@3KuXgtx-0XYA%l&>1p-ke=k)*K|1XDNM&dgJ7CpX)pPwOiuj3pPe zh_~JC9!k6_30jQJBLeec?7)%)w-OWV(GrC&5 z+dxj^Yc-nMPkCUGUr@IoDdS-hhXIJW7-%$+tQ87xLZN^-2Q76&?VP8C@m@JuABr+Z zk<9b89NkVI=l?U3%bOyZ1}7#n_|wfsP3!N?o%mFxc0%GtZ`P==$1>!lYb?i>uxo2A zqQAe+W$gim8n6RYo4d2)R)n105u4?SBcztCHpu>pX%(n}ij_W3>@!Gq4;7kb&dG$> zJNw<8m<_xk7sH;EdxysyJh5R5%(#?rc&l2Y^6{p0M&k$ET=+zGrK$R*Z{+)P($FoN zMi3nfow@Y&0Hn47sJ~A`>)J@P!o&zDpx@Yd7lB_1NbSb`j}Xl0F*&F}W4Mqh-{?>6m`ikOO% zeMeCq`GgI+H)KG~eOx7_bx(SX#?yxT=4DB#)QD`)(u$`+iP$h0W;taKA{xk z7TA10Gp@BJe=NtWj&2Io0kD%;nNdaM&8U8WNb)p5k?YqjnCq9&u?8@sTSQN-sFC);n)kc=pKF35 z@_pQm_wh|O6WrW%$ye<4;WmD~Sw$5>ReEltA!3!(V1307XXm%N#>Na;^snx$j@GC9 z%f`Zi(?baxr1RyC=z|-Demm1_nwQTmF5sV6O@<4Ll{_}-BWeDbU5n->t)aD(~ow&872&L-OFVHTvvn*{w!=dDZrw@w8>l2`jV4= zxpk(+(}YaOg21}Ij3iB8I#>)wGtWXZZ2BK~{_f#hha?=3p}FN0E#feGTfl11V$rWW z^~sIzDtq)Qu84V6p>HDIk|XbynNlojxh=^6cfvwiV@kk*)gYklv!lILWdoRzJ#_gU zZqHqQad@8>TkmJ)E@$0Tr0UPvZz z#@Z%78uTDzQwl`_=^~eg+t6?>Z(feCg)rOv0*mSJwoN6}uqU{^U(Aj6y}g7pb(SOO zp7N?fBv!Vvp1=was-Yh^<+{pzU*aeWWF*ywsDFn7A-t-~g%i2aWFw+`r`OC~tSf{J zfp^I=9kK#{P5Uh`vR`0LKkN)B?m%v7Q&|9=m|!(j1~%d zJJ)fim?Bm9sF8CLl9M=>F`7aN(N^dcsxw#f4}3^I+mXt8JZYHE>dE+;t=Y5pH(g$c zqGCjhkPa703u9M?iN*)w)}HTMj2&Bto(!$VUw%ZNx^(MPG87nWr?A3|6*6NQHg8Dc z)m@+?(Jk)~vi~6}ITiDh0xu{b-~~kktZ9QEvTP3kN1}bw{A^2fy!H@9L`#;aX{H%% z4SUW|70ZvJqxH{c&Z8+?-h1}Tm4x6D4350zjS{#Z!(4;-xqMrJ2SYYyl`^>KO;xl0D3XOn9`Q`*zhYJezqWVHB~&IgPk|?>h|7f9EDgGO zqznb@5;7>H|1p`g))>fw8%&6!)23QuzrGhM#*N~i|MV*jsQ%J8s}=?vb;iNpV%cm8 zH!DUP#!Zb!-3EtB_yT(bcB@7jC!+WDGh>9S2C&gx(6x!$0#&vYwArcT)qA9s^Y_`h z2J3T$vWPFeB*~z%q6{8eM`OHEPA}2TjRn+uULD`r+zn?2t(Bhj zNqfiSL1}T+lhjgW&Ol<}|HnPuv;WY3{c%uX12$@h4r>PYLDBIXY~R8s%saj538DI} zwod6)l62i70*5i9m3e5qF`hx9x+ZZ#y!*fYB!`pds7@y0iYmEyGf2_(0!e}x;)?3u z;y~QE+hVero&T_QDgx?fmbsZ%(3+<2sfpioKaM*o>rDk>=Nxx-#1ws2%#)6w-@J?q zo9*3M6i{hzb@Hk7Fzj8pMNpL^7f*pH?2~tQ9Bs+@Kubs)bV}X`O1f-YnND%h~v4x(XHG1IgO=)_0o`P!DIjgPol+<%zUVu z!k-@Uqr2R>{C<76@R1wP)v6M{9>lx%z=O(KCs;YUcfHR zr-G>9=|uZ8?>!t!rX4V#Rm)w#T5YM1zrDRotViC1;V$VO7Y-srnzx40Nj7V5Z&EU! zeNst@Dr!yeXQ~S-*2_3AcOkd6LiiSv?-cXcn>zkB$PSaM7hu?_V5iYHLqP08bS=Nj zfYW&5#P!?an<@dCmJA#GCs`!%5PFAAE60uFVURW65-E2hHzpOeJDZG5c97U}t|}_< z#imO~rym6CTxL<4uZCgf_=zI3%+e#}+vwbmDAjU<^O}S%saPAP%8?ksGBWTAnbsPi zF_z-m;u<_n94J0+@3sVi)`bQo{hJ^Jnd&B%T+h+y5hVv~!a53Ih1T3Wvgsh3_$%H= zRo9ac$)H=>VW7Xu`h2}xQb!+aBx2sF69Yy&(6vxXvE^IT)|@P$y{tEqxr5DiosjzR z`|ynb9z=<@E_|Y3kJj|oN3+|w->PtAOMiwZ1-$2UDPZXHD8Tu1Y#gMvAGJQ!C`z!K z?6GQ$f))HJ5JfN7BAq9d1s*%~X19-6qR-Y?1m#FCDFdXgy?*GpOcy`bZA_C`?!`!r$HvRt;EO#Mu}~*8e-gXBI%jdwm?>oXrcUn4QUz zzYG5=jGOzCc>V^-#VwF~boA@@g@G=N8&9f;Gui<*s|nzP`!TpzZ>AsHuK$=!DDPg2 zzdDIDBO;kxD3y*kkSOK~LM zXeL-F;KjaIU|gQ~BkR-Z*0mQN#YBeaZ5;ZlT=n+-I`>&}NAa(9&NF&3F{Sxe8lmd- zuWX7_YM5~X*iFk16FVc8mq%O*0!0P2-A~_IlRPxSuYcnx2}d$`De%5pjgNt^xF9t}Ylhk}(-{ zk|#~}@rO9!u$ZaaOPsXKOFwV&g}aKeE`Vti!w7P6(PFtn411XzX;B)(v1&)FleWYQQgSe z$F9QW%3GQqP$PQj0Ws!rXIa$Sw(+sRqufY?pLVd-xvMD*t{>0Ppe}B47Z1$fGd#_R zz-PPbM>~R^LRC1%<0A?6F214*nSbd3-fi}IWpmYjcHgdz?KbfNxWLa6zJ*XxtNgLs znAgZuVw8*%iV_ZLFO+euz#Dhy>rCBFQ0`2G(^4j53niV@g6fL>_x`4hjEcG5Af0xp z)z557F4iH~^(-}p3Eqe1{HI-Ho@h;N4~XVi$40xx!_D=Ln1h@6_PZ_p!|q=AzQO(b z&J1syVTl|$5p(EsD<4;GKFO_T8ZZYj;$qHJryzOsELliX0<@2b8BF5`Mw+?m`$K#v zk^j!7_BL**dlTiQc8qOpk&FVkvD?}-Xti-iT?f;UfbHAT7Y7IGwwKkfU{-XFH&nhH z@@ zVA%2~;vWOCEDFZTZ@OQs_ml;wSi-P!5>3DI>78%{J|MlUlaDAJiIel!VUwMq$PQXk$TsEkBH*6nXb*GOx0dr0^*%EqzTEhs4L@XzS<% zRaUaP9WUUC-fiRm9cQCp@Bw(7*EBOV2M4aOh)6fk2&->kkOxHRUxTKVGXF+W*?WN^ zDm0)1!>$uZ#&@r-Qo_T-uLE5T!TRKE(lcL5W-lTKhkqi8DnBk%(Mhwp!!dwl({HI- zM`~Kh430>m^EISk!hemvWnRr$^un`RuH7MiJ;VGcTS$gv@CVJIKhz0vWlXja(8b;> zq(G)r$>NxX#c{~+Swux^1eZzxTp3QWyP%fYCk8ej#AD&w48pX>%2tF(N66_!*B%!5 z-F%yz98`zgoJ5z&o@o%{$nNueby=&jcG47B?9I=Fn7I4=-;!g1(3y)fkK$onM`&{nqa^pDQk3vt@#db7@G49IXN^ zYCE`qY;TGnz-x+J+DANax8L12TVUkw*v$>RmrRKls{}Hjz}^4MzOH{3>=bu8O$GQc z&Ea95gKeIE59LzZmT~USU`#2ev!h;qRf9ckeDc_FP(BIm%n?2W0jzqAXo6|vdxq3@ zv+7ghup$WdUvXM`RY7*Itx@?uobY*`uESwc2>wLd13uKSS4TTg9!kxvekd%->i|K0 zjj_@QpzoRu!~_A&2a>C zRG=3&%;#p<@+VyxGVl$+`Picy9iN1qWzuh^@Sz6ebH>>c3s_3HZ0?jYjRU0e5ZW(3 z%Snl~NWO3xu3qJ;mfvr>9mgRSU=&lXnSp8FkZE-H((ygzT@4%TEqr|cNwh{hpALc2 z6%uBW_uu?K>czl+IVe6(t+9-qfMXDHmCiz_ywX5d*VAVSxe@=@16YZpX&YK(%HTUZ z4;TBGowx>^WEB+=CSH;pu?dPNa=wNkEXR;7N@%deW`Mi3jN zP9vaXL9xd5HBnj+hz*I1)c82ttwY1n>7v z(w=uQUONwV>)f23;f#!oNcbGc0hem=PZe_(-$nt!fn@aBC5-BGXRaSAeh1SZ8eu?T z4Qm5=wB?5Dyuk@U|E#e-CcBC)`lLiACiCsbXti21Qz013-pamvGe3n}rmbu2mN3Z) zQGr3N#>i@!GZI2b8l?WuTF?zk#BZEj*M7l^P2{ypfH0r5Nm*fl)et zj??0{yVoa|up)c1sDFboG06v9Y0-QFPG7$z8-8E+r{3WDU08IK)d>hi`r+Hbm`$;1 ztb^1~MH{Pql(dB`u5MuFA%9}7E+MRh>oY)%9x}r3!yg>LURrPVQr2sXxgzr=3t?!= zYJ_}jIu)poWH5?wbq+W4R9Kz+4K?L8zaLYGT&$|VFUX{%KC#+bw(4h4Q&U- zJITLq4wm%zeXJ@+E2@Y70RC9fTc^;4j}fLqmgH8hd`7_Qj|Y$7$MdZj&haAaEY{&- zIhOE4=FEYjhR6b2*LBdFMlxR^-l}Oo@1K9i|As~2d^H|#TjV&oldJjfLdMC(#dLi1 z`RO4fHWsVGpq0qbYlo2E{VOH=;mJuQ*bEV)@Vuw06pspnh{d?)a-u%qZH~IGXL9pg zSV>Y#3r24~cXIsWj9YfWvVKf3XA;+NI@qwMzi~k?H7;p`{MuKvxgqOX!L1EvuRkE> zKf?JvW7eb3U7n;JwD1Kcr%&CFR}nYOk;F=fgJyU#6vlHz(E8oG0X)Rc@ds&wy9Vj; zQy4c)ZJvJpNaEjrjyA=5Um?=YQg5QJ?OVU@@&|Ju6HHE}i`p%4oSy$UG&+C}b3v58 zZ*ecg=6O`g5^$yW@x9zvUF5P~ZVW@l`n_ZYPa+r6BT@&d)@S}q-C@u=P0L44L9qd3 zcxGELRk_~-2?vA#H@}cy$wn0lYjN}#Q2P+QI(@G^XUe9V&~A%&54>-Hp?8$QQQLal z(Y%&*@`U+~3NfSyc?2q5#;z-QCb-Z48CXj?#GkuP`GE;!-?ra@^HotcwpzXeI>+yv zHoqm38nG6<2vUsaw`)0#GUVDh33De;z&(Cr?nzQiwPH*7`mwxo0xRx<4=Ztr+EPC zOWuo$W;e43+-{%%y-B=&LX7Xq`>iW#3qKzJLa&M-4xGx$0gSINj~B|NUiJTWq*dH^ z5(?z?-3l}2SqbfF@S9KU`CWhM>&tRx=MY~^aZ<{x`zL-n|8{f7m}oIhOei<9)q&O{ z7WHrchwN0RnUL5*iw%nJ)zy6AH8C&^gunCq_V&fJL? zdyH6WmRwh+B_}C`c-5?ai)lxQS-);_@E(V6=1+u9M`?ne$*cIS@<)1JV2J`lWTm92 z;sNN_V4?Y|c$X5EmP>mStc-d5h&IPGD!7J3nK5DGE|StD>jRh%bI*uuRM(O&N+8urM*N+V1JShHoT` z9EQvKyFkC(020C%9*m(xpp}hd;h_=~TJt_)wO-L}VE`<$eE1N7L2xP5VvO-C3KmEx zN)em`lqV&M#9J0X6fBVRykrHcazOd@o+&ZYXDo#-<5|brCR!bgOf8*A6?Uxs*r@tq z8FP%0!zmyA8C|1Og;l6JNk8Mk=sKZ_3=OkMKD;*47Tn~Ncmwxk#Ir5Lo1`f#!sZV5sg4MCSDyF$GxFba$NqV} zVaSFlX#9MsJkXuj0^?y+HXczEPDQOTU`fu&u6_Jb;=_4>4gp*9=XBisR!O&g9B*ii zY7Fald<3(!gxJP2G6|9`$Q~YZG;04>JxE85Z5GDHU=&h^%Ue{wP@Cv1gb@eh6xQw5 zchq=~&ZNw~|O*LWyX_6W@;p z3|u<~W1pZB;kucAwSTbEQO0b;?5LFJ$Vg*}!<~_`5>;Q8N0ZT*HCgAE2>SP{g|YBgKr*pwVj_M_ zZ>?Ok?WsG%iSLQ6?cqSd>B_}&4*_QD)lJfGX$Wp=YzlwX9axlaiRyQFqZy_PP_$g1 zAo^3EP=ESF(CDzj6cIXi!g~{ZI7eD0AspW_azPJ7Ok5g^)KE4{oCx1*1^-XCl~c4d1cko1l^fNOa^y2lb&8{ zg=#qDY&W}mO2C-Ud0lZ7v9|`{YpDxzUuT5>5}l(?7s+GL$K{iW0OQ>85BlMAS-NR| z*dF2R0iS!{i6+`2;~x)HRcOlojFAg?t>Xu>mU6K~60aIt8}Fk}gBjG}5pE#-gJpOV zx=j_xU)(x=4Fq50%RjL0&to$icruu*UUoPe!PnQbKYw|{(ABg`$}kIs3)AOdLfVCP$6i$}LWCTRh?a!=i^7j@AOG#|Wt(WRD7d zN-`2gE(&grH9)@a&5JF=ykC*Wmat;W=x7T&fm4^<=wzR-1vCz{COsLFZrHSy3ioj^ zbd1hrM1k}>7G3MT>0XXQua7oGjqBeg@MM&Z;FzB^nz7~|UEhQ&?4yr66zzcC5+e9V zjHXQi4L}zgf!_SkY>}b$eQSh&Mo?L#V_>#&O-FAS#@@N`-h-f}WvLsqru>FDjC4Y3 zC()mQ4A}2}REqya)U$*EMEa0_v$*#~rE2+Iiw*pJttwQh&bC1;Vw*e@wO{(A-R{k^ zy}MgC2oHraC1G#@1%A7Sb|=fof`3j>Q;1TWp3l9s>)g=nmfFy0XlY;T!gO!2kZ4CG zv2k#$E{rh|h$u!U#v*8IWpmxjc_%vzP-=SjiQHFfMbBL|gP~V5M;w-iUz}V#O!JA* zbs60@u7Nx+vH^Us^q!XihU@Km(#-}?&S^p;if%EN!t1m?=^q&v8{XJq-*;teSz3SW z&Q?tUD-28fRX$(cQ{ybzJ=Klo?S>WPqjO+KrIPjl5X&Y#U^Kf=pP(04KgnuLj=Vi# z7oapV-br@UL!Y?_bt~oWX-#?Ky=;ma>s2-D4H#AvTWVE(e=GFNGXX&V(nS3PJ^8M} z6V_G0P6zPhu|ntwC^*J<(tCMw-Ar0-^}2Um zF!`f^3?RySw9=gL^9Vj)A=aW_#nln;nawc|7BU$Z=1XjlclzI%$GwIk-_0*bDb9$M z80NlXpgMtv~`ohJ}3_4NM)zAEqPl|eIDue`qb(J%CO1r=UO6Xn1nj?px%{e zM#g-?EOMGp22=pwo{)-a$#}sv#wf415F59!adi2&hn~2+D^5S>k%!_>aeI%nct}5n zs15!AOZfK2Kk@3YTMt+SlUzRNa>~7?uA}JF;5WPf&I?`=-*VSp%_Z@LFQ;J4!jY?S zJ4C3U6cxYVv6mWm_X#&E)5@X&83ked_pbO29q=@pBZw1E| z5j*jm_ob`QhDMogV#pKc#6{SBz#jn|+TW^C;rB%p!fL52w#+tQz|x4nCxi0w5(A2QTcKu^EX2`UyktWB&4o(1*YgV5*#Wt-h-qw`;(!4*i|6erK5#78C@}eGTtI1{=#E5;;>yZ_uV@|kF#OzY z7pekH(7OX%jk%dsz0ceMJNF^$CO|^ zx~chOd_aX)p!mn=$g6$fjtn|oqZWkWFigwALj>NVkVjea6AuU9eu8P2QrJ7^Nv*=o z;@)fwqKhkggLaVGa+1L`Kc*;(b?R-SZi51zsCe>i0`f$w$q%?$tu7EUeP&$(E9?~K^8_@Z(togxq{sZSVe5(^n z-UlYygjUt>^SPLjG*&B3+yK;?cW}FQLIMQnpOw}9|NTx`ABZNC&(?MM<=x`>)3jCx zB4P})8Oc-m>>9ptkMi z=oq-aZ#ATta&0;MhyLgVKrBF-JJjk1y$MGZUrx(o`(2Hng^Vy}p+L-#5gcPnKFnfl zX^8^sJfi_TjI}+5^z_&!L+dnbe(=1hfS1*1_RM0Zw*-?lH>c{2JO%|^yYw$N7qxC* zkHekD+^jH0Iy(9;=;}k?e6W~@%go-MB79wl2oz`H=`y8YeSnZdWF1NQ^u~!u+3ey1RBVsIe4Geg#kyE5-8T|aswh7&!zkmO( zhAq~R(*67s_RgGA4c@yROuuFaWEiG?0%NzYK+qLU$W!S3F2qJ)ThPRxCpao`WkV@= zX=knUn*OZoyW9irOd~DZvuX5?7wiKgWZo^2;D$ij<;}j8o34)EVWL0q4h#-ktMYz% z=yU7E%5)ZQc%?|8AQI~L3z2*qD;wk5Pm_qqBa@bQ5Ba4yYXu(SpE|vq$HN@_ql^)V z>N+*OKeo>@@intDEU(_E(#E{Q|~t4&s+lL*X^91Vf8QpkO} zYfy&9BID1jzsE=NZ!5#_ma!#NEk{$6NYY_QUC&p83zzqv57aJJ)HCv-%IKk(k@@R$ zSKg<9oKTy~8pVgp+DQXOn2V&R$C~Whd68x;q1WjXHt*gZ*nda3(V8nZHuV#Q zX0!Sl@td3&SH!WQo&0gEZwT2xtCX?LJ)DnZ@y!6fgS5_{_var~@H!NrJ{O;K3X^HL zCMZ&*8Xo~`afwyhI7GA-_Wkj>10as@*;j}vr4{+b^*-VB3l^mbwUq)2$4oJwxoy{6 zGU}Yd#6kip6Maszj0y?UO5qkX#3>!Baz_$ZO7fODRq~lmr$Y+IS=+>Z&L~=Zd24#3@-#u-j%ocdC@Emv+D{7xlfgBD}Sy(#-L<$Sr3re6d zQ_iIHt8oJ~a_spOjO^D+ALu_mgkk%l7Qx)`OmmebFxM zGP_mXzr)1^>oAGuXOMC0`|LP{M9H*+36QC|8I4HZl#Vbkwq~d@3;qMt{uaz!{MR~s zGlaXNP39{M-mcVw+U_R;`JM-kX}F6X_jOm*$Es z%uvtN3=bS08v@H?l2s4x#}V-C*{?N^J-fRn%O9pXXQx;kysK}D3aD>CSXt%#Z{=UA zD<^R+S7=addiSk{59UOm!()1kD+s~0)1$h*!js}G{R=$jQ3}0^e9>xP(~NU4LkFg^ z;e0-_AxTW~T>Wlk4E%+uCwPiGJRzZl_Y=Z~r@&*A@L*sRWC ztF_0bCna^_b}*8bFMRsX=|MLV3CdrFX72lM=LU+xiQ96~UYWpV-5O?H)C>Fg z@^Gn2haBuVY5<3bG_p_=e?Pg4%{|Z-bp5gSODVAt0OqWSR1f!9&8`<*6Z3IUIQ`Db zAMo<*NQUWNI*_yQyrqiQ{mEdR5Ju7oWJ+Wyc-p65abRLi?f~q(qn+Fin)r?y>fy|F zc|c!(6Mle%9(&u$Ia%W?gG%N?BE@{E*s$TdW2&O;7+abcAe-7W_ z<~19jfn$xvS!Np8@~zzUTXc$X)<-cC1tGbAa-h14L6iwfah64I`cj9Vv!q5CKesf*$g(L$d6bF{CkEsjoQmQZTqt$l6Kne;RQi)Q%gnI7 zCLPfB7YkMp-Cqp<mM%yga?<;`ZGy|H;BWIF>2h>XSBY-(ea_jl*?&d`ov1z8-c5L8e~-=N8ct1;UM+(Y@?Suvxte9Wrp4oPBiU<8cEaaM<}{aMqjx*c!no zU$-?}8N6W3*R6nLMGyQVas{GjSgR|xZ3<@Or1@875ZbF2g`@>0oOXKVk+`z=& zT8O=nBD$ShF78N+>N_;ChQuaFB}No4lKe^DitIJDrFYStuQW1Z+f0XKv5$OHz{8W6 z08ebyc*NX^-AKFIq6N;7u?V*Ou3tg&dtMQ~_6H+l=Da_4sNBnSYAA%Nvzu*M(Gmv8 z*{ysLhyBZS8rx!W*dYE$nP@epJ~(v{6cF1R6s)$MPJz)DU~7es&N*(am(B6nhDhYg z>B?e}bw(Pz{jYOSQdAHyQFS?-i;f;29vd^y53IT^eiQS)TlX#MZrjBpP=nMpfH6&) zv11}$EO=HG)i=N<59cd;e|<K(HC{?vG zxXbUqRz{jlI((uJSSY?ZdIm#Vt?sONn?Sx|hMBsFhYOd%BGQrLsw06uMaUxWLI(F! z?nK|)Y7>vGO9Izg|II^3o9H!343-76Ymd6?(4=y`mONNZsdPSE&iS3Ax?0+S6{@Jhd z9Aub1)BDhK(<_2)a@F}W9wzEj#Eq?tMB`HBkNqBgQ6GS!5ssbcJf5PYBA^K7M7&k;x8YySb+aV} zw}D6=34VQhx+gIK>vWO1uhoifM2%3}2aR7}hc35WppED zuBrlES#)*u!TA{oE~M-B>SY*Myhq&9A`J+o*kJB;ML4x8PPR}PGWC8R<^KG5Rz#mT`Qe+C(wA>`=^awYaYX<535rU+uBJjk9du|K?lh zSZeZum))5zNh0#O81tyo#NOgyVhX)k(?eraWhxbYg^OuAIfFJ<-L<;Xc)yr*p2fO` zFn|4p<1c%K=)YHKp#HP!_Tgol458F3g;F>IJ(4Ci5(cdE+xf8)K>*GvgMe#xH`NEA zF)eNZM~TRHt?PY+@9_D)uNRXtw^^@Y4Gm8qv%2nzh-_#3-G67|(6r!?_Ndl#fLJdV zFtA-1u#xI2RPyRFZH^2MqYgfGX1}0Vju##Jr@_tCS0DhL<2d0GHKqplh6J=BAs!(? z(aOa2tdtbc+Qc(q1UlV@_e<_mmFELPRdsdXFkWH!$$2&H0JuJh-)6NL=~_W9eDSpD z*y#C|%G-ab{yVmmpOgsiB*1>@AtIawqIcJ7?Q223hHH0oqh9kQ%Vx$pqKrGz?F?lO z<)k;E6`!-G(NKHTxe&w|7~~)EM5b@|ZZlo*7pm9!T1KZnH@O~a3c2jR#p?k)qSmkW zxrODCt&%F@0|30c=p|qe9YwsTtK%c)Hbn$zt}z5(33pJR z%@nNcb+l#({Tq9Q6QXh&v+-9kAVKZVyN_iyW#2rHrZN&E7uuVp3sB|<-b-cqEaVAD zH*2TmFK?OmzDYv81{mN(I(;l#h~6?4cL>%o;k`_(K^zEt5rrJZ5R*gsh$9)X*g-0c`39?{58n2>FzqU`bU9fKb|qfzC70WE4? zazct0>T8Beh5lndoI>;o;)M8oWQ|v;aI-K{mB+Z9BdPyOHN|^%4xa`giyXaDg>sXI zdjJoIDj^}Uet0;7UT8YtWo5-Q@=kMG^arU$*1>_VhNjdhlOf)(MxiCWP6`eQe|I1T(OsXUbZ=lxfhGT)=0jHH=ts_ z>t~|Vs%;vvm!(=R(-khwV7JrTz|%paPF|_UunB+D3)9>$ZK~64g>(2TFe0UVgkZB- zmxsMl-tDJllq%bO(Y13Sk{vnKsiuC>t>~Y5Q?g7vkuFD5fRH8JY2znjmT@+tufGts z>ihR^*mP=w>T=8J)|~6jfOu2-yXK@s zYN8cYCfpo#%kKG*{P>h8|0fradTp+JxI>}+;=UgHEE$%Sn0gXXCEcNE#i4M-m{ z`tCXf)JnSydw*JL)TS>~r4FvKlcAmRkV8VEL$r95s&=N{MncS^ap1GSSVZbr^&)iQAdwoKR z3?|~-H+4RDPIJi$+^#38{yiX7Rm-4bVAP&^h?|d{m!GdArAd!ZfG=f|wLNrgwLVUs z^K^e@_7IF@;MmH{e6Fa}7W-#!WqQu7C1l;^q-{Z1H&VrAyD-}?h*R>Ov@L@VoN6}p z)|rp={sxBq3anirpXEP3+QtAvzifIGTlOCF4>H}t_6$Gn*H`<<-eHpmYf5xDaEajJ zsxk!md;0^k*o*bWy{x3>`ZHfLVGn$^v_^8|OR_h69GPJh01v>#1g`D$5maS9CSbKu z9$|rdf^wGmK@`r|y3$~%!utF8rUjn~1n~e;U*8xqrYCSHwpP}}V#GBYE=1xdJPN3b zQZ=ZgQQ-d41fMSh`i`{!|LaT;A+=`?@HBqvf92S@S^@O!DF2BDQyH9#5l#pnxiw(& zU{$jkXcNYdyqYq12)Mny&7u>)pMHz!+xAU3#4@JD2^$lKrzEm${ooFx zlXW;_ki3(URygHe35Yoo2X4)Eg@_N(#4bpECOjqfiUAQ8y0e!6OEFs(OJCYLf2PBG zapgKvuA^cH?i5LOeuQ^|%o9yz-wZz zv(etsw2_U=@3^aIK}<$m9S6PGnlfMhQ|C1udh73DKJW&2+4_yU-M6k5q%YeW3xz!k zFiExMoN$)Dg7ss(-8hgjhiJqxQwt*l15Y8H2m=h-KJxA@SH~Mr){h`=l-C^E2#Gu+ zV?<>Nhe1$T>!NAFSs2U@mD%!ZPEO97S3pna%d==xb2Ik_;hw@gUu+dtppB~nW>7WX z?L5GPU1)Y@j4YIz#(?o$U0+)~)Y$cVeU~g}10Hs^$%QX);aGFL;j647Psw)g%-m{e zy{y&(=qRIN$RsEQ*q7lyg#D)z3_5A`qyQ;OBw#c&xJ}l5_{zX{p%y3P6(Hy~Tdb$W z@chtl9?~rN45x5S4h{x08sABl=Ga&IjT(%LULb9V4oRXWKQ>~Rx>1GTg{ex-WD%$G zT4D(;Dp3$M9C)M#TuU8kcRXEsS5<2fILVzZ*z30;<%dO^OuMC~jT6N8U&QQ%yYJ*X zcRKHli%bZ{lg8fN_Qf8nEdS0=(D2;keQ%G70U7 zz5h?&2~TEkMVb+`r?Yr;r=o9l)G~`KBIyid&x$40XX8qrcC_H|eYIa9Q!{<=1Ueb* zih6p+rG!So9vdDnOy=@dq9fy0$W5Wml(8dDka8St`$02NT(e*e|AWUTF$YpWJqwP= zGc*(2pHU^HskW86a@1@E@E*5{-|KppB(I4H8r7$XA6$r=no8~N(LoU=8=RuR9tkzX z1@k)ab+os8ps$XL|fQ(8rq*6*UZJ zNB013Ri$I`yz?RT$n|h=Eo4qQZ3pnrW79ksW|hSi6&pXK5M_q|e_BpBM}P&Zq4utn zW3#OIUxJYDpEho8r?%pZa&SY-ZOXfP{=(PP)TFjP8V-mP|FuU2>eJ(ez^QqP(5dO@ zE)1JaZ3$5E>;@Bc38jvT>X@FXb_5hfgZc3t?jDF&g9e#aXxFdRjt0K_wFXTo7n4y? zTs=A(bF@p`db$!A&mu1qjSm|Uc0*tbU3oQEq#xtOUNc^3fW#+SI}O;hKmr((XOzy*Zx+%~|W@qWTDQ?t;O!`bR zmE=Ky;2B%x1S^l1`p?j2p-3jM3dPWYQh%J(!{MB!APWt=Jl(0;6gnfMWk&ZlcK z7uXs>ftVnUp|?HqWXr($glLmO262_29Uhulu=jCw%wUZyo@t zXito6M`p43S56IGe?E>Hd}^rI%6c;VLG_K~ya06b`X*tbodmOKGCX$`^EBHp-<+Bw z8NMCe`zZ3OY^dn{d#Z}lgN}p<#0M3Gr+za2s61is$2{m=t5Y?A?_g%lTxu!Zc95iS z);tqN-^e^jNvP~jTeyfw?%ha&2=6_2yN33Axljkk#Ooh;r+n!3O`2pW66QNqL)#we z0Hz@e3!9OJC%QgW(vVecvDw*?VqHR2TOdz}82sT5Z2hu=P@ndk56&sR*X_CSdA`y6 zQD55GFx9T`@pgYp&1O52x@E%0Tlm+nm6HE&GDCyvX*!;NkO9pcob1EtMv69Z=kQwt z8Fl+p_s2?DV?^g3lwCY`&8k@7%csFPLVwt=&erd8Z{2SrZUM1i$C0bqv%@OEj@@45 zZ%srXS-M#y*Cl{#h%xBfpq_d*7P};Ofe&T;jv#lw!Ujd-B-)*dn-Q(Ub~*e@QDNAL zzU*!XmI=d>YWK=I9o?GfE&Vks#ySFStZA5Ofjw61E$e}se>V$_wJuMU$HsVl;*0D!&+BBkiG4YU znrWoS&pfAV0w{LiTtW^wKLDt1;+gh+(cvypz3Y#5)kd(+5&WRrzX|pEUouwK)l=ad z;fP2v-lJ^Wpp1bN1b;C+2+?s$5&aTXMT0D4ZXI z!YlpbuBp8xUOiYMbxOZXr*59vx~7x5AH(Qh?zPUjF%$3mC;kyTNr_k-?QeIl8| zZc)vJCT0KEMveT<;&hIiRLBR1tD{dv6UXy@>RI!Qu zBR+Tinu;m~9zeq~3PJR^*}4>y>8+57x}BI48Po6|}(2x>9p3BTU_nuy}@^<$xh7jEM zsbD~D$In;lWnE3(yq2z7**+TRRsVfhX(wA_4ubcC)56Ek*IV$^Tyxv#Fx!qgNOc+P z-gbmJ9nhXJhPz%C*X-@Gy+ z{n?0Fb2l~cUBMEfca%K$Tp-jlARAbb!u+}^oYzSk?Wm9QgUUQzNL)~p^4#5?iQtq- zt|I8fPKf>C7Jd zR{=4(|J8cW<8JBS6yU28kZu$cm1v#KeB-z=xx2Zx(H!Z^npZ~VF+bYl)AK_Z zkQpL5+iuj#A{Q7is{$(amf-*}KxHI6<|M)zjWy{$q z2CyghPwo>w-Nm4KYkBKfi1c(8N`C|WmwzTEUv+BdEFp~?V&#V@Am2;v7Sr&%VQ_Ht zlop}tlsVfxc94$M=D=p8K}TI1L+>a&P+xg#N>o_;@)uNrdp_Eix z#|Zs7pN$z{e1i-MMZQWvyx&3>MY>l#WR`$79#LB(GDjyl+V8VKt8DI^Q}TAI)bM9i z58A@@&4e_O$ryYYK5%n*CzCQd0LCFBP3pOYo%*R*XNW>sgn-EW8nIYLlh*QP@q!h_ zQxG5{uN6T)SuK_jz98u4wZ|sh%BnC@0<*Iw&T~_js1`h=7{wrcblo#jn?gwLCdV`I zK70$zZX~_|PO@rodO6Z5ejizC0>$z`8v_!Ac@C&a?3w#hS{gW4AR#`P7V2-T9F{Ac zprOe&+uzU=@q6>ka&;`ed3ogTzplaI90Xmx$Dg@7WyfW%Efv)XblG@0{G#u;RDcdG z(L23UXp|Awnzb9%aPyewjexU$sZs9&es^;9C2rV^m=Wzup$X>O&SlJ72;<^_TuvyFh+M?0J1VD zr)PuK>+9W#B~X5#OwRmzFLzkU9_qaJi#SaRyxa?jCyL+yz(nwGOc{;;F>y(?ik4n~ z9<+oGRXELSsBM+0Z?*t|BB-+?-xUv;7V{{GIR9NvVnz;gPhoZ4sU;>=ad3B-ZYRep z6eeo1s1T>Z0r%88*#DQ2#tJZuc02|}(U01Ajds=DtCC%%7ev2HDiGWDiq1wx{(5-N zHW|U;XB+a#6Cn*EUl@DDU-yq#idfqpJx)SzTv;OplmWXy_-?NA`~sy-LwP zRB+!nDlqfV+vRvm%BED)Uwt-h=0XG@a5fQg5@{x`2q;JZ3aiPBk~z|cR!nT%H6k`t z?{|M4R$|>-2PS*g^fIBfbhuG8KKfNnW0zzkWioke>z*=G?KP(6EtfD<;AkK_|0hOn zfWlucDE`h{I@81i>JVc!?K+?+4#*!AkZH?|+Lw(Lt zg-lgyW=llT~wn++-BE2ahbVA6c>8bb|0-wc8i<6;oR#>r$IF?cU){NZw* zN+w|pZF2wZ{9^?-ze*^TRDh{i_V-whE4>t0IDh_;!SS!4l9lVbcco1oJ{$r_qEsGd z{J_9XXae#t|)KqtJjYaJ$z6&)m=3ljX50i|85{@9RS#;#ditH#ER#v@?&G z5I_6Iq@49P1lSj#4s>t88s0o-wR)*%mbR31U!RMs%(yw#R|=5)EiogBO*(bvD&gxjXi#lzplNTGlQl z3ZS4O!qa|syb-3Rhmb~d$rDro{qaGvy;K;$0{2ms%}Q=YbHN$<`M598R?xzbX!x*I&c9UPvJlNhIF@Qngq$nUQGqoi2Q&2Qo6gl{mTy5O^$L{ z4=VA9hv385KbfF{N$xVt5iKIwmer5Zth+yy&eS*VfdlQ>zIJ{@U-u#*vho3kN+qDi z+O*Cs;Q678I#UW`ixEkkATXr;LCCH*2hM&GMAL7|SZwY*4t*h%1-v>6fbnN*rugu5 z;P4|g1P{q59ZFQ7f=N@|9(sNgyWAJFcSR~xyLPdptYVRsT5pSNv92F2LZn@-KILhT zj0FHE2v#?Pn4;2FwEL#jtD;8+8**tGbxD;wteQ5udoWB4Z0ik?;u00oxU3fJ@ex?s9km%d336~iE#Fq?TW88 z|JMsq#cvAnri8?}W-6$%;dUtaOnqI+Vtywqf2Uovh4Lw|bM2eX-Rl{a95}<`NUAuf zMkvJ-xN{R8mD{g2hY0zuD0bI=nor)G6kLf7ei}wjc_jfukI6nRuRw~l9aLkK)bNi3 z_6thnhro{n7BCl-U7<=@6qnaQFuhT|RP&h@@&iDCVEruB~nO7a1iC(G} zsv4PTE?dr4zT}?BTC;^l>X#fyHT7x)|5SaR_B|no7;5L(V!y}?z?HZR7xhDfdO4GP zEA1!3cZd3UqehX?%KgFN2R%Xt)~o{yq2zsj6V~;uPXde$BFjM`Eyfr_5x;|a|7>w zoA!NJ74gC}_A&ILBtinuzcr#x5>zo!K$dStUw@(;k6lg_9&a|Y?EhrGc;tpoet172 zEXmoS(BoT(duuj(L=04>p6;A0%Td_?XWJ(*@G<*kt3_3pZJlcc_o6&VSxXkn*gzXIM)c!SibP2HokEN( zR0A14t@iEBl>?1q|3;FN`EwjHM5&?0Moto_uR9WcQ9jY=*kP9fRcvq|_?G~QT5Z>w z&xtg0&`8_2NjH zE$Q^67*fCU8Fk6P-v3LIVE-S*CP=&?H9Ym-Epp9cv?lYtKNk+UNBeLsm7TPj3_Y!C5 z&mvz=LL_mgn~!}x^SpU6JnjQJ0XH&oxRa&qGxnOX;_~yyb4UcO;$|clrbcIHi@~gM z>fIE;Ohf>h?QdTst+30Jfw)=G{s(aA>Gc zakVzv0Hirp(tpB}l7xNkW`$5e2^h$-V+j7)XYRV|%{QSl_uACZRXb~Yc62`!4yu50 zf7j6UPVmOwe)i%eq)jyLYhv&7;CG*n!^a96iOdQ`_tyI8-)s}DZ5sw_R$a^=O*G3~ z`xTOyqPtBhTmWNxo}Z}&|`XQz{cYk-&v z#@@r1ItU(lNYo8v!K6EK=MnXX4xHwZ?E85!qXqS-g!ru9oAil_y&D|f9d@>lu_7CC z4j@(#VSc_AUkZV^1|?PSW@njM4hAAQnv1t&P7hin9CiPuhA@}hQbaz#(l=_I47czB1N+Sy)?d{6B~+0iXW=QF)#Do~ zXxMyX=Q#P%pdIsM-;@(O~$9EI_KzZTh zx&}47%_BCb;ma8%44X3C&WVk!WL1~~2M12&BWzo^; zA(R3xy z?rV%uVl#Xc5*dj$+gED8>~r{hX)Mp_hUmV3X2?8?IAo&M`(3sdrwmV++vC8H&_`Zu zjxMC%^vPRkkNGU%Jmx2%A9Q`^m`30li^XNu4545i$Vhcq$Mf7MPn`Yb!C}pdB0Y#$ zj1wPp$1A+yVD-+Orlb}587*U@OvPp<&_;9r>4c`E6JRh)UkdExqqBgkGe@3)nXSWF z;`tOVe-a~<7*$iCge+8m%u?7&{nT8|Dxd6?iIu3eZ4i=VB5>GDwub~_F_i$tw>Jmu{{eI$Af=2IK`6n z|Lm#yT`nxvaL=7ez&o<8P&28Vgq1M|Bhec z7IPI1<^c^B#s`>086DR{pp@#QCqBQwB5Au1=@Mc1|_~?^Lh>t?F zNbYn+MkqREKXF?y6VAs0#PF;rr-0opae7hoAH;Pm6Wjj54=Dc;fEE|tgUDEaF2q|W z67e42G;PmGIG*yYv^!3$^SqXWHU8NH*CMsm0D<^2YeB{*(?#oh zH%`rD1$&n9lbA`fro`sXtLy^}G5#t`GOK}~c-DSv2=x@$oQ%XbbuR4o+W1KGoWC{x zN#K|d{{2fXac@NzMb7LC1)OBY1FVtjqiJL=5omeI0gh@^{&mQ1M{!Hv#?rmJDxE3A z7(V?<$C4eRyK*8eGh!lPJG~+GZ?xtWLKhU3!NoEVBRAQtNUOS@S>h>oA1mg!5jTf} zao!4%;D@^YoM_RmG7g;UbYJsroM}tbA4NwmGG``@MB?&PX#5)k-}bVNY2-25vR?bQ zp5WWg>M@(2+_5qz!HB83+8^Pp-eUyLqgJ4&SVV4nluwJBHS zeIapLwl|!GcTIuh+VQ!x9S+MFQR!S`}1=i znTx&lVB-XI(Mt~M@gCzul5%^OSG9kTet)h={r)%bYn4t^C|C@>z+M0f!%=Q_wS3ROD~I}*i}pwSJD1Ik>HR(rV99E z?KccL#<2GJOG7(5ujk=Ut;3&Y`SPpf&0rLX&`8uTedu{XIZSS!o+sShEXPUo-7FUk z3^pEpJXQi%A&ZM4!h52yn-0#((3u$^ho`Ua6~~6cnkGO*zO{Q(c^!AiV>-L|pME8o zs1{4*PqP<7_H>!+q$p(OWsNg~Kkje+84L^a_{p|e=2Z%4a$Y>xYfq`Qbp-I|YLFs3 zSZuY`13LamcN5!c&fGQDKJS$;i(C0SLztUD2iGlYg7((PfYYo|_TTECnPB`1xnOC} zTE=1T076;U?{ye}OTMZtcn)$EAW!30q`Kgh2w=f8BTjxX1}q1vH0Tu?*u zd$(6N;G1KVQ;v64I~tv2&kAM_ofFX97QhR8$~7{cAa!@OJ@ z{!WUcNmX+vrh#W^;crn7v=O)ok&z+HpJxc5aYWxo5qN41Uv7Qj;z;ryVp6R-DY&I* z_J`Ro-0N*MYIk>4dRAf+G=4Qjr)I5o3>y$2IJF;0WcE9S`NzIp{tY&N$Q zh-GC8IpP?4_#q`GPKrU+*Pl;!{0rY(W1=gKZ^0RcCFK9}aC;_`8NO=a2FvY|7I3Z1 zmGHOut-H31OD>JHwZLYPVfTU0U}43+0=s?(QnF5NM|v=%Wl;XhPD)C_ zF&9Ig+1%?}DxeLHMF+4mC*7NCL{ixjzFjjj_(#XbCjAjRmrXIhWVE%3wY9b5E_kfx z*osO@-qF+?Sr1$o7{PhJbUaPY=Mz0Xe_BJzuM$gd3C+^u(m-{Rs*kGIy>^RwwU zz5VgyhvHdh$vv*m>A%*uVtrJ~W6IrwgJELwj4#(BxsBvlVHZNZpp#4#2TL2{@$8D3 zVR=N(IlKShJ01hn5PMxSgYFN%u_n~;e$7W;fmT*FLE2;r%9XjySQ9?y%nGAXs%o5g zzX;KJQ;!p_OyOl~#Lg&8dKjs#e*Wr-d5#x01GwtwF z;sJ@gO}`ej;m~NN(oT5eo=P~cfZ3oD<0~S)0<9HM`@6KFjYhExk3}ioe{3YV#$0kB z6e|g)g>2MLFa#CX2nDcj5?3_s4?gvQ6M=t4iJZKrofJ(ObNc#>RIVu$=!-E%b;73D z^ZM0I%7lBwa!gjkw#e#2ad8R#fdwK08_1p_fg+BD$fx7d>54F=q^sw&engNmqU8zT zKE5TT#PGM(L1ADhH@=>Ag%g0g*Sjjr9f$B@k8ED6(Sb> zNOeS&ciR!d?CFoB+sMm!K`>Xco789&l_&MhJY4r+A(6?pPOXMI-uCf(Kv?7c>S*_>2KNZUo zhNoN-B%TFy|voWu16ZbtT3y zzxAE(WF57dt>g1Iq;2{i?$34frb+=t=sm+pxxKpe^gq%&7Y@8G?rj&^|5ib-2i|nN zOtkTSc_hyiSA%Pv8=l}>CzdpXwr@O8S-c;NsZ*>n|HuFo$K{*48-6$ok>{Ha#@Kkk zRx$fpDZ2GjiHstGEwiINs4rtLZfl%v(G#OdhA$%XNDPf})WZ z=kFh{bS07H_r9_ib= zi}Pb%9hjTCGv136JpeJHL0a4zj7@ka;%9DYDFax8HUK_WFt4R*B;`&4VYy@dB63t_x?KQh~zD z>wNqDpQ%u~KmN%|n+*Dykkd8(W3ayQY-jnVDD!R+Citn>?v>NzP8;glr=&6~hy zOO3?;dWFO(rfR?4eoaEVQ@(ot?090WOTozJjb5W&$nC914YAYF0=I*U3mr`7M}a9@ z^uzZ>rS@p-abd(!HY)XGPTe|GAl%2W7@LrQ*9VwxwiofbSNjpJZGSRkrz9rgd%)Un z6jOSDJ}4$;b;9@We={E%orN;xk*JuR)i()$R5k{Ql4VBtt(p&VT3vbEpz2m}DofI! z)fdP58Mfz^*XMCX6S9yz#1yr6-n?}`b5mPFD>Bg)I_!mJ6XuS5Sr9@5$k+9qn{koVmd6dwNx%!s;|S{@uM`}p6^?@FvgN(wq4?5Q~r zMAMy9FnM;_cC7D(8in@tLX*ph1CQ?hfu_lM#6$fyG_TT3Fq~Y0Xi$?=1Xb&si>(7C z)zOhi_h$C)`%b?Zc^3X1#IwMP8J=k{@L3eA!rLj##_DljJE5mwQ7qMDUr%^Zjti!B zru08;iJUhIEJ8D!9E?Y95!9^EFe37;&-U3wvs%u@{FW&gMNOM8CR13YK7v(F^_gk} z2{cyHp^~kM-!KJF$H|G^;&y^wDRx7uCIWg#pza?_!W)X;mcp*bVC^piGbI=af?$kb zOFq7Vh)z370gfo+fhdw~H+i>EA-CAbNLo24wB0+wwBij*H)C`w41A>%ClegK>G{v7Ve%c z9)U9cC`E1Ra5x)aNImh2SbS6B&}G% z$vi_Lk$W5Mf|748=63TWU&!p+q^^G=c{W#3o~Oeb3X@~)If8-j8;c3}tFvkB&*Nmw zT$y<{T-7TV%zk@~ZU`ZTZX~$rhW3{-ob{4ueUe8#k--v!i{pq%!rf$s*SWd?e)QJw3=Tc<$-FEE6=JM z&fbfw%WW&m>CxkM3V-6^5V;jUIK*)4` z)qSqFAo|~bYg;h?R5U+7bbC{kEt8!al=YUHeRF65!KfcSE~%){++a1~GG+wJQIOKA zto)T@*%!Y#ydbkp(Z=snOMoKct9p3p{f|{$?#7-x0iJKCT)6Asrwm&v(|9fadr{kA zL&04tR^QG4ueE}w6ENrXdAD)6wI2_?Xn!Vz;+bRvQ~~;AN%)|hk<+30ZU1XaSJ1rn zqkQC-ZHK5HBcJU!wTK*5Hb34Q2$u!?og^*eR$xzaemm`DO?AkF>k+nsEOQJEKO|yZ;@@qNxLwNYEpJ9fk8q^4^_Z9@B(?ceBBO{lM}xc{jof)>G{ku z{8IiulCJq7?*H*G+qRaiWiPB{+g!SAYk8|J+cqy=Tv#@*TIOZDvDX z2cX;QBJ%VcQgDMHSOY-z80*}TlCQi5m4Z`Tiy-nP`Ddy;pnUFL+yz&qf$COP(V_q` zlCZc^DCI>RNcJd<;imyW>d>{Joaji)ZU+)rbbFA=Xrr?|ATLDxEf-AcY?ElB3!y|95Nyr#S6DC~qp z@H>W0Y3%drU2EXw3%_Q79CY!xXgLY7MR1LKl7p?@_<(YXaM>anhel5aRtZv0b|^~h9%T}Ti4A z&PG#9+V5jM2nM9^EEGaGX=sWhjsQZpR8(}%-t*!`%A9AO-F&^X~nE*)QJk7H9`@S^yb-6^E zJ6ZquiB?|7@Kk3N0~{dkyVNo-sCv<&|9F<#)_&8@>jsp#Iowd!>U?=Q!VE_|a-+ns zi@BX>!nr(lkokOZ(CK|KkY;)skr(Vtil#)K$eofKgqs@-1&Y6CAW{H@;6lfXa-+io zKFUks=W*K`zqNr$QaP2P-A1_@=el;5jO%7C!e-?xGCA|Azt@FMbcX2sR(dS`2E-Nu z)m+}V)5GJYf?npBC5jD3dHk5MF7Wk-rdvS}XkMvJ{CIUVpC2P^Idd; zO%n2|UQes%GPjT8J6?frhow$R^p<*FlW`+q&+s>m`Fy zP$Y-$anRmx3)CjIB6{B+4V^&73;y&0CYQDklo>~6aN(7KFnCR=SY6JT(F4Bg+X&t& z-R+SYh8c9Oz!tsL=Q|wTK3D2rWTxc{yWNFGL(>2D;`r5)i!Ld&kT5@yXPC{!zaIa- zr3}xGp_OEs&im1j6sie3G-4#Ii{Wd~Q%iH*KS)ipZd(u~3llmW^On5iDH}!9s5$-N zaJdMsBO$2TmFyqEF@h+Ph>W2F8bmC9;R=%JXw9|W+N>8utto>{azMxO1z<+?s*6jE zXY6Mn^~dfwq0@XOltJBRcq_DQi;~9qz&1B(c>}mc=o+7bL$|(tQdbs6k2l@oDnr_J zedpIc+)N0)9!0~M*hXxDPLyUG?oO#6z~K$WGLdr+eJ5-tE(p0n{_P*3o19@o%Z5rc zEfjdCPFp{1^p5bN6FpQ)P)^whqkiZl8~ zK?T_n9VXVoC=+ZG;Q24Iwn;6Xw&jKwoa|z(o5LMch%KlG38He3>%;)o5Lp)uGa?h+ z=ucRBp%ycwL@dh)NUeG*j=Fry(K~_5{XJ%M23!7-JxAq0c`C0V?(z5T-l2$1OQJ9? zENA4?j%_a z+%QMJGer+@iwd}GhE9oZr~s9g0b0!=nfxMpJF*k@qTpMCx+d75?K|-3KSw^BN)k_z zN_T`{m%*yIG^x9zg@r0}^9miOW{q?QXqH24gJ7hU(+{he2YPIJpn=n?U;x+IF9Ou< zp`oGV=R%)D#lHlCI_avddz2z4_K8ELG}T(mP_@-ic0tqurs<%s7AY4kN$PhFdXEmL zz>vcRUUycuL+e9AUN{u}{#@HU-%N%T)KD|}P&SpzFk<6mNiXho|CaMFSXDqZCI|hg zK1FRaxEukaa>b?mu(sQT?v>Ji(|M~M;~w52%TI-o1)_f|&Our3Usub@?u4s}_xl-c zW!+{njw4R$A*eA2*|Wx8@W02~2W3M1Trbby*p=|vZ3GXQ#)^Y-F+i-d`dvA$)Ve1l zO{ts}JZN)vtaKr%JeiJaYI~E2$zwDD-+uUrI9u|pYx_x$4T;1;61Ny#XkgBWmv_8x zNPUM~dS~XNU-jG?PV;p_L32lDozyMxlX(GZ`h^&R`Q*a3`R##b%3&&KB5uUuNvmVo z>}=6RF|(unMZ=R7NIsD>{nt;j&jl6rmo?kd(kqJw+O7nPuiuQspUGcmE1KvVDv>cL zsDm!nJH`iRX&DT-^$!4oQE)YuCIRO3gM(^ok`4}NSN6jo=1w9me{toTSC0y?E<%R6!3b^Dxyqa|Tg+iNO$;JHe|3hH8YvT*L;BeKg$kmh8 z6_v)yo77VFp|ESH3}fCG0t46?M0q%En)>C%6B~8`{Odp?ma>n9Jkqz?r^hr!MOXw` z$D|NNuU<8jGLU0=d3dEguG#D&d3k!~{GkiL7uNRHV5Gw@t`RX9D5+Fz_fv-WM^Uu4eRD|}S*UR{t*bBvaX=F4 z4m2;6Tk+a7NekP!yuC%&{BsTV=GyN&&& zMfS`{e;cllYMoHxG(*F&Y9C97s_4v`+YWc5Q4`SFWz4%X>KAw7@fuBta6$OJXqsR1 zPup!oQgahG3fx^PnG&{sRW3B_Jf7OC8Y)^Y9yyQVexjE|4+m;jI`XSN6yJe=4*z2C=;@s(J>X;6wZtD83m1Sn+6APu_;C@V#x;`R(80A)^+tm zOZp!Bu5l$i`7<=CQG3PfJQuHf^>d~spo^MV|LyQ5kO@Fr%HiUv1Sq2tlEQ)m9}p)p zd61g786bJOI-_|$(yi7%7(_^9+c_>uccA|&Ea<@6nir8t`UC8NtormiGGO z^sEkIjeIqbl>uHb{mWKWM|}bu{}hO$k>H=5Azd-BD9y|y%tqDAXycwr)=(C!W^Phv zcyUy&Ia-Rxs&d`G^CrS%@{>Wux~tlmKy2mup5DxSV8AkdWp$E%R(TUW+A0DbpHjz!5p5L?(woh`XwEDpnCmrv#L zV(f+_^m4@(uRxiMw5P?>fuZZ}n6&)YPj%2X;2$ggyB;ChMQ^YA&XTx&Bj?L~74x(T z3D+O{b*YN5*j3(>LerFU)L&q#b2;kZqyUc3kAVd!Ut;q_)f79|R8j6qTMqLxLkKW zkJ@Gha@>3+GIih&tWlRKq-JI&k^7tw0qupARJ)7M5Rib{-9W^D~g>lDz3FFz^U zTx_Ip+^UCr>3X5z=S7%p)F^P-3c7qmGVN!b>QOv*kA@Tk-`Cum4;Nbx=KZ1jKR|$)QH5byF5-Fod2;JJ4juUA@wy&L zkJ-%uG1v^dU<(m3LZb*rA%U|)=igte-QT@1qv!OXx6wpLs{8(CyyW~}QDEi<$I-dt z1|M}>?ek30744NVE6P9GhFvoBF|2rmarbF5%lO||Q+gQXcZwJfJbkeW>R4Ul0v3D+ zZ>VF+fMAz$%vP|;@&`j=tzxuague$MjvLicKWN+!tvD@%IkZgMVWS3J%E|ORo#dT& zi`}=z1i2V4dAT8x3Q%oI7xUNeLz}JZ_UW-|`i4}TKT3j_qcij8A#*;E+Y>m6cOKos zS~0Iva?$G>svkKeLW7wD+vR0IAmQfIlCWO4@RwQZD^RrM_EFhDjBv_a`xjxKcq3f| z!Z&}x=5MqG?P;ua{St~YXQ>SA$G3TyM0AkQPzFhj^o|h|A<)!m4X!Ln5U$7Hi16SjZE1DN4ZZMRB zx_rx`>fg?Rs2K!cI&T5tSc7#Qw&|iR@P#xe*1Ue<=dQR9vMkMfv)!vinqp-(dcVeM zKcbS-qkJ1xs!_=*Wman_a7TbSav#DQVE@sBymK@Rfs!r~bB%$++_xUvyKq zE2ouF9=&Pi^B^Coo!7P&0(rtx^`AD^LsT&eVBA{t+pNmoW=R^qAmX%H%_09P^V7*m zas7FL+Ccso-IJ>9_oRY8Dadq3^PgUS-|EFYS(#mlK?FZ0w4?`;^-Ik;^$sLi;m}Tr zmZqoMrX0Z6sBXLiNZHr4*2~>OeJ&5;SCN3dF$|yUzb*9vzS-5ik5EQWiicMf)F1hk zMkLGN*X`j9CE+`tVRBKQeHYmCrfrGu6r1L(tH`ViN#Z7T=%CbNbuQJ z_{_cQ)#u>?{5}TcObm1&rgE2wE?G3`feB_E>9%vC#V;d6&H8EWY_C(d)Xri^rlXuV z1Q1$ev9qRyuc~|!@~c4e62%RE&|&|+fPRvZI5N(%yK?QkTB=RKJ%^Vpi>mWID$*~M zu*N_k?jqF&ca2yS6Qq)IYic{7GU{M~dcN;eB7xTqnjmq-`Lb~kwY^1@HpX)0`o^Z$i)wpWog$x*lb}xon3Jy!>lk%>YVy-C%fmA+iaLT zGh+g<*S2AcsT9@0qJ*cO>^eoaQ~4w}Lh7#0c4R+k znxqG(G_3Xci^uAj{L;Zf=H2U-pGu{yAFb_B!L~Om_j0judISe`eNS7M62}8Oq8|-f z9Wh+P%6l?D3z#uMpQ0 zhHAPgIwZD-w;C;tq91a`+xk@VTwXkSjzlkmWUw5B^+V21i8(EP_6tCQBhm7tlZmam zPiGaz=wTP^{^*CnoZ_{U4cmb$I|2qVo7E+2)ryp8_pm;RfJ6o}3maNy@+Nnhrp21W zJG|H(K0{(6)DzFR<^5ETU}C@+%eA4QxE^3+iI#o2iZjfTCcVJew@3cfvf5t{^nroxlZs6}lY?-?1Hu3kUftDFc4w#iA+iaTIi|kPG--KC=HiiK)Nit^6nlxD9TV2z&_b!?$ zh2SEYFwdeuR&Y#IsdUojB9{%}Yqql?!uW!K!jOBmUO!&KOxj# z>K8S$ORai3(y`{_#O)m(1o=#wuSiw;^eziE{Bw1SJJaLq?q41?YyC-cmZPRVNvO(R zT|DIARMG7w!?t6gPP(w}M(xG35MPs!DJ@1=vSQ(FEGz!B)%1z0KWwbK^IqYneqUFO z5VJ*-tgQwyk(*@36avQwBhv^S8=?nx4{%u#r+;16X$ZK_$JwheTKiX z3nGss`xs2`0Ev|@65m|ldUqh!Irl+Ht#l7ZwQK@yxz-z0=IHOyc{{7|^yh%GOWZd9 zF@rcsfK_Kd+Iu#m8ScJG-muj%t8YiUi9WJGg_oW^Fz=vpSB^WAYLWHVX~ARN^m=_8 zkXY<_5mOC(t#N6%zYu!1Quhq+-M+#v(qm-eGkO*B$}vtnQ!izYqm3B&LDbLh6*=rm zSza}uXvo}BbPc+}LOc8%N9b!6`bCwvn|ZsB$cdWxuir)=EMbZGhb72poGyg?X?Wd!A?f0OqN%TcJUI{<%AdzvB>eZZs@3jRp@Uz<=dg&*)}obE zRObKG@1bD)1GVnC7qdW2rl3e)-fp9j|B97^7WhrSio__yEL=Ti z$G9TfHtuqt&?dQee92mGs2;)p#z&W+lBK)XBiOB?s<@8xT@v%KBIzKp{xekwm)uiK-ET)!#|)bcE_^3kSZlZt3W{njQmt~?{ldr#OEJi z*B)F1AAQ%3;lLZOGZd#9_R%wF4+WANru`BISa=#|)vI`DD4Yjv61waL?uYLlcA9l$ z4Ugb$<%wQo_;!{sf!NC6G&TJc2$hN)kE?=wWKh?EDg@Y3ea!h(H_aCe&KCyX0$A?L z?L!NXk>H|QIUR%-S%`^SFHQL*`RRIyH7rmmkGNea#cgaDF7HH&z<&jzWmty~+^_f1 zecn0<09RHB!G?n5^#2h|bv>&HRDsPIY$_<9(cPeoa0Z;07vHb+IljIV8w1Cm?cHG0 z0T0Z70Y-hajH{!_%`M+HsGsrKyq-L+L+j{z8Dj5a1z5qWYfFFL+Y`3#&-XX-Ev=HU zY7~zBQcmzb6>DA6EDcS<5&Uzc(aBNl=BREHCIldmNokK5`RjLEV?4Vrszk95qK!QR zhx}XXU+=|)iKOei7kLng5cIGf4JW3S-M>HE21nH;u*c(BL+xM-tDqlw4R2?u`@&U| zknUvo-`~{v7|{?FpuzEeJ!&888Wa9ptSwp(d?>kWbxL5JqgP(RCTS7 zfZH}Ak;-H1&i0plo^%?5iOKH&O3T4`Y*+u2Lb1kE^W&)6!lcgZkw_I(Y(|RT*4lu7KfE>&eZ<*?r=gB>&_nDZ)OmFAfkKR*?b;`Fz{f zSniDWdf%p9AalVCZ`-2$LRcrL9A_wS-p84Dj@<_FBNdMKiv8rp^&P*vKGN0C5Uy&Z z1tBiqQGI#(*o$p?M?CVRvepDrO^?pTmWdLvIzsT!o#y?+|9tB z{cbNZC8k{+$QB8roV~Xi_akSyiX72S)fsL44dT*Y{AT;Ec*bU=_7i*92juYVqn$6Y z-S_2f=WcVa!OV`f^dXKV>4aZ^#-{rm4e80|-#WG{uBM?oo>$%MM*o2;Oz>8ozY9K} zrb-0Mm~Y58O++|KBFJFcwKd5Eb0c^lwkG<4jWsIQN{<(?)Lwr zu7Y%h_cvqk($+p`x}bJlx-_Qod`TubfBs;LYQahzjY+&?V%f~V%Z{BniWE+-nV?kb z;jJ8Xc>d&#n7`ZCu*pw*uQ4y_h)2$|bA5`CgXg8_w0g+G>%{A}*IDOhlI$LEWPU8yJ<(*lC4Pg^AfukeU&lV-8uqTmydn4Y$B6DI;p5N&m%Y@>zm-7n z?ng9O#bDn>zxn2tihoTpBa@Q}T7k|DfWI8bVE6LfVgAy?Ex8t1rymtQ!H6rl8>X z*W$C5%1bgSIR0x~RBPYH&WP!258CHnRs^P)#(ti#@9NFdy^qhvjg(8ZnuY)pg(>^f zC@cLWGSN7L_K(hA=AL}$Y}IAo8va=@v5I2>d8{TSA)k8WOZx*JAHr~zf4uTKaR6HgkXGkhy%+gB#)c!L(qdO1H|coPgzk_ zr>|6EXXp !_Sg#$w1xYA%Q2CTYj)sNGn2d*BRuq|!(OraiZb+wBWdct zctM^=)IEZ*?5x*HoT%s#6Smf`q%l7-^x?&?kD*KjxthH92IwUO?Ezg_S+d&+1?y7~ zMgq>=Ozqdg<*RDx!d?TNS>hs~96AK_pG4B!(ikFMD@M=aZEQwxY>PHt&{z!wCDjS~ zf6*x*(!HKxG^X~Ik6L}kx;&B*Te*10IP!PnJBM?!gWrQy31~pBVYf6b%w5n&O6orf zAoVFtoJI*USQ`Tuh5@<+0f}25IjME>ld{-vEULXR4`~jY^K3as-OmlpeSSZGN0@Ki zyJ>L$k(D3By{{h9$^)hI(d5!tYx||>w06jF(S1&qWg*YsQwoQuTy|v_7!upOUG%P= z1CUV)%*U7NwxT%2x1^-CSy}lH`~iYVO_i@x5-H#e+qaNHTGEG&jwbqm>obpZ5l;L} zvW(2M>&rgawH?*gIN8r998TtAa~{=mdVSJHw4x=_+_-a>l5E2_yT_AxMZZago#wE z`cV)BI_!mkYJ^9^Mi|eZ-vYl#nbzl<|2%_qlV&;T(q7)$V?N z2ps-z2^K0JB(Xw4!_NeH3PTd(Lq`V;V?XuZ%BiWXJcrwxYmpoutjP0&N{%n7WSx+c zz02>dlAR$0?Q*5pbuK;W(40&WEQ?0BU?CHbL3P-1z?~CNo{Zg+(Oy| z0WDq#?&KdDq{@&M2OTJSDvyPtC(Lu*J-ZGC?3w&-&S;N-%a8u(#AIhR>@?q40_#4p ziRaG{R&a;HxTyAuX{=9v5rS#xT!^M}CzVJ|wYh{6MBtj>qz}Bj!YisO9HEcbso0zj zwO$8&I@-^(Tb3~;$&y;XcpHAWdX>g4W|yLvP$d;-W4Gl6*}{)T*vB{97cniHbot;w zU9(`QHR-byv{c!6XzM)~A9@!g<}c-J?gZ@)aJr+({wOWDiT&a8B}d0fpL4?1073D^iu88N3JL#w;pY|A{CK|Zz$Hqlfz)%-d~SWK;?1xc$* zd51jfQM)Xx!qe!nYCD1)C`$acK0Q~wUG{WbU$evGC&=ydI612}nNcO^UU+d11; zRlIXQxO3VU%1XKRcwVG0|8Uc9PGHI&=Ev3V57L)Eo`!!10Y@oCXem zeiiD~_aEKSJyug7$$ZgwQIg?r}6J)FgVo7csn_9<{=%k)KTc+2+gKI^|rjeNXA zMP3on^i<7@2fas23CQiKk8T}hF}N*yT|83@-TPvFj#+=*@(13j0W?ojdLaU<9xoQN zK9V;fM#lmg>EZgkYxyGQ)1So2M%M;)#YwI}uBW?Q()=ziMAqT%-dq*dk)YuigrWQ- z|6`8G1Ami3uh4pFkniP$+QaQ1#YQLzgtj02NqKh{>T3DW2C)4jT&@=&*e*<&9bQou z5Qv^j0xJ?MCh2@IOi356MDt4rb6XQ``@U@{S!%DO@f)tiiO?VYyWNw(N$+>s;}li` zT^eiMPw)v4M47PdihgO0*-6K8;b0PB4|}BH!H5GyoOds+XCpyFgy=Ra$-&R0>1)d< z#Q#2Q(!A1$sWhbDtPypBM65ofUr(1XVCX-@c_8W%kv(j^d&$`B+pC|wE$DWFFSjlK zvp^JRGD2GyqOaS-hHh_zOZqCcW0Ivs3|PD0p42XHPp%b`k}S!MdcER|s1PoJM^sX> zya@?V0ENweT4UIj-_>Q*T#Lr@lGnLwtd>9G>>{A_3;>(jgc zNz2%wou6tl`h2r!%Ddp{lTjDj6fCZ(Kx)cQn`!jeIBjn@OLfA9ov`|s^BO}y&379u z>W|A>zDXkYSyJjhr3n(Qqo4D!KWulM`9NIT1M%Xv$3`}KVD8`5Sq}0(v_~TNZI?YX z67xU5R(XSiZUftq#+)O1kCO6U4!%4Xc2KbmZguIgimSTYA<{F}#U{z9&sWSbQi-06 zw1NF_&i)KM-UjYjW{N?I$F9SVeV)2hrAK3MR}Nk#bFu*43PGC~{?*=f8)4(JtDYqi zmque8asIl_k}XkFW>q>he=Y=|t{Bl;K%_ri@yk5@!~UMOU$6<%mbe8OiC+gnspg)N z%w>!lfs3u|fql*5E$JyOipkKOsIuODJybIP1eN2X)HLpq85=e*2HX>ESr8SE&EnP( zn1>n@ziJnlg8%*GRnymjIyA#=;J*f}uYdvqm!EGVRm4PM@j}{_SM`6JJyZ%@n+#Vg z!y8K_4foB=j|WXrMT4dv&|MKuC}X84f(X4OBKmZkj^?4Ypb152Si!IDc}j(u8s@i9 zf=cQu$lzd>Uk~#7CLgFrPFJ95`}N_fR}*|I5Ea_d5%chRbE7%97I?kyks|_ygz~Z` zCL~Z6oJX&f?Hoq?xlw}f%0fimY7gsq^cDwnLT@5K)v&1P1fSkTTy-jzb7EJgG4H1FjD?a1jqyys$&?W01?x8SVzv(RR~A=;w^ zGH)|TVK~4nn6m3>75i|h?ro9*b0odp>x_X--;LPo?AQJufVPM{0sZ5_A5ZlUfol-p z$}*@h8yYnEZ-Se!`-b4xzu!MEo-w_ma8me^@~9Z>svw~HAwZK3%^%Bdd{3`xj*F((qQ* zApzRPi1iJ*Q1HGa`-h;T<3U+}I8;(^n^CB_>Xc6xzh06)b!JL}CLz&jfPYyn+(9g! zb4QVJO(V$3qR1HoR7FFZR;}%8zd9cW+Q(8U@SAbjb*zP6UW9N-8~#E>*ka0|dyD0v0WJ5J4Nphy(Y`s1_s zz)Z0s!_4N8A2CoA`u2OZs>wBfn>U_=viLmpewFJ?%q}}=k*2gdNyM*L_ZJIdfg9*j z+sH~1AXzs`Ws1oGf*XYSNSKCIt&VPiG5TW#RH&?R8%e6LN^{XAp#;67p@$9}+4^kU zKWApk-#++)ria5d3zJ&i_ITu_M-mAyR}NCZrY;!Ki#136?yeAo{}`hB|8hHS zQ3KpMmo94sG9zZ^+4RX4B8xO~32q0KUXyTUNK;r;PB=AMz!d|~!{h1-zxesTK_V&h zMUr;!1M$bq8|jY?>s+OEGAEi~gIf%@fEp!b>VU#tsr$5gdJ3qOo$;BMn?E#vW>Z+q z^re)q-SZW5y)!TgzgY~ab1cYlBJayT*9QWg%_O#8<-o0twR>t;2cVDX*0ytUUXjMz z^y+8G!M0}jP@ZgEB3>_*;BZ>k-LJEZoqJJQBj~z0f-4!pNxu_rb!FEXcq>nIYtj_6 zuVd$W=RT5i0(an2tLrtwP34PeRfZmhBkYA?yA4=%5rN}LQ2Q8&4z zviwEH=C4~Y%%miy5|$1KsYv{@0%u;Q;rTQ0lBpr$XxUf{Rrmoveo)^MTs#bx5Hmj~kBZ8XdhKCdbBk0K(K4#A<~Kq_{B_i!C#vklRA++9~adNKlS)C&Sd!;CT!)q7l12LWFPm7nsg<(2$mb zkwJuJi51{v`q!B4O%%{9@ECL(Ij)wj_`3K?@~o6SV3EeP)>0iipQF#huAm)y{9Jul zl+5AM0e+N7ReIg^96bQ)7^4m8mkl~40bUmw1+m;p?ieDvAnrv%{AHY4G8Y`{Q+d}9 zAcFIRE^w{Osgd9JAsaRWa^-m^s!f@4jr;i-jh&*hv)xEo5plxTN&e8Yu-y1}|KXt9 z%G=Fav4R{PM=Vo!mH0xtBFwUUnCXtR7Qql{LlP%f3{PyNZ9AOs@UKGcks?yjwXyZ8 zH%D=gn$R8-q+FR+$;3(=CzL$GkDHUrd1z^ziy^h>nS`GWSa2L)MRBX@@3=qG5k5+gMn_?3bVIr5f+uz|tnTlF1iTj`lj6*xqtU zim2<&McwGsbcn31GFP*tN=5a^R_6X z67OP^)({xCG6~`i4+vt({L~(sM2GZj&xO%IDU;$ggT7=O#_jbwXlAQ^*A^IJX}%!W zwNV=A@JE$ROFv*lh#Ivsad<}MGGr$9rKDL*Xa`~rp<=g&pEfnd7H>#02R)iingjE} zB1(ZUVw?p({?-1peo$0p)hONf

PC9*?BC{;2IAhxxq*7jQawHZOMNXx~L! zp{!II6(-VSlY|Vc%=4wz-M!wWCN-o-z-=oHYt7FT1E6aQkQ<~?uf+viDy>R)1(%2# zX4F@BOb3Xb;M+IMBQB}=AC{w2$kKmlo*hvmss^?oaSqSgLS0HcXBR7EPrx~t%U070 z+|l&(E1p|*wm*Uh9bZNYDjJ~5LVioh?AYOx)59LGEGA5J5vn(q;eS+5x)>v|L34g= z*ZR7K&s~NDc+KTrIw9(k8*>EPjK*RrmkwG#QCDxT3DirM>3nfA zgZ9)u1F5M!zB5q~Q!qEc)U!&osivVT)%>Yqtbj;hi5I(h$OgsLk-u{0fjsFME)-^t zqDWKdkz~3qeKS4=#NH?k(R50nr-=p)h*w!IYL)chW*yb&j^gs?0~ft6TO#y&Ne;l{ z4#AfK@*#cc2B4>*`&EF#Dy2@&IrCBN*VSB(Q8*d0Oklp#U*i&6Q3GEKS5pe65L^Jo zp=9j28xB-EYEwg6$w6nyi^Hkvh-R+-pDmVoy7>t`3)yk@^=GjKGG&2~f)?4j%o&Y& zmNW(6VCG>L2#4R6 zIyRsE(cN&6GW3z$t@W4S6K_LrOsI-xm1m4<#3H+>ozSXrzgrc%zHAWnw{$rA&Iwapa3n2Tu5kIQL$p?(v~9j1K(Lp zu0abZT-yX2i~%w>(YUh6$?fbisU=Aq;Ai2dBjkstj1sHo=_vg4cgW}P^Cp^Q|2x<= z@Y`S=zW(zDB>lFKXC# zWTXdsSH9w6rdkR;@OlRw8737=h<;PJ**p{07xi2y$6IdoezruM%-J_v^3~Ai{80*g zu@!Ad={le6R{jC^elBb=Odv{n4Re^^hMR9BDLXQrm<2ZTX$-sQxw`|fk6V0RK%1wo ziotXE=DgJfhm-;PSnmtx**ajJhZVQ)Hsu;edeH=ex)-d=@_l*%3lkPXR2pXb_u@ zB{3`ORi(p4|GsHKr73|O0SlON`!le00koO-_v0K;p*CEWeKLcWB#SCZ_)e01V7j%1 zc`)$Ru}G*}JUUJESOyFrg*P>EjHs_#lIEUN6F;{_os?=xT%<9H@BV7zk-NAZ-7^t_ zfOf&3# zv|jTuvtTy*QXxJGTiKN@>=OkoC{}g8yq2R|wCMjnn9 zYs3PcRz5$x#C@4Al7B{G6Wjm$ngFlUsA!2PpO-Bjh_dNxE7k>h%1gHBjQQf8F2ETP zQHThC!w)Q^8ENl3*_g^>iopDyQpS!>Sq9Ww(waXDG z`o~Pn4ehFNDE)yF{suft3q!r#FIEUz*)_>b>$jt$FFfVKb)5 z=ti(lVc`O|TF{k)900TllupjJiwjH)b!yg%SOH)t)2Ts~k1wjq7${eT3b6h%{ z;tI9?$RK{*X46?!()H%}rNoWFIy%BYgiV7tA)1u9s??n_w)Y8y1d$6qnrD{skM z*RA(we?)Z;P>q;`mIbHz89IEu+Wf~gGP71vrk5`-@wldFj^vl6`&&2e{+eq0%db14 zDJDV&Y^l(^VJp{y?l|UMsw6_8>Nb({I*#R<3BN9X4D~ua)irrjRu0(zSP-{S@y`oq zIZh(dOPg0zhG|N2%q}O>7{VSp6Dzl}``fS$c%e{+IXM4C0}ElT0M5CBm>%K355X)@v>bm{ET~LYUVjeH!`+DZn zN>iUma*%j@`%28aJQx{zUG^{X86l}?#PC-T>||c$^aHVxULQ30z8l!RZD*tHICYVyGgg$iXYrZ3m&BFU{A5K*1vPt66niMsaG6##kW0>gbo1Nd? z5j@`h49fcA?-(_=>KEvdKY+m0STd;Ggc7jkMd8qG49UtDmmk8aQ}@1J^!n7Z$TM{=qn~vX57?KMwoonb}(D1gN zS#n;*4I7^6_*q#$4=gBSlZSpx6-x?JNJb8O1_)X!7Kk`xq}&%-e#!D)Or>*=l8)!-wA@QDw;g1_=(U1@Elb40*6-96f zy-eF0JHtFf+1Wy(f~Uag5K=9T`f#ZZL8LXAww_ooS<$IA!ck4`HR0P3N;qKbS_4_K zwHT%t76N9S>Z}2O2zyn(Z%suPYDIiuQNi-f1*%Hu>(JT8IA&-;!|^I+nA}Y3X-zM2 z*|$}Zb(|-`d%IZ{Z>uq0^J(ozZ!B#EiGcbQrcPYJ;jf)u`=6IW>hz73LPkyrGi1?;|A71G z>qg>68D@~y@;r8OtMzv;ro%u=LAjQ_7#2*fv9~_%&0Cf;h$xiyVpVTjVi%<{Nn>i+ za4f6eP+CuBn3UyyX-@Lz{0=Qvbv94L{_C;PYOl)MVkAQW#=h=?DGJON4ELh)#dHgW z>e=P2Y?Ra+&)x)a4>p9QR-x3peLs=*%gWa!Ja4KM-@WiHt>vi*+9G8mfc239Qv9Y= zOkNfQPma263;@iWzkapR0$t$5w@3N(X@e=iE8#k1eUDd16zaIJbR=H-9#JG;&lUHdE*aQIB*!_g-a}i<_4cnP>0>VY0BOQ z8PoS%pzyF+OlQKQJ;R=cC~^q85?}YLx%yAd+O@kXb02CWVQN69sJ^d7zb79ACXV5e zE71wIr=E~;hJV+hrhmcc*-d_jAgPF zn>5QkibfSrvji#~22$O{L@u6tu{?Jdt@oBec`ND62T)}z>{L^p)O1L!F|f#%$RGH8z5HBA<{@wdAB{ME!h{Q%KAT%B!=bHM17d% zqW2xq_6@6=`iC|(j?;y**cvF6fl})c?wUp))U@>$OG8Z3b!aCE)ZzE~CuI3uMNjyQ zD%89XMIYhytL>O%p*%t)DL%aL!jaze@BWE7K$iaCKGe4PpQ)IPxz-URCiRZsePepE zH*bvVRf%>u;5<{%BKSiHwAs1pC^ zSd`DbIStGa>Igh}a?;x&B%2t6&m9V^jl_b8fR+A&337L)q*v)Q{g0&iF6@^|F*} z7*rNgqnU<(h%Gy3);`h$PpWeKifAs>#;B63W2(Y^-OS<-)+qcuWmBVz*zscsVY^4K z%NtqEW4z?N)R;K;r*kp_wwDxaH^i{D&`aQ^Wv)=vO9k3p_QA8>*G5I^b;wL#sD6?E zLA+UgvYQmvLB!!=Ejk-7FnOT-09uGKLd@Nxjv{}7a39#!SVCO2QfZ*@j(JHo@+46W>w3|bW?-f^>W=LtW_Vj>mIgwlOu(LFogUGlw zk)Tb+6W330Kq(hp(yo*)c1?#V53N1jU*GN@rTF2a-jLCl9H%v8Krs8%!`D~l&_V=T zOTMDdv2Uy>-|E&sSi%MWA4lilmgn1t@nv(FYuQ@$)RWz^Z7&weuJ(Jg|_U2odu>1XNU8w5rJKBQc?A`1}n$a*6&CQ7O`<{kUp`K-- zMCvei)ZI)@9)kG_FZ3dXYXnsq8{ip)@5o|)>aN^<3fF4jj0k%tTc=Xoej>kDl_k|> zBmil2jSctZ)m0EB5Coh}DPnLu2;RSmNbtT<@=GI@u#jkxvKp+gdCn+nizma_%wY?1 zm8`>I`M!Y$DFtmuLZV@Zb**UYCfGdKn$>WE*~Qf#NQ{)pqx&l|V*tCj^r9&4#Q_@Y zC@KH3{0e9n{Kqc?;-4qX632j2hKMPpg55t=pR=?A+P9OSSKQT+MNC?6B zQc+{u5eI&^;VbgN*;xAB|G6Qd6Pg_FE}mC<1(b7tfZ*ng(ia`S35iY zssMfO07ys%(O6q}RFr~ZE0e<2T75Vu7+R6S(2!xxFOA?~wbAG{eI%5ry^N2XTHIST zc1)MuzM;g%@PN>lXM?f=576jP(Ycl;KWSYw9pqdf_s2I-)}o=Lk>2t%mt--65DF!( zY{j&6(+b82>ySl3Al&Jb%Qxp!D_BQi(&2_+lOBC?iHY26KrU-`umXDW?qVhM`V}ktRDdh6Xj1h@hc-e%-?;2RTUJIDYq)AgNZ@11M~j!8b{TP+$T}Xsd4bV%eCt3 z%Tl4soGW{-q9w<>@jhOrZDsCDs3;_u^|{KqDjM163XPi7$2IUye_7;6fx#B}cOE8E z;b~Es$BBmIxfhi0naj{b^h#GB8B7VL5%$ zm|L!Id2lK^cSH}~EuBN)fb(eh#rxAuC+q!Lt&GMJm)M!`3#C?vU(qJXuU)J4Y8qS8 zwh+pl_vQ%YO9JGV7!31eOAnSv_7M{z|M8*@Iec@IW(!DbqWp|0Ux$Lv>qPchC=tzW zRNqwL1sOKS2Y+3TY`XQ+Q)@OZ6%}VJ!B@O-c*8pyZhgMqgb zHUIsI^LN2i#X(Zr59htby+)^P`^IM-N>-|h(w`#o(L70?_v$mZn=F+K5s6EGL*F`? zzvjITGV?3GAW%Ue?E%mfR5@7{d4)%9Sm#u#lC%w$M20E)8LC zX{4sok2Wsj=fg{BusV6p+DNAE7sN`z3}|buF+jola;W_xRTmADK6fi^scYkC4DD&M zFw^wat>!C^T4D;G^}cF_6}T{4#BXtAR=qWUW2f>s3aW*Iu=Z01&T{;9F=M1q5j!@= z^2fF%dL8rHhwBB5tBMFBVi1MbWj?!+Nm=TwUZ*dq|8oW&J&44Qemy|%3rg3kJKw|8 zl8nEBHt=~cM~BcS;`iVio_u1k8sEWS$*Iu)jmlZ{j+*V;fqQjbm}#Nk))1y?5E(4A zAc&w^^xz}`ma1lYG{JApqVV+dxy-MX=WuMkS~XwnPG(4@?Ib8vA|WP>;4Xm7eUoUe zjMSF>o=)GbwKf?B>HLc>JA2xO_i)dS0uO*j)bK)&DJ_^yX*roo)+dfO0^a32*42^1 zV7K4$B#w<}01UE7uL*70=+r;+i8_K|64tm{QtNfm+kGn61fdvz5egk%uU~kptn-|- zuE-}CGBIP(?^X`qH^hY!-|{0aK-~v{{}_vaDhRT=N}>kq>L%lO&SKZ- zK-aV5YZZ9XsEgUmi-w9;779%uF+87bt(C?e^s$_tRXNG$X7CFrK=S<&Ee+~TWAO@Zb2Zut>(0el+jp7rQzo9b$14D@kwv*!l{CTGkkw%3tk!0*uV6vpv%MSng z<}IT)__QqJ)tSomXdXTNGcn#dep0=L{z~sA{q*M|_Q2~nyYA_lSrJueSLEzpFyp^N zq1oZXB*zlS?aPpF$l^BSB=~;TTXiMUOs>4R8kF{TcM2k_#6~0APfKtce4|zM+u|TG zZc7Gq(_8X4L=68TQ}Ix_k~FDFA%N#kE9HT=}e?7OiDnQd>z zxu(vz&(vgi4I4n~F@U!d%M?4INV+`2AUtOsmvER@G4HaZ$}Ha2W7=2hDgad>12zs^ zgHq^yZEHC!a@<$vo-hoxmhutFnYnJ#zTjJV^qEdp*HwUC5L*j~kiR_u_Iu-##o8L% zB@V0k3c?s7E@Z%lzcpK`da)Hj9^Bl_qo!u%umtRRY?BO=liR|UXA3LUiKk|kl$tnf zdpdXj5N^K~v6A1Ph&}4yTP+P4^1C37MD3L#FWq(gB6WqRW!G(lAc>kM?jQG!&uK7C z?@;B^bsFD+Upr2ueR7mwjS^4iusC`YPf`qAw$(-_xMQD)zrwOrl&f#0Eix=bASNC2YklCXm9>Np)oX4YWMTx?U|<<q`qi1Hq)YFP2*hP#ENSEg$?d>H&wa4$VPTzDp9*S#< zT6xWA?<*m^eKzK?V9D%;_L*1Wx+%c_OpQl6*gMs40!q@Pv5)iS>)m`_t-p!)T=6Jb zfoWn|PH5p0yAhPiMX~8A{n6$Zs7QV=A2BX9w=~=m&MTO+8EcRz?7M`ewuqwNZizA> z;sk$-7?+9#ji5OY8C3l6RL$-l42m#0HCx9cOuB<{R!67)d{N2?AbPc$7h_LREZzvJ(!x$s4j=YP_CXqOc@VcR#C- zsZr~vTq#wNDBNwHRSRnRAsd)XM>ofsPLMS0TM z<#p!|O4^+=FLpj7cDyZTv$>6!JnY1QS{!pFoyqm2)7UvN-}IjOJFds+2q0QpPN47) z)w;NQnt$v%vO2GZTWH!_>YXm3T8dF=>^Fw7^NNV}dttzmH;68wSuh0)P-3BlG@Kfr z`|v}LUosFhX4riWV1(^S*s*;!B#H~n2Ps7j(JouC8$?r3TScJy6Vq%Yx zWM}?nUgWzAd}S;tRM|h$au?$rVzhJyrIv^y5rD_7i`rgW%cxtG8Kc`mnaWYE{gE&RzbnMh zu&!oODYk#A>h&&@S*BAsdzWVFNf>N{lHc$Pz;=)nSjWzr1(#hL2E5{)oEDbXX%QH9 zc(S*4dR<$zxJcDU+~)^3CiO%ocKNN=W*V3<_B9N0g=+k(giwc8T@;@d!cYSc$>S4< z7-=jpuM8Rh?DU_&lAW19FzVw_iWxTbB0u*SkY>q!<%dV&y6J>KNP}iG(;Q#RI+VJk ziyFW9IdH4d!-UOFm0C(U+p9I&bN6e|0Ccwl%Tg4-HM`t6VtuWpXUtyJ%X+~l>oVuK zM@GV`!>vYw;O|IXMr-=Hw!KQVw?f=&pIxf`GmWRAm*G48+&FN|gi0K@9dG2L@y0&! zuR;M16}*=wGqM=ceK|nr6sb*5FEB5Vt>J zXU9N!cMn%kTP!buuCFO$MU0ji`}7?Pfx>)fBGH#wV<2|5JAfR$A}{RW`yqv6)h>U= zcriD()rK?w@vj#B3@_0-x=(C@^ex3~cg#5*@S86eFT>FZtqi)b=R5W&hdw1gWaFoc z1%zs))fP=f2yo9%M8!B%V=N_b`R+5tCg&S|wwUS4Re~(ZqUTSfxSMdcCgIDk%g6PiL@_7R({D zSqAyg-PNCHAgm=P96a>g^WVU&QxxS`v^M?LV2GEXylg^|3CB*$DK{vyy^idOH^O>r zSuuqCxpils8k$W=lB5VpkAUv0P%g(udEE<_#9^j+Pf1Wv7vNO{@a!!ROEk3I-QmQ% zQ@Q86Wv^Yo+d0jPQO*g-U)FBa|8lF(Kuf89Erk2Ta=4juEP;yP)1A5x@@u!=1? zjeT=)^Tf!dKP|rg`+Y=|_{ry%28I!Kj_i;_{p!SjidcW97mAoBk-qPDhv_}K8H5_r zB>D}eXuX~}WlFBRkgo=g(+KtU%~TgOhs8H+Xgk-gqMYA|0cNqNk$CpwIJNzPCCK|h zZWQMtY02b&~PE2d2D*`+)9rWSe<@1Qa|}s`YcX;RzuGS${CB%fA8gO9DuYGKv~~S2(5@jomgG=nOIvR=S%uPFoZ@!)yuOf?$}Ae zU!#SAKVdYsZ3Xg3X9zQVcJ)m}+pVlB3DRe@c5>f#3HiZ6hlSGWL&!c@Qrs(nf+H66 z&1AF^TaZ>+C-nFH{rwe$!Mh4!0`X_1lBDeTgFJm*utsf8Mh&%mWt3pht*|8%!wwIP z>_3N7aC68n{aOD3hfsVf+uQ+f3Nw6TWJ{rt5oV<5GQ0xCCJW z)^*?aV3JnXGY%?i#3etq&CkPTRnGWMj^{}E7L!f?&}4TuJ-eLXH=NUf=vLj{S?jl& z-TTKH*Co+Zz%kk8?x%#LrK5uuP$?UfVyFXWI^>H0u%@t@?8m6O&YzkhZEP9b8=@ZX zRyx^CpC;IR=BqDu;t)P@Uf;l=;1?A!N4`!JXUm7)5*vg9`MuCm#b@3QA^VQIDy|JU z+|2o5E9~{q#)0S(HA9*56>XiS#01>s{)QJ)GB<|9O691PU+nClf6kxq-DB z?8%_v`EQ7Dr9+WPvSKG@HGwQ8LrxiI)ik@D);4XsU2f{@xR#96Wj5JZXL)%-k+r5s zX`2`eHdP-PiJMBYQiLTeg5jv%LJKG;2?IJK6{#b#c?HR?Hqv3Ex&eO53^yIB zk(i>U3iomD$#ETu;2HeZ$l2~r(*^g*!s3@OIN)XSTKpoP78-l9-btLyqW^XFG~4$o z@?_`-&_4C{_CjW6KJvMrNUL3GSq*K!2E(D`0MY7f-Zxg-6MF|G-i;6akskpC3`UD< zB%aZ!Ug?6Vf(e#hhvUzN>cd2+uFlO}kuFx7u>_t5MZe~(nNt=b` zf0(NXcg#Xe%hP(G001vGFN^X)cc08r=aCQH?rR{{{A_(Yi~cECG#Vh8pH;StPyOuR zAtix+Dif*-^2;r>Mrm=mX0hIgHo!+&?`ss&ocCK1qg{!0ymX}|Y&q4d!;tJ058*h@ z&-?*bo7AdSM%QqzPlzUl7jEsFF+r!vzDn!l1D_$FjckF}z@_rB65JTPZqMz=A*F1! zad&Kx`WUbytO-5n0}(7tpyTPnbXJWM(QHSU%kwUT^L}U-p5`8axc9~RKqv}$2q?)y zo`fSXBtgNZ&E3@>o@bPr zRaWCXTVHlxw*rKAgBOn9Uwt}DK#gbfc;WK*@iC{rr}y*!sW6}DiQq1XSt39hk2SZ_ zdk6ZgAl(4HWtr9<;m2+0Zx08^nCw+uGEwhbnU3_b2ugj>j@x_2iI%f)$0_C_etNUN z=$*@%!Ph<$KdH8Izijt+Eavq)!Yz1Bn`&q_mK-0=DFtyGUB;S&Zwj$Ho`W(a8vm38 zE_XjpE^H)^F2Vl&JAXR1rAG13k32?&zjUF~q73T?`y0_DrZ%zqu|15XP8!u--EQU91>-OZAPPYi(|JcBMjg zZo!%>R4>J(!g)ZmO1^cmTR?SI|1!S7>u;_dQ!k){^mPntPsYK+>y07g{4;=5X$;Hi z!JCuk2h1XjOo61eqM`z;x9;~>4F9)B%PU`p&RaZ!V1FRz1p0YwdBu^TTkrhY5k2qJ z3$B#5@$RuI`dFAq?RT~olQPjCzO2c&$zJi=EZ3bQc{+vS(|{vKl;S&(R9>lyC@!lE$K9hH zsbI8V@ddtGexQxa>%zm})_iHA?EcMnQ|d_Z7CSF8zc~WS4>27z$Y(1$B$-`BGp4_Fh84r*5o^V~3fo zLrYP}TAw6SlM84NayM7H`$Sc9Qu$d*46B&}?TqnMg^hKXD`q^K2pX>_v1wY=;g9&-k&U_@LwWcMERh8vf9czh ziPF)OR++Hn{ugNaQKuFu-bhj@>vk(A&p<=+1e*c>34vZDY;GkuUc5cH6Moy=jA72> z{TX5fv+Qu^LJ6exX5EeP1h3zLHmNW;g~|h+_iQ{eHMY%f`VgJcrg3An^|PNU7}qhK z4MwX&Ga3yd=Jd>T5l7Ago}`96U?Fu01K-*QUN#`n%1^*fgk@h*O*qNf@ViTmZ}(f9 z(Z0JeQKkzR;aCXBJjrw?0X^JIKVSbh9TVI__Fx8ioRQSVxB5PEblff5^oQt^k7f&z zult+}&@nIs&lD?uj3$)d-7jyl>-j8Lb)};%vr>N2=`CXL6{*$l=s@Bkda`Ox?&F)X z9WeCW>(|)RIJGO`^Fbr=wPM%N4VHX>coYT18+|2t(FYlz*~e&hkO0~f@$SpK0#!_o zsAZX1sXtl)i%*@RnJT}$p-1I)fw8!xVG3kQFy2Qim+sH1501fud)6S{&3Af=_y+AM zWUm`XC?LYAN~HX1$AAD`w`)xd`4rc(Jcnuz6*lH>{-I@Z7^=o0QWQ591?&FXd-!-$ zy82c~5nSKAgwOD4|BpkqM|H0?0%*#8sowPWm9I4N3uV<}PFIA8go@D@Xy&ssA;wdT zjDJo;T=s%d2B;*2o;V;leqp;6qEqr7f3#I)*|Gwd%IbEyXg-2|uAf3RQg zfRTzz#+LmxI;!e;v>WosS`s)U3Pb=mI5?pDJ-I@+3uZ#*=aIc{4n7EYHvow$A45p% zyM&KZuCD$%{*%MakSK5FRHxFrJ6U2-el15wK~V)Rc(^Qs&Oh%O8mrN-%6;$tCHp{v z^I}(mw<5;x6O^8iTd+kyh9LRD0!e+=a)g{WJ9gVG+xW5D=#l!i-FeUW=63#ChlH-9 zGP^q|GZRK87?aQKtYF54Opg?Ur+N)LdQ8kxLON>vXz^Y*qh)fbQPC2SE1N0EG zZJx;uii{Lwz0Mp&1>6&3lFk$3HGJIz(-3$F+`nb-LQyQ+sGIvEm?ea3ag>Z4EdPtC zy6XOHgqVxZ*&Dwbd2*$B_a)(DF%o>MTue)_1f7eTiS@O+Pifqp*@fklCAA_wNO?!`8a;j3=fIzs9`6p6_Nb{l zg3JNTm?0~m4NFtg)ch~rDTCJq{@-{e3NYYlTwjMJF+Iz zbcGqp%{&kQ1o3_bIDlO18BeU6Gmn7L=j`vVu6!aq~dlT48MLvlxbGSg^|l<7D(V!dkD*CU&|!^lc|IV$>o zq|?(139QM$6kF_`>@?S>`EGKm4GoCGf|{Slqxyv7(Cm#EwG5DO#N@}&TGYJISPUwH zqVcO4-m8PI0SaKNOUkg{ZgBPRQXv8e%m|>!j~@Ooxq~vowT6AK$0eQhMJw7H!X6#a z?WSp+ceXIVmU}WD%uGH5$zsW_xY08x{Dg8dSUd&qY?%m$vxBrjSDNENU&3nfa z)tScNiZ`MpVU2M_9`zHhsE*SaXJ8tJX0rNECPt_L8p>#DQ*wPZ0D6;TjMTR(q~d=s zkU%Ie)J(Re!qwGe#vT>0unFbO+(aprSQ6l4`5Rr{o8>>I1BPdhK73r}yZ^+jHbx1sbXU;*cJOA&H3Ax=SH~5FkecmRgETCj)S=qLmVw_ju$D-D zt5)U{$X+nQuieb@x}alvaE;Q4yC>X+7ov~e0z~^gVKYgSOHKKbDCqZ&wCn^=8tFJVD=K@_sKMa&)&p84ho!N3OEn56WPB0U@&}h7RXs==k2ndn420? z!@nWOcBn%Bm%8Kmjs*%pnRNUt#jdp4{TUC>pIyIvXJ9x`$#H_;@mIm3?lDM9G)ZQ0 z_rv0IL~nh-zQYs-@Jj}bz*Mv~g~;xu3`4vZrp4Hr{Cfx`_Kl%W4}saaI9q})lSU+FFbfCV&t{!l~1hO zKeK1J6zqC9os5v_Os0Fmr{)~9EZB83tmWMKtxV0Z;=lQYvHSM*Pw(vZgXl^j)eRTj z2>VSk3oSQrozEe~Wz)Wq<-w}zD~Z%ct&XA3U4h(iFFV@BITfi$I++FmlMKl23Z#V# znIa6q<`|Xv$mccmBHJ5o8J0*O_&WqZTu-6Fiy;cESER|J$xcLjNcU|mum1K!dp|UM zYVo+wqSBk|V9ty{;nn|s&*t&R1bN0l`7AzLiwf_lwjOWp5|Oy;C>KW?sel8jT;JOR zn)ulsqvMWv!TxLPu&(x>>bQEi{hYdnsMhY6U@%t7z?7dCDzL7t%2-L*N7l^bDMm?^ zpxwkS31}w*I@2jYWm*-q((I7AvcvL05id*>r z4_!7co*)1Q@bUb_^pF-psm7Dt-PVHP8khtyp!RSJlH)}Xn-aNjs-=m3d2`$|sU;bP zqwB8>`}=CPD@=A8;yZo#9>hkOO;&N~BnTKEf_<)HTffm>0Q5^LxO-l5#d*-y zJ>fqYlrzBdzh0a^cV7k(^%G(R(~T?X{C-I)0QSx`a2_N#StDSS&J5fCuHbRuhr^`3 zUH8#!B)0RJQU<@U<38&CtPWOC$xyG{O}c05lfzo{EG1rOG@QrFN#-~!obC{rk+h=| z{C)e&j=Y)ALR}x*J0ilt zTz0se3n=cR7V7=gnS2+l5?oxE#|xjb3RGdCx{`*5JpUvY1M}<#;VU`V% zrE!b5!`QkhXRif#A^<`(A=WskrZi|bAcLphPA++|cD2ikSjBzvdUAdlyJ}$-bM9fHjUO4nX&!WY+vTHk`GpuHPQBr9SZsp(S zV^z&sS#^nctFMxtB`+Df{kxIKkKCuDk%cjQT5dk%9&=e2x5}v+qR^UQ3+J=KP^mS- z?pIc&J;-OC_Ac92J!@P!*6)71022wpwwK7P)cvMf>)4(SSmnw8XZRapEdQz z!u14+8gQVETO+PLxhB(1FD3J|KA;xDWHtz>Ipv_gD`#zgKvdmTZp=k*kimj%3*z~3 zZGl(CI{dEw5Voi(AOtN34iO3_DF`PtGvXnCnd)@FKiHRu=9scM5Y{Gzp+$fsjzA-z z>%|R{0(dj+xxYc`mkc4N4R8o24~1WEO{K%f(87|xuE1rykgQ7!Q2zMQqx#a*rVGBj zcgf@^&!G>gd?IbvirU{jZsx&k!~h_AY8O@_oOU`NI^KH-9_)sF7h8|f9$ZdcfHtoP z63r^n+aD>&=9nV@0=ZJUbHw!q!{MWN-!}olyMk;MF|_=*Y6QMH{OkJd8-c)x7DyiW zji@L@iVlQ(M&b*wHn|X?c&%bI^+VO3Ahz$l(8NkxNZr8Jt%`{$(qWbFXIqr`}U-?Rks*zA8N6n;y#)tNl_WRte`llQqRq1dR*Ad0~ zVf({>v>T?DUVX7k-@kZaCd4rE|AiVP-HQo%P+aT+{z37|Sc4O$lKA)T_h+GlrFzZ2tPT+8k&-pUOGjC;$0pHWE2+)gzXnfnL)5Ol$F8K(|4!` z``Q74sAZrUPyGxhm5y$jM$i-1_6|#yGVJv(*?7c-^=-3Z8I0dbfS0~?rb|Uk3FlFn zgc=q*vo!xdnDT9-GulKh^drDb9K8Rj;Yqn;$O>ZzuSoxSD~GVB=4$81t~)@8Qi}H` zA$f7tq(}YDgW09Y5-VOUANrWB!0zc;2B*U(cSoDZZ1z|2+4!PCIKSb`Cx&~?@Lmju zH<>1km%frMaaj=JabMf^XXu% z?s(Q2Jfr0@YAvnw;KD+}20I&gJLWM+UFDM4W%Ryd3~t9+218z$cmsdmv8t*CpyX3q zv$uoli&0yPO{Q8&5tkjz3&!kI%JBd(G7)mn{&Lkng4XdsMt00V*(4ERS(=i{eR?s! ztOcxlN1d$8D#>`txLyb1?+E?&75MyMp08Sp63pmtuhPkLJc_Rj{u7)d>Up9L1LIHx zP7@zCx{RtbJV++;mVo41Iq|75Kn#|EMvc=MH;51T5Rg@(26#yfRKwgT&@3ijm)8!j|?3Mw1VQeF^ zOa}SOCQsm<*>!RRiYnvS8pEZo$U6=+pw~h$a!AQCd8S_8l6zaQ`VrUn`g&K*B|aAn zV~(1tzOSiBj!%WRy2Df08!a?WY4^*yrwVQ%2;EV^fS6Q zM7Bxm_6MT^(Mpqwgp&hXU$~Tbs}L3J^5=Gx90RS0F#ba$I~fqRo*|+F{W@8Y-|riG zQQYG*bv0}L#{uaO6gr}>+jmSYu4i?N@-Z4A;WS_x@^MD>S*;V&vWOZroOQ$pwEIuc z#H}?^u1uJ^I@+^7hVzD`1M-=6mDEB)&UD$Z(a2>th~}D1b zSlHs2#Nu;Rc8wpjH=28fN?4$|bdPh?Af(gzueHFo0YSFl!UoU292e8_$vPPDclVqzt}BuHSDBgjND6 zO0>DDZJXSaA`NV%t*&SayGn{eyhap@N%a18b{+t{aF2R@5U>C0BmV40KtW1BhGMKl z%!;sgRV+yn{XLp`D+-l;lBLabQ$E~7T)}C~bMoJMVXa-a)Z*9>7#iTa@9@Cf@uq~g!N6<8_z7Ltd>s#TF?-M zukEC^#BUqjJn{AfKyka}hze(5dErgDsP1~Uj*izMQ#~b*i|RhqL0=$dDNDfD2bw$m zGjC|6_3aOc{9v-Rvgf|_ny0KpijmNUcny!YTE8l+007k7ZKhqp376C;EaqV{A?jD$ z2IbMud&6HB9MuKD>HA&*%kH+=pZT?LKt#7X$zKz8pAGT|aorJnzjg?(Mb-S!7+%?g zp|UNT=&_7LER862!QF^k0)64g4(Wad^q4L;tw<4~J1g)sL;!$b8r!;Ux12&cccO*j zbwyL;vs?NFKjbq~N zoikjS*K3Zush~)jI2T>1GDnHu1a3FZO+O#TiaByYw?W7rP$U=XzZhnN&?p=$;yj6A z^vZu#W;%;1!6h_X3Xf5bZFw%j53@+uh{!mLY)JT$!M@@qrf$;e6L80cOYYI)_N76W zX|Pl6WX0h)6rcVLo8D28r8oU-5{%(mt1dV0n9zNvOY}6W#?HwTv>^EVlYKT25?$`k z{~#R~2^=Qq2XV*v=B?!BoFZ*tX{+euq*^Imd3dKyiMQv-Uolkssc9u-P6c6+Wh5t` zllt)H{JuO#WJ*`UDg+6?lR-e(h(oC5-Y_|WA!u#GcdOLQR=JQu(lfsGwGb0!ZYB3) z!8|9hE)?svU?7Uhkl&}fBvzs3)%s^QMkPv-F=0kt;3D3$k#!PX%WZ6@_q9$@Ch7

Y z2PN|1Kvgz;KPN1$%tWpK0P!5-fo-tEt#PS^`is8o_QyJJ*NT32o(@kS9dHpP$PNMj z{U9n}{b1J%vFdsK4i&75HQx+`W4R85D34i^O?GXbjiCxF`F(0&_?A!fUsm3{xRB&t z)#2NBU`zr)B4oBt7nDx)?5N@1#U<_X(ABhz78BS&KeS~LqK}L^NmV_}vD{*~Q`4J+ z<1fG1x^IkVO(GCf%??%Wi)ps{gn~sCefWeoo<6Gc6B2`XQ{RZtOu-Gjq+jF8&o`}* zTOMx0r#P*j&;JQ+Z)r8TlX3x0#lEdSP}QuSnOKlx41M!IsAX5ug{`}9?O@uba$08AHkpbmb9>hob-Qs7AYc4ex>${N76w{+bo`cipVqt zs5a3@Zcu6?D5TYP%gykH=9h0zbHwqr5oNWM7NMfmq7O>Nh1kRx7ige%lUU(KW1sf(jL|7u&V(mDb5>N$br zlB=UWw%*Kzm)9>;l6|zbq-X&UmiG17nUtTjWKV`>eva-0`nFe(#=GuU48{N7o;N*X z$IV;E0oC{@LHc%l@yCj)#d!_sux%)EUcAUU=>)}Xoq)k=yII9)EY%XV+FnZchrr*G zy^;9VTq+ka{BgVtN*BhU9?pYBz1U^JW235mP5n$il@o8B67=k+oJ_fwBAiIl=!^9o zlAUq+Fz9Z1uuGn!Wzt1UCb4YvkQJ*-tEjQV=f#B2eyesp{lDDfaV(W=<^l-fJmw{F zTp*!vpK$1Mvwf!B%S6dRGM9Yg|FJm!h8&mNCwffgb>3snd1()WIjD!0OKEXcN;}>a z4qw6U#ZZ;$e}1sy?3Y%K%0qaq0u_3CT4TOrz^DR>=j)f%?o6O*P`C*8`I!GZlj~9p z2b3;{p#9WKT_be4AO`f!p?Pv;pR5pH`F%u=o1z>wNp47t;nkg^b1R@j?YdQ^3@6qn zs}i^Ma?`0oF56V$n&^?a=~G1~(BF>w+;Z{@q**<&xI=sDMF&>G06l|>D0<1f(Hz<0 zT?vv}w~=QV&SvRLV6)xxW8RX&m|bq|Gz-LZ*(ui>`ySf z($i9{AAL|`jzKM{`mqZvrNZq7xyKy~0Z&DEGKW+JOHG5Ri{P}6HegwTWdw{~_ z(B;ICP-+(ZgsAu<6|aOtZzMiv=-}dUR?L4WfgtaRO+!@DM|k2*zdv&5sM=lNNQ?vd zWX-2H^ii#_(NvzpjA-+m>c46xo$Sy_qUM=Zha{tg?U$ZMIv=nR3hvKz^%Xj8%r(=q zi!mxbJ==4Q5qL@yF{?7Ma%cU4|382eh=U#iZCe`j&|PvZa?4pHl}Vdgi1_dy zQom#qOCcy7T@e0bQ699Z8@eO}0z+sj+T7w{A8JAt!ZEpM~es@I@CQF`AIGVV&-_@M{$wVsVQ}hJ4Pp&zYzHsy()1%H~ZuVOZ z688<9_OJ0ewn04m49IC_3<+R6pBUR%;~h4ZU>B+;Wr+VfHPCYK!q4;d6)OAtrgP|- z?C4EeY!_93L8@hM*JC?*==C2-uQu4y2&^XaMY!T)sQz?#+@Ny&vq z#P~eDMf_IRD&#{6z$Nq0Va8uw1>}%=NE;rm zyFW+Zjj8!NDmsPrqa~CNf@a=BigK;+bp=Ol=KbmkIcdmy8*%LHaI&yci*6{dOH4RG zre`hSS6+>Q+ZzWK*_@_h+{P~kBvJK7wx3O4-Heqy26FYQBcuIZ;M6w1e#BSMlwJqx z!>BF=w~L&ZZ65|lCst=pvGfBSS1bdd#f?NLPR?vWR39mD^NWzKgHz1?QIl(QLC=r} zHvvhMn)r760Ic&idqP|51ig-g8HsWOqpB&gm9Sph6 zgSc6HFY|gSL#!v%5a3CXGXC(QnGXP8n{vGvb*}6(!|W8sREl=NVm%P|Qrwwo2MIc? zTWsCkDp>`)+M~eGPI50XM`IkAeKfOz4sR88C_dZ1D;iJ}nEPeDRL9%#xJPyAQCham z!1Uz#Te&;szY2!k{(?_Mzx0nn@A2Q?hH~2jl;%ZhM+OYzB7fT>atRy|LF49qDiNdT zP%bE?IxCnbx=yLf&5Q1^7?O*#N_QL#exsFjRvS8RmuztohA#7>g)9;C)KSo%nDlmD zwb^jE5^GP*UMD^_Pn$F@u<>x?jq|v3 znFj{xbjB(?pyD2%&Cam!$h-rz z)=VGal~}x8ZgkAJKY*l43P9T&Oi=wTwz*K%gj=zX=<;xmvRu46z&@hWZHz6wP8C#) zQ>`B%MMw^QB8z@n8imN|GF37`JYfAYR{|3Dh3q(Uh;Q6$f1<|jVRCaxlT;*J6u>N# zKgWB)P@_KtgpS{z5T!MjbwaFpLdt}v6B6^09Iz?HLlmOSC&F6j-F5+Z9*LC5>J7#BLb@!t)+eqpdJ^Qs5`Cz$$ zp>G!n8xvZ3l5!aSpANX3WQkTg^J|P64kjfV{DB@A*BN~(jNshZ-tlc$f^N77&Hz~Q zSMcg9ga zx4k4zI<8PhUzuY9q6BcIjeGP-=|)HRJsY;(PDl07;OyGsKl;e&$@Ref7PNnA9BT7F zrNP;stb#e)7zbu?JpX-iy+DH15dP8YqtmB9O-bgY(<}#Sxa(q8e|2?Wd{>oam{Efn zWXm_rs`!&SFl;O-`)CFkqt>(4ZD%7|!w4VPLa3W=YbauTX;e5S!GBA*W_y|v$n+9F z*@)z2HWr%YG+U?;gv^sg9IMg9qGnEdm^Ore-Fzdbe~N3fm*H%_NWEOL&s zMdbHE>^tB^q$kd|TH=6R29KcH`Vh#{@1|RpH&0@P|&?LJX#?#@N) z8G=4gw^~LPWC(&T3Mag9#wu$c)RGfhEpw=z88KA+V;MFutx^UDt$ z?di@I7pFG!UF-7FHHkRXq&@kfY$CE#Smy%cx6O|KK?DjfJhhAxa59uXFEF2xM;3kt zv67;k5h7eut(`u-t&J%PP;_BbuB6MfIRlo=eJ6;g)EAPFZ9F*`UFYp6RFkAlvO%0x zH&g`)UxGN^n~|CMq9T1-*@JUCT6>_P_)mEbcatbJ4k^6))_8v(RWOw10wM>a%bJ8x8HC|xO{;I=&7o#{f_iYW6$e@5CzCeP9x!9vj7|{qpk3@;K!w;P1YgI)eD!E4|SX%Vz z070x#o!{}N#|!p7Jm^W|K(*2afEiTh3vf-W-7f?rbS;D=xD< z^4il1j3!gOeIo(V0ytGB&-%>H|B-agQF;Gw7+&Vf@EuvX>@L)b#wCOWSBJhUV2*#cdLo7 z_c!(E^S9vley>}DY6HDsf=x9MQ*~G85%Ga=#;0z1gmoD;ZZYZ&))P# zoLN68WjyogALy!{$R-5di<_El|Aq`|!0A}`!kWeQRd^~`%drb|kT@YeBzVy{wUDtg zS99*-WHi471fE0RHl6lAu9{gYRs!IU0QCdPodAW|SJcgIqJsFth7;}O{Hi-l#i35w zK4#?i6!P+bcsOhX9JB~?sJRR*@)h@dI9E4j`c%oE%zg)&xYw_X(4vR4S98BZ_Y)ox z;6?OZ&GB$TS6BM1QQy`%ye+F>#Nf@7ud);fL#CvHGwfZ=3>2LhzI zuo;WE+yY~5eqL|OOTW0|1PVvEHlOv5lQ^O&>ko2N>HTI}&`Y#)LMyE^)=SHl+-9u= zG)NUI00^2VTlkB=4|}MfE_r5}&^Ee8cV^k4EgroG`OBqU=hEu7?#|{FrL%uei8n950A@v}}z7b4p2T z{ONLp=4)YP3{_!_!csDLD+(vYCv9qI-y^-a^C%}n=!v6A>Jr6xxVx)gb8(bU3g?wN z01A*^Db%pMT=2+zQEJ9IlJ+4S%ujttKtk=}S$*h~fWTJricK(6sU=AY6l3329UFP2 zbb*2)PKD<0^BY+nE62ZR2*{%3CfGd?6Xv0R|FJR(`Hictk8aXVzgXq|P$sz`K$Njggo>2ga*oDSfjn3eiYU-J+zx?@*Oja_pwEqre>JtJQ8 zc(l|JELN(5eU{t-YE>q%2xB%XEdx~dv+9~4AX%)2xISBqFMY3)J*z(THzGNU8-wqf zw+!5IlLNKtq1{S)U-t-@vAfG1WN7GE(s*fs9?Uz&(cvsnK zSBd&Yw{Am4RYekR#YA-LvZd;BF+!5h{c{pWV*XMnrSv-Kj>U!b+NV*`pZ-@G0*R|qiwS&LmNR8IDtzgvfS(NSoUSrL0is<0+$S`W zkgab22jY6)MI7PKHfNGTr*~tjCV8bG^Z{%k0tmunLe8a;>wWywt$T)*&asdGUtD)3 z@OJ*;dPjpmzka%&kuV_-jFL;wdi*jh$IEE1;6c+{Iyv5GfE)$+K{7#M7V$MwCC*bD zc~esjVD$;SID`@BK8j^zc42G?O@_$)jls#GXeS{uroDoAgl4S(F0VBj^(UV>#Sf~t zM2Rn5^c2V7!O?HemCC+gXnnIJzDUvcfis)Uu%rfnK`}G4sJRf9od2|TNkxICmd$_0 zI&dx3$L8OF#B5FvP4CD1mKMEpZ4yyV+rTrit?XuQ-I!#QgMIE6LbC8w>h0eMp@UA% z%I6pYmEeW=<8rk+3gIVw_v|M(R*58;{uWSMoSkGID=-`J+kY%spzO#$J0->2(0}2w zB#vq21}p#8CNuycDPIarry<|J6BTSyGy?JlY|)_e!oEY~WZ8ufH&qj} z-e?yf!d(9s`EbQAlj#Wp{b$>tX5V`;Ncvan2QmT5kEIz7l?PyXl`AMRGA&s&)MV>D;K4Xucdc7li494(+*0|h7Dm2%O0^rS#Z)c6*Rm9_T$sds z6ev;33Sa*@(jK%_>kfRYqEzEZZj%+GNK z{`JpXSkPhGINMbWbBI5mZd18OSJ@slhKmo{V+dtX#U7Ws`&Lb10m}LeDb6;%77-zw*D?uXtU26O)_aFgG z4SCg6*&x%4#SPYQ3d4FXt8_8oh7x8X<+Al<@qxhiqI`l5D(0kHcnY|7qd~?r!ukH@ z{E5OFSoZQzH5LD*-81@t*%S%UZ2iuAOjN}_UKfYd4MEkQG_X&$If-Qk033RVFGa7G zcdq@q`J!dgOIE`alPTK9PtGYGw^W>tnlB6)yzr9zq6tePJ87Xmty&LUl|pC$^rKWl?`4cB16kRUn}>_xL3Ka43eHO7xpo z_)E~J0(~+@eCP$lkCJyT{MOqO@(W}J`_F*H%z5Krj0c3&7`_vel(^%CK=uW1Yc*B# znB+QB<)k6V$hr6I^(m1@U#}+V0kD=W1A$w6g9++jm!vL&bkLm72=nKy7&Wy^jPz?; z#wg}^IIPSmt0Nz;Ato>i_I_Lfz=#?X@8x}i#pl2_>Lapz(~vOmqOf;&b7~EqgZR9> z;2~sf@%#JTcs|F@%d<>u2w4Gn)JWjBuo%NrfKn@%kBhRK(!P-2z$0FmvEUpD$q;#1 zHDtG252xJ-XSUY#96~yLtT+mE28Ys4>}s~ekFcXmfFH(aCPeP}Z0SV_hBzFcW)wKKZ;b-XPzul4Wt5(cn#KpJZ9Iqhk>v_k_D zG+9pf!*?HExdWmfamE<8u=f$}b2_>;dupGtC?EZf&J>xOm^mK(WSrzhQB@uZYZV`A z=4SsXhkDJ?(|-GY(^1Vdi6;Z{hswme!6S z=4VSDn^$(90_QCI8#<*CqLvH4wbOr90u{Lm2>U)-(!G0h1wmoeQ`cY(rJ@ilo?h+_ z-^0c!z%NtVf)Sy(vZ0!+pXkB|y?9-sV0LEXlQv;J!o%&Ms=8}3oiSdgEN-tGsJZuz zG|eQyN5MJkcO9gAJk0u;X}wTj@W>!QH{7 zQJjLUI1lDV)>f#J1w>C8Nd{6ZrWHo^ywtohH$m6O_F%YUGUmBhNA@L>pN#$KDknYJz zs7PP`{21S^to|7wMJG;uJrB_TGTk{rUEUv!=*||->8d-Ih+KoQ?#hgqV@LNXl!Wlv z4+L(NA{gI(mb(i7>25a@az}4U0EdhQ-)EAOFRm+EXG4R?+3;`-;Z_CLhN)*bG*_xP zq)XQVsMMt3_KZ|cnwnZ%cN_buh#+~83E*c3PPlp@R@_uIg|Lc>3qZ&-}nhLTY^>@V22FY;$&X-;gzE(?$aw8VggJI{iPbQ!wQX+fJ=xp{cyOv zGWFdqCwcV4sr4f4lb50+uJ$}e@pLF+mzS;g(uD;OQK(vP9zQOyCRP5$-SL2?S=mv| zhtP7m3_g9#X9LBJmB+E3(g?s*xzwgys!aklXz(*oRvnkN=3Z`-Kr-nkRg1m|MLoa% z(F{eFv@qV@)lhT>2Z6VdTAM9AFshmNbYwSBaI+lBB+119BfV1epAMlQyQEU>uZja8 zcKs&i)c_X3f;xQYn4wvP5ii}jtiYWea3r4_Tw(#&8o#*Oek$d*N%RZ0s9OK!E>?Mm zACwimFggNs(wGpIq9!(*i0x_2`Eo)RkR}#S_PrRrx1GNCC0g9P~am z%^tL@=y83GYIEhuA9JBxyd7N<30V%-{S|NEB77Y9SgqFG9pkl2NZfUZKGJ&3{cYZo z>1=`Tj@z)N!_kr+-V2cQaBwrl%li|jGeKK`6Qh2Tf0%kMd@P@^*vU}sP?KPgN6DAm z?hneLmt{{dt{Od8^=8oCIeQ6>6DQ_B0zJn#32@6@Mk_p_cl;S+Y-mtB|z zd05h zT1agsF~Frv`F*?kS-r~hy*)Ur3@WzxN51R?!kJzljygcj=Y8$_p6QdXOP&? z7gk!VrIdfNlxsf$DK`QY<{W%R!b<;?u$J%VxP0mD#dczAu)@u&LPey!rL*UM}bMi2rQMz1d~G!KXJvrMMcGOvs1vs!=pRXp_8U%vsfn85YW_I&8a8^W@h5EJ8WdV$TJyq z>Mr;ho0u5AJYG+=I9o8Yumo1=we?QriyA$<;So$&t$lnlpqc(o_^}_6QwhH}vMzZY z5q*PK!Ho&m6*cOZ_>FX6bsoJA_=ApSGRxvvO|e{GCA=b=WPmK#3QgZYL|-UK#++Z| z1HN)KkPmB`>kJ;6-f78p>7d<=FOO2_XNCl?oBJM9g%(1mCe07X=fMg9G>*!uYwfko z4{oHT|e;#yTJbU_Ik89zH<6puV_0Fe&yBF=`PQWJ1mohYUiwawU2KmFGfDJJ86Fs#a+n*M|W=)=86u7JD6J}Kj#E!^%HFV5T3x|%oBQ+)?x6^_9;m5x&f{^oX-ZK-%M1~4$6hBm%2q2z&24Vm!zA_RBq*rxcu-A z<2iY~)IGkAR&*6cZH{h+ybj`%=rT1o59;U;c)9d{55{89?mn6>?3tWIE-NqJUUle7 zS>qxl1;e7({3P>(K9EczF5#1Ti_h(}1T$#s>r#UaHa9PCZ?ofWo=W24)X)n}4r_t^a(`71xnTw&ardOf z5rKPl!QWto_{2~sJ6!#tw_;$RItngbCY@5&68l!H(2I4B`G^Q6znJ9uoXK9LXNJn899lWIruyQgOE|um8Rex-GUPW;KObYxhKNv|EMp^z^K;U*}!w z@a6*IEbtN&i}YTzd0m4JbG(s$Fz7%@#1n;v!efuFAk%KWU%tN~`}q3i7ZrUjC@eHO zUjf{{*wC1mm{+58)mL2gk=;N{v{bpe)P+{5S^N1dBz|cSVCznE`4={oFQaobKpm#V z5vtps=GIyk}s&}NJ>q3l~!}Eh^^gdEd@0U9 zG~mpvm&{E+s}2RdSo(+pWo~QpOLvM26*?4#R{frF@r_3{@Vqb|{TI0ZGZXZd5i0Kl zens-OY-!Ny+i4%Xy&W}rZv1Cwc$H}tmp%pLOCJHoW`GPW1^+APwEI9dVp(`K5?FVMPAh z`UTMi%=)w2^gkX)1O|+rHXv1?D!=|z6P+q9>xX8m7)wD$>z3d>%dV?x)1sOXkUozw z|M!S2F4VS1p3;}=RR9k668b~*#b{D#MZAPuS9sHZUY?5agFYfbgMxZc3yFo31H3fI zqE9Qc$x?puyB^h-AI2$XK|{Q>CpS1Ysu&!#{SF1uP-(lN(yy&qV*(J^ICS{JJO;!X z-Cne7JfT$0fi>P&bnSlRLzq+L~8@T(^7NzL*WnUAJT5#D0>t z#+^?mbT}N=a}Yug*ZKE75nWwE^!n{MA0A10dHF=S&K@MCAdR8G;M}y|SeU$72rKCSEx~x8nM3Jc9$`5QR5D%G3uPf3zkvxPP_8NhY8B zCS;+J73Yc81HTLc+OT5Ock{32w|V23CpMLK40)e&#hEZ6pn!<@a&| z!@-aaB!oma!}Vm4j|oU@Em!I}f?+48O*ZbIjS4jmgy78EnPJY>Ef4mihq*Kz~-B>YgQa?ydJ?sNmT9lq)#(Co<0UBr#X@GhkAPGg%9#t$tX# zyKRSxyTeR5N`H=z&=S@xawFaYxAJxp%lyensGW2}7EvfWRyrPMlUCSYr$W6$7HTzQ zbXbiXCI0lh6gd=?WmK2a`xENBI27q7yn#$NjyNh(%CRkpwg07HqI0Y>MIg$caZ;#S zQ{f$=wpAI#WUygs@B-JP;4tK&ofeDB_g)PNzI6IQ`JdHomFf#(PTdcgm7>jTH%3Sj z8Z{_@=hB*)i;D`I@t77*aMa{_P{tjVmR2D%0ZOwG zY7D#=pBNFs)zx}L*M8wH_}{G6BShcwsx2HWs|g6U!NqxJoywws`!4yzq-p!>2^rw< z?LSr2sCER5QClcq`EVQGBz@WE18L!Ug7&pt8H(8Q`A)j?$b{{Jez5Y487eS}9w1=! zsW9cVki$yNgaVR-yj%+hNAJVLaO@mjTY2iV1}h5MPGC0zZDKLj%WPS4OCzB+le;t35Io4ucNG`9%2x3@z!3tXeqAf}457ZykXznGiZuL4kv z$;q%*{jM7N!^K8YXIT6sl!_PvE0D%qQzGq?AY@Cc z8SouW0Dc6c5M#uhrQX?MI*hod-dV|G>nII zZwy|hvHVp3yOF#psd3iWa|?%fI#Asr%UUfV5M+FkKdID(kk$g7G} zec#WqH-Jf%a*xvstj;eS(d_nSPLu$8O6`#2xgb*~O$sU)T3tCMh8M7crhbMz*ZS$Z zWgcF2PJW1(^7qNOUqzJz<2UE`rDA@BDOyWvbdu2RU)=dD=2y<#cWNgMtq+sr-0pQd z--&=N+%FwI4*;XM<2F+3q3kj`1`{p?Sl@BY?j!zK`BGf6{DUzT@+ie@D$B*O6wVrY=)j zY0TT?S-sQ7#(=7IqgDD%Og}MXCyB~uwhB-c!j&UJgJ>GN33;c9s|-3wY1ki}8UlA* ztnGrcN5FwI)CRAIIrTBdx0*ukKpsjX7SStL?4ylTuZ&gTaGZ0$4kVx-KsXNo&JHPh zyB7&>8GYh%KnR)1c}}FO+3HfIUFYCNR0hXyaQqx!dY17+ly`Ui63@C?}H=bHX0A>RI>mw^5ka$E?$%Zl@ zE+x{sogv->PBPKtrNawKC%=o~HjRjCG=Mhn4yKX23?Ii^yai1MFrb`H6NzVw^wc-d z06V21Q5*CTz>|4h;BP3}voL6|sS7&~*oO1i?tzfzhfz>$JU%-c#)rUWfNLbZaI9sw6u4)_9)`?Gl_s zmPw?r$}R%(+;jk07_pfFW)xH1&cbTyMPmEL5w^#a7iGDh%C~iHa42-r$I2V@CEM^3 z!(gdFo~38CkrsCC<*b`Y_nnsHV|UMwmsl9z^8+*L8kg0!4*9c$073la5Ug(2W?L9- zYDp*W=P_-%5Z1xbHzD-?fJfk7%Pbpf(INnUILBh9WsR#-q~J7XpJeV3AQbLxkFvO*cuy`zawn zX#;euiwOv#7?jq?0K?OO3F~iW$ipqL9bd(o0j#y)L22JS$R+X52q^>LUA%wM;I_k* zF+vE3D)izAmW(?z$jA2qww4uz>08{E&&I~5lN*A;u53&Qr}~ng2T(CYcXow5uEiGR zTN|%Ao*SxY?f)jqCw3SP-TMuSvfWbOJeix@o|BwgDLyZK{ z2B~#g-n=z7sMEh}kvvQp)Mu+=gIG~`f^#+ry9no=e$OiMx;Y>lp&##z3|>A6*885; zHFKOAkO5-Pb1l%z@Un!L?BJ-%I}uXHlS)_Td&h57#+=zTx!#Z4-Y)cej{nUBl;din zfrZD(=IzsPe#OhB{PaoTZ#ifjUb5j*1!OOfgHf+?_yiJZPY!bMc^6SKhE0P~!Qv3$ zEZzBcIX4?MCcYQ6-cWHjl81XzX=#jeii-fo!I8>XktO8%9V33=FjFcwK3l^uzY`F1 z%v{F)4J#U-8yyI0AP0^Sj$L$uy2uX5|B(6jpLoxV!7a7CGtI}X{`EN&VUO$@=RzDN zfoFSb1&@CcSK<%l0di>9#NIuNa0E6}76dfEicTlfenv&z0Zg(cwXq#t-pxr%uk_Fu zcXrw3&|1i zPDD|o@7(P0PF-6V@~bJ0Z{!db7AF6S38CePFR(W|JG;Fl{KOSC>jC@%Brv8&#)|l7 z(VCPI+kk3!y>>Fa{vJ45QTYRFgU|g~GJ$*9e=Bqp1Gmw3NLJOM)RrM$p2`Tm&WBqR z(P?Tophk})(r7LYc;o0*jw?`y(51jt3%lllQ~2OzlM`x+W71|s)k9Dz`41j(TqA0e zpJxCeF3?TKJD%`h=%DOO96h&xtMV-$@$1-Dc-G8!4VT&PrhM z(s#~7R!dgJ%Exgr-k#k3c8W00HII7gvae~^JdkBLsGrNmLd9Y0TX45lHCRjf;>@!w zjKLUEYSXXrNf}CipSYD6p8T5#IsvJUz+3)38hWU)57ft3JZ1v4B^F03ZRkfLPr_bj z`r7mx!xT)#V)d`Wf0jBK@1Sp;GAP~L+{8wzPGpcOb!G>6)$)J!J{X(0{;tKmM@K~k z6Y#oCctJR~4MQfbN__=z8Zq8`eV(PwPiMjtEi|BU2|QQ7 zb8-RlJU9 z=g;o32P#W?&Tv_T079WlbcoQYpW@hFZOdPu8SManDCSP+k7f$QFri{k5f%?~7~3ax zRDRVoZSayC87Y)<}y>7itw-uycA9vgbmQI9{% zj`_n^8hhWH$0az>gd<#3P`akMW!pQW4UK;g;F~9h`Qb%n10mW?j!3&(3Xr^r6il@+ z6Dibivzp8cl=PU(U9{@TUoAwXPZMi+rn)8I%Rp+|8_6d$t0}Dw0@I)9_UXxs{OBm`Wk`>wsfhL zm7jHQ=hTX`kXhBaqlNy@{Loewc4W|v_gi>V@z9E=5r8+?cJ+;x9jm=VPbXgJapoDWMTp=ElFx4&fXskuQ3rm=G6gdtaGTWfn^;Ehbg`3;Jlz;$OuSi+)mWQ z_6sLi7gFE`5>vZ^?KxgHWdNKW>#2D*I&90J8Wvy-O#xe3$ACXi!lRG^chk{IhVBsH znx`h@=s6k93=n0>Z|TUv*G%6cn`*E8{+3?r)O&>S1ju~KhGBf3RIjy*Bcm{`hek;M zDj?fbB^%QJ+UkA>R1u`Z?IaJ9+!sP@9M)<4-f!${yA3Q!gw>h}*l7S1ymD?nK4|Sx zgxb89o~m7}ZwGN?d_@P8aQum|mRXl6r_(#Fwoa$HP~8|CaQ|`ARlG*iBC&3qrD3Zz zC4oh+@5^2biOjTkNwO;R_P$kZVu%pt!{{fDuYi={zb(W$uJG-SI2UFYPvw(LEiY)d zkB5bY$=-&KL9`_L?hjx`SbMcsm)Awa3*La=zrA)B1Q$Ty((w5aO<*aW{s>Si!%kJq zq6oMb%-|JN6r-F1T#z3*({>^r&PazcXTo=8RKf5{>nYBBzKQ4t^r&D?E)efA_4ey! z(>|~Z?X6oERgcf0n*+IKU!80331vOK*8nvA)K?h&i?9{s!|U#538AcT=^^{`yf#8R zu0K)5!DhaJ!K45Ho8vU$0@D4Gns-|`ly{Yr`XNn|>2XfaC7>`4@MNO6hLi;+k3K&D z`YZFoQ9bkzG8(|QkGxuf#pUgg$ zfFZQm?yqfH#+S)dTcrx}(c!NeeFBWqfl3mcP(qrgUncp)p6=)6R|mwC(VQ%jj)Sn*_vborp*%By`n*|Pk8Z`H zHB{w}pJxHaVu#AWJ?@Y%M*I7hifs9h{JTJdPltk5t`*koJ#gJVVb)E}8VNuEO_g?9 zeZuE<&X8M(nCvk_iw-3bMf z+w~f}U$~QqE~FU@qqQM^DFGB)Wnn33L}6y53T6_&(clYL)y7F$C0F;U&3;Z=gm`-Dyg6rQ=FkYyT`%oG%~6zLG8O*O&6{4)0_d5hj6V)kAhq^dqgGU`NFaT`|uXg4ac0>IXsd{h`^OE zuE(9ZC+9m`P`+Ar&2$*(sG5Ab^)tk`R|zTj{)`|KxlVuTAR`8C0*Io} znNtmiuP;!%o4Bm0sF%#4uYmYJsKdcvX}olG`pZx5XskbNcc-!kpJ2RYOJlGb9jm6K zc@EI@-pZ#|#Bt2am(OdMT*p=o>>5Ucn#_1=)tPfWc}Ol`m2kd0xu*GCmoEPD6}9<}7%voZc$ z?Q3ztbJ)x5Y;iE5$luja$BLbv&X?kQ|L^|cA9IT#J=LGdZXQ>`PdoVo=)^6q=bUF2 z^y;^E&4;XrPW6B0sB9Z}sU>L*KRf-ra$p)MCAlQ~!)Vvzm5{RY(6deA(0(0u_3r<& zOANZFZ%C%4rw;)JX6fb^fborUXWG)-+zs5_wjZx|uO|2|CB5|C|4sVmRaD4cIT3T& z6Zk6;L3s3Kll8=2n|x#9pl?qe5F?j>{Q2%gjwgV@9hfT}!mfOPOByAvqqd+gp zt@wMD24pCa|rK1-u=P-!aqlW0un1=o+yg-_3Yj=d(w zk|`ZrzJ=GboU=G+=ZK0q&Ea#zHGVHL(j=fcx_lm$sL$h7_JYEC(MXZ{Prmdt9;DFN zv;A7H?tR?nxYpExrC3vQJ8QSMarlB&n5f?37{xXBO!GaI%3n+{aRmkPzBv;O@VGl{k`bN1nW)Oy z>i#*?9t~-ofW-t?eW=Puyc}L8RqX!kx}oIY75TuhgTpT7lshm|&qGf;H?YQpx{2dH zER%OC{vc^#MCg8=Xf2I*t}iyv&$&JDEZUS6Dp-J32*QoFDL zzbO_Y_FiMJxhq=8a2htr;c_R#-`n?;*goKxe`>V)O}S-Tq^)1#?;T**v01dsuKQCi z6Y96cWLRP%TFvj@=-{$_0Cjn2NTSBe^X~}N2T&;SbgL`!-EO5`>Ty|u*jLQ&e$9Du zzByOu?GeK3{tPzP|BVNMh#!YiHto~>{r%P3^W{fylHouUrlC>36U!F6{ePdBX$KOk zqV;w!othZMj7GxMZW1o*%EZZ-FSJHR3elyU0p}OBkH-a zhXHwtrA0?NZu_>Fsu+s#QlhZwI|_`%W89xD;!4{wT~P&?l64tnX#>k{%07|m)ikn> z*svkgg&5hHA(<0}6+WkUI=CIpAc;_VCG9ep+j{L9o3Gt`-}5+3G;LOLg+JjFcyJ94Xzjq7uMDx3s~z4o~^GwbI%HI|sQvEvw8o8zEKKFVL)!J1`aRh#0wPulb2N z!xzq-A`C<_w*ZlT2wn>(NWduuRn7><+I6Xd8iOzr5mcYZWrPOzW9dO+VnW2&z+|~nV9D!#@ln^zwvg6i(x}R7dBj7ER}_6PNgrE-Nf%v2 zf8;J4ZxJ+UE~Sf6G0=91Mxx4;KudO9bCvZa@zgdP}XYbh@oBus>zf zzpNREg|OmRWIZ7}056tiy&Yt5y17R_uC3YM2b@iiJN$1de8@$n&14Kbr=H$K2A4g} z^}K!fawm(9cWY5qS(0N`TTA9NiyCCa_z??L_^=?-(gjhk#NG3*i$qs}+vLlFdwz-M z9Mg0+_BWG`&koYHTLVAaIuTsX>0s?+^&|yIW6kE+L@CC7L^p)OSZ&P{@}5vj=o;xt zH@GNf^`panRL*0GYS{ONGF` zmI6O$b5o%MQ>ElkHMhm}6{+GSjCLSg?jdPUWL|^wwLO|$woS_&ixPPptZZ0?b2@VL zyMmIpsL9ebUuA5#2XcVmef`DMz^*$1f6PCK{&EJa%tsb!3}))`r*rZ(xFXi7}~i(W|^+YWniv(E)>Rt2|0T=t8n zA?;B8mOB*fva_@E_Clr3M}ykqK$vpb;7i%?8ZPH82har}{a&smdbAIKd^OP9i>GFT zH6}MV^*hhboB>cKR3Nz69-G@(5FcKFa{H#Y%O5*^(LZ59!ZTq8$KO%|dtpJx)P3Cd zS9SBvq!Z>gShBnRkqS=|U+N$d3t;55-oNTp4pRJeRk)M{Swl+ju4y=F8e@qlO*-dfW4-x0@Nb=3n<@`#`TM|N5yb_5J3r zQ**WbRm15N-rL=|%eHq`VY;gj1x)58jQya(`}398yQ-1l!9?*Kn%PUVQtw_qPlR55 zxMrBk@jR@LiA%WU2{KN!kFNu;*a%iwTiBGh3b|Y=aCCcrN$Qc&(jJaec74rBo5emZJtBTqP!;85$K&?7;rLi9`a`*lcHV&sh0QvCRh6PfZH?e$+VR{i2xgdR zK;=x~TvNJpY)mXet7q}?Yu@vc(1MJT6`^|mex-_lA#EO%7GpN|TXnzF-405NJ0n<0 zAw21;_HF!HL%WejLA*C50k4a_43@#4u9(VGm9j;bQ!7{pAC~kmnA~OrgRYK^BVDME zu9OmGl}Mu( zTXD68_&)oKxj#7%((H`*_Ft4It?c)2LQ#hOKOHlXDk;z#TEC_o$RA`C9E4a3P~Dn$ zd8(NOM%YAdNzHL8gftk#<{ZJxWt7@8adjr}Uuw>pm=G`8AH#Ros04IiA^@ zjsFnAtJiKY?VVlBA0C$Zrm(*Et-nUC-&~T3R6>T_Se^MyCzrG~$!c1fpmXy2ar6B{G!|ma_BoL;C+@^IPs0`>x1Qu z0%0>@-kuYv!FWH9QW<;WZmb~iiz~w0tES}W7kqG2yq~bv5g$kegC#|=@H30k2OShO z>ze|uj^`X4SDCKss=|$g&peE0Xg$Zb!K!?X^1Ou#gA}fPaXDiNrHXee2GO`D;8)zO zWHbRsgugMV?pph&0+-=#0uX-lixwy78QMT-8V6eZH&2e$)G<}Noh zWY;7WI+mRecpukQYl~OnXA-u^6`?T?Qi8RXfk5>i{w<RL3QZcvL_u+@invZ|_k=X3JiY&rgc8y6?%T z-e8?;FE%@@5l~AOh*SjBy3n?AE(NH$`O<{HTJ4)UgXMB9GVfv>drGQKqPk08u|$s#O&QKQ$+%lBA{JW? znah%?H_5k117JT2dkZ)FC)#>h~m8k zqxMh?kCp@p@wx6ojb>j%QPi%Tn~rwJU45nRa(+#Q1zDi1gLLoL8L1^p%IH77rfIpo zV`MeAIvG~Mo3ko8ES3b08^GQL20kH>U;zJ|XFy>{dNU$-({XG3u-%x1&w3uM$C0O+%Woq28CAM>B zS0J`7y9APMl(|yKAJvVY2RI5|UG<0#uS|qQeQT3`nbUIR)vkzGYjM=5G5B{9nH#zb z$d-h!H!@lbEg{IXTkB8fp#HkgavV-+kp>Y`+fW5_(o0Hn@Zn+*L~fb%C1}X;7}9I; z{f(4EhMF?gEaxnnFbEFDbzwN*(CrHIR;RaHXbV|7Za6Ne#mzC*es{w-)>U6UBw-BX zXHEfhsEojiT8O$x^0Vbnj2BHL6`PlP!3%wskmE?{Y9FeA`vK?j6i!PXGJhOvl!<33 zD}-K$MDzD)8Tzf|J#V?C=jB9HVfy|(TcqZ&8Ih6z8kgAo>Ow?rC}obG1L8fcpc=so zsy`Dj=#X_)T=Tiq5sHA~HwA0Ssb@=-lRrgm8Rt&~Rj(vVyx?1yCOWRPd*%cnBF-eJ zes(K4i;ZdB$jiK0pmxe?DE}Na>*}z^Ke6RLf0`>7bkRW{YN89{_Y^-5+D3Tj2GYH= zTDM+~X&*1k>7-1M7`m^#wNqdFb|;95?Ii$8oadP_Cg!8D(%^C6(%tVFMA$`K80o8-~_V&hP3AY0_&1 zE3bt`;1)-`AsY@=^bK0n_P2S4g2yh`JI;TESrJ&r*0Z?M>=E5ahs2m~x$TsMRhGkO zt>Y0HPrTZF91XpM4)~QIO(D+(c}hEHw@NqGl!2k`TO&LlV(y%UOM0t$`?g4JO%?po z3~ZZs#ty!ouZx{|$Xa~IE{&YXemn$B0&*GKk4t)@=U=UsDw^MrIwIvLfKN3$mIvm@ zbOgM~gL2C|hHhI$dv1f;X4RDQr#se3wIy{*{|7%HK}Rl=1Cq-enph3l$#S*_JS)g* zmp53++7dkTCdV35{O-DE1!O{m&w0y~)eIxe{1j^Ggc~i-v;FIFC30Ir@&#@Dcx}GN z@lUs7J+<89EHhoKv2!UaxaHoaSahxMwIJQ3_` z;>tRo7O!hO0?ER0!}r>mFkbwhy!{p=F6Vk|N9d<5x`XlS{_NRdRk1GLmM^(x^qAmN zRl+BG)5+L1{zPk8;EB8uJ+&i(8pDvXR|9GAFRFT0=HEYxEDBI$ks&-a+OXlCX4Zz)k?NH7|+3MAGF7`W}C2tbRoy0WjSJ?zq2HJ`2!;2bT) zhg9xd;;mFv#a&9e?<~iYe)D8iTUYx1>vc3|-YSUrCqdW*+>ggSjqF8(d3?@+T#pqK z!|#lnKE61wy5!D&1EAJkpPao{0)i6|%sN9x+m$76+;nDKloeVBuebZx~ z+K8@tp-ql%8sFJpu6hojSe8FlC$V5Jov&{lv7n+`gn!F2??mtrXWk3Pp7ZWCocfQK za(hb?zlk5k@fWw)7Ee2V{HhEZ@)mv&)Ejf!z{7x$RfHuR|S6LQ5=XvS`WY?vFM%``5Oe z0JLPr-5Fk>T5|O4@Ry^1elLb{(IboDq5H1Y_3HD}jE_H5-+t!t*#wwP1Yss_hI>HSwvz&<>bb0IOVPQYL7)Fhk}`mE1a?L1 z2Y-S6lZa_GW~BL@%lU!^-wO2?qH1@H1g0EV zls70y(G*R^IX$D9V&v+pz(82K3Ydv2UCw^@LN_o{MUaRiQOl3UmR!CMYYA)n=K{LE z6a=V5^`_J1jv(3vvUFE??fM8;BOMudBf=2<^N;;lgRdFvUH;|n6h6_KweCPx`Mmx=Yuip2m;vrKMJdG z^1xrxDnHyYE;c$9D6!4}KGFXNmc@^Msk-ji*6ziTgUlfK?h!^FFa!X-2UmR(a>7SbF$TVDXC9h<2>xZaRnNMuAkw)K; zFHY@~3&1Ho3Yd7}Qjd${{7Qj(HBy@|{kpB7sDpdEVlhM=3l?py?uyZ6ORQ9y1|jh- z%)Ip5KUk%O?BQw zl%)sT<@@b@kk{ijlxh>FCs&?uOKf*Wq3Qc>o{~MYCYTL|bIm>elb1KLb6Ds7-;Rmc z?difU!=cMaG#jyn+%?6xA6WscgXn%sy00RB=o&>+Yv7rTBC}enCf$+h-)pQm=Ff2? zy?||;QjCdP5{$Q=>I|371_E07@<T@({k+I!o zJx@cvBSKy9efFv_RAKW2;jfiXJ%|DAdi9>?I^%GZ={!R{`Vb+9|0C%d9P)a+{+4Y& zVGGN)mzQnZ{$|_OvR%twwrykCcJJ-?{s|B6J?FzCJqr>=Mbz`GKinU<`oY|uZW{!w2y><)&m?fG3^(K7hm)UQn8 zU0yPOzYL2rY^V%`m^Y{V!}lx4R_agYtLF1Ka)>(hIXdDm+wp9^dc94`EUKA(G~GnqH&#@2c6_lT9se=4 z)Ftw;x+xPdvA&ux93!ivO&0k0nG_LoV<>n=M-cZaP#E8NxLDnkvPL1?a~j{@eCQHs zP*8)B@$q0`fj}}*{iW2Fpv%0uFQH=f6Xf6Hwc7&cuC8LCcH%Kb-L$ z-*W5x>w_V%&v?J2$mo8L@xxpX?W12NG0!nn1hqK)k(lX2%EbGUGNBdcnz4Hko-jdB zNwfVZz!mn*TSgw2f2S0tR$MOgolfcRw*{bN>MzHWudrEQ!VvkWjHIA+oPQtMjFruL zW0KK&P=WQxj5&FLz@Lh=4RXXV;dXGU=GNGK;6Y{^)n>_ayXQwJlk{r0qp zw!sI?h%W5^>*&Fw{DhK8bi6dVrXPRe?MM}0nhzMg?Sp3~hczx&(IbZOQ?vGHta2Um zPV23RY_n18bEuY8%M@|PpPN%m@UcX>ow}dN42DY48ks6r>e1i~ZjkyfFxLz{nz8!PHc&PE@@13PA3gRHPVx@kRM z&76|^{cORaR)Wph^k>sADKafmkvlnawTF9CmOPvtLB1cQvyG_^^^+MUL4MVFJM8D* zUP;o7k5zgl+&4QoE(BHYhiLD)?yc!n*D_8}uV_^~r7e)$cYt?KK$NYoEL}5uO}vV= zBshgL>M6QDIFzBpEYyYhd~3&-NuECS|MkOA9K9|1hvccc9r16-_9=D#`=RCB_>*vH z$NHrI5b(gRBJN|eb2lG{g>JR*!9%(r@1P(@`+Op?(zY6=ivmW_p!BUJc!{;QjGl1C z9WxOnMY7#6Sihf6uIFcvjhkp86LSA--?6+Iois^15b}hMiwarkq6|8*@Pk`9a6QI# zP3yHX*=ohJ?ZvrHK>9t``L^G%YS$bqL)Hx8@Zk(>GaR4Wj7r9v^u_Ku;r35DqIDlQ zy6jng;W~Gc1rLsL{Ke0{5L;EW>j0x67y*$fVo{oVIdfksSZjp;P9z1bt83C~1)6-g zqS?ZA{IO-VgN0f~)MSOnV3Jv`ZM74kEf-d9y_qub2WeNpk%@+GwqvqDKje=$JIj3x z?GML(=8bhaUvU*!E70*c&D9AF-$aVId*@xH3pfY_p`QH9YUTo`%gWgUE#njG+yHrDHS#gKVu~M7C7rHf%So|1oI*FRh# zr^7p*62`Pib2{wnYQ!vZ0wv&mUH!>Vs4xzYI-gJCbhzZV1`kRJZlm}C*{ZMWx#bO1 z!gbGoT60~QN3Q;^lR*X5JTvY3SbZ-*RKfsMgt*<)%$4-Fr72@EriVZC1j%O04ZIu> za%5jLzkmWv>m#9*=kKWw%zr6U%``TGMnVKWaAwdc2MagZ9e2RY0rBGW(!>q9Lcz*(re z!auU5-M;!>h9>fpv+dmBdh3F{4oLrdGL6S=J-%}$FHn*czXxCze_GIOM%2o!%pIN;Li;^QzTF!s?EWL@ejzBN#FmOGxT zST8k2hkNE7$fHL7xO5RoPXe?o-zKPt#6V-EuSA^Ofv&OnP0ct=;U`%oExG8Z{|Q3+rxI`I?BC3$X8$__ig)xXpg441 z_HNi-C?H70j=+Gq8Ja>`Um~?&F=u?_vq=n(>2)=FjY1$pcp|v@Kf7(C zaa0NG@5QC*wHV7V-Iv7oT4?VDL3M0)Qt@|xWD}2EY^MLPLP+0!AGD8r79pA%l`kMI zlwvwIr!mYbrXp2}$zaP@0AW+1>*S5pWJ=z5&r2@me)m;`cKi_G)n?uahhT*lBfQa# zfifk+%V^t6cO8<-ixBywcMfGmcLda)X&dhCu8xppJBNi7{#_4>rH4{Ah;7oLFo$Sd z%$rVedrfjt5vRT$+c9;US;)!tEcEB69*G-v4P16hX{gpMLGw0oiK&slmnFpdKblLziR}F z8?)ZJYc-)Aw4Aons%6lx$_ux%##tuq!&5VVFZ2Nn)ArKWW7Ugsqqt!|at11IAl#%kR z{(LtQD2xd!BRO~w;6ZyRTuD?8%#Hg!XP`1cb?l0~qHp&68I^OLxx7nt{0Mr2@y-+_ z4q}^vmZHlwXR2iiOu%Umu)v@%{v&y`om3A?Qqoft>^P?~*n;pM?b8)}kBGP#iEb|^ zrBm(f<9Uy-cdT}E5TGnzeBN;QZf0k9)&%2gs=EX>b*7rsFJA2mc6Trhi5G-#xm`ql zpN+c9MC9*7ue!8Z$oy_ralc<1-$ySU?9rq=a_O8|pqM&P9U$Md>u&sQ)-}0T<_9;W zrzi@GZ7CY1`Oe&Soxen;a+Rv-#qoa*E!lO5O9=9n1k+heg=z7Gi!i5YRL@}y;(8S6 zK6gHm{$AC{*3BdjHk$z?QVE^mHKdsA=yG3Uoa$X86B?ogoU1(`Gw~5J(SFW74qG1W zUWUcVbQoDrl7kfk@DuigUE1h;D4}H4iM3*RaAUO*`W8QDxkxnGr!8yaV!k^}!+B<{ z7b4*sPV<;>U2PlX4tR~_9qD-ZjoXn#>Th3 zArw%CQs+9=FG!|utfke3&`cFqQqO9o)I9LY;VR&tHJXT6{J1hVs9yZiy((uEAq7HL zG7R+&P@5|nc118aq{2!eu8GVp(7JpAU18kC$*N_6x!9N^m(L$Git#ch;t$fV7Bj=B zireWk+^f5Pta?kL`TqieJ+ARSy>mEtHa|j;R;o@IcGk>J%z*Jdx?$DV$}Q8aw|Eip zxjIv7DTi46Z;vHZty&B8;cr4XF9T&iwm-ki*5z13Kk*I6FjM$HodqE7Ep@l?KQbnJ z+~F#b4btY1&tK7tq*^CHW;azv%8|I+z@&DtLRAR4&=;3Ip-LskkIJLX zoOpF`KKIMe$0p?KV^tC}rHp1hcAp9y)P&6J%D&?nvN3@n&y`8Fv{3KM5~ji7uW_-I z-VJOsUIr}eGxDt2lTsF{559}S&iLz|dW)G3UE>jeCj`SXHszlRoL|w7<1rcwL}D=J zg;Ud!6b%KC+bm}60iEM#Hbc(xH};!{AY2PA28pz(F!Yso=JB2DjM06R_8uVAg6p?$ zIl5quB0(VHh|%iaTTfX(^%SiUN%50DejN3X#PfBmX+KjxvikM!{>E1F6aKYEY;{=% zM{k|O6nvMr+0G+!|0cq0SgkfU>J_ctQ}ifw;!JfRfiINAO!h4$^yq8+>IUsBlNhDJ zeh5&@O*JV&<8O$45ftp7Y058fL|3~vTWPf2KOHshYje4;su(JE6vV8$ou882>xJ$K z4dnV|3?VERVstgW9>}W3wSsr5UcDrd#Auf@ZK+Wrre=P0yVj}K;*1t$qX%jc_tjc* zht8lhno}`+9KG5fD-!mqeZv#lDPv(04>w4O9eys|SGONU$@ZI?94=be>`(JtoG}W+ zYk#(kN%)&5#*N0s>Z`TUlx3p0&Gog?Oo43%Z{0SL{M1nC9|0~r_5vQ{nGpj{a$#c4 z+!H{x)p&1Blin;fzQ^muUv90K&E~T9 z@d(U|fqYg(uwoFu>Yr-8z1csVDlc71_SWuQ%x3j71{jcGk>CHQ%)T7S7q1}Zj?&{Mz>pc!nf9$2!xs9Ak*|op!pho(F@qhM{ zVw8CdDWm(@JH!8c8M6ex6rx=moxz(Cu>NQA4Hhj4{14{+ADIM#yO}E?@o%j!+s~egaG(ApF;r z^20^hUe{F!u_j&~u1~Be?yv+jbNc1hQiK|SP5`1l2ob_lXL>Q~jZ<>CLt*CE;L+>~ z-qESy75;wS&0CR1C-1id(~Dx=92*-jFYomp{K%}0lJm_!ERQ^Oa4rZ^fMB07fbcm$ z{rmj`nk*iOhGp5JDkHgP?dZqLD( zi(qN)m%4~K8=1@oXB4d2{oXgoC<}8S0Nm-neRJe`yia6c>}I?B^xws55-z=T5RyaJkRd3nARdyxIFt#(z-b**u_CaY8<6&3|6D!F99-`8r zv*ZIpzYznA7POlJ?=m-x2EEwwB@U#8ZAzj+Ddp#wJAda)r4~Wr7(!luUds}H9xJ>z z&s^7EO1k+P>x^ch-@MVt32M4u54UywbHwYTz7rm`bBhOZ#}K|k28BU?Q^}kS>>Q+8 z6M<@dAn|^4{nPL9Ac^;`I-w!}h{M-)v6N*4&)csO0^dY@RGH@-v36lCxd>IFFVp+l>(1-(0wZ z^zwsy_EPe4z|r4Z_Y!CUF%uE))vEpS-OSFe_~N2pcTh;f-;LcHfG@?lv8|T;H1BEq zQ%h|RekpwTi+UAu$KVIDI>Aw$(;5~%H$u6R$dYC-Ywb?RY#)MevtqmibW5sf5wv4o zyBOu#4PCNA%U9f9y!hbI0e2{AZKNTOh4p^X2G~~aivtaki{I0StHRS19eeZ4Ccox} zMdh?|nfb?va^q_4vY5{Z8<6j^_DElIThp$_j<#E~^8aDYw#It*Y2CqJYgTZ~?9<3u zdO}27P$Z6X6x5@y@wXN{?Rh+5bGU>gY{>P(C8h&uo7@euM zZSU7DN)D&r&mfDQ4Ikhh?Tk!won)4__>1pi&+;|cVCA#EYNoX4GDIj`A{8P_hvxua1gH}1#6ON7-!K-a+&^$(gP zY=apF{ezrcfTme#^iQ?zi{0(JTb9%LDfY_Q+TFj}*^!O3?x7wZw1RD0;ddRM@Ot!l zf++ZUC!yyd|JTC?!S*_l$xp{0YH|7LA|J~L$mgX@LR?O^$mm&$0|9IJdUD6CPD8E$ z7}mdK-sgTeNwNJoL$%UHFfJvI2Ts-f@Mk>M8d@ao4=WBBDh=;^REr|^Vq(;i z0?XQ&+EU;|v`Tgsn1HU8Mq0sQmRFTyC2NYYWwx5yk-AX3IG=#=%eZDA50%z55bE(h z?LNUQ#O-y<++nP23NQwNdGKrgT2CaC|4ZWPaZ2hiL<+PA$zW8ovFb#kM35!9S)B`I zcI9Q>%k85V7H_}SMC>gksLu|6PpH|ixjTd=XjA;~5E_y!NJl5!M*6XwK_gMf_H*KW zO(qynIK4kzq7i&T?!Lw;jRg#p68Sz1Nqw}5Y-^6Gc6dVF(>$sJru5`GA4TmqZbsCdAf$746V21>j48`J z5bM!IMMVs-e~h%!BXAhN``kvc_vLf_7fw{OEO0Tg=QI*j+$zTL(^fNS1$@%FJXQI; z`8z6CoXt+fyG001dG@p591H)Ur5L{${DvlAT)rz18f3e?&^_=C;u3z?Scks6I7p6T z_zC=9-PXG8j(qq#5zfm&{njfDfyh3Yh-b94$Io+k7%z2HdYdX5Aro$v^?Cv`C_l3= z?lxzSf>Cb-bG%roF%)YZr*(El*K;koVX{hAHH2-{j(HmhBv+%YznSr5Mke+CF&_3W zffRuMWh%N?aP^LJCHYwYML)C0;bevO*YeMk)1hUhui1au0xCdUchwkytV>$hXM*ww zPVbK*2As2viXeqq0|KAB9;a=Ggrb=j;TY%JT-_Iuaq(gA2{yZ{0XIo+ zquT6h;C)&Z|0-2q*LOF|DJq4@AoKjU))KZJ{%ozzFzRK%(e$Qi%3BQET=TFMh5WxO zw;5>X;}x-xG#zZ$_b2feX$$h3!)aZ)F{9l70iGL#Cb&bE-J#vlFRvW{-%$L{n}wgUPZ+c}{tUgxcdqtu0r&B2!=)amqTLF6 z!C!}AQ|e{$zjE?_3*&8<@63{c7a|ayu@Q)Y(m9+$ zcbR?4M|R=_9brzVkJx!*&YNmYw#I_e_Sdy;5hkLWba1=|KC+2{g%;PSi}z^*%-*MF z$N^5))_Mdw$=cm(x^hvQ%XM6tRx~-fTu?dTqJ-_Jh?AqNV3U+&WM=L+ET9hFLs}n7HwKbhyjO_x8Y1b19nzy$ipo z7E|)!o2VbbCd_2wodufduP*+IZ{nl&YAgGz+eUq4hzG` z%(gNmoJq*bdkUfmg>8-_@v)pley)?$0Bsl-WyxUOjD1SY9LCjq{|jCi+E<4Q5`yO?U($onDHzKMgIX_zeY~O;>$1eSI_u zUZLL1_Ipi59N;Z_kFw@ci`_}NlbmA}-|CSf>7&nu2lvtgFJSO|fId}~QxW4HbB9S7iQN*=UAVVT#V|R=BGjbX@ zHM6^dA69H#;&*dr!E+Llo;(j`oA%?XWp7zxQmeAhB&`$w;iLMii3FdRlPlI%B`)P{ zt$cTAbmqo&=Jgw~cQ#NZ^@g?nZ5WJhBKf_9#&nT>o7sQsdYNj-R9Kb*3$1BTkWGA8)xrSf;e<$%I_Ipkr%>5W&_!k|yi9;aJ49-Cg{MMsNdsYZ(@OSOe)y zuBC_Ttz2Q`#Y`JIU|LZihG=!WOEywilwDUF9QVr~6NL(Ht0iSN$UoLEJAFXbu#I0$ z&Q&p;Kl@BH^W9T5v}-YuIWy6TzEsxvm7dnO#P5;M2DhcdV%!3Jnj+CBn`FhM>Gb79 zV13yBoo{w&#FYt)L4?v+a40&S>LJ7R&01B?YkQc&aTX^jkgfWQf$}--s?pL&pB$uP zQLtRju^x6H3-tkwbl13IjFGwOuVKw5K>G+6rnxVdmax38%s_^9k`Q{g7r zeuSrtvOx)>Vbrr*3r_zkbVUI=DKI6A-yW9fKdU^7@h_&JVN)YzaK)B~!&S@y9&v1w z6K^RLybJud)VmounDC;DpyoTyYIi^VgzvzIn3Yq1azyL1)jP!{CeWKGp96~~$)4Zr z7f4#+2ffuEbnv!z^LQ;$40wX#VqrIWZVOsCwCoV>i<1&|87C=k2>;4EDWWBF8F)8u zEN(VsEzHV09+|ZN2)`#J-65zK6OjCUTy2o%XXBOI=}2^q2SjwJHkDOauB3YT^)i}r znz04=AC9*sf3!1dpcRByt3+$7fzqJM#-OI}gOr!@T`5dWzrGeWmergt=v*39WmU}& zb~NeDQ~EcszWfc6$|Ei~zUMAR%A6l16g41c4lg_V=bMgHvDwPbd}IalsVUB!lI$(< zN<9U?RDxV`SS~0yo#BCB4j@EbKc{26oiP|AyrP+WDd{~QcuoTs#$|VVh+Kxn>zga% z_;pWvA!)rB(ULvCQr4(4+sl9)QRizP+V@j2PzCZPAPM|OwNUdbLJqrrXrsYA2g3|; zm)GCsOCNo5vC3eNM>Y1{ene6!)8NlPasvU{His5Y{^ZYs=}2YvzdS3|3!`fQPdX~t zpu2?9`MwENz61Yn_1q!Tz6>chH=O?t*OH|oY2KSn9x=t(Id(oCK4iXdtAeiUj5p8x zA z(J5I!1`~7$Y!+TNZ^02Q2u_!E6`(;vJ=v6fBeejBj?y)kJj5!6$gGKUH`!7& z{=p9%+f=_~hCCtyEX${{!wjvz!7V5a4|k*N+do_X%E=y}aOcKF>iH=pN)1x5PUri$ z99m0aV&O6=op&dghsqjH+jFSPVB-%kSrIDFkl#Ak{!**Agsj`d7mt3h8uJU_ zoEwcNAwn?pxF}AYnhjF{tN*(*FE;`*%WM}&RNdO2^8+G|$bq`Q2NVa^qi+!dlC z62p4;K<80fw8-@M&sUrtAt)GZ!L__2r$MfZ!sRf(_zcj#_4aj4s2ootM%0#b3_Mq> zVE0^?GEmxk6t3KsuU2iu)yf)CRO@xbmw)k&=d-%@n-1>x0^dhy6saXAdRy#9$|4MV zx31?47?qTLgk7TAG=M)*7nHyvhgMm&jOmZj1g+WBE1q2fxR0wJKVCrX#2ON{`c)gf z>w%)SHu#w~SV6*%YBg@OJIo3zcBa+_!H)=}Sh@4VO9L!t#({l>taCb54*@@}aT@+wh7z#I%U)5znsmm!z|^x2dwpg>yaIhz?QeUgUB1=Kb`@Na zkSW#ZF%h{y;+-$`Jw)bFJs{w29L{$Kx5G1=kT0r0!GEV=5UDjRz0k3)h6K5%p7gle z$##>-3){$*u5~R%TR6c7$Dh3`>M&StLD=f!t0VZ1>xXN9YUt1bJkPji-F(-SE0_H8 z&Kd`zA$$l|gkJ*Ip5u3EJLmjH-kesd>6ScDMjVbf%!Kp~F?0_veqz#fcC^W;sX5#h zw--WsKCWsVPL)R4Me-F9b=HGXAyG74eeOedVI+m7fft<9O}L#Oe9zWM^@`Bxj<*l0 z6QRYpVzPgklS4)DzEVqee$>TIVjcoXZDuCiQY^`-Ws|rSK%SXQ>t; z9UIC`DtV#OGSY=RKf+!CzG}${TM~5VMSKbc7MxYPU9qG^Kh7hJaY>Qk5Fw|2^=|+> zez8I*#X0_!oYYCtxpL#2`0){`XqHlu$fT@=Z3fpvJ{#4>ojtp5H-glF!EjhMZ{yxZ zKaHIuvLnW*Xq!+NQs{H~EPakp9lg9qJ@zEoY)Wfmp5MXT!9+2{^ ze4o3ihlBEL1ugu_^kJ*cC25)3p!^uXXB_=SzULaYOtNfdH(wD(Gr?=35MS7BG+WfT z_9XlBE6yvMWMAx;s6OJ=)dlAi91l*_4M8WaiEKWs)@`JlU)frMiYW}?!+sy9Sv5l~ zP3^JdvE~X&7vumlk@UAuo953Ryk4Fc&M&+b+})i3wxrc8Pn2=7*$jEeQinT`(cYFt z23QlN41R6M3*L6&$1I^s59H2|)`yvjpa0CD)1v_hD;v#7irK3Gf>TTQ^XduWybpIk z1Aa!xg%WNoh0*@$NhC}QQ!>4IfU2Q$_n5IYojd!p4fOVBJ}H6hBT6!*`Xax5?+u4! zj$^_VaXG{|zUZ(tXEN8z$6KRbQNH|%O|B*XceX-=Sz)%xVis+LHB`iIq{+}llIgBo zTs=Csk~nWY`tT$gS1#|F5OiOG%*}S)UqP{8GT_)uU6U#xL%;fdadcfy?I=4WBa6 zlnkJeYV(PA4NHt4iu2YVP(ekDV#@$v>yoVjwuo!1_(_5dX|+3`zq9(F zYPFQC>`Enc9Nd%U=lyfZ<#+6FP{k%mnkR| zfe+0p=1o+h!s=US}#)vKUhE+(ynK^V~cB*wb%PHdiD8y4dZkPTg# zsEwpFtJLzfyJ?o2?ycUinWxaWk*%QiiH~9%mAY9BneO_dh>KqKlun3tD9yO+2D5el zlFR8{Eh9jCDH$rh4_vYuggxLbH*Zwla#g-O>V-xoLm%v5oIf@CO}Xzav(R9pRNa_e zqOzDoARn&)*Hl%`24M$>!!Cf(kZ^Re1Zu@gt%*(fHGj6ys3ItnL^*%f{_J8Y*Mb5( zrG_-0W))D%m*d^&j!}c@e3o;IkB(zVKgO>)(PJ&9!}T2c-CN)4(e>w#9S+y>^M&tn zMpFpj$703H@=)~Q_g8*m4t-H|CsK!6+Mg4h{Hn6mQ$on*Ni8dVhWd{tvVd4E=R%#f z%07DhzS>-gwf9-}7#wOUZ%4SkH%wPTC@T_DOwBN>U?X|ed!2T*Scmd&UPdkvI!}H4 z6}>gZ)>7+XlgHV$wA$0%L@Qq*RFr8f?cduA0CmnyM_j(wNpAnqfc>-`AttRhox_gY z&ki2WZNgtfK~?G5$m>x&y0~yEatq7c1^G=ZOvS3*K`J-3K_GtR4(gHkS9ZewCTtB! zVhRO&f=Cvs{rlJ4EKs$BYGL8kC0e(HFE)zNIi1CIA!}6q@a51@mwCzI!NGzWmt_Mw zja7Bt79d_@)fN#lF%gup{|H>^w0hq}+lR~{&Jn%$z&>-D&_cG|o|AGqBQ-*c{lKXg zS6u1P%eGX?tvjKvGGiaw)J9OHFX>||SwL2)s$^JGUL)cb|*@(+_&dzvy?&He#Jz1W?L)AHlPIk$37MC<1u!$WWW@D zcBRYi)5rM_?|f(}r8JGp%aJXEw`_GEI*H6`*AqM6ds-|r`s=UYz~a2tAMSL@hkfsH zB_*X zZ3Z5f{4THFe#F>8@gb?_>j+$(EexP}M=NYSEc)nZA%#55cG}nJLw`+U zT%wkMzI=rCHKp1C&FgauwflFCVJ{+sA2`B691j0Zl^t;BmrduqAM!FycSbP8LoGEW zl^3MZZXDNOeuAXp%CV>q9# zm;WL=VqsuunL#vC9yn@RW~5DT_m{n0$EI*Ay%-TeK!nI4ZcbVWQvswVRl9NFK&!RK zXEVJI1%F7(Kcq-3HVzwSg^y2P2!~^xqmhj$8)b?``0Ptk1@(yJj)%PH#^t~;Ys8>5lUB>A|JmW#+l(YQ4yK4tt$Z*@cS z4~A1EE2I2Dw@V`~V`_gyw;Hy(<3+^NH>F9|_ekqzXjvugl!gRY{0s0f>Qb%_5o)~u zM*cVnt9bqufFTtD4jRIXMOdyF?cDXCg-=`T0zOcVQP)%tv02tqe7FS3FuOdbHkJ4=OCbtzYV!97 zK*9V1-6@d6A|@e&swq_e;c!b(^7#KNZ7};9C)75YS-HKEl=xIFxR{X0wX>pfP;L&i zZk6A9RBh6c;ie?c8OCs8De2(!*v4llVJ=FYYkt%!K_}XMWucD0#_s84S~qqu5)KcZ z%5tECMT@EI`7n+OUmWz7`O@`86|cTcGG^eZnlm?EkO-6i+5bJb;@YRpf4g+389O9y z6d)mUN#|Lzp{=V%F$PAxHV3RY`YAl|zzz+c<~j!xcKyAKV^K{#ZF+YRynT2c&M9E< zMKM)9Trj$;vaJr%aRE)O+c&BT@?#xoxaQVY@Y0Ul^||sl)IVk&qU=T8_lD$G-b3DM z?#PT{UB#w9l#}Iwe}!Awy2AJ!JIEQjn7E@CmH`{vtouN1K#j{4(JrN0&mlOq0`6(y zGGQ>;w(J0vNuDfXIl5;yDU&l3P6-v?;?f$wQ>e@jDB0_Pa_lOBX~O4~W>F47I&>`e z8>;b>76+9&JM$MXWj!l5=b-8$R6U)I#^gDf0oF0p_{AA0pSd-Lp{00m7u!-W4ce6^ zEQ|}7;Er(q!GVTHN7MyV!uhkb4aH{Ip+CE~z{tF?%4bAS0a-0kt#oc-)G!(i&R!@j zqHqJJQ&_APR`r_r^rmGn)kcA-69W&7PsWwOH@j)rX8k-lK9>QNA1Rb&QQZ;p7}fnT zTGf~FOCwg3DPFv{+;f8|yuWLbx~~Q#i>u7u-hN1@`4;b8Q35M46$+y(7Om3#=$chn zhIA5ZTgs?yMu^lE`hZM7s%*=olanY9rVQOK2YGZqm56T0#ulFF+Znqz5%m5KvkL56 z{HZ7@qKbEktU+(D9`X^`HJq4<&5g~UiR}2jW;1)Xmobw+{HU4Z>a9hfDjvm`rOAO^ zh!L~gXB}KKWmYsT{p083ix2s+byasveQH^UB}H`$YvZ|BcT{Qr zSa))^GNOK;yB)Hf;t=@DSlmr)id^2bfjrW~1+Q-(W@+vGS<0jetj+~{%x$J~8~Uo=o{f`I+?^WV(CAs7 zkqa0T=8m@qfqXXHWWukL`^vC(c(!Tq0XLx;s%LzN=$qhuT4lJ~0_8cGA9fkYPn-@P z=Uzj`eG(0(Ri20w1?2`%X-Qe(f0W~vqlRtTs+GfSy9&RF5%ta{_=*f=fN$O5Hq6FlkARJL{ z6o0gELm{E^AoZ_yfk`8I5mW{ZlEycZiJe>+UY)b21>Y5nnl&2=Y0*5}OC=~5PzV5^ z%_;Of638ea)md-0WahA;E_diP=@6b0w_F9;0U`4MyctGWg(QztBJpsjU+%(Ouo1)% z>>O-wX5L92hr&eH>fO>oth($xN!-!(FPE#pRv{+(8Z%Ph5CqY+JMX|13i{&fW+ryFGrJ zW3)D)z6=sJyJ)foEjAj0V6uN$ui?!CtXv@Dyhx(jXL_!S_2 zTS&#|&88#b60p%nYSFDZexWNnVX^Ww1b;DlGUxcMLwBWtO>25t6WSSLhECW}Z}#;d zTE`a*5q&~>ezwuzm!&)+93|aNY zImLqh{Kcu`z{)U0^lYWgU>KzQ_GX79cQ=?4QLVF_lP5p9Ww+)Aj#~Oc2U2@ZU0hRf zSU^kCGzawz9gcCnjNJ4`akFJL8dLjxV($;@t4w5KG@*ao{?%VXiXD2E$sIk^O8Fuc(Z}s?_X~8)y|lGL6niU?~Zi zEH*%wPCUT(LoZY!Aml7Biuvep!nHd|vpAbBla>rK;M2$TqIW}b{5onwK#KV4EyET& zzNC5Yawdc~xg(rU@vBS*@I$D*PrFB*jLwYp?lJvACFE6NUF3GY_(pN7$UYt1d_f^= z5RzIOx8n93Rm3kgb?&ekOcUnP@q-F3x`(rY3xCt)-SD<9L4X^_i?)X8_X9V#u{8x} zq;VWMb4vwj(92;Fd~o6QFzKhW`O^a$V2@Y?_W)m9zNG|tH=~G#5R5v!>G*{(teaDG ztDi!@b{7tDh33*=;A@@BNGD<=gOF1@2bwwv>Alh49ZH}93dE%ZIu|1skr)A~l9oO=Bq1vJCNQjN%={Yx^skLb+1psq^ z*`oLzSMD*TO^rSXTr{&6paCr^H8^&64A0nWrlzXRR^hT-O%XUJLaF`NNO##+IKF6C zaCMRU@BnM>h2mP*^C?mJ_Yn2ZbAmG3@tImspN?%cEj4EwYL?Zjmxz2}xnr)G%Tt?K z+#h`G_1(OcQpY1AHIe5qV1`QbUu%$sB`hn$HC_Gv&{kIam8i3II2Eb#9H zGF}6oGqm)y;~Z>mwwLl*!=EnAZg*Jn-MV~2XQBW+ge$YS=j(@EV9kfR zR)WILEteAjMj~Cn;7`EaDKIrWqHq9(m|j|F+l$X!fy4wt80gr9OwUp}t~|HN3+4v> z9x>?zKKH~f1b|TyiVPR;rzSg!h5y;WWq?D&8-~M_1yjSt;CiK7aUi(wbx}26lb)zg-&<(W~#z=kDJjA`N-kpPl_=-+x0}uQU;- zyMDNtb7C0FXG;TbpHlenI_mA+6jc=J(8e=O*iBVlE3O-C;japf|AgdMzKhG1anvly zaj3ZCDKt&Bx~-h#kKyG+q+@}+a~l0}6TEU01k(jV;G^N5WQg-`t@iQ-)2(x2T$!}W zuZb)#+oiTje>lBd%_zoC%^Z@GkXm>$_QJ74d5X2dTOu(^VJy;rK;OQ5EZ_SIwXl?= zSch)5xHSz-R%I1irtDXiG>n255#AD=!M-RSJ~O-4W^b+2Wnr)Qv<8>sjV#wd_Uw@w zOn0z0Gt5p55$?BKhC8D|h_;>?m1vt-V~`OA0=cXN(T)zk;tB?Y^cV2;XMaYF;$-x4 z?6og754c|>9CwecODKwxiz*Olfdzy|=F@7%sF})x{?u$J&k|_fQDbNq?7!jimT7%x zji*N)U>IRF-5U*W0geszUP0f6MD%dn$OPnM{$q2Hm!^Au*}nL}EK-2xD%u8D1@A9H zt66PHVjvnj^;=YKdD0!t0vujS1(jHpKsQt1{Ef6DOrm@ZFzyKqC{Cv^87G`Cs+bcO zJqaCTl`RscV_9}uyHdGmedBu=CVgDpRDy4qO~TgOt(r?nG{pC^uH549oFvm30#?{S z-_N)Z2uiPw`#L10c-^#%pa(2Sdcy zN_lu>#BwSV)Y!-H$gz*0E`WDvX8$q5E0U}NZh7!|QCGINi#7{5He?#DV3kQd5d8Z( zp2nU~GPQiCHbgouP)9x@(>r|4%w=t=wzMk3%DHlVD75d~s&Y-ZZBg%~aBkBY?F`$9 z-p3`O2&itJGOI2Y;A{cP-`jx6bkp1oyo&qr&iju-q>BXajAoVq2>=tm6=PUG{`5{D zk7u(;(`^FFowx+5JC@YiRhSdrusm-h`;Kl7g3YV9cvg_xzMoGHjYg-pNM3iil7Dct zc1{2i0hEJstV_MIII&aqa$a2akKq~QayQ(fXNMqgg;oi&j;fPGm z-q*dw0U((;Dd;(2FL+vD&P#+SfWu30M6W!Ok6Akuy=At$fPJLA=ievjICge?&Z6$V zs-3O2C9&3#@$f28pFbN4zSt$j6?)4oaUH~w3#>JD!sCo)>?$%@1zV{e+~*ATw=f(O zG;g-J6J-FX3Vk4+F#BcE5L!ke@dBs7{pi+M+;Q2ZzF4^CvwJ9Nrx2)Q%8e0{Ms`8< z#5bT_&vSANyYMg`whuEVf7iK>_6!5lQToX5(I(ZaP)gN_RTK0)md6Q9hfLEej%1`S z_sIT}6V>_t3_p`9zVBPp3c96f{xcz2fapMWny}QRG?`melQ6s~CgMux*J$#Q0ek^6 zUVc6=_K{%LM1A&3R(L7@2v~=ik1<8kcSEjYs0`wIK67idiyRXr@HMeQ5_MFhKuDh> zWGSHs3n(%dUkQM>C!L`jipDo(>Nm0$W)P56yUgMe#>ACih&R_V^hMEB8s()|I?-+W zPkZ6cZZ_9LS6M&8BAK8QROE`3b_jmPU5@pgLj(B5d63;(sx69z+>!vB*~@+?WoQ~E zOy)c~Qpt+?!d+ZJG9pn<4 z*-e88z_fz!mHlZWwG+l>Q;Aad>g6!=!qhhbTY1JIajB8n$$3Jb#CS~fW{{NgPHs9kg@M@+igCvI1*lIY6U>^TeBax2W*GOf`i*>jh+NZ29`ET3gcY|MTm5JWQ;g>; zLZ;6l(;u^XEo@06(A#$0LUVkJedR4*BLBk{)SJG+7oe7ip-6f&6_=niFUhZr0FmsW zc|9#(F0LpYnvVs81^+qD<7q;zUd#)#zqaNm#f;BY}h&n3I|k#eM#AGR+_sX5VvA3g~*FwS&R7#P&g{*zvFrzj48- zEo5IzDUyNkn}$iXsx;AsFc4P6Jrl&Zp}>&P^OqN4<$I|^l|>Ay9|sDZry6)$BGIvk z>GY#Xs~gz2Qsh?U9mBx-eIg*hUeB*Fs&n6Ytxts5iFuvk?`7i zN?Q^@oE+BDP|;l*79}+-zg8d2b~tiRg-DqM4APDXRG%>h^O#gdq^oFqxPTRMI^Ggz za0?X2y7ji(bX}CCFJ7>ueF&dW0KWFzzvkEnHNC8^3|F>;TXUSYQ3|ho5O=+DOG6(KP5VI<*`Gy?dQ>ZhXftwwdsgkG$3dbugX_ z%g5F566rjj&{vYU@G+iWj#`cej^a#(?`C+NHpFO#$RY=h?Cch7^SuT!h=_%X@q!@9 zj%&Kx%V)P!=}k}Y_4^?e`fk6dz5HXFccYc{W1Jkl>B47Y$4LG^md?Si?)QJ=pO$fI z*|vSMm%XrT+qM^1EiKzzwzary+sppF`#yeuL66RT?sLCzUC*ndOMQ_owg8IPSQi+wS+7Ne~tFHOwExI`P;PLlQw!im9NjmLfvNu-Sey39gch# zC$|1>CG+ysd?tq!rsP~F3kJ4MEjO#H{Wn&|>xQL+i2W^YHVfaw0k+EKjDH2ztmQ)W zdKlNi8$>o^+T?2@4?(Onx8DPQJLd(8VWOgHe&^$y>LvW1l@z7)dImMTjPJe>dM>5$ zpJ^qH5s#*OA`@s4Egqd^A&f_o!glFyS%M3V{FX%R4GJn!AVO#HU;R3@}i1t==A4V z-?J*iPLaAslg*l_MQl{oKQfQ!Jm!ucU1jGjhD0;DI(T=nWK=gMldB5Q((B}_q3=7- zVVL0B7LAY5hy&1==J}aJD=?%*bIJi^Ad0-AWrI)+zkJmLa_k-=b8}K(ey_3}&iElY zk(}&y+Tj&hYU(GXbBIh#1kC-zq1&<*mg(m{iR=2zvUa$6NW66utnfa}bAo(B_bS$S zf~c=5E2F!Vge+2PI)ocI$J62Bpfb4|$^iJWo-4T)ELy!jU1=JMFla(RRrd5O@Z=sI zD5rsXa;t%_0Tm^Fvow0`K#>h2KpXh3!CZ4%^tF<|`Cd3Lbs7eLH$E7)4z@SrFBy9?RmO63*F}h2J1J${I^{R24cU|T85a`S4h;( zAKrZ5x)MXhX`JpBDK+jB>kOL+0=f-jFDID~uYEd>vD$gubEhyqyaALvj=MpZuR$B^ z1xs&x7?;FltAkvyKyEtU94aW{^d76&p*V#q>ImFvC;=qp>BV3@@YZhKv8MKyV{V>n?S55CXH-Rd#w-3jV*$v zS8P@E0fr8E8QMRD8Pl8R4ZujEpk~2Lb(Y?qow}u==l=-! zY1WN^^XgFr_;7ejH`4P)J4pX7R8dF&lEgN$2K-p&sRQVexIuZDo41ox@iE=j-)80S z5!PJ*wB4w~=@iey!Rf}bZYRYexa>;I$qz3{FVK@FzJxRkuXEPJ6Z7|AyRj~qZ#Wu-p#^2# zru(nLd(VMha4!@Bs^&AT8xjqFH`<(f_3W|Jp~80-EBE)8y-cfg0z&-WJ=LPzSRqMz zJXD*z-)`&>4RQ*ALlPSS$F(_Q-xDUkQIeCtZ2LA)R}tCiME2NIj3-qM!hKc~_rZ5_ zlM7DnN|V;?>A(N^K={ucWN~As4RUwzED)vfRo~p9pGQd?3ie&xjV&yvKM}mzlKRJu z_VG~Xe&GhgMBP>P&|0uyP!N(dvfX)Mku2(nd*iV7vm0v@eVNEZu(S1ua|nJdrQ7f( z2+p{D`%>8zcSqaR(W%qyQ?#AI(#ta??_HC+L8FKk{S#XsN#%_b=BLT5vvSSdHKS;J z=zzVQPkegEg8ARCjo~1VoX*2m9^d8n6XUv@N+{ z4SGyVK+gU9y`KG_Okg9=AHh`;o825btD#?|;3&2Pu$94$(qd5q?8a|== zo#V1a-l+W$a!`iL+F?{3DQrL*pV;hQ8l_O~m(*R%h1l4@U=i%Jm21jG#HR$zZ!~Ec zoLKW+gbR~0ws^Ha0pCX3vGnKM(u*~qe%>6;&os5xNO{!D_iW|wXqvsHb>N2S0Uil) z2l%{DSn#TRs5yn2@E#j<5$i*&!wil>{(>z_sH&YPEBw`TD2nq&_;nltfVF77J4bRG z3kx_Ds=i%KRmxT|HQ#zs?!(w>n67Q3e26V-1Y}-&p*dmlAd&&1*v2CJqCoNG?jYYq zeyx`w#&zQ#m_I)Ej+R`zGssay!BGlxer}^#`FG9K!}a@I?YwsCbM43I?djfQWZ0@K zF#B7qo2aqQ=0&ywg^asRYZvRYJGWn|A|~)6^l_{E5efQw5;nv?;)7SHg%@dde82O2 zRcd(NNiQ#@YuEpldfDzIzuo$^DPJuc(EcpetX&dTruP;-i42{OdVJmJ@+RKKq2!*A zeu{)ss+<6yW~P>^%b)6HiTZU(zS|j8i!_JGI9~BwtT&aLd`)gh6mjKOfR93Og$kDa z?KUOh{LI8bQQuT4W&}?8Wogg0s)Gyd2>n(IVVi*RjiOQYQqo8NwJB z9txz#-#39SNmM;iN`v#6Kz%J%{$|;!M8n==lE&O(S+30t^-LVaOsTLbE=!bSwLn;# zsPNRJjB9mG-h?_>(~b;z9a8S^?7=W(m+W%A^_;~U`T_-kf>n^F|o0q5G;rPsXN zozi~(nRWnz&Ck3~EvYdHb;6SEwoYy%fjorq=#!)dfLzM-A8tzeo`EUXUc{W$j`Y|T z)$=)Q9EAEQ;WLt{%-T#^u$Z{PiPfn6-u9wqfwS9L{j`hwcV!o&UZ z?Ig9xgT-DkDbIh7^nY_RMoxPOFnG{IZe!5*%nj@b%U2NZ2mID?Dznt zqRuyMdP8HULpjfxTEMR>h*reEG@@(^^44LdV8{oeZ_@N$@2}k57z>+ldFX+AC9brm zKU}h-ob+}$O)h2jvQi7*<;|n^YX_=ySeMbLfY6Hgc^jil$N%P%`L9RUut!?{FI}Xs z1K%OXLX9qFoKq294)aD+WK8^G(d*W13L2R#wM!O<;!tVSHo8Y63)A5Q=lFqjWsAXf zk$5ptGRss0^P!lYC~J!!5RGb(gWs^l0p)WRi=pE0eed9%2pqepiz38_iq<&%LS`nCkgOG~Ateb1K(IF&CUcC-I1|yG+f)N-Kz#?_MgSN7YF`hADv**ZM|v@v~(A16h9XuKd> zy8_JeZ$nYikE>_AJ9v2)Ye+@?n+Y4=GJX|W&96*P#iiR+Wg8+Y^Ta>Ct7T7 zye6d3Xwy+%Iosr^w^~Wy=t~D%la(xpGg*x2^v3*WW0S(7&Po={58K@b8yE8DfuMHO zv5Pfps$I<)a1lbwE(S@PBb~S1Al-eLyN6P?=Zo%q?EFF=hD=6H$_<2DBm8 zlEf<}%z7G5c=0oT)GTVak4F-yhgWha7_}NcN9x6g2$cp&;B${!2^5%vLZb+jM#vL7 zDN{UHj{lUHhbR1j{fpAG6_lStVXPlPmJ&zI4{CuQ)gZ;qcZ$<)MPbVN%0w}rv1##A zfPHA)xEI&qU?RkL$uo+^zY4$pX7;PbL1HTXI+Ge(%+Za~zYOTh)Gxia^{=s|f1N$} zqTvicew82SMd9ihFE7JG3H_|tDO4rWDI1rR73Pay`sA@JB>78s&ClgsdU#D-oNh;7 zU13cxS%nmJ31Q0WAjG=sF8&0iJ0P(`jQqy z(TI~Z?Xrv1d0XwkI4=Qpz>&r=>c~zKp=qy%EqUu&4^!UBnT1IC+-q&E$grTs6AaqenL|3Ed78HfIw9a_93fn zum9EE{lwd~uKXG24Qvh)*SaL3ZZqi@y@hc~Ik4;{Dg)%dF}&PBOX7%A(qMTU;?uy^ zdjnxD0OsRrY5CIp*9u_PbuBA`^}IosU>QJ_zu{HIW6gmZiDe8ZyKcqb)VLhrmrpO| z!#;~Y^N&MF1USm@DOUpBOcXbUH3$FUoN@*b*gw(|{e)7ZSnso}9rFtOrMq-y@+P(+9zA4i*`0!pr1kL9dvu@z!edN&I%c*VX+vd0B_>o%C88@| z<-!x+N`Q5V)5RL2(DZj1?=BH9?br+fFA=5YsD6GiYfJgnPDj|gs@%pfAhH=rwgWIL zDb+?H`6sMXRawR+E6FARAs`N~qaffOZrB4_3kry;`!uh&c}}{U>7OYkOO+iG5X!Pq zSW3!L#mCDo7GksK9)tiNVR5Kr#aGfBj*Mj5Eips80KNW%=GJ_2F`534LYUg*Z}rp$ zKxa!Q_Z+D%((IUv68)~=l!39Qwzz_ ze+0CG$onvu;y~W4*#Gum&O@ctW@4X+|6FIwQ4JlcwOuRng2$AJs(~%z}iB9cWcjL055_&end+u*3&O_y$SXC z`NPIyoqAWqFh8G%Krpv&p;$`GaQn6f+q(x4QG|V2Z^Ha*Bc8h=Pvx_ZDlY{Yc|0+ydQ8$Qg#&+1pcol&m(o zwj40UH1M&*Mi6-gaeP?+xQ(b28WOQ&pSoi~X6HZr@kQjeqVxjusEuuN+_#0f%EF}A zDgfM=FO9E{C;TYaYHcZT&x7P3z$5)rO@KI3V+pa#Z8t)+bkAd>N3*os;;@~-qHYD( zd?~E&q3ns?B=yEK+(YZYg5*#bD2d+{7u-tXWJcnmrU;!QYH^za`nGpYWCbgdfAk4n zz`B~IUAZ@Y`)~>N?P`GA0uWnxqWC`DtJOGH)*1EzLclGwHZZ=Rzb^=$-v&^syj?%@ zJN%nh?D;bt2V}N%ACY4N6GXSy?E{tZ^#b)s;EGeGJTKmc1hD>^8!9rno@|Rqz#E_t zLAL^zfGFJz(OS+X1o(owvf@ybne>ktNUeRrU!4!;^j=#iy6f~mfJkF9BmPr);y!G;kc*r^ z<$0*;KfIzQH@n*`_=U2~g4$V47k%3$hL~Jc^saJ}B)5Q@k#DLad+>UBXmKPb6J*C= z0hx{6xOFZd+O*;As$s1%Db?x*V7>;iMkqZZ*75&y;@0(qV9HRpl_LSPfWYrK9iAEG zV7$1YS?E$WEtrJTzSnS957;{K za(->xV`okLbGd0ZptY9R`(FD6h;2@ZI}?Hrzox&vGae$-Ja5U(7AU_ne`UCZpr(@G zm!A8#qRdzwpcW6uL^Rm-r9%k7SWLB)8X;UdD!js~wOnY$Bbd{vA>M=y&%6_%RIW5q zMAboG*p{5ASlu8wo`JHNvQTVS%K6nCd!AmO(*f|KXA1~8+p9W5M}(KlH>$d&^(ryz z2b-hB&BKxOvm&T;UF*(bHxKdym_A{1Yl{frX9L1Ay6G;uX%L4HrE3k^^kNnVtlo_wUj2gVL{50A0j=w?cxT`i(=H-cv6GhlbuMTch;*G= z&@ISMqI_&R_igR|oGnsg51EN^8S6;x21J?7P_F;8Wi0GG;U1?$<6TnR@kTl`xop9L z$E#jZfT50^B;c{HdFS1yC!8qO{3^)$^w?!hmTuYX&B|j@RF`rsBYfGEM-re{8eqcD9D=g?wD4+>ndW0dxBwpAc_Z^O?D) z-yy&{%;iF_O1wM}T)H2DkfP+zu$AS!|dv6LAp14%HcIrUabU(@0^H)klR@Q2^xX z6^Cs}(LCY+xu_>@rWTUpVc@r6@gmLzPq&PbzSzYiu~vg6^osu)<>PA|peyqDVeT*D zqM8YW8r8&EhWw6D2ajt{3(v)>bNJ`n|HyKPzWOnAEo>-9^_vI1r179%f@fcQ&Wzd_ zm@tmOK*4ldeG;=}&T5q_k^oE;1#Bl=88(=e;Jt*F;N|$c5B*;Z64Ai^`w>Db#HM9g zn0MW5*ILGEr>mkd(|;c-N?4D+4pcIQ?T?_UQ3OjzBV?GCMPb0+OW{R8&7{B%eCFdn zvZh+XDQ{zpdkv7F1we!^rLf*i`EXOPZays9C{gI|to1+W*S~3%BZS;?)*mm#5SX)y zYgHbA|H|9{!S?;`85zbvStXaW7aog`T*2!HfZUcyQx6F@oO7%U1LDk|KlQ1eXL@Of z{>RQ)vxjYpzg=R0cTaYEv|7s|jYek5bje@T!J0roU8xYRg2|aA1q$6_hcu|+)8&7A z*vB(`Ni){6fpH6F{>BedpD&ZAFgd#z0Rq?3$B7mJR_!sejhFB9goH*%09{bqeH}(8 zu?@@=UZ^o%!o0Z{D!Rezm?S!M)KJ)Pd>%R)Urd^7j-CI*CaKL+;PM+N8yf-Z?5DS2 z6I+axYUD{BP3c4}By}!DD40u95WV#FzLrQvLqeQkT5-x!As+UgxLdCXRnUb>Z496s zi^4%*;=Y-1>vZ{up>^~6_=Rxzmn9d9|9g@%+WFgWb&mOkgK%DrXt)*C{#f49Ay&>L>t%*tv ziH`9~20u0yT`UIk~ps>OWPd8zR$ z1%Z5C^`DIBI4sNjE;8DY=sWZ1WMhvG*UQO$T3&Dut!1grfr}4lk>KzdU**eUn{EmIqs3>F)}X7>UUs#UefgWtVf|^3hjRhzgGFEF z{B7i#cPA4bZ}NWBbMAt~h@EhOkCEY5VvSk`7UAK%VxFBl$Q{TYVzicn&wuLF9coN? zUTk7gsXNLoEsgm^bmyj_tZJJ>NYyz=RuLhIY!RbTf1gvZWyjD0PlDD-`I zn6Cb~UrxVe*&L?1euynm+PBu>EJ-b!|6EV0quqbzyMAa6=aceT-J{EE=<_JF(DKc!67DnBua(nDhUz<(j3qwWHI?h0_4`rs>jCJxfb-Sn^;umH(m^07^f zIfx)ii?v3~etz*e@pQjvE)VB!9EV!=*Ky@tkBcV;ZuPR${UDwrz5?oK|1d2?Tb&z5NW7WVR+a$<8gsJj zSrK>9PFx%~-ckzbx8M1Wv;@7;Nu*fIP!4c+^UYtaqnC&g!I}&Hl3b@^RFA^g&|I+^ zK=8AqP|fXO^}=m!vKb?GxV2I_5YP40U!z(fR{qi`{Q5z zq|3mBl+?6NaT)9^1k@@NpBs75Cq~NV4&Kb^>g$!}P8lp>u9a*-K{4K_2TI;Fe{$q~ zHsZ?~z5=48=>zn>s_etZhqz?$=p8_v@Tvu*t@X@jrG7av4`f%4mdvrwaegKOW=xUCB)>m`*LDF>Y0M9- z=yzRB;A`Rhr~MAWj+BG@cJ)fuVNh7nHuD~RUl?QhgNh_3HyoXt^G%P-I;o2@AzjJl zhbb%|Sjv;UUU0`?Jalr$gda!%69k`1T}8izmyFJfUt(zK7?Jv3#QwX8ODzr;MRd)L z=~0jda=Ubyt&kf41Bj|}QhpSekb%U4EPvP#L-;8G@w;;LHr$W{E5EGtAU`&3bSGJJ zZXXO+TH@x_dOtoIcG{o!HfTIHAXzinW5)meiO$Z}h>At{c1LJG(4FS>oSBgy!auw7 zzlrQBwgpR|*m03%x9ui)F;a979 z!~gZj4j^nsW}BC3mcP+O*nS!8lIuH=`ir3#2e@Nrp3H-bJn6ln;}K9b(O-vMc(!&e~9ZR?#o&FlEH?a$|!Dl_W7 z-EW3s?bZ4F4umJJ#q|5ArI*yWU}Hr~7#U8Yzb9M=fs_ zFn0x7A-LUG=X)L05GQ-uCq>$2cRN_rXxQBeAv9CnN%I1}`jY~@*M2fq%cOP*0kl^r zP*(3iw;F@PbTD(VoboO&G|8QL7q-Lbl)h=$KINw?GhG;^wS5VwzQHm zlik)T{(*@}*dvo+ig!W&zFfL&h9j(mG=mRsw@JGm0mk+?0{SfmldTpm#A5|9Tbl29 z+SD?yuKE`wDQn~exn1a)0AE%2&s~qeErwN_f-Tf-dnW*NS4p8^L$#h;jxeVZpY|&1 za+a_D2M9r=Hcfz$bPst#6>Wn0-=_uZ=5i7{X+xhy*3TG~U;2yqBXg#hlusY zmfsHk0{x-*2&XMMb6KU&XI3a2`{K$%XyxJj;bESBJtM?mbuJ@r+VaLBhsCOSU5fko zxpNIBx5TMyt0SmQ32y}YVJOlUGy_$UL`%QQ1~g}Y^{Zb&gPm^tEZseq zAr4_(XY6#t&hA9#W95ep)bJq&XPbvhO|iD7VHO0ODtOFPwgP+7B`K`E8?hlxeGywje^jbd@A#H^d zb`KfG5VvdKfI)w>uv7cl0@PAZ)tz6Xf$>5V!4T8Wh23goFJX!C+;r%4|LAY+q_JhR zwZlT1elT^IiwtZb0n_JG3%My0p_*d-j2>_yF~wzhh3{(!*b2I(GtOQa!kqI_#LIK z^v#Z|Aiu;P$*NpIOtl@%fIy z<{zCj7^R#~4D_ri6FBzzn`lOozxIMgb<8}>T^ktjyGLVjk9*w<6#E> zI8N|sN$T%sA=ifr6VxKW+^;45a?jiXR}u79T`GP@K#Zgomf-$D*esMru)f z3oIc`?Ux;*VTHJ6vJk_+3F1jIbX~f7TpSgCZX4uC_XbY-#zsF4;@!^_J+4Okgw3}&^-wiP0Y;me>q7L-U&1}e%eIl@r&FbGoPGxmev|}er`-8z zRDx!tTfaVuPzqGZvtazJ9I1WG?5W_c=(2=0LC4MoS^y61YUxU$c)A~LiF z11Z@-sZ#x1gOGQ{$?yk3o8FW&0P(a%}PM1ils&Nl#h~5_vQx)PWd6!Ho-TJ!d9k80H8)g)efrB(< z3jDZx{N-9xunvr`J{$D6cM`x{gv}D&^rbDJk}oqt^WL>jy~X`-6)Wq-fPiO0n*YC%bXR*($Dfo|LDcQ4aa^9K%ipGzhNCeIX!Z7Jnz)hpBcV7tb`wh}q%zwl6bF%k&|UA)(R zeGn9{dCPpeM+My-Va=WqE4#L-{9xohw4g{2avoyt)f>z+*|uhl1ix|tB0@TVZBX~p zt?BUBZQ*O#VXSg^iWRvZ>xXgrfN`-$)toxfJp}Y3c-JxF=nBnK#i=pCukbV3TIE|MXmgA%e<_Gg^cn=iaczSA3(og>mKCws62}%D7E;C*KYjl(s__r)=uu-u zOZwl)&zF~dwzw66S>!SJWvsdxb)ahhptAwzJl<_LRTVtCf`Tpe>?}WXSrwC9nI0T` z|DRKaJ()HxLr^;orDvn4%)1jtEVA*F#+-Vyj zGxSINsbnY)m<9^}b5|n~(aDHW=w21HPL4PH7Q{3l! z8vZN`p%d&~A>bnEzddre9Lo&#aWP4U#_8*csMY~R%A3#cEI$o?&)AwX(&bqcR5_%5+R()$K|E8zs2 z4fm^9IAnQ8*6Upp66}|SE^ou#^SsspICKG&UUfSHO-f$;3 zQ>YcGu4pLD-DRsL-bYOhVwYs@Z9$)P-w^0;2i=oV-RD|fhgp^m0COoJphJ@qDTkxB zpI0AzIo514SiT%J_koqSq*tlA&*=e^C9Dwai~hv^#;lX#@iqc2$)Z{4Ru(6g#2fW@z#NET-CKE!^WANDnDisUT1Klt)#4BFwSy*&od9U1#EWpyA_ zPip*k)K(gVDm1jQec;LR%I`eZx3rV8IXT&wxDz4$H)byk20=5~S8nc%C?0ug@;g|2 z92#u2gLBJmc1mLzRlA4HTlk%`&^X|&8pF4N;6I4}{*(S9qPo-weFN?-h7J(tJ2NZ= zQi3Imlz~m?rwKf~TpT|yAcW^H5=z!Ced*lN$;$Hz!U;(u?dr&A!s6hj*HQ0PRKkI( z3VCKj0`BHKwy;>*V?-^9E0C7eA{Q*^64v!OGE(00s{M5?=K4dGLWSm;RW!p-GfgG2 zs@iiqU?6eXo6uCA3Z7(S3>m75;*da%Gott15Ymip?Wh*B?}1#$xqLxPEOi(pU*&LP zYwRK*O2}{? zM5UyE_Y>5Lc>*YEBR;(kCZ)sJ(hWn1rG9Zhy4ec;Wn468xm-buluaxV+r+%c=sjux zc-4D0zM9aZs}|bNxQ#{i4PTvOmhw`=-ay-0dm$e(=?o6Yua&Kqrtkr&3=}l>>?R7W z+U{YR|Ie>ZnEn>~c=)%YBgf?)xzHb-4mm6E8`UckAXRyk?b(`TmqAqQ{*&m3CRj|S z*VEX0(Co}Lw=X#4V{rD+hhM(AuA7uo;9Cs7+s$j!LUh=Qv^99mmOi0?SWoJHVPmiqgrg zhNQP_qEUZ`(y$2NRghgYEQYGK5D5xMHNV9|!zV~Q5Dii}O z_T{C?iENFZc$O^co%!7Y=AqubZ-naXj)6#DAjv1B!+K_<3pVzi^e)v#;ip))!Qpyw z`EKJ3?z>yP&-6_O3GG;a^*0U$y7E(HYsdwT3abq+i6*j6UQI{wG2hmJTA;R*d%2%B zo9UV40Lwo{`?q;Vcm~H_`ReuGfETB9KU{W-fb@ZUa3q~BivG_;udC}*OH22f-Hi%X z`giftf4$7N(C@>oP}4#;GMos$63v!3^Xxa^3F0<5NmFnNphIJT{~}{HNNr8-KtLGB zAVn7+f^GQ;lMRrx?w)d-vL2HenSJK4uAoliw)7S<#Zx|10oo{TImF@{k4qu>ua8UnKMhi)4N0d zpyNhZ|KTGk%Wx>uagCv)_8H+QD|01-R(G4wSODwql2=SG`%b|fE~!-`4F#C+KIN>n z^y{q{%`TBSN`TSnRYcr=zazKhJjN;nZ_So#K-^&F^PyPRl{`*cn-YdTk9@Zy9U?{V z$Ze{8ET)=LH$7plkLQ1Ckz&IwhH{L$gKNlN95X*7-s_E&IDLVE2h%SL18^E8jO(1R z@%(Mf_{iS;1c&KV^O7XR>2=5tQRY`g6mK%5D*YA+Z}l0NwhJ1R@AAmt8SyWeJWh&h zH{$&)-X;Os#KVdumJpGhC)>A5oT5iYPt_@7LPA$1vH|Pq#=)N;@dMpy4GHAl@`#K<2SzSl&B%s)Of5 zs)sGiYU_Y5Zy7!VP`@FMMY+tx1y3>UU54WYYBlMY4q+D)MK3Eq_c~U!#R=T8Fzu@xKh| zkNbzbJSeK!#)D`}2hs-R+3?R8t;*BtJV}C8eP@Q#sE& z&Vf2GkD<0}+J{^xzefIrP%h>ifOydixHB%vVw^6*bvb7uVB4H}i)munu16VQC0UFI z#o?=+kpU*dU;t_|+dRQ@>;1_QxiMJAtaZV@oqBgbr7guRb2&)1q{HFSV34%gOJ|`z}}*MZ0T~?dPbtK zlur-UG9Q}LYqrjL02V&Lji9GpMuHAbtO&p1SXLk83R52aV-XZp1D$U`ui_zhauzKp zW&`RBnB~g(eYc~q(XK~9W(nmTdg+(D1x&^spO#r8n_{9=HapCddqE9G-}|;wQC?Pk zFHgTN)>ONwumKFwlMsLG9>UHu3U#)G(eP+D*}^mXfW_^#6P}>JN9sNbT%Gx5 zFrq-F;ju}uWuH6W$W_f!k0|;#TDHc$o`J*^X+gKNYu*W)df9%ezITFf) z4U0XxP76w?Oo1dIOT3e7KuS+x4g11{k!JIpWJi!=MC6>kSWzU2VvV5urR_^UZ9h?L z@>Gq!{0T0`NCu67c&v7lJ!5Uf5l;=DiQRZ&NLHj9b44bSnYpH0qSyD|D!t9$r!p+x z7Vf-Dwuhc{uuofKGp&+k!TF2#IL9<_=+RrM13cWij(WxuX{Dzo1irAp*8YZq< z0mmB9-QNUqtrC-Kr)~4XDY%+e=uHrIQG^JleL}jeH5*h;s!O^uRXwkW6scapkp@b- zS;@pQF&=GPt>}Y(Y=mPw+nd@8M&&M=H951n`_ugaWC`7TQ1w>Tmxj0~Wc8!`DCJ3s z1o5MhI`)LCZ@!0SGtfRbiV`X`Q^r|H!SiRTZ)rG215ft9(yyw&B!I$z^m36rqDfC* zX#DdIST|}=w7tHz$WNY()}E1aVxO*aPw59Ezhy%Y&%bdFr$5?3JU|`DFZpM(R~Fqr zWEH~-Won_Z?efv(a>`D>hm{e~vy%oZmkVBk^P^=6fF6mycEZq>qOSzApV3dC{?22_C(xA79$# z{^Hwl{9$6KOLZkmFI22TRhL>3mAC0tamKGWcEFOv?q|Uk&ikJpS zRRxaE9oHmNJ}AC-d^DisxB&)!^pLt-DKL* zmz+_a39)SUFQf`eq;t>$urBEBC*sNetql_s;1ydd#cV`KK z?4f|{4L#Y|%vV^E8>Cl(?h|zGj;8$+!d-4NKOC2%rRmdyz=oUU>P8@x`f7b@ttiT< zNng|FQ)B{)RR!tZ*VKCbzlVciZD0pH);o7RH(>&!0n%RR{1+BIg%sjTaN#>ImbW~ zrCef{wc&Di!9i=!Te-^$FrnNNq;%S;K(M8#;7)^ zVgtE55ly6J)=JXk;fBs)BYS{K7Huw?NkUL8{pCp8Sz5V@nTgcxe?wgRx9?#Zv;&LS zc@aaDS?1d^ykypPX6-y$G)4pBN~dIxZ;!W0q(rXNS@ z?R^C>&ALRXzn-so{+Hd=3FG%RslL4c;46PZ3VrMHX~*pJFlJm;@n+vSu%G`Ee>rhK zC$u({yA089?JzN0-BG8c1N(Bqoy#9e*ST`QAEOno`N1RnaE@hX~vw@q|R!8k^_ zsUsjD)!=|#>0)JZBH~L;)xzAExHbCp5CQ(=wBoGIls?ZlXn?iAu?)tdCZ>?Lvw1`I zXA{*xsj z+_Swp5Drs=QIOuXbw2vgj9r+VK8C*BB zIHRUR&@~7Yn95WDR-^F^fuVVDSfht3rJy)NCQ&7nATa)jwX605&Zj5#I>Z3bh7Goa zi0kyiw3bn2e%u;@U?Wq9S~AZ=rD&1`fQyQ<y^SO+lK+6Aw}jlJ*6UFBx}#(wv#xoBv& zx58RN=Wn9ZZyY_U*{++M1zEXyD?TaRt0=A!MW{m@0T9{Z zBSK#!yn6sfdJfCgf7k44RZwO7ORn+nIKW@vLRtjDOotWm-e0Vz*f2mv&z&d5-eISeN?^Ba-P6?=HzL-K84NCg0s719{E-6)}5JtFU9V#iuklrK_gravL=0uqsO zCnR}YY+-v6UAzOo!48lb?O@>VWR39_H16Bm@nb@P%oz~G9nP@3LLwE&%tM6}spO^l z(r!givWX~Oggk9q3*g7iq&3|xgnylGKZO+nNaP2DofCuU^-r-Fa~V`7>Gc)5#VR$K z1*LIiE~*9u8I- z65m$-2D(y@pFETDj>@UWNM+aIO_|$Wj`N=;ehG%72|^B%gorY}aV^trBh&7!~lr(=DQ9>Y#S3BZ=z9>%m-{@xtBv+`Jhaq8x!eUOZy9*cy zV~gQ);FV7I4VD8!J-uzg4Wp{$(YU5mYs#NPL@@ffy%ii@bNlT$ znCRLhguxmO3*%O*W3kT_tbqO|hgEd<1CUV$$d-(5WEAoji=yd7g5$_EYoRHI>rGFv zHo2C?QnyXgA4Jf74JNJ1R=XTS6+FRnpZA}( z&fd*7OjBO~7RUTgkWpSn2wLLGNCvkHG7}I7jhZ67Cn)3upj;>yk;%rjIYN7wR=Y9* z&7M7;8}v}T_)yhZx7mN7GlpT7uF7c162g{@beC?Idvpc-^C4M9Sy2xpX#7RwH`T|a zfoSSuNUR#0K0-uio<*c1=nmNXoCQ+^MIfD$+pe_SlCQgg`Nmwr%KbeJsfa9Ec_u$k zBDMn=Yr)3jxDgt@4u4wL%QfL)_CmwJ!-LMNH!7VOTR|siR=!mq(Kqep9>=ML;Q$V! zIF;#M7#m$Lk}AKmsd^)@L7yu&XS&5cF6exouAEfW!uNGFdzHSOC=e8xB18aN?Z>aD zp+x%NssG!6xgGE@HamoAk2!EVwRoK4y#O{=jcfeO(cZ~9*>@s(pFyqq*=o{ZR>HJ4 z84^cA+qZEaU#J_STcU;_(#9M77o2CP~tp`!@$#^VC}atO*B;| zPY=}m&?v+u;O6P4gX#SD9`V~>2m%WO|EZ`p>330J8gCk-{5q7yd(C5viLfx%TvMQO ziR>~kCJ%~qt>P~dg@#NTLxIyj@h>0{r+v0f85I{?P1f+ zU0SDS&xZ>=dnOsFAUx-Z*L_T+a2E741PJ(cI0X^T@|f@ErPe0mZd*Z)TQ7Y9-iVB+ zPe98M+sKx-Pdx69k_)`?X~i;&df6XPJ4U$qaU*AH(Y(gHOT&<>}S9pEqlCF*^r8q)Sx z42e1(FcE_(w$ppF;^hHSl{qRN^&UxdVjaK4UI$cv6F?Mro~mgMejM6*blebovYWA8 zb>fZ*1#wcdP1f1rhthf=!I0mqfv#Kk)zuV3|3}kVMa8v6Su_NKOK^t( z0fM_*aCdhP?iSo3IKkcB9fG^NySr53E`4A3=+FACaqHAQd#^QTQx!YoeQB$O49&=Y z=%REu|LpgvnL3RAB7=>kwUGm0nb%E|ws4lR3Gn%k1phc$S#X_o-w44P3q z1Lw2trG`88n!fUiGYOWEHdk!~6sW3X@>|=c6u3wFmy@Ehs8~TP`apL~HGc?AMci%L zRp;(-59fD%OX!*K+uD4rAMC2fVm_{)EL)Ps)pyom*r&wkJ77`mb%-zWh1^o4zM4G= zuVZ5l8#EFtC484pa z?OKk`ivVm-jL&rR%Dh4xfGWmgSsfQOq_An>aldDKc>`KD(VdqeZCJKjok-~%b|I8m zzdu{s*yNgyB^ekRLQGChUhaev_fDigb3~Pwm2Dp+>qFz?$!ia(qpSM zni>ejf*jCH{DoqC{(t#zy_U!*oqLp~*&1VK%`4H-bqeJKVk&OiSjSG6HMQ-@N0>UY z$kwk2GEg0qE?-<->rX!4kEhT+XPuGS2cZ1(0tTv4SrJlxqQ7gjo&x$A5|4s6mwL1= zT`^X!yVWl{C&YaG5 zO2Kw~Ee~JcU%|i35nG^^doML1_J7~GIjn0H5s^MhSNlTriqDBM{_^*_-F19?63})( zrVGvYZ*v3n$rIc9KV<8|FICTmVEIIMYQON_5}IUqLx{Wkc)JnS##sQPh1a(>O_RJT zlKTC=r~U zGkCP-4rfVznp)vLh@%lmY)V;slp?s{{{iTDCg8iKP%q`ThbbyZTK5AoGm_92-`K2cwCMuICyW4d#Ep z(z002|A0kA49XXe>3sorp=4!c+3y6n3fLq`C)4{UCu4hkygf`eSSaJL{6cK7S;Ojl z0r6bk--pns%M=s1+@6bjRXAL!Y8EL8ry$UZtqX^w3=D0gE9PTUB#+k7&WM5q)usa# zC(W3Hi}``q=r{~_M@S7APb5EN-8FQC$c)T+^;%$=H~VWhYr%pWTy6!PWb8Nl#6n}q6S>9F`+TatKV8x=glj4YKUQ23pN zr>;ZYJ&eP$AB^JT&_}sB0CAO(OeaRce_dDiM|;D@6h9 zh~0d>1~Y+FkBjwk^x8ibvlVcE`fAL5$#!u#p{g><1GYZ?wGTe!5JthWuZVgXt&@OI zEIrX#d(;(rivfn@?{LffPg7sK&qlz}$*ThfpQzg$P`fKS$^*IFYY+^Icg!xt2P5g3zo4vVE*8%bG6pLZA($Mg%aOIA-j9rc+xNs;hk zEmm|;H}Uh9UBxJ$ixYhYuQS$ZcuA`NwDJX6L|H~5u}#3>&og$qodvsJv3tgaW(EgK zJb?9qvk{$XP*$Ps0pD8G$F+Tzx1IUPOylK6Zv{UEXsSbPeVk!Y^pykiv3T(k94G;X z63Zb!6}dM^IxOGc#+Y10yEDy-8Y2ca&5wb1`#7Rd@A;>Pe7^xS^&edk{INZKTx8jF zs-rhA;ALELFFi9T0YQrvWJJL)&c`8B?1+~>na9Bo9c^^e#0@h$wzKSG*}=SPLCw#V zp-%mYqZDIRQi5&L8Pf}N(!0n|j2J%HF>k?|u%$vxL z{JU7aIE%?0d9m&+Rg3+r#)tN|o{LO`td(f7YVNRfRt?S@8&uNUzFc+$8C)3Hlaq8o=9M(C=>Z z{WNb)j}JgxZL_}zE&ZVI1L~D~K&QWv(evI~qs9ON5mzX2*|a1$?TUT2K}mJqfH#*2hu^v37r<`)wtc- z(_N$g3vo4lsf?~TV>;$W*0j*#A zI;|So4}0`K%7qb#qg_4jEk*=bd!6DgTM?5aRX~ji)DltDQ1#XEc0`YHuF&G)Bvv5Ggq!xP$^l z0gyYfOiBItr-~U+Y2v?QE+RJV)f~1YI)S115HrB!*zFngE<3+5_5inY`$zO;@hcOV z0zd`V#d-0}E6}X6KsS7;R$&999ex7X>2bylUZ{fjqk4fJKR2t4TAZKZOW!UP!;XTD zisnM67R>9K&1H*!Jug3CRT$Q2R*l?b-nn;q-c+4FSEls=px6Dab1xXU=J=XO+n-P} zmuS5j!AIe!CpDjmWRo(1iCVBrE``Pwn%rYu_{*xqRvgWh-cs{P#FK7bhdfYf-X@^E zV8Riy{cN-CiRhv-Pc7*v;924qm8r#n9gN%Hw$poI0z6HSxjX94E>J+VlvvR9*}>p9SOBm z4Pn<#?b)1Hqi||7l=9&MEIKk+`Z$jX=#+NGP>{JIp5Ik4F+qQcvpCag3k0iWh2NHH zyNT`)RpD}H^_HOjbQ<*VJ`pqR|9%K(4eZlP{05{Z5uFS9<0Cfp?dsrQomjw`Oa_I> zhy9{A$qaO4#`PJ`;x||m+b7LN+wr3EKaW7v<#bSiB-!{keEoK@A04ARtR_K4YiYE< zUFVw>GGyt#Tsrw@$2?V|gdR4srgOT;k2ZNNHwQ0jI&Bhs>A-pE4S}z6!}>@sTiX!2 z=n?fF%p=Kh^qP}zIQeh&-a{= zQ+Jug;lX96DQ6E_`?W~T=~B*+T6??BxK!>wb|~h%=;$^F_e-9FMn={cF?qZ~vXwYw4 zY(O;zxKMWrNuxuQU zS>C%_=p$F}x5BZN3O^il10(EIFvGLQ=EUi{XXTq0?<^dg{roir?0j&A#HNuJd742z z&0uRkw=5mn5+AONLHN$DkGp=-$DP0l`_PqdhD1YqlbJbo!UP3#nlYUtlY$HcN|w za|9XcSX%a&6(U26$Uo2+2`g8fRG>uOV7io6=79J`j0gKKXi;}3YN#+o!u31Be0!;2sWZ^%ZJBA_c+}{`z6|&Uh&+$-w4t`(l^2HFv#MTDu0M zfAjYLyLgVTh}@zfNca>#=3N|igUWW+%99WMP_B-KV$uhDg%Ky}oa#%JwQ*gcE`GWe zlKK~=9Z&xwT=uw)FIi9Yv-!OTOI${HJ5Znh+|Jg)Fk!fP2~z9rwFhwm&yxTpWDh@-SoZpzvHJC=f-H*%1GlnPko z^e)Sa!lH6nPKLon;5`iWh27tm{Y%GA7|q``VMoen6mkzIm)Yo^4TSTrCd33rucl_Z zYbUzE{=3fH$Wx`+4K`cw?@tF9k4MSzxw1TEu;cPPz*EA+IjiDMLLiq}y1MkMuRgyk z{`YE=#TLBYhZkD=)1AtWQ%~tu^^V(zhGYet!_E7GomtvAd~b$}NDqH5)GcQmb4MUBRx+g~M|*5-Bh z)hA7S@*XyP-G=eJSHFFm=ndaj*s!(r{BdtbXiKq_$Opt-c8(FUS=c@`ct7pM2tc{K zh!cB%KjY@GD+@netYpJB!on&Oh~=;ywjx+vv)b(8cie})1#RHd12kAZrs-N9i-x13 zB;ozEr^#px!?z%C9i8HQdkP{vUo83HngR{Tz5&TU_CU&kW6gG7VJ z6&;?o0j1~HLuX~`Hd@(7N`-U~A%OzNjS{=5;CRIK=ep;XyRW<$GxDfYB!&xmm-ywn zi92|vD5aLwj~UBCDUZ?jjK%kSYd--~u05a$%e|D-N*5C2N-PT1Rj+Y4{xY;6Rg#!( z<5%2SM3U4vv$ck|>SloeSQEd_^Jx1<1RBbRjJC?J`E~AJ6-MVkh~p>@6waah5jF(tY5w4V-{NypbeUHDYQ!SrA^h=^o=c~#$kWMmodz{F zxdcz2PtOCd>9rFffZ|v{O`A9@>j)C}O4R;&Sajzx0z0{v55aQei`eS4I@3Yepu?4A^L zHexk${dBG?oW7Rn;rg{MQzZs7{k|+ao5v&TRc_nFZdjWByHU=^%i0IrB3l{c`E5D< zrb1{bDQHG6c5J!fceJzr?)V$p-9M-OuRCv7+nd4wNG1_XM0RY9cEJyLE{Y5 zJJ#{xU2PZluXoU55Ym#@k=Sy^|C#SZtPYaYE09 zSnBcWGM)r_Vh{@RYPw=g5~{L2I{PC)Pwm|DCD&>;A4Hrfz?c`w#dUeV(I=SA*7C}3 z=k>4!l%?FHG7=S{dQoQQr6ZM|)or(Wuqxr>Wc1s9{$44OTuDFlU~ueQmL#c@Bo&Z- zTX@uMyG>82jBC!i!a`r%CSLfA zlmIK$5CC~D6NpNl)CoSw#%!qsl7Nzu+{R$8zU4ZbnkBt)+EV%9Qmayk{jmQ(GymOt6T6#szu%D;k+ z2f3`XagA=X8X|w^U$scB-QM~*ZvBy%B|VH`Cs>mE-2uU3;)s2osP*n7@4@DK^<Ni#C3&Dq=xNB<;d3dBHMyX8oM1eRCtV^ay@OZ-U5j8{X7@(TO z#5<8lkH*@@u%y&n%Sn`pfuMego1GWle@|<+geL0C_#Wf#Yx(b6KPrdkddO^}%-sa+ zt9-v(Bq(+*A6OQSu2`9nLFn6gk8H_OLamcVHv9bZd7@CknC^m^BpHSG5urxO@K0A) zZIaX=Uh{nT;^cU;?$TnPCa1mJHm3(fO?T$qT6%R++mj4uu@fc?j)4ys@x<0eqiZaONo<7R)(e9k1u71Zh45oiK z=FJ7?@YgtgP+YSpjt5NqUjOm4A5Pf>NR@M{dPKka9Dnycwp~EF#${4|Z20B~A`hD{ z8>AeB^tk)G+HiQ0vvd>b=+6(yrj2{CEX!eZNkG8s0^79tScDbvud}bb*7wztpjkU~~)1OfT z&!QQH1XCYkH;%!fHx2~5o~}5#Rp3RzaN}sc@qeryTw1d z?BE^#7DeWbwQ9=e4Q6{or9Ak1!ww`cJ9|2I{rp*biB78k8*ZY)$z!cV$8oa?kvN zVyDy3uKfwC)=`CQu?JKoIjT0XJ}Z9kU#Lsg6K<_NcKU#aQjw}w-dpM|1WC`$PC-Zf zsfd}Cc;fYMyC*a?PuQoT{5yr_K;*=BGh1V?>d6S73psrkVZ%CM<9Owfuk!AhODjUfAu3ZN~GXykxE#H2UbVXf7Cv8ar z=snnuvCB2{HKhDvCwNvxtY4oT{pYgRW=i;PZ$)5 z678!~M0^0X3l66k-eVxdTaR~Q4?b;MyRTZmVM?U8cxFf^GXbf=pMVue*qcMw%NZ}( zIgYHT%B1h{@dyb}?y0V(jt|0B$;uvBWi=;Fu?`-!3GTN?lKE6F{-M!~iXie=OS=mO13%EJPMGvKZHp_m;Q|_7&bvpxgJE2&e zPcO&Bj)hkyVk}N>VD<0YwcbZfjQyb@S-%EPkx3VVz!GI8o7rmgJdfy!hn@N6^(l8g z6-X*}d&kAc*qTX|8>7WQ)R@Ft!UX8+oily`a%;n8r+|V{A&rWm!g0R-FEmU}v`4fa z2W8dQNj0g3K*ydNq7QggH?Uq7yy|IibX=q-NjMbi&agF*XAe=umhtv3h^6#fZ+FF4 z^KFF0Lug@o8jNuY%CXo7*dRi4x+e-c(I;?`jzL?l?a+Oc0?#ymbmPgNv{{V8n=gF^AJ|Z$tTYJte&Z$iG-VRNM7f4eJ4E$KK|kHZ zPxR|e0yjCFkATB(@=mdxY$$R`<+Q?=PRvhtis765awZg21*5kP3@9AOH!Z=a;W>;& zPBGD1z8~wg2(eQ^CSn4)Tpv`fJGPEo_U+BzHJl%=z2l%c!u0@vSCF`68=KdKR)Z}B z<(S;u3!fAqHoP_LnV$2t^AJusZwCy5lPU@Wt;1|$HQzj1_tFYn){=S~*k=E8HNxT` z;%b{8Q$aW1H(f}c8;OkJtK55vwn>1$>S*sax;<$gor-S2O1hM`1+DA-P3Xhd|j7iRLBrepLn`Q%=*z*n*zBru@TIa^HV*=_4 zXfrXw22U;|mf4W4A1H#A(FQX_Z3>tM)G#Vn4^0<>@U1poN~TGjL2_4=)#UVIO-$4b zg~xyyLu3kxWXK9|OJJKxiTaJZXX8KA-4mF9iS`#} zWk0xaodLf0Y&mf~UKiZ(l(22 z2jV`Q*&dw{2FkjKzX!zr{W)U99+odihv9Syr~YW5Qz--qq}fWC+A+lHG^^s5CLRJ? zy|_lfvBk74-xqchsdJNxXN%^`QhMuNm%tX+SOhtYeTwO&em5vxVLT~GEv^wKAtvN1 zH(5bLOI-K2RY1`6Mlfzg=~Q=3s-yUFO%|Gpq8hFbUn?~i1NM7%hg9%pnTQrR)KBsK{hSA+QWSG%Gl zk&RYMRR)&N$V~z47g}**V1M2|TE)m7wmsVFzU8OPoLy zWm;7XqR@z975=}^e)x==GJE_^ZJ&kN*axWQiQyL=|&YWfN!tZ9nl zcG|JuBAclvg4D%hkqUqY%FnzoeRSPOcXpx6D3O|{E?}gR%Y@28DUo-mdAaGq=_I4q z%~br()V~1TssHeq_sWB{!CVykJ@g?P%bJJZ)0<6)xA@f=J~Tgq2D_>+`9p`M9fbyY zmQi4QJ@w(i@bc{$JLrL-*zo%3Xl+M>%3D|e?N?F%ul(Hl62V`<}vH z$fLHX;Ey&8GXJn;4IdxnK_xbKS{qj&zHn<MQbN~w;1JN zidyc+q}t!|xCKMIb_Q8Qci;JT zuYJ7LC1AZ=BWOMH8ub=19hO`R7w25m1@Dr=%j7eF*mN zLaBy`OR;*c(fAK3SklDTOXCu|sc;z7Qpe%aYz1ZxP=>6I?)PJl*v=XSCL=wWcq3T# zxnZb=1FaLJV&s{DY>G5CJF$5N*s~*Zc>8lzm9Cr)WDkW=!^c;9NpxiVF!LG0NVSxk!J#+-d(;LYQ^p$f*8O8*Ux=e<8UtgQv*kHqxq7A?sdQkGnAMk4L z84^NBTm;IFM~i$K?|Zf>9Ou#!>(rS^2>2(TyTGqJG8x3y3c@kU0zag9pzwWi=iJDS zzNBv?^(0_WB*VYYliu*BeEUOk8m^jcLz2vwpJCt^Q3tWord|NcMij6}LuT?=r4EDN z-QCA;Z;G=)DCWrI~JBDJyW`soZ!zNo24*Hx7xMS!$p7D8yx`JXX zMMvO|Wlx4>t2nUs5fXUw#UR9e4i#iamvj@G>aJ`s$>)~c5s&*fUOOeQCIl$x(9NYH z!N%Ah#Hg!Z`e=N(D!*vFXh%g!Q%$%2CmT*Uc|oc07PjoD_N^HyP3q?Kngg2J}Y}4@7xA)mvHKN`rHSLT*KqA4Z56;iTZk?D_)yhKCowEibq+-hH23j?Y(d z@0~64MyLUmT?&cpvX?av!DdPzJR56L?#zx{Z$>`n`Q&?Sk7*o;ywgo3eG%8uOdP!z zxF7A3ulM-MyQ%M}YvbKIV?-XL=wkCC`?6N{=daxZS#C$Nyl!C7gi56`e8AA-Po6{X z#@jtv)zP!|R8iyKcQ16z&0PyzQ_M70l8 zNaa3XG_QuIL-?Vu+b{52`oQOqMg(M7Fwb9P*UIhUMiv7Q3Rx~Lil}$hKlSTRK3*>5 zs=jLtK~%)EvZqIc8FISUR}_xx6Xoc?@BeyO;l~S04%q)V`N2i>bpNSCV6*bZT*Go% z#J4cF@U;6^+p%f1i}|#MKorIm&MMU^PF>FS{vr9mDkjGXBe5h@`eFqG&tm7}?|Wvo z$j1(w8*?IgC#$sPfz_Va54*WYjXD#t1F6@80jC@RX*+~KEhZkx{>osN?MXZWWmWIp>;wAle7sySKBSlLq1kjP9LykEa^Fmd z%h_TNaDOmdsWS~oyzzs8gu;%PE0*so&h~+~wY5E{$o>l)kAxKRS%|^G!CwgoXjwOb zJ4esfmcIEmO?_Mzr$gQ>gQutGj@hLT*8=3GeYM{d|BK9zxCQV3{;}6x?6GBVmsP|? zU;a5AL4jRgADXv(0tchUu;oJfSJFeGGpIWeo23&W2m6Ds&L_hQvjU!rY_O#Tab-Z| zGJ+*^MB{m+44@h@`MrcJ4)*X_&h^M{9b!M>F~V4-m!bPuO9*wQa-+pmCq)m zxKotfz`%^aebvQI^F(!b21AISrgG~a@hOGdnJlht4vA=fokI4%oJGP46oZ&OV}U-{ zoBHI1Qkn&d@z0l|l#pN6@@*r(`QN;DF8a2&5!vc>%J#Kj%LH)WAsW9K!0)mbBM4#r zP3|NxoMqf{*-~`zMCqcR-dIwrZjQN+!j7h*NV#&TW6Rd5(OY8@kV5kc4+n>LYjIi$ zVBRC96t084j{-W-?CvP&N@ZGUhi zMtAYbU_bgJ1D>ksXdXVMa7d=TDdKzS#!=&N^qs*Fw)RcOp9v)S#5%Fr3$bh6L=W2j z$d^tamgksdr-xe`EyHQ3HK?G@p0!xKzVpYq5`dUby;NGx=H!ZmAK0hYM;QY;GN8(A z!P67)Y0O|sWBam4`1LMfVSqdf_yoi`EEg-_fJZNsOgtJ^Kb_qsprqtSPiF7P2wZqX zMDQWEw2VyTbUBbk!rebQLO^nDmoqZTn{F5qd%ak@wIjdY^aR6o{4~|SJEbZr8j2yF z(x0>ha&+p-TFrLI6I#@-|71;R_V6U|0ggj)@ONinzk$D2jKGmcO#EQG*(8MXHGnMH z{Yt!{NeNXlY8(q*ohv7lhnkP|%JczO$a|fEsEZaiUgd7x&@T@5?93c?x^H$hx~$Oc zgOKdZ!<8sA=#uPjiKz;Cesq&-W$>=P$(o%?M?us=tf$>!lh4w76SdKN8>GaHZ`C8( zcW?su!w&ZHVJvLrr|^Kbsxoxf>m97QVyi>w3ij5N7IF;HkI&_tQyeW882sx{gf=0+N_2jh0J(e_1{oth_hp zZkfyPybZg*hpl8F!;qEKx%9TZ^%RA&sbJQCbf3s4Y_9P4;av-{z7^$g?gfeLGd_3t zdvwKAc7YQ$ghmx5lF6Oyh;+Dfzs7GjIpvpoel*_r$NvZZ_n0BFo@8)yE zW!*1w%21^sJsB(O28%FFSsPpKHs^2DUQI(f@wLK|QNuy~bfq;1N3ho^-xEN}9JYqg zDAJXt)5tIvEZ{AlZv=Y@)ek>8mZu))+?=gp*rUVKh=lc+&Q*VR>UsG;wnh|yCktJf zWlzg*#l?Auykbf$*J<_^6W8oHgKa-QBL7_~Fq2#nJ#z~6tLhaAtTH*zKhto7EM5DX z5o8qR+eUR^!tGKY@NCIm#7W?i)9W>pCu(43rcQ6=&=c^b*L2)kgc2Tg=K3i=h98YK zN3^}Y-QaLMRjA(kWVWy_>veA^2B8Ye+?2(O6SLf*jJ>_Ay2zk@awMW`-GRj1+S;SR zfAA0~cUZ&ej7sFg*z z5*~d>;G&&J@^ivGYIjMgJZ1)_%8w8){rhymKJf>-n8Xf<8BeKi|01&z9IHbSvxHTp zPTHKdd#|o>5$o(C6nRa>4iJ2vo(ESKl@wP0SU(yTOJcS>w$_fauR|?&4L0~!g}@qQ zy$p$19yuLC)4rP5m&!-vi=T2-kU^EvM|mI!g|tW{Sc|$JZEu7{>}10E-VE}HlOdaC zg}71BhM*MD2k&}udk2H7w*@C_Ou*WVCwIV2szk`3<-Qk%UD7ckq_g7RlAGzTBYh_O zi&xpa^~~23#`r80xtFlze`|j6L;+Nqa@bj+Iz_nx&T_<+wq{2G-urb+pVVv2m-2eE zhYp?R9Zx$p?SB1HN_6>-z90S>10(NGe}&w4*%J!7iJ+Gb_kU1IxVh2lzUn=cdIgFs z1J8)4V`Cp3mjaGF!xvU=J7cI_-L2KlYL7UyJX#WIlNEMe+vfT`RTOC!_o4DdcdzfN zx3xUbF7YMzsMe1W278@ivY4*SJf%v63vW0o<;d;P8rsd)O9b3p7DY9KXGM(eDw}UE z&GCZ1Z@u$-`^&m0;qIo}cu+u|?f-vFf9GKZQ|0&IvHALwL zYYYzr_WHr1A|k`BF>F?gupjr}E#mRy&`mGvPts}? zJ6`LG-ZalBM#^ominPa{@jcS;QXxOyU$WJD;sGPya}IP9vj%HKFbDKY#<;Sb(+*Dp zyX787QHd()-XE%RBKg>zwKXp;4Hyf&=dfkmI>Lm1>WF+% zG^%A}9gyonT{lXG_Slhl57-QE+vd}6MK(1nouF<-hlm!xvZmQiRy_X-jg?kNta)hg z|68rdrga;_Dq%7&olz!uqZYL@Q61Wo&7%kT;75OuR=oUsh@pLarzDT@1<{Mqw6sQ2 zIfdN2y5-p=LG-oy&i0#4eL*=9a`F~qO@_a1z$mMBiDMXPzM0RvHdcTN|_)R zM7=jc7L3ao^j%WnTY`1$-H6O~lsE+OI2DmR77KC;^WY35BlwmoX` z+J~&bzfi-tPVW+PZNvm%hylCmxD=Nl@VAKz6Z`l>_`OEZLUI*uBRM4qRtj-c<$Yvw{E0K za6t7Z@jw$Kkio8`Og68*bS%_Ift0iKL$o%^`rSRb;^9lkkJOQI*@%Km|wHhn&qi=kpo0II@~|p1HF1zhT5k6zSwGzgMq$?x-rMBi-v! zmYLs2yQXwe)TLE@GRj2TCb2G`dc!XJ%1);HbX!YAJ#noIjDVr{Ny>=t17{+n7qJNi zQE>ARUBJIREJ52;1rL>zw{}|!ucrqSV7{L?G8u2oxi}SpW{oDQ_h!h-+H9#*n04K_ zbpZ@&Hb?I!PJ564y~%p;SGcuB8Ef04ELERkSBe@f&4k=1@) zO$t0n29cXW3X5htj60%<@}8Sm+FIv+r)_+Y7Q8$^VOegB-LwUl3ZjjrHtDW)zs;^0 zbNSvX1s_?OD!<#MWvWxQc!g`3Z*&P?2-{?=3Y#TIe{J^XZ_a|$32WsyHNz?$IAbeA zm^3|Cu33p*6ob`jtYJ^5b+g+D^%}gN^Zb^HtQKrfIM_#k4@&Em<2N>LPb~!b!Z4>& znJ0hq4AhK0V2&XipcoZ$LK>;MGM4Qk3Wb1z)Hw(cNp}1So zi<%IDKK^{KZ9i=-j;*e%tZN=-cne18))g;B`xRvov3f~k8p}&zt(hYPg-bNDUk`@i ziLtQ?s|=$H9I|S-Fm;5e??3f3O%#G@TutTovqG$n?0!bDhj>;adgn8oYv*1%i7fbl z{Drp(DmII2wj#eWmxT|ot!|Yl?I)0%R}j;>+hyjY2-=Ob+>HpOB(g(A-TPx^Tcf2- zKBj@5odjfFWj^$5j5i5kPdqWw#YJ%`zj1&VIbo=0r(_|ZIgs4RG1y<*wK)evJi(HD zJvt~6k)xln-ofu&xO#B!YQL*_TV5m-SbVxJTtFQq5qE3A-S@XK;RAMs4roD$t zg!Eznn;Yo++pP|6tdxJ-S$noqXu07#ksM}X^Y?l;3z$891gd6-zEUlWqHMprE+um3 zOIQ>V=Qe7G_eay^j>uK7NT0T@oCY>bvuGW^?-a|9w{6er&BpmU4SD@VRBO{o2<*`h zOpA;-US%NOh}jtFx+LfcwW>ccT2(c?KVQXv!{N&Bucv-fZQhg%=#V~t{@&uEnaeE> z5iQK%squ6o7KYYE%VxRY=PQ*BY3)OUpvaf1?=#xusw=t&f3iJ|HA{A)Vle!e)q4|= zlu|JeDRKTBh*qnxQp8ZM^>BVhJECvc^MbMPYF;|GP|i{UdEv5rRC8Q&d#jTwvDSfn7a^toZxA_Ki zfs9i~^%iv}0v!2(42I+>olqys1U$zpL+^PvQ;S09 zinSc%7G_zyravm;Q#*PE=3QQ=W#2I*?p%1u$>L8j?r>&J7Q(32XL00(gr_?_wGGh& zRR4`ZAQ;gei;`lGsQ+8CQ<1N!M-{w_X)0ULa#W$o(Fj7n>*k=Ry> zdnHC}4#0zkBlk+mJ2#P4qtt9B4DXxCMy_@u<7=)s%CLZFL5*g(cFm5S<=3lu+%nm4{;iVSah5$`O2U5xo&6$HfM2g*0@F(SV~r0sIa-x ze8#*sQuwj>N+Q!y3cr|4NN3H>Eg|3*TW(e@Y&%X;_A^&YG(fnn8HNH>KG;GNb#Nt*E<(ZCBvZS_C4qu3{!f)m=~@0#6ameEcR3(+`} zWCIsFo~enF=`(0K#Vg$GH$vgvgcTg$=m{8dzz}AG?+Y3=d~o6vPSJfc90LOb0RsqC zPrk2L;=2b`^1$;0oz70>s5=-#T>Y?iq+$ywGSd2>i;eJF*opRAcW29K{$<#7hwzkH z+ob%WEnC0y$KKnE*7Gh-z$kOPG#tdNosNIeO3`Ku2E8}ngs;WYj=d0K-VJwXKadM5 zjCS`<))VXY7l`65szKx4W8M82%6r)j8@m=#FAj~}J6288>_hyW3f+`7%tuxd(NMS* zY0xt&;T1hoH25{r-R|o@9s3#_WO7A28D&3&qAM`U_n{dpz!oi}N@kzJb8Cd z#=6O+N_>kp08L%Cx$#w@6Kt0!?ooOK4YKWY-%AaF;J=WF)aRu*wp64J!Sg= zws_->_pQO1l`1}srOHMM)GBN&pz8MXT!c5h<$=5fB@%Z~E0*l^i@9%E2x@f*XjtK3 zxaADzlh$y>ykC&Mnaw^Yn0|!+CPfxBjO<{zOA@GI53~K$Xa`Ych%BUGsb`(rFGpcZ zrpqyJI)C-ZPmi!U*g!lexK=fLqG%fK7U4P(94o)$ia$?+kNiiv1d_GUob;bvb`L1P z;EUvXh8V^nr|R^AcA3;v1kLy6s8Kg{Om?AT-|UIynp=WgA8v>xB+Zx9Ujk$manjG5 zIwLm<_fNAS<8QYP&JJe=H6wB7oR*ZvwktTRt9VQA=R1Lv3K_m~7Ka4I9&@$E;({Yp z=hFD}dhI_nhI7cr)9pIpoDRqTzWN42hi}z}qLgB)X%g`&yy})q08(AB9?73Z;Pq^K zhh!peTgAJjP=;-2vy!j1$mh z3SYJ^zn_gsUo3h6XQ`vx6XJ&59Ic?keV1V?!6z(e1~^} z^aU<_0-ko-?3FcF$P*zrykGIsTV$7`Py*wiEbUg?S2f7Qu=f3OlWb(UtkVHrB`79~ z%PWg@iqe}&>f$b7@VR%_ZRy83KZtjJ7!Mj;Ek0GX|4)P_r7G)GTwc|E<{@hqq3I*{ zl41r8z2ms@s&S&yDzw#G^{2CL_Ws=5~o!@hP=62HHR z&wTs+`vdM=CzI66@aL}&CcW*`t$$KqDAhRZ(RnGKI7O=D1IL5%4=8dJYEQP>U?Ni% z(O2>R2?DgKHg&=v46%Nus}2Z*GZnq4SKgZ~-w(L#15#HIVUJzSVdvtrNnZ?VLGF5M z{Fg^2cZuicOtU_vK1!G$cM9r5JNx3l{SY{MK@BsAcfBS+{nX z{4FkLSV18ny({%*hS&Qemviz0i7&WTR>25;z|vlYeQidrNHU%RlYM=uLX!+A^eUB8 zdL5`wly2o9JDN{oKJDJcd0Q5BrTsHIjQ8tPK56d&1b_A_^M2!|$zS_}TNjB{IdAH_k(7th zQNE*}Bm^_e?dtm5o(`wU&I$YswXn1Kw=9ZEsU=$_^zAoCCf9iSZ5p3%Hg)!}t}dA?_+f>yU{8<@NR8l>2-FTzrZA&XOBH(tDZsA3Z8{zd|Yt z`2@-Ts_=_^G#UYOvO|T)T?LxH9QC#dMD0%8TMnM&MO;1BB77a69~Wnl?_MEmki=k>Q^XdpmWyR=d0^?ho}f)ftA5s! z;;k1SC;S@kkJ2>yNABL`hfYri2i zDgA3QnLS-*KM43^7~-@R=O)T&3lcD4+>{Ws5XbUgNlQE`|} zU}^Q*HBOFFD=RC%=6Jk4y9grbCuIK{ zrZO`bPPh3|w)cU0Tg#kHn7wviRR&C$EFp9+Pvvb)z+q@9 zxc({p(MrG^p)Aw``9`Jnc$QMGqt!o?hwz`yP1K3^+=j5HA9}ri!()DwW&P(Y@pGNk zzW#^jcw-eVke+0sX2z*7!q_0=TZc~##k+UP+sU^Y(6Z3=hdO;i_o;x9fzdR|%(DUQ;gzu0$9wT_q#rzG4>j?t*P%gNav2`^s@K$#mq2;3apL^_R=pb^htuveO`8 zr~RwP9py*YLFQSLex1B#-)w~g3e9O_&!;12H(I zCuz}(@N;dsg`ADI^B^&d6(x9E|}90govwOMz8~JUdzIO3>oV=%#v7_ubKxT_$~uvNbrkf20tYs@I+0}mihr(}{p?j=qF9;2Pa~ z_Zdy1>FIoXnQ_Xg$##2vWO#ghWFdQvc4BoFlOh zQ0IP`?qM>s;NWV}S+AAPX116UTEzYqrjPmUxJ)l2;K=%>VPluIrFxT)9?*P$euM`X z#bD$WEYts~rlFMJ{Ve+Lmbcrl_1b_JUn;sb4|y1Fl7+-orq~w^0~SkGGia#bnc!;~ zH9|TD!UVTjbk9b%RZzm1eyT!yu|_B8mSFF*k8m5(2CWZln}3VMA0XpoScU^>qpccg z;NcbX{n7F(x?u(?H6B|bM7ED7f%l6cUgX^iZilB6q9F-BsWcKKtMAp_tNE4T)?NQX zvrPN7gq+Ns?lif^=Ia&JE_V8vw4^YBQW%YoQ1|#hMDjX>)axf(K&K5?|En?+btZp- z@MB!vOFqRDJvvEUn7DW2xM=R|RI@7P6|sB2`m4f0slQSqC)ASQ$wbng_x|-!DJo~u zXSxt^t|5a3^0=L<;qcg*!$igL_5L-HZ~39?C&i{d>gIXB@#uI9o$1A9l%=~yf-4-t znMP!PgBXjF;7)NNF%BtOfn&|3>MNJqEhHmOw!;R|EYdGKs=JB@ykj*Pw{T}pV$=G7 zgVGTh5zq7VMl)#VtV})v(*w7IXDv$R-&=@u3!Q5$`60?gtwwsaXsf;M*p>=(5yQVv z_SaF4@uoNwaT+T&0+ZFcgaCC4|#2XVGb_jMVGQpffX85_V=osiOo`gjv{@bMVFG3A2v4 zW94Tr6f28GF~q%?f_PaBY{k*8BFZw|u!>+rV5a?u)maJw^x+MQ!kC`+@$#cqM#7=j+}u9-zYBRy3^M zK<$m*jSbDTuC4_la|g23PO7)Hdeb#XvUU{cQiBZ)ZCNax z^Ff_8@zq2`c?)m>?D9|(wE6j+=2EjA7!vd$;x+bl5)LY&VV{h(HdQXQBX%>7>BiX8)<4dpRLxv zHYcXvFM2mPLCl8T+49c zi~MMOq^R0*VTH}k{VA=%v`+^PP{zFw_uK1b;)MhG!NB*UV;w+_oQ+Bq5odGHa`dFx zX$#;LVzI?5cey`|n_XWS^BHa@X{A55 zZTnDonoSdFldhJ+3}O$pNP*a8bdH@M;?4mm8PpZ*y3#Tw(OkyD(TzVM zy7ZBDu{AK77W~V+ngQOe+4O_7-$A;{CAPwQh#;=|?p9u!iWuWef4oS2A%Zd;*MIj2 z^_Wo7>M8uJ`^{p@x$DX850cCX2uinD(OO>w{SA67Z}7OO_Lw{EuDacuMJpwIiwd&B z?fFRKVWQXe2Nej#q^}N7ghcG@+MHGh<#l1`SHVn7MAi2z1iI#z+@Nrr{%BSFuLm(Y z!6h)kdr_{5%P9Sp5+M-jN83@o-JO!$tDG=Ir50$szO0=D(6k+yLyqx7^^%Qx3u%gv zx+A}_&t5P*d0ik#PAPm_Ntdm5m0hei+K{VT?Y9P9Kt0%%=s6;vt>H^8`M4e@B#A2PsV?q~~#))?eM-7DR?P61ci-8Md=}Q9%qkO!6V1NrmGb zma#TcEIukgnly}hsk&67rVXU|-#s7|&Yy6T%BlJ&LM~Scy$YWh zy2~OenkNR#6u4NgqS5AMN(>1dmwR9J84VfDPU$J2EBy7q9E`niLv##yD?KcPQlIDY z$}f)5p~fIdG};@)gVKvO)lhT(nNlD76jBYex1D{Z;AA3kW;Atku*JiGdP1-#Pex|c zD;_yD+-98)oAHfX_dknZymanc_;&MWN1Ks54piRb1@Qw zuaRN!22=^zj$=6`Jhz=_!?Oyul+j`C^tpd652MNIF4m!`ms!aN!Eugl@&xOqR0<`k zgCcJXWcgwkmbnonu1@2pG7|(jUM?0ulrn$TwkFoIRG#!ZQ^=&COk~@5WKOkrA1*W& zdD&kP1wvP?GTkaQCPO5vO;MVdkXBZV2-0?sW*GzUb&@s1l-vl>(Fd2k?ybdY{YkX^M0}Xw3l<+6jdO3l)B}$a<67+h~@M? z(fwW*oAMBO9-Ur`-u>Qdoxg!WV6ybkIap8asTi;R%9qW#T%Rsez?41c!pd(c^nH~| z2`+roq_Ew>BjxEc^m+$*+joA5%-k%$G~A2>ys<+zC09TE&i7H(Doas|hLSJy{_q=l zl<&@3rEIv_O%57jt)u75mYS;oQznej#ce9z}EDb~N(w}MZW(e`sB z(||j9U#y>nAe?(3{&>sNEDpev8s*idV;)Uz>ugy4i1^bi6e1=^P^77Kx7n8voX ziVQK60VY0Jxq6f8>!Atls#vw#nDYw+{6RdW>Vv_plyZ)J(CZ`e61C&+=iXX5;2`_- z&~R^)MIb{O&dsPXwIdm>m#YU6=!6=71Y#shtLOHxCRbyn(yDPB04u{y8_=zY?-%Wy z)U!?a^pO3LL%;GU-D3S6)KiS_kNB~D3RFnYHOeiEacjALuCT}DWvy zrcm8I+6)?P|_&tvLFn&qU5e6z^&MT8yd&PIZ1+Ofs!_G4j zYxqR{apKktyv65-zg~;%5k=}vVbIqsJJg3l+h@|MAiwEjzEJ^+iVw)(A#T!i;a`htVq?= za<%p_OE_xi`glT*DQZ87E8dDO<(Ph+OMruVTMq9KykGgm5yIzjokLszruT{%9{<-6*1b_!DZ$re=#N6?MUfRrOF9QPj!1THM&RJt7h@FTHiP zK_0tPD@xbv|G=6C$1af-5FOV#zT%&m2uBgJ`9_Bk3rT#sb_Yh&nt# z)}$g3B}=C((z~;#Ev7-BZ&{Ho-eaBw%_4aBDzB(#XStaS%G=wAsf|nbd*tQg7oMyR z0n;kN37$MhfeN)zk#8J*7(9E_Dc4n_X+$ew#iAGUu{oa3$o%LlhXo) zin8`xN*O|quWoiBiOaY(r&rX`1|dH^p>d)a;kS2g&Xphu)IK-lE%h*Y$`;wOGtJ zsw+d4f-ae*RAJnQrd;2o>|?ep2i%G*fJ5UMQ@wOUX z_Q#jP-Mv~FLbkVg+cXNys|5dZc`Bl~_GSv&Uh3kWlU>^lqJRt@n+zECFqbp&O++jX z8nrR;qfV!1Wb}`65f52@VVWO4W!4FHV8B_RIBYE%v8g!9pE;?<+&)H8m|MY7FW384 ztk1r1I}^~XBL*qta62CIC$6mQpCvhQ1DppcJifHzx$5bIN!o7l$h0t+Yjf{gu~sFh zx=D|l`3^Sv!1+*53#@ql$jQ2sE^p`;D3Q}nb-3uP)cZ4^6TyR2X#1eU1~??`+3f$5 zCmsWrdsjAc4_;oXY!C2L-mP``Go#XHN7!7haZgv$pnSaR;n#6*l5_68!4H#{XLu*N zXa3ls8<}*)9d^%u83VEfi^AyyU(dNz?>v}2Sxm~SQt4;!n6~WR?wla|PKW%M1}ovR z6O6j26mE5Vjxu>J7b+I?oK`(92Nndo>DoAFZE{i$cWe#Yu?Tt5$P$G%oNpO?J}$uS zjZ9!_%gj#Xio}u>J6ywMm7mgH5>-#hYeH-5Wr=#TXVaxQ ztMjNC{)g^@7@YeGoewe-XHZlrHGlvi^xGyR`MLI|s}CpE)Cx={_7CPY17&iFw99#C zt4#^8KJH019@c2--1t~xh+y8MXqn%Ympo=&6$*slRm0VTFnUr>GP`=;sX^MuFaTGx&K$WE8ZEyWbYI2HsMxS?uDiB*BhfFU^i; zHsY+uNJ?UG?S23n_sSXh>~kw7#UEmJJJG=Yb{p~5k`&pE1bC(n!n`Xh-Q4h< zNDXf#=0r455&s8Hotp{@Hd0_eM+wU}%lXE$<(8h4oX#!6+GMzh5s$`m!Toiv`eUTJ zM3jh~$EacYvk*II#H8K>!=V$ets)Aa_@L?hN)hL9eMt8I~|JrTPM&w@J^WZbLu#$Tq=VEHQ+sQPbzuo$_B{m?=77rf!{df}~ zJ++ieQ+Hu*bgZz$6|#BZ1h7Uu3!CB{0yjs8x$w3m2h`|$nHcZw9vaiCdfg%j?s?lP zYGX8GUH@8em>cOKZj_C6*Z)b`E%hLbrOk(kM4jN|gCBRVn3^0;{Nzk@bB^w)TmVlK zDiJkiXN5L3$`@&tYAOp|WvmwVMhK5&=c(}mfE^glV81 zYesS zaM+trm=Pp%M%tK(k}5ed`d14WMoU&V@K~`Hr&O1m8CRTY6PoSsIWmUwX7YL5O@X{p zC&KW5ve>IK(8Kz;=az+O)oeB@#X3voOaFmC@l^>bB{|yRmZbnmYxgcSoS#)l|GIh>)Djd6g!b-k)V}UXb@g(O` zUY&Y&Uq*eTIVLFHhz;oJ&_vfgQK?3K&0?B(Fe~L@b-DBQjPZHVfaGrJ9d1 zudF4D`_(OO&a@&wZ8R;eN{uQEaKGo9l3p`yj8V(>rY-hg$jCh8^I$xyK+5I z*j53CpPP_N#YUp%ML#ZG*HhkYhou&$c#oEcEPSxw*NDyMp!9rjcoHj&w{~Ae9v%`A z+Lw{JMVwYM+u{v5*+dq$eVQ;#_Q&^n6Z>*SyOPZexfrV2{XdfCkcb6VYt0y{$;f6h->O{guK$Mn zt)jP-T8}x{p3O-gMYncz8@DRZsVnB66&QH5_$t$bWM&l@+{l#@Oa*|Y{#2VZ(-tG9 z=5Qoy4u`F!!miLcj#|fFJGRb!-4H&1fdWb~^~Js*{1Q41VG)_GgUP043(RsSqYSp- zpE>62O*)*lIU)Ap;NO`PTy$4Uv*=?;x}th^L}sB=YL;A4FTT&Ce@EU7JC52{?WA00 zH5@;8o?NK1X_T$sAG#GZ8=<+Hbd%Jup?msns9Rj-=*iqcY#5ZUuwr?Rn0Rr0^Va4> zYcTYNlf3UBeN2i;n}+x7J%tM50*&tZkK0?0_o{zk{VYS+{smZtna)}cN>g#^d|aN>-zWZXy*QY@ zROvgZV!2`3x*C7Y;e|xD4{pd*zc%I+y~v<#f$?dp%>oW3OL&FSVjE6-k{!rCYeg8k zK1BPnY^mXKNzy70ic;$fIu8MP+DEsxtwAkRA6s^!BBO41$CZmn32zBH{;9v`9Y3+; zO9fY@2p4cE+b8pZlc>;V5NlWPFE0FySmO@b#Pi^rR|+SzgqjL}!FBE?E}SVTUrMDZov|gGbbISQWL=#E5+`wQkxJU*m1es;%)6d>I%`GY6|prv*8Q+ z!8?{Z@B7<4^(_c)^|~2A5k8)=XZMDuxNMg8Cv444Lc@-`T51}|r7Eo$`}?nA&)TSp zR@GIzSRwO6Wa!?G`dvOJ3vQ+tDCn8ZYE=C^aM<$P_;5mqOea9#e!2B+0MP{HxF8M2 z0^|fthqqanu1{dj-O^Vd+435Y@FMm5iB?PVRCOy$VDtloLhV%%%)d=5^J#2%CZhCb zV%7$^Z$+VzxOV&F&~nR3ESj<+S_|MDY(SF~`5@ z(^p4Q9m}FrVjirJ4Zg}oP$>OAD1gR2(SJGX8#hb%8wVTi_CYhqYa+Gx05En^=`Kxw zK?rMBVzPyG`XPMRk!ZL1jjxE9(mbs-n1gTLyI=JBx)wLc#<;FJf^#fE_XHM)1{(=|v zaL$G9sdTaC{p+zek92H?`~zk@lUz0nHaVMHo>~1nZ#UW4M~?TK`y=vfnVKMsbGb$( z<}6r4Qm$aWR0j{21zdC)V)L8OTxoir^xz5F0^sgce@c-Msgidv`4HDpXL6K<<1fa- z4N8>5#HDV;fSv|0mod)OL|BVN$LBg*v@DM!{wsDyw6fCe$HvyCD-ww`#66neU+W$j z5HM4vJh5AMaS!eLZS+y%D&FyXT%Co8g*ZaBS66V2p$hMn$AaOh8Q`DA(prt&9Im8_ zS8`}omsPt8&{Sr5qH9Br3i(+tXoczm;BK;hw*4UAkKOg8A45k0L4#(%;edvz9+Fdd zK7x4)hl2i%eYgs?9}!ivGxQN*wrwj5C6PQ-RA}_42J6~FnQ$ubA3>=I%S;Wk^a|Jp zp&VolJlOCmyZQw-?F1XEkOSSic0h3f&lW0Cbf7T}F6=Qw96s9ONKT_2tBbi+f*YU5 z3zAa~jRaO9y@U}uPK)ol*Sr>WVhK5OZmEh1|8#&ZLozDADCXRBgjtdfmp4wdk%AA8 zQWfHTWP7u~qnxnnQk}o^+VU@gQDzeC4ibA?2VAZujVm`wh32Bu5&zL5^qoq3TiCPD zRuVc2nvoHBM9Yk)QQoLZvqu8zmnUeI-V>-1c`Q0ANQtd3ye77Wa2|@y2DWxePb$q(L^NxBE*Lbq?h8EBNGK^xKZ0ygkPbX;n+nBjdx_@gw~>+9ovfzH(Ci2&zir& zIVm?9%z(&))qN3CXG8(4e6Mx;9VyG{>wExVltE|y`T;N6XcYGjkZN+zO}jV^mPG}`1j z_6ibEv>q;x)mGMOm+7c-;=F1ZzCGU!Cpxfh{xb{8i~-}GyMbqA3QHX{0wC-EtG57e zBD%-~Q9iUO?y#<+Ec_T4(_a??2&L=c$aUO5HPezQ=?i5 z3;OgsgoO3fnCFSVFkzQB@ePp)+^5XK$2*1miMoWRACbWamdodwYUy;r(32MXE_p$l z)R0Ad4RnjhN~wS;Z>zVT4%VDBe|K36AaH&&&W{7tEci3_zwX$mU(8K#Bv%80TBtv&*1M3ajc>Op=i{rxZ9zujbuahmGnSPlX5{_lYO7cspNPz{^t~j_vQ*omfGlAdW6%vDUxhM1%SC|!Hn^k^Pa`L@m zkLyDIjh6BwmZMWw5=D?&4dWWIb|@JmKSG2b4DN%#q7HoWExDB4idETj1k2lw4!a= zSQT=<%TQv%c?kwYR7`~_9-&v;W>X{ETBg-fJ`@|-T^@S(Z>jqD>@40gS_b?m*Xrdbvnj+|(G-YXBt4|41*5&$Qv{`L%DxqKQ>2#C=<;J70wxqrsSm z7X#HoRN5>%SO1}=WxPKU-Lu|V#8m(D?4{4d=G5p+`SAVSSwI!bABuT7W~kq%n4IFd zI@N}~`SZpsxZLATjB0?{t7)ydT6bws*>SasB17hBsFCl`u-`LdVXzS`%+ zI371SYzfh}Y4HFj?=Lb&2F*FIt#riMjqD#hvr(HVx&6bt)p=_K*zfdC6TDJ?028#{ zHB2|z-dD*&KQ*TQ0|h0c>6a7nyc##axVepZr~E5;e`M*T$_DC=n(*%YQ$X=b<6B3v zWkucb)E7_6Dk}MdUKxKa4HmyoWZx7q*s9&%dZhmqD;ts5zPXfG54I6IGoq%Cxk%t` z6sseDzo08)y{9jEs2cV|b@H&#Fn6Jt@1Zu;>M2>5-Or|JMCux-`DG6V)YYdB4I9z0 zw+IazazHgj!e5o(UexL(2)ZCzdbtsI_XsBiUco27Y-fMUDRp;(HYs)WjC}K_ zUOc#2gqUW9B?O6^`2k7RVBVRQf9>v?XFL+98X85S=_>hI?>| z{cGosZgnWY?ZN}oeWW+ie_>u?fXu8A-EwCUkQq?_PoB=e4^cpcOemhfjj7M8n$iWq zW_IDs{h>(w4vhO)S(~#Bg)uTRKse|3e!-2++cBGYoc?~e_6u`M_B%J}Rz3!JARsk1 zk6TjT5IpWw;AA3ci-(*u97fGRzi)0#~B*${?3dMyPmBo#_o3GxHf_WbtiP-uv{YA?V?Nbod zR>Ezi^`MzlhCMpYy@*aZ8IElp**0B?A|XB1pV?51Y6Jd1`TUmFYU?8qc-LivoSeB~ zLbQJqV7W!z1^;tB(u1)l2-}4^Vldg`IkLc${l={DaPsT0@BW6z_%h0n?X^`|7awnu zG*|9Xw|;*z9U12VUhNf#4~oBdLcAhLMr)iRYZm1_u)kZ27#vVJ0=RUYqUM%ct=VxU0xOpViXiYiYjJKA8>pkB2+lo~jlU zg#i-UUEqh(&aeLIQq;#Lrm$1?D`DvRhAWt&9rxxd(b(5iz0axVmFe>uMD zO44n6GcLHq1F0y;L|q8!^+Bh9@@Mdz?jdq%j=bpOET8?;ZLWhY^9CfwE_(vFiL(K%0ips#>V2+R`709Vs_?fPuBdvw-U_A=|X&-pMMtd(d(n2{`=8v~e zO>0k?e0j^L4A&LBt8`h2+d1!O*ko08kVN!y*c)RB)t1(Yn7%;#GuJi$C$v*Pa8e7h zHbQ{>SC0q_shtC5btJ&ulig+(YJw;189xy4C8&u91B_?Xlb{lh^&RjWQdLI>9s03y zfB1#RJxi-d=>Rzd=^wu&*qahTr~ipiK?cdAYm@K=3gg6&Oq@Zm6DCzJHtrr}jOwm! z_2yl*YtT-Rqr&obF={MXZy7|q@1eUM-wRIQJwK|oFW!5Lp~(E5oDd9Vb~wkR zjRf%tf66QZoqwzW73-P&1lR%*%B&-ne0%o9G0U;r%}2e@Pz#@%?PmA;xo6p&8Kb?m zq)?U9MwwSL+cpJ}!O`-Qf6=U>k|9E$7#78?6>Z@|wAoksL~4 z5*9`ctANg^atz0ZB8g|OQLyc}dEpfWKOd&o_ zMVi0=txHXB*A;N*Ump5->bObU!aMlVS}S(f9FLaE;qVEMj`Bvv8B&~TB*G|V0#3KB z%%_AQ;_H7$CzpUNM9I6>4~XQVgzslB(tCfw8?G)_&~V*Lk5hMZqzKKahNC@#pI`G!Q3!%ny<@Nv%yc>lmy9al*$`=R37rF5IMnKxB|6n^z`2CsX@Z z;8g~u{O{|hXjs#L-!X#0BndHc^*Kp@Mm+J)q^j>4ie8XK)5IzMXN#2one#NXoHoY@ z#zLhR(?!8X7<8H_rVs zS=(vGt%TfNHo%I3j?cV$kw~`a;S{XhdsKWCPS?=Q4Y72hNVe+V(hU$Wr1xs29TD_L z?7-+Ka3*8N9sqsC91(K~igLS-O zW-&#TssYph!RmY5OUClAEL*lC^8AjuBiF)-Z^M30w+u*WYZ^x)UzfX9n_MHxNa=mV zcDpmb##haHGYJN*gsL`W!x&CUzAx>Vg1FMM2VeCWQp>_MyvcUtYv z@1YKpSf9F!#PJLfoF}e^X}ZwC9r$T)90lu-E29G>&SGX<7Pc43QQOtqmlth9Tj^yYbn}=cWw&aN z4_SYvxK8jk-F&-$rI}1v^J&6jHUrD$=`LbC7g_YgE|^v=c3T|*i5!SiNh0I80@=xj z0){u0txj+Tov5bTjTVh{;s}lUVx`of4dn}QdR?&ipyQ1dWF=>&-Lj{ zq$>w!n3>3U;BYul< zsv-=uZzFuXPSf6KtTa=9h0(=zZ}1cMzs`RriNToL$a>Qp3go{A?E&5xbsaLsk~?3N z->o=F`Ddy5!B*>qIF5EImGJ}5jGSri9S`^w&-Y4xeicRVlL-bJ{An8h{y;4_Sw^R* z=)Af1Q2k^|T1Ke{)>q?7mZsR_G6NAS!$8Al39L<%gQEWyY2xr;JKIYg|6A0WKnq&p za8v|oynqCjrhjXK$SttM%cb8-QEuE>S_u0^Z>`mg7?NOC)5hV6jr?eFVBI5*bI(Pz z15P7zQc22D1jFDH7`>4$RsDz4t@s%KVqfQ=i;w)N0$C!WO6P*m9`C@7;#tOa9+@+^ zU%~5iqfL14uG77N+HALl*=HpNE$Dl3C7zA=b1-OF6so|(C`YZ7jD9~8=x3a~cJ0tToE;wgvczLmf@0c1T=?!Zi$i?Mtf^UxtZ_D6#t@kjrCGVP+7$INAdlm( zVz=kcw)BuLnf^-~I<=XIf%CVw&L8|F0W3Q}Y)h#5nn^6)?jVLlkKAYv+==jsc$~!n zC1zX-OWr&?5Ep>@k`7ps1*|5PC81+($Y&@$asCkf9RurEz3ujdm?mS>dz;PipbjBK1a zgmryENHzf*!%iL*@5XO4#q$7dB)|Dg>YAj!j9Qk`$XmO6peCI&$J8=IR zvLi3LM@9LjzM3(_P2(9BC|jVA@q)vd;fu1qcWStJ7c5w5jq1cZdUoPMls#N`jaSmb zFxf--VaI`W;wCMwe`L**E4hV(1JGFJG!ej{n697_sqX=rq&Kt_GD^= zcYLvqXLFT{BLqGLMN(iK%0mS<(Nlez48~!?w%#Ic7rX|)cu5-=?VuYHVT;TzkKF5o z^xEI(0~J^HjX&+!dD&_Ni-cPO)^8fQSwD$C+IdF;)M>EVzk`734iz;5O6wY_OxBbLkL69dP(tDgK)zKzmlLmO!jJoYvl%&+Yd;oqn=CF7Zj(7=Iw~s3G|I; z<85-qJ#Xd{^n9j{xI)M|?OJba$><118G$j5-r_eZyri?VUJoB>QikrlfdX zI^edjbtu9=!ti+0E4|y45Wq6_Zmn1VgTTVv`W_wR&>t>B!LzQ$8=ZhbZ)PfknGtj= zrtT2)VjHR~E$5VfI_3{QNC%&M<>1_>@mAMDFa{@JfgJ~k^+|VJe+OGaANW*qooV3b z?>v&^Ec!0N{P8Qs>2noZ(NlmkHutkns9Ett7AC^i`G09^f4wBP+b4pv!UoHz&$VEyEAc}mA*f-{f*oIFkVr;`oV9HlL~#2)jhNQ=l9G!hLws-$#tI6q;IM^M+!Y&P z$d#)IO{+JT5aErQvlVq`(vvcaTe97W*EdupupCn2??EZZ{awO$F;*oG9H*mgYq)MF z$pum61%0JHy~v!1_uh>+RdJT|<uhmw(U)Jcg3rsEG|s$C>_3wV5Q-qv2B|C1HH{YMVXYyk~0wen`{<8v1#n zpOTJc;-bSN;6Hh$rhzb&aE3-DL{xiZMjJI>5=J`vgT_6!6?xI|)-JP!*|Yy_52Ye(*UR|$X}JjJMa1A5up(A zp{$rI(^8qan>{}y=a#=J#dwYd!*3*=_Iusp%C%&^$OGr?U58MdCxH5jEs1*()a&)F zzVF)0Kf|&a^5GT05+ai(PcQH~RC-(fx$Opw!2D9a6Cz>!IWhw0=A%R? zWFA)2`j^;GyWroHyoPx2Yikl+C5Xm-^On_A>8GEm>E3ZqN~acIO(QegFNLe?kDXst z#r0H=n{1`LaJp??Fpej7!?J4fK(?J8b(Df;Qi@8F0KnW&f^h^cefjTarV8%AB9;-^ zuK`}R7u*9(I7I@gvh@)rHa&NvaC3ePW|r&UIF=jbTbL?ss!Z(-aF;FB+bUe|iW(S) zBOkI0ADO-fiE}ctz(3=QDfNDxCoNTy-MN{zK(9}XL)#7OZ*6+y(?~sid-g!0+V02F z30zkI6Oz%gy}zlvIRwPmE#f(rv3V<$ELRoR;Nf@+9*G_AXa98kU9bW~(LpfHbyL>d zB3c(eBIHM{m3IoE?8Si1rVDW*^+1HqbWs62Zc4m-N}jSE?>DO5$9#}9->&0#zmftA z4$-QX)6ke6k;53)p%gK`(4>!-_&rC!fLgvCrtb1ygU}y z;Y)#qU}@fIbvW*6t2)oKX`R>Sr}X?(E+9W-FAr{v34lSe@R3t6D(b{NS}uW7Bcmxx z4!S~w28K!}WqSr{^bX>6 zE4^(L9=wzjzoB@rmZqU3$bI(w)8T_6zYQdVbMK7F4fvd1{#*a_hzmo1+U_xo#EIB` zy}8_t)88ppKEs`w3!85Ne&z~u|Jwrr``9n#W#ATKx^d=;hOINIvQE1w{nO zfud%;#V*kCqp9*wwu~1D*x|t6mI%HxVz5HsxME~$FB&10fDBR2%fhLkx}AZ!{$$=? zR%OiBxB!jyvEvocm7828T08OndoC@tJ1xlkiz4&Ity;k!w#ED74F@QSh5)rIMn2f^ zjR$NG*|$#)WRy{tABD89t^Ck_R&_8Domuc9QK&Wh0t#$=#icOAu7z_{(uK&V&w>4Z zP;`6XJP|{j%*oLZV9ld7#IQ_|HNvYw8~CR_!4BRfP%un*x1%ch8{T=bwa@h>c=!EM zv!#iOjOzsmv9*Y(soLoD=C5Az%~_K^rltG=_x%JJ7>^QX-`Yi{3!gt5|IA5`DgU8L z!Fl$>+IaKj6UJeQZu;{&>Q;9-d+^8yw&IU$5z-h2Ly{GL02{5?_8@{-L9;1@`w zGvLqj=kgndOlqc9mg)kdpyeRw6M^qc3>)LknN`-i|GjD9fKg|*$Gvoq9=t!$$DION zg`bgy=?}}bJ)etubkmifE88a&>`fTJg#9vjV z=fZ4aKCFy@Q_|y7L}P(8ZW0nf_j-bDbuoaO2dt z@b5kKk{{oF6}q*@%Qw1Q9m1@=u{_Kq^@@nBtuGY>NN{2~=~|w3TFDiyPUiA*uEdHsakf5HnOuDquf-JwZw7jEeg%aQh}KBxlxbh~KST6K zD>{89GNm35)u(X~YI#}VPe>X$%aW*A^E<_+EQZCG4&ukKv&B=Ie9=wN4(pF5dp}!; z$hgz@3A``ZuCvc8L<|&HF<2Z<5m?g(p+*1y@EUzYMJrPf@EUxfNij%WGKt%dD zp(}e;sP{=dk2e=hA+pxMyQL?Zj*O|~n{*sx!ui<0U&S*fzmy_LtYs3Nw3PRv{iKhn z6;|CjVYQ?GPHyK0T=!}@6-?IY+T$d$VnvVMybcD1H%;5WJZrHHK`IREJ!(LZsPI;~bOgADue+q^WMWVcSw#)4w z!jfVRV*WY2$ck%qgO2f-D@19(M&AdGwL22m9N5xzZc0f}n(23RQdg6;25|&nD_x3c*~hOJ|=(1el@#)4gdFl zG@WBuW^cH~C)@6glR4R#Tr=5Cwr#t~HYQHyWZN~_#$?-_-T$2PrH@@#z3+bZbFX`? z-#W+>BLu(@L(Vl1{E5YH&3&7can}-GO6rq@P+YYlL&>dKe^nqx&*sr)N4~$&%UMU4 zqPrOC?`J==wdul`%>=KXBb%_PgS?SPn5ntxC0awnrgRaWOf%R3V0WpXHuRZEvafk2GdJFXT*np zMT2?@i9qz$Zf_x6x}vk5-hrf?*EALaQkazVSf|<+3o5gGLmmI@cbzfQFZ0`g&e47F zAEpK&Q+FGO-Wn?!amS$n;eYh98ZV--cALHqdwj|m`)u$4AXml^91M$P3pz1^yb^2WApfo&SXb zJ~D6hY*tz*JWGv~gWSV|GG{x#+&8P&(>F`KR>hzYX@h{7aAx!r$b87O^^8VST7W~N za~nX(i#@ z@m^q+@(CwR)|@EeubpqIJxgLs0w6p9B2%8?IkrsRow6<;>FtXOH`@Zl{y)DL?u-@| zHmfM%-V=E<-c?Emk>Slk{NZ8R8(-gC*a#z#p#Scd^}X1Y4*06tVXlfiS6m`&0sD`w zCcwXWF?@A6S;|nqSbSu3^2N`na3J%k-%fufmnF813_+HO^dLci&?NS`I~~rF3az}P zP6g%s!SIWQYfdqQ81_*Y>MX&)lhGo`Tcvb!X;Y~IyWfOwMg5$YLEUV?Fz`w7kenhs zPpg-J#Mm^sFI!vG%8nMo7AB+PVUfwNp59=F$a}SaFy<~1z;%-0sV(xIpP5oo5A{Ry zhm*^HBI673eM^G1^+3L7bQQFkECblY!;dN6GBHdEXbu1q7GJ#&qR7s2?vspE)EH^X zS`R^vxXie1KK-DBEP_z^w&gj2WdFA(N~&Kr#fzScute_zA>#p#cIWceg;5{-zu@*rgk}euU`8cUtTTCD4daMRq zI?KU7J+pj8ggNhz47Ew9Q}zM1$wnuO#4WOIpdeQW)SIL`3CB<#Hl?!m zlG$O;ykns5G1+&OcI$N_2Rui2j~nH)w02YbNb*mZ*P)^;ce_3Rl+>g|8s0~9d;S6I zqLn-R18ABto*ZCh%~dJN6abPN>8e&NpRzYd7OM-vOuDr~wHS#;G&eJk!XdRsz`Ag| zai*m8R{oo>l5dC1u8Zl@adMq8z*eggJo4EdRH8^kg>98-Hjqeb7nRWQb6ubnIRT*~ zW;(4t2PXmNHv$mjjNee2Z;;q9V)ieoEWENc#XpONPPh0FDMlcHtCP{ziOAqz_}YoC z1fb#6Fkq*;;qNIqt*glP+|N>}nY)9D!7PRwrdSna4zqAqUz|T+{v)Y7+(rL%U2{MR zuA8u?XJAKC)PtOF%H7=c>jwbs7J;nrb88w@aBhi8QF1j2dYF0>3ej510w0llODbB@ z<928n!DLzdZS0_UC*|7{aL$cM4vczlghOuoo5MnnA%`sAVuq)*a-R3Pq=B#oK4l{y zdSY-pAMl-&iqd)+$`|P{JXp<1o9WMqat-v~x__q zsx}+M;!<|(+}lr!>O2VJxAnvP_!!CzIE7zyOmt2dkpI$e<+;#hq zqEyMx#eAIyrYVesAG;?0tTx-@AeVoSSD&Q5js~azg|W|MJ@c`~pkp)k#h#Yt&DQ8r zw>SVfBlIF}Q9Iqg76O^|C-m-{ahPT{Waq>Wt+VGmZ}?vvE0GfK*V2WLC%ku`qq~$m8_>$6(C2y?GQQ7z_YT}7(4?i?z4+$bHBtasZ2WI_5QqbK=HC-mc=P7`5 z1Fd;k4ckwrX2>6`Vnc{_Sd}u!ItUb*kR&+P0l369_VzsondQCPM8Y&Y|&_ zGjIr7%fUytd>^46jRwX@|Kj?{&TGKE{wC%Ov_jEUyAxGlKu^F2->nrh6wtUo?c$Xi z%$^DvdgW9QW@*h(Y3`qr%B&Vv;EODZ{#Y>suXJB*DLcQ>V`%2CfdzAr(0ZeR#7 z)ov*Zcw1SWDT-BdqymXuECHNSJ*)?0Fzs9)vb9>0$&R>sdS{dwISgIAlk}yY7QDK{ zS@cL58~FMFtMw7Y@?{m`2tbfk@&;PXqUr58J6krJ`FaRm8p(8{9E7AlEyghMgfY^c4M+ZMDT!i2H3UvBY9%&reqi8lEol6Rmq%$%8=FsaKCo13=;B%k7y*vCT*HiumOfeQA-V z*+tE)MX*WDo!$?)TMed?pNxh4);%K`)L!H10(C|v{PwG$)vI8BkDBY#Tv`1C7wO$7 zsEmC_9~KN)mElvS0*qFF1|m5Ks*}|igy~n^V_DExhi=3o0!DOlOVW8=Z7i-O+!{MU z?z3yfSj7pu^46t8ImGYARA`GU2M3GDC*G}HfxIXr9xC9EZ<`BG!Z#L2zU%$92?aD2 z^e=Vvjf_dkFK%zS3F&XWZ6po~Y~0AWTMV<~3Zn;o`a^EMTb_~tsk={B+^R1ESPUZ| zQz(`tbTKO{Da~u0r;TMzP87!SOXA%pX2iIkNHC(0M(;m&H+!zz0x7 zJuNf1NUn`~o(X{6mBBo#omZu-a~!y9K7HJ%D!f)@=Ij~|r=amO=;BY`pU>Y(QfQ`c zkg&mt+5t|Xj2x2d6tXxuuq0OqHw#DE2@EA`DdvfbkkF;to(MIJf0Ry*COE$bdNR6) z+gL5Kdvj`LBQCp^MFT_Dl7DX?E^`Sgxe-O1jCgM3NtjgdgzrNSnz64@<6d%TZxW63 z!9nqK$UV!TPFE=w_BMkTKUV zj-!PZRVhSi3~)lmmI~Nox-HHcQzruC@peLn2DwtJjqcQ{#+ z5Jb99Tx*V}?8wERq{bhSp;zT$u%W+T|Euo-Q?A1BF&PAb}BfDaL z6gZ-;GyET_!3>{*2x;pssOucHo?&K>GC_}=9~A|35T?Ojl@Nj}kfTMbo}){l=^)JL z0=E=DBkWbqt(@;&i=NvHFnMuk4}FYC#OIZqM}^qHpOhN!2w+%&%LtmUMW)}A!zrC; zrh;}?;r0fx70Dpc7V>7VG!XPVz0Tjft8Xnhx><&_7keAHuSTw*=?gQ&O-DYlG?>+xLCP zz)UY^P=+gzW6Q_G&is+=b8cWP#`*2Nxg2FpMNe z_~fDM@OTo3(9`oBBcxMI#ZGw)(fmkO5baH!sqQ~&h7#oB_eO4`Hs)5C2MLjCKplKySX>;`b%uq&l) z1azO6K}Nr4SIq2VfUn~Bp<~W-4BS(3u#XC?2(zeF=e}N{7}7-J2GN++wjg#KBSSkEJTQH0D{^f$jy>!^A*l zpwnkbQz&22Qu1TYIQlSDai}-t@28ho8)tgDkVe4XFbX)UcSMC3>e^X$DZ9`NwLT4jwUgTWQ~Y^Mk&HUSDT3j-$q`b@aJms5M5T41_n4W8!*?IG z{PwW>Nk2eXE2!eh3qhH3c}b&tQteRKWhLO%?um)evyyRhuXm+W4$?Cwde`&VcMZW) z74+rHhl?%}i8f7;Py!;qhB1@Z_p_5@kuO3S!cTyRvYu3T$bHuCA3)_~tPdnjE-oq! zlps$pLs%P}0jVR8q4O+x@lu~v2{aiHTmQqT0{ma;N)5CT6jgk-q4Gmk=1O1|jJB@6 zA^(?X4yoQE>cp*QwZ88?-z|ZhF<`2QJJ;s&NYnDUlDA+8JaXOFo6F7oGEcpMUfoa| zpaeCG(qYrv(g%)Vi=uM>u^Fw}1w*rhWD+U_cKLXi$B9THGK-0yo2%~D%*VBoZ$h=A z=1u|B(9m@CM#aa|8_6lC>ZSe-u`+ zT1s^nE1F2IaB{1-DhG)#Vnrk^xn0l*Do%v9lsD7>yQjRcQC&KZ$|Ej0Mg?RC=GT$& zdBt~w?Ae<)6OZ_B1jDT4@92p4En6g#!Nr447oDxc5(eBC3TWH4Hf8W>1L1AD-aohxrSy#$#-^KcF3|A#>aam@!_K zl7fF5kl~*b_X_4G_s5_AltuJA?VKtBQ;yx4AaTZ@rBU1WxO4S)!D6fH*HgZ8I-V_3 z<-=Y45i7|_B0O-98DM6Z#_l(P>T0m$a5v;0R^@p>Uh0J-Wi(F*M%Jb*Yox{H0{54& zx!ArpOB==g&N-lEY$pz(EVj|!f=c@I2);u$4cI6xq&@9jErIO()8w6zPT&WiIeXx1 z4?O*`&Obz3m7l<=jNk21som)X!}x2e!oQfIvc2V-l`*1+uF%~@55n*8r!v<~qYf`9 zcd=PpWgG(VgB{7JoSpcV;?*ft@q{SUPEw-yZMEoW0lUaA&6)Ud43O@sqJYrd%^d5I zPCVOXsY8tDQQv3;?{}M14y&c7=g;sbLIZLb@SNe)S(V9$?Oc!0X4ioY2KN?Cij9U4 zyOh-5OhIXVs&6cM%NiA2f=q&2l}>vdg>=x$bVqmj@;hSfNVIq07II_T81(z?sP0-0%@AzCn)IS1gbipS>T~^BK+q_qM z)M7Y5z()iu^Xx;fdKivm2`LyH++Pi9^5AgYMmPe0wlB#g^ULjSFq8Y$*4Xu!f0_eW?R@(Tr6XqQgbU~Eg)kQHEI(;)@0mlhuwmi%e-)MW-Mqql3K-=X41lZmFneBF#O?)d9 zu-*et%SRl|t-XKf#nE+UYnUBc_wcip#q#$2?k|V}85cNOTng?l_kaj1fs^cA4`IMD z1)V9UE0KD)6l?KVpu^*iHUIWCW6Zk<@~hhYh~WYN*QZ$Q46kW}{jsT#H z@iR$gao-OqO(rLm8`eg%4e&OYmSM%qKOD@!U0CHPDN3->6701ZthNX8ca!AH0H(#1 zVkbwC^Z|`l^b79~7IqIiOME`>^2H&u#R9NZhTHR>@BY)M?udH*EuJHshO~7yXy((? z(%YRr?+raTy2@*Y>a-pi5qT)A*C9p**ctCN$7a^}Y{{}|;p8?l35z?MDYT2@D1vEG z*Cz)e1?%Fxc#U@H85B4-se);83rYeIhfYMhKNCg9GV8VJj0 zm8ZF?hv^9)_Y?@gR5yi7^`+EdLWW(&&cbxCApoU-3py>^NIe1_1%QzNQwx&oguvhU zp0h;gw_|!hW6UixrK+$pu9i2=n1V54v#SxBh;|yNkW=*}Jom2@B@K$&-r*ilSx5HH zTJAME8wU(fS8eZ}QFo*n2nS%q?LTTK7pjNbLx1uyy4^AF3@mrdlVTif8^5~p*4@3Q z1bQ|>EVAa$eivLeLZ>SUPrW>)Hq$G7@bvS^LfFtonM=BJQ3b?jJo$VlA;-pDFfT3- zfz>AIzzJYh-gh@T)@82#H-YQ$p~tte`o_l^E39=*YzeMwAx(Ts%3mbO>RaqmfmZcu z(MW9&F1Mm5u`+lZC==+yH+#M39kw5G6J*gYrlBrfY)|bN3>iBl3q_el>m&`Podh^} zZ>&ixPdT>S&yKGqA>VXnw0);d_?AiY#1K4M_L;EB)z!|P5@e+t8tRoTAbMGB_cktU z4pIV0wj#35g|Fww6Mu+@SI;E4`Cfv|vWU3`ef~)i|1oCMzxo@YdvLV16#aXHwJTiqR@ul?Fu$-Xzq)9ey^@H=7d6ARRUwIh` zw40jwlz;{xnWZ;a9JlLMToz#MM`a~7S4=Y!lAia25Hxypz@EN5Vl;^Pb-11v>&;6k zf)X8NjNC+Y7B}ZAgwTAAQt4l`n8!iECx^%g0vWF!` zu+8J7*IAWM%Z9wH;_Bx5paJy?U;v#)>cl8^ZOpm)GD6_C@!J9^wO_N#8PDVUAlB@8 zGYoR~YS%P=r+&0T_1S2DC?_8D*e+NlTb8=qdZ3 z(-wCa&^`fco3!tx$8Z~JyU>mV-0UlYOp9OvW<`=Ln58f}Yx{8-mGkpqFV3dLp^50y z?e7ji9nea35g-ltvB-nJ1D@%|omdb2@ zDh4`RvX?+CZLwbINB_an;cD)ATjX#L+LmP`Uj0>ayQ2pzRH~`2c44$U0Cjx?`zA)T zO48Zs{umsfXatXPd1SQli;!ui?Gi5AGMYg!;zLj5t|vXK4fIyfU2(`lw-J1secX@u zB2*ZngdAw{lNIU8GPa<10DihuoW0=pv-x z!Jb$VA9h>2xasq+{j^^Y+Io8_y+fDHhH9G$z?NwW;x@zlISH#I$vq9fu%Z*+yww&Plm3Y^Up5Sp|4C#W%wsYU z53&2~UG%eSFS|HOKIWJ9)*i*<=NzDzskde<;a_E720~sGa%zyyFyg_B^2`M1tu zLvhFey$Gk;e`8LQwAlUb29MlUE~R*DG~h1sbCx!5aa}_9y#fJfghyFt2E+;8CxcOU z+dY^=!}(<+B<}{pqcK6sw2ve!Yej?=;U9?gi6o&W!TzQ;_(y$ICwE%5OMJQIRC0qg z)v3=%0&m~_19y(E)aYYf?U?iC+YERsUzhvQxs>qnjbW>oj@Dw9D+^dig<7^*L$U1v zrf*2C*j2ggNWf06b~}$HmUOsS>bc%>b&R37go;1e>WNuV=JT})_|Lys+CfTCSM7VZ zLKj?uX3O7l>x67aDmq7$nl)YOK27-4ZH>{U?fT`9TUDP9WV;-;39%hzfjMDp9#Ck2 zh+2Sm^%gnbL(e5$wXHF@GUdhV!*Uy3-^Sa=%KwV4G^9Kl(W?X}(kIXamSCJ4LRVIU zzPR(h!+XEc?6#$)Xq7S&YKpvSDjHBjjw7 zSWnA7Is|oF`O0^ZlvZ7G+orqJ?0C%qCK%pu!uWrZg-6osil|seWtZhKV zHw2>J1Qm8hUaQyB(mOlrb-`(y{}|)ZWF$aH{%X2MjlWXlAyK}~VYC=bH8kfq$g5Hx z6(nyY=PD>QE>QCT%Pv9Pb!$AFX0Ipeav9Ew-cr3ob+nR<-ZRppn+`C>N^dm0f zZdCOJ*)xGQ*#QO9f=$RTQV+b%&WKBQ@M4OiA$fBmD7f<3rbwuW4=@V}QST6TK9!wb zG{u+vUU8`jh_jtqFki@nkk2$u@03dlzI1#R?v%Q59+jaF$=1;}pA3TqZrf8ZR0tEj zMLRg?nn3`M8@78g`BYPRg^G){<~(cPvt)+at2m`tBc|!N z#T@>Ce2HqsK2@aMmyMQK9p!c}@b@1jq#gd(y54TP6&*6V>I4l5(6zQVkI;k6(uKUle z2DtE|RjY~9e*)XZ9f@$^@x>+>*Mvv{P`u4Aj!8g0*}4*(cndB+5bW;>KRDA$v~49mbTwz z(Af^;bV5fm0kVm&Z{%Fv_(Ko4 z&ISr8>a3Im$QLHH1bh)HwWt+n=XY?4ys7}=fAxr$%8hGOZ^;-gWqXLyznar^@#g#z}DXM>^s<$h*~*>QmY!8O55s1;GXws$`Y&JX5@_EfIE?+ z$+_FL8btqWlwkMhwB_Dp_rg+lR$zv20gl~KOd^=>9$&n=;#zbkcobkZb!Rl_)o*6C zI$(6b8WrMQT9<^kEmWCMO(ApYEHBl0P>~eyg@U6E=qLXAu&bj-f5cHt7da$Ozbc`8 zx1|Jb%+C0;V;!Y~@SeA3E$wRDjHxlPiXOz>D7f2)37f)t{}0aR7a`%Y`Kvn5O*M4Y z8zT_5k>AdVp<&cRWd9R}5aeGgtbhF_d>pXXN(g7uv;+TPQf2H(BJ;oJ7+a?o=RrHH z5{x~Cy7yg+t}tJnp?u{wo~*(7nyy3Xd+sWwuwP<|yx2lx9vrwbzxB^ub8fxHb@paW z58!b?ZfJ68zy?(U704dETD-0avjvVm+2pe}Nl;czl`mEZADbwstjxM9`&sq`ph>Mf zcz3>a;(t(mm8UXPrZs4>68PTuxw@B`9q&|?)N z+2ET9e=uA~`+U#9Seecm_=ObYc%v;-gSVtza)Bfe*YV7g>a?XET%~oqQP^oH$ zASNY0kX-Nb-;mOt$LEw_X@1}`kbk9Yp1BXNjKt+Jopp3V{*-GW&EsM#Ix)FB{nAu;NJlR2ql>TEq7}!x6^W0b-WCAKb>xKLoa$DGz~b*y@nKJ?5ms3C6e)JY zmvs!9cxs$Gjp2JJ@}Nc0`o_S|?pq}%dfHNHL^-Y^O0G*S$aMtPVTk=s3|B{;=#ceT zn(f(V|EZCm=QkP)(shS1=c+0Lus-O#c$GH$c3g89>vsEQQMGbZ4%}2rY)G!k@zotp zX)sRL{i;8esw(>wAu^(V#+wP{)13hKP$eb{FWyLgBrf zY0hlM$=N(+n%x~E^}5g_LP#9$rfMe2V%ot$S^z4K)K(gb&u%FxT=yAA41+y}Kz^LX zff4EE+hQU(fR+c-u690!t<xf*f*!AP5>59)|XY zs%q7gJvSVbGrc=Ju$`)lWbiXlmwVU%brE?`c{B0z@{pE*2*VehM;mfTl? zAohuO%aFyH&8u}xL1o2;q`Q*a$5gje(U@QqaC?(mE8MGFAXh>XRBv5#Gz(A9Q|G5V zkfEjQTW0aAGe?%cqCgsRfio{l8gph-BKvM&e4NC>J+m*Lkii6X)$J=nX84H_Q)2S! zI+L9KX9bmBE_c&HMP_+1OiJQl5RPqJ32vsxBxai};w#qW{5D6TLabAwI{aLv8Un$~ zNms1UOw4!w$Ny@^+YdYj)%9fM1rjdzcCB9=H-V9MtoEfE*Y5!K_GsUKbWn_`V0SF$&J zWmYE~L*2zH#_ZKvMOYO@DZh9->sJ>MT&!cDqEbDC+(s%8%s3!xBw&J_cE0f?PhI{* z6AcBzRK<%>lM~c^eAQDk+GBsxLXLagjmks`tcFAK*!sm4R-cSg-+JAWbmHl~b>IW) zb*(Y5aheVT!t?cQZxXya0fS5TRYH^<5Y5r2HJp({yLjp`=iyHV%)m%Xmsri}j{env_X7`y4#WlQxCVB++a)e87X`PZnX%w<{;70^y_`=M zx_fSp>6ga(yxvR2@0l6v?Z~0ob&BTRRW4LV%Z58T?-Ec4k^Q~}v7C{e9!p^U4sGxn zDt0ZV&ig#=y5G~klM*)VIb=D~t2lwJjry`BHZmVUy^4BxvW&*l>J;Sr{)%%~krQQI z$G_T0A6+~HYGlH&kW?AJ4v_(MwQIujQW{g&W|^<%p%F|7h())0EcMv3z9&zWbau!XpJ{P+8&so|AGD>Gd4~KEx9s(W_5m<4%7R2_b;U-#9w1IN z-I)}qB2Q*IA*)E^-}5p==1lWw4`(80LMqiw7XzllZXw^E+_cX#X5fyyDXo*#{czu0W83DFWOT zKgLCOCv;4m0mbHpQY8dRmi%pCR}&X%2)_Aii|*tw_(hnmg4Tu5q+;Jvb_9AH18_C6j8OZLqYoJKrHc z_lW$+J@eKbeLZ$fKE1{S$A;wJoB9!Z>o@q6Vr@aj&HQuoBPgU1rpj2J&Aakd#iV}v z55ucfm3E`*V}hb0QA2GbCA?MLV6$D~@&#Z@_6M@gLYV=#e->{{8T1z?7k~`!zu9A$ z#)3GcKog)XsslWj6O??E+m|kGJajZ4j&l_?^%eR^S0vh}yPo5RpK8*V)x}-AUlJdB5C#CWRt35RTlCKL z4cRpD=o9;LGLCUYP6UY}|D}&X!xWJ*a`*axO?<#%zZ`M7ckf+!-K9eG_g5_WlVu^Sq7N{Ty$Jk_ z{-!%h?V+&VbdxJ7jBH5$r~IMFzYnB$S8*y}+}hbzvn~%#oP%EaXxzDqn_c zfPU4ziHvrwRC19?N^3wn%>7dXU{DIsR?1Z{aQRxdX(Y6VA_T#IDn(0CzSKcMQ9P?r ze4E`L5tbVbxTU4d6$|N@QahYj13Zm+YCr`8Y1MrKr15~QB;~g=kEkQzDur-LX3Kv8 zNc#!RrjiguP~uJ9q>A(NsGu8D_`{aI>4CgecbYQ)N+zERf5TT2lX@qzPZPlV&O-;N z|v2a=QLwaMfrk2x`{75qNuXFKVgAfX|s@oD(x-W;oSw0#j-y9ZR=LX zcAOx7BpH8Arh4aLJ@G?Cp#*G`$^HrmZW9#*K>3M%sv_o5M|8DOCiwaz?gvrE=4bni zT{<-}po_7u;!3=!hf*==*}onpxTpRzPzypsZ6a#0Km=(xV{1a)roJP##lonk{yrSu zg#>52SHFDsOcaH+9jJ54pfH?2Erlb$Wh|Y<%lJ+A$pe#{QIb9&k~+9T#yGFWohTLt?^gE{nujSo_KotY@dqEBW!I=ZX5pf>Ozb2$iQZ%t6XKn0sC zBQ1GmqKpC8mI7uC?WGzf{P5ZX@jlCIN_ZFZq_6}iQ&JNK>q6NFqfk8v14QRm!z!D} zaZS(q_vl9jqx?^`q2Pm0BQ3woazmXitNkl=MzHSUe8n&_u#|&YdwyKrCQnoW_ zHaH{YJBjhn*#_gmm-gMoKl#CZ(1rBWd#IddsLmKglC`Q)QmCcHH#iH##f5`R>Nn{r zY;#4E^prOlINe_4F0cMTTbYvjOlT+wVPq(Bge;k{xNfjZnY)kmnBd+{LW8u-cvy}; zPv-dtdzaE)T_BI5%Z#&u+Jr20#@kmGgW{Is7KdVJLD`?hs!x1@L=E{ z^2_zV2N;=%5Q-hj;U4HL(ID5As*uz2oC3=cHq z9CxfyJ`*g4xS#n8bE$ld_|6Sz3E<90blAULZPyd%)$5F321O`n?^o4-xPib^HqQd& z3WwrRIEQ|rGf{%|@^$e?+K9irZb3KMOXYHuo~b*VkuHw1St#mq4aA+Fn^yP*qHz}r zumfM(U?GZQ!2cv9kROCdcnVe9NlfwGSZl7??TWUXe*9JlT*AYjx3)w_L%m13G51yy zmbJau_6j3f62GyS;UJ{iz6w!lS%Q%#3bgp)RVtg&v@ribgz~c_H2q%B&637Gv zED9HpT!!W#;vDLxe+isPcFM~wsiquSm+MEo|5lvRyhvcuOjFw+gw-U;55VaJ1Cn{% zrB4qyXEA^xB@!csuuW0RmCEo!o^Z=;yjzb4qoZR+hgh_;9A`?8xiawDd{MsV=Al1M z-8o9CX+$JSSh@hPgspgvdr_yXu$@IQEri$dy9MkbjjdA23IdC4+`Zjvh;vtir)5GW z%!jiL+D&Y93hsqO%$7sxxk=NtA@*e3bZ|;RPGqy2Z8c^;3k#Fx=@U+x&T9#kK{)ca znP0u;-R^foP+a4YsipZ5tw?OPYmsbk7#ve1pH=jP{(9&RR2D%s(SBrjg5enrq_-dh zj+DDfQ`0Z^3oIFu!IqOXY35+reG8!a1sZ-&|N44dcNtiZ$`!Y?*Gys?9@yq(ec= z^uQ>BMgo&Pt2w$O!g}y4GrZr%ZSvJbbg!UOhZ@mevGokaX%>6Njt`ShP9#jTJ07u~ zUOjtvFylUVL)(ufn(Xk@bh|o$6MYlo1cUBCXMAi1WJ|l}W$pg%cg*a$QajiK)h)}* z2_>H35$I~@=*z%J{+n7sv+`_tx*D0=t|^~*KPAki(3qKQ4Nq!}*Uk5`be(RQrn(`k z-`m;YUlGKn(u+7k$}Og5#h)qXM`mn8uNG947_-{oO(TklJ8*V9ZA0lJMM?&fWPL#i z1cf-82TM~qz8Jt8tLV=MRw&E78s zmN!xoKV1UC#f{cDuhM59eu18NZcjyTV}NM8n|BRrKhjS~Bpou9$Y(pIX022&oxvZ+68iUQp52u7paVNaMWlOgrg{Q+6{!{O4QKw0__~ z7Os8$-5=WzO9l?z+5@^CD7$g4v=LFg0qs(Ytin@Eh20JF(E2u zAGs)t+P)@*lRu5ahCoM^jA&vVqyRgo^1fDW6;}HNVGA4#rfCp9r`qlv39yd{s0+0U zb8SCRV}wIxZ&ZZU-}RekDU)OHo0OV8i>uJ>=fMev&0B=%l_p;E6rCM5 z3)~@Cg$SRHMjORzs}#&9iO~?@KjMFGP4pz1s=SmuV*YKA1B`5T;Ij-`Iy-#!2=#%Y zAY5+XJnr^*{OjEBsGGkRu{ozE=)zKdY6iG^^p)^W@xsOhYi%%IPKKQ#5ahf7y)m+S_`zJK`fmqnu4BWM?^ zbA!f9@M%1lQKfdx_N2Jfq_OZzFFuMAnTQ45z)M&#pL|4$1O5R%j)dZ9iB7qUSqa7H z9w3&PK3hE79j6N!u^JkzoJPKY@M~>x@nMEBi@3904#y5QCqvG%qmb+{#9+Iq7b;tB zcRq=?`j&H??=(v60aDP;>LibOA+W{~=@P3#c z@K(;Ob94fi3l80=BRtUYF<>_S%ID9Dyu4*e$R}DaUSH@X@&PEvI@P=sD1P#_o&#X< zmeXHecw+=FM_|d9R3*SCRREM19&}+2I4GYT$PGv9{}F4U_4?VAFp2|oUa|&XO(^I( z-TuKENDx!s7!U6XUhZ1j+U44=GK=b!13KhMYU+f7V=5zG{cK2f2Z{Yfq?EBtVAC6I z-~)AkPxS5q^62_e0+SRAM&gOK@Q6{H4TZj!Ck0`da_t$;F z4|MX`$8qcg-j6Fdp+g7zS=8%^rw2gpHvRjGM~8<=Phv8T-&LMHpxWX}G~Ze#P?P}< zEJ!J^J)fX0am$TG2fVpNi}u}SoSotSB`elDo`Rk7I=| z##C$iKH}!Uw|na&Y#ooGM8bBYF>wzejT8k!%(D*PZF^I&rmMXxV~F+4$yvQWPoXY% z5}gwW*xy_q#6xCQifbQufZwLTJc&Jz%3Jyl(^!}=xdcGal9dW}D0|+Ll);Gk{>uF; z99z@8C-vs6{{x4o+u>dnIp+NsK-^gz?rPd6pWa3l@2dFjveE6Norln9yNY#{DB5;| z*;9(>Rk;+>su^p3V&tH7uv2fbf=Dloy8Iy+B*6hH+5uwpy1rX!iT7&FMZM|Y-pK}* zHE*jhDz?&wt5mUq%4(V8*){H-hcb>FlvZGJbI2sh)nH9waNC)u8n%#zj?V3Ob8!;5 z98wlTINlTfl<=pE+>qsg-2x?{jse0(JI)kk#`Q<6>{ij~X+?KF%B_>u`x^2j*3Vzs z-M?y%G;X1^aY5m6wzm*~y{E_63cK8(O_dIVp<-PBXrFAz{+kgpxz+CxN@PEodyb1! z@lvNjs8?6k6d{M8H%fQFn|BMwndh5c4;@{gL>lz|701nX3o|!lmpW7nALcRAdIZ2g z1`q5-(XP$Z-Zr+AyAgP|Qh9T%=G+g?GpWxQghV%x+fHC6ZoqQ*O0=!%&ffyg@NOWU z7d-l@RxU%amn_mVxo(1 z>R(YEw@avYnkm-2tKpykpkC!*cNQ73f4B$1$N*y_yV#Di-nTV@voQhqm+-6{ZlIw2Zy;wowgmqFI@!sNHrYCRw)gP(Kh)`iI?XH*Xp!Z=WhscrYHkJrR??6@Ia$mmEbT+snc1rKU z8%P+z7PQ;_)p~iwmYiMc>O*}7wzaULXWw_22!U1POU-%|2Y}Q%7Xx!o)Gd*Z(=}za z%yh!uhqRt17{|GSSKZxm^U=vy)QNCIP)hu}LZJJwUPf?+WJ0JaNccPDjO2zMGE$ih z@%yEGMTKScoA&#fi$kP;Q3bE&NXwb(z*)a-F%=N$(mI?FV~C=_s49NK1|8+adrWKt z_9t+enoV-te+x03xc%y9qqLhtC+uAnalOWNl-_08A& zEFu7p{Z7ch3-c+kR`fR-v}k`t5mBnc!JakUrMB)pk~XSE1er%B3oR1?PrD z11vHNgjye7Z{_j25pPPw(OxaRBF86Yx#uu-bgQM6iW`+a+)6_L@?|M^cZ77Gbyr)T%kWjL7ZeNF5gF8E?<#I#%0K zAT{)raAexBVm`u2KnAxC1<8dR*1^>r7Qs5;y$)CtteBVEoFA=eRR2&A2PS$UCx1N# zjd>Rn&U_n*PN#ChnnDx)HRh(9u4QsXOU@cIdh?)^-+57VHKdi1d)8wV*c>panS~tM z?_ODab4pG}|lJLYgbb6chT&tBLor(#oYS z@(Mk|iu^Gk@pJ-z!$m5FIR>qT6XmekN00N=$(h4l_$%5kI7Iq1h`SajawSU!6!Bl4D)7%f_ zm55;s2MF7^(XwP-g7E_>K8G{~O>QnI=M+dw>!-LUcMF6ND{iQzgq@^pYmTPymD;KV zQH{=rSLZ)eP?0hlC1B{5X;6R&nUV6({a%gAj_i8yv`U24M9KLOXGQ9A~E93v@Er40!kP#qoozld+!rz2Z7%7U} z{*{P^0}j}IVlM(^k40ynIq28^29(|##a$48#zE>fi%WB!!YWzV@0V~Xxl*ak){?LV zMVWje8r#T8SzMT1@YyHJR17B}%f^a+$h#J)Xdc^E%GG=e`?oI@iyoQf?`_El&NVda zGMk+21#BtPj^QdjxFD+IyR~hEIqsFW@TGAgnnU|FXnOay*Cu}Eh%7AZR48>uO*)yz zvpmUlQJquFE{DrC3Dr&{E#0zOs?N8=f*m}ZQx-LrX55ql}Thf83BYJh8SIh|zeW_UDSCgel2Ck$iTZ4dbf>N*y zC#timA36r5Ibzm3aN+uQM4QLT2(mJ#<*ie(RU-VJuFLb!_1GVhV(~2rm~=%<9&roq z+LsGVV!p06!s?1BEfI?E?GQ~p$vuvC`;23q-G(s~`RkX&l*7&5XEuQcm9*;h;u94{ z*Qlu%6B+L$MR~|p5y8@@(Bdp|T}Q$Pr1zY}z%L^9J9HuV&*~7$alse-m_IP5Mwfi! z_{{=QpyDI7u|whfLgQHep1gH+R+8(0Llh`W6d=%(i9;79=rpw*E4N`K3cs=)tWs98 zvf%T$1Pi-9d);k+AsmY3N5x1pgoa9i?EqhHXSz+1{v+>u$ItXKs5}_eE z8202}Z+Az$r{xof0?ahx`FPU>nJ$vY5}E_(drLE>NI(UPQO>tOszf>6c(?kU2M!kJ zOwm?!QcN_QE#n^?x6l~X5A~oEKJ+zyDjzQoAEfi_WnE-;5d=Wi1~Df=A>P~7m)p$H zGQa8E_d{~_vdD|!MT^`vd`<@{gX zM3C^AN7%z?`pJV{FJg=4E@s$e==H#iBeBoFO{} zpUoeSQrkzix3sRvzW3krrW>$;spnR8?5WEgf@s`^vN1sXtw|O{tG=1=zI-Ht4*?h& z#+$4LFox-zuh!;&thV=Ao1FI=?654KJ6KRHuKoqS47q2!y3eRa5;-zx3-kC~CTUfe zDXo+!(E#Kk?NCMBrw!N-CF-uB=)y2F&kcGMW1mcO!5+h?6#)vRUW%E)$OU76-Cpv< zMzk>POj_jAB1Nqo!|08FK{blmvZ8u~CxRxRfoP9rmJ~DV9q{TdSq32564Ar4i?e!A zHQAgBcKGM3ZE`w@6nN|fw|g7sn^Q5t2A_F2s!y?mW;`P7JNFPP`~z^1kC*}>)NVcQ z@*9|`JZ|m9`-5O-prrq91y;c5n!urBKpwKtV<^uzvFxNkrxw?UpfzE_X5tcJ396X# z{53U{)pevu;KNG}e2^t1s9Ou5sZ6>NE*q5TBv9jAj zZP>WWB$1;?uDC++;kSOlUoB0Gg#&CSc1C@uJukgJ{x7chF#{8toie}PV?D16wF3=a z2eE$qBPV;NHlkf++T}gv*WNm1Er`;@BGRnO-Na?vZrpN zH{*Wbm9oQP=h8GRKc0T`plJI-uj(fe=*ndL8zgDFX=Qp!{}D?!K~$*}qTtgQb(iSxg8lUka$IcNdl zO59o>R1p97n98LSPX&^RO98u;t&#f5U#6IANVF1M2p8~g@KRE#V8A`|_***_JYQxV zR7M;-nB{EAR{~leXg~4E;_p#$@cb7+JI$v5uF^Iru#~||VL8)X4hDFqS7+ybXh*5N zPL0Oyy7d&c6t6VCqDW$oszX9XDXe`l3Drpk?pW>1jCSZ_5BlzI7s#i&SaMO2o%7<; zlMGVaMu=JkglnkWZDg&=brH8`_)q&dR zZ&+i!cBC$&s!DATif9smtu#`3yvPn?j<<+5W~Qotdnq0oWv$VVUP^h3e-+kZUJ=Yb zyM^MRi--}td*_Wz%CXt!xc+$7@0}s|?+%{tX?#6b_q3}9qk`s+fz1I(NzHWPu=k#6 zDtv{*;O54VZWQ5QsOHR;UD*AFuJAX;QBbdBg`3a*{Zs>@QM->Jn7>-CJ;Fm%h~$oj%s_VRoFoBqxl z+9@&xLQ=Czi z*#t}g@%qKYNgSkM`qL!E8k%Tk;h##$b?fqMPXznh$y`tuQ%8@;7fsh-Cid^uCx8EO zv74J0%(CzSVQ7)HieV3^OhXq9)r#~{|5ikz^q+>{7UmSa8q+}H1myeoLsJWqz^mQH z<3$t=*S`mnmGFQR>YvNyv@=fk4|rj>DYq=mOqW2{)!3N)z4^~?2KA|%NR?}_Wd&`&6`c0=|5y`9wzr8r+>&HF|3B)r`2+CYxZ%T)4&=gj}N|`oi z`32y`%Eal@_yc`nRM1-9eQt`>x%#mXC@(4g5h(*nS<_yNsk3t4fQ*#=LT zjp()N%-QaCZv8#C#GjpvU-~1}O1`d+J@{y2a`Jq5VEbV{7J>$bGAOR37GCteL-_A0 zKD)0NPQ&;L-H3lX?NO`ame7$2_dK}23Xjbz@8T7t^=PonF}O3%vlZ-zBH8reOViXiQM(j93 z6c7T#vKC55+_Rv^sdK?#V$M+w>}Bb4qbXoRV3tc`;kBX|o628RTp=Trd1V0p0!T$> zCzaNKq&mB42{9nj_SQ}x9RV)0Uyh9FH8TOg9NxPI)Q+q(Ln{&=nf!9)Z`o&#Ytcp> z-xO09_e>!&mR=lXr(nRzSn39b8o1A+&rKgu4tH{7Vzr8?ncRy-u_agx(l77E99AJb zG$>JBhNy1a9jCpdK@$sf8UDuGDIbh7wy>h9B~eoShKa@qw8xA>A2+9BMqaUd2pRXrz zV+zXy=58jp8yxXc(|3t>^XR>_AkY&w&kd|tK)2QdOyXn*t?v{-clL+{5JRkm5It)% zPj5_Bj`;jnyJ~VB;vMEYyd({MCQI8#mX`Do{+Ly6vk5Q>HeNxtC6gb&h7^2{u? zrG_29eO3biy~Ue_#lC-EsKSf%#BG@7Lq1!~AWBgQ&S+jog7WXN_kN@l0<%n<`pzb* z*-sR91tMATL6^yadBwfvT3{dkbuCNshkNWdXw4$Lf(t#4pWk4It#qUErtXSA&^8-kj;8J8EncP zfh`KVE;WsgcN(Enn!!~l;0i|xGk(rhRlB^HsrpHDIqoO9>x#jf-kDSf1%w0G?>_Z3 z-d{4Kh+^+wL%GdK8k7(%AKe!;Crip%d16S&XbO;A-eepFgWAy%?$_&dDx~#PCv`8% z$Ob?449mPWfgjdGi|L35QCh@<-}vXoXz=}|MHQfA!w}@Rx^>W%7i-;xtqK658dC$~ zqO|SYZ;c;7kNKAzEL+#Kq&_b~<=lforHwN{v+?sDo!|m@4$fsRlvDGnL00vx#u`V> zP<#FWpf6u2>3)h{g_wytUM(6kxBpV|cI*7yd+&@IX)usqvYI#(H?MYQ)74$j*XwmJ zKYmf#fQ*oT^XYaL=b1^n7z(IS?E_{g6z-8<$jeJZq-R;f?*OXjS7fdau-$I?x%w#3 z&&9n~Y;qP5-oog_uk?F=;4%9W8b^JPj=VlN{;&j(;GF-e`jCyY&1KNOoiHr zCD&wN7r?WBSb{upzxp>{SPqA7qrGa597`)qRsqx45tT<2Af=f>?sBdS3fIyZ;fQDnlM&5o=H;`$jP{!Rn9*Mb#=Sv~p$ z4_G)T8*K%FI{CC+t6bI32MgiCWKKE50p4Z zLWEC4phSIqFj_P5_+-5ViK`SYAaOhzoY69;{|=wnIH>TC-w;*SpdmsC&dvC2cv4m1 zWTM>O-2m@c`3{Ml7zWRorD*&*0zf$IJzwS---W*;D=&p;;3F0yPLZFF@JDhKd7$IG zYGOHaYj|Y|X=vIB`@URw4qa_>d4rYmaapB`TgKAkv!ncoueqlJ+98j;+vdfuKLCh- zs02v4oQUWn{lIfRP^)Y)Jb4%aK*%7k<<1D@QUh^Vvgjfyj~aT$cfx_S(`V$G!fmfNt#H z1N$pF_(o?NZU)aZtw0h$)55o^>706?ki>NOM=AH{bvGhk>Esxhi))qjezk7_4A@b2 z5!siH7&6}+45&$fChcDUzI1bXv${j?M5;C!?yKw<9Uj?omjU0h)E4hg2a?*gKvP6& zr+<=kT3A75UCYW0fm3W>QaH<(jw+di`IS*%;@LWQQLe0wea5vb`yP?Ab$ z-jK~yM<8jHGJ*lgTOfw6{F5(dGBvxY!s8~U8KQgjY!q0p;uUV53$yZ?4zM0v7^gaB zD+~3YGB4O6kA!+IX{=J^y+RqAO#|X+-J@DDsku>g+FXBSuu#9758uHyW>LNcte+WO zK8=NP(_YzfkRC;(NBC#bAs=9-S`B3p%u6A&KwTL@A84f*0!(yLcTKypN za|%Rl#=|$RyJM*y=BemvMp57F>UB`_-ZwxT1@MeH^Nn-i3dM#{rQ|rEE*a@vfbbBB zeXovM+s}P+Qv`GI+otdD-hBMZAGG|=(62K3;xP)=zE5=q`b7vQz`*-aSaW6A4Ch&z zStTHDqeC4Nac2ShYylCSs|0D;xn7Yd*704eV+|1~2|*^<>?bG-9+u{qkY#*zF40)2 z<-wnpdF1oTIGikJdqn|ma;^pIjiId$n``==f}ttU7=UxoU1BjA{v!Gj?>vE>F(&ll zE||jyOPnv2vxKXP2ppwRDW)$V8bTQIW%$Io^L{1W?x3ZIILD=3gK;k$8KRwkwDIQ~ zne`kxhWG)!(LX8rL*Vc}CJHz&ioPC{WU(iCs;wkvKAKIQhe2zWS;#@fiI zwk9D#qwf8$p^+x`GgvAYI&a!~4;xdv2bX;PlHZNtZ^QJ^PK_(0sXbiieusHXJ%i!w z6xE5BrrwAb=gWOjsvRsZjC2E93@6Ltg#>!pQxR?QV~e(8%|kqXPVNW8TdM4XS4Mmi zUiAx_M@*!S3@B!%vdhe1+vlZ19 zmXSCy8#wQ@C|e)EcnrLfZQjfw2JB{iKPLvl^vccT=^S^q>FV+KPsxRMiCZX{*bm3d zm#WEr&RV@VH;Oed5< z9E14Z9sd<(!i}yam=@;qx8IZ1!E9Xbd+~en4l>!wyUNld>|>jWA&uS56nHOuDUJ|2 z7dIqnR0Xh}z(arPDj0v(edqza4>4kjks559Yy1?wMO<6t(1)JuU9SoJ2Z{{0KvyZF zQ3DTyy^`JgqVeJr1-u!--S`*gbTi8Pa_L_q`LW*?Ne7^Lw1I|6fm10v;UZ#07xcRC zz=U_T=vzvF>>Xzey!tnP7E5Foiy8t^F5Pi{oj$r%WDQpfvRA${lVf$|_FRIeJ6MiD z!?M9iejOFicH+1<`mlw;6|{1lKY*7(|6$uK6!E)8W|b<(+S@6x8HBAN>RJ9yT^q*L z#FlP_nw64KzBb~wBxf{}E$MA%O72fX%w6(}rvPBD!Y&E4u&=&BoJ`1)$3tg1Bf3sf zkE+mP$m#fxm>vcc0ah05b8xJ}%}moU_>xamHW5GGl>``g?WDm!PrQ)7m~(f+)8?4I zGW5UzT}YI>$pCi4R#e=b{X$=~0^YlD@t9PP>fP`Bk}hP;hxMr22G&~%L|oyyDa?7N z5&`cJOQ1acuRw7h-LcB~n~D6#?d=*@&8a2aR|swW&3K8~8ucbmrR*($Iy{Ki$cAa;R67dfvljjjhS6-5T2aw6 zWFPEUd!z<~Q(cqZk(Z}>>%Hy2yv*ns&h7ZAo%FKsLt{w~GrV&{bC9xaO#jew7GfNU z2dG=0_o+?CeZW2Xs><#}Ev0n1%JJS5jvFjEG zZnCT-$&FU6+i)@#-c)M%ae{{lQq(N0G zd^DDIxc*9f(0r+PbhoDO8y!VIWC zkt=~vd}sfRp8e5X1VkDEwh%^$!YGt zgZ$GgU@r7#S>EiJY)$OziG&UFC=x5j(7!xsDgLPLXz@CxRv5`u7erVT*#Ov>q(*2I zs`0;_DW6wK0EU0yJB#@^0!p+@mwpL8Q1wXW!T3dR=%z8t6L=R)9Q`GQDDsDh%W zQT5Jh3tEl~Hrfm4s?yTR0!Q=;z$aGUKHeuhgFVNaC04NtCU4yF@ec@D98LGfN4YMZ zIyP*Zd~uU)H$AKQX1=E5j0frlHCC>}Cf=YuVvVwvahk%Ow_Od&u(g`!xN?WFv zdU`Qo@)#&5ZSgfVnJp_Sk zyT(4IH^+Lv70kvShStIj`Knmd^}R{U=aO0z7o$1B>6^jaCYer!1(q3aS8~ML2OpUg zxf%9qSvMhj%-GCB&&ff`8q^4#^)t7BAPxM4_RS!D(~w)8Lk%SC*_n@h5LGIKPI$D<+Sy| znY`qNgJE5&ZHc8ema9DyeV1ti+A*UBge7zF*Wv*CwbwWa!XVJB7-mM1o5Jt0*v%Q* zqHXQMEMA-rw;X0b@w>n+_(^Y6Oos*Usgb|s0py&if!$;@B@_6M;6&8{qQemoVk+5o z3ZjA-yzArrYm-B@k;Yki8(+SD`64MIsQiuE@jy%iXeqE-nRI#%A%V7Nw}v#CLa*T$ zGj=x1CI;^gGxrqXw>IwDZpIuAz>hoVW!Q7WgQF-hkx!1;dko60(Xy)azB)wf1D|Y= z(GDmK3uCx_;b};qFLO|QY`~KZzYA7NM{T=_&vuE$)>{h8P+KuMRSIWrn!#kSNW9*K z2P@-;UH;O(+Aj(nq;H*CO>m3#ii=gB!w^TQDUF$nZ6(_|r%}E2lu+fs;s+iqCg@&J zh0??UF;J&TOKHi48)XA^8y;lO_hYl)5ZmH(ZUXsznNOrDcIzJ~Y@y?GIT3iz_1s9; zw(Hs^I4|q@)J5lZ9%G@f9l|!7Rtc(F!X;?6-OZ~u^Eppc7-|h@ z{QgSw!bR>%gu-1{*e1+$1hO~Lk5;A z$nJy|{z{yKL{4)Rlx(S#8*<&QWluCA;gI<8lG#T*P+utAa3F*9tz$W-H2u^uVyvtul2FAeS)MGj#|WZ$ zL>3|wl)Z99qky8lgfvJi;NxAzs{xfk1q?fB9!wi)LcL$L_$NB`;lFtR2WEGR_xWRh zON(Hy&y%_k(Ow?r1WZ#+o3MSkib{(f(>;e{un(K>Y@*?%->Yoptc}lge7-a~WY-w; zZjnUy^&Z?JQF6sm>w-k27u9T!W~A{h**lCq8)U3Hk&AzvqZo(#*K?#}LfRmH)&4y= zouaUMk>M)efF@V^PWO)(fg5J=7j?7KTP|L68oD$1wj9+${2vwZpa-Po0dF_atSpOZ zR%7b;0Q#M_qqhp^(Rk6n>|KsNx5GY05#cX)N^edc((WCt4tdHm7R5GcCd{U+M8@a=;%3~6%vL&RbKdsI@k~v4JLK!o#h%V&&2K0P zVSz^MBJ<@KVY+!fCu%4*P$x(YAsJ{**^X>ib-X@By@TDc z2%SOInX>F-{ts8kP-ea@r*Y98^ffaH%f4)Nw+9U_=ceyhcsrtA6Tmp)QQ8Loi?|=w z|3O}3alLalY5JZnvh3JD+439n`tyPgTk+}&luq8i7c~OC2@veKLdj)#TdFoSBrT}& zSMSvI7k3UvOOSQ{B9PO`k{P7(-iq)xsiB&Fz4`O4$%1Imr?qejTd=^hsj+MiUwk{x z4Uh@^j({t?1V8?nCFM3Jg`{>?PZ0udNiA8Vd_2g=Ramygjdo zR2nsmw;B7TS1+rrkCbha479n|Ew$2>cV!{B(ZMGX@QM#Zd^Hup8!Vm}Gfz>DYOS=w zs+FW#!LwE2MyobpWBbSlU1h#r!Bf@&BVTd+B@Zb}*3(;;B!$#))MtNxZ6C=^mHRg!r(LTdk{&&Husnmz*Y-wO-C}r7{TInR0C`G{4B1kyYEMGK}^Z za8X|GYHJ`IokVr(sL@_-sfq0#%ipDJDd4D`yO`koWHXVqZH`Y-lCwK4yzIw|Uq+a# z%##O(qX&>jq%a@nIRx&lmaO(7mM*9UsWb=U%eQYv#rpn0+>u^k-_u(e2k{wy_@*G4 zpEkBcKoC!+T7Kl{C?uvw9weLH0@-~(FHaV&l%ACns&Yi2VK3xjU$q8>h|(9Bz&f}9 zN#*^)Ti`^P$RMJn>JdxIa@l;A)WIn-Y52YbI=14OC3o1>tUbheGzvbHBcXf5v4&xR zHQm3RLxi*G?qSIz&iA;)WTZ+VC`scJmG!oGebox4>#Duy13Y@rVg)sQw;$$ep*b%` z5qU2GB+0JYjWE`o^zk^v7CK?05kav|1Sp}CiGg7)LSi@C&xqpA!Np6*04O8A?2|?S7ipY093UOW(bP9kjYGiwjmR-FwhtM))xagd^SX8 z#RSQbRuIJ}3LS=h7~u|j@?&f51fr&$ z>oKh_3xgR&dq#buMWBOM$4+vV0&P<%tnzrcI@1Z2j2+z_ef4XP462ei=p=aJ#9Clu zC7P-zrz=a1yximGlHV)*zk{z_p&+(>Qt%_bH6KR>L?ml^wiIgkuTRwqrZzhA41QP8RnH|5u{pW{Z5ca*`sm%{g@u2gE(PAH4KIQ?$r%Em! z2B3GKb|O^32VHgiAoDq^NH5dN{-7*M!oM6-pm%aQ|HURk6@_Q`i1&Lw>>0*Km(G+O z$^uIjRE}IkTj@0AKrXCJebtu#?|7jy(X*LfUrF8zsr#{u(FO7Gn$6TSrUiRN!qG;% z+bzmCBlwOBNnQg>s9>=jTg<+)Gj49xNUtBeOlo}JF@tIK-R7R(s4~iMiXwd@i^OSoj z6pU3yVWs6Ik9c@`SyO_Ok^%2bTQ1wbuVwW6B&Mfl2YeGZ+Kl1B%KUOIR5@ z9qiZ~mB?7lCy5 zhck0Be>1IX><%=IdRQ73k#8YsveS!a__;NmzC26{nHgO8km+C1rK+xA9dN2Eg<;LL z#oYq0aFdfiUQ9O}9Cx22gb|0Snu?nCLa4_Bxg6<4va^FTUc}}}*g6sN`=%wPr!QAi zuG_Zy^*~8&mazx~)YO@LCm`#l%9qRf+X1Ecj)}A0Q!%vOzg?fzHa7yXrU`#LHXj!U z&JC6e9p7JE{_I`HRGy(J=$+9)V@GhEbmy`{-CV=f&htbP;mu+Ly%-O_!r0L637&_C z=!pERL;h_B^0Iaa{>n}lS4}70ku_{5{hdAR7kG$QTl~=$Ux+o!7E{&dXm66AB7`$XdQWk4Qcy#Sug|S6wb2U3Ib|L-%6j5e7-ixbxD86doE{oy#+3Pq zFr}e}pC@ZWB3plr*)}#KYUpssb91|?Capt5JAdSEpovH8siO93(|&=A=EzNl)4q_L%tD62muT%nV?mW6&2Sk1(5`}NZ;!D)|ftOUsa0p8Gy!vLAU&!gq1|eEE zN8!iUth2P(M4>Pnglyj|3CuvX^6K!U!rxjC2++~}pJhus#<~Y)0wOqMHy{&}Kp@1q zq|_+C*1n1PP3V;ryjx7a{i4pVMAG9EDIkhS9AE);vHIf_FNuT{=-cRZ2WQ)^MWA zvQyzBt*`}lw;n6c8Cs+IAC-QZ;#a${8bE&C=AG4s3PQ6#w_w2KFI}vh&thh+%GxfP zSt&xwa~H>Hpy1+;FMPhrcQ7^QklXhE zV90Is+=U}L-jHVm?I!#F>`FkHYLgn6idy?prg-x5>Ipj6@K#tCEO`R)XB@}qqfGJ}OG|&_`Z>pGd^iS+s{S8tHJf z^Rrc7z8!}W|Em?)dc7RMP07!pvMF5iSCanc6+Oh)d|tH3WZ5ZV3~aY_t}fQ+g8x=c z((f#r{G{b=VE+Tww(Y8z+O3TH3-_n9ViG7ZCP*}l>+WI7%&u+3DGrQ5I8rC&HFw<> zlJ~r?L?+^Xz~h6NTx^Wo1wS%@Ynm_-csS}8e-md?{Yu#*os%*lJYNbvpVKyJqqtej zijHcnv!fLD@5NKdxt_7P5W^E`;hb^IRp%yv zIp6)i*)Gk13|82m+{plmAmODatAy3i%9xQ6Zl#n>l4PVsp18gKTe*_K+#z0c$>uS< zxu@}>;GD`i`FN0gjhDScni+g9oK5>-0B-nTk<$b;Tz!eLGO%hgDq6+!D)yGKI20`h z^E3g6;K(K@gnD-9@X5H@5u;uxhH5w~LhRhgA(7hfo!O5JoY#s;ubs_xXuPQ=+*0Rk z-ryNStP_mc4$S%}qc_5}=fpRVGb!Jn#+Vd#d~Nw{1iH6XURY(+M_&JU>^rg8z#e2w zF(B3Qm!4wHPrt%(bM)&Q;{2Z7?QcNq>K*-hqPIujb7|WGRIlo+`PzcBxFQBC?$tE> z-ZVmVDbRO;gcu-sy8eLs#tw%Pmi9b)=h(P)X`umk{(T*1SVhID!Tj&o%$tOMi_}`! zk~twjOl-Gf(Ud))$NZ#soMl>VYB znuT(xyD^L2$k`RUoz=6-u*+>Nt+Y<%G8KI66(77&cdz!@;EXlg38>bg={k23qB51A z+{u7Gm*p3RE@S#IErWo=;JD>of{-Ie3QY`~XVknsIjLAHbwO#u7qQKx?Mzzz(`sPe zSp}y0v!3Gu`kiWM3dY3c?k;C@y{nEKPz3_&bjj0XW)9!7ox{jtH3;0}3YJPixyAM} za5S-aWW@`TB8N?k1w;{_g~G}UPDQx5HJoZ=AC>l%^ZF7NVAzVvr72lr=D~|zsdU7U zZy>C^&~{4IFcNDdmQGnCLW`y+G5fYN;P6;Shf0oE4P#4HxUocnxvB%F}9v<*wJI z^flMQvo>UBqS1)k22LEoDtix17VtS`E+#|>BMx+5$qakmIOZvrD}6;Lz#<7KsX5zn z47Lgo?X*T)cf>`Ya2y}h`o>D3e)X$YHnk;^IpPw@iE&sELc; zcbTFP0fKJ#nJ@)S(y-He5(f+o>fjOR1u`LoG{fC_1|LWgW=01y%A$XJm$&PVlImuK zOgcY1F#KGDV8d*yUxp7jiYK@=^7YJGif+-pC;c0O|6UXtY^m$jf95Ov9GIg1GYEz=n)ISdy9Q0^Ylew7hyYa7&6+9V?2wWR<%@BH( zw3ynSVmQgtUj3uTlA`6wIouel{~c*JsLTYc6`qMvrLS6eABUy{X}B|~VTE>QD=0Ci9O$)p#oI*yMK9-v^~h>3W5b$(Tc1~SDko+a ze<1~wDs$SZJ<&e3)w5%#QO1Iazc)q?yY;+Z^_f`lP#0)=r6czMk&ky+ft3!6hnzbhC*cCbxOI0Dl5~x7{kw(H# zQ=#71z>x7Noh8?1knm@n1G4>4owi z*I}D{Q=eq@nU{fkesC<;=P(bHPO2fdt&14DJH)x5)+SGl#y>ywLumi{V6HPM!BHw4 z=qtQ_OO5`6B89=-430q$ND*W^Ra!->@U+KJ78EgmyoZYi*DwLRw*3NGcz7A5ZaFQh zeV(Wk*RWgkJF7ghrhz?C?vJ*h7T9lkyHCA|ej9~aUgGsciM&wl$qGPDUZ20N0w}5p@^P z=>oTh&TIk0&{u{qB@1$jQtVtR^dZxf@5 z12cb&;JqTFy-Vez%j->drFy|w;+%j^!Ze7owePOsb&t10_Vf`~i?PW(^A^07C!uIp zVuU|`jDfK@1h$e}K2rDqBX5wO?=6R{>Ba~xQIaQQ`8hy5FQu51oX@zNJ;rf=nB(KrdHo5!`?g!LNyM>W0A=qgN z$coBu5Hj{tc8)CG!$i3-A)hG}pf}dv1a`BGb9AxeH=)QR_*FzpyG6N!e20T%I_@V- zXqU{bJ3Y7F05#PJpP(SkJLo%;vy}u$u-#TbM^_41_*ucYvI>E-mUjblQ&+8}WlXVJ zHg)6PzoNkj<}FzV0lE8SX>R8%I60TvDz5m|G+F``j2$t%XseJ=y zyYSb1?qurSG6Ts*YM?<0Y8egs5$YtXRBZB7{g5A~<#oTC&S>^<<$d?uQ9q)A@*6t> zN)69#`dKXkRL&PBLhs4fa}6#Aj#t6(*B}=Ny)9X5bd|55+lF%T&YllLct*A^kCjk_ zstiqB<1n1eHNHm*4oeN?;In2$Nu{Bu8FB}f8w5Y9Mf|~je_p8JFJXoSqS-*%)`+(E%g26)?1|v3-qB2ts4_u>?lRc#F$88Qr z+mZ$$AJvO&vweGY=ATB(#`pmU&MBHV{N>11ds_I?DW+#qT={)h=)c)V2EoqkWK8IJuhsee$$FNEf zs!9X~S1svkq5`*6(+9IbmQJ%F;=eBgUVcqV&jeXvNv)WIgSy2O=SO&3c7GMz?HwSW z5AUCPH7~R22aDF}`*ebd##y%d?(Oi^N1iI&-Vf2FFWRxuKN{fq3TRAY611&RwTU8l zF)3V6b$w6_Kn`6MGK={pi;~|lGUSWB^|FrBd$OYYz)-G#(-b+RGQ7KbtVAR=7p*u^&mjW6sIVu{`<%{(2?)4Y69o|lDS$=D4Ft2v1xkBZDv z4n+B*$&H1>aHAD`8%7$O2dxd8Ll*50JBHr%)7euy*FwJGuZ@+~^k19aV5e}AI(_Dv zO`PUWPdDV}<}Q+XFI>1B0*#E?Ra?JX`cFmv`ft-C>xLAN@LqVj;u5}XN^9{^I;?WN zaY}RWn>lNyEfLdu5o+hR8WkyjCc{jBYFDm0JrK2t)6icJwy3d7W&aZS3F=OFLUY#_nX|HaNFg=>{uOGA3-F(II}4GIuE zM}hx1sW|!ld?fSYYnU0v(Wj3IdNs|_D8X{Y8eVM z`9EH7N>6N->!4HEEFnb0(F3RRe+ZYVmJ<>aAG~snKSaJypmJ@Xnl#+q^$fOqyoJeE ze^*KA1K+bU{HtlDk!tc?U#YQtbno#*YUQtlO}IHp|7KxpA_1kdH=Komf3*Bw+&7mq zI&d6hFk$kEVanUa_rT@h)n+$D`u=HEaqVApwfCdjdK-U9YAVB}vZv!w?=f}xynnaD z?&+$5smK?pqbxli@X14}9IN=LzNHVj4W9aI(BK;WDp??W{(g~if{19#`@8K6#8IR+ z_b*zr3mtc;AL3T?PXm*U*hMYOX2eO{Q=?zuigegbAf-=lIe11vvs-baa4&C2%xvM{ ztoVc#L%JDzkZ)RhMzwoMRez+d`ipiwf#!Fd?Qp;a7`S&ezMYM@R++paFgIMYg8xWH zr)~Zk-nwEk$rWOW2>Dknm)d+vjTDGlDD8SS?xZQ!Nr-&?Jy8k6e>2MY_+YP(9~4)i zj)Kd*Eb;Dp(bJpfk4&dKSpv>gb(_@J15T%=F4~lB4eIuC-EPO#+FPNO3KLsFvEc{k zUMA13r-m5uFWZiIEt%-$_BvtON%}9jq0Bh2`$gX|xayza>V1nWU48>%Gyl=!zNu-+%6f z!7F_F%8(IlQ|i)GlrsWFr9$=pI6AASwze${Lvex@FHqdw9SRh8cc-|!yA-$LP#l80 z6nBb4ad&rjxcl7KiiOtNW^6FLy^qAVs=MA1eV=awXJ=I% z939aSFG}RIKf%GlaeKtuRXK6v{XY`K@}sj*#kji9oMB1DlvAnEqGx%;Vtr_BVl-N} z)AyyVTtv8_Y42YbOlAzr5Nr#`Nv-fXay z0oE&=hfDK#qY&qTwHp&ZVr{g!^M<8oUo+tn`#7q;pr72nRxpl*AT<`x;{$@ndjM>M zu_J%8O-`-B_8Q}I%U_zrbJJh@^4Yd|#^YmeCqnIS5bqyZ#5+xWItc3Yq!;>K;TZUb z?jGTQ9}?T-UcKH!vhQ^!(S&^++TY9X=AU2ED(Uuep736KxmzWY?dO8r7%=>^#WN0D z*=9Xa1&8mad9Sp<%m7VElj5E4jXb9!*n-4~Zzz3ece)wo^)_kNZ*O2n1I?dTEXA16 zp2Wpk4DKp=E&#tBeJ}P>|FHC~qvYS$uPU?p-2>6hB zIAK!j?)rl zh%A%J7|Qx%u2EN zvX=NqW_TfLdP3@PardeOd zZqV1##aQ|}UrN2)eOOh`Mk3+hZx4)uwDjE#kiQkW#*}0O;=yDHXrA9Y2uU*lQ@F`+ z%m%44y#~a{QaMkccyeGk%E(#sd`d2L!r|3cov{m7utFbDesF$Sl9g1_UDLWY`Y9xa zEUWw{Cap3=*yIT*czINVp>oFLRb2{R5LL(QpD{yO-wJAhaMCl98{bzgQBbKKm51m6 zy$JW*@QJy-;lrn6$9223w*EFdti%}nb8ObdPb3Y~<8K$w7z`+EU z0bxgh0<;}9*8PG;qpzM|PVV*FAhJU|vZ3Ytn-SvjrXh9NPirP^Noj;X5Dznz{vj7r zg0~;vC!WQmKPhU}%9!PqY{NpV>)#*|e$onj`evRU3o&n{j5lR1m~s>X1gHX;aNN!< zEmU$AVXY9=k;0DM^t3c7LR794|438hdJ&GpF*(Z-<5uLC7EEeYp8pGsZ02p+$o*Du04laljJ@`@d6n%n8B^7rTjO}*kKQ}ezr8f3PZz3g z6uUoruEK>J;z;-W&2ez$O?jIE_ec;&U2Yw8(UK!bg&L3TP8ilU9tdo-5qdB|gkE&P z9|7tD7K%I)9sGYD2=?!F`hrjd451#vG zhbkJkqjEZ6zD?Q}uZQuaf=M-1#YQRKO+VA0HnWa?NSuhFyTvYTeTJnl#DX(u~O?&$Sg{T;RoB#zlPW*%0z3w2J z>gGINCjM9w-UN{!An zSKJ~Ow3&SxI2i?%?TeqF>J|)_#0FhxcjjpeIKmJ_snu@!=q^EX_44o*TgQcRCL242)> z+2%FAO~%TXYU4zT^1e-Y!z`D_;X6E-ddWOkOxf zONpQaljqYdGCf`gp?ISfJQvfyUcFMM0mI(oPG;W|LvXyz95a=7;~5}}ZOyTXK{6Wz z=4{0=h>~xOJ&)D83Oi5)dI_QGEJyvsU`N3Af76)Sc0QW$9HvCtFRskP?R@@kF(i3b zz8k$$)MS38toY^P_eP$H(f619n#vvm;<4C2+2Sy)RRV;e7cDBXnq@kZ`@2t~aUWx<_?jOsdJv!Nq6~lG~;p2u*$EI{IEIDTU+qUgvd6HMX|E)Zyl12=HPJP*Q zsO0Lo>Xqz3)CA2t8`}6I>%+WhsC%U7QA^;}j}d&P)ovRR9mKnx3S@x8CD26sVx+p7cW+vVUf=io9Tqyy3g z^I4XqjgsumhlqBcS487j3RF-Fg z(-9R55Ay%UP5c7IslCJb+M{k5je28X@}=ioThy5!h&64k&Z6Ed2FY?ER{kicWIK1}(3} zd@BL9WDH*|0JH1$Zx&4Q_`0aGeOLXDXMJE-C0_dTL;f^P3oRYv+nxfRvT(~kL1V>^j#H&!KyQU~yV`4r6pdW*!g?wj4Z1T{VCEXKiU&V_$p zyH8v)DKKMjb$+~m-999G{1cpMwK0O~CvV*+AEwq!)@;9l{XSl?7>h}x3@x2PkHDng z9>8VqqA|05$dr-Fx9hmQvl9xvotB2Xb+9fF>hvwS*JeC}V{5*$Lg4k^zkeabaITlP zNBAw?2Ry3fRsM+7w|;awwb1;&V2fJW=yt>jTlR9yzmaBDvqNuJYX=T&XM(dYlNwwTGchikab?2rUMyu_^# zZ@X8YOL8`UMBdYR<9rSCt8W`_wR=gZ+yv~IP?&`zzR3`E!#KYQ=`|tkGu034>RgxJ z1^ZDKp|tiFuD=Yp=gZb*O!S9c(zqtf*iEGiT;<5ixhDWbHmmpM3rJ;=wQF;hi1(&Z zH3fxijfCO;%aX+k%LVf+S0#-4*X!jSp0eQM8mvH!9{T~B9@LjnDxf+oP#fNmhWxwZ zEbrxT-c!71ZVryjE9>|@E+IZaLBUMxrZWD!7;Co2%C-vynXkD9i@D!A4h4DF z8ea=OaBrY`7D(rbzCWKZr7;_QBH(riz+7>bsn+jceWOtlX=J~zkd&R(BerzgG##G} zD_g}NW53A$ja4+78URbFT8ZweeAC3*6wOP;@ce~exMsvs1EZ1Su9Hl z(RA=qEd+YADd|g)Ba!af;LcjT+?-cQ6L1iSw(a_^Y{R77j)wGH4)yBn^@*v;IwI5Z z!EpFe&NN#1K@`U&yj?^@krw)*IOVHv@e5+!!Ls2R5n^B%H;(JcQQFDM>KOIiN%|Z% z{mX8h3HF`cQ2Zn%N_zS0Rhm1?v0K}Hief>+?13+k9~_VBxh-al6gkh^qmuka<5j@r zYDIqKIkerR$;=dQR}V(Qi#p3^$liXZl|npnXIv+P@n}G;d#L?vgEx6v?0kW}25x=p ziIIr^A}*Sy=jHR}1@z|e1CB?)J8VHgsc4k9v!(Sue0cX+Vrs9GPpUIezm3i}$~YRE z#u!@U+i<=0tle1S&iR9NQY|z@*l-A+n*oHQO*n7(4MVN-f&Ak|*GKo<92Tv5p*1^- z1mR_nH;Uo&)c@+wDU>Kwb#z&O5Zi-{&0ryc~B6%jlyV)rIi8=V8KF6yFFIfvfUf7#fR zP@H=NLA2P%VrodV`pl1PR2giM_rOLLRHwV(f$B^Z2L)B7xW||ZMDaFJbYm!Rj6}61;g9l_=?8&x zQ&zlsPH(^waztPhpUF2qxKgHg1#}*ATE~rP{a!~Oj~B?;YE<$rd%iKKPtBnT`R?x` zPg{dLv_8+*QjYq2u+wdXpbOM=B*If_u@@0^83neUd}bSe_H-he$jjMnil4PPrDlp0 zGx7r_Cx<(1_Dh%}8l$ZyoHqVQ?Fy)5Ou7|li+>xOg+UpGxvCK`iL^dG4@$;VJG0Y> zvtrOm4?!o*t#U`J+8F(?N=0_bynCTMQH&lR=NN?))6R=y@) z9>YuDkNL41Q3Yc%lpRj^e+XJb5_@%#>8?!-BiYd;LG2921h-2o|6>sLB*8wvMKC$x z?G3@AVxG)t56sl~DqQ)D<+{Jq`J*Zy*Nf%~o8SLcTCvj_(Px$OH>HC^k+DP&uhFYu+oFv+QPLrnbWM$IJUCxhw~ptX}4~VW)x6P#EkZC{Ea5`>+(l zZ1Af8L(m;Y6GqM?O2G>7K2MHL@`)uBSPEQH9E-y<;G6SMwJr`^y ztyz8cTjECPICBA3CAES<`kq03Oeq1VhxqT^FlxU>5dP#A5|g(@AR$b%us&QKMGxaB zNMZEgv#<`vq*qp?hBt>@r}E073r^^OtR3wU@e-KPt})%Ty8bx#&FZ{Pbw4!~{bb{K z=IXVg74arJIF5u_WyqK)S^jPSS6265DF4e)?0ntap811#0%IUh))FDJo)h6AizELx zUY8hJ?>nAMaf;T+Q8y(IJzG~XgZL~)YY8P!@31H&2lw~#m7 zK%`?Ee#-WbsGiPIM9`EyB*^+%P?gH!C@U?E8gqW7$@XRLL!-J1th3tT%{6Mww>i^Oh>6R|b+T}cbjBZ^Gv5|`SM2fe*ppgXIAaYgtEeHyuL=@C159jV71J5ayU;9@ zw6z#|UOR(!ILOSSZMm6ESmo+c!oQERP0W(KG9B7CpHR-|yUlTte}V3ko?)FEpuFav zQCve^(N$p7SY)hjIdJ#>K$zX?fZXqH+kPdve=E-q$VO-Gepk@^SP+(~?a_s zU9;96y-1x?5{=<7?cw&Ctr2X&25C4Z_LIN${ z$mo?A8-|}EQ(ghcO0E|>ND+ah4i}NVu+SHAXANV=Q%a!|+VCS329V3H(-n<0S<6l- z)HUTCU?l9Xlnx9}#r>?XSdXNsJ5^Fas###AQ8dKz^2 z9%b=qxjERat*}Djmu&kwPt&UU3-gsaq5dn)_Bo{r-odcQ(BV;33cpRQn6l$@4;5|x zefLdA&$0)-z3#parVIHCB7I?p7mp=Cu%53#U++F9_kYg;Y&+k9vDh-)ZBeF^ThQ>o zamjL+hr#)~-0ygy|DcoEYLBFi?v_T<%E#3Q|A6j{qIoljLjq_;Wz1Lt1<##c6Y#a? zgGXFx<7)|IuqFem3NxVusFsGi7}d_t#2YA008rpcGr<(pT$LUh>pG`N&c94}560XP zJ!qYZT3>C#T)h}N4Dx1R0ep-P4U)lVz}uw-w|;;B+NpH!T+Vcxy$+eNIFozI;eQ%t zYL+)J{dIAU-O3U5@nzS8*;@W>E#cBcin-FWf=zwiqO0}QE4SK zz-`{R?4qzK$X6C*Fv^bs@x~CS7q)Uh+xkgtrH;|0FjT`A7F0PJW=}paU6(8)95<|` z0|vKow?ruA^SRaqJ{SK%RT`S|ocCYr*XkT`e^?d$&JvR@zND4;$Z$y&YW1o!I{z=9 zHIiHt%ex#B(MaNOomyFU$g^Z&Dy7DEP(NZM2ia)cYCT#n9`IFu{2>X*E5$1{(Zx{i z!yNJKuga##GM(MC4R8J|C=ttw4@2!^)P}#*HFS`QtX$+TDuxLN9rbr=sHXJpm!7}M zmHj<#BBmnfwr7XGs6rYPcnp4?tqp!YH3bZObh?~>STX^DV&{_=r;^;>2_daJ55Wn^ z=Qg@i*J}N;3U4b{Z{^kLt^{vzKq89I&D0W@m$a(zS=m_kHhz8vEA{7V<&UTKGht@U zj@5c zU{b0YHh-fzDOo(tlY>8Zi)H`h^<718Qq26~r&W`zgyq~^EaFXhebi_E^HM{eC9bka z=-?&7gCieHQhsy0uN0*A2ks0QAzDhEUOd8cWKK5Oe&A36xIc(SF@Hygs(&4SJ``LE z*X{i6BSVqQ*W^|oMMNK69`#$rci5zF!EGbaB$we*fZbTjzHZp*VMKJXBcLhl0A?!4 ze0xw>u}PZ7C_1WDt3$oJV5J>gPIvr8Ef|k4yvL>sT`}&;a$sS9cS7>eo-Z3`ou{rF z`QH>70&~A7*mhl>;wW;bS&e&4jgUk zRrfkQZk7MJMy#Knp2TJ%RIEimD7xpJU~DY#nVqF-Hs)%-e!-B9Gmbiq+$UWW)^3wR z#;v!c-Zh!G(+Vw)Y~nQDm#I}2gJ$w{RLNDG4%+lC9{Q%O_LD#38)sHzDh=Y`I2-C;wNT~{ z436@?u${4BKsT#Zg}zTsE=5LoJd!5?07^alZuQ$xbSdMkCBbET7M z3&Fqf3dCbRzh|?U!0P@H;mRi^BMZ&YA5Nsm1621{m`zi}2ZsexeSQ7S-CbxiGc(01 zU7i-dw^l_9gH{)s+lvjNkzX`W>+9=vcIA#cV>)A1dTp?KqcfLtySpowms=)}JedeN zRn$6vm@yf(A=bOz$ieNR3KT)5dgpINYI#m?$|B6z*uSo*ZaoOt8y8M@^J{$xx&O#1 zNyZAMBcYxPjJfnyBL1+#4tJW9=}TIf3?06_Vi4YB$>jmWUg@G0wg-z(=5Dl~K8UC;GrqWN>;S+Q3op1Gd#at0q3 zM9x>$K(RQyZ6o*e$uBIrHw1MvM8XXJhJouDn^Bw(D*T?DKULAl(odiU9d7$HgNk%& zO}AEc_LhmS`%~2=fjmn-@X};@DmQ($1XJn$^|2N$z}sKq(x7T_b+LYvS+kid?DJk^ zFekNM0e8z?Z8*jWR@9K*&0Ain3=~*5)lK41KnMcKV4cv+MVp2m*FeAITmfg9C31*Uh0Z;~SPVa7YZr%F!c@_7J~R#k<8Y)5Cun^&j~ zs;wnIwxo9p*~sC7mZ6SJ==Cw!VZDKHmS9AK0mk6{0sXh&oW6NH<8<>46x7!emuGyz zb~Q{U5HH+Aue4= za`<|nXR)Am7wCv1k^~mOARdJ83si)D#PKE2@1jR|AUOr;)q~A_FiOGz!;)tu|BvM3 zG%mGzDSVm-J8ZMx_|{=0K?tc3gznFL!X5^%mAdE&}Wb)P2959r9gM% z+FYULlNNy*>657UZewdPn**abxhR)BvZk+h$b`pMw;iE3QQ#!iU$cL;`7ZXx;D3mO z(=AXV3+=vWm=7fqlH_@g$|n{W{4^@nYjYcSV;dhB5OXN*&d9E3x_VjKL4h=L=85d)5ej3lkReZgV>exrDJ%=*fTT4FwtPjU@MGs_8*iTF{VTFADj+ zep;w5U#Jt;%RTO!3M#`(EC6SStmLu|{SCt=#+WP{awFr5K0Ou<6hTy58EVei6q)5{ zT;s$xtz4O!UzGePY0x&NF&Ro$_CAjL^i(588HA z3(s!b3Z8@J9wF*54|L)yT_uO4Xz>^o#z=bZ%#Ie1pd;)4JVKdVfj63_=GFe;`lD;pWf*# z=>Pnw#C@py0(f~2KpYykU1pyeK|71U?Fvm@@Xew;}4ZlnPCafK0dAY?%|!Gp}xRAvT$y37En_ zzTM*%F{oKWa-UMGGXBX@j|Ca0!YUNPQA#40vIV*jUD<3oeD%y+JN1<1R0ZRzWbuOY*%|RG z9U?zk8_IZyv#5`530J6OKk@cQ#OuqG0sM+Y8x0|=X{IT8Ka+T!uyU0)UVL94zQX1{ zk5!w=o*bWQ^43~8YJ6|S61{*k(IQu_t}rQ*X$o+&KV3-BjJ%^L1^n{?^gHbA$cx$FxefmTKO*Ir+%_jv&U;YO_=)y_V9UVM(oKlv$H2+&B>$Z#`9J zFkh*G=XiDsZ!K|#wya+pOw6#$+j6f+k5t!Z?I5Gc?-1j_<6}UNWU!2ih@HZS!n!@Z zfaf+LNUST^#`f7u?x>>PSsFU$!mLu3ct6c}XJ_`gesi@)f8CXERD=x`<0ns5%Q}#iWMyvxqjq-v zL&otHUYd-v54?xc?I~}r-yG<09!Bs;JKuZn`gkGwo+F9nm$5ttw~=ROcx;h-GfAL# z-H}aX4`?epz!A8aLF{q0dou5~qCPm~D{Bq5{1&|3Wq!N$y2xzHL=HPWeqy)Np8F%U zx{~8I{ju8_ttn3X^)CG!gq&e9bPtI&Uec_H{v9x)a?SLtdWsN5xb_jPu8dD-aS7UY zy}d3ny|X+FQma%EeZo9N-hYtr@GY zsHtHTtU|R1-xUK^VLWCxxcUsKdp3_oSFROu0hezYg{{`|Ycjr%5BWuWFVVtrU)FjoV*f4_dmoNU|s}1g_BN9kTv6ah zr-iqP2L^IrM@f<4LJ5lUzlD;Ya@HzfMWy<+r1$At9+tW)I1VcfD7^r^151Qk{#r-Q ze^HZX(T3`x+8NuvBU{uU*gQf1s;oV`i}j9BA`}U{mR>#7ZtN6ZSIpyMI8+NAeLH5B z(gdm?mGSh=*`Wu6-j2*xYYppt5z+MDtNxH&Oji1}7Hb)_yrgjyWoqd<^^R)b^>}Q7 z$@3WXr=}^WnLq>*-jh`4pEMvwF9P4AUK-8fxIZf9Iz36BdyIV--?N|$1)k|dD$#D| z`eDKZH*fHfRU$x=VGS0?kf|8iLrUePKqG;eL-xr~P6da|!e%8PnoD-Qh!chbc3!9Bvg4`m)a({6Q-lErZ?q(x~8{+S4&zN_AS7?*)u*7`DRm(dVT4yJ?GKw``oxbHM z8fGggcd)L^7xq*FH#|yLEtEc8^S)96r7x48whmKPQi*yucHBfz>!oTO_erE`--dGh zFhvwKveTiH=215X+yqE16v$7tWZT-A6-rh-nEuKbrf$|5;SaZTYT1%V>grTvwnS+* z8zwASJDks*Eq1sk9NnI>6a}B2bb7o>!*gRD{nxx0xK|(&>{$1D;=aQx? zUi*E{3$c76cr-3)gR7y8$|IJ@h+glD_B_UZHr{aABI9yOMb0E>EC65I0R zJ(&lQ+x*&_OD~#11Pb~Xdl@{Hp)av61w*4k>up`2z;b9T1yca3zJJ?GeW{EzBnQ|@{8p;!NeAR$^ z2XJ}Za+5aVeamoG6ZQ!$dkCN^FFlfb{(#gjhH~Ud*3*^(2k!T?ZT@l_V%T1dM@l!+ z2B51nmrTz;EvY(V&wbC%@H`S+znbgU4s~{$40CWT1cv6 zP7n~zh2@sO9scSnwn5(~JckV15Jr@r0-WNf&`+RU##@}O6Sv7?F*}u1%Bg-~zssVH zpuSyS%{pC2Vd!`)Do*GmWKW;2=U2+c%0CHSI%r})A7A%rUSF<<=^``>&zMo+ucAns zLvM&xyURbF1WqVdAdM5#>LolwWwvq#W*%H5Ue1zIAO_pEnO$C_`jdE2sY$gxIFhEa zNZui-?3xYGkW$-3NME#y>9(40MBw%F6X8Nc<<&U>EC-OCUalMKxEg)S&J`syKn)_Y zGDz3?95m5a@(}4<=1?1Cm_JB7Ki1-ZgRuHXWNulV!BxY-d|_K0yD`_$=xqe!vy^NL zMePhw0ZtOSggTe-6a6*e^NjOf?Y*E$WVxwu_yhdzW{N=uE79#;Hlh5|zHaj4J z`1<9nfvHZCU!}T{;9Sv%ksA$j93-ipgRS?*tQ9yNm!hbUdT-L7pJh@Fd)f|E4On9Z z5}BY-|UkzZxg>P%YD{)HK?yfjRVLa!jk`US!;@+n|w`nVvCtv%3bHpFW; zclceQ+|vso_*NG8=J?KkdrfcPPi5*>x_q6cTkN2RcwUkXA6`@crh6G$z22NH(b`v> zCS>%tLS9k0IF3can;(d07rX0;WA+V5V+C6Bg^MQK#w@q@CiiF7_~WT*zr4>Q9y8OL z$pz&Yln+v$?(};a-N;|t8q|FfJ=bEo?quT~?qrPQ(scD6V$oLw|IBA&B{LB84_v#k zn6fT^XH;x8E~TG4bB(q}yrJBEY$g6&7dc?hM?}Y6b4vZBgQ>cT0V=jqUWxQO%a6?Y z>JH*=O!k`MbEygY6p9Fs50jq;b7n}8Ht`RwOY(DJqMQBSKa9nZIRURi!S76;r?_5kYSV%K0`d%p<*&N?4?6PX(dY zNxJs5d*9hHQX=sivC2;-dMP-6RmstxTAEjMCZtK$Zy;gf&#|J*Ecj@JZ0gXiC}^H@}F zKfHI%04!B)_n6EqX9cW@`W@AdH&8JI(tOZ&UnD-dox39B1RkAN&@|E6(fM0fL?9^x zP`Rkd6v#ntY`z(Cft5FL-F+*q^$I(Gp}3GtKB!K%PDc!LzyJ%BwhQS)Pxi$-FU4oNgD~5f}};|Q%npPOK7h%jbfYk z%q=6Be=hlinmw@|1y|UAaN(^qH3q<9U-ywdFbTjCZTROO-`iBU2DydwB;Cf6sTR5u z$eca*%cT<+)7ABXIeT~d4&E+DAg*74f_m>9@IQ?v#jQxNz37np8={%gr3P&6iyD&g z0JF*!%RE~-vQ%Khx07X+1fg#1txC4V9%!3F!hHGcGuClYL{r4AMZIU|&WfnCEjZ(! zwqNY@wOU3&bOQ&h9yoeJ4TqFnia802gYtueqp|>e&?9y)*8T`!_o)Tu>y&>pyXt%r>9q~Y5l=>ln?RU zqnVN*ZGi$DZ&_KS28(GC`0sc3_hF2BtpPQ?qXIaV+i$>_M$69iS!NxPtfQ@*$6Bh|HI^)f>=@jHeBxDWT zX%WQNOPj9T$J7&i68EdM6T^fqSa28Be!rq+XaJ?$2?$llj~s0mMFAJWxC9aO)04VX zIpv5a>mZaU6LY-gpPtmwx~pRA$rOVO{^Sl(KRZQkifHIb--eAJ2dS91mev7bug6Qy zH3KEkX7SWt|L4TcPkM5Wva3zwyf&PFZ^ zrc3rFP@1ENwsGh|zdKfJC@*eL6?emk2KreHRQXQuwlx^)UGrWIH8pC?Dtp$cyhz_u zMUVd4+2q0&wB$!-D7N|CVrfqNl4<{tirPYp)i}x2j>E%F!(v2pau4hN!}n7a>y77Y z=gj<{aIv!(m3v8_W_+!^PTX*q^A*bn@ZDNY_OCijp4&+TR&npNirF)ducvyFb0m< zl%aKbxGAY8nrgK>Ui9_Ixj*sqft5{X5^HX+U@VRDO0!B%wyahE57L(2p&wOSpSTf4 zl?yBuX#KhlBOe_ynAGy5X}}pI<>Ce{E?1B~vKyDe2_!%|9hjh@IIGT^!%0+VgoJW_ z-(O(v-5+M5%+Zg_V_l9`>I3;m8H@Zw601qF4xc7 zEPKRnu+kqo^;GxA*SF&>)`af?pQ9?jd1x3Y>9%kFh61D*Y)ay#x6O^8vYDK6k88zp zndHqTwzkM83)MLEriw5hd!slW_osN!aBv`XR-w1T-<;ejUU#%238axJ!m2gD6^bA^ zp2N}^vKI%!r`KHdy0KSHpeFlp7@#MXJ<}6L(ZhG<_RC`oM_mA)-ITyNx=D%_ByX{Z zR@6;sBuz-C-vz`W5`)?ZK!m#8>^ICLCOk04^zt~2#cAX_+tkN3UR~!sCOp%GOu$ zcb88Eess{r8XCaT@-fASbcag9~rdE8PQ+jGR{9tdhXR*uXDv6)) z80ZC?V<@ zEGVt9tYg_%IOJ^Q_*G7N=d*vvL;`1QgrQPsXKW7GVLz*QLvRm|8?i~!&iv?6`8IhKx&m_w z8#rLHDj=eniuSTrru*k`{s>A|)Xo?*T4BcK%>EZ-&gkEQ2<^tC zh#&oW+y+zX5Kd<5ya)*zY~z|U2SI#v%)9?>2f;Y2T}GEcBJU@ySYqNKwA=>skIq?p z18*b+R_f6PFZx*6?|Cop;}YCBrBBEZZXyqx`xq^Lv1JakkSkVrzU(v_&3j{*Ru_7{ z@Hlv=GCVD}-#B88N#n{0kwlWAPHuie>hLTv8wUK6(*dyx*6m{+f{GdC$ffT(qV~$W zr#F6eoTnU>m9BcrpGaahKU50@sp!leG_PA$q$;9)tNB)AbZuAZKK|O2Po%}X-th|2 zG${z}iQWK8Yjx)S_2W~x`JEPmJ7f8JY40?05#29{jl8NFUQuZ9MFsiwcC4q2#Gk^q5e-s8ZX8+Gt2*^6ZpZVKn?``C}5-sCK zx_p&5TaHJy7wUeem?bSJg!K!GPWdw$cPjQvP_()pJV0r)gPuqXpTjPhBUq^xqRD%BRI1@P~ zq#_s?Yp&41^Cd5TIoHR9(snXh?0JHpT%H;zJtvdo_-Tiv#Fkm|Z+~DJNB*b7LU|QK zmhtW&d7Iw3ia|F+8=5Kgef3s@;xfejVp)8)@~i~WrIDhZ-t4kD1-typswJa6OGkQ(W!P=gX2dY;G1*Jzz@X({PhVFcJCMk1 zy8ure|J(Hn2nnWbw?XR*`c|HZ?tmZP?Je~2#Tidw`IOE2X2*kbJ^5~`+tVED5#Rc7 z04_l*XF5c|GCf-4Klp6)(=xYZgM#te1r7#(U(rzx2VmP4Ir3T}wVz4UlnEb-Ct=sv=)4 zJiIffaySt?&~;+ATgUw^|!fE6U9>TJiE%g<_gn?q?Yv84*<1QcC0O4_idL*~V z0yZEmz&GV&>?-%ldAx0BJ3N)-_(Q93Xzx(V=;AiI*xG1AA(prgpWq)%ZUi$ORr~h@ zJ>?2RS*eWxBlH;-=m72DvcWuz0$QXF11v6WRJ!e(tf-DlpzA2*u9IpvPosAG-`Zq^ zBqdq(dG%t(0tk2kw50MClezu>3h%aUeezSH4-z0F1)jhGYB(agBE+{CTV!&pOVLrw zmXxX&UR-lE6Atg}GR31-tVSEIMhe!@ zxuVEwuHo3|;3p9~qWfGF4y*x>k;yV6tU>UGk@N$WgP|*$u1rUg`J}aLa5ltYXcrJj z#XaWLSpWd|eaNqBZ&tL%h|@M#aKmg?zK4NWMaT5zxy%_w_ZJ#x^f z%CGZ6m(7iOi{bffd;H$oAcm~!on)CPfd-}4*I9css`V->&br8w4Bsu%YG&! zW@jy<4f+2?<(<8t^9}As>(ub=BZKuruC&B_BE*d`K&%ivk3iQ5;pEc`gmm8y+8=(y zagZCwW=c0&36nnsqIIb5Rp15I-az2NS|dubj`}j~c5ugLbEZ0hvtAaHj&>Z9`-#Cr zBjjJGU%Nt9w>eyA*t%$`)$#0neXp@DQFqgQQ(vjUQGy!P)|&M|J}=d!qign*zHzNH znVRP)IFe&D*xR7hL$K!YdkP}M58;|)MK3`xlYqwG3*a_#qozH2 zKp&gShAqFP?2xr-+3Uaz9!DmDzCSLBrc9!#W?3o*MHf0D9d<0;a?~7>YVmPv3#r&$ zr?Rcsxw6*qkrQMCb^@04{)%^Sc8kzkzdVetR$0;5Jp-&A{R7pKZB++ zDO|Nw77rj7h=;3)!@y(|03f?v zjt-8NHoyu;epp>8i!sXk-S*}FPNGLfUgH<72Emt%ncQ9r@xWXf#ZdjstJOGeSsbn7 z?%#kZIdFI;W6(T13bPVXDNsskVArDyjl)q9w|jJU{o1D_7Yr!}FTWa`em`5`aBNJ~ zkVHnb;Tyy@W-Pp5NS%s%`n*5D$K4|9$FjIQie$x#cuiQZs`AG@dr@BVl9cIdXAs7T z<%x$Ki!)22hf5V+m)^-%QSn6B=KM@xbo0bwPRZM(um2rH+`4K}m{o<`OetdAjcova z#{NW%(PGww$l~z^E+R!|%3%2lr)Hd?YF9)ladv0Vx6&Q{MWYuvY2BZAo^DW089b z;$A$l5T z=7`qpGn8&;GoYvuaXvd2t64?xnh9I?MRPuwG8TF3KLxD~@Q_J_LmMFltF@LMYBTW$ zq-pjm;kEnQeXrEh`bfD;T+fcEZJ2=~P~;6~uuiXn(Dcfsc`Qz!8VHMq`j3#$YlWWT zsNi`0ok<)wE*;J0%9G}Z-|8arDE9E)bEi3UoAJO!Eg}$W79DM+U6U_v9$&nhWhG=6-JE{k&*S})dF%fi6O zia?;hQPY4~AgqJ)B-v(N?M(8h34*3Nl$XUjzH;&B2E{3|kAUtKtl^-1z_I1d#1#{H zLk!!!692TvJm@&5ORAll-HY~AeA(kHv4`ySqQ2$1*9U>w=}`v^&2}?6wZ@C>0&shS z4dpYsF~8IqENKl@>dg-CCx}Rx$~g;I0L^36Jq5kiHJi#fc1O?sX^>-NRCsfDe4))Y z;dshIROW-(|4=X<`pqjO@Pb*x+(236(q}v>v@r|ej#m4C$p5HaT}a_tN;uxLX$3!p z%{_1vHddVu8^gBiPbibiTUJ-zQ-ur)F(orrjP)n~`kA}#UE6m-rpV!`$_Z+mEdh<{ zP;Q`<3xFg?|H{MnS`f628w{1lR3DF#FQRjbT#h#0e+f3CaJt|hpH{eILiy2aRRXct z5RH42x)14*=;ylvQT5j*j=6KEG4Zv2IoRPa8?4Fi77gtJDQ!c9;r^oWvYMD#P|<$M zqYdb0P+i>VI$3~ypkh)ze@33B)GHjGivdXza?QsY@@#Gt4GcT)z3`Z|1T$QRkjbhF zl6_55zP3kE9OF%(B@$sE_uV6ja3~cwj~&Oi5UBP8z1^&T08C#hK-$3;&xtR-_*$uY zcYu20>PaKzG=n2>t)r#0u9!}^xD75HK#~^We02}~q^^&3`DavB61h`!VB87KF+!K< z28Vv`DGQEQv?3Ir^$13hQ%Uxc&K67qfNwgBc`sr~4K{OpwfaTEe#@9z1~e+cj^}13 zqrm}%0NQYKQFOuq3wu7{Jg1#NFz^v4Y{8ElBRtvUR1-05r*ruIkmFZ~o+X})2H7H) z3QG-;V~H_fP^omB$MACbf!JsB2kYL0dkf~H5;{!ayplmZWifIDeB!qzggKCy) zZnnwv&)}`I(fiAzSE?{R{HkA6#<~ewiup=v=p8{>r~HKv`|kX)9rC|3aT1>n$Qb=| zD}?SAjuwQ?4MEsAI9XcxXlkJ+Kkqv0Lm)?E;MF+;MqV#4rfhz;k^1|<(iPto5dm-; z{YCdK=Bg|0P&ywZ-GN3dh%v?HApeYqm%H>itXXi+4o_s)U`Ri@murWbsEF%R(C{%# zqET=1t^W~0GTaWLk_Mm&{Q>Hx((W$;z1dL)XeF}2=mDhofu1tIXb}3P zL1+=P{nA?sO4K@a#Jfxne>VAJkCk6ER9EvQctDMB_eWIS`*0Q5it*w1;e%n&w*dE~ zr_9W53-D?JOZ>fLg$;y_s;X?d&gszLu480cq4vVLT!%kA1}19Qv~%=Djr|ZYrf^F{ zkXBS@Bs))q^#)^UZ?t9RE^>?Ans7@Jh>@>v$K=jJmWcFRc4jU6)p)VvCQ4X0h#Q?j zw1d1+cR@e*B1Ss|Lee{A8`1kFyMZQG?Ta?1L(Ty=o_pH#r$IZC`a%{bD}?$e%Q#e} zX`Vvo0%e1Rb@aE(47s=Y#bMO##5!fnaGo>|aSYp1Z_lbl(Q=(C^i?U9vAJpA8qZ$Z zpW7Z~;1TuHH7;LHXA5oEsK3Q%o#5+v{M6y7VeM#+bEM71LDZZ@SXNksy?Vs?x7T zVOEBrW5QTu*GBskED(HXp*yKsAI=2QI$dTD#5fw!x^Yo0+>C)nOFqA=_T82T%1heR z&e^LZgThjCPl>_7LdtPHEB^pOH8*IrrYSb|S~|dp4K`6Y5OfFOljDA56fmNTbReAQ zwX$^%%poQY_~pMS?FkeeI2uNQc-N?&;U?pi2Ner(obh*{(5%st@f|pPYTDn?%oQy zL1PGfq1BhsU!UMW4`p)|aO`;MAcWJ~boEfJ=u%%S=F!Iv$>=7eYuHM%oe1`ko|`o? zC}?}V-P=E$!iuF~0EWq&O|o@6K~Ci!^#pab`kXz{G;}xQjO`u~&khc>ti;U-++JDG zvv2)Yk}}k4TPNO#Xmu56{JnGwCB#S0qSa)mYA2)w;xy$JZ?FHV;!4Q2xu>ws^}Wci zoZc_=E#U)$-^*KUSVi~>PkrD2p^a1h!{3D|!u{?6u=*88t9gK{3dv}(dgjf!;)ia8 znQN#F4>)VU;Mh}5D~oPXDq0;`lrv(EMw`-8NY!6g){c2vyD)Jgc_2YhUN1jW#fhd$ z5ml2=VysF)YsJNf=cGO%_LsC#O=b;i)lSC~j_Gf!#brH9y;8PZY>Q!q!?#hL4}Xis zL&K)qkITK`;#G|8+XeN)1|hL--cQ}(y24PIQHR?ee;o5Ma?BYPM>c(G8(5F;9GEof zzW`i%=)0UXXY_Ied08*p)nQD-++w6AFm$T*rW~Q$W_&)s+{np4zDl*f;?I4KjCOD4 z|6J}}V^3i#?&)#yui_+@JSQtdYs!DQutron(ICCRRcl{eiM58N7{MY#tg-Pshj#HF zSBQUL4|e%NO*aLy>DfafHC6gqs6od0N1{JsTz+w+u$yi&Ya(~%M%~i zzSMvA9pw;J+4%PO%;BEO7}qUVs8m*0Hrol<>Ex|*M$A_G2IU@C|7|{zaRK3>CzscJ z{^9C)$Wp+b+In&E&8yLv<@13DsV-&RyYyOz+m@kJN+}KqphSc$_HQ1bl|G{|6sfzL z%O!a4L*UY>SWhCHGJdo;KVehrfUWKf2} zjeV_2AM283ze?Z86$)DOJLT{a#zsY z>t^;0M)$=w#G;=INjY*Rse5m%)-t4Lv>oIbY(3b*gu206Ej&=AL~H37(V(Hggsm1N z3&}8penK^7>69(_8Ng`+z!we7)jp;*A}4xxQ#Q_YI6SQwvN|&;ApFnJ+8Cxi-FPwv z#Hfz`3wBxi5_}`mEl`L{j@{c==N_|f&{gSc zg(K@?5}O-L@9`OFptXYTW}v_lkGbpPdyN^<_|W7w$t&r5v1x_;pqjBL_&Ouz0Hypp z$di?15kKZ{qO>#=mPLFr^r+BP2i9PaVUe@S@9#ur%3y=<#<7jxiKueP*vZN**VhMG zwZGn34L64Q=nfziX27iyQo{R*(MhSfd!NwgC^VDNG?hg@FA(+CJ2eh3PORX6L54~F zVf+B`B zxq3k}#XC})^K#1w>3C`Nwg~A(r`Wd7N5I@hU>gBviCyui!9L*d&`Rv3xyGiBeP*6E z6r_7bLi5mO|9F}OhtpPEw5gOy0Js3?Wgq(Qwtqx-Pq`L*#ef&6+l-O)DdZlbEaM`m zXQgS>3_-7?qA+|V!9-DX33^LEq`S$?U+UJbO|*GPXZAg&b6e z?g5TD!Pmy4U%sX^`9KvzswN|AEn-mCDL>=@0A3_BR|)AJw|f7;*n-V_ycYc$?mFMU zwj{B{Ra&FO37OCSF7anET;Q)wyqlh~t9!xTUeFd9N9&>J6; zF{Moz`S@>S8w|OekgwI?k7XQH5u?fh*<^4V9p5vS$wO2P77`qN-3Lbq#*d0}0 zi!ln$x009ISAYB`b78fR0r{I)zr*vJ5hsQlYpNy+X-B75_jxny*0qF~1o_1HQrsMh z9aiw=s_^Vt@W0;P69gHl4;aki6ZRR0_4>5q{A|rx#MPKfVM2JZ$);o>yLQ5oN1cYMB9e z_%F_)8_YE&R8YAjRau>ZbVnrFEnryQ`xC1E(E{gbFr3)s+NmbTbj6=ODp-{A(Zdh2 zp5R()X>N6t$tn|t-E0)EerL;^7zl@=9*o!At|x05I^s;8u4uooCUg%?zW!6pSO&=) zgK=Tkt+?1Q+HZ~ z08#9LJ7y#Gf%k=Qr{6WEFhb4GRr`}h!jap|kJQYrj*5M?k~EY4!-{|@h;G}qjfJ;7 zwK+Op?8?mQz$liTzPtiYC`#ZKA}RGi0vj*pn*5XjdzR%U`IjMCw*v_Qo4Fj9M!kjF z9Vrf!LA2pPtMns(ElM+PP`E8DI5d0{Z4z{GlD-i9Ua-=HNvjU`cF}p-deb+-*6gP; z*hJ}(HRaYJJpM1v{b^N0sGz)E2H?&scJQb@!7#^;4|R~JmOQ&$;avNQ@_t^m2jmMW zXN9zH?ULe5FQ<~otxy0KM~QEO$nOV8T8ER2MZ&y$t;2^+k;?GxD%BO&3gO0f5X|AK z-L^c4sG~(Hyj`9nI|7pq4n}UaF+SIJaT&TX;X{K0t!Mup!^~b0Z*dsbp;RtP!pvM* z$q8i4CK%DZc+}{Exm~wXP@M^ZJWAF4iiBDV9IcO&$}cu^b!^&MRoOd-TvY1bbqeF$ zrCRpP&03p|C8c~rH4;8)av*A3HIASo7|zgz(}i_ZwLTuT8X#1VcZST^wv@DyVSia4 zuq>;&KY!#)OAsi>>r`x^8QqqZUU&-b@^7@w)+BK6XzifdOyE{&4s>Y2<8492DqjEE z?Po5jrB`s0!ZB5fvKAI0kOeg=VKb+V$O3%qDZym;G2qmFLdQipYY5cMd7Y{fA-P81 z3f(478J-oQOD)xE7+7lD#o?2leD9Kb0lZped#+3aI>DDBH72Xilk(Cwsc@P;2*y4WlNRCz`Gwx773l;h>(GmQ4D3mL@~3mt)hC@d$T3=qChQPMRi zVnVlQ5fM(&X79BpH*c2HIh=>y(+eX*{V5t`SN38bw5L9}hY5g1yN z-a25OA}SJMG_e=o1NX_pty&T^=*Jp>giTKDlx7G<`uRrC4fRyRwkfW zs{?Pol+GCU0($E8dmmmw>V{SU@E*flJzqanQqXnQgdBKgf8~vL*xll4LM`Z!W3m>r z{Te{~_zEKM4?RMlwlUh65Z(rFaTp;a`N6~#c+sDW*y687XZ){qCtbu~1;6wYu*uJ8 zgPWjJkKTYcyPqz{k;pc^94()SOGYa<5m%&31#05;wiJQTbD2&@_fYGd8Adhu9P-Oz zj0-@p51_Hq6xo`^lwp;w{Hf{X>tKuOS{-X_B69dgL2@ z?Cvg&yLWsZZ-S(>&#q{;Mbi_8_p%WHy}J?AZSbwxzpse}wU#L@N;lP`qc#lmqfcI3 zjObx3j7$2=gt@vEzB#-F$c zGW32DICvUV>I_x-em_p46mmKYZ529imd7nm?}HCu8tOGu}N@;GoM*hZk_{1G7W z#J{6?Dz-~ot0v~#)eqrz0^?3Xyd39~9^EF=-u2-A+Io0oq1TtH(@Rx=GSQd+G#=@O z+W)|83cd0P4?B*&4*LW{ARlu}cW7-Ch1j|3Wno}>6yK7wAd`N@>{R0j=hC&>Nyy~2 zFVj*@$}M#&Dd;TE(|Ww=EC(@!@f@0B6q{4? zx;I^p#C6HSh7JZmmSeqZ!*-Pnf#g?mMdVY{9OB3E0DwVc??8_sxjbCPuT5LKGI#-XMr$$8xoqUyOjuaBv7QXS@>$Z3F8Tjk4kbQTIIpZjuDr#L7^K!NdoDpsj=DP`4eEV^U+ zYW5hGA7|Qja5d^moIeBsPR%s~z+0$gb8&#J+5<+7c!8}1KB3_g$(@Nu;vcX4V4`?| znR+{lKy_Ln-V!k>;IV;_2fRjt4_q(~O+5j37F2MZjv`4ru#A>^pwj9x>948xM?b;M zR{Cq8Hg?M0k~VR_9BYmIh)7$`*D$#%;j2Gc2g2dS>#3MHI*IqIQG zYALKIj$cYKpI!t1M4#^z4_Z5g{_gQF^%~uUr)<;D@jkWhVlDb83De_xn<3~t^5?^r z8SMT;nD2wT{37jiCTib9hjF~l9N0auNRbS%p0l93etOiF1dfSn?{~#uPoLSDE8q*h zyCRuGsITd*kO!xjMWd4zov-e_z9vL5((;eZ$N1T%}F_v&pEFx2|WLBlT~+bD3J zR4KmuuYQ*IGDwNa{I7bUyG3vX5~$Z0!rUN^r`pm+_@!r~!yQ(tyLm;@-cYz2m<)Gy z>i?qK{By^=pkh0UAcTcYO3E^b;Z&FZcvO_#Wd&!mFd&BYq<4(5M&(9Ki>ITLxkmPN zalWAFMc#Q}NfBCS<{ zTv+pw`jSb=u56}*;nCec&lQmdlX@cutB7M#tv0;8)6s7}O6Y+Glk6SivD+iD7RvNRr|CregH5J!mPO}GGJSYy8>de zw0nCo&|d2NzU)%LmVUKG`%E8jjrz8Hgf$7qt9s%gM|QoL)aIu5mVQSpMWzEmW3DaRGjQ z`m$<>(J})==c8(5py3dvnq%*a({B1>{>%YDcLi7tt9l1+3?Enjd3-rSv`(wlHnRKM z6|g+J*9S=A09~*j>mA|oCqQmKqQ3U_BGl5-E+}xwC#01z_&C!+Awh2WNJQ{u)f9x+?U*YoC2Z`sR@F3k+%QK_2nFg(SMeXI2KrX>i{D05f zcJ1ex0K5IDIs>vC{^yYn+W})!#B&atxiUeNud=Nm;Amw2@_G+UHJUO+H|&yl=$rh! z-*G4U*M~6P&dLR=!PS~1R`GrF6 zVy0yf=o++#6F}>!&g+tkus6eM{k@tUr0pukoFbh`xOc4HyaAXACeC9N<@YAsn~l5m zuq-)Cz!{^yz9${|3a2-=6x*reY2CU0T74U(Qk5zk&j{H7`X9#hf~P4ys2$Ly^A&Jx zchLbo{Rjxgs*Q4Ib(oa}<^nGtx3}t^@Qqr^`k5Uo6OC3L^VsTF1X@+5^n>i#1Z`pB zV{=kml$14S+9K^W3&gsau4;R*GXhIGl?w{W1XL{g# z4(=IvAXo-j8@HcnHWYRp<$90Nt6X9At>9J^pqXt#>0)k&j#W%{N{lN08EgshqUOi+ z^#a$y@by`IxSFii4OY{z^M%Ka)5IMMzFAgs3nhClfX$9WOz+)Y9ai5H=Fl2mZIxc| zonJquYufQd;JgvFPPvUN3=RNxmS?u((e2>csit za_v2lL|lGP(Qv+PB${13T}=xNp~+~(N9%RE`{hu?_hKe@0ILNpfyBiTL9iHcz+3Ti za_PcO+oXPGj{TMJF8=CJ@pfH!vlWKmqxaLym1RT~HPN098DE>iY79mkr3-sM8a#;he(E*;^zH&WSvHJLG+2HE9U zw~RK1by|yr^6WWhS)`UN_JPlenX(u5q(9>ljFYd+EARlgAT1TQ<6VW6Hkq0Jq0W-m zLQrAG6PsMRq9j}6wN5h3hryr|hk+MRk|=W}yg6USjc>5eRUcI<@<38b`>p}6H1bv; zu{BZfocd0ce_?QDIW+;x2`lR2-k-MD1tgPz`eo^QJ4_?yDVo@ehGH4+82d=m7O{Xj z$7oo6{xF;^GZbqc?kPgzXgeLsCOVs&qbU^22zsA$Hix-7-iZ2e%cu|*FYdG+?7}xW zf{upG>EcXT8M0{Dx{>+>mYNbe>)AuH#qjJ5S!mKi*&=}t`53f&_bYE>;`G6$Y99c# zvf1u+)<$J#E(7)f)O>UA&-d@d9E#UlC5z`4a}lMQJt$>NP0BhZH4UsJ&2@=F3d7q+ zlherx`ovKdeu-92J5$}#7Kz>j#;O}rvrqX}jt!J0-!%!+IP7Exz@yS6^Z^u6va`n~ zsW`3-HlCf+mIvArmqoJlpdv7&GUp6vV7y4Z$j(>jU^PJ1j2-@Rc-t4kX3wI|Niqy+ z>H-2a!7;i|YwhoNz5ct`NG!aAVRqOt5LydubAS0f;58l^+K5@o^|4-owbgGy@vGCO zwnidtQsQzMv^O_bg8j#%$(Od_hur3ue~3w^Rm&WuwWNO%+(;MBxq0~6lIgFFQL8nB z_OEWcEkjm`uIQh(esvnhPq4|^an?}I4;3y+ev!e4{MQyNYx(+-RfMpn&pG{(lA$-u zYHgJp!hXXpH4lW<;h@4%RYqi@zxs6C$F6n14SC^M$^87iMfv<=FP2^pdcO0)di!1n zC8QtmO*+NpRmULQCC8Q^bWj*y1mxzy1AOiUR9Z^3ZVx1@#>03KaQ-`Y(j$((XO%+p z39YVKYaO|tG`~;1f{`)R8#9rtW-QJu@~*)4fg-ku@{ky-?S39FFYank1Y&zeNpM+* z!`U*Kd%_!`Xe%9=c-Y{_d<7i%ocqLLijeS8YyLy%h4lw$$4E79W~MQy{Q~bi{MOlC z40g6Svgir46j-QANJTIjSkc}bdVVXa+4qcq#=f~k=;+`gCNw1sUv9^fHqr9h=Wahn z(&1(=eJTVhHZP0jx zyRRoG)lM;hAF+@ zknB*oW%oi=Jzm~)?2F_8uX`%0sSp#RmEcR-wH16$I4axENR;DvJ{St?Ku3U>J%d&z zl;Ma0%ES_5OytRImonUryR{?>FN@zD{24?BNlfyOa zMMK{+BygAf&XQwFAA$XUoX4kiao3DhM|E`i*rfP>{0m2I8{tQPRYBg_Vcqo)I+_iI z+r%!5!+lwKR$R{wpd5>zy-dADH@Dikq|jYMTE2z;EzNG%G<+@hqzX`ll&qN#FhJGV z-t!Igao=No-1ol?dq)(B*R;SQ&nzsy)$tvJ-y)R>*PsW7mGW^*H-b>+8A@Wktf!z! zaHMBac-Sd9=!n*R1A+{0X-^omaS>_ppwu*`2aBtUX}Q=iPIou2dmvRCm< zE85@4glmHwJwrA;rdzONx&=tL`6WA1p04 zSovg=b4{=zqF3BiV7dZS!E>bZEk5u!9zv`>q;IzWum;;6I7pd$4QB4O^S7dDG`M!w zda*gTf|k!LulL3GU+LY1zz7K!DmhlZx6Q7Jg~OIkqWeT3z6X3zIa8YC+BH&?TjJGgxV6VWG9~&$RUyg#1Ijz;!cZI zTOpM8%|bOF-nIsbf{0b)IRaOS4am;w`YzR1%iTTSoBI&g+fmetc0LE4c>|RztHL#O zD$W^*mcXjSO_^-0#Y|M6U+dXc3Tb~5FcuQ*q^zCO+*Ds*1a>42b_yAgDAI9{8xp1m zLiBE=cw)cUtScTmPhkP0KlQ)}#lp6?5Jf6hl0Vj=|2^yf`_0MqJF>fm!Wv8{$XB6! zpw;#$IaK!uiNvYkTe=q4!4NRVBrwqrNWa)Q1rnZ&ubSt69s8 z5bxqstSEwJ83L|gmcpUlJaP((qT(%`+xroG4V+Df9^mqyhRjB{}-7kZ|8l9=S}29>XDAjc6-3B{#c_-M!Mg*1%R_z-1n~0p%@CU1lLADe?&WMv-rK?cb3O6IKae0 zn9jlFJL-%XnF7Htub(R@kB#aUTP z1t?_4E~Mn0vLk$?K)FaW6_bAJ&q!QY2R!A}KZ;l0e_vxFx*0Q@7iubq62yeASlqO9 z7`GXIn5E#fOO2$OvV;+R9P#}=FU?}NY}Z02-jL#0Gl8y@O#V)l5{fc6iy6W0A61Z$ z)7J|}I~tr?OTJNB>tm);ZE5)Zh9BHH%|itdbWe!Ebu zzUC%sD7165D5XDKzZ$|Ck*BM8EXl)gy62??v=-3$AiIF4hZjz|vg&udz#b@{n=I#k zXeR#~q06(Fx`qSsg2v6PnmJ^*JvF7n(#U||>%bB+n}sN@T&*kRZ`LbQ0q(OkXyn8gW-*j4L0-k{=vprcN%)W`liN4OzcO;vi7?>l=BjnK8 zp|G4cOp>)TI4yB~V#J<5N1#_C$Jfm17(pXy9w=M;P4MHN>oX99dfZ8>*&>?l+jpwY|O71Z(hXn=#k&w?ti{y@xh=6 z=%cv~#z$Tm|35#^G%{6J`4@br9=d+WZD=Wjbsl@siP?7G9G=Ll<|UdfJtvJe(K9Xy z_erFBje{bp6l2yuT*cThfQb>V2Sw>Nl$Oh-FyfRBo-?RR;rquY`7B!*gN1*R2;f|X z4sH*_*Zvs-h@#WLGy9-NkSymYfio)HSwE?E(S_1++X=T^&6Fr3u&2PmvI$&$=80Ei zR@JG%ZAG4ir#)N(o7*`F{BDzG9*j)KAArZZGdW~9sMt{&GRJxlPvUY-&Ks=ekGuGZ`f^nfZfZFj&#{I?&zj^RE` z=3xEnvE9eKjqw8xBCa0MYigUxR(1el;BCu1t7B<^JT)igLf=3~-36NDoZYUR8* zbpS{zKLz1uJlevuWR1a%?L{Eqr)+(SNt;3R`Ewb_vJ4p=!y*|Tv{G{jRPnhykb#dl z`4W{P2uwP?ILry~Uvp5A-g}dzl4u-&Z+^Tey$ND zRP5CS8eXugj_xMgU&u7BshA|JPMeTWPNLpMh^%Ly!lxhi#LKupK~Bh&Zkn>*rXNEa z36>$Va5Zt_)8#M8MBw_Q?UxaJSQz6v0`Q z$4zzSI;yeV^HiO+(o9RnD3B^t*XdJF(S5d5H{O{^EyMw2dePr=PAev-Vl!#z&yv8I zT8c&MbfVXIjWt?tp{EzBl(U>vDWdVv7NsQjrX~BU$=A@n=wKx)SQ;$pY^IILpXHeV z)xKv=w3Ns7q#|XIP)~|ea!MM80Y*%ob^VMTPXjX0R6o={3sEj%adlT!5M@;!nhKza z2s4m9_Ae%A7qfPxhSqFuRUsV85ILkw*b&TGQ}r%88VLjyo|(i+7UC@ri1=((HQ(BC zHdK}+4pz}h$#$>x{rV^=WVFEp`;;QM^}~UiQ92T(PMh!a_@58uy4!(n+SF7V2=`Bi zcUoUVbjk0{%5zfkGI1&RX31Z)$BNCc=Wr@5U6Bjey~5e6J}~gCh^;XM6;i~Qu8=vC zX7bfpS4~{am@;z;PY|B502)EZp7uK9E^S@uuM6>k$1u`L1IWO{PH@0#z&5cRmlRgj z1^GLKs)G}W0fw5n1%~w+;U8OKOoneMK?sv)e-PfDHKsvt6_{(V6X*0bc1@^0Sd@>K z0A8_oT8HGb6Imr&05pA_8{Wa~IN#pGYm~Z49y|P#TkHxK*GiYP99k0@{zR}I`3^~> zC%`s)oO4_;d^X+`hGN)$Wvy!MyCbJ!{_%P#ZF%t5qm2`jCaXS=+gZP^PrrS^IZc9e zyKxh>c@=OW;ViS0bFh^Md{Az>g~jS>yL0A~WAzi>C7xqXySR^kujS`>_LimsM`%Zv z)i_DRh-+Y17PtZ7CuK;0c&Ko}ANWTWM)?HX+4!aN{Vjodp++z})$#R`wk6(RxkpmD zdJ?XZX3GHFw!CP1sxUgsj7 zCqhP1^OWB+!QkDEi&O)VH8u03=OeSk$D**UH4B(Ri@hpF|H<&CkiM z(;WR%c~9oYce<8gdusyZoskkeuvb1Y2plX$bp!2sw0skR9(v`Q@T?j0Y5X}ww9sAb z@ssp97(6DY@DbfaIlNp!-cDFteT8P^B95{~V%ya=0O?it7Msa~$iSdqfrO+#9~#9Z zs_U!F&!MJEytLp?-!dMFO!C5ur<>9gAH*UOX(2vZl|UO+pWp(}eGKgjXw5IyHDP27 zc6FjOO#4s8fe&4(Mg*$G=HOBX)rDb5InZy*(Pe^wC|ytkIEg`=ujh2nnz}w|+bi(9 zNx;d8Az)9S+@8$H0}P+uVVRGNaun`NmdnY!iu;plymM7-&Y;e;Ga~*CjzUBp0+z19 zp7<+g6<`dex_2ysNrv=%tI-QION9>R=?Z2nb3|E}!zL&N(aPx__MH4i5L@~Xxb6X# zm&=zenQseDX0t8uMU&k-yxh-k#^#BVQxnYHDykb@4Hl{?k=9DJ<`bo>fOpOx_3Lp$ z2~v|}KQdN)w2EtQsN3yrNjzvz8Fz{*`UI2iV`d+bTrT zMzQ-X8BgiRbEUSAchqc|HPJ=8;7}y01?MdCll;Y{9M;OT=V`V+XMzKU7v$w5yHz@- zT&+h75xlfm|78xeb1z>bzOGZmL#FXo<2$}3H&rO?0+Ey`+L?q0++YfpnX6dW*_YsB ztjXjomClBPC4ioB^wv9uTbCgOFigo7H^ZHA=3stKW`t^Epa0x=y}6DhU%VO2_8V)k zk7?lJuu1Q}ke}IXlNAC!QB-;@iu?g<< zX(XirprNYH5!s^p1_K_s$~Q^bVk!&rKx~bLiiLj{8dI@~8kFfiXW*4ussw=I;>Y#_ z^HJ7J6k&5^1riHdYf$ma<#=(`%L@>zDU(gs~p0Y8t{7ayHQ9fTVQx_mF$Q z4j$9d4J=?-63c!CNGD4Fbbz?tE414bodM)|eS;YV80xx;^|sK(%IOWJ;w%giX??i_ z3=JU!5h`Vgu-ko@t1RXJ-dvVpN)T-M&ATvY+kY{PXf@%9EYTJOj~3r>w?})5nuf;v zh#0a2ZT6hhMG>pQ>eEqYc62@bJ3`FRRDf)|2`3zbz*s4rJpD#btY-<-28?TQ_ziUQW&Ml5s90|@t z*u07PUa?Ijls z%D=5Mi7j>`m+4QccL2%*6QSP7 zp>7aUjJZU|wqPwW$hiS;kK|$140<_#wOMlR3@AYO-f{v%1#~EkywVkl_J_zjI{y_B zKnrap#YqB;OdoZpmBqr6d9}$W5%BB~xoSwv3h~R#&FNUzNK+WltC)7SGM2Q7 zXZn(^#&7Zb(&kz@gk>xtM(uhIHLG+e4Q)1w@dY!b($D`i#aj9DJmh+`Gj zU;DU*i06QzD)fsfNjib`=1iu{{g#pJ#IboSI766%fakXX!lJXmC(&$N-64nVJle2t zm%C83Q;KT+v}3CEfkO58Sqa-ABMqI!PqG9C+W*4#d?+mWIiO@$zBH7etWh>3y5DS1 zHflNBEd_RjG7jj5ep_4UN z?2m60QW{+oo+pGtZM-hs!f2s~$SM|JE73g%!8b&U$ojt$GCstU9H0$NivQL9sVo>7pCFXzfR!l z5oK6fU2L2yuZS{+m60Oe&m2W@bn&W`on~Z-gW(@8P(DL27=ywiNY14erk$rpk;5-J z;BU*7>UeWyuALPvIVNA2_(2x;x%;N7?Xeu*M$pG$7xk9$y2OL5)F}x^bQuf>tAv?y3yc zU_jOx-rJ(ofaVRSO|F30Md5I3_{isq2cD>Zs>e?YKka(HbxICrkn@fvq!`27XTmR? z@k}3-buO{Rmabf-Xdx>u=C%30iEoF-G%!%B8n9X0|JfWZ@(T}^kzXbooiD8I+5X0K z{Ss{M-)M0?5YUf))%kt}o9iBfZ#a7v;tKQ81HzSf80_9ziI64r?qEd}_PM=b4`MUI z=Rl-kV;s~?Sln)63y8{)jO-95gzU&pgx4CE1nPBpwM^MW~Io4~Y%4v=1Oy#EIYr$Q=<{W`t!smPW2(5AFhH|&6z zvskl?@7t{PgE4k-IyjKG7-z7C*W)+EJ4Eolh+g5+tWH6TIT)8nnXw86>}pSX&O|La ztp=~e0km^0gaCt$|8*?`fSR_zB0A)+nf3AyeC|5X&^UaQXtk^S5#W?gA;Q@Oh!KYF z`+~+rawJ;{vKqee`tr8+5lCC~;_X0_FDG9qo&%{j~ z^EBl_yXn80>VzEf{Z?z)T!vQr=W zTc7yP~y3Iea|fN6S*Wct|&8zkkZW5F4`< zV*K}lCj}6S@j>y`ts_r*_MLI=i0cKRCx-}DZl#)G0xt5@T$Z1$zq7l}0(W*kidu8_ zcU8gCABa}Tws$}Kf7H&4HC3Y`W;FePk8g(GJhY;q{vHQx{D6lCH{H*uOr+re$k=O~ znY?m^&4DlxiDk!DAD9NS(F@nZF9V1O*eaNi%o(G0(`bn?AG^pYJ5y}dPisUKH{0w~ z(uqvqKB>=Kf2h#66|AT@N!XbAT>3VAhCbr!iyR3JMSe1y&)5}2Em64$16~W5fW5K? zIOSzTt#bKkP&Y(EoA?ZN50G>&G zPL~t}fHC2cH##4|V3U+i@Gw6EW?yM#`YJQ6bRF!uGj4busJSPUuc+@&<*2Q2@s==Z zLrx*dR-6^!N}(2oN&rtfFn{O}DmLHd{|S z&kCqx4~4Dw!+OY*0b)L`K0u0$V7CxsU*}LPdTH2zvl^~4TNFH$GTf-PeJm4CriGwx z{B>n=b6rl%Omv{WuH$O+O~(DR!;LN=Y3fkV5Vc(QC()x$ zzjp<6uzW;&bPCTed1;3_zg%MmJ1V%F@qM}UN`%Ys@qH}1U+?;#?E|keRiRx;ed*4! zezR*v?xhwQ66fq`-H8Io;;9M1PMew!yqV z9tM4j)w-c1j~94{*d_n8m9p32Lo4s&TCcW3aKN&hc>Tu;IeELPH$~kk_!D1z_jOCa zJ?dkBTN{)wOeKDMVpKF>rrVglX;n)%KyyBtx;4>R_)Y2lNp*u#Vt31(Bu?e`d&c>t zif2|7zWrOhZI3JKfCnV){GaFN=!9bSm=Y@tW^?rmx{EuqvTrV4+SGBj8;%A?EXp{8 zMlFEjXYr|s=*o#nKcD`$n111X@;Oa5LBkotmZSBAlTS@p?D1WZn}vjCD_TWRPs^cx z?1<}i5<>rNc(iOyn}(CnRM&*Q*m2ryfOu3rffb5V??7~-H4CxzB%uki85 z{HXzNPuT(u3RY>c8KgW0Il1zd{_W@QIo2)a5g(uUw7?SpJfCuIDCqHQCDwi~!dJ_j z*gd1r)wz}cTbgR5CC7d`z`#f$5h9-IXT4+Re1)8(`Tlii6*0qpnwA~W>*PuLv|uK& zICwq+K%o60u72a9LSg>5qKK?H1K&IAL33ZU>&P z*VnRGg9RPGDGCwL-47rpiID72!_{~%?fw`9O6_28gW(mqm$DgpSPD$OY#r2*j8wZg zKmopJ(#b=Ace__T_H`iJOo~?=BCbPXMeNuitCCF~*JNO7$o!&%FB7$ti@DpD2VD>8aZtC^h`AFGE4n1|}e9Du}I`q$X2smu7h9`*l7x~j0Ky0)z} z3?+?}ASEr0bSX$TQqnziH$%5{w{+*w-Hmj2cQ^d|{eBMMngfpZn!TRq&Q1AJc`-bo z9BQxPb+;oX7whbH_F(U0hMzk=NdivOZ$WR6uVzuxN`3@9-_Nt@t}UTnXQ#Ld9lSus zBhVm^Ww}AL_TOkQ?4_5`MX-_y5qH&Bb^YYvz2TW2A{)jKJffAS$;Kl9xLdzgUCa*g zI!oraeji@Jx7Zzg6N0snRu~AKlObFM*~mn%>rp3T(GKA{K4YqqM6-&!Gl0`=W;hS% za-VQb;{t>l1QeDmg^=trU(6BeYQ2B53p!7B|Fx_wq7~2`tz5lG+6v=m>05OrWdBtm zfHlQORWEaLY!}#A?I-=tbRs^+!$7I)%XrGJVsFcxb`}Mg{8);*_3zJbu`hmRG?=k8 zI4Z4}i>&?PaN=EVvWW`sFC{@TqC5J|{|Fd!=LNzvQtJq}VbDfq3UCBk4k?$LbCBA5 z$KQNGZnP={y*z7f)C0JnTk5f2hz9y58~I<@u@U0W0mtqAOLy_oB$2i<^i0#kAE&8` zU|G+Bh`ZsKKp{b`>^HL2-+z@8WdGWys$o=?m2O{|aOA7FGfoU%635=Zo`G7!VM0=i zoCmIg$H^AkKGAT>JMtFK0lWhF)`aIIbc#7DWDfdMFVYcyHcpcDS4rEX^*-Za`yT z`U^4Cth0-nUcl=f^~=|}!TBx)?-RrG*1_3N4e8RS9d)c;2wwDpYnO|mSKwS;(XWv@ zzQ4`)#_93ULXf$#rSLVlr?{lS2mVhBxpOvGL!S#(1l@6isH+^n)UZ4HWP_zssb3q~C*ITOqBNzQXP+s1FnZ8rF;$?<@1Zk$dX-zMlEv-$Ag9!c5#r zEcYU=qdtS7K(MPHAb0g?`_EYg*eNjzrc^ZNnIgizHu@^*h-!2Jx|?;N*Wr9;s@+ES z0-2y)q0XiN5cdk-XLAdK11l~v;9n;!G=9j_@KZk_3AfGXM1(fC|FHCLq@g}!@UO^_ zl-U<7VJm&s&VCL_2fV|IKpeuNVL0;D-78d8hrmU1X2qHzg~N(m^!Bq#UVvpXOL zigE>XNku2O3WRMZrTAG=47EDJiAFV z-JC}n3z(25s^wmWZqpJH@e-jVu)4JPhO6igqv{-5Cj3z{;T2|x{=nc9o8z$ovw$#3 z;o?2rmj^?zIH)FIpc=K$5#_aycRJ+w%`nfZVyZUKFqa&d=4MlMBUM5fMib`rP+jb*;ha?7ubEii|ej+a1IXXjeM( zT{eJaGzp-h|1fLlO8=}oGtI0Qv4wVvtolJ3}vCT64!ixX7+IJKL3!HIloW zM3az64qMUC!|e=SOLjoVHkTofF76$haTRf)zuh;s|CBVlBRy;q@S2329yepaL?f9W zA5fQ=T=`DCQizs@Z>GAgXx%AO&`VevfK2@{8b938_n91l{MWFKzrb^ac-(j?BE2&Lywj2h4f zti}2B)Haj9S@oh5*v_);Gd2#b&-PJ({s~;E3$^I{pw0&t!GSm#5cXR|9%Y^ox;^e zhHKuK2;yrNIoLIGADFL-jnK{RhukthCH~v2o0E6Of+MT^nncX(l#`u~3TTG4oz>Iu z%M39G13jsGotB(NJ3g;$Qt_yJaj6BQ>_{pQmEqcwhQgpn*LU*`T!fl?@ac^7erSw$ zQo|BsM(8r$rd5iATC9ch;V1j{_l)^zyU){8X&}u@mzlEs4!KEDL*R>KbI+k%;69|} zxz3RXQZle7Qx3Fda4@X+15)wL_VV<ppJlII4?ew6m`yeoIPc9o`}U(K3@cmyD7B zGk*iaYot&&P?an6K7itOs=#{#W{RRDDv7P0^+K=d#`cG$hJuQcvQVh%DK=OR6>Mgz zI8R=@DUUyvZkB2~-`SuQq+AW{r!(A!&=Ftf;pol$T<|rw=H$EC&S&G%c-tP%#7JwN zP`!Vbe3u6T0-!1HeL^kM+kVJP5B9cZvMHCU=rn|j$D&CuL94UGWd<*){DlJw>Ho;#1xAaUnm!(r zDrO(sto|YbsP4b;G6SHyC!|%=suS&A^eyev*4S2Sz(WBmoFyOZT?O4`wmk8Kgf7}IA4bS|&mLSwh0|V9UCa~K zduxKBBh-;E%^A^G^O0hA6FM^ zrbk9$7H$-OKB>N=aRy43UyvHYDK#aC6`vvsTU*(G{})xIr%tx2gKLK}G{d`33e- zF*IAVu!)SNqPaAyAfXj-!z>(BOg=P=V5$KW--1qmUE*hAX=|xAOrVFo|F_?x@!^%2 z3Jrh$qsaO#rf@`*&gOs1j+OWQKob)l3QM@9)18qRqciy-D|@wKn<=EYcy)@mE2h&T z(+M@r=XKfA*M%#A<6B~*eu(o21l8P5)B^VIU zUg}%q?lqIV62WPzt4f5IMu366#EA0^{O82}Wn-{)k40ZnZejL2AV;;vP2YQ~#uLF% zbdR8Or0W=Y$<4~a+ZAXsT=7r02{@qb(N**~Zqo1mtKH=+5Yw(JK+skirO|Fb8@WR5 zz!IWuDbP-5k{C#Irunu=pB$!A_M2z8y&vs}q-LL5HjEVg_X~gV5|AJv)|cV8oMp(< zOa0F2vq000zrW#Q_$5x$`ou?tw6paqD!0bBv^SG`uGSK+8c+Q+*xWfjJZAh`&G**7 z+uk0dUVnJ5za@G#*QHMTcYw*axh-25b^T|=>~J%GPwwvi2>yn}yr6Y1yT*;lg<*qE zwYfUSGc}iRQ&nN4%>TokK($nVdjga0ivo_ZpX%glHqag=A7A5DYhPRC=t&-@s=e-t zV@v59ezH{D=A;l9Y*0JZQy+h*%6quJI$Cgk&NOPo>O8R)F~R)%)zAJ53g}CZa#Wf1 zCCYzBy_ZB>i>m)xzfW3%c!(XlaZl5vont5awKI#Xm4Sjo`C6$>kxx87GwOo#h2TXa z5ZGNf5B;5#PdURQdi{u^HoiBlPI;@u!;R&qhH7)rT%TcM-jguu_L6WA)5OL_5U^9~Se)x=voF&VWs?V7rjjt=!6Hl z8WEc~JIm9&UdAkGta@o`*FaHe3^Uc_?&PyNEEERCM&l;YSe z5yzvgNcFnEuiMcY)$w1{^4JNIlpH0IrUZ(Z(s-5N-L5Yvg%2_SYf*lzyQME~yo8-F zhdZcmIQ^z<1Q}HX7kQPCX_z8N#a=IXZt4joK2G4DZ7Fm@*av8GR8{{p1W&`NO#`Je zuq2Gs(=dql^hVd1hz+m$Ol~Q7!>EFP64)%NZSdGw=P>Z7wM4puw>DWu^$Y%p`qV${ z>soy#uVR}9p}LDc48d1UHQNFuJ?WCh^XKRs-3|aJ;GAz{E8by{(1(1INhj)V`D~DF zh%UVCN`k||K-waUbGY;W;TbpO5emHUyODy!TOb2dqzEN6JOhB$DNS(?*!zem z(0e-pvPkm%;STnrr@ziA50LTAq+VZ2C<=CGg>43j9j43%0^Z~3r)!cEs;V7faW7vH z`1S9HUR$DyCD?9$%q_JIuS40v(690J&0dRqNx zDZs-jqT6bUw!Iv%C`Jd`T?Qhy!!1kap=#Fa-OQO(l5BxpX(`UjG8?(ZG>=(`&2aqIRNY>wj@@ePJbHtu4I#oem__qv zjsF>KZ)|+GbN-l2(Qu@R5Qfio7lnYh%XVOX$#_3%M7=D_?D9Vq7(&h4qCUd1aZ6_U zH;qmpOKtG`8Q00uswdAE%N*+`vZcT;Ww)v{?s&w3|H&V6n7r_dr+$$7@l_N|IKB&v zr!O(S)pSwEs$VqXTbUjY-|t4Y*~_62MLDBqb9{mT_@7AqjOekG=8mj8%b9t_mPoc|uv5PHrQ!hM^q&mimcUvZCNlM( ztxI3UW(P9H+A9e%2_(WUbVT=;BX?K~Xtw@!lP^_Hhg0C1||l8iSKnGgQ63Fu zK3g?uJHAqnKs^&?n}@3PhI)F|@1lmH^UFaC?rUh5pAJ(xH7#DnK;7R|oA3EXa>mcB z?af&b5mGI0%KM=bqHByB(qi=%isoGsEWSra$w9kbJpfwSuF4Iy&}mnpv)0+BlHcL- z>J~EPnwH54eHnG6ri9C!JE#Jt)tO+v1SA8E@c{fNGxDmdvkVsFI=8Tv$Dzdw-T*Yj z5bNfTWtwsDxJ$golOol7LinAqn%e7qFhnkq0SjrY)~t{fpQpqLh+WWRhCD?o6B0wr z<7l279S)YwZi( zG+TVw%`9IH*?HypBMv<-F1v}M2HzZ$CrHsNa%SyDo99IiLi5&FN}OZXRcql4LA5~% znScEAs(W3cfgpp8pU;ku9;kMsx7-y4%P|hiRh#i?E_aXo{$zi(YpwW6gjCM!1e&=* z!%31|8@~a;feeOEpMbu5jW34LA|G^CmV@tTAr&tSduHaQx;6yu zLOz}Pg}Z7AL-{+M(N_y=?0Q@zwIwLB3Gh_-)t--D>FSpb0FE69&K|N99_RQg^&>Nh zd)kL7R&xT3O1#REh>PuIblzOK7d|^-@ZI9EM((VwgZnK2vs&STBGKR9f5XP+#y07n z6Ctu(LAtmUujt;zs25011&5GW!-%`>q`{`SaQvH__V=jEawSc(8=iBG2+%DO(>mga zY3`+gWqM1LTLM0Vj*#BG`E+^B`F4UOU!bj+VIzs zFlx_S(2}2#HQ9C-o3QsZ;0t=W3}X4RrdK>RDoRSq+R+L@#RPd|&{+S=YpdEh#3GDc z?2xAhpp0lavu5x))12+~4U4<*l(m8QnSSl1de-mtGg+p|UCON$W(s7C8i!4UQzz$T z(mS~f+&mxRmglQS=&e~{G}VSx6I~&jw%@wGME(2h9qrK*`JH6V#nb1#zDOm5?DFlH z(x`FXQSB^+(YNBqnY^u@-R*nw--4cgH@VI}^_^n@?}wZIft77Yc2OuFb9ynYD>on! zP+4UEtM=%j=3i@pELIl3|J_Dq!PpFDF;=>bFtsuVOGrr2GEtNhw2`Pr&I|YBxM(Lg zS!24GW?7B?%yPc#5;!_=Qek^}tpA)%^jVTARH(f-z3xyhV%Ac99?z72h(>hH(KTxZ zZOwJwR^$q`Oje%d19ud^iC-j>e0Bmi`^9iSGbL()iF^QK|I2d`%C^hOqW=M1m@uW$$Jf zOa6>2oojG@H=CdQWhm!hX-}zL+Og^6$)j^{|Aft}eqW>})MxI<*GE&InLw|h5V)l^ zQEU$)iuk5^|MK19qh5Gn8GKRFX9k?PPMVryLv^P_M_7YumLj=(OEwTbhr-m9LzAAu5~~KEMS$Zc(_c8_Xb)^ah0_{)|zw{6u^#84hB>MLD?Zn z$s3f)3v*p1^7GWa&4F=d_?-ZT`XG7R(2PsGC3$z z2F;(DEdCC%H>gI-?2yql?c5JdpOp-zm0divf8S2jccRGiM{Q}{#O{8PRK8mOYm+)h z$rZ}s&Li*o#5BxqV7PymnUm&#?@qt&&t@a@OeHKVtR{F-Xg{asyP(-heZ8vc06pP( zQj*rPsqjB6Z$SMh)J34d2!%rpd70;p4eZfS-P9Ydiidz`MM)&#dgU1%?MDjbSKeBBbt$e!8Uy_XCcDO{ z8=CX;(_fDOl-)Dx5rM{M@Oa5wZ}T;0sVZ$#I8j$9w@RC%i6jpjz(14PiS5(x6)hmG6}T#V8}GCLIWtDZ zM5oOcBT#7}br*7KR%FyNZtl6je&(;pE9@N=sl`ocd16%OR5)Ja z{^}0}S`DQe2HQ7=An#Yz@q8M3#g!H;1_2(@;-pIZ_2o%Jf`6t6Kpd`O#S=9-IXMDs zBw~|{pJ922>S!qp!?&;KbRR=h`rGMTR@A3WT4_JwbyrHM(3Q`zx$qWOX5`B;0Katrf*s$8%`dgZ6$S-rpcQd3$p_| z&PGd-wjY1Ej!7=5%fyZ9H? zJa6X0{>)V|eYN5Pvy5vd=}A8a;%Ci;mQ5|B8tQ07>k_Mlf$+%a3Ff8G9k?R1Rb5R= zAkBXlK`^nw+9Qg+HL3F`2>s?Tvk{{qxREUrTC4c0Y@fE!VQ_NS^+T(@$Nl;VUUuP^ z7pqaJ9XmE#ePY`=4CSyto5H5uiP;~z9#;;d>USqKpoE=>6x6f@Z|!z@&@_n_$+h;lnc;}GJLQNxW&Z%S)FQ9b_E(vHR29ijl0(M}(PK18^4_~`E#u9nPGwCRo3e1;zMD8enpGBz><#EU{F&bd4{7kUHy;X%j0 zKNiQ2%d1kTt){VfX$qd7jf;4$wZ9_edhW&QeizkQj{kEX-#M-mYr@+q794 zS-8fUl2N=paBtosl#&jX$2+E(3m1e4BDxq;C-qi4bbN0DAImElKINmKSD*(qCz4K7 z;WEXPXsGk!r@`Sj!cUh^Eg9Yukga<7dxDbfR+wriK>M< z&Ja(z?CgH9n<&Noh{NzVSfBq~s=NFbzeaEyd8x>867Q+AwR_|zWyToG>%dhTN?412hSl+ireMJVhC+Z|K!nV_V#h_*bz0lo~^91-$ zt_A|{A6B!;NhSAv)Y+N` zgeZ~p(9s(OXAsoKS-QfyyB{F!s+0DYV5vs<+Khui14p4fp0U-F?IEMkNR7mNNhJsi zH#BBx|BkRqv#M@5s-e#QFD$#V%~f$`c%h*dY4~wcMK)_^)_#Q5R2)1@09bX5ONVeX zD%7uW6B4`LD>`cFK4F2^-3>?K+}|;JHRPRP%Q~+oVjmq5Nqy!z?n+WvH&oJZt-HsN zFFl3l!i1_tD#Ylw+WsgJdiscPSMnA!CO`O>_+`g@9` zdgnfD(N~XUtILasjnPyCcts-My&1!o2G(ue%CCW+Uy0kxm}?1Fx1n@CStkS6PEuxf zAMu>}Ee}fW-icjf}QaUE{La4?hIIry<4c2=K8fPpwf%d?fVxE_t_Se0zt+%ToeWj&v*9jzqrR z9zxQqx}2p$F2y?s5`O8K`wev1UptZXrwB5x?n6?)U<`;rbg{a8J;6)MD+7*BPX$ETCQ;s$KGt--v&i*w$A57bci-u`Xat#V6p2?3 zFUg*on`bI6l^}x-)00KvXk4?+m~gY7!Nw!V*olN&&jCC-%}O)l(xPt^_9rC4r@Eds z*qMv7QFJDnHT39vdQy(Stvn1kU^ebv9vI7MsIt?36f~_v#BkX-gI73JIO5&5GSIT5 z>l7U_XQLL1Zrj$y%6cdI9NgEQyT2i4GAd*fI3y22G(J1^b~IvMT+ASv=CbR}31PJb zc|1Lnu z7O~}wjmsay>&=uStrmYis$g?hZTX%M)U;w;H+p^syt>{Y;ss5BD|Ak`8(rHxJD-zZ za?ag|ugD7x<@oyG7|VVy=Kl^x(0>PmAkB^ZLEt%8yuuuC zjP*P<5Q%iAKi~+qUBR4c$D&m_MtCZLinTiBr;a2S&t%t^ByG=2ivlnNefnor^t$p< z(0Hqndx~1Cerm@5SucdC-lSO#QWMETd~`s;wn+=mLiwqbvF!StkH;JCMUdkrq#s$P zKZNmsGjOUn$F4ub5Snbc8GbT0*XBX!M&5-kqjg z*TS{T!4Q>hPvEM<)8uFY3v}sFdf1JH(&oZSvImZ3^Bbg`dO5HyGIpJB&WVYfJlj)0 zidxhgm_4{|qG=6nBzs&6El0lDdg0GTEWm#$Sg0#|8sQY1ERh4+icrEHOLLc0r0Ik4 z%Ie-nBFc+1P1x##;^RAkh0Sj?Hc4c)EFD()>u7@uUc)-l1si_T0bkRbWwgDa3O6HD zZgu;Oso-PtioFY!J8t2TIqK6cNlt(@4oVvT$Txob9UxG5NACoT4y1$m4ZdCt+=~&? zO*2WsoIMDsd4RjT;>tC!h@XvI#oc$SJil8%{Zmor`ntHq*Vc=&HE;AO%8y>;0KAvr zB%fvaih38N22E{Foqz`!1@p&HqIj0A{qzJ8XrXTGQI>1f^ate>%kC>D6KG+orMTVeg-B7! zn+f5gdFM;t&X4mH&98Hay+b&VLt|-2Z)!1joi##*2DrtcO61KVSm~2&#)*DydFd>y zW05%HfGU*lM%m`toL{%P%i?-hD@awoM+?zW`LQb49tW@9H2LoTWF;C;KPn&T`vBvDi)H=0BhY z=u|>2c&+EIM=_lxZkX@{mzNtB>Vi)A1TeE1#$tK2Hn$R3$0@^$Bi=OUycV|`AJ!KZ zl3!72?hRgcyT=-XyoUpjkdd_^VcVMdWImE*iNOqP8mQO)guhY_SVLP4c-uw{v4M`0jMhzPDUw(7!3-ek;>y91^9TC@VI&G?i=Lhs_TzsD)38c6S zZNRGiZDjl67N}~g^ezg)1LdeQw3|$Jf5@? z)-Kg<+t+I#@Xid2xd9W0*{QgT4;x#^ILvBsZq>ObpHO&2c*wt(YzT-IdF?EtHFyN# zifW=>ec-1it295*L+BmGe>!lrE?D*pTh95DbBBU&nR8oRniQxNl+WsKrC-tASwZ@X zKp`=B#ZquWqe35i!gMHZr~PfWypG&k+L%3@&+CQ_~XG``^#)R%{kpkTE93E z=*J5a0Ubs3l{Hu5^a=2;ZKqZ2)D{6^>$y0;NI}Pm8p@;t%&D?miXaZWUmw2_#FR-J zKQw%fY-=f#hRlMEQ1F=FC7>mTZc)xr6f>Qks88~GJekFV=BvASlo}E3vGK)S9PM+V z6}@W^Ui>rqmMA$v3=JIbi}iSkmA~jMc@i?IhlX9$l}7(aC;uvu*sKG??KtUAA1Raa zVuv>$LSVQ4Xit)ZQTUc}1g)aUwuYfc04r+9$rBOxz$4WGf`nIbTq!oemw7`L6iJz;Rl#p1#XwnYCT0cLPhgkopguNM8p^&N@y z*60M#JPbH#Grfsh*d`d>BfQ&R%qZE!wG={Ut2c=T<-!w+`NmdX|9(|28D@B?eZLrR zF0PCEQ>88|YHxZhSvcq7!(x+BPvpUq84U$O%@g|h^MUm(Gdg+3qX?~8Npz`DtJ$;c zw|Yjk8q^Q)09Kk9`8hO>2`+K0@?4d{pT29XA4Q<`39g_)q>bEQA;>sP$d0jv2-5U$^$(Ge>oo zbRC?SW^%LT=pL2f!G3ip08W_ZUPgkLzy;^tg4eFV*E?TKnPGd{s z>FF{~PyO=ttuD_BsF#6@E6ZtXZ{-Y*(%*Va$=}KHc~9SGyTQftkg+`GhWsD+fee5j z(M-E(hunjJdX@WYwSzwW12V$ymeYgPU<>EdFVS9 zRlQ|TMHtz4kTHs=*_&Kw_v%9shicH!#26B47l&>Fib!c8E1*!Un;DIc6wrSSC?(zzy-kKNMDC`e>q!uuncn_;l<431g z#J*mu|CPnDJn!1m8*Yyr!$iCll;S~-$c5S>*8h}3T{)0aLN@+6%Uk~B7uncKXqv1;ZFs6^iu@^N+ z#gGAvdXx%$hV?_O5`z#5x$P;$I%|4^E$u{Ura$9kZ6cPH#=dd;!h>=wB3yJ#Zf_D) zv6s4r!+6?|#*jb?mmPFj(>Mjqr&|*}wSBX4OJ6~L40&IZI~}3OWggwrkPsj#Pj~d3 z5wlI@L1>;VIv}YI|KzH1oid&@cd68=R^oNP4|~#3R;4&a1SIy~WxIiXu1sCUuz*^Z3tq*US{R0i6eLa^21HnV z^dR=M_lT;*!?o|sfo}-BGL}{P2e@3%-Okin3d+riX$a2J@Ek2#`)BU&X_I<{4_?CO zi=4(5ziEFd)FTzk)ypDNX3TZfx$iHM{^2jU>{T?H=ec^vD3&YeHvT}O;)ObrfxP>? zwiI=zX1Y`5+GTaWXT5OWqTWbh2Uf6oue1N$otCcp+4JCxGkitZI#(sbz@BKHT`zHf z?d3EN@!HxhKSG5q=dO$C@4mMk4G2jFBl}MjpEoV9GiFqIxed=F9iJR;W~F8iJviLn zVFg)1I9)Bp`|Xh=vQ%H(Ewe4dt{GL3t3%G|Uw2M{fkzJ<{in;ZqB=zv^M0-Z^QB)*>;9A`B zj~%azZu^6(fl4Lm=>OWvP>b7-8~3;yHSIMCqnH?2js|KqbV*{mxv+YkdTJ|ckfp72Ps zmqroXAkh&{tw~i$WDP7yR#4*AI48}buD~kZ4ez(1ause>vwzLeT|TNhc`IgiC7F0w zEX&Uw`t{bpe~Pw=RNIQ!^-wA7%FG>rC2iC1?0;llT3nQ3hJqFd^%e$j(o&W>0No<_ z!*(9(8(uGFi#$w?9%hT|1KKqfe2RWOrjzJq7y57cA2YnbFHJIH=a~NScx@{^V!`GX zyEfq;Gy&QKE&Fhc&l6KRzZ4CuC*a90JxG#_a*Nm1sqL7LBy2XCM1h$+WZcHUiKcy| zDwfCjm_mE|i(+vWEmVpuE57)grQc?6}0Ov-oF344Q2jW+hrpZjF{?7HyHMo}5F-dDTR1 zbLuMp(2eB%t?B&uL$1Mx%7;(0b@|NlCw$DhxCOIJ?}1AMz?VxuRtM0P+b++fT>VhP zYf-)W#iU(OM;*pPSmlLUQ`tEo?aULfEIQn6YW3sPqfvrfQ}-yY^}0U9fRW-80I~iW zR|aUfy9&O)OAGcx{x}D|B4Mw?P0`~b)29Rhn{n35N$>sS;fbHY>uc?AxQqzwC!aMv zxZSyZ11qs+cxg~O_m;*PnHrpJ5{|f{`X8N!Jo9D_Y5j%XoF4C%?df1Ethh>k6aBQ~ zv@VWuyG8JPX<*p^B&T(C?==jO_dv!S>h?KU+IG~GzYNKXB@uzhZo@Qlg6q4dMCLW} zcu;Em^AfZjJ@2IAuwXR3119S{>!8KW$#%L`cBi;ra-Kk68At6|yR9cUv z-XOG4>4^h$6Bm>$1*X-ie%Ba*k};(vRnP?Do3E$CtmpY@WF%1RcaN;K--ncUwp1%8 z>c-2n$@=9xJ&a~DC^6bVF=~wz>4*t4R8gBrhNCW9P z+3_1g)gE|kEbHCwRhuGsxIN~Z`IyAC0|AyLr`Nm9G{VE{4gPkFR|1-Pdy{qOgc>cg zAu`S@2S#HiZNb4!uzkjF3& z@l|#!VdCiGXDiuT$WsY^3Oyr`0C}n;Zl#^W-?hpkFSH2bJF%*wNwelz>`etR+q_DgtpW|5V;vxmZOxi=@11f6fXByLl#d(5yRU=91bHsROYr7PbMGve{DJ3qgB;yN(*F^r*_Z@#g$%U#gh!n1Dq}k8 ze6x(UNOHdSn^4Yn@~Blj#bq6c|F!w$JDR4O?uWyqowQnRtdJZ|5@ibs>ofDsC%{Z~ zVGzHzCl%nEZ4o9}47khf9V==reRnz|Q>F2;9~hehX{397`x=Bb(1i&3C4>G^y>|V< ziS7@w*T-6CI4}fsYSM`MrC+)_Hbl^t@Ky2&eegFeMv_hAug7(_A=vx#QxN~z(c*!# zH%_!e2BT_nvdX8e-~Zcoa1V%NDMW`7=uvw^t8V+Q5X$YC)fx$Km`4{dZ0KJfy8`JO zGNYPMeorYbI7Z}FP^nzCd0ZnXj(?St0xPsr(Kq}KZ~Gn!P4O{5{U%!{hW8m59^wJe zxvxreL~@!i2D26hR}71_Lf)buIVSIj6}Y;A5L@P}w-W^Fnv4hQ@dMd%#K zMCL^_=kHL^RAtr9XKm&H`{LO&m`1Hrc)jIms|YZ+ZBaa#;%S*L^~7v<8;a?>peWdb z9Fk{u3-(^lA3yt-ej^96)@f5Rm)jgSn0iR{1XjTP_ub8jXOaX(K2jF$3VM%g-mGn; zwI5Gm7pD9X3tQAw=@o#ynCLlPypG)49(hgi<5~v`8hVDHazROP0;7_|oMPwgn<*~L z*36>A)ch@PvPSyAq8ZSRg8I83nH~ZRGk!$5bltXN25WuA&wBA*GAXiMjQb6*jVI7?P)mrn`PhjJj`|%&y zi-aERs+lYX%iOT_s?l88Wu*1@b*9AexLEkqqq=h(TH!6Kh?DBD9|P&V^h;(VTK-{6 zhYj`(0F#@7XmAytSgi(C0ue}v#fx1XkCi?;8e^kgL;8_(ShE~KvZns}Cikkn@NbIo zKQZ52egE88!W5$HSj6Y8rn@jifw7jRYBML&UPOEXY-JDUqwW5S;CAPZ|B+Q3GzL={ZX=zY-lzbN5}A z<^*Hd*5I3vVqz^)6uBwO?aGPZxzq4JMN7_;U}EOvZ*N*zg4LVc9uJ>1pZjc+-gISG z1X7JsvQmg!moS(>c>$)xXP0=)g(Ir|fT*NN-BD{&JxY_P5UlCPfd1*8lm}WJu(JFL z{P`TV$L-Wx{^NbcDVkUq>W{h1znN+yM^$DF?F6G?-z)!ANR)Xwi24`WE5_g5-%LR% z#M_z!LmgIS5oY#yu1ukr#GWj^tMv|C&Mz+F!+~rt(s}5&={9>TvZzw{BNxyquS9Om2 ziA^(kN*KBJMpbityE8zk5$MlT?W;=W*p^5&_40Tk93wT>$sJN`uOmSl7MW216f@yN zNNIc=hh3pJL8`<`haPwPhu^I;SBXHGtDcHS>L|su!o9B#ex>3#!empjvB{fgpi8wa zE1#xq<}{2H3@Z^R1_Qe9U4>z+gi*)s1?jTblj5}sw|lURmvWwb2xKBz={mrgpGvXE zEH*_|C3}bEb`gUK!*5%H)At|O4c|!C(R6|XktjwY+OpyKT5B>qo)QA{k%*;nj_O)9zniM&vH^SVvsPjh_MKA=WMcf>_;K&W1She zzR(Mg`o1=10s5yV=HTpo-|b=@;MeKD^d&mz0#C9AWWTt0$CDxUEAi&DStR!HubB0# zyJfMym=hmW9<%4&$a(vbfc4VQ6vOlc{+dScwOqi-o`ht#Td*||dc0iYLJ~;w1Z%8~ z(Jp=@*9{~_5jvOYkvG{$CNr)cW@~;Dh=bQ1qB{8(rs@V|s_bgBm*SNOkq&S#!Z_eA z-?1TEr268ZwC+;--X!_{to<#tWre@FaZp7vmx{vs7^i4vvTFyfKz@%ABYp$WyVVZo zxIE!XLskK~LN^j~K@5?U8hn!*6DHHN}7! z*vp3~@z=YeywAgDtYTyKPORdg+W~evN&K<@Aa%0on%&pPI@LpvCe!)Za#ffHvqwRYwt@XW&WIMz3)1qmsn;r-jb}k?$`$h1 z&oV**p`Z5zy`_=kAEMU9Mx(D1)u{ODB$@L?M0yDk3hBC5=R)C>$O{`n=XURg2qo(2(nZF-vwoF7S|H%6-V?1E8YFu@~dY+ zA3cnI#uYY-1XX&dVzeWrlGe)@)-%juTX(+p5}M4P>zvQ7;!!LKRh(K?x}z&Wx7zfw zzAyz|X#34O{;DJZfg%J`P?<>wb`{l4`ytJIkv77m1kz#IcZ9xp94!H1#jnr(Q1LL7 zZNi63dI~#hhMU%Yc+yoSMWVlAzy0pa_THhSS~j>QF!2x#;eBAxw!j9Fd&Xdn;%SD( z)4jtj{yNMr>JQE5fEL~_X{G}@o##hjb4CupVM;?RR*qf>{1g)6jQUuZol{cI2nr7g z``2C;o>%gL(WBk_3R!WB&E&(oup&IGK8Gi*;MN@7r(peJADKZ} zOWYv>4Um9PvnO|m`taWXj*TN7-k)r#q+m#x!2gkS4sLnA?;p>$-Dx!W7`x+sEX6>E*QqC1zna6V?WJ=QZ{9obA2W_JzFaG5}Sv!MCWsdJR zcU&hK0iqgzlw(bD;q!q{$9uxd#zMyNJZpr4P6&?88!aQ~53;<%49c*!scxys?}Wge zcOW8NaNqc-@=rAquEwHfVR8s3+LfvjMu%_T?MFv4(pU6tkiMbba(P3UE7N23a7yI2 z3+>26fvIUob=7GD#g7q)c}Dxcl9aHvo%Nu1x+ArwTVrCwCu$(Z)uR99ANvCgPmMG0 zKC|8b@~@h}C>^Gz<4j=}g&c9Z9XIDj@Z53?iKbHSGEfs%`8uZOS_$x&w8K`U!xjEG&f{!bv&Unt{lXZ4h0J^>n(=S*zGw&+qzFFx zjDf-RM9<3)d=TQPpG9dIP>iNEheyBXxdSs=Mq7tm0)!Q?`vn2fygP;9o}dIRO&zrt zt4J6Xx>etm2}Cw7GFU<#RVToa1X(`S*f^&j?j61X77Eoh@gAKg4L&MqR(qod=bz@D z$ResO#AZE+IA(O}Y=mG&_2I9R9ARU$>whuXXbZHK`_uDu;B+B+EJ1FUYGuFu`ZkGzJ!n$&)PKNL#x~l-dV)i`rE@_QtzuBmemx%YzAI}u>ZM;7L@m> zglGLC>E=-dtXh-y)0a1!q~6Z@0T6TtlVe>O4dOGm}Me`-_cb&h37?(Ly+peuX2fxWmhZB-cjuDijk>X7Nvda)?y z-7MR+i|kL$c<^Ay^bSI9Dr0H=qxEQ9J45&~Y6t0OaZRqf*QUIEl7|avA2vTjb+9*d zT3rx5y3wFGMDjhHW<^0<;nG#gSZ~9asErfq%GH+lrsv#&7us_IGo{apwvMb!WsTj2 z^2RL#O?!LkmNdq>koN`zh`RI^g_i;$;iFL#gwyA88lU4Ke7m|-KJmQyFtb)<$u zTE1n|{U_K7cDxbYO`4fuqftwZ;cD^+UHvL+uXwKXM7f5(V?&Eru!-3%!`L4pc7Jyi z48dJ_sCc{>ym3)&2$pvVVVwo!TOc?%eWj96{FS;{_HEB*aP_A*MFZ*ayE>MgFA`Pt zu=XTjSBb}Gdd*@uNyNdr0Oheit?B+U=2DAUs6kR7?MU?)GQ9AMZE02Ee$KluLa+w- zp2B1hGP$Wz&Utw;2Yi^2sQc$QE_uHJrY#z)-#V>IK8<}oT+~42qHP+K!rEjszRV6p z?NuZP;<;|bjvoCk7*5Fsd~(ub?R$q*HU)N=w3tz(V)kZ_-vAw4)*Ru#&xb+bC@Iw5 zCWZc6mw)8k+z>**PIL-Al%{Pm54}FILLH%o6kriWkP@ct7qN`DDDWjMnzb3 zR0!Q>qz#Rc^DeWJYiBwti`=wWPhS+mP)l!vgns^1X#&qdJYi0BiKvg1B&7B;T5NWL zB$}~??VU{mv{I7`I3&8IvU^rEyKOac=07&6KJ$Bg&W*3P9dR*?RHMQB6y2Mq144mRz_Gx;R@K#h*Pt{e?(P z*+Bl_G%2pqp#%fDhdTJ~R(&X!MtMp$)F?Wcm z6vfX*ZMJe?rS`RxeT{QK)ev=Hg(9q3iqz)Nr#PoT-+3x39D3f?pBi@PIO9E>s6^Ne zS>j*Jr<*RTua{p(Erm_L7x3L`Z%~0qQN@ z(Cl>;3e8h+^-xTPp<=c$$V03)c=qKAA%b7%bDH`pzCP+%%Q4p&9b!H zuG3NKggJ}Y=%x3+@18tjH9x8!@RyRWcBMFz?z!t0yDto$ODFt-k7^e1)w@1ge_qJx z86I>qHmdQUk<}CLQ#njad)e#9Wz{Z}d@Lx6flWRQkFLj(Mjm{sZU1nGq;AH0 zd=9;qrI^*lPv}Y!S8a7i?jZ9l?t#j~Ge_|EdHVxL7BaK%#Ce&P$<@QZS?6oP`d=sf z&7pm!tOSGM9L8_dkFpcur9@CMve;{%5$XvoO5(-2gYUX_IAdtxH zyR15S15GISZkRQb)8Qh(vG7++`$lLt5VC51Uz2u$SsuDrjRcIR{@kE9WI%hOqDF>( z?^To>Lb=xluk@>^pXXeinuGW}1X#I~Du-Pgc3c2?8XFaaJ zLHEwM%MP#U?27KKitb_UUM#eK+nHG*;SBWy{zZxHY==!A{ka0ToTPz{WBTPJ)O=dI zuMigI|Ifyqhx|7*O$`a5;;mG*^bYM7`CX{?8VhoP?|BNFr^pm1x%G+dFu8_PK2B)u zom$EM3)xz)j^amW>4SC|H`%KxW6d$pXJWL3W7E>yNESmjAuLNRrykW{LAJcLn?0RI zw-^6K!PnY@Oi(i}9fPmg`_BZeV{88E%6b4kXRDy67{YH#Wdf5H#t zjjBG6d#fP)RBPQ8c%syHy3$8rtE)YBAzVg*sq2^J*i5>w%(CUJLi2(C&tI5<>U8E% zsG$K!>1kn*)vgJ`AV#C{I_TrvG5P!x8%1YpT&3j@BtQK#9-}H&(k_r6bWS*aQft(C|A^f_6@8Tk#udx_6K zJBfwz-cCEWM+O~#yse~7PIvg5EYGkE?bu^l=F=K2_}&!$UZy#x|MggzYuD#`BQGhl zDED7aAdL{1d^y36OSrc&sUij_qZPp55TIyi8h+H_CQMr3+f3Mx%Av?HOlM>@T_E;; z*Q;&zdj045lZ_<_9TM3;kH+2TXfNhO7hXEdf?^6q%s2+`EIu0PTQ2`R@-_!TqzlMr zBxCvm%mGKP`N?)Nj@-!_l^{$8H~6ABo-px24WdTrd> zGWCL?G8Nj$dH&5652G4w;W&`^%YnzLlNh3oyD6E*=PJ>lxEPbM2*+?BdHEsg8VL4J zBVKMYOpjR#)(St}rL(XufXm>%exwrx)SJWm#6MGi!aw!biDe#o;?JV6+p1GutvVuY zG@7fPdtB_Lk6Q`)P1>`&no*jXlsYsiETghfc9z6!zNUI;j`>>eSAIyq^lR32NcKJx zYB>18!x`)MY9HzP@sLOI)FgZTa-LtuHxP}nW@t$MX(PZFP^u?LU^Kyh5&>(ae~gg9}u2qEA!Z z&2mHPvcp24^YC*hw$>@~x;Wi_V`m1o7ug!)xcCXHm$JTEmddYrAXVKmo8m4^zPNiO z7xMHraAMT*)E=8PX(|0zQNp*=rWGQEZx>qS_`j2baV&1yq4JQ7%B<`SFkE+Ms>4(M zi(=-R5*4aMF^eTL(&ib9rGto*DMv=qx5cVi`9|toD;-sY&j!W_K8+%Ur2QPPPj`=0QC^-F#KFXx`x3oc2uMD%| zIKJ8nH~)vY?4$VWX0nMbzQb`FNj(_Y@w_2l>Hjs;u^uAU+VFwTKW8aBrLWYh;`dPk z=loZ(S5M|jPe_AkvVY&D&V9~$ly9s^%NzlBmdig=M94hwY-4YF>7jKiIjI&s6XxXEr5bn%Ya9vX^N6k-dBZ z$2(~+lZh%pccuLa=~IIN+;%1#XsCG!L~Y~;NQE${@$un|9*ZqvTUL%)-Ket=wn84N zG$t;e^`8oto?0mvRiz*HRA5#y(*8!^@in4bu94~BP>=fcvU_U!xbE}o{vovHuZ$A6 zOB6^w>qlnY>FWPO;EGy6I>a&WZ!wDkbcbWfo0U?N-{GN@6Dku?G_bk#_0uSbfDW3!pKAEZG^&oRFrZ59G?|08}7wsp2E); z*m!Spjp19R__Md*N0ty(TQQOHOX%-G?XRmNcF0jdbdxA+q#UR3TDD3?ULWzj*@x zS-Klw@cu%37g)j~Fb(i+>t7Bdo(|SG5I2?hm0xHt%CXm|IkqJ)HgtjY_YwH+Pv_ia;`_DcdvP|$OvXZ1W_ ziH~of(Zw+umJ?}|U0$2YSzaA(Yqt!;|GXloh(OXCDJmIl6To6%*96=sFL9EAuVrCA z1lKM=Q9x31yYEJz;r`+z|%1I`7g2f6}9jM-dUs%;K1bEw-6LbS~$h2|aMnLTD zQc%oMM3~Y%%vr=$uyvTTTG|k{rzQHtskV^nSe|XCaA${LNh)Awnsm&Y87<@3h=yNh6J@IxSBOPx{$N zw{gU8nagLa3x$nQhN3r{y;ms)W7(j&$nulKkK7ca?oxHXyh`pTJto06FOa!wjyYt0f66wXZhE2=21f1S)+rlDO&bE_nG$NhvXbh^vkVxeC zNcks1%%z8{A5=h8nc5SqqH#{a>G6Dx$VL^%RE8=-$k+0tv-+xWG`O|hKir+&N-lIY z`*HRJnG0nw0D!wU(%XKp3i_PnV6tx6coBO0U8yGQ>m#6V?i4*WXJMzdbJ(CYu_CO%(5d(-*#zqe0% z#DC9a$k6NGe%=e_iI^D`Dp+i#B#*M=m*LnG0K`>VFoEDkYwGO7A>XIMdG#^zI zEL@z%Gzx)|9Co*YVoyjFVyP?Ux@U%hv1WL4a<3?3)&~aIiX!&gpzH%vLRgWG5cN5oG`EOkV`o!SuT?iwHT!DiA}L?p>S#(E#JVGlr9zOeBQ zUs9P}W$9%+uI1T9+_`_OO~-ide*l2;5$K6`V!@Brd~J_Oc$JL#96B_D9d|VDpDE$> zt>9Fpc+^Vdk%^ic*~Za{k&|Q5+r3Tc^V+;z$HJZ88QYuC4F170lOC*}g;-6kNH|Ab z)0}q~F(J>*@mOy*J90Bw7AnSslMKNOX){Hzv%O^e)9kG>92~V`E~=pzT`e`z1m9aw zBSJF|U%#E`3=>ADYEP~O?`&6+f>(8d9&XM6v3P*sBwKIs7rSrBJHua>gM+C-r%bw& zh@f}5pqF7nHBxuyh@ygUlyE3$C@48eF%9RECMTd$eUcfJM(`|0Z@aw6ZVk{l=u;{T zV8&a=ivZ|RU+shf{T5{Xt7Gro=IzkWaf_&n)#O7LcUbi&f#!xp(!LA|pmd3+OO~Jg zcfky_rEeP;XVnxV_|#U)*}ZRhsd%bEX-2grB+J3CfGT%(@vtNlU_ zOTyS8I@RGQ6*@Bi6ZjiOD{>MrK? zziqV8NU$^hQC%Pa2p(<4*>o>{1FCBY-R^%JCmbxm6PiT8s_+--IoC?&vZSqkbxmZh z+tYn9r|qzVwPfI}5{hzfY6CCWkdM3%D%apo;q$>A9!5cop90@@+Q=`voccqsw8PD7|)kAO<;#q!vb~IYdG1_ANj@h)YM@ zaQ$K8A9Rt{PSW4AYk0`hb&i|CqB7$5?u8gbIr(;L3`}DOg1wYict1VRKd@1DhYY~- zh_OMCosUF2#1?(6(Z3Iloz>rsht08^G{%{$OCVw(>SVa2ddQJ&d$P>ec{oi9Sv?)MCm5xP?45(+2V>)>Ys>` z>FYf2bnyZ*)_{O}T4%Q_ zHie@IB)~|w2H;zdGRRu%LR(7)IGkOO=_)K_L2e+JAj%|t8_MaEd!H#o02|@fQWPv*mP3jG^=;6>hNra-P9hocs z1w&aFMTrjYTgFdZQ&7C$_v`H6vQ zyqJ-3`W=ZfWv(C?Hv)@?;4dx zIMGSN%5=6-e-*+D*;J@h8{Ea(lxq!)Gk|MuJo**jXaD3;bl9Hhi@=c2UGc#H!W1?y zY3Kr+PNQiOQIeWPj0a8(^gX#Nfd~w$7T zD01|=Ki41hYS~LcR|3-9Ur3qSc4(UhbiWRe&|$NMS5~X7@5$E{s=a@jW+Cx0Ei|?5 z;Vjwf%5m0r2=ccO{k%M^81S8q{R$|i>IggI5>Y$IJO+DtX#*@O8sZeR>B`?5UhtYj ztEG)$w^3YjU`CN%>E~!&=->Ixr`~D`$tdSIBA^OYjfVR&UII%meb{(Mp~CKGCP?ly zU!Jf%p9pH3k|Q(_6-vR#i#|!GL_H6v>1_`QZ|B2otgik68Zo_S0qoT?&a zsTU_2iq?G-;D2~24|vjCE%I|AtqBLo5PoP^assd>h$&>i9&jWAu}7CsD9b$i%(i&P z_9nM5`N7@8bD%~{#kfc+Fb876y;p%qG?YKz?UHupar7WQYfy& zNbpw6o{Qb$)S(z_75rO%Uxd})V6)?mw4|_2J7rry$4Gt2mNwtAlQBZSw0wxf?Keafj%EVt33xWXULwwOD$0NRtkXf53SmlRxw2YONYoF$gKx>$#h zDd+u3w@~3|KBQgEGZ)nbG}ByrA#Z(R0=ObO1+t1+x%Kt+w~%^U0&}hc01%}zR9>Rh zuU@-~4>3ykZ$4Qz(S+BJVIj#F)kI>zU15OJF!j5TW94HKlGkF!AV5!#JOZjvnrl`c zAtWjbv5z5+*&THK)V8#VC`GY)#?LpRjWN{+$fM7FH6(R%wDa6K`vHU=`SL}jUOD|a zK9Qoh?Dl9Z5pX zerCkYWDvzjAKQS8qN5^GFQk$_Zn672e!PCHbp>#?5=2ijyRkcypRNPgp%U4@!cCOx zS#M_tZMYW?d+M&w@$b*DZIjlhnAJi!_(}R`PAGIu9Os^BT-uUjCW<5PsRcEn(`p2I zjF_CClt@3}vwefZ=}$*0In@b50xJI~j<<64#ApyAIn>=2(M`y@`b|s6JlbIrw*`n6 znk@HpztV99BEW$5bs!F*K^u?Y@YG;MCjpMfpR7i7xPe!5taTU#dwJU39&MTF8|g%Uk`$(^t7Li z_7*CetVQjl(N*J*vkM#bIMFr^7eJr1=WitbZUM};`;}mImNJ9h!o?AlM-Or%DdBKM zTou~Z-|(f5tYyOA?maY0btInVIct%vQ5w?Rlrv##gPsR~b1bR}BwWOtzD0MTXiu&V zUz|hYOyIYE?>mYj?F&?(@Y#sJYlQ%vO$mC!nPN2jAEVKN7hY4sW$wlfzBgIx2@Q@P zb4C0^c42uztTtSuGIQ0PMe7LOp2{T$Tr}+8ZRhrcRWcz9r(B?oVLh#f1eNS)X$zB+ zJz?feY6Wwl2y9Dca~wQyB4l4+~k=Q;g0<5xtMue)s7*6|Dt3LZ|MM9*y+5EpFZ6S z?TlJz`BF*I7Vhg?XkGBA!s96yPtJ9x9^}fbcOC8ytr2p4bQh6rkf)%T{+TQe)oao` zzw8#WBoVG>F~S#euRP?Ek!LLf-%_B&-x2>ouqQx+=F3JnoRmC%#Cx%Bgh>QO#U;>q z8WbY^>8#d3gJ$3^Y3`$*SEhgm>Iiy;RcQ~iGnm;{Yney9vM#myN#eeMFK4#YF zfTBlL9dd*By2r>Uhp3x5^&~>-2Jkv~+8Rx^-5o+68DKDk3DODLh;jP>+mxpG9IV_R z+39kau=0ab0#0D#v69#ATHbe+5(p~no9AVcoNNz%YxAl*cQEG8}tEDS8) zU)s?6mbR!-EY%;>^$r__J-U=>4a*FLL?cxMwE33r8@=o;74bxdBNr&Clu$Rp=mKES zJuo!^^9-psBdU@TN{OP93Ik;uhxp<1mgLtBtt&&E?RfKe67l8i#Eh6w?GZ#m#|$Jepl z-t%qM%;%ZQ(xzNHlbX*rUo;BDIjlv*I{O0sbvU#5hpY3Uxt7JN1^MxxJSp`kg@Hk_hw*SeinT|T@-$C$zX~XPm zF#d!+DDQ0mHdCQMOy%;UY1sI!({2#)977(^@<5|3S%9`LKG}av=XChIS)ITBWxdz- z$RNf&!X~kqZ9UN^I=Ikj57;?<9)x4H4thI%`UD7xZQdFU=Fq0;w!_AVf}VY-x!Cn> zPv}|6z{oa@_&@h!oS*VOh5>V<9S7Dp;1`#dCwrr1Rq#HwPD^}OPl%t8ms#iHsIkB& zB^~Un%546mvfKUPL=nQpXLh?p=W22tLq=dFl4i@+ZS91;S_v~>@5RwzM>S>*iuQ6O z@%*y1r-Be!ol>nU5x&R-!ssV!Bqf^luAe?d6(DV|y^-im>*ziK#Of0! zOL@)6|I!?ywF}B?Js|@Mr^RRo>MItuOl<+CGTcRjwkgFM*ea9?WV!;UbFj>ZkdTcg4%PaDMS& zZD4A}8EacbV8&F@p-Dv~0BfU}F3}8PeD19xuXp$wuej~?cZ*kVYFORU>>oyrvCJ58 z!*b!#o$iSjtHWuY?4dZ=Z&EBF=*!+I61)rZ=zHl0UX*qeL%k7=e`zL#WrfMwW28$O zr4YVASG#j0L~XDXykEDkO2d!JJb&jU)r6lC-iCd4+d}J?9)un)m`fgs?OuGeZro5E-P>NEXeU zXLZ24ijlYf=nv%zeNbPJ%!Wjy=wE(}%F0Ib95jpU_%Ffw|8MV@WuW~TN5(lYxlj|- zzdKgJ6w5_wmzsf~=`3R}l-BJ9koZU-)%2)MQU!FiqH}?(i=!>gR*6k5)2M*4bQHe# zqfb5FAy!TP^ycH5&`S9i`~WFj@nr3FKlm14O^m=4JwEWvj)gVc=z$hw%+-LGXb+1+ zV$R!kvy%Kf6K*nnlv5A~)m5dB@5~v?$Q82c21;t$+JrUt$(;s7N;{43lDEqn^FrmW zSaj8-L#Hr?TWgch2Cbfl4znE%)0r+jtGUmY&Fxn^K?{S#i?^RO55p~oK$juo9YTNp<-Q5*P%0f0q56Khd}4xAiz zs#Nz-fo8j^;z;YXo3vfqB5gi#ypu>MhoWWw5O=b96WNmSRadWylp+Mr&~^KTTxZI| zX^E!^%E{5?Tjs`XTo5aocFn%7cGb{wHy;s&QEGVmR!iCW7Ap#>4-tZ|sQ|93d&W#O zT-Ez$CI$X7AJ4ww?C-~gyTJ({sBTV17GGtp1ZKo}xrjB{d&lHCe>p{2B4o`-IVvIFO|Sv%NrpN^F^K%ri>-x0%)^7QxA z$Gh4yLH+abdXFe%6I3huxEZO`XvPg{jhXag*D5viWExiXdC>8XRN#<$$o@|WuuTwp z{$^}Ds@IQ$yC^lUQoi5E%uliE2akDv(7n6fS4^-Yt1c7xc7gw^kgLXff2ES5Gu!ov zvBRg@hvP+N+`tx>O$Ck)e;Fy$-ELp@)9Nr}|9tbrWPbi}U<@B0K8C`eSI-xJbFMEU zyfjdSRq(T4oc(kIW~MW_9(Kjm7K)PPYKw8@&&EyprKgM2!7^??cXggxtz@l2f15>C zy^4#9&lWv$YUl9T2Y2%=na&{8Wwl7x&+-zP9D(l7)6r%qucx1j-)C4=>eM)nl=*+p zy6UO0b2H8{!p?#s5?-L;lKpTqCL$q(=f{2vfehfqWs@Il_ML*5zEd`+78NNE3_qMt zTZE>Y2C>rq`q0vlbr-p7$A-Q2+#}7OBtlrjfvd8A&jxYhP{`#?(jw87-CRmr&H9{u znGj{TcJ8N2cbVX*9YIInQyf94jMEvc2cO?(324GKtEO+#^K)f$6m=!-lXzum`h9+- z_eRsIRYN|H51;M#L5-b$Ji!(6aMso2t;uWE=4uox0xx9CQMxRME#MigkN)LDkL&9L zYXBprMf`7FIFPgdY5n zPsS{i=ZUDpOer?m)?kG7t=iDY_Z8t`-NTKkiKBW)O+1{F?5Vy@Qg`=+@y+lZUD}ccG*IqXS69=qOm4+XV+6W{ zng0RY<_2eeRcQ?kG2PYu;~n@)7&iLZLQHYERd#Qpl+78&*Z zWK}+7NdTBBN{FKJh}TFb{ZT)iRL*PTAN;^0asYu>zF0uZL2ZEEhcbz`zQ??IB+9l} zTqTS6^x*P(Y+X1k9bR8V-G*tnl~Pxmz}*L7=7UK;`C~fq4SIjPtSM*>@gC`>$Pg z{JCg$c|(=$`Q+OrqJ)~6$WC@<5Qub4cS3uPU9mKUkXy>A0k^L1?_@l$+*57@FUt4%JlC>LDR$jP*=`H1LWq=Fr9`8>T8FEUDYX7gG+d#_=(u+s1AkO z$$^ZAO4X1auPx%~j09@(q&g5J_x%%@6Irx9PS^>lylWVqsQ1}&RJ}B~5S9_=m7Lyi zVRme$*hO{9?F_w8wEwXPPOO(beG`FS(0M^yc6nO^u-g}>^;yoEj_8u$(q0Tn10sB9 zN1Lye7jX`8c(KHnodXS~`v-)un+sQnM|2#WPQriFRg%|_^ zefaPJ%66^{n%`|NOwi{lxV)S}A)OOmIPj$v_jWp8V!B+Nj-7)eq(CZ0yvcqk&--#~ zV|N!`CJww|j~sRLzk|}uWc${{qc&bUJ*seNoxe zC;NvFtjLdRycUe`sF)4Ms2D=VzB-t9d zA?bSyo?sM_zUMzZ?1N=&Y*JF4+KV`+AnK?uslS(%GF7DKh=90M2 z=xY+ZOrY?NTE+eECqllHHi)q;7mgAzTylLpGJj9C`ZIr?8yJ#j4g@UBI*fB%9#rUA zm-G~KX-hS+r`3Z%2vRAZm?FW)UAnK!#=DpGPxqErGuSPj@$xhJS?Phd*1qMuH)Gg5HOV2O&I?I`(yB8Tk%>(Tg1LAII zBWD2BF*6ZqY}__&@_k4F3&ps(P=I$(3WL?EA`q>HO;neL zPn={%ZEj#rP&O(c5T+;EI3=Ek-bVwq^B0B_b-)UX}&l4yS5s^i(39K%?<8lK7-+GVZvxowoQRGjm^A#V6k;ab-kMNr;POebM6`Ed_mYC6^q7L zE-|=OleA!R2HA;e65>W?s5FrrYw-;#o+QB|o~>n>3fh~6&Eyw6taN-k3Zb^zZ7pCE=IcOB_$+t#+|>}rT;%A;FtdvASJP?3 z;{*kU?jRSWmDZy4fhd5CVBJTquW|X?&0aHq{(FgJEgRT$+OooyOY$T`uWv(h^?-*_ zM#g7iPQDr>`IJQe4X;h%CelHBFM*mxwe#JW)cNxp0q_h#{X&Eux@lCuG2Dift>Z8E zVy_JjdgLHF;=o;AAVD}DJP&~o4y=H(xB5-EA zh0aWu2_xtW)h(c9C3`k@#Yr+kJXup(=Mla6cdj$z*A3r>kXldrTW}_5@`>h1M z2r%zlo0PZc1Q#GcH=M-mYplNrzv>8fdmlv)vl(G`yrS)GUTmRSoSMT`b{>gu{yOrI zLdx244Kn~ZB;S@A^i?{8PHPXIiBQkdp}U6Y198CD8Z1d#go0i7I20?EJY_rxc`e$K z;xNblsQ?H94PK-4{@^dHn!5T(w~*hRsI@h{LAxjV(Z4EG?HZ$Su~3AlGcL~)9pHR~ zH|X%j28D*2EL0m}k&=FW+nvZHw4BJ03Z$b-p!je8b#VSU@l6u9E;!!LBe6~;4?G@8 zqW=fC(Bt^N5)EK)!nj#AVL5-&d4y6{EO#`DD!aqVB9o$2Tg5aae$Z0CanZKCDmqxX zJ~$F@Ry8b%qvO=;zu;lB()4}}gw>JNTKRiTBl#73o~1(cv)f%dvQbsr=;~TL%dZus z*3zUZ)Fykc?6nlfAqu&$n9_Mv3{Qt+QP2pS7TR*zjX1m$ta_cA%M-uBa!}}C3Nthp zjwFnKLb!3}e4b}uLmQb#Xl)r= zy@%vX$BiAb)2OTp6uzq2j(`|XRP~>1IxpydlDQxQeCD2U4BQvFxTr6AJx~sZa8Im9 z1s5-ac{ePHp0f^9P}*g-cWzi)#Rp7SK}ky=J#$xceu(`{jW_D;f{#2r2QUgS|KWTZ zKQ^oSr2<6-)plaI_M@)AUkvNBg+Skdy{<~h(d;tkJ}ni|*BEv2`QJyXQWyY^Or65} z(kC6WsW@`F*dHb7!!}*itYZ-fSX57jYSos+Gja^^Sga2XbvE$f18!v6VNU!~&8o}= z(aX=T$MIPGOVYmXmfKby-huD5BY`;14i1RRpBRAx^Y09;U^Sa1{+?T~TSgKE(eie1`rbYw4qx>w9D5b&{(*h z`dv9n4yg)9W~c+q!bag5@5vi|98o&IGEd<8`kMr;>F^3XR3T+I(HnDWN5NG)u-feK z_w#PNQu5q}!67{o3wLdkFsoKg z96FGkTQjfd=zpQw9;H4E%LqfrsD9Ph3|abU4h8?rHob(_clVYDDSnVIw(to7?>CRM@sWw#WgLJqi9fNWY;Ax(JlZMtS{RDx^% z?vlKc6uMu#mPs^oqc(2m1(N-mJ}tTMCyf1XVw9q0IM3Jg({;ye__FWsKx zeAC_Wxz@|lY70FVfsghi|3XCGe+7`OcfCNRPOZ+wR4E*_W)@8tLGb7^Fe^#YZRfQR z=+K*Dmn6>)5N-t_xx-U|r$Oki(A;EC z-R835m>g=TInY!K&rLeqMh;rY2_6Fw?C@P2L$IQgUzh539$&Scy-(%RRy-LR$`*6W);Zj7`^uS6V!n3c z61k^3zfjv}k;!&k>T41sakW>2E>5jB<`bJ;S*G+bZzGBmaKEny4%G&##MLt;P75tk zeXj3!@11d3*6cE?67TzfsH5HZ7ExVE>9YJ2 zOXB@Z^{0lYW1bifp<+b6;{TBU&5*d;RMNQ&tQ6*>oZ~OqENOYMD3s#ME^1#n36{A- z%J=M+HE4;s-oL81?=xj-=eKnN74?k+w)$lV*FM&YV6ICui*Si33*@MPL!-U+4XaTA z!ALL(U!UE+V_p_`MFoC7`}7LoDg2X7h%HcHqe@0dL*ky4%OAYWYO)Cv+GLnapF_FH z8RQ%SNBPZAfcV}sj>N;nnB!$p>mT-XUT5e(ky2i^l}H)h+>z|k1Du&dzc3H0MUvob zG`-vc_zFQg4GDO7?+*4UmF^*08gIw-F2$IGa8#xy3`x@T)poDMZYMzgWl8&U9s`8b zroY3$yGk_L((W3WqvN|h+iEzxZmtrxq!j4aSXm5fv-768@{o7*i7#k;{tac(de82f zyY)fIV*@qc3WL_#z(DE&3|`!zY4nf(`>v0qEG5n#{)>7=fKRMToHK^nUudYcY3HUV z>X#j!AW=!3Bifs9CRA*piD&qMCo z)?va&`4vSV-r6-vPKdJ%O;_}pnNebjxXNU#xK;9T%_WO+Zo?h|2PO(X@)YHqOPveUcCpw&S#iC zgvufaFx`nwwK78_AqFws$p688AQO??1cpu;oBzVwoqrw=+HnoW>(yHPX;X!iqjX4y zn^J!j3JH3DP1Wf~O5-hqEtzbTLh-R}Rx9LIjVn}CvK{~Y#7iM$K$V0FelPe#4R#Vy zo;m%#29eRCRDZbVGs6DQQ>bl}nBJ=vqq9obRqUURGgl%>oh ze571n+It3=#~TCf{77yxD7;wLBAIH(-)7noaRuo6t9II*k733nr@8$f06Fnur-@ku ztD9@JwYIbA`_|m4G>>M!H~+wX8BdGHCA47YR8kejTKYC=F1B^R)452U8+TB?ph}?< zKfCz_**=RUORJK1)OZk^Gxmk{W90f}oB8F|z)lb*CgyO$_I}<@eyS8KGV7E~h#+BXHOrrPwCV`i{26FMr94wfFi^rWuPHoW4_~aMxD~>$ctOj9%d>b_ z1mozG>wytKq*h{lQ_|V4zhia2w?K^C^f)Ailh0+SN%uYEN}__(_w_)k&GLb#0&w1I z;TwOCAZbLJw4|~V3)g)i%eZqay9lsb`Ar2^lf1NG0Brmvq-G1%4R|!ly()9mg_qZ6 zL5_D4riHCArf7B&mKuUhN zxel(IPrgrL(QJil0KkP|S=1q@q4}y-gsPcM6~L`P`NVn#!?nyp1pzYHAaHC#!2q#C zC}T@445yoxPJK!vZ?qz*WO?eUiOPmI{or`=M(*tA9I)qG{rl)4l5HMK8re1#aua5a_pEm ze&UtNQVUs9rG`RPu#^N;_j>K~(OR;M^T$Wl6+X7-`|aAFGWlDNy?qlz%qy|I9f1z? zDq-QBUk@-(ShFAwpR?Ac@CJF-JWOs&T$I7pLgFx zs(CI$(?(?0Vm@rn5z7)f$bKYkXe|QfgRaJ#ULx&}GIzbtS7YW%q~#Y2<++t=4ie4MUWAtS?u<)VJzC6RWgq2gci8v-n^ zb2Qf^j6TOXGqT4Q{ zr;dVJnAt4?sp1353qZ*E2d@!G!G=evJ7?17@rNtzdAzKaf+?x;kN zUsJm%6hDmtn>E0vrMOQEv5IJr%E}<#sW1>i6(}&b!K&iGsvqYUJu&cHmjARq)kfUu z%_?Kj5Z#&|yqz>fS8uc*6O)9)B|a=C8+!n&s<+SF~U&*mnoTH!s|4YuaHl zTZpXL*b5~iV$##7OQIbgFbu(}MWohSz9^JK!fqN`sJ$XkPX(`Jq(NlU@l_X-9(LQz z@s@Ehrx{f)vff?bYIhs7z-yQ!xj707ophCZi0vY2a{8vNG_a4bCRb)dYzg8a#K2s> zbmBkkv>!;Ipr~_J>`kp@lp!uvsoWNh60Y@!)9K}gvrg3#28bj@*n(LzF0%8&k`@N@l+9=P((Q_KzcV*e-*rRZn#!tCbP*iM7wQNc3O+hWud^cepT|3*v701t*u7T9!9{E>+7vs^!Yetc?tWG!U>x8iPcH zh1Yf3JTaFV?Bv@a_Quk`wXOIWuQWRB*tL5<3ZED^J0F7cdER^?BqUTED9#S3u$#yd zQfPLzuy=9_8vI4k(-j0Gb;-xZmhcj{{NVJ$2R(pHz>8h3!xWv?qYu`DKPzpFV>wR` z!R2HcRH6SKY6m3BhLZ?|bRiU?%^;x*%G2ra9@d)fF!Xpm5c;GK)dv*{HGB(oE0}ep zukGUCLj*s}K} zCp&>-Y(3zJn%W;8lR=PNyfA z+{%R*vS3~R)Iqnb;*_hhq=lzeA5S8{#P$=TSaFx%oQ>we6q5RBTNrIq=1(N7s_BtT zZ}t8Fw60lg5RMHf-bSTMH?kf4AM#KbclOWb`#?FyK|g{;Ic|seRus8WlZ?_M|6jFW z!w|qv!tCBcWMk<&baJB|M#qfZMKivnKxGi62=>!?^_KZx^acr6y51~f0ej9 z^m~7kh}6U;3twe5Z@L9sKvy23v$M+}!j0A8`yIbpaIeb)HeSCA__w$Gr$C>g$iDEe z(N(%GR~0#mDYPX!@A#c0Q#t&{n-8vPL~L#>p>QqGKb?jYBDj7NQ73ys&ww;{EmB*O z2s>jBX>)6&Dxekm2PMn;pDy-DOYz#$ZF$&hplMM0TyX&3=r4NS0KIFT!PAXpmV4Az zUuxypZ8wo53mZ{(_JaE=)EVRjp@;p;Xdr|ah%Fbt z>K*>VxD0&k3!lV`-aQ0ixPe5^Oh?e_WZ6KOC8(#pZ*{+S*fOfPhd zCHwnC;ObzK^gHqO-Y6b6voS2<7fyvuAIH5BxPSltReC=HG%R@yAHSG()AdwX2m-GzGI9tn;9VT+LWd3$->1YR3Ew?+Yc^z^Zm8;VVF6>zi9 zg%1qc^`-6la)}>sH*=Z06TbkdbVpP|x74RbJkPE+nZ6u_VsArtNBue9NOh|V8r7pHdHU4=JW?BRj-bA_d4=;wcB4Ovb?)LDT(fqX963Ah&&%r< zPlI@ztykEM&b3f3Gds^9{On)qD!&*aQp%@1toDuI)M7@6_+|3xwb-l?G(7$&?*8G< zI=8%EJT-90bTQ_668!QJJ5(gr$7vq5Mkq3~>%8qd|Yn75ga^hya1632`%=v2GMY$=MdGOqbkf_M>ab4zB(5y15MPvHo6j zLaHDN4VZ!Ev>&?jAtW!Vo%S!Y8pcsl*3AdT0xb~_0=&j@wgRkrh2>b6F!)yQ8~D3X z0a|KF5JSJUc)i@3h@E=5GT4_tnj%MwRx0*H?kNmsZHavM1<@?N6Vel7Ng@3d1mB3U zJaD?gzNK=`UdCe9Zjpb4$=B}@Kx`}Q8LFHo_1r&cz?*UWsPh)7Vga4wZ7L6FLN)v*?Gj085aU_6MFEk%n@>kp~ zr*-95%l)cpzA0vfmuGFrNjOo+XSUfV@;EeeV?NUd6`2}cBlpj(U|w@w7|x|1Az%dg$i3eQ z00UN_mTXwkB9yur6Tv`VyyLVx+iDOPRj@=`Zv@u%M-$54+*0VyW#@^YlEf1Yl221g zZ#ek$RdOu6$;zf^AtR*IY9=yZS**|1=oy!>uan1uasRt#%U6>1I|m+^qmc&u#STjB z@{H|LD>Ya3rUhzypz!`hv#(opImU{dI=rBNttspv8M0!um4atH)Pbi6O0#PVDr;7f zy}ZeJkNPxMWWtZ2;vT!3+Q3*Hg2XlbGiC&2o|k81ofFxRaKJAfOvpK>r7KsV{483yY!m2>Dfx4Ei|S&Cdi$)#zGlTVB2lieJN-P~`y}6K=bt1j zG(39kCW<9U39lFu2GF6P#;U^_1XN&7@>@|;ij{d%W)@OeZ=T=ZUIZ%dC))%0`Nr&B zneWzs=)_~yMh1{^rq}nBMZcGy}0`hD#V z+f=Py6sgJB%xY_XLR;(f9TR_g@&>1beitW88tSxsc;V4;NiURlIuQK5oza8L)^ ztzbW#{>71f!X1t(U_8NqUH6Q{VEdg1c&(eec1n+#f^Ja4=<-S>%EJ5ZwUt zimw@$ce1pdGgag&>{Kv7TuHHoGj2W3Tp}n=vE9_7a9aaC?WHu!0)^v%aSrsva02_w z70F-Ktl&oo*h*%eBD&$qT)VOUq*q(Dlt(%i=T~QhB}BUO4$LtEnH&KYmvX3S6l79( zBN5Zla2tENx5R-4-6Z^Pob*eqh7E;{)~BlKG(trt#R*D7LpzIYph~Mdu;x>aiBz3= z-yPQ>K2G3wt1ye677_LRK08&+@SIXk@R$pc6O+-cg7nG}gvHJrNNBf7!fkg*0y>+r zT3$fQ$alFiP@K2R0tMT0N?~3=MeV{Q%~e|&I2+L7a1fd2%{H1rO!lDNdTH5W7O(40 zS78JD2v&Ug@0$bZcrXS1LUK2XfNH!DNFu8c8$%#yi>-T;;*Fk2%ALh#8zlo_467IS zD?y%bU@Qe%3JO96h4t?4*ElOJ{~+c5JHZ}z2S_Vzm(jM@BupY-nc^2`XN#0$ojbot zV&ej{aMJc;^USG2Mbd>Prvh^@b<7E0vC3v9RxElQ*stG)93^Lc$2)vLTuqoVS-ZgiD6oGvXQx1<2ws*KuSLbnP4P7``;v zJDuSg-V5;cTsKk;9!ER??&I}kDfU`@uz#+sjkHMsYiSafoQW3*V^MQ(F{tDaIsaLK zS3^$3%qDf~6?}EQQj9jNuD==uTH?#FC}OXIfKCZxg2wy^z4TB?<`Af0Z^iK zmEb8WN}TYLUP<*cfoC9eyTer7F^44+k^i97s%LEVrmXRlK|vJlKluWg>7~~R&wQo; ztz+N#&tqyE{1eu~J%ed>q2B81Arp5a@9(#jx|pB=YE&L7v7>wb@3$?V&PcWH;8>4N zawY)YZmGiy)yDL^NYT8+{ps?0uU>q6zHK>f%-;*PFY9B?sP^7vHZ9+HIqcVnH>OCg zt@$Urm9rdAnH9KYVgJf+{Vk8QR=CtxyRvK8;9mbqh8P0OVnIu_77!Hj8ITeZ5(^!E z0>uN+$HXeTVzPl4_~z?kkb27fO3dhHg>^OfGmux@6xLGIS!O*s5VeyyjZyWi z&K;1@-Iz+TvcJC)0i@vxu1JkOJ%hiii;ZvOBI-LgSz)M`eBYaUkTtWdFjAB|tEq77kzNnxll z>EUMd$TVCYAOg}m-8r?0sJWJ_krQs&XWq+DnRaX9#v74mmdoVXS6=x0U1w8MShTn^ zitYj`nb*VxfQ^y@pZV(TOo#DUuX zJ5rEgavmZt;4p~?I)}RDiQR0SW@hz;oPBnuhhshasx~-G3S?v?9pc_ji|y|eawusN zIuc7DOpy%tLq4rT70{u@>gR%0HI6SB=7D%{*jl7&k?dTw;*JN{RVr&swAtRU67yDwmp|th8Oe^OkQq`~&0$VKMS!d@ zGE^)klBxf(Omz>$wy)maKK2!uQ64Vpo;wUb<0uOtk?DEhi>E4`d_12|y4(RA)8lt$ zxgXCA$Knm=x}LBSYDK0tr2dJFg+w9>haHv&rU?Sd3*6&w(4{y{x+Y$@9#A^~Y-u_q zhGTrKD^e(x`Rjz^$sL$NWupvcnDj??uQ=ijVdLbp+HW=|mcWP>mt((jq~Md7icI6R9S?-P(90HQypyMkf9MOGwn>zd zDvC)u;=mgpNVDW5XYKR$2^E<`)Hn{1$0xwKPQJqG&CfePxsWa&<+{_O){=>yyz`gv zHJ#aH5=g#7vlz=+CwWw)b|WNzs$x7_$9Cl5Q~07VKbySr56XxCfVB-uxb_(RUxS;n z$?D%gl8!`Kwgs+Zf5}z7+Gtvk*}hC7X^@}#XlwPs?cecse{CKL5?=*lq#y)>1($-t z;Z4+n@cWa!e5H9(v}(OCiGM17n08G8@IQs+a&Aq7S@zFf8pH$trlV<(Ora?)o8!Ar z5Kh}L!xoSJROq+=Dz^fofjDSa6IGhho!X4Wp!x}-HU<-G?>+BN<*4pcg+w?{<*-Y9 zmWPF-(`)4%NnwG}(b3s>dwmu?CBVXh*6;8QnJd#45fz1`)u{Yb4gSzPF}&K_3zq+nd)+KB_$X~ z^y=t~!+z_aN=ZLDJ%{WJ7&>FdQW^8%m=(;wQ?H61bN&b!tBj^Fs@X;0a`0XZ#;50l z6bxO9Ju-`hA64-jC>)g^HrOrbtWeq$EizWJ4Zu_YE2QZVAYCAICEL6n-j7W4Fc{d* ztL9e5V@93@CAvYa^ILyD>YAlsEZuZ=&(1p42O5U=P#~$vQ0l|puTL!3(Ek|CT|ns{ zPE;&q(2WkneFcAx0A5v9oLpoy2~CQBLhUk21<)8R0pR@ES=o3R4cY~RblE^t75+R7 z--H9rZu(RYfqNLKmk}2@~6;LUQZKnbS_wIl`Vo`$tgTh9SAnuvr zRh(q-m57#CQh)+Dy|2k{524vR7M`Ya7Hp$T6F=|ud^t*NZA4bJg^mTyI4?e+%jM*+ zxe9cl?tfY0CpG@37jt#ydz8&cW3)h^CIn1|%2DX~sK1K<(=5J*ylhMJZy_~^V@)ib^r zSabMTo7el}xeLDg zWB6OIu^|to-Ssxh;nMF{yThUh`uQ?A2NT&4Y210~xY45j(YDJJYt8u!6#~l4Daf(g z%VfH`J+kw0YNk0sU4dyg5k}~9Q^~k~czXOEsTSil+lJy%=>WJtfXsHIyyRkLCw%zg zDt-^D^{MQ{{1cwsi5yIE!Gv8mlu`N`VZqN=NhkVVj7&lboybB!OpU!*l{iUS@|)ob zLD~{g4Ly9JZ$@%x%E#5~XguaurY3$;@$6+lpI@3JLMcJL*sd5}?Y;$1JB5%87qNqI(EsPPRV9?JNxe#e(qApIhGt z`jm0v=4+SeQ^A+;6j(g*o@@39swf_Yhs9Vjny}^|Sv6<&|8cVV< zR!Wk82mRbNf$-arvEM6NsWPxR?b4$Dd&iOx$KF*x^V6bB)Lg((Nlew%^H~qjM6B}v z!OAJ721;9GrHDdMvzImnD$RCPj@9?bK?0M6DmayGf9|1GXI*po&AtV>vX2uMx=!|f zn)Pq8IbbiI&$-W3t`OwuK);ps@xG?AU{J@b-KWwoLBtT%6F(&Cs7R z)6LhAs>7Z9y|4>w{ihLx)~!rL<9}{3Peqsg$T$~q30RM?x42^{!9MWfs5+d4a|YZi zGNwoRwRyNrMJS_TOvV$!wxg1JUbbrbts2D$6YE~vjjJRyI5)iNNv}~N8)skd+Ia27 zIF8CPKJ(;5DYY=x>L2sB1ikd@W6)k1o^vI=uBMvViGk&svxL=q1R%uzAo3)$>*7@? zc9d%TQy(6S#SHakv>Fbz`7B@0TzErJS0`{9D;Vb_X0WN2Rh|mhi*K@haJQ`uBwpUR zwGgeOZ3Q~sg)uZDk6iV3)lY?n4t$@zcloPFe2pNcyF>C z{f6D=7Qd`1umDSG_*HeuOnRPKAF+eg_{E!+rL6O1ug{Cb5=gIpU+sGW@BizdV_>Ov zVdwX70jvPVdTFpU1>+frFI$xIq%o7(tgv|9FFxI!F2ls&vgL?9RGSQ=m879l%+%~A z{D}L=y-+8rm2PBszY)A##~p1(rNp@S?8Ss7GBY4`W@5t(Aj^*2n$q;pXnYqLv-&!7 zBViBT1H%;?BY&8cne6AG)$XYQ&cdQze z8?QGgw2G_idKq;9?6ltb`R2_DH9Mq_z+~+HGoF`7(GasXIB%w@tA;rIye3pf08;iV zBw_RG(I6i7l0mcN;77g2>+0c-1^b9!)_K&1z4O_ZJV25=VMm34td2>$01y%4Y`t8Zew8S_#{(_rDEM6s<9~8Bgk8rE z$YWt`7U#eQFubTmv5x*>4KNR(00bcN6lR!4F}IEYy-Fu&FT@6Gd0)>ALK~B289M|Q zUD*RBg!P|Q$>3Ha!l=|&gl3zekuDhGCwnR`uEm(`zswJTzRpGAB@j zg1RlC`2ZlYF@fLfEr``>4iUJH!^Px)ImITB7p-(V*F%HVuCr9tXtXD;nv9=7>>&jE zW=wT7Lxr+9r6Q9l;1@PCJOVMTK&79@`J1-Cm2SIHjsL&As3AlDYUq4QBdWn{C#2*4 zFU=b7#|NY_hqcJ1;CrC)z8f!zSwOGhUT)nDJt~o)AUxQ^(y3Z&R~%fhRd*EOYJH#1~l}CK>T5^Q&m9C#PFR~CuovFdrn zJmto+;bZ6F# z-`iV~R%R_Wy|UN)z`rUUsVv&S`FrHFC&GlyR(!or8uq)_DRRrUjM-JnF?gJ_yOj(FAP&ZR8Mn(IRLa4IPr8C%H^Fv_+Yjq^%s2YjI#=fy> z!4u=gzA!D*-E4!;Jq&sjK;CH&M=99O}(rMbu6?%HPd)U+i|i zAV`k@;+d$mRwF7r6nB*L*d-!|LtnGl_ZcFD3YOdj_#iam*}ab~@-X%UPPG$$0)&&0 zf~&HwNWpW}wIyD{e=FsL1I;j17bO!Igf*$o@V9I4yB|I0=PludhwA;-*dHt$2zZ!{@y>Vv(9gsJ00j)VWay)U{%I}XPvp9Z zxL?2EL(-X~TkyZ~8Q!N6HJ>URAXNQ${Jf@?FI6F)b4_Xecgq3k*7m(afXFVJSA|Wl z-jmAS(on>E4~o$V+R6C`;P@0pz#`D?3;3ITEZ^Od#4O;%E8fZ&wEr^r&Hfo2{l^1J zB4P266a7xBWcrw!CN%^-RvKTLWCNayE3_=!vZFNFkFGO$8}z0)O%OLnNx>CKP_wu9 zJVSuq5X3^8 z7l&dkgJv}(fq<_}po-${T8TPCqu)C}A~vJodP`bf{Ku@nf2x?dExprY8;AKNi=68Y z%lq|ZmC@RSGvPVKV6|{$x~DF$jFR>6)oPZvFQKmuU)eJj_>#SyL7sY7sDky%ln-n5-QCd zfPb6q0YBhbpYG#Zk&Ti}+2W`jMnD#x~ZKsOF>TnrM zb5@bOJ@22-nC@1b_zA75A0p^Xs6hvB`_MZtZniX}KQ&=Oh& zhgr{Ez2%WB;QtB$Rll!V?w$A4_Su}qY;6Y15K$>1u_hwQ-)jnB`Oo{;=B2Yq-X!Es zI7M18J|bvpF*0mtT0%4e0in`q^2Jvlc8{1gsRrFYC7HTe)MhQFbM zQ3dDWbw#|x$|ArO(|u z=y1OzxjCLkx!OZyP||30h(3TMKTm82U5P37Bh;kozM39YV-(<;kYKlpR7Iu12BcHdatA zR7ay@1yqhITJpuFS72VMh)o!=xtCp0-&^h_Waqo6HkVlo)jj?opDEwIEHgtbF>rh& zr3>T83&_KU4ec^0Rw{OcGv`XHvnSO03_bX{xZzi}bjkYEr{0F57Z>NY_M0^hQ9A9;q3*- zl1mF3GB$i4z*=DR<#>s^9s%GT54W7HPn;tMm1)I)s(7rX*19GtgYFQVnzw*l@f6!v zg`R5W8~rKG{UL9apzE%Dk@?#|VBT5s>Yhq*MjW*bOP^@;7QrlSctf)vB=SsuMsiL%OIo zP6a)EP7X(C3j7@@Nuw^-%f;m_;Q0*C$P|+Xk<8JzZV^C%?VT~F{#N!HpQ1J2wj z|E^HPtsSMZ)l&p$orRWaV9ZemJtywVUa5zyB|@6Ogqq2q=qe;)w^IADf~Ro}(TR~nfhp`e69;4uxx(ztRr+JRJvy}kXwg|B{l;o2d6*MG;U;5(4* zARBc;?q8QmYe>xwoP$k7p4~|qW((D3he>dXn5Yi1IHfvql+0g@_6+3jv zFxIUjqF9Veok3RpeS;)~S$RVqlZ_Oq4+cMqsNrA8`S2piBQw{i=+TT|lK|gDytFQ6 zWD*0k9G!++_u6Brix%cmj2*pF!8nSBkdKCH*Gv;T>%o9lDu?WX*wTn97{aI5B$$a1 z9B+oI&Mc~jMv=KYvi53N>dQS>-4v?6@7ALP9Zp0RZ6K_={5nk;41GmO_{dOH@E)MvDW=gF(Y{BV-yOzI0gTw3wkDx3%R5AB5;FVy*q&ZvAqIRQP$@9mYdA8(giEfsM2MzZe_)OgWEA_Qe2m!#@Ih9*CrMADJDpo1n zGG~jL@gv}kU70!82WSzfYkx^ae!u4JCqL5WvOxDevp7|v`KICc)gA{UG4a1ge0HE3 zldrF@{mBATqwg#Cr5S@#@aXCk#OvzhzOJy($NYM(3k=+EQg#5G|4foE=}*pvipYhE zc*su(_2@C6`-4?_;q?e;Ej;D(v!>V!4Y0zI-?lbev4c(bmiiCkl8K5FpNw5O-SCxMeQX$RuUn$C=;{_-xwPuvo^{ z5OnGg1jrI}r$g%5TEF7LObSo{!Sts~i7LMpZRaLBzSDaTQ^n(UJf9%vbe}B6FCWy) z8X+hd#ByPyfJjnEK1lC5$X#h3PX6e;{!S*JLfIkh&kz;7IR)D%tKS{){Cu}hT$!;X zj#Dkluzp;M{Ob>*OH>EM+!fQ^6PLJA#h0&9frnQA#7O2{tE=`CfJBLBH(mVvOI*eY z$Ik_we6c@Bqj3iNt4w42YZaLV-8oeB;1@`eU7YOx`{xc4C|F||)m=FPR=!3tqiDF1DVhTI#kcW{bQg?IzlPasYbWP7fxxU1Jw4L{?+ zKBci6Th9rou_jMROL}Ij6R7}}nRD_)plw(wkI>SnY97FxP*M=A?5u1Y=dN3uChYRp zrYJ8lcuh{XKOYhqK(w4KfqzYM!3-&+KWSqB=52Nb+&S2LgoH4r^e3$?s4h^Ujk2ST z#Xi90li6i|2o&x+FHa|L%mBYFag@!jRpKCX7-EMuAQ^>C8mkOB(7Rp|!IuC!K69}v za;00SwXt~ZRdFl+N+w-QeL%&-*=wShK$_}C*{GuDvTTGoces|_SuqU;ub39eAKWxKCpF5yfH$%`?LU5{n-Vh`3 zkK;0a-)4VXY1Tm1D60yNH|mdqU%MJe3B#A5k!Jzst^eZb%T71Pt^Ixm@>JM@V)^!V zVUsy$1B*?+=h0W#Hkg}BM?PM4Drjb-qsA4qpbcGIlqotBgT={;l#?nf%Ehq2pdYvz zVV;`rI7IVsocPcZ%Y)sX#!iu13S2{$8|z?E8*jM{9Fo$`53UU}o*4i9ZaXFB&->^o zd`gST|3`A~zIu6KQLm}`r5pt~Pp`_=WF`BO#`GUWW(o}rHE^Ms%cEI|Mkfh&A`WAC zj3J1+5sk{pCdr=41g2tjD0s|aiCLbiekJD9CI_(Yx`eU;HE~dg;FlA2^YNFS?)tQ; zp=)>7B6-ZE1G^y@lcK=&PYm(jUp#ZJhAcbYGMpLTx4LLJ>*TpYysx`P2ybrMHQa&8 z{Sx(k4b1kY>Yu4^s!K7~VM8t&sgFm7+B4NU7E2AEEodYz+H{xf+J@qSil5(xlW||Q zUg$U*c`<4oGEg)K1X&O&Pkme|_GC+~Cf>qP6k~uoh1Xt{eSImo+)x4j7i3!-4b-c) z841P(6Xq3ZLuq#4B$f6}dodVjKkb_go{PYO^sR z_sa`R)d;tgCGp(7H733vwnheCfPs$K(G5|xce@VL$pjoUC<^iZyzOVd_!SJTIW2H! z>Rpj0+4&KL=+Xj>c8CL}oK2eiULGYoD{iJ#UA7i=(mIO+ju|s@bHRhsy+ok(EqP%J z_Tdh_WcfUg-yhg{2!xcr!N!^)mvNF;y9_2!nEhh_47B0th;8!f>I3Nvph9q0JNV5O zv0*G|D~80^Tx5+-`HejsKwHu^k6#rA=bn~oqNj0Ga1Rs+A~lJ)SSL^1Ef9RiEDkl& zUFUF_FLhve;7_Xqp<;orB2iOP%oWTZ$;q%arlTYg?a6adAaq%JO1rhzfB*=XpOt}M zxEz1H!yw^8+O4;T=tq3C@Yv*V(vt_E+cp2GkxRSaA?tb+EqgMf3;=GEx`d+f$Hw)J zC+stwxtOaY^}p+FFACXzq;MF?hRch-uI`ECsoWN^SDT=3_mnK;N`F3>jDfcVOLM2L zN%jvYJJ^riFg_}be6~HckvosGypWVT&4XX8HVf*q@{A0W_s~~S`L;AAF>6)Y#C^uQ*a$$MLZAZ2i3Rql)Oaq9E&4b7)4OeWxSNxvs%o(}3 z<;%W>Rr?K`SR_P;^gmzq;{a0GSY7ZkZPYtLE;+I`|ZvOu(YpPI*QD`tG+O5s5<6+poW z-tX?`(90 z*i)OVvtRzlYuveg-7Ht5Y7%-4CHG19`THk#11EH))c&9Sp9%CI)bmyNJ(nA8H91>o zB3Hy8=}fnN7b^)HV}{lHc)$;gt)|x?htqnTydFikf}I{iDtIe!;CH+NO_j~b7>erE z*X_7WVfEbrq1yWjUZq2R0jx(49$btR^Wr&i9!}z9tkWEs^t$y&2bMtkgDdj`elWZn*r1&F$EJuMxQ)P+3*Q0NJRL+*N98gRT z(cD;5>IF((IQj4}f4dLr`?C@hvz(S0JfVRbtY)=qy*4 z2cR#{3S<;wi3oTG#B95w$*ZbXT@@n4rhzCuyN!s2RXrIFBzet367R*@YK^+SFZY7C zaw25u)&y=$;p7JoMWfFCOaMfq-h4IO^kH5_DMmJbWh?C`OdJugD{B#|WKn0rereGL zwjl)7r0K>2M-5&}7153}edil&E#Wat&-`ufBDy9NZD@{Ni-L?O+eI*6kktNf`mXa9 zpe-8t(ko$eSTK1le#(V#X(;18=_N#=#H-Q^>_7%@wL1Y>=#0b9ipYLjcH$ijx_3rN z-A_DwTl;olJ*c8@r_Zf;kt%dBewfs-R(56i0)$m|tT)Sy{ww8c8>qbHmy%V*e0*;Z z?v^*$maLqjg!&H+hnM1>UWQX78XhiCO}d+Lalm@;8)qv?bnN0pJ(SN>JcV0EM5zmq z`2x}LZ}?1HiFG#kJ!PDI_RU@Z&6uBao>)e%&V zJrBpCD=j*rs6eD}?Vnf%dYB2^YD*+fu+J+C9y3g^JUD4~?LJ?xNBYZkzDbx;Sro3c zJ^ysBBz=gltYq{?_yYXi{YOQ`G_bNl*zS)D6A44^`b8lhPOYn}Fz9=|C!3Sl;d+XD zvet%2uiFgy`g~7e(Ipa!7@$Af|Joe_bHVrP1cx>IdmWlW>+g|oUm^rdu&M94U1y2y zz%)}2KcLMI=(N$)EcO;%k-6~!4wjmclp>_%E^LnDn#F5VkTp+r+0L_Q&Rclhbw5i2 z0p-_#zNp$x-z#^$n1>6yXU5ghuHcviD{19uDvC9qSNNkdHADrZgcx4Ec*(czph-NN?UJ-T_Zxr=1C|=mx-N0@`u$*xZ)8%+j%J$q-tkQ7gTng0+ylw z3nEOYytSI$+1fAjK!9+qASB6zt+l18`}eB|v0HoE&20u`NH|zwEqAN5r4O5u4T*| zA|X1(C%f3-vwPo$tn)I^C{+aMikIS4C452X0x#|Vg5^K-%b6D{=f1}0jJ>68;K=xR zr%hph*eCD)Q3c9htou(}bAjB_E7sztG_|XXy0TPsX%)afuY(*bua9!1-r=Q&s_Rds zS;RTBe=_l-GUoHv4^_F|&LE>4r>+R+77Iet&q)O4v*oen?>Wx z;mluMc|zJM&Q5na^kh3@M+baHI#y%Qoepp@XugW)PuYb1gZ@r|-W7~rH%cSeUi+9q zj7~Tn>^xhNM3#OEykgMMJn0+dGk9?b_(!xX+u}(jx_?tCXG#4c#|=R;r~;P$=|A|q zTs5k`Ln$^QjHL5`A~9*8fgn@R{y1BMI3q@M^pN0K8Z#6M)-r>hO2QmF+2h(g>VGC*x@)Zq3lZjS2KHL z`@x5$TVP|Y*>+rD4taDka&Udh-kiwd)9xLmHmJOdM*g&Hnj;?q$YqCT^xTdW(~|PW3b$15m*}p&@@xkeR3o|x{?3CzMOTlz1$Zs2R} zKU~ZWBrR)dV|(^d^n@44$ve$ihT@$6MDQ)9q(__iwM(UY_hm*-rYy8LgJp~K@EG|iT|G|^j0#1ja+-uvVl-S_X}yZS ze@TB+5-TsmXIT=&!9nAM1P6Ck2!Hg5s6fJhI(5^KaE_m>D}P})LA2)~joriqao|Pw zhv?5lK51)yuuydtBDWTGodWV)f!)_T5+`-P!uzS5H}XHBT*2M&O~ zl%CqzV@Ab>EY?7*q~4967;gCFx%y_fhBnsLG>vJ3)>)3GY+D6Xn|$1_;pXC&%ICHp zkZuAa|LqRg=m=`8cp)LZDq(pOLYJS2&u#$!G#EfV2eEe&wcKt0nY6k26SpNi>x`te z?3}R@XsaJC6O#AJVXR4$_(A?2JRDR=9Tt-0^1+1Wlnrc2Lh>V41DNNVyMN~yOS_!I zx4PRV_r6~iNhvh=+6+VjVLJRRkrc`7RcwK&s(h=9KkOyjO1Lu-#YdZXo>s<_4wpBW zN`;t$gbzvyeNONmSpx=|9kmGSdkqs@krn$ zp4|f3h8G=G)!X;{te$f{QSz-K=wxP98tT2_!e3I@L{x;SU z#)Cmijv#@eV-NZEsLg&xsa6ily5Si70{{dWci{KS0DlAsXVdwzLBQ}^#NM9q;qg(Z$#FMa zcX+i$lT<+&onGe$(%tCj=myLg4M&U*?7QkI{rfkZq7;Coj~ixP8;2uD&L6i+NlJdwiRplP z#V3yhXFG15Bkyz7rPO5!0&ufE0bglpr`P)7xn(ZKfA6m`842Y6t2`_FD2lRu(`Do^ z5sI4y4oq1pibd&+4+6g#-t&&umX$1(+LA?H2aZ+SnMJ7L6qZ|Rz4Izmy6Qhx zGpKVOagyq%?Go7S=EETeafAd(5ckeI*K|&7n5eRYFP0=NsuXA0yM{0gxKgO!BOc@ zDnS8?X&cz?p`Y;SHrgGDd93S8iqj_!#rxZ2nv;$g`P-dG&P%R0SAMIpa%O5bN-!nz z$WOu;3#6PZYhA9h*j4DtU84Wr-IJniPsn15xYF!`5R1OZLYTh};&|#t(H3|;*6n6* z$%zdmgKc6-hFsq~_oiFGWB@Su73R-JT{AYcIQ?J~h!lJc)tX4g@4rmpolKg(I3d-` zB4Ox0A2(n<2bB!X6$Rwh$SLTc=q`UV4){snuG@E9@=4FlN>lyUbAvf%*ftO3=_|Lq zm5AT{{e6ewu7N3=(UwY-#@cF=Ga>kVdSISkGP=oQf8bWPGkEc`%cu9oit7Eagnaw~ z{;aDtz-|jvJ;3i6OrvIi(NeZKPD=mdxTfv+n>wsbT%`ZrDN(awVvpeHrC}={8S<*lg1Bsnse!? z1%mX21=-a4m>=(`Dzw0JMO}%WO52F-J)rvg3-gNUA=ZVZ=ISB|402o=074-znUAM& zT{obLn2|?HSaNr+uBN~A1U~PLrW(FJ-R8m)5)jC>j_|mhA%cN{DU|(r0W(zF*_KF| zcQ{+Q5)r%FiI+9!_f{ykS8b_(nbXsm=o|}3VqlSvNcr64^bR8yxf1}urb6xt3!^qR zIwOrjBdb)o!jXS1hNSVu!`nP@DWN#|0NXDl5|jaW+bTog;TqIW_VP`0t!;W_yE#@F zNs^adIXu`VE-=#cXdl-&xJ+Mnp+gXB2&efgnhwvhIWH`oY6<1e^A*!B$$2a(SFAyR4d-jk(tVon6YP+IoLXDpVtOh%q;=>a43>^}t z>`>Qg?U!IWWjB#`RWNCR0yljQ&L&dax94@g4~uo}#h?yT@?+c|w2Uc6;I5U7wR6)q;C zEW+xVGH1HG4C-w#9PrkBEj{T7vDW8xfnt|g0P{5`N|n0NUxg!g9}fee=>58OH4QUi zWHKk>B4oys?GS)mX;~23bc8bK^S8O!4JtjTGMW=={AmyIL~D!EbdBe0kQ{tAh7vIX zA+i9>NKq&nScXdl-k$^xct;6!mKVEH8|(n+)#brI<)1#p?#3W$%}>x}=kS^B-em92 zaP$vj<_*c7o}dZ^j3t??Npl_F{Vj+EmL$!;?l*-fVl*I?BPMmEHR19K;rMAcbJC`J zFV}sj6a8*$y3`vx*mslXq&J&^V)XxUbPj%Xzi${{c5NA}R?9Y)xn)}?EZa7gYuUDK z*RtKow$0z?`}+@ioz8il`@Wv*dSCUMAA>;M=@|w4_b#m&7Zkn;Ihy(47=|^HgTT-! zOrxAIwOyn>c8UgErG_p77TEhqV;y_z48l!I)34g09YQ|)E5n(mj8R}noSM(`&u3>o zKNuUO^^q_sCK|GTkv|PSOM`e31jOFQ?kTw%D~c}p-0V;A$Q+ylqbjgA4u1Np)-IZ# zDnq@G-GqoA;Lw0431tew&f{ZFEYZko|13%A3Gn+~qAx!28&5n84nvJmjLy}RN={bL zPSxJNY9xlL)LBypq;#3|b6Yc)8dq$?)=ZivQCC^~1e6aq3Yk<{J;l-IU>R$%%b%GiE?v)AIMaEojPEUG==vzm6292 z(~um=qqCEiv7+tyc%!EE@AP;>u-1#Embi@!^}@oZ2|J6Wo=&lFF$4#eC~WsR9(=zz zoD_~H+VDheiu+ASnwlAY2d*Y{*sWCmA)yST%-`l@w4SKW`|*-?&HsV&qi%|uE_*|s z6#VI-z`-5NSqidlkQY%b!?Bwrc~~7m4l-cFR9y0xkO7(8Z&2J4gvEkKb@%M z*@%%hW-#pc;!5@}Svjk2@Lg}%EO0o-*DU(t`vP(tRWvw}?B88!q)lr-6X|kBTxbDL zlqmo>zjtn5N^Ic%RqVRAn6N{lhOg}Ueg0=M*p8i^C67w~u<+a{hnIDU%bb(9i;=cB zH$$rnE5qzQ^ase#^(^MhvE3i$75jU9-QSj2TY_BYng^V!CfBuu!6@pN;aqmbd}$KX zRdB{TYV@98ko54`!t@s!+@v6>qRQTa*)jCIvwq{x_57)&YkIEk{zfIa@ zCvvW~^kD~=CaVRSCnjtTUE4m_r8drID-xzmdV2n1X_u~}=^rQvhF}mNtQ%dmxF#g? z@gv0P4p3v(!v9K%$x!n66-jF{es|A2f#RhU@jsT6!%ua)c5kh5D~((zjKim(iw@?+ zy|VGiy@A;=gW*bQq>e@QrBGY`za2%Y=i(rEZQ16ZudlJ`Ep;L&N__w*-t7kOe8W&R z%jx)+GYimcj!4Mk90-_8MJhBKKxZorUGa}kO-jw9O23KuJ#l5zK~Ol_GNFI|_zn&I zs;;j7=FD2WI#IeiPKy-u^!ik{y5aLorsR7tTBMCc%%GTGI5;D1zi=N*<9OkSRM-&iQhky%-dy7${x9^tz)eVXl%rxonTjAl= zbZP{FBLa*D6buDbWp(!5IXvWeJDi0?Z@s5*&H08;*FxBPUW!#)gL1BRp6{;%`RW3O zp|P=!UE+v@_~ho{-ss_81gCPD6is~|wxP6t*b(A8DpwXX6^keo?^r@pE3lxw7oXLD z4u)U%X8X$Ocuv!tk^^0H6yN%TO>w)QvS~>{nmm8jaeh*$OkQ*Z?oQ1)dWj@(3;c+( zfk7alw&fF2HQnI8`$oslDzoSOwjlEM&3sU!*+HSPxc2udzdAmT$nOHl?m1^8pL0Do zQHtJ^kPH6=gt~7YvU@y&Glu=ool~r4&^08zJ59M8s_H3RNY}eb8#Vb>mv6Yh<9x(T zid5#uU7Slb6Nqn2fLW;I-bv>)x{=6aImL{L5`@)H6suvx*Yf6%xdum%_GxiJqv`O0 z$MDw<8p4w{mlI53lb&Ry9wa**ZQMnQ!EYp!<(r+CrY;~?uy^^IS-u$=vAT+L+%hsL zH^VX5lo5RBb(Jfxq=iQDO+IAJ0j-yiA^UTM(TJ+N_HJ)W_rlfKLLrd0v+qbaxbq^p zX6c8iBejlRCd_u^-e0cu?m>M_Hbw|tOed=QA>2dyr2nB^_=gi9!F+T=wg1wx6ePa$ z_0is%^g)H%o$AzC+W0UE*%b&A>FU(2cBev;w&9+GTjziL=FV3U@-lOCzVt)&(%IS! zj{W!#5~7rvP4<7qy1s@=S}hLPyvDCDGuFziK?fYL<(Vtv@eAH>?Y~T#Xhf}%g1frj zWpH*08l6a-t&aV73T8c0R5H|XaHw?s=>sjo>);ek6}oJPAvZU5XkeL(lI8%7D7DTE zqi)ON?U}u#Ptr&~2)N}xP}-c&NNMhm=gay4E@r3?56wD2R*-BT14bk$Amt7SC7*zy zD4-lUcz_g*m_Po-W9tBmM%h;n1!+|Bn|8Qf$1}Xk#c|i2G~Q$WqXX>rP81ghP|D@7 zl~hcrZ|p0{3`bme&o@k6R4)^7FO5J{LzyU@&t4!O3fZ5g_|3>Ga0Md9`Yd_mt-Ds9 z#Y`cwupt6b-JTI=&pY z5PTj9a;1Lb@_9q>N@C(kB?xUI`Ew(xf}L(_Kq8F$Xt+hlXVV~*3E84Qx8OaedOUn) zQe97+7Hx5F}?wqP@4=u0m69#!=8sMYy5)$(@U z0T^qYpG}d3=E7;yryeqd0LKg&Few#)n}1oK8|OO8)9A{9k{Zfq-#HtGeft5L|J21V(j96zaW15vVw<0NR6GtV0?lZ39Q)yY@}i=cS~Utiz31os{g7>9mZAy}2-Fa5 zZEfI_j*SnG1)_=dmv45)$#}-i2M@gb6A;Gx(wTLO?u##~Beq!qoEhkDgFLtO3ePvA zFI_t?B&-9@yY{y-vBd2nE_-6Uid+qxQM!$1pd7^-9hKau^t>d3CHxc78p0djZSD_e zB#*}ZIh>DsSQMHg(Hb4Vt^0;atq8r+U_mL+@T_k8pBheGnnbMx+v;>8u6(Mhs#*>7 z%qu$H-`^MRAU)oE{~6`m(!wn&TFp?+uUHh-8@6-0JQu(al$Dm8Ea;pE0$`}kDLJZr$5U4J9<79}-@}UW8YE62P^X-!JT?Z1`QXZaTC0>U=Y?-7 zi#|x4f&O*I##DVy!-u~`2~xPP+iVGg7-V5#K}PONU+_jK&#F(Okzt1S>QC)t8Y_}3 zL~_v>PdtM7+d=6pI2{Ug$N!6X$reBq|`Wy*lYY{r`KZ%HmG_HO%2MNXm<;D3XS z7F#3N!SNcn;-Z-<4?#&@%D2w*%gI8kq!ATR=Ow7^)ln7&GwFwz5;JiT5?=uI21grg zj_d*X7&xIgT-OJBJ~Nl|kduf36jYb>OiOL-!5C@FZZY8edn3&##?*)O^-$UMtDwh~ zNDJTC62C(ZG&;20!C6H15r4PQIm&MeWIY={+w|$lJ2K(aSaL*0-^b$_UNe!}Amwbj zz9n6;+n;Y>vW$jWjj45HddW~}D7;}%{nx5cW>?Og7dpID-)$^CR)bU$mRS3j0EhU? zsA7-%$e1{%u=XsJ1NGm(>dCn!8VPi3#CUjkPDPfJDSO`|>hrD*a?Wo2gOp z6`Y@0QwWPn;V9(y;^XL*EZL+qkWX3@EFp6i|D~A4Lf|iTSyUxjP_zxJtj$OY4<*-~ z6^&v^-nIlyf`wXODoW&`a}47Rmr4RexW_c?R8dowejjuryc(?BMWzw2rYS`^ZoO=Q zuzl9DPIiQl#53^F+Z2omHz(92aV9!RT#BbKUo69m?Fe;Z)IfyZd{1_&MNy%jccR`{ z4p(MiA~gRzHhaIn?7_5T9!51K1N>mQLiLo0j@CiMboE zFi+E6VyqWy*n@M*p3P;vT7It(#jbb+R>8%WC%4p3iIn+onIqq{6E(IH?=qH~Hk9ZmEz8coSC4nceL5+!(FaHHIkx@oDg+rB|7xo0U-h;9z->L_FQVO@NFwlm75??9 zO`gv2-V*ht<(ndj1w6GI`Ert`j@Q;Mxa75%W|i9~I(RmbnGQzQ>r9{a!SzzzV6S)X z%tR!aQ2c?KP?YoK>Wq@BCMCn9kzhZ)P>hJk<>(DR9-6hN74O{nHI~fP`-0qS;!)yf z$=9pj$IcE#t;)?gG>=4~tAljbsSi^Tt$YT=T9onZR0zRN4V>d%{Kv3Y9YrTa`>pzD z_&EO-;v2?kMBe>diLrey)}>sNEa`*vD(;Sdca?bJrJN!z=yZ80rQ_rej;C>k9%_io zA)PZbGs{=$@?GBC1Qf|-^#ZIJ2?+l0&vz$q1f1qBo_Z>m%=()rfe09)0*pGXYzRSb zFBe_^1-{vi+es{1@D0Ukre$Y&^fGhM(88FY!MQ)pozDV`yrWj? z9F=M#l|l@{SMX;7J?Ca4z}jbO$%%&UDmakW+vmTH##0azv2)< zI{8NaMF6+|asML3{=F1`US%q&9?jiV!manou zdWrfWDcI#ZWa(wYcA*|xtaBeGR$|k{1}xG=`$*56WvS11tI3XMkLLe@(syB1IU{6zh8^64WLp=*9YP<21_O9-dT5F zF?x+lQ6}W}9eWk4U4w5yk^XsUX5bO%L?e0rrqxdc z#o!wxhT(2Ttl^ZKw^9qFNP@Og_zn&lM7G$sx!K$9>n468xL<1R*ZSIxhWe^E;zWFAIaE5vA63)W^ z?DF2n4zzV9J$G0whX2IfK&zR*G8oK-oe0&A7B@)$HT-yK9uuqcV}w3awGzb!RKmvR zN9O%PEC{}&QKn~LgR=ag85(x{$U3ST26?@ActYyrAy@f1dh=<7+(^7H}=Z|gFxVoe$tV1Th53aD>2wMtn^q)l?D|hwa$TDg}XDMgL<}03C~ums?QCgg@e6zxE;2(iV@DZ)E>ne`;2%Qi4FlRHa*ee>?d2 z#w^K+sOhR`Ik7yFz7OeFFI0g4yT_NdI{0il0>GV%(z~2Y-T&Df}(L0%m`$4Gnl zex&Tk;kK3%13gk(5SCdjY6XiPhsW=y+-^@095Ti<0>2Lhx(E>DMK0Mjb~`n#Ao7ZJ zMX~Ze(HvsPRf}bCbbs1h4*7@P_G$OxLO@@W$Hp~1GHhC=jRQJsmNiFl+(-)YQ?tVs z1af1lG$PFNxenv^JnHSJ%m4^u43Z-QXr-fzR>mc!z zyKVTK%a+dvM(9_07{-DY2h({wh3~#ig>jIXZC}@hPSG-6*vB0YsbC9qx9GyZCLr4; zAPT@(fD|D{)aX%GwzYSi9C0F&zE4CUWa*6{x9FEN4qbH;sPC79$P4 zpRuq!4*-h?=;Qq@M!?5AnavD+X?eK^Af}UWaV5k10?RV8v9Ylz#739TNBR1iWzd3% z(p@BgCXB-g-SokV(vH&vz<=d6_5&5fXK$DjXMTG>PX|N4C4&%jPSNATvNE zH;e@RDZ9LU#z?R(`mN_N6#JDU1ZMjX$|RoV^+rDjx0rm-t=vLQDxYeD+a=5XWlZB3 zWcRwTJjU5=&R#0?st4md4wl1o98qkwmq7QV_}si7D7jT>`oiLg!lU1u;)>&3HmnIN zg!R1?4@Q3PPne>WVvf5;a-$1b#=_&{KC84TyWYu5j>Hs^f!G}K^y6zZ(2BQ9a zfS*9|&B&;h)-<%n{?QmciY8E%RJG!`ue5ntel7jU;e_=+BaZ|F(vb@xb`s?UK5Xz^ za<$0psTMZ0371rpH&Uyg{@U1@0CxS`KPw!K=q1~2-&Dp@xfBlHa9w`~JYC%XoSY zP7De(E#d2Yn44o>b-?L+uLgmg2~Fkk@%Pt2^fK>DB6Ju&ynid<{N9z&m})+-s^kV^ zWICMfRa$@xNHFO$+#xVwP)(JRQY%23o==x5WQBq`LA5B1$iIAEPWi0hx$K28RS=eY zAcJY~ldygl6(d=m2HN9&y3c4hK^g-OCv#M!92qCK{tvB1P` zNa-M#*&a}oo>k0X*pq=wF1{|GCdpBkyh4C60DFBm>%!%u>9%n?HkHqJqwnebfu$`a z-$_N&Ko#j-cifoJ(<_2iX-b%H;7St(x-oweBoFunETsNt>jfdUB4QpjR@%^rmjBvE>vMpu!+u!dqxaeF)Zr3K1 zZusIGLS+dhRvlt8*ZO5I{%+wB0krcl*ky~+CJkwV7P&%{OV52QR#q#4R@WJ#f!VWiJqF&cxn z8Hy&D`#)6@H$;StV@cTo{@g9VjLMxVStkn00YWyozvK z%eXGDq-%}SUwBfYFFL#D>rQ#+gymkrPs6u>z+&RwD=aQfVbQHU41*BRs9#r{TZ5x?(+`tT@Sc+bfrA538DqWdEdocj>e?&D2L6;uYP2ht zD?}C7L8F$(><>qkHrdZh&Tii`SQ}{~R`4$Khn)RU)YzH?n3C4GEwWo5!P?PXFh$Na zVj0TzuL)4W6~@Po+Q@k`I#{p_M=%4U)SWKXxHUswk&2B1M}FZCe&HT7XAU_@`is1K zx^PaQ#Q}U?Y-fz+%CW;{HZV+tW*eI?`;|jB$3sT|$0v+*obJ*uT4tLW5o10Gm=KR1 z#D4mbML;)v`IY8CK4f`V1`Gqycx_Nn#HIRy^UW6xeaEyV?DUyS>P2Yv>daXuJS@G< ztKVb){8GH5qASq8LzF`XB;8Ta*i)x3p`#{|E<^8cVx0rj%_iv^9Jgu@I)gzbqn-SW zdRx7APlZF4PI~I8O5!e&e40Vhk6G~K@V!mfN22*`mu_vV&ZqD_sVJyG8(7nCcbXyXhpz_4;Qb131#S!idOdq9b=tZY7O;tUr=laoyhIq9M@_Lr?=#8knDeFrGfb^*)x~Jq2s!3hzuT=-DVB&S`6-gzx^)T-$NTBmozT|UQQ^0#MpP>R8FpOmlA<9;#ZoQyVBb`GY9%XLA z**fZ27h@<9A+9mPa(C_q0{<(CcSeV<&#wPo5lmh&eHOW}GLYQ+^8OMAp?iv~JN>~h zTlo%`2p5_PSG<4FV|X{8OhDR}Qr}&U4PjVjo_`ec#r3OqKYNd+-GZqi!aAlbCCn&p zV&IR)6tpGY&Y+-zR1QwR+=6)~&Pfa_}PUd(> zt^eXEZMKe-j8#nkRfPUFGP?aw?EuOSp(5~DVXt?Gp*-$S{k&f8dxDT~h9>iJbF*bL zepDO6SyYY|!>xTJCJi?>vK&n25T{!&)lnJJ=1K9N8;_d{N2Mcv?8^aXpqT7k0SbPD zwcuNxUHs$MqIU(#!>#kF40?XKVrnL48$aIsLx4Re+iFCg8Cml@^LRu5#%W;lZdV(E+E5*FCEAkw z`7QNJ+98L#`wlm(tfW?4GhmL zF2SofY*6a}cB*Te@0zFDb=Os*@#U+i)Z4{U!l~ag0pWB~aQ@h-Kx0r&t z-k(H4v0UyHBQ%B(TKUAMKc2c+2;TBwH=&(vr&QwEXnTdX&QhCPQle!L#GJ1=%&SDa zU&B|3UIjXl`f4I)__pjI=7W(o5Z7H1JG~Sh;|k#zhj0PT>IWhV#~3cI|N&yoLfnK(p;H|=`V6F?wfV;>*x=|-zMGF^%WrC~(kt{4L{r15eI@Ta7q;to zV=`f}qljq&S-YoH2_*$h%0qU(0fPE>xb{pYDDn1UR6TDgur7=b=M5*VI)c-dNmwn< zy7AS=3*?7(8wp~V72njCYaG>G&#zjXQ`$3XS2jZsM zJ4V?B;iNI_e7d}@Fw-IEiVZ~ZBPp9V{TwJgH%M^jyy_~lXHhvD1Tf(TaHFcJ)Z!se zgJJ#LBbrA>!AM0m@;rEBHB2`DPC4uTN+Gyxffu_<#$|M9-{2{wcTbI^TO^!-SBN)= zx0Q~L4iYjl=#j)Xr)N8W+4o<_je>$g^o=2p|G%+cbvS_*ZjM7gte=dpCV^U`kx9Aq zUp#eD0$7c z(=SQHJ@*=xWc+owDfqrcE@-h?FQ^j)s#=>yJ@IROVi!j;N)O#%$k%b6p|K;LC(e=A zDJeyccB!Fuuf|Q7N|!Cw`Rr-Ue20e)c}$lZJ*M?>K|dfW`)iFAxBkNDyLZFm)~&OG zq4TZ-&%%P)^VDhEpgh zJ>5qOpC0u~pNE}U)M<(YDz)*xQNVKWG162{6bZ-91Z{2jyrZGM8N(}uHwL4@U`9f> z({H6F6;^(m&FE$Byx|J^v^Cg7oGZAfnu)sm!0XYjk<*!4;zz~BvFG5DZedwMCHG@EK70id38@Y(I;b|dT;g!2 zA(RRXXv`I5Ju89SQ_g?B6W&qYOIyhL|9rcONrtHkh=^!y#&}#U}7}oRbjS(dL_DY*6Z^G`+P(`Hkvd(hGNLCdo6Z9 zFe1hZzmqPraG3FPjV1{*Znd%AW8r6A}AlYSTLkt&0# z#?AKzW>F!H*o8MAN_Ab?moF8?kDEg)ahLZky$dLlgxX4Mr1D-wH6hg1JO%Jdd)$pA z(zp0m%PeMo&K$V@v{Tl}7&XF5!IVvzLfcnJxrIW;EwQdi(tob;;8Q=bW&|3G04!-S zp@US|Gy~?rrJIG~g!^(!mS>{SgE#P9zwG|?-ryzQQ*s>ZK^CYtbl(V=*x+tWl7yqa za)WyOVemJyKG5xZbi_~Cn0}9prg3KVgcub>@MEo3Wq80}Y*1Cz&O`s#NX_cO!GPzM z3=?JsrJc@hZYbF#;$l`=yqMnsL*MT0{(`q1&4MLmTzz6$k$5?0@H_mEaLYYClH!O! z+Z()zxs*T5i&9%Ia5&w4`&p{!esfDGN>lSu{MnjyDbzY^2{(u1C1>=p(UHgY z4B;1V54Nx(wOU2QkPT7CeU+_{$;gyTCV@g4!u|JtO>voM9Gp|!sj&5C_|E$qkH@K4 zTPy#8?59DBn1}poMx?bx!FE^z0}YwV_7+fg?s+wtC8Bb1&p&cKes`!iO-BC~KGnaI zArTRsmwP*$>fD~J2;v<+d(3a#91@flm;MM^a3(*hmDE~3+hG~I=*UtIAYzZA-S|^{ zc52}elB!ScqkFA+D4zfs#64|F)>F4&4nU*#sov#nUE*DBt{BcRVVm-d;gv4P>A1OH zT=(huE4qR4kQMhF3|nO_;mBz)LjZZDuLfnzxDp(XTCKk=+o}V^hFovRA}?lv!Kc)3 z+vVeh70x#rO;GTYV2*IV17DuR*ngG1tJpb0z9=uzAMX&XJ?M>?X3!s_Ib3LLj|c26 zjC6rW-nXWMO=%w1+OTXN1Gy#}Gv7(?xQTBk0xmiNXqO!VG?)~7IqigFo9gUNt>8xN zla-nbAMJG50}HN?o$35{c8d$oj81>3o3rEIDxT8+xVr>Q0sbu_$8kt2`#lvnmw%Cn z=9Bz+Y$y#Ciy&VM{6gV@OEBd$LfbZJ`iDv>W=O6W)-hSRSSFu`9-}Ud$PSBqxpI$k z0-{Ew48cAn9)YKV)rd_A+m1i}WU!7O=OmlbTyKz+NM-Y6h{SKzFP4izSR-f-LTV#3 z-dswGIsy&^mG*fbc)&8JV7d}n^7w{BNFij5r;x9#U+rSJ2ySANFA?@BgFtoUC8qz~ z>zoPeH0@;$SFBh>x~6+vqe>3WLH({JM9%QdA*HBFQPLaYD?L%2-8YpwzV>@)4mp)9 zH9}b$=`~hJdoqumZPUAQ1}SnG+TygoSmAxIc^JQPB&mqTj%$$_#E&Lik|+dEN>pm< zx~=@neqG_!a1uSw6C%BM`6twR{EeOFczlbzP>Xio`8LI=eCrj3p>Z!l?oi=e->`q@h%03CzqjrBxC)`# z*?+iwy{UOZZnVvbKzPL_fMPO&f5OA=i+*HLao6;8eTxN5J_abv{y5JzT|zBb4qHl_ zcJLp0q%CN#c*e_{H8|Y0`_L*UhIZQ3DPOwN2|8naM;_}bpqRcr?1_jOW4xotG8;n^ zX-z4{FwP)3`ziJuAn-lFXrh^C9@&EGRd)u+WY0%9A@VOD?;VX@tV_NHl7Kjj&*g4$NUkgpEr(H$sZgD zG7Gcy{^E6V%BR4kDUL~}`YH7eG-(l|_YcXSWcQI4E&{wQmhjT-x|iUl*yFpjWx;oJ z|3(`PT3m&qIa50O$DVyAPZ{g~Wj_A6H(Qqc^4hKltY&EKpM~iBm;x`Ks@1_}G2p}LuRBwY;pA;Esf-S=Xk+#O9zz%kdkF+>9 zodOLfY}&g~UQkK*Bwd49H;WEgC$Mb?XGP$D3*wi%`*n^FHv=|qTWJ^KpOh&63KHPl zipoX%4Kir`Od)^>ILtR;{S7OXvtX=oj6v@*0)RiGO9cR;=byZ9_i8&HWipvsapi z$gp1t@G#T|&~CE<4sL(b+bZ{7VOreZzo4aRefbvq-#`F2=->TSmp^cV4Wmmx0cB`> z1#`_Bp#^-j`I+G@FdK2R`*rxrSIL})jVuZwlje8tb`T>Wq(RRX6RQ9E3t<)XNgg|a z=h6~imH?rQF!?f)n-t!5@w?o*lNjtLyvM<5ePmgTrMBBY`6nusI$w@g>ZX_Z z!qvYeW4aQ%D(`RbS9OZ3W5tC{S>sy3KQi))iuWfJ6gGU(5WFF3bYZMzqWo=}HH$T( zW|C7G{#;JcS$3Qo)JDj4Z~R_d?25Fwc|ginI$s6JiWX7mEX{Qs1w+4{OA4@monh3m znNfl(>_a@Yvv)I)&_ID}(&|5K`R<+*VuvNGHO_ggDUoZFk#R`Xg|Nhx^o}XD@PAqpb+xziF2@DG6@~-`zpv5F26Qd z`|g6ho-VPK=B6-yPozmuziikHnAvdPA4jT)kpsMZl2W-;Wv;_S2a?~3*$Pb8$ zEAI*WU|L$)d`(<*|L{%|YyaloH1i{=^pvL}G70cx%_Qo7!?wjcgs%xn&X8*wKHC~x zB>vnStg%~9upSm{K(yf)w%ZQ>vaIiDF>SS@{B?h|8f=qUD2BY>2C4)4bF1f|%#?b8 z&TkK-Sg&ZzOE*OA(FuVxC!1TT%`za4J2sJU_Cw2z%n|qDZH`FQw&`Ms#j3t%U`gwe zpS$c+7Y`j_fg83(Dkq~rZ)Er>tKk~m&As5tx6m|YDuvun%D=Exhk;R@U#(Ib0;oPw zbme4c5Bkr)>k9z|>I+7(eZDojehEP%{`&!FVj#f0yLohk0GP2r*4C)}{QOh3zonYp zRb1?m?yvy0B945nN-tE``|TNnx4DWap|-HZN)_*VS1CRH<86nSi@*WNG3Z}~hNZ`hUJn#E$_uH#pZ6wP2fj1sR-q9J{ew#vD-jt3}?^8F{ja0J3=x*74)-A#|1 z*ap>cI-VA!RL`ZET=dPhF&;Hqh0Jwav5V~rCF-keG{WvO#yb~RafI$rNGH;H$lrP836@{Vledu?VGM-MVj z_o7Y^NE_FK?o_C0I*y&>FN)<8qVeiIpW|b-J@GN zHq&1tv~OJh`1AS<@CysA=rk7s?9$+3g|YpEDr%w}lv6pTegekrPkC>=-7mXFKzSfg z$m82!X8!56j1{#PYWvoBTT=Xko4^?_SX~b0;@unA)`U~S0oU4B<5NP2I@E~Z+F=Zx zWjy4>e;1_dq}KUaa0>7iX+s@${l?PWIUy+Z*OrM#U@@^Le_h%VF`1GP^6uF*LJU?? zBM(3#%EDRh0(ncBXYU~sO+vfNUP$}P!`L2MKlnrC_S)hFC|j1#`f`0_vB{ZOvOgz3Y+C(hfM<&i*`elxn{&wM-;n#?LoHIbJ3Da*l#urml>uaFd;n6q0Y;V1fgjpuYZjMKij zvP_iCPfubXp(|D{dP)9NP|(7&z%AoI`Nc)(FQ%`8v}~q-u>fywxA3Vf#70p<#S&2e z>o+~snkL}oHMrNE+G%l9@TSfRGQDZ!IPzh!ndK!IFAtxU)Mqw0N5fhHYZNFy>wqzc zfdu!wOYlrwyg$?Mawb#$8+SK>+tnre*hveb_{yX|ZF+)lLg&V=oK)zDiB|~Qd+MZLk&F6{&F0_CsdmaytZ8g@)4TIFapm-{t*u#_h(;%a z7ugtMt{#S*D1R-nuFnTN&<$un3%LwzC;D;68@W~^wGf@B%yT7=;e;cF5^bh-1R@P< z6b+O~O_Qrq1ii)CHp^WpLl_y8sDeJ%^@5WwTikvB-pVJWSb39#o!SKVRWslzw<9C5Lpi~gbI$}nxaT?#LlHD_zjO>Yp~g} zENop_;4s`PQG=rqN>=&q=)r|8$-8x_#V?)x^R=W)hNmadSaCjUmj99lA46%Jj5U8! zIBkfi%PGmox&im?@JBPB%el6#y*;T9Wu7jev`Q!HTTg^(<1q63+ict#rW0eG`@EJs z9s)pVn92;LaS=_7rRHiF_tj7-&87sinThmZ!`$e7H!kav&>s#+pAU7 zDw8|C=g37=N~$sz;FRUlubSh$KMSUsJA#H1#gFf%LlT;pg}NL|HvJdfJ}X@l-g3hd zZ?Fi=kNJnU_t37E^x}u35m25hA2l+)Mm7~|%-Eo4IF_OR`{8^8I<|R#--AHx*!PSk`j#t&ZgK>;jj2JmIi;B4=ZdL6mSH^Cw9ssQBa_;5GSrZs zQ+`(dTl0^VG3lvCmHNe11!!a4lS}qhGE7sJq-0-ljRw*a0DMht=PA#%EQ|| zo*@2lPxuWsB{!&ax#>vM0G+BiZtfm7=F9%$2PF`BGq;bb!TlIGn^QE-$QN8ILDIDn zFln<{fBQrn4n|@67TxnT*u=_`Ll!e*Mjtu9d-92G*d*lvJj8K>J09$rX;gAs`Wy9& zA2oakTp!O@1S(B82O9p00_3JI2A0F|YayzzO5yk=$M+M4I%2AxXFuyn@7cg6FjHB} zE$sf=dzc5mu7pe^hT1Qaubz27-e0PUzeSIS&8SEgKH;J4A5fNlC z&Uk0vaaMoVA{*|o#Bgd%U*8;b*>3q8^LEY4tiQH&_eYf6?8q@^O_u+gTTiB1`=ZTe z4puD$O}~PUyOr%Jx~Z14bnsjUB$?Y|NSQ}gzvv*vCfbw7nKa$g$m?KU1_T( z8;#HDC>{B?XCS{%;i#`S1kDsXf{pe5<}c$H6xUs93oZiR-Nz51?^0{!wYqA+TdyUf zF)AQK1eJWqVupd-w$EG-dJrnGzCy*L-;4SJD*|$q5ml_6_xnzwnK}owU1PMriUV)$&>7+_re1o1 zo0jQ<=(ufWO!4ev?Taj>i8w7)UrL&;T)!nOb&D!&Hj)nJyQb!p@)%#_fh%CyLI4d= zjAnNa>P_^;zme|oQ4Lx5XZRtmbo9OB&&9eMVts??m|#JCtd}<&$09ISkFzOOorXj> zI+jK?)jA_=BBG=R??T=TzpEi`#^>%rK0W1wqN{RnvhV=D0YPGN0Kz4ZHg{jE#F$ZS zBEIAnN6M#iua1+?{wJaK{t87&{;{3Kh_GqlyMY23)sb3E-n7+^ZA@u+q%B9^(^X<; zPw5e@$u(ba5WBK@K`cPRk<1QQaSkUO<+X8dZaJGO7|gwoB7NFYEGYpqyG>w7NZ;K_ zxp)x&84@<#(2Un}73qK9qX>bIR}sw2R9kU(W|p#=YLk)tW~J7hov2c-8{_g~m*+p% zM9V1yofvfqp{TsHiu5HI%3pI7)ixg&el?W*wS%CE`RwWU>|eV5MR&~~#_CK9KY#8{ zs~N1|M98q>Hm~B&W^N*jJqC~Me9wRo@0nGcX_3TR82MG*4ztr!J|dwM>7hkuIJvPq zcb!({I=soA83xRNa&#Yzz_YpceI7BdQbf}?#ZafU!+B2CfH9ll{c*=7xf%FSHM@KN z0lS~dx}K9SjiPcKB8b`0@@Dy?C6~uPr~wb+J80`F6x6>*x#==8d{&fGHH@pyZa~}a z{<@I@88gglYFBNh`RSI0NDNZpsf5*YQs#yj7I-%ja++8;?&=1fhxOZMd7E8QWMUk% zR#8_e{v-^qKvMR|5`&y^tuer*Vmi|PvT&7oh&FWD2A|943{C--Nsq~;d#jbD6*Pl( zvxg*8#Mh4PAZnfm?$Z|?F1VISzJsOue~{mTLv=$tg?#uy5;nP|ID%41fR6_~3@-8c zEqa3=b3}z!q)DXlXRNW3d$#-s`$NY{c^k^teTrw>AIS8esUNc0Zj4%*%0GQvZ_9BG z*$T%Qt9mNk#@agU;H0S38XQw6AHHnFIkvsonvi-A!7DEz9fUSl3CEPNX#Z)Jq~w$5 z+L}vRCMZSZf4CZ)2n~Bo{Zz!b9bIE4563xPd^lSd_e3gfkgpE+n|!HR;!k^y3jmI` z^L}EfGqao$WN1G5{lt(pdByji67vBJmw(hg4OwTMQ6jB&N8LP&(c4lWBe z*f2T+stq;u%HDFl57v2%?-%MfIctHC(P_?50SDs}3kzv%qvl72;wa%BFK3(t>$1K} zRi?HR7W=1oA7pBdB&x|d?W0(r7{m6^49d>}3rP^!-r>~nXBYta8zlA-3{j}iAINqx zdU-5VnljYxxb$i4FRP1eEz?77vLH4YS8s>e zwKZV6&YEdrH#M{Q^}{<8vr#_wg4Kv`jF(H+EL!pPWtsra|7XVZ<<=b~iwsj&2pU~< zW(9V_|42H=fV|!YjOUiuswdZ~W!u)WZQESNvevR~+g@C@ZMW?A^nd%jFZDdT&$%!B zt`vE<wr7$m$fafdPjX57R-?>Th0LFA3CEG~eW9o~{S^t4&&Sll?7c7?JA?w!$P> z1RlCX@Y8TaZ19{o!q6q5Rla$^5$byW-WA<6tGzx{eWzrtjdauxyxBZPv$vmv`-eZG zl&>s!$W@8N(Xvu<)+V|Ueukzwj`m~04SNO0Lt0^jQB0!^ljrRcn~1kaN-etx7E}wA za%Rr>oLU7i(2f$n95%ciU*S4ppFJ`7lF)j_{oFYay`S?nK#4tg`dEwj=u@q<_%bsz zG7$$M@=ewoXgN)L?e!_Vzmo3=!Lm_Q!_oQm$9hXK+ukLWXyRmkqh63wrjQIj2-x6$ zs@{40q**;xC_0WeN%DBxb6pn|=Qg#{8;Rv5%_Bih|J&ZhpKd3mLrd~fTdsPtr`oAR z^rA!iSDA~IlBG)?;tPvO;69mrt0UYH4SA!eLW9r8c)AZWn}4Y+G0HdjF7Ht&^KO}I zi$Dsxx{=ApgN!Pd(nNt|xy;3+)f_FvYL5mJ(-2<>TS z8jHD&F$rEblw#GT$h;Aekce1V(20qOvmH%DXHJ1-yvxfR7V$8645}fyn$%wy=S&*m z*k)ru(1_bTmzF^rToQc=^j|E;g3Zd-;B z%*phPMnt9W?UmI>BZj{TFZE~|eLbFo+#9cy9@eiatFEZ`bZZ8$d><=kc}oTn$G zqK33G#4`|q>DM{TW|>xWT1e&0<9otZ(oxJ1BWjIkGxjOTe^dxXg2Of1W4H*qv?pub zL$*mqYaT-eNB@E9oY`X-p&olRARJVk>_hoG!}mV(v5X-=ttpLjj^u!##NU-0HnHxS zS-C_CuA#$cztNnKE7I8iUa`l>hQ-bHV3q#**wxw{C<(Mb;REw7f1kNnWzP+PRrm}V zQW@xCgmj$nty5zhCk<5%85%QkrG=4Snm)%|?O@1rXQ>_=Kt;+N83hH!{rQge$`KR( z{Q2&TB#HS+tpL7kV%V7A5m%W^it0@ zT^l7?ze}K9lGMgNk5ASpx)dKkPfXv_^9zmUh$Ps;4mPYfL|ES#4%V2IY`Qlhu@hBW z`3p_ai;6~%Fo54EV2F(s_&rD7G4(bxJQbDVB1Ti&*R>JTO5w#V*1CjkmL9NT5O6YeHQoE3rJ4Z_DV7{^S@ zXd~4LdnRln)*Gvj!r2jZm*WFYj>NF@D+ z*EimI*?u_fMOV)TJ`)M`3Q-sSjJU88@Ehpu6PWi}!ZEkS$0s~|Hf&({DpgJP}-Ty;&&@TK0)KvbQ_eI3R-?~kZR+0xaE4Yda>@^7n< z3ysS%jT4bE&y@}Ew?lJID4RQN^zzvZxEo`VI1Q!ZuPcn-zppehnV$rdPANg(5{QdVq{38hl3o#> zjcx|x@8rZ=Mlyl_7kS9EjzRoeWu@u&d2dVgUwQuiZ8la-6vv-y*C@ZXQTX^d*6HT7 z&y<26kp-}zSx~U<}{L_XB(;E>IZUmw- zBDoug^Ce?r&E5%3)L&i|aWs$-^X<6^E~{zU>>kk%<%_3)7s5b4s|-vFj3|Pn)P76q z(GFe0SMA-wEq7l5!oW9KNv!>@IX#{SFWyhgJi%DA27sPtu<#OS|Hy(sef`fNCzl@wizo_B9Q?o!R0jj4LjKHY)XK5JzQFf?J}+wbst zNKJQwcgD(TZ`Dao{bGM*UYMvqmWP|%x>o*+45%*Xr>qs zr;uf?JmN059nN;IyG);8+SAfNzRQOzEubj8K}kMka)ZDF%V{4c!*|4Os`{irI86#r z9K_#PEOk7(#boRjQ=s`>L1ldNZ!Bm4nTiRhe1|g9db-BS- z0_BE5qyZEVeo?oztg^Y2p6#^12qnVs=xmX{>w`%$klDren{Vy)kV$%j!|7<`?b2G3 zY}s9`MBrC{R}s1?p*u%ECnj3r#2iQg-B=fvV8{CuT4YJy=54GiW6ar$z{Go^3D4Eo zZ`0}^tmbkQ>kI;c*O@6p8OfRs$20g`vA3@GHw0=TrHln(8MPLOt3D_w1k zV1vi63yDJ8iL5l)v#lEUXPP@ac5_|WNZPWn^OK{DgU=mV7=7GPppcS(`c6>&h!AHX zQfMN0+St+}qyhM`4e)|(MfX`mS*TD&RC8YvpN>iX;F&6=(5@~0$mZ%<@TYHPKeMiw zn_yWU{*$2}g^!D08yB{7YFxTNDYp>OI*u*7W~(m`5q>o6^Y;bb2Q)>@()JP;M3Z6! zP{}rrQ(1d%-d+Ba?v&bGe+(s@assQ{atchXbb)#=?S_I$UcfA^`BXY(HVJZ#f}zb+ zIW;bI$B#P#p6{ww77&BFqh&YCDJvpZ1<1hf6d;HXvUd%=R#E{i5YdL_BK&Z@hyFvT zaL#(flSm6vSPIJ9R5VZPh2*oeOIH-N)*kxDcao~#y`9NojEDdW7^{lInuWLe;@ff5 zo6N?L?LMWNbRu(4D%mKQ@eu~j94Xnd)eP)57vGQfBKcZpmT477p2X3bW$Z~aM?N4I zezAAVTaO)T8m)S*5Kl9?bECl#5&HkebL7`X1#X^U)v~(bt7JA+`w>*579|7BO@I?B zX1%*{={ZO3HnzIko0hZkN`GjpI>B52x}kq^ybOAGq^x@Pi9Qg7&p{g4^7-XH;`y}v z%36FoCNmHTO?{G#fSCtWYieWKP1*Q^*GnC>QPJU2bC`+yo5t{sjtnk``P?+OD0l|B z{TQ5rg4bc`8}Ww24a^3c5@XHJ%~>3eVD)R`Y8lbyk8 z#uw=wgyWgUnaez06Hha^K?x`Uy?)n3!v%Vf?N)~em zk0MMMwOAJPOb3}D6c}$zf1P?39La~2r8XR*vWv+NUtsmswOAPre>gD%rH`VgGAwd1 zx|JfNZc47wy1xlbMGj8X*@%k-%+i7SO`B!Mvhcpn(n6 zuHiB!M^I5@F9u^T9|V0DJXxXu{&dhkPdZ2s((e zlv;~lyb<{&tABA4g9p-KdFVP56kAZt=@g@+;N+AN+o7*}Khc#dS0^eIz_D5OeLd!l z8EM7{oG&St9=v2$UWdy*L!j!MgDd?PTzM?sWS7+j)Jm8x*2{BGn%O4p=%{b9d%aED z_LM0pA@iH3n=7zsB9cF(kw6pbnpR}tGL$M_ID*^3zT|zUuU>LAl*Pk`D8+YCypEv4 zdM(6`(ip!pC*+qeWRa>uGk1s~NJ*erxKB3AO-D>}7Zf-yFK%Mb3bMulWM$!J>JM2f zHk_|M{Q(cVYkhNuTSP2BvdCd6jRL)2*yt6965&f5gR&L8qQ&g+;tLP>+2Yt`S0$Qk z+E}3es&4201E?_~KBpaKE;$-Zi+<$KkI#00I{WQLRXsX#O~SiR2DR(BqWVx_>i)3- zi4OWu_!r{lByA2clBg9efl&hxmL}jiF}Rc?d}QNSb|-MCx>jQJH8@txE-*513+lsv zJRY(_+3ELR-jQfeD~sm}kUV{Edw1>2hh8y+)cqtXX7{5WSve7KM|)MqK}BQwuAK!` z59@r5VPTM|_w`fip_Kd`noSZ&6qP0?oP2rkXU0E=*UR_ku2Jky;H{W6?NjtxM`H;2 zw|XB#N@TPD0{4WW;^2=28{7jO*M`o5%dsZ5z`1c(H^9p4ZNYNgPM=F&#um5HvOTOcZ(zkb<c9*i^hojvi-GvzT&7|cV!1jWz|~U*^5}c zjYVE!EmcSGxdAxwHv7HVFlH>$nJJsiFcxXD=3P;}WRO)S64=QBnFa*JC);zeI9q7H z2(=i=+z+3{!%zO%$$0M-Cf2*rM4Gwt)@3$iMl$I)n$0fg5bf&1^TwC8H2`@6Uoz6S z1K>mW5){RC2Y|{P)N<`YA#9=FHWSYA9`F}@!P5}CohqSuy{F%FrQWXeS{x_Iz(CJe zZ*oq2hdSi-Bxn$xpqhi;nkHIw%7SXWwl7YQ;~_dA=#D}t+`C=}oC&?uds*&<5W91u z=dD!qrJ3?fB2SqljsYZ~9akK~=)H zOBoH#J3E-$X8ENA-BPW;*>hu`^yFH-Nw$yy0v7J<7}y0t?$^~ZrI)Q6@Z0tf=D-tu zajc^QL3om(12;W=>uXMX(SW84-&lAt6aCF?g6flVc&x?|9b<_gU@;B@2*vl5O|fj> zcG^8MD%fXB&tH8Acd-nkrd-Vn1d!YZ_y9zT(#B}j%r(GzA{Lqwn zix#szWW%nqZ?QhPvB&g1y>c5a!$B+f^Sgt~2Ow9S(Y<3R5YI*-KOm0Yy&bLG{A0qP zgHx(5{tpb9kg?-0u7F2UK(jGyy5Awca>Xn*RE#y9F2`-lXkS28o^Zq3gD4BBlb`zt ze@+mF@0@1=6ubAgxTiIm8$RJr)0&+=y=EUF?t@N5M;2AxRPeZ|x)Ab%02k}$=tx%G7x0sQIx!9Q1 zec_wL0ElcWI2vqIc{E{g($Wf5KB7ah?LKj_z>fm{NAC$O9=P{PCYeSA<%_&3$ZN7Q zgC3j9G$ZHgE2M=MF)BhHFlbTEbvdBqoX<(pDq~1cgYqTFwR0$k#3-L2QU(jT*nM7I=!HjNx> z2q#J|U3*&s+|mqahrVsiA9)^5;CU?nhJkXt_$M5SApHenMhV0`Wh*D&gh{KEEd8rM zw0m1XAudmX+=A*XFnuv6r;O7=k>YPyMXX3c6grgx7*50Am*=a$gLa>X!(=WOD=2$k zmh!)WR#X%o%wYJ^0M!_316I0B=Kr6K(f>UgFMlr0 z#+R(3eYqV}9T+X-QgQJ8&sX;ZYZc;6JFb`i(A4ek&~dTnK{74G?m(64H^QG%KWIAe zn|@_AcgwJUqCF%1@wSATc^>W*RAWJgNOXKRXK-1vm$2GiUM~&%*WR zQh9?jobDy)1o8$w4jgQb?s8OmL_`bW8V=5ObB-+xSc1WK2+(C!$25N*1C~|+H*_3wC$w%GrGU;Jx+B$ja1^#CX-rGo~ ziT+vsPWEb~g@hoS8@%rAOB$)Yf?}zuhW3i&gG`^%DHz^is}kIcHP~WrJ|+RYpdy#d zL1D>yIqy&=&zhn-S2AFMTC0MZNz8m}c*?ZPcy7{Wpj}sWk>lLl1-+j&f6rFEt5m~r zPW|9(uJg6J9 z1j~dXgS0Nv2t-Ar$y&Zkz<>$W^~~wcA`lYbn9i?jICM~BqeMYkNsA?Gn*{5Y7}I6e z@Vh^MlwOOTf!mig#6{&%g?X$Q3>e2f8)gtuD)gH7Q#_bj==MaV)x-qQx(RokcLDNp=UL zVVbapDX<@zQRw+=>ubyL7iMSj`=X!_)?Rxk0f!a|;GKeY69Oa0y9rn0exD!3EAsFH z1x;u&?P_W3;(cqScp$%wl#vY7(H+j7o71@GbD#}r7{u`Ll zU~7pPs8&^wCjLyB8r0)*HZ!SH%GoP-o6#F zk~^bU%xa)JS{;DYbC4uO>X%|yVJR$?*&~2Q*AuDrEAQa!`Z-*H+oWgeS%NzR75ZyH z-ZgMWWN1C0pa-2qm{LAw+FVA8R@hVp+!uQLD%nwec#~Kio zjO%vv(9$*qmwPpB$kA?K%FW7=UF!}mU&nn!B! zyaG>O#*pz{_)&gBU$wK|@1Jhjo_(lINeb%~aI}&8saGL)ffFA2(5P5R0Ud`rp^hj? zB$3k#M#1#cIv&xNHgl?OY?Yf{PekVE<0VVA{-_k?UNBk3C0RVXVZ`VtI<^iBOOYknjh!mnY~7f? zcA$k=I(guCn zJ~=4&wkp+@@6pf@au)>7+*=Pn7Mo~xgaT``Ehmgn+p@}P*n8TljuSbK^Bbd3X?XZz ziw{1pk`q?RRC_nxXoF$<)dm-2PeevnKOP&Zqq0JBA=*ihSttfHGJApu{T0Sc(OB=S zn=tH+@qO+x|4z7ZWeeTK`_j;ywt-reafEoTn?@JKYhfQQm2&iFb7N>nk?;^!?C4=* z9&PpYa|ugDbo4j=(8};Ox%m-uTPJ060s&iL$3e!$EmLrW4EHFqA zBzkTo5^lv;tp$%ue)_X$c+8WgJ$i`;%5h)T`<-FJxFm%Bbv3C<7sM6ghVd#hngbQP zx7JB>fQ#AmBUtyF`+y5&fs2krGTb%(g$L$m4@$O?$wZ`d|F zrAoRJ)PwQ#Ku@(I+vX@>fCcgai3ek*oekOs=JlTOj~C@4Tb(>KJ6g+kR^6%@t4K7WHRLLn>Ob9GSYFI$zfjZ$t&jzM@g<+VP%TPO9G#F&u3>VKZQ(6yI$ zL`^|)XY0+(GXvOR5O`@#yFh;pMeJWvSKwi*5u(XWZA z@WXVKN}l+S^`zGAFEJQkYDX#g`cZh?UUy!7L`dhc!Lt$ccT6P~l|Pn>UYF)mJPmF` zX)V=W%-k+L`-ke`Z?YCJ8rRh&U)B{2#@O$n_&Qz@0Zp{q?9lYf@e*)*q`A|E1#IKq zytIk88R!SKm<^%DN^^BKcgP!7Y+>coYtO3SNx-@%O8D$bL)VIIi5wv-{Zr>&tv7Ve zJ4di?x&DYgj`6`qi{AkAA$p0{;Hn-~jw#8OL`w+b@`943iDnI=PmumlUhmd%W2kb(r8mE`IS#WLS@;G+e zJsN)V4WTuTOyh8VCU+Ivx)WiCQ7$o2F=54=SOs|Y7#BV1Esg?#Vk&}!4+mMkTX<#>CIcGnKx=qt z$Qb#D2~nO>vco)0n1`}Gy=v`O<+8B>j~(YBRhWcHq>Pj4GiLJqmC)h{%Uf!}mQor- zCTqEJF&o@e=Xzew zS$;owRlii}W6!+;U$l<57l=joC0rPqW8~XYt3s~C=pbCgb*7?5#*q`92tMJ@@AF%M{bE<99?p3)cbl^#C^Pdl7fmio3Z zspBeUUP_v{ef0G6u)CD1e-R94l-;E4zzF)}T)wPJc|<>OwLD~?@)o&#c}(B<-VWJ= zE6m(Z;OlkDFajOW3@2FcO=+c$6QCM2`Ae1N^cvc!b4hz_77Pfxy>1_kvD0gJqLrnGn;hm*_F0(c;C#V(*XRrZxi>n%>P1Vj{8&IZ}*}Y(V z@s7v&=Evo-2S@**ZnzMAJX_(JndeoyT+b-Iq*ebMLMGk~a2@SLFWUD=BpF?F7u}~K z(8(Za->{yIwrdN!4Vo)QE#+e37Krlik`L>qq(Ebu$Z(3H@q^04)tXGG?^>ZK6duGT zn;I0foLV~onM-1VvJ-?a-c-1P>S?&OCw#OODbuzh&y9-rio8A~G&qYy*9%JzI70`0 zbZ9ZEbK&;{xz)>}-l`IJopNxc2W01X@Ll9(1`gUAE8bDd8#5e*y#2mV@m1sD%TiBO zK@qX7SFZse3Mp9`XcA_~r@Bx-jxXsCM;RWtpWj7CaN>V93ga30Ou8>#yCaP*azg-H z$3mjEUm{`C6yXNxd*@T0^>hCU0sHNroGW;KYU}blVE#N0C)3?eMXXdMFaFly{l=$I zX$PQU3f;8fJFKDu?h0VLp$?#T%>Y=1MJyxfBD&Mz6c(8+u25zdsew|rO*=K^r?S6& z&t0FMqUA2Cf3kQ&RUbCecyV2Qoshibc26lH)DAa{^YS)z^C!cvm_0_AFiQpqX)xrZ zyv?s-cBF$yPzZ}gO^6aRcDQMyQoP<4>jGE*M04JI&vUz4T<+-b)O+Ih&HDom0IY@< zuNN9niC#Hb#CiY1_q4yn-7m&Ph=8c$wq00ReZ|_wta+C|4Rv4(JAwHVf|4K*0sSw% zC89II0X#rI^_#R&C<)HkhQbcc{I~!gB~8%7^GpzNMU7c$o#D9IQhu(tkwDkaThYji5`p$z+k}CM)*)@vD8A0_81&y< zQg^<_aaJ>Mw;s`i@{ho0H{hzlUQAMoB&5&j@1$fwI36xRwH97-P9R?B`g~R55prq( zK<8MUja`aJc%yF|BN#eU`xTb2^+gc!NItB=KPphP*7Wj+26irL!F`ljqqd3wk+;Co z^39#oyoI|Qw<%*eSD#Vac|BHa0t;a;IfR%;*6U^6TTLn9h2Fj?Fz5%MXu0{lO2tgt z46eKW*@v}R_xVO`eWgAal3|-x6QWybtB_!veyOiyKuE6MT?oe3ue4SHl=2}G@laW% zep)m{!jE}AzMdPU?R)3LvZ3&IDzM}+daVwxAq2UGgay9Hx&DjEf zleT?)FlKR6!g$c3_p(7gLix?IKx5#Gtk581#ga%$kBinvM^(a zsd25#Bl2t1HRcGyW5h10&q&Ml0r9*dr{kMMK-QIS`k(IVez`2ZI3bV9^p+vFz8E|H zt(612%d;7`ho?twujiYnhl_2WE$=cegMeM?^dvm~Qr2R`xnni5QkNT(q$xaeZ?={( zp;BZqoJPh#xD^;F`1Dzp9PV8GYjodMo9RDTVh!gZ2OPIJKKD|*sI4(USvLz1=T{-9I7xow7 z%IUE1UQ&1g8HC#<5fhM4ZA+qs2}L%!%=-OBS1;{&@K}5rOVWpmJ1er z#2sfC1pph803AibAJo@84y6ZHrg}T)xMBmtJssfL?2CAlwvvF}t^8xc8EXn2=(DP= z&S$D0vUjB^29YSYAVBNk<#hp`k)O+KW7N5PVHn)9CPl(ms z9m6I0YL8(es`3^*g@(iX^`B2)cg@tz1zAsbGlYE=NJok>X(HLpHPc?ux-U9B0gMyy z*>0T!aM+6x5_gXSXqDfEus(L%C*+I8R*G*QH?GSYsZ}$ zA&SMkCNIqcfjVFm0lLpNQXoq_h0}#8dchgRsNYIe>O1S#vE%9F0P!<(qXz zX9!d$@ehQ3m?XjI8@t8{(|KN2nz8^3ctL609I@a)lm}lQ1#-6x8Bu6p~>aEix67!yvz7zeL@ToFZv@*=(TM%Pk%XDImyb7#A&QuDyb@#M-;Ask(g>#)8{R=H z+LsA@kIj0L&9HiW-mp43g+buxG=&t^c|zazlkaRyaGDrN!@uwLrA_>@7n)L^f`X3N zgQdnSgyL@Xk6JZN1S|>*1v~^L@fYQLNAb4!-xwI=&Fa;>?fXBc=gAsu)ZKAPcMJUB zn=8>Y{9l1Mef}rf1Xp3LaAhO)%gJxWt9Hv3jBD#GChUN@{QYR9C%3=&)|zv$!IqsYVX+#<17+iY zLjWavZpqK-PJIqEgc|in zwU=sK8~$W{sTZa2NmNl2oC=b#*>ZXHr~9cH$P532J-0&pTr!wT#!8GrHhnMMll$1M zjAT$X;%60H1~>Xv>|k~K$$S=I?9*>ub*Oo0*z==J6q1aN?s}p9chMrD1}<9BFON>b zQ8*@>BMIcj&(beC#fklb+KP;oY5*gD1d?#Wmo%a)e}=thU>M37TpKHm8>??vC!BHf zGB9jX=&!%{Bm5q?)xp!}8oe46a@TYiYO^s)y^QB2Vda0`2FaU12_Z+BbT2`IQcyJY zFBohq*kFKXl7o%1qQnj{D#o>sQIpcdoNe4PWr5~7$22sZs4huT9tzpV!m`AbbT~g3 z)e3aN^A^Tn&V-lhq)bez`k|e6AUg4{+npw~f@U(6A|T-A4*-Ef9Wg6ZKzF*(fAh?! zc}WfCa|xP}9RdQWF?QCT6A6e$IA``8z=G#laRr091$_pk5Z7I8&_^calB+&?0Hj@z@!DQn4Y7R z1Y9+<7_5A*wzVlmlfx|SC+D0f66|jFWk!igaDIt{2ZcM-e(D;cX;o$NS}Z9s!Z+WdYlIKubB7tX*L{Lax(NoJr@g={i9SF$8fqLU zb%DGr<_&)PnFclL|%qzjaZDCw|_7k3%g=y8fdJ$$S#&*PlZ;4!Wa=3R*(?v{H-zIT9m8i4-5 zpxK@ooAQs1j3ex`+c$}`c6K}pcRDZC}TuK19;oS63&?>#yMgaT7hOh!gxja*#aFba5rqBL^ zXl-)8Xf9c?l@%iJ+XW0ZWyS?|fp?DYbRu|3(6SM3zY~3LqK|8DyoG}D#_%5;J?axV9r4Fq;`9nL3 z&Ri0D>iD;^_enX-3hl|v?`jM#4F3H&O8T`Y#9W*X=C^QP?zk)lf!)5Zd7hik2ng&C zavHi_s_vdrk8i)GYyguN88EB-XJF9|C#ofwuDi{PD}{O#(6Sqb^Hs0EGiteC$S(4s z15wJ7Gd_EdPeq|WFBt0mQ`$Y771vk(Ja0S3|^8Fbk>LcDiW~8 z+8TtALLC)T=Z(bJJz=eoIaU75Lze4;9uP&(V`>Ymr?Je%5(5acVLHl#IxK;aKqNb! ziJ{Pd{_8j=?EG*EQGo9k$8z~|FfdM&Bc;3SawW#G&>-ucj@NJIdHd+1Ro47oDsbn| zzP;iQ=#G__{zc2|VtR*k+Ge||DG3Ky)A$rBt^k-U5Efir!jyoldJ+mhSWuus)%*WdT^g;D! zVw#@H$V-Ss3~7PB?r;ClvUa_MUGQ%lFuzB(3l65cfcJ3$`;SXP&I*`6lB1}tB0O9N z*=k_l zgi!ev4v@EDP{C85X-WbF*CeZ%OdE#|H~>f%*0MQOO7E}O*eTvgkX8N}qCyQmZtLGX z(_`tH*Q~7f?$Kx|2|oYs?7a@rK@yj0n|)ml%n|GFJRP1C74`$c6rj>2pH{SsfAt)l zoZ7|(O!^M=6biblf0p^2=xtc^(Ymcn^c)dATrZ#jzR@?u#GP;O@DNawJ6e~na#e?s zoV5KTq1VR95(-V2f9d}P4@$It9aluO(SuK-fjSwmDH1&dNc4f;a;Df#05Z>m2A~d{ zCoLotuN*KzdtK``9`O|cA^DudB-Km4`X>(FBEb`WpsNbZ>>!D=*YfawRTm0BS{CGdkh4rTI$yvR&@N_d(%4#!$gj_D$(wz)6iVlFTV;8G zc2S+F#!wQyTH<*O03eoj6#-!Bhu?w$(@owWs25poh;dd_uXv-Higw%k?E#-I(}4Vs z1`=H*jTS?{%^$%a9JRFU@Bp>J^hMMP$5J zYAkJb^r;34Mget1BabV2tw~>D`rE+z3tp{9=<>&&t7c$LjN_LtYBPAxgws zWvXfSZ(aG^fLt*D2m?gE{v$9G!M%)UZT8kEqUw|-Uu7G){}6Xt2pztU;QOt#Z&1 z%6&L}2gm-S6e?&@O9SMMeb7-6Rc9it&Wd=b5 zRx*(I&e+wim_gY=}0$o6eY{S;m$e z03YdV%igpXxSh{=WG~)9MLgDoXrPhNx_jbyU3|-E7Vr%=Xsi^@7;?tmP(JL+hPA-^ zYOXZsxzHBpgm~q6yPNoF8tZqit+-X%3@o}*!btJTRXPOi8`LtQgYqykt(P34js5Ou z92%Y{E4YijH5&D{5lpaum}#%#74MhY2q4pUW?12ZTYZ>)Ju6!%vec`ezZc-^@h7WQ zmS3Dy5)&d2=gGjIO8OT8OpTmPy$_9`Krvh*gkV+?Qo0(@DjC3ohJJ^6%vv7TOm14~e7hA$zY4ZNO zju}OYK?7Y~??_H-iBs9by`%`emkk6K>o5RKxcyp{sn5IrK5T5I-+MuRj#X9}u77_9 zcAIGbGj-^_)+Bzk9gpbg`l0vB#(PiKJ4N(Fa@#KuP%wgxBRgH)2PSlbNM9F(EcNDI z&a&OO_j$ik@HiYVNE`2MAFZK%EL1d(A!hUU(O{MBN`rYJv&k%p`kGm*M7>3y|K)!V zc<&8##>K@tGP#DLLEa@$l*4?@%GwaDR71E5jyxmQHcUOeUnWRvAuBn^ut2Fzcr z-O5#G&lbj4iLzj!HX`ma)UUO*LI;cj}*^Lkq7AMPa=mn^NlZiA(s6nwLbI3BH7KXHE@pqI- z)+%Aqo#t^rJ=ywy%bTfz8=Pju)g zReW?u9=>}|QpOT!&HK%}hP+D`S2jn)J5#sh#4f3Y#b$cAz`kgXlb{dzmUgnE68I;o zg8^#-A z)buV-oJKibszs!;PI$P%O81fZZ`+C*{64U&ZGUL&fFL?zPnv>;%g+g^<(S4OF zlR0LeR&mtj?YBGgL$fYR3Ix)AfyvZ)u|I7Yb}-kf^Z|w(q=(O$wUb?*>w+AF1LZF6 z4^B>Rp6vjI7TAweGDyP>0IrWfJs$#-WbvEog@(?dw@!4sEaWD%i{}W0>FzqmzQX&n z-Q*9vv`HyA&DN_i>~xr4|8#qDef-!xvlWTT%yp|`-_q)-j^j`CUj>_|Q;3cSYUn2pM1wJkW+E-61wElc2Jfk@mD3z!^mgmCKzx_^X*%`vRTK{Sbk zAiOLn+UXj;VR)fNasQ3vv)sr~?B~H>!8Kf0SIeK8#{Y@hpiH^7pnnON=<&x87fp27 zm%W{Sd3K{;?~|<5k0vUXO_vv_E?Q!=qF)=(zXjm+LwhoDuCab{f*O2)OsdW|83XFR zTHOSSdN-{{CH9`O7b4-e5aw64F0H<$zEZa2E#^s8_PP(T)@Q-TUQP#3x%-~?CHTV| zK=Ul-4N>KWjBTLazRT>*V9Loa3Xnbk4;UsTlF#pmx2v(3)FVXbb!VRgrh;nFdO_^+U_9CBbC#_F@kt26jNHgnSJW>4@#CrU^j~*D10FOA zIaijHvto8L1ORBZ+T~Z`e;^M0JWQ-&BIIzsI7V~tj>53CA=H*BnZ`&kHj8G~Q*a3+ z|1Ep&>ioQWN6~=sFf}vNqNmjA4b0h&^yD;WWehSTJRiN9ZC=Ujh1Z*d`|V5&g)C%! zwXa~IzXxhB0m62m)mA%;uL@5R1v?alY~n%7Kx9l{X4CxX5Gl~y>Ey8^srpB) zez}Nl3qS+!4U%?`L{l;D{;udQU~i~zI@nLe3YD+FHV}#a_#^9PnC_aCUAsOEAiR}_ zr$GXqQ4xO|;Fcmbrb8Xb9wkC20dHS>lBY6Q#*CX1#dWE3ei+qEps2)FlZa$SZM^CIdgr-L8*ddCYGa1F{`0iu#2K#rrx6Q8E> z`i^uFY#%}2{Al~EGaPhZ?S5KQ4BO&G`<7^)T5gekOFp~TG!+Oz&B;+ez!72 zKbvcfGz-51V)S3M@Q7AfNMFvp4x(9g1{#7O|7!H*q?UkNaA-$&(!u{a<12vd@H*c$ zX$Ja;o4~|}?5($b(qGK(X9)!5*Xsj*8z97&#fknXdE_4x)^t;MvHT0FuylHKyU%j2 zz*Zbr?2Wl>ct9P9)n4xPu|IWMm+#9PQvSR!Ymu6#wK^%!h)jt>9V41_d3(Ko|4QV~ ziZM2}B+>%BtVTw9d=PZGqB@_NNmR#@naH_zYh^XI z_jHIeM|Fc!^*w%_EzNGT`v&)?=dA?Q^~&u!R$I)UX+~QAcRuv?MwZn7L_-5G>;& z3kZ6kkJKnWygec=lir|6PC1(9tWvx!D3bUfPG`3`d$iHMf_xoO@p|CveGgrO|D?!7 zy&ohf)C@-xG3eJw#kmQM@%bKqE%NzWx3x5c>?3K5Ryyq2&MthNh5pM1x7n8Z^%|BA zKVMo;ak4~Gt6%xf?JISAlN?eazkIxj>x-Lwo_>8E+FWD#z@#fSGXf64JM3AC%2N2` z-|yZ1ookV*_t4VO#h2RW4MdG?yh{)tuS_cb7CjCRn#HB0R15<~H}00Ar=&#LvC88g zL;sAa$>mNjQD<=lI!F6QZ`W6Mlg`sH{Kamh zp)gRJL(c6C;qmqjyAz7bewisNFtr^;PSd26zb}cG!Vt#Q@a5 z83-D`O;3QIJaamOqSJX#pAK!S%A6#kXl~q#5}aS#Q!amn%iGCsEsssBgi3iyEPtkz zygglJpp>_4Y={EVDHmybh!(dM5K6XB`)tuT02|41YAAYcIqepQG9sbyaL)Rm{NZ_* zHAL#tTdkW0BNGcQ<0V?lGJbLj)Lc*pmLm(rfrzvPi1H6GAH2kter`VxqxQ}5)#ixe z8;@h)d&di=I2v&zl5EIJs-|J%VvPR$`D2G(ym^wd9R`+Rt)*7p+5T^MM#zk$8dsVt zJn)~D_=PXeMbPrFFiuTAm9QKd&z;E&hnyydmTl)dGpfc>mpCZ;{h{B~740^3TcuE3 zQ77SyX>n76>Uj28{b;s{edN_YcCDk~-F>$a|2yV#50~sq0NX|X7T5muNM>>IQo>$G zy8dV~^SH+`(7R;q6d?%DYe$jiqGp%Lu(=sFu_#=0gtTiATlKe z11H210h_yfk>BhdKL|UDkO4?IBfQyua9whdW%Hn7VZ)B+)6;IrFsv$ks0=1liQhP} z>E@5mKWy!E!W(nNQ-Nc!Q10(7wcllpG|83P8QAj8Q)X5iazQ7(Uo&Aayh--v3pmA0 zLDG30+K`awbCjsg)WS4^FLMq#>!Bep|EV1~Ez?}CLD83iGTvy_E`eA4wKYc5URfD+ z+r0r=stZ3Wh9Mw!fi`U~HpaHu4?j5$fb>8Mtm6iesV7O91qlKBAO=dqm=4!2(=_EU zd&}!M%#)7DjoQ6~J^#2E+K_)~y_6^f%EwsB_6GCWuvcfA#PKe4{XY2_H_h0xmU!jM zrX+7YTq`T+8@+`YB>C(;2j(j(yk_nPFU*8NmQBObUx?@AWI3miv^o2WBg)uykyfszV`oKDYlxu%vgT=6Hwphg`=1z`lf zAbh*>0p{sR#t=*He@Cgl+@td!tC5+wBl4S5TPvblIX#K2fL&%XwR}HLXd{Xg7!e6M z<*a~jO{ygP8^{WG)-k#!()3^LXy(g5!?xsUH-1yfqpWLXRMWYMuC5ox>n;8EpE;onCA7VlR(#sjPK-JUm@kLEa&txjG%DBGx;B zyAz=W&t6#;Satn;{9V~*2+X4Q-r!Z8X~sRH?Fy)s?ZtgU)Lg?jVf(7CJrkpMxI~JB zd9ws1Umb~7$AE5xQXgdW5y~Hdgf~^XK9k^ZM!4Ec(HUt=Sm*Vd^6{m8=e`ct8Le-;-QYJyE#jQ zjLJJGI)5?+I3tIq`X|JSTh2LJ-a`c{kCEN#Ik6 zUksK-3R?oSx=4n1OcY>>E zCy9~guwk$q@>bJRi!r5DsbSpKoKG)(HE&K=Eo}(!JEDjWU{h*@EH}omY4KA(r(VA= zI7i#d%{n$9uvzGZ+ZE&^tA&?gRt(t@i_C@eLHOx4@2A7B43q|2dP|}!==Yw$zRjn= z6|04HI~@cZzeolG`SKai>!c^FkOqz77n1QM!6;cMgFvL-sl(B{OanW2fN-y8m_!Ox zSCAT@zR$Tc&|h3qS*^GM3^44hv@flwrE5_8N=;&v%)dJ8D9{xu#b=9_A7rb{2zi=c7o26T9RykmD z?K7*WcFUf>MziIF{@gh(Zo6rQ;JV#f?~jn4VrY(3PuF%;l>1Z^{Ul4~jSPhp^4q7Y za+3_P9@m;B@^5O>{U7(93oZ^9)x`5YRyvWL&{G%PpbUdJde{g;d18{21W%|YMofAfiL-PX%5oy2^?LVRHMEsvnrq=uRbc+Jakq?5) zw~0K_4&>Ps8I@A5oJLi~)Z}`+Um>k^$VV5mbg4k2MS)0Quj-GBlD~Xgx z18#Yqhi7OM9)k|c?ta{WWVoIjkd6n)A;>@Wa56y1GJKP-DzgQO*Fjq+^FP)fcKWaO zgBy&f%J!J=bW*hOocYG&E7$Q)v%fr)g!7V!-mAkPNRnxkNFf4 z9B78IB|RQXzb=S}=L>E-5vD0mIiL~!FMHJg zf@7Kp_}?NuM%X_${KV+;mmc&T62gXecDEQ-<$+Buz+9<4jj{YYrW-~;`fFT!|K5jZl_%#YIm5@`r~lU1gAP*CV? zP?tkkCMG9)dA_r{(Z3638R_2*jYlXm416Dj|VYQp6(D>Sb>FzLVFOJv@N-qBO+4M z5Y!45=Bw3UqJsDyy(FR$c{t2drNC%JpsqKj1`)U}CpJG;V<#8k+RQ7_9f{*Si z3){bl$zxS~&rGag5A2kIAbYnss7TemtHq$fQ?Q9tJZV*Vfm=1dTpU5w1Rg11(ZEI|Oz*S=4ZR6Ie&SSe`sYlDKNMG* z-1DwvN;fz6e?O<Eev&^u{ z2G;*v!A%cZ_4If5ZkAA-Y4OR3KYT29uo4Z4ue{Z?Jh^8g>F!gi%3Zf>sh377EpgXN zcts0r()-h~%L*~oOMYJ$Q=tBq8C2Y3ut06c5a7nLa-hA#?~%es76tDu;7nWIJN3=w z)2)5;&Vol9@`X3~;U?oMu(kkz-H88P4>)O=1cez@RVbS5rW<~#=$HK^5BkEX7CTnb zOp=5GU%K``(dMDc>-{-;YrL_LVA79lrNRH0pa+zXxj5*d)_%9IB(kQ! zUeh*U~G5Nq}obK^KC6If7yv&XV#HxG#M5R!-i+@VQ26I$ot!KeiJN=X2wt zjfkruf_rhq8oPb>=)>jqx=Nwq_a$qJ4zCK_bd8ii8L!9=!^OB2cym$qhipmf;HM!f zVIaA6Jca%69di3UBa3o`9%!V8cuGN203z&n35_H0FfBeAkWpP z9D0k>4)*o-R{z;|&9ec4o`+lOm{id=aCGnZykq36G+3psm)s@j{)L>Z6nM3Xh8<@y z{!?YBScZ<__62-SzBqlk?Nv1uOk=cbJ_< zrG7eQKq62#6BF1y59dufM6OqdDoe*#{ElWqq>}OoiSy+EU{T*RkmIyxU-iKCZ~&Kw(B zmZEqLHuc<6Jy(L=uBuoq%b(=sUIK-giVQ$lN*HXRg?i4$^gQBpP*j*UKboAqbNzkT zMdrRyWtR_`bYz_gOi0!^$$M*ys^kt71xWQ}`AY!#e_i6QliA!l zkpwOF$|vm4H6E3SnoE_-(-C&|S;VAIZJG{;zI(x(AyQpcwJCraU{0pderUbO>oZ-q zp*UEB$?#|LvNi~lB^jFJ!{j5;1XJ&7K5N$1vbdPJrj^Zg>XT!HRjMiZl#y$2Ss(N- zIRpW!Tdzl97+O?&|)X*DyXU>T)F!`x#Ju6>dMjaFh7mJD(ZW;njn z_~3f73Qvs&S;I6EKD6h_^RgstmY~0j74kA<*HD>T|Kiv%jORsY?ZNY(n<6?UUzpC2 z|7Oza6YfNay3vEJSD<&_9X3@03HeoSP!-@aeqCVP2I zIG!NHzRUyM-PX>}*6EeR*sB0}T&^D4ZG3}{HZHY{+|DES(fClO*sl5f&sD0T7BOPj zq1t#8!)BMMFwm|kz}uTF@xi4LDBfLD70DEyh!od0>s@>S$j>hAp7DcU3dB`H z&P-xGGMf(W)+E1g-D#_mW};jCkyt!4Nn^K|*EY@buw1|XITZl~AcsI+meiWe3jecw z$NjcoLe7NRLTM^g1jyymDMm3#hj8>7Os26 zkU+BQUYzZ9nb5-$${y&dXjp;~q-8X@dASoX9Bl|tc6SfMMM{y|E`I*jnC>kK?VF~z z{JBuGl9caLLqoAbuMu0=W)(?h1ziuJ)(pWjZLVZ(4plptY>MiJNd zQZrB7x;m2eE_7c=V7)M@^i5Q4`|hpaxJ6K2RMxirTjgbcaxy!;c=Cnjk#Nm7){;{v zWzejvm8NPayZJ$&uDSJ0+FJ)Pogokzc)ZfiV@XMTs=nUu*U;QKhMiqu3X8YbRH-#2 zV7-#dP~b~*#p25zKW;eM3m1r^Mg{2NFcc?!+v<$L!$ED^kV#g_3jP;Y>Ew>3pq*bE^# zj(IeLZn9nWu$oJBcKKQ)S3?Z|UyZIKPqvvqG-Ee%Y$at_c<4RU!_L)nIwe9+Or08mo;_x`> znCbJV!+LNRR!ki zRj0C}L5RCg`cp3Uu$2aG6YC1iGj)YZ-+xjB`@Fm&0W$EP^D?9|p`jW0%oGB+W^uhE zi?1pmG4PqMjZ#!}O3csQABDbzc{g9nf*7s8rPI>Nc(+&CS6GcD73keCx-7kGb{v zgOK*RNyHu%`w!6Z*JVqL+wa8M1DsCyNr%!zS10#vO@oDwRW!iVO=K-#$mYSAAC@wZ zqtxztyiu|rwMH>s?TqlYIGLAx^Np`y7yZOKenEToVLK$z$qnzZvl}!;o3R@h z3%+Kq8dCxdtWKQjQcoK{9`~5~`aQM(-E~*g_w3NM8Ql3;O6xhM-+uN$?31`DGt2~W zbMcv!~y^{MuEVwf8>7LzUS-sZGQwjSM_SVPaF3J+e=KyHX zPgCkl>f7Zflg&TjlFjW5EdtZupc;x<#hv0UCPc5BCR3E{%b*nKbjWvXy_p}s$$Gw240jD*5e_!iEkKS z*1$KuplO{mCh_;~WSa*&7D~|vY);`ax9!}?vKSTtpEj5JXhiKZZA{~+hE#!&_;ac5 z8nm&ZqJz`-Vv@P5ulcXlxw==gy45-bLOrU5l>?yy>MCq~e z^k$%C9=oORVD%OkP^9v|$@mJCk>J!BmfFM?;V(dTi~Gf9(v!b8(Mng9_Pzkx z)8_)uJ^NLg@pOXGaxT~OKHw0Yh(=Hpb0|-I2Yza1bH~hhW>p;Rax%D(k~KU8t^Xxj z)mEig8V0l5R|+MZt=3 zDz`&C{vHnl#v-i5h^pkTmO{yLsG7v4a|~NJM(U@9P`|*~Gal8VU+5Wd*Z{EwSYtZ3 zY_gQGnMK355F+v-F72u!$=8{B)IE1)Ri7%$iQhPVr)@2lR&BZih2$S%+lV|22hbrLXC25<g78-vEPqZs2D(U-OT0CzyyErGT{629PQ+Am*oA_9l>^^ZVXLih0 z83J&1WB&GN@5oosa%kx2A69pHS$bxcDq|y7Ma!3#!%E=|%gEzo4iX;^ri%j(KEN)h z;$ZFI`YYKde+pKD5OPG#QV<QiK1kHnZ#*qmX^uN=$DOh#}P&598R=s14PDAcAcR{By0jSZ!f=M%l zBLxcBCM+y`j~n8rDP4_{{a+`ifZCQHY%^Td76vZ?;)&|q6BSZ)dwFM5R9tYQtyRx> zu<_ADeA_acC#RV6#@@LkR}vqxsprQD4*>jgW@`qh-idsO^Be5`&dTqZ0YHY+w_mmiSu2aZ zH(TuhI*$L-?Te-CnB&g+Xd_+E+t~Q$bz5cLCB9I0h${~uBZ9O88UNE20uuWn2%s$F zPLWmH*$eiC5GA4iJKb#khF}_4!@sd@6Z* ze9azrxR}H8@Hyr7J^%H>2@MRWCk#WLIqxhbWo=qSU+RB^r5T@f^jGoM^Z$XK6kE&9 z`RE(C2~;AoY!YwlIbWUH*1cJQDY3y%E4$ZQR5PmD2zSrHisk$FWYYcj#%qjjFA(Pc zq2Rpjtrf_Pv*rBs$N2F^+@EK~^L9Vzc_GY&_c_E)i*pO2I?tKEjmVGG+U8#oswire ziKGkYdDr82c866Jo2cUSid!Ga|1CXn{2D3l9_Tw3hd%$K%pgw)w_BpoXG+eXrOD&fO>f6EqgbQGwa^`%gKhgwIXZ22hDVX{KmEMg0tn)|N zd9p9D5`|VCOud~ZAkHX#HVacuk?v{mEwGq*>_1gtE}9UD_5QJFX@m%j^p@ngJ#q_q z}rYS=NQL z`MT&yjwBIwxdF%FdbPviS_>@Y7*~^a+S&{!pIB1A3Qf99|S%*a38 ztgaQ12MpgJB&5VKV#?!|Jk`AK`BFF)34`m zco{7F2|4bKGO6cpR9~Sfr(NIYfJR8(z!uvF*aX!{7- zSxm9ZUyFi4r} z;~LscEg)|Sq3O7%{b}X*bUm1TG3r>v;5mbM!3I!hveb2rnIb8JbtH=i&e{9ZNVMzf z7e1|qCmS;j+#yp#fL^k7c{$oiOj9~v!xN*!8DJ;a@{DWds#oTSPzDI_h8Id^Ou^X~ zv#Xv-s2aWynWWME#eE4_zDF)_PK+m;y-nVRveZnMKqaYnNu!WWQR=t&yl> z!C|%&5%XydYxIEdsS{%md+HN9i)=SgjHuty<$erKf6~b^(ftvf$psLAsNs&qZ-9gu zV8A(qSl_#6t_3bN5#4YPcAR`XW`R~icUZ%UZWnlfCYb0((9YZ2^@Npd$ZI!*- ze4S*VpzKvA+PeX9gSl@p$*zEeUjK_yirG3r^y|G+ ze8=023Q&M&JgeQ_SvePNq2ZpfL7U@HKqp?3%4!aYqp74>Uv_@OVm8!Gm*!li5U3^R z5APHL+d>wZfszlvSEt8(*eg-8^Hh<8DD0El*PI{XNJ~e*ej(=(`S zbCdpJR)JlmsVcK&CV<^~~VH5zNhA2H1cSHfy1x&5D- zgt~!&0lREdVcpSxED(!H&*=+YFR^1OxI0xQty-?um{Ci;^At2qn;u68sG)w8=Wq6; zKouxZ&6=aL##deFr;p!^KlXeGgeGCDvv8v&o_F)$7E6L0(N>H9up72OF)a`>gaGi8 zF1*7`Asq`DFwxkzK~#UFVCc0c&Ki=JP>u*FMsY&ZVI}(abo+BPTgmSZak;n~D>-GY zySZ#XjPURnZf|A?($-zMW8BeKN%HoWmtr9SgsMkIXdcSe7dZJ5v2$~2dko$QuO?$i zTvlqRc8mt6ULp0aCHj;_g^ivLyRqZ1@HxOp7fjLjUldmFc4MXFeENAE%x1f}`e#&q zZ^oq0q}C>0^k?9vh|-B;kGf;!yX%K-iAK+MRk^Wpug+xj=HW~UB~S=!{9J!I!@7$X z&kj&V2F9?8QfE$zrL&@bqsxO;n$!$;_-mos5^tHH`TGNmhhEDI{s8fp(NJODP2ONeNHbVtO(nAbabRq!kkJnRb2_6o z8woD{$<$Xdc(}T(f2vO$fEuhPYvtOXU0zk#DhaFn)DWAlg@wAnTdnUO5xMoCiA_URlS$w6Z20Qq3 zKDUjVV0W#X&zbfCXpXfiotL0Gf8*}(rzZrD+_jeW-~^0bqC<(55sU3YHU{W$`C=4b zY1sfoKg9U%68i_KCzFIzsJhQ(8=^x1QYubTDUPeBs5Rs(W7eEiHZ^R%U3~_#Rei}I zfTjoiT6Tovri72#U`Dlanouh>*e0y(fJ))Uz9@l;$={5$thJQ@qbBU)nyc)EAL7a~Lytdv4ZYB%MjV!rTvy`kDxlX@t; zqqhpdAvBubWB)aZc;YSyXH4cKCJFr${D&WaA#It5dJRRLJQ2@aZKtceT&G2-pa+d< zS7Hjqz3mYB$w}j2YDz_blv z2`*+BX*Fd`n=4Q4X=e%@0ExjCo0v{U&&QpPjdsDcs6U35E8aGq|yd*0hckqtTUDp{ z_SP_F%FD(^Cu$fj@)e8NZ`+{&S#`fB-d~^{NJ+^ml5be!)BM)7 zSKD8x#+!J5dX>FLRX_qj17Zt|OIs5DFAbR%wtOg1zHb}97u ztmIhHA0`%_$Vi^!DnLp!e@-*MVllA;AVt>QE;eIA8cLvP4`$q?0-E*5B1FsMW{)BH z^o&tEX?1vfi2yv4P_wQ|?XXgcP`9Gja#KO*3j9-PBCZqO2He0~_d8U3TsMa(+eA6l z<(3m6Z%M87P%)`%;H{(k))>`*H>G(Mp~1?x=DCOCKrQn~IGb1-yF5X4F6Bqq`Zc@S zOOgN1b3m)nT=WX6rAk<_u09K3exQGbD1_&m zYF-c74YavpF6VP5iG!WeYOhJ>|fJ@-^`wTNarWJkMQCpj3NUZw`Kg;S$ehFp(C% zq~dk_=s4EV02Q$pt{27zhyEQ;F9VDFplpG0Kfv2X)LwW=?T%g}Kv4SS@6r9Vp66m3 zIw0D2eVZ@)Q=q>6hs3s*JXzdNo3wTjDanecO?%`{O<}wT+u1!`($EZ~Sg|Gv;CP14 zUicNTH9UztfQbx3o!%gM-Z(qC60n-Q-{r^uI@fWnG;kZJ640L>iI4dhAzfc|7Up{3 zm`ejoJmTv7xmOoCM72SLE8Yg;HPm1&P7IEuRb^}4H=h`t)wwOG3ZE;+}3y4DxA z;y7|e`q@cq(8A20SZ)5H%kg7b8L9UpX^Cd_PE<$6Chz;yn!1L~PALr=g zrx_?F*LJb#sKfnNn1q0ZR^;ZXorl0Z<$|rEvhe#MbkIG}ITmjK1k{3!wmN9u<#qeUt?O@kuKTrC+vjvA(&K=) z+btY>HE(u4g6+gHqeZNSH2u&w9~a)v8EX=qMd_A97=MdEGC2&h4F@T#`=w&qj|0w>gz9v)W|FW9k_Mf1Ng6URt$&7sz@Ij3bn*a}THTrDy1J z_0MxUSzY0NPU>80wkOfl(h?RD0!wDp#RnG6VE>*jhPSkEIk>xrU#z#A|79FCVZFPI z(UU+olc?D8JKkV@ymw#uT=E=|sAM^;Pm04&1|-~L-A*7ECYGdD-+X&j%_cEc zZP8Ug#sQjM1v{mI;>2A-gjlF3)nB@BlssegDj~o(!XrN_RNI{OE^t0q{cwD(0E_;N z(S%Y!mbWyQzHC@KI*~v;%ZpBt1BU(OG0{s+7;E#BK1y5cK)mI7$F{xjdVw&rCUW$J zX*rF^TY|4luz_QDqnB-O(1`?ni+x)iE-;6K#&9>2VPw%?3J~0t6MchYVrcnV-v|#L zw4ovKW5PA73?i1?>Vp2JcJvziah^0*+Vi#CWeETW2$mGGCilmFD=1KsP*nPV|LqPOEt zO2hE4myYrdkuW>03;n4X@P|;ZoFEOn{f#3+Ijh`^&~-HoSl7d~-{2eZ7fCNQpt>=x zxw?HR>7sbO*@6KBN9ysURCgxdK_hSE>azQzZ{hg;&K(#NLgj5uKGS!@+@QdDn2FwS z<)T|EfwA`u2d~1OlyylU_V~4zxw&+ts5as2F#&f{qCZ&kC5?9;+V$#f%>C1Tw!-1N z9J{uUKqFNgad-4jMyS7M4f?WvJ}=N~t>aO}O;v5CRL&H~0zN(up?-`Lw###s0zC== zw<3*aB7JiA8zV)M!c8n%L%^UnWH1a8`4EVR*G))S8U69)MO0H0SEs`REeewcg4g{b z1h@~5FeSyTBmfpL<}N%1nSE6^XlOaKonDNVHz1Iw7>V|@vQl5KvN;?M00}=NU7R%Qv?SDvi!`1 zwDXKibz~VElbN)Hf3bXQfTRFv_VVi?T%Jc z!_K3yeM|na2KSX%4R+j^s3+tSjtDe%?fO1tTGBvpvAn3~oU>zp2wMkvT-P5B_$Y zI%P*-VOm%HE^51$TY7!Vr0hISKlX2hYG7glWoKuXzc~+53NBlnaRMX@-G5@( zXpa9Y@?WI-_ITdYg`CLl0{43ubvZ3VDvmGKbDhVAW6(JK9Z!T8dL#HLRDuUy{M*QD z8phy!>}8n*2}*i_pLGIKczU2Sz_4$pLUkwS&hw%aNy#2NhKq&Z{SO{7+G3oGR=3-Y zBH05l0eKJsz;R;Gyd_^_yXVZN=m8p-J*5AHLl!!k|3~C{m*#k=H zXPnH{O9s9WGS=VyT0qNquo7wh3}Mu3U%|F-WJUv=%Vak=lcA2_enV%4UNamdv%(q8}8$I zgh2DFMb*NOvd4>n?2o?V>0}MHCT-+~75wwQIMm#x$L`nfj#b-sP0^x^ zZm(a)!P!iE&G*Vnr-cJQExnKSX(_RCzjDT=5rcLP5MGXk^jl0;Lf`JV+5}>-2b6@i zhl!$DWl+tjX6Gu&vcl$|+!+Nz+EbU>rt>nbw)@h-7j3p-ml_n3INFNrhTe7&E?!OQ z1QL>6;{BSUu_s{F%8u++IN2FDypaX{-escCxt)i9vasMKv6??)SVM?M7u(uWelDPQ z>g6Xe9(`&t9|~#*bdIud&%Y-aYn^lqS4tW+2OVJ5S^l4ZZUBUCzHHHsVt+DLR^Pp; zT=U{Tg=L`>2>A12BWgR+J|qw+xhPd5kE+ojnK5WOEWKk5vABBU>X>Q9Lz6u*DeCQC zOqau9GF5Z6K0Z(}%A?41*=6>|-LEvqrnLaVp3RpDaZ7@|JFoT|$H8V_U z6k6UDG*w+13|A48g_Puugh8WIYY`Rt;74g}APW zzXZOnN$&9hR!FKU4tfBb03hGsl+Gy)WT651oTvp$@s>o#9A$^@B}tck zu*=v?`iR83=0Rb-bkBW*k@O~wz@F&z=xiY7$P=^b>g2+nCS#4eBWZ{97NHSZ0d%Vn*467lfIeMF?wcH&A-~DoaG$ zhx323JBR*)W9Gx9rE37`2{~zWySm$IuQ1d4_1&ZyM71(ia7akV7ROzf+PLjjw{v`( z%`PH%97aUjRV#Hi(%Vguf6nAMH_1bJ+TNR7_t3kFa*KoB4zTf!Q=$*<9yVy2D|Yip z&)BgePjlE^iR1wwRU9R|Un|sG!!M%p;OXTI7e{_DzIOq$@`mlkb6zjDooNckKpA7( zkFpr{>PwOaXm_}AtD6=JQGGY%2DIaqq%%Hz)zy|2kV1AGrPeTPag=20p^Ms%$@yTL_Jd}a^47hNPw#%^unZo4!9`u_jB2 ze4mq8@QD)C6Sw@bB>_R_V^~W>m78FEDoSKH7=2C;K{(9FzVlQHi4jmGW&USVr=|U1s7LE6<_!HJ52iRnKUr^Xz zK772x9@+^84mQ@OhG&Krk?C~5$GF|LyVg&h=qi`x8qbg80%OaV&c>ouLSym=xiU)) zc3@p959s!n%}fG4MN8imn-dck9{Q^aC@M0~6tLkfbkRtB!0{Uwe&JJUZ0KxFSE3Q> zRMuc}*D+#h-#4mc2IT{D8YaT7oLXe<_FAYh+J~h#=i56iHuHb3_7+&3@?&?eMK!*< zHqHqxp4UJ0zTl8V&|;{~5+f8*H1ggx1cF_P{;tPTxN;j`hD~xqPIXxait-|>hLbLP zyav~X7W_+SH`w34pBGCh=5YZe{g}E;`6y-!q`=)b;MlBw{Fh{B7a3r5l!)Urpgj8D zPeRa^o!jnSeyps=>P8?*e~QQtsH6^xT_>W3r>3BcGQx2)qzZ!jwiIjR?ta?2*}TF3 ztMftF$SlpU@AiANE4L0Fr2^lJXHJPuEQ^6jV(k~SrzJbGA~ZgS-e{+cxOsBL^ZCW_!Up}49UH0QbUV&Zu0dzk zv5RxyXv|n6-VVdV1d<+x)qBmu@Ei&fb=W@2nK4KUKWFJ;I6m(QmGT9{Sx=4AMQ9IB zpT#LqHTf!g!r%S+cFH+oqYzu&k!eknHr~FwCuVMvIXT$AOc;260DFlKGP8A}h%|;GCr?}ecO!E!xZ?=2! zj``9H^yA@Qx$CU01hQ;hyGo*FO4uw+L}AXBLD0wY52Rc)k^FFLke7(uaE}|r+8@p$ zvf&xQnJ75%JuF?fAsZ>~bZcErJaSl%O=@*L#T#*ue1AIhH!$iO$9wK+Ch6W9;GKTvF!>9lcDfjdsp>Shy()d|kj+{QL7?s;lkbGbmAwErlJ%!HvKv zOC3VXAPDiBAUF+j51$ed-%3T8-*R~wss>iG zPq<*wr|NBVU4t%nH#R0sl!Q@*n28PZJ4t9F=BN}sB;9ZRkE3f~jI&{aZR|9>*tTuk zw$a$OZQE(mxJ@p$ZQHgQefNHM|KOgzeRgMNXYOK}jK+qN0?bei6XYQXGhS!vmbg~h z4JN45CJ3Yn~zopI@PW(km zkQ)tmf+B|5^WQ|%H&ZC^;80>T4S!yeju1Kz^lYKc-;${QKDoOCw?ureh(p5K)@7HQ zwR^BUZCG#9+sjsm4AFoeBXezNev2?=e<{s5+|9Al1{|YZM|@h6{a-|(MO~18{A*s zxjKY)ZQrldD5OEE;Z`U=O)M^h4=T$ZJnpwyHHnayR2?Y^BAKxP!Qh7}rBamvpU_Ko zal$#Cp2y^TUC%UwFiQGF4C$B4u|Ly;I(}Y&&p-gK_=2{j?T`S|dyl6Jc9&{&XWBg+GI>21oZ7!$iVT1KK+P6M-FU?FpE#SK{}^|w zQz;{hN|&|{RRHub557x36Dk7t70CjdX9tqZ*(C{qnHJg^JiD@aQ{jd|oYJI|XJ9dx zXIf3pQs`Q0QE^R>Q8+&gd<+JZ!jJfCqy^}L>t+ea?d!A^X-9Omp8N-5&gfYWrb8Sy zR`JGg(u;!oC<5G*atXBhmV#ndS5bh-UA{*9m)Sk$CO%BN3>TmTtcpL0`W?2)nK(T0 zu|=d@@d?Ge(VSJD1^+Jdq)ZHcpgU2tz!5R@O@PG&@;Ap)nEMCc5YCu>x6N;mJs$gg zsJ+p6YKtU04H@_x@1&HoE=i<#-4VE>Og!`Vok@$!IWG0SR+6vl5g}c14Q7)zE)cR` z<3l-Lx6AIREwBhzoN?OgQfCN@g@Ia@&krBCFf(`Qguw-MSK#qj#C&O77-N?ajbf?FvKXP;15y*Y zH2ndfxS<$KGu8h5rd{@{uN!K@P?FL|#_%pNGZUt^wpQtnmGVk=fFR1&{lO@>dbLKF z`FOf82w#uf_j^2B{24%p8i4%Ig`Fs=cE|tzRu+{pgqigp7!Sxonx=oZztL$P2;`ml zopr^9awAfr%u33IN8=JNiQfv>e3?2WVz2!PFu8v}L5nA?AG20;>|=p31Kv6E+l;(<>1Goj4%2YVJ2y;A?Nf~(2N%}k+UQoMx)~{xS-%mSRFFv z1-m=TJZ{VVcHcB%H<9-_G=tv|_eqWG_Ylvt{>BvSbeSA5Ten&#voTdc(WjhmyD_jXG{7u*06$~vN`x>bY0v5rK1|RgE*J4G! zEgkn4R;ZhqD$^g>II=Cw_7aX$rbDlLvo!s3b@T=G`8*9V z8MHEEdao;hjW1ZEQ`_c?Mo6uE#I7P&!YF3X%wPGKxW21>oki9dcfOu%N3C7EMqhd5oAT?kjz=j0fGoIj_^ecggu90dC(bLi{`V{;&uhu_`1>J&uef{I^_mTP`=dg5M_YcnX=1cmV~rY z9CYH=OX{fp;_db~FfeGkvUT-1+P*+-epOdT#$5=Iq25nbOZKP+?8aPzWk)B!iAG_^ z%Ln=yhzqPDN1ptpHC2Ec$jx@WiF2Qg|7VpSSyT5p?od*VrB zlO%)i0!SogdXRmCv$L`{`r>znp$G?)=>XZ~A)HmqK}kLGBYcQS zK1}-6$1skYvsE9d4v-L8_e9Iz#}_YWfX;wVQ0)M2`!VQ0`Dt7P3E%K2ZsuKV1wjy%N!(U(Lm0U#$wqf#+n zk+4lnEX;RQbGg7?jo@`Z&Y&0Y`GRPvFsdiNCN{5tI7m4zy;_om$OyX0^hJWTT+C6A zQ)pWHSj~J5I+PrRsjm{Ma6SK|WMe0Bjj=vHb)6^rR$|H*>H9K3yfIMGuh`$@^&kNH4s&l_aE zzbwKBU`#||8ZwbGVr=vKIwQTY;!QDQw8X?>Y#>_807^hkN$Q0sr4!sdAi3zDl9JP+ zhY-7X_m#i(GP%DBRa%ft{T&)bbnQs6I)DW~R}8%jdK-NR4RsGZ45O+=WY0qgiKP^= z+WG#Hs8&g>v^DZAe99#(&I8a_|JoTP^{dY4^r#eb{+CefaZ1SXQ?RkHA4?h5%fS;C z>SW*XprYW;=WuX($40`tFxYc#`N7x;;?vT(EDDvVv zA7>9NBk?|7$=t>gQUytGQJN%iI9iZ${j@F>ZV|{p+EmVd;{Jf*^gV~e zXafi}nf>*+&zUPQFl583JG1Aq*uV6voA0l>;KV{|YdwfBmXruomyXy)S~z?JnIA=N zrQAseb`;UCSV6<+C z9$$rcFtRa9x!<3_47pE)g-w0jOV&}j>7Ix^tO>-&9*~|eJY4fA7A3F#>7m=My^1IC zV8_HaVUw^>1|?@^x($G5b2Y*Li{Q-falEGXY+O2G96|Z++=*w z(eM>ZU=}36Ue6fjQTd!Xr+oFi*;$QD98FtrS=)0>0sTDAbxO%+`NH=r8eFf}Htioq z&x~5OT#j+y z3@GiF@)ODxOkG5TV67zs!_U-gBAHxP?2D0u1M_GyN3zSqc}}nNSLi1(>{Y<`^4$K9 z-f@qcce^#pn>Lu*J@^5qeb=_&G=5y=I6*fwf2+z&Hg+Y)=AYvm;*=ida_CbqPyAX( z$5HrBaJ)baNZEnkyxHYkV87uUOYtS6Al)sPOd!Gv-TU!QINI1Chx0lL-=Y9^3BUQN zTgLGj2WEFE9NB4v56)y0;!AWzyZTs|;SF^^O$<~EnV8J)t*F$ck6im8Lj$jb*?iQM zXOvHyEJ>oue(VKwtW8R3>Lq0ebTN=D@3-EJGj-}Jg_l#JL+(R?Ug4qkDXVWMAd%Yu za}2|EH>7K8fRWN_nMr}NNd)C6&gjh` z%JLv~k6aZ>O%jJd#bcptK>Tn>ma*!|rzW-tUrWy^|AhAW-V#B!nyxEO{P~d2KAulaoo$;yU({T+-tx(aQ#wwi@MdsoQ+> zT!CrBe>EiDVAaCN6jWCi&?5NB5Pb){VNe<#igf)}I*5}{Q;bC%<9=uPY@a=45CQjh zEcsX(+c7CyP~$JY?enl&NaIec{D1`J^_;sA>)$Kgt*)1EPI^e4{G3IJSz?61Z`}c7 znqFTJ)~SUE=rs=-ky zs>p^0=2$|(d|yG=6QxS^YRpl)mc~XfSy|bayF+*cJgy(voL@~3x*{U~Z<~d#N$*M= zqu)aYwimj!uvHZt73F-99Jb61>>Fs9nn6Z)&f|AX-2or4$0caO>sE6?`Zs!!9kACa zGJjcc&z}&99Ok6BF{AQe%v+1kia&n%{Ns=Rw%u90Hmx+Q{QLdU+m>V_(vq}jERaEL zVn}SO`~4djtO%_Aey_c)`!kIlqz(6GjHChwLE}>%JvCN_$TE5bXQ> z^&yewQh0k`g9?wFx<8Tnt@erZY zz?Zq<7I>fomNZ@?jDR70cugo|zveHteChSxUue+F6hAyC;Ft7}L$6ZsPgB7eyG%7+ z`cP~NWm}7wCVb(3-y@N~g-rNvJ|OyDp)ziSOtQN7uas|@8IxL_&~O3#4-=*xltcPv z25aztcd+4N=0DB*aPm&?4_GoaCP8Z#?w9ak4$4=Q6`Vo2v@=7&%dUk@%l8O-R4S>y znsk`vkcg}5P(3}}Uehe!Gdt|C%(^gJ)t(c6U5!UDiYWm$URI2mxe`Cx?V6sGlWl_< zXrWRDmkNn#Po}GKG=d~K3zD7fA)Ye?lS5ghgd+yKnT;ozXy#0O@wy8W@r-4Vdvl+1 zELT2G0WVE-J$C8uH1@v+vajh%!Ub@Km=vW;LN4{RlLmcC&4!AB#D-@S;!K&*1MqucN;fO z7dd_4PB3)9Zw+Y4mfRz6csS9IatWM-6zh#GvPpyh`5!J1JRf)fTJ>*LiyYtdNf^@o zYR?yGp}AA0+8@F=3A=^%R97K>m{^P;grTC|vG_dMuC%&PuB@;3gAh;S<`+Ol;v%^JUiq%A=OsG^UA`z(y=jPfQOg}d6)-2+)+5~f#r=O7-HXb# z&8c*B(%qsduae%hveYetMVy;zZGS({tf~I|%HcYTA-4weNv~WgiUIs$$5mI&8t*h} zcrPtX>E6*~Qy~-2c-sUxfBAm}UOkY!D)*t0$>beCC7N9sie^V!8+=owM?BM10h1eyDcGyVP2sUF><4$#`3m{+PYdR1Q`; z?2B(v!{v(vx?*0p(Pl0CeQ*U41u9JUn;q230`7BG&P(eGWhm{tQ&97$v*e`L#+^P#0j~NlWsw&?KYt2_ z!mbRrk)eLBs7!(H} zgFxWK!JaO`%p;qbfrB!g{Kof5x*Ia+5*^$22CWi$R*(KsyNa}~PKFeoNVpTE-;sec zJs*W|jTYl@`MQ##9~QRT&Y-h>Esp%OWphlbZ2IT|q($7un4Xy7+P<0aD;?(E!e4$g zL#alyXW{GcMxVc|&DO|U@9}%@rfwc9j5G=c$!t0dFK_D|4{#dm5*xS8!FZ227;&1? z(evFI__5yGpoCY}QmBXScfg>ZEGMFFLMM?i`%eZxin{FR<6xAtA)di=EH6lyS>Bmz zDl*XIR{3OVM2L_ra@$v5=d|egpgiqx;oa;~J@y2;zfjBFGsRhf%wv!x9reHYI*-kE z>(w1IcQ4Ja2)q9b#>x)Y_5Za>r{A9LuHGo7 z?AiiW(#^Q3K$i(lJ3%w{^h2cUUW2f`%4nMkhu#mH-%VwcLW|tTs(V9Oyf-IHtVuhy zunJe)lGD60>Qr#yLu(iwcek4W;6T=2S9*n`)u!j0o1lxPxXdieDK!-f)Qld4kn-?D zS9@VG5s|&?gX^}&ml*4w!`#l#8^|mE0Qi3rqP31E@2zj8d=eQ}M*4`2l;-aK|7G(O z%nG1Hdz5Z)`5@giZ-l*V3m>8Fxz{Y zf{b8s_0d`iKdb=LJ-!GFNHIqrCtbN?@IVKkYr{A`x)&7-w!NZ9H8VF<(>gcrTD5Zw z{M>>4JShVgN79;@>h!zKpKIZCe>GA`r>yU@;}^TOD09G`RC(Uw{vHd2jmE!6?pgb_ zPSn)5;#kuzkkJ;{q5i?5yXw<1EY`LU&l;1`3^`dT84>8LOz z3rO+fo`uW+C0eNYsLa$kEVAsWAt$$~JUgG=j|gLS{R>JH^*RI+l8!Gk2D$&ht*O`zkY zmRb`pzNHZ#aI_5ic_1(mOdFUa;;LfeGscH%NEw`hoT|f7Zv)&5#2+j%Y%)UHGp_q1 zN+Si(SH3>zd8c-d4PFes*HP=OY(zHhs!X0unpadnZoR2BPnO^7Xt*2m z=|vX)a)hd2#Dq|AHQfKEDppw^n9DTfJb9~dZ17bo$+sU&8*sULaz2a}`lVuvBTm@! z0e)pXOG+K)2Jfv=@HHkwVD5YAQb|aoEd`UV5jd}?UW*0!Hr4&bp4`~hi{rJPLkBhzAd;YurDf0IhNOzC!a^5 zr*E6$4N@;W_7B_Nvk)baL4)RZaLebyYx!73pJ!al0r11&(FBD3_yp|nJ#v5!%Q^_U&lN(;LyTA{_@xHC-YJihWPYa3)U;U=dtj{)1%=iq+-L?64mTZhiOHKZ+W- znjMD{*3Zd4{baau36cDb8g&A0(SYjcKReug?mI^sazL93T@HGdtiMX!2cz^ZK-@)e zls58FZ@?YmvV(WLu^?lyliAnsNpCAY^#iv{k z;r^)Zfh^R4EttzGj6^ik8Jf~>!f85P$+5({pxd zB8s~+{xWhoGthK?7Tx(UP5;2}^#-x{DPQ$%+m^p4z0Em)!ygF{U+u080m*(={Php~ z$x(sIT!<69z&QGrGP=v)L}_@B`TK+kK%(ZbD5jm19>?$P0v}Hcvq;IJOr%4EOmlka3a0X#YD2~W9Z!+ zN0zoWu~10b>4L#;Mt0yMe*S(vekY9^rHUWsHeCEsB$<|U&{gNUEmt0GZwUO7^o3%q z-@j-3=_IJ-=f+^`Uk^i3gD;U^@gDXSzo+We<2<=*k+R$SI84In+5 z|G^y`YrqnS2hDnyuLHuaauM6b0+!5^Nx2>@<_)X|QD359?7YQTLjog+k-z@6y z#S1}PX~^E8S$TR!f`O>f7kP2xV%K~->i2AqI1hsDcsX3yod&_0epUwGmXq~Vo|jGC ze<_W2UePM@cxNIDNE97UHy0phK7yhcb)X^v&O*jJAYnG@_^MpXIeMB=!3fsq4Z?%H&50@E@?Y9|8{?UJ5+3A57I`{_WhQh*zNNHvaa@p`71|brT<$_ zs>i@N=;7ki29@4)A@T6o67yv3@2M43L`vi6ZTF zs%@9HDG-0}fwcv;q_D2g?7;{JUeQoPteKxHvt1se>%-Ncp3WR^f?;q&>zxz)w6)V-`g1`h$6@A<1Lf(EtBvt!~UwC z51lHT(1PGL^mejiS2D$XV;|{p6^vzY9uJ8W!0$*ZJA(pU%-)#C2o$}97U}Om61Lx!2+cq8>|ZhPnaiq*pl@ng`=6_1bLYGT@YV$WQ+(V;cjdD;FQ8e}X5&v9vmTFE+|l?k zeQLzeSgFJ_bzbC4!qvZD@lE2v0nUww6HwjBPv5znEh8j3^CP28&)JxRQ7o=5m=PR= zLk~gehP`LhIVaOOZY@xxF=xfj`g~Kcpoc~}2(|_adYnBb^61h#;3K0qc`k@(2N2R6 z+JD{c(q8CK7~F%<%X#W=;=-)L69N`njs(k4YLE(j_eYReQtlQEaGN=*x{!@wYu)t#`Z1j zMed+lbV^cU;tcVBn_C~S>bn_8nssv>@C9Aa^${2<$yj9Jb?J<4h7R(ttoLl7qx^TZ znQ?QrC`qM3CwKz)3U?IVR-RTl4fYqYBIR)B25Xu8Gb&jD*3^{2K)4?tj*6G%G%x?qzBus2VwfrF?X^uttI z5x?~{GE#s0WYN3NMai9cFNT+E56U`G31MP4_fSA=%OeHJ4-l&%k^C4_dp}Vw_G-d( zUCQ|%U*NLA9B!l%b2`)Cgy#Bu@~~`SI}Yw2ACo~Z(uL!ZEFWS-hs;oEl-_!APW)P@ zl%9!6#k13$a=dMU3A(-}CLva^GzyR`TRSWgp$AX%CHTa=;W}J)6WEB9FBKGJ_Ut<= znpqj;Q@xHMYKDI!gdi>FK~VkVqU3tch=oNFFO}2d)vL9N{4rhQcm?A!_>6p#Zvuhn zo;2aCFV*5iVzhIS@9OiG>*x4-;MT@Z0u*#&Nnk3F8+3CuGWF;glg2&mmc13&Bjy>s zjdwDAlAijybb0D4L$g*~-tEf}TKt!`^V3yiZu^yxI&kn<`UlqSJ$Y(Yt?RZFgsm~n z&zW3<2X{wiRxIY*nVqJD*^$W+hjSM~kzPmU0cpWcBD`Qka>43GJ&tLbClE*xtKee{>KQ z77-B%NUAmYBDp)5U_f1&J~57O4-z(3R+jV4MH(7gX^82VM_K;E;GB}i3eEeRpaZ203886#Z0oGpM zZP@CF9*`i#`aMxD6qdXJHdCFBSddDr!scrkpN&G*agRZy(+is?0(u2j_y@F2!w56sV~aq^C~X5q~Mv z^+RYtICPF2der61rs zzBv``yI^%am7e%x()3PMkS-Mj`)Fpu<5RY(=m3##*F<>T9G!v$pDK>*hkxM(8Em}D zco)uenQnbo?ydPTUQWg3hSr-WI>zIu=8_eAJ>DtXf7+~%OuP0SluGDSXYQ5~twfEs zU+ftl)<{*NW04kREt&K3`AC@?lZ+kd%B%pjt?TRG{_EH0;3%1ZAL6y(_&>_e*X^%D zVRrW}deFRFbSd>z*o~mt@9g-x#j2CLwr=niZNmbV|DL0(7NpN>z8}-j&{E`vyU4A^ z5+X4fYt&Fj*LZk1|Lk}?#9C>hhVus&B(O3{decDF4K(dPR@nIF;Hy*_5@8kzM*`_K1T`|5(&)7%5#{+mVI zF=i&vJyfq-x7i!J{(KRlPb5CY?p>eGLw1}!85c%b$gJ&XG{Pw%jA32mV z`=*2dO;7(Xa+tFZ7i{Ta5IvyYMJk5vD0sSR4~5}%%OCECWo;e~zw0udXd&3S&)XRn!Ji+H*LCR{Ld;qJNVuk3I92{N%9vQQG-}#m`8*RR3 zsvoG}(ZLjg*`2H*s=#EmuJyghqEnP9Up{%d_|xrMF6e!}%s5`WxEf-_A6A_4mDa$b zF9nnLGMtYmzdug6$=3V7ztHpfJQ=W^);Qci_2L{xi*{{{4cp2{NlBS;B=39zj(~N1 zWG=XRzz+!jk5543>B@=ydGOoo{qyee^mZbD`&GvxJYX7`{VXlyTpE%Ki?+lr zm^Ye=s=i>27R@o?I&wL3Cw`?Zv;XYZ`e@Y0`Z3{-?EnUoP0^NkI%cPxQRlV-DyUU1 z%*xNsWV@7EqQ^!E1%0eoXvf0wB;fL9nNt-2D>NxVlg1J&+>w@CISiJ#sg(vK>Ymxv zW#TVg<0TZuR)2uY2{R>^V~A3(1|akpd2pk2f@x~L9G1vg1i%sZ|k zF^eCjwq;z9WdwnG)CP^D1yvzpY7G&VlLuOp`mCWRkTR6$feymMZz2)KPyN@>7 zpKi0oRWU2cO7X){(^3@N3T&{2J@F*~3`Qgp@H~SzS4ha&< zw;dr(u8x0P`bLAf-oz|NFX>jZCVh!so#I1bk6Y`~3mG>8eIt1Xg;w3R?>s!_N<|jh zjyw4!sa(y>DhJuuY(;4zbstl&(5LRh$~7aQg)BQJlLECfotr;=M)KM0%HW>p1Vp;) z9CXpfuxVNn(lSOiEqBMEvq-r#xA2VSljO@RC@d2UBNC*Uc~+gxAkUkl^IR;TW>M%A zhJITk=J9q%9&cQx(O^WF4F2hlEUxWSqwdxR%>v7bbm9TnZZv7Zc+#u(+W{Zpw9ne% z`s_w<_A8(96&m{JibGY;;It>bzi)f9ZF(a=kp^GHio5Pfg1A!)LQ20Zp z3wMt3IqBjj=WX9c7bO~h!-m>oUDEu?(!(1F7wb!-(uS&TRCCY@1Fk|Wy@k{G7TgvS z>R*AK2y_kT&s0EL%xF}SyFl!15212C)0apNVJ$_8TVs_Nkb5p8kSOUv>Uy-MPi$-~$qF z3IP0J-RX_`96;(;X<|0G2Tz0_Cwo^gpyW?<@lhBPzUb{y;o)Y@6E2$fD{b_2=Lb1d zErVEQk6<9994s*+2vCrh6>VJ4V^oy-ke@dnji;TL(f znoy^Y8t}QN*&}BzvQAoy(gk3^?V*gvBc8kl@$LAbZd`gQga@}b;%%Zs5*qlG!o%4? zae%7_l`YNsWR16jXm};g2$7XkVKhn?75AD`E^M|#BiI&>pm`mqVTT#J@uPqz4l7>Y z+}JFFw)Fr|5#-96#blMM__u?8x%mI|E7;tP=3#AVG8HTTY8VT1I)%YGSO`troCJl28>`8-pE z6ntU#lB;3mG(EW{t|G9u1v$6Am-z;A^%5EXv}?%}4C<+Bn*S1S8t}SRZ}i!w(`g&+ z&}lG50FF=oWB}+4St}{f#FVtO_`Ktm=q3K*?|r)r=b5pi^V#HaX#wp`4cwp58%091 zz7Jqd+55Jhj{0tI_7lAMKE72_D~8ATaMk?HRu`{>(;9C@NKArbny%eSVt6JjQ9!Z3 zmwz)7XUI-ghPcXG!P8Zin}3)#QZWcIEVop7-R@vy6{3XNm2N-OV#g=-P75f=f>v2f z!Um%(kGzV(Z0a2GVFFD;0BU=BG8IVBM1bM04E#^zB{AaX`EiTP+;#n6?|3~1u5v>^ z(;;cfRZo`-cj0+8AUV3Drsm9)&;gP{9lU`Yw}ekNI;{mLcAJxU%@9T~ez3*=94&Kys-@*Tn@P1;j=0hwBCSs>tRY3i?t5NE z+x)Cxnd;|b+yql1jNbL zcl(`%tHeN^K@H!YbDAbNJ*G}NdS7QD3@*N0WJ%q5p`ZuU%RM}zWl0s&RPaV$orGct zNxnA1-YF7ShWw;RGa7Bho~aUa!7C8%w)%juVfIm{uROrrW-R#NpO zdkn0n(aC1_2>XZ#ztffI6kYfR$Ue`GwZ-QL%CMrSA(X}pY0yhM3d*1n%l~T6Sp9QY zS6~e&GR2c0E~Vz2*n4(EDmAY`XE2UW$mz$*P}TEqnO|u>@OWl4Ez9Gg*8r&qJwG1k zZF45`;bXn}_Ytpkx*Z>)PgW>W2kv%kh{eu!{DeJxbWl985L>*U-r7OosS&6c2MNV% zORj0pl-VQhjHszfmAoRss}O$bV)j6ckwlv+{YF|vs=MOyUYNs-rD#! zLH6?ta_%+=`hUF5d1YZBBV98YgX81|7Ebmb~Fr3V=tw=DnC zf^#<4)L%&XuXZemKrM&(oogx0XL?7M2Rpe9Mo{B~> z6re8noKbAww_X}MJpsjNr1N0Hkl$~kI7Yu`v2e)XcyvSVIR*<~RT$)lud+u`AL8Ol zE$fjGPtx!%Lxx+CmcbJjSLn~m5!NuJ`}u;>QFu9g*uB-|h+OQ>l0?Oi(_G1jdcnM` zw~o2+A6-tMJM@E-DEJw9AKtT9Tp4tU)nAHOQW<%BHj7Kr245UP)3cJ_I$WOI#}|Zu zk0f+6!k%^KB}4laFwq#qWH;LMC)lybyB4%5x`a0Ovlw8uTFx5JnlM|WCFnDY)yAjj9$)ore9E41 z9L2VWAt*Ndd0si90*TLK?OOkP1?CeAbH7hq=|hlrhEY@asil#0P=!nIOujX)%-Tc_7CDGyE46 zFU}gWgD?O+)EXwY_>3bgkFd}yC?UIdp8@lCIT_8pz5n`^hEmy9!QjNGp(rsLK?P)+ zyhVU!{_{I%;t?aiU*Ru8AghYi^|js@m19#*Q!aKNnn%_3&BI%`;24GlmgJa=pGSsN zyM?y}u=F+HUjS|Adqd@ztQebBo|;ECYp(|JCVp=6v`Kn`1N zk)M}Ybt*D8VqeXXb+h03-_#fl_^m<&3YSkLa_QTHD9&N$o9(T_-Q?j0px^E`t zu{avRd(uu zTRJXF*j@YJGcgjkg^#=j6<_*xl_v%NqO@@HQxFZ*z5SKp2Ahh6Cw2q=)1#j;q51oN z!B!P3MFo@1cUCc@$tk&J&wIeO3N4bQ2=ymzr<_|a-Ll6YVcyMv2sz{%O;3gPv32FCo2EU2=Z|8V^GW^bOjI!7LVKUlUOVjyXcL%*`2hcx#4tUftJ6yyVV7vrbxBe@it6R+U=gTzTNEBOwiwqvFVeE%`SHm7Zr>u2;%NaA-_3o)4Ih%HzfiViKBib`KFYQxPU410RNc~lIiz5S3yLC@b&hRk`BKG zX^8z1bMWtunT+FaL02c6f?`u%R-o@052!u$R(P-w!$13>X&{HWi{hy{uYc#qWa4ZQ zM#=QIiq>1*1WrG?XNwNn(@{UJ@T4mc-~N|ibj;aS!QYZlqD*sApTA`%Fex|e zH}|40DxLq<5j1{ssb-^u?F`B{!<2vwJtk+TS)@iL1t1a)4*^4tjaOg(T7UY)s-jf> zRDo5nODOt}%pfDNgLIIo=ufn42b<>9E{K*FQHD z@C}mxgykwEqo7(Z7ge5F+PQdpC1+ zFT1@TnSK8~Ai5&bLNjO_MS_rBsg(GMR_F6QaLzo(YiPq{%k* zKL?qT9e(P&QkYyZ6pX$dmi(TPC-8oAEegQZ_oU>m$ma+W_dP*C7kW0HNj$`k?ma!< zP6HA_l8Blb-%>4Njuelu-A6(%*!4MUP7}02G_^5?*Wqks6lbnjyV_`Z`(t8h*C#iD zn4S>L2yLbkO(v9XGO~v_S<1SU-|p!2Vbi?P!Gr#4B20*NwNGS=W5>8C^Z-2KDXaOPgDNQztu}omTjv)LXP~(ZH_$ z<$QGWBU8Qj#*N5s#0-Uc=@AC|Dz_GppJt}N;e2tn)lWJ64)1r&DSY#+1K$Y~i{OHy zk|H{adh3dCoS47T)l|=}5I(iPb>CYVXWTtKd?vG@5({*F=K*_YD<)-5X)L8|pu2gYNR@-1YUgH^{1dlp*m? z6kK>&Q87wRCKqN^t0!`b#ot0t?N!K^0FV{OE^Y~Esh=$}-%inAxzNWcx|6$2|Hu~k zBw+y%wp~3j7<<)^1p)*&TPGGW{VaEnvq_UHi9WL&AXX?%U{;|fUt(;VB^hxVn z+kJgp6>n_WD7P3DcNS=*rkUib?npaeaaBX3gh9mH$A*6X9qMh6SJuh61}T3LRu%(A zZb0YL$+PrU&EJQpzw%AY>h;-71^9dG9%t2CU}2FRMlj;HaBPN65^mg1{r(HP(LOQ3T9SbnoXKxV zoRnkGUvM~<15ZFH5Q2aIG+)3QX9o@vcU6D_KRPk-V;U0CW&ft!KTNr-{0)@f0AZa% z-a>OGZySCOPDO7x00j)E7KgpYw@V++pyIN$1o^co?!&l=Y7skH(X{C83in1A<6)G> z312s7Gg98Iv??vUGc9#!dB^89Q%}!x?oIo#EayLAHpf@}?aPfPd34%e-{EyHX3rly$&|T8qA&p3bbR#LvP}1EX-7TqfcS-kyG(&fH$9MC7{GERsd-m*W zU2C1IJ)FDazajL#1-Ru?>9FA;cbRTE{8gt0`z1GkuQj`z&OeSxGQZZP|4QQ39)2dA z(=wt0>+x`&EK$QPfd#S{vd`(nsmH<{l4T|Q&`_b{Cc673bCS@-%v)rtIcaAmUBgBI zci?Nflb)#LYeLE@7~i z;k39!?eAg=^HT7ZQ;Yi-sZ))I(|Ewd^rntRSqWRWKaN?{s76w2E+_1>ejyXJXW45x zXcPYM6zT&)o5sW1w_ZTlE)-bvCT?m&`EuO*ea12qcI&Y19#Nh9?tJitaA zL7c)ehopcZow%k6fI_9pYxi-en~u%ZB+&>qv~C1@A4Sf2RzjT&nr36v6pbJ}`om`15!v zj0le^x$IDUoP(&OcKGuJOW6sGeu==09ka?8;otYTauJvPQj1l9=BCClxauh(q?;D{ zu)dF+1Omed#+ZX1mH~h2U{ie^ED@wc`&6ryGm0*4(S>rQ(~(UQg#&O)<^Q3~WJe;a ziR5qpiWK=||2-_g$B1{>>21l3!2ICz2wX$JB@e2xZxY+E+n^Q9s}H1VPMZq(aP!q@ z^9&B9)+Y~%5V^iC?uR&p&Q?%a zhn?Kp`HPn$n3$ zqOz^(N}o}xP-xj@ng1ThEZ(<_6{_-u$m4z>-ji|k*YjZ2FQ<4m`p@SRU&Xmd+?Je< zz++!9Jq@`iEi4qM--6PJtZVQiLZ=jX{n)U2)l10rUm^dRpA5$3%R`~o`ZXC{IC|Qm zdVSYlrPLu(Tgv9Qu^gICdI7CM`=1P40lqVBQTFt-j?hXTuk4rbPjcIE^z^)nmZ!J) zK6d%vLN@c5^RF<}`1qCN(;Fu4H-CN*9SHiZ96kXFzKd_=$MJfIpO}I0zsk{y zy{M<}d&Z6c3u;&u?MyL`5INZX{`rS^QN|VrpfU5q;Q0gzh*jMy*`!NHnVbAn|pw!Yoe(X;FfmOqo45d(w4q<#ux?viG@X)aow= zt0@Nhu_DR#v&$v0bi8fyrDu6`+BK7`D_5Cq#>QHvUr?epz_q@}WTJOitRf^?8!NbK zRXmW|47>kaVxK4!zmMmgwQmSwx6#OM-gp>Fk*3y)di9fkcw*2t-9|gRc37I_9Dj;N zsSDF2EnJ3(2*rO!^c1KT&N6S>8^v(>qm5RCcK$P?R1Om^dn~HHMslNudmDwUuP29Fjt%h_I%!Y zuoT~JQDQknYyGL;Q%#S6b28a*J->S0VvosL(S~TO>fQCZA`u$TDG7wT&l2GR(<^%p zB6Wf9&${)N=s#)As@xwnmX|P74AS5rE2P7JBk07NYN}lF7(g8o6}3{fe5IO# zeNAyF!}~;Cz0!3i7&1*CN%0`8OYU@lweMa+^E{$Xa7?TrZP$iU(HvW%s3^bK1_GtO?;J_1v8HeBDI;sAplS z(6W(rhSqH`Qx&+#KI-t3C$UYeh}Jw3F`4NK^lQ2sK(U2`DK!TQqz#6Wlb9kU z072&qQ5#z^x@(@}=kz`V*}v4h*_uyle^HbMH_*XPjzgb+Y*a8-V>ViYcIB3NEsa7U zEBmmO*25zW5|$tLoZWXXwJ)5ud=gWcb!xl27ex+klZS{g>r!`q7l*mhyRh$)jE@dh z(b|jH6#))@0?sNvh|C2vY0Ca5jg;Ei(NEnb80i#=xRYD6_Qeu2(3k%?ePc?iwZB?%2$q=u1zDHU}2=pAHFB(V!bjc2oe*P z_h8J3B&-8dj0x~2OBq>ZU{r}E!10bHgSxk5*CVV|>Z32=Qrf+U;LLm_5WXlOj33aZ zM&ZPT^-yc}0oDeo36G=LkAqWLt6nl1ymlww&`DdUCficUf2OfQ9dC3;Fh~E^5sozP zGqd>dTJW%LOCWUiQN2MUa_pBr% z4VP6V6Of~m(nu2YHW9-X3XT`>)+e?YxLSN`K5rB7cg?FOo`YtbuQ(s{=~e?Dljl!r z(bs{muAQI{x!r}&_STZggtkrI{&Q3-X9Y)sv;RRQ!Sz3Nd|7lVe_=;RPC+x`_f_OZ zn)WRfDFxJT<>{+#&g3tj4D6S<>p+`SciHoYwHhm3=f$*4q|u2j-Va#bZ>&OH7vG4_+8t%hLaCK($2Hh zI}lM?o*o;|rtBD#E$0iQatv1=Pw0OEmakm27O*Z{S9Hm28wHXCe2sq?vsz4R0s2Xg zz(jfpa_UnUUjziBm(gp&c#y90XZO?LjqTsMvo6aHG548Y+d=%G75nc=(isFJQTv%l5%+r6|L z&-u!I;g7|Npd}SfMvwe(RBVUCqJh}o;9gkY4N1w@HaL82VxjYsVpBf0)TD8V zWky}l+bSD@@r~VsD=Cawe7^3R+Q*XDx*Mdq_k&KTUot)K}{ZV2b$xJ|fhQQq*`_8%w9BuRmVa z0ACax77QTspmqPj!zpe;IGsGVoBOVd6HWBBaGCfeZAKA`M7Ex0Gw5e#$FNb&@&R85 zF`m$=kr%9N3(99tlGTdGx~RPv-+U>@ISar;1XVW&+=r3b;vpWb)ph`s;LntRcbTEMFuOd;qg%mDOMJQGnEz4wa~@5GIs* z`W<)`J&UY6ZJZx&O)WceKa3%omQcvU4=RSm_q83J!3*_Iu64 z0$wS;4l$P}HmsdG5&v%lP8?oaREn0I|D3Q{T~$9ULOZP0rl}Vv*RPc6xktr;d{Z z%BY1fJ7ZlT*B3{~p2*LHbpaRrnI(1B{=7ja`QV*|pZJz>`qyQ)v z&n3+J6ByZpu~iia-0H&;@!dcYk@9pM$Z7jB=&5LdE=*E4>=OK=)nVKqGZX)5(aSc~ zbRzxcz4=;eq{1`3VJp$>))%Q#9qx8)ghv_M7TW*p<`X>Wz*nWJ#Kv_9{KUPIy-VID z;tjPFB!jCyukcN|BUwz3uwn0LT2bzkPd_ zZ#O{ljIJPxe{6!xloJU+KO$G<+8A*(1iw1I?&A?GIbx5IR^zTZhYT|$#Om;31{QdK zjwo*Qm$(hK5dD_R=)Y6pVYGwSH)D44ucF$A)rE7Oy$X58P3fo5DNI}d6eMW>7czdv z%S3oj# z=oc}$B^TJ2oQlp55Zm8#R4fwp!~?;`-krdUl4&Vd==!q&jCYU&gZGcS5VE5-uWFaj zr0zY>m_eW`0E_=7qxNdyy1PH9qHe(jV)2udrS>a4v;0n!R(A6~#aDpb0UPC3riQJM zG5V&R93oa__iv5>nTC*zGxHt4`;cgBQq)rStd5!v*U77Da=a7~clZyFOw1QWnXJwy zpe+3r>GWRkBCw5cp6`DOc+|TBt`s0FkT0W?KC|l(k=#>D$ecaR!<;$)Yt2Z&1!MKQ z2{(Fd?PW{MpahSe;bJ)-ld=fl;Ph4?qSd7L$ak0x5J6}QOgB^hY8+sA6M8I_$%d<* z0*~&RkDUtFGVPbD))Z7(nSl3=wks{JVu*(XxsATG0i1B`oim{7 z9JJaob?Q8(@97e7wi=;Q;LY))CZJbUrf%L&P_MiBtKZgS1KzB zvl%=612vInX}?ZYWEvs3lT{RlqIL3q<=yQ0s6md#1mUxW1O^1#2eCTwc^Rcyoguih ziR6jNUSOEXNZ3!%s|zk+l1rjcfXJv`Py=0v;I<>ahb3NWer-Vq2pAtnR0sFz-(V7l zJ5|XXb9ZO$;bT0+1Bm>@c%0m>t_=!MAA#vAFqs+$v1)K7cn4>0#_ky5;B+zK7=t}f z2Ja`?uJ*R)-lMw5MR3Gy%<)2vkydz= z*=|N8UQ|SJ(@n?|+iK5mj6b%mlzb6v@7t6pt3jQPU>TtKEYs9JLsYfP*Heu($ouIQ zht=r64&XC;9fk}I*huw4A3yV+5aePQ}lQA~gefi_4d=A-yH%HT< zq(yeuvpfmN`6`2HqOe>TK~qnBtkQj-5FaDF-m7}y)CQ=Yh@aCi0K~%-KaSl8xDj9- zA~zX8@hkslOzy?5h_=3-3=-OkEL}P*<3DB;u3q8o*vI?J4HN}yoKGP-xXC4#JQzaOVYGhNe+TS{|Bg>{RgrKmy+005^P`kbyWS&z>(HKr$* z$1P9ycq!33)IV6KLUKs-jkP)Hf<@_Rnef9V^ivh&*SpX*ax6#D(WGfB4g%u8E%Unz z4RqmtENcJc>pg1i;griFd~U#BPGPFf?d|)PJb**XcBqNq&8K=G-=81bU@@W=kI1S7 zUjd1y;=0f#)?`Kv)g-4lu|R5`SsEc-$uTVwcyluBNr(UwlH6ONUx9=b(afteW1{$aK~P4fQLE8Pgr-PQmq!$Z_3!7ov-(B^QYe@;{Pa(wO|b{EC{7jwU-lp4_hkGPIs?r z;JWN?RF{ljj|I_W6u`MtUT#mIG=mw%PyBG+3=vrAZ|>&^R9`E~Uu9AwnAu*o8wse2 zEv|X9=^OAsIUgxCw>GEz2_e!KRF>##@%CN;HT;F5-G4>s)kI7{JF?K*92-0TQWr{5?C|5 z-D^GQD1p>pjqyBsnpQ%8$>t!2h%d$NczC}zE!~F~T&Lpn{nIab9_=WsEc>0Hpd1{M^`t((%C-Lj0P z$QUSYj;~(MA}r?(rF_~ocfg;1wRhH6^{c=69cnnP3!mrk{CX$G?{dTe;pIxYTn1k4 zU~<5(;-9rxCjpVS%@S2|a9K$wUVq1h_zOM!H>;h#fBro$W&zBe9}>ex))8=Id1`aq znZ#^_OXRro6+x4HGO6o3WEvNlzC&~WKhb)b8+4=geO7}aSRXOl>_n$jF9SVlK+%n{ zuew+%__wJ6q2tL1RHxPsn-Pl;e8UXj3sX*KD{p+TfZ5_lP8!9)j%d6xvD#lg`JWbo zn~0s%Ccnk{5BChkl`O&@PYJL6lVxcdWToot;>Kt%Kvqa|`S)d;V69;tQ^6kZ=SKtN zkwB*p26S%zH}3ce`gZxjpq_=u9ed(DuYQy2RX&a1KyHyW@E?=?Gq-Wg>~ba9l19V2em*v`rI0hdb@ z*o5!lb9P9bS@JRMA59jFB}5H~X7By?pOyD1gfA95qI<8GEnZCp3g8GklFUX*&E*}D zepwHBicCMFH2i~#i(>C!WURCEd}O#fi*H7UE0_cLyjiFN8y)rMft)O0Ym-$U4l-Imrzp@=E<1IYd&#}ZJZ zYCkcpXpMO&pLGxuH5+YbY5jx5>Rj+|(DJ`JZ7S-V*Vu0qSTis4`8$XHS=YlmMl?kM z#VlofI*>mEWnolB{aVwjFQ*rru%K)9_w}A%#HFTfKw5J$uaWr87R38W36Z>+B6^IScWg!Y;_3BF%;H2qS)(&g|? zl~niITE0n@LRB_8Qt-XP0lYb4H0>(Tw3dLX90|_-59!AbSNy2c@%CA|R$?8Ep!Y>d zkuTh19RSKb@W+Q@R0WlW*~^vRoCoOBWo+X?`E*8qUL%&$M+69LO!PJrfPR!>LFN zKuA~cEQQMiq@h5)0CN9MXN=F6Ub*S=Po%`uYM9weE^QwFh?>EH={S!{XELV0V5?_L zW#ZrpO4FSHqT3lzI^e%|XP9s{EyeBxK?I!bXwUMIKYBVb!BHs26CEFr+nTE|yQ$QH z0)zd(*sBH`4wp4taFM&9mmC0{jM;WZ()RWWSwE?BuNLrc>0U;y4Ql1(oIgDr&K!D^ zb?oA?s{FnG(7ieLTh;bIhXv@GCxi8^W#wvnBA4=}W56$WRO{m@-|l&f-sp7U2GHRP zLe?++1@@OM>h4nkao&QVjdarJd_}z5GtdTd^3JR>uLL*FFw6xNvQ90Mx!)&PMXIao zM*}#(1UjMWd7$XYY)Xa-qwYH`lNayDKTPXXhRpQMdfHZN;H2`apidiC>sf|Vzfg#A#9BG9 zHOtpu%?2Etr+wN_xEokxj74wS)kO>Lpk#LP5>qCn<}{I$84&vdAG<@_dT1g)ke)*m zC@x5zmV3X8XI>YNtKG?(8Z3GtIT{@nUC|Io!^TO5zEgMU+xYx zj_%SCv?p~Hwwz2y1Gc_&DByB^icd8@JNJrIH9iwg4h-r$lMIRf!dFSzA`8S*AM3Mm zD7+YZQI&X4^nOTf2fI6&+1qE4Tpn@8$#ia*-2t%!aKQSJmp7N*_KCo3y3`3xjv0;@ zkQt&KG!VBP+=~8mWyo7UhPR004X6}D1&WxjKOg-*SKb3A5z5LR0Wv4Vy`8kjuY8WB zC@fFQy?<7Fu5Wtza{}tT63#fF(d#bY9CZ=6AaLw1zHn#0?%cG2zVf!`NJS{@1U-Gq zaz*etOo&c`z$p!?)Ng4MCebDlce%KD7?X4Jy0p&(`e*|4q5CT?t6iX6n#>&H;-$X= zNtL;rUsXO4&eHk5>eN%XvI1O+lFJ!C$?3W3*_C!TNEZtTO~(x+50IOHB5(K4xzhUg zTs*UH&u;(!<}w1_TmtvEC&V6}p5hHQvlNySSt_ky>;2^>Het_Rr7R$49)i5RN!w5r z40~hwr6g1p@C)+|Zv+zBfKC~17r<~XN{(H=FG$|^=XhWd&0X+c*C`ohy?MaAZhlq< zKpMmq7R+NHAY38cX5QlEu=(tfBz;ekzNQ0Pr6zY~{$H1;AHe^)ll84kY3GG{HV_uY zk+(H>zK5hq`K$h&G(R@~yySzGeZ;6RmG-^_9C{e7R`33C>@QSo+HUiMas8;qumP3f z1^ef@fWrt236@>VdvpxK2!fEWdO&YwDh16ebMcbK6A_GZdd@I#UQi%MrjKsva{Y(BDffg=UZ zV5Ny|H7KM{GhE*O;*Mh=gv}GLK>eK8&)Ynz$E}k0oJHp@RM>!IKMe%?9N2%ANI=;0 z#~=Fkezwo}Da+?uF3Yolu#)i^5-aq@3{2X=Dvu7y=#R4#uoKkXsN`PFN6s7|E-1xe zYe4T#6BTD25Y;5x?0jrEBPf{z-6C!Xw9BD}Y1OWs30K;M!mbFVl65b#@51htKz)Vk zyrq}a@%0>xXPFi42~FO(5VG?&}x&<2@gp*$#Mye@>ax^oWpl`n{nyHa4yw9iafb#L(N5 zWy!@_bCdB59+fU%pJp$u3tJm4t$ExoWZa(-r5XO;ioTk|F>NoA=#tKkGlSg^l&#{^u##BJ=;u;?Nc)|Jm!2vr-KNd2Rqk^{&&282(URv4Pf zyH(GgKc9UEr=A#K=WHzhl@Rki_RUr*zdwAu18lHrc$-=hsI>p%sUtLuxHz3-Dz-QSvA32+aSoNLg)jX+g9|IRzwq)k0et>;<(2G7`4xd4oJp`V{D+IJ=K{ zIRd3Eq4R#g%(l|2HXE>mr8#SSG9*l&0J`(%gO9iQ6b#!+V*G@WsVo_rU4ui6fT!jV zcA!hZE|qwDs9%r}y-I);JNey52Uu1%X4)oc9(RBKWkCy6w~b zls&*1cGalkRT@Ng*R-XH9$>qee)VS3ixdAuDt*1VqZaF>BjQQT(mWtX5FPqCj)zQd z-xQHmrv5P3Il#__MB6#3b?ZVPbZ2GPUt_eUl2b}3NMCH@dxv0J&1?^h^aL+U;N>c55W`Uw;fV=jXgrI1!I z1)F**8j|}tw`Aqrj*QF&Z!s#{Hhcq|yzgPLR z|G0_dhv2<$y!(Ovp6#xC6@+ISXA-`-BIUew8;b!}x-BsKAZd-WF@0m|)>*F?3(HxZ zi^=Kf6}>|@v3-AB!0c5bk7zX^gs>QBKk{gU>fqA`$d88W!sm0&K={>%`Z)*Qzv zugpddu!5(Vj-1an4y0smuyAn!JW1iynS17cs<>!DgbkO0aODgsi299zAtlwa((M#?y1K_*u6>wX-^^ zd=|zS*6g1xi!opW&|GdM?coFF8*6B)a`yUzv@kbY@LIJ9;6<}xhRw#J%ZQL>FLt)H z7TRb~;b+~0IATf?wCTmq@h)qeYFu_^Ti9Rx+vZa8FW0>sm$N!(;KPizC7fI=(tv{Z zO5Lr&SkEUDVCDw5$)!uM-vyg;aI%qi1`e~lSpP#5Jrnh!ljvWvrA$T;(iL8YpYi|+ zi$0!a!%BbQvOF5+YLbrnFiWHnsv3oW8DK9n30JVBhps(r-ri#%Xqq)TuSp zHF=5Q)LMw+dy}MCeX7JhoC>AH8NjeH&e6`)S-U$$gRZ5gd`8_XbvV;alqQU8* zv!f9iy<(-Gs%LJs)4429d(M8LU>X$2bU3U0YCe`CCY9#UC1O$^ZAZpc9svzLdFrDP z?nX`}L?uTE{1n(J3&+`WjuUZ&B6FJH|2l0SPU<=uKYh$|y!llJz%HppjFLh^_{mr? ziJHj`oi`j{#NMN%SqwQ&zx~sRLD1oYOP}_ zC2g|wYQ6OS5;OdnW#{|FkEZ>=9=f|%+fR+^qyJeL$DE;~W07gRWCr};LyaW`!$_+4 zh8Z?h5+-glmHPlz0FJ>KL4J_w&tSK}Dy@|s6q>}fqH79mes7+YHY#2Upcum6+n#q7 zCA4M~LHvQpR!}8VJOJ|h#a5fcsgR)x!t#v(6$$*c#khIe4*|$|Xwo!#of1hFr)=-M z{KjAhc4R35g&$%sGy;DM35a#Q1zVF^_5q=hA>PP8r^K@2?|}2BgA?vBg20`G%B`&}m|%XpQ8D}hHP4w0?W_Y8!hO{9gEu$IF3Ax1(zQQz(nq@NPATq#-M zACj-n5`-We$nYT^;_H*?^a#<~3vuD=-crb&7M_=Tbb~O=?s#MvWc2rP{M$gTo_~;( z4SwP@xgb0?{nlH?7wxM2*D(*v`PtULuul!}$x;xeOaDDsfSvX0)D3)U89dH)7rt^E zxte>;-+99=Fe%}@J%J7sBtW@dy{l=Cf!Rk*2ZMA7T>sl%^$!K(3T4(u$k{_T>)OFE zq=Q8YzMKzKbDARu1fa`o4oM30={VTDlhgrED@MY44*c}=^!Y{~LaBZWXGGlR*u+GI z4@*sPF)W9GV-*xY^2D){%z!6%{cjdVWk8k@RDZRHc@k8qlp$>GaG@kV(!}$KUy5w|uuRsR z`>nNnX@{)MNFY@Rf7S$@H!$!oBb@%B+|ve&oH$wF86{Yeag%_I{Wm?HgsR5sJ~48u z6flUI$fVInFnv;KzBuL2*K(I7;4T3{2DH;_c-1$6%D6mM6^6?dUU>M4LvbSz z^S^99D}k}Yj__~oM@0LkH%p@UysJjAm8%mfe{8X)-(;~cslks#F##L9iNiFJl~M=$ zT+L!FJ{O`+9A@95WtRzQ5`n?Z{l%i8+vSL0;o6wv`qoW}Z$>w>87oz;SZ)(N=dEyR zp^{!NToaBH$_*#j!#-b3Qnq3Kv__>sVg!rsV?gD_a7A$xeyI#I(Fe^~i?UZpH`EvD zP5xU}phe;X*xXH zCk12>MdJ%JQs=N*<&_N#)ni+kXN{TM>NL1bb<}Fm(1jwM?(TlA_l=Y%6!m#QpC@Mr zby@TLe=?2y{eomQNN5p{j)_r!e(RlNGt&Hn>U?{=$nJ4&Ou%D<%WTw{B{gFOIk|poK7Ip}7Zf+y z{#QGFEZnnuQnMs}tOl=!GNE-|BVs0P?w1sv=gU0{iH^Jc4(lCHj$YD_iF0V8!7;e= zK<#)$ED~jMvXwadHDMQo*gwf)_@$;5&g6dkO>T? z`wDUXbm5@-deJ9L#!Zp;boqf|^kG=d;d_#Jx#V)LWm#h2XJl2Pd+xM`{@DRtk=g{N zoLFw~q%>z(k`;JxmDlR3aNKEbKZrz^ln9r$#6^hx3^09 z^9BEDT0M*EET`x=WZ?tJU~^9(G=~7VGNYI;FFSDT;1`P?g|rbPH|su)`i^%;Wd4x= zkc4d5W2!%^mVeQ;dtVbhw~ZB2nU=}Z4pG^CYNB}L(&zSYe8GZD^!E1|Z-Xql>BTAs zPlXTw;f(I#EUSLta0$)v!dmF=Cakb*?^|DEhkSm-Y5hyx^<+N1AB_^tHKK!6B^Ro^4;&WY0!C%4=0b5tta)ox(9 z`L+Jm{@tlvs-t?yC$jXDcqNhY(BN+IJ zF8>F88$~U~AHIO**o%&Vpj;O42jyiBvPMxI_@KROY*+MsWQKvq*0CVzLJ6xRB*9jM zv14=qTl^E+`0M3a*IO@;uQa}sm{V7)aT7>bj2)!&>@iC*O7(OEf+mk#z}`oy#&lR* z_WlPs9sYb>1spYKRLVL4h>7$}d-pej(=yc)xHoTD<0&bC1A-j-rxS zQP~m-Y}WlgywQo2+`qLq#sxX^puS>?qogv*!Jb`V`K2ruem4kL#VW92k9t`&NT0#E z>oZG13tV{FTbDz0;d9UZF9LtUG#81j7{h2m8BFe23q`$fFCC?Z_vP0;iRrGek?zv+ zJluaUXCRq9#OuseJY1-fG9V6@nLp08Z4b~}&^WjeftYI5W$7=2p0NIzsPt-w@FlMl zlDH5=Vm-@QRw{)K9?S;NUQ5 z{vlZD@JdBI5f>Mq>hkkrQ2T{g@33ZAxm>Q3*dKAe(&BO_Etk5k@ z*7f>9%N`P>7P2^b9bz-91Hamb;IO{b(56xlygL$>)W6V*O#-3K{L9~XCQQI*VQL|7 zER{gV{#^&BGB;jeHWqMQpi5Rr@0g?yi?w_>#7~Od3aQgs>Rz z2`@5S=Jd;hae?Mn0n!LK z;fpLszqLvDVt7k_3t26Ct@~7H17+VrQ@DieI04B=|kuK6q)1 zxZ*T7k2bAIwR*V+cdw2P-7y4`=n_K}+r37k&RC4hGNDnY+yntW)1UpkxcURjN+D%l zgCB7he`4S0%>~CZJ@)nX{Eo;Ao@Pzdz3D#BRr39xMx5R(PN68=ZX5_D!xbCj#5$-7&Xu{Ha4rbU`O8JWQ-V#t zc=bJD95=KPx+Cmj=qNQY{bNO`A}3u0^h#{fa_ToQZrG6&(@OVJ{C!=@eB!!gt)5hfkUhAtihbQ%TJDZ9yxi3x^!rqYBeA~ZGxT3j$`%Bsmgzml4eqZ0Jn_=9MaXJ`)I^SNA*Nol- zIN}M9QA{l24+nNAy$o-2W{S5)hlBpqbuW~w6|cyCb-9FH-uqW+KqTsI{N&<8^~jj{ zt=w8gyKEjAxR1-bsP}xTe*Y5!OlA);(*#5ZYAn|s;^!94M~79hu^87 z_>2m7JePO`&#dre@2tFYMU@!nsS->2*mtQlnLDq@*JYh(bN%$&k{lo_FQt3gS{Nk? zI*WC@Kx-*|cPr2Hbk~G-+gL#J7>~fVd1(`2q5ST)DA5PNYAH@X4SZosR3FP@*A}WF z$Q429i};zUDyVELS=R^z&|wC78B_g?mIp9xbhJvAGkx5da8=d+<*_^UAib+TfA@yI z1f{sHRvh*G45|D#9xhSPrH&axI)rFp7i03=+VhjelD9S{irB>< zRpOZS20yqke224zRAGrTS_%a*Yt=_0bFg zGA<6A?Uq1KHVTIE;BnCUqBAdQ*$#S-q~!Yjz|60pPg(|VhwxF(ytN*=N=-WOx*iV_FNe6*(mnA8Nl`rR6sp-H<|sEv#pw4z=Wn+ ziCCz#>GXR=2U6nr5ht_bjD6cvsz0>?Y`=ReS7q0d7Bqk4UopbSnx~%aFMiKL0)WKl z2_tT{W;+sOPn6oOsKKyq(dVvVgsnMrx@mor-%8ydS;=6;qMECl{IGCpkGRXAKvb2H z_^kI`^G@DOk8k>PP4p6iYMm)iS*!h#n{UG-WH19q=ZSa~@O(kpE@@Alt=8uI?8R_@ zNScsDJ5$pAYWGIqC4LCdym0Co{i2ipj9C7;fV0b&dc_6d^uieoQ!7uNJM(> zX~bvVw%=&$r7Qm97)cNspllV~Rs5DkPzeV3C>wuw30fgPnpwV0-dM)}Zuj=M!HTsr zZJM{R;z}kKLa#KA!iJxfm&m5GW&C20H{^GPDs;Q(Hk(yQ;-zVdQ8O9nil8Y|r6}BT z2cNTIi2_Dz`A%y9C6>B3S%;`(X2wHA#y#{K!)Sf_yM-`&C3EH~z$^Ya1)>S_BHA8j zYFmGMLoyvQmAh z<4l|>wUGAkkl0;se%xniZd7?~tS9_3dRT3zNAmExG`lxdXNfb#!)%8l9?*-h(YFjV z%4K;R&~l&sbl&@*EVD{J;_o-HlFuu3+V-h&G8poqO^KO^rq23Mn*b1&spC1{?$+MK zk8?(+mVYNMy|Id(DH;%o-~U(~a*>kbu0L|9x@1<;VzcCLV2S-U-r4*{R7s*34D9c4 zTXR3Y`~j@nhjYs!XYBmwc5p4145xx*!h?9BnTm94h0AxB@tITY2%~e#aiTM=y~E=* z=eLVORPbk6KA3b4BrJDlafZT}@NgkE=Q$7#M+6`*%>0OX{87^s3(0x0cY|T`#P;Fg7&;6`vC1 z9@bD6-62GNA&7JSHjAXn9DRq*HO>)PWl0g%S(dvN_YE-Vshu2*OAiuzt(t_TJFq$UT=pmwipXvnlb?w$-qh4 zwEy0A;LV{gwKbKgFUV-VgUgwCcmvw$mKwB{_Ik^@W<>8QeKBpybog<2|85w2` zmox0IlMS(=dz?X2C<0-JFI~xcOpSEF&T9p`9pn? zc=r}`-VwQExY#`f1TWD+{8hPiPe-K(GWDMZ0ZQQj% zEb0UnVO|pMKo)iqNBRw|Q^;6htL%K6cl75hf(oiMl#jLw`MUzd+{PZYO*wC`AEcCW zzE0D3!7`1>4W>mc z-Ju?>(F|IMl`b?R@{0KDHYR-s5mMa<(H{G6jnjn>!pmJD(aNZXv+M`ZmefftduD`e zyF{E$9NBA)O@+v=-ov}gpfn+m?Suf8I#pMXmu<9r0fhgY6<5|2^!cEnf52khHB)$} za&h`we)fZrR@%6yZ?8J%;`cju*5(YwOWhqwMUem+U>$Nhr=7pP`a~4eo-h7&o~xZj zTelGQf&k5_s5-T;Fi|o!_Vio34fzIY=?pTEb5%|Q2x)#sgtp4x6%WcQA@^5(c1O)g z++5kQDkF|~fgP459*X{wgPIW^948@^D8C7ur2r~plONkk=^JndTU!Z~e1f-l$9e;% zT7oh5MQSyqi*4LlqPxAcD3KE;|M~&OAZ1`CwAHMhi+P#cU(MSLoyN-41Jky4sSplO zXL68jy!?E4jL8#hA5qbbnyvCG$a3HS19)y>KL*k?mxx1JU5EW9bH#Eu-$1^GB}$L< zB?lL$z`vKZosOf-=7Ub+fV&vbrNT#Em*VbIpveNGCK2@fc*B+@K)_xZ6`+ z3solHW{V$FP{<$<5BQaaIOY|zl6twvSXX^!FU^)C*8U1x!gcVfQK^^HtOn=C`VCIM z{MXU1+w0zAz^>@{1H;AQ)GipMDw-!gW?U*_^m>3vHn<#xjwopnT;;<#nSr_kW~gq`q&d&?pfGr!PQ3$0-MqvS|el zrcD3(*Qg)ESti(_>bT13f}wE4jnJAx3avy7-_5V*@D6;#`ahD+`LEBnjl(UjKH0XG z?OL_Cuv%WWZ7kciy=>dIZM&B7-1|QL0sYYH?!K?@E#qg(I~4xc%&i+=1Oe}VFyOX8(O;vw6|TTH-2KMWlBe08q3fBCWZ~5TzQ!5u zZv+Hh1m)LH%_JusPv>h{(us-r2SZ}`Pr&fs|3yX3AEHBB*3ww7Na*iR@(D?>FrDlB@biR;3)F32Su!#2@f<0DtMr{mctr50n_u-Lq)F?Pb>HV{1}NS)N!R zrtYklM%mnR(aKp@bic%6MqPCHzoeV5jA)5NIa^)NX&}h!tbh@Ax^}O(i4DMq|9kRR zf<4C!N?`$kDz#iT*cZ}vmuu3wQmTm(5JEykZiD42$2TvSU0o(w=utHUg!wF(WI|W_ zeeYC^?_CA?uTFf}xCv73ZHidKsqCQSRAt}YLBkj*X66tb!vc41FGpNue_zp?o)^w6 zNfEKJX>*;;>Q5$kF3iTW-H`VcJdzDj0R!J?x$T7Kp;~5fo!J6{Wu^^Yx>C`m$o;T> z-|TR$ePW;zH59qz2K~5b^JYmSv#e)|J9r|K=^WHDbrO0x4{ZD+I3RgKwt7^< z*R(@jM_|F&DAfVTmYHf7g=+}vSrI4jJHQaskCc5%1RIA5whtS#s(@|NvKX^nZad>x z=#}p)4qXR>LIL_%)C~kDueFPdCS_Gl_ARi1NUm{Zb7h#91ZsZ(N{W(>kN{j)vfmTC z6Pe2f$JRdT*GyKh_A}gpeBS3EZD4n*n(Xp4=Xeu9s{6g?7C2#T-3*`Zg~0@j#R71y zV_?2242^k?$siwX@FUN@R&Llgnzhk_`PY2*Qq&7V_fzx7(mH;!4&JEu3}(u%+VZ-(kKL#;qy2 zovY?0tan#-KOY}!cpsxps?qXd{yAk{MSpG@)8jQp@;PbcJEc%hAR%dcp#rQ7sCE|&BaZikU*UJb;2}lAu+D?(C9YjP>|SszGONR^8i_Vg;Y$*~KiszJ&4{&V0x3yUmAubQ zSI(5-qMmp6w!?BHca4mjx}1$Vo1V$vO2XJjP&-Ia1=!d_HdT*)4~+-8D;U~f;|elR3Jyi3QNRzR|M^=PI5#2s?J+4=P;Z3QgS@!Hp& z=p`^aN|mkTJJiF{(yB-u@(#!W%E9N?%Dg{>x&f9+Oi-ms0f1QNNs;e<`}IoTa=*nM zKeIJ#bJ`(O{C{POgbVmSf`U_Y3F_^_-|lQP$$znJk9%hZE=V$8Rl4AkBf!3bv4#eK z&w!k%ucSQ(N)9}=KUbugC@EOf7bV6Y&o4q#oPdv+sa z_aP8!F4OueRssi-)w`T0RfvQEoPyq>RjqrYKWJO5jBbI-WC%{x*05p)%D{2^WLLNk zk0f<_qW=Y0+aehSxQBd)OvsV=BfM2_OgDDi3>>-*CrX}fV^H^o6ZN1>#4qk(Vgf17FTceS)BuGW4 zd{X%!GGVYrFSOhQJAOh02-CHRFrz@7^F7(4Ra1#y;2T`uWtFK7c`-{7ky&5;et6n7 zetdh;9(w+v{F5dmj1-uDP$)3%brN;-07;u+k&v3L&41ZPvt%VGfF!clX^0?YEKxPf z&Lp5eJ;ZL~pb&p_Na^GV=;OQ1G(CE-qjC6sO+?^4H{_}?cVy8&L0yQFsabqK9-$Zl z*Zmb%aRhw`bsk86+6!cHM6zB=;Hx`{0x*{w%Ee`~h^gKge;Xwoiq9nubl%c? zQT!yUc=n5$6`B~a9TyF&y+yLkh6r_HqhQsNMRM;)MBb`eO0}i9LisVgr0iS_?VUUA zOL)emS?J>r?(eQHq#kMqp{>qE8uV!qevr=YaoC%^N`g^V(j$h-kRM@0Of?f zvf23p2Mhb7dr0EVx3t7`tp*Wejj?YnFzT{H(35`brBt~QKfe4IDj~%PV8CqX7oLQr zlj5yqwvY>nms@Wq#UuOLbU+TDvMh7%h4llQNuSLm+2X_2VExO{@iYn?rh?^~(EKw- zNbRLXa+wRlE+q?16Pe2N?@H4Q-RWZFnuz5wz_S#Vt2d)Rg|9;T znyV86Fjj3$b^^-GEcC+vOu7#{>y7qYEE&7kY~1n z;_y9yjk)Au$dh_X?Qd=5LWwqei z?hdV4OPCP)Nl5;v`}5icMKal3T8jh097d)ONhz@zzfNX!3~zTYxQjbCLcGt_a|2fMmkD zBwG%WxQoN^YYBd&>rl>aj<1igBltP){t`hJQYo5}`m5Pmg@Nf=LjYbC72L{9i z<;;4aUTBdR^1ufO(7Fdr85~OwN8 z+jQ#`yogPMNAbB{@&RjpifI*a?3ehM2Rebl)q@%d!t_)ra~@%w9Yn4C3=$V@0`Yu4 z=4`Y3a)S$}-oS($Gyd^@%eB^P$crmmDGjPPj_bz_+9r2DO)vFR6Hj-IGXnX|LLzS(&2@uHYOTUwh7ug) zUv3`&sw>0j@f_97{wP%aX%$MtY>|?-P72N(+aIuiuwe$4_qR79k)Ya`@0Q>{aL+YV zFZBBsR1@M|jrQK_E>`2$v&N_UbQ5vdE{!A^_D~!^dL&6aM?BU1Z|}em$%cZ4q_;7| z%BiT@A_r36aHHL&a?bL)d5)R}SRLoe4Y2W4zC6~v-ds6SNsa}!eO6lr2W*7ovbvg3 zo3q#%TU<35Z#VC}Z=!2X75V8pLUm=%JK3!aNDrO8#E7>$d_ zHU&NSLKC%c`Yn{hJIvWJZO$LDR~>blfqDeXsZ;aY%@)?cWA9!BS%^*$&Kz~RQi($3 ztE`dcQr=PU4h2J|&K!o*H=Xpr&5e+%lV8MVA8m*n>)P?P1rjAOnH-_GXmSGJ8!hm(<2SrX6FY_A!J$G(|X|8}|*y=$1# zXDSK9H>DU%j;p&i&` zB1etq{!~Jb4hW&@Ym0z&3OU5_4~UPIP>KB|&>dLH`U{O>oTCfH3Te zMd-DBQlR_j+ffBDy1DF+d`*~fKpin;=0Ccg@}a~Gj@mX1P=vryRxYj!FlRcs?>)%-o7`$A&2S;01r`xqR84@G{C{aQRSA6=%T4?>tM-K`~sVe>SWZwuCWRS)aI&s(0JZ(WsBjj!5U}6ClGV zO@j0bZE1Xr%+NEQSet)&z0d0O?0phheus>KQvkx9cNlNAbfurrdwzR(45_$Dr*TAh zPz?6;_<9_}E!P-*ilc!>c04E43|Ls4KNQ1M+MekTs>1-1&6L^ZTP8&b-{_ZGg_I32 zaGaoi@uc~3%#01A3xnUs(hVj3*y!G$4UwCqF~m|fnyRbCDZ5*P+Z51$?dIgFrzJeY z7$a8-JmaqY_L6gt%;Lzq41fs1(+BJ2U7hRzz-6(a;b)aN|NFndTuFH;9)7c8cPO#S zyqkp7Dm%D(+KUPW0Uc^R#P9IlcevaDMMH%YO`@AN)aGW!shHnC0KcebI9I+@_j)-c znqo97@9xXA<#%zQ&;G5(HbDLbih0)2$pNJw+2MHC7+w@#$icbx)(1zI5NE<+@X&F| zwWek|8wAW`s`+J!~2yR9>(e#-L#EQA-0z;)Ses(V06W zey=)&?-zb$VUu|xB?M7S6MVe)!|-(9?C)~of^JV=7+HXUdgj$8pnjAhHS+v;0o53c zfz%y(sw-9*kb5JKZV&Xw;rAoZCs!lZFkz&`n36oYcc6l633&N`LS(FJog0UeY84Wt zl7?2tV&p)F_Sw?m_P@huFbP%@Qt?6Bz`;d@xMS58BZrfPZS?z#qWkS4rUPXr?Z6j2 z!nSknH%x^XqC9=LL~1a`i_uUC5c()g6k5t|gVk*6o&a1x`H|iOz)5^ywUBrBav-FraCtI()^|I zCfy>()piIASi)I`0W9%AvXpa4_JWIBR8NyZoFz`UNWP3sD+7NST}D66=6i38-+>;f zEs>W+kVP&+wd2f$If&$ET-NKnD1)}=XYJVVugkT@Q2J>70>&*p|7t}qjLQQ2#tmh1 zjXzi3Q2t#zT0+U2r1Ie^I6%Jc9^kEV)0}j;NP!P2}RQML;rUNKr`>2{GO-)!HB&_{kcHoHyc0{0Ys!=0ZD-0g#r)i;vbRcEn`G|{+ZBw z354)DvPwz$b=p6(VsgcrToxGVUq?j9yK(r8mb)B446Jan^fHd=6m}l^Ygjy^wlxwM zdb~WV)myymnVVj)zw)JR#aKiNo*b+g* zgnDsI5%&t5MQidx9|@9Drk2f3w$YQL=IdWd*wgXhB8vI><=E*_f3&kxK%UA=0ZU3= zj2E*~ulkEDF)X4hy6RvBzHpvF6{J$IZ;xmx^C(Q%eYt9Go7;$yJg21D{$?^n)U+UY zlULu*v%9XJ_9Q3JGM|QbE%s0>Ra&fbSDS3)dHoKF0i?F!#lJ70sfD>5dn+a(W`E0E zot&3EHNy_Rs$;qp?_J%(k-=pDP}4z_K<8@@5)%1ARx1zWEKr_prvrIdS3aY8 zb5tYoU*Pvgs$;8@XU8b=rLgB2VMIemS-Pf|-FSrGRU^gn+Qb=+|wys_p;fXrS; z`8#XNbkELUV`ewW1pp)rA1f7NwaPJw7&HR#5MjblWilGL)8|STcmVd#5_A2@1&8iu z2Ef_@=+_}sIQ@mSP-Acp)Z$W#KoPtfwRv)>ch^R8P$xCFKTuPBSTs?X}9UuKVT*h!fzGu{rvEZ1%oElwC@gQpFCFFPv zocSTGIf&3{v z5U@a@&jcVsu8AWcoRFaooU)foYMceB5c%DroH-IUWD_}>?e6Yip#ns!w>)&`{=UC# z4Dg#|eg}8o|8n<-kn8>gvmOixQ;>fKZmL$+wgrCgcj|#JGtgoO{9RyI z;|v>1SJorB->DjEha<;EtU6tGp!wQgVg*!QpZ-t=Z|xXJr_K9cMYJdN`i3EuRnu0LFZI2oc zjW#hJdnqr?ivvuHHUUa8&fBzWLH}wNaQQWpf(Bh9m9*gTp{UDFV!KK@L}3NR3WZ4Y zVz{ddQpa7c9-o$ZfLBcgrC9=P{m__&D_vS=>_X1(`H(%Do;kNuiS2&`Av~M&UPjd^H@-cNUs2{Jr?akGc zd;-b7piO@VBRJ$@_FkS%fohTa$AI^Ip$xgn&f4%qL_2@$svPxjd6GNmNsOKntER9M z$G%d*X8#?75*m0UUc{fMZ8ug~Rd_J5@|fu5nzf*s2G^l^hNCP)bEjFk zZ}*59?g9M$tSUoat|mmw)yvrhNL4`S^&|Dc;#Iui_v1>O1Kx}DV=+NytvG|w*hX#c zu4V-vHwk}Yq`G+Rwr%H%D?V@H5QKoZIbeC}pWYG?660z+;Bvk2SH&-4E^%(W)IuL% zxgN#*WG?=sf5Fo8u2e=>pYz+2T20Fy?OWDYkAT@A2Gif^@XOA2i+l_8W_}F53JZ){ zOsxLR&7KF1UeC`275qB0pZ{<65+DO8gnxXoNDJqeq2&1*am%e$Rhh853xLF&`HX0M zMR%5gR%q)tJwpJ#ZI;GWGhKC6J|>#~<=2D$HOXK@f2{P$Shhm}s?KsIKNhbqV#)qx z@#wzQ>NqORyx|)n7e|G}@t3GmHshGBcFPHY0MAYnoN9d}kQ~u^eCHL8rn}}k@67o+ z2nl6cY!M|?QFBezhv5Jx%A5s5i{B{sml#vAGA?G{3nKrN9_gT_nP)N4n0rC`#NrmNuFu^64CL#{vQ1K z8&-$cqU&7)v-*Z{BhVvD@RO$6w){--r%BEms${THq&I|q`i=u%yx#Y>Mx;BC?cKsW z{5hz~Om~0a`gDb{?5w9&=e?Ck)hOQ#Ic_6f!dV87VU`d8Fc$BOA&lc+M9et@JmA+<#UC6$QRoR9)MQ)$R}OVvO&K#V7(z&~}G0Z7rTCYbk%Mf5mD! zg%~KX*WYvyqEqXbY%a~e^gLPcJl+5UPLNgHvM)Ff%m1rjW2W&aRpA(;=&n9+P;bl& znaIs?5e3H3M{D?OtyFZONXQueJI3zob>~x^3#zfFWoJ9l{{A#w)W~!q1aiiF>ASh# zmxL^hbH^HC3S^{;2jx*i<)izzL>IgFfVu)a1z&f=VJUEYm8Yi>J<8 zfl40RVWVog6=V8Mc0oCdHnlW}M+TG&$?pK3ZjuC=0)xu=bm_y@S$P?k_~uaTJ`odZ zxpbqxp4F*USvWryE!x0HTU`W=brRYi*sAdI09w4ojm{gFLfCS#1Erim z95g6rJ5fy<=VtYq=|>sX`{F&PYOwN5diTw8fLl@*9pdYpgIYtU?4V4a*2QAC|7ksf zn)0&-k1Yb}LRRd{L-k%H<6tYQE5toLH~2Fc938Mt7Pcl*c6oT+kiuSF0ufRUeT9w2 zJn|JyqBI%=Vaa2E+1h1;QU0+k0$Dl+shK4--NB^$R}Ph%%==>Q55Zva0tNa~>AgTy|^AV!i1Z=nvT-VKOc8b=Ls-Kf~#cwJ^Yun8|mDd6jEW?M6q6eNNvc zByxs%e|z}49+eQ%2_j0H77gJ)ABn(!VCUqJd|Ku_V3x zA+R+YP8~RGTQzX%cIP;YHSrK2Yg`>niU|4FW|6(ro!}S!hX@_k!|IaO7X>jgA1=0) z!>(&_c$N<}Q%_#1^HztACd6?>k7@nR0qTF)e2WQIc{c1510aAe7jYGS83YW;d^dfD zMRZUFrw41#;UDiV>6-yozkiDl6TgsXuVLlLU7sFt^w`=%r7& z()!{_j%GxCaBKIW=vFwqX=BFZH+pAbLzB>Wt8I0y*6Yf1RWY?R8XegD^6n=jXwpRp9%ge6aaZ$D=&Zp#P1=rqkmFvBJcq229*VMt!be z^+mY~{)gl2zcHNz&0x=>j3$F~9FXwgQ9*9~qSN@@3TxewI{LG}^m@r%uF`Ghf?RKU z#B46IAE{qkHW#yp(AQtt&)a{gRJl#_gdcSKdOSrw3o$k5Y;+a4VSJr2mBz!awLr$O zt1`e0Q>+|h`Sc}^&7l}3Y{yuz8Q1V=5?IK8?!}e;XNT8GRNK-+G(u)<TL@^(!wK=NuQ&-D;pXzQ;dlRNcQg34MjKSyMY%@Y!lwH-b% zAo^YB^Q9!*)dO}xBT?x9-+nl1$F$NSi@t({Knt_rp0Anv``Zzt(oAFym55g*n;DFbt|CXb3;=5Vi+=p(B2uqlZ{9^@1cA>FWf20i#4 zR~sH!*{z9Oag|74JQp7G9_NB%?(;K_9PYT{QYq~lXC0!CpCGtQ53pr zj0@3(M|L3F)@xh zvY5XbqdT&1Y78A9g$}k$$$sS2$1$E%)Uf}(WT%%QEOdGfw#CXd=_QAq3f<0-cF*o` zpykIUKj@*N*odUOF^cu={e52C#zG-}=5ky2cKMMfRKXo1+)=2}q|)^49sieOd=vLR zRIPUqRJ92%Iz5xUN>&$YkL0O1auKm_b1&Mt#}*xx$)8+lM_mgrRY!way2*l#WN?kD4o+A}marm)&%-?1>p(b4N~q z1&mwlkJcfDt)OFm*W?<;kNECfuX#%S5Gqfe2LzBW*R7C&5jy@l z@|O|hsIM-WiMhLa;-?N9@@+6jBbcp4ru!4iq*s zhwblON}{jZQn?zhvcs5KbNzXJ#0@pxfM^VlMDfc{so--4V+k z0Ha8o3w(9-x-H6zzY&lA;&<-D>kK4-Kon4=q8XAe`mpuV8~_BT*z2)h+5vM~>&jxtrC8snbMRZ0klEauw{|bJtxx#<_Gk_ASjIbk^AA2 zCyVQ+*IRr)fdQq2PDcG%^ULfQdeAQt&p%rl%NA7^8p_)2Piw%wU z1HCyh0DNSt>6poL9yy?Y41T@Hol&PQ0meUoX}iVd&p*rpXB}a&h6D74f-#WYT<;#s ziM%FrNfRB-&!}uS?-rUlC-#0h_3l-8x6vh1a|ai0YnVF!r5w10$&yYt0cdhj3hQ9A zMGKm2wrh<^`I`U2C))Y4wj9`M`?nKM)E^^oKxMc4j4GvviI#Ty=gMK=0Fa>i1;LTi-h|6^ zu$x#HowI`oNhJU!)YOpn&IK5z-}~T56r9?B6bpAo*+L$zpf`D)qlCyuW1&-t(Gv<@ z_AW+kO5l5U=q`7ozuvc^HB$Ks!-j5$@~PbXg>fMFg0T;A?Y8uoHN84v9@QC_W)dqC z%_^*-)D-}JIq69HAZ#248mF)Md`y-5^tSXWlEe|mQA6guF*9(VmtkptqAoc>pzR&2 z`ohH911d?WGN~LRzUbn{UeL*<)`GfS!OF^j6~io(R!0FQdqN`;u2|dx1Ixg$GH1tB z9133pg(TD)kXN9O@$JAqfkrVipnxy3Aj>F}oXKCHKME&fE96|VOXuSD!u|FEc&Fkf zs~czWPqxHuTU?_CNU8W`*VC6!`)k(i4bU)*$N&RQH(W&*oA!2F?l5a>rp%U^BKZ2i zpAvOr>#)#d=7mB)!^8ySj-Ojy%Cg!_*)k}Xn61B(_ti>-RMQ5&qEOhI?90pR(k3e0 zzRc_ul)NMnq8QHX{+5UW!l%UdU9JETTq6|oVP|t7n88O&D$c!2?t;wWGhGZdhP(6B;i zPj*}D%nE|=>)DecmJQN3#I?^$phBvNk+LLn2NL>C8D&@Su#3K`IIUL74s9R3X_LqW z2A_&q>hR|9?e=7!S1Z4E$NVjtyKye#Ne$JwIQ4J_Zh%rM43eylG2@?F6CwwMx(i#y z#akj=o49^fex@eR$)>5YTH$a>BCi^P;9+}_xfE-Z7u+R?efG7zm1ywx+s|2lqSkpf= zDsT}L9hb<56pMtsQw4i7iwpp^@1||AKR1TJ3eT~TK>Dx4#`LUE^5Vct#4wwrFmf3otC`F1pot z*1)8Z2P-5>7uGm9ddysRW(G37q+evGw{B0DttdU*UQ*zW-;D2&McZ_~SVpdKZ!M@P z1Km0y?O1L`o9joVN8^ik7`K&YB(f&s3;8^52?Rb%SPfA?{D>-yVObcWTH2B;Js_+sC zDGUxsuvJqcJ#tpV_+_b9+1JY-ar|u2PHPH4bzdCFU6fP;=#|Uofa6_ICTz?$g=iI5 z3iB34gW9Z6*XLy~)82t6N3PmKr>9@Q4DC^C7s{D7=vQ{w)^70rb15C2s#i=$H{Lxw zLg=?`kK+eP0Nf#Q#1p&4K!SzIp3LODeUJ8-$!1j&m7JndxS6%Et(r1Ge>!zpnbC?= zS-(_neb}RC$RN7zYmB62e)qmdpA1mXQfAKIN|%}{TZBk*pqzbL^xsJeM#X2<N740 zT`4#Je}wOti^R^)AcA6=C@e6B24ez+JpApAaJ`u8CX}(~A5q3C}iUYZw1B#7TINep;R= zKWEcr&rq%*ma(5rRE9y$jN`XNu!+vtJN+L$lb z0PWt@DGL&S5VUKG(FHw6d(nz~+pf;O^}n}qfKVT^VjYTf`1N_M^%=R)FAU0(`dG_ zzfnkp22nY<5$L{%Zm(?YADQ=ShIAAl83^B5Igu29$E`O9Q;MLSIX|yW>7~sJgVt;w z!VRZ%bqk7kr3H`7MUtQy_y!?F2Fq}pn?vI5V0Rye#kW2=J6ef0a@cw3Qaxd26 zF`6nh&)1cxCeo+H$xeXjj#>;?g=`B%@0pzXb#??X?iG?}&o+9ZxOmtlyY-%Qr_M5~ z?iauT*mI`Q8X2{RWoie=O%Wd&6j<&bl-@*j?d&a2Q6#Ld8(&WqZn5_%hIdCKju<;? zr@@H2teH8Aj?PfgK@oaw5TW9$km6iu*6G$G*vxu1T@xeG?2;7vw?Mtd!9?a`v7o*) z0T8P@eSR*R=fo6BRG1B4WmX5pg=_kaFjJugU?-Ow7oil>^c>e zxxwAu9$ngFHY|oYS1UZ2kC#KDD@VlqFlg#EeT{G%y5&}C>Ak7J0!n7qo0N#20fwZ=1wNw>l8T>0I^iZN}*m^OG(Hu z$BWTWGmCKBza#zs_?;o$PCCBg_IA;lD`}`AZ;YEP>)+M!zr~GR480VliQK%m)!>P3 znREt^A&Is?O7GrcrPuZtPGbKbsV%4snbH;>6k5NB`3^ihO~g&yJWQ2FQbpE^&q}8% z*{d%qgrjwxnl&EGCTk&@M;1tY*)yt0@2<`nCnAlE1g(gT6D0?47hJg%=!{hMbh%PC zf6DJJeXVtqEQ&UqtmSNk+>pSQk z@W7yu0(R&wz^^)Y!<{fiSoPU{a)&8)@&b6#RAd>+>g59kn?C*yATbsKJ?+nn4x&_% zT>(Qo|2?c22sYpcIS3`_O15urZ=1zgGL3%XFP1*oNCq-K=3hwWKO}Y;!lh}@XFe*+ zQ!uRy?J9E4(gm!XG_ho8!hwDv>mRkwm~!&vmk(a=o9>pC>k;b_@u3k*>j=5vdc?$} zxzxnP+0?#Ep#d4enxB`}*VoG*-rvnTd%xsUrgYz`g;BsbCUYH*rN5myTlXKP0GCOr z9g`77+Trgh+h-fNHEAEshDfaUgw8a-khg*!?+q_50Z>q_C47T8BlZAq*L5bC3jgAl zPn!6L@2iIa+rl!VW~jzrTfS_abl83mCdS7w6W>3@mS0LXba=?EvKuq}1A%tgotO5* zwEBf+_|#7Tff-QT#qO;wHkMG>?od(onZkah?rI+KY+#a*2aPdN=W%R{zA1)hE|&P(Yhf-s7`-3Q;?5R72AEyD0)44EK7MWTrmtQ zc+iZEiHVtpt=VG819*sH46^+&mT1$l9ZS17WXcE60YP^+f8NE2*)9VMN<$T9N~tc7 z*5teY79cS^yq)Sjfz&joG!>T|cgHkasA-I5$p^~KrTph*d`gniLxpk(a=Ez>E29eV ziUQg+47>VUd(*qeGCmWoyL?X*aV<6dgmtRl8w8?-x!^0B7Z}-JK)OC9>sx(&@IvQe z3lCApiCKf%;>!?0See`Qa@yS^NL{m{-af7GnQ9Bk_#@YD3^sis@J%U>s}czSgwkddC5IzU6cH;AW{)PkR~weHy>k{@WYR_6Teo_|C_er6;g zhtwB}nU%Uta^DC`KY5XGX?~T=TEcZXP(*_SW%yUM$FOA!;!C zl3&a+4(8QDXuZ-h@Z_Fa#Ohc*7(Z(7d)4JjePoUv8{+Ehw*}sTp+Vz*?XuVVqon8W z!>dDBcp`k?Ac;)SLf&>&@Z^ISdgKy8R`Sw$QF7W>dRZ6d-~k~E5X?rSJ%xj7enMqd zr{wo>b%Z*_6_Tv@*5BiI<*3fQoMltwt3CHOw8%^rChXbF$G%BXn{s8v*$r<~M)8?l zdv-Rvuzog;yKVh_B)Pwe1^5Yx zfpSh^)BmN`#MJVBMA80K%zG`rn4AWxX0g3)oLRRlrx8mCm4hYQ2B&xzd=6V0j%n0k zFq_h#=3lAhNSwjjYto11>VjI%R2p=+ZyjA^_*S2$yxYh*wjjMGrDs#kBDiPQX>)|D z$)AIB=u|6!8EZGzkG4?!*Q2Xo!l%r7A|Ux-wOGGT1S#?_Yvc4AEz<1kNA3R5_lYp^ zt5swpQe%s}@b3-j%R}w}(ILkkm^P%RkMSL~42}PRwMp+T;NcmIWTb<}Grr89#v~j= zC_xguJK0}}Nhpnkt<7VZFRhmbu=Wv7=G% zuQIekXY>Szzf3mgn+Wnn{E;K3DXq#W=H`=g5ZqGXzT zcy|b;2hXIL#QU?7ZO@cw9a~(W-B_y63H~?Q3Jzp5xZp9%8l#E(~dJbAVixJrpXWC9l0`(LODb_jks zYoJp2tvT6tis^OZY2xj?m|3Q|@xhAxwv{(qPAw>ofUBlV^UG6hX2UAV3;ia*{6ZRO zw!Z#;&Z=B?w%y3P+&HHe=r&i=`)U>ibn@li5|2-31niG!+C2-I_Pbv7mj4JG)9N|9 zk3);aSIRN4i0tfIp*2!h2>N&xw^9FM%|mgPN#A4`93C1PdgUJSZIw=CriqP$+egu0 zyvAAD*V=5Nd&}5rcz@i+)9lD^eYKM5NhexhhZnYip?8Pf|5M#MIXmb-Hg^Y(bqDy{ zV(C8S4Ri9A_6a*r(-&%TPwR_GY@5ZU6(*iC@R74DJlv#M0w&K?+NHp-u5G~WAdt~j zX{0`e`N`!usRflNq4N_B+0AHw%@8p=LeTaDK_M|9@vUt<;r5w8zb9zA>kI!=R=fLk z<^cP3Y_L!&k9K{%xg-fyk(B>gn(@UFmh)g^C2bwp`iDjk66fFzx1T>1+|Ll)A11?eRnTxrOk2^CxAU&6c&PA7o8G6JxI0 zAB+pVyYqXs{-8FzC*q1qu(GSGwv_#by74tSB2vRqcCNpw*S?(7oWPK-g?wU5kWT4BnEe@x0JD$G6qBz)|#{bRJN5XcVKLt9mqgCs&L<0R-nVrOiQv(aU8^Vfhg zr>2SeZL7`pGp64z8CTEyL&5}sjpUkO51w;&x=KQ;B*(M2#FTF1w(st7iR{!rdW`9R zLqHdk$>(z6$U79~ppb=kq zLKRXyoK$#V3cIA32|M=;aHod2OP(xUa8`plf7?B{wiM=g*oiF6)s>A{GummF-%Raf zwQ&y*G)DD(a(SOJEUUScwC|iR@2IOHPxR&jM#)RkR~&4S!WV@?YjIXbo{t5 zt6EXr;e7~Q;6K~YIvqlz)L*s z;4FU|cs?r{K;>EWnz3Gp;b!r<^0$_z=eQ!s?=bw0j%gx}_( zoOu+{Am^f_)vuWoe6Wf%K@#2{+|DWJY4@SroD=j7cJ2+u0kjru(S37}=sMV86n8Jy zOin%X>m)McJ~~f4f%aynC)Fh4?D4iEiJPwUqm6ProN+7H56`pQ?K-;=H_yLl0EVp$ zN0gSba@T&uh^hqM|w$=#9_@z*TrNZN1(mwBNXeJBtNaLoNCxod<unWJor0f}Y-Mf|5l8z$$II*fxvQP;;_Y=? zR)gl9x(!z;CF9YB7Jma6g4b;Ubh6iE(oMrjWw7*j#*Ox8iEDTN$p+UV^`F3V*z295b3N$WSA!)>6Ld8&=IN zD#q)#vr7*d%yTq#U2y!eX}(l<4A&BH%S+fav``8gS-_0ER1!#IzsI)lfJ%B9pLI6r z#GU3-_Dhqa(9hA=-6$?-%Z^iyJ|8ta_O(gEkp3)5i;N*QQ5zF{-->xbWeKjbXA%~2 zca|ya?_zg~BXf=-A$vebN2`kE+iA$iYNxs11sYLBMA9{xxUz}hy{I)9?a@e$;|Pxn zCN{p;%FbaQs38gI$cQ7!%+YZ?&ttOF;z}<`hPx!$dRZhIyFQ&ar2I?A5iU9@*f+ah zf`enKMjA@EUglA(GK_-G;{>%1lo|aW@(bJPR1L;_Yd=|h&MzGbi5K*jsCpB|wPu$= zEmvC32M@@E)p<}O0Cu4g7v;ZI^4(B{0NK;F0h} zfD}+1deP2DT0`le)oSA)B$8J_ zX(9SanKwjA_O!l4l;EuVaS+!8ORSQxbNhjwZ=&xTN<3p&P?w8_XhdveYF9HvFP{~Z z5K5l#oU@+FchG1Xsp~Wr!|S#Wgru5|@DeU5A1D7h4^75LCbkG<`OP|=Z6g*tjY#T& zjsd>c*;f70j)6>z-iuyNe!Z=+{zy4l&R1?k3$AYES09f6dxdsNr6J@UgOk*xqBU2H z>Zrhhl-Y-)7w7)N!n0*oh4NoGoJOJ+Q+|=r@)@uH4u+B>pbHin(8c8D?_BrEzl-$c z!}t$_QJXV)yWUnOx5`!b!iU5VWnTUPMFb+wwIWSnytyWXeP;Ol-m6v?~9JH54s{+ zy3}-&SjGAr;Ajd(QKl+9f#JY_+xh*^$7WS6ESee@W=na#%8xo$_)nvh6mRL8VV#m% z8Q?<=&#Ei6uh9$rtEn=jGiUk=6c<|P5V9t_Qbe0Hi3jGWBJ^}E=tCMs_%;gA@Tr4( z#L)?5)`HQN{P|y?2Br05ScfLxsbZ+X9_U7t202os0i$(on{yxi7hb+tdBi3+d1UVD z_OE~GjKqDMK&o>XRH}A5#fQjz$p}9v%k1=0mYzvbH6l8PYgQVAV3%i+;e#IUa+bx5 zepcIw&b9o$qsT52YI7@26{Sf1ZSkw7rxIHPfQtm;1@C`J4l^Q1mlk99!ipzt`yWZ? z;Lzv)cJW%v_Ofl;TDEQ5UUseJmThj?`efTSmR-;N{XP8+pYHc{o%6cR;hGNbx%jok z=A8s@KKV;p8P}%!J82|Y0A%n@$qL`fb^lCIQkj*M9U0lKLK@SEZV;Lth4N?`}~vwyY(|3$ww%!N8=& zMbzLr6l;(%G$8YiTa2RS;Vn3NJ1Y}TcI(f4R8ynhwbnD{8zNw1C}yQT-tUPB*2t=8 zt=B32U9T2)ysDVLv2!xY0-5FGXv*h?&S<^*@kzjz{M`7z59n|%9ke=i&9Km6YLvMe z9OftmOZE9%9Ma08v6~}lrI0u;YSbtX@TjPQ}?T3no;(jFf!(;KJd_} zRS+Yl)9?T4H}>8c#w>Q|Cs__ZfN&*c&e~jc(I;ce>bFr+jRHd?H1L&UgNpcR3-9Oy zgbP@sN2<8)`B5NUHoh{=QpZH;Ja2fzALY~h)*k@`iZO#j?lzVjyUIA@8O)bM4{Xp+ z?r}o#vz*YRM=VW^1{%gNNWge1J6g2Cy{G1kwO9$CxwbFeANNGOep+P1!roi8p>=04 zlLZeEr4Cra*_;^(>ZL58!_v=`xZU+DPZF~o)fWyNecHA`#%qsFfr&9yQ<5Wana%ZS z_tem(?vIA2YJ~l@7GXtDH$^8u1({g;GikOqab8b{@DxX!H<(_WxPp)#gRI$!8RE$=8Y?M{QIw#gI} zmD7UMB#H|tuKG;LzjC!I_III2aOCfi#4z*TqtQp?uvoou zs2=>un2T~@=fOIB7A=wjxd+|}TcxG?ivV8*B|lmZ{RDmPV4nA+FA$qyHKOEldzTjT zJvv%(Y53fl;jucSwSv?J`P4D_%Bf?Z+D1%1e6f;ynWW`hG_UH#=-Z13&Lcie_K}fe zc(>1JqeGOyp;^u~ESQi27YqL_ioJ;bPK9;z9GyLoKCk(~WByy2d%T$aRZ!YD=J{LQZ+X@E zqkL_1FWkN@e-E0c2wUVVC1f8Kz%O}#Vy&~c<{L<_iXSn)jH~;5Yg4p{&`N&Pj7Q=I zEH}Y(G9Lxr)@6Z00eBPzyD(7ledb^x-E5!oo4V`N+>2@E1TopoPQ^%-9PzM*t8VRL zUvs^USUE22Nl@Pjn>ZS42oqL*1NB8cnblwqryXT1L!>|5yZKV0L7zn??>#G@`BTdH5l^e-Bc2gdq zMj#eK8)xFoV)GI;`m%Zgln)e#GKn9h$21oxDpeow%zwoO7Sh zCcLs&CKggnR6bB_eZBS|0x@^@65Z=7o8I{EMZHWjHS-Z2?oi$QYPRt1GE6k9->9J( zt6{A-oY;8VakZvq|6Tp}cH`*BUr`=w==Q^tE*DAs4Xuqo{FqFY&rgK%BnJ7q-O3W9 zG`Kx@XiGH$`JRs5{{+w}DMjT>B*{Kj{Xbq^ByLsn`A;Pj{#(|+;r=H(rfvt^yokM| zSkZV$_KnLq5$Ltvq;Gbr`U$h%X=N0)yKBCc!=)0Ou_&W<_&+Bq5UEUhS0QHJGZ*8x zHeNd%LY##bAFo2y)GHuWKV2}~KK`rWlUMC;U|q0M%h(G4SxxiEoLcSbz25R^A|ip= z+gw|A><$X{cYB|=2Ue$G1pH7v8b-Zrr!hLQ-}ww>T`5Y$Fuf-y`Vu_3lr$7*)r5Mg z`|z49<-!JdLo>fB8@1JfmnkA&Sr%Q;1lmY25N-P16K;CJ4FsNYMXFqzGe^HN*tbm9 zv-SpFk-Ma`g(iIcLRC2V%l@9_kGVneCj4Avmi0Jp&TD9B8ANj2y4}UjBVbB@%TbyE&*ac4OpES;%ttkvu}|}5^ZV8)fSy)m4D95QQj~ z_{!*!MfSK+`e-+zgi-TC(&cZJI^kz9Fl9*3wBV>7qi}^83(E~6nKW_z$0*%_SYUT) z=uO?){SC=T0vlwLEB~wN8C4`?#anK?5>!jk9AuS!$vDzJsr)T|RwLe+4f>zM%Y3*p zydK$;edELZ+a6_r6-y0)a%)_^l>iMp9*du;4@Zp4Sa{_wHD{b*WVH@cp8Zr6Jr9Wpx;;|V+_Ek^#^Gv=I^ zUhE(UZ*yHmI4Ezd@Gad$0VgW^P0=xe)X#I8p)sWuV3bX6>)DgA29=!;h?ZCreD!22AGP8$WA?X`?cLJ+ZZ z#@IuxRNMW~WN``PZFH~PP|llKkOq6?Qx*B6(!aMgL#;FaWiVVzdB@>`%-L@bpVhQm zGc#J#IVZ%F-{p|=!*XyG+j#P!=w>rPa!8hS6exYFb3|jpGK_AO6zmKk-)%NbhIS28 zJi{3nEf|cywY_dWo?iX-VL9vx8bu}cXx4O4%IVYBlwn;<97`B?MZgX4Q*GHpMP1xz zg|N@S8~o(Mu;V&g`x$kE@07DPg)<&5KwXOkTHNN3HD=@vd}5XGs!hYF5q-iA0pE!w zxy^XR+4kSGe;zg~G@I59I>vG4kNaC)_l1c<tL5`=xy)0?dRv=FO}XDO8Y-6TAMaN}7UO$AZ`aJt z%s#~LZe_$IYe!Nk&=+VcQCfx9)s0)VBq1g>RU2<`NI{)iCtuV`3hLDE{xQDCr&yCj zr$lYJCT?~w`jHei{At7-t-dt$m%|nx)LexMcF>)<+Tx~)4)}N~PT5l5C}72D#?o6G z>sYiM=UAVtfn@q6rlx&dp{HH`jXuN^u70Q4F{ew6PmNlM;CLhHVAtc)Dm`%EwYr@~zDYkL||0G(CXKl_#bEG&H_6P_J>lwUu~&xM>A(Ifsq4)7$? zM&EiQ=JJ;$QgwReWtU-NG{2l@3D=AfWK0h_b6isL%zK)8FSW%6s9;C>PrR^`Pq~{~ z_1}iEA1$k$3YFuzj=NMc4{X=>hPBW7LU?;17(HM4$Kj{L6SA zQ^m|i8F)mBf*7gNur}!}pjlXkTen)Bgf@5rg`|TBrYP3ikzAo>qIF%Zcb+rG5e+6f zer-md@6OI%Yp9OY8ZvkKGbVr0EVC5{#NS2`$)%eTCITM;Zmi`y5Fp|WH~_vmvo|l~ z2j8TeZB3xLB3ImgiDswX_6zN|i!C38J_A<6Y!T8N#V*%?N0#a#4`b^3l*nf1@@Fp7 zs6_2T5$^r(qHo6^Z`VmkQgAZ_PNuohf>VyBN?d{_7|-|F{h$WnA09A-qGO$l((Cty zedo)2`=rLiP`;A!#q=u;b>A{yH8JSof2_Gkebfhf-6obi!XHLJQ)?158ZA50L3uDP z%UX&%_`I%!taQuAKNqImWK%djC%}Hbb0&QWp-rA}xpO-CxqBsz)beJ??={#3s=%-R zZK(ba_gs(oT#DLI0=A~a+wYZU&<+!SLtyB1a)S`tJ>Gy{ZU45=Ha}ZlRmO3^yj)AW z!U3wAGfIfsZHpL>RRSBc4z^b>DW|Mjx5&ae)Np8O&U)Dv@wPB2#!sG7T)n$0f`;OBFVYiFD9-+=HxLNoe=~P2T$GvcsHeYvcpM#jUI~ zvK@core~B)GLz_irsh%epaX^ew7HpWm)9L|rRPSi(xNrX9#R6s_{fKSIp;hhQNi2u zOw}bUgyXMJJ$|(+U#SYO>~YRI#_P%K6n}jp)gutv4*gsja;DGCGz}b3)(9_~h|GXt=#3!+^ACm7eq5$xm_`qU6ta4IOA#mJ#L_+u#&syC~>vBCY$!7yGP zoyHFsF|aQWqcmsQ8m}4teLSd)T5BioRt{N2DYf=RiCbaIsmh0hztV9LPAx!kva=aE z-w>`kc%MHHze&|XOsYqTv>y6JnBN7$?y|Wok~EpRBYAmTLE^DG)iP}!pmznyCaidKs^pPFRg5>bAK+ zCiv#-h7QO(a&UW-JnUaKqDcwuI?dkr?0guRBVgl%l0a$rQIEZ95?M{$F}!(5MJ%dy z_@Sz#$@ePX0kO^j)3frj6eWpl&9@Lg(J-Uy-aPc{)zwm0>%;_TmFSs@siug!N^U_8 z-^8_PC*w*JHf3A}Ye?GHWYZXh2ct``0`lYs!aHy!C1~2Apa%bw$igLOb0y8qW{F#- zwrZS^v%7CYyMVKP4R7*W-Jb=uhJn*zx5#B(&f2Wva+#V1fAgi59~B8Z zN`9visScjLXre$9Q~y0T7Ck$p4IMmQ0F}>8mz)?dF9cYz@pgWqy?#8!v&lUzE`*_{ zE^%F%inON@Sga)m@vSs*Hbl_PvyjHb4`DRkldl=;S;cUZNkGzTFZHHqo|>zf>l$27pY-vETn)#;9A?{X=WbG*)^5Sf$kdl$+UC!`F{0T zzjV8lV0ibo0{?RVj@>zN9Ryf0gZNL8kv}3|PBy3>-XoTBkMEP3?ZTi2zB`MQvL=^n z1Yc}LhW3GdA%K3l)KiQC0F}n{pHf^H+WYvF_Ghe1e_4<6=Shu-Pir%qlwBNfcISTk zw7JX7wZctaNE5tkn_))OyAx#o+2oOqpocQ(^0&C9Lq*XJ`G!+dhNk>|xC|7~8?}Wp zWvi`W%6Koa2PuMEDX`6*Yjn}y{z+od?;OAGagBm``E-y(Msexfh;%<-M_lNXwfe zsn<{aZ%qG8Q;-VcfB7Oh;qR?eeqduvB!JN-7uU1jD z;x3DD@6~O&aru`Q?L_}v3SCMYBbxLG1jK6@>-u~;GBl=1M{JyXGvM!o+^kn$lASDfnOb`zD8w+=;V~v9Fl{jk@vw9MF?#xA zuD5=n0(yu8-)Z#uToWqhWw&C!>;Xk3O66~NKy?cx@wt}YPblw-z?lq|PHu+W!PDuxOB% z!#SxTWzB~0Jb~#&EH+J)Rg}A|RB_I|Hh^NP?Dpp;-Sy-I`PS#?)5#Ln02MGw7L0L0 zU<^5Nfp~(Rz>%wiqB^@P12zMNW$D?G1h0EP?N;nZaNS?CJ{uh&*~FRH&zSL@n!>eT z)qz5Swpw%jtjU?FmG`=-N)3{)0dk`6&nkO-`2)^0s<4rObWCre~s zhL8nLcUhQk=twhra$dWn+@(tzM@}`)ZyK zj(xDn2_U*^ENCib^YDwQv{wEo&c^}LwRPKXZIxZ8WM+0YYn8AD3of}3pr+dJ;??VG~=WR`PCw-$AbHBD?sWVlh-G zrmLbDbm;CR8)T3w1?kEp6*MkCdJb|ndn8siC5KfKbbKPC8rurY1`Et6b)Y&0m0FL{ z&8c-8F*r_Ve{31>*e4A=U~{Vu?h42(bC>4Qy8US;EyN8DvDh4@kijnNQ|BVJLh1Hr zJ*6kmuGO7s}rPTY zdy`+aMdmN~`k@G@L^+XFG>0v85cOW~#HNEd-5Pga7ddM-sc&+^taTC}MVAxmA}nMJAUj}0q^`u8*8Fnjsop26bloCh zUb0b1SzYEV&HwPXz8P}<^RG{C^}(1YEa!$QjpAY6Tum2?Bn5TAL)=w3S;8+?%Pyk5 zy-^hpD{;OoCTd8$P8L)%8`;K)R!`80+3Vb8jkN}B%~F7iiB`X{TEc^Vo`klxqX2SB zK<9DYpOi%Bj5QHMXH>9KDFwsg8w~P$;S|%YOD7Rue0%#yx?;XwIhp)wgWrx{9l>t; zNx1ErA*E~haA0=W>p*1W4i;_E&eL29IZ3D09Ln8ykn6`oZ4rZ_y%gX@SK+As`=Hh} z_dYs>PA(^L(oP+EG_MsL+#vTFRlyI7R|AE(Eo#gcBn&}-9u;=oj(ydLrdfYW_>MRd zbo5=%Zq43~@55Uzjskt@rqYm=;x4DG!7!{{@;j+DZ^?{)E}B*`upc+q+m4c&vm=Hd zKUEFQ_}l3U<|wWiaR&%vd@ea_-MV2nGYxK(0NDQRQP$x`3Q^B-w4-65(mMDct$m7)i*2Of)!S!K1}rw4qfWHGr|OpM3+{5SIik~Z|~OPY^YB$vEfI=-U@ zw*x2Qfgf)I8?Cp&qbY3T$jFG98U>NtV&l#aFVE0zelLOx%!^yaMH>F&C!&3)!Tq`l zi5tl^$pV&(Zk>t%+*1JaS$+3DhOBYu5(av%!uFGC$&yYw+v{5arA~) ztR)Qgh*2h@kWd1ss*7{Sko_v}fsyGns+}0#p1(&}Pr(Z;BZ z19^W~ARy&0+m}9NUDwA6tPtnTeYdTSLXNBwR$y;P#cr?NjrmaG{?@2w!9Ycz-!Tt>yk`N*@AwQ1pPVEz{zVy>*hehZTsl^mYP?Qsi0zi%u}=$pc}WiWaqYgpcX|PJvV9`9B>ZpuA$qbq*2iGR^w0Y^VR@ z-J;%bG`U%I(ARk`1p;LixBT)7p6@^$rP4r@sdNe*cCqae#@L>Ti34ucb`GDTM}@sk zyg6xbfBWRw-))DP63(#0_#(xt?gr!yspf2BQa_(BGi4G?uB)x$JL#)!Mnu~8jzw%U zH5Jy7lyzcO(6(Qi5~nn$BdHkw}BcN*LB8Vs53n z&zaVy%V=^sR9rciIkR*yrNbe{@1@}hyi?%~JVuMtXAgEcm{S-)FqpFF#QP;dWxC4l zi(P%aHUMw!bSkHKxnIREt45?}rV@ zQ4(ruX=hkVBIAa@I)in6MhxP`mR%4^cp4eENw`48IIUMe}iKL1S(%Kd)+rIn@;lKFEZ_TxM&#?Q++we!}* zmsYu+nkTcR5#LpzN6Lb~VU>q)OYMy)@?yJ{TH&PUwD8xu4+fg)AZWm2?m1T-P>(Jv zvxaLOnTa-nkF5GmmDSp8q$!IE*FqpVQT(Yq{%7C~fAw~rSBN1unN9VTXb?n1N;8kA zL2}c4wcCv-0^9Y|xml|3v%MT11j4t~Qg-J}^&-?h4ON;$SWT~8(|+LzRp-@tShgsI zug#QW@=;miR&g#fM!ShT`=#2oMUnS*b(tnARjv6LnP$IV?mxZl(7oc@!KJ`mC|bm8p@l({8u+J2-;GZ9Zo@w>Li7^EJ-=w#=*hK5W@Cer!Hh2yg4dS3Hz!k zzOKF#OiX6tK(a4Fq=-_9m_li9d%Z;z*EO(i@dC%FkmjyM{wXN#RcbGYMp-oCTp`S;ndJ;9M@qXZ)CrgC4mnHlS4-d zPpV`iTqc5(ry9x7T}0To+#qeMoqE%r7ELND53vL1%*}=@cWCH{(s)3QSi2{hW)dpO zV-IFr)9h=h<3W}_XcRdp3CuZ%@)tPY6VSx@GLn0Zpv1UZ^y(Fj!=mJ7vsOHV@bO|T zH;_!~2~ZWa4(g9xeoF3{g569x>qQNEAbf#HkdwcyK$|2;K=4qo1HE1+GdPSyUuY4q zoBkwjB5Sic1ZO&wJaS<38W0WG1`U?SA`(qbntR393~X+HBF!ib<2|18hZqQAkTRo~ z_7Ao=5hvjlu=YIHOEV1`eZ1|AvCm0Ta}Rdc=dz{00bZrhYu-=4QxDHoW?SgND~y5R z-Z`Hn={2A84j4_|$CiKXGZMXs)XHy}+`$%dPggbW9gN@jUEEy$P?}b*(nyZvC5Y;+ z>Xd4)l5!MaU08;V0{p$5TD0;?=M&gQE3sc!Gcs;jqCbbpDkX_(r&)jvi?Bbc5RC^t zKQXP(gaTl2QRzGTd{X#iGI#U_tW8R?`7@q6Io(RXiphF}G0kiQ?J!1~h&+9c~GD;8o#L?&o-`#e&QeCP+a)ny#DJQxK zcT+g50)xI%tps9)mgZAW*N|aL9&?FPBQP|ZtE5ocm|4Mg}dS{5S z@9(=|(U3sx7TN!0!LaLAGHrcj4P;zWSSRPIEZ<+WsW2ms-Z9s0PyKT~r1zg-$Z9#0 zWr_|$jxVfFO-c_*hj@K?oBOEZBa?5wJu}QatNTyx-jgt^Iv0g?b;-B_*u6q+&iuD- zP^;r%?}(Y2*fkHTkl{{ijt3*gCY>!;r2^nM8Bf6m5MlE{)~%K&NMUE1ZN>Bf)3C_Sk>yMW`rHZ%M2+)+g}mJn2-MtTFXRvfA%BJ=eq}&Ad4!VR$=NpD z3Xw*RnCi;{Q*2X;T_!A4|H$VnoGmEAyGXwcF@;V^L!eu>lx*$S5UpZe1 zv9tj6b|ni2?ljb`M(<64L3*#lN*0?pFR*V4Biiofs;`j`lQ}HCqx4Cc4x8`Lg^0@u zWS_Y9PkYWE7iw)(Uho;M9h${9F%5Ioy5KFQ(dDgmo2E1o$n*NpQ>)1Pki`1$1-HTj zeu&EO&Sze4_t1B>gxF3MvM008`S1SAJXLJX)sRa!S zvg8>;Eh>adESZ%HbC^bX$_#!JVCGzwJyY=DK|8~Pyff*u^w(4PH0GZOs5McNy z0*a* z7cTnGapfpyaA!ct)6H%{wf%xSlDHl{Mj)ahLtD>BH=B>g|L=+Y^B)^zquebD`@&}| zeepWAb-W6(LrPf~gp6=_7|cgcDd~<~BRIXRqb2F%cV+&PSZB75qIdba1iGzByBlaR zN2Jxz*g+W@+>4h8{wyFAZ>0KYh(5(}tbA@phP*$u_b5A}d?u?< zZ2>axm=uBU_t=OxSa23Q2%Tt^epRS?nE-)N-yrF*08hkXdLibRL!+0S303Q@xD=1! zHSn8_8y7=-lo?CLj_R3MLYri7>c$-d=X*`{#IriKdJlEGC?ftU9Bld!|M)@Ue?C(kNHi zij>iueQW)$+TnknI0@RAL~&*X61odY6*1|IezbbufV%QSrV^Z&2+3Q~3;-+cP*EIf z-F%+ZvWZ@;uu5sslfMu&%8nsOo-OqFLy$sm9EY?-zEUaG4i0p41gxOrEboNG>}Mu& zfmHhmUH@@Emzsi<;&=td5yql~$3V6-JJ#P{i(6Y`5>Bm!%gJN#3s9YUd@AwlE6>G; z)I19qf9We5W`*M=&@~#r2)N^q12=ZSiI+T~L14k%g_}!*g@T;(AB0hu6l1S#VT19o zTXv&uy<&n#Y2(eAbXpvEg)wGKO(@|W(fqS>)7eOz7EY4g>{vmm)h`9A#~Cy|s8u?Ia1Pm6KjE9inzA$2`IcNZULASno`(Z1x*%(U~e>t?|qysMs6!X)-IrCAr-2Zu5KS6CW!{T7=9Kyn1)V4tU_HG`)b0|vGFEYWn3~E`sMJpNK?}`ofd9gJ`~Pi zKbuwxC$6jIhcoxEv73l5X{OL(76(KARYj{s)(gA~-tD#@3Qq7 z{lEF{FH5!FX~Kwet zZ?ZeoCE%e3eQo8A)NHa>v$pbIie( zcQmxBF++d;lQo9hy7~LC>{09I!DLh^3esV`-Z(s}HRR;9bBt8vuG^+cOU(A69Q^Q& zgddn^@B8+tsZNyz4}H%?)R=zl(1w|WcSW$Bf%te{(N73vUZ*xOEit4{&L3l&t{YmZ z)svW;F3+#S$x<_UjKZn*fc5z2?NAR3lAO^z^eODs6gaG*Y zRJ`M^KiqlGuF>(kr4r0pq8H>p+~Q799ciz*>rl66`lv!u0$EQq86!Czx={EWd;Q1B zN*{qoS7CRL3rq%}Okyp<`lCeF)wZXZSMlk&^Tz(WoYs!Pg#}G4{@kF4&MCBN72WR! zC7GA!@_9wQy^J49aB%Ov7x1Su!}Mmw1o!e7k78*)1gbz>NfFxn9lz6s{fK`=uZ`O~ z-dO^(n&2I7= zz9Moh(++8~hA@O#`je&V+t0Mlb=71w2_=*#48Vh*NXj z^xL+&;htGV3XSNe8Mffu|I0MMSz@fD>pIiAzl`uYd@+X3Qqm+r#3&cV5ihIz{9q&yPoAVH;>oov(E$B}iJ^DVWT4B59<)FEhi0XynUw@oaY=4bTD1ZN;UW`wwJmrtM^G9aD;<}$2!^< z(rUp&RUtP7U1J?N9*KK%hD|@R48?(~nV$hosk&|Kh^1MCm{0J!1|S30a%74=F1nGt zeKu*+jci(>>N8YmA?+81tU}+RhruNi-cP^Eow- zaw-?I!xo})D*AlAHH7oj?)^ldOP!vo4>yxY0;Bnzy*tkCwrEs*=Te9SEj~kEx<6-5ey`Fw%tMch^IST$e9ni^JFAy(wV=7S z{irnzm>wZ(bN*E!b1{@sF3cja_TJt7QTUD*ia&i<6q1cX=X!NR)<*8qcpQx7kEhZ_ z8hvyCjNRkjH4MZik8YSIc1;l%d-?&Hd9_m5nT3*mLXRN1yEE&e0-d08F%;q3VoYn|Yg&)n05(hq6Cq&i7yJrJ=9Y z|NRINk^@{Cgqh5Lz6+i5)zig)cb%%Z8WUWW`pgt-@_CA}Nyq>sO zRimUScN&{#);s

NJWHuw>B;ZzTFffLEjetM|H4>i_k~U`%0bZH`<23@CSY^LnoQO$*| zs^@ubMxV_X%rPj7zRR+QB5_JC(Rf}hXqIp_mI+u{pQD7!J*XVN;t-2 zf)MCi2YYU+G+$Uqe{0CGn~hXHm6A|XqGmFzF;I7(2FUxa{`5a`sSni{V-QU3h_ z*Q{7x*;frh5_gsgrb%_^Oc`B|Z7bE&>{fo(>v7<&_m7Lo! z&`YOnz&DAx`#s&8szh}It8cWqNc@7$7m}sQAqICJ?|>vqMOwyA3dt{j7V>zZkPA?F zX!#zmksj_R>4zXef0q&CWA8osbxK~e3`yPa1Qfng=oewrD(&o%lo4L48OeSDHmP5?0L)>mr;^g5d# zW5S?3@Je%)RiQc?5q`TK+7P|x+0^?E&q36t;Fx~nc;--bD1=1p7%0@^FFnE%`u_-< zy@G%488$j&R;+dKT8AXf9BH%ANnKqNNT=DM`hVCi*Arq=KlpwI)u2i-r&cM2(k{sc zq=X4NiQ`kvN%GN!xeZ>Y|3>p{ClN}W=k`BCeV5a^PGhsRs4yt`?z2Vl-^%MI1#BJc zxqFN@R8x^=W2qTYmpBv}Lk)3tYPSRVws+c7F-C)vSyIipa|d1`e%}=b4$48ES^1Wa z>IwHsVNFdF)Rv;%J%A}@<=b+qy;%+}WkU2g`Sx&G9C3UtZ~WZZrnSad3g;dM-}iH7 zOv^Hux8}sap-eai%Uxc1=hFp5WkUUTRP!HY9HdrtMd=vv&hFM6b+8M~ zXz>TrEV3uSJXBVUl+kEmf#efu1Kfq|1|rSaixXfUKFniL`-u-AxGc2BsKMw*Hscuh zX6b}{Q}6NrvF0)KF~ZI@nHHt2cLat_0cJ@rF8#Zr(E5Gc9`}O#=M18g5tEy~w7+Zu zad(&Nu}iGm0e0v(@MO=8@{#Pf&9lfmQ=R^J!UREGVz9V}kav|Wjl_(rRxb=FRu*zh;toW zwS=HO`HdrCAGuw|Naka%5~>4FCGx4s-R*Z6Xa*!7_;=O#*A$Ojw^2`a8ePAbkK|;e z6qzWE^SoTUw%n4GC|h-(MOzg0(8WA!w=_|JnrDTu!Ph{cO*ps2iAW{6^o+~AYK5F&# zx&cU)*qpv?KFO`FuGGp{Ue<-Wu&6RM$oUwlrvCDJ{f<3&*)IAbm#6|+reG>!wA?A9 zwVIQ|URNO@f!>f)@8_@E;-G&??Rv(n*V3|vfluLVYJ+=5B46%=BLR!^HM%${*)y*6 z&o6K!0i>3X0*S#N6=1!c;t~?;U*ea9+aar-l(gI8?WvsKAo=K3be6-v&DX^cN77$w zi9IsHdpB16fVWCMb1n`b+6qV&r!0ea34KTQRqI7KYMH59lrTjsLi zgMSA3BhoSGrE(Xw$|bqt>^&%be|dh#{y5T-XSMER3%mr<)hyh^6b*!-^dtKm)3Dx#k-no4NqtB7ZU zDZG9^9)nGE9DhtYJKYmzUI=6qUx@Lg(1pk$SG~eg6iaz#6(?MMy(aGb@qOJ|96rGJ zF}m>XIpjur=X83cH@bn+>?Y9bJ<7vs<{eF`?FFU0P#=jjY&c2%kn z%l`$L?lpu8WMSzqZQK5$3sk^uxBC_c_csZnq6T2xTH81>4sz2^5i6Ow903W?uZa(e ziVy4D{L9IQy40NczYIW<{!h27ZV^a`@*EDv>esu%+Z!pYW^$Dy`+Su-cMl<)wZL%d z7`)<_|A07BsWtJCyX$Z}Uv)g<85knAfxMQfhUQw32H(x_$F|a9uv|nWCbZ~%?)Yb7 zl<6`t=m#M2=CykdE&j*}rnBMBu>P>phDgLF=2gYmov7~e#CQa#8oQq%>b%1(N7P!Z z-J*6(y6I6XD9?joW7KB$LVeMUgLf~l%PnQYbA(&*{Zr7?yh@sUG4sv#Ttsg$4gJm zulKvXvWxk>L0_z#cRW<`jxkKQ9ygf*GsS!?JuU5CNYH zJ(nb48)jnkUuKG9@4lwa@$c<;ZX7X5d@55Log95CGc|*bSMlatxLojqHxJ-J#(~6n z9A_$Y9dN9{(RZ3kV}e$%QK27E!51r%*3DPDu{*c-`MrHJt0@O`!vk1u!<90chtnMF zEVRzO{dXQT4S2uje<=^*_zNdjQ~KLPHbQbO!@5GgRQ|68S}L`OnGdX`HwC0KK59p^@tYwLgjvPyqqbc$4=9%XZ{NrPT4f~I&=Pw z=!3=O6$%Qgh5?m<1jeTAYsfcoo+l~a*=XI{beM#73zp&cE&PrcNo_eZiONU@k@^Dq zt}~=68y+10_^301XasXo_7@~HY?}VoSnA@x3H{o3auftS>N7FhbC#TAJlIlQJq(xK zo@jU;6i4EUNRhKh_Z#Hi7lU*d#oOG5Hsm_iN#FLD$ZEpQAbRuQMctU170{sXZMNjA zmVzK^tQ71beDPQqG)=(~%ZS;${LxXc)R!yFIHUNl5fm$X!23(uu0&$mVL{3aPGXB~Ur&P; z;J2y#;r;%<=NSm-F=r2tq6P_kyo4Yykwyyf58}3L8&n%u(<+Hy@4z6H0#c)NZf}@T z3}LWRoo`A&a;XWAr|nD_@2Kj+2%%<}Lt&M+0)yA*4cUdBm#}R&JdzaN*5C4nt0&QvuP2j*t4iu@Bnjv>lIDf-KWFF|A;rV5 zRv=CZe5n=0zrsT|oO&`TGsRQU5c7s*WhpScz%wci;0pJMSA}xQ%2%eVuf6x@y3X@BHW1ckA+3n9h!a{hcs#4E!wqM{j;-0D#sZje=Ai8B283;t zP1y*w=mrag_s+j3KF9(SJtE#s}up z#mFl%7vpxusH|-_h(7xMq0yzd7zR}T)xB7b8W`^LX>(+%QcC?-8*}NCj#p7^vrqVf z;}@B3myzF^bNcr9uKe{la8{=QIUOy66XxQuAWZ>l)7MCS77G{vp1W|ZS+UElZHu5A zEZ2+P3aq*OMDI4>=Lcfv(M;!m)wwtq?6i_@(?ofLc>%(`%OgI-X@S=ml$c zQ|w_!#T^j+yf}XZN)j(DbegPeyRFpMrWc|F22$b@D^B>u)*i``| zL8v7ZQd`j*6W|jzV{jUCFHjY_jbV?F7o!q-HMs!`cp}5 zEluLZ6KSb)E;9!tEEd`+AKZ{4T(+b-M0&&-)qz~0`arE=W?#0t@m!M2iHiJ?`IupOQ>H|d)n-v zv$4fSLeMFDK!$FL5x9}?>A#LTbK1JOZpXlDnb#2e3K=!t-d-AmX+9u$FsA13u-g@7 zM|{1?745kH-gT6KjtpTVYLN!gdN;%2fjrIl<}k3w9c)D~j0clB?jO>n`5!OnK6=YN z_`~yt@y;UDm&qm4!#Vy>cPpc-%)&~PFG}6p4!a>HLNJ&^5G-o9f~NTF-$gGEsa1=(WKoj}cbO*%zLFg;;j}DgOQ}73Q=_)*vDv_4 zkd0MBvzv-9(!{lPK!6k-z-^?~1$<)`p{x@st;;RTwMfaM(SI3%dAZ=CaS4&X2twD! zHT(t@HW>fgQhPHG=fx&g$UJILvulaw;SY|n6BMl>qMKZzp0#D=I#|-fHu#7NM-6tZ z@<=OVi=8=m!<5kCurS+pU?C${+wd@@&NyOK`T9xyA08f_@AY)8 zcysu-%x0}Qk=;s7tId&Op+Y^z<#ZWn%qdbrd@_9S$X%l=Rhm?GzO9Y1k`;|(5!VPRpG;QK?`Nyc4EM;tC&UezlsTgs+!bfPWbuY%+G^T+cAONF6Dt283P4VA>Ag+hkeH=Gi|AOu0xb zNtW>XNyw16P(yZ14u!4WTFjg18-J#WusH+VM6z&%cM>C@Bm8r(s(2P6PAEk$*9-0Z z825#0Zgm)8gwzd|ZcCOP-uvY>E7~^H*vO)0*TqDAJgrT>Zp!zgyvvPR6lQmGXJqe_ zXF7C;Sj`uHaA3Ruks|%q-x!I}F44!PoF~E5l{L$xA)*fzG0+dl-h6x72~`u` z8qC}iP|35^EV6rePYPk19n2Cx2;KYhn$KUh^f)3=iw zswhqI#VPHCB5gU^7%)1WZNM6?=9Lv2LUwTP?qxksk@Dil7&Us6}of%=cSSa`Y z`WUZEw4#ssDUB2135!jd|hogo)JV_Y9^pP??2@+KEtCrybpfa{2t?5 z`qB`ScEbXsdQX`%dnN|8fiuY4v9YMn zPd8L6tE;m$`r(*9EG#EvpBFRK_y||DnSCE2G3?)o-y+305HU$K+HKTH^xCmo0-e(Q ziptoHU)$DEY`CiyIvq`qOqd3L>qNE*w=f+odgs*qB&x^au3UsU-!A}f6b|C~F<%&B z&)m}Q+4aMhcztz&{`Z#-cRA@57B^>mSfUBCZ(uq# z?4P+eSluw;T#AgE5CinjKwnPFiy{;*e7ES;ZQX@EOHoa_!g*1a*wZ>`K;qui0o@&u2d2pBndh&>3>9Ce7asT&K;xZ73 z<>s&lNppci^WMnA6yit*f+snlGf95KnY03ylci$)@C0BW9-STSLvhJT{Sj*k|L=p3 zxp@dj+jxeTp_tS}RzFxl7c+NP)=-gL$i*2>FDgSWYGbbo)cZ~VbK}|5xdf<-ns<$e z>(15FdPeE<`-tihzPY;!s&PD>-5W5Q+JAVuq)u)TWqxSvkVO0Tl?MjdqP@CwsWq7^ ze9h+|V#>GvehIE|90+bHHGnThUuKEIdIVwZ`qy_G`MV?x#c!$nuu;8lgHb zHnGH%4&OUw9#UFN8M;tnA!Z_V6IexcOipexaG5nYI&!Qw@#!ijS4qN3n+#Snn&Ei3 zilakZVVvn7EnlQ$%}yWUTmMcGx63py41bT4!)NqF4lN0tfbGHg`DfAjF-P=KLhsq{ zI=a#4kfUo%a!Q+;6J4{5BeEchDdl^MfMdO4d>Rg7T{S_^BvAxVF2G7+DUC;WlH7C1 zyG0F($CcHQu;o;17L(Z!O6Zt0nEU`gAXwQ8cJlK^t+pzc~0YrMk<_v zhy5pZT$U%j1m{pgHh5^8+thBU!cr*rD6?0eC9G**E$+EbXwPmRE3^UX04W>Qa8fJ& zzQO4a&M8<>2GI1PHn{jE8O*PyA#==;FhuB}QmG$3^mm^zmM*W*9d3LWRJxWAEvt1g zL6&>2?;{rRr|K(_6ObC>a{U>;ORaNKfG?LAdKS9uPfqZ38#?}Vc*xDp!L-^rrn&p} zyl7XW_k~34aaL8D<)Omv%{y4fud6iQE zdnuIeNIhLOxwR1#UIVbBqg;M8kGtC{FJzyqYw=P;Gvir`j6=Aaq*CZ`wY4JB}NSHpjuXZi7YsDc|)(wQE@CspLc-Ln^aFF)ly4y8r;ML zF-|58gKm)3Y_gSEfu&vi^Icd9sUSa*ykZyfZf~jQ*K_f9{aEOHsbc*5Xc4NH@7uHd z>`%MUP^bq_&YZa}{1Z$SOZ?W6^j}}cy7JYB;|0LL^X5W<^?Bai)$33XV}Xs2uB&UGfku z1WUw_co7I$Pi<8;+WU|D6>Q%0-7wSj8LLFqwfy?w^5u?7uA9JnK9+dsDkDY}0AFC> z5=_8z>+p1y~fQzp*0E8Zyf}(VPtPR}PlIbCC z+~#TUa9?Wib>Af-bt%}xf!d{SM(3M)4NkQGWQ%9fKWGgbNuhQmV%XOe3}WMMpe_$g zP37l-zm=71QY5YKrbE{FC#*nTpK~Mt@u>a}f@&kKM9`m?vdlj3w|xdY#igLOm#4MA z8G)GhMqtTGwl~=aA?7;hOx??0bLkh2&v2Rr9ld-$($=+K6xQ$OzH)L8x^Sh`*)iDb zF*?+cCwO1~r|!c0_|XvZdoHF%@M$=TG~v!V>kC-;xWfHMHf61>^~m_K?OdF zM2R$3|EW2K|D1q}PJ$5(>`V$_8FEx<6jU&1~Uwj%6xFfK7Zb0?RrYLm-Q) z*XW3Oz-^5}%BX_L^FJm_g|csid^M=q#x?6D!3!+5th@rt#%0^t=0O8Al!wslR>{o- za^8iX%#}P3Ks_aSwR1dOeZy92gf?qd`J6geoix1skRrcc4CFzT!>&bAggj;g)!FH zsKRxvt-^&j`Hx3}xhQ*khj0LTkL=26{^Av{%g*=u8o}(a8GAxTdre7z)lcKCjrzRn zl3h&x4^uR!h^Qj1qd}*tv14P~+FASj^EH{-0qVfd&^VbHTupUbAqXr?1KTBIDQ7yM zZrtMb%K>o#i_mY;#{T#FJI!Q{L0kLdWp+HZwIFD@W66!oHHu@r1xOqYl9CSF@$^Q+ zMVCV`P(C_|K!MCxMFvr?msri0XPzjMp9rmj6tq~z`k^_(h@)pt4@Zs@&h1{WcrUEn zJGe0@`k|Cke7W>SDxvVnv_e5ws_i70w7RiKCXUz>Ly&y1dFei~B0^UavbLg&|r!|9f#tMj3k(@aMm#S6%DD#%G; zWlu?+{V^~HoT4K8_XwvGx(pSA;cU0%M4U=CO+Ft?ZH7MZJcRQr4$&Y(W}z@vvZ&e* zhwWt%8AM2@Om&4v+UUN1?^GZr09Wpw4!*%773##t&(4P1X32d}u9X1mPMLZA zf0QA%ula2zvmZLSOp4*@U^&`sHrc7n#?ZRmUTFfp?=tE&dZt_0bZU`{m6`=j|IpI} zQQmxDl9`tYTehe zRb`NKt)V`5x=r6R*<0c6y1?q_wXm8?RC~5ITmz2Dg+v8wN4Nbj*nY_yvd7yzL9m@J ze>mz-ZS&b%sD5_yvK5#4U5NR{6sQG@;>-c~;gQnmykFdhc;hugu|vMSI%<^RrAdte z=7HyXJhhQ#wN^7e<#U}s%8LSM6iYObPIhMf7on(aok0EG^SL&M zT!R+OgwZOeAwVX(K{$S zh_K^6tByJfzJsCHvG>P)AmAV$-1T^Wx$^}|w64#$=Liw4JK^XH;Ap(m z2SZ>LMqQ3F=IRt4;_7@|H<@a3y7DjTw9wf`Bx<7O!)+3(B%2~Sf54<)S9jK#%`N4d zd%nS_wF=cnuQ2Q^m%+Wq^Yq?f z>pTLL*N#~9>~(JZmJrf_W$x%|q}Ck=TfTPY=q&X%5XM0G@eKM330(A02+sDG=n~Q&Y`$r#Qgk2X2pBMKVfvA2JcKIJ{Fk`* zVQ|z-h~Oq;J1$ZD6hQRF_h=c|VZ5m|mA08fWw7J|lBWH;*Jglto20J(?QlJB9D@n( zcIELtzlYTV#5$G0J7w{KC#>Fesjg&@ssU^;E z9jZb}fHpvOM=KdID&KM}kr_pE_4vuh;t%Ye-`}vkpRNNZvpVFF@Dj;Ii|U+ZwJ=bN zP=67172#tOD;A}%q7TkPkp-&SvBKZ1_clL>|Cs}8x%b#PvfyontIDbig!8g*CtmT2 zPye^crnpx{YxO2RyoHr2BB$T+PtAVqkpPxT!KE z_CaXDbwN4?s}JD9j06Yv+~4+*0K{kvk&gGE5j)X_mq?5qpX?%14$eCCAb&Na(ozgu zzsFU$xTe1;fCn!F($O;J!*!;xB*vZ#jZyT&I;T7!g1^l^-kO6^JOt5vlaq=@AgwqS zs8Q$7NX^Y4U;9&H@vki52SzwiY`$UT=2oRRmY{DRK<)%)f96k0*g3(*pJu8K+_x30 zAa4p_O%nn3)vR3<3^WLmW?nL%aU*T%a=EeWgw0N*j#>*8Vj|iV>Er}LO}nny|2_R% zzX>M=elV0P*_&b#XMSPW|Byvx!b;aW(7$6mcHJWpm_Zl ztjy{Tk_&$pxJ)m;?;OyKDP=|$i!Iu}l$RMPEdq$4WLhv6(sDO8svIKp*|otJfEy(? zs%F|WAKC2;_q6Kc`re)0nr134=lVC+PhCD73(<6J-L7!Q)>@Eg^$r zi#jKRJjGbiNBC4Cm%|aB_gnsv>S!`Qt5YjJ%W$+VsE4zqGZ~sYQ^F3l9tmd>eQL;7bXR(v z?zv{efBDaM#l(*Ym}?k;fO1eRmrbF&HfXs!U4$8e>9S zwmsu=E#vUVKR$^znT4+=Gm5Q8QI{K)3%gP}Ze&`)#-RSUTcQ1E6>ObOu26Utw77b9 zlNPNOI2a-$lh!pa#~1FZTQx-cL@36IXymFV8?J>ZVzo(Ua97%0JjeaX$!5}MnKJ?D zs#E9n!^b3Qkg^G7fwBRRd94&vP|vM(oGB8r+>|Fpg%}L2yw#Jxztc%ODzYJ=?bpQf zpcGP4LQwDt&9s?{o^y88XG4mP`Z9FWFuVW+T0dL&Ur^lV+NlsM#ybwIn}!OJ_B1e{ z94iz{5jY*+johCCOl{&naiGQ83zcN-#}D3u` zAsN_5+oBrriXDFzvK5Bn=%sb(n}L*ZWM@nJOl6?*{MR+b!{ZHaO1fK&h%B-X6`rLULcP_0c??g&p|U%cLwD~i)_g*AZO$o^3gD6M*i_p zYXn76#s%u~xDKY&c)8plK&`ma+Lu2X19Jl!I1}6I-P_*y2NjYB(R=Qi0v1&!O(LA2 zdm4cY>Yg8P91;VJlwK3ET3pRfzrU4u<+`HKT7L8HnuUjV94czTIWNbPPQVdc+(1p5 zS$)1+OkaLHeV1Ov&kOwu*`>>SG`|t;?eXtRkOIUiqUD4dSP`ebfOmnYw%pa)qPMC( zZV4UoIjT|ogtl4%y=D`q+Y(0(@N*{+(d8NvemSSkg6sd5=Gm|pX#c620t20nL^}0o z?v;dRr4T;%YlVUzQRYk4x)U4c$H$UX^6+#wCMNTwZw$FCxL;-3h`YUp<$HLwaa_RV zEH&6Fd#~5}u2S*YEE6%AHgU<4Z7gSo5?ogvB=Q+!tE}*|uoP*etG&3(4A)kFnK$f; zkXxD}vRkUB-1=8Xt2MeN?HWDZ55fF;A7L-INvr>!Z}IH@t2nhP9CUa-6as+2*w{}h z`BOXr?1ja@S91>!5EObgmS<%V4Sp88%gcdPiXr#F$P*M@iFGbezptm)S1r@1p?Ah# ziyqqc11JHdk|?a@3hFq;MVJ}K-pBLVH^y(aD_I57XN!Ld!+ki3KzQR*kuFeGjwZ_5}6#bhk#sYkg zdc&byi!N#j#-UnaZIX0I#wCEtJqK`Vk6uZ426&_}uT=l$e?_k*HTWm0;>WZ7RH@QK z@FE2dTdEOv4MhK&5uXMsP;Sr0QLvB#nY=#EZaAMgg)e3JxF#|gSl?cJd#IHcWmIw% ze8%GWSxu*svf5%bIlSI|Qp4W*bFDGkA2_}+bbvh{hDcsST)20dBMylxE{;QJ78rc~jy&23t*ZNqRM6_{^IwDdckRy%#7vjmh% z-=-7C9i4|}Y|NJ1@b*WNqUcx4iCRh!1iT-msA6YUJZY)@5f8bJBxiE8HJ4OcteDePH30=OzY%3??B6{VMPeNQrs zbw$Cd0t1H^*&zdXdF}IhRcm6+-}6mldm_CBr)|OiM%<`s(Fd}Aed;Zk;7_FngP|}o zj1{en+=hMl2B|N%yxMa@t;$*>b|yjnn@QE2vlBCm-!1HOEwu(@S_)30Xq6ep3t7>c zf`HzB&6-)1#YNeYybQ7ilk4T*CR$2Uy3X@p? zY^YF=8C<2_(czwlWb&uB``|HBwpr-)V}Gp#re2p4Yz7oO5CKOP+irTFHAe&RPPbXh zQCfgh&ePZZ?vWh|N&8Y8+_s8xVHrMnUatbhP9R=?i~AStbEjBoh{Aen23r8cF!5Lr zcKG_T1{$m3ow*C+?)%>_po92IC$V?P)2wf}KupEwYD87?$y|JID=h78s%i6m&QA|17VebFE*kSbv!;Z=$h3pF&Ba>XPa##up;*j zFvn&3Rda1*nnP&UT7*1<_z&>q-mR|2tpINC) zF2P++BvIzDGR=HFe}^`MI6s9bAFv_)%tn!I>#vJPf^d=ZBLbTK77-~io733`5|3xu zjAzb1dYgE~IQM}+fb2@&*QcMvLLq1p4b^{M!#TX6R?gN9m|VLFRMr~#a9Nm#ZKcMP z?p8_*;i4lTT^q_xS=NO%ru+W&A@#9-X=mxS&oR{RR>9~RA1Rgo<3klI$$6qqE$mB` zi6iLsB&U8ZI5l1K<9vOkESMVfx~v$qwxKYZz(lpcjxF#=w9ZKXG_M}3s#zaEA|Mv# zNPpLNFzMhp1=U{D7m8`Bb57vO#9xRrQ*UfDU0MOB%8YiO7i3$(hF213dO1P?>Sb{c zH8=r-IX|k+d*!IFU4nr}gBb&(Vh-oxHsZ+1Efp7Gme=aD`Jb!`C?|eS!t=vuiF>oK zkxTMTG25zfzKX<3$4uBJ(vpqi#94tTX%i>F94@;hGT6yrz(RTMPV>!B&Kx~J;i%7=CI-`Q(%dSqF z=O{Ry)_^8ivnYLtD38Hbp~}#`w%J9zF@$T_aW^X$%Mp)Y^$#(H)c~tEMXS6G15I!!h_S(Y)rXavI?t)>9ORICEws z>6#Dd$(R?~*u(mXMVvrKm;wL;eRBArTobFmm+L8gC2z2N#;W?SZSSu32764TCZ~O1 zeJ{N*OBVslv={BPR^x1`)Ak0>guN85r>mbXeyFYVOTG8MW(>&W>ALZwtEv*I4QPVU z5hu{DWoP^dH;mOK=pZw0|qHcW*Q^?S{|`X@b}YAgmhDMS{f5V zOj-uwA)CAw@BnUX^gyRLK!iqr4Ufh-+>%u|ng#KA!<~6H=hE6BTk&`oc%!DlyU^vd{q0>9-i%K=dp7JeN6388P|rR>BgCrWz7z&STR)HTa@t7Vx2F z0_svMlu%^|QGUkfh=_PezV8aqm0+SO7!Nwgm|dI|<9~8>4^-et4%OA3#G#7wvGpct68?1Wo0zxt{BMW=KrO zlcU7P0T9NQ&j&N!u7&FlCON1>ED^nfK$x0iLlKU;Jfp=!yH35f&-}NrbQIA7?^|iXb4Y}h;l;6%T$j^xXmwh@H`#8a1DCM0nwpvlFgy zrwVu(;*~$1Qy+p-`cY&)!>7_*8H?+~_or6(e8thkFrRlA93nL7iOLSya=959VB)HH z=Sgy+k7@rTl>@kr5iX2qI$G@$jx9$diIbJrYx&OVH?d{>PhWOEsSLJpyt3#jfNI1> zEXWjX6F+rai7c;X-P>V?3D#7I9RqP#c;9^PU zDC5>SVrgesu%c)k>;J=8@S?8bbYlfSXdri*b&HLJ>E`TNMlEm-{{e1?ylo&E)lj$F0PVqD8xVpo)!%jAvy^~j z=io~P9QkWtejm|jZ#+)iJ!X|?Qu4yIV^iATNTjit#dA$j0sVMUR=J6scJEFT`(FR8 zKoM0$xK{9fpoe+-Q8<{qP0iDALDqBj-3<1kTOPseUcYcd8u@zdjmlY-J0D6E)!~>n zT#2`0CsBTarRL{*n;4{GOP!6RwgFh3w|_0Mi$ndQ`;Vt)sFz1!RL8Zrt!fm44WDFz zF6By(@pOJx_OtR2ao-RZSO=Em#U{k|R08p`VSp2q;|?8@AZcC5nzRS(!(pYRrPIF~ zeo-w~31if075cYSV^;kD#PDWD;>eG8ha%<+Bw}v6Ggxq82fSSqW<1LiX~o){OiZ^v zo-YJ>oNv%fe+?4Xvxrp6G~5TcdGB7bKR@cB65V1tR3eTk=kP0FJQZ=;UpBX(|K2&< zf%(=SUVXH;sTi;5W7J|lQ-JiDfFFb4R+HPHMyqA>kFv9)q<4_3iB%k*@0LyAa2{Fx zvC|3(q-%`=Vrbb;j6|x$nAriW{B#o6fhWzBN7#kH@`@Y5G+;Lwp!e7Ho+nfHcvyRw>bF0)h-h$_Oqzkl& zb@i(?tStQ4h3(=(885}{X{h)G=#8wkgmQzSr7mfdr<)UC--JhpGXx6Zr~7?93$C}} zqb-~#`FZlbc?EtW#S>o~nwjWu)OOhPU*+=LO1SVvobtroc@?1F zPD!R2jjs4dYAP$gh2qX)eV^8CB_AXodpctY{v8*G_VoBTU1vOeG@3wl zyElyTe0z+p+x{yoATY4Z%YA^+E_p`q^WJIode`$2Ct0K<$tXBp8Y=C`2t;1&w&0&C zf?>mzoj}Su8UqVX^jh{d$4>5+VUFPU6K!%(TZkSkg5kCKM65pW6Kqc zAfAB)XzW{rO74k+amd^Mg;Wp@6_>TGl9CzgXKV^;u#AX3Q4>5ug#!dGf~675oOO3- zL5;x6$l*{Yj25Pk$NNC>p3sKr3eHLyAVbj;R&J-?St7KiAQ@NgeTjO{e?BL&l(fHL zLwWHV8n}^}9h7ED`&4~k9m;{oC~MoHf~u$~#T#R^rR}pOS}yN5|@`}D7FmCByb z*NOr->4jK=38}SpbVpBbDL`dYk4wf>5xWH)a0hJtk^0VbEw>28TAtM4_?0qN<=kqj_1Y&Z1TYe9m?zowI2x zt7NY4zNPL=q}on%uU*Gv*7vwfCT&|>-XU^l3+E(I$bSKjP0=Af=rkW2PtSciU!S~u zK`c}#UGTrQRXH12={>BN*wC5XYd%vzOD#o3Y3pDF3rT6J?;AR;hp+qSsEBC0Eu}8; z>xUvWBo8vI-|ocLEi@S#8DDI}1QG+-diBWSk3(mG*}>B#uR&-qTg*7_t`EVy*t2C< zhk}Z8rPI!q)1P@EO;w$z4LUy=`o1R*^)gW%^kNPinD5hpAdp0iom505n(-_^JWI#E zkl4ZguMS+A11a2dgjB){@m{y?Cvu(#fbs*l2&WTr(ExamRsE$qDPSB&gv3ZiK_FeD z^zCVniWfwW(-N=+b9KSY)o;At>+S?#tktyWCq+U+vs5z2=GLa%K+M<_fha;F1s7%} z@^P#|;8xAP^(=Yb0P6P?r~l25q(@^PK16~4#t$2Cgbc!)=%&m57pOJKe?qh)c1VaB zhs#9N`OVde`1w_DhjGY##xnz7wNzWpkr`i*(9Jab$Gj@3uf7BS>oKt#@?qq;2jt2$u{205A+47-2-nzzPj$Y%e zczRHhPEv}Vt1P9A^coqy+}m5oKZVme`tOM8?6%464Q~QP*H>{t$g`$*?S<|Qp1;Vw}B%{<#nmrEYp zjMUy0O6JE9wc^?wZn;MzGkSmcFyCEe?%@xiRtkEYGF+`DLc**HmFlc{BweKJv(PV; z5mwWPS`er4-}2WWjs}Xr8lzTPPnU=2P{;_N7U0j2?h+4lFcpqt4X+&vaMKFrvH-7H zBXcz*@sGCx?w$x@bK7?tZ5lXYw~iJyH>dXi#qoFuVn~X`>(3FF&Lcrew%5ybAhI>8 znvuqX-OuOAzvD5-GrB{x$kUbHvlTLkK`{b-%=F zrGWNm+W|WXXlqmPfDWKf72-?Jcs5z+*Yse?HMZp}y-n5{caoC%nTado+szMMm^V`Fed z>wS+5YYzQjw$;sGLOOKw0C9^?E{{2Oyd>iaH-s=4)NFtrf(eC-nMed6XGpDHIp%&g zz|D8;+rztPhyk+jN1G<}aQ07Qvg*U(T%@-iy(^z|Gd*6HAZoDUPwUU#n+Agk0|dc< zboF0D+kcH*zqh))ri`R(W1OP(PhCU#gn!E@#c@)L{OP8gERw)_S$7k}N*nNhL;2F& zik&Yh@6=omIjk!MiblQ<(0*O-8^3)|B6|;_!xy*s*GR2Sn~wT$zxN{F%p$=DCZzXt zTA-|n5h-H*5FdJm6VOPI%LVFdJ6oB_A<{!@W@4aJ^B22EM?9V3MU~f%5SXS4TGDj6KPP{5RZA19M8^vg zD2yDL#tX~rG9o&M0!|11|7^D!GB#5hC5>D=?ojhk#Id)XF8;XpgBt(nMwS^k44LiT z9v(%r-loyr{f!Hv=UpK825sLvy*@0QB<1xF1zCxSidk3E>UV#~WUPEdOj2}zJ>h4a zrZBDiR3`ubOOR*x-tkI>uRy87#bPW@)S>VF?S+YlVoZ%q%wBk*v5G{Gc$L&kW6@0O z0n^A-?yN_K30=qjS;)+@kJ+2-`O5}&7<7AnxTHH!198(;Kk7Z$%-1)j)mG*rrng69 zF<=)5Zt!J7{X^fc7tLZ61Y-!8;f=ADCC7Hjip#JV0&$>PFv%(UIj+}}7?uYejxksg zhkzR%!PopnKXCUc)G%)U$Ca)WjPLf@dWSI}hQtiGHR_pi*@MTTfcGZn)8emb= z8vV`D5^bMvk@Jbftu4g()wOo((yNC)x&b1AUP;^{+r{87EidlSu!SHg3(5@sI==|( zYw!a?P4}YK5Afl$N>Swz=Q8q$FewCJR6IL+C1p#qZkwol5S8>pB4d z!?p}hTt^-053UxmO>nqA7~xv&#LOQ0v@__jKOS*G2YKG?65a#qC7J8UX#9!kz$%I9 zxy%2h|20avZC+943z z1j04|y+U*AB51N$H4nS(7HyBFxT+g4({-Jg(lqiMjx^Wi8_2{hO*ld$BN%| z8oSz}U1KV1X-v2Or}}C`Onvt^0)uIU)G#2!1TY&xsk}<|KZ`M8wGtM2Z`#H_=WHwE zIfB&Jk*dI?_Jqyc(oPbbA+!Hks!hu@)$ORsfdG?%4#QP=T`Usz_d~3rbSf)XS1(Dy zODi&l*LsSYAgu90BEV03l7$?o1-lX&!6Y_ij%(k?m)X;nH_ku1Q*Jrl^1UmF5FvJZ zI$}SI;1X%D7PT;dlK4!4!xe`t5~j3PaL2xFIm_^3JvH24efhN0inCgf^Ws$K13aQU z@B@i@Yv5Q$kbAid=)v(Z7Od%i$rVDXr9IxJ$pVGD>vJncb^gY=YL@+QnOGS+rHX|A3S7%`4UJdnIwo5JxU_?am$p3WaO zV0!?pV`^l$?W^Y9E}1Ixzc0;FYQzRZ{*PWc1S10y$pSFh4I&hn;%mV36_g1KJn;QzqYMe0g`r1%YPvS z51UO?zwQ#h!f{-v}V}XdO4tdd8HR-hvbNC+_hnk$%9K%!rg3_#uMjXno4?TtrorRjyR7LrSY% zMBT%{$TzFL{jK?g93$~u53_*tpbS-@oxAC<1?9(|8LPP7Tsqo$cCwrM{;&H{{vA!{ z|NWqBw^(a`y^jyGqL`GIovw9)05YN@aL_x|XkYWBQ~~C0Z$vxJ?gS2>{(;e=2ZNQO zJSS-Z_CfkC;&!7Fz_)e8!t5Y364q7skZ*}Lpe~rvYd^pZ*`XI*#4YmSmVP_WW`rJ;m3}Df> zNkd=4W7FK1*Ah1|^_PCWeAcM15=h204;MGasLR)IJ#ONjw=_s#+X_LDsGhD}-8-7r z$^(#SRKXnYo>YqO`8~tT22B%<+5U}2j^BFPsjQ;ut*IBrs}f8`{3anVM#2d%vk0ny z{5kJEQ8^+t)(~^+y#p7W4JPkV-v$DTdAu{&Mi1F7J;{m5H6=D>sHn9>-}i^k9iMz5 ztaEoir`1negSF#0(SQ3GJSx|z-|cVBLBm_fI-BU^!}Z|ON}224$;mPr47ettAew9n3TLy3Qz&Y`-k!@IGnGqK!0{5LRx0Xk zZ;%>Xuhv5C_S6zgRa|)|8R~e0RI_vmgBHS$WmN{5B{ePG8LHW)F+#+akpD!{DOPvR zR-=oObEs(u2T`Xv9{g5%h=fISqN?WKZ;_?@_aX3UgiqM~-z{=9o1JxmMm=*Yx~p7k zCzsUFS|^#sOpSiv{q>1Ai`VRNm8Ev95`8%<0c%u{z+=F+Z2pFAc$oA zW0CM_^)ue6xgcow)qDsl90Oajec7opU-{cs`CEy;)A#K*3X|%dwZ24rkJ(T1wlk6Y=_dX#HZuK@Kr<7Ihnq8v^7A^vP2n`Kr`OUT`6MfjRq$DWt;Z z8#=izAOrqH*tbPTm;-cO=Mi?l&-yq3M4b)v()nQ&u1-CcIf`n}*{wCEhSB?k7*fNL zc}l<~FnQWDa!&XAe3&!m+IP64%WleesNLdkNC>Ov`%zIP8%DLggTQ0J4eQD@C5`aw zN*6X;dma_x1`ZPqnK*a3cnNfmcy**S#>N+k_P5MFsfaVMDP|A0cv{e)pSe_skjH}v zjvK)K8=GVet!(o=21kD%N;^Yc+i3)48}DacXuJRZ@I-_fu1P$sGv*3R0t14g`O~FO zqMr_%eM|_~9=jawR}gOH@5{(va_m1~KJ5N%qe{NNPWu%`Xe8`yuj24v&{THf@2+J? zsh~z8jn#XO3O9=c4=`)Eq0B={Q&kmQz(D|p7Dq*(LL!-Re!m##d||s)Z$R8*hry(u z8Yymib3FP`Gh08u=ndr*)_wi9CLg{+cM^iCtBga-Jp5ZV`tUHFVPz5c@4HP#I)VM* z*$Wqe3H`s*ap~d+IeEdiLsjXyQT%X(+-1543o+ie5BpN&m3q_S=eww*M~9#n(!*uv zW}6X><}%3$vmbRAIec!ioQ(YWQtY=c18UtOZJO>UGiPQi_mh|sqw`XNjH&j*TNZB8 zTKl-8&8`gGVqKYQ*H2=h!yL z8gRF3lhyBl1*dE-CBpA$e{2pXu+Pnwy7)Max(>G(?lMmjSD;q)uZ#vbOAM>}_ z*7aOXnK(W1txMdfRnvPB2?}(G7Ug#)kf`P9C>!!L7iU^g&+~!fWLN8a26S`4>VAb@-aRd8UTi zuysJ%BzrfK8^f=pIX#)nMAyXI@(~wd$s-otnqqSA{E)d6|4_*>;HUAOrqqLV_6l=^ z9aJGVSHS#V^=q^c!X5`rZYlXozF&0SFRSxlP2(di-(B|tDGVAm^e6*NZ8H=Kg`obB zEYPUYE&td>%jT%-3^anCW|1%=5oG5LT}}YgH6Hqy8x!w`Dm)?!)b~@WjF_cP=+lLc z+T88}e8K6mvU(L9JL5hkj z?^-VFsR@5jfeT`|Y4Ey+Rt&7?nUh)7@NTw?uH;7!I^EvkKE$ISpxmO4_iE%mha4E@ zrw4I~y@5GDY$kseNCJPIj1(VlK%UQA@+r{e<+Ac~DKqH&`QaIx+xyKIchY-y^KPFw z;xuw>I&2=asq*RLyw;hrpZ-1ln2z#yDGau#B15@oeaexR&f%|QnhDKlriPmDg?#}3 zuuGhO`#~AJFQj|k%b>l^I?o_qugs)5#`f93|1%trT^YoiE|5LgHldpmCn91a6U` zx5UTi78`2iQn7(XM1h6v>p2dnoYx|eZoca7g%R_T$WgB7TiQ4VqxO!U2(ya%&BS52 z27luuB!4Qf$jzUqep>(fSHPtkuZLv=WDe0DJ?!@|Je+FeaM~QP?Fyf;{Cw@Xhk&UV=ezES(d3p8n4%p(DGO8rx;E3QcUqqYw}!8jy_w z=?mG7Avu)D_ExLRHOWc~Hh%@;Fd=SxZ;wO{yS8|q!U z-+@^a*2zi$`{iA-IvQ#*AbP!fdVqG?ZsH^g0GB!vefbrku{`9KMPiv&YI9+k(V57x z#IpEo9|C;@!1S)b3J^O|fj3$G@|NZsv@e1mcIcXb5-aL}BO_M|1&o@0|;%4TUM&4Bpa zfqqj2mLqf1)hzaHKp-sK`77}RBm?SG$wn0;m6xERe+L_8e`L0FE!G*dj`x6<^Lrmn zi~IH0(7h(-07p#lB7tly+(1mi3=EE=09Xf0u9Fw zDi3_@nXEb%2FH{|**?K!ipOQ}Z)d;`Ky5C%Ne5LRKx>z?)|0jQ>ms23Th=)r+kk$X zL=a&n^+*z|JNQEo?t^#ubMW#?l{3JRz~rTwA4chIg6VDY25nfXq7!C|1 zWv)9DzpDzBw^TS9fR}uHTBNkc0zOyKfIl6rTweAn*2(B3i2J1}N(P5Vdl9>+TG0*; z0yv%P`Wg_x5QS)d&|j4J4%|jZmaL=GR99dNPWfPb8-pt;1gkjX-NWq?Y=w@}Lx@?* zM=xbmnYZbIO*6Qa;+S#zz&H-Tn#<22WgoRAQspE$P)+ckZ6Xz@`arC#FdI@4-1$JT zW=!Wd{RT%RyS;u_;cyiU;xt>CxY{*{z(d_J7#pK1T5H!K_jtOIn&}RRKmG4%T^w7z ziED5N{+-FD*&e4e=GuPE{%}H?#SAJUP(P=PS0F!H$vz{0w6@VM5M|akh)+AK`My$K&KXsK^HIq zI0alPH6y5?Z>jd5d>_R-0LBhMcPOjdT!9!7ISW8_9Z;QC3glW9P-c-Y;Ba%U$^DvV+)|s(IXgu4QA@T zhROp0N6RH(D_SDn8-RN3!LNH#t-ok`G(`&jo@Bp2&iC)Mcjnc9>Z0??USjp{8)t=zOzR?L5{@-)A40a_E80p)t;7k6-(^ZvD0KpSO9V$uB&YT{NG zxH4&bd7Vz~^lHk}qF-Q%;hzbuW0*AJORdKyB{Jt|c#YkZIqL>K{ig6CZoE&_iB3{7 zL<=#LthLjjek{)m$0b87U~#lD58=QB zbL}5dd%3rFSs&;er*W{=74VbX69-VTM7?V_^MO6Y$FT*5Ip9Eq@S<+^p2LYSf@)tt zml9XO>#;pwPI@nAYWNT;Tj`1hkt4kY>z$Ob*T^57C6k*^l-q*-K+N0V{x)5Haq z8x1Q2y^aX^OQ0K+GTgnlUv1wH{%e{qd`7v5hIZSJew8W;lz;#{j7j3v%%^XSGiI6L zF{=|9RM@chmok5K4&Mya?{(hS(tlY>~2Ft$>-48k&b?@aN4&;RDQ$zO)_sQy>t! zD^+9GM4wr{!q> zb<*K<*t|q`jb0GO(QZxPfFdEE$LlWPV$)m7a0KH)-)bQnj;;xR(>Xbwz;HAhU&1mL zEW1JdWtB+vEXL*dsNsr1IZ6`-yFy z7TS8UK=6wY-HFgg>PgQPC)RjcUqaGsK~ykb7ucwN9`~nj3F8%K=dgDdV$QT1g-z#$ z%T9Y^mXZYMob&NtduEbIoc5ME{-A349>4pV0*iC8kk)`NTActrb7>L4!BAU~8{I-` z0Z66qD)c#CSWXPtz|K^*ykI_trhLwMD=vTvPrIaxn{N0#v{I{G&-=u^*h>8E=_B?W z1Q<=^1Crd&hew3IRd{Qm^t6>CoK+uB8WR(YQ*#86!nWQt!Z=?4`R@o`0)y1XzMK$# z3xGVT$|bh15_WVGuQsfI%Lau?+pqbW;b{uxi95)==Mw2~h#1NEs!q$oqqhJ!!6N1et#esp;^Kkz5(P6u99R`&Q1OzoOSd4unja`3~f(;@>rC<~Y zCIOeJNJL!pG^VS)QEo}TAN*;x_pdU^?Vjdhet)7jC&{pFKtNShCdh7YXt?S9`4Ss^ z5%BBOi=XGTId}1RcpW{DF0NE9#EcaUG*F6g`q++FT$xt9;M_kn8&!dIrZRISe6t&j zW<;Jn`#T~uCpwf~Oo7STdYt4clt2RgA?nzEX>k149o4UbBo^tFFQ*WR#`y4rB?nt6 z7%PYkBFC#fT`aou&Ex}(wAeTH})WSS%6l43@sI>Y z14tmC>0m>=3U*gG58pH77Z-GEM>5h?UdD#Srr&Z5)fjRUZi z5+SZun-S=2{y1SQIk&Ea)wrv*;3v^@2Y_-ES5=L|fPIt#sMMHl0It}jh#pNCcuNm} z437w!3j3AQB>9y?%OX(1W58z^csVGA{1zA>mXQh=Up{~S=&@(-UI~oc?!LU;_{p=J zqt5o1ut8r4;!m&BjGHl~vhEOfJ$S0cx*ZBJN#zp;#)$B9`$QzjR6| zRM^#>Tp2{P1Fp9Oh{BG53_0ay;|^-b6wv5(Aq1`P0{Bp8&tu;S@cMwHN4aO+A7CjU zDul!V&IWiN017q$*NPGNdrcj2aBhDO!OGYIA;&QU>CBk<>n~NL9u;O2qvrK5ShX7} zt3cUDTU@IUf03B^b&-L>(9ZT4|!ybnG!cGi0n$EHR>^j zB|vwc!fl!F!x$AHm#aY(=8>OJL2K*|Wr-vgxv+he>r~4W_7H^VdkSZAzWD_IW;-=q z93zSybj}p$ceR6~XH76f=A^XiVsFVFj~n`B8XQ?Uvfu=bA0hx_ZG$@X87TcnHHYc9 z0eiG!o(_q@3dThqhyakGc@=ONu(y}V{Lc>st_r<PTP}8Xa#Y5O7)~GrsrZTwtc}s`$mTD~Y4(VP5=u_%NFO zW4StnG@DR*Jv<=4q*T?h63d{oZIf?MRyO~!06KN34Htcj(xN0 zzBs^WoYA4PRs$Yiq8R5H89EW(%^y%i+s$~PoD-uK7e@4|whEIn^A{ZkVIPq}ioAXn zCFl#r$!6jvPYXqcr>WkiofcP`p%RE7+S7e%>CJ7x5YnSk?o*CL6Gn6>45E};6nBJ& z0o$+c!N$zt+ID+&>j(;;`J)cD=X>lLQ3dymlyU0T6gGXJ|%h~U8Qfa8W&TnzccmQjh( zORN3gu$=Ut!Y*CDw}^ayEG60V^Y$MJZ6O!t6N;d=U6mnQ@Y;p{IEzrB-pZ6R?kdT~ z$HQe2j1e@&k!jn1@UfD&8t5Q=9ldCcKv`3KUBD?^#0uh)^Y6GwSLMTUx!Sb7fB);*ye8+JBs8k)^SlI7EQT{r%{v< z&7Zt^N@lG#*WdXG^*m$>hItAgfS=ESAE24K4Bx0cC_rw`I32kQkg8D(8vmf1GD2Z^ z{Tk~P_W=;+rh-!hl-h%$H`Z5NL{yuttCA;59vH~B+hI!k^)b_2OJs5hm_S_U1#`L9 zgy4HsaHuR|;|>CsU;6|QW+_f_C5c~8dh*;&ks{69sC~vj{zHg`bDM4aEymHnzR!Z4 z4^QhuX9GeXh$@MPlMN7HAxV^&^Q;&zC9v6Vq~r69mKR{T{3n%|Un zvcjTM9+gX^R)kv5R%=a1@2Tu-o|s-kT3#)LpJ;ye?5hd)3W?r|(Z?$g5|-Vq7>1Xv z>)&0STt4BCu#$e+wGy#<%^olOVOSh+U?mbKX$x{$)pLz*e>Q**%slP~WjNd=jwsOOyZo_Zt@1`S}8<5fwu=BT#7+MvJz5arcWcDF$(OY!u`c9=mY^5UFluGR|UZ z6sz?`@l95G<8IKEU>FI&En(MFX&*sI94#$UZdOl?tc)?6Rtj#178KbCdq9YcnpP>x z&su@Vja&ifAZ1@wKb|?W-Kq^{j%tKHFxaVQoFyD0To9x-qs#okh8^Es6WII%YJ03B z^q-U(=u`EVjlTpbhnKO9t>s~&!YD<>6x6&!=uIZw#Tq%AkoK?$=M(Ym_7qy zga&u)%g+`1Roee{2WTxqQ-AE>m}eZmS;DOF(cyO_ym9u?I67NRf?i$S))vL)vb@Kt z+!Kugo4=X444<<{O|~*}bO^T1yUe-tg!S)65zlcU&b)2ULmJxv9U@9_E$fQy>lvjkxH;n7SIP^*sylRK^J^oFptpSyU5(ZwE` z>$W?>Y}-p7Jc<`B*$o#8SXPIOaZ`G7Jo>sQB7GL(clqm-0b4~`91FYjCHG_mZ$wOd z=xm>34+0YjutAnWkE9G~^Lan(R-Bh)&we{zM@KnpO4_Ud1}ZO-2rul4y4rnm4g7yQ zwfR6;TFky)-a=~-Kb-^dP|!am-p(``a)8tkv-x7B5Zu>@xOu?7M}{jo7QhV=HDT%Q@Zn=CQ(ZEys)6}`cb zfz-uH_K7^#l>i)n8DaFM#XTNF80dsh*bOMVImx_u5HIxBd2*xG*PVEgFOOsV+Be58 zj;9H0Dg>6rLj@-o%2iYYOD%9TEhwTOq>rL3;y!bn8GQ0` z;6>%r()tQ(zzUj58Il66!pO7mL4`4Z3%*UAW4i)l3AhK}QON`PDe)r?Jh7u!%^*f7 zDP8-_i6=xvDmeR`Z0BIQ#G{Yj4`+%^2<`IDr_c;`$@>11fYZ8MTGc)&aoLQ&o;k?O z(t}OQ4&5G_Om{NWOLPlmqCB+&Qj_UKzp_IhIzRn{8ezuXzuEcHHPXS3rMj3+iZ#I= znbw@1vFY94`XVxhm32RMzQ54}ii3XIngG>r*a9m3`p^rCNWy3X;rU#I0ADAz zYelM<8;wwpz~SVtT=U=_cG{ItVUJ$N*NgUs#*huAR%@JnO(lHO1GQG*BcK$i?z6-I zH0(yX555DFnf<2{yWc=gKdZ#3=L>>i7{mgOg1M+)n0=E7yAeEmAy6kfmiZ#R59Dg1 z-TTNxJX)ffJrI9Kvt8o;W$H@;g@euQFg!E0F(f-U6SDZOam_gL_x78{5YUp!tCk{_ zTlD=&ZI8`Bw1a_ewK=Hsorsx8zrYGt<^UJK4M~O^Q7gIL@4?al;riyIXa(8`Q~tZC zPdu5Mb#|YScx0&7*l+p0OaXpT#(~F3(>Q~a8t3H@&{PpfwM-s#$g9oo-$B3<^Wn;( zloYZ&=7%l5aU;gNE;g3*i~nwJbF#VEBEgy+ie+B|fW(eOi~A9G&is;?6aE-uy7ylp zMuG_&(CM`A8pqz*d(p)MFcP@OO!hoh4g7W9tWenPFICvcWY#aFg~Iayc+NG@^Gso9 z6O5_nrJR#Copv3+qj6vtw%Lt@s?H}ego@?>DgrUy;k5@t{L+L!u~2&%l6db*{~wtSN-qP3HIOIiM=0suOJ4@U-EXT6mxSw;)YCt zX*=gGi5z4qN(_fe|NHZJ(=SWbB8JX(I!0+STMAPWkqV2s31M>bRc3FQmiX2O8>>Ou zv+lsfOcRM9S&zc;#X&+71my6p-QSZYvpCs9k_rUiUw6m{fpoq}{UvGmnp3})gYIiUgtW}CvXKf`vlp@Zy$9AynV$75)q|0$)^rf?9F`mP~IzAYAARzOl` zfDo8{>p4`Cd$`N|5~3Wff8J?~^e@M>kknv^PqM(lt?!Qvm8Sc`bDP37 zFuUtdC+$8&J8SN6?RbCtry)*Ju9AQ z@#ot6&Jm3gTm2!|xu&b%=0ENRDs6d?_ACmt2GP?Z#^Fr|-tgeP$+xx+2|;eM{nLa3 zxAKzMitg8=2B62h;AO(pZWnc_K?u`lo}!Y4w6B1Vlh3-@@s^d;p2(z_HWoW*eSCqd zTOTLX3l%r*LkKs}lm}z~|Hr!-FnhV~wLM3i?$1nJta|yA`v>Mivs>zEWT@ zEIA5QaURq_=9lx6dAdsKi=&$+yRX$qLB0cl`bnn7#2HL$^<}0lER{5RK=L z>;w>M(-9+;l`A{Q-(X*=8(sW!hJ|$q^l>KD65sv~4|{z3M;Q(zj+_k*tc1}^DmL}< zcwJ43u|A%NWm{a4KRh7K>_DS!9?8_Q}XAit{Pe@x90Yq>t+5Gu{Z{y*ys(N%;Ry@t;qIrXTxJI?&2*q&9 zc>+390LRyIs7~_)k;H|k5I#u)z7lf(&u#)+)f0o^)7v)mGkntE;i~={zsxmau(w@b2KL&jdCrKkJtp`E3z*`-j+4|TAq-tzK=UGtc4a*P@( z{0V2t#p#werD6`_gU;GVM-NPI4UXQuHjRULO+A9nQQXF8W>Y`*eN6vNzP#F)>jY%H>lQaFzFLGnzhtKENgKi-gP#QoYc6iy^uD z!`K~Q2qtaF1aL)IST4PuANsV6}hERh`6Z;~= zC6TTz8fPRb6H7UbU_vwseZv+h=u+h`ERu=&3+uv-*_OKQfbX=>Sv%>7XR0)H!Ta({ zrIiCYV}j=QUhWV*g~j{_;N&P!_?V9jw5x7k>5>VUXG!A@`k#w%S$r1F-k>0AO3%eF zwc3m)_%=K2O{~h`M@v+|zMBF8rkR0d&gGv(;v@5vv3j;TcE3b@XV>$eQAn}8JZ^)z zR-WaqZ1IE5l`y0Ja=X?-yVhMB67g8guA5HkBzV*vBMLQ@_D zYpa)=_FuGb1sGQ(J9IbciM_mff97g`zKZ}%y28=NOa-ajuyv0N_S;PZzzzq8)6~-t ziPxM!$WuqC9DuwQdC?)-OIBx$?*Aga>JtvcKoBy zPbjCH9D)#n93&_rz#JbGQWTV~`rU~jM=z^LM^~5CDdYBi@fNQ3F$}Wz$Jy&kQ&RY5 z@>;F&gWF;)n%i;;BWu2%btAd33+%6;b*KhUn|0d*H|r_&4zhjb<1G{`kVIN9i`2dj zDSIcqqq`3F4y%xgtT6}Y+Aj^Br+OU9lB<7gZ$*YY(iiLlb{Fy;(s&($Ekz_Z|6oa# zWIH~hF|xm<6Q6IfvISWx@Z5%N)H1wwg;@-e2bpeUGiWTZby-h+Yq*rpVCV0h_jT5P1-ooNhOVUp{sHm=e-pi@BN2F-W<}su3UV5l2cbJu%5Odmo`r_-ofA z=2xj$%C-79XZTf4-oex9k{xf&WvqY#`0OLf#nbS95~x0|%o<4{d6%r$Jwl9Ll!1=V(a@l9BcaYa<9EN`^EOf86)KUMwZQ!C+@AX~u_pgPyBl+V>3*!wLJLdnYcuWYTGIo~N>@?Vo8f;4H7elENS zR-gT=h#fq&{C2j6vwyYHxa)1$QYA%`N2{ieBH!w;MIx{q?~!wlb7>p0;aOj=boQii4z20bdUq zjYC(GE_}Y1KVMX;OAf$Rf_LhjIWSv;oux7vS}AmAnqYK^k@X`f(oU@_2&#@p%7v_0Z4R z=J6I{XgZrS8VW%deNFuec6`S+v@RJc5FU1t%a_`6_CbZ@CZoyjh})WQ);k?e<~0;h zNb9e)+oF?I${q?gLK!#TVIZFumn2pB`Rj0_>JIB_v0RI- zdFC~+))5yW6hTFD@fQwx@Si7nvQ~?-o}6hYpLGwi-K+FR+Cf3DR85?7Ci2JoBX3#4 zP!lWttWbLb%k|r{Jvnkp$Wj|Y!m1T-7VL=9@Uve~TN8peEW8!ZegyJ5O?WnB-+A*8 z4QWEL?6>fHGXmD8)&9VKyZw+{>GaJ+okMW>+03nTk!hk^UhmKTFuQO6u4NH~H88~h z-QJV5t&Au)bba@u!xt3+nMzXo7Vqa9SRnT@;1BJ*yvdwQ1h{Uc0>Q`DrEyl0^ALSG zr}h0H$(rz2<3UrCJ(F{PYaSg=#UroL;dTa?A6n&RRNTmDiT=Pno59vqSgbi;LJgcA z%%-ATATOL+&98pGhNfo7&xK#@aC$(4s|Xh{9&NDeq)BD(RmJ%l{1@ezZ<>)1vV$>n zB1RPghVNz?g>(eLUYJJs$)BAuku^pkO(PHPWj1L^%$9eLnPgfME%B5<#h$C<-S4*z zTk|RPXo8alhq6&2@qWB9m*?MkEu!nQlVfIOgcIy@O8Y!%tv?T}NUXy;cDFO8*kJ#Y)V%p)D&$JVaEF zcf5p&j1#hgJ=}SOau1KU?Sn0pF?#H4;#eSS<7I@FlV%M`U93a~S|*bI6SdKG9ti56 znAKMq4NiCDjDySa^^n7S4!g(=pdZOXfZ(*%t;yRhB) z`Sc!{V-qlbL`I^>+i{CB+wv^Nz6JYI=QkKI%nrOXkia3P2G4pZQ-pjr^NoIKc3Zvd7?b%2V6< z*;gS;mZNfW@sNP)AX}}D86FyIy4YSUo`bMIe(mBF9ZMQJZ|Nq97=#B10~Zh$JLwa?R2F{x?iMnrUDd-e)NT&=-P(TI1~9 z?{nK!F0U`dA@Q=Gi!)j73UTC`D3#i6`edUJKhyc~S0iN6u^Nj1f|r|ZkC;y9bR}$Y z+i25#;s6BN-WHGs|FiB{cOpKwdS?_3*MEoPe6o{r<0%kc>CVfn(~|WWvNo4`PClI?IY@vcDNa2nEFE)1~u+w(1=DoumJ5X5Y!%8zHU?M_vQEVmiQ=5 zG?}ozcFC{FHd=;>yD?hoS>bTIOfP|wwZG1dZqmDQpX!cCUYpOmD-JBMgrlc^?5S(Z zap6wuMs?%s$D#Be5Omn6c8b_5mS(;~G6-FtbyNMgI3!MTi(vF2y}G)v*JyR2Q|TXW z^L#}-oV|Yy!41RMu(L_c7t|-{;FqL11bMv&Jj3gmj&!>-Le9k9k94!rF z@7e}o;iq=K?mfY$Nk>aU6=13*zZQ#V#Z6GVPZKYbb&xwBiCW;tw6>Edv$)1+|H4+W zD>}?zwmz^?4>-wEJwi3Fc?JXv`uk}f+FTA0qp{|O9FEutYme2#8Lx``Ob0dy-0^lb zTt*8zK;JAPcpfNd8Ihgo9~#hPvU1j)EZ`I*Sla5;7nM219~zO`#gHc{h!QkH;zY>4 zey2@!?kzjVNcg+-mGU0@U^>ze4|y*uzowhv>$11i5TBTDtb{RsUIs#Q^MZ>tU-F3h zk=43b!K=t6aVr89>0GpZQkGuNpKU^$->eLaC|)JsC?iogw>uBdOPwXzysocE>;*er z?n2&43%P_kr#d?UHv4x~uUPc6hw>tnH;5UFyt2$yYd$F`-%CdXjQ;tMI%Z+WKeh z_!Ej9TDr7f6=|R67@! zCK40)G&Hs1y$%Nqi=gpar&?Q^7P`{5oUP_%x$Q=Yo{6sF_NoC9MTj zQ)P&}v)__x^gJpsSXEfbQ`5a_h)!a3o{!(jJf7cR0b;!FYuk~?k3PF^$AHA-Ju=t; z3<_Om`2-B=(71EhQ0kM{7Ti9W@dVrRm!8qu0rFD&-Iof|8{gDDW3|F<%jwBtvXWzi zPy6ThRCv`(?0!8{lWR4Y{&>mnnBmgE3mA3WY#$^loWdCDWlzS_-&8J64K_PWzJ_i2 zYla`py9ts z?{VZoWu$}Cs)K(oWeG(Mk6K34b$ZAw!c}ljym0h|!DD4#@k^_(squ!=^`2XphKaL0 z+i4osuq1iEV`I+wOPAuK(<*^UeLd48OZgxnZ`DNRAp<;Uv~r+C(;H>%b|BYCwu}J* zjw~Al@u*0Z8~xtN(T?i8=`rnW^4#B37BK*!5bsFc5t zrzpK2C$dWw=v!~#CZsr^!_Ru-jH`$McRI+rlShEn*@;ULvO^Aw4$>tTXWjLWP)9j1 zha6q@T>M6W{D4!Df!M!Jp7K>;Ot!k4?B$o5i8FQhAe?sHcKNomgCjW6Qi5I0K zyaUP32FbDU3=bO}q8VlQnT2z767FJ4Z*nHD5zx zpYLMp7@I8HFtK256Rs>o;K&aZ$`ux@1`hHFrJHXWcfTaTFoy*4{{Hv8F0URsH)OS= zqj|N5qg`*pJni@csvUpntl(5-&GN9RbSx?eWmvaHX+Lk!x#GyOv%E}MNAvgC#K_Tx zCFuxVJC4Yb!!9T;lKuxHTrAz?epd@O($Yy(psn8Mr0?IWcP1b*O5LE~YMM1qBzb;B zLeh{(sqIS>1cv+g&Sz2BAMBcouD_8)lD0I#*w_xv=<{11XwftD_l#j8js&0QfN`mi zHh9Coe-ay8#38MkX4_mKR&Iuz$3b7NEC-I_8k5YVrZ07*pcb7uRtq+pt|!zl?M`0s zyzSqrJi!6)c3{)KA*+wcLYTv?eQ~4_2{Op>2FSt(ZYHKHpdkSv zi$GXQ}pA;AAq_i+|z|VwvmCoHdUz5L+v`StU zK5#!zx=Zcv4$`r?AN*{zj1k!}K$3p~j5^@+OFfzYPR##zWH#08r2H7+ns&WD0I}ji z2v0e13JZIpvxFS1JcODqZjdHXEbnh6h!DIbV?L2~mLR^c<39syb0g(gmP22;HxIW{ z5+VW(#+1v>k+e|KG1x$IRKG&}7R}U1zV9?FX5Od++UWWGq@4gnQFr`m-tZi2N}0vWvEDkVavphe{z)&A|7b4r=o1idXMCA@Ha~RABbXS zk`AoUUF)Ndh8>Z68FyhRBYGTtf`RA8)!5D3Ph?qIQD3ADvwmQ}7wN!8YJH`ad4D}h zdMQ8E5$|)cXzE01xD~GQ_dDZ9q(oM4tzOM}tWBy>67Tig{V~Q)|Ch$dAl?tRkYU<} zV^kGgU|LR5FirEQ-O=WpL6s#b=sjAbxij|9jIkbkSsR+~1o6uIR#vJx#b0oz))CHG9d~zvV6DUe$!0 zePuhHi%CiKI@|&&UD@ex&L0Kk<~bHZ%i`Pf!LxJ=^%RSqBo02ZPF(j} zvPB5zj71E>a-~9H@^n|btINjED(7@^ED=qD#k4$V^UD(bA(szf!^+#CuxXy_`&*SU%=#pItAjWW8Z*5$53lR3o@f+` zYcVuRMY_P`)pT~kOq}u`jPiZ{)KRg^p%rvYMqBTv@JTE-gm{~7l((KJoLUrad@!i9 zw9aa!41-W#?*r1dM>Q7oRKu@rF80PjcP}Fm{X@_M;jtB{*;yf=WnOBZNYn%u58<_I zkaQMlJDO}G(6z<->h5s<1W|RHaCQ;=zBtYkNK{2+6rvCIzpl2YsnyhaJ3nPjkg_g3 z^j%JXW*1?TYjuUCmQ3@vg%go-_N#-Dw`wUVlCK%#Y_{9i?TJlxKD@dr#wOJ5tMng!>`qc`Ykf+t z(}qb5hdQK#*A@AJicZ=NoGiln!4%;`n7`vllOj@C(~t>=@*4-7Q~9j|a}3iGmhD!5 z6j{dD9PP(tG!)3wzSQ%`QERi4F*6qh4pR--uR1GS>Mu;`yuDUhyfPY&#tJ0%;qTnn zvR>gM_#VO=L^4xqlp(z77fMKp$$|n0@=~yA&0nN=>t4R$wa#4y#|B}RKfzK#oNH!O z#g3Br)Yh3ABnJ}6Mz=AHdi{*mvL0tE`d{hQ2N4Gw(&AoN-DE^GAobRvmQk<86f-!@ z5Yfv;dh%rIu&EfwOH4jL?%f}rXw0Q_VwqTDp4dQJcz^mNe3we=-|_J}sDi74=}wEz zPTP{Xb3F5L6=|2P2&toYzgz)XI+f`a3IWCsBkv%; zsv-+zenCaC+_m}{N>dP&t#_26FIC(2w6*Sy$9gR=nSZJdk*wuy_n`pZk<>zC-lME{ zt*BfinCb@BCtL6~iAHVr%~O~zr4Kz{xkMAslkEOaPIja@{@!6oumf}3_rNUod5PRn&rbfTr2v;*Uf5jrS zoM}0b5s{q7MOrJSk97H<2Pu5e_T}L7jc&FivvOB?+Q#2Oj61bE6j`rKlkN@HJdCqj zMR8_anonEZXg0yO(oT?B8-1+1*uc>gsZXSQYV~B-w3^(`%l|nBONl061p7M(JyH;} zv1Z1$s9N!>j-Dh^LTg#<^9zjIX|_xNy4#7O_GQtF%cHkoEnwC?VHL|hXkD>rKIUERs;4YMn~pnSyhYa z=?g_+D)dG((68@B zJ0cS`Ga^4?$Ctj{K5B5r2H4UR>_iDlqPNR%=N5E1eTeRj(rFfn*>VFzwtSpJ+(1lX zV7LS?RH6rAah0?mv`Iu6hBo*dwWL*ZR@v|LayfJ@qlu*l&hco(tL76y*1elR_-q38 z$V%1OP0wuYf5s2J&hyu|X;M>;Ybg&WF?M&DQy-b5QUWam>q-quj{uBxPNdgUP6=Rcl~=^~$4u z5PK6-aI2)Adp1!GzW%)bt(f!G_TM6|Hl1z{lj6b5)jcEWyc_7$0Hnji|0bU`gx@c| zQHm6*yT*~rM`?aMYdl^Y;E7(~y@c{Hw-vaZ}xne$B%4DZ4&R zsq!s=`{@<0oe}Otc#;8Ewn>$50=C(Bo*{lcQ8?g!ss#JfLuhQNtgiTvS8R&}Y7wn+ zmaIAFWXz~zfrZ1!QvBlXkH*H=fM5+6nE1jMSUA0iu{8P_V+7=-K8tkx@FS9%!w-e! z-KJ;a{QqI;9Na2x->?5nQ4PHX%MfiChdi>KV2N}%USoGko zOV;>^?92AUoUJM@FjE`s02?5NWDILbgB%Y3O7lA7&$hkvbWO09_Nt;5j?i~+rf_^~ znAEaAmHY4N`aGG`xa+Q)UKz_{AFgXdPW^|P!m+9)!_Kz-2Bo5Yehjbv)-D>` z4RFnI?o1Y=!2a?{Yb^1RAF{ra46~r&xsa<>Q~wSkMSdO7Rw8D+w(}l;H^#slZgdR? zvm8xt(a-${Z{vEF*ROnCXgD5Wi8THMYG*le{bxUdA5z2J&^_m?kUwy$CXG7Wg6pq3 z%u5WbbxI4#U}6-N`x}}LeIB9)v$v_c8v*lKB+we$lr|3s!)a{(M8yhKE?N2 zhu@bv2^uy`{kKm-ey*R)SF1+$1iwGj;-0kXay_Gh2Oj02+=Fe!#_y}Zz@)$N&f?+v zn%rJFHRG&AQF-#sb3Xwc&7vD3w6+7DzWvtz)Q?YYt@W(Dcy`mA4>#NKd#nfuH}u@u z$^Bp{D#)|Ot{0915^h)4tcUvS)%p||G>r?8D~u+#wf%>Jb`A$2)&4y4svfHkUgErI z!XEZ2*Z3+#I;wQ&tkLXy5DZN+t zG1Bw4zo%W1(xgY$z_>I4luUuPiPYgMT05~PX(?-#lJr}**V>*zttVPGD6vroDq-9Y zm*a#P&t$)5Ex&@AGhRLp7*mi5(lfssX=P`(*^4fpbV+dkIpAqNDuwS(F}LcuQlGw4 zCYv15HEr!yB#Y@oJ)vv&DJA3hYnVSr;;-CNDN|)IY8?&BKWwed=@bwvdarxUA08Lm zu}(Zor=ShG7T2q|UkSrP7M)cl+o#Q^O4QiH7)YG^OXA+FSccMSZtpgP`tHt7XgVrl z?MyFyHm!~vG=KX37M9O6vTCW*=4D?>tu7lRb&7^s^)W}3m`>b>i?b239R#0a(0E0h&tmPlGRE{xvaOxFnB>IDYR|2=BBtd{O2*ZC z-UoF{(3;e0WmNlkDSjRvjRklOJxyE8rc>}{n77>e{I2m_3&nSr!9fcWYAcfAA_=kY z$0H|%@3^Rl+9~V?a`&nnm?R`@AsFEni=|Y}I&RfIj;S{U(EBr&5XN`CJqy+iqxH2toR0pN{Vf+gC~YYmRqnnSQpbEO?Y&lWU+Hx7~h^xF_^y zP87L#%eMFOBpGDSj3@~3a?)C+DP(#G!2&$_*RA^z$&0_s;xwimPK8P-ga?T6KF|D} zh{ql&M%%;~(D}AAaRYPHc}Exb!G9FPzcSh*6ole8HzGa0TvQ}tD(y~p%)FBPc3BG> z-}`6>zhSsmuO^RL7aJztof-^NtBAaLCqx+GrF69PU{*iE77H?TDsr)m%ET)8{;8$% zA@(XTjs3c)trUo**euR#B`CM(#S5a^lDeUi0`s*Y7{_)Zd84w|YJC z>2+h`#~)oan3HbsK{<>5y%aMpH46Ek#ypHu_Owq*bcz3rI#3#|*VU6rS*?)YTP=_2 zs2ugO!UlWlU~&BUhwnS4v5E=>x%{EDt4_u6Op@Cz&3e~+2#9>H+buZ5)`V2{>krZ0 z?|cp9DEUPXYP7|QsZxd0_A#ICjgu^`5!VCc0uUW`YsO)d2)s4f>A2|h`Ftj%Mt-*# z(d%;*N2K%M9U$wPF!<7#Xzw*9zR99Q0Mni617$rp*DUqFF$FrQ+hoZhs>T!q;-b>)Z3LvKMU?i}uV}=X>avf-{8|F_#KcJyWL251BXxb%*_|W( zL;Q;Q_NuH*GlKObSERIuLqE)KR2?J^Ngo zCWupUyV|Q;HQ(?}=J^dtEpWC0ff&wByNtwpoq)jRjSi*uj5c{(P2!uP&c}-xDeDXt5AnMio^ocEDM6 zBjSGIO!c6YhdFPwOcoD^7FeUN%IT-t-4rN zSU~#m=1(!lrC8YSIDyqvajaEhH6ww{$VO>OqK2YO7bCcF3~pJJ*))BM!QUOfF13 zFD;Vcd_?51+r8NFM#*Zt&PH32?5-YYsjZlSrzvEu*;VPhHoHP#hUtys!N1kJnU0)z-IhokMhgml2Bm@uNC*(Z7seAelQWZP$`xz^LP4BK=$p{u`0kO;9ggdEW{3L zIbs{RZJr|Z&Z5t1QKE6|bRQbLT;(Po+e@m>?)LMX($qQqD!T4=<|J6Cwe0FN`<80i z2>DQTl{z>X>*6qC9SwFsg(hQu33k6#l}H$0PZVF?PD+6#&fl$*c%_Vs*6ra6JqlIS zds8)m<80W1WcmE{3GVeXeEXyVr5>+mfZbz;bB-a-L({@Bb161MY# zoH8mh;)Iw{K#YUA<82&<@}DP5#M4_5^K0ej^sf&)e)}NCqS1-zk>c9^DiRI> zkRYi7o7Q+Njjyw&HF73<-5#T|`q0%`aqAh7D*Kb8F1?GM76(VrEOYFPeM+Mp zdy{}p{lK`0gd4{nF zyZ=p5T;g`X!w2!1d%pH~lgs64_3q{I^$r)Vgz7>uHKn04nqFIk(m9!7U07@oI$<20 zku4ReGtF>OEA}({-YWhN8DBps)p|n1T(~yvZw8(%!^YwRrOEPzxwTy$-bVzig_Njy zq8G`ySm)coK6bOx!1yD5E_!GgcHzJwil$E5Q=;)9S24g5NB04f4K6X_K`q+UFWooR zH*4U9E^8hJ|{xUkfpI$F!eG*GAEb)_W3%wJ6w^|Wp732tYTe-M<}^3YR9>*_pjS>U|IMKv>8{RNxM3LB?-$W4_N z6lf*c%yq_-?*-krnhTa^nW0u+FaG#JZOTgTHeCShDvI<0Hh8Pyfiw@cAw*ZAB`R%EXE^?=fyM^&J*2 zK)?gE+D$AvrT?nRDTjaH-110RVRfJ1uGb~bvMywYfa5|=B)!x_5~uzPqEj;t3d}UU zx?E7U=_$(=Rxd$o*chzX--&NS=Gf=ub_1_$U{$qCNVpULw243Hht#!Qe=>Y6^SU1bJ=056iE$ZgAJVV0< zLbG3_>2ft^Fb}fdPb;AT$!yO@n@lZM091W3DVIc@pQ3YEWqG>if@=uzds}K7QHP6s zl!elu!y})@Y?|BjCFd@~paOxJc(xG-&F2>ep%OzQox=EM3?ik#&ln|Pq8!{$E*c>E z`a`)z9`8OfATlzFi3daD>1=T_f_J%B2E2~N)k>Bej^@}Tq}zfPr+l+oGn0vOtIML; z%h;yH8h@)Q$cm?jo)nIjnr!U?wfKO`8l)Tk@TO2FGbA^QV$C#a`gK%A?R@(?J@<5U zN6tuqC>p5`m6!EjHE6jpE5%lq0e!fM`rnX}TyLdh382hTRr(j{o*#b=Nf%yh<2}|! zGT(Xp^jsrT zB?E{Qjn%}H!X+>qFFDegy!yv753Ie0&M7ZyW<;cHu{_rqcIM@dgd}FAw_1}r^0Lk) zTlW$bPy5uG`g?0dbg4o*Yt04j$>@g_RH#~u4lYJ`IqdOLQl~gnq8o4H3D;yD z?~Q*?L1iL%qfF0LZQiFr0gK|C1%GezBW0glCnj&2?OHGhLBY&Uk1R!Tk-`G+!6te! zIO=^q8I=2XYnGk~4*}@Lf&9Qda9CnldN=hp)<-8St@pK|?GL$sC|P}6AHka-!YBQ6 zMjiDs7J+M1?a;hgN6L!Qlm~E~*RHEdhA-DM5uS(Y z0QysKuD}7XZpNizyy?QyqVTbG9rD``x^Zb{6NsywH+V-!UNiNzlIverr;2(198~9p z^aw#+=W13;{hw0M`lV-!Ax91SP(}Q853G}wp{##LQ?7zONO4!|-A+ZY`})-G_Hs`e zMs44rr#PK_ISloZqI`PPhrsT?QlKhp22m-Cn=4Zo2gud|qKv27t`Gr~S5IrlzvRkS z%q(;WN3QZj(`0W9mzv5BLFWD?IU1|oS1FzQ=uA6}^ODrfvLsVf*?ut28Y7{bfO;)F z+r$mm+UYmhY40)R&d^KLZv%yvDXpFm9bQx6Vm`j~16G;m>HDW32VZY!Xv0aL2fP8~ z>E4hj26fJFt&YaO;0GB*OX0|8+S;uK+RG&$d()6qI@H zJtR;XgvnKe4JS1OowSZs8=6Y;RusJ3LG5Ku39 zcXVZxC5J*l?g{GiB7_pfo$;M@R(aVFqfj(yGm3zh{XSj$izJ|qr0D`!7gv_S{`2D2 zYG8jFl>4|OFx`tRJ2dPOp>v8&jZwH$V}@YCQSFx@#H3U&9$0t8n5XiDO6MVsf@aQY z?!uC)pOEpDS&2B7Z9{mV8+ZS7yU8=+=+9kwiGSpcVau3DQKVu$itV8yG*#8*HtY;K zt^vE0D^7z)oTZr!`JrXy>$Y z6TF11^jN*mn>=KJ*4bfP(eU=AW<^9ZFPOlWCcne^U6vB+M#D zXe>FJMPtA2;@1fcXNo9p&e#wUi-g3-DQ^992 z%zCQa-eA2?J|MryzLb~cm7qiMsvTo$9Ci5(phybA^H@u%LXI?fhhqpu_gvbfAMyc}N{Q78@nvfH+RL*v!mde#)S zf9MF@bVJVwnc4VZ9`8B(kQ3#|{QI6NOSG!U$`a^9J*PxwU|~y}XBJCf9eBvralS2x zj@u|c{q%Zm)FEy`L#HLhT#dfy47{54G#^jCsS-x4@e!m$s~$e`0ogJ~c#iHvsG(1+ zunX|6eQR+!XFqUo)i*4zdocuf5N_*ZA~qIZuT=0Cx*z9-35=4re0dP_p<-F5;S{1P zR+-(n0lFBD+w#lphM$8;-CiAp}u__0C5wgZ>HepNMJa4Tp)p? zd8oZ8Dwjq!M(XJ4c%ffs{3>CmLvPD)(rYZQ^cZY)@B)~pmoqTB-=(}LhVG;uA^|E~ z`S^n3?JgDjm1irRdDt6zd?$=Qv`P(M;}c8b!#k44#+N9a{!yL6e<=seNh`{#tgfCg z-2te|UF?VO%t1^vV`44_jBq?r_;iuCg+?YmGqT-J`0yMOBIgR9T+*}JVmRo=)(yzD z|Dyl`9B}E&!Z`?8YrdiQ;g4oHKJgw@MuNoU71^XT(3IbB7eq-v&+iU@gaL=m)+VN6 zGTe+~V5pYYZ{r{=`3)j!Ki7TN^I6nZzn2I7Z8eh~1_P@M4oFw#rZtb-I54|e%1is~ zH5_`RNiKQz6qEDj`UgQBHTS1avceh^s0ut0->%C~V5wirJZ;3U$JUlvny;>Quy()P z^013+<>ke{M-z5!_6sVu=27IRc0oo=xcPZ6I&=lf=;p*X2*y9rJp>+-6_| zL0(cmnIBwi4pThsrGLPp=6h)|}4#KNY@u10CAxbXSR_DiLBBmv4dl_h{br54B`}GAue^{g| zLx4ne(?Fh@I2dny_ndsOqRjm&hTl92@p$+( zy0Zf=4(@0A&gl8a^8HcMyud?y`}*d59?y*2UB=kk=(pIiSjVEQDMxi zRqrp^pKg{x{|WBpvE&yK`SxVseqQhL2-fVwa;R1uNZT{uVKhaP>L%f%K?$w(iY>I> zv`e7M$D8qtv_%hZBWa*X5YP{{BdW7UEDWa3gM5B$`PZ zRT_A4d>SpBE-}8-Ik2f#tfwnenK@0Vt#AoGfgJSa?KQH++LCyH)2xU&|3Wwm?(on-2vAG!Ictl3r>L+p+=v^MK^iuh(`!LSuctzpp#ZP&d#%?@A2O?PWh@&nU z@mT|$0RIac4pz&Jm>2L&B&^I_-s)9`LbobDWcH;nd8pOqVa?m+clbXqi)Hwkg0TR6=q@bKcISO$P!+`#jp#js8?UJaV8{E zc2Qhk9tPP*G}Qme*q@$=L`Gps@Y|dl6u)oq|C^`zyxzm$IS7TSvpf>4cH{qtgh<@7p8nd9np#4>@px}zls`0GUx=d9Hb7a2pOhJnqZ z0XrSJ3Y3R|rodYrra^PCDcVZ1|I&P)N?+A^FG!8=vmIzrQ*Hn8^c@~Z+5PvHn2;~k z+py*zdx?YI-xMERw7t3FmSkEEGc0;oH;PoH`?>^c4yZ>O=WR%6LKV9%xVsTf+Lp+! z^~K{CZ=^z!@oBmNcsxsVm zv2SPnlPs5q7{36v%rPNTR*|mNUdq46ffl1A-RtM;u-3-z7K*1g`3Ko{^2v_*L_>8;Ve_MY$*!s()D3h=Rb>7$q zb*HuPyz^E(3FTU^sr11sTkdA%GMW_rIv&V>aH`|NKZ z7&&F(>c!sF>h3hWkY(@tt+$HX#XOK>z|Ou?qd4iBgu!maMxaBpLeYe2>u~(36=#~9 zF_;0bNt2*-tXX@H?8BSi*xOb`E{gPk?6)H}AM0Apwd2iO)vo#|euQbX&<7`0Ti z@&w(3jh4}^)`n<%w*)2Mm>XhiWg~L?16zOXE9c2DS#?i}7n*Z>Jg2e1k`WO}o@*pw z4y&S%e;_-Z0NaEE(T{rTJ5Xuk-Hw51+rgJ_O(nnKRtj6d8i6aMlZ7oO%KY zxX%L0%&p^gzPAg*%8=CCu4K8*1k+zrqGs>+X=A!rJGOmDnpnJDiyrIZtd>C$c88`> zxX?R39^IeQ+R-oCe|x0u&bHHcNQ&c(rT;}1BAzef(Ho`>HcY-T;GxiK(cq58RV(SU znhV9iKc#ItCG#X~$i`9u{w{F#t5itOP~e_32A#deeq(HkpY{Qw42}h}+ba(sp1sOy z3B=wQb8jE&rdekN1O(mSf+anfa;l&08xHXHyPNUqU-tj$*MBZk-CkhP+TV?$3@ zz7E{VrRR0hH`7399G$8F=L(_QpAUDZnH*#_OMOX*8rWX?Q23tdu#&8o0uMlA-4pqg z>O5S%)L+DyErzCbkjEIOlDVT^5k|FKS)ELA4nW+996cTRdQ{3sQ$k?C&}SOy|Ew-w zZ0~EyBSAR3ATQGO+U>IAI|XEBw`M$>641OGKE66N?_)XD!^`&5Ws;;F5~nrI`b}gQ z#6UM};{R-o;d&W}XJm+VshHDVpSBKK61CeXE?cOkYrHI%ICL~fJ@F`|;_6#3b0k`C zzk(TZ&Dv=y1&gh%0wJF6{!rc#em2Jv9;5%bzs1vGP{wPA@K3^@16s+PO}C<0214jr zP@TOF=0Ds#wzn{J(X z=9=5nA?pimjNsiCm7sG<8_QEor0-Y9BbMXMdNhjzLgEQVLDY{ zgHX1Jm&UDk7b9T^Os-4+D7u)P8TWa}+nBGz5}Ii~4ms}n0SGVSAK$aWgcI>DVl#1uG3 zW5RTEMz>~~9}C9kK49~z@$h1~J*3mBzDixlI{Xf%oHFJ*V@tn;_vH7S1WqVghepCK zx;+b5Z4Q(yMde)8two$Y6&sn0Odt)W^YDeLLibS*qkhBXV=|ni`>V&#pN9|JxwX%OTjF<3&PET`bFlb8K#a_8o3vEQP z@0IukIZaf5Ecz+rT9_X?TR_Tgjn9<-OMfm-7|p4^6+s(q?Txugw0eCM#}Bq0bc=Gx`L(zmxhJy%mmD*TP@R%ZnQi(r}je*6wk7%J4*T?g;G8vpG^JS{S zz^ih}R$N@%$S9-^3ra+VRvk{2OfE})mpHk-)?WAgIy4a1g8Fn+sL;YVXEp;17U_I5 zQzA_O$SKH)csg7_$q}|dItM_Ck2+wXwKF3zX77%RW+>B9IBmflmi%G;Ygry=+f8f1 zv4eH#f2OuMX1Z*s^e%GuTlFVz`qU#oj(1Y1{&k>3dg+q9&YV%BFtuh<3Z1FH&dskI)Xdn5&Ll_c*A5_h zTu8v(`P9zN_&O|H*HB-Z0$hkXrW8W<3?Zp0;`o9DC;p-dBI01_+N*2OBq_Lu!mee! zmti2x{WdEA*;b^NSXRLO!cMHz;aamo5`fAqJD#RmqJ$$qI33-LwWfm4t<3!? z21k$Ho^IdKXoaz6ZGQ1Nwv>la9!=T1%~IQV<|w;1h7;xtl=GfELJ8L^T4ONZ!QP)H z1Ek47ppAt3mfaKV97}TzpWTLOez5nFGN!WNzt}{bQJ$WsVxj@*!1RU7`FLuTUA1h} zcIOm-sf)-I4UH@=R)f7ad@8qCNEh`$qGXgBZ~#eJ?J+Gl4)}Q{&*TRmlNh9?lrGr1 z;KR949J%j*44!aHw;HLHo5I3; z8@xl}OsL=#@ghdq6!XAi=1<}=;Ev9Pg)c z;z*L>;;arI{x7Lc{O5n;bb@kr^&M7%S=lfgR~t!oLfD}n0xzNES;Kh~PBu^#i%3RsvnJQUk7dbG=1 zFT6lgghD5={vi5(hg>PnI}l^*wbC&yjs4)E{@Xfy9hl)fS$>+M>KFZRg^L;yeZeqJ$~3`i6q=`9|W;a5~_6{VLKaE zPO0JP^f6knUo52<2ggs#RT(%tR+~_g1cfP3Hb3;6;fq_rM?K{RY6DVwlihCP+Eeok z6KE1&;zR8$TOJwfz@G!PIwPQCo?;k(L zP3%d&doL&lMm|AyX_JkC>a01DDkBfxixw-nimX%mF%^vcs^X<&;-jx)L2B;-g>adU z5V{|)Oa?ke-`1wgPw+H++DSMY&N(N~yN9i#(@jI;jKhU-ruphL^D-Kuy|EIii`wEw zu%cONY!SPw5$sNj{%$YmpAoi`L^*)*Uz)`fq0Vr_h#1&$K77#V$r2gV@g`PEY|;d? zVoHyCpW2n4&fK9SNz)5@WdErg0$pbNIN@=R*L(0n!otgYqT!f;z@cbPBDn1qPa*pi z3oF?1;2kgW>TrhCYN_Va)6K!q_CR=QpRUZ$pG2cc^x=(`i`z%D#c&?1Y2sttD-~>+ zZVyNB3~LJ+;};nqkWzL#p6{tvRcIo$THe=No?|;ObhD*eg^c0bN{L=KYI7%#60hz# zuUO-bl%C}4QkPk`)CF>+EO>l>%{3XVYU?p7BfNaspg`R>VFnZ9EtZPS*Q!NmLmgdl zv(5v8(kZsyC)Yc0)i!uk0-F0_zX{DS{wsyzmlU*Nd>+qk=(4$;ho{CY#Sp!?n^M?% zd)>`0bJS#xaX7IT$Wc*rMwJ4h@Qs=*-iUOo9M1fw1%q+6%8DP(sNQBue@1O#nhAkj zpr34X-)ej@7`tF3;FZgNa2VXl@X_Q;YU4i+wB&|{Mh&(hmkKx)zI;I_<&MJ0)qAay z_Ro_oso^~JA_B5D_2!ZMf>(_1!T}nRmwgRGs~gnutDeR1CRSMyO29wv)9s&e$l@_I z2-obK!IB>6p!GGs!zE2&bkwqm=_e21C~iUedi>~WU#r6}7Frao`dHd~wa?KE76Z7g zRN3|6(JJy66tqc3OqXva5q`>^f_kSq9mudeQ+8m0e-IMSj)qR4tj_8hL=83U&|54z z{I0=N%f@dQx#41?804V^9B78UDm@FEypPXMxa~5t-bKPsUHHz#^d^^MHebuEfe+yM&q|MNv*?c79q%NOzfKoUa0B9i$iRKqNG`a?ka%jJKQeA^; zaP`l{xh9~=blR_bM{U0%kY}XvV4n9NaqSJp|NIwjP^lR4wFz3HPY(ZS^;e#-{=O~W z?n=li9g-h)VA0KXas zR)3(ev7AQ<&n>=L?wZ5C4OYHxGl}Q<^8EDduX!R85ItK%;8K-c{LiSmH?s@P4q=1 zOuw;aOI978 z%CYkvIhppXe8IJ$Z)@iE0DP_}yidXpIT9Yl*%`Kq&y{rpOU4d;_@Q0@$>s_3^P)3Q z(1br<-lHK|^CCL;kGFm1sFQh!7jacJ!*;%siyLWc%{G>WeP>H{ zU=Tvn<32b_P?x&)`TZ`~0n1r;K}G<7Ep2rpiBoL**7a$zPN~SjoTANAb(@9nAX0BU zkI6x5)&l@&kG_cNt!D<&Fr^2?`wiq`u@yZbbhuxL z42EzfMX4t{*c+|FH_?2?XXLio>0wNQc3aRn59LjVP|}iOB9gV7OxOqMu}8LZ-W$Hw zIQy_>$e2iAq&FRvhLcM=PUo#EXgQSwsdrTG)J zeEhz3nrTUFx}Q?_8Sw4)e5g{Yre!q9=?kA!zu+CXSxnpcJxM(nSId00SRl8=hJ&iZ zbH9(3g=D5tfiR$&g0y*)>q^f*3W?yHFL5Pw$XkWh-06MwsTJK~Kd=li`HP*QZ9Mxj zbpJuPr4KzW=id{Cod5*>5vs>!4C9#DM6=5hU*TUnooxQ2O2xxoy#!edR)fCBMan27 zxqlRvPe4HGCK)mi)P;Vqj-t}6;&gojSUh_td`ykLFM#6;|gonieKzh^wo^?le!a(C; z!-$9AMrF7wQ)e-MQA73!W<-BXdkWsQAZ(YCaZmU5QvjCT$$oI5vYMkVvhUj)R=d6) zPey%5i=aJwJlK7E&gT85-RFc`#%i%jS&^bPlR~>+!L=V~J5VeLJZz!1Txe-uw(@c# zeO}FX!>c}1Zm~gh|7t-9tVtBH55Cn>`rr5B|6t4aGq+F5M!Q%RKV|uPU_5MJNQVa1 z&^;g!AmkEF3TIt|Q!qLlJ2v`lR?%a*YfUHYyCj;W%o~{JGwMvk)_BN>TU<{QJy*|) z<<|Na$6+c!Kna+6J^zF8?%=Bnv7Feyv!u2}y*wiA>O9lTsNQZXnC8Wcn48!L1<%9w!_xBnJSO%@FZ-~AlFWdzG^qXgH@+LiE$K<9Pg6V$0?#zAt zvpdh8V$Plo`_k}-x$=PS@!Vc4bi6b!L!rg#6Q@Yjp#Rp`zxFy8E0SP#Z=0j^53b&A z*H`6RMz_<9K=&kr78fltDhm9LK-gy+J5AHzI#ox84y3Cl0pmqw1%&<4bI?@M1xvY+KxvcoW~gMew3P8?brS=n$0C43As|tdjz8 zO;7vyVYxU*HW3AgUuORMNeW)$qWnhxK)$-&l^MMvb$5#l&-}$(3)G^{pFpP%0W2|l-f_-cbw{Etsv3Gh zSjVYWttG?j1q{X$Ew`s@Yr(2B8>TZySsHJ|D01@3qd8 zHX;*|TTQf!URpHb0l=$*>9?@ss$g&jXnZFv2;dwB)f1%#2$oqLnQqc`3^Y}6%M{DVTN zQ2$X}3)7Nudid*iZu`Hz^0DVqf$K8QK={3uGOI0e6_ASx72Am~{KGfmA!PHs=Kq>m z^hU2;XnVpP!?Y6eAkH5&8LH#$$SJ6b)+&kq-1@^4iCoF+7tE2Ujk#8&?#O!4&!|;( zw=#)06;_}cZF0!^HwD;U{z`?K|8#2Pu2`ANe%dEyKe>GHpa-fl#zxE86)fc5WAZ)` zK_8>rdjnp1eL`TEj09m=8>Y&unJ?Rl%Tkb@@UA@TJLvy!1U0q1RE*^BEq3hcC|m6g4#XN`6Wr=S?!zLdTCZW{>VpY zJ>Le|U_Eh#U+8*=01Mt6z;{~nA{*3exm3VgA@YsZeXFUtllp{10TWnoBjiEz@#a|~ zZkvdS-R=EE0KJg4+0-7*q$utBa`aZ}cBAC1pqoL@$r*Ue0?!Xd|Mx`j_ z?EZGWJ6l}M>HAL5T)DA7H7nQ}kr{>wUp6;qTwx4Y<)E z6%K9}iwNmt5rA2mD(W+LkZ;Y{E$Mrosr0p^lUeRz$o}Dm=yHxZoUHGNB>DNYZBr;? zIM?Iq5z%Y(5o@Kf5I%O^&p>&?{^uPmMFRj+A%(UxUN<7iWk6{1Ik^&b$V!5+xzS>^hRJSc!+?w{su zatxxXefN_}HO^XbPG0kt?$Zx!w+w6lQzki`_EDIu?v9Ye3Cx5SNtvbT4-CI9=J$P! z_#Qmp;&M$v!O%3Rm>{Z#bHs(MOUFaxw#u9_<^-PUg@LUs&Y$WtZsY}~(H&D(D9va> zW9|e&g$~9sUM$n_3w?WZ%Dl;+C7$}Xbthr3Q0Uog(y7lXWnOGoUl=HLM2|x)eRWC- z4sms7Da_WCli!lqTwia7rj`is!hzlf3F8Jdpk~Ejk3SG2&*aWi0BEKh#{qV+cMgcA z1G($ma@AluePq~o!0jjXI{qg2S`6D!>nNTRZHSzDj~o^oiw-`zf+Dy!5}n{ONFCG4 zh}z1R_W(QheCK$QMVm1dE3qbPmY*i68xo^TsLn~Db8gGrQ)YLV;t{pgbVpPh_4La* zYN@<=L_LoZMwl5?Q7;^dUXzZp4<8KPQTQvAaFQV2~^Nr$a4vQD5(k;hrCcyP~y@iA7rGT50Y%3joL5 z8UqngPSq~rypmX%8iqGhoA1ZLPVaWNdbyfx>0;vIbh$dzhY{Q<&ILd5T@^?gX&zR% zukExT$?f?Tu}XI@6E-bMvY8^|&-*^0utj2pE~W{w%}MtJXM3_&gDQ)HKPSOiGQ*8SYp6@bw`l21WBvnUPRf|6egUjsQ_cA* zOT3nYxE7av3P8RboD}uENdRYJV*77KDu7=Duj<^9{4rcvZRSii`80Z+K$ z-48_4HDIaF>b3XJh5lTAjVQ5Q(=Zh+3V6Nd7^XbPC0m7`LF$U{Mno#5o?WAY4SSJ6H>+l) zd8a^owSeBVH*y`K_%lWiM_PaA5Ad-~9+QFdH&2-$6kaVW1b^g`(*hOnf;yBKXw z%pu<{TQH~qykX#u=8oTwV4<-4V*Qx&N1BGlF)m_{5 zw*fc|f%r6x*aQmh9Y6|5BH{wIr zxt>~Htk!Fll?C*}{^rF)7nszxENj9gC%7aA6DlpCH!+(R`P1J^A^9?#9(T8hAd;xO zi$DgNPWeG^tz5yAm6o`a!KS_i5M#2WCT(~_r2C_yG# zs{cvA*x3Z8HE>s=S2FBYeA3GBor{Kv{V>&!?hlA!kVrOTJLZM(lZEm3pHhsiVdtRY z_7;gd@S^Q^{H&YPGoaatiFmAhLkF`~Fh;_ak~}l?s;(^r2&>M112lQ2+PlBCw5PT} zt#VNO2to?jwU25;(4jSxzf%oA_**_uZYDGLqeg4aR`6m`;RRy}6EDEv{B0V*>1nqn z&JxQ1A4_K$RaM)y?OQ|;5Rh(=?(UYB?(UNAZfV$?lF?5VxggGy?Q2gcW0Pv2O~k6Ref#%W>(nmee7$vcihYu`9%eceXHKajVh|Xi)tf3B z>l}2zUQ2*~xDZ+gMCNb-(DPmsyga8V(${h`DO{|c0F#u1$K*75sZw(OWTH(bZfkv{ z#th4t_AH*7Xznkr`}3K)PgBTFY>|;2F5PyJhY|Y16~8F%w6KhTmlq^&w_l|XObBnu zXK&c>M+X)T4)I8IprC#biuB}Jo?5BfT(|!H`}Y9f{C4+~F+HD)iwn;OHNNV*D*%}h z6Z3srX|Agw&ci3_b+QsP^JEnh1!L~ewH)glq<%;T~N#`N+W8isoe26VC z#`B)$y|LAV13`XBJeKYR+`@x%s|u#eUmD;kG9p zT;o$lQ%+eD*V1~PC=6Eqj6st0QHmT@po=``_|;i`jHTD1@b=47QtO7;@DdQ@sIg+h z+e0~rT7wG3)3zLAGXJYSnW8aB_h=WE^O-Oak>t)Cz>e72JlFKN#F>~L*aV;e`3rYU zD@T`tJ3YScuOH~i0D?FX&bCUt>js&^aQ1LymWAW@X)h}5bsi6-+bSU=Wln80h!tC@ zyrO=S;zaD#(2eG=&z>_F{Q?hpwNAl=;FIcXGo!b;ZY38Am`D>i;&I&1Pmg~3negd#D>$Z{8+&}Gm6{gi?Y)d4ROI%g- zw+^e7xqC&W((dJ-2-o;7OHM)MRE__$V+tda`1Yy8F@)J|9HC)Do_7;z%MTdIkXT&aO|iGQUkm~;M$bnX zUZfzn&VQf%orvY68lYr@5%AmqP%P_$QQz_V*}Y!l-2=T$GUL0)8CETSFQE(`OWQyH zVc8_7$cm5&Hd9RewNeglO{5L8TK1Nra&_?UAQWoXTM{Bg4b-0i$ZlvH?zCYVB(eWq zAX)-pxaj@-v9{q!3!*IURg@qb^UB%qi1p+VY_l6l!d;T}^#Imh|BI!i(gn7o!P0>| zw+lxO=M+%E0mlk#AiPDcTB|Tl;|l|u#lZk4PYa0orDC-M4hD5k&$eW5Zq=3x=UKJI zlN1OL*6I0qUA#=fQ|+q;GAY;TA;{_O<<3MV01jqH?xff}R~_sA{HR2U%UXIKw8{2N zsYetwFIKDTx_-|Tyk<23LUc_GK{y;a9WkxQ38N{g{P1l5WRXJU=m;+yt>0YBRX(rW z?N6r7>mK!bTHv4rf6rOUPgn8{|L_0bBtYXlgv`wbR#%DOQHcj4F{qVF6o_yM2)0jG z+rP=lnMshNp`od~zV1!uY%e$3cwX-hSDfVLe#zu>LsV7Pn0kp@l(4Rwhz;BIk)Qz2V@o<7BtJO$@ar~%9OO$j<8N$6UG(SalAqzS1?i*Sw!@HCst`80&E_hdg+UhuO}f>rzpFXCwo;F% zfieE4qko_Eo7(kju$tw;$RumNJy+q?=a;_$`s(w^IQK5%%k8gyZadWuxwo!U>9=

yHd08fwiRulKD94k5{P8uPSm`bt8UkDvJO}7-YmtqGp;*X!7uaAp%jVZq{V`Wgn!P)RkuhI3dKyf z`upF9BH;|h5b>vO2s!V3OKvx)|2}EU`71}r5C7)oX5ygB@6o-({}~?z1?BimN2h#J zLd{|yR_YH_f%nu4!8DRNbZ2TreriYo-Eh~TP@rd(ARW5#y4KlX6MC4n-97GH!eFvB zpRi!P>CCKOh6%@emA1v7Lin`}CUdPjSPzttvgAzJMS;ugwmS$XpcXboNmOQ!Ba!oLIKnk;TIT@!wNZa225 zziAXei}Nw4ik-trqiOJFVS2r%@mF=?|FWe|w{lXiw4K1GxEnxtvu#kCR?tw<8RbaD z47!8hoVk(WK+{e!4PsX)(O(psm{)=e&~F1ueMlPi3tPGH{g0>mZ0$<^tg(5r%Rqe-^-OhJDl zJmxhgBrUskn`Hi1k4!-q0urDDjfgz~FWA|EN!{O_Guoq3;(UYc@dlTKHG7MUea#aM z-8eakZfM|0ZdN$`mqf2As;m1L{(3f%#=eB0Zf3TlSPd5c2c&-rOG@gG^4EKNdug=U zEXW(Z-t~t);S?m4d1ynt4SE+-nMd0^&L~gUHitVniSX4cXX8AUh!=ZfS!@tHEAD)+ z3tQ;Fb-&p47uS4olWn`3#-L6HFifOP-#StU$PI88(AkhQJXuNFKMPuHGSQi z<0a^^%}Mc~U$j-I6@iv+_&6QE0#BZRO|$)rC;9Mc2vF~+pRhS;^R4|i^y6eAl)n1- zLZM&ov+GJYg0e}B?YAC)=LMXlw_VdJ=Ik}zBPmYy0qiy2z#N`7)!YS&Oj^Ik?Y^kZ+V2fpLjl`x%j8+o&(tF3^wvVq^kcb`{Wu# z1_pqcCX*+F6_}jueJ;!Q5QbB8z&fDfbxmRR7C>B(4y|1x1+Ze+>aLsT=4GX&5*9GI zHp^Ao-40BGd}zC-A1J zDf52!7Tow!{RP18;ENr3tH&|3GhdwyPq(=0`uBm_RP#iofkfHx2qCyTY(<N6N1yx!gZ#GSzc1N&3CaS8@ zO23W@D!22JNYoguI8))%rn9~i)8cAOWhqf-2ogn$!&qc8!)%tv>m}~A7;h9$sY{S{ zt^S?LtUBln5oogCXjmN>v@#UPzNfFJ8M@FtDJk_6>_1b;A1 zVcWZ$p9OhU`ooz!O|R_*F2R5Q=Jwxb01FkgzVYSLUt%yz$%w$7wFD)*NNkIuj^Gwj|q1s0j$a?0IBkLm!$e6Bo!oxaHl3zuAjn;Glke- zB>r1E0a%Da8=FaNBXVI4n|@Fsy`>`NP}t*rIYJ+G$E)O7%^U4v$m{oo;A3>6T;T7f zaC91uxyz_`5ab)~J|S*p?gD)}4)#Ax`|1ZR?80$#Nc*A@gFca5rk&;H{q&x?Jm zb~&Wz0$)tcPZ{d&Aw)a%5HOCYA%MS;Vj}DgYhSo?NiaY+|CipAp?MbXnTS)w|8Gjr ziOsP41&o34j5d#vl(6x3%KiejwZ}xiS2?2qn`_Zm)tO5|>%dQ_Y~;+fdi_ znQMFc?$Nqz$hJ+(GeS(Z5N*n&(X?}ii4SYgrMNEfmQLXI`>7mpcMyn>p2J}4{r?N< z$^m+?6spFy8hO+0yc*y%pPZR;Ep$`pQ_IUhaKrE-cHds%t;b;?;{2FsQpfmRkK6QT z>}@&ixp+RZ1N()e#m~!)9KUR@Ep6E>JZaU7ab2Sizcj}sy278WgQGrtKzs_;sI1qd zs2q(GiH@=!0K7<{fB$*0zd@;g{4ha-^>`O8TBojGXD7&P#IQNk;&w!Px|(2kQ&*lB zuO6(>q3ZU^cyK$a*fi#L-%2VS8MowzoQmHhb=ug;|h)5rz@;B2@ggE~&LBO~HWPN_*N8diP+xLFdD6RNb zO2K&|@Y?woz@Qw|(#q=pb$Vozr$f#pTQ7U3T@M*Ny&^14|ZiA4l`o#=9`%e3*4Y5BI$Plag}n&VCSB5 z^q97FJ!oieor`a<1v1CNZYpJwS`Tt;0ipFhfa$gO7LFN2DxL|~FQjA+YammZ016q;moK*r%XH|@{0SopC^sTW!*w6X zy_sk=b$$YY$odaylzY2I2cfNUEyd0n+AaR4RK@PsCZR2;4wn*JgEClwz*VK)U%PJJ zs`p0>t?IH*owMZ|bOX&@NU1pV9V?J5>>D+M+e4%-eeD5K;CiqZcBHjH(sR&m!9kyZ zy@1MsKYdT}?Xm{Yu$>@8;kdmM8nt9+{|a@rgl*uPS0~E_b=+)F%z?)A2Z|wsjV3!7 zxILa1DdN)`JMi7H`;}8MO?h$JR=ehq6F6B= z8@buhwiyqzT^i+H9GxFh){fbv)<{+@`d3>tjrF>E?jzJS#%wi z#P%}Bnu7Lede{;T#?;{LlQCS(aOs&T?*3e`ODnLJLae2~SGH7V>#HY0#KGWATJQ1= zPh!U+=Xpi(HYk>i+AjHzxJz_@AP2{IoM4QM>vT39VsL~(>Cvn8sdZuHFK=$Gcp@s4 z7idf{EkH(7gh^KZ%8OTD=(lvQwmSZ}j5 zZp+!GwS%#o=zjgDf#G?7hee-@+@KR8l4O@UqJVdTr)Vhq{ECQ8`vb`1ZvsdUpj~Dj zFV>hbZt(5)eAg8A6KY^q_Aj*;Uiq%;rX$eE5t6a}IwPd06jt6x?qnlWJv-xkJ%G$O zPN>>abigMZ31ov6T%pu}A}Dpnwgx^uGkhI(hL*j4C5HOKYYZEY7OJnfa}wFzNwmwz zH;x29E|8GTSl6nh-3+kUgkpbE;ZWWvrPM3bygxKg0(gs6H#KFx+Flq5Z~({)vHZK# z;yd8?i}z@bRaNwO9KOw|ap;$}Z(rDNa?WW};`o_RNj{FB4<6|H%!JY+Em!za zvu={zbs7w@zQcWjw!gp5#&q)r%8=3G2#jzMYVgJ2YK?|r&78&xfTAoM{5>ai-5gK= z(CXroC$EqFXfy-(Bh&7Ef69c2!+Z}~_+M!$Uu6mm-sOX5F;@9HMdVDkZ z$l15*55U1YrdJ#+(MnAN{D13VD z3Nt@8%H>A+KMEqjTbJxN?`cU@asACd0Rnm#29MSic}CCLhqX$JmULg#^srWW_|wbA zD`5DuV%O;2y2ng|KO6uomQX5%Ke+X*RI$HdIhYdmn-{I3Wd^#t61;Qs-sM0D1W32w zFXYiMX$?{l1b4D2zx{XF@bQVXPt+jpeL5_IbiDAK5DffZ!i1xfxF77kk^wvnP2cll z?rPx4O^IYn`D>|^U)9>UOo`}=oas@5{@Rru5^?IF#E#HqV3npUO z!4lN`Kb-R6W3Iq)JrGz9oRi>(Y;q2>pOGR$M66)|^F@N6W8O{&_R zbX*jHsaU`0LKq#G-=jqku89`*-j`ADZObvkj?AgPNQ0;JnB)5ZLc;_9fY%YRpD{U9l=Fn zPvn87!Z_&kpg{BDp~#YtG9WNurb_B&yL^sUL6WUo|4&OIP8>(^@B8|4oIh{lfXn}t z37s0sHL>p5z`kpKPf_4ztnDW?&1D&1?Wl2c-czh!mU*?rIn16$?I!sgn9?Uh>N8q7 zIIQjrs{R)i?u7S(!wq{)9^pj2C8*^dtqH2GD+2z7LeL$zT^7y3xeu+#gA$1V{#K;4 zU#4h<3&9ouey(uwuDoXu!@_=4F87t5SlgQdVGK?Xb;2U>PmMe`(V#B5K-5mXimHNn z$wjrB8U%1O0N~npm=?1awpPb}s%n#XX2n^kW+`2DA(UmMURa|jlx)9+IZW;-7|!m{(Y*7gQX$45p7PTKfRkro=7rh* zr?>bVYfy&@m|lN%epHkwNM!7?xe#*iwi?Uxh(xP#V2_R*l;Wb;xX8-s#=bUS0YbA@ zM?|&IQN%ldOFcevL!(h%sz>71foUQtS`;+0kQG z5UpOv=ZqIeU4AqPnQv0a=Hb&M!u=i_Fin=E#lDSoA7#!ZFfi^v4($xxHl&UVa=Wfu z47Q21qja;-Fl+eOC4o-y*(OElmSxdq#Fsi)Wm`RSWE#i&$2>kK%I{1t-T@)ycD)8Q zG%fGzz;ff$8wvDNcin$(%{fAjc&HBG7}AEg6`!cxq#~K2{~_J;2b1`X^Y(etqM}gs zGNa<-O~zyABdgb6B(~K3%cqt8j}EWIS2x{__^~wN4f7>&GPaWPQK!>ES(jfC;gntz zlv%>=$edTnXxN-W$9hr#mbDPCI&WDtTWq=c+AC+Qpvuy0A1h1kP%{pUxrgiF ztCgDSyknehau$(U(WEZ!`*AwFfI!khX3xmMvzlLXE~J+AlTV{~AJgy1jXEoW-^CV4 zMX}48RjOzj{=qc^R-6za?4*bm#*&$)0D{nUE(D>scCkbt#mCBvH4qwlEyHCY&}U7p zUnfdfo-)&(-=QngLSDDTY1p3=uWB}*kwv|V@$Ow^_%S|MYSk9lWBYwyLwxAeVe@!AOv|{~7$U!M*fEOH z_m41F@n}8`>79KT(W^+CjUb^LDa=vEo!H`F8a6bZ9uFl?ak}cuBEc5k?*cZ4;OFy& zE8rQ`L{KNN6q9YIpaWQn4~&O(2w0#erLc^cQyi=xDlc5n*;yCA9RDi8iNE6S5RQT2 zkZ3{^^6!Q$6y40Y+vgHo%CQM*>gQXDc^xd-+7EH3e~GluL=-LyQ`#%c!JpPs*!Zi{ zdx$j^-b9_>btXg(rMp7nWW4?IS9hA2Rcq^jn614?_}Z|4yS}#E&yL9TWE%CM{6nyJX0%S{|8w+DLG?<3w^<-aW!+rCY(^AmaI}>E z=c)U%y}UADNVP;~27o+Eo-ds%t%|3hgm@0BgQf{xC$`aM;;m=u_(-Z(;eZ13)ee^` zhPR{y)7vycQq8HBcGU0e+6CF1zjYfJCJuDK*q<-Nri?uL70HO>vWYgTOXtTpYe7cV zN-JhRDc&GySY4E?Cf#6&*-g7BE#@rUn2FXU&R8r==oNclJd-LDAI9D5t?)z6e%tFa zYR+1KNi+QentriX2@df3V>5ji_59h}8d5LQ1#_p zR$k>hIm#1e?8k*<=k!)#%UMnvvRHt}ue~Sn@Ic{dUMpKXgyU)NS$kS>US4!Lh-l~& zPtJ_FWUc9bTpq7-D~RUhU*1o&r1`=Fw1p+tpu5J)wSy?hu*=w>7C%}d7fGhACab&= zx9HA3B<5)#MSOyE>kPq0(Z{;H^M{w0RmN-aG_JG`9^1E?l-eo-EikXim^a_n9z~fH zndJ;AQCI=Qjf;>GJ&|iX`kXZwbAU97?(xK6*ov9n@bfi4S!UaeBA=U*I^;?xQ*OLX zU5N%Bsr7%A>bi@7*KyfOEw3M8$OLtIlfN`wH0MTcSG=!b{QSgYR84ZaImeO`b9f3) zEA$h3T32iqm|}}IHMAejvV{Yt{8MNP)=)CP+|nF_(Rnj6n#w|dB$?(~#U0VEsP3c} zDDQLb`6itjj87q)j!;eA-6>F%Mqlp-i>P{w5@|t+2BG6W9Wk>pe~dNK(>(jfsk&EG z(Nps^9sj}fZ_iBH+MqOEGzj3MmPv?fJuf6FKcJba6es&7i(`0t@yD*szCldCGu>I( zA9#^sXYaZ=-t)Y+N_brYi(hDVo}8fQ{j~@2?#)YNyMGR^4y>0R-I4E8o-Z4vA^W*F0>vDu=Mk z7ZvLdZTYF0E-OxL5=DryFoW5B?nFhU^Zeq#yco;07Q)msN1qRBEb+s)s5+^sEA~_d!U5w%SG3t>ZXWl{ZQWk?C>T15 z+r?i-q=~%8a*j}yF&zv$`xmjRZ0W=ZNwB^DP!EF{m&_e@lo@cFgCN;h+dvY~Oel0Z?|GFKQL3JeIDHYG;p%x^2%t-X9>`p3n^Bi1MW9Vmc0SDQ^-3|p0r`+b!U@y zPd2@`(fZf>HwG>~f*&h~wHA@?mV}9%=$PI0Uz|$c-6X9ve9o~g_wUNHwJkQd0^%gC zAF_7mm0ESIPQ^E7rYJt)RcBXa7%CTPk*1fh7$U{As+en39x<^d5Y=T1R7Pm1^ekHvQk9{S_h zm=P{%TTq&u-hI1u&S5mdZtn_;hM)Cb)1dwePZc(poxg+asSZS8=2JE^US>Y2(MM5Z zjZj0(G}ZmqJU_#ZS1?gTk!;G)(VoNk@fuQxF9pai@8^tWEc-dvwxCeJ-2@`}(iv+0 z`qsDid;*}@c#0Nm8omEMfFlAB>(((j)!KeygUd+cv;1e9%Bdsl4co?@XRU&^EH3!f z@*zOniYN?sRN{Qv^ClhZKN1~+*Zx`)U`Pu*FA3vnl7LUO!sF|w40r9L9&g;8L0}4> zy8JuigdXjY+Q7Mz^hEu7M9i3LS^6qg_n&X!!hb-q5_#-<(t?J) zad3W?Elk)~q6ifJz8GZj(YnFkewrOUVF#QD2(hv>5o{ypRq5OBuedmq+GH+OLGlnsUp3 z_deX3YPuwCC{RPP7+h=Vz9nvZ8A+kTEj4L+R+V=gn9rm9MjK8QJ70?)=JBAwV&pH~ z>r-;7%m?7P5m{h~)4I}`XO{ZYku68HXz6v0MIr+d9Cr3O9R5lX0G&&dyThix%$$|H z+*2HiWFYL*9}>u0ko;wTQ7$xc?I-TFb!7b7`_H^$6yFSaeDT0UP%NlhR zASKNi^9&wl?mw6a#YC4);gQ&ydGM4&Bmpl^VkTJ0PJO>ZSY7n{f^!YT^HVpN4@4;~ zSfBUZY+``LQm*;JcSLR2xZ5MGI$MOB1V|oj$Q$^!{V7+C;wQ!R^1qBZ3;_3*JRU?3 zme6sDiac>Jh!9u3fEoNj+sR@p*-hBKI|wZW{M(xm|(gbi@5a&bG=GD#64-V5EoEeT&|_s%=uo!5x>G z8M9C=?EF88alGag1liSDk*WJ_qFbD&eVFv)2W;h`x_RZ%KznRXysv7aL#8GR>*gvx z@Q;T2ip}G@y9-3q(R71I3m^{Pb(t} zd<$?oAlGx|wWI;BG|I)ex3@csve{`|VX# zY^zH&k=|m$!1^Cb)&zK=6~f_X3YC zm(pOnI|spu?d|MY{$_{eNPfFto9lORu)JK_?NF9$G6$E>duslvHC>3ypwZ2P1$KLm z5T9lcsEi}JtEt1$@eQ6K(~9aAx^lcKmEj11;E;SBs^?aMx8^8^ap4!zFq>Uj^-N9c z1~!B~hxw5G8vyXO#2@c`*`{Kj5t0X3x6^v4!t_orr}$op#asmCwAetp*SS0}=wmc5 z;%Ia8m7ze~w2+uOGJpccfIp!)Zm+TbvuU!l!eE)nh;cWsBXtFVeBZ@Mq)KQaC^uaJ zOGC7E^F?JbR4Z@H!$|_z77qC!y=4uhsfZ&C>J&-ApJ9Y?+86F6d*9@sHo!T+hmIq; zPyvje+Tlz1cU>0*xL~2!AFct{X9%yv8|RBBAi|^Qr=zUbIS`25p$a(z@S%WZU1hNA zI{Tx*PNWJHio<(xUi{5ArGp3^;`8k~as&hYwiBMg=>8-`BlPF_+Gdm}z-Aa2WDt&G z_qjj7g0%K)Ge^|%wg|l|E3tQkZ0{mVy}*DNQKt|U|Kx1%fA8A+zOh3rlVdbaca2vg zMrN_*YD9pk8Z6y&To*u)BfQ?^ql!6lY%>Zd6^6H~p-VhzI~lR(FA0{cp;1;p_s0Hr zr5KZV`+vnR2zyFhxk&7wYzcU!RAKwY(mYc{l7p5}^UAhXCLM3#ZdqXnV58OMaZ`L2 zl!q7$Bnc~Pd167KOf7Lyj5bzKtS%YCQoRQ#k7&o1Ju#geZ z)GX=TeeMmv-T$aDG7@>3>AhBY6mWJK-u516&;q-3w*915IM1gGA01uB0CK z`YjyWqz_nm)u~ruZm7+^}HZvV-s?8VuOS0@SadUVljuSr7L0IC@`6bk77y@~_e|6Kl>*uvZM z+Yf7ZcxJwHD{`z2!euGrZzk$IX372~t*5+c5^99Ok_r*(+=EdVq$u0ziW}Lja~7Z$ z67IN%HAc?|<>!UMpAo6@;(U?#Z*pHS{8@t{%ulE8QsZK~SPN-SVU0mY3Qb7>4q;<^ zH+Bcs0z{On^7{+K(RUOR4jNr$r5a+G8U9YyN7nqxfahlr;6Wt=6!S$q;|F)7bk$j) zx7^z$L6A%%{kP`;%icW`J-TE$klEDjg*TJd9?l>(9OlW$w4IVPjXank9XAlo?bsON zOXt$KF8}PrRtD6#Q!(wq@~HhwXAhN!Bb@0wTeWJ7&F=j`Yobh=obD+Lklv50l<-h^ zoj+~L-UzL=M#Q4g8?@o-dLFXZhU<|mblRY9&e<0OhigGh!QpgKWqZwT(q6ecY1GOq5DOHQ=(L;$ zgci1UCRveNE~vZ|iYoB7WO+1eR;r`85$rL+bNthZ{5@Y82z0$knbDYyzp~p??EKG# zQi}ezB)4oBPZa-m>3j8XF`k}sm4#tFM}uT%C$z`Il+wC#1$~7l^5NWTx|PTZr82So z3u8_fDS1)m$;L;9awsT-W5psze7HM* zG9YUhPO!uI=t=I6!0~zjP=2b2Cg1+JpC~K;RyyIh+!mSnp8_;{s#vJy=ZF**@JcIr z;JN9^3}#oVBQzX%6PvWI0$ift(ou=6Ra~ z_BWTRkGdv8x@Ez+xt+7tAR_OCH-^p|F$f$?XP$(D#xy7LO0fV};u}28p5mepfw3z3 z96ti949UoHO~yW-5t^H#ICYR;ieT2e4+PpdQ;a5p(f zoad=eF{~f~M%(4^V(}H%ixiw_+nDzK@>aA75J0r%L$m&m6}Sbfl~Uylrf1gKD{yeP zDmQ_!`A}8mw5tSKd`?KGh!vnslfyItDk8*4N{^JV9}-QmA{G-lsGuK4 zJSE{cSS5kQ*W1KV<&E9G9sk4@zhGj%FI#6;A-AFosI;4BS{-ChoplbyZVIfP63!g1 zlX3ZDv|aQn2bt0F;pGoT?alyjyo7bBYGC=Io7}C7aKzubd%<=gTzJt*kMR!;3BF% ziW2=in*WmeXK*Fc2h9w%<)c0@r2dg3ls$?ez9 z`hEFwaIL8Y&RHZ^eY96u*i_%Iv!jKIqc;sjTkj2#4{&hh^6y+xhF8vWLT&zJOjqJ@|v+T~&a=(Ss#gn?%G|Jetwh>t-ZJ(IL8 zWs3&j!o27eBJOYE|uT^7C!Y(JoLVn5>?W(Il7c@|Ul#fSua$g~iaa?9icXmA!l7!WO*MY6n^uUH#HkVrT$W1Fc4_nHF?!m`@}m6Nj>Oe zoq!`a4P~X~mq4N|-?;>ez!5q%84`=OX*iQ)2?H}G#F6W>e|huulGvQv**P4sS>`T3vGdcSZyyW3m6rj}Z}O7DO#awU>0d1;%agzQ%{d2+ zX(t2v{p-~P81LQ$4&RBg=`-c6S$5=`5(1sk~q>?dO zYf5gy+@Eh=Y5G1qAX`+ekE^D+0Ybk*b4y~GQ;ihl4>mhFXWCUzOo(I7M04&8dSVcUJcpB8)%5lB98@5 zm_j`eikvmguIs~AJQI|X2^ujbX7wYdo3H4}+y@N+!n%TvR#z)CjBI@XCvM>2&Y|hhw6kd!&S4keY@5Kqizw0uY0Q_QUPcUXActMqVNKkrRwH6~!W`1T#zpjrKcpQd z>GA!5G#h&CWsqx%zxRmz%;R&aS!UD#tfRrkc1ksm&ogmzR?1Z9TTFfq>+SpRdHHnmjhV7>qhE@MAqT}@OEC@8;z)uM`}J~_Mx=rQJ$Z?02!Pbk&%zfg!gO=kQqFk zFLQlB2_yDQy-9txyBU$7(QV{#U7l0lw`)FKBNXD#+O$F&xxAL%pm?`N`6mY2Nsox$ zX~SLp?#$ms+h0(GcTUZfm(oPwdJ^ceDCHIEM__p??t`HAM{zaJ9JdZQD28F%#vTnU zc0qtMtROzmTH!%9i9Qf0_STX(dzWN)!~oy5+KE&868k4t{2#Dwh3|2eVkv+gMZV1Q zSgBq+%$Pbo*jattQ8*abx3)u_6hQFc* zB<6h~|FE;t^Ce1#O4VfBA~ZHdeSjfL`g6gILCv>!pPXyI8kS@;EX!sqh0MUco@i}% zhJtZOM^_7eN z#QMR5zIIiLC>cJ_L-gT zs5#vrAwcCh5epO|fBpX+t{2A{BzNlk&^y_nVmSl|g7$UR<{NeU!fn>Wk zIHmL{flW}p$T1#<<#ZVrzA4f<{Z=Y#_FHeK=serZ*)k?VEt%9o%z3{xV@m?WHdWVQ zf*50}Db|iZSR}zGUsBAj^!*7up3zx355;g~8&HUB*fNXRZf5v;VUb$ zOrx~;PJ8l_NTW*V|C`B?#nFP5p2F8hXJW7~p$qsTXNo%LE;Jwj3(sNK`u}zdzsIh# zVVbK zkQONR*)~@rVqaCbb|xH5^85WuJ+HRgW93{+A_1Ktx6{=V(eCG0EQ(LFikH6=NnWCb zY`}n0o~#Izo2sF#fvBDD>V(N;5Yg&=g)=t&{Jnbc^<@~=RcGLFJ>dFL*!TV}ls9*2 z8WMYV_ksbo7y}lWnSF_@3Zb)g#KYgtD}L!>xVhTo*^j4JJYAo4Cmj_R4Pz4cf6mxD z?HiY>E_`7O==I&_ZDNOOyAIR%aGzrR$A*AKK-~J3qU7H4m2{tbNscr@LyC*as7bs- zqbulsqv4NEo(cbnFi=+59_-g#F?C(vDdp(RAOj^U=#SeqAOBIKdyz}ila}iTl(t31 zePhH^Z^WoCx_TJ~_>h7=Vxz+En@Rgf+8VxBa0(G}?pyFpOXRE%GFRL-8 zd^tfdq`uN}8nr)sa8pSbBw3lhJqk@G~2#N&r!9G3J4UPAxKSM+82H(38 zeL=5zEe^HZ?ZgnjSJGZ!TN?fd3HeOF@1{JpB|zli8y@0l?+>y_ud}U?Svz`tc_a}f zB5yqb+E2#zCHR}KK9)HV*O6OI1bB0y_QR5%7h4NR{^cRr<0qTG+SVz(a5Ne!S0O8j zh3%ekUNK)?PYb-_c>=t%D1cq|W922xbr)Z@^Ipl`;QxkB{~IPce}KwhO9-CSwYb0H zL4>lM-@-cL5O-uppzC}d#h?BNdmF}Bp6C3J?KmW`ON)5XAElp)rpi%XnZs5c$t^>@ z{~waa(#_2|c`CXIf?Ft|Qt~Y02EQ*FLy@M}^V7ORnrV#O6sQpl-R z11gYf&Uddrq-zzX=s=QEGdELF~auKJc@lPop=kW@5gtB!2GfA(n= z&@E8bcUWZFfqnRKzMg}b^?dj=M>o}>U?h%pncYj3{1gf2bu4O z+wxrhp*wdAjzW-YbpusdeW^Uxr3Eqh^Z`R1Rdq7mW>(!wvIJ{c(CTJ*T=G3paO!7{ zpYpAx?=4XDL=S0yi_@|G5$jw%iF{~E%xwADSwX*!wt*`1|sm*qr4S55Y zAymzeaTi2$O|I7-vHg9;@4v_5B*hc2`oodyO)540d}#ZJJgJjw55{lFroh+KKWM~q zG0~gbrSO_jV<%koMMV8W%x>Xz9+LDCbfvb`ay@f^{f$pNKWOBLfX(>B;n94MyFL_a z%ANQ(cC5W{@3I{ACUjeq6~dM{NKf-1DAun~m|fScpQJGw zqp@wHanjhfZ98dft7&Z8wr$(Cb?(037$?6VBf0n9Yt1#+HP;7jn=+E4l#8#0nmVrY z`G7r~J|Nlmk=-F3VkMYZ|L6xtP>e|GXgCku?3=&v*�W-uup&8joW_;InelG&24mJWuH* zoz)qoj@sK)Hp?HP1kZ1U{?}ToO{wVU zxUVNGF4qYvg~|YN-IJ}fN+2S96}PLS)^i57hQtTx9A7s0>IW01O-<+Xp`7arsTYzj zAEVix%;@mN=Iy>u&^K>%_G=k$rG;m|;&9?Z6aFWVQH7%`p~YW5LMV^sSV0fSu)si> zQ33q;c7XpCRcbiED{rr@u==h2>pm!H5O8p*&;qL^V>^o#MOyBs z)B3@~(oUxvIA#7%bza9sF-&$l`_fS~-b|+}IMg35+b034&p_hya?3l^;P~-3=b-){ zWvL(AtJJa9x)RONjv;h;#^evfS>!d7yhZ?H7qa@|?8ru6wjb%f{<1j!O{mhii4}!G zW-97HPSHJUYZ?&@|LNC1RX-)Mf3}W~8gU()5ZQmXiIhpeYW*Dv27`7?l{y04Ugw|R zK@($+v&naGj{SQ1XPAf-g@Z39K1=w)D6qHJKj4l5HN1WM}!iN!>fYb{!7VXzTqi%)-Q)%sDi`mse^@t2z+A1O&+VG=S3b5r_b-|s3MdE z*-$KFSX2bp*jUO{p$0)P)ni+q9|%QN8)J@B7)eFpIXF-2!74zEruleV4hd{F3v{>$ zL(`$`y1^1jwS%~Gq52wx*-R&4Q^zcIc+0|k(BTDk?gziOjpsq+1WlKga_qL0jRhln z!j%DFUjR0>k-f<2@V<3adIdAK$maPQBs#pI7BPGkWBjc@kiDEbZ~I-6(H?gbiSMm~ zTHy(dxNm>f$J_mlM-(Ay2Xv30z|z;K2kt-a&+m@Q^6exdp5k$N1rk!%OAc*Q4os;& zKUVv3G`j51^$rL8f5=>|fn?56AjA%^)&<4!22@{8B0p-z0aN~h9FC92T8-05g|8Kj2*C#f9gZ+I^B z-$^@7!x*m@{hljmQTeYnfp0T@%th@=r~r;DN? zZC-l@@G53zY}P0`A$Ym`X0!by3FxQtg8>+4I=(@~VJ3i`{gUCR=8`FCi2~UF`mY|M9IQiJEplyb#CU@Ff^aq3%o$*dpOdvng3u(2Kgj@>-b01sNXjP zk8C6L0b06H^3-<7aZ?mqFz7KKMjO8NBGeJAD+(>~{kvgY!3sHmeO&AhES>4+eA7)_ z1wat@v1-eztdQ*K{I3n7>qPt4Cx>#TnLKVmhHcL>|G~mLH~ow1DH~}ADUsCuKkT+4 zsk~X6e&=(}aTy7;Hz@5kd6b*C>ZJ#ptkW4CB};-HXaY8ts{O?o z0duCo%kJoJA{~m!+$L>|OIs2+8J64b2Ih9P0t&U9)8a=pK$O&QF(3W-&>r*zkb&X< zxWp{l^IthMqso+hOoi<0umN|a&}P%WR^y)jZFn`B0|`H5^)4b(&%k<9MQTJu3&<;7 zoX08GzcGL8qZ+V>)l=YhVkp_u3qB1*V<+qUiFgt1m}%_l?2Vu*q3#9Z2FYF{x~}Bd zKKYL})qvH((-X?d^;duo!GYQ87$+|r(i40gV{iEK79NOxwmt!>riI}exppxMa;HB+xJ&J*T6CN<2PIJW;WA&D(={K z#|X}YvczBk)z3PigypUaAlj=@hIGEXs-5nK@%6=87ZBkzG*WLpgdKBhkRItLika(v{!i&3*qe ziq7nEOZ02TeGiLkW$MjCKyd<4Kv~<}tzg9{JVP#m)?thfvCv3VQAc_PcU+z-Y82=S zrs~19Hy*}<0|_L7XmiMD<}^;1FcOSOWYbT zZ6FI;t38GsQ461eC)d}Mi=5TPSml!nX%ZQR_9csT!QQwD;eh)Kl|8%kiB`U2&KM#x zIc?zg+aPBu2|&A7j~mm{Sgz>j_BaXCuRH~$bpYfuJ2e&vY=;kt0V2_F%w!+BH8SYQ7-yX{ky4C<$&h#y{P$26rzA$mp+d)^|LSYuS-TN*qB4K7}3NWWT z!AXc6z$!-l0|te82Xs;&CgVGYGom35e-|__<*&?jx*?h_(ZDM?$u0JO9&3^-a@eG@ zvs|kLAtH*d8B@xZs9{nrP5fo!|I2`#mS>c1tL*`fQrvY$Otqtq-#1w01^IZn3hl81 zW58252v>OMvjSD;@DEDqd_|~?5L^u(GpOlCy7n>0@P99`rJ`gM&}}Ff(Y?Y6sn1tk zUpt>sL(2^0Si~mx$}8SPNaMUlsDPMWa{(k!*H|2)7hs&td;st-P*X+xLymlkyb~T1 z{T@d8paX76!M6uJRai4+iXd>qzSL&vr@peSpMYW`q&tKsLCJK%TS%>m6fhah|x9i=~C z1b+|gYm_*g#?6(VO!K;P?oi2Gaz!MxO>XZ@WDViPrDfDUdoDW}&^Z|6)}4U;-L*=V z(IH(3;8(xcx4=J~kU#o%o|Ck7kt&{99<`}m+i5fEQUx(j- zdb!pu2L!ExvEV~Wn0EOiU|FWuq3#i$(fx(v(4=S!z^2LnU6w(jQ{)=&w;Brqs-KHP z*6L%Y|Hdp-fh^z4+9W(|b(|zMtD+#4n2Cf$qDIA*YSAlB1tc~$$Kag1&_+43vW`4E zd7@IQ_4XYFMdeX4mjQh(?lD=a704JR4Rb4%Wvto|?qqa26~utT`#`R{gFDr8wMpM1 zMr?C`K@k>alyoxw0ZeD}Km*d}k;?V%rK&F?!X2)r4Z4`25td3FB+`h($|bdb6Tvya z!QrPBVsGeE&hfHHY}Agzx^zV9;8_kgBa-`*)#r+|-7_z>%JE;^#8RAYMT~|6)TvAh zRl$s*6Ss}$9^j~|GMVZE$Z+RA;R7RjZBR)u#PMSt7T&x~E|bLrZeF4-D?7qxmlKSv z1~^S0`VV)R?`opL&y{OXL1zr;_f_%*p4YEPTam|sfkKS(FC zo}57AAOKUz7t8}ltDmFr9P6tv^d^Osr1V#qcI+a?lMHNVJ>}O*ROd?b497QX~HrzQITK2l#L;T{Au$N53H>->!p(!~I(Y z-yy(vZtXwc;5c93WZ43kI6Iy|*}%k%;t*K)fxSdIG3?58K%A+W>xOU0hv>sA$=U$U?K7*o%+4I&D`{4=_cipA9 zp&;qoyB)v$)oeOez>cp^5KmqiL8Vgq&lwHNUI{$rKOH+$7bdt8#Y!#C)sfGNs2)VB zafM()PO3kcz5Jrz7{$$>Z{or4kOilY^&?igD+eJ<(r{vAz7VrJTe*B1KI8azvzDUqCB zcgvBv;uKt9X`;sYowwBo12K5TI>qqiL0xHh3|2ywsRyD;vT^dPu_HW74ag%R&=%M&b|9CQb(^lO69r3z_E0I++r(gqDYS* zxc*!@*2bU}ZlviY{kG32WTxqrH){s}Vv5UpJ~ky)Vwp-XI!7scC1}~}K~ALsu3Z+N z@2~1F&)2^%o?yE-6{dVN!ia5|rSKK0!n&g7(;!JQ zRM^BcY=ToN1y3K_4-k`1M57q#pPoM8AtVXS&@}l=BP6oKkBuwJ5)?}~v^qk|-r}54 z=`7#RzYOkwWq+~neOV*EVo+U4%DXn;D`@ZEDxD|UAJXm(p8 z;+v)o(4F;|rn*?r{4@ZDf67rem4KarR<^BE&oDUn|L>aQ~#fN}rr1ATBojFdWL{Uh|? z!>AV?ns$Tjh2X+x%91PEJgoniq|)-C<%yc+xEYLmXRa9{2Dq}AEj8D+f#baQ78Scs3+ZIGFd zhNpJ$Z%bQqymxL4LbW#D_kUaKj&Vb|202vey@dNhuf^sjFUKK0W9#%jKh~-*?vUXTyp?q}mdKbqM+>`?i&8OP~#ZRq_Pe zM5LKDhUM6hu5znQ7m?PvXkZJHg_xLIWo$w&=eYM*z+h@fG+6bhA7oO5YQcY1UWI^y z-yJ^cZ>{ffbbK;3k>R~Ee~%ieb4*C*sf(HO&rg@fe$BBmK9Fulgl&p4e4ZSTaHgGh z&=Z0R`;eS;NW~eD{E=;C)KQpr4|J__Bt~Eo72dzHA z{%OjZoVXzuCZI=841}zinM8a8ra-Qm-^a-JHFE!6>L>JT=wEa)IZ`aP^3V)!o4KK! z9Q%2VU#hAv{=HZE_3rjc~DlLtv+zJ}x2H5R;ex z5el`wc}QDFOeB1teQ5Z0dCXBuDK&;IL=hX^B@of3|5422bm23Iz?*`fVJI;(G|1P6R6A9U~$h7()G$t^BU!k z77*v5YR67u4YXg`rBE6D?Nd0cGG7e!aPzsbOTKb-p9sh0EpTohnjzt2MuWDT*JogM zbRM#_Gdw!{dE!fsv>Z0&Zrm2`MBH0Z+qR5Gm=iCWoIaQurFvXb1$EL){>VsnAh|!% z>Gk;z?rDlYFmrI!d^Bv-ADiSoZErR96U8~2jxRzeLe|kT0?U~3>OTJ`J*a!;U0)b0 zh9kHhyQ4~_Tf6BbW4~8qQOt;YoRlm*hWDivHQ?jNj z7Tx5fOYcI0mzu@LuOvBPSy+X=$BS+)7p7BC7??@eT0Y-kRXwPt#=6%!PCPh>fSzYp zsJGLQp63*^Vl+YNyZCGOn@mZ2Re;nmTnt5}LgUxn9piLyh#vZQaW|PsNZ+=8;NP}3 zFoelcKnhb9HihTsMCgop9*BEq4BA)lfp^SS_@my5mB2{jzHTU$cIk$ZA{@rm6C-`_ zJb;*x|2Ms1;kCan`~;QbRhQ zLsg%)W}}vq5!aHsVB5nHd(x~vFa5SYY>$sE<-d;oPr-94E52*xD4OBNJU=I5ssc719NH_+4iP61Z9>>ORDyWqW3Iuut+MaG2s{T1UgKvs7by`zn8k zHnXNSt9ckhusGj7zQsz(mg8*qR!FMSh_|gJiT_@r)>I4moLASrCuFmu}-0J*tRkBiNnx|Z*BC*03e6Q!vEi~K6 zTGIzUL}36 zZo-qjJI+}(HvW$c`105r&$M%Wwa{Q_1Ao^FZcTodRN{5}^?!aow(>KDIDw!@AA?q> zF>%hOGbbQK1eclKaCuAAsvLwFeJHMQ1OAnkCtT0!6pQd%VI`iT`!7d{PWY3&ym-uO zG(|b_fs))>IfW&`Ic2Z=C!dJ+PInyzf41CNLA`b6jzE9v8U#)_jdSwisXWleEe*uc zC9LMEe+4T!k#AhAsAnBtg$9k3vyJI3bR&ODxzF906AW_N9Rqip1)+Z`B-_{}VPPjB zK)nLpoEzHbRN#M0wQ({K7iKfgHq}a$3HqaksYvg^L@MZW*ud~s7Q6JZipP$lX6H{$ zGeYyF4ESEL`xZeJ(+McAaACac{IzbD1f+Ns?0ECJ@*W zo3Yz#5A=eZx&ji0M`}wD6{-o3@SUX(USds+O$;wWwbxsn3gVC1O5g+gg_{0b@1lJB zPI#X-7lytODCGt~yy^HR(TwO-Y|=YrEBc^*k%`DRRC2&V^6hSR5abt*_M5Oa_!ldR zaxA5}gSS*A87giDvi)VmjPxbr;==0eY$@V|4xfti4eoi?X6VTvR2Q>59ZhgO-fP;a zdB#i&MSg9SBR$8ZGB{IF#VSR?%lzGBYp7?V1E(OUUB_^)sg&cwbStk>19OR`VPQ76 z1tPkrU2s;%ZIkn{9bB)dG`!qc1s(x!e9bk4XS|e-qUP>injOyFUz$Hu@4`_n$0or4 zK8MS-B!ET=o8Ne0J#DTfnXW;8N7?DzkAh~AmiIAi;@(LwtfpgU!c^Fx7I~yods(A+ zMkgRG3IB2EIx*)>m~mdFb)@$Zn$g@T>w*^p8#B}&e8*@dty_;hLo*dakDSb9)xf+O zWg+9;xDh>kOS}*62mReqZ!VYXVSqd*-{3duc&IJ+%5;GaoxmTbn-EA5LLv&}ogVp6 zsObEG(&kXGeo+!Rfm>O{`er&fseAcx;jGoQ*sOsu%-;E0Ff@ap<%Un<;l!Sdz0GC5 zD-K!<3c*kG-MLM3!Ly%P4))xqVXQ)pgv6r#M9VJ4Q6rwe_D?LOCVeeKY>pO#3*f46 z2hV?ISrG=27~F}_r0z?tvPR;$j_x0a-$8j(TILcQb=_z}K#!IN8Bw4A_4e0PZEO(E zICN*vxv}M}`#>jGK-qfalBO1lj0J+gXr;_nM}Env2z9~6OOh7(c*7>uCA_rxwq-Qj zhf~1~X65R3`8XLe+`{Z=6)<9Ur78rA$Q8QDw?{-CwSu&A?r(_IBT}eHVJA_i0U5Y* zt&g`J)SNvT)8&e^mt=ig^qBfRGYpi}lU;Qe{G#a&_648)H!~y6?(2}WZ!n%*0qfK7 z>097)tCjg@raD!2SFeim=49pA*0a#Z_Jc7l@hFSmx4<#FNLlI?Q{lAMajWnl_D-}@ zt%n*%Aob-tq%vI{Q05!vtDk#Wq-@%M-=*-?AEUyckH(k?;oi3Y$6&r>r&P^2lziiP z;sjcQwer&hLFc)BP%WiL;)IP<=MX5ygi^w+ORg_nldd93{0fZ9Hfp&%q|W}c)ZB4t z|4KlV{gkQAnQ0m*x}S$55hk0>R@sHee<6$yzkd0m;$P}74Sx3fdN_c+NG@7S>5{9q z6VMPQxv~Yc|6>gyQCuz$jp@mi`fv+@wqldaAjd-y)7yy;S39c%PA@N%xHH%ND_izj z&Q@8=tRwApqb@D7^Squ3htSxw_H1eoDa))P1Kbe=wafOf@QvG(pd@xv#TlL|&bAXO z8~hYwjo~hCF?*%dc2B%uLuz`vGnc-!Yy(_%-2U4Ft_~j8-WWx#`H=_#*UnWt$Y~zl6O9(c5fezP`*ZW3Iv&AyMZQK9KIF*Dael5CfQ(=>CF@K?2uvXF(e~@MT zM$v-MFaP*S^^SBF7!Wa~x1DPvu|ZEnHTjbTg7|DQAGcpvNoU!Rr}3FCXD87e{>Dnj z#hL7aEXCFB66hLEPEO4DD%~?LVUa9X;cQ{*N()LJzMnXxc#d~@WBGFqkyBKh&F2RH zNY=^>D{{>;T#D>-yBv5s1mfF5ZTc;Op{@k)-M*?~4MJK|u^N66_4oHrXF))?)5pRj z-6uL}t@@ylFKIoGas@*fOuTaEJf%-EuuQxyMM}}qoHwFZ;e#E#p382q$D3g6lU^2i z*EAn_Ob&_xtv+?-lwg>HyQwiTdG{G?iv zk$VOqiC9ASr-*5DgV4}wbvdOM;r09F+VD8E-r~ZNpdr%pgl~)`jDAVOIjz2&mWN`; z=B90I+?|iP1^O>q>>YeV-&vW$@;E#REU_zZ69!DU5&|63z@F!C6e=GdIvt`Oi%)$! z`QaJb;2wsup;6KUXG`+2Si-{=n@I@4t@ek6@XE;4xFM{&n{@~R^4Be|s0P_z>oV(x zyWcJfrwJ)gN0(THh8Ss^A4Z&0ZVqe86Ess1&mz@D2dA&K1hw2i(VtfND|iw?PU^LN zyjAEJ-rY#bVeW=bpjP_}7;-Htgk0?Bne)w4@JRTvhjkIJ0e!FsOL`QLfQw(3e zbpGW1HUBqR(yFQ0Jebx;@=nX@iNg^g!n6Fy-(e*1+l>{yHsw{oC7|Ne6dys@=mJ+W z!$-H-K7FjK&g<6hdNMZD{Xv_DsO>R12X_H1w-yo_mLz_{RgI4$m*8u|GxZ4O#^L=AZV@9DD_WNFjvpTjY&NQO#k42 z9k^nLmblp=*ocvd@8E}OJAP4Vc2QB7#e#&RrEOJHEK$^|ifB%6E$kJE!=8Nr!UZz+ zU;(_|+TW)fzM2;8vm?hKOh$b9$OfxO3>u$%I zDDO?p*$?yC*;>0RJYR&lY4L}1qgkd}yD#T|_|5;_W*GmPW9c5y7x^N=5?dGWV&v%P z_L%AzSq6;nq@?vu^X6`)>0?8e0^?3tn}0mtZ{?vfU#>T-1q;8@Wwb&y$3p;nO|R!S zSa4)94wt+CY^2*qWo=`@7pSm_yjV}o$0}qiI}*nvIjR^bazy(i#rX@;aTO7E`mr`8 zIp_&93WZee2jAxsia&zV%i1;=S~sPr+^A59+Sgdhf){M=YBn8_P8)zx%o9nd)6jgt zvAP9jW8_-EH9;yox==e}TTDph=bsf1XbAPFp(O3f`7RwsjGVLlMh?Vw@I9?yiUG2q z>m6yZ!PUvZl7@DM(n9q=E_N|1KSMoEk4Q63KRST}q!i^21SSewWi;bY{dCtX&Y||q zGr)zVcv~jgskrTJuwk@@)1AOD4(h#WNNE9$4sOKqkg|c#5rUy9YHD#fOZK+T(G~{m zIV?t6ypy_j$X71Lvc~Vw@9&$0+(pINd3n*N3oj-_$~{lU6t#|%sT!w2@(nm!h&{lm zfZOQ-a}AbQ8AaUUW^{u2vaz}tbwnqojw)?D zXn6pXQbLHxQBp4;Ma;p%mB-g@5n*>`*_f{@<{(v1f0@X2yCrJ6QZ}ANtZ&4 z#|?XvYUXh;J73C)UMIMzbD8%gdsf(0@WX{|`Bm=+6-6Kfazv_pgQ?t&Hq_CJ!~;#u z_PLUkZ!4B`Ixa^W0Vp&EPu&hvI_)n*jXRNM$KEsuHKu+cE4QD~bld@-$%YIKBcP`% zRZ!3$IMvPM-#gd*d#GQh!;p;md105HHhQlADmtnUC)P0akZqC9Z<1BraEbOmHl#1h zI1*P*TNTdjq*B{3#0b-=ev}Q4pJDg>f&oz zx1W2u0k3G1d3rtzp75A(Kx_A}dUwZCyU82bu@{GeNpY7dMQ&}yDppVM#kJWwq+ng0kvo3V&)%QGvf^INlt$T^4)`=Iiz=3e9OF1dnt` zuW_$VJ8Z0pfD#O%-}2L~(D+%izYJw;wH9(`x!B5R0fYOL7!K5`)RrwSk9EO?t%U(r z^5bS`(znKGqL}nWp)|dOD}}_$KTo_T`OU(hu?LSBOf}wQ6H9Usew|e{=Hqxa*6L%C zGW?X>`y5Op5N)V516FHm-JJ>7f>#JMJQ3$o>TE%oadcdfay~h`reu()X_#2fmGIuA zV2NLJl@v}*s#Uhp$OZR=tnmrV+h2)UdX~iuEoD@(XTU(G^QAZWrHL66E_hoXXrgaX z=N1G-6gMRmZVW_{?40&HzF%#6Q$p$A!~_;mfr)JVq`thK$BIUiZ&VPUac)5os5wI2 zG=00x`bKM^1=R8fcegOj51xmLOlz-pjNnhQ*G) z0`C)rn}HOQOhsE@PZGJFQ^yaErWY#YT$H57F1XAl+07o~U0$l%DT|K8)f@mJ6CSUU&0$YYtuVAXQ8P3qL};%2dU`@C+jzGNOP(^HbaD3v)V&Q zd0u`iNo&K?v~hh)ZWtB#(Jw-xC(z_PuM;TOFBgK&eYdJ}bQODd@+#lfLIlm(6s2(l zmxHwbg~$H=W=QqQ{GwPoZo0*l`M)b&5xcC7Nf>k=131Zt=1EiZhEuZk-0`d0gqL@9 zmkjy(Vj%VUpy)s4AXR(=#3X_-aKZ=vf#Cmp8c=|zLCGHiM*Bb4j~Dx&%DH|O;I~I9 z-urudiZ}Noo?=KvY%P6?9k#q7YASWY%`isV)^84^hLi9uSam(()TY*)q$obP_f4=zwsOI*YgA-u5oazqP>qAR?-2X2ecr zYo|?Q5>f~|dN|M9J7$TgZLy!mMX-5wJliSraGX;U^h>{Ln%a-3C_IRoDUZSJ?;e?G z2bbwMRMs=dY|`)PLYY;|#Z1;H%3Oea;I=`T8dHbO($QUx-S`T<)2U0n^fybdV z@YGwAQ3=@-yyruFT9l;VCU2qCzp;_4RB5NJU}HVsoI0{v%I zU^0*Un$4G29Fua|A%;ii;^8uFQ*pFV}Te`75BN{>h^`>#<;w zGjdB-$qY`Wsx8porEa><2@}2<{&7k>olNH)o@;(^SCYASdec=$6_s9FiXr2f_-wp8 zYyqQr%AoW8W&39rwaRAtHzCDMWGZ^jrUR@-+3g62{*d&2L5i_o!PU;aykKq}yNi8G z_aeq?Fmh})JJ8E)LG(Kz+RWfcBgS#RKk+nbs;8}1~w z8{}-mN8&Rwh2}+nTWSUK1qB7MGI{oB(QvVEQQAraMftDUu}6ScBY;rr&-SDobU7*) z2(Rx8Y$lZj^;|XxU!(WaC9Khr(|}miq4x9j{1C704I>GJLD2e7XS^QQ7G0kGpuj&y zp&)P3Y!p=l1ebrDwGj1_eMN!3$dK*+Xwk3J*3M*48A>D(_lgPgaN&7o2cFoK_&U>J zz>p`8NX^-jZoD*klGLgSzcb))M4_Dij%OeRdx&q~qs5=3nG*K}p8vK_vPG}x}qv2-BNbyE4*DWkbbu_BUKfzw! z*<|`!kFyX>LN8#6=st5n+#ENDd`YLjaHh>MXr$614YrQzy@j}n^UC2HMpK)^naSjL zGv@jlg6N?r!$$&A!l+8t`UM6LA>RU7X|FT4HP$zdrpiM(q=qyVKTNs$!@5ev?`W3c zs?A;=^icSlBbBxzjI)9}tEv*sZC(3hhW8H*de#|FD=4F6jI7^e1D;PLxq;gYcvdL> zX|tpRyu;xbr{u2qUH0)?2e3AzbgE)KX85+$_I^Bqduk=bYsE6 z2?bzKD?`?6*4>F)lQ5+&48)#KB5YA>#B>Cz)HAA&20X0pb^Kk3(}b4zA30HBZ1KAK zj$iAlEeDY%HTV48ZBg5{e0r_EiI#^L=c1~L_;NmW zY=-*4w)W+FK|{gccE*QA#WfV|Jp^QHElP}4P4o{te9{1uAyuPO-ULX*hRi&jfY&kP%~b?4((Zb-VhcMFP)v;wLEmjzDxvFAUi z&*AOO)1xcV(J3ej3A6SGzajg3f4gUt#4zj_%AwMoH-i)A=2b<>pQX33-aPSiB_XIP zvMHZa8|=UOtiJ*yQZ>$QkrNRRpcXpKkm=Xw1i-w#_vn(b@v$(*iUXa<@Db-6jigIK z;;pu_|B5nYvYx9|LnvIP1HGl1?}hj{5h(I)#lCK3gwn-&&#q$p=K*K3)vk~7Qcmd+ zk0R+M@flM?nBb$KhFI@(1vo$ZuDr*TOmx@wFP4R z?TABgQ2ObD-D+$-+hII)ZMwTjdGzJ`q^h;s@?)9mUs1Vg2r*)yr897=KSoBivP6vA zA*s{Z_tp%YY2P@vcj?$$dE4KAVmVhqYI~>Y1COn6yRxAc|}?>m{?FVM|a(I3T@rTRNBl)zyj zUU$@KGsUB4D47AJ>G+8&5`iKA`U$VQ2lqDVT;mZ7E2ujPQ;F0g|7XU5GTnovyP9>9 z!h#~r4v{I}G3z-bAqvWQ-_6bLgA#Au%fF~8%XgtcmP{g)yd{^OGj+cj(8z9C$9md5 z)Ds(-f@)H8^nQ-`HqNo_yJHH13YH+emA%>C9Lh6f$h?q?<>ac&l@>3QV*y?&I9#5c zjEGJV2QQe0=$3jOSGPv_R0GT-rohayL}&Hz#C4Z?_>aEb6Y8iUKPDHUnx|h8@o`B5HADheyg4q|{2p=8m`ll-l zR{aweMps7d{M&pP23_kzO67~?Kv=42W3ZR9>@n&TP}iYp+ZJ}foc}tJk4g>K>GQweq3z3%tDrgornQ@(LDu63=Yb+P;qgV zZ@VR)1jxDZ#lPzvvSAlnCw9)Y5+|n7I*=Z@K%c5c+Rb8UxM3B?kj>V|R89o1$BFl< zZI)fMHZBI$Ga_C8h|t_p20;}QwoK2C{yFZcohzhJ4Bo0P^E9DyrAuueVRlY%Sd2h%cf-U| zwRnwU%p}Hq+>rq(_+wdx`e93XN=7nh`71*sO))qkpx4IlV?aPhg|rmyW`9OUoCbCH_P%v}{>dE(F6ww@lHmp`LV0r|^i!uD>gzDD zlFELG?~kXIp*hS$Z?A2=KJcFohWmFZ!@P|e*St4NLBT{v_PrH@B{C`592GFxIh6K8 zUv_rd#>K~jRCER1iu;YS8S7i9z~uU3y z4teKvp%p9)_51E2Wf718Saco=HkxKks!=X{FtAAAoX!hD{%kqpYIn8c2O5^q=N`?w zAc6{0{BgHq8C;nX5!;4*U0>DbmYZ$opjxbvyX-ue3V~h=5o{|)mnrY;ej$r7XTX-E zlg{(Y9u0nGMH!0?-oVzosJ?L9p@tnJQ9%I)eM1f2O^hCW|E<2zvWd;)X$V=i0{A)| zKDa^VCw`#T?=FKB2>l-Qarni~&5Ygid3EwgTOVN|IMqzy1Xo}3fqVk9G_#Z&v%sU4~KstAkYkdBQ8l#pN$AB&kG90BTJ zf2B(BrCmo=VD{t_)OsMlkb@jKiWM%QryA&VWs6l8A`|(}gZam6k!qeTq&DmYGbFVl zndq3(_xzvl?MKKU#RmSzZKWn)c!FtaypY50Fm(+?ByiCy3%L~b2&ah)35Io0Y~KNcJ6DKmn{Q53h#|Y#*x{QZUUe zHyamZWFm5~UvxBAeEe)&#>WpGovN97b7L`hhdGJX^(NX5v8AP{pZr-h(q<`LY9}o1j1RV7 zw+HlIaM8g_2Se4C#Lh+ImsEmmeru*Uac*1#qqhet-KDeA9;XVau8<-WzQVVhvWVD#+V=x=*q4%{Pz$2|h z@gsMcIr&5sUx$9nJ2>e_W>fV$o3?<}&ctg@xYK-!i~x6PW+3 ztJrD=JD#&Day~*51a*D zYJpHV-4*PZjm)`Nab%fBhiNved$r&Gpp?si19|A&aF3zl1-aEXpV-Z04Nc9}6c24b z37Qkn6PO8Wf5R=uuSq~x3(e-qTVMia?>JGOutMd8xGxu?Y$4WftIJG2^hbQMfdymS z+V)3*?5~p-k&VQEp#|69aS^jTS|Hog$M2<1Ex$h9F+W~*q&d*!#M!;ZLz=aE(%s=t za!Fl;2PA%EyHS3_Orn($;*iu#ojwX8%KVh+x{>6zG1yv15et`|eBhpCm8pda)SD8% zc17qk^Vi; z$s6cq-w)_UndOitP8FX16pypOkLLP5G$GYTKfwR%zjCxCuK&=>P^FS=<9l>>G!+^n z#;;^XgG_`{0!6bm>;#Ua39Mc_7*N$R*Isj`Ee{&m3zB=cIOs6%a_Xq%G0d}!6bAQV z1s%)MYQt-!&(M;sh3~(U%wcJWu#mp|^^FK&&oVgc|8RSvVOhE(9gkwL@b>k1hXz+j z*H|?r&Zs1eeKPBR^{M||V6{tx%+U5Bkxx2Os4~nbO6CtI9Tc}CI2k|<**rZ3?We{Z zrL!d)JH$B>w9F}K0xnv-u#DA^24g63k~J(g*1Lksge@+Z_W6ApF${k4i6)c|KM5@e zdV*^KS?XK&RWJ+aNKHM9e_Y)4w}~&X%WxN_^0o}1PZbVf=_*i8?>~pbaeMjCo5qsL zQx#17pmuP2K}pEK0)p536Ev$Ek)^a!Ys=7BuBDa|P$m$u)BLBhy1F+$NHv-cx$z2w z$7~undo}<^$ki6W<>wR%O>$T)EMBNazC3!?#MAawX7T4nLJg+5jINP^J>eSWL#-73 zIio~?JLOvxoWQDqU>+E;rhW3dTpCflRtiD23`O2v=FTSyxG4Pcs3+$hh?h*&_#D{D ztgvaIK#F9_u+&btAnR6X@<3_zm+t%OJw5t0Oa+`J7A-W;*6=F2Uuc(#Ga!-Hp@n~E zt$6$>Wv?BKJ?IcQx%#vn$>QUKlQ^Bb5bf_DrV)XIOSCj6`PHlA-Tz1#vh6LPB9(Ht-M z9XEV@O=l3lC!-alKt}A>JTrTJnDC>h73T5{ zm(V2h`TwzW7Hm~DT(|Z?y1Q%BozmUiDa}T@yFt1`N$KuXkOqMbNOyN5-5qD~e&-i> z;kDOVbB=M3F=00)WbIgnCF#Y-7@M$xCKP3DRjKdO9OnnsRre}&X0Ipj3LSswy!zk! zafn5UnG88uS?^z^py!8)JWR5cQhez``NViITm<5Z-o=)-jjlF(*k>s-2c_1PKIJ=S zKfF!O^``UjfB1MGQ@Mr<@_P=R*TPC$zBz(8teidZ(^7D(Wam#Sx|hq#D5)Nq4-f}x zZklfzJDQxoEb_s_U>-LErlou`S zH(l3o2OA0}#o#VNuoV&6QwijJ@@^moU;)0pj2qXY0rGka%;*JW{$D%^t0ALicP?N7 zglR)(|5S2_$CO~*iZ>DpTq>uXJK2maa~3Q%CfLvE;uXIXE@Xr{?DSU8V^^*Hi<<1WG}c}WQkE-bTH~(x z_^F1`2io?Whz(ce02wuAcioh|+T8zXpQz~@ENbE!nCPiJ5mjt1g$vXh80RcHQ0JeTFyq| zbT&j8`ztev(MgRCT|en!Iou>>U43b>=5IA+4!)|jAa~rN(up&EtT9NKfl)9dtnnC ze{enmbhP|lx?TO1zk`b7J(vHnG&qTo|A3~{z6(-a?7C-~`o87Q+vNCw^*sq!VxF*wuV3|p^EbULrP0a3dt&nz4w zfj#?y@cx#-EN|XDwu0HuNFlk{My`9P!w~oU@MY_>feRtjdZk4vxG$PoRnRi_he72^ zM{fjJ4rO3&Hm?M9b*LgY^z}(AFf#kp%p0`1nJwDx$CNWJcrkSXFzHRgEt?D!;-R#y zL*;h1QOaiePDfWPJR6=T2%(aoM)7n{7bu+HyH$ic!In9!&^QZn$L6y4g;^$VclcVK zuts#cOz;R`vR}_jOiZ1;DCSj4uI{QifWJ0|YgTq9a1m$0765(GvCee`|Co``y%?vH zm`#l&k{m%kE+~j=dp08TkkL~w0+MWxDF|Z+HoBJmp?Qv8pYbp{!%1lIjgfvv-eT3} z>gk{S;nkR_=^d71&U$s!iBP9S*%Ha5PA@{iAA6ka-wYjAeSP$;j;y2lkDeF$k5-n- zHSF;-4#Y+bMBH;3M{qdj!s3LC4bm`+{yY2~ys@TAMVQjZeRTZ}?95cs)p@;$P}E0*lnAPe}WS3LP#6 z&%YIBkC{wAcYJCRruoToSF^1z&Pof}JupUyrn=GRA*H86q;zvO!AY4IK5$S^?_{Vg=oYq)7D0Yde?=@K) zwV(Cf%9+c|o;)v)KjVX4ej3UC{hcj|g!C2~=)^lpNQwP74ef^Ea{e)P#p@TW1n?p@ zDSEIU&GV9K-xlO1FfL5L4bU;QQ<=3YBX5R-_7e>aZTp1tu_Q+XvAa#@ClN=zxC0eV z+e$~RaCaNt&r?43y!`6;|E}7cY!j}~&UaYW7pXo)CR{djP+E!R8BiJVfYUa{eiij* zyS3smQ4LF}#4B{i&JANcsc{>zVk2dVDfoi7;!O>#7%*z^JoIBeBB@NFN`b^B<7xa! z>S&#qrK^tM0B@z`m_oUxC9-o@y4QRv0ODt&%~^p;-+7$wgYG{R*He$P?_#cnK3uSA z`v_jIys_dcVjo3CCu~CXHTgabMMa1G4d50N76%3-lnU8G|7hHaGMzPr383Mz+JBYW zFPS*Q%NOlQ(?MD525@zTsDLYeiL(3E^8tWPb`bJX(-9-hT@NOPlbOHxE#qNd(K{2I z`-n+-ch)KC=0$;LunbV7vN(PHxxoFIJS#P&GK%Op6D}rsaRo#CeSOf@AhhTE|26Px zHZ9;(zu=ZjwHUuA?7L|HbEg)moLmz4AY$rZL|iQY&(&53CPB8`)Km*|FXes^wl5x3 zuih5LHKe)oR$@j3ncY2|X=GuT**d}ctt`;t;XES0Y}&$|8~!TP)4Cm`Zq{E7 zqRL$^M!q@>xUdBjM-@T(7?*;I4JwXW8-WN3otvMdpoHoNk;&qPWO8j5 zvh7~=Bd!W)Aqf&$441VQ-?XMb8d-m|xK>`gyH|}sp0VFD7h*rP%SM7(kwUuBnR=SR z7SGyctBe6GX;>P=#csv!*7&}>n7o<@RO+}2&Sb95%D*mCd4NQ>) z6n<_*)3N&NLU$j?)}n_ZX4DkcN2ngJY zCN}(NkE}O`1-IrPnP5#~h5rj(E?NONcCBEYFN%usGtDqF%;JR@g#8zKFqV7x+jnwC zitp&BFr~BwnqlA3F`Iz1j5l?(O{YSYA4e)vf>imsR)JFgo|dCtkU}fc*xtdw>WNOX zdxwP4>%|*KAf4ulw`w^kmkcxV0G7O8RvA@kR}u%{M8#xIXS*k-8E_To*0C(!2Zm!8479i_P~a6I?p1^~)ax9Evb z!4Hi3iHe6KHf>Oo`SmL|D-AD&CR*$2PNu(z22OY9v~PwAmmzcLKVhcwclTS|V**gw zG~j`<^@LU9tzAl^uSeX<&#Nc%%8$rGWnzx&@}6Yu zJD&kW38y2Z2!F|ScX_{Jfds3OYDeNbsj9#2_fO;h;JK83$e4re5aX+M+8D#&>zVM| z>P4DR+wWjyw9m!E67lPri3o2RcITQBe9Zo3^kHV{ zx{6`vYEZO$5>I1389Dw*?w~B!EtdrX%~eF5AB7a+@`TSe#dxQ4&$eHQ0Yq$V&#vCp8Gf47wM5OPeRvSZ!cl`7NyR&3>TSvHr zl&&_G=L8jBcujU|E+dl`oriK)W>uAnlV{*yH4)eE;EDoXXM3F=?oDqRMkMb@Ie~S6 zZ6&YG0}^@Q*u5oK8y0H2c)9x7xZK(lYLu94bmVxv`A*!O7K)I5VQ8iGCd)%!Aw_%j z;oC}65ZT40H60{gV}EIaCl!V_VCXS)nxzIE3rHqU+WRRu_&Y2si7hqIf#>VF3GcST zjwNEo$^XQVz2GA5!20*xg38wZX84}Yv<|m0QHE*?61KPVl#t;oF;l?r7&Q1_iV>D& zq%vWJgrXu;*-s6SzORelho{wk$)y-{smRF|3!jbc4on6203%Em8kfWZX)i#1m`GO^ zUw2UE<`*sO7`!RQ^=sALZ+$wxHdg9Y4Z~!4q*P15M#Xje-0MA>6o2s=(`q(1=^V^} zo#<~kWlQYcmKf~8k@a$bT?D3hAo>zX^i;xiHsv|xkBk(>?D3|j}BOW)x1-_kD@9&h$m$SgKitz|KANIgsXX-#;bfy;K`KRn>b*x#h zeB!d3v^34ugR8KcyiGE_BlMBpv(#}Pr7*fc7$TlzsVRVB7W<#dMh-GVBV@-HW^_+z z=3aHJ)u6A?Xi8W8Ln`Fx<2CjA+{N6$s2>S+o@KVJTEh_7xy-i>&vMoZ1KOII20D)L z{?sM^cbD}M;ua{7Ms*kBA51uD%cP>OfzpZ2AE%eQQ*n~|J%fsV4-A$~QT3DR^ z3x34VZu7wDtJwF`<5!HwN4+wmY_v-k_g&V2V%Z~7_m%wqw)Xp zPjPVmbe8{Ocq-XI$lHa~blY)>e^-OF+@-G}Lo$6EFWUM1vvwM2Ca}ZrkV(9y&EO~0 z^)qFVPu@NOamTKNwUzpTB$;f|O@U}!*<>VI8H)eIx9HO*)T``ta8M{;{1V#-8S$of zgPM(geAx>FvmnXIWmF2eh6CZ9hFTbMjYP&!`S5cl5(TI(X~bi%+S5cX=O-A@$&oo8 zSXp%|Nu@5ce4Av$Q(R#`W<&yku6D*;k!$&IRL;(EUCgT#>jo_m4jZ=Y=R-g6<(_5* zK+oS@1ufm^%|pD>x`P=I(E%&HzWq)6RPQ_Xm#E#i#1{7vBG%I@6x!ajJM4y=(RJ^u zYoXKit%U7Uv%dA!*G)rYQtyt3y)Pv*vT}o8YtQr@PKwx^D{bhw0~YmC#JZoi9*Fn2 zqEW(Z5ItJor4y?61^*CdAI7t*O!Z43hEaO1s+EaWxH1en`1=(4z0m?UZs7ND#LaTg zv-Z60EA`@lZFxEy@*`XdZB(AIb3<3Klu{u&KgxdF3hQZ$#LGlqsJ{%6rh{sZew(6D zY?Y#^C8K7Q8R~vZb~6`nqMO()0)3K`A-FiLR0GWi3L`94z_DLKClLM{MW0*U&Jgl- zR<|#LWsy19IyY{C%?1yp3Y)0td@?{2={TShx6)y>`1KKJVeb6w_riuRP)Z{0_)86c z+5}g*wktFp%~^igQVYb-gloxd)D1n*a$w32JToNsLz&s|-+q(WnDxaiy4eJI-C}jw z){8=p013@hejBEt*ASHtmB)*n zBePpAd6`GvqcO16WkE=#&^s*Ci|^{zG}k8(XuG6S74^`Qm6h%7jhI3y1mO}X z3CwQI0rK)xy-oe}&>||ol=+s5v>xu*sZ&Tg`D%2p&b4@_-%pB#fjqjbML!bphgEH6mCtsoD9z`Ngve z&?(8*Bqx+TT$pZ{grM~MPl`!50|Whs0dXMG{l$vsX(b6uZFNx3X?6Au`bRM0Q#!O< zP9wwvq_ODNu!i=H#Wr|I>0cL{%l#)Zdw4THqODcZoc?8tihavq0bm=TQFKb9vw=Y@ zkY-=I8&$|h9Jwci3uA%mJB8Ac(AOtvw7tR=a6qJNRWFiCTUt?4yno^qyu>ZpxMxJ~ zE_y+yb?^_kvoE455mQY6oLO)~{_4ovehF0ov$m=rxjEPtE~a2~EISI}*k`^^W*#9Q z`8ijj>lT2BxDU<${Uv+RA}sG^^5}O>6!TqLcLD7Bzq`Fjf!Mkq@tIKBboZYDOEiBq z(qh5Iksf9FSg389MH>Cu5vM=`-cC}gq~7{lOD}% zQu0k^M!Gz9v`sV~x<$HW<(2<|a2>eg7c*@y%Lf3Pm;*?O@-{}Dp`Hnr@WskswGeR1c>`A@I)C&n$n^Jgu)6M+lIV{a!$5#WavhkM2r z2S9GJ8%NEfA6sq!&v~Ki{8OlWA3tuoFIW%}JJ~&ndZ5_ECWcy*7n7k#AUCx9H^JJc zH0mCgeI?z=KqP-A51{WS=&HR&b7%F@1PD3W!xjDvH-2#?le4_dhmAYNlV>#vfqc zs4Ek=S;u3{74h$1YZ+iK9;ASTU6Ee9>(A!FFewn3hAa3Odws=uX>u9hXzXqMgd!Dl zByN=$Jpa8jv*jv58v{jgoku+}l_$DcQ6&0Uagi%WEUMsuzlItj3Fts8e8Zl>clh(- z*rt6V!Ng~HXuhttrN3wM%h4N{xg6F}cS`J|@#dw9<+p_9UzNL8^AXpdA@}uxZ3S!Z zfSa=TxAPrGx+ejF6yn;D7mM z^WCtcPXaDS+m$R^L832{2#pz<-7_7OEvIwM=g^Awostu#*8@aLmY5$z zPiH1yPX2ja(c!CqZKvIv9WvSYp0n`g^U;8mMP}XG(8c|p@o_0>%p5GlS+w-Ujh!$S zn5|Twe$Y03Ow3_;6^D$Azs>=Kk`s6o=6##aMu9WcjVqsF=)cM$oBG}eUSB*umGwb;pfql4tef20?Pj8M1e+@lM>UHNUpJ9SB<2huZnqtmjz zG}F#|yoAVje7AlMEC*|X`}R|MH)TlluEShkeOKyCrN)$1XYbLIB6-NnTDYzp--L}{ zACZI+NltHK0kGMGEfk5f{X3i1bzHt$9{G*a_~Ru3TA@fCYw?+=L&adx>L3vE#WX9FDa$x zGv&hm)g&#eRg#ta$)r^3g**6LcmmJ@^2vwWmh%rlqs3pvaT3x@{3PVu7_WH{pBdRre1j&`^gyxmW1^?B6!?V^SQ84L$wzt3G)}D6twOQsH3;^XwMzgbodebat zOU#uTwm9@+V*(Hh>*@5>7DQxf=2-Ttb9^6Sc$~lly!3f%uEtjo`ct}jnq^*W3PNb) z_^u3Z+m9x5Yt9A?AWb9s_lxN-S zklLE=h>D8M#VH4lKHDS`HQ*ZBnvD}KqFl{l1st$f28y>u!YRvQ~-2O0M;LnYxetMX+a5MKp1{< z?E4<00mBIY>1;o;>YB-pknxKA^`85C^l7DmCDPKb4EW;8)jjZfI~MQnXJQ;e5%a(j z;e7zBn>_W;_vm}YLsmj5+ZR{_^px|FD{9iU>j7YHZ1={0PAMKq8#lIss{!M&1hsQp zq$uQn@iqf$D0++9*>TLH>?2fL+XmM$flcTgi7%CzJ(WD@4xtS1h!=yc~ zp@L|CYK8_K_bRFi*Uj2Q{G`yEUeBHvH;0%88vkQ!w%YLDBMyp@=tMQZJHZ8pYRb9e zz6U#92hYOGWF&&OYK&eJb4Z)JFVS2G1xHC&%SzP6(m}{h+n7a-UT;1@Igew~g)@6&p3=%}Gyc{dX7IbxHMe zoWTyJn*pWoun*P>wX#qlN7lwE)`QVv8>Ll6W?E>f9;oQc7tqnP<$b3gnheg(#IJhx zkknK{b#;!ih_=!J#!uFI1nR|d5gEL77c@AB;KJ`4+qY0C8IdB*QK6+dtKON>cyiA4 z%`ShiNWu~It6l|gY*HkWj8L4m6KM_4^xa@@N$m3~Yf(TF{E-ng2T@99cat~U%j3>& zecr(2MJiqlZ16J~U-qvl-Ckb%;1q4^u^Amlit=;Bi$%Mk@SB_8kKM+myfl&XD{`lj zY&?8lszf$EiNy`clBA`3iS9#vSRRy?1?pg2&*tH;y~Fl}Ypo^@@7hnwBT29yX0IfZ zxtNO4UNC*=EDF#*VhQ0VPe`a0Qp|*hhJLg?vQ|5yy$HL6g(;VWTN3(of52qLQ|4#ij+ zL_snVGTC};T!-dxpsQOs_>v~Q@CM>1PL8m;jS;FxQp9h7%Wu*ribvH~GU0A|Bc5I# zxYPA0xI_Kz%*@H|hT37(!HP1uoNDA>&>03P=EwP@yJq)0_NfDhF6Z0-fSaK=?l+&* zx}vy>V%zH@Hg+`Sey&fxNy_`soObPfJUXd__k@yz_hJ_y0G2cgU`ca6WW3K1ihqcl zIcBoL#l9W=`jakQL#rh>9oe6XssPRbHNG>_va$m3UNRv+x-#Tjl#1j3w&p2Q9s zNLS!hPN92V8Wiz)n-4ybtk!~cFvN?GAl!dtM~j~1;e7~$398e~7I*)l^Ur2i)xQkN z+Y$bPtBF|o=epzgS-?)9opoiP*gk;xzi=)0hgpRxX&?cid1{&K|z{n%MjK;x|v zk`wCGv)4r4<%@?d-4qy{r4qT2-enSi4P1)1ISe!3Pk(z!@R&IrPKbuf*V?TY5NW=jAwqaUEN$u)#JNKxChdl`?B9`quvsm6A3Kzi7uX4Z8>HJ}^DGOPfUJ|n- z2KsRL_R$KbV67JFQ>l8gSs51wKOJczg(1q_aSPGWA)c-EIUR*7_jIs4PJ6+HZ)l8N zR8^LTGngyT`KCb+@3R_jR8EMNmASs7o9N$8dH6!WS`EAnNVw829yG!)H!uNe`A$sb z85Eb}COR!lw9QEu!W=af75@px5$Zb!KZU@Zq2r}N2?fDcy`U~#R}3JWCit&yy)4ru zGtj51z<2PCQB@210B2sRw%oqnY|gp0v-OQ7W9NfPd>iwi*|0a{8j=tdE*8e!$usEiF0&|U2YrwxdTuwdKUdT%bjJ=E) zIjCa#HbJ~&j;lTZf=V?|I<@c5A_O5nih2)-0a?c~O!AQ10du^A{LO(B-|y2c2Z91<#6*y%%C~ zH_~R|ka?*e?i8jQJvB3w#)^{R{agLcWDcwTcAS&?A29z%kwn_@$M}gf9&5{(17rYm z+`E#ki8Hl9Jup{+j98{xf3JfOnNXAgoB!wSGGCj0Z~c8VLV17LzWP0=HhIyn!22rA zlVY5uZ@hlAs~(~t`#1D-#0sG*-jveCW=Q#-^L94=P5~jKxTE8wNQQ~?G>N($QXP}m zy#;HXGgmvrh*0$84^j_0oWUkE+` zfwH=)eQiYnL1J`|Urc@ZwnDm#df*#TJO)|Jzitq~f^$BKr0sVkLN4UkVl8{o|64Ef zyxg_xsKR6&84;+cOAPSnQ@N1J4BZ>_BRq8odG=qb+>N{A;kUB%8cPFgDHQi|dlUqG z7Mmi4b;{OUa}+6Bz>t2H7TV3LTUaV1i5=c5-pA& z=}z|nhlOM_2EOrlsL)I9pR(DcZ*)DhimC8E_{Yv{*R<- zp6j@NUm1dQ4c{AjmR%sIUA8LD;8An8%o#%QSHW1_BgGS07Kb*uT;Hn=5}Z{XqfBZ< z5xXruAc*Q_u(m0G!o7pbfaWONx51FaX5x(2Kr*R#E=6RnJEc2Ev7A6F8_5iVu_gx! zacdrs?3-|n4bOlz#LD%c$ooFeWBlKu%2ZTEO|`OrAD2r2rT}4nOp17LdyS{cqv}yn zXqn`x_vMmD?i^`{g@>dVhOSzuAT9WbLJ#uI*i~_r6AIPvP`oVhYV&7+zE%H+p!P9N zw78NaF7f^2IimY>n(w~VGBwA{t>cD@UQjjr21%H$aSrk)5fEiJNs4yIi(AR+R)xaI zb0H|G9&`-Q6Y@8SiTeD*rNMhR8FG-6hrK$vvG7b2riEyOs_G2;AVecYm;2kR)6YLOo^C!qTxTWCQ*qUc1FQ;Rw1eK98h?jZ^!a6w;CS%i~@ zoBR)kjOxl*o?T_!bcs=!MaHZ3QJo}P7~1=ib$MGJV1>;G#idZ>vBkC>dYH$a5q07p zTyn09es!dFP!}OvZ+z3}kR|{wiAZQU2lg)y&eYK%*t=iwJUr6_oKeAC^ZFDI{yNK8 zQaF6MXcc-GKf#E}GdxXm<%hl2M>frQan05dvT0fl=xTW+ogFo9RIMS?Z9bJpUbat& z-!2B}j5N7eVMj-h)#j@3gF^~hV!rGLcqB5|5Qa?@NhoElviiQU65ondlDbRH!_dsK zc=iBt+otO-`xJnie@_oLbX76gN)1Bi-@(sl_J^0A&5Zkd$9}!%W4%7QaNH5OWC@Ss z;{4<}nY}O^Dd$Fu%{e*HbEUE20l|9bxYVmgdbYyOh+?aRvV9B%#*Yx)nYql*7(y;^ zsO)ZMS`~PkH?TRTzPvVG7DQYH*BtPc6kGXKMOi*I;3_n;IJ-DxHQ4e3L7}gS-cHkG zfD*YqhnjsfVPeO=!Epl5%z^24-LN6}>mvmApF@}5M2Nm8=_GU*Voas=hW;0G|DF`A zfRzkIJ$dg#27xC|2S)5_LM}`PHW#C(CQgALWLI>1lpMmthz@|Am*>O28KKFD1)iM~ zQ}K9Vff0XujGRhQOzt2;?q{W3b##M;Yx$u@+@^jlV&)h3;~S}B%mvg!cIk6pBE!_* zs4jdDca%IFS9%EtkDbH~&A=|8&D1-sjd*G|2Tv>_5IFRBHgdc2*bh>HzW>YgD-;!j zKl0_PAG9zF1%^U)6VU8Cj?UpS*-`(C8K@dqEp615f9dSIHaq})rZ%X#9n3q~i)}zr z!nfl34`^wk4vaU8L<+nUefOmS8GVfUw~p)2{##ncNpo?c$;gwloG??Dq_7sqZInQG zfivst3WX$Cq~Gk}YxPP?eo+fXzXK^xoL1)^u(WZvNo{rsR-cw^J#9f`Z$RABWX#qj~w(yc(sop z;G{56598-ae?)Tde@z_eltfHqxeE?6$1A&J@noN}NW>b_+8fy9QQ@can$(LoXXZrK zU(D1O=^%=HK~j-8K%eYb-x_O?yh{skWH0^)rrPmhi-7@)eR|ozXX@biNIi5xKthG> zCYHtpw9gO#AvC)_0Nx9CtvTp|!6M$@T>Uj+FmRUKL0P}`$dpWHNtMBKaI%fgbodiP zj&F;i{@afRSGtVb#)w4FIE0fiKJoWP@o0h3=Dlb}fJ@U*cFpW3?tvrgZyu5P8ACn_qYiKjCa^z_K zOp8u^iZj2spwp)>&P@#%UY=jfd7hgbgk-{^smaZkuB&1qa(>oWJI?P%0(4_%{<(io z;Oy`VgvQyYCCOpzTcxj=_%yW}$qrd`OxwzYgOyoi(3}L}=8t*C6=9aPN#WKl!K%a0 zc^+3b=phF+`T6lz`i>L(Yr+WKMbb$1&r8S$M0jE8WwQ+ozePTWQ4Wu7EnNNrRBy(m zdqe-&2%eWohcBGhIjd3OksSEmQrgnEjR|}|ubHMazGUaQ=%F}-{-@wGWlg}2v2lZ` z(CQ%W6I~^d8~RUqnFll8Ff8}mPv2YTd*Ak*IRN66ZmHNh9}@Y!he;t0Za!1O6l4jn zdG3;+x!hSh0ZjA>gHWju#|!?Ak4AzBqwXj4=IR=po4b7~R28k+BP3zrEO2wHuM*#X zaa;gri$lxCpO@b-0E#BdyAe;lyTpju{0u7OqSl1ulmQB3e%R^?4F4%r&A+%gIlsA4S`Y|Z&jw|C}IptZx>NC zYr!gr?)L2r9ql;as!e^fL%MLNrBzR*wktLub=%4G$bC{#l?{Sa&*&_&8xu5#Rqwku zX#j5XzTwb4Ye5Hklp>YwXiX=l^_jOJ9^i7WqrbXpkte66XPY~%!vKZp>)(Jsfezm2 zU6#$#^$gSZ#GiyVmplk0dI6E@p!qr{nSD4_gLC}S41+CQoR%=sAK(YY1&9G&Dm z&|s@OV_QhKhU9`F;$^J@)Ej*BJ>AZfWoQlbBSK&ok0u+g8HE5@8B@&?{5wGeomIe~ z44=(H1R7+1bO~%HoIjeP)9{ZD0QIcs7{q--wE!0S?p-B~V`^6D8@biK0@vo*evP`Y z)FtU=j0&;;2NZgeyUBu_Zzf1qTB{oA`yCEhqfwpJ+d9T?U9N z46RX%6)w=py8OG`H^amQ{42O)yW)c191yrTnBEtBDKgK5uO>_XYAOy6ZFUYNTXr9b z)k{B@>KQas*C$^%BZX=n@K?~^HTC%^{)sCTMyG@fd;NJH19W@>k(I80X!WA4C$n|drKD`Y*1`bX zru1cN6udE|*6*|@B*8$;QsSV9y3|bU4rL`dC_J9uf{fr}W7eQd{{-?m))($UsY_o;QcHaFmFRTlaJ#iL%GeZPvX+_&atY(XFF%hAElT08N9sx@_* zG~{piDll&zQbGW@!xoDA{u5NkNX5$j7R?jo$HBjC`>wtO^YOGql@oY9Yd1<hLIA_PUQlE>GH_N+e}N(Uy+s_Og^uV zX5l1-|K=|tB@7=z#Q}bJoI@F=0fX{;OyiCcZ{rsmYbzJ=>jx25Cf$Qv+U z_SRp`z#Qrq9H0z+&NmN0#G*;xs1ZLXx6T8rNjRL~4BD9ogaXm@h2(u!_%jAK$})Z_gg<+}3TmTKteqH<^S2vASnv7ro)Cz2VeemG0QIOdI~3#9kL@l#;c+$pn65=1 zMx9n_HW^kEDFU0N{s zCWzV+<4P^nQoT;~zgPq6vAmfs&nC|#RnMo72|AJRNgXmfzQ4?ymJ<1E5R>Yal~bCJ zV6=8NGtp$_(zwF0S<=be8Gpu~kZl&4(3%zHqi~XRh`f>lI&lA4FtF-|gS8g)H<4li zP>~oc0IKXXyWuNBz znGuT+o_2rdkrCb>2en>QGW2uAKwe}%s-dN&g_&sr3XtFo*R`7L0$GrfD!`D61L@g4 zH(e=vYKZc?0pskCn`)2|?=pLTraD#a>7GFJsD&S)^|49a7aew6A0k5yeo7Ac|CsMz z_Zlt++sN`KY15;S-8yS+r3O}VjD;o@p->^$c-L`)^aIn;>J~a<3F6JjS5nq`GuT`h z_0>T{|9!V$U9Xn|lChSRojJq)M$ti+zT(N%HABMHlhBxmhtu0ue^T2LJMfO!59(GsI;DZ?z@5x_}2=ImpA3NKKPqt1)B@TvB_w$s`)zX--S^6Bycsph03?iFXiOzMsK{yZGv44J?g z1HtJ>LoVV8t+w%Bb5AXxU>RU7zOIhX7dSf#@K;l63!4Y_RERA**{qtM@?jyFRkaDB z6*EK^>WlC*p~m>w=jB=k1AYIB%L2hjv3UGn^uVj>9a;8p7)xy`b|~zaIJgnEGVRoh ztE8la5l^SJoqTfiS^yN`4P`zWop1M9INe~De7-pP^#oPxzMrMP3Dn~1a>%%_%>js- z_~-*#RNGCaW>(QqZ%G{|a-RvjNXMl&14HX`P??nOk$MGG`pmA*Y0ExW0!5dTc?rLM ziENK$<0bN=GFOVz?mLbtj`!b8-+^$hrJh7MaN(|0Qwy48b7hv4nMt(Eu5Nmx{kryg6YEN?8I==G6j59Rcor~@kt zvK0%ZD@alw_sp1t`-jFj109dSeoDIXP-~@BPPYG3AR&O=qlH`@9Hut==RVU+FV<0F zYP*XIwk#SONC~6hw5~X=Bz9ISnl=VoO*VSeoNKbXle}RWN^^+;ro^?givfcJ@m_~* zpLB=ztXe6I<6xcH9{V9J3k`pqy_i7XF0sD%f25XuT;3+1D~actM~gi$T>xn!ZZ@Q8 zQdyT?HP7U~2K<9ip&(m!=Y6(h)jE4}k`f0QA@=q?O-1`w_y8R;g@T1Y_m;A{=RFR# zleN$IS<6|=C6$r+Ebr|)AH^~!{N)A8Y=>yXAc}rpC}_BhBA@1SUB&5Eb`0p zSs))C>>Thr;A%->UB~0}&SIkxO;^NeFa42kS%rJ6iSF#a+Mwcqq=h?U`fm#TJbe?K znW?5%bH1g9+0s6oU1_f#$*z%Z&sM_Q&{GbSg96d=qv_UU^wQYH*$tgx&GvDO5c#D( z+Y8~_Mwh}50f+)Lpn;IzVE5Oz2=_tpp%y481iBTjeP{@D%R|lv z2Ru+Hv5}+CXGIj88z!!pI5FVvxwnrP*xvcZauTY9b#ZaSyxHOX7CwU;3SE`T#xr#D z^xkbcX+saI^CJ`pQA-Jk_1xE|z#&YM*zypW5smb}Gq=?dEBqD*6~esd9+W`G7bQY| zHu^0CvDwM~Mq{w5{dzfSszDZ_lHsCeIzDgL4Xgw4^~RiiIJfjwKs-89kx3okJAWw| zETIE*4>ll(5}DF#TjTUwSh^U6YIZf?sE4wM%6wwrFe7ZTsL-p&>y0@&j&CkSNp(e* zIILMDtROoUEP=P33tT&R*T?a}A;33C_G8$FW>*>87yr~SO!iJCsp&1YiP@rruIC#( zL8$vo1ue+b6n;sG-+slB(psikMWSfsW86bc7E(kJR*w9EU^{!q1Mm-*Y(Lispfm2k zcHA|1tUMmRws{?bYM`Qkd1a572o8nBy>$$rqE^OiC2 zyXk#hIQNo`dWN|K=A(}|YnCJ=a4{JzjztnV5vC^-CqSDl@69!;FLREnG4KW(IPaD! zomjTR#&t6c**9uug6H`Cq@PZzA@wq`-d+xuPK+vVcC{(Oh(ar-Nui5IhzDO zi3C`*Q{upQ8vU=}rT!0qITiWTf2Yeb-Xq9(CZ$flw~!95v-;9Q;nSjz zP0nVo1rK|pA5!WRtfrIBe)=3d5Sk*2hb&A2E!z5FEC7~a@GXOCjb#UwGV@OrkQX!( z@o;3Zg=06-Tf?8ei`(cQrIY~IHQz~3e*T&0Fl?Sy+MUlyhI|Lup#4ju#S{%Q%hKs}Fge_0Ii({BckT%Hm%VWe~hT5&xU{*U?Br zJ_YVBYaF=^_DukRH)U1EYd z{juw?7|XUeoSSn-np@x!%gIs%NNc4J zJ*L*~EuO@4@HUq$ccGuMmc5C{-`?Rqi1f+cESm|)5#@YGnf$lP zZXZs)D8ErD8FARyw*D?=TR-1OVJf3~D7JhBz*GVvXg#9|3XYLnT`k33tw{F@D&yp@ z5!vGmXMEFI-3$FP$%wwK^C?b%+7qPV8y(2TT5Cb1E<$QDy@Opi=)eb#(#Zg2zYX^k zjo{u3Ej+8{GC}Cn=hf-EVc}))WdY5cGLny-D7eASrp#nBINjfksqqa13obQFYGlBR z<-mPrNw~m=3>(282O*cS{YCaHs`_9jQIj`tBF5KXxJD2~QHxsON2~j2el>IEl7<#G z(eJc;huA5M?#h1-j z#((Y>uZYz0*kO%a#tkjr5|6(*c6yqXSUV~I_5X;FRY-{^Za?XOJ&j z4qW6H_tT1}9id6M{uD9zBjm2-gd-n^&|^EH%~juV)F)^U z+5fjzsRL_O0o`NXI~tH|_YXcI5b8!8My-@WWXMAwWNxIT*6!8me#9rdA8W4gBfPp& zOhbRJI3p(mVg45yh+6Nu8qJ+~ZTMwI`_;lI_Bwl#G`#rwpV?GgYrwIv-vdR>c5>wO zb`xr_xBF{Whx3RLwAcP^3@#FhG}n7+2yZ3|c)!kchXp9qHon|PS0X_HPKqD{gO^## z5%Ge1#Z`)k^VfZaDw5wMhOw|_9?QGU^>3(H>sPv8JVj1o`h@1E?@X-EP~0%Z*r8MW zXl%L5gC8tuw*O?t9q@I0jK*b!D*sNLD~@EQ<8c*SR{sMu*ETM56Ydw;s>|DcOnjPk zy*p~o}PYK9vEU|*9Qlog8bvF+)d*|L&8tZw~ z4iYxX|A;xsXtx>*{4PmzA<6^n?+6jOua8gwJ%5Y%`Y~SXKBiAVWxXQQ@gafSC>93s zuQKT3qTCCpCz+F^(ro|$oR!Dm9;IaLi5V>!1!xd#7sz|0X4Gi!myM?{6!%NQ@ zGs_94u_rG^={_mZz#@s?&}(d%5Vxv6E<1R64HlrJM90(d=*R8aqg#N2LoT^?4V>%bCgT1AyHH23Cm!8^KVl^(1iLlQ%ZQzjA`CziTb`s!5<@EJ|#3vs#NO3hlqxi;3k zBqDI{b`{wzD{tTXJ%=b3suU-e7o2vD^aaAMdJ)8OoKRi9=0}dZg^?xb8-49C&A+na zJ&hW7E$&A-H&j}R_oO^P4K6VWgF2e0)?U1bBdta)*cv$mhvdIl;`bHwi>X|J5$WFps%guTC-iX%cv6ly*mOiilE*oVdA zVCGaHHk@`xh=g~>^_Y=aD+zo$(nweuM&|&!@$GuKB7f+`G4|$b*gJGszRHbVX)EGq z9Gcy3vKz>_0rTmen~{XX6-4%d&|=}B1s&*R9eeTBAvJ67^$Ufo9BXNmWhZeXUPfd= z(|NBUXdqjKYK2`gcK*8-*#5EI#0D8keWBZA1+qP}n zwr%^HzTeOO$?BSwne4OAeP0)TGbbHqinpL#Qn8gRqB@w-T+_c62ur@bP~Wo=CfV|+ z3mx8*x1fv$sM3Usw`3FPETZ}sFc07C?c?HBMaRUzmMGMQgTS`O+FyRd^CwQA`LxPQ zvpWlWTSxfd^Z4FC*q8Y5Ut%TIUvg@8Pz@41@v3RbYA^%>D!+SYRgVjpK zuZ}pLp-D4Kz$?V)ujY@0@_ts>JI)(#w8-W3laBY7&!3#YvF=lb>~M_y6y<*pUUBcz z=SB(>{Z2=h7AY4YW0HT>YeX54XB2BpVW+0nR{GuFQ@Oa_fQZQ#YT<1<9c*5pJwNlH z%I7B3;oL{qolDg6>|C!>!ZQNl40;Pn9Xp|9&_rJ-v$h&}wkYBD7_kx_ON_6pq+DENh-i)4 zML4VZ3hsgzC(A(D>{83sKc8&@Wjh3CJZ=qSnfL2Q2E=hO?>9zu>H%QNxV~IjLUj}T zPYHPim~8yOFD#qOL6it6iJ;5CE&Bj>5xcj|E#^2ITPoD$ZMOk^Oaido-&i8zkbLCM zpxyeS+v^rhPDIn`K2GRxz8MdjxpeMU*RI~oIz(NS%y(mdrqB;Z z6Dhn;!B8{mGLsa^zBcb^1Pu{Zo}e1Q05bOchgU?C+0dDOI2;L*ov1-H$s!_or9BUv zUP8ETA%ni)Zuo?fq?X7Q@)ik3m}Td(u>~?y3_YuQoY4s}QhkbABskq?K|SbAj?tr# zS>9+{BP_;aXs>S1+HMg4Le2(4!!5@Te)z38IKDzm-l|6F1GIQEL=mXWmy5+St$ zFCx)@4E3w-b~mxCpdle9P#j*VhfN9rhEOpe6U@LRmz8ax4Kd*9*YjFxa5)(Y698E1 z^k4h^nmH5tz8)lt5z9bG(}S^l+lX7PObjI$RzIN8;u6xza;*~ew4wA@4gY4EC)CD8 zBC~j2Xa#$Ie}DjRdS7TgkX>zO%a@jwMc&wFvKqgnw5DV-Wmi_ZxL(fM;k*B7==mav zXMsxQZw^iz-ys3KDK}Me7j4ieK@QmStP~M(d<<8DB+Xxp1+Zbcfb++fX!rPaat{CA zt`x+Gzu&1MYY_(4>trh<_Pgn4Uq5q8aw7EQ(}yo`A_ycD>R+>csQa$hw)s%)nj<;i z>h%ip8fgg?k#6NpK3y-u{Vy7cKtQkf&Q<@)Kw^FRlfKIYnCAPgU+tq)G6VXP4gaQj z{fKLV4O|}1;>9bxz=(v5KqWjraerMOq<5Fg)wES?0KBWS#YNOWD6#vU#sUF{;!N0) z``jnGn{J$w9we~9>*Af5d6@n$uy)x1XleXl^0On-=gy+VM*NDqDqmS}RedLiZlcO$ zlS#aIlA~CC<#y!pC7Mt`xOtBMr>kYarIFM*6L29v_v>NXZ3%6&A|AyzfPoE2?3-7_D;;keW@``Ix5U6n@qVLS8JTBc66*2j?PLLHdQSfQaWMRsr0d!||{?gG9nV;@ws2nW)s{A2)LnfSdN8 zQ-^CAMvVfnNWV{=Ml2h;EV5{tdzx`{V2p1>t`b%19f$WcaqNyv89H8lC}zdxT=I#4 zXu}Dax_^&Dq8qfLK^YNEy6VNns{Xv>il-voq~Sm;Y#nh&Kz&e!`${+SXN}s_6U@L! zZuE{P<5c)U1}A=)wv#1ljk&TS6y$9^h!Sb~4u5xK z0rl!|lRgRfG_4%6+n^BOK|}^x!tZ(BQ$^=8Ko#weCfmv`!Uw^x9c!W+KvB~$Nr08& z^_(yzIslL+EA)sJol9W=4=R|B;;_ZAt-aj^vAKj$BrJP>uPK1<1fp>H6ef(tVW=OO0|i}hWJ z--dK2cYLShzVRxYwQn16 zt$YBw0`?JT`&KhyLcmn-J!>NN=if-Sd1ISA?*MsmO}u$jw_4HPx@O-6wT6t8pXc)R_#emF z#HmjWe#y37*ARRlc+HMmC9^gAM27h$JGb;YRvV4Zo(W`++(7JbW@bcF**7omo|?pa zpeq9)3owSHoLonRZ7)8k=~4j0GW%}Z?YIFKH+PN8jsElVGd?9HbOMD^Z?Rn7bd&X# z&g;|pMjP74+tb(ORh#2kV4lbw&-0|L05obVxJw4Qz>@Y3fuzmX&vFSYJ{=K1l1cB+ zTQmSDnx}+wvVE0w9{@W)dUEz$L*7R+dTOy>tE!t&JRUA7iC9>2cr)(46?r)W`F zNU%}|X;bfPPTR@q-Y!x54P1b$kwWl`HD*@{ukgxL(RW=S^afxR0%FSC1j6BcY-b;P zR8QLjDtS92`rBzNYR+$&%Y29^XA^m4J79^?Xw z)ZkZ_DT`o~sa~ry_(EA-f=2#+{yHgnd}3 zG)&LqCBQ4^bV7;x^sBsmxZ+|}v1kdIp1$@Cp4{fF*Wfql=(+bi6I6~G=?QBNye4qY zF<+#B=I-#m&W~rS5i4=Q#Ll$fPrWf{C)yIwc3M)I`_Cvw|D4!>4m}qb`*^qKOB56# zNb{l|A0O%H=>97!qfF1pxVb-3C|7SdP13n8RVvF>Z!p*4)+v(4AqxW+He&0^SRTEn zqTPfqZ2!-}a#@9?4`a$i^U1dq`qF3&)vv|Xw%rv;Y>Bn<&EPsZFq%V{#I1O)ny_6f zYwn>1R67=N9kQ{aEbnhYEQA-$nJ|&THZ)v>e=@nk$d3(@#@?Ltm`7+%tDYPKOVuS> zIQre?2Hf5)Ap>jaY+OhPP4;Op#$S<$o}?~wosVQH4NRzyszFigM@hh%U*F{dBiYdf zhGGHb$i1+fQ20cg#KHc2a7`yh5_A@pw#kZsm&i949t&6GLJKk1sOQu*06=V*8|7JR zDjM4xXk!zPhwExaBE!Svln`BnNAp6Gt8cJvNB(n3FjB|4cN)iyCO2B=ABU;t<1>|g zP;gub;K!laM@0xgGTN^Gb=C3mUn6~yJ^_4o@@=H__ILiY01|Y!vgi%4dX;z%PF@_@ zg90Wu~epX42D%LDn$5$IBV*BiIvMG!0nG_T|GnPHio3j_f2vN9k;R2{UPP zTPwKkHok}(BeCP^lVvx673J31`~UQRMwmI_a;)vu%Os71=hveBvwN5Dfg373;|58+ z?@-H62mkLTkipo_1IOlx=W0BOK3ZiYF2fmkXT3_juMl=>wlE zSX>h$CvAE?+vMYV_zyHEp`)7$K^;|DlKtc)=$kEH2dE*D-}r|H*s%r*0GMC=LR22n z#RAAISjiu+TP=yT2_i)rQpn1D2vRjT>I(H$(-VqPwgr*deD%?U%UBh3}*Tn&IiTs}ilwQ1Q`y=V!#KbvTZBBrmj7+*r!6`*KKQYu zSF98A)TbhZ$)VOWaDdvNLVz17gBz6X27A&)l3+QSE9pwq?oND%-EK3CMRZi}7hgQr zkD2gy{CZu+>8{%O#I>A*m2aLE?)Nvl_d+l9Ef>fO(YI?7L|`L3@UO!zJnT;I*1?!7 zU~sctNColMpDIB|V-c+EmyYigOvd@P%$5$EXxogj3mShIuCaeW;I{Jg#c%YLsE$2< zz{jL7M?_zU3{w*fGvN!Uw!6Xm6L+v4BIY%Onaf)gu2T2&E{KxIsleFuFis_{+fUwc z%$?xX)OsGu0Y<9XApo)7{XFp@q=VezHjE^Pw|inB?|;TzL!3X@0_wpxHn@pMVx-iE zxcBO~;`p;?Gw1ufQoBuRW(w>UTD!~mx#1^9EHPR-_D}^Dx)`Ns3c$}L+3mD@^8;+# zA)n{#Eq5wi77L{)^YimIcZX7jhK7I6kCEjCP&^H+E--p@@aZ}u)N{TUYt-DcwxKJc z>@JuZ7z|bbm!DGKL~#fmD4ggvxSrrkMs|5=-#@I+dHrY!;deGvzF%1fs2@yw2_ji} zy3H|jbv*%w9tXrJE~eC1jj{4m_Y)Sgk&1b9HJ^ zaEa$fAV~1K59DS|cv+K{tt>S_G_Gjl{FkMn3fBs;3l+FSl@+-Q>jP9>w_jnF;M{D^ zc;?msq+6FX8WDzB3R|l-s;`vkW*JCm+NprJhYOmYyYq9dOj~EWV^Vxbm_)!9_a{%W z8F2l>!Q(?PJpKusd`bW#eVDaOG`&kZ;ihergawLE2&zRcuub0u3JzO4BS8}E;dMiWJBmgecw&$`PaTq+^X)6XI#xwJBLqHd$y<2fKEwRGU=5i)ITbD zP_c1hq}>N#wO|IU4`>%Zl-CI=($#s8;KOG~^Rt-2|eAZeOCI z1eQ*bI5lpBl8eD@=9lT*m=iKM=wUdP>s2H4#(G zB;Q!yDM*P4MuFn^*i3Ko#umNK>>G;pe!99dhJob~{5t10A$xl&mDtm364R!pPSr#+0TVc0tkC z>Q|9;27`@#9OOk3Ew|u8Y3-$6$y{~|vGj%QkdDqLzo03$Rr;L}wY#k&>j-qjmcUvy z-DyKPOqGPpiQ87_U3DWRwYjaI?0P$yI!txnf!05j^Wj;w6J3xH?2VJzN#cAzI+{uO zX%i?wVc-KyfrX9gp{^!_xxqP;cfGqvbC`UEWfsbs*`r^Tbg9}>1r=w98%Vy zDgxOFvW7jQ27KY8;FSD+EMEdk5?oSyGyw~yn!#vhZU83wFt!xL&&`wR-qkbOv)1fb zBkzXZRmUsOD1S^Moax#*7=Y8%?6|fAn`2O(Q~z}CZ0mBMlz*UlVP;yEBlnc5^C6|r z>#fQ80)N7ulY^-qAUDxif4tR{we#`D*6!NJ8nIBSbQ|@)q0hmS86Zf&Bjj`!&%5;{ zJ0mJW92xM%FDuTK%Xkb+UBJ(Gq2o5ek>Vcp9hdwWbrB1(K z2h{ry|0U=}Ik9R5_>?TJm;e0znTo%I3v*Q4Zht+N4dDS<49pey7wF+m4t<{A-50;N zW{8bBWK6*g>hv{>CLT+MVu!163aGabrWLGN34lq#nw z+DAH4z38`dpPaAOWU?Q@_#FnTz)s8D%)ha30M7*nDf7QHkr#v734Bh|L)B|BF#RIC zy?G+5i|JKN^V9eJg!mTsC+4T+@^lbMNkb>6((tD-x=Y)G*@2vbU65K{Kb`ZRm$t5! zp2+0C)20o9yXP9B%f@~@cx>$OB-)p4Am2+ZMFL}PW_I8f39;`@sz(sh()?xPOb1Sa1ZM=i|tg=<*7HJ#+BX3*zK}`72qW)<0>O)RVmYmz>+G z&o9^3sO={>&GC2oIV&4R!vc@qNeq#b*Nr;3Wv*L%)XWrr-+<2z;{Rm3unN!7eF8JE zbSI`Sf&Eym1i{ltieM%Hqa+vv^=6i zPeNXFf3jZgdWNd4+!`-d7{apCcLNead3 ztOO@<_&}Vuwld5xQH58@QxbZ2I-Go2vFqv)w248$9d%_V@Oct_TDxN3A&{@s&WH{^ zkt=Az8HJtMS~QVbD}5divSLMG-Xw~!T6wgw{T8B7riC4&C|rLX=YbQA$=uV(wu!WM z@^E07yh0RyzVj}S$FX-nU-8$-T6IC8y$t(e+;G*DaKPS3*WhhM`ENm>ac>Om@VHf^GQ3CK-ly%mlz{(VpL&>)k2@xY|dzj7YLUjP(!V&>M{g>}q z!%th|3BXwxcqP<&KDy)^%#~j6cKWI;32N)=Zq8R2i%Ux6IFo_@kcS72j*F3Ag~^YM zbc9M7Z_l0%QRA3fMy7J*NcZ!{{RupG?|#8OlEwACr7B=mk#XvR<|JF)&}@G|4$u$q zneRi-awq<%`NZ0I%sKuZy4wuXFT5K6MW$BC#!Av@zD#47AJXbnL`dj>K@OvMa9mh@ITW0D;WsT^RR$?J=pLFwxcnE+fRLKbh0u1%nen3(7Qu&k%+ zIFJ_%yxyt;cyBd51_W-BmZY!Q6V@+tQ-7NfclbR1d@N6Z%63^&2dC^eBbQ@!1Zij} zQbt%Isq;gf>v;KiMIlO#qKL#S6DJfD>DCr#Y#(Hq1~C`UMRjU z-wanzX7(TAt1IUNmGJvsZ%q2!zRI@ui>|>iD-94C4qVpreRDPi#_SK*+x?L$FXx+G zAz;+bw%r?4boi=D=Wq=DESJjzj~DybdG2D{I=DnYC9(LmGCP1gA)^Qb~UugI{LCD0CU=m+093g4D3pz{{rA%|fT|G-I<#&Y1%cp%@ z4b=pHN9q&L&rTsXH`MhF*Rw?us0fzQ@ZJ2nxZV$fy*C$;>!XL$0dfAi?E6Ap;Ax)r z0yuFx(`kHjYWvG|_H-W4RCgW<3Mhp_$($%s>GbD?FB#nK*Bwy2Pd?RFhZ7aEwR{Dy z&-WKL+ua}Y3kx${-ri(b8U~;IGmPmW2)qIX@&j^`Dl8e-2UT6$pW!GJ5I_#Gk%85k zD^EMB^jk~L-?cKHrcS$aTm~WB&X<4gqujG%;G6J%$r%h25rA9`d>W{eH8|B8@b zBu*)qNFW8Mvgn)k%*QY4Q^&~glT|4Fq&CB*ovn_%Gsf@@hnYqJlGM)C4MUQY@={0z zfm1sTPk)8MW(!IAs3Nk?<=7TMoPs-Q*4`qjl0*()nRzs(?aYWx+jFu;le1Mf%2`2V z&=95JzUG>_IjV5S>u#gtD~=#(VK;AbQ-y_ULe_uA9(Yp_+Samv-~W9K$h(0@on2HE zw9)1~SJ^$8!Pz%50;$#N;8RdQ46IcAAdyIHGOt3Tvz2QR=(S=n7>&F8`+qbxHZq|n zI7}yzD}dh}PULCb{)EF79Elz68n(*DZ|)Kg=QC8Vm46a}frpbDqIeftusqz8wly~t z(!t_)aA0_FQ$$I1Y~<5(Oc`>O=f_;9xn?z#&)vWm!Soe=j(>pssjTU*h~Fz>2DW1` zF}yK9`{z8m#9!!$Bu_B_aot{n6sLcpZ+09j+5jN3I&rCm6nb8?9Cdnp`tFs{>dGdJ zTyq_fin-$iKEK3&Kn^Kq4rP}rYW%Kj+fM=;r8V^d^D{|NK!8qFy3`0p>ICr&&oc_HwUgPvs7+VX}@jny$9V)6gG zFNFe*M?W1-=OLZ1HihbpC$5iYh%`GrFb)q7m)o3af$i3Z1nqq~yS=Wpb=35<>?*VO z*&?;6sj2SqbPm&YgTYWF{P%)}(A@mOOV+Z<@T|Io8-NPxmUz#R>W#w9Ej3>`fks%V za~D*aT~QV}cwxSe$)1>-EYdsmDw()qjbN2=D%gZ)=<;RJrr;A2P zG;55@`GiieM984lS#Ev7`S+CcHz!&S=n`G+1|~Nti?NC-y!-#24n(5A4@llu-_I%{ z)6eg;cA!0N`VTp03Y^aW)l6|~8|xR>#&2;xpnn+T0SGY<;l_0FLJq1_iH{rpA=1$B zx9PO{r>Jv1z#0l@#!AMQX1|vV^MZZQqbylnM)gQ}amHM7de4~L`nwI^!n4mUn^beWS z;qf&gOa;J_AdW0m&roS2d|&##z5XdSgn-1>Gn57q7~WH`bL{!}#|APL5S%Z6p=WC< znnhOePH=k|2O^BnsFUOG{%y`Wf0XE$83n1^ZUOk5mzla#bR-u84b4%{#UC!aq{+8+ zfU~T+xX-(mPZ^^MA_@YuEvVRe!7$l(nt;5?zhc_=b{)RcXd6xap`BY@j8aau11P19 zuI=C8%9%f!^0d$2qGE~+^NnVGr@mRN+!QUt%$VB{oqIJxMYyktb=uz!hu*PurZ$n) zl^X>Dto7LviW(#YM867!U$$rFk0>)0}EKp zRX|sTn6jawyFOMMiq>1r6f7orb^jqynr75&D=Et~o})@zXnUg#-D)GBoOh&j zl#E4vMI%WF1aev6!z_1B0RbIvpaO;T{GX+bqS_jCPzC6&b(fmjT3sOYM7h&<%7B(J ztb-}@n}cWm@Engzk`MqD6?hzO|3 zapu;_yVr_J^&*5Kw*og#4U)bn0DB!n(u8C-z(=Ud3L5BO!RJcuq6d^@Xfl^vLx6x! z^{^cSSmw8!8w^_F`2SSQbWJP^PQ2)lw|NUO2G07G)xHI5AW${aN(3xwbdNFbx$?%hmF*ARS zKXMi>Qd6S$&Bu4)ciHY?D1s60JK8iJEV54V>~rxJ2nS#q$#n@SS#?;ifO(82f)R-M zGzwtll=5G%C5rt-xRK^Sf@a228Z0L=VDsLOe^!*V?!YGSX-BJ?pP66i2DGF#dpA%_ zrY3P`&d$=$fO1LjFK!pyO-zXRnn<7ngxf1F;~LG-K1ta1Y*d#ven1sK|JS{`V>V@< z7DMDq<@E#LvgLR!X&V3B9!5!pIi|K+qCU<(c-785l(gJEt6 zxIsSVX~f7EHI+qvzq}Pf8nEChiA#8ZtZe$;?5MD~A>H!D0aJoeyN=f23nts7Pj1v9 zl-n!7XG7}LDBmOwOgp6^=9&YHUKm1n%v;4z!;@n0ChzD!!8B5UT&eh=({9%vnr!4g z1#8VGm6D}_5=&;}nIy*}BV)t8Sveh1lp`}scG{_^o|$GlS&q&J!~LxAPbBDX-)nxT z(mVX*@s6Ersj(V6P44vmeR}}m5i&E9yl98P(;o64g&YtPvX==TDov}?)6V#!`3;tD zpH|h=(#rOIMjqN;EiLqh;u6s7>wi*zD}e&M z5PA#Qmg@~5HO}fzuBnEtU3GOldI!-s0HjKADL;AiqyXJFD4M%tViL{QfmEV{{4t4N5pvcf< z$Luegdx6(?5wIR^DiQ?izLYQS99sj6N{!FGp&vhf6i=e517q_{>(8wLr_X3s;I4^4 zNIZd+f9?`6fAuIO0qu!1m$>hNg|Fxxg|tmxmv`Vpi`s$l)n~eXBb-5Y{IZ&Y-wT;^ zLZkTFbt+0_2RaE^TxomrGx~IkbtC?GT*6Va3j{nNSA2r80*+BsKM50$|H?YZ71uj~3I)}BR2m?73|I+G_WGn$nL3@|C zd+sLOu9B|&A19!cG`k9k<;mIBnm;bU+4)1GA%z%9$cj9-z+=f%-(i-j?wlAJ8xL{{ z3c!_u(wnR(pPN|AOF6)jzxZwJuz3oJZO?!U<%CvVm<0kNB2Z~*Y1L*M0(p7)=}Ilm z*OaA^e`HS1 zuTn~f&{B05a{v+}TURIFQ zjkfU&b#CyMa!#&QUrZFtdn$>oF_3n=@%ZS!RjIN+6hFZHcNWQctu{n+fU2_UzY`=_RDqgIq2Sz$>L z=$I%535B8d3=RE!I9=!~Q>n5!U#5S4ebop485|i|m~jb|di^0U!>{CZdXxtl`I&p~ zyquHTc9U~T=kiOKpPnBt{+xo9P$19D@$Wq{H$S9w;KnvA%&=1UP7-0-pD}W-!vhQk zdHwxSZ8|DqXlKGi2Fk#{MOstxxoSG@;p$iE5)L}^j*Nu91$!D_l*saw{*eaix>sQh z$J}K^^C%o|hz7AWfRF2*1y7*DI95L{p|uJ|?|R3PO&M+_@-u;Cdz7Y0GX)i##4Wkn z6QYq@GtkY_yi_r%9Q$eRsk$5hV!gZvREp7C3?!V8DTG)*M)OA>hZ1l$U%eq32cp+D zW99c>3YoNYRn-^JV>%~9(smFUGTij)@lbV0O|_Dh7ji5TG+BXd+fSM(>F7W+o6|u@ zcWoUhQL20wYA|f{3&-1gVFij0M6|=BqrHVvX~%mLX{-VgVOdORbb8-Q6-#_MoiDe0 z{C=Lp>bGByI-;oZ_{A6hCl~dkL+2Cu&7wZ>C`aHTp2~*YnH42ot4W#g1)M=zH)qV z&1fx9eB&Wq$|-w7?D)!i3X2Ts5$h1_G>@br70?+BK_Z2RhyTwP3|joaU~{oh3hU&o z(ekv_x}bhQM#7DjRO2V`W%q@YtD$E*E1S>Cf1*4#4O)L(yiGPTa*&jY$j^3f?{GuA zQjc!?!=hNO&BT}3rk;|TmVH-Ea{Ig;Sl8@)K|i0bxIk#ineD^1+4&4Vc`m0@l3mzw z`g_>n=?=2H+O5m9GUjxx*065SS)sE>Jc|98Ef;LOJifefI-BlqY8`Y&*suQm$-8Y^ zF)iYtV=a1mdbW-|!oc~=|3DT7ML~={lq7$>T+o~H$xwB$Lrb|WCc3$sF6=xM-AEL< z-U|8`R0Js2aeI?L{8$V4_{!{``MMXccwhf6G*&`d{kt{;P3l<&ms5#6Jk&8hA>5qs zGm({=n7|MJ40P-j4hBVaU&<6|$j8g>CLl%l*QyTXdsT z)~|{oho8kUE!_ zXi+g6uz!<7DgNlUc#@RB?*7Q5-L<&DJs+B=wXuJM7U|wCaRn}HdFrJlq^^zYUDxWe z>s?Bjch?m|EjLXb#g+NchuD656NJgQ@%Hsu!R2*wLAv1Jz1w5Fx~Nt$?dZYzZf{M~ z8g4-e2DcY-z9C>CohwrigRX8JiyR%H6DxBH|4#Bl1MR%e*(lIJatx!{>DWC&4 zQ~=d0hM*(1fDMX+aw*k(+w+V45bi?+cz%?j zji6ZkQjeeRP})r$2ewl+6^a^|C+{_FE)H8NxJ{?(uPaqJeBb?)?f^Y(*69q9)~#u!W=K+HnD@ty zK%#u%eBK46x467o3U}0%_*B%H=bzc21u6xIv8S_BZ>?*W?_b%n%hi>5qwynQ7!I8TKp(jeUs6hb}(PcFW)%Le70GH`p3_odN(p>1vh0T zUe7g`wDQ%GQbzdlqrHRJTkq@grFWesF9?6r>yV~8;@zH9er?6$=H`p$T{g&eSvW4U z8;i|Z7RKMTKSHd1ZbpHYMM_X&ZYqy9kgfi*@)eKJQgo&fEfM(29NgVG+YeEZtMj~l z+#yp{*XnsJ9wsZXayON2_OOB{9-de9C1PTOp)4YoxU^_03GOXO7%^#>nX;-g33iW7Y}Cp@jWXy!Fv578_!m z93-eBRotRyQsQX+uti>pZ+II7zgsR~wYx~%?k$0OlnA-TrQ@7Uv{_fT)y?5W*y zh(%WfeQv+PFU2yx1=oevYJoSj_B8kRrXQ2%L1+&w*0@18P z?4-AhwWs;Eo0BgW#Z%K%@i$GoORQV~BcDIsA&y`fY_SxSWePFYF=+Q&7ic(=M9 zndS@SnlOu31r6cAU%%vbS9iWux4xmY*kgI_FcU3~F-P z{6&mq2#9#b}Ji~H1%F%`|0JnUaDfbe580~o*^|BfBx7@Y~Ner>vnogIRKv zT@dF9cE~}BReH-Uc=}p2*}Pd!y?wpqLbg?T>e5*a9pIL+K^pfi)?(2{l{W*=#SLS5=Za8u97!4r@c}$bUT~ zOy($f(@A!ApcYl_1Pmkg(SSmQ15wir&64L1b}Xsj@S5>j3~?Vf+xlqgBK zwO!xzo@)D+fhM3fmlyFD>l9vRf2qz_94>JgD&NLeOP*WWC|#4WE>Q)S`!#mZQbuoQ z!qDbgS$4Dp57S#Gu6cdgcO*tFKUZ-!HG;^Kgt+*@R44X2-NDV6VLAM^Q>TTrCBEd< zJ?j$9%_32lv}K9wF+jLOSKlv3yUQ(lCrYs zkvIw@b7!?`rRUoR?vR&H^$wc(%IqoPE4STr3WrP^xXegKYoBdxFy?YOqeU{>MwWIvNpF-EWQtJnt0vz!5le|*)!z9qlp)6N%R=M`-=jK}>yPn>8biSM&h^X+v zkTmV$J^5sj^x@aitKUD@;jr6%v^y+%Lys=><$V)jV|V7t1@y;pSYBTOgQCm-EtcR>|{QQl5Lm`c_P zkb}m;$@4ePf`KB0T))5PX-xJPFMPA8_4REccZ zBIgO-DE1Sx6)NqB@}S}TZdreSm8TS@G0gsR94}E6snXX+&-lH*Iq8%{6K~zc_roGV z$u-04FAO8}gJfox<=Ux2aOk|zp0Q(fVA>{>6g8W}F)ATpZvK(7a$&K2z!M$6t|-cdlH(mib`Rw@G`}=5Z7XQVCVma)=!Pal?+i7&^90e7g24)6< zuN4U?uMrYaC6$dWoqt>Y&YZTX-@5E&z3sg1GtUpUsLOeL7#O&=dp|&8uV!~5a@+{& zdiECf$Zu=G`#eDcV2tf9(pk5;S!TJPO?G>=$<3~0%C3_FQ{*@?O@fs_z9Pt29o>e6 zO8mvz(mUu|2OQ4Tyi`lmWJ=I}d%D~~E>0{5`Ya6PDD-$eyDygt`dt{AC-zqt@aj(N zy`bNl$RJk@hYvm>lxZ73s3+quXBMgc3g4WTacmghvwX4Oojh-)it55y0taTms{7$w z$gdH{XM42KNboJi0QGpxQgI}5^;(mt*bSXFX)$#3^Q|wQp-Oi`ip$iE z!CqvV0K5EuU#6-)ew)1pd11x!Y|__QO)V`wOUsD2r>nhNqXS0H%|KYJ{3@w5mIEw- zl0XpW*Wz-LL$Vm55mAmu$&7{&-k%>N93oZjgbeCQqXnMH0w#lHf#epbZSZY-4kt&o3qJflli zOBwBHEZIQSy~A?{9y7xaxvAERZIb7=S;cWqZpewZ`J_aUE*uZux;sSU$9Jp?d+#VO zB@w*=f7oWvOI)l<5(Pn*-;-Bup}gSKB1!t70Of#=>h(ouo$J90&X;SzF86MkMb*~% z7f`bb;n-3%I7yDW*s6w*Jf}l@`(&r-Qf~h1HNMOW2((N(U zbZRbSK;hd{7;c*T9D|~DWzwdVfI(@PNbOM*R?ssr@Nac`794rL-p5!pLIam#y+H6% zuEWhSo>GMdwkdW#Fbh8Z_riVcdeqs1P9B^+bqw5~Ee1hu{Ww@uzeS*#oM%Valf4Pv z_QM&zsn}z>nrwwOxof-LjU2nZWJG`M9StvY+)xlpMqh@+DQ8mfxBlyZtq~AYxh;>| zCU0>co(75ob>umx2vK;l&$}V->c8u5N_cZ69rsP5E8marG|KmLtsS(I-gU7pqJu^w<0z#?zAqQ2ydPx-yukuXrw*F|40lMB-)5^riMb z_lY_RbAGgp^j(?RDMEcd>qVw&XMoKRZuAx4d7F9~EE5&gEml-Mop&-SX{^(rm-IYa z&nC&?(@TE8_--aIS?yRo7|q*OFgp8mFDRM+$!!(KMRTG41LcftiPoIxv*U|efh3|o zR4}Z0%AsPpO~fowy;#=igzt~4AhPtjY^9dP8L{$A5YAi}UU2jGu{n8lbY^p83Z+ti zAl;g4v(sz3)yYkvQW;Jl5ZJxBi7SyvwFCUpi)*R)->aQ94{_eTBM|O!gwF+qkd!2Z zl1H%q(rKB}LM@U^X&+9U5O_WnM(Fw|^&Loi=1{WeM_%TUDi z>3%M(1P&qepH=rc&F%8utpF8np5dR;m|^`4M;oTvgT^?WH|JC3@rx$Kg@ah0rF^NC zRcZMXLfc@vDaw>{ZWv>1DZLAD*jX0Sek{ihqr%Rx#G_S^R9#eDOTk6$If5@sxuHt$qMW_1ykjjHI2j`aa zK#vX|L$D-I50+%_R?nzF`CyPHxgJo$C}9_;^ZrH2-1Mg5M(fGb^Y;FkR^EDs$-Gjt zi;nC5Jp46@YoJl`dbRhwiFNIoTQ)?iOxSoX535ed!}@s9mYIEpc#_`oAq&+CZ9EuQ ztSY(WAHyT5c^1ZFb5qJ*T(ysC1VchX0{l^c7mmV(Wn^UhyV;HbhsPrb1kwsHFTtRY zfqDFQ!i;nh|Lbp*bNe1L1!KW}K_WJa?@8KjdHRj(>t$lHbq;*k9ff6(?#H8^Fcum} zDGVh~!o(&0JjE(i$e%Aq`RP(~jg~@6Tm2msq9vL(m8#!VrcNWHq$Hwp6k9w>E48St z=Dn?++L$_BFF_)$yM8nk8s)GoXsGlr>^0RGjOdxQ8})dV47OP)JkThND=i~*;b8h@ zrAhVmrO6a5InnU=|Ci+RY_{@5$1toy{GE90PRwjGgkJ>Q%bQ;#Vo zKALjhU7*}3RRHyqELJEoRJWFICAi$%ZAnBfOI~n~l#qhkY!7rEP7_p_MDjEPa<$2H zdcODguj|j`d}8|gy1`H@c4Hd@160hQmtuRDir-Q?U!mx0YOWvPdMiYx{O9ae2-bMG%=VY8m>uE`!$|K$=M#P1)rp0oYwo1Pa`w+3Hk)e&J33M6u!$ zq%cXKY?^i)=k{XDn~Tjw4O8ZV2_zK9ft+Iq6hb@lL+Vg{YKVr3gQK*}Uv^rs{lRh^ zh%u@|vyKpqM2OfU+5`=^`nMW#;BanxvJI=rPKeu-51t*8@X~DeTzDT$I*M~Z83XMq zame2p+ftx@5cyI^pn==wfPoJS`^#?%REIon`7IN!-HI#mvk-T~&9`{?__vgL@CbNP zYC-;C(EtUCZ%obWB&*rZW6Rs@R%^0i*ix>H=Y8s;oTHJ_J$L6DjQ!8b=%HL|ks^eN z9N{wKi&)=F-x5Vk>5Yl~%5>LnQCO{z202o!&ZZGw>?xxO&^@cGR8mc(9qd74|*TYTK~*sZY(jO+0b4o|8Rve?#p}Yls8`R9*fG`YEG1`!_CtW zhh5Ii)4zVdq5P|5hi<93Y$z>B_}+f*eqY9eo5QyQ@e5*L-wK+};0Xd9v*c-SX>{?s zg43tJ7|zkIrl!Dt*^rfiO$B6fy(?Mmkx^dxysn*YhVS?|KzC4-5FAYQ6YT?9l7I{na8>RVORKBM9;b=6B z(;g54h+F8y#*c(1UTyBXK1yreld~|=Dqy42!X*7GrboM+pAWf8QS_8nwp5*-d@f6V zDMc#@*r{zH;O54koZOr(nKD+#P$(<6vQH6VU*|Uj#d~$%$l~wLXV*TaqN6|IhueeMs!%2Vh zg8yUbECZr?zpnjLN<=_JI;Fc)N?`!$?(Xhx=>`#o?hff58l=0syK7)*-sAsyKhDRQ zIp^McuXU|`t7B7^d`gI)q*syx<+~oWe9a&48)YRa)SVLOxH`gH0|j*r4Fq}dBZII( z-kr}EvyTV!+FCcHLW#S?B=$hsg38lRk08V$+O+v%K1V5tEPwl#og3da6f+BC`?TlTvYM(h-;#ng)!duuEZv z<)#=buZZx+zKi~LktR>eCnTtUPYb&B%rxYcuX5J?T&Dhxk`Cv)Z_&!sG4%dK*A>_4 zfe9>ykB4j8_p8G*R~1QZ_Wbr`Ne~>w^ zA;*E&{R#R`$97;rK23*%4MtF3IbqG9VMqj)#yC&4BO6xaUPwWSLu#uyUi87EYac9* z-_@uyBa9>Hjhkf6mPSp_+`{L|9+848nxZTr;kFggCZzamvBS5?%?e4B(2Yeae($r; z*2Q`i%qyQ#HRceHj&l(Z=<`}Da&O8sOn$VSrY_%fnoS+}p`LURzmW02Uw##cmi0OQ zHu@FKHVlqnjFg7-^A~>ubzU332btslI7=4`*(wN|qgUyjQ=AC=)=~E@MdeGr;}xe2 z+!0>E+!RMIR61-Eb-hk8f07&ns|8-6;3Ko?eIrZTw`Pr7Q{MZOZY`V238aVbp1dJ*avTk52Ij-N+jv_**&I%zAFHL2aI2^r9@5qkgr)xW=s zR%f>A6bj-{W)7yRmmPta>GA<~>8sXq6Eyj$ z^_zoulk%+Y;4cA(vZLE*7a8>ni97013 zSC!KUnZUTh#2a1|(@-5*En2V4DOq=OcvI*T0Wdtr>0uUxfKq6xyT6^b+6wfxcF|+b z*u1)GTNNufJAc5AS*D1UQe_CUjGpDK{&>E7{U70)1Lb$umQ*Qn%Dv5=q$Z!o6#fde zaa!Q07ifOmLC6GdM|%DYWJC4?08I|BCSCg8x>?^c`z)M~jLhdllTu9>#Om^m+cUcS zLsM!23f&vn@Q0>jOir?GxHGe{mHsld?^Q|;65ziawtC1`FJFT$CVsSDqV&DIGG$Bj z)5+ITtwW6Vhqm1vUz-yI>hxzkr628n(b+kQhJ&+`Zp2u?9U`( ze}`D^oUoV(v7dfFKNquevWhqxe`YfogNGxBqvg~2;~M5?YiopvV&$YBR;e+fR8Gb3 zBlyz(26x_-%Qj<{QyBXyaUdKyEu`~HrDxi=e$4ow4%ZZBnXm71@2|(DI@C;7T@B7V ziZ)8rxQh1g=1N})R%^EMSArJTCh^bk|L9b#Gt|rHoY1JVXrGrtu^Lmga8UytY&{FOgz378AAnwFxc_BP(O344`sG;r@Oa1+nE%*{9t7E$0!c214 ziWU@D{$QzE6cj0?Ydma7!rt9nQ1w@drj4 zy?H!#SZsGOHv7d=zn-Vf^m)tW-)%f!%{8PwRcq^%%9PzmO@<=M2!U=DcE5DtI4idvm)tv6Z8nJU<5U1A#OI=~^DNP)g>Ekg&p*56svI*}eI-1sNA1!|$ zBbP~(RoE)37H?etMeo0*;TEz~X;+m{{*yQJqQk!_#2YCeZP`#BMJ15X=fsyt*w^I( zk-%pBSU|l;NR*Tf-@CY~#!|fJUJ`|jQ9!Z})9KAl#ry=nOmI^ijGTsOMYh0nqU{Nf zG)J?4;0i~OYbbDIDpZ+SkNM)Tn-?V$Jz)Z3FJ8?=-T(M0-)hU+)FA4?rcUkZ^2tNa z!o}i=)1zFIa@sR~D!QNfX%}ng?QtV@#sLhQ$W17zci}Uo4Uu3E9|)A-%eo;;OGl3x zYhvK%7sR8+*CF;qEp|%ZwbBi*S!3mA z*UlA5K4M}(@uA?M-*v&#o~D7JZGgh-ZO?p9!imIWf|GmC;6j@;^4-VDBrFVU|3iV7 z?rw}%Ny;^NBX;sAjcF1aL@q@i#&h=<=5>9={d>i4ssq1A-wQE6>eidA{iPVzGvXrs zN+*WTw^J>0e181x6qGlUB=}~^iS=^*$xkJR^X26WPk~$cVwJ)n$7&{Y@2763P6ARv z!knEMFCN(x@&CRw&koqEST8hmtAS+rKMIwSRc*2Y&w-U(5VULVlYKB>cS2-VAtpG?Q&u{wwd z14}*W73<{3JtZ4EF17}(EEpu)!u4A+0#RpEM4u>oAlKPgR{#^WKSmZt|7gb z9+S2WCm6EL=8ooPOW^Sc@948l=w~|Xm9Nivs7)UyghcnF>;4$;9g61ir2kMaEPZuc zNmH&iE>XHEd(I^_3ZBImbzF3srYTMNx9P9S?2?q1;G^nliq zkF9tFWy|a2QN5Lx{-98+itM3wvgjid-_lzm?EBeej4_s*X-+KB>p4b6A=_BiqZKHi zv~V(`Ut_KPr)+z7F1r$F*3 zf4nqWl|ZIyO7&JFh5+vKvSwhS9;Z5XX7d7c_JMTdGg(RO{jT97yNDFR-iY0AnXN+zubBc%BaW5Y zE%1&zlcz|NO=le@{n-d(Dv_OS8X6w0##5&74`nUQRWhpdQJRzD6-H;TTc}je=jfmy zabc@}cA5KkbZEx3sI4r`TYslVM)2D}gRUhD1}P}7K-+#G-~&(>&)XjBN0+pSWjn-b zrY_&-THFNL`HI_rE9T&0UAiI zAU`9ap;C;sxf#b}zfs6lmOTU@xgJzCwS5$-zB0vJvEnRL9LG&804@ZJxgej$Xs z*AzhFc&-4Hc4B`9Pt+5TepS{o4?pCYL#8p7CXcWAG(fp>^&0YADEnjA zMeXgIw5O^)Q43m$C>f}g%ACKHpyN! z?v{sveAkK&&|V7nRKR17c-Vdhr9d6gguFgvBPc3WWNJu zy6N{&f7G4J+XC^;5}bDmWr!K8lT~Go+Z|T-3MZO{#+w#0wDY-l+dH5`t5)&G%v`(*W3^;Og9&yeCZ z2-yt7*RP1ze!A4L*GU86h#A^B2n5iYWt7iuAdUI%39@I?5Z8WaZ6S<%Ymu(_eGdJqmVa zo^s0UA6s3=(Or3Ke)xB~ilh!y1|x(TSvDdhPrD&^i?|eg3_Iw_^ms6|FArZ}@hW;s zEeS>zD?bW7FLa*KZo36m{_6Bzg{SQ}ENW6tx>#EQt=TN~rh_Ol&28p_s-{Mf>v-0~ z=7sETsv>k)!72I$=5dDkQwj%coQQ}D1{dUO`7k_KBhT$o(P;5dyGeZt78Tt}KaU4( zBLJOg-U~hbgY|p$MY7OlmE7U}gKbcw*Yj6@g-O;G8_%O-PB{VB6Q+QGI*pcQiO?Yy zPVU4^B!nQt`G?wxx8z15wEulPU!p}PT=m4zymO1yzJ#+rDc^~jL0%t;PYtyM7w@k5 zD0)S$v%JIh+(rAR{l3^5ZMp;sfWicKE+9&GIa5DY+Omy__88+Zonm7{wdh^U9R0#z z&-=+s&vqVc^Q>6{Vhm>m51rS5qpf&)>&~f}80w^AW9(t4>mY0?%Va#Bf?#_Cwvu67 ztcBrkIpI3)jRYtbI08ID0evWRXi(XW?!TT9NG2Q9!OVw5>UkC3368wvoB$(Ewd674 zw;+8SMt2Pxty;OVZ;5~D1_@z^6l_M1jhwesnf3!cz0PPySQriAjMYg#W#U|+`AYk5 z{YCtWCSDX>9T&KW1)JpHoaygjMqHx_OX_-fo}C zW&fcBO=;uX&f`8zs0&eKIm)etAp*RDV;EIWhfl}5_?8jzAK7dctHoY2BLZByH=IL4 z6cj9wfov;WgE68c|Lw~C(F0AaJWZ15i9f`34e8t)DAjb&xbLCAgcc!s*a<4|T- zHkehbNv4V#@$%zg@l=)SMyz*FJo4M^fWN=@C!f!6OS@SI zvrh6u2AODu>f)*T=M5~%w1EFadPWo(VeWixx|1ii7K^=DA!3l-mrC#TjZV`WGtd5G z&UVfZTQ8@~ZO_$bu(pY})WF;9YuOJkqrCq?z3|@YkBF|<2tNyc`2%+XOvc!q;sv+v z?e@~BSm$sjhi8A-6i#ep=9KY_ZJXf7y*X3ddpW%i$@8dGrVy}_=4VXIKmfk%>d}(S z?fE4H`Qj&WV|KsR_VlskMZr_w``Ut2#8Us6*Uc}VID8!R-SR}GS*C)tq z{Hb6+S5I*jvIKnpEcQl{FBIBI8>A05Dolyo<=nI)lX{nDiGlx0{W4JM<+hG~;au`! zF!uyFEWaYa%-Lq>lvuj1L}k?}W{M3g?wdnH(a%=BDh9WQxzezX8W^Z!=a~fq4w{yJ zTPBhFan+rvND4ww|vz;AzelwzMRKQ}BC1@%wMhsf!nUzxgci z9Codle-2eH-j6~s^Ao-Il&o!>7kt5;bf?=x z)n)=ulTQ6Ef@K-~IF(Qrv<>8^rI!?~d#y$`CB{Vv^8@0 zvinVUqUD9}2E{jdxv=8H#kI^^2|Vy4f7p0eXewrY8$I<)R|+JQpC@iRyp4#uz@UgdiNm-TB-_BC^qOkH9c>OJ!xCQe1prBWG9?KLnoq%qqg6slPL zPqQUc(jyM3F>^B@b~s{bz`tfrXI9Sg2#16R4|byse}A0%Y@q`z zm&s2(%SASRQEc9w&l0%k{q;4z%KmRh*t_Z3Q%c3OeRRF8{laOM#&yM@{@EhzBX1>C zl(FacfOcyE@j2{0-hYF;?!W{T$HxJ{XxN@d`xz|uw(?ba-5=wo18Bing23)aykpb~6`%?a>m2jF-Np9W9|qhxCG!a~r^^34|9|3B?&YBZd> zN50{F#Ok_rYL9JJi_d9TbL{PBerpdqlsXd zw+Jb61<=It%F|tjJ)AY)J6PlDL`1-t2aB<9Y6S4e|w8o-_J^(r5LW+}SD8N6% zs(wh{nG)*?-o(3#7mlP{T-Th()#|&-oMd{<6B{in?s{$>zqv8UVn?a_mZ18H{_S?S zFHbIW40gEgkF7ZMEQ+ae(wWNj3O_$F5^BrFNMBpw!pe4G6(p`SN-|(r6--gs8!1i~ zh~=NlS}lwn&g}gpdr!E-Iy}@`WL$n3psPD^&YUYas6%FUfyaHG5S>&Hl?>S zX=C@Hl=qDvnnw5p>6~j()M-i)a|m~OfCpS6HBTlz7egSzK2=Y-NifH1J3uTzM)9gX zG>)$(=^b<4TP2jMe9}xwPJvD1(6P1KeIwbOedmguk65i6r z3Yu78*?SZt%5BjhSAjU-M|VF>$4`{%!8L~z^T=|IuScE_y#zUV)wL*wu(>vz6*jlz z|G6y_w;Qa~GG^_sqHJR|*w%0E;yC>CXhV(mhnMm^CPZgt@`UdcKHaVI>%DBAGAnBd z!d7~R?WTc;cAJAm2?_B3zKG|dE4GS75B-yq#F3b27f*NJ?+27qs=cG^uQrpc)kuv0 zLN@iUofYLo0y^61^XAt4sNZ`1&t0|i7tl$Q?O!qPk`LDjPZpg}Cns4`R|QTzm6Niv z1l>{41?r~1P5&LiZKJLS_p8aS^u{sbWEXSBmC7kX}AMXT-n8u4ORgIVmBiXo-n|I-bx#A&%$q$ssq~9eB$D2)`gg zc+Pde*78fNT+JWpnw=5LXv5~(L8C;lhZ>NV*nx~N>%wp4>h>khDgROUa34Wq5;phb z^G;K-xt*PEj+QbOCwe{TBPtjf!+(dh?6FHQiAhN=;r&0v#(mZM_gq>c6ZnrNJW!%R z{TPfp#Q$|~x)e>cL0Nzu8i~{wG(IR$l)jZ0Db}h!c>bn`AHJ(gYoEwfp7LB(n5@{e7&cY8~P*i?76>8|(%?~iXDR+LLM_`6)<=T4SN zq=)8RMa5Tg=1U@PQ&;o-#S zrKj7+z=-o(Z2ENcDD+cUE1twX8AukKnsA(GcKnAB%vq~DU73q);E(|@s7W~6Vn$}-1Gp*Zf; zinp}i>|E}NhF)%jG_bXzBn(AwMtTmu;a(T7%qLY+|KYA_rwIX98rzQW^WY1O9DQJ? zFUUy(JmC+21c?E+wku7|-vSa1B2H>$$-kwLK5qE$r5*A|>WkcEdUCbh=a8pa%sGvE zOnzB3)^M)ERcrXxnP;L%6-Fb~8{n`-OD(-&#xLj_)+3`%b(vP}^jKY&JiS%iz0PZj zoO%?qay`Bg7>e&lqm*QOyvrDrX?r-WAEq%8q_v27bf>~cCncNu3UHsw+ofv#&l*2S ziCYbbu6AfP%tN2hB-xALMjZXb3nUQ=q}{%-vEe_$)_dC-L431FK{>hVnu2DHCa<@f;^7>lk;1$A$YlUWqN{vQ+fXj8d#!f-B-J zJ6FYhp^Jkt_WOGO4!7XizT3sQUhxf=VJ%`<#uXArMzlmW_&B3>uGE5A^3BBDugsG&oRPW zHzrSmd7=Is&hm#Dk?G9(oy!uUq#{ zkZcOaS)nF?!e#jUhv;ArO76C(H&6pz@C(IM{a*2K4B$LEo)ZoF&9+IZ2sm|sL;ePpS{1%bjNF45^)0x zVn>eK@L<9~JQ457pC+r{n?bs+ErBVN?==rq*FXL5AQ*z0)r)H2NqT6c(4%K=ihld( zwyA~B<(!!1<=NiJ*v7zAjv=+X?Ch4*-!h*SsJ9XgR!t~VAe_}oi0>cvU5%K>FEe7Q znFU(=FiF(mfl3>VDw4F<6+lM>EAX^nC9#Ei$)1sNVn@OL_8@I9kM>nPbVyQ~g(+$Q zsxVpwZ+IpUKAIfhRAdfu_lVf-tXvK?czXD5KVUVU@rqGaWYd!g?%#2U<^o@IWNDe( zc0BW13J7nfhH06y_BRS~hbHnv-D5Bx6J}#iy=G^e#b6aWYCHQXrJscp86YaTK_Ioy z^R5Q2UXDb5dtg$u?A;D-vfM`2o(x5>7aC9shxRc58J9wcH)5|w?w5wc!TYqkHbysF zz67QWapLm@V6suIbn!4AEVVlOj6obcN-z7(s-0jRf6w8jDR4c=aI;Bf0za;q%g}-O zGgID7$+Oj8{PYFaFEWkvc`Pg(-_rb}tDp}bIF57yq>h$5N-cI(OmChwdCEnwm4qJ% zLQ&i_*>9vOe>FV_h4W6({{?*Z;G@OG|$-a!a!vC-R4iiEnkI*l@BwDR1q`cerN1u!$>j zSpHezAQW^)>FQdt`D9^j7^Vv3(=Hoki!g9MzGti+HI1$yPaWLudClk-ZL4|v&y9+@ zJVqn~W}bVoA2UuJE-{#H5@5;}PQ;PpLKN3I8Qc^8y4%m53B)nY&q-V0M#Xb5O*5*3 zpCvzn5O+$5R}#rkWhDOcsONm|l-2a)Q>f<@#Ydl&W*;?Hvrb2k^s*|rzUO5UfMrWv z^2zBkNER|I=V#KWCIw!Kw~QV~H=o4abY*_6X41yH+PT2afbNS%M+k?H4@G%G-6AJ{ zf_YtoCY0#Pbo@6UcTcXMx`*%Q3|XF(S|>|_5`MZ?3Ln(6j9LF2S_GeL=dEdnywaMO zFuC*l`~7MND3aJb)BTW`2$@1RFo@#;XrsD6lk;GGgYw}EG*qBx2rQZ;&CeO4ef%=;gko#0LBv^`(mb1^)ZGoZ$n^yKNR z%;CzwA67vbsy{jYx;08~1GdjiXz|-}%4^z4To&e9Aq+D6xljd*x?*|ItXm(#M*NnloXZn0}X39Y7~Cx3gc zNoGso@&-`{`HkH;jyks;bKU<;`)fHp+5&1MzG9XE-0DX4hu|GjTZdA4liLkO1B0SuN!_Uujz{EIVW~`^ zn;!pi98BXzE8b4;n9=|`TM!~ zBNaDhIh+75nx_H7zj8l4_qqh-^^01-ws32*0b}#!fKsgS9u*+-EFd~~aqpI9o(u7@ z^)i@DNFy1R+;e$1At}p~%?qSYyvCHee-U`XY_nQot8M&5vgq<0EL%Rk`~03~zbk)K z$Z+WlgGMv+d&JIrcdRf48{fd!NNz}nLf{B)bE{kcF}5ZK5m8`s&kLBn#c8Lpe;yA1 zn-v!mZ^S{<744$qLbfU9F1|wKKUfbujE;nq_Rr!y$kuiBvh^Y;$^Ss9qpnObCg_)* zZ9s>CLHQSGt08Yy8wLytIbLS$3WX(<8m%$z?@P4MF>p)Et#|VWvet=vBlmyxYmPfM z*0IJ*4*y-Qy2I_}R9D>f>q+e#Q*blF!@>yW6;UIn5_7|>;N)CZ7ONc88{S3&Cfg_O z>yH=}KH-FD)6xH)94yJ`W|+&H3o}c;OFKqe@Da$}P$?meoCaLEB&gVF z))?I@xNCb*#6HSCYdYN=>vg>Qdxko66xXo2x{IZ&3Eh*Si6L%gj3qdvES`8A8vML< z&yzBCCnyGzu7j?ubiOmG-P_p|1wpX^4}3P9V8GI|c7=gH-W!OEkVOVpAUY{Ke~8gG zneO>}unsy|_k-KSN2h2wQbZN&gNS+gGWVh$@R~=b1S7X+&8QOV3HhLVsb&SJ)<5MC zFQJ*!#8wiwL?5m*&YVzyELm8&zdw?s>Gz&$8~{WnGTRzJJy&}j8k962EpXM^-z+fF zzLPq_#=;AJE{n}`3(jJN>`EV=OY7yZd9b>!exsc@TWW3Hv$QJ0X4T-So+fE>`Yy3^ zz+Xyv*=8rL)|`&^MBuCcjPQY~bl_LFPbXRiCrTRxN~H>z zgVfsXssI6sfyG>?cOn`gWh;XMD_g!7k7cNuCdvG?;&PyNYMRJuk{nfc# z1#|^v)LS|njc`W3vf$UE$BF``1)jNFu#eztzhC}fw>#&OvhaBc5l4%i+-ZBj_A>L} zwk^38nr>%iagxD!~%2U(%v4I=7LlZ!0q9p-|J74XQX$_n;PphSV zlqD*$kNGLAH@StG>0K`+q*bay)>avbP@{gx^Y6W<{k0XjYU}SuKLiKX#bxB73aV)Y6ZPMCX_Q z)=Yua{&(9puv&gXeDlU7#Uwnnq%Uyzq7dxV4|MB@gA(59;8RSRktQbT_#QHH%bx1g zu)&ZtGcV|$W|48?|Un(2DX9b-2U--p+X3c7B}GURfz=E#TB+|0Uj zc0a-eM`Y9`)t_@52Rjjg1ml~18an7f_vCZk&6WlwV=dSzn#uaz4{van?CFTtXE|@Q zS)TNgwNi3y#pmnR_-8-jHDVp{O2q=Fw88X>4o~Y@9bx(`=Ugk9K+RsAXog*Bj;pxZ z4s9uieds;NTw%7w)9bP;yD<_`)eKR{pqS>PY5&40)@TfyhC?& zUiFfC6dNy(oH~1hyzBk%q9cMz{9TXf3WL=5Z~>Uyp$GGe0R{{EJA3-m@D>VTlzEvDj*&Nt}k!BS<^>CTk7D=je$} zMzHnbwg;dEC%p^_%)x*{{si%O7s8SJVO<JlQ%Oo>%V2TRlxq63c$#x4bs29@nt|xF(4Yz*>d?()EdhJb%W-$S$}ckih72 z)*`vph-XDp)!ppv$=E&8ozcX5=Ye%}qvetAm)U+5a7Mz~z5Bi4D75@Q=h6)7e2-j4 zZRbn908q1^7$4{Q_4fUGp>5=wYmiTYr&)uOGG?qT=ZYTIS;N>JPu%dT4cHTO)!l|; z6SG%5dY6Re%SB9e=Ep+R7*Mf!mbsB`+|jQVd@VW)_1BUC$A(6z+_64cKQ~y?+n&{l z=NX5AAJC=eocD>`+0QGbueQ+qcxHjf57CH!y;u#v6SjHRX+apnE-$J{)lY|+!lS@DX1#a+t8 z+R#5!#yVa;0Ck-dpoTqXI;DOR3z+adqqQ(#08#EZ$^~+xz?F_ds;g;%x=MVx`iDW> zWrm!6{|aW?qn>$I5xN*xR*a4mde3Wb8ppCN=coO*l?1T`q+WiF)&GiTVvGj*xRoS} z*vC;Q9U$9JzTWniJ~~Z$c~bAtr|^w7XrYXqGzXLj;7G>%2P6}w*SHjOXcU{gjse0A z%x8Doabv_Tf9;JX<|o1!nPD)4pMqrHl;%8zP8l)(MQ^zlge~*!@L+34y0miCnI=ye z7*7&MpEpIWWc}xi;Ocg)^(-oUYVN zV8T4$E{6+g%w&viv-L1qZEd?VwI?^%@KuJH4eqR*W$ z)4lpQTMAh>xx?w5P5Pn(a*>;E_jPD^QY6NE@L=ST4PBlqlG=jBQ|*ov#Cr$(A0XA7 zQ)PnfB)xW($01Q{lF6NA_)Oxp>u^oXsi2&+1Oa633A6Wq>>n7rw6rDucWu{ z+>x_>ed9^4=CW~AX3E3?LWu8>V?pNb6tb&O;Ze3=!JZayH81bJ*x5>h`_o;$_w{6+ zII6iu#nY|5-9c0jpEI|i6*ggjmk6~C;vuMZM(njLB!k{X!@*-{u7af_eKp8+}U|^mHEnF1e6zk(rCAHNJNFsD#t3n${j>a zhIixTV$CUi!41y#CL>#PRjjdd5pMo+YFTfHFjL@;WMt)*oUMNrGGAK5DP_15LBdiU z&s3&7gNmbZKJAJozGlcf?Rm{1I^%*vY4$ho{DevI=iOgZ3~!;>q%~jU7cD;k(^qYE ze+T#yU4!Mg>8p`FC7KGxSp_)6NhFf=)_)}lx)x@BSpK40BPn050d9ZNR(u=$T>s z_HmLgY!wu-G?d%c!{HWWx#)o7CD>vab6>r*{aaf#1Q_GXbP9hF+6y_3Vq(N8u(0i9 zoi88F(g*iOqLH()Y1(EU0pgBkVxzrl(;R&;ro*CK*(=X~^AJkT6k2^-3p#Kj-twWG z1;bn1G#p~k@tRAv3!r;<0apCQaU;q{OrpSlBwxwm2kCWQ!shq9`8RtOzmwn5o$6|P zLNg~NhGqmCVRU1s&147(y^s0lDMgjgg_f;i?V7?L%}>BS8`6kGW&m`3UtGGGKcEY{ z^z?sJBi=j3zv-6{+E49jFdnEt^lTyxeCYP3do7`ue&N# z#xl(o^x25qRcai3EHy2APoq`~Uq{2w@QEPlV9ZW99t~`em+Pb{X|wLC;j7!?Dv_LK z<;1pUbgQR0G&n=2^AQnWuHgsr^Ho&^+P(Vk(69q?-0D1LMO7-n4JNh~BSWLOa6IEd z#d2fcWL$hw2DbOW>JA>KebyC8*5hNJQKfCG=JN$#jq5jVn`wgeyFXi!LK|vV=;xS% zHOuKYHu{{V+VEe#=xZ>ai@!bMmd+0wM@H_tenG#!>8S39hVA_}toaVVsACBh z24+8fW9vK7h73VjDD%fI*XkN7UvU5UaJ%@T=ExLQf4h1(mQc8H{OqST>W0Y^gLCfjC^?^7k ziLRgzqs{*Gqu`WrcYn>?P56oVqZdAE!DMhD`BAX-^ur zx{kY)!&Tx~MheH3K`7+d>}+_OgsM?#mJYnb#jo3O5e6{16=c?@i<5J8QAv^=gfE=&wc;M9aJhU42YX*g{? zq3Fbe^VZOD?dANxG6nDceB3O9Vc-#=_KYl5$YtW1?XL`ghq2`{518J&owf$pu9k@$ zx8LV95ZcdsGP+?@-EGdh-rcnHPTd_zpr7cvd_v``#{MIJo@N;q5!SupPoH1?&Ku2& zA4{nGB;2~$W*XX-_jhr>MzkEQVoKz~?-G$1N5EnK$!CT4^_wSrt1^C7JL84RFX%_1 zYeB%@om!76DF}zIj7%v%(Djkg^8)oK^)EX&cl_I;+@0v;*6NJ>`uNZ2V`sWO#w zGwQg%++ih3FEPaG5G46)#n()f>Ku=4fzJs%6w77Y+{|^<)hTYzJHEuGacqKSAHzTe z?wsi;S1*T{waeEUrr()RY^d1*>F?iwF50wDlMN5t1)p1q38ytkDLH@}o-L-#@iw5x z{$&hPnKll_-l|1sp1IWY;pO2tn`5o*kq+YQGVN_N;w?(JG1rOQ@2VF#XVr`{o7$9Z z*)zq{EHy9hIvFHGqgM+L;KKiVFz|h}MBWFD0W%C5K^NqOp6-IG74y5f4<5aDQ{>0^F!vRNx|65s) zd~qN~tGJQHhV8Ht?x=DZ|1I}q9N6s9RW5p8w^rx;{i!VitY+Zgmu4s`l{I*pqmt|T zxYPkYxdmcCWLBMgBm8(#Z^#6VkjsFdp3%LVoTU?=7RT?~YW z-GD$)2cWIT**r@osjWD`C(`l0gtsH>&L44;{EDJvq|6v6H)M-*tJ^yK^U-Uj>^!J9 z5}llm4zW@fVH0}jW$$9r))%ur{~PWsH}P@snOgzaP%L4n-|(gFCBzJhNQ zuNAUFnVt38bUR^_Qmwwk|M{)|*Xn@<6>X5?@EA=ckDU3-VI_N6B^vW~F|PI(93hIa zk-m?t;9=-Rk1y%-*&4n)Obncz3--%(<>!EyUXO zxa&nUx~E?$3$ppzj(1R*gG50R9ofcN%)&lb-j zZ};4Sa_J_MWpmb*N7#c$gyT82(rV4WcQ=O~cHtDNsg0fTj(~t&xlC1g=mo>|q-o@5 zi~8FKS4Nl+TDE1zr_@o$#!=g7T}(}*xlX_Q%ix~IDzh62j=Kgk4%Z~AcKo6$hBUvXSuy~6~kQNV+P=Dh1EB>dO;E#;@K{RZ@75zgz zRcW_yHwF<_^hUORNmNvITs2fB;T;>^0hL%{~o$+aIDb((ysJ#w85$6bl=Llg~_XY z;{XMSa=~=^Y^UcjwIBi*UYmS8ca2vv-gky&aSpoP7jVBNcPXQdVlxplL6X({2>gQQ zwg-^|&{?(Oy<`%tHs?`>Sa%`)P632`6wySm#BZ@^so^*O*4}giw0_Mc6)Mq0E64g~YalVQerTKXR72{0=8K?t@X8Mt^E_I`2>~|) zu1-}xjCZlGO*JIVnPTjECl6oAVuwcjgBK|)4+gf?P_Fa{ zq0Cn{h~7f#@*-D1bfhq04cun(qZlm)_Lth959P>YUQ zjH3CI3+2L+mhkbKZQ@cBa_#c^{$EtNpb&A%@zE$$1<`Zjh~Kg+jr~qeBjf+1JkY;W zp?sV8+`|4p;#QB*wk!ZSzeLk%4;?r#KkEmDo$x+WHt@M=e|N5Pg&TeVs!`(8%5@C7 zEYUyDZtN$g+Vjc{ZR9kpF*ni zb*gk`E=k%-nYGgrw_~QrRh37HYv{hyruegWOghuz#jUm*ICP)fGH9f%60au#XV4O* zfvIUa)wzOazav}8c9S}o7{=dHf)KH^P&8#nMW!eOX!4Pc^T(FHGih&N35nnmMY2l% zHMckulePZ2Il|eMguT|~23_e%LA5+tAFJVUQAXGuSfQIjA6C;urqb&sxT~OL9d>3W zmm?}Q{%wadk31KXdd<`RzI>VIzs(*qPR{mw6;b#kR zvc2f&?R-rExW*vuHOqQjK5@wK(2+8O{%B+8x>kIWVm41J89>dQkuA)s9)OVJEuZ3bq4WBg&fK0X?A|{74t}3G)LW6FkbgZ zM>#l@*%RO|xxS?Pawe0n-{vzJRux+75B6qR+|~ap*vrCV(A!bGC8o;7{!gDRQ%v0! zN7s0x!^Kz8i6&ySd(3`!^bUdkWlg`& zwytMnQ-oaJ#L#@ubBv_^s6vjaWHZEs3A1X0F&eH#4{1ONtkZz`Fs)mlt7Fo39GyD! zWbS&;`gzk_@6=y*joK>9?ySC8xKV8_z6hJnf&qO#|B_}runN(7*DW3dkJv>?-LXrf z^%-7DK_8<}wMF{$Ux`&~aXqr|L<<|}`=s0wMJHIvkLw7?qk9ZbZzGga>wA|)o66|u z0@>I;H3wDZ$q-q1-mJn3LA_yz-QB7Ac$ABS=y7xkO1pe<*IP)NExb?9?^94s3SV?s zSeJwEXVW{BMxLPgO-hSL@@{=5PMr_Kcy)$wn)>dJibnx}F z!vi>k)Ol&z_C=l6*M0U5fIG2jX8Ey*2T{8d3jfEC$oz{PXnlep{EsuwDx(ZAyOI~k z4KTpkYg*DYK@TVgK}OvoBnW@8+jG46UG;Fx3CuEE>89WT?sVeF$oC5JC;&aV655*Y z**f#(Xt-4{L?oU*<*k0?AFfMf_tWZd^3bo|Z&gfyOQh%=btnc({hier9dXiJubr9YE^%r=4? zPD;-j&jtw{V`YE(nCJ!>qERPm2pEz8`;UrauT@sTOj0Hja{*G1RuEcKS@Uv%#mKfr zM9$He4D{t$_X<~UzCNJTi{SGq9ElNj&s^L!pz)m5C=(~}txu2i3nXMPkkhy`c*%-P z+V#5c^6!V!ZbLi*TNz5tCt*sA+vX$@(9b`vO~ z1Wqh8+WKX6&NQCnUNxiVPKSp2N|4W8N3m(0R?L3@H(7IME4r}LI!iV*beQ3{4h2P? z;>6=C@27MSM$lGQvU%FHWzj%mp_@YdRZpw$myqK*b*@>pf&b+z2(&lsw%2ss4`*$m z)mmwI5<~!*dN+hjC%XT|W1%BT&sLH8>pM5swOa~QmE>wk?})8U*M&i&9DDX+Y9@5@mJx65&t6 zFG7rmURYGNYEIgeM{dD#T_q$;gxkOm)sb$?%drAV|#!GD)6YyyJov&D&hRbbTBMqnT0fwX5u02{vZ)l?0 z!9ZWt%8x+cik$s#O*O2Fe3x|9?W6lS-(1CpR7gi%mePypYo`t%5X8H?h))9W6TwF* z<>`tBNeS}LP-3p1WkmT{|LCNr*A*6%Jy1TJG?$~Zn>cTM@O?fC;*cRQH)mpR0+3-)!_Mw* z(=@}Xv5S)o!#!jsKEVp?t?~+(m{(hWz)7+(ew{p>N37~g0#&Pyn5WM@ z<^XfM{xSL=7w(lowa&2^SG&nB9mM(8e{?6&5M416^In$4_MlsW{VD@uTi!{k&8NN} zLC2#H477jg7)!SZTe6!3W6Mr`5rEgZ<;)wZDkUYe7;5-D<%O4TDYIv^C)&d@Xdl4+-8$1MH;Q|fn$?-21ZFd7I&CLN#Sb2gIlbLb9BZ# z-#bFFv@2&Q{SCsJ7zWb>bA)`(lA6U+%XK&JWi+4QgSzX8%P?~d1&BlBG!$`^&zRxi zI;|O@%q!@#w`{(KrDtZagIJ%^Nk8`Nb*B&7OjJ)|psDx4Q*|PL3-#!>t6H^KIk-j% z;uGdJy<}?Z9(>GkUC75aQ~!tgHil$_t(1YY*(;pOJhsT?+YGnL0=d2SSuL%ZRQnJ? z2Q$}kjz6r*u26smD=SqgM4X8;bR2e8pLt>7zowsRk_;u8CuR5))G!DB6Rf$57(phDTh|*eyZn<9;sQ zw70|t-m>AB4;mdA@p~ivg;1=hwd)>&Ej#_`sGlCgj?|UtFF`oRv zTN`Q);Li}Vi_*pNA?q~Y&Q(SHuO1xvy`hg$&|v=HhT4RIkszk8*FqnP>OWK(2q*xW z0P8~6XU{68YBhBOrpqO)$+}Oba@>QYr4|A6Ko}#P>fz%Fwxc(+diKfWj)ak+%rNwl zu9RwPtrj6MEW-<8VJY{&5Nq?`pKe%q{%k_iY_zr{h|D*h(8Y+NmivAYe`|G3=tiPJ@# z5LN;7GTMI1s2}`ddg=FV1<&=FrK@@K=`P z+t)(f?q^hhnttsLnJUHIFlm=DbZEv?T3U;RkG%ZG>!q~&;fE9MuZ`u2A9Ekm)}U>g zAQYn)rZhxo{g`92aKdgloXLBGiWez4emC3?v=xehLioFSf3cUX{+h09foiz!kQUOM z_WJ8zvec8ks!0 zPA7b~%&r=5sht+$Q&L7)5DA)$+^o_+jfG=`wm@ID8b?|SGD)~-*a~ASPG8W2(c^YO zI&yo1{mxC@raw*tS|s`RHauoM!il`g`kV4xZW`5ibw@m}MgA&OnM-h&J$eLLp!L#z zEA=$mP<@vMIEIyIQT0=^hrDdWm9@*XvQo$QJzQUHl$PMYB7=&b=_|0_`r}KA#=)lY ztJ|A`@hlASR%2>EuhjQNI8E=gMvee{ute7}^io!3JQy~dZKmnj42Hz(odf;d`!p*u zc`bJorW8*)Yq806ya}njMV6n*9;bv|-^Z{~>I}kw;Pd$x>nFRs#YxwXaK2%BCHFd6 z1H#By>Iv_)+2d^xMdoC#R3UqZupbTGn)Lc%KUrsXY4QAWVR#sGrN64AC**PKF3Y$h zDZ64MA#^JL&u}ZAdw-mfVSt>J6iWwyvOY&$IIj87&_89R159%CU%!Im_LC$j@jUrU zX^@FkAsaR!3CYK~rc%ENBvT$BedwHo+ai`l0eWhBdD6O3X=A{h=ncJ9z&mHR)8m$Y z{5CISUs9Qe@()oyij^&Bypi|&HFC_c^iXwSPS~oRjFi{I644+DSHA!xhS7PA%>ZJt z4xmnA1=*0AJRD>-)M9w9D+Ih27mzPA_V!(k$b-@>WM|Ma^!CT)H6DUQal95HgakFp*x)$Cs2T3&gmq zhw`fD^Kts; zgm7h5sZf7*?2CA?t1pqWcXlAWkHn74q@=KvL18Li5nX+VEysG?w3_l(oDD*)+BCPnzi3-)g2#MnB}@$Rqvh6pdE;E(9|^((UH=e@^7E9P{|h`wZsJRacL zrwHKAJQh1H?FhLBOpt{`K^8u8cf#o=(!wG)SN)*wYYZID09Yy5W9sb8&co}*ZB_$C z?j}~U^!urAmc=U{W%9pn0Srv;?5X=S)UzJn=Mp4KmHJHv+lPA%I#vC45EV1)%L2+5 z5GfY=&1$48P0Y+%;%~?zkLSTY6lI?yQCXxSm{FIlrx4DgvnxIQ()05aUgW`Sh;Frm6;*^{wTD&AZ2Jke zVX@b&<@;RBo!I9Yab5Fj6tZD|Def^~VVkIgLkZhYr|-$+czq2&wDlXp;Le?XUy|7p z#b6m4Ps_^lK%u*|r&p^Af1l^IAKZl0{m1MP?^ca}phc^FK!{=X`?knv$KH+$J+X3Nl< zr=sgOFfWPw4zRL*SMUhHZ?^aRnxZP8X`LukaX2gKSTc(|BmYq5z(0?T5fw80&HrN- z7gJBDSa;9XUB7oiBwLny@kYFS0&Urz3#M(erQAHv{wOQvxZJZh9yPkR;?$vB>S&Aj zVeUJuIt-{*9Rjz)at9G7cW&k65KJ{8-vj~*FZF==y>(ZWPqLd@2XDfidAbfA-C;kS zXt+8%4Mt~Dl5;$gwr4*}Nfmzp+&_X}gjxG1kDKz<564-)Lgk)&qyUp4Gv?^<#rbi?5`zX*AGOWRrGA8<-)k&k{SBvi(){ z$Z`io9(}&OGRZ~V7_j#>x@#J)-QXWKzdW^iw7x9%5Da~*lzT}*-+j3e7u>B=dEy26 z@wi-)dW~Z<)R5?xV^)jBPyx2b=80O%{N{@yG32n`!^iajvGpWQKu@7aQ;$VeHf+XS zf(5V|Dbgk-oZqx7h={lAujJ zbne8TDYg%?G_{Z4InTKPwJPYnD*FxGbWuivp#yBcH&LPGy?k?GxyMi0g&qS+>ozWf zReGT(8h&uYLYp(xJ{d7{7tXQ@t=OgIvC-fB20A5#XVwye02AYT=v~NYl0%@w@6akz zI+~k4OBDRR(?$Ytd$?{T1+v8C;XQa5(dGoK#zmM6^lDk9)r57c4n)alX1=*1D2HGq z@7C?ic2$DWCkmSoX>gAgHyL5_a_r$co(O39o%;spV|i_)vpqlzP@LIIk($ z;7UOCLh6p;4|b*einsYl+p;bktiszaw!D%02Ol*|Wm<8%SH+QLFL%QB8&CN7uN^{p zs)b2KV3-}kpQ)3Kcx$-3_oWw1_G#vfP3_YQW(qO4)^<8zd5F#{_vJVAM$aZ@{bm5X zcj+YJ<#|yTS`@( zz31{^hLy5pBa*OIwRFK*#cN^E$8)nfRAs&WPth&#+U`bggr$ebyU& z;gtZA(b|4=+45(ROAX&#!7(%U6XGpWs<@tk7n%X^yi%iwEVb*28EgiOc!w+6kV4g^ zQ~lS3VKzyY0PC2OHMeWN^<*5_o7}N>@Mvro{Zq$We=(oyE3Ag3RB&SOJr&_;A5nnl z$I(4xR;4Qxu}tz_x}I$1)9Y0b#YN3s@~J3*PO@T_e8y5mbl;X zhy&V*2hj(THOjO55We=%_`PJq1wPbaDd>`;OGfWuUeSSvM|yrO9@M#d!H_;HQ}Yk0 zutZdYHW%fm`7%#znCE8Bt2&@x_fhL~&*~o%=7{0)%*5Q_R^jtSmQ`T(qFi*`_jgEhkaG}{aOG8oy8Xxvq zR7DGo-kR&KiGz6lER2YbZm$;W&$lc_%k)H^H3_rZB@l;MutDMk3G-!w^s=r^x{-~hnH5A3H#`=`?Hi#|z&Cnfi%kii6-DAI7C38IyFGC61I8v%8rbDD^Ms`Y|Ebw zN@=S`MowwkX*6eSlk;_rnvg8G5B`Wgo*`3BGpKP;$I<4tei|#01fZHv9`#Qd(>Q_E z(cmelQKynXC78i<8u16Xl5QHz{KOWu}b0oHzJ{- z`+tx7JbHT|uDzRCCnYV-^frmrrp_u_PgG8xjY+)+#*I@yq_$l>L@(93j*xsqLD9kR zwG#0;G*paAe0+gkNQjJ0yHu*x5B0iOje(4kQlVeECyEj&FYGf@YloJuEh#CaH%fuWlnGGkg29IGdKUx9o65vY&XM6 z%NR^v=}_ffE(5sNh}fx+{@-jHrS7lvX5pd=-D7*GwW&=2lw?+^Skb&b=FK@qFvs?N zY+Huqy)+<@hQh ztVxI8MK&llbba~T7JPAunON%tWCLK4W z@mbTMZhkgdSVW5-Ntw_75N60iBgR?P<)~>IO2o5JJEAnJemgJx}zl{7#9 z?TrDh&YP{k7dh>VYBH1zr80mkb4tE%f2Q#a>!E|?&gIZEYrU@qBv-DMU~JAM$p{aK z7Hs?Vb2o1s-x+us#*-@R-ux{8+MNAygVv@l{?IpR&nsUy!Y}-0xa(u5zXbnMZ#AM* zX%SxS4-eqg1^kIgL(^=N_3piP5f^r>`4`@~m>0f*UnZF!>ggS!Q~uuo@FvxRqWYzm z)&US|mFf`a%&3vP;j2BstNZQy_wV^+vz=i?1jNLjRtM7P-FIho8sD?%>+A3TzU7Uj zwrSdi5@qM$7>0Yhv+I=qMarq)_US&6n6>OegUdbf>$5y4i~sSu>h`Z1>@-1#?v?(} zCRSD<5+5-a=i%O2$2U2TG^q~11AUYYPh)c-5aKCsDItCyr`^vu+Lv)8v$gYAtbM0d z^rog5)l(*Zo~-#51SQ!oi}epZ24gin-cK?q)yaR` zeq3ezz3~NkRfLA}Y- z1?GsUs*drki;dJF`tWI7%i*Dwu)Gy+(rWB6Bq%YXtYQT_F#7&9#LaExdfC4 zlApUU8a8{-S~L>DK#OG|&$vBMr}`~{*U4s3rA7bupFj5#+>X{N##m&nt8T1ZImngS4HO>?0Hb zDyPl57%x;k&<8;}D$E-+4m_6iXaIZCbKGBs(dP#(`%(4m5SaQ0$GWz~< z?A^+kUA?pZ{lgQA1&W~bqkK?*TZ^3T8g=hmmSk|w_M*3d5wO(Ss{%quAO=CmkX(#< z#@3c8jroizSex3ICX&%Hn1!wase;*Ea% zoQ`3J5C(=lTVu~&?AZOP_FVHmC=0vTJ>F#(-m3Sf7*;H*jfnr|d-m%UwWa*;rFhZ? zml?>AnVDS5Qv+lw2?BJn}dHs#pyC~wK>>+yFXm+xy zFJ&4tza}KC5j(C&ESQs*Bvlve0bs6o76sh}DzH6&97v5dwsbvKPbB^3#6EL$m+izX zVz(x2){d?@*&sg z_|zlxzUf7|nIH{ha#`}BFdG!H)@E>e|KS!x^#)RfUhOLplk$@%PhLVwNPuu~ck*N3 zhdA~Ax2~_RJ&xt--f>rm+p7w)+D?>sobA)jH!mvP%((uszYq;^z)E}O_n=~%XFlk1 z9?Mu}M>Z@it6%`i(9+~)AmLH3#`T$)KP^cjz%=^MW8=r%tSHTEK)dtnRFjeAq1}rR zDz<2_Ay!+hRA;;vr;UEr>qXsJn7JHsI%Y=l)^dtGNsM^{v5g~8RZ}0!B#~L_EvP!& zO5w4R-15WC*Q89OC~sd{J3bEJ0s%E`j0>q-TU!bhIhk+FdeK|<;md8bmWu^z72|u; z(;ay5&({W?vI+`BV_!7sT=5JOM^wnZXxC!hJQ9}xik`~i{?NBKd~`L9&MV=^ni^$t zivOkdYZ*I1nSJ-59Y9Vh+h#=$f`uBKSH7cxQQ1ui2}ISUu{BiA$$IU=I)b;Hj;ArI zS$Zu%Tcy;Ib4oX5aXbybc={&*xBbkuDc11m87ublWn$O$xdZ41uzZMuG49#ts^*PY z!*w^JdyOyM9DO6N?Nj(#-fvQuW*mJ|54y63TW@H6UQ}|r3?wbhIE?L%=(dxr>E+$RyN+nu={=Au#2i zN%{sc%D%u&H!(f5wos^Pn-nJ#q-mAoivz*l@lgn9>n1W)@$tz#_fFzdTW(IV=GuPbEex(NS-CqD*|fryXs~bSM@h4SDmPor+$94 zSa=bif`Xo5KGY9x|2W>Xt$mz=&to;g&!JpODt0S0Ix;}>5pOkW!N0iKb(4sS1j9(& zAVGHqG`95Z>b}Ip9DDE*zM}{iEDOXZB4Xn|-4Z;w(~_D$-+C}4Z`ULHCOj&v>!|#5 zXgHx@vQhhsuXmd>(>r$s*y2}lXFr+&v?(@jd??7G!3?qW zQl&|i(ibr?F=?Qb5*YZMfPyd}B&LMhD*EDPAd8iil_G~g6)P%I5)6^Qp}t~66IMoq zM@i_I-;vDC!`~m*cBHrzctLSeSil}%T?%Czf&Mu`pqx$mh8pRs1gU0XK+2YJm?fk*ZJH)7v! zO_BT=Y0pP|$%!=M;xgFLRGr~ucdqW^(|ipEIN(mc2# zfOgDzoYR!%Ai*vFQkVc9c3WdCs~6|d86wt#hGkjRuFc@b`}qbJCS7qNLc&AcT80*k zLxA&eEVu8Afmw{$Uo8kmkcw}8hU$uqP*c-%E`Rj6e&W}HUnb5hx_H8JaF(qi>K{8e z@lCJ7boNC_dbuGODlz(fF+`D|U@sCaHwv^sK6BB-dlQ&qj>m_FR4!gAmqcGCFg3oY z^gLQcEzQVE{p%v1nG~;i`85Fj2qTGS$@)ICJ9d#h!)w{UK6rbXnwim~0wQE^KXV#L z*4T^_`rI5W_o#Jf5WK6atb|nUJf-{R%8l&n?UeJ1&3(TyGBO!Kn%F&lv1H`#=C*2| zr*UNc&5xyzLU3Y$+kUEiF=MZ*b)wo@eOL8gzf6~_@q2Ugfvv4=aiM|fI5$~B-cZ0T zq=r_B&a;gJ;O*~};gNB_v}vn0Zoi!_=KIEPuA>8^_398C2L~trCOGupmsmzz9Kpru z4x8xSXkm4;J-^#OF0QTyH@a>?{ExknfQjiX^Xgzm zgk;bI*u1d9zh7p)Q=UxJ_iNOp)8QX^VorUX@P1LBGkmMT^Z+KgoG_B=Wk2K$b z>y3kyS0a~@*aqIduhf7m7gyPxGT)PN?GbIT`#07WYVBqOSoQ&M7@L!mlT6?pSt_4( z{N{|xe?H5VZrb(mPoEClt7c4Lcw*uaE(Kp32rlD5wgRS`)yOet!~XqZLPOPV*#cPd ze=gYV|MssKyZ^p1_=&ER{d;YIP^(7U2jTzxCV?s|0O5XY^t}`)P|gTX<+r5+&XSvt z@6*SR7;kE9fJWRLkskkpQ%2`H^j@(*64c!lQk)sBK za0>`@Ya96n0l9A2I+8=LR`KE-DcS;%_8K>Wue+a%EdePLP9|j_`Xdu%Y-c5{8Ha*++*Q__3hw8vFeL*3aDAB~NTpP>Al)SKP+4Ed}_?zG-q z3xkc@@6P^le}Dhw+F*t{3K@=_2}B~Aj~cOl#n;B*Wr_n4-R>x~Or@!*>F?cLPNi*X zA;-TA8sE2rSN#kmW9B!?nc_Ur<>lPK?_(FLWCiEt(WeSJJmliyVmD|co~d*AY{>OG z`(?b}B(M;wFbK(Ple@}A$|H_9zq_L-lX!dJu}Uo|xXhEohaqXH~pC@wCp(*8x@U2M~?nbhYlt5cMM_D|1|b1lAl z7vyGU%Tqv_6Hx||AEt@HLC%S`wkH!E9rT(vxX0(qvkiGi>AS-Z@z>y56id`~BMz?Df~A0Ja}64s}1zxAIvPjAG)`^p4) z0A$Z4kyX9DO%s&bjirbAj*(ilPX?th0mPDEhwl{ zbWF475k&me<}(WfbP7z3_Snxg@}EvuS&hJ@Aq!?;#hCd2S-9y+i;4wbq`~$;njlg0 z?#>R?*3uFk6%*Bq_i~4O%jX|MM<~i1H9)SJ`a6yhG?Xq)aAiV55g=i1MBMC zr6uWWP0z_kjR(r(hNi>&ghbG@N!_LzAE8kN620CA0bGC&Sc!3IUu^P73oEEt(k zlUZ6{4+U0waRjEI#y!~6a}V5Jy27+8-~K=;l`jr)JN9({DrXRR^eXFqA4=r+`6~ne z(LT=`^&@xHi`ZX##c~g)tF04=v+?fVZ$H_ZeCDxlyz!1Wv$PEw>(vE<#l(CY91SlsV15Q%NO zIzLi3VUt(t=_xESYHdNwc|HjH)x>W*0cCWPs@($K`)AIpkfFmHm5s}@>|+Kv6)^Dl z!Hr@dwctcwt_;x zxg9SoW_j6#peH(cePgDs?EUPs-+&kjeTJ!M4)(0BY{O2r<-QF8Dw~nQ7?cvz8cJng zL3*R(vzH1uC(){TGV*IYz?A>#|GbyrM`Ix(;{O@~zTiWZEU`pL@7ejeJX;nf4q5D< ztQE9H!N8my?FB_5^&NK(o${BBEETo2NiUp9fdG)Mcovfi^~TpTZX!WQ*tnDb;h(T$ ze&8sJtH+OF(J@2cfLR@x1W^*~q~NWL*9){Qojjnuy}eWgkYI#sC_)B^&&1!~HS4T( z;|zJOK7TWIWdIfPDMs?C7br68lt1zD^@VL=W6suutl>defT?YnEHlajHcqXf!l4!Q zh}ZIm+{Dn)-@DPa-pUa~5)xTrzWHxbh26GartnzAql7`M^aqbYHSf|chDJPzq98o2 zu(Og%ex_3@YQH78Dc>GAX_YJIxd>9OmLqs?uUv6=Wmwq#aAKCjz8fQOhD*R1KFR0% zq4dwY^ilEj`zeqqzOKH173UOov4u-1&Jmw0z!8^jd%J>I-<*P4*i8|kF~O8-iVm3oTv7%^I%)?- zFUeQV&SHT-lv^>Ek_;M*3f70 zy|ZKy&&q1V;*R1Z2=UEO7bo2O{E11IagE?{a7De2W3_ngrYr9P6CZCxdTZ3ABUpYY z1c&@^a0tBvfp=6hdMHtn9Z}ZD@gtQV8@OHRlg)8h@bm#Fbuz(k=w%}1_M0A4QNiY^ zK{R=`7(#qe(usJq0$D%)JC*o>`*_Voj≷bx%aO8Ll=f??esP%w9{ic zZS79E{bO5V8^n3k6I9`~Z+NF2RjnH9m_nB)2KM4y6#>HM#+rkXvg zjy?21=8{iI;(HKvELE;yG@L698(hex4LkerRj;n}_*NYV!ZvQKc4r&CcJjmKDEMvK1_vKrBKJaCTU(Ex(;9kH0{2LJ@jVoI z`KK>IEyLHz;K^oD=5rTJv==_eQBmv-M~YV%QiFgoiSe+@29tn~Ad8;v@->u^uvPDl zwR~DnNUt&Rf%4r%Ak0*&Rf&|_r5nd3Ea9x$3XQOp>-bSTK%aS07+&#ppb!{rBSdZMCd(#h&G6Hl4M!0bdKVt$@;o)$N+aa2hl= zBiBSkMTt;wVDY0|U0fn+%FAP^m(D?k($dxns8B(DU);kdbdTxejEvV(@!edbtJ%?? zJ|bHi8wy)fQ$oF>*Ev(*(09$Z_@+5zcE`kcfHT{V-e#11Z}Dx-%!q6)Eg$K%_?%CH zcMtr|8t@*Hl9HytJ5cGB8@KDw)92~jyB9K5YS7dz=67|LaRU;Hiaq!)KDaN*Oqo&Z z6j%|3Rt29O;1Wu*vKC0sKEL)76;sX>A)ooCSEmDftd6j7lMV*PM;$%AnJKU&ot*mh z+ybnu;SPqF)gW<=na>NoVWbBBJN``%FaVoGH-`S(IsSi#A22JUup9ZUZ1Mjo6O!qn z_&-XUGX5(cV?;?nv^5+|;Gr)g9NxcESLW$T^S(e5HZ8d!;AYwH{zj*X^6jS}S>c}g z=jEk#;QXDbLY}FrSza7(yqzqi0(n#fSluVU3)g#{uvS_QAVmL3(NlpoE&BVxbfl#2 zuxft!1Ws}X0`VXqAYiJ-mUMHnYzPkd`zxe&#Pgv40yPDOO{c}D;rI%i_Vu|Y!H+wC z|A&0te^zLiNw>-E+m*g~qpYl~OUyry&j>Uk(f^CZ?dr@)G2;cv!-o%nmij~Gj2A&h ze&<2n(7!hVUl{*C&nYc+KLx~99sT_ZUuJffmTogKF`=D#fQRbLKZ_@)ki7WMEl27c zj98{PTLj+z{0k%+J-|W%Ga3YLO)Dgn?49(_orC^K;32gj(lT2?Br>b!-YHN@V{DxV zy|?4-X(HtAT*rr781+_v*9hd)b=3YYqr|TO=b*Mh7T4~#`hy(GqR!Zk?Apq0jtmL8 zCt&yMQxb=sGv41jGmi1UZQy_1;a^+)e<>gw2axan{nq~Z<9}OT_HR9 literal 0 HcmV?d00001 diff --git a/docs/guides/custom-solvers.md b/docs/guides/custom-solvers.md index 40e840d..0f04ba2 100644 --- a/docs/guides/custom-solvers.md +++ b/docs/guides/custom-solvers.md @@ -19,7 +19,7 @@ def custom_solver(params): return predictions builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(custom_solver) .with_data(data) .with_parameter("alpha", 1.0) @@ -29,8 +29,8 @@ builder = ( ## Examples See the predator-prey examples: -- [predator_prey_diffrax.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffrax.py) -- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/chronopt/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) +- [predator_prey_diffrax.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) ## See Also diff --git a/docs/guides/diffsol-backend.md b/docs/guides/diffsol-backend.md index 91925f9..05db37c 100644 --- a/docs/guides/diffsol-backend.md +++ b/docs/guides/diffsol-backend.md @@ -14,7 +14,7 @@ ```python builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_backend("dense") # or "sparse" diff --git a/docs/guides/index.md b/docs/guides/index.md index b371d3a..a1ec664 100644 --- a/docs/guides/index.md +++ b/docs/guides/index.md @@ -1,6 +1,6 @@ # User Guides -In-depth guides for making the most of Chronopt's optimisation and sampling capabilities. +In-depth guides for making the most of Diffid's optimisation and sampling capabilities. ## Algorithm Selection diff --git a/docs/guides/parallel-execution.md b/docs/guides/parallel-execution.md index 8882c80..dc4fa86 100644 --- a/docs/guides/parallel-execution.md +++ b/docs/guides/parallel-execution.md @@ -16,7 +16,7 @@ builder = builder.with_max_threads(4) # Population size for CMA-ES -optimiser = chron.CMAES().with_population_size(20) +optimiser = diffid.CMAES().with_population_size(20) ``` ## See Also diff --git a/docs/guides/troubleshooting.md b/docs/guides/troubleshooting.md index 6b1d8da..fead903 100644 --- a/docs/guides/troubleshooting.md +++ b/docs/guides/troubleshooting.md @@ -3,10 +3,10 @@ ### Import Error ```python -ImportError: No module named 'chronopt' +ImportError: No module named 'diffid' ``` -**Solution**: Install Chronopt: `pip install chronopt` +**Solution**: Install Diffid: `pip install diffid` ### Poor Fit Quality @@ -29,4 +29,4 @@ ImportError: No module named 'chronopt' - [Installation](../getting-started/installation.md) - [Tuning Optimisers](tuning-optimizers.md) -- [GitHub Issues](https://github.com/bradyplanden/chronopt/issues) +- [GitHub Issues](https://github.com/bradyplanden/diffid/issues) diff --git a/docs/index.md b/docs/index.md index ad25c0a..5f9481a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,17 +1,17 @@ -# Chronopt +# Diffid -**chron**os-**opt**imum is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. +differential identification is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. -## Why Chronopt? +## Why Diffid? -Chronopt offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. This package instead introduces an alternative, where the Python layer acts purely as a declarative configuration interface, +Diffid offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. This package instead introduces an alternative, where the Python layer acts purely as a declarative configuration interface, while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below,

- ![Paradigm Comparison](chronopt.drawio.svg){ width="100%" } -
Conventional approach vs Chronopt: the optimisation loop moves from Python to Rust
+ ![Paradigm Comparison](assets/diffid.svg){ width="100%" } +
Conventional vs configuration approach: the optimisation loop moves from Python to Rust
@@ -30,7 +30,7 @@ while all computationally intensive work (the optimisation / sampling loop, grad --- - Get started with Chronopt in 5 minutes with a simple scalar optimisation example. + Get started with Diffid in 5 minutes with a simple scalar optimisation example. [:octicons-arrow-right-24: Quickstart](getting-started/quickstart.md) @@ -62,38 +62,38 @@ while all computationally intensive work (the optimisation / sampling loop, grad ## Installation -Chronopt targets Python >= 3.11. Windows builds are currently marked experimental. +Diffid targets Python >= 3.11. Windows builds are currently marked experimental. === "pip" ```bash - pip install chronopt + pip install diffid # Optional extras for plotting - pip install "chronopt[plotting]" + pip install "diffid[plotting]" ``` === "uv" ```bash - uv pip install chronopt + uv pip install diffid # Optional extras for plotting - uv pip install "chronopt[plotting]" + uv pip install "diffid[plotting]" ``` ## Example: Scalar Optimisation ```python import numpy as np -import chronopt as chron +import diffid def rosenbrock(x): value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 return np.asarray([value]) builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) @@ -110,7 +110,7 @@ print(f"Success: {result.success}") ```python import numpy as np -import chronopt as chron +import diffid # Logistic growth model in DiffSL dsl = """ @@ -124,7 +124,7 @@ observations = np.exp(-1.3 * t) data = np.column_stack((t, observations)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) @@ -132,7 +132,7 @@ builder = ( ) problem = builder.build() -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, [0.5, 0.5]) print(result.x) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 9120487..588f65a 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,6 +1,6 @@ # Tutorials -Interactive Jupyter notebooks for hands-on learning with Chronopt. +Interactive Jupyter notebooks for hands-on learning with Diffid. ## Learning Paths @@ -8,7 +8,7 @@ Follow these progressive learning paths based on your experience level and goals ### 🎯 Beginner Track -Perfect for those new to Chronopt or optimisation: +Perfect for those new to Diffid or optimisation:
@@ -101,26 +101,26 @@ Complex problems and advanced techniques: ### Installation -Install Chronopt with Jupyter and plotting support: +Install Diffid with Jupyter and plotting support: === "pip" ```bash - pip install chronopt jupyter matplotlib + pip install diffid jupyter matplotlib ``` === "uv" ```bash - uv pip install chronopt jupyter matplotlib + uv pip install diffid jupyter matplotlib ``` ### Clone and Run ```bash # Clone the repository -git clone https://github.com/bradyplanden/chronopt.git -cd chronopt/docs/tutorials/notebooks +git clone https://github.com/bradyplanden/diffid.git +cd diffid/docs/tutorials/notebooks # Launch Jupyter jupyter notebook @@ -134,13 +134,13 @@ You can also run these notebooks in Google Colab (coming soon with hosted versio By completing all tutorials, you will: -✅ Understand Chronopt's builder pattern and API -✅ Optimize both scalar functions and ODE parameters -✅ Compare and tune different optimisation algorithms -✅ Quantify parameter uncertainty with MCMC -✅ Compare models using Bayesian evidence -✅ Integrate custom ODE solvers (JAX, Julia) -✅ Make informed decisions about algorithm selection +- Understand Diffid's builder pattern and API +- Optimize both scalar functions and ODE parameters +- Compare and tune different optimisation algorithms +- Quantify parameter uncertainty with MCMC +- Compare models using Bayesian evidence +- Integrate custom ODE solvers (JAX, Julia) +- Make informed decisions about algorithm selection ## Notebook Structure @@ -157,7 +157,7 @@ Each tutorial follows a consistent structure: ## Alternative: Python Scripts -Prefer scripts to notebooks? Check out the [examples directory](https://github.com/bradyplanden/chronopt/tree/main/examples): +Prefer scripts to notebooks? Check out the [examples directory](https://github.com/bradyplanden/diffid/tree/main/examples): - `python_problem.py` - Basic scalar optimisation - `logistic_growth.py` - ODE fitting @@ -183,10 +183,10 @@ Feel free to reuse these in your own projects! ### Import Errors ```python -ModuleNotFoundError: No module named 'chronopt' +ModuleNotFoundError: No module named 'diffid' ``` -**Solution**: Install Chronopt: `pip install chronopt` +**Solution**: Install Diffid: `pip install diffid` ### Notebook Kernel Issues @@ -222,13 +222,13 @@ If MCMC sampling is slow: - **Documentation**: Browse the [complete docs](../index.md) - **Examples**: See the [examples gallery](../examples/gallery.md) - **API Reference**: Check the [API docs](../api-reference/index.md) -- **Issues**: Report problems on [GitHub](https://github.com/bradyplanden/chronopt/issues) +- **Issues**: Report problems on [GitHub](https://github.com/bradyplanden/diffid/issues) ## Contributing Found an issue or want to improve a tutorial? -1. Fork the [repository](https://github.com/bradyplanden/chronopt) +1. Fork the [repository](https://github.com/bradyplanden/diffid) 2. Edit notebooks in `docs/tutorials/notebooks/` 3. Test your changes locally 4. Submit a pull request @@ -253,7 +253,7 @@ After completing the tutorials: More applications and use cases -- [:material-github:{ .lg .middle } __GitHub Repository__](https://github.com/bradyplanden/chronopt) +- [:material-github:{ .lg .middle } __GitHub Repository__](https://github.com/bradyplanden/diffid) Source code and development diff --git a/docs/tutorials/notebooks/01_optimization_basics.ipynb b/docs/tutorials/notebooks/01_optimization_basics.ipynb index d916426..61743db 100644 --- a/docs/tutorials/notebooks/01_optimization_basics.ipynb +++ b/docs/tutorials/notebooks/01_optimization_basics.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:26.832609Z", @@ -40,22 +40,16 @@ "outputs": [], "source": [ "# Import plotting utilities\n", - "import chronopt as chron\n", + "import diffid\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "from chronopt.plotting import contour_2d" + "from diffid.plotting import contour_2d" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Define the Objective Function\n", - "\n", - "In Chronopt, objective functions must:\n", - "1. Accept a NumPy array as input\n", - "2. Return a NumPy array as output (even for scalar values)" - ] + "source": "## Define the Objective Function\n\nIn Diffid, objective functions must:\n1. Accept a NumPy array as input\n2. Return a NumPy array as output (even for scalar values)" }, { "cell_type": "code", @@ -87,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:27.533691Z", @@ -96,19 +90,10 @@ "shell.execute_reply": "2026-01-10T22:21:27.537575Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Problem built successfully!\n", - "Number of parameters: 2\n" - ] - } - ], + "outputs": [], "source": [ "builder = (\n", - " chron.ScalarBuilder()\n", + " diffid.ScalarBuilder()\n", " .with_objective(rosenbrock)\n", " .with_parameter(\"x\", 10.0) # Initial guess\n", " .with_parameter(\"y\", 10.0) # Initial guess\n", @@ -122,12 +107,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Optimise with Default Settings\n", - "\n", - "The problem class is constructed as the core object in chronopt, as such\n", - " an `optimise()` method if provided for fast optimisation. This uses Nelder-Mead by default:" - ] + "source": "## Optimise with Default Settings\n\nThe problem class is constructed as the core object in diffid, as such\n an `optimise()` method if provided for fast optimisation. This uses Nelder-Mead by default:" }, { "cell_type": "code", @@ -240,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:27.973637Z", @@ -249,34 +229,16 @@ "shell.execute_reply": "2026-01-10T22:21:28.084924Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "======================================================================\n", - "OPTIMISER COMPARISON\n", - "======================================================================\n", - "Optimiser Success Final Value Iterations Evaluations\n", - "----------------------------------------------------------------------\n", - "Nelder-Mead True 2.866e-07 130 290\n", - "CMA-ES True 3.681e-08 95 571\n", - "Adam False 6.927e-09 2000 2001\n", - "\n", - "Final parameters:\n", - "Nelder-Mead x = [1.0001253 1.00019857]\n", - "CMA-ES x = [1.00017279 1.00035395]\n", - "Adam x = [1.00008317 1.00016666]\n" - ] - } - ], + "outputs": [], "source": [ "# Define optimisers\n", "optimisers = {\n", - " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", - " \"CMA-ES\": chron.CMAES().with_max_iter(300).with_step_size(0.5),\n", - " \"Adam\": chron.Adam().with_max_iter(2000).with_step_size(0.25).with_threshold(1e-12),\n", + " \"Nelder-Mead\": diffid.NelderMead().with_max_iter(1000),\n", + " \"CMA-ES\": diffid.CMAES().with_max_iter(300).with_step_size(0.5),\n", + " \"Adam\": diffid.Adam()\n", + " .with_max_iter(2000)\n", + " .with_step_size(0.25)\n", + " .with_threshold(1e-12),\n", "}\n", "\n", "# Test starting point\n", @@ -408,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:28.447521Z", @@ -417,42 +379,7 @@ "shell.execute_reply": "2026-01-10T22:21:28.460670Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "STARTING POINT SENSITIVITY\n", - "============================================================\n", - "\n", - "Start 1: [-1.5, -0.5]\n", - " Final: [0.99938601 0.99871559]\n", - " Iterations: 60\n", - " Error: 1.424e-03\n", - " Success: True\n", - "\n", - "Start 2: [1.5, 1.5]\n", - " Final: [1.00023483 1.00043031]\n", - " Iterations: 79\n", - " Error: 4.902e-04\n", - " Success: True\n", - "\n", - "Start 3: [0.0, 2.0]\n", - " Final: [1.00004402 1.00004508]\n", - " Iterations: 65\n", - " Error: 6.301e-05\n", - " Success: True\n", - "\n", - "Start 4: [-1.0, 1.0]\n", - " Final: [1.00040626 1.0008615 ]\n", - " Iterations: 105\n", - " Error: 9.525e-04\n", - " Success: True\n" - ] - } - ], + "outputs": [], "source": [ "# Try multiple starting points\n", "starting_points = [\n", @@ -462,7 +389,7 @@ " [-1.0, 1.0],\n", "]\n", "\n", - "optimiser = chron.NelderMead().with_max_iter(1000)\n", + "optimiser = diffid.NelderMead().with_max_iter(1000)\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"STARTING POINT SENSITIVITY\")\n", @@ -536,4 +463,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb index a59ba93..7a471e9 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:29.582370Z", @@ -43,13 +43,7 @@ } }, "outputs": [], - "source": [ - "# Import plotting utilities\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from chronopt.plotting import ode_fit" - ] + "source": "# Import plotting utilities\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom diffid.plotting import ode_fit" }, { "cell_type": "markdown", @@ -118,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:30.052995Z", @@ -127,48 +121,8 @@ "shell.execute_reply": "2026-01-10T22:21:30.079161Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generated 100 data points\n", - "Time range: [0.00, 4.00]\n", - "Value range: [0.102, 0.856]\n", - "Noise level: 0.01\n", - "\n", - "True parameters: r = 1.0, k = 1.0\n" - ] - } - ], - "source": [ - "# Time points\n", - "t_span = np.linspace(0, 4, 100)\n", - "\n", - "# True logistic growth solution with known parameters\n", - "# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\n", - "y0 = 0.1\n", - "r_true = 1.0\n", - "k_true = 1.0\n", - "\n", - "# Generate clean data\n", - "exp_rt = np.exp(r_true * t_span)\n", - "y_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n", - "\n", - "# Add some noise\n", - "np.random.seed(42)\n", - "noise_level = 0.01\n", - "y_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n", - "\n", - "# Chronopt expects data as [time, observation] columns\n", - "data = np.column_stack((t_span, y_noisy))\n", - "\n", - "print(f\"Generated {len(t_span)} data points\")\n", - "print(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\n", - "print(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\n", - "print(f\"Noise level: {noise_level}\")\n", - "print(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")" - ] + "outputs": [], + "source": "# Time points\nt_span = np.linspace(0, 4, 100)\n\n# True logistic growth solution with known parameters\n# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\ny0 = 0.1\nr_true = 1.0\nk_true = 1.0\n\n# Generate clean data\nexp_rt = np.exp(r_true * t_span)\ny_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n\n# Add some noise\nnp.random.seed(42)\nnoise_level = 0.01\ny_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n\n# Diffid expects data as [time, observation] columns\ndata = np.column_stack((t_span, y_noisy))\n\nprint(f\"Generated {len(t_span)} data points\")\nprint(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\nprint(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\nprint(f\"Noise level: {noise_level}\")\nprint(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")" }, { "cell_type": "markdown", @@ -228,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:30.232622Z", @@ -237,36 +191,8 @@ "shell.execute_reply": "2026-01-10T22:21:30.236189Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Problem built successfully!\n", - "\n", - "Initial parameter guesses: r = 50.0, k = 50.0\n", - "(Far from true values: r = 1.0, k = 1.0)\n" - ] - } - ], - "source": [ - "# Create builder\n", - "builder = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(dsl_model)\n", - " .with_data(data)\n", - " .with_tolerances(1e-6, 1e-8) # Relative and absolute tolerances\n", - " .with_parameter(\"r\", 10.0) # Initial guess (deliberately wrong)\n", - " .with_parameter(\"k\", 10.0) # Initial guess (deliberately wrong)\n", - " .with_parallel(True) # Enable parallel evaluation\n", - ")\n", - "\n", - "problem = builder.build()\n", - "\n", - "print(\"Problem built successfully!\")\n", - "print(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\n", - "print(f\"(Far from true values: r = {r_true}, k = {k_true})\")" - ] + "outputs": [], + "source": "# Create builder\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_tolerances(1e-6, 1e-8) # Relative and absolute tolerances\n .with_parameter(\"r\", 10.0) # Initial guess (deliberately wrong)\n .with_parameter(\"k\", 10.0) # Initial guess (deliberately wrong)\n .with_parallel(True) # Enable parallel evaluation\n)\n\nproblem = builder.build()\n\nprint(\"Problem built successfully!\")\nprint(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\nprint(f\"(Far from true values: r = {r_true}, k = {k_true})\")" }, { "cell_type": "markdown", @@ -279,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:30.238643Z", @@ -288,62 +214,8 @@ "shell.execute_reply": "2026-01-10T22:21:34.759178Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "OPTIMIZATION RESULTS\n", - "============================================================\n", - "Success: True\n", - "\n", - "Fitted parameters:\n", - " r = 0.996728 (true: 1.0)\n", - " k = 1.002875 (true: 1.0)\n", - "\n", - "Optimization details:\n", - " Final cost: 8.220e-03\n", - " Iterations: 545\n", - " Function evaluations: 3271\n", - " Time: 514.311 milliseconds\n", - " Message: Function tolerance met\n", - "\n", - "Parameter errors:\n", - " r: 0.327%\n", - " k: 0.287%\n" - ] - } - ], - "source": [ - "# Create optimiser\n", - "optimiser = chron.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", - "\n", - "# Run optimization\n", - "result = optimiser.run(problem, problem.initial_values())\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"OPTIMIZATION RESULTS\")\n", - "print(\"=\" * 60)\n", - "print(f\"Success: {result.success}\")\n", - "print(\"\\nFitted parameters:\")\n", - "print(f\" r = {result.x[0]:.6f} (true: {r_true})\")\n", - "print(f\" k = {result.x[1]:.6f} (true: {k_true})\")\n", - "print(\"\\nOptimization details:\")\n", - "print(f\" Final cost: {result.value:.3e}\")\n", - "print(f\" Iterations: {result.iterations}\")\n", - "print(f\" Function evaluations: {result.evaluations}\")\n", - "print(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\n", - "print(f\" Message: {result.message}\")\n", - "\n", - "# Calculate parameter errors\n", - "r_error = abs(result.x[0] - r_true) / r_true * 100\n", - "k_error = abs(result.x[1] - k_true) / k_true * 100\n", - "print(\"\\nParameter errors:\")\n", - "print(f\" r: {r_error:.3f}%\")\n", - "print(f\" k: {k_error:.3f}%\")" - ] + "outputs": [], + "source": "# Create optimiser\noptimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n\n# Run optimization\nresult = optimiser.run(problem, problem.initial_values())\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMIZATION RESULTS\")\nprint(\"=\" * 60)\nprint(f\"Success: {result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" r = {result.x[0]:.6f} (true: {r_true})\")\nprint(f\" k = {result.x[1]:.6f} (true: {k_true})\")\nprint(\"\\nOptimization details:\")\nprint(f\" Final cost: {result.value:.3e}\")\nprint(f\" Iterations: {result.iterations}\")\nprint(f\" Function evaluations: {result.evaluations}\")\nprint(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\nprint(f\" Message: {result.message}\")\n\n# Calculate parameter errors\nr_error = abs(result.x[0] - r_true) / r_true * 100\nk_error = abs(result.x[1] - k_true) / k_true * 100\nprint(\"\\nParameter errors:\")\nprint(f\" r: {r_error:.3f}%\")\nprint(f\" k: {k_error:.3f}%\")" }, { "cell_type": "markdown", @@ -513,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:42.612961Z", @@ -522,51 +394,8 @@ "shell.execute_reply": "2026-01-10T22:21:46.725244Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "OPTIMISER COMPARISON\n", - "================================================================================\n", - "Algorithm r (fitted) k (fitted) Cost Iters Time (s)\n", - "--------------------------------------------------------------------------------\n", - "Nelder-Mead 2.527654 0.604107 2.331e+00 37 0.204\n", - "CMA-ES 0.996760 1.002815 8.220e-03 57 0.529\n", - "Adam 0.953633 1.070344 1.648e-02 1000 0.374\n", - "\n", - "True values: 1.000000 1.000000 \n" - ] - } - ], - "source": [ - "optimisers = {\n", - " \"Nelder-Mead\": chron.NelderMead().with_max_iter(1000),\n", - " \"CMA-ES\": chron.CMAES().with_max_iter(500),\n", - " \"Adam\": chron.Adam().with_max_iter(1000).with_step_size(0.01),\n", - "}\n", - "\n", - "initial = [2.0, 2.0]\n", - "\n", - "print(\"\\n\" + \"=\" * 80)\n", - "print(\"OPTIMISER COMPARISON\")\n", - "print(\"=\" * 80)\n", - "print(\n", - " f\"{'Algorithm':<15} {'r (fitted)':<15} {'k (fitted)':<15} {'Cost':<15} {'Iters':<8} {'Time (s)'}\"\n", - ")\n", - "print(\"-\" * 80)\n", - "\n", - "for name, opt in optimisers.items():\n", - " result = opt.run(problem, initial)\n", - " print(\n", - " f\"{name:<15} {result.x[0]:<15.6f} {result.x[1]:<15.6f} \"\n", - " f\"{result.value:<15.3e} {result.iterations:<8} {result.time.microseconds / 1e6:.3f}\"\n", - " )\n", - "\n", - "print(f\"\\nTrue values: {r_true:<15.6f} {k_true:<15.6f}\")" - ] + "outputs": [], + "source": "optimisers = {\n \"Nelder-Mead\": diffid.NelderMead().with_max_iter(1000),\n \"CMA-ES\": diffid.CMAES().with_max_iter(500),\n \"Adam\": diffid.Adam().with_max_iter(1000).with_step_size(0.01),\n}\n\ninitial = [2.0, 2.0]\n\nprint(\"\\n\" + \"=\" * 80)\nprint(\"OPTIMISER COMPARISON\")\nprint(\"=\" * 80)\nprint(\n f\"{'Algorithm':<15} {'r (fitted)':<15} {'k (fitted)':<15} {'Cost':<15} {'Iters':<8} {'Time (s)'}\"\n)\nprint(\"-\" * 80)\n\nfor name, opt in optimisers.items():\n result = opt.run(problem, initial)\n print(\n f\"{name:<15} {result.x[0]:<15.6f} {result.x[1]:<15.6f} \"\n f\"{result.value:<15.3e} {result.iterations:<8} {result.time.microseconds / 1e6:.3f}\"\n )\n\nprint(f\"\\nTrue values: {r_true:<15.6f} {k_true:<15.6f}\")" }, { "cell_type": "markdown", @@ -714,4 +543,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index b7c1c67..51c7ae7 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:48.226019Z", @@ -42,15 +42,7 @@ } }, "outputs": [], - "source": [ - "# Import plotting utilities\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from chronopt.plotting import parameter_distributions, parameter_traces\n", - "\n", - "np.random.seed(42) # For reproducibility" - ] + "source": "# Import plotting utilities\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom diffid.plotting import parameter_distributions, parameter_traces\n\nnp.random.seed(42) # For reproducibility" }, { "cell_type": "markdown", @@ -83,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:48.861095Z", @@ -92,57 +84,8 @@ "shell.execute_reply": "2026-01-10T22:21:48.866205Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generated 61 observations\n", - "Time span: [0, 0.999] seconds\n", - "Noise level: σ = 0.1\n", - "\n", - "True parameters:\n", - " g = 9.81 m/s²\n", - " h = 10.0 m\n" - ] - } - ], - "source": [ - "def ball_states(t, g, h):\n", - " \"\"\"Analytical solution for ball trajectory.\"\"\"\n", - " height = h - 0.5 * g * t**2\n", - " height = np.maximum(height, 0.0) # Can't go below ground\n", - " velocity = -g * t\n", - " return height, velocity\n", - "\n", - "\n", - "# True parameters\n", - "g_true = 9.81 # m/s²\n", - "h_true = 10.0 # meters\n", - "\n", - "# Time to hit ground: t = sqrt(2h/g)\n", - "t_stop = np.sqrt(2.0 * h_true / g_true)\n", - "t_final = 0.7 * t_stop # Stop before hitting ground\n", - "t_span = np.linspace(0.0, t_final, 61)\n", - "\n", - "# Generate clean data\n", - "height, velocity = ball_states(t_span, g_true, h_true)\n", - "\n", - "# Add measurement noise\n", - "noise_std = 0.1\n", - "height_noisy = height + np.random.normal(0, noise_std, len(t_span))\n", - "velocity_noisy = velocity + np.random.normal(0, noise_std, len(t_span))\n", - "\n", - "# Format for Chronopt: [time, height, velocity]\n", - "data = np.column_stack((t_span, height_noisy, velocity_noisy))\n", - "\n", - "print(f\"Generated {len(t_span)} observations\")\n", - "print(f\"Time span: [0, {t_final:.3f}] seconds\")\n", - "print(f\"Noise level: σ = {noise_std}\")\n", - "print(\"\\nTrue parameters:\")\n", - "print(f\" g = {g_true} m/s²\")\n", - "print(f\" h = {h_true} m\")" - ] + "outputs": [], + "source": "def ball_states(t, g, h):\n \"\"\"Analytical solution for ball trajectory.\"\"\"\n height = h - 0.5 * g * t**2\n height = np.maximum(height, 0.0) # Can't go below ground\n velocity = -g * t\n return height, velocity\n\n\n# True parameters\ng_true = 9.81 # m/s²\nh_true = 10.0 # meters\n\n# Time to hit ground: t = sqrt(2h/g)\nt_stop = np.sqrt(2.0 * h_true / g_true)\nt_final = 0.7 * t_stop # Stop before hitting ground\nt_span = np.linspace(0.0, t_final, 61)\n\n# Generate clean data\nheight, velocity = ball_states(t_span, g_true, h_true)\n\n# Add measurement noise\nnoise_std = 0.1\nheight_noisy = height + np.random.normal(0, noise_std, len(t_span))\nvelocity_noisy = velocity + np.random.normal(0, noise_std, len(t_span))\n\n# Format for Diffid: [time, height, velocity]\ndata = np.column_stack((t_span, height_noisy, velocity_noisy))\n\nprint(f\"Generated {len(t_span)} observations\")\nprint(f\"Time span: [0, {t_final:.3f}] seconds\")\nprint(f\"Noise level: σ = {noise_std}\")\nprint(\"\\nTrue parameters:\")\nprint(f\" g = {g_true} m/s²\")\nprint(f\" h = {h_true} m\")" }, { "cell_type": "markdown", @@ -267,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:49.152830Z", @@ -276,55 +219,8 @@ "shell.execute_reply": "2026-01-10T22:21:49.634461Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "OPTIMIZATION RESULTS (MAP Estimate)\n", - "============================================================\n", - "Success: True\n", - "\n", - "Fitted parameters:\n", - " g = 9.8035 m/s² (true: 9.81)\n", - " h = 9.9841 m (true: 10.0)\n", - "\n", - "Cost: 0.183349\n", - "Iterations: 197\n" - ] - } - ], - "source": [ - "# Build problem\n", - "builder = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(dsl_model)\n", - " .with_data(data)\n", - " .with_parameter(\"g\", 5.0) # Initial guess\n", - " .with_parameter(\"h\", 5.0) # Initial guess\n", - " .with_cost(chron.RMSE(2.0)) # 2 observables (height + velocity)\n", - ")\n", - "\n", - "problem = builder.build()\n", - "\n", - "# Optimize\n", - "optimizer = chron.Adam().with_step_size(0.05).with_max_iter(1500)\n", - "opt_result = optimizer.run(problem, [5.0, 5.0])\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"OPTIMIZATION RESULTS (MAP Estimate)\")\n", - "print(\"=\" * 60)\n", - "print(f\"Success: {opt_result.success}\")\n", - "print(\"\\nFitted parameters:\")\n", - "print(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\n", - "print(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\n", - "print(f\"\\nCost: {opt_result.value:.6f}\")\n", - "print(f\"Iterations: {opt_result.iterations}\")\n", - "\n", - "g_map, h_map = opt_result.x" - ] + "outputs": [], + "source": "# Build problem\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_parameter(\"g\", 5.0) # Initial guess\n .with_parameter(\"h\", 5.0) # Initial guess\n .with_cost(diffid.RMSE(2.0)) # 2 observables (height + velocity)\n)\n\nproblem = builder.build()\n\n# Optimize\noptimizer = diffid.Adam().with_step_size(0.05).with_max_iter(1500)\nopt_result = optimizer.run(problem, [5.0, 5.0])\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMIZATION RESULTS (MAP Estimate)\")\nprint(\"=\" * 60)\nprint(f\"Success: {opt_result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\nprint(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\nprint(f\"\\nCost: {opt_result.value:.6f}\")\nprint(f\"Iterations: {opt_result.iterations}\")\n\ng_map, h_map = opt_result.x" }, { "cell_type": "markdown", @@ -337,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2026-01-10T22:21:49.638310Z", @@ -346,72 +242,8 @@ "shell.execute_reply": "2026-01-10T22:21:58.286103Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "MCMC SAMPLING\n", - "============================================================\n", - "\n", - "Starting from MAP estimate: g = 9.8035, h = 9.9841\n", - "\n", - "Running MCMC... (this may take a minute)\n", - "\n", - "Sampling complete!\n", - "Samples shape: (10010, 2)\n", - "Acceptance rate: [0.284 0.295 0.258 0.24 0.248 0.267 0.284 0.24 0.262 0.277]\n", - "Target acceptance: 0.20 - 0.40\n", - "✓ Acceptance rate looks good!\n" - ] - } - ], - "source": [ - "# Rebuild problem with GaussianNLL (required for sampling)\n", - "builder_sampling = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(dsl_model)\n", - " .with_data(data)\n", - " .with_parameter(\"g\", g_map) # Start from MAP\n", - " .with_parameter(\"h\", h_map)\n", - " # .with_parallel(True)\n", - " .with_cost(chron.GaussianNLL(variance=noise_std**2))\n", - ")\n", - "\n", - "problem_sampling = builder_sampling.build()\n", - "\n", - "# Setup MCMC sampler\n", - "sampler = (\n", - " chron.MetropolisHastings()\n", - " .with_num_chains(10)\n", - " .with_iterations(1000)\n", - " .with_step_size(0.035)\n", - ")\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"MCMC SAMPLING\")\n", - "print(\"=\" * 60)\n", - "print(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\n", - "print(\"\\nRunning MCMC... (this may take a minute)\")\n", - "\n", - "# Run sampling\n", - "mcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n", - "\n", - "print(\"\\nSampling complete!\")\n", - "print(f\"Samples shape: {mcmc_result.samples.shape}\")\n", - "print(f\"Acceptance rate: {mcmc_result.acceptance_rate}\")\n", - "print(\"Target acceptance: 0.20 - 0.40\")\n", - "\n", - "if np.any(mcmc_result.acceptance_rate < 0.15) or np.any(\n", - " mcmc_result.acceptance_rate > 0.50\n", - "):\n", - " print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n", - " print(\" Consider adjusting step_size\")\n", - "else:\n", - " print(\"✓ Acceptance rate looks good!\")" - ] + "outputs": [], + "source": "# Rebuild problem with GaussianNLL (required for sampling)\nbuilder_sampling = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_parameter(\"g\", g_map) # Start from MAP\n .with_parameter(\"h\", h_map)\n # .with_parallel(True)\n .with_cost(diffid.GaussianNLL(variance=noise_std**2))\n)\n\nproblem_sampling = builder_sampling.build()\n\n# Setup MCMC sampler\nsampler = (\n diffid.MetropolisHastings()\n .with_num_chains(10)\n .with_iterations(1000)\n .with_step_size(0.035)\n)\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"MCMC SAMPLING\")\nprint(\"=\" * 60)\nprint(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\nprint(\"\\nRunning MCMC... (this may take a minute)\")\n\n# Run sampling\nmcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n\nprint(\"\\nSampling complete!\")\nprint(f\"Samples shape: {mcmc_result.samples.shape}\")\nprint(f\"Acceptance rate: {mcmc_result.acceptance_rate}\")\nprint(\"Target acceptance: 0.20 - 0.40\")\n\nif np.any(mcmc_result.acceptance_rate < 0.15) or np.any(\n mcmc_result.acceptance_rate > 0.50\n):\n print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n print(\" Consider adjusting step_size\")\nelse:\n print(\"✓ Acceptance rate looks good!\")" }, { "cell_type": "markdown", @@ -846,4 +678,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb index 8a7b5e9..165a3b9 100644 --- a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb +++ b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb @@ -10,7 +10,7 @@ "- Use VectorBuilder for custom ODE solvers\n", "- Compare Diffsol, Diffrax (JAX), and DifferentialEquations.jl\n", "- Understand performance trade-offs\n", - "- Integrate external solvers with Chronopt\n", + "- Integrate external solvers with Diffid\n", "\n", "**Prerequisites:** Tutorials 1-2, basic JAX or Julia knowledge (optional)\n", "\n", @@ -20,136 +20,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Chronopt's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n", - "\n", - "- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n", - "- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n", - "- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n", - "\n", - "**VectorBuilder** lets you integrate any Python-callable forward model with Chronopt's optimizers.\n", - "\n", - "## The Lotka-Volterra Model\n", - "\n", - "The predator-prey equations:\n", - "\n", - "$$\\begin{aligned}\n", - "\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n", - "\\frac{dy}{dt} &= \\delta x y - \\gamma y\n", - "\\end{aligned}$$\n", - "\n", - "where:\n", - "- $x$ is prey population\n", - "- $y$ is predator population\n", - "- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n", - "\n", - "This tutorial demonstrates parameter fitting with three different solver backends.\n", - "\n", - "## Coming Soon\n", - "\n", - "This advanced tutorial is under development. It will cover:\n", - "\n", - "### Backend 1: Diffsol (Built-in)\n", - "```python\n", - "builder = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(lotka_volterra_dsl)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 1.0)\n", - " # ... more parameters\n", - ")\n", - "```\n", - "\n", - "### Backend 2: JAX/Diffrax\n", - "```python\n", - "import jax\n", - "from diffrax import diffeqsolve, ODETerm, Tsit5\n", - "\n", - "def diffrax_solver(params):\n", - " # Your Diffrax integration\n", - " return predictions\n", - "\n", - "builder = (\n", - " chron.VectorBuilder()\n", - " .with_objective(diffrax_solver)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 1.0)\n", - ")\n", - "```\n", - "\n", - "### Backend 3: Julia/DifferentialEquations.jl\n", - "```python\n", - "from diffeqpy import de\n", - "\n", - "def diffeqpy_solver(params):\n", - " # Your Julia integration\n", - " return predictions\n", - "\n", - "builder = (\n", - " chron.VectorBuilder()\n", - " .with_objective(diffeqpy_solver)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 1.0)\n", - ")\n", - "```\n", - "\n", - "## Performance Comparison\n", - "\n", - "The tutorial will benchmark all three backends:\n", - "\n", - "- **Accuracy**: Parameter recovery quality\n", - "- **Speed**: Time per function evaluation\n", - "- **Ease of use**: Setup complexity\n", - "- **Special features**: Gradients, GPU, stiff solvers\n", - "\n", - "## When to Use Each Backend\n", - "\n", - "| Backend | Best For |\n", - "|---------|----------|\n", - "| **Diffsol** | General purpose, fast, built-in |\n", - "| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n", - "| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n", - "| **Custom** | Non-ODE models, complex physics |\n", - "\n", - "## Example Data\n", - "\n", - "The predator-prey examples directory contains:\n", - "- `generate_data_diffrax.py`: Creates synthetic data\n", - "- `predator_prey_diffsol.py`: Diffsol backend\n", - "- `predator_prey_diffrax.py`: JAX/Diffrax backend\n", - "- `predator_prey_diffeqpy.py`: Julia backend\n", - "\n", - "Run these scripts directly to see the backends in action!\n", - "\n", - "## Installation\n", - "\n", - "For JAX/Diffrax:\n", - "```bash\n", - "pip install jax diffrax\n", - "```\n", - "\n", - "For Julia/DifferentialEquations.jl:\n", - "```bash\n", - "pip install diffeqpy\n", - "python -c \"from diffeqpy import de; de.install()\"\n", - "```\n", - "\n", - "## Key Takeaways\n", - "\n", - "1. **VectorBuilder** integrates any Python callable\n", - "2. **Diffsol** is the default - fast and easy\n", - "3. **JAX/Diffrax** for gradients and GPU\n", - "4. **Julia/DiffEq** for specialized solvers\n", - "5. All backends work with Chronopt's optimizers\n", - "\n", - "## Next Steps\n", - "\n", - "- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n", - "- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n", - "- [Examples Gallery](../../examples/gallery.md) - More backend examples" - ] + "source": "## Introduction\n\nDiffid's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n\n- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n\n**VectorBuilder** lets you integrate any Python-callable forward model with Diffid's optimizers.\n\n## The Lotka-Volterra Model\n\nThe predator-prey equations:\n\n$$\\begin{aligned}\n\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n\\frac{dy}{dt} &= \\delta x y - \\gamma y\n\\end{aligned}$$\n\nwhere:\n- $x$ is prey population\n- $y$ is predator population\n- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n\nThis tutorial demonstrates parameter fitting with three different solver backends.\n\n## Coming Soon\n\nThis advanced tutorial is under development. It will cover:\n\n### Backend 1: Diffsol (Built-in)\n```python\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(lotka_volterra_dsl)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n # ... more parameters\n)\n```\n\n### Backend 2: JAX/Diffrax\n```python\nimport jax\nfrom diffrax import diffeqsolve, ODETerm, Tsit5\n\ndef diffrax_solver(params):\n # Your Diffrax integration\n return predictions\n\nbuilder = (\n diffid.VectorBuilder()\n .with_objective(diffrax_solver)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n)\n```\n\n### Backend 3: Julia/DifferentialEquations.jl\n```python\nfrom diffeqpy import de\n\ndef diffeqpy_solver(params):\n # Your Julia integration\n return predictions\n\nbuilder = (\n diffid.VectorBuilder()\n .with_objective(diffeqpy_solver)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n)\n```\n\n## Performance Comparison\n\nThe tutorial will benchmark all three backends:\n\n- **Accuracy**: Parameter recovery quality\n- **Speed**: Time per function evaluation\n- **Ease of use**: Setup complexity\n- **Special features**: Gradients, GPU, stiff solvers\n\n## When to Use Each Backend\n\n| Backend | Best For |\n|---------|----------|\n| **Diffsol** | General purpose, fast, built-in |\n| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n| **Custom** | Non-ODE models, complex physics |\n\n## Example Data\n\nThe predator-prey examples directory contains:\n- `generate_data_diffrax.py`: Creates synthetic data\n- `predator_prey_diffsol.py`: Diffsol backend\n- `predator_prey_diffrax.py`: JAX/Diffrax backend\n- `predator_prey_diffeqpy.py`: Julia backend\n\nRun these scripts directly to see the backends in action!\n\n## Installation\n\nFor JAX/Diffrax:\n```bash\npip install jax diffrax\n```\n\nFor Julia/DifferentialEquations.jl:\n```bash\npip install diffeqpy\npython -c \"from diffeqpy import de; de.install()\"\n```\n\n## Key Takeaways\n\n1. **VectorBuilder** integrates any Python callable\n2. **Diffsol** is the default - fast and easy\n3. **JAX/Diffrax** for gradients and GPU\n4. **Julia/DiffEq** for specialized solvers\n5. All backends work with Diffid's optimizers\n\n## Next Steps\n\n- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n- [Examples Gallery](../../examples/gallery.md) - More backend examples" } ], "metadata": { @@ -173,4 +44,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index ba17ab5..2bb03ca 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -20,32 +20,14 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "While `DiffsolBuilder` provides a convenient interface for ODE fitting using the DiffSL language, `VectorBuilder` offers maximum flexibility by allowing you to use **any** ODE solver. This enables:\n", - "\n", - "1. **GPU acceleration** via JAX/Diffrax\n", - "2. **Specialized solvers** from Julia's DifferentialEquations.jl\n", - "3. **Custom dynamics** that don't fit the DiffSL syntax\n", - "4. **Pre-existing code** integration\n", - "\n", - "In this tutorial, we'll solve the predator-prey model using different backends and compare their performance." - ] + "source": "## Introduction\n\nWhile `DiffsolBuilder` provides a convenient interface for ODE fitting using the DiffSL language, `VectorBuilder` offers maximum flexibility by allowing you to use **any** ODE solver. This enables:\n\n1. **GPU acceleration** via JAX/Diffrax\n2. **Specialized solvers** from Julia's DifferentialEquations.jl\n3. **Custom dynamics** that don't fit the DiffSL syntax\n4. **Pre-existing code** integration\n\nIn this tutorial, we'll solve the predator-prey model using different backends and compare their performance." }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Import plotting utilities\n", - "import time\n", - "\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] + "source": "# Import plotting utilities\nimport time\n\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np" }, { "cell_type": "markdown", @@ -175,56 +157,12 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Fit with DiffsolBuilder\n", - "start_time = time.time()\n", - "\n", - "# Combine times and observations for DiffsolBuilder\n", - "# Data format: first column is time, remaining columns are observations\n", - "data = np.column_stack((t_data, y_observed))\n", - "\n", - "result_diffsol = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 1.0) # initial guess\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.5)\n", - " .with_cost(chron.SSE())\n", - " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", - " .build()\n", - " .optimise()\n", - ")\n", - "\n", - "diffsol_time = time.time() - start_time\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"DIFFSOL RESULTS\")\n", - "print(\"=\" * 60)\n", - "print(f\"True parameters: {list(true_params.values())}\")\n", - "print(f\"Estimated parameters: {result_diffsol.x}\")\n", - "print(f\"Final SSE: {result_diffsol.value:.6f}\")\n", - "print(f\"Iterations: {result_diffsol.iterations}\")\n", - "print(f\"Function evals: {result_diffsol.evaluations}\")\n", - "print(f\"Time: {diffsol_time:.3f}s\")\n", - "print(f\"Success: {result_diffsol.success}\")" - ] + "source": "# Fit with DiffsolBuilder\nstart_time = time.time()\n\n# Combine times and observations for DiffsolBuilder\n# Data format: first column is time, remaining columns are observations\ndata = np.column_stack((t_data, y_observed))\n\nresult_diffsol = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0) # initial guess\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n)\n\ndiffsol_time = time.time() - start_time\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"DIFFSOL RESULTS\")\nprint(\"=\" * 60)\nprint(f\"True parameters: {list(true_params.values())}\")\nprint(f\"Estimated parameters: {result_diffsol.x}\")\nprint(f\"Final SSE: {result_diffsol.value:.6f}\")\nprint(f\"Iterations: {result_diffsol.iterations}\")\nprint(f\"Function evals: {result_diffsol.evaluations}\")\nprint(f\"Time: {diffsol_time:.3f}s\")\nprint(f\"Success: {result_diffsol.success}\")" }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Method 2: VectorBuilder with JAX/Diffrax\n", - "\n", - "`VectorBuilder` accepts any callable that maps parameters to predicted outputs. This enables using **JAX/Diffrax** for GPU-accelerated ODE solving with automatic differentiation.\n", - "\n", - "### Advantages:\n", - "- ⚡ GPU acceleration\n", - "- 🔥 JIT compilation\n", - "- 📐 Automatic differentiation (gradients for free)\n", - "- 🚀 Fast iteration for gradient-based optimisers" - ] + "source": "## Method 2: VectorBuilder with JAX/Diffrax\n\n`VectorBuilder` accepts any callable that maps parameters to predicted outputs. This enables using **JAX/Diffrax** for GPU-accelerated ODE solving with automatic differentiation.\n\n### Advantages:\n- ⚡ GPU acceleration\n- 🔥 JIT compilation\n- 📐 Automatic differentiation (gradients for free)\n- 🚀 Fast iteration for gradient-based optimisers" }, { "cell_type": "code", @@ -295,12 +233,12 @@ " return sol.ys # Shape: (n_times, 2)\n", "\n", " def simulate_numpy(params):\n", - " \"\"\"NumPy wrapper for Chronopt compatibility.\"\"\"\n", + " \"\"\"NumPy wrapper for Diffid compatibility.\"\"\"\n", " return np.asarray(simulate_jax(jnp.asarray(params)))\n", "\n", " # Warm up JIT compiler\n", " _ = simulate_numpy([1.0, 0.4, 0.1, 0.4])\n", - " print(\"✅ JAX/Diffrax solver ready (JIT compiled)\")" + " print(\"JAX/Diffrax solver ready (JIT compiled)\")" ] }, { @@ -308,39 +246,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "if JAX_AVAILABLE:\n", - " # Fit with VectorBuilder + JAX/Diffrax\n", - " start_time = time.time()\n", - "\n", - " result_diffrax = (\n", - " chron.VectorBuilder()\n", - " .with_objective(simulate_numpy)\n", - " .with_data(y_observed)\n", - " .with_parameter(\"alpha\", 1.0)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.5)\n", - " .with_cost(chron.SSE())\n", - " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", - " .build()\n", - " .optimise()\n", - " )\n", - "\n", - " diffrax_time = time.time() - start_time\n", - "\n", - " print(\"\\n\" + \"=\" * 60)\n", - " print(\"DIFFRAX (JAX) RESULTS\")\n", - " print(\"=\" * 60)\n", - " print(f\"True parameters: {list(true_params.values())}\")\n", - " print(f\"Estimated parameters: {result_diffrax.x}\")\n", - " print(f\"Final SSE: {result_diffrax.value:.6f}\")\n", - " print(f\"Iterations: {result_diffrax.iterations}\")\n", - " print(f\"Function evals: {result_diffrax.evaluations}\")\n", - " print(f\"Time: {diffrax_time:.3f}s\")\n", - " print(f\"Success: {result_diffrax.success}\")\n", - " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" - ] + "source": "if JAX_AVAILABLE:\n # Fit with VectorBuilder + JAX/Diffrax\n start_time = time.time()\n\n result_diffrax = (\n diffid.VectorBuilder()\n .with_objective(simulate_numpy)\n .with_data(y_observed)\n .with_parameter(\"alpha\", 1.0)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n )\n\n diffrax_time = time.time() - start_time\n\n print(\"\\n\" + \"=\" * 60)\n print(\"DIFFRAX (JAX) RESULTS\")\n print(\"=\" * 60)\n print(f\"True parameters: {list(true_params.values())}\")\n print(f\"Estimated parameters: {result_diffrax.x}\")\n print(f\"Final SSE: {result_diffrax.value:.6f}\")\n print(f\"Iterations: {result_diffrax.iterations}\")\n print(f\"Function evals: {result_diffrax.evaluations}\")\n print(f\"Time: {diffrax_time:.3f}s\")\n print(f\"Success: {result_diffrax.success}\")\n print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" }, { "cell_type": "markdown", @@ -404,7 +310,7 @@ "\n", " # Test\n", " test_output = simulate_julia([1.0, 0.4, 0.1, 0.4])\n", - " print(\"✅ Julia/DifferentialEquations.jl ready\")\n", + " print(\"Julia/DifferentialEquations.jl ready\")\n", " print(f\" Output shape: {test_output.shape}\")" ] }, @@ -413,39 +319,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "if JULIA_AVAILABLE:\n", - " # Fit with VectorBuilder + Julia\n", - " start_time = time.time()\n", - "\n", - " result_julia = (\n", - " chron.VectorBuilder()\n", - " .with_objective(simulate_julia)\n", - " .with_data(y_observed)\n", - " .with_parameter(\"alpha\", 1.0)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.5)\n", - " .with_cost(chron.SSE())\n", - " .with_optimiser(chron.NelderMead().with_max_iter(500))\n", - " .build()\n", - " .optimise()\n", - " )\n", - "\n", - " julia_time = time.time() - start_time\n", - "\n", - " print(\"\\n\" + \"=\" * 60)\n", - " print(\"JULIA DIFFERENTIALEQUATIONS.JL RESULTS\")\n", - " print(\"=\" * 60)\n", - " print(f\"True parameters: {list(true_params.values())}\")\n", - " print(f\"Estimated parameters: {result_julia.x}\")\n", - " print(f\"Final SSE: {result_julia.value:.6f}\")\n", - " print(f\"Iterations: {result_julia.iterations}\")\n", - " print(f\"Function evals: {result_julia.evaluations}\")\n", - " print(f\"Time: {julia_time:.3f}s\")\n", - " print(f\"Success: {result_julia.success}\")\n", - " print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" - ] + "source": "if JULIA_AVAILABLE:\n # Fit with VectorBuilder + Julia\n start_time = time.time()\n\n result_julia = (\n diffid.VectorBuilder()\n .with_objective(simulate_julia)\n .with_data(y_observed)\n .with_parameter(\"alpha\", 1.0)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n )\n\n julia_time = time.time() - start_time\n\n print(\"\\n\" + \"=\" * 60)\n print(\"JULIA DIFFERENTIALEQUATIONS.JL RESULTS\")\n print(\"=\" * 60)\n print(f\"True parameters: {list(true_params.values())}\")\n print(f\"Estimated parameters: {result_julia.x}\")\n print(f\"Final SSE: {result_julia.value:.6f}\")\n print(f\"Iterations: {result_julia.iterations}\")\n print(f\"Function evals: {result_julia.evaluations}\")\n print(f\"Time: {julia_time:.3f}s\")\n print(f\"Success: {result_julia.success}\")\n print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" }, { "cell_type": "markdown", @@ -656,45 +530,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "### When to Use Each Backend\n", - "\n", - "**DiffsolBuilder (Diffsol):**\n", - "- ✅ Quick prototyping with DiffSL syntax\n", - "- ✅ Standard ODE problems\n", - "- ✅ No extra dependencies\n", - "- ❌ Limited to DiffSL expressiveness\n", - "\n", - "**VectorBuilder + JAX/Diffrax:**\n", - "- ✅ GPU acceleration for large problems\n", - "- ✅ Automatic differentiation (enables gradient-based optimisers)\n", - "- ✅ JIT compilation for speed\n", - "- ✅ Excellent for high-dimensional problems\n", - "- ❌ Requires JAX ecosystem\n", - "\n", - "**VectorBuilder + Julia/DifferentialEquations.jl:**\n", - "- ✅ Largest solver collection (stiff, stochastic, DAE, DDE, etc.)\n", - "- ✅ Advanced features (callbacks, sensitivity analysis)\n", - "- ✅ Best for specialized problems\n", - "- ❌ Requires Julia installation\n", - "\n", - "### Performance Insights\n", - "\n", - "1. **JAX/Diffrax** typically fastest after JIT warmup\n", - "2. **Diffsol** excellent balance of speed and simplicity\n", - "3. **Julia/DiffEq** best for problems requiring specialized solvers\n", - "4. All backends produce equivalent parameter estimates\n", - "\n", - "### VectorBuilder Flexibility\n", - "\n", - "The key advantage of `VectorBuilder` is **total control**:\n", - "- Any Python callable works\n", - "- Integrate pre-existing simulation code\n", - "- Mix solver backends in the same workflow\n", - "- Enable advanced features like GPU acceleration" - ] + "source": "## Key Takeaways\n\n### When to Use Each Backend\n\n**DiffsolBuilder (Diffsol):**\n- Quick prototyping with DiffSL syntax\n- ✅ Standard ODE problems\n- ✅ No extra dependencies\n- ❌ Limited to DiffSL expressiveness\n\n**VectorBuilder + JAX/Diffrax:**\n- ✅ GPU acceleration for large problems\n- ✅ Automatic differentiation (enables gradient-based optimisers)\n- ✅ JIT compilation for speed\n- ✅ Excellent for high-dimensional problems\n- ❌ Requires JAX ecosystem\n\n**VectorBuilder + Julia/DifferentialEquations.jl:**\n- ✅ Largest solver collection (stiff, stochastic, DAE, DDE, etc.)\n- ✅ Advanced features (callbacks, sensitivity analysis)\n- ✅ Best for specialized problems\n- ❌ Requires Julia installation\n\n### Performance Insights\n\n1. **JAX/Diffrax** typically fastest after JIT warmup\n2. **Diffsol** excellent balance of speed and simplicity\n3. **Julia/DiffEq** best for problems requiring specialized solvers\n4. All backends produce equivalent parameter estimates\n\n### VectorBuilder Flexibility\n\nThe key advantage of `VectorBuilder` is **total control**:\n- Any Python callable works\n- Integrate pre-existing simulation code\n- Mix solver backends in the same workflow\n- Enable advanced features like GPU acceleration" }, { "cell_type": "markdown", diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimization.ipynb index a542a35..5608a42 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimization.ipynb @@ -21,11 +21,11 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Introduction\n\nChronopt supports **parallel evaluation** for population-based optimisers (CMA-ES, Dynamic Nested Sampling). However, the effectiveness depends on the evaluation cost and backend used.\n\n### Key Insights\n\n1. **DiffsolBuilder** with `.with_parallel(True)` uses Rust's rayon for parallel ODE integration\n2. **Fast evaluations** (< 1ms) may see limited speedup due to efficient solver caching\n3. **Python callables** cannot be parallelised with threads (GIL), but **multiprocessing** works\n\nThis tutorial covers:\n- Parallel ODE fitting with `DiffsolBuilder`\n- Using `multiprocessing` for expensive Python callables\n- Understanding when parallelism helps" + "source": "## Introduction\n\nDiffid supports **parallel evaluation** for population-based optimisers (CMA-ES, Dynamic Nested Sampling). However, the effectiveness depends on the evaluation cost and backend used.\n\n### Key Insights\n\n1. **DiffsolBuilder** with `.with_parallel(True)` uses Rust's rayon for parallel ODE integration\n2. **Fast evaluations** (< 1ms) may see limited speedup due to efficient solver caching\n3. **Python callables** cannot be parallelised with threads (GIL), but **multiprocessing** works\n\nThis tutorial covers:\n- Parallel ODE fitting with `DiffsolBuilder`\n- Using `multiprocessing` for expensive Python callables\n- Understanding when parallelism helps" }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:18.792193Z", @@ -38,28 +38,8 @@ "shell.execute_reply": "2026-01-10T22:22:07.286361Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available CPU cores: 8\n" - ] - } - ], - "source": [ - "import multiprocessing\n", - "import time\n", - "\n", - "import chronopt as chron\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.integrate import solve_ivp\n", - "\n", - "# Detect available cores\n", - "n_cores = multiprocessing.cpu_count()\n", - "print(f\"Available CPU cores: {n_cores}\")" - ] + "outputs": [], + "source": "import multiprocessing\nimport time\n\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.integrate import solve_ivp\n\n# Detect available cores\nn_cores = multiprocessing.cpu_count()\nprint(f\"Available CPU cores: {n_cores}\")" }, { "cell_type": "markdown", @@ -155,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:20.020454Z", @@ -168,58 +148,8 @@ "shell.execute_reply": "2026-01-10T22:22:08.804066Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running CMA-ES (sequential)...\n", - "\n", - "============================================================\n", - "CMA-ES (Sequential)\n", - "============================================================\n", - "Solution: [1.14387427 0.51852859 0.48038414 0.75238014]\n", - "True params: [1.1, 0.4, 0.1, 0.4]\n", - "Final SSE: 11886.345\n", - "Evaluations: 801\n", - "Time: 0.98s\n", - "Time per eval: 1.22ms\n" - ] - } - ], - "source": [ - "# Sequential execution (parallel=False)\n", - "print(\"Running CMA-ES (sequential)...\")\n", - "\n", - "start = time.time()\n", - "\n", - "result_seq = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 0.8)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.3)\n", - " .with_cost(chron.SSE())\n", - " .with_parallel(False) # Sequential evaluation\n", - " .with_optimiser(chron.CMAES().with_max_iter(100).with_step_size(0.3))\n", - " .build()\n", - " .optimise()\n", - ")\n", - "\n", - "time_seq = time.time() - start\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"CMA-ES (Sequential)\")\n", - "print(\"=\" * 60)\n", - "print(f\"Solution: {result_seq.x}\")\n", - "print(f\"True params: {list(true_params.values())}\")\n", - "print(f\"Final SSE: {result_seq.value:.3f}\")\n", - "print(f\"Evaluations: {result_seq.evaluations}\")\n", - "print(f\"Time: {time_seq:.2f}s\")\n", - "print(f\"Time per eval: {time_seq / result_seq.evaluations * 1000:.2f}ms\")" - ] + "outputs": [], + "source": "# Sequential execution (parallel=False)\nprint(\"Running CMA-ES (sequential)...\")\n\nstart = time.time()\n\nresult_seq = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(False) # Sequential evaluation\n .with_optimiser(diffid.CMAES().with_max_iter(100).with_step_size(0.3))\n .build()\n .optimise()\n)\n\ntime_seq = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"CMA-ES (Sequential)\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_seq.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_seq.value:.3f}\")\nprint(f\"Evaluations: {result_seq.evaluations}\")\nprint(f\"Time: {time_seq:.2f}s\")\nprint(f\"Time per eval: {time_seq / result_seq.evaluations * 1000:.2f}ms\")" }, { "cell_type": "markdown", @@ -228,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:20.380944Z", @@ -241,67 +171,12 @@ "shell.execute_reply": "2026-01-10T22:22:12.217981Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running CMA-ES (parallel)...\n", - "\n", - "============================================================\n", - "CMA-ES (Parallel)\n", - "============================================================\n", - "Solution: [0.8 0.3 0.05 0.3 ]\n", - "True params: [1.1, 0.4, 0.1, 0.4]\n", - "Final SSE: 29190.956\n", - "Evaluations: 9\n", - "Time: 0.14s\n", - "Time per eval: 15.72ms\n", - "\n", - "🚀 Speedup: 6.90x (with 8 cores)\n" - ] - } - ], - "source": [ - "# Parallel execution (parallel=True)\n", - "print(\"Running CMA-ES (parallel)...\")\n", - "\n", - "start = time.time()\n", - "\n", - "result_par = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 0.8)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.3)\n", - " .with_cost(chron.SSE())\n", - " .with_parallel(True) # Parallel evaluation!\n", - " .with_optimiser(chron.CMAES().with_max_iter(100).with_step_size(0.3))\n", - " .build()\n", - " .optimise()\n", - ")\n", - "\n", - "time_par = time.time() - start\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"CMA-ES (Parallel)\")\n", - "print(\"=\" * 60)\n", - "print(f\"Solution: {result_par.x}\")\n", - "print(f\"True params: {list(true_params.values())}\")\n", - "print(f\"Final SSE: {result_par.value:.3f}\")\n", - "print(f\"Evaluations: {result_par.evaluations}\")\n", - "print(f\"Time: {time_par:.2f}s\")\n", - "print(f\"Time per eval: {time_par / result_par.evaluations * 1000:.2f}ms\")\n", - "\n", - "speedup = time_seq / time_par\n", - "print(f\"\\n🚀 Speedup: {speedup:.2f}x (with {n_cores} cores)\")" - ] + "outputs": [], + "source": "# Parallel execution (parallel=True)\nprint(\"Running CMA-ES (parallel)...\")\n\nstart = time.time()\n\nresult_par = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True) # Parallel evaluation!\n .with_optimiser(diffid.CMAES().with_max_iter(100).with_step_size(0.3))\n .build()\n .optimise()\n)\n\ntime_par = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"CMA-ES (Parallel)\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_par.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_par.value:.3f}\")\nprint(f\"Evaluations: {result_par.evaluations}\")\nprint(f\"Time: {time_par:.2f}s\")\nprint(f\"Time per eval: {time_par / result_par.evaluations * 1000:.2f}ms\")\n\nspeedup = time_seq / time_par\nprint(f\"\\n🚀 Speedup: {speedup:.2f}x (with {n_cores} cores)\")" }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:20.695642Z", @@ -314,68 +189,8 @@ "shell.execute_reply": "2026-01-10T22:22:19.043950Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running CMA-ES (parallel, large population)...\n", - "\n", - "============================================================\n", - "CMA-ES (Parallel, Population=16)\n", - "============================================================\n", - "Solution: [1.18267573 0.602622 0.37072684 0.67821184]\n", - "True params: [1.1, 0.4, 0.1, 0.4]\n", - "Final SSE: 8171.282\n", - "Evaluations: 801\n", - "Time: 0.26s\n", - "Time per eval: 0.32ms\n", - "\n", - "🚀 Speedup: 3.81x\n" - ] - } - ], - "source": [ - "# Parallel with larger population (more parallel work per generation)\n", - "print(\"Running CMA-ES (parallel, large population)...\")\n", - "\n", - "start = time.time()\n", - "\n", - "result_par_large = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data)\n", - " .with_parameter(\"alpha\", 0.8)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.3)\n", - " .with_cost(chron.SSE())\n", - " .with_parallel(True)\n", - " .with_optimiser(\n", - " chron.CMAES()\n", - " .with_max_iter(50)\n", - " .with_step_size(0.3)\n", - " .with_population_size(2 * n_cores) # Match population to available cores\n", - " )\n", - " .build()\n", - " .optimise()\n", - ")\n", - "\n", - "time_par_large = time.time() - start\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(f\"CMA-ES (Parallel, Population={2 * n_cores})\")\n", - "print(\"=\" * 60)\n", - "print(f\"Solution: {result_par_large.x}\")\n", - "print(f\"True params: {list(true_params.values())}\")\n", - "print(f\"Final SSE: {result_par_large.value:.3f}\")\n", - "print(f\"Evaluations: {result_par_large.evaluations}\")\n", - "print(f\"Time: {time_par_large:.2f}s\")\n", - "print(f\"Time per eval: {time_par_large / result_par_large.evaluations * 1000:.2f}ms\")\n", - "\n", - "speedup_large = time_seq / time_par_large\n", - "print(f\"\\n🚀 Speedup: {speedup_large:.2f}x\")" - ] + "outputs": [], + "source": "# Parallel with larger population (more parallel work per generation)\nprint(\"Running CMA-ES (parallel, large population)...\")\n\nstart = time.time()\n\nresult_par_large = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True)\n .with_optimiser(\n diffid.CMAES()\n .with_max_iter(50)\n .with_step_size(0.3)\n .with_population_size(2 * n_cores) # Match population to available cores\n )\n .build()\n .optimise()\n)\n\ntime_par_large = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(f\"CMA-ES (Parallel, Population={2 * n_cores})\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_par_large.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_par_large.value:.3f}\")\nprint(f\"Evaluations: {result_par_large.evaluations}\")\nprint(f\"Time: {time_par_large:.2f}s\")\nprint(f\"Time per eval: {time_par_large / result_par_large.evaluations * 1000:.2f}ms\")\n\nspeedup_large = time_seq / time_par_large\nprint(f\"\\n🚀 Speedup: {speedup_large:.2f}x\")" }, { "cell_type": "markdown", @@ -384,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:20.944100Z", @@ -397,82 +212,8 @@ "shell.execute_reply": "2026-01-10T22:22:30.395476Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing 20 sequential evaluations...\n", - "Sequential: 0.20s (9.8ms/eval)\n", - "\n", - "💡 To parallelise in a script, use multiprocessing:\n", - "\n", - "from concurrent.futures import ProcessPoolExecutor\n", - "\n", - "def evaluate_batch_parallel(params_list, n_workers=8):\n", - " with ProcessPoolExecutor(max_workers=n_workers) as executor:\n", - " return list(executor.map(expensive_objective, params_list))\n", - "\n", - "# This provides near-linear speedup for expensive functions\n", - "\n" - ] - } - ], - "source": [ - "# Define an expensive objective function\n", - "def expensive_objective(params):\n", - " \"\"\"\n", - " An expensive objective function that simulates computation.\n", - " In practice, this could be a complex simulation, ML model, etc.\n", - " \"\"\"\n", - " alpha, beta, delta, gamma = params\n", - "\n", - " # Simulate expensive computation (ODE integration with scipy)\n", - " sol = solve_ivp(\n", - " lotka_volterra,\n", - " [0, 100],\n", - " [10.0, 5.0],\n", - " args=(alpha, beta, delta, gamma),\n", - " t_eval=t_data,\n", - " method=\"RK45\",\n", - " )\n", - "\n", - " if not sol.success:\n", - " return 1e10 # Return large value for failed integrations\n", - "\n", - " # Compute SSE\n", - " y_pred = sol.y.T\n", - " sse = np.sum((y_pred - y_observed) ** 2)\n", - " return sse\n", - "\n", - "\n", - "# Test single evaluation time\n", - "n_evals = 20\n", - "test_params = [[0.8 + i * 0.02, 0.3, 0.05, 0.3] for i in range(n_evals)]\n", - "\n", - "print(f\"Testing {n_evals} sequential evaluations...\")\n", - "\n", - "start = time.time()\n", - "seq_results = [expensive_objective(p) for p in test_params]\n", - "time_seq_python = time.time() - start\n", - "print(\n", - " f\"Sequential: {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n", - ")\n", - "\n", - "# Note: Multiprocessing in notebooks has pickling limitations\n", - "# In a regular Python script, you would use:\n", - "print(\"\"\"\n", - "💡 To parallelise in a script, use multiprocessing:\n", - "\n", - "from concurrent.futures import ProcessPoolExecutor\n", - "\n", - "def evaluate_batch_parallel(params_list, n_workers=8):\n", - " with ProcessPoolExecutor(max_workers=n_workers) as executor:\n", - " return list(executor.map(expensive_objective, params_list))\n", - "\n", - "# This provides near-linear speedup for expensive functions\n", - "\"\"\")" - ] + "outputs": [], + "source": "# Define an expensive objective function\ndef expensive_objective(params):\n \"\"\"\n An expensive objective function that simulates computation.\n In practice, this could be a complex simulation, ML model, etc.\n \"\"\"\n alpha, beta, delta, gamma = params\n\n # Simulate expensive computation (ODE integration with scipy)\n sol = solve_ivp(\n lotka_volterra,\n [0, 100],\n [10.0, 5.0],\n args=(alpha, beta, delta, gamma),\n t_eval=t_data,\n method=\"RK45\",\n )\n\n if not sol.success:\n return 1e10 # Return large value for failed integrations\n\n # Compute SSE\n y_pred = sol.y.T\n sse = np.sum((y_pred - y_observed) ** 2)\n return sse\n\n\n# Test single evaluation time\nn_evals = 20\ntest_params = [[0.8 + i * 0.02, 0.3, 0.05, 0.3] for i in range(n_evals)]\n\nprint(f\"Testing {n_evals} sequential evaluations...\")\n\nstart = time.time()\nseq_results = [expensive_objective(p) for p in test_params]\ntime_seq_python = time.time() - start\nprint(\n f\"Sequential: {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n)\n\n# Note: Multiprocessing in notebooks has pickling limitations\n# In a regular Python script, you would use:\nprint(\"\"\"\n💡 To parallelise in a script, use multiprocessing:\n\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_batch_parallel(params_list, n_workers=8):\n with ProcessPoolExecutor(max_workers=n_workers) as executor:\n return list(executor.map(expensive_objective, params_list))\n\n# This provides near-linear speedup for expensive functions\n\"\"\")" }, { "cell_type": "code", @@ -573,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2026-01-22T19:00:23.060489Z", @@ -586,94 +327,8 @@ "shell.execute_reply": "2026-01-10T22:23:09.260026Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing DiffsolBuilder scaling with data size...\n", - "\n", - "N=100: Sequential=0.11s, Parallel=0.25s, Speedup=0.46x\n", - "N=200: Sequential=0.67s, Parallel=0.22s, Speedup=3.10x\n", - "N=500: Sequential=0.17s, Parallel=0.22s, Speedup=0.76x\n" - ] - } - ], - "source": [ - "# Test DiffsolBuilder scaling with data size\n", - "data_sizes = [100, 200, 500]\n", - "scaling_results = []\n", - "\n", - "print(\"Testing DiffsolBuilder scaling with data size...\\n\")\n", - "\n", - "for n_points in data_sizes:\n", - " # Generate data with different sizes\n", - " t_test = np.linspace(0, 100, n_points)\n", - " sol_test = solve_ivp(\n", - " lotka_volterra,\n", - " [t_test[0], t_test[-1]],\n", - " [10.0, 5.0],\n", - " args=(\n", - " true_params[\"alpha\"],\n", - " true_params[\"beta\"],\n", - " true_params[\"delta\"],\n", - " true_params[\"gamma\"],\n", - " ),\n", - " t_eval=t_test,\n", - " method=\"RK45\",\n", - " )\n", - " y_test = sol_test.y.T + np.random.normal(0, 0.3, (n_points, 2))\n", - " data_test = np.column_stack((t_test, y_test))\n", - "\n", - " # Sequential\n", - " start = time.time()\n", - " _ = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data_test)\n", - " .with_parameter(\"alpha\", 0.8)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.3)\n", - " .with_cost(chron.SSE())\n", - " .with_parallel(False)\n", - " .with_optimiser(chron.CMAES().with_max_iter(30).with_step_size(0.3))\n", - " .build()\n", - " .optimise()\n", - " )\n", - " t_seq_scale = time.time() - start\n", - "\n", - " # Parallel\n", - " start = time.time()\n", - " _ = (\n", - " chron.DiffsolBuilder()\n", - " .with_diffsl(model_str)\n", - " .with_data(data_test)\n", - " .with_parameter(\"alpha\", 0.8)\n", - " .with_parameter(\"beta\", 0.3)\n", - " .with_parameter(\"delta\", 0.05)\n", - " .with_parameter(\"gamma\", 0.3)\n", - " .with_cost(chron.SSE())\n", - " .with_parallel(True)\n", - " .with_optimiser(chron.CMAES().with_max_iter(30).with_step_size(0.3))\n", - " .build()\n", - " .optimise()\n", - " )\n", - " t_par_scale = time.time() - start\n", - "\n", - " sp = t_seq_scale / t_par_scale\n", - " scaling_results.append(\n", - " {\n", - " \"n_points\": n_points,\n", - " \"time_seq\": t_seq_scale,\n", - " \"time_par\": t_par_scale,\n", - " \"speedup\": sp,\n", - " }\n", - " )\n", - " print(\n", - " f\"N={n_points:3d}: Sequential={t_seq_scale:.2f}s, Parallel={t_par_scale:.2f}s, Speedup={sp:.2f}x\"\n", - " )" - ] + "outputs": [], + "source": "# Test DiffsolBuilder scaling with data size\ndata_sizes = [100, 200, 500]\nscaling_results = []\n\nprint(\"Testing DiffsolBuilder scaling with data size...\\n\")\n\nfor n_points in data_sizes:\n # Generate data with different sizes\n t_test = np.linspace(0, 100, n_points)\n sol_test = solve_ivp(\n lotka_volterra,\n [t_test[0], t_test[-1]],\n [10.0, 5.0],\n args=(\n true_params[\"alpha\"],\n true_params[\"beta\"],\n true_params[\"delta\"],\n true_params[\"gamma\"],\n ),\n t_eval=t_test,\n method=\"RK45\",\n )\n y_test = sol_test.y.T + np.random.normal(0, 0.3, (n_points, 2))\n data_test = np.column_stack((t_test, y_test))\n\n # Sequential\n start = time.time()\n _ = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data_test)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(False)\n .with_optimiser(diffid.CMAES().with_max_iter(30).with_step_size(0.3))\n .build()\n .optimise()\n )\n t_seq_scale = time.time() - start\n\n # Parallel\n start = time.time()\n _ = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data_test)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True)\n .with_optimiser(diffid.CMAES().with_max_iter(30).with_step_size(0.3))\n .build()\n .optimise()\n )\n t_par_scale = time.time() - start\n\n sp = t_seq_scale / t_par_scale\n scaling_results.append(\n {\n \"n_points\": n_points,\n \"time_seq\": t_seq_scale,\n \"time_par\": t_par_scale,\n \"speedup\": sp,\n }\n )\n print(\n f\"N={n_points:3d}: Sequential={t_seq_scale:.2f}s, Parallel={t_par_scale:.2f}s, Speedup={sp:.2f}x\"\n )" }, { "cell_type": "code", @@ -749,7 +404,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## When to Use Each Approach\n\n### DiffsolBuilder with `.with_parallel(True)`\n\nBest for:\n- ODE fitting problems where evaluations are moderately expensive\n- When you want automatic parallelism without code changes\n- Lower overhead than multiprocessing\n\n```python\nproblem = (\n chron.DiffsolBuilder()\n .with_diffsl(model)\n .with_data(data)\n .with_parallel(True) # Enable rayon parallelism\n .build()\n)\n```\n\n### Multiprocessing for Python Callables\n\nBest for:\n- Expensive Python simulations (>10ms per evaluation)\n- Complex custom objective functions\n- When DiffsolBuilder isn't applicable\n\n```python\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_parallel(params_list):\n with ProcessPoolExecutor(max_workers=n_cores) as executor:\n return list(executor.map(expensive_objective, params_list))\n```" + "source": "## When to Use Each Approach\n\n### DiffsolBuilder with `.with_parallel(True)`\n\nBest for:\n- ODE fitting problems where evaluations are moderately expensive\n- When you want automatic parallelism without code changes\n- Lower overhead than multiprocessing\n\n```python\nproblem = (\n diffid.DiffsolBuilder()\n .with_diffsl(model)\n .with_data(data)\n .with_parallel(True) # Enable rayon parallelism\n .build()\n)\n```\n\n### Multiprocessing for Python Callables\n\nBest for:\n- Expensive Python simulations (>10ms per evaluation)\n- Complex custom objective functions\n- When DiffsolBuilder isn't applicable\n\n```python\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_parallel(params_list):\n with ProcessPoolExecutor(max_workers=n_cores) as executor:\n return list(executor.map(expensive_objective, params_list))\n```" }, { "cell_type": "markdown", @@ -850,4 +505,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb index 5257a88..edf95a9 100644 --- a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb +++ b/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb @@ -24,7 +24,7 @@ "source": [ "## Introduction\n", "\n", - "The **cost function** (or loss function) quantifies how well model predictions match observations. Chronopt provides built-in metrics, but real-world problems often require:\n", + "The **cost function** (or loss function) quantifies how well model predictions match observations. Diffid provides built-in metrics, but real-world problems often require:\n", "\n", "- **Weighted fitting** when measurement errors vary\n", "- **Custom metrics** for domain-specific requirements\n", @@ -54,7 +54,7 @@ "# Import plotting utilities\n", "from functools import partial\n", "\n", - "import chronopt as chron\n", + "import diffid\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", @@ -67,7 +67,7 @@ "source": [ "## Built-in Cost Metrics\n", "\n", - "Chronopt provides three standard metrics:\n", + "Diffid provides three standard metrics:\n", "\n", "### 1. Sum of Squared Errors (SSE)\n", "\n", @@ -193,7 +193,7 @@ "\n", "# Define problem\n", "builder = (\n", - " chron.VectorBuilder()\n", + " diffid.VectorBuilder()\n", " .with_objective(linear_model)\n", " .with_data(y_observed)\n", " .with_parameter(\"slope\", 1.0)\n", @@ -201,7 +201,11 @@ ")\n", "\n", "# Test each metric\n", - "metrics = {\"SSE\": chron.SSE(), \"RMSE\": chron.RMSE(), \"GaussianNLL\": chron.GaussianNLL()}\n", + "metrics = {\n", + " \"SSE\": diffid.SSE(),\n", + " \"RMSE\": diffid.RMSE(),\n", + " \"GaussianNLL\": diffid.GaussianNLL(),\n", + "}\n", "\n", "results = {}\n", "for name, metric in metrics.items():\n", @@ -386,19 +390,19 @@ "\n", "# Fit WITHOUT weights (standard SSE)\n", "result_unweighted = (\n", - " chron.VectorBuilder()\n", + " diffid.VectorBuilder()\n", " .with_objective(linear_model_hetero)\n", " .with_data(y_hetero)\n", " .with_parameter(\"slope\", 1.0)\n", " .with_parameter(\"intercept\", 0.0)\n", - " .with_cost(chron.SSE()) # Standard SSE\n", + " .with_cost(diffid.SSE()) # Standard SSE\n", " .build()\n", " .optimise()\n", ")\n", "\n", "# Fit with weights\n", "result_weighted = (\n", - " chron.ScalarBuilder()\n", + " diffid.ScalarBuilder()\n", " .with_objective(lambda x: weighted_sse(linear_model_hetero(x), y_hetero))\n", " .with_parameter(\"slope\", 1.0)\n", " .with_parameter(\"intercept\", 0.0)\n", @@ -626,21 +630,21 @@ " return sse\n", "\n", "\n", - "# Note: Chronopt's built-in costs don't support parameter access yet,\n", + "# Note: Diffid's built-in costs don't support parameter access yet,\n", "# so we'll compare by manually adding regularisation to parameter update\n", "\n", "# Unregularised fit\n", "initial_params = np.zeros(degree + 1)\n", "initial_params[0] = np.mean(y_poly)\n", "\n", - "builder_poly = chron.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", + "builder_poly = diffid.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", "\n", "for i in range(degree + 1):\n", " builder_poly = builder_poly.with_parameter(f\"c{i}\", initial_params[i])\n", "\n", "result_unreg = (\n", - " builder_poly.with_cost(chron.SSE())\n", - " .with_optimiser(chron.NelderMead().with_max_iter(2000))\n", + " builder_poly.with_cost(diffid.SSE())\n", + " .with_optimiser(diffid.NelderMead().with_max_iter(2000))\n", " .build()\n", " .optimise()\n", ")\n", @@ -836,7 +840,7 @@ "for w_smooth in weights_smooth:\n", " # Construct custom cost\n", " multi_cost = MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth)\n", - " result = chron.ScalarBuilder().with_objective(\n", + " result = diffid.ScalarBuilder().with_objective(\n", " partial(wrapper, y_poly=y_poly, cost_func=multi_cost)\n", " )\n", "\n", @@ -844,7 +848,9 @@ " result = result.with_parameter(f\"c{i}\", initial_params[i])\n", "\n", " result = (\n", - " result.with_optimiser(chron.NelderMead().with_max_iter(2000)).build().optimise()\n", + " result.with_optimiser(diffid.NelderMead().with_max_iter(2000))\n", + " .build()\n", + " .optimise()\n", " )\n", "\n", " results_multi[w_smooth] = result\n", diff --git a/examples/bicycle_model_diffsol.py b/examples/bicycle_model_diffsol.py index 1d8808b..a03f2a7 100644 --- a/examples/bicycle_model_diffsol.py +++ b/examples/bicycle_model_diffsol.py @@ -1,6 +1,6 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np TRUE_L = 2.5 # wheelbase @@ -36,10 +36,10 @@ stacked_data = np.column_stack((t_span, x_obs, y_obs, psi_obs)) -optimiser = chron.CMAES().with_max_iter(500).with_threshold(1e-10) +optimiser = diffid.CMAES().with_max_iter(500).with_threshold(1e-10) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) diff --git a/examples/bicycle_model_evidence.py b/examples/bicycle_model_evidence.py index 33769ba..00998bd 100644 --- a/examples/bicycle_model_evidence.py +++ b/examples/bicycle_model_evidence.py @@ -1,6 +1,6 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np TRUE_L = 2.5 # wheelbase @@ -36,11 +36,11 @@ stacked_data = np.column_stack((t_span, x_obs, y_obs, psi_obs)) -optimiser = chron.CMAES().with_max_iter(500).with_threshold(1e-10) -cost = chron.GaussianNLL(0.05) +optimiser = diffid.CMAES().with_max_iter(500).with_threshold(1e-10) +cost = diffid.GaussianNLL(0.05) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -52,7 +52,7 @@ results = problem.optimise() print(results) -sampler = chron.DynamicNestedSampler().with_live_points(128) +sampler = diffid.DynamicNestedSampler().with_live_points(128) samples = sampler.run(problem, initial=results.x) print("time :", samples.time) diff --git a/examples/bouncy_ball.py b/examples/bouncy_ball.py index 2965422..bc8b98e 100644 --- a/examples/bouncy_ball.py +++ b/examples/bouncy_ball.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -30,14 +30,14 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # Configure the problem initial_values = [4.0, 4.0] builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", initial_values[0]) .with_parameter("h", initial_values[1]) - .with_optimiser(chron.Adam().with_step_size(0.05).with_max_iter(1500)) - # .with_cost(chron.GaussianNLL(variance=0.01)) - .with_cost(chron.RMSE(2.0)) + .with_optimiser(diffid.Adam().with_step_size(0.05).with_max_iter(1500)) + # .with_cost(diffid.GaussianNLL(variance=0.01)) + .with_cost(diffid.RMSE(2.0)) ) problem = builder.build() diff --git a/examples/bouncy_ball_sampling.py b/examples/bouncy_ball_sampling.py index 70294d4..48cc9ef 100644 --- a/examples/bouncy_ball_sampling.py +++ b/examples/bouncy_ball_sampling.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -30,20 +30,20 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # Configure the problem initial_values = [4.0, 4.0] builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", initial_values[0]) .with_parameter("h", initial_values[1]) .with_parallel(True) - .with_cost(chron.GaussianNLL(variance=0.01)) + .with_cost(diffid.GaussianNLL(variance=0.01)) ) problem = builder.build() # Setup sampler sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(100) .with_iterations(1000) .with_step_size(0.25) diff --git a/examples/logistic_growth.py b/examples/logistic_growth.py index c55ed85..3c362f4 100644 --- a/examples/logistic_growth.py +++ b/examples/logistic_growth.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) @@ -15,11 +15,11 @@ # Create an optimiser -optimiser = chron.CMAES().with_max_iter(1000).with_threshold(1e-12) +optimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12) # Simple API builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(1e-6, 1e-8) diff --git a/examples/model_evidence.py b/examples/model_evidence.py index 69a95a9..4ac136a 100644 --- a/examples/model_evidence.py +++ b/examples/model_evidence.py @@ -2,7 +2,7 @@ from __future__ import annotations -import chronopt as chron +import diffid def rosenbrock(x: list[float]) -> float: @@ -11,17 +11,17 @@ def rosenbrock(x: list[float]) -> float: builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", initial_value=1.2) .with_parameter("y", initial_value=1.4) - .with_optimiser(chron.NelderMead().with_max_iter(2000)) + .with_optimiser(diffid.NelderMead().with_max_iter(2000)) ) problem = builder.build() optimised = problem.optimise() -sampler = chron.DynamicNestedSampler().with_live_points(256).with_seed(1234) +sampler = diffid.DynamicNestedSampler().with_live_points(256).with_seed(1234) samples = sampler.run(problem, initial=optimised.x) print("time :", samples.time) diff --git a/examples/model_evidence_diffsol.py b/examples/model_evidence_diffsol.py index 8919b3a..955f9c6 100644 --- a/examples/model_evidence_diffsol.py +++ b/examples/model_evidence_diffsol.py @@ -2,7 +2,7 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) @@ -18,7 +18,7 @@ stacked_data = np.column_stack((t_span, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("r", initial_value=1.2) @@ -29,7 +29,7 @@ optimised = problem.optimise() -sampler = chron.DynamicNestedSampler().with_live_points(256).with_seed(1234) +sampler = diffid.DynamicNestedSampler().with_live_points(256).with_seed(1234) samples = sampler.run(problem, initial=optimised.x) print("time :", samples.time) diff --git a/examples/predator_prey/README.md b/examples/predator_prey/README.md index 767e29a..5388abb 100644 --- a/examples/predator_prey/README.md +++ b/examples/predator_prey/README.md @@ -1,7 +1,7 @@ # Predator-Prey Parameter Identification Examples This directory showcases how to recover the parameters of the Lotka–Volterra -predator-prey model using Chronopt with several ODE solver backends. All +predator-prey model using Diffid with several ODE solver backends. All examples share a synthetic dataset so that the optimisation results can be compared side by side. @@ -12,7 +12,7 @@ compared side by side. - `predator_prey_diffrax.py` – fits the model using the JAX/Diffrax simulator. - `predator_prey_diffeqpy.py` – fits the model using the Julia DifferentialEquations.jl stack via diffeqpy. -- `predator_prey_diffsol.py` – fits the model using Chronopt's Diffsol backend. +- `predator_prey_diffsol.py` – fits the model using Diffid's Diffsol backend. - `synthetic_data.npz` – cached dataset generated by the script above (re-created on demand). @@ -24,7 +24,7 @@ compared side by side. ```bash # Base requirements for all examples - pip install chronopt numpy + pip install diffid numpy # Diffrax example pip install jax diffrax @@ -35,7 +35,7 @@ compared side by side. ``` The Diffsol example only needs the base requirements because the solver is - provided by Chronopt. + provided by Diffid. ## Generating the dataset @@ -69,10 +69,10 @@ performance across solver backends. platform has compatible JAX wheels. - Diffeqpy: the `de.install()` command downloads a Julia runtime if one is not already configured. This can take a few minutes. -- Diffsol: Chronopt bundles the solver; no extra setup is required beyond the +- Diffsol: Diffid bundles the solver; no extra setup is required beyond the base dependencies. ## Further reading -Check the top-level project README for more background on Chronopt and links to +Check the top-level project README for more background on Diffid and links to additional examples. \ No newline at end of file diff --git a/examples/predator_prey/predator_prey_diffeqpy.py b/examples/predator_prey/predator_prey_diffeqpy.py index 1d67721..a31620f 100644 --- a/examples/predator_prey/predator_prey_diffeqpy.py +++ b/examples/predator_prey/predator_prey_diffeqpy.py @@ -1,12 +1,12 @@ -"""Predator-prey parameter identification using DifferentialEquations.jl and Chronopt. +"""Predator-prey parameter identification using DifferentialEquations.jl and diffid. Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in Julia via diffeqpy 2. Generating synthetic noisy observations -3. Recovering parameters using Chronopt optimization +3. Recovering parameters using diffid optimization Prerequisites: - pip install diffeqpy chronopt numpy + pip install diffeqpy diffid numpy python -c "from diffeqpy import de; de.install()" """ @@ -15,7 +15,7 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import numpy as np from diffeqpy import de @@ -67,15 +67,15 @@ def simulate(params): # Parameter identification result = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(simulate) .with_data(observed) .with_parameter("alpha", 0.8) .with_parameter("beta", 0.3) .with_parameter("delta", 0.05) .with_parameter("gamma", 0.6) - .with_cost(chron.SSE()) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_cost(diffid.SSE()) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) .build() .optimise() ) diff --git a/examples/predator_prey/predator_prey_diffrax.py b/examples/predator_prey/predator_prey_diffrax.py index 51e4ab0..98cfae5 100644 --- a/examples/predator_prey/predator_prey_diffrax.py +++ b/examples/predator_prey/predator_prey_diffrax.py @@ -1,12 +1,12 @@ -"""Predator-prey parameter identification using JAX/Diffrax and Chronopt. +"""Predator-prey parameter identification using JAX/Diffrax and diffid. Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in JAX 2. Generating synthetic noisy observations -3. Recovering parameters using Chronopt optimization +3. Recovering parameters using diffid optimization Prerequisites: - pip install chronopt diffrax jax numpy + pip install diffid diffrax jax numpy """ from __future__ import annotations @@ -14,7 +14,7 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import diffrax as dfx import jax.numpy as jnp import numpy as np @@ -67,7 +67,7 @@ def simulate_jax(params): def simulate(params): - """NumPy wrapper for Chronopt compatibility.""" + """NumPy wrapper for diffid compatibility.""" return np.asarray(simulate_jax(jnp.asarray(params))) @@ -89,15 +89,15 @@ def simulate(params): # Parameter identification result = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(simulate) .with_data(observed) .with_parameter("alpha", 1.3) .with_parameter("beta", 0.3) .with_parameter("delta", 0.05) .with_parameter("gamma", 0.6) - .with_cost(chron.SSE()) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_cost(diffid.SSE()) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) .build() .optimise() ) diff --git a/examples/predator_prey/predator_prey_diffsol.py b/examples/predator_prey/predator_prey_diffsol.py index 4dc015e..b3eda65 100644 --- a/examples/predator_prey/predator_prey_diffsol.py +++ b/examples/predator_prey/predator_prey_diffsol.py @@ -1,7 +1,7 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) @@ -32,7 +32,7 @@ # Simple API builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ode) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -42,7 +42,7 @@ .with_parameter("d", 0.6) .with_parallel(True) .with_optimiser( - chron.NelderMead().with_max_iter(1000) + diffid.NelderMead().with_max_iter(1000) ) # Override default optimiser ) problem = builder.build() diff --git a/examples/python_contour.png b/examples/python_contour.png new file mode 100644 index 0000000000000000000000000000000000000000..24ab72e466bf499cf3b7b9259ff0b96a26bea729 GIT binary patch literal 386289 zcmdqI1zQ|lvo#!o4j!BkAh^4G(BSUwuEE_M0txQ!?h;&s1b3I`g-8-oCckdvh;9!7H;Pgs{fPdUh zqUugccBW3Q2H#BH$rw1AOubEfA+%(RmgXT|L^f|0rE8e-&4S2{{Q_H#)fuwC`cb!QC~zwM2dIBZI&A= zywd7S|2#;2^5%8yzsk=w-< zbiKti%uTd}A^YY(h4G=`;eZ0EB%!<0)y=#Z{s2F} zcg^09?0*ap$e9=C>52jv3`9i&9xctXrt9;AGkAIZg*-Xa=umgMP$oyE zOqJ1TUmiT)`9cO(F6lX%uiEVLhYUCz>Fa|+L_!Kq6j2V{WS{inV`YuE2z8b=x*~7u#mg>j+Au(@K2hV`o=AnBP1+)O=~y^SKv2ZN;Y7MqSJE zhlGV6M2<07ZnOy&+L04@R<2Ae1D&q5(|i)p($XSgQczM-lHYMYSyBN%RqD2XWM*N( zwB1{3usZL@*6Yd^3=A>~8RjD)A=ymcvidzXR(ZKMK0mJ}?f`f)uX=T^LJMKgb-q%E zd6|f8`EaU0>YkbPpz4^#|JB%kS-|c5vvejeZmaVVOlxavk3bur$uI_|(|)WXT?)7* zju-o53e9`HPGfH`y5Y*J>jNeAmTxBakKr8-U{`I~w?wrQkM>WTRV_5y;C<&Ec@-5E zEoqB>dU{gS(OJE;8BJv?o4PqZJ|3P2p`b9_TwT%3&d-}=>OILxNtHSEh9G^*@&saI z=V&~gOX;+=zrVkL(Cb{#B2O&Zn7`<+yIPfQ#f;V9_H3P*T+4FY)8k=T)iB}RAC7g( z8=R;45+#aVu8ReUmyp4Dv&mf19bdgRw;IK&ZD5A%3>Fylh7^>Plq6Ip#lauq{8oPh+)EOifBsJ8;qM@X~0tUStl=g7iWHKg;i2 z-CZ9{PbuVy4XswDrD+`i;k9EcmExP=01SLeFc5~ytGuPf-QwG~+Lif`ES{kUAPvx{ zKy~;GJ%BaiHTHn77YqyxB%B)A+E(`LkphYPg*(=Q^KG)uu=Moyj@aSh;RQxVM-MN5 zxe01&YKm2+P}ra(rl&`KdAaNCjl^bvB10{el*bK{gjasZRxBzm7C#TRoGJb=n!$rz z3fcG=jl=Zb(9m!=DQBy9WkuU64E4vSPy)661Tu(O6{$p8|N44XPLJ#2YQ$MKetv$%aix@)MBYM~G#KDw z(QWfC;b%#TKmLI2z*4p(DsB}j0Lh7o!EkbO!^1OyydSPV%vgYNzo;lUIn`$)3OU1n z95V7OhqU!O0fPgqZ zD9TFg{EFsrb4cbzetC5T4e0~5ta8CphzRb;?m>)xBD+4Cmy^%QF{C6Sf~eGPA*LPN z4tflY5P2HyK#=bSUknoV)|rh5&4G&ztZfW z!tYh4T46W?N}w|CgD(QC1OMs^q}G6MqNBZ`C{i}o*2D9&b940Vj(f5V7_uy;qm+6m zm=eWkTkbc9DhI&)m11LM4Z4wsc!LCzP1uBldZi^L8XYpq3F&;EjZ@Ck)6-JE_d7{b zSw0UEhjtM&k_kDZMy)Oo+k=?4)1#x(mE1cwNAm^9$jCcKh`7umoG`q={3}@Z zd9<+zgh2|radL8!JX>qm($pcBO;>+e0e5)at-2OKLqjK=nyIU+mjJPr&A!!r_vMM} z`QfNa3MdKrYYb{?^Ow%@3S>v^n7%KhS$M9lt|j9FrKRU}^cvVPF|@O&^hpJ_anKzh zKzfZAsB}J>E9ma-Ry=}+fk|w%Uaa-v^I8Wc;)N`RELy?G(m8oDDwsRay3||#kNxs5 zWGOZaTaK%imshcmWgz%%R2J11fc~P@Y@aiFviWXiW~Sb2^wZ>6EKNf=3IdD(@PO6dEfu)AI(dbF42OWzqo{(Heg3Df8G`R;PL^Z7PS zAC(TpI!|c(8!02B{4npsN{i!8h5JHjz4xPwf$tI8f+_{X@?f=$6qdO$9fTGMSxI2+ z_sxf!BS9%CsnaXp=bL$bef`fXYrkBpCWcj2&iUdP9kxW0fuxqu2wWvW4jYPK9oYBv zah-WyH1AB{1)TT9-?+Hi=B~8qSgu|pWco<#qiQ;yspF%E>F6zLy-=O=bek(08Pq7N z0R#%8#ZNCQJsW@1v#qm#^49Ob|CI2UjY!mFR5cZF00LIsUna{L%I-aJ0^lL^{$l_? zXDMl=L|1^Oy?wR4mYf_4FCSlmM>;7VGUkfmIz>2a3={~!$c9#4F{cKa%Qub5}~>>8~UvM{-Z=Bm*;J{vOy_*KtD-%1dNJwLW-Ob)5%)9XO9i- zX-*AyO!Gu&$e~mcy~^QzTwGj=(L$%MZ-O2qRx7FLkvSSa062=N!S_G>LKcVp3jd`Y7w2S&N{6V+L7*(yHx zzh3FeMQwZ)UpmBS=pKTME9leTO5z0yVDXGg|55mKQ#`cN9uT1`sccrI&r1XXKBL8& zhtvxmAr|J~m)urZrQM}Qn~`!739R^XJq?~@Fpi;vB>tehM&IDa>3xiXlOf@!`-7R1 zIK`L)AXx8D%FAh6j;IxnB_cA>`VK;71)o~aaSVhvh+FIe?))OP#ieI)I5p##orKPRen~{B*91{x zT`QsmLdQVDBFC?1@q2qz;0v25?A2hEoyXe{s_a}9N#_)i7Y`pP4!V}LQd(NP)mb>E z|AI!CM#S(x)nyUj8g*;S^^W$Hn@Kfs+&&whgcOZ3=Hda>yrz;?>gP8smtqrf7idRT zZWYFt(q~B?j-Akly4u$#Vy>s`ggEJ0Mm>lcy-*gw9n;XPUts-c?kwQjAgH3NCezk< zh|~KPMM1>H8~qGD0(3z)n*CQ)VQkf`!@K4 zoi|-^5WI#lLNUCX`c8a@Ke;+nZoXZ$5Y~S$djNc~oswS+pmUua8|$U|aD^;d?PmE+ zqrQRV+Aem+8uzALC`OIl-#892#-^q{01e4*RoB#1P4!a6v}{E}SWX;>!X+vA)`L~L zJs6X8);m+8WMV69Jrhm7){tTzaz;qLCZX&wtFRf4zBQK4l{aJ5cLaDt4KE9%e45kU z@z*;cIei3Dribi|guk)&uVBqZrwE7w#UwN7Eo4jo!$)&7{ZAADpaWz_Nisb>y#m75 zjc&*{2(IF@J~yWt7N^!%V>F0S=wvX0jTMec%S{<)t)|i4KRH?R^a51WoYv-FzmnFG z0eB7+05x~5pN0|KGWiJ>WjtQs>R8TDeQu2;dL3hag6K(404A*X0PN0stqs=_ffg$Q z)6w_o;%h<^*~=#YCROo1%VIe=I0(JhwxK{PeMtENy}Z#KIC5sU+$aOs?u^JskLJn; z?XlV*M`5z2^vF^GuzIeK@PA5M=+lXvZy+%DS)k!fG>@v=hi7LSv{!=yRh4PlX6}@XS9&hrtI-mF&K`g z+VS~%aBy%AP@^GjYy>NyiqUk&^pjt=lp-M_A_ie_NxaWs7A->I2WLOA7s5p#nD8!W zwixun1V@O})Y?4Wo-j9sp12TtU;pUwew$EXhyN|#q56g^FVBx72;2*;e~*t5---*3 z4S)l{-|=~Z*weL|Y(XRTeo=u7nS7qqH|a8I?3geEdcMyM)qG6qZn#|ENJ+`a63XH` z?swCuJNbEdc#^psP472wnx1=61b)+^S|8z@yN8`ip!U-ciR*ErEt zx4*xc*V|0e^ZD_BM;%ET?p=(9-h^6;;AKZHm#OVTcqcpsA9mIo1VYEKg^Y|v3r20U z(a_RDIOBL-3rC1hpQ&wXGK1$(fe?sXc%WKKdK-Ph$MHN41vfWNFc>WE4sQ^-An>kzxo1u+}^!md&;YqJzh!Z2Ysz2>3kUnraq;vP4DorSkLXko8as(YG8n zx(#WluSqkd7sx4Jtiw_`9Sq$!X#f%kt~5+5!?}*SyuQYWNd9>S`A-&vLXu2yp!z3Y z<;s2o5TXHNM4&i`b74TW5s)K5{UGI#jSWTKxfkZcM+5eCXXmaUP3ges4MghaSJFFZUvwO&Xt zfwF_s@q&UIl3W&l@ZZ12o17aDVJ$5!^75sKw8i=lsSEPBDvbM>LH#_uV(P5DCk8}N zDxYao%ZuyPtIftUFTKAxI-=i7Y>iB}xjTv>g$#b3S_|%HZL5khLwO*&&^Orq{evVM zQQgbQ!NET$2&ynFFbhRle)DB_cem$H8b>g@@Q?#8j|=T5ZYQ`>NUZAfmhmhBy$(c7 z=VDeCc6O|IQ}yD$J4r>-es95sSb!ji#SrM6>X9j=Y+m$@6MnQX$`QMKAsIPyWIM(HAo;BjEOUtjKwNZKp|oczgQb91u*&?@L= zr>EmuY-D8qOf^`|53lY5KRz;_#pPJI;;h&1q4F^9`%atP z=H?ba;)-8dTYr7IlWcMTG@B`4P{Ur@MY0*mH~-RKWsQg16Pkt-fPW;F_5d7J<-yC` z!Xn`fWK#2mw(VsK`1-ujY7Bk`AD7F^OX}4zz;pl}(J8g(d3bo}*^|=A68H-wE#`GK zCLk*4*x1+-egQQ1YWL4SG?$m()YP=#O+9JueB&7a$otl`b8w)M3g}C1K10kJgEA@3 zZ}lga`JL-j|7hQgwA>EXJ4lAjFh5TcMxN5x=wE0-q0iw94WYWa`#SX| zBUlDJ#nwGW-z}MypEf)Nd|$iU{3VU=bsNb{v!(Ks{Y9TON7Q^ z`tLWko&)~HkAgEF_hkb4+0ba|=nnSZ5Dk=ITWD-3`r8I78+@vgkumQ}OyRnDepI$b zJ?CY{e}2(7JK(>^6#l%Za7^A0?l0;Ngq@zG_;*MsIsXcXpDm%Itj^{t(ok)(Vq?Gb zlm9bXwqpTVOB)yj8E{L%OHi;RryxzTL2 z2K{n53HU%FrhMN55Iqu%1a`YMTE`6ctKIGpB)n_`VYIq`Ec@>{6nI!G3-<1%46LmY z0lKYkYfHakJ71Y0|4C9tCRjKWx%&sn*UvOG2x8G6p#g>%3J_!E1NR9(6-Dchtmdd* z0F@Ui6Z-az`RVCuTWPgc)rc`NhKEeXjBv{>c4>C7>095)XCaT`AX9@TsySTUrY3Tu#>5Udnw0quB zw-NuJ^TN$$^V6{96)yxu5%7JKmX;1$o}8Ri@yNuaQHAR#H@-QXm97Cz&dp&Zk~FUY zsjKXo_g~BX`&udlUF0ePXy`$&fCW{2tSv0405A(`l97?=o|>wiy0LG`uMR%i-u^el z{(ZIlNX^*}X}i`20@4gM15U{>Ft9iEy2r*kEiUx$S#qK)kjn8lY=Ed>Z0bu{+KZWiK)q9yW+=gvjn&M zcOP5yEk^(2b&qcD9w2stmY*MPAc3J4kH|Y`!z=<~$?>oMI|!&8cxC%iLB?oXKz8go$;~Aemz50xXpCP~6$7BZYKW&^ zU0*A^|1!+G0Hma09W`O$yeIb4z*0&@(arUW^73JtazGhZYW~wHr-4A##4>!w6J6)0 zq32H3E_YX1&?7KdQ{wr$(vBfL`nL(e_I{5aZWUe0r_+dm1iabzI^;ddVdNXUjD424 z&f$0poAufPS2|J=>AJaG)5$!t`^t*+i98WsN+TVm8Hy&!+BDJIb6;tnNxVgMC}aIm zXk%=8=dQttP2Xf+Ku{}2hD$k^Z$z%Ugs*1jj*T^~D*`<$u+nF?sRXgZ({OX*mSyJ- zkKOv6fL$u|2{&tK>*%Qw_C{BUo9W`X-glTddYiRH{#k4nZ3({y>~WS6^5olKIl9o! zI`6lF@sj%FbBg$>*(X?|als32bf05C<3!2ZVCU8zj>og?$tN!aNx#>ZT)1r4BuCQu z$IPR~`ArYLG~9IG;vA#p)Wk>6F*OFgUNe50&wCejmnMPu9^GFBc`aD9;8A~*9o6*N6D|EJCKP|2 zfKqQQ+W?Z%n}i3)>d7o`W8;h>*+c;P88S*HYJ+C}Rp{^p011h@yXTvSEjxGPpCSX< zvaHPpK9%?5g>s40bA9BH#GD@*0mG%$Y>BQ zmC#f&b48B>%JFH+-gobJ{k-)TAG{@Clugg#8$lG5E*IRB8}Yn`?S{_&v@8uKA~C)a zjNG+nX2G9=r&iAVN>CJ_(#?WnwiJr|CQT^NfY+o0NN`bdY7F|FeRyt7X0+JgmVWjmoApVpxS(Jsv3h!;Y z&sj}aD^E@`KTg+BN00X%zw)E9;;A}y;?z$CULdq(mzbib#86RrKsbD@3NQ_f7;hNo zb=^0Z%FsAF9*UHI`b$=~7R5jgdi1#M6aDQAPQOpvb5aAJ1+Ax;$iJT`7Tnnh*HXC4 zsd6U-n{094GI2h|I^3_cWf1)ppf;U5Gnj)ePY;DRsxIdlPwtd?iREil3%F(W<#0WO zeI9*OebDCE`nhQ#`N7d^knUXhp}iF?rKfFNRu^lc{i|AVOc~0{P`u(Dax%X+cNC!j z0Z=g^f%F>;G#L^I*2^_m3YP!EBOoN$c9zjuiy<4I?(q+WmYJPVKD*KW=9DwVMEcQz z9fY3C`=FU7bk%4r%6&M&)e=*KZw5-`#-y&{s9OKBp9vo zdPe~+bmG8DrV+0~^*Qqz&|rAzTrl&9hn7?DS|BDi5j6?VHkHUH<3T>%EBqa-wAf-{ zvz(0s$6E(NaXIR&rJjx|a;(*Csm|{`CnRN0DV@%QGq{!gE9J@M1G!KXyc3oK1X?Uf zFQ|^!?~PX&SVk%lUm6xAWg*E(?Ct=O35b3{3bezXOPPF9$&LtB@|KN;fiC&sREl7ODRQV zSJ2ed5WALLZIE<4o6HjX!4#$SNJSF8IAjx7ZN!|er9wY7aEr5RH%appLdTh>)Co>Z zHT5}caq4I<-+2m^Q18+|=jhDOjn-!4|9~glV_$|~tzRTgJ|i6V4H=-t>s}d`9tydF zD8=_Jv7xuuKi%UU3k=J{E^Ck}$aGD`Bw_0}sxgb(G6GlPp4q|-q)XbKoKlM2h$zc; z&e?3hdfp1{^&icjcT)-c_88MeeS>E~+(T3z$b{+KFl1FDQ-x$t^K&4WdJ8F7EeQXj zs@?`3Ogib`={1Qx#@afj^C57iZGMO3StZBdFRSS&?Jy{|wS$&VhDFqNXW&9cxBJ{J z$7PW`rl@qIDzF2daPp(6tatEMRC0O!Cr(#pUCXwBmG7Mhpd7W7&X%$I&^WxsTsqy} zNQ;%A4DDl!HKaN3T9wm|4oXvsUs@k=nFFBGn_ZJxKSg9E!5Muq_1)tX>J7mx_+h?& zHOjxaFk-2^Bbp5qE5Vr$bC=x~KND6eF`0g>BoAequ;7@lqOV~WZ(M&zz0=}%JO5HJ zczdd&;N9-99kSxw?|U{|b}Ft?!KaZheV`OhU1F`-V22E8Et6uB^N5KZU_DUnJ$F}C z&~pOz{6@IlRZ?=^m*P_NU7^DXzF7L_i- z>J#n8{|*E>q?(h}&kR8higPfK(kOF4TO*l8uxPfX3K_t1)&5lBz-^oiEsDt!axegG z_mYYUmr?!Q!w`?|%nYLyb-UKAebFnfZ2i{S(<-vnA6bQ)pePEBnfcq?Vw3rgSY?;~ zf;h{jvx=CP8yfG3>D{~m@A99|2(-px;2LNOttvqwhhuMlc-gbC`uck6rdH>JDd?!E zVXshPlASWH+}zw@zCdo*Q$%OzlE&6i-sVr9S_L+ovc_guTSApR)3mq5enl}Va^}-D z1==7=lwq2i7W8j3L^s9h=`C?u-P1zd*|HzS`f**}R6{xv$>ug=0c`MbLo33wnx#T`AN?rNXQ;F}Dh zY{w?4wvHM{_~hg)R949_CVJ^`H2Bf}M!R;#xl!Fe5B|s3F)2(DF$UG+29E$Gjt1-X zPaz)F=suJV);(~76xwi?c-Vk3R%8^qbi~F;x zK*;hNdEKZ_Jy)CD8y{-W8ph-?NO(a;=~A#pod zk<6-MZl)Xc*baIDXDqI)3 zvYSQp!oz%vd~41SPy9Zt)Ba2&zW0Z0_zHf9T zhyIpKC*(wJq1#FeUq^k9U2<40HkO=?1p78qzv{|i3W;LrlvWM?lQCL<;m!im}v&;Ypry-_Ask4MNRV9^C7 zwPB~R8)1^v%8|o!Ye+9nBg$?4D_O~!mb?Cr*MfNCFhNci)F+1wgOLY;9qe9Qf%{To z9$74RicJDze&w-Ztq(s5%s}BE<~Ls0Twp4g#m3AqMd!y?{g*+P%aHwr9;ez`y1&<0 zVh4xRR3PMDjIgB&fX4LnGiJ?U%HZIX(dL4Dv7C6)1DNia*dn)mRq6jd}?hTPrOso+FQCc?IK|l zDC@{rYOIz&iML+d4}(;@J}7mS6Vq+wu{hPyDj6sagiw1eTha(5!fwxpNZYGHnevzW z6LA?Ewu?E?j-8HrM$Kq{md4bZkuc!D{(v>Yt@~yj*DDmCd=6=ygVdguYN>2n>BG#6={Q^?1@G^tcoNji5jH9Um1P9Wp#T z-2UO(LcPfrUtLS9w*D?RkK;D+3)L4J9=euG#phw z^c4{(W2V&z7iivouRu};bB_Elov3Zk(LN~S<=v~P!vN=fv(&nYVC8(A8V*}63beA+ z-O0BrhFptV92YX)P$5#uuVgVhW;CxH7o)6NAsEw6{%|SN=j&n|ho{%>4jBpgy|h3uI-S9GYB7!!KaJGGt+XRyA|n+e?cRp1)vB&$ zWQkYGrQB}FM^tA@5|<-`I*U*(X7BO-meorg(Q`K_#6M5SfFNzVL38dE5L~)(jF6=s zN`>bdN6kB88BsG`5j2Ae0vr9gTb&nms%O)m#8BNy{+WpYD_6tvCCIwX^EN=#)LP3# z*5SkG-CbZ0cKp+utWrF7mBe7+-$`hUSpzCYfI)^KjEi_jD8$a`9ujA6X#W$5V)>B! zi7me}6DulHyY8^CNaG&$n;-t5n=~FWw~F;DNo_3H;HG|{X4g78HHy`1%RJ^j}-C`4lv9#Ul&QDEs@&{!0-D$+E z-_f^>W1L*qLt!cnJWGpnk9fc=ciuRo(fsOTwKIEX`n_CU@O?)jLU;McKen`*IA60n7bOAvvGmf^4e}~8-0OPn=&3k7bN(r zh1)Np(8zd|MYz!y7)+xh+OaCkAPDV`{xe3+l+xwj6RrbQ3APaD0n zU0vB_-A8iNe-Br9L~iRvkB1_|4$78e_eCF5oNJiBYx|(%^d^Y~5udwm*G(wd(?zOC zE;}D1>LD3Xgu^W!3%DyJi=YR_S#vI`CP{RpATt@vr<`EKKxs0{%=igLfmtN-Z<}z=EGI*PuD;r96Q4P~<)Dt|_DCdugQZ7Z0Wz!JQz7@~lzAS_G{Y z1QV;V?Wmw;PrSq|vz_TTkU|uLxi{WbVyp_96kSJEZTXJjV+YsIJ4EGY^K}7&U$%0S zR7=NfaLJy^Iqr_sWhL9zzULWyYfjav#fn%pb(_f-+J$c|_}e)Zi6yZl3UBYMt3kaH z)AriX!Wn(ZEv((OhH|?l+nSgMPzc--`#<`kd^<$AAX59A8hkbSFivXV+sC_&*sm7> z?-Vw)K@hOnf>RYt-?Kn&J#Jyz(lCxXfOqs%0#))%z!Zl+9k$t9T76ZB%W*&-nFd1H z7$RxJvD&_WX_zK*8X#Sx`h!^CJvydbsntpRfOJMROM_PsG(6-mWxxCKY*k`^CmAt% z)8NEffOh)6F+^&JvurrO$}9Pi5?iG(d*`ZAP4m5^zKX>i-d#@Gj6*Yy2WDyDyfoef zMw(lv4`fH0C4VJ>lCT$N0{ZPb#rmjUt85m*i9TtPIu2?ER5*v?Iv|8{q_WNW3Gj&p zcED&kY2&+MJ=xZ!-A0;R)=Ov$PI^1dGH~$)NWm_f> z^{Hu0H#^C@?ao z+&AF_XJ`39Hp_Tc2EVl-WUR0p_u=t9W_p+_GT{|2p)+J=No-;Wrn6N?#rHB!H8DLa zbU}^$h+&HvVl}bDusT=+&qYgjm4m5y_2PP|l7!|3xlCPQPrn1yIfNrdir|Dn`6dYc z(E`3tx)lrc+63yWgiPUl;jpJFYkH;J`W|8R!zH_sI*0m`#&{ExrIa&7opzwW`E(5` zqO>uXkZRK)3f3*sA-y2zp8lw>PWu21fJ@Q#NJrKSs-6D=_6+ExS>=SLBT5U(SB8r) zQ0n15d6Zm)oe^~@H>$$DL8>nZu$V!Dxzp##z@fQ&naI&fqqaGl9Z|E7XL)YCWDaf2 z&>HgAKHWC6f1u>CIBR!%3w@2OE(Z)& z-tG%0=1p*!So7-P74F*->6$%0?oG-uF z(L9D*D?pKDUoH(rwKfD35tOemsSEaGP)k2pvN{SpD$tV#QQIu`lH90N*$fIdp z)MvH=ZtuSaF6JW%@y`upY-njUie9`aIY151-`$zqBmoScFV65AyN6s-w{DZ_wbtbw z-u9SNU;N+MBaZV&Rn_My>93Gj-yo=*e^@0cZfxPI60!I+vYF3T?mJH|c++3ciN z=O5~J{`gA&MfKxyWxw8$8q7mihlhrOh(+n5$hpAQ#cSM+;URx9!Zjo=A?~q68+cKeRP1e9oeqkv8FQj|xtlv6VwjDn? zFeWZiAlV=hNoZpcc{bPcFtAKaDvho19rW=1{<}t4pW?2U6M%j(HaRu<4uPUEf$~8_ zV>q)m8%2R;OoTp&#k&mg@xr_HF&=k} zOkT5u1*M1UP}~(e5E0>ewp48T&wko8jx~71o4=m0v(R8G(O#_94Ytf1NfnNTA$vE- z$F@Lgne08k?Mg2d*my(F!R;@F%+U9|szUR&rj9M@E za-jmt-I~hiL+-gH;QPQ%N>=N=;rs)*SEswEZZX%?#{gu#P?S0MrxWe9-sRo$_v7;G zKk2k!#(mNTs<-a_`-R3Ah3Zaa;tZ9?@8=oO+n&q2is*lNJ(1IptA}Gw@wNP8tX3J8 zbiV&WNg7)>N|Z@8f=o@H^>5{Jr2fE6U|c4%8t-(9ydvS%MT^%Ibz@b5$U3?^h@?x% zsa6_*M&n<7zJ2b3=we(TkQ7lw<<`1uFA?m;qvK#8$^?h|cLn8z?}fuHlYyrt-B()f z>hwGeA-czy*C4CKiC0RF#eA)i`1y3R;;Vfm`X|m|s?WivCIZ`Z(TE0qHVpD1*klX^ zQwN{CM%Rz75Z{dMA8cfobEon?vs~GxY@23eycXdzy?O@*dgqFlrVi=t-{A|Ho-Jh6 z(f*)QE}aTv4kqNd5XT{J1|s96E!FxDb;m*Izo4F&I2%OW&xprckfjl{hpwAKoJg06SmkY@ZOD>im;e>On zOD#OoS!V<0<}~G;usfk1hsf`l>}`}j5INedm%pLMQ0p_=*DTj6b0-4DqXtIqnjC4| z>`#bRB8)oYE-@5fDsG^i6OxhnmIcSVS2xyXL+y`pDkGliI5jWmytS^(;AqHrjm)u)@CZ*KHCO)3+UrDH-hj8nr^j8qm1I5 z4Q$$s$jUI~W6N%J<3~+Iz=FgP!zVNs7bwrfUhf3C$ueqA5;liMwz}h~`(Lp*gJZh0 zDv2^pkaW@Tb}U|xzN?=khd*I<8|ToGcz4OivHe_kA%kcnFHH=S09??fcii?3)M(!c z4f8Rzx_=rLku~AUh-kR^8w--GSapd`#e-I`ALZ3>&++55t2u{x?_y7z z9dP?)F-ta2XYz=dt*~sNB}}I*`Xa+qi=Rlaje*XCOVDZHLL;8&aCRjmHz>i8$!q+Z z^SS2!@QPgfz4&2hHZzzoH2u2Beiz5nFfJ5@VpFuY+r0A-GxWh=y_w6?%Ypr?!b2uE z4%gUw&3elL+Fo2*=aNawpQ?Y-x{qtDnXU&`2ko`P$053>6xQFdw*t+rkmLSdUqj;0 ze>0D9S%I{_^-T8E%?Bn=RcTUxnoiZVwjtK!}Wq>*bhWj>nL_tE4YHmSLK^D@FxYDMw%9)}0QwnJl^w}c1i`T+h zAKW+QOi9dW`|=M1?krp~HKy?PL*Xc&Z`hXwHy8yU8X~6;`kV+SK-{U6d!5&9**Ie` zHS-t9{AW$AAfVVPnBg2RXzgDm_)(0CV@8mLRm4EET4;V)4JVMD{HkHpIBcKQOU{_i zm-5+p>!)Qu-XIoaF7k+6vr;gGOM8xzor~CSI~l*8LFc1&&;#Pp5G~JGf9`OvlRd{i zU%W~D5s2ttQ-vu=T;`0P z*Ua4{VYaA^3}zsFT#N-m?Ifk31&Ye#)N$DXV1MD`$ae&yMFOTTa@hT3Hzw^eUT58N zX_bEJ>B*y9vdPt;mY|gIoP8^O06`W9XToIsy~~6$Yi2RXR@dV*WqMzfR=#>PDZ(`m z{K8PFyLQo{-}er$y4$w&_i(G;n$(|5O30TRLxNLiCIJ$im(-fz8mj#lEkUPnSqQA_ z^6L*LWGZ;H7a(>kkHAh9Nc6x^YUF&opm(X2?1*NJajInyBUJ&F*5|{io|3i4Nkgm( zmCpALR954|f za$`_UVR<`bPI7utuuy6xj`TAA(SQs6jObf?dwITkxZDLFvAe%)H}W0qi#|mVlGc&` zRWp&wn(oG!Ii;6sUldszc6`AgaD&A`bu2N2)=(RNEIXY*TYyZe@U+bllL>!w5qsS* zwX|z*4=VYe@J;W;Oh@$MOu}YKvOjE-#>*e^SdEvFLcgV0H=_gFLBY4&4q;Z}!5OKF0|_}O&*D{#NT z43(S4=1Cuh_zoH@#wO8^7yBKmVq`a=%;u=)i=Iqp zAJZao^Qn9$>6Gy?)0Ngln7v%fmzLS?4WEpzaJ7nHy`O`YJf(jU5b>)hXq0;s;Q$(X^iqLOx`3P%ZU=0XRIdOwb4)*MQ{Y6y{|Pn3 z!IEsEguTGB$;Z|mn08=K?spT3&o?hC9$g8)-!z;SjN(lAPHc7QxA&(~o9`7y&p+*X zgZRH1GPZwbSab8)Aa+XFa5S2fMD!RAIX2chu6{>mSJ{-{fUv(ZuP*sRxl1GO{KSd- zK~p-lNggVdQQFNW`I?Tw{nYT>q-3uyZdw-GJ((^9shTMm>m=T)^Unxf5SY^C{rcK3 zQ<;M!c;{E?FHZ$;omxb$@r-JcQg=9w=4)(O{wUm)5Bk*Ph4j-PRGIg%8)N{-q~E%?(7P?lCie^;dmLybcTv7LK91_mM^ zvKeZr+8LX5=lU6)Z#n;=pnMDehVha4Dg%cuhvpxDO6g&mSkN>p*Z0&fQ;CVFFyH5D zbmDWQtucnx1s6x^bM$_9E;q9pq^|&ED&GoEj)Oz1?%%l96VcS!>z2E{)~1WiD2q%Y zCi>iX0{l3SHK8*egL5#<{F3T8aZbwF8g93F+NR#WK&q&{DQ?)9%9Md_)m?XY?OEat z#}_Al(JkzQ$mC-7OhGcFP4;+jfDhsONgaqJ%e{W0RwsDBdL}!6IF_J>{PSlVy4EJp zWm~-_fOjliGHAYvx^j6k)~DENN5bt44`8@6V&IaI*g-OsV)$WBo3AzG-kV>yl`VaH z;x_CV921rgfi)*^3h9Ocu0wV@A5Z@bQxdKYp~~7HzDJPNY{is7q*PmdtusuR{_XyK zLL|Q2UsrXIgHmtBA;Xjo2PT56K9vn#8I#sHTIY9TD4EE5&~j3l+HXaeC!(}lf4;io z(+Wfb(=vWPm@UVF8kVa41bfo`DW2t`hbXbUfq-x~wJ{8KayIA=__9nh+&5L{uIuVy z{zEtFbF9qhVXhrq8n0NO>2~VPkvk!9cPQJ_0B-P+Ia@H|$z5Ht2@4jF z>Y}iO#uTBkU=ZQ5CYxm={yyt;>807@gk)n}_e?I{bGw8O2TG*63y-rt8Hf}eUY%Cp~RG0KyDbMRe6NAGd)MRZRida2w0%XP- zuRkH8RMxy`-;x*BBW<7#aHWq0HY}k2JQPGdG-Z4n&G8rLQG1O5v zO>1!8R9<@Ot^@0TbJ$P)*b< zZ4~#37$pNUKev_?ioU+V*@@A9(dVEdB z5%!Z9|8ng{IzM@2ZyA||Q#LG3%bGf~j;6}qs=dFDO>gT+ZX$O8S#uL_KG}q>zFt3V z9*1$}$qMmd=8wM;$o`HRsc*1->wUk-}lOfn*IS;N;4`=qFUz!>xN~ z;zH!7F?l_Yl3k;$%3JI&NsL13VJXyKhu*rG`*Q~lz5x3eOd}eq=G*W4TnJw2Mt!lz zGIOFhfkx=6QQ8Z%WUC67%_w${Ah`r|Q6`*g2J^2K2l7KQ8M|uXEX1)G=Fk-RwFbMr znqP^A-z|aUL{C}OY5YImjhA@@gz*BI@id7fM2!*euG?m3OKS|iW_oqMsL?Quu(a* z)W(8zy=>l57Ryij%0xmXpi#{#H4Y>WM97F}Q+DM_Z^Y;hj+#VRWXFNEEhvD*Qi7tW zRD=Vq=_ztv?{J=OXviXB^GFAmCqA!t5NzW-h+XGagxgK#ioU@pvgQ{|T1^w9~5d*l8p+Fy<5W_x%W zaSvhgbFv!)r2-V6i%Gs!rhiB99I~Mh_7QEEa)5}>9xz``Zlt&ha*e3?JxYkGIM68R z%QDrfj}uyyal0Y3Y%F^5X^7dw;{ng#zIy`IR!4nSoY}lfG^IJs`R2!FJ+1laA5^&2 z&w1v8!Iq4OlP-Ztb31?Rf636Ki%&9Ty^go!!77C>iBvx*b6x5kG3_yott}c}AQLI6 z?{3e_@s|_^A;w`g-Zf;fz+$Cj|5}FI$wtRtb_$(XDu_YZlJE0vnQGi_m9=Qs9SAv# zH&eC3TLFUTdQaQdb52ce4iPQ}q&aUjBI`nQD0gu5O{M+V!+z{1y~#J+$kn-Oc9#}h zVRsr{jsl41y2d3%DZOJfCUJwpENNY&&;qHt-2G1M1u7O53lSNWwA3{BSZm$)id#EF z{;bNhajKZ{G#6sFe1GdJePH&sMb)69<#O4Ulf0IGz+a}b@m0cJez9Iq<|N}}+rfP# zR-QQFf!~ltn|??kwjuG^g02#@ z-H5C4(a15@fpBBbS9{sNyrj%3r1vA*>MGp~lT=l^PV5@&YKX{?@#ppqy8Di|&!I_8 z`&FD0Hn+{EFgM+>nlE2&ICc7O(=|w17_l+DQ5%eF*BgPj`y=IDwLE8XL8bQdnJ-W( z30aARGp5^8D=>*~nr?LWZk;z0yKOo}s^Y85<$8rJVznLC77_hnTi*hPoXksfm>kdh zJsrU)m<&Yrkwn+Nz;($Q$JJ<@Vusc&GQRQF#jEu4cevEtGyl$2n|rK=tpv53LM1TaL$@-x_6%jcYL&#rTM4Xm-+annl?$N5t&C{ z8Jzm@{0uDmOnMzcn&Zd_v9;Q25Has@98X5sq>RAJBgZ%e1;c})N zH8(D-1|}R-_g8o^P?qALNDpwyl}%RSxpkP{&FuWwnrt|H?>ya4+y+}{dzEF} z^d8^Zgq$mcjnGjVuJ-?4?`!{Tx5YHO_#e(!G1S|x{%}dqmQ#7?=FLN>4Z8YitMFEz zXq@i!io!oy^Uf3dFPZDf3U_U^U)z7#5P~R;Yw`!btFDqb^het$=&#EqbyBCg4*xvX z?TOS%>czRH$9-}qD7P`Bld=-lQF+!KU8kd#hzDSX*{JN#7uTfED|*BZ^>_|yso zw!We)6Pf=bwBMI`Bh(kd)bZ&%nV2e;z?gB(h~hH)Qlk(CKSRiD*Xdoyi>-usXV^wZ z2W;DoDu_%&7RI6~$hy?8-#e%-gxH8Gq)GzGl;62R#7G!*jtW@lN(n2s#luw%_Dsjb zQFE3@_5jOyP*r1 zqI|!&1@)>|X216|;ZS2t@Xwp!aoAFOe9cD=g|sbu@wF)x$zv7a^bo%8?1bkA9gos0 zFi{6&49EA(ZUZG71wh`=fS`gk&*A=T1$D}iO1m_D_`s@~@V{~BkIJVW%YnNvz82+z` zkp4K`+Rp8)ts%^BfpWY~5sGp%;?5F&XcuYk7peDBmkrTNq`I<|vyZ=%o8@}#S52>4 z@xyS9XF641wSAUQE4RVWQA`cUkqc$e{*Lo4VvFY$qiT5S8A6X6na&bl;|l`+j=8Kg zoS%3FNsZ*ONHL?yN+V*--koS3$phhd(Ls5BX}{U z3X4qORD|ac25a>DUXvuY#6ei~KHpB7zzY*H+}-GE5b!A7uehIUzUK;9GRGdZZjVg2 zjlWmTTz2G#w07mR79TU%m1Be>IqS;+Z5q*sE;T0Cf=D;UMY-+z^fSrQ7`hp*O)U4f z^ZFRMRq^d0v@K!f@?_GM0eXTD#vxzrN z@T|tqHb{>YbC>K)nWRs`NO|NGl3{Wgq9+&@w1_iEwq8$y!mAS+GavpsnS|kODh2ak zHZRA0f5cVk_3rc|P-APN;+dmW7K*A+TvFNxMuAA9TVQm-CPB55!a?vCjD|dABvt&c zY>cEYa#vyT=5*5lKxMaFg2GvzMJar_+{10_Yt0fKc9((EkAV}2rmAX^W|ScJT7lDR z$8SLC;8zUQG}!2zf&yw(h-la0rqUWrC7}9^-|HyR6#o3XTGK;Y28ziIY}n^$ z`2>QN?29_vQY0DP%~gz1Pon%MaJS)eTBHtJ6!-rx5Z(Cv`4jLH6LB1Tus8ULYLg%P zc6z=CrOE3#0EE8anywhDq9m2jgGoZ^nJ(NM$8a)O@Vz45#>L6;neCTf zpWTO0fBIEIQ!W-x!vZ?m*#LjnM@Z|GLV~y7CLD_dQxBceDq&`@e}lbnD;nzR$D?}t zFpaHnt6kc{w2dXQWP8%5#n$cQ69m5f_vIv5MY1;WA(n{sr2>-ZONdkM>WArb!(vl> ziY|p8ezs2VqP59t5^B&y4-wxuQ@}t{jY`DM(q%;JmuN+vx3^%0uuJzVFCb@lzcq%JV|+SnrIEm>lzj&XE*R7G+dDHVK#nP2!&E^5PCHP z7Z9D+@&_HENoV?V&0BspX~{@pquSmgeb_0awTsaS=;T`E^|ZCY;Abk$S!l|LCe#Xk zEW`$C3es=zmS$A*I1Y=xQr$>j9x-z01Q8PppB z9)j>(n<8ywD*ELn$8cuTWr4U_YuH%z$g;En% zDeZ*yaTv-44<?o!mU2WuVV_H_ui?Np%m8=x57(v zur%*n^p-d_shF(|=ZX|SQoSkInaB3lMw8hhd!JP<*5M|1{qW5R86`hq5hXSDk5OIK zx7kU>JaXhCD^5J@1Jy6K+@puVLY0F%Pn~^*VVDE7h7|rFJD<{Fj#3mb*T5LREl=Y8 zbCtCc8P|fx8+7ll1Z{ssm9KTCd@6fb4$}FTN()(G#vjV%FAA`<#^q}-8Y+*6tC5IT9dkKoZo_VyZ(5R z#O#}9l14R2trZfFPbBWw!n~g_i4!5vR-gxO3P9uqo4*9D3%a=`H>6{1amV-TG?_W;FU+jkg zmN3ddJ9LfMJ0T%bSF>ilu>d0C(iR6+%w0JVk3w>-xjP2`9;Jd%))|a;VZ&CNthO{K z=rqtX(-B~^!|xL4ZlJ@dUkAnV2F3Y58R%2GtV z53%!ps4iyq=dvtf+It9LaPrU<;W0Fmp_x6X2b7%bYCX#nz#;~Im?EK)LS%kLRGwKp&Wk|edx3ek1^G%1! z26ZA{FYQg9v>Ze@J`{L`*5WT}z3AW)I`45L2biQ6lM?x#3=QE!+hki~i(r@F*FIyt z&(R5alTIN&3yIY*{=PQQL|kc;l#5ScF&K^gp2=Ts*?uHzucqv)Iatcz`L|z!v^+_% zn)Hm+U_81uSpR(SYP1z|kl^U$Y(w8VC0X>7M8>h#cMFxbt2fc(?(kD{5|~phVv)^Z z(z8_Jsxjye1iqk)pZfbbM)`2C7W|9G4*hpGBja^f#I1Df{ZnH!0gjXXSSpnqZcDHn z^q<9?>}&z&Ls(_vr-;(QC0rBE{L~NzqXY%Z;_1>)b^2c_MkD2|L2e_1n`4*at~pWb z)Z9ZqHrt~ZDsi)LV2M@INO;0&{vs> z&#Kn%Z$G}&(;uDMwcxp^939;2AC701r2*WutO|qdjFx1(MN#sB{I}z~?nE&ia?wkI zsY%U{T*;7^kqkux%fCba?K_@jH(v+^2LGD-RsBL|vQaHy>-Z2xsr&aWGy1^LB*;4I zAEQk~z_y(LnT~OQbqtTo7UpD4ymu3HcEqfKF5CU)iN1>TvuYkgFn3T}e$Fh>!Tj6z z|0)Wva@jGo9m5tldNWia6F_O5soA$%5!RT5NX{E_Rk1n&Rq@+%^*}Q$NTFP_YG>rB zJ+%sZkYZ$uqkFicE`dp;{H;gr z=T8ZDPgRR82MCwnC!9B*)gC!Bb1wVpZ!RX!H>V@**r>{f&P{+y4C*RUli{|bWmWNL z5hFOZJ+(jb3bx+#S7I4_!CmZ){qah>fF}2S^)+WL+m6qFSx8*wWpuNglrmOs#t?i+ zwYY5Gads|WPP;P%^bhNc|0>t~sg3|bZN1Tj*vbs{#^{c@fQ%3JIx=rT@qlH7uCFmm z5beHp%J_>14431IU+QL;53Gz#VbnK*Los^5oh-^DTibmbd!;7{eh)15aW4eRp|giY zPLmmeH1gK^72@~EXw5x{uqAhy z&f?;mxptiO>gKcS>^XlJUm6j$PxjcVr97-<@dE{i&i*qsx?8sDOmIy05(+|%etC^Y z5LAN#7kvOCYfOy#3qWwYoKFLSWeLG3(|7W%MQ#{tYzj4wvTxIneZ{N37{qef~3WyZ>jwGm` zpiAhh7xgbAIW$UCCcku~^g?X42n!XVG|~D_jb+_Vrbdq*#IqH%AYK_s2w7Sv1?unoZgUw#+Lp6eFDz2M7)B<^h zex-M64svh17H0gatj_cwGlBGE7WfO!d~`qnBW=w6iwDZ%ySGMus~m%C2~W#2jn;$a z&0iFefMGn9%FT#!iK?b9<%Wf94YKOMq(4m?9Z^=rA-Qv!AgsTT`~qc+oLf zPFlk;5AY=hB<>kyoySw2fyY|(C7{@8hV}6$kr-ZI%lLGd_OCoPwi99QfySu~KnU$I zFTOr59c}SsKxAdptiON${>E*CatDn#LCHLxYZn`(7+mD15=+Uv<{O~<;4ij`y%EP} z^qq)@N>XkwkV^PuMs#<``g(JuBOcS6z$4?h{l91tBX9X&=%&|{)YK1;>=nFEgHtle z8DiI z@`sBMi^1D2VBMweKMF@SblP!`VR5hhuS~6X{p05eQj&UY<|!^I>4n!ccPQ$R!Vqj-T&vZ_%8BE zGW#%g(?+M(5u)dOq0MWlGY-Sged@> zt}iit&ApsQWpkKnoL}!ug{Em%i}q{AAH#~P`{TT&9AcbD6CSBP{>BH4ilIq4*OGuE z#0aey7V~nk5P>K6OtGQR7x{8F(hh9GWcPG`vAC96iCxbp@SKTsM<(-Cp@7^$!Iakc zyHF4&7Sw#`A|$-!a+qEGN*!&>%5l|7aYh`a4{O%jshJQ%h@1DWcRXr&?SKz8g5|7w znOkjX11TxetG!;hAcIbSGiTi8k3nWhzf1_C7x$jGUw5;RiNIyq)C1amdY9N5v{7ea zsrXCs_@{1P!1JB5KVxan27M)M!h4ljb2spND(_z2`NPkVIkTGKMC@`tGND#%O zC}Shu;XrB>){JSeIZ3(MyJT-B#lnZ$-7HXO#`MAZ(bJb~NL#!wxVMyT_CgktWt;%0 z0^tv#<%eaUDWvJC^ZRata9dE3R```@XJhr2=ORhJW%K^L*vwpveOLXgGPy$wVit}NoT-JSfVhhG^~ zucI>w@O9XtnBZ^zoRY-wlQ<%o(xVSX$&gz=NpemWg7CR#?L#h=p5DOERW9JqnTg2u zdR?6<3@o)!=TM16DV)9g=Ho6n(pkfF8sCLt|u-nF5&AWEDL= zOgC%*|86+x*tg(uo^HvN1_^1s1$xNq84M6vVh&eCsksZJkDn^iHr*V#C``fFn*Syd z+$-F8!-Xqy#oH)Aq@1o`dL0`o(C*@f94kNDN*Kn3>q>pGcT%tS!7%b9l)97vS!J;tfbZaPquBD0tJFvBpfRzMEGv^Nf~&P$TJnU~m*{t}*CLrA71Ybit*6 zGa*WE2RETSt|M*W4$3AL1-6u=wHjg()$wVMa(?%zoV_sbYh<&O(Ke$9F-T&i za#!o4k`Wm%`z|gw4^|SzIalgb|GO#`zsb_9AZq6=^8dz0FM3;}3R5F?wqn$%gtR!J zg5t8tup2KQI%waNf7fLuB}Jfl-D5!&PhtByS4TxhbhLrd_TtEW8#V-va0CkXJwVed zGq5&NX_@%%`BY~GGO3{I)5D10nxduEfHd#JhhN>wOB5{8`g5I~^ovHlcqB(;^y0U} z>5RMQzKpt+>qO-*kNW|o4(NV-)knk+S-_M*jpE0?;PIxgos6P=WZ2+j()>nZ{ zz8->i!8IanORL`Wes7XZMo&Ay{RUVj8K2nViNk`@FoU@VD#Br34KK)22&7eW>eJ4J z6%kf*ciAhcG!c!dz{jkbAxo`s9NFh9^$bhxwN{H9gSnWMg)mg3dQ|G`TyW8B;Vnp? z*}?^b^2*5^$+UdVqQr=&p5&EJfHL^%8pY<_NU4|^R25&^dB2-I>#|!aA*8)Ye67CA z(@5xoNmzhiw0RIN_cM3olk~H5TkE$z5KUFF!|{kb8^8?Wh}oxtG`=#T#r}q z{wLj9L%yKC6|0h2A)8XQ)4(&ye7}>qZd6T~J;sYxM%Pd62r4Cyn&>}kO>UPMi7$CB z^vwPVhN4Q2+=#PyX|q9-qQhzWLrQHV(D)T$!LJVSnu5woiS*!7+P|bZmJP-vt(J}c zaL4t5c9pG$_ED6}i6*NF#6^QR8NMLT3vmyc(jR>%`6V>|>4Us+=W7Y$O5-2kHd`ig zi{aYX8l!YT;gG?G5OqBNgP6%~P0=uyBil9D5E8?724R3$9~kEat@s#3s0bY1Yf9_$ zE{iN52eeS1d-Wf9l+eoqO6Do)BCPCP--5tgIa-;368 z3~9e77F_wf;?7i$z+Iaw7i?=}O5H&uXmoxB0U{nWY+F73kjIL$@F^z*1xX@|&t3?G z?aDl4bjPRfDYD3V>gcA*?ti&3)Dil!bO&8zkE#BJTZynKo`GaJ4k-6?_M3ph;60pk z$yQGysp#Hlba^MrSF?xII#cR8au|~dZB|-t%iPQ5c~#IkL_T_RdcmR9D{AtG7qdex zo{fKwZ!=Heu2PMIxPx0pdmhK*embZLJMfH*$a;dQ{t1}Mv{Y`&mklTYWCg?AjpA+C zr+{Y93y(nC56v&9nTXS`Rp!#B&otVPvoi#}<-33@%OmSrduHLSOZ!vAcvTcgSARoJ&TSP64D{f@wlRct@Hj$fWF|* zVH?BOFBG=guYYlsprQQvLy!>ZC6SR$jOxK&z-a35ig}@7if>&5(1hFi?kcI6-T?v?-D919wHB}YFiw=xzHKJ zvOv(`fOsfVd+6%3*?<^ZHdc%m1_TqrRWW5;Zb4M4l7fprOYs853!-d}ww>r~?X7Oqu_N@7sUw zRb`t&VX?zq*v+HY&AM!f7?Ra@jm5AUP}TlD7oBuibV@vZAYWmUzZ^qT_-4WtUibY` z&vb}JyA99qsJvs@!)#63i$c~by4L9!@eYxe8-MIWde_=8x2$o+4uGR6pAT7aup{v~zGMjL zx(n|!i4Udpi7Q^x0iR{PU5;fpg3k#;VXm)IkIlOJqJ(Rw?pVy=Be%4nv{PRJmW2u9`bl+%QOe#I{x_-CrX3_sAR z_sxHGynpx*bPN?oq}%wbQS-M489!*#O@LLT=7QaN`oHnj#&Cg+9bp4ebdbJONV~#V z7kOes$^FDA#iGv~7ob)e#Of?-G0$I+>^jrrat`GfcD_(gTdLmZ^ZS2IJTLAS_E%pv zT3E&DAgJKdthPh4ix0F=pwsS8;2F!d5kqu7I2NV(`o0t@AA|~0i-d3eJfg8AwHcY` zLG2?@O2OO2n64G}E_Eh$ObqJ>2+%iO3qSizTPgH(*51SXzKilh3&${dGsN_Y)*U)5 z>PR+-_O)DQ5}E7uT9q?|oCzWx$m?Uoj13~-n;{988uu-}+;gk~u2~G~w8TR!wIa+g zIj70ewj$1=eE3e@M!%^yaj5f_l&JVE_R4=LU$aj_!1p*Xne!eWKO$5rKY8-)>`!-U z@Rj~AC(m-KyX<%5-ApVxx=v;7P~rW`EWv!F651>8+Y9DcRl#plaFWitQN=I#qW$?^ zJe*dM{tDJztBmKLBv%?8cyCr7p&5gK`G;P=hv&mX^Ja$g(XQ8P=^=@jza0zpB}Y|8#c|eTQC-nlB#*9 zg$F53gIj~7e)jEL4xiAy??U-nm%&HM%MXn#R5ubrX!N{h7`y#DA`4joKmy;MjpHloHF0au=Siy=^W%`!C;%)=c&yPu!JD3}hCLM_X@S~yaNL`n8 zEzV+CVMp)^Xux(FAHa+kMib{^Dh}1CLU&3AHLWj<0eQ6*WsAO`BYl$$S!>Q6f3X510+g@)c zQV_10#3=^DDm9rzW9wfjvWH4Zt{HExKoYo0wu>dqdw2dHAqDih1JA?+l4`i1I;ElU z4B})?O!$QVU-*ht+prXw_YidapsnR3ADnGLNy!qOI%6!^S7VG@Tzh3d<)OEG{ZlxA zGDc$8omXO)dV5|OJM0ri1?ENqk(qd>x79V;w z-X9_9U`5^g=e8b;MfO*mW)sAX#R~yMT&+r<+)~~t=_n(UKBx$JUy`*0Ow&Yo$kn(U z;dUwXmfRHBDFb!M=BH1KX#+Zz*eA0C zt2N+*@}8x{c}Q;QePOm^+)yQ~C{^FR0pVIU^}5uG{}G`#lR{h(_op$7_g z>#^h|pHU=>z<}Cidx+GM`kg(gbI2`mXBfjN;*M*6eld)++SWLizt308O(mN1S)|R& zttIpF8+CD1t$T$*xhO1Pm0O?p&x1*q<`jY0e_xoL+cu80nPVomYEXTsFe1K57=BZ()wJ>8G8i8ny_h`fa(YjMhb9z?vaeGHFmDriZ>=ciAMR+}3iS*M zwh6B|QSn*dXF{o*vh5NAfLa`%){dAbPQkc4#Yxp(JEvy#xH>MgT!KPnJLsKJH>yk^ zMMbT}_YL)Q1?AIN_f7D0(rM*lyRo8!3C`ZYWK4FaEAeNAdTh$?^N03JB9htdAEz|` z2%i$f^&&NHoD;faiT9L`4Y|5GZ zGu!bB+FWp?DVax?cUx>Tks%d8rg9B-uR~wCN3RTdO9SqPt88BDUH)aO=zr{3-)h21 zqtATyNy7lyE2tCx=5YqIriM1(Y|M%=XVMSSMolH}-_?Br=#SW?v<FVXO}XfBOq#>i5Kk*EogX7X6EM=T+rF>?fjdn1CbD% z59uVe%rG-4B$ZErO!H%Z`^k;w3d`sMtDnR_QIcqP!n?>=@=X@!WtH12>TAQUkG4&e zFC7G~TO7cSjmaGbiIhe$>H9~+TLMYcq?7NZ2(tD&^FrhYQrP8SOP3g%_WJwU04#?p z>^R3JJn{9_zWPf3-+sws_6H@W@`w%RIQ)h5sQheuu>8{Ivq$5)qbu`@ESSA#^{ciL z1tsyGgz(v+g?~IhJUm`WDwGYVV1s5_U;qvmc%N5pHxf}B4_Bmnt8QYjgvdXcfg5zV zpbhpfLrMc2e*Ve-{>A}lZzUehX)x%x`kiZMku${Qapl6oq(5 zeUPr-iq+eBY>FlT{?oNwx)bQz_$ zTr?EVStlKh<8AzZy(;C+X2|BC&qXp%zm+_1iT>yvL;4yHmRVC>b6Bn5P%@hA^=M3P z1Xdi(S#(s(UxpL-W6Zo*O@>ci(m(K2zQ2c{EWT6&TPtkdl$ikHHC&X@)50{cL|vfc z%yuqJz;}41_x-?MH2gCIaoL|C`AEZw*98Y3QW9u}=B6qlcR1ht2UmFHp>g+)4yG*) zfv;LOs8EB+Nc-<6ur2tS%^vs-cFTzdw5q7T!o3g*Hx9yd&HJ-b_?UdIM;`{Vr39A{?{ zV}OuuV(wXA5Srym11wI{G-{N5`+~$by`F_aK5nO4a6xS|$#szN2dfAVfSd`L3fC6h z0Wi=eA_SH3LQJK((^(_K#auqTuH1(mcBrTZ$&y{}OJVN&MsT(f-Tyu0?!!URuMw4( zdayYI8}_rL?TTpnxjW1?@P{QcMzvDTaTIg#?~|`BERvT_7{P+^nbHlm^Iwh0?cw04 zSXA~`gXHU@NU}3cFKAC8OIwB(=Ir521QXWVN)U#*LSgT5wvD5wjIEFNcI(d8EDv&dVF9mBX2T6yVq{XNt$Ca7{ z90Ppwu7l)zuoi5rz%MJiGnwOalnkg#OsnCx@4s(1SmvE9*% zMs=kU?&)wX?XRUISZQ4I8??!rPm#c#-6Cajsr@?_rwAzrb58u&xDR_{v(xl@s|Cip zr6hF2;^bNg;a|d_zu2|`zCuKy0KDg0(Ga51QfaRt-v-+A>K$X!@OHrcSv=yT^jfQ{ z!O=mp~-X$%lApvU*V4UgI!eHT|CD1rpoSwhk6=x7Wi?3@46>F$6fje#_#o0>Qm5DlHpAao-(8nJsK{nbjl*L zmC#`}ckw`r-5h_W?KdA+?48r@8%s=si|vsZZ!j5ey&4X^P^m_O27+x@*ILtE-Wf{V z?*aMEbAbl&z3w~yUp|CHN%KTT;*wv_>Ranrptty?y#%2htXaCTQiYYtRCTUcJxu|( zq>m=a-WAdD$9OHixY3@I*M2(9d9?B%-n+zmRNl)~r+vRMq={a8AFXG-)2nD8VE)Vw zV1t9fowiEiN?xmT{xRr7YG1{^{!5YXJ4_(B^%z|{Zh@AFKr#N+hP?~sdoYU6M)am}DpiOpe@HZ5O1AuYW&cV3=K-FS52WXZG0D0y}$f}@Jn~+L4GM1l0e|OKS)8MXAbDTTNp|d zEEUI*RkS(QA3^oEXc2$>wsE{yURR9mWQx#H{o4GM_UrqNBOM=n6XlTT(p$UXpF&I@ zGb;#6hEa1)Gt4aTx4Ws|DAtYSFolyl?kBwTNZd$Ft%dF0%|zhdbsbB5N^9uX&L=Y$ z7>tO-kp!lL_JDJpnw)15ry2OA*4$3e`OKK4wR8K=-;yI?U~(trW6EHxl!~;JEwK?9 z?&~jnk-T$u|GA#oR9@CvV&tX_$u}uHuE|(;CgWS7c*m??un@;#bSVEoKhbi-Y+5c^ zmk&Si6x$oSY)kWJ6r`}))%i<(`tzHRCd-XqCX&}g7HpnGR|3a?FNtMJvuLW`4Yw?< zOg^7CE0Hs?kjcVB`dAz+&<8Y3)_CL&hcz2y*D~K?lA(qB>p?)EYWa%~+ZhwCWfBXY ziL+KmO~1nf%A!vTDznPwO`Yo>7s?t6ot=-(z8aVW`FS+sP2stJ3~PE8vBsqLs`oey z{tbSSDgzkaJyJaAebvH4ow83;gLh~}4QeStajcTib==+nI)l04d=5=en-5t%_Z=DG z22WQ%rtgm8m_SNrNpb}C^^B95Mii5YO10sN_7~kRhdw!uhVE7cd?aXH@BQ4p#+|(o z8fyPnM>HgRx|hK3ZaRMYsE~v*0Vy8>NbOu=aP@Zk{cZhg@w$fhtW-GW4x=8hs*B$h zb2httc&2b0c*w>Duk4yqi=8mDi#{}kzuh)Ihe0A=011f&4 zd}-QdI=&-s9HI`c(JXvemRps3`15oe&hHW9)!}{L)53-s7q{i^Oqn0E)^H{jN9!2C z0}6Gsd8}iUew@l%JC}Idzpd|iTjEn-Z?`&lfArJIv!d&zZ|t+w*mhqSc*9&3oVJVu z@I``y4s*Jj5Hz;C3lkE+kifuBk^H)Khim`Y-eq*|&hn%;K5bm@%Qs<0{b6?M2S;bW ziZ1vSA{sS>lew%;akxDU6#%02d;RaGfGuxs$?&(5@#U`}1jg+~&&Vf8cI8&5g#Mgb zw5Gp@GJq|h0D~Lsys0X$B)nLjV;HvNoyGESL0KX^dRCXrX@+|`hpp}Sa}Ed{pevaH z-XtuQEvyIG!+}>!39$)dY0-ibJ^)C|4< z{7;S11!3+;lxW^SjN+CwBlPqbUBOmQo+2}rQ;lQ)@cx2C#!zuN?X<5QoA({w>3tGd z{hLM`Yz2p|RB0UDz>{kpX`4K!@h$b2Q@^L1ky{^+@J40R^XUemTUYk0Xc_BI*O>Dp7JvwVltSv*mWj^@~1c8bZ zZ`RD~^Yqx^hE002evXmt?UM`Zk_@?JULS=i)Zc6wL%#UP?}UAGi`-v10*e-YwV+Za zCJjYbvbjWhS;}-}2o#V~5A6bi>7MxaV>l&+qp)Ua^5|O2W{2;Z1s0k_I8P&a(?Ou` zaBp+YYFdJoN}eG>>K)|)R9@L;IEPEvl)guLL<+Q0N;?7(m&EUuV#I>*v00p9Vc9h) zOjLJjrq^fvUnK?z+F_31<_tzRd1mo$b--*W?fx}>EHbEgnR3AQZQ%iUD*uEVgO5FU zpwuC(qX8kVP#Xf3*V>zI)t~Ruyf9w7rzYnnwHM;ScbCKpe+S=5Z$>L!8m{560YgNo zMDwuq0lau5xPuD$w;f`YktzUK@WHN^ia0k5C$N~Z*Mdxs?y}C$h6zwU9OL3kO%J{6 zK_9?tRX}3g|4X+JI&61tQf^NcTMt54vpc0jo3;nC-#P)@6gT?z)B$hc`>L_1?`NGO zaEbiq=fXk-b33QactcW59iL7<5b5el39ZE&ItfFDoj{hZ)J!RBT(wcnL~L6ls`ji$ zkh{2Wq-1B@k^4rp1WdCFEAZ9@8H;|j?$&M?iFw8nQyDA2kYgyDpOeUe_+hnWnRmWRk~~pW$_LI-s>EA9;xiK#!;;N3z%! zA)6!lsb#|Qs@tq}W?Nz9P=KYQRt%3TxaI zu>sdi#cUAewU(!e5vB|DL0r5(`Ck@`Xiq5H5B?_$YK)3$>phlLqz17wQ_E@(&CtRW zdaxs>pOcn%JQ_RhJHz@7mxt`K(fy@ zBBKI#O-kyRYg666n{e8?1lPz*+Ei4bA*FcSNyO})SpV`Um)6hJOT_AW-0(Hhe|tIt z_n9mFGmKSmlN?|9suuy;dit_I%Byb+VZ+>?K-jLAtzE+V!cbr#;OzbUsIessK1#g3 zoBkzQxtZ60cCrRJ{vv64Di^`+pQW{Lxr|N_Cf%$%?{-dQA+o6i5?P-2MsYo#jAO8l z8gC$gS@BQR+83OF%hV7J3|fo{SFdGoT7Py65k`uFM_ft--9x^-UF)CH&TB_ZIT@3m zOtJekgl^JBKUTpSQjTU>vIlN$1d|?eZo3B-+>eiY2>;ft}iO{y`KL z-5cq6_<$a?EiJ3<44n*9ww@Sy3BAca|Fc7L===p`dd8$(Mx-SuBX$bKvRunE5Mfx< z=*!aLKDQALI!Q^ybblq!6La%>{zQpTg|zOtT^XotD-IX~U{Y##RONq(*08CalpAx6 z%FF(wJ>eeGJ$Yi8EX7;|WU`+oW z-Z_^3nBXL&LOm|!r$~YANgdk>q=7*n7ci%9GuYR4@I7ARD4LhE>^L78R4NVqpRt&R zQ@%9ewM{i7v6@V6$$pgg{l;A|T#8Czx+sTAL9e^KsY}=L6(z*sTZgO6@d~cCOjX=~ zVt@a=F7E|d(Jk~TO9MI$KcU-ZrEwvAP-XeE$xsr%W_yKRSx)g`h#}mDP~};^KTv*Szl1#P%G5M@0m^Zt(c2nKP%f7cizVpR z-HlEG$P8ghwPjepJ?{zpR?$Z3*(`rT5iN0YGxT*y!?b0uu%5hQw3WaYqMoSE z(q}SBQ(n)rkOGJxM4aQySyn=+q}3`{j(}0tpLz4-Kmx;|rpPJ;T6LdPZP*CMg~u&s z_A^9V-X??85CS9b%9P#^-kofOR!As$O#ExFaZ~@RU6JblfW_Z)c_nit0mdBO`~@h8 zf3k&PGX$cWAP5+Y<>#Q7QvtFkxRU1J-e!NopMnDG;FKJY2sLw_nbb5Gf}#b&bDyr(2g$jv@=!ZX>(8C&*OZ2_G7dZx%1_F6xMR%AHLyN>(ITP z(`d5|KKh2s$~zFP5KsQt3M91Fln-%_h#ULDbT(0({{&4bypCyARY47W%c&Vz28#p( zbILQ1jAl*_&g!Rr0mW2dX`=Gn4^&)mYsPXvBH%PoN;4H1W%yFp|J4PA+|Z#I z1YNtn(y22jIWJDD8?HjT@edBjopz~1{9n!ZZHZ>lA|MQy4$ShYq$Uy^2QG%Pb%9t5 zbg%>(y>h9|9Cl#M0+CVJ9`Kq)T<*NBB3QTR9i^ks-#T8P)t76r!Cs%n(*4O>1E%i zPonOvTPV{=!abxwTLgW1^VupiFoglv40ax^s(z2omnMEDp*MO87S8@cY|;;pqEb`~ zkkiCAlC!S#^8R7dDjGq{;O&oA!*o%*h_mJ3laO4_X11t5_CnGXReO>;$ca>xzC{R( zj#AJ<6qPMAj*swS;xMkony;NYJb;;#%tuUxUG~WYeI7EUuN|R3t&I>HBn&S7FL1GQ zn**rT2v{!pQ44FM{PW;|#Uh4ZIQj6rxn@S@4{$Hx!7!;QjnUr#cwn0?f_^t=nV?z) zRUY+HIZV1N`=PqhZ!DLFrX=T#>>EG(4@=zYLXDQ~IaH?kMaaNd-NZ;iU-e%;&!1TB zfyL1H((m2(0rOhwLBE8ax-YW7oq3V~+c-5-RkY?)-M>DFd7-(1RcU)6;404a^sNu% z==HhEUV!O~UEWJbAo_6w277ic{Xj!>Yx|PElqo{XrigbK%i=uj;Y*3(0zOVR0@JeD zoReGqY;6cbbRM<#;BULDm&8ZA%-Vq>t+fvCdlDU3qng)Y27IuXtlA~Rl$|&Z?B9Jz zSYG0c)VC&RNls+4qYi848GjsN;)d@ByqXs#SIuFw`H6-bm)Wh>Ws64I<;8hh6nA{^E5=)ayz7!YT_=Iw~kT|^_tz00O7B0TgF~2NMFZ6fU9QIVNrL5 z<(i(OahrbjjkWG8<%az0pk&h=HG;f1qOQ@8`@%VtDtsz-X{3 zHivdgEp0$Tr2<~xfyU#3U^1*!ZQN>62b)gkRXQV#c8}Hs;93D^zrU&w_YE8=k#~Ic zqr~$OsX)HB!>;RF&LFHhokh`xR2cRkV?F@Z!nrgC`)skstrWP`cgQ=VDpU=BTv-Rg z!^bOfmogEg6h6Bo7_8)cQ;ea;>o%e(=x1C7LkNbF4y^fynB0IZ5ayBjun$Fe#(7ZS zMDEc;=5;VM-E?3lbk}LS*GPoYhU7e+AFaRbCP4ix)glIiU}9u0B&znl<-RRn6aEYR9xK5cHpEg)Lm(fY6 zeo%PSw=q`vQU;XKt|Ae%@R~|1i@qX_G(0c1w7fEqxJ+4kP@`cRsE1g8w+%s?gd&IE z1H1}+4|~h8=$-~NZbJ|c1V`JfIOWoiN0v4MmAuED_uIb2|J&*v$rDEoYg^KCC=19MOQnqxq#VDLyeJO|guS3I%S81M@LU;g)F< zO?gyZ1S*{-GeYyG2HfZRC)^v+2fa_HG4-1f-OhakXOZfGI56FBLl|VHj#f4i?fOxw9BC z0vuD*OVi2P7Ku;1Ig+vgTT_EQY~4Y%?^59Zg0aJsx`rud5*naGC|~U+FbLc=z(A>h zogU47IU*pzNM(@PGBXnrWK0og>fNlHRmy$EbD1Vsq&S);*-vMwA;-Zd%h3AiRI}O?z zVE(0S)ChS31I#kC>I5du&>A_{|{H4*kxRF0d$6=Vg6JGk{e9la1=V3D16ylmB5m3R3rv31yp~ zu}c{+{=B_(_{Y;QUg@q0TG+g31E<7|hzX4wro|?`ZAmuQ1Gf4@2v5+hH-b@_)#M5 zlpWxnkEm6zC-=u1xU-v>ANIQEormP$_l>0(?Hb2GsmYjk;AA1@e?*MGULPCEJ;!P2 z(jXdGZzNitAF;6h`J!fL&L|q5d33)9vi^If9=EUz{#vyV|=`my{o*cMp9KZ1r!xf1s_| zU=G1g|Ky|hE>ko%!SEA8TXXI>K%~7#AuG438Rb>Y`IMrnn8{A@K9;h%?wWcz_m&{U zjZu!*$?7sFXD0JKCAD1fsKUg`tDD=xl7xw(_D(V4ez50+T6ZutR>y7MB zS%&WJ=d+FY6g4+pTCll8@(b@}B}(z`qTFQ7*0aiby95Ag<-26Rf@m{TUu7S#wi0p1 zL8#sL#d}4-G2eTN3BCVGPb-cE#VmNxKEJt&vC z9#gF<{&=LOpsPU<^3&Ju^Zo8?V*@jFz?sUOXR^C>3*1d56$nM2!`THt$odke(=+On zXL`_|VO$PssF#Q&EJqZdfq<7EN4pC4^?+LQt`7g3=gs^GOe%!Z(LOY!%CcLDKcf;j zzfoT@%tIgV1vj9^DLDbA)d+>2B#c{f_EFp@g5ce`!xWN0bKK??)4~Jck-q~@o*udo z%4c*hbu2qcfRyWQ@YG)(?w~hrdTy8W-{$@>_b!A`ouRiN(5IH^^b4&H-!`W?XF5P- znsZu`2-TzjlrcR2b+`G8hW4S<&F)ai<^1U*0$m%FYN8ej&+~a~1W4`sFo2zpc`_Ji zNIGfy8Vt8H4N;=kXfwMMHB%@^YrApUANSMS)pAM$44om(fVIV@#1ie82s6A$Kmn?cC5XxQmJfE1<<_e?0pFSTO=l|2H6~^d>77&*ck2yC0c<%9nYo7a z_r;4Ru}dc( zKTsM@pFhIXsc-cYz*VwuFec{wj=1{W0F~|#!+Wyfwz{kR#ASrNc%-CgL*>fd_a7ni zdeXtc9BGD$hzOH(VQ{?~`xsa4iIl`AxQMh?SYWrxQumnyIR{-Xv%EapC{91PB;y8w zm4WrVFuKi|xFPiC={6ASwfQ{!;=}vE8P2D{!TPOYn~WJXjp-vPL1v0C*C(ztU4!$( z80FA!HD~y%w{TPH56uWyA3j^7arzy7TPCOMkoag+%tZ3WWyxK$?-x;ccrheo7t}K8 zrbuKR6T$Bb^H$)Kxe(h+6V5#&8ax!u4Aad;L_Pxss^zs`(r5# zR&oaIYSnREX)#LztINOkL*gHA$xv@rEbjK>`szsttHI{2#8emBL)XiK`wYzVaGe-n zUBG_wUi;VX-ud_1^)cn^%IVzgWW4A7o$j#BSaDMnN=R-8`hWsDg6{Z0l4)oCuLl(t zHE5bvWl5n#CLm<69vUh5%xt*ja1SgSm5Zk6swom~hvM*c?E#JA-Ly9fnrXUpis=A> zw@yg?7gLQUCW0FHmR(RU zz}CC^MvXa{Cl!CTL(YskoE#PXnqe?4PE0_l&3yLsi3>j%aznO0n9h42V5gI{=r_q# zSMDK`6k9qt(7uWz(PCGDLN<&P`m}6vwWe>##F+4~DL)w;_Sg_iyb|}Z14Y|B6(sv3 zweRTj0hK6u$a5PZYp7Jb zP_;^%>mt*g1!o>xN3XjnEw18)1ZqiD<-BJ)gD(~tQ|g#{rR?d?ic9Z~dv!5+z858? zMStqvAQpPPtVd73*6;{F{}1T9uv~bsV*1UvuZL zCP!(Hw{18bc;i1%>#fKLD(1Sd1`Fbo%BB@5wab_XW1)g=a>m0Rk+9qjv2B9W?Uap5 zBpcN%xqN=mOK8jU_Pmo=hyPTuz~k3W7!R$7Sh@O1YqioGgkt^je)VA7>Yl^jOY>Vb z>`*vBROX6kQ0$vbPy_K)F%b~SP19EB5rsaRv7t2>2w?qXr(?bva1wucweax$YA|(h z{F_Rpi}3v{P=0s@0*oiX_Sj-X?B2mILEIr1k! zT|AlXtX#r3XVhw%!4^PYHVYeN?gDjGQ^{TNzj_5A9G|3qzma@$b*AF`YdC=(kttG; z;DzQ%j;#UaZr@MY`@!@wO?1UqivS1~(W6J;0>630Zghd6$6bb1lDa8amX=XJvFF1l zd2pUTcEhGsm#~b9bKgT$2%X=%`T}??g$?pzu?jQc-m+#72U@~D-<80T(v{|g2fLds zJUA#FL$kLZms0##yzuENy!`Yy%t;rq|6TsuuacZ=jC$8qT&=~$ZQTYj-nLBo#u(O~ zeKFI#ilr+1os8qW1QzID9$d>`KMb~v8<@0i1Z~uN!sYElNTHQNn>J8`p49PuXwPWR zrS6$i?^FA=_AH#+_l*<-vlgD#+zwoqXa#rb8%ZXnC|4XY_`-LdVA-8Hz?R!!xUuaU z{O_?3_I8e+tMv^o^TtyS|B}=6Lb(L5TEib@_f`%?)d^fbIL22x`I@rx7dOCoP=F-4 zYWbGSap5@&I}yq>fk&vnGo|DcA~tDk_O5oDBtmXj6OE3|F#2?tM8CgV{1bd8Y^t|l z4Of_ykU~;N&>LT>&Vho4hfvd1>bq-fQ%hSin;WR6vZJNERjx7FYs>vzjhBW}Ws&}4 z6rw$=Imp(!Z#&9U+c5LhN#ng=Qlc{tDVFHnpfm@L^>~<6If=rQh)=P1X?s5(L0Ai_ zeD_qTPeWQn_sNHWw;@em197VcrJOaS?vc>dhiJ6BfiuZqrp$Od&j(=^;U)wNM}_gt z>48l~&`pBIG4fk@H-*RK)4L9$4~~n{X~#e0na{9XK<~w5vj^MH|UNNUT8}+SfB1|Zp zVPF$Z%gRHa340u^z(hYqyk~Xcc25#xvr-IPm*U^T4t$o;-znD~mnwK9t(a2Q( zJhF-_-Yk?yIFV49&$<{2IVlWb%mYRH6|O&hnlzXbqvZw z6>YViDZ-Bj&){GZ>!e(`QH3`Lj?GqUXY5%(2#Rtyht#KyjiyTns2>N+8QKiC&}{#= znR-?gbN8pzX>C^F5v=d8qRfcE;3ye-l-+cQHR0`6->8KrlWAa_Xbg`IYb3-`Fh4uJ=3s%$X!+sEuSw}It0oCc=5+Npg%uM0+`Yz|WSl9I4WPFjs#zk9{|&fr+2 z@V7;zkGf@9n)8A#g?N2P-o+5sF$c7%!HSn4Z__sCdy;H{2(v}Z=v;G@m-YL$a>xrF zddx=FlZJXV4lx3KVsaVSsG0)bR4Lkm+J)#xjy`f5l`kY=X$>c{$DaFKA*!+b9U0Iq zjH7a%L%UN~Gnl7Dn}{7kBF!Fy`nT;9j=>6(Y#YAS1qw`k5fAg)Cr&Xv_PQOkZ-5!2 z$p)>HCAaXfr%2+#YxTKGJ(&eotTxDomuR(FrR7WJH5GOu8Zpb5&DNY7&;LFJPzOOG zmEJ64q~QCs0m*}YY}SQo8L0^BZMhDn7(&Q8-a1#8I3XdlB#MSWpM~zhqhnpAWk?n; zsOgxTK{Bxv4l2TANQkAP?&y`Q*6PFz2#+J=c)9-!>7MwxVE%!C;GY8qr2Ko<+q!>A z;q7urPzk^Z>ivG-HP*u1Vf!m(p^4FObhKyXY0H2fDr{}H#_O*dC!fsXay*aL^|FPd z(c+J4!`BG;alb~M&b5Ze<3`QOy03i%tYLfFO0}oEjGY}^KqzLZx=^4%K|d}M55a}+ z3c$6RlG*<}GC1s{4Uk8M!z*yoUf^|VX;!H~P+a%N#EkQrh5%v049Xt@z+{MDmGX*`1} z4#I4a&zCRXYX4UD(6BgUlgQy@B*N4|XNdH-6RRD*Jin0_*L1V$w{;L)UL4vG(OauX zuCOH#X^pNwjV9KPrpBZWczQjyuQ2{a=Ajv}wgwdBQusO5=~zz}p}jQghc+oWQ*`?! zdiD%1``pID;dsA}>7bk(Tf>dAn6_mlLqFQrFNw$*o3wzarJtyqFBH|~rCUGz|6dVT z=WaZA$}qeUc22qQ7!aJ{aqrudSAt@J-5%j>bQccd=AKo@ml%?cCv+4 zFQ7U2y=HsMKRZyJWfnPNl?2FR{3|+sh9f4d1g2%?!K@5U^=ilyo81#b0;qjl)nvK3 zVy1F)Mxm9j8KgPFVqXVdr!wX-a9ka**SE)e>nz@@VuIpDmqcZAokSP@8KT^XOl!>l zxLAN8Q2tf(ns=QgG6~w!Q(F*Rufx70UT<4`*m*d>2uCtO00Z>i0o7gSZuF+G8`uaL z)GGkjFVCCiUgPfP#2AA?YRq1T0;)_<|CuK9kh|pS$i0Z z%YT?a+`+_D(#u#C1tuZHaHoau=Pw+9{)67@pRJCjb(*SUtCj`gpvF$WAmTMrNcQ?4pOCQM_Ybh_xN5>VXVW@@Wn3Q}4bRsm z-jmu}DqOj4+JC_22RMC+32$hWT>g+iY|L42W^OUDczq@CiA<~iYOtzk-m^I@mlbd{ zIIR9lg8>M_;5D*9kP|_`u5Hlfr7|}Z+UA}BqTwZLf!e>fn>0j>ar+N@Nic^c9M|^8 z5}NyT>F@_5ei}?4t)gRr*^wAl_oY6SP@?CMX=Kcd@@&{|7p|*fbm%MR~d)ikE0?IZM~U!6-HRG==}-VpEY~wy$fpV8-Lz4YUd1^ z$2{@6?WZxnUr+W9Fwnw04B+Fcyay(cSIOxZGWvah8_AU&atBfJmqb+FP@Nt_^?p%6 zt!7;&F8|k{1laf)XM2+4M^%j~eQinC@os<2R<#q+K_Ww)l-o>}T;uP4k;3Tw$(t1V z?I@g+TLI!RD|0Xua_dKlRQmyfIzBwZKt9C*U{}oPU@D07U7)+I{Z&`q8K-s=&u?z^ z;K2QO-m&9yWJ1z6KGLgbn@$-2wXiC+74mD)26Zz*(PjtV!?93uiH0RX9Nx3pJv!3(s6ifqV_;XmtUUyBz8QtTV z(^L2%*@J$~T1sS28zK*guYX-kDXrykw};T;kqudFH%ibn(fv=lA%ZDuRl)@M`9+rc?G?qJ zO6m7k>C(I=J3R-ZK9^(C;en+NU2lhrRl-DPXUI5;J1^m@&osb-@=-_|pPqp%-acU< zy2P>tDMdma{)n$4_FEslSp9~}{SvxsuXu#N=4;armvureq9J6p*L$|WG94QqUvSL- z@coRnd+(534Q|#?siRa(kL=HzDD|?uCdxMjw(r*iwlqT$BRsJIsB6KzF^V%GvPacs z)-9WG-(zlQrHWbx+ljP2KoVd)ZkqP32(4(W31AL4wU!eCK4jlC8 zS{c{mma6Pwjt4M6Do23J{VQsZU)aUQ%VN3w%Af6bKUdMtULOt4Y1^Sm($az|IU`c~ zL#J~>kG=^UD)5TkjyLvfUPm?P-2ZfnSD3~CjSjuNYz1O+tC4&?j!aIxI$}A^L<}O7 z>qS!Ixs!9xD0I3fHx%$|;hW!`1^GIzVd>VP`zk>cJ*6SlN={3SCI_kvnX7nAR{5+A zbf+VpB+u3DzXp6(&4Eqr&c*Q8-uIvLY?jn34m)?YLaywe=;}Fx!73o#_ z8>!L#BMk-Nq4Ybfjx4O~o1i3jyS|HTn-My?pPvJwJ=GNv!fP8n92cW4Vi&BCz(mpQ zlfi><9cqcYn_|DeH??-AU)!Sh`zAlPJi<;G()oS?hujC<;NQF#-fRReJd{UJ?cq^F zDpw#fCq=1bb#^Djb%JS@Zb7HsHn8Z!fL)^Ye5A+ zGC_x+l(5Z}&}o(eb7>vz|665A##K_vhw6K&gM)YX+DZJ2cW8P4z~EqS_vgF4gR5%@ zAeH|a8VY@UdMZ4X%WwE_zT)u0h7Y5u+V%dhcBA1f_DM=g`imimUQ{i%)2gt_uGCq7 zdZt1`r6);`%N)D}F&2FpeWC5=SbSEQM#$igSOM=unCtwTq$l9-%lvrj48I+{1*O25 z9vRj^%-n??yn;kGyL|+8(3Vb`;3$YLXM-5f?dgN^kG`-!03&QP5IoiSUw09a2csL5 z>h6%Q z+Xv{N9{#qnj1PY4#o|_Bp5SrRpyH8~@l$NO3n^m1v)EJ$=X+f4h9GdW56DqxfcvY` zF3S!n;wUl^=?tuuK-kOTudi)5fSInPgNk0LMN(MUuH*EgsELv0(h?-MpD_qPB_1W3sXA!R^ucw0N>} ztv?=h6cgLSRXMF|$l!1s%aA;u-@T@gX^i4``^gV|!{4`>i`xCyElCRLXiH!4V8zNi zw}??wO^eJ#u8>#u271Xfw&eCrBhDk7uUo&=5pp7uwmvlH69x6uHg#=($KM;n+xDJx zH;Z)Q+KEM9HPR_w8)ZZI0q!O${cPsSVmb_Q)AK9;=owSbtyR#ip!~Bf8rEdkKiy!X z$^6|mOcjfUs|;6LTf4Tl_NRT7*6E+ij55dN+(M{aJG_<0zyAhCA1wb7nMkK=YC&fjsDEQ;;m4y?X3cFwEFxmZY%JhXqk?>SaB-< zj#{<_c&!jb`tye9lh2sZG_X=ee)j`k^l61^_V-q}LP;6y+cFPWvst;Y=JV97^n3=S zR}LA{>*8}xkZU&sQyZcM5z~H@hvyLZLyp*apf_(h*Mw=++uY6%-s7p#Swhf930v@Ujvc^^G!o)9a?; z_4C+KRC+_Z-1r-OSe4oPXkF5OW9z(QFBlWX_{j?GQ;ZBjf6=}%`SWmxc)~=mf(zlb z+cjYS^A@=m0SA;@Y%o{HdM@7%Z_SV!Wd3`<4B5+TT1cK)ufq>%DL1)^)yISxk>cL( zvuFdj3LOB}50CRLIOeiSQt<0@JMbUFvWD=}1AqPvOe+sNoV0L`dzKJ%PVfI?zZb$H z*y#5vH~KmKUlaq7W$<|Kx}a1`*=p}3iB2DfhEjzRaBb5)QZqO!Sl2k;2378sdul_} z=27KThw8$1@QipT&=R~tq`cXW!gD81Nd!Uvvi?Pn5gQuU{4;^LH5t1yuw_h*)AiCC z@t>E>F)uE#IXlALbX&ABJ@q0>TGRHI{Dp!%>&_F5jcH7e?JzPF|0FSohkA0dV0DKt z+$KJm3%10xT`^pW zruamTrGhc2R~xmA4z-zOxoiFB~Q9Z&| zgn7JHOe^31RvtgR;SqfMgC({KG+k6xNQK@Z*|ro@7KTdeMEz@tM91W337kaJg0~bU z9XDSXX0AlB6q)a(G2xYW4~9*9JLCpsdmVc2u06^5^t78)8~Sbo3nU0~rU+NDS;>+s z9PJ&PLwx%L;=ZGKHW16i*WYEgnZN53QD}|*Q>*v~$dK@AqGhYc`4)A(>pGqAY~bQ7 zuGY+vY4?(EFv?t#O6@n*DQET2Cd`D^l5iV*Q9J+&UcfM`^2FSlnQPg`b>Ufhkc1wu zh)9-+p-~x_MW4YzJ&yU6we;2LjE5tKHzfb#weC`e4!;23-lVS!c7;kuym&iinx5Ma z!?)v+DI_Ugc*`fqvKZ@WcoKK@gj{EIADLLduW$Uj-}$F-actucT*5A)tSyaa^KSxh z=SULF{3d)Lgv<>Er|WxXpWC)2$S@4XiE$^A7raT)NWT0oXM-*=T%o}X3Hh$CKO&qg zFXldq0(1;RC{RbREzbz#lb2}6(28*N7cZ;HE>=JmX^TTYSNN~qPf;sZD$cX*9|kLu zBIT?Z8A8X>gj2w^*5Ey}l2?~W$Nw~o^x*jk7l^dDoj*^1BrsNf{gF^Ka{GhPf%K_z z^pwIXJj`aNM1seKBQ^S&nrxjGD^CD6cH(!4!6`W*txB56Z%3Z@P*rEP2xiU%1UbB+ zr6uf*c2|*nMn7Dm(KN=m5CrUEA9;AH43*04r^}a4ZMasy49ZzsKDrHYuuDcUsfAJ+V80BY-#589lM|v-CXlerFxZ<~HO_op>DHa`vfx0DXn@%n_E%F*;@*K@@`$`}+n-h@BL+ zR##o|mE~)922wd6?!wH>Y+u(T|4f0Rve{Pw(jNT&w@#+LX+XH-%nvbsVbDJ>8~Nzv zcbPdhGIFI5P}Utsi8V+d35TwrAo+*9`Hy@5_F?GFs{l&9kvB&5#tzcPAMR%1H7xED z@KIzV(i&2VQ@oam0{Jz9&Zpx0ZmtZu8auIN7e!sTB%(E}tJC5eRae8cusj!uIDY?U z$$4(0GkDUIL43&Sy#=8{z2D;Zi3QGz6_T=Mb3IRo_*k=ZmO3|Dx{eoy`}2w3m5sHh zIj5$orB^LnyAyGlMSMfpo83E0fITC5aPQ9>&vdmu%){qb#l23coJb3+Q5d!mkick0 zl6A^-q7FVBxBosK(fZ*Q>w~YV_XTF~`Jt*Y#lwVx6z5rAOrIFMP=$uoPHm9L2T_D7 zOxt!cf`(RcY~Fe+N!A&><$MPZjp~893CdH>!uT136t>69F_d(%0!;hY2GAHjocN_+ zufP*)pO{OZhsSF~p%vp$Irb-s=ia#OdRyrx)vxyH2#Fa<#q4C3_AgZn5-7H!#eum| z?+Y}?s?)Rxyo$f`K3^PS%CcW%_dBz5`Muz_WHXts)I%;IWHTtyGfccd(Lz0&8U%vb zK5yKvETYZ@Z=FBq7pMcu$Ov0)f%kJL`n#V;p2%Jl%L(ykxAv-Gls0xM&wW%GR4O-c zojl6busMUo_6>50l5Ih2_y7VX34aNZH`}0w3A4VY48i7j#%NuEHnGL)&9!sM56^AS zul7=&T+7pjNAxsV8zyZ9{N9375Inc?>kp)VFU9YZipn{TypmsrqwT-EZ5JlOOX8{< z*^{baAkLd+Ta{C5fkMMGmnTK4R$w!6y4els)_?T2Z23Vv*2SLl>6NL8WykGes!jIW zDh+L5D8rj9Tuc6asSHr$2i_#f=UkukTx!iEY&5nQIO8L=@VouaT`G_8E&+ha$qo)$ z3eXOkc7|IW!7h^7&|*zd^MZ`Z++fF32?;QCdWxl4?;?n^Hov5i<(;B4E@?Gb3Xn{1 z|AoJ*%`OTvqarUP&cJm54uXpKc0!%srFI-7Ug;DGD^SynMon*pr;hc_ zqmMs2RSp`>;kudr(#ftZib&t?BUnTINe%rbnf#KdJ8ivU-U?+#cM1j*rv{*5@$C3x zn2+)VaPD>_E?8o>Q9Xt@8h%=j}S~*@36p&W#_}$}CzaRD;`CFBw}w54YbNYP6Lr zLGA5$yBl=c@|%ef?aK_CnwZ>hfCT-GKx+YA-U(N!_Th>*1m9Kd98Ly?>`ZTnA{e&} zCRzM0lp7^T#$P7kw4PPqs(P$nnb*5-lh-&bD(R(4nQxL+N7gLHg!P2<{}`&Kw>6I3 z_}XV2(MxtXpVw$H+fEq?nziNstmNVw;*#=F+;Ez@)auahSq9nU)so8QI zq`J|@-0P-LIr&fkJCX4y|Jy`PuRS02LB~R553&oU9riR7OM_#uA&z?)aGLO1HCsya z&tV|yjMV-ODPSG2D}~5^L}lM4VZF4bLVs_zO!UE>TA_kRKjFEcsz#;OLDevk-c<{e z<@cl&k096fpQxx+Q?p@zC|;X9FE6i3r3R{46qe~{OKojWhv)6po9D-woOUGQe6E0J zh%+U~V!w-i{kg84>XX$fPE|3wglmz_M0-dI_oc@fYl7m(YffK7Sj5R@Xd(|?`?Yw$ z{`;LI+f*L5h20G73m{{hjD^yZ8ipQJDOV(F?(|em`wWF8r|6g;5qQWu?(_Y;@kHZO zH9J`!sU0>N(~}Uf=l4O0Ujnx)85Y*NLN&)a8+RAZn<3YW2fRb^iB~w3c^^Q4-EhnPqekaiyhGw5CmKbE(yFRtrj`Q0n=4QRQ z^$f+~@LOIw#bMtL?bC`QgI$h1!^;3<_O8EBZSq(DY&JXlpU$Mjw5u>Ibd-Uj;s+Bv zY>DRupmL^5Z<^AkMk#$vm*q$D&F@8#JYDp6^p))`%CusaJpx_ZCM#vGvBeZvD*Nh8 zVfj%~G?3Kax+!J9qM-#+%I9wH4ac2-s?5JUALS{Q$Ps#YdYZoYfwsD9?#_CHDqm?M z$~0R@Pv?((6Z_bc)qX%x?hImdZ}9k|48Ms{mLeL|)dixsXTm&c<#!+y&?Kvw@Afd- zJX8sUAIY;RPb(c6>F2L1V`SF1PyeM=cyv8s{TM#p z59;+uPR0}QdA0=CakuXsYA7*LcEru#gYC5E%o9XP+t7A6q4sB%U7j!R7e9_ZKyBxn z*xa5AkL;vYL~(bviFlg~Ei&}D%|uDYWv4z1A#z=iO;({B_$4OW*fi#)wfrFX?81#_ z=!`pF@>CqQ)Gg$}2)mZ=>hMU+3%sFtlYg*pXv$O7WT&S!1WjcwFN?S?kL(IAzCWVZ zhB6)&!cbScF)YWuE)jkE;K}2l6g}s)TbmL_a}ix6Wg~F{N%6@+=Ir?Eo0JJbwT^TS zlkbI$#-`L>yoKlHyP{)B71ERxRI~g+BFVhEO9W0)+>`qL?;+{wCam>xIXu17@#^cP z(sRYK%(km+Pft(ZI5@CoW@eUK9m$mWP$MED=%^XLeRDn>Tc}W1e=#*l=C>C`NJ!8! zITqu1%u%ILSr9{*+fV)vs`t-4<7Q)>b?EGYXk88sj+gaWvO$Eaa3Du8ZRgJldkvVF z)$7-eNBhlVrW$}M7C#}4u)<;At%Y5hVx1bM%K5>5qla0XD4$Amm$g5>KM23s`TR3S zS|olB`!zB)f=5Ze4qghqpm+uBS;psa5JO35cdRA--TuE&=oA)D2<6VqX_^6SBtf5# zXxs!CrXF`jqAtG_>sJagvVM`losTYWW_nT?mJh8)1(I=&mOd*=ch6vkIOq3C^tXl# z^)D&(y5QZ>E+db{m+FiIb@3P^91IgPzsmGtFJV?VWOQb+V`5Gn&iX35x;Uq{L>dM2 z#@9S6JEcx6K#7~Z=9pU=O{X)t85Tg|cMq0mV`pKA-~U14`SHX5>8$e2;9%Pb(daxC=Cc2EAZM#o@M>~9Q=?&ckmYi3uJk5pYe(XH6;t#x>!z&Hu%-#<+t*x!UR4de&I5@<+{ocP~U<6~*s?~12zCE*sBH{+R zgPux9JEBAQKX;y94z#nQ7sn-Hm)|a2_Eq^J)a~!$1-R(N#1bHQ3smub2@r7M{7#mez)P~H$`R8ZROiiUd)fi@_(uB95W?SUgV z`h2B6)Z*{hkgY?irZ_O!t6fjD-(=O>H_xxskuA}BTbz<0$HM`73fU4lUA@8nW^Nhn z+rK}Dr#5@t^zVJ=Oq{U1K=-z*-~zhdtXq`Ks6hP9Z?2of!Et@-Vgrjc0IXQPNeunzLqW$KsoRmC*vV z=bJpm*$7k#!ERp&!qwGP4{(oGsMKIS-|Wo7%&8a7U>Thl3PmA~?G1uy@E;x??w^}e zz8G$G2j$K(hGE#a^$w=~lxx!uhJp8`6GEwj(`o=#D<4kdB zq<`lXHK!O%IdzssTx*h++w5<2S~`KVd7wZ3;>Y_NMOR2K7>i3ebS+<4QxC4y=m!Ra z-qIUYb@XAGel_2N`$>q<#PcKR_3k@wG{W`p@i%=w^b9oMhGZfb0ovUCV;~rf^k5A7 z!R(vGWQA|hX6R?`ctVg@yY5`eJ*q0v{WBij z8bc&16oD9uuj*C*Uzk;O9O>sl52IYmDsWkFlM`{HF&Y$pr^A&ebm3GT=I6kh2GR;OTA+Ld*T`oGxM5LT$MoL9wZf@>u4UKBO zyw!4VE^!5~(-q63VSG{W7e!_=6ZPm-V&r?Rc~PWQhPEEvX<6lT;}$)175$lc6qFEhEy59F_?@iUz0a$y4`Ap>iTeh)Q#;6 zz{KhWxZ`Z8u$Sthl6l`)557svp1EtZ1h>$aYIV}_y_DJ=W-qO@jp+ZbcEq2oS(zhq z(nkb7=1aM{V#-J-F=MJ#+HgaYf0tNINQtD%Nqha`+sj1DcGqnKIfKcBRF?ONc#*bB z-D~FTA5yD-_4{u1)z=kr%f7CRcaV6;_=ql{Hh!j9-H2WPbbV_jW&v`!m#Wb0!JQ()#A=33vZTPc37#!3nyi+fA7Kv#>ObpViAfeCM zVpUKUzCUwr4x3dnT?6^QU3}!vxHLsNTYe@0!cOkMdnRfuVyWCGK{X zW02){C2qo1?0&NXR-ZRL9~sJLbi#XCe}PN~9E-nYPQV>5J)eb!wzj%oqU+3o?~0A} zbquybuQ-tbZm_dlWW$Wz;Cy7v@bL|OM6A;+glKCezf|qi=n}8<@k)TJwY15eTX`o$tLnF$^3J(D*_M@m`Ri8!^6>J14 zx0_zR>fY(`(PUiRkf2wGkr*x5%N1h6`=aX}j6eToBLzV|@mJz%ynjbb zunQ>&@Xw=mz&1T@#PRyJm8O1n%AdT`BngK5zk%PXT|;I3A5rK0R%!dS@liF^Q--Ou-z_fNQw>pre)t#f_Ob8TyhGM@1KmV6ZRH90B! z&MUpSL_tE%oo7CC>SfDSN*~(wgazYzJ+U7pT&Yke1Dkm7w3N-i*7RI?l2e*`%lopq zPl9)~t*T<1f2#Z%FR|Xw8{Jt!a^%@#TtV{iVi&(*cyp?IbdD4WjOCVW%RUkaR?i!3 z7DZCVTN_Dt1KnM8FE%IVRTq+OP}$@6M61r;Da?0bg#tpE0b1Q+dJ2k_wq&d}Q(j=| zot2$rPJ9nC=T_s+4ED*p&nu{9|X(dVY7 zrWOyD6|ci=zjwoTDK1IhtQ7|BP8BH=;1?xNbAPc)t9(nk&b+MDT5jfM3@-%H^Qq2 zatx??J>uxN>6(DSzTV1lQ zmtwOkL+kl%zcY>>+D27r#Q_J88dNpilC_@f&?-GtW%M!oXLocu`M?rX{dH7rzfv8T z1iify(C_?E+!4%V$ySA^N<@}ypFJELiqpCs%LVEjF$4c4&Ua)Mx~{Hl!dP9sQf41*@!4VOBy7Q$8?+tR-NI$E z>UE_}Jqe)7XE)xSzFV$8VyvGTzF6AR6r|@gMPtwd{^{u8qt^otcV=_g5r{<-L}rLx zJ&G)pd3$R3{^C^=w?v9NFas3H9j~QfyNw2Hs`+=P0V}nV+FMpYCxrxY>nAlm?XHl*9gU zOAi%|!)(p2cu9DZpSd4EL2^L`Ue@*q&5sW1cWZV|lbIF&?;`jcWQUl-Oc#N2?<^IGQT3LNa!!wvB zv5wxBseaS)qe&?B`ZYzx{M%=dKcr}4dayUTU>r- z?Wm7JZ)~1sldfX-ja&E6R*$5-Ozb}6Im%V&(BtQS!C(D)+{S{a-+byqQH-`XpmVkJ zTWluiaO=r=JZj&_PRj(nxhjTs_jxwR4az~E)f#*aaGB1LWNRY#dpIXam*MogMqH#tGz=`REbqN~+zPK& zIwHl)sQb%dx6!?_f=wY$9xWl8$qf*AyG3&hNTg9OdTkdDzTF|cp)OL3R4B1OHhkWC zqrl_u9?~<0g874ARvPV{z-))L$lwbJT1UwTNy%_Na>P)Su%HK*M8jXib2@am3CS-#L|0sSo_jheq8N^lt|#vF?L4~wP9&CPn!pQZm~Cs%j;5r9qu0C zLG;KOrb!f1q^wglU+Loxt_MFl?aZ7}JT|`MB{c58m0z{^9;>yz=xglus1|F=>hSeo zVjVTsJZ`&4FL4M(WN6i%z!T+aySu0yT^CLd{(W!+IKGYJuW=4}331&jUtqzn3`BeX zPXivG<)FlLJXs~t;r1fDKc_QXswI>1R=iT4Z8N429@m~hN-R$lj9djU8Z-dl;o+9O zP~m-7!lMZn(o04zhM_1B=IVdTe}kdmxzyIwG`!sG;q-g=Zqrw#u5)uM!KXJIyhdkETr}84~N) z&w-vr&4JgZ`OM|~nDZR3er++isHGEvJSUr|r~yACxKx;f~?y)UNda_QItjm8SSSzBTh{YhE2Kz;I`OhzA;fy z<5jgcZI15*$7LteWS! zrp^*Q)3Eonc%pL^AM3%TDs7QXJ)<>LDfKSs80WarOkHk$-PyW^({Id+)-&7%UE{BEIcQJXFJLTH@I5xxO$&-cNt8-yw;y`1B8hA>e%Qa?rGO97S{_52 z@{n^hsHK1c;+xIx(p}QJYzl=nWQsKSKz*1fDlNS*q-Y_v`NuqC zvQ0XjRn^SG;nm)e+b-X1q*Fc22*333u-rOOiIJVdfIz+pU)$)~E_0^L20ux`=kX7# zVZ%=n%na7?jSes6cc0Df&#YF9KOo!5Wgo^SqX?NGd(N^2NLX3X1zs<|wnK0rVJuwS ze7}!(D$#1kz0sbzIkc)O;FF`Te?%-m)p#Iyx91l>KYM)&S2Hq|J^xmX0O3I?oGY;a zInGPiJx}7+S=mxGH51Z$3D;w@nPo5V~=;+Z?{_O+|z&U$9 zRvMSobasR6^D)ID*X3vmpY)g3*0YMu`C>lmrA^a~-Mxg&Q6?t3)p;@efKg7rW5g<5 z8QCw^zYRYXPgbIA`{1xpSc`9VYW#c7>NzEJHMq}zn`&vTE~@|VE=dle=NIaE-caMYxj|VKVggUVB5PJ^p|)}T`$F-Fb+^4)vG}-vepRrHX>{ja0r3>L0G`(Tp(cz4jbBWfVUAmF{?gn#8@f9oO zpw@uM!KTU&KW8~Se6m(qjVv*s9{u@p{qXa_YiGOV3H=Q97J| zw308vK9KVVPL#eceqv6z`BIs17tM5cnD9c}$LNanovV35E>tTt?HNY&m^3|$oEkG6 zoj3qf{;BpC=-fuNvqMPZ*Bobmvpzw(igN*P{?t+3o>ZOjwMVbjJX=XG@3=YP*ZaXD zTnrUti17Y|PC-FYq1%oj9P|T1SMc%ip)=^=4TJ%V_cx=Wq8?u^x-^Bd(s8R1N9@c2 zG+&f)J1L!h2^+fY5yMs5RG+{n>D4KrlVHXNelAFNK z&2;}Z6AE&D8U6b2_qN6`T;@y4rlAh8KQQhM{5^A)jPcaVE8=nH^AdwV_qM*z<-x5_ zh4A(9fHqd@k#RScm3cC4k{$iFl^R{Rr0^4Za`T0N(W-u@`-kAY)993f(4seu*Xi|P zlxN#)0$G@2HZsni^B32;S|aS!>dbO^wCOo34i=E#s~FC!LU9EBe;mUX8ZWND^x00Q z<2Kf-ei0zGR)0ia0z>_uVId9Bn(yMDFuR2@xAOfQN0SaP-ENpRx?cX^JSV17Q0Oiz zqiNP+d|)Sj2=qs-e9|KBLV{@^$O5|~ug_1bFmxzxSQyEv`wXtMAn zo9Y=}x_S^{WJW;O%YJEG$IRu3=-maZ%z8w(Aru)B@+1(V^=e3_Og4WOl;~@E zE&*lORl$eC^7f^@2w97w0kBcpUQ$SVbgJb<6Yb=oNR1wn(Yd*^&`U$YB)`G6Q)_pc zj52BF%R#xydR;7{0X<0Ylj%$TdnEHYr&ZqCd|q7B9#{;z|0IAV$NEZ*2Ewpd&5_lr z^&-(I*k{%x1`rPJH`pfmt8=4aVraiHYTBBgTd2M<)JS>}Moo%vK@n&Q6_edv8(P0?!K zg#>eDC}-mQr|XDArj<;>5wAPM7ZH{t?3o%_-G+uu_~>?wCo7pflP%j2ErdtXp$Yi6 z1v&b0I5oM6l(j{>DcA~aRR1CUZ7azpSqduL;jJW^cZeL*}OSDT&)VM7V;;?#Lb z>{JM5^T0xa)NQe3CNgwY)F?7z2fx+6XtVNCqRY0BKWchSE}zxWTjBQ#D82ik#2sT7 zFDGenDdbDay$xqX{3I0Ug=9Qmmduy$Uom|9cb;SfL4Ba#-f5P>qew5Pj_Y51xS;*G;VRm$mftt7DB3H zzmB=1_FdLqi79ilH=_Pd0Y5s22Pxi3k;`0J{H~JxSrTU$0pjKU0{*fxMOrEE=P;^_ zf6LBnLEJGM%6F@KsnKYu!QjsK5PYwMaS5uqhDdL<^ybn?OMfajZe9WJ$NpFKE{t9O zc$vIoDD>Cc zB%grPWnXvjI7DHQf=r9!c%W39|zw`HzxVrAJFRPhfvLUSpLHku~wqLHf> zQ7bmQZaDZ&6@AYv6jxU_OC$`*PoB9u8M6n6Enxe5bpFEFG|`LXoUFI24hJuEnvBli zP?`5Do3!_4LR9^$fxB3~3CMI-lS~mgqBogg6P~nfPbzQ9{-`9SXu7hX??>+lT%Xq) z(W19%kYIRPN}JKodIe5YcXH(kQ^*)7PD&7^viFczjm>3lryZ8_Cv}2?VPF`$QJ#%DQ z?Xf9&?Vb6275{N#MW#^~Q9-ChKteVZGEL4f?OH>;V%tkh$|dShz?0Q45bKYnM8W(N z6)YtMmGbZ7x(m2gg^ljXzGo|sI-8yGSTi zT`8vJf{@A^qFvt}E&aB4l0#rQD{t8|2f3WU%5Hy+#Ovd!U8sAOTLH}zULG}4Q?*M| zBNS3lCa#q1-^W9W&Jd=1fke$V2gCvp-YTL3H(wE#zt#Q)Ea+aIE5fTfU9Oph4KJ6n zH8?LPPuOK-?7Co?5Ev+|{NtsE0DcM4--?4IQv7wFb)X^4VA35EeU6&;$|>)bJJnyX zlDSReV*BrcoNY-}AB86>!$wzMMM*#nxE3Y1*>*R5Mgbe!C1;2Gb&`(re69b#zXMab z(uAtQ{&aYQp>gbRl)?V9;ldkeKfQxLHd*I!xJ}~&zM)f7w9zX<1A=C^ zx6W=UB>G`zoMr8aLmoO^aN=|x+ni5v=!Y&tj|q(96XoF`a{eFWSfdDFdda5*0+cMK zwG)XYhwYQe@J|Da7=^v}yz{g*J({-)wW>cTk4k$q4aKPQ453aTL#6Mbh)JD4^ z;;gocB&L$EeD+V zE^K4_TW;07-3##+(XwHHUy-ZFDS?M&&*0%KWFpR zcppeuBfLTmAE7s8Wb$yn9bf9ivN!{Xj?(p%sO7oG=}-j7p;e%N1A9BH9;Ls;dok|g zH4-?Tilft1Q}Sss9#4(&%`N1Lu$kM8AIvj35ft zI{y4(q?tQcNRu6>H$4JYr)Jg%+~KJG)thi!VOCSGws4uph$Ig@-!BK3O}Aw7m(6dJ zfcr!g3y!vahJEKFa27MdpbV1xB~*pi^*8bC11ke`bD)hKbfUXevR`Nbyb|Y{$R?lWRr4 zws#W}LK&Nxp!1uBNk^g2OC{5VI>37}3aFKNO?y^m&-{3_-Hl&ChbmzSuHGJedMU9` zfP$v8DtqOOJ|~x;#T#yK6k@aB6w^9=0|~6`YG2~GU{r1aPM_oTm6I!Vpz+(9q^Wc~ z7)+9abgMU4>z&NRg%bb1%0I z+H;9+Y|>DE9o1ICasps8>VRt*^R?*BXzz;oaVOqdp|?m?r4vm8nxw}7KDw;%RPC7i z#>t_~BNTb&#P_uIqFq>`c8uo|K8!^-IDxE(*DLAAip2~#i*6Hj+uYmp2^eebjJIPZ z)9(+(InJViGFS$%K8m&MoX4p5!ItE)O5}Ee`7R%;?MCv&1m=27X9I;7cQiGSz$4{8 z;VVwb4i#za)U{m)B~&v4?oTH+CtFTuQfXat1wx`p`w|xc4PvsU*Bt+$^YdNam(-zz znRs)}UvU5qa5F&_li$rmoEuMm>n z>R?DJ{DzVO-@mW=CSZ%eTtp0Zb)62*1v5G z3z-|HTho??GfWlrmMt?!{CEKSnNN#EM}qGN6FdFObBHlBOs)aj{&V&JmN>nLcVgL8 zYfK0^OYLCecnPk#3h&=E+zte~shV!4eLK^#Fjl$ANR{R`Wl^L61h=0pma;$W5kWYq zxei8Zwdck`b*jz_YFvuB1w#cYMG&avYv+Z&Od6h_&17w zF}c@^@V^Z@P3e3EQ2!R&fp^i%4jYn zu2QN=fONrcYT>M6lE!5K!1!pvXUOUlyMRSBH_bW?E`U?Pf?XMm{=tn@r=YnmQaaun zgQrIN9NHYLP~#QLAdsh4#;FsuQt*iXR0)?@l71^P2#Lf34=kW@%&rsSc$Mq46R&bM z{7MZgRPGH)Pz0(tNE&Ty`MfsDx&)NbVn^SD{cNxv>hB4}rr)kL;Dp$3!fKUFrKPF@ z{*iXL2iPkzP3CsBYUxQFM6Vk(Q3+LWmmYi+MBLu*f$X|OQ|nf(U!%qcyV(C%%KLAF zPuMmEH2MNqS=7{vys1VgRa1(eja|^kR%Y{1n&}Jz90kasntaAvF6^>8!oz1q6NM4; zI2V>eC27}*+xddHqYnzZ#eq@vn&%OWuGXQevX3DLHeDCWUe09~=t$=;n6TBG#Y7Ag zt{(#24n=_ust_sD7uKybWc%-{XC{-KOm{GK?oe zZ$VQLrK*ah^Yi%}MFp?R7qj$5gN+$scXG@ulWXMvD4ys;$`m`j0M%monjO_2_)7wf zEPXv20n?3!Xs&Bx=rUgrb^AM!HzZoc0J^WdYgNCf5}GT1B#X2oz4Ux0V`eO0WQ7C3 zzIAD3h8?o5p-O-8?T4sML7~PhNn)})PMN{lTMzig(>@iMS`S@5xeVH0sdFs;*%F6` z+~%;gCYyhY8rJrUQX4BYO0+;Vueh)nikf=L6pxVV<@Upg=ldF7z z3Wt+ZSB{f%!}J;t2|I7<3hBIZ|g~ zap;_;(-(8&^)U)Fl0SKJ;>D@fcjI&X5m;Q!+=$YYXY+gp$#aj#T{5#bv~Ht6 zrf~GT`YfZ)sn5^vCvK7<^cWr)mfU?=a+fJUHT>4=Y?R z{etZs&n(^oea^)NiISg$(nD3gmIITe$0;kjR#UcC6}q4uVqXRlYkdt5S33gqvde%! zuS=O;zA6$=hKYgxp`!r)O5oo;+WMR5pTUrFbPvGJFWl1Pd%$#}_>sR7qi#RmmHYVl zPSA;+<}NJOa*c)-(J?FS;$QDksMGRk9Y~O3a(8apyP<~+Q1X&jq8a^mvfUQhYGEXZjD zFb{<7{6=#UxyAmM6`7W&fDcm5vZv?YmZIYxhO3clk?Z~ohxMGD>VV$Ad1uDW?;(=La; z_j^K{S-wI1Onl}|g1>Za(fBA%sE`Gxi*jh*;dN$!RI^X=E#ix4(bc|ggr;RZ7Io*h z5T!lJ%1WJuT}2M5%65++J9p0rmtJ7!he6qx9p|x=KiOR11#d5)B>|88G;r5P@PC^+7NPf&N50*%e=&cUL=|sdv zFX>m9dz>hrpAlQ7kVc4_D;x-ZAgsLtGwM9Yg(W9%5ORg;*vHLK=8Dg<56zP%mL&(uA)?+~MnrUj+M8q7$c>Uc zkL*%=Y#R8SMRl_(ogT_mG)`C=(VEW|TiZEL3$sQ`1y9--8}{L2+A*x;_CyAX&W){G zZ6K^SF+1^2o|db_!y;P5VcQSfWPF2}&SGO*V%|$$IMfFFOuk7dQ_+D=mn?CI-T4U} z5JPH4q2q|sJ+<)(n?~@waEGHK2MoxyIng66l+P5jN=b zpj*u3O5H!i!!OSKfm6HVnpKE@>pU;*9|u`t2KAC*`ZvTYVc6!O#s(3FMQ=S8412{D z0AKuAP-FXbHDBxusH#Fzhz7`H_KN+ul=~vod}C#iESMA* zi}~UZMx!E#$u;bS}|3+PZ6T=6O5v2Q;eStXM$WA^Ye%`nf zRgNPpvkJTBwAk|EI6#4x#Eg3XG^Vh-!gs1cUeO3KE;lxUXw(mAIPI9^7b{DkOF859y$tGZ8T_ zLEPSY-6x@$;tt>ajs&@&tiCh&kwkCP6c=`}$RJ>t>5w-=FuQ}ddgdyW1(2YNX?H&X^dND zsPweLF@xO3uR>l3f5hK{EHJW$Gdz|GZ)!H$2!!7rb~Ik!z7)U4kKiA2=IvO7fPI&( zwWH54r?&h2N-b334ojR%fB)IcdaE5CjKyze)<@C?8Y5FNTNbglIpi56zfO+zb7<73 zG<%lh6^9c9Bx3$x4WzbW81PJemlvg=<}@7?{tfzKx7ZCJZ*;+i2<@R^;_aAE4xk7; ztsK{QoH#ICzJ|{Qf<|)fN0&J06lti|aA@B)*hi z#yM^ePJ}(~4&{ovaj_az8C}-`P}7|TWQHny4QZBJ`UO07sT$=|r4BdG&27_dF77Q; z1BbxAy+8T_mmhYO2j+xplX5=hd8L481THt$ zz&hmG-5qyEWBqQf*#QzRSsluZ2dybstV3lBdM^b2(@;%BJG&WSyqo8aj5y9s1YBU( zoG|zSXB0jMB^dj3%4g-O+us;v^t&?_Q|bx9lTcj0+`JxHS!;}D@4|nf!&QYC{qlNk zX1!!I4*3#b*JOEOD{3=HHaE$f3&)MU4Jl>i+3Jc7!4_(FsBZ5OT)hj~xW$KzC8|GbSYG&hw(0ACnOTw>Ujb<}2FNqf z=$2va%h-#~5GygXy^gPv5mg+Ln#_|@m;=W2?3f1v`2aB`?g@Irf1Bo>FpAK7)f~38S<(Fs8><%lr9~Yu$mnEcI3XlI7a53{=NX^9S~bmEJ?vbMFyB2U)gl zIUKI)*6m8W53_={Emfk5LBK^*R-Y2Da)c zfhx$p@QAVkwmIA_z!>%Eu@N|#;Y%j3{bzX(1el z-d)U63+;Pb;G~n{TpP!yzm5BlP4n5)r*3D zTHwz2jW!{fOSN~V^fVlaFG$6Y`Knhm0Y#qGoR!x7pq3&qTkjP~6yT2wl&Nb1)kj%*A|d zbhf7BNbHSOyr!eAdKe%vJa!<-iILYSFXy$SMwzReaRU4oB8PXh#X2l?4S7&_GvvA< zy0`Rr-#})_=^r_aK)7Px7$wLCbF%E&xD9?c?5T^JqX10%Cr$>hiSJ+gxW`L$hqL^? zkWuW63%zie!wIxKkiK&P7cC}pbnp+qu^&@(=1UAM#&Pjy0NZ~4nz}ncxi2A$Gl3D) z>(AUKxlYz)yQk9>!Em*WECSeXIUDTVYBL~>Zh9Pf+UurFYKEj4pjv<5<23Nu`n`|vEz+#l>n-<(_(|D3HBxjg zhPBR^N(29Rh?hFyX1CnNMe&Y;RpNqdY}OF6TPA}_ak7j!G)VsZN~&bizwveQ>7}(- zoymr7ad*9ATHDk`Io3#&R8I*`kF)@c=bPOBlB?xx6#8TS_9xf{r9>xS`T;?3ch1bZhE_=|TCkar=f3#&O0*kw|B_PucG;NWM$e zbyt)x__($E&a~l;|4qiaKGNnW4BCCZwwmrtEba|HshnYS~&+eSS}Mjaf} zm8BTSoVTr2Nxp#M_V%~mYotlvQ7HqQ##pr_c}!VMw7BNO+w!Dgl)lBS)7lma=hxc> zrHh&B%BphG`tN)n-p~a{Rk(wELA8yJkHT+Cqevl7jG^=ikM{@D(*u2pMk^~j1H@l& z@I*9xf-Xnc6&PN8VR(_0|7g{OIhoaxxlNP={?f)83F!nzFc5ct^^{7)_KuH94hw!b z;gpkxPwX0z&`VSAmKn^2b9{r=s#t58O5DbyS z3h>+FQ9-Y6J7bstG|m?)U$~a2MkfR8v|K;Q6LkC%%r{%8OJPsbzx08Srzy|L@=@Oa z!@E9G7<6HOC$zT=E(A5UmQifVE_1&LRq=a;aRv9yxb{g!e7_v*0dYV;h@#D#r>+lV zhX-~snCF_VkBmN)mUrGa#RrIVXH=RAkDG}*4+QW`nwYpkWeC~NlZ|*BCo3XC(LZO1 zg*8>|!V|h2s|R_>Pk79J&&``XUU|vU7KE8w{LynNOTuWR_B-OOgJ)bvo0w^fjs0UQ z*^roNy)!h{b07)#_XV+|z{SiFdoM;|qWce6t_BL6hpNlL^$zm(Vh`ac;ohkg zUa582u&^3a+5rjk3qh?VR1E*B-26p1n$6BD6@ey93)w>_Zk)8JB+Jgq9iKdQH%);) zH=prOaq}Ch*!g{B5YI~0@@YX`%lS*Gp^x3Qi{oPzUXp!J zkb}I#o9(l4j0fOvouQPO@t(;ZExHiLSDIC;pfAFjcUDTVsG(5y)B>nfeo;uM2>@a_VC z{Dp$qpRuMx8G6Y(y~C-(y)X3=)+lc7lnz05kQo?ZJI~!8yJr5wnG&infE?%`be{Gv zN3~%Nm_Nl@Cy+~V3o=QIJw;OM&bfIAq=;StH=hlv9$xIgcHyeesj|c}XgmbTE??Cb zE6cYW;ucsaG2FN~Uib3Ru8(6HNc`oo%foFD?c{*k>bSYM@Xb(1sAL*?+aA@VGLKsy z6S_Kn!gc>q|113dPC1Njw{XSuAAnkgF2L&Ge5*>rflLj@lswh;=7Koje3oq5wBq=< zsIu!Uz9LdqglaZy$tBo4Q+i|~3`{nlbSd_Z<%De;p077_bxP#tbEhULftVT?Y#pWm zO1}%(ypTOP){?c_+)w3+D}~z*S%T$7)AS8VlvYW|D{4hf*DE@$o!zmX2e5BY_BSPa z?l|x6kO2{Mm->2TT>{ac*ifDk${j%$8&cgvNZGmS^}N3C9q?^PtO3$X?1yVh-@XoN zu-wfxN+T_|WEKq_U6Wj|D2o{%hEPv()%FA%6{Hw848E`nUxtJ(uE|H-9*+w=T)`hX8 zA+Pk?ERQ0!`MJPQ;X3v5X^2YWM>p|9145w+m!9AatY&Nm8gN-Bj*fEeW1i1I*K>HFpHpS z?L|-Y0w3@O9n&a*CSta?EjU!f1C5?}NgAG8a^-%{cnj&A)1NeUUNxen-u`S?c8xpx zO^*X1@@{4{=)+4P!%FN$x+StMIfcOXeL?xm=a6kC(W?^N=?jShva&s`jMLL}#!FdS zP_!RDK&Q`^Irra%4(O^EwV3fYKjIYtqG0o5px=!~)XJ5pg)*?di0j12_M6TYI@cOu zt*s#&cEA{=Ypc7@+ojmH;XCX+vC#6mV(n`$_Iezc(ej~0o=o}pSvP+A{OOaVh@c7^ zs5D`Oz?Oo4sH=8p8C=u2dJ9@U--wJfMzmXiHVva9RE&KJ zlkNRjix!E^Gf_BbVzg66f{H9qzo_y4E6`kN{S=4A86;1>Nb1l`uyfRh$!M+_UfFy| ztc}NKH@AQV#(0Jn|2idwT0o`RCVz7DeRVS3_qW)(2#Fzn@D0Dnk>C>kwh6ZqMxW zlcm3Jy5@c*?&SS0|1x*^14NFCf^6;-%N?Hir9xpD9QuRhMVtvtau-{uS8_r+I<4m~ zZ}R+2Be?;~T^zIs#+HT2p?3Bcu3ud3B(m@HwbCn(5clFuZgMb>rn&)DTEx*jY&IQy5uo|M-!6uua;bG$(O4TDWVSDzKJnPx{Q zHs!yWVI;mC)?xd7j+7-8h~F&l%P0>dd7$8yJ;-%Jv5Fczg0zys5^DMO+ciGp$HV&s zgT}0Xn5+>)G06snJ)2s`7yY|uRfCyC!uD%D{GbCIUJS2@TG5f&SE!NIR=rmCAmRO| zK41G7AQC(M7`CalZ3)%E+hNdlo`g0tihzS2%Y{#>|41{~`zu-p4n)&{W%?RipHOQC zNG#W&C*>W?9zyIh13^Q~JKb=K#e{sDM$@K_NY6ae;GL;M$|94xC2}N^J1jfXf`cgokld$dKuJsT@5%5q{%i1qizzwBq$nH2SwG5B94m> z^d`1A7Q%74L^Kzpe=*-Q01OllO!pA4*A*|2`u3@mgolXQ{bT>DEB$pqQ4X4A^u=kb z4PlQwG~?n>q=Dr0)NPUPY4f=*u^Sgs9`2*jUC4!M=?0#GCF?bBVmy` zh+j6Xuic99-!0ysnV>=1P#Nr{ECJJeSBR$7WBvyBiiHvK2Uq>-ShzX?;h_H*w%+BN0X+=H$HYGgl=Pq4AYyf z?AT~ugQAK`L!rg{oZs6G|DXvw^;o*slT|kIIni)#fl`!-SdC+5wOM4u$b6BWi|z4v zWuAO4Hm9D`caxYqx<;sV^tkZA<~>rKBavPmwMzI2IqTNISBBUH?TIOL=^V~A)Hiuv zmR-LH`>^x=b&p2zt0EfLV6^&ci8O=(JY=a1p9Q0Ma-d{t#ub6yJGUWSf*CdFY=@-gu_PzO`D=;A^p-;4 zo#}E_#N??Vrfh?CM=~Z;K@JGA9n}3sk=m77QzKy!H4|LLZ0vy3JooL^8xevQV`Jyo z=h_+CsxS=sWPFE@9St>`1W10-94J%9)iX@EdJ|ox(N6%1CM<)agAMs=XkNWv8bvV$jby z18IIUD^5HZ_&gCQk1vmxtp=S3j_^{*5kAPl15Dz}Cu@#pU04L0k@%QXRCgxCqX32| zy~y<1*w$fRVW!ed?Bm3}sR3r@QBLn`ay9Gd-6ji6hOj5Jb;Y_rssCsO{V_g{CrX8f z)6H(DCRd8S6o}CS7)KLm0x$L-eia*z3=lXtsVe@l+~9BB6oDcqKHoR~cg8i@>-#M0 ztaeg6Mc5jKmiwC4cH&NvI5#av2>c^G10+;_ARO5cEIVm&y-qUu770ubLx0;wK=(vc zkGhdu%pBtByJ668Dni4sK|L?$(7U^O6aRO?9d|x8;0L;17_ECljXrbvh%3Ovb%ch^ zLt&_8Kzn=Z=dUXw5VP*cCOAOUHx1no%7CU+7YS{3-^MuCJ-86Yd(Nj`oSdjNxnX>5 zE8rmi*npyOjr2DJwN-p~IbN2f{UG_YL_fHr*-Il5sM{YV@O3#saEKsH+5zZ$0DB%5 zv2ue}*DEQOuYt3aobt3ksDVGROc1?&;K~B;BB_@NjvK24+iAoNSlRwx@mP_j-Hwyb!Ekke>0lK&0jO zP3V}s^`y!7zCt}*Z{d1@$SaIrx-^&TV!3ci6JiyEK@ZtR5QEltBrfgR%~^0}f;-YJ=t4?weSWoy%aWtA=bet8^r*Qk zfP2Ey5o9DhMRd-cO>oZmC;Jf@jkFGds668TNIJ`)I=XHNzX>6@y9aj&?hxD$dT@7l z*93QhySuwvaEB1w-Q6AT@ZI@O6;&H%)?VHH^nfn9bt!7S|NTX&O55jD>}yL=aCQ(q zhLE(GWexxah!{UAiir2Pu?X%7>=OO(950(<4BTo*w#2z?j$JN)Z!rHtOZmo4MH>Q% zk1-?fxeNI}$&JZMOFLF235}&Fq3p1>o`E>0^QGNRr5ZD3EF-GjF97N&`gGxzX;-Yn z>`nuGZI`6qE@@bAOP9H#TDgD{Ju7+|-rc*LDlH=~mUQl3@b9$@$xa56>$`LOif9#i&(tiQ7=v&fr4hVDxFAX^Dnlx>GK@`Mj^6k+hyz{S2tdP!l%c{%X9mu z8nZ${j$=D~j3bMG>+kCF*m=p|o3>f22Q}}VRd1ET=0&SVD30cS;=ZPizYdfXME<-n zpq)Eitqj1*08)-omF`Xl&&lmrNNcMxQ@gR{N0`PCBFL~Z5aIcGYF$B$f~DQ-6*KN_ zA)14v|0E>So9Z72o37s18lz zS?6Y&33a;M;MNghIGY790j=@Zp!_VHsEE~qyB6n2S)2#RNtB5kl7QG0NxP?|coeli zwkKM0$X_$s-VAB|KPRH?-^_k46k8+{qF48`*OaagmC&2zyv5l!%*3xZyYB_lR;1vW z9{;+HMBu(7d2{=g<$6(Kk_+@sAxJ)@Y@^93VX2pSTCTmJ6XG5$!QSb&li8|0+K z(izG~1|Ud9+%H4j?!3om6z9l0JVv7^iCy@XOr$cz`?*lz;l$Us`WtBb)Z?kokZhLoQ*CU!0NN5;)yU2M5RL-jVHmSA#+4EPKworH^dr0^y-klif zjl_i4+Yq{y1dgFv0d8Ua`A@)uj51~V-6UC~r`$sp{gpk#rsik#`)jaLG@;&UiW&ey zG98$spJOmQ%5WD)(tl5oZjQgm{}a9C{qmCXNO#)ifYL=2syDfAx9X7iRibLNm4(v& zY7@fifpL5O*T3DR@mvxd**x*@1^F(H5_08#`NnlgRcqXuu7VdJ2 zmn|p0R#cn)!!m%p>(OrRzxqUMfeh6M%T%_mGx5#bzy-^*uHEm_SkiKf`3sb}f#$FA z-?MmB3HAQQ?Wb9!cj7Cl?6jl4JiW@U-V4pvS=b)h0oz)9=SMdZ3o90irJ+31XKUQ~a9c81gI-udquMbpUW z3r1QW#eTe1F>KiW&E2?XpV;~jw+${{->1}5agFm|fLGRVg|Skt(eVprIqpw)+9$vs zouQy8TIUXKw&xIJ2F>dLcXY4Qg=B2Ib=|h>FHPIE0-)3g_`o+dr>!t(l{3eDn*K1X zz!FC|8_neXOBwAyJYgWCdC=;>X}DLdsK(#+I%meWUS2c9FL54)fIBOeS`xLVrZqn) zmAa4GA4HWwc|k)dwTnDiSriCrQ3L{j;!|I3xdVc8osuK|R;b~gkt)gUiR`?gHIqxB z4#P%j%>Qy9R9RtPkBkXXb<|~(%-fsYTnWqq7X={5rX22z-T=6RbePIm=q0XEKRX4o z{lu7I8aC;q?mv4K)h&it={U@|X<9ri&a&HA?XB`SKxzW`!@{79HuNeMz<0F~$^%R5 zHF-mbK|bkqPf46~IuTM5_7QK6r+F!TFMQ2^<$L^|>8>?(jg7J~o~gPGey=8Lfyw!# z=6>|W^KEj?m{+ATTd?H{d#MK*Pn&0ZY-4KSXbwYIqh9Qkd6DCY^!iqC@In_4kwhil zV<{U%{=zHt2vZIw$P@s!Ln-2Mb`f^2!lK1ww_XWRvP)^R>}I4Za4{iewD-q zIAtBA?ww}wyCJ?eyg;W;BU?6tD2xAd(x~S@;~z%gfB!1U8^6{r^Uk7_VruHo$lnIl zL}U^=4|}kS4)y%|@qY7_Dc0mW#^*y^5;vuQot6;qloydXBBLb1aYy3-t6gjA2!hl+ z8OU9I!r149{<0w;>XKt6I(z0HRpHr$cJcbhIho4`Z%_78L~dE2#wn91RtHA6Zf5d7 z8iuK6^SZeP`3@ELNMC+@65fhLB%ptl^;~n;v{o$Ns9brLCDWbn7DMvAN+H}EMQ**b zyQcY>^X(hrAkoTTe*oo!T)zQIilMIeUA(s>VUeA_GJS!6DY-PcZ^A$QHO1Yqq#rG) z| z2f(+yn7heZl#J6*AJ z{kAB&uX;J(&=TI9_t!E=io_U_(wNmMi4OWg7j49PzM?$b^fRO^grT<I`ByBA_axk8^+x;)Lu)u{|7SlYBL%~F>B)OHH#>M+xyi*)OvCSj{+w1!& z;f5~i4>gr8wI$0v`H-VD42Y&~tPJy9EPLaRYt9Fy$da~uJYpi93k(iMXQc$k=~P@< zwLvKEn+iv3b!Ri2*1jSLNq+9}n=Wv? zU*z>@NL821H~-hjKr>Gj9PFyZ_{Fc02;8>Ox0AysQ*CKqD#C6xr~se3NIsroOAO%d z40Pw^ihPp4T0^U9*ySE5qBXIPbxHo3ul?2>*{JG4GXKSBWx=r)3Nu=3yd=F?!o+Nv zuJ=;-DEUMeH@TMH$dSwg)uKbii7<@U20I+uwi+3cK2r3NlefeA&3i5|W^PlI>$^OvN zvBfZLx}#FiFGL%&cxE@J#xyfC9|?&P6?xn)VhM`kDn9>Syj>hbTAZJ5I@0~#{(1ug z`0@;OM?QjsRO-rblp}<9KTyG5O_*NUZ{4jcfuwxNYS983Go2FVRMql`#Duc!`a}zA1!m?{5&%2? z(sB8%slqI#C;2|l^OucA`2*>HRic6L8p3}Aom>PXTkdTIdj93Hjl?tdcX~uM4aR1< z=&hIfmrpI+Ls6O?qnl3VEVI=tZDDlNd<%f<}Gve>U zed42RD|%ppaesS&^isVPC8SwXF4xI?eO^m&>w~UL2_cMI-K^scfy2z*&ssD`4#qk;+Gx2 z&4%D`%mW_=ByZfkWQNAvg6Z>)_bhvHDV+jKK}qHH=#hz=0TxWl*)X;WxcKPrl< z{;=`&CW6qF> zYJk37yG-jZ0~6S%>l=dBo$9X5><21Mu%O6N#;{6mR4TaqZ6nZ}U7Y>nM|d=SE-NzF zjz~W|*7#xDU|Q?;TpVY0a4E;gyz{x!Vy;t*z~9_<_O~|-%r#H=cCfqunBN!rAs=j` zK$XZ+$K|ROj|km&gx_(;Mh^HiFTbD6{bFF|+MIeqf03fhx>6-Bwiy`FlB&&?`&*E+ zxe`mm*yIb56renk$NZx?deg>lc0>aN!0zWIV>g}7-&YOyBcgW=??>bh>&+uR=NGSS zPnm^+nL_!I741i@E)&R#f&WPR)BzCAtysPVHzmA#~nQos8iLpBXAQGzKIf~Bq@QhqEoMybzaEVM{s z_%)gJwjD16K2{kZh?x!U%{F4Wi;L%WJgnQ*RHgzi2VfO7`y77SlUWH2 zwx68(*NkX!tlD#G8C~OI5B-*&vuFvMqbJM@nlILQsxs91@Gcs`Mc$Uxas>Vce&g>v z*+>|0&lD?-5TEv0U8Zc8XOrbpJg+|mdohD_(3G^hU~zFMCl%oakCeLse5-0e4$=?= z&jXSGz+@}B`OF=A<2RFCOr&}c+78N6|6<_QLL>zy<437;CIL*W$Q>8Kn=|0+6IfQCS zb8uTd11;Uub&9F^DC!Ofi7=4#HCVr*4$8;M5_aF?YjLF#_j)As5IP;IZfAGjWYIwPfDNwQ&vO8VCdct8mBVA%IcO zOLB`ASqx*3YvFI$Nf`qqA(~!?JCr@cBI0EX$dwJzAXI+m%$O2yMX%nSS=-4w$qGI% zl?&HO4e2U>DFX(XwgF^^dx!L$TYHqp+AyvY0$TS@7w`CIzECa<_rS+rFg2&8tra6v zA+!${Z)m_yg~gbtqy^nai)h`q2MBP{p?ELeq0nX60H_3{Up0vA$->1 z_qTfi$qNJ+ZnFTUt{4GTCT4d+4;Hm>We`9_9Y$#gPGAuzz1>)2q41vhVgy=Pihb8Z zzv>YAwBZQb0^=VtdxCl^T=$M8BP-+;+Tt8<^$)EB`-Nv)~?B}{QK za2QQUX0XlN`KD}10L)aK|~SD z+l+YPD$3j|wg5gDJKEp-YfTR-Ulw6xDw9AVb3bd+8YyWW%+T@qe&8D5UZ=X0!kHoW zd=+<0yL}HJ5p;^-2S_OK7;KSj!xhq(As75Gq-hjphO?Z;ZMg?Bc@Ag7bb4D05o|85 z^a$M*;i>9_aR$c6Ui`PsK4nX0$9+%`)P~EvYtJPHw+^uZ>^UBiq|lf0={m;#-tl>?jQuz&?gh zK$)P(!t63eqD-;sEv@*}_?YkWU7`MDRx?^ZJBW;xMLtz*_Q)8;m$AW37@BOq zyAeMYTB=94$K`{piK1#prpM&yN*;pBTRXvdXT**IalfxO=hOQ?>TU0X^-oZ)g)Y=* zI$^yf5LkalQdM1HYEGsI%P6cRseR;&NR6(ERsIg!45eB8DL+eIK0c4P-Vqi(B&4TP z#KJSr(JWyF8ZY#oUz8sm`_A$Xcsf{0XTjQAAf{SAAE$w^ti~cM9^W0BsNi(}m<$I2 zYH-fk+;4N6WIi_pO1$;9qi-Hj+p}_9q5ht<_%{N(MFVv?JfvB5M4*#%}NATf%nCz9CDA)WCtH=7XBUZh_YwQ0%Tb<5=SRobOqeTnxVz^!Ub2k5f~qD)UT>iNax z60y|C^3#X+3EiBIT%cG94(od1PGz*>kw+!hjk@qI30r+-C>I<0x?6i^+IS&AQTyB1 z%n)X2S#UU+4>+g{eU1lsGYxtf^@n#d9()vR8|Ie`%rahg_}5oX-@}7PvP_c zw!I#t7Pf}|TLChv-eU@Zb z#z4My%jRAnwG)!M3xMt}HP%C29Rj_E9rgZYctHa&JkG{3)+;E`Il<{x*Pa?C?j7fC z+!b)9^Ux`Zc;jp;CFCdB+LQfmqNq7D$X6kbO|4Nrv3fApezDHkG2A){)f^-9+7W>| zh-c`Ims9?!lpKK9ST?vO-g7*0f?ZUm0OnXPn5O=X0b*Vh>upVtZy@RVfyg(~WruYMq~z*c3iV5&FhaQ3)=wR;QxVyJ#k9k0p*=o8%!=9P#(VF@5#xWdh1c3RJ&}!5 z$4lEq%I6g87{sWCr~v|hCGEB=rk|% z+|4CEM7FW~*&?C2#oAS@9V1AYZVP?|uXlcyal!p~tIG2ILYr=Kj$?9KuT1;g#ztN!A3Y6;B&EDUQTORuU*> zM_atfRT~1H51*$#;iS1w2WLM6@p8wpb9BNr6`Qfj@2^Tf3V+UnEcTp-p^tE#o*39Z zCvu`VedYKP>Uo%>)JUckbV>RP;Jq??bs8Ly{WEnq8qREXe&g@CH?pB)?%4J@fbY~4 zE3K9Fy(s+66{$0Rb-SVueqB$JDgV75M6*8Me_+W;*jF{h6YVO+l5*DDhP3e9 z1bAjT#PO7$4bo+g3blPNAO#GIc0Ld53zo9REC#gzi^t>^Tax-POa1M!jFrhglNBHX zLAv>-WP=_(6hRWQU;>hXFrxMb#y-|JT zEn2ew)2=cR9DRl6=ZxpnY(TC)xm9^^pQ_O}nZ|ZZx-&kU6k^NVa<$Qw^U+SlDNWL- zjHpZ6R_m#){ZaW6@ZqmjDPO*_jd~blRMSv)QK}H*!e-qx^+kFpOZ;2}%T9vUS`@9b zF+;qzAd7LtS`rl`=GiCeFL_wWFTq%ASau0X0H{6!e)3TI{_+`iZR~N` zwzK=he&W%^N)%kF>+@3^&&}}QDYC4)1FOqsnD)bh0`iXSsK2W~44xFb#WOaNIDM4+ z)-=A5!<8C>yFIeNJg8*w_zkg9;fL79B+;dhBp*c~JFj^>$SQW#RKxTndD1f;lU_&J z;J->5#;gvZizhoRZWn|>2oNp3fqR7Gz*~VJfS(C$X<4@@QFntN<$nqJ>}??YU`ZeR z$-Jx!UBrEG>lUu_a{uon{Lzm_3M>N~hvjmfGJcS_JW7dv-O+dEGDhlwgn!gg7Hn}v zr0D$={YD}y8AZ)bcZ4?<2_Hj7oppMpP2^Q^!JwSk4&_HSy-~5tMO3-0k5V)+6#u>s z4uC|j+>0aX^zb00aKyo6_*~C9&PM@)m5u_#!I*2|mOY)<8&} zcs80kCa8@(uQp2XiAzu6_*MO_8Mq+hY}vi1?iSR7f6JF0FonW}%on~skG9_oj`oVPmy9Ty5tYk2ePpHC4U2Sfz@0qt%A%i~hM_85s} zb>WGMQOF_*)LZ`D!UpTkn{CQn3j?si^e)er?5!$uVsqxzjWl)H?Z5)ks-&>B)I~Sh z7q;)N^gD6y8X6p;uZ*&ciYTXV)8e@{G+-4xsHSkmqPl7mxy{8kk~DgB;--~Bu1CV_Y3^9HIL8YNV|mxz&Rs9MQbNSOz)ar?NNrybmNv; z(&Co!xX_C;wDK&lZH^IA3x1=C$eNyn-6|f>>)s~=dw{~H@N-ei1O4g zsAAAnBedTUUyiSyGRXE=d2+llpP8&vJ2VRb@Zee?4mK63Co8E;mlx z_XNAX@Or9z>m5BaeC(QRIXLD-&E_i5HYuLZhHXXI$TO+Zi5Og~c!6pArr54_RV+dt zGsb;#fKk4Xv*Za910IU^D>^0^ftOA!M%}LMMptL6(8{Qz$AKMj7hCQzbdZn-JiBlO zU8=n)B`g~Z`4U3vznDnr5(x4i&y~C49p#>>*(#*7LP`gz#=Q}%sOJ>+Pj#P}$1$U` zZj)!2cGuTN3-Y|t1M2JtK%K~H`SOMH1-qpQ#t0^@udlS1sJ`Eb3B;F3^~MEll7Oc0{+*d~gd!ChxHrxxXR z5`;Mu$rKo-j9FdLt9VF2+06$b550(CML1VtmICB6X$u&3Qmn*JwnYq`lWdIMp^-#e z8!bj%ONG4LB+XS0HcW;-vcCcZ_l&%Bdk5H2!8&yR-mB<-Q1K(#2)t9=Nr(> zmqOPuv|FWuC`tz^OcE4Z$*1h|37SqfqDY(V_dr2S^r~R2C=#O5jqzY)*VmjSN(_5k zJ)srHVL!66W~9a+qA^50sa(%&8Ve)v9l>4>~Y^16f?IKhsmNCKvjd*p8ywuD42!HHocv#_dJHi;6 z=y&WUB4!s`uZXlvqt<)2gI|P5>kbYqq+98>Xxd5_!o{|ScwgM08CPYIM75|T<)Ow8 ze^I9mnz*G^qG>gsXAKC5KY+%g;;Fq~fF`k6diJpm1cd3leL0&I$;?*4G~eC*BXCZE zF)1s@HhC!z){{d}sCwX? z8;u@t*+EC`*Jjjk{V#^Ha(~HZcnElmkn)d`FQc>aHx~gR4rnP1jMT<5qFM-wH5Y&l zLvX5j>7{c;r;dH>5d+1h975O1AY-xPKc(e4RKU^*6at$zKOL^net*B;lN5}0;&e@{>1!ar|7R6yb4tsy~DkSDk0 zJPmt3Fp~;J6UTO)tZSg>wwh)z1$2mteb>R4c#aFhvMlx&J&WI4Ok}L#bAb&B>|Ifr zOy^}bchqp~Ioj=8u9H%J@B zarBysaN@nk_XuY!%-rYb%CTcAQnRRbz}#emS75#q--TQ-K^@D8j(^P)>ZpJd%^Mrh zvQ4E!jL~^aWs})f);S`%vn!&FDi>pJpu*&69tx1so2_{gzig)r$uJjvl%1}4;y5ld zq<6w)Wu(xi?r}t<&G+QzybNvozCRNczMlqPtGYk7d`uZKZ-H~{Gx8za_WsK{1%8Nc zFT?(knpjSE)_h^3`)AUABJjA^#57zmkENiqJEW*DnPBtRloX@Z@vifIimwuz)7Rpi zhoup9!3<3A6vQ1qE=dpHsBv8Z*SxTycYUKYVN>pY^FE{Q*?#0QUiD?u5TDm(z7@Dr zA&YRQgVjk;VE7I*G17kKm{TWgMqrC1QQH@8@13TvwF!sTO#$Mc^vJ7pMEk(mRK;9%>;f3`F&8C>4nx zGpE$_3mS|`h zVhrJHM57o6pN~SFizL<7`Q>VpUsxGf3I1e^5kysE^Uv?K`F_K_^7MZ*VZ`3TmN)*h z(1VE~^{Ii{gCk`;M&wrvk+XHlKLf^4Wabmz;un~ZT;rL>Ih>9=xIP56K^U1fT7OTR zyKh-s?d-60hwKR~A{}x|;EY4&6Z4m9Fl5Q8pqG%N#GA70mY*ghz#LNxO8_2@3}%df z)Q?QG#tox_M2wy=q%2p+q97GRzD5i(z-AY3zF)e4eFK9XPOi64j_$zp6BV?`BXz3D8yhVM#N7DRoDr?o2VNn{~&W z?DPU5{f_N5XIb)zW^G5W4XouUkwspL?Ud z+W&pK9+Q5Cz^Qdcb~V#0HY4u=hIWnFE8DSVjXWoojtjvJeG#mw^+_i~*~OJD`xTZG zokWddPR|?exryDuk=l_b_4d`zY&31MzcFRvOVSRcC7MDBrtzP1{ieOuzHjc>HY5{Z{7OE<)`D-tVo^qX>32{G4O2Vnsb0^XnZ1us-rNDZgU zg3PeuK1$B1vqtZC>L_Ecu~5v@;T;DE!k9{~iu7mw)ov&0 z5*F`eRf3q_HWRsWE_Nv~aC_N+1l-SqUDvOdlUlk~Iw%wW#)(#Z2j+Tjybh#ecAAZi zIX}puK&BwAQvPd!i5PyD&c?tY^hcP@aG`!54B8y5lq4~)vviUu--4dac5n6{x_I8b zCc{Ti9QTzBX$09Pw>0fFLfV9lH8OE1iZoQ4rEA$eD21P`r(W}s_K)*4;ey}QL&Q%02T3zX#c!7&6UW(dfn*bVV1i&)O@fe_nnfF z!8`q#2s9*L(dzI-)!4fe+wevF%{O*XAZ}8-Pve+RFBn*_KJ-FkrcLK^f~nEpZ?Rq- z;L5G1Do`PCR(iBB)?XJmMQ;GAI4?F#p4Mcs@~Ud5SZsWKoJ1e)dOvUg<(}UZF$%B&S^8SDH$-1hdIH%fO7uC!_cAzQhF)v3Kr+}f zlr_eZx^hoyM#9GmB&>op^X}X-k32izK1QOGPf>R~u`FVw91_`;RDOcX-c4xe;}YaA zPODt5g*(sCUw6~u>vP@Tb^TJ~Jx<(5*bzmkTSzo?e>kuk3^-@8$2G1k2g{Ki@1n@~ zrWNCFGIarNIWt#KWU-XAEsckxF~`lO+3DCII0!0ZpZ{B(Py;u{RJk^vjpjz<7>?cY z-JU@HxwiMuX%w7i+>-Fzp($?zaj!B?cduu9X*r(iNDkF8P#7jHY}3@;3OkJlA2JR@ zVIW`%Z;sxJgd@EZ*`6T3C13O|YOagRm)CB*RJ|3yiC1XMMf8Z=2gukUsQb%|oE5zb zPEF;~9!kA(b^IP~9$g5a2fhceFwwz?4DF^xe>ctnjN$`8;j)nt>tO#e(s$#SCZio= zt98U`lto^2bH7)^o-}9!z>K7Z%Sp?drHZ zqQZc1A2Z?|7?95I_Q{mw;j==tB*FBJO)lGQ)T39gQWu!O#5G6nx|}c8o_#d)wB}e& z2LlXb#S)E*U_VMd$uy8O|6)07&h%6YJiMY{g`GfY5IezDz2tqZcFx-A>-lpFB`TU@ zFt#lD(hea4<6@E#ks@d5@p}^ddnj+NwGGCywM=-Z(s1S8!(AsB*96l9^guQFt?$EfA1#5M3j{m(u6CDTJ1{&qda&JRjU95No6mX|4 znoZE9L!&${u$Sl-{D_!F0cu?+28$JISUs5XoyJE` z=kh{`KH~#F`=qAeX15-&U1^T`CR^5Sh+L_~MO7&JUW{|l>62306WGAf|N4>`#>caF zu6o!QAFm+vJuXw7u?Ti{L<*B6=2@=#(N}hhy%?f2ELNd-^!`j*O3X%(5-e4L7ClG* z6ef0DACA?Z701JbrcSJko>fQpThr}-v*(WX*?>TA^!43lhF(O(!J$D&CPRB{d$v$u zV<5|Yony=IbJJ$anH^4+=LRoaT`y|&R?d&=^P0hsCEw5Upvy$a4qpeuugj%BEQb+T zT01yMnuBV@x_=w>mI|16N zI;_*5)bG5ly=A^;))m?En?luxmQfONYwM-coZ=>@g1&iw5MCOfN)n~wx593+ORZ)#}bR%Ywc-VyPH1_^CB$g%?KTm7Qguy%$>GHwE0H zA2Ni5yyg%hFoIkIdi2s`S4ULnX;(gJ0nk|+y<2-KxaP_cj*qc)Sa@DyiVqIBu~uXZBIP!K<}pAqBL3%P9&jW_ zE+5wA+cQG_D6Fwgj#kV9r>N&14FYzrDA;y$5(o}cM8RB zn1!asQ-phuBHqJDD#=Qlrd~{{w>hEOTCx$Jf)*@`gl3koG2RQoE&?=8aa65sJsmDn zJ7{@$_48A~pC1^Z<9Q&KQP}qPs`x|kB%o<{9NW>o z`L4Ms?p(3i+6TZg6&8+}*!CG@P={qJPyMm7j&uE17If}=-`7jo?$=YSE-OQ@`v)=r z-H1xi(i=C=iIqPqtzF8?KN8L@YeM5Icw;5$ergPsSQCj9l;4W;y@N&Rw)k^J3dh9G z;AQDdCG_(rb1d3nh{G_6lz-Qh_vnu1itHKDTf(s(vZn!gI+S+rG*vbpue{fSVgrd=b3ymJ3e=hXukAYyfc`eEFWitfciD60!?PwIy+pX^7&f!rc)q@hmT5~kN*|0 zPT|CG9GSO4O?e0gPM9+*ryW20He_*VLFJwoUPkK=sm? z(vJ0KlZVe;slf3nbkIDU21ilDi9ymfig({w2cC{l>t@YnEB{9G&T-;?>_Ayzt|F zY6P1!xMiMC-%ywYaiag5v(*lsjR>HG!;M+yUx)KWRR6UrU?bLdW=4^B`nP~S=xq%y z4-4r}bAf-$vwKGJmv=TVSGu;tJ40~*??*Y|Jnd5KqzQDNQIW?QRy`XBF9njsqTi?P zj99m!J3@$ixjT-QD^6#_YCT-^R_$dVf=3*tJVK?`2nON7_4|) z7`Y|87V1tbjEs`!L#+Ps_NDO5)(b8P_p$@c5jQ3R_DA}WAhQBsvO)Y8YlQCGuK804I3~h(fF~$=m~ zOwIc;?pW%8^6yKFmMYd{Vobyi+anP82wi@#2~P553`ERv7$kBl8$IU5oNL`7NO>b4 zWn(G$gFVeY7R=*$_5gHPF-#h<=&ECbq za$hzpfz*JjD$b*j^b5q@GqiLPv}M9Ka3xhx+`-h0?k!6W#x4kk&T&G)B~Zp1{6wG# zYKNVhyi%6bFGMu@U80nz%UYg%HVhhR8UKChQmmXHS3&CjwyKHqY=J>`B74r#VHS)2sqYB0~LqD-oHky>zX(D^eTmsmSYY~ts%N&G;)3p$&NYjGr zyEZfgZ0{-)E29_!VP-K!1;GP^{;10?ul;478XBxZw4(zw>rk>#zk9^XZBWZHFr;cD zS=q3Z#W`GKNl@8e&!2c=9Opu~h2Fn&02n5YwY;`(+-Pe8#rHtpq zsR{tW3_Cgzm=C_a1Li5ArYljwD`%R@4BQD}`R%7bGb?a9Cq2c|JF5;^`&J{@jcR2G zs&E)hjX?In+AYjz^wOaVq8`G0K85ihjKWo`wE)w4!4m1%f)8y~{}D z(sj0y-!l2#qV_N&r+iIRBOX1!@whiSG8s`43gvu0Vw#d=&hpmNM0bcii(4U+DgZu_ z<`joh_5t)jDOuc#igp4Tjwe#`98jn9z9js`X2!}xw}hGeyB%{?VXN;pQ~TU*xvz>x ziL=1i*7&$&<~z`(gv;2z>)gtNmz%|7D<+vyJnZYS*$zvZ_5ZI{h>Uft3Z%G)tiS!Z zkU!+PujW5%N4#@c6Y;|iq!*(+ZCgca5f$*Dx?vWPJUaU%q0AL)`Rmph2NHz8Tt4;V z*(_(LXe)ODw7$Nh!X;ViVXjlMT4B#z`{$kxC-HN_b=e;em3gtbV~ZEzsONc4d-!NmR$fwyR%i~hp`nt)^Aey zClq^sr8k^R$X1mgIQLB`HF+i}-@&x6=J&PRn}~A#Q9|Pr9Z&YwsjHIq=0@dHh=~|k zrx&TY-cm9;ew`iU$I+=)V|JuOrMHH1(D-baKyn|tR7=0z5?G{dl>Hd-YTrB3gDXSv zBg12PY&V)$SJ&fRp5`m_BbDNs2Y|cPdbyDkc~T^%rbY02u3>X6{GPncfgiDr-NXwX zlQro6dte(vRTP==-iK^Iuv#U3Oa3tfsj77xL*IpM?NMLoZyF)ZR45SYqVdcT^fX9& zq2)WSyO^!`=^E~^p0D}*DOL9(F7irXK48~yEG-LmMcwFNR;~@gtlEwjge0>Uay+Ld zq~_-rGShB1Bs_1EjQjk7q3B$%`4evWHwJdlIe-8tAL7d>R!A-0O0^YSfKPU|DXZ3$ z{e|1GPvR~egjx```Igt#bn;E+81(Q; zy{%McAFo!e9N{z%Dl!Q;_xDXS5*GcdbQ~*fO}q8=u50w9i7U#Ka=q0+Rbee&A2e5y zRe11g0A1S!sYkbFE;FlbVLfDEzG|1TwVIQ4Fz#2d609_A0Ly8ZuaZVOvQ)d0alUL^ zGklYvd5rx%!xRiRACT^n8rU>TmtW1r$&d+pS&Cdv1ZVbr&GgV$tXQ#*a z3*XnF+<*NtEYDGmJO5#$juM5ZO1Nd|w?-rY-QzFLt)kz+*d$!nDFXeCcS)%Sn61 z9Y>3GC6*(KR56@+U$!T1hMrPU!Lc&xqSRCUsrFAE&nnKa`TM{!E_N7WkHI)r3(DaBBovp1etJiU6G)pERS5jyg|7#Jp$7IV=2X>Iai%8kL|Xij+?d zdzI9T6upxF4F{vrlH7^Z#qe2AjWs2iQSi3qh=x34ua$Ed%q#FV4-c*sLQ1B1g7(C_ z83@`ksS}hO>%M+>F`KZy*1`?gk7nC`EXWot(}*Q89-1(0cA&ZE^OHc4aPR_%1VwUL zmxJ{k55J+>3msyG)_uYc4e_Tc1F3L1nP3ckS0jUATB@Yt0L}gp_X3_f(s*+z#KvHnr$r&zcXH~?<8uou%b{58OLv({ARa@i7_ij%+ z7%ZzqtB&NE-(uxX9H#1GwW=)!z~5t0eS9c&S>8~vs_imZMP2{NYpR7R z7wbacz484S_yy2Htb(2*AFq{dsy*)bf!d5v+JMN^Y0=%8=7?&%`Kq?@3XBTwG$LgIM^c!I zq@vG#4$CyN<$vJ2IugH9bfd3mT=*|WCD(F(jlyJzk1HdIwdj`TzA5Brl( zzCJ@o^}ly&Z@Ducds*pE6E&o;{J_P)NJq!BfTWuzf?Oo?iDoS;197n+bk8b=`e;Nz z%AMBFQsY%3R*#P72P$PmZ&~wqZ#G}g#yt}a!~+ZKf@8r-08U$fah2?`A*Tw+u8YNU zSrdW?u=aCaynO!-*hxZ30qElKG@?81oI9TxD|rO=jwQ$Cm$^XFH?aW=T4yT2dJA}G z<<3+=>w*`EK3d$`H{3qjY@S61+i$WmQorL;6@tI`zU+L0P*Avj>lKMetD_VT z+xy?yk?M8n(R-of0U?M3Hjxgvr;~5&FQ`#pFOMAN zKbaKI{&3um+JSR=8hru_-os2#mlA+4v@u2sp#pBwVq6p=V6^~qOd{Es`mNREA}2K7 zm~#B#WgH&+_{wn64F>&KkGt|O)K{Q>jl5H~3sOe(|GQYinG8JSPT?m-y_j{B>b{y? z6PC8a8%ybBs^tMLi9uVKiGw2``DBkL+O`x^9T_Y8A^nsKCP%0 z#4L>AufGUd>-+JmBp)b`RAtt_*MbEN!g1n=Q4UZ_WBi8(}lqima-J5Y?6E-=PX&5(kfnlX@93%+~Q<9f43`#Tq+bcuKJE1NsU zP!E1Uw8tX_Rnvjoxxbv|K|TDm2+ALD!5XL~S@!*bA(oCauafsU@ej0L2fc}N$5EWs zB=nFg!=RgJ)s09~oQ_y42q&Dv?`+)G>rnua5XZtdkcAyuM;qx&!PIJtb}noV7hzD2 zwgK*P`JGkL?sj&E`EvQJPZ{=t^>1Nf!X^dXgr(FCOU-f|?<~T7;)7V086c0nBXsA( zhpjh#$ghOX($GW~Driwsda?bJpDUE}Ma6N4hwf$Uu)|ngcg{bMX{~P7-|6=eLcWFF zY&)Gna#WViHL;DXi(Qr+@Y+2OoPoA(=tBI{27HQugTY7qkG(k>=pe63?Rv#xZ@Nh% z|LH}#$(Llspv)ntD`0&pFhWl zU#41&k*33;+IG=t{aH3V+U(qtu(nugw!As?YRAWQq)8mpIQ51b@lI&Kmc7vq=8VO1 zb4BJbmO*#*D<>Or-TVKpJAl=uk|5PQG}Soy1DGN%R4po<2Qr^;%0A9VHx}k#EI#s2 z=YKO(31g3W#?zkPjsnDquzmh3hA&ife{22#tWlrAM_xod5NlgR-WPj<`1d4U88tFtN zzA3CM=GqhP1)a%sUQz!V2gCI6Ds(yRUluHay?nDv*fIL`_FS127XQ(K1nl8Yp+D4m)>|4Bd3ftgI>$nkFEI*DkOu%c_f!xj?5^ z1+Lt)Ypx9o%^qMK)XE_-mn$A)SI+J{6~edpWU@rAw$drB&tdiC9G

@LjN3*Pm+9gZH@gR%_5iUr zpQV6h%vMv-Z;0UDRx{TmgTwU4JlLfGw1wqSB*K@^j$4PEM0fk<$DBPisU*4~(QhPU zz9WQi+d-X_U}ewDKTy&%8AR(m=4}ryaqQ&h2#cN;Lbm6{#x3l(terkvhTO^^QnPjjrvG-atbI3P-0W2ey>7S( z9<6bs&eNelU;!MDCzO;;UQ=QyD9A#_v&W114wr7H71Ckg>HxjHFrqzwz@Y^hU!&G_e}NLZ z!Iqm9s`J-#(?5QNavm}e4$4={pmzP_mN=E|j(=`nz1A{Qm4YAW05;0%1J4ZE{#+deLw}BU6(9uuD?=+`u*mBNmTvHkoV`1|;6a1HP;Ef1x zh@#7t2EbIK)y%_gZS|;Kt2d0g^hYJ5XSAH+XBCIXUE9F(Xc7IKaD6RkIri^0$$_G- z1NVcLDNX&~gU3noCm;1@TDnIf?!P(q__iKWLf^=I>RtP*otF-GmX>L%e}dhW13$CK zo%Qd@+6XhxEmLxX8a&<+bSTi9ycrt#gAMV1h;V<-I{P`T>WmA4y0!=JgdhENFyo&r z4l3H%_V(s9msw<8*)NPDR~2UOVEGzov{h4%_$%eY+m3)n7~Y!HUU~F6MQ7=n_EaNI zrhpfI6+^41EK*1M1pB}xYb`=y+qcg^aU|bZgmBK}iS~xRp0kkFGpN%*_0FsL5Lf`$ zQ0vTKN2mb%5I%TR;nkE}F*fi}jdsv!Z@UH+Bql?3lD8e{?zoyKn$pcAEuaO$l&My= zj+R*z5^G;#4IDk7+PIA_G3@_#lP3*Us>G%exK_DRPJ;rVAQj>SrYQzYQ3*(j*6&#! zX<0_f`VnI@yULZ9`sFb?ieU~=un0g`7)baps_)QTXV!JbLVqxu-Y3fwN(`+xh6MJ!vax$ATA)#+Txfa5z#G*EU5YCAQll0>D*ZyA zj0Q&(6z!z}RzF`o)7e_o8=Zy0jxon|S4Xz&Fdaf7G`PcA!Jx*f{!OUtimEQm%lZ@9fN>5=6Qej8oQ8q=xFE7F zL@O;O@6U%&lVNtjUlZHeoNm7&>{D}^c$OUQRA{?iE1QNCX$91P&}s`$SNlC+N6uC7 z!Rn=U`dZq1G##r<`?BW5c+eJk!&jUJF3YrX1n&(ZYAT08hKop zLV*NOER#(OQuZ_u%QtZb!a~DKJ=QACDwsPd6lT|O+t3cb!6I1R>Thz$+8I&Vo9KlS zB&N`djd+Ii{u`%Cvm`8+elZ1GhJf!mGxh5h74HAOWwyVkdpzJqIXL3R>3f0D${&2cex+_~SbRI?c;*qUU zQI*y!1+9=N;>eqCf7X1IfulIkKmdi zVnZ4kypIP;?^`*CV(I`Qc$YsZBqTT^z`5SGWV*Bl?q5^kU)S0)b|D-wk2(vkebY`ini0xPp|^V5DzQt}LdAxdXKBCD9IS zPf#uId3)NpYf_HXP(sMp+`emHV_hrEp#ViSjJxZHg7;~4g_9j+4HR>9swk=DFKD&C zGCr8H2!*|HbyDA015A#9g$)uC#r1BN@wzT?P!&jpPxAitA}h}5UyXChYv#44j!mBP z@e5s6h6fwu6~#?vhlbmo=pJS5eIFU~Hht-r7|TRjN_BQ?$h8Pl9suvPxUf=QHm0)c zVng`-F*=P0b_M7aZ@4N5^8wI+80~5C*I}9Pk++Of@m2_eP ziOV~OV~rnTS5tuzdTF{S7bFVol*cNF^h&4OP*d<@kFPiB_5CqlT>Nw!wSx}3&4P6^ z`+BzaWZeX2OGq19SCDo6TMyLJ;$y(x_|h0E4OWxJC_cEIABWs*dtgLM>8W`#rRW8Q zQo9ew<%(p=(jIzeX0Nk(L>Jwb=lYi*$EjHYq2zr@Tc5WHlHJ1AnXln2oaj_BuopZ@ zbY1CXK>9oh_|3z0Kd~|^8GNi;D~mOhavJ+Sw8eVXj=5~S#bX*q{CXTW=1t=rXA@dA##{J{bM2zs&AdM%fbOgxM703VcHwVropYJ?_?_|Rjs4I{E{_DZ2*oU zNHHB`RUM9Q>!n7o$s@u9Bi#3RIW5cQD=8tNW$NYRZ}-_&C%)yIW))v|Z|%P}cNA1{ z83j!JRpl^Xlxdk_7$b()F@DE*Yg&L>WYy|-PvE;a4KXk;8^(J}WhMke(y*roMIM*& zU!wvkVq6%Z7{j187`r`WqhMkJXb{tB8V0@R03xH@!U(nH?Kh?2FQ^SdBaLDy7XBgL zk=OpBtnL4-c^)6r+?uS+ztZvS1tPLOf5RI1Nh0KcSPgra3#a@cz_?VlSmqJ?9!ZRP)%OBCmt2skq(^U6s?S7koM9C`T;r(O}Fqes~c&gs!9; zfN?&JX{p?z9XO3G&luS-&i>FslWEhB)lT7eQ2@O?jt=DeT*d2W{|G)JrJLk;D8lxX|u3U zJwayh1UkK-*>4syPyRFRb>4CCsd9B-2RSs@1K_)SE=Dn;x-=#SHU}qxD(g+hjbp~c zEhde!c9?{Yh$_Ctt+vrV@+rMCYSWF~X_oDjM?D+j*1eI--<)yZFllc_%E$#Ts6EG- zx;$m&8~I(J{B_?ca)YpH%PxGq)HWH5weQwSR*Pk#LC1T_%^pmQB(<_v?ArB8j!#i* zlJqQFg&2=nT=f=#ebCtkobMk;P9$yPV7HJjI2sxU#vtwLCR5sUUNlWsMw-iQaaGP?f!#R1V2TfCXvw}0)R4?yc<5YegzDd z!2f-hl8PB!5v2PvsJ#k0O9SiGpyiw6J)QA)Q2|D66^K@yBiHu1R`s{h=%FsmLB{)(ctKSp zX6W$Pd(EQ1VX|4sO)A8BJpPO|16kQtD6bu$U>eS`;k-Bn&!jysNAq+=QmZWhy^>hO6 zYT~t{Eaq5!gmP?K>AyH*WikGOhQ8E4hfirleOX>?>E9AL-m7KPQLDb?3~pw$yX}0v z`i~>_#9okvN3qS~OL|PRxm|OUjrgwWzf7_8sUZwGTE|ek3)>z>R4Jee@Oy)W$tp02 z8dkj|7c=QgEh8@tt$rO|o5HS7z@)ftKjeF7dy)JeB?nOf=4~*(_mVuoQ^{2f5?2}o zbuOyXDL9Z_>BQ-Z8^r&lK>ht1BbQesJcMk zz6Pmor!;642Re(@D(cG$piX}rsX?MtYf@^&DA|M>F;p?Dq?KV>E z(ifSz-ed*kXfM?JjNZ4u-3;>gJf>4~K24m4S9YEE=!n!uCC!G#Jaid){^1KdrGRcm z6o%(0cxBQIDAHZI27jMZHi5(+nk*q=Ee!kQ{3JI z?h*2%V*t8On7F?F;)z{uhgo=b=?a?0QK>(&@6V%+b@}ua#OxY#0sb;@ucMgng+T+{I*lqH#pXTG!WHiIL;wi0E2I-7{fK(w~OWQHJrL@JZ7S6~bgPhW{XU;3WryK+g6R4-hRZ@guoy?lZjRm62#@X;Pbx*oQIBjrSUMU+ zq4|Zd7nh(-=@cQ;1>=Sm_IxtvdA-S(y{!<>pv&`(09thn!`J?XV-uxxOi5PYu9`@8 z?OFfoU1juJ7NqY+ViwU2+{#D`Kcy8I_>cgQaK&#e?B~SG5WBQk^6kB0vsi88!h~yl)I^TPi zl^aMvj}CZ@N6q|_@7yDh5pM9w3?k#$%(4z;g)T&vc`3L`A!cr$f+asQ)d=i!n>A#LChV^Sgs$@suW@49WX$e ztPr|(;fvmVWyPL;vfYu_e^d;cByKCh5rkqcx7Fou*1Z%U-II^{>uHg3+gXCr>XyQU z&D_7P9dsVTu%wSf?wJ@FRca#!_dhO?{Pmr348@jBR=P9C!0nSwWJ0W;oHdG$kuFuQ zk0ntPrK50n#&Q$DqSMAx%rmhCAZ(7{R;K-n(+z8o*JlVirk7p>{oscznXX8b&}LQ{ zZj6>_$lMy4_|n`9HzIwdQOxK*U8y%TH-{G#6tu9k%rzN_D>3L2g8NJqT0*<|qJ(J! z?#vX_-y2O-srbPs^A`qfeFulV-HoHZu?O zS?g`e1eU8eqI9N@dU1n1UP-Qt-1k#^f7pp$KfNnn1Us2N^g@ZThHEW{ol{5&giCvL?F#Y}4K~HiHpGZ3g3M)%%+4zcZb7~CEv3eZhh)RUpD<4ybn}G)M7we>eyQa z$ksyzNNfz&G+nvlG(Y)4R$#-o4fJ2z=U7%fvQ``=d%xX+CNz+a!);XA$hSnu6(ukI z7!>ID{lzut5|pdJw9i9oV84gJP`u5xQSDtF-D&83lwfduoyc~zp(hfb{mYf#-Q-95 zvh#@T=jYQwQPwZ*Iv=pH#I<28Yuo?*5n{ulqf^3Qwzun?p1-`k1DR1k&v3!1hH&sc zU5!6RO~X?SH+>FH>+iD!L!+Vw?k~WQqdA&&kO$_;Vr4*UEAMKP?bxE)STc*j-@`)$ zQc_Y^zo{(2zGmoW`?u$l&1wUpnVA`$u&=-@<|3;5-sIa3_ul?SmrCFynJ?ZSW5VWm$Zl^gWP{WJWMC5q7&bK93+ z+;*)kD=jT;f$?6$o>IpcC$~VJrUe){B)J<-5&gIb?j9_BcGzLFx3W1-{3I z==?i7dhm}#-P4@M=NuF`8)2OETZCa&>v(=FVXoy8Cl5jlO+ zsMe2~EtIQATXWnQG#o&BbXU+DXcO@;tD^~s%ZScUAgoSP-9xHpA@kzk(U2m>Si|vd zeEWWh@2%R`7ZlE{u%kv}LbU3LVQ)}S5%=|?kXKLhtBC}MDfXs^P!`i%z_ORV&U)7q0`hakz<9JAdOgJ?d&FlAy<$)2Yd=Rp+Uieoy zXi%!fNyPMq+R3G=c*;L2)LPwe*_kk^B}E>7%hF6J?$7Op0-{w>8VUB2D&8@**g@_O z;X2mBtSTxJ$GN|FLAh?PX26tO_sdy?R#uL!(A3_WLwDinKK{gOqXh;$ZgQG@e;F+^ zXNY*h>Des9WP2Dv!EfZ=mh9B_^gL$1=CIpUXV9WbD?5#88O9hkRjy;BN+^bn&HJw+EipV-`6Xos?62-H`wiOc z*1cG%liF*s3G#4ob~X;DQBza9xd4+MOlBJA1M65@0^mI!3+Ycc+Hrk-eNW#N6&2;T zc27^o&ts5OhN6iBat+s)XQp$-Ao~hWhZ(rz$;g}x5MxEJ&)2hhw&WV-$`UUTaPb8d zg7PWj061cV&u6ME=u=Hmy!;5r?d)D$ko3fYVf#P0i6_ZYE80{u#;)lwN))Cq@jtxj ziM>m|-%bhhG@WpeY=km26j3#NxFp%{t&APn`QRs{2#BBVNu8IUI`8P-FBRcwW|^Fh z|6H-p4BQoH%nCJGNy|~qRIS05i9`#@9DIgT5?+|CPkAwFSIE#ke+j^1e}-08b5 zb*1aMOz+LeLeeofg7DVpbeiK1!$JqmUqu(+8(7+A*eQKuA^B$eLw~YalhFI&20LWx zG7r)5`y%+)zo&wXjO_B}Mm!irhL8k@R{6hr^C`uLEG*??HtR*kJNv;%{92O{jMnFZ z=QJT7J}B(A*}m3lfxZB@g79*GOKNhxr4(-nSU)b$I!F%)5|u z{d1M4$AH0Cch+U!@ zF);hEYG&!rx%u93rBgUbjBi=gX;4LIoZ3qR@jwO{wjr0pKvH9C#fefaZ zVis0aRn>4NzhYCf@z4dnk1D)eeBCH3+=@x42N{pPT-yi;1Y_~|M6{hoWKq_7Z zj=9fEy=(Y|WNJI;;b0wOCm@n=K^A2XXDZQ};mN!Fo6j#6#rIN01hT(_T+-OvrfO)Xr?*;-FXF0LQR>6ENHRYr|Y|TPzw4zLu|~xH8u#Z zRED6_l~e;|vt)h`EU>4xnATtyID8Ag=bg+5I;nrp4(lAAe-~YkqutCz;2#r^4QjcG zBpi|Z%;$yx?p8Yf;W;nHrK0U93-WyWEJeZMWF1{eXi@QpPLXITU6|&0UTqVZkyAXJ z+U0nGF>IY)NZ9YAYR?h(_o7X8}(0zQM4O^Wb@d1{U5g}wdbYzBPWG87;q1DUs(ITnlYY9565ZUQ_$s@kv5^rn{ASbYVY&z$BQN{ZVf%nMzlhZWN*W zCttp)m|Q6sd)BdjCmR^W6(J^kBMMn=rHp&-lvkioXJabAgk*%bg(lZ}J5J>KTpbjQ zpl`V)s7;zqWZ@@OffRz;lvNn%194H6$y*)BMRY*!ujuB?*8_81jv3L@QA%}Pk)Pl( z4zd|E)Gwmk_C~QMxnX>=ClQ-qdb6^^e}T5lFJ8u`-j&t>yNF)%RN zK4+T5qHU}&fXc|7iy~9FhVi-bD7U*(l}NjcMlb(+!CLtbC2MyyT(PaUu67M_BuHvE z=T&B2x4(8=XS-)&5K>X& zKP#YZhfABZ&$#;IHccVMs&RLCj^kD(Y}FsODn!5gLc9R&pQ9WZZz6S{GR7G*yv3CQ zAqUJ=9cOz;sWEm!Ju4j`=+>LNu;AM&la*a^rDY)7!j2>ifDS<-bi9{I_t;`Q@NIph z-TihupjX_t7B_vX z!|N8Bl4ULd>S$!${+JCyx%#$RPbXP=GPvpO4`sTt<$qrl2pu|+)nGMozAVhl- z^+wF2pl0n%t5-5fUYQ4MlJ=}+9omV53Pb?``#n6F{qN#pbTYWY(%+~Mc?5`ODZuW{ z?sxgNG0(}sz)f&@_{PAPR`y8Qb%Qc-7jtr!Vsf92;$~hIZd67CJp~Z;cJ)sgT<|;a znMNda4gEb`<5Gukf1D%%4Kk9otXbl7e=b4HlepMm!$wuE zvmtwlcHu+bsT+>ds+U;3fnM=tjrS)rBQR+cDIXXXs`PYh(P;2_o#~-9v%|4GlUI;@ zD}df{%`4Bot3l8&561yCRfZX8sw9)9HfKtkOVrKo z9UI4S%}LzZC36NzQ7^6g>t^%!3clPEx6D<4o%~wgz#ZZ8pGhUL?s(a?V^zUo8vGQCPSz`hj z%(C>7=^b_u?~D1_Hj>T_51pl=&TzsFTZw}U`3sRy=!=?8e=+x*P!cFNj<7La1a((B z7A;;OU6L_T8{Omf`seKtox_=F0Fg&sZS`PQnOAfeVL@Rzme`7wH{PE%@3$LN=YHPPBLNR1N~+acL0Ww@G#U_7{(k6y&kbjB{@gL-mR4EA=Ie+((xa z=8QTLKeWei=-f98w>@Ge#G?BFT`kYK_i@)<4}J&;Y_^P^zpujWf&~hwULu! zGaU`xniKdVD7AfnUh5!>K(apbavhhId#_Ft^Hgz3>FYI+(H12X{{lG|dwOC_{`2R- zId}O&AG2vIlQy-yMMR%5d->yDB!@L)=4Pwg34YS}-$XiIXVZbpQt#EX3$wY5ZvvQf zHGEO7zx91L12`IWLtCR$plHsqHYD69AJy3M zzJmT>GO9V)u)*sJ?KD<3J5@A9I;}OH-h^xCUEoQC1OgUs=wA6ig3NO}dkVqJID z5CffQ_^e7=uItOq@`nPG`BJv|=5lIt5|XB{7&yRy0LTW_*Okw`)k$(wgK2!3=xHRB z$5?R1ySd_0e0HT*5>`ue?Ub2eI(PK@_-RYzL?<$~3RVS*!Np& z1G&@gWV8lF`p!W9(y3eej3FZVBJodHs`fjaL%w?K6Fik62DSZ28Jsj|dhC>KCE}Vm zM`8N7*1K3)ZQkm~2b@We_Eq;JyF60092Q*`nvWFs&Y}hTH;+TEMVZ9Ej?Lv^VXql~ zv|K(q9Svf-5O=`FWHTJhQA20qTqfH2pNElOlVoWMjE;JLaP<&M++0g5b^gbeW+j4qhzQFX2#W-3A{T9%R!+*oWj2W$D>;QfGO$E3MQWFZ)N-zOi&k4rjC zg*A6bc%~P>J&>>Il<(wv#-yT~6P)KCJ#7$n=c6x{fnLXmkqxUrs!A=~QLejF@w$s+ z!97DL2?($M7|LccHj>@G;8q$DqG`NGP{sztJX8C$e_8f)jFV_cOaFIfuOGHiTLYln zAa7VmqPe{@7A{L&R@^B8pRpV6qE%<+Ul?HqEyAqTa^HY6&7ncMn}Hj@*L10}GS!Zj zGk9Nn@bbIn*gfB9uc@u=N$0lzL7$nOO-jI}?lTFHuF`EyAc=pzIf|d%aWfhwm7Pic z{ow+TZ#mpIGMkW%8N<^*M$sr^9CBPy|22RM0sih1<8b~vEDEHDAF5h5Vk{nhKU|Nx zzvgwjq(2XfLUE%g{Dq z1)IM3;^lF$jZiqt)$kz-ezmE7ckzeG8Rh_bW7elk<(eNt>wI1@k3uuoGmdfGtC5Z& zwR)%;2^d0C+pK)GA1Gcy7Du8`eu{9Cm1LBm*@mpc=r$+3R)p zHX#wMKWr~|=uFkRP=n7BL-T7bdB=&jqIL5ow*qMFr#~+2&gGo9>>;$fyrSRFKz!d%k@#yt5R%E}IOU}t` zQ2Dptx;lSCHUw2zr=M6ziYW^GjEPLMy(T+H+7rR%q8q0iC5u$p7;I?JR zIFN7&LW^;2B+%oyFS@$V;4Rtx^OITN<;NfWHFp&!JSUeQ=aUIT`0(;J5B0?~cf<_} z-CgNRovsLW`}e`P_j(~G5S2j={e6B*eo+na`jVwyDx{nxkn0a;N3Q)-LgeHuS0{`O zHf)KTYbouF=JdpP#i)UWUk?~odCN+dASt<{?hV>^K zSiOhvPd8q@e#2^I%cHOS`Y8!fG@*;VW7J5VOhI zI(QHD@A9T-;TkWd4-;l5+OcTgJKGRvXfai9) z@&}KNVh4z%pny*u-T31ZyW5*C4~~;|>B%nl@R*O%DD9PI8@ZK!+nwuQN!TDP2J>H@ z6T69GY!|BdaD_;nyUBq^HpN>z<$c2_wED9q}z&Bs&FO~;ZX zJoC*8x1DIT(a8_|Y>m$PV;1x}F2BT_Un4S>{zl2R=G+#BSx~^wPvCua1#MegH09ge zFi{Q5Lq9`?3}FY@d_A)~;{}nF8ArO0`)6j(D~cNxD;)_qeAnAb zwD}5~$X0HK@v|6FRfk_>+IRW7FdP%;_5Wj_e#&Vu{LqTo73&;O>#;=F+FJsBCIXAd z-dj53U(hoHwVONA=_KzP#3ZqePT#4))Q)Tn9t6=T=RJ3JZM_jg_BF1n%yyL)bUrxB zd6qBxf!hn1LJBSQ!_nXIq4I`ddJCuOY5cvJlQIS1^j3UA)=*pXRA?Bzk)^b>q|k_rIZYQrf*yfV+o4k`~GSX9nyO)d%MH}EsyGzYfh&>M<#;& zo?nPneCeJ=O{Wr3H_V$wB625(6R0rNE~ZaCQwbywJ8}e%`4_eZhXU)YvJF8tsVKuE zfzNIt&Zim4bNAUl_*Yq=Cqd?B`0EKj-5+36f9cmcaO&=;Nsce&1BXz@YorrUwpOK& zjWf!@;u(?gxShGDqSjJM4hs_qyA-)rrH-x)IE2LRW)6~?PJ>7MbA1uR0@_3!^FJQ4 z*I8n0;>2aEIUOEM4J0T6cN9cSl`}0>UM$9xs3@PHLIcLJ1IryYU(|Xevj2>izoM#? zOTC2o6dVC*>l5a~)gvMG=S}K^HSw%q3Dj#b&7AnX0g6}19))l?*sF|&5PpslE3eKc zcUZW@(RAK2FRk=?bAq+SONAqjdlXem!qSG#$X+N`&b}VAG<`8c^x9|zV{;5#d9`ik)k1$?8kad*?df%YtX{)+AO5)}AdXSn^cO2phgD;UlG=h~4IytqPBPOy7uq672y$Xb7p5!2^{MJ~5s34~}7}3{-r) zcEJ#ZUjP@jd`*m3b0f5~eP5(#@ny49|HfO!K^s%HkNu;Wo)=6*z@D*Si4XlCO-m^$ zxbJ)X#W0skw(=&s0}SPQR6%$@AVx;|CIJA9$FsYb|N6l8LbpYu3yx;FW5mLiASW6~G^fc*RQFxpT+U|hj zOa3gX{xNa8xDpeuT4OO6f_hn?WA|~9H=Ku-nkM#Q^s0X&g-86&uKRTYIE3qtwePpB zlopw^d$iFK88hH~MJdH(z?|D$5flHvnYpNrJbpw(|OLQkMCUU8h>MG(fK;fOU<=O7(TOi<-3 z^p6iFlR(FI{Y)B7n0mC<%tEb@R&aGzq0=1w>NvO5;pt#48NZiD8@NfktPEBh$DC2@-ANr9&=WSt zGhdc5E{V?_26!q@Y&A@Uu(_lKq)Pofr&GBSgTemk3t6uL}Iq$ z;s@IHk}UcA64sdM{ztOu-WzTS|4GTH=G7%5T9Hw_NHR>^>u4I4bzS>Y$nkiHIwr{P z8DHuia=_oj?2W0foO0RL{Vj z;HNNv%k$>u=IMJ+PtQ0@_?sZ`7;CXg4;i|_7I6PU!=4FUPG0TrMIFpy-fU5Gp@H3A zB-4P0a>*==eyWUYzVb{>LLjZp=cQ~1#yD^Rt=w%e_U>SdYqgy3jVgNGM+I4UJT}S# zEmyL3ap!$Y#8kGXFH{NBy3e`aJD(|n!5rL* zOO+O?!BG^mk)TNC_>NX)%&3-1qE_tHU+2Xaku6hjTNkxIQ?e%?U;bpzm8513CuQC{ zYYX^r+ZiNdEXM`Bk*u|({50w+8YU7JO5>E1lqcQxe<9EJB)fS6x7?!~R0$XfeX3s) z&)*4ESZ=uvgfjp@*BingR7EGX+LNPB-Y{11)M)_-3^a}6mVE`r2UNe(HA#(+P=pj1 zr6w!zc>5?6UQE;tZ+Cx!(4)wJ>3@TDJ>rG4NckfF*}L<~3c&Q?vYc}5&dz3bH~}YM%QQ`_({Yy-O`Ih_2@N0_79tS6 z`HxMIQikS2vXTKhnX1`L$i z*eaUNaw%8QKd;M1qFPD>jmw(`Wl(%@E;<*!*ZsLfqt${k^xve*#^#}x6p%a54WpzBA`^gU6IiVN_wKGI8>$QFDFlKtn_j!jfD25sdH+ncN70~&8;l~2P zW(ySwwB<1l$R6c~%UIem8I7qllCls}1`>T1ifU#7mjiW1zY`bkcRWOI#Qs%t3J$Rh zlT*^cV%z5KBcy?&<@_w;1g2dsUb167^nXDjbu_55vfg#;wLE5B`#DU1l!2tR2rZ*6dAPF{<4%nF-6(z`+1qmn5GkH-sk=08ErFEQjQaGw_y5$aQZrA8L1b2yfSl2!cR=LL86^J-y-uW{KxC8j1O_r$r> zfF%fzFQXhF&3)-$JbxtT4ASjHHB~)`y5UzxPp!q?g!c2NDz9s2dMd2l38cZ~2RG zbYE?`8*AZp#K(bqZaaAWh_;lr?-c3u4$!#tL$-i-)XL5SYEGa4U<|uw@sao^dKW}J z@U)dNFB|^x#wiE7|BgI2M3ai3=kb6DB|Q)PjkmCoggz!0+mn6Chdf5?iu$2yH%OKT zR-*}tT0mMYg{2(9o{Aa%OO7PcRwr96G&7Aby1BOh9zUqmg#NMNt;<>ZKaS2Zpt81& z!tYE|O*PeI*JRtCPPVPdwr$(CZELb^+qUc5^Znny&e?eGwXU^pWa)^guQ5(j1LW;; z#rDknOATiEGv_jdc7q@-_JzMKa1@J_NoO`z?@JbIYTY}}%WN6pq)wUmu8h1V`)i|- zDULxQ4>bj)30u&h;hc+GQmNxQQ-g^)$sLHwF}F}pUK?9Y%{ELjid2qYphRJtO?kSX zB{!lKcMs2Qju$d)owDMl#pdjFwI1&sqcuxR)aa7#Cv{(Q^n}Ir0V5W_>_KNZT2c&r zw%z3#SGebyq>P_(92|D7#!c9D9>iM*T`}-Vpg;d(9@ZIyib?iI9zJY zKxa6fuMleprT8MCBpJtFF4q}7J)C1<732{pbhx^IA?(mw_m(c>v_C=R@lqVGmevIL z32-F4k3XoLe$uKU)P|AF(Tvi%#F<*3ber)=2eH10q+^;px^e*r+xGuWqcZ6(2*d{> zG`dzTKvRtTxiI{iH`2eaAVtm%{|tRv<9F5uXuRir75)yG1!ZQ+I67tzI-iYsPcO%2 zJYDHI~)qlekrA1S`Fls>XvvBq#y^2R1(_e5lj73Bib_>becZw(Y!1Sdld8twQI z3}Pa071(SpNSZR5hykX3eOC}k+ZgPjXg%q-)u zaacB{tYc8%Va6MQy-p`mQUUz-Sr>8Thd`+b9^PLJBYDQ;Imuxg`$dyAHjf~6GsXch z)mma)R*&H}(LFDLlfsO!9(D($^+mfF5E*-WVC!$iV%azOtxd5(^C-UYY75jV%Uo}) zl_*5z$hE&XvNp+Y0{vW_?>~wJN?nU87tVO#E;%C7U6*CYxh}ZE9?tJtryR5fI(0IO z*9)8e{w2l%avtO>k+y<^*SJF&w2;8%euGY#MNe(#%dY8^b1?A%&NZt`cWGBD5Y{ww5yVS}UX{{B+0mjK~&euEBPeODa zO7tv2H#p2?O{N7TN4Y^B!3o?hzkBf~5;@$Cv$3X-nP&SCbu>JJKNz1XlyeNRSc8k- zoM?GLLlhsKm@HLc6!(haf7@IhDm8jiJs`zfve7W?C6LO}LKCUONh-!#^jnJ~Aom|? zF&K>mw%mD`oAed=-JN^U#$$c8%%XgxBw!1mfty4M>L+X}*6#56VQ|MVtFJ$9!Kz@? zbap(?3DLFzN-aa^YMh+Ua8DU70D24#VBeyS_>Ei(A}$LI_7k!u*M?4zp0Lm9FBTof zSDGE`K9*4TS)aFSke)Muu0HewY3t4Zx6o|l=NnOI`XUbuR*KLU4 zd?`P190QrUpI4_N)q38Hh5P3V&bHZOo_IwL`UM-xAPBiAXu7*OR%Af}@@$hG)!@LC z%4tfQcZ0Vd`8Q)yYw;fq%$QSnYdN#jlq>{##D{b0D~1Q=2Xn6AHsA+WuPm=Nnf*z~ zawir|6jsoq@LyhJ=1&InB88t8(-!SmwjrTxVS!e?MUUj91^LV`5U%J9!wJeeZ!hrj zfzP`MaFJgNosKSJ<6$;CKPAu$@^+B$B~D`gnP&KTt2%YS<{IyIEB*eNfEq(nah6uf z9CR_dOpdXv*R%Ydk1G>gGL>LFtIzc*sFyXLG~LP-`A;kQKM8*0F?-!Y}1K?3K$ zRBz&6zFfC8`#L!kjSn_5PcghuQX{cgRGktRgO<$RD!kcz&A*a1J*oeCNI-m_ayVB9 zNh{9;!#2UTwcPyjo3fC}=O>UB|`@nmmoaeXr`oZdOsHQ2|e?8W)-nO~2Z$KiB> zzl;w?pZ{j>UF*2h1~H9)c+KxesHcys)f-N`##w5*(y_Rd*&jttX|< zLeX43saB4$KR9quiQN`*e(SWm(DFJIeXZ?SO|6yl9h-wC5TFf?uux@G{^Wsvk!Scv zccB(|8H06vZJ9FASS#-eSDxI^83#Nr0tc*k!BLv_qF!KXpm!DP&(>(rrdCn zP+H=2IQ~+(?lSO^#kmnAS?C?Bsr3J2|p z5KFt;Yl^znYMxDM#C{lKOHZ1U+hdK7EciGYs-9!!0C-GJt&tZbG6Pli%=wh@ znqW+9pIb1a`~ea8GqgqW)?`IYzi)uDZqDw=G!|R?Es+z(;}^*oW;a9gEUX^wCNUd# zAU_C6Z0q{T`n5$Y_;#oOftPJ9BFLgK!NVV*7zeYyv zTXNbfGV#EGRF0mccI^ea37)r@rx4(-wbW{T2_qg`9vTHd1<#XnMcKB0iEDPEed6Z! z#1Ztc-u9d4-9Si47FF1A)Ebdl=C8bpYYv6}Zr-2Fq-MmNTj=O|-V9N&jumlYDpe=J zhtNP>#qx;HN?0V(1>;!Jz~*)4&wJz}aRpVTw}!RlM)=f5>#MrcZICm-D)=YBS2ee~ z-vzEwwWD(+$KmJw>^gqqqpzTEtO^;qAGI@Ms{7oA&9WsZ)Hs(rYRgBGx3EaV=@~eH zP?zo-(Mek-@s^V)S7Z8}Hf?Fly#gzAP3}mZ^2-tZvYW#Wl=$OjK6Mave0b5c56#TU zife4f#2$fyZN6mE37j^|%{#%)Sx*9LA6nv$o!oCD4P&P*Yj1W3&k+KK304+ z@zcnYLjg(^`&&GcmXyV&5@mU~C*U~If<{pasMPy12)Vqj5Z&J4Mu{h6;}pJ^#Q35L z4@I1ykZY3hO$#1E@1MrUnk^_X(14{qUGp#BlE93(*gmpfXondD^xi;2Q(bC#^~GG> zuBB#3djR3bF1K4KK*b0Uv8iol2?=i*@vzd-KviWB&w&O3Lb*UIq3{D0UD>#>aUfo6 zY$3vhqUp`9YE1V{O?OF7rUmmJWuLG~U`Q+22uL#+zz13y-oFg0ss%GJ-Ovg8YWB#2Za=Xqkx7S*G zOuY*}DzlUC9=d<3Opy?oBARpC6|J&gckJz_^Mkq{3;#mi?T{oo9q~`fCKJ|cIbiH` zi?&)#Z*ZgcoVH|o?`S};Bx{+Vuc=_1c?yazT9lTZSq6z~?RzXmbVDzpY_?!fh!#}@8@aKBdGEK4 z(rovPnc~fIsh&q+$4uS8Rb4BhTK21~e#*b1Eg$=OK%-ypu29au-BmfCn-}>Ja$362Mu-< zThyyEUT-5a9x9RHYAOWEx7m=6MiaU0oJhzqK953Uy+Q0kDbV znouQML@`n|W<$b~TCLaq8FcS73A4k!O;<0(n^2;#Ou|;X1|t>cuv{Tk+H>N2gem1y z<*^b&geV~daux=pzb5b@1aZ(16N(znjeu*b282DYRRY3g^yt%(e0!v`XR|o?2-{!s3 zky=I=o-9fp8tk@yO$mz1JP#OCeUiKaY%lBA*7*EHM5|4&f&SB)C0rX+MrFaA!>5WP zCR?Pyu_d232J{`B2z+h%-+1Xmv{UG9t{@ zTYOHC@m#oEV1Rw*27^wN&@1z%s5{?CwgNtzLsR#Pta`Fn%#T{hU(7KaXK3WnvAfG2 z9}gBQs=Akny=%L_)ZqtdY+h|{TnWIFfJG{aLr9x+v3y;J58&-6LJ1K2s@IZ%r2z)5 zT;S{el^aoS5U$1xf}Z};&Yx`_&xhGQ9uAFG3*V^5)#K4EH}Vl}_odRhI*JL~YsF}t zSmsJhlSv*kGN&qP+x-1wla*L?HQDgDgPcY4SWJvHLx@L)v*G*-71122Pi!E+FgWv| zmuK?1XuWgMSxpE>1(vTA-907pLyA}4-tVr$`&TR%U2){+BRHuIwZ{-Ocg0Q2s^iPM zJBg6YYOy+&^_&L757b&lODIvbg_lBFP#oMRPsgeg?qd`zn7|Z0926@gCGkL3u?LTm zqC|;)(2OWi`SK7x5iB=V@zUvRDYYxlh;Lize_p2@agzV{UIf8F%r7(NlHT?bjN#A9 zLLG$5150mbY^S8qG z3z3HqM~?k-562@ikH;1CY2kH57l9D?v}?4V(uLKtHLsx*4OQ}8a*E2GY@)YIo9a$Y zv{alrQ5mk=#})C{!&^br8@W;vV??UJ=Z1b3bx(^UYxC&OusbAhj#V3@o#OX@=f$!4})Msi8=0dC5A zz&>RPuFhNRakmvZvyeSDrzZ&9keT*#4<;j2&-)%iuIAn-f)(b&xReI)44B~Au0`D3H8ld$WrbGunc1^ zoZr|}y{NV_c90l{umSkl~U94PpXnvRduLM5wUu}u+TJX4ju`kwWA|X&>PcjB; z(v7Ne!W6WgQ8G%zaYJ`=NQzr7HiibL+_0b zWBA2Uy)QktsWiQ#zh*+q+Y_Wp)7D^>WbvY{FtPGoDn+RL>Fv~GWn%~KdVfnkFW{8H zV!j~d{?r#GV>A&T*vMh3GT;JB(qAa*1H6Vee|+Z7BI8+3 z)_Jb6TyXS{qVW+o@2U@j>0R$G_hMkhckt@&LP_>4RLPe2=X%<6p zTu&JCWRPhCvyo8ccg%ve&tyEbhQxv6Sb(_`%~v-cfA)n>(b8(^PXjYW!Xou7zxX&m zYNI*xkuWP zAkp9o$XguAliwkd0uCXSerGZ@YBrYVfXl1W6r2<nP0m@fyZ}GxOZL_#kWA>*e z#L~&09Y*8v_pm}V$T@{FZKd5u!TwBxUS~fm=&Easp0b1lo%7_1Cz2(`*w~_8qKznd zk+X`TV3o;Bi(yn>XfgM=>&$c~`Ll6Sw)S0rjnWK;AQlSK5-=ATsHlv&{DPiXm34*1 zMhJ@3z78c}eyP!jrs7&e(W(eXYrD@Wo(k%DjL zo^nj56KJJFSt91X$qRc!aX1%X&k^v$WmUNHG&R5%+A6bUbO4&S<8>@kecL&MAal(n zvVzf1wY@4F;3~JLqmtOPx!l2CoA81fjWhj?`2W^+G`nnnv5%2RVbHykklv&GYh~G3 z5f-y*Ez-Aru7ah0;OH$o?Y@L=k3E|xtUI}FHb$2WOah%KmiD4P&Tntr=^cp5D!DQ2 z6b!OxQCwLh)UZY&j#-`V(qt+ri{xOtrZ(#W%jcp20C2zV{U*2qw;nvox{sx(bU&hV zKBq)xl+QMy0m|&qLHl_$df5+sbU09qk(sVtSb23eZC0My7j<%qHXa5?_`HQEiO@_2 zn&8RV{U&yxIIbM@py~#s-Jbmt<+fsWIqAJwXC@LcsD)ce+@)c=V1Wg?0R+ZpdJvx} zoGrV`4{OWTuUy*6VsL4WHn6yg2rPI|MV2f8ASE=h*EVn5z zmk~h$9L5Pre=;F~vwN*0GD}hUTR|NZd($O@WxCSYyL^55my@NN$=L~Fgs%7exq>2` z8|1wXiiPk8`4bx)7;e91%|tz$uKOh>haj& z=FY^zM;zE=^D@G%QhWSMo&ljJ7x-55{zOJ${i>tZK}2$A5gNg`+GAzuMuoZCA1{yP zD@8|3rrHhsL%*|#Ygb=^5Xsffy>K6o+i0jdz&n_X9#(DAn~1UvjKTK{$K)ovuR%D} z>w0a*zfk71iaxDfyI+&Y7ydx!9))>^zJdj5dh=`^!Y}*M=E{!nFt*3=!xSFO^{o+L z`ji=KU@B>~Q~NPQxgBRmtYMyOuSVXBJa9PCa_bANf>Q6R1$p@FQZX|h1rot62AsDP z&nN|+Z^*le%g`wdn16@3Nv$g=H_CPg-dc{NBhYwYS6h&Lew0o(LsQIC4t{t6yhQq@ zm#S0)(ZTED<#+W_pg_5a!3b7aF@{;8L`B$5GlzheeW;DneH6j6qQ*=!qVupvaM#`e zGv45}uYT((;MJLOC2KW=#EJ+p=AQtFiMjt1lWQ;-4T2R5-jeZc5Gm?{|`jzhwq9oKdq$aL1QQnL zbDZ<*3nAx%`{w|wfh(+&KtO4Kc3AYt9^iP|zK*O!JiXTys<4>D9Eu8D*5NX$m%Mp$ zB0GOgin@wgbt?{A!&MkcjY(^HM?b&if+Q|o9-hz{AEI7!5L03n;D?ttPyhKBBi(2u zRn)|4vbp1rRsl3Us1G;3@titkCi5-EzaD#X=Yl5yY~b zUCyBWutJU32wpb$1Q?5KDD}A=m)G1$S1%Y|pOF@0+2p!-?LGT-@OuNtGojz7*);bV zAjoj7j%e_s{s};fQy~Zu3-6UR9DcgbGQG)oj4&_~Eq`*L1;vl^5{J0bbuH!kZ~^UZM#=Zu<7$+L=)Xz7!T;<%leA zz^v$PeArl@TU5;mg=#gG8lr4%bdhJSd-=>0B6+|8FiLi%gW|;vXOiIdgy1=fEVr9l zorYUUI-Anq$7Fo{MWT{3OPfKn^MHR!q3$(cO z@vUC%9Jdig3YReIETB&6Y3uFxv7t1EzXjZ8-zN8c4J2X5wZRnzJp~qfvVs1)9 zt@u@7j2A)1-T%JT8ZHZ-IPVJIZJV?;vwX*i5sq$c_Ru2J>ML_IsLwhYZ%-o-?35N1 zM^kc5^?r*#aMXlb^pwn@QXqSwkjrR+wT`7EQ>3Hu12}r53!_OPtpJ0~8hW&^gq(#| zXzNn2VIyR+HRx^;sIY6n%F>$@6=Owu+~Bb1x4ePg;M+%f^gbw;$mbqTRyzrXC}vp0 z=gjiGxG@2ufCoZnf?o7>mZd(4-8okJu)(Z2+fbfB6ID%LNwwSDY`^*mki*KSjo}n1{E{$Q4;amCa>rj$Q)olu4&`oa zbNw(irHA-F^PHVE>qt&ZL8ZBoaW0CoXX8an4NUt)+=CNJWOA)PDXoKzuKH!rM3wqt zQ3TFbRI5Q6eJ^0n&V&~(*?vxs7}PQ3;<|D_hbz$FH}V@01mx6U(-)yJ7(PbV56=Lr z1_yqg=#J|i`ye)xa&ciFtla8z>F%VIDTqeQrkzKVVFXqfXs5?6R~@uLwkpkgLm{gi1wh?zTXRj&eb zV3U9uzaO zGBL^wW4ue|zX%4UShb>1n1m9Mq>tJ=V_!BlhdL8>m^c*^bas5>3iz21?o&_!OBH4l z{MECz*jOVjWbUxy{I|=lxhNyv+~jMh_#SmvO!`>t~!-6eZOSuh-Y-qydX|NBaF3LNg(ioAw`DpPbcq zmVY@!KrB)}{$deJg&+UQz}JJ&SFnyIl(&(ikFi4GODZ^>zBNAiN&2~7M}OTwB`21J zGZ&!2q4-*9gguCz}L-Xt|=1e_1S$$y9orjmFCJ+p&?#e5QVPE zEHf~{m@eD}h7rI4)+s)xYcyjTZOU;7LL=NtBY2Q&LE4Jii1$|xX3iHbC@$n--Ua4*Thza6h`&rl6)c&hVq9pu zGOKeG+UK#VkKm8aH@ES4Sq>DcMELPm6`ie_$Rn;{@oj_tZ6Wqseg59UR4gY>uNxYz z(1CmN)b~ysj@c8TA*?F&BoWMi`r*ZJRT5Z(UkU!Od<>$&Y&$yufb2@49(M= z#aywsPn#F26sdQy{S&&FfRlgxn7m;LwGn!-55-`d&(yX>9Mg=e{rw%I%CXc`3rGlL^l%xRAMaI-$8>UM}0Sm~?R+Z&3D zt%1QJVF9?qmy+iBmE~kj%f$_#&;8@p3LX|PA1*--87y3)m#pn5=#;D80ZUkCf8@Lag~N8S44&YVu|SEyE*0+uJGW*h5;$FNkGMr^uhgT z6q}1molpx4wFEoVMU)k9&0yB2j4~=ke6#(o@S2o>#^2r5c7Jwu=mfH0h+Iqgs~K)v zh+y5+N1H7;4h8+3l$TA|0xu@Z?lg=GEtCoc`f&XfPSl{?xy;)sq=Jp%P?7f}M_rN# zzOWqyDF#(GhrJ<<+uNW?sI<^;VAsbyYZe_YAGlaWrqT!lLZ=6fJMyO=uXqPPmh$UM z-Y&C&ob3^3YLp|1!Gyn$`t{rJ>gGf9452~9-SkpD7hG10A4XYja>>)FQ{M4gKo$ne)5Qq4#7;H|s&Jj8;=G$UkNJa2Zz6oOrzgOy<% zG8bayGl4Kt-0I+zXW2cv6E-wkpxL06?IH@$ZOxaskczmsNuI9?-`31spM5Rz`dfAS zY_zCqsUh4;3kZeg{h{CRi(lf2qJ5iA&QcU-bpN;lhx{6umuNs-n2T3i%1DtKKQOw) zTK?1Fa)j`~wSYxpOW@9+Q5!{fQ|`V;52)aH!~oL3UTLl50HyOrN;YCJnF@8_AXc>e zUZf0|Qhj)QguAtCP(K|Oj`{H`=qM^^lVoy_YdAXITK*i!_^GdUTvxA%eWAR38Hyp5 zwUlT=Y6mhkaZmrH%JpF344rcjIc5Z}mp$&Ri@O-esLkrsvWDd+@MVgp#mlhEu2}}O z8eHx7DyN5BA`TyH8we614XV+V%3;4Y;&uaJPiux7E&^8Uzp-CVmnTnEZlT$G<~kuH z&YteKP~4Da`0DwFLj|{HC#5XwC!8iOS)G;rfq}cZ;eZub=jh(ESkpc@BVFD;)Np!lN^JK$6fv4MN6j(`}n8 z!eSZL^UdF+G9JFGKffJg4Lq!dJ6&%$GH|Uh|JHjool*p`_HDiCY(NeHQG@{{n}ONX z1A#qo?A7ttV>SoNF=VFFTH7bX$|I0lZDgEtmL$IZezk=mU6WF8Edv&we78@|-6{$wxO`whlkKuQ|9FKXa0O1$lUg#46X`8sXU%MJRkDoX!k9w(SK}l5oJvUtr7;TG95Rk@yCB*9TsE&OOre&^{^_YYG4c##gab+|R7(Xu zyZ+^)sauU|_>wZ}^XF8(uOMUN%D_TERW%;3 zJ(!i&HDvJI&QvO1Sy;#GSG35hbqoXi>#anidGPI*zj z8LewJT$Bt?)_-*YeER0NHNe6MjR?u>)&a+PRijg5_ijlxcxlMh8^rW;GLBW$Dj-jmpe5(yx6(bLzSsAJ@zvH z6cOiDElTkvgHL9xhm;IMW z_925#+JEl#JO7if7SXcDD9e!Lz%){47*ejRS#oU-8)H&ZeNE>0Cs5hm=6vY@y@w60|YPiHjv8F4;7w@fR^IGvGvIO`{!94P%bCf~bX016& zyCcr;cR2DK>s(IS76r3;oRc=DyxP$egqY{}V54Sp&hEw|0 zA;%vwLHe@ukG8k1o~u3gA#VFK+jsiWymNgsZko z5!zXQ%gr@Z_yCD5e3GMYt4s)UJ~@7b+|sIc`UY5O0~@e(Jl4Nb>luU<>!6S7iot}; z&oavWSAkv%u2)WLGiB*Dp(j@!ROPrSk#%qOn>>fQR}0-Vam|1?s6c5uVS2)iR|=9B zXcn6N2o_?wLap)G?UDtaT zuDU4CneG0i+Qh9gJAz91VcET8dT#`$EW@$HlxPMXk_gi#2evgsW}!>5%S1H?-#Y=i zO^0#AmWWxS?MbBayZIv5abN|@5}_QcY)+RW$_Bp*fr{g(9Gdr4@=&d({^ZYw37B_u1`Qg6AzIG2cR1qBgek0| z-N*O2pO;zN2Su}VC-9>j#5s38Ha;}x{$ z+gm@hgyTU1A`fFRo2bxEMEGd7wa*%vEE8U916@#*yyo6;0(JK;K?4s0po4JSJvMj_ z+SFs#`w(-Hpd^Drdu%+gb4ZDaOy;7OytsK$1w;Yb;PFvo11jEkP(sP@h{VHuT0~z6K)dq6qt@r!T8h$~+-U-9XL{Q?gj6 z*=fxpYw>*Ku?+2+FYRLl`0@)9w&1@Ep2_6&TwpB#n0l{@$S*R2hfo8l6JD*4chJBT zy(Z8=PShhm6>SF*Z?4YK{Xg-PvQ;9>F6mOMZTe>O2t@S5MM&6m(``U%DUWu>KWG&j zG&L2uKu8H#bxk8EgBb7WKOyncb&>6`)Ibtnox&w02!zikU~V#(CdxrSRs~)izx>o{ zT-YM!$<20(pv=>sYg!?lw6%SN))mv#5a0-Kcp?qqZr`vLbo%L5|faY`5~E`V5QQ$2?1cxmgNVJY<# zE?Z1j)9?yW#f%)jk9mw`H3Mx~yZ7{U?K1h!JQ*IYcx&#V4zv8t4L!s+dAmb16+`U5KP`0e-8sH==I(vujm;Z|6y=U;mt3BtiDgOo|+O;0Q@@ z+P&M^acc&tj}E_qLDn4mlD0^tHRmsjH?nLk5NI(CERNnRivB8io6{+-$)2DDn4?f# zgB_#rHY_i>8nzkD-vz3Krx3M>728A=(Ur(Xgzjxm_J1?qLYwDD{_GO6fA=(-e?$<0cZ40R;EaEXcmCmw_6QictVTS%DzFPjgMBGLmiAG%Zp#Mv+s@d_Ojy0 zt`-$GDx$E5oL44{xKS{ln~q0lPnW+<5R;dsA7zv8&1|1GBUv%Lappv-oyuCSt&B4| zC7bAp)ni|Lef)rHc6>uc1v&>(aFXzib=W#R-Xj}#xsrj}dYoSg+%hxn9YFDL6Y9AW zG8(+uBzhK0l;1%^Y*=l<9oBXs2(83G4U;?Y3c3Q1*Hh>1Ki)(Rq8Qg7VSO3y9j>=A z;4CsRAuJX8L{d|1x35v!G30te`bvc4r!VV9=uLFUU`6MDYA%_Si14qA_>Ws>l>~BE zlc}3;Wq61iWIi;K+55or+*Sykq|U=d-z&I0pD>XiUTWgy@jHasa!t?cWn}@4%fs+~ zeW;*sdicY8nL37A7`0-}=x!me**e&Yr{%uZN0xZwQx=0oav9$!c-=zjCV-M-f(onN z$Bg73*i<1zda-KE+s?!jF9@`Lxxm{9%?1adbQ9$MT(8JYR11rm|_{(%pk3bouU&kYkH*gn8BRCCBwVh67JrA$GQKYV!( zMrq?-MaSZHOmhsGhxmvYA!>HMq4XQrbc>)V8@fvfj8D$u*gyy`+6+nl2`UJmB4OkT zl@xLK|I;klNWz>E4`G*Cc_hymIlcH$XLT5+^a!GGc&y7)3fKv`^ib5_8b9JDN4hH_ ztv-rFuL7Wl8MA4t4ZVBxacgw23cAYZz?5L|dKq8r_(_w^z7a&vR}`+oZ3}bSNAAnQ zlu`Kfr`*eHsnN_IfGcZ!H3mgDtk297N(bj$YSC)L+|hS9!dGrPDb&R9aD-WQ7qoa> z!VL7D_0cr@hG)?yHb`uT9r9kydy3#gH`5Tw_2fFfQAv($4|E)s{}MP>tv4YqASY*t z%xDX8tEq|sGJX08`MeZiveR!@)ny@HUF_%zOjq%-mYrk#`?9`A~WJN$S@mqH90}ZgHt~c-Z}v1=~!sd zBUE6Xk9;`?ssL1&P1DwRri{LKDa!-~0j}O61CRN@+W5DG(**GcjzOoa?qlaF2@K13 z>||o!@OMw)J!Ele;g+4v@K4=wk&UFdha73kTcIhC`HI>W5U3G>?H|FgRzhLF(GRkK z6#q7HIR5vPwahQ}I`g;m1&{DtA^+N>37%*ixNc*Zg4`ag;vfl)XiU7S{ibT2kq(b` zC}xI2_(&Wt+S=<%Uag6$Avpbl9gk&(^4gD1#{=98jqS8o$<<{3xM;zUpSutSG1|N) zpYoEo=daQNc>7rGjRy%tX8)o5BkR48?R)ghlw#EL>WN;eMJS86`Y>#~-a`4^Dvj5; z){5J|00^9t2cgL-%Rzc<0Gv$sXkJ2R=iq+7U_0by_kAjm_U!*F|Ba*f zUrDH;g#YZZr?*?<*)3l5CPv3SJv~!Olwr|lkt*VMk+QC)sxUU7KscEKSzZ{O{sr{# zWSnp`a%4|pq8TJ*D_+4QS#>(SecVE5Wch{SUx0qjx7nB|_ca4GTdzC6d-dvyrizRa zsK=IifBu84x%<-cdZj)@ITDsc?eECHFIm9c{{r~=uRGjL3m$>+@-xLF065&D&>*48 zkTXlP5hVIOpoR)>EG_JiHWp3G4@nsE_+dy~{(D3*^Qk4+CqUZXCo7N2$=TXYX>Ba! zpz@IBofLYMGqk(*Y!+#{iKj=j`WHFHv4U-K=oM+~q}xe*GI%$8-6KRoOLPK0{6cxP zKmdtc8L2a!VX(URM3JIWI76ul@^0gUk7C%%)a+yg!#oOJZ~sYQPvEocD#&ivu->Z8 z_9JJFO;gPVpitd^5<9Ol?~$4kA^-nP!DbFr%piG)V6&$=##{u19_2W4cY?KW%G3oe z*rxj&>bF!Sscc@Xq==Q5lhezy$f!_|K|=3Rwc z78F{$4SL#+FrFTkX)3;>xk%|&VNI&OzWj>%yDo4M6ne%+W=!Ss5Rg(EeG_EkgPU& zBKU)Q*&--9sJJu!m)wWemfLD~YpDS0=HxJ@&EsW%|oa_Z~pJwv|Uuy;C9}^ul+2w*9*lZbf2>B&P~fqFBQA zBUUsiQ3P}SohTt%21!h)lsuI%N}{)hqo=z9?vY#Z;BtMT{MkZpkz*)@6syT*r6WdK zNftZ_ccGo(bRF$%Irr_{B~({Uo69u-Jw_)lxjo=+X(%O{8wtwJA zZ;37QGQGgzOTl5f(i$cPhz6rww5%V=LktsZ@>LC}$BaSAu;<>$lnWrCy3)Zy#&2uN z68Z~q|EWcUc)U1)SXR^sDl;6gZY&KH8Ze+QZNmW&2oS`iPrpRQHad~o`egVR_M z!RP!l%RQm$x0Hm+6ykEjo>xBkf&`pUIT>|@ptgE^MPWv=qv`jF%eKr%sgXpC^`Zuu z=f+O2S0Cbcm#BjFfSbSK-72`mEf%SWLlp^C>X;E8W6i7UzZkyJs<$DZa4!06&f&P1 zu6KP76|{z!EcC)bzC{r*%gEJ4w>~g0eY}jsV6C;PYY+N!J8$`}r$eX3YvY1t>{+Hg zi~xGyHlCfb834OPl$17(tp#)gys~RJ?}GG-&xC_e^^UyQP^e%*8Ai{XR*kfr{a>gF zUMjtVAJx|^0fpIz_!RzD49q5K8LMe#tIC(fL${-q__XHHA{fVHB-g^agqUTVwhxxE zat;|59f(T{XoX*2$ra2dW4WyT{NG*W=`dcwT*8sYAsgk3q(at1(#&GXnD^cQ$wS zDlUNpuQyj$F|v7?DgJ#ky>(ngO{Q5yD?DxWYYO;rWM8R^o3-)eKA>QK5RG6Q=e0$# z2ii9d#D5w8W2O~@@(E?bs)*5mR4^->6N(66cw!Z!Dcmq($m4@jOL+gsh#)$m9&y`z z0BuPpMf(R2X^f+R3K8u;R<_SmifUgLJ|AVh7&|ljE$x@3Sroa}gC(mowZMKp za6Hr&(q5%6BLe-bELI%&PjVv6fd>mcO}yScq&?%Ea6+nwQrxgez1I9vIwk)o7{}Il zFy6}B{vPhv=6qa2(QI$lzj~n z|I=@-&aw?MBQ-eyocxh;MA5{fXaB1*^oKI>9BIio1xm7%rmHu1(5^46oE#?(hXYK8 zutj%hMbP!uwb{eQXeu$%>*q41O_gFN*Kk&{h{zGbJXn&{(GiuB_Z;W>l8(W#47*Q(Hh)|c_uyg&VIE5v534nmT9Vz+ z9VT3r0ul-a1Xb%(!b|da#Qc$ECgxyupqa}7t22hgpDXX{!Bw6f&>e;-H1(Y#)jYF@ zcL3Fs0=IFh=HJIn0hW`!^QckV4kJ9HA0oUDB%GwoIoB_-Ho0XENY~id9G8{?gCYesQeJKx&2K%gi}z_(}JKYSL1B}zO5 zW3Q~kRGhr!ji>E)78ZTpy#{wg9y%U>3-8U1O3@OyRitwag5RIr@`esuByL@27(KD} zcCk5aQHbNFuUVGNK+XFv4dkhqcmM!(i+!ImEzi~x(8l`9^1v)kSbB5orh?S=*dDR; z|0h1o+p9Wdyq)9GdPa5{(`3B)(;GqB8f9-1V_I0+;|C(q@iDdl{NKhl zMxBXbc}O80jMLq&Vl7YhqE+(U+kk(uwHOP}jgnA*p2zT{L^8aBqBlnotVLw4A}pR( z;zNpSg~i(s3(xNTVw^Pm;(YWYVd`xd8Y48T4(Qu&*u!Urp71=dtIxKG#BIM;GEcY_F8!+Glw96zwUTF=HhP&o;jC$i!j8a);@) zP1=|>R*cIQWS-~=)*ps!jFF};H#GfjSt*F^m;jU>gjU~8ZIuQlVNpKsk+`hkAu>Hf$&;LS&vw?>(%=TZL~6`x?uThj_u%NkU!;4%%4fJg*hreR zAJEAz%>)Ux{>RZdg-6zHQS@)bcE_EhW4q&YY}>Z&bZpzUZQHhOqvG7t_c>oZ>mD?`Mt_6c9Kt(%?qXu_p;r9dRGq;T#1n zUOX@^XWo@1PVT9pw9!K5xwa}w3&Ci%8pjQ`X6bn<-WWk!_l9f}k;6Fi{0CiIfe=cM|8(+d zEWz6oUWA2X>UPep<;Wu^pGkZ9#&3jxB~@;hymVRDHmJjFUZ2m6;a(h~9cl z^qh-RRuhBG8H%x~?PWI_i?2$j6ZtXPFz!*n51vZr0~d|Bxw3(xSJ3hw1V< zw7|;KQ7`lzY*yl!*{yFG#&0hp7=9K4owX%CZ~A>-T?Ok4G!^#|iAl_1+%^gIo_yuw z+zwKz^8HW$zr0KYHvhe)TCP^q=i3QPk_&{cECmKoA?qcbLvnJ{;eQEsCJg$C^aXUvgZc|`Z5pUu5VHhG$A3z!XH>yi| z8!V=AQn3oGxv=F9Dg&y_-L~Cr4(&YE#4nTN(y(jp5O5mDl{bs<;*itDDIeldBp@}{ z4Htt|>*|3dDiKX)8u?h5u#09aec7p#=dmzBX%ls@6G8eL#{(KH?8r}&(%v9=t4mfl z=!2VLk$M0t50Fy~1fkAv=I0`_Hd1SzjWU&L zp3MJ$lsxmBY1i0tMEn;yKA7bnw9i5K_BTd`r}RN-6HE2^J*kfoWn=H?ml8us4mG

QM!g&rUl8m*%>;`q7B1h%;hsKoO8su;SaDMjQD6W8?a1uzxZrI>0X^(P>axRe43xlW>h)g zVuNj3SfeNi{T4|An(!^FA*#;OavyTGZT`<8gbd%)l8<3%xx|K+j`Bp7jUZDodxx-iUuR!&!*KxJ2$X%oL7^wshmcFzKF4I- zX5~X*k9JPNDJV)`pG3>IbzuZdC-MVv%7W}oEOEsS#XKo>_0 zkiG*t*=C}MRXp#Qi*eB2@MT3zBf^fMoN19|A~&{$4k{Ej8B9}k{<`~QI{lzxDy^eB z7;kmL!xC6>p`+1?0IBg9s%UpceuJ3`#YHY^xOq0I30KPlwHeTXS5NJlMOV03rh*QN zxr5)w%oN7?eZVfHcHD^m>ShE~GNprbaNnNM6b`BY%;5rCBU%{%F?(Fps!8&cu%)bs zZVypcot6u=YuPA~m0#lIZ1_hXOltoy9+a^*YI>4PZn6JS_XJs$b5u6rGTbC`U{$8y zb@%;{3^D5v)BpIvIwm2VOI{5IXe$g;>0t(kn+FEdBg3)<1Uu2a11hG?%h3vP@;R783C9Fv0 zKA&#}{@c6Bcxx}z-><4n4L@{PUbu@c?WYMuNl5Uja+8XZQy2E%FgVe#tRRC+~evyw%u0EvBIv7Xm7nr_}siQuF7 zG3fD`p#z?ilz{W)LQvJfrEC}iLQ%m5FB*_^-$L_oyO-S9{k`x?58O4eEW{kCBI7Q6 z$w7fRW%om=21(@ef2adQtMPZSlD+@At2J9PpLndI>Jd06=O1#bjtKX#c1-3WHqRdT z3}VZqM~v1{oS=ITcd}|{lckyd(#3yj1{M+tFuPy-@ODj*Yw7mE&lDANS@5?bKhc9g zwh4{jG76{P4kFi?VzSkhG%~W*=+~fyPDQ{B_TfU*u#bGxvRqbhzWwmQs5IhYadWC_ z%}o_|s(`xYgCEi0`wLI=f^bmu?ZxUFM21k_Ca7XH(td%M+2sS(OF3Cw4NB0i3^7&V zsuJn{W?IxZQ#pJh1_s1#SDO=vVL=dZf6OK_u$hg(fiEF_AsGF6S1a{UW2ub8A#hmN zpC8^y)GCmb+RZ}^mUGk9x$g+#trr1bcnJbAV1CkULjQvTEIB+Lz(uWM{=Ea1t{6;hZ_sr3-zm<7D?|8@GuajD@< zpzy3Y;la%BQ_|lak5^Ub*mQok9}S{ON&)IDka|$cuVjyQ&r^oOXvA%k$?$1ALAak4 zImsl+-$SC`MFhqf54fP9Ulp6b=RY%xIMAezP!+B6lcH0r|a8QLe=1%H~;I5K5u23GL z;V$*|5ytkCh#)o;bOgxh!?y=az@!E-e=zFYk*r8@2OYzJN8b#SlP2Cy0x+}-&yFVgx5oNu%bMFDR>t#o z*rT00&kIeFqx=5<_92<;fO7=?@mJ^d&4PK3(IANvgVuvpM(J?3>nq{9IKws{fBt_) z934JZ752NN_&UE$^@^uu8rF`C5#+tUfTp7Ov;~lq$#Sn0ucj42Bco`D%7Ec!;8t8z z0?^E9Iwf$i2&_P}8`5c-%0ar=qzAQ%D9kq+wOa78nNg`+A(I^)%VvHx2o&l7s&Xl% zBc=Bx51n;)1ao)&zL#U#6AE}d*Rs%0$kk)sj_h%9>Cv?a*svi$^5O}yN*Z?yuq3`& zXVEEFgpF^z*KOJA)yPo1h-=C@2NnNGv|H;&dy3QV#itLk)8Ft{a7=e87AXp_wVo~p51PzaG~;P%j2pgM9mOd z%BxCTzarO8vrRJ4Z{>XrW<9mHfuqF6%-1Q|pY{ug;kOmH|D#c(-aMuCBWq0(xg zvazw5?(}wlbQTo_2h!r7aq?JVU4FCo;?g?{giFH&v;s)ie97M&OC*zD5_y19WJOifqieCFwAmg z?k@^mTw*ehmu>cQ*N*?v(wnt*m`2bSj=FI;$SVy%ClRq?Ps=;p#&OFoyhxX|sq+Nn zpd9DPVfc)4eLb^Q{TK758+0A4%lP>5K}f9qfV2%jsZM9_)as0&am>k(*9zxZ8eFp0 zI)+^_3cgK7*B{TfSZ{ek{T+jnRa;8NZ7^H&ID!x+BQ5+^W9qL}43n7A5TJ5&IZ_Es zlM{uxVD^Zq4&eg1-9gep=hC zLja6!n@evv8fBb-zaPGUAO6LfGowLQS6381Dc6r5y(=p}`llSfK7HA2mXW*tK7`BF zE0^yqEW*eGwz~Z#ma24d^2OF1k0!HaOG};lL2?Pd``3c|*`OiqSHlUF612WT>SLn% ze+duQAp1xnSC1f8u6?^}Og5hxx5EG7PULpy?lg36cHbLdr>Wwcg~RK6k4TBMIn~V8WMx(|MUdBH5$E(umxKQ^?!A)3w^;p?@F`vdaEGs~`M87u+nRxti#?tP-eZ)Vj%>Kz4NR4E^JAAUvG%j*d`PpEbuyrU&S5Sy+fy z4VDaAPm}9=#YIgGbJ?-hlr4phMj>&ucdB|U$|;L1kkWSJ{I znEB6F{d1Aw(Ud&64}kW1ZZCo5F>Pr7U56@OuLe>N&+ix%{s6|;AcE3x*Kzv@LNE`a z(P=%3>;KD>O830n9nCb^MpUd!Pb15uG9fv8)cK&Mf8rrXAHJ=jyonL=5>AiwDH$`@hGwY zQ%w=DS|$@`e1E#(?N8>y@Qr5WUvXo)H?uN-X`*A&3i&u|S&UkpDC={NZItDS`s3iy zPyH;b*GFbm;%FDN#}pDzX=z&Og0qPTgtE-ITRv{{@#t)x@3j3yuu$k0t)o~le#(0Q z{Le!L?cZy~SO4Dm_@>T@!IEJynlH~{WZqhbJ&z||N>+)_R&kuUB{w)4YpLx!fDv+y zrg>Cbr8P^R(5p}WneO_LhtCJ{SVe9%Vdo%_X}tl0UPWCbKLhG6YT95DaG@iV=oIveYQl+#Uj!V! zyi!4W&LtCGMGXm%@dN*9PDiyk`}e;9M3rVyg3Go*e3{)E9cwZHrI!GhS!K_rnuKR{ zcD$S~diCaCYv;HVt#dt3wlM*s!dm-&9J+?(9vthLM#A+)GlG+3OrvhU(B3$)=U1@~ zV$E77DY`YbyXxUWn(g(*;ZHvKWXc5(S@ZUIG9}a9?jnLVwuK01VtKtLLK{U%#;!=P zy?&Rmy1jiz>d3dz>KvrI<|(lY3bi^HZHpiiP8a?vnMwoqSxbnuh0)IR;$*Kc%Q4Tw zISX2XW1chW^N)gmxN?1acX=O&R7tCy1kgnstw}4sn$<`!`frYfC0PeIPZ) z`oZnS$P=J==8#S@#%4&!Th9mpA?oPJ)V^m~fL=OJ!>FCAjW%73N_jY5ky9*jO5NE@ z3P~&qkJgahQeQgbtE@=ULLY$e5mNiDB3OhsfoavGbahh~14^=iz8+IkYykWhAj*ak zp1gV^Um54p;qNsuB}2w`wSry$t0>QDqnk(pSn!w*T(iPpgnkb+Z)e!!_}S(b;%#G! zqc}@^yX2Y)QNL^k*+7Ijp&4?lSj#OU%JD;dQY^D;v^K(CvjL0bF`gH~W*bE!sVdDC zv)24czrUrMXey#>KMz4Vc`C!KquVfl4e@-1W)hOL1qv_~#zaibDYG48=NRF|0K586 z99r!%Dh`IMpr9}Pnz57Rw`Y)|tCe0H?dpP1#4po}j}#ga7b5W|-G8~}VBv3Hj0}n$ zavC@zmUma@#s8d1)Yags}U85ent?f<%S^|#SFe*=>!WFhFM__$nfWw@S=T`oM;NoV#=sc_NK1l1j z<8j6K&l&Wlv!Zd%@UozX9JCQf_jpH)5bG=P{UWH#wB^Jb9}0ZZ9cyV0^J6H1DcZt$ zaNyBT!W%Iwym~wN@xJ8H?qBDo^Rp?KROaEgwp7hgNirsAWAH1Yh1&t8s}3t>;q*l6 zea6l7kW=T06e|GkEa=g|iO<-qKsM5b)O9{VhTmb;CWmIl=rMH zvLVIqevSk78Ni)1*Ls9P_;?{QD;F;M{vxNgxpbYAm?>CPtP-!l1J?XmZQX=9)9QdSA znE38OFQ-0m1f}dn?DYni9peEMwG2ur|0#Z8*`K2$^*4P(t_la!<~A`o70kBG@mIov z7(EMswN}RktCe;Ctq0W>C6c9nEM;4?(RZ@=X!MjSNkv2zxQn6xfb8QMlLHZJaJ4&w zENOJCqM^N&b2hgH&WSe8Agn3Is(&qi@|u&^U;YD)$8htw|P5&%rOt@^Bje8$(df(DwnJx~a zj58;1{fb%Zk+Pc8>d%YD1?R<^{U?|=`SS$tnm#f_a9X@?P<#+nB7D9ZcA3DQHjQVo zGZjEPuKg}{C!c@#26^f(Jp5Uf-}3o#hXuOc+L{`|25a!!)PElwnVF#OhRyuE`*v`q z+xjwmyyfusw@Ns5z*AX15Wa6_;vFUiqs`^x+kHg$4AEOhcglQOSy2#Ch9YFP8Av)Qd;mb&XR*k}HQ`;+XNRb5#o61pR|R;Y%z(})jHAFd?|ke+N^ApmR_T%T zbQNNX-lO>Rc`_H(0tL|JRPWr#XDvqpGR0;zP=A5^-?%dRN&Bym2ui8aZ&cu3c32WF zgo~JvdV7ksW}YVOG^&hHUt|n5C!Db&#M4dU5aPy9w_ww(L{af6+|yK}ISd+=c;kEz^Z?gN{M5SCf+`G58JcN{lJj7 zEu%>~D@l^O{?(|5%@&=uGWm3C(C>W+^w}N!YVUwJe7a0QMK)J}c>$AGYTETX_darp zW`*fd6B_`KK`dt%Kk=fojH8+LOe3lX{z_U1zQZE!@`wK*+aTX?==uR7C~sXj=mZbE z^5juJuw~iiwL-blVp;LzYJ<1^RWXq6yHx9qz^Py3^%0SFO5FG@BYGJa!sH0}P2tSt zE`XV;iTXZp;`2IV#p;I%K*2zI5yS~VnYw)<73Z}@U)IY3|E#QAC1~~}tGDG`! z|032u>~nA?En>6nCCMW6e~BuX%E^;x#2uke4etvFQ)ul6I1`z?5rR|g zK)2uef<@OZt(55oAf*~&m<{y#fJLhVs~s-clp5a~egN4+kWy~;N67od-VY9)CcqJ~ zf5utIoDLYuo4YyKSHS>Y3FAGWJU9IF%}_JvN_0xXP2?>wr^PcN6GI$r&EbhYsskuZ zrF0^yL4&BPO?Tm2v3zSA`p_iySWmr@d`{`1vxNa*BPSB55h+v=Mt|8?Qh}PCTa*c= ziA=lKGsR;ie_u>-Zn)*zh*Ym=S!os7sz_&mS4L9T%p?Mx7QX#q$l`B65f^D@ ziDKX?FS*c~b$2iJ1*C)8Vg#seK^CCz8GHLV=^^mJFwOdJm17gBy4xUd#cT3sukdLN zV$MjCWmy0xqW~Fia5;auS~aMPxEVb&C8OVm{7{74x^ad0!R+*99J6&P4H*RorZiQ`&CkR4C-5U)R zHR+M}&r#fw=cgt5X(nmo`!5q>L!M7D#c6|1WgQlS!N&0wM^%LAGgZ-Ib@m^|cy77_ zM?WtIeozKfprg>n^efskci3?3h2mexj)KNZ*Yim@4WaI$?fh`Yu>Lt_b)P*AK_&;~ zFl0e`%*FC8ck4!b89n>Nq&6sMv^8-QD6^%%hSO0*fQAIW5jcrXF=u;p^8kE|Lp~iA zRg_#@uc(vY0}Wrz>pC_n`{BZRG8My-Ic$o`@1*4kYlzkyLaDBZiessM7b~)wq64Dp zLC{>Wd~Vnl%aNsQg+eRx{Aj;^PEMBE9^J4~Kke^QgNbILGpfLUEmQ@p(p%;I2eIO; zAqSj4v6y^#^*yJ+s>L+J#>$jNUi^CxZ@nuF`#|9)&? z`0qQmWbAVT-1Pv(qEogmyrO!}XNYY7w}8ssN3;!TpU2i|LE7qo4#kRUleFbCf!hFj zEHiLm@%wlWAulq#UPRiBmrJ%vH3x43C{oJqo~Mk5iEy}#+JwN1-B8Hw~_Zx%5h#OX_%Y6CgO z&DX*-M7F0Rh#gZ~Sy453sW;k2Amt8zu2FW&;9sP{F!9JhA04#>Fy4~9_fCY2i7fu# zZ#}RWsNTzo37>wyGSkxPMyvDl^Bay(_(FN7L@+xVx+Zf4V%K2K_xb)eUE_ddJmv9T(_dR?f55tyIvL}ORn?wg62VB8b9%y>dC)d9c|39e}Pfmn}&-x+8iCbdS8 zS4F9(Js(AM2g@HUa2ZkBRXKp}fVl%^p))d;sXiK^>g%s1Eni-E)e}F@IP7-yK)bE$ttrWWas<*%En||P z0jQD;2J90VaFBY*|7-vpUQznh{xSq+e`f>ZjWcnFrDCb6B3XeGc!}U{`irh)26lBG-g4o-zE`TouGkA z6_dPsvS&7*Ziy4_6dG7qS(%M=iMzGkuU_r@63#e}-0>zMB`<1(z`iFRtOHGO#mkD=&tNm&SND2ia7yQwXc_~R3VCDAsr%Wk01~!@id$2 zpP-hu!vUXc?Fg3F96~twBBDtosgObeq^?VrGRhs={a;|TA&M6qO#ciY$JA)?MM_92 z=7bMFiL;B<6JUMp$>4YFbw!gAJt9J_a1T|I4a z9zOHkodr&l5FM#*Y=iIXTmItN&;FeT3Nrwi9X`CIVvkyATo$S4U8F_!C!Eg8w7k#` zL|#_Xgx(F!3WvnvF+z3WS0S{HCuaakQQ`J0URc0phXGd6-#=D=hWKb;i6iM&Hd!Lx zQVIJ@vgxacj?kulncoj}WUEip#n-+`YUX88hmv%0b*J*cd=FYRjl|JiiPUkeu@kS{gct0kIoYMLW;W z;;rf&ntAV}M+o&wT~?Np&mkX6C>C|%CuNCtWUKuoeQD;tKX6<@>WQyPJDM`_A4hhh#MgUjs+HoKHv7iLg8HCo$P!6Z2)(a#a*f~3Sx?_DUM-xJB0>JT!@#i!A z4HYRfP>(l+j2*R2Of&DVn=uufcV7(Ix^|&YT+5@xxX_E0W+ut=K|iMlUP$dYKZ?an zFO4Ko26-KaI|F?%1ksRp>D0cZnXjU%Rj;tx4gDun-=7vcc$#p_xnOVm+{7E6AL+?2 zlEUp?a;o&|_+O-egYH4S0-gqGC6u>M3dUlZSd8T6dPn5_HTiY)exrStY3B9eZ0rGl zHn!mS=w(SL(LHDMWY`WmvspO2g9018cH%U)MEOcW9A&> zao>$DNE-vDsjS*|P9x;b5Tcr_?2^qq)FN*9ohaWAY^Z9@atteS8ODgznIIsj+L8J3 zaVBq6In41_K!BYYQ0wfiOap%9z&%C(^b3`Ap#{hq3 zhlZ(S2#uP-(8vCl*Lmt1C4<0-e&AtbnUz9i6?dOhJ1zR9K(r%9 zmx|72ky)hxK{IQcWDjrxrQ`7i{63csnT>H7ZO)5TioW~X=Y+mB;gvgEmPjcUbeJ-+qUM6XN8cv(?yHDD@QnvL!mPwtv5$u1U4 zU)nKB4{VzdcD&nGP^*PuB3^{@n6bY|ZDP)cAz^Y`)O&-tSPgL_3dz0;6mRdPdK^!{ z491Ew8?M*;611el3(JF0bDBlz1f`kp!uag`fk0^+g#J5Pq_>p4kY}BKmV}(O;h)IL zZzXSktc^IHV%z3>Nm2pzc1xj85lK-O=V>7iHXFe^jVbW5Du5{iAE|*YlwnqKO z<=|y6&ZgMorM?UFCAn%2u@S5Ia?IS~3Ss}qmuJw?W%jLv573s7FhIR%V93T+@%003 zI>)^Mf^{yx%y_&LhyxbaS=&f?ku?@nCVGs-?|K4$k(D6rnr8-Dp7x@JQO@(mhDSK9 z9E4jwm@*TKh0+NK8Jm$IL1V_bWF21jkalEPEDuR+xUOYTq0nd{*+>9LF8D%_v7_ZF z!rc6B*)&>fqD)BB!{Gsk?$!HdvtZ?J*tjR@a>Pm$lAwo%(ay|ptm2+Y6V5^Ax_aan|87_0YsShvpQIfBpYLx~`(HK0dt6$ctUtmbgu>XO1$;tRR zm`L{xEZnr8-xY=pl*bKv6ge$=s73=q*`mf~=UzGR_!fu3at5=3 zf!^hVjCauq|8vs%*)at>C`0SUOv1X{aELC9b$%2j$8s93q>|Kq{D*2>-xE6XDcrel z9vrmdZb_n1`o}LO86<{o&yaj=4u>I3i>F_98&O!0`!3UsKcDmUr8ldfar^Vs>BPVj zGXF<=OiY;mD}$BA

Ha_l9rwhQH$YNuOU|`(|eoK7iN)T2^-Uk8T04d#W~f1x?qP zrKRE1g>@;iozIWAajmDfC;JAgg$TY_6F6OAVbHahNG!&>ix%HE4y&cA4+Wy@Tj8`C zxW4KlR5DN?+ym&CE)nGv5FpzN9Ii{BgY-1Zv14MgqkL%USc;(shCU2vjkmt5i>|Sk z|9;{4LF+O7AH4Z;7jmPW5jS)(8{M0Jgzi$$5B4q}?&dA^nL?-B1jP+ao}_FgaDz6+ zApS&4Z!+vj9&X+?ou5bczeoexG6G z4wSD~ts|w`G5iV8cIE&>-`USI5mfP)oM3Tq~z{CK0 zhx`BXHYtqylb==c+1ydc2{zd>j60&lGK89U&=Z-Qp~=Y@R*Mzk60wB6YipUdp~qLe zyu8=d|2?0m-a&TS5epaIfgi+;74(PonsN7h3=6xFK7kc;-5mLKX>@s_v5buF6hB1_ z@UK8{ALpwT(2P(&-%=AoieT-mY1CT%wRti6*#vM7u$1WW@&o9Bn=xV^)+uQNbP%s8BFMBZI z#88qH+wdN}FAsig1P+3`nYsH<`R9hT`}mu?UrkQ(J#`$hPxq~ku^Q^-UqhJh!l$h` zdgdhH@}UvX@$e9sTBG~mz{2ov@|q!4wQgZ(dvTavS+Je!wD)}Sky4%KctT@6ts}p2 zQDfn9b#u1G*lFa^k8Q1c#}-&mZttY$nr9|AHi;G+R$59h%jr&#V(IYne?%3h>hBCj zFL^v|_v^dT&rd5A#}JY#Cq$L+9z@JsdKM;)mlWoU5-3@gT0$d54Y+?PzC%MxSO{VZ zcfAX44H&s;<} zsF3-pZNG>(vpvsr{=W5ItW{ZVynaaXwckNT9x(Q5-xpf!3BkIXlXb)#zKMlCRo&ey zyyogDy+T(3FdkQMR9$H3P>m9qD)*#){63P|_4Ux#)AwNO9vqlOHdJ;W1gVfX0Y@(`E-qpY zbsuYEQVyWWKzJ~EV2kt79)$J+ipODt{dl>NdF2R1Zj=JcnNIs&ICj25#?x8_6G9yX zYtgX_Xq7!l-?HHns)pBZxMf?$bmr#aDZST3G9;zte_cHDu|W}MN&^S?CuMKF&Z|@f zzTvizVol?Xw}s&n%k|cg56v8v?wboH2P?{Od^7}if-XGHSU#iZ#E{uG+Ij<3o$l#a zl6z||GdC?EtgcqE;1@zPIC6{B4y@G2&4ERjw_6iUl(uL~g=`5LD~QyM$HOipj@S(_ z0sBW#X1v)I+ksP%lBHU+K)+AWO{hNS3r!sXkE3EE#FL8U`6d!>L|ZwT0gW5m^e89JaLP!PP5JiR@E}NM7!pd79g_Ivu9L`*$qI5 zfE(ItB(8bSY2z3f^Ma`P*Kzq*_$-TjCgu>&&&-io?>yEku#OswlAifu1)?Yd9!y-^-R^ro z4TsUgz7RN7Rj=E9n7;mgLlcwWHrKOR_)|UWYI=pVl&7c(hk>b2k!9Bvn|I)$!iXGWi3uHK6lBv*&yrNEBlJE&K>lX zHJ@Vp>=r|xS9*7J^lcWz zX{th`#XD;Qf)0;V_#S@?1<~1i=K9kgg}Pb6WpJ?5lOTBDKtyz%p^GS|2cZ$bf zgGWi_qU4nM`Q>m4Vr}_I^;OX;(Qritg)XAG>)-`cy4I*&RlwU$B7S%?-)WLDA<4CyU};oct*WEXc`1G6Y`WMmC_<1^KH4k) z6}xLj$4-gye|;-suL}4}NijEQZuNJ>B-PsHT$0g+6(4~4spLN#w zo?EtFoPzDW^d$v{?(LZvn2t&}9uh$fS8s|(mTTT!BEj(q7?B0S7j-pd`Z;E* zn(FL~ai}sZOOhOOjlbM2UUsXAcg{D4+yo1%Tt7$Cg{38t7=FamuT_UlSdz}$+D9v$ zK`IR|b|3c86Sd$38aefhoc(yqW2MKTm#j6PIw!l<$>k&})69ib#b+E>=5Wexay~T7 zTAkedKFhk;+cw6k)KNhlkufs;vn`wyS4)0UUj`{Q(AC(^R;Nkj(P9q?8bdJARg;j<%OHrDJ1Qyj=1vMn=*IbvtlVY2D`07KR-z!F=RbtW683o zxdoLcQ}2Pea397Ofcj&`%U z6T{c^)Uu%*Ilpu!TY?wuL}b5VG%+#RU0$ts$o5w~@33XfqaLzzqEjqJaSW;=XhP%U zBLsfSxf*rsdP63347ldnu(wfG2W>Z+sUJZ}Gs)GO*Pia(cl_f`Tfa4)1nP#Q!nvq1 zj!+qi-jpaCs2(zshX2TYc%qRA0(aOW!XfSWTW3C7SNyeoa5;n%YF3$lk`jqv?I(O8&%+~q1GD2St_ZuarFkIc%v+F)qERa}VFgdrmwm)mjkBUol&$F>3S4u5)UzqgGui1!}14=V3623G8C=#+l zzq7IS&7+bKfBV8o95+J;Lx~e})1{cvpkN`s{NUhCiBSH2pT15xNdBR227G?T*?E4a z4c)wFJN6d!TjZPx@niPIMxXnYQ_9du35w>~!GzUh7>Ls1f6IKC;}Oj1zV)?uS7WD% zL6D`TL~>()H!7Kfj6#n@&lgxibbj}{B#+%d&)x-Rxf?7 z;$pg?M>jC?oMZby&TWO+@LE{=K6|loS}byor`qtXL8OA2^p$~|o3K$^E#$j{wlhd5 zhmKM7f}L52m>pTTT9x6FeVp6*65$xRa&<@L zjR$|Mj^wl3zeX&J-4fZjT&C2_rhbV((5fpt3p_3MaKYc|$1=<=ut75?zYf1WW4&+s z^v}o8#u3lH#7?_<&_-#gu?60W(s8vN(mhU^rXnJjaa4u9NKj&GQ87}_IHkKGo4fC0 zbU^D$G!9Nl!&#LvVuHkOMv|*kSS#;?*>3!N}6yWKgR3D5iD7E2mN2pE}`o2LWJQts( z!`C;cZX2&SzXu;I!%e)0QDo@+pzI=fEw8(|My$p9hTbO5Kc_Se_FwdP zJh0X%F0nF&)VtJi#$E8<6m%~Kga$@E`PBEuGVMJFa$wf7rriv7cr2<3JOkLt-0Fu$ z@Uxn%v9<0kze!#15Alo>H6$M7kO80S)v}>gf%{Hd1GV0J82D?9$}!*u<54lkRvJV; zBz$COh$@eJGI6)Al6YwMR%nh0IdgsZ|PX zer^!Lw!slkfZy7D&|_uokIV-%LWBR~Bn~6|hieQ!^-GBXNpoM@2HtiogTzURz0nUX zs2S6c;Rm;)h1o&+)v6Ow09XF$r`jc{tfQ+K9IqPMY)k?MUD z!q6zyb)Gyfx-ErQ*(D-!2}}1m zQn{`J2QxQAt0bNLNkJfgMScK*jDg5(b#sQzx;PK!0~4`1B&R{n*N*{Xb|n&8fdF3{ zJ>pt43^B4-ZE3B=@tKgn)4<&O0E*hMY@-R%)=|IQl^vZ?I|^+#sv!fxpm)pfR7eX4GW>-l;t}LD^@s5!q@RP%q(K#Ho(-e@@YhNPxBari}IGRPwpY`U@ z#X3K`cgJ1rD;!!!akY=vVQg`=g)&;+CWyc$@y(+zPUZBfXHQ3}^XiLIcmqnLR? z#5Gx}hW?PonV;FWZhx03qVN$SMlKXVUW6Y(cht3j4-A@=9ib`ICOFYbjgD`FyhOl< zWGBB&j(>;a_Y>IWx+Au(@CWK=dp(`-g}L!bX+egaO{B7gbh0Wk2<l@Z1t>ERHUe_qfC^>2py{4(!M3gNYlBK7uGu~r;;TW?`yE;tYV?ai>y z4)yINDwX=}gztQJ@VuxYa0$%CLyeNDOO`BEGz{Z6VOC(Vl0;PSi(?UJFV(f^@i3fB z&Ql<1IZIE5xo_V^%TN2uT%oNh_F$%Fam@9PG2zafU|CY|wOrf&32B$KEJ#>r6%P14o|%Ma%F;9NEjqa zlHHXOD%#x{TS=vW!tJu_v&cN zOepDl52>>Y4Eggqn7-f%fV*wbdsz4$HC6pyI!DY)MWcY#Xz4A{TV%fT1M#1;K>Hqz zo16eu(|j~IiNXqB*>o@?>GgTGGq?gn=lGL-*z=;ZFr^FLM$92J28F5*_hpDTC7}|? zz5+3*YNJ_lsi5NDS`37V!__0D1Z0hsb|_Na=N!M!%DN$&5$~-UYXY_tB_h|=g@Cfx z?#Q@sM=7rEGhE)KY>7fi&bf8f`r4qq1izBiLIZrP@5a`VcY3uOWH6QInbQW>eQ5wq zN2m8u3hI^%UPhg%KS5>AKpp&Y?n!~)SpA7zGQu2AuXCuWf111r^=-W(*9TgIcljz@ z&fI!@oj=$)_rSR7sm2>@4TH@=74DvB6W?cd`($|8?U=#ZX77BnM7tBcFud6?|JY?d zn!Tw()B3)8v>K8FB7~R~H)xtd^l}QvxZx;Z1|Qmz^8b7>l1M)NBF?%nFt~3DRI*0U z{QGrc+(PuMcIvL%6(ZW}ck73{o+amy+JtXf0-AM|echM67kho#8!GU5CH1ND-|VB+Q0(Udql7Lq#71;%Ir8xIkp{@~^*$Ou z{~LU+M3(@)(ioDmo@Td-E!(AD>hA#Ba`f>bWNKrc))&-CyY8T83&ZA;^`vUGjFia* zOYH(det*&JPS1V*COH?3Gpkwl#olLIk(om8PGQG^mT|6N742oA<=|5}TEm=SQHwLI z&-Z&@oB$kMje~zP)@Re;Xw8!H%P2=f?2P6S2uBnmzl{Gr3F(tDjoR@ji;nimB(wJ} z_LJp}_fkN;py1_3@lUp+q!ObOo~hUO4E^vWwvFm`A}qdBMS~B?NU*VUgF{^>4%4P% z`2#<+PlyfSE6MYJz7P|}!~QR-&cUzJsQvnrZQHhOTa(?1lkJ*p+jf&DPc^wF+f6*# zO|JLM^V7V4!~NO!zOJ>tYwaGTiXGy+lpp=k@QnZs~^F7js1fYw2{4h&Xc1 z4wvp;M%bNnqu(KUb;DO4viMSaP<3l(^}09-?0XVT~PRLYBYiu^TEsgHw&?s98nTB_RU|F?;o~aUR)lxBW($ zV7G-KXPG07w_y&(4xWf)-_;Mou~aMaxR}xyZ0&Um>lON5L~~c1f(|RnqRf9Cgu{lu zS>A~bFn>ol+osvs%NaQjS`Nc8BXOK-CLXr;D9-zYfKOh{EkFK-1!iOo#(HUv@31!i=Js$u%B2Hxhc45dk+KURZP{}?rU#^xsb5z1Vx~*4`PB8 z5(__$2Ml-KCPb`aCuZ+o@>HvQmbiqBSA4FxH>R7KfBj)RO^~? zr+u`froPc>l-a{FGKrhpI2WD*uFY|)?TS}$Dasr!kr&KDYC?b$`F;)C(3=j;zg5|3 zC`^dwM$l5Tp7D?*Pf~vthq@H7xK^}2)R_lc>biKPi5#{hmmn%%hs-4KG_NiY>>fkE za4#wU4HFA1`pi#G5{q2ed)r$kq;AiJsqh1H*N;5dsBw;(^*d|0933DVP71Ubr(-!Q zV7bGxdgqq{@W-B`VdKSD+MN78(4#Nki(s!`!~&`JKETmD7#8Rhq$W-#Yi5|^PV`P_ zWg|-#PhVYgw_M<+>w#DXl4Q*EFIhrI-So6YQLisRPotsy)red3?`WgG3gf`YN(0w7# z(qpWB$ZV}GlT%!?3<=L85Ky0%G5ZS*y7dq5fOw{Lf}X89duPyye9QWr#_%kv({vt)fH?x#c0KQpV-!J;LNB}dlTI+m=P97NjMmW zCyd*^KbZ-+ay>IA4`*}9psxt&EIEGMT^=wr>nznY@ZP-a0-PA&{r;wxPgpiILR~05 zFE8c8Sh``gjQAkP$1qHV)Ohtsnfgtw-8C(Bn^4~q*#RMej46%iBqy(i)8(AB5JT^xD=c-A!PTP^rmk8Wlsg3)Cc|GemJj) zY+-Xx74e;tdr?_EoDvZd{pX}y5*H0EjiBgi^wiI(g|*d+mvF&~VoSC(sH)c#Ty$7t zQB|C**&C6t=p<=h+bg|J1j%-F?YtPzjid<`q2S;G+rFsgr{gE*GPtO8PGCXrcNnbs zhNMrvF~a7~wCfwLD4#};Bhh0XvH1lYCx8a!gB%#k`5NJ2J6v^Z@*B|F)3_2q;U6eP z2GraALVi(ROnRpeiIp?@gLVn@gWs9G4m;U+UG#Ri=p*uSjxc0jpKQdFX5QTzo_zRC z8+-I4F1^YMpl5(0kPsK%37O;ciiE&z+?VEi=^8*o03ES}9Nv-TmiBpwlBNVCW}?rB z#gCIM+M4#nneRbT%2}SFlU{#y5-q~T-ODk-9pfd8$rjE3YpDE3*@RNEyyGN&N(K2N zjJPET-Hf)huygF}CVQ;}+Zdq&_e8(6`da_YwXLV-CPS}ZpViS~F)D$6>f4>glA%MU zI@3(DsH=QDbmcL5`GCTru^$MkO}xgeD^My9-Fq8Q!n&!BS^MF)zK#dSk>Af2%t4lX z3srdn%a6euupC{BNXRDMoPBn}$rnz>I{>T~PNe&Q=kAY;`jrve)>`>ZJWA3o)~6GU zPznZ>z2PEXk#lqDQ0!gIWAG?@26E9R>sEB`AsX$<8SRyC!z$GnoG6Zb4z1QQ#~I zLJe2A%Jw~(GMv|*$wdJ{%p9Jd7Ud-DB%i<&(K#Fr30p?`6q?Z$Bxraei zB1=*2_4xfS)8i;P)eIBixqPaMosKU`UO6w-!q&-G?Gl6)=g>UPp)comBh$UOD)5~ z5Q@V>vP+5zg}|D`NvD^#U4F{ND=Iw(ODFC^i7k9u!Ug;WlP;q+$I8G_X*m0tm%A-z z{rK%YzF{WKu0zAr>9CVWtGz*>dxB&g?c6SUYD`_#K=2xo0K336&iz6IXU|fu{QKQy zDr@X%JHAMdRMRkxqm$_AR#@|=7Ys4G^Gc!ifPK^azLSFr>Jl@w76hEIRENpaj&x`p z;IM~04x*8@{!y8E3+3nm{+}sfMVz?~GKA)>6#kOS*b9(OwJ`+na-tMUcNM57x=+K1A)MgC& zD@7~bt8>;=H*6?$M8U`X1ZmJtHkV4-B=;P$bk-F+-)8L<1#qz&a)KklAC!fM|H#$B zqL>wIHy!_8L6^{8k*TM0~4T~0hCB!`O%+ppVzt@ zxv;tIp=>BXS_ni%XTPzznu%p?$iQiYXqb$lQ4`DW@&u=$8X9ArJqAPQfldb-Cp7dq z{*HC%(rCyB`WkxE`|ImUYo=9?(Lu514QHB*Jj;lzFDKQ2)iJtCoi1x*jX81uq)q6x zF(w}O^~FBKWj3j~w^+u)HszIwmx}y-f=Hn+=cH?DwGDYv=35)W&!`ox27@CNyUpd2 zuE8g*&C)qRN;a@9(b?wXo;NwRl#1DrDq=m`XwO`TB0b?0oTRbwG66WNQ0b(UR-+B= z!c2yo4EFNP^5{31@d zL>nr{?|`sg6;^6D-3ZL2InG@#eQ!HL`sJN`eOhwPbZ$5_cXooTqrM?mM9%LWi(;4B zf_~RfDcZRvadBtMkPuzV5iL8{7hL7r-Tn9iR;OR9hhM*_Yb2UWBSWc&5v;4$!9Yp0 zt}CsrqqgTmumUEbFm`w%bQuf%dNNd!gL{BM z4k_w%I~PQ|5PS;7vt1Dg--F?fFsxYnP4IJ@oNdm7phk&(xtXYr_`bhW?xL2hsaa8c z@II#S{B4@_KqZE#_4l>KrC{;=jfRG5k=AUqvZM*ww*{7qA6dXg^n+;A_r@q%%_{4u zoxd_uzlN8Id5?uh$r4hq1=cM&n!Z1AV1JfOW&K^m10LmEwGH$k0Vd^B074p30ISo+E(N-)}#7D!#x`VZ(P;>}e z6A7!bvPQK?R#PIahnXK*@3YQ$Pf~*mIXfB-sV=^3wYKOAu@%3XK6%8JP}e@Tjd^T*J0c|DicmS zvKq*tq(9$$6Z7d_#hvQrKc$?wUW(U}GVe=Iihy6D^v9UlD{+Bb4cI*Ji`37Uxx%lo z>spN=+g&S*13Ji_^{c|m7*>BaO|5(vm_o3K0aW=#VmHRvYmURsr;a*%dY$+>0Y32K z%Z$&7Pp(~crt5ZRd$pif(jGJDP2Y5`tsY~ESmVjby6!gSAHz3U z`Q69TFC5Gp0WnnTD@qcU311?VCu_vl+IKEtMs8_z0WSg%lDFy`Zh1IR!H-O+gO2FP zta=}gHwxR~t(^Zm?1V{KF36Hfl|ps=N7UD!{DuGD+F*V)zB`{ywG$wti`4s#43^@M z$TSWu0>hdA8mZ}1Hou$5RyV9kT>TmsF4u|EEqMfEqUE|{*u#KI?R7i^K48N|Ko*~z zpXsO#ZrZT{hjtLHX?FrMTS|SQy9XFT^@pJ9mWpR^jLl8AmmK3%7H>BF+Si7M(b@Xc zw%=5l#f^Bibu>Q84|(+3K*%Y_qwOV6*>-SjzCnRV&*O866js!C>kJCU3}5FxCgMEb za2Tt6a`ydY!;eV)8o6m7>&q4N#T+gAg{CRsvN2%U&C@U7faQ;K2|6@xqtB=8KMtyu zBVwW3py70HNzLPs@Mmoc9JZG-J$i*H_v}t%=Au8-t5M)DSH{xVsmgY9fy8tD0BD)E zCV`}6^b5_AJie!_1jK}at1W#r9Eypl`NieIx;?;=P=TzRlFT-o?==NEvsG9L}-tgLu=9g&xz}4K&3Q} zAoaogtLmaiXXV%*!~~2hl3hh6q_nZ zkq7V$mq|&NuQT;<)f&eJH&B!Jm#VCE_|p9iOKdBp6x!pc7()*{jSP8@VTreHOyj2Y zR$(UBZl=-PuI>PK0&W>xt(MX#oYiALCTar@bY)R}@=P4f>l{{*{pqhS{TXN>=^jWU zUCiUy!fEmR$G^P?zSZo4vwYR>A&;_%efO+gouWt7d34}w;RJ*z|)0dm2luYaQ5 ze~Cv~DM7Wvk7fG!Zo{ZB_x8^-)c<~nKA(8SD}^xn6%aQ&*8`_GfzZ2$54R@_8u{HP zEiB;&-<95!zq7hbie^`7t!(gJgt=1~R1V0gZ!$OaiAY^Y;1N~2UII%ckXX5lktRzWwE3DRGYv7UV8M~ zfj0jqil_XyDE_GgWq6+%H#{dkvG}*M6RDkwPs1>G%SGe`7K0f6s@reu_*|UuG7Jyq zi-<2+(IJkB$;EWaFIRuc8Xzd1)Qv%gOZ8H!9ZY+H^Xt%iwSll2CF zFJ|5u_s#)P>rrW4S@cjohT8Wcb8rKe^R6%^j6d^*)@q}^7pz?{&y#0rr7_bW;e>NJ zJk$#ab8OCI9b})B{t~$hBoZB}=%OK}7|r-0`=0hv^##E2*-;B_(T=`IJg*6T8h;OA z|Mqt0nx@~PNO9T%uk>8$>b!(KI3OL`9k8mSA`T)3i86wch!o| zRFHV*DltqeOR*^cWYOoCraR{GCH)e1q-1%?$Qf%=nT690N^%owcr3)u_-fA29;&K3$Tp!u9n3%y6vasUzqudfxxFte3v4@{V z$^Vs2F~pv6K=XU!Fzyc}lZEzESUPwC&w7lA>(?$PHcJcZ*ccCpQWqI=Zeck8+y`=I zv37vL4>M;w4%&^UZ!st=W7<04qu6gOL+D5WLCZ(Q7VHrxW=?918`dvr9|0N zcjuVHus3cXT|LE4cTBvp=smU6|;VQ!%srX z1p{MBI76M0u%!zwzh8x|dD{6f)8Mx|Q16Zf()sL$D>A%YOU6I9^=%AbSnK$xp=0C9 zb zQis^HLxLLwreT;wwu@-zsDXDo?D3V8{ot9(>lu~<$z5~tN>rXhH4sOvKLB<&Ju0^H zD_aMOw3y2#J(b))t8_m#M?bNwFFb8JRLLxX>M63cS`&^!@?lc}i?o5@0KV(_9Ks}H zbdP|xnkb?2;4$U+&6weAEin(%L+@_psnFS$Q<}En>1eRqPZQ7+%|jk&To*bJ%^rw5 zk~D1U%&jY?2>Ay7llU~}hd6}J(vyeDx|oB~!+`I59)dl0>UL3^Ek8QlxL4xH@!bh)c)R%Ma#n}c|`|P4J+fvXm zA)`j4;V~}V#ECkhMAk&o)N5VxHM2VLVt{ub>_|`oJVaylt|3}SJLiPY+^VRdpW#Gj zZ7%(ekd5&bk7_E{arU5(jiHseuma{Q&ByZ^@v%lsP3_R#a@#BB(V8;L znB#KP#^TVv10%`myrH}h!5QYLtft)=eI{dN+MV`LLq#WvD0k>eqJ}Wc|JZj0#fZU6 z5nR=amhj+xPDyM29j)yktER{1B0Q>6mco7(B-5c5-WT~_#bA5DeleH>zu0{A-nnH^ zpPw@ou@MQD2wU<+;P59j0O5F24IzfOOobJ^=R;lEXWiKc>oCPyA;r^#&xU^y=f+ch zzh9cgJFFcL&no-Fb+oH|#@t>4#D*p@0^`*nrhpszetj`fhwq@#9oQK7u4W!>o{To? zJ?Y_Mg_yhDHR>7}Ie)P+RpVsr1Kvn(7%i!$`dRc>fYOYkv}Y85ev_OE1)c;+<)fra zxU9+DwsklPH={wnX)D^6j<|lzG_s^(j6mQP^xVh$WffJaYU=NQ%sf z-@S$Fh!w8SwPvgH&*Tm0m6;DeL0PQGcQb+0lj>cj|LP(Q@*rHMo&e}DiHz+ySoKRJarKv%q-)4usL^C-=sqZl=c^{4toBi5 zJ$co8B*8m74h7?4qDLZV3a+<)Wmt+lc|E;r;;mkavrn|#aUI8X2bzO@D=rtm9E;-} z>|@;zZ}Dd|$}30Ezz4lWk?-Dr)>1Ld$s}r{XG&S=#5&Tmwh$5Jl8M zmnDDwc;$L`+0pN4s;${j>G|1^LddCdPcM)Fkjp1K&Q&ib*Q#W7;Xr7}|Acso(@24s z-TEzfm7ybx>z;vm%R6F7e|4E?KD<@>fqP?xmEO8l=(fLF=D{UE+XQ#xZ%K(oqHvyUUYDqmT^WRi{G_1wEN%;%} zz%t%bI_9t-8g(NKW`><1`}+$lhTrxb{-!9M_%b{S;dQ@{VCy%A4YOcBaqd^0(&gB1 z0oKLX-;l}7otuY9!Qo&qnk2OY4SZ`q2@shElxRQ5Ebn|H9W%V$BVaMvHb5z@u!uBN z6>#(jjSrPqH0uXA+nwv?Zh1h_MLi*qV4bE*6^t3qwEO-bKW~4isON(7cz^9L2ye%T z7_Y!FzF{Iu<&M;-0qMXCX@5C0okfO`j!*Xq)2~i;o4OnKeuL;uL;lRq(HAogU%vBjc+1W2cHN}!1Q$n)MOG)Fh4!8BuW{ai&BMnXu z4MBAzVw?L#kOn{`x}mFHfTI&)>WqKg1&z;cLJpfni5Ub`h)|;Y zzefQnh?JuOS~n35OsteC5e-&45C?a$JdrzHq&+T=CDfRZ9zMFAqYrMVrG+dGG9W?v zW9%h~T77P&#euIjv;UmErNDXX7WDj$&x&&LFwZ(NX4~?7XCOShOO?&!-#ICI(Gl@5&ZmZSU;kW!gdW`~ zU=#q{LefCqiyom^YS&}}d0hPQ(6z2gkU zBaQ2&O*VQPkxWGge27b1QtOedC9tsdAMzt9WbQe62BeE)V+Om4fEFf@AMzs(v@p#+ zN+tg@@&Unh!BPiTDGfN`*#Tc$JW(mnKB<#{jW4OPnr8syAd29wt#?}qs_?|YEZg0Q zCcbPS&W)#sv^gK0X?S?=_~|dyPQ<+y_Ga~R$Po1*u$ab}^`B|BTCbJEw=3Fx2B-GW zjWGFOUGgfJ{kFqf!_YpM#06{!yp9sw`*uzM*ggEo`Pm?OL*xzeROVdTfKuQt!$fNh zQo|#mv}h`sl8y{7<6*P^8VOj5ddu;Jvp$%aV!aS#L!j%Y;*_ssqvnZjs$Q$QPJgRzT`b!J%{3)m9@?VNv;LsA&dwrt@>UEfiJNB)Qj@ zh)Gd{Nc^dEhIi)uj$Yp@G@z&9fc>!Fn9ckHDs>#1n>+r)2Ydko z)HeXP-lv3SyFc52CW%IdAW1Zylwh#`V0&W-3(3^~Kw)ZRql%x{gTcz!IX?;Nn-_l{ zA@AY{zEcq$r+`%_`D9ZcG@(kqZS@9v7v2Q3boztztBAB40na;vd-no5fTf-!bWf?~}x+N$*-~7f|*7_ZU<~i-jZf)?0BXmjQb~!f;#wQp&v@`R`4c%_E5STy! zkszp;bFExY?>K4+_r~!lfXiGo9rFg{y{_x#75-kAllk@kaq*Z%hN4mava6@0k~z8_ z4r*A<(ybw!;=(j}@tr${?M@W6(MfPqtNgNMIj5dzu9x65DUSQuxyV{TDeYIcK=1m(`csQ2>jFXLajYm z-lp=};E*g}8=FfaYl)FDL>)Zx?(02R?%;oVt7S*NO^~zU&^Snq#AWfH<>)Uc$JiLf z+UX&bg`GoN>e^PhA}O4K?@#H$>++7&F#s~~>1w6INT1*#XZJqc8>!A1rgNpgho%35 zQ;*C!#!7q&$rS>lVh}A8#!ch@s!qiko!oeULj_ro34cP!bm_!w=T~3P_^E;RdrMjLEW= z)I?EH=5KS1h4Xkp5{=MRKhH?-TqavwX;WRSw%loLvyJ;*UmG|ft~<3|(DmBgyZcdh8unXykgUwETmwTO1d7*M`BAWyXD_l3$wXO(|o6e0c+J7wT-5&ajc~{>u-ZJW+fPG#nar9;RqpF zV`0o4zo~F~3`Q(=;Bq(IW4J6qd-v(I;;w<^NKOO^+qMG*y4lL+a;yTrNh45F$A$DR zyB%NIuKUw+SiYI7qwFve-L=h36?S{7quaFvEi{i58H@+oZ6LL-+tU?GQDf;{cJa@&CZ!G079{N}~N*qGBYY zvEaDwqE+OD>X(5$8Wyb4!?hpMYhl!C(7D%xn~q?7y*Z~xyOx16v~tJb@!f4x3WC>X zlgqhmT$)r~c21#{pkqVXb zS4)+vP4qNd8aLi+f(|U*mys+3b=!^9!}j>(jWaiP0e(m$xkjhg=Kxq+16@tHd4uqQ za$h1RIqQ5L6Br{QlY)|ymdaSDmvHB;p?3En@7!yz3H)Y~CBzVE*Y&h}4#6prfnkU< zq{6VNyqWUGeeg?!98(WVwp%C8{nVygVpT8X|u`p@ z6fTE`WIj|8vv!8ZX{^^v(XnIql%sxbH#m>!8t_hC4Dxu{G2FR+fv=PBh_6aQQg~9;?(hi*Rg000e7$d0o&E z=llNASEd$!(zBRZ`w2aHjxV+?>2hG*p)em`%O3)2ZXgv>T3O{HFr=`LHQ@Mqm?HO# z9~`;k>RT@(899?DKC_@X5kdPmf6WEcN-ob(UAg~?u?W^g zsdM8atIlN6j1BA({-ejlWKUF@ijEVp@+Hptk?ye(M(D zS?RQ6FgAmWa|SJYe`|E?f>lx9+~w23co9(zvV`hE+pJ0Irbj3we6y-7?L+vR#Bmtw zauj^bP^rV|^_wlEZTHK}A#P(KO}n(BBq1n_9z2b)GesNZ=Mc{HT_R!Dp7$0H{u*H@ zoUQBnhFaj1AqU=l++<^ztWvcLv0Zlm!5YK_eYJe^^BNEMaT^jD2vWeLH$9gArqCJb z(|%99ki|(t%IqFvIST%GhM%tg@y_Il|8uZEGMek#^Lk$seA{?pzR~?~`t=Li?g(~z zv(0K78&*!_5v5JOk#AgCNGlm#J`-b#T?f?&O@9-6TOGm!vp@)7y@{aHyvVUQBo0VD zkO`>!wJ|tMIfvYcX4=p0p~+=oCnSh|m@fUB;q=euS0I(g;qsJ}zuGtQhkBCt^s!}@ zo%Vz)Uo{!cG!MHa^&P8sEip3L9yqZ(SP*3A1s&UT=OF#7|9`ojg+vFp;L4(}J}9(D zb%nv`Xej|t+i-JztaG}}%;RAyBy7(&x=Slt%0e@9$vH98!KC!uXdZhHYZ7ByXQ5UPT5!2Au+iO5%eiYz!iI+14i~QPSFY&D57e18popObhsv@>{uPG zDzI_CkLPn(kVzW!cw8|XShiDelh@;l4rU@NGqIW%F}Wd9^?sd7Al*jYxbqU_C3{CPTt9YF`IZJ;{JQ6zjauvJV8sUWyL$z{+ZePr!=KqQ@3P6)vG8!r^6zgXo^BvK1D+qu46V z3B(a!`TDqQr^gKqJCCkO{2Y;1inZ0x;04Z`9uN^8jRo2xM((Tu$Rf;yKS&-0}&v=nyvV~s#)-Wq9KVK^VdtvzC+nW9jjd_J)rHd8Z9x8QS9UV``GQ^L zR2*|%vUnmIv>0xb8LCkT8-Gln^Ls{XHV0;K%-v{5kp35k z)Z%3RgJNOv@zjn&e3EiR=<$j=lB>tWUxd?c3Ps>9ZcnAbM^JzyWZ#U@g&-=M5bj{g z|K~;H1^6Ep{(KIYU2%(xL(ZM3(`!;ra z)p-SOgp;oURFd}D>dq?=BUHgUNiDMoyVI(u9qcNWoBMG$^ui!4d^-?PQSkfr5rgU1 ztA@PxchL~$?|;oSz1A@hs~ft$RZ9s1sNjd5$S2UUZZ6nzae3MLjfGh)=T$Ht+2~P6-yz4NuR`YOi`B|MEdzVqPF$JN? z!cW|7n36&&(8clpAC~}K#TI40>raW#k!LuTNa`{g1gz0zK_D|b@|Nxzwb10#a34`D zIuA21LUKziBB$qeOkw*h0sf^O^tHnaIOzes78VGZ;rqH7MqSR zNPAPDtPcireo2v4UqVcHiGID=wu?tqMR+Uz5+O;#Tv|sKo%72hpcd~tfel@Mj83o^ zn<1khJMn?7Ua&$UvlkGz*u?QzNTuv@z2d)Ei@76xihsnL!$8?erW_o+_ktt2EL1RjiR-!0JYz*e6d7F z-l3HYkrwPUvE0`!4=mA^ahI?Cuc4UdH+KBB{T}#;B$#-aFJe4Ga-5}EV_>8D#b1~n zA72&nN1m0BV}qKzTn}}c;XtpeAIvo-!PPpyz@4+1z&{Xppn~}}%?o!wSY}*L{RhL* zf-;sKh z*TL|}kqj1m=XlZiUjKLO?I0DLy>NGC2-(=?ry?%tN4X}+(&}+S*&AFBhffF3`*dXgCi^(6|^k4g+hmt4T@lHzlmROiloBJgYxF6ug6_0Q?|VbSvZs- zRnoWgoFu=hjrgX_)8C@63T4j<0x_32E@%0Y36c+%ZMvf4%tXipT-puFlxf}`C|n|pyzW=ow2OHTswMK@dbZD$lnpEi={pFRATZ)sNI+q@~%=s_8q*ADkM2Dv4wk#|9no zv(kxW>g&IpHW2*3h{FmkwaN$R5^!SCt#RKP`Z_Qk5p^Y*GQDsU90Gsa zx6_Tu=&|{TT;v6g;j?JYBGw3XWT+%7jb^A$oHv67^=#h?T8ex_Ahh(@%Y_KK+8rja z%h`{vf7&NYaxnPA(N(fHqot~L0KDLFrU#`|P%gWKz5E}DwuB!eS71%sssh7cmCLcE zIh!L>_d)!X=&}w>r!@b}+#mIj63W>}#>v%}&o# zD;xG%SZr{3kAV62%Jc10=d&!{F{r&pwbh5tDXOa?-)wSp(_4OS)FAtm8=5P}l?8&1 zj2lkF*M2bL;mlQDDJNRzh(CDBTbO%~m4m0Rb%3I%s0zLZ9WU?GI-GA3Eqg&VCRz($3f9!mc?7K^#56NyyPfk zuY*aykb*Gn*(?P^B{u_6`IAV9=pPa}O#BwocQEWd!E7rq(p7||N$1lffCCBBOxCC9 zjxQp(2X{zhleKOg(eukcbOGzwD?dBYdqosxldf@Rtq6*x#&JJ?PHTAj?WB{w7vUPm z*kZNnIJ7B+soRc{St@|pAZ0(i_skVMT? zzdc^pYmN!1b_t4jiq=9iI0bmYhrE9MJWI3W)$xU@@g|dlvxSnR`<+47Xr8v~{E--Q z2%gWlcN$J>@zLMEq5lAW${=tOfBi>`2z>^ILytUxoQ(}<7ahCqk<0n#R`AwVpo~UZ zKWMAG4%BIHFa<9Wf{8s8jUvX$_{SV$xpwJ%Btjjmhba*viY=!&WpfTx2~@rfB5fim z)IpCWgVQ$Wbr;i99V{y^Ok!xnN5Ikl@vYh%CS#0&#O-LdRSVP_pM#Wi|%Kw$D6 zNrKY*+i#H_Qy_l+mkagEJ~v^t;FvY(?&Ll(!PPm2fPT0eS9Y%_E)ph?yfoKlJvF5L zXq;J>H1(Th5R^dGrCo);aJ<*c(FPhqONreNWoHAd`jd!1s8z?)m_v- z-aIq(bU#T4zVEOIt7cyw<(%|)n>nXe8NplF>J;0>sW(&_B)2iQZh)VpL3sY|IiqAM z!HDQg$A1Q8%82*)ou(SoFbiVc0e{pzH5{tfbxfy%W^?JEFy0WENWP;yuymWn;k0oM zuW4WuS`8*7-*ern?4IDY4tUHQr8j`~f4{?$K-%yLr;=h1ZlY|h;SDw-(#bR5dqRZE zMuGACW_M9dK!9Bz7IkpwChFifbUZaEfD2A+a6V*0^!h*EO9o}>XJu3l3|V26;UjR| zGIhQK*Qa-$W8)^}emWj7cY(h^FDC`F;x(uzN#0t)Gjn$~X1dW-dzvM^9d^4FwWd!9r5tQZD-&@jsBl@;>VeC!2y5>mP0ZXklJYoNItJ|ECN#}yH15N4qO>5oBFE5L;|#Q3z1(KeaJUY_OAl|r=LynT5QDzD zrK%TNxUSByhfkQ09uPxDMFibFd<~|@K zqUGE?2di}_K@X(#e(dp=nxB9W3^y;6w-3Cks$*Oc6P#D!CXO&{hX$Dt%MC067mO=1 zNDUtcNxxWDtJoQTYgfdA?T~r1ky`@CMzKKrKwp6u=xlC8n!!RaJ zA|q$e)00nP2Hqh!cR?0uwYD^M*JsZJjGh}&k4K?`moHZq{8SgauBKpusKoZFH}^g)sJmsG#|l-8+B1e60|H%&{18C4r(NU z2Ov3wbK+pC7{l~=!}-cIt3+gH@@9UR>GuIuV_luZ{j&eRpwxNd0oMjrDc@h)-A`41gz_1{XVSf))zBgeKD18Ddprt%% zse`X`=|6z>U^25(O6ur*{7tLFQ};<&R6df157ex{V`sJ6LM1d?t$}ovHMsxzF0TP) zM@>9K#Tmn}y`BhB6S?yMK00BA!Zutuby9p0=AfLvcL}>3!iv_AU2HRqe7$%(sye4_~q+2t02d?P1)zt;-pWc4(Keg}CR&Nlne^#&Wh4Qddqhx4#moCoZe z+^E`>oFPLvSFO?1RQ8-%XLS&{$n{GGHmaw|oAkWsV6yz$tj%H|PG|_YDH4pY#ao9S zD0fx53j2{$*AF1+B5ZI6;%9bdew1koAa4%^X(Z?Z7%DtMl!%V)LQ^LHSCHxfIkv^A zZrY5m$Snw-l}dFDS6L>U5wdsfkD`B#kk^j!3hrIu1gL|GL8NPV!H<)o zb84jLP^&2rpVxzD*I}hSp|Em}0%gQUYteYDKmbc7!dsfs1}ePtQ;ph2sd zEUde9`6mKHM|n`Kjo4hHHW8hDJdSl!TEvWkHB%IsWqSk*Q@ zb1}}Adg#h>?62AX;_wK!pppP^wl(}errt3yuC{^NZEQ?ztFhDAw$Y@qnlxr(+qP{r zY;4=M)ueH9cHi%u^F2S?pOcx{bFcea*GgPwu@9%1g_WLXsRmALDcfKppXv9J0jEt- ztpim0MxqmezZw$>8=?|31P%wY%)uvh{IaR<&zSo81=FX~0w&?G43o}y1}blZu`Vt| ztuRXoz1vQft5A_q3jI=RKSE*Ae?e=isdTUMN0+K)Ww=v4**iSQx6pq{qei#k@w9K3VM`of|MSdLm){M0gzSl@wG7B|pQkSUo(aD@Y$kcm{Q)_17E#nY{vNhR zNuPsoN#M1A=I&_w06D^33h9J1Kg4YV^u%+d4N@m6dXjJ4uQd=ZzBvLMs;fYt2F(|tNr$YANN99$=kmQ{_3}0e&8w`t(azKthtD!+-k%+*Z21Fg5%}{ z1$@uS16rr?Ap`jTrFVkPZ(Gh8ox#Cc-PF<6>J(z%F{OSZm<)b`hxYXs$W=i>Y)vs5 z)aQlEPbIx8*TeeX7$)=&;&C%@o^0n~zq0MAChlx%6wZ_Ue!rn7pwjedE?w7b0; zQOQDyDNfJBjK;ye{>G5;DTjor>ei!FbtD#tmeL9W+k^Kg|&qcJ|~0SY#)C>{{5?ZzK2PZ;FDo4E(xSZ+{Uc5(j4F<Gu&1?O?!)Y+!6F3J1Z(qbdwF~UAteRRI84bLvn6pMhxoJkGhDvd;eh=T&3?{ zlkAmaVH6E4On$>N9-2Ey^|2-c+Wixnscp`8q=Q^pG4@eMyY*)mK9{Rhl00LF3f7&W zhKqB|C1>S1;qv#ZUyU#B-L+gD1%S@+VaGDYEhR?68Ur>RXHtbS{8yW)y^iJmvo}mf zf?r3VjMX)+-qSvmqWl?0XzM^zxm<&oD+&JPyb~8?5z5bLrfiw=0Ya+)J}i$ z*fQG|I$-g0ABBXOyaZbdBXq;ORLGvISi1sP$X0zGRjF3JcHxO>dkfh_ny;0^(aOjV zhvP%$-hf<+2b0o=N-Ta#d@lRmkhck8RD%hs)9ztjwUW3QC9KY!4YY=09X?a>A}SuW zwpH^k(3868fxerMuDvJEqdgaECy)qkzW0X;H6i4@s8^2jCv^JqB?~;X#Ouj+I&sLP zKu>ajtClfsYjpg86M5NAXwT9c6azRxIEe|6#~jv? zN--rRB&al^oCmTRDDLz_qdTcIi11-#>AX$sI@}*YW6-ve`wuvElrE}z!6G>!{zsZQ zB$=x}ahH%c--`jL=7XRII>CU5j=TNiKZ+WZ=upjKYgs+uU0^JpJbpcWiPS zRW@fK`PZ}?1}_1%y(Ckse)&D`KL%(Y%KC<$gOX6TgjkeyN+xqS`Z+NQLaBti9YG%; z?j~^HFcRYW#mhM>eXCrtbAzLjmeJa0qK@{SqMEnyu{0>RJ61q2g0{hX@tuO0@ZYxn z^sMeM9)JDLzKFOM20;3A1jO94P;ePOZkM=ND^($0CoIbXD;Fh33F1X)I9&s9mpNR0 zC)kdmR-S*5diob-foDDCXqwU6!b%5fhBu^yk;Yb)dq33~6{bZ4v_P2rK99{?FB|Yb zyps53s2Q*s8#chVWN#rUL(w;DGHdVAOF8r6qM&Sz^UE<-LM*_6%3r#u!^kPk{m;fh z04M6-*8pHbfN})qV#N1nb55YYxtAYX_=jt~tOt~(nQk?GN$jIr=Ib?beK+%(#PpmY zjHRvJZ$Ea>3xAF!psU+>soUDB@ittp-{pIR);^CV{EHVg+Auh>YZ1JG6$lv^fPxA$ zS3d{Lyfu8y4iZ+gpf`Ka4f*{iW@g&3GtW2|s{ONG?Up$N(C-zwSbR!xj%75_+_*H5 z$)Lnhvj_h>&g~D}FJYbBjDFS%wR=2#hp~3qgn5}YuFHMk0q{nQ6HDBuLw}A7J>m++ zso=3cNsap|@}fza2<;Isw#L?TL65IISL;6v*FI&~PKd6YVr}yn_9&+sHm7Yys1$v} z9pB=O{A23d%I$g%cd2KtKcog6kA(|#gz(({9@16w8yu-PTPkC!(_QynyfAe<-w?WX z)eW5OEIQ|v4-*SFDoJ|)FSFz#+o=~Nz)6KeyGv&gq}AWqgM^=tDi?D{7rKux3T=wF zNZHJ&Vx7SMcOY{Y^(lf|Lzle%B9>~dCL@Wm^nwx>rt|2?rEk0vO;=f0hb(WnRm526 zH$U$L_*oHh1Z(>^s2Pr<0a(@FmV&z2z~$q@?LllAJl){Cjqk$HP+!XzpElW_TQ9yz zqON3Qfxi)Q2pRdj9_&&znIUrEp6>{cbgOsLU-bGVV0lFkB~Qu^|4}!1t~Ce|;j>+C zyS!pfPj|h<%`t+#fN%!%%&?pC5mgoh0wM>Ym!1hjB{>vN3Mw*W{H%w)9&VR3CSi5G zATy38XN%q^Uq=x)DfS8LQHIr&8k^_?$w3jerDDV-(kc9Bh6VH9!EqMj3u zWEB0BGqW)3i*vctb?E? zvq%`$+g_o^%n6L6(o#6{^AmVtMzzG&$`e$0x?DxfiS)&(Z=ZNuFKGWVTw{Ed(X7#P zmRn~HO!3>@4F&GAfaAOU{k9%t2f7RHGp+>XopN&)$EkCz!~~Z5f#06EoV6IiKh`2a zw&wcrR<>V~@^#TivrO2%k#HJg;wP7HF=N*THU+BBmWU!SrzC&Amlg7>dwSqk$7h5{ zfEl?H$hZNrQ;4aKj?WrIgfL!vmAbM5S(2LLcZFNd`FF=#pU6v`eA!WfkiH!V=}F@I zw0dSCJ(3?%!mK!s=#K0Mm#B|i3DwotiN7GO(!M^9V{-Zj@&maf2Cg#n|8hw`AeYSf zPcC^x);~i5TlL^0z2U+P@2*GZ9|=oxj|?eI?We}{Gd}o`gvLTKsM4-)3nltUv3`*A z*GZ@i=Cbg_?C9)8irqUFZ>{^>xwK z_;ml&_c{y}`DzF84iBoNPQkBty9n8O_k(+5HP<^gC0hMVxH|-O(QTxjO3Qs57n()KJ#MVi#LV8+ReWq|C75Uc1p{`K`Iip52S zH6jJdeTBMUto2!F<-Wz}I8mJj81iN0;f5F{yJO{JbpdYZSm|sU3R|BvpOJ$75fkgh z1}XBl`hSPYN{!$)&{U%|F9hUP7Q$NkCUVFVbim|zE=NR;AhZ|!(0o7O29b#xv-ELZ zG4ca%=iU5ewkw>;7G3NYw}$L{X0s{?%-li-yXSj7;&uL=AQ3(f)#X+XAt8uNAwYGO#Ty!rM`*uf6FG~G5)4`1`BJO}I*@t` zdps;Ul{wROzAyiDgB+KU32EQEA2Ntc^+@Gd%(FBdCnP7ltkP%MIfcr6ra8x}DykG| z$hez-Ok&Z{f>r4t3B~IMeADYarBYK=>dOC7FJxE5s&EUh!;?Irb(hBR@Cx}G%?lfsq6Gh>-l!WKFHh%NWJU(U4{wJFQHbRe-M z1~Z8dlV>RD9Uj<4F5u;P8sY(f4m~F&>~61$(=78MMb8JyMTSsnHdm=g9iYB7 z-JIz|5&-o^q{{>kwis{;mFKu};-;nHaK4~=z6SONrI({2*WiPzlD{Y6>#pjwJJrj49axu z@`4Rs8c1<-IHBBJ|L{cwVfS6-=9c;bQD3g^#H^Yp?Z0lf8e2#@mEh6yR;X-_{&U|$ zk)Qzohrc4z8|Zipi@+NO=xonhD?ZR&< zs;%{Dc&knA0ioLz&{xbw*Va>9%u>V(eH3b9N zzX3@}(wEbgjsx}MChK%|3*8DsXzT>i*MTsNri%A}t&vRQsEf;G!YbL6AbO#;1*EJC zi_!M~$Ov_9ttE#Dpecl29-Ji|)aR z{EH~m1j4y($7uJIT3eY??)|idM>Swq-uzpR-U6<|MQ@YVfb%=}*D4KFzsOGR~# z#eJB_({t#X7E;zSmKXFbS75^Vu43~>WcSp05a#EKuRAPQlmLel12{7Vi*Ks0+wNq} z|JR&3*MY(LGd*(8L?#=mPcuP4#&y+x^+EG;VovF9W-4b0yyR3^63n#KD1eKInchao zXOV5jFTP$SuHA+#pui%e>-ys=EcUKX5Rp{>9}jXK!6;O-Jpui+0BGN~v>@|0W*rOuYKJB^y1yjmoU8K_kZbOj}q&RLpmF}Sxa;ijG{l_a=xt8RYN z`sUmflGPARBD~K(CL#J2Dd6Z{gt`PvNiETvtGH!}8W+>!Kjf4gsP zuXuy&hbl5Il^^)5GFzHg1&o5Jms*tx>&X&+PcLuG$j+}P!$`u&Ei8nD5{LGe14BBd z;#N1wlau)c03LC`Ep%c(2CXNc<56u^u{Pn`C9A(2*@KFzFK1;yshH2@o9HN4>mop$ zFcCFC8#tSlwaz+?p007vGU%tAb9JCzmcN9=R5!hjiuJYo$@qH1z^W+p2t-*OIqH!a$B_61zTV&(?pc* zEuvOa=L%cJVj^T=RfvR8IZ`>OP!Ljn|9^`<*|yVJe4unEw ze7Ppb0J;H5aTeB+lTQud|Al#Qsc5x4Jv%Kd`O@QtgOkd<(xq89f3p04#F|J#IjrAYg_G#Y+ zT!l1r4VL1PJY~=KJ390Ci%+IjLUmtoBvRD@Q>mX}Vi|%ADlc*5Unl1DXy7ijR9kt7`;@;6n~)WhwE@Xt#Npn=+^?36{A?rb(i)2o~&~rm9w8kb=GH} zz8x9F*6y#oHTNvN;9KcR>Z3=XM2iIBz3NJ#g$lU=Nu_nhY4mYzN7gw82F|LmRd*%P z^nBbV+IANyH%kz7+)Ecy;K!QnJ2;Knw;K&Fs%Iki7;4E zrbhSBZM*!%t1bo~D1aWLePzE=*EbVr{)kTyleran^(I+n!r1BxB;)&n1`<|=MF}sQ zWZkWfr6@n81(a)vopBjuMJ(Zf#HZu40~;sTewCC82B3KYtrfTSU2%L3oD7C6i1{k` z>G0rb!A}7r=jc6^*MN7|F=->YOa)waLsB)Rgxy(-gJZ5QS(H4tfSSKL=W3^}Hm4C~ zk$LtecA&Q*`U!}86>l)s{qsfR|4H} z<@18HoI=nbv6C5@(Huif%(71}!u|I|kr+-6rCv1@U1}ayFyrS7Avc;e1~&5~2J2Nd zupH@7+1^)S-kZO5#8$fM^0{t01<+yx&P5`Ww`i~$ApFGBCH|;Z`ENmp`vxp3BSsRW!mWt7tj!Z5RaznIGC7(S!a`j z4mh;4KmBk(;_i+VsU%t0TI<)0@z)a(W z5R{7nOh$x|*f0>9C(=s&UKTsnu7l?kywC56TCw)LMU9fxxqi-nReVIR``n%=t*(w+ zPAl`p^YSxrD{K6G%{hJKcIcn&M`c!1KQN#75}VEFWM7%)1CM1Isa#Gk*xn9eZ(69^ z-=?9*Yr|){^c=Ue7;6t+am9dfx|t#_?pOP&xJkzljhrIC^IZ#InL#1qdHUK_;Lq`?6+Lll zj)1vxIR{M$)Juq{s4S8vB?~E31hq#k5Gfzy5*Eo=ldZeohEZ~Y+M3E4*?`-op@<>+ zI%#02uE0uG)B8{`!6Af!Gp;F2LNJUlnqY(szf%$}E;Th&y+R|;W}}Unnc26vn5wd} zlG**%1QN1#jQSng$H%9w^gU~ByXPDk%%LTkEEX4z`MUv*Iy2%-uy{~$sFL=LtYjooW6wtD&gZCc zVP;%KFeGd?n2CCL?yr!JOAkuaEy z{Q>hH%aj+r97AM(aG7n63oQBRR32g5BT*uQWHgO{qM)%u`+*jZ1-{7Z3K^8JswQE9 z#)|RT22|;0A=#CFhibd6;YDD;@VG%v4t`TjbgM`3>^hTxqsf)tde==hheojoOJvrJ z^l9c|d(5V!76tFI*K6)!BX3q^?cM--Yx@Z{>?}kBfdU!xse@|*Jvmbu9pvur?sSg)P^2vc${TPj>l%1~n+U!ZZw6yeb zmRQcvr#Prq}`$3M0W$uQchT7CQQ#dY#O6!F`iak}c6t|0bv*8dFn{ov9z*5Nr# zZC;EvyOu^L?tJa8i(t|+cCJVf$&k>9#guYU{pPBw-rD{d+rO}Y{gNC;0&vi2QC!6l z*pOjo!uu^$XzuL|#oU>zsBk%-&SwtcaaxBSPGl5lH<-=eup9P5uD1=?JiKuw(kO$g zSBzGL&q5X~5`o_n3C&8w_VR*-50nMKo!Abqynb}4fT`j6E1!J=a*u*sIPF5kqVnbE zafF^5`$~FhZOqK!AOV&jfRcM9i2==&>3%j=tI+~?&G8-#Vah92UGT>Sasy$(LGORP zX2(Ij=1rm2aI@02LcT{tPy(RoD>9{O)sr7`-4t>Xp-_~&(^T{4Hc4zRjks^AA4{`D0f*hS;k;C6TqE}h09&~e7X{<~2ue6d@q z+eIKje-#nlG$u6=A;%MOnR*oFSp9Izw`e6M`(@%}(Bnbzx>>3+^=zU1fjha|rRYg5zofaOW2( zL~5&NRB@~usY*$tp~qp%sneFB1h)Tj#QTo z^jB;`F*C;Lqgd9Q*%!ZMf95bkx2I*quhXntW;c<*M$B5vdLR>baWU|$KJO;o(fMIC|g(&6H=G*EBd) z(!M&>%rd6k%-XoH6k@s>byd2U1MDkVRiR_jSR^82Z%5v#4LbxsG&}aL^>o`_kgo)S zNCa4*!r|eSR4^uycCE>TvpjhPWx_V$B@S$}RB0K*-Hd2`xql={gOUB1)tb z%-b^@xT@kHV9^{yf|j5Q#%-5cGkqk#sc3euw^OgrkeWI^DVHb^T3A?2*Ym{hQAkcI zLS$op=(~jl`^gU7Tl(ddmj=c)of`G%8D{ssb=O?OBa6N5QUb?=2zRu7_m36xXI?lk zY8qxlnbgxfH8_Z$wl>8vtcStVGb)lQ4zvt;7LFs#mb+N?GZ z;W?E{XOWuRC}7pMwm-xTF%q@}AO~dhxQIvsPKm4y7w&b8^vRa>?wg5}+wmpqMD?U; zXDzyaNdY0mUud{3IE3h4f{=^H=eUbNWfkTA`6!6#Mp*_a2+4E#sombc0*) z_Y}Br<2}Exp<|Tk3ijWjFilsqMS`iRdM_qq7qzCslMXJ6bzx@(=OVS0agbWurIEW~ z_b>F}6E}Z*>^-I>p(DJUv3ZFN;C7eIIX{%vS&9XUNcE%@I2`u3*W=4AQoSf$fc z+Ca<9j9Ok^PNQ8A?D=T-tYs+t&hPlAQv9^pcI&u{uH<_7y+vkETwXXfGdtJ} z=3!UTfBIh7Av4;kIf$Ow`Y-7tbYPzH#B8b7AjU(E&)|`-9YqPeI6O3BBfB~%B^J?? zlCrRKM#na_Y$)^cK=MPSaN*hqd#V+RR)p+ z$d&RF?|#?xzwx;bz%6QJ3~FcrYmN2&w=m6he}b6xm4NM%hAwh?r$738BV=iHvVLNk zJOA?)BjZS@$A?fR{l7PO@Tt&9c->=T25r?;3W` zxa1O%-Gfp1ACDh`9wHx+v9YN06`CMlBkNZV+xH0L?T!H)E~Q_I*=a}K zn9qF<{wy&gX1aVrs+2We9Cl%zpA*wEAs{P22cZ&#X$lBm13OO}@}LYXs+hM55fbYhu8x(ZP`YW|I=02=QIbDnDht}G>4h`R<;Ae`|% zRwKX9Xs7OyZ}OrutWnL-GJs~3R>s0|3H5i@T3?61b{ug5!3sQtVAC5O!1?$Erk+a> zxm(UQaY9eVZPvbEy9yeNT=PqfCz+dAlt&EMkSn)2EV?3W!95blaB-rEi`mw5Hvp2e z(=|2U#<;}H;CEp2)I-a966eIo!)?O==h$*o&AhD`#?GOY#J<} zX&^!6yz)C_rm;Z4Hy`=@W#2y`Qng41v%}*7xwyFa@jdGY-DFf&7GX3IWfi&K^|c)i z9^QN1wpxRk{N3?PfJ-Nxekb4j{Cp{_)4?d_`AP$|8o5lDoZV+EW{J~FQ6kO^S9M&K>M=Vr$F_M2|MP+j{gZ(^e0FEv_RA?Uv_r3 zFz-ZRVKF%e1H@0BQ}#V+0*bxQT|+Iy0B<{vw-+oI=j-GQ44&B$!jCx=$6|#>7;JVE zUVC8o<57$3lv4hlanD66Oq;d^skybCyQCy9h6Q*o;=9yK zGGilK7LCH*TDOZGx)eFU0R11}SDSn--x1+tV;*%V--D~}VZkxE{Y1h?GBLhALkkuy zMg)~t(j*Yf*a<1p0wXjNTol;-_+mPi9F&to+|=sz_IzLB!)v!K)Y97e)})n?o*sU8 z=addbiL9@$-)OVJbM!?aM}S@_kAb5s9~Rm3t3ecgq{M_K9Q`FC)pHl9a(-q;mCb( z@-V^y!%ew06+P{l!RVJLbwG5rnEDTVb-+jFhd5bDk_SSIS&+xg?qmpG%stK`fr4Y3 zD2wKF3p>1`0?tojS&29V5{wxfJQSnxIK<(gm4!brMShIx~AsGo{zq` zc+lo;z?W=fBEHaK`Ha~u;MnpyD`TE^(GHl4T9CuQhZx$DNACF*oDUGpVM}n?S9$zMTLeVw9~O64*)>L?%j^sT7sSHcKgNu#~Sz805rmb1Eqb{kR(5`sBS6#us3 zJ2UE7`pmjB=x!SoYPZTM2fi@JOdx8D+mu>rw;}&p@Z*DOdLFY&nH)GV{@eNWc>b*L zqdgpDs=m!VJixPt{Ztphi`rG>DLq-`jjXu1(C}?vw8c66!kd)+#3br7K0$7I_D9p_ zt>xw?9iqB2k%L_v!IBEKZ;Ghqe`HcB0Kt_mwjAN24sFiW)bjf|i>x(S*2&<4CCQBO zE`^B~87=2bt)0ie{Nj?7P;-+&=r)gPGJ< zEOyHCOG8-zZho<{#$Oox)X%1Y5QPOa*!;OrY`6IQf^_JBrn1E@8=`!`ey;!Y5V-_~hDLDP4!(q8pAN z(iy=w#9yatu)l|zL)_g@rVt~LV+!2mZHZqF+$$f3HE>ufBGjCqCk{u`u)iFRUQ$%q zmIk?>J2Aq_CeUCkpeQ7O2^u(8RpziR|v#9ap(#PslkE>V*-aYl0|W=%dzrlRCQ2Rm$6gHRad!QugjRWZ>_{W34 zcxhhXMba+Lrj3br{&`Wf7dMKpl?i~AudaZ;uI_l(PxE-eTi=Aun*HYxJ=qRLTjfFH zXRIb2j`Sx<1ioy!`z++43phmbhbS-p_lxMOISL4f^573XQd+|5p{U8e$5lJ;lB~$) z2>KKYYL=_D5GI-AT7J)A?PC zq|YQ{-U|k5aFa)kvBASLjV3KXzi`a<0PFVjTlP@Eqp-lQiPDyl2Pv|#&MttT3TY)E zalbM1Cd-Pcrz4W(J6CKq$qA>|>74H-W#3;Cg+f*R{ums&lR8ks(nE;+QF<&C&?ld< z()Dm6oWXNd5a=1H(!4%V_g(IqU~FDvd1D4X5fW=)@=B*4(NREDM<(-)*NHd7WTa!r7D@xrMg+#hg6kk|g6pF1GMfkUdyWWx~6;St! zpdtne4uN%SywX+6Vy`e254z^Szb*T8{v3LA<-$fs1P!%wRkE~?svj67e2G^8vRNTq zr7OMRt5||g(y!*RWVzDlJ3u72*8B%E6G#fvRaKT+?01sOV;y+>%gaxRU3(My)awU| zuK-dFEMA&ho;Ftv3m5>CXd=zk#gELN`S6ivq>2ZyA213+KcP9e$atr3>|wJFMwr&_ zr$IuXl)1Jc6a(XLGE{+DWAVYgLXyJC+fJva6$Eu*CT!Z8v~XU8NdJ5}BUYQ&NU(ic zA!ZgTyt1)nhU#b&XDK1Tl~dzuwfR@8>mUaWv(&aym}I%JQ)n3y#v0SPXrn$qL+0l3 zXHO}F1^ncpXSM`ppJ@e1FSs!v`<5A~k9(Q6^<*ZXa*ZvIPsz!v%n#IXL7JKU1-2=0 zH&Aw=d9$7Q*nPA->_)KVkhV!XPaC!PH)=Xx)5`)ML!q^uU?e_0GguP)8xVv{*>=2H z!hXqfcyl;rX=AwHD;ZAUYxno{bS2obhww3VGkz)Y=EaR>@fa(@mdEYtMlTx|`l zHQPG&Giz&GNbzbLsX{`|B=|Od1n_b|`zl{0CoSCxYGa4?BZ>F+F94NhSd|>1SSR?% zUu-5l2ke|FVjo}?vtfrVv=I1L=X0};L&w6V z%i}OJ%?SHP=xUTY6Iq;_7mAl%Wg~VhKuDfzeHL@2k_b+L=Fs5 z8do>)a<(A{RVO;Bj24UGqF3^=!9|2z4&v9)WB=f!I665Sx=QWn5ialRADHk_;gPff zHuUy-JJ;9^_;@1qY|BXjg5b>V<(qic@P@= zj5=)KYGTg*Jfcs=?H*f!0)=>jcXXcF6zlWx&c9fxop5AJB;Xm!@9}p!CxgQ(xXtr zN^~MA`H8iK$g5bF{W0Vi;TLbDKYT79yL57wokYN1rVthjK=S}u@b+IDB(n>AxU=G? zl*W>xVpT(RjQ^$pq{``oB6ZUg~A@8w^4wxMGVQL9&a6H{5dIS+5Z9Vi-q! zgg%4F|6V2Rs@nDKk^Z(k`JU??f@iT_#QD9c5}X5R#XjQ3FY*pCea!i4oJ~jMse#ck zpLBcT=c4Eu7-wy{s>JCej1RcI1S4Txic8_x#5Qi0A#ZV%TBm?{jKE-60E)J8KUh0( zhG#F*a&y0%B~H0C6Sp;)tF+!lDo7S4Im-Mk!qd9GoF8u zIcOE0>>>xEf3P1veq;*z5E1ga1lH6r>FVkp%VQ7{61KFr_pLr}-71BqHho_x&v~CH zRW7LYetY(LcHOr_L<7&pg)U|PRInJ1Y?`%?n>p0;HrHqhAfr z*qA({(Oj0akM2MHkWCo_shNO@7gZkNJ@{PH*)y36ew@EtmsVPH4}3rjii0j3hPdYE z)oblU1-}5V;i<0c+bdmSBYWVR(g__Q$x)z9^n`(OCr)zIdW0KDQxa(U94_1T0hPNM zD-+qYPAQ6u^AN^gMUy4wT${6>PC~H1kuZpNBzy*Ngryk1LM|0E%?5E(_G5!6;MQYlnq)0$<6Rb9N|Zw1^DSk#I|01pX9#3DyeX$B-6K!?!~IF_UFqi zw1`E;#j{w!Vxf3~-h47i&&}c=X>8^e?%63R*x=ycs6<2x0mEUb>cw)n%gf8%GczFd zig;F9Zj~S-7L{vZ#lPUftQX%>F6_u^1B)Y-NS+WOZ`Qzs`Eoa5B7d3>qlw9-X*Gie zzj3Fq9pYp9IpzrDehuoqKpUYPO=QpCFeB=`76lSG=IMoD_sD%-J&)~x2-kYnEqVt% zlZbHD!1H){0Yi)Y0C9Zjqqg5tSSS5^ZTHYJ$*dxCoU;p-pEJ@qG3$2DP3WE0gKTrW zYa#pjWfl9DUg)SQ>r%>esqJH#kAMECn8OD#hl(ElwP$kMx*y?EOW$ATYrBr$ge|H_ z7R%CD03b;YJa+j^H3#?-wabPFF2446XvONEdvAMF^w?WdG@1n%69;6`RD1Ezx6MZ% z?j6p5S#2o5Vg|BclpLLvnfA2^{!bGp$(GptX?y=w?5hyWH( zeuNDP4Xpx-y1rQB(pjV~u^5iTgDMn&DQHHE{Qie824Qv+tPF(&=w}r2PvKFxE3W?O zrb#&}GPq`n9WRb1vbhdJ4fac(nHx&-4#=YrWh?=H8SnKcr5vy4<4G)Y6zA>O3PA4_ zDWWoNxWjzp!a7-NKs87T~bB(j26RLBEU%uJfIkUJ%KeY@z<(eZ{1047Sx*RFM?7SI`&&NCf~UZ)lb zL4;&c1uDifP(YI%kYdzJ z{HOS0>F<&eK>$gU3gD1%G)Z7MdL;KCNJ}Q)yBO`AZ4+vd88pPYws-{>`1+RvX@`ve zjck2gYJ&ju0WdPMY+~$q4bu>t#Qw*O#hua6z}*ChFe9+>4iS&$k}sFL{A-dySbPq6 zZ|%wj*0(3f83h602(Q*yf1!j<8F5pyzun&hv)uZZ)k;oM5d!}&ig=hxu*wDE&9Wz^ zjTPg*B~Ko(ZWm^CZaCLk3<%lJ0-?lrcb>3Yd=7EH2|hlqRpa>=SMy-z(k?JW02?8B zA`G$?78Tg1H@BE*kjWHBBwDbCyNmsTx+e3zaUi>cB0fGwuF zb1?k|rVJ+we~qPz#ZJe(3{yI6%=vWM>+w+`^-R)YCJ(Qe>4I_r-=WO%1c!Bxq!SW8 zL$Z{HuGe`%D3O-Yqq@s=C(lV#CD9%THyqvufgU$w0aG)s^LtME!?vKQ6h!h;`KXC^51-IosMB%6Uj zhmmo}E=%Vyb9VOHJ63iSj2m7tdnx*40xW6JL1$;*^Sz@~?9LZZp?{{|UHv%F_Ao*ahH~l$BQo#}0?w51u;?a$VOOk* zEREfKw~gPEU4EsxtIr zRUbEHl%?TkAk-HO+@6#5@%(v@*~#>Fv+_cxk|4eEcvX;JEXS56#clldcacV8v~)T; z?J8Ctf{e$*x-weV;oFrVk-0hf2{WZ6JB$Oh!|h<^Cc$_ zgGRrGGnL%lqYRWjNg9;a1U(t^_O8pJtfqh0+Hrs~x9tf2F|>*!$ZV@kP28qEf$syW zNL)KsD5bqIniWv!Jp+2BLs)E#1kqHINlI% zb?&}+<1qQaVy_G|HQLYh#JNz!Iin@0g7An!#vV3Bf!fG{Y+84}5&}eVTRw1BmUFRA zLMI`@1BE0pYThib5ql)_PDf~T0xdw1F8Duf5k=r#Lv_hhvESjpTE+2Mbd$nJeG_vjUshWE zZ~eQk(@cx8ZYRhb&2d_VK(66maMAz5k$%4m{ya~1hIv7HUFuU{g&1(s->6`$NY|M0 zM0Px(@uh*y+VF^8*xNuvj%^OtovUi3*LUVCwZ*j?f53II9bQP4)`UCL4(`iqU?@nv zzlVj*^bQG=S&6wD+%*2U+pvps3CiH|O9#<)@L5t-?B4+C&nYC_Dw&A<&wMY3VFoUUSbd`+lQ`LP?PQ~F+04Bn4)~AWD=kTw{Dp#DAHMBzpHbDFVQ-6D9kHZj9 zim=}gVhLKz`%@EAiey#cq-mH__F6iDrv4zMj6J*QX91P zX5{)UZDHjK*xUDfeei=aiB(v_0&`v|QNHoFJ*CvZAj!?5v?x{V$l ze|pk0E(kPt&1PB(Y*xd6VC8zjIZ|WQA`pP=R1C?stp7U$6g!4zdcTtWgw^%+%!;B> zJF%uUU9et(U3J@c`%Jv2e9F~$w720WG zCcqMmT~o=AD~1v zLw5w06;9%Uu%{I1g@*eY#t-?P6%0hqaIh7moh!y){`@ge0=xQ;IU1VuVRaNLqc2PV z@rljhU<5esvsQDUJhIv2m{7A4LO!Z>^`xv%P|Im7B;K(E_Rcn~#?J0)W&$+bDfe#*tbOZ)^+grEHtL)b?r@zY!Zocb3^3}vU} zRcCR`-}|PoR^4?YG9G=wx|Jsm(-Fq@mCW(;@yYeptn~^1aZT`Zm3H(Q=l|A=L4AWZYIQxoY!AJQbDG#J=Y(y#AXBQ%k$! z>iV>ys?5${0vZ#8;Raf>cb5sN^r7><%^4Zzium-K>p@Ml%e})^&$6nSjWL7;*=$jt zVHvIUOt}ItkY6;ahy#?rl=vi-C}G(UlE?UQdrE2G(<&GL;QCSuS&AgzD#`x$F!z|+<4b%1A@ zIcWKUf4uc$)kccVPO#zWKg<~p=dp$rg1f#y$UoH!FkfKu=;XQihl(~Sq9h^Sw&@%`c6gMbmIpHGf^tj1)ak?ao+tg$n3Chc#szlFD2<7YS-htX5aphyZ^D-`i{yErXcNU(<1O8iR?p9a{Iw zezyExpzde&#n?gT3WA_)N$DHfQP$GDev38*6YX_r(jHBZ*6Ww6#;%X-^6ACgYslKP zc+B-k({QylO}H4agzOuER|oAk6&d^;#I{orCevd+kmUI#T9F;EI%=J(vOM>9>3fY( zs4<&6XM4#WpM*gwc=fDI4F%lt?Jja5JgAJ2Q7v!zM;qz(FGSQJ5>uJ7lO&O#+g~GB zxot*?G_kVMOknA-e#H(Xw>vwEzU#i{!A!~UgoKPpFAnWH7pT>FK9wm_aE^R_<}E}|5`fHmH%G$*z0ZBWbaMlyi8{HW_FNSOk zUN){u9yFxv;uu3D!HRi)T?;XHon*R|90s7MpCAar^b*fIW zJ_juJ%XRm+w{1n(^` zBC4wtu~U>bjlmni+;)<1LgdMVJpaAnaRrRYV}+4;_5Mh(3dz`ZG?uDiG0n#lv4^S; zN-K7zo_GPWwhaPWMw=ZP+0>TsgKAg_vzK@~8(0=`J%uW~X#~c!6nM!#zY4q-&s|IZ z3d7rc*Xg4VD2mf+(~Z!1Lr#yJ^Dd*SvG4xK5xk^a-fsb~wH=#Wqp?FwfpzZt63*Bqd0+mmRJfN#dVj@CwxU_vv%dw zGiFnDRnR{T2@?Y~GcZzk&HSJ_45~K?frzX}mm*hZFN}RIuT%eSMBJarrIZZ*44`R4 z@ZrtObyA1e3lQ63?MFhz4lBOPULGfc1upS3yuwGY>`hbS>V!=3A5Tp}sA6~*KfI@- zr#;yMHk+dvGaM<#h`8JzGqxw=>=+@SgT-d=e0$)KDNiBMbSp+a*J_@@;*0Lyvg$7> zL@NiU+My749;A(+dMb*%uZ88v7Q5Ff6OY`yR(j)2PrG3eG_kmPpS2M%!IQRziXc@g zqYKni4D-9jv6j=b{8ZKo6`W-aO%s~(gyV-?{%eQ}p`oa0_1Z(+LR{b_Tmfzl>%Hum z;FlK!qBC95ux`tv1^fWTK`hM0Si%x@T;kE^Kzt5=xTe&DWzz4xrBS^%zwngWXWR9b zvp|?7T>Z1GJGHMN@xOnCbA&&j%l^2zkg0Zy%AWlVQw}#P8e6!9!mAcA6O)Q{k|bF9 zxpje_c#PPFb#!GYHkSJQ(w=!L6dj@iKO*zyJc8>rZ-83$Q4UuX;;<05(|(apT{hhf z)_3IF2p9aQwJ7y8d=XZ<^(6Q3Ox=6&gIBGCcSo)bB(>M`Crncg3QKD2|xgH&zw<_Wqg@F)z>G zTz`iZB0h64C=)4i{AIj{Dh6XH_zZVeeeDisy?;H9hYnUJM)H(QU7q7uF`T4Vkt=Qs2I3`#&gZv_3)1NSFiC>>7s;^H_ufF zgB?tG;bcGncn#!)AtQC&S`BK-$C%tcUD<}Ov1>W%#Q&A{cJ{h&& zsYJ6KS6^h?tvF$`Q(^slu9HFk0Za>4PUX>qy0vu@*FfxE2iQR0#pa($(jswKc?*VZ zp@fNHyAUu{yuL>Pyi)5g3Hz|NBmN_YYBSW4w(ujnAwJJWB3U?7GS3LyY1j|0rNH*` ze2dhKp?+{Ui^RbMeQUX+h22y4B*ybWnqcH2?|B#Dal8UZn}+NKk1^q^PMv&TskdEyu;y3*-$d=oEf>m}TqosG^-H<7X|dKAH#Me82_4xEb!vSk zh*#Nv=J>aHCice}9(Ff!X(#tdcByHM;{Y2b-(sil`~~P9$(4p3y2tB~yqys5bsoNT z&eYH!ebxZp?9}?iLLO+ZD2Q?&AvdLUlwOy@F@2r-#TupANEtD>;y7W0syM7JMrx96 zBkqLDdLPM~bv_}c&W-j)2D1WVC$2C$ZszZN6Q7{zs24KKdzI(q!VFd_s=ugI@f8oF z$=h3f_n^8kx=_)gx1{cG9zU@1o=RA2nqx492VHD90#=ZH7Np`oZd+2d<*p{6Ykn~% zR6Ug_r~TexOt3i^&qB@IxZmq0yBPqnrgvD-%)A!SA3I)7!JYBR{+_G3N2-SAPx~Zu z?39EuJQ;SGzVgBbDq|#%=O&4SLyrQf^OH>=8MLkAEq9K?=+Mg8d=CDyf0xNVbL1c1 z)jfaUy1RZTaflwaSS+(Q1+h)35&1?52$j+kkzpqUn@Z*d^}+x$8^cljf}0(cK9xZN*qFtjQ@|MYrL9SXzGR9Z54?V-Q9<;YI{nA~+FtIpnVNfKct)fufQ-83u7 zW=-+1cQ0;-?QVsr!E}g)1BLO;*B8eREzT$UdC|?6FA%R0<}jwJW5@hxTis+frI)!s zcUJket_Y;38+E;nNLSE!J?}96OunkJzRa@+Ygux}SF-q~N|V-Zyjfd$(mEd~saJr`h~D*CgS+L0d1gq{ zsXV;PaJ7%6*w`tIP@+2L4JL5uaMrwwpkb z49{8KSB*)kPsQHF1soln^l=Q7#U(&1(t6x(hI2v*d8* zQ2Pe`LF-!>TCwyEFwH+i#8RY;3Rw74@ni@NfOyt=svE;D56!>^xbOK08{nN=_!G>T zL!8IwWyb$Kb%C9ovZQ!dF}w2yjTaaL?_gKPk8PHEZnxcFYfn18u}ep@Clh03Ic^#k z?3TZ&eCI+yzcY@r1eMj=2;MR=b9ENA`-b6bRy5N4v0TlWuQL}`W2CTVKGAWpeEqQn z71RRB2E?`EZQw5xhqS4DzUI!pAKNp1Kz6Ps2An>%YU00y|<8Xaig%F-G#EzX>RoND)hM~*z8VZv zi*BFc{e6qhB4CW%7y`N%+>iDS_$yO^lrB52I#3GK&9nYNDEQZI%yy_D1g!v)ddC{& z!0_%5M=%Lhin#5YZ(I1|FHS5XF0Xhtx-woVh_NJXo)-**&TqJLKWJo`mx;ljyFabb zH?yaE^8%z1E}!-8dlI4irEQr@|3nWpPuv6dA2z~rHI7P^)_Dv#fomVs5-8Tvr|}(w zn_g1$?|aC3*7vah-ql-_AoOPTRqbd{GxD#ZveNJgCiH@-4(q>i&TvAU>*yiN#o+Yg zUOt-1GHLwxQdpw=v~qeN)7dazNYivtma{I6->P!hPChOi!^z%aO|8spxu8GoAR&=q zn3Icz4oc;zYES5$YpwGXhtUrOyaLCoCFJ)`#h~i`*7zi>9AP$=59AKMTpsKU zq%7=gJ`Yeqjq!hZ;@$Yux}LjYM&kZi!FnTT-THQO)poDhR8Y9ht~J8l_7_Ts&0fxT zcuDVsS={&iC4N-b0bic}?MuWTJRE_q%tTL@IXu35_&tMBX6wyhU8%Ssq7)66~?LKsfi9 zm&J|)mh3~x`Zrf0`Yje`q}q=!0{N_ z4GwA;3yn;%MpE^z%;0uka+hD^85ZqRdUQvp=K->&w38kE*ys1Xg|hO@!uqCKks$<3 z!<$Wew@%|kuxQDfhpnV@;jdTS-D;L##KggK4njxLx6zICsJ(z7#ai>3ORxz@p@~71 z8nY=iC(L;;)~2M5sP&<@iQ`jx!$HzyETiltM z8Mk}p9`XXDJD(R|?4X1(ayKrT9u|8Jw5U&a;Na|=Y+3_j6LQ4ifCL8cJ}Mvy}b?%9NqChd@z-``d3faU@x{b3sP!73gJ0~h;b8U4d9%6Pa zXPLOXCa`^q807!P@ewMBo_evq^1@GEzZ5nHoS??kNSGm8hgkV0*PDORUblM^vW{g#%&k{gdt{cYhZhU{u>-RPAS*VOpvOsH4? zR>lyRqp>(CoXwTBz3pvQ?IEH&LFcbV=(#a$(0*bRewVprYOkrQ>objC&9}*SNIMbm z%SGogBeBuiK~xFQkft=~tztOlY~ZjuM>4V0;&a98=NccF2a&wFPHJjLmUp?{&q63d z(21H_of=4-b4B8&T^r!SW9dhD8_d;7SFIP~$s0%A#*1JGP}#kdzTnMOEA2>m^hRKB z=j@pkORDig!*X=05w60f08{Sk`$j>3eu;ah_?bNcCMwj!N3Fy})11#_#Z(fc(nN3s zkv8eX*+XecI*DkE_W!40ZQgUqGQsRUT_&VSqnLq8B9RaDmJ@`juJ1LcMy93vOpfnB zZ@=k{y7{#b8B;+y&6zB4$oFm5Bz{pp2-gUay{fs$l!z+Irk`JQLVv~`jojk;n)%0iYovlvRHrT zuk`iPL{}j5h=W*W9bWNg)DxD>z~@9UlDABx2ujWM6+_u^SAgOmY(FF0lrdhK*mjp* ztX4`_4%xZ`d+)4}h;ZrE{ImKn_-#uucFuY&sN3~7NXU?vR!6eBHHOi>o})DLMNX=H z`?`)PagF1LnJ@5+h~xdce^wkG_WzxTMUvvaJN~ZJ|1Jk;GlTP)Ws$h&2HqDBl%x)6 z;N6jus(kY$Z#DcS@!1Gs2Q&6r*Fpi(x;BpT^vTDK<+S6zgw^|5!)e-Fxwo}jffLFi ztoTO^lx}x`MRnr5)YB7;ilD&d*3D#t`&bzCudx6qzOLv}ONe~C6E7&-=!lTME6Jza zt24#xyZ1~;xY~7oy432L7&|Mi8W`_W8F@aUJ|uWyS~YG2Qd&?t3vNp6X(*E{$aXQ`FWsSK=QybN9EYbs!*9W~pGc68rozp{3`>_WQYd z1&tv=HbjvC+X;3;*f6I?ogwvC6EC)~#uio$WnOCb$5#SSGHG?F)X?F=&Dy`y@Oppe zB}iIRF830b?a;{m56TPM5H)}<9qKN#u(br`(zNtiErzn_PynNqc`Q*cCFebxL!UTP z=65kRwG9t-+!Q{*9AKXF?gVAH{WvJgWHVPT*M;A@LXQNA=jlTtL0| zG=y%ddjIkBX4MzjSXe$*e!1(B{&nH)w=pZpVyHat6`s^-PEcysVgpj})Bsi-_ho;! z-Gjf&vCCzLO-gMG+wI{Lh~zIs^spm=)g6P|kF@@U77i&Sqv!RjZp-44ulkW1PN+d1 z=vfoC6%BQwW<|=R$ii@4YON!%OqBY*VPl9|>w-~R#2Ng~o_+jzBwD2X7`vj&Purm-asP36nx5^#Fl z=-4ms(vVLr&Pj9maX}2hni|zgwAe?cBg&$0ZHRK`&Uekhd2b80Mm?`hLj?&1-+zQx zIauh(uptBIa9}?$KF}$jQ`3|iTnHH*(uZto?dM(^ox&HbdFr3Ym?0B1i*fff#oB-R z-uGMCmDBJgLtAekZ)s#!G*~PX?n20fEfxx>8b;z7oZ^?EGk?YKjl4-GW86z(xad}s zDXLSOUf;W0&|DkB@AIY%0l)1ym|IF^Sp#9CX~ z|1GB0fJMnXKHsigk}Kx^F=>06`v#F2x0BWn^}%NwP4YIFFCUB<%6oL9jW_;c!~(&R z8pYF{rMDeg2WFm7V|zyd>*2_00;}%#CO&UnzEnhsdWl=k_^Rb-(w#d%N6AhOyBAzB z7|7(xGRNc4UNTNc$>&wrle|pwetINW%$bZ2?vDF@)tTlv?eHes3cCd>XP-vdp>oG_|{W zMIL`QBY|K}3+iUWRTNAKVI;~w$*wGBXgPuPVsYcR)b@-!zANjJhj486395y8%R@Y4 zCgF;>^y;|u=HSj?FqF6bMVo|`G&)2F*?V)kES@Q}G|##%X2q_}im5_o48PwpzU|Kc zt*JsI5Rg0NtJNCVIXL*Yw{wjqPy{?}dLo=HRf{V9>Snrs730Ud+AURFe*gV7Ua`V}6}Yi}vq`1QJm=Hu1)}Oj-Hv!4bNO)Nle~1ZO!ZCkDQl>z zUsXw9P9rfqS8~k4u4Zbp|43o?^f2TE#xqap^7UwKlPjAmWh1Cw@QfmgaiIrTrG=?&Q&wB$g zp;9B^j4<%~4%$~|aq4AOzqm0g3PtRYL9(`(8CXZQc`*0Nss@p674t7?+5b)hLxn8U zq0@giX{sn&CD;0b6P@=Q`TgZ6yT$Pc{`c?Sh=_=PESIYEC(>Blo;FaFs!NBBgLo&F>m!>boY^b70>p^BJlf_SWH@C%K{kmVw1fUtKA_!m;3D}1n6wK(Al z{A3_&h+F2}yXI3{Edr%ZbV@(lxM5)i^81o)xm5&DwY0J23bhE_h!wuADg?;r(wrA^ zE?~H7#^uaie~!@`+SC1_uxTYzER}DyF(3bQMvuDc3Q4SQ+8K1Wwnn=-j%qGTy_-Ad zo37FYZ8-hp%-B$)PLiS`CWrN{eN3ZT;+Pv(;I=93276i{Bc{tN*~ zsF1Ex6sgjkauo#!E|>9oqy5F7wV8T>uu^m}LpY0z=EGQG=F1t}n?-Qrv8s0Tgbxe? z@zrF~gQMUu3*J~*SSV^9XO+!z4QBFxgM;@*6R9Y-8$6$Ev^v~=+IGE=s#IzO%48Po z3mwK=QSUit#?HSHlcQe3XVM=2*6$C3(_QV5dmQ}lC7b1V`nyR-yZv|gj{hG;PnCzs zq)Jlq+6dG~`C<=uey&sd)%w4kA5U_{IZgZxg%mpE$!!?%povN-jE&nl@zqa`5y#z9 zJSrVdT~e7t6*9b%jcH0kks=57PDkz)sc&diLsXR1URlqECk+#7Z`~JW8y|2eUZd8z zIF-Xr1hI-Nhqj)P8QKTBxOKa>=l;|4$TKe!@1OGW*YKeuHX7Z)R=}E4 zx!m8&b;dAdDwSpKpIEjXet@d8G)FO;*BcQPHRy7qJzuF*0V*r~;)G<6Mh93~8K?-c z9m7c|X|x4a1LT^_{HPvpSYaT;Q>ye^k6IniJPCd*^-ug2+*?!-Aai5M`SJpx|L<)K zDZ@^Fcb~XR=57=q7>VfJ2fihgDiF^c1%d_X@AYXPYn#SV?s}2vXrt%N1HK1d69mmd zUeUAejaZ`^amKVk+Vg-bmxzC7m1XqH3D7=We&`RqVPJ2((d$(yFM_t=2&_B^(1E)A zAtozg$xG|zG_2X{cAQLjDo@PpVmQ7$9j>y*Y*41Arsu1TR4d;Unr${}oK6&+b^T>{ zJnm~ctg?>XNC=!{rqvmkGNqzH$OvHy=F|m`?z3DBj^U562sz&F zp_;VX#|H{tA{go^MsnuzvdC4%v97%@@$zIkXfqcU>2ed8vI-FI(D>kl3YR2EUOP&L zQ&=DRSA8O7H`DSjB|mWT3WP+2N`G(XbABNRrcM2;iX576_}8J(PFz%h9rCg!ng{gZ z7-kYA7z*5ZUk>XXuWC#@s<^7-8nbJ=!5CxL_o6zh__0pzG%>0{+$VpcdE=O~<~KIO zT|1H!Y&I0W((rsE#!)dbptfRCzFnUV@ZnQ4D{W-c>-$r}XCYDGZsrNYIDfsr+%2~` zTlyaj*D$JY-YO2qbRh6Pe;Wu#3l0bXy*-+WrqyoS9*!jvhmU3A#ZBXOcd+6i>#uaZ z+AO_PAY;jajfN?eB$=aBE(1eDL))K7M+1E9eYt|c%-v7-CyEhxe7Go7DoP-&TCEPb z-oc;j-meOAE7GMW3a$^Q%3k*~qHYI?s@tch&_JZiFLF74@VD2;i}J8~Nq#ztXgt2b z7W+N9d=WS|H@BY6P24~jG?lUYtL`rm3$doOitG#y$1-F5J4X9mq30J8o_&r+z;cP& z4r?-*k-C2;lf_;7>Ef;f#A+xlDTL+EX=<@2OPzdvyjcw40~fqlL2ZQlI3G0Usf?~v z+8#M`D>(5iWIThWUVO)<%Ld%$RlIhfC2!Z)^$gC_GD=Bi)GwY55fwL-)OoaTR-CP9 ziccpJrns@P8z=MpdZ2kjz7#VYrg-Q;u~}JVE|+hL)U%P(RELe(^N6M4Tt(FR9}$uc zLiWzoLU4`uhW<0JI4_Zqf2MX2v+zI~Nz3)@@lC%eI*%SW?nd+*JWA_btN!8cCb`bieu#hFJafdkFZfuQEvCgv8Q6mn1EL$8jjQXcp9Yp{Wkp29?Ix^ ztr-Chleq_wcu%)Fnu1}Vkh4v8e7v4jGgvLxfPs)vXk=yRbb1UxBol8F_fpqdtuRW{ z1J+7$e_&4BXfK0E1oPglZ)zG`ZM0l?&*Je^0nf$do?a9KkOY@!!?7o``D^E%qKG1{ zOt4g9H^RsB6=W>#x1k9ldZgm1mo1k;-YS9t)P*rP+vp)tv1YA3!sTIu-sqi$i}TLmQj^q8&Gd-3o3J zyB&q=KP;jV{LAZ+tK`LFRYn1lT(uH?knXaWbLr!@ANT#^u2!ghk;fA=CrI>z+n212 zjXXGMLs#^IjjC^TFfDwhPe@o~=Q%5Ux%M)o3=S11(U_5s2QLD%<-1OFW99N4%5bg)>jB+Ac=fK&UI5ptnuND={kU2NzI6$~!^X@Q{;nUK za#dPI9o~9wEzqo?XUWwidhsQy)jDMX(!am6A{E5Zx{Nqics^g}LfeSR@z?3V_M$=k z8&6>nG{*G49U-IanFj)Cmbcbw4TGVF#USF)navO(5O6Eptw+Yi5uixLxAlQvQ<@#_ z?-zVRKvfLCi`9rQ2Cp`l=YMtqMw_yp)W}G9-;Y|%xvp0bef^!|xi3epPA}NUrc%7; z`~4AQWl+ga){!zyMk7ca&kL6Q$xO}8rH8X6<($jrEg#AIC*RMPqhCcT0pUEMDgGFrN>!}JB{@tI7w(gF%@HtVVtKo#j=E@XH7W;15OD zNT`XKzjsAv_pXu)&J7M?W=-NwyML)VlDXRO2lr#af~KxjsD74)sxc7gNRVohV@$vvvCSvHs{(%7STG?VFMNsi3LYNKJX_#g2) zBjVxv_ZP>96zXcjp{QH{Q9tE)Eg>sWw#En82R{Nl+5V08=$eZuqT^+Ths@U|>owH$ zRN{xa-7o1n?*Gg~FLy^|&)T-Owk*{0hmt`XG#U+J@x8=nHNgL~D9HqS0-Q(1jJd=m zMj0#*7tLFPyt+vNu*FPfvjw4PKSpppy7<*Rn~f>x^@B?s zpWBN-~rNf+qbQ6=$p3Y{(6eF3hoSVLtRN|R!B%zak=&)Y(s?L@@x8|5GQiES^ z2b@D5#7#Z{Be$b;%?SS~p}3vUa82Wu;whvVA*0t6`?3)}2pv=$BvhXF%>bHxFR3>( zB2InB>%9fSkKh!Tf*Hvc;-oS5tT6;IVs(MgsMH}qb?2Sgarhks zKk8_twlBNxzZby?( zY++q}eeZLs_H>J~@q`n`DwFB$)wA)#j#PT{`R|}nae*WC^dN)Yt^6C6>{q)a4RY}o zpG#g;=~wV8E`r0Mgj=0h0{V&A6T6sa1PUs4 zv#kvEbqvzpRxEX40WBkGG7VAx7;IpNqUQOlFiEmBk>J?h@pu!pRGpx@LFNfNCnx?B z1qN*$TM0x4xB|5{x5sn!G*GK_Hd_@w5*W#_zhuJS<>LfMiR;Hx^fAWd22K~s$zCwy z1dJ(>u&ERZ#Li!ncASx>nPA%`$K+%r`+;ewFdmld%pgttbsHs>mFv)C-||7vdfrc{ zt!SJhsfkK>C9}aki@Slvs$#FpfjHD=^qp@9ttZ}1-_SDhr>+zK8Z8Slb+>T+SKlaj zd#XEdOSEjJ(K2gJ_tBo6COyk86&3RG^XxsoP{wh(P&agNf^kg7Q=5O&K;T8ByzlD;4@ljlD?=0qXzeCc^b*E|dbDu@WXsmHb)Zh@*YA`|dlb zR|94)!D^ZiN;zt;DvW0@&5yMj#nYFdzxdn|YYekZoq(P;Hw%|$!4>TzU6fn zS!VkS#r6X|e#nSCCnneIhU;;l+j_l4K3<-r;4Bl?{uhfSDYpruO1TP6O4xg6oH+>y z>4?p0r)P5-?ZAGa3pz<8OGXHF#;MrOJkINp?w~a68o2kq_OX=#;Y&h890$*}OrV6C zE_h;n*;v~AjrD2$hxL5E+7R# zv^gj~9CVTHL-cO>bl`3K2&(|MnH73{CVXMqVzscTZKL&73H3Yzd zYqinlCq`GH6D^YeSx!*q%G^nuJ0*j2dvBL7PQ+du*&S{x3keskC#~`%!(h5r_#Iv? z3e40osGg5FeLf$6eLfv5g7w~FI!l%gLe{|{6{;vXF=s(of=b9VHIW-JM@+KQ$7&#P z(+`ud6d@5bCMz&8Ujg{fH4El$a%_Q^o5K(?R91eQf@M8yWrZPQL>vqfiAYks5#zOz zWUiy&62Al{AhH5ucLDo;^xdN$tyO2iVYYQ3LcDk%u3R0hTf6m_rXqhAL@~~KaQ5cu zKmM>-R}JN%8DHDI<&_rH-e8%He{I%eGmX-4KI5N~zE^KE>2@x)L$hDzbGOab+#p}3 zz342BM8VG~YBP*4Gqzq__qcPYrtn(5z-(s^C~6_YlN0svQyIT-+lp)$@2k{S-b*%X zxXaimqr+Gv?LIodkiK=e?UDj;N{NkY6}QB~)e6^YDZYL_Jd0eV4@aW5SP=xSuXkVU)s;nW_4YH~sF2dQL`sP{90-K~*_ zDgl0fLILT7FU^58Y3Lsr3pel|1h?I7JPSR24}r6k%4$b8DH+!?8FmUMlUn}LD~IC*?R;k+<@G`&7u zMFZ1c(P`)T`FY(xk5<>KEEC9FFmz)KVwJV=HINpuWlKf zRL+Ov<2oiM(bX!8OvpF<-dt<1F0GlVH8QemzSPuDu3v&_W&oCh`!2d&qfu{yuSBCu z&VP3|={%G7VSD%Vk8QE_oY4>LW#^wDpS>yf(yHw3?@NT~QX+LjK$~4{liRCLX=t)_ zRcz-qB`oxp#9R6CF;VoCRCtIaN50U;yhEe9;#Ivt>vOq$BEhE)XT9qk(iC2&H`TXl zcRltD5(-*!!=tfQp_c@TQM_iPH#Ad-NkU198Fl;>S#y!B0(=G7j|+>t5T>36p`vnK z=OPs2$h?i+oZ-;I*~fAPGaoP#FX}M}`h~(Fll+tDj}WwiDJq7@k#)iW;rjXcj+JR~ zn2M*HTgf>Fl!utW>HOkctOkG3dFp;XQV0a)n4W!jJo#Q$_X8XD?26_7eY9 z2PQDRf`Ck?n0Ai`$|l>bD(JE?yY}ZXk;jvhlVwx%(d4v;xU`2P4TSF#9YiZ6%+I@d>8ON+mDw1{FEraIB^{hN|Gwsifo>|hJ{a;jwmk-Kfg$0K^suJV ze?I z+&#@Zr1P3#v~_ag2BiB04#fK?kFgc%#aDJlYUpN9TtqdR7U_?U`cZEG(^DJ-;Mkr9 zc`PJ2R_6;{Ji$Rbv6|YR9J%LL^Rr$+GKz0&gRFJ@c2Ye4;f?Hx^ee@@#J1%~9&z!;Y(dvAo zU3DXy-JTG@BQVmH$#7sP%*X8r3589y5k1~a_?LBSC53_(dP~WiIN>?OLh~zW03|l3 z$oVEN*F{-10;>*hv@4Zad$fAnf3US^yN23}=DPSuKiOwWts7YptaT=j!jg2sW;)9D z=_Y7ApL)1nKJq!1D(+tVCg1MZ%Z7Z5+H9cI2y=!u&dl>g+9_$OZ1JORZ!FG{-Tl=a z2srYm|yE?&CN@1m1ht|X@EwEPZ4VqU+I@PvLyqBHT6;Ghu!KKaaiX1Dp zAtI)?7BE}FmMNJj`EJ4H4bFn4D)%sbeBg$Q=r7*Cxch$6LNEsb8q~}`^kmQ`EgumQ zLe70ZYmza{o1@3;s@82LQefn470rbzp4|QUJ&QMykYm`Y_vcSH)UryR{hed5AL%V~ zY8YvadcEJ3GOjTJ44g3L@cAXhq=9M^Wv+sFBDGX9%fTjLVwy@5fa-3GFRDM2NI zHVPA|4Joi`!BH%4GHBC(_f=~d?AUmGM0t4g? zo7`Lz({Bq2)jCaf=CDDt6P)rQKC*4cJko(m&evLjQ-3`DqJ422ka>GOCTO5hmMQdB z%=pYa+r1xnV|RVfKX1?uuFf$jBy=l>0(BZJTcnOsmweTGk5P6zL{_DLxCt09u@uD_h5}!-e7IB@|Em|x6Yj;^ zJu{k5#DuO~)Nj>0zzJgV=)B^BSEC|@^W^J!FIsttgF=HwxH9sL3nRWF{Xf{qWJ65z zEy4d&*XsscqtPO;&UnnCNVm-iO0if*`RNWo8(l6|NuIS+Y1I{g=Bh`s*k5`u`v;C* z;-`kj@9)BiRH`bzss=`Cd$oo`2K8AH;&yV3V@5m=Cv!izTrVRgJR~B(z=20wu{ovJ z^9Mw&#V+^N%j>%L2j$%`$EK+P04q`A-{%p_k@p|=_4g~|OY>48Suuh&0QhBrzlDO7 z)~O%K!pU?Tz&{GvTa|;bR)32~GR=}KT=7F@0lvfd>kkun+2G3lXyxtHqZ*k?U)^yrfS zokb5^MA}=^Sa4>n`b8;M1j_l0p#$aJ8`K_9EwML#F;3!iMGd*eGuKk3S| z?WLJ!2|N79*^>kMU4X_1oyu$7R%b`M{WY9g7_L1Q+Hi&J%iVDh_@lXFek!=R)Bfua znY-nrFs!Orb@Ml_+e0K{$*YLEl6xn!!{JV^w@IAU7$6D{dQ!A*ii)r2A)RzbQ?^WP zId)>*exOF|y$So<&cTHrtjQVv*L2>F-91wZo}C8)K~U~*le-`;8QQWM$@-T!fZ|f- zFxhT$uXlQ)u~^K@Y0neq7n#lEg@lC2sk2xvR`gbBGk{+Jf3+41luDHvEGsWDRumYl zPpg*I`tv37X3I6XX|#S()m^U=;A(%t6p*Oww3}_TQS9RIm-B?fuK^6Q9FT#4gv~qk zGO5?vWl}k{p_LmpyIg2A02qLDI;(KK$%Mg~8ISKL_R&=K5P)neKXEeHZTEm>?iGw_ z7RzK_AI}gqW*M;?jwR77&~vNH#$k|vlt*H*?E)pZWH_cL*RRt>zoQjCY!e$ z_K4k%t_X+2V$}miPm4D0PePF>?9gyDy1^=4C>u;S4-bQpI11V0fN>?C!GXlgfF+K(TC&=D_GMR`pH#Zp|4sc}fi<|^9K#?`xeI1r$C%MsrOYzQE*K+gqR znx7y;^wF8xc^H<33%~PuhUJxg|A}IrrlE{vR<0)L?=kQn54ca4ww4!+XF>v~6#M4^ z!3eP$7UDHv5}-Tk4vsTMp7Nx_D1ctiD%)yYe_wHq#+xma4wF2W!oDB(e;7S ziXLl_;+L7H)Bes_3aZ*LY7;hgisGWOIQ*eIRR3>0lM4+<@KX#dD-3Bd${(g-m{phZ z=NmzRVIx=VmsijZ zH=aMwND?D{ybORP^8sK=tQyElDalDXUT<}@s-!oJn<2${BEBZhW`X)Q;z;qIud3i+ z`nwx1a!8b9&S+EOJ884^IleIa#MwQ7s9gJr(FHs=H5!~N*Rksxe!A<}Q(!kDq8f@%~ zdGC{T(jT{+RGFhiaq-$vNWvl_1dM%l;YxRC*uTPtuhFpc;-M#PqR1a42olPa&=1Pd zL|-rml<_0$vaGS94L?%@r&+96-sW-KMci)h1 zo|2(xH}CnjeO3*^;deC;^?c5xHhsn?|%@e)Il zu2|u4I>`xiWpK=Owv^e1E#!2)_{HKa|F6`S#kAf?C92l{;3C#(Bn_%C357ynlJ|{GyQacwUnP!;$x~> z7}t+BJO4(H#8l$>f+0~wbQ;4adINfPsw=9xy%#p7j1Y2yV%I-CSH^$l ze31!r5OhE@&*W$;mExkDHLJDdSEPRvz9}MH_Mf_BrZgf)H~4)TsA1h>bVB>}+N-`F z(r!hD6>`hBp$9nKj3Aaq4>byJzH`)g`AE?~2N-^74HSF({7Z1(nBPlq7Pk`)iWs?w zT(oBq@&^_C&mNSr);QGS`azMke%HI_$qVU&3++gN^D@7ss`WXze%b|=vQL`%HQezx z=&#}8QTJ|7Rf-4cAZBC5UsUNSL~~CInlo2B;bgJz=+c{W+Bh%A86b#oxqG$7Hr?QMTj@a~ z-|NLeFLTP_jiWk6!A%IgcVsg#Ykt+c61u{9GvepEPhT`H?2faQ2)gY`U>@_Bu}y_}2x@!+23L)X6v zLrRbPMV;)SJgY6c!L1cDHm8lkG^NdNA$g$mQ0TCK2xZ`MD#UgY-`L`FBJV%c_E;UQ z6M)5p9`pCVLG9W6jCEyIjmTHWb&j_Fx&Opyd7W+RA6T4%2OgBj$ET;1p2adfKD*jm z8y&;;FH{UD+r6jczEzh;JN@P@rMWXA5bq8tb*d3~LkJ3hQDXlopXVe4bmZ{t!f)RR zx`&MT7CQYs(~ilmZv(-3SPWj=RqL`rYr|`|tj}cRvq*IJNiMd#yR=SYwPyN&4e=UpOJO zQfEH%bscI&W@s>KRH0Ro3cVy5zr`!Q>Gt8yyK*ciHV#goGcL@A_J+N+>JIKi`4#z= zI5(agbE^{b6*DK+l*F1H#g0odcYo8li&?$meLkmem|ri8uHDdu8sY){GE?wMg%?fa+4?_!D_ezlEuBC-a3rlSmvwObAkFGv}!%Uajno6?80 zOehqs%(+sD2POdYD^UJH!;`PNZ` z0FNt;kBK!3o8!#x16gg?OBX@wPMzeI3%1eqK5_zv-7nU$r{d!pw2uXKusUn#Lv48J-fU|GQ_a2QV1~RP1+)80NlSGxidkJfufg`! z?~e#41ZvRw!9pMLgasz$e4Mg7b$SOHnxDZR`khj@!)DYll_C{#d$kjBAYZLJi+skF zv`mCqQ&Ht-`iaLRfWD2Dli=Acmj-*!#x8b}%&rz=$`;K-6BdeX<)d@dX;~H^T$MyI zrZfQ)E;4iQ^I%?$lfl`W&F957d0B5luF!_-jYtM62{+_cs^J_g-85qb3J(Nl#z$n_ zxAFa9@K4ewp=6&~`I%PT0b#!Si&~-}5J`I2fV3@@InZ z?LE?Xz7~u*nQ3)qvU*222G4Id+S)Uk0rpHeQd%1Lj$#8WzNrdFfc#ut8o!CD-G$^> z6&qJ+eOV{WA9Oxd^?vIEHZQcy&1kBUcMF#v_=<~)8*cyjsuspLc)8ygYEFn{@dS2g z?_B1vqR*18j2blCq)9f~vVT|2c`s%FS+eoF&~g{PdT7rT?SH>@7~p~{ltEmMi(gTT zF+x5DfGDGI*Gfv`y~L|7&n6NDg&C+t^IWk_sVPoMrpq_f=hS?~qxn7i%lY>lqtOp5 zlJrU-$^H<0kS0bmr3~J++09NN{=*F$U#YWQWzdObvge!^xKQhWqwt#5A zv*U-wL-O3BqxIN!%u%9a(aTRIwhSL0(0mrQYIB-%&84VLZXbX2yO?|=I@&*%0l>m$ z*>zLXZR0E`4+=~TFE-!5z7=@m6s7fp9dTm(AEJ+^@~Lx}D|4@g<+I4?@yS&dtD zPDP`*KZHFqko?oLR2b;5scD86Pd9Xn5|(;##1a4C9LrVjci@dn(VJ=1uoV zAJTUsqOlvUukQm*c~A`22~Cd+lllZcy!hship7(m%5x6-o$8d1)C@JQ(ZZraWhZ?3 zizqsgt@IEf_xjuNq}u)w2ELoqS{%KY=S3xNjqLtCzll0SzX=j7b(E%5ijt9^^9y&= z#yv$ikcirZjzFKhKIBE*OaHe0@ktr1``DfFB|OiWv^4diA8)K$h9TMFLvKPJlXt+I z)_lVnSi9Qh<7&_RnR*a?VQiVR)Z~}qWHgU(l3KiZ`!3olUJuG7@Yve}WwJ+$TZ>8J zuEpM-rCv;cSV=|wqQ8v*7S6Ct-`3)NaWY(C8LTFud%wCH?whXNO$kjC?De=Jc&w05 z!$3*7hxOHReMCCLxwpmpPu-u7*#o?84{v@P1`0%WH53(O=-MZtk6yqj#d;9!kTdQ#VaE#FP5B@bV{4{;n^m0E(WwN&$uX5mJVWGn& zPbK3!`F&Ijaa=Pg99H;infMw)g<)HDzDE(mHqMTpb*KjRb7>^r&9`sNJky)tqxi#H zvHm_0#?Bi|mcRLUOgF2r4o6h1lic2Tqt)T$y3fBS)1lh#jA1gL^H0JL}EqxSq8UM44NPc#L~L3Kv|D7s2iOsw-a z5w@0A`Tl#w6d_OP2kD%(UbjLI06_N}-r$Vq`+#uyPv81+8`*a};+%x+J_MOVH6g?* zwSQdg+o|DF-b$j`%h~3T3$Z{TE=>p$+riZXl1Z*nU<~m29KWG;!f)%$%yDl-M26y= zgK~ay!v4|P3&NkE6k1dd@AL`imzA&~ZceEtY^bKSlv~;~`lg@O;_Ju!!nn6Fp(1FK zO8-{}0_=WZB}#lb=+t!(+cIz+zin!2T3XA(IZ)Ts)V#70+maw6e{@&yIMEJ8j)@=@ zH{!F7rUM1vmY-!*Dp5w9Rt@Z_e5ycC3cqu;;H^n(fR2A1baAUw(v+`BO=ORd@LP>u zj3GCXH7OD}%TSSCqIgWsUt}bQcZHCXpJ0;^kR! z!1eyR%!T0aCr6MYA~QjGnSg%4$iZWS_P-OTkF%+Df|aKd^Jj28AYA*ER0-VO-oe?0 zm-5263!Fx}i+=4Q{v_h}fqkAfxUkMC8`cKdVM$(}{g58m&-}*>l*)5pb4)yx?spIq%})V))*FmCO(v=rPA;8h3bmZf(Hewf-7k zWY8;$1in3<;x^tA`m#~2z1`BwrskfNXeieG#NP2)dG^Jls{0(<)kaoUzPBO!!D=sU zyrOj`S%f+&hsh4uBKrOB`3ka`Z_k?NQamzdmZ!QjoH*09ImzU{91YJq+xqI1J6UOv z-LL7yW&wkrIc~HhnvK68=$boB(K8$vsb!?tubeM^K-9G2M^ZmMYp8qb*|YVuy_JDE z{aOg^ZB3VKX)lU)s^G7!a_brn$k37XZ;}9p0S8syyE&mV0c|@!!c2zOD|Q88vYkBk9k&T^q2bAhwk9tdFX+x)@37^)+!<70n_%}+mB}#h zE)}1{{rLuG@!@na74y`b9Qs5a>(DBTNt2$-M_b8m7Tx|kX(-Rg>hwl_)SKRQU@Bm) zzd6b}Iy;3KvJ5G^RD9w+{-`F+F~Is1HN*5awDh2s?@=_j+~5iEp_uqMOfC?MEGRUv zD__k0&xG2|%%PV@Uh<1hsk{g`uC&b^GpIRDciPW5u>79f@BU-{SCS~|inqBbh zWA`RxDk`elj)!L2+UEoMxX^&B!2u$|C(#Tj!heX<;(E#BL4Q4{Ys_XQ_&pYrCej;8 zY}0$kYPMR;2OWpapyNIu&fcxDrf(cEl8T~}ecR-QD0M@aBY|>#eSMcdtC&u^Q(r)% z)gJ)DHotlm!MJ4^c=u$cpB#E1wTZA5Nn${}8_&Zqovn2v9ceKB^JSi$*_u&|s_~L< zTba2y!D($mNk+Fas_y`e%S>C$m|xOQ)I)7hxb?d;k>1fohJUEshnIf-kcl#{VuRNk z`^rvA9_^{(Ho0oqEiKi57+Y)EW2%09BVgN%MDaF}tOmEa+P?3feo$*!Ze6_p6iz^&fmwh1 zj^d%QEE8$!-6yO%*lKEOV}2c^$C9Z=JpVz@{cL`W%&j?xf&_9$Ni_NMuTcJ z(s)1N|E7mT54H{CUVq~f+mYka>wGyWO0vAVv=h&7T=C)|Wm-_ub0N}EQXEa?Z34cxWhr9@lX|%*T2e`ZCO;coD;L!LP7HlY0u)Ar>bS+{Cx_+zeMV3=fawDF-TDAv~QA)_!J& zY2-bP`a+W;cLArW^%U1Ej2_9c?DRJGPpMQ5V+E<G5F}VZYjGKeNp!37a z%ttmLg!^f)0kI9l5Mn6@I%oYb>~^7Ozj^*>hzIVWG!L(`AW9YXx>4MSVEQbA)v7_J z4#0dEP`)_J2FAQ@`Z@FLq4Z#KtE_NpW6lKGxU|9hNw-9^XZnwbd3SgOu94&30gsTIHr;`@@f0HDa>>7YcI}WpyYlPM>TvDq zcTY^5`t$74odZIqX&VWSfle4D$Gb{3VMgO;j~>N4eM~b+G~&W>sX`~xbrUB2l{l6-oO*6&MzFKp|bKyReMmm>SKY(?MwdsyS3 zSW59)UVDdI2&XUS^Yje%tu#R<&lL3iCKY_<9SieMR zlKZ^C&?UXJLLH6-w9P+}vt>GTOZ+)iv9#ZAY!T zb55t3G9}M54^r`A;9T)q15(CuJ`lF+FTIy$am8nOU1IvDYT( zwMtsHW$&!r+5dLl;6x4KV`;AfaZS10c@-j)vuS$t+26kwa{}#05Ca~l5hbOMdNz~! zA&?zs4AWMa42joLmNG;Cdu1li&kTUM$U_T!`K3lJl$=iIy?7uosb`T`qo{ILXePmH z=^oQ3xVPw3xEpr#keEGs6FdFpj{Ok4x=P(1lsif6@zTRanV2f?zYin7FQO3sP zil*7Y7VLGkwKSJnrluQHmtVeMYHmD!^v@A-j*M94d?v3F@<98_sY}?n_2ZKh+fE#j zw{J&|g&2)R=j$D@zz&((DINu1bapTUWQagcgf409VIT0%F&TPn{IuA*?7trR3m&@$#IArR{wZH-#|Vu(RxAEgj9If9yUD%#W$abtt5*^+akclBVG#yP z=s?0^=9U)iw?h)8r=*c8zb@-73%ld8e#Q zz@ponylWpq#j4wUB^}RC{qIq3g!~EMr1!(+XsP{MRM85ctTaz8FM-`~7qcS?(SMKF zfV=b@6U|U}1jo~-cYy3hI(;Z&$;s>Xx~3kUDTyz;NB{Q=(}P<-D?M?%Jh2*=l>Y>F zpkaFv0drnC_~hi| z?{nWjLo$(&{o9R!brXO~!I!%a zb$541DBZA*GP)P;ul2>{xFyAGD0aDA95Ysbnh(Upr~3|?Mj|U#zx|hMQ}v*`_x1

z9swQ6w#_DHFI zJ53xEr2DcbLhdKKl85%jwJ=lfes?ID&E=rSWU=-r7&1N>;`jP`HXM+Iu$sC7<4Um! zdb&N{XXKScJKgA7p`j+pJP9%G&2_w{P6xedK=Vs!r)$JMTvsp*d=;x^HPiTPp+Fp@ zt8Y#X9Rp)Wv&uYX^%7QgmMIw}bCDX&yQ3M}q}TxMJLF9O>?DL@)fG6KJUlF{KJM9? zfj&%&2wr!fl|!W)kKwFKoCfnVW{u|2h;$swlGp7eRVbBk@h7CHST2Pxi@Iv7>fq-Z z`Ut4Poflj&Fz_fv$MaQm&jgW6N~Ogl3#=Q+q0WEq>zjdwpG6Z25)%FwgA^Pl*rZ(M znK!KJ-NIJ~D8)KW`4d%I3XYDBvXhs=WIVV)P?b@C3pj8Ft346h4dY~{fcDm#e z!{+;{g`pJwoNCkQL0S$vubZp0MIStf5#{Duku{2X3oJs$8PWtu8u@AEA&$T2NB~1i zX-DA23n^nV=rv-}9yY-dh_QW>8xH`u;@{k{pi{VXG!(tPQewhPIXR zPr95U$3R+MmgWnFGq)hC%iG&~%%R<*@gp~(@$U=nL7T|EI7s(bOnha(t(ERD{9S3; zgu`+m;Gdd|F|^?A;->3fv}0lk+vtf9+27f*C_l}PYiSPQNM^7HZCW*2fD#*ZHt~>~ zJ|W)^fs7=UWzZvhak{0wy`q|m3MHx;@N8vWI?|LHPraHb)=t{AN6J}i07-Eug7b|V zFR@KI6G<8J!kP~b4yL|)p8r{kB1h)}thg!YAz;sqAat-_87F6mhjkTGsjCGSQypZ{W-w(pl< z8F)^)1it#03w?qo$ouu5UtShmf_dm2qq6+%ZkMl=7kB{TxV@sQtt2HPhC6!d_ogdy z>i0J`aweb}Jz_Ap*RA$^+%w9OrW1|yp{Omerm38+op*^iz&s{+)SvJ=#TtHm);%VP z6A^7_%S%Ue#qzY*C4`hKuqkc9e!H+5%CRU)hyUbBwx@=sX3oxOdwV;w<&I+un9;z= zP5MtAP{aB|i~=>=(AxUIyq3Clcqa(9TQvj@hOc)=_3vBWZQeVStlyUD(*5^`!oa@yn`LPY} zekyQ2O8`hIJB^whnwgsV4pfbFv)!JTdo|mL%;~SdMdO8Pp+8un^Xc%To%|fB`;;eC z-g&5HpH%!w-xpCggm%*>C8YO2mLf?4w8M+VSCT?wJh*ygT2xpiK=%59;(!{6NG zewxqHY1cX1-HicI&(5(tC6!;&)!Gp2xn|~Bz|k9bZUEP!Y z)i1(6yLNIZ$HtG-l_Px~Vqy~ujRE{dYO8l{U<3qI{y}iw9nxb9pp3TeAOL zqboijApHcZvC178?+(5c7Q@+(N`A{HRtbQ(`t@XQHUk)2yzo&7Ei2KgOK%-wA)1zZ!_J+(k`>5khJ=Y=;h zFH|5_o{ZPEP}3qr_=wO1!KFHHe9RKhVGF)dt!05~(ZSL;s(gdk$9g&-{>?PQflUWz zOIVgw>%RTl7g;_S8`C8#z0or!*^OAp9os$s5gB z!CVBh9VzURstOUznavvDJIc7NX7ql3#oYF^u_^6Uq&e!?oBfl?8SDud%A1oVY4xl) z&uI!GbV31fIrno5jZIH=oqjtyp+hgrEU=c1{@r8N;IxN?lMuHcoiGHUnYEG=w9i#a z8KK`=Hj`whKSKO&*zc;|PuDt7_&*8gqD|l#MNy z{7Qd#-nJhKwZ9w$%^N3t#^$3?u=B>l=LqH7J-NZY$yPo*TH~=zB=xf0Gm$|2zDtEa zYAp6^y;-p;Qt%kNI~K3vWp>XFA>Wd+$3t%Fg`UeethOPZ|eZ`n`P(m*=*BM^p3GHRXwiT1H_ zcu#RKMYG4bUHK4~r$7_)^MU!k4{&ucJI0&XpL3HuV1+GfUeg;1r(?ZTFmp^D^F_;p zA3#pZ=9))qC~waXZH^`p4dJxw8J2ti^w>!(@P zu$mg4L~g76L$~Df*m!lrRnG4^>b{`=_uTy{C zvjL^zdU~QN+9P_5Ng&q`VSX#K9sq9c)A^@LQ7o(^zJ4+RK!GJFvLRWXgnelbOa4Aq zBCam(_hT=BU8mbjI}G@Egw1bcb5 z_@E~U@*m#=3q>=S$N4{7-_{38vEOtM6OqTgjU`!IZ1c(gkiC>SqfErcWUToZaJ8i& z&s{+!wB3xIVhOn7i6stXikE8-a+khy3MG81efwf)gV63s3!Ycya=1KojgGt`a{fN{ z%5cN&Oh1d>vRd*e(_`nm#yDLWs^Eu%7Z>%$9d&J^U)u@Px!^2@veiv*Y%e{S&iMa|uAc}AxL7$qOt}w-$ zgiBNJw3q70lr=-4P$exd5Q4^$8D5j0T+S#`1vGyopU}_@#0WGe}~~N-E4ovmI{4V)Hmec9TT(ShLdATiMTUKpETJP#QajcpYS#w2R3-|-=ey7kzQO$y4_f%J7 zW23?8upJX!x0O-q&LzkLw)uH@gPt%4LUH8fha6IN6#o4D`STc9R0U}dihRSBouuXf z(~m2eK@5`Z02~|2w*G(%RwhJ5DS>3sA#2Um#YL|xn1uM1&C}m=bv~fTDd@2VsJil- zsyh3Pt|u?|XQ~S7HRQAD9jP-7quE8`P|itO=H0-LE^cilWS@o?($?M6tg|zAL{x*7 zF2AR2?6BVblm%Jk0$T@0r;k|DqoD6};b>)!IC6jiLs;8O|Yvga%cBZv!;LHfBCzTM^^JXFF zMWtJ@Ko~;1-x&%Aes4`WDSVE~u&*G9cT}&%l0FB1#L7G(PNQIO$Jw7HqGc}};o zeO2f=K7+$RdLuV^$@yvS!`|iEQHAS~iHf`l@J=yj7ze|}=D4lwC_rbVIRx56*5A_g z&LNmZY_nidmMZ{BQ{GU4eARB=p%rliDaRCW@^C9A|JHZYF+B}-`8(>J_DCC&jW2=U z8r5K$aN6pT@rUHy(E-oP!P56njLGbLXCFS3-IGl}+Jb#%iw7=3y&omF5ul{b!o$Rb zwYJQZlZX#0*-JAiF=ipGu3YSR>gt7nX`K-VEyGTUwc%VdN1E0aC~+MkccumgWQ{p! z=(DhM#ynF&xsi#7gER2Gh0}fZZ(TnN>UtVi6@D%fIciV~!a=raG^r2DqOqeMkI9Ljk~wi!Cxwafx}OyGdQMkcCFPGk zRTDM7b^>*UeN|LU?BNjkJ84$CZ>r-8%7d%kQnNu;R zF~Z!QAJM^g%BkY^G@CEi8hb(IY|JEeQkSeNc~WmA4|C=;#tT(9x}1<4V{C`}{s%~7 zLZ5AW_#aeI63L_ad;FhJ+JF9UqzIT<|Brt_fxE_JfE0=HkqNliL=H88g~)EbKQC1c zXcNDF{n|KoPD4$dzAKV5xr0a$#7B-zblHOYz96B?V~~-Gfmp*8CN3_X0if8a8fz*` zD=XX8o=3_Bsv!l)p&#)jkwbpy$>QaHi#UK>#;$=c&+^u2PGYy|U~-K8)lMduoFPuKid!u1JBquNQn+2-p`FV<$YQ;vSNH|7^_+KQJ$saWq_~?`;l2cF& z7piky-CXW(m#Gw}<}D&Y)#0Fjf!Us)01Xfu{}{PT=Ti)L%TEZA5Ura6xuQ6@A|w6Y zSmvNd&))$zc90Y1=OQ1Lh&-ge68R!%b6W}?>8V?QYp#H>oxmp#{vX}+pV$4cNxXrZ zRl#L(aq+Jm#O8bh&(+lx#C-ysCqt6nKge*8H*zOl`1&}i9uA`2RJ|-vW3+#N;{$R1 zjeJ{!-ta$YGS=Z@8w#)*#stG60``||FOJOs1FPWm=ucm)ar>#fQ1EV;8~JE)Wtz!p-R&=7L0Z~j+IeHpm@{XPCy9R2?qc>n+A1Ky0+ ZR0%O6nxqv)J9oe#DJCac{8rEVe*n|7%i;h4 literal 0 HcmV?d00001 diff --git a/examples/python_contour.py b/examples/python_contour.py index 6c5503e..8144922 100644 --- a/examples/python_contour.py +++ b/examples/python_contour.py @@ -2,7 +2,7 @@ from pathlib import Path -import chronopt as chron +import diffid import matplotlib.pyplot as plt import numpy as np @@ -15,7 +15,7 @@ def rosenbrock(x: np.ndarray) -> float: # Setup builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -23,11 +23,11 @@ def rosenbrock(x: np.ndarray) -> float: problem = builder.build() # Optimise -optimiser = chron.NelderMead().with_max_iter(500).with_threshold(1e-8) +optimiser = diffid.NelderMead().with_max_iter(500).with_threshold(1e-8) result = optimiser.run(problem, [-1.5, 1.5]) # Plot -contour_set = chron.plotting.contour( +contour_set = diffid.plotting.contour( problem, x_bounds=(-2.0, 2.0), y_bounds=(-1.0, 3.0), diff --git a/examples/python_problem.py b/examples/python_problem.py index 65552e2..4de56e3 100644 --- a/examples/python_problem.py +++ b/examples/python_problem.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -10,11 +10,11 @@ def rosenbrock(x): # Simple API builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) ) problem = builder.build() result = problem.optimise(initial=[10.0, 10.0]) diff --git a/mkdocs.yml b/mkdocs.yml index 6b7ab6e..d83766f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,9 +1,9 @@ -site_name: Chronopt +site_name: Diffid site_description: High-performance time-series inference and optimisation toolkit with Rust core and ergonomic Python bindings site_author: Brady Planden -site_url: https://bradyplanden.github.io/chronopt/ -repo_name: bradyplanden/chronopt -repo_url: https://github.com/bradyplanden/chronopt +site_url: https://bradyplanden.github.io/diffid/ +repo_name: bradyplanden/diffid +repo_url: https://github.com/bradyplanden/diffid edit_uri: edit/main/docs/ theme: @@ -124,9 +124,9 @@ markdown_extensions: extra: social: - icon: fontawesome/brands/github - link: https://github.com/bradyplanden/chronopt + link: https://github.com/bradyplanden/diffid - icon: fontawesome/brands/python - link: https://pypi.org/project/chronopt/ + link: https://pypi.org/project/diffid/ version: provider: mike default: latest @@ -195,5 +195,5 @@ nav: strict: true # Fail on warnings watch: - - python/src/chronopt + - python/src/diffid - docs diff --git a/pyproject.toml b/pyproject.toml index 24d034c..ecde2dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["maturin>=1.9,<2.0"] build-backend = "maturin" [project] -name = "chronopt" +name = "diffid" requires-python = ">=3.11" dynamic = ["version"] license = { file = "LICENSE" } @@ -31,7 +31,7 @@ diffeqpy = [ [tool.maturin] manifest-path = "python/Cargo.toml" -module-name = "chronopt._chronopt" +module-name = "diffid._diffid" features = ["extension-module"] python-source = "python/src" sdist-include = ["LICENSE", "README.md"] diff --git a/python/Cargo.toml b/python/Cargo.toml index 32f36cd..f7f26fb 100644 --- a/python/Cargo.toml +++ b/python/Cargo.toml @@ -1,12 +1,12 @@ [package] -name = "chronopt-py" +name = "diffid-py" version.workspace = true edition.workspace = true license.workspace = true readme = "../README.md" [package.metadata.maturin] -name = "chronopt" +name = "diffid" python-source = "src" artifact = "dist" @@ -16,7 +16,7 @@ extension-module = ["pyo3/extension-module"] stubgen = ["clap", "pyo3-stub-gen", "pyo3-stub-gen-derive"] [lib] -name = "_chronopt" +name = "_diffid" crate-type = ["cdylib", "rlib"] [dependencies] pyo3 = { workspace = true, default-features = false, features = ["macros"] } @@ -27,10 +27,10 @@ pyo3-stub-gen-derive = { version = "0.17.2", optional = true } clap = { version = "4.5", optional = true, features = ["derive"] } [target.'cfg(not(windows))'.dependencies] -chronopt_core = { package = "chronopt", path = "../rust" } +diffid_core = { package = "diffid", path = "../rust" } [target.'cfg(windows)'.dependencies] -chronopt_core = { package = "chronopt", path = "../rust", default-features = false, features = ["cranelift-backend"] } +diffid_core = { package = "diffid", path = "../rust", default-features = false, features = ["cranelift-backend"] } [build-dependencies] pyo3-build-config = "0.27.1" diff --git a/python/src/bin/generate_stubs.rs b/python/src/bin/generate_stubs.rs index 254f01f..fe6dc72 100644 --- a/python/src/bin/generate_stubs.rs +++ b/python/src/bin/generate_stubs.rs @@ -4,7 +4,7 @@ use std::fs; use std::io::Write; use std::path::{Path, PathBuf}; -use chronopt::{stub_info, stub_info_from}; +use _diffid::{stub_info, stub_info_from}; use clap::Parser; use pyo3_stub_gen::Result; @@ -38,7 +38,7 @@ fn main() -> Result<()> { } fn post_process_sampler_stub() -> Result<()> { - let sampler_stub_path = resolve_workspace_root()?.join("python/src/chronopt/sampler.pyi"); + let sampler_stub_path = resolve_workspace_root()?.join("python/src/diffid/sampler.pyi"); let contents = fs::read_to_string(&sampler_stub_path)?; let mut lines: Vec<&str> = contents.lines().collect(); diff --git a/python/src/builders.rs b/python/src/builders.rs index 52229a3..b95c9ec 100644 --- a/python/src/builders.rs +++ b/python/src/builders.rs @@ -6,11 +6,11 @@ use pyo3::types::PyDict; use std::collections::HashMap; use std::sync::Arc; -use chronopt_core::builders::{ +use diffid_core::builders::{ DiffsolBackend, DiffsolProblemBuilder, ScalarProblemBuilder, VectorProblemBuilder, }; -use chronopt_core::common::Unbounded; -use chronopt_core::problem::{NoFunction, NoGradient}; +use diffid_core::common::Unbounded; +use diffid_core::problem::{NoFunction, NoGradient}; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -333,7 +333,7 @@ impl PyScalarBuilder { .push((name.clone(), initial_value, bounds)); // Convert Option<(f64, f64)> to ParameterRange - let range: chronopt_core::problem::ParameterRange = + let range: diffid_core::problem::ParameterRange = bounds.map(|b| b.into()).unwrap_or_else(|| Unbounded.into()); slf.state = match std::mem::replace( diff --git a/python/src/chronopt/_chronopt.pyi b/python/src/chronopt/_chronopt.pyi deleted file mode 100644 index fff8acb..0000000 --- a/python/src/chronopt/_chronopt.pyi +++ /dev/null @@ -1,441 +0,0 @@ -# This file is automatically generated by pyo3_stub_gen -# ruff: noqa: E501, F401 - -import builtins -import datetime -import typing - -import numpy -import numpy.typing - -@typing.final -class Adam: - r""" - Adaptive Moment Estimation (Adam) gradient-based optimiser. - """ - def __new__(cls) -> Adam: - r""" - Create an Adam optimiser with library defaults. - """ - def with_max_iter(self, max_iter: builtins.int) -> Adam: - r""" - Limit the maximum number of optimisation iterations. - """ - def with_threshold(self, threshold: builtins.float) -> Adam: - r""" - Set the stopping threshold on the gradient norm. - """ - def with_step_size(self, step_size: builtins.float) -> Adam: - r""" - Configure the base learning rate / step size. - """ - def with_betas(self, beta1: builtins.float, beta2: builtins.float) -> Adam: - r""" - Override the exponential decay rates for the first and second moments. - """ - def with_eps(self, eps: builtins.float) -> Adam: - r""" - Override the numerical stability constant added to the denominator. - """ - def with_patience(self, patience_seconds: builtins.float) -> Adam: - r""" - Abort the run once the patience window has elapsed. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem using Adam starting from the provided point. - """ - -@typing.final -class CMAES: - r""" - Covariance Matrix Adaptation Evolution Strategy optimiser. - """ - def __new__(cls) -> CMAES: - r""" - Create a CMA-ES optimiser with library defaults. - """ - def with_max_iter(self, max_iter: builtins.int) -> CMAES: - r""" - Limit the number of iterations/generations before termination. - """ - def with_threshold(self, threshold: builtins.float) -> CMAES: - r""" - Set the stopping threshold on the best objective value. - """ - def with_step_size(self, step_size: builtins.float) -> CMAES: - r""" - Set the initial global step-size (standard deviation). - """ - def with_patience(self, patience_seconds: builtins.float) -> CMAES: - r""" - Abort the run if no improvement occurs for the given wall-clock duration. - """ - def with_population_size(self, population_size: builtins.int) -> CMAES: - r""" - Specify the number of offspring evaluated per generation. - """ - def with_seed(self, seed: builtins.int) -> CMAES: - r""" - Initialise the internal RNG for reproducible runs. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem starting from the provided mean vector. - """ - -@typing.final -class CostMetric: - @property - def name(self) -> builtins.str: - r""" - Name of the cost metric. - """ - def __repr__(self) -> builtins.str: ... - -@typing.final -class DiffsolBuilder: - r""" - Differential equation solver builder. - """ - def __new__(cls) -> DiffsolBuilder: - r""" - Create an empty differential solver builder. - """ - def __copy__(self) -> DiffsolBuilder: ... - def __deepcopy__(self, _memo: dict) -> DiffsolBuilder: ... - def with_diffsl(self, dsl: builtins.str) -> DiffsolBuilder: - r""" - Register the DiffSL program describing the system dynamics. - """ - def remove_diffsl(self) -> DiffsolBuilder: - r""" - Remove any registered DiffSL program. - """ - def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> DiffsolBuilder: - r""" - Attach observed data used to fit the differential equation. - - The first column must contain the time samples (t_span) and the remaining - columns the observed trajectories. - """ - def remove_data(self) -> DiffsolBuilder: - r""" - Remove any previously attached data along with its time span. - """ - def with_backend(self, backend: builtins.str) -> DiffsolBuilder: - r""" - Choose whether to use dense or sparse diffusion solvers. - """ - def with_parallel(self, parallel: builtins.bool | None = None) -> DiffsolBuilder: - r""" - Opt into parallel proposal generation when supported by the backend. - """ - def with_config( - self, config: typing.Mapping[builtins.str, builtins.float] - ) -> DiffsolBuilder: ... - def with_rtol(self, rtol: builtins.float) -> DiffsolBuilder: - r""" - Adjust the relative integration tolerance. - """ - def with_atol(self, atol: builtins.float) -> DiffsolBuilder: - r""" - Adjust the absolute integration tolerance. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> DiffsolBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def clear_parameters(self) -> DiffsolBuilder: - r""" - Remove previously provided parameter defaults. - """ - def with_cost(self, cost: CostMetric) -> DiffsolBuilder: - r""" - Select the error metric used to compare simulated and observed data. - """ - def remove_cost(self) -> DiffsolBuilder: - r""" - Reset the cost metric to the default sum of squared errors. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> DiffsolBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def build(self) -> Problem: - r""" - Create a `Problem` representing the differential solver model. - """ - -@typing.final -class NelderMead: - r""" - Classic simplex-based direct search optimiser. - """ - def __new__(cls) -> NelderMead: - r""" - Create a Nelder-Mead optimiser with default coefficients. - """ - def with_max_iter(self, max_iter: builtins.int) -> NelderMead: - r""" - Limit the number of simplex iterations. - """ - def with_threshold(self, threshold: builtins.float) -> NelderMead: - r""" - Set the stopping threshold on simplex size or objective reduction. - """ - def with_position_tolerance(self, tolerance: builtins.float) -> NelderMead: - r""" - Stop once simplex vertices fall within the supplied positional tolerance. - """ - def with_max_evaluations(self, max_evaluations: builtins.int) -> NelderMead: - r""" - Abort after evaluating the objective `max_evaluations` times. - """ - def with_coefficients( - self, - alpha: builtins.float, - gamma: builtins.float, - rho: builtins.float, - sigma: builtins.float, - ) -> NelderMead: - r""" - Override the reflection, expansion, contraction, and shrink coefficients. - """ - def with_patience(self, patience_seconds: builtins.float) -> NelderMead: - r""" - Abort if the objective fails to improve within the allotted time. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem starting from the provided initial simplex centre. - """ - -@typing.final -class OptimisationResults: - r""" - Container for optimiser outputs and diagnostic metadata. - """ - @property - def x(self) -> builtins.list[builtins.float]: - r""" - Decision vector corresponding to the best-found objective value. - """ - @property - def fun(self) -> builtins.float: - r""" - Objective value evaluated at `x`. - """ - @property - def nit(self) -> builtins.int: - r""" - Number of iterations performed by the optimiser. - """ - @property - def evaluations(self) -> builtins.int: - r""" - Total number of objective function evaluations. - """ - @property - def time(self) -> datetime.timedelta: - r""" - Total number of objective function evaluations. - """ - @property - def success(self) -> builtins.bool: - r""" - Whether the run satisfied its convergence criteria. - """ - @property - def message(self) -> builtins.str: - r""" - Human-readable status message summarising the termination state. - """ - @property - def termination_reason(self) -> builtins.str: - r""" - Structured termination flag describing why the run ended. - """ - @property - def final_simplex(self) -> builtins.list[builtins.list[builtins.float]]: - r""" - Simplex vertices at termination, when provided by the optimiser. - """ - @property - def final_simplex_values(self) -> builtins.list[builtins.float]: - r""" - Objective values corresponding to `final_simplex`. - """ - @property - def covariance( - self, - ) -> builtins.list[builtins.list[builtins.float]] | None: - r""" - Estimated covariance of the search distribution, if available. - """ - def __repr__(self) -> builtins.str: - r""" - Render a concise summary of the optimisation outcome. - """ - -@typing.final -class Problem: - r""" - Executable optimisation problem wrapping the Chronopt core implementation. - """ - def evaluate(self, x: typing.Sequence[builtins.float]) -> builtins.float: - r""" - Evaluate the configured objective function at `x`. - """ - def evaluate_gradient( - self, x: typing.Sequence[builtins.float] - ) -> builtins.list[builtins.float] | None: - r""" - Evaluate the gradient of the objective function at `x` if available. - """ - def optimise( - self, - initial: typing.Sequence[builtins.float] | None = None, - optimiser: NelderMead | CMAES | Adam | None = None, - ) -> OptimisationResults: - r""" - Solve the problem starting from `initial` using the supplied optimiser. - """ - def get_config(self, key: builtins.str) -> builtins.float | None: - r""" - Return the numeric configuration value stored under `key` if present. - """ - def dimension(self) -> builtins.int: - r""" - Return the number of parameters the problem expects. - """ - def parameters( - self, - ) -> builtins.list[ - tuple[ - builtins.str, - builtins.float, - tuple[builtins.float, builtins.float] | None, - ] - ]: ... - def default_parameters(self) -> builtins.list[builtins.float]: - r""" - Return the default parameter vector implied by the builder. - """ - def config(self) -> builtins.dict[builtins.str, builtins.float]: - r""" - Return a copy of the problem configuration dictionary. - """ - -@typing.final -class ScalarBuilder: - r""" - High-level builder for optimisation `Problem` instances exposed to Python. - """ - def __new__(cls) -> ScalarBuilder: - r""" - Create an empty builder with no objective, parameters, or default optimiser. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> ScalarBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def with_callable(self, obj: typing.Any) -> ScalarBuilder: - r""" - Attach the objective function callable executed during optimisation. - """ - def with_gradient(self, obj: typing.Any) -> ScalarBuilder: - r""" - Attach the gradient callable returning derivatives of the objective. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> ScalarBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def build(self) -> Problem: - r""" - Finalize the builder into an executable `Problem`. - """ - -@typing.final -class VectorBuilder: - r""" - Time-series problem builder for vector-valued objectives. - """ - def __new__(cls) -> VectorBuilder: - r""" - Create an empty vector problem builder. - """ - def with_objective(self, objective: typing.Any) -> VectorBuilder: - r""" - Register a callable that produces predictions matching the data shape. - - The callable should accept a parameter vector and return a numpy array - of the same shape as the observed data. - """ - def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> VectorBuilder: - r""" - Attach observed data used to fit the model. - - The data should be a 1D numpy array. The shape will be inferred - from the data length. - """ - def with_config(self, key: builtins.str, value: builtins.float) -> VectorBuilder: - r""" - Stores an optimisation configuration value keyed by name. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> VectorBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def clear_parameters(self) -> VectorBuilder: - r""" - Remove previously provided parameter defaults. - """ - def with_cost(self, cost: CostMetric) -> VectorBuilder: - r""" - Select the error metric used to compare predictions and observed data. - """ - def remove_cost(self) -> VectorBuilder: - r""" - Reset the cost metric to the default sum of squared errors. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> VectorBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def build(self) -> Problem: - r""" - Create a `Problem` representing the vector optimisation model. - """ - -def GaussianNLL( - variance: builtins.float, weight: builtins.float = 1.0 -) -> CostMetric: ... -def RMSE(weight: builtins.float = 1.0) -> CostMetric: ... -def SSE(weight: builtins.float = 1.0) -> CostMetric: ... -def builder_factory_py() -> ScalarBuilder: - r""" - Return a convenience factory for creating `Builder` instances. - """ diff --git a/python/src/chronopt/sampler.pyi b/python/src/chronopt/sampler.pyi deleted file mode 100644 index e5b66d2..0000000 --- a/python/src/chronopt/sampler.pyi +++ /dev/null @@ -1,83 +0,0 @@ -# This file is automatically generated by pyo3_stub_gen -# ruff: noqa: E501, F401 - -import builtins -import datetime -import typing - -from chronopt._chronopt import Problem - -@typing.final -class DynamicNestedSampler: - r""" - Dynamic nested sampler binding exposing DNS configuration knobs. - """ - def __new__(cls) -> DynamicNestedSampler: ... - def with_live_points(self, live_points: builtins.int) -> DynamicNestedSampler: ... - def with_expansion_factor( - self, expansion_factor: builtins.float - ) -> DynamicNestedSampler: ... - def with_termination_tolerance( - self, tolerance: builtins.float - ) -> DynamicNestedSampler: ... - def with_seed(self, seed: builtins.int) -> DynamicNestedSampler: ... - def run( - self, - problem: Problem, - initial: typing.Sequence[builtins.float] | None = None, - ) -> NestedSamples: ... - -@typing.final -class MetropolisHastings: - r""" - Basic Metropolis-Hastings sampler binding mirroring the optimiser API. - """ - def __new__(cls) -> MetropolisHastings: ... - def with_num_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... - def set_number_of_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... - def with_iterations(self, iterations: builtins.int) -> MetropolisHastings: ... - def with_num_steps(self, steps: builtins.int) -> MetropolisHastings: ... - def with_step_size(self, step_size: builtins.float) -> MetropolisHastings: ... - def with_seed(self, seed: builtins.int) -> MetropolisHastings: ... - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> Samples: ... - -@typing.final -class NestedSamples: - r""" - Nested sampling results including evidence estimates. - """ - @property - def posterior( - self, - ) -> builtins.list[ - tuple[builtins.list[builtins.float], builtins.float, builtins.float] - ]: ... - @property - def mean(self) -> builtins.list[builtins.float]: ... - @property - def draws(self) -> builtins.int: ... - @property - def log_evidence(self) -> builtins.float: ... - @property - def information(self) -> builtins.float: ... - @property - def time(self) -> datetime.timedelta: ... - def to_samples(self) -> Samples: ... - def __repr__(self) -> builtins.str: ... - -@typing.final -class Samples: - r""" - Container for sampler draws and diagnostics. - """ - @property - def chains(self) -> builtins.list[builtins.list[builtins.list[builtins.float]]]: ... - @property - def mean_x(self) -> builtins.list[builtins.float]: ... - @property - def draws(self) -> builtins.int: ... - @property - def time(self) -> datetime.timedelta: ... - def __repr__(self) -> builtins.str: ... diff --git a/python/src/chronopt/__init__.py b/python/src/diffid/__init__.py similarity index 87% rename from python/src/chronopt/__init__.py rename to python/src/diffid/__init__.py index ebb5280..8ca3963 100644 --- a/python/src/chronopt/__init__.py +++ b/python/src/diffid/__init__.py @@ -1,22 +1,22 @@ -"""Chronopt public Python API.""" +"""Diffid public Python API.""" from __future__ import annotations # Error hierarchy -from chronopt.errors import ( +from diffid.errors import ( AlreadyTerminated, BuildError, - ChronoptError, + DiffidError, EvaluationError, ResultCountMismatch, TellError, ) # Plotting module -from chronopt import plotting +from diffid import plotting # Core bindings - optimisers -from chronopt._chronopt import ( +from diffid._diffid import ( Adam, AdamState, CMAES, @@ -26,7 +26,7 @@ ) # Core bindings - samplers -from chronopt._chronopt import ( +from diffid._diffid import ( DynamicNestedSampler, DynamicNestedSamplerState, MetropolisHastings, @@ -36,14 +36,14 @@ ) # Core bindings - builders -from chronopt._chronopt import ( +from diffid._diffid import ( DiffsolBuilder, ScalarBuilder, VectorBuilder, ) # Core bindings - results and problems -from chronopt._chronopt import ( +from diffid._diffid import ( CostMetric, Done, Evaluate, @@ -52,9 +52,9 @@ ) # Cost metric factory functions -from chronopt._chronopt import RMSE as _RMSE -from chronopt._chronopt import SSE as _SSE -from chronopt._chronopt import GaussianNLL as _GaussianNLL +from diffid._diffid import RMSE as _RMSE +from diffid._diffid import SSE as _SSE +from diffid._diffid import GaussianNLL as _GaussianNLL def SSE(weight: float = 1.0) -> CostMetric: @@ -116,7 +116,7 @@ def GaussianNLL(variance: float = 1.0, weight: float = 1.0) -> CostMetric: # Modules "plotting", # Errors - "ChronoptError", + "DiffidError", "EvaluationError", "BuildError", "TellError", diff --git a/python/src/diffid/_diffid.pyi b/python/src/diffid/_diffid.pyi new file mode 100644 index 0000000..ab356e0 --- /dev/null +++ b/python/src/diffid/_diffid.pyi @@ -0,0 +1,956 @@ +# This file is automatically generated by pyo3_stub_gen +# ruff: noqa: E501, F401 + +import builtins +import datetime +import typing + +import numpy +import numpy.typing + +from diffid.sampler import DynamicNestedSampler, MetropolisHastings + +@typing.final +class Adam: + r""" + Adaptive Moment Estimation (Adam) gradient-based optimiser. + """ + def __new__(cls) -> Adam: + r""" + Create an Adam optimiser with library defaults. + """ + def with_max_iter(self, max_iter: builtins.int) -> Adam: + r""" + Limit the maximum number of optimisation iterations. + """ + def with_threshold(self, threshold: builtins.float) -> Adam: + r""" + Set the stopping threshold on the gradient norm. + """ + def with_step_size(self, step_size: builtins.float) -> Adam: + r""" + Configure the base learning rate / step size. + """ + def with_betas(self, beta1: builtins.float, beta2: builtins.float) -> Adam: + r""" + Override the exponential decay rates for the first and second moments. + """ + def with_eps(self, eps: builtins.float) -> Adam: + r""" + Override the numerical stability constant added to the denominator. + """ + def with_patience(self, patience: typing.Any) -> Adam: + r""" + Abort the run once the patience window has elapsed. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem using Adam starting from the provided point. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> AdamState: + r""" + Initialize ask-tell optimization state. + + Returns an AdamState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial parameter vector + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + AdamState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.Adam() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate_with_gradient(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class AdamState: + r""" + Ask-tell state for incremental Adam optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.Adam().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... # Adam requires gradient information + ... values = [(f(pt), grad_f(pt)) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + + Examples + -------- + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... print(f"Need to evaluate {len(result.points)} points") + >>> elif isinstance(result, diffid.Done): + ... print(f"Optimization complete: {result.result}") + """ + def tell( + self, result: tuple[builtins.float, typing.Sequence[builtins.float]] + ) -> None: + r""" + Provide evaluation results (value and gradient) for the requested points. + + Parameters + ---------- + result : tuple[float, list[float]] + Tuple of (value, gradient) where gradient is a list of partial derivatives. + Adam requires gradient information. + + Raises + ------ + TellError + If called after optimization has terminated or if result format is invalid + EvaluationError + If the evaluation failed or contained invalid values + + Examples + -------- + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... point = result.points[0] + ... value = objective(point) + ... gradient = compute_gradient(point) + ... state.tell((value, gradient)) + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value found so far. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def current_position(self) -> builtins.list[builtins.float]: + r""" + Get the current parameter position. + + Returns + ------- + list[float] + Current parameter vector + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class CMAES: + r""" + Covariance Matrix Adaptation Evolution Strategy optimiser. + """ + def __new__(cls) -> CMAES: + r""" + Create a CMA-ES optimiser with library defaults. + """ + def with_max_iter(self, max_iter: builtins.int) -> CMAES: + r""" + Limit the number of iterations/generations before termination. + """ + def with_threshold(self, threshold: builtins.float) -> CMAES: + r""" + Set the stopping threshold on the best objective value. + """ + def with_step_size(self, step_size: builtins.float) -> CMAES: + r""" + Set the initial global step-size (standard deviation). + """ + def with_patience(self, patience: typing.Any) -> CMAES: + r""" + Abort the run if no improvement occurs for the given wall-clock duration. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def with_population_size(self, population_size: builtins.int) -> CMAES: + r""" + Specify the number of offspring evaluated per generation. + """ + def with_seed(self, seed: builtins.int) -> CMAES: + r""" + Initialise the internal RNG for reproducible runs. + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem starting from the provided mean vector. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> CMAESState: + r""" + Initialize ask-tell optimization state. + + Returns a CMAESState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial mean vector for the search distribution + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + CMAESState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.CMAES() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class CMAESState: + r""" + Ask-tell state for incremental CMA-ES optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get a population of points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.CMAES().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... values = [f(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + + Notes + ----- + CMA-ES evaluates a population of points each iteration. The number + of points returned depends on the population_size setting. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the requested population of points. + + Parameters + ---------- + results : list[float] + List of objective function values corresponding to the points + from the last ask() call. Must match the number of points. + + Raises + ------ + TellError + If called after optimization has terminated or if wrong number + of results provided + EvaluationError + If evaluations failed or contained invalid values + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration (generation) count. + + Returns + ------- + int + Number of generations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value found so far. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def mean(self) -> builtins.list[builtins.float]: + r""" + Get the current mean of the search distribution. + + Returns + ------- + list[float] + Current mean vector + """ + def sigma(self) -> builtins.float: + r""" + Get the current step size (sigma). + + Returns + ------- + float + Current global step size + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class CostMetric: + @property + def name(self) -> builtins.str: + r""" + Name of the cost metric. + """ + def __repr__(self) -> builtins.str: ... + +@typing.final +class DiffsolBuilder: + r""" + Differential equation solver builder. + """ + def __new__(cls) -> DiffsolBuilder: + r""" + Create an empty differential solver builder. + """ + def __copy__(self) -> DiffsolBuilder: ... + def __deepcopy__(self, _memo: dict) -> DiffsolBuilder: ... + def with_diffsl(self, dsl: builtins.str) -> DiffsolBuilder: + r""" + Register the DiffSL program describing the system dynamics. + """ + def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> DiffsolBuilder: + r""" + Attach observed data used to fit the differential equation. + + The first column must contain the time samples (t_span) and the remaining + columns the observed trajectories. + """ + def remove_data(self) -> DiffsolBuilder: + r""" + Remove any previously attached data along with its time span. + """ + def with_backend(self, backend: builtins.str) -> DiffsolBuilder: + r""" + Choose whether to use dense or sparse diffusion solvers. + """ + def with_parallel(self, parallel: builtins.bool | None = None) -> DiffsolBuilder: + r""" + Opt into parallel proposal generation when supported by the backend. + """ + def with_config( + self, config: typing.Mapping[builtins.str, builtins.float] + ) -> DiffsolBuilder: ... + def with_tolerances( + self, rtol: builtins.float, atol: builtins.float + ) -> DiffsolBuilder: + r""" + Adjust the relative and absolute integration tolerances. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> DiffsolBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def clear_parameters(self) -> DiffsolBuilder: + r""" + Clear all previously registered parameters while preserving other configuration. + """ + def with_cost(self, cost: CostMetric) -> DiffsolBuilder: + r""" + Select the error metric used to compare simulated and observed data. + """ + def remove_cost(self) -> DiffsolBuilder: + r""" + Reset the cost metric to the default sum of squared errors. + """ + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> DiffsolBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def build(self) -> Problem: + r""" + Create a `Problem` representing the differential solver model. + """ + +@typing.final +class Done: + r""" + Optimization/sampling is complete with final results. + + This is returned by `ask()` when the algorithm has terminated. + Access the results via the `result` attribute. + + Examples + -------- + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Optimization complete: {result.result}") + ... break + """ + @property + def result(self) -> typing.Any: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class Evaluate: + r""" + Request to evaluate objective function at specific points. + + This is returned by `ask()` when the optimiser/sampler needs function + evaluations. Call `tell()` with the results after evaluation. + + Examples + -------- + >>> state = optimiser.init(problem, initial=[1.0, 2.0]) + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... values = [problem.evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + @property + def points(self) -> builtins.list[builtins.list[builtins.float]]: ... + def __new__( + cls, points: typing.Sequence[typing.Sequence[builtins.float]] + ) -> Evaluate: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NelderMead: + r""" + Classic simplex-based direct search optimiser. + """ + def __new__(cls) -> NelderMead: + r""" + Create a Nelder-Mead optimiser with default coefficients. + """ + def with_step_size(self, step_size: builtins.float) -> NelderMead: + r""" + Set the initial global step-size (standard deviation). + """ + def with_max_iter(self, max_iter: builtins.int) -> NelderMead: + r""" + Limit the number of simplex iterations. + """ + def with_threshold(self, threshold: builtins.float) -> NelderMead: + r""" + Set the stopping threshold on simplex size or objective reduction. + """ + def with_position_tolerance(self, tolerance: builtins.float) -> NelderMead: + r""" + Stop once simplex vertices fall within the supplied positional tolerance. + """ + def with_max_evaluations(self, max_evaluations: builtins.int) -> NelderMead: + r""" + Abort after evaluating the objective `max_evaluations` times. + """ + def with_coefficients( + self, + alpha: builtins.float, + gamma: builtins.float, + rho: builtins.float, + sigma: builtins.float, + ) -> NelderMead: + r""" + Override the reflection, expansion, contraction, and shrink coefficients. + """ + def with_patience(self, patience: typing.Any) -> NelderMead: + r""" + Abort if the objective fails to improve within the allotted time. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem starting from the provided initial simplex centre. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> NelderMeadState: + r""" + Initialize ask-tell optimization state. + + Returns a NelderMeadState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial parameter vector (simplex center) + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + NelderMeadState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.NelderMead() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class NelderMeadState: + r""" + Ask-tell state for incremental Nelder-Mead optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.NelderMead().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... values = [f(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + """ + def tell(self, result: builtins.float) -> None: + r""" + Provide evaluation result (scalar value) for the requested point. + + Parameters + ---------- + result : float + Scalar objective function value + + Raises + ------ + TellError + If called after optimization has terminated + EvaluationError + If the evaluation failed or contained invalid values + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value from the simplex. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NestedSamplesIterator: + r""" + Iterator for NestedSamples posterior + """ + def __iter__(self) -> NestedSamplesIterator: ... + def __next__( + self, + ) -> ( + tuple[builtins.list[builtins.float], builtins.float, builtins.float] | None + ): ... + +@typing.final +class OptimisationResults: + r""" + Container for optimiser outputs and diagnostic metadata. + """ + @property + def x(self) -> numpy.typing.NDArray[numpy.float64]: + r""" + Decision vector corresponding to the best-found objective value. + + Returns + ------- + numpy.ndarray + Best parameter vector as a NumPy array + """ + @property + def value(self) -> builtins.float: + r""" + Objective value evaluated at `x`. + """ + @property + def iterations(self) -> builtins.int: + r""" + Number of iterations performed by the optimiser. + """ + @property + def evaluations(self) -> builtins.int: + r""" + Total number of objective function evaluations. + """ + @property + def time(self) -> datetime.timedelta: + r""" + Total number of objective function evaluations. + """ + @property + def success(self) -> builtins.bool: + r""" + Whether the run satisfied its convergence criteria. + """ + @property + def message(self) -> builtins.str: + r""" + Human-readable status message summarising the termination state. + """ + @property + def termination_reason(self) -> builtins.str: + r""" + Structured termination flag describing why the run ended. + """ + @property + def final_simplex(self) -> builtins.list[builtins.list[builtins.float]]: + r""" + Simplex vertices at termination, when provided by the optimiser. + """ + @property + def final_simplex_values(self) -> builtins.list[builtins.float]: + r""" + Objective values corresponding to `final_simplex`. + """ + @property + def covariance( + self, + ) -> builtins.list[builtins.list[builtins.float]] | None: + r""" + Estimated covariance of the search distribution, if available. + """ + def __repr__(self) -> builtins.str: + r""" + Render a concise summary of the optimisation outcome. + """ + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the result. + """ + def __bool__(self) -> builtins.bool: + r""" + Return truthiness based on optimization success. + + Allows using `if result:` instead of `if result.success:`. + """ + +@typing.final +class Problem: + r""" + Executable optimisation problem wrapping the Diffid core implementation. + """ + def evaluate(self, x: typing.Sequence[builtins.float]) -> builtins.float: + r""" + Evaluate the configured objective function at `x`. + """ + def evaluate_gradient( + self, x: typing.Sequence[builtins.float] + ) -> builtins.list[builtins.float] | None: + r""" + Evaluate the gradient of the objective function at `x` if available. + """ + def optimise( + self, + initial: typing.Sequence[builtins.float] | None = None, + optimiser: NelderMead | CMAES | Adam | None = None, + ) -> OptimisationResults: + r""" + Solve the problem starting from `initial` using the supplied optimiser. + """ + def sample( + self, + initial: typing.Sequence[builtins.float] | None = None, + sampler: MetropolisHastings | DynamicNestedSampler | None = None, + ) -> typing.Any: + r""" + Sample from the problem starting from `initial` using the supplied sampler. + """ + def get_config(self, _key: builtins.str) -> builtins.float | None: + r""" + Return the numeric configuration value stored under `key` if present. + """ + def dimension(self) -> builtins.int: + r""" + Return the number of parameters the problem expects. + """ + def bounds(self) -> builtins.list[tuple[builtins.float, builtins.float]]: + r""" + Return the parameter bounds for the problem as a list of (lower, upper) tuples. + """ + def parameters( + self, + ) -> builtins.list[ + tuple[ + builtins.str, + builtins.float, + tuple[builtins.float, builtins.float] | None, + ] + ]: ... + def initial_values(self) -> builtins.list[builtins.float]: ... + def default_parameters(self) -> builtins.list[builtins.float]: + r""" + Return the default parameter vector implied by the builder. + """ + def config(self) -> builtins.dict[builtins.str, builtins.float]: + r""" + Return a copy of the problem configuration dictionary. + """ + def __call__(self, x: typing.Sequence[builtins.float]) -> builtins.float: + r""" + Call the problem as a function (shorthand for evaluate). + + Allows using `problem(x)` instead of `problem.evaluate(x)`. + """ + def __repr__(self) -> builtins.str: + r""" + Return a detailed string representation of the problem. + """ + def __str__(self) -> builtins.str: + r""" + Return a concise string representation of the problem. + """ + +@typing.final +class SamplesIterator: + r""" + Iterator for Samples chains + """ + def __iter__(self) -> SamplesIterator: ... + def __next__( + self, + ) -> builtins.list[builtins.list[builtins.float]] | None: ... + +@typing.final +class ScalarBuilder: + r""" + High-level builder for optimisation `Problem` instances exposed to Python. + """ + def __new__(cls) -> ScalarBuilder: + r""" + Create an empty builder with no objective, parameters, or default optimiser. + """ + def __copy__(self) -> ScalarBuilder: ... + def __deepcopy__(self, _memo: dict) -> ScalarBuilder: ... + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> ScalarBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def with_objective(self, obj: typing.Any) -> ScalarBuilder: + r""" + Attach the objective function callable executed during optimisation. + """ + def with_gradient(self, obj: typing.Any) -> ScalarBuilder: + r""" + Attach the gradient callable returning derivatives of the objective. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> ScalarBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def build(self) -> Problem: + r""" + Finalize the builder into an executable `Problem`. + """ + +@typing.final +class VectorBuilder: + r""" + Time-series problem builder for vector-valued objectives. + """ + def __new__(cls) -> VectorBuilder: + r""" + Create an empty vector problem builder. + """ + def __copy__(self) -> VectorBuilder: ... + def __deepcopy__(self, _memo: dict) -> VectorBuilder: ... + def with_objective(self, objective: typing.Any) -> VectorBuilder: + r""" + Register a callable that produces predictions matching the data shape. + + The callable should accept a parameter vector and return a numpy array + of the same shape as the observed data. + """ + def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> VectorBuilder: + r""" + Attach observed data used to fit the model. + + The data should be a 1D numpy array. The shape will be inferred + from the data length. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> VectorBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def with_cost(self, cost: CostMetric) -> VectorBuilder: + r""" + Select the error metric used to compare predictions and observed data. + """ + def remove_cost(self) -> VectorBuilder: + r""" + Reset the cost metric to the default sum of squared errors. + """ + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> VectorBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def with_config(self, key: builtins.str, value: builtins.float) -> VectorBuilder: + r""" + Attach an arbitrary configuration value to the problem. + """ + def build(self) -> Problem: + r""" + Create a `Problem` representing the vector optimisation model. + """ + +def GaussianNLL( + variance: builtins.float, weight: builtins.float = 1.0 +) -> CostMetric: ... +def RMSE(weight: builtins.float = 1.0) -> CostMetric: ... +def SSE(weight: builtins.float = 1.0) -> CostMetric: ... +def builder_factory_py() -> ScalarBuilder: + r""" + Return a convenience factory for creating `Builder` instances. + """ diff --git a/python/src/chronopt/errors.py b/python/src/diffid/errors.py similarity index 90% rename from python/src/chronopt/errors.py rename to python/src/diffid/errors.py index 6946247..e22c963 100644 --- a/python/src/chronopt/errors.py +++ b/python/src/diffid/errors.py @@ -1,30 +1,30 @@ -"""Custom exception hierarchy for Chronopt. +"""Custom exception hierarchy for Diffid. -This module defines the exception hierarchy used throughout the chronopt library, +This module defines the exception hierarchy used throughout the diffid library, providing clear and actionable error messages for different failure modes. """ from __future__ import annotations -class ChronoptError(Exception): - """Base exception class for all Chronopt errors. +class DiffidError(Exception): + """Base exception class for all Diffid errors. - All custom exceptions in the chronopt library inherit from this class, - making it easy to catch any chronopt-specific error. + All custom exceptions in the diffid library inherit from this class, + making it easy to catch any diffid-specific error. Examples -------- >>> try: ... optimiser.run(problem, initial=[1.0, 2.0]) - ... except ChronoptError as e: - ... print(f"Chronopt error occurred: {e}") + ... except DiffidError as e: + ... print(f"Diffid error occurred: {e}") """ pass -class EvaluationError(ChronoptError): +class EvaluationError(DiffidError): """Raised when objective function evaluation fails. This exception is raised when the objective function (or callback) throws @@ -65,7 +65,7 @@ def __init__( self.original_error = original_error -class BuildError(ChronoptError): +class BuildError(DiffidError): """Raised when problem or optimiser construction fails. This exception is raised during the build phase when invalid parameters @@ -84,7 +84,7 @@ class BuildError(ChronoptError): pass -class TellError(ChronoptError): +class TellError(DiffidError): """Base exception for errors during the 'tell' phase of ask-tell interface. This exception is raised when providing results back to an optimiser or @@ -161,7 +161,7 @@ def __init__(self): __all__ = [ - "ChronoptError", + "DiffidError", "EvaluationError", "BuildError", "TellError", diff --git a/python/src/chronopt/plotting/__init__.py b/python/src/diffid/plotting/__init__.py similarity index 97% rename from python/src/chronopt/plotting/__init__.py rename to python/src/diffid/plotting/__init__.py index 2c73e1a..8335511 100644 --- a/python/src/chronopt/plotting/__init__.py +++ b/python/src/diffid/plotting/__init__.py @@ -1,4 +1,4 @@ -"""Plotting utilities for Chronopt. +"""Plotting utilities for Diffid. This module provides convenience helpers for visualising optimisation and sampling results. The implementation only depends on ``numpy`` at import time and lazily @@ -14,7 +14,7 @@ import numpy as np if TYPE_CHECKING: # pragma: no cover - type checking only - from chronopt import Problem + from diffid import Problem __all__ = [ "contour", @@ -70,7 +70,7 @@ def contour( ---------- objective: A callable mapping a two-dimensional input to a scalar value, or a - :class:`chronopt.Problem` instance whose ``evaluate`` method will be + :class:`diffid.Problem` instance whose ``evaluate`` method will be invoked. x_bounds, y_bounds: Inclusive ranges ``(min, max)`` spanning the region to sample along each @@ -116,7 +116,7 @@ def contour( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: # pragma: no cover - import guard raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc xs = np.linspace(x_min, x_max, grid_size) @@ -193,7 +193,7 @@ def contour_2d( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() @@ -294,7 +294,7 @@ def ode_fit( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() @@ -359,7 +359,7 @@ def convergence( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() @@ -420,7 +420,7 @@ def parameter_traces( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() @@ -502,7 +502,7 @@ def parameter_distributions( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() @@ -598,7 +598,7 @@ def compare_models( import matplotlib.pyplot as plt except ModuleNotFoundError as exc: raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" ) from exc _setup_plotting() diff --git a/python/src/chronopt/plotting/__init__.pyi b/python/src/diffid/plotting/__init__.pyi similarity index 100% rename from python/src/chronopt/plotting/__init__.pyi rename to python/src/diffid/plotting/__init__.pyi diff --git a/python/src/chronopt/py.typed b/python/src/diffid/py.typed similarity index 100% rename from python/src/chronopt/py.typed rename to python/src/diffid/py.typed diff --git a/python/src/diffid/sampler.pyi b/python/src/diffid/sampler.pyi new file mode 100644 index 0000000..6054e40 --- /dev/null +++ b/python/src/diffid/sampler.pyi @@ -0,0 +1,353 @@ +# This file is automatically generated by pyo3_stub_gen +# ruff: noqa: E501, F401 + +import builtins +import datetime +import typing + +import numpy +import numpy.typing + +from diffid._diffid import NestedSamplesIterator, Problem, SamplesIterator + +@typing.final +class DynamicNestedSampler: + r""" + Dynamic nested sampler binding exposing DNS configuration knobs. + """ + def __new__(cls) -> DynamicNestedSampler: ... + def with_live_points(self, live_points: builtins.int) -> DynamicNestedSampler: ... + def with_expansion_factor( + self, expansion_factor: builtins.float + ) -> DynamicNestedSampler: ... + def with_termination_tolerance( + self, tolerance: builtins.float + ) -> DynamicNestedSampler: ... + def with_seed(self, seed: builtins.int) -> DynamicNestedSampler: ... + def run( + self, + problem: Problem, + initial: typing.Sequence[builtins.float] | None = None, + ) -> NestedSamples: ... + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> DynamicNestedSamplerState: + r""" + Initialize ask-tell sampling state. + + Returns a DynamicNestedSamplerState object that can be used for incremental + sampling via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial point for the sampler + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + DynamicNestedSamplerState + State object for ask-tell sampling + + Examples + -------- + >>> sampler = diffid.DynamicNestedSampler() + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class DynamicNestedSamplerState: + r""" + Ask-tell state for incremental Dynamic Nested Sampling. + + This state object allows step-by-step control over the sampling process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> sampler = diffid.DynamicNestedSampler() + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Sampling complete: {result.result}") + ... break + ... values = [negative_log_likelihood(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or sampling complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion with NestedSamples. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the requested points. + + Parameters + ---------- + results : list[float] + Negative log-likelihood values for each requested point. + + Raises + ------ + TellError + If called after sampling has terminated or if wrong number + of results provided + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def num_live_points(self) -> builtins.int: + r""" + Get the number of live points. + + Returns + ------- + int + Current number of live points in the sampler + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class MetropolisHastings: + r""" + Basic Metropolis-Hastings sampler binding mirroring the optimiser API. + """ + def __new__(cls) -> MetropolisHastings: ... + def with_num_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... + def with_iterations(self, iterations: builtins.int) -> MetropolisHastings: ... + def with_step_size(self, step_size: builtins.float) -> MetropolisHastings: ... + def with_seed(self, seed: builtins.int) -> MetropolisHastings: ... + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> Samples: ... + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> MetropolisHastingsState: + r""" + Initialize ask-tell sampling state. + + Returns a MetropolisHastingsState object that can be used for incremental + sampling via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial point for all chains + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + MetropolisHastingsState + State object for ask-tell sampling + + Examples + -------- + >>> sampler = diffid.MetropolisHastings().with_num_chains(4) + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class MetropolisHastingsState: + r""" + Ask-tell state for incremental Metropolis-Hastings MCMC sampling. + + This state object allows step-by-step control over the sampling process. + Use `ask()` to get proposal points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> sampler = diffid.MetropolisHastings().with_num_chains(4) + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Sampling complete: {result.result}") + ... break + ... values = [negative_log_likelihood(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate proposal points or sampling complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion with Samples. + + Notes + ----- + Returns one proposal point per chain. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the proposed points. + + Parameters + ---------- + results : list[float] + Negative log-likelihood values for each proposal point. + Must match the number of chains. + + Raises + ------ + TellError + If called after sampling has terminated or if wrong number + of results provided + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def num_chains(self) -> builtins.int: + r""" + Get the number of chains being run. + + Returns + ------- + int + Number of parallel MCMC chains + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NestedSamples: + r""" + Nested sampling results including evidence estimates. + """ + @property + def posterior( + self, + ) -> builtins.list[ + tuple[builtins.list[builtins.float], builtins.float, builtins.float] + ]: ... + @property + def mean(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def draws(self) -> builtins.int: ... + @property + def log_evidence(self) -> builtins.float: ... + @property + def information(self) -> builtins.float: ... + @property + def time(self) -> datetime.timedelta: ... + def to_samples(self) -> Samples: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the nested samples. + """ + def __len__(self) -> builtins.int: + r""" + Return the number of posterior samples. + """ + def __iter__(self) -> NestedSamplesIterator: + r""" + Iterate over posterior samples. + + Yields tuples of (position, log_likelihood, log_weight). + """ + def __getitem__( + self, idx: builtins.int + ) -> tuple[builtins.list[builtins.float], builtins.float, builtins.float]: + r""" + Get a specific posterior sample by index. + + Parameters + ---------- + idx : int + Sample index (0 to num_samples - 1) + + Returns + ------- + tuple[list[float], float, float] + Tuple of (position, log_likelihood, log_weight) + """ + +@typing.final +class Samples: + r""" + Container for sampler draws and diagnostics. + """ + @property + def chains(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def samples(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def mean_x(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def acceptance_rate(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def draws(self) -> builtins.int: ... + @property + def time(self) -> datetime.timedelta: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the samples. + """ + def __len__(self) -> builtins.int: + r""" + Return the number of chains. + """ + def __iter__(self) -> SamplesIterator: + r""" + Iterate over chains. + + Yields each chain as a list of samples. + """ + def __getitem__( + self, idx: builtins.int + ) -> builtins.list[builtins.list[builtins.float]]: + r""" + Get a specific chain by index. + + Parameters + ---------- + idx : int + Chain index (0 to num_chains - 1) + + Returns + ------- + list[list[float]] + The requested chain + """ diff --git a/python/src/errors.rs b/python/src/errors.rs index 157da4a..de4a67c 100644 --- a/python/src/errors.rs +++ b/python/src/errors.rs @@ -2,11 +2,11 @@ use pyo3::exceptions::PyValueError; use pyo3::prelude::*; use pyo3::types::PyModule; -use chronopt_core::errors::{EvaluationError as CoreEvaluationError, TellError as CoreTellError}; +use diffid_core::errors::{EvaluationError as CoreEvaluationError, TellError as CoreTellError}; -/// Get the custom exception class from chronopt.errors module +/// Get the custom exception class from diffid.errors module fn get_exception_class<'py>(py: Python<'py>, name: &str) -> PyResult> { - let errors_module = PyModule::import(py, "chronopt.errors")?; + let errors_module = PyModule::import(py, "diffid.errors")?; errors_module.getattr(name) } diff --git a/python/src/lib.rs b/python/src/lib.rs index 828d30c..d2452f5 100644 --- a/python/src/lib.rs +++ b/python/src/lib.rs @@ -10,9 +10,9 @@ use std::env; #[cfg(feature = "stubgen")] use std::path::PathBuf; -use chronopt_core::cost::{CostMetric, GaussianNll, RootMeanSquaredError, SumSquaredError}; -use chronopt_core::prelude::*; -use chronopt_core::sampler::SamplingResults; +use diffid_core::cost::{CostMetric, GaussianNll, RootMeanSquaredError, SumSquaredError}; +use diffid_core::prelude::*; +use diffid_core::sampler::SamplingResults; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pyfunction, gen_stub_pymethods}; @@ -39,7 +39,7 @@ use samplers::Sampler; type ParameterSpecEntry = (String, f64, Option<(f64, f64)>); // Import objective types for the problem enum -use chronopt_core::problem::{DiffsolObjective, ScalarObjective, VectorObjective}; +use diffid_core::problem::{DiffsolObjective, ScalarObjective, VectorObjective}; // Enum to hold different Problem types internally pub(crate) enum DynProblem { @@ -57,7 +57,7 @@ pub(crate) enum DynProblem { } impl DynProblem { - fn evaluate(&self, x: &[f64]) -> Result { + fn evaluate(&self, x: &[f64]) -> Result { match self { Self::Scalar(p) => p.evaluate(x), Self::ScalarWithGradient(p) => p.evaluate(x), @@ -210,7 +210,7 @@ fn gaussian_nll(variance: f64, weight: f64) -> PyResult { } // Problem -/// Executable optimisation problem wrapping the Chronopt core implementation. +/// Executable optimisation problem wrapping the Diffid core implementation. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass(name = "Problem")] pub struct PyProblem { @@ -227,7 +227,7 @@ impl PyProblem { /// Evaluate the configured objective function at `x`. fn evaluate(&self, x: Vec) -> PyResult { self.inner.evaluate(&x).map_err(|e| { - crate::errors::evaluation_error_to_py(chronopt_core::errors::EvaluationError::message( + crate::errors::evaluation_error_to_py(diffid_core::errors::EvaluationError::message( format!("{}", e), )) }) @@ -239,7 +239,7 @@ impl PyProblem { DynProblem::ScalarWithGradient(p) => match p.evaluate_with_gradient(&x) { Ok((_val, grad_opt)) => Ok(grad_opt), Err(e) => Err(crate::errors::evaluation_error_to_py( - chronopt_core::errors::EvaluationError::message(format!("{}", e)), + diffid_core::errors::EvaluationError::message(format!("{}", e)), )), }, _ => Ok(None), @@ -399,7 +399,7 @@ fn builder_factory_py() -> PyScalarBuilder { } #[pymodule] -fn _chronopt(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { +fn _diffid(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { // Main classes m.add_class::()?; m.add_class::()?; @@ -461,11 +461,11 @@ fn _chronopt(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_submodule(&sampler_module)?; m.setattr("sampler", &sampler_module)?; - // Register submodules for `import chronopt.builder` and `chronopt.cost` + // Register submodules for `import diffid.builder` and `diffid.cost` let sys_modules = py.import("sys")?.getattr("modules")?; - sys_modules.set_item("chronopt.builder", &builder_module)?; - sys_modules.set_item("chronopt.cost", &cost_module)?; - sys_modules.set_item("chronopt.sampler", &sampler_module)?; + sys_modules.set_item("diffid.builder", &builder_module)?; + sys_modules.set_item("diffid.cost", &cost_module)?; + sys_modules.set_item("diffid.sampler", &sampler_module)?; // Factory function m.add_function(wrap_pyfunction!(builder_factory_py, m)?)?; diff --git a/python/src/optimisers.rs b/python/src/optimisers.rs index 5043383..4cf537b 100644 --- a/python/src/optimisers.rs +++ b/python/src/optimisers.rs @@ -2,9 +2,9 @@ use pyo3::exceptions::PyTypeError; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::common::{AskResult, Bounds}; -use chronopt_core::optimisers::{AdamState, CMAESState, NelderMeadState}; -use chronopt_core::prelude::*; +use diffid_core::common::{AskResult, Bounds}; +use diffid_core::optimisers::{AdamState, CMAESState, NelderMeadState}; +use diffid_core::prelude::*; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -28,9 +28,9 @@ pub(crate) enum Optimiser { #[cfg(feature = "stubgen")] #[allow(dead_code)] pub(crate) fn optimiser_type_info() -> TypeInfo { - TypeInfo::unqualified("chronopt._chronopt.NelderMead") - | TypeInfo::unqualified("chronopt._chronopt.CMAES") - | TypeInfo::unqualified("chronopt._chronopt.Adam") + TypeInfo::unqualified("diffid._diffid.NelderMead") + | TypeInfo::unqualified("diffid._diffid.CMAES") + | TypeInfo::unqualified("diffid._diffid.Adam") } impl FromPyObject<'_, '_> for Optimiser { @@ -53,7 +53,7 @@ impl FromPyObject<'_, '_> for Optimiser { impl Optimiser { /// Convert to the core Optimiser enum - pub(crate) fn to_core(&self) -> chronopt_core::optimisers::Optimiser { + pub(crate) fn to_core(&self) -> diffid_core::optimisers::Optimiser { match self { Optimiser::NelderMead(nm) => nm.clone().into(), Optimiser::Cmaes(cma) => cma.clone().into(), @@ -178,11 +178,11 @@ impl PyNelderMead { /// /// Examples /// -------- - /// >>> optimiser = chronopt.NelderMead() + /// >>> optimiser = diffid.NelderMead() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -305,11 +305,11 @@ impl PyCMAES { /// /// Examples /// -------- - /// >>> optimiser = chronopt.CMAES() + /// >>> optimiser = diffid.CMAES() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -423,11 +423,11 @@ impl PyAdam { /// /// Examples /// -------- - /// >>> optimiser = chronopt.Adam() + /// >>> optimiser = diffid.Adam() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate_with_gradient(pt) for pt in result.points] /// ... state.tell(values) @@ -450,11 +450,11 @@ impl PyAdam { /// /// Examples /// -------- -/// >>> optimiser = chronopt.Adam().with_max_iter(100) +/// >>> optimiser = diffid.Adam().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... # Adam requires gradient information @@ -480,9 +480,9 @@ impl PyAdamState { /// Examples /// -------- /// >>> result = state.ask() - /// >>> if isinstance(result, chronopt.Evaluate): + /// >>> if isinstance(result, diffid.Evaluate): /// ... print(f"Need to evaluate {len(result.points)} points") - /// >>> elif isinstance(result, chronopt.Done): + /// >>> elif isinstance(result, diffid.Done): /// ... print(f"Optimization complete: {result.result}") fn ask(&self, py: Python<'_>) -> Py { match self.inner.ask() { @@ -511,7 +511,7 @@ impl PyAdamState { /// Examples /// -------- /// >>> result = state.ask() - /// >>> if isinstance(result, chronopt.Evaluate): + /// >>> if isinstance(result, diffid.Evaluate): /// ... point = result.points[0] /// ... value = objective(point) /// ... gradient = compute_gradient(point) @@ -588,11 +588,11 @@ impl PyAdamState { /// /// Examples /// -------- -/// >>> optimiser = chronopt.NelderMead().with_max_iter(100) +/// >>> optimiser = diffid.NelderMead().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... values = [f(pt) for pt in result.points] @@ -699,11 +699,11 @@ impl PyNelderMeadState { /// /// Examples /// -------- -/// >>> optimiser = chronopt.CMAES().with_max_iter(100) +/// >>> optimiser = diffid.CMAES().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... values = [f(pt) for pt in result.points] diff --git a/python/src/results.rs b/python/src/results.rs index 6ae8bdd..902ef66 100644 --- a/python/src/results.rs +++ b/python/src/results.rs @@ -2,7 +2,7 @@ use numpy::{PyArray1, ToPyArray}; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::prelude::OptimisationResults; +use diffid_core::prelude::OptimisationResults; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -18,7 +18,7 @@ use crate::{PyNestedSamples, PySamples}; /// -------- /// >>> state = optimiser.init(problem, initial=[1.0, 2.0]) /// >>> result = state.ask() -/// >>> if isinstance(result, chronopt.Evaluate): +/// >>> if isinstance(result, diffid.Evaluate): /// ... values = [problem.evaluate(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] @@ -55,7 +55,7 @@ impl PyEvaluate { /// -------- /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Optimization complete: {result.result}") /// ... break #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] diff --git a/python/src/samplers.rs b/python/src/samplers.rs index a51a2e9..1bfc918 100644 --- a/python/src/samplers.rs +++ b/python/src/samplers.rs @@ -3,8 +3,8 @@ use pyo3::exceptions::PyTypeError; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::common::{AskResult, Bounds}; -use chronopt_core::sampler::{ +use diffid_core::common::{AskResult, Bounds}; +use diffid_core::sampler::{ DynamicNestedSampler as CoreDynamicNestedSampler, DynamicNestedSamplerState as CoreDynamicNestedSamplerState, MetropolisHastings as CoreMetropolisHastings, @@ -34,8 +34,8 @@ pub(crate) enum Sampler { #[cfg(feature = "stubgen")] #[allow(dead_code)] pub(crate) fn sampler_type_info() -> TypeInfo { - TypeInfo::unqualified("chronopt._chronopt.MetropolisHastings") - | TypeInfo::unqualified("chronopt._chronopt.DynamicNestedSampler") + TypeInfo::unqualified("diffid._diffid.MetropolisHastings") + | TypeInfo::unqualified("diffid._diffid.DynamicNestedSampler") } impl FromPyObject<'_, '_> for Sampler { @@ -70,7 +70,7 @@ impl Sampler { /// Container for sampler draws and diagnostics. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "Samples")] +#[pyclass(module = "diffid.sampler", name = "Samples")] pub struct PySamples { pub(crate) inner: CoreSamples, } @@ -225,7 +225,7 @@ impl SamplesIterator { /// Nested sampling results including evidence estimates. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "NestedSamples")] +#[pyclass(module = "diffid.sampler", name = "NestedSamples")] #[derive(Clone)] pub struct PyNestedSamples { pub(crate) inner: CoreNestedSamples, @@ -394,7 +394,7 @@ impl NestedSamplesIterator { /// Basic Metropolis-Hastings sampler binding mirroring the optimiser API. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "MetropolisHastings")] +#[pyclass(module = "diffid.sampler", name = "MetropolisHastings")] #[derive(Clone)] pub struct PyMetropolisHastings { pub(crate) inner: CoreMetropolisHastings, @@ -457,11 +457,11 @@ impl PyMetropolisHastings { /// /// Examples /// -------- - /// >>> sampler = chronopt.MetropolisHastings().with_num_chains(4) + /// >>> sampler = diffid.MetropolisHastings().with_num_chains(4) /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -482,7 +482,7 @@ impl PyMetropolisHastings { /// Dynamic nested sampler binding exposing DNS configuration knobs. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "DynamicNestedSampler")] +#[pyclass(module = "diffid.sampler", name = "DynamicNestedSampler")] #[derive(Clone)] pub struct PyDynamicNestedSampler { pub(crate) inner: CoreDynamicNestedSampler, @@ -553,11 +553,11 @@ impl PyDynamicNestedSampler { /// /// Examples /// -------- - /// >>> sampler = chronopt.DynamicNestedSampler() + /// >>> sampler = diffid.DynamicNestedSampler() /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -583,17 +583,17 @@ impl PyDynamicNestedSampler { /// /// Examples /// -------- -/// >>> sampler = chronopt.MetropolisHastings().with_num_chains(4) +/// >>> sampler = diffid.MetropolisHastings().with_num_chains(4) /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Sampling complete: {result.result}") /// ... break /// ... values = [negative_log_likelihood(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "MetropolisHastingsState")] +#[pyclass(module = "diffid.sampler", name = "MetropolisHastingsState")] pub struct PyMetropolisHastingsState { inner: CoreMetropolisHastingsState, } @@ -685,17 +685,17 @@ impl PyMetropolisHastingsState { /// /// Examples /// -------- -/// >>> sampler = chronopt.DynamicNestedSampler() +/// >>> sampler = diffid.DynamicNestedSampler() /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Sampling complete: {result.result}") /// ... break /// ... values = [negative_log_likelihood(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "DynamicNestedSamplerState")] +#[pyclass(module = "diffid.sampler", name = "DynamicNestedSamplerState")] pub struct PyDynamicNestedSamplerState { inner: CoreDynamicNestedSamplerState, } diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 9b890ec..c27fb3a 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -1,12 +1,12 @@ [package] -name = "chronopt" +name = "diffid" version.workspace = true edition.workspace = true license.workspace = true description = "Time-series optimisation and inference toolkit with differential-system support" -repository = "https://github.com/BradyPlanden/chronopt" -homepage = "https://github.com/BradyPlanden/chronopt" -documentation = "https://docs.rs/chronopt" +repository = "https://github.com/BradyPlanden/diffid" +homepage = "https://github.com/BradyPlanden/diffid" +documentation = "https://docs.rs/diffid" readme = "../README.md" keywords = ["optimisation", "time-series", "differential-equations", "sampler", "inference"] categories = ["algorithms", "science", "simulation"] diff --git a/rust/benches/diffsol_benches.rs b/rust/benches/diffsol_benches.rs index bb1a20d..7a8b94f 100644 --- a/rust/benches/diffsol_benches.rs +++ b/rust/benches/diffsol_benches.rs @@ -1,6 +1,6 @@ -use chronopt::builders::DiffsolBackend; -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion}; +use diffid::builders::DiffsolBackend; +use diffid::prelude::*; use nalgebra::DMatrix; use std::time::Duration; diff --git a/rust/benches/optimisers_benches.rs b/rust/benches/optimisers_benches.rs index 1f4e56e..03375e3 100644 --- a/rust/benches/optimisers_benches.rs +++ b/rust/benches/optimisers_benches.rs @@ -1,6 +1,6 @@ -use chronopt::common::Bounds; -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, Criterion}; +use diffid::common::Bounds; +use diffid::prelude::*; use std::time::Duration; fn bench_nelder_mead_quadratic(c: &mut Criterion) { diff --git a/rust/benches/samplers_benches.rs b/rust/benches/samplers_benches.rs index fd30d3a..8d74010 100644 --- a/rust/benches/samplers_benches.rs +++ b/rust/benches/samplers_benches.rs @@ -1,5 +1,5 @@ -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, Criterion}; +use diffid::prelude::*; use std::time::Duration; fn bench_metropolis_hastings_gaussian(c: &mut Criterion) { diff --git a/rust/src/common.rs b/rust/src/common.rs index 5346843..2c45560 100644 --- a/rust/src/common.rs +++ b/rust/src/common.rs @@ -32,7 +32,7 @@ pub struct Unbounded; /// # Examples /// /// ``` -/// use chronopt::common::Bounds; +/// use diffid::common::Bounds; /// /// // Create bounds for a two-dimensional parameter-space /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0)]); @@ -56,7 +56,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-10.0, 10.0)]); /// ``` @@ -69,7 +69,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0), (0.0, 100.0)]); /// assert_eq!(bounds.dimension(), 3); @@ -89,7 +89,7 @@ impl Bounds { /// /// # Examples /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let initial = [0.0]; /// let bounds = Bounds::unbounded_like(&initial); @@ -123,7 +123,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0)]); /// let mut pos = vec![1.5, -10.0]; @@ -156,7 +156,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// use rand::SeedableRng; /// use rand::prelude::StdRng; /// @@ -205,7 +205,7 @@ impl From> for Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds: Bounds = vec![(0.0, 1.0), (-5.0, 5.0)].into(); /// assert_eq!(bounds.dimension(), 2); diff --git a/rust/src/lib.rs b/rust/src/lib.rs index d6ede1d..e57bdc5 100644 --- a/rust/src/lib.rs +++ b/rust/src/lib.rs @@ -8,7 +8,7 @@ pub mod problem; pub mod sampler; mod types; -// Convenience re-exports so users can `use chronopt::prelude::*;` +// Convenience re-exports so users can `use diffid::prelude::*;` pub mod prelude { pub use crate::builders::{ DiffsolConfig, DiffsolProblemBuilder, ScalarProblemBuilder, VectorProblemBuilder, diff --git a/rust/src/optimisers/mod.rs b/rust/src/optimisers/mod.rs index d712461..8d81fc4 100644 --- a/rust/src/optimisers/mod.rs +++ b/rust/src/optimisers/mod.rs @@ -52,8 +52,8 @@ impl ScalarOptimiser { /// /// # Example /// ``` - /// use chronopt::common::Bounds; - /// use chronopt::optimisers::{ScalarOptimiser, NelderMead}; + /// use diffid::common::Bounds; + /// use diffid::optimisers::{ScalarOptimiser, NelderMead}; /// /// let optimiser = ScalarOptimiser::from(NelderMead::new()); /// let result = optimiser.run( @@ -86,8 +86,8 @@ impl ScalarOptimiser { /// /// # Example /// ``` - /// use chronopt::common::Bounds; - /// use chronopt::optimisers::{ScalarOptimiser, NelderMead}; + /// use diffid::common::Bounds; + /// use diffid::optimisers::{ScalarOptimiser, NelderMead}; /// /// let optimiser = ScalarOptimiser::from(NelderMead::new()); /// let result = optimiser.run_batch( @@ -137,8 +137,8 @@ impl GradientOptimiser { /// /// # Example /// ``` - /// use chronopt::common::Bounds; - /// use chronopt::optimisers::{GradientOptimiser, Adam}; + /// use diffid::common::Bounds; + /// use diffid::optimisers::{GradientOptimiser, Adam}; /// /// let optimiser = GradientOptimiser::from(Adam::new()); /// let result = optimiser.run( diff --git a/rust/tests/diffsol_optimisation.rs b/rust/tests/diffsol_optimisation.rs index 300ed33..6003559 100644 --- a/rust/tests/diffsol_optimisation.rs +++ b/rust/tests/diffsol_optimisation.rs @@ -1,4 +1,4 @@ -use chronopt::prelude::*; +use diffid::prelude::*; use nalgebra::DMatrix; #[test] diff --git a/rust/tests/dynamic_nested.rs b/rust/tests/dynamic_nested.rs index 0b977f5..e584042 100644 --- a/rust/tests/dynamic_nested.rs +++ b/rust/tests/dynamic_nested.rs @@ -1,5 +1,5 @@ -use chronopt::builders::DiffsolBackend; -use chronopt::prelude::*; +use diffid::builders::DiffsolBackend; +use diffid::prelude::*; use nalgebra::DMatrix; #[test] diff --git a/rust/tests/dynamic_nested_advanced.rs b/rust/tests/dynamic_nested_advanced.rs index 7de0c13..d130439 100644 --- a/rust/tests/dynamic_nested_advanced.rs +++ b/rust/tests/dynamic_nested_advanced.rs @@ -1,5 +1,5 @@ -use chronopt::prelude::*; -use chronopt::problem::ParameterRange; +use diffid::prelude::*; +use diffid::problem::ParameterRange; /// Test evidence calculation against known analytical result. /// For a Gaussian N(x | 0, σ²) with uniform prior U(a, b), diff --git a/tests/integration/test_diffsol_dynamic_nested_parallel.py b/tests/integration/test_diffsol_dynamic_nested_parallel.py index 56a7543..cf5d431 100644 --- a/tests/integration/test_diffsol_dynamic_nested_parallel.py +++ b/tests/integration/test_diffsol_dynamic_nested_parallel.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -16,10 +16,10 @@ def _logistic_data(n: int = 40) -> np.ndarray: return np.column_stack((t_span, y)) -def _build_diffsol_problem(parallel: bool) -> chron.Problem: +def _build_diffsol_problem(parallel: bool) -> diffid.Problem: data = _logistic_data(40) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(_logistic_dsl()) .with_data(data) .with_parameter("r", 1.0, bounds=(0.1, 3.0)) @@ -38,7 +38,7 @@ def test_dynamic_nested_diffsol_parallel_vs_sequential(): initial = [1.0, 1.0] sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.3) .with_termination_tolerance(1e-3) @@ -66,14 +66,14 @@ def test_dynamic_nested_diffsol_parallel_vs_sequential(): def test_dynamic_nested_sampler_parallel_fallback_for_non_parallel_problems(): # Scalar problem does not support parallel evaluation; sampler should still work problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(lambda x: 0.5 * (x[0] - 0.5) ** 2) .with_parameter("x", 0.5, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(24) .with_expansion_factor(0.2) .with_termination_tolerance(1e-3) diff --git a/tests/integration/test_diffsol_sampling.py b/tests/integration/test_diffsol_sampling.py index 84c550c..00fad37 100644 --- a/tests/integration/test_diffsol_sampling.py +++ b/tests/integration/test_diffsol_sampling.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -27,12 +27,12 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): data = np.column_stack((t_span, height, velocity)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", g_true) .with_parameter("h", h_true) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() @@ -41,7 +41,7 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): initial_cost = problem.evaluate(initial_guess) sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(2) .with_iterations(250) .with_step_size(0.25) @@ -84,19 +84,19 @@ def test_diffsol_dynamic_nested_sampler_produces_evidence(): data = np.column_stack((t_span, height, velocity)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", g_true) .with_parameter("h", h_true) .with_tolerances(rtol=1e-6, atol=1e-6) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.15) .with_termination_tolerance(1e-3) diff --git a/tests/integration/test_mathematical_suite.py b/tests/integration/test_mathematical_suite.py index a61b21e..515d924 100644 --- a/tests/integration/test_mathematical_suite.py +++ b/tests/integration/test_mathematical_suite.py @@ -4,7 +4,7 @@ from functools import partial -import chronopt as chron +import diffid import numpy as np import pytest @@ -59,9 +59,9 @@ def ridge(x: list[float], alpha: float = 1.0) -> np.ndarray: return np.asarray([value], dtype=float) -def make_nelder_mead() -> chron.NelderMead: +def make_nelder_mead() -> diffid.NelderMead: return ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(800) .with_step_size(0.2) .with_threshold(1e-8) @@ -69,9 +69,9 @@ def make_nelder_mead() -> chron.NelderMead: ) -def make_cmaes() -> chron.CMAES: +def make_cmaes() -> diffid.CMAES: return ( - chron.CMAES() + diffid.CMAES() .with_max_iter(1500) .with_threshold(1e-8) .with_step_size(0.8) @@ -161,7 +161,7 @@ def test_python_objectives_converge( ): """Ensure optimisation reaches known minima for several analytic functions.""" - builder = chron.ScalarBuilder().with_objective(objective) + builder = diffid.ScalarBuilder().with_objective(objective) for idx in range(dimension): builder = builder.with_parameter(f"x{idx}", 1.0) @@ -196,7 +196,7 @@ def test_diffsol_logistic_convergence(): stacked_data = np.column_stack((time_points, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(_LOGISTIC_DSL) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) @@ -207,7 +207,7 @@ def test_diffsol_logistic_convergence(): problem = builder.build() optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(400) .with_threshold(1e-7) .with_position_tolerance(1e-6) diff --git a/tests/test_docs.py b/tests/test_docs.py index 9c770e0..3ac02b0 100644 --- a/tests/test_docs.py +++ b/tests/test_docs.py @@ -1,15 +1,12 @@ -"""Documentation quality and coverage tests. +"""Documentation quality tests. -This module validates that: -1. All public APIs are documented -2. Documentation builds without errors -3. All internal links resolve -4. Code examples in docs are valid +Validates that: +1. Documentation builds without errors +2. Notebooks are valid and well-structured """ -import inspect +import json import pathlib -import re import subprocess import pytest @@ -23,16 +20,14 @@ class TestDocumentationBuild: """Test that documentation builds successfully.""" - def test_mkdocs_config_exists(self): - """Check that mkdocs.yml exists.""" - assert MKDOCS_YML.exists(), "mkdocs.yml not found" - - def test_docs_directory_exists(self): - """Check that docs/ directory exists.""" - assert DOCS_DIR.exists(), "docs/ directory not found" - def test_mkdocs_build_succeeds(self): - """Test that mkdocs builds without errors.""" + """Test that mkdocs builds without errors. + + mkdocs strict mode validates: + - All internal links resolve + - No broken references + - Proper navigation structure + """ result = subprocess.run( ["mkdocs", "build", "--strict"], cwd=ROOT, @@ -40,268 +35,34 @@ def test_mkdocs_build_succeeds(self): text=True, ) - # Check return code assert result.returncode == 0, ( - f"mkdocs build failed with:\nSTDOUT:\n{result.stdout}\n\nSTDERR:\n{result.stderr}" - ) - - -class TestAPIDocumentation: - """Test that all public APIs are documented.""" - - def _get_public_apis(self, module) -> set[str]: - """Extract public API names from a module.""" - apis = set() - - for name, obj in inspect.getmembers(module): - # Skip private members - if name.startswith("_"): - continue - - # Include classes, functions, and constants - if ( - inspect.isclass(obj) - or inspect.isfunction(obj) - or isinstance(obj, (int, float, str)) - ): - apis.add(name) - - return apis - - def _find_documented_apis(self, doc_path: pathlib.Path) -> set[str]: - """Extract API names mentioned in documentation.""" - if not doc_path.exists(): - return set() - - content = doc_path.read_text() - - # Find patterns like `ClassName`, `function_name()`, etc. - # This is a simple heuristic - could be improved - patterns = [ - r"`(\w+)`", # Inline code - r"##\s+(\w+)", # Headers - r"class:\s+(\w+)", # Class references - ] - - documented = set() - for pattern in patterns: - matches = re.findall(pattern, content) - documented.update(matches) - - return documented - - @pytest.mark.skipif( - not (ROOT / "python" / "src" / "chronopt").exists(), - reason="chronopt package not installed", - ) - def test_core_apis_documented(self): - """Check that core chronopt APIs are documented.""" - try: - import chronopt as chron - except ImportError: - pytest.skip("chronopt not installed") - - # Get public APIs from chronopt - self._get_public_apis(chron) - - # Find documentation files - api_docs = list((DOCS_DIR / "api-reference" / "python").rglob("*.md")) - - # Collect documented APIs - documented_apis = set() - for doc_file in api_docs: - documented_apis.update(self._find_documented_apis(doc_file)) - - # Core APIs that should be documented - expected_apis = { - "ScalarBuilder", - "DiffsolBuilder", - "VectorBuilder", - "NelderMead", - "CMAES", - "Adam", - "SSE", - "RMSE", - "GaussianNLL", - } - - # Check coverage - missing = expected_apis - documented_apis - - if missing: - print(f"\n⚠️ APIs missing from documentation: {missing}") - print(f"Documented APIs: {documented_apis}") - - # We expect at least 80% coverage - coverage = len(expected_apis - missing) / len(expected_apis) - assert coverage >= 0.8, ( - f"API documentation coverage too low: {coverage:.0%}\nMissing: {missing}" + f"mkdocs build failed:\n{result.stdout}\n{result.stderr}" ) -class TestNotebookQuality: - """Test Jupyter notebook quality.""" - - def test_all_notebooks_exist(self): - """Check that all referenced notebooks exist.""" - # Parse mkdocs.yml for notebook references - content = MKDOCS_YML.read_text() - - # Find .ipynb references - notebook_refs = re.findall(r"tutorials/notebooks/(\d+_\w+\.ipynb)", content) - - # Check each exists - for notebook_name in notebook_refs: - notebook_path = DOCS_DIR / "tutorials" / "notebooks" / notebook_name - assert notebook_path.exists(), f"Notebook not found: {notebook_path}" - - def test_notebooks_have_metadata(self): - """Check that notebooks have proper metadata.""" - import json +class TestNotebookStructure: + """Test that notebooks are valid and well-structured.""" + def test_all_notebooks_are_valid_json(self): + """Verify all notebooks are valid JSON with proper structure.""" notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("*.ipynb")) - for notebook_path in notebooks: - if notebook_path.name == "utils.py": # Skip utility file - continue + assert len(notebooks) > 0, "No notebooks found" - with open(notebook_path) as f: + for notebook_path in notebooks: + with open(notebook_path, encoding="utf-8") as f: nb_data = json.load(f) - # Check structure + # Validate basic notebook structure assert "cells" in nb_data, f"{notebook_path.name} missing cells" assert "metadata" in nb_data, f"{notebook_path.name} missing metadata" - - # Check that it has markdown cells (documentation) - has_markdown = any( - cell.get("cell_type") == "markdown" for cell in nb_data["cells"] - ) - assert has_markdown, f"{notebook_path.name} has no markdown cells" - - def test_notebooks_have_objectives(self): - """Check that notebooks start with objectives.""" - import json - - notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("[0-9]*.ipynb")) - - for notebook_path in notebooks: - with open(notebook_path) as f: - nb_data = json.load(f) - - # First cell should be markdown with objectives - first_cell = nb_data["cells"][0] - assert first_cell["cell_type"] == "markdown", ( - f"{notebook_path.name} first cell is not markdown" - ) - - # Check for objectives - content = "".join(first_cell["source"]) - has_objectives = "Objectives" in content or "objectives" in content - assert has_objectives, ( - f"{notebook_path.name} missing objectives in first cell" - ) - - -class TestDocumentationStructure: - """Test documentation structure and organization.""" - - def test_required_sections_exist(self): - """Check that required documentation sections exist.""" - required = [ - "getting-started", - "tutorials", - "guides", - "algorithms", - "api-reference", - "examples", - "development", - ] - - for section in required: - section_path = DOCS_DIR / section - assert section_path.exists(), f"Required section missing: {section}" - assert section_path.is_dir(), f"Section is not a directory: {section}" - - def test_index_pages_exist(self): - """Check that all sections have index pages.""" - sections = [ - "getting-started", - "tutorials", - "guides", - "algorithms", - "api-reference", - "development", - ] - - for section in sections: - index_path = DOCS_DIR / section / "index.md" - assert index_path.exists(), f"Missing index page: {section}/index.md" - - def test_navigation_completeness(self): - """Check that mkdocs.yml nav includes all main sections.""" - content = MKDOCS_YML.read_text() - - required_nav = [ - "Getting Started", - "Tutorials", - "User Guides", - "Algorithms", - "API Reference", - "Examples", - "Development", - ] - - for section in required_nav: - assert section in content, f"Navigation missing section: {section}" - - -class TestInternalLinks: - """Test that internal links are valid.""" - - def _extract_md_links(self, content: str) -> list[str]: - """Extract markdown links from content.""" - # Match [text](path) but not [text](http://...) - pattern = r"\[([^\]]+)\]\((?!http)([^)]+)\)" - matches = re.findall(pattern, content) - return [path for _, path in matches] - - def test_getting_started_links(self): - """Test links in getting started guides.""" - getting_started = DOCS_DIR / "getting-started" - - for md_file in getting_started.glob("*.md"): - content = md_file.read_text() - links = self._extract_md_links(content) - - for link in links: - # Resolve relative link - if link.startswith("#"): # Anchor link - continue - - if link.startswith("../../"): - # Relative to docs root - target = DOCS_DIR / link.replace("../../", "") - elif link.startswith("../"): - # Relative to parent - target = getting_started.parent / link.replace("../", "") - else: - target = getting_started / link - - # Check if target exists (handle .md vs .html) - if not target.exists() and target.suffix == "": - target = target.with_suffix(".md") - - assert target.exists(), ( - f"Broken link in {md_file.name}: {link} -> {target}" - ) + assert len(nb_data["cells"]) > 0, f"{notebook_path.name} has no cells" def test_readme_not_in_docs(): """Ensure README doesn't conflict with index.md.""" readme = DOCS_DIR / "README.md" - assert not readme.exists(), ( - "docs/README.md conflicts with index.md (causes mkdocs warnings)" - ) + assert not readme.exists(), "docs/README.md conflicts with index.md" if __name__ == "__main__": diff --git a/tests/unit/optimisers/test_adam.py b/tests/unit/optimisers/test_adam.py index dabe27a..c937133 100644 --- a/tests/unit/optimisers/test_adam.py +++ b/tests/unit/optimisers/test_adam.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -16,7 +16,7 @@ def quadratic_grad(x): def build_quadratic_problem_with_gradient(): return ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) @@ -28,7 +28,9 @@ def build_quadratic_problem_with_gradient(): def test_adam_direct_run_minimises_quadratic(): problem = build_quadratic_problem_with_gradient() - optimiser = chron.Adam().with_step_size(0.1).with_max_iter(500).with_threshold(1e-8) + optimiser = ( + diffid.Adam().with_step_size(0.1).with_max_iter(500).with_threshold(1e-8) + ) result = optimiser.run(problem, [5.0, -4.0]) @@ -39,14 +41,16 @@ def test_adam_direct_run_minimises_quadratic(): def test_python_builder_optimise_with_adam_default(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) - optimiser = chron.Adam().with_step_size(0.1).with_max_iter(400).with_threshold(1e-8) + optimiser = ( + diffid.Adam().with_step_size(0.1).with_max_iter(400).with_threshold(1e-8) + ) builder.with_optimiser(optimiser) problem = builder.build() @@ -60,7 +64,7 @@ def test_python_builder_optimise_with_adam_default(): def test_adam_falls_back_with_numerical_grad(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic) .with_parameter("x", 0.0) .with_parameter("y", 0.0) @@ -68,7 +72,7 @@ def test_adam_falls_back_with_numerical_grad(): problem = builder.build() - optimiser = chron.Adam().with_max_iter(10) + optimiser = diffid.Adam().with_max_iter(10) result = optimiser.run(problem, [0.0, 0.0]) assert result.iterations == 10 diff --git a/tests/unit/optimisers/test_cmaes.py b/tests/unit/optimisers/test_cmaes.py index 0fe839a..1f0baa2 100644 --- a/tests/unit/optimisers/test_cmaes.py +++ b/tests/unit/optimisers/test_cmaes.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -10,7 +10,7 @@ def rosenbrock(x): def build_rosenbrock_problem(): return ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -22,7 +22,7 @@ def test_cmaes_direct_run_minimises_rosenbrock(): problem = build_rosenbrock_problem() optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(400) .with_threshold(1e-8) .with_step_size(0.6) @@ -38,14 +38,14 @@ def test_cmaes_direct_run_minimises_rosenbrock(): def test_python_builder_optimise_with_cmaes_default(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(300) .with_threshold(1e-8) .with_step_size(0.5) @@ -63,7 +63,7 @@ def test_python_builder_optimise_with_cmaes_default(): def test_set_optimiser_rejects_unknown_type(): - builder = chron.ScalarBuilder() + builder = diffid.ScalarBuilder() with pytest.raises(TypeError): builder.with_optimiser(object()) @@ -72,7 +72,7 @@ def test_set_optimiser_rejects_unknown_type(): def test_cmaes_result_covariance_available(): problem = build_rosenbrock_problem() - optimiser = chron.CMAES().with_max_iter(50).with_seed(123) + optimiser = diffid.CMAES().with_max_iter(50).with_seed(123) result = optimiser.run(problem, [1.5, -1.5]) diff --git a/tests/unit/test_diffsol.py b/tests/unit/test_diffsol.py index 5ed2c83..404162d 100644 --- a/tests/unit/test_diffsol.py +++ b/tests/unit/test_diffsol.py @@ -1,6 +1,6 @@ import copy -import chronopt as chron +import diffid import numpy as np import pytest @@ -22,14 +22,14 @@ def test_diffsol_builder(): # Build the problem builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) .with_parameter("r", 1.0, None) .with_parameter("k", 1.0, None) - .with_cost(chron.SSE()) - .with_cost(chron.RMSE()) + .with_cost(diffid.SSE()) + .with_cost(diffid.RMSE()) ) problem = builder.build() @@ -44,7 +44,7 @@ def test_diffsol_builder(): # Test that we can optimise the problem optimiser = ( - chron.NelderMead().with_max_iter(500).with_threshold(1e-7).with_patience(10) + diffid.NelderMead().with_max_iter(500).with_threshold(1e-7).with_patience(10) ) result = optimiser.run(problem, x0) assert result.success @@ -62,10 +62,10 @@ def test_diffsol_builder_remove_methods(): data = t_span**2 stacked_data = np.column_stack((t_span, data)) - metric = chron.RMSE() + metric = diffid.RMSE() builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("a", 1.0, None) @@ -94,7 +94,7 @@ def test_diffsol_builder_remove_methods(): # Change cost builder = builder.remove_cost() - builder = builder.with_cost(chron.SSE()) + builder = builder.with_cost(diffid.SSE()) problem_5 = builder.build() # Check that problems are different @@ -120,7 +120,7 @@ def test_problem_optimise_defaults_to_builder_params(): stacked_data = np.column_stack((t_span, data)) problem = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_tolerances(rtol=1e-4, atol=1e-4) .with_data(stacked_data) @@ -128,7 +128,7 @@ def test_problem_optimise_defaults_to_builder_params(): .build() ) - optimiser = chron.NelderMead().with_max_iter(0) + optimiser = diffid.NelderMead().with_max_iter(0) result = problem.optimise(optimiser=optimiser) @@ -154,7 +154,7 @@ def test_diffsol_cost_metrics(variance: float) -> None: def build_problem(cost_metric=None): builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) @@ -166,9 +166,9 @@ def build_problem(cost_metric=None): return builder.build() sse_problem = build_problem() - sse_problem_explicit = build_problem(chron.SSE()) - rmse_problem = build_problem(chron.RMSE()) - gaussian_problem = build_problem(chron.GaussianNLL(variance)) + sse_problem_explicit = build_problem(diffid.SSE()) + rmse_problem = build_problem(diffid.RMSE()) + gaussian_problem = build_problem(diffid.GaussianNLL(variance)) test_params = [0.8, 1.2] sse_cost = sse_problem.evaluate(test_params) @@ -189,7 +189,7 @@ def build_problem(cost_metric=None): assert pytest.approx(expected_gaussian, rel=1e-6, abs=1e-9) == gaussian_cost with pytest.raises(ValueError): - chron.GaussianNLL(0.0) + diffid.GaussianNLL(0.0) def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: @@ -217,7 +217,7 @@ def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: stacked_data = np.column_stack((t_span, y_true, psi_true)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -227,7 +227,7 @@ def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: problem = builder.build() optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(500) .with_threshold(1e-10) .with_position_tolerance(1e-8) diff --git a/tests/unit/test_dynamic_nested_sampler.py b/tests/unit/test_dynamic_nested_sampler.py index e79739e..50d4819 100644 --- a/tests/unit/test_dynamic_nested_sampler.py +++ b/tests/unit/test_dynamic_nested_sampler.py @@ -4,7 +4,7 @@ import math -import chronopt as chron +import diffid import numpy as np import pytest from scipy.stats import norm @@ -36,14 +36,14 @@ def gaussian_nll(x: list[float]) -> float: return 0.5 * (diff / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(gaussian_nll) .with_parameter("x", mu, bounds=(prior_lower, prior_upper)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_termination_tolerance(1e-5) @@ -84,14 +84,14 @@ def exponential_nll(x: list[float]) -> float: return lambda_param * x[0] problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(exponential_nll) .with_parameter("x", 1.0, bounds=(0.0, x_max)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.01) .with_termination_tolerance(1e-6) @@ -123,14 +123,14 @@ def bimodal_nll(x: list[float]) -> float: return -(log_sum - np.log(2) - np.log(sigma) - 0.5 * np.log(2 * np.pi)) problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(bimodal_nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_termination_tolerance(1e-4) @@ -163,14 +163,14 @@ def pathological_nll(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(pathological_nll) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.2) .with_seed(456) @@ -195,7 +195,7 @@ def high_dim_quadratic(x: list[float]) -> float: """Simple quadratic in high dimensions.""" return 0.5 * sum(xi**2 for xi in x) - problem = chron.ScalarBuilder().with_objective(high_dim_quadratic) + problem = diffid.ScalarBuilder().with_objective(high_dim_quadratic) for i in range(dimension): problem = problem.with_parameter(f"x{i}", 0.0, bounds=(-3.0, 3.0)) @@ -203,7 +203,7 @@ def high_dim_quadratic(x: list[float]) -> float: problem = problem.build() sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.15) .with_termination_tolerance(1e-4) @@ -227,14 +227,14 @@ def sharp_peak(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(sharp_peak) .with_parameter("x", 0.0, bounds=(-1.0, 1.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.05) # Small expansion for narrow peak .with_termination_tolerance(1e-3) @@ -257,14 +257,14 @@ def large_offset(x: list[float]) -> float: return 100.0 + 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(large_offset) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(111) @@ -285,14 +285,14 @@ def simple_quadratic(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(simple_quadratic) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(555) @@ -320,14 +320,14 @@ def quadratic(x: list[float]) -> float: return 0.5 * (x[0] - 1.0) ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic) .with_parameter("x", 1.0, bounds=(-3.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(777) @@ -359,14 +359,14 @@ def narrow_gaussian(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(narrow_gaussian) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.1) .with_termination_tolerance(1e-3) @@ -388,21 +388,21 @@ def simple_problem(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler1 = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(12345) ) sampler2 = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(12345) @@ -430,14 +430,14 @@ def multimodal(x: list[float]) -> float: ) problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(multimodal) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.5) # Allow significant expansion .with_seed(999) @@ -458,14 +458,14 @@ def bounded_quadratic(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(lower, upper)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(444) @@ -486,14 +486,14 @@ def simple_problem(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(666) @@ -513,7 +513,7 @@ def nll(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) return ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() @@ -521,7 +521,7 @@ def nll(x: list[float]) -> float: def sampler_config(seed): return ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(seed) diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index fe4ec1f..f857506 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -1,10 +1,10 @@ -import chronopt as chron +import diffid import numpy as np def test_builder_exposes_config_and_parameters(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(lambda x: np.asarray([float(x[0]) ** 2])) .with_parameter("x", 3.5, bounds=(0.0, 10.0)) ) @@ -30,7 +30,7 @@ def bounded_quadratic(x): def test_python_builder_rosenbrock(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.2, None) .with_parameter("y", -1.2, None) @@ -39,7 +39,7 @@ def test_python_builder_rosenbrock(): # Create the optimisation optimiser = ( - chron.NelderMead().with_max_iter(500).with_threshold(1e-6).with_step_size(0.15) + diffid.NelderMead().with_max_iter(500).with_threshold(1e-6).with_step_size(0.15) ) results = optimiser.run(problem, [1.5, -1.5]) @@ -51,14 +51,14 @@ def test_python_builder_rosenbrock(): def test_python_builder_bounds_respected(): builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(0.0, 1.0)) .with_parameter("y", 0.0, bounds=(0.0, 2.0)) ) problem = builder.build() - optimiser = chron.NelderMead().with_max_iter(200).with_threshold(1e-8) + optimiser = diffid.NelderMead().with_max_iter(200).with_threshold(1e-8) results = optimiser.run(problem, [0.5, 1.0]) assert results.success diff --git a/tests/unit/test_optimisation_api.py b/tests/unit/test_optimisation_api.py index eb9e279..9f7db58 100644 --- a/tests/unit/test_optimisation_api.py +++ b/tests/unit/test_optimisation_api.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -19,7 +19,7 @@ def _test_optimisation_api(): # Build the problem builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_config({"rtol": 1e-6}) @@ -47,7 +47,7 @@ def test_diffsol_builder_allows_multiple_builds(): stacked_data = np.column_stack((t_span, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("r", 1.0) @@ -70,7 +70,7 @@ def rosenbrock(x): return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 builder = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -92,7 +92,7 @@ def exponential_model(params): return y0 * np.exp(rate * t_span) builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(exponential_model) .with_data(data) .with_parameter("rate", 1.0) @@ -110,17 +110,17 @@ def test_all_builders_support_copy(): import copy # ScalarBuilder - scalar_builder = chron.ScalarBuilder().with_objective(lambda x: x[0] ** 2) + scalar_builder = diffid.ScalarBuilder().with_objective(lambda x: x[0] ** 2) scalar_copy = copy.copy(scalar_builder) copy.deepcopy(scalar_builder) # Test deepcopy # DiffsolBuilder - diffsol_builder = chron.DiffsolBuilder().with_diffsl("in { a }") + diffsol_builder = diffid.DiffsolBuilder().with_diffsl("in { a }") diffsol_copy = copy.copy(diffsol_builder) copy.deepcopy(diffsol_builder) # Test deepcopy # VectorBuilder - vector_builder = chron.VectorBuilder().with_objective(lambda x: [x[0]]) + vector_builder = diffid.VectorBuilder().with_objective(lambda x: [x[0]]) vector_copy = copy.copy(vector_builder) copy.deepcopy(vector_builder) # Test deepcopy diff --git a/tests/unit/test_python_autodiff.py b/tests/unit/test_python_autodiff.py index 9b6e129..4e516cb 100644 --- a/tests/unit/test_python_autodiff.py +++ b/tests/unit/test_python_autodiff.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -26,7 +26,7 @@ def _quadratic_gradient(x): def quadratic_problem(): """Creates a 3D quadratic optimization problem using ScalarBuilder""" return ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(_quadratic_objective) .with_gradient(_quadratic_gradient) .with_parameter("x1", 1.0) diff --git a/tests/unit/test_samplers.py b/tests/unit/test_samplers.py index 43f846c..dba5fca 100644 --- a/tests/unit/test_samplers.py +++ b/tests/unit/test_samplers.py @@ -1,6 +1,6 @@ import math -import chronopt as chron +import diffid import numpy as np import pytest @@ -12,14 +12,14 @@ def quadratic_potential(x: np.ndarray) -> np.ndarray: def test_metropolis_hastings_runs_and_returns_samples(): problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(3) .with_iterations(400) .with_step_size(0.4) @@ -44,14 +44,14 @@ def test_metropolis_hastings_runs_and_returns_samples(): def test_dynamic_nested_sampler_runs_on_scalar_problem(): problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic_potential) .with_parameter("x", 0.5) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.1) .with_seed(99) @@ -67,20 +67,20 @@ def test_dynamic_nested_sampler_runs_on_scalar_problem(): def test_dynamic_nested_invalid_live_points_are_clamped(): problem = ( - chron.ScalarBuilder() + diffid.ScalarBuilder() .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) - sampler = chron.DynamicNestedSampler().with_live_points(1) + sampler = diffid.DynamicNestedSampler().with_live_points(1) nested = sampler.run(problem) assert nested.draws >= 0 def test_dynamic_nested_requires_problem_instance(): - sampler = chron.DynamicNestedSampler() + sampler = diffid.DynamicNestedSampler() with pytest.raises(TypeError): sampler.run(object()) # type: ignore[arg-type] diff --git a/tests/unit/test_vector.py b/tests/unit/test_vector.py index 8629500..c49298e 100644 --- a/tests/unit/test_vector.py +++ b/tests/unit/test_vector.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -17,12 +17,12 @@ def exponential_model(params): # Build the problem builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(exponential_model) .with_data(data) .with_parameter("rate", 1.0, None) .with_parameter("y0", 1.0, None) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() @@ -35,7 +35,7 @@ def exponential_model(params): assert cost >= 0, f"Cost should be non-negative, got {cost}" # Test optimization - optimiser = chron.NelderMead().with_max_iter(1000).with_threshold(1e-8) + optimiser = diffid.NelderMead().with_max_iter(1000).with_threshold(1e-8) result = problem.optimise(x0, optimiser) assert result.success @@ -55,7 +55,7 @@ def model(params): return params[0] * np.ones(n_points) + params[1] builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("scale", 1.0) @@ -80,18 +80,18 @@ def sinusoid(params): return amp * np.sin(freq * t + phase) problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(sinusoid) .with_data(data) .with_parameter("amplitude", 2.0, (0.0, 5.0)) .with_parameter("frequency", 1.0, (0.1, 3.0)) .with_parameter("phase", 0.0, (-np.pi, np.pi)) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) .build() ) x0 = [2.0, 1.0, 0.0] - optimiser = chron.NelderMead().with_max_iter(2000).with_threshold(1e-9) + optimiser = diffid.NelderMead().with_max_iter(2000).with_threshold(1e-9) result = problem.optimise(x0, optimiser) # Note: Sinusoidal fitting can be challenging due to local minima @@ -114,7 +114,7 @@ def quadratic(params): def build_problem(cost_metric=None): builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(quadratic) .with_data(data) .with_parameter("scale", 1.5) @@ -124,9 +124,9 @@ def build_problem(cost_metric=None): return builder.build() sse_problem = build_problem() - sse_problem_explicit = build_problem(chron.SSE()) - rmse_problem = build_problem(chron.RMSE()) - gaussian_problem = build_problem(chron.GaussianNLL(1.0)) + sse_problem_explicit = build_problem(diffid.SSE()) + rmse_problem = build_problem(diffid.RMSE()) + gaussian_problem = build_problem(diffid.GaussianNLL(1.0)) test_params = [1.5] sse_cost = sse_problem.evaluate(test_params) @@ -157,21 +157,21 @@ def model(params): # Build with SSE builder1 = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("a", 1.0) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem1 = builder1.build() # Build with RMSE builder2 = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("a", 1.0) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) ) problem2 = builder2.build() @@ -188,10 +188,10 @@ def test_vector_builder_with_default_optimiser(): def linear(params): return params[0] * np.arange(4) + params[1] - optimiser = chron.NelderMead().with_max_iter(100) + optimiser = diffid.NelderMead().with_max_iter(100) problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(linear) .with_data(data) .with_parameter("slope", 0.5) @@ -214,7 +214,7 @@ def wrong_size(params): return params[0] * np.ones(5) # Wrong size! problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(wrong_size) .with_data(data) .with_parameter("a", 1.0) @@ -222,7 +222,7 @@ def wrong_size(params): ) with pytest.raises( - chron.errors.EvaluationError, + diffid.errors.EvaluationError, match="Evaluation failed: Evaluation failed:: expected 3 elements, got 5", ): problem.evaluate([1.0]) @@ -237,16 +237,16 @@ def model(params): # Build two problems with same configuration builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("scale", 1.0) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) ) problem1 = builder.build() builder.remove_cost() - builder.with_cost(chron.SSE()) + builder.with_cost(diffid.SSE()) problem2 = builder.build() # Should produce same results @@ -261,7 +261,7 @@ def model(params): return params[0] * data problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("a", 1.0) diff --git a/uv.lock b/uv.lock index 5b9b837..de4eaaf 100644 --- a/uv.lock +++ b/uv.lock @@ -259,86 +259,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, ] -[[package]] -name = "chronopt" -source = { editable = "." } -dependencies = [ - { name = "numpy" }, -] - -[package.optional-dependencies] -diffeqpy = [ - { name = "diffeqpy" }, -] -jax = [ - { name = "diffrax" }, - { name = "jax", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, - { name = "jaxlib", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, -] -plotting = [ - { name = "matplotlib" }, -] - -[package.dev-dependencies] -dev = [ - { name = "pytest" }, - { name = "scipy" }, -] -docs = [ - { name = "matplotlib" }, - { name = "mkdocs" }, - { name = "mkdocs-git-revision-date-localized-plugin" }, - { name = "mkdocs-jupyter" }, - { name = "mkdocs-material" }, - { name = "mkdocs-minify-plugin" }, - { name = "mkdocstrings", extra = ["python"] }, - { name = "nbconvert" }, - { name = "pymdown-extensions" }, -] -examples = [ - { name = "diffeqpy" }, - { name = "diffrax", marker = "(python_full_version < '3.15' and platform_machine != 'x86_64') or (python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.15' and sys_platform != 'darwin')" }, - { name = "jax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'" }, - { name = "jaxlib", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'" }, - { name = "matplotlib" }, -] - -[package.metadata] -requires-dist = [ - { name = "diffeqpy", marker = "extra == 'diffeqpy'" }, - { name = "diffrax", marker = "extra == 'jax'", specifier = ">=0.7.0" }, - { name = "jax", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin' and extra == 'jax'", specifier = "==0.4.38" }, - { name = "jaxlib", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin' and extra == 'jax'", specifier = "==0.4.38" }, - { name = "matplotlib", marker = "extra == 'plotting'", specifier = ">=3.8" }, - { name = "numpy", specifier = ">=1.24.4" }, -] -provides-extras = ["diffeqpy", "jax", "plotting"] - -[package.metadata.requires-dev] -dev = [ - { name = "pytest", specifier = ">=8.3.5" }, - { name = "scipy", specifier = ">=1.16.2" }, -] -docs = [ - { name = "matplotlib", specifier = ">=3.8" }, - { name = "mkdocs", specifier = ">=1.5.0" }, - { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.2.0" }, - { name = "mkdocs-jupyter", specifier = ">=0.24.0" }, - { name = "mkdocs-material", specifier = ">=9.5.0" }, - { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, - { name = "mkdocstrings", extras = ["python"], specifier = ">=0.24.0" }, - { name = "nbconvert", specifier = ">=7.0" }, - { name = "pymdown-extensions", specifier = ">=10.7" }, -] -examples = [ - { name = "diffeqpy" }, - { name = "diffrax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = ">=0.7.0" }, - { name = "diffrax", marker = "(python_full_version < '3.15' and platform_machine != 'x86_64') or (python_full_version < '3.15' and sys_platform != 'darwin')", specifier = ">=0.7.0" }, - { name = "jax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = "==0.4.38" }, - { name = "jaxlib", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = "==0.4.38" }, - { name = "matplotlib", specifier = ">=3.8" }, -] - [[package]] name = "click" version = "8.3.0" @@ -522,6 +442,86 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/20/5b/3866770a8e01811e767bd1b560c39f65dc4b57f74e70a2865526f7177999/diffeqpy-2.5.4-py3-none-any.whl", hash = "sha256:8214d747dfacfb5fc49690e0a3b8182c14d82e1b609d045086c036a1bceb9370", size = 17342, upload-time = "2025-09-08T01:33:37.032Z" }, ] +[[package]] +name = "diffid" +source = { editable = "." } +dependencies = [ + { name = "numpy" }, +] + +[package.optional-dependencies] +diffeqpy = [ + { name = "diffeqpy" }, +] +jax = [ + { name = "diffrax" }, + { name = "jax", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "jaxlib", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, +] +plotting = [ + { name = "matplotlib" }, +] + +[package.dev-dependencies] +dev = [ + { name = "pytest" }, + { name = "scipy" }, +] +docs = [ + { name = "matplotlib" }, + { name = "mkdocs" }, + { name = "mkdocs-git-revision-date-localized-plugin" }, + { name = "mkdocs-jupyter" }, + { name = "mkdocs-material" }, + { name = "mkdocs-minify-plugin" }, + { name = "mkdocstrings", extra = ["python"] }, + { name = "nbconvert" }, + { name = "pymdown-extensions" }, +] +examples = [ + { name = "diffeqpy" }, + { name = "diffrax", marker = "(python_full_version < '3.15' and platform_machine != 'x86_64') or (python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.15' and sys_platform != 'darwin')" }, + { name = "jax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "jaxlib", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "matplotlib" }, +] + +[package.metadata] +requires-dist = [ + { name = "diffeqpy", marker = "extra == 'diffeqpy'" }, + { name = "diffrax", marker = "extra == 'jax'", specifier = ">=0.7.0" }, + { name = "jax", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin' and extra == 'jax'", specifier = "==0.4.38" }, + { name = "jaxlib", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin' and extra == 'jax'", specifier = "==0.4.38" }, + { name = "matplotlib", marker = "extra == 'plotting'", specifier = ">=3.8" }, + { name = "numpy", specifier = ">=1.24.4" }, +] +provides-extras = ["diffeqpy", "jax", "plotting"] + +[package.metadata.requires-dev] +dev = [ + { name = "pytest", specifier = ">=8.3.5" }, + { name = "scipy", specifier = ">=1.16.2" }, +] +docs = [ + { name = "matplotlib", specifier = ">=3.8" }, + { name = "mkdocs", specifier = ">=1.5.0" }, + { name = "mkdocs-git-revision-date-localized-plugin", specifier = ">=1.2.0" }, + { name = "mkdocs-jupyter", specifier = ">=0.24.0" }, + { name = "mkdocs-material", specifier = ">=9.5.0" }, + { name = "mkdocs-minify-plugin", specifier = ">=0.8.0" }, + { name = "mkdocstrings", extras = ["python"], specifier = ">=0.24.0" }, + { name = "nbconvert", specifier = ">=7.0" }, + { name = "pymdown-extensions", specifier = ">=10.7" }, +] +examples = [ + { name = "diffeqpy" }, + { name = "diffrax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = ">=0.7.0" }, + { name = "diffrax", marker = "(python_full_version < '3.15' and platform_machine != 'x86_64') or (python_full_version < '3.15' and sys_platform != 'darwin')", specifier = ">=0.7.0" }, + { name = "jax", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = "==0.4.38" }, + { name = "jaxlib", marker = "python_full_version < '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin'", specifier = "==0.4.38" }, + { name = "matplotlib", specifier = ">=3.8" }, +] + [[package]] name = "diffrax" version = "0.7.0" From 9d99b10257069c763834dc70a279e7cdfa686cff Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 12:29:22 +0000 Subject: [PATCH 11/18] docs: updates stale 'planned' information, optimiser guidance --- docs/algorithms/optimizers/adam.md | 4 ++-- docs/algorithms/optimizers/cmaes.md | 27 ++++++++++++++------------- docs/api-reference/python/samplers.md | 24 ++++-------------------- 3 files changed, 20 insertions(+), 35 deletions(-) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimizers/adam.md index 3b79cf0..9ae2134 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimizers/adam.md @@ -74,8 +74,8 @@ The algorithm terminates when any condition is met: **Learning rate** (`step_size`) is the most critical parameter: -- Start with 0.001 (the original paper's recommendation) -- Try orders of magnitude: 0.1, 0.01, 0.001, 0.0001 +- Start with a conservative step-size (i.e, 1e-3) +- Try orders of magnitude: (1e-2 to 1e-4) - Too large: oscillation, divergence, or overshooting - Too small: slow convergence diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimizers/cmaes.md index 5d868ec..3f3efcf 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimizers/cmaes.md @@ -15,7 +15,7 @@ CMA-ES samples a population of candidate solutions from a multivariate normal di | Function evaluations | $\lambda$ per generation | | Best dimensions | 10-100+ parameters | -## Mathematical Foundation +## Algorithm The algorithm samples offspring from: @@ -29,18 +29,6 @@ Where: - $\sigma$ is the global step size - $C$ is the covariance matrix -### Strategy Parameters - -The algorithm automatically computes these from dimension $n$: - -| Parameter | Formula | Purpose | -|-----------|---------|---------| -| $\lambda$ | $\max(4, \lfloor 4 + 3\ln(n) \rfloor)$ | Population size | -| $\mu$ | $\lfloor \lambda / 2 \rfloor$ | Parent count | -| $\mu_\text{eff}$ | $1 / \sum w_i^2$ | Effective parent count | -| $c_\sigma$ | $(μ_\text{eff} + 2) / (n + μ_\text{eff} + 5)$ | Step size learning rate | -| $c_c$ | $(4 + μ_\text{eff}/n) / (n + 4 + 2μ_\text{eff}/n)$ | Covariance path learning rate | - ## Algorithm Steps 1. **Sample**: Generate $\lambda$ offspring from $\mathcal{N}(m, \sigma^2 C)$ @@ -54,6 +42,19 @@ The algorithm automatically computes these from dimension $n$: 7. **Update step size**: Based on evolution path length vs expected length 8. **Repeat**: Until convergence +### Strategy Parameters + +The algorithm automatically computes these from dimension $n$: + +| Parameter | Formula | Purpose | +|-----------|---------|---------| +| $\lambda$ | $\max(4, \lfloor 4 + 3\ln(n) \rfloor)$ | Population size | +| $\mu$ | $\lfloor \lambda / 2 \rfloor$ | Parent count | +| $\mu_\text{eff}$ | $1 / \sum w_i^2$ | Effective parent count | +| $c_\sigma$ | $(μ_\text{eff} + 2) / (n + μ_\text{eff} + 5)$ | Step size learning rate | +| $c_c$ | $(4 + μ_\text{eff}/n) / (n + 4 + 2μ_\text{eff}/n)$ | Covariance path learning rate | + + ## Parameters | Parameter | Default | Description | diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md index 9c271e2..07b8c49 100644 --- a/docs/api-reference/python/samplers.md +++ b/docs/api-reference/python/samplers.md @@ -2,9 +2,6 @@ MCMC and nested sampling algorithms for posterior exploration and model comparison. -!!! info "Coming Soon" - Sampler Python bindings are currently in development. This page describes the planned API. - ## Overview | Sampler | Type | Use Case | @@ -16,12 +13,10 @@ Samplers complement optimisers by providing full posterior distributions rather --- -## Metropolis-Hastings (Planned) +## Metropolis-Hastings MCMC sampling for exploring parameter posterior distributions. -### Planned API - ```python import diffid as chron @@ -65,12 +60,10 @@ print(result.acceptance_rate) # Target: 0.2-0.4 --- -## Dynamic Nested Sampling (Planned) +## Dynamic Nested Sampling Nested sampling for calculating model evidence (marginal likelihood) for model comparison. -### Planned API - ```python import diffid as chron @@ -191,9 +184,9 @@ print(f"MAP estimate: {opt_result.x}") --- -## Future Tutorials +## Tutorials -Once samplers are available, see: +The followings present sampler functionality: - [Parameter Uncertainty Tutorial](../../tutorials/notebooks/03_parameter_uncertainty.ipynb) - [Model Comparison Tutorial](../../tutorials/notebooks/04_model_comparison.ipynb) @@ -201,15 +194,6 @@ Once samplers are available, see: --- -## Implementation Status - -Track sampler implementation progress: - -- [GitHub Issue #XXX](https://github.com/bradyplanden/diffid) - Metropolis-Hastings -- [GitHub Issue #XXX](https://github.com/bradyplanden/diffid) - Dynamic Nested Sampling - ---- - ## See Also - [Optimisers](optimizers.md) - For point estimates From 3cf19345196f24907f5091d079d00d623907720b Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 15:41:16 +0000 Subject: [PATCH 12/18] docs: optimizer -> optimiser --- docs/algorithms/index.md | 8 +- .../{optimizers => optimisers}/adam.md | 4 +- .../{optimizers => optimisers}/cmaes.md | 2 +- .../{optimizers => optimisers}/nelder-mead.md | 4 +- docs/api-reference/index.md | 4 +- docs/api-reference/python/builders.md | 2 +- .../python/{optimizers.md => optimisers.md} | 8 +- docs/api-reference/python/results.md | 2 +- docs/api-reference/python/samplers.md | 2 +- docs/guides/choosing-optimiser.md | 4 +- docs/guides/index.md | 2 +- docs/guides/parallel-execution.md | 2 +- docs/guides/troubleshooting.md | 2 +- ...ing-optimizers.md => tuning-optimisers.md} | 2 +- docs/tutorials/index.md | 4 +- ...ics.ipynb => 01_optimisation_basics.ipynb} | 10 +- .../notebooks/02_ode_fitting_diffsol.ipynb | 44 ++++-- .../notebooks/03_parameter_uncertainty.ipynb | 36 ++++- .../notebooks/05_advanced_predator_prey.ipynb | 133 +++++++++++++++++- .../06_custom_solver_integration.ipynb | 4 +- ...n.ipynb => 07_parallel_optimisation.ipynb} | 22 ++- examples/logistic_growth.py | 4 +- .../predator_prey/predator_prey_diffeqpy.py | 2 +- .../predator_prey/predator_prey_diffrax.py | 2 +- .../predator_prey/predator_prey_diffsol.py | 2 +- mkdocs.yml | 14 +- python/src/diffid/_diffid.pyi | 6 +- python/src/errors.rs | 4 +- python/src/optimisers.rs | 44 +++--- python/src/results.rs | 6 +- rust/src/common.rs | 2 +- rust/src/optimisers/adam.rs | 10 +- rust/src/optimisers/cmaes.rs | 14 +- rust/src/optimisers/mod.rs | 6 +- rust/src/optimisers/nelder_mead.rs | 8 +- rust/src/sampler/dynamic_nested/mod.rs | 2 +- tests/unit/test_python_autodiff.py | 2 +- tests/unit/test_vector.py | 4 +- 38 files changed, 314 insertions(+), 119 deletions(-) rename docs/algorithms/{optimizers => optimisers}/adam.md (96%) rename docs/algorithms/{optimizers => optimisers}/cmaes.md (98%) rename docs/algorithms/{optimizers => optimisers}/nelder-mead.md (96%) rename docs/api-reference/python/{optimizers.md => optimisers.md} (96%) rename docs/guides/{tuning-optimizers.md => tuning-optimisers.md} (92%) rename docs/tutorials/notebooks/{01_optimization_basics.ipynb => 01_optimisation_basics.ipynb} (99%) rename docs/tutorials/notebooks/{07_parallel_optimization.ipynb => 07_parallel_optimisation.ipynb} (99%) diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md index 9510652..de856a3 100644 --- a/docs/algorithms/index.md +++ b/docs/algorithms/index.md @@ -14,7 +14,7 @@ Gradient-free and gradient-based algorithms for finding optimal parameters. Simplex-based gradient-free optimiser for local search. - [:octicons-arrow-right-24: Details](optimizers/nelder-mead.md) + [:octicons-arrow-right-24: Details](optimisers/nelder-mead.md) - :material-chart-scatter-plot:{ .lg .middle } __CMA-ES__ @@ -22,7 +22,7 @@ Gradient-free and gradient-based algorithms for finding optimal parameters. Covariance Matrix Adaptation Evolution Strategy for global optimisation. - [:octicons-arrow-right-24: Details](optimizers/cmaes.md) + [:octicons-arrow-right-24: Details](optimisers/cmaes.md) - :material-alpha-a:{ .lg .middle } __Adam__ @@ -30,7 +30,7 @@ Gradient-free and gradient-based algorithms for finding optimal parameters. Adaptive Moment Estimation for gradient-based optimisation. - [:octicons-arrow-right-24: Details](optimizers/adam.md) + [:octicons-arrow-right-24: Details](optimisers/adam.md)

69uG7N3A?w{i@`s^GCMeu=_etxi3Rxtp_urj+#gMDt zXP&a76^U4iRKn%`&1uz*V95U0V2S@Ou0*ez1V2c=lU++eMy zr)0u_(3pgdW+`o_+8mAOoHKyMqwG{kJWYK%N#5I;+tqLW>gdQL0Qt0&=YS zJaK69rd?uS1?b=QI7JOR(;T>*QoKKOlb&z(NXr9z}*z9E4UQK-G+%IHv}6xxBz1 zNtrDg2eE6ANS4}|6P)SKbDI_>s{?G0b+zeQGABCuz*t3CU5hE~SFmw7$(g(0R$j2s zo5H;h`{$OoEm6U^+G0Txn`&Fc}M_DPXiU&vl>HW6h-C^K0hc7G`mH z-F}AmVdx>g0JwUi4^aKGm_x#WI^u|J75nnOp4{HtkPdCT)EKwiD&%?R84^DdPyQ`E zO~KfdcHfcTtOx*T*5slTtj>@;%^g`cyKzkN&?tMz3ifvSg$W z zrfs`;QrvRVgnac;i*hthQk`|KSby4l9~*g1i7N|iW z=5*ffa1Xx5flh1Q%{3%S08d(KrUp(-c^fe`fBpC%jKkI4Rry%iFk8MO0KHI5^!C1% ztQe1_Rl*DGi%ejvaMx_My)=r{l(P{?Ujy4mf#lP}ueRvbTnVsmEi)OxxT&lN&L#s< z3ma{ZKQN*;7BBB=ca{wbgL7 zMn|kFcM#Sw;xjXf;bLd{>+9&{{}CEo=m&0T&=s$qQj%3wGS*+*y_aGcMQ&NR8dU=7#kpWb^^)ovZ=?@!Y5z70k~ z1MjE2wE!8~8ohsvlkuySsU*E$)*1{k1i62)2ywoz zEhdv`f(eK>Al6a;sY5vj8Ej{{1t9QW>KpCpsecn}zIW>0$)0}>JRDE^Hg$QkDK_!? z`zL@NKB5=A`jg^}Qe%4S)UunTZ%o;;=IwR`_89s&xJW5W-ALLOIkm$Ho2}HHrKj^K=tFS-5>CV6a>uX+gZk@l z>9HN+?*_c|p(2p&!jh=YzB=VXjv-`q?p~+l%j?&F@oZjWzpFhWdaX3$L@HCtg9a*K zJ?8MG-tG~UeZ|PM+HfkvQxTNDZ^`|c5^F|5`Ncq5W+BxvS0Zh3aCc9h-e~RbuQtvu zm{XFLP}oWGkgMCBGqEoG)+wt#S0TES=3xnWMRuucUM){A&hT;W0HK4`Yyju%kk;+2 zw$=6Qzw+*xpw$0vRk}dmyDi$@g--9hE0rF;!$taw3Fi5nI_ucaPUr=4)BVfF;7bm` zRdB%F3vxXJ?EopXYC0OCqajiQ^<~V%hH9I=FHBc8$YYH-dU;aF$f)!dan(M+J?-{4!k%?c?-vr|?$(yT2Tc@p%`wu7~(y@hN>#1Ybk& zR(OWXXDI&4DSI8Waio-&2&>eM`?FG>DeWzf1rl0WXac40t8y&JicmZhIU#Ikab;c- ze;>7=NbXCyQk2n(eJ472b2d6otOn^FPfuo~v%pCs@yx`j)4- zA3RYf3aPZF5|qv>r*r?PjZ|hmppA!kuwYh(%>_xgAYM6||7+8m)pS#koOc^uzLp+@ zW;X+RvbIkiqz$&PxF@3ER(EV|uVbarI3F*(`S2{m#F#}>M}-s&3No*^rvZwmRDbyX zUCh%wB#diLB{uo9UvmWlS`rxgm31v%hBtVlU z+P0&$Ze-=A5pWtfzY>T>rl8x!S&~^sbogr3p``bgyU~n?|Bad;ha%soghS6~|D7418E)JzorMWeL%bLEV)1BCNW zaI-=UiJiRF_1;~&=Dk*)@uokKgfh`O=B^Hi;NXz}jFmY&E_Di)m}YUx>)nv_lhj%WEA3{V zIq&YCSg7<>mrm9&{$i)$J1SR}!uy)1!xeFp(Gusyc<+|y6CtTswROIG7UpLGBlZ_y zIFuvH{rTqO31U8z&WB4t86iI5C$1Q-NWwkLm4wIKHC=Vi4=JLr9dOS;bntClOWLX~ z_ALk^zePJ_o#IHm&cG4`qGyrFxq4fV&*Y?22h{mmr=w-0);8UrWSKnM2ui7ehnMP+ z+i3KxQ|mpMaHEu?WHmI_1RpJ$v)-Piu>BL7r`_{Dv~L10gWG)JP6F10SFwcTl z36674JBtPP^5wPwInk=(d1l^P+gryWOVY(AXscVy1n&$SjV@+)cgUSUN0+obdHE~e zV}r^ZZ?YBX{`AS(Q?dZV?v(Z?!S1wTJ^p1FFe4&-Iez#xl=bUZKd1XM-q^d|_N+b_ zc2*lXyDM6%k2~9>)jjdaqRX0 z^}HEJv7m+hk&a}blD0Fh)yccR-19X-Vzk&peXG-B2v`>G53Z`g+RM6hED$x>>>1~S zU(uC-Ij3wBBiXThzaVv?Ctfg`aZxKdCAN7ujxe~Q)lUq!pp}YZW!|3-yVFO;N?&Vi z>MkVrS!XBD96pM7u{*+q4~`8bPmxO0AT>Ce<$C%^@Yd3Jy*6`q9A9r5hwWMMRMy$Z z{j$bS0mzR-D4Ash4*hy&BtW((n!}i- zAQeg>nV~G>EqJ7*EQ-!e3sS?qvVYVv=thI7lz`w5X?hVYS9dU5&p4#Vd*q%S9v;KJ zogu$4$Zff^nDM2thPTI~)td9YiI@KLUiDq}es$&dlECYxWHeqjeUYjs>uw&|N%_z( z1eV@PYLhpw0h`WY#cUwC`s1i_@!}b<+KG={u;&%<@t;x(TMwh@q_?W(xjgrGEVd8l_PDT`3t(0q2Up5(m@R8b zpr}$~b;Zh=WGrs)mq5Rmq=OHqIfe`+94bStA||^78H?Nxj>$T0Z%K|Bf0BQ>0W8v& z4_uFzO7+1Chp+ltK|BOdJn@bCjtV&#X$qI<#6>1wi=5t6HvYk(>HM#L6#Mc+z|c7Q zv5OFtxu8e^3l&*>SLjhCDute!9%$|;q7~2J z;6>oX>WINzuT)|G))3IithPOZGGgCy&*gcG z%}B)MC7|Vu6Pr_i@#LG-3gd6hk}ANc$}~q(J*EmmCPF(P38V; z(rOKi;R6Ikk^^8lv5IhtN-+oHY4TR>Q@w3p02!d|*#b31PNCw0f3IpkyI0of^ayl| zK$D<=)$hMigj>o$o;fGP(_&g;? z`>5t6?HR3XrGwpM_F~M&aZ?10v(w>NwuhM$yA~VV77b6a6xr*T0_54rxdLGEAzS<3ovm$mTwm@A zY5EOeN|10+=F-zUlVzW9ycUN&t)dqD zg7Tqj8jwXt`vIGEL||I#tZkI`W0g~E4; zaMM>Od~%e}ZQeMZ+?-7unncxIq22Zbx%cwA^Xj~bFTm&g&tZI_Az9-oOB#N*_xBX- zsW&G&$$A%+X7hdNUsMn>oHM9NEOJ0BAOE=7=HXF(T(b#dawAE9x*m7gd)J6hEH2qX zm4TkfW=qww#w45+Yk@i~KPb8XD#+t;@L|nYeaYoPFl6>6xw@M8l}Z{Rn&9qU>x?bW z-fap_YZ;tvK?ai?Cc>QhxXDL7FMk`4(R04FBM#}v=2x=v6^BzJ--+1>={GNzyBJku zNOGl4$kdR*vBk*SJ)vn4&n?qZ1U!u}!D+rt{P}7<`j!}Of>#F~zGE(jchIw7+ za4|pm%8BBJ!0}=o+078|;10f{ML7+ihpI--*FZ#~S7x?auLxdut?e5xC2T@RcM;l) z>X|Qu4x>OrZq@QkkPm?tl%4Zc4!+PpFSy?$u|F5WduVRR7ThtzHW-7JoF~tvJMmn(rj9jh+=3x{}kt@$Q zpCGU<5jAf-5<{qVXx3G~IVaeP&k<;)7%XUT+rlpVzLP>`QZV-&VPBaD`~!eys4)Dc z)tcEXy!b60u(ZbD7?gh60i^0Q@@{Wq&VHkhQtGq^GVueb?;4A4_Dh#^dV3*G#b_C^ z*TGPdgZ)@((>EzJNBKn@)oTq@if#H~Om|QH>s=)6b2Da&nNuCW=Hl}158|`T4FBS? z{k1b%snrdNB{La0CQT1{7F_zWCHRIKeU!X1p4dSjR{SC%hS;2B+0AtEFOPx&eQcKw zO>(li(w_pzTQ?7&6TZs2KjrS=6P*Se4R>it^Rj2(FP5YQw%Xt{`xe7LqrIN?;b=DY z+w@ikHyaw)lP44IQgIy#4PhMd{bl0GGw#d6>{-32E*}FRn(fj-m@)!)n;!ABmNdPK zm&z^~0EA=PVLssdoaw3zB#dNyH4=)$*E{#}-nY!MWb3&k1Z@E7-P@{_QAoj3JdesG zuAObdZDl#2qg?=l{A=%$4)7+sJM2L*Zb-m!`(?)yh*IAB;#_iy|_r+IUK zA1!IS&WN)U%c04!&JHROU;P7?T;jidJ#?L+T48LVU~rBDR-P-tCkq6x!4# z5ig9zFgnv&^;rFO(CQ7(FL}Bb#VU|c7A$P!XQ4Y-1u_b@+C9StiQ||%)5Q#l5um}s zm5c2InXqSWv169|6%Jz$#7Nw2>F)d61D&iDu={5r9~zVsXIKcoXFV~rxY_H=zOpRl ziG{c(q+e!yik33)PL{5;GKK_OBsU+qa|YJv6tOt@n=38J_?YI~au`6^j?_c?wG)l7 z>`RAl%MN#U2s5^HVAh_OQoRE~z#sC#NM>6G&IkPuFhMZWRS31lVuVk&c5lUnKs4C} z&dWEy#{l)ho6f}hx>`c)@4JgF?#(~!Bh<||*SH66#N=Kfs1LN4z@-}IsR%!!8DX02 zSe2eqOQBa#0i6Vyb7g#DUbUXKwi!!2bAYeZSqtg&8gzD?}(4@cOint+Wa zN9*!RJJoMSZ{!9bFK(niYYJtS{<(qe7A%~>TP7x7+t44_tbfXVkuD zr|iKiF92w(ip|rSeWHmS$mDMP%Y%C%gGwO7@VoN-5O7V%B?CON4;{5b1U(lC-R1}q z8T1xraW;&a1dmQCrt=l~X;uW%3??!GsLgF?rh5{;UK6ywAB!b+Zu;=D*I0eF0$PyC zeAQ>}H)Mee0f;7;`?pkW1S@*LKa=fbhgA`Z*t#`Y-IGpLn0~GZ>DFxBhfq9kb*@(=K{R@1m%+z?#_NG6C*ns?{q4qs7guLv1`CQZYM z6xf^$>Qmi?{WupN4OkxqvYss$02^_~ zJE|AsGoQHNT&>A$Xp;+Ko!dUT6~2g;P=+8>Cq0MM_9&9q*0i30eIeZ{ofUSI)KZr> zv(k=dDocf*crIULX*7@NIou}ce1o=p`igxBGi*RTPE4`Y>XEa`<222Yc1o5*Z`o(?lV>c%BT^(>~a>Tx34hNNhj zA%{{F49gJ^oFSBW+DUZsN^+>$nFskv@6|wum~HZ>w_K5TI$a&^3%>(~A^G9qxBpBg zdGt2C#xGiN#9t~5Z(0--YR+^*@a|UW72^sp1P2BkGiGZMbpV&VSO_QdV$|J`FtN0R zso#?EABag(|4{z0mkRNPE7_5J*z{JW!^KJb)<6;M2VtrNV&O`QAC1Mh3+#&6Z-lfY{y8kbqKK7j zCi+)?ioF0TZvOI1hR1a47-fyPMkOq}Gq6w>w>xvnN|5QbR9`1rsri9iQ?>4p?K*k{ zcnCRQodEI;JFHE>9`(_uOcG9XB7QPkfA|#49dUPxb^5r@wf8NNOfysY;gn$>GJVw( z_bvx{);ba4Ua-_uvd+uA+rnETrqe{Va{KlcVu_;?k*hkE&bC9 zHyy6eMXI7fF0hvgUw=r&3!TqTfDiFW?G2K9r4q7fvtR;F)xFqZ95q~^bGFY75|zA~ zE}{Q=csqJ8w*V_!Cc5z5aqi!oVrp!j3QAt~2^Puyj2!+6|6nn__13F+L_6! zEkZo6w&Zlr)Iq(+9Kp^URZM{|KV6!-?VGgy7Is>KpO@|?xx(r9qnLsv4Abe)b4 zRJdJ;BF{MkG@k8xOVXIr?yl%Yu#IR2p+blN;gOmXY4Oh`ODF7>nC=Lar^y0B5jkoW zM4pbLukfWh)0wCjL(PA>esA4qve{8;75ov?e@-Qywn|26dOUn~AHO_GyBwVPH{Cu? z(bD)5v0Qulv$6b;)e!k4NCK0wyIl%X;{3>e8f}BFH3&DnETOxL+`~(w?GDzw5ud*( zCT+U@OfEbV!*}}_#^qLnD+?BCV8kw8Pwm3{J3tHa;V){I9g6l$x>oZcjiWLho@fLI z4n4k~zLCf0EB^%w*1I#%=5Vo8asKz|7<9nNJ~VzSw4gSEEq_Kx2~38$WM_gNE#b~x znVSn++@FyeCwCSY4NH^$y5n!Y@Tx`zWekMSd6iGJ1V!-W*|2}mV)wst&2&y03)lmA z%v!EsG*xYf^mSj{AfVDF=U<=@?fS7@&?{6_%ZXZ&X0fJD__)ZZ@w+?9s0C!5T8%+$ z=mlVR)~D2?Y^Ck0S{;q5HU5;DDxaa^F99Pg8xXQ}=fzAC=uQD08mHCu<4e=wyj0yO z3BzlbZHjXKO52SStSOh2a#=0GJ=jTbYj?#`mCZ%MZeS?QzuXg(P-gkcbaryiioz4ya+;z#@+V8}pc5SzB{Zn2ST^4)5;Ee83-et)u4)^HsABvO;IRTmJV z&6mjn9pKcatFE6f$Bq)}H4s)E$~{2PK|&KRWCMa`Z)UD(cDW!F@P(lOsA-0U*}o-g zx=Xh7^7Iw(ze9m~-JUXZD1&N@>5YnXp$;~{v09)+oohjYf?8Payn$q)+8C;cp>SDN zaGpoIC~?T}Yn#*o33b{i)#@QRVD@2XFu|2r9NudH!2}$+hjpxg z#(KPq!fQ7Gwz=0Tk}+=W^7&KMWwA!s-po8OubezW!jr4G{myV`{Ia% zGtZyiKEz9TqDCMyEO7iAp*Q4WWDHbHL|)3JLv>F~%TJ&M1I;Frgz#K>d7$Hn`~kF!ixf zxaTI+^{qsrV9%n7ruO_!I4q+9o)|5IG&Fl)v)#khQq!8A;z*gen4@b5luvke++RKc zkE5(i2!g$&?vY#=V_W`jnNYBWe4KQarAb^Y-JR+RD09+WTB65E`@~;`@HAZINcB_{ zJ&0*6*aT%DdOtJ6Vtx4x(eNt`k@HlGtR@a!4=IU%ZA_rE54;V(GAirW|2Xvp!T>Fj zA=4rnWMw1^vTFRB-{w!^M8`__1q%=F^hhWkT>k03FYDCwW2e4a-A`K!(XB=(r{CuW zw=OQ|oS24st|1?s3G+|wc0Zz2+pzku5Qx5KKUd838L`W=j;OG5eNTuG&xsvw6I-gO zuJEPJ>eSs7;C^&@F1Illi3KQU9y79pp0TXXPE2*BgPkV+VS^|C0K+I4mwR=*URkI$ zQ=7F_n(lkyGIwVOybZU4DN#Cr(?Yf~pW7}!uGz_wKWj@`+p8PMm+W?B?w(#4mmlXt z6NeoL`>ht}D3vi$Br)=W!wETwzj>MXTMgj23O@X;p`o+RbvKm@bpiDBhj52O{j~;T z;kx3h^lATDA~*kzC0>NSIW`}PwOsIt|1Evh5iS37MvH{4SX6XVk}ouYmau3WcqvE| zg6RVocU7#-RoS-(s2XH3NJ%uA>2MMk5TPwTe>0rP==jtCxf@M~1q!P2yh?}nbBuC&1qezJEQ;?N=-Q)|`7%K^t@ z;0?Eh0b6XM(N4S)jfIX(uzx_!C0dp9)-H@xgm}E~t?F_Y{)q`yj;w`Pf+&W*BhYmI z*|weivGw^8G@Lv?MM>3u6)q@ zG@F@UHV7AQHB|+ZnOTM4#zyT9V+=-9uv(=-x+64z+F;YaDR&}{WtuV?h9A9gN2~19 z>pjSnYjLkH?cTUs0*Yb-Vr*<4LkemjQ}fNlN8p828L+W2R{4{EOC_*SM4Z*A&QF&TP(dLt8v?^#G&m1tpcCntY4SWkUjFX<4|6bFSrMnmZ(<&m?!c$<> z{=oW*yiO2NuEooh$#i6mEm)OxHA)p8@`SAlmlfXmeV_Vph1}m)w=`L?I}%LSf=LP< zY>fjDN3kg)#{jqOea7HAJsH7`U;eYJ!}snJWIR?Gk81nE#QJLzoqrT_?dXj#2?8Ja z+mvrkt$|d3vKbX|e~91xa(TNv3#K#;+s%=)?hGZ7g?h2G`iQN`6q*P68*abrdSQm@ z?+gV0P~SYD|43g@R~a#<={?c41qiXKgev~sqR8A->w3ynpw7Lc$)sUBUEwAV9KP>EEk=w*qoG-*ZW7z#qMyR$a$(Fcp2sY;1YekXuHC(u#V(^K1T z)Hh-5M*{L-if1evZYy?Tm~s_Bt{8l6F?3GEfa$h4h&iumcVUke${Z+m(Rt`s{i4;L zXeW%5&DwAe!3{KVNLubaoW+SI~axjbwv#qS!2_J1&F26Jx$x0h!^U{cQrXE@9Eg z4SOX@39B~$GbS3W<1krlH{P}BVB@gP#$`@gPTh-vJ=SC{9hAY!ll03$O$`r;n~ODs za#h3uOP#}T7`{RYOdZ{!v`6tbwoGZq(jT=|ot9L|u_yBd5WgL7xTUn+f}6w~W=`(T zw9d^RiE&OA$1>mm@?LMT2{$TbI>rFpy-d4})(_P22@kVb7>b-nt-!%Mw@Klt; z_KdK~!E{#_)*Ru2BBDf8IM1Q?@F%YQE3H*kVreKb*rR5WzpM*Kz>po5AB^S*=~Jv* zhf|!{x@8qkqv#}7_T7ErKW0{AaCp-l+DzZs(1^R7oZgMTY~N(HoGfSi%a5uovMWQ; zt}K;}eW3ZwDz(uw;HP{bcW>~@#UR|};m_*vh&4|odVo~G=#AtLIm5lwk)0R6XQXK>@F`7WAv z|F_EZgq9{w5oJ6PX(`&Qm95&`|F3a-3XPn~HtbK^c$=Q@p`5A6ZZS z8XJ_E=P|-(`uL-GyWVx-PvL#^h&U)fbNWd{YR>5pQlo~o=WjH-xqDNVz+B@fT5x}K zi$O#nnyr-0UpTc!2$qWRcnT~TjIn|uYNaTNb4XlR&PXYF^=zG*{Jrpwpcb9kv?8Xz8VY&yd7N8)hkj zt8lcwBg|0^MW5Bzo>W>`Hupa3fk|(9i*`lK=)wzQB~Mlh>&OP4)9y~dUYp_7nLa*T zkLZauJn22L97&}{z5em`40FZfWhk_fze=cx{{DJ9LCWd*j3=$t z-UmPcZvG(JR?ys=$J0{Xl*DI z^U}41wEn9>P5)jb9{Xt@2>bLwsg-iGaLYdJ^b^3Hc;9dcE)_@Wf(X#}@Ko*uTdIoK^bo4{_3a9mS+?MH-KaU=<3&!J}h zqLs#z-Y{x7{54#AIejvG2dRb=$>>f)ckrL=S|JveJ1g%Zj%~msc9FIITDhhQw+T^V zW-7QPU;v>Azc8=;nruTgnG=Twkmri$Eu(c`zI^JZBZub(ovA4EPb#FlJftrmW58?f zjc;Ng+zJAs-gt%r2{?ycP6J)_`}&MX=PKkY?~8fOVud1u);s&yj5nFt{Lh#E$e$fq zMeqnZ8={P5_Xo6uIo$VdNsP0`+MAILDKKH@U z98jaWVq07Rk#{tgyU`(10+BlUqxGQ#W(@gWmxQY@bjA0E`OlA`$);i%p{NG{>mcEC ze=#)G>uwwgy&u~O zSp|Q1&dAzvv;QGJj$5x<$;Rwq>TWQXUCKp{x8)Vsu-9a74i5M%Xu$NZ_Me^}Q4--c zhbz#?Qj=Vf$$e2zj|9oBxcf9!z6d?3Ho1NOb!Gc!~w{8*DWPxe?=pvgt5KY`x~CJ zFWqC>1yYO0JD=$?4ULM3zit!IcY)K4Q1y|*bqWd1Eht0f4^R`D{uRUF>Rrs~YTsQi z%a)JE$9?e`YMTWr~f7!Lm4quo;dyShqWQmN|bsvyTWqoBdIS!pf- z&zNU%H0e-lcmGWFaNp8&onjs;N(fU(o;q&jc0VCs6{pbis35Z&kJQC@i|<@t+LX=C zu%v=@_9ldfW}4YQUvAVDr?kpii$&Bd7-=eXlc{B-<`~LQ44;xfpM5rp9Vv?ND(5DX ztP!#lSe#^B6OoCoOK*6KWSWr4VqfCKS(s-`a0R0;(t$goq?#$qDl>Vjgmr*?%n=Hz z#V;<>ym6#X&-hkuM#QWQ@Hn;cqj~BK)EPepdbC zQJQI%XP6^hFR45=8p&e^V7dZwR6(T|*sHa)MZR=O zP3M-VPdl4TqdzxL5gG2JNTlgk&^xg#SK{O77trphM_M}wmBh2e&i14&a{zJcc1mt6 z?cPCfTQ#$zY1XZf>zg_wk zK4R!@NhTWf3wKCo+S}12r@fA3+sD-zFS`lUb47fLc%P`|KA=Hra$>s2$y}GD;w|{@Snj{^9m^92S+JEP;JmxZ zT-AWDW@K~-mU41JAZs9Fcbwo8)b$ARyX$<-gV3kQKdpi12A#B6K z-6DoLu5sGipF`3l6d0Qnb&IU7SYyK&6E;e$U76SREcv&wc%k}h`|GleIc-+^KzV1U zgi4D`q01vPa8+*I>~gzgdx%d4~Swn!-luev8-5Dva-GFkSfqs9|@&~JE`qV~_*}*{5hwrNiIA z!AD9|p+c9q-FqZ0Dr}`gtaCY^f+_Ryn%&#?3Yf%7Uve5t3KHP?r6bQv#%o}-?ED9^ zmun$aOeWfr)pcXsS)@HMU|&50rxK~x`bjZQ9&p>RARl(6(Z{o?;kKfCQmWb(kbGR` z0!ri|t#=gVsIwu|c+;ZL_Qwr!2>E=!@?Lg~`&_eBEA(i^E+Wd0VTY>FF;u`>VKC}X z%CaSW>G}Zi_-}NEC@Gen%`;l5{KBV5qVGOXmu{|U1#g(ODi0D0Ki4qVecu&eI|nua z$L!Nmd51goqC``LhWf~c3&KRSwQ+A>DGD8is~=7OKEyd~-@Vd8;YapRf^yGL4?IFc z6Ukv=re2oMl75E@=sP+@E6$S>O$uoD2iku8BVE7)5|lpeP4%Dxfnm5LHLUoc|Y_l=iI_Ma#@GypjyMhI+XOEQ!v+s#AnVH?XA?)*Sv{! z-=bC4t#Q%uGK-x&s5Y#w*eYyqt{pwxl8OUWrmeqGq$MuAnBdOX{pF&`YMyvz%UE>= zTSFJ_X=Bw62Nt|bX35{&5oS%wtB;ugb7OewNr)S;OZ|`dgN95|ve3VlZigskgfV3B zHnYPm>WG$S`=kuveCr6wI7+=rE8a zLx8{7;>k0Ok`m-lL4Y)j@`iWyOZ*zp;pXB}g0VCbH-Do=N)b7Buhmpqs^77I=4B46v+r^q@~>+3pUzh2iqI(ShEiyr%4z(SoRNN^ombNfO3eHMYGV z^+GV>?_I;0lpq74{WqiUV;4~o7xGd%?JiH?m05?v&;t`XhR@fJi~xFzDGNgptQLQ&Em#o{PN|z= zulgeeUFGZnN@?CIQRHX3r;B*?+?DJPkU^4YL~pqH+2+YWEghzZ@@LNnm&CP>FfJVM zD(qxr+x`3{ESq z;#cJ5Zch(fOpiISZ?=n^%UPvkhK4qT&*OpKGN#=tK*5FO$C&6Ysf_BGzg;MWq(B0G zgR?Ff{_LAVBSo_wuOHv6H@$TW_p-RX`-*6{_BwYzrM-ZY#9Qbq>qpoV#OJSKiIeG#ejG{FB&3XdQ@Fn8ERgIfWNGGph`zftt6M z7F`P^^db0PVx%VqigRE^I4yNOwXV2I24c-wl0iEQYwUl%mQ(yAkRR2O(R`sbSkS8f zQQoZctFKs&p2?=tukWk8V5)vmecXO1AJeC#{cFAH?T=%Bi<^O-eE7-woSd<^nf+8* z^<_t#wrhQ>(4SewZ5Wq%78i0*NKK`?(<`BtoRxUe62UiVc;A6&7*(Y*=J9j(TC?*r z(or7orot%}|EPjlyqq)N_Ep2w=a2Dp^E{upM-=|slcT|DK}-7Ffn9$PqPXi>1728f z&`<3gP7&+r*Xk(|3ifw|9gbb-yxyBuUHSFtVUo!yVLm<-gOPNo0GMfIs}raM5oZ~{ zh*++H0TOm3;sp~oDi+V@d$_o^%9uN;Q|1X6A+-9lwO;_~*#?my5|>J5j*lw~%S@;O zSO!dmCNPTFo`*q`@AigkLWsv#sk866vn$bDwnfT+lhv;tYIhocl{?=M0F@1={@tWa zrCtAW1Ed&h8s8~#;OCzzPA#EF|Dqr@E_c8_d07NhFLQlNqD>acpt7dXNF~o zw;A*1dTDTsL8oLY0(JUEIbS?t=&V1|i;ZI%OL0>Tko2^Q98yDJ<9E6~-w_Bz!{-Wy zrug`7fomWZI&nC_>DNQ@lOi@Pms-juGp$<K|7Il-=UivIJ3=A^$> zFeK^%LkV_AUJLb42ua6b!``AvylgsZ9)-j8DPtA2-QkY#57YX~3-N&vl~mt&i3$M< z4mWM7lg>mg=@X>K)F?aOjNzoeQpJUE2)qttSFb+_2ncRiYn?%068Pj*7IwGgVOo8- z339H-SoK}WJO(f@#z=Zc@>%2)4RDxxRd{<-6(<-|QIRO|@`|Kj#QUK`b8b`CRn@AZ zA1N^Iq@`_8a$Rw!>W~G^86jd8Za|w8SGK(TOXK8nND-m64A(oS-6%DI;Mgq%qMWVV zO({@akwLTFDOE+_cuOPr<`fn_Y`MxJ2HEZk$OK`Ekap)Muv}qA%F?VdJHq5+-mWg` zvrFU>VSK_|rsQk+sx1pL2~Py(i8;ORQ`PM+lTExTp-kL}#OgscjwLCDMmr$WwvA+GuGSANUwOx?6MYt7C5B4R`=JNy>@*CmtF=LC4HL=e#X_4r1W(v~ zNC3#wo2N&43#1ZnSW2|Mg5-Vv-wehR{ul_)@9tqcl{P0Af0xrrnQdXvr)ZNxm+b1X zS;E%_nfu88Ot}|}_kz}r0_=E-h6K#s@|W4oF+iS~Ky>NnwU1b&vh=2oH?JNA&CYy5 z&FY9BpeF+ozp^z>qfoA;Ha~9}xV`s{*sZx0 zhPD4YLHie8R>T>*W~?F2>fbFX)+>Wao+k_OAPf*b_NiB7i}}7dm?BNIhVAC#_?;<2 zoRmBE;jus%800msHYpbZ8g!HX*I5f~)Ev3hZslgJPc!_uxl^UE(7(mQcSQ?^+& zS3x2@O7K^b{1bBiq!a2Gaf9QI;wd$c_x_=_($EBDE+8+w5$e&fw)c8dos&h|-+0(NQh5oppdw<;qdN4|pDBj-?*Dp5^ZJbflI zMM#Kmkvl8z7&7R$#lT{D9pd-SLtK*`^qbL!Wwy1-&m1;U(f@IDO@WcMOK_FVCfV5D z*tTuk$;O^+Fflf^ZEKQ=&5do_ww>Jh@Aoth^LF~2{<^BVs$2MUS%3 z<#MAR%C8vqL%&+1qD1V-|M+rqUnPT&RGQglLvhE)A0kvf{||ev3X-@-6AC=L6nW*$ z1iry?A4oig`4I9wu2#GYyz%!nlMcB8Qpg(c zDOguSuK#!|eNNq9$)$SDl>u;eUI^`g+!OYT%I%=E4d-adKUq^COCe8*rXB#m<*I$? z`^r%hX{;T?V}}r-x&9fOK9}RZ%{d)3qM|9kZO9~9_HYwcX z|8-=-{lP`FYHK=?h%T?EBA=3P-#y9pnzc6E%6~h1dkLpCfjQ}LFybpN--a(})$DPD zU7;^~`-=>3jqOAw9R=d<_iqNXO)cPYRRNRp?v^RbXMaE=bGTr_i5vUBEu=D&p%^7V zIpD!|4X@16qSwU-;_Md?kLem1-jFNpqsPLJ!ZZ7!3hG~r^Fw$X$A4Tp-SME`PPOpX zLATOT6Ca}PYhp8W*OQ+7KBLF#;QTZQ)D=i> za~=LgXfrhV@5$ry(b`Z&b^E*1_YsqJ|Nn+bvBZ|ey4v46K;c0o6?A`@bawcq3o23z zrKau!Y3#wA)`9Y*)#X+V!qn{eq7DBcCCHYbQ}!mkc?Wm(4fVa9d*J#8B(Sr)tUYyg zLVYy!@5Qy3+RIr8*R$`K> z{C)n7CwP;rtun4q!wZdlI=L0`xjkQ0nreVvE=Gl6FTlK}{1NNIfV!vNJkPM1+VlMp zT`6yh*CYEGM`H&@#Us=c%Au>5*EW6&V#l}L!CVy0YCBmvYYRvmGm=E zhd}F%Rv2yIo`iO2z_Nw;oh0qEK zkmFW$ME?%A{8r*pG=SI4%87^^p_xUW%B%-6>^|OUU=~pfBtF6hp(s*Wi-orGCMQNGNP^cM*;M6&<<}s2 zdZjo0O37(=(!@SO-t#;*@%vu-HAkjGBlq>q^?D~!(q7qBqLk@4GKOueFKW?hP(hwsmbmM7FEwypL9HA zne$C8hs|XbV{ZCL%*_UeOQksYS6`xu2xUl~3S*-j3r z)mmyUlT*L)veR!(w7Xp&^a_l$SnlOL&lh@VvUuS(LMS3mCS5s_5fh3tt^6Ru|SkG5~zDAQ> zpEw*{r?GKT4gs6X6ntAI2`=B9xs}yX74$ZKcS+PnkkId1;drf2;Oyc6hw`FCP8f8& zKW0?GX+w->p`3ho5HEI?px28>h3Pqm21yl)IzPm5QT4@fiR0joVd+kiO}*t|`F);( z+9m3B@bdk0IOmT4RmixKqy;W~Gl8Ytm3#F#Mcg;7JQloaN+O;SmY#C0=x<6X)Ny3T zp)dg-l4dOi@&XG2#7bs+sZ>y;mD}8&5J<*~$x@Tz&;TqvG6gem?oU;6;UGnGx!i5# zO#x>SP{bI__26A)@_NfW3+AyK zM&17M)w4wmevz>e5fP)6!v_-vOu$F-)1Uhyf`__mb_rys83pUkohUAq0^J$Do_RFr z3gUDwLg%D78fn`%#SAG+Gr=_LG18_X$uvCCMcQyl7}{2q%d$r@Sc zjlZpU@2@V$#;9;s`y+m>#{J!eV`;eV@y9?_kxIY%hrWP@+?r^XRB7&?X5mL#i#Fx1 z$%brfl@+)-eUnq$m4{;D&^FG#`5*W}Iy7Dp5ZYflLGkSQhQA~fxz2*(!7a8%yo6l( zQ7uvBr9%3ZiLUi}Oi`{%&jdriT^JcyRBTGC$U-2pfC3rBEga&7pvI`uF3STFLyK<~+)qS8~fQ((# z^0^U&m5!As4yhDWoIjLm#(Ws`a!pHi%inAtcFdpR{aVC~w640w2DGg794wS^blQvz z&u_HBWqIz7>%m))SlmnWz9*10peN&ysh*_q{!kr{eH=mQi9e;yOUMOq_Ld&4$;|j` zE??`8wk%(4zh8JboPgXsces=)TvWH{Nkt~bfGt>Z|23qB3rpW+3Lcr;U z8}bfxxlDY3G&bbeUTwWmM4Vv`q9JVO{fh~0sH!@k`{vEI-v0hqs=^z^rz>m6hK+zK z8>_7N+s?S@`pRHOC-(5%_Cw9FphrmKRoighMfW*vIT=+FqK-dDUUlR8O>1>~m>=Q= ze@!e;wE$}2V~6`a&S&EVM*|x5YR0I5lA>#lQe`wRjOJg3uNRdcTTzm0vvtN^CW8sF zks~nGf9!lSHso4Ll_=L+)vWbl5)-kMXFBY6Xx}}o>&hdH&>#7VnJcoJBWqutI>~UO zF{svGzQ0_BT|sn^dF~|dX@aO`Tgems(iQe~Lus<~_XUtzX|Z$(yp%GbhIbiccoER>fQh{RyL z(;FqujLAG`mD^d(GRdth#P&8S*HwTt9{@em+-Q>%?S|?WHH{<;1!V(9e|ULw3tMc_T_wO|FO3%6?kWW+jTzK6uL^y?y*DPyBON|k9utVDQFqkXUjTHe6^!C4eHGT~nui)N8{Q?oSGg334J zmNarxz;E_NCMdh)PW@r=-=ozFh|+WV$>Z^g(J0>+%v6iFRDGclbPr!(0%*bM(TUp%}Za)JS`B`kyunye#( zO!uQqM>u_;sV`E z{;$9kur+F=2TmxRki)^0NPAHlofX*tLTn3_%eYO6jv5b?AuyzcF#Le<(GnvZ#i2_| zT-@~i{e(d?1XRbWUL5|t83@9VsK9&0nuzjW*5J~r+u0E|w&Uv~Q%V*@owd;>c!IkT z3yev8EfUhR@wmF#O(;S>_YvG%QF$#-^pKI_UX5>6Ky8Nyrg(~fGZKE`!m?(8iv|pk zaVtyUigA}tFC6ieoW4-AZ^8-H!Q{dB)y%NO^aw`3EFVjytFT&$N3Yn0(iH#6m5ZGC zp`VqF2bkd@N*?5~KKl1*s9#P`(w5!T5)lxYwSk8yuwR=)#|>}O1FIlhksaN)9g7RR z#>%6IPfH%?4a|(uN`Q5|>*o6j)C)DYZ^5kjMM6Rnk7XZiU|2N*tBcx~V?LGy6OZXK z{|sfWU1c~$fVbB7LZ4J_645b(S7#(DE62-?2{+-ZI*Qw{8o7iEX=p5y>q3&_p3j#l zYh*(!Q{KOiKKTIX(!JGPM~|qn=TOT$K&({J`||Kej{6whUakft%0Kj!m~zOn?Fm$^ zG^5(@cl?4F17o>kj?YU=kVMB6-07&GG$m)E;1_quiU7nHMc2T>K)*_Z4x=~Ob?Y*t zH)h%=2CkARO!VRC!sRMq*#s)XuL4YLme)v(yQhPe zn@#kjFGX^oS?VZZv1?Ln8W7b4WjMX3>Pv1_Z7GfxwfZ_%{TRr>S||C`OxZ}S`oz5d z32`z=9B+aqVE<@{H9bt2JTZJbUvis%H9;vicvf+%Bi6=I494D3xBU<2V=p2iSx>iH zY$fBzvjkN3@APPTV>goV+Sp`Lv)JQ^bFHv2YiM@-o}(Xg@XUAcx0_t)gnT3nzI#1i zW3@r-^uZ11W6kx}#Q?*%jI&9M+ZUXcDmX~|*v|Ze4R4g_B|YtUj?n zNB-PgYh0%=>=i$2`ZRdw>J;n~nJKZ+SCFU7&Z(qFxE9fE55S6t*_yFeWj#_?H{QgK>K;HaM+Eo96NW`kJ!Kh=_{6|1KF`wp>T-@^MlGwbK6ZX#K8$L)c6mf zTT5?MV!El3RCr@gk6$wE-Xgnv-XnH~#PSJcx>%0tO}`ZQ-#MRlm3UkKwmOGuzzssq zAxiQ@pHa*REU!m*8S@R!v2j#3oOF1cgpb(yhNmC4mut2tbw-5dvfR#*eyF|80N3e{ z&BVjKlY{_01O7b?e0suA@um%Z0Jy=8t)WpeEZM$tQvg8HDDEw)PT)yG_q+>#s?|rc zrsHBath{P5O8ZQjZKIK^uf^+;leIv@#?~HO-u-OZg~Cu;kh2}#E+}`n}kW^SfKYXp5^^fNctFbr+?@4RC>MRAds1p8*y$gCc`>%z z89T`K@NO}xewBMo|M6e_>`r78s;jG~uv(%xpRa}j&^UDAcj-GYs;neBi9y&D*Rl7Qqd}B@y?7M>Z|o?!RL!q2S}`u< zV70=+!~pl;L9f52>$44s;aVB`f?FQtire{mIVZZ~9%iAwLwNFZQ=ytDn*zIP33|Zzgnc!iIakn{i&^UJy+FxT?^CXq7sVmUbBKeuM zkgAh^z|?09wLQC0y{Pg-{-Id5dG*MRe_mk4 zQoj)X)EaRVGU&>*f0+MNVeFCP1}Hgb`LvDZ9_4hI@z34@ATc5*(UP!g_~MI{UO%Px zQHw8tcmQx*OK7)thv)Nfq`bCAouG71yS?T=LjX8djSb793CU^imCp=|zPF~`S>(&Q z*JH$2ipPgoH{*`ja+(1BgMlL}OzMa)#IgOA5xRe*jl!z$tl6Xw(__)e@+P6SRc+H ziRLC$Y;_kC>3jqF9wG6qYJ}(`2CRfQD>rNPwlXfI1+Ted24DPA#{+Syp z;*v0|{1%MlHju~zu{8?dU%6~{ta|59c)?mE_rqV=J2Z;ar8FP*d4die47%3qgS9}C z(UzG_l~YC5n&DET#e#a0Y-$zk8|e8=?H*HzY8{A>Hnqu}=p!Eu;1k-M1xwPIS|5>K}Lxy-M1;|WjxV?E-PDNgzOJ+Q(H|38%;|-L|Wv<oVVoHin2*+9$eeYD|W1l9a-x6dl?C4#`Gtg zLO6K%zMX++`-gKqR}T*oaH(dsQ5@k??adDNkyxK+5G81WkhF1Qa;&;#9)5(#KS$^G zX+%S`#6rJBYV@pNe+yILruI7R-(oLPz0S2*Lkv2!vPlp)N4;#AQaP#9V!g(-QTt)a zdvbyliJ1SKjrhycxvWu4&Czl7$s#Mm&&up}p*b^jCc_gXNFTxif~{)C5P(ZR0^6Dj z9gTO{*#C9ONh}={@S@nY2)Chl2i^OFCwp`0AJ~+Ym8PY`;CrDixUS(-4ytv*8@^i6 zMU)J2j&^CdLFor7S-d4VJ4iAK$YOlKm8;P^JQG6cP6RgeOvuGfeNZCvM+HXAg;I$u zUM;!edi=|2EnqnEK;qv=>l^##iLtRyY`KmzVPeRdj9jYQj#{lsma-4$!)@u_(CQP* zQK-nC%ZXBf!Vrh)cy-zC(9AM~ElDha76tEA+YFVgFqea2FDVs}NxhNmR%SW%@Jz5y z{#(MzPYOV!4N?-K-WV7=k@jC0$CZMC;n?n2TwbKnM2i~zd-FN?t78l;ICdYU;PO8Dn1p`JJ zBK(op#8^0a%YOkdsJ|^Ohr~}z5-5YEuWEMAI4X7w@Z0_jgR|k`tu8S=RvxT<$}6P7 zMkqIzlX4W(yVaB^YJ>hO3iehTb7&icnYyPhSJcHf!4+HmZgX&EWFRz=dNWU=2yQXf z>mTa9m*qb7#-DYj}q2UXn6>jHZcg zRP749=BQSUi`s{qUj~RNs`TAnUK7XuR;haLuUg^V4V9@7>ksQa?|S{4cNzDpR8PZL zql#b!Q2ZXV$>?80e!kSy;utrt*VIu19mV7@*JUn&}iLvE0d8dY*b86 zsz^kG6UR>wj&3I$)(85(zLBzdZ933 zRgrN6U!r~_P}cC8@zj38_+Y}l)MxcerLs0le4vK6u_9sHWgCu6KX^3Dc>6`u=86{W zMGTC$6>v23$)T*Pz?q0XLkIsJ<*iJpb|WCH5q=pBa&C(ZWInX_Ykz#u11BXamg^3; zO#%$OLf|Cq{#e_#a`+YHns6IRD)vYCDB-*9No3l02z14raA}J3`^keJ+^{zf$Y}S^f${{&OaHq06K&bHfo4mNdAlR0 zT5m|q4+u=}tsVAp$BL4$=w!@LSKRtDD5~S+ln8)*2*~OwS+|}hl@t^g7bmBJ>XDpK zTkKT9kro;N3GI}}(`mj^_v(i9wRKrH*LSkK1}cr{#TX=*q^m1_Hd9w9HQ)Z%w?_6Z za$IFPC9f;c7k#~mSR-0`EJXpM>>B18tx}_HZzlI^cewgwbXri-D_y(9TFt+3`RsBJ zSF~t<&K__fmN+L@sREwa=rn0rz*s^u|DGh)fe<4+n#7yi!(aK43VL0UAf!ssTx~()`hkU#Zo*)?t)b zG(F(#U`~$9(L~4}XBq!UB~XJv&?R9@jc zw>=n7CT6J7e-!CX9+H*(ktNy37JW4EwFEw>(APa$BdUd?t=@Xt(NI9B=V0hajW>fx@~O zjC1t)2SL%%f49N09cTuos&qp7tgXahRs{avvO3P?)AJ!(+Be%nG-fFDvbV&%6ifLI zdS{Vs<(b%F)_L7AZV%)oB;1IQNVQ0Hxy4>Eb~1g)_!ku$(@8GpBS9bb>&n05GGE)> z9mLS6ztiDj{aZ^y+iAyiGB?m!U5Li*8O@+$**3}sy}m}u;BF1+&2<{cCY@^iIY7zg zmwVx)K0bS1S41P9p_-LbebwQ!j~9iYh$3Ev(Y4;>7i$IC5(+ihvJsItkK~5c*MW*) zs_Z;4_0`12FNYf`09dZrT^jwPR1Or;KK7wMx_mYN%W|yI=R>TiWOKGf$jxN9WGPz> z^|S@upTV!&@;~%`Wc&4Q{Nk_fpJ%yW#ttGPA|}HLp>Hb%QmC@o{P+w8AUl29-FFE4 z<<09}@GgdLD_Sl}U3`hVu=H7Y;TK{tJMm4AUU0>BSaL^s zj4H>3&)(*bLm>b1Cs>YmRmt=j$ERpvE$Q4BQfz^tr$E=YT7(DXnCWDk!I9FHKkK8f zJu3=W8(v)crO4s0G=eY;#s^NDVH3UWD-)9&OUZ@Z{|%W1=*i>jVx8k7_8$n@qls)f z881zzDVqRKWnC`suP0`K(h2>1#hgWt{FV%L zIgNxb*yxPmpBOj*gz=ioIHd!DF_0hV=-94U3-XscI(q#}BoO%NRJR%t6Yz)oZ+%^Wl6q<}U_vPtkp|o#5eh|sTP_u;#rDt&Z zU{2_F$T=8^Y5YKkp-)4jLUJ;#1+x45V>(1iyxV$}5@qD@teDm1+_xbf^LV%Hp zsjIjO&TfFmcG*7Dn`6!E?ey0w3$WG+1h=?%!rN1sWQ`pzYkqzZe|4q7$9}w>HdK| zqt`~Zwpmx?b%9Z?NWE)+>L}}cv*Y>A8~T`?DxAxko^h~j0Nm;RgwW&uZ2VENVVAuN zP{ZN$;edBc8pOB*B^qVkx=goA#5g$P$~B6%3YmRU~ea7IK<})1KQ zZ1u#|g&*Y8(O0Tkiqao}-J^UZpUoc$0kDXE{rXFl74rD#T4%k0z~g-SmtJZ57vk{V zP;!A~T7}{&C*tsb4uGMBY0Eh#OMa9(Ln%6NE4?GeY=MD}Uja@BJ1-OEhY2_~VwtHA zcKpP>6v}%mGqyZYRa^5VBTg10SE6dp-NjhKudTpIC}L?@rk&2%@{Tz!Z!d zJuGk9sm_jaWVjrQIpHr7*ucwD>-g=w49*R>PCd|IbM0+Ad$Lnt~`oT zCGp{~#p#tiDQfw4rLZ}X+AaX9Y*n!h?ZcyRd$|rdUDHp`s-4aRass5BY6k#)n7+-d zO63Ja80F3)q0CS|WtidJ6!(CwM4Tr$+~0Kyh>_91$oi&ptuINFG~$6z130G2TVf)C zNqZz;n%N+;@1i~t7iBY@{H-UYDp@Ok_G`W2K07bw61gI3q<$+KDu)y=Rk#{s(xXag zcV>8X`R0wlREe8|$!LZWv$VzjG9CoD*dd2LeBp+>Hynm^8XFaLMWrG@&H3E(fQ7y? zB-nCj4JJ|<>fURm;IX~Y?u4pWz$4y7GKPGy`R=DnM9#s=F!i2sHWktPod+IwAI%A^ zX!mD5!-Fv_Pt;R|T9d)Q`(nTeb7|B;sA7YZ&tJc9b_c?xaM}~g^p)wib6YJ`z?++! zpDx$K>FevCzI1rKeAm#>@O*t#g81&ZT#Pp?!F|PhYq?N(X?DM{#eU zLPUxI+jlx=xOdviH^%D4(d)fE9%&B%CR&rfEnpf)>lJl8$vMg-`=Kuu7CkP$4RkC1 z(HCkPCMgZ2!*QztHfg9yCPH06D+-gbSSSecJz3dh(WHhQmb#Yw+acJ^M*d@@xUyum ziI?0KH)x2cC!Tc#4Kt&}!trf(f{BAU0uN&n_bL3kZ#Jf|waSh!+cgjN?7L=;g`07x z@8b>+%(^N_aQduk9k7aHd%FK{Jts-jZ(Eb!OG9ljC7-cWww6a*+SUKuOsw#^!?@9L zP3hZ9(fFf2CKGK+cqy#aJWVNU7vOp_^6_!Yv4xid0*h~aA6Y%j3wrV#=G3k+$pKZcanqsvDiN^Bx@;!^cVQiZKY~_jhH%EhFBMp4Gj-is@ zZxOaAz<50$$Hc6i{--v{U*-gSb@uXFX?T3W;5a}@sy#0I`6F$wKjiensekGN2j>*Je+^p@^>+}8wH^z<6@XFiCO{r{Du+l+!zhtjjQhR^3t znt~|c`G_PWBxEHJR9$T!M+gO1)K+Gy7AEki?fn)8T1@_}ML68ubV*32E4gw5iRkiT z3L{cm?Qa%8VPY!A{?>4wGU1w6ka6oAM;FF6Nps(cT)xgPi6eA5P{ zFH-y-$Y^=O*s$gH^oYLR<{Dm^-ASxK^wsQ|)8j&Obd?XM$P7jbUy4e^qF`LIz*&cd zDr7}<@^LdI@+(;;7Oq=%lR;f%=wbYO+r4^E`Jua$mRO2p(<}Uu& zEaSbtl-1!1Hbo?h3!V}4UECQoH)50*wK7OdnKxM&>Go6^T#@=oriE~P9^54Hy7(6& zUx=XQb%St$L{7BG#wV)KI|nXC134R^xXI^+W(NAPvM*7F;TcM@vzk&el%XzDzr0Ur zA#B)Bo%14y)0>)Pt|M#028(i6iTVj>2m^J$#l!KKkom_&Z4OWIdF~tPnLl`nO0l(OYe}g*ef#MyAydmP^Zplm9B%)guog|5>Uklz;>_%#$ z@vx1CvdH6S@5+TLGpWOqO?&F|H61*!OLGW6=FoM-5gaBDJGdZ~Oqn1;SPy%RevgEY zUzYE$=8r=b$6I3gwIv2%sTQcSg71J~puCBkfR}ijBpMpl)?yaUL1@N*J zY>)fhw7LQI@I{;RiadOvA=@w?C_Fq%>&u@fJe9AbkfK!7q6$#WDm;bZLbXRmww`5i zaLJsGb^G+=ihv-6SjCZv933uyp!a@tIeeg!v9q#seqc#jojRiHhR4zR7O&{xdf=?A zRN-)jZL#Qs5bhQSFwTB23=kI(-xwd!vik3NM}TyBy`a4^+m@I_f8ZNGN7e)kAl0g( z^E)9kLuXH}L1&8&Po95h=E~D}Z^R`j3Yi40IGBC@RdCTpKlq_%8$YeoQV3BNS##YS z^*Ba3)-=Lv)u^CoZ!^|`MSa^L2#PN6ASUG*G&yDc#Cdy&ah{Qe@qjyLvp>f#g@|rb zWrIr^y`FCh7kmfvEfKa8HW*RC*|D#l?i(r~OGF}d4)iibEFf)S`mS9XWu zS0?LTiCF#SD=ZGN+SBEKt5Jk9;XYRg68DxJiV}T^&gZXE=fkBdP43Ump!3GJPx{Ju zm@m51V^XloyUU1GF(p9Bqk^AeOrfw+tjFTlhYmpLt{g%7hET=EUq&kv8Efi;)1Tkt zrUa=`rgna4v2gvc6CJ_^O*)F3@3QU7je3cHllDZ4BlZ}#Ic($XRD|^tK98e$; z%Z1RRO8HzqMgl0#-+5l^4GsYoTJYl{PY;8^F4>G7=^XkY1-G4FZwVUD6>(KCT!OMF z-GZYKdsbho_0WQ8A>24g&^S~suEOu8;90zkC_1NzYNW{f4}{4l55ExyUQ{Y!9q##{ zc5_ECe{wyGi2dC+gVo&0#G-qk@|}m?tR0z(E8J0Q4z7&33~UVo9CW$?rs9JrQQ1@9$Q7r5nU`wb^nkmxJe~EHM6fkN%NG4hMC8j#id_ z$X1P!;t}7~!f79ka+Xk2;1p`jzcV07>lib_$Ha>R?=H5v4L2`d4=2L-F4r z64em@dA6_3#PXHSHHxs)TdWsI~Eud*IP1BDCmzMj?#OG!!g7=SE0!wOU8c zuzG{B8&0F+HLG#-qg1t%b$EVz&PWK(rBvAevecf7!jF8 zCQY0qm?HN*Z+t00Sj9=T7zf^{xBkm7*A#_;tnpQ9oOoNMtBKsmw+Wy0Mma9lrum#x z`LZ+mC*S?n|0>)jUfzQr`%@3s>2N_hP8aiGlZ?DYl4r3KZtf)E_xFzezDN4Uh02Yp zKhjwH3b0)V8?^NbkU4@SM%jHUL6J7A0e_?8nnd17Hnnp&65rUH!$LQr|ImnjHJ)_( zE_1(R&g(X?XG)VJT*C&F77(Ieo1qNs{Ev{2Me@#G9PD7nS)P->Zy5Pr8HeSLcOBKGum(qWi&hWwd!-;}s$rq{6EwObVlfg%hau3%1`MmClouRjcXv3-fF~d&`bRBL?7zYTFUc_-I0miYbDZ zG2Wu)K*K<<@xfG=h3|r70|}UR<+;^oNlF|ir2)6J`!z%Bbis*D($Dfq7mKJKb1KNrLy zz0$;LtS?ha^bT{upxR_ZG~3A5*K*`p8+R|EP9PA@JJ2n^6IQ~w&$t*CSsP9?_vk!z zGFc--vdgr6_A}w?X$CXk@v;Fu(#E>bHRAehC3>-a3#)Oy#aPemZ>7|s+Xceb0I^Oz z1c{oKwT{Z{^mm=Y0;N)3N@@H+);riAN<+Vk0IGx*QvLsRZftyr{@?(NVj?&}Rl7a< zF7mi0`ib({{-z3>kMOziC7ZWQ|2{S>6-Ow{Uck%5NVV)#9!|UD?ObEQf>y%}3`F*%^`UzvW)S5vNSIYTXV|0=+z|`u#pk-b4+E!{U-sd?a!aqN#=!C zOoroP$rh9{>SKCVCZv=@yeS|US6@M(yu}8%W7m3teqAD4e!a;x=z@I&;$54XAplIr zN3GCrV|Yr-ml27`UO-+F%Wvs~+p^9hT~Bc5sx{oEb^()5Pc^#$F3?@?FUcC$fpv7l z^Rk(s2sG)+toE(>!Bd`QC2r*}4A`3tSDp^3(?Zv)$?&k7_lOBmC;Vk_I_Ve987k;A z?@|JG2%fI_&-^}ZXS8@2WP_z&sp5LQ6Eg=ESx_%aes|=U2@J-uv7sL~q7wsEgiuA) zKdFe9JP?d~gnO%BS1AX+rAKWQmQc=D*x?o=gve1`Zkn?h=-nM%BlMe z^Hw}$O=KMXD2St!sn(g8fkN6KR7!}5f-D9vL-{1YMhlDnsl&71jnT%G0T8)6Z)|I} zwTF6#(mUuMrmPeY1ER5bcdkD#t}unK`?JkKK;fJvBARm?>MOOyJ@uJPv~n!!uRmuK znQd5}R4h+6Kl!(&T1g|K^d#}B@W>7HXI#g#4cHG^JBB0H4$pUt|1hE(5G6Rh1yT=( z-)|c`E!EJ(UCWL1?Sv0gm>k4Cnr>~cR#N?GINLv{lOmQ$m4MM#N1u(AL}Ryz(B49fmxaFJ7I8?FgDWp;X~@Mm#^W!gMIID+t>DQiVML=u1wB$wYiO8*(L zG6S&;BijOhYQ`Q$Sm=Vy(m_plg=-1skl09Jh(fJ!(QcPJ)YPxgV}vmKLWeN0HTlWQ za^VU=I??VzzjL6-?Nz_LLrxA2g$$R*A65tV;9_&0nu9i6S?vi1NiFvUH9c^_QVkg@ zY}dC4S;t+Kjm=Qn+V*;T4l@3aaeR#79u>75UDtB1hWVrxWM5qjUbtaMnIjPLkHU(F zMUSO(d_3ZyhXxqrM<@JwzO_uC)4`zAY3N;TcC0p^Ajsfy{7xaC@eia{HrWC^A%p?? ze7tec@S9F#G0!yDi9__M5t*)9-vwz|Xit6nePL94{_-w2+MXPHf(yDb*t zhHQoS0pfU}+~}?s4npK+E?}SkKK_Xmu@MJ@vcjIpOe1v+t(MA?_p$o~5DNN6Tc zC!3n0<2=8G=JsTy;$F!P$P`>hXwwJgYyZHb8MtJ2RI56%8=qE!h?_P@Lr>I9>*Q^` z&)H1SZ&IfNYAkxCT_9GVexE}DZwBstE^4Zb zc|CnIa~9llFU4I`PEN-(+G4TtH`%p4bZAsf<(Z*edtLWl#VPIfn7BwPXP6svbg42_ z|Iw&kEu_UxKpr5hn1pbf%-Vkntp$-^GhE_#rH7~>Q=gx!Si}?W!Aha2x9__Q1o~?% zHDw^Bci4JIFepz2g#ms_8pNR0-{sN6_3Oz(Q9RIaXt%M zYenTXy51e~&&m0vsda_f+7hHD)zN|r6u_8Rzd@1H%e6e49=z8B$h^L6_$=BhzF>lb zpd29h-8&F&#g$nO4Nko8Cf8Y}Lr2gUSbEh(G*q96%OJDfx>ceL1+$E`7_NMA*z0w` zZ1HAX7EPjCyjs>dg2^$*m~oNhbwK<`=_5|SGa z2bO2#XKuOyLnZgb?n+Cg93JTeDgDH&vg_L|nF!#0TuB}NOM~#p{ZP7vH45`i*8X;1 zsw^tnzgUS`BxQj_0F*x8-rF;;8Bi$8PwE9`D&!rHgP{UTVz{y++#Mo6DnLSs_MM`{ zk1PnJUyBUpa5LWh-cWC^0Z-;(H5A7OcN2p$ohL(*00Iz8&E_ME;1sJ=`Sc*0CH^Wn z`37AmCU^~i)gGitIIahWD$j+L-L`5ABx{Jr3a{3wnHD7cvRjt`nUEQ)O8E9)+bH3%Jq^I{xOdwWNRFqGe z*w{prYt<6VB+-uIt&WcVgu{S9$U2R`ZNb`se0-Mbqu-Xk*~gTJINoqO#g=O^=LMEH zba;IfI$_IIJ8YUxL5xM2zh2b|uHrYl?7)l@NN1vqfy`?OYp=iA{$U?O)VSd1)s}9{ zm>*j#nzkkRipY#=m+PsGUx-v#$BH6`E>X@^aN8~zm^XC-T_v18iD(@tISq#0J4rH= zrXWIF!mTkW)_zc#0FkEwOBkfU`Po9KRg~t??{~RP2W&H{FK-q&%%cV2utZK%Oyf)G zdI7KFhX}09bGTs{1(to%69QZ?6@dyE-XKajBqvY_lk09OX-aT z4+QiJUBmoyLcBo=#IuRrChpyfZ~Sw|`tf3Yu&%o!7Wr_&&qRYrWNTAayP%n_+wzX& zLU{eItDdJwsJAt`6YP3z<@xAAohL7 zHA6$CG!F)ph9R-F{aWsH*dK3sqj@KjFKO9vf_>UE5mF{o(e}*fV%ZHo(|5$0AT0I1 zQo#L|zTQ^74cThB+%&rmgU4|k0VN00d;(`SpdU}JmVSQV0$v!6Q!+g7u`&P66paPO z0jFmRS41VSlIbb2!TCyjz)Oh5B{o=x+cj}|N)VU}FQ3h&r&(Sb1Mk>l&wY(D{k5gU z!f4D6CtJWRuv)L}Qa<6&b*qp*?ReH0qh&0r!|pBvwSdoaSTkya;r=0=4G9(Gi|``o zGczOc-LPv{F*GfRc=RY`bGB9rB;MqN|J+=BtLqE#I(9fgmz86*h>D;0xeoy<*>sW= zsMZ@D@{jYYJ;=Ni_Yv3Hz45vqc4~qYQA8TPl###IeI^Q1XA}?KVvk#J$a--AM2a82 zc>6>o=Fe|MmV8pFNrTbnS08IsMy*F?<}98PBr>a@`>n~z@H$|-I}evg{R0k?KR|Hp z&HBMl^dW0nxdG|Kq{l_i?DdLyHCFF3xcisKWM2??3U)2=u*nm}8_GAe1S$ zSsnJ~L%TkTLvkHylH!U80|+-!QR+U+WLg-6>+sZk;A9mGopSMExN*U7)qj9YZi?vd zy>v*7_kXU6n?aglVk@a1!Za`;o(gsTzdrUT!MmZ@AH$v4fN>RD%nDdEo-e zvFhokjB0vf8scVK+z3YI+-3a|48OK}NVD@NVB{n0=Fizx!}$nQ3jA(JcL9l$qeD=y zFl6s@-2ldRb{3E45;e_PG$$pdKL85V@-fV&3sHe?QVj!)tCv=lGH)tctrOpA^v6%% zjO-qvk~D!kX~*7X|GyxN9S$6x>_)!v6n>^~`l(qno{5j@ZGMm0*0f4afQM%2+@2N+YNo53@_3yx`U z`$-0xj0^-=K1Zc;YtIx1VN5$69RhW^#-Rnd5Naf=l?0yH z@z?r3GIbs-%I)Wn+r_s{PPrpTeswAh4+yGszeHyvyOCJl&}5i7APYeIIJ zf|S+T(^n0o4!4AWOv(SDU2=COx+#fW%&N@qJLr1i23=Mb}W% z{S3W=X^V>j0Obmt-Pwxqb=BXEI+EWd;r~2h5F2a1-f{{H?ljU|3ofM#u8>pxj6!#r z`nmz0Q=tF@z)Pw8!}bCGw^=6S?tKmAtX=I7D);mZ6kjClF8VCiTE|Pb$~lY|Kf{*X z?%270K;?M>U^=Z?S?aO- zdn#Ihk<(sW3_JZPn`k-Ov-h4QY@vTKlA3s_-V6uOg{2=$@8c>jA-`lXJe*58-$UJz z`ZS>yBR2nD2GvWWhL9ETW8#IrR_epIpG=umFx;TuMmKxrE{KVPD6#1#j0$VenFXsf16tlNL`{sOT5Y zBq!=?Pkxxy`GIDS5{dt&JUCI3-3z~yt3t-B z=3UvK0jSyf9_PY`5M6Lvs$!VRq({583IQ{IVVK63SE7RB65`qQ=9m8loiY;7Vv$5k zA8AXNycGnICLJs-jfo_sKGmM)_WZ6Phn>|D#rBkT=TZ|of zC#f;>g%n825;T;*BGex(+fKk6VfYAXA%D~+Q_JlGX?n6AH@5=HUb|!}~k=2Hk*aD|ijZaeZ{rt3(vYMI+Eg3B*8oRRY$92SBj=93IF%v3TK5rX7 zVd@uyCAhjhpe-2W*m-*Yj3chT z?+py9FcuM0+wrUCop3*pFd9fB^H2Xs7v$LYT`YAQJF`>w4;R(3WdMAUal)*SRadW0 z^2{IZVp~CTzY0$V&&vB&1Y}PfF`?@W{<$@b<9Td@l9B@6X~JmC2;D_fuDD!h0X=8& z%cPQ5B85?`$NiY;3eQk=LKrdwXE+uACsxnOIX6$IiQO1hM4K3+t&_;^i^U z&bM@m5?kRtp-SXFu^ba6Z+6BxGNNwDW`oaZwXTpE&8SeqDsf zngPg#!pWt8X~lSseY5E}d5$@ir#gtPfpVkYLo#@&JvW}*`(E`D(02cs#X27WP& zi7pMQmpNGo^3<)%Gm^7uQ;1B>q@~b&hu(l!}NOu@k>cq z$i;${r!>jsg}l)%xenv$7&YM$QP`etHnxjwz#IrbkE+g=ebidr@7NoH2zSYd0=;oQ zRF3hk?NYWII5pnVf@0#K^x_(!A3N|8A~!?6;h8X`l!PLY#-)ZYEpsf!x}W#gHlnQ? z!6c0T{3PJ4(AW1A;PQC;TDiEH)j^(`fEmXV@E zBW@;O{DlSvnjsw2atPFE9s!lnPoQLZ@}39V6@D5g#|tM(!-EM#tST!Fqzf$o0@WCJ zlV_Q3wz%i-&=#ERR1xADR_gA65xV9_s+h59GCd()xjb9%B6+AkUly0$l0!Cy3c>7j zP*XCLV3OcNMB0lj`EAx9Zfq$dup1eBcKhr3;9w*g`Q9vPO9`*n3TrAW40Ku;KkZ`7 zFTpw6a{i)_eh+UVUHkn`fFx+D{W!M6z(gzd^=&R1v&oCaVory-LZZo1Fk)>vNkzvr z3(@V~@Qs+(7?|$PG5L!kaf?kCsV?0JbGmg26_Drdf|%8>1II?c@rw|P@Kg#VR-Y4! z=Dz1h_(#dkOH3KzSuIJ}Uee0URgp*j=_)PMucdD+WZ4)fWr3a76NNw`O^}N5bULJ` ziIpPTH;w;0`s64JB3ax&-Z|v|m!SP-DAFoX`&yZhH1;tu>A*s2ZZi1~gSYgb+ z-A;@^|1g0>HY>1K3y91aQ15)!kZD57WGVciRg! z$42pIVn1Xd{V9!WqJmqXc?k}8f@}V|$aAh{zMK={YE=2=6(X7DXw#AIaw`GO_6jaO_F5_VvK(G+*&!?NJ_%qzl>F17~6cdwhdp?_z@-g>FvAFA1zW@ zO_=NZEvOKA_tfJWafHHj*|V3rgcmmKh=G%0clt19lVfSjbwOoYvpT%Iy_VBMmv7UX z*s}ro+x69`;+Vp*r59^Kd;s-XJ#nesg8aikUmqc>v%4PXMcG`2sBV0QKF{c92`K%Ux$W)jn7L`zGc6wT-(tM(T8YKV>9*e|XwwkWyvfZB*5`_?zc??ocY+LXojd}fft||QkLiw7AjXk_hZ~YHm zDG3A*HsG4s=jwF8LzyPAB7S+V+h+DQ7dS+Z%l8@gT4{u5$GP z#bdxC?g zOlOmg`0}RbEuQS6@Ev*X9aTtI+XtU@iFqnB7fYho56~>r-mrd1`LKt7I$2R+x1wVfA8$f zs(w-}kHNKJZ~UZR#r=^z?QU~Iea?5#=DXq@%8K9?f%?vj(CN`Io4sZRM#rlrk*I4cn>4 zBE_kGqAVDPs4Qyp+b74XfcefaM(#%nulc-8v8xOcMRJS2fJBmiBAI1SjDTFGC26eV z|1Q=+7`vaB@ZL4?yfuTRD*7COh$p&Y}~)nvAAE9zZ^3wGSg zV4Q!PUcbBrvmC5xq^;#{oQl}p?ew)s-1fw@V>=|Rz%}eFM3{4sEsGO@S?Kmn55&19 zA|YeO2_QmnwLGkCh%M#$e8%b5SteuNQvRavOpILF;8-7M6pFTOGqspaqg9u$b=?a#GhJcWz_>o>}xG(q5Zzk|G}|?B4_?u&5lDmRSVAjqL8ooZ>@mHO7QJ@ z*NF0HpTI?vF@?r+($D2GnsIyuuA28cMBwVI+wLh;cHMZ>+BNdzRZ^l&fI)xcH{BnS zeP_f$S7jC|w98HRD8^?b5J@;k2%oNLLy3YB+fli?z z+m4~dz7A&S2@x_LO@Pdm@=i0>)gb79Ohj|C8ra@RZauEB4Dou$fsoqRS{M`XY&rI? zVyh@sq&@amw~m<>=>sb?!rj;%@>s_!b|p3$I2@` zNDoTAExj%X(G5Gw-f8TRM^{&3)qYWm&G$u`^!N)y9)=7x97lvG`1H6l zfT)5Hk;;M^oTdsKtRm;lOgCvMD;6ri2XadcP9l#k@^4_Qchz{1o9I2Wa%H~Q{F2M{qE21s>OB=O0c zX{yBBC7?0h6a=4}><8>885R6GyoL=5^%y`><{kRpmiJpH<*TA5XT)Jg;jQt8k?a$f z8jJ6ym_nHr_&ondZZP}_V(_Dnj1;-71Dd&a+|8CER>WD`Zy6Ry%nFlyOZ?2im+|^c z$L8B91$X$cTceRD7F8lytU2bMoZOwy&yUB<2@PMsObA?@FYP& z(=1cHLGvC>&@VvwM=0cj-SrJ*C%PyskX0q65Vl?w!BLfNa|JDdeNz&3iH zA}ma+uFZqNEuJmc?im_^4c51rQ1N62daIflg{SL@rPD~dg=LEQ;IK~oWO@VcM=mPb z$xY+|f&5_>KzP$LYlF^Q#!BeMF+609b~C(Ob@R*B;ZC;1l%CxDZ!?8kmf7WjR~TK> zReka_$A5>gx8vx1df&t~cZ0E-YfRqRst?2~k;ejMf1vZl<`C6?7hm#QMb#tPebWUA z8%%L&`x^I!SsLvxi{7-4238pQGvK`^HdhDCMMuT5)+MtPTg`P)JHC>XAEDgbX`ZAa z={I?R0;Do2lnRkd8!ASSw0SQ@OtT!)kA}$Fna6gZh3D1ebwA`svOLn3pi4%N9LbAQ zjl&`aj{O*`D!lOx!lGmwp3Zhd(3frXbuml8RRw;B; z3yjOPh5+>1hL3L!>^+K2l}asq@cW-t4x(U8^l&m{OPDEpIgw$lK#?T)y`niLuPdaa zqN1y`POYkuJerc@986mar0)`KF0ug*m&^#g64wQUfXx3e-kIzcluQFdO}e-Rt>N+9b@o6Q?7;+>V3+KiQ^^N@eXYj zBizsCa;tXI+kx~lkzj78Oln&BHh`bceYe~SM6`uw>ObUwAOM=!f=iVRHeFq{RyFC@ z_cRY{hLq0#{(S_{7imD+$59k0g2FzSM^7dI@H)!uoTI+q%z?`Da761O+eZD5KNc&gvw9g8sJW>RYml5dlRXAQ5BcxZ^LU~V0xuOQWCYLkWUf~M2YbxcCoMb zF62aVBYkxI$f;#JLf`*zM4BcZnKkM-7WG}$h$3>alF~U=a0M;q3B6*meXSU+<>Iw2_)xoTT zrPfiKGE*WQz*&JFEXR~y-NgJnTMf69!g(j9%JsBQit#7T_=s@(Aeoa%R)ueXgflhDu zx0l<_^878^tjX3Vb5#;{Fk2BJf0{YEjlDXLnY``?ThlY^g+OUW!jdO#XLOcs?4~eR zf)cu9?wNEw8O`LzekU%Fpy0t4RivQ4&dte7o=0RZb^z5uV(;uyU;C#V9#haQi<1F8 zcD+Xm?Sy)8&@W(H4Iwjcb-xR}Z$eQ{@}w@YuLj9ojxG~_1{=ND^YQGi#Z+!Ky##6e zex+UtkHqz~lm+hO@`17)ZHAZHkoGLO&(|dWB_<78PR>8+^t@udFzOPo(viacU`E=V z0z*hW1SV+N?a)|N zvE6fkd5v?D~)r&Oo-l5Jb zK^{Fo2g#E4K7NhCP!PC(XpZ0)0;Ks#|1huj@Fpj#>W>kDB}>iFifo=UjE7Z#)QKi= z{NJa7tSIqYeOOp?{S?}H#@Jl8Zm|!kXlDP+eOj?74$ShCAUbHJU=Z$kdwZb&;WJi4)PtN&g^$wKhqf`f-BU?P}hA`%bfh zN4Lxc?-`&r8<5mn#Q$aZBWqBeg!!RC3~7P)gxL3Mb5r|NsZrZW+bQdYoa~qMLzXdw zNW6q7MhHZ`&x2iOa@kuB*qzjr6K<$eX)jF=LBVa4>?013Jz|``8^8?_A?t6adVBFs z!jH9Gu0RP$r53gQsHPL)ZC;7ZMH73$)ga0&&ERnD%*2i7ekSB25P$n;5ku1-5Am&b zqCFqt!E&>{jD)h2{tZ0lJ;`pbf;d>NA8IWf$C7J`Oav?~E~|~(!~$Wx9%=NfNy;L~$43Ag2o|fJx5UN`CKRm5b$#h}+Jhb&{9W z1b0kEFK6VwY2-rv#RA&>$_>KF?zYi#Qci&}@QfX6W!YZ9S#$ScYk!-mCy>vR|8fAv zn{I~}ohsF3CO(C`TrSJ)Z}He|k<=JVpSwd6xkFd+sVa`urb!~701!9gYq`!3jr0Xt zzjtuNJ~fZ5!rD@ftS+SFnaGyTkb}EiIGUL-v@nm?vExq@e5ip$9LWfs@1m=%S~cvrP)UU-MPYC-^P*V=18sU_oLwf$AfpXnw?{X+T37dEAEOG z615Q}-MQrHEPnXaI`TP0s(sO?P z2+Ey)BK;xy^ji6u@5YW@oH%6HBsI=XJ5~`$v)O^Clu-6>&}ztR1`~NwFceLO7_ev( z6oE{>tM~?O53}l%3wN!6 zY;C0vD@9&g)-b~7`#~P*XBOuWDk36;&y`qtLGLzQhkM_6p*ijz_~u86ixbqU#KiJ&K-W$ns_>fpn-2?; zBUIuxmc?C4tVoZmdsCx^4GxC&`{(i7!JE{*qiWj%w&Wj70^hDOEuOiJQtMi^u<_EI zner>(YfeA15h z9L0TBYIX`Ia3Hr1#s!HAXKS)tCgnm}3oaon6${fSkk%h48rE5ev4$rXj~4lGxjiX%-ww1);n`oOH5{!1`r;oJ72+!eYdR+hatGwuS7^0K+rT4tSE-QbLABMR()M{{^!#_^-+pf3w;3}d`IweF zxK^U&fW1Fwb4{&pRunMVWl%JdZTXK}e%D|>+e90ug7tdp3DH7$qr-KQOZdiZcjk{F zl;`fwA7&r>ZR4K}m0V!}IezBXZ0N|!k?iIEsDckiY+M#5tKoOH>ikm*S2%niHs= zP9+ZJ^1$pKebBC3=X}%qgW38GZudR)JH_8R#U|NkvxHLA+bpCRuWINUlpPDKI3AdS z`W%^-9pq3_?EFnEbsPm|c4_)y^(U+e%? zg2?DE2{1dE1S$kSdX^E{7CD*WC~57 z_&&Sv_T7g)4TRvks`PxQcRE1P%OtQ=5WDuOMk9p6$RPG?Cq<@@CThz6FmuGq(okYIIZa|4p-%U{hv;xQ(aYK?p@iiub@;p zXyAle(>}D`yW&O+=WZ!%QT-j8eHbA4nFFn>HBLy{Eo_PGpWD zT<~{2*Qq(#tGbv^u_*C-daqS9xWsY2uyDK0^#(>8BC-l`GW8|`!^Z1CjElA~Fi|yC z{l*`oZcQUbm1S^T%wqQx@YwbgNMTx@4>WTq|B1f*uOQ$};6~RklcO=405|rSTGKhF z=QJT9iMjGG5V<4D5GXaYFw(=J*CtD5NObCrf^3*lLJ;x1d|(JWt4|4s{ewdEeR$}N zEXq~;t;+%JxEbp4@@7|GkRgIoH1=Wi^uxCPdBa3^uWmOq7VIp1;Yt<$q z-7O(feKG|nF``df^D=&kmPLnd8Urp1i`3z7@c;x^OxH6E}70>J1_*FY{p zAjQm%jK20h&=iR%@^TFwu($9-1h$5!M*Jx#+$< zc7FcK={?xKFHNrU)sY>BTFKD~y`dc%r4Zodzg1G9n*ONj;W6PXgt)ij?)zN$9q1EOES_(zR@kFbWh_J)+3in*LIEy&@ zr`NR~fel!L74dC(>_o*5z=&J-yslgtHBlsjR5s!g+%gSuU?+u1;NB2z(6x zvjZ4_4gnbPHis7r0~n~~GsPA(^iaQrnReB9OM)>Lg6Dr1#)XvD&7x|2sl8aMlJe&w zewT$d|5O%*XVb!*LwuF_dqphv(jxN&^x(BdNol$q?^yEc6>h7y8o}+grrqo)a^XX?IQsE6$T zFCpljq8Oq{ksAZ1J-Zl2QF5;05%r2=*N9@yDTUtp^+7B}9guVu$6mZt2xRrMe$J49|U}b$vowxWK9nYj})F!EwTAezBJRe5PX6ii0M;*+= zDRcxFXe8p%PiY+eA(B#?o&S)M>+MlgzkYtb>Em5egD8l2nA^{$^}i^{hn(q&Yua+7^6~ivozA#5zvOema4?MRjPl5j9YuA{)=Ph#xY&r92 zvS+M3yTQQh^DH!3w)u>o`mC6ttu6J=XXvqchXO%eJtMj-V)aav8*QB}BUzZ2*!k-K zI4hWJYLsr5mk8mG)*EL6G?`rWF93|;K86|@bo=0!*XL7<_0$JebAvs6a*BXx#M9LS!PmmTt#GK|MlydKy_8$siMNyq7e4ntQ5th| z^5EfP5MisNWL-fXM8QK{y{zqrUmFegm!S+y6S@K$acK(&=`YOYxJLoAR7SChRg~(G z)Jge~RbiHhr=fAj>gRtr8(YY4mb2+i063@wrLbO?C_~!RCtWr2g6>YQ;qPi7U7K%8 zU~mYl-an7X0<2$ssbn2I5h2c`T`i#l_u?&2cwd{R!rkTS4clW#&ZzoA z2Sob6NF0MD`v`S92lPH(=stC1NEfbi6tO$*~+ zg+{?5)lNpazi}0^eq<>_=E;&zadW)=#25l{NU6r(Do8P=3PPY{Mo6U@X;kgY2FooT zS0|bMPPSO{s8|#Q3!>Ffh^++QA2+?SZ;Koe;%5PmJvpd>^_rUN0vTUmAR#!q)0pcg z+|l^v$FW)c;F7T&levM_1ydIOc9^Bu)pq*LA{X5dVhMo4T88hW90DqYq(U&{+W?o= z9O>cAbv)h6fZQ=Dg7)l3j&Qpm6-M1}j#jJ*)RQ7gWJ=AnfE}Z^tJ-3@C5SG8USBDIUQp%{B=YUIp(kUk4Zjqy=eotd_hKY51u@OM+ z>1qDr8rC3LTk!3D;%&gfkU)RoV%!TUb?7r%`nzk?%i+xQ=?@@uGaH?Fb)}d#kkK3C z{l)n=c^2!>YTo#P;Yg|I>Ss#S4Yg>^((I0mw3QU+`?LDK)Zn&k^$wQ11vK&Ht|F>jgT)tdeUSLnHFTn$L$MDZ38%5>X@R!G1T@=?$sp|`8 z`($Jbd!$L>jhp59*sov(?ps**zv1kh&5u|Ryc5=D3)JDW`hDjs{jW_n5_`#e#vymV z#rJ&Gn#15jpnh)6LZ>tgr&3`SFq!yYD3lO5C6qNiz92hpws;>MPG%+2AqLEu{FMY= zcbAu!=%Om1Uv7cVY)mwYU%mc$#Sap*I&9$g#z(aBz6+gJxHQ>#uN7%}OT|FK6S*0O zuDRSzH8E|%{8+b_wYbDlU@-~N<vhkJ|Ju>5m7dCAqQ$9oC}cL<%rnr^*Z5?F~%L z^;`WMHqSw>yZLpHK*mT`Y0B#-jewHB6VHovA?C1C&M+4B#8L`L=-nfZ{bRDa}x%k?5-2T#h?-Zdx#G4Aen97o8>nIeM+{ zGDrNW5f0O4H6AJC>XT>fVSL>rD)#LfnG2v0);K>wQu)SiO?Wg@=5Jzw;JWs$F3OD7 z$Ps?m0^V^|Ub8a8M@lNQ2-Y~IYi7bf^Q(Zak^b^}Ok;1T`%&QzClN2NHrIy$wbRxi zFktDj{cmtIzCYMQ&O{jqodZwe;E8I{!XhA=b?Yx}Ac+<~6p50KNf-Ln#-`|%Q0T%1 zENrt6nVKw%-<+YVH1&zF4-gdOh$7Cn#ZY56M}*3>CB^}xT*WMLGPQ)u-I;I2-p2Fw zcr!jz;cpv6oifw~)-%jfT5a7j(QIu3i0jfUWMSRm_xCDa*EI_-epHzTekr)G%Bx5A zS#j_as`v9b^xCvS^t@%4+pm-EBa1ef zJ!@^^sPpLOV(ut}#SUoP&sYV}ag}XiwjX<8zVrql%v#!y6{spEFA9jKOWoZf z*(S^>WxiN&k=9GDFJ{ex8p^H+$utFuA2YHR^@}ngY!Ak}%iVw;cMc^+B}!7+fggPB z>rG4#wN7dY7sNY$dYOvW7(XQB>1%JL{U$x|M!Aus(o+w6P>KH#7> zLH*{>Xr%=0VZ>Tsq7xy%#7HgS275T&VBX2yPb^TaJ-I@P1q}-16l|6*)-(}^#R$rf z!~TT@QKW~I|JLhCTHanwE`$WzZp*+F~CuSKLajf6O^IH z)Q$6Y(9A(;AsN(cX17OHiz&e8P-=6li+Uq+Hb-L|7e?0tq2Uri4uNbafrubwF&0H)7SYx7?qf2Zhynwd zEa0R5qz6dvBaJ6RdwGE*-|ss=tE)(uY7{ha+N;d6G+nQXZ&wbK`m*jRDMeEc(`?eqJk(0kD42SGtdY2ND>R<$ zwpd>TUI=eNM7)ETjzYndy2vc)JGQk{NYpr>IqPj!z>rq1 zEIpX^Pe}DsAQ^7_$%_z$)MucbH9+5J--(|DLIAY>j<(V^#u2CVl8}5OS=pZU9g~FU z`MEwZzi#}_>a#FkKLP);jU?lQ{`LmKYC=&VC93W56%*&L{?$@m=J&EDxBV}Tf+5$2 z=@`F(*sRD7P_j%B!e@`8$%P-aN?5@s{S_C~{`9A4ncy$Z|`SzL_-s|iCPOvVGZLv@mrMkR0uXVP=Yp>{Jk_={Xu+0fUri8bTj7QZ}%gmW_x z0T!{Lh;rpADsgVX9}<4NL|1*~$HeY)1W9smWkwU^iR@qwySXb#7&d8>2OSE9ENwv| zl4S2{PJ@cV@Ii!l->llX@MU2|FO|;aa++G9d_LQ^1?^46`S*?w#*&m2Z6N2zfI69~ zWx6F*iI+^(xY)&ZehyX-ZuqA zD&NFyE{dfKkyO2KZCve13`)ew?bQ*;!iT}LRD|Z|==4J*K4NqQ?2=Vy9#a7&UqKDZ zj2n;B9uvLx7b0D)LU;e$t7*u&uAw7E z%AIGClTzngkwBUcdQis#5hgTP-3={LpcCW@qu@US?Y}pc8^}Bn9wl4kjfyYYX`!zG z^Bm4}smCAE1DNsKMHy~KFksMS;}H)pcY=-QNj>6XpT9Xr{Duh< z=Lqg0yzefbU%?{2jWRLaVbuwUF8;9INkulqppT7fpwZK@4zMNe82ZQ zK2)>LrUnJs2-cD-Kn*o6QX?oE<_c8$Dmt*#7FfY^N5%zD#0JDpjK~;yna3e}{rOH% zZQaC=|L!2WY}S;AI_^A0)J`EmA=>Q9zCpTe4(R<*jk~EREy#MT9vy0+{3G&1!+qkS zQZ$*jy9zbu@R3hWFqFhy#u~bNfLsD; zR-Gb7>h1ap#5h&S5SK)-HT6l0tbtrRb3C29jZ%5O60t+K@k$H5yOe~$I12LeQF@Oc zced?BJjq}rBa;IjXkb3phtM0(qk|$MX7}Y-ZEQ;|NF*P>OoZq_d{5x)=I~Ks4}N)# zOh>EEeoHj?WOXDL+0l3^Q$`^~ku>Zyr#-w`4LYt!_9q}x`PA8yoVx6BP#wh0oV_&e zFSL?4b?x(cYGM8j6*b+oR?0BsnmW}n+n37%a0 z9qDht)hr6fkN>7PI6o9Ur))U0@aKz0)V~pO&h>(9Vy7?e_Ev&W{J*VExnk6*oPaY>sg_f=O6jD@cVEij_k0mo?ZN)Q+8% zs&590j8wwaRsjiR-zgs3kh>gOqL^r|1A~tOxogwpMNfIV>kEe3oHUwDi74#1j;BT! z%cPOULYP@vFRuCsYI|WEsTg;KY?wD~Ew~GZtj*rAicpn6Ic(ty5PlZRA)UaO<9*B% zb)J84)8>){Yjx^oRvyG1SK%FDYlcKTO8SHOOt_4q29aGtSd~*n;~0=7{H~*g~u-^miea9 z4$s1+aG!6TJ!;9Hj8qJxsI7(b+m(16}MF@(tjdaiXv{I%x-O zez^Xxr(S3sGqLHnF}@F?z}{=SNBhX-=fnNp#O3$!tjR~9@7TDn>~aU^?9G92kMQC9 zRS1UMt?7@)@VS%l)MwL1>1N#-jlZc2E%;fcvfV)R579`D1&40<1zH zP#&0?^@}2Lxd`wn@hk*lr@*!urM#ZRe+)>sf+g*p1Xlg!Yx1IqSgo2m5RNu4TxyH1 z{ACe!w9ALT!5Q~}x7WX`6r?whJV15f_@a6(m(s zyXU82cN)LFie@xS6I{vh2>~Al{?UAA3LCpcyFW1FUM_fu2({M)hmF-DO|Vr6PRBqE!e3kf-cGmnEUrnT^cKb2=Q!t4K=!h#&XR%ho=_5lq3J7iY$E%sl@5sr|Kq6*|X9<7nm# zaJ4T)VJI>P*ktB@!;ZKc9=uhTo^3&HEdUUak`$>PdKfu(+f(FW{ar>;(-3Tr8*pfHcv-+q zq1;09U9$|U9+oCENbs0bP4BX$SNPaPT4h=c`D5ZLkQh>6Mm9=GCrXI9 zcun;X-R2t3){(WDs|}V`-eT_inDFhB)tEq@NAn8A{vJO~@YW#6^eA z=O+$V$LEqgKEJn4+K`_JrttS8&SNBW--UZjcPQC0pB#zrhtV=$xoYW6&58q1~$wu_zQjvxI_`l=G7RM?9 z#2Vvy7p6;q>v{JmiMy((Zj=s@!1qv(Jgzo5kG^PmZ4ZVyGz@Wk|GWn#-nfT=sMN zECxAHrdO;gb1eLnLB@E*zqQ2Mn`G_jY!s!tV|?KnH6Uvg)I0Becl$)UMT}ShK&2o}@uv6!N_oco36C=}t$N@~tJ`oAQNQ5?tDq{NnILt{XMfO{pr3*Ib`KQI} z<#mXkgTJjI&U_e+>uav-%Qlk&8sJy3?NebBrycoiCBGDN6K@|`;x!<60m^c8XLWSLJ>C>oxQ#O9ufs-Z z5bp_1q9IDquEii#A^%b@#m8+;%mbMH8u^%zSPW$wGaIM464f|0Q0pgDLgbt|ucm2u zAIC$YBYUFMQV2|_Q{m|Ci^6a$4Mb~EVW`{9;54F(3ncV$XrV=*Tx#=`Mipix?$EF! zeF>(w+vG;DWch*ib{y@4rYv~-W11`Tz{!=j_%tExew0XB&^QTgdm;!k6nSQ@fjEF# z_?58*aDL$*sPjq-iOxq=W^{IsK=PnXxzp=TYy|B7wqu3enLHNZAyAiVr35Vd0Z*<~ zhDqdrY5T9y zUtzt3Lmou7+cST6jv#oEe1Ou8S!%r!_14WS>(A9z=u+y8{?$py@UlJLQd|9zF(}Da zcKd!oHKDiR|J`{I)S_LUK3W^EZ*on^bdGuU>HU{@Afp~SaQ^%_P+c{lHdBZi|x(TzEk0}?>jMr+iC5&WqjHn@Vu*;%U?BH^nlDX{d| zS06_%XnQd1Ev~}Q^V^I1E^X$^`apx+>4O+J_|}KR21DWce|AX?G7@+WjCXN+YwG1H z0!w%CDD2eO9mFPv@f9Xt2eU1fKYdZpyTnj3m)&AyNCVj7o_a~!Q|4)p8Ys?Vo5 z>53{cbOT+T%}hYdK>WVJw1?QT-5Q-hl1kE_^QIU#Lwb!6nC^uQ49&pB0j3nYK^yUZl8FOs$^P_1s z!X>*HzZO90kC8Fze%^f`E=b6&R~C&nJ**AK$;*y{gL7jKaogR!e2IboE}9zJ`Y2xh zngidij*tE{tr!ED@#@@qx9+%Fv;)Xj8SQQFXBchQo(- z3dw^>qKj*avg?h&iiBG_04bEjiV&%LG6N7OULj~lK@T`Ot ze&=7gOUcZnc`7EbQ&4Ald_1As2?%cO;j*2*F0JRTN^OA>aAp1#5ku3tyuZOTR+#Gf zby#e&-~agy>Fd+{i$IjLRJ!x0s)LzNyIHRFt8Q7}sO(e6GSFH^gLAt8z8-U>3zQ34 zpG7h;#6M{f4Gg$!r}=+rZ#aL+2eN3(|Hsl4VX`KJbcMi8)(SuQ4wf9`#Z=i`-FrcY}FmalylW+o$qi|~*$n%9ijoQz9>8jrx$e^$$5H|%u8cD;Su?~tSO++;;wko0d zDJA)~^PxhS-3{}^=U(2(S(PH>)~qyJ`Y`9ZJ18yPo&b5}3ywrV)$|3%$`_Xi85 zkYeJPGq8>FmvfV@h_1JiS&2030pVwE~35S)SsssITC z{)0Tk9T((^%%5M(TU>}s=O&UqBS&J1*5Z{M>bLVgds8JRZavOKRmWfcAJ{Xc(vn?hxnZ=h)T#Dd{ zU6e^`RRZU#bMJwc+Ry*?nblxhJ{qZ?QZZh()&*em9ux09O{7Ixl)w(f`tS(({wOC7hL19>TcgVq8$Qk$yvKu%oCz$7DG3Q7YbH`Xe8cnnsYjea5>fr z(Thf_W;tfdd`zF#`(U||)o@xRi=t3fg~U)>u~PNMdKYo-$nMk;e1D-scj=NX#L}Pw zlZllyZVv$!w(yaTpf}mGj`@KI+t+39a`9}8Bf-V1N)fBQZzh3tc72RvOIa{2Mkcqb z9=0Pqj9*WiLmdcA;X4JZ<4IIt%x=4$pK<+^r-f&5b$IE~oV{xehhmeWxO{ye=!tv` zrqUKbC918zEXc{VB+X$W!wwukGoi`UdSFO2}iV-te z^r3`zj9C^PAD1vhx;saB(TcB?DsMFXy7*6Jg)R@0-W+-BUI|~L*AYv1;8|PMa#Up3 z;dTQ5jAooZWw!0Ix$OK*DI1NDZ!9QkIu?gsh|k`awQrktX>>Q_Y|-l~5F_}~4$2?)TVh zzd!AhnLO=L-7ztpDzs7Dihx*oc+jCFW~WvNSf!^loh#yyrAV9f(o{R@YS?!v3<`;h zyCUFBAWj?YYj%1w!^GG|ChQ?lK6Tsc`~GaX8__xycocYK3Ku(N*c27pU;5Dn|1sqp z3~51RF6$(2u@@Zj>Bji6N%lITN#d|%cuO#kCmyU8+nO3Lf-_aLj&zs$^xv$9<3ZoP zHVr2Oeh)U4yFrlJv{~JiML@^?g5l0ef^YPTyJWctxta~-MQK^w+%hI^aGz}nOYIle z$T=C98j&iCY4h_{S&Wx>+@h>|yoL;&iF5_xA1R$O`Vzcbzs~EU*cwZ2kfS%0pw56@ zcoji+Os8DH-;gRPG9oFJWZ(>Ih)wzm{Z&~d@}p$d@bZq|Bw2W_YW1R;(#UlwEam5d z&}WJVCmM^5YMO`?F7+pmTQ(R5xPwK!Z9!{9Wihoy^W zHEEgL)glUM{N14ibA|fDU6w5G0zPRkJux6U|I!c_ng||aUB{CmaBb()Yt|*fLNAWB ziQJ8bYKC`xAM(Qax1TqK(i(lri;;UG{}= zTeA)lYG|?MPIoSd(dDR{ERh@NDjPK~3AU{;gmk`QRNe7>mw*^h9(@!i+mBr8Fd5TE zOZII_8q^8zl&L!uzwmSGuU`-6T3b9NGMbzUSt z;n9)in0M;5`(>w{Li~-?bEc>FGaCYvx%LOGyM{6~`a))mCp6^(X(f*-d2RzLSWsMv zs}{YI1oFM3#Q94*`BtyjU$gM7__;$jYW;dRz7&Sjy#WrRLvt2)+nB<&dFw@Z67TOx zr>JnOcQM!;543d>|x zW-YerQr}e0n~VpNBdPOBL$#qXm5D)x&Y$1kGCkoHY-c}!u0Q`h{GsWBv^m;iAkOpe z#A=nU>;%1Iq~=%&+R7E~Z~rRZ->#jL`lZ;Cz#^^jXUAETvuIL`jmE+`vDJ`~bHBtA z9QoH36sx?^eho01DpEg{rPA^7BJCt|AKdGuNOmR1>j~j^#2l!s0NTV~;;X78Dk-v6 z)MP*&7pQV_V{cnrillnb+hYi;nfjcI}FM$pati4jG8mO$0 z7uaL4eFE7>sCoJ>1Taz$*ItD37mO54-9D}Oq_TIHFI4eU!g`TbZca;@lkWS_hb%Mx z#01S^d{Y&YPpY;6_~{$i$mq-9sajq<-J)huV?o_Rn|g2rswUA zvhTwWp-e{zH&b`b*ZeU-*#S$Ju4U_aNCYq$19c8L#K9(3${bBnmkqiC9JpLlTXNSu zqp79sCVH~)ctL{Bs&p7MZO)HG*pV+-<%S%zb;Nv-N|8TFU&7N{ioIp#>*Gpl^&&b# zkl8$X=@V(eS|(A;jmQ73t-J?wfo^(>r_ZRV2(ku9} z%aLf`pr6kU7LU`_LX@sP`K!obc3hG7_=2zw8%%@Jv#-g8Y}11M(KO*-UYFhtSJ-Xw z<}0qB=BNnTi6gb4qm?)n`Y*noE~pk7lF(b)2DcQC2bMG*rcdhSCKa&OKUh71e)3+@mO4|}HKNp)5KAbzC{C`? z@u#pC^rm0o3i!#_EP6tb;d>_-Pw@=*1{H!8c%uB&yVqyU7l1HkA z+CZUwoHza+xnmY6%5_aPI-tf^-86+*mFZz>)u8t^Oy~D=XB$mNxYy2yJkiLNG!8Sw z`-_gCR+`hpiJ~m7S|R2}$jVMjfk~v6jjkOqVL0EA=S`k#vmkOgTR?&``p9e^jHta9 zw;IiKKVB4b5Nlv$(HY$4lBH1@myn2+9liEO@I%`AP_x!Pjxgp2gJOXbdW-l07~GeJ zMWcq7{Our@Zp6M!6}McI>FVo;Z@&*WieeDc{>n(cQwS)Q6bMr~Utl*r>iJ?{AFEv|?#;KChE`c4DFhmm52vn6rFt{QS2V2TJ z=8}nXKKRTfgg`c37Gy-0Cq~sPBro z%O;u0BBI7~cL=4z74M5EH&5<{tjKQ8h*Uj?#t@nMbEnl_D{y0dM`D>e$A;p2psZ5W|yYW0(W4nLakFfQ9v%Rf`zQ>@|4#DEDwe<*h^O`g9IQVfzviDx&4%U}o== z|8Vhc2v-gi4*%LhG3M^_t+8hOK#&89M1OGBaUvIQgR)ihaLISlua5Wl#DcO%0A1Yg zo+DW#r>gq zo)vUtfI}%g2_Q@l}RJvSUS| z3BXe_7kd-f?VPVt`IrSK6d4)%pi~KXXVMFFp`y15LC}Rw_!fN;^5bpJdxG~hk$)Fg zy`&kCF@8iOl1w~)>ZQDIR%)UbV#q^jf~>T$2kKNBS_L*N%nc7%0JzjHY(lPTFgE^b zU{rr0_(J0Z`-NQ6>vvTe{9==BEcTFW!mGZCuP?pTw?3qy8?%X+WFQ~=p5x&2LJRwo z`hR12e$ImWjyA}Jiy+HS>7*_PvU{4E&0Rj3)xeGzGdG`+c*Y`q!~{NeXsM8oKy5hC z=!Dta@XJ~fWof?>6>Ym?=PI>s1{aJzn{Vc7a1`f8rgYJ=b%~8qIW-_=K>=v$+xC2( zjpJsgyZK+%#gs#5AjCZbr8JVB&46#oMyrlcBX&5iM~>lEYd6qqv03vtTF)Qx1#;nk zNz?*E&xcbr$bFKvf*>lRl{FX!fAVR*^le-@m9*ew+{Cj z>ow1siGS#+|Lhrai8|_p$y5i|la8e_b(pBXSAMPi96c6aV554mP-oBj@nh#>gHn4X zj%5+ck%?oD50BwoIm&*`X}|&>OutFkXZ+Hn#QoVAYx8qvu-Q7q0S&qx`HsceSxB`? z@58FtwnEY6O`&u017sRcO_W|~q>EP2{~=JcuXGt=S@DS8!vEo!i)e2VKnTD<(!;4r z!?ie>|3ftgp=wt%n{ge+-@Ul&4Td$L6s+~A|9wGSmo83doXQmkc z?|+U0`8rQTUr^3EGbVOnp-K}A8Voa+?>#qdGu7bBuwVUi%=X7Tak?~0f<&+1BZ?!$ zpg{4;N2&yRCL}h3y(t_ytM%jm=S zU~pxTQvou)D`xO-GbJ1B3|!N=mEmGwyM|D%(z$R`s0{7ZP0LRGL)*6ERnZ*)SsV6J z6LTFpkj zl*Bk5F8((>bWQ2KhI%2iGq{cWFtzq80{FfttrwY(2=;bxdFSAd^k%@1i_BZvb=U>% z53tX>Qmk@hObzdjvYh}FK@X<3pE6O#6v#9A5~7o+4&R_fr}NFq6r01eP*0^+P&>+?-iU|&F;uN5a&2KBka;bY{qo5+6)y}1Ak zA%afQM1=FL-dI4$+4#QI>5e))yt<`|HZh$uI8P?TGzjdKf)Y0bTsk_sSP!XvINu1G z$AMgLi>K!oZ{Fe^*=KXM`-`qo*RYQ*s9%Spgt%-ozMS_VX2nJbH@Y=1Q%bg76;4|c z>}^I<{WF}P1Z55Xl7UC>cj4DyFV^Or4~e$M#L_Unmd2Nv_S$jKKoiid@*$y*&d6J& zOz0?N1i_HV0|?BH5O{0?={LW_y4BWmYF(Vs8!677zvKM!2sufnYsKsTsbeX9HA|Y9 zo;CyLCy7kc0UVr&nUi!0v1bx>`W0|~gO>{*ip+EE;MHX#E)F|xf&km;3)+MQUW4fvcds&dy+0!<9?k=bLD zPetUBiuqjejc5!cOEF{5dkvwBF+*#1Uds7&XA>)McNav*fQ*dZCLF~$*4G(~5NeC8 z=hpY^*{jE^A0F#beo;Czu?{Csn1i~$VFwBe3Z|1uJRjM7xoj2jT=7!lM|Nk!&ni(( zj>_aP7PQJ~!OSc_1%8okBs*R_k*)W*pi;RRd;=H8Mdk&;>8c?SQ>UP>}((Y)>s%P za>8t4!q!Rv4gGb=_KQWqBd2rtHFtECZa3%POa_^C!!#=2*G%Pzf}-oQ=ibk=mmJ-- zWJ_BW1uJw?pVM6eF+P+K^1uzb9sHx=08_y#u{ZgSAaG*D<(kW_L`2_ntU=wurP^{+ zRRZI5sop_{ZeKX#e<)||foA&|*~Ognrau914JqbWd^wy*W*Ed?h;-Rq=-!4uh6;jV z`Uvzj^3)pE3C_IE7)Dsqjj%}&qRCXk6|C_LBSUyspMWwwB7<`Pn8=fqcBJ1}pglrJy$ zF>SJ%k@bdKB&6GjZ^FR;rAmrboU*om1Qv-A*>v~$K-jFzHlEo`3usKzd$kz$;$L&) z)>oj0v}U~Q{0)o%vvn}^H6+ea2r7&z0mjkWv{@)YGQSw49^Xe4l`L#vOI{ytho|!4 zrt8vqMDgacF5XOYA{WctFIcjV7#%-5OsxbKr$Le8Ha~0by2c($iqSppsfmC%`rnQR z*46yHdMHGlJXBgFeQsA+bA|Q(m7P}d?43c6t5XI#&6!YUin1~(K`gpt;^4@=Z#W;s zgOQhEea|!ku{>2ZJKPUGpKthOTrxesvn9_>Ok*7Kt(``+lJ zP0h))^J)`@7d>G-e@!%+q30cPYJRUD0%Rq_qWvBQBp57afF=3Q(sRh_D9(Pz&`;mNE1Vnik|3)wRM6AqHSnV=hpf5ItbO6?^4?pbGY@c~ZAB{{f z#*Cg_xo0zfkJWTmRJJLYc^uG*DQ#O88zNB#Y|4@q%4-cU1ufTBg(&aK4Zgz(fCmy? zp!G<8oyY5>fKazy>PjH|4Bt+_Z32`D*Xg$pRMGyqVik$sx;H2DiC0l?@H z%~j}rWOebdl2^~6;D=Q=9woQWv3Y`w!yvR-N-B{c=B}P{zBc+kxWLf*j}kjaWfJ9d zC@)mRuaB?qhKE{JX${B<#FV&72TV5q0lCtd-xYWb-=_O_&zoq9NI|YD?w^SI&XGrc zkJrcS_TXzS8CFozveRht@gX7GSXHOW(`JBu7hUf**PJ(pM!=p9h0+)BFB=;i zFGU%!WghmT1ZGV})2}C~wWkMFCR!hxCoAgLenXT+CNrdjh3wuhhfV(FHH0TN@)^&b zG8=>V^CzUnSxiRravSA5UsR0M)3}45eY_oJ1%6oPx##g;p#f+LK z;al&Y|LJJ~EY|$~1hF#{-d{pTZwk2IT`(K8s?5-a-RQ5aLkDBZ4`Cfmi zqFD`kuj72fNQlc{#M|ST{N;BiYI84@zEC+G%$Ve(wj(u=0R&`i&v)hcGk}CrWs!1u z6~aNC!yom2C2XCM2S8b5hg8eOS?-x$yI7B=fnfl%x#Aa@MU1&FJqAGF6;7ks1in35 zOI!A2>G69*u#Qb(rjEm$Chi_wr0}lun{rdNQ@odu&c+?e`7WC)qd^w~jMX){4M8O^ zsNz#tx-T`^*io2#4>JzZUT1>w#LWs3Kg)F-fOS;sBGCVTMiV_Q(Ar3KvMyRyi<$+9 zE6}yVQ0q(OimDxE@B}NvIEM287LAHC!Tnj8K1b-G+)Eu=dZ^R&4W(85xaj^zHJbMs zlt{|;P-BNWhd+BC7DB@vTZy9qH*dj6D+~~$_v{eeGKCu1#>Ho046W^A?{D(0enFiCzKLB1*AlwG#Sslz8XS|vWbsY0=S z=vx9ACGXUGy?A{e-z#Kbwi?{f>YlsuxzsKDeVgZQZw}{E)LNL)TX$kN!z0LDK33t_WS>m)?PQ2Rf_s7wUi0Rf9Q%#O)? zQSAJI)QPN4pAYk;P9jc!c-%aFtjXyOl{qkWyz2*ovG;Hn&^Qbg*QaCF_wiURHk0Xm z_<+Muc+n<3UXFR#@w6m7ux8d{zJf8I$h*w zuC!M66|nP2bosub+i&^&%v>T?VHYVwSh;J+lN}PF)~QDuS7jG}qz%r`rSmoJ_=XYc zlRkTmq69rjQ+HWs9?az&jF8C(cD$}$%-SRHV?|o#qD9ZUD zkW})=^MN@Qd{vg^SRQlW(l<)FKML3i|T7_qa{u+|0A(F>(oA$5&gZMwxt`Ut({R&xIK4yfqv%gW$W;?q z59BLM5aUcY8DY73VeNlSB}`fXcn?_9JA9#l4JjCF#!PXdfTz-`LxmIP$2vEA|d9eoTkbcF?@F7 zK*I$@dkyi62TA>tjZ_Q%%Wx@+1vr0=Q$`0fXPWg=^+)Q|Il!BKQdYe2@SD*aoL1uv zKKjs!cyHo1RQI-)^C!Ws*N2^x8v)<6d>U7#g#e%G;i`*RKje}6dLd}U=NdyIbP8Ik zA|#-8yoX?Pnjwh$jVm>>4no+7?uoE3GIxIr`qdi>#Q2lHN;;V<&~b*pw>xy9cIq`#fw|_yiF1~bQT;aaJZzCLk%&tH5fJ^FKvNtj zwFpJXMeJkIWJB!VVqbG#vU7wY%#W!6w^5t#f3LZ3oKkl{hW}GFAD5zc(eq3-w=6JA zM*Q>zgapbQEN=}LmMi|ef$a>9<7F#RbzC_Q=e^ZIO|aTe6{BXa^pC*%Lj4_F!$U4r z%-8$gV5+OIV2qiaIAZ^K{7t<)AZ1;WsJo?qDJV&8r2j!N129$R_e9g+BV+kjZ+u1M zT&?|c^m>p$-a{F4mo`$udI&k@vLb9XQ)I?DeX7{hU2WmVum?lepor4P;hk?q48#-q zpieF~my1__3OYsM8OeYZv7qM{c({E;>_^qzUZl@Z{TkxZ!jTtrLu90*G~LFGhs78H zGYr;ZR#P6L8WMaONM$;`srtOfV*hQ_+%0`8I%TmXrbcB=c}9L6Oi zwdv==XJ;at&3R=e0|H>VK) zsAdH+mnuub)JT@w7W+zeheINBrKkULq@_GQ`1TcNOY4*J!}=60vdXyI`^(BZ)W# zBJ;N-v{ahxWe5XtONh?&w=_(`kehIy0F{=y?f{3}w4~!qJU6bXs~zttlhb(cyrK*e zf<3(U;6*!LQ+1y>v&sPWMfbXOqMi`lBrg?~EC(_NsN9pjNWPGDl8#v*jNgmUe)DRG zX`cA!4QzsDBu} zjgi?jh>J&50r!pSItCS@z9s(>CS5PQP<*DGMug@X5%fI2%-FZEgV)#rAyOG z(_74q7qlfF$Lw{{K38vu!Xu=#lfNbIcq!UyXB^Y8ly0wX$LRQdU!F#Kr~m5jEKImR zywKQ+uP6LK>ZGxa^+`T#$HyKG{}Aj%(yL@wlEguu#s+M2Oxa}~GN|eE$$8@uC;{Mk zTGRNC@|b31nmpolkEr+8JDiT#W)m`Nge_rXpeUcU>KtF5QbxVc(})SQ#sUEjKO37P zEyi6s8R3BPl29lkKfvn?&m_G$4RI@}BaSm%twvD&6alxpAPM*x#Y2r2rHP$uGPzPs zWso%YL%PIaPEQcM9>2(&>dF^hvm2)-eW0NMfX;yhdl<8IoWD@w$uTJ<(zPiZBweZZjmb$>5?=#sr#x5 z0Rj`dmz*y@9_KGf&pAjrZ$gf+^^VB^zfUG5x8Ef%kuU0BCBr4~Gqf?Z4&>6ZuOvCL zzwP_=etk<`|4eZyd_FN_a^=obd+7l8N=S6 zs8rb|AL_g9_mm3&E*@>k_sEp!vLC&EN&X-x-Gp%5THHh=F_q1j9C#c&97(O1d5 zX~!9%!Pw7P3Y_~x(QK_NkY!}%P&xaBqW#$?aprplYHKI0fq34#2XIRZXok8E))hiUgWEbdD}R_-0mQ0ZaQo4o(cc|Y## z>g%Tvaoba!>-$Ib-DD*YJ|;bK$c|Z_MhPYfk4Vu<5A}M4YA|i>sJ(daqu;%4NObwX zr@!WQ@s-6r?M-mq*ZzIKg~)^wBbXYJeSCi=W;LJsIc#-gz`JQam4&|IY--e_==G!p zz@VUcoJ_$r{6JDY>D2PdTlBzWORA4vKwWlBdKkC0z`THVrzvFW0-I4TowQXG+T^}9 zkD4=k#XX%P6S$RrSAyzR?2#WME|dn8HG@d4d&}po#GvC82d5XV<{RG@?qHaPfxC2X|-O4 zllA7nq61h#*?8^^fLy~8nD%6fjCScQ6d1oWF2U)ygz8w^m?I=gmp7&_@3#^ge2NZ4 zN`DJ!mN6*;Vf{xQG@K>{&LkRK4^K=BUMo@IZAHVN@_D;Qrff(4qumVCXddo)MNmpC zG#DNVaop1)u+#O6$vd2&Hi^S=wLG0_qsH&<#DWCrxhorY*s#_R{97RF#BbO38tPTh z`%J%QL7~R<1okzboZJfk?C|_n^9RcrV%d=q+Ui>M3zRi#VER54X<=LFDO7dc9I^9t z^mEF5Uq{gvmrz7B9w>uoL~vwmbRuls2?JuB^N%Ih7TYQcOy0*f{Uc-U>KygCi4aVW zp%PnvHpzjSvDk_$K<0k(5+Z&v4A2a4q0Q!%rqNVK{ka%_QczyB9y%N@Y2_t!4K3X& zJa`s3Yh!k{g)jqvMub@pbc9`Dg(6Eh=ce}(;c)Orp@r{&*T?k886nZmF+tDx_q3qd z#(zk_@R1EOXRiTE-`tE8@bWe15DRd1^a|dI@9ZV!7G_zO!WO#7Va_*KLCJ|+ut6*d zmrV%QpKb(*-)}i?JTe-+$&s!rA#4^uY`nCX?Sk>UM(n|5@51NSz}MG6Vdpe6FzXAU z^$Hz*$@u-8iqwBpm{Ame8s zvZG!%rgkh^sI9Fv1R~$b5TCI~r8A@XcM9}a6>z?D;qe_;~ z?SE86fE zf&o)Ra3^#1lJvtgoXJqhtXgE=+;>iDgT^*#VCI*jCw@&^#HT^@CoZook2YK4CcCMX zY0GGCojKXg`g|416O0qIDXQVT(NlED(oD9LT=;9VA)t$0Uv1KheW1v8Kuen_dgU%- z3nYkh@UuhXggDbw4S4Gu$W!LovGD?8q7Nuwj=2kEy-`C~RYM^u949kK@u`T&+;8wG zn!Sdq?I1B9(S~Ql%-O!Q504A9m#>#xOX!FgYrp|lEEm&;gnuNDEQp$njI8jy?D@Y5 z9_dIq_BX-VPVWw!OpG5`UL_24Ch8Ax^L8qCq1qWSHxbS$1yZ;AXkPh@|K?*rD26;3 zhqn})CeepYHUJW*25|8NTg8pY#xV?};Kc;P`$^$gmPfK5x$2d8r}rj(bR}agv;PR) zA7}~*kBh63G$;Qh=mkIp{MgG_Wg-9yb-AqzO&L`sF%simU16P`1-*8yWj}WM2(m9= z+B~>bz5}-=M(o_FK#re|GwT~zI3A2ms~PfmzPFgcvk2avg+KDA`3BTEtKuuZPi@@i z{EAoq6xo$&fz)vY!BVrm`L~?oog`LS<*W=tvWlrMfrim)=qGo#+nq`P{mcE`z%l}W zLiOTt<2p;M@s^v1~ah0P7|W$)58CPOQ7RU&;236l9_qV) zFoPIGKZ-6H&CN=#_o9!8AFdCt8V(Qllr5F8b}p;C?tCSbOE5=-&qBW*;F3|JW~dHe zAlB;qwvCdKk#IVlPJ!7G)pD?Ck=X9bLmZijJ46Qxc>S{ZDO>#4V1t)h{^$XKt15_= zQCYra`Oy=QvpPfq77p{toZQGLY~c|q@vwq;F1E|U|8qL6-O8`80v&UfKEE1r6fI2V zZ)%3bNoAAvxORG7p;v@Bl$nExIdNG2f2~l>cxlPsR`8MBHG}j{JbAjKqM~Gt3D%s^ zKUVEaPWD;8SY857ZO(q_1A)I~X-Xw9xu0Z)aEEwK^l!;HhgDn5#*;v~LcT^rlvf_w z&%)Ri789_wR zXWLn(&N_it`LhD@JH>$`3NW?BFNI{K7Hv|%p3O;TrVO5#+qd2I$gIclSerc{84MI} zVEoH)yb?Zg&MfDnrH&C|sHFn1V|4tt=}oQg;00GDkH@tQ;74y6L9((eEP}d4Qc5y1 zuplrP?!yPtFVro||9(rD``(BeGu4jcV3tPWvU#w5vx79p2FYohUbXhD-5DP3>Xy=2 zL0S(#e9UaW5iEvAX_*z%&ii2gf}kJ2ZuylhsrH^PSRoxS5XQJtNs2;eIXS4c@L*i=`#WqU=+6p+-#DxrC)lmu zRub! zNTP`ezCMf``k1$|=bX3}?s?HPB@u>&NW*OUR<=xd4yB%AH_H}Ruag;8V%bDLnl~Y3 zcB)x=@+mD1{46HV(UNjQSR9x~++Q?yt!WcW_GmXq&5i|^bMEWyLm+{AV_*`gh0d*r zhNxik-sVDR+PA+R4K@5#DKP^x)(d zHV-k)myBKM2&9P9nwFC-tOY!E*~ankqV1#z(qH#=-EDpu7x4-xRx0>>_HAXk2Hfe= z@Y2PY@)q{ly`c=k*Xi!kWS$ILT^^y%LWo)HpI=p!V&6i@Utm%)EJ_zXpyxPh9^pby z?CtA%s$y=A><$$w7Q!18@iiP_iI~z~k0E->dsbzS^H&#<6nbuj_aQXH zmEeFXk^?WE$}KkA}Z_A2!~VHjLmAbD~)4PA&J#| z+q!=rSETHab;N}Q0w^5E>=wjI)Xk(kcTyv#8uy(;P1?_B?V^YZtM1`E5%-OtzTA3f zOh@26(!av5e);6bY`_5DWW+V=4A&_q6rjZGDHnbFC;3-VLdv}}`Q$!s>GHjTzf2#+ zY}4ripkA0ydUTXvLaWS#(Yi)(_RL??!T;%Rw|Z|yo`_RU>ht=9j9=h+62|C@S0Gvc zVHA#W%O@&wDN(tc!oreW#Oyo9anA%xYvRkl+cKuedahf%&d1xvQf0{i+ zY~%Xm*+IRrbz9e?DP7ma@XX)cNs=mq)SdXk2P_!@7BU7>##295d}ir z#W{r6G};cd_s{aEAvOG8*ZbvcMDTafXjGIGl*|*}K;(!i@CNht2+^OffXI6!x$yzrlCG|Jz0o$%syu#JRDu<`0 zH&82b6K83}1jKwcz@lIz=nRMVJt*_d22O~Z$TbZj*4~oW%)y>`y{1eP`APDWsPyFg zo0T2DX|%&O7%F%D@AcU!qc*zOQ)j(M{sHc!AhoPTS>bIA=x;sQhNaY5Ya?{aeE3aZ zrbNcEx7UKPzJewWObyX_tDGjOb_x%idufUl48ptBKc_j79$(UQMl0rvEmwfQqkf$i zsRqWmemK-#DG?fhbEPrFU-wnqI?wp*dbz`i%PyyTK#y~M=s$4L)LqgkzJUEl2-kea zR+II(RRS|avj!ZypPD2~MVBPos{dz0?3gnDT1&sBnkOn%;W038W-vz)*Gx*EHEZ#&fuEG=zonXx>0GzMo^Kh6?{rrzDsGd#@F~(| z&A&9mtsT3qcLZqUJ`mkcisMrlKFlw|^F$;m@?)tZ_!&*O;glh@`OWB--o(s?RsaeKTkJ#WC(y>E+07eDti8~X zd;q;3QI3#LF!K^Ff7e^uqqMKjyE&Pf)Rls=2WuuxZt3hH@&et7^Ha;Nb)4_;YCH zhXGp>tfx92Q&dXpww>J?#NR|qA4Rb)|B}>-FA4V)1pb=;W;VH{q)u(?c4yPWQRA|Q zk+v&DyQ1FO%@V35ES2$eW-V%e{M`Uw{0;MC`vf93-S_(phI+p-b!ut8YISBu4w3S4 z3P+0pNBLrM#9Q1Ng5JJtbBjW|vP3~a--pT5Peu9BO;9Ee_YDrQnvC7C^K*s#p+z3K z^G>}iii-5C=hO|X4~6b?umD4Yy_tCSgRhx!%4KY&W$9|!f+h#$+-=54(3Ncza)!G2 z-Kr%UzeZAQZcoHy|FgSo7=h)om^!vz{|BQ^XF5T@eSw@(=Gw?gA|)aAp66kCfmpGE31N; zmh7f9jI&}MLp1B>;GulGAFGA$xX9d9>EqfE(_jPzmtIp|`7z%1o4x*!?`OEZKa(CM zBr#8MDXzQYlEt%+jjK=%EDDQ=%wIjL2o*u>78;0deI`89jHL5qD2|b>B#m=&r4&AY zsS1<65XLJfznE9`onDdXYRBMlSiK=zKoR`GgGIgh;*+4QE2ElFlfO^TLswWZ_bni1 zpwg^*;I>D<&4YdKYhtZIPs1hb>Q{U_Av6>IC@iZ`Cze~#Szi>uVy9dL_`%has8rC9t&a>cMq#4LBM>;j?g!h2pmkU)0fm`W}D z-cPH{c;|zm%|*kFuuS!KpZ#<7?sV8NpPK5-U#0jv20|xrwacbDk8bOA+T5;4xJt4E zj!OoA7&l(Nm6y%WMykXT|J^H%9Ze*ox~4S2lHcs*GUzrPDpT0%dCS;Rk;&eD_g1S6 zCvE?h%l2MbhA40KrcEvPwUG9L2hI72pOr6Z`O60H{o@3e&1$w|Y^TN5hr`uWroif0 z-f?B^22$H6PT>WZmmrEkGJvms$p3g_7#GdS<+lA>jbL+-YOpA%&xK*;lXI|dknB=1 z_@|+cAj-oz;8ZLfku)J>CBl4_c+D6b|6!`6-0CVfZ=nrhBI*&DTIVsu=M2!P`)z${ z#BZ?uLCK#D`4+p002o$$A{s`Vn^#W5vwdRkYZ@X0@5tDY)={WlE%9}i&3qa2FSZ;h zA_rNz4H1tNy=_+g??UhnGfmVJ7@bdSoO&IBSm5;oL9@-0H^Y5wJzU0m-~OqX-4}8! zVAI^?XngR9<@+ekebjGq#%>Xt*c%*az&$0{69{NCt zw&{1f3S4<;x9khpAMo6V8Jy{%=U9XB)qEwa4ecjMy1Ym)PZC9cTbZ(z9 zbza^z`W$GiqVcr^YaD2l8=%9?aio*D7yW$c(t?Ccis@!hr1*T120jL()1R#;GR{Y% z9H%-H_93&wL;|Q<;c1OIDo#pJvgjOHmp6@O=(%E=ol@V}t*AOnlez|jE+ZffpojuJ zjrvv-FZAAQ`%>$TB7||%4~>^$H|B9%67nw9S@WxqJMY=v(R(mrO49D-c>zr&j`)r8+yH4V5@cQUXkBy)@?kpghnnmD@#WGeu`2A@+GiPmj z1$(3QvRyyzd7rL2G3pTy{6d5{I^`N$n)l!HZ+{Bx%EBPqQ}}bj*MM(LoJcuOAzlKB zz!GMEOxS;DVdHLBX<2G^?KLB1DlMK91+gBa2pY|PVv?AecV|0#l$uw)t=;X2wG%KB z#Ei8U_-0lX&9^iX_nCLym3Ye!h6(yL>(GyWKQk}pXV?91jI$=I08&Lkr;(1P2Y%$R zFV!a#jV3Ffq9mzpV`x7b?4b4TC)~uRM?;A!E44`ADKX{|bI}ua)os4BZd6ZN7HVXT zy{c4_aEf>vWvKU+^u{wj$PN6zjn{gg&#UXuk^hcXb#)zv*zrq!kNP+wgNhE#IYP)V!F z@(9z9(n!zGnp4*aN=8ex<`_G}^@h(|Ta2(*X2zVieb2-pb})h2@h+>9bUYYVAy^rq z8*`0XD;fL9!Jyl7g?mtYm8qSv;n}+yWxR7oXQ5s~|9>2v1AARvw}#uKN#EF3W7}?Q z+qSKpq_J(==8kRKwr!sMo!_wLHP;;Xcz~Tu^+{b8y&%;9f7M%9g=2rv%c~me`T2^% zrqk^|y3Qs@&jyx~!#{o!l6@$T0eU?wI+v?MklsN{*U+2!+~4@|rrURK``mz)&=p@P z(*=-KxjS{Y+z6d2{%W_sfXtLmHq@4*r>TurmSpkvnmj694Sd%AFQZA;b`gDa zA_UrXqdKcCsI#sfD{fx6L0w}j(&aWWiK)!Ryq;+zO|H`G^MV%4`b8MqBM*-^{xboW zf5;8o>apGa2Kv6{53%f;OVNAsi>1U@(J;Vs8Gx0!DQ7R1?c?F3g_SIPo!q|-C4h(P!?+^Z4YInvC>U_|z zFo$SO5ff>c;jgN3FC?U1Y6YT`+duJuLPysB0&bBz&{0ht55}UQ8eka*z3hu0t>SB0 zt?AJT6xIfN3Xo(;Vt&)R&?1yc^e-3>(wa9 zkBfbt;6_Kw$~1PmxV|qxKzR>*R(B-ppXdMor?D*6tkQ_kyBN~#>LFraW|lcu`AaH5 z1zVQMgq$~a`gSPB;kMT-d3}F3x}I8m89}_B0Dl!@%Rjso*qBk;U>KDv|8M{@5JDf3 z3)cVL5{usp4@7&4V?wJLA@5a>J#jq;wU+DAWXzgzPokUGEXLy$B(^Yn(fy%~^>eSh z7Nu!|4SL^sP2HG9B0iHI8gOOa$vq7cQSz^I-6bQT*&KqEDRXah_QV8;(i-R@FQ}#v ztHBo=<_iu-s-TGYsO3$~T4N|7NhZ<{#h;5C(og17Qi~+Wni#TF5x_zmOs0HAK!(@(*ES z9kW#`!Lf+2>FPqh?3g)GpAl_!PTQc)^o%47cmH#k^3-1k(puNxausb|5KsVT>#dNabTH#ySB%W(+#WXBI<5Wre~RUEu0(U+R3QcR&=yW z^;aizAYUxNPUggQYC)1Ij>ZzC31s9W7yFP7M;%@~tgd%L3E=QDZ2+Z6p;`S+8{o;MSM- zhKM>*)IGy_Vx=|AB_+B9ZzGJUu^+KkWfF7i=RDx%y;5*mCpgJ{?~9P17HZN1ziBUoeYT86TDXN&^4 zoo(%z@6cWu6y~g}&_^qHPCh>W+QT!uE*Oz*3%wXIyE*Rn^+YUS zo^t;BoQMY*qjrTe^zP=Q3*ts7GUKnH(P{s+1Z4;Z6(ZRNQz%@yl|sC72Scs>vH*{m z=u_I82}WsgPyGfUG%qGgRRPDW@V?%!e=nn(GqlprNw(O{b%=vCRVb#0l;{2>c$<(I zi6iD_fqN-DeK5YyhIt`5{1&-4xhtDAAAtp|Pg+rAb^quBd`GkABpiLFWb^i~lE)Y* zcO$K-Reqei6C+VRRBC3u^3J+s1Ke}+rU@(Z=s3z=&&%}kQ7tj{-@BG|Wi!A?cu&?_ z@f{^RP66*c|@cz z2AVyX$)>8M!UC`^3;p@T+3M~icrXZUy$+MgsgsPRK@^G_aR?$9{jRB4%y3F6Q3gOl z8h6$+@N=f zOzzm$1cst@2r^Hs-nnH=kGorx`{|sn`lmLHeg9kCou)j|sPqX0ycl$D;eHR{A6p#< zF+rTA-A*@u3`&_qar;~Me9(>N(kn5khk(Zbxpai0 z#-`ieM?}ZW90YwJ0%QXq5%b+z2baTpTCfWFP_n}DCa20TmS=)U-iYT`QJD50TR8c^ zHyg|RAjoYgg2Q$RR7RR{vkB=xFuu=c;6$LK(ro06_u;j0O}Q{3Zrww8bA|uJ!B#mr^i2pC5THT9o2emg^94VUw93+ zXyoi;f5SBtF(x$TdJP!y;x1gh~CQapj5ZFENoOBhz>){gc;=z(HIan9= z!?_=Zv^Of$ZHB^{)|@}Bn`G7U2YH_rR|8h}51g zy4#FHu?Kyq?-xF>{&z(9{amZsV);6|-`^m#?822anQ+#yqguAF`2b-k&?A{iB3&P? zvvM^M?9DcxlpTr;N4iUcoeVnXd$(E~t?ZX!E(7x1HgUI4Z6;yRP3|tpsmaxP9dI$W zd~v6soU^~>C{T?7So*{n2IMkp!IKC3Y1g_w|1J#xd*L0@4+MjSZwqulxv~FrxQe35 z`r?OgN{If*)YGQKh^$&l#7!YDy;Keha3lEFAH8^~MK@XB9HO z`2c`ttf#hndkw_Ath6%uPL`vpjPRfR9*gL=2VYX^=k&rim5-UqdLZHx2nFwY^eyEa*Yl;7DScW-NDxWY}2EuXYR>)L24NYwbzO*=4urL0MaYRc^e&E~3wbc)nOM?=t_P zra2_EQ3~s)%Fwzl@GZlCyf*UUldP+XQ59N$OL_8Q$Y>KMV9qt=OHrvqit9*y+jSIV5;8pSIfrsOhr}{=)G^N>1kd|7l=`MNJL4Zgqm0 z<}=QO0@BR%YBX@`!LE8AU}sm1ddNDeMY5a|+vaJciYJ8;GSQG-FCVus?6R~I3FxV%B)jKlqZz3- zZK*f~%j2d+K@GC<0?kGOvPwhqcOvwo+82bzb!AqpBu`rw_~;Q5 zv&ELXy(G*b`9jemKuiWB%NMTLis^b(F9uY4y2I`eM9~=?Zb2zvK-{N~eivW4O?UXj zO_&n1J<#=HB&o)}+4k@w1bAE4#AT{;s5mkzCB5?V3Pj-|>T+`;i~n|Q%v>&_<~aom zbhCFermW@?DFLY#8A^SK4-j#UD59df)&)xB{@6ulGL+}s4jpU2{r`_c=-QGsj(zxg z@Z_0*Kh|D`M_}Cg;Zzx+)#>(V_dGsMu7td82++28o^l=6qR=!(4fm-^q0(hu4)rHo zu|h+o@oGL0N2@_ULArQb5K_a+sm#GS9@;^dc;3Y#p4AxA&e-SnM&%+ZY4S&h$u-%8 zzH)j}w88`q5p67|dxUOO9j!5f6`DNc0k??g{#>ltBJR8hH1#s5h(4@LYO;lKt&@bG zTm;$8I4E5Ei4fg-i0wnoEawbePD@RWgY_SVIAYq0jZ-$W9`<-xu?e{?Lwy zJK$O*m{$};=3TbB-u|tk2<~vQrQ%rynn#}mzCz8*x2g^`EmCM(zx7~bl)9v8rYbST z$ihrtb^lAKM&N@%wiIi_$+Pj@BDj&O)TxDPFMtn^Y@n236_b8r zjZr`D7Fl2t8!I^m_2Jwp_^}GJIdE@sqK3e%84{-NLESk>mfGok8;NfIUcwv99|g>U z+LnV|hnA8$MmZ^~n#i9FplgCqUe;+;n<*Y>Jqn(ysNEtKbb^;O%hFv#T7>t{k83j5 zR+MXZpUC|)U-i)~T^8OgGPBY1KVI{7{Q6#J>?p2h%cQS1+{1<1U!Qns3sk9It0|z- z?25RF>#^;+0l~+9yd2LDjE$CyCbS-w2gLUPY|zEY`08{hI9(2yepwq&;~(mf_v1O4 z?TRj~iiVcM5?M?ynyrMZ`4LUF*yK{v|85u;F)(0`^_l zJe86&)T^O-Mk*zz=V=OcExPz_C{(6V`wB(}f|fV^?vUNglCw+jYfi$!#bdS-PTH*a zlWk;08REG?xmC$-$x2+a#*01qzwcek<+HwdkV~F)de!0Ntiw1`^8_G4;3EQe%@zG< zlegB1wr1u}cnTdXP{oLGNhRhUV&Wom%ZYK-Q0e{Wq{eyu0KI&gyuyIQ^zUhcj)NgD zxNF!>NG#1jaSNtiL%_ATv2!@M?H(Me{ zkVv86j~A#h9uXEsb%l(CgmkjnKt7g88NlOlm+N>q#=yol)EiJPgE)R><5B`j8f(G; z^(&bQmyp+VdA#l6JrPW+&pU1Ab#NVCO`37|{B4RUD-pn9OG_qm^mF9#v}CD{oN5F3 zkF@<0=INyG4i_?0BtS(SsZY@i8f|1K=}RxexwgA`xN@q@rp<^Wml*pUdAN=cW5XLo zj3Y-xJ7e4}p{CoLplZH%ifTmZS*5xXwEu8*54~Mo*9qTta{mP(cf}V{5DgUE(ykXw za4!jkGG8moZaFEuoFw_sh{A@WHrzF(ejuRR$|DVM-abXL$L>O%#xJi3U}Med-6v)^iyvT{Kn!-(LhgA+NjX zfBn`32HvFvF0OF0w;_5xzyw z_4sjYbvIm>UX`x5kx@erPHm6qEEQULKVU9};btFI5<&y zeh68=-lQz#o`V+6lS5L8)X*}_e3_yOE(s52pwq2A!*U6$a=W*xMPj?t4)b;n{JUig zooR_$Vt>VRqSwI2j9sIU_`Ik>j04)(KvU?nu|UFN{!mj>v)<~!n$D@H&b__e7kfBg zMhN(gFw|_be)Do`1o-;Ea=Y5-Emx_S$rJj`!OdN9@zsEhN2Mw{3h1yfeJ~lsXg(Fm zvMf@lf#kOc+f1$lB_V>|RVIFS1~x60Fqw@33bLh!k0XLn#G&_Pd?0U_Ut73tu2=1CEx>1`K5u+&XoIu>e zl25-u`v}Q6W`Gn&wtF;IAPjGYvW9hjen0ZKJo*Ho%&KM4E+VHyH47tGO&eT#OiGML z(VTv}pZPrfYjVeYJN+MZeR*AwJWN{4_Ul~xgx?@&1V+*eK!CgI4z?^;v{6iJQQ{S) z%@M6g;TLlfOrm5*6=a_D{txo`S=f7~`dz7Fl?mjzgo)N!L6-ke$F>^OYzBC%M`kLN9R$$@+Awi(t z=+cSuwwq=zAO#Eb_EeTGl1xgy6IV%1PdD5jD%1(A)@diAqk|WV#ud=l|5L73Ge!2z zY&JDeZvx;s?}o1h}K%pDShzq zLa+!C9DG4}ISX1(bX-3yPZGQ~0I+-{&vd%y{$;|rR8(XXNk4Q&%GVi9)LnPDmrfn_ z|CNqPmo_DJu0-t)rKCUNMQM=sA$n3T7zThD`fcb;ES0wYo3!y{7E=iCv4zo+*`7I_ zJbWNIN!!D*j>2l!>n}{?;Jl#n8l>nY3NtZz_}aO;XI3-9)W!ZMdOh?c*J5wHu+{?u zs+@V5jYHm~C1{Kt=)@`Kg#;;w=x_H%aonTf{2MHO=<*>|q-#1?4J*s&G#=jLjKSh= z0ic%k3zp)3a;919BkBHLs!gfO52 zZ>H_yD2y<>+3b@55FATr*L@^YNfp~~6VYmMkm$oD+>}iL|K7#<3ITiZ-{YU`vk;yA zxP;reH**Dv>)S%C!la;1=Se5!&VV( zHUQ;Bru)5q;ljMfiTG2%xZ!SmP%d#TgE(AURFZ97zK326;bojE5r%F5vt9Qxa^c(@ zt6aM!6)B(hhkqa|;I`GPdx%0+uxq-82=MX%aSe*&$#5gMeU+MXQ+vD5Tnz1U#s?{i zdLQt#W|+*OK4Qj?GRnzN&@($aJ2fg+yMHvzRq1u($Kdn!U9JP`oKF>#Hm0NTxDm0i zM!#}URSNCr=H>*)lW8>w&p@|M<&KEDme&5^cqFx~F`4uEO+?S|^|rzF=K9Emvz-MZm&6lfYW2cs0~ z`YA1;sTLBa^?4ZqIVYLAZH`bYfzskjjcHA{a5|wF{*!$M%#XfJm7ro6+^(Z>VLu{c zo-WW9hg~+PoInxA+kk8H`vx2+kJ+w3c=}~=afze9&UR$cGGvB#P}C_)J@9UWT(tT` zFdtC1ATC2IGa5cdetcZvwl)IbIB3 z0eY|Pl_$vs&+~bMxVYVs352hhB=xq-F>vp49%z>@i^-}{x?^l&5)_U`ZMf6pSBZJg zE=HvUqxJpusbQ5>9^oz=jmueUK1*aeM-Jy9=HcN{_wD<5hL%i!ET{ZU)fAEPw?Og3 z3&radjETF|=}@?v4p3*ke|7&-nF}UN8AB;2`!H`e8g;qi4mq8$8l*x79_%LE<- ze>!^AN`uk8tMwUW%iMhDzKtWBPwXI`HV(k2f7MAuqKFqyN~%@9eQq|aaJ_viOg78(7_B@7bzk2tY*Q_WU{sT2XVrMaO{Ite3bX8-a(oZ zts+qwQ6VY;MVKjI_yBire!Tf=m2UjnOm365u25RgCDQvYr(s=bXKRHIjygo7sz%-E zM=$ja(=A3;MlnRBKtDWyV>~B!Z@7a|N(9OYzM>>QRzE>Vuuu(GmhA3~z>*Nhm^=29 zKWp7A<7K}}S+IIACP6*R8-?o8JmR0472=mse@#y9HPps68^_(br?UH5G)cSPWoaP$ z2*fDW?t5T-qL};$eqDi2cf+#Cz%F+Q)N#uTKkR-K80m( zUNhNvQGs&2sMtpP8zS=~K={(jIr`FaBqa9(O7UiD;U0Z+>E)GJzJjxcCfVNhn>27j zyFV&!Ip1Z(y?IvN1P8=o7dr8=?k2m4-{Ph2`2T64bH<~ zf!eK(c`_Mn-PJ-O{H#6y(wpM$kD;<(K0a~Sbf-VMsL;mIMIs;pkpOK-;G#med(2Nz z{vY8R#a0CaTqMZ%0VDMz?r*~Ke|oRbxRe6z32R1P_D0H>Egsn^hU0>dLn<>ih}Q_Q zwKPc2HRGn$o8yHrRExCJQM0n!7+8^fVY^e1fZp>-vDqs_%{+;1(29HFK{@?L1%v9J zgx}!-*=^Ra?4s>E)uQ+6$g%nImRV&PNbI7QzRZuu^+BOjdHx`X!`XJygw9)|Q7rX8 z?MyvWVyK8E%%y@({N`+7bFMu}QmW zZivs4GAk0Jk5;X{WT<(^cWHI%9y`%w#CPH!xxZeMtM2!idS(xdmsHR%FDTXxJtG*? zSGUK+l7MsOdae3pkM}z~o+u1Q(X~Rvy!+w-;OW@y`G55>JJrjz((nhbG3-hyAyn?* zTDSrb9fLBc!7yw3aF)9$_1~I5;)f!m$5vN;{eUAu*CW9(;#5cSNEJL=c?|+u)*5vI z?cQ9+8IzzGk!?_WcnX+Qj)GIp?uOapWQ5y)wY%$B)C+VWT?O&Jo1vh_W}-1OUiPI* zFYv~*$@lbP$jKDdwmTeeko7{?OU8#-94Q}zd~Y3tlD>R{Gq*<21qBIb$c{_3Mh7xV zOI796JGhEF93%qnnW*J$Ybp!6D~kAAuKid&ZDZ(4G5{M*A`b*&a!wA)q<06OKLb}nqbig7IPaY8!Dng4Uh`ul**%yhY#)n zITdpVudI+50_-A)oXH=8gMhd6Nip?#M4v(Y) z9#^{UKDtdoHmg#C?Ee<zz|<2r46w=TF-iU1bPOz z;aRYlTQHA0TsQ_48wX5@(cwb!gbuJ01^=>Ks$JUmcQ1;6pvLND(lKJaJU~84m&3h` zLaw;a(Qu>P<+Ur{sHsL2nMzAtlB|L|Z7nK84l|2Nkz%P8>~f!2&G?B%hvSEwwRRm$ z3`EU$Ef)CvM;fuv_oTn`k*=tXd`IL(na7>uV;^`XZ!d;}bL1;$Oa1H1DYYv>Yq)Au zd9G2!nf1l|w9NF!Yy84rDYa`Oky45N_C7;#vnODR9pBK!;ktv|;61fS7*b(k3bj0< z4fRdNn`OPZB;G5Wf|L#bzytCJhO9Q271(aJT^K)neR>532G%wh5K3DVJ)4UAEl?O@s7{RP1m|So3fm_s2)**Ksh-R8>0S!~SxqJRPT~Q+To6 zL3PfD@kHP^I#%#|Ahr944G1B9Ss79pBzaYugJmHXn8KlBS;fDW=0n+23h1JGD!n@y zv5aPyofEs|jSLJTPO@M-8YTL$`))V}sW7n+;#^x1tzmgPpHt#C9`E#P)v1Lu?ySzl9_;{<|XF3a}qUh)F-bp(8xA_qn@z)qICGZad47veIhTL zw8HxY3kk0$XsO+tj5p*ijO`AUz@~9mN6UL$&ITdh zOv^=?S$}=}Gp3UbsiyKwUa^|p`4l^I#ocS^xgzxeEv)KvRHjKn5vZeLK6Du_WpOP{UVfSeODZ0E#D*1eGH8ZA$FxqdNlSu9;S` zj;bj~wQ3-^P6I-b18tI6+@Fd=QD zw}rfNQb5x(nCA(=z+03)PTR}?iZFHqXQHVtk8q@~h}*1_mbsc4#?SJwAF$6;U5qaI zFW*DSA0IeZ8qa=+x;Y8~c5GeJv(kN`-+Zn^Il~pQHq_rY%={1#XLDU#UTm8C%!mD? zc~eygS+6%7sB3L4;*U#Nh_fEkLZU(Woz9sW3_cLiww$zUKODoFgtx#xKCb1o`8@GJ z3@DuE4dm4lQV?hf3G4?!=LOPWoPs}yr>N~wKiBK@a^cI@i^)K+q(59`pv0P!fnST6 zASNTH5FW-U3LPs*DMKf352E$C&({(0cwIy{s!tz? z!*a2d>0O6OjBvVmoE^VFa}asvdLGro7d?;SpwzP{e&(~a6_i;M&qT*;Q+nA2+7R>m zNV5)VPwFY!#|J}WNF~!4gkvdkCQ&K{doy(Ven-=4HTU%N{KJY`Us>6iC0^~2`!eP8 zej9jYDH6Tf=$4ZufY7V1! zCi9|GauWMV<;(j!-Q`fa*uoL{v;;Q^rOZEA9c1-NiPjzdav4Uu3u0=-{UbTCiZ(c2 zr{o_g{yX&e z#LO0ZIbr5PQNGEiPhtw0ktcn)mp@MFZ55^jJ7np;H`CmpvA0qoDx(Q%_hl=#csHCX zl;`ms0FomuFqxfNMXt`GB-qGIO3oCAzTb$f;5+V7thJ71F-AsaNGK5-W3<}5i z_wGp@x4o02PrC`pH}Nc&J{lobVva0(tXg9L)4Gpcwsb*E#@JxLcXjx#&g4?G!-zic zt6Sj)e|K0XMHd*rKh^`wYmaD>_aEmixXleBiaqju9ZGOZP@XlF`!&|PzS02_Egy;H=)hroqKlzj5}ar0 z2Lc^<&`JX zPc2b_bW6R!>u(c~N63I%=bq^h6#9OVL(8hsxFfvL<&C4Ot84G<=|GKFY7BST`Qp&bH z?y-jnOupEr>PcbpI`!jG8V;3%=~o_tZ>dVoQwVk+6c!~dB2ei=fA+tQ!d>F?qhoqf zNfIPX<@XIp0-N$V%rr1L^4Q0ecgfZLn29u);wo)SMWpVz%f>4OO_&f%HWwr!L>sUvZ$4_8ln12|b@+b%g!jC+ z|Fc^t{%Hom|P->|kFX*kI{$C7_Xn}@WT2ggkK zN%-9DaWkV5ggK>#8>I%KxCdyf2a>sG(IOm|h>v>Ap;BulHi6IWYp|G*)&Rzmz)?O2 z?;{UgcQiAP+FEYu(b-Th z`8g?HZ8-+f*!VxIkag5?x16_2dVxIEsn6Iy#nU$ziyz$gx+8=PkJrmZd{X7}OXdSU zCHW|EmnGCIp!Vls!GiP83RD_(`6D*YyP^1|Puc%og^Hep zM6Wf{hKj~=({uB)R3`deyl(kefLGl;VfR?=>Q?iu=woVoNwAFmK29pRR$Fkb_aqV? zVb|MVLJxbFlC3uBf1~UV$SK@vc!c*XR}&@X4#pB9L|!HDAGVwZ=vGKT*I%a7#I!Ib z6sXc#prhOR6wsulEcVUV(cSYj!`tGm@b%EiuK-fJt^`J6l)Ph38HMrQ&FEH;22U;= z4^h~}5LH_k6i^&iq!?78k<6!|aK76L%(| zdj;=%)kClr*^myOf~zss#jpd411YQYD?$8Fkk2FnFF#FgKHG&n{xhQ(&#K?If_13% zyx?CrM$Bn%#Bhzlq8*#;btefIBu8)zMv)6u1}|gCE`ORDoI+kNn==nc1VXuNGpjk^ zcHyYaGx7V1z#a1H$$>Kn z^jd7zW61|~!erYPyV&7aP>89n@->I!?M`Is0dA0sU|6!uQ#62n4M<)~U>w8OzEO z3Nd`X+H7$KWys3qOnr$834w!l@-6PSMA+=M6S836^`!v?NG~f`}4qw?WwtZ0TX3p@d%@4sx8w(8sBt3p*4FWu_v9uESz$g*#b6Dg3vDs z>$s438x8qD_%&MjPNZ!*%p)xNgnDstkio^eo(c|)-B?DV0B;ynl^O;+pvMwFJ&6sb-F^fwR9f{!psKH56`oyuxW*d=vYJ0_Ktyts?V zsGxiU!BF#-}eAozToCD!e~YA zwHbA&`iPn^3UWZYd#p?c*5-I)1%*fTXOAk)WAYJSVRdq`anK$^{p`_@xh@JPbu^CE zMl1fhuw)o0i2Bb+oE&o@QSy=c>)ImG5y=i{znz))jA+I8sfwfvO009Ceo>vAN}rT( zZ9Ze$;Ro%}T;e{wW}DoE5)}=wvk}8QPvykdPD@vu4<>9Dsx{L;jmZol`uy=#Dhljv zUwJkcjQ9LeUYX+z!xT@soKODs^!)AcxDU?4OlPqG^Y!(8Vy9NC79NfUL`42l6qvO^ zRI47zL4O3w*KFCH5}+L^3ORC;{))GhhKq5%Z0ja62m|Wz8J)}cm{Bnca{mYJbw^dw z37UIOBO-NjA2DZsLBQNsWiT3?<~m}}!*M!((qYdC8%4z{%PNcG>=uqk@wUF>X86BK zgwV_cI!#y`$vVnX(o>sQ+azFy!YuOTG3n;4Z90=b?+-l<;hGL@mFpJ{|5>n`zQ)A^ zeN?QdF>u~%i=RjNsU~-InvBFmGBI@~HDtB0n8et690m|iP_F4JBIEc`JFQdkWh;fP zPNzSMpC0rrczsdeG*v?E`dFm(7!uL)0s$Z}cT4SR1X48yPOSFC2ZJ1&7xM2jYyYw& za@JRIIM4zEKZ5QI3p>%#$=7marh_%Qyk+~6P}muZN`B*S9_=4TBvS>hLaCZla3E0N z+^IRUrBYK}!MV(cbkA-cc6AbG$T1bgvn-HZ)h~q?)shZ(Rv_&1Ix@10Lau|>BY!{| zhHgLxV3_9cD>!oZvX8uf8IUI4B&DlfCIGo(LD+i!j6aJ8<V`Ld<<||Io+>+-3rI9~A~LDgf1`CHQ+^gg%lw_^t3tgk+Xw^X z*^@KF!eqgpkFT2*1A+7IUk6c=*D2^#ybm5_UqiK@l1iMc*IPf}ew|vyZ9zf5UdYcD z4$E)78=Q}AG69^HMfj@ICjc*v7iRp36Isn-p zY+~Mvo}sCPd4BOOfTwcKge|4l&?(V&nE0VM19Bquxc2B z9hERwr3{h9ec5E{h^VSgv|nekB~k9Cdbkr1G-P?^}r21nwzAGm`FAaZn_*U?mI46fKh3bI|UxU2MLp z#Al*iYejS1AVF0pkbL*bUsP%fy@zJEK%`q2e1^(XxDySxPm8PBSm9UB%A8^;-nago zgV^5AJH8BJSWpoQbJl{s7435a{|6y5@ys4FxLle3o z{J#BXL1Pk?1$dk&ekt?g_Xz(48JgguyUfA3#dbA#$QXh7i zIV2R>t{XkCei>i+zrv?x0~FuKN2%PboeJ^;W~7o)hD&r9tsRQ$bg~u;KV*AID3a&K z*vvA1HQNR!5klXvR;^3u~>dDMSTw>3B zhnnh19_MYNs+mG93Sm>8O}Vi4oL}m|ZCL@xIy8wx2lS7OpTzw=g@hIq%#Bl<{Bn@n z6a_pr+~~5GTL7cK%xH05lT@9EFCM;`y2!-kj9}+1aoTt`)?{p5AJ#GcPJ=C`aQ>uw z`AmgeKR5hD_Sssfms4^kn2ST>KT4-P4Cj2{Ab*QF&^3G0rOCt`*o5RAj>`^E@}>dt z3vJ{-o6R%NcObmtgVGMlBx(36Xd6qiO+j`TcdL^@gkmYdV$oEe@Ap})&^VswN0geB|5x5{hC(N8nePwoY=z@a~Db_5Jr@O&~pxE8)r;e@nO&d zku@zAhWjSx4LE%%B-O+-TAfS-q6PBs9a7o(LZzsP)19j$IB}{pV1QJ4OVsN=+3v32 zr0iST1%#EVktU~=cd^d0>Ej)cMW>bv@BubTTa8g5!GC96NNb&C6qUgkQ-fYm>ZgNP zY^^iSH+vMdQEV0bQRL0>P{SvEhO|Tr_D*L#s)!@^Wuum%-GjhY7bVA_;n#mpJQTUY z=E1h+MPX-Aud2@{txyw23fM~Q1yoVfripdM&b7F>C&T$B{=n zo!l+o$vHI@^QT|+{wS38BVMO^H;8LMI|6D<37`A|U#}$XcGsx(NFUT9&=APL#ANv0 zZM(_!a55Y4>2udmK@=3rDtEJkPk1efxQbxB3a@xIviib&}yReu(f@`;DCu0xP1Z(>Aqo= zFl;lkeJStgx5&>4+y_tEK61PZ zF=L=-Uu%2%4Xip@Gn7Tyh4^S~wj*@h?E}|QlRhz_vNr9LTd_~B#BmZtlU1N&#bC(v z27ZF0r&MSZAVsX*M=-|_>0mbGR}OBSVo(@e19%~|<5bdT%eb4SLC68U)34>R8O zg8Qx%q`&`ZQo9N!x3mk>X-c4!*^@ySTD7vcW>&~2<2-l~2%gBR#7Es}!1?=Ni?I9ZA*o6;2`|Ia;h_qYHY;J@bH_N~Q5{&+U+dXzp3jo$F?Zw&nC^=^6w(1o<*W2lMn1yRY;? z9Up^_ie%GeRE}1Kg+E>MrI1Qu3lH&k3ek8L$17noMiD_Jg^A&eWpwSWEZwYBq>u-_^>cO_< z>p;kS=~ry19;Z{ZG!-dGWNPrBVyYR{7FN6;aLbvrb;UJeQNa_psx68+qKg)0go#ND zqWU*v8y2wB$t4~)mE`jI47BKlRG^tdowFN=q+>)h=sQNF_qv)E=B+wgZi+`nND`XI z+in^m&Zc>S#*l4zK8^9!U!2gZ-9yafQDgk`H^6har_W?|gi`AQR5Teb4xzIbg&ei} ziplaNWg{!v++Nta+iHfw+}Ns%(sV8{<||cXJZiC_ZYMujEbs5B9w}d~EaluXm-?__ zN=o!MZ4iO^zN@w>DTeK)H3JGZ%4OsblZs@oQD?NEW%9@4nD5guYKuD~Ie2unvkDc` zaTr9Qk#E|2k3XVo1L-vHY>5lgH6!rM9=7DM^Y1FM=oSU+a~>VXFofezhftj5^0LlI z9s3AR|1E{QN0V9P^860nU>b~4$iDzt*G=z~!Vg+H16 z_8P_*YiOp5lu*Go@1*Ke;eB~ZbrZa^8A$(|2Cd`8alYO2 zBUb3J3lYtNgW5Iv_h*fxs+ z$&bSbCf7vAydn_d7y{O5Ghn4yrq;^TyHHQ-@&V`y#1tICqqVsTZ? z)$F{g(PKl4Z2FrUsd{b9n;k|HzRzfMK^!%g8a%NHqpZrKa_uHKFvfc<*R42&js7&= zQ`NFf97_yG+cqC4w_-dVk^YP9)arGveRm&O*I+1}JH=zy?aaG%1B3CyN73swx3Z1p z1-lFUz1V~R7Iwugcbht+_tCLirDg`JMZq&~iSvA!VQ6K$UN3ZsT(Ax>yPzZ5ePvp{ zu^NPzMVqHzi|A+Ql2>Zg{gkZDZgn7dr}6AlEMZBZVIEXW@u`-4ParWE$8*D;%@t=~*cN#R(@ha9O=wWcg7>1Ycu^MmjojBkAO5`P> zPI|FS)>lq&SK0EnBVRvB3|{$CE>s(Qd?eiP6^;GbTTv zDOR4*VbRGHhu?SL<451O&-gqAb=~warRMy>MLGjBvhvyFnpj3|UVAGRXO&7#=(6LH z>E!ao>K~T}BF1wdl!ggw$dp(k^BsKIO9R^AJVLgezZ;=HowlxK?S# z(IIwwdG;9#wJL8T_rkg9kv>o_k#*D6jj9F%QByMg#` z!bXSR5ul^sooFlBaJY^r({}Y~40ehmsVQd8P7N<1<)X6dhHwIM+tY`4)hLogp@&FU zyVVr>-?7RfdRCLO@DlC4c#S~oQK3j>DDu+U!wui7=2F{HqyO`gg*6&I5@lx5?OlHl z68R;)e4{O=`ZLPVRVVTRd&S3M)5N{I-OyA!%y5Is%r6I6u`ukjLw-^+^Fd2H#8}n_ zAvf7ZfevrtS0CV&4^9@raC>$&K~^Q4M{vE^Z=y|NC!v)^?10!BlL6=e_hur^o4+c} zS1^=|fqoeFAfi>{@rg)Vcpvuo z_azuZ*Epcl9X$RNjq_fwlf#x%1Qo4 zKns+Pa9I`qe}bgErJL~8e?;4~(EE_sd?7LCLDY^fZPSMIUvmhkE-J~a%6}pq#S$R$ zAGL7W>A{fE-7VCwg<{BDipAruz2=cc&_pseX&BP4(ibFm$_qEOJdimgr;32>#86zG zVal)>Kb=(iaRU{(OZED4gTIFJIr(D?Ba@uVd=7OpRiSF+Q4M_4muRoFhx#oo&0OIg z4LlnS1F1#6jRNX-BN>EfK#Od*g{S~|01|f_O#LWU-s~n1ZhV~-TCLE7USIWh^U^B1 zwR7#S$Oi0myToqdg`-Xm;M;rN7J^+x}Pg444S-%Q6Tx%DS)@M6ysvGA-7~UnJKYom8)f$6n7Y zdemi?-IP4IEO5`72b&_t3-J{C)#AlN0#`C#hly1f0?*Dof3;xx-)hrPt_Ns){6-%E zUzQ_gHDF&r#K|LonXu^$9z%+YNasn}Emx@r8O<^*292edI8kL?@y#xa3w6dMO%b1LjB zx4nmyB1I|Qr5Lvcbge(1TIQL=hKN&F>vafu=XSSS{8Un|R3j|=!FKYkOTvfHad*<+ zKhWbCTICD)l?C4$z2<(A6`$V^#;Q4Mu)<{7{;h0rN)^OVl-!2@_dPT@nhHqTm_2ag zd^1CyYkD1#8Yks*>2P1`IM94NzgZls_}`%fJB_K_dB4^fO@2N*R83lWaA!IC>U+KB zl*!?TjRPlxPoW}|?nG`^e}klwPcP4@P{BirNQp4PxT?@WbAJA12d#WM0Q~=41nim^ zYrbIg@D4-{QgL5C%lR(~R@yD!Xvs(|*~&j7ZO5!EzP02N^M5umI%52K+Nc?!`S0}r zUYt`XW?fE8-UCX%_J3|o*GMe#fb7@Bl65Gk@KI+~seQ6lmqvarsvY8g@Q z`SJ?k5NQ(bmb$xtFSa7}AR5)SFCiMYLI6-fbb{ub9tU`4>dZ(U ztZR)qWe=F#?lR3yEu#^VVdC<=Y^iN}7I z`s7Nkbv=g3)DE@Q>T1d;2*XKul{&mmGSg^v61@X$g7O(&eeNRiw&u!c7&%>oh0Ow) z_G7nf!}rVMuq9`x{VJXf%_CUJBI>LN5G_e=fclAvE=R6TIicfd9))s(>pvv6CG<^| zD*B_-8yikRpxsJRl$}gMuxd0_eth>>h_Wn>%)aie0!aD0f?r!%UVmG5$V=7qpvrSn zvbq@hTF>lb*0hvZH8ZoB2t(?;p7Jzojn053oUuPVdo*TU?oVe5y1;3l+3%hX@i@#q3jZFo3r^HY9}3gr z|G{7O{A?SzS4@c`m!}Hmkh=NWi|rInP>y2gsaFkNlh%eUMSMaz4lCq7-9R&pDax`e zerTpA1Pk1337azUsaVmmRdRL5he2LCy`LU}9~n8n(ht_CN>~<&1+d{;95t4}XNZug zdkm-HKr2CB!BCh7#W(gt{z2%VvCo<}{o}*o!+I1RB6&=CTN8@q3>A89I?4rk@-Ul^ zvh&N@96&nDj+;brheJe8iqRlex>pBjZ*$j3_XGQ|>o>0BciRtA>TO8v#E*|ir1iM! z7QIfx-U1+k9X`9LEVEvo#3B@}m|+ymOFg!~$&S4NR9V!0yoP^ox9jHm>#ZLbPtzv| zN9pvy#(0xwyE5gCG@iAfOcpV=oc=Rb?_*m>cnm?T*YYZ?T<#O3C)uY$0xtbyEDwqT>)nQy>+6gA?a?T#miM$u+_2Fhrj+Qj*1kL-s)W$R>`Lia>fh8j{T}b91O?fhDIA?qd}@ z?4l)6-2=;V-XrIFuH6xv#^tmK`cA7{su$83&l#=zy zS0+e5K8Ne**h_4bUef>UBA{)b3lwYllzf`*@1Nfu_A+14>i&X-r7mV@kM7kHtI?x* zR^WC!XRK4A?&tZAIi7(P(<5V6)8A^;PFK{z0h%v5^Q=tePmo)|aGtj=x>7C~9~{xa zW~ePMr?`xqqy`X~22r63WvKU~0ejjXnl{zj?duO5#$j_PxCX~0fnE|q((e#OrBx+~ zmi!^61?#jD-V8;FQ$k*E<#l%p?r!TUaKG1C;8iJb*}=u1dZmwSQoK_{9% zzaPIzG8>1)nD-sa1+@&-x;h?mUWl7LMyQsSX0*71Pu~0VF}>Jlj&X<_Qt^%<`Fx|Q z8_SPLNNIuRoSvAKxM~9X$*hZu#Z!85+-k^))GA+_&-ld_+wzAKxXE$nsX_r{(09A+ z%tgPK&%|gkwO|>_evvPQ;(8wp&r-e(Y%|0^BFQxA?S*E5||=gCulGxqp@bFng9`2l(@Z(r)}W&lu{)^E)hUb zD7>5Wp0gQwcyeCM8CuN zk$rdWh}P;rVs_r!Ivu-TF)286jLnMZB55mFHqzLv!Da07)}*4fv=O42Zt`;qwsjNh z=Y!^~*c)`*9f8|dmr9fwaQ41EasN@ZHM=*eObNHFg6@a8KKyG%V@eES%kJ{14s^NE zyyh=XjR3VnQu%GG_=sR?+c+@xT5WCm>8*3Rj@F2;azNK#`lW!9%u+H8p*(UP6B9eks||{Yr;By^13JvKNPLLI|m@l z>?)k(dG+i;T5PKcj`st%A2$hm_`8AXBv$MJ%JJZfXWsyosfp9g34!<96%s2|($6>V z)-*T{%#^TDxdix()unmS+)&2k7Q|i%>Z*R(Lh`H4R?k@26tq>;PVPqW1bbCcTbEJ- z-~XPhA412DnDTkw{CcC>C^AxHMTN+J=@NLfntN~IcuNe4*gNjfr^0+h)O0Wk$CKR- zkC*OH>N6KaktKV7UG{^$PXg;ulX4w5q2Ux)r8mm5SfqSP?@0H+aWb;Y1`E|FC5|^U zF3O4}E~4w#!vlXxI(*vROZz70+r@FfPbp58q^t>`078~&$;2(TN?5fpGG559uqR>e zUD!aW){4@!X>Cjs5d&Pb<$X3;jp9j-cBcyN(ds`^K2$K7&lbb&*)(I*rVr!wn^}K* zDGDyK8q+fkT+(B`(KJ|VEB!}AG1HVYLb*5l;Bvb@_5lz}6G;lE+GhKs`y`^wd5;GFW2DHIt>qnjjwhzt;s z5A?4fa5DGA}5|v?k0digk3%hbvmuhsI zv&|K?v$WRbUblNBu7q3VFF5{_zu9PPgxW69NjQ4S0t^cg^1*O+VVxv<6lZYKyqLkV z5L3dCm9LcX#6JC|<#p4*E{h9sC%S>1Zr#6}{Ly?1g0--{AG<*`b=?fgT20JlVT*5; za=M&W0lw63=qevp?Q-H)TWS}k)_NT_@J$V=t5La?h~YX!DgJm)X!AvApdK|>ep+Zb z9Da3*Co_<{F&HPTMcs08Xn&Jow9Ck%z*L$W zD1xnax6NG3vG`y#Jt5B62=n-r>(D(i;>>6dQ6$vAQn738FZTt0n|#5SI;4!SGR3`l zsUqm@h@YQU>fF!|WC{K=K7B5394s z{$0MoLx{3T&>a1^UipLWpGn#4yhL~RZ8%i~`0!}1d65j?i5=T#o~EpI==qGrjEURC zIO&vc^6W0N7XFS<$IvED?o*g6?E}>3x3dDP!pam%L6{v|Xj3)Bv(G4c7q`o96`F1s zvDuvh?>Pg5MEvC`1;2`|NTf;{MqdK zss~qxX0exyw@}xAtWs$R$5NhxVMqPlwcJp2el$pt&C}u<9YJ#2{W>DWwjAo!VKt6? z-3|~atVX7SMJ$@vb8Tpyr*}-=j+1S$Pj^N}ztS63{T4u910WG5YXYD|oK0a*C4J(m zoG#kgTM0)!Km%oKJrD)k8p(Ghh@Kq&Mlz!)&(i7e#AcfDFjiStHebSII;dVZFJ_Uv zT+ur@XF>1E2FzQktY|gj9`oJZS;FLZbQe ziTB&qj1;tb=!iG30cw`wVU+b}!#+j6(q`TECFyVxVfU(m%4_d{n(*@P5y^YA?!Y`M zR3`w~4Pv}fGTMi$P)NljsUk;baZ6VTezI!L#F>azSd2J(C~)m=KWzLOousK@XQRpp82c~d;p zCn?5@i)CE(0};4IaA732aE$YXOG0QyweHsNN1&>0TRg_Wv`{P*oh~gLEEqi9C0M9j zzMFF^T7?#E4r2o`2Bq%rMrm=OaFJ0(PSm2w zr%VI(M&{s_>v$&*2xp*8Jefb*BvDJ$>Wnw8v}Niu;&y~8OKt9hp+X4k`bjVnS*@ivEtMFDiWRF%0ylM_p_@Ypr+DxeRa9zoy`b+-MCOG@`ysA;b)N&(A!bN(D8&@ z!X!bdAGTD9ag9oG|AXpgg6m*`+;eKtn;ZoBKmZASbQEiHFGlrI> zHa}KEY0*xf%0Ey`0{j(Br|n|HIR`a$_k(fAJVptO#$8H2O6C zKB!L0>c0t|uMeO7y`mQ!YGj6~CLZ;}1B?*8x&BCGcejH;1dpR-_k>7~Uo%s|YXvsr z1MGu_F|kQN8Fr*bG@wN9RanOcqAnniQ{j?Ht$uO7cxAcd0V6c0Xn2IojjyZ*4=xa& zpmJ_65OrourRI8%pZaZc5Tu-NXKp0oo?Sj$_L+>H@U|TG;%x$^<-j8{3QSJRXe7Cz zFSd3cuG!8%ytyo_PQxIQMpdX@8y>G}BI3C%^+!@{sM;!^=_N1b>0b=Nt8y>(Dnk3o ziwPdPOE!;grh-)#mx>LOF0M|2Bg-m98ew;EIM%WxkZ#9+GM`$ewx`cS3p99Wg~>iTq^rwaFat)6 z#~2+twYC_LykoS_j`56OPND?4+pyhMH9(qX8M^|fLH>dSW}MKnnoQ3qn1l`Y`Cr{Ece+cVmH#@{m)M>ag-X9L~Zbq$jl;d z?80KFSQj!4Vgs7q)Qwbm*G3yruoYN8ibydtMDv;hO5F=K(i6r#K;9Ef_Td@XmfX98 zzl4Pmc;0|{0u*_Xp$r$c@B148pn-BY9~C+~e@eUkc@2b|LFavG)n?CnY^}>PUa$4^^cP1d{FoU*WlP_gol%(*PzXyE!NfPdWsRV{3L{^YKZtb%6qa%A4ts1; zMF~Z|?Mnw{WVV}FPh@NDqhM8%)u>b^NkX>jmB?o(mA3G}s*>&^SU}-Ev}eyJQIn#e zl~`V;d0s@lf4;r5i3j4W>!?hz@MqI26&D&Pz(S%1#$1{d&&(F`Qu(NBJ`d2%A{h)T zRM2k*?Tmxpp>)TZ7!w?nR-1;cPddHuV%M%M{60f{@K3)XT}Ejv(v`t&aXTlZQkA}f zZ}YmRqf!?4!nZK(0}$##Iq#jbDy{OUCroS;&U@}7mp2342T>>+h8sgjXRt< zMqI*L_jmfEKMq-vG0l90;jAfZ>Ca%bZ}uIHARv7l4%MRLp+~!r_x^7)#t_H^?BNF) z)mO5LFQ-`a92+6PqJ?{J+Rfj!6QzoKFj}od1~l8lr4X?0hr*}-|}6aKmj>cZ6Ovr!}f zP=^Ks=8wlJ^?JN@ zZE1=GcCek>%#Ob6+V3Lq(%w|?!Y7mRB-zV0;B)j=Rg5+I*{VZ0!((dB`4RT-bhha7r%HG-Se&)GymTDikAza$v#O?RMFevcRvn&SbmIO# zbaYmkPj?|xL#MH3PkK-*P_Om|o@L&D_X#O-O&eK7*6Dgk#Rga)Ia{w_&!^8MM{)x& zl);8ss&kBX3(~EcZ0ns*zv3pW0gd*#AXMSUsdaU9G+Ds8))elz74P)~-cJp;; z{d%wW)0N?PF7ouzWcdd3N34tmqrThqoY@dkDWgvR#RooWC;G@r@EkDbe}};6qRR4l zhWRsg*vUYC3N=urumW#|03_)rsyyfL0QGu(=Wu+!d#m!8&o2A4cDPN?Wcm=MA*-%f({MNWx-T52^1$ zxf>$b9qedY%C}hHKYyAIGSXBtvqh6hxF97r&3ELT^FulCc$R|~?9ew59IWsST~Uyq z=6WI*S23I&L&An0iEh+V9Xhc=9)6zv#!Nds*_KchYaS;+MKM%DiDRnnZG4O~&WO;x zoYG;3Fe6gUN;Zxc@)DrT&ss-Xl*R3hRE(lMaQLd0)~uwN3+FfDD5B0-X1$9>p;ibj znM^;5#Sghs>u}z5Ui~0f`C9^^+YCjIF}QoXo&MrrAFON+A`(H6LK{`14G*!g%oR93 zGXImu7Lg+1g*FFWBU19>eQqlWZ^$%eXqY>(wz7hP+cmg^bIF-92fWAHcm^pau5=k0V$L zBWOKNLz=R|m(eeKROuTE-$D>@XrByaOeXN4Y|Ykic2<{jp zJDAc&Tzz3MVl{kuSeU2ok)+I7eP++@t|*6btH1@AIOCZQ4Z0>y_w&qA^OxIut|a8Q zzx*X&O43PDy=q;TaiR!TP+!~G9=6jrwr<5Zd=B|!Gr@tM8%Og*0wqh-NM0eo>#mMw z#Twu>Bk13OeoedUQA{3^hu(6oa~>_0(l+@)Nb#6d83T^%?TvVCm`JR!6k63O)2Q17~fJW#g#6LP6`O zn}$U#FQ^k?AMQ_`7H>;XD)lMfJj}^2Fli12^PC^FCoc6KE5j7ee;I*p4o12eC({6q zG_6X`G(R6#X4@5KOocw$qOgvr}iq1H+kjjrk1<%J)LylZIHVC^uRwdH{*#f*6JYr~g zP)$Z}ExRjUedbr^mvhs)4S(WVGUhC<`BO#|FxJiYipTbVeZfAPbYu;++e_OAcV>2O zy`9u33~KVf^QL2_9GfPd&YX;FECK9vaKfE+TD6#s*9~v-&vg$fDvwz0eoV!K4)8-i zzU!NWb|ZFu$fHAZK6fh-0zPUsz1VzC05H*VY_7lA$yCM+F)rh)N+NKJIxvM0&i8D6 ziG{e^zcP+%E<1%BpFXh%ZkJuke|91Z(HA?hn`7oJ-UyUEc_@?In~ds9en=bVp~-srVb?jCOvU7r z?E(DV#%c>sCpqAk=QaKJSAVci2Rzk$R;;y$N5U4^e}Fuu`*`Dg_|FTc)7C*)?QjaS z6@Hl5gnBT-0^#dZr$s0HI6g!E-}Y0Um5PTf@sv+O({1@jkp_uBME1J=JoL+mc4Ccc zm@65WT62r7w7z&jX;nxYRB$jxHujGo6hfk`jYo`k(WvGqLq)`ROz2|dov#H2td{J} zJ|fm8*kIF7)8WSj3?BjAqUnJef$>Iv$j~u$>1@G+?Wkz1v@~TNR&V{S!I05CWbhK>?IfC0SD1rdxCfW}Mh6adF}` zN~5dTxWpHnz}aJ2-N3*~)+EQMgoT^5c(+P)zUh%+h(!J<&a3p(2OAOTuOgC*3vZFbMKmli1^2 z-N@ne_~B&eR&WT5bAt?d22e7)Y-8sYFwt>z*e6fCw|f zNzN~0>AqS4&$shw+iqJIi<+aa)py+9fN9PB%=hv&^LYbq+o zK*`~;NmxbM^MkXp;aC;0h9`b0bSVgJ%)A?>1Fe?-<{W)~`2$)V54`8_ygXo?r&%nR zK8<@1VsL$!EQBG>iGX}Fn)0-CNL@h(zmSW+7!1kdQrm zs}V%)!oQ7A{{hQa%KQBy;YULt2u3d1n{UjSu1~$~qz<;B9bYn!gbH|W@xI}z@NC`Q z-Cj?4y$Kqy@j^Dd+!y?4ty%GztJ1&ojHhEbcj&%ZK=jHbLoIkOOg5Fv4HnC;Szx^I z@A!^q-Tk+NR2P$MVF=wlyJCg&cNSYUV2#>jR_X^_$ZPh&-m$zV$*F=<9o)DLWmR-+ zl>S3**#@3^^g&a)^jskd$0=SX%7~4#viF9wDRO!nmMnV8)}SqapND=H%nSgu5vU=A z9g%(B;?bu>%Qmw<#jHg|#WrWI`Dr$A=#`31A#Z%nzN+Lu$ZP>Z8?xmT9eRl1F!f6z zXCLf+PsJDG)jpLwLv%0Vm+A>GHq&61Lql|fliZ;QnSaSp?b`6K{C+_|85Xb)Si_3% z43@~yHyWY?Pb9sXHPGC>NoZem-n?L~S`;>qo{^WV4TsHXPNQavV^X%Wb}LD5+zf3o zM9)fqLAK4mdiJ@yB8bUq51fyt&Ue|SvW|+7LCNU|*^j5a&~C%TuHx;lt#5WX+T#5; zdsgBB)tV+81^WCM1r+J6hUj@}Voh|vDh6YTG^p)`|6QTA`OXJo-+iCwUuY92_5LHE zR^1x}(VIp$a0Q@|N5Eqx&1m_KxiiD3U*JY>>|SF7-}Y=EBw-?TYjCEBB9`%t^I|R7A=au%cnD6B1J}k&r161dQ$U#i1=C7Oei;!n z+L-nMSV0%w6(Gac#GgKm{(7O+-l`O)s^tJrgryGPHI6~0Wwlk-qpq$MbraCwYT<|! zFVU76-6iADeAdL3{8R_APioyLhNpO2@mrUMSkd0ia@20~3^uuR`v;Bl&x}X>jt^Dl zO$=gLa`?8Q8ZC67fGw-5j$s$MdFy$*o zwY(?Io>{pJ$SEAulVf&=4PP?Reyqhwx&*9Kj1#pZq7-242-nt${FCS&6#;Rk`U#9H z8xRYJ#zmud=d0ivHG}I~d`A5vke@(c_KnR&R zT`CN09_X(l_u6xV^_(I@CS8tY*cp4bFDtt!o^W6hdEs$k65xfqiA_3wiTNf5{QxmfR3?d!UOjgZcf1-8U?IGq}ZjlVC0TQV%7}wnXMq_J{U)fvG;Y(Z8 zUgVrlXTRi0xo%zdsb!9qIv{k_fZ(5S+*VHy~f*^SFc742o%F8cc`Q7ndl};SDRRx}vA@~9A2uxIcXLTKGk4ntk;0|j->8XI z%1};hnj{cr!g3!r`$z<)AXl8NP+cMJUhnyDGiS}Xy9r=@U7&;z_X?YLAL5;u&F4N6 z)pq#m0l@j5Q@ryJLeV%RT*Tjf-QUwZ!hBwzWXMoTPdML(mUQEN9f{?#wTJaM=;5(O zLIId*{(sN!FBF<}->%;%Rm;9Sr@Z9<(PGRGxhK8Jz=~L96pL4xL~JI4?Ud+bE)YqM zKw)k$-HzyHj)BLXoKc!-$k=k&ZKhud>?}$dCa(sk4H8XMjDW?>>0}3Rn_cFujf9}G zsJCC-O&o#emyy|(i>-RmKMVEwQwnAeLg`}^P1Fs303S(vpT&l?Hn)dTl_32DWdWlD zn_$T`F3_5IH2M@J5|hb!adkZ*E-qet7-m5kahCe`__KUS|l> z`R1W_#)K0oFTFlSVaRCR>j)o{scbM*u}5G?diwDDyhS7vspSR0HzMe~$4*7v#XF=Hclf5l2s-gu9FDm(ybBs|zGkGTn0D zQHAzVOD7zjR(7nVmo2&$)cKf>lONbv50$a&Cz5OL;M`25#QMPwV*sF9j4|8X__y~_#a#`X&h{PKvfA9$=OWg1gSfnvw!FGJ*}I*sOe z0o0{rTxJi(vk@)`TLGcc6u$@By!n$?{qf0KZ}-G1z>Qw^X-g@s$^Kp z`cDqqe<7-x9Ie|l$oBmnMh*GLMQs7;grbG}Hs)grWaqkFSM^kHz@b0(+l3qp^Qhud+=750CwxqZ)&Jzd4q_t(5$;H%et zf@)2rRIQ)xUippR_1IL^EwziGtjFPv>bzKF^*Umk&-u+p17@qZ*)LB>au`b0?&)oc zpA&CStloO-a?%m^$3DC~$kit2jZ0_abLQ$q>h)K(IwP;YkKqfED-M*qjrV>L#1wZPH} zVM>tIpiu7kCfbIwHCR$fJsl&*EFFHuI@)dB1Ry0oCF+amK2=$^;R4AlkG@t$MT!Z? z>*?_%4V*xDPsUe zTOCT?5rJn+HyhR!C^KML)?qdLR~&%Wc(qHPm{Pc)kU>KmPYUQ^>yRPHo&99^u1kC) zSQ&uc(SLm{4M)|w4bXNKxw>FsW(u2-hGNCucZd@7BruU4@P zN@^?weIo0jQl_LupHu(gL+t@iN;*HBPv#r+C*7!QUY1gqaVq7i zW5phI+;pR_e-VYJ5#M+KV7&yQo5YnLPoljxL~=m`TuJc|xQEuY4b`#`7V*c~_eYYl zmuIYYOk$fiiKODmT?ApF6Rh z0BdI$R>LeSwG!4`#Dxgt-wU|v<2s$$P2X=kK)n?W|BwuLEwBycQ)1_Q9?l5vR@(zB z0(9+sLl57!)zU!Z+-}*K#P$6?;n)u6clFK+>PYF3NW6CMBes|yRCb$kwB&8*56>KR zyNI#~QIfliUx*A-b_TxL2+ELU)%{s+pa#g(^pdsQ^=eH+5WA?)xUO3&`ap}SH&|%& zx;y^gb}!GQ2i%0QYR%srDZ)7>6`PmE;7X?leBrK7P$TUb{MqSLFs@)P|DJX8{4B`3 z6PIjQ#wK5F8QKC0%n+0gN{V%9S~Bby3T} zRx1`IlMU2RH&=HQCKhu*Ik{b(Av#W~lu)H5A2)uQqNu__i|z8zM-=D*Sj7jf%Cuxl zfq*Kh%0S$Yfl9h`evJ~nI*g+f zih*bm9gfHfsI>RFlK$eT%D*IFzvu&wG=69IKoQA`*jyW0usn?1{VuYPtMw1cqxMHZr!4Z&d-m#pH z<=_%R!Fv6p(MMtK-m(yQ;mW6ra85UR{_d!^?20fF`i6@yPkz&ay%Y9?QbLNK3z)8# zjLEO)^9UU8d#*)Fe;y*yDwkk7&+zRVBMsQ;w8ToRYNH;ZlHXT#0ZKlMz+j&7u>J=r+nOvCvKD<2xpod`P5O6C(%zBqT}S+ADOIds0CA(LuOZX6&(+5zkZ<(=B&V}Gsv z$D777B&r^pPM0#;)te8(xg&%H##?>8p4G&1&c?EzgJvEBUqGjIp!9 zp0SuSFbKR61*0I{-GyhxLNJV@TW|aL5+?==gXn zOmcIaKwF_$CoJ?4bw8IkAa-m_5^TDi>oJh!Hapmn+i({Hi{;- zTuOyj*WPi7z!N9hv)FH)6WATPcGf>*C>6ow4aALUN;yL;2BK%HwgTX&zwBYSu>ho(;LABa4s3NTcUw{=8eEV{Utwd^=>WYd^17C49s5ji#c4)GLcPB zYG{I6ZeR^@743L8we65C#|3L14o6b0`9%&l*9RxeAjd)K%l_jN2uL;8x+0T_73H5g{=SlHdb-r3?TjgRgioQye{7Q z%)VM(Iwg(&M}VNFw}bxUS+zP?u=TwQ145)%4Yl46SD^9X-${ZN$A{ws!}luN^OP}N zp#jrc_pa;i1Hk6CK8?VprRfqJ)uBhR%w)(BGc(wWYsn(NpDw%g!;fRCh%ETi%D1vr ze}%tX%T>M=B}$MjQH;GmgL8-+XjkiC(NopDv!5#lFnNAgg7OU{dwirH5y0&#B1e;W7c))nks#xrw|+1evO zDeHoc?3M<~&=`)k_}pOL;^7@6LK}t}JCcf+nLpuc=f3Fg(0@_3q?D6uvwx(g;ihv9 z-HqJ;$9;7v!L+Dxb_*{pTAe8CwT(fq9|Rdoz-0w4LuD{pHg^r<|B+02P3>@owuDl{ z;J*A>qvvLmzxPot>q4nYe!x!1{Rk|`dVTzeI2zcMh_jp&1{agssz>R>>Y09!?<~n} z3Qb4aUDkR9-R=$uzwk-Z9cJu~6<1Zy+5x%I-6vME;~I&kiy8M(W;qY1FRWvkcL zuJea`MsqnH{rbbwRZ~b=pjm9pZ7#7Pr<+}BI@Jj8=9a=RjGI=ft0F4l86~NC=x{U| zmaAF8=-JBdJ8}fuRErLP#b_&(CRs;tMnd@&A;CMWKbhOfR{^9-ri^8{gXbMZ}&yQks>AZ8f;u7|q#oCiMR#P;w+&{vamnuP9x{ zZO3j>@HcUjuc>Vd(Pa9j81)i5rgd=A6}=)UiZ#7#KHRsTks4}{7B?0&+e)- zvq4OrWqd&0t2I)fhws1Z6Wqr2I%vopUXjT%dj;+Cs-$q(=Rw&N?UD)ft;x>p zBpQ6rMgxau3G(7gqra>z$Kop;jgzm?0gg2e!J3B{AQA0a1m3d6vrhw>?nq%CT3;4o zs)cmP_YBL%MEv}gfmE)pHAE$kPi#5$9_%oOgI__m@d8P_U)Ow)#G7u|Fl#E;ZtW9DGl=EMyk6fC&s>d>7^oo< z9y5*K?~1EJBRCW3pEg>>A?7V_c+k>9wX1^$mZ>9(bWU@1d^c@DAGIQJV5Q~LNLP&F zK2KMNEo}}B4GR{!hyZfi&gjb$L0@NsI8ZUZH~FA$2!ymaa(WLj+iCgv0IxhaFZ*)! zJ)L>4wh_u+n_#Zt^gZTYs$x0oyo3J67$V0MH3jQ0^25HYdq$tmFi?D_z+W7zSGVEw zsX{3b5!1XXerMQY$|WI|d`CLf&`5XSpdrC$0My=)5kpQ~`fi{GE?$N0 zyhk}RTnNsHgGBG^N`G0kxst)6spsNi z7T%iBlYWbA^D)jpEuYF!a)!ro+bWLR^1U~%WUCVE{LM2wx=VBnBUvj;Cg}UEO?Xz7-Zz8j)ww?-IkuF-rXeBPfK!&7>}@&QF4k z)>!)5253xedcl!fN?a?8NNgV?P>l!=4_4ZL`XzPpDD3?_WnQRhGOZ!)gPGj^b<-1y zqw3a?H$N_GpMIFC_lQBS=ImNUom1Sve++MVN-Jb%F}K_~+AAZJIOw*f7o93gn*#Yq zZ*9$WR&GeBJIOX@C&RyvF8UZ1-A&&Y|3J(3Q^hWu0GSu{IRvsf?-V}u?{&GVKz!|6 z%j$nCw{WYc&pL%&K`sY0x!Pd5qNv_XMtkE2e#CNZ^@ndeMvd9MO$as{M5C&d!Id5E zTvlHD2JN0@9yzq!l2O$Q`-8gt-VqOi;V-m>09HV_)A{<$wLBHK;`+io>Wu1^0nR?m z;4r7waMzI1!d;z_AEhd1#04MgPFBPkQMxE-JM4QQC11%#Ewq)IEQT!i@r{)>eHSiA z0~rtr3q0g=Guh5KvNc#30~*INu%IJP{BX<2L*tYGAttGGOsTa~mDzz7oxxt6XdH{; zEGz2y#cFSyg4^jubImnP%0TuYC7i%*h0Tz z>6o6HLf&x=np@nEBvlT@zHpLCd?9m=AA;6Ww*GE&m2`NVtaOb#K$8qprVme_n}kt@ zlwK!tB%LMsF})-MaT(wxI`o`p621+*nN|mZ*rHK&77`6PT!e_PKl@Grl+FFyb5PMW zZcC~XR@_gs)7zTiY(5^#!aFwPZJUtQFDEb3o~o$W$aujrNUUtEl4!`a2IwlPi&}#FXfq3p7dBsd@DXdjFoqj6v z9P2H^@`-ao;#rG!dbShqf(0$U7pUZ}E`|{S9 z%oZ?z4>-{1dl!M;Q1$}8t53P*kNwNCbMx^OCRtJX1d|wc-xl5MR+n$cQS1xtj%<2z zM@IL!C#6tkUaF=V-fF5lRk64MfZ$_tSVVbDhg>Z;JQCB$J(Ivr3f-9umT4hfpT;$; z8bW&u1zIuklv|lLg+=K?3y3o~OmUa!B~I#;QlegrdKibPfb7+slq$R)dP`KVK*wh! z%2UI-`T{iOZ>Cf=IU2YZlc4b(%*ln*6!Evts9cN5toEq8VmGpSKgkfM8?Ag|CA)E= z!=-&D74*My!PR($VEH~1H#c+Gw9(6BhaaC`ByGkV^C11~sO#CycB@aAbFS{Gj6+~>&; z3Tq3YRzFUb1N4@MD!D^rPK+gZ$4d#H-qzFztJ#0FTo?qBD;h))_Xk}d%r63G48a>Dd`)CvBGzVx zHRGfw%qhUSRjlN` zYD++!<_dz_68g;bg1YN=t|0S3kkuOJ!3+Zmj%z5=Z40Lo1UP8e7c4V8G*^J;Spz`t z!DK3!$(lueRdPR=U>;M5^DlMifJ;SX!u_%vO}tK`daoN>Dy}>39SmF$8{pXurK`P; zgm*FluBRx>8g`l_^&4<8GtQVpSVs*G2V1Gvy^!8n#sjmX&wFCQ z*U>m6I7ZRtms7H6-u9f*jghT&xfHsl?m_Z|1TFzli(2{?Zur&HzYzSNS=P=;BdL{N zuMBF*;TslaqJw=1H>HpFgFI=W*;p7sqypyNZ?7cmgV*T7DvEWAWc62lS6&0q=l%GC z%66KR3Z#TAddY{kHyO*AbT;y_Ns8YBmJ75!^0>E%73#+_R?Q6aJ}DK6#sPpw!qHgo za!a46X#9Tw%dzQ@3cv3}6X)F#jeeu|xcrDjjY-ZUBr85DFd{o8Mz8N@A5;?qrTmC6 z&%)gkXT&O*dD9}mL=kzj8Af?fcbM+iaddK}VvO63bfzxNzpi`+!7U`?FH0)2jvEIH6 zdG+l;)`>QbA9XgP=20G>k0iv{R+v;PS}uDWY$`p4j!$;)|6Ku~`+7QuLyV?env5yE zP5?o622$-JUgano{F6laCtX=?Abfx5Up?O4rDI$#mFbvd>T2+g-*#J~#)9m?-Jxg` z^@&k$_U1cEX~ti*Z^I+5hQ|MEZ;&Ee`@9A7FFlKixy64l#k_~2F6MK!-Pvi2s;SI-)ox+y*l?#XNxG zS;U}Ykjaa~q`pUJ1d56%CFxvp&nqeh#;^uB_4u7HUjkCb6_l|+U)*ldSZpH4^C2#X zV-;fsFmacqgsW=2KQ*j4v~`ES^zrpTJ8LcN51Pjbi&8LKt6|+|d)T_7(Cq`>N_}*s zE&DMIX%W+0;~+yjn7UtM=_8a{g$2hBnkBD*w-N)6-_&autW>vDNFT440(yf|t?2XKlC~sS}K?vg4mIjWdab@;@gLaSS{W#G+K>r?R*GV?y~& z+p}MXf7S?PRP{jX4GQ0nu5_Kb?Zov) zxv7h_xg39J1?UaiY`Dfn$V`A3D9m+vJtHp8+y$Ui6kA+0oMsLwpwIlDwnsj&r5?I* z4=lQ)fD#=zRGr-&+2;S98X*%@VkyKajZ z4w4ehhPj`&?;MwJCF4^hsIP5ax;_5*`;=(dInu1E?qrxP$ER(#7ugOg@@FeW`keLt z8`0&;*4KJ?mk%>0h^=vvvh1kp{a6}kEkub$<;;06Wv6r&^8e(Mp0fbej^*z7k);_? z3$n~dx7l7*J9RRP0UPLnGMw=-O9rLm>VFeB1XDMd$`D4EZJiI1&&wAIf;OlbSxXF; zeG?@=nY1jfdy|Y}$mudHszk2L+0{a`WG@R(W14F&;N@ngP82YB zg_RD57kYf25z8h#%{7f3c)2!r^Evs<%`PWINEbVNllQ?5!rAS&FIrTiXH7Y&XcaS4 zc3_%0Fehs*{8CsnzYuw*c7XEW@f;uv^Ot^7$NRsIO)qQ_(|_b?xBKjzyW^z~o{8R@ z3*zf6fl8Axl22E<{QpfeIqO-!tY(X>m9v09=({@_4FY3x4|_U=H%s8FHdC!<#dGw& zCGFEm5exz&a>iE?*-L)!|4!|eGfrn{KH!FzR`=J(8iY+hul4ma6KnO}8i1G%y;>`B zMn#f0CA^EwWQyGIm zm!=9;&EMuv(8PCI8M>DaWUuz-lK9r57tZ5me~R&yQmEip?X%lK7&1BoJY9f2x4$4d zy4f@6NqGb$k;Z5>_=A@J*gu&s!L~s%k~b7^>!Y6Il_?wfuvsvpU+H@P_`Qc0X=5JZ zeKQ2GLR>yF3|9;|aO5shtm4>7zIPF~`P5aL9N5g5bjY@Ldv{R@)HWQ$u7ty8_|M2siib{wemz{C zu+t8%atAy{P6d_d+J;)0l<*bU-+0{J9(j^ckxL%B6`)b;wZZ@|s%Uw=PU0Ig&_sy5 zp53;{>|x%t3rSt|A=;;ot$Ho%DF!x8=_j<07n&2SN!5S*GFoR2V!!|O!EmqC9zZ)h zZ4df*{k$aRV=+*U$wAoge1raSd4rxjj&Nb)gjvFB*qA-aI)Wiq)INH$(~+{=LFcV( zd{CMwqPK^%u5Ng*2!u9&w4CBs^<=6+xFgWHCRPN|aRCIDu)n4oEj9UCa(lnKzp}B7 z0%1g=$JX`b2BjvHgF4-!vpGojK!COSuj%z!K9TN04pSojFp#Htzs-1KjBdr4QX@&u zc$HqeMB~9}U;({-#z<3uCU}m`fRHH+)3G2I{GcFOj+1`5W)cB#B|%qOlGm}f-Lmx; z4b|Tfo@D(uSHv!%-pR57H)=6og71Idsl18fn`Cc-CL) zyAQhwnrY3>7%|HVa{-J1KnOOx(Jx@CpCCMHW z2!@3IA;uijJI@KEL?2_;=S}`5kN%oJHpZftb?s1<>8P~0 zTz3oq;Ca4^iniC@IkXPR~2#g>KPx|0nAy1ZpAE4lh{|neH&BFL*1G%f> zT67}464GI@5b7g3JSPN=F_gdO>0CLmJqHfr7u)tJdNvL>39HIeP;X>=TG61xrMDy`}QEQhkI%xtxr{(Mpn&h;Ued{&N2C0F1KovD7B4ykk=`p*(|Fm*TlKHsZxnK^z6KXwXoR|mu3$ytTmH6jPJ z`{GCWySxz12i{2RP=sE|4N+@5I*+1n8uCM&KToVMOR7phz0o&6soHY{e1ZdtQYaQKc4V2Ei=AowjR0w?MOjKYdbZltCWS^6hxf_Ykd-hl}~mL5XHDNa{DWnLgr zk}xXrwLPJ&BOFl~_fBAOomo$ppxatW!()}{+BCaKv_P$EPLvt!@T<*((pl3Iqgiz= zFrlv$rEd-r-UnDzgmb8X1$p&uAOI;iA9cA3TJN|;0V>W746)S!O`Tl{YYOPaM|v&) zCc)`vf04D#W`WCd2uH+LmfQjj9^YjDAyJdVoM}r_KDN~|%p~;KJE{Jl5PE_j5NcTQG5~|QA?9AP`|QZe zvm>oIuH->p@p8+}ze3Rqta2*$ky6J`ep&?z`KqIPa^k(HE?JQVsN`w&kim=%m-P5c z9=LisEYU9hR^T7ys!!}f&@vi1h`*>B9g|!eBj1bYeLG7RFW&C=;MzE+0_9*_;lvI5 zPq#~orPGr8E+6AdUSXqu(W=eeoATKSbO+v3g;G|47Gu=7vbtVTj+nzJWY*{#6bqsC zzQQ0cXW78PWHD`|Hg$il@W7Xpw6eq5VqmrbpX#;&dKm;15!3?66EPHXNUiF&wt>sb zOB<$_CC~Rbt|uf$2$sHXjwwz0hrB*U`=e~{$2Ja9_5C`HTzG?41%kcl93Gd?QKR!n z{>;<0bH@6@k`=Rw^9l#HIz|2&eQCZ+9Ii~&7u$IE`smx=fs;9QjxVRVn6iM0lF^Z) zCGhX+1UhHu11;|Z#7c>{g$RY_IqPxvTAMk7p$D?O_OmZ~WZv)hXbsP=Gp${2V9jPH z2I?*@LuEs^F##pungudQI6+)P>(lKa_X7tP@W|VYcI@Szue-OOcVw&}1@Ys$?eWdA z)YTaX8xkS=&r&CG*9;$7#;1ZW`1$0nq1>oXWJuX0!qvm>Nyl{+9&@8I+m)+~7Y1Yd zwa%wkAx0Tfq3cRE_*Ori*ikJ_ZF|sC7Kr19%@k>c2GL*!h+eJIw|UU&D8#}=*x*Q}(vGf( zd7MVUXDB!Jw`yz{S2W|GL~;7ZRX-R);VpP`=``=j{~-YlI5 z2e)B7oSxTL<867z?5%V_z;uj2Yqy1H!C>^h#~9DJyF~)@g>Bk6TibAh@~DTB0o18= z*n)&yHO5Z9CfJo!(r`JQzQTZSo#$Gy>0sVT;f0tdBZPJ^pGu)bBBt)@-{RQc<{9cdHbc zLE;chs{k>yx$T=0^{1;A2iJR|M-6Jpv%eG5J|O9tC(ha(Qp=izg1b<5s_5AnOWt)u zCXQ3_-E56xP8Vx(hB%ioE(+|Z)+kqLu5xYF%AAgvKCR~|4ZeUzN@ZSw#EA^GoN7yw z9Ee3reRSY7YSi~OQL0zat+&>M>hk$CoifjnJH?d(vPCMxdjGW+K3mSo^i8pJ=cJ4b zEf{}i^-q94i0j~r71h_9=-=59w!FobTH0#wJy5&7iY>%8F2hv)eJS9B&agGSRGCMs z=~^1*kn~($Ug(HiDl>n^R7)0~bjZ+*{X)HXAmVZxrt3A$-cU?;moyenLYfZ82l&|;(m9;R#syW?QVXRJ z(RU`-?P)AgDu!oXoDFW90gI z%DVds#$C;nu%23Y_Xy)3n8=Wf;1HOseRtgJ0!kaut=mF2&2l|2$%`(#7~>idN0E1pg6r5 zD82MXzl+|Duu8ewJ=iPaLg^?emrU43kMxES9N8Ow$ssH>n};pLB<-DjbLgZX#E+%$ zrAJ6B$8()aC#!j;A{sXtOU8r{j~EbeF7Qo(KAx?Gmbu4F$uE@P|9Fn3YkGNFj7@iD zF5jad+YK_Qb2zqad$ zcFs*8sc}Ah%5f`S8M#_4z^*S@%;{ch0s?Nq3F6A=Hgb6pjCQw=g+rXRDmBqbuhy1A zO%wu#_56k$m+ z6`PMEzEp`|kDq|Cjn7$sTdAhFyvL!mqzvZ;RF_A zDPG0GZ$Dkj#2MDYEd0z6$Q=(fMC1BjjajyV*XnBZ#|sK{*rNA!3P|xvzP?zyv|n#r z*7Pf4?d<%S-^zJf!tmB8DMnn2A>gm%U{@ip@v-UA(hT${Ip)C!yg8+(Wt>UWrjT)14z z^=rIv%-6nz@AhYpjeFCNFxFKLm;F9$+jo09WNEDAnjknUU;0lpd?d#{>02UgI8nHLYi%pM}fSCeB0= z$ym_vay}V0I^ye9Im49uWBCEu7tc8cd)d;VZlQk07QWEG2; zbI2No&7@0(iW2&UjVRZ`WPg=EqAp4+eK^i8FU(Kd=V|A1u`@YrAVn?01~eY6&?RwE zl$@oylrVsd@(3&qq!m4WM4&iu$MHy_p#_6eh!slB|30!Cto>s}DvIX8Z@yEn%Xpd# zGdzRk)sGm(6CtkjSj8Lu$2#j^^jZoTq@8~jx|r%Z2v5!ulCJ=Jl8*w^M+V!;Q` z4PKhm!1)qdOn+}t-)y}84gfl6s83^VwkhI5^imx@y^NycIx6L?i0Phel#{^GNF%WraHf}ROYe^l5SssNQ(ps z!570&6+*?q-@>-GkC~Fpla8F+NVWk7n`bpfU|otof2;E!cE@9nD%I+7CwcS7c9UIk zNvNm)u%TExrn<((BzEAF%`yrE7yZnJPkFP_Cz*>^Akk?fq7&H)_}Va!L?fAC^H3PQ z0h`9-{fClpblI2Hv2bK;HI@KCP+_`SwZEZ|+fakA;^dt%NdiqNkR43c%7-?u^f&2a zrCCtIL$^5}C!*(RJnGGlIGf)4K_*08wU)wKQBnw`8txa&PftjI6+zwRVYEjHM?mgB zUr793-1;nfu!ncj`B?YiO~~P19vYOPvc|Nq9^-qu*JS#;v; zm(S5bZ>XeZmr47UgC0Ai_nFg@_YI-u5jFkR4ZToGrCP3AAPDcZ&b%r`6utv)0@T3c zQj&e@bDkO&ES=Oa$-gAaldY_Ppzg0he#g}4N2fIpvw?@lT7We=rE%GWn}4QGvg`$$ zz3xNwu!YH>2B~BkGx%P&dCcEhp~*G{+hYHUB;#zhz3+0<(zT(l14-Z@dO}TxaoYis znab;kp#0S)ktTG#brvY~P;k^J>EQEe(u|lrU;={*G2v-Yf2V)BeD}s+u(TnJ5h;7L zWia<(7K(L^OsU#? zQ+J)aFG4a)tX>cpJXUgSvpOOQznuZHcLF#f@XUza29bo`zer!yQE@E+Ht)y{CNFcz zvQF7)vF+8Qpe6Y&1JVEVzACqgsyN|MV-5ct>vS%@-iGcNK9mD)RwoOOgk!ORvbMVkYSJW`bh*>AHXI=&HrH ztZWI#bSKyfa^2So=ZWq0v#>0=`)ufzpFfb9lLJW%)GmxO>ySbuT` zlnpbyK54|eEVaG2CBpSRIoYUiJhs9m@koI#i7MqX*3~C9*|pWxpHKSBF;}a!CcQ$27X5p#p~y}wW)*GuKO0Dye?7|V6`IOCzlE&3eeWFRJH!% z8))s;rne}Q;o58wE)+t}DnakWIeQ6;M)1p~YEhFVyW8sL$HqtA;gG+sr$FI(Z+3;- z&YtlX(Us22uWXRhXcF<_aE!#xu&= zGv}seM%uCJAL`qqZP#>FxKiGQEYpY4{LJ6r?hB9i71b#PT3f-C%7Msj+U!riM>~Bl zijt;ldGzqx3^u{q-Tw;aZWYURk(YN!$FueDT;D354o$U2S_E+W|5{uC)4INHJz>fa1*njlXDLyxzLF2OmjCbmyMX9Gh5H{WVxm~8LeXCkApa`~HDYKz@9wd&hlRD%khYPa1RSl*SO zO_hQm4FObO27Dm5`E)~f`id9UPr8lLW40Yzo^0OfhaZ6#%$Mqy?_5lSihrkX3Z7rN zkZAnfL!7e?lV%EpWlQ`Yq+^KWgi?!K#J$<{XcSw1!g>x39CFZ9)1~@~y%L!aymQ7-SNKj~Y86;)7OYWIwWT^?% z6w?l8+h_RT!#!c}q%(z!?ys|Jl*m%+iNQl%(isJ@BPp~LA!M#T&wNZg*HIjmbtfN9 z!d3{SHO{>-4D3n$vCY&LOR?}hmD_4|I~uQ{KnD^%SfYi^wTrmoAD0-mdyg%ishbnR z80j13;=;>xZ0KGtAO3IC|7C6s#jMAo4>@M=@hovYJPgqEkjpNJC8Y*P=~60e7~MH} zeLQ1IYpacXeBNOUlQ~$WLB|Y*RhZdJ#wM+pIPo|UuK9hWiE(#`L+?%H^iO?#eXrI4 zjbcWUG7dR}ff8*Tu9qKk^N$ zUwHAYE(mR|d&$0!R-EoRoU)a`!eQ((=o%eb^)W1kV_GSbI6Ww5)*{boYjtb&E-RMJ<=Ny^o6=D+?V zoK}y!{1s_zLR1KvuKZ9|-rG_CjfifiaVGe}1|C}r>mu_;1olPP32f6*NF0(N`5Rn2 zlNa{*D(7Ag+X8G;c%JGVwVgUMYy|zYVW@iyDRyIhY~Ho^n@=5MQ6(Z)OI6<-Pq_D# zg@WF@Hdx-~XjyfOn^(UUo>Vks=Pa^vYJL=Jj|kUqI6l^G@F26&*6}EtOg|jU$3LnREnFX|CBhoG)Mdf&ZoPN}S8`BehiQ$h;4R3GW7Y7N}-_yM&HC4NMOC%Ss#4*2DqJ-kiT{#*e z9dIbo2(Y*+;%TNrgrw+*qx$K|&{uS%5-pWWNCi4^O)#@c6`@SRZzQt#yBRsLpZ zkV{#J7Lx;&$G$P>THhswz(d`&ij|{0hlLJ24p2SDAubAOe^98u_%7n9=DN(nlZ}dK?Q}uy;jQ%4wsABv@RMpDTMR!>_qyJ#djkqSf!M^? zYHB+k%L%FQ(TCBR$m(O7zc_o0{{=xEQ8;e_u(w)s*7ZE5W`{bOrTQ~7moNGUM#Eyz!?nIYqP*f?G zS3N@@EUn?QcnQT9Z!XdC_I#dp1GGhPVVzh&F{6mo--kKjD4Mbo&OYPuC7D@y4ECpn zZ83O*q3xoH(dewOA7Ivx zkXXY}eA|WL;jjCtiq%qKbj2qb=^aeNdPTeHn?YHgQJbOM(^R!p<3p7ryU?m6n^=|U zz@J9F?7oj@nU-v97ZW@TGT8AYtrWgB9*f>od6ozr7xBu}|A?(%s;`xrDjf&?yDd@s zc~76f;HB?2lJN82K?6680kij5sn7E}3S&}eSkn$*j1EF(kuyWmvmPtRBySV z-QIYIu^z*QScBj+{}*;{aa@Da5+yZKW$qtHN^QFQZh*)-kJ|dndvcsFf0)r>sIzk! zox~=b;OGys(dP_vl;H-a_ZM~@UJ*m30HlVU#-3nrT}iZyRy=&67gU#5j%=;-rN z^4u%?;^*aAkQ9bKarU)+;gt32dF~XFz7wz+($-=P7T;UGb+|j{!)j%K zjpF4vDQ`Z{@>I*wpR(XDZ+pAOqSTApI#!qz>=1lE%V@fF)p62MZZw#{##M#1M^b(Bo<~ z3_-+c6pcx!7da6i*9B@=Qj{YtQ@@^5sq+f7v#-^na%*IY)!rVSXsNr{ncySD$D%uF z&Wj+_ozTn~xqs|iFS}rxu`gI_-CZr$=wsM=U$cv}RbX+2W-e*w%AKXFW7lI!^DTvU z0=@*FcCYVz#WZA*W>Gz*Y4|l%n0uR0;Ro0q&=<*Rd)Mjc#Kp=Vj}1(V!Z{ylU&j+e z$kyF*qma!tWO%SHhi>TFho?*Rl$;0>YGE8e7qDNqdi7#l@iy@$S~-5TPao+wvkBP9 z?FFKD1PjK|Ua$6h#{k_<`up|Ys2w>fbTP&VEH@kRsMWAzN{hb?F|7A}P}sH@342mc zE|kC_@wLYk`^$QG_lWrv?Y{BjeZ+CbvMJTik^M%^WGz9cuu=iLu=q@CxM8I@vO z*!?XX6=$`K>FKG7u+z}>+_@xSjLNs?-jQpjWG!|?r`bqNZgXJf0vq$yo3h!_p_}q@ zxoK*P$#rm)b>mXKDVO$4C?2K^)RIYO{K1j3n|zB)Kz+ANco96;?9Z6_7MPh2KN?KG zSv=>K^?lC#?cF<-%^+I4=d4%9^3!_~G@C?dR0w}+7aXr`%y!@iNoX&;C8;B- zM^YRpoRdZ?!7isiH3f<-`<-KtEz;jgbY2brn&u=1u|Uv6ugdb&XWlrHPWXl-rjk3xxAU9uPqLYI}}9c++>ws zJHXa#zCGXWlnt-!x0rC_uO%Bl#RtG}=uxt1{V{(E=4?KtB&Nj*gRHJ)lafZ1OMyyQ zfus7Y#+X=GOhT*fGgv zb=Tc#+4auK=jnrjW20#-z{`zx(Fxn9IS0qZ209mx4>*(C?cJG&|HV3M%I%5Z8o6qH zIHgVma&HL^g&x1pX)4VNIvC3R?BdOuiZou_C zvT;7$9|A|mHT{WNp}_LA4$N>D%R<08dGEy@flkRCninGQhTe~^vnu@e64Vi&dbNuZ_z(7fR%8+@Wh+Vj6o zJ4aMW+2d081?Xj*cmN>}Bv21*7h7O*6npZjmTYu{OK~XRG1hhRH8A7yr55j6?6fPT z`oeLH3x_U?_s^0s49lw)NVq~rUAfwVLIcj+JQlgQa z28!+VDTak!6sXc!&QZ`2@FNpw_@XUlt*bMZ*G^xoz@Qel&lHqB??!(G$0E?KnKoA` z6=ib{z&fnB$LljcF)nW=fGit^1+6w@OUPgd;aXVwyLPKiAq&%_^@s|ja#>$$dzG+K ztooulAZ&JC|995w(qnJ;9a=537Y}+V_*{QVP-@CLLKjh^Z#{g?FSW)EEq&yAy_AQP z5m=HUqvNmbzWdSSRn56s*_HD^r#(rjBzZebFDHA;+s2Xn`KJwYpAYuqYH-%Q5u4uM znv$W%$Mz|$;t)iID^}(xD1SZT`r1L^(lf#0y3O1zzi%~nv4N8USz%c#lyB{G}GVg!dt!`BB7S{-;eseUSj(34d3PFF|ad z4AxEz|DG$|PF=F*F`HNh*hDN|^5i<{Xe{#$K@WQ(#z5}Gk)4(Tk}zQUum4UxxIXOfQdthO>*8wDnSiveBa zlHlZGVJ@->0rbyS*y#>?rlf%{F)^|3^_5Nb*fZ+5QHo#HN3JMKA(YuJ+6U2AAhG9) zU3GrxATwZ?i%sr|&uua%*|67NbganK8$5neXP_ z3}pctGod4g)d|E(NAUeNIFvpx)UyX1D9V+YJcF2kkOTWk(t#j`2@3A>%Xaitr;ZZl zRze=MTZAPk?04=3x3)^bOo%L(T~ z?4F0pl}v8SlGOWOHaND5mbZEAvs<(_?{(-X^5M5eSwU4k z3-5vM7OQ7a9}R}hk@FtB#_mwKL|SG`6M-Sp3MqIg!)J`w`wf+qNd3_ovWPh;_toop zn3?tA2I>8^vv7mH$C(Jl=)5&_sxHCaVW-L*k;2Y~)W#mklEqoh)VPp9d;QQ~Q#Y&}m?)Fp@$cSX zl8;=MQamM0B^2XV=$gbbN4WCxK4vnVz3gC4H3J4@*S^uwCAs z=tq;@+pazDkC8?b#9V{eJ|0o}yLOE$8ej$aB}AC-mQH#SG}Wu07bCsBtF(%toxb5@ zHUF2F5pSx`!LO-;T!Sc)hkUVf|0G<_#5Yh{3Wx20!-F{FL;@KU zpXjlv3x$gI)xIcEqJ~9hV=kFPIK0wScTe6kt-j4{V*or^A4R*Yzq~`bc&@Z{yh&hB zIX-R89?0$g%#Vdb&Nt9K$^{?CB8G`HALrg59h{txG$*yHK|(6k$2hclI#2P{I|$2v zX%t(xJw6LRdejOF*be>3Q;jKSm?EwZQ8{UXk?2^IfY&d~n)#&%X4aJO&KxI~Gvo(m zD|68qFxN&~V_P-Pt9y`mmMoI^a-eFi^^9F0S12Bex}k`LW>*)ZBS{m&fq}m}SrBaT zCi~O9+G6-`(}vLNg2ETmbDb86ju9u`L7_eTzmZO z=rrS}@5cexXE$0e4AB=CQQTiYZFh2Oni2ZcYHaCu>_vTdoi?}xCkrYeFLc?a0$|$q zd2{dbh?pJAc1|vgb7yE&v8;IhbJs%%Whgbs(FuKXd3^IXQP4ZZ6-WU!^sO1ckz|~? z;T$GN4RfZ?>One36}(lj85`Hi!w}3|nt#7&6lM|^AzUHnyHs4Ii}9c-WiAQm_OBeK zAnT8TFbmG__7Vez#DjbZuLh;z94axFR`Ifk2ieS-N_~7H(J5MzqFx`s^V}iqba&<@ zkI)2Qc5%VirzTZK;X9{%NuEcPW$;h!I8cKzNC z(wy0eC~88{e3TM2fmK~|D8RjqKq~(M;`3-icDqRMH9=b2fP6dIG%Iph1d&b;#ap-9 zDVrNRdHc?4K4Ut?U#0Q+zN_nKJQn_}4?dPQ+AV?-n68G`cZ1js?}soulWy2Pu3FY? z+;{Off*^6aKRmxTcmzFS1bY^uUD?{(0HXC}$`(j{K2ZP{`F4NOK82}fZxC-(kvcR) zLCfwUeGY7(JQ4hnHK9@sY=_iS&?-AWg$0_chOUukUoSwqF{6;V7G;=Cs zwo3dim^Lf?Uysshe61-p` zpXgL_|6P6Eb3QOn-fd?A5k8d2&IcZ-zDOf6jI}|=0w#(3q-qS>)}0%(DQV@f9oT8c)BL3S_Sxbb1rs+= z_4z`PFv%)q{G)RyXQfxv<1>Rbh&#`a0~55*D9niBy&3V|QVl%$NM5D}scz z)-7w20_^LI_B1HB-W^x>I$yECk} zkI%|wpjx!X>rp7;WDEC{chiH7UMHU>_Xu~pHXTR<1&uC9lTqi4;y`FoeXj1`wi^T02aN#>*zkcpiA#go+JL)WY`Aut6<`S%xRN6x_Hxj#+y z#o@?BRF~TuB)VnB%5g?LE`lnV3Kh@W18a}B2P;A)_oSyKAZoY(e+TYwwV&Qd2NUlK zG?LQE`(XsI}k)`hskgl;gam zDxMJP`FAB}(dsNq1`KCfZpD_e(B-B#=rMC-Sk3$QV@?4nyV_cI9md>p6doDmW5anY zBi%irss)@PD@NmZSqrn7BA9c%@#QS8zsBMHg|kW$za*3^6vL(Ff3nOs{#b{c04{4Y z<0W*q8c_>d4a?9-f;AyMg*=+SbmF;~dY$hK(iXphRv!{y*hIF5mVQd2QgD0T7^H&i zkLxs!VR7j7c}x3A^m4|qf$t-Ej#eyQzTdx;u+VZy;aDtm22Tu?+S>Ed`>WUawa~Pz z)Qk``V>~o!>7k_>4vEH?uqY9O)R%N^;9hE?hV77UL)7-RI0=f_7})dpL;bHV*7g0wYY{0(L^OtWUG z1v0`>H^>Y{{OLdeSNIX3-#E0|YQt$^2fNrMSWX@Yl_Bk^wb;_+kl>m1aH)ZEgz!P* zE0JuCV=ejy`lKB0W;x&f3xA-o2v)!K1Kk&MP%KsbTtaxSf1vsGJ8G`fR5B*Uafi7f z5uDU&dh%mE{KM=lpTIZtv@;OnFMEr9w_`_xMmV!67;nUonw%ks4~wAEyh64F|A$u9 zHxmuJB)NonxwL@)bYOF7wKAyRcS)flr2)hUm>N=;PX-mg>sofr5ZIigs5GZl7z8j?%DPM5AZelF8s5%05(T4 zaks=Ozih3L;(f3dgaph8;;>Ie zgtVO=?NE6`?&)})TLK?3c?=LLTn$)nub`eq3X|6f+ zr?|n##x~nNLJY3nv?V?aZcM93;*yRX{pih=?W88QXrV8imyH|?RoQJq8}oO0WrmR^ z;v{$T)!CfEYnmiA8j&5FT35)lu#H3K^ZheS6o_v}4S^p=yXULsAo)k~$f@iOVY_^n1H!VMt{#vHB!b z?V$V+D{n)mvq26kMzn^&f>iydcQSINM3un%$-9bu(l7HGMg|8tDMI5WT26y!8D|>q z?mnEU6vopo-Wi5K*%E10WjEOtEmy^o-v|R^7|$}zJMwOt=j!<3fL?Wd3BeO5F}*jp zRFCsb19FTrj*6D()+HLYXXFC;^6pasph8(I>5O~B%CjVNjo_BaGGgSU{g-KX?4|Yf z=Gt2J%Cc7)RHuS$w9F^I&Z(mOsy0XBQ5M2M+?UP;waxQFpe5Z&eK)}kl(BX#ilRa# zOG3QTzRfuR$V%Ou!}v6*BVlAQy1T8T-mf&fCmBa6^Yc+C%Va~;Z}o_yOQ_m13UUE} zGIOZ{P%J48Tf_snjO^O+%MPy|yP`~7g#;p8@(X5U7pwjtrNhj7gDZO>+vj~e6-4OW zD0qg;fExtQ99Xip5nfv8D~qs`2_ z!tS)SmAm?40)(X*D7`nofqP|2DYZ)qqogxYJ7UkmQ{(x9@4a&Of5v|Hap%c0{kC$b z7>=H!>T;v+Yd}+~QpAlkyK7?~Yf4ux%gVc|J{(TVno0}{Rn*kSqj!-vMx~gHW|?r@ z`dR8EO-b~T0&$ChPQ))OpsZFb+eQ_%a*g4zZ=}koF1z&yjF=K_=wBYH2C<5}R#Qz? zn-QGaEk~DBYkD2UUM1lwUwXRN;*ds!37M&1_90|wdIfM)y#H8beI-^ifW`?FeY(NL z?-ynwS~E-dLTv*neGyt**J-k-dET78wDapoiOz}>&)A9dQu;GF=aJlyu>p`@^)sM) z^)HyHTF!$Vqx-(sPay$t9leVgV*TaX*Tl0#fl80l*l1G3`F#=HWwEUlC z!95KHwaFL?&{hEfyES(`d6`F( zlN-|K89gn?aMzZdpUX&x>5V1lrX||S!S(hNoX6*?%{To$R&}OI6Jza?=nz4}`wsg^ zn^U9@xzv7>4D5YV%0@ZY_k44Yh1GV4+X1I|NLsLRosG^!QGzPLh7FIy2Cu1#M_s7bQx3Z~loSTg^5gadMbY z%zD-7vFf_RKTd3UIWz|Mj451O;`*vLOxMYL!T zuJO=9SR%8645}qdmFNgnY?-BzKasb|`XrIDlkK(414YM(b0bQCzN%V{!mquBH2rwK z|IN#Fp}FCN51_Imj%Q+$j}`&Og|B>~M81pm2`(-*W?v!iU5=j=v1-oUB>(S^hf!t9 zP%n}&G`}bduIMx4=OL43S7#ElGQTSAPW^>nULJL}V-&@6vYwS}NtR+%w_+hJ$BQKT z2l$?EA9!h2#KMl>Um8w-mHE{|FT}C-99AfM{S=`zP-Y?o?puOt)Slj(Sd7MVJhe@H zN&I(=7j?I!-1rxIhkLPtE>A$HiLOq6aI`WP;tqOD$mN>pX19nO0+xd)NT-m+A&0JY z$-*OUSi*EA2QnR8amhv)i`nA25@n^qWLgpXH`=Xq@Mme2YkVXr6<-X&-qZ z0qMN&3Zom_L!6;r@ojyqI+CTO#VAiV@1*=iRNYa5dP|C-VFTOkX4oi)ieIP&(c8+6 zc*>ZdaHw;kRPP66%QmM+GHPzm=CRz$#ptq!KG%}3LSB)Bt+P%Zubv)=qap`C3bngF zwwJ23&z;Q%@{oQYJJgvO>v|WSle*E)R0Z%TWFo^zG5spxqtAkCL;ukWR+Kn?K^}R? zg;RU&>GnwhhpdXuUfE6YIgwj6vs?#QAM=*)OOb?UY@2*jP%JWcEmlm8%v^3^^YmL| zHA}i%eF~_}8};tsBp^A)(_cksDyKEd4r!K94LUoAB?VooGUqDQGWQ*ZzMF9OoZ?~a zL=h0U4g>F$(e&mX&7uj2Tqk!`^E=z6`q$G(X~m-pRFTtrTJo9}2`hp)RB=rICJxsw z6xOS=r+$n{TQxgz+A8>c$aH%dgQAkL!6Y$set-L1cDJk+%tL6%AsZ%Vsqx3ieFqx+kFK?|w?UyK?^ZaZR%L5%vcVJZ5cbc=cRTO%hUaOtT~-7xs@e?7W>b?4#y? zu&v0L83h+N4gfA+;D0%?nZHFTjF$BCSjh2(!kBu1(4Jdm2@On!zVj+8WvGeD)`u|Z zRe1HzgaKllo*9lviCzN3PhCCnYn6ch*1VtuNCLACwZjhly)#wat6D;fnL>h#X|Oo- z(t$BFzQ8{Pa(QBgOcvU3KtH_T5CQeHqoDacok{Q}3j043{h#~&PUKddW^lvFn5f$Co-y?DQ$U$X2RihZSl2jf)M*dJ3Hb>jlXnv?gk z=<2HjY9))()nWkYavvcE$th>K;*iEJ3leH)suxONq(y1O1B+%x3VzF|7maE}pqalS z(J&cvM4-Q!%MhfE8B&SMg8MWv>ic9&S^E79_xz+&D9J(=)$gPrKn91ci_8=J95nsQ zRH8QrkVpS0&|yu(x^6)d=kwL>3gD@DjO8rW@Ndz~5%{fpHDRb}A=KfB_P{0@T}RVs ze<5wwUiCjWGsSA{6gVa#l{ID^^pj-Zu3}C%a5JnLjoebU6V@sOj;5+vgnhckW2EI* z1T$i#E|(P&2*l(OW)q*>Kf(9kNPSO@>|r)%{LAfI)#Vjdo{!~wX=#@0(_K)fN2xvb zXY(Fw^_%P0M|u)@X1J@br>Dw%;Z`0|Q$em!H@Bg?feSQ*YB|F3;_EeNUh3D!N#_ga zvzQmGF&CZUwe$9PmvCn33Vwf1*iV}dqh^+Skeir+vD=&SnerYHWV<hGgU%lU- zVZ&*0wRv2J7|e^|Z|tbNcMA9fjP^z{j;=A0B24i#Y|qZQW|mBmq*7hwj*N2ybr?lv zG(tOk#iWY%*12sj<(6hHzT0#|42+I!emI_;Yf4&PgcLPpg$p8eaY^Uma_r9Tm{-UD z0?FY3gm?UM{kS<^Qgb=-`__koyRNz7DT}4aHwT$V3G&hS)?!CIymp@J-X}%LX z+(f(%Q4N21PPUd=n44ZA5YP@lsew`e4bXpEEgTqPHD{MeZ+;kG@TMP1`T49?YG)hq zh#5BDYAQhGazpuB3lH;^q7mZh9VpCWPt%5*H&Ogj;Z9zZA%~5VoVC3g6)H!9mrdjG zf*d^I?wV8R9NapbOsaqIq3QM)?6>w^+Cras_!L%Z=uI?uu%%W9L zwS0t0Blmlwv?DQM4cHy=4Z(T~eTmChO)T?)eTMs)7fa|4`gT0({dC1{hi5%oZKBN7 za(Aq=J&gVcM>V$HME$%?h&-xvS4Em8~$-$@|S$U_d6@5|N$7ne|~T&r8;PV;J9 z#@_E|%YOdg10dvzB}xQ(7ZV`p1u{!QTk6Wi`8x*!ANGYnlLOs7PGGi}{mn7Ex8;nn zJWD-J+;M%TanrZt=xdyajG!jrWVzY8Zx|d^s1TlWgML)GM4{?lD2LTkW+Ra>Wi2QQ zT$Al)48tI>Piotv9oK=;p_o*i9}lRW%5VKRWOcX+bJd*h6J+A_5_DCW>V zWA;-;(Zt0!3>k0(pQ)8RRpXyc`=WVDnAQ(HHKpBEW3}X3jNjWFwHzmr(Apj8Si znL-JsThw;ne~%R$k!#7w{!3^O4FZ2LzB+5M+kv|$-7VUt$#86Wxo50Yw#Bqz{OGQ4!GnWUM%#Cw%ix0i2Q{#Uet!juGIaC^)xXN9cB2OX zpS{6wY^~X%QA7n7JWj8ZINIUZ0vSZrM5B$S{)h(YXuxDNjlU7WD2cFxHrcV;A~y!e zh-Avo3(z@jj`t|-$d$^~5qw3~WgFedXFhX$uCO}V1^r~kI;D2*y=zPbj-AFEB9Y#= z2o^o7+4O#w4&>I$mwO<^%KomG)NL+r>M5MYTgI_h*}qLc=6TuLDq+>D$UaD{%>Xhr z$uI9T_?7PtPJK%)@8=r)$}_u<8LHPrSi9J?r{d&4ry_@`PP}okjWe!j+(Y<8Jh`Obx?D9;ZNJ)3GGdVFdj7SPz~QzTST} z9gT`h25K6*pxL?5eE()WJbEP|nkdYXb;?0{}|`e0)*kanK8` z>9DAi;RJ;q9rp()&NeO*r<57-euEQ|@6Y8w&8pP5f4M8iF&*!&0B&g$oOC$U=q|x) zC+9b&s{wS6bxF#xKC&>N}82>ksGIFSvEM`9}U zr7O95Ka*+I11&1jG3Ft{%0o*vf+^2>?Vcn~CvxE{+om1W$EOzGQf37zlcwf*ec$%& z=&jHCN5KQG`gR`PcRD;FVMlkue)3H?OQw(wbM&-`fkiNV2?XmT-wIhpCN>4CE?^je z7rHJOx_u!my@0)+=b_vQhw$<9ac#Ubi!WrTOXqrpwObt`UqeAr0CF#ULg77BRZKkV zX!BpE*X}^=NY5U?hPdp~|JoYGqf7@FY_Ctm@q#M85;9zm8+c;o;nw9YMZ z+sU`d^Mk6hf7oY1YnbB^5G#i_M!bzm_8rWSRqP>jAF1(Lv5jpr4Uls$hP!`JsR^8t zs8Fv!Jyk^EfL+Kq_)TN$eWlX*OMD+m8|8oPI^pnW>>Y|Aia(kEz%?^pvW(9q!~W0R zitbOx&d-m)c$puB+k#XPP1e1=aP1kOj$*||Xf@ETe`&HcTcoxb(`tS^BS%owUSH|I zH0oVZ|4zkd{$~IJnO4%)bH-7p968_QMB9Q0C70H>_0Fg7m6`1sg=@qdUjN|MJV#0+ zt2mCWA+t=FHR2?qX#*N9E-%74%YE_~bu;|9&t_=Oj=WY-Z@p|TKmAucTM_>#M?s9l z{U=zUV&yWOir~O3LD;4oY77?1?r|3Iu$zV|iJ@}DO`1Bd@~u={h6xjPUF6Mh3V`Az^!rVsob}=yYOsi--2=jtqspj0HQf&(9?5I$sCEc@%6Ayo z$6Ih5=kVH4^iWQwUGWK9$V!0&yCM%Dc-N@ORqag9!!G=-?jrnTCVoXUok@4i*q~ap z`h>HT+K7tLSa0qFt>zjJ?-#Nar21n|Ph!pZm$97Qzp6RN5US|OL_l#uO*`k&hga_1 ztO}xM{pr&A)>hv*`tFCJS3nR$nw#YIodimDO%LIjjNQ;_W621})WBEmq!L!`w znl?iL6*E}A=2hA|y%HbnmL}9Egpa8~KZ<;K+GENS8SR_?M@c7)`$Hk>n3{$HD_%dH zFW1Qwi@*N?S;F%0@OZrcY1CX33P-0`QcTjmw_f;jKH*Ch_^F`pSbKXvCt}fT3-9#m z+^09?6p+E@t8TsZ^R4`)nN=7FI(~edFNol>+M=8aR{2WF=40~oV&c_klM`Bs@r9cS z41EBBWgwofnPB(lF2eI~EF+?I7h(o_hS3-O@>kh}lF?ven!C-Puq;wAcBE412J|yD zjgP}RsaJ$BI)h*I1u=%T2>n{B6&-^D7^^Crju~1rj?IBwyq>Ui78CO_5}W;5w^1y4 zp^y}6Aei^(3*N^b;Wifq(ZrUB)-XJu?g$*slpeflT+e-66%4ivwvr`962NFYd(LO` zL9?-^=9OeJzYR|H>=FIpC9k6*TEuG%C#k8SQnInyB2Hmy95~4U7LUsoa6lJ1e7;b3!sI6O7xYuy z8riUQ=o91YBzLhmLCwjHwzSKF_3ut|vi3oW0;iw*i8}Qa?o*m@m3NEghB33B%`F3( zv2wiEwa5nSk%mh!AD$;O890wG(|{7iaP#56npQZJ;1v*}yO$eRjl%(WH z`q7uC{*~*AdFrZ&2)MB$8>+k7;059)lY=&sE+`H4IOY;q*-HNy3rp8pz z8gYOn0&1SHY?BjyOw*-k4yVUi!g53z&jKbs3?C4jRP!P|a>(1DNOdfnYc`xiX?)VY z-8no}l6-Im>b%dQE79lrCaWV&mx>8-JoO0vAMC6_M19KrtlKFCa3d{ zwJ$kC9kMx2sxwO?hC1uZ=*dyVAMN}*ZAo&%A)G8I7f?+xt(b&6rlD)tB%IVX8lgbh zdFh*%*^{+Es&N7%s4@!8o(Tpb`0HtXDFV>gb-l`fw}4${gd}vd*)o^>FOV#E&E1i8 zU-anzsYQxPITlS&)V~50w;oqq8yC!2Ho=3Y84o2WJ`8V`2Xz1=d1eVx@*=PVDM&Iq zm!91YBxdyRePhV6AFyn01gAqD9tU%f!+w*61utcW@OKssrRZ9AE6b56M;6z5bo(%lg2!l>l?rDPZ!i&vI+luc zZ_)q9i==&oRoHyqp5W*i-4O04o+a=V@s*Z^c>}dPAgUu4yEVs(Nb!aFp$&(t8ys9V z`(zGKvq;`Ij9RI)v<8++yCKM>V)%=15B9?X9&d<0t$yDYsHUcjzm69&9!+NSP3H*Q z-0X?$kHkY|W@fUw-IG0BZoml$2s~I@ZMKJMb-3-!k#3fzVLM%}he{;V5I>h`^LWja zM1^d+bXgI69KpX~JpC2n^h#n>2N_KLLHpMQcJ;(VXTA!?r|I5XX~?l?_Zy_`MI{?w z!=40{Go+0fbMSbMHqcaE5NP#|onsREwqA40R_e$$qi?v+;G-@(!fT{-dC~W%pd0M`3WgqP09$U-@f@+M?e2zzY5dU$nn#06|zXh5`MvRsRUykwGW? zsv)gHq_bwl?OH&c`_rhVW6(t*`?mnid(@=IUog}jBxjR4iMyr+9u*8!OC?8NZ;HW` zywsiT`uK;??2hcn{p=PbdT87j4$~im%R1ZbBAj&1v8B~W>4mFk5vPY)~$?*a3STk>+f zxI)fdXKPL!)k_-e=3KzaWK%+;PdW{;F7j8+sCC}4d0sjsqXAyQ{Z{M7zJ)xNnr<)hN9NI7dky9bZDC|IeAdTD4Cd3+DSAEX zve|6N9;n~I+VG3a$rnvNX(M@AU#JcQA}os)9H7^p7l_4C*o^+V{3}RvL0%_9$!TvS zbWPKa5kW0&b56RzTtuZz#Ts#i?EA34iDQ_{J>f9BF6C-}#w$Y?m4yVn=|Xb9$JoTZ zlMVtaTsM9eJQ2F>p{uVbM@i3N_#9Qe z+_6Q0n}^BqsSkCt?MswKQ+$&Se$oNKxZW)ehOaYi|4!R%c80!gCd#`9s?F;Q`4&z7 zr33EfA2uXd)G=DSr)8(R{=w@EQ?E;t@~8mr;DbYpd>Lf5|7adp{<{N7Q^2Dp2jHqm zQ&KOp##JT=S@xc}01Y6Rzm2img#))2RTo;(m<|3H3oSm8pf4@OGBArewq6wsogSt(U3C?R)T1-B-=-GXj=hvDkEAhHQhgg8~QU#To z=w*V4_V4`V``aG#Snvf$e|U~7H+aPTOO|)eIsL*9dx9DJT@E!)t0jF85r_FB9D5Yj z#)EI5shthP#IxVsE>}AY8%LBwR(ccXmbo(>a#7&1YRXWSLx8?21m6tMbN;_v?H-9i z<9kC0X^xeVkQJqTO6_hpU(}bbm&!8!VrNI_5K?iu{)4db4>yLu+n9MP6(M_yNF`|R z_Bt;{l?>93Q&U}Zv6LHwrf1QUTL7=eFCLlI^_B?X#|KU+tJj+g2@_0?M$sSU&r2@| z2#B4cKTJ^tq-tua|LMA{HdTZ~|IWHLH4Hdhx!ekrVyR-3u<$!hu&EKJAQAjmiM#Ho8v*mpb9)+KFhpX%N=RZg0Be z7CT}Xoe{jg0;{U>mm+||ooze}m5|Bf0`LC&dMBj>8$8XrH4O+wGIRqdi*3Rn^vklof5h>qnZ#f}popqVnuVj@Z# z{T=9!%4ts<(Awy&GF!p{A+_$inwnq%6jRiG>mu?FX~(patbGZbtJ?&gh9_>kI(v6x z8%C5B_r#w+OeUi64xKp=oS`XsWoi1B+l@rvXXYo$hEavveOBe3a*37#L_u<$>H!DH zGnnWZX^}=z0k6V1sHo6O0GW#0mbtl%*{E>eu=t*)yXb|1N<(f1pJ&0`c9|Kn(cMO8 za=&WULj)2}j*&Rxq2Uy(5yPyO`sC5Nwhqj{cT^>pZ;hD;ahhf5|8mjRo-x;rIFqG8 z3GaW2UvqV~0v?)lY$;Qel0M=L0T&)@z$~BUIBE&O`E0=Be}vqm;hElRfg}v?-lm)# zcVJ`hAp0STCCcD0g&w*gzf#URec>95&R_&cxzFTR$~A0KUO5*QC&Q40cO?hz4Hg>f zG_YhIELJh+I|vxCleIg(2L>WmX(FbkK0R4?KXQGp0OmCzE4eQ`4(RRmZns6cnws+s#gV~Ie~DYXc0v4-M`cQdDO9qN*GPC@;5%C-uQP%FOt>> zi@tRCSyu0DSPiJDAU=3|kQ1pz@@zH%xhT;Vo@WS5@*I0J0}^A&tzQt{S+{W-9@qm% z+5C*)w5!F^E;wDe^}y{$Knxu}VVysf+Zb2e{r0N9mgoLu1V^Rb zv7gu%++WxqC)D#gU-(G-&fuDP{P_sF^h+zU-iyk1@#`niO=+$jRwrYrH5v=E z&T`=!nRX^pLo`8nB>3XubyYy-Z}SxKMPqXTna}Abn^MMapkAm6g?)rG7%Fs9fuv5j z&Ph&H<-Ao5RgGuV^gG4k4h!YJt7o9v;P%m@_PR@|Yc*fo(Z;;pVK;O7@`$r5-l7_i4GuX#j5*IlP; z_zw}_MAo@~YSo?-g_AY0*5v`u$?jM}k>x93NWIy1wr=TSmALe%rlw}JnGy@};?xK9 zqZs#7ifsjS%6s4!D9f$w)&BAV@woMA`6X5Zl~kiwnNsm+3Mtfi4yc<|QS@OQl6s-22I4dRY7=05U!MU;Gs&PYG3V^Pj|tO=Cg3+|zv`8Dm0<3IN^s@1CsN(=J!n2;#BE4W8fO@o=g z#Ep>2#dhgz)Y$Me4ijjUz%fxA*G|So(!#bNyZf`nR02CLghb3g;TtZ)vkCFZ6g1Q6 z`Qb)FBQ2_l&170(ofB>hn5QtqD<=kd?=sJCVj&mi6sn&>MiuDB$Y7UbHYaWT@*o#@ zt)g5Rlk^SH7!N1gJ*C~&NT^%Mr@7CYNm0^79pnq91p)39UHbifn@2Z_>~|<8UBl65 zM(n{s=f$4-WHm0C8)rZ+QiejjSuSC$nHeQiEMq7`w1*RWwo;eTc?ZT`XHdpi;6Je} zpb)4GrVzB4y`b)zi|G9Qk^3yvB1J?7-+1~@G;Oxo4jWFS#6p&iV!T4~AVKRe0@Mfn zNpe#v8x`NOp{G0NGB2r>rwl3$P6zlizo+U8>10g|HB}|j(o*o$S1f7LO3l|-qf+?-*fAS%pf=4cQ^sg|w{G<& zDF7_W=Eo1bC~nvLsf7gI-oS5cX~P5ecb^zQInUmE=~Dk)1m z)s{-#9p^j@;F*DTXpiyNT|@obX7PuqtfMAXIDk5Z=ROt{oCX>4ttz3ZAd`*YF42{` zH!>Pr@wMk*Ka+|hoN&KNa1!C#YE4%%S*9OUM_BelqCWOZi2X}4p@an^u~jx&&6Rb? z7yTkY)ArgCgVrP>^)1*FGvNsE$>}WgBnG}xfWdMh@SIHangz=KX9Z|Pl2@0h zl~$y=Dl5G}hR8Q%tq1dnTaiefgf~yPEeW@OzHu^TejPuJ&{;yW*!;v3 zy&x8k%S3~7(FqylX1;H9KMKZT;gjT0 zRv-as+qQT3gtA$GKceuQM5vDW1sZ?jkVoWO;TeJRX1XjIpe^=IM!r4iPXVeCQ|MIu zy-$C&(%SQ*M_ZwiobsqjS*rjFmNwBZ1Cw5T6%s_a=+8U+Coi&R)6CER0D+>Xe_&we z?^)N6cEeGkACSt`I(&zIzwo)l)m5&2!J$%~1XN|MH`{wcQAkx7@k6y@V`ED?H);*X zNM6f;cZ^>~trv*TW8nvhPJs>bou zv1)OuGCv&K$+6>8`(i17c=KUc)DTTzi7*Y)_n2ckpZ|?_hGx_zdbDKzml~IHZKJ1# zG{U)KLmT1acw2-cwH^APf%P*$ubhu`5>Wej9m0XrfYJbyIxgfB`?jJw`!i>fNaq_s z?AJoX#Mr;m^cCm8k2#57l@q~o{xI8slCb*9aHL>|c3=M;KBE=-1x+$lVE^rd@+3*B zfWWia{bc7;pu4Eo(nP10M$#<{>*d?fY+FtWGABB*66+!1$e2h~YT1ahWU=CxE};GU zpAr(uKGJ3LdV52u0X5kRw9*5Wq^vH>FtmsJv4a3ZtFyL{0rMkHR^3Fl915t)o)Shj zj*Qkhf+au=@w}N<0^L%wRWI^y(@ z$gM9GmrOYNUv%M8zDUBRa%Qnf6X(?W15{nSL+NuQ+)>PI}0 z{y&^Hj5Kl>i&7{zHT25nFh-dOkkbcd|JzJeYS2=3z%LJA7GmfZ#P7b+Ms5*htEYFc zFfT@NkKX7XWAUh5wjtMl!1|(gBsyE;L7XrikCFPP8P6e+#P%mgcAgqW7CUg-L>(`Z z3FR10 z1n=GNegtK8_Uq1R9T=b#3@|eoAz#_Ky&O|NSjbi2gl`t1XhMak?W4O;6y?Oy#F0;l z#gR4*e90-v0i<%1`OvM2jjmfVgi1D=M@s=ovbcIpm{64+#)&o<-&~c_FVG+OKGpPyXq`R!yBb@DK77Q z(jgNr1gUlTf;Xncx)RBv#c$smI1cG7++FB*dxgh>{g87SC@kB%;2cnv3>XsJ7Su$QFR3%Vlc(OtOflo&u{%Ryl_;z2n!0AG(;yS z%7GjuZ)LP`h-Wpf>8n28wiP&wm%l&3k!go&bWpAwn_Fjx^SF+*XfmMJ+d+nt z=_^{tHH+2LTR?F<-agilS)iwhC5 zmSY+Dz9Pl%OKaJ!V!6C(gONCcPp)_M7oJ4Q--8!xw+GiAPuE|bZx1A9-P#Pt(7$2Q z`Ltx(9gGHYIA3N9h9cW}B4VdWAlNApH>V#TiH*QTRJLBwi)z^<=zJv}KUfKpVD7h``Zw>Iqb4hgQ>MKQ znJ^z37ixC*-9o`g&Ol$tD`x{KCR1N0jmR{~A>+{Sk@mpbpSKaw^G135EZ-J@Obp@Cx3?-CmnlWu}@&oFP&F} zCA0TKX(%Q8r-dL&9Du+&;4LH>9?43pxCsovF~w1hKZ<3)Pba0|G9^VK76tuV0`S%d z7`_lo6$oM!snsAhT}tW#M5Fuz*=zdRfNuoFrQn&rt$dbR524@S!!nly|okj!(~-)68f8;XQH zB9DPXD)Lw^wl&K>>3n;bE@bJ(;`5$vD@$yZ zMESi9(5=T)DLK$BK_XvFCtDnL?+c*l?o|5l?ix?~gijf;0b5A-d7(e@Xx8<1ce)rR zw>E!V@a^g-#9PR90$Gk6fZWS=R4BZo%uFrYgRV48XoWW7qq*hspMDpX*kvvZDNU}0Qu=0O>7ulKP7Pr z(j{R$vgEG+I9DBoqemC6-&q2Ew)FHAq%SOvl?vDw5vFD2EnZ&kNTsqa@CEikgSylAdeJC60I20eg3Znxx|$Q1A5h3I=_%(+JzyBRTXFgzEZpzDyaHY z5XCOim&=jm@lS0CcF9$1n~{u`<_@Ku&SJ1v$}SJ918Jh~j+=;XgS&9V*aDh|4frTH zYJ2xqUP!5#G2jL%Qrd@#H-Lp;3CSeUXn3-?;+|sMg$w8=eeD^Hi-8sxk>w_`+M+2W zN5Oa5s?)SQFkc4kSDuXR^b{rzm6pj4Yk|i&tGz*MxYT~9`fe*WiIhb_nAH6r07*c2 z#jFG9`vgtqAu|}D$||Ivq?HTyxzLFNxM=?w^q$vLC3+|s80kwN6BDnuJ8!a}F{r!HS)mlu~9bW@ME&(IcBFMfzZ>R=Qk7iBGXGptM;2X-0y>*vB`?9q5FoCibFM8Sm$D!Z*gg#WaM_j|mb zNpGL@Ety$aUmi(Zul@kuvZe+sh=$fbF++`9wueCiK@*1)$lEdc^|pBeRW7;%tst%{ zB4)&#bG|~~YqYa%Dc!o>*w>Ih3nNBNK4dN1JAqsai5YVpp zCk$y314@MLAfQM(u;k%&&{K`&DPI&d#4owB80-kI>79elfc7l}y2R_rMJInQ^-m!Y zwZ1z2MMRocEW0 z2k!~(Ih-VNc1nqML#LS;u|L9^oDt!xm=|;DlSaQ&CXgNPiv!0+24F1to?$rJIq7X9V5K~m_c~?*)%=5AY7>-b>!5g(K zK|KF$CbL$E5s~0G+TX$GPA2=CUC4`-*_>$BBB{pbB^U4aq0nIDa+-I&#Pl#QWmv`R z2|NRXZ{St0$gtOMv;RU>clv`(@g%(-FE$1HX!@{9Eg~@K9xgwMn(GyQhpqkXAr^Tf z{utnIjQa|IIF>@_5`i^RG(5FB)&|@kMsr1}>iZOdxkq`|^K8C1g~>z$bR=WW&=l7V zv>9z;{HN4~?h!ltvo6o(pg9olS`nn%+4-sBN`nrMh@#^i-X@-D<(2SZq&Mf@I`#qg zGBEI$X#d+~k8<7&p$OHyQtlyF3KfmX5jyutfJ2*S?^{T|r>d`JY{rNyJ%)K8IkdWb ztSb?@<0oaD+A3Qbm*7Ek_(vPIpV-f^I^4Ki4VVln^CFu|94tpsOd+ce$$xQp>u#ur zm(xS;N!Az6(p1?2;^43Ekp^i_DWt4*d0lPJ_I651 zCW7E&DN)GlF>dnjcHdvhU<7eB*1hEHc=R2?Dc8nT%aJXN1HXhqPdh#?a@t1fKY}~& z>odm73KHt74Ywo2!+?18G!#eI8S#JD{NIJZqyb=vYZdCLfQpjtO_m%(Ea=nqq+4W5 z05HtQS(ai9G1|}LchX5Q$Ib77rUq8xH4K2lfF_4*bZ)=r2QPpz^H-h@WZ~#XAvW*{ zTE6-k|0fa6*|4d>tZ#(N+gIfjVv1!+HTOUsj!+LS+BrdCo;njpS&BQF89@+)_OxtF z)z%9XMe=j5Q?uo7<6c$Dg<3=D%Le`4oU)tcDw=f|1xuy7(2()jlN+un zFtMP2TT>^~BJS7`ZGJH$58B7n)tbv(siiy?E0Ko*!G9SBO%_V1m=jl=mub;&p=G>E ztq#!a*3+RbHCpyKhH71TMF{Sqr9kzgl}W(ZG|bW9an;Er(BoL^!sU_df-0c&20O2u z#4o<9E)6tFSh)Uxi%#iAmgCB4vm&mD8m$l{r~PY~)Fjw7SNGm#oUqgH8P&>d;&>Vp z57$}Gw6CE&h!mwg+Pc&8d;BeJFq{y^JpZ&PF>rxUt`z40THpI*0Ej%L&*cR6(n1M) z^uYX>KdU{*V!x(S*em5AkzikC1+5uo8q(ZINt$fM^|Alo_08$pzvNm0ZuT`H%PDE# zfQ2-zJ*}$4{d+OkY>78*p4&!t95W(4AQ^eU2;g2jT@|&k8qCdl*cWe0$dy@zlbsIb zMkcw5T}+QU`C6>7xcuI^^DRZ~KH2ORI|to8rAgv6%sT97*?wYowO;BsU^kFBj}z!5 znDa2veRv$<%I)t;h9rXJ#IRWC^T_5nToc<{yVfm0?KB+Yoagr@gB2@CJtFtGCaUtFlzv zdBT`%3aR`{#t(~)(M4RKP% zq@6cS00*kq_06^J#R^oZ?`S$-sLTwNh?{CYsmGjonC1Gl%I@{;;|h~|FXH(eEMP7b zl?c>n0c=e#&8lt!6SGD;+7X{thcW}en2UR=T#0iX_uDe^j!fA+iOyJ2`(e738r4aT zhQQ%Jc)1qLYao*>1O6)K@tx*_XAMwV{Y87a2Tu|;WXXs+8p(kVk1n^}-|z9AL{U#` z4ErXS%B}d-*q!US3y^z%_1eF~)ol5Gb`f3Cdc1zutcn(>xOtsx0x_`vm_eAeB75Aa zPs+9vSE|&;yh|TF($eSlChUZ^;V94dhz&XB3!iywcRL}$k}dzE5Dsj|>auO5E&6r@ ztXs-b_-HIxgr#qbC_*L4!yOYnI)g$}8U zt={}S?*tQOH#Tj_e^Z8j(L}frbbH@mRSrDbkNjJpbP7b+{gRDYkhI(T3*Dpc(q^Q) zKda0{V*Z@6uud*)52SNr!aC?(}3iLx9I zpv*wM+@C&P&LChj%CSB%<@ePH`F}c|^(Q`2#!yyP1~am+HBYz;o9ggDgIok-$UfYL zr}d=a&cTL1y`MfFN$*;}vh(ij#`XB_3^|Dz_WZVAu*Y_c{Fjo+VLqv(AW7b zq}&(0qC&$lc_KgMX*$;+`sHF}?HJ?^*0|p#{0(XTTj3l3g7_7Ihh`$?gQjlrLM9Y>wAQcC=a?fr`l7ChnWTxQ9`kq|wVn$v1=W09aCV?FNC z^;|Yu>w3@ORA?WBcIPKv{Cd6?ZHK<6CX2s3`RIrGNJ6DMeq4G%XS%*ndzRXDA(VL4 zN}8i{vsGS@S~Fb36(guun?|u8svV64bxBWe7YJT@WH!btPn&sYA(IfzzZ@ulTW+;R zyT;#72bZ(2wm>I$l7q8pgPf9iiZ!A?r#RFUO>!(*;#2+}yi=7gD~#=F)vSwiCkFJu zon>z0<-wODhz~if#6!el6eqN3)p1@Y(atv;z)xtI1AJ3Vr2#)k8A( zc|GN?oY9;x#J3{P8Kxi6f0bJnLYLpd@Jzc_>C-=L&yl!0XU95?rikiN)|XElyW*In za|eiccp-InYrD69+aFEN<|(QnI*|M`zC`_{*5<*wX|1O;m3kpOQ)ol zWl5ef0dG2K+cVUA8M0iFO5#+uzq@Su=P_^}Wc&fL7&5=(`NUCdv);n({lwhV-0W~X zOVVV~2lMj${M8;VT_~?lhEiwRJ`OpIi3JDaC6VoK&kbR8)4iYPI^=ko=nUh}iHp@!uQ0w`i22{q36zzAsZo#sh-L72kVCB&txMJa{JwV>S4b z8Yf&Fm&LiiAnnIAUo7CTrO3(Lij%bKP3rJC3b*b5N_Se5te~UhIpzfHLAz^kTeh~x ze4heK#{`J|oELroeWSe?h~8A2K-I8FsCRrvWX}gmd_(EQr8`Gr@T&PB#f}E$E9A z9FJtuYLa~qcO7dAoQw!WiRGS5mGFY9U|g`1tjM!a0;!~mGtqA>ja#aBFM|{B%f%7N z3wabY&SPI<9Vb2VvtWWpGh>SOMk&~&qVqpkY4sa#+k1Bhdzx}K2h@1D1lHvv)A`Z| zWUI~&Bc{Sj3;TKX$86G^*r@MaKmf~KpFyDBC%LgAWpA197)Skzxl!*+nQe5&Mi6lb zSXAqXa0Ura`AazD_(O$~mFTiFDZp&H9S@?*0@AgG^MO2cG2h2t(7_)y*&GNAfL-Tt*`A&FI{`RXRIB7#TB-5(n4!0|>p zi_a}4dd%nbA(~@$b$y);BK)JrcnJRR8)=1lWynIQqSLu+1FQm^Ar*9RS* z&fsRJTfNy7-UFSmA|e99K#{b!tg3`ZAK1eVVX#{9Tb@3Gc7(N%S&5_je+Y|(!yUlg z=Fi^arTg0n|TIL)_%_~8f-#-{Kjmc8Bc zc*$KO(|vs|-YH@4BA_P1xCie*AoWHu?Nr;>%IH~9yIA*pKIWk0XxdD5WKTIubsSEk$FwcI zt9J4}jv5g27@Y~?ak!vMYOp4-h3a{hx*7m@t<1Ih}@U$ZV0aI4Boe)=y7?0@yJMXg`@KvF*~7So6E_Iir4BBcO4gsCiw z7d5%B1XHbA@SQ4X1Gpl!fyh^$&RxuG89MYm!7?hXHu6tr_TPD2;QG5ye`#AMilA*8 zME_~n;vJK#V+Q02I4yyEOC5e}+%-*cKX#;J8&Bg_Nx3SD86Nu1AO5bAOl) zs6xZ|KGjZ;`4>BUE_nn{Bs0{#A6N%zU~-BqUr#ci7^X8y58g)o1UwRhoY1_t{N zzPRL@q27#56un0&&7If383uTz%Y#iKh9EqaxcG83Zl%fPR70&?Mf{FK8@e$D^yTOWJP!GfM`1T`>%PQRUfcE?H=KAEE z^TECBp(AYyA96-2ZQw<6MS;ue&1fpwHL3q$PvW?rjG{;Z^7rSIL4?w?)uwtz=TGp( zBv&f9UG6VLd#})YrGqs#tvz2@rYIcLG}H-J>_7J;VmpmUXx|Uo-!2+jmo#Pa*0)0n zbV(DCO&W3|lPY@V3B`N==&@0N7z%oZA;VH~_O(oG@n3DL-c>^3tV2oTj zDw){)+oU<)s2;FCD0k-VZL zsaO0C*n|TlY%mcrJcHZCDcvIu;T*`D$~w1KbKkwe(AFO))}b2n#vZ6ii#+=_B6m8) zsskh}dRGIAwKQ1XT}Ijlsqetfc#;60aonark5OB+$BmKq=YM)I zSz9B-*vM2VSg5}1*2=BwF4P&-bCM{QBZ97!7lE_pXWMli>h(?o+E8sbnX;~1fwMiG zp23>68=jwqWuIooO(8y)De3TYB_{42?}g&@#Lt}SZP~%aA^^MH*&}kU{r#FfiW0F;pt#9lamc62Yg`|sCTD-C67*mDpkxCFj^>) z=XO2AagyTqdWZ@RRHZZM3xHPcbhUB*y9kF)Dfe{bJzf`}`erxSYl-j{-)1pk6F~35 z5U1^NcMZ8FF(bN@)vi^79z8cV=@i{B{?VTP7mIFM-+sP9moT^l%An^tNLcl7Cv}j! zudHY4JL}tJa1}2qixWO4y8yg*D!jOwr{J+iGcLv?{Rx~>o9SX7E_F!};YXmA zV!zi0!G%^Q#MJlJS?;M;c@L~%`I5cc*TG$kgJOWYciS zzUaWZ;{HYg`qnHv%!Q(4%*%?K!$1c>EBg&#TdN+-#zCV&-(ZbTVU0DVO(koF9xNU2 zzr6Cj7wBQ3p`Ylba8zQwzCYFqS|5RzYq$OUz}NT=hCO5#Rs_2Oe&&y|zo2CEE-Ikl8-^Ve_Q-V_ zK84CBzFYb`K$)9-=mIv*MSYd-H# z?Y2cB>q&BE2#^8N0|Xn3MQ29@n2Ri7QtSO6#IAy>6D`3oDAv<_3oke>{p7uiN5T|C zuUxs`SC-IoDT0P?e#??tmqyFzizg9s)EF`w^h#~K)v6Sdi=;pd^LwhHqY&N9$ zYs7b_zYKCX?pYpMj^~QPRF9{Kb~a!FB|KA%codqkDX~}~2@isk!AHDjNLvGYGDLgpAscEo;8kP6*D;iRwB$cW;4-0 zXT8RqPMyp9)^W2#mveuatA&;E0L?NcD*Ve6-jeM88M?c*g8C$2qq2Kq)59P$ZzH~{ zB5Lze@V$PKscP|yrI^X8V&6<+7xi2%oNoTA6;zftI?B8mcSn$Mw!fT$gzHG_(Fh(@9l4gT5ARQq|0WbBhg@tqE6% z4iNM7E^^tWxG~L)?^*u_bju=#L(N}rk)@5cQaEx2$bAg!NWqIVBDJy3u>nmv+y06~ zd@A>N=8NvloZvXwz##2~LswEweCR$m!hu#ss?-6~ttIV6nWL_?PYm}NUz@u;Z?Mo^ z2zjvalY_;G+%(5wp8@XEN^`=@^o;=z?iU@JrIUEH%G5#QE$gkZLtIXHd7X;oq`?}p z|5$1PpA{UttcH>FN7YDd@n}>xKge=naOQr!Hx}fTRl&|Bu)joU;7yd z3KZN&32zvpS1Fz9K_qtg#3pkHAzK5g2juw(-gxKnK@z)bh;i>OE|w!6b74g7;=p28 zyG6k)5X&(#OT)MsY*zlokKr!k7wJ3-bR@RY_~RB%3;IO&vO_J4G$8f$?@8=1o@M7x zmn3JyyK7tMzuSo}m4otjeh2q%dsDgwnJ`;G$TAi;{FbekV8cr-awt|wj-V-qrsF4p z(QSIgb48Ml;H|d$wPKeviIlqkRhi6r{L^7iyo6sP5xw`f_NrdsmpkiY95Ynrao#rk z!ePc60*s&iJ*rhG`e9PMyG8db20-{W^KhS+_#Av>idG+dgzu=~XLyfnQP z(9SuxK^a*!UD5*S;;{1vGagnlh6>P^aChH+1C9pV@evp3Je6x-emq~|yvvnLonL=Y zFr~=f5eExnbte*N7C!W$>poB6Ii|_O#djZ5_S0q@_)5?FSbJ%S;gB}Z?)@zw4nSXj zUM@T8_+hb8j)XDQ%=XA!eQAB6_1Y6{gwOf8Vt=BPJ8BMLuoO9*RQJ6Ea|fSCqMFn4 zL6+o1f*9x zy+PwpRA|};ZZN*GXQM}W{`AjT0Ovy=BYgY%T5HY@wz2Tz?Hzyh!;UL8IFxv6Z@t#A z%s|UWLd^`ydiVpK3;f=#^r5GP&kMox(c4%K07d>kH94rb8vu}A!6t7>70L;8y+^^> zeB-wAY`3I9X<10ozoQH6ri<;H37d&M=B)TaFY^4Yc~PhkS5=26BE!F{buC|+meIGo zFD&Df+--nHHxTa#kSI~yp3^^S&k(>TB$a6)_DS-q?Y88j65tJdeU$81=xyL>y<~kG z4(>Fi%J~oCzyGAggw-1J%t;pa0WR^kHKZl=adD=0FAW%LvuGXZXsfwV({1TZW(ys= zeML}=mP7ziCeWg85cln`1w=meXGN`N%bggT#Nz=52pGHa0U`i&_5;jkDvt>wRUMNV zTD77gV@%rLkvaP^=w4&md@n65lIg#$)bw{UM2}vN|JmqBnH!xLr%CSoJbR8HP7crV zxO(z4%fpe?T(NFU?@X^raA-t0!~S=jD&a=wofJ7*Gx1UcCfV#tlQ_QQ+>8wtCxjd&e&tP8wfyB{sycGW)V;UN-z}S%kaB29L?Y=w%r9YOV5_-V2fHe(i7H%L)BDtY; z+(9Zh!-s4KT^V+*6q30hVuxj`FGrv{lTHP5NMtXxgI3{E%^1ECTC5Dy4*Gxisct>d zdh?54o|J|m`!=gE=NYD5B40APEQr~Nkp(GxkYUtngMUu*&)@r+auQkndyt_}dk%;N z6GS}Cpf%p_9MeH z$C1L6*#LR?gOo zmkyD0(4HIg8>UeSMIO)??P*Ijv${X-@&o?gUrS}1(w`L)uNEij*qr(?8IxJ?#*^T> z%|;0&jIMH5FAdY^B0fIePh-L6EIuQTwRf+`Z~0jz+kR;Xfeo3XxCs=MSU{c^ug-_i zU}0{$%fUYltd@nSVv%&o=e6gl?*f}a@E+D{dOFB-H&M}V)%MX}e9s(BW0~Sr=WA+`Wps_a2R#9A77=FC3JLW%%vt$!Jy)VATU0Dt>wb{h`_u zf2|8#zgeD;v1lCU+-z_L9>k@etThq{8@+C6A-@Cp&SU7vtPth6^n9Bvzq@UEj>!Xx zsRheX(%?I-61^a)g2#k+4=Gog!@>!Dnjz~Y9r;prbbd+7vU6ug%tnZMpYihtZwdkh zA$hJ&%g`XH$&1WITE40vBJ*EZ^_!1#Y@B20-Sy?c|6T#;MgNZ_=CXAj(}D#@y%dYk zDy0q-(-`Q9W+Y=_G9R0!cVdzdLV!rsvip+K4$D=|?cHH=2cAWHR{lw_h#xH@C~lPH%52 zZ4NOV_OISv@(rKi~vxST8|e~wSg9urmGlXntwqtJZ-gyRRJS( zDrJXaL=s~dkK|^2>Cd}Ff&<B6_7#thI$tldU)C z`)S+7y0(rnxZK|9D?_6Hj-cD~*ls;z?vlLeYZ6Jtj1E!?Ok{1ejj{a}ygKY8j^rN% zT`v6#2S^7se$>93yBXF6zX7qkc;p7uD2C@&loFwE+X`iFTOHr#6eGFCF+DbpH^%Bt53;|JTTTQFc zbQ6>j?Yt%263nd!D-?7GUscvH{C`NbJGUNTfcdMjiq%LUf%&OZ>O=&V?;ucw0=~hU z8mM%rjJ%qRSzG>dZ%_1iCUM7zE!wIRZmpX$`~PkTWV?W8jkj=~Wi+FGY-4cHkGA0Q z1(nJ*C&2O?rkiwbB(Be}6b~UPf6kjh@?t;J(tN;LHV`01b&?s?`lk$Gw;}j~G+4fw z+O~%@S7NbAb)Ft2BK4Fry97y0AyGV`!4Pw$zmR-5tPmjuCe&)ZTj}jhbZ0aFO`cR+ zWsr-1R3i{j{`J?^Toa6EQ+04u*!6?_iUZd*fwZaQdT7jaQjDgD+<#z~KIv`^H#~Oo zz!X=HulU4^IA9yu65q+PVq1k+0UV{m6g^`^ZJqFk16oqWhqE-l9mGXA%hJUClSpLl zuM|=2-E(8u2YnYl~bxq z6bc+ZS%!DH&U|Uj4}Kr(TU_giKac{Z{zTY~S8&vZol#{uI%%Qt1p62E+ZJ?|mV9+L zWoqU>Nu?M~(tzm^1)yZ>%ga~=>kEzJ7F({i(i|GxnREP|$3j!PmFLk+vMWHe=#ype zxMhOLn<#5hadZ0-WqrEAkE6Rl&;t??2QlHXQ=@s-0s@Ml@Gmx60*Wfe=y389Y?8)v z(O>V<7v)Ke7hW}qcX+;q`568%R$aLGvhvytP?g6RJsAnw0;HpDiX!B*C0c}Mm1q2n z7XXdepS&Xgqi4xh$Ae#(3Cx|)GLO4~4iFrGv?C2vvL=NX_o&81&$P|z38a}iH$l&A z8ZuT*w7tK68Pf&+PL0}3^}}7?Pu%WqabxswWgriCh+Ur+hY48F+L?>d3wl(R}XB+Hrc|fTpTnqQZiTTn1iw-T57jaHKVnvMpdV3t8&xXH#O^%dj z%Juto_`9?Xlu9xft7w!oYJr56hE%*>uS=lBuRuZ+L@Bs&-@Wnwf<^r|uHiM}847IX@B zc*``Qc;Mb&@?XvNq?^AfyIn5BnvP>1#xTII(^dFtKy%=!3q9^Iw1Gl7a|hbV#t^W6 z38Yq}7GmSm9L| zw95YxW!h+iGRbexQ+5`oo4^|)2^Gs&B70x`hm~QAoe`|LQYdT)TtVl%G~kO{;79)ZlH+HE%dW2-)kX`i1MV#lXP)l6 zyTZN$6jm)&)G94?WgZDJviR}Yct&A=j*PJwA-)JA*q>N|_ERHM2lJz+;`HNIm(b z^%U7di z$RuaGXliaWE~(uY{6~Y`$%8=_J{N!2!{M}>&Zt=8uD)rYczf`HBeyJS(8sMCw1-y= z4QoD@5DXoBG(N#p^l1uV0QZkwh|4h4D?hGj1(Y#bAcc(qwviDEk7d^6_e5^XOp$u=tZpFQJk5_od9baRs>ALkCAfua5wF=@_1)P*3w_X zQShU+x2A+>kFSJVgsZ!f?_X#H?Xuv5%)LLnwt!~4H+g|D`GcI^G4GLpV{VSRTHH!< zBM~Q9(SnS8?)(69%P5IV)AcQdlv5rFHx}TmeEnsQWTqIEbmA1rni(`*(qUt@yRfb( zR}~+mgWBD)+W30FPYP=e?SFw$b>K+ay%(tg^d|=ag$%U4AW0uMw z%0`Al42A?c``1TP_Jh6np--BGC}3CmT|r)wk3zoPUPj5c8T_zVaN-Yr{c>@@r#q@- zVafn+azsdU?J$n(q%28(p5X`Or|@-WruXBmTgXbiguoJmF@ho$^Kw!O;XFfA{D<(t z3tC@jZO3eh6GO@-xwPCqgm>(oB($1vPe6P6G5J`bE>oR@{~fcoc=Qsk$F*?grGfV^ zg1(C%mH_1%;yhR&8V$WIv-c9N%WT6-*q3;?yQh1-%U(TO5an12$S~`J zGf?o%Gw1sui~upQP89~6DTgw^=F;uMvDiZ3Nr3hDbA6=fXTUOYf0l*?fKnwagkSKa zEVx^q+0VSZKqC6GoDI+Kna+~TaPXW6R7P|6LI6mzqE3zj&7h28p)Lp0XK zAL!$fYHJi+c`aM%qW>_uj40%8Pc(dU!&~~)!H*||d#2@z$TfWpiWH@E6TW7xU~<<& z>B{W19Rr60bWknA0d{yflFVocYXj9kyJ-QbOM_u!M{HKbn_Z!oo zUq<+oqQI{iwx1flLUdGDuMk&cSWhjVuXEqhpMscB!@$K|Q*BXUK%^D7Gdn)U)N=jK zybGqby)K=WTH|Ph{m2sP&uuh)w;E#r+WH+D6#=FH%&Vw=!f`oDl)vq}DL3jDQi*r} zo)4BFx%d+uIhc8Wf6vs+t@}aYCtOL}andUE+tb@>s$rGy>hKxnz-LC;X0{0-SGwM9 zh$SnI)i)jwFewzeMp|yv14|6!k$%YXj_&vvNNuM?@_jjY_8hv@uwub(;x(lCgP8c! zzWPhK_QA^iJeeyXZ?Yf3n$!vbu9pRX4EbvPG$7q8ON%iah@$T`XN@(Xlk@GWWk`et7`*nZ`$gv&S3MzE>n1Dd{PPc&gji(TMLv zLOlUdMzSZeD#$`)#ZvJXBL(myCDHrqbg1uiY~jTN3NF%5=hhK$P*jW~1|vn3EkKh2 z+E;3I(1I}31mz~^5(M=z^Ysl?s{KNYG`pEyg40a^VmMJT35C?(+6Ok^0C!2c=MAPk%% zU&`r26kbkxq`c=Gr89BseE3p z8-8GA#5nf>WNzRT@`I0eRs*S}V&bH&D(V)thVE<3NW zhB)k|L|amNlP$I?xNOzxb^|HFC&Q5r7h=1xs#|Y7_3n*4jtIk3YJK;Ok4AG;Klzqg z6vvXqxz)@wPHJHq>hj4Ne~A13g9h_r}hT>a50u9}HwFzOV>ieXVZ?T4-gf zdh`z^m}sA=v13HWiiKkc7k<^#qf=85ZMyeMjM`>*Q@!AYi#|+DDwnDkl)c~V(52Xs zpKrJR-A3e$qy*{z(V%xdYz#)zS(kFg?)W8Top9*Pen&V)kU#>I1&uMnB}MA#cIvTX z+Tv#=A@*T3sW@XP!e~BT^;gXYJMyZlz6*0>9XI=Y#*>Poeg6Qb-S0HN1=QJQ;#~oO z+Zj($r<{e}a#uAvk>1W`BKc2MzWl1$J=?A6bo}B%`hhTXgEJ3BhCAFHW_!-*v+hYk zHyvJF1~0AvR@gedh|Pd0uG)hVixr9HRr$Nd%-HnM(^Rxve6$QKTUOC&a=rSKq4h^Y z=I=LpZ*lG)KOkLir0$)J$NGVPHs>;Ae4%B(Y;dfaye%~`I*UW-D;TKZFJxg_%Vb#w z^5_?2BFiy0u(&y#*|z4be%qQIr+IBBX>3{ir_jN@0latbYF}-=cKxlRhmp{YFRLuBeFp)_3+n+&Hr9Ca&3{Y~q5u24y2>h`)=mxjx{fAgvfiKH_jqRZ1zsaJ1gqPJ0ioW$cVhJ}KFAr!s z{TP>?i*xuom##DVK+hkA{OK_k(3uxZGUKI_X=bx-37(3!kgd5yTO`r#yf3&py5Y2a z5x($lSMff#117DghY0B)$*>v&kQNj3!B%L}@;=8mSmN!l5&JJ2Ax3fPc2J5Lq56@7 z7xNX-bUPQpuTKoHfMrf>ZI5@(kKL+fozt#XZqE@;u-Bfc>%A_;+FRK@7kI`w{Ygjr zn{K9O#JZ5DVBJZ}$RF5if4uT-az;bgA@0pIvs+?2P6kf6B?O7lqMayM^HFWNYgvum zAes7$(W&agqzqAok5J;X%NGuq-fsB7Hb(5kG~=%zx*|m7Bi3YfAXI+%zdfF=!!8UY zwO%JABhY|vRdBh9*CK~f(Lj6= z`;);yEi3LFoQQ=>m{Ab!_xsDJ2EGpTk+3Y?^-)0=WrgMLAQe0+m>2n}18=tGzv{Qf zykz5l0Y*Iif~*%g9sF|#!v}-OT>(XGH+3d z&P?$7IevhHX+ywGabGV3gB_{VE$83mLJTbVzm#`aFa1C9M>>P7F-e7G;8yB1;sVTO zK+et1l{;4*=zx+|mYh=&o*?qWjK3YUD8Rk!3cPZTCc=R#T2gKD9mknSX|lDRV%iC& z`zV9B5i`Wgvsp~^uO3YAOng%ug)6yC*<4OhIUPQyXYF8FtY2A$wlZ+=R4{8vM80iq z=O?`&p4iz;pc;1_0&#N#Jw(a*(cSi04>dOG7wZ2dvjq9LtBbg?Q zT1YDrZFvQU2M)9=N7?$|8)wMAL_u6=xnc-&c6HhXiMvHL?K3rCj#ZA>8EwqR<5!cF zN)57bVQBnvlBgkM*O!-G-5q$l!+HkyX-VBu#6#KI?@Np<-}687R9dqwlKg{6&Wei z0Xg93kmf;~4mJ&uKBk(iXU$E}QC1!! zC~e+gAxr%@7R-lKn~J$NXI{5KcbnXDV_tJn?;UOpwu%K^REYJ(3!tS=vkC>z2gm;} zB$36=lNTHvXxT8MM>`n7+&_eH<#o?ol2+_ zyXnSDNLJPX9~W2NUZpp=^z#?iyyE09qhm~Ed%ZR?>V@hO0A}h1SN3phLcxrkR?-+f zp3F*vl76lpsy-ZwGJmBgCU!3G;!B(?m4m;eAY}AfIoSa%mU!%kH&%a*{E;@Mo#(7e z8lxr04z>*4fa~7aW~$6MUfE_*omGQ>uUgTVz>#9uFeG4-~6F4EG)FuN)K~iCj>U?8@(9A@lwfuY!nL= zFg}5b_P&}Vk|rr-Ztk18!m@@Z@Jb|VSQG_Std-9xW;DhGF~c7ei%3DD5~D zLnT4CB+;J{hA7X^wBv#GKM+Na_zo*`7C$`*_P&y;TRUoTM2E`*H|9W9LBseJ_6^^k zQr5h^VBmNi67Gux#9w0(^6W7@s#oA+4@W%?dB*y1Jy+BeS+U*qyP^Gl=}bsx(I-ZL zwP~g2RU9sVY2YK)1m?WZ3dSp*9m!)*`_c?8DS1ly-R-m zXcQSLi3+0}kQNP5*V^;Tl6+ey0lK9kmD*{UAyJZ2cM$Zcbz9~O)8l4qkr-;dQOSV! z%*Z3a{x|Mr^?{b(JMGR1^8^%keSdi25evC`CS+QGz1{dkhpozHf=HA96&cEY3+Bx%knQp z+=zXOZIdSSOFZ@U4Ig;QIHVWjhk#Eg4mR0I7=syCVU!wd5~rV#!eiHA8Fr!eR6HY1 zOcXIN_~st$;X1K`qY4!M*R^7es+47tob_*)H=! z^2}@YG9=!|jqWEDpa(ZJ7hP(Kmr6$6)&U$uCMmQ_bZCl!4yA*jR>_LWjEkOc7CE^i z5Eza~^qcJT_t633JJ2NyNz$AN&o&gpEBnE1_TjlHnRov(Z+rT%=P{_xh1eQ#(_d<0 zsK9~Uvs#sNfxquL0Vvdc1`szP(jffw3qBj7w~uGtMJ#FHh9M*J8Bk~e`L{SJA>7|Z ztd%A&QA#LnH)m9in|NKDzXW6#jh8?lx=Kid+BN>xv937pS?f<)A(#qdW}hJ^?+fn2 z;e-1#^>vm4;Ea^C63(pX`qp)gQDUQab?dbY(rpk?4jSoba1}89!{@<*Wc?>NG2LoX zNt5e9SxLOnat?J8S;lz<#Q?Q%PD_&~W~+&FAy|xfKZfCfFt`G9k?K%{2&m16_*$g^ z9Jjl_Gh(Ha65iefyMil_4!U5X(XCH9yq-cx`WB}0^v&@EnUUZQf@@;^yks}tCPzBT zQ?^3hEP#<25=F;7G(z^_g8ip;a7d|UZZu&=*z{32aM%6*fz7Jwm1a(ktw2#VEk@}* zCY^pz>_YO6z*p1eP!DnuyDeI6NU!x?3%fa&&rHyqe(lb-tm*Q3XNn5vly`S?Vi4g& z&%*`9Vet!Kh*BPXgyk>{nsR!I9CHnHF={n&tQch1sZtJWW8My>t^Uy@k#no~Q<>bA z{txjj8fGNPQyC0AmaJ|F^!Z;e!~;og;GJOdlcVWaDo^ts0m87n1i3KoR20OMF=xL)cNSS#_XSsyZ zDrUX@d+zbJT9t0hJAHo@jpz0nV|u?x+dplMo~}Ti4qh=W_Q>sPtyuI=>2Ue|y8=IA zI@AieIInq|1?NKvPy2J0D&Dp~?-R%cA^eU+rpd5$zQAN*a-Ew6JC&Thhnr^h>(~q` z-bD7+6l5yM!%Y)VD5XuPFU=5pPV0S!`SXM&aB3ST&V=K68;o)zGBejyW6GLS+*|2!{A_!WDqzI2793u`XT=vofWU*aG;Zk`4~N@HWwZ87N5NmHkSva#@WL_ zC+Mp}ckA8do08~_B&I5A!@pQt|0mOK&R!7&9`To}0 zZI&84=KljSLCwD2zW$7yxw2!>=vEjwsztih?|%Ip2c!0$c=J5*0=!7c0*E>=133kw zX^P~;&I@oJ62u*A(5@!DHpt%ElN@x>Z`IiyH%{>`hN)rW(Q|yqRNW?J`q*@J=W>#= z>tuu9ZYR{#tt!{WzU!a>(JnmQhTk5iGzGWQ1rFtsoY}?O)msl}&K6X%Jht@Mtqs`1 z+U%q?SS2i0sY$AA^~4=IML{9O2UZ-#Jo{y$#a;q5k~7Fsk1hTv$0j|X1?izRC)fg) zLXl+^pGeH8->w!KcC5?X9_4G5R!TVge-V1Ul4wc!JQogJWiBS6V38E`zN6PQg?~5e z-asW`@bp?gwE8%6J|-pd(m94gyocKKk=db1`D}p`Bbfjrgxo78vklOFTt{wkP0k;; z!LzvXfdoh-by{tSY&zNDHppLUz8MLs53fJPb3uz5y;of~*dkxyJWR}z7A!7wH8t#1 z563o%$K%PdDICR1a5j+x_3WODqMwyAo9eZ##mXBj+dGR!3hTA21@G-MxaXVPPt)2t z8ZHA4xXAUIS{J+>kv64aSCG4q)}4O$4)t`!Xh@#N&);E~`9hZAlz`FDRxlW#tCkPl_H}1icGGafwkVK_78pe~RuG4` zoP(F~LhjfAqYf~1Qtl2~fawD4is3KJjeYge&$x?1Zl4xgrn>g{{QfJqc$dSFt+OGDiXxn3~Y`W%gtqSFeJtMS07;3Vuq+bd#Fuc zOz@F?1d!V7qHLmZa2210k^n7w`b_GEkxqkgdTdNZ zSq)*b@aEY&#BMl+BkNBv=aX>EC^=|BUaMtw)N5N4_1e^8jbj=Il;<_4l&L^EO*J(r>jPo3ZVr7fS?7*Wj zw;c<42c@LAV!hB5B_u|1=)RC!Da>qv)aZxkDma~fmK%Le# zSu-YyO+trT?N-&{wQU9`T2tPYbtgC6I(tv#QuN#FlYRvC&ZSnQXJt&T$R>RyLjdntO(x4 z9W(PCh8`nYLe)HZ@CJs1Lh<`o;?Li{dtKOh{|$6jCw~3@1AU8yc=96gebcCRS%f%oKFCsluRzz3ac~UWjJ*;UocZ(W zU|pyYa!Q1XXLNr4{DmWXu0dNn3mWNGM((_dad6RsW5)1soI8Aj%jyW)YK1Cw|GrO^ z0p(&$Y~hI4PZAd?*>Y$j2ihrBOk| z00R|83{33q?rz0y1QAsBe}0Gm-0to`7#j@a{T?1Z4!GUj-FtV>d)`2`G-{?Zf>YZs zz;)z8&KP9?1)Xtc4SDi_!hClvJCLcGYXVKaX1BLv;l+}moTNF6r(}v$q;@N6t&`3k zAxjL|gffEb$8RIpWL2sYV|1AcUBadP*O{#=BcMqi<@8X+Jxy{+T|&pKs#!g<8l=2?dgD3nUw(oI zmmcHJxd%*@lTnnbRt9bRHAlNaEzzu7!%PP+XY^W2$3~cQB<^2KJqW*9_lB6_F^0YW z=>;k6g2;L!!!}4n`$V_V7&dnRM@<+1H69D6FPE8}MoK)h&>845ygl}v>hgi>jQpVHy00I;;fjP> zS@J!5E2Y+=&!`y-Coyu0h(^w~YihuChZUN3ZOD{}rd=F)8CIky_1tUExn@&Vl-)p2 zTsd@;*WgDArDCO1SL0B-rTgfGJcZT#qb{h~v?|gI+6QFcmdtIUbAR3rOSJ6O1epQl z5zg%7jMq=Z#GP6`H7qz_i@Gh9lb`ea!5hr#?ad*A8?>$g-z{^pMcL_Yu@NT@-v0Bm zx_Ul@ZkvtrnI!U*f&HiMz&Vf`vcAx67}_+EZ^10PK_x_8)~&$HZ{lczA$96wVK3#i zBdZEzEffoXzXg)N090s|{1p&rK%ibP*U!H*f;_@04>|sT7siJ{_-&qeKu{QhGN&O> z%Xp}89D`VjydxcfZ2b+drdoXu(l`JzZTjjIw z{rdR}4mv)#bbxE$AUlrp7V`}4uO~O3vz!oV1_5o$K82BW~qOi`c=P#Ew^yA5n&ml}w8qDeK8 zcJc(oD;hL`CBp9fo-1q;D-S^;3T>~^xC+b2p|2W^tFlS3vci3{`^KD^_RA ztUYTm(f8#FoVHz?6|JAO*8NYq@K0L=Plxe%G)x0|FH_l~5=Tf?UF5b)g zn=+M4VW`Oftg_pHq#s-&ijGktb42-2R(;$Uu|(*l-<4fU2)>7$P_J!mJii^on&4!e z9~43^t&E~fsJS825fvEC$VcgOg`T@^tD1~#vgql;Ytf_q?7iOsMT!;S*c*NH`F1$k z2Mdn7pv-@%H-bI9@&t3cI&lmbL3=G(J6%@e?u{52Pv9z)hfnB$sdKrG z2UP&=JAKQwc=%j=e%7l|4l6vy=GeuD15X}eYV>x_*w$gA(V~3LvT$#$+-6J4URs0g7J-- zqHK|(%#Nm(%7++TAnErM=%;8qmm*=|9fea8_35RG7s2L51}In{cVu(X&FId^3s@g= z48N0wm~AtFNx>C`4FUVG#`phC8vn<1mtoME(iO_YGTR75E#?wJ!{_vcf!hd$P5Lc+ zyW{p*ZvFFkhhZ3JH&m|2B;}AvS4TX%6~h@HVLk{3o?|mi4A~F#;x#eNSfG!gb27K` zw|n^kreKgnrP>t`cxIklHaJr2Z1C8MEem$>wpWDiqdPL|UV3dn83j%5*0^j=wJu)N z{HHUk4+|Kluw_*NO`K^md6mA7-o#spOcQxP_8KA*=so?fP6cK=DqE#A`=XF%RlQcz zwTx_t@1K9ftH*Dd<%t@-==;kDZhy$9|i7vRQ;J2I_#ZTh#sN}F(=eUy|#l$#`ZeUVjWP@_pz1e~6SXSZI! zVXzmE$R_0(5#-Xx0*#rin8~g~kFP)DH3|}uRIOoUM#P!-aK;-6HIIl)^Ev3qDIK8u znh>jSZ1Ug9`CGbnEI8rHBkLqV7DH;JlUA0^n6D4r+5M0atgw$jq#t)18g;A-pM7?j zw%MNFe;tl`Tm@Exwzc3JZKX5|?~$mB@R_}aGp09F8r4y@7RnJFU0%5QJJ#XsW(n#Kc})1 zLg8-+1o{4n`FBQ;M>ypnR~+!kpC$uI602DSyFsl4cXE~+;eYNFf-a`01Wej=fN|^g zGKnOTzoyk1p=#vQ&HJw~&0zy)%xrQ`4C~VjIUO`X6WtV222_CiP82Ruklie46)&tX z%ZgE7AG~-V&iA3RRX@RDl6(cXE$-u1kcK9OE zj7QJmw%r1)S6Gw?T9!fTYG z38U9Od#D$XPeu0n$y>L_ipPl3>3j*+;q?`o=lL)+13Qg;kD z9hjkvt*inmFercM#)-SQe&RM>+~tN8X9fie7ex7LWf&<{vOIkiLy2<5nPNn85Y+m?|`L=ef!T+*G>pOHQrJh_u$ zLc4)2VY|UZ<~dI)sb%JCm@TjK@OCg+G7VRc+=P#Q5HCB5jz=QY62B+$2%y=)Q`xmg znn#3pR2HdFcpU@+mn-%KQy*EZQP~4(vZh*4qRWT|u|4uLR2&X*ciUVGl5C!2^ zD~XYO5R^FyLB7hFLYYO|2DNrUJ-+Te%?rt2_Oxen%u;eu5A}MC|@!GN5u*jj*2u!+xyP2}hhzlNA&0O)N{vJ$$taq})$wg!nk+>8PE=@?0ii|9LM7~O5ThJaXHeSuUUbtWkL>nkzM*Oc9{T zB2Ap1+OVtqb;5DKG_h5h6 zQKs}t4HTq3(n1HlCw9${qKq=s_JkhBO51Q=IJXD`ruD)U&oLaU7=g~oz*!+Uvf&i( z-;i2jto?A78AmE3zbU~uAtNJ=ESeUpXCd5e^Pj+PNzlL7cNv*3@|sAqd7~D{d>mQf z;G!HVvK+eU&1YnkBr4V_53jxUs8FLEvuP69B&{l|)~GUk4mzPkS+26_Q1}UWO$g#W zAE{J(>!)ocUu?Vz+k;br&?U)GDvzKdh4g~#kj9-I@l?v5Z4@{OgQjUCv)CVg67y%S z+BmgYOW1W(9^si&luANj5`>>7vcPbt*Mp#_ z5d`%AxqSYeUgQx@dB`;feDW7F;D>}~K7S*qmEMYf9Z$b}#>{;?ap%K(sH^3JcJ-QQ zT(%tE#mD33`?t9G`X&0*sEwulvfhjTw~PFA#|YfGFSe#su22H&=TA)29;iwmOp-(s zfe5C<(bsV9_$V>3hwCTqa>nh#W}@?uRtnqKZlf0B)OPM_Z~rMhV7yp3W}(X%@-*yytZhu5D>wbf3fG8t&7jee7RWN3n@3LtWTGLL9tO=QQ* z`>*lx;cJE4)szh)dCS)*i;6YMp<>PQjF72NvmC0_tHel->}avd$o1;tzH5wZJr{i? zO#+Zw2Z?CvG^}lgNzR+6?qbQ@)x0J)NixM`si90l`qF`G%u*{$ZAD?&mSIMW1g7!= zH$8v6xW~72w&>LaQ|66<*MvoQaaU}6776@EjP!*r-#z~TuEYHq0iIs6iR^}h zXZC@UjyI2NqoM)|2qz+m?1GlP+<0UY1(;K2leCTQ?5<028tTV8S7rxI#^Ara9#XIc z&*iG3C^IeKq#HYq(+fIg{0x@iV7Ry@o}fLpGt5H`GfmFCeDoHxIy-YnP-f;< zGHKIG%OXFkk2hi@)I@~}7r@fPwkY>s`acU5g@o^kn5+}X?wv%aQKvi>Z!lEJ>&vyf zF__`Bkuzp#pp7BDRFfP_4-a*n~A1Yy!;2)`Inbvls0KGaHY z`)B6g=|LXhl!sh%zzY+PL-^T%GbSh<4MG0&*AeGr$O;Rm>?W6!N3izBRWvPE0haAL zp>>5y?C*%QN&Ni_)A#IPq2rOdBhmUVmONDmTXZ!Z0PPOSts9jGZ(e@J`IGn2zFR$% zDOZfOFp-p=@Z#wQ9Nl|^Rro%C{2rysXrgVGI_T810U9=|f`WzAmFD?A5P1&sO~W~3 z!>9Me6#LAnVg80U&pu#gM@M}BCRQC5Emj!8r(EQ=y-9;s{Egb%bE=^Ua~4g;fT=y@ zmh4jo)2VG2c)_?-r%eIA8JWm>jt#`o$TOUAB1Nbzi6-On;$+l0xD4~-j8UG3|1lTT zX_td|!P_*jPG{dixtYKtrI~u`PCW#ba z&qe4)eEY_XTN=hRj8zp3O412 zYFBSm84HiQWIE@F_Ooek4?MW?lrwI>W+po6D6hSjibtq`L=uM1Lovi4d(rrs?dS_6!0zp1ovg#QR7@z;u384Gy~nHEP@i)&s;^bNc>@q{G#u>ATWfTY2 zpMw946`Zlgo$A7ChlRo{P>-%Z!`!aUoUmFV`uq+$pnhBVGBfD0XodaGIc^(X+W{@$ zyv002+fI`Omtp>Faw7?vq%S<-nyCi32(KutuwBnvo%cWLf~pNFEA03=xBD`dnTH`p zBKTAiG)WsURU0F%bl8MbS)jn&olEv%+oHX^NwYL44`_fvPu8@T5t1;cf{s}tP>6t{ z3H{F>Kauz&iH+0$6Zx6a;w)qnY~u{l zRe_t?boi>*7>hI`OBs3LAZd;l zj-=ne(QfrJ)GARDYX%QPq5P^9{CD{5eb_JH)(iVu>7t{GDHxKHeqn4Mf3{B@d`B?F z+0MudxN`0>lY-Z6SP7kbHALG^bx^6Qcyb|4HYtOAVmFU{yzwhZeDhed(SM9|QdW(! z=(TuFS%zakiyCR+uTbW50N0zZ8 z3984YFyD+I=|>Xm26^M^;hVfwd&-BB*9J!W*LU(_90)tcs}!dOr%JUHXVd(d^pl05 zuXEeXn#ximp86Z~#3<__N+q6X@=jy{sR|xl<*xe6Qjc^S*@3kUl1fDttrvMvJ}49D z;JV{X(e$?lL9)HlaZLGOWCzSBD5r_YAId@_b)GCx%NkkOTJ&rzlh|2pAAzj_T#`YA zW<46garY~}BR5Y@WKnb9X`L>0B9#fajqu0wJ1=EE z4k?np$0qRhqy0uxmQ}Xgf~)=^kmAW{iv?2vy?XQp<~^KwWK-1!|H1o!Badvlci|x{ zd%N?Vlk%F(LZ_#Bo|9G2q^lFYN|DwRob@oyE@Ns9dVh^NI`AILA;JWmhO|azar5+j zSoHVi*gXHMT^>QloLDBHGCvJn^ZK=|_k zgx@@YBvPZQ0pySoCIP=C?lT0%fC84oUunYV-OW`6v_m>mN^S1pllT``LNbw8}41=Hmzj_ z-SpaH_*^c5_2K1bc#L0^X7*27C{|hv?mMhm`<*1B7VW|21-mlzSOp3dfNPWm>b2!c z|7r4OJHS(B+f;h@FJ->a1XL2AUVnzAwkyj*Q54h*s6%H~U#xk!2oqgL$y5!IdV(s` zC)6}z)}9YiY6nHyyr`~x$we=gwbEmB$5e?^Qa_Ld$h4a?^lQ(-Du)P`m6V6v4^l4? zF=;o@3uCIzM5y)pG|8qKO{%~+*Z}LF1!J1exD1m`bWK0K;{ptunzIHrS!5F_lV&WK zh-FuOWs*%7_Fcua#-=jKCL$PS2N}S7zXM8FDv4do4q$d$8<}JiovW00w90;iOtOi{ zo&_h|(nU5MSaXa$k1VpOVaIxSdh-R3_@P{>1t;Bb`M@=qWK+*^o#DFOijhr}S!Ujy zPd1T_+3$!mk8Gk0JL|q4yyxtw(;BAB)6VlDJF3k9PaZ+kV|1s#TJU5M->#z;@*Zon zm5xHlCi-0PF%03{>yUcaD%4PEWE0`wrt=)Ki7<3p_iQ7Z9^QS8gS)PA#`@{DP+79+ z(en=+vWYNyK+7CWHhoO^j-3yQJ@qbK;o*L04!kbs0 zu`}{K?%(3y+-C+OX7xhVx?IJ}I{#?AekOYbUQwV`Zp-CUStzXtL;`smEaO=rGK0x( zBhj>L1D@4kg*|_=m0nb;RUS^8&HiL@iLl&q9S(0enW4ubrOmvZ)=bqTZSAlf=qdNU zpeksWC`&YG&%N%^WS4A#)Fh=yXcX08O;)QPEMk*d>3t)z5fbsFU&{!~%-1M<8$_UU zh+a3j{O3^pt!>bEBvQ&wZy2ifExMeSx)p)7p) z_zj_M+Iml!G6v$R=7w(OqkIfs*1JSQV0kUG>a63ZVnC#Cq&%3 z!4{@PD4>=PBOCAu^PCJ}ay;;3H&C!U1e(SWhU`)x0Rm^k_JsQY}XpGm48WWSs0B&>oZ0UNuo*T z`Y;Wd&KvievV>)SW!6G(Emqk2LkniUCJDK8XwL=7NlBh_Ou2>S|9fo7=qae8NutNJmtvDp7 zG-#hMp0f(0D^G_-FE{L7evsKH^N{mE1opL~w-7XECB{~p1q*FA><>GZ=5bFgdK4@j z{$w%iBTN{fBhQ54;ngQ_9_j<5HrBX)?6%CmY1*wJf-m`CPViKwoRn1nFtw2>&l)ST zsZRITXG$MxO}lyW4yHG^z{#!LNiBiS|0S1vP_b5drbr?(TM~3W82U|s@sg>3wNgL2 z_7t;PTVv0PLo(a+D%Yuq8^`bPe%EzGdpJi~z3cBk>ck^` zsj<%YpcBt(Otw3#KJyU!@f&BH!qA;In=)hKZi(%Tgn_l|qIBW33F{n( zq_ebK#xj~H7@P%%|Nov{C`=artlj`>#hf81wFxMw4g9P{PqnR`SYWQ zx;m1QlNm8puT*I`cIu9Hl`6|@x>FVGICLFeQcXQUjw8^bp>l(-<$}EG3L7N*_p1_(VN6@N6Ew}`XQRub>+i%9s)!Z9ptHt{0rt|lPqRdVT&H8%b zuGAz*(M>yd&p(8@wi~BGi6(7+2kcR=jr?skk$yW6YO|iF-l7bqY8gBRsj}RxyE8t& z<+>G>zKwa152U!ccH|aLZNGri z+b`hj2QCRA3uHqgHB+C7-O#90y-cl06VbHJbrY}6Qd*@m#bYd5_iZNgxu}sX*kl#X z?z|-P-;;ufEVI;RNx4w#=WS)rDcPzf0-aYx^970N}#krix1lt z?Lmm;T3!nsp`&ga+`sUMr%0mLH`!whI_b87{U9$!c1tVC%va20t$HHspzAO{o^p!R zQQij}pjDDfHnBkQ9xgoF9odPAU?!VwTCZub@ha?C!mV>JU%f0A9(QHI=@|hjXRZ5t za_riqU=29of~xg1e-Hff(OVdGaYjO1BIo^Ea>@ne#DC3yN!yzo}KoEBDYoAamHSJmLTbRTve5oa}O6jaC7#Zyr%2NzfDd2}B$ z_HvbqJM_k)N!jwqO%NvRg7B*~P{aUgnkEoZb`#Wj*KQ)82rMxC=WPhT+5^9CK~ORh zf&xwd>G^kB$RnKckSh=PF5=Q5ipC}j#simaV$FN##2D07xdl) zo2+I*)si3!SKvuEl&YAq$EC1e^B%6e)%p&DTfrf6uH5Zll$&JI(VkZ;PMIw(+pOd! zvocZvPZMJzo>I%tDo{ureJ1t5C@YU$O*!Kbt%N2scBk`}#zf(gV;gpA5dB6+5Z-y||R$@dQg0d$eP~(PA=0=d3$^GO} zoQg^D6sxP{Ln#dny#F50q_KnwMKv+0Nehgq-w4GDW$p$%E3tL|RrpCs{Fk_lMALez zaokBFG+;lD?70D#g=1K*Q(B;jIjsQG`$ae5WKSYHov%X_-o5#Xwb64Gx}tX~`WoD) zaS54TBih1r{#Ym*`_`Vo0wakgNdWa)*M#35D}_$z7tamjt!US!=Q&`{LcHYx-AKBP zT)-3+yl~q?j=YJ|-|+Ct6PWjO<%Nb*tD5y1Be@#{WA49#Nhf@Y)Dl!OkocEg04Z}5a`b7 zm+Cd<=Bc|_Xt+#f&J&^SfEF-bGL6}8X_B))>?jt`St;{4C99tG+SyELNraTFZ6gxL zd8>s?E1x9JMPG*hlwh9fQlvVnRHp)tZRA?K2(+DUJFO9O|25kNDh`kYohS7BTFp}I zzWY`m!F+=yylVmxW)6{NJnPn*r|)3a-GvvxPPV)BTyrT{JH{WKR*{NkRMwX8K7ha?+a83v_Rb_ zB8)S(P`f{d)Xmtrqv{~l5-FCFzd8X45kS6b5EL~63U-A+jeG4Bkx!yVI^p+C5XRa= zpy>cXk#YFf=ijLzk8sLEt~}s{$=@J+*MU-M{)`Eu#HzPD?wotTo7j2m zw#jhT#qG2AVclmQZ$e4dAM3S7a+7eBRYFa5(z!`wX)#}^n4FRH;MtuRtZ|OS@l9va z{5!H8=^76}tKLns?0Q6*S?8m#;K;^P?0ZHsv?|rEz(VTjt4X&83L!QAbr2ayq!N)b zbly@%khJA0Ef5LOd1xDS8roK21m?X953$aDbE>vC5z(Y)HW)faeNzn_|M2o7LT$oj z<{A-7lq&{9KYjEV(;}R+J{czuDjnb7; z$g>EqZCGi?wIvhiHIqt9`dUY7Ng|u1L%!*8efHb)GM=Pya@%>hk6geZw`kwmtecH) zBk2Ax{h;l49}|F+QCw25ZU5$Q+GMH_66DgMo3I-?pEFjaZUqD$aa3w6-OcllU@Xjc=eh&LZ%F=EaN{E|>NjU3Pt#uK_BC&TXO zL4@BDHw2c}D2&tk(@-E^?rdXAvB=p0zitC+Wr0GyAt=}d$X^#wD-D5we?OM|)don| z3PI`pDIw$k`20IH~ zui+?NRtrCW{(`!CepUxOd+ajkjBbl*rhODj61;!)8Pj`GptP9iC|junmK?G{;Y`|2 z(j(tD>f(ae5)%o!vGv+vj;37{M|j14io={vjx7H}5=McOFld^#+>iU^eJo76IO5gA zH~h&5YqiT5Np$`AZP@nrq^n%uaN6_ho#1^*KZTo@`6?+txz;cC}Hj zO>H(Q&S((vzXl46reJF#Z;52Ocm83zY;uys3t1g0bBoRudJhx<%1a`GY0H9LxN(9X zj4f%)8fH3(Ws;@V_(p`;Cchns^w}CX-UjZi5MHwWR&IgzU(R_wxRZn){4`rtm)7$Qn#!lk_ze8SYBLHH^0Inl05U4`daDiVGx4r1$mF)>p>JwG-t znu5|LR5RQ>J%~+AM7!0?@cozg9GcUr9ZcJHL~e%g_d5vV7P7)A2;%^?ia>#8EKD5v zssi~c0Qu_yzn=j=q9Ke61PZi=TFKQ}x}7m9JpY!3Ji;jtx%Pk;{x}Tb7jCjvX3Q-$bPEj<;!jHtC7&bc;pCxQaom$m|kHrwEB1pmHYPON7ot8$+ zc*uf%*s}Hv#u;|SI74yW@7}0OSm?GBeRP^*u9J?!w=H_zDJ-(y$Qc``-wCtkDL)tM z{^ci_+slKK^HZuqaRi@oL9xF~CNi3pWYU$J8%35fk6l)1)~#WtEMunKoOumPJw|te z;v36_mGz}mR@UPo4(ChsC(;v&7i5k2^~$o53W6qNZ;);Nf-{%G?$2` zzJEv}S;r7rHYpTb+72cH9UpU-8M5Pp5dSqE!E;w*|LUVM|GwXpo|v&fABBn(V!_@^ z&BBl@p{NjTo5LnEH1CmmWqCqe0@k{0!j^@*dHobk`Lv(cB~JZwH^Ulc7~7=fqUP^o6-7rN+iokj-W{64WAi!j4y40H_T zUl>Ub!sqY7y7_y!O@dCk(48v256Nn{nQz3&L*mBA{Q2`?`Q}-uR5g1Du)pE`r*9ZO zdj-oG6+y3GbIhIC134M1u3d)9N%7)tK7jzICQL>7qKdmeQ#Jgt$^Cu<{5S~Vk0{`$ z$O#|-)aV#g1E~32Pyk4N52!bVpr{!H1+&(E8l%GVZ&}DAobr%s4|r-A5WedK2qh+j zMP@-w(>OafD-R05y6xu?yq;@KSnE3hHM1aqIHQRF_5<}ELup&};5Csso0Z4E!@O&yKL_3wy6J^6B!y8@$Y|^nz?LG-8R2p|bk@eolEbMG}%wUO29(Y2>|^EBh+5`p$htrpc;epXBE#%S2Fp2C%5q^5~p zeh|BtAK($lk|0%$p4lJ_o|U?Fj&SedBi8bE?I_pID@mH^JC9^!s_2})dgLa2CoPsq zPLXBLG-NtD>9%Eo<3UC%5FN_RG9*v{w(AaS{8zg|s@MBd+s-o*OuFrjKz85>E+cXI zz%^WyP-{qrp}Ry`vi~QtNjk)v)LPT$Ph^TOdf$lHavtV~%Llkb3Sq|liA+Hy4a#gH zvPs%TM`|L!!%iqswiwa^saZ|CJM$EZ{igJQ$&!p)sfoOr+s&Dy1g6*Nw%rn~dNqL} zuw~I+gxGUG2V3`U0@p}mrCt}F-FpqAem?k>B>r5e-Lwi8>`;tFP#)a4^c3a>+>A$R z`ZY9H-Ii%m+>`QN1oiy+n7XNqRMTQk0yS1^uV017U&QB8|C)6W+;<50{0)*d4&5%Mg<*UM!P691E2=ZmU!y-n7=ijoBM>ypnS0C`nv97@Pb>e}1 z|D~v$q%JCX$XSR_NW#!rA@~+Az6tEvz7E_>RP)1d^O{pwx#%D!&(_B934A$n;*X#3 zciM&vryn38J_%aIG!XkW#Rf&IdaeOuP^pS?R`*^%`+ynRUL2C6a;G*>tbb7&4=mLZ54;eFQf8aT{-FVz%(O3sVKii(9Ae!Mfi(9!WIKXB>vl z9hl+Aqe-g$Aa9=iCbOANM>n2=$Cv<~Vu7q{R9Bu|4Q%u}srhPa!0uFoltm!55VhM; z=8GbgEg1o+5+7fCifhMi% zwvd9U-LnsjAb4^Al}sWdqo~%XGP8`40D``-Wz1@bnsfK93e)5)ukDuEuo)}A_UfbFj zHg_PFTda}!9#W{cFXT@m)mbNWf^QDLfL+h7f%62MFC_9>^`caOw}ln zP12+r<)!%_aYCsIX_vl`1(C=mX|k&Qpq6lq%y?M>SqY6hJL0X>_854Mf!^IZ{oAkp zjA<1wjFsExKvOIA^DaB_=}SC@8ilfaQ4w@|HHC$Na+PVy#)%hCF@DDu&e(<_BhbE5 z+D(@^9m3?V5U9x}wS~!Xz^_MuKpha!@t7Zi{FSpU3pYl^=ik!EBb@S(s}FeL&l?ba zhpzQ&eDqPXN@ZDi%^@8fGagQuU^!{Zh8AIpslN--*_Z->89^mfzhj@PHr9z*B&WDl}N}zaI zEohb0M6uGE&@82i;$<}1M8B|RVQ6R;VyYYZPVXbBfbti}&%WnVPnl>7lZEU&VBdcy zGtwaOdm?^(PeM{c60@Ct`y9u<;y%YC?#p+?eU8KDcVF4$n~0`wpSfzj%tE7RA!aRW z(5^Nbb*zW_?dw3Rr1Ho_dTnHz+q?2GQz)hdYG-wi3qPKnW6Y}e2lan!k#1CeGAWGM;`!9ER*zJJ4acfMNhun@5Q}Wu+VnpDU(Rqufn?Uh887aN z$qGV)_I2RB&z@NUGXmOImVMlD_xvNyc=wSVV6}RtLME4#F*vKUGv3FDv%2VU-M7p| z%bty(Ohhg|fK{H{1(@M8dSZ&5;;sAX#o_4d@V4WU95b!@LuXX>H07lSi^Gp2e1|wl zo|Ivc0aH*xiDY3`z+iV24n7r=#dS)S!oE@ZxkiaA3E|JX5dPQ&{EkVn%HbCT>dje0 z9rD+PfUI*Wh!_>0e@i2eaLPljKHzB*BmB@G_$_hMpfMb3#XPcev+|$-kh*D@Q7Gah zCgnZa)q(q5)l@lcU3VJG=kG^f-4-x$7|i+IgWIn#XF>=D3~vP^>y(w#xNiw4`hVL7 zg)HaGucovQdm>8a>MXzmiPcV;$YMiEC+SHdwYfQOF_W7dI=1Nyyv7Ca zg1cL2H)fN;tPJW-4KMD!L{vaDBgp>N97mJgzLR^R_rz|0RX}7FXd+2DMfWc~!h=gs z*!PTvkmp8_!d$&$bXb8Fw%JBa(%80b+qP{xY3wFxY@@N0#dwryih@16PP&iu|= z`ID1%&ff3yKD)r`rxb95kL;wWasp(beD0H8`i%Py?+j++7cpY-ho=l8C(Ec-%JS1p z>j6?4GHUa$IL^}2wNVv1aL}omBR;M|pJ)NJHPWI|b5_JKt4=4hQB~QR&{?)uS-tTw z9%7^cBLH{dA8mci^=s-SbP=t_{~BslrD#KL}N*^>$nm9XAaJm~HgY`awnR z;#;){ekv9!0fN^*^%JRQp?H5AOIBOaE*XbCElbY_>zL!;6NihsmXCIfyO>Sq`l#0` zScx7fZYlGvBv#z?kkNKaR4!0(tN6TZQH925ojGEl`&&A6PIoZI1XB%)znkt1^fjf< z4`K5o59}TJv5pyC^HHNv6zHfrTM;BZqO6O$e~RHKeCjvMXrM`+XwTb-yuQ$W?TZw; z(%ostnd?BbDS8x?8@#R3pZ>89EkP&U^5Z-VX#Nqh3sSs|tn3HXL?h+bUs2Rp#CFkv zVg39Xm5WePs)lSE89voj827KFwTOS!+iZ$3_#E*KOq7tq3kyW>LB66E%O$~i|J)kK z?*oO*mWdBajWxD6EK!bV`u$By-)F9*!jp$qi-0&?~`blKkS(zJvg@s~@Hk{=xAksI8Lw#j}KWSBks z!t7fmyL}l1v&NkEtW#HI9R;<$rm6Usj*_rjjkD>c7Ajv0aB?Gyb)_F~Z;T2ArC@3& zWA~%aL@ghEUgBZureq273Z1gTBUy67{Gy6f)3ijF*>ojinNBVG zDRDHsWXzfyNRD()I2)PA%*&SRwbK2h7dM1YnNeDuUmPK!xgLk( z$;tzw6TL*_VAEzVpMXZzyefbu5@xar4eIAlsGW`Wv!Ef?C|Jz-Y)~{q^TnMthd!vyvCKX$!E!Q z8>9YI3%xAd6pn|Y8i1(K1pADST9QxS?+2#noxLG*2d4a_U7d1*zB&L<5 zBFDSB7@Q$FSm#^|5_8$rbMyS_aQ}vkGEWs$(64_+?dQwta=MyInQam$Eh*VmZNF;T z?uQ*AgmrTCw+o869L2}Db=3JA5jy@}j6mz&{GR+*GRV1wIUZ9^$pp*oR1eS1Fe>TsQg2)p5=vp9M@9HI`Mg(7L~x z%rQC2#P}rTKKS~*jh0!f@Voke3hVX3wRXGjO*A5siD5z`e;IUW4yASj%UF8e2*>-U zUjx+^@CVm8h;AP;H4|i;W9FvY*=jXzZoW((M`M#za9!9+^O-hRdhDVyJbLFs{egG7 z##U@Klz;jN{2`a)=slN=pRB zv-$xX(n7MvM8bw%S6<>}rLkBnjt+L>-i4{vz%1au?UO7UYmocIus(_|TJ-lIupx6_ z(5v0X1jo$j5|n>nrj3oAw`&>D(i6im6>hO>ikEzg#R^a7>>6^a5(G3Kf#UTO`kOKO z{C+*Qb3zZk(s?@bGgtgO&FyvE$DgOX5;Orc8^zgcVpnH~qRR4G z(mat$A7`{bT6L(V-;mkd=BWN6^`t~cRaMLV@qik}1)VV|0a8|5{nsPYe1ikykJ&Q? z?-Y&tVlHGeNpE-b zh>|O24!@nRdOYv`gS2INlI2!MD{Y8na}<3lvhgA_v*MQ+l0Tj!m(a9YJT343TGBE5 z^Dq;DglJjo@kkJo)~#qgZqJ*KpB?qnNX92*1phiytdm3y=3CqjRlfQ?+|d1x3Vs&P zROpC%n-WF-V~{Yvgx4M;9{7aov}e*>fL@?Mfa-tOUm>{&;jnHNK%^DC--xOC=x9y_ zA&C&M)i8zkIj{KP^;=QF;U@PAc?sDwlvUy#wtywKz1WVJJI^CVY#A+@jvh*SSyF^A zjxgUnl6z}sf@G02`jwU?w4!A3GRI0RCWC=?ovt~**e{Ro{> zFLhZn0)@U;C^902b~2GMkDf?b#gs{6!WR8OXJ8waT;pSLQ^mMo>5Qcw_66G_Uo!@n z)WVVex_HlGR{U5`G~-i${EN#tV#`8YBa-uIc-p)EZ}0EiM&ULVYs&iLvkrGI{g7ce z!W@wpCVc~&8BK04wj@y+b-N*IKl@!~Z{>o-)^mcolHdVXeb>f8e8gM7Rm@b)Va&{cgHYT{zfov4eWbpo}>? zzhYR*dcDPGaf^k>pINO`jgyA!B-ISHg-7GK8WG#a&0%mf5|$JXUCZ&GG4E-P3gVYrBQ^oTVmm z86?pIMS++6uprL5dwOwiEI9P)1y2QTYt+ebt+@&CRgE^yC;=U=FWI%^rYIO7Aln^E z^de`HP*%TbiXy@hD-|=xW3n$LpUSyB4Iy(H>cfPpv12>LS z*=O)}GU=bwBlg1!y}4(WDE4KN#)NBBO>)GFD7Iswpbk`T&1XazPw}XElIEHS5%+c# zB){gbIX6ACWKej+Xy3FOh}NU^piqyb52f_|yoZMNxGrB-9IXi0NbRoTL$Y+@0RBBO zZ}BRmUlCuTX%jPaI?*i-@Fj0d!!I6<1*O}lRqb%yg}dsTkg{d)1oazf;0h$AsoSz{kxd6@&RxJ6>PU ztymGc3?QAbgTjUg-*~=L@W5DUO(H-e6CxD+8AXNm`w~V@_;xVTI{;3~E})PDUW@$y zdlrWP83rh$yx$;Lx+?<8-=VI+$Iacpj5J`9BRd&9$6?Pv*L!6So#!uto^Sb39!a?LM=4m*6rgi%l;% z$lmLTHPxFJTk9ULs;o6k$-E@LlEHUgYB-ojYdfY9- zb(XcJ>qD@Nk1b{2fO>a6&2;;>E>)={f!rPyQF%??CizY2#V*2|svlhk7PD30b&5r5 zUMM!6OWCJ?meGlPMaLgm<1eVjVvx;1MRkva>P~UfnF(evC}SJ5 zM%q`5^NFEpNfaiFlDx=IBb8rhcofm?ncF4X zD4<0|oKJwXS^w+%?~R?#WWC+CZkuP8V4s8GQQNZBS}to7)y5mA%a>Wm>ODTUt{Pm1 zAup8L?|0AKUTaP~?Zh8zx!}#fhAJl&WK1Zr(k}0H;>X7er{22A@*iYZr`w(0Z}8$n zqnPTZ zZv!|_gt=zJn+R5gEt=FVxX$n*#3vtF{QT-vWnsI!u(9;N{y0S-V5j)!)-_Yhy|=RX z_f_}~Q@Y=5(blb;Z#pKu>y$;34Pva&ER=p>TS~WwdwE~))PhIAfrjFVc~gP>XoNLDa|W$;091#|!LXZYM|EDbi*8@20VkWo=78 z1--w}t5+(OJ|ZQs>3=i!8RBSd6E@)%gvfFCXh+p+wV1I{tr7>g7AGP}5+)6ZP}efI zN{^UpxQBM6WnE_aQQj*}mu%81J%VIExN893++N2^$2_$vHI9zSA$T342$-Do0N%=C zqa#k|J7{#PZ!<~ZjMYLV;hLKUlXMf2!|R&xzjBI(ZkRg^*F}(%sucCQ6ua;$GL)kc zszs^OK=oNDZMX%c8fzxv*{U!2ZB~=xpqa*9Uo7K!Z2p{V3qe~LgDJ2b;zdoNo}*KQ zeibjbi&iruKp_)+8o-d?fk8R@D)^UE;Dd{a_6Mck=XVf6U&sVH!6!=?1$UUa`pnp@ z&=J8~`~SX!5B$-|e*0hDqD-ddxUhgdKeKkUpsH`Qhg>AzXV&b$yG*xTaZX3y+S zw>|uP%aoV(hy6~r%TM~tfeJ)@ia#XgUCsL?Hoj0;sz|>p{`y%MN3Y|lA7t<+yNq~g zt(MI|riA|@V>W~HozmPNeMDf%kMg_6;cx~y6;^XN&7Z|e8+jwc%sdSCaANk60SNOs zpuS_uN_e|!=NY!GK>N_8b4(nc^(XSh18N`?fMzz;@)3TPHVI3Zl+HyX^?=Mnf7I&% z7!-RYnPkE;#3~*2&NI?<2W8^<`Mw<CwK%9ft+RBzEE6)Ov^jVVCnphn?NIom~?{aSojbH+xL zCuhS+tz1YV)D(xi^zaU3N$IY9tE25o2o3MNDH9 z7HuHrRU$4&hLi3|o~W8|iFqo(1mxRSF**5k6<;pqylNA;kVaD~5feUaC&TdP_g=E& ze;f?*11s0eftS}|sZ1nSQ@rrGQuj<6wT>a9dZP`Am^aRNJ_v0K@fc5e=#hef>e4Spx{NHiO9gE z`yu~h^xv4j@i5L+RMKf0DQTKu&9O{eSNYpjyRiNGv@9cbd8>l%{gIe~AB*~g^O{x3 z^Dcq13Hf-rg7Cns%8#+WXh;or-Z|-%UX<}@Ot^!BCzrcp9SFaB7uS|*w~V+yt49aO-NS$n8 z%Qvt$P9p^?Z$D&Ur%vwuIcWQnS(gpg=dR7n6Ulbo9IyOKipW6vKE|<%6x=C!oH0;r zzdDI~(m~rtX}PR7la;^mkEX7=4v--wfwj6N(w3D|`E2)nOE%@jjp7L&S~OvgEDWK# zr_Qxp46y)^`R`WrigNdoXPF>MU*nO^0g$BG=?MZ)$1^k6taF97EdRFpfu_36yP+17 zk1P~yx#@&~zFiARWGlLMK@?~oc?^9Nvu7h7G{>=0jIP+F^T4y?23^Z}maYthV&JL+ zhbH~x3D>hXyd`gcAaHUG7{dVRe-8>bc1M$O)+$+=D<4s(#JozAs;thMAMD01sA*Zv zu$@(hD_gx(k&d}!8nRIf8c^*{wxo60G$l^pDfVmXcHpf0A|*U!g8=+f$)bmK^iV^W z_giH;uxw;>u$==xKuiG2?k(#2RVqUaq`v?rv)+lzn7r-cyO^rc9Q zTDO)b8YYWpUa&UNPJM|Hbv4@0`?=H{pV70A#{yT$P36icxh>_HzqTkN?!fPXlDpV{ zA~gX5t~qEO2eOP#mTjsjx9RP#|Eb=Lysc8guaJYABlc*T=$0zP^WBeEjO4#1>-(7r?#Kfjg#B6$k;x?Md3_UDEiQJL)xZa!FX8Jzz4V8Dq;bVdbZftF zv10!0?e|!-=bl=tjaGclTcxM1b*|p&O37*wniX5+%i>uYPXUd4J5bYTnpzt=XMS`z zJoDjceULE={Q>S*nuEz^i=9ObNcC%0S6 z8VM1xO>G88ZLG#343UJ)Z||GxPMG@n}}izt9U znXDx74ubt#;hGq?vDStuxwT6b$h}L*FiD*|1&zUiGuve+J$Y06=O7-4@3MUbg*KkkGE(U4i&rzPY>U z`%kF^a0>YIIgIVCk79B|#ef6an@xRxuE`mc1FO_7c@{w7w>Zdg95;OlOv({|aUg+* z|Ds!qnXr}DHf`cV-De`$2RHAzjMA0nVK6tstg&!>=$Z-%Fl@fCE{h?&GR;&mwqwI{f zeqDn(ljNgZ8?p1-9&uLaaP9eJPu=9Smob`As2=j3viaX@k!N~BwYPCpt3l#d8N2Fq z#8S`mVcE=0=ip$nVpXqfEQtZ7N<~x^dnMo|t-7hm7fO2*+1^jo>4pfQqf1@)VH>lU zKYWIpv&eU}uu79a^NZ3WR3~oe1m*P@`=OF6{3sT*-N9ROnl&+$rZ$G{L__}Il&k%N z^!96Uvb3$-5A3lYQPm&Khl0@unRO*x4}Dqn(}4*80qIOcx`+l~W`AF+=3|@dS66s)5FdtZFr!!k>!z zgeGr0oEw^8Av^(PJAB;pqP=M7b=RGpeN1;Vyfjjx#&hV`Sq$;_htkb&&8Jh&D`WB? z{)bRhc^njffmx7g43GdU_Mi0J2nl@$oBu=)AEltbW8VxJuB3_jo2Cs)P#^ocf}RiM z2(R+o3yTCgy*cvvVGCKFHuf_^w(f^&mZX$}Z`+wBv9G4<-%cGqK$+Y16CGsl> zDfv6z!!@%quby>jhUB(e)^TLpC_WB2=j=e)o}E=y2ga-jw=@na(M)0(FOPdO?mpCu zLA>F)Mn$>edIPbpDReZkOf)eK`7|!hd668UJCR$;|K^QQYCe{+PwK+5{W}mk&_Q`Z zUgjk)+YyhaQlbbqkF9Q)Oe`kB`lnYYN0KyETVRo5RKU~m5mkACr}G0w-j2t(jGO3o zFThjq{_|HeyD;Du#4seHPm^1%bw2(IwCd9j9uO=g2zNcvxJs0&;>WvktIr0U)r!*d zMauYVH=?R>VD3RM{bIsG^#JK1>t0+`-A_4dn=2VgHcI6dloeQ;fjKqWdhAb2R_=~> zkidhASV`Z%BEKhnYH$DYk*?}jUsI*M=C0=?S8K4I#so(@Sgm?I9et~@VL$dUZJ$H% zArJf6PkMdv{B7y(aVxH=9u^M!Fb@DQj#uyz`BrOY>_F|k7ka~{(@3*be?MHWUC_~> zOCu$$iy0LJzb#5yw#Ph!<A^VQAFN-es%cwxMn+1D);*2Nfb8^fB*n5$@y z*DKj-uY}7M*BITV@$=8_TU1u@S(zvpmWD50@*x^Fe@c44AL%u4sllprt}Ck&qL6`h z;eC|^b}8>zAPr3ZIRpw4*n!?R`^HqDoVFiXKq9!E&9w#9`=pBCdy4`O$wENkOtt#n ztQqEZ?ReBo2F9N-pav1qn8i?@w4?S|`(58iavx>X7V%gEev+ykso}@EB~5l0{%)~b z-;$*c=50(6i3Uc6kdP44o4Sk{GCedBy-q#Bm_1Q>R;}Qw!Q3h~eRk;W6o4=M68(!- zqDF7c=U;`}1&WQ!KWW9dj|z_KER^=i^gAFCE%fR(s~zPRv7F-Wjl&T%bbsHnW) z)Q)Jfo)$zC+rl+kEAf0Ehui)v8AJszF2Aq};+;|e6FTFjbeB@48WGx*R->Rp*UTM< zf&#bf^1BX@%{%7a|JC9B1VNAAZZ_m#_6Q~0e_CG_1~?V+J*EJXEf)^jYS@06L?}3L z$BmxfpIhlXVTRy-$KSGp!RA7s@zXTGDIK8N9SI^c%*mgB$2V95KAJ?e?%u)MHhM&$ z1ZkZ{EL8fQqZV0_NV2}cVaETIO`7F=g+TX}V8)MJ zsIm`LitR5OrkmqV60L&6a|L*~^==*xPlT^tv)eJ0x%Yq7wo_syM{2DzsNu%Pgc&vs zGm0qPcX2)L*L!{0HAVy9jRzh$r^g4+U$BjKCDu^_-NVj`@Xk-Veg52RN}KNPMVMnW z83RGgeRuAWnyL;;Z26gU?my3mV*DPKeV)DmZ%(UF1_jZLE#bpV%4V{Znq@WD0@witM?y zr+Xh7zOMIlOCd?Dfia6*-&d}Z%|Sk|eLXQYTgtt-gqy2S&Gc^4u$o zK$9}qr?6VFmM8yN#JH$IHI!S+IUSFeb%DvSZmYRf=Q59|a0wq*hGx=^(9IR+Fa|8> zKW9CO3)$OeJPf$482@A)AXWacF3SHAa~J0`s-vNOFlB4R;)oIHE3E3(Bh;D}{io`` zgxI7SJ0jU-c<;8kk(ccR5~-uXoyY;qur0!eFSua^_th;`?3-fde2}-l7F3P^HYD`D zG!$IQ@V%;<&OjI2=OoaSypqSgA|mno4M!rSvR&2`phfAcCTTZ#?`F^7j_d~RsCfAm zLq+>x)Ko68f(`K;?#H$j4?8$! zp}sMB4UD&h( zDjA`P-thp`X4nCzLG}q-tdObKw4TU|oq4~uRS+t8U_vW>IT>*Z{JOyO87g3ybDhK} z?Tbv2F(up=iKS(Q?fZ?ocG>_b(kPo--C$u= zvWMrCRuhyr-!`r6(mp3lHPH6eu)rAgM|$8k#q7e{$`8(ON`1b#iJ$t#t~2m%&4Vpi{c! z&k0LOsDFcUl3XvP7Gu@PWBlX2Yq<3`2d14@jXHvi|nDGDH7DN6Q~Uj>ha5%%Og zXg{GtY?u9|?fVULfDL8$f}wFNcD45|WyQv*EsEwLJL0^m#(ur^EcnCyUG7szS8NCu zM2yu~ZF;S?t6UjmekY&%^_6X-=IKtS_t2T$A9q?-%5-cND4)RuC&ii^9;8}B32HD1 zx!DlK?tU40wvi3NG&ukk*!>oE*(6>XezOb?U%@*`<61BpXQ%ORrykBaHg?}U$HVGZ zW^<|s^g*|qM`Zrsl&ygg(fr=C`9l5-S}GYvptdnbDo__SQ{vuz+2osMrQPt#RElTc-wRD>5cdz@f$ zN}3DKRXcTXO=)h_Sve4+q9zJH@)VIG?iz{kuo4MWqm)ezk@;s<iS}euAj)i9#SB@i9I!L z&!z|VgMC*orgrtp7ufW&2VOV1F|Y~9%xHX}CD@vH#c~4xX<-*JykBdwNPMIH5(*Ci zeJZTr!UbkU`e|R~6(_{@?g_R%B`q*RO7P4ZTZIKC3PV6q@l91!1}?+z@V~nYcf_Dx z63ERTGS`{P&kc)J z{`lsmXUaZ0hdu)o{O7DIxDJzOtaYOxlBx}gEt7)@`CCvyk!{@o&FzkcBpOb&@C2DE z%7=G>79scGqrHyxUy&3<4BG+Qj>MEXrqkoVCM9H%(Z}m$`|Rx#H%U>j;4o=zo#C0i z)^(+emrMQxnQwL!l@?By^~;g7E=j*o`u(DY%Ej%KJMlVu%70a1I!cMm{Ys6AoK`hO zs1fwAG^I*03pf(dn_2X07|Xx&D@-g?$?CmfBwsYW8yl8IK% zVst>erQAn0U+3hwgXwD-?mBzUXAmU!!8 zsvl+noPC0*i2pO_=^>5OOm;us15myy0fmnMX-Dg5?-XD+Gu5Tg8w$%%30bi9e_rAI z1WiIf@_;UH%$#79t@uG~W@#KBwWHwmRXWqX&)wzi_q&5PBN-D_(pt;<)r%*1HMm#oyy5GfSE3qU{vQN z*&l#7fHa&<+0(h;sDqFd$wv`QW!orNF~}*mo<)OBrA8^80x(9#!HUStd?wIFcQR#Y zCnHXudN^L968lez>-R<>yLnjgY7!)=J}PES8{3epl;~YlO*y_V*;YKRg^w{|;fWIL zSOwlnv_P#y21?{i+`2JJZ^pPUPk5r#%00Aj(OQ2Hrl8B{5-HD%Y=0CeotRH2IQt2Y za;x+DrJ;4aoLezB7+Db>)nxB1brlNuSRx3rIB)m6Xht9NYM9laX@ll*?$&1w0%r^D zr;%?01BcX?93`&P5)CphKU@7juuT7()4{@(VMh+o1mtv%D6 zUXr+;eur{80o;rFZA8+~*;va)ig`1Y_u?1xAQG&9j|LQ2NSZ%^CDyfx=Rzh}*FaagP} zn7)oegK38t4>8eQ&*zB?H{1R$pGMF?jJDeG{t6PQCiT9#hKGMb0`yBJq?S+n*VZp9sZ3t$}2lIs5IZwJpPAr1VoKo?+ zDsUKsNZ3yjF)yzvDL5QmMICd7i&ZHraH%xy95~(BZc{fW5RPgUcFLMdU3XjfZH}*b z$=x(PxgA)G8V?4BSxnPeqY#Bbg)Mcw5fHWKX=t?3ARv}pG9kUgcoOJ$ddoaFtQpbf z37eX;vR)|RqfwhX{=s#5Uuiml9QzrM%ELEq@}Eti=68w!>P!7ov7Sp;P zkIUhm{NjBO-!1^S8%>S&wGT0VXEeDiZC~m-z<4WmL?@BhKd7CTAzZYQtZ-cq1QwT> zHGpxt$FB;O_#r8Z9H3t(zwpc1tpTVnkwsOZ&~52nPmk{6)@ipbcP5WGHZeKn#>XEC z?-!&P!#d$O3P;}a%?83V^HFrIDygTe%<39 zDmiLYJnN5>BQ<#4j|`A3w1gB)qWisl|DT5=kPfdG-X~xaX}$rAI7e+9_Y;Ya%v=1m zdSBv&wo{YQcJ`~Y)>xId!afaoFIF~GiK?pWY;>8R((+vse0#;4fVcN6DRCBwz0;UF z<-iRQOZc$9s$^gdAnYbtaXVTX>Mf7${2`# z0601m2sJe%u!u4iNqA)Q_I%eo*hFw-SaU%DZZ}lpQ1*_LI7hO)JcWPShaRN@`)Ze{ zS7$xpwG?-tRvy{mYpL)(?|C#0IEuhFZ%ay=^g0aw^uYQ1(HAI<3;yakrfz(?zeV>Z z|F$d9Z#l`~bPF zHHg;;5-~^3$zr3)4Ei~td6Xc*@p*v#JhIKKkywwjP-Z+F-(&)*9fRVR2aT8eDrzRA zV1fi@rA3J;hj0bbBMMEieYuFxxd;J$ACQxJi4SPu3CMm;bg8HBvF4FP!D3|g;i3U1lkZCyl)Y;_u3{`Nuh>BzqWW9Q@Hp>Gv z=P*1@+juyeo_fG9ww^=xELvO4dg4Wd6ZXD7;?fsPR8(Njx)c;wvG1SA%L^;fghyXX zAX=RHUMmX(cD=y;mG(_V-w0EBc5e@S>P2H^gqlMtV&%T4PU;ziTy^T zPBF(dZb>ZD#wFAg#E0{vy~b^Sy9<~yJC*SgWSbd@HiNqDI-s~VXOQxcPaO3QJ7sm z#`4|8z&DQLmufLiO%vsWKd{wrt^6SqxWgW>O>&!kcL!)pBSku)kvcwWEfVX zVIM|_sD-@=xM`Hv2_h_v8U@r)G`4C%i(}Tl`oPg`GJRJqOOaIl>+&cD<7(d$3d*gd zC2rANk__P@)*uR$Stw{lM=3x|!m4M!_gCJ=aPD55n&p@_y$n;W@jW6WgSK;8l>@J0 zgKa@C75kDWYbC1*abOW`_+d!C;Gd|e;2QHoA0ADEE^BC~bpk}TZPd+fh7lPRBMKOm zIeRXHZstd;)k?8sXUwIgD)D?edyw@0AO}>i0UVyGW}7;2)eA`I1}l}_(o`>i8*I&f z*NP#%f2CDv0vg}7bI$#fEjH>9S;DPMmTn}6UV9pblwq;x)f-^MfGlmQz>cCM*ZT)q zsTYNXSJtYT8b&PE%AI{QC7$tTW=xmg=ioz52TcV(VYIe^0EJ2c0g%NGbsu+dh#py)2}{hPOorWWPrU!NDP}-b^AyXJf7&nqWrdxx{zp7`n`w8XC z7b^X`EpBP3uOwjq!SWT1V;n=;t8_oMuo%$FqW!ChmoR)d)yp#QykA1~u+^Lgy}j-N z^xnmW&k+Wzg03>$M&SdE1Ppm0pj+l(TPF^A|LL*z4 zdVL_0Cn^ds@CHk8!#oV0*THk_CJq?m8wNgr?oVY9zs|HXer;DUsvV3S&AF|fvt{+` zt6QZs?4M~A5L#bgO%o!WGD>+BK;C4R-`svUewC4ReqW;HX`}s+k))@1YJ1;W0A3^^ z!Ny~^n0Nm@B{UF5d#Ln|4K0Ol$qCjs0bx1I6?h%(X)&i* z!PDt;fCpp{{XeC3ob!6l*jJ!xz2&$pMT87W(J9S#H@XLz*K(I2r;YHb4 zD7C4S=wa1T8A~kbZPjzk~}BOL(eOQ-A`P@_FacaXAH7O z5!%YuAL%(t5xAy%dk{je`e0f11$ub|p0%6a%AXH=@0I$p#LjohTiO)hIX1E_em^VqW&9VBzC zL3N2|ynJ$YdtLp>@uZidk2?s0RT3s|sEs5^*_ztEQ|rcxyb988SNEjqQlo}BT;gh+ z_B2E<+US6PyxL+IW}{v)YyJ|>XlcBrbmbbCOIhc@@xk42)#fnXg=sT zq&aF35+9#N{+`H?;psvO3aqy_|NFf=7RbN}*ZYY3HCH2qcDmlE66_NxH1i0t6MD*M z069c&-kPYOOJ!0OPP8{BdpZL-TwY&UMIdNFwv4Fz;&a0e6+*K)Xhdi-s%}`W$!=Nk zVIDx!Q@M_FL`w5;%Gcku>9_4LN9GhG*eP5K)t$7@1|fDw9WSU_mwO$j@irhd!ABu# z3J|U%bWppmIDHJaig1F_gB`W+@4J7F$-7J8Ras>4f9*>1(FMAK+Xaz2W&d4w`hI7S zwFsMT^${}~ke37h$s&@lF)&zeBKOf2bppA&RZ5|ya}8b3LjiGzHzV>JYsBcHqc_5f z{`yT0hB$q1;w?{MH+`Z!U$Y5-l{ygrt!|HR_hvJumKyH9xYDg>TcM#)TDq9soA#^@ zNKtovWF-1#99|j~SX=F(2k3Qmo=QZ^CQm+>_*(?E*(Y0OBR2?OxyJ;nNy=_;^7@@1 zWyW+8C9Gv^v4G^;gj6TD%~iYYBZ6U7ZrQS*3rDCj|5c@bPhe2eT)unkJ{`K`*mc>M zS~ZOV5NKh&X-12M*`&p@>S^kZ>z$vtc@2156xLEr=)?Y9V9BX z0n_FA-sQZ5;s0i3#lgApWk15q*N~=Iz{P%iPOfY73y^_!;Qw*jOl!WyHbE;z74*AM6NOBAvr@10>F)St)4Wd~P{*!RBV zGTgn8hSYFQWJWvVL7F%@%-mIrm&v1YS=BssIPXOsufY%f5Z12AAU)2I`^C9U**}}- zSh6m_iJyb7=hp+!Mg5Wpo>-#a{}n)0O45H=^#GOWzLJRV_DnX7CeWTyar&@{Xv2es zD}BN2$ChiQcmR@0Wip?!aT+`;TumS8^@b#-d2A5BO3_lK&eEdGVh#TA2PykC{(-D` z!mLC`{bM)8$>ktQt9z9ntoDm$Rn~;!k3B+b!6CHLH9bD}2^xngV8gd+ewoI5F_GZL zgvx~ZRdexQR^(}Gl@F*0fLQZSuY?%UkCOFspc5$7L7Yc~{UQD<)Hagsobu-qP}=l6MKqkf$AT*mPh$N95ZQ^aZ zKm^nYnNpShvmKVR)-8X?pK2l4)TTEaNaoAzD>+2f3RE7<$LYnsOt|Kd1A*2kwRmdN z7zAs^Xg0cdl-I&LuW9`I29SD^jVkx$pH3OB41tDiCUESPt{${lEHhg(Iq-Ev48YjR z_xmD!YeHY3>0dD%EA`i<1PTkAjyp08cZhjw=ovj(Hsskw4K*`-m<@w&RVNF~d(zxV8R!ESp% z2!HG^V2lwIAB6nu<+lzthyAJiYVY15cE#@oy&11$S8}A(5iU_OS%#iU4j%%%>#wrV zuLSc<$ysh#0y7iyeHB?su7e>-p}&yNBUAn!Riy}dd!AS5P~&`HC#ntO;~7yCZ-L`S zijPga`(8LoWlvh}61>CkfEnx&Lj4&@+E3a$_qgrMIUfS(kpdRd_VQ)vmNeYkWDRHA|Ze&3$Kgdu9RL0=>X#w#-dYcl)6f zw;BdGwpeTa*~YXGj6AW~G7YQ75ac^4upmWk?c`IDaB>fYa?KxL`ZqmDKzpn%5SvG) zKSYzA9EKem#wP_4TAgH%a3J~ldY@d3>hJY4A=&;Xz_tSR?TwxD*+<~U!rt?Aj<7C^ zh59$yR#uU9HC9yLk58GGFxvpH>QP_N?+wY|O1H6jmhB0@XDXdtaFk~^fFgp4)^x#b zGQgVUYd(r)hD`(ZIz+36=oCj1kkam>1|UAz2fs|{wT}A>ow-KEU1tRti7;N|C74~_ z`^x1VuxkX0R^n*f;Sk9%iO%dY+d+p2J89Y^kx;FZf120R5_j)`mv)~0db$}dq5=Ru zpN!z%5i+fA2h6%qVD2iK#7;9cLG~&7rX}hyOx6q2v`r<**5Us0Rq?dhwt$v!vnIDA``hz>lr8WjrFTSSWwscVR?CbLFW7BSHI3)~ZVNF|Rq>>$o1S@>vw*Bdhr32w~ z&e

@@ -123,6 +123,6 @@ Each algorithm page includes references to original papers and implementation de ## See Also - [Choosing an Optimiser](../guides/choosing-optimiser.md) -- [Tuning Optimisers](../guides/tuning-optimizers.md) +- [Tuning Optimisers](../guides/tuning-optimisers.md) - [API Reference](../api-reference/index.md) - [Tutorials](../tutorials/index.md) diff --git a/docs/algorithms/optimizers/adam.md b/docs/algorithms/optimisers/adam.md similarity index 96% rename from docs/algorithms/optimizers/adam.md rename to docs/algorithms/optimisers/adam.md index 9ae2134..49f1052 100644 --- a/docs/algorithms/optimizers/adam.md +++ b/docs/algorithms/optimisers/adam.md @@ -137,6 +137,6 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) ## See Also -- [API Reference](../../api-reference/python/optimizers.md#adam) +- [API Reference](../../api-reference/python/optimisers.md#adam) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) -- [Tuning Optimisers](../../guides/tuning-optimizers.md) +- [Tuning Optimisers](../../guides/tuning-optimisers.md) diff --git a/docs/algorithms/optimizers/cmaes.md b/docs/algorithms/optimisers/cmaes.md similarity index 98% rename from docs/algorithms/optimizers/cmaes.md rename to docs/algorithms/optimisers/cmaes.md index 3f3efcf..3945716 100644 --- a/docs/algorithms/optimizers/cmaes.md +++ b/docs/algorithms/optimisers/cmaes.md @@ -136,6 +136,6 @@ result = optimiser.run(problem, initial_guess=[0.5] * n_params) ## See Also -- [API Reference](../../api-reference/python/optimizers.md#cma-es) +- [API Reference](../../api-reference/python/optimisers.md#cma-es) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Parallel Execution](../../guides/parallel-execution.md) diff --git a/docs/algorithms/optimizers/nelder-mead.md b/docs/algorithms/optimisers/nelder-mead.md similarity index 96% rename from docs/algorithms/optimizers/nelder-mead.md rename to docs/algorithms/optimisers/nelder-mead.md index c2623fa..11fbc78 100644 --- a/docs/algorithms/optimizers/nelder-mead.md +++ b/docs/algorithms/optimisers/nelder-mead.md @@ -107,6 +107,6 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) ## See Also -- [API Reference](../../api-reference/python/optimizers.md#nelder-mead) +- [API Reference](../../api-reference/python/optimisers.md#nelder-mead) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) -- [Tuning Optimisers](../../guides/tuning-optimizers.md) +- [Tuning Optimisers](../../guides/tuning-optimisers.md) diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md index 99997c2..3469e43 100644 --- a/docs/api-reference/index.md +++ b/docs/api-reference/index.md @@ -22,7 +22,7 @@ Diffid provides a comprehensive Python API with full type hints and automatic do Gradient-free (Nelder-Mead, CMA-ES) and gradient-based (Adam) optimisation algorithms. - [:octicons-arrow-right-24: Optimisers](python/optimizers.md) + [:octicons-arrow-right-24: Optimisers](python/optimisers.md) - :material-chart-bell-curve:{ .lg .middle } __Samplers__ @@ -130,6 +130,6 @@ print(result.x) # [optimal_x, optimal_y] ## Next Steps - [Builders API](python/builders.md) - Start building problems -- [Optimisers API](python/optimizers.md) - Configure optimisation algorithms +- [Optimisers API](python/optimisers.md) - Configure optimisation algorithms - [User Guides](../guides/index.md) - Learn when to use each component - [Tutorials](../tutorials/index.md) - Interactive examples diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md index 80a78db..2bd4f05 100644 --- a/docs/api-reference/python/builders.md +++ b/docs/api-reference/python/builders.md @@ -246,5 +246,5 @@ print(result.x) # [optimal_y, optimal_x] - [Getting Started: Core Concepts](../../getting-started/concepts.md) - [Cost Metrics](cost-metrics.md) -- [Optimisers](optimizers.md) +- [Optimisers](optimisers.md) - [Custom Solvers Guide](../../guides/custom-solvers.md) diff --git a/docs/api-reference/python/optimizers.md b/docs/api-reference/python/optimisers.md similarity index 96% rename from docs/api-reference/python/optimizers.md rename to docs/api-reference/python/optimisers.md index 65e73ae..40e8af6 100644 --- a/docs/api-reference/python/optimizers.md +++ b/docs/api-reference/python/optimisers.md @@ -69,7 +69,7 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) - **`max_iter`**: Maximum iterations (default: 1000) - Rule of thumb: `100 * n_parameters` minimum -See the [Nelder-Mead Algorithm Guide](../../algorithms/optimizers/nelder-mead.md) for more details. +See the [Nelder-Mead Algorithm Guide](../../algorithms/optimisers/nelder-mead.md) for more details. --- @@ -142,7 +142,7 @@ result = optimiser.run(problem, initial_guess=[0.5, 0.5]) - Omit for non-deterministic runs - Set for reproducible benchmarks -See the [CMA-ES Algorithm Guide](../../algorithms/optimizers/cmaes.md) for more details. +See the [CMA-ES Algorithm Guide](../../algorithms/optimisers/cmaes.md) for more details. --- @@ -212,7 +212,7 @@ result = optimiser.run(problem, initial_guess=[1.0, 2.0]) - Stop when gradient is small - Smaller for higher precision -See the [Adam Algorithm Guide](../../algorithms/optimizers/adam.md) for more details. +See the [Adam Algorithm Guide](../../algorithms/optimisers/adam.md) for more details. --- @@ -290,7 +290,7 @@ graph TD For detailed guidance, see: - [Choosing an Optimiser Guide](../../guides/choosing-optimiser.md) -- [Tuning Optimisers Guide](../../guides/tuning-optimizers.md) +- [Tuning Optimisers Guide](../../guides/tuning-optimisers.md) ## See Also diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md index 926bc60..00ce41d 100644 --- a/docs/api-reference/python/results.md +++ b/docs/api-reference/python/results.md @@ -307,7 +307,7 @@ print(f"Time per evaluation: {elapsed/result.evaluations*1000:.2f}ms") ## See Also -- [Optimisers](optimizers.md) +- [Optimisers](optimisers.md) - [Samplers](samplers.md) - [Choosing an Optimiser](../../guides/choosing-optimiser.md) - [Troubleshooting](../../guides/troubleshooting.md) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md index 07b8c49..1d027b3 100644 --- a/docs/api-reference/python/samplers.md +++ b/docs/api-reference/python/samplers.md @@ -196,7 +196,7 @@ The followings present sampler functionality: ## See Also -- [Optimisers](optimizers.md) - For point estimates +- [Optimisers](optimisers.md) - For point estimates - [Cost Metrics](cost-metrics.md) - GaussianNLL required - [Choosing a Sampler Guide](../../guides/choosing-sampler.md) (future) - [Algorithm Guides](../../algorithms/index.md) diff --git a/docs/guides/choosing-optimiser.md b/docs/guides/choosing-optimiser.md index 410d0bc..e664dfc 100644 --- a/docs/guides/choosing-optimiser.md +++ b/docs/guides/choosing-optimiser.md @@ -27,6 +27,6 @@ graph TD ## See Also -- [Optimisers API](../api-reference/python/optimizers.md) -- [Tuning Optimisers](tuning-optimizers.md) +- [Optimisers API](../api-reference/python/optimisers.md) +- [Tuning Optimisers](tuning-optimisers.md) - [Algorithm Details](../algorithms/index.md) diff --git a/docs/guides/index.md b/docs/guides/index.md index a1ec664..bb6772b 100644 --- a/docs/guides/index.md +++ b/docs/guides/index.md @@ -20,7 +20,7 @@ In-depth guides for making the most of Diffid's optimisation and sampling capabi Parameter tuning strategies and troubleshooting for each algorithm. - [:octicons-arrow-right-24: Tuning Guide](tuning-optimizers.md) + [:octicons-arrow-right-24: Tuning Guide](tuning-optimisers.md) - :material-chart-bell-curve:{ .lg .middle } __Choosing a Sampler__ diff --git a/docs/guides/parallel-execution.md b/docs/guides/parallel-execution.md index dc4fa86..0e12ee0 100644 --- a/docs/guides/parallel-execution.md +++ b/docs/guides/parallel-execution.md @@ -22,4 +22,4 @@ optimiser = diffid.CMAES().with_population_size(20) ## See Also - [DiffsolBuilder API](../api-reference/python/builders.md#diffsolbuilder) -- [CMA-ES API](../api-reference/python/optimizers.md#cma-es) +- [CMA-ES API](../api-reference/python/optimisers.md#cma-es) diff --git a/docs/guides/troubleshooting.md b/docs/guides/troubleshooting.md index fead903..818c973 100644 --- a/docs/guides/troubleshooting.md +++ b/docs/guides/troubleshooting.md @@ -28,5 +28,5 @@ ImportError: No module named 'diffid' ## See Also - [Installation](../getting-started/installation.md) -- [Tuning Optimisers](tuning-optimizers.md) +- [Tuning Optimisers](tuning-optimisers.md) - [GitHub Issues](https://github.com/bradyplanden/diffid/issues) diff --git a/docs/guides/tuning-optimizers.md b/docs/guides/tuning-optimisers.md similarity index 92% rename from docs/guides/tuning-optimizers.md rename to docs/guides/tuning-optimisers.md index 654294c..e59a331 100644 --- a/docs/guides/tuning-optimizers.md +++ b/docs/guides/tuning-optimisers.md @@ -24,6 +24,6 @@ ## See Also -- [Optimisers API](../api-reference/python/optimizers.md) +- [Optimisers API](../api-reference/python/optimisers.md) - [Choosing an Optimiser](choosing-optimiser.md) - [Troubleshooting](troubleshooting.md) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 588f65a..5ea3dff 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -12,7 +12,7 @@ Perfect for those new to Diffid or optimisation:
-- **[1. Optimisation Basics](notebooks/01_optimization_basics.ipynb)** +- **[1. Optimisation Basics](notebooks/01_optimisation_basics.ipynb)** --- @@ -79,7 +79,7 @@ Complex problems and advanced techniques: | Tutorial | Difficulty | Key Concepts | Prerequisites | |----------|------------|--------------|---------------| -| 1. Optimization Basics | ⭐ Beginner | ScalarBuilder, optimizers | None | +| 1. optimisation Basics | ⭐ Beginner | ScalarBuilder, optimisers | None | | 2. ODE Fitting | ⭐ Beginner | DiffsolBuilder, DiffSL | Tutorial 1 | | 3. Parameter Uncertainty | ⭐⭐ Intermediate | MCMC, uncertainty | Tutorials 1-2 | | 4. Model Comparison | ⭐⭐ Intermediate | Nested sampling, evidence | Tutorials 1-3 | diff --git a/docs/tutorials/notebooks/01_optimization_basics.ipynb b/docs/tutorials/notebooks/01_optimisation_basics.ipynb similarity index 99% rename from docs/tutorials/notebooks/01_optimization_basics.ipynb rename to docs/tutorials/notebooks/01_optimisation_basics.ipynb index 61743db..b7f06d8 100644 --- a/docs/tutorials/notebooks/01_optimization_basics.ipynb +++ b/docs/tutorials/notebooks/01_optimisation_basics.ipynb @@ -19,7 +19,7 @@ "source": [ "## Introduction\n", "\n", - "The Rosenbrock function is a classic test problem in optimization. It's defined as:\n", + "The Rosenbrock function is a classic test problem in optimisation. It's defined as:\n", "\n", "$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$\n", "\n", @@ -76,7 +76,7 @@ "source": [ "## Build the Problem\n", "\n", - "Use `ScalarBuilder` for direct function optimization:" + "Use `ScalarBuilder` for direct function optimisation:" ] }, { @@ -142,7 +142,7 @@ "result = problem.optimise() # Uses initial guesses provided\n", "\n", "print(\"\\n\" + \"=\" * 50)\n", - "print(\"OPTIMIZATION RESULTS\")\n", + "print(\"OPTIMISATION RESULTS\")\n", "print(\"=\" * 50)\n", "print(f\"Success: {result.success}\")\n", "print(f\"Optimal parameters: {result.x}\")\n", @@ -156,7 +156,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualise the Optimization Landscape\n", + "## Visualise the optimisation Landscape\n", "\n", "Let's create a contour plot to see the function's shape:" ] @@ -463,4 +463,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb index 7a471e9..c95948f 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb @@ -175,7 +175,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Build the Optimization Problem\n", + "## Build the Optimisation Problem\n", "\n", "Use `DiffsolBuilder` for ODE parameter fitting:" ] @@ -204,18 +204,38 @@ ] }, { + "metadata": {}, "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-10T22:21:30.238643Z", - "iopub.status.busy": "2026-01-10T22:21:30.238402Z", - "iopub.status.idle": "2026-01-10T22:21:34.759904Z", - "shell.execute_reply": "2026-01-10T22:21:34.759178Z" - } - }, "outputs": [], - "source": "# Create optimiser\noptimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n\n# Run optimization\nresult = optimiser.run(problem, problem.initial_values())\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMIZATION RESULTS\")\nprint(\"=\" * 60)\nprint(f\"Success: {result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" r = {result.x[0]:.6f} (true: {r_true})\")\nprint(f\" k = {result.x[1]:.6f} (true: {k_true})\")\nprint(\"\\nOptimization details:\")\nprint(f\" Final cost: {result.value:.3e}\")\nprint(f\" Iterations: {result.iterations}\")\nprint(f\" Function evaluations: {result.evaluations}\")\nprint(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\nprint(f\" Message: {result.message}\")\n\n# Calculate parameter errors\nr_error = abs(result.x[0] - r_true) / r_true * 100\nk_error = abs(result.x[1] - k_true) / k_true * 100\nprint(\"\\nParameter errors:\")\nprint(f\" r: {r_error:.3f}%\")\nprint(f\" k: {k_error:.3f}%\")" + "execution_count": null, + "source": [ + "# Create optimiser\n", + "optimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", + "\n", + "# Run optimisation\n", + "result = optimiser.run(problem, problem.initial_values())\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"OPTIMIZATION RESULTS\")\n", + "print(\"=\" * 60)\n", + "print(f\"Success: {result.success}\")\n", + "print(\"\\nFitted parameters:\")\n", + "print(f\" r = {result.x[0]:.6f} (true: {r_true})\")\n", + "print(f\" k = {result.x[1]:.6f} (true: {k_true})\")\n", + "print(\"\\nOptimisation details:\")\n", + "print(f\" Final cost: {result.value:.3e}\")\n", + "print(f\" Iterations: {result.iterations}\")\n", + "print(f\" Function evaluations: {result.evaluations}\")\n", + "print(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\n", + "print(f\" Message: {result.message}\")\n", + "\n", + "# Calculate parameter errors\n", + "r_error = abs(result.x[0] - r_true) / r_true * 100\n", + "k_error = abs(result.x[1] - k_true) / k_true * 100\n", + "print(\"\\nParameter errors:\")\n", + "print(f\" r: {r_error:.3f}%\")\n", + "print(f\" k: {k_error:.3f}%\")" + ] }, { "cell_type": "markdown", @@ -543,4 +563,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb index 51c7ae7..8189aff 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb @@ -203,7 +203,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 1: Optimization\n", + "## Step 1: Optimisation\n", "\n", "First, find the maximum a posteriori (MAP) estimate:" ] @@ -220,7 +220,35 @@ } }, "outputs": [], - "source": "# Build problem\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_parameter(\"g\", 5.0) # Initial guess\n .with_parameter(\"h\", 5.0) # Initial guess\n .with_cost(diffid.RMSE(2.0)) # 2 observables (height + velocity)\n)\n\nproblem = builder.build()\n\n# Optimize\noptimizer = diffid.Adam().with_step_size(0.05).with_max_iter(1500)\nopt_result = optimizer.run(problem, [5.0, 5.0])\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMIZATION RESULTS (MAP Estimate)\")\nprint(\"=\" * 60)\nprint(f\"Success: {opt_result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\nprint(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\nprint(f\"\\nCost: {opt_result.value:.6f}\")\nprint(f\"Iterations: {opt_result.iterations}\")\n\ng_map, h_map = opt_result.x" + "source": [ + "# Build problem\n", + "builder = (\n", + " diffid.DiffsolBuilder()\n", + " .with_diffsl(dsl_model)\n", + " .with_data(data)\n", + " .with_parameter(\"g\", 5.0) # Initial guess\n", + " .with_parameter(\"h\", 5.0) # Initial guess\n", + " .with_cost(diffid.RMSE(2.0)) # 2 observables (height + velocity)\n", + ")\n", + "\n", + "problem = builder.build()\n", + "\n", + "# Optimize\n", + "optimiser = diffid.Adam().with_step_size(0.05).with_max_iter(1500)\n", + "opt_result = optimiser.run(problem, [5.0, 5.0])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"OPTIMIZATION RESULTS (MAP Estimate)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Success: {opt_result.success}\")\n", + "print(\"\\nFitted parameters:\")\n", + "print(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\n", + "print(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\n", + "print(f\"\\nCost: {opt_result.value:.6f}\")\n", + "print(f\"Iterations: {opt_result.iterations}\")\n", + "\n", + "g_map, h_map = opt_result.x" + ] }, { "cell_type": "markdown", @@ -619,7 +647,7 @@ "source": [ "## Key Takeaways\n", "\n", - "1. **Optimization** gives point estimates; **MCMC** quantifies uncertainty\n", + "1. **Optimisation** gives point estimates; **MCMC** quantifies uncertainty\n", "2. **GaussianNLL** cost metric is required for sampling\n", "3. **Acceptance rate** should be 20-40% for efficient exploration\n", "4. **Burn-in** period discards initial non-stationary samples\n", @@ -678,4 +706,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb index 165a3b9..f576140 100644 --- a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb +++ b/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb @@ -20,7 +20,136 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Introduction\n\nDiffid's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n\n- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n\n**VectorBuilder** lets you integrate any Python-callable forward model with Diffid's optimizers.\n\n## The Lotka-Volterra Model\n\nThe predator-prey equations:\n\n$$\\begin{aligned}\n\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n\\frac{dy}{dt} &= \\delta x y - \\gamma y\n\\end{aligned}$$\n\nwhere:\n- $x$ is prey population\n- $y$ is predator population\n- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n\nThis tutorial demonstrates parameter fitting with three different solver backends.\n\n## Coming Soon\n\nThis advanced tutorial is under development. It will cover:\n\n### Backend 1: Diffsol (Built-in)\n```python\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(lotka_volterra_dsl)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n # ... more parameters\n)\n```\n\n### Backend 2: JAX/Diffrax\n```python\nimport jax\nfrom diffrax import diffeqsolve, ODETerm, Tsit5\n\ndef diffrax_solver(params):\n # Your Diffrax integration\n return predictions\n\nbuilder = (\n diffid.VectorBuilder()\n .with_objective(diffrax_solver)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n)\n```\n\n### Backend 3: Julia/DifferentialEquations.jl\n```python\nfrom diffeqpy import de\n\ndef diffeqpy_solver(params):\n # Your Julia integration\n return predictions\n\nbuilder = (\n diffid.VectorBuilder()\n .with_objective(diffeqpy_solver)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0)\n)\n```\n\n## Performance Comparison\n\nThe tutorial will benchmark all three backends:\n\n- **Accuracy**: Parameter recovery quality\n- **Speed**: Time per function evaluation\n- **Ease of use**: Setup complexity\n- **Special features**: Gradients, GPU, stiff solvers\n\n## When to Use Each Backend\n\n| Backend | Best For |\n|---------|----------|\n| **Diffsol** | General purpose, fast, built-in |\n| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n| **Custom** | Non-ODE models, complex physics |\n\n## Example Data\n\nThe predator-prey examples directory contains:\n- `generate_data_diffrax.py`: Creates synthetic data\n- `predator_prey_diffsol.py`: Diffsol backend\n- `predator_prey_diffrax.py`: JAX/Diffrax backend\n- `predator_prey_diffeqpy.py`: Julia backend\n\nRun these scripts directly to see the backends in action!\n\n## Installation\n\nFor JAX/Diffrax:\n```bash\npip install jax diffrax\n```\n\nFor Julia/DifferentialEquations.jl:\n```bash\npip install diffeqpy\npython -c \"from diffeqpy import de; de.install()\"\n```\n\n## Key Takeaways\n\n1. **VectorBuilder** integrates any Python callable\n2. **Diffsol** is the default - fast and easy\n3. **JAX/Diffrax** for gradients and GPU\n4. **Julia/DiffEq** for specialized solvers\n5. All backends work with Diffid's optimizers\n\n## Next Steps\n\n- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n- [Examples Gallery](../../examples/gallery.md) - More backend examples" + "source": [ + "## Introduction\n", + "\n", + "Diffid's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n", + "\n", + "- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n", + "- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n", + "- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n", + "\n", + "**VectorBuilder** lets you integrate any Python-callable forward model with Diffid's optimisers.\n", + "\n", + "## The Lotka-Volterra Model\n", + "\n", + "The predator-prey equations:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n", + "\\frac{dy}{dt} &= \\delta x y - \\gamma y\n", + "\\end{aligned}$$\n", + "\n", + "where:\n", + "- $x$ is prey population\n", + "- $y$ is predator population\n", + "- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n", + "\n", + "This tutorial demonstrates parameter fitting with three different solver backends.\n", + "\n", + "## Coming Soon\n", + "\n", + "This advanced tutorial is under development. It will cover:\n", + "\n", + "### Backend 1: Diffsol (Built-in)\n", + "```python\n", + "builder = (\n", + " diffid.DiffsolBuilder()\n", + " .with_diffsl(lotka_volterra_dsl)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + " # ... more parameters\n", + ")\n", + "```\n", + "\n", + "### Backend 2: JAX/Diffrax\n", + "```python\n", + "import jax\n", + "from diffrax import diffeqsolve, ODETerm, Tsit5\n", + "\n", + "def diffrax_solver(params):\n", + " # Your Diffrax integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(diffrax_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "### Backend 3: Julia/DifferentialEquations.jl\n", + "```python\n", + "from diffeqpy import de\n", + "\n", + "def diffeqpy_solver(params):\n", + " # Your Julia integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(diffeqpy_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "## Performance Comparison\n", + "\n", + "The tutorial will benchmark all three backends:\n", + "\n", + "- **Accuracy**: Parameter recovery quality\n", + "- **Speed**: Time per function evaluation\n", + "- **Ease of use**: Setup complexity\n", + "- **Special features**: Gradients, GPU, stiff solvers\n", + "\n", + "## When to Use Each Backend\n", + "\n", + "| Backend | Best For |\n", + "|---------|----------|\n", + "| **Diffsol** | General purpose, fast, built-in |\n", + "| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n", + "| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n", + "| **Custom** | Non-ODE models, complex physics |\n", + "\n", + "## Example Data\n", + "\n", + "The predator-prey examples directory contains:\n", + "- `generate_data_diffrax.py`: Creates synthetic data\n", + "- `predator_prey_diffsol.py`: Diffsol backend\n", + "- `predator_prey_diffrax.py`: JAX/Diffrax backend\n", + "- `predator_prey_diffeqpy.py`: Julia backend\n", + "\n", + "Run these scripts directly to see the backends in action!\n", + "\n", + "## Installation\n", + "\n", + "For JAX/Diffrax:\n", + "```bash\n", + "pip install jax diffrax\n", + "```\n", + "\n", + "For Julia/DifferentialEquations.jl:\n", + "```bash\n", + "pip install diffeqpy\n", + "python -c \"from diffeqpy import de; de.install()\"\n", + "```\n", + "\n", + "## Key Takeaways\n", + "\n", + "1. **VectorBuilder** integrates any Python callable\n", + "2. **Diffsol** is the default - fast and easy\n", + "3. **JAX/Diffrax** for gradients and GPU\n", + "4. **Julia/DiffEq** for specialized solvers\n", + "5. All backends work with Diffid's optimisers\n", + "\n", + "## Next Steps\n", + "\n", + "- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n", + "- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n", + "- [Examples Gallery](../../examples/gallery.md) - More backend examples" + ] } ], "metadata": { @@ -44,4 +173,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb index 2bb03ca..4979633 100644 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb @@ -405,7 +405,7 @@ " backends, times, color=colors[: len(backends)], alpha=0.7, edgecolor=\"black\"\n", " )\n", " ax1.set_ylabel(\"Time (s)\", fontsize=12)\n", - " ax1.set_title(\"Optimization Time Comparison\", fontsize=14, fontweight=\"bold\")\n", + " ax1.set_title(\"Optimisation Time Comparison\", fontsize=14, fontweight=\"bold\")\n", " ax1.grid(True, axis=\"y\", alpha=0.3)\n", "\n", " # Add value labels on bars\n", @@ -583,4 +583,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorials/notebooks/07_parallel_optimization.ipynb b/docs/tutorials/notebooks/07_parallel_optimisation.ipynb similarity index 99% rename from docs/tutorials/notebooks/07_parallel_optimization.ipynb rename to docs/tutorials/notebooks/07_parallel_optimisation.ipynb index 5608a42..e18810d 100644 --- a/docs/tutorials/notebooks/07_parallel_optimization.ipynb +++ b/docs/tutorials/notebooks/07_parallel_optimisation.ipynb @@ -476,7 +476,25 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Key Takeaways\n\n1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n\n2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n\n3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n\n4. **Profile first** - measure single evaluation time to determine if parallelism will help\n\n5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n\n## Next Steps\n\n- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n- [API Reference: CMA-ES](../../api-reference/python/optimizers.md#cmaes) - Population configuration" + "source": [ + "## Key Takeaways\n", + "\n", + "1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n", + "\n", + "2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n", + "\n", + "3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n", + "\n", + "4. **Profile first** - measure single evaluation time to determine if parallelism will help\n", + "\n", + "5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n", + "\n", + "## Next Steps\n", + "\n", + "- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n", + "- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n", + "- [API Reference: CMA-ES](../../api-reference/python/optimisers.md#cmaes) - Population configuration" + ] }, { "cell_type": "markdown", @@ -505,4 +523,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examples/logistic_growth.py b/examples/logistic_growth.py index 3c362f4..f6b5bf8 100644 --- a/examples/logistic_growth.py +++ b/examples/logistic_growth.py @@ -35,9 +35,9 @@ print(f"result: {results}") -# For now, just print the optimization result since Hamiltonian sampler is not implemented yet +# For now, just print the optimisation result since Hamiltonian sampler is not implemented yet print(f"Optimal parameters: {results.x}") print(f"Optimal cost: {results.value}") -print(f"Optimization success: {results.success}") +print(f"Optimisation success: {results.success}") print(f"Iterations: {results.iterations}") print(f"Optimisation time: {results.time}") diff --git a/examples/predator_prey/predator_prey_diffeqpy.py b/examples/predator_prey/predator_prey_diffeqpy.py index a31620f..22b28f7 100644 --- a/examples/predator_prey/predator_prey_diffeqpy.py +++ b/examples/predator_prey/predator_prey_diffeqpy.py @@ -3,7 +3,7 @@ Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in Julia via diffeqpy 2. Generating synthetic noisy observations -3. Recovering parameters using diffid optimization +3. Recovering parameters using diffid optimisation Prerequisites: pip install diffeqpy diffid numpy diff --git a/examples/predator_prey/predator_prey_diffrax.py b/examples/predator_prey/predator_prey_diffrax.py index 98cfae5..53e0d99 100644 --- a/examples/predator_prey/predator_prey_diffrax.py +++ b/examples/predator_prey/predator_prey_diffrax.py @@ -3,7 +3,7 @@ Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in JAX 2. Generating synthetic noisy observations -3. Recovering parameters using diffid optimization +3. Recovering parameters using diffid optimisation Prerequisites: pip install diffid diffrax jax numpy diff --git a/examples/predator_prey/predator_prey_diffsol.py b/examples/predator_prey/predator_prey_diffsol.py index b3eda65..9c57caf 100644 --- a/examples/predator_prey/predator_prey_diffsol.py +++ b/examples/predator_prey/predator_prey_diffsol.py @@ -54,7 +54,7 @@ print(results) print(f"Optimal parameters: {results.x}") print(f"Optimal cost: {results.value}") -print(f"Optimization success: {results.success}") +print(f"Optimisation success: {results.success}") print(f"Iterations: {results.iterations}") print(f"Optimisation time: {results.time}") print(f"Eval per ms: {results.evaluations / (results.time.total_seconds() * 1000)}") diff --git a/mkdocs.yml b/mkdocs.yml index d83766f..8242c49 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -147,18 +147,18 @@ nav: - Tutorials: - tutorials/index.md - Notebooks: - - 1. Optimisation Basics: tutorials/notebooks/01_optimization_basics.ipynb + - 1. Optimisation Basics: tutorials/notebooks/01_optimisation_basics.ipynb - 2. ODE Fitting with DiffSL: tutorials/notebooks/02_ode_fitting_diffsol.ipynb - 3. Parameter Uncertainty: tutorials/notebooks/03_parameter_uncertainty.ipynb - 4. Model Comparison: tutorials/notebooks/04_model_comparison.ipynb - 5. Predator-Prey Models: tutorials/notebooks/05_advanced_predator_prey.ipynb - 6. Custom Solver Integration: tutorials/notebooks/06_custom_solver_integration.ipynb - - 7. Parallel Optimisation: tutorials/notebooks/07_parallel_optimization.ipynb + - 7. Parallel Optimisation: tutorials/notebooks/07_parallel_optimisation.ipynb - 8. Advanced Cost Functions: tutorials/notebooks/08_advanced_cost_functions.ipynb - User Guides: - guides/index.md - Choosing an Optimiser: guides/choosing-optimiser.md - - Tuning Optimisers: guides/tuning-optimizers.md + - Tuning Optimisers: guides/tuning-optimisers.md - Choosing a Sampler: guides/choosing-sampler.md - Cost Metrics: guides/cost-metrics.md - DiffSL Backend: guides/diffsol-backend.md @@ -168,9 +168,9 @@ nav: - Algorithms: - algorithms/index.md - Optimisers: - - Nelder-Mead: algorithms/optimizers/nelder-mead.md - - CMA-ES: algorithms/optimizers/cmaes.md - - Adam: algorithms/optimizers/adam.md + - Nelder-Mead: algorithms/optimisers/nelder-mead.md + - CMA-ES: algorithms/optimisers/cmaes.md + - Adam: algorithms/optimisers/adam.md - Samplers: - Metropolis-Hastings: algorithms/samplers/metropolis-hastings.md - Dynamic Nested Sampling: algorithms/samplers/dynamic-nested-sampling.md @@ -178,7 +178,7 @@ nav: - api-reference/index.md - Python: - Builders: api-reference/python/builders.md - - Optimisers: api-reference/python/optimizers.md + - Optimisers: api-reference/python/optimisers.md - Samplers: api-reference/python/samplers.md - Cost Metrics: api-reference/python/cost-metrics.md - Results: api-reference/python/results.md diff --git a/python/src/diffid/_diffid.pyi b/python/src/diffid/_diffid.pyi index ab356e0..99d2b73 100644 --- a/python/src/diffid/_diffid.pyi +++ b/python/src/diffid/_diffid.pyi @@ -126,7 +126,7 @@ class AdamState: >>> if isinstance(result, diffid.Evaluate): ... print(f"Need to evaluate {len(result.points)} points") >>> elif isinstance(result, diffid.Done): - ... print(f"Optimization complete: {result.result}") + ... print(f"Optimisation complete: {result.result}") """ def tell( self, result: tuple[builtins.float, typing.Sequence[builtins.float]] @@ -464,7 +464,7 @@ class DiffsolBuilder: @typing.final class Done: r""" - Optimization/sampling is complete with final results. + Optimisation/sampling is complete with final results. This is returned by `ask()` when the algorithm has terminated. Access the results via the `result` attribute. @@ -474,7 +474,7 @@ class Done: >>> while True: ... result = state.ask() ... if isinstance(result, diffid.Done): - ... print(f"Optimization complete: {result.result}") + ... print(f"Optimisation complete: {result.result}") ... break """ @property diff --git a/python/src/errors.rs b/python/src/errors.rs index de4a67c..a56ad97 100644 --- a/python/src/errors.rs +++ b/python/src/errors.rs @@ -71,11 +71,11 @@ pub fn tell_error_to_py(err: CoreTellError) -> PyErr { Ok(exc_class) => match exc_class.call0() { Ok(exc_instance) => PyErr::from_value(exc_instance.into()), Err(_) => PyValueError::new_err( - "Cannot provide results to an already terminated optimization", + "Cannot provide results to an already terminated optimisation", ), }, Err(_) => PyValueError::new_err( - "Cannot provide results to an already terminated optimization", + "Cannot provide results to an already terminated optimisation", ), } } diff --git a/python/src/optimisers.rs b/python/src/optimisers.rs index 4cf537b..fdffb45 100644 --- a/python/src/optimisers.rs +++ b/python/src/optimisers.rs @@ -159,9 +159,9 @@ impl PyNelderMead { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns a NelderMeadState object that can be used for incremental optimization + /// Returns a NelderMeadState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -174,7 +174,7 @@ impl PyNelderMead { /// Returns /// ------- /// NelderMeadState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- @@ -286,9 +286,9 @@ impl PyCMAES { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns a CMAESState object that can be used for incremental optimization + /// Returns a CMAESState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -301,7 +301,7 @@ impl PyCMAES { /// Returns /// ------- /// CMAESState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- @@ -404,9 +404,9 @@ impl PyAdam { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns an AdamState object that can be used for incremental optimization + /// Returns an AdamState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -419,7 +419,7 @@ impl PyAdam { /// Returns /// ------- /// AdamState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- @@ -443,9 +443,9 @@ impl PyAdam { } // Adam State -/// Ask-tell state for incremental Adam optimization. +/// Ask-tell state for incremental Adam optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get points to evaluate, and `tell()` to provide results. /// /// Examples @@ -469,7 +469,7 @@ pub struct PyAdamState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyAdamState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -483,7 +483,7 @@ impl PyAdamState { /// >>> if isinstance(result, diffid.Evaluate): /// ... print(f"Need to evaluate {len(result.points)} points") /// >>> elif isinstance(result, diffid.Done): - /// ... print(f"Optimization complete: {result.result}") + /// ... print(f"optimisation complete: {result.result}") fn ask(&self, py: Python<'_>) -> Py { match self.inner.ask() { AskResult::Evaluate(points) => Py::new(py, PyEvaluate { points }).unwrap().into_any(), @@ -504,7 +504,7 @@ impl PyAdamState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated or if result format is invalid + /// If called after optimisation has terminated or if result format is invalid /// EvaluationError /// If the evaluation failed or contained invalid values /// @@ -581,9 +581,9 @@ impl PyAdamState { } // Nelder-Mead State -/// Ask-tell state for incremental Nelder-Mead optimization. +/// Ask-tell state for incremental Nelder-Mead optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get points to evaluate, and `tell()` to provide results. /// /// Examples @@ -606,7 +606,7 @@ pub struct PyNelderMeadState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyNelderMeadState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -632,7 +632,7 @@ impl PyNelderMeadState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated + /// If called after optimisation has terminated /// EvaluationError /// If the evaluation failed or contained invalid values fn tell(&mut self, result: f64) -> PyResult<()> { @@ -692,9 +692,9 @@ impl PyNelderMeadState { } // CMAES State -/// Ask-tell state for incremental CMA-ES optimization. +/// Ask-tell state for incremental CMA-ES optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get a population of points to evaluate, and `tell()` to provide results. /// /// Examples @@ -717,7 +717,7 @@ pub struct PyCMAESState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyCMAESState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -749,7 +749,7 @@ impl PyCMAESState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated or if wrong number + /// If called after optimisation has terminated or if wrong number /// of results provided /// EvaluationError /// If evaluations failed or contained invalid values diff --git a/python/src/results.rs b/python/src/results.rs index 902ef66..38deada 100644 --- a/python/src/results.rs +++ b/python/src/results.rs @@ -46,7 +46,7 @@ impl PyEvaluate { } } -/// Optimization/sampling is complete with final results. +/// Optimisation/sampling is complete with final results. /// /// This is returned by `ask()` when the algorithm has terminated. /// Access the results via the `result` attribute. @@ -56,7 +56,7 @@ impl PyEvaluate { /// >>> while True: /// ... result = state.ask() /// ... if isinstance(result, diffid.Done): -/// ... print(f"Optimization complete: {result.result}") +/// ... print(f"Optimisation complete: {result.result}") /// ... break #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass(name = "Done")] @@ -212,7 +212,7 @@ impl PyOptimisationResults { } } - /// Return truthiness based on optimization success. + /// Return truthiness based on optimisation success. /// /// Allows using `if result:` instead of `if result.success:`. fn __bool__(&self) -> bool { diff --git a/rust/src/common.rs b/rust/src/common.rs index 2c45560..874fc89 100644 --- a/rust/src/common.rs +++ b/rust/src/common.rs @@ -24,7 +24,7 @@ pub enum AskResult { #[derive(Debug, Clone)] pub struct Unbounded; -/// Represents parameter bounds for optimization and sampling algorithms. +/// Represents parameter bounds for optimisation and sampling algorithms. /// /// Each dimension has a lower and upper bound represented as a `RangeInclusive`. /// Bounds can be finite (e.g., `[0.0, 1.0]`) or infinite (e.g., `[-∞, ∞]`). diff --git a/rust/src/optimisers/adam.rs b/rust/src/optimisers/adam.rs index 0d1b2a3..27812a4 100644 --- a/rust/src/optimisers/adam.rs +++ b/rust/src/optimisers/adam.rs @@ -80,7 +80,7 @@ impl Adam { self.gradient_threshold.unwrap_or(self.threshold) } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (AdamState, Point) { @@ -211,7 +211,7 @@ impl AdamState { } } - /// Get the next point to evaluate, or the final result if optimization is complete + /// Get the next point to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { AdamPhase::Terminated(reason) => AskResult::Done(self.build_results(reason.clone())), @@ -400,7 +400,7 @@ impl AdamState { // Convenience wrapper impl Adam { - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// The closure should return `(value, gradient)` for a given point pub fn run( @@ -438,7 +438,7 @@ impl Adam { } } - /// Run optimization with numerical gradient approximation + /// Run optimisation with numerical gradient approximation /// /// Uses central differences to approximate the gradient pub fn run_with_numerical_gradient( @@ -729,7 +729,7 @@ mod tests { let mut positions = vec![first_point[0]]; - // Collect positions during optimization + // Collect positions during optimisation for _ in 0..5 { state.tell(result).expect("tell should succeed"); match state.ask() { diff --git a/rust/src/optimisers/cmaes.rs b/rust/src/optimisers/cmaes.rs index 2530ab6..c3ae1c0 100644 --- a/rust/src/optimisers/cmaes.rs +++ b/rust/src/optimisers/cmaes.rs @@ -77,7 +77,7 @@ impl CMAES { }) } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (CMAESState, Point) { @@ -274,7 +274,7 @@ impl CMAESState { } } - /// Get the next point(s) to evaluate, or the final result if optimization is complete + /// Get the next point(s) to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { CMAESPhase::Terminated(reason) => AskResult::Done(self.build_results(reason.clone())), @@ -663,7 +663,7 @@ impl CMAESState { } impl CMAES { - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// This is a convenience wrapper around the ask/tell interface pub fn run( @@ -770,7 +770,7 @@ mod tests { current_results = points.iter().map(|p| sphere(p)).collect(); } AskResult::Done(results) => { - println!("Optimization complete!"); + println!("optimisation complete!"); println!("Best value: {}", results.value); println!("Iterations: {}", results.iterations); println!("Evaluations: {}", results.evaluations); @@ -889,7 +889,7 @@ mod tests { #[test] fn d_sigma_matches_hansen_2016_formula() { - // Test case 1: Standard parameters from 10-dimensional optimization + // Test case 1: Standard parameters from 10-dimensional optimisation let mu_eff = 4.5; let dim_f = 10.0; let c_sigma = 0.3; @@ -1515,9 +1515,9 @@ mod tests { } }; - let optimizer2 = CMAES::new().with_max_iter(50).with_seed(seed); + let optimiser2 = CMAES::new().with_max_iter(50).with_seed(seed); - let (mut state2, first_point2) = optimizer2.init(vec![2.0, -1.0], Bounds::unbounded(2)); + let (mut state2, first_point2) = optimiser2.init(vec![2.0, -1.0], Bounds::unbounded(2)); let mut results2 = vec![sphere_cmaes(&first_point2)]; let result2 = loop { diff --git a/rust/src/optimisers/mod.rs b/rust/src/optimisers/mod.rs index 8d81fc4..a071ae5 100644 --- a/rust/src/optimisers/mod.rs +++ b/rust/src/optimisers/mod.rs @@ -48,7 +48,7 @@ impl ScalarOptimiser { /// * `bounds` - Optional parameter bounds /// /// # Returns - /// Optimization results including best point, value, and diagnostics + /// Optimisation results including best point, value, and diagnostics /// /// # Example /// ``` @@ -82,7 +82,7 @@ impl ScalarOptimiser { /// * `bounds` - Optional parameter bounds /// /// # Returns - /// Optimization results including best point, value, and diagnostics + /// Optimisation results including best point, value, and diagnostics /// /// # Example /// ``` @@ -133,7 +133,7 @@ impl GradientOptimiser { /// * `bounds` - Optional parameter bounds /// /// # Returns - /// Optimization results including best point, value, and diagnostics + /// Optimisation results including best point, value, and diagnostics /// /// # Example /// ``` diff --git a/rust/src/optimisers/nelder_mead.rs b/rust/src/optimisers/nelder_mead.rs index 051aac2..ce52862 100644 --- a/rust/src/optimisers/nelder_mead.rs +++ b/rust/src/optimisers/nelder_mead.rs @@ -75,7 +75,7 @@ impl NelderMead { self } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (NelderMeadState, Vec) { @@ -98,7 +98,7 @@ impl NelderMead { (state, vec![initial_point]) } - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// This is a convenience wrapper around the ask/tell interface pub fn run( @@ -202,7 +202,7 @@ pub struct NelderMeadState { } impl NelderMeadState { - /// Get the next point to evaluate, or the final result if optimization is complete + /// Get the next point to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { NelderMeadPhase::Terminated(reason) => { @@ -721,7 +721,7 @@ mod tests { current_value = rosenbrock(&point[0]); } AskResult::Done(results) => { - println!("Optimization complete!"); + println!("optimisation complete!"); println!("Best value: {}", results.value); println!("Iterations: {}", results.iterations); println!("Evaluations: {}", results.evaluations); diff --git a/rust/src/sampler/dynamic_nested/mod.rs b/rust/src/sampler/dynamic_nested/mod.rs index d8ff260..845a5ac 100644 --- a/rust/src/sampler/dynamic_nested/mod.rs +++ b/rust/src/sampler/dynamic_nested/mod.rs @@ -5,7 +5,7 @@ //! **IMPORTANT**: The objective function must return **negative log-likelihood** (-log L). //! Internally, values are negated to obtain log-likelihood for nested sampling calculations. //! -//! This convention aligns with optimization where lower values are better, while nested +//! This convention aligns with optimisation where lower values are better, while nested //! sampling requires higher log-likelihood values. use rand::prelude::StdRng; diff --git a/tests/unit/test_python_autodiff.py b/tests/unit/test_python_autodiff.py index 4e516cb..fb05e15 100644 --- a/tests/unit/test_python_autodiff.py +++ b/tests/unit/test_python_autodiff.py @@ -24,7 +24,7 @@ def _quadratic_gradient(x): def quadratic_problem(): - """Creates a 3D quadratic optimization problem using ScalarBuilder""" + """Creates a 3D quadratic optimisation problem using ScalarBuilder""" return ( diffid.ScalarBuilder() .with_objective(_quadratic_objective) diff --git a/tests/unit/test_vector.py b/tests/unit/test_vector.py index c49298e..24a13c9 100644 --- a/tests/unit/test_vector.py +++ b/tests/unit/test_vector.py @@ -34,7 +34,7 @@ def exponential_model(params): assert np.isfinite(cost), f"Cost should be finite, got {cost}" assert cost >= 0, f"Cost should be non-negative, got {cost}" - # Test optimization + # Test optimisation optimiser = diffid.NelderMead().with_max_iter(1000).with_threshold(1e-8) result = problem.optimise(x0, optimiser) @@ -95,7 +95,7 @@ def sinusoid(params): result = problem.optimise(x0, optimiser) # Note: Sinusoidal fitting can be challenging due to local minima - # We just verify the optimization runs and produces reasonable results + # We just verify the optimisation runs and produces reasonable results assert ( result.success or result.iterations >= 1000 ) # Either converged or tried hard enough From 380a43bf73c8b32ea84bd0f124b20fe98a00f279 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 17:48:54 +0000 Subject: [PATCH 13/18] docs: remove tutorial numbering --- docs/api-reference/python/cost-metrics.md | 2 +- docs/api-reference/python/samplers.md | 4 +- docs/getting-started/first-ode-fit.md | 4 +- docs/tutorials/index.md | 122 +--- .../notebooks/04_model_comparison.ipynb | 102 --- .../06_custom_solver_integration.ipynb | 586 ------------------ ...ns.ipynb => advanced_cost_functions.ipynb} | 35 +- ...rey.ipynb => advanced_predator_prey.ipynb} | 16 +- .../notebooks/model_comparison.ipynb | 35 ++ ...iffsol.ipynb => ode_fitting_diffsol.ipynb} | 29 +- ...basics.ipynb => optimisation_basics.ipynb} | 28 +- ...tion.ipynb => parallel_optimisation.ipynb} | 37 +- ...inty.ipynb => parameter_uncertainty.ipynb} | 52 +- mkdocs.yml | 15 +- 14 files changed, 85 insertions(+), 982 deletions(-) delete mode 100644 docs/tutorials/notebooks/04_model_comparison.ipynb delete mode 100644 docs/tutorials/notebooks/06_custom_solver_integration.ipynb rename docs/tutorials/notebooks/{08_advanced_cost_functions.ipynb => advanced_cost_functions.ipynb} (99%) rename docs/tutorials/notebooks/{05_advanced_predator_prey.ipynb => advanced_predator_prey.ipynb} (91%) create mode 100644 docs/tutorials/notebooks/model_comparison.ipynb rename docs/tutorials/notebooks/{02_ode_fitting_diffsol.ipynb => ode_fitting_diffsol.ipynb} (99%) rename docs/tutorials/notebooks/{01_optimisation_basics.ipynb => optimisation_basics.ipynb} (99%) rename docs/tutorials/notebooks/{07_parallel_optimisation.ipynb => parallel_optimisation.ipynb} (98%) rename docs/tutorials/notebooks/{03_parameter_uncertainty.ipynb => parameter_uncertainty.ipynb} (99%) diff --git a/docs/api-reference/python/cost-metrics.md b/docs/api-reference/python/cost-metrics.md index bda1cae..e5ba79b 100644 --- a/docs/api-reference/python/cost-metrics.md +++ b/docs/api-reference/python/cost-metrics.md @@ -223,4 +223,4 @@ For heteroscedastic data (varying noise), weighted metrics can be important. Con - [Cost Metrics Guide](../../guides/cost-metrics.md) - [Builders](builders.md) - [Samplers](samplers.md) (for Bayesian inference) -- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/03_parameter_uncertainty.ipynb) +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/parameter_uncertainty.ipynb) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md index 1d027b3..ead0b94 100644 --- a/docs/api-reference/python/samplers.md +++ b/docs/api-reference/python/samplers.md @@ -188,8 +188,8 @@ print(f"MAP estimate: {opt_result.x}") The followings present sampler functionality: -- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/03_parameter_uncertainty.ipynb) -- [Model Comparison Tutorial](../../tutorials/notebooks/04_model_comparison.ipynb) +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/parameter_uncertainty.ipynb) +- [Model Comparison Tutorial](../../tutorials/notebooks/model_comparison.ipynb) - [Choosing a Sampler Guide](../../guides/choosing-sampler.md) --- diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md index ce2fd7c..e16aac1 100644 --- a/docs/getting-started/first-ode-fit.md +++ b/docs/getting-started/first-ode-fit.md @@ -226,6 +226,6 @@ For more help, see the [Troubleshooting Guide](../guides/troubleshooting.md). ## Next Steps - **[Core Concepts](concepts.md)**: Understand builders, problems, and the ask/tell pattern -- **[ODE Fitting Tutorial](../tutorials/notebooks/02_ode_fitting_diffsol.ipynb)**: Interactive notebook with more examples +- **[ODE Fitting Tutorial](../tutorials/notebooks/ode_fitting_diffsol.ipynb)**: Interactive notebook with more examples - **[Custom Solvers](../guides/custom-solvers.md)**: Integrate Diffrax or DifferentialEquations.jl -- **[Parameter Uncertainty](../tutorials/notebooks/03_parameter_uncertainty.ipynb)**: Use MCMC sampling to quantify uncertainty +- **[Parameter Uncertainty](../tutorials/notebooks/parameter_uncertainty.ipynb)**: Use MCMC sampling to quantify uncertainty diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 5ea3dff..c069ea9 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -6,13 +6,13 @@ Interactive Jupyter notebooks for hands-on learning with Diffid. Follow these progressive learning paths based on your experience level and goals. -### 🎯 Beginner Track +### Beginner Track Perfect for those new to Diffid or optimisation:
-- **[1. Optimisation Basics](notebooks/01_optimisation_basics.ipynb)** +- **[Optimisation Basics](notebooks/optimisation_basics.ipynb)** --- @@ -21,7 +21,7 @@ Perfect for those new to Diffid or optimisation: **Topics:** ScalarBuilder, contour plots, optimiser comparison **Runtime:** ~5 minutes -- **[2. ODE Fitting with DiffSL](notebooks/02_ode_fitting_diffsol.ipynb)** +- **[ODE Fitting with DiffSL](notebooks/ode_fitting_diffsol.ipynb)** --- @@ -32,13 +32,13 @@ Perfect for those new to Diffid or optimisation:
-### 🚀 Intermediate Track +### Intermediate Track Building on the basics with real-world applications:
-- **[3. Parameter Uncertainty](notebooks/03_parameter_uncertainty.ipynb)** +- **[Parameter Uncertainty](notebooks/parameter_uncertainty.ipynb)** --- @@ -47,7 +47,7 @@ Building on the basics with real-world applications: **Topics:** Metropolis-Hastings, posterior distributions, diagnostics **Runtime:** ~15 minutes -- **[4. Model Comparison](notebooks/04_model_comparison.ipynb)** ⚠️ *Coming Soon* +- **[Model Comparison](notebooks/model_comparison.ipynb)** ⚠️ *Coming Soon* --- @@ -58,13 +58,13 @@ Building on the basics with real-world applications:
-### 🔬 Advanced Track +## Advanced Track Complex problems and advanced techniques:
-- **[5. Multi-Backend ODE Solving](notebooks/05_advanced_predator_prey.ipynb)** +- **[Multi-Backend ODE Solving](notebooks/advanced_predator_prey.ipynb)** --- @@ -77,13 +77,13 @@ Complex problems and advanced techniques: ## Quick Reference -| Tutorial | Difficulty | Key Concepts | Prerequisites | -|----------|------------|--------------|---------------| -| 1. optimisation Basics | ⭐ Beginner | ScalarBuilder, optimisers | None | -| 2. ODE Fitting | ⭐ Beginner | DiffsolBuilder, DiffSL | Tutorial 1 | -| 3. Parameter Uncertainty | ⭐⭐ Intermediate | MCMC, uncertainty | Tutorials 1-2 | -| 4. Model Comparison | ⭐⭐ Intermediate | Nested sampling, evidence | Tutorials 1-3 | -| 5. Multi-Backend | ⭐⭐⭐ Advanced | VectorBuilder, JAX, Julia | Tutorials 1-2 | +| Tutorial | Difficulty | Key Concepts | Prerequisites | +|--------------------------|------------|---------------------------|---------------| +| Optimisation Basics | ⭐ Beginner | ScalarBuilder, Optimisers | None | +| ODE Fitting | ⭐ Beginner | DiffsolBuilder, DiffSL | Optimisation Basics | +| Parameter Uncertainty | ⭐⭐ Intermediate | MCMC, uncertainty | Optimisation Basics, ODE Fitting | +| Model Comparison | ⭐⭐ Intermediate | Nested sampling, evidence | Optimisation Basics, ODE Fitting, Parameter Uncertainty | +| Multi-Backend | ⭐⭐⭐ Advanced | VectorBuilder, JAX, Julia | Optimisation Basics, ODE Fitting | ## Prerequisites @@ -94,8 +94,8 @@ Complex problems and advanced techniques: - Jupyter notebook environment ### Additional for Specific Tutorials -- **Tutorials 3-4**: Basic Bayesian statistics -- **Tutorial 5**: JAX or Julia knowledge (optional) +- **Parameter Uncertainty & Model Comparison**: Basic Bayesian statistics +- **Multi-Backend**: JAX or Julia knowledge (optional) ## Running the Notebooks @@ -126,35 +126,6 @@ cd diffid/docs/tutorials/notebooks jupyter notebook ``` -### Google Colab - -You can also run these notebooks in Google Colab (coming soon with hosted versions). - -## Learning Outcomes - -By completing all tutorials, you will: - -- Understand Diffid's builder pattern and API -- Optimize both scalar functions and ODE parameters -- Compare and tune different optimisation algorithms -- Quantify parameter uncertainty with MCMC -- Compare models using Bayesian evidence -- Integrate custom ODE solvers (JAX, Julia) -- Make informed decisions about algorithm selection - -## Notebook Structure - -Each tutorial follows a consistent structure: - -1. **Learning Objectives** - What you'll learn -2. **Prerequisites** - Required background -3. **Introduction** - Problem context and motivation -4. **Step-by-Step Code** - Fully explained examples -5. **Visualizations** - Plots and diagnostics -6. **Key Takeaways** - Summary of main points -7. **Exercises** - Practice problems -8. **Next Steps** - Links to related content - ## Alternative: Python Scripts Prefer scripts to notebooks? Check out the [examples directory](https://github.com/bradyplanden/diffid/tree/main/examples): @@ -165,65 +136,6 @@ Prefer scripts to notebooks? Check out the [examples directory](https://github.c - `bicycle_model_evidence.py` - Model comparison - `predator_prey/` - Multi-backend comparisons -## Utilities - -The notebooks use shared utilities in `utils.py`: - -- `plot_contour_2d()` - 2D function contours -- `plot_ode_fit()` - ODE fits and data -- `plot_convergence()` - Optimisation history -- `plot_parameter_traces()` - MCMC traces -- `plot_parameter_distributions()` - Posterior histograms -- `compare_models()` - Multi-model plots - -Feel free to reuse these in your own projects! - -## Troubleshooting - -### Import Errors - -```python -ModuleNotFoundError: No module named 'diffid' -``` - -**Solution**: Install Diffid: `pip install diffid` - -### Notebook Kernel Issues - -If the notebook doesn't recognize installed packages: - -1. Install packages in the correct environment -2. Restart the Jupyter kernel: *Kernel → Restart* -3. Check kernel selection: *Kernel → Change Kernel* - -### Missing Dependencies - -Some notebooks require optional dependencies: - -```bash -# For plotting -pip install matplotlib - -# For Tutorial 5 (optional backends) -pip install jax diffrax # JAX/Diffrax -pip install diffeqpy # Julia -``` - -### Performance Issues - -If MCMC sampling is slow: - -- Reduce number of chains or iterations -- Enable parallel execution: `.with_parallel(True)` -- Use fewer data points for testing - -## Getting Help - -- **Documentation**: Browse the [complete docs](../index.md) -- **Examples**: See the [examples gallery](../examples/gallery.md) -- **API Reference**: Check the [API docs](../api-reference/index.md) -- **Issues**: Report problems on [GitHub](https://github.com/bradyplanden/diffid/issues) - ## Contributing Found an issue or want to improve a tutorial? diff --git a/docs/tutorials/notebooks/04_model_comparison.ipynb b/docs/tutorials/notebooks/04_model_comparison.ipynb deleted file mode 100644 index 6ebe716..0000000 --- a/docs/tutorials/notebooks/04_model_comparison.ipynb +++ /dev/null @@ -1,102 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 4: Model Comparison\n", - "\n", - "**Learning Objectives:**\n", - "- Use Dynamic Nested Sampling for model evidence\n", - "- Calculate Bayes factors for model comparison\n", - "- Interpret evidence values\n", - "- Compare bicycle dynamics models\n", - "\n", - "**Prerequisites:** Tutorials 1-3, Bayesian model comparison basics\n", - "\n", - "**Runtime:** ~20 minutes\n", - "\n", - "**Note:** This tutorial requires the Dynamic Nested Sampling feature. Coming soon!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "When you have multiple models, **which one is best?**\n", - "\n", - "**Dynamic Nested Sampling** calculates the **model evidence** (marginal likelihood), allowing rigorous Bayesian model comparison via **Bayes factors**.\n", - "\n", - "$$\\text{Bayes Factor} = \\frac{p(\\text{Data}|\\text{Model}_1)}{p(\\text{Data}|\\text{Model}_2)} = \\frac{Z_1}{Z_2}$$\n", - "\n", - "This tutorial will demonstrate model comparison using bicycle dynamics models.\n", - "\n", - "## The Problem: Bicycle Model\n", - "\n", - "A bicycle traveling at constant velocity $v$ with steer angle $\\delta$ follows curved motion determined by wheelbase $L$:\n", - "\n", - "$$\\begin{aligned}\n", - "\\frac{dx}{dt} &= v \\cos(\\theta) \\\\\n", - "\\frac{dy}{dt} &= v \\sin(\\theta) \\\\\n", - "\\frac{d\\theta}{dt} &= \\frac{v}{L} \\tan(\\delta)\n", - "\\end{aligned}$$\n", - "\n", - "**Task:** Estimate wheelbase $L$ and compare different model formulations.\n", - "\n", - "## Coming Soon\n", - "\n", - "This tutorial is under development. Check back soon for:\n", - "\n", - "- Setting up multiple model variants\n", - "- Running Dynamic Nested Sampling\n", - "- Calculating log evidence for each model\n", - "- Interpreting Bayes factors\n", - "- Making model selection decisions\n", - "\n", - "## Preview: Model Evidence Interpretation\n", - "\n", - "| $\\log(Z_1 / Z_2)$ | Bayes Factor | Evidence for Model 1 |\n", - "|-------------------|--------------|----------------------|\n", - "| < 0 | < 1 | Negative (prefer Model 2) |\n", - "| 0-1 | 1-3 | Barely worth mentioning |\n", - "| 1-2.5 | 3-12 | Positive |\n", - "| 2.5-5 | 12-150 | Strong |\n", - "| > 5 | > 150 | Very strong |\n", - "\n", - "## References\n", - "\n", - "- Skilling, J. (2006). Nested sampling for general Bayesian computation.\n", - "- Higson, E. et al. (2019). Dynamic nested sampling.\n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 5: Predator-Prey Models](05_advanced_predator_prey.ipynb) - Multi-backend comparison\n", - "- [Choosing a Sampler](../../guides/choosing-sampler.md)\n", - "- [API Reference: Samplers](../../api-reference/python/samplers.md)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb b/docs/tutorials/notebooks/06_custom_solver_integration.ipynb deleted file mode 100644 index 4979633..0000000 --- a/docs/tutorials/notebooks/06_custom_solver_integration.ipynb +++ /dev/null @@ -1,586 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial 6: Custom Solver Integration\n", - "\n", - "**Learning Objectives:**\n", - "- Use VectorBuilder with custom ODE solvers\n", - "- Integrate JAX/Diffrax for GPU-accelerated solving\n", - "- Compare different solver backends (Diffsol, Diffrax, DifferentialEquations.jl)\n", - "- Understand performance trade-offs between solvers\n", - "\n", - "**Prerequisites:** Tutorials 1-2, familiarity with JAX (optional)\n", - "\n", - "**Runtime:** ~15 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## Introduction\n\nWhile `DiffsolBuilder` provides a convenient interface for ODE fitting using the DiffSL language, `VectorBuilder` offers maximum flexibility by allowing you to use **any** ODE solver. This enables:\n\n1. **GPU acceleration** via JAX/Diffrax\n2. **Specialized solvers** from Julia's DifferentialEquations.jl\n3. **Custom dynamics** that don't fit the DiffSL syntax\n4. **Pre-existing code** integration\n\nIn this tutorial, we'll solve the predator-prey model using different backends and compare their performance." - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "# Import plotting utilities\nimport time\n\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Lotka-Volterra Model\n", - "\n", - "The predator-prey dynamics are described by:\n", - "\n", - "$$\\frac{dx}{dt} = \\alpha x - \\beta xy \\quad \\text{(prey growth and predation)}$$\n", - "\n", - "$$\\frac{dy}{dt} = \\delta xy - \\gamma y \\quad \\text{(predator growth and death)}$$\n", - "\n", - "where:\n", - "- $x$ = prey population\n", - "- $y$ = predator population \n", - "- $\\alpha$ = prey birth rate\n", - "- $\\beta$ = predation rate\n", - "- $\\delta$ = predator reproduction efficiency\n", - "- $\\gamma$ = predator death rate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Method 1: DiffsolBuilder (Baseline)\n", - "\n", - "First, let's solve it using the built-in DiffsolBuilder as a baseline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate synthetic data using scipy\n", - "from scipy.integrate import solve_ivp\n", - "\n", - "np.random.seed(42)\n", - "\n", - "# True parameters\n", - "true_params = {\n", - " \"alpha\": 1.1, # prey birth rate\n", - " \"beta\": 0.4, # predation rate\n", - " \"delta\": 0.1, # predator efficiency\n", - " \"gamma\": 0.4, # predator death rate\n", - "}\n", - "\n", - "# Time points\n", - "t_data = np.linspace(0, 15, 50)\n", - "\n", - "# DiffSL model for fitting\n", - "model_str = \"\"\"\n", - "in_i { alpha = 2.0/3.0, beta = 4.0/3.0, delta = 1.0, gamma = 1.0 }\n", - "x0 { 10.0 } y0 { 5.0 }\n", - "u_i {\n", - " y1 = x0,\n", - " y2 = y0,\n", - "}\n", - "F_i {\n", - " alpha * y1 - beta * y1 * y2,\n", - " delta * y1 * y2 - gamma * y2,\n", - "}\n", - "\"\"\"\n", - "\n", - "\n", - "# Define ODE for scipy\n", - "def lotka_volterra(t, state, alpha, beta, delta, gamma):\n", - " x, y = state\n", - " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", - "\n", - "\n", - "# Generate \"true\" solution with noise using scipy\n", - "y0 = [10.0, 5.0] # initial conditions\n", - "sol = solve_ivp(\n", - " lotka_volterra,\n", - " [t_data[0], t_data[-1]],\n", - " y0,\n", - " args=(\n", - " true_params[\"alpha\"],\n", - " true_params[\"beta\"],\n", - " true_params[\"delta\"],\n", - " true_params[\"gamma\"],\n", - " ),\n", - " t_eval=t_data,\n", - " method=\"RK45\",\n", - ")\n", - "\n", - "y_true = sol.y.T # Shape: (n_times, 2)\n", - "y_observed = y_true + np.random.normal(0, 0.5, y_true.shape)\n", - "\n", - "print(f\"Data shape: {y_observed.shape}\")\n", - "print(f\"Time span: [{t_data[0]:.1f}, {t_data[-1]:.1f}]\")\n", - "print(f\"Number of observations: {len(t_data)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Visualize data\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "\n", - "ax.plot(t_data, y_observed[:, 0], \"o\", label=\"Prey (observed)\", alpha=0.6, markersize=6)\n", - "ax.plot(\n", - " t_data, y_observed[:, 1], \"s\", label=\"Predator (observed)\", alpha=0.6, markersize=6\n", - ")\n", - "ax.plot(t_data, y_true[:, 0], \"--\", label=\"Prey (true)\", linewidth=2, alpha=0.8)\n", - "ax.plot(t_data, y_true[:, 1], \"--\", label=\"Predator (true)\", linewidth=2, alpha=0.8)\n", - "\n", - "ax.set_xlabel(\"Time\", fontsize=12)\n", - "ax.set_ylabel(\"Population\", fontsize=12)\n", - "ax.set_title(\"Predator-Prey Dynamics: Synthetic Data\", fontsize=14)\n", - "ax.legend(fontsize=10)\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "# Fit with DiffsolBuilder\nstart_time = time.time()\n\n# Combine times and observations for DiffsolBuilder\n# Data format: first column is time, remaining columns are observations\ndata = np.column_stack((t_data, y_observed))\n\nresult_diffsol = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 1.0) # initial guess\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n)\n\ndiffsol_time = time.time() - start_time\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"DIFFSOL RESULTS\")\nprint(\"=\" * 60)\nprint(f\"True parameters: {list(true_params.values())}\")\nprint(f\"Estimated parameters: {result_diffsol.x}\")\nprint(f\"Final SSE: {result_diffsol.value:.6f}\")\nprint(f\"Iterations: {result_diffsol.iterations}\")\nprint(f\"Function evals: {result_diffsol.evaluations}\")\nprint(f\"Time: {diffsol_time:.3f}s\")\nprint(f\"Success: {result_diffsol.success}\")" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## Method 2: VectorBuilder with JAX/Diffrax\n\n`VectorBuilder` accepts any callable that maps parameters to predicted outputs. This enables using **JAX/Diffrax** for GPU-accelerated ODE solving with automatic differentiation.\n\n### Advantages:\n- ⚡ GPU acceleration\n- 🔥 JIT compilation\n- 📐 Automatic differentiation (gradients for free)\n- 🚀 Fast iteration for gradient-based optimisers" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional: install JAX/Diffrax if not already installed\n", - "# !pip install jax jaxlib diffrax\n", - "\n", - "try:\n", - " import diffrax as dfx\n", - " import jax.numpy as jnp\n", - " from jax import config, jit\n", - "\n", - " # Enable float64 precision\n", - " config.update(\"jax_enable_x64\", True)\n", - "\n", - " JAX_AVAILABLE = True\n", - "except ImportError:\n", - " JAX_AVAILABLE = False\n", - " print(\"⚠️ JAX/Diffrax not installed. Skipping GPU example.\")\n", - " print(\" Install with: pip install jax jaxlib diffrax\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if JAX_AVAILABLE:\n", - " # Define dynamics in JAX\n", - " def lotka_volterra_jax(t, state, params):\n", - " \"\"\"Lotka-Volterra dynamics in JAX.\"\"\"\n", - " x, y = state\n", - " alpha, beta, delta, gamma = params\n", - " return jnp.array(\n", - " [\n", - " alpha * x - beta * x * y, # prey\n", - " delta * x * y - gamma * y, # predator\n", - " ]\n", - " )\n", - "\n", - " # Configure ODE solver\n", - " solver = dfx.Tsit5() # Tsitouras 5(4) method\n", - " saveat = dfx.SaveAt(ts=t_data)\n", - " term = dfx.ODETerm(lotka_volterra_jax)\n", - "\n", - " # Extract bounds for JIT\n", - " t0, t1 = float(t_data[0]), float(t_data[-1])\n", - " dt0 = float(t_data[1] - t_data[0])\n", - " y0 = jnp.array([10.0, 5.0]) # initial state\n", - "\n", - " @jit\n", - " def simulate_jax(params):\n", - " \"\"\"JAX-native ODE integration (JIT-compiled).\"\"\"\n", - " sol = dfx.diffeqsolve(\n", - " term,\n", - " solver,\n", - " t0=t0,\n", - " t1=t1,\n", - " dt0=dt0,\n", - " y0=y0,\n", - " args=params,\n", - " saveat=saveat,\n", - " )\n", - " return sol.ys # Shape: (n_times, 2)\n", - "\n", - " def simulate_numpy(params):\n", - " \"\"\"NumPy wrapper for Diffid compatibility.\"\"\"\n", - " return np.asarray(simulate_jax(jnp.asarray(params)))\n", - "\n", - " # Warm up JIT compiler\n", - " _ = simulate_numpy([1.0, 0.4, 0.1, 0.4])\n", - " print(\"JAX/Diffrax solver ready (JIT compiled)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "if JAX_AVAILABLE:\n # Fit with VectorBuilder + JAX/Diffrax\n start_time = time.time()\n\n result_diffrax = (\n diffid.VectorBuilder()\n .with_objective(simulate_numpy)\n .with_data(y_observed)\n .with_parameter(\"alpha\", 1.0)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n )\n\n diffrax_time = time.time() - start_time\n\n print(\"\\n\" + \"=\" * 60)\n print(\"DIFFRAX (JAX) RESULTS\")\n print(\"=\" * 60)\n print(f\"True parameters: {list(true_params.values())}\")\n print(f\"Estimated parameters: {result_diffrax.x}\")\n print(f\"Final SSE: {result_diffrax.value:.6f}\")\n print(f\"Iterations: {result_diffrax.iterations}\")\n print(f\"Function evals: {result_diffrax.evaluations}\")\n print(f\"Time: {diffrax_time:.3f}s\")\n print(f\"Success: {result_diffrax.success}\")\n print(f\"\\nSpeedup vs Diffsol: {diffsol_time / diffrax_time:.2f}x\")" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Method 3: VectorBuilder with Julia DifferentialEquations.jl\n", - "\n", - "Julia's **DifferentialEquations.jl** ecosystem offers:\n", - "- 🔬 Largest collection of ODE solvers\n", - "- 🎯 Specialised methods (stiff, stochastic, DAE, etc.)\n", - "- 📊 Advanced features (sensitivity analysis, callbacks)\n", - "\n", - "Integration via `diffeqpy` provides access to Julia's ecosystem from Python." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional: install Julia backend\n", - "# !pip install diffeqpy\n", - "# Then in Python: from diffeqpy import install; install()\n", - "\n", - "try:\n", - " from diffeqpy import ode\n", - "\n", - " JULIA_AVAILABLE = True\n", - "except ImportError:\n", - " JULIA_AVAILABLE = False\n", - " print(\"⚠️ DifferentialEquations.jl not installed. Skipping Julia example.\")\n", - " print(\" Install with: pip install diffeqpy\")\n", - " print(\" Then run: from diffeqpy import install; install()\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if JULIA_AVAILABLE:\n", - " # Define dynamics for Julia\n", - " def lotka_volterra_julia(u, p, t):\n", - " \"\"\"Lotka-Volterra for Julia (u, p, t) signature.\"\"\"\n", - " x, y = u\n", - " alpha, beta, delta, gamma = p\n", - " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", - "\n", - " def simulate_julia(params):\n", - " \"\"\"Solve ODE using Julia's Tsit5 solver.\"\"\"\n", - " u0 = [10.0, 5.0]\n", - " tspan = (t_data[0], t_data[-1])\n", - "\n", - " prob = ode.ODEProblem(lotka_volterra_julia, u0, tspan, params)\n", - " sol = ode.solve(prob, ode.Tsit5(), saveat=t_data)\n", - "\n", - " # Convert to numpy array (shape: n_times x 2)\n", - " return np.array(sol.u).T\n", - "\n", - " # Test\n", - " test_output = simulate_julia([1.0, 0.4, 0.1, 0.4])\n", - " print(\"Julia/DifferentialEquations.jl ready\")\n", - " print(f\" Output shape: {test_output.shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": "if JULIA_AVAILABLE:\n # Fit with VectorBuilder + Julia\n start_time = time.time()\n\n result_julia = (\n diffid.VectorBuilder()\n .with_objective(simulate_julia)\n .with_data(y_observed)\n .with_parameter(\"alpha\", 1.0)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.5)\n .with_cost(diffid.SSE())\n .with_optimiser(diffid.NelderMead().with_max_iter(500))\n .build()\n .optimise()\n )\n\n julia_time = time.time() - start_time\n\n print(\"\\n\" + \"=\" * 60)\n print(\"JULIA DIFFERENTIALEQUATIONS.JL RESULTS\")\n print(\"=\" * 60)\n print(f\"True parameters: {list(true_params.values())}\")\n print(f\"Estimated parameters: {result_julia.x}\")\n print(f\"Final SSE: {result_julia.value:.6f}\")\n print(f\"Iterations: {result_julia.iterations}\")\n print(f\"Function evals: {result_julia.evaluations}\")\n print(f\"Time: {julia_time:.3f}s\")\n print(f\"Success: {result_julia.success}\")\n print(f\"\\nSpeedup vs Diffsol: {diffsol_time / julia_time:.2f}x\")" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performance Comparison\n", - "\n", - "Let's compare all three backends:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Collect results\n", - "results_summary = []\n", - "\n", - "results_summary.append(\n", - " {\n", - " \"Backend\": \"Diffsol\",\n", - " \"Time (s)\": diffsol_time,\n", - " \"SSE\": result_diffsol.value,\n", - " \"Iterations\": result_diffsol.iterations,\n", - " \"Evaluations\": result_diffsol.evaluations,\n", - " }\n", - ")\n", - "\n", - "if JAX_AVAILABLE:\n", - " results_summary.append(\n", - " {\n", - " \"Backend\": \"JAX/Diffrax\",\n", - " \"Time (s)\": diffrax_time,\n", - " \"SSE\": result_diffrax.value,\n", - " \"Iterations\": result_diffrax.iterations,\n", - " \"Evaluations\": result_diffrax.evaluations,\n", - " }\n", - " )\n", - "\n", - "if JULIA_AVAILABLE:\n", - " results_summary.append(\n", - " {\n", - " \"Backend\": \"Julia/DiffEq\",\n", - " \"Time (s)\": julia_time,\n", - " \"SSE\": result_julia.value,\n", - " \"Iterations\": result_julia.iterations,\n", - " \"Evaluations\": result_julia.evaluations,\n", - " }\n", - " )\n", - "\n", - "# Display table\n", - "print(\"\\n\" + \"=\" * 80)\n", - "print(\"BACKEND COMPARISON\")\n", - "print(\"=\" * 80)\n", - "print(f\"{'Backend':<20} {'Time (s)':<12} {'SSE':<15} {'Iters':<8} {'Evals'}\")\n", - "print(\"-\" * 80)\n", - "\n", - "for r in results_summary:\n", - " print(\n", - " f\"{r['Backend']:<20} {r['Time (s)']:<12.3f} {r['SSE']:<15.3e} \"\n", - " f\"{r['Iterations']:<8} {r['Evaluations']}\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Visualize timing comparison\n", - "if len(results_summary) > 1:\n", - " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - " backends = [r[\"Backend\"] for r in results_summary]\n", - " times = [r[\"Time (s)\"] for r in results_summary]\n", - " sse_values = [r[\"SSE\"] for r in results_summary]\n", - "\n", - " # Time comparison\n", - " colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\"]\n", - " bars1 = ax1.bar(\n", - " backends, times, color=colors[: len(backends)], alpha=0.7, edgecolor=\"black\"\n", - " )\n", - " ax1.set_ylabel(\"Time (s)\", fontsize=12)\n", - " ax1.set_title(\"Optimisation Time Comparison\", fontsize=14, fontweight=\"bold\")\n", - " ax1.grid(True, axis=\"y\", alpha=0.3)\n", - "\n", - " # Add value labels on bars\n", - " for bar in bars1:\n", - " height = bar.get_height()\n", - " ax1.text(\n", - " bar.get_x() + bar.get_width() / 2.0,\n", - " height,\n", - " f\"{height:.2f}s\",\n", - " ha=\"center\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " )\n", - "\n", - " # SSE comparison\n", - " bars2 = ax2.bar(\n", - " backends,\n", - " sse_values,\n", - " color=colors[: len(backends)],\n", - " alpha=0.7,\n", - " edgecolor=\"black\",\n", - " )\n", - " ax2.set_ylabel(\"SSE\", fontsize=12)\n", - " ax2.set_title(\"Final Objective Value\", fontsize=14, fontweight=\"bold\")\n", - " ax2.grid(True, axis=\"y\", alpha=0.3)\n", - "\n", - " # Add value labels\n", - " for bar in bars2:\n", - " height = bar.get_height()\n", - " ax2.text(\n", - " bar.get_x() + bar.get_width() / 2.0,\n", - " height,\n", - " f\"{height:.1f}\",\n", - " ha=\"center\",\n", - " va=\"bottom\",\n", - " fontsize=10,\n", - " )\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Fitted Models\n", - "\n", - "Compare the fitted trajectories from each backend:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate predictions from each fitted model\n", - "t_fine = np.linspace(t_data[0], t_data[-1], 200)\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", - "\n", - "# Plot prey dynamics\n", - "ax1.plot(t_data, y_observed[:, 0], \"o\", label=\"Observed\", alpha=0.5, markersize=6)\n", - "ax1.plot(t_data, y_true[:, 0], \"k--\", label=\"True\", linewidth=2, alpha=0.7)\n", - "\n", - "# Diffsol fit - use scipy to generate predictions with fitted parameters\n", - "sol_fitted = solve_ivp(\n", - " lotka_volterra,\n", - " [t_fine[0], t_fine[-1]],\n", - " [10.0, 5.0], # initial conditions\n", - " args=tuple(result_diffsol.x),\n", - " t_eval=t_fine,\n", - " method=\"RK45\",\n", - ")\n", - "y_pred_diffsol = sol_fitted.y.T\n", - "ax1.plot(t_fine, y_pred_diffsol[:, 0], label=\"Diffsol\", linewidth=2)\n", - "\n", - "if JAX_AVAILABLE:\n", - " y_pred_diffrax = simulate_numpy(result_diffrax.x)\n", - " ax1.plot(\n", - " t_data, y_pred_diffrax[:, 0], label=\"JAX/Diffrax\", linewidth=2, linestyle=\":\"\n", - " )\n", - "\n", - "if JULIA_AVAILABLE:\n", - " y_pred_julia = simulate_julia(result_julia.x)\n", - " ax1.plot(\n", - " t_data, y_pred_julia[:, 0], label=\"Julia/DiffEq\", linewidth=2, linestyle=\"-.\"\n", - " )\n", - "\n", - "ax1.set_xlabel(\"Time\", fontsize=12)\n", - "ax1.set_ylabel(\"Prey Population\", fontsize=12)\n", - "ax1.set_title(\"Prey Dynamics: Model Comparison\", fontsize=14, fontweight=\"bold\")\n", - "ax1.legend(fontsize=10)\n", - "ax1.grid(True, alpha=0.3)\n", - "\n", - "# Plot predator dynamics\n", - "ax2.plot(t_data, y_observed[:, 1], \"s\", label=\"Observed\", alpha=0.5, markersize=6)\n", - "ax2.plot(t_data, y_true[:, 1], \"k--\", label=\"True\", linewidth=2, alpha=0.7)\n", - "ax2.plot(t_fine, y_pred_diffsol[:, 1], label=\"Diffsol\", linewidth=2)\n", - "\n", - "if JAX_AVAILABLE:\n", - " ax2.plot(\n", - " t_data, y_pred_diffrax[:, 1], label=\"JAX/Diffrax\", linewidth=2, linestyle=\":\"\n", - " )\n", - "\n", - "if JULIA_AVAILABLE:\n", - " ax2.plot(\n", - " t_data, y_pred_julia[:, 1], label=\"Julia/DiffEq\", linewidth=2, linestyle=\"-.\"\n", - " )\n", - "\n", - "ax2.set_xlabel(\"Time\", fontsize=12)\n", - "ax2.set_ylabel(\"Predator Population\", fontsize=12)\n", - "ax2.set_title(\"Predator Dynamics: Model Comparison\", fontsize=14, fontweight=\"bold\")\n", - "ax2.legend(fontsize=10)\n", - "ax2.grid(True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "## Key Takeaways\n\n### When to Use Each Backend\n\n**DiffsolBuilder (Diffsol):**\n- Quick prototyping with DiffSL syntax\n- ✅ Standard ODE problems\n- ✅ No extra dependencies\n- ❌ Limited to DiffSL expressiveness\n\n**VectorBuilder + JAX/Diffrax:**\n- ✅ GPU acceleration for large problems\n- ✅ Automatic differentiation (enables gradient-based optimisers)\n- ✅ JIT compilation for speed\n- ✅ Excellent for high-dimensional problems\n- ❌ Requires JAX ecosystem\n\n**VectorBuilder + Julia/DifferentialEquations.jl:**\n- ✅ Largest solver collection (stiff, stochastic, DAE, DDE, etc.)\n- ✅ Advanced features (callbacks, sensitivity analysis)\n- ✅ Best for specialized problems\n- ❌ Requires Julia installation\n\n### Performance Insights\n\n1. **JAX/Diffrax** typically fastest after JIT warmup\n2. **Diffsol** excellent balance of speed and simplicity\n3. **Julia/DiffEq** best for problems requiring specialized solvers\n4. All backends produce equivalent parameter estimates\n\n### VectorBuilder Flexibility\n\nThe key advantage of `VectorBuilder` is **total control**:\n- Any Python callable works\n- Integrate pre-existing simulation code\n- Mix solver backends in the same workflow\n- Enable advanced features like GPU acceleration" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next Steps\n", - "\n", - "- [Tutorial 7: Parallel Optimisation](07_parallel_optimization.ipynb) - Scale optimisation with parallelism\n", - "- [Guide: Custom Solvers](../../guides/custom-solvers.md) - Detailed integration patterns\n", - "- [API Reference: VectorBuilder](../../api-reference/python/builders.md#vectorbuilder) - Complete API documentation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises\n", - "\n", - "1. **Add Gradient-Based Optimiser**: Modify the JAX/Diffrax example to use `Adam()` optimiser with gradients\n", - "\n", - "2. **Custom Dynamics**: Implement a different ODE system (e.g., Lorenz attractor, SIR model) using VectorBuilder\n", - "\n", - "3. **Benchmark Scaling**: Test how performance scales with:\n", - " - Number of time points (50, 100, 500, 1000)\n", - " - System size (2, 5, 10 state variables)\n", - " - Problem stiffness\n", - "\n", - "4. **Hybrid Approach**: Use VectorBuilder with a custom callable that switches solvers based on problem characteristics" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb b/docs/tutorials/notebooks/advanced_cost_functions.ipynb similarity index 99% rename from docs/tutorials/notebooks/08_advanced_cost_functions.ipynb rename to docs/tutorials/notebooks/advanced_cost_functions.ipynb index edf95a9..777201a 100644 --- a/docs/tutorials/notebooks/08_advanced_cost_functions.ipynb +++ b/docs/tutorials/notebooks/advanced_cost_functions.ipynb @@ -3,20 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 8: Advanced Cost Functions\n", - "\n", - "**Learning Objectives:**\n", - "- Understand built-in cost metrics (SSE, RMSE, GaussianNLL)\n", - "- Implement custom cost functions\n", - "- Apply weighted fitting for heteroscedastic data\n", - "- Use regularisation to prevent over-fitting\n", - "- Combine multiple objectives\n", - "\n", - "**Prerequisites:** Tutorials 1-2, basic statistics\n", - "\n", - "**Runtime:** ~15 minutes" - ] + "source": "# Advanced Cost Functions\n\n**Learning Objectives:**\n- Understand built-in cost metrics (SSE, RMSE, GaussianNLL)\n- Implement custom cost functions\n- Apply weighted fitting for heteroscedastic data\n- Use regularisation to prevent over-fitting\n- Combine multiple objectives\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic statistics\n\n**Runtime:** ~15 minutes" }, { "cell_type": "markdown", @@ -1068,23 +1055,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **Built-in metrics** (SSE, RMSE, GaussianNLL) cover most use cases\n", - "2. **Weighted fitting** essential for heteroscedastic data (varying errors)\n", - "3. **Regularisation** prevents over-fitting in high-dimensional problems\n", - "4. **Multi-objective costs** balance competing goals (fit vs smoothness)\n", - "5. **Constraint penalties** enforce physical requirements\n", - "6. **Custom costs** are easy to implement via `__call__` interface\n", - "7. **Scale and normalisation** critical for multi-term objectives\n", - "\n", - "## Next Steps\n", - "\n", - "- [Guide: Cost Metrics](../../guides/cost-metrics.md) - Detailed metric selection\n", - "- [Tutorial 3: Parameter Uncertainty](03_parameter_uncertainty.ipynb) - Bayesian inference with GaussianNLL\n", - "- [API Reference: Cost Metrics](../../api-reference/python/cost-metrics.md) - Complete API" - ] + "source": "## Key Takeaways\n\n1. **Built-in metrics** (SSE, RMSE, GaussianNLL) cover most use cases\n2. **Weighted fitting** essential for heteroscedastic data (varying errors)\n3. **Regularisation** prevents over-fitting in high-dimensional problems\n4. **Multi-objective costs** balance competing goals (fit vs smoothness)\n5. **Constraint penalties** enforce physical requirements\n6. **Custom costs** are easy to implement via `__call__` interface\n7. **Scale and normalisation** critical for multi-term objectives\n\n## Next Steps\n\n- [Guide: Cost Metrics](../../guides/cost-metrics.md) - Detailed metric selection\n- [Parameter Uncertainty](parameter_uncertainty.ipynb) - Bayesian inference with GaussianNLL\n- [API Reference: Cost Metrics](../../api-reference/python/cost-metrics.md) - Complete API" }, { "cell_type": "markdown", @@ -1128,4 +1099,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb b/docs/tutorials/notebooks/advanced_predator_prey.ipynb similarity index 91% rename from docs/tutorials/notebooks/05_advanced_predator_prey.ipynb rename to docs/tutorials/notebooks/advanced_predator_prey.ipynb index f576140..dc38320 100644 --- a/docs/tutorials/notebooks/05_advanced_predator_prey.ipynb +++ b/docs/tutorials/notebooks/advanced_predator_prey.ipynb @@ -3,19 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 5: Multi-Backend ODE Solving\n", - "\n", - "**Learning Objectives:**\n", - "- Use VectorBuilder for custom ODE solvers\n", - "- Compare Diffsol, Diffrax (JAX), and DifferentialEquations.jl\n", - "- Understand performance trade-offs\n", - "- Integrate external solvers with Diffid\n", - "\n", - "**Prerequisites:** Tutorials 1-2, basic JAX or Julia knowledge (optional)\n", - "\n", - "**Runtime:** ~20 minutes" - ] + "source": "# Multi-Backend ODE Solving\n\n**Learning Objectives:**\n- Use VectorBuilder for custom ODE solvers\n- Compare Diffsol, Diffrax (JAX), and DifferentialEquations.jl\n- Understand performance trade-offs\n- Integrate external solvers with Diffid\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic JAX or Julia knowledge (optional)\n\n**Runtime:** ~20 minutes" }, { "cell_type": "markdown", @@ -173,4 +161,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/model_comparison.ipynb b/docs/tutorials/notebooks/model_comparison.ipynb new file mode 100644 index 0000000..36bc5d4 --- /dev/null +++ b/docs/tutorials/notebooks/model_comparison.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Model Comparison\n\n**Learning Objectives:**\n- Use Dynamic Nested Sampling for model evidence\n- Calculate Bayes factors for model comparison\n- Interpret evidence values\n- Compare bicycle dynamics models\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, Parameter Uncertainty, Bayesian model comparison basics\n\n**Runtime:** ~20 minutes\n\n**Note:** This tutorial requires the Dynamic Nested Sampling feature. Coming soon!" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Introduction\n\nWhen you have multiple models, **which one is best?**\n\n**Dynamic Nested Sampling** calculates the **model evidence** (marginal likelihood), allowing rigorous Bayesian model comparison via **Bayes factors**.\n\n$$\\text{Bayes Factor} = \\frac{p(\\text{Data}|\\text{Model}_1)}{p(\\text{Data}|\\text{Model}_2)} = \\frac{Z_1}{Z_2}$$\n\nThis tutorial will demonstrate model comparison using bicycle dynamics models.\n\n## The Problem: Bicycle Model\n\nA bicycle traveling at constant velocity $v$ with steer angle $\\delta$ follows curved motion determined by wheelbase $L$:\n\n$$\\begin{aligned}\n\\frac{dx}{dt} &= v \\cos(\\theta) \\\\\n\\frac{dy}{dt} &= v \\sin(\\theta) \\\\\n\\frac{d\\theta}{dt} &= \\frac{v}{L} \\tan(\\delta)\n\\end{aligned}$$\n\n**Task:** Estimate wheelbase $L$ and compare different model formulations.\n\n## Coming Soon\n\nThis tutorial is under development. Check back soon for:\n\n- Setting up multiple model variants\n- Running Dynamic Nested Sampling\n- Calculating log evidence for each model\n- Interpreting Bayes factors\n- Making model selection decisions\n\n## Preview: Model Evidence Interpretation\n\n| $\\log(Z_1 / Z_2)$ | Bayes Factor | Evidence for Model 1 |\n|-------------------|--------------|----------------------|\n| < 0 | < 1 | Negative (prefer Model 2) |\n| 0-1 | 1-3 | Barely worth mentioning |\n| 1-2.5 | 3-12 | Positive |\n| 2.5-5 | 12-150 | Strong |\n| > 5 | > 150 | Very strong |\n\n## References\n\n- Skilling, J. (2006). Nested sampling for general Bayesian computation.\n- Higson, E. et al. (2019). Dynamic nested sampling.\n\n## Next Steps\n\n- [Predator-Prey Models](advanced_predator_prey.ipynb) - Multi-backend comparison\n- [Choosing a Sampler](../../guides/choosing-sampler.md)\n- [API Reference: Samplers](../../api-reference/python/samplers.md)" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb similarity index 99% rename from docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb rename to docs/tutorials/notebooks/ode_fitting_diffsol.ipynb index c95948f..7712d57 100644 --- a/docs/tutorials/notebooks/02_ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb @@ -3,15 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 2: ODE Fitting with DiffSL\n", - "\n", - "**Objectives:**\n", - "- Understand the DiffsolBuilder API\n", - "- Learn DiffSL syntax for defining ODEs\n", - "- Fit parameters of a logistic growth model\n", - "- Visualise model fits and residuals" - ] + "source": "# ODE Fitting with DiffSL\n\n**Objectives:**\n- Understand the DiffsolBuilder API\n- Learn DiffSL syntax for defining ODEs\n- Fit parameters of a logistic growth model\n- Visualise model fits and residuals" }, { "cell_type": "markdown", @@ -509,22 +501,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **DiffsolBuilder** is used for ODE parameter fitting\n", - "2. **DiffSL** provides concise ODE syntax: `in`, `u_i`, `F_i`\n", - "3. Data format: `[time, observation(s)]` in columns\n", - "4. **CMA-ES** typically works well for ODE fitting\n", - "5. Parallel evaluation improves performance\n", - "6. Always check residuals to assess fit quality\n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 3: Parameter Uncertainty](03_parameter_uncertainty.ipynb) - Quantify parameter uncertainty with MCMC\n", - "- [DiffSL Backend Guide](../../guides/diffsol-backend.md) - Dense vs sparse solvers\n", - "- [Custom Solvers](../../guides/custom-solvers.md) - Using JAX/Diffrax or Julia" - ] + "source": "## Key Takeaways\n\n1. **DiffsolBuilder** is used for ODE parameter fitting\n2. **DiffSL** provides concise ODE syntax: `in`, `u_i`, `F_i`\n3. Data format: `[time, observation(s)]` in columns\n4. **CMA-ES** typically works well for ODE fitting\n5. Parallel evaluation improves performance\n6. Always check residuals to assess fit quality\n\n## Next Steps\n\n- [Parameter Uncertainty](parameter_uncertainty.ipynb) - Quantify parameter uncertainty with MCMC\n- [DiffSL Backend Guide](../../guides/diffsol-backend.md) - Dense vs sparse solvers\n- [Custom Solvers](../../guides/custom-solvers.md) - Using JAX/Diffrax or Julia" }, { "cell_type": "markdown", @@ -563,4 +540,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/01_optimisation_basics.ipynb b/docs/tutorials/notebooks/optimisation_basics.ipynb similarity index 99% rename from docs/tutorials/notebooks/01_optimisation_basics.ipynb rename to docs/tutorials/notebooks/optimisation_basics.ipynb index b7f06d8..a24c1fb 100644 --- a/docs/tutorials/notebooks/01_optimisation_basics.ipynb +++ b/docs/tutorials/notebooks/optimisation_basics.ipynb @@ -3,15 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 1: Optimisation Basics\n", - "\n", - "**Objectives:**\n", - "- Understand the ScalarBuilder API\n", - "- Optimise the classic Rosenbrock function\n", - "- Visualise optimisation landscapes with contour plots\n", - "- Compare different optimisers" - ] + "source": "# Optimisation Basics\n\n**Objectives:**\n- Understand the ScalarBuilder API\n- Optimise the classic Rosenbrock function\n- Visualise optimisation landscapes with contour plots\n- Compare different optimisers" }, { "cell_type": "markdown", @@ -409,21 +401,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **ScalarBuilder** is used for direct function optimisation\n", - "2. **Nelder-Mead** is provided as the default optimiser - good for small problems\n", - "3. **CMA-ES** is more robust for global search but requires more evaluations\n", - "4. **Adam** can be fast on smooth problems but may struggle on complex landscapes\n", - "5. Starting point can significantly affect convergence speed\n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 2: ODE Fitting with DiffSL](02_ode_fitting_diffsol.ipynb) - Learn how to fit differential equations\n", - "- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - Detailed optimiser selection guide\n", - "- [API Reference: Optimisers](../../api-reference/python/optimisers.md) - Complete API documentation" - ] + "source": "## Key Takeaways\n\n1. **ScalarBuilder** is used for direct function optimisation\n2. **Nelder-Mead** is provided as the default optimiser - good for small problems\n3. **CMA-ES** is more robust for global search but requires more evaluations\n4. **Adam** can be fast on smooth problems but may struggle on complex landscapes\n5. Starting point can significantly affect convergence speed\n\n## Next Steps\n\n- [ODE Fitting with DiffSL](ode_fitting_diffsol.ipynb) - Learn how to fit differential equations\n- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - Detailed optimiser selection guide\n- [API Reference: Optimisers](../../api-reference/python/optimisers.md) - Complete API documentation" }, { "cell_type": "markdown", @@ -463,4 +441,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/07_parallel_optimisation.ipynb b/docs/tutorials/notebooks/parallel_optimisation.ipynb similarity index 98% rename from docs/tutorials/notebooks/07_parallel_optimisation.ipynb rename to docs/tutorials/notebooks/parallel_optimisation.ipynb index e18810d..b56d358 100644 --- a/docs/tutorials/notebooks/07_parallel_optimisation.ipynb +++ b/docs/tutorials/notebooks/parallel_optimisation.ipynb @@ -3,20 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 7: Parallel Optimisation\n", - "\n", - "**Learning Objectives:**\n", - "- Understand which optimisers support parallelism\n", - "- Configure population-based optimisers for parallel execution\n", - "- Benchmark scaling performance\n", - "- Identify performance bottlenecks\n", - "- Apply best practices for thread-safe optimisation\n", - "\n", - "**Prerequisites:** Tutorials 1-2, basic understanding of parallelism\n", - "\n", - "**Runtime:** ~10 minutes" - ] + "source": "# Parallel Optimisation\n\n**Learning Objectives:**\n- Understand which optimisers support parallelism\n- Configure population-based optimisers for parallel execution\n- Benchmark scaling performance\n- Identify performance bottlenecks\n- Apply best practices for thread-safe optimisation\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic understanding of parallelism\n\n**Runtime:** ~10 minutes" }, { "cell_type": "markdown", @@ -476,25 +463,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n", - "\n", - "2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n", - "\n", - "3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n", - "\n", - "4. **Profile first** - measure single evaluation time to determine if parallelism will help\n", - "\n", - "5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 8: Advanced Cost Functions](08_advanced_cost_functions.ipynb) - Custom objective functions\n", - "- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n", - "- [API Reference: CMA-ES](../../api-reference/python/optimisers.md#cmaes) - Population configuration" - ] + "source": "## Key Takeaways\n\n1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n\n2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n\n3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n\n4. **Profile first** - measure single evaluation time to determine if parallelism will help\n\n5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n\n## Next Steps\n\n- [Advanced Cost Functions](advanced_cost_functions.ipynb) - Custom objective functions\n- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n- [API Reference: CMA-ES](../../api-reference/python/optimisers.md#cmaes) - Population configuration" }, { "cell_type": "markdown", @@ -523,4 +492,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb b/docs/tutorials/notebooks/parameter_uncertainty.ipynb similarity index 99% rename from docs/tutorials/notebooks/03_parameter_uncertainty.ipynb rename to docs/tutorials/notebooks/parameter_uncertainty.ipynb index 8189aff..dc267be 100644 --- a/docs/tutorials/notebooks/03_parameter_uncertainty.ipynb +++ b/docs/tutorials/notebooks/parameter_uncertainty.ipynb @@ -3,15 +3,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "# Tutorial 3: Parameter Uncertainty\n", - "\n", - "**Learning Objectives:**\n", - "- Go from optimisation to uncertainty quantification\n", - "- Use MCMC sampling to explore parameter distributions\n", - "- Interpret MCMC diagnostics and traces\n", - "- Calculate confidence intervals" - ] + "source": "# Parameter Uncertainty\n\n**Learning Objectives:**\n- Go from optimisation to uncertainty quantification\n- Use MCMC sampling to explore parameter distributions\n- Interpret MCMC diagnostics and traces\n- Calculate confidence intervals" }, { "cell_type": "markdown", @@ -209,17 +201,10 @@ ] }, { + "metadata": {}, "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2026-01-10T22:21:49.152830Z", - "iopub.status.busy": "2026-01-10T22:21:49.152632Z", - "iopub.status.idle": "2026-01-10T22:21:49.635464Z", - "shell.execute_reply": "2026-01-10T22:21:49.634461Z" - } - }, "outputs": [], + "execution_count": null, "source": [ "# Build problem\n", "builder = (\n", @@ -233,12 +218,12 @@ "\n", "problem = builder.build()\n", "\n", - "# Optimize\n", + "# Optimise\n", "optimiser = diffid.Adam().with_step_size(0.05).with_max_iter(1500)\n", "opt_result = optimiser.run(problem, [5.0, 5.0])\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", - "print(\"OPTIMIZATION RESULTS (MAP Estimate)\")\n", + "print(\"OPTIMISATION RESULTS (MAP Estimate)\")\n", "print(\"=\" * 60)\n", "print(f\"Success: {opt_result.success}\")\n", "print(\"\\nFitted parameters:\")\n", @@ -644,30 +629,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Key Takeaways\n", - "\n", - "1. **Optimisation** gives point estimates; **MCMC** quantifies uncertainty\n", - "2. **GaussianNLL** cost metric is required for sampling\n", - "3. **Acceptance rate** should be 20-40% for efficient exploration\n", - "4. **Burn-in** period discards initial non-stationary samples\n", - "5. **Credible intervals** provide uncertainty bounds\n", - "6. **Posterior predictive** distributions account for parameter uncertainty\n", - "\n", - "## MCMC Diagnostics Checklist\n", - "\n", - "✓ Acceptance rate in [0.2, 0.4] \n", - "✓ Trace plots show good mixing \n", - "✓ No trends in traces \n", - "✓ Distributions look reasonable \n", - "✓ True values in credible intervals \n", - "\n", - "## Next Steps\n", - "\n", - "- [Tutorial 4: Model Comparison](04_model_comparison.ipynb) - Use nested sampling for Bayes factors\n", - "- [Choosing a Sampler](../../guides/choosing-sampler.md) - MCMC vs Nested Sampling\n", - "- [API Reference: Samplers](../../api-reference/python/samplers.md)" - ] + "source": "## Key Takeaways\n\n1. **Optimisation** gives point estimates; **MCMC** quantifies uncertainty\n2. **GaussianNLL** cost metric is required for sampling\n3. **Acceptance rate** should be 20-40% for efficient exploration\n4. **Burn-in** period discards initial non-stationary samples\n5. **Credible intervals** provide uncertainty bounds\n6. **Posterior predictive** distributions account for parameter uncertainty\n\n## MCMC Diagnostics Checklist\n\n✓ Acceptance rate in [0.2, 0.4] \n✓ Trace plots show good mixing \n✓ No trends in traces \n✓ Distributions look reasonable \n✓ True values in credible intervals \n\n## Next Steps\n\n- [Model Comparison](model_comparison.ipynb) - Use nested sampling for Bayes factors\n- [Choosing a Sampler](../../guides/choosing-sampler.md) - MCMC vs Nested Sampling\n- [API Reference: Samplers](../../api-reference/python/samplers.md)" }, { "cell_type": "markdown", @@ -706,4 +668,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 8242c49..eb0f145 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -147,14 +147,13 @@ nav: - Tutorials: - tutorials/index.md - Notebooks: - - 1. Optimisation Basics: tutorials/notebooks/01_optimisation_basics.ipynb - - 2. ODE Fitting with DiffSL: tutorials/notebooks/02_ode_fitting_diffsol.ipynb - - 3. Parameter Uncertainty: tutorials/notebooks/03_parameter_uncertainty.ipynb - - 4. Model Comparison: tutorials/notebooks/04_model_comparison.ipynb - - 5. Predator-Prey Models: tutorials/notebooks/05_advanced_predator_prey.ipynb - - 6. Custom Solver Integration: tutorials/notebooks/06_custom_solver_integration.ipynb - - 7. Parallel Optimisation: tutorials/notebooks/07_parallel_optimisation.ipynb - - 8. Advanced Cost Functions: tutorials/notebooks/08_advanced_cost_functions.ipynb + - Optimisation Basics: tutorials/notebooks/optimisation_basics.ipynb + - ODE Fitting with DiffSL: tutorials/notebooks/ode_fitting_diffsol.ipynb + - Parameter Uncertainty: tutorials/notebooks/parameter_uncertainty.ipynb + - Model Comparison: tutorials/notebooks/model_comparison.ipynb + - Predator-Prey Models: tutorials/notebooks/advanced_predator_prey.ipynb + - Parallel Optimisation: tutorials/notebooks/parallel_optimisation.ipynb + - Advanced Cost Functions: tutorials/notebooks/advanced_cost_functions.ipynb - User Guides: - guides/index.md - Choosing an Optimiser: guides/choosing-optimiser.md From f083198996f1e25307630c226d4f91a746acfa1a Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 18:08:26 +0000 Subject: [PATCH 14/18] docs: case-sensitive fixes --- .../notebooks/ode_fitting_diffsol.ipynb | 29 +------------------ .../notebooks/optimisation_basics.ipynb | 8 ++--- 2 files changed, 3 insertions(+), 34 deletions(-) diff --git a/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb index 7712d57..47d9ae7 100644 --- a/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb +++ b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb @@ -200,34 +200,7 @@ "cell_type": "code", "outputs": [], "execution_count": null, - "source": [ - "# Create optimiser\n", - "optimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n", - "\n", - "# Run optimisation\n", - "result = optimiser.run(problem, problem.initial_values())\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"OPTIMIZATION RESULTS\")\n", - "print(\"=\" * 60)\n", - "print(f\"Success: {result.success}\")\n", - "print(\"\\nFitted parameters:\")\n", - "print(f\" r = {result.x[0]:.6f} (true: {r_true})\")\n", - "print(f\" k = {result.x[1]:.6f} (true: {k_true})\")\n", - "print(\"\\nOptimisation details:\")\n", - "print(f\" Final cost: {result.value:.3e}\")\n", - "print(f\" Iterations: {result.iterations}\")\n", - "print(f\" Function evaluations: {result.evaluations}\")\n", - "print(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\n", - "print(f\" Message: {result.message}\")\n", - "\n", - "# Calculate parameter errors\n", - "r_error = abs(result.x[0] - r_true) / r_true * 100\n", - "k_error = abs(result.x[1] - k_true) / k_true * 100\n", - "print(\"\\nParameter errors:\")\n", - "print(f\" r: {r_error:.3f}%\")\n", - "print(f\" k: {k_error:.3f}%\")" - ] + "source": "# Create optimiser\noptimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n\n# Run optimisation\nresult = optimiser.run(problem, problem.initial_values())\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMISATION RESULTS\")\nprint(\"=\" * 60)\nprint(f\"Success: {result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" r = {result.x[0]:.6f} (true: {r_true})\")\nprint(f\" k = {result.x[1]:.6f} (true: {k_true})\")\nprint(\"\\nOptimisation details:\")\nprint(f\" Final cost: {result.value:.3e}\")\nprint(f\" Iterations: {result.iterations}\")\nprint(f\" Function evaluations: {result.evaluations}\")\nprint(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\nprint(f\" Message: {result.message}\")\n\n# Calculate parameter errors\nr_error = abs(result.x[0] - r_true) / r_true * 100\nk_error = abs(result.x[1] - k_true) / k_true * 100\nprint(\"\\nParameter errors:\")\nprint(f\" r: {r_error:.3f}%\")\nprint(f\" k: {k_error:.3f}%\")" }, { "cell_type": "markdown", diff --git a/docs/tutorials/notebooks/optimisation_basics.ipynb b/docs/tutorials/notebooks/optimisation_basics.ipynb index a24c1fb..b52fc68 100644 --- a/docs/tutorials/notebooks/optimisation_basics.ipynb +++ b/docs/tutorials/notebooks/optimisation_basics.ipynb @@ -119,7 +119,7 @@ "text": [ "\n", "==================================================\n", - "OPTIMIZATION RESULTS\n", + "OPTIMISATION RESULTS\n", "==================================================\n", "Success: True\n", "Optimal parameters: [1.00082688 1.0017059 ]\n", @@ -147,11 +147,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Visualise the optimisation Landscape\n", - "\n", - "Let's create a contour plot to see the function's shape:" - ] + "source": "## Visualise the Optimisation Landscape\n\nLet's create a contour plot to see the function's shape:" }, { "cell_type": "code", From 0848a9db27824af27a74f17bf95c8449272900c1 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 18:28:04 +0000 Subject: [PATCH 15/18] docs: adds architecture diagram to readme --- README.md | 4 ++++ docs/assets/diffid.drawio | 8 ++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 759e859..a24b4f8 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,10 @@
+

+ Diffid architecture describing the two optimisation approaches. +

+ ## Project goals - Speed and numerical accuracy through a Rust core. - Modular components with informative diagnostics. diff --git a/docs/assets/diffid.drawio b/docs/assets/diffid.drawio index 3882c4f..4f7bc83 100644 --- a/docs/assets/diffid.drawio +++ b/docs/assets/diffid.drawio @@ -1,6 +1,6 @@ - + @@ -8,7 +8,7 @@ - + @@ -31,7 +31,7 @@ - + @@ -43,7 +43,7 @@ - + From 074e736d555f78b3d88672c8b46b403467fbedec Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 18:32:49 +0000 Subject: [PATCH 16/18] docs: updates architecture, removes dark background --- README.md | 2 +- docs/assets/diffid.svg | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a24b4f8..25d5607 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@

- Diffid architecture describing the two optimisation approaches. + Diffid architecture describing the two optimisation approaches.

## Project goals diff --git a/docs/assets/diffid.svg b/docs/assets/diffid.svg index ef51290..b64369b 100644 --- a/docs/assets/diffid.svg +++ b/docs/assets/diffid.svg @@ -1,4 +1,4 @@ -
Conventional Approach
Python (interpreted)
Problem Definition
Optimisation Loop
(for i in range(n):)
Sampler Loop
(MCMC iterations)
repeated FFI calls (N iterations)
C / Fortran Bindings (fast)
Forward Model
(single evaluation)
FFI Boundary
⚠ Bottleneck: Python loop overhead per iteration
Configuration Approach
Python (configuration only)
builder = DiffsolBuilder()
  .with_diffsl(model_code)
  .with_data(observations)
  .with_parameter("k", ..)
problem = builder.build()
Problem definition
(declarative)
result = problem.optimise()
Single call,
returns results
One-time handoff
FFI Boundary (crossed once)
Rust Core (compiled, fast)
Optimisation / Sampling Loop
Optimisers
Samplers
Cost
Metrics
Objective
+ Gradient
Forward Model
(DiffSL/Diffsol)
Parallel Batch
updated parameters
 \ No newline at end of file +
Conventional Approach
Python (interpreted)
Problem Definition
Optimisation Loop
(for i in range(n):)
Sampler Loop
(MCMC iterations)
repeated FFI calls (N iterations)
C / Fortran Bindings (fast)
Forward Model
(single evaluation)
FFI Boundary
Bottleneck: Python loop overhead per iteration
Configuration Approach
Python (configuration only)
builder = DiffsolBuilder()
  .with_diffsl(model_code)
  .with_data(observations)
  .with_parameter("k", ..)
problem = builder.build()
Problem definition
(declarative)
result = problem.optimise()
Single call,
returns results
One-time handoff
FFI Boundary (crossed once)
Rust Core (compiled, fast)
Optimisation / Sampling Loop
Optimisers
Samplers
Cost
Metrics
Objective
+ Gradient
Forward Model
(DiffSL/Diffsol)
Parallel Batch
updated parameters
 \ No newline at end of file From 246182aa820fbfaf8ff8708caf84bdb85c09b03f Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 18:57:42 +0000 Subject: [PATCH 17/18] docs: cleanup and simplify --- docs/examples/gallery.md | 41 ------------------------------ docs/index.md | 34 +++++++------------------ docs/tutorials/index.md | 55 ++++++++++++++-------------------------- 3 files changed, 28 insertions(+), 102 deletions(-) diff --git a/docs/examples/gallery.md b/docs/examples/gallery.md index f59d7ec..4d2a036 100644 --- a/docs/examples/gallery.md +++ b/docs/examples/gallery.md @@ -113,47 +113,6 @@ pip install diffeqpy python examples/predator_prey/predator_prey_diffrax.py ``` -## Example Categories - -### By Problem Type - -| Type | Examples | Builder | -|------|----------|---------| -| Scalar | Rosenbrock | ScalarBuilder | -| ODE (DiffSL) | Logistic, Bouncy Ball | DiffsolBuilder | -| ODE (Custom) | Predator-Prey | VectorBuilder | - -### By Algorithm - -| Algorithm | Examples | -|-----------|----------| -| Nelder-Mead | Most examples (default) | -| CMA-ES | Logistic growth, bicycle model | -| Adam | Coming soon | -| MCMC | Bouncy ball sampling | -| Nested Sampling | Bicycle evidence, model evidence | - -### By Difficulty - -**Beginner:** - -- python_problem.py -- python_contour.py -- logistic_growth.py - -**Intermediate:** - -- bouncy_ball.py -- bicycle_model_diffsol.py -- predator_prey_diffsol.py - -**Advanced:** - -- bouncy_ball_sampling.py -- bicycle_model_evidence.py -- predator_prey_diffrax.py -- predator_prey_diffeqpy.py - ## Contributing Examples Have an interesting use case? We'd love to include it! diff --git a/docs/index.md b/docs/index.md index 5f9481a..33690df 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,8 +4,7 @@ ## Why Diffid? -Diffid offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. This package instead introduces an alternative, where the Python layer acts purely as a declarative configuration interface, -while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below, +Diffid offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. Alongside this approach, Diffid introduces an alternative, where the Python layer acts purely as a declarative configuration interface, while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below,
@@ -17,46 +16,31 @@ while all computationally intensive work (the optimisation / sampling loop, grad ## Core Capabilities -- **Gradient-free** (Nelder-Mead, CMA-ES) and **gradient-based** (Adam) optimisers with configurable convergence criteria -- **Multi-threaded differential equation fitting** via [DiffSL](https://github.com/martinjrobins/diffsl) with dense or sparse [Diffsol](https://github.com/martinjrobins/diffsol) backends -- **Customisable likelihood/cost metrics** and Monte-Carlo sampling for posterior exploration -- **Flexible integration** with state-of-the-art differential solvers, such as [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) - -## Quick Links -
-- :material-clock-fast:{ .lg .middle } __5-Minute Quickstart__ +- __Optimisation Algorithms__ --- - Get started with Diffid in 5 minutes with a simple scalar optimisation example. - - [:octicons-arrow-right-24: Quickstart](getting-started/quickstart.md) + Gradient-free (Nelder-Mead, CMA-ES) and gradient-based (Adam) optimisers with configurable convergence criteria -- :material-function:{ .lg .middle } __First ODE Fit__ +- __High-Performance ODE Fitting__ --- - Learn how to fit differential equations to data using DiffSL and Diffsol. + Multi-threaded differential equation fitting via [DiffSL](https://github.com/martinjrobins/diffsl) with dense or sparse [Diffsol](https://github.com/martinjrobins/diffsol) backends - [:octicons-arrow-right-24: ODE Fitting Tutorial](getting-started/first-ode-fit.md) - -- :material-book-open-variant:{ .lg .middle } __Core Concepts__ +- __Uncertainty Quantification__ --- - Understand the builder pattern, problem types, and optimiser vs sampler workflows. - - [:octicons-arrow-right-24: Concepts Guide](getting-started/concepts.md) + Customisable likelihood/cost metrics and Monte-Carlo sampling for posterior exploration -- :material-code-tags:{ .lg .middle } __API Reference__ +- __Flexible Integration__ --- - Browse the complete Python and Rust API documentation. - - [:octicons-arrow-right-24: API Reference](api-reference/index.md) + Integration with state-of-the-art differential solvers: [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy)
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index c069ea9..9358645 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,7 +1,5 @@ # Tutorials -Interactive Jupyter notebooks for hands-on learning with Diffid. - ## Learning Paths Follow these progressive learning paths based on your experience level and goals. @@ -12,23 +10,22 @@ Perfect for those new to Diffid or optimisation:
-- **[Optimisation Basics](notebooks/optimisation_basics.ipynb)** +- :material-tune:{ .lg .middle } __Optimisation Basics__ --- Learn scalar optimisation with the Rosenbrock function. Compare Nelder-Mead, CMA-ES, and Adam optimisers. - **Topics:** ScalarBuilder, contour plots, optimiser comparison - **Runtime:** ~5 minutes + [:octicons-arrow-right-24: Optimisation Basics](notebooks/optimisation_basics.ipynb) -- **[ODE Fitting with DiffSL](notebooks/ode_fitting_diffsol.ipynb)** +- :material-function-variant:{ .lg .middle } __ODE Fitting with DiffSL__ --- Fit a logistic growth model to data using DiffSL and Diffsol. - **Topics:** DiffsolBuilder, DiffSL syntax, parameter fitting - **Runtime:** ~10 minutes + [:octicons-arrow-right-24: ODE Fitting with DiffSL](notebooks/ode_fitting_diffsol.ipynb) +
@@ -38,64 +35,50 @@ Building on the basics with real-world applications:
-- **[Parameter Uncertainty](notebooks/parameter_uncertainty.ipynb)** +- :material-chart-bell-curve:{ .lg .middle } __Parameter Uncertainty__ --- Go from optimisation to MCMC sampling. Quantify parameter uncertainty with confidence intervals. - **Topics:** Metropolis-Hastings, posterior distributions, diagnostics - **Runtime:** ~15 minutes + [:octicons-arrow-right-24: Parameter Uncertainty](notebooks/parameter_uncertainty.ipynb) -- **[Model Comparison](notebooks/model_comparison.ipynb)** ⚠️ *Coming Soon* +- :material-scale-balance:{ .lg .middle } __Model Comparison__ --- Use Dynamic Nested Sampling to calculate model evidence and Bayes factors. - **Topics:** Evidence calculation, Bayes factors, model selection - **Runtime:** ~20 minutes + [:octicons-arrow-right-24: Model Comparison](notebooks/model_comparison.ipynb)
-## Advanced Track +### Advanced Track Complex problems and advanced techniques:
-- **[Multi-Backend ODE Solving](notebooks/advanced_predator_prey.ipynb)** +- :material-connection:{ .lg .middle } __Multi-Backend ODE Solving__ --- Compare Diffsol, JAX/Diffrax, and Julia/DifferentialEquations.jl for predator-prey models. - **Topics:** VectorBuilder, backend comparison, custom solvers - **Runtime:** ~20 minutes + [:octicons-arrow-right-24: Multi-Backend ODE Solving](notebooks/advanced_predator_prey.ipynb)
## Quick Reference | Tutorial | Difficulty | Key Concepts | Prerequisites | -|--------------------------|------------|---------------------------|---------------| -| Optimisation Basics | ⭐ Beginner | ScalarBuilder, Optimisers | None | -| ODE Fitting | ⭐ Beginner | DiffsolBuilder, DiffSL | Optimisation Basics | -| Parameter Uncertainty | ⭐⭐ Intermediate | MCMC, uncertainty | Optimisation Basics, ODE Fitting | -| Model Comparison | ⭐⭐ Intermediate | Nested sampling, evidence | Optimisation Basics, ODE Fitting, Parameter Uncertainty | -| Multi-Backend | ⭐⭐⭐ Advanced | VectorBuilder, JAX, Julia | Optimisation Basics, ODE Fitting | - -## Prerequisites - -### All Tutorials -- Python >= 3.11 -- Basic Python programming -- NumPy fundamentals -- Jupyter notebook environment - -### Additional for Specific Tutorials -- **Parameter Uncertainty & Model Comparison**: Basic Bayesian statistics -- **Multi-Backend**: JAX or Julia knowledge (optional) +|--------------------------|----------|---------------------------|---------------| +| Optimisation Basics | Beginner | ScalarBuilder, Optimisers | None | +| ODE Fitting | Beginner | DiffsolBuilder, DiffSL | Optimisation Basics | +| Parameter Uncertainty | Intermediate | MCMC, uncertainty | Optimisation Basics, ODE Fitting | +| Model Comparison | Intermediate | Nested sampling, evidence | Optimisation Basics, ODE Fitting, Parameter Uncertainty | +| Multi-Backend | Advanced | VectorBuilder, JAX, Julia | Optimisation Basics, ODE Fitting | + ## Running the Notebooks From 03139ea4c6f35bcd193df90cf6b7bd5ecc9bd359 Mon Sep 17 00:00:00 2001 From: Brady Planden Date: Sun, 25 Jan 2026 19:00:34 +0000 Subject: [PATCH 18/18] docs: updates README --- README.md | 18 +----------------- 1 file changed, 1 insertion(+), 17 deletions(-) diff --git a/README.md b/README.md index 25d5607..e8a135d 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![License](https://img.shields.io/github/license/bradyplanden/diffid?color=blue)](https://github.com/bradyplanden/diffid/blob/main/LICENSE) [![Releases](https://img.shields.io/github/v/release/bradyplanden/diffid?color=gold)](https://github.com/bradyplanden/diffid/releases) -**diff**erential **id**entification is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. +**diff**erential **id**entification offers a different paradigm for a parameter inference library. Conventional Python-based inference libraries are constructed via python bindings to a high-performance forward model with the optimisation algorithms implemented in Python. Diffid offers an alternative, where the Python layer acts purely as a declarative configuration interface, while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below,

@@ -24,22 +24,6 @@ - Customisable likelihood/cost metrics and Monte-Carlo sampling for posterior exploration. - Flexible integration with state-of-the-art differential solvers, such as [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) - -## Why Diffid? -- Optimisation based workflow run the forward simulation thousands of times, a performance improvement on the process can produce results hour or days earlier -- The Rust core provides a high-performance inference loop with fewer runtime errors. -- Quickly integrated into Python workflows, and later use the rust crate directly for even higher performance. - -## Documentation - -**[Full Documentation](https://bradyplanden.github.io/diffid/)** - -Visit the comprehensive documentation for: -- Getting started guides and tutorials -- Complete API reference -- User guides for choosing and tuning algorithms -- Examples gallery and Jupyter notebooks - ## Installation Diffid targets Python >= 3.11. Windows builds are currently marked experimental.