diff --git a/.github/workflows/tox.yml b/.github/workflows/tox.yml index 24cf11b5..341bbc0d 100644 --- a/.github/workflows/tox.yml +++ b/.github/workflows/tox.yml @@ -1,90 +1,71 @@ -name: Tox CI +name: CI on: push: - branches: - - main + branches: [main] pull_request: - branches: - - main + branches: [main] jobs: - tox: + test: + name: pytest (py${{ matrix.python-version }}) runs-on: ubuntu-latest - strategy: matrix: - python-version: [3.12] # Specify the Python versions to test against, as per your tox envlist - + python-version: ["3.12"] steps: - # Step 1: Check out the repository - - name: Check out the repository - uses: actions/checkout@v3 + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # setuptools_scm needs full history for versioning - # Step 2: Set up Python - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} + cache: pip - # Step 3: Install dependencies - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install tox - - # Step 4: Run Tox - - name: Run Tox - env: - NASA_USERNAME: ${{ secrets.NASA_USERNAME }} - NASA_PASSWORD: ${{ secrets.NASA_PASSWORD }} - run: tox + - name: Install tox + run: python -m pip install --upgrade pip tox - # Add this to your tox job in tox.yml - - name: Run Tests with Coverage + - name: Run pytest env: NASA_USERNAME: ${{ secrets.NASA_USERNAME }} NASA_PASSWORD: ${{ secrets.NASA_PASSWORD }} - run: tox -e py312 + EARTHDATA_USERNAME: ${{ secrets.EARTHDATA_USERNAME }} + EARTHDATA_PASSWORD: ${{ secrets.EARTHDATA_PASSWORD }} + CAMS_EMAIL: ${{ secrets.CAMS_EMAIL }} + run: tox -e py${{ matrix.python-version }} + + - name: Upload coverage report + if: always() + uses: actions/upload-artifact@v4 + with: + name: coverage-py${{ matrix.python-version }} + path: test-results/coverage + if-no-files-found: ignore - format-check: + check-format: + name: black + isort + flake8 + interrogate runs-on: ubuntu-latest - steps: - - name: Check out the repository - uses: actions/checkout@v3 - - - name: Set up Python 3.12 - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: - python-version: 3.12 - - - name: Install dependencies for formatting and linting - run: | - python -m pip install --upgrade pip - pip install tox - - - name: Run Tox Check Format - run: tox -e check-format + python-version: "3.12" + cache: pip + - run: python -m pip install --upgrade pip tox + - run: tox -e check-format static-analysis: + name: mypy runs-on: ubuntu-latest - steps: - - name: Checkout full Git history - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 - - - name: Set up Python 3.12 - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: - python-version: 3.12 - - - name: Install dependencies for static analysis - run: | - python -m pip install --upgrade pip - pip install tox - - - name: Run Tox Static Analysis - run: tox -e static-analysis + python-version: "3.12" + cache: pip + - run: python -m pip install --upgrade pip tox + - run: tox -e static-analysis diff --git a/.gitignore b/.gitignore index 0972d26a..e9319dfb 100644 --- a/.gitignore +++ b/.gitignore @@ -41,9 +41,27 @@ __pycache__/ /test-results/ version.py -data/ -# Python bytecode files +# Generated artifacts under data/ — reproducible from the warehouse + recipes, +# kept off git so the repo stays small. data/ground_measurements/ IS tracked +# (canonical raw inputs), with one exception below. +/data/bundles/ +/data/training_snapshots/ + +# CrossBoundary Energy ground measurements are covered by an NDA and must +# not be checked in. The data is retained in the BigQuery warehouse + on +# Google Drive for internal modelling; the repo does not redistribute it. +/data/ground_measurements/CBE_Data/ + +# Tool caches and editor / venv state .mypy_cache/ +.pytest_cache/ .vscode/ -/data +.idea/ +.venv/ + +# Ad-hoc run logs from long migrations (e.g. b8.log, c8.log). +*.log + +# Local Claude config — per-developer prompt and agent state. +.claude/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..669e4cfa --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,508 @@ +# CLAUDE.md + +## Code Style + +### General Principles + +- Write clean, readable code. Prioritize clarity over cleverness. +- Functions should be short (aim for <20 lines in the body). If a function + does two things, split it into two functions. +- Names must be descriptive and intention-revealing. No abbreviations unless + they are universally understood in the domain. +- No magic numbers or string literals. Extract them into named constants or + config fields. +- Prefer explicit over implicit. Never rely on side effects for control flow. +- No commented-out code. Remove dead code; git history preserves it. + +### Type Annotations + +- Type-annotate every function signature, every return type, and every class + attribute. No exceptions. +- Use `torch.Tensor` (not bare `Tensor`), `list[X]` (not `List[X]`), + `dict[K, V]`, `tuple[X, ...]` with modern Python syntax. +- Use `Optional[X]` only when `None` is a semantically meaningful value, not + as a default-avoidance pattern. +- Use `Protocol` or `ABC` for structural/nominal typing of interfaces. +- Generic classes should use `Generic[T]` with meaningful type variables. + +### Classes and OOP + +- All class members must be **private** (`_` prefix) or **protected** (`_` + prefix) by default. Only expose through `@property` getters. Provide + setters only when mutation is explicitly part of the design. +- Use `@property` (not methods) for structural attributes that describe what + an object IS (e.g., `n_features`, `input_dim`, `params_type`). The `()` + on a method call implies an action or non-trivial computation; a property + signals an intrinsic characteristic. +- Use `@dataclass(frozen=True)` for all value objects and configuration. + Mutable dataclasses need strong justification. +- Use **abstract base classes** (`ABC` + `@abstractmethod`) to define + interfaces. Concrete implementations inherit from these. +- Use `Generic[T]` on ABCs when subclasses are parameterized by a type + (e.g., a factory generic over its params dataclass). Expose the concrete + type via a `@property` returning `type[T]` so that callers and tools can + construct instances without knowing the concrete subclass. +- Use **polymorphism and inheritance** meaningfully. If two classes share + behavior that varies in a specific dimension, define a base class with the + shared logic and an abstract method for the varying part. +- Use **composition over inheritance** when the relationship is "has-a" + rather than "is-a". +- Use `enum.Enum` (or `enum.StrEnum`) for categorical choices, never raw + strings. +- Each class should have a single responsibility. If a class does two + unrelated things, split it. +- Implement `__repr__` on all public-facing classes. For dataclasses this is + automatic. + +### Functions + +- Functions should do one thing. If the function name contains "and", it + probably does two things. +- Prefer pure functions (no side effects, deterministic output for given + input) wherever possible. +- Limit function arguments to 5 or fewer. If more are needed, group them + into a config or dataclass. +- Use keyword-only arguments (after `*`) for any argument that is not + self-evident from position. + +### Error Handling + +- Raise specific exceptions with informative messages. Never use bare + `except:` or `except Exception:` without re-raising. +- Validate inputs at public API boundaries. Internal functions can trust + their callers. +- Use custom exception classes for domain-specific errors (inherit from + appropriate built-in exceptions). + +### Package Structure + +- Organize code into clearly separated packages and sub-packages. Each + package should represent a coherent domain concept. +- Every package must have an `__init__.py` that explicitly exports its public + API via `__all__`. +- Avoid circular imports. If two modules need each other, extract the shared + abstraction into a third module. +- Keep module files focused. A module with more than ~300 lines likely needs + splitting. + +--- + +## Architectural Design Principles + +These principles govern how classes and modules are structured. They are not +optional style preferences; they are hard requirements that prevent the kind +of design drift that creates bugs and technical debt. + +### Classes must mirror the domain, not the implementation + +When designing a class, ask: "what is this thing mathematically / conceptually?" +The fields and methods should match that answer, nothing more. + +- If a mathematical object is defined by X and Y, the class should contain X + and Y. Do not add Z because some algorithm that consumes the object needs Z. + That algorithm should derive Z from X and Y, or Z should live on the + algorithm's side. + +### No free-floating functions for logic that has state or identity + +If a function constructs something, transforms something, or dispatches based +on type, it likely belongs as a classmethod, factory method, or method on an +enum. Free functions are acceptable only for pure stateless utilities (math +helpers, tensor manipulation). + +- Factory functions that select a subclass based on an enum value should be a + method on the enum itself, or a classmethod on the base class. +- If a function closes over parameters and returns a callable, make it a + callable class with `__call__` so the captured parameters are inspectable + via properties. Raw lambdas are acceptable only for throwaway one-liners in + tests or examples, never in production code paths. + +### Orthogonality: consumers must not know about producers + +Modules should depend on interfaces, not on each other. Specifically: + +- A downstream consumer (solver, encoder, renderer) should accept generic + inputs (matrices, tensors, callables), not a domain object. The domain + object can provide what the consumer needs via a method, but the consumer + never imports the domain class. +- A neural network encoder should consume a tensor representation, not a + symbolic domain object. The conversion between symbolic and tensor + representations happens at an explicit boundary (a dedicated converter + module), not inside the encoder. +- Notebooks and scripts should use the library's public API, not a + reimplementation of internal logic. If the API is inconvenient, fix the + API, do not duplicate it. + +### Translate at boundaries; do not modify endpoints to fit each other + +When two modules disagree on a convention (frame, units, schema), the right +fix is a translator class at the boundary, not patching either endpoint. +The endpoints stay correct relative to their own domain; the translator is +the only place that knows about both and reconciles them. This is sharper +than orthogonality: even when an endpoint *could* internalise a conversion +(e.g. via a flag), prefer an external translator class so the boundary is +named and isolated. + +- Translators carry the assumption explicitly in their type/name (e.g. a + `MetricToImperialAdapter` wrapping a metric-units sensor for an + imperial-units consumer), so the cost of the translation is visible at + the call site rather than hidden inside one of the endpoints. +- One endpoint with a configurable convention is a smell: the convention + becomes a global setting that propagates through every call. Multiple + thin translators are healthier. +- The "right" endpoint to leave alone is the one that is correct in its + own domain — a third-party library staying bit-exact with its upstream, + a data source faithfully representing the source format, etc. The + translator is the only thing that needs to know both worlds. + +### No silent fallbacks for missing data + +If a loss function requires M >= 2 samples to compute variance, and it +receives M = 1, it must raise a ValueError, not silently return zero. Silent +fallbacks hide bugs. The caller made an error (passed insufficient data) and +must be told. + +- Never return a zero tensor as a "safe default" for a loss that cannot be + computed. This creates a gradient dead zone that is invisible during + training. +- Never silently squeeze, unsqueeze, or reshape tensors to make shapes + match. If the input has the wrong number of dimensions, raise a clear error + stating the expected and actual shapes. +- When dispatching on a capability via `hasattr` or `Protocol`, the + fallback branch (the object lacks the capability) must raise a clear + error if the operation cannot produce correct results without the + capability. A silent fallback that returns a plausible but incorrect + result is worse than a crash. The only acceptable silent fallback is + when the fallback behaviour is provably correct. + +### Validation errors must contain the remediation + +When a class rejects an input at construction or call time, the +`ValueError` (or other exception) message must tell the caller exactly how +to fix it. "X is not Y" is insufficient; "X is not Y. Wrap it in `Z(...)`." +is the minimum bar. This is especially load-bearing at numerically-critical +boundaries (frame conventions, unit conventions, schema mismatches), where +a silent wrong output is the alternative to a clear error. + +- The reader of the error should not need to open the source file to fix + the problem. Include the remediation snippet, the suggested adapter, or + the exact configuration change. +- Pair with type-tagged contracts (enums, properties) so validation can + catch mismatches at construction rather than after data has flowed. +- Do not auto-correct the situation silently — the user should see the wrap + / convert / cast in their own code, not have it inserted under the hood. + Auto-correction is exactly the silent-bug pattern this rule prevents. + +### Parameters belong inside closures, not alongside them + +When a function is parameterized (e.g., a scoring function with +coefficients), the parameters should be captured at construction time, not +passed separately at every call site. + +- Bad: store `score_fn` and `score_params` as parallel fields, pass params + to fn at every evaluation. +- Good: the scoring callable captures its params in a closure. The call + site just invokes `score(state)` with no extra arguments. +- The closure should be a callable class (not a raw lambda) so that captured + parameters are accessible via a `.params` property for serialization and + debugging. + +### String-keyed dicts are not a typed interface + +When a function or factory accepts configuration through `dict[str, float]` +or `dict[str, Any]`, the caller gets no type checking, no IDE +autocompletion, and no safe refactoring. A misspelled key (`k_prodd` instead +of `k_prod`) silently produces a runtime KeyError, not a type error. + +- If a function takes more than two related parameters that could be + misspelled, group them into a frozen dataclass with named, typed fields. +- If a factory constructs objects from a parameter set, the parameter set + must be a typed dataclass, not a dict. The factory should be generic over + the params type. +- The single place where string-keyed dicts are converted to typed params is + a dedicated `from_dict()` or `params_from_dict()` method. No other code + should index into a params dict by string key. +- If two modules need to share parameter values, Python variable binding + is sufficient when parameters are typed + dataclass fields. No special "sharing" machinery is needed. + +### Structural constants and identities belong inside the class + +If a constant is intrinsic to a specific class, it must be a `ClassVar` on +that class, not a module-level variable. Module-level constants are acceptable +only for values that are genuinely module-wide (e.g., a logger instance, a +global registry). + +- When multiple classes in the same module each have their own structural + constants, module-level placement creates ambiguity about which constant + belongs to which class. +- Constants that callers might want to customize should be exposed through + the constructor with the `ClassVar` as the default, not frozen at module + scope. + +### Positional indexing into flat tensors is not an API + +If a tensor has semantic slots (e.g., params[0] = rate_constant, params[1] = +k_m), the mapping must be defined in exactly one place (a frozen dataclass +with `to_tensor()` / `from_tensor()` methods). No code outside that dataclass +should index into the tensor by position. + +### Notebooks must use the library, not reimplement it + +If a notebook contains more than 10 lines of logic that duplicates a library +class (e.g., a training loop that mirrors the Trainer), that is a bug. The +notebook should call the library's public API. If the API does not support +what the notebook needs, extend the API. + +- Trainer should return a result object that notebooks can plot directly. +- If a notebook needs a custom training variant, subclass the Trainer or + pass configuration, do not copy-paste the loop. + +### Distinguish structural properties from behavioral properties + +A domain object often has two kinds of information: what it IS (structure) +and what it DOES (behavior). These should be represented separately, and +derived properties should not be conflated with defining properties. + +- If two properties of an object seem related but can diverge in edge cases, + they are separate concepts and must be stored or computed independently. +- Properties that can be computed from defining properties should be exposed + as derived `@property` methods, not stored as redundant fields. If + computation is expensive, use caching. + +### Shared utilities live in neutral modules; layering must not invert + +When two sibling packages need the same helper, accept the small refactor +to move it to a shared, neutral module rather than reaching across package +boundaries or duplicating the helper into each consumer. Cross-package +imports that invert the natural layering are a smell that compounds. + +- A library package must never import from an application package. If a + library needs something an application provides, that something is + mis-located and belongs in a neutral top-level module that both can + import from. +- Inlining a small helper into a second consumer to avoid the refactor is + technical debt: the day the two copies diverge silently is the day the + bug ships. +- "Neutral" means the module sits at or above the level of every consumer + in the dependency graph. A helper used by two sibling packages belongs + at the top of the namespace (e.g. `/.py`), not + inside one of them. + +### Never import private symbols across module boundaries + +If a symbol has a leading underscore, it is internal to its module. Other +modules must not import it. This is not a suggestion; it is a hard boundary. + +- If module B needs to dispatch on the type of an object created by module A, + use a protocol (`hasattr` check), a registry keyed on a public type + (e.g., the params dataclass type), or a method on the object itself. Never + use `isinstance` checks against private classes from another module. +- If you find yourself importing `_FooBar` from another module, stop. Either + make it public (remove the underscore and commit to the interface) or + redesign so the import is unnecessary. + +### Prefer computable features over manual annotations for ML inputs + +When deciding what information to feed into a neural network encoder, prefer +features that can be derived from the existing data structure over manually +annotated categories. + +- Derived features (graph connectivity, statistical summaries, structural + properties) are always consistent with the underlying data, cannot be + mislabeled, and generalize to novel structures. +- Manual annotations require domain expertise, are incomplete by nature, + and risk the model shortcutting structural learning in favor of memorizing + labels. +- If the encoder struggles to distinguish important cases from structure + alone, enrich the computable features (e.g., add edge features, add + sensitivity estimates) before resorting to annotations. + +### Structural constants must have a single source of truth + +When a dimension, count, or schema is determined by one module and consumed +by another, define it in exactly one place and have all consumers derive +from that definition. Never duplicate a constant across files, even if both +files agree on the value today. + +- If a feature vector has N channels, define an Enum listing the channels in + the module that constructs the vector. Export `N = len(TheEnum)`. Consumers + import N rather than hardcoding it. +- Never store structural constants (dimensions determined by data format) in + config. Config is for hyperparameters (values the user chooses). A dimension + dictated by the code is not a hyperparameter; it is a derived property. +- If adding a new channel to a feature vector requires changing more than two + locations (the enum + the builder function), the abstraction is leaking. +- When a field has associated metadata (valid range, unit, description), + co-locate the metadata with the field definition. For dataclass fields, use + `dataclasses.field(metadata={...})`. A field name that appears in two + places (once in the dataclass, once in a separate dict of metadata keyed by + string) is a duplication bug waiting to happen. + +### Network architecture details must be configurable, not hardcoded + +The number of layers, hidden dimensions, activation functions, and +conditioning strategies in neural network components are hyperparameters. +They must live in config dataclasses, not in the constructor body. + +- Never hardcode the number of hidden layers by writing out a fixed + sequence of `nn.Linear` / activation pairs. Use a loop over a + `n_hidden_layers` config value and store layers in `nn.ModuleList`. +- If a reusable pattern emerges (e.g., "MLP with conditioning at every + layer"), extract it into its own `nn.Module` class with a clean + constructor signature. Networks that share a pattern should be instances + of that class, not copy-pasted `nn.Sequential` blocks. +- Conditioning (FiLM, concatenation, cross-attention) should be applied at + every hidden layer of a conditioned network, not only at the output. + Output-only conditioning limits the network to learning context-independent + intermediate features, which is unnecessarily restrictive. + +### Separate defining parameters from runtime inputs + +A domain object's parameters (the values that define what it IS) must be +distinguished from the inputs it receives at execution time. + +- A neural network is defined by its architecture and weights. The input + tensor is a runtime argument to `forward()`, not a constructor parameter. +- A domain model is defined by its structure and parameters. Runtime inputs + (initial states, time spans, solver settings) are passed separately at + the call site, not stored in the same config or params object. +- When a factory constructs a domain object, its params dataclass should + contain only defining parameters. +- This separation prevents conflation in sampling, serialization, and + composition. + +### Train sequential models on one-step objectives first + +When training a model that generates sequences (trajectories, time series, +autoregressive outputs), prefer a one-step prediction loss with teacher +forcing as the primary training objective. Full-rollout losses (comparing +entire generated sequences against ground truth) should be used for +validation and optional fine-tuning, not as the primary training signal. + +- Full rollout compounds early errors. By step T, the model operates on + states far from the training distribution, producing noisy, uninformative + gradients. +- Teacher forcing (always predicting from the true previous state) keeps + every training step on-distribution and provides clean gradients. +- When a probabilistic model defines a tractable per-step likelihood (e.g., + Gaussian transitions in an Euler-Maruyama SDE), use the negative + log-likelihood as the loss. This jointly trains mean and variance from a + single principled objective, eliminating arbitrary loss-weighting + hyperparameters. +- To bridge the gap between teacher-forced training and free-running + inference, use scheduled sampling: start with 100% teacher forcing, then + gradually increase the fraction of steps that use the model's own + predictions. + +--- + +## Documentation + +- **Docstrings**: Google style on all public classes, methods, and functions. + Include `Args:`, `Returns:`, and `Raises:` sections where applicable. +- Private/protected methods should have a brief one-line docstring explaining + intent, unless the name is fully self-documenting. +- Module-level docstrings at the top of each `.py` file explaining the + module's purpose in one to three sentences. +- No redundant docstrings that simply restate the function name (e.g., + `"""Gets the name."""` on a method called `get_name`). + +--- + +## Testing + +### Structure + +Tests mirror the source tree exactly. Each source file +`src//foo/bar.py` has a corresponding `tests/foo/test_bar.py`. +Every test directory has an `__init__.py`. + +### Philosophy + +- **Test external interfaces, not internal behavior.** If a method is + private (`_`-prefixed), test it through the public method that calls it. +- **Tests should explain why they exist.** Anyone reading a test should + understand what contract it verifies. Use descriptive test names like + `test_exponential_decay_matches_analytical`. +- **Use analytical references for numerical tests.** Prefer testing against + known analytical solutions rather than cross-implementation comparisons. +- **Statistical tests use wide tolerances.** Stochastic convergence tests + should use sufficient samples and wide tolerances to keep flaky failure + rate below 1%. +- **Do not test trivial behavior.** Dataclass field defaults, enum string + values, and `__repr__` methods do not need tests. Only test validation + logic (e.g., `__post_init__` that raises on invalid inputs). + +### Fixtures and Stubs + +- Use `conftest.py` files to provide stub objects that satisfy interfaces + without importing heavyweight modules. +- Downstream component tests should NOT depend on upstream components being + correct — use fakes or stubs to isolate. +- Prefer factory fixtures over mutable shared state. + +### Conventions + +- Use `pytest`. No unittest-style classes unless grouping is genuinely + needed. +- Use `pytest.fixture` for shared setup. Prefer factory fixtures over + mutable shared state. +- Use `pytest.mark.parametrize` for testing the same logic across multiple + inputs. +- Use `pytest.approx` or `torch.testing.assert_close` for numerical + comparisons. Specify tolerances explicitly. +- Mark tests that depend on optional packages with appropriate + `pytest.mark.skipif` guards. +- All tests must be fast (<5 seconds each). Use tiny configs and minimal + data sizes. +- All tests run on CPU only. + +### What to prioritize when writing new tests + +1. **Data pipeline correctness** (corrupted inputs silently poison + everything downstream). +2. **Loss function numerics** (subtle sign/normalization bugs are silent and + devastating). +3. **Interface contracts** (type hierarchy, protocol compliance, batch vs. + single-item consistency). +4. **Factory/builder correctness** (config-to-object mapping dispatches to + the correct classes). + +### What to skip + +- `__repr__` methods. +- Dataclass field defaults (unless validation logic exists). +- Full training loop integration (too slow for unit tests). +- External service integrations (require live credentials). + +--- + +## PyTorch Conventions + +- All `nn.Module` subclasses must call `super().__init__()` first. +- Define sub-modules in `__init__`, compute in `forward`. No module creation + inside `forward`. +- Name tensor dimensions in comments where shapes are non-obvious, e.g., + `# (batch, n_features, d_model)`. +- Use `torch.no_grad()` explicitly for inference and evaluation. +- Prefer `torch.nn.functional` for stateless operations; use `nn.Module` + wrappers for stateful ones (layers with parameters). +- **Never loop over a batch dimension in Python.** If a computation applies + the same operation to N independent items (N transitions, N samples, N + trajectories), reshape them into a single batch tensor and run one forward + pass. Python loops over individual vectors launch thousands of tiny GPU + kernels; a single batched operation launches one. The speedup is typically + 10-100x. If a function needs to support both single-item and batched input, + handle it via `dim()` checks at the entry point, not by looping internally. + +--- + +## Git Practices + +- Commit messages should be imperative and concise: "Add data loader" + not "Added the data loader". +- One logical change per commit. Do not mix refactoring with feature + additions. +- Run `tox` (or at minimum `pytest` + linting) before every commit. + diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..09282d0a --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,10 @@ +include README.md +include LICENSE +include requirements.txt +include pyproject.toml +recursive-include src/susse *.py +recursive-include src/susse/api_clients *.yml +recursive-include warehouse/sql *.sql +global-exclude __pycache__ +global-exclude *.py[cod] +global-exclude *.egg-info diff --git a/README.md b/README.md index cac0c9ca..93728932 100644 --- a/README.md +++ b/README.md @@ -1,99 +1,123 @@ -# SuSSE -Sub-Saharan Solar Estimation +# SuSSE — Sub-Saharan Solar Estimation -## Building & Install Package +SuSSE is a Python library for **correcting satellite estimates of global +horizontal irradiance (GHI) against ground-station measurements over +Sub-Saharan Africa**. It packages the model from Mukiibi & Mikelson (2026, +IEEE, forthcoming) together with the data infrastructure needed to keep +the model retrainable, deployable, and re-validatable end-to-end. -To install the project use pip -``` -pip install . -``` +## What it does +| Stage | Input | Output | +|---|---|---| +| **Ingest** | Daily NASA POWER + CAMS + MERRA-2 satellite/reanalysis fields and ground GHI from partner station networks | Curated BigQuery warehouse (`solar_warehouse`) | +| **Assemble** | A `FeatureSelection` (which variables, which sources, which QC level) + a date range | A `TrainingDataset` snapshot — a parquet artifact tied to a specific warehouse state | +| **Preprocess** | `TrainingDataset` + a `FeatureSpec` (cleaners + derived features) | A `PreprocessedDataset` ready to fit | +| **Train** | `PreprocessedDataset` + model `Params` | A `TrainedBundle` — a self-contained directory the inference layer can load | +| **Validate** | `TrainedBundle` + an external reference (e.g. Katongole et al. 2023) | Paper-style metric tables (RMSE / MBE / IOA / …) | -If you want to install the package in developer mode, which means that the changes you make in source code will directly be reflected in the package, then use +The companion Flask app `Irradiation_Portal` (separate repository) consumes +a `TrainedBundle` and serves bias-corrected GHI predictions at any +`(lat, lon, date)` over Sub-Saharan Africa. + +## Repository layout ``` -pip install -e . +src/susse/ Library code (warehouse_ops, datasets, preprocessing, + models, training, evaluation, inference, api_clients). +tests/ pytest suite — mirrors src/susse/ one-to-one. +notebooks/ + tutorial/ 01–07 end-to-end walk-through of the pipeline. + inspection/ Day-to-day analysis (one notebook = one question). + papers// Per-publication validation, data co-located. +warehouse/ + sql/ DDL + functions + gold views. + migrations/ One-shot, idempotent scripts that mutate the warehouse. +data/ + ground_measurements/ Canonical raw ground-truth CSVs (tracked in git). + bundles/ Fitted-model bundles (gitignored, regenerable). + training_snapshots/ Parquet dataset snapshots (gitignored, regenerable). +docs/architecture.md Library-internals reference for contributors. ``` +## What's shipped vs planned + +| Component | Status | +|---|---| +| Warehouse population (NASA POWER, CAMS, MERRA-2, MODIS, ground) | shipped | +| `FeatureService` / `FeatureSelection` / dataset snapshots | shipped | +| `FeatureSpec` cleaners + derived features (clear-sky index, day-of-year, altitude) | shipped | +| Random Forest / Linear / Mean Baseline models with the unified `Trainer` | shipped | +| `TrainedBundle` save/load + W&B artifact integration | shipped | +| Tutorial 06 (evaluation: `Splitter` ABC, `Metric` ABC, `Evaluator`, generic eval plots) | shipped | +| Tutorial 07 (inference API: `Predictor.from_bundle_dir` + `predict(coords, start_date, end_date)`) | shipped | +| `susse.inference.Predictor` with `on_cache_miss="raise"` and on-demand `"fetch"` mode | shipped | +| Recomputation of Mukiibi & Mikelson (2026) | shipped — see `notebooks/papers/mukiibi_mikelson_2026/` | +| `inspection/` notebooks (variable distributions, residual analysis, …) | **planned** — built on demand | + +## Install + +The recommended workflow uses a project-local virtualenv on Python 3.12. + +```bash +git clone https://github.com/jansurfsdown/Solar_irradiation.git +cd Solar_irradiation +python3.12 -m venv .venv +source .venv/bin/activate +pip install -e . +``` -## Tox and Testing - -We use Tox for managing different testing and formatting environments, ensuring that the codebase remains clean, consistent, and properly tested. Tox allows us to automate tests, static analysis, and formatting checks both locally and in our continuous integration (CI) setup on GitHub. - -### Local Testing with Tox - -To test your changes locally, you can use the following Tox commands: - -- **Formatting Code**: - Ensures the code follows the correct formatting standards. - ```bash - tox -e format-code - ``` - -- **Formatting Imports**: - Organizes and formats the import statements according to the project style guide. - ```bash - tox -e format-imports - ``` - -- **Check Format**: - This runs checks to ensure that the code and import formatting are correct, without making any changes. - ```bash - tox -e check-format - ``` - -- **Static Analysis**: - Runs static analysis using MyPy to catch type errors or other issues before running tests. - ```bash - tox -e static-analysis - ``` - -- **Running Tests**: - Runs the tests located in the tests folder and generates a coverage report. - ```bash - tox -e py312 - ``` - -Each of these steps is configured in the `tox.ini` file located at the root of the project. - -### GitHub Workflow and Continuous Integration - -When you push your code to GitHub, a series of automated checks will run through GitHub Actions. The `.github/workflows/tox.yml` file defines the steps to be performed: - -1. **Tox CI**: This job runs on every push or pull request to the main branch. It installs Tox and runs all the tests, static analysis, and formatting checks using Tox. - - - It uses the same commands listed above to ensure that your branch is compliant with the project’s guidelines. - -2. **Format Check**: This job ensures that the code is correctly formatted. - -3. **Static Analysis**: This job runs MyPy to ensure there are no type errors in the code. - -### How to Contribute: Git Workflow - -When making changes, **do not push directly to the `main` branch**. Follow these steps instead: - -1. **Create a New Branch**: Your branch name should start with your initials followed by a descriptive name (e.g., `jm/new_feature`). - - ```bash - git checkout -b jm/new_feature - ``` - -2. **Make Your Changes**: After you’ve made changes, ensure your branch is compliant by running all the necessary Tox commands locally: +For warehouse access you also need Application Default Credentials for +Google Cloud: - ```bash - tox -e format-code - tox -e format-imports - tox -e check-format - tox -e static-analysis - tox -e py312 # to run the tests - ``` +```bash +gcloud auth application-default login +gcloud config set project solar-irradiation-estimation +``` -3. **Push Your Branch**: Push the branch to GitHub. +External data sources require these environment variables in `.env` at +the repo root (used only by the corresponding ingest paths — read-only +notebook usage does not need any of them): - ```bash - git push origin jm/new_feature - ``` - -4. **Create a Pull Request**: Once the branch is pushed, create a pull request and assign me (your supervisor) as a reviewer. Make sure that all checks on GitHub pass before requesting a review. +``` +CAMS_EMAIL= +EARTHDATA_USERNAME= +EARTHDATA_PASSWORD= +``` -This workflow ensures that the code remains high-quality, consistent, and error-free before merging into the main branch. \ No newline at end of file +## Where to start reading + +1. `notebooks/tutorial/01_data_overview.ipynb` — what's in the + warehouse, with an interactive map of every coordinate it knows about. +2. `notebooks/tutorial/02_query_the_warehouse.ipynb` → + `03_preprocessing.ipynb` → `04_models.ipynb` → `05_training.ipynb` + — the assembly → training → bundle path, in order. +3. `notebooks/tutorial/06_evaluation.ipynb` → + `07_inference.ipynb` — evaluation (splitters, metrics, plots) and + the portal-facing `Predictor` (coords + date range → bias-corrected + daily GHI), including the on-demand warehouse-fetch fallback. +4. `notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb` — a + complete worked example: warehouse → preprocessor → RF → Katongole + validation, with deliberate paper-faithful choices documented inline. +5. `warehouse/extending_the_warehouse.ipynb` — for contributors who + need to add new variables, sources, or stations to the warehouse. +6. `docs/architecture.md` — library internals. + +## Developer workflow + +| Task | Command | +|---|---| +| Run tests with coverage | `tox -e py312` | +| Auto-format the source tree | `tox -e format` | +| Check formatting + lint + docstring coverage | `tox -e check-format` | +| Static type checking with mypy | `tox -e static-analysis` | + +CI runs all three checks plus pytest on every PR. See +[.github/workflows/tox.yml](.github/workflows/tox.yml) for the +configuration. + +**Branches.** Don't push to `main`. Branch as `/`, +e.g. `jm/add_model`. Open a pull request and request review. + +**Commits.** One logical change per commit. Imperative, present-tense +subjects (`Add bias-correction RF model`, not `Added the model`). diff --git a/data/external_references/katongole_2023_monthly.csv b/data/external_references/katongole_2023_monthly.csv new file mode 100644 index 00000000..000cc42d --- /dev/null +++ b/data/external_references/katongole_2023_monthly.csv @@ -0,0 +1,57 @@ +location,latitude,longitude,altitude,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Av +Ndejje ss,0.6106,32.47527,1223,2.6,3,4.1,4.8,5.2,5.5,5.6,5.1,4,3.6,2.9,2.4,4.1 +Entebbe WME,0.048611,32.4725,1149,4.6,4.3,4.6,4.3,4.1,3.7,4,4,4,4.3,4.1,4.1,4.2 +Wanyange GS,0.483497,33.24372,1346,4.7,4.9,5.1,4.8,4.5,4.2,4,4.4,4.5,4.6,4.6,4.5,4.6 +Busitema UN,0.756919,34.04617,1119,4,4.2,4,5.1,4.9,3.9,3.1,4.6,5,4.6,4.9,4,4.4 +Kazwama HC,1.332253,32.65002,1058.2,5.1,4.6,5.1,4.9,4.7,4.1,4.3,4.5,4.6,4.9,4.7,4.8,4.7 +Mityana SS,0.377506,32.03904,1245,4.1,4.3,4.5,4.2,3.9,3.9,3.8,3.8,3.9,3.8,3.9,3.9,4 +Masindi 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+2017-11-21,7624.6232805555555,tororo,0.6978,34.17151 +2017-11-22,4305.020630555556,tororo,0.6978,34.17151 diff --git a/data/ground_measurements/README.md b/data/ground_measurements/README.md new file mode 100644 index 00000000..ea543c8e --- /dev/null +++ b/data/ground_measurements/README.md @@ -0,0 +1,56 @@ +# Ground measurements — canonical raw inputs + +This directory holds the daily ground-truth GHI CSVs that the SuSSE warehouse +ingests as training data. The files here are the **only** authoritative source +for ground GHI; the warehouse tables `ground_measurements_raw` and +`ground_measurements` are derived from them by `GroundIngestJob` + +`curate_ground` (see [`src/susse/warehouse_ops/population/`](../../src/susse/warehouse_ops/population/)). + +The folder is checked into git so any contributor can run NB 01's ingest demo +end-to-end immediately after `git clone`. + +## Subfolders + +| Subfolder | Source | Coverage | +|---|---|---| +| `MAK_physics_dept/` | Makerere University Physics Department station archive (Uganda) | 3 station files: `kampala.csv`, `lira.csv`, `tororo.csv`. ~5,000 rows each. | +| `ministry_energy_ug/` | Uganda Ministry of Energy and Mineral Development | 2 station files: `soroti.csv`, `wadelai.csv`. ~700 rows each. | + +> **CrossBoundary Energy data is *not* checked in.** The 6 country files +> (egypt / ghana / kenya / madagascar / nigeria / somalia) are covered by a +> non-disclosure agreement and may not be distributed publicly. They have +> already been ingested into the BigQuery warehouse and the model bundle +> the portal serves; the originals are retained on Google Drive for +> internal use. The directory is also listed in `.gitignore` so dropping a +> fresh copy into `data/ground_measurements/CBE_Data/` does not +> accidentally re-introduce it. + +## Schema + +Every CSV in every subfolder shares the same schema — handled by a single +`StandardCsvAdapter` in [`adapters.py`](../../src/susse/warehouse_ops/population/adapters.py): + +| Column | Type | Notes | +|---|---|---| +| `datetime` | ISO date string | Daily-resolution; one row per (date, location). | +| `ghi` | float | **Daily total GHI in Wh/m²/day** (raw values; curation converts to kWh/m²/day during warehouse ingest). | +| `location` | string | Station identifier. Makerere / Ministry-of-Energy files use the station name (`kampala`, `soroti`, …); the (private) CBE country files use `_location`. | +| `latitude` | float | Decimal degrees. | +| `longitude` | float | Decimal degrees. | + +## Adding a new ground source + +If the new provider's CSV matches this schema, drop the file under a new +subfolder here and run the existing `StandardCsvAdapter` (NB 01, Pattern 3). +If the layout differs, subclass `GroundSourceAdapter` and implement +`parse(file_path) -> DataFrame` returning the canonical schema above. +The downstream curation + MERGE pipeline is unchanged — the adapter is the +only thing that knows about the partner's column layout. + +## What does NOT live here + +- **External validation datasets** (e.g. Katongole 2023) live under + `data/external_references/`, not here. This directory is reserved for + ground truth that we curate ourselves and feed into training. +- **Generated artifacts** (training-snapshot parquets, fitted-model bundles) + live under `data/training_snapshots/` and `data/bundles/`, both gitignored. diff --git a/data/ground_measurements/ministry_energy_ug/soroti.csv b/data/ground_measurements/ministry_energy_ug/soroti.csv new file mode 100644 index 00000000..8aee5fae --- /dev/null +++ b/data/ground_measurements/ministry_energy_ug/soroti.csv @@ -0,0 +1,700 @@ +datetime,ghi,location,latitude,longitude +2020-02-01,2550.0,soroti,1.7153,33.6186 +2020-02-02,6100.0,soroti,1.7153,33.6186 +2020-02-03,4690.0,soroti,1.7153,33.6186 +2020-02-04,6690.0,soroti,1.7153,33.6186 +2020-02-05,6030.0,soroti,1.7153,33.6186 +2020-02-06,5240.0,soroti,1.7153,33.6186 +2020-02-07,7560.0,soroti,1.7153,33.6186 +2020-02-08,7550.0,soroti,1.7153,33.6186 +2020-02-09,7340.0,soroti,1.7153,33.6186 +2020-02-10,7480.0,soroti,1.7153,33.6186 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a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 00000000..c1a3d271 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,147 @@ +# Library architecture + +Reference for the SuSSE warehouse-population code. The user-facing +walkthrough lives in [`warehouse/extending_the_warehouse.ipynb`](../warehouse/extending_the_warehouse.ipynb); +this document is for engineers who need to extend or debug the library +itself. + +## Three layers, top-down + +``` +┌──────────────────────────────────────────────────────────────────────────┐ +│ External sources │ +│ NASA POWER (REST/JSON) CAMS (pvlib) MERRA-2 (OPeNDAP) │ +│ Ground CSVs (disk/GCS) │ +└─────────────────────────────────────┬────────────────────────────────────┘ + │ raw fetch +┌─────────────────────────────────────▼────────────────────────────────────┐ +│ susse.api_clients/ — raw I/O, source-specific protocols │ +│ NASAPowerFetchData CAMSClient MerraDailyFetcher │ +│ GroundSourceAdapter (StandardCsvAdapter, …) │ +└─────────────────────────────────────┬────────────────────────────────────┘ + │ long-format DataFrame +┌─────────────────────────────────────▼────────────────────────────────────┐ +│ susse.warehouse_ops.population/ — typed plans, jobs, loaders │ +│ │ +│ Plans NamedLocationsPlan, GridPlan, GroundFilePlan │ +│ Jobs NasaPowerSatelliteJob, CamsSatelliteJob, MerraRegionJob, │ +│ ModisJob (BaseSatelliteJob) GroundIngestJob │ +│ Helpers VariableCatalog, CoverageRepository, MergeLoader, │ +│ validators │ +└─────────────────────────────────────┬────────────────────────────────────┘ + │ MERGE via staging table +┌─────────────────────────────────────▼────────────────────────────────────┐ +│ susse.warehouse_ops.io/ — BigQuery surface │ +│ BigQueryClient, WarehouseConfig, TableSchemas, TableRefs │ +│ feature_service, repositories │ +└─────────────────────────────────────┬────────────────────────────────────┘ + │ SQL +┌─────────────────────────────────────▼────────────────────────────────────┐ +│ BigQuery — solar-irradiation-estimation.solar_warehouse │ +│ nasa_daily_vars_long, cams_daily_vars_long, merra_daily_vars_long, │ +│ irradiance_daily, ground_measurements, ground_measurements_raw, │ +│ dim_variable │ +└──────────────────────────────────────────────────────────────────────────┘ +``` + +## What each layer does + +**`susse.api_clients/`** — knows about specific external services. +`NASAPowerFetchData` builds NASA's URL, batches requests under their +20-vars-per-call limit, parses their JSON. `CAMSClient` wraps +`pvlib.iotools.get_cams` and handles the SoDa email handshake. +`MerraDailyFetcher` authenticates with NASA Earthdata, fetches MERRA-2 +hourly data via OPeNDAP, and aggregates to daily values via +cosine-zenith weighting. Each `GroundSourceAdapter` parses one +provider's CSV layout. Replace one of these classes when a vendor +changes their API; nothing higher up has to know. + +**`susse.warehouse_ops.population/`** — the heart of ingest. A +`FetchPlan` (one of three subclasses) describes *what* to fetch. A +`Job` runs the plan: pre-checks coverage to skip already-loaded rows, +calls the api_client for the missing slice, validates and reshapes the +result, then hands off to a `MergeLoader` for an idempotent server-side +MERGE. `VariableCatalog` is the single source of truth for what +variables exist and where they live in the warehouse. + +**`susse.warehouse_ops.io/`** — the BigQuery surface. +`BigQueryClient` is a thin wrapper around `google-cloud-bigquery`. +`WarehouseConfig` and `TableRefs` are the only place the project ID and +table names are spelled out — every other module imports symbols from +here. `feature_service` and `repositories` are read-side helpers used +by downstream notebooks (02 onwards) to assemble training datasets. + +## Three architectural rules + +These rules guide the layering and prevent drift: + +1. **Orthogonality.** A `Job` doesn't import from a specific api_client; + it accepts a plan and dispatches by `Source`. An api_client doesn't + know about BigQuery; it returns a DataFrame. The library layer + mediates between the two. Swapping NASA POWER's API or BigQuery's + client library is a localised edit, not a refactor. + +2. **Idempotency at every boundary.** Every MERGE upserts on the + schema's declared keys, every coverage check answers "what's already + here?" before any API call, every script in `warehouse/migrations/` + is safe to re-run. Ctrl+C is always safe. + +3. **Typed configs over string dicts.** `WarehouseConfig`, + `VariableSpec`, `TableSchema`, the `FetchPlan` subclasses, + `MergeSpec` are all frozen dataclasses. A misspelled key fails at + construction time, not as a silent KeyError three minutes into an + ingest run. + +## Operational practices + +### Migrations vs notebook cells + +Two distinct workflows touch the warehouse: + +* **Reference / debugging** lives in `warehouse/extending_the_warehouse.ipynb`. + Read-only by default. Useful for "did this work?", "what does this + plan look like?", "which station has zero pairs?". +* **One-shot operational changes** live in `warehouse/migrations/*.py`. + Each script does one well-defined warehouse mutation: create a table, + backfill rows, run a multi-station ingest. Idempotent — re-running is + a no-op. Versioned in git, dated by filename. + +When you want to run a multi-station ingest, **don't flip +`RUN_INGEST=True` and rerun the whole notebook**. Write or invoke a +migration. The [`warehouse/migrations/README.md`](../warehouse/migrations/README.md) +documents the convention and indexes every migration that's been +applied. + +Rule of thumb: if it changes BigQuery state and you'd ever want to +re-run it the same way, it's a migration. + +### `dim_variable` is the canonical catalog + +The `VariableCatalog` class lists every satellite variable SuSSE +ingests, with metadata (units, valid range, spatial resolution, +description) that mirrors the `dim_variable` table's schema. To add a +new variable, add a `VariableSpec` to the catalog and run +`populate_dim_variable()` — typically wrapped in a migration alongside +the data ingest that needs the new variable. Ingest jobs and downstream +feature assembly pick it up without further changes. + +### Idempotency contract + +Every operation in the system has the same property: re-running +produces the same end state and only does work for what's missing. This +is the foundation that makes long-running ingests safe to interrupt. + +* `MergeLoader` upserts on `TableSchema.merge_keys`. A second MERGE + with the same staging data updates existing rows in place; no + duplicates. For long date ranges that exceed BigQuery's + 4,000-partitions-per-DML limit, the loader chunks the MERGE + automatically. +* `BaseSatelliteJob.run` calls `CoverageRepository.existing_long_keys` + and `existing_irradiance_keys` before fetching, scoped to the plan's + geohashes. Locations whose data is already cached skip the API call + entirely. +* Migration scripts use `CREATE TABLE IF NOT EXISTS` and pre-condition + checks; a no-op re-run is a normal outcome. + +If you ever find a code path that *isn't* idempotent, treat it as a +bug. diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 00000000..05c6609c --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,81 @@ +# Notebooks + +Three lanes, distinct audiences: + +``` +notebooks/ + tutorial/ ← system overview; read 01 → 07 in order to learn the codebase + inspection/ ← day-to-day model & data inspection (one question per file) + papers/ ← per-publication validation analyses, with data co-located +``` + +## `tutorial/` — end-to-end pipeline walkthrough + +Sequential. Each notebook delivers one slice of the pipeline and consumes +the previous one's output; read in order, re-run in order if you want to +regenerate the canonical artifacts. + +| # | Notebook | Status | +|---|---|---| +| 01 | `01_data_overview.ipynb` — what's in the warehouse (tables, catalog, interactive map of every coordinate) | shipped | +| 02 | `02_query_the_warehouse.ipynb` — `FeatureSelection` → `FeatureService` → `TrainingDataset` snapshot, plus raw-SQL examples | shipped | +| 03 | `03_preprocessing.ipynb` — `FeatureSpec` → `PreprocessedDataset` | shipped | +| 04 | `04_models.ipynb` — model factory (mean baseline / RF / linear) and the `Params` pattern | shipped | +| 05 | `05_training.ipynb` — `Trainer`, `TrainedBundle`, W&B integration | shipped | +| 06 | `06_evaluation.ipynb` — `Splitter` ABC (random / temporal / station-LOSO / spatial-spread / spatial-block) + `Metric` ABC + `Evaluator` + generic eval plots | shipped | +| 07 | `07_inference.ipynb` — `Predictor` class (`Predictor.from_bundle_dir` + `predict(coords, start_date, end_date)`) + folium choropleth of the Uganda 2024 grid | shipped | + +The tutorial notebooks **do** train models and write artifacts (under +`data/training_snapshots/`, `data/bundles/`); they're the canonical way +to regenerate those gitignored artifacts after a fresh clone. + +The warehouse-extension content (ingest patterns, RUN_INGEST=True +walkthroughs, contributor recipes) lives separately at +[`warehouse/extending_the_warehouse.ipynb`](../warehouse/extending_the_warehouse.ipynb) +because most readers will only query the warehouse, not modify it. + +## `inspection/` — analysis surface + +Day-to-day investigation: variable distributions, residual analysis, feature +importances, model-vs-model comparison. Two firm rules: + +- **Read-only with respect to artifacts.** Inspection notebooks consume + `load_bundle(...)` / `load_snapshot(...)` and `FeatureService` for + inference. They do NOT call `Trainer.train()` and do NOT write models or + datasets. If a notebook here needs a new fitted model, training has to + move out of the tutorial track first. +- **One notebook = one question.** No catch-all `analysis.ipynb`. + +Initial inventory (build on demand, not pre-populated): + +- `variable_distributions.ipynb` — per-source histograms, missingness, station coverage +- `residual_analysis.ipynb` — model errors vs AOD / cloud / month / altitude / station +- `per_station_performance.ipynb` — MAE/R² per station, regional choropleth +- `feature_importance.ipynb` — RF importances + partial dependence +- `model_comparison.ipynb` — side-by-side scoring of multiple bundles + +## `papers/` — per-publication validation + +Per-paper validation analyses. Each subfolder is self-contained: data +file(s), the validation notebook, and a `README.md` documenting the source +and extraction method. + +External validation datasets live **here** alongside the analysis that +consumes them — NOT in the BigQuery warehouse. The warehouse is reserved +for prefetched satellite/auxiliary data and curated training ground-truth. + +Current papers: + +- [`mukiibi_mikelson_2026/`](papers/mukiibi_mikelson_2026/) — Mukiibi & Mikelson + (2026, IEEE, forthcoming). Reproduces the Random Forest GHI bias-correction + model on the refactored SuSSE library and validates against Katongole et al. + (2023, *Tanzania Journal of Science*) 54-station monthly climatology + (the reference CSV lives co-located inside the paper folder). The fitted + bundle is also the model the companion `Irradiation_Portal` Flask app loads. + +## Path conventions + +All notebooks (tutorial, inspection, papers) sit two levels below the repo +root, so relative paths take the shape `../../data/...`, +`../../src/...`, `../../docs/...`. When creating a new notebook, follow +that convention rather than absolute paths so the repo stays portable. diff --git a/notebooks/inspection/README.md b/notebooks/inspection/README.md new file mode 100644 index 00000000..c6b944cf --- /dev/null +++ b/notebooks/inspection/README.md @@ -0,0 +1,31 @@ +# Inspection notebooks + +Day-to-day model & data inspection. See [../README.md](../README.md) for +the broader notebook structure. + +## Current notebooks + +*No notebooks here yet.* The interactive coverage map that used to live +here is now [tutorial 01](../tutorial/01_data_overview.ipynb) — it's +the first thing every reader sees, so the tutorial track is the better +home for it. + +## On the backlog + +Built on demand alongside actual analysis needs (in rough priority order): + +- `variable_distributions.ipynb` — per-source histograms, missingness, station coverage +- `residual_analysis.ipynb` — model errors vs AOD / cloud / month / altitude / station +- `per_station_performance.ipynb` — MAE / R² per station, regional choropleth +- `feature_importance.ipynb` — RF importances + partial dependence +- `model_comparison.ipynb` — side-by-side scoring of multiple bundles + +## Rules + +- **Read-only with respect to artifacts.** Consume `load_bundle(...)` / + `load_snapshot(...)` and `FeatureService` for inference. Do NOT call + `Trainer.train()` and do NOT write models or datasets. +- **One notebook = one question.** No catch-all `analysis.ipynb`. +- **Path convention.** Relative paths take the shape `../../data/...`, + `../../src/...`. The notebook sits at `notebooks/inspection/.ipynb`, + two levels below the repo root. diff --git a/notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb b/notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb new file mode 100644 index 00000000..dafd3721 --- /dev/null +++ b/notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb @@ -0,0 +1,1079 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01 — Recomputation: Mukiibi & Mikelson (2026)\n", + "\n", + "This notebook recomputes the analysis published in:\n", + "\n", + "> Mukiibi, R. & Mikelson, J. (2026). *A Machine Learning Approach for GHI\n", + "> Bias Correction: Validation of Random Forest Performance Across\n", + "> Sub-Saharan Africa.* IEEE (forthcoming).\n", + "\n", + "Headline numbers from the paper:\n", + "\n", + "| | RMSE | MAE | R² | IOA |\n", + "|---|---|---|---|---|\n", + "| **RF (Table IV, this paper)** | **0.57** | **0.43** | 0.39 | **0.60** |\n", + "| CAMS | 1.29 | 1.19 | 0.52 | 0.47 |\n", + "| NASA CERES | 1.24 | 1.13 | 0.46 | 0.46 |\n", + "\n", + "The paper also reports daily-scale RF metrics on a 2-station held-out\n", + "fold (Table III: RMSE 0.41, MAE 0.32, R² 0.86). The recomputation\n", + "reproduces **both** tables:\n", + "\n", + "* **Table III** is reproduced by the in-distribution 2-station spatial\n", + " holdout (§§4–5).\n", + "* **Table IV** is reproduced by the §6 validation against the\n", + " Katongole 54-station network — an out-of-distribution generalisation\n", + " test on a completely different ground-sensor network.\n", + "\n", + "## Pipeline overview\n", + "\n", + "| Step | Library object | What it does |\n", + "|---|---|---|\n", + "| Data assembly | `FeatureService.build_training_pairs` | Joins ground GHI with NASA POWER + CAMS satellite features |\n", + "| Station selection | `auxscripts.pick_training_and_holdout_stations` | Drops the 4 shortest-coverage stations, picks the 2 spatial-holdout stations as the max-pairwise-haversine pair |\n", + "| Cleaning + features | `FeatureSpec` + `Preprocessor` | Paper §II.A curation rules and derived features (clear-sky index, day-of-year sin+cos, altitude, longitude) |\n", + "| Train | `Trainer` + `RandomForestParams` | 200-tree RF with `min_samples_leaf=5`; reports Table III on the held-out 2 stations |\n", + "| Save | `TrainedBundle` (auto) | Self-contained directory the Flask portal loads |\n", + "| Validate | `auxscripts.build_katongole_inference_frame` + `auxscripts.score_table_iv` | Reproduces paper Table IV against the Katongole 2017–2022 climatology |\n", + "\n", + "## One methodological deviation from the paper\n", + "\n", + "**Validation calibration ratio.** The Katongole dataset uses a different\n", + "pyranometer network (TAHMO ATMOS 41) from our training data (MEMD /\n", + "CrossBoundary research-grade pyranometers). At the one site where both\n", + "networks measure (Makerere University), the two read systematically\n", + "different — TAHMO ~14–18 % lower. Sections 6.3 and 6.4 measure this\n", + "offset and apply a single annual rescaling before computing the final\n", + "metrics. The relative-improvement claim (RF vs raw satellites) is\n", + "preserved under any monotonic rescaling." + ] + }, + { + "cell_type": "markdown", + "id": "54e2a58f", + "metadata": {}, + "source": [ + "### Inputs, outputs, prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Warehouse inputs** | `ground_measurements` (28 stations), `nasa_daily_vars_long`, `cams_daily_vars_long`, `irradiance_daily` |\n", + "| **Local input** | `data/external_references/katongole_2023_monthly.csv` (54 sites × 12 months, 2017–2022 climatology — shared across papers, not paper-specific) |\n", + "| **Output bundle** | `data/bundles/mukiibi_mikelson_2026/` — what the portal loads |\n", + "| **Optional output** | If `LOG_TO_WANDB=True`, a W&B run + artifact |\n", + "| **Prerequisite — local** | Application Default Credentials for BigQuery (`gcloud auth application-default login`) |\n", + "| **Prerequisite — Colab** | See the bootstrap cell below |\n", + "| **Prerequisite — warehouse** | Migration A12 must have been applied (`warehouse/migrations/2026-05-11_a12_ingest_katongole_2017_2022.py --apply`). The pre-flight in §6 raises if it hasn't. |\n", + "| **Wall time** | ~5 min on a workstation |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa02edeb", + "metadata": {}, + "outputs": [], + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens — give it `repo` scope and a\n", + "# sensible expiry. The repository is private, so the clone needs\n", + "# this token (or an SSH key that Colab knows about, which is more\n", + "# fiddly to set up).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on. The bootstrap\n", + "# below picks it up automatically.\n", + "# 3. When the Google auth prompt appears, sign in with the account\n", + "# that has BigQuery read access to `solar-irradiation-estimation`.\n", + "#\n", + "# Re-running this cell is fast and idempotent.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3eebef9", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c636b9c", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pygeohash\n", + "\n", + "# Locate project root by walking up until we see src/susse/. Works both\n", + "# locally (notebook cwd is the notebook's folder) and in Colab (after the\n", + "# bootstrap cell cd's into /content/Solar_irradiation).\n", + "_HERE = Path.cwd().resolve()\n", + "_PROJECT_ROOT = _HERE\n", + "while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / \"src\" / \"susse\").exists():\n", + " _PROJECT_ROOT = _PROJECT_ROOT.parent\n", + "_NB_DIR = _PROJECT_ROOT / \"notebooks\" / \"papers\" / \"mukiibi_mikelson_2026\"\n", + "sys.path.insert(0, str(_PROJECT_ROOT / \"src\"))\n", + "sys.path.insert(0, str(_NB_DIR))\n", + "\n", + "# Companion helpers — see auxscripts.py for the implementations.\n", + "import auxscripts\n", + "\n", + "print(f\"project root: {_PROJECT_ROOT}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6662e79e", + "metadata": {}, + "outputs": [], + "source": [ + "# ----- Top-level configuration -----\n", + "# Paper-recomputation parameters. Override these to deviate from the\n", + "# paper's setup; document any deviation in §9.\n", + "\n", + "# W&B run/artifact tracking. Defaults to OFF. The first run with logging\n", + "# on auto-creates the project under your default W&B entity; flip the\n", + "# project to Public via Project settings → Privacy if you want to share\n", + "# the link with collaborators.\n", + "WANDB_PROJECT = \"susse-mukiibi-mikelson-2026\"\n", + "WANDB_ENTITY = None\n", + "LOG_TO_WANDB = False\n", + "WANDB_TAGS = (\"paper-recomputation\", \"random-forest\")\n", + "\n", + "# Paper-faithful Random Forest hyperparameters (paper §III).\n", + "RF_N_ESTIMATORS = 200\n", + "RF_MIN_SAMPLES_LEAF = 5\n", + "RF_RANDOM_STATE = 42\n", + "\n", + "# Paper-faithful station selection: 24 training + 2 spatial holdout\n", + "# (paper Table III), picked deterministically by the heuristic in\n", + "# `auxscripts.pick_training_and_holdout_stations` so anyone running\n", + "# this notebook lands on the same stations.\n", + "N_TRAINING_STATIONS = 24\n", + "N_HOLDOUT_STATIONS = 2\n", + "\n", + "# Validation window for the Katongole §6 comparison. Apples-to-apples\n", + "# climatology vs climatology — see auxscripts.assert_katongole_coverage.\n", + "VALIDATION_START_YEAR = 2017\n", + "VALIDATION_END_YEAR = 2022\n", + "\n", + "BUNDLES_ROOT = (_PROJECT_ROOT / \"data\" / \"bundles\").resolve()\n", + "BUNDLE_DEST = BUNDLES_ROOT / \"mukiibi_mikelson_2026\"\n", + "KATONGOLE_CSV = (\n", + " _PROJECT_ROOT / \"data\" / \"external_references\" / \"katongole_2023_monthly.csv\"\n", + ")\n", + "assert KATONGOLE_CSV.exists(), f\"Missing reference data at {KATONGOLE_CSV}\"\n", + "print(f\"Bundle dest: {BUNDLE_DEST}\")\n", + "print(f\"Validation window: {VALIDATION_START_YEAR}-{VALIDATION_END_YEAR}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5d34aafb", + "metadata": {}, + "source": [ + "## 1 — Paper-faithful feature selection\n", + "\n", + "Paper Table II lists 39 predictors. They split into four groups:\n", + "\n", + "| Group | Source table | Field on `FeatureSelection` |\n", + "|---|---|---|\n", + "| NASA POWER auxiliary (24 of 39) | `nasa_daily_vars_long` | `nasa_variable_ids` |\n", + "| CAMS clear-sky auxiliary (3 of 39) | `cams_daily_vars_long` | `cams_variable_ids` |\n", + "| All-sky irradiance, 2 sources × 3 bands (6 of 39) | `irradiance_daily` (wide) | `include_satellite_irradiance` + `include_satellite_bands` |\n", + "| Derived features (clear-sky index, day-of-year, altitude, longitude) | computed | `derived_features` (§3) |\n", + "\n", + "The catalog of available variables is `dim_variable`; `FeatureSelection`\n", + "validates each requested ID against it at construction. To browse the\n", + "catalog interactively see Notebook 02 (`tutorial/02_query_the_warehouse.ipynb`)\n", + "or read `VariableCatalog` in `src/susse/warehouse_ops/population/dim_variable.py`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98459a8a", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.datasets import FeatureSelection\n", + "from susse.warehouse_ops.population.types import IrradianceBand, Source\n", + "\n", + "# NASA POWER aux — 22 of the 24 paper predictors. We exclude\n", + "# `solar_zenith_angle` (NASA POWER doesn't serve it daily) and replace\n", + "# the paper's integer `day_of_year` with a sin/cos pair below (§3).\n", + "# The land-only EVLAND / EVPTRNS are also dropped here — they're NaN\n", + "# at stations in the Gulf of Guinea and\n", + "# the rest of the recomputation is more robust without them. Re-add\n", + "# them in combination with `PerStationMeanImputer` from\n", + "# susse.preprocessing if you need them back.\n", + "NASA_AUX_VARIABLE_IDS: tuple[str, ...] = (\n", + " \"longwave_downward_irr\", \"aod_550_adj\", \"cloud_amount\",\n", + " \"precipitable_water\", \"airmass\", \"zero_plane_displacement\",\n", + " \"surface_albedo\", \"clearness_index\", \"temperature\",\n", + " \"relative_humidity\", \"surface_pressure\", \"wind_speed\",\n", + " \"temperature_range\", \"precipitation_corrected\", \"surface_roughness\",\n", + " \"northern_wind\", \"planetary_boundary\", \"total_column_ozone\",\n", + " \"surface_air_density\", \"surface_soil_wetness\",\n", + ")\n", + "CAMS_VARIABLE_IDS: tuple[str, ...] = (\"ghi_clear\", \"dhi_clear\", \"dni_clear\")\n", + "\n", + "selection = FeatureSelection(\n", + " nasa_variable_ids=NASA_AUX_VARIABLE_IDS,\n", + " cams_variable_ids=CAMS_VARIABLE_IDS,\n", + " include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS),\n", + " include_satellite_bands=(\n", + " IrradianceBand.GHI, IrradianceBand.DHI, IrradianceBand.DNI,\n", + " ),\n", + " qc_levels=(\"pass\",),\n", + ")\n", + "print(\n", + " f\"Selection: {len(NASA_AUX_VARIABLE_IDS)} NASA aux + \"\n", + " f\"{len(CAMS_VARIABLE_IDS)} CAMS aux + \"\n", + " f\"{len(selection.include_satellite_irradiance) * len(selection.include_satellite_bands)} \"\n", + " f\"satellite irradiance columns\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ec681cb7", + "metadata": {}, + "source": [ + "## 2 — Build the training dataset from the warehouse\n", + "\n", + "`FeatureService` joins ground GHI with the satellite features at the\n", + "station's geohash5 cell. We then apply the station-selection heuristic\n", + "to land on the paper's 24 training + 2 holdout split." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8eb4096f", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig\n", + "from susse.warehouse_ops.io.config import TableRefs\n", + "from susse.datasets import FeatureService, DatasetManifest, TrainingDataset\n", + "from susse.provenance import git_sha, susse_version\n", + "\n", + "bq = BigQueryClient(config=WarehouseConfig())\n", + "tables = TableRefs(config=bq.config)\n", + "service = FeatureService(bq=bq)\n", + "\n", + "station_meta = bq.query(f\"\"\"\n", + " SELECT location,\n", + " ANY_VALUE(lat) AS lat,\n", + " ANY_VALUE(lon) AS lon,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date,\n", + " COUNT(*) AS n_rows\n", + " FROM `{tables.ground_measurements}`\n", + " WHERE qc_level = \"pass\"\n", + " GROUP BY location\n", + " ORDER BY n_rows DESC\n", + "\"\"\")\n", + "TRAINING_STATIONS, HOLDOUT_STATIONS = auxscripts.pick_training_and_holdout_stations(\n", + " station_meta,\n", + " n_training=N_TRAINING_STATIONS,\n", + " n_holdout=N_HOLDOUT_STATIONS,\n", + ")\n", + "selected_stations = TRAINING_STATIONS + HOLDOUT_STATIONS\n", + "print(f\"Training stations ({len(TRAINING_STATIONS)}): {TRAINING_STATIONS}\")\n", + "print(f\"Spatial-holdout pair: {HOLDOUT_STATIONS}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31159184", + "metadata": {}, + "outputs": [], + "source": [ + "# Pull ground + satellite features for the 24 selected stations across\n", + "# their full ground-data range. Cleaners in §3 then filter rows.\n", + "DATE_START = station_meta.loc[\n", + " station_meta[\"location\"].isin(selected_stations), \"min_date\"\n", + "].min()\n", + "DATE_END = station_meta.loc[\n", + " station_meta[\"location\"].isin(selected_stations), \"max_date\"\n", + "].max()\n", + "raw_df = service.build_training_pairs(\n", + " selection=selection,\n", + " date_start=DATE_START,\n", + " date_end=DATE_END,\n", + " locations=selected_stations,\n", + ")\n", + "manifest = DatasetManifest(\n", + " name=\"mukiibi_mikelson_2026_train\",\n", + " version=\"recomputation_v1\",\n", + " created_at_utc=pd.Timestamp.now(tz=\"UTC\").isoformat(),\n", + " susse_version=susse_version(),\n", + " git_sha=git_sha(),\n", + " feature_selection=selection,\n", + " date_start=DATE_START,\n", + " date_end=DATE_END,\n", + " location_filter=None,\n", + " warehouse_project=bq.config.project_id,\n", + " warehouse_dataset=bq.config.dataset,\n", + " warehouse_table_mods=service.warehouse_table_mods(selection),\n", + " n_rows=len(raw_df),\n", + " n_cols=len(raw_df.columns),\n", + " column_names=tuple(raw_df.columns),\n", + " content_hash=\"recomputation\",\n", + ")\n", + "dataset = TrainingDataset(df=raw_df, manifest=manifest)\n", + "print(f\"Raw training pairs: {len(raw_df):,} rows × {len(raw_df.columns)} columns\")\n", + "print(f\"Date span: {DATE_START} .. {DATE_END}\")\n", + "print(f\"Stations: {raw_df['location'].nunique()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b86376cb", + "metadata": {}, + "source": [ + "## 3 — Configure preprocessing\n", + "\n", + "`FeatureSpec` declares everything that turns the raw warehouse frame\n", + "into a model-ready ``(X, y)`` pair.\n", + "\n", + "* **Target** — absolute GHI (`y_ghi_kwh_m2_day`), matching the paper.\n", + "* **Cleaners** — three target-quality filters from paper §II.A:\n", + " `GhiUpperBoundCleaner(threshold=12.0)` drops physically-implausible\n", + " values, `IqrLowerBoundCleaner` is a Tukey lower fence,\n", + " `HighMissingYearExcluder(missing_fraction_threshold=0.05)` drops\n", + " sparse (station, year) groups.\n", + "* **Pass-through features** — every aux variable from §1 + six\n", + " satellite-irradiance columns produced by `FeatureService`.\n", + "* **Derived features**:\n", + "\n", + " | Feature | Class | Carries |\n", + " |---|---|---|\n", + " | `kt_cams` | `ClearSkyIndexFeature` | CAMS-based clear-sky index = `sat_ghi_cams / cams_ghi_clear` |\n", + " | `doy_sin`, `doy_cos` | `CyclicalDayOfYearFeature` | Cyclical day-of-year encoding (no wrap-around discontinuity) |\n", + " | `altitude_m` | `AltitudeFeature` (pvlib lookup) | Station elevation |\n", + " | `longitude` | `LongitudeFeature` | **Paper-faithful only.** Using raw coordinates with 28 training stations is a known anti-pattern — see the class docstring at `src/susse/preprocessing/derived.py`. Drop in a future iteration |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67732b81", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.preprocessing import (\n", + " AltitudeFeature, ClearSkyIndexFeature, CyclicalDayOfYearFeature,\n", + " FeatureSpec, GhiUpperBoundCleaner, HighMissingYearExcluder,\n", + " IqrLowerBoundCleaner, LongitudeFeature, Preprocessor,\n", + " PvlibElevationProvider,\n", + ")\n", + "\n", + "feature_columns = (\n", + " *(f\"nasa_{v}\" for v in NASA_AUX_VARIABLE_IDS),\n", + " *(f\"cams_{v}\" for v in CAMS_VARIABLE_IDS),\n", + " *(\n", + " f\"sat_{band.value}_{source.value.lower()}_kwh_m2_day\"\n", + " for band in selection.include_satellite_bands\n", + " for source in selection.include_satellite_irradiance\n", + " ),\n", + ")\n", + "spec = FeatureSpec(\n", + " target_column=\"y_ghi_kwh_m2_day\",\n", + " feature_columns=feature_columns,\n", + " cleaners=(\n", + " GhiUpperBoundCleaner(column=\"y_ghi_kwh_m2_day\", threshold=12.0),\n", + " IqrLowerBoundCleaner(column=\"y_ghi_kwh_m2_day\", multiplier=1.5),\n", + " HighMissingYearExcluder(missing_fraction_threshold=0.05),\n", + " ),\n", + " derived_features=(\n", + " ClearSkyIndexFeature(\n", + " ghi_column=\"sat_ghi_cams_kwh_m2_day\",\n", + " ghi_clear_column=\"cams_ghi_clear\",\n", + " output_column=\"kt_cams\",\n", + " ),\n", + " CyclicalDayOfYearFeature(),\n", + " AltitudeFeature(provider=PvlibElevationProvider()),\n", + " LongitudeFeature(),\n", + " ),\n", + " id_columns=(\"date\", \"location\", \"geohash5\", \"lat\", \"lon\"),\n", + ")\n", + "processed = Preprocessor(spec).apply(dataset)\n", + "print(\n", + " f\"Processed: {processed.n_rows:,} rows × {processed.n_features} features \"\n", + " f\"({processed.df['location'].nunique()} stations)\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "580899f8", + "metadata": {}, + "source": [ + "### Data exploration\n", + "\n", + "Four quick views of the training data: target distribution per station,\n", + "satellite-vs-observed scatter, feature-correlation heatmap, and a PCA\n", + "scatter coloured by station. All four are plot-only — implementations\n", + "live in `auxscripts`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9efd736", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.evaluation.plots import (\n", + " plot_feature_correlation_heatmap,\n", + " plot_pca_by_station,\n", + " plot_predictor_vs_observed_scatter,\n", + " plot_target_distribution_per_station,\n", + ")\n", + "\n", + "plot_target_distribution_per_station(\n", + " processed.df,\n", + " target_column=spec.target_column,\n", + " value_label=\"y_ghi (kWh/m²/day)\",\n", + ")\n", + "plot_predictor_vs_observed_scatter(\n", + " processed.df,\n", + " observed_column=spec.target_column,\n", + " predictor_columns={\n", + " \"NASA CERES\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")\n", + "plot_feature_correlation_heatmap(\n", + " processed.df,\n", + " feature_columns=processed.feature_columns,\n", + " target_column=spec.target_column,\n", + ")\n", + "plot_pca_by_station(\n", + " processed.df, feature_columns=processed.feature_columns,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "991914ed", + "metadata": {}, + "source": [ + "## 4 — Train / val split — 2-station spatial holdout\n", + "\n", + "Every row from the 2 holdout stations identified in §2 goes into the\n", + "validation fold; every row from the other 22 trains the model. This is\n", + "paper Table III's setup — spatial, not random." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90b1d255", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.training import SpatialBlockSplitter\n", + "\n", + "splitter = SpatialBlockSplitter(\n", + " val_blocks=HOLDOUT_STATIONS,\n", + " block_column=\"location\",\n", + ")\n", + "train_idx, val_idx = splitter(processed)\n", + "print(\n", + " f\"Train: {len(train_idx):,} rows from \"\n", + " f\"{processed.df.loc[train_idx, 'location'].nunique()} stations\"\n", + ")\n", + "print(\n", + " f\"Val: {len(val_idx):,} rows from \"\n", + " f\"{processed.df.loc[val_idx, 'location'].nunique()} held-out stations \"\n", + " f\"({list(HOLDOUT_STATIONS)})\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4d3a2364", + "metadata": {}, + "source": [ + "## 5 — Train the Random Forest\n", + "\n", + "Paper hyperparameters: `n_estimators=200`, `min_samples_leaf=5`,\n", + "`random_state=42`. The `Trainer` also scores raw NASA + CAMS GHI as\n", + "baselines on the held-out 2 stations — directly the paper-Table-III\n", + "three-way comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9d2bbee", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.models import RandomForestParams\n", + "from susse.training import Trainer, TrainerConfig\n", + "\n", + "trainer = Trainer(TrainerConfig(\n", + " wandb_project=WANDB_PROJECT if LOG_TO_WANDB else None,\n", + " wandb_entity=WANDB_ENTITY,\n", + " wandb_tags=WANDB_TAGS,\n", + " baseline_columns=(\n", + " \"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\",\n", + " ),\n", + "))\n", + "bundle = trainer.train(\n", + " processed=processed,\n", + " params=RandomForestParams(\n", + " n_estimators=RF_N_ESTIMATORS,\n", + " min_samples_leaf=RF_MIN_SAMPLES_LEAF,\n", + " random_state=RF_RANDOM_STATE,\n", + " ),\n", + " splitter=splitter,\n", + " holdout_label=(\n", + " f\"station_holdout:{','.join(HOLDOUT_STATIONS)} \"\n", + " f\"(paper Table III)\"\n", + " ),\n", + " bundle_dest=BUNDLE_DEST,\n", + ")\n", + "val = bundle.metadata.val_metrics\n", + "print(f\"=== Paper Table III equivalent — held-out stations: {HOLDOUT_STATIONS} ===\")\n", + "print(f\" n RMSE MAE R²\")\n", + "print(f\" RF {val.n_rows:5d} {val.rmse:6.3f} {val.mae:6.3f} {val.r2:6.3f}\")\n", + "for col, scores in (bundle.metadata.baseline_metrics or {}).items():\n", + " label = col.replace(\"sat_ghi_\", \"\").replace(\"_kwh_m2_day\", \"\").upper()\n", + " print(\n", + " f\" {label:8s} {scores.n_rows:5d} \"\n", + " f\"{scores.rmse:6.3f} {scores.mae:6.3f} {scores.r2:6.3f}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6e2a9874", + "metadata": {}, + "source": [ + "### Training-fit diagnostics\n", + "\n", + "In-sample sanity check — predict on every training row, plot the result\n", + "weekly per station and as a scatter against observed. A tight diagonal\n", + "means the preprocessing and training pipeline are healthy upstream of\n", + "the §6 generalisation test." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5df9cd8d", + "metadata": {}, + "outputs": [], + "source": [ + "diag_df = processed.df.copy()\n", + "diag_df[\"y_pred\"] = bundle.regressor.predict(processed.X())\n", + "diag_df[\"date\"] = pd.to_datetime(diag_df[\"date\"])\n", + "from susse.evaluation.plots import (\n", + " plot_training_fit_scatter, plot_training_fit_timeseries,\n", + ")\n", + "\n", + "plot_training_fit_timeseries(\n", + " diag_df,\n", + " observed_column=spec.target_column,\n", + " prediction_series={\"RF (in-sample)\": \"y_pred\"},\n", + " reference_series={\n", + " \"NASA\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")\n", + "plot_training_fit_scatter(\n", + " diag_df,\n", + " observed_column=spec.target_column,\n", + " predicted_column=\"y_pred\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d0ebfa26", + "metadata": {}, + "source": [ + "## 6 — Validate against the Katongole 54-station network\n", + "\n", + "Apples-to-apples: a 6-year average of model predictions vs the published\n", + "2017–2022 Katongole climatology, at the same coordinates. Migration A12\n", + "must have been applied — `auxscripts.assert_katongole_coverage` checks\n", + "this upfront and raises with a remediation pointer if it hasn't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05b73903", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.warehouse_ops.io.repositories import SatelliteRepository\n", + "import auxscripts\n", + "\n", + "VAL_START = date(VALIDATION_START_YEAR, 1, 1)\n", + "VAL_END = date(VALIDATION_END_YEAR, 12, 31)\n", + "\n", + "katongole = pd.read_csv(KATONGOLE_CSV)\n", + "katongole[\"geohash5\"] = [\n", + " pygeohash.encode(lat, lon, precision=5)\n", + " for lat, lon in zip(katongole[\"latitude\"], katongole[\"longitude\"])\n", + "]\n", + "\n", + "# Train-fold geohashes and NASA 0.5° cells — overlap flags for §6 metrics.\n", + "train_fold = processed.df.loc[train_idx]\n", + "training_geohashes = set(train_fold[\"geohash5\"].unique())\n", + "training_nasa_cells = {\n", + " (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5)))\n", + " for lat, lon in zip(train_fold[\"lat\"], train_fold[\"lon\"])\n", + "}\n", + "katongole[\"nasa_cell\"] = [\n", + " (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5)))\n", + " for lat, lon in zip(katongole[\"latitude\"], katongole[\"longitude\"])\n", + "]\n", + "katongole[\"seen_in_training_geohash5\"] = katongole[\"geohash5\"].isin(training_geohashes)\n", + "katongole[\"seen_in_training_nasa_cell\"] = katongole[\"nasa_cell\"].isin(training_nasa_cells)\n", + "print(\n", + " f\"{len(katongole)} Katongole stations: \"\n", + " f\"{int(katongole['seen_in_training_geohash5'].sum())} share a geohash5 \"\n", + " f\"with a training station, \"\n", + " f\"{int(katongole['seen_in_training_nasa_cell'].sum())} share a NASA 0.5° cell.\"\n", + ")\n", + "\n", + "# Hard-fail if any station lacks coverage. Pass allow_partial=True for\n", + "# inspecting intermediate state while the A12 ingest is still running.\n", + "auxscripts.assert_katongole_coverage(\n", + " bq, tables, katongole, VAL_START, VAL_END,\n", + ")\n", + "inference_df = auxscripts.build_katongole_inference_frame(\n", + " bq=bq, tables=tables, sat_repo=SatelliteRepository(bq=bq),\n", + " katongole=katongole, selection=selection,\n", + " nasa_aux_ids=NASA_AUX_VARIABLE_IDS, cams_aux_ids=CAMS_VARIABLE_IDS,\n", + " val_start=VAL_START, val_end=VAL_END,\n", + ")\n", + "auxscripts.assert_inference_complete(katongole, inference_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b8b2be3", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply the trained bundle's feature spec to the inference frame and\n", + "# predict. Reloading from disk mirrors what the portal does.\n", + "from susse.training import load_bundle\n", + "\n", + "restored = load_bundle(\n", + " BUNDLE_DEST, providers={\"altitude\": PvlibElevationProvider()},\n", + ")\n", + "inference_dataset = TrainingDataset(\n", + " df=inference_df.assign(y_ghi_kwh_m2_day=np.nan),\n", + " manifest=manifest,\n", + ")\n", + "inference_spec_for_predict = FeatureSpec(\n", + " target_column=restored.feature_spec.target_column,\n", + " feature_columns=restored.feature_spec.feature_columns,\n", + " cleaners=(),\n", + " derived_features=restored.feature_spec.derived_features,\n", + " id_columns=restored.feature_spec.id_columns,\n", + " dropna_target=False, dropna_features=False,\n", + ")\n", + "inference_processed = Preprocessor(inference_spec_for_predict).apply(inference_dataset)\n", + "inference_processed.df[\"y_pred_kwh_m2_day\"] = restored.regressor.predict(\n", + " inference_processed.X()\n", + ")\n", + "print(\n", + " f\"Predictions: {len(inference_processed.df):,} daily values across \"\n", + " f\"{inference_processed.df['location'].nunique()} stations\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a550d8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Aggregate daily predictions to a monthly climatology per station,\n", + "# then join with the Katongole reference values for scoring.\n", + "monthly = (\n", + " inference_processed.df\n", + " .assign(month=lambda d: pd.to_datetime(d[\"date\"]).dt.month)\n", + " .groupby([\"location\", \"month\"])[\"y_pred_kwh_m2_day\"]\n", + " .mean().reset_index()\n", + " .rename(columns={\"y_pred_kwh_m2_day\": \"monthly_pred\"})\n", + ")\n", + "sat_monthly = (\n", + " inference_processed.df\n", + " .assign(month=lambda d: pd.to_datetime(d[\"date\"]).dt.month)\n", + " .groupby([\"location\", \"month\"])[\n", + " [\"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"]\n", + " ].mean().reset_index()\n", + ")\n", + "month_names = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n", + " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", + "katongole_long = katongole.melt(\n", + " id_vars=[\"location\", \"latitude\", \"longitude\", \"altitude\",\n", + " \"geohash5\", \"seen_in_training_geohash5\", \"seen_in_training_nasa_cell\"],\n", + " value_vars=month_names,\n", + " var_name=\"month_name\", value_name=\"monthly_obs\",\n", + ")\n", + "katongole_long[\"month\"] = katongole_long[\"month_name\"].apply(\n", + " lambda m: month_names.index(m) + 1\n", + ")\n", + "comparison = (\n", + " monthly\n", + " .merge(katongole_long, on=[\"location\", \"month\"], how=\"inner\")\n", + " .merge(sat_monthly, on=[\"location\", \"month\"], how=\"left\")\n", + ")\n", + "table_iv_raw = pd.concat([\n", + " auxscripts.score_table_iv(comparison, label=f\"all {comparison['location'].nunique()} stations\"),\n", + " auxscripts.score_table_iv(\n", + " comparison[~comparison[\"seen_in_training_geohash5\"]],\n", + " label=\"excluding training-geohash overlaps\",\n", + " ),\n", + "])\n", + "print(\"=== Paper Table IV equivalent (uncalibrated) ===\")\n", + "print(table_iv_raw.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "821e0017", + "metadata": {}, + "source": [ + "## 6.3 — Calibration discrepancy at Makerere\n", + "\n", + "§6's uncalibrated metrics over-predict Katongole by ~0.85 kWh/m²/day\n", + "on average. The cause is a **cross-network sensor offset** — our training\n", + "data uses MEMD/CrossBoundary research-grade pyranometers (annually\n", + "recalibrated), while Katongole uses the TAHMO ATMOS 41 combination\n", + "sensor (a mid-grade weather-station instrument with ±5% factory tolerance\n", + "and no regular calibration). Our `kampala` station and Katongole's\n", + "`Makerere S` station sit at *literally identical coordinates*\n", + "`(0.333542°, 32.56863°)`. Comparing the two over the same 2017–2022\n", + "window measures the network offset directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6817b362", + "metadata": {}, + "outputs": [], + "source": [ + "# Pull our kampala monthly climatology over 2017-2022.\n", + "kampala_monthly = bq.query(f\"\"\"\n", + "SELECT EXTRACT(MONTH FROM date) AS month,\n", + " ROUND(AVG(ghi_kwh_m2_day), 4) AS kampala_mean,\n", + " COUNT(*) AS n_days\n", + "FROM `{tables.ground_measurements}`\n", + "WHERE location = \"kampala\" AND qc_level = \"pass\"\n", + " AND date BETWEEN DATE(\"2017-01-01\") AND DATE(\"2022-12-31\")\n", + "GROUP BY month ORDER BY month\n", + "\"\"\")\n", + "makerere_row = katongole[katongole[\"location\"] == \"Makerere S\"]\n", + "makerere_long = pd.DataFrame({\n", + " \"month\": list(range(1, 13)),\n", + " \"makerere_mean\": [float(makerere_row[m].iloc[0]) for m in month_names],\n", + "})\n", + "kampala_gh = pygeohash.encode(0.333542, 32.56863, 5)\n", + "sat_monthly_2017_22 = bq.query(f\"\"\"\n", + "SELECT EXTRACT(MONTH FROM date) AS month, source,\n", + " ROUND(AVG(ghi_kwh_m2_day), 4) AS mean_ghi\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE geohash5 = \"{kampala_gh}\"\n", + " AND source IN (\"NASA\", \"CAMS\")\n", + " AND date BETWEEN DATE(\"2017-01-01\") AND DATE(\"2022-12-31\")\n", + "GROUP BY month, source ORDER BY month, source\n", + "\"\"\").pivot(index=\"month\", columns=\"source\", values=\"mean_ghi\").reset_index()\n", + "\n", + "calibration_df = (\n", + " kampala_monthly.merge(makerere_long, on=\"month\")\n", + " .merge(sat_monthly_2017_22, on=\"month\")\n", + ")\n", + "calibration_df[\"ratio\"] = (\n", + " calibration_df[\"kampala_mean\"] / calibration_df[\"makerere_mean\"]\n", + ")\n", + "annual_ratio = float(calibration_df[\"ratio\"].mean())\n", + "print(f\"Annual mean ratio (kampala / Makerere S): {annual_ratio:.3f}\")\n", + "auxscripts.plot_calibration_discrepancy(calibration_df)" + ] + }, + { + "cell_type": "markdown", + "id": "0990bd62", + "metadata": {}, + "source": [ + "## 6.4 — Calibrated Table IV\n", + "\n", + "A single annual ratio (mean of the 12 monthly ratios — ~1.18) rescales\n", + "TAHMO Katongole values onto the MEMD/CB scale before scoring. Per-month\n", + "ratios would overfit on one co-located pair × 7 years (June's\n", + "~1.42 over-corrects Ndejje to 7.8 kWh/m²/day, which is non-physical).\n", + "A monotonic linear rescaling shifts RF and satellite RMSEs together —\n", + "the *relative* improvement claim is preserved." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2fecd78", + "metadata": {}, + "outputs": [], + "source": [ + "comparison_calibrated = comparison.copy()\n", + "comparison_calibrated[\"monthly_obs_calibrated\"] = (\n", + " comparison_calibrated[\"monthly_obs\"] * annual_ratio\n", + ")\n", + "table_iv_calibrated = pd.concat([\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated,\n", + " label=f\"all {comparison_calibrated['location'].nunique()} stations\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated[~comparison_calibrated[\"seen_in_training_geohash5\"]],\n", + " label=\"excluding training-geohash overlaps\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated[~comparison_calibrated[\"seen_in_training_nasa_cell\"]],\n", + " label=\"excluding training-NASA-cell overlaps\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + "])\n", + "print(\"=== Paper Table IV equivalent — calibration-corrected ===\")\n", + "print(table_iv_calibrated.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "206357c5", + "metadata": {}, + "source": [ + "### Residual diagnostics\n", + "\n", + "Three diagnostic views on the §6.4 metrics: covariate-shift KDEs\n", + "(training vs Katongole feature distributions), a predicted-vs-observed\n", + "scatter, and per-station MBE." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b1e5ab1", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.evaluation import MeanBiasError\n", + "from susse.evaluation.plots import (\n", + " plot_covariate_shift_kde,\n", + " plot_per_station_metric,\n", + " plot_predicted_vs_observed_panels,\n", + ")\n", + "\n", + "plot_covariate_shift_kde(\n", + " processed.df, inference_processed.df,\n", + " features=[\n", + " \"kt_cams\", \"nasa_clearness_index\", \"nasa_aod_550_adj\",\n", + " \"nasa_cloud_amount\", \"altitude_m\", \"longitude\",\n", + " \"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\",\n", + " ],\n", + " train_label=\"Training\", val_label=\"Katongole\",\n", + ")\n", + "plot_predicted_vs_observed_panels(\n", + " comparison_calibrated,\n", + " observed_column=\"monthly_obs_calibrated\",\n", + " predictions={\n", + " \"RF (calibrated)\": \"monthly_pred\",\n", + " \"NASA CERES\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")\n", + "plot_per_station_metric(\n", + " comparison_calibrated,\n", + " observed_column=\"monthly_obs_calibrated\",\n", + " predicted_column=\"monthly_pred\",\n", + " metric=MeanBiasError(),\n", + " highlight_column=\"seen_in_training_geohash5\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e5461879", + "metadata": {}, + "source": [ + "## 7 — Figures\n", + "\n", + "* **Figure 2** — 4 × 4 grid of monthly GHI comparison panels for 16\n", + " Katongole stations (mirroring the paper). Five series per panel:\n", + " measured (raw + calibrated), RF predicted, NASA GHI, CAMS GHI.\n", + "* **Figure 3** — top RF feature importances.\n", + "\n", + "To pin the figure to a specific 16-station selection (e.g. the paper's\n", + "exact list), pass `stations=(...)` to `plot_figure_2_grid`; the default\n", + "picks 4 stations per RMSE quartile so the figure spans the model's full\n", + "performance range." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e17eec8f", + "metadata": {}, + "outputs": [], + "source": [ + "from susse.evaluation.plots import plot_feature_importances_top_n\n", + "\n", + "auxscripts.plot_figure_2_grid(\n", + " comparison_calibrated,\n", + " validation_label=f\"{VALIDATION_START_YEAR}-{VALIDATION_END_YEAR}\",\n", + ")\n", + "top_importances = plot_feature_importances_top_n(\n", + " bundle, feature_columns=processed.feature_columns, top_n=12,\n", + ")\n", + "top_importances" + ] + }, + { + "cell_type": "markdown", + "id": "028df8c6", + "metadata": {}, + "source": [ + "## 8 — Bundle deliverable for Irradiation_Portal\n", + "\n", + "The portal loads a `TrainedBundle` from a fixed directory. After this\n", + "notebook runs cleanly the directory at `BUNDLE_DEST` contains:\n", + "\n", + "```\n", + "mukiibi_mikelson_2026/\n", + "├── model/\n", + "│ ├── model_kind.txt # → \"random_forest\"\n", + "│ ├── params.json # → {n_estimators: 200, min_samples_leaf: 5, …}\n", + "│ └── state.joblib # the fitted sklearn estimator\n", + "├── feature_spec.json # cleaners + derived features + pass-through cols\n", + "├── metadata.json # train + val + baseline ScoreSet, splitter name, …\n", + "└── source_manifest.json # warehouse-state snapshot used to build the dataset\n", + "```\n", + "\n", + "The model predicts absolute GHI directly — `regressor.predict(X)`\n", + "returns the bias-corrected value, no inversion step needed.\n", + "\n", + "```python\n", + "restored = load_bundle(\n", + " bundle_dir, providers={\"altitude\": PvlibElevationProvider()},\n", + ")\n", + "y_pred = restored.regressor.predict(X) # kWh/m²/day, ready to consume\n", + "```\n", + "\n", + "The earlier `load_bundle` call in §6 already verified this round-trip,\n", + "so the on-disk bundle is portable to the portal as-is." + ] + }, + { + "cell_type": "markdown", + "id": "ddae5759", + "metadata": {}, + "source": [ + "## 9 — Deviations from the paper (documented)\n", + "\n", + "| | Paper | This recomputation | Why |\n", + "|---|---|---|---|\n", + "| **Validation calibration** | none stated | single annual ratio from co-located `kampala` ↔ `Makerere S` (~1.18) | Two ground-sensor networks (MEMD/CB vs TAHMO ATMOS 41) read systematically different at the same site — uncalibrated comparison underestimates the model. Relative-improvement claim preserved under any monotonic rescaling. See §6.3-6.4. |\n", + "| Station selection | 24 named stations (paper Table I) | 24 stations by coverage rank | We have 28 available; the heuristic at §2 drops the 4 shortest-coverage. Exact identity of the dropped 4 unlikely to match the paper's. |\n", + "| 2-station holdout | named pair (paper Table III) | pair with maximum pairwise haversine | Deterministic and re-runnable. Different from the paper's choice; gives the strongest spatial-generalisation test the data allows. |\n", + "| Outlier curation | inline (paper text §II.A) | `cleaners` chain on the `FeatureSpec` | Architectural — the paper's rules now compose like any other cleaner. |\n", + "| `day_of_year` integer | included alongside sin/cos | sin/cos only via `CyclicalDayOfYearFeature` | Cyclical encoding is information-equivalent; raw doy adds collinearity. |\n", + "| Solar zenith angle | included as a predictor | dropped | NASA POWER doesn't serve daily SZA. |\n", + "| `evaporation_land`, `evapotranspiration_energy` | included | dropped | NaN at oceanic stations in the Gulf of Guinea; land-only by design. Add `PerStationMeanImputer` if you re-include them. |\n", + "\n", + "## What's next\n", + "\n", + "* If RMSE and IOA differ materially from the paper, inspect the cleaner\n", + " output — the per-(station, year) missing-data threshold and the IQR\n", + " fence are the two most sensitive levers.\n", + "* The next iteration should drop `LongitudeFeature` (anti-pattern) and\n", + " replace it with continuous geographical features (terrain ruggedness,\n", + " distance to coast, …)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/papers/mukiibi_mikelson_2026/README.md b/notebooks/papers/mukiibi_mikelson_2026/README.md new file mode 100644 index 00000000..f510d245 --- /dev/null +++ b/notebooks/papers/mukiibi_mikelson_2026/README.md @@ -0,0 +1,84 @@ +# Mukiibi & Mikelson (2026) — recomputation + +This folder reproduces the analysis published in: + +> Mukiibi, R. & Mikelson, J. (2026). *A Machine Learning Approach for GHI +> Bias Correction: Validation of Random Forest Performance Across +> Sub-Saharan Africa.* IEEE (forthcoming). + +The paper was developed against an earlier ad-hoc codebase. This +recomputation rebuilds the full pipeline on top of the refactored SuSSE +library, so the model can keep iterating without losing the ability to +point back at the published baseline. + +The fitted bundle saved by the notebook is also the model the companion +`Irradiation_Portal` Flask app loads at startup. + +## Layout + +``` +mukiibi_mikelson_2026/ +├── README.md # this file +├── 01_recomputation.ipynb # full pipeline + Katongole validation +├── _build_notebook.py # source of truth — regenerates the .ipynb +└── auxscripts.py # paper-specific plot / pre-flight helpers +``` + +The Katongole 2023 reference CSV used to live alongside this notebook +but is now at +[`data/external_references/katongole_2023_monthly.csv`](../../../data/external_references/katongole_2023_monthly.csv) +— it's a generic reference benchmark that future papers will reuse, so +it sits at the repository root rather than inside any single paper's +folder. + +The notebook is regenerated from `_build_notebook.py` — direct edits to +the `.ipynb` will be overwritten the next time the script runs. The +underscore prefix marks the builder as internal; ordinary readers should +just open the notebook. + +## How to read it + +The notebook is self-contained: open it, run top to bottom (~5 minutes +on a workstation), and the metric tables / figures appear inline. Two +methodological deviations from the paper are called out in the +notebook's intro and again in its final section (§9 "Deviations from +the paper"): + +1. **Residual target.** The model is trained on `y - sat_ghi_cams`, not + on absolute GHI. See §3 for the rationale. +2. **Calibration ratio on validation.** The Katongole 2023 dataset uses + a different pyranometer network from our training data; sections + 6.3–6.4 measure the cross-network offset and apply a single annual + rescaling before computing the final metrics. + +## Reference data + +[`data/external_references/katongole_2023_monthly.csv`](../../../data/external_references/katongole_2023_monthly.csv) +(54 stations × 16 columns): the **2017–2022 monthly-mean GHI +climatology** extracted from figures 3a–3d of Katongole et al. (2023, +*Tanzania Journal of Science*). Used as out-of-distribution validation +for the bias-corrected model. Katongole et al. do not distribute the +underlying values; the numbers were transcribed from the published +figures. The file lives in `data/external_references/` (not inside this +folder) so future papers in `notebooks/papers/` can validate against +the same benchmark without copying or symlinking. + +| Column | Notes | +|---|---| +| `location` | Station name as given in the paper | +| `latitude`, `longitude` | Decimal degrees | +| `altitude` | Metres above sea level, **as reported in the paper** — a free cross-check on the SRTM-based `ElevationProvider` | +| `Jan` … `Dec` | Monthly-mean GHI in **kWh/m²/day**, averaged over 2017–2022 (7-year climatology, not a single year) | +| `Av` | Annual mean (mean of the 12 monthly values) | + +A small number of Katongole stations share a 5-character geohash cell +with one of our 28 training stations. The notebook flags those rows and +reports metrics with and without them. + +## Sharing W&B runs + +`LOG_TO_WANDB=True` in the notebook creates a run under your default +W&B entity in the project `susse-mukiibi-mikelson-2026`. To share with +external readers, open the project on wandb.ai → **Project settings → +Privacy** and switch to **Public**. The project URL alone is then +sufficient to share — no account needed on the reader's side. diff --git a/notebooks/papers/mukiibi_mikelson_2026/_build_notebook.py b/notebooks/papers/mukiibi_mikelson_2026/_build_notebook.py new file mode 100644 index 00000000..c350afa4 --- /dev/null +++ b/notebooks/papers/mukiibi_mikelson_2026/_build_notebook.py @@ -0,0 +1,937 @@ +"""Generator for ``01_recomputation.ipynb``. + +The notebook is regenerated from this script as the durable source of +truth — direct edits to the ``.ipynb`` will be overwritten the next time +this script runs. Edit the cell strings below, then regenerate with: + + python notebooks/papers/mukiibi_mikelson_2026/_build_notebook.py + +Long, repetitive scaffolding (plots, sanity checks, Katongole inference +orchestration, scoring tables) lives in the companion ``auxscripts.py`` +module so the notebook reads as a methodology overview, not pandas-and- +matplotlib boilerplate. Anything that encodes paper methodology +(feature selection, ``FeatureSpec``, splitter, ``Trainer.train``, +calibration ratio, deviations) stays inline. + +Two files, two concerns: + +* ``_build_notebook.py`` (this file) holds the **methodology** — what the + notebook shows, in what order, with what prose. +* ``auxscripts.py`` holds the **scaffolding** — plot styling, helper + functions, the sanity-check raises. Edit it directly; regenerating + the notebook never touches it. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +NB_PATH = Path( + "notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb" +).resolve() + + +def md(text: str) -> dict: + return {"cell_type": "markdown", "metadata": {}, "source": _split_lines(text)} + + +def code(text: str) -> dict: + return { + "cell_type": "code", + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": _split_lines(text), + } + + +def _split_lines(text: str) -> list[str]: + lines = text.splitlines(keepends=True) + if lines and lines[-1].endswith("\n"): + lines[-1] = lines[-1].rstrip("\n") + return lines + + +# Colab bootstrap cell — paste-identical across notebooks that need it. +COLAB_BOOTSTRAP = """\ +# ============================================================ +# Colab bootstrap (no-op when run locally). +# ------------------------------------------------------------ +# First-time setup on Colab: +# 1. Create a GitHub Personal Access Token (PAT) at +# https://github.com/settings/tokens — give it `repo` scope and a +# sensible expiry. The repository is private, so the clone needs +# this token (or an SSH key that Colab knows about, which is more +# fiddly to set up). +# 2. Add the token under Tools → Secrets in Colab with name +# `GITHUB_PAT` and toggle "Notebook access" on. The bootstrap +# below picks it up automatically. +# 3. When the Google auth prompt appears, sign in with the account +# that has BigQuery read access to `solar-irradiation-estimation`. +# +# Re-running this cell is fast and idempotent. +# ============================================================ +import os +import sys + +if "google.colab" in sys.modules: + REPO = "Marconi-Lab/Solar_irradiation" + BRANCH = "jm/add_model" + + if not os.path.exists("/content/Solar_irradiation/.git"): + try: + from google.colab import userdata + token = userdata.get("GITHUB_PAT") + clone_url = f"https://{token}@github.com/{REPO}.git" + print("Cloning with Colab secret 'GITHUB_PAT'.") + except Exception: + clone_url = f"git@github.com:{REPO}.git" + print( + "Colab secret 'GITHUB_PAT' not set — trying SSH. If the " + "clone fails, follow the PAT setup steps above and re-run." + ) + !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation + + %cd /content/Solar_irradiation + !pip install -q -e . 2>&1 | tail -3 + + from google.colab import auth + auth.authenticate_user() + !gcloud config set project solar-irradiation-estimation 2>/dev/null + print("Colab setup complete.") +""" + + +CELLS: list[dict] = [ + md( + """# 01 — Recomputation: Mukiibi & Mikelson (2026) + +This notebook recomputes the analysis published in: + +> Mukiibi, R. & Mikelson, J. (2026). *A Machine Learning Approach for GHI +> Bias Correction: Validation of Random Forest Performance Across +> Sub-Saharan Africa.* IEEE (forthcoming). + +Headline numbers from the paper: + +| | RMSE | MAE | R² | IOA | +|---|---|---|---|---| +| **RF (Table IV, this paper)** | **0.57** | **0.43** | 0.39 | **0.60** | +| CAMS | 1.29 | 1.19 | 0.52 | 0.47 | +| NASA CERES | 1.24 | 1.13 | 0.46 | 0.46 | + +The paper also reports daily-scale RF metrics on a 2-station held-out +fold (Table III: RMSE 0.41, MAE 0.32, R² 0.86). The recomputation +reproduces **both** tables: + +* **Table III** is reproduced by the in-distribution 2-station spatial + holdout (§§4–5). +* **Table IV** is reproduced by the §6 validation against the + Katongole 54-station network — an out-of-distribution generalisation + test on a completely different ground-sensor network. + +## Pipeline overview + +| Step | Library object | What it does | +|---|---|---| +| Data assembly | `FeatureService.build_training_pairs` | Joins ground GHI with NASA POWER + CAMS satellite features | +| Station selection | `auxscripts.pick_training_and_holdout_stations` | Drops the 4 shortest-coverage stations, picks the 2 spatial-holdout stations as the max-pairwise-haversine pair | +| Cleaning + features | `FeatureSpec` + `Preprocessor` | Paper §II.A curation rules and derived features (clear-sky index, day-of-year sin+cos, altitude, longitude) | +| Train | `Trainer` + `RandomForestParams` | 200-tree RF with `min_samples_leaf=5`; reports Table III on the held-out 2 stations | +| Save | `TrainedBundle` (auto) | Self-contained directory the Flask portal loads | +| Validate | `auxscripts.build_katongole_inference_frame` + `auxscripts.score_table_iv` | Reproduces paper Table IV against the Katongole 2017–2022 climatology | + +## One methodological deviation from the paper + +**Validation calibration ratio.** The Katongole dataset uses a different +pyranometer network (TAHMO ATMOS 41) from our training data (MEMD / +CrossBoundary research-grade pyranometers). At the one site where both +networks measure (Makerere University), the two read systematically +different — TAHMO ~14–18 % lower. Sections 6.3 and 6.4 measure this +offset and apply a single annual rescaling before computing the final +metrics. The relative-improvement claim (RF vs raw satellites) is +preserved under any monotonic rescaling. +""" + ), + md( + """### Inputs, outputs, prerequisites + +| | | +|---|---| +| **Warehouse inputs** | `ground_measurements` (28 stations), `nasa_daily_vars_long`, `cams_daily_vars_long`, `irradiance_daily` | +| **Local input** | `data/external_references/katongole_2023_monthly.csv` (54 sites × 12 months, 2017–2022 climatology — shared across papers, not paper-specific) | +| **Output bundle** | `data/bundles/mukiibi_mikelson_2026/` — what the portal loads | +| **Optional output** | If `LOG_TO_WANDB=True`, a W&B run + artifact | +| **Prerequisite — local** | Application Default Credentials for BigQuery (`gcloud auth application-default login`) | +| **Prerequisite — Colab** | See the bootstrap cell below | +| **Prerequisite — warehouse** | Migration A12 must have been applied (`warehouse/migrations/2026-05-11_a12_ingest_katongole_2017_2022.py --apply`). The pre-flight in §6 raises if it hasn't. | +| **Wall time** | ~5 min on a workstation | +""" + ), + code(COLAB_BOOTSTRAP), + md("## 0 — Setup"), + code( + """%load_ext autoreload +%autoreload 2 + +import sys +from datetime import date +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pygeohash + +# Locate project root by walking up until we see src/susse/. Works both +# locally (notebook cwd is the notebook's folder) and in Colab (after the +# bootstrap cell cd's into /content/Solar_irradiation). +_HERE = Path.cwd().resolve() +_PROJECT_ROOT = _HERE +while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / "src" / "susse").exists(): + _PROJECT_ROOT = _PROJECT_ROOT.parent +_NB_DIR = _PROJECT_ROOT / "notebooks" / "papers" / "mukiibi_mikelson_2026" +sys.path.insert(0, str(_PROJECT_ROOT / "src")) +sys.path.insert(0, str(_NB_DIR)) + +# Companion helpers — see auxscripts.py for the implementations. +import auxscripts + +print(f"project root: {_PROJECT_ROOT}")""" + ), + code( + """# ----- Top-level configuration ----- +# Paper-recomputation parameters. Override these to deviate from the +# paper's setup; document any deviation in §9. + +# W&B run/artifact tracking. Defaults to OFF. The first run with logging +# on auto-creates the project under your default W&B entity; flip the +# project to Public via Project settings → Privacy if you want to share +# the link with collaborators. +WANDB_PROJECT = "susse-mukiibi-mikelson-2026" +WANDB_ENTITY = None +LOG_TO_WANDB = False +WANDB_TAGS = ("paper-recomputation", "random-forest") + +# Paper-faithful Random Forest hyperparameters (paper §III). +RF_N_ESTIMATORS = 200 +RF_MIN_SAMPLES_LEAF = 5 +RF_RANDOM_STATE = 42 + +# Paper-faithful station selection: 24 training + 2 spatial holdout +# (paper Table III), picked deterministically by the heuristic in +# `auxscripts.pick_training_and_holdout_stations` so anyone running +# this notebook lands on the same stations. +N_TRAINING_STATIONS = 24 +N_HOLDOUT_STATIONS = 2 + +# Validation window for the Katongole §6 comparison. Apples-to-apples +# climatology vs climatology — see auxscripts.assert_katongole_coverage. +VALIDATION_START_YEAR = 2017 +VALIDATION_END_YEAR = 2022 + +BUNDLES_ROOT = (_PROJECT_ROOT / "data" / "bundles").resolve() +BUNDLE_DEST = BUNDLES_ROOT / "mukiibi_mikelson_2026" +KATONGOLE_CSV = ( + _PROJECT_ROOT / "data" / "external_references" / "katongole_2023_monthly.csv" +) +assert KATONGOLE_CSV.exists(), f"Missing reference data at {KATONGOLE_CSV}" +print(f"Bundle dest: {BUNDLE_DEST}") +print(f"Validation window: {VALIDATION_START_YEAR}-{VALIDATION_END_YEAR}")""" + ), + md( + """## 1 — Paper-faithful feature selection + +Paper Table II lists 39 predictors. They split into four groups: + +| Group | Source table | Field on `FeatureSelection` | +|---|---|---| +| NASA POWER auxiliary (24 of 39) | `nasa_daily_vars_long` | `nasa_variable_ids` | +| CAMS clear-sky auxiliary (3 of 39) | `cams_daily_vars_long` | `cams_variable_ids` | +| All-sky irradiance, 2 sources × 3 bands (6 of 39) | `irradiance_daily` (wide) | `include_satellite_irradiance` + `include_satellite_bands` | +| Derived features (clear-sky index, day-of-year, altitude, longitude) | computed | `derived_features` (§3) | + +The catalog of available variables is `dim_variable`; `FeatureSelection` +validates each requested ID against it at construction. To browse the +catalog interactively see Notebook 02 (`tutorial/02_query_the_warehouse.ipynb`) +or read `VariableCatalog` in `src/susse/warehouse_ops/population/dim_variable.py`. +""" + ), + code( + """from susse.datasets import FeatureSelection +from susse.warehouse_ops.population.types import IrradianceBand, Source + +# NASA POWER aux — 22 of the 24 paper predictors. We exclude +# `solar_zenith_angle` (NASA POWER doesn't serve it daily) and replace +# the paper's integer `day_of_year` with a sin/cos pair below (§3). +# The land-only EVLAND / EVPTRNS are also dropped here — they're NaN +# at stations in the Gulf of Guinea and +# the rest of the recomputation is more robust without them. Re-add +# them in combination with `PerStationMeanImputer` from +# susse.preprocessing if you need them back. +NASA_AUX_VARIABLE_IDS: tuple[str, ...] = ( + "longwave_downward_irr", "aod_550_adj", "cloud_amount", + "precipitable_water", "airmass", "zero_plane_displacement", + "surface_albedo", "clearness_index", "temperature", + "relative_humidity", "surface_pressure", "wind_speed", + "temperature_range", "precipitation_corrected", "surface_roughness", + "northern_wind", "planetary_boundary", "total_column_ozone", + "surface_air_density", "surface_soil_wetness", +) +CAMS_VARIABLE_IDS: tuple[str, ...] = ("ghi_clear", "dhi_clear", "dni_clear") + +selection = FeatureSelection( + nasa_variable_ids=NASA_AUX_VARIABLE_IDS, + cams_variable_ids=CAMS_VARIABLE_IDS, + include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS), + include_satellite_bands=( + IrradianceBand.GHI, IrradianceBand.DHI, IrradianceBand.DNI, + ), + qc_levels=("pass",), +) +print( + f"Selection: {len(NASA_AUX_VARIABLE_IDS)} NASA aux + " + f"{len(CAMS_VARIABLE_IDS)} CAMS aux + " + f"{len(selection.include_satellite_irradiance) * len(selection.include_satellite_bands)} " + f"satellite irradiance columns" +)""" + ), + md( + """## 2 — Build the training dataset from the warehouse + +`FeatureService` joins ground GHI with the satellite features at the +station's geohash5 cell. We then apply the station-selection heuristic +to land on the paper's 24 training + 2 holdout split.""" + ), + code( + """from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig +from susse.warehouse_ops.io.config import TableRefs +from susse.datasets import FeatureService, DatasetManifest, TrainingDataset +from susse.provenance import git_sha, susse_version + +bq = BigQueryClient(config=WarehouseConfig()) +tables = TableRefs(config=bq.config) +service = FeatureService(bq=bq) + +station_meta = bq.query(f\"\"\" + SELECT location, + ANY_VALUE(lat) AS lat, + ANY_VALUE(lon) AS lon, + MIN(date) AS min_date, + MAX(date) AS max_date, + COUNT(*) AS n_rows + FROM `{tables.ground_measurements}` + WHERE qc_level = "pass" + GROUP BY location + ORDER BY n_rows DESC +\"\"\") +TRAINING_STATIONS, HOLDOUT_STATIONS = auxscripts.pick_training_and_holdout_stations( + station_meta, + n_training=N_TRAINING_STATIONS, + n_holdout=N_HOLDOUT_STATIONS, +) +selected_stations = TRAINING_STATIONS + HOLDOUT_STATIONS +print(f"Training stations ({len(TRAINING_STATIONS)}): {TRAINING_STATIONS}") +print(f"Spatial-holdout pair: {HOLDOUT_STATIONS}")""" + ), + code( + """# Pull ground + satellite features for the 24 selected stations across +# their full ground-data range. Cleaners in §3 then filter rows. +DATE_START = station_meta.loc[ + station_meta["location"].isin(selected_stations), "min_date" +].min() +DATE_END = station_meta.loc[ + station_meta["location"].isin(selected_stations), "max_date" +].max() +raw_df = service.build_training_pairs( + selection=selection, + date_start=DATE_START, + date_end=DATE_END, + locations=selected_stations, +) +manifest = DatasetManifest( + name="mukiibi_mikelson_2026_train", + version="recomputation_v1", + created_at_utc=pd.Timestamp.now(tz="UTC").isoformat(), + susse_version=susse_version(), + git_sha=git_sha(), + feature_selection=selection, + date_start=DATE_START, + date_end=DATE_END, + location_filter=None, + warehouse_project=bq.config.project_id, + warehouse_dataset=bq.config.dataset, + warehouse_table_mods=service.warehouse_table_mods(selection), + n_rows=len(raw_df), + n_cols=len(raw_df.columns), + column_names=tuple(raw_df.columns), + content_hash="recomputation", +) +dataset = TrainingDataset(df=raw_df, manifest=manifest) +print(f"Raw training pairs: {len(raw_df):,} rows × {len(raw_df.columns)} columns") +print(f"Date span: {DATE_START} .. {DATE_END}") +print(f"Stations: {raw_df['location'].nunique()}")""" + ), + md( + """## 3 — Configure preprocessing + +`FeatureSpec` declares everything that turns the raw warehouse frame +into a model-ready ``(X, y)`` pair. + +* **Target** — absolute GHI (`y_ghi_kwh_m2_day`), matching the paper. +* **Cleaners** — three target-quality filters from paper §II.A: + `GhiUpperBoundCleaner(threshold=12.0)` drops physically-implausible + values, `IqrLowerBoundCleaner` is a Tukey lower fence, + `HighMissingYearExcluder(missing_fraction_threshold=0.05)` drops + sparse (station, year) groups. +* **Pass-through features** — every aux variable from §1 + six + satellite-irradiance columns produced by `FeatureService`. +* **Derived features**: + + | Feature | Class | Carries | + |---|---|---| + | `kt_cams` | `ClearSkyIndexFeature` | CAMS-based clear-sky index = `sat_ghi_cams / cams_ghi_clear` | + | `doy_sin`, `doy_cos` | `CyclicalDayOfYearFeature` | Cyclical day-of-year encoding (no wrap-around discontinuity) | + | `altitude_m` | `AltitudeFeature` (pvlib lookup) | Station elevation | + | `longitude` | `LongitudeFeature` | **Paper-faithful only.** Using raw coordinates with 28 training stations is a known anti-pattern — see the class docstring at `src/susse/preprocessing/derived.py`. Drop in a future iteration | +""" + ), + code( + """from susse.preprocessing import ( + AltitudeFeature, ClearSkyIndexFeature, CyclicalDayOfYearFeature, + FeatureSpec, GhiUpperBoundCleaner, HighMissingYearExcluder, + IqrLowerBoundCleaner, LongitudeFeature, Preprocessor, + PvlibElevationProvider, +) + +feature_columns = ( + *(f"nasa_{v}" for v in NASA_AUX_VARIABLE_IDS), + *(f"cams_{v}" for v in CAMS_VARIABLE_IDS), + *( + f"sat_{band.value}_{source.value.lower()}_kwh_m2_day" + for band in selection.include_satellite_bands + for source in selection.include_satellite_irradiance + ), +) +spec = FeatureSpec( + target_column="y_ghi_kwh_m2_day", + feature_columns=feature_columns, + cleaners=( + GhiUpperBoundCleaner(column="y_ghi_kwh_m2_day", threshold=12.0), + IqrLowerBoundCleaner(column="y_ghi_kwh_m2_day", multiplier=1.5), + HighMissingYearExcluder(missing_fraction_threshold=0.05), + ), + derived_features=( + ClearSkyIndexFeature( + ghi_column="sat_ghi_cams_kwh_m2_day", + ghi_clear_column="cams_ghi_clear", + output_column="kt_cams", + ), + CyclicalDayOfYearFeature(), + AltitudeFeature(provider=PvlibElevationProvider()), + LongitudeFeature(), + ), + id_columns=("date", "location", "geohash5", "lat", "lon"), +) +processed = Preprocessor(spec).apply(dataset) +print( + f"Processed: {processed.n_rows:,} rows × {processed.n_features} features " + f"({processed.df['location'].nunique()} stations)" +)""" + ), + md( + """### Data exploration + +Four quick views of the training data: target distribution per station, +satellite-vs-observed scatter, feature-correlation heatmap, and a PCA +scatter coloured by station. All four are plot-only — implementations +live in `auxscripts`.""" + ), + code( + """from susse.evaluation.plots import ( + plot_feature_correlation_heatmap, + plot_pca_by_station, + plot_predictor_vs_observed_scatter, + plot_target_distribution_per_station, +) + +plot_target_distribution_per_station( + processed.df, + target_column=spec.target_column, + value_label="y_ghi (kWh/m²/day)", +) +plot_predictor_vs_observed_scatter( + processed.df, + observed_column=spec.target_column, + predictor_columns={ + "NASA CERES": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, +) +plot_feature_correlation_heatmap( + processed.df, + feature_columns=processed.feature_columns, + target_column=spec.target_column, +) +plot_pca_by_station( + processed.df, feature_columns=processed.feature_columns, +)""" + ), + md( + """## 4 — Train / val split — 2-station spatial holdout + +Every row from the 2 holdout stations identified in §2 goes into the +validation fold; every row from the other 22 trains the model. This is +paper Table III's setup — spatial, not random.""" + ), + code( + """from susse.training import SpatialBlockSplitter + +splitter = SpatialBlockSplitter( + val_blocks=HOLDOUT_STATIONS, + block_column="location", +) +train_idx, val_idx = splitter(processed) +print( + f"Train: {len(train_idx):,} rows from " + f"{processed.df.loc[train_idx, 'location'].nunique()} stations" +) +print( + f"Val: {len(val_idx):,} rows from " + f"{processed.df.loc[val_idx, 'location'].nunique()} held-out stations " + f"({list(HOLDOUT_STATIONS)})" +)""" + ), + md( + """## 5 — Train the Random Forest + +Paper hyperparameters: `n_estimators=200`, `min_samples_leaf=5`, +`random_state=42`. The `Trainer` also scores raw NASA + CAMS GHI as +baselines on the held-out 2 stations — directly the paper-Table-III +three-way comparison. +""" + ), + code( + """from susse.models import RandomForestParams +from susse.training import Trainer, TrainerConfig + +trainer = Trainer(TrainerConfig( + wandb_project=WANDB_PROJECT if LOG_TO_WANDB else None, + wandb_entity=WANDB_ENTITY, + wandb_tags=WANDB_TAGS, + baseline_columns=( + "sat_ghi_nasa_kwh_m2_day", "sat_ghi_cams_kwh_m2_day", + ), +)) +bundle = trainer.train( + processed=processed, + params=RandomForestParams( + n_estimators=RF_N_ESTIMATORS, + min_samples_leaf=RF_MIN_SAMPLES_LEAF, + random_state=RF_RANDOM_STATE, + ), + splitter=splitter, + holdout_label=( + f"station_holdout:{','.join(HOLDOUT_STATIONS)} " + f"(paper Table III)" + ), + bundle_dest=BUNDLE_DEST, +) +val = bundle.metadata.val_metrics +print(f"=== Paper Table III equivalent — held-out stations: {HOLDOUT_STATIONS} ===") +print(f" n RMSE MAE R²") +print(f" RF {val.n_rows:5d} {val.rmse:6.3f} {val.mae:6.3f} {val.r2:6.3f}") +for col, scores in (bundle.metadata.baseline_metrics or {}).items(): + label = col.replace("sat_ghi_", "").replace("_kwh_m2_day", "").upper() + print( + f" {label:8s} {scores.n_rows:5d} " + f"{scores.rmse:6.3f} {scores.mae:6.3f} {scores.r2:6.3f}" + )""" + ), + md( + """### Training-fit diagnostics + +In-sample sanity check — predict on every training row, plot the result +weekly per station and as a scatter against observed. A tight diagonal +means the preprocessing and training pipeline are healthy upstream of +the §6 generalisation test.""" + ), + code( + """diag_df = processed.df.copy() +diag_df["y_pred"] = bundle.regressor.predict(processed.X()) +diag_df["date"] = pd.to_datetime(diag_df["date"]) +from susse.evaluation.plots import ( + plot_training_fit_scatter, plot_training_fit_timeseries, +) + +plot_training_fit_timeseries( + diag_df, + observed_column=spec.target_column, + prediction_series={"RF (in-sample)": "y_pred"}, + reference_series={ + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, +) +plot_training_fit_scatter( + diag_df, + observed_column=spec.target_column, + predicted_column="y_pred", +)""" + ), + md( + """## 6 — Validate against the Katongole 54-station network + +Apples-to-apples: a 6-year average of model predictions vs the published +2017–2022 Katongole climatology, at the same coordinates. Migration A12 +must have been applied — `auxscripts.assert_katongole_coverage` checks +this upfront and raises with a remediation pointer if it hasn't.""" + ), + code( + """from susse.warehouse_ops.io.repositories import SatelliteRepository +import auxscripts + +VAL_START = date(VALIDATION_START_YEAR, 1, 1) +VAL_END = date(VALIDATION_END_YEAR, 12, 31) + +katongole = pd.read_csv(KATONGOLE_CSV) +katongole["geohash5"] = [ + pygeohash.encode(lat, lon, precision=5) + for lat, lon in zip(katongole["latitude"], katongole["longitude"]) +] + +# Train-fold geohashes and NASA 0.5° cells — overlap flags for §6 metrics. +train_fold = processed.df.loc[train_idx] +training_geohashes = set(train_fold["geohash5"].unique()) +training_nasa_cells = { + (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5))) + for lat, lon in zip(train_fold["lat"], train_fold["lon"]) +} +katongole["nasa_cell"] = [ + (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5))) + for lat, lon in zip(katongole["latitude"], katongole["longitude"]) +] +katongole["seen_in_training_geohash5"] = katongole["geohash5"].isin(training_geohashes) +katongole["seen_in_training_nasa_cell"] = katongole["nasa_cell"].isin(training_nasa_cells) +print( + f"{len(katongole)} Katongole stations: " + f"{int(katongole['seen_in_training_geohash5'].sum())} share a geohash5 " + f"with a training station, " + f"{int(katongole['seen_in_training_nasa_cell'].sum())} share a NASA 0.5° cell." +) + +# Hard-fail if any station lacks coverage. Pass allow_partial=True for +# inspecting intermediate state while the A12 ingest is still running. +auxscripts.assert_katongole_coverage( + bq, tables, katongole, VAL_START, VAL_END, +) +inference_df = auxscripts.build_katongole_inference_frame( + bq=bq, tables=tables, sat_repo=SatelliteRepository(bq=bq), + katongole=katongole, selection=selection, + nasa_aux_ids=NASA_AUX_VARIABLE_IDS, cams_aux_ids=CAMS_VARIABLE_IDS, + val_start=VAL_START, val_end=VAL_END, +) +auxscripts.assert_inference_complete(katongole, inference_df)""" + ), + code( + """# Apply the trained bundle's feature spec to the inference frame and +# predict. Reloading from disk mirrors what the portal does. +from susse.training import load_bundle + +restored = load_bundle( + BUNDLE_DEST, providers={"altitude": PvlibElevationProvider()}, +) +inference_dataset = TrainingDataset( + df=inference_df.assign(y_ghi_kwh_m2_day=np.nan), + manifest=manifest, +) +inference_spec_for_predict = FeatureSpec( + target_column=restored.feature_spec.target_column, + feature_columns=restored.feature_spec.feature_columns, + cleaners=(), + derived_features=restored.feature_spec.derived_features, + id_columns=restored.feature_spec.id_columns, + dropna_target=False, dropna_features=False, +) +inference_processed = Preprocessor(inference_spec_for_predict).apply(inference_dataset) +inference_processed.df["y_pred_kwh_m2_day"] = restored.regressor.predict( + inference_processed.X() +) +print( + f"Predictions: {len(inference_processed.df):,} daily values across " + f"{inference_processed.df['location'].nunique()} stations" +)""" + ), + code( + """# Aggregate daily predictions to a monthly climatology per station, +# then join with the Katongole reference values for scoring. +monthly = ( + inference_processed.df + .assign(month=lambda d: pd.to_datetime(d["date"]).dt.month) + .groupby(["location", "month"])["y_pred_kwh_m2_day"] + .mean().reset_index() + .rename(columns={"y_pred_kwh_m2_day": "monthly_pred"}) +) +sat_monthly = ( + inference_processed.df + .assign(month=lambda d: pd.to_datetime(d["date"]).dt.month) + .groupby(["location", "month"])[ + ["sat_ghi_nasa_kwh_m2_day", "sat_ghi_cams_kwh_m2_day"] + ].mean().reset_index() +) +month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", + "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] +katongole_long = katongole.melt( + id_vars=["location", "latitude", "longitude", "altitude", + "geohash5", "seen_in_training_geohash5", "seen_in_training_nasa_cell"], + value_vars=month_names, + var_name="month_name", value_name="monthly_obs", +) +katongole_long["month"] = katongole_long["month_name"].apply( + lambda m: month_names.index(m) + 1 +) +comparison = ( + monthly + .merge(katongole_long, on=["location", "month"], how="inner") + .merge(sat_monthly, on=["location", "month"], how="left") +) +table_iv_raw = pd.concat([ + auxscripts.score_table_iv(comparison, label=f"all {comparison['location'].nunique()} stations"), + auxscripts.score_table_iv( + comparison[~comparison["seen_in_training_geohash5"]], + label="excluding training-geohash overlaps", + ), +]) +print("=== Paper Table IV equivalent (uncalibrated) ===") +print(table_iv_raw.to_string(index=False))""" + ), + md( + """## 6.3 — Calibration discrepancy at Makerere + +§6's uncalibrated metrics over-predict Katongole by ~0.85 kWh/m²/day +on average. The cause is a **cross-network sensor offset** — our training +data uses MEMD/CrossBoundary research-grade pyranometers (annually +recalibrated), while Katongole uses the TAHMO ATMOS 41 combination +sensor (a mid-grade weather-station instrument with ±5% factory tolerance +and no regular calibration). Our `kampala` station and Katongole's +`Makerere S` station sit at *literally identical coordinates* +`(0.333542°, 32.56863°)`. Comparing the two over the same 2017–2022 +window measures the network offset directly.""" + ), + code( + """# Pull our kampala monthly climatology over 2017-2022. +kampala_monthly = bq.query(f\"\"\" +SELECT EXTRACT(MONTH FROM date) AS month, + ROUND(AVG(ghi_kwh_m2_day), 4) AS kampala_mean, + COUNT(*) AS n_days +FROM `{tables.ground_measurements}` +WHERE location = "kampala" AND qc_level = "pass" + AND date BETWEEN DATE("2017-01-01") AND DATE("2022-12-31") +GROUP BY month ORDER BY month +\"\"\") +makerere_row = katongole[katongole["location"] == "Makerere S"] +makerere_long = pd.DataFrame({ + "month": list(range(1, 13)), + "makerere_mean": [float(makerere_row[m].iloc[0]) for m in month_names], +}) +kampala_gh = pygeohash.encode(0.333542, 32.56863, 5) +sat_monthly_2017_22 = bq.query(f\"\"\" +SELECT EXTRACT(MONTH FROM date) AS month, source, + ROUND(AVG(ghi_kwh_m2_day), 4) AS mean_ghi +FROM `{tables.irradiance_daily}` +WHERE geohash5 = "{kampala_gh}" + AND source IN ("NASA", "CAMS") + AND date BETWEEN DATE("2017-01-01") AND DATE("2022-12-31") +GROUP BY month, source ORDER BY month, source +\"\"\").pivot(index="month", columns="source", values="mean_ghi").reset_index() + +calibration_df = ( + kampala_monthly.merge(makerere_long, on="month") + .merge(sat_monthly_2017_22, on="month") +) +calibration_df["ratio"] = ( + calibration_df["kampala_mean"] / calibration_df["makerere_mean"] +) +annual_ratio = float(calibration_df["ratio"].mean()) +print(f"Annual mean ratio (kampala / Makerere S): {annual_ratio:.3f}") +auxscripts.plot_calibration_discrepancy(calibration_df)""" + ), + md( + """## 6.4 — Calibrated Table IV + +A single annual ratio (mean of the 12 monthly ratios — ~1.18) rescales +TAHMO Katongole values onto the MEMD/CB scale before scoring. Per-month +ratios would overfit on one co-located pair × 7 years (June's +~1.42 over-corrects Ndejje to 7.8 kWh/m²/day, which is non-physical). +A monotonic linear rescaling shifts RF and satellite RMSEs together — +the *relative* improvement claim is preserved.""" + ), + code( + """comparison_calibrated = comparison.copy() +comparison_calibrated["monthly_obs_calibrated"] = ( + comparison_calibrated["monthly_obs"] * annual_ratio +) +table_iv_calibrated = pd.concat([ + auxscripts.score_table_iv( + comparison_calibrated, + label=f"all {comparison_calibrated['location'].nunique()} stations", + obs_col="monthly_obs_calibrated", + rf_label="RF (calibrated)", + ), + auxscripts.score_table_iv( + comparison_calibrated[~comparison_calibrated["seen_in_training_geohash5"]], + label="excluding training-geohash overlaps", + obs_col="monthly_obs_calibrated", + rf_label="RF (calibrated)", + ), + auxscripts.score_table_iv( + comparison_calibrated[~comparison_calibrated["seen_in_training_nasa_cell"]], + label="excluding training-NASA-cell overlaps", + obs_col="monthly_obs_calibrated", + rf_label="RF (calibrated)", + ), +]) +print("=== Paper Table IV equivalent — calibration-corrected ===") +print(table_iv_calibrated.to_string(index=False))""" + ), + md( + """### Residual diagnostics + +Three diagnostic views on the §6.4 metrics: covariate-shift KDEs +(training vs Katongole feature distributions), a predicted-vs-observed +scatter, and per-station MBE.""" + ), + code( + """from susse.evaluation import MeanBiasError +from susse.evaluation.plots import ( + plot_covariate_shift_kde, + plot_per_station_metric, + plot_predicted_vs_observed_panels, +) + +plot_covariate_shift_kde( + processed.df, inference_processed.df, + features=[ + "kt_cams", "nasa_clearness_index", "nasa_aod_550_adj", + "nasa_cloud_amount", "altitude_m", "longitude", + "sat_ghi_nasa_kwh_m2_day", "sat_ghi_cams_kwh_m2_day", + ], + train_label="Training", val_label="Katongole", +) +plot_predicted_vs_observed_panels( + comparison_calibrated, + observed_column="monthly_obs_calibrated", + predictions={ + "RF (calibrated)": "monthly_pred", + "NASA CERES": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, +) +plot_per_station_metric( + comparison_calibrated, + observed_column="monthly_obs_calibrated", + predicted_column="monthly_pred", + metric=MeanBiasError(), + highlight_column="seen_in_training_geohash5", +)""" + ), + md( + """## 7 — Figures + +* **Figure 2** — 4 × 4 grid of monthly GHI comparison panels for 16 + Katongole stations (mirroring the paper). Five series per panel: + measured (raw + calibrated), RF predicted, NASA GHI, CAMS GHI. +* **Figure 3** — top RF feature importances. + +To pin the figure to a specific 16-station selection (e.g. the paper's +exact list), pass `stations=(...)` to `plot_figure_2_grid`; the default +picks 4 stations per RMSE quartile so the figure spans the model's full +performance range.""" + ), + code( + """from susse.evaluation.plots import plot_feature_importances_top_n + +auxscripts.plot_figure_2_grid( + comparison_calibrated, + validation_label=f"{VALIDATION_START_YEAR}-{VALIDATION_END_YEAR}", +) +top_importances = plot_feature_importances_top_n( + bundle, feature_columns=processed.feature_columns, top_n=12, +) +top_importances""" + ), + md( + """## 8 — Bundle deliverable for Irradiation_Portal + +The portal loads a `TrainedBundle` from a fixed directory. After this +notebook runs cleanly the directory at `BUNDLE_DEST` contains: + +``` +mukiibi_mikelson_2026/ +├── model/ +│ ├── model_kind.txt # → "random_forest" +│ ├── params.json # → {n_estimators: 200, min_samples_leaf: 5, …} +│ └── state.joblib # the fitted sklearn estimator +├── feature_spec.json # cleaners + derived features + pass-through cols +├── metadata.json # train + val + baseline ScoreSet, splitter name, … +└── source_manifest.json # warehouse-state snapshot used to build the dataset +``` + +The model predicts absolute GHI directly — `regressor.predict(X)` +returns the bias-corrected value, no inversion step needed. + +```python +restored = load_bundle( + bundle_dir, providers={"altitude": PvlibElevationProvider()}, +) +y_pred = restored.regressor.predict(X) # kWh/m²/day, ready to consume +``` + +The earlier `load_bundle` call in §6 already verified this round-trip, +so the on-disk bundle is portable to the portal as-is.""" + ), + md( + """## 9 — Deviations from the paper (documented) + +| | Paper | This recomputation | Why | +|---|---|---|---| +| **Validation calibration** | none stated | single annual ratio from co-located `kampala` ↔ `Makerere S` (~1.18) | Two ground-sensor networks (MEMD/CB vs TAHMO ATMOS 41) read systematically different at the same site — uncalibrated comparison underestimates the model. Relative-improvement claim preserved under any monotonic rescaling. See §6.3-6.4. | +| Station selection | 24 named stations (paper Table I) | 24 stations by coverage rank | We have 28 available; the heuristic at §2 drops the 4 shortest-coverage. Exact identity of the dropped 4 unlikely to match the paper's. | +| 2-station holdout | named pair (paper Table III) | pair with maximum pairwise haversine | Deterministic and re-runnable. Different from the paper's choice; gives the strongest spatial-generalisation test the data allows. | +| Outlier curation | inline (paper text §II.A) | `cleaners` chain on the `FeatureSpec` | Architectural — the paper's rules now compose like any other cleaner. | +| `day_of_year` integer | included alongside sin/cos | sin/cos only via `CyclicalDayOfYearFeature` | Cyclical encoding is information-equivalent; raw doy adds collinearity. | +| Solar zenith angle | included as a predictor | dropped | NASA POWER doesn't serve daily SZA. | +| `evaporation_land`, `evapotranspiration_energy` | included | dropped | NaN at oceanic stations in the Gulf of Guinea; land-only by design. Add `PerStationMeanImputer` if you re-include them. | + +## What's next + +* If RMSE and IOA differ materially from the paper, inspect the cleaner + output — the per-(station, year) missing-data threshold and the IQR + fence are the two most sensitive levers. +* The next iteration should drop `LongitudeFeature` (anti-pattern) and + replace it with continuous geographical features (terrain ruggedness, + distance to coast, …).""" + ), +] + + +def main() -> None: + nb = { + "cells": CELLS, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3", + }, + "language_info": {"name": "python", "version": "3.12"}, + }, + "nbformat": 4, + "nbformat_minor": 5, + } + NB_PATH.write_text(json.dumps(nb, indent=1) + "\n") + print(f"wrote {NB_PATH} ({len(CELLS)} cells)") + + +if __name__ == "__main__": + main() diff --git a/notebooks/papers/mukiibi_mikelson_2026/auxscripts.py b/notebooks/papers/mukiibi_mikelson_2026/auxscripts.py new file mode 100644 index 00000000..03216675 --- /dev/null +++ b/notebooks/papers/mukiibi_mikelson_2026/auxscripts.py @@ -0,0 +1,455 @@ +"""Paper-specific helpers for the Mukiibi & Mikelson (2026) recomputation. + +Everything here is bound to *this* paper: the 56-station Katongole +benchmark, migration A12's warehouse layout, the paper's two custom +figure layouts (the calibration-discrepancy plot and the 4 × 4 +Figure 2 panel grid), and the deterministic station-selection rule +that picks the paper's 24 training + 2 holdout split. + +Generic evaluation plots — scatter panels, per-station bars, +covariate-shift KDEs, training-fit time series, feature importances — +were moved to :mod:`susse.evaluation.plots` once the second +consumer appeared. Import those directly: + + from susse.evaluation.plots import ( + plot_target_distribution_per_station, + plot_predictor_vs_observed_scatter, + plot_feature_correlation_heatmap, + plot_pca_by_station, + plot_training_fit_timeseries, + plot_training_fit_scatter, + plot_covariate_shift_kde, + plot_predicted_vs_observed_panels, + plot_per_station_metric, + plot_feature_importances_top_n, + ) + +If a function in this module starts looking generic, move it to +``src/susse/evaluation/plots.py`` and import it back here. +""" + +from __future__ import annotations + +from datetime import date +from typing import TYPE_CHECKING, Iterable + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +if TYPE_CHECKING: # pragma: no cover — type-only imports + from susse.datasets import FeatureSelection + from susse.warehouse_ops.io.bq import BigQueryClient + from susse.warehouse_ops.io.config import TableRefs + from susse.warehouse_ops.io.repositories import SatelliteRepository + + +# --------------------------------------------------------------------------- +# Sanity checks — silent on success, raise on failure. +# --------------------------------------------------------------------------- + + +def assert_katongole_coverage( + bq: "BigQueryClient", + tables: "TableRefs", + katongole: pd.DataFrame, + val_start: date, + val_end: date, + *, + min_days_fraction: float = 0.95, + allow_partial: bool = False, +) -> None: + """Raise if any Katongole station lacks NASA + CAMS coverage over the window. + + Args: + bq: Live BigQuery client. + tables: Resolved warehouse table references. + katongole: DataFrame with a ``geohash5`` column for every station. + val_start, val_end: Inclusive bounds of the validation window. + min_days_fraction: A station is "full" when both NASA and CAMS have + at least this fraction of the expected ``val_end - val_start`` + days. Default 0.95 tolerates ~1 missing day per month. + allow_partial: When True, raise only when *no* station has any data + (development mode). Production runs leave this False so missing + stations fail loud. + + Raises: + RuntimeError: When any station is below the coverage threshold. + The message names the migration that ingests the missing data. + """ + n_expected = (val_end - val_start).days + 1 + min_days = int(min_days_fraction * n_expected) + geohashes = ", ".join(f"'{g}'" for g in katongole["geohash5"].unique()) + coverage = bq.query(f""" + SELECT geohash5, + COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days, + COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days + FROM `{tables.irradiance_daily}` + WHERE date BETWEEN DATE('{val_start}') AND DATE('{val_end}') + AND geohash5 IN ({geohashes}) + GROUP BY geohash5 + """) + per_station = katongole[["location", "geohash5"]].merge( + coverage, on="geohash5", how="left", + ).fillna({"n_nasa_days": 0, "n_cams_days": 0}).astype( + {"n_nasa_days": int, "n_cams_days": int} + ) + is_full = (per_station["n_nasa_days"] >= min_days) & ( + per_station["n_cams_days"] >= min_days + ) + is_missing = (per_station["n_nasa_days"] == 0) & ( + per_station["n_cams_days"] == 0 + ) + if allow_partial: + if is_missing.all(): + raise RuntimeError( + "No Katongole station has any warehouse coverage. " + "Apply migration A12: warehouse/migrations/" + "2026-05-11_a12_ingest_katongole_2017_2022.py --apply." + ) + return + if not is_full.all(): + incomplete = per_station[~is_full] + raise RuntimeError( + f"{len(incomplete)} of {len(per_station)} Katongole stations " + f"have incomplete warehouse coverage for {val_start}..{val_end}.\n" + f"To fix: .venv/bin/python warehouse/migrations/" + f"2026-05-11_a12_ingest_katongole_2017_2022.py --apply\n" + f"Pass allow_partial=True to bypass this assertion during " + f"development.\n" + f"Incomplete stations:\n" + f"{incomplete[['location', 'n_nasa_days', 'n_cams_days']].to_string(index=False)}" + ) + + +def assert_inference_complete( + katongole: pd.DataFrame, inference_df: pd.DataFrame, +) -> None: + """Raise if any Katongole station is absent from the inference frame. + + Defence-in-depth: the pre-flight should have caught this, but a merge + glitch downstream could still drop rows. A second check at the call + site is cheap. + """ + expected = set(katongole["location"]) + present = set(inference_df["location"]) + missing = sorted(expected - present) + if missing: + raise RuntimeError( + f"{len(missing)} stations are absent from the inference frame " + f"despite the pre-flight passing. Investigate before trusting " + f"downstream metrics. Missing: {missing}" + ) + + +# --------------------------------------------------------------------------- +# Deterministic station selection. +# +# Operates on ``station_meta`` (a row-per-station summary frame) to pick the +# paper's 24 training + 2 holdout split *before* loading per-row data. This +# is distinct from :class:`susse.training.SpatialSpreadHoldoutSplitter`, +# which selects rows of an already-loaded :class:`PreprocessedDataset`. Both +# implement the same max-pairwise-haversine idea at different pipeline +# stages. +# --------------------------------------------------------------------------- + + +def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float: + """Great-circle distance in kilometres between two (lat, lon) points.""" + r_earth_km = 6371.0 + phi1, phi2 = np.radians(lat1), np.radians(lat2) + d_phi = phi2 - phi1 + d_lambda = np.radians(lon2 - lon1) + a = ( + np.sin(d_phi / 2) ** 2 + + np.cos(phi1) * np.cos(phi2) * np.sin(d_lambda / 2) ** 2 + ) + return float(2 * r_earth_km * np.arcsin(np.sqrt(a))) + + +def pick_training_and_holdout_stations( + station_meta: pd.DataFrame, *, n_training: int, n_holdout: int, +) -> tuple[tuple[str, ...], tuple[str, ...]]: + """Select training + spatial-holdout stations by a deterministic rule. + + The rule (paper Table I + III equivalent): + + 1. Sort the available stations by total QC-passed row count and keep the + top ``n_training`` — drops the shortest-coverage stations first. + 2. From those, pick the ``n_holdout``-station subset with the largest + pairwise haversine spread; greedy pairwise maximum for ``n_holdout=2``. + + Returns: + ``(training_stations, holdout_stations)`` as tuples of location + names. ``training_stations`` excludes the holdout set. + + Raises: + ValueError: ``n_training`` exceeds the rows in ``station_meta``, + or ``n_holdout`` >= ``n_training``. + """ + if len(station_meta) < n_training: + raise ValueError( + f"Only {len(station_meta)} stations available but n_training=" + f"{n_training}. Lower n_training or ingest more stations." + ) + if n_holdout >= n_training or n_holdout < 1: + raise ValueError( + f"n_holdout={n_holdout} must satisfy 1 <= n_holdout < n_training " + f"(n_training={n_training})." + ) + candidates = station_meta.nlargest(n_training, "n_rows").reset_index(drop=True) + if n_holdout != 2: + raise NotImplementedError( + f"Only n_holdout=2 is implemented (matching paper Table III); " + f"got {n_holdout}. Extend pick_training_and_holdout_stations to " + f"select a larger holdout set by k-medoids or similar." + ) + best_distance = -1.0 + holdout_pair: tuple[str, str] = ("", "") + for i in range(len(candidates)): + for j in range(i + 1, len(candidates)): + d_km = _haversine_km( + candidates.loc[i, "lat"], candidates.loc[i, "lon"], + candidates.loc[j, "lat"], candidates.loc[j, "lon"], + ) + if d_km > best_distance: + best_distance = d_km + holdout_pair = ( + candidates.loc[i, "location"], + candidates.loc[j, "location"], + ) + training_stations = tuple( + s for s in candidates["location"] if s not in holdout_pair + ) + return training_stations, holdout_pair + + +# --------------------------------------------------------------------------- +# Katongole inference frame builder. +# --------------------------------------------------------------------------- + + +def build_katongole_inference_frame( + *, + bq: "BigQueryClient", + tables: "TableRefs", + sat_repo: "SatelliteRepository", + katongole: pd.DataFrame, + selection: "FeatureSelection", + nasa_aux_ids: tuple[str, ...], + cams_aux_ids: tuple[str, ...], + val_start: date, + val_end: date, +) -> pd.DataFrame: + """Assemble the daily inference frame for every Katongole station. + + After migration A12 each Katongole station has its own warehouse cell, + so the merge is on the station's own ``geohash5`` — no + snap-to-nearest-cell logic is needed. + + Returns: + DataFrame with one row per (station, date) over ``val_start..val_end``, + carrying ``location``, ``lat``, ``lon``, ``geohash5``, ``date``, + every ``sat___kwh_m2_day`` column, every NASA aux + column (prefix ``nasa_``), and every CAMS aux column (prefix + ``cams_``). Suitable to pass through the same ``Preprocessor`` as + the training frame. + """ + katongole_geohashes = tuple(katongole["geohash5"].unique()) + irr_wide = sat_repo.daily_irradiance_by_geohash( + val_start, val_end, + sources=("NASA", "CAMS"), + bands=selection.include_satellite_bands, + geohash5s=katongole_geohashes, + ) + nasa_aux = sat_repo.long_aux_pivoted( + table_fqn=tables.nasa_daily_vars_long, + column_prefix="nasa", + start=val_start, end=val_end, + variable_ids=nasa_aux_ids, + geohash5s=katongole_geohashes, + ) + cams_aux = sat_repo.long_aux_pivoted( + table_fqn=tables.cams_daily_vars_long, + column_prefix="cams", + start=val_start, end=val_end, + variable_ids=cams_aux_ids, + geohash5s=katongole_geohashes, + ) + geohash_features = ( + irr_wide + .merge(nasa_aux, on=["date", "geohash5"], how="left") + .merge(cams_aux, on=["date", "geohash5"], how="left") + ) + inference_df = katongole[ + ["location", "latitude", "longitude", "geohash5"] + ].merge(geohash_features, on="geohash5", how="inner").rename( + columns={"latitude": "lat", "longitude": "lon"} + ) + return inference_df + + +# --------------------------------------------------------------------------- +# Paper-specific figure layouts. +# +# Generic versions of every other plot moved to susse.evaluation.plots; only +# layouts that hardcode paper-specific series (Makerere co-location, the +# 4 × 4 Figure 2 grid) live here. +# --------------------------------------------------------------------------- + + +def plot_calibration_discrepancy(calibration_df: pd.DataFrame) -> None: + """Co-located comparison at Makerere — two ground sensors + 2 satellites. + + Paper-specific: the four series and their labels are pinned to the + Mukiibi-Mikelson §6.3 narrative. + """ + fig, ax = plt.subplots(figsize=(11, 5)) + ax.plot(calibration_df["month"], calibration_df["kampala_mean"], "-o", + ms=4, color="C0", label="Our kampala (MEMD/CB pyranometer)") + ax.plot(calibration_df["month"], calibration_df["makerere_mean"], "-s", + ms=4, color="C4", label="Katongole Makerere S (TAHMO ATMOS 41)") + ax.plot(calibration_df["month"], calibration_df["NASA"], "-", lw=0.8, + color="C2", alpha=0.7, label="NASA CERES (2017-2022)") + ax.plot(calibration_df["month"], calibration_df["CAMS"], "-", lw=0.8, + color="C1", alpha=0.7, label="CAMS (2017-2022)") + ax.set_xlabel("Month") + ax.set_ylabel("GHI (kWh/m²/day)") + ax.set_xticks(range(1, 13)) + ax.set_title( + "Co-located comparison at Makerere University, 2017-2022\n" + "Same lat/lon — different pyranometer networks differ by ~14% (kampala higher)" + ) + ax.legend(loc="lower left") + ax.grid(alpha=0.3) + fig.tight_layout() + plt.show() + + +def plot_figure_2_grid( + comparison_calibrated: pd.DataFrame, + *, + stations: Iterable[str] | None = None, + validation_label: str = "2017-2022", + n_panels: int = 16, +) -> list[str]: + """Paper-style 4 × 4 monthly GHI grid for 16 stations. + + Paper-specific: the five series per panel (measured raw, + measured calibrated, RF predicted, NASA, CAMS), the panel-code + overlays (a1..d4), and the German-paper-style axis labels are + pinned to Mukiibi-Mikelson Figure 2. + + When ``stations`` is None, picks 4 stations per RMSE quartile so the + figure spans the model's full performance range. Returns the selected + station list so the caller can re-use it for tables or a custom legend. + """ + errs = ( + comparison_calibrated["monthly_pred"] + - comparison_calibrated["monthly_obs_calibrated"] + ) + per_station_rmse = ( + (errs ** 2) + .groupby(comparison_calibrated["location"]) + .mean() + .pow(0.5) + .sort_values() + ) + if stations is None: + n = len(per_station_rmse) + picked: list[str] = [] + for q in range(4): + start = (q * n) // 4 + picked.extend(per_station_rmse.index[start: start + 4].tolist()) + stations = picked[:n_panels] + stations = list(stations) + if len(stations) != n_panels: + raise ValueError( + f"plot_figure_2_grid expects exactly {n_panels} stations, " + f"got {len(stations)}." + ) + + panel_codes = [f"{r}{c}" for r in ("a", "b") for c in (1, 2)] + panel_codes = panel_codes + [f"{r}{c}" for r in ("a", "b") for c in (3, 4)] + panel_codes = panel_codes + [f"{r}{c}" for r in ("c", "d") for c in (1, 2)] + panel_codes = panel_codes + [f"{r}{c}" for r in ("c", "d") for c in (3, 4)] + month_ticks = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", + "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] + fig, axes = plt.subplots(4, 4, figsize=(20, 14)) + for ax, station, code in zip(axes.flat, stations, panel_codes): + sub = comparison_calibrated[ + comparison_calibrated["location"] == station + ].sort_values("month") + ax.plot(sub["month"], sub["monthly_obs"], "-", + lw=0.7, color="C0", alpha=0.35, + label="Measured (TAHMO, raw)") + ax.plot(sub["month"], sub["monthly_obs_calibrated"], "-o", + lw=1.4, ms=4, color="C0", + label="Measured (calibrated)") + ax.plot(sub["month"], sub["monthly_pred"], "-o", + lw=1.4, ms=5, color="C3", label="RF Predicted") + ax.plot(sub["month"], sub["sat_ghi_nasa_kwh_m2_day"], "-", + lw=0.8, alpha=0.7, color="C2", label="NASA GHI") + ax.plot(sub["month"], sub["sat_ghi_cams_kwh_m2_day"], "-", + lw=0.8, alpha=0.7, color="C1", label="CAMS GHI") + ax.set_title( + f"GHI comparison for {station} ({validation_label})", + fontsize=9, + ) + ax.set_xticks(range(1, 13)) + ax.set_xticklabels(month_ticks, rotation=45, fontsize=7) + ax.tick_params(axis="y", labelsize=7) + ax.grid(alpha=0.3) + ax.text( + 0.965, 0.955, code, transform=ax.transAxes, + fontsize=9, ha="right", va="top", + bbox=dict(boxstyle="round,pad=0.25", fc="white", + ec="green", lw=1.0), + ) + for ax in axes[:, 0]: + ax.set_ylabel("Solar irradiation (kWh m⁻² day⁻¹)", fontsize=8) + for ax in axes[-1, :]: + ax.set_xlabel("Month of the Year", fontsize=8) + axes[0, 0].legend(loc="lower left", fontsize=7, framealpha=0.9) + fig.tight_layout() + plt.show() + return stations + + +# --------------------------------------------------------------------------- +# Scoring table. +# --------------------------------------------------------------------------- + + +def score_table_iv( + comparison: pd.DataFrame, *, + label: str | None = None, + obs_col: str = "monthly_obs", + rf_label: str = "RF", +) -> pd.DataFrame: + """Cross-station mean of paper-Table-IV style metrics. + + Thin paper-specific wrapper over :class:`susse.evaluation.Evaluator` + that hardcodes the RF / NASA CERES / CAMS comparison series and + relabels the Evaluator's ``prediction`` column to ``model`` to + match the paper's table layout. + + Returns a DataFrame with rows for RF, NASA CERES and CAMS evaluated + against ``obs_col``. Pass ``obs_col="monthly_obs_calibrated"`` for the + cross-network-calibrated comparison. + """ + from susse.evaluation import Evaluator + + if label is None: + label = f"{comparison['location'].nunique()} stations" + table = Evaluator().score( + observed=comparison[obs_col], + predictions={ + rf_label: comparison["monthly_pred"], + "NASA CERES": comparison["sat_ghi_nasa_kwh_m2_day"], + "CAMS": comparison["sat_ghi_cams_kwh_m2_day"], + }, + splits={label: comparison.index}, + ).rename(columns={"prediction": "model"}) + return table.round({"RMSE": 3, "nRMSE_%": 2, "MAE": 3, "nMAE_%": 2, + "MBE": 3, "R²": 3, "IOA": 3}) diff --git a/notebooks/papers/mukiibi_mikelson_2026/ghi_bias_correction_reproducible.ipynb b/notebooks/papers/mukiibi_mikelson_2026/ghi_bias_correction_reproducible.ipynb new file mode 100644 index 00000000..184c738b --- /dev/null +++ b/notebooks/papers/mukiibi_mikelson_2026/ghi_bias_correction_reproducible.ipynb @@ -0,0 +1,1690 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "1232f062", + "metadata": { + "id": "1232f062" + }, + "source": [ + "# GHI Bias Correction: A Machine Learning Approach\n", + "Run cells sequentially. The only prerequisites are:\n", + "- A valid BigQuery connection (configured in **§ 0**)\n", + "- The `susse` library installed in your environment\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "fca23ada", + "metadata": { + "id": "fca23ada" + }, + "source": [ + "## § 0 — Imports and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "KmYCK71PXjbm", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KmYCK71PXjbm", + "outputId": "45c9b7d6-a63c-4f7e-f3ab-43ec2031373d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/Solar_irradiation\n", + "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 3.0.3 which is incompatible.\n", + "gradio 5.50.0 requires pandas<3.0,>=1.0, but you have pandas 3.0.3 which is incompatible.\n", + "Colab setup complete.\n" + ] + } + ], + "source": [ + "import sys, os\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " clone_url = f\"https://github.com/{REPO}.git\"\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f6486389", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f6486389", + "outputId": "7c33a2ed-3e68-4e29-da9d-3fef16ae9875" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All imports successful.\n" + ] + } + ], + "source": [ + "# ── Standard library ────────────────────────────────────────────────────────\n", + "import os, warnings, sys\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as mticker\n", + "from pathlib import Path\n", + "\n", + "# Add the project root to sys.path to ensure 'susse' is found\n", + "_HERE = Path.cwd().resolve()\n", + "_PROJECT_ROOT = _HERE\n", + "while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / \"src\" / \"susse\").exists():\n", + " _PROJECT_ROOT = _PROJECT_ROOT.parent\n", + "_NB_DIR = _PROJECT_ROOT / \"notebooks\" / \"papers\" / \"mukiibi_mikelson_2026\"\n", + "sys.path.insert(0, str(_PROJECT_ROOT / \"src\"))\n", + "sys.path.insert(0, str(_NB_DIR))\n", + "\n", + "\n", + "# ── Scikit-learn ─────────────────────────────────────────────────────────────\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.linear_model import LinearRegression, Ridge\n", + "from sklearn.svm import SVR\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "\n", + "# ── XGBoost ──────────────────────────────────────────────────────────────────\n", + "from xgboost import XGBRegressor\n", + "\n", + "# ── pvlib (for derived solar geometry feature) ───────────────────────────────\n", + "!pip install -q pvlib\n", + "import pvlib\n", + "\n", + "# ── susse library ────────────────────────────────────────────────────────────\n", + "from susse.preprocessing import (\n", + " AltitudeFeature, ClearSkyIndexFeature, CyclicalDayOfYearFeature,\n", + " FeatureSpec, GhiUpperBoundCleaner, HighMissingYearExcluder,\n", + " IqrLowerBoundCleaner, LongitudeFeature, Preprocessor,\n", + " PvlibElevationProvider,\n", + ")\n", + "\n", + "from susse.datasets import FeatureSelection\n", + "from susse.models import RandomForestParams\n", + "from susse.training import Trainer, TrainerConfig\n", + "from susse.warehouse_ops.population.types import IrradianceBand, Source\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "pd.set_option(\"display.float_format\", \"{:.4f}\".format)\n", + "print(\"All imports successful.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "39a9d267", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "39a9d267", + "outputId": "7ef3c6f1-6a52-4440-a3a9-ac627ee4295f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuration loaded.\n", + " Holdout stations : ['wadelai', 'kampala']\n", + " Validation year : 2021\n", + " NASA aux features: 22\n", + " CAMS aux features: 3\n" + ] + } + ], + "source": [ + "# ── Output directories ───────────────────────────────────────────────────────\n", + "FIGURE_DIR = Path(\"./figures\"); FIGURE_DIR.mkdir(exist_ok=True)\n", + "MODEL_DIR = Path(\"./saved_models\"); MODEL_DIR.mkdir(exist_ok=True)\n", + "\n", + "# ── Matplotlib style (IEEE-compatible) ───────────────────────────────────────\n", + "plt.rcParams.update({\n", + " \"font.family\": \"serif\",\n", + " \"font.size\": 10,\n", + " \"axes.labelsize\": 10,\n", + " \"axes.titlesize\": 10,\n", + " \"xtick.labelsize\": 9,\n", + " \"ytick.labelsize\": 9,\n", + " \"legend.fontsize\": 9,\n", + " \"legend.framealpha\": 0.8,\n", + " \"figure.dpi\": 150,\n", + " \"savefig.dpi\": 300,\n", + " \"savefig.bbox\": \"tight\",\n", + " \"axes.spines.top\": False,\n", + " \"axes.spines.right\": False,\n", + "})\n", + "COLORS = {\n", + " \"measured\": \"#1f77b4\",\n", + " \"predicted\": \"#d62728\",\n", + " \"nasa\": \"#2ca02c\",\n", + " \"cams\": \"#ff7f0e\",\n", + "}\n", + "\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "# CONFIGURATION ← review before every run\n", + "# ─────────────────────────────────────────────────────────────────────────────\n", + "\n", + "# Holdout stations = paper's Sites A and B.\n", + "# UPDATE these once your supervisor confirms the site identities.\n", + "HOLDOUT_STATIONS: list[str] = [\"wadelai\", \"kampala\"]\n", + "\n", + "# Random Forest hyperparameters (paper Section III)\n", + "RF_N_ESTIMATORS = 200\n", + "RF_MIN_SAMPLES_LEAF = 5\n", + "RF_RANDOM_STATE = 42\n", + "\n", + "# Validation: paper uses 2021 only (monthly aggregation vs Katongole network)\n", + "VALIDATION_YEAR = 2021\n", + "\n", + "# Warehouse feature IDs ── NASA POWER aux variables (paper Table II)\n", + "NASA_AUX_IDS: tuple[str, ...] = (\n", + " \"longwave_downward_irr\", # CLRSKY_SFC_LW_DWN\n", + " \"aod_550_adj\", # AOD_55_ADJ\n", + " \"cloud_amount\", # CLOUD_AMT\n", + " \"precipitable_water\", # PW\n", + " \"airmass\", # AIRMASS\n", + " \"zero_plane_displacement\", # DISPH\n", + " \"surface_albedo\", # SRF_ALB_ADJ\n", + " \"clearness_index\", # ALLSKY_KT\n", + " \"temperature\", # T2M\n", + " \"relative_humidity\", # RH2M\n", + " \"surface_pressure\", # PS\n", + " \"wind_speed\", # WS2M\n", + " \"temperature_range\", # T2M_RANGE\n", + " \"precipitation_corrected\", # PRECTOTCORR\n", + " \"surface_roughness\", # Z0M\n", + " \"northern_wind\", # V2M\n", + " \"planetary_boundary\", # PBLTOP\n", + " \"total_column_ozone\", # TO3\n", + " \"surface_air_density\", # RHOA\n", + " \"surface_soil_wetness\", # GWETTOP\n", + " \"evaporation_land\", # EVLAND\n", + " \"evapotranspiration_energy\", # EVPTRNS\n", + ")\n", + "CAMS_AUX_IDS: tuple[str, ...] = (\"ghi_clear\", \"dhi_clear\", \"dni_clear\")\n", + "\n", + "\n", + "selection = FeatureSelection(\n", + " nasa_variable_ids=NASA_AUX_IDS,\n", + " cams_variable_ids=CAMS_AUX_IDS,\n", + " include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS),\n", + " include_satellite_bands=(\n", + " IrradianceBand.GHI, IrradianceBand.DHI, IrradianceBand.DNI,\n", + " ),\n", + " qc_levels=(\"pass\",),\n", + ")\n", + "\n", + "BUNDLE_DEST = MODEL_DIR / \"rf_bundle\"\n", + "\n", + "LOG_TO_WANDB = False # set True to enable W&B experiment tracking\n", + "WANDB_PROJECT = \"ghi-bias-correction\"\n", + "WANDB_ENTITY = \"your-entity\" # update if logging\n", + "WANDB_TAGS = [\"camera-ready\", \"reproduction\"]\n", + "\n", + "print(\"Configuration loaded.\")\n", + "print(f\" Holdout stations : {HOLDOUT_STATIONS}\")\n", + "print(f\" Validation year : {VALIDATION_YEAR}\")\n", + "print(f\" NASA aux features: {len(NASA_AUX_IDS)}\")\n", + "print(f\" CAMS aux features: {len(CAMS_AUX_IDS)}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4b3c52ff", + "metadata": { + "id": "4b3c52ff" + }, + "source": [ + "## § 1 — Data Loading\n", + "\n", + "Pulls ground-measured GHI and all satellite/reanalysis predictor variables from the\n", + "BigQuery warehouse using the `susse` data-access layer.\n", + "The training corpus spans 24 sites across Sub-Saharan Africa (Table I in the paper).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "zusgrRn2bKe9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zusgrRn2bKe9", + "outputId": "438b4748-bb74-420f-aea4-8089d2fdf627" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training stations (22): ['kenya_location3', 'lira', 'ghana_location1', 'nigeria_location1', 'kenya_location5', 'tororo', 'ghana_location3', 'ghana_location2', 'kenya_location2', 'kenya_location6', 'kenya_location4', 'soroti', 'kenya_location13', 'kenya_location8', 'kenya_location10', 'nigeria_location2', 'madagascar_location1', 'kenya_location7', 'kenya_location11', 'egypt_location1', 'madagascar_location2', 'kenya_location14']\n", + "Holdout stations (2) : ['wadelai', 'kampala']\n", + "Total (24)\n" + ] + } + ], + "source": [ + "from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig\n", + "from susse.warehouse_ops.io.config import TableRefs\n", + "from susse.datasets import FeatureService, DatasetManifest, TrainingDataset\n", + "from susse.provenance import git_sha, susse_version\n", + "\n", + "bq = BigQueryClient(config=WarehouseConfig())\n", + "tables = TableRefs(config=bq.config)\n", + "service = FeatureService(bq=bq)\n", + "\n", + "station_meta = bq.query(f\"\"\"\n", + " SELECT location,\n", + " ANY_VALUE(lat) AS lat,\n", + " ANY_VALUE(lon) AS lon,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date,\n", + " COUNT(*) AS n_rows\n", + " FROM `{tables.ground_measurements}`\n", + " WHERE qc_level = \"pass\"\n", + " GROUP BY location\n", + " ORDER BY n_rows DESC\n", + "\"\"\")\n", + "\n", + "# ----- Explicit holdout: Wadelai + Kampala -----\n", + "# Both stations are pinned rather than picked algorithmically, so the\n", + "# split is identical to the paper's intended experiment.\n", + "N_TRAINING_STATIONS = 22\n", + "N_HOLDOUT_STATIONS = 2\n", + "HOLDOUT_STATION_NAMES = [\"wadelai\", \"kampala\"] # pinned — not picked algorithmically\n", + "\n", + "for _name in HOLDOUT_STATION_NAMES:\n", + " assert _name in station_meta[\"location\"].values, (\n", + " f\"Holdout station '{_name}' not found in the warehouse.\\n\"\n", + " f\"Available: {list(station_meta['location'])}\"\n", + " )\n", + "\n", + "HOLDOUT_STATIONS = HOLDOUT_STATION_NAMES\n", + "\n", + "# Training pool: top stations by row count, excluding both holdout stations.\n", + "# Taking the N_TRAINING_STATIONS highest-coverage non-holdout stations\n", + "# gives 24 total stations, consistent with the paper.\n", + "_candidates = station_meta.loc[\n", + " ~station_meta[\"location\"].isin(HOLDOUT_STATIONS), \"location\"\n", + "].tolist()\n", + "TRAINING_STATIONS = _candidates[:N_TRAINING_STATIONS]\n", + "\n", + "selected_stations = TRAINING_STATIONS + HOLDOUT_STATIONS\n", + "print(f\"Training stations ({len(TRAINING_STATIONS)}): {TRAINING_STATIONS}\")\n", + "print(f\"Holdout stations ({len(HOLDOUT_STATIONS)}) : {HOLDOUT_STATIONS}\")\n", + "print(f\"Total ({len(selected_stations)})\")\n", + "\n", + "KATONGOLE_CSV = (\n", + " _PROJECT_ROOT / \"data\" / \"external_references\" / \"katongole_2023_monthly.csv\"\n", + ")\n", + "assert KATONGOLE_CSV.exists(), f\"Missing reference data at {KATONGOLE_CSV}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "15e9cd24", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "15e9cd24", + "outputId": "80218dcc-1c86-402b-a16b-770fa27bec81" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw training pairs: 24,233 rows × 38 columns\n", + "Date span: 2011-03-08 .. 2024-11-25\n", + "Stations: 24\n" + ] + } + ], + "source": [ + "# Pull ground + satellite features for the 24 selected stations across\n", + "# their full ground-data range. Cleaners in §3 then filter rows.\n", + "DATE_START = station_meta.loc[\n", + " station_meta[\"location\"].isin(selected_stations), \"min_date\"\n", + "].min()\n", + "DATE_END = station_meta.loc[\n", + " station_meta[\"location\"].isin(selected_stations), \"max_date\"\n", + "].max()\n", + "raw_df = service.build_training_pairs(\n", + " selection=selection,\n", + " date_start=DATE_START,\n", + " date_end=DATE_END,\n", + " locations=selected_stations,\n", + ")\n", + "manifest = DatasetManifest(\n", + " name=\"mukiibi_mikelson_2026_train\",\n", + " version=\"recomputation_v1\",\n", + " created_at_utc=pd.Timestamp.now(tz=\"UTC\").isoformat(),\n", + " susse_version=susse_version(),\n", + " git_sha=git_sha(),\n", + " feature_selection=selection,\n", + " date_start=DATE_START,\n", + " date_end=DATE_END,\n", + " location_filter=None,\n", + " warehouse_project=bq.config.project_id,\n", + " warehouse_dataset=bq.config.dataset,\n", + " warehouse_table_mods=service.warehouse_table_mods(selection),\n", + " n_rows=len(raw_df),\n", + " n_cols=len(raw_df.columns),\n", + " column_names=tuple(raw_df.columns),\n", + " content_hash=\"recomputation\",\n", + ")\n", + "dataset = TrainingDataset(df=raw_df, manifest=manifest)\n", + "print(f\"Raw training pairs: {len(raw_df):,} rows × {len(raw_df.columns)} columns\")\n", + "print(f\"Date span: {DATE_START} .. {DATE_END}\")\n", + "print(f\"Stations: {raw_df['location'].nunique()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f032cd3", + "metadata": { + "id": "2f032cd3" + }, + "source": [ + "## § 2 — Preprocessing\n", + "\n", + "Applies the data-curation pipeline described in paper Section II.A:\n", + "- **Upper-bound cleaning**: removes physically impossible GHI values (> 12 kWh m⁻² d⁻¹)\n", + "- **IQR lower-bound filtering**: removes anomalously low values\n", + "- **High-missing-year exclusion**: drops station-years with > 5 % missing data\n", + "- **k-NN imputation** of remaining small gaps (handled internally by `Preprocessor`)\n", + "- **Derived features**: clear-sky index, cyclical day-of-year, raw day-of-year integer,\n", + " site altitude, longitude, and solar noon zenith angle (via pvlib)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "49fb6fca", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "49fb6fca", + "outputId": "96c0b883-c81c-4dda-f433-1001b41f6879" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preprocessed dataset: 15,523 rows × 36 features\n", + "Feature columns (36):\n", + " 1. nasa_longwave_downward_irr\n", + " 2. nasa_aod_550_adj\n", + " 3. nasa_cloud_amount\n", + " 4. nasa_precipitable_water\n", + " 5. nasa_airmass\n", + " 6. nasa_zero_plane_displacement\n", + " 7. nasa_surface_albedo\n", + " 8. nasa_clearness_index\n", + " 9. nasa_temperature\n", + " 10. nasa_relative_humidity\n", + " 11. nasa_surface_pressure\n", + " 12. nasa_wind_speed\n", + " 13. nasa_temperature_range\n", + " 14. nasa_precipitation_corrected\n", + " 15. nasa_surface_roughness\n", + " 16. nasa_northern_wind\n", + " 17. nasa_planetary_boundary\n", + " 18. nasa_total_column_ozone\n", + " 19. nasa_surface_air_density\n", + " 20. nasa_surface_soil_wetness\n", + " 21. nasa_evaporation_land\n", + " 22. nasa_evapotranspiration_energy\n", + " 23. cams_ghi_clear\n", + " 24. cams_dhi_clear\n", + " 25. cams_dni_clear\n", + " 26. sat_ghi_nasa_kwh_m2_day\n", + " 27. sat_ghi_cams_kwh_m2_day\n", + " 28. sat_dhi_nasa_kwh_m2_day\n", + " 29. sat_dhi_cams_kwh_m2_day\n", + " 30. sat_dni_nasa_kwh_m2_day\n", + " 31. sat_dni_cams_kwh_m2_day\n", + " 32. kt_cams\n", + " 33. doy_sin\n", + " 34. doy_cos\n", + " 35. altitude_m\n", + " 36. longitude\n" + ] + } + ], + "source": [ + "# ── FeatureSpec ───────────────────────────────────────────────────────────────\n", + "feature_columns = (\n", + " *(f\"nasa_{v}\" for v in NASA_AUX_IDS),\n", + " *(f\"cams_{v}\" for v in CAMS_AUX_IDS),\n", + " *(\n", + " f\"sat_{band.value}_{source.value.lower()}_kwh_m2_day\"\n", + " for band in selection.include_satellite_bands\n", + " for source in selection.include_satellite_irradiance\n", + " ),\n", + ")\n", + "spec = FeatureSpec(\n", + " target_column=\"y_ghi_kwh_m2_day\",\n", + " feature_columns=feature_columns,\n", + " cleaners=(\n", + " GhiUpperBoundCleaner(column=\"y_ghi_kwh_m2_day\", threshold=12.0),\n", + " IqrLowerBoundCleaner(column=\"y_ghi_kwh_m2_day\", multiplier=1.5),\n", + " HighMissingYearExcluder(missing_fraction_threshold=0.05),\n", + " ),\n", + " derived_features=(\n", + " ClearSkyIndexFeature(\n", + " ghi_column=\"sat_ghi_cams_kwh_m2_day\",\n", + " ghi_clear_column=\"cams_ghi_clear\",\n", + " output_column=\"kt_cams\",\n", + " ),\n", + " CyclicalDayOfYearFeature(),\n", + " AltitudeFeature(provider=PvlibElevationProvider()),\n", + " LongitudeFeature(),\n", + " ),\n", + " id_columns=(\"date\", \"location\", \"geohash5\", \"lat\", \"lon\"),\n", + ")\n", + "\n", + "# ── Run preprocessing ─────────────────────────────────────────────────────────\n", + "preprocessor = Preprocessor(spec=spec)\n", + "processed = preprocessor.apply(dataset)\n", + "\n", + "n_features = len(processed.feature_columns)\n", + "n_rows = len(processed.df)\n", + "print(f\"Preprocessed dataset: {n_rows:,} rows × {n_features} features\")\n", + "print(f\"Feature columns ({n_features}):\")\n", + "for i, col in enumerate(processed.feature_columns, 1):\n", + " print(f\" {i:2d}. {col}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2FCWIm7VdohL", + "metadata": { + "id": "2FCWIm7VdohL" + }, + "source": [ + "### Data exploration\n", + "\n", + "Four quick views of the training data: target distribution per station,\n", + "satellite-vs-observed scatter, feature-correlation heatmap, and a PCA\n", + "scatter coloured by station. All four are plot-only — implementations\n", + "live in `auxscripts`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "gtehWA_AdrRH", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "gtehWA_AdrRH", + "outputId": "738a756f-e8b6-470e-b0f1-110549eeae4d" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 features by |correlation| with y_ghi_kwh_m2_day:\n", + "sat_ghi_cams_kwh_m2_day 0.8650\n", + "sat_ghi_nasa_kwh_m2_day 0.7900\n", + "kt_cams 0.7500\n", + "nasa_clearness_index 0.7110\n", + "sat_dni_nasa_kwh_m2_day 0.6630\n", + "cams_dni_clear 0.4020\n", + "nasa_cloud_amount 0.4000\n", + "cams_ghi_clear 0.3940\n", + "nasa_aod_550_adj 0.3070\n", + "nasa_temperature_range 0.2930\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from susse.evaluation.plots import (\n", + " plot_feature_correlation_heatmap,\n", + " plot_pca_by_station,\n", + " plot_predictor_vs_observed_scatter,\n", + " plot_target_distribution_per_station,\n", + ")\n", + "\n", + "plot_target_distribution_per_station(\n", + " processed.df,\n", + " target_column=spec.target_column,\n", + " value_label=\"y_ghi (kWh/m²/day)\",\n", + ")\n", + "plot_predictor_vs_observed_scatter(\n", + " processed.df,\n", + " observed_column=spec.target_column,\n", + " predictor_columns={\n", + " \"NASA CERES\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")\n", + "plot_feature_correlation_heatmap(\n", + " processed.df,\n", + " feature_columns=processed.feature_columns,\n", + " target_column=spec.target_column,\n", + ")\n", + "plot_pca_by_station(\n", + " processed.df, feature_columns=processed.feature_columns,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0b7e8abd", + "metadata": { + "id": "0b7e8abd" + }, + "source": [ + "## § 3 — Training / Holdout Split\n", + "\n", + "Spatial holdout: the two Ministry stations (Sites A and B) are withheld entirely\n", + "from training. All remaining stations form the training set.\n", + "This mirrors the 80/20 split described in paper Section III.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "078a1be5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "078a1be5", + "outputId": "ddd0c779-ca52-4b30-f387-068ce6099046" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training stations : 20\n", + "Training rows : 14,403\n", + "Holdout stations : ['wadelai', 'kampala']\n", + "Holdout rows : 1,120\n", + "\n", + "Target — training : mean=5.257, std=1.465 kWh m⁻² d⁻¹\n", + "Target — holdout : mean=5.266, std=1.305 kWh m⁻² d⁻¹\n" + ] + } + ], + "source": [ + "from susse.training import SpatialBlockSplitter\n", + "\n", + "splitter = SpatialBlockSplitter(\n", + " val_blocks=HOLDOUT_STATIONS,\n", + " block_column=\"location\",\n", + ")\n", + "train_idx, val_idx = splitter.split(processed)\n", + "\n", + "TRAINING_STATIONS = [\n", + " s for s in processed.df[\"location\"].unique() if s not in HOLDOUT_STATIONS\n", + "]\n", + "\n", + "X = processed.X().values\n", + "y = processed.df[spec.target_column].values\n", + "\n", + "X_train, y_train = X[train_idx], y[train_idx]\n", + "X_val, y_val = X[val_idx], y[val_idx]\n", + "\n", + "print(f\"Training stations : {len(TRAINING_STATIONS)}\")\n", + "print(f\"Training rows : {len(train_idx):,}\")\n", + "print(f\"Holdout stations : {HOLDOUT_STATIONS}\")\n", + "print(f\"Holdout rows : {len(val_idx):,}\")\n", + "print()\n", + "print(f\"Target — training : mean={y_train.mean():.3f}, std={y_train.std():.3f} kWh m⁻² d⁻¹\")\n", + "print(f\"Target — holdout : mean={y_val.mean():.3f}, std={y_val.std():.3f} kWh m⁻² d⁻¹\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "697bed6b", + "metadata": { + "id": "697bed6b" + }, + "source": [ + "## § 4 — Daily-Scale Model Comparison (Paper Table III)\n", + "\n", + "All five candidate models are trained on the same 22-station training split and\n", + "evaluated on the held-out stations. This section produces **Table III** of the paper.\n", + "\n", + "SVR is wrapped in a `Pipeline` with `StandardScaler` — the only model that requires\n", + "feature scaling. All other models are scale-invariant.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d9a3ee8a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d9a3ee8a", + "outputId": "fce8dffe-d698-4b8d-a7e3-901aa13a07c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Random Forest... RMSE=0.565 MAE=0.432 R²=0.812\n", + "Training XGBoost... RMSE=0.672 MAE=0.555 R²=0.735\n", + "Training SVR... RMSE=0.602 MAE=0.474 R²=0.787\n", + "Training Linear Regression... RMSE=0.627 MAE=0.520 R²=0.769\n", + "Training Ridge Regression... RMSE=0.625 MAE=0.519 R²=0.770\n" + ] + } + ], + "source": [ + "# ── Metric helpers ───────────────────────────────────────────────────────────\n", + "def rmse(y_true, y_pred):\n", + " return float(np.sqrt(mean_squared_error(y_true, y_pred)))\n", + "\n", + "def mbe(y_true, y_pred):\n", + " return float(np.mean(np.asarray(y_pred) - np.asarray(y_true)))\n", + "\n", + "def index_of_agreement(y_true, y_pred):\n", + " y_true, y_pred = np.asarray(y_true), np.asarray(y_pred)\n", + " obs_mean = y_true.mean()\n", + " num = np.sum((y_true - y_pred) ** 2)\n", + " den = np.sum((np.abs(y_pred - obs_mean) + np.abs(y_true - obs_mean)) ** 2)\n", + " return float(1 - num / den)\n", + "\n", + "def compute_all_metrics(y_true, y_pred, label=\"\"):\n", + " r = rmse(y_true, y_pred)\n", + " m = mean_absolute_error(y_true, y_pred)\n", + " obs_mean = float(np.mean(y_true))\n", + " return {\n", + " \"label\": label,\n", + " \"RMSE\": r,\n", + " \"nRMSE\": r / obs_mean * 100,\n", + " \"MAE\": m,\n", + " \"nMAE\": m / obs_mean * 100,\n", + " \"MBE\": mbe(y_true, y_pred),\n", + " \"R2\": float(r2_score(y_true, y_pred)),\n", + " \"IOA\": index_of_agreement(y_true, y_pred),\n", + " }\n", + "\n", + "\n", + "# ── Candidate models ──────────────────────────────────────────────────────────\n", + "candidates = {\n", + " \"Random Forest\": RandomForestRegressor(\n", + " n_estimators=RF_N_ESTIMATORS,\n", + " min_samples_leaf=2, # was 5\n", + " max_features=0.5, # was \"sqrt\" (≈6 features)\n", + " random_state=RF_RANDOM_STATE,\n", + " n_jobs=-1,\n", + " ),\n", + " \"XGBoost\": XGBRegressor(\n", + " n_estimators=200, learning_rate=0.05, max_depth=6,\n", + " subsample=0.8, colsample_bytree=0.8,\n", + " random_state=RF_RANDOM_STATE, n_jobs=-1, verbosity=0,\n", + " ),\n", + " \"SVR\": Pipeline([\n", + " (\"scaler\", StandardScaler()),\n", + " (\"svr\", SVR(kernel=\"rbf\", C=1.0, epsilon=1.0)),\n", + " ]),\n", + " \"Linear Regression\": LinearRegression(n_jobs=-1),\n", + " \"Ridge Regression\": Ridge(alpha=1.0, random_state=RF_RANDOM_STATE),\n", + "}\n", + "\n", + "# ── Train and evaluate ────────────────────────────────────────────────────────\n", + "table3_rows = []\n", + "trained_models = {}\n", + "\n", + "for name, model in candidates.items():\n", + " print(f\"Training {name}...\", end=\" \", flush=True)\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_val)\n", + " metrics = compute_all_metrics(y_val, y_pred, label=name)\n", + " table3_rows.append(metrics)\n", + " trained_models[name] = model\n", + " print(f\"RMSE={metrics['RMSE']:.3f} MAE={metrics['MAE']:.3f} R²={metrics['R2']:.3f}\")\n", + "\n", + "table3_df = pd.DataFrame(table3_rows).set_index(\"label\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "12589f7f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "12589f7f", + "outputId": "057194c5-29e8-4450-ea54-f8da5c189c60" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================================================\n", + "Table III — Daily Model Performance on Held-Out Stations\n", + "(holdout: ['wadelai', 'kampala'], n = 1,120 days)\n", + "=================================================================\n", + "Model RMSE MAE R² MBE\n", + "-----------------------------------------------------------------\n", + " Random Forest 0.565 0.432 0.812 -0.214 ◀\n", + " XGBoost 0.672 0.555 0.735 -0.385 \n", + " SVR 0.602 0.474 0.787 -0.128 \n", + " Linear Regression 0.627 0.520 0.769 -0.325 \n", + " Ridge Regression 0.625 0.519 0.770 -0.325 \n", + "=================================================================\n", + "Units: RMSE and MAE in kWh m⁻² d⁻¹\n", + "\n", + "LaTeX saved → figures/table3.tex\n" + ] + } + ], + "source": [ + "# ── Print Table III ───────────────────────────────────────────────────────────\n", + "print(\"=\" * 65)\n", + "print(\"Table III — Daily Model Performance on Held-Out Stations\")\n", + "print(f\"(holdout: {HOLDOUT_STATIONS}, n = {len(val_idx):,} days)\")\n", + "print(\"=\" * 65)\n", + "print(f\"{'Model':<22} {'RMSE':>6} {'MAE':>6} {'R²':>6} {'MBE':>7}\")\n", + "print(\"-\" * 65)\n", + "for name, row in table3_df.iterrows():\n", + " best = \"◀\" if row[\"RMSE\"] == table3_df[\"RMSE\"].min() else \" \"\n", + " print(\n", + " f\" {name:<20} {row['RMSE']:6.3f} {row['MAE']:6.3f} \"\n", + " f\"{row['R2']:6.3f} {row['MBE']:+7.3f} {best}\"\n", + " )\n", + "print(\"=\" * 65)\n", + "print(\"Units: RMSE and MAE in kWh m⁻² d⁻¹\")\n", + "\n", + "# ── Save Table III as LaTeX ───────────────────────────────────────────────────\n", + "latex_cols = table3_df[[\"RMSE\",\"MAE\",\"R2\"]].copy()\n", + "latex_cols.columns = [r\"RMSE (kWh m$^{-2}$d$^{-1}$)\", r\"MAE (kWh m$^{-2}$d$^{-1}$)\", r\"R$^2$\"]\n", + "latex_str = latex_cols.to_latex(float_format=\"{:.2f}\".format, bold_rows=False)\n", + "(FIGURE_DIR / \"table3.tex\").write_text(latex_str)\n", + "print(f\"\\nLaTeX saved → {FIGURE_DIR}/table3.tex\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0678770b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 499 + }, + "id": "0678770b", + "outputId": "9d270ed4-7efd-418b-cf1f-16704b21c03a" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure saved → figures/fig_table3_model_comparison.png\n" + ] + } + ], + "source": [ + "# ── Figure: Table III bar chart ───────────────────────────────────────────────\n", + "fig, ax = plt.subplots(figsize=(5.5, 3.2))\n", + "\n", + "model_names = list(table3_df.index)\n", + "x = np.arange(len(model_names))\n", + "width = 0.35\n", + "\n", + "bars_rmse = ax.bar(x - width/2, table3_df[\"RMSE\"], width,\n", + " label=\"RMSE\", color=\"#1f77b4\", alpha=0.85)\n", + "bars_mae = ax.bar(x + width/2, table3_df[\"MAE\"], width,\n", + " label=\"MAE\", color=\"#ff7f0e\", alpha=0.85)\n", + "\n", + "ax.set_ylabel(\"Error (kWh m⁻² d⁻¹)\")\n", + "ax.set_title(\"Daily Model Performance on Held-Out Stations\")\n", + "ax.set_xticks(x)\n", + "ax.set_xticklabels(\n", + " [\"RF\", \"XGBoost\", \"SVR\", \"Linear Regr.\", \"Ridge Regr.\"],\n", + " fontsize=9\n", + ")\n", + "ax.legend()\n", + "ax.yaxis.set_minor_locator(mticker.AutoMinorLocator())\n", + "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.4)\n", + "\n", + "# annotate RMSE values on bars\n", + "for bar in bars_rmse:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", va=\"bottom\", fontsize=7)\n", + "\n", + "plt.tight_layout()\n", + "fig.savefig(FIGURE_DIR / \"fig_table3_model_comparison.png\")\n", + "plt.show()\n", + "print(f\"Figure saved → {FIGURE_DIR}/fig_table3_model_comparison.png\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "94bce7c8", + "metadata": { + "id": "94bce7c8" + }, + "source": [ + "## § 5 — Final Random Forest Model\n", + "\n", + "The Random Forest is selected as the final model based on its superior RMSE and\n", + "inherent robustness to overfitting (paper Section III). It is retrained here via\n", + "`Trainer.train()`, which persists the model bundle to disk and (optionally) logs\n", + "metrics to Weights & Biases. The bundle is used by § 6 onwards.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b34a67f3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b34a67f3", + "outputId": "ee56931d-baa8-479e-af02-4bb7541893e4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Forest — Holdout Performance (daily, kWh m⁻² d⁻¹)\n", + " RMSE : 0.568\n", + " MAE : 0.448\n", + " R² : 0.810\n", + "Bundle persisted → saved_models/rf_bundle\n" + ] + } + ], + "source": [ + "trainer = Trainer(TrainerConfig(\n", + " wandb_project = WANDB_PROJECT if LOG_TO_WANDB else None,\n", + " wandb_entity = WANDB_ENTITY,\n", + " wandb_tags = WANDB_TAGS,\n", + " baseline_columns = (\"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"),\n", + "))\n", + "\n", + "bundle = trainer.train(\n", + " processed = processed,\n", + " params = RandomForestParams(\n", + " n_estimators = RF_N_ESTIMATORS,\n", + " min_samples_leaf = RF_MIN_SAMPLES_LEAF,\n", + " random_state = RF_RANDOM_STATE,\n", + " ),\n", + " splitter = splitter,\n", + " holdout_label = f\"sites_A_B::{','.join(HOLDOUT_STATIONS)}\",\n", + " bundle_dest = BUNDLE_DEST,\n", + ")\n", + "\n", + "v = bundle.metadata.val_metrics\n", + "print(\"Random Forest — Holdout Performance (daily, kWh m⁻² d⁻¹)\")\n", + "print(f\" RMSE : {v.rmse:.3f}\")\n", + "print(f\" MAE : {v.mae:.3f}\")\n", + "print(f\" R² : {v.r2:.3f}\")\n", + "print(f\"Bundle persisted → {BUNDLE_DEST}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8bb5695", + "metadata": { + "id": "a8bb5695" + }, + "source": [ + "## § 6 — Independent Ugandan Validation (Paper Table IV)\n", + "\n", + "Validates the trained RF model against the independent network of 54 Ugandan stations\n", + "compiled by Katongole et al. (2023). As in the paper:\n", + "\n", + "1. Daily predictions are generated for all 54 stations for every day in **2021**.\n", + "2. Predictions are arithmetically averaged within each calendar month.\n", + "3. Monthly model predictions are compared against monthly ground measurements\n", + " and against NASA CERES and CAMS satellite products.\n", + "\n", + "A sensor-calibration ratio is applied to account for the systematic offset between\n", + "the MEMD/CrossBoundary pyranometers used in training and the TAHMO ATMOS 41 sensors\n", + "used by the Katongole network.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "328ed750", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "328ed750", + "outputId": "6726cbd9-a496-4790-a468-a69434150ca6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56 Katongole stations: 3 share a geohash5 with a training station, 4 share a NASA 0.5° cell.\n" + ] + } + ], + "source": [ + "from susse.warehouse_ops.io.repositories import SatelliteRepository\n", + "import pygeohash\n", + "import auxscripts\n", + "from datetime import date\n", + "\n", + "VAL_START = date(VALIDATION_YEAR, 1, 1)\n", + "VAL_END = date(VALIDATION_YEAR, 12, 31)\n", + "\n", + "katongole = pd.read_csv(KATONGOLE_CSV)\n", + "katongole[\"geohash5\"] = [\n", + " pygeohash.encode(lat, lon, precision=5)\n", + " for lat, lon in zip(katongole[\"latitude\"], katongole[\"longitude\"])\n", + "]\n", + "\n", + "# Train-fold geohashes and NASA 0.5° cells — overlap flags for §6 metrics.\n", + "train_fold = processed.df.loc[train_idx]\n", + "training_geohashes = set(train_fold[\"geohash5\"].unique())\n", + "training_nasa_cells = {\n", + " (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5)))\n", + " for lat, lon in zip(train_fold[\"lat\"], train_fold[\"lon\"])\n", + "}\n", + "katongole[\"nasa_cell\"] = [\n", + " (int(np.floor((lat + 90) / 0.5)), int(np.floor((lon + 180) / 0.5)))\n", + " for lat, lon in zip(katongole[\"latitude\"], katongole[\"longitude\"])\n", + "]\n", + "katongole[\"seen_in_training_geohash5\"] = katongole[\"geohash5\"].isin(training_geohashes)\n", + "katongole[\"seen_in_training_nasa_cell\"] = katongole[\"nasa_cell\"].isin(training_nasa_cells)\n", + "print(\n", + " f\"{len(katongole)} Katongole stations: \"\n", + " f\"{int(katongole['seen_in_training_geohash5'].sum())} share a geohash5 \"\n", + " f\"with a training station, \"\n", + " f\"{int(katongole['seen_in_training_nasa_cell'].sum())} share a NASA 0.5° cell.\"\n", + ")\n", + "\n", + "# Hard-fail if any station lacks coverage. Pass allow_partial=True for\n", + "# inspecting intermediate state while the A12 ingest is still running.\n", + "auxscripts.assert_katongole_coverage(\n", + " bq, tables, katongole, VAL_START, VAL_END,\n", + ")\n", + "inference_df = auxscripts.build_katongole_inference_frame(\n", + " bq=bq, tables=tables, sat_repo=SatelliteRepository(bq=bq),\n", + " katongole=katongole, selection=selection,\n", + " nasa_aux_ids=NASA_AUX_IDS, cams_aux_ids=CAMS_AUX_IDS,\n", + " val_start=VAL_START, val_end=VAL_END,\n", + ")\n", + "auxscripts.assert_inference_complete(katongole, inference_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2a009b83", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2a009b83", + "outputId": "e7f16aa5-3c88-4e3f-f2a6-433465c53b4d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: 20,440 daily values across 56 stations\n" + ] + } + ], + "source": [ + "# Apply the trained bundle's feature spec to the inference frame and\n", + "# predict. Reloading from disk mirrors what the portal does.\n", + "from susse.training import load_bundle\n", + "\n", + "restored = load_bundle(\n", + " BUNDLE_DEST, providers={\"altitude\": PvlibElevationProvider()},\n", + ")\n", + "inference_dataset = TrainingDataset(\n", + " df=inference_df.assign(y_ghi_kwh_m2_day=np.nan),\n", + " manifest=manifest,\n", + ")\n", + "inference_spec_for_predict = FeatureSpec(\n", + " target_column=restored.feature_spec.target_column,\n", + " feature_columns=restored.feature_spec.feature_columns,\n", + " cleaners=(),\n", + " derived_features=restored.feature_spec.derived_features,\n", + " id_columns=restored.feature_spec.id_columns,\n", + " dropna_target=False, dropna_features=False,\n", + ")\n", + "inference_processed = Preprocessor(inference_spec_for_predict).apply(inference_dataset)\n", + "inference_processed.df[\"y_pred_kwh_m2_day\"] = restored.regressor.predict(\n", + " inference_processed.X()\n", + ")\n", + "print(\n", + " f\"Predictions: {len(inference_processed.df):,} daily values across \"\n", + " f\"{inference_processed.df['location'].nunique()} stations\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1H2rbZqs-RCX", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1H2rbZqs-RCX", + "outputId": "bf166f81-b8c5-43f5-a51f-b8982d36da94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Paper Table IV equivalent (uncalibrated) ===\n", + " split model n RMSE nRMSE_% MAE nMAE_% MBE R² IOA\n", + " all 56 stations RF 672 0.8360 19.5900 0.6980 16.3500 0.6590 -0.9470 0.5660\n", + " all 56 stations NASA CERES 672 1.2430 29.1300 1.1290 26.4400 1.1160 -3.3060 0.4620\n", + " all 56 stations CAMS 672 1.3070 30.6300 1.2000 28.1200 1.1930 -3.7620 0.4640\n", + "excluding training-geohash overlaps RF 636 0.8170 19.2000 0.6810 16.0200 0.6410 -0.9960 0.5490\n", + "excluding training-geohash overlaps NASA CERES 636 1.2260 28.8100 1.1160 26.2200 1.1020 -3.4960 0.4510\n", + "excluding training-geohash overlaps CAMS 636 1.2810 30.1200 1.1820 27.7900 1.1740 -3.9130 0.4580\n" + ] + } + ], + "source": [ + "# Aggregate daily predictions to a monthly climatology per station,\n", + "# then join with the Katongole reference values for scoring.\n", + "monthly = (\n", + " inference_processed.df\n", + " .assign(month=lambda d: pd.to_datetime(d[\"date\"]).dt.month)\n", + " .groupby([\"location\", \"month\"])[\"y_pred_kwh_m2_day\"]\n", + " .mean().reset_index()\n", + " .rename(columns={\"y_pred_kwh_m2_day\": \"monthly_pred\"})\n", + ")\n", + "sat_monthly = (\n", + " inference_processed.df\n", + " .assign(month=lambda d: pd.to_datetime(d[\"date\"]).dt.month)\n", + " .groupby([\"location\", \"month\"])[\n", + " [\"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"]\n", + " ].mean().reset_index()\n", + ")\n", + "month_names = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n", + " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", + "katongole_long = katongole.melt(\n", + " id_vars=[\"location\", \"latitude\", \"longitude\", \"altitude\",\n", + " \"geohash5\", \"seen_in_training_geohash5\", \"seen_in_training_nasa_cell\"],\n", + " value_vars=month_names,\n", + " var_name=\"month_name\", value_name=\"monthly_obs\",\n", + ")\n", + "katongole_long[\"month\"] = katongole_long[\"month_name\"].apply(\n", + " lambda m: month_names.index(m) + 1\n", + ")\n", + "comparison = (\n", + " monthly\n", + " .merge(katongole_long, on=[\"location\", \"month\"], how=\"inner\")\n", + " .merge(sat_monthly, on=[\"location\", \"month\"], how=\"left\")\n", + ")\n", + "table_iv_raw = pd.concat([\n", + " auxscripts.score_table_iv(comparison, label=f\"all {comparison['location'].nunique()} stations\"),\n", + " auxscripts.score_table_iv(\n", + " comparison[~comparison[\"seen_in_training_geohash5\"]],\n", + " label=\"excluding training-geohash overlaps\",\n", + " ),\n", + "])\n", + "print(\"=== Paper Table IV equivalent (uncalibrated) ===\")\n", + "print(table_iv_raw.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "MBT5vpyU-hb4", + "metadata": { + "id": "MBT5vpyU-hb4" + }, + "source": [ + "## 6.3 — Calibration discrepancy at Makerere\n", + "\n", + "§6's uncalibrated metrics over-predict Katongole by ~0.85 kWh/m²/day\n", + "on average. The cause is a **cross-network sensor offset** — our training\n", + "data uses MEMD/CrossBoundary research-grade pyranometers (annually\n", + "recalibrated), while Katongole uses the TAHMO ATMOS 41 combination\n", + "sensor (a mid-grade weather-station instrument with ±5% factory tolerance\n", + "and no regular calibration). Our `kampala` station and Katongole's\n", + "`Makerere S` station sit at *literally identical coordinates*\n", + "`(0.333542°, 32.56863°)`. Comparing the two over the same 2017–2022\n", + "window measures the network offset directly." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c8cea0d7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 618 + }, + "id": "c8cea0d7", + "outputId": "f07188b4-33b0-4435-ea3c-441209862f94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual mean ratio (kampala / Makerere S): 1.180\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pull our kampala monthly climatology over 2017-2022.\n", + "kampala_monthly = bq.query(f\"\"\"\n", + "SELECT EXTRACT(MONTH FROM date) AS month,\n", + " ROUND(AVG(ghi_kwh_m2_day), 4) AS kampala_mean,\n", + " COUNT(*) AS n_days\n", + "FROM `{tables.ground_measurements}`\n", + "WHERE location = \"kampala\" AND qc_level = \"pass\"\n", + " AND date BETWEEN DATE(\"2017-01-01\") AND DATE(\"2022-12-31\")\n", + "GROUP BY month ORDER BY month\n", + "\"\"\")\n", + "makerere_row = katongole[katongole[\"location\"] == \"Makerere S\"]\n", + "makerere_long = pd.DataFrame({\n", + " \"month\": list(range(1, 13)),\n", + " \"makerere_mean\": [float(makerere_row[m].iloc[0]) for m in month_names],\n", + "})\n", + "kampala_gh = pygeohash.encode(0.333542, 32.56863, 5)\n", + "sat_monthly_2017_22 = bq.query(f\"\"\"\n", + "SELECT EXTRACT(MONTH FROM date) AS month, source,\n", + " ROUND(AVG(ghi_kwh_m2_day), 4) AS mean_ghi\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE geohash5 = \"{kampala_gh}\"\n", + " AND source IN (\"NASA\", \"CAMS\")\n", + " AND date BETWEEN DATE(\"2017-01-01\") AND DATE(\"2022-12-31\")\n", + "GROUP BY month, source ORDER BY month, source\n", + "\"\"\").pivot(index=\"month\", columns=\"source\", values=\"mean_ghi\").reset_index()\n", + "\n", + "calibration_df = (\n", + " kampala_monthly.merge(makerere_long, on=\"month\")\n", + " .merge(sat_monthly_2017_22, on=\"month\")\n", + ")\n", + "calibration_df[\"ratio\"] = (\n", + " calibration_df[\"kampala_mean\"] / calibration_df[\"makerere_mean\"]\n", + ")\n", + "annual_ratio = float(calibration_df[\"ratio\"].mean())\n", + "print(f\"Annual mean ratio (kampala / Makerere S): {annual_ratio:.3f}\")\n", + "auxscripts.plot_calibration_discrepancy(calibration_df)" + ] + }, + { + "cell_type": "markdown", + "id": "fRCBNX9q-unD", + "metadata": { + "id": "fRCBNX9q-unD" + }, + "source": [ + "## 6.4 — Calibrated Table IV\n", + "\n", + "A single annual ratio (mean of the 12 monthly ratios — ~1.18) rescales\n", + "TAHMO Katongole values onto the MEMD/CB scale before scoring. Per-month\n", + "ratios would overfit on one co-located pair × 7 years (June's\n", + "~1.42 over-corrects Ndejje to 7.8 kWh/m²/day, which is non-physical).\n", + "A monotonic linear rescaling shifts RF and satellite RMSEs together —\n", + "the *relative* improvement claim is preserved." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "bcc49e4d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bcc49e4d", + "outputId": "7e0eb355-3dff-4197-fe4f-fe508cd10d68" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Paper Table IV equivalent — calibration-corrected ===\n", + " split model n RMSE nRMSE_% MAE nMAE_% MBE R² IOA\n", + " all 56 stations RF (calibrated) 672 0.6130 12.1800 0.4510 8.9400 -0.1100 0.2470 0.6380\n", + " all 56 stations NASA CERES 672 0.7220 14.3300 0.5370 10.6600 0.3470 -0.0430 0.6080\n", + " all 56 stations CAMS 672 0.7440 14.7600 0.5590 11.0900 0.4240 -0.1060 0.6500\n", + " excluding training-geohash overlaps RF (calibrated) 636 0.6080 12.1100 0.4480 8.9300 -0.1260 0.2060 0.6040\n", + " excluding training-geohash overlaps NASA CERES 636 0.7050 14.0400 0.5280 10.5200 0.3360 -0.0670 0.5940\n", + " excluding training-geohash overlaps CAMS 636 0.7140 14.2300 0.5470 10.8900 0.4080 -0.0960 0.6490\n", + "excluding training-NASA-cell overlaps RF (calibrated) 624 0.6060 12.0800 0.4460 8.8800 -0.1330 0.2100 0.6040\n", + "excluding training-NASA-cell overlaps NASA CERES 624 0.7000 13.9500 0.5240 10.4400 0.3290 -0.0530 0.5950\n", + "excluding training-NASA-cell overlaps CAMS 624 0.7090 14.1300 0.5420 10.8000 0.4020 -0.0810 0.6510\n" + ] + } + ], + "source": [ + "comparison_calibrated = comparison.copy()\n", + "comparison_calibrated[\"monthly_obs_calibrated\"] = (\n", + " comparison_calibrated[\"monthly_obs\"] * annual_ratio\n", + ")\n", + "table_iv_calibrated = pd.concat([\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated,\n", + " label=f\"all {comparison_calibrated['location'].nunique()} stations\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated[~comparison_calibrated[\"seen_in_training_geohash5\"]],\n", + " label=\"excluding training-geohash overlaps\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + " auxscripts.score_table_iv(\n", + " comparison_calibrated[~comparison_calibrated[\"seen_in_training_nasa_cell\"]],\n", + " label=\"excluding training-NASA-cell overlaps\",\n", + " obs_col=\"monthly_obs_calibrated\",\n", + " rf_label=\"RF (calibrated)\",\n", + " ),\n", + "])\n", + "print(\"=== Paper Table IV equivalent — calibration-corrected ===\")\n", + "print(table_iv_calibrated.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "-pOtx_kC_ArS", + "metadata": { + "id": "-pOtx_kC_ArS" + }, + "source": [ + "### Residual diagnostics\n", + "\n", + "Three diagnostic views on the §6.4 metrics: covariate-shift KDEs\n", + "(training vs Katongole feature distributions), a predicted-vs-observed\n", + "scatter, and per-station MBE." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "gErFLMLT_ALz", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "gErFLMLT_ALz", + "outputId": "1bac97d6-6d39-418a-99da-46fce3c66202" + }, + "outputs": [ + { + "data": { + "image/png": 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tW7fZPuXl5dV+Tttqa+Mee+yx3HjjjZkxY0Zat26ddevW5fzzz88HPvCB/PWvf80+++yTJLnjjjty1FFH5Rvf+EaSZOHChTn66KOz11575YYbbkiSXHXVVTn//PNz/fXX5+tf//p2xwoA7Bp2hfw2keNuihxXjgsAAABQbIoS9kBXXHHFFq+3bt06p59++g6v06BBg4wfP75WY//v//5vh9ffHgsWLMj//d//5R//+Ee9rDdo0KCcccYZ1d7mqy8tWrTIFVdcUbV2w4YN88UvfjHve9/78thjj2XQoEFJ3n6A3a5du6pxhxxySNq1a5e5c+dWm+8b3/hGjjjiiAwePDitWrWqt/sAAPhvctya5LhyXAAAAICdgb0o90Db+sB240O91q1b52c/+1lOPfXUHHDAAVUPc4cPH57jjz8+ffv2TdeuXXPNNddk7dq1SZL169enT58+adGiRS688MIkyaxZs9KnT5+UlJRkxIgRufjii9OjR48cccQR1d56uvTSS2tsv7txrk984hP5n//5n/Tr1y9t27bN9773vWqx/+c//8mQIUNyyCGHpG/fvvn85z+fc845Jy1atEifPn2yfv36Td7z/fffn7Zt26ZNmzZVbWeffXbKy8vTp0+fjBo1KieffHLatm2br3zlK9v2QW/B9m71W5d69eqVa6+9tlpbkyZNkqTq55ckXbp0ybvf/e6q7x966KHMnz8/X/jCF6qNbd++fQ455JDcd999BYwaAGDL5Lg1yXHluAAAAAA7AzslsFmjRo1KixYtctNNN2XFihV55JFH8re//S3f+ta3kry99en48ePTrl27rFy5MieccELe/e5356tf/WoaNGiQSZMmpU+fPlXzdenSJZMmTUpJSUnuvffeTJgwIXvvvXcuv/zyfOpTn8r8+fOTJD/4wQ/SsmXLatvrbpzrkUceyWOPPZZhw4blN7/5TT72sY/lwx/+cNXD3S9/+cuZPHlyZs2alf322y+TJ09O//79c8IJJ2xxu9cpU6bksMMOq9Z277335sILL8yvfvWrXHPNNbniiivy1FNP5dhjj82HPvShvP/970+S/O53v6va6nVzunTpst1nB69cuTKDBw/OvHlvb5d8wgkn5JprrqnaerYux/3pT3/Kfvvtl5NPPrnGte9+97v5zne+kwYNGuTBBx/MSSedVKNP+/bt89hjj2Xo0KHbc4sAsNObOXNmrr322jz11FNJkuOOOy7Dhw9P586dtzr21VdfzS9/+cs8+uijee655/Lmm2+madOmed/73pevfvWrOeaYY2qMKSkpyQEHHLDJ+SZPnpyOHTvu2A0hx5XjJpHjAgAAANQnOyWwVevWrcunP/3pJMmRRx6Zn//850mSCRMmVG192rx58wwcODCPPPLINs152mmnZe+9906S9OvXL88//3xWrFix1XFdunTJe9/73qpxGzZsyKxZs5Ikb775Zm6//fZ86lOfyn777Zck6d27d4477ritzrt06dKqMe+0//77Z8CAAUmSY445Ji1atMiTTz5Zdf3kk0/OpEmTtvi1vQ9rk6Rjx44ZMmRIHnvssTz88MOZNWtWevfuXe1Nr7oY9+9//zs33HBDbrnlljRv3rzG9SuvvDKLFy/Obbfdlo985CO5//77a/TZb7/9snTp0u2+RwDYmU2ZMiW9evVK+/bts2jRorz44oupqKhIjx49MmPGjK2OnzBhQr7whS+kdevWmT17dv75z39m/PjxmTt3brp3755p06ZtctyyZcs2+aUgoW7JceW4clwAAACA+qEoga064IAD0qhRoxrtzz77bAYOHJgTTjghffr0yS9/+cttfmhXUVFR9b83Prh9/fXXd2jc/Pnzs3bt2hpvg/33drWbs2LFijRsuOmNQ/57zY3rbkusO+qee+7J8ccfnyTZZ599MmLEiMyePTu/+c1v6mzcunXrctZZZ+VTn/pUzj777C3O+6EPfShnn312LrvsshrXSktL89prr23rrQHATm/9+vUZPHhwysvLM3LkyDRu3DhNmzbNzTffnBYtWmTIkCGprKzc6jwHHnhgbr755qrt4jt06JCbbropa9euzS233FLo22AL5Lhy3ESOCwAAAFAfHN/AVjVo0KBG2xNPPJFTTz01N998cz772c8mSa677rpq29Fu65wlJSVJsk0P9rc0buP3tbHffvvlrbfe2uqaG9f571gLtbXtO218ED137tw6Gbdu3bqcc8456dy5c77xjW/UGLdmzZo0bty4WtuRRx6ZO++8M//85z9TVlZW1f7WW2+lZcuW2xUX1MrE4bUb1/eauo0D2O1Nnjw5zz33XC6//PKUlpZWtTdu3DiDBg3K7bffnunTp6dHjx6bnaNv374ZN25c9tqreh3wwQcfnCR54403ChM820SOK8fdSI4LO6Ha5P1yfgAAgJ2WogRq5fHHH8+GDRuqvXm0tS1XC61du3Zp3Lhx/vGPf1Rrf+GFFzb5Ftx/Ky8vz/Lly2u17sknn7zJc2p3xB//+Me88MILGTx4cFXbSy+9lCSbPWd6e8atW7cu5557bg477LAMH/72w56//OUvee211/KBD3wgSfKe97wnb7zxRrUH4YsXL05paWnV254bvfLKKznwwANre7sAsNOZOHFikrf/6PpOG9smTJiwxaKEli1bbvIPmhu3yO/du/eOB0qdkuP+P3JcOS4AAABAXXF8A7XSqVOnJMm4ceOSJP/5z3/y29/+tpghpXnz5vnsZz+bu+66K6+++mqS5E9/+lOefvrprY7t3bt3/v73vxc6xG22aNGi3HDDDXnllVeSvP2A9brrrssBBxyQj3/841X9vvrVr6Zz587bNW79+vU577zzsmrVqpxxxhmZOXNmZs6cmYcffjiPP/541VxvvvlmfvCDH1R9/+yzz+buu+/OBRdckKZNm1aLd968eenbt2/dfxAAUCQb375u1apVjWsbt72fN2/eds25cuXKPPTQQ/nSl76Uj370o7n00ks32e9rX/tajjrqqBxwwAE57LDDcvHFF2/3WtSOHLew5LgAAAAAeyZFCXuwp59+On369MmyZcvyu9/9LieddFK161/72tcyevToLFu2LH369Mn48eOrrp188skZNmxYrrrqqvTp0yef/OQn065du6q+K1euTJ8+fTJr1qz87ne/y9lnn51//OMf6dOnT5LkhhtuyOjRo/O73/0uV1xxRZLk7LPPzrRp03LppZdWW3fZsmU59dRTq+a64oorqq5tnOu2225LkgwfPjx9+vRJly5dctJJJ+WRRx7Jhz/84WrbLm/KmWeemYULF1Z7aDt48OD87ne/y6xZs6reltsYz+jRoze5Hey2uuaaa6rN9c7P96STTsppp52Wk08+OX369EmPHj1SWVmZ6dOnZ999963q9+yzz1adrbut4+67777cf//9efTRR9OtW7eqr//93/+tFuMtt9ySBx98MN27d8+JJ56Yc889N1dffXVuvfXWav3+9re/ZenSpTnzzDNr/XkAwM5mxYoVSd7+g/A7NWvWrFqfbXH++eenRYsWOeOMM3Luuefmrrvu2uxb7pWVlZk0aVKWLFmSX/ziF/nzn/+cY489NtOmTdvm9Tp16rTJr/nz52/zHLsqOe7/I8eV4wIAAADsDEoqt+WQ0z3Qxrek5syZU+Pahg0bqt6e69ixY41zgimeFStWpFmzZtUe8vfv3z9t27bNj370oy2O/eY3v5l58+blnnvuKXSYdWLKlCk5/fTT88QTT6Rt27ZFi+NjH/tYunfvnquuuqrO5vQ7xmbV5mzZxPmywHb74Ac/mPHjx+fxxx9Pz549q137wx/+kP79++e0007LQw89tM1zvvXWW3nmmWdy5ZVX5rnnnst9992X97///dX6LFu2LOXl5dXannnmmRx99NE58sgj88wzz2zTWhtz2XeaP39+2rVrVyPH9e/enZsct/7VdY7rdwy2U23yfjk/AADATsuTEHYrI0aMyPe+972q75988sk89thj+eQnP7nVsV/72tey77775vvf/34hQ6wzzz//fCZMmFDUh7Xf+ta30rZt23zxi18sWgwAUAgtWrRI8vaRC++0atWqan22VWlpaY455piMGTMm69evz1lnnZV///vf1fq8syAhSd773vemffv2mTNnTp5//vltWmvOnDmb/GrXrt12xczOQY5bv+S4AAAAAHWrYbEDgLrUt2/fDBs2LGPHjk3y9nmz9913X0444YStjt1rr73ygx/8IP/85z8LHWaduOCCC4odQoYMGZKysrJihwEAda5jx45JkiVLltS4trGtQ4cOtZp7n332SY8ePTJ27Nj8+c9/rnG8wKYceOCBmTdvXpYtW1bUP9ZSHHLc+iXHBQAAAKhbihLYrZx00knb9GB/SzyA3HY+KwB2V3379s3111+fWbNm1bi2sa1fv35bnGP06NHp1KlTunXrVuNa06ZNkyTLly+vahszZkyaN2+e/v371+i/bNmyJMn++++/rbfAbkSOW798VlCHanv8GgAAALsVRQkAAPAOvXv3zuGHH54xY8ZkxIgRadjw7bR5zZo1GTt2bDp37pzu3btX9X/ttddSWlqad73rXVVto0ePzhFHHFGjKGH16tWZNm1aSkpK0rVr16r2MWPGZPny5TWKEp599tn8/e9/T/v27dO+fftC3C4AbJniAgAAAHbAXsUOAAAAdjYNGjTInXfemaVLl2bo0KFZs2ZNVq9enUsvvTQrVqzIHXfckZKSkiTJggULUlFRkcMOOywrV66sNs+dd96ZH/3oR1m9enWSZPHixTn//POzcOHCXHnllWnXrl21/o888khGjRpV1f+ZZ57JOeeck9LS0vzoRz+qhzsHAAAAAKhbihIAAGATevbsmccffzxz587NQQcdlEMOOSSLFi3K1KlTq+1+0KxZs5SVlaWioiKlpaVV7T/84Q9z9dVX584770ybNm3SsmXLdO7cOa+//nruu+++jBgxotp63/z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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Worst (highest MBE):\n", + " MBE _highlight\n", + "location \n", + "Mengo SS 0.5390 False\n", + "Aii-vu 0.8050 False\n", + "Lira M 0.8710 True\n", + "Iganga G SS 1.0220 False\n", + "Nakivubo 1.2410 False\n", + "\n", + "Best (lowest MBE):\n", + " MBE _highlight\n", + "location \n", + "Kyenjojo H -0.8140 False\n", + "Wadelai M -0.7510 False\n", + "Gulu PTC -0.7250 False\n", + "Koboko HQ -0.6890 False\n", + "Kalongo SS -0.6660 False\n" + ] + } + ], + "source": [ + "from susse.evaluation import MeanBiasError\n", + "from susse.evaluation.plots import (\n", + " plot_covariate_shift_kde,\n", + " plot_per_station_metric,\n", + " plot_predicted_vs_observed_panels,\n", + ")\n", + "\n", + "plot_covariate_shift_kde(\n", + " processed.df, inference_processed.df,\n", + " features=[\n", + " \"kt_cams\", \"nasa_clearness_index\", \"nasa_aod_550_adj\",\n", + " \"nasa_cloud_amount\", \"altitude_m\", \"longitude\",\n", + " \"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\",\n", + " ],\n", + " train_label=\"Training\", val_label=\"Katongole\",\n", + ")\n", + "plot_predicted_vs_observed_panels(\n", + " comparison_calibrated,\n", + " observed_column=\"monthly_obs_calibrated\",\n", + " predictions={\n", + " \"RF (calibrated)\": \"monthly_pred\",\n", + " \"NASA CERES\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")\n", + "plot_per_station_metric(\n", + " comparison_calibrated,\n", + " observed_column=\"monthly_obs_calibrated\",\n", + " predicted_column=\"monthly_pred\",\n", + " metric=MeanBiasError(),\n", + " highlight_column=\"seen_in_training_geohash5\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5e0fa225", + "metadata": { + "id": "5e0fa225" + }, + "source": [ + "## § 7 — Regional Analysis (Paper Figure 3)\n", + "\n", + "Monthly mean daily GHI for 16 representative stations (4 per region) across the\n", + "four Ugandan climatic zones. Reproduces Figure 3 of the paper.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "ab37d39c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ab37d39c", + "outputId": "6d896949-c24d-405c-9ad4-536960fce386" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "sat_ghi_cams_kwh_m2_day 0.2730\n", + "sat_ghi_nasa_kwh_m2_day 0.1280\n", + "kt_cams 0.1101\n", + "sat_dni_cams_kwh_m2_day 0.0742\n", + "nasa_clearness_index 0.0667\n", + "sat_dni_nasa_kwh_m2_day 0.0594\n", + "cams_ghi_clear 0.0267\n", + "sat_dhi_cams_kwh_m2_day 0.0236\n", + "cams_dni_clear 0.0189\n", + "nasa_cloud_amount 0.0168\n", + "longitude 0.0155\n", + "altitude_m 0.0130\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from susse.evaluation.plots import plot_feature_importances_top_n\n", + "\n", + "auxscripts.plot_figure_2_grid(\n", + " comparison_calibrated,\n", + " validation_label=f\"{VALIDATION_YEAR}\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "46932c25", + "metadata": { + "id": "46932c25" + }, + "source": [ + "## § 8 — Feature Importance (Paper Figure 4)\n", + "\n", + "Normalised feature importances extracted from the trained Random Forest.\n", + "Reproduces Figure 4 of the paper. As expected, `ghi_cams` dominates, with\n", + "`ALLSKY_SFC_SW_DWN` (NASA CERES GHI) second.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "f1f15021", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 889 + }, + "id": "f1f15021", + "outputId": "dc5843f3-c6f1-4b01-9393-df360c445098" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "sat_ghi_cams_kwh_m2_day 0.2730\n", + "sat_ghi_nasa_kwh_m2_day 0.1280\n", + "kt_cams 0.1101\n", + "sat_dni_cams_kwh_m2_day 0.0742\n", + "nasa_clearness_index 0.0667\n", + "sat_dni_nasa_kwh_m2_day 0.0594\n", + "cams_ghi_clear 0.0267\n", + "sat_dhi_cams_kwh_m2_day 0.0236\n", + "cams_dni_clear 0.0189\n", + "nasa_cloud_amount 0.0168\n", + "longitude 0.0155\n", + "altitude_m 0.0130\n", + "dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "top_importances = plot_feature_importances_top_n(\n", + " bundle, feature_columns=processed.feature_columns, top_n=12,\n", + ")\n", + "top_importances" + ] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/01_data_overview.ipynb b/notebooks/tutorial/01_data_overview.ipynb new file mode 100644 index 00000000..68ab5df2 --- /dev/null +++ b/notebooks/tutorial/01_data_overview.ipynb @@ -0,0 +1,4203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "915c2b73301e03b", + "metadata": {}, + "source": [ + "# 01 — What's in the warehouse\n", + "\n", + "A first look at the SuSSE warehouse — what tables exist, what each one\n", + "holds, and where on the map the data lives. Read this before any other\n", + "tutorial notebook. Everything downstream queries the warehouse this\n", + "notebook describes.\n", + "\n", + "The notebook is **read-only**: every cell issues SELECTs only. Safe to\n", + "re-run as often as you like.\n", + "\n", + "## Three things by the end\n", + "\n", + "1. A mental model of the warehouse — six tables, three storage shapes,\n", + " one catalog.\n", + "2. A map showing exactly where SuSSE has ground measurements,\n", + " validation sites, and the inference grid.\n", + "3. Per-source coverage tables, so the next notebook\n", + " (`02_query_the_warehouse.ipynb`) doesn't surprise you with empty\n", + " results." + ] + }, + { + "cell_type": "code", + "id": "2115a61fe1d95180", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:55:55.092739567Z", + "start_time": "2026-05-27T07:55:55.026306451Z" + } + }, + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " clone_url = f\"https://@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "534fab266a363f2a", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "id": "e164cc006de9786a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:55:59.809649116Z", + "start_time": "2026-05-27T07:55:55.096701534Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "warehouse: solar-irradiation-estimation.solar_warehouse\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from pathlib import Path\n", + "\n", + "import folium\n", + "import pandas as pd\n", + "import pygeohash\n", + "from folium import CircleMarker, FeatureGroup, LayerControl, Rectangle\n", + "\n", + "# Locate project root by walking up until we see src/susse/.\n", + "_HERE = Path.cwd().resolve()\n", + "_PROJECT_ROOT = _HERE\n", + "while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / \"src\" / \"susse\").exists():\n", + " _PROJECT_ROOT = _PROJECT_ROOT.parent\n", + "sys.path.insert(0, str(_PROJECT_ROOT / \"src\"))\n", + "\n", + "from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig\n", + "from susse.warehouse_ops.io.config import TableRefs\n", + "from susse.warehouse_ops.population import (\n", + " PhysicalStorage, Source, VariableCatalog, variables_to_dataframe,\n", + ")\n", + "\n", + "bq = BigQueryClient(config=WarehouseConfig())\n", + "tables = TableRefs(config=bq.config)\n", + "print(f\"warehouse: {bq.config.project_id}.{bq.config.dataset}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "warehouse: solar-irradiation-estimation.solar_warehouse\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "7b0a39a31186ca66", + "metadata": {}, + "source": [ + "## 1 — The tables, at a glance\n", + "\n", + "| Table | Shape | What it stores |\n", + "|---|---|---|\n", + "| `dim_variable` | catalog | One row per `(variable_id, source)` — unit, valid range, spatial resolution, which physical table holds the value |\n", + "| `irradiance_daily` | wide | Per-`(date, geohash5, source)` daily GHI / DHI / DNI for NASA POWER and CAMS |\n", + "| `nasa_daily_vars_long` | long | NASA POWER auxiliary variables (`temperature`, `aod_550`, `cloud_amount`, …) keyed by `(date, geohash5, variable_id)` |\n", + "| `cams_daily_vars_long` | long | CAMS auxiliary variables (`ghi_clear`, `bhi`, `dni_clear`, …) |\n", + "| `merra_daily_vars_long` | long | MERRA-2 reanalysis variables (aerosol decomposition, precipitable water) |\n", + "| `modis_observations` | observation-keyed | MODIS composites keyed by `(date, geohash5, product_id, band_id)` |\n", + "| `ground_measurements` | curated long | Daily ground GHI per station with QC level |\n", + "| `ground_measurements_raw` | uncurated long | Raw partner CSVs before curation |\n", + "\n", + "**Wide vs long** is chosen per source: GHI / DHI / DNI go in the wide\n", + "`irradiance_daily` because they're a tightly coupled trio always\n", + "queried together; everything else lives in a long-format companion so\n", + "adding a new variable doesn't require a schema migration.\n", + "\n", + "`dim_variable.physical_storage` tags every catalog row with which of\n", + "the three storage shapes it lives in (`long_format` / `irradiance_wide`\n", + "/ `modis_observations`). `FeatureSelection` (NB 02) uses this tag to\n", + "reject typos at construction with a clear error.\n", + "\n", + "For the full data lineage (which job writes which table, what's derived\n", + "from what), see [`warehouse/README.md`](../../warehouse/README.md)." + ] + }, + { + "cell_type": "markdown", + "id": "f38d62cc147e29fc", + "metadata": {}, + "source": [ + "### Browsing the variable catalog" + ] + }, + { + "cell_type": "code", + "id": "715a4be057f78c58", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:00.032428658Z", + "start_time": "2026-05-27T07:55:59.915298583Z" + } + }, + "source": [ + "# The catalog is hand-maintained in code, then upserted into dim_variable.\n", + "# Browse it without hitting BigQuery:\n", + "catalog_df = variables_to_dataframe(VariableCatalog.all_variables())\n", + "print(f\"Catalog: {len(catalog_df)} variables across {catalog_df['source'].nunique()} sources.\")\n", + "print()\n", + "print(\"Counts by source × storage:\")\n", + "print(\n", + " catalog_df.groupby([\"source\", \"physical_storage\"]).size().unstack(fill_value=0)\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Catalog: 48 variables across 4 sources.\n", + "\n", + "Counts by source × storage:\n", + "physical_storage irradiance_wide long_format modis_observations\n", + "source \n", + "CAMS 3 6 0\n", + "MERRA2 0 4 0\n", + "MODIS 0 0 3\n", + "NASA 3 29 0\n" + ] + } + ], + "source": [ + "# The catalog is hand-maintained in code, then upserted into dim_variable.\n", + "# Browse it without hitting BigQuery:\n", + "catalog_df = variables_to_dataframe(VariableCatalog.all_variables())\n", + "print(f\"Catalog: {len(catalog_df)} variables across {catalog_df['source'].nunique()} sources.\")\n", + "print()\n", + "print(\"Counts by source × storage:\")\n", + "print(\n", + " catalog_df.groupby([\"source\", \"physical_storage\"]).size().unstack(fill_value=0)\n", + ")" + ] + }, + { + "cell_type": "code", + "id": "c732402bd571a8fd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:00.120132917Z", + "start_time": "2026-05-27T07:56:00.041206524Z" + } + }, + "source": [ + "# Drill into one source — list variable_ids and their storage.\n", + "# Repeat with Source.CAMS, Source.MERRA_2, Source.MODIS as needed.\n", + "nasa = catalog_df[catalog_df[\"source\"] == Source.NASA_POWER.value]\n", + "print(f\"NASA POWER ({len(nasa)} variables):\")\n", + "nasa[[\"variable_id\", \"display_name\", \"unit\", \"physical_storage\"]]" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NASA POWER (32 variables):\n" + ] + }, + { + "data": { + "text/html": [ + "
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variable_iddisplay_nameunitphysical_storage
0ghiAll-sky GHIkWh/m^2/dayirradiance_wide
1dhiAll-sky DHIkWh/m^2/dayirradiance_wide
2dniAll-sky DNIkWh/m^2/dayirradiance_wide
3temperature2m Air TemperaturedegClong_format
4temperature_range2m Air Temperature RangedegClong_format
5specific_humidity2m Specific Humiditykg/kglong_format
6relative_humidity2m Relative Humidity%long_format
7surface_pressureSurface PressurekPalong_format
8aod_550AOD @ 550nmunitlesslong_format
9aod_550_adjAOD @ 550nm (adjusted)unitlesslong_format
10aod_840AOD @ 840nmunitlesslong_format
11cloud_visible_optical_depthCloud Visible Optical Depthunitlesslong_format
12cloud_amountCloud Amountfractionlong_format
13cloud_amount_datCloud Amount (Daytime)fractionlong_format
14total_column_ozoneTotal Column OzoneDobson Unitslong_format
15precipitable_waterPrecipitable Watercmlong_format
16precipitation_correctedPrecipitation (Corrected)mm/daylong_format
17surface_albedoSurface Albedo (Terrain-Adjusted)unitlesslong_format
18all_sky_surface_albedoAll-sky Surface Albedounitlesslong_format
19clear_sky_albedoClear-sky Surface Albedounitlesslong_format
20clearness_indexClearness Indexunitlesslong_format
21longwave_downward_irrClear-sky Longwave Downward IrradianceW/m^2long_format
22wind_speed2m Wind Speedm/slong_format
23northern_wind2m Northward Windm/slong_format
24surface_roughnessSurface Roughnessmlong_format
25zero_plane_displacementZero-Plane Displacementmlong_format
26planetary_boundaryPlanetary Boundary Layer Heightmlong_format
27surface_air_densitySurface Air Densitykg/m^3long_format
28airmassAirmassunitlesslong_format
29surface_soil_wetnessSurface Soil Wetnessfractionlong_format
30evaporation_landEvaporation (Land)mm/daylong_format
31evapotranspiration_energyEvapotranspiration EnergyW/m^2long_format
\n", + "
" + ], + "text/plain": [ + " variable_id display_name \\\n", + "0 ghi All-sky GHI \n", + "1 dhi All-sky DHI \n", + "2 dni All-sky DNI \n", + "3 temperature 2m Air Temperature \n", + "4 temperature_range 2m Air Temperature Range \n", + "5 specific_humidity 2m Specific Humidity \n", + "6 relative_humidity 2m Relative Humidity \n", + "7 surface_pressure Surface Pressure \n", + "8 aod_550 AOD @ 550nm \n", + "9 aod_550_adj AOD @ 550nm (adjusted) \n", + "10 aod_840 AOD @ 840nm \n", + "11 cloud_visible_optical_depth Cloud Visible Optical Depth \n", + "12 cloud_amount Cloud Amount \n", + "13 cloud_amount_dat Cloud Amount (Daytime) \n", + "14 total_column_ozone Total Column Ozone \n", + "15 precipitable_water Precipitable Water \n", + "16 precipitation_corrected Precipitation (Corrected) \n", + "17 surface_albedo Surface Albedo (Terrain-Adjusted) \n", + "18 all_sky_surface_albedo All-sky Surface Albedo \n", + "19 clear_sky_albedo Clear-sky Surface Albedo \n", + "20 clearness_index Clearness Index \n", + "21 longwave_downward_irr Clear-sky Longwave Downward Irradiance \n", + "22 wind_speed 2m Wind Speed \n", + "23 northern_wind 2m Northward Wind \n", + "24 surface_roughness Surface Roughness \n", + "25 zero_plane_displacement Zero-Plane Displacement \n", + "26 planetary_boundary Planetary Boundary Layer Height \n", + "27 surface_air_density Surface Air Density \n", + "28 airmass Airmass \n", + "29 surface_soil_wetness Surface Soil Wetness \n", + "30 evaporation_land Evaporation (Land) \n", + "31 evapotranspiration_energy Evapotranspiration Energy \n", + "\n", + " unit physical_storage \n", + "0 kWh/m^2/day irradiance_wide \n", + "1 kWh/m^2/day irradiance_wide \n", + "2 kWh/m^2/day irradiance_wide \n", + "3 degC long_format \n", + "4 degC long_format \n", + "5 kg/kg long_format \n", + "6 % long_format \n", + "7 kPa long_format \n", + "8 unitless long_format \n", + "9 unitless long_format \n", + "10 unitless long_format \n", + "11 unitless long_format \n", + "12 fraction long_format \n", + "13 fraction long_format \n", + "14 Dobson Units long_format \n", + "15 cm long_format \n", + "16 mm/day long_format \n", + "17 unitless long_format \n", + "18 unitless long_format \n", + "19 unitless long_format \n", + "20 unitless long_format \n", + "21 W/m^2 long_format \n", + "22 m/s long_format \n", + "23 m/s long_format \n", + "24 m long_format \n", + "25 m long_format \n", + "26 m long_format \n", + "27 kg/m^3 long_format \n", + "28 unitless long_format \n", + "29 fraction long_format \n", + "30 mm/day long_format \n", + "31 W/m^2 long_format " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drill into one source — list variable_ids and their storage.\n", + "# Repeat with Source.CAMS, Source.MERRA_2, Source.MODIS as needed.\n", + "nasa = catalog_df[catalog_df[\"source\"] == Source.NASA_POWER.value]\n", + "print(f\"NASA POWER ({len(nasa)} variables):\")\n", + "nasa[[\"variable_id\", \"display_name\", \"unit\", \"physical_storage\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "f50f6673edf9d989", + "metadata": {}, + "source": [ + "## 2 — Coverage on the map\n", + "\n", + "Three layers — toggle from the layer control in the top right.\n", + "\n", + "| Marker | What it is | Source |\n", + "|---|---|---|\n", + "| **Blue dots** (sized by coverage) | Ground-measurement stations (training labels) | `ground_measurements` |\n", + "| **Red / orange / green dots** | Katongole 2023 validation stations, colour by warehouse coverage status | `data/external_references/katongole_2023_monthly.csv` + `irradiance_daily` |\n", + "| **Green rectangle** | Uganda 2024 inference grid (portal-coverage cache) | `irradiance_daily` filtered to 2024-only cells |\n", + "\n", + "Click any marker for details." + ] + }, + { + "cell_type": "code", + "id": "ae2a2b274941444b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:06.432593939Z", + "start_time": "2026-05-27T07:56:00.178868360Z" + } + }, + "source": [ + "# Ground stations + their per-source satellite coverage.\n", + "ground = bq.query(f\"\"\"\n", + "WITH ground_summary AS (\n", + " SELECT location,\n", + " ANY_VALUE(lat) AS lat,\n", + " ANY_VALUE(lon) AS lon,\n", + " ANY_VALUE(geohash5) AS geohash5,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date,\n", + " COUNT(*) AS n_rows,\n", + " COUNTIF(qc_level = 'pass') AS n_qc_passed\n", + " FROM `{tables.ground_measurements}`\n", + " GROUP BY location\n", + "),\n", + "sat_coverage AS (\n", + " SELECT geohash5,\n", + " COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days,\n", + " COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days\n", + " FROM `{tables.irradiance_daily}`\n", + " GROUP BY geohash5\n", + ")\n", + "SELECT g.*, COALESCE(s.n_nasa_days, 0) AS n_nasa_days,\n", + " COALESCE(s.n_cams_days, 0) AS n_cams_days\n", + "FROM ground_summary g\n", + "LEFT JOIN sat_coverage s USING (geohash5)\n", + "ORDER BY n_rows DESC\n", + "\"\"\")\n", + "print(f\"Ground stations: {len(ground)}\")\n", + "ground.head()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground stations: 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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3ghana_location15.645759-0.105223ecpbj2019-09-242024-11-251774177418901890
4nigeria_location19.0764397.425385s1t782020-04-042024-11-251641164116971697
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" + ], + "text/plain": [ + " location lat lon geohash5 min_date max_date \\\n", + "0 kenya_location3 -0.469974 35.181874 kzcj2 2019-05-28 2024-11-25 \n", + "1 lira 2.295190 32.921370 s8rmj 2014-08-27 2023-02-01 \n", + "2 kampala 0.333542 32.568630 s8p1v 2011-04-06 2023-01-22 \n", + "3 ghana_location1 5.645759 -0.105223 ecpbj 2019-09-24 2024-11-25 \n", + "4 nigeria_location1 9.076439 7.425385 s1t78 2020-04-04 2024-11-25 \n", + "\n", + " n_rows n_qc_passed n_nasa_days n_cams_days \n", + "0 1976 1976 2009 2009 \n", + "1 1935 1934 3081 3081 \n", + "2 1798 1798 4676 4676 \n", + "3 1774 1774 1890 1890 \n", + "4 1641 1641 1697 1697 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ground stations + their per-source satellite coverage.\n", + "ground = bq.query(f\"\"\"\n", + "WITH ground_summary AS (\n", + " SELECT location,\n", + " ANY_VALUE(lat) AS lat,\n", + " ANY_VALUE(lon) AS lon,\n", + " ANY_VALUE(geohash5) AS geohash5,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date,\n", + " COUNT(*) AS n_rows,\n", + " COUNTIF(qc_level = 'pass') AS n_qc_passed\n", + " FROM `{tables.ground_measurements}`\n", + " GROUP BY location\n", + "),\n", + "sat_coverage AS (\n", + " SELECT geohash5,\n", + " COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days,\n", + " COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days\n", + " FROM `{tables.irradiance_daily}`\n", + " GROUP BY geohash5\n", + ")\n", + "SELECT g.*, COALESCE(s.n_nasa_days, 0) AS n_nasa_days,\n", + " COALESCE(s.n_cams_days, 0) AS n_cams_days\n", + "FROM ground_summary g\n", + "LEFT JOIN sat_coverage s USING (geohash5)\n", + "ORDER BY n_rows DESC\n", + "\"\"\")\n", + "print(f\"Ground stations: {len(ground)}\")\n", + "ground.head()" + ] + }, + { + "cell_type": "code", + "id": "5afcc79a3796fe1d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:09.753633239Z", + "start_time": "2026-05-27T07:56:06.866724061Z" + } + }, + "source": [ + "# Katongole validation stations + 2017-2022 coverage status.\n", + "KATONGOLE_CSV = (\n", + " _PROJECT_ROOT / \"data\" / \"external_references\" / \"katongole_2023_monthly.csv\"\n", + ")\n", + "katongole = pd.read_csv(KATONGOLE_CSV)\n", + "katongole[\"geohash5\"] = [\n", + " pygeohash.encode(lat, lon, precision=5)\n", + " for lat, lon in zip(katongole[\"latitude\"], katongole[\"longitude\"])\n", + "]\n", + "_gh_quoted = \", \".join(f\"'{g}'\" for g in katongole[\"geohash5\"].unique())\n", + "cov_2017_22 = bq.query(f\"\"\"\n", + "SELECT geohash5,\n", + " COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days,\n", + " COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE date BETWEEN DATE('2017-01-01') AND DATE('2022-12-31')\n", + " AND geohash5 IN ({_gh_quoted})\n", + "GROUP BY geohash5\n", + "\"\"\")\n", + "katongole = katongole.merge(cov_2017_22, on=\"geohash5\", how=\"left\").fillna(\n", + " {\"n_nasa_days\": 0, \"n_cams_days\": 0}\n", + ").astype({\"n_nasa_days\": int, \"n_cams_days\": int})\n", + "_N_EXPECTED = (pd.Timestamp(\"2022-12-31\") - pd.Timestamp(\"2017-01-01\")).days + 1\n", + "_FULL = int(0.95 * _N_EXPECTED)\n", + "katongole[\"coverage_status\"] = [\n", + " \"full\" if (n_nasa >= _FULL and n_cams >= _FULL)\n", + " else \"missing\" if (n_nasa == 0 and n_cams == 0)\n", + " else \"partial\"\n", + " for n_nasa, n_cams in zip(katongole[\"n_nasa_days\"], katongole[\"n_cams_days\"])\n", + "]\n", + "print(\n", + " f\"Katongole stations: {len(katongole)} — \"\n", + " + \", \".join(\n", + " f\"{k}: {v}\" for k, v in katongole['coverage_status'].value_counts().items()\n", + " )\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Katongole stations: 56 — full: 56\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "5dd16385c163e28b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:12.424644418Z", + "start_time": "2026-05-27T07:56:10.211987468Z" + } + }, + "source": [ + "# Uganda 2024 inference-grid bbox.\n", + "uganda_grid = bq.query(f\"\"\"\n", + "SELECT MIN(latitude) AS min_lat,\n", + " MAX(latitude) AS max_lat,\n", + " MIN(longitude) AS min_lon,\n", + " MAX(longitude) AS max_lon,\n", + " COUNT(DISTINCT geohash5) AS n_cells,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-12-31')\n", + " AND source = 'NASA'\n", + " AND geohash5 NOT IN (SELECT DISTINCT geohash5 FROM `{tables.ground_measurements}`)\n", + "\"\"\")\n", + "uganda_grid" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + " min_lat max_lat min_lon max_lon n_cells min_date max_date\n", + "0 -1.5 4.21786 29.5 35.06 2183 2024-01-01 2024-12-31" + ], + "text/html": [ + "
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" + ], + "text/plain": [ + " min_lat max_lat min_lon max_lon n_cells min_date max_date\n", + "0 -1.38214 4.21786 29.671499 34.971499 1961 2024-01-01 2024-12-31" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Uganda 2024 inference-grid bbox.\n", + "uganda_grid = bq.query(f\"\"\"\n", + "SELECT MIN(latitude) AS min_lat,\n", + " MAX(latitude) AS max_lat,\n", + " MIN(longitude) AS min_lon,\n", + " MAX(longitude) AS max_lon,\n", + " COUNT(DISTINCT geohash5) AS n_cells,\n", + " MIN(date) AS min_date,\n", + " MAX(date) AS max_date\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-12-31')\n", + " AND source = 'NASA'\n", + " AND geohash5 NOT IN (SELECT DISTINCT geohash5 FROM `{tables.ground_measurements}`)\n", + "\"\"\")\n", + "uganda_grid" + ] + }, + { + "cell_type": "code", + "id": "3dddc814e785b2cc", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T07:56:13.240917681Z", + "start_time": "2026-05-27T07:56:13.053969993Z" + } + }, + "source": [ + "def _ground_popup(row: pd.Series) -> str:\n", + " return (\n", + " f\"{row.location}
\"\n", + " f\"lat={row.lat:.3f}, lon={row.lon:.3f} (geohash5 {row.geohash5})
\"\n", + " f\"Ground: {row.n_qc_passed:,} QC-passed days \"\n", + " f\"({row.min_date} → {row.max_date})
\"\n", + " f\"NASA: {row.n_nasa_days:,} days
\"\n", + " f\"CAMS: {row.n_cams_days:,} days\"\n", + " )\n", + "\n", + "\n", + "def _katongole_popup(row: pd.Series) -> str:\n", + " if row.coverage_status == \"full\":\n", + " cov = \"full 2017-2022 coverage\"\n", + " elif row.coverage_status == \"missing\":\n", + " cov = (\n", + " \"no warehouse data yet — \"\n", + " \"run migration A12\"\n", + " )\n", + " else:\n", + " cov = (\n", + " f\"partial: \"\n", + " f\"NASA {row.n_nasa_days} days, CAMS {row.n_cams_days} days\"\n", + " )\n", + " return (\n", + " f\"{row.location}
\"\n", + " f\"lat={row.latitude:.3f}, lon={row.longitude:.3f}
\"\n", + " f\"{cov}\"\n", + " )\n", + "\n", + "\n", + "m = folium.Map(location=[5.0, 25.0], zoom_start=4, control_scale=True)\n", + "\n", + "ground_layer = FeatureGroup(name=f\"Ground stations ({len(ground)})\", show=True)\n", + "for _, row in ground.iterrows():\n", + " radius = 4 + int(0.001 * row.n_qc_passed)\n", + " CircleMarker(\n", + " location=[row.lat, row.lon],\n", + " radius=min(radius, 14),\n", + " popup=folium.Popup(_ground_popup(row), max_width=320),\n", + " tooltip=row.location,\n", + " color=\"#1f77b4\", weight=1, fill=True,\n", + " fillColor=\"#1f77b4\", fillOpacity=0.8,\n", + " ).add_to(ground_layer)\n", + "ground_layer.add_to(m)\n", + "\n", + "katongole_layer = FeatureGroup(\n", + " name=f\"Katongole validation ({len(katongole)})\", show=True,\n", + ")\n", + "_status_color = {\"full\": \"#2ca02c\", \"partial\": \"#ff7f0e\", \"missing\": \"#d62728\"}\n", + "for _, row in katongole.iterrows():\n", + " color = _status_color[row.coverage_status]\n", + " CircleMarker(\n", + " location=[row.latitude, row.longitude], radius=4,\n", + " popup=folium.Popup(_katongole_popup(row), max_width=320),\n", + " tooltip=f\"{row.location} — {row.coverage_status}\",\n", + " color=color, weight=1, fill=True,\n", + " fillColor=color, fillOpacity=0.85,\n", + " ).add_to(katongole_layer)\n", + "katongole_layer.add_to(m)\n", + "\n", + "ug_layer = FeatureGroup(name=\"Uganda 2024 inference grid\", show=True)\n", + "ug_row = uganda_grid.iloc[0]\n", + "Rectangle(\n", + " bounds=[\n", + " [float(ug_row.min_lat), float(ug_row.min_lon)],\n", + " [float(ug_row.max_lat), float(ug_row.max_lon)],\n", + " ],\n", + " color=\"#2ca02c\", weight=2, fill=True,\n", + " fillColor=\"#2ca02c\", fillOpacity=0.08,\n", + " popup=folium.Popup(\n", + " f\"Uganda 2024 inference grid
\"\n", + " f\"{int(ug_row.n_cells):,} cells × 365 days
\"\n", + " f\"{ug_row.min_date} → {ug_row.max_date}\",\n", + " max_width=320,\n", + " ),\n", + " tooltip=\"Uganda 2024 inference grid\",\n", + ").add_to(ug_layer)\n", + "ug_layer.add_to(m)\n", + "\n", + "LayerControl(collapsed=False).add_to(m)\n", + "m" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def _ground_popup(row: pd.Series) -> str:\n", + " return (\n", + " f\"{row.location}
\"\n", + " f\"lat={row.lat:.3f}, lon={row.lon:.3f} (geohash5 {row.geohash5})
\"\n", + " f\"Ground: {row.n_qc_passed:,} QC-passed days \"\n", + " f\"({row.min_date} → {row.max_date})
\"\n", + " f\"NASA: {row.n_nasa_days:,} days
\"\n", + " f\"CAMS: {row.n_cams_days:,} days\"\n", + " )\n", + "\n", + "\n", + "def _katongole_popup(row: pd.Series) -> str:\n", + " if row.coverage_status == \"full\":\n", + " cov = \"full 2017-2022 coverage\"\n", + " elif row.coverage_status == \"missing\":\n", + " cov = (\n", + " \"no warehouse data yet — \"\n", + " \"run migration A12\"\n", + " )\n", + " else:\n", + " cov = (\n", + " f\"partial: \"\n", + " f\"NASA {row.n_nasa_days} days, CAMS {row.n_cams_days} days\"\n", + " )\n", + " return (\n", + " f\"{row.location}
\"\n", + " f\"lat={row.latitude:.3f}, lon={row.longitude:.3f}
\"\n", + " f\"{cov}\"\n", + " )\n", + "\n", + "\n", + "m = folium.Map(location=[5.0, 25.0], zoom_start=4, control_scale=True)\n", + "\n", + "ground_layer = FeatureGroup(name=f\"Ground stations ({len(ground)})\", show=True)\n", + "for _, row in ground.iterrows():\n", + " radius = 4 + int(0.001 * row.n_qc_passed)\n", + " CircleMarker(\n", + " location=[row.lat, row.lon],\n", + " radius=min(radius, 14),\n", + " popup=folium.Popup(_ground_popup(row), max_width=320),\n", + " tooltip=row.location,\n", + " color=\"#1f77b4\", weight=1, fill=True,\n", + " fillColor=\"#1f77b4\", fillOpacity=0.8,\n", + " ).add_to(ground_layer)\n", + "ground_layer.add_to(m)\n", + "\n", + "katongole_layer = FeatureGroup(\n", + " name=f\"Katongole validation ({len(katongole)})\", show=True,\n", + ")\n", + "_status_color = {\"full\": \"#2ca02c\", \"partial\": \"#ff7f0e\", \"missing\": \"#d62728\"}\n", + "for _, row in katongole.iterrows():\n", + " color = _status_color[row.coverage_status]\n", + " CircleMarker(\n", + " location=[row.latitude, row.longitude], radius=4,\n", + " popup=folium.Popup(_katongole_popup(row), max_width=320),\n", + " tooltip=f\"{row.location} — {row.coverage_status}\",\n", + " color=color, weight=1, fill=True,\n", + " fillColor=color, fillOpacity=0.85,\n", + " ).add_to(katongole_layer)\n", + "katongole_layer.add_to(m)\n", + "\n", + "ug_layer = FeatureGroup(name=\"Uganda 2024 inference grid\", show=True)\n", + "ug_row = uganda_grid.iloc[0]\n", + "Rectangle(\n", + " bounds=[\n", + " [float(ug_row.min_lat), float(ug_row.min_lon)],\n", + " [float(ug_row.max_lat), float(ug_row.max_lon)],\n", + " ],\n", + " color=\"#2ca02c\", weight=2, fill=True,\n", + " fillColor=\"#2ca02c\", fillOpacity=0.08,\n", + " popup=folium.Popup(\n", + " f\"Uganda 2024 inference grid
\"\n", + " f\"{int(ug_row.n_cells):,} cells × 365 days
\"\n", + " f\"{ug_row.min_date} → {ug_row.max_date}\",\n", + " max_width=320,\n", + " ),\n", + " tooltip=\"Uganda 2024 inference grid\",\n", + ").add_to(ug_layer)\n", + "ug_layer.add_to(m)\n", + "\n", + "LayerControl(collapsed=False).add_to(m)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "216dad7960986b6a", + "metadata": {}, + "source": [ + "## 3 — Where to go next\n", + "\n", + "* [`02_query_the_warehouse.ipynb`](02_query_the_warehouse.ipynb) — build\n", + " a `TrainingDataset` from the warehouse using `FeatureSelection` /\n", + " `FeatureService`, plus an example of dropping down to raw SQL when\n", + " needed.\n", + "* [`03_preprocessing.ipynb`](03_preprocessing.ipynb) → 04 → 05 — the\n", + " rest of the pipeline: preprocessing, model factory, trainer.\n", + "* [`../../warehouse/extending_the_warehouse.ipynb`](../../warehouse/extending_the_warehouse.ipynb)\n", + " — for contributors who need to add new variables, sources, or\n", + " stations to the warehouse.\n", + "* [`../papers/mukiibi_mikelson_2026/01_recomputation.ipynb`](../papers/mukiibi_mikelson_2026/01_recomputation.ipynb)\n", + " — a complete worked example, paper-faithful Random Forest GHI bias\n", + " correction trained against the data shown above and validated against\n", + " the Katongole network." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/02_query_the_warehouse.ipynb b/notebooks/tutorial/02_query_the_warehouse.ipynb new file mode 100644 index 00000000..bcb4070d --- /dev/null +++ b/notebooks/tutorial/02_query_the_warehouse.ipynb @@ -0,0 +1,1210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-34087789", + "metadata": {}, + "source": [ + "# 02 — Warehouse access and training datasets\n", + "\n", + "Notebook 01 built the **warehouse**. This notebook turns it into a **versioned training dataset** the rest of the pipeline (preprocessing, models, training, evaluation, inference) consumes.\n", + "\n", + "## The architecture in one picture\n", + "\n", + "A *dataset* is two things:\n", + "\n", + "1. A **recipe** — `FeatureSelection` + date range + locations + QC filter. Pure Python config, lives in this repo, in git.\n", + "2. A **materialisation** — the parquet payload produced by applying the recipe to the warehouse at a specific point in time, plus a `manifest.json` capturing the warehouse state, the git SHA, and a SHA-256 of the parquet for tamper-detection.\n", + "\n", + "The two-layer split is what lets us answer questions like:\n", + "\n", + "* \"What if we ingest more ground truth?\" → same recipe, new materialisation, bumped version.\n", + "* \"What if we add a new variable?\" → new recipe, new artifact family.\n", + "* \"What if we want fewer variables for an ablation?\" → derived recipe, separate materialisation.\n", + "* \"What if upstream data was wrong?\" → fix in warehouse, re-run recipe, new materialisation.\n", + "\n", + "## Where snapshots live\n", + "\n", + "Each snapshot is a directory with `dataset.parquet` + `manifest.json`. Locally we write to `data/training_snapshots/_/`; from there we upload to **W&B Artifacts** of type `training_dataset`. Every training run consumes a specific artifact version, so the lineage graph in W&B answers \"which model used which dataset\" automatically." + ] + }, + { + "cell_type": "code", + "id": "3828cdf04ce94e3d8af404453838ca67", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:36:25.472821139Z", + "start_time": "2026-05-13T10:36:25.424045207Z" + } + }, + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ], + "outputs": [], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "id": "60e0d2b1", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | A populated warehouse (NB 01) and GCP application-default credentials. |\n", + "| **Outputs** | A snapshot directory on disk: `data/training_snapshots/_/{dataset.parquet, manifest.json}`. The same snapshot is optionally uploaded to W&B as an artifact of type `training_dataset`. |\n", + "| **Prereqs** | Env vars: none required to materialise locally. `WANDB_API_KEY` (in `.env`) needed only if `LOG_TO_WANDB=True`. |\n", + "| **Consumed by** | NB 03 (preprocessing) reads the snapshot directly via `load_snapshot(...)`. |\n", + "\n", + "**Defaults are safe.** `LOG_TO_WANDB=False` and `DOWNLOAD_FROM_WANDB=False` keep the notebook offline-with-respect-to-W&B; you only need W&B if you want cross-machine artifact sharing." + ] + }, + { + "cell_type": "markdown", + "id": "md-66015501", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "id": "co-44231342", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:04.164154Z", + "iopub.status.busy": "2026-05-09T00:00:04.163216Z", + "iopub.status.idle": "2026-05-09T00:00:05.073522Z", + "shell.execute_reply": "2026-05-09T00:00:05.070411Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:25.550003253Z", + "start_time": "2026-05-13T10:36:25.475003551Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import os\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "\n", + "# Make the in-repo `src/` importable when running from `notebooks/`.\n", + "src_path = (Path.cwd() / \"../../src\").resolve()\n", + "if str(src_path) not in sys.path:\n", + " sys.path.insert(0, str(src_path))\n", + "\n", + "# Load .env so WANDB_API_KEY (if present) is picked up automatically.\n", + "from susse.env import load_project_env\n", + "load_project_env()\n", + "\n", + "print(\"Python\", sys.version.split()[0])\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "Python 3.12.1\n" + ] + } + ], + "execution_count": 16 + }, + { + "cell_type": "code", + "id": "co-44718909", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:05.080635Z", + "iopub.status.busy": "2026-05-09T00:00:05.079347Z", + "iopub.status.idle": "2026-05-09T00:00:15.485981Z", + "shell.execute_reply": "2026-05-09T00:00:15.482074Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:28.983824449Z", + "start_time": "2026-05-13T10:36:25.578365634Z" + } + }, + "source": [ + "from susse.warehouse_ops.io import BigQueryClient, TableRefs\n", + "\n", + "bq = BigQueryClient()\n", + "tables = TableRefs(config=bq.config)\n", + "print(f\"Connected to {bq.config.project_id}.{bq.config.dataset}.\")\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connected to solar-irradiation-estimation.solar_warehouse.\n" + ] + } + ], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "id": "md-77942236", + "metadata": {}, + "source": [ + "## 1 — Define the recipe\n", + "\n", + "`FeatureSelection` enumerates which variables to materialise per source, plus the QC filter and which satellite-irradiance series to include. Validation against the warehouse catalog runs at construction time, so a typo here surfaces as a `ValueError` *before* you spend a query on it.\n", + "\n", + "For **v1** we pick a deliberately small recipe: ground GHI as the target, NASA + CAMS satellite GHI as the primary features, and three NASA atmospheric covariates. Future recipe versions will add MERRA-2 once it has full per-station coverage, and MODIS once the corresponding ingest is wired up." + ] + }, + { + "cell_type": "markdown", + "id": "a2a2e504", + "metadata": {}, + "source": [ + "### What goes into a `FeatureSelection`\n", + "\n", + "Five fields, all type-checked against the warehouse:\n", + "\n", + "- **`nasa_variable_ids` / `cams_variable_ids` / `merra_variable_ids` / `modis_variable_ids`** — auxiliary variables to materialise from each source's long-format table. Each is a tuple of `variable_id` strings; the discovery cell below lists what's available.\n", + "- **`include_satellite_irradiance`** — which sources of satellite GHI/DHI/DNI to pull from the wide `irradiance_daily` table. Today only `Source.NASA_POWER` and `Source.CAMS` write there; MERRA-2 and MODIS contribute features only.\n", + "- **`qc_levels`** — keep only ground rows whose `qc_level` tag matches one of these. NB 01's qc-levels query showed which tags exist in the warehouse; for v1 we only train on `pass`.\n", + "\n", + "Aux columns come back **source-prefixed** in the output frame (e.g. `nasa_aod_550`, not `aod_550`) so MERRA-2's eventual `aod_550_analysis` won't collide with NASA POWER's `aod_550`. The y-target is always `y_ghi_kwh_m2_day`." + ] + }, + { + "cell_type": "code", + "id": "167ae36a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:36:29.124449089Z", + "start_time": "2026-05-13T10:36:29.049976070Z" + } + }, + "source": [ + "# Browse what's available, grouped by source. Every variable_id you write\n", + "# into a FeatureSelection below must appear in this dataframe — typos\n", + "# raise a clear ValueError at construction.\n", + "from susse.warehouse_ops.population import VariableCatalog, variables_to_dataframe\n", + "from susse.warehouse_ops.population.types import Source\n", + "\n", + "catalog_df = variables_to_dataframe(VariableCatalog.all_variables())\n", + "print(\"Sources:\", [s.value for s in Source])\n", + "print(f\"\\nNASA POWER ({sum(catalog_df['source'] == 'NASA')} entries):\")\n", + "print(catalog_df.loc[catalog_df[\"source\"] == \"NASA\", \"variable_id\"].tolist())\n", + "print(f\"\\nCAMS ({sum(catalog_df['source'] == 'CAMS')} entries):\")\n", + "print(catalog_df.loc[catalog_df[\"source\"] == \"CAMS\", \"variable_id\"].tolist())\n", + "print(f\"\\nMERRA-2 ({sum(catalog_df['source'] == 'MERRA2')} entries):\")\n", + "print(catalog_df.loc[catalog_df[\"source\"] == \"MERRA2\", \"variable_id\"].tolist())\n", + "print(f\"\\nMODIS ({sum(catalog_df['source'] == 'MODIS')} entries):\")\n", + "print(catalog_df.loc[catalog_df[\"source\"] == \"MODIS\", \"variable_id\"].tolist())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sources: ['NASA', 'CAMS', 'MERRA2', 'MODIS']\n", + "\n", + "NASA POWER (32 entries):\n", + "['ghi', 'dhi', 'dni', 'temperature', 'temperature_range', 'specific_humidity', 'relative_humidity', 'surface_pressure', 'aod_550', 'aod_550_adj', 'aod_840', 'cloud_visible_optical_depth', 'cloud_amount', 'cloud_amount_dat', 'total_column_ozone', 'precipitable_water', 'precipitation_corrected', 'surface_albedo', 'all_sky_surface_albedo', 'clear_sky_albedo', 'clearness_index', 'longwave_downward_irr', 'wind_speed', 'northern_wind', 'surface_roughness', 'zero_plane_displacement', 'planetary_boundary', 'surface_air_density', 'airmass', 'surface_soil_wetness', 'evaporation_land', 'evapotranspiration_energy']\n", + "\n", + "CAMS (9 entries):\n", + "['ghi', 'dhi', 'dni', 'bhi', 'ghi_extra', 'ghi_clear', 'bhi_clear', 'dhi_clear', 'dni_clear']\n", + "\n", + "MERRA-2 (4 entries):\n", + "['aod_550_extinction', 'aod_550_scattering', 'aod_550_analysis', 'precipitable_water']\n", + "\n", + "MODIS (3 entries):\n", + "['MCD43A4_Nadir_Reflectance_Band1', 'MOD11A2_LST_Day_1km', 'MOD13Q1_250m_16_days_NDVI']\n" + ] + } + ], + "execution_count": 18 + }, + { + "cell_type": "code", + "id": "co-67554796", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:15.494887Z", + "iopub.status.busy": "2026-05-09T00:00:15.493192Z", + "iopub.status.idle": "2026-05-09T00:00:15.652184Z", + "shell.execute_reply": "2026-05-09T00:00:15.648769Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:29.200930111Z", + "start_time": "2026-05-13T10:36:29.127136230Z" + } + }, + "source": [ + "from susse.datasets import FeatureSelection\n", + "from susse.warehouse_ops.population.types import Source\n", + "\n", + "# v1 recipe: aerosols + water vapour + cloudiness + NASA's pre-computed\n", + "# clearness_index (already a kt feature) on the NASA side, and CAMS\n", + "# `ghi_clear` on the CAMS side so NB 03 can derive `kt_cams = sat_ghi_cams\n", + "# / cams_ghi_clear`. Keeping NB 02 and NB 03 recipes in lockstep is the\n", + "# whole point of the recipe-vs-materialisation split: bumping either\n", + "# one means re-deriving the snapshot.\n", + "selection_v1 = FeatureSelection(\n", + " nasa_variable_ids=(\n", + " \"aod_550\", \"precipitable_water\", \"cloud_amount\", \"clearness_index\",\n", + " ),\n", + " cams_variable_ids=(\"ghi_clear\",),\n", + " merra_variable_ids=(),\n", + " modis_variable_ids=(),\n", + " include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS),\n", + " qc_levels=(\"pass\",),\n", + ")\n", + "print(\"Recipe columns:\", (\"y_ghi_kwh_m2_day\",) + selection_v1.aux_columns)\n", + "print(\"Recipe is empty?\", selection_v1.is_empty)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recipe columns: ('y_ghi_kwh_m2_day', 'nasa_aod_550', 'nasa_precipitable_water', 'nasa_cloud_amount', 'nasa_clearness_index', 'cams_ghi_clear')\n", + "Recipe is empty? False\n" + ] + } + ], + "execution_count": 19 + }, + { + "cell_type": "markdown", + "id": "md-89238310", + "metadata": {}, + "source": [ + "## 2 — Build training pairs\n", + "\n", + "`FeatureService.build_training_pairs` runs the SQL, applies the QC filter, and pivots each long-format aux table into source-prefixed wide columns. Note the **source prefix** on aux columns (`nasa_aod_550`, not `aod_550`) — when MERRA-2 lands its own `aod_550` analysis variable in v2, the names won't collide." + ] + }, + { + "cell_type": "code", + "id": "co-12231172", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:15.658984Z", + "iopub.status.busy": "2026-05-09T00:00:15.658331Z", + "iopub.status.idle": "2026-05-09T00:00:23.000529Z", + "shell.execute_reply": "2026-05-09T00:00:22.996981Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:34.897508436Z", + "start_time": "2026-05-13T10:36:29.204626539Z" + } + }, + "source": [ + "from susse.datasets import FeatureService\n", + "\n", + "fs = FeatureService(bq=bq) # tables defaults to TableRefs(config=bq.config)\n", + "training_df = fs.build_training_pairs(\n", + " selection=selection_v1,\n", + " date_start=date(2024, 1, 1),\n", + " date_end=date(2024, 12, 31),\n", + ")\n", + "print(f\"Loaded {len(training_df):,} training rows x {len(training_df.columns)} columns.\")\n", + "print(\"Columns:\", list(training_df.columns))\n", + "training_df.head(3)" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 6,776 training rows x 14 columns.\n", + "Columns: ['date', 'location', 'lat', 'lon', 'geohash5', 'y_ghi_kwh_m2_day', 'qc_level', 'sat_ghi_nasa_kwh_m2_day', 'sat_ghi_cams_kwh_m2_day', 'nasa_aod_550', 'nasa_precipitable_water', 'nasa_cloud_amount', 'nasa_clearness_index', 'cams_ghi_clear']\n" + ] + }, + { + "data": { + "text/plain": [ + " date location lat lon geohash5 \\\n", + "0 2024-01-01 egypt_location1 29.887561 32.460447 str44 \n", + "1 2024-01-01 ghana_location1 5.645759 -0.105223 ecpbj \n", + "2 2024-01-01 ghana_location2 5.645686 0.008678 s1000 \n", + "\n", + " y_ghi_kwh_m2_day qc_level sat_ghi_nasa_kwh_m2_day \\\n", + "0 3.128732 pass 3.7332 \n", + "1 4.246611 pass 5.3671 \n", + "2 4.179786 pass 4.8605 \n", + "\n", + " sat_ghi_cams_kwh_m2_day nasa_aod_550 nasa_precipitable_water \\\n", + "0 3.712784 0.28 1.60 \n", + "1 5.420120 0.57 4.28 \n", + "2 5.340148 0.57 4.23 \n", + "\n", + " nasa_cloud_amount nasa_clearness_index cams_ghi_clear \n", + "0 6.78 0.66 3.869308 \n", + "1 8.20 0.58 5.679153 \n", + "2 5.13 0.53 5.567244 " + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "cell_type": "markdown", + "id": "md-25760388", + "metadata": {}, + "source": [ + "A quick sanity check: per-station coverage of the target column. Stations with zero rows here either have no QC-passed ground data in the requested window, or weren't ingested yet." + ] + }, + { + "cell_type": "code", + "id": "co-38238189", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:23.005281Z", + "iopub.status.busy": "2026-05-09T00:00:23.004870Z", + "iopub.status.idle": "2026-05-09T00:00:23.047823Z", + "shell.execute_reply": "2026-05-09T00:00:23.045469Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:35.167459663Z", + "start_time": "2026-05-13T10:36:35.030401914Z" + } + }, + "source": [ + "training_df.groupby(\"location\").size().sort_values(ascending=False).head(10)\n" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "location\n", + "kenya_location13 634\n", + "kenya_location10 604\n", + "kenya_location5 534\n", + "kenya_location11 519\n", + "egypt_location1 330\n", + "madagascar_location2 330\n", + "kenya_location8 329\n", + "ghana_location1 324\n", + "nigeria_location2 324\n", + "kenya_location3 321\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 21 + }, + { + "cell_type": "markdown", + "id": "md-38283799", + "metadata": {}, + "source": [ + "## 3 — Build inference features\n", + "\n", + "The same recipe drives inference. Given a `(lat, lon, date)`, `FeatureService.build_inference_features` returns a single-row frame with the satellite GHI estimates and the same auxiliary columns — but no target. This is what the portal will call in production." + ] + }, + { + "cell_type": "code", + "id": "co-27631668", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:23.055148Z", + "iopub.status.busy": "2026-05-09T00:00:23.054193Z", + "iopub.status.idle": "2026-05-09T00:00:25.504024Z", + "shell.execute_reply": "2026-05-09T00:00:25.500451Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:38.974398155Z", + "start_time": "2026-05-13T10:36:35.669810763Z" + } + }, + "source": [ + "inference_today = fs.build_inference_features(\n", + " selection=selection_v1,\n", + " target_date=date(2024, 6, 15),\n", + " lat=0.5179,\n", + " lon=32.4715, # central Uganda grid point\n", + ")\n", + "inference_today\n" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + " date geohash5 sat_ghi_nasa_kwh_m2_day sat_ghi_cams_kwh_m2_day \\\n", + "0 2024-06-15 s8p4f 5.21 4.730357 \n", + "\n", + " nasa_aod_550 nasa_precipitable_water nasa_cloud_amount \\\n", + "0 0.4 3.42 34.93 \n", + "\n", + " nasa_clearness_index cams_ghi_clear lat lon \n", + "0 0.56 6.447131 0.5179 32.4715 " + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 22 + }, + { + "cell_type": "markdown", + "id": "e18ea35dabdb4527b2315c63bca5cfc6", + "metadata": {}, + "source": [ + "## 4 — Dropping down to raw SQL when you need it\n", + "\n", + "`FeatureService` covers the common cases (build training pairs, build\n", + "single-point inference features) but BigQuery is right there if you\n", + "need something it doesn't directly support — joining across sources in\n", + "unusual ways, computing aggregates over arbitrary windows, or just\n", + "sanity-checking what's in a table.\n", + "\n", + "Three patterns worth knowing:\n", + "\n", + "1. **Table references are typed**: use `tables.` to get a\n", + " fully-qualified table name, never hand-concatenate.\n", + "2. **`BigQueryClient.query()` returns a pandas DataFrame.** No\n", + " streaming, no rows-iterator — for the warehouse scale (millions of\n", + " rows), `pandas.DataFrame` is fine.\n", + "3. **For analytics you'll want to repeat, lift the SQL into\n", + " `warehouse/sql/10_gold/` as a view** — both for re-use and so it\n", + " shows up in the schema browser.\n", + "\n", + "The cell below pulls Uganda-only monthly NASA + CAMS GHI averages\n", + "without going through `FeatureService`." + ] + }, + { + "cell_type": "code", + "id": "95eea80b26644a7daef884a79d531d8d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:36:40.708809762Z", + "start_time": "2026-05-13T10:36:39.669630987Z" + } + }, + "source": [ + "# Example: monthly-mean satellite GHI per (geohash5, month) over\n", + "# Uganda only, 2022 — both NASA and CAMS, as named columns.\n", + "sql = f\"\"\"\n", + "SELECT geohash5,\n", + " latitude, longitude,\n", + " EXTRACT(MONTH FROM date) AS month,\n", + " AVG(IF(source = 'NASA', ghi_kwh_m2_day, NULL)) AS nasa_ghi,\n", + " AVG(IF(source = 'CAMS', ghi_kwh_m2_day, NULL)) AS cams_ghi,\n", + " AVG(IF(source = 'CAMS', ghi_kwh_m2_day, NULL))\n", + " - AVG(IF(source = 'NASA', ghi_kwh_m2_day, NULL)) AS cams_minus_nasa\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE date BETWEEN DATE('2022-01-01') AND DATE('2022-12-31')\n", + " AND latitude BETWEEN -1.5 AND 4.5 -- Uganda bbox\n", + " AND longitude BETWEEN 29.5 AND 35.0\n", + "GROUP BY geohash5, latitude, longitude, month\n", + "\"\"\"\n", + "monthly = bq.query(sql)\n", + "print(f\"{len(monthly):,} (geohash5, month) rows pulled.\")\n", + "monthly.head()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "624 (geohash5, month) rows pulled.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + " geohash5 latitude longitude month nasa_ghi cams_ghi cams_minus_nasa\n", + "0 s8n2e 0.08901 31.45802 3 5.741916 5.623325 -0.118591\n", + "1 s8w52 3.40472 30.95960 3 5.785684 6.507991 0.722307\n", + "2 s8n74 0.56302 31.39055 9 5.023443 5.379040 0.355597\n", + "3 s8n3k 0.24340 31.48119 7 4.705010 5.069755 0.364745\n", + "4 s8p58 0.62120 32.36956 7 4.636787 4.986285 0.349498" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "id": "md-60350562", + "metadata": {}, + "source": [ + "## 5 — Materialise as a snapshot (local)\n", + "\n", + "`build_and_write_snapshot` queries the warehouse, captures provenance (git SHA, susse version, per-source-table modification timestamps), writes `dataset.parquet` + `manifest.json` into the destination directory, and stamps the manifest with the parquet's SHA-256 hash." + ] + }, + { + "cell_type": "code", + "id": "co-45104859", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:25.513392Z", + "iopub.status.busy": "2026-05-09T00:00:25.512366Z", + "iopub.status.idle": "2026-05-09T00:00:32.746017Z", + "shell.execute_reply": "2026-05-09T00:00:32.743703Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:50.881920944Z", + "start_time": "2026-05-13T10:36:41.639231105Z" + } + }, + "source": [ + "from susse.datasets import build_and_write_snapshot\n", + "\n", + "SNAPSHOT_ROOT = (Path.cwd() / \"../../data/training_snapshots\").resolve()\n", + "SNAPSHOT_ROOT.mkdir(parents=True, exist_ok=True)\n", + "\n", + "dataset_name = \"susse_training_demo\"\n", + "dataset_version = \"v1-2024\"\n", + "snapshot_dir = SNAPSHOT_ROOT / f\"{dataset_name}_{dataset_version}\"\n", + "\n", + "snapshot = build_and_write_snapshot(\n", + " feature_service=fs,\n", + " selection=selection_v1,\n", + " date_start=date(2024, 1, 1),\n", + " date_end=date(2024, 12, 31),\n", + " locations=None,\n", + " name=dataset_name,\n", + " version=dataset_version,\n", + " dest=snapshot_dir,\n", + ")\n", + "print(f\"Wrote {snapshot.manifest.n_rows} rows x {snapshot.manifest.n_cols} cols\")\n", + "print(f\" to {snapshot_dir}\")\n", + "print(f\" hash {snapshot.manifest.content_hash[:16]}...\")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote 6776 rows x 14 cols\n", + " to /home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/data/training_snapshots/susse_training_demo_v1-2024\n", + " hash 753794249a639859...\n" + ] + } + ], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "id": "md-22767296", + "metadata": {}, + "source": [ + "## 6 — Reload from local + verify\n", + "\n", + "`load_snapshot` reads the parquet, reconstructs the manifest, and validates the parquet's SHA-256 against the manifest. Tampering or corruption raises a clear error before training silently consumes broken data." + ] + }, + { + "cell_type": "code", + "id": "co-46538051", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:32.751737Z", + "iopub.status.busy": "2026-05-09T00:00:32.751334Z", + "iopub.status.idle": "2026-05-09T00:00:32.899132Z", + "shell.execute_reply": "2026-05-09T00:00:32.897185Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:51.104169634Z", + "start_time": "2026-05-13T10:36:50.983433807Z" + } + }, + "source": [ + "from susse.datasets import load_snapshot\n", + "\n", + "reloaded = load_snapshot(snapshot_dir)\n", + "pd.testing.assert_frame_equal(reloaded.df, snapshot.df)\n", + "print(f\"Roundtrip OK. {reloaded.manifest.name} {reloaded.manifest.version}\")\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Roundtrip OK. susse_training_demo v1-2024\n" + ] + } + ], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "id": "md-64571449", + "metadata": {}, + "source": [ + "## 7 — Inspect the manifest\n", + "\n", + "The manifest is the single source of provenance truth for the snapshot. Every field is JSON-serialisable and survives the W&B upload as `manifest.json` *inside* the artifact (a subset is also mirrored into the artifact's `metadata` for the W&B UI)." + ] + }, + { + "cell_type": "code", + "id": "co-63034158", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:32.903213Z", + "iopub.status.busy": "2026-05-09T00:00:32.902691Z", + "iopub.status.idle": "2026-05-09T00:00:32.939204Z", + "shell.execute_reply": "2026-05-09T00:00:32.936803Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:51.184101013Z", + "start_time": "2026-05-13T10:36:51.107541691Z" + } + }, + "source": [ + "import json\n", + "print(json.dumps(reloaded.manifest.to_dict(), indent=2)[:2200])\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"name\": \"susse_training_demo\",\n", + " \"version\": \"v1-2024\",\n", + " \"created_at_utc\": \"2026-05-13T10:36:50.703206+00:00\",\n", + " \"susse_version\": \"0.1.dev402+g2f3975185\",\n", + " \"git_sha\": \"9dae482d4217ac6c8e52ed806c94f237d6008f56\",\n", + " \"feature_selection\": {\n", + " \"nasa_variable_ids\": [\n", + " \"aod_550\",\n", + " \"precipitable_water\",\n", + " \"cloud_amount\",\n", + " \"clearness_index\"\n", + " ],\n", + " \"cams_variable_ids\": [\n", + " \"ghi_clear\"\n", + " ],\n", + " \"merra_variable_ids\": [],\n", + " \"modis_variable_ids\": [],\n", + " \"include_satellite_irradiance\": [\n", + " \"NASA\",\n", + " \"CAMS\"\n", + " ],\n", + " \"include_satellite_bands\": [\n", + " \"ghi\"\n", + " ],\n", + " \"qc_levels\": [\n", + " \"pass\"\n", + " ]\n", + " },\n", + " \"date_start\": \"2024-01-01\",\n", + " \"date_end\": \"2024-12-31\",\n", + " \"location_filter\": null,\n", + " \"warehouse_project\": \"solar-irradiation-estimation\",\n", + " \"warehouse_dataset\": \"solar_warehouse\",\n", + " \"warehouse_table_mods\": {\n", + " \"cams_daily_vars_long\": \"2026-05-11T17:09:29.099000+00:00\",\n", + " \"ground_measurements\": \"2025-10-27T12:11:17.877000+00:00\",\n", + " \"irradiance_daily\": \"2026-05-11T17:08:51.623000+00:00\",\n", + " \"nasa_daily_vars_long\": \"2026-05-11T14:57:41.087000+00:00\"\n", + " },\n", + " \"n_rows\": 6776,\n", + " \"n_cols\": 14,\n", + " \"column_names\": [\n", + " \"date\",\n", + " \"location\",\n", + " \"lat\",\n", + " \"lon\",\n", + " \"geohash5\",\n", + " \"y_ghi_kwh_m2_day\",\n", + " \"qc_level\",\n", + " \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"sat_ghi_cams_kwh_m2_day\",\n", + " \"nasa_aod_550\",\n", + " \"nasa_precipitable_water\",\n", + " \"nasa_cloud_amount\",\n", + " \"nasa_clearness_index\",\n", + " \"cams_ghi_clear\"\n", + " ],\n", + " \"content_hash\": \"753794249a63985933e3745331115d01f85042be4851c352f135101658392f39\"\n", + "}\n" + ] + } + ], + "execution_count": 26 + }, + { + "cell_type": "markdown", + "id": "md-00219479", + "metadata": {}, + "source": [ + "## 8 — Log to W&B as an artifact\n", + "\n", + "Set `LOG_TO_WANDB = True` to actually upload. Default is `False` so the notebook is safe to re-execute without hitting the W&B API on every cell run.\n", + "\n", + "The upload creates a `dataset_build` run that records the recipe + warehouse state in its config, then attaches the snapshot directory as an artifact of type `training_dataset`. W&B assigns its own `v0`, `v1`, ... versioning on top of our human-readable `manifest.version`." + ] + }, + { + "cell_type": "code", + "id": "co-64781738", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:32.943264Z", + "iopub.status.busy": "2026-05-09T00:00:32.942938Z", + "iopub.status.idle": "2026-05-09T00:00:32.982221Z", + "shell.execute_reply": "2026-05-09T00:00:32.979907Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:51.258248725Z", + "start_time": "2026-05-13T10:36:51.187377520Z" + } + }, + "source": [ + "LOG_TO_WANDB = False # flip to True to upload\n", + "WANDB_PROJECT = \"susse\"\n", + "WANDB_ENTITY = None # None = your default entity from `wandb login`\n", + "\n", + "if LOG_TO_WANDB:\n", + " from susse.datasets import log_dataset_artifact\n", + " artifact_ref = log_dataset_artifact(\n", + " snapshot_dir,\n", + " artifact_name=dataset_name,\n", + " project=WANDB_PROJECT,\n", + " entity=WANDB_ENTITY,\n", + " aliases=(dataset_version,),\n", + " )\n", + " print(f\"Logged: {artifact_ref}\")\n", + "else:\n", + " artifact_ref = None\n", + " print(\"LOG_TO_WANDB=False — skipping upload. Set True to push.\")\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LOG_TO_WANDB=False — skipping upload. Set True to push.\n" + ] + } + ], + "execution_count": 27 + }, + { + "cell_type": "markdown", + "id": "md-28526173", + "metadata": {}, + "source": [ + "## 9 — Reload from W&B + verify\n", + "\n", + "`use_dataset_artifact` downloads the artifact contents and runs the same content-hash validation as the local loader. Inside a training run (NB 05), pass `run=` so the consumption shows up in the lineage graph." + ] + }, + { + "cell_type": "code", + "id": "co-17692815", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T00:00:32.986448Z", + "iopub.status.busy": "2026-05-09T00:00:32.986064Z", + "iopub.status.idle": "2026-05-09T00:00:33.022340Z", + "shell.execute_reply": "2026-05-09T00:00:33.020523Z" + }, + "ExecuteTime": { + "end_time": "2026-05-13T10:36:51.332282669Z", + "start_time": "2026-05-13T10:36:51.261517205Z" + } + }, + "source": [ + "DOWNLOAD_FROM_WANDB = False # flip to True after a successful upload above\n", + "\n", + "if DOWNLOAD_FROM_WANDB and artifact_ref is not None:\n", + " from susse.datasets import use_dataset_artifact\n", + " download_dest = SNAPSHOT_ROOT / f\"_wandb_download_{dataset_name}_{dataset_version}\"\n", + " redownloaded = use_dataset_artifact(artifact_ref, download_dest)\n", + " pd.testing.assert_frame_equal(redownloaded.df, reloaded.df)\n", + " print(f\"W&B roundtrip OK. {redownloaded.manifest.name} {redownloaded.manifest.version}\")\n", + "else:\n", + " print(\n", + " \"DOWNLOAD_FROM_WANDB=False or no upload yet — skipping. \"\n", + " \"Run cell 7 with LOG_TO_WANDB=True first.\"\n", + " )\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DOWNLOAD_FROM_WANDB=False or no upload yet — skipping. Run cell 7 with LOG_TO_WANDB=True first.\n" + ] + } + ], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "id": "md-17582536", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "Notebook 03 (`03_preprocessing.ipynb`) takes a `TrainingDataset` and applies preprocessing: clear-sky-index features (`kt = GHI / GHI_clear`), missing-value handling, train/val splits. The `Preprocessor` it produces will be stored alongside the model so inference uses identical transformations.\n", + "\n", + "The **versioning discipline** to follow as the warehouse grows:\n", + "\n", + "| Change | Action |\n", + "|---|---|\n", + "| Bug fix to one variable's curation | Apply migration → re-run recipe → bump materialisation version (`v1.1`) |\n", + "| New ground stations ingested | Re-run recipe → bump version (`v2`) |\n", + "| Add a new aux variable | New `FeatureSelection` → new artifact family |\n", + "| Smaller subset for an ablation | Derive a child recipe → separate artifact name |\n", + "\n", + "The recipe itself never gets edited in place — that would silently change what `v1` means. New variants get new names." + ] + } + ], + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/03_preprocessing.ipynb b/notebooks/tutorial/03_preprocessing.ipynb new file mode 100644 index 00000000..a8015207 --- /dev/null +++ b/notebooks/tutorial/03_preprocessing.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-67575976", + "metadata": {}, + "source": [ + "# 03 — Preprocessing\n", + "\n", + "Notebook 02 produced **`TrainingDataset`s** — versioned snapshots of raw warehouse rows. This notebook turns one of those into a **`PreprocessedDataset`** ready to feed into a model.\n", + "\n", + "## What preprocessing does (and what it deliberately doesn't)\n", + "\n", + "`Preprocessor` is **stateless**: applying the same `FeatureSpec` to the same `TrainingDataset` always produces the same output, with no `.fit()` / `.transform()` asymmetry. v1 transforms are deterministic:\n", + "\n", + "* **Pass-through features** — selected raw columns kept verbatim (`nasa_aod_550`, `merra_precipitable_water`, `clearness_index`, …).\n", + "* **Clear-sky index** — `kt = sat_ghi / sat_ghi_clear` for any `(ghi, ghi_clear)` pair you specify. CAMS ships `ghi_clear`, so kt against the CAMS satellite estimate is a one-line derivation. NASA POWER ships its own `clearness_index` directly so we just pass it through.\n", + "* **Cyclical day-of-year** — `(doy_sin, doy_cos)`. Adjacent calendar dates sit close in feature space; Dec 31 ↔ Jan 1 doesn't have to be re-learned by the model.\n", + "* **NaN drop** — rows with missing target or features get dropped. Imputation is left to model wrappers if their data argues for it.\n", + "\n", + "What's **not** in `Preprocessor`:\n", + "\n", + "* **Scaling** (StandardScaler / RobustScaler / …) — RF / XGBoost don't need it; NN models do, and only they know which fold to fit on. Scaling lives in the model wrapper (NB 04 / 05) where it can fit on the train fold without leakage.\n", + "* **Train/val/test splitting** — splitter design lives in NB 06 (currently a planned notebook; see this folder's README for status). The plan is to defer the `random / temporal / station-LOSO / spatial-block` choice to that stage so the splitter never leaks into preprocessing.\n", + "* **Lag features and station encodings** — added when concrete needs surface in NB 04 / 05, not pre-emptively.\n", + "\n", + "## Persistence\n", + "\n", + "`FeatureSpec` is a frozen dataclass with `to_json()` / `from_json()`. It travels alongside any model artifact so inference reconstructs identical transformations from a single JSON file — no joblib, no pickle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70d908b104b64e9f865afe939fce50fd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:07.169444243Z", + "start_time": "2026-05-13T07:20:07.090611725Z" + } + }, + "outputs": [], + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "id": "cf01ce9f", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | A `TrainingDataset` snapshot from NB 02 (default: `data/training_snapshots/susse_training_demo_v1-2024/`). If absent, the notebook falls back to building a small one inline against the live warehouse. |\n", + "| **Outputs** | An in-memory `PreprocessedDataset` (X, y, feature names, source-dataset provenance) and a `FeatureSpec` JSON at `/tmp/feature_spec_v1.json`. NB 04 / 05 will pair this spec with each trained model artifact. |\n", + "| **Prereqs** | None for the loader path. The fallback path needs GCP credentials (same as NB 02). No W&B usage in this notebook. |\n", + "| **Consumed by** | NB 04 (models) reads `processed.X()` and `processed.y()`; NB 05 (training) wraps the same `Preprocessor` + `FeatureSpec` into the trained-model bundle. |" + ] + }, + { + "cell_type": "markdown", + "id": "md-75962193", + "metadata": {}, + "source": [ + "## 0 — Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-05933550", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:07.851735373Z", + "start_time": "2026-05-13T07:20:07.181700716Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:52.747324Z", + "iopub.status.busy": "2026-05-09T00:00:52.746465Z", + "iopub.status.idle": "2026-05-09T00:00:53.478877Z", + "shell.execute_reply": "2026-05-09T00:00:53.476871Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "src_path = (Path.cwd() / \"../../src\").resolve()\n", + "if str(src_path) not in sys.path:\n", + " sys.path.insert(0, str(src_path))\n", + "\n", + "print(\"Python\", sys.version.split()[0])\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-55982336", + "metadata": {}, + "source": [ + "## 1 — Load (or build) a `TrainingDataset`\n", + "\n", + "If you've run NB 02 and have a local snapshot, this loads it. Otherwise the cell builds a small one inline against the live warehouse so the rest of the notebook works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-11867428", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:12.001678946Z", + "start_time": "2026-05-13T07:20:08.044868622Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:53.490662Z", + "iopub.status.busy": "2026-05-09T00:00:53.490146Z", + "iopub.status.idle": "2026-05-09T00:00:55.473847Z", + "shell.execute_reply": "2026-05-09T00:00:55.471764Z" + } + }, + "outputs": [], + "source": [ + "from susse.datasets import (\n", + " FeatureSelection, FeatureService, TrainingDataset,\n", + " build_and_write_snapshot, load_snapshot,\n", + ")\n", + "from susse.warehouse_ops.io import BigQueryClient, TableRefs\n", + "from susse.warehouse_ops.population.types import Source\n", + "\n", + "SNAPSHOT_ROOT = (Path.cwd() / \"../../data/training_snapshots\").resolve()\n", + "preferred_snapshot = SNAPSHOT_ROOT / \"susse_training_demo_v1-2024\"\n", + "\n", + "if preferred_snapshot.exists():\n", + " dataset = load_snapshot(preferred_snapshot)\n", + " print(f\"Loaded existing snapshot: {dataset.manifest.name} {dataset.manifest.version}\")\n", + "else:\n", + " print(\"No NB 02 snapshot found - building a small one inline.\")\n", + " bq = BigQueryClient()\n", + " tables = TableRefs(config=bq.config)\n", + " fs = FeatureService(bq=bq)\n", + " selection = FeatureSelection(\n", + " nasa_variable_ids=(\"aod_550\", \"precipitable_water\", \"cloud_amount\", \"clearness_index\"),\n", + " cams_variable_ids=(\"ghi_clear\",),\n", + " include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS),\n", + " )\n", + " inline_dest = SNAPSHOT_ROOT / \"_nb03_inline_demo\"\n", + " dataset = build_and_write_snapshot(\n", + " feature_service=fs,\n", + " selection=selection,\n", + " date_start=date(2024, 1, 1),\n", + " date_end=date(2024, 12, 31),\n", + " locations=None,\n", + " name=\"nb03_inline_demo\",\n", + " version=\"v0\",\n", + " dest=inline_dest,\n", + " )\n", + " print(f\"Built {dataset.manifest.n_rows} rows x {dataset.manifest.n_cols} cols at {inline_dest}.\")\n", + "\n", + "print(\"Columns:\", list(dataset.df.columns))" + ] + }, + { + "cell_type": "markdown", + "id": "md-38759260", + "metadata": {}, + "source": [ + "## 2 — Define a `FeatureSpec`\n", + "\n", + "The recipe below is a sensible v1: aerosols + water vapour + cloudiness as pass-through features, NASA's `clearness_index` straight through (it's already kt), CAMS `kt_cams` derived from `(sat_ghi_cams, cams_ghi_clear)`, and the cyclical day-of-year encoding. All of this lives in this Python file (and therefore git) — change a feature → bump the model version.\n" + ] + }, + { + "cell_type": "markdown", + "id": "10748830", + "metadata": {}, + "source": [ + "### What goes into a `FeatureSpec`\n", + "\n", + "The four fields below cover everything the preprocessor needs to know. **Every column name you reference must already exist in the loaded dataset** — see `dataset.df.columns` in the load-cell output above for the full list. A typo raises a `ValueError` at construction.\n", + "\n", + "- **`target_column`** — the column the model will predict. Always `y_ghi_kwh_m2_day` here; that's the source-prefixed name `FeatureService` uses for the ground GHI target.\n", + "- **`feature_columns`** — raw columns kept verbatim. Pick from the `dataset.df.columns` list. Source-prefixed (`nasa_*`, `cams_*`, `merra_*`, `modis_*`) so adding a fifth source later won't collide.\n", + "- **`derived_features`** — a tuple of `DerivedFeature` instances. Each one declares its own output column(s), required input columns, and how to compute itself; the preprocessor iterates over the tuple and emits the columns in order. Concrete features available today:\n", + " - `ClearSkyIndexFeature(ghi_column, ghi_clear_column, output_column)` — derives `kt = ghi / ghi_clear` for any `(GHI, clear-sky GHI)` pair. CAMS publishes `cams_ghi_clear` so we derive `kt_cams` below; NASA POWER pre-computes its own `clearness_index` (already kt), so we pass it through in `feature_columns` instead.\n", + " - `CyclicalDayOfYearFeature()` — adds `doy_sin` and `doy_cos`. Drop from the tuple for an ablation that strips seasonality cues.\n", + " - `AltitudeFeature(provider=...)` — looks up altitude in metres for every row's `(lat, lon)` via an injected `ElevationProvider` (e.g. `PvlibElevationProvider` over Open-Elevation). Not in this v1 spec; see `notebooks/papers/katongole_2023/` for the validation analysis that exercises it.\n", + " - Adding a new derived feature is one new `DerivedFeature` subclass; `FeatureSpec` and `Preprocessor` themselves never change.\n", + "- **`id_columns`** — non-feature, non-target columns kept on `processed.df` for traceability (joining predictions back to dates / stations / coordinates). The model never sees these; `processed.X()` excludes them.\n", + "\n", + "Two persistence-relevant defaults not shown above: `dropna_target=True` and `dropna_features=True` (rows with NaN in target or any feature get dropped). Imputation isn't a `Preprocessor` concern — RF / XGBoost handle NaN natively, and any model that needs imputation can wrap it on the fold-fitting side." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-12523627", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:12.358294192Z", + "start_time": "2026-05-13T07:20:12.081265017Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:55.481397Z", + "iopub.status.busy": "2026-05-09T00:00:55.479789Z", + "iopub.status.idle": "2026-05-09T00:00:55.535905Z", + "shell.execute_reply": "2026-05-09T00:00:55.533159Z" + } + }, + "outputs": [], + "source": [ + "from susse.preprocessing import (\n", + " ClearSkyIndexFeature,\n", + " CyclicalDayOfYearFeature,\n", + " FeatureSpec,\n", + ")\n", + "\n", + "spec = FeatureSpec(\n", + " target_column=\"y_ghi_kwh_m2_day\",\n", + " feature_columns=(\n", + " \"nasa_aod_550\",\n", + " \"nasa_precipitable_water\",\n", + " \"nasa_cloud_amount\",\n", + " \"nasa_clearness_index\", # NASA's pre-computed kt — pass-through\n", + " \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"sat_ghi_cams_kwh_m2_day\",\n", + " ),\n", + " derived_features=(\n", + " ClearSkyIndexFeature(\n", + " ghi_column=\"sat_ghi_cams_kwh_m2_day\",\n", + " ghi_clear_column=\"cams_ghi_clear\",\n", + " output_column=\"kt_cams\",\n", + " ),\n", + " CyclicalDayOfYearFeature(),\n", + " ),\n", + " id_columns=(\"date\", \"location\", \"geohash5\", \"lat\", \"lon\"),\n", + ")\n", + "print(\"Output features (in order):\", spec.output_feature_names)\n", + "print(\"Target:\", spec.target_column)" + ] + }, + { + "cell_type": "markdown", + "id": "md-07589597", + "metadata": {}, + "source": [ + "## 3 — Apply the preprocessor\n", + "\n", + "`Preprocessor.apply` validates the input columns, runs the deterministic transforms, drops NaN rows per the policy, and returns a `PreprocessedDataset`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-57462031", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:12.683404018Z", + "start_time": "2026-05-13T07:20:12.366966908Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:55.544641Z", + "iopub.status.busy": "2026-05-09T00:00:55.543683Z", + "iopub.status.idle": "2026-05-09T00:00:55.696644Z", + "shell.execute_reply": "2026-05-09T00:00:55.694917Z" + } + }, + "outputs": [], + "source": [ + "from susse.preprocessing import Preprocessor\n", + "\n", + "preprocessor = Preprocessor(spec)\n", + "processed = preprocessor.apply(dataset)\n", + "\n", + "print(f\"Source dataset: {processed.source_dataset_name} {processed.source_dataset_version}\")\n", + "print(f\" content hash: {processed.source_content_hash[:16]}...\")\n", + "print(f\"Processed shape: {processed.n_rows} rows × {processed.n_features} features\")\n", + "print(f\"Feature columns: {processed.feature_columns}\")\n", + "processed.df.head(3)\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-38163333", + "metadata": {}, + "source": [ + "A quick check on dropped rows: how many input rows had missing target or features and got removed by the NaN policy?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-81787070", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:13.121167119Z", + "start_time": "2026-05-13T07:20:12.874564809Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:55.699960Z", + "iopub.status.busy": "2026-05-09T00:00:55.699668Z", + "iopub.status.idle": "2026-05-09T00:00:55.737141Z", + "shell.execute_reply": "2026-05-09T00:00:55.734218Z" + } + }, + "outputs": [], + "source": [ + "n_in = len(dataset.df)\n", + "n_out = processed.n_rows\n", + "print(f\"Input rows: {n_in:,}\")\n", + "print(f\"Output rows: {n_out:,}\")\n", + "print(f\"Dropped: {n_in - n_out:,} ({100 * (n_in - n_out) / n_in:.1f}%)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-21354269", + "metadata": {}, + "source": [ + "## 4 — Inspect the model-input slice\n", + "\n", + "`processed.X()` returns the feature DataFrame in the spec's column order. `processed.y()` returns the target series. This is the contract the model layer (NB 04) consumes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-64880193", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:14.184977076Z", + "start_time": "2026-05-13T07:20:13.948822683Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:55.742930Z", + "iopub.status.busy": "2026-05-09T00:00:55.742272Z", + "iopub.status.idle": "2026-05-09T00:00:55.834517Z", + "shell.execute_reply": "2026-05-09T00:00:55.832596Z" + } + }, + "outputs": [], + "source": [ + "X = processed.X()\n", + "y = processed.y()\n", + "\n", + "print(f\"X shape: {X.shape}\")\n", + "print(f\"y shape: {y.shape}\")\n", + "X.describe().T[[\"count\", \"mean\", \"std\", \"min\", \"max\"]]\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-62512244", + "metadata": {}, + "source": [ + "A quick sanity-check on the derived kt column: physically, daily kt for sub-Saharan Africa typically sits between ~0.3 (overcast) and ~0.85 (very clear day), with a typical mean around 0.6–0.7. Values near or above 1.0 happen during cloud-edge enhancement and are real but rare.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-88488135", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:15.416395509Z", + "start_time": "2026-05-13T07:20:14.688815927Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:55.838342Z", + "iopub.status.busy": "2026-05-09T00:00:55.838031Z", + "iopub.status.idle": "2026-05-09T00:00:56.770818Z", + "shell.execute_reply": "2026-05-09T00:00:56.769179Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 3.5))\n", + "ax.hist(processed.df[\"kt_cams\"].dropna(), bins=40, color=\"steelblue\", edgecolor=\"white\")\n", + "ax.set_xlabel(\"kt_cams\")\n", + "ax.set_ylabel(\"count\")\n", + "ax.set_title(\"Distribution of derived kt_cams (CAMS clear-sky index)\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-49770888", + "metadata": {}, + "source": [ + "And the cyclical day-of-year encoding — a quick sanity plot to confirm we're getting a circle in (sin, cos) space, not a ramp.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-00306625", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:16.944174589Z", + "start_time": "2026-05-13T07:20:16.300091696Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:56.776095Z", + "iopub.status.busy": "2026-05-09T00:00:56.774559Z", + "iopub.status.idle": "2026-05-09T00:00:57.073268Z", + "shell.execute_reply": "2026-05-09T00:00:57.071917Z" + } + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(4.5, 4.5))\n", + "ax.scatter(processed.df[\"doy_sin\"], processed.df[\"doy_cos\"], s=8, alpha=0.5)\n", + "ax.set_xlabel(\"doy_sin\")\n", + "ax.set_ylabel(\"doy_cos\")\n", + "ax.set_title(\"Cyclical day-of-year encoding\")\n", + "ax.set_aspect(\"equal\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-12714057", + "metadata": {}, + "source": [ + "## 5 — Persist the spec\n", + "\n", + "The `FeatureSpec` JSON is the only state the preprocessor needs. NB 04 / 05 will save it next to each trained model so inference reconstructs identical transformations from a single small file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-83565703", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:20:17.246486381Z", + "start_time": "2026-05-13T07:20:17.116778378Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:00:57.077220Z", + "iopub.status.busy": "2026-05-09T00:00:57.076903Z", + "iopub.status.idle": "2026-05-09T00:00:57.110147Z", + "shell.execute_reply": "2026-05-09T00:00:57.106926Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "spec_path = Path(\"/tmp/feature_spec_v1.json\")\n", + "spec_path.write_text(spec.to_json())\n", + "print(f\"Wrote {spec_path} ({spec_path.stat().st_size} bytes)\")\n", + "\n", + "# Roundtrip verification: from-json back to FeatureSpec equals the original.\n", + "recovered = FeatureSpec.from_json(spec_path.read_text())\n", + "assert recovered == spec\n", + "print(\"FeatureSpec JSON roundtrip OK.\")\n", + "print(json.dumps(recovered.to_dict(), indent=2)[:600])\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-72803405", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "* **NB 04 — `04_models.ipynb`** — `BaseRegressor` ABC, `ModelFactory`, RF baseline + a stub second model. Each model class accepts a `PreprocessedDataset` (or its `X` / `y` slices), wires its own scaling on the train fold, and persists its weights + the parent `FeatureSpec` together.\n", + "* **NB 06** — splitter design (random / temporal / station-LOSO / spatial-block) decides what \"train fold\" actually means for fitting scalers and validating models.\n", + "\n", + "The discipline going forward:\n", + "\n", + "| Change | What ripples |\n", + "|---|---|\n", + "| Add a kt feature | Append a new `ClearSkyIndexFeature` to `derived_features` → re-preprocess → retrain → bump model version |\n", + "| Add a new feature kind (e.g. distance-to-coast) | One new `DerivedFeature` subclass; `FeatureSpec` and `Preprocessor` don't change. Add to `derived_features` → re-preprocess → retrain |\n", + "| Drop a feature for an ablation | Derived `FeatureSpec` (omit the entry) → re-preprocess → train alongside |\n", + "| Switch clear-sky reference | New `ClearSkyIndexFeature` (different `ghi_clear_column`) → re-preprocess → retrain |\n", + "| Tweak NaN policy | New spec (different dropna flags) → re-preprocess; depending on data, might require a fresh `TrainingDataset` |\n", + "\n", + "The `FeatureSpec` is never edited in place — that would silently change what an existing model artifact's \"training distribution\" was." + ] + } + ], + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/04_models.ipynb b/notebooks/tutorial/04_models.ipynb new file mode 100644 index 00000000..12663cae --- /dev/null +++ b/notebooks/tutorial/04_models.ipynb @@ -0,0 +1,628 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-28976848", + "metadata": {}, + "source": [ + "# 04 \u2014 Model layer\n", + "\n", + "Notebook 03 produced **`PreprocessedDataset`** \u2014 a clean `(X, y)` ready to train on. This notebook builds the **model layer**: a typed `BaseRegressor` ABC, a `ModelFactory`, and three concrete models that exercise the abstraction's extremes.\n", + "\n", + "## Architecture\n", + "\n", + "```text\n", + "BaseRegressor[P] ABC, generic over typed params\n", + " fit(X, y) -> Self\n", + " predict(X) -> pd.Series index aligned to X.index\n", + " params: P typed, frozen, JSON-serialisable\n", + " save(dir) / load(dir) dispatched via ModelKind on disk\n", + "\n", + "BaseModelParams ABC, frozen\n", + " kind: ModelKind drives factory + persistence dispatch\n", + "\n", + "ModelKind (StrEnum)\n", + " .params_type() kind \u2192 params dataclass\n", + " .model_class() kind \u2192 BaseRegressor subclass\n", + "```\n", + "\n", + "## What each concrete model exercises\n", + "\n", + "| Model | Why it's here |\n", + "|---|---|\n", + "| `MeanBaselineRegressor` | Sanity baseline. Anything worse than this in NB 06 has a bug. No scaling, no hyperparameters worth tuning \u2014 exercises the contract at its simplest. |\n", + "| `RandomForestRegressor` | Realistic baseline. Tree-based \u2192 no scaling needed. Exercises the abstraction with a non-trivial fitted state (`joblib`-pickled forest). |\n", + "| `LinearRegressor` | Exercises the **in-wrapper scaling contract**. Fits a `StandardScaler` on the training fold + an OLS regression. Future NN wrappers will use the same pattern. |\n", + "\n", + "The architectural decision (carried over from NB 03):\n", + "\n", + "> **Scaling lives in the model wrapper, not the Preprocessor.** RF doesn't need it; Linear does. Each wrapper owns its own scaler so cross-fold leakage is impossible by construction \u2014 the `Preprocessor` stays stateless, and the train fold is the *only* data that reaches `.fit(...)`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ac92b819e9e451084740ea238391312", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:10.880382392Z", + "start_time": "2026-05-13T07:23:10.805519466Z" + } + }, + "outputs": [], + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools \u2192 Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set \u2014 trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "id": "8e40524d", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | The canonical `TrainingDataset` snapshot from NB 02 at `data/training_snapshots/susse_training_demo_v1-2024/`. The same v1 `FeatureSpec` from NB 03 is re-applied inline below, so this notebook is self-contained. |\n", + "| **Outputs** | Three on-disk model directories at `data/models/_nb04_{mean_baseline,random_forest,linear}/`, each holding `model_kind.txt`, `params.json`, and `state.joblib`. |\n", + "| **Prereqs** | None for offline use \u2014 the snapshot loader works without GCP or W&B. No environment variables required. |\n", + "| **Consumed by** | NB 05's `Trainer` reuses the exact same factory + fit/predict/score/save flow, plus a `TrainedBundle` that pairs each fitted model with its `FeatureSpec` for inference. NB 06 replaces the inline station-LOSO below with a proper splitter abstraction. |" + ] + }, + { + "cell_type": "markdown", + "id": "md-16361538", + "metadata": {}, + "source": [ + "## 0 \u2014 Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-69761720", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:11.924845788Z", + "start_time": "2026-05-13T07:23:10.884966182Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:04.637978Z", + "iopub.status.busy": "2026-05-09T00:01:04.637283Z", + "iopub.status.idle": "2026-05-09T00:01:06.021244Z", + "shell.execute_reply": "2026-05-09T00:01:06.020210Z" + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "src_path = (Path.cwd() / \"../../src\").resolve()\n", + "if str(src_path) not in sys.path:\n", + " sys.path.insert(0, str(src_path))\n", + "\n", + "print(\"Python\", sys.version.split()[0])\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-69505387", + "metadata": {}, + "source": [ + "## 1 \u2014 Load the preprocessed dataset from NB 03\n", + "\n", + "This loads the canonical v1 snapshot (built by NB 02), applies the v1 `FeatureSpec` (defined inline below \u2014 same as NB 03), and produces a `PreprocessedDataset` with `X()` and `y()` slices ready for training.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-42950000", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:14.624929788Z", + "start_time": "2026-05-13T07:23:12.134493142Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:06.026516Z", + "iopub.status.busy": "2026-05-09T00:01:06.025987Z", + "iopub.status.idle": "2026-05-09T00:01:08.121344Z", + "shell.execute_reply": "2026-05-09T00:01:08.118824Z" + } + }, + "outputs": [], + "source": [ + "from susse.datasets import load_snapshot\n", + "from susse.preprocessing import (\n", + " ClearSkyIndexFeature,\n", + " CyclicalDayOfYearFeature,\n", + " FeatureSpec,\n", + " Preprocessor,\n", + ")\n", + "\n", + "SNAPSHOT_DIR = (Path.cwd() / \"../../data/training_snapshots/susse_training_demo_v1-2024\").resolve()\n", + "dataset = load_snapshot(SNAPSHOT_DIR)\n", + "print(f\"Loaded {dataset.manifest.name} {dataset.manifest.version}: \"\n", + " f\"{dataset.manifest.n_rows} rows x {dataset.manifest.n_cols} cols\")\n", + "\n", + "spec = FeatureSpec(\n", + " target_column=\"y_ghi_kwh_m2_day\",\n", + " feature_columns=(\n", + " \"nasa_aod_550\",\n", + " \"nasa_precipitable_water\",\n", + " \"nasa_cloud_amount\",\n", + " \"nasa_clearness_index\",\n", + " \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"sat_ghi_cams_kwh_m2_day\",\n", + " ),\n", + " derived_features=(\n", + " ClearSkyIndexFeature(\n", + " ghi_column=\"sat_ghi_cams_kwh_m2_day\",\n", + " ghi_clear_column=\"cams_ghi_clear\",\n", + " output_column=\"kt_cams\",\n", + " ),\n", + " CyclicalDayOfYearFeature(),\n", + " ),\n", + ")\n", + "\n", + "processed = Preprocessor(spec).apply(dataset)\n", + "print(f\"Processed: {processed.n_rows} rows x {processed.n_features} features\")\n", + "print(f\"Feature columns: {processed.feature_columns}\")" + ] + }, + { + "cell_type": "markdown", + "id": "md-18396784", + "metadata": {}, + "source": [ + "## 2 - Station-leave-one-out split\n", + "\n", + "A random row-level split would let the model see rows from every station during training, then evaluate it on more rows from the *same* stations - the model can memorise per-site mean shifts and look better than it really is.\n", + "\n", + "Real deployment is the opposite: we run the model on points where we have **no ground truth at all**. The honest stand-in is to hold out an entire station, train on all the others, and evaluate on the held-out one. The proper splitter abstraction (random / temporal / station-LOSO / spatial-block) is decided in NB 06; here we inline a simple \"pick the station with the most 2024 days, hold it out\" rule for the demo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-78941589", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:14.882033891Z", + "start_time": "2026-05-13T07:23:14.685413328Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:08.126671Z", + "iopub.status.busy": "2026-05-09T00:01:08.125893Z", + "iopub.status.idle": "2026-05-09T00:01:08.189188Z", + "shell.execute_reply": "2026-05-09T00:01:08.187508Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Pick the held-out station automatically: whichever has the most rows\n", + "# in 2024. Keeps the demo robust to changes in which stations the\n", + "# warehouse covers, and gives the densest possible held-out time series\n", + "# for the annual-profile plot below.\n", + "rows_per_station = (\n", + " processed.df.groupby(\"location\").size().sort_values(ascending=False)\n", + ")\n", + "print(\"Rows per station (2024):\")\n", + "print(rows_per_station.head(10))\n", + "print()\n", + "\n", + "held_out_station = str(rows_per_station.idxmax())\n", + "print(f\"Held out: {held_out_station!r} ({rows_per_station.iloc[0]} rows)\")\n", + "\n", + "train_mask = processed.df[\"location\"] != held_out_station\n", + "test_mask = ~train_mask\n", + "\n", + "X_train = processed.X().loc[train_mask]\n", + "y_train = processed.y().loc[train_mask]\n", + "X_test = processed.X().loc[test_mask]\n", + "y_test = processed.y().loc[test_mask]\n", + "\n", + "print(f\"Train: {len(X_train):,} rows from \"\n", + " f\"{processed.df.loc[train_mask, 'location'].nunique()} stations\")\n", + "print(f\"Test: {len(X_test):,} rows from {held_out_station} only\")\n", + "print(f\"y_train mean = {y_train.mean():.3f} kWh/m2/day \"\n", + " f\"(std = {y_train.std():.3f})\")" + ] + }, + { + "cell_type": "markdown", + "id": "md-07874045", + "metadata": {}, + "source": [ + "## 3 \u2014 Train all three models via `ModelFactory`\n", + "\n", + "Each call follows the same shape: `params \u2192 ModelFactory.create(params) \u2192 .fit(X, y)`. The typed params dataclass is the only API surface; no `model_type=\"random_forest\"` strings, no `dict(params)` to misspell.\n" + ] + }, + { + "cell_type": "markdown", + "id": "07a95079", + "metadata": {}, + "source": [ + "### Hyperparameter choices in v1\n", + "\n", + "The values below are deliberate starting points, not tuned \u2014 proper tuning belongs in NB 06's evaluation loop.\n", + "\n", + "- **`MeanBaselineParams()`** \u2014 no hyperparameters worth tuning; predicts the train mean for every test row. Anything worse than this in NB 06 has a bug.\n", + "- **`RandomForestParams(n_estimators=200, max_depth=12, random_state=42)`** \u2014 200 trees keep predictions stable while `.fit()` stays fast on a 6k-row training set; `max_depth=12` is a heuristic ceiling against overfit on this dataset size; `random_state=42` makes the notebook reproducible.\n", + "- **`LinearParams(with_scaling=True)`** \u2014 OLS with a `StandardScaler` fitted *only* on the train fold. Scaling matters here because feature magnitudes differ widely (`kt_cams ~ 0\u20131` vs `sat_ghi ~ 0\u201310`); flipping to `with_scaling=False` would let high-magnitude features dominate the fit.\n", + "\n", + "The full inventory of available models is `list(ModelKind)` \u2014 currently `['mean_baseline', 'random_forest', 'linear']`. Adding a fourth (e.g. XGBoost) is one new enum member, one params dataclass, and one regressor subclass; the table at the top of this notebook walks through the recipe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-22309762", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:17.037130626Z", + "start_time": "2026-05-13T07:23:14.894304906Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:08.193057Z", + "iopub.status.busy": "2026-05-09T00:01:08.192626Z", + "iopub.status.idle": "2026-05-09T00:01:10.285844Z", + "shell.execute_reply": "2026-05-09T00:01:10.282316Z" + } + }, + "outputs": [], + "source": [ + "from susse.models import (\n", + " LinearParams, MeanBaselineParams, ModelFactory, RandomForestParams,\n", + ")\n", + "\n", + "mean_model = ModelFactory.create(MeanBaselineParams()).fit(X_train, y_train)\n", + "rf_model = ModelFactory.create(\n", + " RandomForestParams(n_estimators=200, max_depth=12, random_state=42)\n", + ").fit(X_train, y_train)\n", + "linear_model = ModelFactory.create(\n", + " LinearParams(with_scaling=True)\n", + ").fit(X_train, y_train)\n", + "\n", + "print(f\"Trained: {type(mean_model).__name__}, \"\n", + " f\"{type(rf_model).__name__}, \"\n", + " f\"{type(linear_model).__name__}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-02873723", + "metadata": {}, + "source": [ + "## 4 - Score on the held-out station\n", + "\n", + "These numbers reflect *generalisation to a station the model has never seen*. The mean-baseline number is a useful sanity check (it predicts the overall train mean for every day, blind to weather); the satellite estimates (NASA, CAMS) are what the model is meant to *correct*; RF and Linear are the bias-corrected outputs. The visual comparison below makes systematic biases easier to read than the table alone.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-24555260", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:17.509280333Z", + "start_time": "2026-05-13T07:23:17.109822893Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:10.293557Z", + "iopub.status.busy": "2026-05-09T00:01:10.292089Z", + "iopub.status.idle": "2026-05-09T00:01:10.678979Z", + "shell.execute_reply": "2026-05-09T00:01:10.676509Z" + } + }, + "outputs": [], + "source": [ + "def _score(\n", + " y_true: pd.Series,\n", + " y_pred: pd.Series,\n", + " *,\n", + " mask: pd.Series | None = None,\n", + ") -> tuple[float, float, float]:\n", + " if mask is not None:\n", + " y_true = y_true[mask]\n", + " y_pred = y_pred[mask]\n", + " err = y_pred - y_true\n", + " mae = err.abs().mean()\n", + " rmse = np.sqrt((err ** 2).mean())\n", + " ss_res = (err ** 2).sum()\n", + " ss_tot = ((y_true - y_true.mean()) ** 2).sum()\n", + " r2 = 1.0 - ss_res / ss_tot if ss_tot > 0 else float(\"nan\")\n", + " return mae, rmse, r2\n", + "\n", + "\n", + "nasa_pred = processed.df.loc[test_mask, \"sat_ghi_nasa_kwh_m2_day\"]\n", + "cams_pred = processed.df.loc[test_mask, \"sat_ghi_cams_kwh_m2_day\"]\n", + "\n", + "# Score every series only on rows where it (and the target) are non-null;\n", + "# some grid cells / dates can have NaN sat_ghi.\n", + "rows = []\n", + "for name, preds in [\n", + " (\"NASA satellite\", nasa_pred),\n", + " (\"CAMS satellite\", cams_pred),\n", + " (\"MeanBaseline\", mean_model.predict(X_test)),\n", + " (\"Linear\", linear_model.predict(X_test)),\n", + " (\"RandomForest\", rf_model.predict(X_test)),\n", + "]:\n", + " mask = preds.notna() & y_test.notna()\n", + " mae, rmse, r2 = _score(y_test, preds, mask=mask)\n", + " rows.append({\"model\": name, \"mae\": mae, \"rmse\": rmse, \"r2\": r2,\n", + " \"n_rows\": int(mask.sum())})\n", + "\n", + "scores = pd.DataFrame(rows).set_index(\"model\")\n", + "scores" + ] + }, + { + "cell_type": "markdown", + "id": "97e13a7c", + "metadata": {}, + "source": [ + "### Reading the score table\n", + "\n", + "- **MeanBaseline**'s negative R\u00b2 is the right sign that the LOSO holdout is doing real work \u2014 predicting the train-set mean is *worse* than a hypothetical predictor that already knew the test station's mean, because seasonality and station-specific shift differ. R\u00b2 < 0 here means \"any honest information about today's weather improves on this.\"\n", + "- **NASA / CAMS satellite** are the *raw inputs* the model is asked to bias-correct. Their MAE around 1.0 kWh/m\u00b2/day is the bar both ML models have to beat.\n", + "- **Linear** halves the satellite MAE (0.56) \u2014 most of the bias is linearly removable from the seven features we provided.\n", + "- **RandomForest** halves it again (0.37, R\u00b2 = 0.86) \u2014 the non-linear feature interactions carry real signal.\n", + "\n", + "Two cautions before reading these as final: the held-out station (`tororo`) may not be representative of every deployment region \u2014 NB 06 will fold across all stations \u2014 and station-specific biases that *are* learnable across other stations get rewarded harder by LOSO than by random splits, so this is closer to a deployment estimate than a fitted-data estimate." + ] + }, + { + "cell_type": "markdown", + "id": "md-42210604", + "metadata": {}, + "source": [ + "## 5 - Annual profile at the held-out station\n", + "\n", + "Daily GHI through the year for the held-out station: the **target line** is what the model is trying to match, NASA / CAMS are the **inputs** the model is asked to bias-correct, and the model lines (RF, Linear) are the **outputs** to compare. The mean baseline is shown as a horizontal reference - it has no information beyond the train mean.\n", + "\n", + "A 7-day rolling mean below the daily plot smooths out single-day cloudiness and makes systematic biases (which is what we're trying to remove) easier to read.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-33389263", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:19.286427138Z", + "start_time": "2026-05-13T07:23:17.638789686Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:10.687810Z", + "iopub.status.busy": "2026-05-09T00:01:10.687175Z", + "iopub.status.idle": "2026-05-09T00:01:12.180573Z", + "shell.execute_reply": "2026-05-09T00:01:12.177926Z" + } + }, + "outputs": [], + "source": [ + "profile = processed.df.loc[test_mask, [\"date\", \"y_ghi_kwh_m2_day\"]].copy()\n", + "profile[\"NASA satellite\"] = processed.df.loc[test_mask, \"sat_ghi_nasa_kwh_m2_day\"]\n", + "profile[\"CAMS satellite\"] = processed.df.loc[test_mask, \"sat_ghi_cams_kwh_m2_day\"]\n", + "profile[\"RandomForest\"] = rf_model.predict(X_test)\n", + "profile[\"Linear\"] = linear_model.predict(X_test)\n", + "profile[\"MeanBaseline\"] = float(y_train.mean()) # constant\n", + "profile = (\n", + " profile.assign(date=pd.to_datetime(profile[\"date\"]))\n", + " .set_index(\"date\")\n", + " .sort_index()\n", + ")\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(13, 7), sharex=True)\n", + "\n", + "# Top: daily series.\n", + "axes[0].plot(profile.index, profile[\"y_ghi_kwh_m2_day\"], color=\"black\", lw=1.6, alpha=0.9, label=\"ground truth\")\n", + "axes[0].plot(profile.index, profile[\"NASA satellite\"], color=\"tab:blue\", lw=1.0, alpha=0.6, label=\"NASA satellite\")\n", + "axes[0].plot(profile.index, profile[\"CAMS satellite\"], color=\"tab:orange\", lw=1.0, alpha=0.6, label=\"CAMS satellite\")\n", + "axes[0].plot(profile.index, profile[\"RandomForest\"], color=\"tab:green\", lw=1.4, alpha=0.85, label=\"RandomForest\")\n", + "axes[0].plot(profile.index, profile[\"Linear\"], color=\"tab:purple\", lw=1.2, alpha=0.7, ls=\"--\", label=\"Linear\")\n", + "axes[0].plot(profile.index, profile[\"MeanBaseline\"], color=\"gray\", lw=0.8, alpha=0.6, ls=\":\", label=\"MeanBaseline\")\n", + "axes[0].set_ylabel(\"GHI (kWh/m^2/day)\")\n", + "axes[0].set_title(f\"Annual GHI profile at held-out station: {held_out_station}\")\n", + "axes[0].legend(loc=\"lower center\", ncol=6, fontsize=9, frameon=False)\n", + "axes[0].grid(alpha=0.25)\n", + "\n", + "# Bottom: 7-day rolling mean.\n", + "rolling = profile.rolling(\"7D\", min_periods=3).mean()\n", + "axes[1].plot(rolling.index, rolling[\"y_ghi_kwh_m2_day\"], color=\"black\", lw=1.6, label=\"ground truth\")\n", + "axes[1].plot(rolling.index, rolling[\"NASA satellite\"], color=\"tab:blue\", lw=1.2, alpha=0.7, label=\"NASA satellite\")\n", + "axes[1].plot(rolling.index, rolling[\"CAMS satellite\"], color=\"tab:orange\", lw=1.2, alpha=0.7, label=\"CAMS satellite\")\n", + "axes[1].plot(rolling.index, rolling[\"RandomForest\"], color=\"tab:green\", lw=1.6, label=\"RandomForest\")\n", + "axes[1].plot(rolling.index, rolling[\"Linear\"], color=\"tab:purple\", lw=1.4, ls=\"--\", label=\"Linear\")\n", + "axes[1].plot(rolling.index, rolling[\"MeanBaseline\"], color=\"gray\", lw=1.0, ls=\":\", label=\"MeanBaseline\")\n", + "axes[1].set_ylabel(\"GHI (kWh/m^2/day) - 7-day mean\")\n", + "axes[1].set_xlabel(\"date (2024)\")\n", + "axes[1].grid(alpha=0.25)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "md-81224579", + "metadata": {}, + "source": [ + "## 5 \u2014 Save / load roundtrip\n", + "\n", + "Each model persists into a directory holding `model_kind.txt`, `params.json`, and `state.joblib`. The package-level `load_regressor(dir)` reads the kind tag and dispatches to the right concrete class \u2014 no caller-side knowledge of which model was trained.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-40442913", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:20.686260611Z", + "start_time": "2026-05-13T07:23:19.891083592Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:12.193418Z", + "iopub.status.busy": "2026-05-09T00:01:12.192445Z", + "iopub.status.idle": "2026-05-09T00:01:12.828559Z", + "shell.execute_reply": "2026-05-09T00:01:12.826190Z" + } + }, + "outputs": [], + "source": [ + "from susse.models import load_regressor\n", + "\n", + "MODELS_ROOT = (Path.cwd() / \"../../data/models\").resolve()\n", + "MODELS_ROOT.mkdir(parents=True, exist_ok=True)\n", + "\n", + "for name, model in [\n", + " (\"mean_baseline\", mean_model),\n", + " (\"random_forest\", rf_model),\n", + " (\"linear\", linear_model),\n", + "]:\n", + " out = MODELS_ROOT / f\"_nb04_{name}\"\n", + " model.save(out)\n", + " restored = load_regressor(out)\n", + " # Tolerance accommodates ~1-ulp drift from joblib pickling/unpickling\n", + " # of linear coefficients \u2014 well below any meaningful prediction change.\n", + " np.testing.assert_allclose(\n", + " restored.predict(X_test).values, model.predict(X_test).values,\n", + " rtol=1e-10, atol=1e-10,\n", + " )\n", + " print(f\"{name}: roundtrip OK \u2014 restored as {type(restored).__name__}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-75081077", + "metadata": {}, + "source": [ + "The persistence format is stable and human-readable \u2014 `cat model_kind.txt` and `cat params.json` are sensible operations. State (the fitted forest, the scaler + linear coefficients) lives in a single joblib blob.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "co-22133777", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T07:23:20.901380675Z", + "start_time": "2026-05-13T07:23:20.771542057Z" + }, + "execution": { + "iopub.execute_input": "2026-05-09T00:01:12.833173Z", + "iopub.status.busy": "2026-05-09T00:01:12.832771Z", + "iopub.status.idle": "2026-05-09T00:01:12.876520Z", + "shell.execute_reply": "2026-05-09T00:01:12.875297Z" + } + }, + "outputs": [], + "source": [ + "example_dir = MODELS_ROOT / \"_nb04_random_forest\"\n", + "print(\"Files in\", example_dir.name, \":\")\n", + "for f in sorted(example_dir.iterdir()):\n", + " print(f\" {f.name} ({f.stat().st_size:,} bytes)\")\n", + "print()\n", + "print(\"model_kind.txt :\", (example_dir / \"model_kind.txt\").read_text())\n", + "print(\"params.json :\")\n", + "print((example_dir / \"params.json\").read_text())\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-97655213", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "* **NB 05 \u2014 `05_training.ipynb`** \u2014 the `Trainer` that orchestrates `ModelFactory.create \u2192 fit \u2192 predict \u2192 score \u2192 wandb.log_artifact`, plus the `TrainedBundle` value object that pairs a fitted model with the `FeatureSpec` it consumes (so inference reconstructs identical transforms from a single artifact).\n", + "* **NB 06** \u2014 proper validation splitters (random / temporal / station-LOSO / spatial-block) and the metrics class.\n", + "\n", + "The discipline going forward, mirroring NB 02 / 03:\n", + "\n", + "| Change | What ripples |\n", + "|---|---|\n", + "| New hyperparameters on RF | New `RandomForestParams` instance \u2192 train \u2192 bump model artifact version |\n", + "| Add a fourth model class (e.g. XGBoost) | New `ModelKind` member + params dataclass + concrete subclass + entries in `ModelKind.params_type()` / `model_class()`. Three lines per file. |\n", + "| Switch a model from no-scaling to scaling | New params field; the wrapper's `fit/predict/save/load` already accommodates the change |\n", + "\n", + "The model layer is single-responsibility: it consumes `(X, y)` and produces predictions. It deliberately knows nothing about W&B, MLflow, or the warehouse. Wiring those concerns belongs to the trainer (NB 05).\n" + ] + } + ], + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/05_training.ipynb b/notebooks/tutorial/05_training.ipynb new file mode 100644 index 00000000..dcb4682a --- /dev/null +++ b/notebooks/tutorial/05_training.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0027206d", + "metadata": {}, + "source": [ + "# 05 — Training: Trainer + TrainedBundle + W&B opt-in\n", + "\n", + "Notebook 04 produced three concrete regressors and persisted each to a model directory. This notebook adds the **orchestration layer** that pairs every fitted model with the data it was trained against, scores it on a held-out fold, and packages everything into a single self-describing artifact.\n", + "\n", + "## What this notebook contributes\n", + "\n", + "```text\n", + "Trainer\n", + " fit(X_train, y_train) → score(val) → bundle.save(dir)\n", + " optional: wandb.init → log_metrics → log_artifact\n", + "\n", + "TrainedBundle (frozen dataclass)\n", + " regressor # the fitted model\n", + " feature_spec # the recipe that produced its inputs\n", + " metadata # provenance + scores (see below)\n", + " source_manifest # the source TrainingDataset's manifest, verbatim\n", + "\n", + "TrainingMetadata (embedded inside the bundle)\n", + " created_at_utc, susse_version, git_sha\n", + " holdout_label, splitter_name ← how the val fold was constructed\n", + " n_train_rows, n_val_rows\n", + " train_metrics, val_metrics ← ScoreSet (mae, rmse, r2, n_rows)\n", + " baseline_metrics ← raw NASA / CAMS scores on the same val fold\n", + "\n", + "Splitter (ABC)\n", + " name: str # configuration identifier\n", + " split(processed) → (train_idx, val_idx)\n", + "```\n", + "\n", + "The bundle is **fully self-describing**: dropping the directory on a new machine and calling `load_bundle(dir)` gives you back a fitted regressor plus everything needed to reproduce its inputs (the `FeatureSpec`) and its lineage (the source dataset's manifest, verbatim).\n", + "\n", + "## Two architectural decisions baked in\n", + "\n", + "* **W&B is strict opt-in.** A default `Trainer()` performs no W&B calls. Set `TrainerConfig(wandb_project=\"susse\")` to activate `wandb.init → log_metrics → log_artifact`. Notebook re-runs don't accidentally create runs.\n", + "* **The bundle embeds the source dataset's manifest verbatim.** That's what makes a bundle self-describing — you don't need the original W&B dataset artifact (or the warehouse) to understand what data went into the model. The manifest does carry a content-hash, so if the dataset was edited after training, the lineage is still detectable.\n", + "\n", + "## Splitters\n", + "\n", + "The Trainer accepts any `Splitter` ABC instance. Five concrete splitters ship in `susse.training`:\n", + "\n", + "* `RandomSplitter(val_fraction, random_state)` — uniform shuffled holdout.\n", + "* `TemporalSplitter(split_date)` — every row on or after a date goes to val.\n", + "* `StationLOSOSplitter(held_out_station)` — leave-one-station-out.\n", + "* `SpatialSpreadHoldoutSplitter(n_holdout)` — hold out the `n` stations with maximum pairwise haversine spread.\n", + "* `SpatialBlockSplitter(val_blocks, block_column)` — hold out named categorical blocks (countries, climate zones).\n", + "\n", + "This notebook uses `StationLOSOSplitter`; NB 06 walks through all five with comparable folds." + ] + }, + { + "cell_type": "code", + "id": "8d80b8e6a3324625957b70c2c0a92a0a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:08.099372727Z", + "start_time": "2026-05-27T08:05:08.044497514Z" + } + }, + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "c7b2b521", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | The canonical `TrainingDataset` snapshot from NB 02 at `data/training_snapshots/susse_training_demo_v1-2024/`. Same v1 `FeatureSpec` from NB 03 / NB 04 is reapplied inline so this notebook is self-contained. |\n", + "| **Outputs** | A bundle directory per trained model under `data/bundles/_nb05_{mean_baseline,random_forest,linear}/`, each with `model/`, `feature_spec.json`, `metadata.json`, `source_manifest.json`. |\n", + "| **Prereqs** | None for offline use — the snapshot loader works without GCP or W&B. |\n", + "| **W&B** | Strict opt-in: `WANDB_PROJECT = None` by default near the bottom of the notebook. Set it to a string and `wandb login` to enable artifact logging + lineage. |\n", + "| **Consumed by** | NB 06 (evaluation) replaces the inline LOSO splitter with the full splitter abstraction. NB 07 (inference) loads bundles via `load_bundle(...)` to serve predictions. |" + ] + }, + { + "cell_type": "markdown", + "id": "da4bcacd", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "id": "92746995", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:08.604700982Z", + "start_time": "2026-05-27T08:05:08.102362829Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "src_path = (Path.cwd() / \"../../src\").resolve()\n", + "if str(src_path) not in sys.path:\n", + " sys.path.insert(0, str(src_path))\n", + "\n", + "print(\"Python\", sys.version.split()[0])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.12.1\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "3a0c7c3c", + "metadata": {}, + "source": [ + "## 1 — Load the preprocessed dataset\n", + "\n", + "Same loader path as NB 03 / NB 04. The v1 `FeatureSpec` re-applies the same recipe so this notebook is self-contained — you don't need to have NB 03's output kernel-resident." + ] + }, + { + "cell_type": "code", + "id": "5ae013f9", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:10.022658161Z", + "start_time": "2026-05-27T08:05:08.662629860Z" + } + }, + "source": [ + "from susse.datasets import load_snapshot\n", + "from susse.preprocessing import (\n", + " ClearSkyIndexFeature,\n", + " CyclicalDayOfYearFeature,\n", + " FeatureSpec,\n", + " Preprocessor,\n", + ")\n", + "\n", + "SNAPSHOT_DIR = (Path.cwd() / \"../../data/training_snapshots/susse_training_demo_v1-2024\").resolve()\n", + "dataset = load_snapshot(SNAPSHOT_DIR)\n", + "print(f\"Loaded {dataset.manifest.name} {dataset.manifest.version}: \"\n", + " f\"{dataset.manifest.n_rows} rows x {dataset.manifest.n_cols} cols\")\n", + "\n", + "spec = FeatureSpec(\n", + " target_column=\"y_ghi_kwh_m2_day\",\n", + " feature_columns=(\n", + " \"nasa_aod_550\",\n", + " \"nasa_precipitable_water\",\n", + " \"nasa_cloud_amount\",\n", + " \"nasa_clearness_index\",\n", + " \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"sat_ghi_cams_kwh_m2_day\",\n", + " ),\n", + " derived_features=(\n", + " ClearSkyIndexFeature(\n", + " ghi_column=\"sat_ghi_cams_kwh_m2_day\",\n", + " ghi_clear_column=\"cams_ghi_clear\",\n", + " output_column=\"kt_cams\",\n", + " ),\n", + " CyclicalDayOfYearFeature(),\n", + " ),\n", + ")\n", + "\n", + "processed = Preprocessor(spec).apply(dataset)\n", + "print(f\"Processed: {processed.n_rows} rows x {processed.n_features} features\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded susse_training_demo v1-2024: 6776 rows x 14 cols\n", + "Processed: 6776 rows x 9 features\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "8bf14798", + "metadata": {}, + "source": [ + "## 2 — Define a splitter\n", + "\n", + "Same station-LOSO rule NB 04 had inlined, now expressed as a `Splitter` ABC instance. The Trainer accepts any `Splitter` subclass, so swapping in any of NB 06's other strategies (random / temporal / spatial-spread / spatial-block) is a one-line change.\n", + "\n", + "`StationLOSOSplitter.auto_pick_largest(processed)` is a classmethod factory that picks the held-out station deterministically (most rows; alphabetic tiebreak) so a re-run produces an identical val fold." + ] + }, + { + "cell_type": "code", + "id": "8b7c5c59", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:10.814643303Z", + "start_time": "2026-05-27T08:05:10.075516328Z" + } + }, + "source": [ + "from susse.training import StationLOSOSplitter\n", + "\n", + "splitter = StationLOSOSplitter.auto_pick_largest(processed)\n", + "held_out_station = splitter.held_out_station\n", + "\n", + "print(f\"Held-out station: {held_out_station!r}\")\n", + "print(f\"Splitter name: {splitter.name!r}\")\n", + "\n", + "# Sanity-check the split shape.\n", + "train_idx, val_idx = splitter(processed)\n", + "print(f\"Train: {len(train_idx):,} rows from \"\n", + " f\"{processed.df.loc[train_idx, 'location'].nunique()} stations\")\n", + "print(f\"Val: {len(val_idx):,} rows from {held_out_station} only\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Held-out station: 'kenya_location13'\n", + "Splitter name: 'station_loso[kenya_location13]'\n", + "Train: 6,142 rows from 20 stations\n", + "Val: 634 rows from kenya_location13 only\n" + ] + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "5092ff5c", + "metadata": {}, + "source": [ + "## 3 — Train one model end-to-end\n", + "\n", + "`Trainer().train(...)` takes the preprocessed dataset, the typed params, the splitter, and a free-form `holdout_label` string that ends up in the bundle's `metadata.json`. Passing `bundle_dest=` writes the bundle to disk after training; omit it for an in-memory bundle.\n", + "\n", + "W&B isn't touched here — `Trainer()` constructed without a config performs zero W&B calls." + ] + }, + { + "cell_type": "code", + "id": "6a3d3fca", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:11.486740345Z", + "start_time": "2026-05-27T08:05:10.873002696Z" + } + }, + "source": [ + "from susse.models import RandomForestParams\n", + "from susse.training import Trainer\n", + "\n", + "BUNDLES_ROOT = (Path.cwd() / \"../../data/bundles\").resolve()\n", + "BUNDLES_ROOT.mkdir(parents=True, exist_ok=True)\n", + "\n", + "rf_dest = BUNDLES_ROOT / \"_nb05_random_forest\"\n", + "trainer = Trainer()\n", + "rf_bundle = trainer.train(\n", + " processed=processed,\n", + " params=RandomForestParams(n_estimators=200, max_depth=12, random_state=42),\n", + " splitter=splitter,\n", + " holdout_label=f\"station-LOSO:{held_out_station}\",\n", + " bundle_dest=rf_dest,\n", + ")\n", + "\n", + "print(f\"Trained: {type(rf_bundle.regressor).__name__}\")\n", + "print(f\"Bundle: {rf_dest}\")\n", + "print(f\"Val MAE: {rf_bundle.metadata.val_metrics.mae:.4f} \"\n", + " f\"RMSE: {rf_bundle.metadata.val_metrics.rmse:.4f} \"\n", + " f\"R2: {rf_bundle.metadata.val_metrics.r2:.4f}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained: RandomForestRegressor\n", + "Bundle: /home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/data/bundles/_nb05_random_forest\n", + "Val MAE: 0.3729 RMSE: 0.5946 R2: 0.8583\n" + ] + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "73e91a7d", + "metadata": {}, + "source": [ + "### Bundle on-disk layout\n", + "\n", + "Every bundle holds four pieces. The model subdir is whatever `BaseRegressor.save()` writes (the same layout NB 04 demonstrated); the other three are JSON, by design — `cat metadata.json` is a useful debug operation." + ] + }, + { + "cell_type": "code", + "id": "82fbdbe1", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:11.604118233Z", + "start_time": "2026-05-27T08:05:11.545686560Z" + } + }, + "source": [ + "def show_tree(root: Path, depth: int = 2) -> None:\n", + " for path in sorted(root.rglob(\"*\")):\n", + " if len(path.relative_to(root).parts) > depth:\n", + " continue\n", + " rel = path.relative_to(root)\n", + " size = path.stat().st_size if path.is_file() else 0\n", + " kind = \"/\" if path.is_dir() else f\" ({size:>11,} bytes)\"\n", + " print(f\" {rel}{kind}\")\n", + "\n", + "show_tree(rf_dest)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " feature_spec.json ( 610 bytes)\n", + " metadata.json ( 926 bytes)\n", + " model/\n", + " model/model_kind.txt ( 13 bytes)\n", + " model/params.json ( 186 bytes)\n", + " model/state.joblib ( 32,862,193 bytes)\n", + " source_manifest.json ( 1,558 bytes)\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "ba0405d7", + "metadata": {}, + "source": [ + "## 4 — Roundtrip: `load_bundle` → predict\n", + "\n", + "The whole point of bundles: hand `load_bundle(dir)` to inference code with no compile-time knowledge of which model flavour was trained, and get back a fitted regressor whose predictions match the original byte-for-byte (modulo joblib's ~1-ulp drift on linear coefficients)." + ] + }, + { + "cell_type": "code", + "id": "71be6179", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:11.902217548Z", + "start_time": "2026-05-27T08:05:11.606770622Z" + } + }, + "source": [ + "from susse.training import load_bundle\n", + "\n", + "restored = load_bundle(rf_dest)\n", + "\n", + "# Same predictions as the in-memory bundle, on the val fold.\n", + "X_val = processed.X().loc[val_idx]\n", + "np.testing.assert_allclose(\n", + " restored.regressor.predict(X_val).values,\n", + " rf_bundle.regressor.predict(X_val).values,\n", + " rtol=1e-10, atol=1e-10,\n", + ")\n", + "# All four pieces survived the roundtrip.\n", + "assert restored.feature_spec == rf_bundle.feature_spec\n", + "assert restored.metadata == rf_bundle.metadata\n", + "assert restored.source_manifest == rf_bundle.source_manifest\n", + "print(\"Roundtrip OK.\")\n", + "print(f\" splitter_name: {restored.metadata.splitter_name}\")\n", + "print(f\" holdout_label: {restored.metadata.holdout_label}\")\n", + "print(f\" source dataset: \"\n", + " f\"{restored.source_manifest.name} {restored.source_manifest.version}\")\n", + "print(f\" source content: {restored.source_manifest.content_hash[:16]}...\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Roundtrip OK.\n", + " splitter_name: station_loso[kenya_location13]\n", + " holdout_label: station-LOSO:kenya_location13\n", + " source dataset: susse_training_demo v1-2024\n", + " source content: 753794249a639859...\n" + ] + } + ], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "ea4d3565", + "metadata": {}, + "source": [ + "## 5 — Train all three models, compare against the satellite baselines\n", + "\n", + "The Trainer also scores **the raw NASA / CAMS satellite estimates against the same val fold**, recorded in `metadata.baseline_metrics`. This gives every bundle a built-in answer to *\"is the model worth deploying over just using the satellite?\"* without re-running NB 04's inline scoring.\n", + "\n", + "The trained-model rows below should beat both satellites on MAE / RMSE; the satellite rows are recorded inside *every* bundle, so this comparison is reproducible from any bundle directory alone." + ] + }, + { + "cell_type": "code", + "id": "6f455ba7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:12.608510657Z", + "start_time": "2026-05-27T08:05:11.906330778Z" + } + }, + "source": [ + "from susse.models import LinearParams, MeanBaselineParams\n", + "\n", + "bundles: dict[str, \"TrainedBundle\"] = {}\n", + "for label, params in [\n", + " (\"mean_baseline\", MeanBaselineParams()),\n", + " (\"random_forest\", RandomForestParams(n_estimators=200, max_depth=12, random_state=42)),\n", + " (\"linear\", LinearParams(with_scaling=True)),\n", + "]:\n", + " dest = BUNDLES_ROOT / f\"_nb05_{label}\"\n", + " bundles[label] = trainer.train(\n", + " processed=processed,\n", + " params=params,\n", + " splitter=splitter,\n", + " holdout_label=f\"station-LOSO:{held_out_station}\",\n", + " bundle_dest=dest,\n", + " )\n", + "\n", + "# Tabulate val metrics for the trained models PLUS each bundle's\n", + "# embedded baseline metrics. We pull the baselines from the first\n", + "# bundle (they're identical across all three since the val fold is\n", + "# the same).\n", + "rows = []\n", + "for label, bundle in bundles.items():\n", + " s = bundle.metadata.val_metrics\n", + " rows.append({\"model\": label, \"mae\": s.mae, \"rmse\": s.rmse, \"r2\": s.r2,\n", + " \"n_rows\": s.n_rows})\n", + "for col, s in bundles[\"random_forest\"].metadata.baseline_metrics.items():\n", + " rows.append({\"model\": col, \"mae\": s.mae, \"rmse\": s.rmse, \"r2\": s.r2,\n", + " \"n_rows\": s.n_rows})\n", + "\n", + "scores = pd.DataFrame(rows).set_index(\"model\")\n", + "scores" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " mae rmse r2 n_rows\n", + "model \n", + "mean_baseline 1.332483 1.630380 -0.065287 634\n", + "random_forest 0.372898 0.594594 0.858313 634\n", + "linear 0.556482 0.812901 0.735172 634\n", + "sat_ghi_nasa_kwh_m2_day 1.037429 1.336287 0.284371 634\n", + "sat_ghi_cams_kwh_m2_day 0.981366 1.230592 0.393100 634" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "a90346ae", + "metadata": {}, + "source": [ + "## 6 — Optional: log to W&B\n", + "\n", + "Setting `WANDB_PROJECT` to a string activates the full opt-in path. The Trainer:\n", + "\n", + "1. Calls `wandb.init(job_type=\"training\", config={params + manifest snippet + git_sha + splitter_name + holdout_label})`.\n", + "2. Logs the eight scalar metrics (`train/mae`, `val/r2`, `baseline//mae`, …) in one `run.log({...})`.\n", + "3. Calls `run.use_artifact(dataset_artifact_ref)` if you pass one — that's how W&B's lineage graph learns *this model used that dataset*.\n", + "4. Calls `run.log_artifact(bundle_dir, type=\"trained_model\")` so the on-disk bundle becomes a versioned W&B artifact.\n", + "\n", + "`WANDB_PROJECT = None` by default so re-running the notebook never spams runs." + ] + }, + { + "cell_type": "code", + "id": "7a90234f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:12.852636739Z", + "start_time": "2026-05-27T08:05:12.731755306Z" + } + }, + "source": [ + "from susse.training import TrainerConfig\n", + "\n", + "WANDB_PROJECT = None # Set to e.g. \"susse\" to enable.\n", + "WANDB_ENTITY = None # None uses your default `wandb login` entity.\n", + "DATASET_ARTIFACT_REF = None # e.g. \"/susse/susse_training_demo:v1-2024\"\n", + "\n", + "if WANDB_PROJECT is not None:\n", + " wb_trainer = Trainer(TrainerConfig(\n", + " wandb_project=WANDB_PROJECT,\n", + " wandb_entity=WANDB_ENTITY,\n", + " wandb_tags=(\"nb05\", \"demo\"),\n", + " ))\n", + " rf_bundle_wb = wb_trainer.train(\n", + " processed=processed,\n", + " params=RandomForestParams(n_estimators=200, max_depth=12, random_state=42),\n", + " splitter=splitter,\n", + " holdout_label=f\"station-LOSO:{held_out_station}\",\n", + " bundle_dest=BUNDLES_ROOT / \"_nb05_random_forest_wb\",\n", + " dataset_artifact_ref=DATASET_ARTIFACT_REF,\n", + " )\n", + " print(\"W&B run completed and artifact logged.\")\n", + "else:\n", + " print(\"WANDB_PROJECT=None - skipping W&B. Set it to a string to enable.\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WANDB_PROJECT=None - skipping W&B. Set it to a string to enable.\n" + ] + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "8590b61c", + "metadata": {}, + "source": [ + "## 7 — Inference reconstruction demo\n", + "\n", + "The portal-side use case: someone hands you a bundle directory and a fresh raw row from the warehouse. Without knowing which model flavour was trained, you should be able to reproduce inference identically.\n", + "\n", + "Below: drop a single row from the held-out station's `dataset.df` (raw warehouse columns), apply the bundle's embedded `FeatureSpec` via `Preprocessor`, and ask the bundle's regressor for a prediction. The fact that the bundle carries its `feature_spec` is what makes this work — without it, you'd have no way to know which derived features (like `kt_cams`) the model expects." + ] + }, + { + "cell_type": "code", + "id": "ce1a9ba9", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-27T08:05:13.125145878Z", + "start_time": "2026-05-27T08:05:12.974350424Z" + } + }, + "source": [ + "# Pick one raw row from the warehouse-shaped TrainingDataset.\n", + "raw_row = dataset.df.loc[dataset.df[\"location\"] == held_out_station].head(1)\n", + "print(\"Raw row:\")\n", + "print(raw_row.T)\n", + "\n", + "# Wrap it in a single-row TrainingDataset-like input. We re-use the\n", + "# loaded TrainingDataset's manifest because the apply step only needs\n", + "# columns that are present in raw_row.\n", + "from susse.datasets import TrainingDataset\n", + "single_row_dataset = TrainingDataset(df=raw_row.reset_index(drop=True),\n", + " manifest=dataset.manifest)\n", + "\n", + "bundle = load_bundle(rf_dest)\n", + "processed_one = Preprocessor(bundle.feature_spec).apply(single_row_dataset)\n", + "pred = bundle.regressor.predict(processed_one.X())\n", + "truth = processed_one.y().iloc[0]\n", + "print(f\"\\nGround truth: {truth:.3f} kWh/m2/day\")\n", + "print(f\"Prediction: {pred.iloc[0]:.3f} kWh/m2/day\")\n", + "print(f\"Error: {pred.iloc[0] - truth:+.3f}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw row:\n", + " 5\n", + "date 2024-01-01\n", + "location kenya_location13\n", + "lat -1.491302\n", + "lon 37.052862\n", + "geohash5 kzdre\n", + "y_ghi_kwh_m2_day 6.311958\n", + "qc_level pass\n", + "sat_ghi_nasa_kwh_m2_day 6.252\n", + "sat_ghi_cams_kwh_m2_day 6.89512\n", + "nasa_aod_550 0.24\n", + "nasa_precipitable_water 3.26\n", + "nasa_cloud_amount 35.06\n", + "nasa_clearness_index 0.62\n", + "cams_ghi_clear 7.600264\n", + "\n", + "Ground truth: 6.312 kWh/m2/day\n", + "Prediction: 6.057 kWh/m2/day\n", + "Error: -0.255\n" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "8ede41f3", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "* **NB 06 — `06_evaluation.ipynb`** — walks through the full `Splitter` family (random / temporal / station-LOSO / spatial-spread / spatial-block) and the `Evaluator` class that compares predictions against multiple baselines under multiple metrics.\n", + "* **NB 07 — `07_inference.ipynb`** — the portal-facing API: `load_bundle(...)` plus a `predict(lat, lon, date_range)` helper that does the warehouse query → preprocess → predict pipeline.\n", + "\n", + "The discipline going forward:\n", + "\n", + "| Change | What ripples |\n", + "|---|---|\n", + "| New splitter strategy | New `Splitter` subclass; Trainer signature unchanged. |\n", + "| New model kind (e.g. XGBoost) | New `ModelKind` member + params + regressor (NB 04 covered the recipe). Trainer + bundle are kind-agnostic. |\n", + "| Dataset re-materialised (new ground truth, etc.) | Re-train against the new snapshot; the bundle's embedded `source_manifest.content_hash` distinguishes it from prior bundles. |\n", + "| W&B logging convention change | Update `Trainer._log_to_wandb`; the offline path is unaffected. |\n", + "\n", + "The bundle is the unit of inference. If something here feels missing — a metric you'd want recorded, a provenance field that should travel with the artifact — extend `TrainingMetadata`, not the call site." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/06_evaluation.ipynb b/notebooks/tutorial/06_evaluation.ipynb new file mode 100644 index 00000000..2ce455c7 --- /dev/null +++ b/notebooks/tutorial/06_evaluation.ipynb @@ -0,0 +1,1055 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "654ad5a0466e77b7", + "metadata": {}, + "source": [ + "# 06 — Evaluation: splitters, metrics, evaluator, plots\n", + "\n", + "Notebook 05 produced a `TrainedBundle` on disk. This notebook works on\n", + "that bundle alone — **no training, no warehouse queries** — and walks\n", + "through the evaluation surface a downstream notebook\n", + "(`inspection/` view, paper recomputation, deployment readiness check)\n", + "would use.\n", + "\n", + "## What this notebook contributes\n", + "\n", + "```text\n", + "Splitter (ABC + 5 concrete subclasses)\n", + " name: str # configuration identifier\n", + " split(processed) → (train_idx, val_idx)\n", + "\n", + "Metric (ABC + 7 concrete subclasses)\n", + " name: str # column header in score tables\n", + " compute(y_true, y_pred) → float\n", + "\n", + "Evaluator\n", + " score(observed, predictions, splits) → DataFrame\n", + " # one row per (split × prediction); columns: n + each metric\n", + "\n", + "plots (susse.evaluation.plots)\n", + " plot_predicted_vs_observed_panels(...)\n", + " plot_per_station_metric(...)\n", + " plot_covariate_shift_kde(...)\n", + " plot_feature_importances_top_n(...)\n", + " ...\n", + "```\n", + "\n", + "## Three architectural choices reflected here\n", + "\n", + "* **Splitter is a class, not a callable.** It carries its\n", + " configuration in fields so `bundle.metadata.splitter_name` records\n", + " what the val fold actually was — re-running with the same name\n", + " guarantees the same split.\n", + "* **Metrics are objects, not free functions.** Each\n", + " :class:`Metric` carries its display name; `Evaluator` composes them\n", + " into a score table without the call site repeating column-header\n", + " strings.\n", + "* **Evaluator is the post-training counterpart of\n", + " ``score_predictions``.** That helper produces a single\n", + " :class:`ScoreSet` on one ``(y_true, y_pred)`` pair during training;\n", + " the Evaluator scores many predictions across many splits, post-hoc.\n", + "\n", + "## Notebook structure\n", + "\n", + "| Step | What you'll see |\n", + "|---|---|\n", + "| 0 | Setup + load the NB 05 bundle + reconstruct its preprocessed dataset |\n", + "| 1 | The five concrete splitters — what each one selects |\n", + "| 2 | The seven concrete metrics — what each one means and how to read it |\n", + "| 3 | The :class:`Evaluator` — one tidy table over splits × predictions |\n", + "| 4 | Generic evaluation plots — predicted-vs-observed, per-station MBE, covariate-shift KDEs, feature importances |\n", + "| 5 | Where to go next |" + ] + }, + { + "cell_type": "markdown", + "id": "ebe61e629d27ce90", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | The NB 05 bundle at `data/bundles/_nb05_random_forest/` and the matching snapshot at `data/training_snapshots/susse_training_demo_v1-2024/`. |\n", + "| **Outputs** | None — this notebook is read-only with respect to artifacts. |\n", + "| **Prereqs** | NB 05 must have run successfully (it generates both inputs). If you cloned the repo fresh, run NB 05 first. |\n", + "| **Wall time** | Under a minute on a workstation. |" + ] + }, + { + "cell_type": "code", + "id": "5a38520f62ae4529", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:09:48.309818181Z", + "start_time": "2026-06-09T19:09:48.184404424Z" + } + }, + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "9d05dc443fb9ceea", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "id": "b968dfef8b0a5ea2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:09:50.087384592Z", + "start_time": "2026-06-09T19:09:48.341737634Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "_HERE = Path.cwd().resolve()\n", + "_PROJECT_ROOT = _HERE\n", + "while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / \"src\" / \"susse\").exists():\n", + " _PROJECT_ROOT = _PROJECT_ROOT.parent\n", + "sys.path.insert(0, str(_PROJECT_ROOT / \"src\"))\n", + "\n", + "print(f\"project root: {_PROJECT_ROOT}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "project root: /home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "75d27ad7aaf2a95c", + "metadata": {}, + "source": [ + "### Load the NB 05 bundle + reconstruct its preprocessed dataset\n", + "\n", + "The bundle gives us the trained regressor and the `FeatureSpec` that\n", + "produced its inputs. Re-applying the same `Preprocessor` to the\n", + "matching snapshot yields the very dataset NB 05 trained on — without\n", + "re-training, and without the warehouse.\n", + "\n", + "NB 05's `FeatureSpec.id_columns` doesn't include `lat` / `lon` (it\n", + "didn't need them for station-LOSO), so we enrich `processed.df` with\n", + "the station coordinates from the raw dataset before exercising the\n", + "spatial splitters in §1. This is the pattern to use whenever a\n", + "bundle's frozen `FeatureSpec` is missing an id column you need\n", + "downstream — copy the column over, don't rebuild the bundle." + ] + }, + { + "cell_type": "code", + "id": "1c27c14a4a826497", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:00.514460463Z", + "start_time": "2026-06-09T19:09:50.405634660Z" + } + }, + "source": [ + "from susse.datasets import load_snapshot\n", + "from susse.preprocessing import Preprocessor\n", + "from susse.training import load_bundle\n", + "\n", + "SNAPSHOT_DIR = (_PROJECT_ROOT / \"data\" / \"training_snapshots\"\n", + " / \"susse_training_demo_v1-2024\").resolve()\n", + "BUNDLE_DIR = (_PROJECT_ROOT / \"data\" / \"bundles\"\n", + " / \"_nb05_random_forest\").resolve()\n", + "for path in (SNAPSHOT_DIR, BUNDLE_DIR):\n", + " assert path.exists(), (\n", + " f\"Missing input: {path}\\n\"\n", + " f\"Run notebooks/tutorial/05_training.ipynb to generate it, \"\n", + " f\"then come back.\"\n", + " )\n", + "\n", + "dataset = load_snapshot(SNAPSHOT_DIR)\n", + "bundle = load_bundle(BUNDLE_DIR)\n", + "processed = Preprocessor(bundle.feature_spec).apply(dataset)\n", + "\n", + "# Enrich with per-station coordinates from the raw dataset so the\n", + "# spatial splitters in §1 can find lat/lon. The map is one-to-one on\n", + "# `location`; pandas .map fills NaN where the key is unknown (no\n", + "# rows in this dataset, so no NaN).\n", + "station_coords = dataset.df.drop_duplicates(subset=\"location\").set_index(\n", + " \"location\"\n", + ")[[\"lat\", \"lon\"]]\n", + "processed.df[\"lat\"] = processed.df[\"location\"].map(station_coords[\"lat\"])\n", + "processed.df[\"lon\"] = processed.df[\"location\"].map(station_coords[\"lon\"])\n", + "\n", + "print(f\"Bundle: {BUNDLE_DIR.name}\")\n", + "print(f\"Held-out station (NB 05 splitter): \"\n", + " f\"{bundle.metadata.splitter_name}\")\n", + "print(f\"Processed: {processed.n_rows:,} rows × \"\n", + " f\"{processed.n_features} features \"\n", + " f\"({processed.df['location'].nunique()} stations)\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bundle: _nb05_random_forest\n", + "Held-out station (NB 05 splitter): station_loso[kenya_location13]\n", + "Processed: 6,776 rows × 9 features (21 stations)\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "a7e6143d9989e73d", + "metadata": {}, + "source": [ + "## 1 — The five concrete splitters\n", + "\n", + "Every concrete splitter implements the same `(name, split)` surface:\n", + "\n", + "```text\n", + "class Splitter(ABC):\n", + " @property def name(self) -> str: ...\n", + " def split(processed) -> (train_idx, val_idx)\n", + "```\n", + "\n", + "Five strategies cover the common evaluation scenarios:\n", + "\n", + "| Splitter | What it answers |\n", + "|---|---|\n", + "| `RandomSplitter` | \"Does the model fit the joint distribution?\" — weakest baseline. |\n", + "| `TemporalSplitter` | \"Does the model generalise *forward in time*?\" |\n", + "| `StationLOSOSplitter` | \"Does the model generalise to *one new station*?\" |\n", + "| `SpatialSpreadHoldoutSplitter` | \"Does the model generalise to the *most spatially distant* stations?\" |\n", + "| `SpatialBlockSplitter` | \"Does the model generalise across a *known categorical boundary*?\" (country, climate zone, …) |\n", + "\n", + "The cell below applies each one in turn to the NB 05 dataset and\n", + "prints the resulting fold sizes." + ] + }, + { + "cell_type": "code", + "id": "e6e78b58add61eaa", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:01.184842961Z", + "start_time": "2026-06-09T19:10:00.672794807Z" + } + }, + "source": [ + "from susse.training import (\n", + " RandomSplitter,\n", + " SpatialBlockSplitter,\n", + " SpatialSpreadHoldoutSplitter,\n", + " StationLOSOSplitter,\n", + " TemporalSplitter,\n", + ")\n", + "\n", + "\n", + "def describe(splitter, processed) -> None:\n", + " train_idx, val_idx = splitter.split(processed)\n", + " train_stations = processed.df.loc[train_idx, \"location\"].nunique()\n", + " val_stations = processed.df.loc[val_idx, \"location\"].nunique()\n", + " print(\n", + " f\" {splitter.name:55s} \"\n", + " f\"train: {len(train_idx):>6,} ({train_stations} stations) \"\n", + " f\"val: {len(val_idx):>6,} ({val_stations} stations)\"\n", + " )\n", + "\n", + "\n", + "# RandomSplitter — 80/20 shuffle. Note: rows from the same station\n", + "# typically land in both folds, so this is the weakest test.\n", + "describe(RandomSplitter(val_fraction=0.2, random_state=42), processed)\n", + "\n", + "# TemporalSplitter — every 2024 row to val (note this dataset may not\n", + "# have any 2024 rows depending on the warehouse snapshot used; the\n", + "# splitter just reports zero in that case).\n", + "describe(TemporalSplitter(split_date=date(2024, 1, 1)), processed)\n", + "\n", + "# StationLOSOSplitter — held-out station = the one NB 05 chose\n", + "# (auto_pick_largest), so the fold matches NB 05's exactly.\n", + "splitter_loso = StationLOSOSplitter.auto_pick_largest(processed)\n", + "describe(splitter_loso, processed)\n", + "\n", + "# SpatialSpreadHoldoutSplitter — the two most distant stations.\n", + "describe(SpatialSpreadHoldoutSplitter(n_holdout=2), processed)\n", + "\n", + "# SpatialBlockSplitter — example: hold out the Madagascar-region\n", + "# station, whichever one(s) live there. Block column must exist in\n", + "# id_columns; in this dataset the natural block is `location` itself.\n", + "madagascar_like = [\n", + " loc for loc in processed.df[\"location\"].unique() if \"madagascar\" in loc\n", + "]\n", + "if madagascar_like:\n", + " describe(\n", + " SpatialBlockSplitter(\n", + " val_blocks=tuple(madagascar_like), block_column=\"location\",\n", + " ),\n", + " processed,\n", + " )\n", + "else:\n", + " print(\" (no madagascar-* stations in this snapshot — skipping block-splitter demo)\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " random[val=0.20,seed=42] train: 5,421 (21 stations) val: 1,355 (21 stations)\n", + " temporal[val>=2024-01-01] train: 0 (0 stations) val: 6,776 (21 stations)\n", + " station_loso[kenya_location13] train: 6,142 (20 stations) val: 634 (1 stations)\n", + " spatial_spread[n=2] train: 6,127 (19 stations) val: 649 (2 stations)\n", + " spatial_block[val=madagascar_location1,madagascar_location2] train: 6,127 (19 stations) val: 649 (2 stations)\n" + ] + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "6600f54480a12943", + "metadata": {}, + "source": [ + "## 2 — The seven concrete metrics\n", + "\n", + "Every concrete metric is a frozen, stateless dataclass implementing\n", + "`(name, compute)`. Constructing it takes no arguments today; future\n", + "variants (different normalisers, different bound conventions) can\n", + "land as new subclasses without changing the call site.\n", + "\n", + "| Metric | What it captures | Returns NaN when … |\n", + "|---|---|---|\n", + "| `RMSE` | Quadratic penalty on errors | Empty after NaN filter |\n", + "| `MAE` | Linear penalty on errors | Empty after NaN filter |\n", + "| `R²` | Fraction of variance explained | `var(y_obs) = 0` |\n", + "| `IndexOfAgreement` (Willmott 1981) | Magnitude agreement, bounded [0, 1] | Both series collapse to the same constant |\n", + "| `MeanBiasError` | Signed mean offset | Empty after NaN filter |\n", + "| `NormalisedRMSE` | RMSE as % of mean(y_obs) | `mean(y_obs) = 0` |\n", + "| `NormalisedMAE` | MAE as % of mean(y_obs) | `mean(y_obs) = 0` |\n", + "\n", + "`DEFAULT_METRICS` is the seven-metric tuple in paper-table order." + ] + }, + { + "cell_type": "code", + "id": "82ad3f3b8ca4216b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:01.938676363Z", + "start_time": "2026-06-09T19:10:01.337788502Z" + } + }, + "source": [ + "from susse.evaluation import DEFAULT_METRICS\n", + "\n", + "# Predict over the LOSO val fold so the metrics have a concrete example.\n", + "train_idx, val_idx = splitter_loso.split(processed)\n", + "y_val = processed.y().loc[val_idx]\n", + "y_pred = pd.Series(\n", + " bundle.regressor.predict(processed.X().loc[val_idx]).values,\n", + " index=val_idx,\n", + ")\n", + "\n", + "print(f\"Held-out station: {splitter_loso.held_out_station!r} \"\n", + " f\"(n={len(val_idx)})\")\n", + "print()\n", + "for metric in DEFAULT_METRICS:\n", + " value = metric.compute(y_val, y_pred)\n", + " print(f\" {metric.name:9s} = {value:8.3f}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Held-out station: 'kenya_location13' (n=634)\n", + "\n", + " RMSE = 0.595\n", + " nRMSE_% = 12.725\n", + " MAE = 0.373\n", + " nMAE_% = 7.981\n", + " MBE = 0.178\n", + " R² = 0.858\n", + " IOA = 0.960\n" + ] + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "57e31ae2414ddc8a", + "metadata": {}, + "source": [ + "## 3 — The Evaluator\n", + "\n", + "The Evaluator builds a tidy `(split × prediction)` score table over\n", + "its configured metrics in one call. The default metric tuple is\n", + "`DEFAULT_METRICS`; pass a different tuple to focus on specific\n", + "metrics.\n", + "\n", + "This cell compares the trained RF against the raw NASA + CAMS\n", + "satellite estimates on three different splits — illustrating both the\n", + "\"prediction series\" and \"split\" axes." + ] + }, + { + "cell_type": "code", + "id": "506a40add36890c6", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:02.515771885Z", + "start_time": "2026-06-09T19:10:02.048234035Z" + } + }, + "source": [ + "from susse.evaluation import Evaluator\n", + "\n", + "# Three named predictions we want to compare.\n", + "predictions = {\n", + " \"RF\": y_pred,\n", + " \"NASA\": processed.df.loc[val_idx, \"sat_ghi_nasa_kwh_m2_day\"],\n", + " \"CAMS\": processed.df.loc[val_idx, \"sat_ghi_cams_kwh_m2_day\"],\n", + "}\n", + "\n", + "# Three named splits over the *val* fold for a finer-grained look.\n", + "held_out_df = processed.df.loc[val_idx]\n", + "splits = {\n", + " \"all val rows\": val_idx,\n", + " \"2023+ only\": held_out_df.index[\n", + " pd.to_datetime(held_out_df[\"date\"]).dt.year >= 2023\n", + " ],\n", + " \"first half-year\": val_idx[: len(val_idx) // 2],\n", + "}\n", + "\n", + "table = Evaluator().score(\n", + " observed=y_val, predictions=predictions, splits=splits,\n", + ")\n", + "table.round(3)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " split prediction n RMSE nRMSE_% MAE nMAE_% MBE \\\n", + "0 all val rows RF 634 0.595 12.725 0.373 7.981 0.178 \n", + "1 all val rows NASA 634 1.336 28.599 1.037 22.203 0.845 \n", + "2 all val rows CAMS 634 1.231 26.337 0.981 21.003 0.959 \n", + "3 2023+ only RF 634 0.595 12.725 0.373 7.981 0.178 \n", + "4 2023+ only NASA 634 1.336 28.599 1.037 22.203 0.845 \n", + "5 2023+ only CAMS 634 1.231 26.337 0.981 21.003 0.959 \n", + "6 first half-year RF 317 0.608 10.938 0.309 5.553 -0.025 \n", + "7 first half-year NASA 317 0.938 16.877 0.657 11.831 0.314 \n", + "8 first half-year CAMS 317 0.998 17.967 0.726 13.075 0.686 \n", + "\n", + " R² IOA \n", + "0 0.858 0.960 \n", + "1 0.284 0.790 \n", + "2 0.393 0.865 \n", + "3 0.858 0.960 \n", + "4 0.284 0.790 \n", + "5 0.393 0.865 \n", + "6 0.823 0.947 \n", + "7 0.577 0.846 \n", + "8 0.521 0.886 " + ], + "text/html": [ + "
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splitpredictionnRMSEnRMSE_%MAEnMAE_%MBER²IOA
0all val rowsRF6340.59512.7250.3737.9810.1780.8580.960
1all val rowsNASA6341.33628.5991.03722.2030.8450.2840.790
2all val rowsCAMS6341.23126.3370.98121.0030.9590.3930.865
32023+ onlyRF6340.59512.7250.3737.9810.1780.8580.960
42023+ onlyNASA6341.33628.5991.03722.2030.8450.2840.790
52023+ onlyCAMS6341.23126.3370.98121.0030.9590.3930.865
6first half-yearRF3170.60810.9380.3095.553-0.0250.8230.947
7first half-yearNASA3170.93816.8770.65711.8310.3140.5770.846
8first half-yearCAMS3170.99817.9670.72613.0750.6860.5210.886
\n", + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "a328e187b7f34b28", + "metadata": {}, + "source": [ + "## 4 — Generic evaluation plots\n", + "\n", + "Each of these helpers lives in `susse.evaluation.plots`. Inputs are\n", + "plain DataFrames / column names — no library-specific value objects —\n", + "so the same calls work in `inspection/` notebooks and in\n", + "`papers//` validation flows without rewrites.\n", + "\n", + "Five plots in display order:\n", + "\n", + "1. Predicted-vs-observed scatter, one panel per named prediction\n", + "2. Per-station metric bar chart, with optional categorical highlight\n", + "3. Covariate-shift KDEs — training vs. inference feature distributions\n", + "4. Feature importances (top-N)\n", + "5. Training-fit time-series — observed + named comparison series, weekly-resampled\n", + "\n", + "All five plots are visual; the figures appear inline when this\n", + "notebook is run." + ] + }, + { + "cell_type": "code", + "id": "a739b38c8d32f2eb", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:03.315759729Z", + "start_time": "2026-06-09T19:10:02.782733386Z" + } + }, + "source": [ + "from susse.evaluation import MeanBiasError\n", + "from susse.evaluation.plots import (\n", + " plot_covariate_shift_kde,\n", + " plot_feature_importances_top_n,\n", + " plot_per_station_metric,\n", + " plot_predicted_vs_observed_panels,\n", + " plot_training_fit_timeseries,\n", + ")\n", + "\n", + "# Build one tidy DataFrame to feed the per-row plots.\n", + "val_df = processed.df.loc[val_idx].copy()\n", + "val_df[\"y_pred\"] = y_pred\n", + "val_df[\"seen_in_training\"] = False # only the LOSO station is in val\n", + "all_df = processed.df.copy()\n", + "all_df[\"y_pred\"] = pd.Series(\n", + " bundle.regressor.predict(processed.X()).values, index=processed.df.index\n", + ")\n", + "all_df[\"seen_in_training\"] = (\n", + " all_df[\"location\"] != splitter_loso.held_out_station\n", + ")" + ], + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "1afcec8502457d60", + "metadata": {}, + "source": [ + "### Predicted vs observed — one panel per prediction series" + ] + }, + { + "cell_type": "code", + "id": "afab0df5c6c503d2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:05.811267278Z", + "start_time": "2026-06-09T19:10:03.992752510Z" + } + }, + "source": [ + "plot_predicted_vs_observed_panels(\n", + " val_df,\n", + " observed_column=\"y_ghi_kwh_m2_day\",\n", + " predictions={\n", + " \"RF\": \"y_pred\",\n", + " \"NASA\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "20b88d773a425c9d", + "metadata": {}, + "source": [ + "### Per-station metric bar chart\n", + "\n", + "Bars sorted by metric value. `highlight_column` lets you mark a\n", + "boolean attribute — here we colour each station by whether its rows\n", + "went into training (the LOSO station is the only one that didn't, so\n", + "all bars but one are highlighted)." + ] + }, + { + "cell_type": "code", + "id": "9527ff1f73c96f87", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:07.051664362Z", + "start_time": "2026-06-09T19:10:06.311891697Z" + } + }, + "source": [ + "plot_per_station_metric(\n", + " all_df,\n", + " observed_column=\"y_ghi_kwh_m2_day\",\n", + " predicted_column=\"y_pred\",\n", + " metric=MeanBiasError(),\n", + " highlight_column=\"seen_in_training\",\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Worst (highest MBE):\n", + " MBE _highlight\n", + "location \n", + "kenya_location6 0.165 True\n", + "kenya_location13 0.178 False\n", + "kenya_location11 0.179 True\n", + "kenya_location12 0.438 True\n", + "kenya_location14 0.443 True\n", + "\n", + "Best (lowest MBE):\n", + " MBE _highlight\n", + "location \n", + "kenya_location9 -1.164 True\n", + "kenya_location2 -0.339 True\n", + "kenya_location3 -0.306 True\n", + "somalia_location1 -0.207 True\n", + "madagascar_location2 -0.085 True\n" + ] + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "5fa5f27f687f217c", + "metadata": {}, + "source": [ + "### Covariate shift — training vs val feature distributions\n", + "\n", + "Overlaid histograms. Large divergence on a high-importance feature is\n", + "a warning that the model will extrapolate at inference time." + ] + }, + { + "cell_type": "code", + "id": "ba620cfcffbe3501", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:10.905420735Z", + "start_time": "2026-06-09T19:10:07.431548948Z" + } + }, + "source": [ + "train_df = all_df.loc[all_df[\"location\"] != splitter_loso.held_out_station]\n", + "plot_covariate_shift_kde(\n", + " train_df, val_df,\n", + " features=list(processed.feature_columns)[:9],\n", + " train_label=\"Training\", val_label=f\"LOSO ({splitter_loso.held_out_station})\",\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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1dTViY2MtjrFz585mBcZ7z8dT80qfPn1My86fP29IMqKjow3DuP+55f2kni/++OOPRrt27YyWLVsaV65cMX1++fJl08XRlJQUo3jx4kZ4eLjpvPfjjz82ypQpY2pvybG8nwoVKhj/+te/0vRz3LhxpmXR0dGGJOP8+fPpbmP16tWGu7t7mr8XMuLv72/MnDnT9P727dtG2bJlzYq297p48aIhydi7d69hGIbx119/GcWKFTO7Ga1+/fpW1UWQ8zA8Ahymfv36Zu/9/f114cIFSdJ3332ndu3aqUyZMipcuLD69OmjS5cu6caNG5Kkl156SVOnTlWLFi00YcIE7dmzx2xb8+fPV+PGjVWqVCl5eXlp4cKFOnXqVKYxXbhwQefOnVO7du0s6sNvv/2mI0eOqHDhwvLy8pKXl5eKFy+umzdvmh5liI+P16BBg1StWjUVKVJE3t7eun79epp4AgMD02y/YsWKKly4cLrH6MiRI7px44bat29v2reXl5eWL19u2veQIUO0atUqBQQEaNSoUdq6datpW/3791dMTIxq1Kihl156SRs3brSozxn558/T399fkkyxHjhwQEFBQWbtg4ODzd7/9ttvmjx5sllfBg0apPPnz5t+7pUrV9bs2bM1Y8YMPfroo3rmmWceKGYAcAYNGjRQu3btVK9ePT311FNatGiRLl++LMnyHGKN2NhYNW3a1GzZve8lqU6dOnJzczO9/2cOyop/5olChQrJ29vbbHv3y93FixdX//79FRoaqq5du+qdd94xG27g+vXrGjlypGrVqqWiRYvKy8tLBw4csOo4vfXWW1q0aJF++ukn1alTx+yz1q1bKyoqSoZh6Mcff9Tjjz+uWrVq6aefftLmzZtVunRpVatWzdS+YMGCqlKlium9tceuTp06cnX9+890X19f1atXz/Tezc1NJUqUSHebZ8+eVceOHfXUU09p0KBBFu3PkjxtyTEeNGiQPvnkE928eVO3bt3SypUr9eyzz1oUAwDYSm7PqzExMSpbtqyqV6+e7ufJycmaMmWK6tWrp+LFi8vLy0sbNmxI08d/5uXUvPLPXOPr6yvp73O6+51bWqJXr15KTEzUxo0bVaRIEdPyokWLqkGDBoqKitLevXvl7u6uwYMH69dff9X169e1efNmtW7d2mxbD3os/9n31H7er+//9MMPP2jAgAFatGhRmr8X0nP16lWdP3/eLM/my5cvzfn/4cOH1atXL1WuXFne3t6moTlSf26enp7q06ePFi9eLEnavXu39u3bp/79+1vQY+RUFG3hMPdOtOXi4qKUlBSdOHFCXbp0Uf369fX5559r165dmj9/viSZZkF+7rnndOzYMfXp00d79+5VYGCg5s2bJ0latWqVRo4cqYEDB2rjxo2KiYnRgAEDLJpBuUCBAlb14fr162rcuLFiYmLMXocOHTIVFPv166eYmBi988472rp1q2JiYlSiRIk08RQqVMjiY5S6b0n65ptvzPa9f/9+07i2nTp10smTJ/Xyyy+bitEjR46UJDVq1EjHjx/XlClT9Ndff+npp5/Wk08+aVX/M4rVxcVFkqwaa/b69euaNGmSWV/27t2rw4cPy9PT09Ruy5YtcnNz04kTJ0zj3QJATubm5qZNmzbp22+/Ve3atTVv3jzVqFFDx48ftziH2MP9cpCtt2dJ7l6yZImio6PVvHlzrV69WtWrV9e2bdskSSNHjtSXX36padOm6ccff1RMTIzq1atn1XFq1aqVkpOT9emnn6b5rE2bNvrpp5/022+/KX/+/KpZs6batGmjqKiodE8m0+urcc/YdfeT3vqW/DzOnTuntm3bqnnz5lq4cKHF+7OEJce4a9eu8vDw0Jdffqmvv/5at2/ffqC/LQAgK3J7Xs3snHXWrFl655139Nprr+mHH35QTEyMQkND0/Qxs1xz7znd/c4tLfHII49oz549aeY+kZQmpxYvXtzs4qgledaaY5lePy05n928ebO6du2qt99+W3379rV4f5bo2rWr/vzzTy1atEjbt2/X9u3bJcns5/bcc89p06ZNOnPmjJYsWaKHH35YFSpUsGkccC4UbeF0du3apZSUFL311ltq1qyZqlevrnPnzqVpV65cOT3//PP64osv9Morr2jRokWSpJ9//lnNmzfXCy+8oIYNG6pq1apmA3jfT+HChVWxYkWLB/Ju1KiRDh8+LB8fH1WtWtXslXr18Oeff9ZLL72kRx55xDS52R9//GHh0chY7dq15eHhoVOnTqXZd7ly5UztSpUqpX79+unjjz/W3LlzzU7ivL291aNHDy1atEirV6/W559/rj///POBY7tXrVq1TEknVeqJdqpGjRopNjY2TV+qVq1qutto9erV+uKLLxQVFaVTp05pypQpNo8VABzBxcVFLVq00KRJk/Trr7/K3d1dX375pUU5JH/+/KZJsixRo0YN7dy502zZve+zm6W5u2HDhhozZoy2bt2qunXrauXKlab1+/fvr8cee0z16tWTn5+f2aSglmjatKm+/fZbTZs2TbNnzzb7rFWrVrp27Zrefvtt04lj6glmVFSU2rRpk6V+29LZs2fVpk0bNW7cWEuWLDG7UzczluRpS45xvnz51K9fPy1ZskRLlixRz549rb4gDgC2kJvzav369XXmzBkdOnQo3c9//vlndevWTf/617/UoEEDVa5cOcO21rrfuWVmhgwZounTp+vRRx/V5s2bzT5r3bq1fvrpJ0VGRppyaps2bfTJJ5/o0KFDTpFno6Ki1LlzZ82YMUODBw+2eL0iRYrI39/fLM/euXNHu3btMr2/dOmSYmNjNXbsWLVr1061atUy3R3+T/Xq1VNgYKAWLVrE0yx5RD5HBwDcq2rVqrp9+7bmzZunrl276ueff1ZERIRZmxEjRqhTp06qXr26Ll++rB9++EG1atWSJFWrVk3Lly/Xhg0bVKlSJX300UfauXOnKlWqZNH+J06cqOeff14+Pj7q1KmTrl27pp9//lkvvvhimra9e/fWrFmz1K1bN02ePFlly5bVyZMn9cUXX2jUqFEqW7asqlWrpo8++kiBgYFKSEjQq6++apMTmMKFC2vkyJF6+eWXlZKSopYtW+rq1av6+eef5e3trX79+mn8+PFq3Lix6tSpo6SkJK1du9Z0nObMmSN/f381bNhQrq6u+uyzz+Tn56eiRYs+cGz3eumll9SiRQvNnj1b3bp104YNG8xmA5Wk8ePHq0uXLipfvryefPJJubq66rffftO+ffs0depUnTlzRkOGDNGMGTPUsmVLLVmyRF26dFGnTp3UrFkzm8cMANll+/btioyMVIcOHeTj46Pt27fr4sWLqlWrlkU5JPViY4sWLeTh4aFixYrdd38vvviiBg0apMDAQNNdq3v27FHlypXt2c37yix3Hz9+XAsXLtSjjz6q0qVLKzY2VocPHzbd5VKtWjV98cUX6tq1q1xcXDRu3Lgs3b3UvHlzrVu3Tp06dVK+fPk0YsQISVKxYsVUv359rVixQu+9954k6aGHHtLTTz+t27dvp7kDKLulFmwrVKig2bNn6+LFi6bP/Pz8Ml3fkjxt6TF+7rnnTH9r/Pzzzw/YMwCwXm7Pq61bt9ZDDz2kJ554QnPmzFHVqlV18OBBubi4qGPHjqpWrZr++9//auvWrSpWrJjmzJmj+Ph41a5d+4H2e79zS0u9+OKLSk5OVpcuXfTtt9+qZcuWku7m1GvXrmnt2rWaPn26pLtF2yeffFL+/v4ZDgWRXX744Qd16dJFw4cP1xNPPKG4uDhJkru7u4oXL57p+sOHD9f06dNVrVo11axZU3PmzNGVK1dMnxcrVkwlSpTQwoUL5e/vr1OnTmn06NHpbuu5557TsGHDVKhQIT322GM26R+cF3fawuk0aNBAc+bM0YwZM1S3bl2tWLFC4eHhZm2Sk5M1dOhQ1apVSx07dlT16tX1/vvvS5L+/e9/6/HHH1ePHj0UFBSkS5cu6YUXXrB4//369dPcuXP1/vvvq06dOurSpYsOHz6cbtuCBQtqy5YtKl++vGl8u4EDB+rmzZvy9vaWJP3nP//R5cuX1ahRI/Xp00cvvfSSfHx8snh0zE2ZMkXjxo1TeHi46Vh88803ppNcd3d3jRkzRvXr19dDDz0kNzc3rVq1StLdou/MmTMVGBioJk2a6MSJE1q3bp1Vd+ZYqlmzZlq0aJHeeecdNWjQQBs3btTYsWPN2oSGhmrt2rXauHGjmjRpombNmuntt99WhQoVZBiG+vfvr6ZNm2rYsGGm9kOGDNG//vUv01ARAJATeXt7a8uWLXrkkUdUvXp1jR07Vm+99ZY6depkUQ556623tGnTJpUrV04NGzbMdH+9e/fWmDFjNHLkSNNQOf379zcbiia7ZZa7CxYsqIMHD+qJJ55Q9erVNXjwYA0dOlT//ve/Jd29EFmsWDE1b95cXbt2VWhoqBo1apSlWFq2bKlvvvlGY8eONQ29JN09SU5OTjbd7VO8eHHVrl1bfn5+qlGjRtY7bwObNm3SkSNHFBkZqbJly8rf39/0soQledrSY1ytWjU1b95cNWvWTDNOLgBkh7yQVz///HM1adJEvXr1Uu3atTVq1CjT3cFjx45Vo0aNFBoaqjZt2sjPz0/du3d/4H3e79zSGiNGjNCkSZP0yCOPmMbFLVasmOrVq6dSpUqpZs2aku4WclNSUhx+YVSSli1bphs3big8PNwsxz7++OMWrf/KK6+oT58+6tevn4KDg1W4cGGzgqurq6tWrVqlXbt2qW7dunr55Zc1a9asdLfVq1cv5cuXT7169XLo327IHi6GNQNsAQAAINdp3769/Pz89NFHHzk6FORwhmGoWrVqeuGFFxQWFubocADAIcirsJcTJ06oSpUq2rlzZ5YvUCPnYHgEAACAPOTGjRuKiIhQaGio3Nzc9Mknn+i7777Tpk2bHB0acriLFy9q1apViouL04ABAxwdDgBkC/IqssPt27d16dIljR07Vs2aNaNgm0cwPALyHC8vrwxfP/74o6PDc7gVK1ZkeHzq1Knj6PAAAJmoU6dOht/jK1askIuLi9atW6eHHnpIjRs31tdff63PP/9cISEhWdqfs+eNH3/88b65P7s54u+QTp06ZbjPadOm2Ww/Pj4+mjx5shYuXJjpGJAAkFOQV+9v2rRpGcbbqVOnbI3FUTk/O3L7zz//LH9/f+3cuTPNnD/IvRgeAXnOkSNHMvysTJkyeX6W42vXrik+Pj7dz/Lnz68KFSpkc0QAAGucPHlSt2/fTvczX19fFS5c2Kb7c/a88ddff+ns2bMZfl61atVsjMYxf4ecPXtWf/31V7qfFS9e3KJJVAAgryKv3t+ff/6pP//8M93PChQooDJlymRbLI7K+dQYYC8UbQEAAAAAAADAiTA8AgAAAAAAAAA4EYq2AAAAAAAAAOBEKNoCAAAAAAAAgBPJl907TElJ0blz51S4cGG5uLhk9+4BALmQYRi6du2aSpcuLVfXvHc9ktwKALAH8iv5FQBge5bm12wv2p47d07lypXL7t0CAPKA06dPq2zZso4OI9uRWwEA9kR+BQDA9jLLr9letC1cuLCku4F5e3tn9+4BALlQQkKCypUrZ8oxeQ25FQBgD+RX8isAwPYsza/ZXrRNfazE29ubxAcAsKm8+ugiuRUAYE/kV/IrAMD2Msuv2V60zUhycrJu377t6DAAp5M/f365ubk5OgwAORT5FUgf+RXAgyC/AukjvwK24xRF2+vXr+vMmTMyDMPRoQBOx8XFRWXLlpWXl5ejQwGQw5BfgYyRXwFkFfkVyBj5FbAdhxdtk5OTdebMGRUsWFClSpXKs4/eAOkxDEMXL17UmTNnVK1aNa5YArAY+RXIGPkVQFaRX4GMkV8B23J40fb27dsyDEOlSpVSgQIFHB0O4HRKlSqlEydO6Pbt2yQ9ABYjvwL3R34FkBXkV+D+yK+A7bg6OoBUXKEE0sfvBoAHwXcIkD5+NwA8CL5DgPTxuwHYjlVF24oVK8rFxSXNa+jQofaKL1vcunVLAQEBCggIkJ+fn8qWLauAgAA1b94803XHjx+vH3/88YHbWGrHjh166aWXHng7x48fV9u2bVW7dm3Vq1dPiYmJadpMnTpV5cuXV8mSJdPdxsiRI80+e+utt1SrVi3Vr19fjz32mBISEiRJmzZt0vjx4x84ZgBAzkFuJbcCAGyP/Ep+BZB3uBhWjJ5+8eJFJScnm97v27dP7du31w8//KA2bdpYtI2EhAQVKVJEV69elbe3t27evKnjx4+rUqVK8vT0lCS9vemQdb3IxMvtq1vcduLEiSpZsqSGDRtmWpacnOw0t/U/9thjmjFjhqpXt7xP6WndurWmTp2qVq1a6c8//5S3t7fy5TMfLWPnzp0qW7as6tWrpz/++MPss/3792vatGlav3696bOoqCgFBQWpQIECev3115UvXz5NnjxZktS0aVNt3ryZR4iyIL3fEQDm7s0teU16/Xem/Epu/Ru51XmQX4HMkV+zP79y7poW+TVnIb8CmbM0v1p1p22pUqXk5+dneq1du1ZVqlRR69atHzhgZ9O/f38NGTJETZs21fTp07VmzRo1bdpUDRs2VOfOnXXlyhVTu7Vr10q6eyfyxIkTFRAQoCZNmuj8+fMWtzl06JACAwPVoEEDhYWFKTAwME1MV69e1dGjR01Jb+LEiXruuef00EMPqXLlylq1apVFffv999+VP39+tWrVSpJUvHjxNElPkpo0aSJ/f/90tzFq1ChNmzbNbFmbNm1Mia1JkyY6e/as6bOHHnpI3377rUXxAQByJ3IruRUAYHvkV/IrgNwpy2Pa3rp1Sx9//LGeffbZXDtmyaVLl7R9+3a98cYbat26tbZv365ff/1VoaGhmj9/frrrlC1bVjExMerUqZM+/PBDi9uMGDFCY8eO1W+//aaCBQumu96uXbtUr149s2VHjx5VZGSkNm3apLFjx5riTn1k5t5XcnKyDh8+LC8vL3Xt2lWNGjVKk8Ays3r1agUGBqp8+fIZtlm2bJk6dOhget+oUSNt3brVqv0AAHIfcmv6yK0AIM2fP18VK1aUp6engoKCtGPHjgzbfvHFFwoMDFTRokVVqFAhBQQE6KOPPjJr079//zRD+3Xs2NHe3XAI8mv6yK8AcrK0l6gstGbNGl25ckX9+/e/b7ukpCQlJSWZ3qeOFZMTPPnkk6aC9KlTp/TUU08pPj5ef/31l4KCgtJd57HHHpMkNW7cWF999ZXFbXbt2qVu3bpJknr06KH169enWS8uLk6lSpUyW9alSxflz59fVapUMV1BLVGihGJiYjLs1507d/Tjjz8qJiZGPj4+6tixo5o0aaL27dtnuE6qxMREvfvuu/ruu+8ybPPOO+8oJSVFPXr0MC0rVaqU6cosACDvIremRW4FgLvFtbCwMEVERCgoKEhz585VaGioYmNj5ePjk6Z98eLF9cYbb6hmzZpyd3fX2rVrNWDAAPn4+Cg0NNTUrmPHjlqyZInpvYeHR7b0J7uRX9MivwLI6bJctP3Pf/6jTp06qXTp0vdtFx4erkmTJmV1Nw71z6uGL730kt544w116NBBa9eu1dKlS9NdJ/WPADc3N7Pxf61tkx5PT0/dvHkz3W3906VLl9SuXbt0t7Fr1y6VKVNGgYGBKleunCTpkUceUUxMjEWJ79ixYzpy5Ihq1aolSbp8+bLq16+vPXv2SJK+/vprLV++XJs3bzZb7+bNm4wJhJzhh/DM27QdY/84gFyK3JoWuRVOy5KcaClyJzIxZ84cDRo0SAMGDJAkRURE6JtvvtHixYs1evToNO3vnVNl+PDhWrZsmX766Sezoq2Hh4f8/PzsGrszIL+mRX7NAzh3Qy6XpeERTp48qe+++07PPfdcpm3HjBmjq1evml6nT5/Oyi4dLiEhQWXKlJFhGFq+fLnNt9+oUSN9/fXXkqTPPvss3TY1a9bUkSNHMt1W6tXK9F5ubm5q0qSJLly4oMuXLyslJUVbtmwxJbK+ffve9zGkevXqKT4+XidOnNCJEydUrFgxU9LbtWuXRo4cqf/973/y8vIyW++fyRIAAIncmorcCiCvu3Xrlnbt2qWQkBDTMldXV4WEhCg6OjrT9Q3DUGRkpGJjY/XQQw+ZfRYVFSUfHx/VqFFDQ4YM0aVLlzLcTlJSkhISEsxeORH59S7yK4CcLktF2yVLlsjHx0edO3fOtK2Hh4e8vb3NXjnRhAkT1LVrVzVp0sR0lc+W3n77bU2ePFkNGjQwzYh5r1q1aikuLi7NFUtr5cuXT9OmTdNDDz2k+vXrq1q1aurSpYskae/evaa7p8eNG6eyZcvq8uXLKlu2rObMmXPf7b722mtKSEhQly5dFBAQoKFDh5o+27Jlizp16vRAcQMAchdyK7kVACTpjz/+UHJysnx9fc2W+/r6Ki4uLsP1rl69Ki8vL7m7u6tz586aN2+e2R2YHTt21PLlyxUZGakZM2Zo8+bN6tSpU4Z3jIaHh6tIkSKmlz1yU3Ygv5JfAeQOLoZhGNaskJKSokqVKqlXr16aPn261TtMSEhQkSJFdPXqVXl7e+vmzZs6fvy4KlWqJE9PT6u3l1vcuHFDBQoUkIuLi2bNmqX4+HjNnj07Tbvp06erYsWK6tmzp81jSExM1IABA/Tpp5/adLt//PGHnnnmGW3cuNGm280r+B3JZjxikyPdm1vymvT6z3cHuRX3x++IBRgeIc/Lrvx67tw5lSlTRlu3blVwcLBp+ahRo7R582Zt37493fVSUlJ07NgxXb9+XZGRkZoyZYrWrFmTZuiEVMeOHVOVKlX03XffpftIfnrzsZQrV478eg/yK+4nW39HOHdDDmVpfrX6TtvvvvtOp06d0rPPPvtAAcLcjh071LBhQ9WrV0/ff/+9XnvttXTbvfjii3aLoVChQjZPepJ0+vRpzZw50+bbBQBHYXbrnIHcCgA5Q8mSJeXm5qb4+Hiz5fHx8fcdj9bV1VVVq1ZVQECAXnnlFT355JMKD8+4iFO5cmWVLFkyw8f2c8tTovZGfgWA7GH1RGQdOnSQlTfnwgJt2rS576yZqQoVKmSXK5X21LBhQ0eHAAA2w+zWOQe5FQByBnd3dzVu3FiRkZHq3r27pLt30UZGRmrYsGEWbyclJcXsTtl7nTlzRpcuXZK/v/+DhpynkV8BIHtYXbQFACAvY3ZrAABsLywsTP369VNgYKCaNm2quXPnmh6Bl+5OOlWmTBnTnbTh4eEKDAxUlSpVlJSUpHXr1umjjz7SggULJEnXr1/XpEmT9MQTT8jPz09Hjx7VqFGjVLVqVbP8CwCAs6JoCwCAhVJntx4z5u+xsayd3fr7779XbGysZsyYYfZZ6uzWxYoV08MPP6ypU6eqRIkS6W4nvTH3ADgxxtwDMtWjRw9dvHhR48ePV1xcnAICArR+/XrT5GSnTp2Sq+vfo/slJibqhRde0JkzZ1SgQAHVrFlTH3/8sXr06CFJcnNz0549e7Rs2TJduXJFpUuXVocOHTRlyhSeZgEA5AgUbQEAsND9Zrc+ePBghutdvXpVZcqUUVJSktzc3PT++++nmd368ccfV6VKlXT06FG9/vrr6tSpk6Kjo+Xm5pZme+Hh4Zo0aZLtOgYAgBMYNmxYhsMhREVFmb2fOnWqpk6dmuG2ChQooA0bNtgyPAAAshVFWwAA7Kxw4cKKiYkxzW4dFhamypUrm4ZO+Od4b/Xq1VP9+vVVpUoVRUVFpTu79ZgxYxQWFmZ6nzq7NQAAAAAgd6BoCwCAhR50dmtJCggI0IEDBxQeHp5mvNtU/5zdOr2irYeHB492Anb29qZDNtvWy/zFDQAAACs555+Qloz7ZQ0LxggrWbKk/vjjD7Nlly9f1tChQ7Vjxw4ZhqGOHTtqzpw58vDw0PXr1zVw4EAdOHBAd+7cUYUKFfTtt99Kkvbu3auhQ4cqPj5ehmFo+PDhGjp0aLr7/fTTT3X69Gm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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "82c1dff4f2946c44", + "metadata": {}, + "source": [ + "### Feature importances\n", + "\n", + "The helper raises a clear `TypeError` if the bundle's regressor\n", + "doesn't expose a `feature_importances` method (linear models don't);\n", + "that surfaces a bundle-vs-plot mismatch as a load-time error rather\n", + "than as silent zeros." + ] + }, + { + "cell_type": "code", + "id": "d612b6af68458cdb", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:11.473873496Z", + "start_time": "2026-06-09T19:10:10.977541028Z" + } + }, + "source": [ + "plot_feature_importances_top_n(\n", + " bundle, feature_columns=processed.feature_columns, top_n=12,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+P0+fPpX+/ftLsWLFxMzMTKysrKRixYqyefPmFD8jERFpUomkw+gfRERE3wCVSoX+/fsnu/SXMoeuXbti69atiI6O1nUoRESkp3gPNhEREREREZEWMMEmIiIiIiIi0gIm2ERERERERERawHuwiYiIiIiIiLSAPdhEREREREREWsAEm4iIiIiIiEgLjHQdAH0etVqNR48ewcLCAiqVStfhEBERERERZRoiglevXsHOzg4GBh/vp2aCnUE8evQI9vb2ug6DiIiIiIgo07p//z7y58//0elMsDMICwsLAO9XqKWlpY6jISIiIiIiyjyioqJgb2+v5GUfwwQ7g0i8LNzS0pIJNhERERERkQ781+26HOSMiIiIiIiISAuYYBMRERERERFpARNsIiIiIiIiIi1ggk1ERERERESkBUywiYiIiIiIiLSACTYRERERERGRFjDBJiIiIiIiItICJthEREREREREWsAEm4iIiIiIiEgLmGATERERERERaQETbCIiIiIiIiItMNJ1APTtcBi1R9chfJV70xrqOgQiIiIiIsrE2INNREREREREpAVMsImIiIiIiIi0gAk2ERERERERkRYwwSYiIiIiIiLSAibYRERERERERFrABJuIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCnYKFCxfCwcEBpqamqFixIk6fPv3Rutu2bYOrqyusra1hZmYGZ2dnrFmzRqNO165doVKpNF6enp5p/TGIiIiIiIgoHRnpOoBvzaZNm+Dl5YWAgABUrFgRfn5+8PDwwM2bN5EnT55k9XPkyIGxY8eiWLFiMDY2xu+//45u3bohT5488PDwUOp5enpixYoVynsTE5N0+TxERERERESUPtiD/YHZs2ejV69e6NatG0qUKIGAgABky5YNy5cvT7F+zZo10bx5cxQvXhyFChXC4MGDUbp0aRw/flyjnomJCWxtbZVX9uzZ0+PjEBERERERUTphgp3E27dvce7cObi7uytlBgYGcHd3R3Bw8H/OLyIIDAzEzZs3Ub16dY1pQUFByJMnD4oWLYq+ffvi2bNnWo+fiIiIiIiIdIeXiCfx9OlTJCQkwMbGRqPcxsYGN27c+Oh8kZGRyJcvH+Li4mBoaIhFixahbt26ynRPT0+0aNECBQsWxJ07dzBmzBjUr18fwcHBMDQ0THGZcXFxiIuLU95HRUV95acjIiIiIiKitMQEWwssLCxw8eJFREdHIzAwEF5eXnB0dETNmjUBAO3atVPqlipVCqVLl0ahQoUQFBSEOnXqpLhMX19f+Pj4pEf4REREREREpAW8RDyJXLlywdDQEOHh4Rrl4eHhsLW1/eh8BgYGcHJygrOzM4YNG4ZWrVrB19f3o/UdHR2RK1cu3L59+6N1Ro8ejcjISOV1//79L/9ARERERERElG6YYCdhbGwMFxcXBAYGKmVqtRqBgYGoVKnSZy9HrVZrXN79oQcPHuDZs2fImzfvR+uYmJjA0tJS40VERERERETfLl4i/gEvLy906dIFrq6uqFChAvz8/BATE4Nu3boBADp37ox8+fIpPdS+vr5wdXVFoUKFEBcXh71792LNmjXw9/cHAERHR8PHxwctW7aEra0t7ty5g59++glOTk4aj/EiIiIiIiKijI0J9gfatm2LJ0+ewNvbG2FhYXB2dsb+/fuVgc9CQ0NhYPC/jv+YmBj069cPDx48QNasWVGsWDGsXbsWbdu2BQAYGhri77//xqpVq/Dy5UvY2dmhXr16mDRpEp+FTUREREREpEdUIiK6DoL+W1RUFKysrBAZGZlml4s7jNqTJstNL/emNdR1CEREREREpIc+Nx/jPdhEREREREREWsAEm4iIiIiIiEgLmGATERERERERaQETbCIiIiIiIiItYIJNREREREREpAVMsImIiIiIiIi0gAk2ERERERERkRYwwSYiIiIiIiLSAibYRERERERERFrABJuIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCTURERERERKQFTLCJiIiIiIiItIAJNhEREREREZEWMMEmIiIiIiIi0gIm2ERERERERERawASbiIiIiIiISAuYYBMRERERERFpARNsIiIiIiIiIi1ggk1ERERERESkBUywiYiIiIiIiLSACTYRERERERGRFjDBJiIiIiIiItICJthEREREREREWsAEm4iIiIiIiEgLmGATERERERERaQET7BQsXLgQDg4OMDU1RcWKFXH69OmP1t22bRtcXV1hbW0NMzMzODs7Y82aNRp1RATe3t7ImzcvsmbNCnd3d9y6dSutPwYRERERERGlIybYH9i0aRO8vLwwYcIEnD9/HmXKlIGHhwciIiJSrJ8jRw6MHTsWwcHB+Pvvv9GtWzd069YNBw4cUOrMmDED8+bNQ0BAAE6dOgUzMzN4eHjgzZs36fWxiIiIiIiIKI2pRER0HcS3pGLFiihfvjwWLFgAAFCr1bC3t8fAgQMxatSoz1pGuXLl0LBhQ0yaNAkiAjs7OwwbNgzDhw8HAERGRsLGxgYrV65Eu3btPmuZUVFRsLKyQmRkJCwtLVP34f6Dw6g9abLc9HJvWkNdh0BERERERHroc/Mx9mAn8fbtW5w7dw7u7u5KmYGBAdzd3REcHPyf84sIAgMDcfPmTVSvXh0AcPfuXYSFhWks08rKChUrVvzkMuPi4hAVFaXxIiIiIiIiom8XE+wknj59ioSEBNjY2GiU29jYICws7KPzRUZGwtzcHMbGxmjYsCHmz5+PunXrAoAy35cu09fXF1ZWVsrL3t4+tR+LiIiIiIiI0gETbC2wsLDAxYsXcebMGUyZMgVeXl4ICgr6qmWOHj0akZGRyuv+/fvaCZaIiIiIiIjShJGuA/iW5MqVC4aGhggPD9coDw8Ph62t7UfnMzAwgJOTEwDA2dkZ169fh6+vL2rWrKnMFx4ejrx582os09nZ+aPLNDExgYmJyVd8GiIiIiIiIkpP7MFOwtjYGC4uLggMDFTK1Go1AgMDUalSpc9ejlqtRlxcHACgYMGCsLW11VhmVFQUTp069UXLJCIiIiIiom8be7A/4OXlhS5dusDV1RUVKlSAn58fYmJi0K1bNwBA586dkS9fPvj6+gJ4f6+0q6srChUqhLi4OOzduxdr1qyBv78/AEClUmHIkCGYPHkyChcujIIFC2L8+PGws7NDs2bNdPUxiYiIiIiISMuYYH+gbdu2ePLkCby9vREWFgZnZ2fs379fGaQsNDQUBgb/6/iPiYlBv3798ODBA2TNmhXFihXD2rVr0bZtW6XOTz/9hJiYGPTu3RsvX75E1apVsX//fpiamqb75yMiIiIiIqK0wedgZxB8DvZ/43OwiYiIiIgoLfA52ERERERERETpiAk2ERERERERkRYwwSYiIiIiIiLSAibYRERERERERFrABJuIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCTURERERERKQFepNg//nnn/jhhx9QqVIlPHz4EACwZs0aHD9+XMeRERERERERUWagFwn2b7/9Bg8PD2TNmhUXLlxAXFwcACAyMhJTp07VcXRERERERESUGehFgj158mQEBARg6dKlyJIli1JepUoVnD9/XoeRERERERERUWahFwn2zZs3Ub169WTlVlZWePnyZfoHRERERERERJmOXiTYtra2uH37drLy48ePw9HRUQcRERERERERUWajFwl2r169MHjwYJw6dQoqlQqPHj3CunXrMHz4cPTt21fX4REREREREVEmYKTrALRh1KhRUKvVqFOnDmJjY1G9enWYmJhg+PDhGDhwoK7DIyIiIiIiokxALxJslUqFsWPHYsSIEbh9+zaio6NRokQJmJub6zo0IiIiIiIiyiT0IsGOjIxEQkICcuTIgRIlSijlz58/h5GRESwtLXUYHREREREREWUGenEPdrt27bBx48Zk5Zs3b0a7du10EBERERERERFlNnqRYJ86dQq1atVKVl6zZk2cOnVKBxERERERERFRZqMXCXZcXBzi4+OTlb979w6vX7/WQURERERERESU2ehFgl2hQgUsWbIkWXlAQABcXFx0EBERERERERFlNnoxyNnkyZPh7u6OS5cuoU6dOgCAwMBAnDlzBgcPHtRxdERERERERJQZ6EUPdpUqVRAcHAx7e3ts3rwZu3fvhpOTE/7++29Uq1ZN1+ERERERERFRJqAXPdgA4OzsjHXr1uk6DCIiIiIiIsqk9CbBVqvVuH37NiIiIqBWqzWmVa9eXUdRERERERERUWahFwn2yZMn0aFDB/z7778QEY1pKpUKCQkJOoqMiIiIiIiIMgu9SLD79OkDV1dX7NmzB3nz5oVKpdJ1SERERERERJTJ6MUgZ7du3cLUqVNRvHhxWFtbw8rKSuP1pRYuXAgHBweYmpqiYsWKOH369EfrLl26FNWqVUP27NmRPXt2uLu7J6vftWtXqFQqjZenp+cXx0VERERERETfLr1IsCtWrIjbt29rZVmbNm2Cl5cXJkyYgPPnz6NMmTLw8PBAREREivWDgoLQvn17HDlyRBnJvF69enj48KFGPU9PTzx+/Fh5bdiwQSvxEhERERER0bdBLy4RHzhwIIYNG4awsDCUKlUKWbJk0ZheunTpz17W7Nmz0atXL3Tr1g0AEBAQgD179mD58uUYNWpUsvofjly+bNky/PbbbwgMDETnzp2VchMTE9ja2n7JxyIiIiIiIqIMRC8S7JYtWwIAunfvrpSpVCqIyBcNcvb27VucO3cOo0ePVsoMDAzg7u6O4ODgz1pGbGws3r17hxw5cmiUBwUFIU+ePMiePTtq166NyZMnI2fOnB9dTlxcHOLi4pT3UVFRn/X/ExERERERkW7oRYJ99+5drSzn6dOnSEhIgI2NjUa5jY0Nbty48VnLGDlyJOzs7ODu7q6UeXp6okWLFihYsCDu3LmDMWPGoH79+ggODoahoWGKy/H19YWPj0/qPwwRERERERGlK71IsAsUKKDrEAAA06ZNw8aNGxEUFARTU1OlvF27dsrfpUqVQunSpVGoUCEEBQWhTp06KS5r9OjR8PLyUt5HRUXB3t4+7YInIiIiIiKir6IXCXaia9euITQ0FG/fvtUob9KkyWfNnytXLhgaGiI8PFyjPDw8/D/vn545cyamTZuGw4cP/+c9346OjsiVKxdu37790QTbxMQEJiYmnxU3ERERERER6Z5eJNghISFo3rw5Ll++rNx7DUB5Hvbn3oNtbGwMFxcXBAYGolmzZgAAtVqNwMBADBgw4KPzzZgxA1OmTMGBAwfg6ur6n//PgwcP8OzZM+TNm/ez4iIiIiIiIqJvn148pmvw4MEoWLAgIiIikC1bNly9ehXHjh2Dq6srgoKCvmhZXl5eWLp0KVatWoXr16+jb9++iImJUUYV79y5s8YgaNOnT8f48eOxfPlyODg4ICwsDGFhYYiOjgYAREdHY8SIETh58iTu3buHwMBANG3aFE5OTvDw8NBaGxAREREREZFu6UUPdnBwMP744w/kypULBgYGMDAwQNWqVeHr64tBgwbhwoULn72stm3b4smTJ/D29kZYWBicnZ2xf/9+ZeCz0NBQGBj877yEv78/3r59i1atWmksZ8KECZg4cSIMDQ3x999/Y9WqVXj58iXs7OxQr149TJo0iZeAExERERER6RG9SLATEhJgYWEB4P191I8ePULRokVRoEAB3Lx584uXN2DAgI9eEv5hj/i9e/c+uaysWbPiwIEDXxwDERERERERZSx6kWCXLFkSly5dQsGCBVGxYkXMmDEDxsbGWLJkCRwdHXUdHhEREREREWUCepFgjxs3DjExMQCAn3/+GY0aNUK1atWQM2dObNy4UcfRERERERERUWagFwl20sHCnJyccOPGDTx//hzZs2dXRhInIiIiIiIiSkt6MYp49+7d8erVK42yHDlyIDY2Ft27d9dRVERERERERJSZ6EWCvWrVKrx+/TpZ+evXr7F69WodRERERERERESZTYa+RDwqKgoiAhHBq1evYGpqqkxLSEjA3r17kSdPHh1GSERERERERJlFhk6wra2toVKpoFKpUKRIkWTTVSoVfHx8dBAZERERERERZTYZOsE+cuQIRAS1a9fGb7/9hhw5cijTjI2NUaBAAdjZ2ekwQiIiIiIiIsosMnSCXaNGDcTHx6NLly5wdXWFvb29rkMiIiIiIiKiTCrDD3JmZGSErVu3IiEhQdehEBERERERUSaW4RNsAKhduzaOHj2q6zCIiIiIiIgoE8vQl4gnql+/PkaNGoXLly/DxcUFZmZmGtObNGmio8iIiIiIiIgos9CLBLtfv34AgNmzZyebplKpePk4ERERERERpTm9SLDVarWuQyAiIiIiIqJMTi/uwSYiIiIiIiLSNb1JsI8ePYrGjRvDyckJTk5OaNKkCf78809dh0VERERERESZhF4k2GvXroW7uzuyZcuGQYMGYdCgQciaNSvq1KmD9evX6zo8IiIiIiIiygRUIiK6DuJrFS9eHL1798bQoUM1ymfPno2lS5fi+vXrOopMe6KiomBlZYXIyEhYWlqmyf/hMGpPmiw3vdyb1lDXIRARERERkR763HxML3qwQ0JC0Lhx42TlTZo0wd27d3UQEREREREREWU2epFg29vbIzAwMFn54cOHYW9vr4OIiIiIiIiIKLPRi8d0DRs2DIMGDcLFixdRuXJlAMBff/2FlStXYu7cuTqOjoiIiIiIiDIDvUiw+/btC1tbW8yaNQubN28G8P6+7E2bNqFp06Y6jo6IiIiIiIgyA71IsAGgefPmaN68ua7DICIiIiIiokxKbxJsADh79qwyYniJEiXg4uKi44iIiIiIiIgos9CLBPvBgwdo3749/vrrL1hbWwMAXr58icqVK2Pjxo3Inz+/bgMkIiIiIiIivacXo4j37NkT7969w/Xr1/H8+XM8f/4c169fh1qtRs+ePXUdHhEREREREWUCetGDffToUZw4cQJFixZVyooWLYr58+ejWrVqOoyMiIiIiIiIMgu9SLDt7e3x7t27ZOUJCQmws7P74uUtXLgQv/zyC8LCwlCmTBnMnz8fFSpUSLHu0qVLsXr1aly5cgUA4OLigqlTp2rUFxFMmDABS5cuxcuXL1GlShX4+/ujcOHCXxwb6S+HUXt0HcJXuzetoa5DICIiIiLSGb24RPyXX37BwIEDcfbsWaXs7NmzGDx4MGbOnPlFy9q0aRO8vLwwYcIEnD9/HmXKlIGHhwciIiJSrB8UFIT27dvjyJEjCA4Ohr29PerVq4eHDx8qdWbMmIF58+YhICAAp06dgpmZGTw8PPDmzZvUfWAiIiIiIiL65qhERHQdxNfKnj07YmNjER8fDyOj953yiX+bmZlp1H3+/Pknl1WxYkWUL18eCxYsAACo1WrY29tj4MCBGDVq1H/GkpCQgOzZs2PBggXo3LkzRAR2dnYYNmwYhg8fDgCIjIyEjY0NVq5ciXbt2n3WZ4yKioKVlRUiIyNhaWn5WfN8qYzeg5rRe08zevsDGX8dEBERERGl5HPzMb24RNzPz08ry3n79i3OnTuH0aNHK2UGBgZwd3dHcHDwZy0jNjYW7969Q44cOQAAd+/eRVhYGNzd3ZU6VlZWqFixIoKDgz+aYMfFxSEuLk55HxUVlZqPREREREREROlELxLsLl26aGU5T58+RUJCAmxsbDTKbWxscOPGjc9axsiRI2FnZ6ck1GFhYcoyPlxm4rSU+Pr6wsfH50vCJyIiIiIiIh3SiwQ7UUREBCIiIqBWqzXKS5cunS7//7Rp07Bx40YEBQXB1NT0q5Y1evRoeHl5Ke+joqJgb2//tSESERERERFRGtGLBPvcuXPo0qULrl+/jg9vKVepVEhISPis5eTKlQuGhoYIDw/XKA8PD4etre0n5505cyamTZuGw4cPayT0ifOFh4cjb968Gst0dnb+6PJMTExgYmLyWXETERERERGR7unFKOLdu3dHkSJFcOLECYSEhODu3bvKKyQk5LOXY2xsDBcXFwQGBiplarUagYGBqFSp0kfnmzFjBiZNmoT9+/fD1dVVY1rBggVha2urscyoqCicOnXqk8skIiIiIiKijEUverBDQkLw22+/wcnJ6auX5eXlhS5dusDV1RUVKlSAn58fYmJi0K1bNwBA586dkS9fPvj6+gIApk+fDm9vb6xfvx4ODg7KfdXm5uYwNzeHSqXCkCFDMHnyZBQuXBgFCxbE+PHjYWdnh2bNmn11vERERERERPRt0IsEu06dOrh06ZJWEuy2bdviyZMn8Pb2RlhYGJydnbF//35lkLLQ0FAYGPyv49/f3x9v375Fq1atNJYzYcIETJw4EQDw008/ISYmBr1798bLly9RtWpV7N+//6vv0yYiIiIiIqJvh148B/vp06fo0qULKlSogJIlSyJLliwa05s0aaKjyLSHz8H+bxn9GcwZvf2BjL8OiIiIiIhSkqmegx0cHIy//voL+/btSzbtSwY5IyIiIiIiIkotvRjkbODAgfjhhx/w+PFjqNVqjReTayIiIiIiIkoPepFgP3v2DEOHDlXukyYiIiIiIiJKb3qRYLdo0QJHjhzRdRhERERERESUienFPdhFihTB6NGjcfz4cZQqVSrZIGeDBg3SUWRERERERESUWehFgr1s2TKYm5vj6NGjOHr0qMY0lUrFBJuIiIiIiIjSnF4k2Hfv3tV1CERERERERJTJ6cU92ERERERERES6lmF7sL28vDBp0iSYmZnBy8vrk3Vnz56dTlERERERERFRZpVhE+wLFy7g3bt3yt8fo1Kp0iskIiIiIiIiysQybIKd9LFcfEQXERERERER6RrvwSYiIiIiIiLSAibYRERERERERFqQYS8RJyLSNodRe3Qdwle5N62hrkMgIiIiytTYg01ERERERESkBUywiYiIiIiIiLRAbxLsNWvWoEqVKrCzs8O///4LAPDz88POnTt1HBkRERERERFlBnqRYPv7+8PLywsNGjTAy5cvkZCQAACwtraGn5+fboMjIiIiIiKiTEEvEuz58+dj6dKlGDt2LAwNDZVyV1dXXL58WYeRERERERERUWahFwn23bt3UbZs2WTlJiYmiImJ0UFERERERERElNnoRYJdsGBBXLx4MVn5/v37Ubx48fQPiIiIiIiIiDIdvXgOtpeXF/r37483b95ARHD69Gls2LABvr6+WLZsma7DIyIiIiIiokxALxLsnj17ImvWrBg3bhxiY2PRoUMH2NnZYe7cuWjXrp2uwyMiIiIiIqJMIMMn2PHx8Vi/fj08PDzQsWNHxMbGIjo6Gnny5NF1aERERERERJSJZPh7sI2MjNCnTx+8efMGAJAtWzYm10RERERERJTuMnyCDQAVKlTAhQsXdB0GERERERERZWIZ/hJxAOjXrx+GDRuGBw8ewMXFBWZmZhrTS5curaPIiIiIiIiIKLPQix7sdu3a4e7duxg0aBCqVKkCZ2dnlC1bVvn3Sy1cuBAODg4wNTVFxYoVcfr06Y/WvXr1Klq2bAkHBweoVCr4+fklqzNx4kSoVCqNV7Fixb44LiIiIiIiIvp26UUP9t27d7W2rE2bNsHLywsBAQGoWLEi/Pz84OHhgZs3b6Z4b3dsbCwcHR3RunVrDB069KPL/f7773H48GHlvZGRXjQ9ERERERER/T+9yPIKFCigtWXNnj0bvXr1Qrdu3QAAAQEB2LNnD5YvX45Ro0Ylq1++fHmUL18eAFKcnsjIyAi2trZai5OIiIiIiIi+LXqRYK9evfqT0zt37vxZy3n79i3OnTuH0aNHK2UGBgZwd3dHcHDwV8V469Yt2NnZwdTUFJUqVYKvry++++67j9aPi4tDXFyc8j4qKuqr/n8iIiIiIiJKW3qRYA8ePFjj/bt37xAbGwtjY2Nky5btsxPsp0+fIiEhATY2NhrlNjY2uHHjRqrjq1ixIlauXImiRYvi8ePH8PHxQbVq1XDlyhVYWFikOI+vry98fHxS/X8SERERERFR+tKLQc5evHih8YqOjsbNmzdRtWpVbNiwQdfhoX79+mjdujVKly4NDw8P7N27Fy9fvsTmzZs/Os/o0aMRGRmpvO7fv5+OERMREREREdGX0ose7JQULlwY06ZNww8//PDZvc+5cuWCoaEhwsPDNcrDw8O1ev+0tbU1ihQpgtu3b3+0jomJCUxMTLT2fxIREREREVHa0ose7I8xMjLCo0ePPru+sbExXFxcEBgYqJSp1WoEBgaiUqVKWosrOjoad+7cQd68ebW2TCIiIiIiItItvejB3rVrl8Z7EcHjx4+xYMECVKlS5YuW5eXlhS5dusDV1RUVKlSAn58fYmJilFHFO3fujHz58sHX1xfA+4HRrl27pvz98OFDXLx4Eebm5nBycgIADB8+HI0bN0aBAgXw6NEjTJgwAYaGhmjfvv3XfnQiIiIiIiL6RuhFgt2sWTON9yqVCrlz50bt2rUxa9asL1pW27Zt8eTJE3h7eyMsLAzOzs7Yv3+/MvBZaGgoDAz+1/H/6NEjlC1bVnk/c+ZMzJw5EzVq1EBQUBAA4MGDB2jfvj2ePXuG3Llzo2rVqjh58iRy586dug9MRERERERE3xy9SLDVarVWlzdgwAAMGDAgxWmJSXMiBwcHiMgnl7dx40ZthUZERERERETfKL24B/vnn39GbGxssvLXr1/j559/1kFERERERERElNnoRYLt4+OD6OjoZOWxsbF8ljQRERERERGlC71IsEUEKpUqWfmlS5eQI0cOHUREREREREREmU2Gvgc7e/bsUKlUUKlUKFKkiEaSnZCQgOjoaPTp00eHERIREREREVFmkaETbD8/P4gIunfvDh8fH1hZWSnTjI2N4eDgoNXnVxMRERERERF9TIZOsLt06QIAKFiwICpXrowsWbLoOCIiIiIiIiLKrDJ0gp2oRo0ayt9v3rzB27dvNaZbWlqmd0hERERERESUyejFIGexsbEYMGAA8uTJAzMzM2TPnl3jRURERERERJTW9CLBHjFiBP744w/4+/vDxMQEy5Ytg4+PD+zs7LB69Wpdh0dERERERESZgF5cIr57926sXr0aNWvWRLdu3VCtWjU4OTmhQIECWLduHTp27KjrEImIiIiIiEjP6UUP9vPnz+Ho6Ajg/f3Wz58/BwBUrVoVx44d02VoRERERERElEnoRYLt6OiIu3fvAgCKFSuGzZs3A3jfs21tba3DyIiIiIiIiCiz0IsEu1u3brh06RIAYNSoUVi4cCFMTU0xdOhQjBgxQsfRERERERERUWagF/dgDx06VPnb3d0dN27cwLlz5+Dk5ITSpUvrMDIiIiIiIiLKLPQiwU7qzZs3KFCgAAoUKKDrUIiIiIiIiCgT0YtLxBMSEjBp0iTky5cP5ubmCAkJAQCMHz8ev/76q46jIyIiIiIiosxALxLsKVOmYOXKlZgxYwaMjY2V8pIlS2LZsmU6jIyIiIiIiIgyC71IsFevXo0lS5agY8eOMDQ0VMrLlCmDGzdu6DAyIiIiIiIiyiz0IsF++PAhnJyckpWr1Wq8e/dOBxERERERERFRZqMXCXaJEiXw559/JivfunUrypYtq4OIiIiIiIiIKLPRi1HEvb290aVLFzx8+BBqtRrbtm3DzZs3sXr1avz++++6Do+IiIiIiIgyAb3owW7atCl2796Nw4cPw8zMDN7e3rh+/Tp2796NunXr6jo8IiIiIiIiygQydA92SEgIChYsCJVKhWrVquHQoUO6DomIiIiIiIgyqQzdg124cGE8efJEed+2bVuEh4frMCIiIiIiIiLKrDJ0gi0iGu/37t2LmJgYHUVDREREREREmVmGTrCJiIiIiIiIvhUZOsFWqVRQqVTJyoiIiIiIiIjSW4ZOsEUEXbt2RYsWLdCiRQu8efMGffr0Ud4nvr7UwoUL4eDgAFNTU1SsWBGnT5/+aN2rV6+iZcuWcHBwgEqlgp+f31cvk4iIiIiIiDKeDJ1gd+nSBXny5IGVlRWsrKzwww8/wM7OTnmf+PoSmzZtgpeXFyZMmIDz58+jTJky8PDwQERERIr1Y2Nj4ejoiGnTpsHW1lYryyQiIiIiIqKMJ0M/pmvFihVaX+bs2bPRq1cvdOvWDQAQEBCAPXv2YPny5Rg1alSy+uXLl0f58uUBIMXpqVkmERERERERZTwZugdb296+fYtz587B3d1dKTMwMIC7uzuCg4PTdZlxcXGIiorSeBEREREREdG3iwl2Ek+fPkVCQgJsbGw0ym1sbBAWFpauy/T19dW4zN3e3j5V/z8RERERERGlDybY36jRo0cjMjJSed2/f1/XIREREREREdEnZOh7sLUtV65cMDQ0RHh4uEZ5eHj4RwcwS6tlmpiYwMTEJFX/JxEREREREaU/9mAnYWxsDBcXFwQGBiplarUagYGBqFSp0jezTCIiIiIiIvr2sAf7A15eXujSpQtcXV1RoUIF+Pn5ISYmRhkBvHPnzsiXLx98fX0BvB/E7Nq1a8rfDx8+xMWLF2Fubg4nJ6fPWiYRERERERFlfEywP9C2bVs8efIE3t7eCAsLg7OzM/bv368MUhYaGgoDg/91/D969Ahly5ZV3s+cORMzZ85EjRo1EBQU9FnLJCIiIiIiooyPCXYKBgwYgAEDBqQ4LTFpTuTg4AAR+aplEhERERERUcbHe7CJiIiIiIiItIAJNhEREREREZEWMMEmIiIiIiIi0gIm2ERERERERERawASbiIiIiIiISAuYYBMRERERERFpARNsIiIiIiIiIi1ggk1ERERERESkBUa6DoCIiAgAHEbt0XUIX+XetIa6DoGIiIh0jD3YRERERERERFrABJuIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCTURERERERKQFTLCJiIiIiIiItIAJNhEREREREZEWMMEmIiIiIiIi0gIm2ERERERERERawASbiIiIiIiISAuYYBMRERERERFpARNsIiIiIiIiIi1ggk1ERERERESkBUywiYiIiIiIiLSACTYRERERERGRFjDBJiIiIiIiItICJthEREREREREWsAEm4iIiIiIiEgLmGCnYOHChXBwcICpqSkqVqyI06dPf7L+li1bUKxYMZiamqJUqVLYu3evxvSuXbtCpVJpvDw9PdPyIxAREREREVE6Y4L9gU2bNsHLywsTJkzA+fPnUaZMGXh4eCAiIiLF+idOnED79u3Ro0cPXLhwAc2aNUOzZs1w5coVjXqenp54/Pix8tqwYUN6fBwiIiIiIiJKJ0ywPzB79mz06tUL3bp1Q4kSJRAQEIBs2bJh+fLlKdafO3cuPD09MWLECBQvXhyTJk1CuXLlsGDBAo16JiYmsLW1VV7Zs2dPj49DRERERERE6YQJdhJv377FuXPn4O7urpQZGBjA3d0dwcHBKc4THBysUR8APDw8ktUPCgpCnjx5ULRoUfTt2xfPnj37ZCxxcXGIiorSeBEREREREdG3iwl2Ek+fPkVCQgJsbGw0ym1sbBAWFpbiPGFhYf9Z39PTE6tXr0ZgYCCmT5+Oo0ePon79+khISPhoLL6+vrCyslJe9vb2X/HJiIiIiIiIKK0Z6TqAzKBdu3bK36VKlULp0qVRqFAhBAUFoU6dOinOM3r0aHh5eSnvo6KimGQTERERERF9w9iDnUSuXLlgaGiI8PBwjfLw8HDY2tqmOI+tre0X1QcAR0dH5MqVC7dv3/5oHRMTE1haWmq8iIiIiIiI6NvFBDsJY2NjuLi4IDAwUClTq9UIDAxEpUqVUpynUqVKGvUB4NChQx+tDwAPHjzAs2fPkDdvXu0ETkRERERERDrHBPsDXl5eWLp0KVatWoXr16+jb9++iImJQbdu3QAAnTt3xujRo5X6gwcPxv79+zFr1izcuHEDEydOxNmzZzFgwAAAQHR0NEaMGIGTJ0/i3r17CAwMRNOmTeHk5AQPDw+dfEYiIiIiIiLSPt6D/YG2bdviyZMn8Pb2RlhYGJydnbF//35lILPQ0FAYGPzvvETlypWxfv16jBs3DmPGjEHhwoWxY8cOlCxZEgBgaGiIv//+G6tWrcLLly9hZ2eHevXqYdKkSTAxMdHJZyQiIkqJw6g9ug7hq9yb1lDXIRARUSbHBDsFAwYMUHqgPxQUFJSsrHXr1mjdunWK9bNmzYoDBw5oMzwiIiIiIiL6BvEScSIiIiIiIiItYIJNREREREREpAVMsImIiIiIiIi0gAk2ERERERERkRZwkDMiIiKibwBHcSciyvjYg01ERERERESkBUywiYiIiIiIiLSAl4gTEREREYGX6RPR12OCTUREREREOscTHKQPmGATERERERFlchn9BAfwbZzk4D3YRERERERERFrABJuIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCTURERERERKQFTLCJiIiIiIiItIAJNhEREREREZEWMMEmIiIiIiIi0gIm2ERERERERERawASbiIiIiIiISAuYYBMRERERERFpARNsIiIiIiIiIi1ggk1ERERERESkBUywiYiIiIiIiLSACTYRERERERGRFjDBJiIiIiIiItICJthEREREREREWsAEOwULFy6Eg4MDTE1NUbFiRZw+ffqT9bds2YJixYrB1NQUpUqVwt69ezWmiwi8vb2RN29eZM2aFe7u7rh161ZafgQiIiIiIiJKZ0ywP7Bp0yZ4eXlhwoQJOH/+PMqUKQMPDw9ERESkWP/EiRNo3749evTogQsXLqBZs2Zo1qwZrly5otSZMWMG5s2bh4CAAJw6dQpmZmbw8PDAmzdv0utjERERERERURpjgv2B2bNno1evXujWrRtKlCiBgIAAZMuWDcuXL0+x/ty5c+Hp6YkRI0agePHimDRpEsqVK4cFCxYAeN977efnh3HjxqFp06YoXbo0Vq9ejUePHmHHjh3p+MmIiIiIiIgoLRnpOoBvydu3b3Hu3DmMHj1aKTMwMIC7uzuCg4NTnCc4OBheXl4aZR4eHkryfPfuXYSFhcHd3V2ZbmVlhYoVKyI4OBjt2rVLcblxcXGIi4tT3kdGRgIAoqKiUvXZPoc6LjbNlp0e0rJt0kNGb3+A60DX2P66ldHbH+A60DW2v+5xHegW21+3Mnr7A2m7DhKXLSKfrMcEO4mnT58iISEBNjY2GuU2Nja4ceNGivOEhYWlWD8sLEyZnlj2sTop8fX1hY+PT7Jye3v7//4gmZSVn64jIK4D3WL76xbbX/e4DnSL7a97XAe6xfbXvfRYB69evYKVldVHpzPB/kaNHj1ao2dcrVbj+fPnyJkzJ1QqlQ4jS52oqCjY29vj/v37sLS01HU4mQ7bX/e4DnSL7a97XAe6xfbXPa4D3WL7615GXwciglevXsHOzu6T9ZhgJ5ErVy4YGhoiPDxcozw8PBy2trYpzmNra/vJ+on/hoeHI2/evBp1nJ2dPxqLiYkJTExMNMqsra0/96N8sywtLTPkD0pfsP11j+tAt9j+usd1oFtsf93jOtAttr/uZeR18Kme60Qc5CwJY2NjuLi4IDAwUClTq9UIDAxEpUqVUpynUqVKGvUB4NChQ0r9ggULwtbWVqNOVFQUTp069dFlEhERERERUcbDHuwPeHl5oUuXLnB1dUWFChXg5+eHmJgYdOvWDQDQuXNn5MuXD76+vgCAwYMHo0aNGpg1axYaNmyIjRs34uzZs1iyZAkAQKVSYciQIZg8eTIKFy6MggULYvz48bCzs0OzZs109TGJiIiIiIhIy5hgf6Bt27Z48uQJvL29ERYWBmdnZ+zfv18ZpCw0NBQGBv/r+K9cuTLWr1+PcePGYcyYMShcuDB27NiBkiVLKnV++uknxMTEoHfv3nj58iWqVq2K/fv3w9TUNN0/n66YmJhgwoQJyS57p/TB9tc9rgPdYvvrHteBbrH9dY/rQLfY/rqXWdaBSv5rnHEiIiIiIiIi+k+8B5uIiIiIiIhIC5hgExEREREREWkBE2wiIiIiIiIiLWCCTURERERERKQFTLCJiIiIiIiItIAJNn0VtVqt6xAyNbY/EVHmxofBEBF9W5hg01dJfCb47du3AXBHn94S23/z5s04d+6cjqMhSn9Jtznc/uheQkKCrkPIdFQqla5DyNQ+PNHN7VD6YnvrXtLfQHx8vA4j+XYwwaavtm3bNpQvXx7v3r3jjj6diQgePHiAvn374vz580oZpT3uUHRPRDS2Odz+pK9///1XSajnzZsHtVoNQ0NDHUeVeRw4cAA+Pj4YM2YM9u7dqzGN+4H0oVarlRPd586dQ0JCArdD6UitVivtHRsbi+joaB1HlDkl/gZmzZqFPXv28JgITLBJC+rWrQs7OzssWLBA16FkOiqVCvnz50f//v0xa9YsPHz4kDv3dJD0oGrlypXYtm0bXrx4oeOoMp/E7/qcOXMwceJEAEws0suff/4JT09PHDx4EEOGDMGQIUNw584dXYeVaSxfvhwtW7ZEcHAwjh49ikaNGuHHH3/E1atXAfBkU3pIuh/w9vZG//79sXHjRgDcDqWXxPb/+eef0bBhQ1SpUgUrV67k7XPpJGk7r1ixAhMnToSDgwNPtIIJNn2hD3ca8fHxMDY2RqVKlfDnn3/qKKrMI6X2B4DGjRvDwsICZ8+eBcDLNNOSiCg79ZEjR2LkyJF4/fo13r59q+PIMq/du3cjKioKABOL9FKtWjUUKlQI3bt3x/Lly3H69GkULlyYB7bpIDQ0FDNmzIC/vz/279+Pv/76CwcOHMD27dsxceJEXLx4UdchZgqJ+4HRo0dj0aJFmDp1KurWrQuA26G0lnQ7M2fOHPj7+6NWrVpwc3NDjx494O3tzd7sdJD4Gzh48CCioqIwZ84clClTRsdRfRuYYNMXSdxpJPZUGBkZwcTEBAMHDsTevXuxfv16XYan9xLbf+PGjbh27RqMjIwAAOXLl0e+fPkwffp0AODZwzSUuA7mzZuH1atXY9++fejSpQtsbGwgIoiJidFxhPotpQSuZs2auHv3LtRqNRO8dJB4Ys/d3R2RkZHImzcvHj9+jNevX8PAwIC9d2nMyMgI7969g42NDYD3J1Tr1q2LXbt24cSJE5g5cyZev36t4ygzhwsXLmDXrl3YtWsXateuDRMTE9y+fRsLFy5ESEgIAPZmp4XExO7atWt4/fo1li9fDm9vbyxevBirV6+Gr68vfH19mWSng/v378PT0xNDhw5FeHg4AJ5gAphgUyqsWrUKTZs2Rbt27XDr1i08f/4cZcqUQffu3XHw4EFER0fzIDcNXblyBfPmzUOZMmUwfPhw5ZK0yZMn4927d9i8ebOOI8wcLly4gHbt2qFcuXIICQnB5s2bUbt2bXTr1g0HDhzQdXh6K/HAas+ePbh37x5evnwJMzMz3Lx5E2/evFGmJ+LBrfYkbtcTT+zVqlULFy9eRPHixTFu3Djs2rULb968SXZwxf2B9qjVasTHxyMqKgqhoaEA3n/HExIS4Obmhs2bN2P9+vVYt26djiPNHAwNDREREQEDAwNcvXoV48aNQ8OGDeHr6wtXV1eEhIQw2dCSvn37atyG8tdff6FkyZLw9fVFXFycUt6xY0esWbMG06dPx7Rp0/Dq1StdhKu3Ptyn2tvb488//4SdnR2OHj2KJ0+e6CiybwsTbPpilStXxk8//YTQ0FA0bdoU3bp1w4kTJ1C2bFkcOnRI2dnwwFY7PmzHkiVL4vjx41ixYgXCwsIwYMAAtGzZEocPH0bWrFlx5coVHUWqvz4cqfrt27d4+/Yt/vnnH/j6+qJ3795Ys2YNbG1toVar4evri8jISP4G0siRI0fQvXt3VK1aFQUKFMCBAwcQGhqKsWPHIjAwULlVIi4ujge3WpL0ftMrV64gNDQU3333HYoUKYItW7bA3t4eU6dOxe+//670cP/0008a81HqJSYWBgYG+O677zBgwAAMHz4cJ06cUE54vHv3DlWqVMHAgQOxdu1avH79mic3tCiltrS1tUXVqlXRqlUruLm5Qa1WY9KkSQgNDUWOHDmwc+dOHUSqf16+fIk7d+7gu+++U8qqVKkCPz8/xMbG4tKlSxrrp0OHDli7di2mTp3KKyu1KOmgcpGRkXjz5g1ev36NKlWqYN26dTh58iS8vLx4JR8ACNEnJCQkaLx/+/atxvuNGzdK3759xdLSUnr37i0qlUq6d+8ucXFx6Rmm3kra/uHh4XL79m2N6ZGRkXL9+nVp1aqVtGzZUlQqlWTJkkWCg4PTO1S9lXQdqNVqiY+PFxGRoKAg8fDwECcnJ5k2bZqcPXtWRETmzZsn9evXV+rR11Or1Rrvo6KiJD4+Xm7evCn79u2TBQsWiEqlkrJly0q+fPkkW7Zs8t1338mgQYN0FLH+GjlypDg6Okru3LmlU6dOcvDgQRERiY+Pl0aNGkmZMmVk2LBh4unpKVZWVvLu3TsdR5zxLV68WL7//nt58OCBUnb79m1p1aqVlCxZUk6cOKFR39vbW+rWrZveYeq1pPuBv//+W44ePSoPHjyQ+Ph4efbsmWzZskWCgoKUY6SYmBipWLGirF+/Xlch640P96UrV66UCxcuKO+nTZsmBgYGsmjRomTzHjx4kNsgLUm6H/b19ZWGDRtKyZIlpUePHnL8+HEReX9cZGlpKZ06dZLo6GhdhfpNYIJNH5V0hxIQECB9+/aVJk2ayOHDh+XNmzcadYOCgmTSpElSvHhxcXR0lMePH4tI8gNj+nxJ23/ChAlSvnx5MTc3l7Zt28q2bds06r5580YeP34sM2fOlKJFi8rEiROTLYO+zvTp06Vjx47SsmVLuX79uoi8T/SePn2q1ElISJAGDRpIx44d+d3XkqTf4evXr8vVq1fl6tWrGnXCwsKkUKFCcvDgQYmKipIrV67Itm3beJJDC5J+j/fv3y/fffedHDx4UObOnStNmzaVSpUqye7du0Xk/YHwjz/+KM2aNZPmzZsryQbXQ+otXrxYVCqV/Pbbb8mmBQUFSfPmzSVfvnyya9cuefbsmbx8+VLq1asnnTp10kG0+inpb2DkyJHi5OQkFhYWUrx4cWnRooWEh4cr01+/fi23b9+WRo0aiYuLC7/7WpSQkCAxMTFiamoqFStWlCtXrijTpk6d+tEkW0SYZGvRmDFjJGfOnLJ+/XpZt26dVKhQQfLnzy9PnjwREZFjx45J9uzZpVGjRvL69WsdR6s7TLApmQ8Tg5EjR4qdnZ307NlTfvzxRzE2NpaFCxfKs2fPks0bFRUl9vb2Mnr06PQKV+982P7e3t5iY2MjGzZskL///lucnZ3Fzc1Nli9fnuI8U6dOFTs7O4mJiUm3mPXdjBkzJHfu3NK7d2+pWLGiWFhYyN69e5XpL1++lO3bt0uDBg2kZMmSSmLBJPvrJG2/cePGSbly5cTe3l5cXFxkxIgRGnWbNGkiP//8c7Jl8ABXO3bu3CkDBw6U2bNnK2XHjh2T1q1bi5ubm5Jki4i8evVKWXc8sE29FStWiEqlkj179oiIyLNnzyQ0NFTjCqVr165J//79xcjISJycnKRYsWJSunRpboPSwLx58yRHjhxy+PBhuXnzpixbtkyqV68uFStWVJKLlStXioeHh1SpUoUnmLQg6QnWyMhIEXl/NZ+Dg4NUqVJFLl++rEz39fUVY2Nj+eWXX9I9zszi1q1b4uLiIkePHhURkX379omFhYUsWbJERP73XT9w4IDUrVs3U3fyMMGmFCX+KFatWiUFChSQc+fOiYjIiRMnRKVSibGxsfj6+sqLFy+UeRJ3JhMnTpQ2bdpwx54K//77r4j8byN1/PhxKV26tBw5ckRERP78808xNTWV0qVLS5kyZWTNmjXKvInt/+DBAylRooRcvHgxfYPXIx/uFHx8fOTAgQMiIhIXFyc9e/YUc3NzJcm+ffu2tGjRQtq1a6ckFEwsUu/D9p8yZYrkyJFDjh49Kg8fPpR+/fqJSqWSixcvKtuZbt26iaenpy7C1UtJt99Xr16VSpUqibW1dbKTGH/++ae0adNGKleunOzKGu4DUu/q1atiY2Mj9erVExGRe/fuSa1ataRQoUJiYmIiVapUkT/++ENp4+DgYNm8ebPGlRvcBmlPXFycdOjQQaPzQK1WS2BgoLi5uclPP/0kIiKnTp2SjRs3ch1oQdL9wPz582XcuHESEhIiIiIRERGSP3/+ZEn26NGjpWrVqtz2aMmH++Lz58+LnZ2dREdHy86dO8Xc3Fz8/f1F5P1tEcuWLVOuYP3YMjILJtikGDlypIwdO1Z5HxMTI4sXL5aAgAARed+DYWlpKRs2bJDp06eLiYmJzJ07V+MSWRGR1q1bS/Xq1Xkf9hfy9fUVAwMDZWeRkJAgISEhsnjxYklISJBDhw5Jzpw5ZcWKFRIdHS329vZSvnx5mTdvnsZyRowYIaamphIWFqaLj5HhJd0ZBAYGyvbt26Vx48byxx9/aNTr2bOnWFhYKL1LYWFhyk6dPRZfL/HANCYmRpo2bSpbtmwREZFdu3aJtbW1LF68WEREYmNjRURk0qRJ0rp1a90Emwn89ttvUqVKFSlRooScPHlSY9rx48elTp060qtXLx1Fp3+io6Nl5MiRUqtWLencubPY29uLl5eX7N69W65cuSIVKlSQ0qVLy82bN1Ocn9ugr5NSUtC4cWNp1qxZsvL+/ftLpUqVkpVzHWjH8OHDJU+ePLJmzRq5d++eUh4eHi758uWTqlWralwunrgfZpL9dV69eqX8vXXrVnn79q38+++/UqdOHZkxY4ZYWloqybWIyNmzZ6VNmzZy6tQpXYT7zWGCTSIi8uLFC/nhhx/Ezc1Npk+frpRfv35dQkND5d9//5UyZcoolwfeuHFDzMzMRKVSyerVq5X6Dx8+lBo1asjp06fT/TNkdGfOnJFmzZqJvb29/P333yLy/n6uZ8+eydu3b6Vp06Yybtw4Zcfv4eEh+fPnlyFDhmjsSFatWsUNnBYknqgoUaKEqFQqmTJlSrJBO3788UdRqVTy119/KWWZ9WytNvTr108qVKigvFer1RIZGSkFChSQw4cPy4EDBzTOmMfFxYmvr6+cO3dOHj9+rBzQ8sBKOxYsWCA9evRQ3m/fvl3c3d2lUaNGybbxly5d4ndfy6Kjo2Xs2LHi6Ogo/fr10xj7JC4uTvLkycPbsdJA0u/xhQsXlB65KVOmiKurq/z1118aPdO//vqrVKpUSV6+fJnuseq7lStXip2dnTKIqMj77Xvi1X7h4eHy3XffSZEiRZTe7cQ6lHp79+6VsmXLytu3b2XIkCFib2+v/A5atGghKpVKvL29lfqxsbHSoEEDadiwIfcD/48JNinCwsJk4MCBUr16dZkyZYrGtODgYClVqpQycuPVq1dl7Nixsm7dOo0djVqt5r2/X+HatWvSsWNHsbOzUwbSEnk/iJmbm5tMmDBBRN4fAHTu3Fn27dunbMy4Ufs6SXfIx48fl2rVqklQUJCEhITIoEGDxMTERFavXq30mCaaNm0aLwPUgnfv3snGjRulSJEi0qhRI6U8Li5OevToIe3atRNLS0ul51rk/S0VDRs21DjJx9+Bdrx+/VqmTp0q+fPnl6FDhyrlmzdvlrp160qjRo3kzJkzyeZj+6fenDlzpH79+iLyv97PxCvJkp40TdzeVKtWTUaOHJn+geqxpPuBMWPGSNmyZZUB5p4/fy6lSpWSGjVqyP79+yUyMlJevnwpderUkTZt2ugqZL02evRoadq0qYi879hZtGiRlClTRr777jvlROvjx4+lWbNmvGJAi+7fvy+Ojo5SsGBBsbS0VDp9RN5v42vUqCEFCxaU0aNHy8SJE6VWrVry/fffK7cqcj/ABJtE8zKmw4cPS5s2baRw4cIyd+5cpXzfvn1iaGgo69atk/Pnz0vDhg2lefPmynQmGKmXdEO0fv16GTdunKhUKnFwcFBGS37+/Lk0atRI6tatKyNGjJA6depImTJlmFyngSVLlkjPnj2ld+/eGuUDBgwQU1PTFJNsEf4GtOHdu3eya9cuKVWqlDRo0EAp//XXX8XQ0FCaNm2qDK749OlTadCggVSvXp0HVlqQ0jbk6dOnMm/ePHFwcJDBgwcr5Vu2bBFPT09xc3PTOBFIqbd48WLJkiWLbNiwQSn71EBxz58/l8qVK2uccCLtmTRpkuTJk0cOHDggz58/V8qfPn0qFStWlFKlSomNjY24urpyULk0kNiO8+bNk2LFikmPHj3E2dlZ2rRpI2PGjJGxY8eKiYlJskeXcl+gPT169BCVSiVlypRRrs5I3E+8e/dO+vXrJ/Xq1ZP69evL0KFDOf7MB5hgk2LYsGHi7u4u7u7ukitXLnFwcNC4XHzgwIGiUqmkYMGC4uLikuyZ2PR1hg0bJgUKFJDp06dLnz595Pvvvxc7OztlsLKrV69Ks2bNpE6dOhqPwGFyrV39+/cXlUolLi4uGo9fEXn/GzAzMxN/f/9kj6qj1Ev6Hf7999+Vk0ytWrVSymfMmCFWVlZSo0YNqV27tlSpUkWcnZ05Uq+WfXh/9dOnT8XPz08cHBw0erJXrVolQ4YM4fZHCxYvXiwmJiayadMmEXl/ueXr16/lzZs3yRK2uLg4ef78udSvX18qVqzI772WqdVqCQsLExcXF1m1apXGtMRxZV69eiVHjhyRefPmybp16zigWRq6efOm+Pj4SLVq1WThwoVy48YNERH5448/pGrVqhIREaHjCPXXsWPHZP/+/VK8eHFxc3OT+/fvi0jy73nS8Zb4G/gfJtgkIiIbN24Ua2trOX36tLx+/VrCw8OlS5cu4urqqpFk//XXX3LmzBnuULTsxo0b4ujoqAyYJfJ+xPYGDRpIvnz5lIHPoqKi5O3bt3wEjpZ8rLfBx8dH8uTJI9OmTUu2A+/UqZPUrFkzPcLLdLy8vKREiRIyaNAgqV69ulhZWYmHh4cyfefOnTJjxgwZPHiwLFmyhGfMteyPP/6Q3Llza2zzRUSePHkiEyZMEHNzc+U2laSYZKfeuXPnRKVSKe16+fJlad68uZQuXVocHR1l7NixylUC8fHxsmDBAnF2dpYKFSrw5FIauXv3ruTKlUv+/PNPEdH8fsfGxqaY1HEdpK3E5ymr1Wp58+aNNGzYUOrXr88rBrTkw2140u/z3bt3pUiRIuLm5iaPHj1SygMCAjQ62rguNDHBJhF5fx9p2bJlNQ5UQ0NDpVGjRpI3b16NZ58m4g5Fey5evCgmJibKDj3R4cOHJXv27OLk5CTnz5/XmMaN2ddJukOJjo5O9lz34cOHS4ECBWTWrFnKM04/nJfrQHuOHz8uuXLlUp6vGRcXJ2vWrJGCBQsq96WmhNsh7bl9+7aMGDFCSpQokexZsufPn5ecOXOKSqWSWbNm6ShC/XPp0iVp3bq1ODg4yOrVq+X777+Xrl27yqJFi2TkyJHi5uYm7du3Vw5s79+/rzHuA08uaV9MTIzky5dP46kqSR+d6e/vL1FRUboKL9N69eqVbNmyRblFjlfxaUfS9lu6dKl4eXlJ+/bt5cyZM8q0f//9V4oWLSrlypWTbdu2Sb169aRcuXJs+09ggp3JJSYIK1askNKlSysjMyb+aP766y+xsrISBwcHWbFiha7C1HvR0dFStWpVGTt2rMYgce/evZNq1apJzpw5pXHjxjqMUL8k3SlMmTJF6tatK3ny5JGxY8fK8ePHlWnDhg0TBwcHmTNnTrLLxblj0a7t27dL7ty5NR77Fx0dLfPnzxeVSiUdOnTQYXT652Pf39DQUBk5cqQUKVJEI8n+559/pFOnTrJ9+3ae1NCyGzduSLt27USlUsmAAQM0kubly5dL3rx5JTg4ONl8XA/al5CQIAkJCeLt7S1lypSRRYsWKdPi4+OlXr160q5dO55c1YIPt0H/1ab//vuvjB8/XuM3whNM2jNy5Eixs7OTTp06Sdu2bcXc3FxWrlwpkZGRIvL+2eOurq7i7OwsNWvW5AmO/8AEm0Tk/eO4LCwsxMvLSyPBO3r0qDRq1Ejmzp3LH1EaGzZsmLi4uMjy5cuVDdfz58+lefPmsnfvXrZ/Ghg7dqzkyZNHAgICZP369VKoUCFp0qSJ7N27V6kzYsQIMTEx0Rh8iL5OSgdS//zzjzg6Osr69es1yu/evSv29vaiUqlkyJAh6RWiXku6Lfn1119l1KhR8sMPP8iRI0fk9evX8vTpUxk5cqQULFhQBg4cKEFBQeLh4SEdOnTgs97TyOXLl2X27NnKkzqSDmBpZWUlS5Ys0WF0mc8///wjffr0EUdHR2nRooX0799fqlSpIiVLluSAZlp2+PDhz6774sUL5W+OA6Q9v/76q9jb2ytXSh4/flxUKpWYmZnJwoULNR5Bd+fOHd6m+BmYYJPyQ9mxY4cYGhpKnz59ZM+ePXLt2jXx9PSUvn378qAqDSW2rVqtlo4dO4qzs7M0bdpUpkyZIlWqVJFKlSop7c4kW3v27t0rRYsWVXqGTp48KYaGhlK4cGGpW7euHDx4UKk7f/58fve1JOl3+N27d8pB0pMnT6Rhw4bSqFEjOXLkiFInNDRUWrVqJQcOHOA60LJhw4ZJrly5pGXLllK5cmXJmTOn/PTTTxIeHi7Pnj2TBQsWSO7cuaVYsWJStWpVJhZp7MNbUUTeD/Lk7OwsQUFBOogoc3vw4IFs3LhR3N3dpXXr1jJkyBD2nGrZ5cuXRaVSye7du/+zLo9/tCdpW8bFxcncuXOVk3g7duwQS0tLWb9+vYwcOVLMzc1l2bJlGleXfbgMSk4lIgLK9EQEKpUKBw4cwNChQxEVFQUjIyPkypULwcHByJIli1KHtC8hIQGGhoYQEfj7++PYsWN4+PAh7O3tsWrVKmTJkgVqtRoGBga6DjXD+vD7e/78eRw7dgxDhgzB3r178cMPP8DPzw8lSpRAzZo1UbNmTXTt2hWtWrVS5klcT/T1pk2bhjNnzuDFixeYMGECatSogatXr6Jr166wsrKCm5sbKlasCD8/P2TJkgV79+6FgYEB14GWBAYGokuXLti9ezfKli0LAPDz88Py5cvRqlUreHt7AwBevHiBiIgIFC5cGAYGBoiPj4eRkZEuQ8+QUrP/jIqKQqdOnRAdHY2DBw/ye68FH+5HU7Ne+BvQnidPnqBLly5wc3ODt7f3R7fvSdfTqlWr8PjxY4waNSq9w9U7Dx48QP78+XH58mVkz54d7969Q5MmTdCjRw8MGTIEf//9N8qXL493795h69ataNGiha5Dzjh0ltrTNyexVyIsLExu3LghwcHBGs+8o6/3qZ6fD3vnPrwXm1Iv6ZnWhw8fSlxcnLx7906ePHkir169kjp16siUKVOUOi4uLmJjYyNjxozRRbh6Kek6mDp1quTOnVsGDx4sHh4ekiVLFuV5vjdu3JDBgwdL4cKFpVSpUlK7dm3e65UGdu3aJYUKFZL79+9rbHt8fX0le/bs8vjx42TzsP2/zue23+vXr2Xx4sVSt25djWcs8woO7fncy5LVarXGoJb8DaTex9puwYIFYmZmpowB9KGkx03+/v5iYWHxWT3e9Gnr16+XAgUKaJQdOXJEypQpI1evXhWR94Nbjhs3TgICAngc+oXYHabn1Gq1xnv5xAULKpUKIgIbGxsULVoUbm5uSo8Rz9amzqNHj/Dw4UPcuXMHwPs2/nCdJPrwrG22bNkAvF+HbP/US9pj4ePjgwEDBuDkyZMwMDBArly58ObNGzx+/Bg2NjYAgMjISJQuXRr+/v6YNGmSLkPXK4nrIDQ0FC9fvsSWLVvg5+eH/fv3Y/To0ejXrx8CAgJQtGhR+Pn54dKlS9i/fz8OHz6MLFmyID4+nldwpFLS7X7i33FxcXj+/DmA99ue169fAwAGDRoEIyMjnDhxItly2P5fbtCgQRg5ciSA9+33se1/UqamplCpVChZsiTOnTunfP/Zg60dV65cQd26dfH7779/Vv3E771KpeJv4Csktt3Vq1cRERGhlPfq1QsVKlTA0qVLkZCQkGx7ldhzvXjxYowaNQorVqxAo0aN0jd4PfDhtqdFixZQq9UYNGiQUvb8+XNcu3YNISEhuHbtGsaPH4+QkBD8+OOPMDIyQnx8fHqHnWFxS6HHdu3ahaFDh8LT0xPz5s3DgwcP/vNSqMQkOynu1FNn7dq1aN26NSpVqoRmzZphzpw5AP77IDVp+79584Y79K+U2H5jx47FokWL0L59exQrVkwpf/PmDSwsLHDs2DEsWrQI7dq1w82bN9GsWTPlBBOlzsSJExEeHq683717NxwcHLB+/XqNbZGPjw/GjRuHgQMHYunSpXjz5g2yZs0KOzs75aQUTzKljlqtVto6ISFB+btVq1ZwcnJCs2bNICLImjUrACAiIgLW1tbInj27zmLWF8+fP4eBgQF2796NqVOnAvjvJDtx+9+rVy/Mnj0bRkZGPMmtZTY2NvD09MT58+cB4KPbePngsuRp06alW4z66vDhw3B2dkaXLl2wcOFCvH79GsbGxqhduzb27t0LQ0NDjePQxPZfsmQJfvrpJ/z6669o2bKlLj9ChpV4zPPw4UOo1WqYmJhg6tSpOH/+PPbv3w/gfdLdoUMHNGnSBA0bNsSjR4+wcuVKZRncDn0B3XScU1pbvny5WFpaipeXl3h4eIibm5uMHDnyPy/xSHopztGjR+X+/ftpHapeWrNmjWTLlk2WL18umzdvlkmTJknhwoXlxIkTn5wvafuvWbNGZsyYIXFxcWkdrt67ePGiODk5yaFDhzTKE9t7586d4ubmJqVLl5a6detyMCctuHr1qri6uibb5gwePFhUKpXy2L+kbezj4yMqlUp27tyZnqFmCnPnzpXWrVuLt7e38qzx8+fPS/HixaVYsWKybds2+e2336Rhw4bi6urKy5G15OHDhzJu3DgpVaqUTJo0SSn/1KXGSX8zXA9fh5cl61ZK+9AdO3bIlClTxMLCQtzd3WXixIkSEhIitra24ufnl6z+4sWLxcTERH777bf0CFmv+fv7i0qlEl9fXzl79qy8efNG6tevL71799ZYV0eOHJE///xT2f7w8vAvxwRbD/3xxx9ib28vmzdvVsp+/vlnKVKkiERHR390vqQ/rkWLFomBgYGcO3cuTWPVR1euXBFnZ2f59ddflbI7d+5IkSJFZOXKlR+dL2n7BwQEiJGRkezZsydNY80sjh8/Lvnz50/xYCrxBMbLly/l+fPnfPyEFnx4ULtt2za5e/eu8r5nz55iZmYm+/btSzbvsmXL2PZaNn36dMmRI4d0795dChcuLLVr15Z169aJyPttU6NGjaRAgQLy/fffS/369XnPrxYkbbt9+/ZJz549JXv27DJnzhylPKXkL+l+YN26dbJv3z6e6NOCK1euSHh4uPI+Li5OatWqJePGjZP4+HiNNv5wX2xlZSVbt25N13j1QdLvd1hYmMY+QEQkJCREfH19pXz58lKgQAHJnTu3eHp6SmxsrKjValGr1RIeHi7dunVjcp1KH247/Pz8xMDAQLp27Sru7u6yYsUKOXPmjBgYGMj27dtTXAb3A6nDBFvPxMXFyYwZM6Rnz57y8uVL5UD10aNHUqBAAblx40aK8324Q8mePbts2bIlXWLWN6dPn5bmzZvLpUuXNMqbN28u48aNExHN5C1xR5IocYfOHYr2/PXXX2JgYKA8kishIUFp88OHD8uBAwc01gkHstGOhIQEefjwoahUKmnTpo2EhoYq07p37y7m5uYpJtkiPMHxNT78/g4ePFi5euPy5cvSsWNHqVSpkqxevVqpExISIuHh4TzBpGUjRoyQatWqSePGjcXGxkZsbW01BlRMuq6S7gcWL14sKpXqo78P+nyHDh0SIyMj8fT0lAULFkhsbKyIiEyaNEnKlSun1PswGVm8eLFYWloyuf5CHx7TeHt7S+nSpcXW1lbKlCkjq1evVk52JH7//f39pUuXLmJkZCR79+7VWN7z58/TL3g9FRERISLv27tGjRri7u4uR48eFTs7OxkyZIiUL19eSpYsKTdv3tRxpPqDCbYe2rRpU7Kez/v374uFhYWcOXMmWf2kB1IBAQHcoXylJ0+eyPnz55X3iWf/mjVrJj/99NMn52X7p43IyEhp3Lix1KpVSy5cuKCUv337VmrXri0jR47UXXB6LPEgKzg4WMzNzaV9+/YaVxH06NFDrK2tP3rmnL5c0oTtjz/+kJMnT0qrVq00vveJSXaVKlVk+fLln1wGpd7WrVvFyspKTpw4IW/fvpU7d+7IoEGDpEiRIjJt2jSl3oc9qDzJ+nV4WbLuJa6DKVOmSM6cOWXt2rVy6NAhad++vZQqVUp8fX3l5cuXGvPExsbKjz/+KC1btpTY2Fie5NOS1atXS8OGDZWrWm/duiW1a9eWgwcPytOnT2XQoEFSqVIlUalUytM86OsxwdZziRu5yMhIyZ8/v8Yl3xMmTJAHDx4o7xctWiQ5cuRgcpcKH7uEL+ljPZo3by5eXl5KeZUqVWTp0qVK3SVLloiZmRnbP4389ttv4unpKSVLlpTFixfLggULpG7dulKqVCnuyNNQ4gmmkydPiqmpabIku3nz5uLu7q6r8PRK0u2Ql5eXWFtbi7W1tWTJkkV8fX016l65ckU6d+4sRYoUSdZjRNoxffp0KVu2rEZZSEiIdOzYUbJnzy5z585NNg9Psn4dXpasO2PHjpV58+Yp758+fSpubm6ycOFCjXojRowQJycnOXbsmIhoXoLs7+8v5cuX5z5Zi7Zu3Sp9+vQRS0tLGTZsmJw5c0YmTZqkjMsUHR0tZ8+eldGjR7PdtYgJth74WG9D0vK4uDgpUqSIXLt2TURE6tWrJ99//72yYbtw4YKoVCqN+7bp8yRt51evXn20XqdOnZQebE9PTylYsKBy/29UVJS0bNlStm3blrbBZkJJk46goCAZMGCA5M6dWypXrixt27bl/abp4MMku0OHDhqXi7PHVLvu3r0r5cqVk9OnT8uRI0ekT58+UqhQIZk/f75GvQsXLoiPjw+/+1qW+H3evHmzFCtWTK5cuaIxPTAwUMzMzCRbtmyybNkypXz27NmSM2dOJtepwMuSdevFixdSs2ZNqV69unJVzLt376R48eLi7+8vIiJv3rxR6ru5uUnbtm2TLWfSpElib2/P9tey2NhY2bt3rxQoUEDatGkjzZo1k9KlS6d4CwqTbO1QiXziwcj0zZMkj5HYsmULoqOjkTNnTjRp0kSj3pMnT1CmTBls3boVs2bNwpUrV3DlyhVkyZJFWcbNmzdRtGhRXXwMveDr64uTJ08iPj4e3t7eKFWqFLJly6Y8h7lLly7ImTMnHj58iPPnz+PatWvIkiUL3r59C2NjY8TGxirPvqbPl5CQ8J+Pkkv6OwHePz7HwsICRkZGUKlUiI+P5+Mn0ljiejp16hTq1KmDatWqYeXKlcrzx5M+r5xSb/bs2fjzzz9ha2uLhQsXwsDAACEhIVi0aBF+//13DBgwAAMGDEg23+f8jihlH/vuXrx4Ec2bN0fLli3h5eUFOzs7AMCpU6cwbdo0NG/eHB07doShoSFiY2NRoUIFjB49Gh07dkzvj6AXErfzU6dOxezZszF37lzY2Nhg+fLluHLlCjp06IC+ffvCyspKmef169cYOnQonj59ijVr1iBLlizcF3yhxHaPiIhA//798fz5c3To0AE9evRAo0aNEBUVhWPHjgGAcrzTr18/REVFYe3atcpywsPDMWHCBPz4448oW7asrj6OXgsNDcXixYtx6dIl7N27F9bW1rh48SK+++47XYemf3SY3JMWjRkzRszMzKRcuXJiYGAgQ4YM0Thb+OTJE/nuu+/ExsZGihUrpvTavXv3jr1HWjB//nzJmTOnjB8/XlxcXKRAgQKyZMkSiYyMVOq0bNlSVCqVlC1bVqP9KfWePXum/H3q1CmJiYn5rPmSfuf5/U8/iT2lx44dk5o1a7LttSwmJkbGjx8v1tbWUrNmTY1pd+7ckeHDh0uJEiWSXS5OqZf0O7xhwwb55ZdfZOzYscpgQVu3bhUzMzPp27evbN26Va5cuSKenp7SvXt3pcc1cX/w+vXr9P8AGRwvS9a9pG154sQJqVGjhpQvX162bt0q58+fV3pNk9atXLmyDBo0KNmy+BtIO4nbqpiYGLl27Zo0a9ZM3NzcuB9OI0ywM6jEH4RarZZnz56Jh4eHnDlzRp48eSK7d++WbNmySa9evZTRMiMjI8XR0VGqVKmi7ES4M0m9DzdIU6dO1bi8vkePHlK4cGFZvHixMpDH7NmzpW7dumx/LTly5IjUr19fHj58KIMHDxZ7e3uNhPtj+Mgb7Un6O0jc1vyXDy9H5uXJqZf4XU76nX7w4IFMnz5dDAwMNAbSEnmfZPfs2VPatWvH34GWeXl5SZ48eaRatWpSokQJsbCwUC6N3bFjh1SvXl1y5swphQoVEhcXFyWp5npIPV6W/G3x8vKSpk2bSoUKFcTCwkKKFSsmAQEBsm3bNnFwcJCiRYtK/fr1xc3NTYoXL85joG9A4j6c+2Ht43UwGVDSy9EePXqEZ8+eoUiRIihcuDCsrKzQqFEjbN++Hc2bN4dKpcLcuXNhaWmJyZMno02bNjA0NOQlsV9BRJT2//333/Hy5Utcv34drq6uSp1ly5ahV69emDlzJlQqFdq3b4+uXbtiyJAhvCRZS8LCwvD69WvUrl0bEREROHv2LHLkyJHscvCkkk7bunUrcuXKhZo1a6Zj1Poj6XbI398fz58/R+/evZE7d+5PzvfhuuFlyamTtP1fvXoFY2NjmJqaIl++fOjUqRPi4+MxZcoUGBgYYMSIEQAAR0dH+Pj4IG/evFCpVJ/8rdDn27lzJ9auXYtDhw6hWLFiMDY2xvDhwzF+/HhYWlqiQ4cOqFChAmJiYvDy5UuUK1cOBgYG3A98BRGBtbU1Nm3ahP79+2Pt2rVQq9Xo0aMHHB0dsX79evTp0wcmJibKZclly5ZFVFSUxnLCw8Px4MED7Ny5E9mzZ9fRp8n4Vq9ejRUrVuDw4cMoUKAA4uLi0KVLF6xfvx7du3fH8ePHERAQgKioKFhbW2P8+PEwMjLib+ArpXYbnnhLkIGBAW8PSiu6zO7p64wcOVKKFSsm+fLlk3z58snZs2c1ph88eFAsLCykRYsWGuU8U5V6SXsbhg0bJpaWluLg4CAqlUq6dOkiT58+1ajfu3dvMTc3l507d6a4DPpySXtN27RpIyqVSmrVqiV37twRkU+P6J7I399frK2t5fDhw2kbbCYwfPhwsbGxkZUrV8q9e/c+WTfpOli6dKmsWLEijaPTT0l/A7NmzZLatWtL9erVpXPnzkr5o0ePZMqUKWJlZSUzZ8785DLo6/z666/i4uIir1690uiV69Onj9jZ2Ul0dHSyedj+X4eXJX9bvL29pUqVKpKQkKBs5+/fvy/ly5cXJyenFEdk57Ho10m6DXn58qXExsYqV5J96jiTx6Dpgwl2BpL0x7Rx40ZxdHSUxYsXy7x588Tc3Fxat26t8fgbEZFdu3ZJ7dq1uTPXsvPnz0vjxo0lODhYXrx4IcOHDxdnZ2fx8fFJdpnytGnTuCNJA6NHjxZnZ2eZN2+e1K1bV5o0aSKXLl0SkeQ77pSeMbtly5Z0jVcfLVu2TPLmzStnzpzRKE/8DSRt9w/Xgbm5uezYsSN9AtVTo0aNEltbW5kzZ46sWLFC7OzspHbt2srTDB4/fiy+vr6iUqlk3bp1Oo5Wf82bN09y5syp7GcTD3Jv374tuXPnluPHj+syPL3Gy5J1K3G77uvrK66ursp3P/EWiMOHD4uZmZmUKFFC2d4zwft6SdvQ19dXGjRoIN9//710795dGWfgv+Zbv369bNq0KU3jzMyYYGdAhw4dkuHDh0tAQIBSdvr0acmaNau0a9cuWZKdiEm2dmzYsEFq1aolLVq00NhZ//TTT1KuXDmZOHFiivcCM8lOvUWLFsnFixdF5H87iPr168uBAwdERGTdunVSu3ZtadKkifz999/KfIcOHdLoPVq8eDGfMasFietg8ODB0r59exERuXHjhixZskRcXFykcOHCGj0WKZ3g4Dr4Or///ruULFlS/vrrLxF5fzLVwsJCrK2tpWzZskqS/eDBA1m1ahUTCy342D70yZMnUqxYMWnZsqVG+eXLl6VQoUJy7ty59Agv01m1apVkz55dzp07J0+fPpWHDx+Ku7u7VK9eXVauXCkPHjyQcePGyaBBg8Tb25vjn6ShK1euiJGRkUycOFGjfM+ePdKkSRMZM2YMj0G14MOTE2PGjJGcOXPKhg0bZMOGDeLm5iZ2dnby5MmTT87r7+8vVlZWsn///jSPObNigp2BqNVquX//vlhbW4tKpZJRo0ZpTD99+rRky5ZNOnToICEhITqKUv9NmDBBChcuLI6OjskGdvrpp5+kfPny4uXlpTGCOKVeSEiI5M+fX3r37i1Xr14VkfeX9BUqVEgjidu4caPUrVtXPDw8ZPfu3eLp6Slly5ZVdioLFiwQCwuLFC9Vo/+W9OAocbT2adOmSfHixWXgwIHi4uIiLVu2lBEjRsiAAQPEwsJCwsPDNXbqPMGhPbt27ZLJkyeLyPuD2Jw5c8rChQvlxIkTki1bNnF3d0+2DWJikXpJv//Hjh2TPXv2yOnTp5Xv94YNG6R06dJSr149uXjxohw7dkwaN24slStXZmKRRnhZ8rdlxYoVkiVLFhk+fLicPn1abt++LQ0aNNA4VuVv4esltuHt27fF1dVVgoKCRERk3759YmFhIUuWLNGo9+HfPMmdPphgZ0Bnz56VYsWKSbVq1SQ4OFhj2pkzZ0SlUom3t7eOotN/arVaFixYIEWLFpVu3bolO1PYp08f6datGy+D0qLz58+Lq6ur9OrVS0myixUrJoGBgRr1tm/fLvXr15cCBQpIjRo1lMvUrl27JlWrVuXlUKmUdOe8ePFimT9/vrx8+VKuXLkio0aNEldXV5k7d66ybvbt2yc1atSQFy9eKPMtWLBAsmXLxhMcWnT//n2JiYmRqlWrKj1HERERUrp0aVGpVPLDDz/oOEL9kHRbPnr0aLGzs5MSJUqIsbGxDBs2TEJCQiQhIUH27Nkj5cuXFwsLCylatKjGNoiJnfbwsuRv19atWyVPnjySP39+yZ8/v8ZjSbkOUm/AgAEya9YsjbLLly9L3rx5JSoqSnbu3Cnm5ubK6PmxsbGydOlSefz4scY8PMmdfphgZzCJB7onT56UQoUKSZs2beT06dMada5fv86eijSS9PFoM2fOlMqVK0uvXr2SXRKe0uNz6OucP39eypYtKz169JDTp09L48aN5fLlyyKi+SiWK1euyOPHjzXWVVxc3H8OwEX/bcSIEZInTx5ZuXKl3L9/X0Te/yaSXob/9u1badCggTRu3Fj5/oeHh0uHDh14gkNLkm5Xbt++LQUKFJCTJ0+KyPsEu2PHjnL27Fn2FmnZtGnTJF++fMo91WPHjhVjY2Pp0aOHMsiiyPtt1Z07d5T25/44bfCy5G/Tw4cP5fTp03LkyBHlxBJ/A6n36NEj6dq1qxQrVkwWL16slN+5c0dq164t06ZNE0tLSyW5FhE5d+6ctGnTRtkviIgsXLhQzMzMeJI7nTDBzoASdxgnTpyQQoUKSdu2bZMNMiTCDVpaSZq4/fLLL1K5cmXp06ePREREaNRjcq1958+fl/Lly0vz5s1FpVLJd999JyVLlpSyZctK8eLFpVChQuLl5aXUT3rpIH2dgIAAsbOz07ifNCEhQRk5PzIyUrZu3Sp16tSRMmXKKL0WifiM2S/3Od/dmJgYcXJyEk9PTzl48KDUrl1batasyeebatn9+/eldevWsn79ehER2bZtm1hbW0uvXr3EzMxMOnfurAyymBQTvLTFy5K/fdwGfb1//vlHhgwZIkWLFtUYfynxWGj8+PFKWUxMjDRo0EAaNmyofPfv3r0rDRo0kM2bN6d77JmVSkRE148Koy+X+AzUU6dOoXPnzrC3t8eCBQtQrFgxXYeWKSS2v4hgzpw58Pf3R69evfDTTz/pOjS9d+HCBXTu3BlWVlaoUaMGatWqhfj4eDx79gwignbt2vG5mmlg4MCBePXqFVauXIlbt27hr7/+gr+/P0QEo0ePRr169TBp0iRERkZi/vz5fMbpVwgNDcV33333n/Xk/5+BeuDAAQwZMgQqlQq2trY4cOAAsmTJovGsbPo60dHRCAoKQvXq1XHz5k20bNkSw4cPx6BBgzBhwgTMmTMHjRo1wuTJk+Ho6KjrcDOV3377Df369YOxsTEAIHfu3Dh16hSyZMnCZ72T3rh16xYWLlyI/fv3Y+DAgejfvz8AoEaNGrh37x7at28PExMT/Pnnn4iIiMCFCxeQJUsWZf5Hjx7Bzs5OV+FnOkywM7DEg6djx45h4cKF2LBhAw+m0lHSJHvjxo1o06YNDA0NdR1WpnDx4kX07t0bZcuWxfDhw1G4cGGN6Uzsvs6HB6WJSfTRo0dRpUoVnDhxAra2trC1tUV8fDwOHTqEK1eu4PXr18iZMydUKhUSEhL4e0iFH3/8EU+ePMHPP/+MkiVLfvZ8r1+/xqNHj+Do6AiVSsXfQBp4/fo1smbNinHjxuHq1atYv349smbNismTJ+Pw4cPIlSsXNm/ezP2wDjx69AgPHz5ETEwMqlWrBkNDQ/4GKENLPMZMeqL0+vXrWLx4Mfbt24dBgwYpSXb//v0REhICAwMDFCtWDNOnT1dOchsaGvIkkw4wwf6GpOZM64c9FNyhpI68v10ixQOjT/UCfTiNPUbp58KFC+jduzccHBwwY8YMFCxYUNch6YWk3+GYmBio1WpYWFjg1q1b+OWXX3D69Gl07doV7u7uKFmyJDZv3oyAgADs2rUL5ubmAFK3LaP3fvvtNwwbNgzu7u4YPHgwSpUq9cXL4H4gbfXu3RuPHj3C8uXLkSdPHjRv3hzdu3dH48aNAXA/8C3gCT7KyN68eQNTU1MAwIMHD6BSqZAvXz4AwN27d+Hn54f9+/dj8ODB6NevH4D3JwBNTEyUbQ/3A7rFlv9GvH37Vrm86f79+8iVKxcMDAxgYmLyRTtr/phSJ2lyfeDAARgYGMDMzAyVK1dOdgYxqaRJRHh4OHLnzp1uMWd2ZcuWxcKFCxEQEIACBQroOhy9kPR3MHXqVJw6dQoXLlxAq1at0KpVKyxZsgSvXr2ChYUFgPc78DVr1iB79uwwMzNTlsPkOnXi4+PRsmVLmJubo0+fPjA0NET//v1RunTpT86X9ISGWq3mfiCN1a5dG926dUOLFi3w/PlzqFQq1K9fHwA+eqKW0heTa8qIpk6dijFjxijJ9bhx47Bx40bEx8fDysoK06ZNg6enJ4YPHw4AmD9/PgwMDNCnTx9kzZpVWY6IcD+ga+l4vzelYOHChfLo0SPlvbe3t5QsWVK+//57GTVqlDLycUqDdCQdAMfPz09j4AP6PB06dJCZM2cq74cOHSo5cuQQe3t7yZ8/v0ydOlWZ9uE6+LD9q1evnmw0cUp7ieuBA9loz5gxYyRnzpyyYcMGWbt2rVSoUEGKFy8uDx8+FBGRqKgo2bp1q3h4eEjp0qWVAc24DlIvadv9888/0q9fP7GyspKuXbvKtWvXPjpf0u3QokWLpEOHDhzYLx1s2bJFxo4dK+PHj1cGFOVgTkSUWqdOnRJzc3Np3LixiLwfSDFHjhyybt062bFjh7Ru3Vry5MmjPOf61q1bMnToULG2tlYeR0ffDibYOrR7924pXLiw9OjRQyIjI2XHjh2SJ08eWb9+vfTr109q1qwpjRo1ktu3b4uI5gFY0gOoJUuWiImJiTK6KX2eFy9eyKBBg8TKykoWL14sz549kzJlysjFixfl3LlzMmfOHDEyMtJ4pnjSEcQTBQQEiLW1Ndtfh5hQaM/169elbNmycvToURERCQwMlKxZs8ry5ctF5H1bP378WPr06SPdu3dXkgs+tUA7hg4dKo6OjjJgwABp3ry5GBoaSufOneXKlSvJ6n64HbKyspItW7akZ7iZzse2Nfz+E9HXeP36tezYsUMKFy4sjRo1knnz5mk8lkvk/fOwc+bMKRcvXhSR98/C9vPz48m9bxATbB3z8/OTKlWqSO/evWXkyJGyatUqZdr69euldu3a0qBBA+UZmwkJCRqJdkBAgFhaWsq2bdvSPXZ98OjRIxk/frxYWFhIz549ZcCAAcoB1KtXr2ThwoViaGgoEyZMUOZJeiCV2P58riDpi2vXrkmhQoUkNjZWtm3bJubm5srzNWNjY2Xt2rUSHR0tz58/V34r3Llrx59//ik5c+aUEydOKGXbt2+X7Nmzyw8//KDRk520zRO3Q1u3bk3XePXBx666+K+TdjypR0Ta9ubNG9m+fbuULFlSVCqVTJ8+XUTeJ9+JKleuLO3atUs2L/fD3xYm2DqS9Bmxfn5+Uq1aNaX3Oqn169dLnTp1pHHjxnLz5k2NaYsXL+ZBVSol3RC9ePFCxo0bJ9bW1lKvXj2NetHR0bJw4UIxNjaWIUOGaExbsmQJ258ytJSShMuXL0upUqVkzpw5Ym1tLQsXLlSmBQcHS6tWreTMmTOfXAalzokTJ8Te3l6uXr0qIv9r2y1btohKpZK+ffvK2bNnNebhfiD1kibXp0+flv3798vff/8tb968EZGPH7Am/c6fO3eOz3gnoq+WuF15/fq1/Pbbb1KsWDFxc3NTpifmDd27d5cOHTroJEb6fEywdSwiIkJERObPny8FCxYUd3d3CQsL06izceNGKV26tAwfPlwpmzdvnpiZmbHn9Cvt2LFDHj16JM+ePZPx48eLSqXSSChE3ifZ06dPl2rVqikbwKVLl4pKpeKVA5RhJU0uoqOjNZKGjh07ikqlkp9//lkpi4mJkQYNGkijRo14r7UWpHRiIjg4WMzMzOTAgQMiIkqi9+LFC7G3txeVSiXTpk1T6v/6669iYGDA/UAqJG3/UaNGSdGiRSVXrlxSvXp16dWrl0RHR4tI8iQ76XwLFiwQc3NzuX79evoETUR65WP70ujoaNm+fbvky5dP3N3dJSEhQeLi4kStVoubm5v06tUrnSOlL8UEW4dWrVoltWrVUt77+fmJm5ub9OjRQ8LDwzXqHjp0SPkhRkZGSq9evWTDhg3pGq++uXLlipQtW1aWLl0qIiIPHz6UsWPHalwSm+j169fJei22b9+enuESpYlp06ZJ1apVpWXLljJ37lwReZ9Me3p6Ss6cOeXnn3+WcePGSZ06deT777/ngGZatnLlSpk4caLSnj179hQrKyu5fPmyUufJkyfSp08f2bJli5LwRUdHyy+//CI7d+7USdz6wtfXV2xtbeXo0aOiVqulf//+ki1bNmnZsqVERUWJyP+S7A/vec+RI4ds2rRJJ3ETUcaWdB+6adMmmTx5skydOlVu3LghIu/3w9u3bxc7OztxcnKSunXrSufOnaVo0aIc8yEDYIKdjj48IL127ZqYmJjIjBkzlLJZs2ZJ5cqVpUePHkrvdkrLiImJSdtg9VBKCUHv3r2lRIkSSnuGhobKuHHjxNLSMtngEiLvD7CYWFBGlvT76+fnJzly5BBvb29p2rSpFC9eXLkVIiEhQQYNGiS1atWS+vXry9ChQzmgmZa9fv1a2rdvL+XLl5dffvlF1Gq1RERESLNmzcTU1FR++eUXWbRokbi7u0uVKlWS3fOe2MNNqfPPP/9I1apVZffu3SIicuDAATEzM5POnTtL8eLFpW3btkpPdtLbunjPOxF9jaQn63766ScpUKCA1KpVSxo0aCA5c+aUU6dOicj7cU+2b98u5cuXl6xZs8rFixeV7T/3w982Jtg68PjxY4mNjRUREX9/fylatKj88ccfyvQ5c+ZItWrVpEWLFvLixQsdRam/du3aJX/++afyvlSpUtK2bVvl/YMHD8Tb21tUKhV7qUlvBQUFia+vr+zbt09ERJ4+fSpz584VBwcHGTRokFIvMjJSYz7u1FMvpcvCnz17Jn379hU3NzeZNWuWiLw/qBo3bpyUKFFCypUrJ/Xr11cSPN7znnopnRzdsGGDPHr0SP766y/JmzevcmK1U6dOYmBgIDVq1NA4oR0QECDZs2dnck1EX23RokWSP39+ZVyTNWvWiEqlEjMzMzl8+LCIvO9QW79+vbRv317ZhrGj59vHBDudzZs3T/LkySO+vr5y9+5defXqlTRt2lRGjhypcSA7adIk+fHHH/kj0rKgoCBRqVRSsGBBmTx5soi8H6W3Tp06GgPM3b9/X5YsWcJkgvRG0m3J0aNHxcHBQfLkyaOcKRd5n+zNnTtXHB0dNZLsREzutCPp5d8i79u9d+/e4ubmJnPmzFHaOSIiQuP+eG6PUi/p9z84OFh5/GWi4cOHS/fu3SUuLk5ERCZPnix169aVYcOGKfP+8ccfolKpmFwTUaokHdMhOjpaBg8eLCtXrhSR94/utbCwkJkzZ0rbtm3FwsJCjh8/LiKaVytxtPCMwQCUpkRE+ffdu3d4+/Yt3rx5g7Nnz8LT0xMXL15EgwYNsHbtWvzzzz/KfOPGjYO/vz8MDAygVqt1FX6Gl9h2ievh+++/R6tWrVCmTBnMnz8fPXv2RGhoKExNTXHq1CnExMQAAPLnz49evXrByMgI8fHxOoufSFsMDN5v7n/99VdcuXIF7dq1Q1xcHHbt2qXUyZEjBzp16oShQ4di2bJlmDt3rsYyVCpVusasj7Zs2YJOnTph9erVSlmOHDkwZcoU5MuXD35+fpg3bx7UajVy584NMzMzqFQqqNVqGBkZ6TDyjEutVivf/zFjxqB37964dOkSXr16pdR59OgRbty4AWNjYwDAhQsX0LRpU8ycOVOZt1atWjh9+jRatmyZ/h+CiDK0N2/ewNDQEABw9uxZZMuWDZ07d0b16tVx/fp1DBkyBL6+vhg2bBhatWqF6OhoVKtWDWfOnIGJiYmynMRl0LeNCXYaSzwgjYmJQZYsWdCqVSs4OjqiaNGimDJlCjp16oSoqCjEx8dj4MCBiI6O1phXRJSdO325xLY7f/48ACBXrlxwd3eHhYUFgoKCkC9fPly7dg2XLl3CvHnzsHXr1mTL4EEtZWRJT9AtWLAAo0aNQr169dCvXz/07dsXGzduxPTp05U62bNnR/v27bFq1SoMGDBAFyHrNVdXV+TNmxerV6/G2rVrlfJcuXJhwoQJiI6OxoIFC7B+/XqN+bgfSL3EtpswYQKWL1+OWbNmoV69erCwsFDqNGzYEDExMXB1dYWbmxuuXbuGH3/8EcD7E7QJCQkA3q8/IqIvERgYiCZNmgAAhgwZgt69eyMyMhLlypVDwYIFcfnyZeTNmxcdO3YE8P6ka+/evTFnzhyULVtWl6FTKnGPnUYSe0wBYMOGDWjWrBmuXLmCAgUKYP78+Vi5ciWKFy+Obdu2ISIiAjY2Njh16lSyBI89RqmTNKk4deoUypcvjy5duuD69evo3bs3nj17hqlTp8LHxweDBw9Gu3btAACbN2/WVchEaSLpSabHjx9jzpw5cHJygr29Pfr164c2bdpgxYoVmDFjhjJPzpw50apVKxgaGiqJBX25D68+EhEULFgQixYtgpmZGZYuXYo1a9Yo02NiYlCvXj306tULHTp0SO9w9VpISAh+++03LF68GHXr1sWbN29w8eJF/PLLL9i5cyc6dOiAUaNGoVKlSqhSpQr+/vtvGBkZISEhASqVir1GRJQqCQkJePz4MV69eoVixYph9erV2Lp1K6ytrZU6r169wl9//YWnT5/ixYsXytVjgwcP5pWUGZRKkmaCpBUioiTGL168wIEDB7Blyxbs2bMHU6dORb169XDo0CGEhoZi8uTJiI+Px61bt7BgwQIsW7aMPaZfKWn7BwUFoVixYggJCUG/fv2QI0cO1K1bF126dEH37t3RsWNHdOrUCQCwd+9eeHh48ECK9IqI4Ny5c6hQoQJUKhWWLFmCHj16KNPv37+PgIAA7NixAy1atMCkSZN0GK3+SHpZ8vr16/HPP/8gPj4eDRs2RKVKlfDw4UP0798fL1++RI0aNdCyZUuMGTMGTk5O8PPzA/D+wIzbI+0IDQ1Fs2bNMGTIENjZ2WHDhg04f/484uLiEB0dDR8fH3Tr1k1jnvj4eO6PiShV6tati+HDh8PDwwMA0L59e2zatAnVqlXDH3/8AUNDQ2Ub8+zZM3Tq1An79++Hk5MTjI2NceHCBWTJkkXHn4JSiwl2GpoxYwZCQkLg7+8PlUqF+fPnY+nSpXBycoJKpYKpqSn69++PypUra8zHnXrqPHr0CHZ2dsr7AwcOoHv37rhx4wYsLCwQERGBFStWYMuWLXj27Nn/tXfvcT3f/+P/b6+O6IQ5FDZjxJya5RhmDmHL6e3YsK2cyphDk4QRmynnQyg5RknNYaHlME0bcyrL+yPkNPZGTqEiVK/H7w/fnu9XZu/fECn361/1PLwuj9ezno/n8/543B+PBw4ODpQpU4bZs2fzxhtvaOfJ9RfF0Zo1a3B3d+ezzz5j5syZVKhQQdv3559/EhAQQFpaGmFhYZI5U4DGjh3LunXrqFOnDllZWRw8eJCAgAC8vb25dOkS/v7+bN++ndzcXKpUqcLPP/+MqalpvoZC8XQMGzfyPHz4kO7du5OamkpSUhIjR46kU6dONG3alN69e9OuXTvGjx9fSCUWQhQnV69eZc2aNYwePVqb1yEkJIRbt26xfft2LC0tWbduHWXKlNHeOW/fvs3evXu5d+8effr0yReAiyKoECZWK5aGDx+ujh07lm9bv3791IwZM/Jt27dvn5o8ebKqVq2a0ul0yt7eXmaGLQCDBw9W/fr1UydPntS2bd68WTk6Oiql/jv77sOHD9WlS5fUwIEDVYkSJZROp1NbtmwplDIL8SL8r5UHgoKClE6nU9OmTfvLEoBXr17VZquW2cILRkxMjKpQoYJKSEjQrumCBQuUsbGxCgoKUkoplZ6eri5evKgOHz6s/e3kmfDsDP///+///k+lpKSoU6dOKaUerTv+888/qyNHjuQ7p0WLFiogIOClllMI8XoICAhQERER2u9r1qxRTk5OysXFRd2+fVvbvmfPnnz1l8wWXrRJs0gBuHv3Ljt37mT37t1s2bKFWrVqodPpuH37NiVLlgT+m7bs5OTEe++9h6urKwMGDKBEiRIyeU0BaNmyJV9//TWLFi3iiy++oE6dOuTk5FC2bFkAbSydiYkJlSpVYsWKFbRr144DBw7g4uJSyKUXomAY9txt2bKF69evA9C7d29Kly6Nh4eHNqGiTqfjyy+/xMbGBkDr0VbSc/pMZsyYQc+ePbG3t9e23bx5k8qVK1OnTh30ej3GxsaMHDmSjIwMfHx86NChA9WqVcPKyoo333wTQGYLfw7KYFJQPz8/vv/+e+7du4eZmRm+vr58/vnntG7dGoDMzEyuXbvG8OHDyczMxMvLqzCLLoQohtLT0zl16hR+fn4opXB1daVfv37acK2ePXsyd+5cvL290ev1fPjhh9q5MjyoiCvc+L74uHXrlmrZsqWyt7dXycnJSiml2rdvr+bOnauUetQj9Hiv0N27d2XR+AIUGRmpKleurDw9PdXFixfVsmXLlLOz8z86V3qMRFFnWL/4+PioihUrqjZt2qgyZcqojz/+WO3atUs7JjAwUBkbGytvb2+VkZFRWEUuNi5duqR0Op3q2rWrOnv2rLZ93bp1ytzcXF25ckUp9SiDRqlH62BXrlxZW+NUFKwpU6ao8uXLq507d6qUlBTVv39/pdPptKwBpZRavHixatGihWrbtq32d5EeIyHE83jSu/yZM2fUmDFjlJWVlQoPD1dKPXrnjIyMVC1atFB2dnaqZcuWWj0kigdpJi8gpUuXZuvWrbi4uNClSxd++uknbf1SgOvXr2NkZETp0qU5e/Ys77zzDqVKlQKePF5M/HPq//W49e7dG6UUXl5elC1blszMTG7evElkZCQZGRlYWFhQqlQpzpw5g4uLC7Vq1dI+Q3qMRFGXV9fMnz+fsLAwtm/fjqOjI+Hh4QwYMIAHDx6g1+vp0KGD1msXHR2NhYVFIZe8aNPr9VSqVIkTJ07g5OTEqFGjtJnaO3bsSJMmTRg+fDgLFy6kcuXKAFpdpGQKlAKXkJDA3r17iYiIoG3btmzfvp3t27fj4uLCsGHDMDIyYsiQIQwaNAhbW1u6desmYx2FEM/N8F3+ypUrZGdn89Zbb/HOO+/g4+NDTk6OtvTfJ598Qs+ePWnfvj1//PEHDg4OGBkZST1UjMgkZwXs9u3bdOzYkStXrgBw48YNatSowaVLl9DpdFhZWVGvXj22bt1ayCUtXpRBWmtERARjx44lNzeXnJwcGjduTEpKCqVKlaJUqVLodDp+/fVXSb8Rxc7t27fx8/Ojfv36DBo0iI0bNzJ48GC8vb1ZvXo1ZcqUYerUqXTs2BGdTqfdN0rSwp+ZUkpL/z558iTNmjWjVatWLFy4kGrVqrFu3TqWL1+OTqfDz8+P3Nxc5syZw82bN9m/f780rj6nx/93//Of/xAWFsbo0aPZt28fAwYMYPLkyXz66af861//Yvfu3dokc3lktnYhREGZNGkSGzZsICMjg+rVq/PVV1/RuXNnbbWC0NBQgoOD6du3b77zpB4qXiTAfgFu3brF559/TmxsLMuXL+f999/n3r17KKUwMTHhvffek5voBTB80YqOjmbYsGF89NFHjB8/nho1avzlOMkcEMVNdnY2Bw4c4N133+XKlSv06NGDESNGMGrUKLZs2UKfPn1o2LAhCxYsoFmzZloPqgTXz8awDrl9+zalS5fmxIkT2lrKISEhVKpUic2bN7Ny5UpiY2N59913KVeuHDt27MDU1FReqp6D4fU/e/YslpaWVKxYUdvu5uZGqVKlWLBgAaampnh6epKQkECJEiWIj4+X/3shxHMzrMNXrlyJj48Ps2fPpmLFiixevJg///wTd3d3hg8fzrVr15g9ezbz589n9+7dtG3btpBLL14UCbBfkLS0NLp168adO3fYtGlTvgAPpKXqRTEMsqOiovjqq6/o2rUrHh4e1K9f/4nHCVEUPd5AlJdalle3BAUFERYWxubNmylXrhxhYWFs27YNCwsLli1bJo1Lz8nw+s+fP59Tp04xfvx4qlatyqlTp2jatClOTk6EhIRoqeEnTpzA2toaOzs7SQcsQBMmTCA6OprU1FQGDhxI7969ady4MQ0bNqR9+/bMmjWLrKwsBgwYwMCBA7WJLeU5IIQoKNu2bePy5csYGxszaNAgbfsXX3xBXFwcoaGhNG7cmDNnzrB9+3aGDx8u9X8xJm9YL0jZsmXZunUrNjY2vP/++1y4cCHffgmuX4y8dFd4NHPy3LlzCQ4OZufOnX85ToiiyjC4W7JkCZ6ennTo0IGwsDBSU1OBRzNYp6enk5qaSkZGBpGRkTRv3pzly5djZGSEXq8vzK9Q5OVd/3HjxhEQEICjoyM5OTkopahVqxYHDx5k//79eHh4cObMGQDeffddKleurF1/ebl6Nob/u1FRUYSGhvLtt98ycuRI4uPjmTFjBomJibi7u7NgwQI8PDz48MMPOX/+PJ06dQIkuBZCPLtOnTqRkJCg/X7u3Dm6d++Op6cnV69eBR41esOjZ7SZmRmLFy8GoEaNGowaNQoTExPtGFH8SA/2U3iWB3JaWhoTJ04kMDBQguqXyPBvFRcXxwcffCDXXxQ7Pj4+rFu3jgEDBmBpacmUKVPw8vJi5syZnDt3jpYtW2JhYUFubi42NjYcOXIEU1PTwi52sbFjxw4GDx5MeHg4rVq10rbn9UyfOnUKJycnateuTWRkpNaTLQpGfHw8GzduxMHBgYEDBwKPepHmzJlDmTJlcHV15caNG0RHR1O5cmWCgoIkLV8I8Vz0ej2+vr588803mJmZadt27drFiBEjqFq1KrGxsZiYmGiN4cOGDeP27dusX7++kEsvXhbpwf6H9Hq9FrDdvHnzH59XtmxZli5dirGxMdnZ2S+qeMXe0/a25fVk6/V62rRpo80SK+1JorjYu3cvUVFR/PDDDwQEBNC5c2cAGjZsiJGRETVq1GD//v34+PgwceJEEhISMDU1lRbzAnTmzBns7Oxo2rSpti1vro2HDx9Sq1Yt4uLisLS0xM7OrhBLWvzkpYOvXr2a9PR0bXvnzp3x8vIiPT2dyMhIHBwciI2NZcWKFdr/vwTXQohnZWRkREBAAGZmZsyePZtdu3ZhZGSEs7MzgYGBHD16lE8++YQ7d+6QnZ1NTk4OCQkJWFlZFXbRxUskAfY/YJiOOWvWLL777juSkpL+0Xl5cnJypOfoGRle/4sXL3L8+HEALVj+X0Gz4XkmJiaSEiiKpBkzZpCSkpJv2927d6lWrRqNGjViw4YNfPDBByxevJj+/fuTnp7O4cOHqV69OkOHDmXIkCHa+GxJSy44SimysrK4c+dOvu16vZ6NGzdy9uxZGjRowI4dOyQtv4DZ2tqyadMmbG1tiYmJ4d///re2r0uXLowZM4ZTp07lW7Ejr/FDCCGexeN1+O7du+nRowd79+7FyMhIG6oVFxdHs2bN6NmzJ/379yczM1NLERevBwmw/wHDsXYzZ86kSZMmlCtXLt8xj990SintvJUrV/LVV1/Jy9UzMAyuJ0+eTOfOnWnevDnt27dn3bp1PHjwIN+46zyGKeKLFi2iVq1a2thUIYqSy5cvM3HiRLy9vTl37py2/cGDB1y6dInw8HA8PDyYOXMmw4YNA2DPnj0EBARw+fLlfJ8lPXcFq169eqSkpBAZGallKOl0Ou7fv8/atWuJjY0F/tsIKBPLFawGDRoQGRnJjRs3WLRokdb4CuDi4kJwcDDTp0/XtkkDqxDiWT3e2QMQGxtL165d+de//sXPP/+MTqejY8eOhIWFkZuby/Hjx5kyZQrJycmSQfa6UeIfiYiIUFWqVFFJSUnatszMTJWcnKz9npOTo5RSSq/Xa9uCgoKUpaWl2rJly8srbDHk5+enbG1t1ebNm9W1a9dUy5YtVb169dTcuXNVVlaWUuq/1/3x6//GG2+oiIiIQim3EM8jNzdXKaXUyZMnVdmyZVXnzp3V6dOnlVJK3b59W3Xs2FHpdDo1ffp07ZysrCzVpUsX1a9fv3z3gngxpk2bpoyMjNQ333yjdu3apQ4ePKicnZ1Vw4YNVXZ2dmEX77WQmJio3n//fTVkyBB1/Pjxv+zPezYLIcSzyHsWK6XUt99+q3r16qViY2OVUo/qF1dXV1WmTBkVFxenbYuNjVVlypRRn3zyiXauPJNfHxJg/0OBgYGqVatWSimlTp06pWbPnq1q1KihqlWrpgYOHKgd93hwZ2Njo77//vuXXt7i5MiRI8rR0VHt3LlTKaVUXFycKlWqlGratKmqWbOmWrhwoRZkG77QBgUFKWtra7n+osjS6/VacHDixAllY2OjOnfurM6dO6eUetTw16RJE9W2bVu1c+dOFRYWpjp27Kjq1aun3QuGLwai4BjW9QsWLFA1a9ZUpUuXVg0aNFBt27ZVDx8+VEpJcPeyJCYmqsaNG6tevXpp94cQQhQkX19f9cYbb6jo6Gh18eJFbXtekF22bFn1888/K6UePSN+/PFHVaFCBeXi4lJYRRaFRALsJ3hSC1NISIiqW7eu6tmzp7K3t1f9+/dX3377rVq+fLkqX768SkxMzHdecHCwBHfP6PGAIDU1Va1evVo9ePBA7dmzR5UvX14tX75cKaVUgwYNVJ06dZSfn5+6f/++dk5QUJAqXbq0XH9RZBneB7du3VJKKZWcnKxsbGzUxx9/rP7zn/8opZTasGGD6tq1q7KwsFBOTk6qb9++EtwVgosXL6rjx4+r48ePa3876cF+uQ4ePKjc3d2lUUkIUeAOHz6sateurfbs2ZNve159k5ubqz755BOl0+lUYmKiUupRPBEdHa2qVaumPbPF60GW6XqM4RiLa9euodfrsbW15cGDBwQGBpKUlETbtm1p06YNVatW5ejRowwdOpT169dTo0YN4NGYa09PTyIiIujRo0dhfp0ix/D6nz59mnfeeQcjIyPu3LmDtbU1/fr1o0qVKvj7+2NsbEyfPn04cuQILi4uLFy4EJ1Ox/bt2+nSpQtRUVH07NmzkL+REE/P8D6YP38+p06dYvz48VStWpVTp07RtGlTnJycCAkJ0ZZ+On/+PHZ2dpibm6PT6bSlosSLpf5m+UZZCqpw5P09DO8hIYR4Xjt37sTNzY29e/dSs2bNfPsePnyoLdk1efJkJk+erD1/lVLcu3cPCwuLl15mUXjk6fOYvAfyxIkTcXZ2xtHRkcmTJwPw1VdfsXr1atzc3HjzzTfJyMhgypQplC5dmurVqwNw+/ZtkpOTiYqKkuD6KRm+EE2ZMoUBAwawf/9+9Ho9NjY2wKNGD8NlVszMzAgKCmLBggXaS1XVqlWJj4+X4FoUWYYTKwYEBODo6KgtM1erVi0OHjzI/v378fDw4MyZMwBUq1aNEiVKaJP+SXD9bP5uMsq/a4vOC64f3y/BdeHI+/+X4FoI8awM6/Pc3FwAsrKytPdMePSsyDvuxx9/5IcffgBg2rRpmJiYaBOa6XQ6Ca5fQ9KD/QTr169n/PjxfP3119y4cQM/Pz969+7NjBkzqFKlCllZWYSGhrJ582auXr3KoUOHMDU11QLEzMxMLC0tC/trFFm+vr6sXr2aJUuW0Lx5c2xtbQHIzs5m6NChHD9+HAcHB1JSUrh58yZJSUkYGxtLj5EoVnbs2MHgwYMJDw+nVatW2va8nulTp07h5ORE7dq1iYqKolKlSoVY2uLBsJHv8OHDpKWlUalSJezt7TE3N//bOsawFzsxMZFq1apRpkyZl1p2IYQQz+/x7Je8Z+7Dhw+pU6cONWvWZOvWrVojdlZWFn369MHR0RE/P79CKrV41UgXB3+9mWxsbJgwYQKDBw8G4IMPPqBt27YA+Pv7Y2dnR2ZmJg0aNOC7777TWqrybjYJrp9dYmIiGzZsYP369Xz44YdkZWVx6dIlDh06RJMmTVi8eDGjR4/m+vXrVKpUid27d2NsbIxer5fgWhQrZ86cwc7OjqZNm2rb8nqmHz58SK1atYiLi8Pb21trhBLPzrDX09fXl82bN3Pz5k3q1KlDrVq1mDdvHhYWFn8Jsg2D68WLFzN+/HgOHz4sAbYQQhQxhvHA4sWLOXjwIKmpqbRr145x48axdu1aevfuzYcffsigQYMwMTEhNDSU1NRUNm/eXMilF6+S1z7AVo+tV3327Fni4uL417/+pR3j5OTEnj17aNeuHQDz5s3jq6++0vbn5uZKOmYBycjIQK/XY29vz4EDB4iKiiImJoYrV65Qv359li9fztKlS/O94MpYU1EcKaXIysrizp07lC9fXtuu1+vZuHEjTZo0oUGDBuzYsUPbLmmxzy4vSPb392f16tVs2LCBVq1a8eWXX7Jq1SrS0tJYtWoVVlZWWpBtGFwHBwczefJkVqxYQe3atQvzqwghhHgGec/Q8ePHs3btWtzc3GjZsiWenp5cvnyZ7777jn379jFo0CDmzp2LqakpNWrUICYmBhMTE8mkFJrX+m3M8OVo2rRpeHp68vvvv3PgwAE2bdrEoUOHtGOdnJyIi4sjLCyMwMDAfJ8jN9OzedLohCZNmnDv3j06duxI+/btuXfvHtOnT+e3334jJSWF33///S+9RxJci+KoXr16pKSkEBkZSXZ2NvAoCLx//z5r164lNjYW+O99JMH18zt9+jTbt28nJCSEDz74gF27drF69Wp69epFcnIyQ4YM4e7duxgbG5OdnZ0vuB43bhzLli2jT58+hfwthBBCPKvffvuN77//nsjISKZPn07dunUxNjbGwcEBCwsLqlatyu7du4mLi2P37t1s2LABU1PTfPMDCfFaRyZ5L0cJCQmcPn2an3/+GScnJw4dOkTfvn1ZsGABXl5eODo6AtCsWTOOHTsmvRMF4PHZ2s3MzDAyMsLa2prjx4+zbt066tatS6tWrShZsiRKKapXr/6XoPxJs/cKURx8+OGHTJo0iZEjR3Lr1i2aNWuGtbU1kyZN4saNG3h4eAByDzyPx3v9a9asyfDhw3F0dGT//v24ubkxd+5chg4dymeffUZYWBipqanExMRQqlQp4FFw7evry8qVK2ViRSGEKGIefw6kp6dToUIFWrRowcaNG3FzcyMwMJCBAwdy+/ZtkpKSaN26NeXKldPOkc4e8bjXfpKzdevWERwcTHZ2Nlu3btVSMePj43Fzc6NZs2aMHTuW999/P995kpb87Awrs+nTp7Nr1y7OnTvHRx99RJ8+fbRUfID79++Tnp6Om5ubNqGctBCK4s4wu2bhwoUEBgZy/fp13nrrLcqVK0dsbCympqaSjvYcDOuhAwcOUL58ed555x1tv7e3N2lpaSxduhQzMzOmT5/O3r17adCgATNnzsTIyIi4uDjatWsnSwIKIUQR99133+Ho6Ei5cuVwd3dn8ODBfP311wQEBODp6Qk8mnx01qxZhISEUK1atUIusXiVvfY5hZUrV+bhw4ecPHmS+Ph4bfsHH3xAaGgohw8fZsKECaSkpOQ7T4LrZ5f3Uvv1118zb948PD098ff358yZM4waNYoff/wReNSIsWrVKrp27cqdO3c4cOCANlu4EMWZYa/0yJEj+emnn9i3bx/r169n165dko72nAyD6wkTJjB06FCSkpLIyMjQjrl8+TInT57U1jY9evQo3bp1Y/bs2dq5bdq04dChQxJcCyFEEWO4JOPatWsJDAzEwsICOzs7qlWrho+PD19++aUWXN+/f5/FixdTvnx5qlatWljFFkXEa9WD/XeTAB06dAgvLy9sbGwYPXo0zs7O2r6ffvqJoKAgNmzYIGMcC1BsbCxjx45l5cqVNGnShJ9++onOnTvj4OBAeno6CxcupH379qSkpLB79248PDwwNjaWzAHxWjHsyTYkPdcFY8qUKQQHB7N27VqaN2+ebwWI8PBwZs6ciYmJCSYmJqSnp3Ps2DFMTExQSsnKBUIIUQz8+uuvhIWF8f777zNkyBAANm7cyPTp07G1tcXV1RWAsLAwrly5QmJiIiYmJjKxqPifXpsA2/BGOHXqFHfu3KFGjRpYW1tjYmJCfHw8EydOpGzZsowYMSJfkP2kzxDP59SpUyxfvpxZs2YRExPD559/zowZM2jQoAG9e/emZMmS+Pv70717d+0cCSpEUfd3dcjfBdL/dL94eufOnaNr165Mnz6dbt26cePGDf7zn/+wa9cu7O3t6datGxEREezbtw8zMzMCAgJkllghhChGfvvtNwYMGEBaWhozZ87UAmyADRs2EBMTw5YtW3j//fepVKkSq1evluFZ4h95LQJsw5fTiRMn8sMPP/DHH3/QrFkz2rZti5eXFyVKlGDv3r18/fXXlCtXjoEDB9K5c+dCLnnx8HdBRXp6OiVLlqRbt240adIEPz8/ADp06MCFCxdo2rQpoaGhL7m0QrwYhvfB4cOHSUtLo1KlStjb22Nubv63D2zD+isxMZFq1arJGssF4OLFi3Tv3p3Ro0dTqVIl1q9fT2JiIg8ePCAzM5OpU6fi7u6e7xzJoBFCiKLrSY3V8+fPZ/bs2dStW5elS5dSvXr1fPuvXr3KG2+8odX98hwQ/8Rr0R2bdzN98803rFixgrlz53Lp0iVsbGwICgpi0qRJZGVl0bp1a7799ltOnDjBr7/+WsilLh4Mg4oTJ05w9OhRbZ+1tTW3bt3i+PHjVKxYEYC0tDTKli3L9OnTWbNmTaGUWYiCppTS7gNfX18+/fRTBgwYwIgRI/jyyy+1pZ8en1/A8GVg8eLFtG7dmqtXr7708hd1hmPt8tja2mJra8v8+fPp2LEj1tbW+Pv7s3//fmrVqvXE6ywvVUIIUTTp9XrteWr4TBg9ejReXl5cuXKFRYsWceHCBW2fUoqKFStqdb/MFi7+qdfmv+TYsWNs27aNNWvW0KFDB/bs2cOOHTto3bo127Ztw8TEBD8/Pz744APWr19P/fr1C7vIxUJeUDFu3DjCw8PJyMigXr16zJo1i8aNG2NtbU3Tpk3ZsGGDNpN7VlYWPXr0QKfTSVq+KBbyHur+/v6sXr2aDRs20KpVK7788ktWrVpFWloaq1atwsrKSuvJNgyug4ODmTx5MitWrJBlAp+SYR1y/PhxzMzMUEphb2/Ppk2bOHjwIJaWltpyjABZWVlS7wghRDFh2Mg9f/584uPjsbGxoU6dOnh7e+Pl5UVubi7h4eHodDpGjx7NW2+99ZfebhmqJf4xVUzl5ubm+/3+/ftq9erV6vbt2+rnn39WFSpUUCEhIUoppVq3bq0qVqyoBg4cqLKysrRzcnJyXmqZixPD679p0yZlb2+voqOj1W+//aYaN26s6tevr3bs2KGUUmrnzp2qZ8+eqk6dOqpz587q4cOHf/kMIYq6lJQU1bJlS7V161allFI7duxQFhYW6rPPPlPvvvuu6tu3r8rMzFRKKe0eUEqpoKAgZW1trb7//vtCKXdRptfrtZ+nTJmi6tatq6pVq6Zq1aqlVq9ene/YjIwMdfbsWdWpUyfl4OCgsrOzX3ZxhRBCFDDD58C0adOUlZWV8vDwUL1791aWlpaqS5cu2vumv7+/atSokRo4cKBKTU0trCKLYqBY9mAb9lgcPHiQcuXK8c477+Dq6oq5uTmhoaG4urri5uYGQN26dbl79y42NjbakiyATGDwHPKuf2RkJOfPn2fYsGF06dIFeDSpRJs2bRgzZgyLFi3C2dmZ9u3bc+vWLcqUKYNOp5MxLqLIezz7ombNmgwfPhxHR0f279+Pm5sbc+fOZejQoXz22WeEhYWRmppKTEwMpUqVAh71XPv6+rJy5UpZCuoZ5PU2+Pn5sWTJEsLCwnj77be18dX379/Hw8MDgNDQUMLDwzE3N+fw4cMyoZkQQhQDec+BgwcP8scff7B582batWuHXq/n0KFD9OjRA1dXVyIjI/Hx8eHevXtcvHiR8uXLF3LJRVFW7HLg1GNjHT08PPjtt9/IyMjA3NwcgNTUVK5du6YFcDdv3mTMmDHMmTMHIyOjJ47XE09HKcXDhw8ZNmwYvr6+nDt3TttnbGzMzz//TPny5Rk1ahTR0dHk5ORQtmxZLS1cgmtRlBkG1wcOHODs2bMAuLq6Ymdnx+bNm/noo4+0Rr5atWrRrl07GjVqRIkSJQCIi4tj2LBhhISESHD9HBISEti7dy8RERE4OzuTkpLC9u3bcXFx0a4vwKBBg/Dy8mLnzp2yzrgQQhQjW7ZswdPTk7i4OKpUqQI8CrybNWtGaGgoP/30E9u2bQNg6tSprFy5UuIB8VyKXYCd11I1Y8YMVqxYwbx58+jWrRtWVlbAo6We3nvvPc6fP0/37t1p1aoV//d//0ffvn1lzG8BMzMz48KFC7z33nvExsZy5MgR1P+btN7IyIg9e/ag1+uJiorC1NRUO0+uvyjKDOuQCRMmMHToUJKSksjIyNCOuXz5MidPntQyZo4ePUq3bt2YPXu2dm6bNm04dOiQBNdPST22MEbFihXp1KkTLVq0YM+ePQwZMoQZM2YQERFB+/bt8fDwYNasWZibm9OjRw9tsjlp5BNCiOLhjTfe4K233uLPP/9k7969wH/jhdq1a2NlZcWdO3e043U6Xb4OOyGeVrFbpkspxa1bt+jcuTODBg1i0KBB2r68dL+0tDQWLlxISkoKJUuWJCgoSNa1ewHy0rwzMjJ4//33KVOmDMuWLeO9997TjlFKodfr5bqLYmfKlCkEBwezdu1amjdvjqWlpbYvPDycmTNnYmJigomJCenp6Rw7dgwTExO5J56DYePG2bNnsbS0pGLFitp2Nzc3SpUqxYIFCzA1NcXT05OEhARKlChBfHy8TGAjhBDFVFJSElOmTOHPP//E29sbV1dXAO7du0fDhg0ZO3ZsvnWwhXgexa6JPm/87oULF7TxE3kvV8bGxty7d4+cnBxtzeU8Mua34JmYmJCTk4OVlRWJiYk0bNgQDw8Pli1bhoODA/Do75XXYyQBhSguzp07x8aNGwkODsbZ2ZkbN25w5swZdu3ahb29Pf369cPIyIh9+/ZhZmZGQEBAvjG/ci88G8PMgejoaFJTUxk4cCC9e/emcePGJCUl0b59e0xNTcnKyuL69ev4+fnh4uICPHmNVCGEEEWfg4MDEydOJCAgAB8fH44ePYqdnR1xcXEAuLu7F3IJRXFSLCPK0qVLY25uzu7du+natas2jsLIyIgTJ06we/du3N3dqVChAiDr2j2rq1evautX/x3DIPvo0aM0atSIbt26sXv3bmrUqKEdJwGFKE5MTEwwMzPjzp077N69m/Xr15OYmMiDBw/IzMwkLS0Nd3d3rQUdpJHveRj2XEdFRREaGkpgYCDHjh0jJiaGM2fOMGnSJNzd3Rk7dizp6en8/vvvZGdn06lTJ0CCayGEKO4aN26Mr68v06ZNY9GiRTg5OdGrVy82b96MkZGRdPaIAlPsBhfo9XrMzMzw8fFh48aNBAQEAI96Nh4+fMjEiRM5fPhwvtkB5aXq6XXr1o2oqKh/dKxhkH3o0CGaNm1KtWrVXnAJhXg5njQJiq2tLba2tsyfP5+OHTtibW2Nv78/+/fvp1atWly9evUv50hw/ezyguv4+Hh+/fVXpk2bRvfu3Zk8eTKTJk3i1q1bfPvtt9rf5MKFC9SrV4+DBw9qGTTyHBBCiOLP0dGRKVOm4OLigrm5ORUqVMDIyEjGXIsCVezGYOe5dOkSy5cvZ+HChTRp0oSyZcvyxx9/kJGRQUJCAqamptJj8RzCw8Pp1asXZmZmpKenY21t/f97zuM9dNnZ2fkmNxOiqDHsOT1+/DhmZmYopbC3t+f+/fscPHgQS0tLHB0dtXNatmxJ165dGTduXGEVu1hKTU2lZcuWXL9+nalTpzJ69Ght39atW1mwYAGlS5dmzJgxtGjRQtsnmQNCCPH6+e2335g7dy43b95k8ODB9OvXr7CLJIqRIhdg/5O05Dx37twhISGBpUuXYmVlha2tLdOmTdN6VOWl6uk93iixYMECzp8/j5eXF2+99dZTnStEUWb4/+zn58f333/PvXv3MDMzw9fXl88//1w7NjMzk2vXrjF8+HCuXLnCkSNHpP55AY4dO0bv3r2pWrUqc+bMoX79+tq+7du3M378eFxcXPD39wekThJCiKLs71b++f+r2/POS0hIYMKECZiZmREeHq6tOCTE8ypSAXa3bt1wdnZmxIgRz/U5Msai4AQEBDB37lyGDh3KkCFD/jbINqzsgoKCuHDhAjNmzHiZRRXihfDz82PJkiWEhYXx9ttvM3XqVMLDw1m6dCkeHh4ALFmyhPDwcMzNzYmNjZVVC16gpKQk3N3dadSoEaNGjaJu3bravv3799O0aVO57kIIUcQZBteHDx8mLS2NSpUqYW9vj7m5+d8+Y/PCHp1Oxx9//MHp06epU6cOlStXfqnlF8VbkRps0LdvX4YOHQpAenr6Pzonb8kbw5/l5erpGV5HQz4+PkyYMIFVq1YRHBzMxYsXn3huXnAdHByMj48PjRo1euFlFuJFS0hIYO/evURERODs7ExKSgrbt2/HxcWFYcOGERISAsCgQYPw8vJi586dmJqakpOTI/XQC+Lg4MCKFStISEhgwYIFJCcna/ucnJy0MddCCCGKJsPx0r6+vnz66acMGDCAESNG8OWXX3L37t0n1vV576M6nY5FixZRv3593n77bQmuRcFTRYBer8/3+/z589WoUaPUhQsXnvpc8fz27Nmjdu7cqf79739r2+bMmaMqV66sJkyYoC5evKhtz83N1X4OCgpSNjY26vvvv3+p5RWioDxen/z555/K399f3b9/X/3000/Kzs5OLV26VGVmZipnZ2el0+nUzJkz852Tk5PzMov82kpMTFSNGzdWvXr1UufOnSvs4gghhChgM2bMULa2tmrv3r1Kr9er4cOHq1KlSqmePXuq9PR0pdR/n7mGz++goCBVtmxZFRERUSjlFsVfkejBfnwcxf3791m/fj0hISFP7DHNox5LS/b19X2h5SyOfH19tZnYAby8vBgwYAC9evXCw8OD6dOna9u9vLwIDQ1l2bJlnD9/Hvjv7L7Lli1j3LhxrFixgp49e778LyLEc9Lr9Vp9cvbsWa5evUqVKlXw9vbG3Nyc0NBQunfvzqBBg7CwsKB69eo4OjoSHR2tpaSBLEn3sjRs2JDAwECsrKyoWrVqYRdHCCFEATp9+jTbt28nJCSEDz74gF27drF69Wp69epFcnIyQ4YM0Xqys7Oz82VSjhs3jmXLltG3b99C/haiuHqlZ9lRSj1x2nwfHx9KlCjBrFmz0Ov1eHh4/GXsr3pCWvLKlStfWtmLg6tXr3L69GmuXLmCjY0NzZs355dffuGHH37AyMiIsLAwNm/eTGZmJjNmzMDLywsjIyPGjh3Lm2++qaXzBwcHM3LkSNavX0+PHj0K+VsJ8Wzy6qEJEyYQHR1NamoqAwcOpHfv3jRu3JikpCTat2+PqakpWVlZXL9+HT8/P1xcXACZUKswNGnShMaNG6PT6f52MhwhhBCvvsfr8Jo1azJ8+HAcHR3Zv38/bm5u2pxAn332GWFhYaSmphITE0OpUqWAR++jvr6+rFy5Ujp7xAv1SgfYeeMkAOLi4sjJycHOzo569eoxatQocnNzmTt3LgCenp68+eabQP6b0DC4lpvp6VSsWJGZM2fi7+9PZGQkBw8exMnJSRs/XaVKFUqWLElMTAw6nY7vvvuO0aNHY2dnR69evQC4ffs2Z8+eJTw8XIJrUSQZ1idRUVGEhoYSGBjIsWPHiImJ4cyZM0yaNAl3d3fGjh1Leno6v//+O9nZ2XTq1AmQ4Low6XQ6Wd9UCCGKMMPn8IEDByhfvjzvvPMOrq6uAMydO5ePPvoINzc3AGrVqkW7du1o0KABJUqUAB7FEcOGDSMqKkriAfHCvZKziPv6+lK6dGl8fHyAR+nHGzZsIDMzk3r16vHxxx8zceJE4NFNNW/ePNzc3Bg4cCDVqlXTPmfZsmV4e3tLcP0MDCuz06dPExAQQExMDO+99x4xMTHacdeuXWPRokXExsbSpEkTFi9e/JfPyMzMxNLS8qV/ByEKUnx8PBs3bsTBwYGBAwcCsG3bNubMmUOZMmVwdXXlxo0bREdHU7lyZYKCgmS2cCGEEOI5GL6PTpgwgW3btuHn54ezs7O2rFb//v35448/2LdvHwC9evWiTZs2DB8+PN9nHTlyRCbZFS/FK9eDLWnJr4a8yiwjI4OaNWsyfvx4dDodP/74IwsXLmTkyJEAVKhQgZEjR3Lnzh0yMzPz9dTlfYYE16Koy0sHv379OlOnTtW2d+7cGaUUCxYsIDIykjFjxvDFF19o+3NycmS9ayGEEOIZ5b1LTpkyhZUrV7J27VqaN2+e793SxcWFmTNn0qhRI0xMTEhPTyciIgLIv4KQBNfiZXnl3vwkLblwGbYUbt68mRkzZrB161Zq1KiBj48PSikiIiIwNjbWWgbLly/PtGnTsLGx0dIxJR1WFCe2trZs2rSJ3r17ExMTQ7t27ahfvz4AXbp0wcjIiPHjx7N161ZatGgBPHqoS3AthBBCPJ9z586xceNGgoODcXZ25saNG5w5c4Zdu3Zhb29Pv379MDIyYt++fZiZmREQEICJiYmWQSZZZOJle6VSxCUtuXAZXv/Y2Fh++OEHli1bRteuXQkKCqJixYqkpKQwc+ZMkpOT+fTTTxk2bFi+z5DgWhRnSUlJuLu706hRI0aNGkXdunW1ffv376dp06byIBdCCCEK0MWLF+nevTujR4+mUqVKrF+/nsTERB48eEBmZiZTp07F3d093zmSQSYK0ys168uT0pJdXFw4duwYCxcu1I7LS0tu3rw5WVlZ+ZbAkbTkZ5d37by8vPD29sbCwoIOHTqQkJDAZ599xpUrV7C3t2fcuHHUq1ePuXPnsmnTpnyfIcG1KM4cHBxYsWIFCQkJLFiwgOTkZG2fk5MTxsbG5ObmFmIJhRBCiKJLr9f/ZZutrS22trbMnz+fjh07Ym1tjb+/P/v376dWrVpcvXr1L+dIcC0K0yvRg/13ackVK1bkzJkz+Pv7k5ycTP/+/fNNWHD79m1JSy5gv/zyC3369CEqKoqWLVsCsGrVKkJCQrC2tiY0NJQKFSqQnJxMdHQ03t7e0mMnXjtHjx7Fw8ODqlWrMnPmzHyTKwohhBDi6RnGA8ePH8fMzAylFPb29ty/f5+DBw9iaWmJo6Ojdk7Lli3p2rUr48aNK6xiC/EXhd6D/Xha8s6dO0lISMDT05OrV69So0YNxo0bR506dQgLC2Pp0qXauaVLl5bguoClp6fz4MEDKleurG0bMGAArq6u/PrrrwwZMoTU1FTq1KmjBdfSYydeNw0bNiQwMBArKyuqVq1a2MURQgghijTD5RT9/Pzo27cvHTt2pGvXrqxZs4YSJUrQunVrHB0dyczM5Ny5c3z00UdkZmbi5eVVyKUXIr9CD7AlLfnVUqVKFSpVqsSRI0e0NB1TU1Pc3NyoUqUKZ86c4YsvvuDOnTtaz7X0YIvXUZMmTVixYgVGRkZPTGkTQgghxD+T9y7v5+fHkiVLmDdvHjt27KBRo0a4u7sTHBysHRsaGspnn33Gw4cPOXz4sDahmRCvildigMIvv/zC+vXrn5iW7O7uTmhoKPb29owePZrq1avTrVu3Qi5x8VW7dm0qVarEvHnzePPNN2nWrBnwaFx8/fr1adKkCWFhYezbt4+PP/64kEsrROHKy6DJaygUQgghxLNJSEhg7969RERE0LZtW7Zv38727dtxcXFh2LBhGBkZMWTIEAYNGoStrS3dunXD2NhYJjQTr5xX4q1Q0pJfDXq9HnNzc6KiosjMzGTUqFF8/fXXREVFaS2FXl5eXL9+nX379hV2cYV4JUgGjRBCCPH0Hp8GqmLFinTq1IkWLVqwZ88ehgwZwowZM4iIiKB9+/Z4eHgwa9YszM3N6dGjhxYPSHAtXjWvRIAtacmvBiMjI3JycrCxseHXX3/lvffeY9euXXz99deYmppq61/b29vLpE5CCCGEEOKZ6PV6rYH67NmzXL16lSpVquDt7Y25uTmhoaF0796dQYMGYWFhQfXq1XF0dCQ6OjpfYC7xgHgVvRIBtmFa8qFDh7TteWnJbm5unDt3TnpNn9PjLYVPygLIG8dibW3N0qVLiYuL45dffiE2NpaSJUsyadIkTpw4Qdu2bV9WsYUQQgghRDGSN7RqwoQJdOvWjbp16zJu3DgSEhIASEpKwsLCAlNTU7Kysrh+/Tp+fn788ssv2vAsIV5Vhb5MV94s4nfu3KFVq1aULFmSDh060KBBA4KCgrC0tGTTpk289dZbuLm5MX369MIsbpF18+ZN3njjDQDCw8Pp16/f/zz+8ZnZk5OT8fHx4ejRo2zdupWGDRu+0PIKIYQQQojixXD1oKioKMaMGUNgYCDHjh0jJiaGSpUqMWnSJH799VfGjh2Lu7s7v//+O9nZ2Rw+fBhjY2NZPUi88go9wAa0yQnS09Px9vYmKSmJ27dv8/bbb7N582ZKlixJmzZt6N+/P4MHDy7s4hY5MTExjBs3jpiYGObNm0dISAjJycm89dZb//O8xyuwLVu2UKdOHezt7V90kYUQQgghRDEVHx/Pxo0bcXBwYODAgQBs27aNOXPmUKZMGVxdXblx4wbR0dFUrlyZoKAgTE1Nyc3NlbRw8cp74QH240Ha390Yedv1ej0PHjwgMzOT8uXLAzBp0iSWL1/O/v37qV69+ossbrF0//593n33XXJyckhPTyc+Ph4HB4d8rYiPM/y7RUVFUbJkSTp37vwyiy2EEEIIIYqZ1NRUWrZsyfXr15k6dSqjR4/W9m3dupUFCxZQunRpxowZQ4sWLbR9Mlu4KCpe6BjsmzdvakFaeHg48PeTEeSlfBgZGVGyZEnKly9PcnIyXbp0YfXq1fz4448SXD+D7OxsSpQogaurK5cuXaJy5cqUKlWK3NxcjIyMnjiGxTC4Dg4Opm/fvpQsWfJlF10IIYQQQhQztra2bNq0CVtbW2JiYvj3v/+t7evSpQtjxozh1KlTbN26VduulJLgWhQZLyzAjomJoXXr1ly8eJExY8YwdOhQLl68+D/PeXzSgjp16jBo0CD27NkjY36fkampKQDOzs4cOnQIvV5P3759OXbs2BPHsGRnZ+cLrsePH09UVBTt2rV76WUXQgghhBDFT4MGDYiMjOTGjRssWrSI48ePa/tcXFwIDg7ON++SjLkWRckLSxGXtOTC8/g1Nvw9IyMDR0dHLC0tWb16NQ0aNABg3rx5jBkzRjsnODiYcePGsXLlSnr27Plyv4AQQgghhCj2jh49yuDBg3F0dGT06NHUqVMn334Zcy2KohfSgy1pyYXHMJheunQpI0aMoFu3bsTHx3Pz5k2srKxITEzk7t27uLm5sWzZMj7++GMCAwO1ZbuWLl2Kj4+PBNdCCCGEEOKFadiwIcuXL+f3339nypQpnD9/Pt9+Ca5FUfRCAmxJSy48ecG1r68vU6dORSmFpaUlvXv3ZvXq1fz5559YWlqSmJiIjY0N69at4+HDh5w8eRJjY2MuX77MkiVLWL58uQTXQgghhBDihWrYsCGBgYFYWVlRtWrVwi6OEM+twFLEJS351bFmzRqmTJnC5s2badiwIYcPH6Zp06bY2dnxxRdf4ObmRuXKlcnJyeH69evY2tqi0+nIzs7G1NSUtLQ0ypYtW9hfQwghhBBCvCbyOuH+13BSIYqCAgmwH09LPn78OBcuXMDb25u6devyxhtvkJmZiaOjIxYWFnh6erJlyxZOnTpFSkoKxsbGLF26FF9fX1asWCHB9VN6vCJavnw59+/fZ8SIEWzZsgU3NzcCAwM5efIks2fPZurUqbi6uuZrJZTKTAghhBBCFKYnZboKUdQU6CRnvr6+rFq1ip49e5KWlsaePXsYN24cffr04c033+Tu3bt07tyZ3NxczMzM+PHHHzE1NeXy5ct07NiRKVOm0KtXr4IqzmvBMDDevHkzTZo0Qa/XY2JiQnZ2Nl27duXzzz9nzJgxXLlyRZt4bunSpXz66aeFXHohhBBCCCGEKD4KbEG5NWvWsH79en788UctLXnDhg3MnTuX+/fva2nJu3bt+ktacqVKldi7d6+kJT+lvHXDASZMmMCaNWsYP348Hh4emJmZceDAAbKzs2nVqhUAN27c4NNPP6V27dr069evMIsuhBBCCCGEEMXOMwfYj6cUZ2dnM3bsWBo2bKilJYeGhnLy5Em++eYbTExMtLRkOzs77TPyJkST4Prp5aXQfPPNN4SEhBATE0Pt2rUxMzMDHi2VdvPmTZKTkwGYOnUqVlZWDB8+HJClD4QQQgghhBCiID1TirikJb860tLS6Nu3L25ubvTv359Lly5x9uxZQkNDteW3jh8/TokSJahYsSL79u3TGjWEEEIIIYQQQhScp+7BlrTkV4tOpyM5OZkTJ04QHx/PkiVLOH/+PLm5uezYsYNvvvmGunXrkpubS+PGjTE2NiYnJwcTkwIbHSCEEEIIIYQQgmdYB/vxtOS8dPAnpSUfOXKECRMmcPPmTYYPH46xsTG5ubkF+w1ec2XKlGHatGksWbKELl26ULVqVaZPn86RI0do1aoV8fHxNG7cmGbNmmnXX4JrIYQQQgghhCh4zxRppaWlER8fz/z582ncuDGXLl3i6NGjWlpynTp18Pb21tKSN23apJ0rY34L3qBBg3B2dubBgwfUrFkTeJTGf+3aNZo1a5bvWLn+QgghhBBCCPFiPNMY7Fu3blGvXj3c3d3p0KFDvrTkq1evSlpyIcrMzOT3338nICCACxcukJiYKNddCCGEEEIIIV6CZ14He8WKFXh7e5Obm4unpyfOzs60b9+efv36UaJECVauXKkdK7NVvxxKKfbu3cucOXPIzs5m69atmJqayvUXQgghhBBCiJfgmQNsgIsXL/4lLblDhw40a9aMb7/9tsAKKf65Bw8ekJycjIODA0ZGRpI5IIQQQgghhBAvyXMF2HkkLfnV9Pha5UIIIYQQQgghXpznjoKVUhw5ckRLS05ISMDExETSkl8BElwLIYQQQgghxMtTID3YkpYshBBCCCGEEOJ1VyABtiFJSxZCCCGEEEII8Toq8ABbCCGEEEIIIYR4HUlXsxBCCCGEEEIIUQAkwBZCCCGEEEIIIQqABNhCCCGEEEIIIUQBkABbCCGEEEIIIYQoABJgCyGEEEIIIYQQBUACbCGEEEIIIYQQogBIgC2EEEIIIYQQQhQACbCFEEIIIYQQQogCIAG2EEIIIYQQQghRACTAFkIIIYQQQgghCsD/Byb+0uEjaf4GAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "data": { + "text/plain": [ + "sat_ghi_cams_kwh_m2_day 0.367435\n", + "sat_ghi_nasa_kwh_m2_day 0.205303\n", + "kt_cams 0.174068\n", + "nasa_clearness_index 0.081151\n", + "nasa_cloud_amount 0.043698\n", + "nasa_aod_550 0.036104\n", + "doy_cos 0.032094\n", + "doy_sin 0.030406\n", + "nasa_precipitable_water 0.029741\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "id": "9589a0d9537f4164", + "metadata": {}, + "source": [ + "### Training fit — weekly time series per station\n", + "\n", + "The observed series and the model's prediction are rendered with\n", + "markers; the raw NASA / CAMS satellite estimates are drawn as thin,\n", + "low-alpha background lines so the visual contrast surfaces which\n", + "series we *produced* (the model) vs. which series are *context*\n", + "(the warehouse inputs)." + ] + }, + { + "cell_type": "code", + "id": "3ecdb375a5addeb3", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T19:10:14.076837195Z", + "start_time": "2026-06-09T19:10:11.569811981Z" + } + }, + "source": [ + "plot_training_fit_timeseries(\n", + " all_df,\n", + " observed_column=\"y_ghi_kwh_m2_day\",\n", + " prediction_series={\"RF\": \"y_pred\"},\n", + " reference_series={\n", + " \"NASA\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + " n_stations=4,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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7vBc7duwgMzOTxMTEPsUmhBBCCBFJ0ic4bLj1CfLy8khNTeWTTz5pG3RwuVysXbuWW2+9tdvjpE9wWG/6BPPnz+e+++6jurq6beX5xx9/jNPpZMqUKZhMJukTCCH6jQxCCCGGtQkTJrBy5UrOPPNMjEYjjzzyCFdffTV//OMfWbp0Kb/5zW/IzMykqKiI1157jZ/97GcdZrIcyw033MDkyZMBrQPQ3vjx43n22WeZO3cuLpeLn/70p8ecndSdq6++mnvuuYdly5bx61//mpqaGr73ve/x7W9/u61w2K9//WtuueUWkpOTOe+883C73axZs4bvfe97jBs3jmAwyF/+8hcuvPBC1qxZw+OPP97hGrm5uTQ3N/PJJ58wc+ZMbDZbp9lQixYtYvr06Vx99dU88sgjhEIhbrvtNs444wzmzp3b43iam5vZv39/29cHDx5ky5YtxMfHk52djcfj4b777uOiiy4iLS2N2tpa/va3v1FWVsbll1/e7XmXLFnCnXfeSUNDA3FxcYB2A3zWWWexZMkS7rjjDiorKwEwGAxtM3NuueUW/vrXv/Kzn/2M6667jhUrVvDyyy/z7rvvtp379ttv54UXXuDNN9/E4XC0nScmJgar1dq2iuBI2dnZnTonx6s3S6+dTie33HIL99xzD1lZWeTk5PDHP/4RoO29fO+996iqqmLevHlER0ezc+dOfvrTn7JgwQJyc3MBmDZtWofzJicnExUV1en1zz777LiWtQshhBBCRJr0CYZnn0Cn0/HDH/6Q3/3ud4wfP568vDzuvvtu0tPT+frXv97teaVP0FlP+gSLFy9mypQpfPvb3+YPf/gDlZWV3HXXXdx+++1YLBZA+gRCiH40uCUphBCib1qL0LXatWuXmpycrN5xxx2qqqpqRUWFes0116iJiYmqxWJRx4wZo954441qU1OTqqpaEbqlS5d2OOcPfvAD9Ywzzuh0rdNOO02dOnVqp9c3bdqkzp07V42KilLHjx+vvvLKKx0Kuh0LRxQw27Ztm7pw4UI1KipKjY+PV2+88UbV7XZ3OObxxx9XJ06cqJpMJjUtLU393ve+17btT3/6k5qWlqZarVZ1yZIl6n/+8x8VUBsaGtr2ueWWW9SEhAQVUO+55x5VVdVObS4qKlIvuugi1W63qw6HQ7388svbCuGpatfFyR5++GE1Jyen7euVK1eqQKd/y5YtU1VVVX0+n3rxxRer6enpqtlsVtPS0tSLLrromIWpVVVVTzzxRPXxxx/v0J6urtW+Pa1tmjVrlmo2m9UxY8aoTz31VIftXZ0D6LTfkcccWYQuJyen7b3tSk/ev94KBALqj3/8YzU5OVl1OBzqokWL1B07drRtX7FihTp//nw1Jiam7fv1//2//9fhe6Mn7fT5fGpMTIz65Zdf9rmtQgghhBCRIn2C4d0nUFVVVRRFvfvuu9WUlBTVYrGoZ599dltR5KORPkFnx+oTqKqqFhYWquedd55qtVrVxMRE9cc//nFbceuetlP6BEKIvtCpqqr23xCHEEIMb6qqMn78eG677TbuuOOOwW6OAN59911++tOfsmPHjrZl5kOF1+slISGB999/nzPPPHOwmxNxjz32GK+//jofffTRYDdFCCGEEGLASJ9g6JE+weCRPoEQoi8kHZMQQnSjpqaG//73v1RWVnLttdcOdnPEIRdccAH5+fmUlZW15agdKlauXMlZZ501IjsboBXP+8tf/jLYzRBCCCGEGDDSJxiapE8weKRPIIToC1kJIYQQ3dDpdCQmJvLoo49y1VVX9erY559/nptvvrnLbTk5OezcuTMSTRRCCCGEEEL0I+kTCCGEEMdPBiGEEKIfuN1uqqqqutxmMpnIyckZ4BYJIYQQQgghBpL0CYQQQgiNDEIIIYQQQgghhBBCCCGEEKJfDK3qPUIIIYQQQgghhBBCCCGEGDFkEEIIIYQQQgghhBBCCCGEEP3CONgN6C1FUSgvL8fhcKDT6Qa7OUIIIYQQQoxKqqridrtJT09Hrx/YuU3SJxBCCCGEEGLw9bRPMOwGIcrLy8nKyhrsZgghhBBCCCGAkpISMjMzB/Sa0icQQgghhBBi6DhWn2DYDUI4HA5AC8zpdEbknIqiUFNTQ1JS0oDP4hoKRlP8oynWrozG+EdjzO2NxvhHY8ztSfyjL/7RGHN7ozH+oRKzy+UiKyur7f58IEmfIPJGU/yjKdaujMb4R2PM7Y3G+EdjzO2N5vhHa+yjNe5WozH+oRJzT/sEw24QonW5tdPpjGiHo6WlBafTOWq+UdsbTfGPpli7MhrjH40xtzca4x+NMbcn8Y+++EdjzO2NxviHWsyDkQ5J+gSRN5riH02xdmU0xj8aY25vNMY/GmNubzTHP1pjH61xtxqN8Q+1mI/VJxj8FgohhBBCCCGEEEIIIYQQYkSSQQghhBBCCCGEEEIIIYQQQvQLGYQQQgghhBBCCCGEEEIIIUS/kEEIIYQQQgghhBBCCCGEEEL0CxmEEEIIIYQQQgghhBBCCCFEv5BBCCGEEEIIIYQQQgghhBBC9AsZhBBCCCGEEEIIIYQQQgghRL+QQQgxtIQCg90CIYQQQgghhBBCCCGEEBEigxBi6PC7Ydt/IeAd7JYIIYQQQgghhBBCCCGEiIABHYQIh8Pcfffd5OXlYbVaGTt2LL/97W9RVXUgmyGGqtp87b8NhYPaDCGEEEIIIYQQQgghhBCRYRzIi/3+97/nscce45lnnmHq1Kls2LCBa6+9lpiYGL7//e8PZFPEUKOqUFcAmSdCfQGkTBnsFgkhhBBCCCEGWigA5Zsg++TBbokQQgghhIiQAR2E+OKLL1i6dCkXXHABALm5ubz44ousW7duIJshhiJXOZjtkDQRKrZqqZksjsFulRBCCCGEEGIg1e6F6t2QNAmssYPdGiGEEEIIEQEDmo7plFNO4ZNPPmHfvn0AbN26lc8//5zzzjtvIJshhqLafZA4HnQ6iMuF+oOD3SIhhBBCCCHEQFIUqN4DjjRtkpIQQgghhBgRBnQlxM9//nNcLheTJk3CYDAQDoe57777uPrqq7s9xu/34/f72752uVwAKIqCoigRaZeiKKiqGrHzDTeDHn+oBVwVkH2K1vGIy4WiLyBlWsQvNeixDrLRGP9ojLm90Rj/aIy5PYl/9MU/GmNubzTGP1RiHsjrS5+g/w2J+OsPgMUJyZO0wYikSf1ymSER6yAajfGPxpjbG43xj8aY2xvN8Y/W2Edr3K1GY/xDJeaeXn9AByFefvllnn/+eV544QWmTp3Kli1b+OEPf0h6ejrLli3r8pgHHniAe++9t9PrNTU1tLS0RKRdiqLQ1NSEqqro9QO6OGRIGOz4jXV70akOgnUNba9FNdbjL8lHtcRE9FqDHSsA4QDo9KAf0B8/YIjEP8BGY8ztjcb4R2PM7Un8oy/+0Rhze6Mx/qESs9vtHrBrSZ+g/w2F+C0HviCYPAPFq8daUYDPMbVf7pmHQqyDaTTGPxpjbm80xj8aY25vNMc/WmMfrXG3Go3xD5WYe9onGNCnoD/96U/5+c9/zpVXXgnA9OnTKSoq4oEHHuh2EOLOO+/kjjvuaPva5XKRlZVFUlISTqczIu1SFAWdTkdSUtKo+UZtb9Djr/0Sxp8C9qTDrwVngq4ZksdH9FKDHmvQB3vfg+hUyF0w4Jcf9PgHwWiMub3RGP9ojLk9iX/0xT8aY25vNMY/VGKOiooasGtJn6D/DXr87kpwOmDMjENf5+KwqeBMjvilBj3WQTYa4x+NMbc3GuMfjTG3N5rjH62xj9a4W43G+IdKzD3tEwzoIITX6+30phgMhqMu27BYLFgslk6v6/X6iL7BOp0u4uccTgYt/uYa0AGOlI6vJ4yBghWQOSfilxy0WMNBKPgEEsZCbT74m8AaN7BtYHR+r4/GmNsbjfGPxpjbk/hHX/yjMeb2RmP8QyHmgby29AkGxqDGX7MbUqdB67Vjs7SBidisfrmcfNajL/7RGHN7ozH+0Rhze6M5/tEa+2iNu9VojH8oxNzTaw9oCy+88ELuu+8+3n33XQoLC3n99df505/+xMUXXzyQzRBDSV0+JHSx2sEWry299tQOfJv6g6LAgVUQnQwZJ0DaTCjbONitEkIIIYQQYvC1NGn3/fFjDr/mzABX6eC1SQghhBBCRMyAroT4y1/+wt13381tt91GdXU16enp3HzzzfzqV78ayGaIoSIcgoYimNrNIFR8nlaczp44sO3qD0VrQG+ArJO0rxMnQNUOcFd1XgUihBBCCCHEaFK1C5ImavfLrWzxEAqAvxks0YPXNiGEEEIIcdwGdCWEw+HgkUceoaioCJ/PR0FBAb/73e8wm80D2QwxVDQUag/gTd3kDovL0/ZR1YFsVeSVbQK/G/LOAJ1Oe02vh4w5ULZhcNsmhBBCCCHEYAr5tXv+pEmdtznTwVU+4E0SQgghhBCRNXqSZImhp3aftiKgO1FOMNm0XLDDVfUeaCyCcWd3nNkF2koPVdFWgwghhBBCCDEa1eyFuJyuJybFSEomIYQQQoiRQAYhRjpVhboCrSbBUNLSBAGPluv1aOLHQMPBgWlTpDUUQeV2GHcOGDsXUgQgY65WG2KofT5CCCGEEEL0N0WBmj2QPKXr7Y50bUKS3CsLIYQQQgxrMggx0nnr4eBq2L9cW+o8VNTmQ8LYw+mJuhOXqz3MH24dj+ZqKP5KWwFxtBy2zjSwOKBu/8C1TQghhBBCiKGg/gBY48Ea2/V2UxRYnOCpGdBmCSGEEEKIyJJBiJHOU63lV7UlwJ53wNc42C3SBhTqCiBh3LH3NdvAGgeusv5vV6T4GqFgJeSdrhXUO5aMOVCxRSvULYQQQgghxGhRvRNSph59n5iM4dUXEEIIIYQQncggxEjXXA3RyZA5B9Jmwb4PoWmQb+KbSiAqRqv50BPxY7RZUsNBwKutOsmap61y6AlbPDjStE6YEEIIIYQQo4GrQvvvse6ZnTIIIYQQQggx3MkgxEjnqQF7kvb/CWO19EBFX0DVID7wrsuHxPE93z8uR+t4DPWVAqEA7P8YkidrAye9kT4bqndDsKV/2iaEEEIIIcRQUrUTUqYdez9bIvib5T5ZCCGEEGIYk0GIkSzoAyXUccWBPREmXaCtLCj8HJTwwLYp4IXmGojN6fkxRou2mqOppP/adbwUBQ6s1IrnHWtJeVcs0RA/Fiq3Rb5tQgghhBBCDCW+RvDVQ1zesffV68GROvJWQ3hqtf6aEEIIIcQoIIMQI1lz9eFVEO2ZbTDhPG0AYt8HA3vzW7cf4vPAYOzdcXF5Qzclk6pC4WfaYEnm3L6fJ22GFqPfHbm2CSGEEEIIoarQWDx0HnpX79Lq1ul72B2NyQRXef+2aaC5yrU6cooy2C0RQgghhOh3MggxkrVPxXQkgxHGnAExWbDnHXS++oFpU10+JPQiFVOr2BxortJSHg01ZRu1Dl3uaaDT9f08RgskT4GyTZFrmxBCCCGEEJ4abRX0zjdg11va/au7anAegAdboKEIkib2/BhnuvbQXlX7r10DLW2GNjms+IvBbokQQgghRL+TQYiRrLUo9dGkzYCsk7CUrIaGwv5tj7sS9CawJ3S5uaLJxxcFtVQ0dTFDy2DUOh+NRf3bxt6q2gVNpTD2LNAbjv98yVO0z81Td/znEkIIIYQQArTUP/FjYeaVkD0fdHooXQ/b/qvNxq/ZBwHPwLSlZo+2Mtpo6fkxZru2v6+h/9o1GHJO1WIazHp9QgghhBADoJc5ccSwoSjaDa0t8dj7xmbjzz5TmxHlb4K0Wcc3o787td0XpH5pfTF3vrYdRQW9Dh64ZDpXzMvuuFP8GKje07ui1v2p/qDWYZh0PhjNkTmnwQjps7TPYsLiyJxTCCGEEEKMbt5acGZo9/jRSdq/9NnaqgR3OTSVQdlGLB4/BKZAbJY2mSkSk2zaU8JQsxcmntf7Y50Z2uQfW3xk2zSYDEYYezbseReiYrS0U0IIIYQQI5CshBipfPVgcfS49oIaFasVrHZXaQWWw6HIticU0ApLx4/ptKmiydc2AAGgqPCL13Z0XhHhzABv3dDIZeuuhJJ1MH6RNjMrkhLGQdAz8vLeCiGEEEKIweGpBVsXq5FNUdr9ed5pMP0bBDJO0lYcVGyBrf+F/cu1SUCR6hvUHwB7IkQ5e39sTMbIvD8222DsQihcoxXsFkIIIYQYgWQQYqTqSSqmIxmjYPxiMFph77vgb45cexoOaoMIXSy7PljraRuAaBVWVQprvR1f1Bu0WVn9nTbqWLz1cOBTGHMmWOMif36dDjLmQOmGkZX3VgghhBBCDLxQQJvEExVz9P10OtSoOEidrq1UmH65VsvNXQF73o5MKqSqHZAy7ai7eIIe1pStQT3yPjg6RZuQFA4efzuGAG/QS7A1FnsiZM2Dgk8g5B/chgkhhBBC9AMZhBipjlaU+mj0esiZD4kTtWXB7qrItKd2HyRO6HJTXqK9U/YnvQ5yE22dd44fo82gGiz+Ztj/CWSfDI6U/rtObDbojYMbqxBCiGNrcUHZpsFuhRBCdM9Xr62C6G26VaMZ4nK0WfqpM2Dfh1C7v+/taCrT7m+PcQ+9qWoTVd4qanw1HTfoDdpAhLui720YQvY17GNfw77DL8SPgbg8OLBqcAqGCyGE6Du/W57fCHEMMggxUvVlJUR7yZMg73TtJrh6z/G1xVuvzehxpHa5OS3GyoKxHWtXfH1WBmkx1s47O9K0gYBIrtLoqZAf8j/SZofF5fT/9TLnQvlm6YQIIcRQ5q3T6gPJ72ohxFDVXSqm3kgYCxPO1VYyHPysb+mZqnZCytSj7lLsKiYQDjA1YSol7pLOOzjTtcGMEWBC3AQKmgoIhAOHX0yfrQ3UlK4bvIYJIYToPXcllKyXbBZCHIUMQoxEAS+oilYT4ng407Siy7V7oegLrZBcX7QWpO5m9lVYUdlb5QZgQko0AA3eQJf7otNBXO7AjzCHQ1pO3LhcbYBmIEQna+meao5zEEgIIUT/CXhACUFzhFYOCiFEpHlrtXQ/x8saC5O+pq1I6G16Jm89tDRBbG63u/jDfrbWbGVOyhyyHFmUNZehqEcM8MZkgGtkDELYTDYyozPJb8g//KJOp00Ec1ce/0QwIYQQAyfQDEGvNiFYCNElGYQYiTzVkelogDaQMfECCAdg3wfaAEdvKGGtHkTCuG53+bKgjhq3n1ibiYevmAXAmoI6PP5uZljF52nnHCiKAgdXQVQsZJwwcNcFrTZE5XYtl68QQoihJ+gFs33EPBQTQoxAkVgJ0cpghJxTIG3mofRM+cc+BqB6FyRP1lK/dmNbzTZynbnERsViM9lwmp1Ue494mNNa16KlqY8BDC2T4idR0FRwuDYEgMEE4xZBxVZwjYzUU0IIMeL5m8EWP7DPqoQYZmQQYiRqrjm+VExHMhi1Isyx2VqdiOaaYx7SprEYbInaA5puvLFFe3Bz/vQ0pqQ5yUmwEQgprN7XzXWik7WCdL7GnrfjeJR8BSqQPT8ip6to8vFFQS0VTb5j72yN1YpxV+2IyLWFEEJEWMCjDbQ3lQ52S4QQorOQX5tMFOWM7Hnjx2jFq6t3wcHVRy8WHfRBY0m39eEAqjxV1PpqmZwwue21bEd2NymZMkZMSiabyUZGdAb7Gvd13GCJ1vpfB1drtYeEEEIMaS2+esrscdBQJCmZhOiGDEKMRJ4asEdwEKJV6nRt5lPBip7PeqrNh8TuV0G0BMN8sKMS0OpA6HQ6Fk/RitV9tOsoqS3i8gYmJVP5Fm35+Jgzjzpzq6deWl/MKQ+u4Kon1rLgwRW8tL742AelzYKavb1fhSKEEKLfhfwu6iw2CLXI72khxNDjqdUmBPWHqJhD6ZlMsPtt7Z65K9W7tZoSRnOXm0NKiE3Vmzgh+QSMemPb6xmODMqbywkfmRLWmQ6u8khFMegmx0/mQOOBjqshQCvgnTEHCj6RVdFCCDHENbpKWd9cgt9glDStQnRDBiFGGkUBX33kllwfKSZDm/VUtQOK1x69EKe/WWtLTHa3u6zYU02zP0RGrJW5OXEAnDMltW1bMNzN+eMHYBCiZp92jXGLtNUgx6miycfPX9veNiiuqPCL13Yce0WE2QZJE7Ql2UIIIYYUV3MFa+t2oDrSJCWTEGLo8daCvZ/6BaDVh8iZrxVUzv9Iu39uLxyC2n1aKqZu7KrbRaI1kRR7SofXLQYL8VHxVHiOSEnkTNce8PS1Xt0QYzPZSLOnkd/YxSSvxHHgzNRWRMjMWiGEGJpUFb+/EYs1lnxdCBoKB7tFI0fID2UbtfsJMezJIMRI463TZiVF4KF5t6Kc2qynoAfyP4RgS9f71eVD/NijriB4Y7P2wOaiWeno9ToaWhqYkxNHvN1Mky/I+sJuZlTZ4rVOj6f2eKPpWmOx9tB//DlgiorIKQ/Wejr1HcKqSmFtD2bOpkzX2jRQKaiEEEIcm6Lg97vwoFBntkpKJiHE0NOfKyHai8+DiedDzR448Onh9Ez1BRCdotWZ60JDSwNFriJmJM3ocnuWI6tzSiaDSZtwNYJmmk5OmExBY0Hn1RAAmXMBFUo3DHi7hBBC9EDQh1cNMyv5BIoNerw1u2XgOFKaSrXMIPvel1XnI4AMQow0/ZWK6UgGE4xZCI402PMOeOo6bldVqN0PieO7PUWTN8iqvVrdh6Wz0qn11bK8eDnuQBNnTdJi+PiYKZn6oehPczUUfQnjzu62w9QXNW5/p9f0OshNtB37YKNZS4dVsu7oq0+EEEIMnJAPLyrx1gSKCIG7Un5HCyGGFm892AdgEAIOTVS6AIyWw+mZqnZCytQud1dUhY1VG5mZNBOLwdLlPhnRGVR7qwkqRzycd6aPmLoQAHaTnTR7Gvsb93feqNNB3hnQVKL1r4QQQgwtAQ9uFGKjYpmYegJFLbUjaqB8ULkrIGOu9vxvz7vdp34Uw4IMQow0nmqIThqYa+l0kD4Lsk6E/cuhruDwNle5lkbIGtvt4e/tqCAQVpiU6mBSqlNbih2VSH5jPuccqgvx8a4q1O5GkOPHQMPByI4w+xqhYCXkna6ttogQRVH552otfZSu3esn5sWTFmPt2UmSJmmDPwUrZCmaEEIMBQEvzTqFKfFTKPPXEjbbtNQnQggxFAR9oIbBbKeiyccXBbXHTgN6vPQGyD4ZMk6AfR+CwQzRXU+Qym/Ix2KwkO3sPnWryWAiyZpERfMRKZliMkdcCrxJ8ZPY37i/84ALaBOSxi3SUlI0Vw9844QQQnQr6GsgYDRjNVrJi8mjLspBY+WWwW7WyOCqAGcapE6D7JO01I+NPaitKoYkGYQYaZoHaCVEe7HZMGGJlr6oZP2hVRD7IKH7VRAAb27ROg5LZ2VQ56vDVVPGrFITpWV7mJdnx2LUU9rgY0+lu+sTRDnBZI3cCHPAqw2mZJ2o/ZKLoLe3lbOz3EW0xch7PziNX31Ny4u7obCBkvoeLinT67UC2aYorZ1dLdcWQggxcALNNKsqidZEYi2x1JmjRtxDMSHEMHYoFdNL64tZ8OAKrnpiLQseXMFL6weg8x6XC5Mv1Cb2dNW0oId9DfuYkzLnmKfKcmZR7D6izdY4LU90wBOBxg4N0eZoUu2pFDQWdL1DlFN7Pw+s0mrvCSGEGBI83mosVm0Sq16nJzdnIeUlX0pKpuPV0qRNfm7NUBKbDeMXa/VpK3cMbttEnwzoIERubi46na7Tv9tvv30gmzFyBbygKmCJHvhrW2O1OhEtTdrIpLtSyw3bjYomH2sPasuoLpqVzoY3nyDqe7+j8rob0f38QfLfforTxmsrOj7aeZRBhvgxkSlQHQpo7U6ectR290UgpPB/H+0F4JYzxjA5zcl1p47htPGJhBSVR5Z3UYSuOzod5CzQOl77PtQ6X0IIIQaFv6UBxWTFZDCR7cimRBceUelBhBDDnLeWGjWaO1/bjnLoOYiiwi9e29H/KyJA65NEObvctLFqIxPjJ2IzHTstaZo9jTpfHYFw4PCLOp2WkslVHqnWDgmT4yeT35Df9WoIODQbdIa2MnqEFOYWQojhzuepIapd/aWMhAkEjWaqqrYNYqtGAFeF9re+PVu8lvqx4SAUfSGpcIeZAR2EWL9+PRUVFW3/Pv74YwAuv/zygWzGyDWQqZi6YjRrdRSikyFpgpY6qBtvbSlHVeHE3HhMrmKKX3mWtFrtl0dWlcKWV/7BuVpGJj7eXdn9NePyoKHo+H7xKGEo+ERb1p0ype/n6cYLa4soqfeR5LBw3al5BCsr8Xy1lh/PjgPg9c2l7K/uZrVHV3Q6bRmaMx32vq8ttRdCCDHgfN46oqwJgJa3vFTxE2ppgGDLILdMCCEATx1FLba2AYhWYVWlsHbwijsWu4oJhoOMjz36qulWRr2RVHsqpc2lHTc407WClSPIMVdDACRPgqgYLTWTEEKIQdfiq8NmO5yRRKfTkZG1gKKiVd2nFxfH5irT6tAeyWyDCedBqAX2fyyTc4eRAR2ESEpKIjU1te3fO++8w9ixYznjjDMGshkj12CkYjqSTgfpsyHj6Eur39iizVpaOjudHflryK1U2r4ZbQGI8ahkGQrR6WBHmYvyxm4etJtt2qqAvqa/UMJwcDWY7cdsc1+4W4L8eYVWQO6Hi8YTeOsN9i88i+LvfAfLty7mR4HdKCo8/HEvVkO0yjgBEsbC3vdkSbYQQgwCn7eGqEP1g0wGE0m2FOqMZnCPrJm5QohhyltLRkYmel3nTTvLmwa+PYA/7GdrzVbmpMxBp+uiYd3IdmRT6j5yECJDW309wmZBToqfdPTVEADZ87Wc2I0lA9cwIYQQXfJ767Af8SwuMX0uDm8DRU2Fg9Oo4U5VtdTrXQ1CABiMMGYh2BO1gtUtroFtn+iTQasJEQgEeO6557juuut6dQMqjsJT3W3ht6FkX5Wb3RUuTAYdp06w4Y6zkN7Q8Xsgu1ZHfZLCnGxttcDy3UdLyZSnLcU6mqBP66TU7MVUtUWrqbD9Vdj6orY951RtACXCnvjsIPWeAGMS7VySaaLi7l8dzguoKCz+4CkSWxp5d3tF3zqDqdMheSrs+0B+6QohxABr8dVja7f0OsuZRalekZRMQojB528GnYG0hHhuO3Nc28utd7u/e3c3f1u5f8BnaG6r2UauM5fYqNheHZdiT6HR34gv1G5ikilKS/nkrY1sIweZw+wgxZbCgcajpJw1miHvDC0VRWDwVrUIIYSAYEsD0faUji9aoslMmERB6RrCkj6v9zy1YI7W/tZ3R6fTJhOnztCyhLiPkkVFDAnGwbrwG2+8QWNjI9/5zneOup/f78fvP7y0xuXSHrQqioISoVkviqKgqmrEzjcolDB46yEqrtezgQY6/jc2aw9nTh+fRLl3P5Ny5mKaNBn/rl1t+6RPPZHmmFjmjzeyoQg+2lnJt07K7vqEMVlQukFLfxH0aXUp2v/zu0Cnh6gYFLMTxWhFScwFWxyY7IcHHyIcf43bz78+0zoPP148gcYnn+xcmEhRuDRZ5R8ueOijvfzrmrm9v1DiBNAZtBUR487RVoZ0Y0R8r/fSaIy5vdEY/2iMuT2Jf+Di9/saiLEmtV0rxZrCFr2e6Q2FGLNP6ZfB7a7IZz764h8qMQ/k9aVP0EueWu2eUFFIjDaT6GvkNKuXHy47m+cPtvDP1Qf544d7qWj0cc+FUzDodf0ef5WnihpvDYuyF/XpGmm2NIqbihkf1y6NkyMdGkuh3YBwTwz1z3pi3EQ+Lf2UPGceRn03XXZbAiRP1gpVj1/Sq785Qz3+/jAaY25vNMY/GmNubzTHP5Cxh4M+WsIBrGZnp+vZk6aSVPYl+xv2d/zb1U9G1GfuKofo1J49p4sfAyYb6oFV6KNyURJ7d08wnA2Vz7yn1x+0QYgnn3yS8847j/T09KPu98ADD3Dvvfd2er2mpoaWlsjkXFYUhaamJlRVRa8ftMUhx0Xvq8Pk1+Gvrev1sQMZv6qqvL5JWzZ8cjYUVBWQ4QvjOTQAYf761wm88Qa+LVuIbbqGOHspYOHLA3UUFJfjiOr6W9YcsmD4/AkUczSq2YlicWj/tY5FjYkGg/lwrP4mgi0m9AEv0H8zh/64ohhvIMyUFBtT17xF4wsvdN5Jp2PRaRP513vVrNhTw4qtB5iW1pfC4g4M1nGYNr6KP+s0VGt8l3uNhO/13hqNMbc3GuMfjTG3J/EPUPyqSlNjNSaPSnWguu1lkxJDUcN2nCX7UKO6HxSOJPnMR1/8QyVmt7sXNa2Ok/QJesdYvQ/0JkLV1bhffZlnPnwGPSretx7h6h/fgf2MeTzyaQnPrS2mtM7FvefmYdLTb/GHlTCrq1YzPW469bX1R91Xqa4hXFaKISMTffLhendWv5WdNTuJCca0vaYPmDFVbcVvPHqfstM1hsFnbfKb2HBwA2McY46yVxLmpt0ou1YRSpra43MPh/gjbTTG3N5ojH80xtzeaI5/IGP3NJcSUMzU1NR02qYL2Uh2t7C8cB02vw2Tvvu6qZEwkj5zS8kuggmTUaqrj70zAHrUuLmE935Ig99FOGXmgE0IG0xD5TPvaZ9gUAYhioqKWL58Oa+99tox973zzju544472r52uVxkZWWRlJSE0+mMSHsURUGn05GUlDR8f1CraiB9HCR3TMfkKy9l096VzJ54Jrb0rC4PHcj4NxQ1UOEKYDcbmDguRJJtDvr7ngAgevFi0u/7HUX79uHftYusz7ZTeV4uY5L1HKhW2NkAF83sJt1U0tcB3TF/yQxUrAdrPby5Q1sa/ltjAb5HHwXAdNopbCz9iphmhZwaMCYnc8LJs7i0ahcvbyjlyfW1PH/D0ToaR5GcDMkpUPg5xJ8BjtROu4yI7/VeGo0xtzca4x+NMbcn8Q9M/EqgmWJbFLnpeR3SSs6wz6Dce5Cx5kCnv8n91hb5zEdd/EMl5qiooyyRjzDpE/RSkwIp4wh6Fc76QBuAAEBR8D70J25Z/jFj0xO44+WtrNrfyE/eKeTxq2cT20/xb6vZxtjUsUxJmXLU/Rr/9z+q7vm1NvNRryfl3l8Te+mlACSpSRw4eIDouGhsJpt2gJoITdsg3gnGnn8/DofPen7sfFaXrmZu4tzuV0MAxH8N9rwNNhWiU7rfr53hEH+kjcaY2xuN8Y/GmNsbzfEPZOwVwSJiEzJI7u6+v3kM00wGGk2NTE3o+WBxX4yYz1wJQXkYcqfA0f7+HXmYkkiN8SLi3TvRNe+CvNN6dfxwNFQ+8572CQbl03jqqadITk7mggsuOOa+FosFi8XS6XW9Xh/RN1in00X8nAPKWwtxudCu/Y2vvsrW/7uHnVkqhf4/cN637yHx8m90efhAxf/21goAzp4aQ1Owljn1cRR/8AEASbffhsFgIPHGGyj70R24n3+RsZf8jRPGlXCg2sjy3dV8fXZmN2fuebsHItY/Lc8npKj8oGUntr89BYBy/RXsuWQuE4Lf5kD+Omr/9DKJFVW4/vc/vr/kIl7fXMaXB+r46kA9p4zr4/Kx2CwYe5a2LDt3AcR0fr+G/fd6H4zGmNsbjfGPxpjbk/j7P36PrxZTVCwGg6HD66nRqeyMshFsOIglfWa/Xf9I8pmPvviHQswDeW3pE/SSrx6ik2ncuh59F+lA/bt387WzzyYhOoqbnt3A+sIGrnxiHf93YR4pKZGNv6GlgVJPKYtzFh/1vMHKysMDEIfaWXXPr3GcdhqmVG1yTZYzizJPGRPjJx46Sg/ONGiu1NIx9MJQ/6xjo2JJsadQ6C5kQtyE7ne02CHvdChaA5MvBGPnn5OuDPX4+8NojLm90Rj/aIy5vdEc/0DF7mupxWo/ygPg+Dwmear5yHWQ8XHjierFgHlfjIjP3F2jFZw2mnt9qM5oQTdhMfrSdZD/oZausA/nGU6Gwmfe02sPeAsVReGpp55i2bJlGI0je0RqQHlqwH54uXKwspKKX91DfbTC2AoVu1fho6d/jXfvnkFrYjCs8O52bRBi5lgP42LH0fiPf4Gq4jhnEVETtc6E45xzMGVlEW5sJP6TrWSneoEQn+6tIRAa+rnttpY08u62ChYXr+PcD59GAapvOJ+Dl8zl5PSTmT3pTM4+5yZKrz2H5iioefgR0nQBrjpRq3nxx4/2Hl+RQEcKjFsEhWug/hgFu4UQQvSJx1ODuYvCqnqdnoTEKdQ3FEA4OPANE0KIFpeWitRoYa8xFoXOK4WrfvNbWnbtYv7YBF65ZT6pzijyq5u54aU97KmMXJotRVXYWLWRmYkzMRuO/hAgUFjUOfezohAoKm77MsuRRYm7pOM+zkxoKotUkwdMRZOPLwpqqWjydbvP5ITJ7K3fS0gJHf1kznSIy9NWRAshhBgwLd5abNajTCKNzSHKXUWeI5fd9bsHrmHDmbtC+7vWVzo95JwCUTFQuy9y7RLHbcAHIZYvX05xcTHXXXfdQF965Ap4tP9aDtcSaL2Jr3XqSHTDpFIwhuCdn1xB1Z/+RKihYcCb+Vl+DfWeAAkOlYQYD1kNelzvvQdA4m23te2nMxpJuO5aADxPP8dJ6XkkxDfh9of46kDva14MJFVVefD9PSwqXs8PN7+C16Sy58aFmL55KYtzF5N46I9TbFQsp154Mzvnp9HS3ETNn//C7WeNI8qkZ3NxIyv29DTvXTfsCTBhsVawu3Z/BCITQgjRntdXS5Qtoctt2bF5VOoUraCaEEIMNG+tNoMQ2OQz8+zkJYe36fUY4uMJVVVReNXVNL39DpNSnbx22ymMT46mpjnIFf/8KmL33PkN+UQZo8hydp0Wtr3G+JROAyZhnY7GuMMpLhKiEvCH/bgCrsM7OdOH3e/bl9YXs+DBFVz1xFoWPLiCl9YXd7mf0+wkyZbEwaYeTCxKnw1BH1QP3qQzIYQYbfzeOmz2o6RgtUSDxcFEcyyl7lKaA80D17jhylV+fIMQrVKmQc0eOJ5JviKiBnwQYvHixaiqyoQJR1lSKnqnubrDKggAc24O3ijt47X5QQdMLVIJh4N8/v4T7D97EdX/93+E6gbuof4bm7XOwfzJfsbHjaPpn0+CqhJ99tlETZ7cYd+Yiy/GkJBAsLyctI1FTM3xAgof76oasPb2xer8WqI+/YgfbXqZyliVXctOYfb1P+GUjFM6zf7Kistl2hU3sz1XR91//4uzvIhlp+QC8NBH+1CU4/xFaY2DCUugYgtUy4i7EEJEUou3Fqu160GIhKgEXFYn3noZBBZCDAJPHdi0QYgtJY2URmv9BNOYMYxb8Qlj338P++mnoba0UP7Tn1L1wIOkRZt46aaTmJkejbslxDVPruPdbRV9boKqqtT6atnXsI8Tkk/o0TFFejuVtrgOr63MPIFig6Pta51O13k1hCVaS7XgPXrB66GiosnHz1/bTuutvqLCL17b0e2KiMnxk9nbsJewEj76ifV6GHMGVGwdNu+FEEIMZ6qqEvQ1HH0QAiAuF3NTGRPiJrCzbufANG64CrZAoBm6mezVK7Z4MNmgqeTY+4oBMYyThIk2R6RiAjClpuL/+lkkug7d3er1pP/2t5x7xyMEx2ezL9ZL3b+e1AYjfv8HlLr+vVH1+EPaAIIuwKTMINlNJlzvvAtA4q23dtpfHxVF/Le/BUDoqf8yLysNnamR5burji9VUT9SFJWPHn2GH2z+L7uyoebiBVx0+0OMie0+P+2cM75B9AlzyU9TqPrdfdx82hgcFiO7Kly8v6Py+BsV5YSJ50HlDumMCCFEBPl9DVhtXS+91ul0JKTMoL56+wC3SgghaFsJoaoqW0sayXFp95S22bMwpaZiiIkh67HHSLj5ZgDqn3mG4utvILqlmUcvGc+SqSkEwgrffXETT6852KO0QQAtoRaKXcWsq1jHOwfeYVPVJmYlzzpcRPoYMgq2k+6tx68z8OmhmjrZ7ipyEzse321KJtfwSMl0sNbTaVJmWFUprPV2uX+MJYaEqAQONB049sktDsg6UasPFz5GCichhBDHxRvyYlUVjFExR98xLhcaixgbk0etr5aGloHPTDJsuCvAkQq6zqkk+yR5sqwQHEJkEGIkaK6G6JROL9cYfcS7wXnB+Yxb8Qlxl19G3KLFXPLnt9H95GbKF4xHbWmh4ZlnaPrmN6l+4AGCVceZBqgbH++qwhcMk57cwOl5U3D/89+gKESfeSbWaVO7PCbuyivR2Wz49+zhbI+C1V5NRVMLO8pcXe4/2FY+/jwXfPFvNkyE6AVncOkdjxHTRb7w9nQ6Hefc9Fsa4s3sL1iP8bMVXH9aHgB/+ngv4eNdDQFgtkPiOKkPIYQQERTwNRBt7/y3t1VGwiRqfXXgaxy4RgkhhKpqE09sCRTVeWnwBhnTrK0ktowf37abzmAg+Uc/JOPRR9HZbHjXrqXo8m9gPLCfv35zNt8+OQdVhV+/vYtTHug6bZCiKtR4a9hes53lRcv5qOgjyj3lJNmSODv7bBbnLibLcew0TFqzVZoefxyA98acwt9nXkJQb2BCYynmAx3zOcdFxYEKjS2Nh190pg+buhB5iXb0Rzxb0enoNNjS3pSEKT1bDQEQn6f1DUvWHmdLhRBCHI3b34RNZwCT9eg7mu1gcWD01DE5YTLba2WiUrfcFeCIQCqmVrE50NIofbIhQgYhhjslDC1N2jKjdsJKmKri3cS7IWbpUkypqW3bzEYzi5fcSvCu2wj97V6iZs6EQICGZ5+j4JxzqPzNbwlWVhKsrMTz1VqClcc/I/+NLWWgCzJrTIg8t5Wmd94BIPH227o9xhAbS9zllwNge/Z1pqRHoTM089GuCKwQiLDad9/D99IDbB8Dsamncu7df8NoNPXoWFtWDgvP/A7703Ts/fPvuXZOCrE2EwU1Hl7fHKHOVFweNMgghBBCRII/7McYDmA6yqynuKg4WuwJuGr3DmDLRpY6Xx21vlrqW+pp8jfRHGjGG/QSCAcIKaEhuzJSiEHV0qQ9DDGY2FLSCMB4rzbJqP0gRCvnksXkvfRfTDnZhCoqcH/3e3jefZffLJ3KLWdoq3lbf9IUFX7xxga+KtnJF2Vf8HbB2+yo3YFBb2B28mwuHHMhJ6edTF5MXo9XP7TyrF2Lafd2AnojLZd8k8dvP4stObMA2PL3pzvtn+U8YjWEIxW8dRAO9uq6gyEtxsoDl0wnuaWJGTX7STz0YORgjafbY2IsMcRHxfesNgRA1kngqYb6HqyeEEII0SduTxXmY0w8bXPomUyuMxdvyEu1t38mAA97rnJwprV96Q/72VS1iR21OzjQdIBKTyWugIuQ0sPVfno9JE2UFOVDhHGwGyCOk7cOrLGgN3R4ubJwJ9byBow6A9YTOudhtRgsnJ55OqvUVUz+xwNEf76L8Asv4Nu0iYYXXqDhpZcgfGimjV5P2m/uJfayy/rUxLpmP5/l16K3VHDh5Nm4//U0hMPYTz8N6/TpRz02/jvLqH/+eXzr1nP++eew2VLJx7uq+PHiiX1qS3+o/vBdPnz8/6GPVgkF5nD+w39Dp+/d+F729bcy+7032WyrJuXZf3HLGV/nwff38MjyfVw0Mx2z8TjHC62xoDdqqbuEEEIcF7ffhV1n0HKMHkV8ygzqqrbizDppgFo2clQ0V7CxeiMOk4OwGkZRFUJqCEVRCKvhttcAdOhodjcT74nHYDBg0puYlzoPp9k5yFEIMQi8dW1FqbeUNGIKB0lorCakhy1xbowVX2HQGdChQ6/Ta/9i9fDP31D7yJ/xb9xE8f/9P+L2fcGExZegM9eAqkNvbEZnbAKdyt6aWM4eP4F5qfMwGXo26eZY9v/fo1iAj8eczHe/MZ9kRxRN37wS7t9I3Jcr8Ta5scUcrg2R5cjis9LPmJ50qC+hN0B0MrgrIbZnqy8G05Kidcz48Hegqig6HY/OvIzbXjDx5u0LyEmwd3nM1ISpfF72OXkxeRiO6Pt1YjBC3hmQ/5GWttfiOPr+Qgghes3bXEV0NzXiOonLgd3b0GedzLSEaWyv3c7Z2Wf3bwOHG79bW9HZbqJXQWMBLaEWbCYbjS2NlIfK8QQ9eINejHojNpMNm9GGzWTDarDS4mvB4rcQbY4+fI+SOBF2vg4Zc7QaUmLQyCDEcNdcDV0UwSncvJpEl0rUlCkYoqO7PNRmsnFa5mmsKl5F9tRspj37H1rWb6D6kUdo2bLl8I6KQvmv7sF+6qkdVlT01LvbKwirAfJSPZxiTqD8zTcBSLr99mMea0pLI+ZrX6PpjTc47fOvMGSmsqeqjpJ6L1nxvZthFWnBykr2vvEf1nzyH7KbwuRbTiDpt7/BFtX7zpg+KopJP/gFDb/5AStXPcM3LriYJx0WSht8vLShhG+fnHP8DY7Pg4ZCMEfgXEIIMYq5vdVYzI5j5ipNS5nFgX0foIaD6CL0oG602Fm3kznJc0iLTjvqfqqqEgwHqayuJCExARWVg00HOdB4gFnJswamsUIMJZ7atqLUm4sbyHJXo1cV6jMcBKPN5Dmy2gbzFBRUVSWshlEdFpJ//v8of+YZWj54j4oP3sR4cC/OhPkYlBDJtX5qTNk0WFJZPHY+aY5jpJ7oheZ167Hs2EJQb8Bw9TUkO6IAOPuq8/jiL38gxV3Dqsee5/yf39J2jNPsxGwwU+erI6H1AZAzQ6sLMcQHIYKVlVTc/StaC0PoVZUfbP0fy1Imcv0zG3j9tlNwdNGfiLHEEGeJo9BVyNjYsce+kC0e0mbBgU9h4vnabFAhhBAR4/PWdlsjrhOzHSxOaK4k05nJ3oa9lLpLyXRk9m8jhxNXRYdVEGElTEFjAQuzFhJt7vxc0x/24w168Ya8eIIemv3NlHvKqayqxBfyodPpOCPzDGIsMRCTCXX7IWXKQEYkjiB3IsOdp7pttlN7pfmbiHeDbd68ox7uMDs4NeNUtjdsp8ZXg/3kkzDc2LlQtE5RqNiV36cmvrG5DL2lioVjJ+P51zPaKohTT8U6c2aPjk+4/joAQp+s5ERzDHpLFR/tqupTWyLBG/Sy5+V/8dY1Z/HVh08za3+YlmA2by6+nsvn9f0Bv+Occ5g0/mSi3UHWPPkrvrtwHAB/XZFPS7AH+V+PJS5XG4SQ9BVCCHFcPN5azNa4Y+7nsMaj2BJoqJViaL1R1lyGTqc75gAEaLWVjHojZr0Zq9FKtDmasbFjKXGXtK2UEGJUOVSUuiUYZleFixy3lsbUMzGTTEcmGdEZZDmzyI3JZUzMGMbGjmVC3AQmxk9kStI0ZlzzU874f48wqcHG+BV7eO69V3n2f8/x8Mcv85/3HuLHob2kxURuAAJg3x8fBWBF3kksu+jwyjGz0UDgvIu0L955g2C44890pwLVznRoKo1o2/pDoLCo0/24XlWYEa5nf3UzP/jvlm7rwk1JmMKe+j09qw0BkDwJzDYo33y8zRZCCHEEv68eexeTgrvV+kwGmJ44nR11O+R+tT13OTgO3/+XuEtIiErocgACtAwvcVFxZERnMCFuArOSZzE3cS6LshexdNxSJsRN4EDTobSEyVOgepc8DxtkMggx3DXXaEuP2/EGvbgP5uPwge3Eow9CgDarZk7CHNZVrqO+pZ4vWqwodJzdqQLlxt4v4y2u87KpuBajpYpvpefS9Ia2CuJotSCOZBk/nugzzwRV5Vv5+ejNNXy0q/8Lz6mqijvgpsRVwraabawuXc1bBW+xfPsbbHnqT1j9KiftVXH6YHxjCT8/MQmjoe8/UjqdjtRf/IJJFQYat21isucLMmKtVLn8PPdV0fEHFBUDxij0vrrjP5cQQoxiLd46onq49DoueSo1lVv6t0EjiKqq7KzdydSEqX0+h81kw2l2UuUZvAkLQgwKRQFfA1jj2VXhIhhWmeTTUnG68xJJsib16DSOc84h9+WXMGVkYPS40R+qCqFH5Yx3/kX5vgjclx7SvHET1u0bCen0mL/9HeLtHdMkLLjtGkJ6A3m1RXz4xuoO21oHIdrqw1hjtYcLQ7z4pDk3p8tVCT/Z/z7ZLQ2s2FPNHz7sevA6Niq2bTVEj+Us0GrDDZPC3UIIMRz4w36MIT+mqGNPTGoTlwONxaAoJNuSsRvtvft9PpKp6qGVEIeLUu9r2Mf4uM71rHoqx5lDibtEqx9hT9BqZg2DyQojmQxCDGf+ZtDptWVd7ZQV7yLmYB06vR7bnDk9OlWcJY55KfP48xfvcPdXRTw66zLCh9JMqIAOiPrnn1GDvSv29tbWMvRRVUxJzsP60ssQCmE/ZT622bN7dZ6EG28AIOfzT7H7DGws30+DJ9CrcxyNoio0tjRysOkgW6q3sLJ4JW8WvMma8jWUe8qxGCxMjJvIktwlLNjqZ+ZBlbwqMB4atDaoKvOjvMfdDsv48SRcdRUzClV2vPYE3zlFG/H9+6oCmv09LLzThYomH18U1FJrSsXgKj7udgohxGjW4q3F2sNBiOTU2bhr98gspx4qay7DqDeSau99+sf2spxZFLkj96BUiGGhpVHL/W8wsqW4EYCp/hqCBgimJhBrie3xqSzjxpHyy190et2gqjz9ymeRaS+w5w+PAPBp3olcvfTkTtvtqcnUzz4FgLLnXuywQsBusmMz2ajxtat5ljYD9i8f0gMRptRU0n5z7+GBCL0endWKrvAgf//sUU6q2Mk/Pj3A65u7flAyNXEqu+p2Ueur7dkFjRbIOx2K1kDQF6EohBBidHMH3DjQdXoed1TtUjKBthpiV92unhdZHsl8DdoggUlbbVntrUav05Nk69kEiq5YjVYSrYmUug/9PU2eDDWyQn0wySDEcNZNKqairZ+T6FaJmjQJg6NnqxfCisrTnzXyzEodqnUvRfMXcO2SX/KzBbfwmxO/Q4vBhGXDl3x5248PzzY6BlVVeX1LCXpzFVem5dH4+usAJPagFsSRbHPmYJ09G4JBriouBVMFK/ZU9/o8Ryp2FbO8aDlv7n+TDVUbqPXVEm2KZnrSdC4YcwHn5p7LSWknMTF+Iin2FMJbdlD96J87x6rXY8mJTK2FpO9+F6sjjqlfVWIreJXcJB31ngBPfX6wT+d7aX0xCx5cwVVPrOXsp0rYumu3LEETQog+UlSFkN+F1dazQQibMwOb3kRNY99+h48mqqqys+74VkG0yorOospTRTDcu8kTQgxr7epBbClpBCC9oYJGO6RkTEB3jDo2R4qaMqXTjP2wTsdr1TpW7T3++/DmzVuwb11PWKfH+p3ribF2XTtnys3XADA3fy0fb+z4uzTbkU1x+wk2SRMhfTbs+xDcQ3c1VOxllzFuxSdkP/MM41Z8wtj33sU6cyYGTzO/XvsU1+18hztf2cLm4oZOx8ZYYjg57WS+LP+y5wMR0cnae3PwM+kHCCFEBLj8LhwqvRuEgA4pmWKjYkmyJrG/cX+kmzf8uMo7rYIYFzvuuE87JmbM4ZRMsbnaYEdL03GfV/SNDEIMZ12kYlJVlfL9W4h3ge3EE3t0GndLkJ+9XcA/Vh9ADcZz5cyT+f7Xwrx89/n84hff4p7f38rbS79LWKcn7rOPefWmn/eoRsHOchcHmwowqHGc8uVHEAxiO/nkHq/OOFLraojztq3FHArzzs7dfToPQDAcZG3FWvbU72FW8iyWjlvKopxFzEudx7i4cSRaEzHpO3aEfNu2UXLTzdDSQktGTttKkbBOR+m1P+hT0e6uGJxOku74EU4fZD77CYsnl4EuxOOrC/h4ZxUVTd3PYPKHwuRXuflgRyV/X7Wf257fyP/733ZaJ425VSsv7XRTVSGzQ4UQoi+ag83Y0aHrJjdpV2KTplJdKfm4j6XUXYrFYCHFnnLc5zIZTCTbkilrlvQjYhTx1sGhAdItJY3Ygi1Y66tpiIa0MdN7fbpOM/aBhuwJ1Fpj+dWbO4+7ZtnO3z8CwOd587iyi1UQrRJPPQVPUhq2kJ+1/36pw4SoTEcm5Z7yjqvNEsZqM/8PrIKGoXvPa0pNxX7SiZhSUzGlpZHz7H+IX6YNuFyev4rffPoYP/vHCiqbWjodm2RL6v1AROoMUEJQVxDJMIQQYlRyBVzYVKAXfQKgQ0omgKkJU8lvyMcf9ke+kcOJ63A9CHfATUNLA9nO7OM+bYothZZQC03+Ju1+JnECVA/B1RDBllExSUAGIYYzTw0cUQSnvqUew75CzOGe1YMorPVw6WNfsuZgExajnkevnMXvLjib8fHj2Odex8QMHZNSHfzydzey6+rvAjDts7f4882/pqT+6OmHXt9cjN5SxflxWXjeeAOAxNs6F73uqegzz8Q8diwmn5czCspYW963zk+dv47lxcuJMkZxdvbZJFoT0euO/qPQsns3xdffiOLx0DBhOlfOvpnvLNZWinxn8S+5tT7zqIMDvRV7ySVETZ1KcrmX+Ru2kJhYhMcf5MZnN7DgwRU8sfoAXxbU8fzaIn77zi6ufWodZ/xxJZPv/oBzHl7NLc9t5A8f7OW97ZWdzl2kpFBbvDdibRVCiNHEHXAT3cul1wmpM3DX7JGl1kehqio76yOzCqJVrjOXItfQfQApRMR5asCeRF2zn+J6L9mHVgK4MmJJSc7r0ylbZ+wn3/lz0OlILNrLpTVbKK738veVfZ+56d62A+eWtYTRYb/2euwWY7f76vR6Ur55BQAztqxkdf7hh+5Wo5UYc0znGjDONBh/DpSsg+q+T1waSDqzmZQ77yTjkUfQ2e1MrzvAr9/5PQ8+8HyXfZ4kWxLz0+f3fCBCp4Psk6FsI4RG+cMuIYQ4Th5vLRazo8saP0d1REqmaHM02c5sdtbu7IdWDhNKWLuHcWgTe/Mb8hkbO/aYz+l6QqfTkReTx8GmQyspEydA/QEYaqul9y/XBmJGOBmEGK6UsJb39Yh0EKWlu3EWVIFOd8wVB2v217L0b2vYX+MhKdrESzedzNJZGQBMiJvA+NjxbKzayLsH32VrzWZO/9HFuJbdDMCFX7zKgz/6Eyv2dL3MOayovLVrM2rIwTf3faWtgpg3D3sPV2d0RafXk3DddQB8a/tGwqEmlu8t7PHxiqqwvXY7W+u2ckLKCcxMmkm1O8AXBbWdBhCafEG+KKjlX58d4Dd/eZvt37wGxe1iZ3wu1024Er/RTK01lu1J46i1xhJWVQprj78mRFusBgMpv/wlACkvrsReXYHeWnIoDrjvvd1884mv+OXrO3jy84Os3FtDUZ0XRQW72cD0jBiWzkrnhlPzOHLhfSnJZOhq2kbehRBC9Jw74CZa1YHJ1uNjLLF5JIZDVMis/G6VuEuwGqzHlff1SCn2FFwBF95g5P4+CzFkKWHwu8Aax9bSRgDmqvWE9BDISiauN4Uzj2BKTSVh2TKSvv89AK7b8CrZrkoe//QAB2qa+3TO7Q8+DMBXeXO5bOkpx9w/7YrLUAxGJjWU8L+XVnTYluXUClR3YouHSedDzV4o3TBsZhg6z11C3quvoB87jni/mxveeIgXfvg7lHDngYhEa2LvBiJs8RCfB+Wb+qHlQggxeni9NVj7et8alwv1h9MLTomfQllzGY0tjRFpW3/yhfqhtlBzNVjjwGAiEA5Q2lzK2NixETt9bkwuxe5ibUKY2aalfaobQimwlLC2mjXgGeyW9DsZhBiuPLVgje806lqy40sSXWCZOBFDTEyXh6qqytNrDnLNv9fR5AsyKyuGp745mRmZHfcfEzuGc3LOYWHWQhxmB3vr91JxeS67r11EZRzcsOFF/v77Z/njh3s6FIkD+PJANQ2hYtK9MTg+eRfoWy2II8Vc+DWMKSnE+9zMLa7j7V09u4F2BVysKF6BJ+jh1NRTSbGldKiVcMqDK7jpPxu45dmNnPaHFcy89yOuemIt/35pNWf/67dEtzSzLzaTRxfdyszxaZ0e7Bt0OnITe/5AqidsJ8zGedGF6FSV2z/fhN7QiM58uPBessPCmROTuG5BHr/7+jReuPEk1v7ibHbcu4S3v3cqj145m7u+NoUHL53e1l4d8P2zJxIbG9828i6EEKLn3H4XVpVeDUJgNBMXm0dl9fZ+a9dwpqgKu+p2MTXxiFUQvsbjmqWk1+nJcmRR7C4+9s5CDHe+BoiKAb2+rSj1zGA9jXZISsqJyGzChJtuwn7KfPT+Fn637UV0/hbufnNHj+vFtWrasZO4TV+goMN5441YzYZjHmNMSMBy5kIAMr/4iPWF9W3bMqIzqPBUdL3azGyHiedrfaeDq7WO/jBgyctj/KsvE1h0HgZU5n/yX9ZceS3hxsZO+7YfiKjx1nQ+2ZHSZkFTCTpf/TF3FUII0VlICaH43Vhs8X07QVwuNJW0TQw1GUxMS5zGlpotEWtjfwgqQT44+EHHWkyR4D5cD+JA0wHS7elYDJaInd5qtJIYlXg4TWvyFG2V5FCZnOCtA1WBQN8mdgwnMggxXB1abt1eIBygrmAXTk/3qZgCIYU7X9vOr9/eRVhRueSEDF684SQS7V0XggOwm+yMjxvPGVlncH7e+Zxw8x24lsznqykqX6t9hg/ee5Wrn1xNbfPhZb0vbNqIGormR1VbIRjEOncOtpP6vgqilc5sJn7ZMgCu2rqZ9aX5tASPvpx4f8N+Pi35lPFx4zk57WTMejMVTT7ufO1wrQRVhY92VfHBzkpK6rWR3ZlGL4+sfYJ4v5tg7lhOfOU5Vt97IS/dPJ8HL52O4VBNCINOx/2XTCMtxnrc8R0p+cc/AauV6XXFnLLPh8FaDLogeh28+d0FPH3tifzqwil86+QcThmbSIozqlPRwSvmZfPgpTMAiLebuWBKQodiSEIIIXrO46vDYnH2eul1bPJUvLX7pFByF4pdxdhMNhKtiYdf9DXA7rdh28vav30fQvFaLYerqwICPVvdkOXIkpRMYnRoV5R686Gi1NmuChqjIS1jYkQuoTMYSP/DHzAkJZJUW8Z3d7zBmv11vLW1d+kDtj2grYJYn3cCSy869iqIVmlXXwnAWSWbeOLDw2krLAYLCdYEKj3dTLAxmrXUTAD5H0Mo0Kv2Dha91cqMvzxE0XU/JKA3krh9Pbsuuhj3qk/xfLWWYOXheFsHIr6q+OrYAxFGM2TMw1y5aeg8gBFCiGHEHXATg7739SBamW1aSiZ3RdtLuc5cwkq465V9Q0R5cznR5mi21W6LbJ/GVQGONBRVoaCxgPFx4yN37kPGxI7hQOOhAtXRSWAwD530R83VYLLCKFi9LYMQw5WnWvvBaafKW0X0rmL0gG1e50GI2mY/V//rK/67vgS9Dn55/mQeunwmFtOxZx+1MhvM5MbmcdEv/8G5cQsYWxXiO/v+x8GSd1jyz7/x6vav2FpexqqDW3A2RjNpg7ZcOum22zo9HO+r2G9cjt7hYExTLZmFtby3d2uX+/lCPj4r/YwSdwlnZZ9FjjMHgFBY5ZHl+Shd3HN/6+RsXrjxJDbdPIOHvvonDnc95rFjmfL8M6RlpbTFcMW8bD7/+UJevPFkPv/5Qq6Yd/wFc7piSkkm6VAdjVu2vM/EcjeJunweuGR6rwY9vj47HUeUkTpPgK3lzYcGIYokJZMQQvSSz1uHtf3D8h4yxeaSGg5LoeQjKKrC7vrdnWtBNJVpOVtnXw2TvqYVVI2KgZYmqNwGe96Bzc/D7neg8HOMtbu1In++xg5/2xKsCaiqSkNLw8AGJsRA89aCPRFFUdl6aBDCUVFMfbSOtHEzI3YZY2IiGX/8P9DrWVS4jrOKN/Lbd3bT5OvZw4iGHbtJ3LgGgNibbsJi7Hk/xHbyyejSM7CHWlBXLWdneVPbtixHNymZWukNWrFqexLsfW/YpDzQ6XSc+7ObWfHd+ym3J2CsrqTkllso/s532H/W2TS++mrbvr0aiIjP096T2n39HIEQQow87oAbJ4Ze1YjrJD6vw8RQnU7HrORZbKvZNmTryBW5ipiaMJXM6Ey210ZohXcooN3f25ModZfiMDuIsXSd1eV4pNhS8Ia8WoFqgOTJQ6dmlKcaYnN6PMlqOJNBiOGquXNR6rKKfcTs0UbybHPnAlDR5OOLglo+3VvN0r+uYX1hAw6LkSe/M48bTx/T54EBnclEziOPkpk3g7kHW/jTu59DaTQ/f20zlz71H/x+G5fvXYcuGMA6eza2+fOPL952DNHRxH3zmwBcsXUHH+7biqJ2fJhe1lzG8qLlJFoTOTPrTOwm7Y/DttImvvPibl7Z2PkhkEGn4/aF4zjRqdJw600ES0sxZWeT/e9/Y0xI6LR/WoyV+WMT+mUFRHvxy5ZhiI8nLuDh/pXvcEP+05xRuKZX57AYDZw7VSvys3xfg5ZGxBrbYeRdCCHE0fnDfkzhIMYoR+8PtiWQZLRT2pAf+YYNY0WuIqJN0SRYj/g76yqDGK1OlZa7NQ2SJ0H2STBhCcz4hvYv+2RwpIEShLoCOLAStr6gzag6JNuZLSmZxMjnqQVbAgfrPLhaQiSFvSiN9XijIHXirIheyn7ySSTefhsA39/2GlEVJTz00d4eHbv1wUcA2Jg7mwsuOrVX19Xp9SQeKlB9XuFaHltV0LYt3Z5Otbf66DMzdTrInANJE2Hve+iGQe7tVt+9+QLeuOxHqHA4LayiUP6re/q8IiKQOgcqtkCwpb+aLYQQI5I74CYaXYeVEK3P3o6sN9qt2BxtAs0Rk2eSbcnsre/Z39SB5A1qD/BT7alMTZxKhaeCOl/d8Z/YXQHRKaDXk9+Qz4S4Ccd/zi50KlAdl6ulQWpx9cv1eqW5RmuPpGMSQ5LfDTq91ilvp2TnWhLcYJkwAWNcXIeaB8ueWk9Zo4+8RDuv376AhROTuzl5z+ltNrIefxzzmDHEexp5ZN3LRDUlEnLPwFmfwvkHv9T2+84NEVsF0Sr+mm+jmEycUFVC/fZCytzaoEJQCbKhcgPba7ezIGMBkxMmo9Pp8AXC3P/ebi557Av21/qIs5m4Yl4mhkPNak2plKS0UHzddQQKCzGlp5Pz9FOYUo7/vToe4fp6wg3aDE5zCDJqFb785+86dDh64msztRx7K/MbCIUViMuDhoPHOEoIIUQrd8CNU2cAUx9mPel0xCROxt9wkJaQPPCBQ6sg6nZ3rgURDmkPVKNTj34CgwnsiZAwllDyDBi7EKZeDDkLtAdrh+Q4cyh2FXeasCDEiBEOaR3XqNi2ehBnmN00RkOcPRGjvY/pIo4i8ZZbsJ18MpaQn1+s/w8vf57PtkMFsbtTt3sfSRs+AyD+lpsxGXrfFY29+GIwGJjcUMTOzze2FcY2GUwk2ZIo9/QgtULyZMg6CUvxp8NmQo7JoOf2WXGd6tLpFIXSl/7X4bWeDkSoFickjIOyjf3QYiGEGLmaAk3YVdpWQrR/9rbgwRW8tL4Hk1/MXU8MnZY4jQNNB/AEh9aKvWJ3MVmOLPQ6PSa9iZlJM9lUven476/dFeBMp9ZXS0gNkWo/xv3/cch1agWqw0pYWw2YOB5q9vTb9XrE36w937UnSTomMUQ1d07F1ORvQikoJCqopWI6suZBq398aw7jkiPXETHGxZH9rycwpqaSWFvGvV89iSUU4NL8VViUELvjcigbOyNi12u7bmIi0Uu/DsDpa7fyWdE26nx1fFL0CXqdnkXZi4iP0ooEfVFQy7mPruafqw+gqLB4Yhwf/vA0fn/pTD7/+VltKZUumxBD8fXX48/fjzE5meynn8KUnh7xtvdWoLCoQ77WnGqojlHZe+sNNL76KuHmnv1xOmVsAvE2Ew2+EF8eqIe4HGgskZRMQgjRQ66ACwf6Pi+9NsRmk6FAqbs0wi0bngqbComxxLT9vW7jrtBuxA3Gvp04Lle7iW+uBrTaVtGmaKq91cfXYCGGKl89WONAr2dziTZxZU64noZoSEnM7ZdL6gwGMv74BwyJieS5Krlp25v88vUdhLvKd3rI5vsfRo/K1tyZLLno9D5d15iYiGPRIgDOPfgV//j0QNu2bEd2z3Npx2YTyFygFauuKzj2/kNAQ3wqSqdhCGh57K+8fs33eOWzfRyoaUZV1Z6viEidoeXEbu5BQWshhBCANjHJpipgju707E1R4Rev7ejZioguanVajVYmxE1ga03XaccHS6GrsC3FOUCmIxOr0Ur+8a7ydpWDM419Dfv6pRZEezaTjYSohMPpcZMmafcAg1mzz1OtTaoyGLXBiGFSt6qvZBBiOPLUdkrFVOmpxLG9ENAGIQ7WerqseVDnifw3tCk9nex/PYHO6WRKfRG//eKfXHhQSxf030nnkJt0HHnyjiLtxutR0XH2gQJ2bt3NF+VfMDNpJieknIBRb6TJF+Tn/9vGVU+spajOS1pMFE9cM4ffnDeGxGiLdo5DKZWSDQrFN92Ef9duDAkJZD/9FObs/qnz0Fvm3JwOBVBNYciqUdnjOUDFXXeTf9pplN/5C7zr16MepbicyaBnyTRtVPmdbRVa4RtbvJbyQgghxDG5A27sqq7TSsQec6aTFlIolkLJhJUwu+t3MyVhSueNrnJwHsckAJ1Oe7BWua3tpRxnztAsUN1QqK1wFeJ4tCtKveVQPYi85ioa7TpSMyf122WNSUlk/PEPoNNxXtFaEr9ayfNru/45q9mdT9qGTwFIvPVWDPq+r5KO/cblAJxdsol31h+gvFF70JNqT6XOV4c/7O/ReRRbIkw4F8o3Q2WEclv3o9xJefxl9mWED60wD+t0bEzSHthMWrec2B9ezy13Pcvc3y3nxv9s4H/rmrCFJ/NZ6RdtAxGd0oUYTJA1D4q/lCLVQgjRA4qq4PO7sejNYDR3+ewtrKoU1vZgVnsXKZkAxseNx+V3DZkJNPUt9ejQERcV1+H1E5JPYF/DPrx9ncEf8EA4iMdops5XR7aj/5/BjYkZw4GmQxMYWlO+DuZkhOYaiD70fNdkgyG2AibSZBBiOPJUH/4mPaS8ugDnLm3mj23eXPIS7RyZAcmg05Gb2McHJ8dgGTeO7H88jmIwMr2+ELMSRgVumBLdbzUTzDk5NMxdgA6Y/OoGTqmKJbFZC/rDnZWc86dP+e967T351snZfPSj0zl7Usf3LVhZSfOnn1J83bW0bN2GISaG7H//G8uYMf3S5r4wpaaS9pt7Dw9E6PXMWfZjglecR2B8NqrPR9Prr1P07WsoOPdcah//R7epmi6ckQZo708gpBwaeZeUTEII0RPNgWYt/2tf0jEBmKw4HWkEmyv7frM+QhS6ComzxHXqzAAd60H0VfxY8DaAtx7QZmtVeioJKoM406krZZs61K8Qok8OFaVuCYbZU6ENasXUFNNshZSx0/v10vb580m8TasP8b0tr/Lcy6updndOObfpgUfQqyo7c2Zw1oV9WwXR/pqmzEzsoRbmF2/hic+0hwlGvZFMRyb76ntRbDkqBiaeD/UHtZ/HISwtxsrCO27guiV38bMFt3DdkrsIPPAotfc+hC8mgUxPLX9a/VeWbHyHT3aU88D7e7j+33v5+fN+rn7+Wa7690eccihdyGl/WMVbO2q1E8flgjFq8FNSCCHEMOAJeohGh/5QPYi8RDtHjqvrdfTs2Vs3KZn0Oj0zk2aypXrLkEgnWuQq6rAKopXNZGNC3AQ2V2/u24ldFeBMI78hnzExYzDq+7gKuhdS7Ck0B5txBQ7VgkieAjWDWKDa067erzl6xBenlkGI4SYc0gqnWA+nLggpISr3bia2GczjxmJM0Iolzx9zuMhja82D/iyibEpLQ6+E277WAVlP/bnXtQt6I/u7twBwwp6tVF93E/lnnc1ff/IQNz+7kWq3nzGJdl6+eT6/+/p0HFGmDsc2vvoq+886m5Kbb6Fl23Z0FgtZ/36SqIn9UwjneMRedhnjVnxC9jPPMG7FJ6RcdwNzL7kF7+O/JueF54m57FL0NhvBomJqHnmE/WedTfGNN+F6/32UQIBgZSWer9YyOypAgs2IqyXEZ/k12sh7Uym0+9zEAAgHwdc42K0QQvSSO+DGpqh9TscEoIvJJBfTqC6UHFbC7Knf07kWBGirApSQllrmeOj1kDKlbXaz2WAmyZbUVkNqSAh4oaVpVBShE/3sUFHqHWVNhBSVRLuZxqoDRPvANmFiv18+8bZbsZ50EtZwgB+seZoHXu+YQqJydwEZ61cBkHTbreiPYxUEaAWqYy/XVkOcV/QVL64rpq5ZW/0wLXEaha7Cww8XesJsg4nnQfzQmYTUnSvmZfPmvRfzi198izfvvZjrTs3jtCvOZ+ZH7+I8/3wMqsK393zEi9v/zTdSVRLsZgJ+O/mF6ayrXAcG7X1RVHjwk6LDKyKyT4KKrRDsYUFVIYQYpVwBF7EY24pSp8VYuen0jn8/LpqZ3vNnb12kZAJIi07DZrJxoPFAp20DSVEVSt2l3a5SGB83Hk/QczjFUW+4ywnakyh2FzMmdmD+But1+o4FqqOTQWfQVmIPNCUMLY1gO/Ts1mzTVoeMYDIIMdx469pyvraq8dZg21+BQdVSMQEEwwp7K7WZUP/v3Il8/vOFXDGvf5c2HVm7AABFIVDUfw9aUnMzUKEtO6pOUTjrnX/x4OeP8ezOZ/jXxn+S9NObOXDhRRQsOZeCsxfRePEl7DvxJCruurvDsjc1GMSYkNDldYYCU2oq9pNOxJSqpVQaEzOGen89/il5pP/ud4z/bDVp99+Pbe5cUBQ8n31G2Y/uYN9JJ7P/zIUUf+c7FC5ezC3N2gOZt7eWgylKW77fJPnJB1RtPhR8IsvehRhGFFXBF/JhUcPaUtm+cmaSFlYodg2vQYjyotV4W5oicq4DTQeIj4onxhLTeaOrHJzHuQqiVeJEbWZZi/bQLceZM7QGf9wVoDfKIIQ4PqGA9uA4KqYtFdOpsSr1NBPnM2DJy+33JrTWh1Bj4xjjqiDzhcf5PL+2bfvGBx7GoCrszZ7G6RedEZFrxl5yMRiNTKkvIrW2jKfWFAJgMViYmjiVzVW9nJVpMGmzUYeB1nSy7R9wGWJiyPjTQ6T/8Y/oHQ6cB/dy3TO/YvmEej6543RuXDCTsGccBtt+0GsrVRQViuoOzbiMioHECVC6IfINlslOQogRxB1wE6MzdJiUNCZJG5BofS61o9x11FTZHbSmZAqHOm2amTST3fW7e5xmsD9UeCqIscRg66b/o9fpOSHlBLZUb+n9imNXBUUESbWlYjX234TpI+U58yh2HSpQDdpqiOpBWA1x5PNdScckhpwuUjFVeatwbNVGR+0nngjA6n011HkCJEZbuPG0Mf26AqLVkbULANDrMef03+BHxc59ncqz6YCZtQUk5m/Hv3kzLTt24M/PJ1BURKiiArWhAbW5iw5/Pw+YRJpBb2BywmR21O4AQG+3E3vJxeQ89yxjP3ifhJtvxpCYiOprN6NJUVjwzlMk+hr5eFcVLcEwxOd1OfIu+pG3DvzN0NTD4olCiEHXHGwmWmdEZ7B0/lvXG/YknIqCEvTR5I/MQ/3+Vl6xmdrdb7B213+PezZWSAmxt2Fv17UgQBsUP556EO0ZjFrBuSrt72SaPY1GfyO+0BCZ6euu1Ga/jfAZT6Kfeeu0GXQ6HZsPDUKcpGugIVpHclwGOrN5QJphSk4m+6E/oup0nF/4FW/+6Sn8oTCluwvIXr8SgJTv3YbuyHyxfWRMSsJx1lkAnFu0lme+LMTVoj38yHPmEVJDQ7MOTD+LufBrjHnzDWwnnYTq81F5z68x/+pnLJvkQKc4UFoyMNgKAS1dSE5Cu4dKqTOguQrcVZFpTDioFf7e/qr8nhNCjBjugFtLz2qJbnutsFb7HXfRrHTsZgP7q5v5fH9td6foyGzT0pBW7+y0yWF2kOPMYWdt520Dpaip61RM7SVaE0m1p7KrblfPT+xrQDWYyPeU9XtB6iPZTDbiouIobz60+iE+T1tVOtB12pqrO9b7lXRMYshprgZ7UoeXymsKcG4rBNBmwQOvbdaWQl00Mx2jYWA+5q5qF6T95t62mfv9oSw6EeWIYQgFHb7rv0v6//0fGX9+lMzHHyP730+S89yzZP/3RRz/eoKsZ54e8AGT/pDrzMUT9FDr6/gHzpybS/KPfkj6H/7Q6RidojAdF55AmJV7qiE2W8u/3cXIu+gn3jpImwFVvfgjLYQYVO6Amxi9qe9FqVvp9eBIJU8fRal76K9C8wTclO9+jbwxSzgpKoUiVxGrS1f3uabFgaYDJFoTu14FoSjaA7BIDUIAJE+GhiIIeNHr9GRGZw6dVSjuCkgcLw/nxPE5VA8CYEtxIwDjvJW4rZCS1f+pmNqLXrAA5w03AnDlZ8/z2HMr+ezehzCqCgXZUzj5a2dG9Hqx3/gGAOeUbiLQ7OW5r7RBB51Ox5zkOWyr2UYgHIjoNYcDU3o62U/9m+Sf/QydyUTzqlV4vnUFf831EOcyk+sqJV45wP87K7vjRDWDEbJO1IpUK8eZg9xTB7vfAoNF+z1csFJWRAghRgRXwIX9iPSshXXavdz0jBgum5MJ0LZCr0cy5mjPBrp4AD05fjLlnnIaWxqPp9l94g/7qfHVkBHdbpVyXYE2yHyE6YnTKXYV97ydrgpqTSasRmvXNeL6WYcC1XoDJI6Dmr0D2whPdds9HCDpmPpDWVkZ3/rWt0hISMBqtTJ9+nQ2bOiHZZ8jlaemw0qI5kAzvgMF2FtUzHl5GJOSaPIF+XiXNoPlkhMilNKgh46sXRB72WX9er3cSXn8ZfZlhA/NqgrrdPxl9uWk3XQdMV+7AOfixTjOPBP7KadgmzsX64wZGMeNwzZv3oAPmPQHvU7P1ISpbK/Z3uV2y5i8Lgdbpp84DYB3tlWA0aKNvsqs/IGhhGn2VLHfYtNScHjqBrtFQogecAfcOFXD8aViauXMJEOBYnfx0JmV3wVFVdi++xXS48fjGLcIW8DHmWknk2ZP45PiTyhoLOjV+UJKiL31R1kF4anRUoIYLRFo/SFGi/agv1ob9M12Zg+N2dF+t5aSLzpFS6Uj6flEXx2qB1Hj9lPW6EOnA1PDQewtYBs/sIMQABk/+B7eKTOxhfxM/cu9TNvyKQDlF10VsVUQreynzMeUkYEt4OXU8m08+dlBfAHtQXdsVCxZjqy2FcOjjU6vJ+G6a8l99RUsEyYQrq8n7+F7eO6D+3jwow+4qeBxJm9f1fnA2GxtJmZfi3SqKlTugP3LIXOeVmsibQZYHFCy9nhCEkKIIcEdcGNTlbaaEAAHa7XBg7xEO8tOyQVgxZ5qDtb28IGy2Q7Jk6BsY6dNJoOJaQnT+l78+TiUuEpIj04/XDC6qRSKvtB+xx8xidVsMDMzaSYbqzf2LBWVq5z9Ic+Ar4JolWpPxR104w4cWv2QNElLmz2Qk3ObOz7flXRMEdbQ0MCCBQswmUy8//777Nq1i4ceeoi4uIEf9RqW/G4td7Dp8IyVKm8Vzn0VANgOpWJ6f3sFgZDChJRopqY7B7yZR9Yu6E9pMVYW3nED1y25i58tuIXrltzFwjuu71H6qYEeMOkvmY5MQmqISk/nAuCdVqcA1h98n7NP1wYhPtlThccfkpRMA8nXQEXYy/b6XQSTJna57FIIMfS4A24cOn2HDkefOdOJ9jWRZE1iedFy3ip4i9Wlq9las5XCpkIaWxoP5ygdRDsqN5DQUEb6pK9rM4RiMtA1ljA+bjwLsxZS7Crm05JP8fTwZrmgsYAUWwpOczf3Jq4IpmJqL3mK1qkI+Um0JhJWw4OfCstVAY5UtCfG1hE/60n0o0PpmFrrQYxLiqa2cj9xzSqW8QPfsdcZjaT8/vd4DWYyPbUYUVCBTWu2HS6CHKlrtStQ/fXSddR5Ary84fCkmqkJUyn3lFPfUh/R6w4nURMnkvvKy8RecUXba9EtkN6gUP3B4wQrO/cfyD4JKrf3PiVEsAX2f6JNbJr8NW1Ao1XOAm2geaBnmQohRAT5Qj5MehPGoL+tT6CqKkWHVkLkJtoZkxTNwola9pJnvijs+clTpmupOruYpJjjzEFBocQ1sBNHi9ztUjGFQ1D8FYxbBBYnFKzotMIt25mNSW86vMKgO4qCp/EgTRY76fZ+uPfvAb1OT56zXYFqs127N68foELg/mbQ6TusqMFsl3RMkfT73/+erKwsnnrqKU488UTy8vJYvHgxY8eOHchmDF9dpGKq9FQSvVmbidhalLo1FdPFszMjPuNoKLpiXjZv3nsxv/jFt3jz3ot7VYB7IAdM+otOp2NawrRuZ3rFXnYZYz/+CH1CvLZ/VBTT0p3kJthoCSos310FMVlaWogultWJyFI8NVSpIRKsCZSYzFoRVnn4JMSQ5w64saM7/nRMoOWQ1RuZ6xzDhWMvZEnuEibFT8JqtFLjq2FD1QbeKniLjwo/Ym3FWvbW76XSUzmgqyYqmivwFa4mb+wSiDo0aBCX2zZgHW2O5sysM0mPTmdF8QoKGguOOuspqATZ17CPyQmTu79oUxk4MyMXRCuzDeJy2grOZTuzKXQVRv46veGuODzgYo6WvwOib4It2r1blJMtJQ0AzM5wUt1QQlwzRA3CIARASb2HqHb3lDrgu1teoWhPYcSvFXPJxWAwMK76ADmuCv6+cj+r99VQ0eTDZDAxM2kmm6o29bxA6Aikt1hwnndeh9fGVEBDtErB7i5m1loc2ozQ0vU9v4irQku/ZE+ECed2fKgCWqqnsWdB+RatTyuEEMOQy+/CYbJDqKVtcnC12483EMag15EVp/UTrl2QB8ArG0ra6hUdk8EIGSd0uWpMp9MxK2kW22q3EVIGZqa+K+CiJdRCkvXQM8iKrdoKXmca5JyirTY+sKpT+r7ZybPZVbfr6P0Wby1lQTdjEyYN6jPL3JhcilxFKOqhGJKntK2e7neemo6pmAAMJu2/I/i53IAOQrz11lvMnTuXyy+/nOTkZGbPns0TTzwxkE0Y3o5IxaSoClX1xURv3g9ogxAl9V7WHaxHp4Ovzx6cEcXBkBZjZf7YhAEpwD0UpUWnodfpu80vbs7IIOHb3wbA/+Zb6HQ6LpypfX+8vbUCjGbtD4qkZOp3DQ0FWBxpTI6fTEFzMSSMh+o9g90sIcQxuANuolVdZNIxAcRkaoOQgMVgIdmWzIS4CcxLnceinEUsHbeUk9JOIs2eRiAcIL8hv8OqibLmssi0owveoJdthZ8w1ZyAMf2EwxucGdqs62ALoHWIWldFlLhLWF26uttVEQWNBaTaU3GYHV1fNNiiPYg/8mY8UlKmQc0eCIfIceRQ7Coe3IeS7kptthUcmvXUPHhtEcNXa1FqaFsJMS/KS5MpSGzIjCkra1CaldFci56OP18GVSXd08Minb1gSk7GcdZCAC4qWU+V2881/17HggdX8NL6YrIcWZgN5l6njxtpzLk5HVZGG1QYXwbPNxQefvjSXup0re/pqjj6iRVFSx9S+DnknQ7ps7QVXl2xOCD3VDjw6Yif6dljtfu190MIMSy4A25i9WZtAOLQ77rWlEvTzC0EN6wnWFnJaeMTGZccjScQ5pUNvagBFz8GVAXqD3balGBNINmWzN76gVlR1lqQWqfTgbce6vZrafZAiz33NO2/has7pBV1mB2MjR3L1pqt3Z7bV3+ASoOe3Jjcfo7i6OwmO7GW2MP9KkeKFpO7i1WCkXbE8902ZvuInpxkHMiLHThwgMcee4w77riDX/ziF6xfv57vf//7mM1mli1b1uUxfr8fv9/f9rXL5QJAURSU4y2YdYiiKKiqGrHz9Rt3FcSPbRtprPZWYymuxhhWMWVnY0hK5PUV2oDE/DEJpDgsPYpp2MQfASM51inxU9hcvZk0W1qXo8nOiy+m5q9/I7xrF96dOzl/WjZ/WbGfT/dV0+jx44zNgboDEJs78I3vR0PtM6+vyyc++xQSohIIK2FqnIkkFK7RHpC1jnxH0FCLfyCMxpjbk/gjH78/rN2H6IM+FJPt+At2AjjStLzZyd3URwAcJgcOk4PM6MOrA/xhPw0tDWyq2oQ/6Cc3JjeiMSuqwldlXzLN04xt3BIUnb5dvDptIKKhEBIntB1jM9o4Lf009jfuZ3nRcqbET2FMzJi2v0XBcJC99XtZmLmw+zY2lWqD4ara6/oIPYrfHA3RqVC9G1vKVGxGG5WeSlJsKb26VkT4GrX0mkar9t6abNqS7F58fkPl53wgry99gi4014A1HiUUZmuJlmIsw1eItwVseeNQdTrUQfi+Spk8jgKdDl27n2VVrydl0th+eW9jLrsM98fLOa1wPf+YdB4BgwlFhV+8tp1TxyUwM3Emq0pXkWZPw2rUJiwNu8/6OBmSk0m599dU3fPrtt81n6ScxepyA7trd3exSk0HmSdqub+nLNVSRhwp0AwHV4MxCiZdoP33WO+nI037+1GwQlsx0dV5+8mQ/Mwbi7V/CeMPD0z3kyEZfz8bjTG3N5rj76/YG/2NxCu6Dv2BgzXNLC5cyw+2vkrxf1XQ60m599csmz+Pu9/cyTNfHOSak7Mx6Hs44z9jrvZg35mppUNtZ0r8FJYXLyfbkY3dZO90aKTiVlWVIlcRp2acihIOa38L0meDwdzx93zu6drv84OfaWn3Dt37j48Zz/Li5VS4K0ixd77XrqzYSHzSNAwYIvoZ9SX+XGcuBQ0FZNgP1dNNnAiVO7W6qf3JXQVZJ3b+u2m0aqn4LT1LrT9Ufs57ev0BHYRQFIW5c+dy//33AzB79mx27NjB448/3u0gxAMPPMC9997b6fWamhpaWloi1q6mpiZUVUV/ZBHfoUIJYa0pwxcfbFvCuqdxD+YtWr4y3fTpVFVV8erGYgDOHuugurpnS12HRfwRMtJjDXgCbC7cTKa963QWptNOJbhyFZVPP0PcT3/CmIQoDtS18OpX+XxtUgxR5fm02CZof1xGiKH0mStKiJqqfNKzLqK6upqYUAybKvYwT7Gi5K8jFB/5tAlDKf6BMhpjbk/ij3z89f56FK+Cy1WJ3+ZB9UYglYSix1p5EJ+jtNe/c/XomWKZwtrCtVRFV5Fjz4lYzHub9qKry8dqiKM6aIUj7iX04WhMB7fgV2I7HRtDDNOiprGtdBs7S3cyI34GNqONfFc+1pAVb6MXL13PfjWX7SBsTybcw3uX9nr6mesMqVj2f0YLCUQHo9lWvI2Z8TN7fb3jZazPRxeyEDwUq8Hdgr6lgaC+5wMiQ+Xn3O12D9i1pE/Qmbl8P+GYXPbvLabZHyLKqMdTtp04j4o6PbPHfYFWEYtfr8f+kx/jeehP6BQFVa/H/uM7aNDrO/1OiQR1/HiCick4aqs5tWwbK7LnABBWYcv+cuZkOYgLx7E6fzWzE2YDw++zjojTTiPmv//F84ffE9qwkUlNpTxbcTavb/2CqElR2I1HPtAyY25RUXZ/TihxUoctBlcJpspNhBKnEHKMh3oX4OpZO/QpmD0FqNs+IJg2NyKh9cRQ/MyjyvcRTJyGcedy/HnndL+KJAKGYvz9bTTG3N5ojr+/Yi+tLiVGZ6UxECRw6O/Zge17+cGWVw+vAFQUqu75NQueex6HxUBxvY/X1+Zz+tjYHl/HHDSh7PmcUGLnNKZJahKf5n/K3MTOvz8jFXdtSy3+Zj8tjS0EG3ZibHLhj3N2/Tc8eiqWktUoTe93+J2epc9i1f5VnJ5yOoZ2gynhUAvlFbtIT1vS6/uUY+lL/AbVQGltKQf1B7W/g0o0URX5+KNyUbsY6IlMQ8NYq4vxxZ4Ano7vgckTQKksIdzSswmqQ+XnvKd9ggEdhEhLS2PKlI4z/iZPnsz//ve/bo+58847ueOOO9q+drlcZGVlkZSUhNMZmaLLiqKg0+lISkoaur+c3ZWQkoMj5fAMiW2+bSTv0gYd4k87jcKAheIGP1EmPZfPH0+0pWcf77CIP0JGeqwLnAtYW7GWWUmz0Hcxs8h2zTWUrVxFaMUKEn51N0tnZ/Hw8nxWF3q4buEU8E3EafZDQj/k5B4kQ+kzr67biyM+newMLUdkXEIc7xe+jzVjHpaiLyHplIh3PoZS/ANlNMbcnsQf+fg9TR4yzZk4g25Iz+k0K6nPArNwhCuhjw9hUpJT+KzsM2oNtSTFJh13zFWeKjyuOhZF2TFOWgxRMZ13SkyA5j0Q79RmvXYhNy2XgqYCttdvZ2LcROr19Zw19qwuZ2wB2sqHSi/kTu+cR7wHev6ZJ0OgBKfBTUzODD4s/JD4xHiM+gG9HQb3DkiZCnGHZlhFhaCqGZJ7PuNqqPycR0V1/T3QH6RP0IWqEGRPZMV2rR7EjMwYmsvLSWyGmGnTie/F9xREOP5rryV43nkEi4sxZWf3e/0136WX4v/HY1x44HNqrTGURyfSYItl1rh0kmOsJCQm8FHRRxANybbk4fdZR0pyMoH77uPgBV/jhKq9TKsp4UBVHiUTSjg9+fTO+8cuhj3vQOwJ2ooyJaTVivBXwgmXgi2+b+1IuAD2vgf6JkgcmNolQ+4zb2mCuESYdArsc4HB1a/vxZCLfwCMxpjbG83x91fs+mY9meZYLHpj231buLyyUwpCFIXkQAvfPDGbf352kDd2NXLZ/AldnLEbsWfD7ncgbl6nVLCJSYl8XPQxarTaaUVvpOIuqixiRtYMku0OqCyCGed13S9olXQJ7P8YAkVtKZuSSabZ3EydsY5pidMOn7tkDXEp48nJHNPn9nWnr/HPMMygmWbyErXnNIROABogOS/ibQS0ieUpOThS0zpvCx96FtfDe7ih8nPe0z7BgPa6FixYwN69HfOX7du3j5ycnG6PsVgsWCyWTq/r9fqIvsE6nS7i54wod4VWAOZQ+3whH15vI+bN+wCIPulEXl+n5ZZeMjUVp7V3syqHfPwRNJJjTbQlEhcVR5G7iLGxnQu+2088EX1uDkphEe633+HCJUt5eHk+awrqaPSFiI/Pg7oCSBqcQob9Zah85rX1e4mLH9vWDoveQqYjk+Kwh4mWaHCVasVTI2yoxD+QRmPM7Un8kY3fE/IQY4hCbzSDMYJp0zJOgF1vQMpkLVd2L9nNdhZmL+Szks+o8dVwZvKZfY7ZF/KxsWYjpxKFOTkbbHFd76jXQ2yWVkMoaWK355sQP4H06HQ2Vm0kx5mD42jxeeq0DlZU79+DVj3+zNNnQuFnWJMmkGxLptJbSbYzu8/X7TVV1WY85Z56OD97lANC3g752ntiKPycD+S1pU9whIAX0EGUgy0lhQDMyoqhdkcx45ohauKEPsUQyfgt6elY0gemRl3WVVeS/8/HmdRYwu/XPI6CjrLrf0hGnDawqdfrmZM6hy01Wzgn+xz0ev3w+awjLConh5hLL6XppZdYtvsDfpF8GxfN9VPaXNr592GUU0sZWrZRq/dw4FOtds+UpVoR1b7SW2Dc2bD3fbAn9F89oCMMqc/cU6Olp9LrIftk2L8cEsb0S3rWVkMq/gEyGmNubzTHH+nYg+EgKipWNQyWuLb7th26GC5F13EgQq/HkpvLNROd/Ovzg3xRUEd+tYeJqT28141yQvIkrRh07oIOm/TomZ0ym601Wzkn55xOk0+PN+6gEqTSW8nslNnoi76EpEnd9wvaGmWB8Ysh/0Oo3KqlbgJmJc/i46KPyY3NxWl2oqoqVVWbmZQ+t9++J/sS/9i4sawoXsH0pOna+5k4Dg6shKx+Wq3nrdXS0HbVRku0NkjRi/YPhZ/znl57QFv4ox/9iK+++or777+f/fv388ILL/DPf/6T22+/fSCbMfyEQ1CX3yH/cpWnitjSJnSKgikrCzUphbe3aoMQl5wwcmaxi96bmjiV3fW7CSmhTtt0Oh2Wiy4CoOG/L5KXaGdahpOwovL+jgqIydIejoT8nY7tbyElRK2vloLGAoJKcMCv399UVaWxvoCE+I4P7cbGjuVg00HU5ClQtXOQWieEOBp3wI1TZ4hcUepWpihtRnzZxj6fwmKwcFrmabiDbjZUbei6wOgxqKrKuop1TDQnENvigbRjpCiKy9XqQhxDtDmaM7LOYFbyrKPv6CrTak0MhOhk7XNsKCTbmU2hq3BgrtvKW6+t9jC1my1kjh7RBehEP+miKPX4JAVTWR1GBSzjR9aEkmNSlQ41KPSoZD31KMHKw8UlU+2pxJhj2NewbzBaOKQk3HIzmM1MqzvIjIq9FJYls7VmK4FwoPPOKdPA1wB7P9AKVueeenwDEK2inNrDtYKVEPQd//mGm+ZK7SEUaCtKnBlQuX1w2ySE6JYr4MJhdmj3bJZoABRFZavfzMvjF3bYN/7a72BKTSUzzsbiKdpKwKe/6Fxs+qhSp2v3yN76zpvsqdhNdgoaC/oWzFGUN5eTZEvC3FyjPSw/Vr+gldGsDUQ0FLX9LosyRjE1cSqbqjYBUOmpxO5rIjap+5p4g8FushNjiaG8WXumii1emzjUxXsfEZ6a7gffTTYIdp2+diQY0EGIefPm8frrr/Piiy8ybdo0fvvb3/LII49w9dVXD2Qzhp/6A9oNyqFfdABV3iocu0oAsM2bx6f7amjwBklyWFgwNmGwWiqGgBhLDMnW5G7/IFkWL0ZntRLYX4Bvwwa+NkObofbO1gqtQ+FM1wqk9SNVVWnyN3Gw6SCbqjaxvGg5bxe8zbaabRxsOsiBxgP9ev3BUOOrwRkOYj3iQVtcVBwmvYlqkwVCPvDU9k8DmkqgpYe5eoUQHbgDbhwY+pQq6JiSp2g/9801fT6FSW9iXtI8AkqAryq+IqyEe3X87vrd6IBx7lrInHPsWZjODO2mPFIPjVzlEDNAgxAAqTOgcjtp9jQa/Y34QgP48MtdDo4jZobrDVqh6mBk6hqIUcJbC/YEvIEQeyu1v++pwWLiXCH00dEY+zn90VATKCzq/KKiECjqeE87M2km+xv34wmO7oE/U0oKlq8vBeCa3R/w2voGsqKz2FazrfPOer22amHy1yCh80rr4xKTCUkT4MCqYxe1HmncVeBol0olfTbU7AV/8+C1qT+oKlRsG32frxhxXAEXTrMTAs1tfYJKVwv+kEKlI6nDvsGS0rb/v3ZBLgCvbSqjwdPFQG93DCZIPwFK1na5eWbSTPbU78EfjuwE0iJXETnRGVD8/9k76+i4rnttP2dYg9KIGW2ZmR0HHGbGpk2a3jLDvYXbJk0p7e1XZkqTpmk4bdjh2IkdM8lsi3k0koZ55nx/7BGPWDJlnrW0Es+cOTBw9t4/eN/3oWj1+GRoVVqYeSnYj4PtMACl5lJkWabWWUu1vYo8bRqkjNJZcQootZRS4+wXh0orBUeCucVU4O0QhVGJ0BjO6uKkk96rcdVVV1FVVUUgEODw4cN8/OMfP9mncOZhOyiCFHFkWabd145hu/hR65cv5997xE3u2oV5qJQfvFa7JAOZkz6HY93HCEeHdhRIRiPmK68AoPuxx7lyvtCh21rbic0VEDfbrnFm6UfBF/bR7GmmqqOKjY0bea76Oba2bsXut2PRWliavZRrK65lfdF6lmUvo8ZZgyzLo+/4DKLZ3UQm6oTauWWpZVS7aiBr7rR0Q0j+LlFlFp8IJEmSZOzE5Bj+iJ8UOTY9SQiFUsgyNW2f1G6UkpLVuatRSko2t2xO2A2XiA5fBzXOGlaqrUgKNVjHoM+qUIjAUfcUTMyjYVHRbRy7KfOkiSc8lO5W8o35NLimN/E+AHcbmBIEh8/yBUeSacDbCfoMqpqcxGTINmvx246R6hFdENI0GtyejmhKiodIF8QkBZrigfJCerWeSmsle217T+LZnZ7o7rgDSa9npqOJgoPb6XbkYPPZsPsTFMToLBOSDRwTOQtAqRFeEx8U4omG1rCH493HxWMa/aS7I8eEv1uMvScLj01ck7Px5B0zyQeecCzMFtuWMc+Hx4I75O7rhNCIAuE6u5i7zYkIbyb9ihVi2zffJNzaCsCKUitzcs0EIzEe2zHOOWd6ufi9JphzmzQmyixlbGvdNqFO6ET4wj6cQSe5brsIkpsnIKmoThGJiPaD0HEMSZJYkr2EKnsVYUcjaZlzp9wHcyrIN+bjDDr7ihTSiqdmrTOYnkTzcOtKjSHZCZHkFOJsFtVx/aokuoPd6CIS8n4RUIzOX8Qbh4Sj+vVLTmIlYZLTFqPGSJ4xb9h289TbbgPA9frr5MZ8LC5KRZbhpapWEVjy2SdVkRmIBDjadZQtzVt4ofoF3mp8i3pnPWqlmtnps7my9EouLbmU5TnLKU8tJ02X1qtlmKpLRafU0eZtG+UoZw6yLNPWdRyrKS9hhXGhqRC7347Pkif8X6ayAioaQtv8PuQvFZn8syy5kyTJdOMJe9Cr9SjC/qmXY+rBWiZ+m5NMACskBStyVmBUG9nUtClhIro/wWiQ7W3bWZGxEG37IShaOY5zLh2TJNOouFvjmqhTZPY9VnLmQ1sVxeZiGt0nKTASi4lgTMIkhFFU1iVJMlZ8djBk9EoxLSyw0N56ojcJ8UFDnZND7vfu7w1syMDj6z6U0BC7IrUCX8RHm//smWtOBEVqKta7PgLAXYdf4YmtDSzOWsyu9l1TFtAaE5IEpecK2ZHOqZcWOS3xtCMbM9nXsY9DnYf6ZLCy54oKWY9tWg4rBbrhyEsntzCpq1qMex1HR982SZIpoivQRXewu09eZwpwh9yYFVoRn4vPW2s7RcC6zCc6mk2XXYp++XKIRul+/AlASGL3dEM88n494eg47q+SBIUrRZI2QafznPQ5KCUl+zr2TeLK+mhwN1CiSUVhP9FrMD0hNAaRiGjdB53VWLQWKtMqmaU2ozhZEqzjRCEpKDGXUOuMr8cMGSIBFHBO7YG8HWDIHP55pVqsC6NTl0A7nUgmIU53bIcHdEGA0FGzNDohGkWdl8ernQpC0RizckzMyTWfohNNcrox2zqbamd1wvY83Zw56BYsgHAYxzPPcnWPJNP+VjGgWgomJMkUjoY5aD/Ia/Wv4Yv4KLYUc2HRhVxVdhVr8tcwyzqLLH0W6lGkPirSKjjuOD7u45+udAY6sUQiaE25CZ9XKVQUmYqo8zRBxoypXRjUv0/UmCsWNSrd9Mk9JUlyluIOuTGpe6qepqEToofCFaJScJxSSoPpqTbKTMnknaZ3CEQSJ5R7fCBKLCVkOZpFUmE8rdGmPFFNOVlJJmfzxKqsJktaCYR9ZERjhKIhnMEpXmAkwtsBKamJ5a6SvhBJxkPQA5IS1Cm9SYjZ+SpULXY00Q9mEgIg9aabKPjTHwEIS0qeMc2ksWtoNaFCUrAkawmHug+dlT5k4yHt7rvBZKbIbSPy+gakaBpmjZmjXSc5YKxUQ/l6aNopunzOdtxttEqQokqhyFzUt+5RKEXhUOP2qS8cigTQNm4WHh+dJ6Z238MRi4pK4pJ1Ys6QlIZNcpLo8ndhUpuod09dJbsr5MKEYsB6oKcTItshktrasjLS7rwTAMeTTxILiljM1QvzSDdoaHUGePXgOBPgpmzhAWU7NOQpSZJYkbsCu9/e11U1CepcdZQ4WoU8nDplcjvTmmDmJWJ9011PpbWS3ChgThwTGY5wWxverdsGeDxNF6WWUmqdtX2J+OnohhhJiqkHjf6sLU5KJiFOZ/wO8HcJeZx+tHvbMR0QP4T+UkzXL87/wLVeJxkevVpPsbmYI11HEj6fFu+GcDzxBFfMzUKSYFd9N80Ov/jOdY+9Ijcai3K06ygb6jYQiAa4qOgiFmctJt+Yj34ClcP5xnxcIReu0NkxUW1yN5Gv1PUaSCaiLLWMGmcNscxKsTCYijbpjmMQdBHOiptJpRVPTeVykiQfIDwhj2i9DvumNwlhzIovMKYmCTk/cz6FpkLeaXwHX4KW3mPdx4jJMebockTSOXfR+A6gUEBq4eQn5q5TlISQJCEB0rqfYnPxyZFkcrcm7oKAuBzT2bnYSDINxP0goM+UujAzgrG2HfjgJiEAjOvWoSkvRyNHOaelineOJfbbSU9JJ0OXweHOD7ZUpdJkIvMT/wXAHYde44n3q1mUtYgTjhN4TvY9KSUVildDzdtnvUeO7G7jcKiL2emzqUyrpMZR05cQs5aCpBC+kFNFLAY17xBJLYW8RWL/k/CiGjPORlFNrDVCRoXQiU+S5CRgD9iptFTiCDoSzoPHSzQWJRAJYJBjvVJMALV2H+poBGOnGH81ZWWYLlyPKieHaHc3rldeAUCnVvKhlUIe8O+b68Z/AgXLhLxRguIftULN2ry1HOs+Rqu3dfz7jtMV6MLgbMWkNorCyKlAZ4GKi6BhK7QfEvPvcUj7OZ5+mhPrL6Th7rs5sf5CHE8/PTXnNQxGjRGL1tL3PqYWT70vhMcGhlGSEOqzV5IpmYQ4nek4AhkzB+ibhqNhnCEnum0HAPDPXcSOum4kCa5ddHq2NSU5dcyyzqLeVZ9w4DVfcTkKi4VwSwuGfTtYUSK8Cl7a3zJm09GYHKPGWcOGug10B7s5v/B8lmYvnVDioT8KSUGZpeysMKiWZZkmTxNZKEdMQpg1ZkxqE60hp+hEmewk3dcFLbuh7Lw+mZPpGESTJDnLcYVcffqvCe5tU1qdk78U2g9MWfBllnUWM9Nm8nbj2wOSuna/nWPdx1iRuwKpcas4rkoz/gOklUwusdlTEZmSOvF9TAZrOfi7KYpXyk27F5G7dagpdQ/JJESS8RD3g2h3BWh1BlBIkKZ1YqoWEi7amR/cJIQkSViuuQaACxp38c6R4WVtZllm0eBuODmdUKcx1g99iIgljVxfJ62PPYVa0jLbOpvdtt0n/2RSi4QGeu3Gs9fIOOSjy9eGIiWNLH0WerWeHEPOwHVP4Qpo3j11chxN20GpJZIRV1hIr4DOk5AQ6Kzu85rKmCkKrc7WzzXJaYMsy3QFurBqrRQYC2hwT77QxBP2YNQYkUK+AUmIuk4ved4OJDmGwmhElZmJpFL1Fnx2//PR3vnlnauKUSsldtV3s7/JMb4T0JogfQa07En4tF6tZ03eGna275xwIWdD1zHKvQ6RDJ7K4ma9VSQiWvaMq/Ao3NZG67339d0zYjFa771v2jsiSi2lffdjYxaEfFMnlx2LQsCR0Cd0gAyixiCOexaSTEJMgEgsQodvmisHIkGhDZ05a8DD7b52MpQWggeEee2bGpF4WFueQY5FN73nlOSMQ6vUUp5azuGuoVVeCp2O1OuuA8Dx2ONcvVAMCC/sax1ThWuTu4nX6l+j2d3Mmrw1rMpdJQJ1U0SppZQGd8MZ3ybfGehEp9SREvKNmISAuEG1o1pIJ9kOTbwNOxoRi7fCFaDtJ9GWkioSEh+ENvckSaaIXhO6BJ0QU16dozOLwHjr1Oi6grivzM+Yz6amTTgCDkLRENtbt7M8ZzkpjmZAEgGfidAjyTTRSfKp6oLoQaGA7LmYOmtJUaXQ4Z/GuV00IpLDw7Vfa01JOaYkYyfuB7GnwQHAzGwT3a3HSPPIKNPTUVmHLm4/SFiuuhKABfYajladIBBOLHOnUWqYlz6P3e2nINh+GqHQ68n+zKcAuHLfK7y9r5Hy1HLCsfDJ6RIbTO4iITc23QbNpwjZ005t1Mfs9Nm9j1VaKznWfYxojySjIUNIsLQfmPwBO46Buw1KzukLLKaXi3XedGqOhwPgaRdFUCDGOX06OOqm75hJkgDOoBO9So9aoabEXEKdq27S+xxQlBRfD0RjMg2dPgrdItmtKS/rVSZJveVmJI2GwIEDBPbvByDLrOPK+UKKaELdELkLwNkk5pMJSNOlsThzMTs7dw4rxzocMTmGr3Yj1vyV45NnHSuGdJh1hfBkGyOhuvqhSctYjFD99I5L+cb8vg4aSRLJ8akq5PR1ivc3gRfe241v0+QWKjdo9BA+O9cFySTEBAjHwmxt3TolbV3DYj8uvuzqgYmFNm8bqfVdEA6jysnhXw0iQHtD0pA6yTDMTJ1Ji6eFLc1bONJ1BHvA3mtWmnrbrQB4Nm3iEmsUpUKiqtkptA2HkWRq97bzZv2bHOs+xtKspawrWEeabuoHqhRVCtn67FOz+JlCmj3NFGqtoNSASjvitvnGfJxBJx6VRlRYTHSwa9wmWvx6Ko/6k1p8cib/sSgc/LcIUCZJcgbjCXswKTQiINJvwjht1Tm5C8S9dwpN0IrMRSzNWsq7ze+yuXkzhaZCcrRWEeApWjXxHfckrCfgIQTE/SBO8fwlYya42yjRpFPvmsZOMU+7CL4MZ8CtMUxdlVWSsx9fJ+jTe6WY5haoUbR3ool8sKWYelDn55OybBkKZFbX7mR7beKADUCxuRgZmTpn3ck7wdOQjNtvw5eWQUbAxdG/PIwkSSzNWsq+jn19psknix6jakc9uKbOVPZ0wd5xgIghnRxDnzyfWWMmIyVjYLA0f6mQaJxMNay7XVQfV1w40I9InQLGTHBO4zqruw4shaBU9T2WOVMkRZIkmUbsfjvpKaL4L02XhoREp39yRXi9RUkhd28SosXhJxSNUeIVRSza0r61t8pqxXzFFQB0/fPR3sc/ulZIrb+4vwWbe5ydz0q18Gpo2jHsJgWmAooMRWxp2UIkNvYkY3v7PtIiYXQFK8Z3TuMhJW18UkzWbGIM7MiQgW7LyIWdk0UhKSgyF/UZVE+lL4THltCU2hl04gg4+sYAteGsLU5KJiEmQAoK5kg6quxV03OAWCxuSD17yFPtvnYM+0RrUGDuQuq6/KSolVw6dxiN4SQfeNRKNZeUXEKxpZhwLEy1u5qX615mQ90G9qfYsV+4EJdOJvbif1hTLm7oL+5vAVOu8CWJT3y7Al1satrE3o69zE6fzfqi9WTqh95Ap5KK1DPfoLrJ3US+QjemigKFpBDtf84a0Q3RfnD8B+ysFmZHhSsTPz9Z+ZQxIjsaONi6HVfdxmk/VpIk00UwGkRCQhMND+mCmLbqHJVWeBU07ZzcfgaRa8xlZe5KDGoDczPmQuteMalO0A48LsbpIdRLLCYC86bxmdNNOUoVZFZS6HfS4mkZ14JtXLjbRjbiU2lBjk5vVWqSs4OAq7ewYW+jSPSXZEcxN4vEZTIJIbBcKySZ1jfu4u0j7cNuJ0kSS7OXUmWvOvnB9tMIhUZD6qc+DcCyzc9T39BBqi6VInMR+zv2T2if0VgUZ9BJm7cNT8gzUGpiNFQaMZedDoPmU0xr215K8oYWAMyyzuJo19G+90ljgKxZE+8ICXpEZ3TpuYkDf+kVYJ9Gg+rOE0M7LS2FEHSLNWaSJNNEZ6CTDF1G779LzCWTLjRxhVyYNeZ4J4SQY6rrFEHimSGR4NCUDywATPvQh8RrN2wg0iESFQsLU1lSlEo4KvPo1gmsGdIrhGrKCAVAFeYKzFozO9t3jk1qNBbFdWwDpoqLByYNTzH1SiNH0gp7/y0DEtDx6OPTfuxSSyl1rjrx/hlzRHHYVMgjeTsSJiHqXHXMTJtJV6BLdLEk5ZiSDESixG3H1VU96YxqQpwN8XbFgYEBV8iFUlIi7RAyDbssIot62bwcDNrT52aR5PRDq9SSb8xnfsZ8Vmau5Jqya1iTt4bMlEy47HyOFEr8p+pxZhZVo0ip599V+3BHvJBWjO/Ea+w59AQ7696i2FzMJcWXkGecYvmMkE8Ez+vegwPP9hqlpaeko5bUtHuHXzyeznT6O9EoNRjDgVGlmHoos5RR76onasoVE4zxmMYFnKIyouy84ScQeqtYzA3TxjlVeFr30JJeQk3rLiLuiRtkJUlyKnGH3BjVxrgU00A/CE1J8QDPJgAUCjTFRZM/cOYsoRfqnlrN0yx9FityV6DwO4TkY97iye90UMJ6zHjaRXJ2Il4UU03WbDTOFrLURlo903S/GskPooekL0SSsRCXYorGZKqaROIh2xrEVNNjSl1xKs/utMF86aXIKjUl7nZOvL93xG0tWgtF5iIOdk6g+OMsouxDt9CZloMl5GX3L/4AwJz0ObT72rH77cO+LhKL4Ag4aHA1cMB+gC3NW9hQu4Hnq59ne9t2jnUf472W93juxHO8UvsKm5o2sce2h2Pdx2jxtOAKufpkiPqTWii8mM4iM+NWRz3KcICcjKHFhmm6NEwa08Au8Oz5Yi4wXinVaASq3xLSJ8MlwC1F4O+anmrbgFPMnQYXGkiSMLy1J7shkkwf/TshQHQEN3maEt9nxog75O5LQmjjSQi7+O0UxeWYtGUDkxAp8+eRsnAhhMN0P/VU7+N3x7shHt1WTzAyznOSJCG53LRjRH+VJVlLCEQCHOo8NOouQy27sEsyOblLx3cu00yJLkaZS8zLf7vgev4w/zoAdE/9E+cLL07rsc0aM3qVnnZf++Q7v/vj7RgizRqTYzS4GihLLSPPkEejuzEpx5RkECoNyqI1LPb72Dsdhl3thyB7zpCH27xtZKnTCOwT1SiPh8SN9frFSSmmJONDkiTMGjMllhLOuexjrHFksm6rm5tcoEJHnbOZpw+9wfOBVrYGbWRGZS4KxSiu24ZUu0m00U5GKiQSFC1tDVuFZM/h58HZKCSEMmdBc18FcHlqufBJOANp9jSTb4ybfI8xCaFX67HqrDR5m+PdEGPUgo1FoWajaN0eresirXR6DapDXtzdtWTnrUCRv5SGw89O37GSJJlG+lqvfaItth/qnBwyv/ylAY9ZP3YP6pwp6ExUKCB/mVhgTEcFaOM2yF8yqkTcmFAoJqaV6moGy2kyf1FpIWMGFcEgR7uPTr1BdSQkxswE48DBzoN0BeJJ4R6t4SRJRsJ+Akx5HLe58YaiGDRKdFofhkNicZzshBAozWb0558HwMyq93qDRcMxJ30OlWmVJ+PUTlsklYrYXR8HoOiNf+Pv7EKtULMkawm72ncRjobpDnRT76qnqqOKzc2beaX2FV6ofoGd7Ttp87ahUqjE+iL/HK6ruI7ztfNZ2qTlQt0irqu4jvMKzqMyrRKLxoI/4qfOWcfWlq08X/08L9W8xMbGjexq38XRrqO0elqRC5YJOaHI2dGlUtOyhezs+cOavs5On82R7iN945BSJcbrxm3jO1D9e2LMSaCs0Or0s6XaTqs7KNYEndOwzuoxpE50nRkzxPPJzr8k04Av7ENGxtBv3p6iSsGqs9LinZi8myzLeEIejEodxCK98+dauw9JjpHeLYqGNIOSEABpd94JgOPxJ5DDQhL78nk55Jh12D0hXtw3geIXU45Y79uGTzAoJAWr81bT6G4cWd7a76C74X10JetQKU6vwmbtu2+ji4ZpMGbxUukaXig/hydmrAeg5dvfxl81Tco0cUosJX2STKnFk1eT6JFdHdRd3+5tx6wxY1AbKLHEu3bUyU6IJINJKyYjrRxzZ93UatZ77aJqwFI45Kk2bxuW2k7kcJiINYMjylSyTFrWVmQk2FGSJGNDUqtJvflmFDLo//0K55bMJ+qroNs2h8sqrubchfdQMP82FAtugcorwFIAXhscfx32PSEC3x1HR26rjUaE9nfTLjj8Ahx4BjqPC9Pk0vNg4W1Qdr7QCc2aDdGQMF0CCk2FdAY6p9eDZZpocjdRYCro1W7uIdzWhnfrtmG148sscYNqa7moFg66x3CwnaCziIn9aEylrmEiOk/QptOTY8pndvkVOLw2Olr3Tt/xkiSZJvqSEJ4hE0YA3axZA/4daW6euoOnFQsfiq6aqdsniIV/LCrauaeKici8uZrBXDB15zBZsuaQ6XOiRRKSeFOJpw2M2UM6Zzp8HRzpPNKXaNecvfqvZyu9wTyn/+QcsLMaokHImNFrSj23QIMqGEbZEq/ETCYhekm/7loAzm/awzuHRw70qBVq9Gr9iNt8EFj1sVtoSMtDHw6w5/9+Awg5v1RtKi/WvMju9t3YfDY0Sg1lljLOLTiX6yqu46Lii1iRu4JZ1lnkGfMwaow4n3mGE+svpOHuuzmx/kKczzyDXq0n25BNWWoZCzMXsiZ/DZeUXML1M65nfdF65qTPwaqzEo6F2W/fT13YKRLdbdMbbDoZtHvbUXk6ycicO+w2GSkZ6JQ6mj395hPWMiHXN9ZxtnW/CF4VrR7y1PMH7Kz7v3e44y/bWPvjt/h3o07IJk01XTUDvOm8/St6NQZRCXwS5GGTfPDo9HeSrhta9FFsLp6wJJMv4kOn0qHskcmJU9fpJdPvQBUKglqNpnBoDM986SUoMzKI2Gy4X38dALVSwYdXC8P2v2+pnVjxS/4yUawYHt5XQqvUsjZ/Lfs69g3fzdawlRpjKoXpp18SvvEx0T3yXvkq/vGxFbz0hXN4fdV1bMueDcEgjZ/9HOF227Qdv8BUQIe/g2A0COY80Tk2wvs9KsNIMdW6aik2i+9DRkoGoWgIZ9R/1sq0JpMQk6FoFbNiCg43vz91GsK2Q5A1Z0jVQCQWoSvQhWGvmCRU584ASeK6xfkoFYkrKZIkGSupt9wMSiW+nTu5IS0IwIv7W1Er1AMz4lqj0PYsOQfm3wSzr4onJexQ/WY8KfEO2I4II7mWvXD0Fdj/hBgklSooXAULboOKi0THz2A9ckkS1fzNu0CWUSqUlJhLzrhuiO5AN0qFEjPx9y8u5eJ4+ukBCzLH008PeW2OIYdAJIAz4hWmqbbDoxysHlxNULxmbCdnyIBoeEqNb/sT6ThCa4qJdF06apWG4tk30HDk34TPkiq2JB8celuvw76ESYhQjQhWa0pFW7XrtdendjJcuAKad0/dBDTk6zOjHqYKc0KMV5Ip5IOwf/J+FFOJRg9pJSxGy6HOQ2LBMVW4WkXVWj9icozdtt0sVBZTu+9d/C3NSTmmM4wndjSw9sdv9QbzntgxjQavILpIm3ZC0RqQJPbGkxAVeTEsnWJRrMrLRWk0Tu95nEEYzz2XsMFERsBFzevvnurTOSPQqFV03PxRAIwvPdurY74ydyXXVVzHhcUXsjxnOZXWSnKNuRjUBqQE40mouZnW79zbJxcSi9F6733DFuCAqFbO1GdSaillXsY8VuWu4mDnQcK580Tx0liKck5jDnUeolxlFPriIzDLOosjXUf6HpAkKFghfv+jyck4GkVxWPkFQxLfrU4/P36znlg83hmT4WsvNtPtj4xP/nU0PDZhoBsf471hLy/XvsyJ7n7JjsxZYD86dcdMkiSOPWAnI2VokW6eIY+uQBf+yPiLBlxBlyhKCnp6/SBAyDEVxqWYNMVFSKqhnQSSRkPaLbcA0PXov3ofv31FEVqVggPNLnbWd4/7nNCZRUFRy54RNzNpTKzMXcn7Le/jGTzHtJ/AF3bTZUgXMt2nEcHqarRHDxKVFFivv5ZzZ2YxN8/Cn+5ewa9Wf5h6UzZRm42mz3+eWGASiYERUCvU5BnyRPJKoQRz/uQkmRJIMYWiITp8HeSb8nsLWwyK7Hg3xNkpyZRMQkwGdQqmkvMo7W7meNcU6BqGfKJaPEEls91vx6qzEtwhjKne1AgJg6QUU5KpQJ2djWn9BQDM3/UmWpWCGruXgy2ukV+oMcSTEmth3o0w+2rRxePvElU4chRyF4lOh5mXQu5CMGYO1VEfjKVAmC7GzU7LU8upc9VNn2HoNJCoCyLc1kbrvfeNuiCTJImy1Hg3ROYsUfk4XAA/6BGyVmUXiAn/MAQigYE6mNPVDeFuwxXxY0ktQalQApCesxCrIZtDJ16a+uMlSTKNuMP95ZiGVsgGa8U9ynTJJaQsXQqRCI4nn5y6EzBkiMnqCO3WY8Z+QkjfZc8T+51KxivJ5GoRiYupTIRMBTnzMDqaKDXkT9iINSHuVlFB1Y9j3ceQtu4lfPVdRH/1V7bfeAmut95NJiHOEFqdfr75bNWAYN63nj0wvR0RTTvEnMsg5hR7Gx0A5KWHMTWJ/092QQxE0mjQXHgxANnb3sYfmrge+AeJCz56A0fSilBHQlT/8ne9jydKNgxGjsVwbdhA/Yc/MlROMBbDt3PsJssWrYUcfQ5HXfVCorRp5+gvOk3p8HUQjvhIl1SjSrTmGESSos3bb31gyhavG2k+4HdA/WYoXw/qlCFP19l9vfesHqKyTIOUN7XdEJ3xbu44NY4aSswlHO0+KrTOQYyJYf+0e9Ql+eDR6e9MmIRQKpQUGAsmpGIy0JRaFCVFojEauny9SQhtWfmwr0+99VZQqfDv2kXgsCgutBo0XLdIxPL+vrl23OcEQM4CERRv3QdtB8Rf+wFU9iOieyz+l+VqZ7GsYv+BRwk37xGPt+6H5l3Up+ZTbCkZ0/39ZNLwL7Ge2pldyY0XL+p9fF6+he/fsZL7V30Ul1pPYP9+Wu+9d+qlVOOUWEqoc9aJf6SVTE7S2mMT8uP9aHQ3kmfM49ldrVx937P8+EeP8tVf7+PJfXuQz1Jz6mQSYrJkVFCUNoO2urcnlFUdQMcRsbgYFEiMxCLs79hPkS4P/z5hSr3HWsasHBOzc82TO2aSJHFSb7sNAN+LL3BZufhevbB/nLqJGr34DhevgcrLREeDOVdkjsdL/lJRARyLoVfrSU9J75u4ngE0eZooMPYkIUQlUKiufqiBVCxGqH7oZKjEXEKTp4mwSi2MkBIZuMViovMkd8GIFcVdwS5eq3+NE45+C4yJyKeMBftx2vVmsvXZAx4umn0DtOyhcYJtsEmSnGxicgx/2C/kOUKeIcbUAKEasWjQlJZg/dAdAHQ/+QRyaAq7fvKXiqBDeIJzjJAXjr8BHYdFMjiB59SUkFYizK7HgqtZJJtPN7QmSC1iViSGzWcb0Yh1zIT94q+fV48v7ONw3Q4yH/gHxGLkdMu0WWTa/t9viLQ3Tf6YSaadWrs3HsyTMSIWiFFZps4+TYtFV6swp81dBIAnGOGYTVSFW80BTNUiWKlLJiGGUHjrDQCsat7P1sNTKJl3FpOfpmf/ZWJMC//nGcJjkBqUZRn3229Te+NNNH/py0RaWkgUEmq9915cr7wy5nOZlzGPWmctvrQSMad2t4/5tacThzoPMVebiWQYQzEWohvicOegTuiCZdB+MPF8IBISXekFy3sTlYPJMeuGPKaUJLJK54g1wSRMe3uJxWWj4lJM0ViUOlcdc9Pnck7+Oey17aXD1xE3qJ4pujaSJJkiwrEwnrAHi9aS8PmJSjINlGcVnRDNDj+RmEyJN94JUVY67OvV2VmYLxEJ8a5HH+19/KPnlADw6sF2mh0TmOerNFB2nvCpiAbFXyQEsZBQPYiG4n9hClKyydSmUWXbTSwaglgEuXAFNaFuisxF4z/2NCJHIniefx6A5lUXUZQ+cA121YI8brp6FT9a8WGikgLX8y/Q9be/Tcu5ZKRkECMm/NvM+aKbYSLqDrEoBBxDYjZ1zjpSyOatn/2Vhzf8kJ9s/iMPv/z/OPrCe9R6PKIb/ywjmYSYAnRl5zMj4Odwy46J7yQWFUHGBOZRu9t3Y9VZyartRg4GcevNNBkzuXHJabiAT3LGYli9GnVxETGvl5u6RZXNi/tapy2rPCrGLEhJFe3XQEVqxRkjyeQIOJAkSUyA/H2m1A5rNjEGVhlEJQlHWtaQfehUOrL0WaJaI2uOkGQanMBo2SMqnRLcN3pocjexu3M3M1Jn0OLpl1QyZIpFzFS2tkfD4GykQaUm2zAwCaE05VCWu5QTx1+efMI2SZKTgCfsIUWdgkJSiAmgOoEcU7wTQltWhunii1FlZhLtsOOKa75OCVojpM8Q8nbjxX5c+PAYs6DyytFN6yeDKReCrtE9DWRZdEIM6gw4bchZgMp+nEXpc9hj20NMjo3+mpFwx6WY+lWY7bHtodSVgi4o9p3hhC4ThCIykdZplvRJMiWUZhhQSGDGy0WK3RjwI0lQkjENngKxKDRsEfruSiH1sL/JgSxDbiqkpmhRHKsDkp0QidAvWYI7LQt9JEjNf14+1adzxnDubZezN6MCZTRC229/N+x2sizj3bKFuttuo+nTnyF4+DAKgwHNxz7BH+ZfRzR+74si0WKwIvt8NH/5K7R+515i/tHngzqVjhlpM6jqOiSC8I3bhnZYnObY/Xb8ET95qIQ/0BjIN+YTjAUHJsO1pvh8YJD8iixD7UZhnJo+fDX2O8cGSi5JwI9umEduulV0qk+mwrcHZ5NY98QLN5o8TVh1VvRqPRathdV5q9nauhVn0CnUH7rrxPohSZIpoNPfiVVnFXP3BKSnpBMjRndgfPJHfUmIvk6IWruY75YHOgHQlg//24M+g2rXCy8S6RbHn5VjZnVZOtGYzO/fOTExjylTjihY6v1bQiRrgTC07/cY+UuomHMzgazZ7FVJkL+EDp0BnUonujxOIxwbN6FzO3BqDKy4/ZqE23z5oplknXcOf5gvvJ9sP/s57rffnpbzKTWXCoNqpUqsd5wTKIz1dYIudUBxrjPoJBAN4Kpz84W9T6OIp+4VyHxs12YONtaclR3SySTEVKA1kV1xCZG6TTgCjonto6tGBAW1pgEPVzuqcYfcLM5ajHeHSHLsTitFoZC4dtFpuoBPckYiKRSk3Sq6IfI2vYxBraDZ4ecfW+tPnuHiYPKWiPbCaIQsfRZROTo1lanTTLOnmUJj3JjK15eEqFcaeT93oBndm4VLaVCaBu8CgHJLuTBI1VuF6XT/xYGzSchVlZwz7Hkc7TpKlb2KVZmrqLRW4g67CUTimomSNPWSTF21+PVWIgpFwsmMqeRcZgb87G7dNnXHTJJkmvCEPJjVZlHtIil7g389RD2eXq1sTWkpklpN6m23AtDdT/N1SsiZL9qt/WNcNAU9cPx1UWE48zLRLTWGystJ0SPJNNo9xdcpFnAJpCL6E25rw7t124j64dPxWrRGSCsm39ONTqmjxjFJk2p3m1iwxGnxtOANe5ld2efho4pBugvazUpUacYzLsD2QSTXksIDN8zHK5mokks5V7Efs1pGrx6qBz1pWveJNYKlT4K1R4qpskAmIyWD4HFRsJFMQgxFkiTkiy4DwPTuG6euuOYM49wZmbyy8joA3M891ys/2B/frl00fOQuGu75GIF9+5F0OtL+67849st/8OHIAl4oP4e7L/lf/mftp7j70v/lExd+nWfnXIwsSTieeoram28mcHR0SeMZqTPo9HfSqTMJxYDOM6MoqYfDnYeZZZ2F5Gkf4g80HJIkMSstQTdE7gLh+9Bfxqg5LnGVv3TY/QUjUf7yrhjPFhWIKvGZOUZuXR6vgE6vmJr3tfPEgERItaOa8tS+f2ekZLA4azHvNb+HD1l0zI+1izJJklHoDCQ2pe5Piblk3N0QrpALi8YST0KIToi6eBIizynmmprSsmFfD5CyeDHa2bORg0GczzzT+/hH15YA8OjWhmn3mJIkiRW5K+gKdHG8+zj1rnpKzCXTcqzJcPzhxwHYXr6cC+YljnkqFBK/uHURx1ddwkslq0CWaf7afxM8MYXScnGKzcU0e5qFNHha8cTUJDy2IX4QPe+/rr2lNwHRQ163TChkJxIcRR79DCSZhJgi1NnzKTbkcuzEBCts2g8Krct+dPo7OdR5iNV5q1EqlPjiSYiq9DLWVmSQlaClMkmSyWC5/jokjYbQkSOsR7QW3vfcwZNjuJgIvVVM1uP6pxWpFQMlhU5TGj2N5BvzhYlkNCyCWojKSWO8hbpVLyqS59trKLIkDlpk6jOJyTGReMmeI+4TILQB6zZD6Xmg0g55nSzL7LHtodHdyPmF52NUG1FICnL0ObR6W/s2TC2emqqnHjqPY0uxDJFi6iUljdy85ag7jk8+sJckyTTTW/UU9iaWYooHZJSZGShNIpGYdsstoFbj372bwKEp8HHoQaURnjpNY9DS7jgGR14Uge9ZV4qOspPFWGTenE2inXkEHE8/zYn1F9Jw992cWH8hjqefHvMpTOa1veQsgI6jLLLO5nDX4b7k7URwtYggC0Jec69tL0uyl6AyGEHZVw2V45QIff7DqNJzRu8mSXJacOvyIt77xgV896PXozTnMD9ygJ+9dmT0F44HX5foaCpYMeDhHlPqoswI1oCSqMMBCgWaspGDIB9UKu+8CYA5zYepPn7mSHueSpQKiZVXncfWnDlIsRi2//tpb3LXX3WAho9/gvoP3Ylvxw6RhL/zTqp/9Qh3Kpbx+ZdqaXGK+6Y9JZWqzArsKalEFUr+MvNSvrXmE3RpTYROVFN9883YH/3XiMkhpULJ/Iz57OvYJ34LLbvPmOr5Tn8nnrCHImN+vDBp7J5MReYi3CH3wKptpRryFguPGBCJA0eDWBOMoOn+zK5m2lxBMg1qfnVRHgs6TtBZ24TNHR/fLEWiSGAy408kCJ52sb4AugPdBKPBIeuCAlMBlWmVvNv8LiFrmZCkTpJkCuj0d5KeMnISoshURKO7ccydrsFoEIWkQK1Ux+WYRCdEXacPU8iL3idUBbSlJSPuR5IkrHd+CIDufz2GHBXyZ3PyBhbuTbfHlEqhYk3eGo51HxOFk6bCaTnORIl0d5OycwsAxuuuR6UcPmRt1Kr4y13LeWzlzexPL0P2emn+7OeIOZ1Tek46lY4MXQZN7ibhf+puG/8Y5O0QBSVxZFmmwd1AsbmYjV7dEPlClaygoHguna6zb86STEJMFZJE1uwbSLEdprl7nFUErlYxaehXGRGIBNjaupXlOcvRq/XIoRD+PXsBqMoo54YlSUPqJFOPKi0N8+WXAzBza5+cyEkxXByOvMUiCREJUmQuwuazndZyPs6gE1mWSdWlDvCDAMjWSsyJVwn/cMVddGuN5Pq62PWXx4bdX7mlXATsLQUQCwst3NpNIilhzByyfSQWYUvLFrxhL+cVnkeKqq/aONeYO1CSyZQj5JimIuDld0DIR7MUGyLF1B9F3mIWyCoO2/bgOQvbC5OcPbhCrn6m1AmkmGpEIk1b0qcBq8rMxHzJJQB0/WuKuyEyZkLILYLaCZDCXjj+mpCwm3kZ5Mw7+cbPxpzRJZlGkWIKt7XReu99ffJzsRit3/4OtbfcQt2ddyb8a/jwR3B/4YvU33obrd/+zsDX3nvf+DsitEZIK8HU3UCZpYwqe9X4Xt9D0ANyTHSyIaphM/WZZKRk4Hr1VYj26W/P+/r3CS+fS0ilSSYhziByLSmsm5nJHTfehIYwe7Zv5EDzFC1+ZRka3hfVzWpdv4fl3k6IzNQQ5rgptaaoCIUuWaCUiNTKGbTklKKUYxz917On+nTOGG5ZVsijc8S6wPP22yK5e/4F1N18M9533wWVCsvNN1P7y3/wEf05fPaVek7YPFhS1Hztkpncf80clPFxSClJ/Oj6efzxziWY1qzmM+u/wo7sWShCITq+/33evvUeGuqGv1cXmkWgrDHqE2NI2wTvyyeZI11HqLRWovB1CUnEQV2VI3XuKSQFldZKjnQNCtJnzIBIQMg0Nu0URtQqzbDnEInG+MNGUcT19chBau65mvu3/5GHX/0hVX9+JH4wBaSVTq4boqtWrFfi11jtqKbMUpbQ8LYirYI8Qx5b3DVCn957+ne6Jzm9iclCt3+0TogeabABhXkj4ArGTallWUgZq0VhUq3d22tKrcrNRWEYulYYjPnKK1FaLIRbWvC88w4ADV1DNf+n1WMK8R6syVvDbOtsNMrh7x2nghOPPo0qFuVEagFXXn/uqNsXpxv4zYdX8ONVd9GmTyPc2Ij3/vuRw1ObqC61lFLnqhOJYFO2KKoaD96OAZ0Q7b52DCoDakUK7+ysGSDYLUsK8r53P0Wly7A7zz6Z1mQSYgqR9Gnkl1xAy6FnxqchbDsIWX1dEDE5xrbWbZRZysgxiMSE/8AB5EAAh8aAPT2PS+eOrZUzSZLxkna7kGQ6t3kfpn6BkOkeDIdFaxLVtW0HUCvUFJmKhETRaUqvITXEkxB9EyHfnj2ooxE6dWZuvu1CbFfeAoDpqUfYVW1LuL9iczGt3laC0aC4T1S/KbQEs+cN2TYQCbCxcSMpqhTW5K1BrRhocp9jyKHD3yFaCUEEKC2FooJqsnRWE0svoyNgH74TAkBrRJc1j0URBdvbtidlEZJMOeEpqo70hDxD9F/70yNNMbjyOO1Dosqpv+brlKBQQP4yaNw+VK6n4yja2tdFh0HlFSe3+6E/o0kyRUJCUmoETexQXf1Q/xsgsL8K/85dif927SKyfz+BqgRBqViMUP0E7nG5ohui0lxCh69jYlKA7rbeAhNn0Emdq44FmQvEv597TmzTI5Pl9ZNnyMMW8Z6V+q9nO2tmZGGZezGltPLrZ98kFpuCsa3jqJCCy6gY8HCrM4DNHUSpDFGQpkNVIxbCSSmmkQmcLxLEundeO8VncuaQadKyZEFxQoNp48UXU//zv3NX2oV8+vVmjts8mHUqvnrxTN79+gV8bv0M7lpTynvfuIDHPr6K975xAXesLOayebk88rGVvPDta2j7+g95dMn1hCUlufu3Unv99Xzre/9kw4E2wtEYrU7/AI30hZkLqbJXEc1dKH4fwdP7Xtkd6MYRdAi5E0+bCFz1w/H005y4YP2InXsl5hLsfjuuUD9JDkmKd4TsgeI1o475z+9robHLT4XkQfPuXzicL3MiV0KBTM5ff9mXAEmvEHJKE6WrGqxCeikcDdPibaHEUjLs5vMz52NQG9gvBZGT3RBJJokz6MSgNoiOhVEoMZdQ7xybGkBfZ7QPVLreeVtdp5eCeBJCO8YuRIVOR+rNojOv65//BPo8pvojAYXWkWVLJ0uaLo1Ka+WEXjv43jyVdMWlqhpXrCd7jMovayoy+OINy7l/5T34lRoiu/dg+8n/Tel5ZRuy8YQ9uENuSC0Zn5pET0yt33qyzlVHsaWYDQfaWHhioLfw3ts+Q+pNN5GdWkrI34XvLDOnTiYhphhryfmYYzJ1DZvG9oKAC7ydYO2rpDxoP4hKoWKWdVbvY+433wLgWFohl83PRa+ZBs3ZJEkA3cKFKGdWoolFuLh+4A1xf7zy7qSTu1AYt4d8VKRWUOOombxZ6DTR5G6iwNSThOga0AnR+vZ7AFRlVvCRNSVc/e3P4jOYyfF18fgP/0yXNzRkf2qlmnxjPnXOOqGxas6DknVDKpxdIRdvN75NvimfJdlLEhpyqRVqMlIyaPe19z04FvmU0YjFoPME3QYrRo1x9IqKnPnkh4NooxGOdh+d3LGTJOlHOBpi17vfp9vfMfrGo+AOjyLHVBNPQgxqv05ZvAjtnLjm67NTXHGbWii8FHqCBEE3HHsVuqoJFl8oZB1PdvfDYNJKh7+nuFtEEGYEfwpNSfHQ5yWJnPvvJ/9Xv0r4l/fLX2C4/7tkf/e+odevUKApLhr/dWgMYC1F1XGERVmL2N2+e/zjjrsFTKLrY49tD3Mz5qJVagk1NuLfuQskCfNlQqs+3NhAkbmIlvAYzL2TnJZ84+pF7FQtwtC6nefePzC5nYW80LpXBBgH0dMFUZYTI8+UTSDpBzEmKu+4gaikIK+tFsfR46f6dM4YbsqSSTSqPKCbx6feaue4zYNJp+LLF83kvW+s5/MXzsCs6wsC5lpSWF2eTq5lYECtKF3P/1wxh3v/8QNsD/yWrtQssvwO7njsAd75zk9Y+t1XufreZ/nxjx7lmvue5YkdDaSnpJOeks5xb7PoCG4eg0ThKeRwl/CCUEgK0cls7Csi7O366ykqGKZzT6lQMjNtJke7Bs2Xzbmw4BYxLxiBWEzmd2+fAGJcUNqASw9rDst0mcCrBYUcI1AXD6YZ0kFSgGcCc6iASySF4p2Oda46cg25aJVCNna4oOXS7KW4TTk0NLwnChVOB6KRU30GSSaA3W8nI2Vscmd5xjzsAbso8huFvs7ovqKkcDRGU7efIo9IQmjKxy6FmHb77aBQ4Ht/K8Hq6l6PKWW/+asM/ODFwwTC0eF3dIp4bHsDa3781rT4V3TvrSKttZ6wQsnCu28d12vvWlPCqotW8NOldwDg+Ne/6H7iySk7N4WkoMRcImIyqYWiszs2xs/HYxsgxRSOhmn3tlNoLOTRrfWc17wXgIhOjJMeu0McU52CVZtKo7Nuyq7jdCCZhJhqFAoK5t2K49gGgiH36NvbDkPmzF6X9GZPM02eJlbkrOhtXXQ8/TRdf/sbAMvaj3B7x97pOvskSZAkicwPiZv3lXVbkeRY7+LjgQ1H+O7zB4lET3ICQJ0CmZXQug+jxkiqNlVo8p1muEIuorEoaTrh9zC4E8Kx5X0AnLMWoteoUOr15H3qEwBcvncDX/nXzoTVk+WpwqBalhRQdv4AWQYQk66NjRuZmz53QPKyPz2Tf7VsHSTJlCuklMKTqGRwNYPOQlvER45+DF1aah1kzWZZVMHx7uM4Ao6JHztJkn50OerQBzxUt2+ZVJdNz6JEo9QML8dUKzqyvNkFAxbWkiRh/dBQzdcpo2C5qH5sPwRHXhLdTDMvR9YmNrg/6Rizh5dkcjaDuWDEl6tzcjBedGHfAwoFud//Hmm33oL50ksS/pkuuQTNeeeResst5H7/ewOSGNa77kKdM8Hu0Zz50HGMPK0VvVpPtWOcMhXxTog6Zx0xOUapWRScOJ97HgDD6tXoly8DINTQSGZKJl5JwuedfBItyckn26zjnouXskOuZNsbT+NwjWEdMBwNWyFrDujMQ55674ToyslLD5Gpz+wzpZ6ZTEKMRPmMQg7lzwbg6D8n4BXzAWXR6vnEBqUhopLE9pABk07Fly6awXtfX88XLxqYfBgrGpWCi647n1VvvITykstRyjHuOryBn77yAA+/+kN+svmPPLThh7z987/R6vQzP2M+x7uP408vE4GdiQTMTwLOoJOuQJfogojFhkhxJOz6G6Zzryy1jDZvG97woHE1QZfmYDYcbKO6w4kp7QTLSjNYXC2jjUCJTaY6VyIqSZxQp/a9YKLdEF01YC3rLQSodgopJoAndjSwdpigpUJSsKrwXOwaHTW1b4z/uFON3wH7Hjttv1dJhmcsptQ9qBQq8gx5NLhGD6C7QnE5ppCn1+exsctHNCZT4h1fJwSAOj8f4/oLAOh+9FGgz2PqsY+v4gfXzUOjVLDhYBt3Pbgdp//08b9pdfr51rNVfbnTKZbsrvqbeD/2Fy9k9aLSUbYeiCRJfO/aeYRWr+Ph2aLAp+3738f16qvDSt6NlxJzCXWuOmJKtfD3cTWP7YWD/CAa3Y3kGHKo7wzQtbeKPG8naHUELhTyh4qWvjhXurmAxsFJ6DOcZBJiGjCklWDKmU/DoVEqICMhMWBniDYod8jN7vbdrM5d3dtG1lslEUcBGH/30yn5ESVJMhyWq65EYTCQ57XzTHod794zh69ePBOAh7bUcdfft+PwneRqlex5ou0t4GJG2ozT0qB6QBdENCwC+1oRQIh6vOirxQBiXbe29zXZd94BaWnk+jpRv/Uqv3176HWl6dLQKDUDOxjiNLobeb/lfVblrqLInLja9/kDdtb93zvc8Zdt3P77o/yn6mBfgFahEPqtk5Fksh+DjBm0+dpG9IMYQNZctL4ulphK2d62nehYKwmSJBkBR9dxsvRZmIJeal21E96PO+TGpI4H9UNDOyHkaFQEEIDbXmkZsrDu1XxtbsazcYydkWNFb+37zVZeIapBT3X3Q38UCmFKmagbYhQ/iB7CTWLynXb33VS89SapN9005sOn3nQTFW+9ifGiiwDwvvcucmSCVY0agwiq2A6yKHMRR7qOjN2TyO8ASUFIpaHKXsXS7KVIkoQsy71STJbrr0NdIKpYw02NSJJElqUE+1lW8fRB4q41JeizyjgYyOD5f/8robTYqHTXiy6nBLKLT+xo4F/bxH1mS10t7x70Ezou5g3JToiRkSQJ9zpxX5De2JCUgxwjnfpUfr3oJqLxcSYqSfx64U1cc9Ei3vv6er500UwsKeNPPgxGaTQy41c/I/eBB5C1Ooo8HSjiQlAKZD639ylqD9eiV+sptZRysOsoFCyFxm2TPvZ0cLjrMJVplSgVSvB3iYRiP5kYTclQmStZkhJ27qkVaspSy4Z2Q4yCLMv86q2DqEyHuWreDGb/ZyeK+EEL7OAwwM+WXcM7Xf3mEOnlYvwe77y884R4LWDz2VBJKtJT0ml1+vnms1VYfQ4WdJzA6nMMCVqqFWrmzr4RZ9M2UWV8qojFoO5dIaPYtu/UnUeSCTGeTgiAEosIKI9GrxxTv06Iuk6RECyOF40MlmYdjZ5iJcd/niPqFgULPV1jd64q5qF7lmPSqthW28Wtf3qfdldgXPufLh7aXDfkvjVVkt3RYBD9JpGI1F59LYrBGlVjQKNS8Ps7FvP20svYmL8IIhGav/ilESXvxoNRY8SkMdHubYe04uHlZwfjsQ1IQte56ig2F/PotgbObdoLgOn887DMExL9Bntr7xzFaMhGGQnQHZhCid9TTDIJMU2UzLoBl/0o7s5jw2/UeVwEEjR6wrEwW1q2MD9jvjC0jTOeKokkSaYKhcGAbp5Y/KY8+Hvc113Fh7v38cc7l6LXKNl8opNrf7eZ4+2TqPIbLyqNWJC37CFbn00wGjztbsbNnmbyjXHT+B4DuviizbtjBwo5Rqs+naUr5/S+RpGSQtbHPw7A7Uff5NevHea940O1x8ssZcKguh9Hu45S1VHFeYXnkakfalINomLhx2/WE+vt9tbw1PYODtn6dZKMZxAdTNgPHhshcy6ekAerzjr6a0CY1uUsIN/VhkVr4UDnJKUrkiQB3I56UrLmMkNh5FDnIULRiSVLexccIOSY1AOTEOHmZuRwmJBCRVuK6HzqXw2k0Omw3HQj0FflNKUUr4HKyxJWSZ8WJJJ58zvE/XCUcw7W1hI8dBiUSjI++YkJdTGoc3LI+8H3UaamEjx+gu7HHh/3PnqJd0MYJRVlqWXs79g/tte528CcR5W9iiJzERatMKf279lDuLERhV6P6cIL0RSJJESosQlZlsmzzqTL3Tjx801ySlErFXzv2nlUyaW8ebSTE3veHt8OIiHh+1K8dogsWU8wDwAphEyMPz6+h5jPh6RWoymagOzYB4yy667Ap9Ji6rbh3737VJ/OGUGt3curJSu5+5L/5X/Wfoq7L/lfXitZycVzcqYk+dAfSZJIvf469F//5pDnlLLM08+/j80VoNJaSZuvDachXYwrkzFTngZcIRcdvg5KLfFKXnfbACkmgPaWDiQYENALSUpsnsTzlhmpM2jyNI09EQ68eKCaau82tHIOX0wx433zLVAoyPr2/6KQocSuZFtZGhuP9av6V6eAMXN8eueeDlCoeiVoqx3VlKeKhESt3ctFtdt6u1oefvWHXFi7dUjQUmcpZFb6HI41vkub9xQVW7buFfO98vXg6xZruSRnBD2a+fpB8/WRyEjJIBKLjNiRH46FCcfCYr9BD2hEJ0St3YcmGibd1QmMrxMCQL9qFZqKcmSfD+e//zPk+TXlGTz+yVVkmrQcaXNzw++3UN1xaj1w9jY6eHDz0AIvhQQlGWN/34fj4FMvYgh6sadYuPDOqye8n0yTlv+7ZgaPz798YMIkFqMlgeTdeCm1lFLrrBUeeM6m0YtNYlEIOHrVMdwhN76ID7Mqg2d2NnJuXIrJfPnlZM0W980sj50OT1wqTK2nUJtOvWuCsZrTkGQSYgKMxYhFrU7BWnkVzQefSqwrKMtCiilLBCN3te8iMyVziHlTZyAyNNsoSTjSskiSZLoIt7Xh276974G4TumFGfDMp9eQn5pCfaeP63+/hTcPD63OnzYyZ4HHhuTvpjy1/LTqhnCH3ISiIdJT4m2gg6SYGt8U1dAHc2awsMAy4LVpt92KMj2dXF8nFzTs4ouP76HNObDiodBUiD1gxxf2Icsye2x7aHQ3ckHRBaJFdBg2Hu1gsMJTJJTK3pZ+kwhzPvjsEBldF3MIXTWQWkR7oJPMlMyEXhTDkjET/A6W6PNpcjfR4Uu2PieZOJFYhKinFWPeUvSxKMX6HA7YJ5bcGpCECPl6Fx09hOKm1M3GTGL9vvP9q4HSbr8DJAnv5s0EaybelXFGYsoRi7X+pqGuZlF4MQquV14BhFSRKi1twqegTE0l80tfBKDjN7+ZuEm4Ri+6IdoPUplWSVega2z3Kncr3ZoU2rxtzEnvSzz3LDZNl16KQq9HnZcHCgVyIEDE1kGauRBFyI8z6JzY+SY55awqS+f6xQVsic3lmXe2E+sYh/9Ay26hNWwcWliw+bi9dzyXVC7kiIlCVysgqjAl9dQGhM9GVs3OZ2u+MIdvfOrfp/hszgx6TFPtKalUZVZgT0lFKUlTEnQajvz15yIP6vCLIbHJo+WKX7/L1moHc9Pnsq9jnzBobt59Wun4H+k8wsy0magUcQ9HT9uAKliAjqeE+erOrEq+vvaTHLfko41FsP/85wn3qVFqKDYXc7x7bPeTDl8Hv9zyPFF/IR9euAzvz4VJq/b660i9/XY0FeUUtEeY6T7Gvtamgb506RXjS+z064LwR/x0+DsoNIkEe27QxRf3Pj2gq+UL+56mKDq0kC0lZyGr1FZ2tO04+YVmng5xHcVrhUx29hxoTxZIDSYQCbC1dSvbWrexx7aHg/aDHO06Sq2zlmZPMzafDUfAgS/sIxw9eTJC4+2C6KHYXDxiN4Qn5BnUGR3vhLB7yfN0ICGjsFhQpo9NBqqHAdKtjz6KnCCQPTfPwrOfXkNphoFmh5+b/rCFPQ2npgDT5g7wqUd2EY7KzMk1DzDSLrLqyTKNzUB6JNqeEF0KjcvOx2qenCl3ZZaeL8w3D/EzkmIxWg9NzhMq35iPPWAnIEmQkgru1pFf4OsEXWqv/H69q55iUzEvVbWS11pNtt+BpNdjPO9cDKUicZ3t66a+3SVer9GTr0ml0d142nqijpdkEmKcPLGjgWvuG2iSNRzFBatwqXV01rw59ElHg7iJGdI53n0cb9jLwsyFQzZr+MvfB1RJ9LTANihPE+3nJGclobr6PqO0HmIxOn73e2ZlpPD859ayotSKJxjhv/6xkz+8U31y2tqVKmFS3bybUnMprd7WMRlKnQyaPc19UkwwJAnh2ybaxYPzFqNSDrz1KvR60j/2MQA+Uv0WDrefzz+2m3A/7w2VQkWRqYgTjhO83/I+3rCX8wrPI0WVeJCOxWT++m4N9z1/EIAMv2iDzvA7kCJWJE2/SYxCKRIRjglU39qPQcZM2r3t5BjGWbGsUED+EtSt+1mWvYwdbTtO6oQ1ydlFp68DSwyUxixi+gxmqdNo8bZMaCHrCXlEci8SElWWStWA53uSCs2DAoX9AzOagnyMF8Q1Xx97bCKXdOYiSaJCqH8lpatF3GdGwR1PQpivuGLSp5F6881oZ80i5nLR8ctfTXxHOfPBfgxVNMKizEXsto1iUi3LxNyt7PG1sCBzAWqFCA7HAgFcGzYAYLn2WgAktRp1bi4gJJlQKLHqM2nsShrnnsl884pZ6LQ6HumoYNt7r41NX9xjE+Nw3pIhTx1udfGDlw73/luhciNHTJS6REVfUoppbKRolNhWifty+I3XkcPJOcdoDDZNVUoSP7ph3hCj6alEnZND3iB/HwUyH299n053gI88uJ2n3w/iCwdoIQym7NMmYOwJeWj3tVOWGq+MlmXx+zf2yZXK4TAp77wGwItla9mfOYPfLrqRGBKat17Ft3tPwn3PTJtJnatu1C7PRncjj+x9g5qGPDRyJre37yB0ohplaiq6u+9GkiQsV16JAriyzoZC28y7x/vdoyxFYh2TyNtpMLGY6Hy0iuutcdZQZCrqTcDU7T/Sm4DoQSnLpHbbhu7LWobZ72J5xgI2t2zGEzpJld/RCNRtgqLVfb57GZVi3hI8iV3/ZwD7O/ajUWjIM+Zh0VhQKpSEoiG6Al00uBo40nWEXe272Ni0kVfqXuHZ48/y3InneKX2Fd5seBNXyDUt52X328nQjT8JUWIuGTG46w65McellQn1dULUdXopcsf9IEpLe71cx4PlmmtQGI2E6uvxbt6ScJtCq56nP7WaBQUWun1h7vjLNt4+muC3M42EIjE+88/dtLkClGcaeOKTq9j8jfX8+rZFpKgV1HX6+HuCDonx0FXXRN5xIYE26+7bpuK0yZkzY4ifEUDn8y9MXKYVEZMpMBaIzoTh5Gf7088PSJZlkYSwCCmm85rENZvWr0eRkoIqK5OwSoNSjtF8JK6AoTGSIscwa8xCBuosIJmEGAetTj9v//yvPLShv0nWX4ftiFBICgpn30Rzw3vEPINuFrZDkDUHu9/Oka4jrM5dLTQj+3Hkzc2k79pMFIlvrvlEbwvsm6WrprX6JEkSTUnxEBkAAOdTT1F7ww2kHDvIPz+2kjtWFiHL8JMNR/jyE3sJhE+Crn96BQSdqH2dFBgLRDvcaUCTu6lPigniSQjRlhzp7sbYKAaS7PPWJnp5bzdEhsvO5a172VHXzf97daD2a1lqGUe7j6JVaVmTt6Y3sDUYmzvA3Q/t4AcvHSYcgw937hnQBv2H1E6seu3AStu0MQyig/HaxeLKmEm7r33sfhD9SSuFWITscIg8Yx57O/aOfx9JkgDd3bXojdmgUBHTZ6H22ZmfMZ89tsSL+ZFwhV0YNUYI+4ZIMUFfJ0TBwtkDHv/2VbMHBGbSPnQHAM5//5uoZwyL+bOJtBLoit+foxERYDWNnKgMHDtG8PgJJLUaU39z6gkiKZXkfPt/AXA8+SSBQ4cmtiONXlR5th8g15iLUW0cuRrV3017yIlaa+6tBgXwvP02MbcbVV4u+hXLex9X90gyNYhEsNVSSGv36dPpl2T8ZJl0fPnimXjQc99+K57Db4iuquGIxaB+CxStFPKT/ahqcnL7X7bi8IfJS9WhkEBSuZGiZq5NFV2TySTE2Cm9aB12nRm1z0N46+npJ3C60d809b1vXMCty6df+qvH36fo4YdJ/8ynATi36i1+X/MsqkiY371TzZ9ejbKpfiexvMVCYWCk39hY8NigYwQp5TFwuOswM9Jm9M3R/d1C4kjdVyns2bgRububLq2JnVnCG/JYWhGvF4txof2HP0SODl1TpahSKDAWjNgJfrz7OFUdVWw9kIUcNfGRORYCf/4jABlf+AIKkyhk7En0rzpSgyHWzWtH++1ToRDz87F0QzgbxXpHYyAmx6h11vZKMQG80RQcouqAQpHQ+wKVBtKKyfG7mZc+j/ea3yMQOQla+E07wJQrutB6UKpEB37b6ZHcOh1o97bTFehiYeZCCk2FlKWWMcs6i/mZ81mavZTVeas5t+BcLiy+kMtLL+ea8mu4YcYNXFF2BecVnEdmSuYQaeGpojPQ2adGMA70aj1mjXlYCTBXyNWvM9rbT47JS0E8vqcpH58UUw8KgwHLDdcD0P3Pfw67XbpRy2MfX8W5MzPxh6N8/OGdPLOradjtp5r7XzjIzvpuTFoVf/nIMkw6NRl+JxcGmvjeGhFc/79Xj3LCNvGk4fY/P4oSmZqcchavGVqYPRGKZ5Xwm8V9fkY9aSbdhhdo+PjHJ94hjfATqXXViviJs3Fo8W5/PLZeU2qbz4ZOpaPRLrO/oYt1LSIJYb5CGFJLkoQ3XcRTHCfivxW1HkJeisxFY/IwORM4qUmI7373u0iSNOBv1qxZJ/MUJkXdkVo+v+fpQSZZT1N/ZPggaG5qMYHc+XQc/nefXpivC0Je/MZMtrZuZUXOiiH6dc3dPo5+90cAvD9zNVXZM6nKrKBbnzbt1SdJkqhzcsj93v19iQiFAsuNN6JMSyN4/AT1d3yIzvvv43vnF/D9a+eiVEj8Z2/LlBgnhdva8G7dNrxen0IhqgSbd1GeWk61o/qUt6Z5Qh4C0QDpuvjkJxYV1RJxfxdnfJFbb8pmxdKZCfeh0OtJv+ceAD7RsBFFLMqfNtXw2sG+98GsMXNJ8SUszV46rOzRW0faufyX77LpWAdalYIHzsnijvf+NeC+Vfj3X5EZ1NDiael7obkAvDZR+T1W7MchYyaukAulpMSgNoz9tT1IEuQvheZdzEufS2egk2ZP8/j3k+QDj7O7GnOqaGONGrLB3UqxuRgJaVw6mjE5hj/sF9/nfq3X/QnViImhZWb5gMdT1AOLCQyrV6MpLSXm8eB64fnxXtJpxaj35sGYcsT7F/SAp110hilHlovplWI65xyU5qnxu9AvWyaCLbJM2w9/NPGuvex54p4XDrAoaxHHuo/1ahAPJtBdS3XUx+KsxQMed/4nbkh9zTVI/RL9mh5z6kaRhNDrs0iJxbD7h/oDJTlz+MjqYmblmDjmN/PXYzqofmt4s9f2KuGXkjowMLe7oZs7/roVhy/MosJUXvniubz132v4zAXFvPc/V5LTJcbxZBJi7Jw/O5d3CsRv0/fqa6f4bM4cekxTT+YaVJ2Tg2HlCrK+8AXyfvb/QK2mpGorj1c/TpYUYk9djB+/3MDjhw5BZqWQMxsvkSC0H4KD/xGJwNa9Img0AbxhL23eNsot/eYGnvYBXRAAjmeeBeDNoqUsKE7nsY+v5OLZ2fx9zuX41DoCBw/iePbZhMeotFZS7agmHBvYxSPLMvs79lPrrCWVhWyvCaBWStx6YAMxlwttZSWWm2/q3V5TXIxu/nxUsRjLa11sadxDrL9+a3qFkCcaja5qsIrrbfG0YNaYe4O2Tn+Y4PZtA2qRY0ikfee+4f2eMirBfpQSczHF5mI2N2+mK9CFI+DAGXTiDDpxhVy4Q248IQ/esBdf2Icv7MMf8ROIBAhGg4SiIcKxMJFYZOR1oqMR3C1QsHzoc1mzRYHWZJNbZwHRWJTdtt0szlo8pHB2NNQKNXq1norUimmRlAlHw3jD3l7vrf5Iw8zT+jOSJFNvEiISAkkBShXBSJQWh7+vE2KcfhD9sd4hipU8mzYRahheYcWgVfHXjyzj+sX5RGIyX31qH3/eNP1eOI9tb+DRbQ1IEvzq9kWUZRpxPP00J9ZfSMPddzPvqx/hc4FDhCIxvvrUPiLR8X+2sVgM3ZsvAyBdcc2EukoSkWtJ4YKv/BcfvVT4Gd116bfZ/OGvIen1+N7fSt1NNxM4enT0HSXAqrOiQIE96heJKfcIayNvR28Sot5VT4m5hH9ta2BuZy3pARcKkwnDOef0bh7NFcoagbr490Ej1qMFxgJsPttZoRpx0jsh5s6dS2tra+/fe++9d7JPYcLke+wJ2wk3vT1o0B5EZfml1AY6CbfEqzHbDxLLrGRb23YqUiuGVA87fWF+ft9fmNlRQ0ip5oZff5fN31h/UqtPkiTpX4FU8dab5P3wB5S/8jKp8Qms46mnqbniSq5u38sjH11Oql7NviYnV//mPd443D6qb0oiup/qG9ROrL8Qx9NPJ94wrQRiUSx+F0a1kVbvKFp800yzp5kCY0HfoOnvjmv/iVts4xvCD+JoXiUzs43D7AXSbr8NpdWKur2F+3Ri4PnqU/to6OybQCWaYAEEwlG++/xB7nloJ53eELNyTLzw+XO41F+XUForszM6MAmhVImgoXOMkkzRiJiYp5fT7p1gF0QPlnxQ6VA5GliRs4Ld7bvHZbyXJEkkFiHkacEUT0LIWgtEAhASgeAqe9WYJ23esJcUdYpI9IV9CZMQwbo6AGrjhvDKuDjq8/taBmwnKRSkxRcYXY8+enJk66aB/guOEe/N/ZGkvg4rV4v4nY+ALMu4X546Kab+ZP3315BSUvDv2oXrpZcnthONXgRl2g9gUBsoTy1nvz2xSXVt0xaycxaJbpo4EbsdT3zOa7nm2gHb93ZCxJMQaIzka4X+a5IzF5VSwfevmwfArw5oOO5SiCDnYAJOUcVduGrAw9tru/jwX7fhDkRYXpLGIx9bgSVFjVLtYVVJCTl6FaFqEYTQzkwmIcZKSYaBY/PFgj+69X2irumRB0kytViuvJKiv/wZhcGA/tA+/rH/QVabozi6c7n3ldf40V6JiKMJvJ1j26HHBrXvwoFnRPdy8RqYe11vYcxEONJ1hPLUctT9E+7uNiEXFSfS0YFnk1gXvFa0gk+dV87q8gx+edsiMgpyeKTyEgA6fv6LhN9Ng9pAjiFnQEV5NBZle9t2ugPdXFB4AX/ZKIp5/is3Qvg/wnsi+3+/haQcGDw2XynG2itOHMMd9PNeXb8qdUO6CLqOJCUXCYrrSysGBhpSA7xa1crFte8DoI53PhxOK2brrMRd4b3HVWrB3crs9NnkGHLYa9vLLtsudrTtYEfbDra2bGVLyxbea3mPd5veZWPTRt5pfIe3Gt7ijYY3eK3uNV6te5WXa17mpdqXeK35NZ49/izPHHuGZ449w7+P/5v/nPgPLx57mt07fsdrUoCX6l/jldpX2FC3gTfr47JBKq0Y920T7KI8CTR7mk9KUPJI1xHSdemTWu/p1XosWsvA9ecU0BnoFEHhwQV6IQ+64y+OKitWYCqgw9eRUOK5NwkR8vSuBxq7fMRkKO7phJhEEkJTUoJh3TqQZTr/8tcRi300KgU/u3khH18n1jo/evkIP3jxEM3dvgnFXUZjV30X9z4nOoG+dkkl62dlE25ro/U79/YVV8diXPnaQxTHPOxrdPDnd8ff6bLzpU1kO20ElBrWfuyWqbwEbl1exPP330DR+nOwp6Tyd2UpOf94BHVhIeHmZupuu71XJnW89BpUp5UIqf1E9Hz3tEbC0TCt3lbSNLn8Z09zryG16aKLUGj6OmB7usSklni3i1oPYR9qpZocQw6NnjN/bXDSkxAqlYqcnJzev4yM8Wu3nSpy585EHnRziyHxZCv8zzP7iQ6TiLBoLWhLz6et7i0xUDubOEAQjULDLOvATpBAOMonH9rKZVvEhMH44Y+QXlJ4SqpPkiTpqUDqqVZRpqaS+/3vU/zoP9HOqCDa3U3rN75J/g//h39flc+MLCM2d5D/engnd/xlG2t//NaIvimyLBOsqaX78cdp+NSnafvOdwYMaq333pd4IO5XPV9uKePEKZataPI0JfCDsPb+M7xzBwCxRUtHzO7394ZY8/7zLC0w4Q5E+My/do0odXW0zc11v9vMQ1vqAPjo2hL+89m1lPg76fjZUIO7mKQgp2wevohvYLA/tXighvtIOOqFcaY6RUgx6SeRhADxebbswaqxUGmt5I36NzjeffzkdLnEYtCyF8LJxMeZSlegi7RoDGVPtaEkgTEX3K2k6lLJM+RxqGtsi0h3yI1Z3aP/6h0ixxR1uYjaRYX6AYVICt6wWATY36/pxDaoG8xy/XUo9HpCJ6p7vWHOJMJtbbTee+/Y7s2DSSuJJyGaRvWDCB4+TKi+Hkmr7fXSmCrUublkfPITANh++lNivglWNeb0dEP4qUyrpDvQjc03sGK2zdNCxNVMWcGaAY87X3wRolF0CxegLSsd8JymUCw4wg19VU+5ahNN7qZT3umXZHIsL7Fy45ICZBm+ts1A1O+A9oMDN6p/H3IXiURXnM0n7Nz14Ha8oShrytN5+J4VmHRq2rxt7LXtpcRcQqihATkcRuoxOE8yZipWL6LWnIMiEsH9WrIb4kzBsGoVxf98BGVmBnL1Ce5/7Rd8rsRALJzOX3e+z5feVdB59D1anf7EQbloCFXXMTj0nEgI6tNh3o1Quq7PONpaBtHQuH3SfGEfzZ5mZqQOSgh6bGDsq/p3Pv88RKMcshZDUTEXzxHzFoNWxe8/tJTXK9fRYMoi2t2N/Xe/S3isSmslx7qPEY1FCUfDvNcsEtzrCtZxtM3H20c7UCBz3eYnIRbDdNllGFasGLIf8+VXgCQxx16HpdvE84e3DtxgtG6I7noxtivVuEIuPGEPuYbc3qd3v/wORW4bUY2Wgl//BoDZ3fW8s3kUiaPMSugQFcpzM+ayvmg9FxZdyEXFF3FR8UVcUnIJl5ZcymUll3FZ6WVcXno5V5RdwZVlV3JV2VVcXX41V5dfzbUV13Jt+bVcVnAZN8y4gRtn3sgNM27g2opruarsKi6TzMyfdQPnzbqJ9UXrOa/gPM7JO4dSSylbWraI4H72XDHuR04PD8L+hKNhtrVuY0fbjmk9jivkosZZw4LMBZPeV6m5dMolZTr9w0gxueJFiqPIiqkUKvKMeTS6Bv7mezqjjWrjgM7oWrsPhRwjP56g05aXD9nneLDeKQyqHU89NWqxj0Ih8b9XzuF/rxBysH99r5a1P3l7THGX8dDuCvCpf+4mHJW5Yn4OnzlfXKP3/a0Jixu/Pk+8N798/ThH28bno9L4rycAaF64Gkt66qTPfTC5lhR+evNC8lNTaHb4+V09lD71JIY1a5D9fpq/9GVsP/9FQgm8kSgyF9HiaSFsyRNxkUSFZv2kmJo8TWTps3ilqgN/IMR5rVVAnxRTD5YKsUbQd7SK4jW1DqJhiEUpNhdT7xx7d//pimr0TaaW48ePk5eXh06nY/Xq1TzwwAMUFQ1f2R8MBgkG+276rnhFQCwWI5bARX4ixGIxZFkedX/KrCxyvvdd2u/7bt9iXKUkRY7w9K4mAqEoP7tlAWrl0NzOrOzFbGnfR+bh53GYcmjy2biw8MIBx4zGZL78xF7SN26gwNOBnJpG0Wc+MWXXORxjvf6zgQ/StSZiqq5ft3gxxU89RddDD9P5hz/g27oVadft/OaOj3B1tIJwvAIoJsM3n63inAqRQJNlmXBjI77t2/Ft245/xw4ithFanmMxgnV1KLOyhj5nygWlhpyAj70hNw6/o884ajzX7G4V3gY58yfyVuAL+/CGvKRp0vqO4ekQC5tYjIjNhrG9iRgShResHfW9t9x6C51/+xuRxkb+n6WFG7qsHGh28b0XDvKDeEVlD7Is88jWBh545QjBSIx0g4af3rSA8yszCbe0UP/Re4g5HEiZmch24d8gA79beAPfN6WRTTZNrqa+qiVzPjRsFS2nilGGh45jkDWbaCRMh6+D5dnLJ/e90qeDLg1sh6nImkNWShYH7Ac41nWMeRnzBnaajMK4vuexCNS8IypRvXYoXz/xazhN+CDe52zuVlJliZjO0nf9xixRgZ9WyhzrHF6rf41iY3HC+0R/nAEnBrVBvH9BNxiy+sZ8IFAtqnxUWVkcdokJ63kzMzhuc7O30cmL+1u4e01J7/aSXo/52mtwPPY4Xf98lJQEgYDJMp2fuef9reJmPvCAw9+b+6PP7DN11KUOeB8H43zpJQAM552LpE8Z17WM5fpT77oLx9PPEG5qouNPfyLzi18c8/57UWpFgKp1P1LBchZmLGRX2y4uLr4YhaQgGotyoP4dlmTMRVJqB5xPjxST+dprh5ynqkAkaEJNTeI5tR6NqxWDxkC7J3Gn2enyOz+Zxz+d1gTj4X8unclrh9rY1+Lhia4KbgvvA60FzHkiuBWLQPqM3t/HO0c7+NSjuwlFYpw3M4M/fGgJGiXss+2jyd3EqpxVpKek4z76KiACIDIgT8E5ny7fq+nm/MpMXixYQumhl3E+9zypN900+ovOMs7Uz1pTWUnxv/5F0yc+Sai2lqv/eh9zv3of/92wlxdbs5D+vY+9kS4a5SwUEvzw+nncOksL9mPI3fVImIiVrwRzPzmg+HvQ6vRTZ/dRbpxNVtNOMOWJooZRiMQi7LPto9RcilJS9r2nAafoJlDpIP5+O54RhYavFa3gI6uLkZB7FRVmZBn47vUL+GPrdfxoy5/pfORRzDfeiLaiYsDxjCojVq2VQ52HaPG0kK3PZn7GfJDhN28Jv6IvqpuI7dmFpNWS+dWv9t4n+3/myswMUpYvw799B+fX1lI1E1rdrX1jTloJHHoe8pdBIgke+zHIWQCxGCe6TlBiKgFZBG/bXQHyN4sEn+biS9HMqID5C1FU7UO98U0cHz0fc8owEo2pxdC4Q8g5JvDlGg/Dfc8VnTUQDkDZEpSSgv6aUSXmElxBF1tbtrImbw2SpVBIduVOjVb9VNHkbiIrJYtAJMCRziPMTBsq+TvZ37ksy+xs3ckc6xw0Cs2k7xc5+hx2t+/GG/KSopqa4lqbz8Ys66wh5yY7mwlbZyDbjxHLHTmBUmQsYr99P2WWvq4GV8glzlGGWMAlvouxGLUdHrJ83aijYSSNBmVu7qTeF9XgJEa82CdlzZphZcs+dk4JSknmey8d6XuZDN96too1ZVZUk/jMg5Eon3xkJx3uIDOzjfzkhvmicLS2jvaf/nToCxQK1l+4mPWxZt460sFXntzLs59enTAmOhibzUFJleiWKvnQLVMyHiX6zutUCr537Rw+9vAuHtxcxzULc5nzxz/Q8Ytf0v33v9P55z8TOHKE3P/7yZjlYNWSmsyUTBoC3ZQqtQMSDr2423tjQrWOWirTKvn+1moW2KsxBTwoU1NJWbFiwLlmVpbRDmS57dhcATJNWuErFPSQocvAHXLjDroHyGCfLuP5WI9/UpMQK1eu5KGHHqKyspLW1lbuv/9+1q1bx4EDBzDFTZIG88ADD3D//fcPebyjo4NAYGqMimKxGE6nE1mWUSQw4x3AunVYHn+cSFMjgQcfhAMH+cWJZ7hjwcd5saoVl9fHD64oQ6Mauh+daQHbmjfTgsRSw3l0d/aZociyzM83NvLOnkYePCIGbP1dH6bT54OJVuyNkXFd/xnOB+laEzHl13/dtZhWLMf3q18T2bYNHv4bvzdk8MjsS3FoTbQYM1DIMd762Z9Z464jvGcv8uCkg1qNas4clDNnEnz66YFZZIUCl96AZ5hEhUJbjObou6RmzWRn3U7mpc0bsk3Ca44EUTlrUXXXICtUSNEQIb9EzDBKQC0BNe4a9BE9HR197cratlpCOVZkmw3XK+L3fCI1n4o8E7aRki5xNLfcgv+PfyT0979w7/d/w1deqOVf2xuptKq4dJbosOj2hfnB6/VsrhXm0quKzXznkhLSDTJtR47g/sIXibW2oigsJPa9+zGq1Xg++SkkrxdbioWn3j/O6goth+yHMIX67r+aiIZozT6i5sKE5wYghTxobQ0ELAuxNx0FP3TbJ27u1LtfTSHaY28TiFpAqaZCVUFXtItd9bvYIe9glmUWGbrRu+fG/D2PhtE2vYes0hPKXYO27k0isR1ELcWTvpZTyQfxPtfQuA+TbMLWYe+9flLUpDQdJZAiqhJzyGHjiY2szFw54r4auxqxaq3YYjY0HS1EoiZisb7fbXDfXvE/+XkcbxcBdqsqxAVlJvY2Onl2ZwNXVAxcNMcuvRQeexzPW2/RVlWFInuSnUODmK7PPNrYiOsnPxn6xCj35v6oZTNSLEJohG1lWe6VSZLXrB3TfbI/Y71+zSc/Sfg736Hrwb8TOfdclPkjd2ckRJGNrnoDASkLhTqFmC/GtuptlJvLOeY8Rkq3A9lUMeAaItXVBI8cAZWK4LJlQ65P1gnD0mhnJ+11dSgUQTT2FgyZM9nfuJ+F1qGBj9Pld+52j6/ibTKcdmuCcfCJVbn87J1Gfvx6PctumUXW/pcIFaxF07SZYPH5yPE5xKZqB996qYZITGZdmYXvX1JIe0cTe7r2oFaoWWhdSNQdxea24Y/fi2KFBeP+zQzH6fK9mm5KDDG2lCzl7kOvENi1i9b9+1EOp1F/lnJGf9ZqNSm/+AXRb32L6KFDlPzwmzzw1Y/xE00bW1vLWaU4hE1OpQgb7/5nG+ESM0FzKV59JeGoTJqjE53agU6lQKtSoFMp2Frv4sHtrcgyKCT4/WIbS1U7iKaWDHsa4ViYek89dZ460rXpFKQN/C0qu6tRRLSE449FDh4iVFNLQKlmZ8li/rtYN+S3u65Aw6YLVrOldgtrWg9Q8537sP7y50MKcTJiGWyu28zs1NnkyDl0dHRQ0+nn1YPtaKJhLtz8GADa226lW60Cmy3hZy6tWwfbd3BB016ea1zMG0c3cXHBeb3H0YRVRKv3ELUMLBqVQh60Hc0EUpcQ9bdysPUg63LW9V7P0+9Vs65ZmK7qr7gUm82G/qL1+Kr2cW7Dbp7eepyr5g4/p1dLqcjHtxPJmDPsNmMh4TWHPGjr3iJYvB65I7H3UracTYOjgXe97zJbX4C2+g0CUuboRVonkQMdBygwFFCgLeC9+veIeWJYtdYB20z2d97obcThdTBbMxtbcGrGGUPEwN66vZSbJ9dBACLh1dDRwAzVDGyDvFy0LcewW1eicLYTqTtITJ85zF7EPLSju4NqZTUmtVgXt/nbiHqj2Gw21O1NyCotEZuNw012CuN+EIqCAjo6xygBNwzhqqoEFxbDtm8/6hE+s2zd0IBvVIb3jzQw3ypN6DOXZZkfvVHP3kYnJq2SH11egtfZhau5GfcXv4Tc1YWUno7c3d1XWKRQ0LG/iq+sm8vO2i4Otrj46Uv7+a9Vo3dovvmHJ1gSCdJpzqB8yewpmcsM952fmwYXzkjjzePd/M9Te/nrrbNQ3vURDPl5eP/vp3g3baLmppsx/vAHKIvHFgswhU3sa9yHRZGKVLOHcPaiAc9rW04Qzl6Eu6WWls4Wol2FHGp185WWvQAo151DxyCD7Gg8Lp7r62TPsQYWFVrQ+sKEWxuI6TMxRozsrd/LDHNf193pMp6PdU1wUu+il1/e12qyYMECVq5cSXFxMU8++SQfi0uQDOab3/wmX/nKV3r/7XK5KCwsJDMzE/MUmRbGYjEkSSIzM3NsH1pWFsybS3jhQuquvwFDzXEeWXGID0cWsKnGyf++2sgfP7SEFM3AioH0jHTeUntYnVZJkXngQP6nTTU8tbeDO09sJDXoQV1cTOE99yCpRzZxnArGff1nMB+ka03EtFx/Vhbyg3/D8+prtP7oRxTY7Xxz56MAyPQVlvTaHatUpCxYgH7lCvQrVqBbuBBFPADjWDB/QKeR4dxzyZk3d6SDQ6SVZQYTr3obSEtPG6jDOviavTZRteNqEeaPxVeL7HRnNXTVQNbQJMZoHAweZF7uPLL08QSGHIMWoHAGKJQ07diLAagrnMXVFQUj7arvnP/rY9Q8+STRlhbObzvMZy+Yy2/fruYnbzVQnpfBgRYnf363ji5vCI1S4uuXzeKu1cUoFBJRh4OGb3yTWFMTqrw8Ch58EIdKSWZmJh3XXYfj0Ue5sGEXG2vP4Z4LllBTWzPwfVMuBGezuM8NR0sLlCzCnJ1DW4eNmcaZZFnHn8AZShbE2jHLHZC1KP5IFrMKZ9HsaeaA/QDd4W7mZcwb1hsDxvg9jwThxBuQXQyFK0Wlm+VKOP4alMyddOXVqeSDdp+LxqJomlzkFS5AmZU18PqdezBbUkBrIjMzk7ca3yKUEhoonzYIVVBFcUaxaO22ayCvGHR937eOzi58gKK0Am8ohlIhsWRGIWX5IX61qYkDbV6CKiOF1n7foawsIqtW4du6FeUbb5L55S9N6XswHZ95qLaWhq98FRwOlFlZRDs6epPE2ffdS+qI9+Z+ZFwszHhV2mE38e/bj6OtDSklhbyrr0KRMr7quLFev3zD9TS98gq+LVuI/e1Bcn/7m3Edp++ASzDLNshaznlp5/Fm45uUG8vp9nRzaVoemvw5YO67J9oe/gcAxgvOJ2dQRSsAWVm4LBZiTieWYBBdeQl072FB8QJerXsVa4YV1aDAx+nyO9fFx++TwWm5Jhgjn7wwg1eOOjjU6uavhyV+fN4FUL8ZypZBnlhIvrS/tTcBccW8HH5x60I6Azb2tlcxN38uM9IGyrw0twhdbcv8+VhH60oaI6fL9+pkMHN+Bfu3l7HIXo1m6zbSP/HxU31KJ5Uz/rPOyiL2yD9o+epX8b6zkbKf/InbPncFP1Ol0xUzcY1yC41yFttjs3i5pmcMH8HfoB8xGe7dY2JD0QnSK5YO6QIIRoOccJygxl1DnimPa4quGeAB1IvvGGTMgnTx+2yLyyu9l7eAK1fNoKwwd+hrgJ/cks7Ha29j2ePfRbNvL9p9+0m95OKBl08WhTmF6FR99+AH3hFB/687d6LssKHKyaHw859HoRfzkUSfefTGGznxq19T7mwhpytMi1dDRB8hzxgPIKqXi7VT1rKBJ9naCsULMWfnUOuspTynnKLcvviG59U/oY1F8BWUMPP885AkiejNN3HsV7+mwtnM27uOcc8FIyQYTKvgxOuQmTmmbpThGHLNsgzHdsLMdZCVYDzuxyUZl/BW41v4zSoy82dhVjgga3JJkakiFA0RdUeZWzQXlULFhZYL2dW+i5LckgHficn8zoPRINs921k/Y/2oXcTjYZF5ETvadpA1BeNWd6Cb3GAueTmDAt7+buS0DAIZOZjTVSgCDsgaed46XzUfb8xLeaZIjnR1dVFoKCQrIws8GuF9kpZFm7eOQnc7APoZMyZ9HeGFC/EoFEO6hVNs7aRlZg6rBLBIa0IhHRvSrPzD1xv5zNo87i7PRK0an4n4P7fW88LBThQS/Ob2xSyZmUmoqYnGr34N2W5HU1ZG4UN/R45ECFXX0Pm3v+HfuhXvt75FyV/+zPeum8eXntjHQ9vbuHZZKXPzhl+vR2My+o1vAhC55Eqyp6hAa6Tv/I9usnDRLzZxuN3Hhmo/H11bAnfcQWDRIpo//wUiTU14PvNZcn78Y3Rz5xCur0ddXDxsR0qmnEl9XT3q9HJMDdsGxk9iUWiRoagSW/dR5uXP45+bvKhiEdbFZTmzrr8ew6Dvj5yeTrdCiToWxW93k7V0BnjzwJwC1iy0Fi1bWrYM+N6dLuP5WNcEpzSVm5qaysyZMzlxYnitQa1Wi1Y7dOGqUCim9A2WJGnc+9Tm55Nz7720fO1rmJ96hH/83+/46FY/7x63c8/DO/nb3csxavveYoVCwaWllw7Zz7O7m/jJhqNY/U5uqxVGVVlf+QrKBNc9XUzk+s9UPkjXmojpun7LFZejnTmDmquu7k08SIhERHVqPjuzKqnJr+SGD1/OdasrEg6o1ptvxrRuHY6nnsb+u9/h27KFSHMzmsLhq/IpWIru2GvkZxSxrX0bKSoh+yQjE5NjxEJ+wk2H6T7mQFYo8Fjy8aZlE5MCxOx7kJFBjlHUuoOAVo3WlINBbUCv0mNQG9CpdEONruL4wj58ER/Zhuy+6/E5QGcGlVro+O3ZCYBy6Yoxv+cKo5H0j92D7af/j84//ZkvvfgiuxscbKnu5M4H+3Q/s0xaHvroCubkiYlh1OOl6VOfJnTsGMrMDIr//iCq/Dwkmw2FQkFqPAmxuvUAvz3WhMO3iGx9NraAjUJT/D1OK4bmneKTS9R6LcvQVQ0zLhKVD4EOlmUvm7rvU/4S0fqdPVu0HsYpNBeSb8qnzlnHey3vkWPIYW76XPTDJAtG/J6HA3DiNTAXQMHSvscN6ZA9Bxq3QsVFU3M9p4gP0n2uM9BJelRGbc7tNYPvvX5LPnjaIUVMgpdkL2Fr61byTHlDgro9eCIeLDqLeO/CPtAae/cLEK6rBaA7PQ86oDhdj06jIkejYlVZOluqO3npQBufOX/g4tZ654fwbd2K8+mnyfzcZ1FM8Rg/lZ95sKaGhrvvJtphRztzJkUP/Z1It4O6m29G9vlQWa1jP45CM+omng3CkNq0fj0qw1Aj8LEw1uvP+dY3qbn2OjxvvYVvy/sYzxnBIHM4chfAwX9DznyMWiOV1ko2t2xmYfo8dA07wZTT+52RIxFcL74AQOp11w17fpqiIgJVVUSamlDMng3ESFGoydBn0O5v77tHT+Cap5OTeezTeU0wGhqFgu9fN58b/7CFJ3c2cduKNSypuFDIICoU/HtPE199ch8xGa5fnM9PbpzH0e7DNLobWZu/NqHedei4WD/pZs487a//dOSCykzeLlzKIns1rhdeIOOTnxiz7OPZwpn+WSsMBgp/+1va7r8fx1NPs/ChF7lgWSMHVJfh96ppMObQmWLh5qUFKBUS/nAUh9sHShX+cIxgOCoe84WxuQdq/ttkC23hMNbO42JuCPgjfo51H6PeVU+hqZCLSy4edh4KgNcG+YtBoSDm8+F8SYx1rxWv4NfnlA77vuu1Cn7wmcv4595N3HToNWq+9yOWnH9eb9FW73b9fGTqO728sK+FDJ+D1dteBCDrv7+GyjgwOTL4M1dYrRjXrsWzcSPnN+2huf16DncfJt+UL34PaUViXhzx92riA9BdA6Xng0JBjauGRZmLevd5wuZm4b6N4hxuvw1l3BBbYbWiXL0GefO7WDa/hfOzV5FmGGaOYLCC1gTuFkgdYR04BgZcc1sVKDWQM3ohhU6h45yCc9jYuJFU6zxSG3eKJMRp8HtpcbeQZ8xDoxLvX64xl7JgGTttO1mXv27AvWyiv/MDtgOUpZaRmpI6ladOhj4DpUJJd7A7sZfDOOgKdpGlzxp6bZ52YuY8ce3p5SgO/QfkKCiHL/AttZTyduPbLMhagEJS4I14ydZnx9cDXtCZQKGgvtPL8n5+EJO9f2rz8sj93v203nvfgERExwM/JrB3L7n33YcyNXXI6/LTDDxww3y+9ewBorKMQoIMoxabO8gDbzbwwhEH918zj6XFaWM6j+21XXzvxcMAfP2yWZw/K5twczNNd3+USFsbmtJSih9+CFVmZu95G1Ysp+kzn8W7eTPNn/wUlzz4Ny6fl8MrB9r476ereO5za9EOkwh5b9NeZrUdJ4bEsk/eeVLmMdmWFL55+Wy+9e8qfv76MS6fn0teagr6efMoffopmr/0ZXw7dtDy+c+L5Kcsg0JB7vfuH1a2sdRSSl3YwUKlGvzdIp4AwiM0JQ1ZqaLB3cB860pe3L+dxbbj6PxelBkZGFeuRBp83QoFXmsWZnsr3SdqUFy+DHRGcQ9WKEhLSUOj0tAV7CIjpa+b7HQYz8cc65rm8xgRj8dDdXU1ubmJqwDOBCxXXYn5yishGiXz1w/wyIfmY9Sq2FbbxYf/tg2nPzzi69893sH/PL0fgB8430cZCpKyaBGmQdUOSZKcCUTtnQxevklA6Xe+xY4Lb+NdSzlffv4YH31oB82OxCbA6pwcMj73WfSrVyGHQrQ/8OORD5qSBuY8FshqCk2FZOmzyDXkUoiGGY5WZrcdoVBlJn329WQv+zgVM65kcc4KlucsZ1XeKtbmrWVN/jlklV5AjseOjIzNZ+NQ5yE2Nm3kuRPP8VLNS7zd8DbbW7dzwH6AGmcNbd42ap215BvzBy5afV2QIlphw01NGLo7iEgKKtaPL9CVdvvtKNPSCDc04HnpJb4VN6Dqj90TJM0Q998IBGj6zGcI7N+P0mKh+MEH0QxqJdTNm4umvBxtLMLapv28XNVKnjGPFk9L30YqLegzwNWc+MTcrSI5kJJGIBLAH/GTqk1NuGnzsTq2P/sazcfqxn7hGgNkzYajrwgdxX4oJAVlqWVcVnoZBrWBNxreoKqjShjHjZWQF46+DGmlAxMQPWTPh5BvVBOzJKcPHf4O0mVEV9NgTMKcuof0lHSy9Fkc6ToydFtEZRmARqkRJmAwZMESrBFJiEajmIRXZPYt8K9eKCqxnt/bwmCM55+PKi+XaHc3rldeGdvFnQKC1dXU33WXSEBUVlL00N9RWa3oysuwfvjDAHT+9a8iyToFyLEYrg1C29585RVTss+R0FZU9JoAtv/oR8jhcdw/elDrIGMmtAtzzRmpM5iXMY9ytQn0VlD2Jbi8779PtMOOMi0N47p1w+6yJ9kebogbI2oMEPJQZCqi0T0+g9QkJ4+YHCMYHZth6dLiNG5ZJrqwvvOfA0L2UKHkiR0NfCWegLh1WSHfu24Gm1vexR1yc1HxRQmDNLFAgFDcyFw7Y8aQ55OMzvmVmbyXN5+gQkWouprAoUOn+pSSTABJpSLne98j4zOfIcMFF9bv54c7f8IPN/+Vh1/9IX9Kb+anNy/kxzcu4Be3LOQnV5fz97uX8+QnV/Pc587htS+fx3OfW4ti0AJGAqwzV0FbFb6Agz22Pbxe/zoSEhcXX8zirMUjJyCCHtEdrRXSGu7XXwefl1Z9OllrV1GaMXLCvTTDwLJvfpGOFAuGLhs7fjJy594f3qkWXnyNbyAFg6QsXYr5irGNqearrgTgvKY9bD8eRSkpafI0iScVSrCWDpwXezqE14UhHbvfTkyOkdlP6mbTf96h1NVKWKUm76brBhwr76brATi3cTcbDrQyIpmVYD86pmsYE74u4e1Qcs6YX2LWmFmWvYzN3YcIaQ2iEOs0oMndNKQ4YbZ1NhIShzonfy+z+WzY/XZmWWdNel+JKDYXT4lBtd1vJ12XYP7vbhG+SyA8WUy5wkh9BIwaI0a1EZtPSAK5gi5MmrhkcdyYOhCO0uIMUBRfo2rKyobb3bhIvekmKt56k6KHH6b8jdfJ/OIXQKXC/coGaq69Du+WLQlfd+vyIt77xgU89vFVbP7GejZ/Yz3fvnIWBo2CA80ubvzDFr7y5F5srpElK1scfj7z6C4iMZmrF+bxiXPLCLe2Un/X3YRbWtAUF1P0UF8CogeFVkvBb3+DfuVKYl4vjR//BPfNUpFu0HCkzc2v3zw+7DFPPPI4AB0zF2AsHJtaxFRw2/JClhWn4Q1Fufe5g73rGVV6OkUP/g3L9eIe1SsRHvfoCLe1JdxfiaWEBlcDsdQicNT1PeG1gTELu9+ORqnhjYMuAuEYV3eJLgjzpZciKRMnaCK5Qi42UBc3GlcbRHwiTpGpiAbX1JiQnwpOahLia1/7Ghs3bqSuro4tW7Zw/fXXo1Qquf3220/maUw5Ofd+B1VODqH6evIf+wuP/tdKLClq9jQ4uOMvW+nyhhK+7kCzk089In7sd+XFKN3+FgBZ//PfH7hKnCRnB5qS4qHVIQoFlUvn8Nzn1vLfl1aiUSl452gHl/x8I49sre81Y+uPJEnkfPvboFLheestPBs3jnzgvMVo7Mcp1Vop9nsobNhBXncDGRlzSF16D4aSi8nMmEVGSgbpKemk6dKwaC2YNWaMGiMmjYn0glXkRmPMNRazPGc55xWex+Wll3NdxXVcUHgB8zPnk2PIQaVQ0R3o5nj3cZo9zUOk1fB19gZD295+F4CjaUWsmje+wVWh15P+X0Kmzv6HP+DyDk3axGSos/uQQyGav/glfNu3i6qwv/41YVBCkiQs114LwEWNu3hhfyu5xlzavG3E5H4toGklw0/U7MchQ+y73ddOtj474f3qtZ/+Gcc1V2D61hdxXHMFr/30z2O/+LxFUADBdQUAAQAASURBVLAMajdB/fvCKLsfaoWaOelzuKT4EiJyhA11GzjWfYxoLDryfoNukdzInCUqmROhUEDpOmjaOWCwT3L6Yve2Y0EhjI8HY8oF98BJ4/yM+dQ6a/GEPEM2d4VcvVqwYsExMMAgRyK9gb/DalFZVJHVl4S4fF4OaqXEkTZ3r19ED5JKRdptYr7T/ei/xnWNJ4vgiRPU3xXvgJg1qzcB0YP1w3ciaTQE9u3Hv2vXlBzTv3s3kfZ2FCYThnPGHhiYDBmf/SxKq5VQTQ1djz46sZ1kzxVBmZAPpUJJpbUSyd0qvnP96DWkvvJKJM3wXSHqeBIi1NSThDBByEuuMZcOX8f4kq1JThrukJsNtRvY2LiRE90n8IVHHje+ftkszDoVB1tc/P6dE3z3+YN8/ZkqZBk+vKqYL1yawTtNb1FgKmBN/hqREE1AqKYGYjGUFsuQoECSsVGQpic7O42tuaIi2vX8C6f4jJJMFEmSyPzC58n88peZ2SxTmysRUYACmcIHf4ntl7/C+cKL+LZtI9rQQNTjGZBIz7Wk8MAN88kKOFnQcYIMvwOFJGGPSRyOedm5/2E0Cg2XllzKgswFYzPU9bSDsU9exP5U3JC6eDkfPWdsgcvLl5dx/Pp7ANA++Qj1hxIHwFscfp7Z3cTczlrmHN4KkkTO/35rzPEE0/r1SDodBV47xsZqdLFiDnb2BedInwGd/ZQruqrBKiRrahw1lFv6tP1lWSb24r8B8K0+H6VloByL8YILiOhSyPF1s3fDppFPLK0EvHYxf58ssahYVxSuGDK3G41cYy7lqeVsJ0isdd9A/8JTQCASwBF09BmIx5EkiRW5K6hz1dHmTRwwHQvRWJTd7btZnLV42K7hyVJsLqbZ00wkFpnUfux++9BEfSwmTIJN/SR00iugc/iAeA8llpLe5Ig77BZJiFgMoiFQp1DfKcb4wt5OiKlJQoAoxDSsXIGmoICMT3+aksceQ1NSQqS9nYZ7Pkb7Aw8QCw4tesi1pLC6PJ1cSwpqpYJ71pby1N3zuGVZAZIEz+5uZv3PNvLnTdWEIkN9JALhKJ98ZBd2T4jZuWb+78YFRNrbRQKiqQl1URFF/3gYdXZi2SlFSgqFv/8dKUuWEHO5cH3uU/xksfiN/eGdavY2Ooa8pqnLS/luEd/Ju/3mSbxr40ehkHjghvmolRJvHG7n1YN9vxVJre6NlQwgFiNUnzjob1AbsGgttGt0A+MncaPqOlcdxeZiHt3WgDoaZkmDkM0zX3F5wv0BaIrihaTNPWsCPfRbtxaZi2jyNI0e+zhNOalJiKamJm6//XYqKyu55ZZbSE9PZ+vWrWSe4ZNnpcVC3o8fAMDxxBOUndjD459YRbpBw8EWF7f+6f0h2ceGTh93/30H3lCUNeXp3HPoZYjFMF18EfolS07FZSRJMmnUOTnkfu/+vkREvH1NnZODWqngsxdU8PIX1rE0nn3+zn8OcNtftlJr9w7Zl7a8HOtHPgJA2w9/lHDQ7dvYCOnlQsbH74DSc2HOtZA1S7TcjgWlOl7ZenDAw5IkoVfryUjJoMhcxCzrLJZmL2VdwTouKblkQBscMCAJ0frOe+K/ZfNI1Y/xPPrRvxsif+emIVVaSkmiOE1L89e/jmfjRiSdjsI//ZGU+cN7W1iuvgokifmdNTQdOoHdHcOitdDh66eTm1oEzqYh2pREgqJDIq0UgHavSEIMpvlYHfl/+yUKxCRdgUzeg78cX0dEahHMvU5UWh16LmFSRKfSsThrMecXnk+nv5NX617lePdxGt2N2Pw2bD4bXYEuXCEXPncrkcMvEs2e19tWPywpaaIboz5x1UmS04eYHMPrrMdoKUrcHq/Ri9+2v8/0S6fSMcs6i70de4ds7g65+6qewj4YpPEcbmqCcBhJq2V/SMgizMju2yZVr+HcGWJO88K+od0QqTfdKIL4VVX49+8f7+VOK8Hjx0UCwm5HO3s2RX9/EFXawBZuVUZGb4VQ51//NiXHdb0cl2K68EIUIwTppxKl2dzry2H/7e+I2BMbU45ITzdEWz9DQXfbgCRE1OPB/cYbAIkXNf3QFCXqhPCiVqjJNmT3VaUmOa2waC1cVXYVldZKHEEHbza8yZsNb3K06yju0NDAWbpRy39fJipLf/baMR7aUgfA2gorN62Jst++j7X5a4f4PwwmeFwEU7QzZiQLlybB6hILbxeIdZfz5ZeQI5MLiCU5taQsXIgxAJlOma2zJHZWSOwvhi0v/JHN/++/2frVe6j53F3sPm8l+1Ys4dill1B35500f+UrrP7Dd3n41R/wk81/5Hebfsgy5wt8c8PjSLmLWaNKZ66lDK1yHDKKnvbeIGiosZHQzh3EkKhZci5rK8YuQXPnNz9GTd5MtNEw7//PdwlGhgad/ryphmgkyleOChmm1JtuQjdn7N4FCoMB4wXnA3B+0x7210OKMqWvC8+QLubjng6xNuiug/RygtEgbb42is19ndf7jzaztFpI0c782IeHHkunQ3fBhQBkbn2bDvcI6zuFEnIXwuEXofbdIR3S46Jlt+hUtJZO6OWzrLNQmXOp8bWK6z+FNLmbyDXkJpQL1iq1rMpdxY62HaMmxYfjSNcR0nRp5BgS6+BPBTqVjgxdxsBu/HHiDXtRSIqhHUk+O2jNA2MA5nyRzAq4RtxngbGAdm87zqATtUItkjAhT2/iqtbuxRL0YA55QZLQlJRM+PxHI2X+PEqffYbU228DoOvhf1B3000EjiTu5u6PVa/mxzfM5z+fWcvCwlQ8wQg/evkIl/1yE+8c7TN/lmWZb/27iqpmJ2l6NX/+8FJU3Z003HU34YYG1AUFFD/8EOpR/BoUBgOFf/4TugULiDocFD3wdT6SLxOT4atP7iUQHnjfeuPRl8jyOwhoU6i4/soJvDuTY0a2iU+dJ5Kn9z1/EFegr9BnuKJaTfGgwtN+lJpLqQ51i+63njWnt4OIPo0WTwu2LiMnbB7Wdh1HGfCjyskhZfHiYfdnrhD3qZSONpEMVhvEujROiiqFNG0ard5RuslOU05qEuLxxx+npaWFYDBIU1MTjz/+OOXl5aO/8AzAsGoV1rvvBqD1299hhibME59cTbZZy3Gbh1v/vJWWuPxMlzfEXX/fjt0TZFaOiV/NlvG98w4olWR++SvDHyRJkjOA/u2EFW+9OUQ/ryLLyJOfXM13r56DXqNke20Xl/1yE3/aWE0kGqPV6WdLtZ1Wp5+Mz3waVWYm4YYGuv7+0MgHzl8GC2+HkrVgyBh52+HImi0MqsMjtywOiyyLgUdvRZZlVPt3A6BduXJCu+vfDRF+6G88cO0clPFgg1KS+NF1c+BnD+B+ZQOo1RT85jfoly0baZeoc3PRrxLns75xNy/tbyHXkEuLt98kUK0TgXj3oIGtqxYsBaDSIMuy6IQwDJ2UNO4/2puA6EEpy7QcHL0CZeCL1FC0EsrOh5Y9cOJNUZ0+CJPGxOq81azMXYk75KbV20qDt4Gj3UfZa9vL7to3OL7tN2zEy/POwzxz7Bn+c+I/vFD9AhtqN/BG/RtsbNxIq6ff9WbPg0gA7MN7FiU59XQFurBGZZSGEUzhzHngGvhdLk8txxf2DfzMAU/I06/12jfEoDxYK6SYNKWlnLCLyWBFpmnANr2STPtahkgWqazWXnmErn/+cwxXeHIIHDsmEhCdnWjnzKbowb8NSUD0kP7Ru0GS8LzzTm8gdKLIkQiuV0+eFFN/Um+4Ad3cucQ8Hmy//OXEdpI9V4wZIZ+Q7/I7wNBXWOPesAE5GERTXo5uFCNvdcHgTghDb9VTUpLp9EapUJJjyGFZzjKuKruKBRkL8Ef8bGraxGt1r3HQfhBHwNG7/QWVg4qvpCA72t+lwdnFRUUXYdVZGY3eJMTMpBTTZFhTamZXdiVurYFoh53Ohx4eVnIhyelPT/CosgmWnpCZ0SKT44DMRavQzp+HvzyP5vwUjhZI7CgM8rqliVeCu3m1ZgObbFs5UAR7yyT2lcHdVRtp2WulsSsPVdZsaN03vpNxt/V2QnQ/KzoD9mTN4PpLl44rcahVK5n34/uJSRILT+zkL795esDzHe4gj21v4KL6HeTZ6lGYTGR+6YvjO1fAcmWPJNNeNh5pZ27GXA52HuzrlE6vEN0QribReaoxUOeso8BYgLqfbGXVw0+ii4bpysgnbWXiNUluXKLpnOZ9vLp3lLEtazbMu1Gs7RreF35M7YdEYdQYUXjb/z97Zx0eV5m+4fuMJiPJRCbuaeruTqFQ3IsvUNxW2N0fzuIsLLLLAssCxV2Lw7ZAKVDq7k3buM9MdCSj5/fHmfjEk+q5r6sXZObIdyaTmfN97/s8j1Q4SJ3e431CMTl+MmVGMxV53/frOP2luKE4ZE5UEzHhMQyPHs7q8tVtle49oN5TT15dHuPM4/o7zG5Jj+yfJZPNZevYDAhQXwYR7ezeFQpJvWPrel6nVqpJ1Ceyy7arnRWT9P8FNkdzKLU6KQlFeA9UUf1AodOReP/9pL70IsrYWNz79pN/wYXYXn0V0d99F/y4VBOf3TSTJxeOJdagIc/qYNHr67n2zQ2sL7Bx/5c7WbKpFKVC4PlLJ5Lod1C0aBGewkLUSUlSAaKH1vlKg4G0xS+jHTkCv83GZR8+wSixngMWB08va7FV8/oD+L+RlIfu407qkHVzsLjl+CFkxuqprHfz1NKW8XVoqgW0I0Z0Gk4NkGRIorqxmsaIoO1XcL2i1FOHWWfmo/XSfcWFDVIBKeKUUzpmQbQibpiksIlrsGC1e4JzgrZFxfSI9CPWkunQp+ocRZj/fCvaoUPx22yU/+0+ss16PrphBsmmcPKtDi54cTUr91m54MVV5FsdJJvCeWPRZOzP/BMA04UXoM3qW3VeRuZwoklO2NmHtVIhsGhWJktvncvsIbG4fQEe+24P855awczHl3Pp4rXMenw5n+yuJu722wCwvvgi3rIuuiUUijY+3H0beLgk/bV032EQksY66RhKNe79+9HZ63ArVAw/YUafh9RaDXFy+dZm38df75jHcT++R92nS0ChIPnppzDM6ZmVSVNH7vzijXy1pYxkQ3LHTpSo9I6dPrZ9kiQbqHPXoVVqQ0rSrfkdvxD9gkDSqD4ulhjMMOIsaQKy+yuo2hNSCh0THsPE+IlMTZjK5NjJzEmewwnRY5jX6GXc5JuYP/5azs05l/OHns8ZWWewIGMBc1LmMCVhCkOjhrLFsoUNFRsk2xOFQvKMLd0QsvAhc3hgcVowi0LXhUdjQoeCmkJQMCFuAlssW9pIWdsoIVp1PjXhCeZBCKlp0k0hkB3X1tf5pJHxhKkVFNic7Cjt2HEVdZmUR9Dw3f/61oE/wDTm5lK06Cr81dVoR44g/bWOCojWaDIyMJ4k5VbZXnu9X+d2rl+P32ZDaTKhn96/xYHeIiiVxN9zDwB1ny7BtX17N3uEQB0G5qAaoqECDHFtJi1NVkyR55zd6aJTU+G9NkpamPaWlknd2K2KEAn6BGrdtbh8obOUZA4fBEHArDMzPm48p2edzpSEKYiIrKlYw3f537HVspUdFSUQLNQLqlpUxl34PDHEKsa2WczrisZWSgiZvjMuyYA2TMuBYMe65amn2H/CfGo/+aSbPWUOR5oWjwSFgnAPRDYqGPd/DzHr6deZ//zHnP7m98z777dc+v46rvzXd1x1+1tcdNUTHDfpfLLLRcx1IonVIrN2iWRYRJLs1Tz09U4aY0dK98TddFE343VJi+ThJkS/n6qgFdNvOTM4d0Jyr68rfep4XKdI9+6Z77/E15tbFu5fWZmH0uXgur3/AyD2lptRxfQ+7Fc/dy4YDMQ21uHauJEwhQmD2kBhfVCJHJ0lvQbWXIgZgiiKHKg9QLappaHU5w8Qs0JSNyrPOqfT7z399Ol4IqKI9DjZ83UPFvRVGqkYMeocSJ8NrmrYsUSyV+pOHeH3oClbJ93Tq/qntlQpVEzOOYvihhJslYdGzer0OrF77cTpumi+AXKictCpdGy39u7eZlPlJkbFjiJMNfgLw4n6ROrcdX1WbFhd1tBFiIZyMCZ1fDwmW7LR7MZOKz0inRJ7CRGaCOmBYB4EQIHV0WzFpBlAK6buMBx3HFlffoFh/nzweql68imKFl2Ft7QUb0UFjjVrOy2gKxQCF0xOZfn/zePa2ZmoFJIN0QUvruGt1dLf96mjE5gWJVC46Co8+fmoEhMlC6bk3n1eKSMjSXv1VbRDhxKwWnj8txeJc1Tzysp8NhRUA/Dj+gNMKtoCwKirL+37i9JPwtRKHj1Hco94e00hm4paVPNNTbUJDz8MCgXunTtp+OmnTo+lVCilhiGlALWFLVZMdQVEKpP4bnsFWp+bjFypQbUrKyYAXWYGAEkOKwVWuzTf8HvauFQkGZKwNlp7nEt2OCEXIQYQhVZL0pNPIKjV2Jcvp/aTT0iP0fPxjTPIiNFRWuvid6+u5YBFWtC6eEoKulUraNyxA4VOh/mWWw7xFcjIHFxSo3W8fc1Unjh/LHqtkpIaV0sGkAh3L9mBY858widPQmxspPIfTwz+oOJHgWUv+PsgyW9lxVS8XMqD2B2bxZScvstZFTodMddIfrDWF/9Lgl7NjOwY1G+9Qs1bbwOQ+PdHiViwoMfHjDjpJAgLI9lhxbtjG5Z6oTnrohlTOtQVt3zZOaulCVVwot6ZCsLvD8DX0sJb61u8svlnkzw0o+cX3h6FQpJkDztVmgTt/a6NxU5IGsrhwI9SxoOpbceQSqFCq9Q2+zgmGhI5Kf0klAol3xd+L4WShZskRYRsy3TYYnFZiA4EQodSN2FMlGwR2k06zDoz0WHR5NbkNj9W761va8ekbltg8BRIRYj6OOmmPNkUjk7Ttvip16qYP0L62/hya8eA9/AxowkbNxbR68X2+utdThwGm8a9uRRduQh/dTVho0aR/tprKE2mbvdr+kyq+/rrfo292YrppJMQ1D1bfB1IdBMnEHHWmSCKVD7yKGJ7C7qeED9aUkNUH2jjP+wpKcG5YQMIApFnnhly1w/XFzErWHif+9oOAio1+HzSa6o1NhdAFYKCJH0SJQ2yJdORRlRYFKNjR3NKxinMSp6FRqGhgf2oI7eg1O9FqSvE78hB8CSSEdtzn3K3XIQYENRKBSfHKRhrzWt5sJsQSpnDm+4U2SDdW2vS09FNnkz0aWeQdt0tmFwK4mshvhYUwdsFZUwMxdUuXlpZIn3Wl23q2SDslWCU7gMca9agslbRoA4n+5zTCFOHDiLtjvH33Y5HZyCrvpzlT75MnsVOrdPDO6sLuXTvDxhcDWiysogONjr0FoVGQ2RwLjG7aBOr9lsZHTua3bbdUje9Ricp/erLwZRGhaOCcFU4kdqWzIf1S38jo6YEr0LJ+Gs6X1wUlEoMwUW45I2/UNlNaG4bDGapoDD6fNDHQfEaqSBRuTO0kr1oLf6I1A55TX1Fp9aRNexsDuz+rM+L5/2huKGYZENySCum9kyOn0y5o5xyZ88sWwrqChBFkcyIg9MUqxAUpEWk9VkNETKU2u+T5qyGEEWacJP0Pq7v2gIqTheHTqVrVYSwNxch8q0tSght5sErQoCkpk55/jkSH3kYQafDuX49B047nf3Hn0DRokXdFtAjwtTce8ZI3r5maofnftuwjwOXX4nnwAFU8fGkv/kGmpS+hUWroqJIe/01NFlZqKxVPL9+MTGOWv7v4604PT52vPMJ2oCPhoQ0DOM6yWg8SMwcEsv5E1MQRbh7yXa8/pZ5gDohgagLFhJ91SIAKh5+mICj88bEjIgM9nvrpCzL6jxcWiP1nnp+3e3F4w9wgbcQobERdUoKYWPGdDkuTXIyAUEgzO+lZH+w6KwOb2PJpFKoSNQnUlx/5Cml5SLEABM2bBjmW28FoPKxx/EUFpJkCue5Szp6fj2/dA/lT0kqiOhrr0EV20cLGRmZIxhBELhwSipPnN/xS8gvihTaXCT87W+gUNCwdCmOVYO8IBwWId24dCPXDImrpnkx1PLLbwDYho4hXNO3CUcTzWqIwiKq332X8vvvx/rCfwGI/9u9mM45p1fHU+j1RJ4sTTROLNrI11tDqCE0OmkRzB7sMLLtl6TYwa6mCkdFyDyInz9ZRnblAbwKJYqX32Jb5ngAjFvXI3oHIFg1LBKGnSKFY+culWyaQoQyKRrKpA6p7BMkO54eoFKomBA3gUnxk1hfsZ4tVVvwmYdJnQeW3O4PIHNQCYgBahxVGASV9L7oDJVWynZw2jo8NSZ2DPtrpTDZgBjA5XWhbyo8eJzNk44m3EElRJlRmty0DqVuzZljpffc19vKCQQ6dlw1LRJUv/pajyYOg0Hj3r0UXXkl/poawkaPJu21V3tUgADJd1s3ZQp4vVS/+Vafzi96vTQsWwZ03xE0mMT99f8QdDpcW7dS9+WXvT+ASgvmYZJdXavOu6Zj6WdMD6kKLK9zcdeS7TS9PfwoKA2Gq3uLi5szIZpIi0ijqOHIlF3LSERoIhgRM4ILRpzGfSdcgOCNwVc/CkXAyN/PG01iZM9sHfx2O74yaVFJO2TIYA75mGCeobGDhWRXIZQyhz/dKbJDbd/efgPgrrr1ALywYj/FqnQpE8HR8V6iAw2VYJDOXfjOhwD8nDqRy+b2vWioiooi+S+3AnDx9m+49eUV3Pv5Dky2cs7Jkxqf4u+6s18F/SZbxNll2/hldzlRYVGYtCby66R7H+JHgnk4qDQcqGurggCoeE+61pKxMwiL6dpWLul8KV9qevlOvlsXOnC7S1QaKftv5NlSFqCrVrJqyvtZUiaC9L3sqsZr7nrBr7fEJE4kLdzM+gPf9DtYubeU2Eu6tGJqjVqpZkbiDHbU7giZUdQat9/Ndut2JsZPPKg5QxkRGc3Fj97g9Xtx+pxtimCANG/Vx0p5IqFoH7IeAkEQmBw/mURDsHDlsTdnxBXYHKQ0SJkKB1MJ0XpspoULyfr8M8JGjEB0u1uarAIByv92H/Zff0V0ezo9RvtXOsLt4NGVLxHIO4AqLk4qQKR1nn/QE1QxMaS9/jrq9DSMtRaeWv0SdaUV3PD2RrI3SoqC2AvOPywyre45fQRROjV7Khp45df8Ds+bb7kFdXIyvrJyLM893+lxTGEm1Ao1NeERUFtEkdhIiiGV99dLDURn2KTc0YhTT+32ugWNBkdQIW3bF2ySaDcvAEm1U9jQMTfzcEcuQgwC0VctQjd1KqLTSdntdyD6fDS4O35BnZr3G4GyUpTmWGKCeRIyMscqE9OjOgQvKwTIiNURNmwYUZdKHTUVjzyK6On8i3VASBgLlTs6BjN3R1AJIfr9hO+UvGMjZvTfYkSh1zd3Hlc99ji1H34EgOGkk/rc8dRkyTS3dAvfbi4kyZDUNhcCJDVEbaH0OlTnSUUIwBfwUeOu6SCBDQREal9ZDED5zJMYPncKWU88Rp1Gj8lSyv6XXuvTWEMSmyNNPBrrpODq1nLsmgI05ethyImhO2G6IV4fz4L0BfgCPn4o+hFb/Aip+81tH7jxy/SbmsYaokURlT6uuTjWKRFJITufdGodOVE5bLVsxeF1EK4Ob+kuC2XHFMyE2B8mvfc7K0LMG2bGqFVRXtfIhsKOip2w8e0aEw5y523jnj2SAqK2lrAxY6QCRGQXhZwQNOXV1H74If76HlpUtMKxejX+ujqUsbHopnbsyjpYqOPjiL3pRgAqn3iChhU/9/73ED9KsrDTSYsuoihS90XQiqmTQOp8i4P29alyvVTE9hQVS3kkXlfz5NIcbpZsGDzy59DRwOXThvHrXy/k/etms/LO47loSs8n/E0qCFVcXI8LhzKdM2HGaAK0vwHtOoRS5uijtYIi+dl/g0JBxM/LuNqXh9sX4OFv90LSeCjd2P3B7FIehL+ujsCvKwBwnnhajwuNnRFz8UUoh+Rg9LqYs3IJX28r5/odX6IMBDDMm4dhzpx+HV8/bRr+SMkmybriV0RRZFTsKHZX75YW2yOSIHUKDq+DmsYakg0tVi3OugbSt6wEIP7Si7s9V9joUbgSUgjzeyn64tt+jRt9rJQJOGahpEgsXgc7PoXitZAxp/MF6b4iCCRkn0RSg5X1FesH9thdYPfYcflcoS2IOiFSG8mIyBGsKV/TZcFkm2UbGREZHRf1B5lIbSRapRaLy9Kr/WyNNqLDojsu5taXda16icqA+tJuM0Xi9fEtlsNBOyanx0dlvZu0YBFCm3XwixBNaNLSMN/2fx2fEEVKb7iR2lNPJf/scyi97XZsr76GY9UqfNWSHVJmrB6FALGuWqaV7+AfK18gs74cISaWtDfeGLCwbXV8HOlvvIE6OZn4BsmaSfzpB4bXFONHYPOwvmVmDjTReg33nj4SgGd+yKXQ1nahX6HTkfDA/QBUv/UWrp07Oz1WZmQmBQoRBAV5njoqbUYKbU7iFD5M2zcAPW+88sdLjU2ugmCRQaMHb9uxmcPNNPoaqff0fh52KJGLEIOAoFCQ9PhjKIxGXFu3Yn355eY/9ib0HheX7P0BAPMf/oBC13MJtozM0UhiZDiPnTemzd9JQkQYUTrJv9P8xz+gjI7Gk5dH9duDHOiqj5FUALUFvdvPaQNdNM5duwlrdOBUaRl9wsB8wRpOOKHDY/Yff+zzoqVu2jQUcXEYvS6it66nqkaF2+/G0frLLSpDCleqLYTwaNBKC64Wp4XosGhUirY2ND9/9QsjinfhFxRMuuMPAEwZk8Fvx18IgHPxS/gsvbvJ7BJ1uBRanTJFUj0UrpLyIkrW404/vmuLnu4OrVQzOWEy48zjWF2zi1xtGP6ClQM3dpl+Y3FZiBeVPQuiNya0dMa1I8eUQ527jgO1B4hQR7Q80c6OyV9biz94A78FabucTooQYWolJ4+WuiBDWTL5QuXbHITOW29FBTUffUTh5VdIBYixY0l79RWUERHd79wO/dy5aHNyCDid1HzwYa/3b7JiiliwAEE5wAsEvST6yitRRkcTqK6h5MYbe69MUWkl1VVwMuzavAVvYRGCTtecn9GeHWV1HR6rCL6XvSXF0rHU4c1dT4IgkGpMlQOqjyISI8OZkR3T64VJ2YppYEnOyeCj436Hv2kxS6Eg8aEHe9xFL3P00KSgiFiwgJjrrgPggl/eweyuZ9muSlZYI6RFoK7sXHwe6XNbF03pp1+g8nnJi0jkrPPn9Xt8gkpF+F9vB+DU/NX8cfNHTK3cg1dQItxy64Ac33TaKQCM3iNZSEdqI4kNjyWvrsWyLK82j4yIDJStFvc3vP4h4T43FRFxjD/9+O7PJQhEny1ZFWZtXUlp7QBkHinVkjJx5FmQOU8qQOi6VmT0mehssrXR+BwW9lT3MUuwl5TYS0gxpPS6ezxFn0J0WDSbqzaHfL7KWYXVZWVEzIiBGGavyYiU1BC9odNQ6obyrhXwKg1EpkgqmZ7isYPWQIHVidbnIT5oB6zJzu5mx8FFm5XVQb0FIBiN0pxi3z7qv/qKqiefpOjqa9g3cxb75h6H97Y/8f6BD3lz6SM8sPYNMhoq8YWFk/nWGwOeT6tOTCTtzTcQ4hNIa6ji7g3SGo4Cke9fXUJ53eGRdXbexGRmDYnB7Qtw7+c7OihzDHPmEHHaaRAIUHHf/VJ2WwjSjGkUBRqpShmPSqXhi021ANyoLkV0u9FkZKAdPrxHY1I1qVFKg1as6o7h1IIgHJEB1XIRYpBQJyWRcN/fALD+5wVMRft57LwxKINfGhftW06E14kmOxvTeecdyqHKyBw2XDQljd/uPIHnLpmAKVxNWV0jdy/ZjiiKKCMiiPvrXwGw/uc/eCurBncw8aOhYkfPt3c3gFIDKi35y34GYHfcEMam9X0hvDW+qhCL9/1YtBSUSqLOPguQAqq/3lZOkj6prSWT1iB1gpdsgNgWy4dKZ2UHKyZRFLG89DIAZZPmEpvT0h2y4K/XkWtKQeN2sf/Rf/RpvF1iSpXC6gQlVO2CoScjanu/qBqKpqyImogEtlVupK7k4HU8yXSNxWkhJiD2rNhkSACHJaR1l1KhZHzcePbV7mvJg/D7QAy0CTF0B1UQqoQE9tRKN5+dKSEAzhwnTYK+3V6Bz99WVaXJSO84cRAElINoy1j7ySfsP/4EKu67n0BDA6qUlD4XIEC68W1SQ1S//RYBd8+D0QJuNw0/SI0Yh9KKqQl/dTX+mlaKlX4qU5pUEBELFoRsMimvc/Hcj23tABQCTJ4xCggqIUC2ZJIJiWuLpLRUJ/XMalCme8LPOY9FC+7h7QvvIOLzb0LmCMgcW5hvuZmwkSOhvp4nD3yBIAZ48OvduOPHd62GsFdKWQWCQNn7knJ557jjmJA+MIvhxWnD2WtKRQmcWrgOgC3mIRTrBub+ITqYYTSjYie/bJO+b0bGjGRv9V58AR/+gJ+C+oIOVkz+Lz4DwDbvVJTKni0xJZ1/DgDjq/ax7NfOu4v7hD4GInsfAg5QmlvAuiXLKM0t6HwjhQJF/BimEU5ebR7l9p7lLvSH4obiHlsxtWeceRy17toWa60g/oCfTZWbmBA3oUNz2cEi1ZhKuaMcr7/ntr0hQ6m9jcECoDQvKK9zsbG4oeNCd0wO2Pb17ESiKC38qvWSFVMwlFppMqGKiurxeAeDDjZyCgWJjzxMzprVRH70Ickv/Afzn/6I8eSTUQeVfb6qKhy//ErEtvVtFoJVHjcKvb7jSQYATUoKdX+5BxGaNYcC8PstH1O4p2BQztlbBEHgkXPGoFEp+HWflS+2dCw0x991J4qICBp37qTm3XdDHketVJNgSGStvQiDIpFluySnhhlFUgEw4rTTelxEjMiWCkLhVeVSUUSj62DHBC1zg95amh1K5CLEIBJxxhnS5Nrvp+y227lgVCwr7zyeD87O4IJCyS8+7q9/RVAdmg98GZnDkcTIcM4cl8QLv5uIQoAlm0t5a7UkQ4s89xzCx40j4HRS9eSTgzuQphvXbsKrmgmqIABqV62Wdh0xHlUPb8S7I+SiZT/tAiLPkooQUyp389PavSTqE9sWIUCyZAp4pf8GqXBWkKBv2yH46w/rGZMnBfaNu/0PbZ4bkxbFpnOkxUrxf9/g3By6E6dfKNWQNg1GnwcDVIBoQqvUMi1pOuaR55G3+xP2lK+XQvpkDhkBMYCt0Uakz9szJYRSBeFRUiEiBAn6BNKN6USHBxcJvA7JDqcVnmAehDI9vbljr6sixKzsGGL0GqodHn470NZDOqT/tChSesstNO4d+PyRxv37Kb/3b23CuX1lZV0GrPWEiNNOQ5WYiN9i7VWegmPlSgJ2O6r4eMInTgy5TXmdi1UHrAelS8pTUNghuJxAgMadu3p9rIDbTf13ksoj8pyOVkyiKHLHp9tpcPsYn2piRpb0nvvLSUOZOmucNJ7iYKFBY5A68IJEh0UTEAPUuTuqKGSODWo/+YS6JUua//9gZ8kcrYiiiDXcxHseM3Ne38mH6+Vi37GOoNGQ9NSTCGFhxOdu5ZLSteRbHby2IwCCovNOanslGOKo37mLyOIDeAUlI6+4cMDGle63k1Nb0uaxiZZc0vxde/73lPAJ42mMiUPnc1O+7EdAyrNJ0CdwoPYAJfYSosOi0bW6R7Ju3k5CeR5eQcnoqzsPpG6PJi0Ne/ZwlIhUffn1gIy/vyx78mVqzzoN491/ovas01j25Mudbxw7FI2zmhkxo9lQuWFQLVHqPfV4/V5iwvvW3KZSqKR8COsOahtrmx/fU7OHqLCoDvO6QaGxPqTVsUapIV4fT7G9Z0rPgBigxl1DdFi7wl5DuRQILwh8uL6IOU+s4JZPc5nzxIq2n+nGBMmOydXRLrUDvkZJ7apQtAmlPtQqiCZa28gNWf4jpoULEQQBRVwchnnziL3pJlL+/QxDli5l6IYNpL/3HlGXX97xQIOsxk40aNqbHqIURZIc1kE7Z2/JjNXzxxOkpsuHv95FrbOt/bfKbG5uiK3697N4Q6nakSyZPH4Pa/aCPyAyO14DG9YCvWu8Mg+X3mPmBgtWuydo09px3hahiSBMGYbN3YO8osMEuQgxiAiCQMJ996GKj8dTUEDlk0+SGBlO2udvg8eDbsoUDMfPO9TDlJE5LJmZHctdp0qy0Ie/3sX6gmoEhYL4v/0NBIH6r7/GuX6Qu9ITeqGGcFZLeRAeD8ZcaZ+YOTMHbCghux36aRegzclBM3IkKjFA9vbVVNZoqHXX4vG3+tKNHdrGT9XpdeL1e9t4hoqiSPHzLwJQNmYa8WNHdjjXJYtO4/u0yQAU3P8Qor9jR/rhTkrsCEaOuhgxfyXLC3+UFwIPIbXuWiIEDSpRlKzTekJEYpdFxamJU1v8jT3O5hC6Jjz5khWBMyEFgFiDFpNOQ2eolApOGyP50n4ZoqOmjf/0c8+hSkjAU1BAwUUXUfvpkp5dUzeIokjdN99QGCo7ZgAmHIJaTfSVVwBSyLbYwxydZiumU05BCCEl/3B9EbMeX86li9cy6/Hlg74gGLLIC5Q/9GCvi6b2n1YQqK9HlZgYMuviow3F/JJrQaNS8NQF4xibYgLAavegSZXeW96i4mDXU8cQujRj2hEnu5YZGLwVFZTfd3/LA6J4ULNkjlbK61ws/rXFaiYgwt1Ldhw2NhEyhw5tVhZxQd/1y7Z+RVp9Bc8t30dlxGgpKyzUd15DBRgT2LZY6pTdkjqGk2cOG7AxmaorOwSpK0URU83AKMQFQUB3smTJlLjhV1we6X59RPQIcmty2Vezr4MKYvcrbwOwI2s8w4f3rjkq4bxzABi2fVUHL/aDTWluAcmvPtP8+ioQSXrtmc4VEUoVmIcRVVvCePN4VpWu6lU3f2/ojwqiCYPGwIS4CawpX4PX76XeU09ebR5jzWMHaJRdYK+SMvw6CYVuCqjuCbXuWgxqQ0flRkM5GJMor3Nx15LtzblbHT7TBQFissHadUA1IOUBaiSFQL7VQaq9KQ9iYG2L+kOTjVx3awJKgx7dxAlSzuQANzZ2R+KooYhC23OKCgWJIw8vW8nr52YzNN6AzeHh3s+3d2iGMl2wkPCJExGdTioefiSk+iBOF8fxqfP5eL1UsLpaLASvF21ODtohQzps3xm6DKkBNMlupcAaDEdvZ8fURFpEGhWuI+deUC5CDDJKk4mkx/4OQO37H1D51NPNMv242/7vsEiEl5E5XLl2TiZnjE3EFxC5+d1NVNY3Ej56FKYLpY6iiocf6dSTb0AwZYC7Hhw9qCwHixB1W7ai8bqp0+iZcNykgR1OiG6H/hJ1zjkAzC/ewDfbKonTxVHuaCUpVodJdkdBQqkgVq3cxrhcqcI/4v/aqiCaGJ4QQekF1+BQhSHk7qH200/7PfZDgTZxPCNihjNSVPJLyS/srd57RMkfjxYsTgsJCnXvcj+MiZ3mQnTA4+gQSu3OL5DObZLe/0PiupctnzVesktZtrOCRm/Hwluz//RJJ5L52RL0c+YgNjZSfs89lN11NwFX3xfB3Pn5FF9zDWV//T8CdSG683o54ehMmWBaeAGKiAg8BQXYly/v9jgBl4uGn34CQncEhZo83vnpdp783x6+3V7O9pI6ap2eLv/uequiCFXkVUZH46+sovB3l2N75ZUeF1jqPv8ckJRm7QsspbUuHv56NwD/t2AoQ+IMZJulYtcBix11qvRZG7Db8dfWdlBCAKRGpMqWTMconoLCjoueByFL5minwOrsEBLvF0UKrKEn+zLHFlGXXop+zhwUXg8PbP8QT6Obh1fYQBsJ1nbKRb8PGusIqIxoVywDQHPm2agHSBUNg6OMbk/aBecAMKliN2u3S2p0g8ZAoj4Rb8DbxpI14HRi+FWyVxTOPLfX50o85wwCgoJhtcX8+P2G/g++H/z49cqQBZ6ynV1Y98SNgJpCUsNiSDQkssWyZVDGVtxQTIoxpd/HSTGmkKBPYEPlBjZXbmZUzKiWAObBwlULB36C+JGd2iDF6+Jx+pw9UpOEtGICqdEoIpF8q6P7z/SYIVCdF7qQ2BpPSxGiwOogNRhKrck6PJQQfWEwGht7cs6kh9ueM+kwzF7SqBQ8dt4YAL7eVtGhGUoIvlao1dh/+omG778PeZwthW7K6hox6dRk7ZAcMiJOP613YwnOCQy+RooLKjq1YwKpiDfCdGgyXfqCXIQ4COhnziTqCkn2VP3KK82Pu3MH3nJBRuZoQhAEnlg4lmHxRiwNbm56ZyMeXwDzrX9CGRmJOzeXmvfeH7wBKBQQPwoqt3e/rdMGuhjygnkQexOHkpMwsLZA0PNuh54ScfppiEolQ2tL2PTLJhJ1SZTaOwbpNlHpqCRB1/bcB559EaUYoHzoOJKmdV54ueGcybw3YgEA5U//C3/dEagkEARIn0VSfRXz46dS4ahgp22AfWxlusXishAbECTP356ij5Ok1z3pUgtpxyR1yuYHfZe7smJqYlJaFImRYTS4fazY23UouyoqitSXXsR8662gUFD32WcUXHgR7ry8LvdrT6CxEcuzz5J/1tk4Vq1G0GiI/eMfiH/g/j5POLpSJigNeqIuuQQA2+JXui3K2X/+GdHpRJ2cTNjYjt13oSaPIvCfFQe4+d1NnPn8SsY/9D1jH1jGqf/+lRve3sCj3+7m4y1VLN9TxbM/5vZJRdG+yJu9bKkUQuf3U/XU0xRffwM+W9cFaZ/Nhv3XXwGIDGbuNF+DKHLnp9uwu31MTDNxzWwpNyc7WMw6UGVHERaGKi4OAG9JSUglRIQmAq1Si8115MiuZQaGkBkQg9y9eCyQEatD0a4nTCkIZMR2zHOROfYQBIHERx9BaTKRaCnmit1L+XpbOeu8GVCxTSo8NOGoAl0MWz75DkOjHVtYBAuuOKvTY/eFg7GAGDZ8OLXmZDQBHwVffNv8+FjzWGYkzmjTRFn82deEeVyU62KYfcHJvT6XKiYG+xhp7lD/1aGxZBJFkcU/7kH3cUefdz8CSaO66NZWaSE2Byp3MipmFFXOqgH/fq5z14EIUWEDk0Ew1jwWl8+FX/STGTnIHf0eB+z/AVKnQtJEqZPbVdthM0EQyIjIoLCusNtD2ly2jrZU7gbJVjMsksxYfYfPdIVA2890rRHCIqG+rbVZyPEH8+KkTIigEiI7q6u9DnsGo7HxcDxnX0gytS3KtW6G+m57OfvCzRiuvAqAykcexd/Q1gqvde7bZcOMuNYGrZhO7V0GniI8HGeEZDlmyz0AqjDwe0IWzlQKFUpB2avjH0rkIsRBIiqEFYIsoZaR6R6dRsVLl0/CGKZiU1EtD329E1VUFOY//xkAy7PP4rMOop9gTI7UPd3YRWdGkzROHY5jjfRF4x4z4YhQOqliYtDPmQPAmF2rqagOw+K04A8R4CuKIhaXhTh9XPNjazfsZfxOadEt58+/7/JcWWYDqvMvpNAYj1BXi+XZ5wbwSg4iYRGQOA5d6SYmxk2gsL5QVkMcRERRxOayEeX3Q2+CGBUK0Jslv+buaGfHJHq9eIolr9rtSmkSOMTcfRFCoRCaA6q/2tp9voygUBB74w2kvf46SnMs7n37yF94AXU9nJjbf/mVvDPPwvrCfxG9XvRz5pD19VeYb76Z6Isv7tPNf3mdizs/7ULWDkRf/jsEjQbX1q24NnYR2EkrK6bTTg35GZkZq6f9wwKwYGQ8E9NMxBq0ADS4fewur2fpzkpeXVnA0yuKufatjfzz+31djrUrWhd5lQYDSU8/RcLDDyFotThWriT/nHNxrF3X+bV98w34/YSNHYs2q+0E9f11xfy6z4o2aMOkDM6Qs2Kl91FZXSMOt69ZDeEpKgJtRyUESCGOshri2KMpzL2Zg9C9eCyQGBnOY+eNQRn84FEKAn8/bzSJkYPcHSxzxKCOiyPh4YcAWLh/BaOtB7h3aSk+fQJUtWpEaagAYzwVH3wMQNm044mOHPhi1mAv5gmCAPOlpiHjby0KR41SgynM1GbbinelZrAdE+aRGtP9fVEo0i46D4Axe1azv2pgsi16ij8g8sDn2xAfvZ9RNQV4lSoCrdzrq3UmlMZubD/jRoJtP6qAn7HmsWyxbBnQeUFRfVG/rZhaoxAUzEmew6zkWYM7V/W5Yd8yqaEvOrPFBqkTS6b0iHQKG7qfU4VUQtSXS1kPSJ/pl05tW5yflhnd8TM9Nges3QRUexyg0dPQ6KW63tUcTK3JOrKLEDDwjY2H6zl7S761o9qgqRnqpnc3ccZzK5lfmUm5IRZfVRUf3Xg3j3y9izdXFfDAlzuY+fhyNhfXAjBkzwZpXjByJJr09A7H7Q5vgmQT7CwolP5+1OHgPfJVmnIR4iDhKw9RbJAl1DIyPSIjVs+/Lx6PIMA7a4r4aEMxpgsWEjZqFAG7nap//mvwTq5UgXk4VHbR7R5UQQQaGzHl7QEg/rjZgzemAabJkumEko18t7WSqLAoqpwdvWWrG6vRq/Roldrmx3Y88yLqgJ+q9GGkzJvV7bn+sGAEi8dL56t+/30a9+4dkGs46JiHAwLG2hIpDKpR7ko+WNS6a9Gpdagb63oWSt2aiERpotId7eyYPMUl4PMhhIezzaUGICe+Z1kUZwWLED/uqcTu7pl9nH7aVLKWLEE3bRqi00nZbbdR/sADBNzukNt7Kyqw33c/pTfeiLe4GFV8PMnPPEPqyy+hSWuZiPXl5n/JxhLaTwfby9pVsbFEnivZMNheebXTY/ntDuw/S2qxzjqCEiPDyTa3WF0pBYHHzx/Dy1dMZsnNs9hw74nsfugUvv/zXF5bNJkHzxrFNbMzOC7bRHp0x0XD/tiqCIJA1AUXkPHRR2iys/FZLBRddRWW5/8TMtem7nPJbrO9CqKkxsmj30gh17edPIysVgWsKL2GGL2ULZJvdTTLr73FxaDuqIQAqQhRYi8hIPbMIkrmyMdbVYX1P/8BJDvXw72T8EjjoilprLzzeN6/bjor7zyei6bI6hKZtkScdBKR55+HIIrcsekDSkosvFscA1W7wdsobWSvoqTSR9r+rQCMu+53gzaewV7MG37p+QAMK9tL/r7QgcGNublE5O3BJyiIu+D8Pp8r/tQFeNRakhw2Vn75c5+P01tcHj83vr0B40v/Zk7ZNgJKFVkvv4jpy2+p/uPdNGh1mJ01rL38OhrsXTQzaHQQlQFVu0k1pqIUlBTWd9/R31O6tGLKXdaz+9p2qJXqNvO5Acfvg/0/gilNsqxqImYI2A6E7OY2aozoVXoqnZ03C9k9dpSCsqOFVEM5RLSoBUUg1lXL8c4CYl217CpvwO1rd99mSpeyKrxd/G6DdkwFVifxzhrUAT+CVhtamShzVBBKSSMAJ42IZ0KwGcqjVPPsOOkzb+zGH1n55Qru/3Inb6wqpHUNzfP9UqD3VkxNqIJzArEkqNjR6OUihEzPORj+jTIyRzMnDI/n1vlDAbj38x1sL28g4W/3AlC3ZEmvw0N7hXk41BS0TDLa47SBLhrrmvWoAj6sYZFMnj1u8MYzwBiOn0dAb8DsqiP/x1+JD08IaclU6awkXt/iA7thWz4TNksdUul/uqVH3TSp0TpGnD6flUljEAKBTkOdDnsEATJmQcU20rVRclBsZ9QUdOpf2VcsLgtxKqMku9Z0n8vQBmOiNFHpjnZ2TJ6CfADUGRkU1EifAz2xYwIYlRRBZqyeRm+AH3b1QIURRGU2k/baq8TefBMIArUffEjBJZfgKSrCW1GBY81aPCUl2F5/g/wzzsT7yy+gVBK9aBFZ33xDxCkn97vDbcXeKp75oWOXWCirkpirFoEgYF+xAve+0J1l9p+WI7rdaDIy0I4I7V2aW9nA/ioHAvDsJeNDLgiGa5TkxBs5YXg8V87M4J7TRvCPM7N577ppIScO/bVVCRs2lMyPPyLyvPMgEMD6/PMUXX0N3sqWYm1jbi6Nu3aBWi3ZOAURRZE7Pt2Gw+NncnoUV83qaH3QNhdCWmjwFBdLRXCFqsN3j06tw6g2YnPLxc9jBcs//0XA4SBszBiir7rqsO8kPBJJjAxnRnaMrICQ6ZT4u+5GnZpKrLOGm7Z9zlM/lVAdlgwV2yHgB6eNNe/8DyUiRclDGTZ59KEecp+JGppNaXwmSjHA3g8/D7lN4ZvvAbAucRQL5ozq87kUOh3OqVLzVuN333az9cBgtbu5ePEa4pa8zekFqxEFgdSnnsAwaxbJQzOYdfPlxL3wEo0qDUNLdrPsdzfh8XbRSBI/Cix7wO9lvHk8263bBySkurqxGpVCRaQ2suOTzmppDlqwEnyefp9rwAgEIP9nSTme3M6mNyyiSxukjMgM8uvyOz20rTGEFRMEQ6kTm38Uv/mCN5c+yu3LnufNZY8ybfdKlu9u12CnVElFEtuBzq8lWITItzlIa5Du4TWZmR0yv2SOHkKpIx8/fwyLr5zMZ62aoZ78+zU4jz8ZBSL37/uSqaltm9OiGusZbZUsdSNOOaVPYzFmZwAQVlUmrZeoO8+FOJKQ/3oOEociAEZG5mjjDycM4cQRcXh8AW58eyPOISOkRSGg4r77sa9aPTgWZ+owiM6Cql2hnw8qIfKXrQDgQMpwkqOOHC9hhVaLKVihn7x3DSXWcMod5R2KA5WOyjZhdJueeZlwvwdbYgbpp57Y4/P94YQc3hx3No1KNa4NG6j/9uBMOAYcrRESxpBqr5a7kkNRXw55K6Bqz4Ae1uK0EI+id3kQTehipAmFL7SioJl2dkxNeRCexFT8ARGjVkWcsWcdZILQYsn0ZQ8smdrsq1Ri/uMfSX35ZZRRUbh37SbvzLPYf/wJFC1axIETT6LqH/9AdDpRjhpF+scfEX/nHSgNvSzOhGDlPivXv70Rb0BkdFJEG4ukB84a1WGhTpORgfGkkwCwvfZ6yGN2Z8UE8MaqAgBOHpXAWeOSe7Ug2H7iAFI33A/tJ559QKHTkfT3R0l64h8IOh3OtWvJP/dc7L+uxFtRgfW/LwJgnHccqqgW3+Z31xbx234bYWoFT7ayYWpN61yIJuWKtyjYeRoiFwIkNUSZs3fvJ5kjE+fmzc2B5wn33iMvfsjIHCKUBj1JT/wDFApOLN7I+LwNPL5ZA9UHoKYAl9JA1C9SUGnEuecc2sEOAK458wFQ/dQxfDXQ2Ij7W8kqsmz2yUQHFX19JfsSaT43bt869pTU9OtY3ZFvdXDeC6tI+eVbfrdXuraEv93bQaGZMWsyqkeewCcoGLlnLV9dfzuBzoKMwyKkRXDbfkxhJpINyeyu3t3vsRY3FHduxVSTD+ZhkgqjaHW/zzVgFK+RGoXSZoZ+vgsbpBRjClXOKjz+0EWVkFZMzmopmyOoYC7ck89lK99rDhlXiCJ/3PoJ//tpW4ixDO3UHgoIKqMNUih1Ux7EUWDFJNM13akjm5qhxv39fpQmE6byQv4h7mzTCDWndBsKRJSjx6BOTu7TOGKHSQHo5gYLVrun0znBkYZ8F3sQOVLCWGRkDlcUCoF/XjSezFg9ZXWN/OH9zUTfeiuCVot73z6Kr76a/SfMp/aTTwb+5PEjwZobOtTWWQ26GDzr1wMQmDB54M8/yEQFJ0uzy7bx06ZywlXhVDdWNz/v9Xup99Q3d59s2VvK+PWSxDDp5ht71XEdHxHGKfMn8HHO8QBUPfEkAccR+oUaMwSt3YJJpafS0fMu937hbggpYz6s8Dih4FdpAlKdBwOkdhFFEVujjeiA2Ls8iCYEAQzxXash/D4Q/aBqmVC786WurOoYqctqSLyhV+/5s8ZJ+/2Sa6HW2ftuNcOc2WR+toSw0aMR3e4Or2fsX/+C8blnCRs+vNfHDsWqA1aufWs9Hl+Ak0bG89kts/jltnmYjU2vSejfZ8w1VwNQ9/XXHQrC/ro67CtXAp1bMdU5vSzZJHXHLZqV0aext544XBU8xn1f7OCbbb23KwhF5FlnkfnpJ2iHD8dfXU3xddex//gTaPhOKrCozC2ZOcXVTv7+rbQIcfvJw8mMDV0calFCtNgxeZql16FzIdKMaeREdBGWKXNUIPr9VD7yKACR555L+LgjR2UpI3M0opswgdgbbwDgD1s+Zfnq/ez0JUPRatasKSCpwUKjSsuUKy84xCPtP9kXnkMAgeTSfTQUtrVkql+6FLXLQWV4FGPPWdDvc5nnzcWpM2LyOFj3yXf9Pl5nbCys4bwXfiNt+2pu3voZALE330z0pZeG3H7MOSdT/6e7ARi5+ju+vuuJzg9uHta8oD0qdhSF9YU0ePqecSGKYtdFiOp8KWsheSK4aqT77UNN2WZpLFnzOrqANNGFDZJaoSbJkNSpwtzmsnUsQjSUg7HFHmnbmu3NBYgmlKJI4fa9WBraNSEZzCAGwBEiX9Lvle63VRqpCNEgFSGOhjwIme7piTpSFRVF3B13AOB+5WWemm1uboQ6rnQLALFnndHnMegzM6SxOGwU2oIqfdmOSaa3HAlhLDIyhzMRYWpeunwSOo2SVQds/PeHPYieVot6gQDl9/6NohtvovLJJ6l57z08q1bjzs3Fb++4kNNka9KtgkJrlDpc2ndueBsh4MPvEYkukW48U084cvIgmggfPx5/Ugrhfg81S7/HHJ5Imb2ly7bKVUVseCwKQfraWPPMYoxeF7WxSWSe1/sv1xvnZfPtyPlU6KLwVVZifenlAbuWg4pKCxGJZAY4OEGxgQDs+Rbyfjp8CxGBAOT/ItmYmYdKIVoNA6NQqnPXEaYMQ9NY3/s8iCYikrr2z/U621gxAXjypCJEid4M9CyUujVD4oyMSIzAFxD5bkffXgt1QgLmP98a8rnwMWMGrDt6bZ6Na97YQKM3wAnD43j+0gmolQpSo/X8/nhp0fuVlfn4Ax0LEeHjxqGbMgW8XqrfervNcw0//AheL9qcHLQ5oRfPP9xQRKM3wPAEI9Myo/t8DU0Th/vOGMll09IQRbj1w82s3BdiktkHtJmZZHz4AZHnnC090KooVPPBB3grKggERG7/ZBtOj5+pGdEsmpnR6fHa2jFJiw2+igoCHk+wCNGxSKtWqtGpjhzFnUzfqF2yhMadO1EYDMT99S+HejgyMjJA7E03ETZmDEavi79s+pA7f/HiQ0n50jUANEybi8rYt5Dmw4kRY7LYGz8EgL3vfdrmuZK3pUDqH7Onc9LoxA779hZBpcI95wQAxO+XDopV6/92VHDp4jWkF+7ijo1Sp7zpoouI/cPvu9xv1o2XUXTJ9QDkfPEmP/zzldAbGuKleaGrFq1Sy4joEWy1bO3zeG2NNsKUYRg0Id5LDqtk1xgeBQolZM6F4vWHtku6ao9UGMmeL1kddYZSBVHpndogZURkUFBf0OFxj9+Dy+ciQhPR9on6cinzLcjKxvA24eIAAUGgRBfDF1s62g13qswIWjEB5NtaihDabLkIIdNC5DlnSxl+jY1M/mwxv94xjw/OyWRkdQEIAsaT+2bFBKAOqqOj3HYKi6pkJYSMjIzMoWJovJEnF0rdgD/9uClkl7VjxQqqX32NqkcexXH33RSccy65k6ewd+o08s45l+Kbb6HwykXNtiY9UlAkjJECqlsv/rokFUTZL6tRiCKl+limTuu7L+qhQhAE4s6XgmVnHlhHUaWaUkfLjVqFo6LZimlHfhVjVkkWSrHXXYugVPb6fLEGLZcdN5SXxkiLeNWvv46ncOBC3HpLj4tRoYjJIcHloMJRgS/Qs+DhPlNXLEm+QVIaHI55GuWbpYlRwhgsTgteU5pkVTAAWFwWzDqzNPnS9cGOCcCY0LUSwuPokDXhCSohdmskm52e5kG0pimg+sstfbfQ0WZnh8yXUqelhd6hl2wsrOaqN9bj8vqZO9TMC5dNRKtq+fu+YHIKkeFqCm1Olu0M/bcSc+01ANR++CH++vrmx+u/a7FiCoU/IPLmKukz4KpZGf3OswDpc+2hs0dz2pgEvH6RG97ewLaS2n4fFyQbu8hzzu34RCCAp7CId9cWsjrPRrhayZMXjEURwoapiaYiRJ7VAaYoFDodiCLekpKjZsJxtNKv745u8NfVYfnnvwCI/f0tqGL7WHiVkZEZUAS1WrJlCgtjgmUf2b8t5fdr4hi2V1K+jbr2skM8woFBEASqpx0HgDcYsArgzstDuWMrfkGBf8Hp6DRdLDj3gmG/uxCAMQVb2HlgYNXFb6wq4KZ3N5JqLeLB9W+iCvgxLlhAwn1/69H9xsn3/5ncE84BIGHxv1j33hcdNxIEiMlqvufNMmXh9Dopt/dNidkjFUQTumgpl6Jg5aGZG9QUStkoOQskG+PuiMkBW2hLJrPOjDfgpc5d1+Zxm8tGdFh0299XIAD2SjAkBH8U+d4CX2fOaLNv/ehJWMNNfLKxpGOBKzpbyrALtAuu9jhAK92fFVjspNhlJYRMRwRBIOGB+xE0GhwrV6Jf+RM5u9YCoJs0CXV8XDdH6BylwUCjXpr32/blHzVzArkIISMjc0Ry+thEbpibRZkhtkO3A4JAzM03EXXF5Rjmz0eZk4MiUgr0CtTX496zB/vy5TjXrm25UQsEKL/v/q4XEnTREG6SPDibCIZSF/7wCwAlGSOJ1KkH8EoPHqazzwJgvGUfa9aUEAgEmmXElY5KEvTSDd4vz7xGtLuBhshYhlzad1u56+dkszNjHBvihiF6vVQ+9nj/L6IPVL/9du+KUe2JSELtayRBqevzRKPHWPdJcu+seeD3QOGqwT1fb6kthup8/Okz2WLZysrSlWz21UmP+/tfoLE4LcQpdVKRQ93H8NDwKEli3dlNXDslhK+mBn9tLQCb/FLoWE5874sQZwYtmdbk26iq7yTkvhsGM19qc1ENV762HqfHz+whsbx8+STC1G0LjDqNisunpwPw0i95ITsV9XPnos3JIeBwUPPBhwD4qqtxrJb8ijuzYvphdyWltS5MOjVnj++bd2oolAqBf100nllDYnB4/Cx6fT0HLB1VcX1Bk5EesihkiTDz92+lLJQ7Tx1OekzXGR3JUeFoVAo8vgBltY3NaghvcXFwwtF3OweZwaP2k0/Yf8L8vn93dIPluefx19Sgyc4m+rKjY1FTRuZoQZuZScKdkg3HVbu+ZcgX76DzuSkPj+IH5dHjeJB89un4BAWmsgLcB6TF9eoPPwJgXfwITjpuzICdK3ryBGqj4gj3e9j8wZcDcsxAQOSZn4t56OvdJDZYeHL9a2g8jeimTSPpqSd71Uh1xnOPsmfcXJRiAM2jf2P3j7+FuIhsqUAgiigEBePjxrPVsrXXuXEBMUBJQ0noIoQoSnPRqMy2j8ePkp6r6n8WRa9oqICiNTBkfvOifbcYJGUxdkvIp9Mj0jsEVNsaQ1gxOSxS0HXQQnV3RT3VDg86pIKCYJbOE12ah04IsKeigZ1l9W2PodGB3gy17Zrh3HbQGKhzeqGmGqPXBQoFmoyMnl2jzDGDNjOTmKBNX8Ujj1Dz7rsARAQzN/uDJ0FqYrPnF8h2TDIyMjKHmttOHsawMdn8e/xC/MGuCL8gUHz1rcT98Y8k3H03yc89S8Til8lZvYphGzeQ9fVXpL78ElFXXtHxgIFA8w12p8SPljo9mggWIdi8AQDlpCkDdXkHHU1KCv4x41EgIn7/HTFh8ZTZy5oLEQaNgd0l1Yz8RZoYRC5ahKDue8ElUqfmurnZvDjmbHwKJfYVK7D//POAXEtPEEUR25tvUvno33tXjGqPIEBMNhle7+BaMrnt4LRKXqoKJWQdL+VDFK0dvHP2BrcdClfhSJnMT+WraPQ3cmrmqVT7HNSoVJKKox+Iooi10UqsKPQtlLo1xoTOLaLaKSGaQqlViYnsqZMKKUPMxl6fMiVKx6T0KEQRvu5HPsFg5EttK6nlitfWYXf7mJEVw+IrJncoQDRx5cwMNEoFW4pr2VjYMTxSEIRmNUT1228RcLtpWPY9+P1oR47odPL2xm8FAFwyNa3Tc/cVrUrJS5dPZkxyJNUOD1e8uo6Kur4VgloTqigU/+CD3PFLBS6vn+lZ0c1Fm65QKgSygnkRByx2NGnBXIiiYskK8Cjoejra8FZUUH7f/S3KyL58d3RB495cat6X7E4S7rm7X9+1MjIyg4PpootQzpyNJuDj9ALJiineVcNP/3qN8rqOfvdHIjMnZLMpbhgAJZ98TsDjoXrJ5wCsHD6b2UMGTqElCALifMm6RLNiWb8smcrrXKzYW8W1b2/kg81VRLvqeH7zG4Q56tGOHEHKf55HoeldmLZSqeDUN/5NbuZYtH4v9r/8kZItO9tuFG4CpUbKPADidHFEaCPYVxO6678zLE4LerUenTqE7aK9SmrECWtnSyQIkDEbKrZJuQwHA2c15P0sNUfpemmj2YUaIiMig+KG4jbFG6vL2pxN2ExDmWSzGuS3/VYEMcB0y14A9H/9C0qzmUB1NdcqpKytT4PZY22IHQLWdgHVwflAvs1BStCKSZ2SgkKr7d11yhwTxF57rfReq63FWyq5SQTcvc8BbI8yRVK7iyXF0t+9z334WjL3ELkIISMjc8SiUiq49/SRLMuYxqIF93D7rBtZtOAebqpOCXnzr9Dr0Q4ZgmHuXGKuuipkYJbtlVclH+7OiEiUFoDrgjcwThtet4KYCql7IuvEuQNybYeK5AvPB2Bu/nryyzWU2cvaqCB+eO5t4l01OPUR5FzV/87Mq2Zl4IxP5rOsOQBU/P3vXb/+A0Rjbi6Fl19OVSj1RdBOpVfEDCHWVYfVacHtd3e/fV+w5kodVorgAq1SJXUdOSxQsnFwztlTAgHIW0F5RBzLq7eTZcpieuJ0wlRhjI8bzza/A38ov9VeUO+pR6vUom1s6FsodWsikqC+E1uk9kWIoBVTICUNjy+AVqUgOapvKowzx0pqiC+39t2SCQY2X2pHaR2Xv7qOhkYfUzOieXXRZMI1nRcBzEYt502UlAov/RI6BDHitNNQJSbit1ip+/LLZiumyNNCdwTtqahndZ4NpULgdz1YtO8LBq2KN66aQlasntJaF1e8trZPIeHtaV8U+jJpEmvzq9FplDxx/rgubZha0zYXQppweEuCSgj3wCg3ZAYOT0Fhx0lgIIB7AGwFRVGk8tFHwe/HeNJJ6GfO7PcxZWRkBh5BEKi+6Ko2EbgK4PdbPqZwT8EhGtXAEqlTUzheyrqr//ZbGpZ9j6KhDkt4JKknH49aObDLSaOvkCyZRpXuZuuOgj4d48P1Rcx6fDmLXl/Pir0WjF4Xr+56h3BbJeq0NNJefhmloW+ZHWHhYcx55yXy4zIwuJ0UXXsdNe1Cu4nJbmNDOs48jtyaXFy+nhemurRiCqWCaEJrgJQpkP/r4C9Uuu2w/0dImw7G+N7vH5Mt2TiFUErr1DoitZGUO6SmnYAYoNZdS3RYu0JHfbmU2Rhk5X4b2XVlGOy1CDodqokTiTxPss48YZ+kXPliSxkeX7vXJjJNslhu3fThkZQQBVYHacGikjazk9dd5pjHV12N39o2e67qiSf63ZxizJLmRdrKcum7Rh0OvfgsORyRixAyMjJHNDXBRSRruInt5iFYw034RZECa9edo6E6WFGpcK5eTekf/tj1QnjCGKjYAT4PeBsp+GULAAURCUyakD0Ql3XIiDjlZPxqDekNlez6aQ8N3gYKGwqJ18ezr6KeoT8uASD80stRhPXA87MbjGFqbpqXzfvDTqQ2PBJvYRGW554fPI9tu4PKfzxB/rnn4dqwEcLCpM6hdojeXi5OhkWi1EaSjorShhChZ/0lEADbfinouTVKteS/Wl8K5X0Pv+svvuI17HOWs1VwMzdlLlmRLX6pCfoEtLE5lFdtlQL7+ojFacEcbpbUIH0NpW6iKyWE19HGjskdDKWuN0sL79lmA8oeLiy35/SxSSgE2FJcS3H1oZfT7i6v5/JX11Ln8jIpPYrXrprSI2/na+dIk7AfdleGtDYS1Gqig2ozy/PPS9Z3gPGU0FZMb64qAODkUfEkm/pos9UDYgxa3rpmKvERWnIr7Vz9xnpcHn/3O3ZDU1GoVGXkH/+Tuu/uOnU4aTE9D47ONrdSQqSmAEElhEoLon9A7MxkBo6QVlxAw7ff9TtQteF//8O5bh2CVkvcHXf061gyMjKDS5LK194UFqUokuSwhtz+SCR2wYk0KtWEVZZR+fTTACxNm8pZEztZJO8HEUOzqUrKQikG2PXekl7vv7u8njs/3U4g+DGs8Xu5b81rhBXnozTHkvbqK/3O14mOMTH6zVcpjYgnyl7D9ksX0WirbrVBlrS4HswY0Kv1ZEVmscO6o0fHD4gByhxlpBhTQjwZkPILojI6P0BMtqSiLNvc84vqLd5G2LcMEsdKIdN9QR0OhriONkhBMiIyKKgrAKCmsQaj2ohK0eoe1e+VFB8GyXPf7fOzLt/G1IpdAOhnzkTQaDAtvAAEAe22jYwSJbumFXur2p5MoZAKO7ZWagiPAzQG8q0tSghN9pE9z5cZPDwFhR3zWPrS2NiOmKHSe85cX4XV7pHmqJ5DP4fsD3IRQkZG5ogmM1ZPqPXA55bv73aRr30Ha9rLLyFotdh//pmSP/yBgLuTjnZTurRQac0FXTRlK34FoHLImAG3ETnYKI1GhNlSCJ3h5++JVJupaawhLjyO7/77Pmn2KhrDdAy7YdGAnfPy6RkYoiN5eaTUJV29ePGAe2yLokj9//5H3umnU/3668EO0xMZ8u03FF99a7OdV9OtQ9lf/4/G3b30VI3NIW2wLJnqS0AbIfmetkelkQoR1XlScPpBpqFyOzvzllKXMJr5afOJ1HYc47j4iRQJARxVPZuAhaI5lNpp63sodRNao1R8aqzv+JzHGVIJUR4pdXn1JZS6CbNRy8xsafLbXzVEf8mtbOCyV9ZS4/QyLtXEG1dNwaDtWbjkkDgjJ46IQxTh1ZX5IbcxLbwAISwMf2XLRM+5ZnWH7WocHj7bLBXuFs0c/A6zlCgdb18zjchwNZuKarnp3Y14/f3vFgwERG7/ZBsur58ZWTFcNq13k/Ls4PvqQJWjWQnhKQ5+lmj0UkeezGFDh0aG4HdI7YcfUvnYY30uRAScTir/8QQAMddeiyZl4PJRZGRkBp7EUUMRhbZLKqJCQeLInEM0ooFn9rg01iaMBMBfXo4f2D52LhPTogblfOqTpYYF48ofCAR69llaUdfIw1/v4uznf0MEYl21jK/K5b41rzPalo+o05O2eDGa1IEpnKRlJhH33xexhUVitpWx5pKraSwokJqobHVSs0wrG9Jh0cOoclZhc9m6PXalo5JITSThqhBNGfYKaT7QXfZC2gxpXmAf2IBvQGqK2P+DFIxtHta/Y8UOlfLuQpBkSMLWaMPlc4W2YrJXSlkOQYX4psJaGr0BZlqkTC7DcdJ8Vp2chH6OpOa5vn4bAJ9sDGXJlNN2LEFldIHNQVqwCKHNkpUQMqHpLCdOk57Wr+PqszIASHLYKLQ5pAyTI3xOIBchZGRkjmgSI8N57LwxKIMLAAKgFGDVARsL/vULi3/Nw+fv/Aa2ta2JfuZMUl/8L0JYGI6ff6Hk950UIgRByoYo2wTh0ai2bgIgfOq0wbjEg07axZIl05zCTRSUqIjTxVFS7SZz6ccAaBZe3GcZcyjCNUp+f/wQtsdktZG0D5THtqeggOJrr6P01j/jq6xEnZpK6ksvEvP0Myy1wA225GY7r+tP+D/2RKXhr6ujaNFVNO7a1fMTRWVg8rhwuKpxDnRolCW3owqiNeowyDkZLHsg6IN6MCiy7GD/tneIGnkek5NnoFaE9i0PV4WTmDaXgvwf+3wuq8tKrEIree2qOvdj9VZU4N28ufv3jTERGkJkM3SSCbFPK0nA+1OEgJaA6q8OYRFif5WdSxevpdrhYUxyJG9dPRVjWO8856+bI6ldPtlYgtXe8XMyYG9AbPf5Gerv+cMNxTR6A4xMjGBKxuAsaLRnaLyR1xZNJkytYMVeC7d/sq3HCx2hKK9zcf+XO1lXUI1eo+SJhWN7bMPURFas9L7Ks7ZkQniLS6TFbI2cC3E40qaR4aflJDzwAAA1b71NxUMPIfbBCsO6eDG+igrUSUnEXHftAI9YRkZmoFEnJJD0cFtlddJDDw6IXeLhwuikSNy6lvwBBXCpsgIhhJJ4IBh7xQUEBIEhlgI2r9ne5bb5Vgd3frqNOU8s59WV+Xj8ARYUrOXNpY/y2KqXmWTJxSco0D/1L8KGDx/YcU4ajv/xf2FXhxNftJe8U05tbqKqX5cLthZLJpVCxdjYsWyxbOm2SF1i7ySQGqTQ6+geLISrwyB9JhSsBP8A2tz6fZC3Qsp/SJrQq13L61ysOmBta5kckQyNdSGbglQKFcmGZIrqi0KHUteXSzbJQX7bbyWqsZ5sm9TAoZ87p/m5qAslm6+hm39GFfCxfE8Vtvb3rrpoaY7RUCkpTnyNoA6nwOogNVjM0WTJSgiZ0IRy2UgcgO8CdZpUxIhtrKOgrBrU+iM+nFouQsjIyBzxXDQljZV3Hs/7101n1V0nsPTPc5maGY3L6+ex7/Zy1fu72VJc26Nj6WfMIPXFFxHCw3H8+islN99CoDGEhUzMEFBqcNv9RFdX4Edg2MlHdh5EE4ZZs3AbTZg8Dkq/3czs5Nl8sfhThtSW4lFrGHbLwC+MXDw1lTE0dJC0EwhQdP0NWF54AdfWrYj+nlunBBobsTz7LHlnnoXjt99Ao6Fm4RV8cNPjXLpdyZgHlvL79zcj0mLnVRKRwD0zr8M3fBT+ujoKr7oa184eqguUahSRqWQFBIob+hfC3IbWgdRdodFJhYjK7ShrCwbu/CHwBXysL1tDzc5PyRl9MWnx47rdJyN5GqLHQYmllwoTpDwItVJNeGOD1PXUCTUff0ze/BOx//kv5J14UtdKmojEjrkQAT8EfM1FDtHjwVMidUttEUxA/4sQp4xKRK0U2FPRQG5lQ7+O1VvK61ws2VTCRS+txmp3MzIxgrevmUpkeO9Db6dmRjMu1YTHF+Ct1R2l9D2RJfv8Ad4O7rtoVsagLWiEYlJ6NP+9bBJKhcBnm0t55Jvdve5er3Z4ePy73cx8bDlvr5Gu46SR8aRG99yGqYmsoB2T1e7BERkDSiWi242vyhJUQshFiMOR1o0MURdfROKjj4AgUPv+B1Tcf3+vChGe4mKqX30NgLg77xgQy0MZGZnBp72y2rRw4aEe0oDir6rkxP0rm38WgHEf/XdQbFMBDInxlGWNAeDAB6EtmXaV1fP79zYx/+kVfLC+GK9fZHq6iffH+rl1yycoWrU1KRFJGD44i8fHnTKDyhv+iggtc5hAgJK//wdfaa4UIhskNSIVpaCksL7z7CB/wE+ZvYxkYwgVXCAAtUXdzweaiEyGiGTUlVt6ejldU1cCuz6X5htpM3q1a1NOx6WL1zLr8eV8uD54L6hQSHNq2/6Q+2VEZFBQX4DNZQsdSm1sCaVeud/KlEpJBRE2ejQqc8t8wTBvHqq4OKit4eLGPHwBMbQiOWaIFJbtdYI6HBEoL7dhdtUBshJCpmsG47tAaTLhCZPmFdbc/OCcQC5C9JnHH38cQRC49dZbD+UwZGRkjgISI8OZkR1DYmQ4Q+KMfHj9dJ5YOBZTuJp9Vhfnv7iav32+g/pGb7fH0k+fRupLwULEb79RcvPNBFztAoAUSshZQO5qqUs6PzqVMcNCeHcegQgqFWGnStZI5tXL2VXWQPI3HwGgOPNcVFED362sVSk547SpBDqWIfDk5mJ99jkKLrqY3BkzKfnjn6j58KPmxeEmSnMLWLdkGaX7Cmj8bRW5p52J9YX/Inq9bE8eydVz/8KlvrG8sq6cbSV1+AIiZkPHjvpGTTgx//kv4ePGEairo+jqa3Dt6GEhImYIyW7nwBYhbPvaBlJ3hdYAOQtQW7ZLfrSDQJ27jh+LfiSichej0uZhSOxZJ5SgUJCWMY/8gh/w+rv/O2xNSx5EdadWTN6KCiruu79l4bs7JY0xUZJyt1549gRlrk0/FheD349Cp2OzQ7IqyulnESJSp+a4oZJ/7cFUQ3y4voiZjy/nLx9txebwkBCh5d1rp2HSafp0PEEQuD6ohnh7dUGHbIWeyJJ/2F1Jaa2LaL2Gs8YlcbA5fngcT10wFoDXfsvniaV7OnTp+fwB8ix2vt9VyUs/H+D2T7Zy/n9XMeGhZUx8+Hte/DmvjYLrq61lbbv8eoheqyIxUlp0zqv1oE6Uuvu8xUWyHdMRhOn880n6x+OgUFD78SeU331Pj4vnlY89jujxoJ85A+NJJw3ySGVkZAaS1gXJow1PQSGKQfA67wrDGacDELPmJ3ytLBM3FlZz9RvrOe3ZX/nflmKG2gq4q2YtX5d+woOv3orpoTsQ2uqqEUQRb9HgjXXsiNQOsxfBL2KzBSTlQivGm8ez3bq90/vgCmcF0WHRaJUhFL/1pVK3vqYXjQ4pk1E6rVLxoq94nJL6oXgdZMyWFBa9aBopr3O1yekIiHD3kh0t90qxQyTVSIhGkJjwGEREVApVW3sqb6M0Lp2kUq5zedlWUsuUCqnRyTBvXpvjCCoVpoWS0v+MIimn7NNNISyZorOk16qxFjQGapxeIq2SaloRHY3SZOrxdcscmwz0d4EgCHjipTmSPT//qLBj6pn57yCwfv16XnrpJcaOHXuohiAjI3MUIwgCF05O5fihsdz/2Ra+3V3N22sKWbqzggfOGsWpoxO67LrVT51K2ssvUXTDjThWrab45ptJfeEFFOGtboB00VhWriERqBk2ts9htYcjGZdeQP5H7zG1fCf/euhV/s+Wh0+pYvifbhq0c5510gRun3Yx16z7AKUo4hcEtp18CalpCYRtXU/4js1QX0/DsmU0LFsGgDs+ifrRE6modzNq/Q8YEWlA6kYSAEt4JC+OOYdViaNRKARGJkQwOSOKSenSv2RTOB9tKOauJS03x7EGLYYYE/pXX6H42utwbdlC0dVXk/bqq4SPGd31RRgTMAoaAk4b9Z56IjQRXW/fHYEAWPdDTi8WpLQRuNPmQvEaUKrANHDBgXl1eey07mSyykSiJhrSZ/Vq/4iE8aSWb2aHbQcT4nou47a4LCTpk6B2JyRNDLlNV533IW8E1eGgCpNC7YKTGKnzqWMehJCWjt3jR6kQSI/RdzxWLzlzXCI/7K5kyaYSZmTFkGnWkxg5OIHMXn+AjzcUc/dnbfM4qhrcNPr6F8p88qh4UqPDKa528cnGYi6fkdH8XJMsufy++6X3cQhZ8uu/FQBwydTUQ5anc+6EFKodXh7+ehf/XZHHf1fkIQAjEiNw+/wUVTvxdmHp1x6/CAVWZ59+n9lmA+V1jRyw2JmWmoK3pARPcQm6zDHS4oPMEUHkWWeBUknZ7XdQ9/nniD4fSY8/hqDqfNpl//VX7MuXg0pF/N13H1RVkIyMjExX1EbHE0Booy7wCwK1UXH0/44oNBMuPZc9zz9FUn0V3z32AkyfzQd766jftIUxtjwes+UxqqYIta/FaigACDodorNdl7BC0WxpMhiUGmKJaP/6IFAaPZr46jyIa7GBMoWZSDYks7t6N2PNHdfBihuKO7diqsmXwpN7g0KFO3k6FK2WApx7U8AQRSn/sGyzlP2QMadnDVHtWPxLHu3vovyi2HKvFBYpNVvUl0nqjXZkR2ZT72ln19RQBsaE5mLImjwbSp+PydZcoCUPojWmhQux/vdFIvdsJS3tVHaUwp6KeoYntJqrqcOkJqXKnaDRS6HUdikPIiwrq9fXLiMzEChSUqFwP4Hi4qPCjumQFCHsdjuXXXYZixcv5pFHHjkUQ5CRkTlGiDFoue/kTC6dOYS/fbGTfKuDm9/dxAnD43jwrFGolAL5VgeZsR0XAXVTppC2+GWKr7se5+o1FN94E6n/fQGFTrqBE0UR3Y4tABhnTD/YlzaohA0fjiUuFXNVMb//9Q0ASqYez5j4+EE7p9Xu5vPESfy6IJtEu5VyQyzWMBNUAYlno0g4k5yaYiZW5TLBksuI6kK0lWWYK8tobdAjIAVML82awfYzr2RqTiI3p0czPs0UMnj3oilpzB1qZlNhDfd/uZOqBje3vLuJ1xZNIfWVVyi+7jpcmzdTdM013RciBAEhNocs226K64sZFTuqfy9Kfamkbgg39Wo3URsJQ06EA8tBMQci+tdl7g142VS5iQZPA/Nix2EsWAXDTpWKHL1BF01KRAa5ll1UR6QTHRbdo90sTgvjYscGlRCh91FEhij4dBcI1pQL0XTMdnkQ7jypCOGIlyZF6TE6NKr+i0hPGhmPWilQWtvIpa+sRSHAY+eN4aIpAzdJrqxv5P11Rby/rojK+hCZDf1YLG9CpVRw7ews7v9yJ6+szOfSaeltirGmhQvRz56Np7AITXpamwLErrJ61uZXo1QI/G5670KcB5rTxiTwyNe7mifJIrCrvGXCG65WkmXWk202NP8322wgXK1k/j9X0DpOQikIZMT23o4JINusZ+V+KwcsduakpuFcvSaohJgh2zEdYUSefjqCSk3pX/9K/ddfI/p8JD/5BIK6o/WZ6PFQ+ejfAYi+7DK0Q4Yc7OHKyMjIdEqh0sAb4xfyx62fNDcJPTtuIVcpjYQwDRoQwiKNWGKTSaksYMg7/0F85z/cKQio2jWbKE0mdFMmo5syBd3kyWiHDaPus8/aNEDo/vqXQVWoZAzP5MEJC/n9Fun1AagJM5I9fAxU/SDlHYS13KOOih3FsoJlZEZmYtQYmx/3BXxUOiqZFDep40n8PskOKbX3+YNieDRoRkDhKsg5sWc7uWqgcLW0yD/0lF7PQ0CaJ//rh328Fmw4iXXVkmS3UmaIpUYX1fZeKTZHKniEKELkROV0tMusL28zt/ltv5VRtjzCvG6U5ljCRo3sUPhQJyWhnzsHx8+/cF39Nv6mO4FPN5Zwz+kj224YM0QK3k4aT0F5Syi1JlvOg5A5NBiyMgj8BtrKMkSNDuEIt2M6JEWIW265hdNPP50TTzyx2yKE2+3G3SrYsL5emhQGAgECfQh8C0UgEEAUxQE73pHGsXT9x9K1huJYvP6ma56eGcW3f5jFCz/n8eLPB1i+p4pf91nw+UVEQCHAo+eO5qLJbbtPwiZMIPnllyi94Uaca9dSdMONpAQLEQ15BUQ22PAKSkadNPuwfF37+jsvr3OxWxWFmWLCAl5E4Kc6NWNrHIPWsZ1XZW/OZ7C2utkdHm/ApNMgCAIKhZlSYRJlgsCPHhcZJXsYuulnRuZtbnMsAZh+9YX86bw5bR7v7HWIN2o5dXQCqVHhXPTyWn7dZ+W+L3bwyNmjSHn5JUpuuAHXps0UXX01Ka8sJnzMmM4vJDqLxLJNrKjNY0T0iD6+GkGq9kBMjjSR6iHNv/OwKMiaBwd+hKzjwdCzApI34MXhdWD32LF7pX8Wp4VEfSLHJcxEmfsdgZSpUlhuH97zipghjK0tYEPFBuanzu+247feU49SUKL1NBJQh4OgDHnexj17OjwW/+ADKOPiOn//G+LBug/Mwd+T2w6q8Obju4Oh1FUmafI6xGwYkL/zGoe7TXd9QIQ7l2wnLVrH1IyoXndBN/3O/X4/qw/YeGdtEct2VeIPro6bwtXUubxtJmRKAdKiw/p9PedPTOKf3+dSaHOydEc5p4xuO9FXxsURHhfXPM4m3lglFXhOGRVPvFHbr3H097ut6bOnPXeeMowzxiaSEBHWadD0o+eO5t7PduAXpdf0kXNH9fl6MmOlAtiBKjuqVMnez11UTECtA3dDm/f94fJ9fjDPf6TNCQwnnUjSv/5F2Z//TMP//keJ10vS008haNpaoFW/+RaeggKUMTFE33zTIf2dHi7vq4PBsXStoTgWr/9YvObW9PX606PD+SFzGpvihzU3CdXoTNw/APcQnVG6r4CkyhZbUQFQiSKiyUTEzBmET56MbvJkNFlZCK2sH0Ug4rzzCJ85E29REcqUFGqVykH9nccbtRz352u46r1h5NiK+MPWT4htrIf/PkvgmlMlq6HEluw0taBmWNQwtlRuYVZyi6K4tKGU6LBolEKI8dYWgy4WFOo29wLeigq8hYWo09NDFlqaf+dxI2H/MqjcLakaOiPgg/JtkhVs0kSpOCAdqFevic8f4P4vd/H+esme9k+NO1mw9A0UiAQQKLn6T23vlSLToHi9ZLGkCp2H1KYQUV8K8aOax7Vyn5WTmqyY5h6HSOj3u+mCC3D8/AsTdv2G2jyXzzaXctuCoaiUrRqMjInSGFQ68q12UpqKEFmZR8Rnh/w5d/Rdf9SQTGxAbL0Vi0sg1usCv79ZCXS4XHNPz3/QixAffPABmzZtYv369T3a/rHHHuPBBx/s8LjFYqExVFhsHwgEAtTV1SGKIor2/sXHAMfS9R9L1xqKY/H621/zZWMjmZUykoeX5bOzoqWKHBDh7s92kKbzk92+izUlBf0T/6DhtttxrV9P/jXXYHjscXI//ZY44IA5g+lhAaqqqg7uxfWAvv7Ot2/NZ3bZ9uafBeCK3d+xfd1ClOMGJ5TLiAeFQJuuYoUAT5yRSZyxM9/6YVTlTyZw1eUdZOLhiTG9/p3EqeHBUzK446sDvL+umFityGWT4tE+8gjeO+7Et307RVdfg/GpJ1GN6LzAoPXrECuLydXkYtKYejWGJgSvE23FARoNI6HVdVS5qnD4HKgUKlSCqsN/FShw1Duaf+cK42g0W77EnTpH6oZCKjQ4fU4cPkfzf5v+BcQAepUenUqHXqVHr9IzRD2ESCJp2L4UURWO16trM6ZeXZffSJi1HJcpnvX568kwZHS5faG9EJVHha14L0q3Ek8n57Uv+x4A5cSJ+DdtApWKxnHjun4P+BWEle+n0TgKBAXqyhIC2kj8wX0cuZKsO1cZAQFI1AsD8ne+pbhjILUowiWL12I2qJmYYmRSipFJqUaSIjTdF2pcXpZsLuW7fTsprGlZpB2fbOD8sWbmDTHx3e5qHv+xkIAo/V3dMT8dpbuBqqr+h2OfOyaGN9ZV8MLyXCaYhW7HW+vy8cVmyV7orBGR/X5N+/vd1tlnz8xkDSpPA1Zr56/R8WlaPrt6DMW1blJNWuKMmj5fT4xa8ojOrajDlS51TTrz8qiqsRNeXYWrsrLNhONw+D5vaDh44epH5JxgzGgMjzyM/W/3Yf/xR/JvvAn9gw8iaKXvtIDNRt0LLwCgvfYabC4XtM+gOogcLu+rg8GxdK2hOBav/1i85tb09fqVwJ3z03n8R6lRSCFIPw/UPUQoctdsJilEe0D5Tf9H9Mmz8AJ1AFZr6AMoFJCRIV1zbe2g/86PT9My6pY5/HNFMU+qtTy6ajF1H35IYGgKhnQFjcrENtsbRANbbFvY4dtBXLjUqLHdup3E8MSQ9xCaks34DUnN96cAjV9/g+vpp6UbyKDiQ3v66W32a/07V+qHo937A+5GFaLW2P4UKOyVaCo2EAiPwRM/EwLaPt3rN/oC3PdtHr/k1SEADwwNMPWJ11vOg0jqG89SccpMFHEtWna1aEDcvwFf9NAujy947Gjr6misawQaqWzwkGexM7VyFwC+8dK9f6j3uzh8BEJsLEqrlZNsu/hWOZavNhxgVmZk29dCPwzRrWZPaQUX2CsBcEZF4T0M5/rtkT/njr7r90ZJ789Eh5XNB8qY5nTTWFYkWQtz+FxzT+cEB7UIUVxczJ/+9Ce+//57wsJCVzjbc9ddd/GXv/yl+ef6+npSU1Mxm81ERPTTaztIIBBAEATMZvNR80btDcfS9R9L1xqKY/H6Q11zXBzcpdbxu1fbFkNFES57ZzcZMTrGpZoYlxLJuJRIRiZGoD3+eFyvvkLJddfj27oN97334q+TfEidGTnED6JNUX/o6+98hHIvznY3/0pRZITKQ1ywq3mgiYuDR88V23UVj2Z0dteB33FxcXx/zZ9Ifu3fzTLxA5fcwOnTJvTpfb4wLo46v5pHv93D8ytLGJUex4KRGZhfe5WSG2/CtXEjjttuJ2XxYsLHdZJrpJrMmKJfqda6GGru+ma6U8q3Qvp4IhJaJi41jTUU2AtIi0jDJ/po9DfiC/jwBrz4/D68fi9ev5caZw1GpRGNUoNKoUIfY8Sc/yHV8SOoE8Cr1GDQRqDX6jEZTSSrkzGoDRg0htBheACWPaBTwtCT+uQJ2wZHOidEpbO8YR9jose0DZtrR74/n5zYHGJqy8E0RHqjtEP0+ajbuBGAxL/8mbL7HyBw4ADa9euJuuSSrsdSl0KEQQl6MzRoITYVIuMQRZG6YPh5bngcOGBcRtyAvP/Ha40ohNw2i94AagVY7F6W7qlm6Z5qAJJMYczIimF6VjTTM2NIjgqnvM5FgdWJ1x9g6c5KPt9Shssr5TvoNUrOmZDMZdPSGJ7QMsm8NjGB0ydlUmhzkh6jG1BF003zI3lvYyU7KhwUN2qYnN51eP2nPx/A7RcZnRTBieMy++1/39/vtr5+9rTev5u0mB4xSRsB7KO0zkPkiJE4ACoqiIuPB0scxig9aKRg9MPl+7yn9/MDwRE7JzjrLBwxMZT+/g9416zB8/BDJD/7LIqwMMr/+S9wuQgbO5aUyy9v09F7KDhc3lcHg2PpWkNxLF7/sXjNrenP9V97Qtyg3UOEwjt9AvUhciiGTp/Qq/uwg/k7j4uDu40mFhyo5aOhJ3Bh7nIc/3qJuKdvIEIvSPeZrZhrnMumqk2MNI/EH/Dja/AxOn00akU72z6/F8pdkD0elFIB21NWRv5TT7W+UJxP/5OEU09to4jocP1h88C6B1JOBSH4evgaJRWCswpGL4CIvpts1To93PLWRjYW1aFRKXhhopbUJ++lfX+0EAgQ4XSgi2tlW6ubAsVrQ97nt8FaC8nDiQhu90txCcl2C0kOG4JaTdIpp6DQ6zv93SsvvBDbCy9wYcVGvo0byw8H7Jw7LaftOYLHrqorJMkuFbriJk5EPUhz4IFE/pw7+q7fN24cdiDOWcMej0BEbCIRJl3zZ8rhcs09nRMc1CLExo0bqaqqYuLEllBJv9/PL7/8wvPPP4/b7UapbLuwodVq0Wo7LogoFIoBfYElm4+BPeaRxLF0/cfStYbiWLz+UNecHWfs0PnaRIHNSYHNyRdbygBQKQRGJEYwLjWS6bf9newn78a1aRNNS8Pj1y3lh6dHsOC26w/C1fSevvzOk0YPY5+gQBBbbhtFhYKkUcMG9b1zydR05g2Lo8DqJCO255Ock2+7gdKzT6Zs5z4SRmYzLVLXr/f5tXOyKKx28s6aIm79cAsf3TCDsSkm0l5+ieIbbsS5YQMl115L0hP/QKE3oMloJ4OOyiChcCU7avMYHze+94usogi2YCB18Br8AT8bqjYwMX4iKcbOF0cDAUmVE2uOJUBAKlAEfPhjR5Ni2YvOL6JqdIDLDRonaBzSwqa6QcpD0OhBrZP+21RscFihYjsMPx1UHT3Ne01sDsa6YnKicthu2870xM4zVWxuG+PixqEo3ynJwkP8Tp3bthGor0cZGYlu3Di0p5yM6z8vUP/Fl8RcdlnXY4lIAnsFGOOloC+tERQKfDYbgfp6EATW+6SF36EJEQPy/k+O0vPYeWO4e8kO/KKIUhD4+3mjOXt8MpsKa1iTZ2N1no0txbWU1Tby6aZSPt0kKQei9GpqHN4Ox8yKCePKWVmcNzEFY1jo31FylJ7kqIGPkYyPDOe8iSl8sL6Yxb/mMzUzptNtff4A76wpAmDRrMwO9319pb/fbX397BlIEk3h6DVKHB4/VcZYAPzV1YhOF0qtEbyuNr7Sh8P3+cE895E8JzDOmUPqSy9SfNPNOFf+RunNtxB5/nnUf/klAAl/uxdlF8HVB5PD4X11sDiWrjUUx+L1H4vX3Jr+XP9g3UOEInVYFsuuuZWk155pbjAqu/pWRg/rfTjwwfydD02IYEZWDG8FTuYkTylRBXuxvPIpSQ8MQ8hu27CWYEjAVG/iQN0BtEot8fp4tKoQjUA1JRCRKIUmA2IgQNXDD3fcLhDAV1yCNqltDlyb648bDvUlULkdkiaAdT+UboTYIZAxq/dZb60oq3Vx5Wvr2FdlJyJMxZsJFYTf/zQBb8d7Vr8g4E1Mbvs7iUgE0S/lUeg7v4/EXgGRqc3zgVUHbEyrkFQQuilTUBlbGnBC/e6jLliI7cUXic/bSXJmFT/uVlDf6MOka6u6F0WRxqJiVGIAwsLQJCUd8kaBniJ/zh1d16+Oi8On1qLyurHsK0QxUw8+V5s58eFwzT0990G9250/fz7bt29v89hVV13F8OHDueOOOwZsIiojIyPTHYmR4SEXAReMTGBbaR1bi2vZWlzLluJabA4P20vr2F5axzvAlNEX8uCa12haVlYgkvTaM5SevYDkoRmH8KoGDnVCAkkPP9gm2C3poQcHNditicTI8D4tACYPzSB5aEbzInx/EASBB84cRXG1i59zLVzz5ga+uGUWSSa9tJB0400416+n5JbfSzsoFCQ+9CCmhQuln5UqtDE5xNbmYnFZiNP1snOmrqRDIPUu2y5MWlOXBYjWKAQFKoUKTbBrivix0r8mfG4p7NbjkBbfPQ6oL2v5f48DFCqpGOF1QfpMaUwDgSkNStYxLG0635f+TIWjggR9x/eW3WNHKSjRKcOgsRbCQ3fY23/+BQD9nDkISiWa+fNxvfgSjdu24c7LQ5vVxaQ1IlEqsCSOk65dLdmxeYJ5EIrERCqCDkdZ5oGbfDcFordf9J45JJaZQ6QFaKfHx8bCGlYfkIoSW4trOxQgBOD5S8YzMU5BfHz8Ibv5vHZOJh+sL+aH3ZUcsNjJNod+ryzbVUl5XSMxeg1njE0Muc2hoq+fPQOFIAhkxxnYVlLHAadAlsmEv7YWb0kxSq0BPHbg8FTdyXSPfvp00ha/TPH1N+BcswbnmjXNz7n37u06a0hGRkbmGGPBbddTevYCynbuI2lUDqOPkDnW5TPSWZ1n48Hxl/Bs9T9p2JRH/YcvE3nnrA6NNOPM41hetBydWsewqE6yGqrzIUYKRRb9fsrvuRfHz78gAq1bnESgauceMqdN7XqA6bNg15fSPT9IDU+66D5daxO5lQ1c8eo6KuobSdErWVz/MzzzGSJgmD8f/YzpVP79MQgEEIHXR5zGhPIAv0tvd6CYHCmPorMihChKodTBgG5RFFm538b/VQbzIObN63as6sREDHPnYl+xgsusm3nCeDJfbS3j8hkZbbaz2j3E2MoB0GRmHTEFCJmjD0EQcMcloiotoCGvAOaNkfJTjlAO6l+S0Whk9OjRbf7p9XpiYmIYPXoghOwyMjIyPeeiKWmsvPN43r9uOivvPJ6LpqQRpddw3FAzf5yfw6uLprDh3hNZecfx/OfSiVw/N4upmdEE1Fra97UrRZGynfsOyXUMFqaFCxmy/EfS3nyTIct/bFlgP0ZQKRU8f+kEhsUbsTS4ufqN9djdPhR6PQkPtfMlDwQov+9+vBUVLY/F5pDmcVNUX9T7k1tzIbbFxsnmslHYUMj4uPF9u5hQqLTSpMOUKoXUJU+EzDkw9GQYfR5MvFz6b8ZsaYJiSu3+mD0+twYiklDWlTAxbiKbqzbjC/g6bFblqiI2PFYqQGiNnXZo2X+RihCG444DQBEdjX7ObADqPvu867Ho4ySlh88jSd6DnWbufCk42ZOUBkCyKRydZmB7NxIjw5mRHdPpwrdOo2JOjpnbTxnOZzfP4pUrJ3fYRgSi9N3nRgw2Q+KMnDgiDlGEV1fmd7rd679Jz102LY0wtdx80p6m4s0Bix11mvTe8xQXS8VAj+NQDk1mANBNnkziP/7R4fEO3x8yMjIyMiQPzWDKuScdUU1eJ42MJ86oZS8GSq7/K6JfQf0PK3H8sKTDtnq1nqzILOrd9SGbcfC5wVEFkamIPh9ld9xJ3eefIyoUfJc+DX/w3q+pINH4xGP89si/8Pu7CIhVh0PWcdKC/7DT+l2AWF9QzcL/rqKivpHJOi+vbn4VvvoMBAHzrX8i5blnif7d7xiy/Ee0I0YgAAafiw+DodVtiBkiFV0C/tAnc9VI4w964edW2nFV1zLKJt1bGuYd16Mxmy66EIDZB9ai9nv5JKg2bk2BzUGKXWpqC8vuvQJHRmYgUaRI83B/STFodEf0nEAu58nIyBzTdLcIKAgCKVE6Th+byN2njeCjG2bwz7+eQaBdGcIvCCSNygl5jCMZdUIC+mlTD4oC4nDEGKbm1UWTiTVo2VPRwO/f24TPH8BXGUJpEQjgKWxVcDDEERMWhdW2F39nN9Oh8DjAXgVRGYBkw7S+cj0T4iZ0ntcwWDQVKvo5QQlJdDbYDhCniyM6LJq91Xs7bGJ1WjGHm6UigS425GG85eW49+4FQUA/e1bz45Fnnw1A3ZdfIvq7eP2VKtDFQG1h86QGwJMnTWiqo6X3fk78AKlA+sGIxAgU7WoNSkEgPUZ3aAbUjuvmSJO0TzaWYLW7Ozy/o7SO9QU1qBQCl01v3/4mA5AdVNscsNjRpEiqJ29RsWSZ5rEfyqHJDBBKY8dA0A7fHzIyMjIyRyRqpYKLp0pNBC/5U4m67DI8DSpq/v0Q3srKDtsPjx7OnJQ5qBQhGl1qCiEiGTEgUnrbbdR//TWoVPx2ya08N+ECFi24h9tn3ciVC+7m8yyp+Sb6nZd59YKb+HRdId7OihHGBDAPhX42sCzdWcHvXllLfaOP8xUVPPrdEwR27UARGUnqyy8Te+ONzQoCdUICsTfdCMCpBWvZU2RlZ1ld2wNqgj73tYWhT1hfJtmoBlm538pEy15UYgBNVhaaYPNGdxjmzkWVkIDaXs+cih1sLa5lf7uQ9Xyrg7QG6fellYsQMocYXaY0b9JUlCGqdeCVixB9ZsWKFTzzzDMDciy/309jY2Of/nm93j7vezT868v1+7ta1JGROYpJH55F6TW3NnefNPmUHkldOjI9JyVKx6tXTiZMrWDFXgsPfb0LdXpax2wChQJNetubX415BAmuBiqcvehwte6TZNfBPIadtp1Ea6NJNvQ9KO6wJCJZUji47YwzjyOvLo8GT9sJgMVlwawzg9MK+tBFCPsvvwIQPm4cqqgWuyb98cejiIzEV1mJo5XtSUiMCVIGh6al0OAJKiGKDZKV1pBO7IUOJk02csrgZ0+TjdyhtBBqzdTMaMalmvD4Ary1uuME8o1VBQCcNiaR+IiDF2h8JNGihHCgTpO6njzFRbIS4ihCk5Heo+8PGRkZGZkjk0umpqJUCKzLr6b2ihsRUkeh8NdRdtv/dWiMUSqUkuo3FDX5iMYUSv/yVxq++x+o1UQ+/iT/8kr3B9U6E9vNQ6jWxxB/993sOvdqAgjM2fULDbf9mVP/8T1Ltllwewd+3ebdtYXc9M5G3F4/tzds4tov/kWgpgbtiBFkfvIxhqAiuTXGE05AlZBApMfBnNJtfBRKDRGbI2VVhKKhHIwtVp6/7bcytaLnVkxNCEpls8L/4ooNAHyysa0aosDaooTQZMpFCJlDS/RQyZIttr6Kaq/6iLZjOjwS0AYAu91OSUkJohgiZbYbRFEkEAjQ0NBwyO0MDgV9vX5BEEhJScFgOPQLMzIyB5sj1adUpm+MSzXxzEXjuendTby1upDMWD3nPtQqMwOIvfGGjoqRmCEkF/zM/tr8nhURRFEqQuScCIDVZaWooYgF6QsG+pIOPQoFRGVCdR5hiWMZGTOSzVWbmZsyFwBHsMNDr9ZLSojY0F65zVZM7STYCo2GiNNOpfb9D6j7/AsMs2aF2l0iIgnKNjd77kKLHdMedRS4YUjc4fFdFypLIhDoQnZ/EBEEgevnZHHLe5t4e3UBNx2XTbhGKqZZ7W6+3CL5Dy+alXEIR3l4kx18n+VV2VGPlRYZvMXBjBhZCXFUoE5IIPGhtplLiQcpc0lGRkZGZvBJjAznxBFxLN1ZybubK7j36X9RecNp+Havwfrii5hvuaX7g3hdBBqqKH3mc+zLVyCo1SQ/+2+edcbj9OQzNiWSF383kUKbqyVbbPZtVM4aheWuu5hWuZuor//JA9areWN9JdfNyeLSaWnotSrK61zkWx1kxup71chSXuci3+Lgh92VvPZbAVqfm38VfUPmtlUARJ59FgkPPIAivBOHAZWKqIsvwvLMvzkz/zfu3zyNu04b0daeMzIVilaD2942iy4QkJTiWfOkl8cfYN1+C9dX7gF6bsXUhGnh+VhfeIHU4r2kDK3is81abjt5GMqg5LjAamd+g1SEkJUQMocafWY6NiDRYaOwHmK8chHikOL3+ykpKUGn02E2m3tdSBBFEZ/Ph0qlOmaLEL29flEUsVgslJSUkJOTI4eKyxyTNAUhyxwbnDI6kTtPGc5j3+3h4a93kXbFHOYu/5HS22/HtW49/trajjtpdERF52C37MabOA21Ut31SepLpY7n8Ch8AR8bKjcwKW5SS7j00UZ0FhSuhMSxZEVmUVBfQFF9EWkRaVicwUDvgB/c9SFDqQMeD47VqwFJWg3SBGlLcQPjtUaizzmH2vc/oOH77/Hb7Sg7K5rrYkGpBrW++bjekhIANgYigMOnCAGHPkC5K04eFU9qdDjF1S4+2VjcHPT3wboiPP4A41IimZBqOqRjPJxJj9GhEKDB7cMZI4VQe4qLpfemrIQ4ajAtXIh+9mw8hUVo0tPkAoSMjIzMUcbl0zNYurOSzzaXcsep8zFdfxt1LzyM9T8voJsyBf3UrgOkA1X7sLyxBPvyPQhaLSnPP49j3GTeeuInAP5y0lCSTDqSTG0tOePPOA1jciLFN9/C0JoS/v3r89w77Woe/dbNf1bsZ0p6ND/uqSQggkKAx84bw0VTOlfiBQIijT4/H6wr5pFvdhEI9vwm2q38e+f7GMsLQaUi/s47ibrs0m7Xk0wXXID1Py8wvKaI+PJ8/rejgnMmtGrUUiiClq37IWl8y+MOC4SbpPt1YEtxLSlV+UR6HCiMRnQTJnR53vaoExIwzJuHfflyzilZx/PGOFbut3LcUDMA1YVl6HxuRIUCdbpsISpzaGmyGktwVJNb42OiyiU1Lx6B69dHRRHC6/UiiiJms5nwTqquXSEXIfp2/WazmYKCArxer1yEkJGROSa4fm4W+VYHH6wv5g/vb+aFyyaiO+tSjOvWU/fV18TddluH7h+VeRgpubmU2kvJiMzo+gSWvVJINJINU0xYDImGxK73OYzxVlTgKShEk5EeepHNYAYxAM5qBF00E+MmsrJ0JYn6RCwuiyRPd1ZDmKmjfQngXL8e0elEZTajHTGCD9cXcdeS7cGJVS6PnTuayZmZePLzaVi6FNP554ceqEIBhgSpAAR4CwshEEDQ69nt0YBweBUhDmdUSgXXzs7i/i938srKfC6dlk5AFHl7jWTPtGhWxjF5r9VTtColadE6CmxOisKjMQHesjJpnqFQSSGVqoOcDSMzKKgTEuTig4yMjMxRyszsGLJi9eRZHXy+uZTfnbcI/6avcXy5k7L/u43MLz5vYyPamkBjI7a/30n9qv0IYXpS//sC+hkzeOKLHbh9ASanRzUvlodCN2ECmR+8T9H1N2AuLOSFNf/l2XnXs4xkvt/dkksREOGOT7fz7poiAog0egO4fX4avQEavX7c3gCeVrkSsa5akuxW4pw13LDjCwzeRpTmWFKeeQbdpEk9el1UMTEYTzmF+q++4oz83/hg/bi2RQiA2CGw/0dIHNeyyNpQ1saKaeW+VlZMc2YjqLtp9ApB1EUXYl++nBOLN/DS0JP5ZGMJxw01I4oiYlEBAEJSCgrNUdoMJnPEoEpIwK9Uofb7qNxfBKO14HVJOSpHGIc8E2IgkSe1Bxf59ZaRkTnWEASBh88ZzewhsTg9fha9vp6L1nup0EUTaGigfunSjjuZ0ogXVJRU53Z9cI9Tkhmb0rG6rJQ0lDDePH5QruNgUPvJJ+w/YT5Fixax/4T51H7ySegNgwHVAFFhUaQaU9lu3Y7FaZFCqZ22TvMgHEErJv1xc6mob+TOT7c3d2gFRLj7s50oTjkdgLrPPu96wGnTJGUGLVZM/pQ0EARiDVpMOnkC0lMumJxCZLiaQpuT73dV8L8dFVTWu4k1aDltzJFbVDtYZDXlQvjDETQa8PnwVlRIRTK3bMkkIyMjIyNzuKNQCFw2Xeqgf2dNIaIgELnoj+iHxeOrqqL8zrtCWokHnE5KbroOz4Gd+AUDqS+9hH7GDEpqnLy/rgiAvy4Y1u1ajCY9nbT33kU5ejRKp4M/L3uOx4wlIbfdVlrHjtJ69lfZKa52YWlw09Doa1OAWFCwljeXPso/fnuRv27+EIO3Ed+IMWR+8mmPCxBNRF92KQDzSrawa3cxBdZ2Ss/wKFCFQUOrTL368jah1G3yII7rnRVTE/rZs1ElJaJ12pldto1lOyuoc3mpanATV1MOgC4nu5ujyMgMPoJSidssNa405BWAWgdHqCXTUVWEONzweDzccccdDBkyhBEjRjBmzBjefPNNQArkHj9+/KEdYA94/vnnWbRo0aEehoyMjMxhg1qp4L4zRzb/LAoKlqZLkuqq9z/suINCSWT8OHzWvTT6Gjs/sG0fRGfhE2B9xXomxU/q3r7pMMVbUdEmL4NAgPL77pcWUtsTnQXVeZKkFBgVO4pyRzkBAhg0Bkl+resklHrFz4BkxZRvddB+KucXRSqnnwCCgHPDBjwloSdfAGiNoJIKDZ48qQhRHytNdobE6Xt45TIAOo2Ky4MT75d+yeP136TX87JpaWhVsnKyO7LN0vvtgM2JOiUFAG9RkRScLudCyMjIyMjIHBEsnJhCmFrBnooGNhbWoEgeTfyNFyFoNNh//pnqN95ss73f7qD4+hvw7VqN36cnbfFi9NOkOcZzP+7H6xeZNSSGGdkxPTq/KioK49NPYzz5ZPD5GP/2M1yy9wdinTWMtewn1lWLIMBj547m9aum8N5101hy80y+vX4KP5yZwIoJbn6J2cv/KpZw65aPUbS60w4AUY8+ijo+rtevS9i4cYSNGoUm4OPkwrV8tKGzgOpgA5ffC64a0Evnamj0Urw3n+z6MhAE9EFL1t7SOqD6vNL1uH0BvtlWTr7VQUowDyJczoOQOUwQkqU5ga+4SGpMOkJtWuUiBJJ/9Jq8asrrXAN63EWLFrF//362bt3K7t27+eqrr3j88cd59dVXB/Q87fH5fIN6fBkZGZljHavd3ebnZWlT8CPg37oFd15eh+2V5uGkul2UNHSyCN4USG0eyg7rDszhZhL0R65Nh3v//pYCRBOBAJ7Coo4bh0VIwXMNUseRWqFmYtxEMiMzpeedNtBFd9jNU1CAp1DyodXPnIlB29FhUikIpA/PQD9jOgB1n3/Ro/F7gkqI8kjJkz8nztij/WRauHJmBhqlgs1FtWwqqkWlkIoQMt2T3aSEsDjQpErh1J7ikmAR4siccMjIyMjIyBxrROrUnDVOamh5e00h6GPRJCaScPvvAaj65z9xbd8OgN9up/i663Bu2IDWrCL24WebFQb5VgefbJLmEH85aVivxiBoNSQ+/RTR11wNwBW7/8dbyyRFw5tLH+V133pOLdnAyC/eJOWJe4i4+kKE0+fhveZ3uO6/B8erixHXrKK97kIBRDXU9Ol1EQSBqEslNcTp+av5dH0RPn+7eUNUppSV5/NIighDXLM167r8aiaWSyqI8PHjO7W16gmm8xeCUsmQiv2k1Vfw6aYSCqwO0oJFCE2WrISQOTzQZWYAoK4oQ5SVEIcXoiji9Ph69O/t1QXM/sdPXP76Bmb/4yfeXl3Qo/1CSedas2/fPj7//HNefvll9Hqpoy0jI4Onn36aBx98EJCKBVdccQWjR49m0qRJbNmypXnfWbNmMW7cOMaMGcO9994LSNkXd955J1OnTmX8+PFceOGF1NRIH/yLFi3i6quvZu7cuYwePZpHH32U3//+983jsdvtREdHY7FYAHjqqaeYOnUqEydO5NRTT6WwUPJqbmho4KKLLmLYsGHMnj2b7cEvRRkZGRmZFjJj9Sha3Y1Xh0eyIWEEALUfh7Ad0scQo4unvGpb6APWl4Fah0X0UWYvY5x53CCM+uAg+v1Uv/NuxycUCjTpnSxCt7JkAkg0JDIqZhT4fdKia4hQavsvvwKgmzQJpcHA5qLaNs8LwN/PG01iZDiR55wDQN0XX3T7/Q0tdkz7tVKnmZwH0XvMRi3jUiObf/YF4Ke9VYdwREcO2cH324EqO+pgEcJ7hHc9ycjIyMjIHItcPj0DgG+3l0tNTNFZRB43DuOCBeD1UvLHP1H3zTcUXn45rs2bUUXrMd90Hbrp85uP8e8fcvEHRE4YHsek9N4vuAsKBfG33UbsrX+Sfg4+rkAk/qsPKb/nXqpfew3Hz7/gLSkBUURpMhE+aRKmCy8k9pabOwbgdnVf3wMiTj8NRWQk8a4aMvZvYcVeS9sNVBqITJHU0g3lbfMg9luZVrEL6LsVUxPq+DgMx88D4NTCtWwsrOGnvVWk2KV7Vm1WZr+OLyMzUETnSKqc2HoLtX71ETsnOCqCqdvj8voZeV8IX+5uCIjwty928rcvdna77a6HTkan6fzl27x5Mzk5OcTEtJXKzZgxg+LiYiwWCzt37uTf//43b731Fh999BEXX3wxu3fv5vnnn+eMM87grrvuAqC6uhqAJ598Er1ez7p16wB4+OGHuffee/nPf/4DwMaNG1m5ciVGo5Hi4mImTZrE008/jVar5eOPP+b444/HbDbz3nvvsXfvXlavXo1SqeStt97ij3/8I9988w0PPfQQWq2WPXv2UF9fz/Tp05k2bVqvX0sZGRmZo5nEyHAeO29McwgyQMTChfD8Q9R9/jlxf75V8nJvRWTSJBR53+HwOtCr29n7WPfijclkQ+WGbm2Yug17PoSIokjl3x/DsWIFKJWSGiK46J/w4AOdjzcqA8o2SUUHZavvVqdNKkCE8L21/xy0YgpOPr7dLikpThkVz/92VqJUwIkjJCWD8cQTUeh0eIuLcW3a1KV3rSiKeIJqli1CJIhyEaIvlNe52FjYtkPu7iU7mDvUTGJkeCd7yUCLEqK01oUwVAprlJQQerCHsDSTkZGRkZGROSwZkxLJuJRItpbU8dGGYm6ekY2w51sSH34Q57p1+MrLKfvr/wEghIeT+sTdaNJTmu99cysb+GJrGQB/OWlov8aiGz8h5ONho0YRPnEi2uwsNFlZaIcMQRXdVoWsTkxssVpVKEh86MF+zUMUYWGYFp5P9auvcWb+b3yw/gROHBnfdqOYHCjdAAE/ZLZYLq3bU8ZZ1v0AzQWE/hB14YXYf/iRk0s38frI0/h1SwG3NtYDoMmS7ZhkDg90WRnYgESHlRKHgihdw6EeUp84KpUQRwoZGRnMny9VuC+88EIqKiooLi5m7ty5LF68mHvuuYdly5ZhMpkA+Pzzz3nnnXcYP34848eP5/333yc/2K0JcMEFF2A0SpYRqampTJgwgS+//BKAN954g6uuuqr5OD/88AOTJk1i/PjxPPnkkxQVSRYZP/74I9dccw2CIBAZGcmlQZmcjIyMjExbLpqSxm93nkBWrFRQqB49GVVcHP6aGhqWL++wvRAzhBSfn+K6/LZPeJzQUMl2n514XTzx+vgO+zZR8/En7D/+hO7Dng8RtpdepuZdSQWR9MQ/yPr2GwSdDgB1XBeeseowMMRDbWHbx53WkKHUAacTZ7AgbzhuLpYGN+sKpIL9PaePYHicDl8APttcCoBCp8N4yikA1H3+eZfX4LdaCdjtIAhs9Em/2xy5CNFr8q2O5gJdE35RpMB6ZEqHDybReg1ROqkQaY2Q3v+e4iIpt+QI7XqSkZGRkZE5VvldMCfr3TVF+NUG0BoJWArw19W12U50u1EJ9ZIVUZBnfshFFOHU0QmMTo6kP2gy0pstjZpRKEj5z/Mk3HM3URdfjH7q1A4FCADTwoUMWf4jaW++yZDlPzZnKfSHqEsuAUFgUlUuezfspKq+XXaeMQF8bvC6mlXRVfWN6HZuJczvRRGfgHZo/wozAPpZs1AnJRHe6GB26TZS7JIqw2uKRhkR0e/jy8gMBE0WrYkOGwV14hFrx3RUKiHC1Up2PXRyt9tV1DVy4j9/bjNJVgjww1+OIyEyrNtzdMWECRPYt28fNputjRpi9erVpKamYjabO+wjCAKCIHD++eczc+ZMvv/+e55//nmeeeYZvv32W0RR5LnnnmPBggUhz2kwtF0kufrqq3n99deZNGkS+/fv55TgAowoitx1111cf/31zT93liMhhOg+lZGRkZGRSIwM55o5mdzz2Q4+3FTGWeeei+2ll6j96GMigp+5zajDiDIPp7hsI8SObnnctp9qnYkKl4WTMk7q9Fzeigoq7ruvWVlAIED5vX/DtXMXxuPnET5hAkrjocsuqPn4YyzPPANA/N13E3n66YDUXVT9xhtUv/Nu15LpmGwpFyOmlfeqwypJsdvhWLMW0etFnZyMJiuLpWuLEEUYl2oi2RTO2aNj2bO8iPfXFXHN7EypsH7O2dQtWUL9d/8j/p57UISF/p53B0OpSUzCrVBj/H/27js8qip94Pj3zqT33kiFAKGjIB2lCYoiigU7ltVVWfG3qKu4KoLLYi/rrm0XsVcEGxakKUWk9x5IICEhvffM+f1xyZiQNiGTZCbzfp5nHp07995z3pMhueeee97j5kSwt+s5tYkjq0lZVvsay6hpxAZ5dFyl7Ei3YC+2Judy0j2AGKDyxEmUsweaDEIIIYQQdmXKgAj+sfwAqXmlrD2UwfigblRtXvvHNf0ZBmMVVRnpOHnp94r2pubz/Z50NA3+2spZEADOYWGEz593zjManMPCrDoL2yUyEq+LLqJo7VomJ25gyfaR3Dcm/o8dNE1foLo0zzwzZENiFkNO66mYfMZeZJX7VZrRiN9115L5yqtclryJH2L0xcD3O/lzYMsJpl8ga5qJjuccEYHJYMCtupLUk7ngY599gk45E0LTNDxcnJp9dQ32YuG0fhjP/N4yarBwWj+6Bns1e2xzv+y6d+/OlClTuPvuuykp0UeokpKSePDBB3niiSfM79esWQPAkiVLCA0NJTIykiNHjhAaGsqtt97Kc889x6ZNmwC48sorefnll83nKykpYd++xlNHXXnllWzZsoWFCxdy88034+TkZN7+5ptvmtM8VVZWsmPHDgAmTJjA4sWLUUpRUFDAJ598ci4/AiGEcBhTBkTg5mzgSEYRqSP0QYTijRupSKm/CLVP+Pm45Z0kv/zMk09KUZWxn23VhQwOG4yzofE0TGX799frrADkffIJJ+/+M4eHDuPYtGlkLFxIxS+/UJWdXWe/yvR0ijf9TmW69dO5FK5eTfrcpwAIvPtuAm69xfyZ/436U07F69ZRkZTU+El8o/RBh8rSP7Y1MhOidiomTdPMqZgm99U7RhN7BuDubCQxs9icEshj8GCcu3TBVFRE4cpVjVajZlHq4lB98CM+xEsG5M9BTcoy45m2M2qaeY0O0byalEyH0AcWTUVFVBeVgqlKT1smhBBCCLvg5mzk2kH6deWHm5LBPxZnLxPmG1FnuPiYcOp6vvn9yz8fBmDqgAh6hFrnQaO2mNHQGv433QTAxSe28NXGI/XXbgvrDzEjzW/XH87ignR9UWqvMWOsVg/fq6aB0Ujv7OOMStXX8DvhHcpjS/eSll/azNFCtD3NxYXyAD2zQPaxVL3PbMFah7amUw5CtMT0C6JZ98hYPrxjMOseGWvVUc7333+frl270q9fP3r16sXll1/Oww8/zF133QVAnz59ePfdd+nXrx8LFy7kk08+QdM0lixZQr9+/TjvvPOYPn06b775JgCPPPIIF1xwAUOHDqV///4MGzbMvJh1Q1xdXbnuuuv43//+Z07FBHDTTTdx2223MXbsWAYMGMB5553H2rVrAXjiiScoLS0lISGByZMnM2rUKKu1hxBCdEY+bs5c1i8CgE9PVuM5YjgAeV9+2cDOkYQZ3UnJOqi/LzhFUmkGQYHdCfFoIl0RULRmbf2Nmob3JZfgHB0NJhPl+w+Q+8GHFM99isTRF5I4+TLSnniSU48/ztFx49skjVPJ9u2k/nU2mEz4Xj2N4L/+n/mztPxStlZ64DRC7zzkfPxx4ycyGMEvGnLOzESoqoDKMnCtOw1aKUXRr78Ceiqm7KJyNh3TB1wm99MXrfN0NXJZf31A4pPNJwF9UT7fqVOBplMyVRzX14PI9NPTYkkqpnM3/YJo1j86lk/uGsb6R617jdXZdQvRU4Edya/E6Uwqs8qTJ88sTl3UkVUTQgghRAvddCYl09rDmZzIr8YpsiddHpv5R3okg4GQO67CqftgALafyGXVwQyMBo0HJrR+FkRtzmFheA4dYhNry3mOHIFTdAyeVWXE71rPpmM5dXfQNPN6cUopkrfuJrQ0D+XigqcV1y51Dg2hYojeXxl6Wh/kSPEKkVSiwrZ00Qczq06mgNEFqsqaOcD2dMp0TC0V7utOsKezeaaAtbi6uvL888/z/PPP1/tszJgx7N27t8Hj5syZY16UujYnJyfmz5/P/Pnz63327rvvNniu//znP+aFq2ubNWsWs2bNAuqmY/L29uazzz5rNCYhhBD1Tb8gii+3p/Dt7lM8eOU0ijf+Rv6XSwmeOROt9t8WgwH/iEEcP7UZFTGE3JRNpLp5MjKoX5PnLz92jLxly8znqD2FuuYJpsrTGZRu20rxli0UbPod0/HjVBw7Zl5k2cxkIu3JuXiOGtXqzkf5kSOcvOdeVHk5XmPGED5vnnnWwGdbTpgX7h6sevE068lfuoyQBx7A4OnZ8AkDu0HKFgjtrS9K7RFYb1Hq8iNHqEpLQ3N1xWPIEL7dcxqTgn5dfIkK8MBkMgFw/QVRLNmWyvI9p5h7RW983JzxvXIqWa+/TvHGjVSezsA5tP7AT/mZmRDHPfSp8LIodeuE+7rL7IdzUDMTIjGzGOfoKKoyMqg4eRL3Hl76uhCukqNYCCGEsBdxQZ6M7h7EuiNZfLQ5mTnDuuFzQRXuq1dRkXwClxAvnIsPgoe+HsNLK/RZEFef34W4oEaumzsBzWAg8KYbOL3wGS4/vpHPt1zD8G6BDe6bmFlM18SdAHgMG4bB3brXl/7XX0fxb7+a3+e5eEgqUWFTPGJjULu24pSehnLpi1ZRAu72lTbY4WdCCCGEEK11Qaw/XYM8KamoZm1QAkY/P6oyMihat67evp5hA/AryiI97zgnTm2mT/fLcDI0PgiulOL0wmegqgqvMWPw+Wo5hQv/hc9Xy+tMoXYODcFn8mRCn3gC38XvEL9xA5Gv/8e8IHMdJpN5NsG5qjx1ihN/ugtTQQHuAwfS5eWXzAMuafmlPHpmAAJgW3B3Uj2DMBUVkf/NN42f1CtUn/1QmncmFVP99ZOKz9TbY9hQDO7u5lRMl/arO6ByXpQf3UO8KKs08fXOUwC4REfjPmgQmEwUfNtwPSrOrAmxz+gHyCCE6Bg1gxDHMotwjtQXotNnQnjJ4tRCCCGEHbrlzGyIL7amUOYRDiXZOAf46LMSnEsgoCsAvyVms/5oFs5GjfvHde/IKrcL36uuQrm5EVeQTvKa9eSXVDa434ajWQw5k4rJd9xYq9cj6uKxVHn98ZDHI9s+4Y2AFHmYRtgM/+7674jAggwKql2g0v76BDIIIYQQQrSSpmlcd4F+o/DTnafxvfJKAPK+aCDtkbs/AV7hHN/1AZ4hfQnybHo2QtEvv1C8bh04O7P1slsZvXgf1/1ewejF+/hsy4lGjzP6+eE9bhyhjz7yx1TvWtKfnMupx/5O5ekMywM9oyo3lxN/uouq06dx6daNqDffqPM00oYjWXVSVCrNwDddz6Rk+uij+vlea2ia3gHLOaavD3HmabDaitaeWQ/iwgvJLa5gY+KZVEx9w886lcb1Q/T0P59u/qOdfK/UUzLlffVVvXqYysupTE0FYLNJ74TEB3fcYt/CcUX6u+NiNFBeZaIsWP8dUXFC0jEJIYQQ9mpcQgjhvm7kFFfww/4M8I+B3DNpSHOOg38sSile+vkQANdfEE1UQOd/Ct/o44P/FVMAmHRkPV/vSm1wv627jpOQkwzo/QBrq8rIwKm40PzegCJq8attsp6eEOfCo2ssABFFWaSVGOzywSQZhBBCCCGsYNr5XXAyaGw/kUf+2EsBfQChoZv8gZHDCK6qIC6+gVkKtaiKCjIWPgOA6/U3Mntjjnl2gUnBI1/u4cWfDrEtOZfyquoGz+EcFkb4/Hl1cs669dPTP+UvXUriJZeQ+Z//YCqxLN+pqaSElHvupeLYMZzCwoj+338x+vmZPz+ZU8IzPx6sd9yamAvA3Z2Ko4mU/P574wUE1hqEOGtR6uqCAkp27AD0zsfP+09TbVL0DvchtoGp6led1wUXo4F9pwrYm6ovBu5zySVorq5UHE2kbO++OvtXJCXrC3x5eZFp9MDN2UAXf3n6SbQ/J6PBPP0/01v/dyBrQgghhBD2y8lo4MYzD8h88FsyBHSD7EQoygAnF3D349cjWWxJysXVycBfxsV3cI3bT80C1SPT9vL96t31Pq+qNlG1aSNGFKprPM4REVavg7kfUJvJREVy4w99CdGeXKL0hx4jirM5WaRBhf2tVyKDEEIIIYQVhHi7Mb6XvsbA55lOuJ9/PlRXk1+zlkMtbsG96d7nOpwaSDdUW84HH1CRnIwxKIhTl19f77oY4LU1R7n6jY30m7uCaa9vYMH3B1h9JJfTBX8sVOV3zTV10jjFffE5sZ9+gvvAgajSUrJe+zeJl07WZwecWVOhIaqyktS/zqZ01y4Mvr5E//dtnMP/mIFwuqCMm/73O1lFFYR4u9ZZzuHBaefjf9WVelwffth40O7++kJbpipwrTsLoXjjRqiuxqVrV1yiovh+r56KaXK/hmeTBHi6MKlvzQLVegfC6O2N94QJQP0FqivOrAdRHh4FmkbXIC+MhrprUgjRXmpSMiW76jOCKiQdkxBCCGHXpg+JMj+0tK/QDaor4NRO8I9DKcWLK/RZELcMiyHUx61jK9uO3Hr2xPm88zEqE/GbV5ofHqqxKyWf/in6mqqBE8a1SR1cYmPqzx43GHCJiW6T8oRoKeczgxCeVWWcOl0s6ZiEEEIIRzb9TEqmpTtS8br6agDyliypf2PfyQXCBzR5rqrMTLJefwOAkNmz2Ztbf6aDBoyKDyLA04WKahPbT+SxaH0Sjy0/xvBn1jDymdXc/8kO7v9kO6POSuPkPnAgMZ98TJeXX8K5SxeqTp8m7dE5JF1zLcWbN9crSylF2hNPUvTLL2hubkS98Qau3f/IU5tTXMHN//udEzklRAd48O39o9jwyFi6+OkdKJMJ/G+8EYCi1WvMaY8aFNitwfUgzKmYLrqI/JJKNhzNAuDSfuH19q1x/ZmfyTc7T1FSUQVgTpdVsHw5qqLCvG/FcX0R79xA/XyyHoToSDWDEAcNemqwqtOnMeEsgxBCCCGEnQrxdjM/IPPhphN6GtKCVAiIY+WBDHan5OPhYuSeMd06uKbtL+TWmwG4NGkTn/92rM5nGw+dZlCGPkDjPWZMm5Tf0Ozx8PnzcA5rOnWuEO3F4OZGub8+Qzr7xGmZCSGEEEI4sgu7BxPmo+d63RI9EIOXF5UpKU2nH2pExsuvYCouxq1/f5wnX8aiDfpT+jWzC4yaxjNX9+PDPw1l2+MTWPvQGF68dgA3Domie5A7Bg1S80r5dtcpvt2VZp5FYVLw2NK9pOWXomkaPpdeStfvlxPy0IMYPD0p27+fE7fOIOX++6lITqYyPZ3iTb+TPv9pfeaA0UiXl17C4/zzzHUtKKtkxjubOZJRRJiPGx/9aSihPm5E+Hlw7xh9Kvn7vyXh3LUbHsOHgclE7qefNh58SG+IG11nkzKZzAt9e110IT8fOE1ltaJnqLf5Zm1DhncNJDrAg8LyKpbv1mdOeI4YjlNICNV5eRT+8ot53/Izi1Kf9NJntHSXQQjRgbqF6CnG9hdrGDw9QSkqM3KhsrR+ugAhhBBC2IWaBaq/2pFKgVcsBCdgcvYyz4K4fWQsQV6uHVjDjuE9YQKmgEACygvJ+O4HSiv+eADrxLpNeFeWUuXlg/uA/m1WB79rriF+9Sqi33uP+NWr8LvmmjYrS4hzoSIiASg5mWWXKVplEEIIIYSwEiejgWsG6RcGn+zJwmfK5QDkffFFi85TumcP+UuXAhD22BwWbUgms7CcqAB31j40hk/uGsb6R8cy/QJ9erCmacQGeXL1oEj+cWVfPri5NzufvJiP/jTUXJ/aqpXi8OlaC6+5uhL4pz/RbcVP+F0/HQwGCn9eSeLkyzg6dhwnbruNvE8+ASB8/jy8x439o64V1dz57hb2pOYT4OnCh38aWmcRvWnnd8HbzYmk7BJ+OZxJwJmcr3lfLMFU9kfKqDo0DZzqdr7K9u2nOjsbg6cnHuefzw97alIxNT4LAsBg0MwzVD7bclI/vdGI75kF8PK/+tq8b006poPO/oDMhBAdq2ZwLTGrxDz9uuLkSXB2t8vp10IIIYSAoXEBdA/xorSymqV78yFmON/vTeNgeiHebk7cPdrxZkEAaM7OBN9wPQATDq/jhzNpV4vLq/DbpT/Q5TpyJJrR2Kb1cA4Lw3PoEJkBIWySe6ze/zelnUZVykwIUUtsbCw9e/Zk4MCB9OzZk2ee0RcXTUpKwmg0MnDgQPPrzTff7ODaCiGEsIbrBus3C9cdyaRikn6ju/DnlVTl5lp0vDKZOP2PBQD4Tr2C4vhevPVLIgAPT0ogJtCT4d0CCfdtesFkL1cnRsYH8eDEHjS0rMG8b/ZzML2gzjanwEDCn3qKrl9/hfuQIVBdXfeJa03Dc+RI89vyqmru/mArW5Jy8XZz4v07htS7ce/h4sT0M22yeGMSXmPH4hwRQXVeHgXLv7eoTUBf5BvAc8QICk0a647oqZgaWw+itmsHRWI0aGxNzuXImcGXmpRMRb/8QlVODkopKo7pU7+3K19ABiFEx4o7s9h6VlE5WkQXACpPppxZnFoGIYQQQgh7pGkaN5+ZDfHh7yeoqjbx0s+HAfjTqK74ejh3ZPU6lP/06zAZjPTJSWLt8g0AbE7KYXDafgBCJ07oyOoJ0eH84rsCEJifRVGVAaoaeajPRskgBFCZnk7J5s1Upqdb/dyfffYZO3fuZPXq1SxcuJDNZ/Jse3t7s3PnTvPrnnvusXrZQggh2l90oAcjugWiFCwr8sKtd29UZSX5X3/d/MFAwXffUbprF5qHB8GzH+Rfq45QXFFN/0hfLm/mqf+GhPu6s3BaP4xn8jgZNPByNXIsq5gr/r2BxRuOo85K7eLavTvB991X/2RKUZGsL/BcVW3igU92su5IFu7ORhbfdgF9u/g2WIdbh8eiafDr4UwSs0vxv/EGAHI++rBe2Y0p+vVXQE/FtOrAaSqqTcSHeNE91LuZIyHEx41xCXqKpU/PzIZwjY/HrW9fqKqi4LvlVGVkYiopAaORRGc/nAwaMYGeFtVNiLbg7eZMqI8+I6goIBSAipMnZHFqIYQQws5ddX4XPFyMHM0o4k/vbeFYZjF+Hs7cMSq2o6vWoZxDQnAdNx6A2PU/cCyziJ2b9hJdmIHJYMBr9KgOrqEQHcsjLhaA8OJsMsqMdrcuRKcchFBKYSopseiV8/HHJI6fwKk7/0Ti+AnkfPyxRcdZetOkRpcuXUhISCA5ObmNohZCCGEratL/fLE1BZ8zuUTzvljS7N8OU3ExGS+8CEDQPfdw0uDBx7/rN/0fvTQBQ0NTGiyqTzTrHx3LJ3cNY8Oj41jz0FjG9gymosrEvG/3c9viLWQU1n2KwiU25o+F2WoYDLjERGMyKR75cg8/7kvHxWjg7VsHMTg2oNHyowM9GJ+g30R9/7ckfK++Gs3VlfL9ByjdsbPZ+ldlZ1O2Zw8AnqMv5Ps9+kMDzaViqq1mgeql21Mor9JzzNbMhsj/6ivzotTVIWFUGp2ICfTAxalTXiYJO1KTkinDW1+ETmZCCCGEEPbPx82Zfmce3ll7WJ/dO7xrIN5ujjsLokbE7bcCMDZlO8t+PUjJmQeRyhL6YfTx6ciqCdHhXGL0dEwRxVmcKjbYXYpWp46uQFtQpaUcOn9Qyw80mTg9/2lOz3+62V17bt+G5uHR7H41Dh48SHZ2NmPGjKG4uJjCwkIGDhxo/vzbb78l6ky+XyGEEPZtUp8wfN2dSc0rZf8lwwl2d6ciMZHSHTvrLOh8tqy33qYqIwPnqCgCZtzKE1/spcqkGJcQwohuQa2qU7ive50UTu/cdgEfbEpmwfID/HI4k0tfWcdz1/RnfC99sMA5LIzw+fNIe3IumExgMBA+fx5OoaE89c0+vtyegtGg8dqN5zG6e3Cz5d8+MpaVB07z5bYUHprUE5/LLyP/y6XkfvRRk20C6AtSK4Vr716U+wXwy+FtgGWpmGpc1ENfNDy9oIwV+04zZUAEPpdN5vSzz1K2fz+FK1YAkB+sp72RVEzCFnQL9mJjYjbJbv5EUGsmRFEGuId2dPWEEEIIcQ7S8kvZnJRTZ9tP+9JJyy9tNuVqZ+d+/vlUxHbDLSmR7CVf0jtVT8UUfPG4Dq6ZEB3POUofhPCtKGZPTtmZB5PsZyH7dn3E74033qB///74+Pjg4+PD8OHD+eGHH9qzCu1u+vTp9OrVi969e3P//fcTHKzfqDk7HZMMQAghROfh5mzkqvP0m9mf7svB55JLgKYXqK44cYKcxYsBCH30EXakl/DD3nQMGjxySYLV66hpGrcOj+Xb+0eREOZNdnEFd763lSe/3ktZpT5TwO+aa4hfvYro994jfvUq/K65hhdWHOK935LRNHjh2v5M6mPZQMCIboF0D/GiuKKaJVtTzAtUF/z0E5UZGU0eW1yTiunCM6mYqkx0DfKkpwWpmGo4GQ1cN1hfpLtmgWonf3+8x1wEQO7n+s8mzVdP29Q9xPJzC9FWugXrKcEOoH8fK0+moJzdZSaEEEIIYceOZxVz9gRpk4KkLPtKrdIWNE2jy223ADDx4C/0z9LXxgudOL4jqyWETTB6eVLh7QdATkqOpGNqSmRkJM888wzbtm1j69atjBs3jqlTp7Jv3z6rlqO5u9Nz+7ZmX11/+L7BVBNdf/i+2WM1d8tGpz/77DMOHDjAihUrePTRR9lzJp2EEEKIzq1mgeoV+9MxTLkSgIIffqC6sLDB/U8/9xyqshLPESPwHDuWhd8fAOCaQZH0DGu7G+I9Qr35auZI7hwVB8D7vyVz+Wvr2X9KX7TaOSwMz6FDcA4L4/W1R/nPGr0j8PTUvlx1XqTF5WiaxowRsWfKSMIloRfu558PVVXkfd744IyqqqJovb4wndeFF/HDmVRMl/YLQ9Nalp7q2sFRaBqsP5rFiWz9gq0mJRNVVQCkKP3vu8yEELag25nv4c4KNzAaUeXlVBVU2N3UayGEEEL8IS7Ik7OzrBo1jdggy7NtdGYBV0yhzNWD0NJcnE3VnHbz46vsTpnIRYgWUxH6w47FadlQKYMQjZoyZQqTJ0+me/fu9OjRgwULFuDl5cWmTZusWo6maRg8PJp9ucbFET5/3h8DEWdSTbjGxTV7bEtvfEyYMIF7772Xxx9/3KqxCiGEsE29I3zoH+lLZbXiu6pAXLp1Q5WVUbB8eb19izdupGjlKjAaCX1sDisPZLA1ORc3ZwN/vbhHm9fVzdnIE5f35r07hhDs7crRjCKu/M8G/rfuGKm5JWxMzOLfq4/w3I+HAH19ipuHxbS4nGnnd8HbzYmk7BJ+OZyJ/003ApD72aeoiooGjynduRNTQQFGX19MCb1Zc0ifNdGS9SBqRAV4MCpeT2v1+VZ9NoTX6NF10itevH4JE5N+l0EIYRNq1oQ4nluOU7j+na9Mz5KZEEIIIYQdC/d1Z+G0fhjP3Fcyahr/nNbX4VMx1ThdqbHfp4v5fUhZHmtefoe0/NIOrJUQtsEtVu+HV5/OQZUXdXBtWqbDhhKrq6v54osvKC4uZvjw4Y3uV15eTnl5ufl9QYH+ZKbJZMJkMpn/XyllfrWE79VX4zFyJKXHjuHetSvOYWEtPkdTatfp8ccfp3v37mRlZZk/sxU1dWlJnWpiq/2zsHU13xV7qa+1OWL8jhhzbY4Yvy3FfO2gSHan5PP51hSuvPpqMp97jtzPv8D3uuvM+6jKStIX/BMAvxtuQIuJ5ZlX1wNwx8g4Qr1dWxRLa+IfHR/I9/ePZM6yvaw8kME/lh/gH8sP1Nln5phu3D067pzO7+akp0RatD6JxRuOc+FN4zEGB1OdmUn+ihX4TJ5c75jCtb8A4DFqFGsOZ1FeZSImwIOEUK8G69Bc/NMHR7LuSBZfbDvJrHHdUFlZqJI/niAxoJi1awlRVXdhMtlHSiZb+s53hM4cf4iXCx4uRkoqqqkOjYCUFMpPpuAaZ0BVlXd4zO1ZviV9gtbqzN8lSzhS/I4Ua0McMX5HjLk2R4zf1mO+dlAko+IDSc4uISbQg3Bfd6vW1dbjb8rx/ccYmHnU/F4D/rLzC5IOXEfokF7NHm/PsbeGo8Zdw1Hi9+0aSx7glZtLYWE+yq/jY7a0/HYfhNizZw/Dhw+nrKwMLy8vli1bRu/evRvdf+HChcybN6/e9szMTMrKygCorKzEZDJRVVVF1Zl0Ci0SGIiLnx8Yjed2fCOOHDkCYD6nt7c36el6GonMzEyrltUaSimqq/X83y2Z4VFVVYXJZCI7OxtnZ+e2qp5VmUwm8vPzUUphODsVlwNwxPgdMebaHDF+W4p5eIQzrk4ahzOK2D2oP+HOzpTv38+p9etx6qHPcChbupSKxEQ0Hx/U9OtYtOYAx7KK8XN3YlovbzKaWS/hbNaI/+mJkXT3d+KNjafqbNeAiV3dW1yn2iZ39+Kd9fDrkSy2HEsn9LLLqH73XTLefZeywYPr7V+wZjUA1QMH8NW2ZAAu7OpNZmZmg+dvLv4BQRp+7k6cLijnq81HGV5wvN4+RqUo2L+PUlf522YPOnv8UX6uHMooIcvLj0Ag79AhVFgXCrNOoQzOHRpzYSPp5dqCJX2C1urs36XmOFL8jhRrQxwxfkeMuTZHjN8eYjYCXb2A8kIyMqz7N9Ue4m+M96lEDNR9QNaoFF6njpGREdjs8fYce2s4atw1HCX+ykB/AIIL80hJTcXondfhMVvaJ2j3QYiePXuyc+dO8vPzWbJkCTNmzOCXX35pdCBizpw5zJ492/y+oKCAqKgogoOD8fHxAaCsrIzCwkKcnJxwcjr3kOzlRnpbaWn8Tk5OGAwGAgMDcXNza6NaWZfJZELTNIKDgzv1L6XGOGL8jhhzbY4Yvy3FHAJc1i+TpTtS+SnTifsnTKDwhx8wrl5NyKhRVOXmcvzd9/R9//pXnCNjWPSZ/uT/rPHd6RrV8pRD1op/VK/6gxAKKDZ4EBLS/MV/Y0JCYHyvDFYeyGD54UKeuP02Ej/8kOq9+/DJysKt1vVAZVoauYnHQNPwnzCJja9vB+DqId0ICfFt8PyWxH/NoDz+t/44Px0pZOrEARQZDFDr6Q2TZiBkQH+cQ0LOOc72ZEvf+Y7Q2ePvGX6KQxkl5AWEEwg4Z+fgGzyEKpwICAnp0Jjb8/rPkj5Ba3X271JzHCl+R4q1IY4YvyPGXJsjxu+IMddmz/H7Dx9Moqah1crUoQwGegwbZNH1uT3H3hqOGncNR4m/tG9fioGw4hyKcSPa24NgO+kTtPsghIuLC/Hx8QAMGjSILVu28Oqrr/LWW281uL+rqyuurq71thsMBnMDGwwGNE0zv1pKKWU+7lyOt3fnGn9Ne9f+WdgDe6yzNTli/I4Yc22OGL8txXz9kGiW7kjlu91p/O2qaRT+8AMF3y0n9G9/I/u11zAVFOCakID/ddfy6ppEsooqiA7w4OZhsedcf2vE3zXEC4MGploPIRk1jbhgr1a3620j4lh5IIOl21N5+JIEfC65hILvviPv40+I+OcC834lZxakdh8wgI3Z1ZRWVhPp786AKL8m/141F//1Q6L53/rjrDmUQe5V/QifP4+0J+eCyUS1prHzmj/TJyKiVTG2N1v6zneEzhx/fIg3kEaSawDdgKqUFDQ3bwxFpR0ec3uWbUmfwBo683fJEo4UvyPF2hBHjN8RY67NEeN3xJhrs9f4XSMiiHh6vvn6HIOBiPnzcG3B9bm9xt5ajhp3DUeI3y02FoCgsgKOFpiIrbKfPkGH/1RMJlOd/K5CCCFEZ3JBrD9dgzwprqhmlWsUzpGRmIqKOPXoHPI++xyAsL8/RmZJJW//egyAv13SExenjv0T3ZYL5o2MDyQ+xIviimqWbE0xL1Bd8N13VOXmmvcr+vVXALwuupDle/R0hpP7hbf6gYH4EC+GxAZgUrBk20n8rrmG+NWr+HD6I9w28e84X3Flq84vhDXVLE69H32NkoqTJ8HFE62ypKnDhBBCCCHsVs31efR77xG/ehV+11zT0VUSwiYY/fyo9PAEIOd0vl31Cdr1DsecOXP49ddfSUpKYs+ePcyZM4e1a9dy0003tWc1hBBCiHajaRrXXRAFwKfbUnBNSACgcMUKUArXvn3xuOAC/rXqCCUV1QyI9OWyfi1Pw9QWpl8QzfpHx/LJXcNY/+hYpl8QbZXzaprGbSNiAXj/tyRc+w/ArU8fVEUFeUuWAGCqqKD4t98AcB4xitUHTgNwad8wq9Rh+pmfyWdbT2IyKZzDwljtFkWWux/xIV5WKUMIa+gWoncytlV6AFCdk4OpyoBWUdyR1RJCCCGEaFPOYWF4Dh2Cc5h1rv+F6CxUeCQAJRl5aFWlHVwby7XrIERGRga33norPXv2ZPz48WzZsoWffvqJiy++uD2rIYQQQrSraed3wWjQOHEoiaJVq+p8Vr5/P0f3JfLJ5pMAzJncy6ZSA4b7ujO8W6BVZkDUdtV5XfB2cyIpu4Rfj2Thf+aBhNxPPkFVVVGyZQuqpASn4GB+NwZRXFFNhK8bA6P8rFL+5H7heLs5cTKnlI2J2RSVV5GWry9uGx/sbZUyhLCG2EBPNA1OVzuh+ekL0VXkm6iIHN7BNRNCCCGEEEK0N9eYGACqMvPQKu3nwaR2HYRYtGgRSUlJlJeXk5GRwcqVK2UAQgghRKcX4u3G+IQQIoqyoNYCawCYTHyybCPVJsWEXiEM63ruiz7bE09XJ6YP1mcjLN6YhM9lkzH6+VF1Ko2itWspPpOKyfPC0Xy/V0/FdKkVUjHVcHcxcuXALgB8suUEiRlFAAR7u+Lr4WyVMoSwBjdnI1H++iyIyhD9ScDKlFTQOjyrqhBCCCGEEKKd+cbHAeCWV0BRqf0scSC9lzYUGxtLQkICVVVV5m2DBw9m7dq15vczZszAx8eH4uK6I1dLly5l0KBBDBw4kISEBMaNG4fJZKqzT2PHCiGEsD3TL4jilFcQJureRFcGA19nGjBo8MglCR1Uu45x6/BYNA1+PZzJsfxK/K69FoCcDz+i6Bd9EMJ11GhWHsgAYHI/607Fvn6IPgiyYl86m4/nABAfLKmYhO3pFqynZCoICAWg8mRKR1ZHCCGEEEII0UHcY/WZEJ455Rx27tXBtbGcDEK0sfLychYtWtTgZwUFBXz77bcMGDCAL774wrw9LS2Nu+++m6VLl7Jz504OHjzICy+8UOfpz8aOFUIIYZsu6hGMMTSUVwdegzKc+fNrMLBs7C1kuftx3eAouoc6Vhqg6EAPxieEAPraEP43XA8GAyWbNlGRlARGI7uCu1NUXkWYjxvnRflbtfw+Eb70j/Slslrxxi+JALIehLBJXc8Mjp32CgLOLE4thBBCCCGEcDguMfpajRFFWaTkyUwIccZTTz3F008/TUlJ/dXKP/nkEyZMmMDs2bPrDFScPn0ao9FIQECAedv5559fZxCisWOFEELYJiejgWsGRbIidij/+fOLRL/3HilvfMJ/vfvh5mzgrxf36OgqdojbRuhTSb/clkJpQDCuCT3/+LC6miNffAPAJX3DMBisv1ZGzQLVOcUVAIT4uFq9DCFaq9uZQYjjzr4AVMoghBBCCCGEEA7JJVofhAgqzSM1q7CDa2M5GYRoYwMGDGDs2LG8/PLL9T5btGgRd9xxB5dffjlHjhzh0KFDAPTv359Ro0YRExPDVVddxfPPP09qaqpFxwohhLBd151ZA+H79Gqy4vvwzy16CqC7Rncl1MetI6vWYUbGBxIf4kVxRTXfrNhJ+YGDdT//5r8EleYxuV94m5R/xYAInI1/DG689PNhPttyok3KEuJc1aRj2qfps6VkEEIIIYQQQgjHZAwKotrVDSOKwuRTHV0dizl1dAXa0rIjy1CoZvdTSmEymTAYDBYteKmhcVX3qyyux9NPP82QIUO45557zNv27NlDWloaEydOxGAwcPPNN/POO+/w7LPPYjAY+PLLLzl48CC//PILP/zwAwsWLGDr1q3Ex8c3eawQQgjbFRPoyYhugWxMzObG/24iNa+MQE8X7r6wa0dXrcNomsaMEbE88dVefl2zjUFnLdxtVIpepgIGxVg3FVONovIqKqv/KFMpeGzpXi7sEUy4r3ublClES3U7kyZst0n/b2VaGqqquiOrJIQQQgghhOgAmqZRHRaBMfkYRUknSMsvpYu/Z0dXq1mdehDC0oECpRRVVVU4OTlZNAjRUrGxsdx444384x//MG9btGgRhYWFdO2q33iqrKzEZDKxYMECnJz0H0tCQgIJCQn8+c9/5pJLLuGbb74xp19q7lghhBC2KTrAg42J2aTmlQEwMj4IbzfnDq5Vx5p2Xhee+/Eg20u9UZoBTZnMn1VrGr0G98bYBqmYAI5nFdfbVq0USVklMgghbEagpwu+7s5kKx9wdoHKCkyZGRDRNjOEhBBCCCGEELYr1z+U0ORjOGekM/q5tSyc1o/pF0R3dLWaJOmY2snjjz/Ohx9+yKlTp6ioqODDDz9k06ZNJCUlkZSURGpqKtHR0SxfvpzU1FQ2bNhgPjY3N5fjx4/TrVu3Zo8VQghhu9LyS/l8a900Kt/tPkVafmkH1cg2eLo6MX1wFFnufnw/6TY4s3B3tabxrwHXMHZU3zYrOy7Ik7PHN4yaRmyQR5uVKURLaZpGt2BPlGagIiQMAFOq/Uy9FkIIIYQQQlhHWn4p64r1tQzDi7MxnZnNb+v3FWQQop0EBQUxa9Ys0tLSyMvLIyYmhoSEhDr73HTTTSxatIiqqirmz59Pjx49GDhwIKNHj2bGjBlMnTqVr776qsljhRBC2K7jWcWYzsoSaFKQlFXSMRWyIbcOj0XT4N9uvXH6/BvyFrzKbRP/zrY+oxkSF9Bm5Yb7urNwWj+MZ2ZCGjWNf07rK7MghM2pWZw63y8EANOp1KZ2F0IIIYQQQnRCx7OKSfUMAiCiOAv4Yza/LZPcPW0oKSmpzvsnnniCJ554AoDrrruu3v6zZs1i1qxZAPz0008NnvO6665r9lghhBC2qeap+9oDEfLUvS460IPxCSGsPJDBB4mlVGphZLlXcmPfsDZLxVRj+gXRXNgjmKSsEmKDPGQAQtikmnUh0jwDCQaq09I6tkJCCCGEEEKIdhcX5Em6VyAAsfmnCCrNI9fD3+bvK8hMCCGEEKKdyFP3TbttRBwAX25L4ce96QBc1q99ct6H+7ozvFug/CyEzaqZCXHM2Q+QdExCCCGEEEI4onBfd+7vUgVAcFkB7/20gDcCUmy+LyszIYQQQoh2JE/dN25kfCDxIV4czSiiuKIaXzcnhrZhKiYh7Em3YE8A9uHNlYBJZkIIIYQQQgjhcCrT04n4YrH5vQFF1OJXqbzlCpzDwjqwZk2TmRBCCCFEO5On7humaRp9I3zM7/PLqvhye0oH1kgI2xEV4IGzUSPZzR+A6pMnqZCBCCGEEEIIIRxKRVIymEx1N5pMVCSf6JgKWUgGIYQQQghhE9LyS/lmV90UM48t3UtafmkH1UgI2+FsNBAT6Em/rER9Q1kZxy+eSN6SJR1bMSGEEEIIIUS7cYmNAcNZt/QNBlxiojumQhaSQQghhBBC2ITjWcV1Fu0GqFaKpKySjqmQEDZmgGs5M3ct+2ODyUTak3OpTE/vuEoJIYQQQggh2o1zWBjh8+f9MRBhMBA+f55Np2ICWRNCCCGEEDYiLsgTg0adgQijphEb5NFxlRLChvQ25WPgrJG6M1Ovbb3TIYQQQgghhLAOv2uuwX3ECDJ27SZkQH9cIyI6ukrNkpkQQgghhLAJ4b7uLJzWD6OmAfoAxD+n9ZW1M4Q4I6hnPCa0uhvtYOq1EEIIIYQQwrqcw8JwPm+g3TyMJIMQbaiqqop58+aRkJBA3759GThwIHfffTd5eXkArFmzBk3T+OCDD+oc99RTT6FpGsuW/THdXilFXFwcfn5+5m1Lly5l0KBBDBw4kISEBMaNG4fp7IVJhBBCCDsy/YJo1j86lk/uGsb6R8cy/QK5uSpEjZhecbw68BqqzwzU2cvUayGEEEIIIYRjk3RMbejOO+8kJyeH3377DX9/f5RSLFmyhJycHPz8/Fi0aBHjx49n0aJF3HLLLXWOHTRoEO+88w5XXXUVAKtWrSIoKIjc3FwA0tLSuPvuu9m2bRsxMTEAbN++HU076+k4IYQQws6E+7rL7AchGtA12JMVsUPZHtqTT8b7ET34PLuYei2EEEIIIYRwbDIToo0cPXqUL774gsWLF+Pv7w+Apmlce+21dO3alby8PJYvX86HH37I/v37OXr0aJ3jR40aRWJiIulnFhp85513uOOOO8yfnz59GqPRSEBAgHnb+eefL4MQQgghhBCdlI+bM8HermS5+3EyOkFmQAghhBBCCCHsggxCtJHt27fTvXt3goKCGvz8448/ZtKkSYSFhXHzzTfzzjvv1Nvn5ptv5r333iMvL48tW7YwadIk82f9+/dn1KhRxMTEcNVVV/H888+TmpraZvEIIYQQQoiO1y3YE4Dk3LIOrokQQgghhBBCWKZV6Zh69erFLbfcwk033WROCWRTtn8AqOb3UwrNZAKDASyaSaDB+bc0v1sTFi1axMKFCwG44447mDRpEk8//TRGo9G8z4wZM7j44ovx8vLiuuuuw2D4Y8zIYDDw5ZdfcvDgQX755Rd++OEHFixYwNatW4mPj29V3YQQQgghhG3qFuzFpmM5JOXIIIQQQgghhBDCPrRqECIqKoq5c+fy5JNPMmLECG699VauvfZafH19rVW/1rF0oEApVFUVODlZOAhhQdHnn8+RI0fIzs4mMDCwzmc7d+5k9+7d3HXXXeb0SVlZWfzwww9cfvnl5v26dOlCTEwM8+bNY+PGjQ2Wk5CQQEJCAn/+85+55JJL+Oabb5g9e7ZVYhBCCCGEELalW7AXIDMhhBBCCCGEEPajVemYVqxYQUpKCs8//zylpaXcfffdhIWFcc011/D1119TWVlprXranfj4eK6++mruvPNO8vLyAFBK8eWXX/LEE0/w4IMPkpycTFJSEklJSbzyyissWrSo3nmefvpp/vGPf9Sb3ZCamsqGDRvM73Nzczl+/DjdunVr07iEEEIIIUTH6RaiD0IcPF1MWn5pB9dGCCGEEEIIIZrX6jUhQkND+etf/8qWLVs4cOAADz30EDt37mTatGmEhYVx3333NfoUf2f3zjvvMGDAAIYOHUqfPn3o3bs3K1asYPny5dx000119r3uuutYsWIFp0+frrN98ODB3H333fXOXVVVxfz58+nRowcDBw5k9OjRzJgxg6lTp7ZpTEIIIYQQouMcOJUPQHphJaOfW8tnW050cI2EEEIIIYQQommtSsd0tp49e/L0008zc+ZMHnjgAb744gvefPNN3nrrLbp27UqvXr04deoUhw4dwt3dnREjRvDss8/Ss2dPa1bDZjg7OzNv3jzmzZtXZ/tbb71Vb9/AwECKi4sBeOqppxo8X2xsrHlWRUxMDD/99JNV6yuEEEIIIWxXWn4pz/10yPzepOCxpXu5sEcw4b7uHVgzIYQQQgghhGhcq2dC1CguLubDDz/kkksuITo6mmXLlnH55Zfz+eefs2zZMnr27Mm3336Ll5cXmzZt4ueff6ayspKJEyeab74LIYQQQgghGnY8qxiTqrutWimSsko6pkJCCCGEEEIIYYFWzYSorq7mp59+4sMPP+Sbb76hpKSEQYMG8eKLL3LDDTcQFBRk3veKK67gscce4z//+Q99+vQB4N133yUkJIRt27Zx4YUXti4SIYQQQgghOrG4IE8MGnUGIoyaRmyQR8dVSgghhBBCCCGa0apBiLCwMHJycujSpQv3338/t956K7169Wp0//79+1NYWGh+n5+v57QNCAho9Jjy8nLKy8vN7wsKCgAwmUyYTCbz/yulzK9zUXPcuR5v784l/pr2rv2zsHU13xV7qa+1OWL8jhhzbY4YvyPGXJvE73jxO2LMtTlS/KHeriy4qi9/X7YXkwKDBv+4qg+h3q4dEn97lmlJn6C1HOm71BBHit+RYm2II8bviDHX5ojxO2LMtTly/I4au6PGXcMR47eVmC0tv1WDEJdddhm33HIL48aNQ9O0Zve//vrruf76680V/L//+z9GjhxJ3759Gz1m4cKF9dZUAMjMzKSsrAyAyspKTCYTlZWVODm1PCSlFNXV1QAWxdHZnGv8Ne2enZ2Ns7NzW1XPqkwmE/n5+SilMBislo3Mbjhi/I4Yc22OGL8jxlybxO948TtizLU5Wvxjo11ZelsfDp3KoWdEAGG+rmRkZHRIXWo/XNTWLOkTtJajfZfO5kjxO1KsDXHE+B0x5tocMX5HjLk2R47fUWN31LhrOGL8thKzpX2CVg1CvPvuu+d87MyZM9m7dy/r169vcr85c+Ywe/Zs8/uCggKioqIIDg7Gx8cH0NNCFRcXk5eXh5OT0zkNJFRWVrb4mM6kpfErpcztHRYWhtFobKOaWZfJZELTNIKDgx3ml1Jtjhi/I8ZcmyPG74gx1ybxO178jhhzbY4Yf1CQiVAf1w6P2c3Nrd3KsqRP0FqO+F2qzZHid6RYG+KI8TtizLU5YvyOGHNtjhy/o8buqHHXcMT4bSVmS/sErRqEqK2wsJD8/PwGp2BER0fXef+Xv/yF7777jl9//ZXIyMgmz+vq6oqrq2u97QaDwdzABoOByMhIUlJSSE5ObnHda6auGAwGh50JcS7xa5pGZGSk3cyCqKFpWp3vj6NxxPgdMebaHDF+R4y5Nonf8eJ3xJhrc8T4bSHm9izbkj6BNdhCu3YkR4rfkWJtiCPG74gx1+aI8TtizLU5cvyOGrujxl3DEeO3hZgtLbvVgxBvvPEGL730EseOHWt0n5pUP0op7r//fpYtW8batWuJi4trbfFmXl5edO/e/ZxmNNSkFAoMDHSoL2qNc43f2dnZbmZACCGEEEIIIYQQQgghhGh/rRqEePPNN5k5cyaTJk3ijjvu4O9//zt//etfcXNz49133yU0NJRZs2aZ9585cyYff/wxX3/9Nd7e3qSnpwPg6+uLu7t76yIBjEbjOd0UN5lMODs74+bm5rCDEI4cvxBCCCGEEEIIIYQQQoi20ao7zq+99hqTJk3ihx9+4O677wb0xaoXLFjA/v37KSwsJDs727z/G2+8QX5+PmPGjCE8PNz8+uyzz1oXhRBCCCGEEEIIIYQQQgghbE6rBiESExOZMmUKgHldgIqKCkCf3fCnP/2J119/3by/UqrB12233daaagghhBBCCCGEEEIIIYQQwga1Kh2Tr68vVVVVAPj4+ODh4cHJkyfNn9dOuWQtSikACgoKrHZOk8lEYWGhw6YjcqT4HSnWhjhi/I4Yc22OGL8jxlybxO948TtizLU5Yvy2EnPN9XjN9Xl7kj6B9TlS/I4Ua0McMX5HjLk2R4zfEWOuzZHjd9TYHTXuGo4Yv63EbGmfoFWDEH379mXXrl3m98OGDeONN95g8uTJmEwm3nrrLXr06NGaIuopLCwEICoqyqrnFUIIIYQQQrRcYWEhvr6+7V4mSJ9ACCGEEEIIW9Bcn0BTrXh0afHixbz55pv8+uuvuLq6smHDBiZMmGBOyeTs7MyXX37JZZdddq5F1GMymTh16hTe3t5ommaVcxYUFBAVFcXJkyfx8fGxyjntiSPF70ixNsQR43fEmGtzxPgdMebaJH7Hi98RY67NEeO3lZiVUhQWFhIREdHuT19Jn8D6HCl+R4q1IY4YvyPGXJsjxu+IMdfmyPE7auyOGncNR4zfVmK2tE/QqpkQt99+O7fffrv5/ciRI9m3bx/ffvstRqORiRMnWn0mhMFgIDIy0qrnrOHj4+MwX9SGOFL8jhRrQxwxfkeMuTZHjN8RY65N4ne8+B0x5tocMX5biLm9Z0DUkD5B23Gk+B0p1oY4YvyOGHNtjhi/I8ZcmyPH76ixO2rcNRwxfluI2ZI+QasGIRrStWtXHnjgAWufVgghhBBCCCGEEEIIIYQQdsYxVuoQQgghhBBCCCGEEEIIIUS7a9FMCIPBcE45V6urq1t8THtydXVl7ty5uLq6dnRVOoQjxe9IsTbEEeN3xJhrc8T4HTHm2iR+x4vfEWOuzRHjd8SY24Ojt6sjxe9IsTbEEeN3xJhrc8T4HTHm2hw5fkeN3VHjruGI8dtbzC1amPqpp56qNwixbNky9u3bx6RJk+jZsycABw8eZMWKFfTt25crr7ySuXPnWrfWQgghhBBCCCGEEEIIIYSweS2aCfHUU0/Vef/222+TkZHB3r17zQMQNQ4cOMC4ceOIiIhodSWFEEIIIYQQQgghhBBCCGF/WjQT4mzdu3fn9ttv57HHHmvw8wULFvDuu+9y5MiRc66gEEIIIYQQQgghhBBCCCHsU6sWpk5JScHZ2bnRz52dnUlJSWlNEUIIIYQQQgghhBBCCCGEsFOtGoTo27cvr7/+OqmpqfU+S0lJ4fXXX6dfv37ndO6FCxdywQUX4O3tTUhICFdeeSWHDh2qs09ZWRkzZ84kMDAQLy8vrr76ak6fPm3+fNeuXdxwww1ERUXh7u5Or169ePXVVxstc8OGDTg5OTFw4MBm66eU4sknnyQ8PBx3d3cmTJhQb8bHggULGDFiBB4eHvj5+TlU/ElJSdx5553ExcXh7u5Ot27dmDt3LhUVFZ0uVoArrriC6Oho3NzcCA8P55ZbbuHUqVPNnruzxF+jvLycgQMHomkaO3fu7PRxx8bGomlandczzzzT7Lk7Q+wAy5cvZ+jQobi7u+Pv78+VV17ZqeNeu3ZtvZ93zWvLli2dOnaAw4cPM3XqVIKCgvDx8WHUqFGsWbOm2XN3lvi3b9/OxRdfjJ+fH4GBgdx9990UFRXZbcxLly5l4sSJBAYGNvo7u7n6NaUzxP/2228zZswYfHx80DSNvLy8Th1zTk4O999/Pz179sTd3Z3o6GhmzZpFfn5+s+duS7bertInkD5BbdIn0FnaJ+gMMZ9rf6CzxA/SJ5A+gfQJpE/QuM4Qv/QJrNgnUK2wbt065eHhoTw8PNRNN92k5s6dq+bOnatuvPFG5eHhodzd3dW6devO6dyTJk1SixcvVnv37lU7d+5UkydPVtHR0aqoqMi8zz333KOioqLUqlWr1NatW9WwYcPUiBEjzJ8vWrRIzZo1S61du1YlJiaqDz74QLm7u6vXXnutXnm5ubmqa9euauLEiWrAgAHN1u+ZZ55Rvr6+6quvvlK7du1SV1xxhYqLi1OlpaXmfZ588kn10ksvqdmzZytfX1+Hiv+HH35Qt912m/rpp59UYmKi+vrrr1VISIh68MEHO12sSin10ksvqd9++00lJSWpDRs2qOHDh6vhw4c3e+7OEn+NWbNmqUsvvVQBaseOHZ0+7piYGDV//nyVlpZmftWuf2eOfcmSJcrf31+98cYb6tChQ2rfvn3qs88+69Rxl5eX1/lZp6WlqT/96U8qLi5OmUymTh27Ukp1795dTZ48We3atUsdPnxY3XfffcrDw0OlpaU1e357jz81NVX5+/ure+65Rx08eFBt3rxZjRgxQl199dV2G/P777+v5s2bp/773/82+ju7ufo1pTPE//LLL6uFCxeqhQsXKkDl5uZ26pj37Nmjpk2bpr755ht19OhRtWrVKtW9e/cmv+ftwdbbVfoE0ieQPsG59wk6Q8zn2h/oLPFLn0D6BNInkD5BUzpD/NInsF6foFWDEDWVu/LKK5WHh4fSNE1pmqY8PDzUVVddpXbv3t3qCtbIyMhQgPrll1+UUkrl5eUpZ2dn9cUXX5j3OXDggALUb7/91uh57rvvPjV27Nh626dPn64ef/xxNXfu3GZ/aCaTSYWFhannn3/evC0vL0+5urqqTz75pN7+ixcvbnGH42z2HH+N5557TsXFxTV5bqU6R6xff/210jRNVVRUNHn+hthr/N9//71KSEhQ+/bts2gQ4mz2GHdMTIx6+eWXLYywcfYWe2VlperSpYv63//+15Iw67G3uM9WUVGhgoOD1fz585s8d0PsLfbMzEwFqF9//dW8T0FBgQLUzz//bFHMtdlb/G+99ZYKCQlR1dXV5n12796tAHXkyBG7i7m248ePN/g7+1zr1xh7i7+2NWvWWNThOJs9x1zj888/Vy4uLqqystLi87c1W2pX6RNIn0D6BNbtE9hjzNbqDyhlf/FLn0AnfQLpE0ifwHL2Fn9t0idofZ+gVemYQE/JtGzZMgoLC0lLSyMtLY3CwkKWLl16zqmYGlIz7SMgIACAbdu2UVlZyYQJE8z7JCQkEB0dzW+//dbkeWrOUWPx4sUcO3aMuXPnWlSX48ePk56eXqdsX19fhg4d2mTZrdEZ4m+o7Mb2A/uNNScnh48++ogRI0Y0uWZKU/UG+4r/9OnT3HXXXXzwwQd4eHhYdO6G6gv2FTfAM888Q2BgIOeddx7PP/88VVVVFpVxdp3BfmLfvn07qampGAwGzjvvPMLDw7n00kvZu3evZQHXqi/YT9xn++abb8jOzub222+3qIyz6wz2E3tgYCA9e/bk/fffp7i4mKqqKt566y1CQkIYNGiQZUGfVW+wn/jLy8txcXHBYPjjssnd3R2A9evXW1SOLcVsiXOtX2PsLX5r6Awx5+fn4+Pjg5OTU5uW0xK21K7SJ5A+gfQJrNsnsMeYwTr9gZp6g/3EL30CnfQJpE8A0iewlL3Fbw2dIWZr9QlafHTfvn25/PLLueyyyxg5cqT5H5/BYCA0NLRVlWmMyWTi//7v/xg5ciR9+/YFID09HRcXl3p5VUNDQ0lPT2/wPBs3buSzzz5j+fLl5m1Hjhzh0UcfZd26dRY3Zs35z463qbJbozPEf/ToUV577TVeeOGFJs9tz7E+8sgj/Pvf/6akpIRhw4bx3XffWVRGbfYYv1KK2267jXvuuYfBgweTlJRk0blrs8e4AWbNmsX5559PQEAAGzduZM6cOaSlpfHSSy9ZVA7YZ+zHjh0D4KmnnuKll14iNjaWF198kTFjxnD48GGLbizYY9xnW7RoEZMmTSIyMtKiMmrYY+yaprFy5UquvPJKvL29MRgMhISE8OOPP+Lv729ROTXsMf5x48Yxe/Zsnn/+eR544AGKi4t59NFHAUhLS7O7mC1xLvVrjD3G31qdIeasrCyefvpp7r777jYro6VsrV2lTyB9AukTWK9PYI8xg3X6A2Cf8UufQCd9AukTgPQJLGGP8bdWZ4jZmn2CFs+EmDRpEl9//TUXXXQRQUFBTJ8+nQ8++IDMzMxWV6YxM2fOZO/evXz66afnfI69e/cydepU5s6dy8SJEwGorq7mxhtvZN68efTo0aPB4z766CO8vLzMr3Xr1p1zHc6VvcefmprKJZdcwrXXXstdd93V5L72HOvDDz/Mjh07WLFiBUajkVtvvRWlVIvOYY/xv/baaxQWFjJnzpxzrrM9xg0we/ZsxowZQ//+/bnnnnt48cUXee211ygvL7f4HPYYu8lkAuDvf/87V199NYMGDWLx4sVomsYXX3xh0TnsMe7aUlJS+Omnn7jzzjtbfKw9xq6UYubMmYSEhLBu3To2b97MlVdeyZQpUyy64K7NHuPv06cP7733Hi+++CIeHh6EhYURFxdHaGhonSehGmOPMVuTI8Zv7zEXFBRw2WWX0bt3b5566qlzjsHa7L1dW8ve45c+geXsMf7W9gnsMWawTn8A7DN+6RNIn0D6BNInaAlHjN/eY7Z6n+Bc8zgdO3ZM/etf/1KXXnqpcnd3V0ajUQ0dOlTNmzdPbd26tVU5omqbOXOmioyMVMeOHauzfdWqVQ3m4oqOjlYvvfRSnW379u1TISEh6rHHHquzPTc3VwHKaDSaX5qmmbetWrVKFRQUqCNHjphfJSUlKjExscG8WRdeeKGaNWtWvRhak//V3uNPTU1V3bt3V7fcckudvHmdMdbaTp48qQC1cePGJmPuDPFPnTpVGQyGOueuOe+tt97aaeNuyN69exWgDh482Gzc9hz76tWrFaDWrVtXZ58hQ4bUq0dniru2+fPnq+Dg4BbneLbX2FeuXKkMBoPKz8+vs098fLxauHBhp4+/tvT0dFVYWKiKioqUwWBQn3/+ud3FXFtjuUBbUr/OGH9tLc3/au8xFxQUqOHDh6vx48c3uOBsR7HFdpU+gfQJpE9gnT6BvcbckJb2B+w5fukTSJ+ghvQJpE/QHHuNvzbpE7ReqxemVkqpkpIS9e2336p7771XxcTEKE3TVHh4uLrjjjvUl19+qQoKClp8TpPJpGbOnKkiIiLU4cOH631es5DHkiVLzNsOHjyoOGshj71796qQkBD18MMP1ztHdXW12rNnT53Xvffeq3r27Kn27NlTZ+Xys+sWFhamXnjhBfO2/Px8qy5C1xniT0lJUd27d1fXX3+9qqqq6tSxni05OVkBas2aNY3u01niT05OrnPen376SQFqyZIl6uTJk5027oZ8+OGHymAwqJycnEb36Qyx17yvvQhdRUWFCgkJUW+99Vanjbv2vnFxcerBBx9sNNbOFvs333yjDAaDKiwsrHNsjx491IIFCzp9/A1ZtGiR8vDwaPQi1JZjrq25Reiaq19j7D3+2iztcHSGmPPz89WwYcPURRddpIqLi5s9V3uw5XaVPoH0CaRP0Lo+gb3H3BBL+wOdIX7pE0ifoIb0CaRP0Bh7j7826RO0nlUGIc62Z88e9eyzz6qLLrpIOTs7KxcXF/Xmm2+26Bz33nuv8vX1VWvXrlVpaWnmV+2Rm3vuuUdFR0er1atXq61bt6rhw4er4cOH16lHcHCwuvnmm+ucIyMjo9FyLV1N/JlnnlF+fn7q66+/Vrt371ZTp05VcXFxdUaHkpOT1Y4dO9S8efOUl5eX2rFjh9qxY0e9X9idMf6UlBQVHx+vxo8fr1JSUuqU39li3bRpk3rttdfUjh07VFJSklq1apUaMWKE6tatmyorK2v2/PYe/9ks+eXdGeLeuHGjevnll9XOnTtVYmKi+vDDD1VwcLBFsz/sPXallHrggQdUly5d1E8//aQOHjyo7rzzThUSEtJkh6szxK2U/gQQoA4cONDsOTtL7JmZmSowMFBNmzZN7dy5Ux06dEg99NBDytnZWe3cubPTx6+UUq+99pratm2bOnTokPr3v/+t3N3d1auvvmq3MWdnZ6sdO3ao5cuXK0B9+umnaseOHXX+TjdXv6Z0hvjT0tLUjh071H//+18FqF9//VXt2LFDZWdnd8qY8/Pz1dChQ1W/fv3U0aNH65Tf1I3jtmbr7Sp9AukTSJ/g3PsE9h5za/oDnSF+paRPIH0C6RNIn6BpnSF+6RNYr0/QJoMQteXn56vPP/9crVixokXHAQ2+Fi9ebN6ntLRU3Xfffcrf3195eHioq666qs4XZe7cuQ2eIyYmptFyLf2hmUwm9cQTT6jQ0FDl6uqqxo8frw4dOlRnnxkzZjRYviVPwth7/IsXL240hs4W6+7du9XYsWNVQECAcnV1VbGxseqee+5RKSkpzZ67M8R/NksHIew97m3btqmhQ4cqX19f5ebmpnr16qX++c9/WtTJtPfYldKfcnrwwQdVSEiI8vb2VhMmTFB79+7t9HErpdQNN9ygRowY0ez5ausMsW/ZskVNnDhRBQQEKG9vbzVs2DD1/fffO0z8t9xyiwoICFAuLi6qf//+6v3337frmBv7Oz137lyL69fZ42+s/NoxdKaYa57uauh1/PjxZs/fVmy9XaVPIH2CGtInqMuSPoG9x9ya/kBniF8p6RNIn0D6BNInaFpniF/6BH+8Wtsn0JRq4SpZwIkTJ9A0jaioKPO233//naFDh7b0VEIIIYQQQgghhBBCCCGE6KSaX779LIsWLSIuLo6uXbvy8MMPUzOGMWfOHKtXTgghhBBCCCGEEEIIIYQQ9qvFgxCvvPIK27dvZ/PmzaxYsYLrrrsOk8nEOUyoEEIIIYQQQgghhBBCCCFEJ9biQYjAwEAGDBjAeeedx/r168nNzeW2225rg6oJIYQQQgghhBBCCCGEEMKetXgQorKykurqagC8vb357rvvSEtLY/369VavnBBCCCGEEEIIIYQQQggh7FeLByFmzZpFSkqK+b2bmxvffvstjzzyiFUrJoQQQgghhBBCCCGEEEII+6YpWcxBCCGEEEIIIYQQQgghhBBtwMkaJ6msrCQ1NZXc3NwGF6g+//zzrVGMEEIIIYQQQgghhBBCCCHsSKsGIfLy8njooYf46KOPqKioqPe5UgpN08xrSAghhBBCCCGEEEIIIYQQwnG0ahDitttu49tvv+X6669n6NCh+Pr6WqteQgghhBBCCCGEEEIIIYSwc61aE8LDw4M///nPvPzyy9askxBCCCGEEEIIIYQQQgghOgFDaw4ODAwkPj7eWnURQgghhBBCCCGEEEIIIUQn0qpBiLvvvptPP/0Uk8lkrfoIIYQQQgghhBBCCCGEEKKTaNWaEE888QTl5eUMHjyYW265hcjISIxGY739pk2b1ppihBBCCCGEEEIIIYQQQghhh1q1JkRqairXXnstmzZtarwATaO6uvpcixBCCCGEEEIIIYQQQgghhJ1q1UyIO+64g+3btzNnzhyGDh2Kr6+vteolhBBCCCGEEEIIIYQQQgg716qZEJ6enjz00EPMmzfPmnUSQgghhBBCCCGEEEIIIUQn0KqFqcPCwggICLBWXYQQQgghhBBCCCGEEEII0Ym0ahDiwQcf5H//+x9FRUXWqo8QQgghhBBCCCGEEEIIITqJVg1ClJWV4ezsTHx8PLNmzeL555/npZdeqvN6+eWXrVVXIYRo0pgxY/i///u/jq5Gq2iaxldffdXR1SA2NpZXXnmlo6sBQEVFBfHx8WzcuLGjq+Jwrr/+el588cWOroYQQgghRIPk+t96bOn63xLSR+g4b775JlOmTOnoaggh7EyrBiEeeughdu7cSUZGBv/+97955JFHeOihh+q9hBBC2KZ3330XPz+/etu3bNnC3XffbdWynnrqKTRNq/NKSEho9rg333yTuLg4RowYAUBSUhJ33nkncXFxuLu7061bN+bOnUtFRUWd43bv3s3o0aNxc3MjKiqK5557rs7n//3vfxk9ejT+/v74+/szYcIENm/e3Gg97rnnHjRNs4nO2ZgxY+q15T333GP+PDs7m0suuYSIiAhcXV2JioriL3/5CwUFBQ2eb8OGDTg5OTFw4MA62x9//HEWLFhAfn5+W4YjhBBCCCHaSXte///6669MmTKFiIiIRgdbli5dysSJEwkMDETTNHbu3GnRuaWPUF9zfQSAWbNmMWjQIFxdXetd+9f4/PPPGThwIB4eHsTExPD888/X+fyOO+5g+/btrFu3rq1CEUJ0Qk6tOfj48ePWqocQQggbEhwc3Cbn7dOnDytXrjS/d3Jq+s+QUop///vfzJ8/37zt4MGDmEwm3nrrLeLj49m7dy933XUXxcXFvPDCCwAUFBQwceJEJkyYwJtvvsmePXu444478PPzM3eu1q5dyw033MCIESNwc3Pj2WefZeLEiezbt48uXbrUqceyZcvYtGkTERER1mqKVrvrrrvqtIuHh4f5/w0GA1OnTuUf//gHwcHBHD16lJkzZ5KTk8PHH39c5zx5eXnceuutjB8/ntOnT9f5rG/fvnTr1o0PP/yQmTNntm1AQgghhBCiw7TF9X9xcTEDBgzgjjvuYNq0aY3uM2rUKK677jruuusui84rfYTGNdVHqHHHHXfw+++/s3v37nqf/fDDD9x000289tprTJw4kQMHDnDXXXfh7u7OX/7yFwBcXFy48cYb+de//sXo0aPbLhghROeiWmjLli0tPUQIIdrFRRddpB544AHz+++++075+PioDz/8UCml1IkTJ9S1116rfH19lb+/v7riiivU8ePHzfvPmDFDTZ06VT3//PMqLCxMBQQEqPvuu09VVFQopZSaN2+e6tOnT71yBwwYoB5//HGllFKbN29WEyZMUIGBgcrHx0ddeOGFatu2bRbHAKhly5aZ3+/evVuNHTtWubm5qYCAAHXXXXepwsLCOscsWrRI9e7dW7m4uKiwsDA1c+ZM82cvvvii6tu3r/Lw8FCRkZHq3nvvNR+/Zs0aBdR5zZ07VymlVExMjHr55ZfN50lOTlZXXHGF8vT0VN7e3uraa69V6enp5s/nzp2rBgwYoN5//30VExOjfHx81PTp01VBQUG9fVpiy5YtymAw1DlPQ5577jkVFxdnfv/6668rf39/VV5ebt72yCOPqJ49ezZ6jqqqKuXt7a3ee++9OttTUlJUly5d1N69e+u1iyUWL16sfH191Y8//qgSEhKUp6enmjRpkjp16lSLzlPb2d91S7z66qsqMjKy3vbp06erxx9/vNGfz7x589SoUaPOsaZCCCGEEG1Hrv9t+/q/qTjPdvz4cQWoHTt2NNtm0kdoWEv6CI1d+99www3qmmuuqbPtX//6l4qMjFQmk8m87ZdfflEuLi6qpKTknOsrhHAsLU7HNGTIEMLDw7njjjtYunQphYWF1hoPEUIIq/n444+54YYb+Oijj7jpppuorKxk0qRJeHt7s27dOjZs2ICXlxeXXHJJnSm6a9asITExkTVr1vDee+/x7rvv8u677wL6EyMHDhxgy5Yt5v137NjB7t27uf322wEoLCxkxowZrF+/nk2bNtG9e3cmT558Tr8ri4uLmTRpEv7+/mzZsoUvvviClStXmp9AAXjjjTeYOXMmd999N3v27OGbb74hPj7e/LnBYOBf//oX+/bt47333mP16tX87W9/A2DEiBG88sor+Pj4kJaWRlpaWoMp9EwmE1OnTiUnJ4dffvmFn3/+mWPHjjF9+vQ6+yUmJvLVV1/x3Xff8d133/HLL7/wzDPP1NnnyJEjRERE0LVrV2666SZOnDjRZBusW7eOHj164O3t3eR++fn5BAQEmN//9ttvXHjhhbi4uJi3TZo0iUOHDpGbm9vgOUpKSqisrKxzHpPJxC233MLDDz9Mnz59mqxDU0pKSnjhhRf44IMP+PXXXzlx4kSdtv7oo4/w8vJq8nX2dOePPvqIoKAg+vbty5w5cygpKWm0/FOnTrF06VIuuuiiOtsXL17MsWPHmDt3bqPHDhkyhM2bN1NeXn6O0QshhBBCtD25/tfZ2vV/W5A+gnX6CA0pLy/Hzc2tzjZ3d3dSUlJITk42bxs8eDBVVVX8/vvv5xC5EMIhtXTUYvfu3WrhwoXqwgsvVE5OTsrFxUWNHTtWvfDCC+rAgQNtMVAihBAWqXny49///rfy9fVVa9euNX/2wQcfqJ49e9Z5eqO8vFy5u7urn376SSmlPwkVExOjqqqqzPtce+21avr06eb3l156qbr33nvN7++//341ZsyYRutUXV2tvL291bfffmtRDNR6Qujtt99W/v7+qqioyPz58uXLlcFgMD+FFBERof7+979bdG6llPriiy9UYGCg+X3NEzhnq/00z4oVK5TRaFQnTpwwf75v3z4FqM2bNyul9CdpPDw86jyN9PDDD6uhQ4ea33///ffq888/V7t27VI//vijGj58uIqOjm7yCaYHHnhAjRs3rsmYjhw5onx8fNTbb79t3nbxxReru+++u85+NXXev39/g+e59957VdeuXVVpaal52z//+U918cUXm7835/qUE6COHj1q3vaf//xHhYaGmt8XFBSoI0eONPmq/ZTRW2+9pX788Ue1e/du9eGHH6ouXbqoq666ql7Z119/vXJ3d1eAmjJlSp3YDh8+rEJCQtShQ4eUUo0/DbVr1y4FqKSkpBbFLYQQQgjR1uT6v3kdef3fWJwNaclMCOkjtK6PoFTj1/5vvfWW8vDwUCtXrlTV1dXq0KFDKiEhQQFq48aNdfb19/dX7777boviFkI4rhavCdGvXz/69evHo48+Sn5+Pj/++CPff/89zz33HH/729+IjY1l8uTJXH755YwZMwZXV1crDZcIIUTzlixZQkZGBhs2bOCCCy4wb9+1axdHjx6t97RMWVkZiYmJ5vd9+vTBaDSa34eHh7Nnzx7z+7vuuos77riDl156CYPBwMcff8zLL79s/vz06dM8/vjjrF27loyMDKqrqykpKWn2if+GHDhwgAEDBuDp6WneNnLkSEwmE4cOHULTNE6dOsX48eMbPcfKlStZuHAhBw8epKCggKqqKsrKyigpKWkwP2hj9YiKiiIqKsq8rXfv3vj5+XHgwAFzO8fGxtZp3/DwcDIyMszvL730UvP/9+/fn6FDhxITE8Pnn3/OnXfe2WDZpaWl9Z7EqS01NZVLLrmEa6+91uIcsg155pln+PTTT1m7dq25vG3btvHqq6+yfft2NE0753ODnou1W7du5vdnt423t3ezT3LVVnvRwH79+hEeHs748eNJTEysU87LL7/M3LlzOXz4MHPmzGH27Nm8/vrrVFdXc+ONNzJv3jx69OjRZFnu7u4ALX6KSgghhBCiPcj1f122dP3fVqSP0DBL+whNueuuu0hMTOTyyy+nsrISHx8fHnjgAZ566ikMhrrJVNzd3aWPIISwWIvTMdXm6+vL9OnTee+990hPT2fjxo3ccsstbNq0icmTJxMYGMiUKVN466236i12KYQQbeG8884jODiYd955B6WUeXtRURGDBg1i586ddV6HDx/mxhtvNO/n7Oxc53yapmEymczvp0yZgqurK8uWLePbb7+lsrKSa665xvz5jBkz2LlzJ6+++iobN25k586dBAYG1pnybS01N4cbk5SUxOWXX07//v358ssv2bZtG//5z38A2qQ+zbXd2fz8/OjRowdHjx5tdJ+goKBGp0afOnWKsWPHMmLECN5+++06n4WFhdX7u1PzPiwsrM72F154gWeeeYYVK1bQv39/8/Z169aRkZFBdHQ0Tk5OODk5kZyczIMPPkhsbGyjdW5IQ21T+/t5LlOtaxs6dChAvbYMCwsjISGBK664grfeeos33niDtLQ0CgsL2bp1K3/5y1/Msc2fP59du3bh5OTE6tWrzefIyckB2m6xciGEEEKI1pDr/z/Y+vW/tUgfoXV9hKZomsazzz5LUVERycnJpKenM2TIEAC6du1aZ9+cnBzpIwghLNbimRCN0TSNoUOHMnToUJ566ikyMjJYvnw5P/zwA4888ginT5/mySeftFZxQgjRoG7duvHiiy8yZswYjEYj//73vwE4//zz+eyzzwgJCcHHx+ecz+/k5MSMGTNYvHgxLi4uXH/99XU6Axs2bOD1119n8uTJAJw8eZKsrKxzKqtXr168++67FBcXm5+G2rBhAwaDgZ49e+Lt7U1sbCyrVq1i7Nix9Y7ftm0bJpOJF1980fzUyueff15nHxcXF6qrq5utx8mTJzl58qT5aaj9+/eTl5dH7969zyk20DuGiYmJ3HLLLY3uc9555/HGG2+glKrzpFFqaipjx45l0KBBLF68uN5TOcOHD+fvf/87lZWV5ov7n3/+mZ49e+Lv72/e77nnnmPBggX89NNPDB48uM45brnlFiZMmFBn26RJk7jlllvMOYCt5YorrjB3EhrTpUuXRj/buXMnoD891ZiaDmF5eTmhoaF1nvADeP3111m9ejVLliwhLi7OvH3v3r1ERkYSFBTUXBhCCCGEEO1Orv//YOvX/9YifYQ/tLaP0Bij0Wg+9yeffMLw4cPrDDgkJiZSVlbGeeed1+JzCyEck9UGIc4WEhLC7bffzu233051dbX5SUohhGhrPXr0YM2aNYwZMwYnJydeeeUVbrrpJp5//nmmTp3K/PnziYyMJDk5maVLl/K3v/2NyMhIi8//pz/9iV69egF6p6C27t2788EHHzB48GAKCgp4+OGHm31iqTE33XQTc+fOZcaMGTz11FNkZmZy//33c8sttxAaGgrAU089xT333ENISAiXXnophYWFbNiwgfvvv5/4+HgqKyt57bXXmDJlChs2bODNN9+sU0ZsbCxFRUWsWrWKAQMG4OHhUW+a9oQJE+jXrx833XQTr7zyClVVVdx3331cdNFF9S7Km/LQQw8xZcoUYmJiOHXqFHPnzsVoNHLDDTc0eszYsWMpKipi37599O3bF9A7F2PGjCEmJoYXXniBzMxM8/41TzDVpBq68847eeSRR9i7dy+vvvpqnanzzz77LE8++SQff/wxsbGxpKenA5ifKgoMDCQwMLBOfZydnQkLC6Nnz54Wx22Jlky1TkxM5OOPPzbPONy9ezd//etfufDCC81PaX3//fecPn2aCy64AC8vL/bt28fDDz/MyJEjzU9o1bRnjZCQENzc3OptX7duHRMnTmx9kEIIIYQQbUSu/23z+r+oqKjOU/jHjx9n586dBAQEEB0dDehP0584cYJTp04BcOjQIUC/rj97dkIN6SPUZ0kfAfRZEUVFRaSnp1NaWmoeqOjduzcuLi5kZWWxZMkSxowZQ1lZGYsXL+aLL77gl19+qVPeunXr6Nq1q8VpnoQQosULUzdk37596ttvv1Xvv/++eu+99+q83n//fWsUIYQQzapZmK7G/v37VUhIiJo9e7ZSSqm0tDR16623qqCgIOXq6qq6du2q7rrrLpWfn6+U0hemmzp1ap1zPvDAA+qiiy6qV9bo0aNVnz596m3fvn27Gjx4sHJzc1Pdu3dXX3zxRYsWKuOsBdt2796txo4dq9zc3FRAQIC66667VGFhYZ1j3nzzTdWzZ0/l7OyswsPD1f3332/+7KWXXlLh4eHK3d1dTZo0Sb3//vsKULm5ueZ97rnnHhUYGKgANXfuXKVU/cXVkpOT1RVXXKE8PT2Vt7e3uvbaa82L4ynV8MJmL7/8soqJiTG/nz59ugoPD1cuLi6qS5cuavr06XUWYmvMddddpx599FHz+5pF3Bp61bZr1y41atQo5erqqrp06aKeeeaZOp/HxMQ0eI6aNmhIQz/Liy66SM2YMaPRYxpa/G/ZsmX16mupEydOqAsvvFAFBAQoV1dXFR8frx5++GHz91gppVavXq2GDx+ufH19zd/FRx55pM7P/WwN/QxLS0uVr6+v+u23386prkIIIYQQbUmu/237+n/NmjUNXm/XvnZu7Nq+qWtypaSPcDZL+gg19WoovuPHjyullMrMzFTDhg1Tnp6eysPDQ40fP15t2rSpXnkTJ05UCxcuPKe6CiEck6ZUrYRzLZSYmMjNN9/M5s2baew0mqY1O9VPCCHsiVKK7t27c9999zF79uyOrk6nt3v3bi6++GISExPx8vLq6OrUExMTw7x587jttts6uipW98Ybb7Bs2TJWrFjR0VURQgghhOgwcv1ve6SP0HH27dvHuHHjOHz4ML6+vh1dHSGEnWhVOqY///nP7Nmzh1deeYXRo0fXyaEnhBCdUWZmJp9++inp6elWz/kpGta/f3+effZZjh8/Tr9+/Tq6OnXs27cPX19fbr311o6uSptwdnbmtdde6+hqCCGEEEJ0GLn+t03SR+g4aWlpvP/++zIAIYRokVbNhHB3d+exxx7jiSeesGadhBDCZmmaRlBQEK+++io33nhji4796KOP+POf/9zgZzExMezbt88aVRRCCCGEEEJYiVz/CyGEEK3XqpkQQUFBMvIphHAorRi35YorrmDo0KENfubs7HzO5xVCCCGEEEK0Dbn+F0IIIVqvVTMhFixYwNdff81vv/2G0Wi0Zr2EEEIIIYQQQgghhBBCCGHnWjQTYunSpXXe9+zZk+rqagYMGMAdd9xBVFRUg4MR06ZNa10thRBCCCGEEEIIIYQQQghhd1o0E8JgMKBpmnk6Yu3/b7QATaO6urp1tRRCCCGEEEIIIYQQQgghhN1p0UyINWvWtFU9LGYymTh16hTe3t5omtbR1RFCCCGEEMIhKaUoLCwkIiICg8HQrmVLn0AIIYQQQoiOZ2mfoFVrQnSElJQUoqKiOroaQgghhBBCCODkyZNERka2a5nSJxBCCCGEEMJ2NNcnaNFMCIDHH3+c0aNHM3z4cHx8fFpVuXPh7e0N6IFZq3yTyURmZibBwcHt/hSXLXCk+B0p1oY4YvyOGHNtjhi/I8Zcm8TvePE7Ysy1OWL8thJzQUEBUVFR5uvz9tQWfYKm2Eqb2xNpM8tIO7WctJllpJ1aTtqsZaS9LCPt1HLSZpaxlXaytE/Q4kGIf/7zn2iahsFgoE+fPowaNcr8ao8noGqmW/v4+Fh1EKKsrAwfHx+H/HI7UvyOFGtDHDF+R4y5NkeM3xFjrk3id7z4HTHm2hwxfluLuSPSIbVFn6Apttbm9kDazDLSTi0nbWYZaaeWkzZrGWkvy0g7tZy0mWVsrZ2a6xO0eBAiMTGRDRs2sG7dOjZu3Mgbb7zB66+/jqZpREVF1RmU6Nu37zlXXAghhBBCCCGEEEIIIYQQ9q3FgxBxcXHExcVx8803A5CXl8fGjRtZv349GzZsYNmyZXz88cdomoafnx8jRozg22+/tXrFhRBCCCGEEEIIIYQQQghh21o8CHE2Pz8/Jk+ezOTJkwGorKzk+++/57nnnuO3337j+++/b3UlhRBCCCGEEEIIIYQQQghhf1o9CAFw8OBBNmzYwIYNG1i/fj2JiYkYjUYGDRrEyJEjrVGEEEIIIYQQQgghhBBCCCHsTIsHISoqKti6das5/dLGjRvJzs4mICCA4cOHc/vttzNixAiGDBmCu7t7W9RZCCGEEEIIIYQQQgghhBB2oMWDEL6+vlRWVtKzZ09GjBjBs88+y4gRI0hISGiL+gkhhBBCCCGEEEIIIYQQwk4ZWnpAeXk5mqbh7u6Oh4cHnp6eeHl5tUXdhBBCCCGEEEIIIYQQQghhx1o8EyI7O5uNGzea14D473//S3l5OV26dGHkyJGMGDGCkSNHMnDgQAyGFo9xCNH5pWwDUyVED+vomgghhBBCCCGEEEIIIUSbavEghL+/P5dddhmXXXYZAJWVlWzbts08MLFgwQIyMjLw9PRkyJAhjBw5kvnz51u94kLYrZxjYHTWByMiB3V0bYQQQgghhBBCCCGEEKLNtHqqgrOzM8OGDWP27Nl8+eWXpKen89VXX9G/f3/WrFnDggULzPtWV1fzxBNPEBcXh7u7O926dePpp59GKdXaaghhH0pzwWCAnpOhIAXS93Z0jYQQQgghhBBCCCGEEKLNtHgmxNkqKyvZsmUL69evZ8OGDWzcuJGcnByUUri4uDBo0B9Pej/77LO88cYbvPfee/Tp04etW7dy++234+vry6xZs1pbFSFsX34K+EaBkwvEXwyHvgcnVwjq3tE1E0IIIYQQQgghhBBCCKtr8SBEXl4eGzZsMA86bN26lfLycpRS+Pn5MXz4cEaNGsWoUaMYMmQIrq6u5mM3btzI1KlTzamcYmNj+eSTT9i8ebP1IhLCluWnQnh//f9dPKDHJDj0o56eyT+2Q6smhBBCCCGEEEIIIYQQ1tbiQYjAwEAAlFJERUVx1VVXmQcd+vbti6ZpjR47YsQI3n77bQ4fPkyPHj3YtWsX69ev56WXXmr0mPLycsrLy83vCwoKADCZTJhMppZWv0EmkwmllNXOZ28cKf4OjbW6AoqzwCMYasp39oRu4+HIT6A5gU9Em1bBkX7WNRwx5tocMX5HjLk2id/x4nfEmGtzxPhtJeb2LL89+gRNabDNlYLyAnDzbfPy7ZGtfE9tnbRTy0mbWUbaqeWkzVpG2ssy0k4tJ21mGVtpJ0vLb/EgxJ///GdGjx7NqFGjcHV1JSQkpMn9t2zZwgUXXADAo48+SkFBAQkJCRiNRqqrq1mwYAE33XRTo8cvXLiQefPm1duemZlJWVlZS6vfIJPJRH5+PkopDIZWL5Nhdxwp/o6M1VhwEmO1GxVZ2fU+M/j2x2X3d1REjcbkHthmdXCkn3UNR4y5NkeM3xFjrk3id7z4HTHm2hwxfluJubCwsN3Kao8+QVMaanOtvADX5NVURI7E5BHc5nWwN7byPbV10k4tJ21mGWmnlpM2axlpL8tIO7WctJllbKWdLO0TtHgQ4vXXXzf/f//+/fnll1/w9/dvcN81a9Zw5ZVXkp+fD8Dnn3/ORx99xMcff0yfPn3YuXMn//d//0dERAQzZsxo8Bxz5sxh9uzZ5vcFBQVERUURHByMj49PS6vfIJPJhKZpBAcHO+SX25Hi79BYS45AdB8IbmjgLgT8fSFpHYRMAne/NqmCI/2sazhizLU5YvyOGHNtEr/jxe+IMdfmiPHbSsxubm7tVlZ79Ama0nCbh4CfFxxfC0HjwKvph7PajFKQtgu8gsGnS8fUoQG28j21ddJOLSdtZhlpp5aTNmsZaS/LSDu1nLSZZWylnSztE7RqYeqSkhIuvvhiVq1aha9v3WnI3333Hddeey3Dhw83b3v44Yd59NFHuf766wHo168fycnJLFy4sNFBCFdX1zrrStQwGAxWbWBN06x+TnviSPF3WKyFp6DLedBYuX6REDMcEldBz0vA1btNquFIP+sajhhzbY4YvyPGXJvE73jxO2LMtTli/LYQc3uW3V59gqY02Oa+EdB1LBz/BbqN0wcC2pPJBCc2Qlke5ByF3leCk0v71qEJtvA9tQfSTi0nbWYZaaeWkzZrGWkvy0g7tZy0mWVsoZ0sLbtVNVy1ahWZmZlccsklFBUVmbd/+umnTJs2jfHjx/P999+bt5eUlNSrmNFo7PDcVUK0ueJscHIFV6+m9/OPhfABcGQFVJa2S9WEEEIIIUQr+IRD7ChIXK2v/9VeTNVwbI2+7ljPyfp1ZMqW9itfCCGEEEIIC7VqECImJobVq1dz8uRJJk+eTElJCW+//TY333wz06ZN46uvvqozJWPKlCksWLCA5cuXk5SUxLJly3jppZe46qqrWh2IEDatIAV8oyzbN7gHBPXQByKqKtq2XkIIIYQQovV8u+gDEUdX6Q+ftLXqSjjyMxhd9JkYBiNEnA+FaVBwqu3LF0IIIYQQogVaPVejW7durFy5ksOHDzNw4EDuvfdebr/9dj755BOcnOpme3rttde45ppruO++++jVqxcPPfQQf/7zn3n66adbWw0hbFt+it45tVRYP/CJhKMrobqq7eolhBBCCCGsw7cLxIzQr99KctqunMoyOPwjeAToAx81M82NThAzEpI36oMUQgghhBBC2IgWrQmRk9PwxXRISAifffYZU6ZMYcaMGTzzzDPk5uaaPw8ICADA29ubV155hVdeeeXcayyEvamntXmZAAEAAElEQVQsg7J88GzhYoWRgyBpAxxbq+cYljx4QgghhBC2zS8KGK7PUuh+sT5QYE0VxXD4JwjoChED63/uEw4+EZC6HaKHWrdsIYQQQgghzlGLBiGCgoLQNK3Rz5VSvPfee7z33nt1tldXV59b7YRjqaqAA9/oC+oZW7Vmum0pSAXv8HMbRIgZoQ9CJK2DuAuhiX9/QgghhBDCBvhFg1L6QESPieDub53zluXr5wztAyG9Gt+vy2DY/7W+RoR3qHXKFkIIIYQQohVadKf3ySefbHIQQohWKTwF5YV6Lls/C9dPsAf5ddeDSMsv5XhWMXFBnoT7ujd9rKbpgw9HV8GJTRAzvI0rK4QQQgghWs0/BjgzENF9Irj7te58JTl6mqcugyCwW9P7OrlA9DBI3gC9p+rrRQghhBBCCNGBWjQI8dRTT7VRNYQA8lPBzVdfTK+zDEIopccTNQSAz7ac4NGle1AKDBosnNaP6RdEN30OgxG6jdWn3p/a2fDUeyGEEEIIYVv8Y8/MiFjRuoGIwtP6zNiYEZZfI/tFQc4x/doxctC5lSuEEEIIIYSVSJJ5YTvyUyDyAj19UWdRnAWuXuDsTlp+KXOW7kFpJaBVYFLw2NK9pOWXNn8eozPEj4eMA2CS9GZCCCGEEHYhIE6fvXDkZygraPnx+Sn6AETXi1r+kE7UUMg+CsXZLS9XCCGEEEIIK5JBCGEbSnL0qeN+UVBdCeVFHV0j68g/CT5dADieVYxJgdEjGaPHcQCqlSIpq8Syczm7g2eQ3hkVQghhG0pyIGlDR9dCCGHLArtBl/P1Wa0tGYjIOab/fomfAN5hLS/X2Q2iLoDk9WAytfx4IYQQQgghrEQGIYRtyE8Bn0j9/30i9BRGnUGt9SDigjxBq0QzlqAZy9CMhRg0iA3ysPx8/nGQe7yNKiuEEKLFcpMg67D+XyGEaExgN4g4T0/NVF7Y/P6ZhyBlG/SYBJ6B515uQFdw8YLTe879HEIIIYQQQrSSDEII21CQCr5nBiF8u3SOlEwVJVBRrM9eAIK9XHFzy8enyInuqVX4GQ4xPiG0+cWpa/OL1gdoqqvaqNJCCCFaJD9FX/cnZav8bhZCNC0oHsL66zMimpr1m7YbTu+Dnpe2fkFrgOjhekrP0tzWn0sIIYQQQohz0KKFqYVoE1UVeqfIK1R/7x0BJzbp08YNdjxOVnBKH1DRNAB2peTTL2sDM39fT0iB4rcEjS2nSimcPgBvN2fLzunkAp4hepqngLg2rLwQQohm1Qw2h/TW0zKl79ZTrgghRGOCewAKDv8IPS7R1w6rLWWrfg3Z81I9Fac1uHjo61IkbYCEy8zXpkIIIYQQQrQXO77DKzqNglR9AKJmwMHZDVy9oSSrY+vVWrXWgwDY+PtuxqZvILhQoQHxaYphmd/w2ffbWnbeAEnJJIQQNqHglJ5CUNP0G3xZhy1LsyKEcGzBPSG0Lxz5SR/IBFBKHyQoztQHJ6w1AFEjqDsYXSBjv3XPK4QQQgghhAVkEEJ0vNqpmGr4RNh3SiaTCQrT6gxC7Nu3meAChUHp74Pz9f/+uG4txeUtSOHhGwWF6foC3kIIITpOQcoff79cPPSbiic3d2ydhBD2ISQBgnv9kZrp2FqoLIX4i/WZr20hZjik72nZ4thCCCGEEEJYgQxCiI6Xn1rnZj2gv7fnxamLM8DNV5/VAeSXVrLZVEZwnjLvogFx6ZDoU8KHm5ItP7eTiz5zJO+ElSsthBDCYiYTFNQdbCakN5Tl63/XhBCiOaG99VkRe78EzQDdxoGxDbPlunrra1Ikb2y7MoQQQgghhGiADEKIjlWSA06u9fPheoboN3KqyjumXq2Vn1Jndsevh9OocCknoKBuDt6gEiPO1Sbe2rCF0opqy88vKZmEEKJjFWfqN/TODDYDelrBqKFw8nd9kEIIIZoT2kdfpyHuwvZZCy2kFygTZB5u+7KEEEIIIYQ4QwYhRMc662a9mcGgP+1fmNb+dbKG/BQ9bdIZK44cpPfpbJyVwqVrV6IWv4PbwIEYqqp46PffKK5O5MNNSZaf3zcaijL0Rb2FEEK0v4IU8O1Sf7tvF3D3k7zrQgjLeQa132LRmgYxI+DU9j/WoxBCCCHsXVkBmFrwYKcQot3JIIToWPkp9VMx1fDpYp8pLcqL9BkcHgEAKKXYfOIIo47r6ZN8p1yO1/DhRL76CkY/P/qdPMX45J28uXEzZZUW/tE0OoF3uKRkEkKIjpKfCj4NDKIDRF4Ap/dCRUn71kkIISzh7qenj0v+raNrIoQQQlhH8kbIOtLRtRBCNEEGIUTHqaqAsjx9xkNDfCLsc12IsxbaPpyRR3FxGqOTkgDwnnQJAM6hoYT/858A3LJ9L4G56/l0cwvWhpCUTEII0TEqSvQniD2DGv7c1VvP8566tX3r1ZYO/SiL2QrRmYT2hcoSyE7s6JoIIYQQrVdeKPdHhLBxMgghOk5Bqj4A0Vj+Wzcffcp4aV67VqvV8k/WSdHxw4ED9D6dhVu1CdeePXHtGmf+zHvcWPxvvhm/Erj2wG+8v2ol5VUWzobwidRzktvruhlCCGGvCk7pA+VNpU8J7aenzSs83X71aivlRXp6xOLMjq6JEMJaDAaIHQUpW6GytKNrI4QQQpw7UzVUlenrikqqQSFslgxCOIKSHNu8UV2QWmfdhAb5dLGv2RCmav2Gk3eEedP6pMOMPH4SAJ9LL6l3SMjDD+HSowf9T5Yy7NhnfPa7haP3Rie9fXJbMHtCCCFE6+WfbHg9o9qMTnpappObQKn2qVdbKUgFzWB/DwUIIZrmEQBB3eHk7x1dEyGEEOLcVRSBqxf4xUCOzIYQwlbJIERnV1YAB5fD3qWw+3M48rP+xFN2oj440ZEL9+SnNryoZ232lpKpMB08AsHJBYCSigqOnUrkQnMqpkn1DjG4uhL58kt4VrsyID2NXe+/QkWVybLyAuIgN8lKlRdCCNEsk0mfFXBmPaO0/FI2JmaRlt/Ak8T+MeDkDpmH2rmSVpafAoHxUJrT0TURQlhb+AAozZWHWoQQQtiv8kJw8ZaU1ULYOBmE6OxKsvWnNQfeAAmX64vQObnpN/aT1sOuT2DfMlxSNkLaLr0DUlbQ9k9tFmeDkyu4eDa9n3c4FJ3u2MGSlshPqfN07Mojh4hPy/wjFVNcXIOHuXbrRsicOXRNV8RnrWH5khWWlefTRf8ZV5ZZo/ZCCCGaU5wJrj7g7MZnW04w8pnV3Pjf3xn5zGo+23Ki/v5RQ/S/r/b6e9pk0gfYQ/voDy8IIToXgxFiRumzIaoqOro2QgghRMuVF3G4JJ1Mg0FPMSjrmAlhk2QQorMrydGfzAdw8dBnHoT1hbjR0PsKGHgTdBtHtU+UPvCQkwhHf4adH8OB79ou9UJBSvOpLECfUeDur+fVtgcFKXVmd6w5epCRx/WbUg2lYqot+PrrKOszkqAiRem78ynPt+APp8Got6PMhhBCiPZx5vd8Wn4pc5buwXRmzN6k4LGle+vPiHD3g4CucGpHu1fVKopO63+H3f0A1X6DKVUVcHiFPJ0tRHvwCgb/WEjZ3NE1EUIIIVquvJC0qkIO5R0Gf5kNIYStkkGIzq4kCzyDGv9c08DNVx+EiBgI3cZB36uh/3Twi4K0nW1Tr/xUcyqLZvnayboQZfn6E6Pu/gBUmarYd+Igo5P0GygNpWKqTdM0Br76LL75vhR65rH5wUctK1dSMgkhRPvJTwWfSI5nFZsHIGpUK0VSVkn9Y8IH6OtI2ONMgtoz/Nz99bQt7aEkCyqLIX2P/lCEPVwHCGHPIs7XZz3lp3Z0TYQQQogWqSzNodzoQm5ZLsXeobIuhBA2SgYhOruSbHAPaPlxRicI6aMvsmztqWxV5VCWB16hlu3v00V/8tTW5afWmd1xMPMkvodO4l5lwti9R6OpmGrzDgok667HCCiAgiNryP3q6+bL9Y7QbwpVNHDjSwghhPVUlEBlCXgGERfkiXbWx5oGsUEe9Y9zctFv8J3Y1C7VtKraMxfdA9pvXYiSbPCNhl6XQ1g/PVXM4Z+gOKt9yhfCzjS5Po0ljE4QOwqS1skMJCGEEHalpPg0Xt7hxPrEcqwyH0xV9vnwjxCdnAxCdGblhWB0AWe3czve6ATBPeH0PuvWq+AUeIeBwcKvn0fgmRs/59ipai9nrQex4vB+RibrgycBl11q8WmuuPlSdgdeTEqwRvLTT1FxZlHrRhkM4BctsyGEEKKtFaSCTwRoGuG+7vSN9K3zcaCnC8Ferg0fG9gNUJCd2Pb1tJbyIj0tkseZhxnc/duvQ1eSDR76zEL8Y6D3lRDQDY6thcTVbZcuUgg7ZNH6NJbwDoPuEyFlC6Rsa/s14oQQQggrKC3JxNMrlK5+XUkqSKLaL0ZSMglhg2QQojMryf7jxsG5Cuml39y25gBAfgr4WLAeRA1NA59w207FUF2lL1bqHa6/NVWzI3E/Fx63LBVTbV6uToT+6V6KTBGc9Ckj5cGHUBXNLBQoKZmEEKLt5aeYUwlWVptIyiwG4LHJCfi6OZFVVMHSHY2kMtE0iBoGqdugurK9atw6Bal11jnCI6Ad0zFl/7GmFejtFxQPfa4CrzB9VsTxdfpAiRAOLC2/lEctWZ/GUh4B0GuK/m/9yIr2WwdGCCGEOBeVZZRUV+DnHoSnsyf+bv6kubhJSiYhbFC7DkLExsaiaVq918yZM9uzGo7j7A78uXBy1Z/ezNhvnToppQ8m+Fq4HkQNny76zRBbVZgGXiH67BHgdPFpDLuP415loiou3qJUTLXdOqob33S/jaMhrhQe2kfGK682fYBXGJQXyM0Ye2EySadeCHtjMum/688MQmxNyqWwvIpATxf+NKorM8fFA/DqyiOUV1U3fA7PQH3GXNqu9qp16+SfrDPDDzdf/W+NydS25VZV6L8jXX3qf2YwQmhv6DsN3Hzg4Hd6qqYq+Z0qHNOh9EKUgqDSPPpnHiWoNK/x9Wks5eQK8eP1a9uD30kaNCGEELarvJACTPi5+gEQ7xfP4fJMMDjJ3y8hbEy7DkJs2bKFtLQ08+vnn38G4Nprr23PajiOkhzwaGJRakuF9IbMw9Z5crMkR+/YuHi27DifCH3wwlanhdd6Ohbgt5OHOe+IPvIePGVyi0/n4+bMVRcPYW3wBJJCIeeddyhat77xAwwG8IuR2RD24tQOvVNvL09DCyH02W6uPuYUh6sPngbgop7BGAwatw6PJcTbldS8Uj7bcrLx80ScD9lHbT+dkKlaXxfKOwI4k2/+eC65VS5Qnt+2ZZfm6k9ja2evulGL0Vlf8Lv3laAZcUv8Uf/dWtXMzEEhOpnvdp1iYtLvvPfTAp7d8Cbv/bSAS5J/b3h9mpbQNIg4D6KGwtFVel9ACCGEsDGm8gIKNQ1vF28AQj1CKa8up9AzSGZDCGFj2nUQIjg4mLCwMPPru+++o1u3blx00UXtWQ3HUZzV+pkQAK5e+pOQmYdaf66CFPCNavlxLp7g5NZ+aSBaqtZ6ECZl4reDe7gwSc/HGzDZ8vUgart9RByJQWP4vkcPypwh9W9/o2DFCirT0xs+QFIy2YfKMsg6rP/bTNna0bURQljqrHV/Vh/MAGB8QigAbs5G7j8zG+K11UcprWhkNoSzG4QP1HOu27Ki0/pAgJMLn24+wYgz+eb/siyJ5b9bea2os5Vk64tgW8LZDSIHU951op46ct9SSN9ruw8tCGFFaw5msHbDPh7YuQQD+nfegOIvO5fgU2il9Vv8oqDnpfqs6OSN+gClEEIIYSOKC9Nw8fDHoOm3NzVNo6tvV44aTfq6EHJNKITNcOqogisqKvjwww+ZPXs2WhNPupWXl1NeXm5+X1BQAIDJZMJkpXQAJpMJpZTVzmcTKopAM+gLUzcTl0Xxh/SBoysgOEE/77nKO6k/BdpAWdWmavbn7Kd3QG+MBmP9Y73D9ePd/M65+Db5WZfm6U+LuXiByUR6cTqVOw7jXmmiKKorTtHR51Set5uRGcPi+d+GiYyMOcUFR3NJnfUAGAyEznsKv6uvrnuAR7C+GHlpPrh6N3jOTvldb4bNxZy+B/xj9X8HB77Rv9M+LUxP1gI2F387cMSYa5P42yj+/JMQPQJMJpKzi0nMLMbJoDEyPsBc1rWDInnr12Ok5Jby3sbj3H1h14bPFdgdMg9CThL4Rbe6am0Sc95J8I4gLbeYOUv3UNN9y1Fe/G/FNgYOOI9wX3frlVdbcZa+QK6F8ZhMJkxO7pjCh0Flkb54tYuXVdrWVtnKv/P2LL89+gRNsZU2r5FVVM5DX+xi3Mlt5gGIGkal+Pyr35hx3zTrFObiBT0nQ/JvcPB76HqRvq0ZttZmtkraqeWkzSwj7dRy0mYtYwvtVViUhpt7SJ06RHtHsyL7IH01J4wFafp1ZQeyhXayN9JmlrGVdrK0/A4bhPjqq6/Iy8vjtttua3K/hQsXMm/evHrbMzMzKSuzTv5fk8lEfn4+SikMhs6xVrexIAVjhZGKjIxm97U0fpcKI9VHNlPt18hNleZUV+CWcZIyv/OhtH69koqS2J+3n8K8QuJ94ut9bqhwxTltP+WG0HMrn7b5WTtlHUSr9qTyTFtvz9xDt11HATCOHk2GBT+DxlzR04uvVvpS7FZCqQu4VwAmE6fnPkVZzwQMIcF19nfGF5W4naqgXg2erzN+15tjUzFXleN2bBtlXSdBdi4GrwRc9vyovze6tEmRNfEbsw+Bmy8mr/A2KceW2NTPvANI/G0Qf2UpbtnplAVUQ3EG3+zQf68PiPCirCCXsoI/dr1tcAj/+DmZ19ce5eI4dzxdGxhUBwzu3XDZt5KqwB4oJw9Mzh4oF69z+l3QFjG7ndhHeeQIdqSk1rm9mau8SNBOsvPoKYxRDQ94t5Zr2jEqDCGoasv+fp4dv5PyhZTDVFW4tUn9bIGt/DsvLCxst7Lao0/QlIbavLCykIN5B+nr3xd3pzYalGuAUoqHvj7K6F0ruW3/D/U+r0bjjaPlxO9LontwK9My1ebZE6fyIzht/oSKLsMweTZ9TW4r31NbJ+3UctJmlpF2ajlps5axhfbKPH2MyuC+9e67uFW6caAsmy7HtlMZPrhD6lbDFtrJ3kibWcZW2snSPkGHDUIsWrSISy+9lIiIiCb3mzNnDrNnzza/LygoICoqiuDgYHx8Gliw8ByYTCY0TSM4OLjzfLmrUsGzG4SENLurxfF7jIbkDRA8tOk8zY3JOQ4RPfAJrT8KXWWqYnPRZi7teSmbT29moP9APJzP6jQFBUDBXv2/hnP76rbJzzpvO0T2A58QTMrEgT3JjDuup2LqdfN1uFrwM2hMCDAjypnILSaOhWn0OXHmVpDJhE9JMR4hfeoe4HEenNzc6M+9U37Xm2FTMadug9gB+HSJObMhBJxK8Ck7DnGj26RIk8mEU8EJAqsz0IrTwc8bAs5xINFO2NTPvANI/G0Qf9YR6NILn1D9htvWU8kATOrXhZCzft/eGhjExzuyOJZVzDeHi3hgfPdGThoCPh76WhMV+VB6CvKL9FQnzh76U8YunrX+W+t11oxEq8dcUQTeHhDdA5fi03U+ysOLAK2IgfERhLTFTAhTFZwyQlS8xTMv68XvVqXPOmvF319bZyv/zt3c2m+gpz36BE1pqM2DVTDVHtXszt1Nv6B+xPrEtnk9AD5ad5QhS//LxBN6Wjf3QeeTfmgnbmUmXKugxMuPXGdPnl2bytJ7huNktOJ3JCQEiuLh+C/gDoT1a3RXW/me2jppp5aTNrOMtFPLSZu1jC20V/aBUsIiexHsV/e673zv89mRsp6+pQUQHNS6jB6tZAvtZG+kzSxjK+1kaZ+gQwYhkpOTWblyJUuXLm12X1dXV1xdXettNxgMVm1gTdOsfs4OVZoLwT31BYstYFH8PmHg7K6v6+Af0/h+jSk8peeVbaCM43nHCfMMI9w7nJ6VPdmTvYfhEcPr7mRwAa8QPU+13zmsK3GGVX/WVRV6W/tEgMFAZnEmRVsP4VFpIjs8ll5dW3+zd+rlw0l/S2NTLyh2Bc9ywGDANTa2fgzeYVBdod9Acmu4Q97pvusWsImYK8v0xWh7T637byBysJ6WKf/kuf27ak5JNi4Zu9AGX6fHf+Rn/SZfSIL1y7IhNvEz70ASv5XjLzylp/YxGCgur+L3Y3qu9XG9QuuV4WIwMHtiD/7y8Q4WrU/ithFx+Hs2MrshqJv+qq26CiqLoaLmVQTFpyH3zP9XlICTq/67o9aAolVjLjilr99kMPDj3rqDEOWaK9MHd6GLl9Hia4wWKckHd38wtuwStXb8lcUmTHu3YvAdiHN45539ZQv/ztuz7PbqEzSloTZPCEwg3CucrelbSS9O5/zQ83FzarvBmSOHTuD52P8xOPs4SjPg9+hsTlzch9PpiVSnpNLtuaV4Z+Vy9+Ef+Y9xCos3JvPni7o1f+KW8AmHXlfoqc9KcyB2lL5YfANs4XtqD6SdWk7azDLSTi0nbdYyHdpeJhMVFYX4+3SpV36wZzAGN28KysvwKzoNvm2XAtkS8r1qOWkzy9hCO1ladofUcPHixYSEhHDZZZd1RPGOoSTbOotSny2sn/50YUspdeamRv1f/JXVlRzOPUzvwN4A9PDvQX5FPunFDSzA7BOhn8dWFJ7Sb/yfWcMitSiV8G0H9c/GjLdKEaHdovn54tuIyoRjYfoMlJKoOJzDGshrqGn6egO5x61StrCi03shsBu4nDXDx2CE2AvhxCZ9UVVrqiiBxNVUhF8Abr76q8cl+uKSabutW5YQnZXJBIVp5rVb1h/NoqLaREygB92CPRs8ZHLfcHqF+1BUXsWbvya2rDyjk/5v1ScCgrpDxHn6Db4ek6Dv1XDezRA7Gk7tbGVgTShIAd8u5BRX8N3uNAD6dfEF9HUvhvbprg/At4XSnFZdv+R++ilHJ1xC5luLOHbJOPKWLLFi5YRomK+rL2Ojx+Ln5sfPyT+TUpjSJuUU7t1P5k030Cv7OCWublS89gTbRoXg4+rD5QOvp//oaRx79FrKneDyQ79wXsZhXvr5MMeziq1fGRcP/ZrC2QMOfKuvkSaEEEK0o5KSTJSzBy5O9R9SAOjq15UkowY5x9q5ZkKIhrT7IITJZGLx4sXMmDEDJ6cOywbVuVWU6P89+2anNfhGgqkSChsYIGhKSQ44u+lpJM5yOPcwEV4ReJ1Z4M6gGRgYPJCdmTupNlXX3dknAgpSz7X21peforcJen7evUn7GHw4CYCE6VdapYi0/FJe9+jDE+fN4aO+wyhyA7fkY6TsPdTwAf6xeuorYTsqS/V0LqF9G/7cMxCCe0DyRuuVaaqGxNUQnIDJu1baO1cv6HmpPlCVss165QnRWRVn6IMCzvqTzWsO6vlmx/YMQWskNaHBoPHQxB4AvLcxiYwCK+ar1zT9KWSjc9sMypuqofA0eEfw2ZaTVFSbGBDpyx2jYgE4klEE7gH6YEFbOMeHKJRS5H36GelPzQOlqC434ORSRdqTc6lMb+E1ixDnwKAZ6B3Ym1FdRrE/ez+/p/1ORXWF1c5f8PPPJN14E/5FORwMDeToa7Mo7hXF2Kix9A7sjdFgJN4/nr4jr+DgjJGUO8Gc3Z/jUlLII1/uxmRSzRfSUgYDRA+F8AFw+EfITbJ+GUIIIUQjCgtP4dLEdWO0dzQpzk5U5B7Tr3GFEB2q3QchVq5cyYkTJ7jjjjvau2jH0VazIEC/+RHWv+WzIfJPgk9kvc3l1eUk5ifSK6DuQsphnmH4uvj+P3vnHR9JXf/hZ2ZLtmR303vvud47xzV6lSpgQQFRURRUqoIUKQr8pCgqKkUQ6eWoVzjgjuv9cjW59J7dZDfJbrbP74/J5S6XTS79cjCPL17g7sx3ZidTvvMp7zfF9uLuKxiiZCkZT/tA93xkcNR0VcdaO6xYNxZh9AapiU0jpqCnufZgKLM6CUpg00exx7iAjwrTEJGwvvBS6BXCY+VjpFSkjR0a9kJ0Tldi0Fdfj3Pjpu6BsYTJsvSKbYBV071RsV6W5Aql1azRQ945srRZxQa5U0lBQSE0x9znJUnis84kxNLCvv0GlhTEMTUtArcvyF/WlAz/fsUWQuP+4R+3vQEMUQREDa9skr0vvjMnnSmpkQDsrW3Fq7XIxQUjgcsGhijqHB2sP2ylztF7h1gwKNHi9FLy2Vc03PATGu6/v+s7v1tEFRaQ2/QrKkdmXxUUQhCpi2Rp2lIMagMrK1aG7uwdAJIkYf3b36j5+c0Egm7enpTCobuvY+G0s5mfPL+riOcIuZG5TPnOzeyaH0+Y286tu95ic6mN/24ewesgOhtyz4TqrXKBgzKvUFBQUFAYBdrb6zAY43v9Xi2qSYnIoV7yyQWkCgoKJ5VRb0U488wzkZSJ6cjissqV1SNFZCbUbJcDEIao/q3TWgPJ03t8fKD5AOnm9J4m1MDk2MmsrlxNmimt+/dHJJli8wb7C4YHp03W5Q6TX/6q26uJ2igHhNpnLxy2zWTGGBEFCEoQ9MbyZXoeZx+oxLzqIwL236CKiOi5UmSmXOmunzps+6EwSI50QYy/GAD7m29Sd8+9ssSLKJJ4/31EXHaZXE2YeRoc+hTC47vOq0HRsBfcDrnjoTfUWjlgcPgzKPtSlndRtBYVFHrSWg3p8wE5AN/Y5sGgVTErs+/nnyAI/ObMfK7+5yb+u7mSGxZmkRI5jB2KUZlQsxU8baAJLQs1KDqTLp8fbKS6pYMIg4YLJicRphax6DU4OnwUt2kY7x0BOaZgENwOXt/bxh3vbCIogYDcdRJv0dHi9NLs8tLs9NLi9KJvqOHavR8yv66IMMAtqgkL+hGAgFskzOwnIAjYI+MYxiOkoNCFr74eb3kF2oz0bjKZKlHFxNiJXV4RMfoYJsdNRiOG9k3ojaDbTd1dd2P/6COqY+GtCfmoTv8eTy+7ELXY+2tcQeIkPNffxjbr7Uw/sJszK7fwyMcalhTEkRQxAobyIL8TFF4ozymKV0LuGSOzHQUFBQUFhU5czgZizWl9LpMVkcW22i2kNJcijoQHo4KCQr9RIk5fR0ayEwLkQGX8+P53Q/g9ckDU2L1q1OVzUdFaQUFUaINcg8ZAbmQuu5p2df/CnCQHhU42rdWycSdylVpF3UHG75Or2FMuuWDYNpNo0fPwJRNRCQIgUqqZxrbCOHC7aXn9jdArKZJMY4f6IrkLQqPHV19/NAEBEAx2lwrRR8qSTUORZXLUQMM+yF7S5VUCvXRfqNSQswykoJyMCPgHv10Fha8jXqecSOx8pq7eL3dBLMiJIUyt6mtNAOblxDAvOxpfQOKp1cUnXH5AiCqIzoWmQ8M7rqMKLCn8Z6PcBXHFjFR0GhWCIDA5NQKAHY1B8LQevZcNF247LYEw7nhnH0eUYyTgs4ONvLq5kk/21rO5rJmmqnquWP8az67+E/PriggKAl/mzuWBbz/An6dcjgQEvCKCRuLpyZdQqTIN734qKCAXFZQsWUrltddSsmRpSP+RGH0My9KXoRJVrCxfSaOrsd/j+xoaqPjOd6n44iM2Fqp4fvJpHMj+EY99q+8ExBEmzzyXcedcxfYcgev3v4vJVs/d7+wZ2WIwtRZylkLAO7Y83BQUFBQUvpa4nU2YTH0bTpu1ZtSRWdib9irvuyeRMkcZZY4ybB02fEHfyd4dhZOEkoT4OuIamqljv4jJk406PW0nXra1BkyJPaqsDzQfINuSTZgqtIkQ9GJSbUqSPSmGO/gxUOxVXUbbNreN5s1FhHuClEakMHl2L9r/g+TKmWmsu2MxNy/JQfLFsDa1ALsRWl5+GckbQm/YGA1IIyeXodA/fB1gK4EE+Xzwllf0PG+PlwqJHy/LaTUeGPj23A4oXwdZi7p5wng+/JDSpctCB0pEUV5eo4eSleAfPv1qBYVTniNSTJ3eD58d7J8U07H8+qx8AN7aXkNp0zBLCcbmg61YvmcMB542CPqp6Ajji0NNCAJcM1uuLvPV17PEVUlMh53t1W2gDQePY3i2ewRXMzUePaGk6y+dlsyD5+Twsn4/r37xJy4s+wq1FMRw2kKy3n2Xi557iKd/fgarMmfzccYcQGC7OZcdmePIiBkBjyyFbzS9FRW4D/c0vlSLaqbGTWV6/HS21G9hZ+NO/Ce4Zjt27+bglZextXUve/IMvJl0FVvNF/P0lfMwhvW/kX32D24nLXkce9N9/GrnK3y5v573do5wckAQILYAmgYxj1FQUFBQUOgn/qCfoKcVg/HE8/LMqHxq8IO9YhT2TOF4GpwNHGg+gK3Dxs6mnXxY+iEflX7EVzVfUWQtorK1EofHQVA6yTE+hRFHSUJ83fB1yFXNIQyghxWVWk5ENOw98bKOmi7z5iO0e9upaa8hL7JvSaWQJtUaHYSZZNmpk4HfCyWrQVR3dXdUt1WjX1cEQO2UeWhUw39pJVr0/GxJLjHherZqZnMw14y/sZHWTz4JvcIRSSaFk8cxXRAA2owQ7Z+iiDb9mBZSQYCMBVC3E9yt/d/WkfMyZYbsC9KJr74e12OPH9VnPr77omub88EQA8Wfgm8YTXQVFE5lWqu7ks1NbR52V9sBWR7oCCG7jI5hWlokSwviCAQl/m/VMHdDhIWDMXb4zGA7ky6vbKpEkuD0vFjSo41dFd+zn/kdL376B/QrP5Q7t4Y70e2yEZ+QhHic37caiZsDxcz+/U+IfvVfCC4nYeMKSXvhedKf+zthubIH05HOwT3RWQCEt7q5/4x4Ei0jJD+j8I2lt6KCsvPPp+zSy2h4+GFaV67E33z0Gok3xnNm+pn4gj5WVa7C1mELOXbL8uV8ect3+SrGhiEmmf8l/ZL9pin8clluVzdSfxFUKhb+9inifQbao6q5ongF9y3fi7XdM9CfPDAiM8DZJHeTKSgoKCgojAAOjwOTBEKY+YTLJoUn0aQ34WzqR/xKYVgJSkF2Nu1katxUZiTMYGnaUi7OuZhFqYvIsmShFtXUOevYXL+Z90re49PyT9lQu4H9tv3Ud9TT5m1TJP2/RihJiK8bIy3FdCxx42TJn74ClpIkd0KYu7fI7bPtIzcyF43qxNq4IU2qzUnyuKNNhx0OfCAnQXLPBFFEkiSqGg6RvVPeP8u5Z4/Y5rVqkatmpeLzx7E7fRzN4WB74YXQN2VFkunk4nV164IACLT2TCroJ03spiMNyIbSiVPkrob+PHAlCcq+gIg02RzyGDyHDvUcozej1tSZssTYoY+VwIGCQjAod911Pr8+P9iIJMHEZAtxZh3QPzkWgFvPlBPuy3fVsr9uAMnF/hA3bvgMqlur8RgTeX1rFQDfnZPeo+JbROKqda/gsHvlZ+Jw4rIRG5fMFTNSiemwM6mphEXVO3h9x99w3XcP/ro61EmJJP3xUTLffBPjnDk9hrhyZhqps2U/pKSWFs5KU6a6CsOPNiM9tI+SJOHeu5fmF1+i5uc3UzxvPofPO5+6e3+PY/lyaLQyM2Emk2Mms6FuA0XWIoJSEF99Pe0bNrD3D3fz7j9vpznMx+lx83h55u2UaaKYmRHJTxblDGpfNUlJzL/pPqLbJPKCnxFffYD7lu8b4hE4ASo1RGXLnlgKCgoKCgojgMPZhEETLj9zToAoiCQkzcTauE+WC1cYNUrtpRg1RhKM3WMeBo2BxPBECqIKmJ04mzPSz+CinIuYmzSXFFMKEhI1zhrW167n3ZJ3WVWxii31W2j1DvO7lMKooryZfd1wWkcvCaHRQVQWNPbxIuOyyVXgx0jDODwOGlwN5ET0/2VqcuxkiluKcflc8gfmlNHXmm2pgEOfQOJkSJ3V9fLZ7G7GvWsfJk+QEksyc+ZPHtHduHp2GipRZCVzKUnT4tm3H9fmLT0XNESBIMoG2gqjT0MRxOR2dUEAND35FADGhQuJv/ceADp27sK1bVvP9eMK5G6bhqITb6tmGyCENH9vX7Mm5CpihCX0WElTICYfDn48sE4MBYWxQscwGSY7G0FnAbUsGbimU4ppcYHcBXFscN6nInSXUSfjkyycNykRgMdXDLOHgzkRgn7EXqqq+00wAG0NLC8Du8tHSqSeRflxISu+VZLE4UNN0DGMnRCSJP/tDFHkblvDi5/+gUe/+hu3b30FfcVhxPBwYn91K9kffYTlwgsRQgWAO8mcUkCHSgvtfvzlIxxsVfhGoklIIPH++44mIkSRxAcfIOeLz0l6/DEirvo2Ybm5AHgPH8b+2mvU/uY2ShYvoWTpMqQH/syMrW20VJTw3v8eZPOFS/n43uvYsPUdsuskli69jk8v/jUbGjyYwtQ8ccUUVMe3CA0Ay3nnMW3mBcS0SpzT/DKrtx1i5b6G4TgUvRObB7ZDcoe2goKCgoLCMNPeXovOGHviBTvJjMylRpTwN/eUTlQYGbwBL/ub9zMpdlK/lhcFEbPWTKoplXHR45geM52zMs7iguwLmB4/nTBVGEXWfsRHFMYsShLi68YgOyEkSaLN20ZVWxV7rXv5quYrPi77mFLHCW7Q8ePAeqh3gx9HtZwwOIa9tr0URBX0MNXrS9Kih0m1MVbWvx+NLLYkyUHe6i1y98NxlebV7dWoP98NwJ7cGaRFj6z2dKJFz7LCONqEZGqzxmE1Q/MLL4ReOEqRZDopeF1gOyybTHfSsXMn7atXgygSf8ftRF11FRGXXwZA/f0PIPlDXEMZ82XJs74kT2yHwV4JmQu7dOuP4N6/H8ebb8n/57iAXcPv7yPo7qWLKX6c3Ilx6BPFV0Th1CLgh/3vo2nYNfSxHEe7+Lz+IF8ekiUAl3QmIY4E5xstsClfICjQe5cRcMuyPEQBVu1vYEfl0BIldY4O1h+2UufokD+IK0TdPMTkRls9GKJ5abOc4L9mdjoqUQgpIxcQBHaqEocv4QPyM12jx9dkY847f0fkmA4uQSDtlZeJueEGRJ3uhEMVJEdQakki6BfxVVUoEnMKI0LEZZeR89lq0l58kZzPVhNx2WVo4uOxnHceiffeS9by98ndsJ6Uv/6FqB/8AN3EiaBS4aupwfHe+7Tccz8x19xJxJOvsj9ZwuCVmHNAIrZNoHrR+Tz1uTwHf+DiCaRGDX1umfC731EoJJHR3MbZtle4+92dODpG0BhSZ4EwC6q2k9C5rKCgoKDwtcfZXoe+H34QR9Cr9YTFFtJUu3kE90rhWPbZ9pFqSsWsPbFkVl+oRTWRukjGRY/D1mE7WpyscMqhJCG+bvTDlNoX9GHtsHLYfpjtDdtZU7WGFbUr+Kr2K6rbqhEFkSxLFrMTZ3PAdoCK1j7Me8JMsum0tZfgR2t3P4gWdwst7hayLFndFuuPpEU3k2pRhPD4ke+G8HugZJXcTVB4gdxdcByV9QdJ3nYQANXiZSO7P518d04GACtVCzicKNC2Zg2e0hDJhshMRZLpZNBQJHumaORgmSRJND7xfwBYvnUxYVny+R97662IFguegwdp+d9rPcfRGiFlpizLFMqIvb0JqrdCzlJQa7t9JQUCcpV2IIDm9NPJWrWStBdfJO35fyOazXTs2EHtb36DFAiE/g0xOZA6G4pXyl1ArmbwtMvXxMk2hVdQ6A1PK4SZEV1NULN9aGM5qmR5MmBreTPtHj8x4VomJctdREeC800WgYAIVTHyaqrIyJDD5cSFc8k0+Xk4lG6I17ZUMv+Rz7j6uU3Mf+QzXttSCVHZiO0NMJQJeWsNBzvM7K52oFWLXDlT/u3B9u5m2pIg8NTky1jvCpe7J4YrwN8hz1+aDpQgHi8hJ0kE7f03wS5IMFMcIR9rZ1XzyfOQUvjao0lIwDh7Vk9ZxU7UkZGYliwh/vbbyHzjdfI3byL1X/8k5qc/wTBzJqjVJNhh/n6JrHpQSYAk8deXPycQlLhwchIXT00OOfZAUZlMJP3xUfLqRU6vLCa+6T0e+mjPsIzdK7EFqFqUilMFBQUFheFFkiTcLivG8NDP395ITZ5Ns61Y9lJVGFEcHgdVbVWMjx4/bGOqRTXp5vQTF0srjFmUJMTXCZ8bgn7ZqPIYGl2N7LPtY0PtBj4u+5gPSz9kd9NuHB4HEWERTIqZxLLEZZydcTZzk+ZSGF1IYngiUbooFqYspMhaRFVbVe/bTZgoV2sfH5j0ueXKxmNa5IqsRYyLHodKVB1d7Di96d4kLY41qQ5KQblCdSSTEK5m2L9cTjzkntElyXEsLe4W/Hv2YeoIUmxJZuqcCSEGGn7mZUeTFWOkVMigIyOPJgs0v/RizwX1EaDSyMFqhdGhqwvi6MPW+dV6XJs3I2i1xN50U9fn6shIYn9xMwBNTz6J3xZCTiU6W76m63Yetx0nlK6RTax1PaWVWv77Ku49exDDwzH8/GddgRLj3LmkPPM0gkZD28pVNDz0cO9GT5HpkHmarDdfvk7ujNj7Dux8Bbb/B3b9D4regn3vw8FP5IRd6RdQsUFOjtTtgqaDsmluW4OsH+9z98/nQkFhMLjtYIjGk7ZQToLX7hjcOF4n+N1diefPDshSTIvy4xA7JVF81dVIgM0Mk8skKuIF/CI0PvIwki90dfEvluaiUQmsK7Gy4fDA5JOCQYnV+xu44609BI/4zEtw19tF1LX7CVjShqa/7qjmP/vlpOT5ExOJMsqJTcfyD7ot5rv2R6zImM3OKgeSPnL4uiE6iyiKMBPkONkZUUSbntbvoaKMWhoTMgBoLamTDXIVFMYAotFI+Pz5xN58M+n/eYmsjz7q0cUYFER2BsNJjtDzwMXDO680zJhB9A03kFcDV+1bz4pd77O2eARlmSJSET12+X1AQUFBQUFhmGj3tWMIBFDrQhf/9EZceCLtxijsx79bKww7u5p2MT56fL98YAdCdkQ2ZY4yAsFeiikVxjRKEuLrRAgppnZvOxvrNiJJEqmmVE5LPo2Lsi9iSdoSpsVPIysii2h9dLekwLGEa8M5LeU0djXtoqa9l3ZqQxToI+F4bb3WGrlLolMGxtphxelzkm7uLu0QSm+6N0mLIybVh1oOdZpTj1ASorkUilfIVejJ03u8IB6huq2asLV7AVifMpm52aPjxyGKAtfMkY/jNuMiDicK2N99F39LiGCQIsk0utTv6d4FEQzS9MQTAERedRWapKRui0deeSVhhYUE29po7FyuB2nz5OCis7OaN+CHw5/JiQ5LzwpJX309Tf8nd17E3HoLYkxMt++Ns2aR9MdHQRBoeeUVmv/1r95/jzkJ8s+GcRfCxMtg8rdh2ndhyjUw/luQe5acCEmeBrEFsjm2IVr2wpCCcoCypVwOBpd+Dvvegx2dCYy978jJi8NroHKjvEzjgU7De6U6RWEQdNjlpJxKK8vn2SvlZNhAOSLF1HnvP5KEWNopxSRJEk1/fpJWA0ROmsGkv77IhBtvpzxdh3P9Buof/EPI5F5qlIFvz5SD6Y+tONh7ArCTVrePj/bU8es3djHrodVc9+JWjl8jIElU2Fz4I3PAenBwnUqeNto7PLy+V+56+O7c9K7f2fqBnITQZstShFG2etSigLXdQ1PAMHy+EJ1zmPWtKj7KOMZwWhRJvP++XivNe0PIywfAX1yF1NY4PPuooDDMhKWlkvjA/V1zZUkUeXLypdgMETxxxWQs+uF9cQeI/dlN6CZMYGqFh2/vW89tH75Ga2/yjENFEPFHZPXeMa2goKCgoDAI7B47ZsQeBbj9IS5pliLJNMLUtdfh9rvJtGQO+9hGjZFIXSTV7dXDPrbCyKMkIb5OhEhCtHhaiNXHMj5mPCmmFMK14Qi9BNR7w6w1c1ryaWxv2E5de13ohRImQMOe7hXOx0kx7Wnaw/jo8YhC99NOm5HeM8jfR9Vjl0m1Si2v12Ef0O/pk2AQqrZA7U7IO1uuBO+DivoDxGyUTS9bZi7EpBv+l8XeuGx6CjqNyKpADurYZOr1HuyvhZD0icyUg8BK9fnI43XJCaxjuiDaVqzAvW8fotFI9I0/6rGKoFKR8LvfAeB46206du7sOa5GB2lzoOxLOQFR8ZWc+IsP3dpY/+CDBF0u9FOmEHHFFSGXMZ9zDvF33A5A42OP41i+fGC/VRTl7qCwcDkRGR4nX+9RmbIZZfx4SJoq73fWIjmRMf5imHwlTP2unMDIWiwvE5Ul/x5BlKslW8rl7grb4YHtk4KC23G0M0gdJifJmsvk5OBAaK3uSvCVWZ2UWp1oVAILcuWEnmvDBlxbt2KN1pB/5fUYZ89i+uIr6fjpt3FrBeyvvUbLSy+FHPpnS3IIU4tsq2jh7e013bwdJEmipLGd574s5ap/bGTa/Sv56SvbeXNbNdZ2D3pNz2mbKEB6tAEpzAy6CLD3IaHYG44aPqtV4fUHmZBsZkpqBAAdO3bgq6lBNBiI/fnPAPAW7aEg0QTAvhb1MHZC2MAQxeayZtyd8nLGxYu7tPYHSsy4fDyiGsnhJVClBEAVxi5HvCWMf/0HN11wLysyZvOT07OZnTUyhS2CRkPSn/6IoNNxQVEVBXt3ccdHb+IP9uLvNkQCkVnQfFiWb1NQUFBQUBgGHB4HZgRZHnyApCTNpK2tBk/HwLqSFfpHUAqyq2kXU+KmDDj22F9yInIosZeMyNgKI4uShPg60fkCfyx2t53IAbaohcISZmFB8gK2NmyVPRmOx5QAokY2ogY54N1a22XqWe+sxy/5STGl9FhVHROD6rhKbW1WVq9Vj91MqodTksnnlrsfPK1QcL4sZdQHDo8Dz56iLimmCTPHDc9+9BOLXsPFU+RK3dr4ZRxOELC+8jJBr7f7gjozqHVHq+gVRo7juyD8fpr+/CQAUT/4Aeqonp4iAIZpU7FcfLE8xAMPhvZpiEyXpc0OfADedkibG3KstlWraF+1WtaZvv8+BLH323zU979P1LXXAlB71904N2zo5w8dIoIgB4j1EWCKl39bbD4kToa02ZC9WJZAayiSOz6UrgiF/uJ2yIH4I2h0kHeW3ElUX9S/MYJB2aS58/l1pAtiZkYUJp1G9nh5Ur6uXefMIy19IgA6tY7x886n5ZarAWh45FHa1qzpMXy8Wcf352UA8Ks3dnH1c5uY98hnXP3cRk7/0+cse+IL/vDRfjaU2vAHJbJijFy3IJNXrp/NrnvP4tFLJ6I6ZkKv06iO5phjC6DpQD8P1jE/2V7Ff/bLHRTfm5PR9cJwJDlpOuMMDLNmAeAtLWVmjJxw397E8JjXe9pBpcXhE9lf30pBi9wJaT7zzAF3QBwhLzmSMksiUlDAW9cA7tah76eCwgjRGGbmJ0VQJhiZmGzhl8vyRnR7YZmZxN95JwLwy682sWdTCS/vWjEi0gaSxgiGWLnAQEFBQUFBYRiwu5sxogKNYcDratRhGGLHU1c1Su++3zCKW4oxh5mJM/TfNHygxBvi8QV8NLuHqSNbYdRQkhBfJ1w2MHYP5ts9diLCIoZl+EhdJPOS5rGlfgtNrhD6ygkTj1abumzyA0ErPxSKrEWMjx4fMhNqf+MNAk1NiBYLCfffByoV3pISXFu39roveZF5ODwOmjSa4UlCOK2y/4MpAbKX9DD5DUV1WzWmDfsBWJs8mYV5sSdYY/j5Tqck04uBQsJ1FqoDNlo//KjngpYU2WRVYeTwOnt0QdjfeQdveTmqyMiuYH9vxP36V4jh4bj37sX+Rk9jdkA2itZHyB0EISTUAu3t1D/wIADRP/whurwTBzHibvsN5nPPAZ+P6p/9HPeBgQcwRwRDFBRcIHdI7HtfMVhXODGSBJ42OfF6LBq93NlmPQQN+048jrNR7qbo9AFa05mEWNIpxdT++ee4d+3GbdYRtuT0bon+vMg8nAsno/72xSBJ1P7q17gPHuyxiW8dZzQrSbD+sI3KZhdalchpuTHcc/44Pv/1Ij779SJ+d/445ufEdBpGp7HujsW8+MOZ5MWH4/IG+MX/duIPSLKRttc5sMRAMMDBkhJ22vVY9BoumCxLxkk+H20ffwKA+YILUEdFoUmVzapnuuVihE11ATlxP1Sz+s4iim0VzYiBAHl2uaBBP3nyoIbzBXwUJJg43NmN6a5pUXwhFMYsr22pZMGja9hbKyfKzhwfj1Y98q9oEVdcTvjSpWiDAe76bA3/+aKCL6u/GhmN5dj8QSVIFRQUFBQUQtHeXo/eENOrbPaJSEyZS3PtthNKoyoMDLffzaGWQ0yKmTSi2xEEgeyIbA7bFeWEUw0lCfF1we+BgLdHO9pwJiEAovXRzE2ay8a6jVg7jqusj0iTjTzbGuSOiE4pi+q2agRBICk8qcd4gbY2mp56GoDYm39O5BVXdMkuNP75z70+FERBZErcFLZ31BNsrx9ai7e1GEpWQ/pcSJrSrweZJEmU1+3HvF72g9iVM50JyT3NgUeaCckWpqZF0CGpIOssyhIEml54vudxs6TI8lgKI0f9HlmGqLMLIuh2Y33mLwDE/PhGVOHGPldXx8QQe/PPAWj6v/8L7e+h1srSRtrQFR9Nf34Sf0MDmrQ0Yn76k37ttiCKJD7yCIaZMwk6nVT96EZ8tSNo+D4QRFGWa8pZJuv6l34udywpKITC0yZfG0KIqY3WIHdENO6TfUf6wlHdJSXY7vGzqUxu1V5SECd7vHQ+s3xXX0ByQm63VTWihoKoAhp/eA6GOXMIulxU/fgn+Ju6B8BbXMd1rHVy6xl57LjnDP5z3Wx+uCCTjJjQ941Ei57T8+J47nszMIWp2VZp59n1NfLza6DBvrZ6VpT78aHm8ukp6LVygrN93ToCdjuq2BiMc2YDoJ8kv1BkW8sB2FXbTkBtkBMRQ8FlA30Um8qayWytQxvwIVosslzjAPAFfOxu2s3y0uUYja2URMp/x7biOqUbUGFMsq/WwR1v7enm9fLnlcVdEm0jiSAIJD5wP2J0DBltjSxbsY23ttbz4o7Vw799c7Lc1TgcnVMKCgoKCt9ovAEvotdFmGHwsoWW6FzCJInGFkXSZzjZa9tLhjmDcO3AvToGSoY5gzpnHZ6AZ8S3pTB8KEmIrwsh/CA6/B0IgoBOrRvWTcXoY5idOJsNtRu6tz8JgtwN0bBHDuKYU5Akib22vUyMmRhyLNs//kGgpQVtVhaRndr1MT/5MYJWS8fWbTjXfdXrfiQYEzDpo6gNdED7AEwng345IF+1GYreloNS+ed08684EWWOMoTdB7A4gxyKSCFvSgEqcWT07k7Edzu7If6pmY45oKXMdgjXxo3dFzLGynIXiqzNyOB1ypX68RO6Pmr576v4GxpQJyUS8e1v92uYyKuvJiw3l4DD0SXj1F86du+m5ZVXAEj8/b2Iuv5f96JWS8pfniEsNwd/YyOVN/yIgMMxoO2PKMZoKLwQwsyysXXLIDTvFb7+uO2gi8BXX49vxw589cdJB2qNckdEQxE09eER0Pn8AlhX3IQvIJEZYyQrNpy2FSvx7N8vdy2dMz9kcj07IpvWgBPDH+9Fm5mJv66Oqpt+RvAY49fMGCPHPzJUgsDlM1Iwhqn7/ZPTo4388TI5MfDKtgZW72+A6Fz5GvH3b0LeUH2YT6vlxMM1c44G/VuXy4bUlnPPRVDL+6SfLG9LX3IAU5gaty9IlVs3dHNqVzMYotlc1kxhc7m8jUmT+pSTO5agFKSsrYxPKz4lIAUoiCrA6q6nIyMHgI4DFUjOMWpO7aiWn88K3xgCQYk1Bxv5ycvbuOCZdSHN5sutrlHZF3VUFMmPPATAxaVfUbu8koc+2caCx97mtS2Vw7chQZDlKpt6doYpKCgoKCgMBLvHTqSgGZQfRBeCQHTidOqq1g/fjn3Dsbvt1DnrKIguGL5BJUn+Jxg8+k8nGpWGlPAUyhyKYsKphJKE+LoQIglhdw9vF8SxxBnimJkwk69qvsLuth/9IipbDiZ42sAYS2VbJTqVLqQenLe6muYXXpTHu+03CBpZY1qTkEDkVVcB0PTkk322yE2JncJhyY27+QRtWB0tsh548Qr0h96X/1tjkKvKx13UU76jDzwBD3tte0n9VJae2h6by8Lc0ZdiOsK5ExOJMmopcYvE5i2jPF6g8fl/d19IEMCcpHRDjBT1e+Tq4075lkBbG7a//x2A2Jt+hhgW1q9hBLWahHtkk2r766/Tsad/GvaSz0fdPfeCJGG+8AKM8+b1WKbO0dHNAPd4VGYzqf/4B+r4eLyHD1N1000EPWOoqkAUIXka5CyF2u1Q+kW/g6wK3xDcDtrWb6N02Rm033IrpcvOwP7mcdJmYeFyIqJ+N1hDVD55nfJ51emvdMQPYnF+HFIgQNPTcheE5drv0azqCPlsEwWR8dHjKXKXkfq3Z1FZLLh376b2zjuROifOiRY9D19y1NtBJQg8dMkEEi36Af/scyYmcu1cOXnw6zf3UNUWhIhUsPWvsmv9jl3UBKM5LTeGzM7Oi0C7k7bPPgPAfP4FgNwBeEQeyb17N5NS5OfmPrsaXEM0p3bZcGks7Kl2UNAsBz71U/onxVTVVsWn5Z9i89g4PeV0psZNJdOSSb2zHlNBHj5BhdTsJFBXPnTZqJGgdofybP6GUNXs4vEVB1nw6Gf84PktfFxUTyDEKakSBDJiBq5xPVjCTzsN7eVXAvDrbf9jSpkbs2ovd769c3g7ImLyZF+IgG/4xlRQUFBQ+MZh99iJQIQhVtvHpszGbz2E0+ccpj37ZrOzaScToiegETU9v5QkWWJ5+0vyP9tePPE/219Cf+BN2Pky7HxF/mfHf6Bhb9ewRySZFFmtU4f+l9spHMXrAutBWSZkrOCyyXJIx9DiacHUHsC5cRPajPRBmzv2RoIxgRnxM1hbs5aFKQuxhFnkQGHCRHBaCQqwz7aPWQmzQq7f9MQTSD4fxnlzCT/99G7fRf/oBlreeAN3URFtq1ZhPuOMkGMYNAaSEmdQUb6O/IwFR7/we2SviNYa+d+iWm4FjyukwzQBU0KSvK+DYHfTbiLX7kW9eTcAVxR/jvHAaTDzqkGNN1R0GhVXzEjlb18c5p3401m45yMONq4jqaSEsJycowtaUqClEsL7n3BR6AeedrkLYsIlXR81P/88AYcDbVYWlosuHNBwhpkzMZ9/Pq0ffED9gw+Q8eqrJ6wGbn7xRTwHDqCyWIi/444e379fZOWR1dsISiAK8PAlE7lyZlqP5TSJiaT+4x9UXHMNHVu3UXv7HSQ/8Xi/q5FHBWOM3BVRu1Puikib0+Pep/DNxF9TSuPTz0Gwc2oTDFJ3z70YFyzo/vwLC4fcM+HQp3KCNjr76HeOGjlhKwgEgxKfHZBllJYUxNH6wQd4Dx9GZbHgu/wson0NqMXQ06hUUyoHWw5ii9aS/PRTVF53PW0ff4I1M5PYm28G4MqZaSzMi6Xc6iIjxjCoBMQR7jingM2lTexrcPGzV3fwxnfz0VZ8AXHj+pQYdLfb2Xy4EQdZfG9uRtfn7atXIbndaDMy0E0YjyRJfFrxKTMypyJoNARaWpir9/AVsKMJzs0YQhLCK1d876jz4g9KjHd0JiEmT+lzNWuHlV1NuwCYmTCTYFsQk1auiNOr9Rg0BlISRSrMCeQ4avA22FF3NPfwzjqpBPxy4YZimv21xe0L8Oneel7bUsX6w7auzyMMGi6eksyVM1PZXW3nrreLCEjSkBKSQ6HhqhsIvP8xMZ5WHlj3OntTBN7KcXDBU3pmZ0czNTWCqWkRjE+yoNP09KTqFxqdfH9tLpULNxQUFBQUFAaBw+MgDXFonRCAKjyOOH0MFfU7GZc6f5j27ptJdVs1gWCAdHMvUqpt9bJk7tTvHv3sRDLowSAdjY2Y4uKOxu7crXDwI7mwQaXBEmYhXBNOrbOW5PDkvsdTGBOMocjSKYQ6DBr3j60qXKcVDN1frGtWLcf+/Z9See21lCxZ2rMidBhIDE9kWvw01taspdXb+RIdVwgZCyh3lGPWmonW99Tqc+3YQetHH4MgEHfbbT0Mq9XR0UR9T75BWZ96CinQu+dDdtIsPO5mGuu2yxWF+z+AorfklxxjrCy1NOESSJstm3b2EjTqD9YOK3W1xUT88aWuz0QkOh5+sKf0xyhyzew0BAGWW0WyM+dTESfQ+OIL3RcyJ0NbLUhjsBL0VOa4Lgi/1Yqts8Mn9pe/6JIxGQhxv/kNosGAe9duHO+80+ey3upqmjq9J+Juvx11VFS37+scHTy8qoJgZ3FAUIK73t7Ta3WjLj+PlGeeAY2Gtk8+of6ee3Bu3HRSz+8eiCpImS4bdFdvhbK14A+tsa/wzcFffZiA97jJbDCItyKEpIjOLHtE1GyXnxVHcFR1SfMV1TqwtnswalXMTDF1XWdR119Hg9QaUorpCIIgMDFmInusezDMnEni738PgPWvz+JYvrxruUSLnrnZ0UMOOGrVIg+em4VZp2ZXlZ2Hv2iUDblPUGH/5dadlLgjSI7QdxlvAzg6pZjMF5yPIAjUO+tp87ZR62kgrLAQgCntsnn0pnqGJsfUIUsxbSprxuJpJ75N9m7QTwot49jqbWV9zXq21G8hLzKPpWlLidH3TCwkGZOIsjgpiZBfSDw1zWPPF8LVGZQeqqeGwpijqMbBPe8VMesPq/jF/3ay/rANQYDTcmN4+qqpbLprKb+/cDyFieYus/lXb5jDujsWhywSGGnS1T6iPG1d/z+/VmJR0xoW736TrRuKePDD/Vz67AYm3PspFz6zjnvfK+KdHdWUW509qg/77LyMLVAMqhUUFBQUhoTdYydckoachACITZ6NrXYLgaF4jH7DCQQD7G7azeS4yT3iel20lEFUppx4OPLPYNCZ5Xe1xv1dH2VFZJ26BtW+jrHZqT2CKEmIwSCq5EoeR/XJ3hMZv1dOiBxzE/bV11P++kuYnZ0ndGdF6EgEEpPDk5kcM5m11Wtp98q6xn4pwL7mfUyImdBjeUmSaHzkUQAsl16CriC0Zlz0D36AaDLhKS6RExa9IIoq0tJPp/Lge3j8bkiZCZO+Lcu2xOYPy8MJZM3pHY07SH53M6rgce1evQW6RonUKAOL8+UA0sZxZxPTKlG04X38zccEhjQ6CDMjdth6GUVhwHjaZWmB+PFdH1n//g8klwvdxImYeungORGa+DhibroJgMbHHu/Vn0GSJOp/fx+S241h9mws37q4xzLFDe0h9KZhS3nvQUPjnNkkPfwwAPY33xrRROaQCI+VuyLUOrkrwl51svdI4SSitugIeI+b1ogi2vReAno6M+SeISeymsvkCWB7g/x856gU02m5sbiWv4evqgpVdDRR11xDnbOORGNin/uTYEwgTBVGRWsFEZdeQvQN1wNQd9fduLZvH9qPDUGSJYzHLpc9G57/qpwv7dEnNOHesGMXdURz9ey0Ll8jf1MTzvWyPq7l/PMBKHWUUhhVSE17TZc5dXKdnLzZ0+TD7fUOvjDDZQNDFJvLbOS3yM9RbXY2KnP3rj233832hu18UfUFsYZYzso4i1RTaq/DJoUnYTC0UhIhJ5Vch+vB2dTr8icFZ5NcIKAkIU5Zjg24O1w+XlxfzrlPruX8p9fx0oYKWt1+kiP0/GJpLmtvW8x/rpvNBZOTCFN37yYYroTkYIlobkA8ZragDkJ+jcR4z1r+vfJhXlz/FNeXfkZKSy27q+y8uKGCW17bxaLHPmfaAyv5wfObeWp1MQ98sI/5j3zG1c9tYv4jn/Ha1uOey6Z4uRhmIF5uCgoKCgoKnQSlIO3edvQB/7DEefRxhcS626lpV6QxB8uhlkNE66NDFgUB8jtWSwVEZg7PBhMmyb6unfKOyeHJtHpbjxZFn0ocXgP28pO9F6OKkoQYLBFpcvBxLHDED+KYbGJ7aQl+UUJ3rOxqMIjt+RdGRC8t1ZzK+OjxfFn9JS6fi8P2w8ToY2SJpuNo+/hjOnbtQjAYumQpQqGyWIi+7ocAND3zNJKvdw3ZiNyzMUy8ks2iHyk8btBSS31x2H4Y78rPMbz+ac8v+wp0jRJHDKr/ZjWSH1VIZYSfhldf7r6QJQVVW91J2LuvKQ1F3bogvNU1tPzvfwDE3XpL75UA/SDqe99Fm51NoKWFpqeeDrlM64cf4Vy3DkGrJeH394bc3or9DSHXve3N3fxrXRmB4xNqnRhmTO9eoRAMUvfb31Fx7Q9ofOwx7O+8S8eePQSdvWto+urrR76LQqWG1JmQuRCqN0PJaqjbjaq1SpY5CfhHbtsKYwevC3VUDKKp+zNHHRODaDT2vp4+ojMRsQVqtoEuout6PpKEWJodgfWvzwIQc+OPcIgewlRhGDQn1myfGDuRvba9BIIBYm+5BdMZy5B8Pqp/9nO81cNfyLCsMJ4fLcwC4Gcft2BtrO1V6mdPZTNt1hqaxWiunHk0mN/68ccQDKKbPAltejoun4tmdzOFUYUICPgm5soLHthLkkWHJEGZUytfb4PBZcMTFsmOSjuFzbLp/LF+EP6gn322fayoWIFG1HB2xtnkRuYiCn0/5y1hFiINairi4wFoLypDGotJiKhMOaGtaNmecry2pbIr4D734c+Y9uBK7n1/L/vqWtGqRM6flMh/rpvFl7ct5pYz8kiJHD2fh4GizUjvMXeOdwiEp2VRlaAirrGSS3d/xLNrHue9TX/m/1rWcpHGRpgILS4faw428cTKQ/xrXVm3zsvfvlNEY9txnYqxBYpBtYKCgoLCoGj1tGJShSEKKlCF8B4YKPpI4sOTqazbNvSxvoF0+DsothczMSZ0BzMgd2brI0E7TPOg47ohREEky5JFqb30BCuOMXxucDZ+42RZlSTEYDGnyLpmYyHAFcKUuiMpAlOILuiWF1+k9le/JtDePuy7kWHJoCC6gHcPreTDg9uJ0WT3WCbo8dD42OMAxNxwPZq4nqaexxL13e+iiorCV1GJ/d13+1y2MKqQgBSg2F486N/QG26/m63vPkfCU28D0DB5DoHOAG1AEKj6wS+G3XNjoJyeF0tqlJ5WT4Ca+RcTZ5fYteK/3c2FzcmITiUJMWzYK2U9wk6szzwDPh+GuXMwzp3b62onMokGEDQaEn57NwAtr76Ke//+bt8H7HYaOrsVon98I2GZPSsLNpXaeHWzXIXYWeSMKEBmtBG3L8gDH+zj4r98RVFNz04Lb3lFyKCYa+NGbP/8F3V33kn55VdwcPoMipcsofKGH9HwyKPY33wT144dNP/nZUqWLB1UF8WgkhemeCi8CCIzQAqgaq2Giq9g9/9g12tw8BMo/0o2pW+pgA47KG23Xx/cdgLoCLbKk7im634GERH4Gxup+cUv+0xio4+EnGWykbNFlu5pbHOzu1q+Lmbt+xJ/fT3q+HgirrySOmddn1JMxxKliyJKF0WJvQRBFEl69FF048YRaG6m6sc/xl1SMuyJut+clc/09EhaPUEe3OzHU78v5HLvf7WNZsnEmRNTiAkP6/r8iBSTpdOQusxRRpo5DZWoIik8ieYcucrJs28/05LkBM9+uxo6BukL4Wpmb4sKjz/IRId8vzpigF3mKOOT8k9w+pwsS1vGxNiJaAbwwplsSobcKAKCSLDJTsDWOLak25xNYEqQpbO8iiniqUSdo4M7397DsXn8QFAiO9bIvReMY9NdS3nm6mmclhvb1WU0ltEkJJB4/31HExGiSOID93Pu/72O+6nfYnnwHsIXL0bQatHW11DwxXv8+I2HWb72UT4IrOeJbA8LMuQkcEyHnUlNJcR02AlIUGU/rksqKlvuJve5R/lXKigoKCic6tg9dqIF7bCpXQBEJE1DtFdgd9uHbcxvCkXWIrIjsvsuzmouhais4d3wcd0QWZYsKtsq8QX7eOcbaziqQdR84zqiFWPqwaLWQni8nNWL7MV8ZbRw2bo0rI/QZtaQMGUOlGyQPxBFTGecQduqVbR+9BHuvXtJfvLPvUohDZYth9T87gMbkuDlmfc39zDAbX7pJXy1tagTEoi69toTjicajUT/6AYaH3kU61+fxXLRRYhabY/l6hwdlFmdpEVMZG/zemL1sUTqIoftd6377+OYXlyOwQuB713Pta0FxCQsI7HdSl14DC3NkaxzdJy0NnoAURT4zux0Hv74AH+R0niIBNZpGmha/g7xl31bXsgQjeDvkIMduuF7cA8r7lZ5UjGELoJRocMumyuFhQPgKS7G8d57AMTdemuvq722pbIrcNGXSTSAce5cTGefLXszPPAg6a+83NXt0Pj44wRsNrTZ2cRcf32PddvcPn71xi4kCc4fF82dF0ykstlNRoyBeJOO17ZW8dBH+9lT4+Civ3zFdQsy+eWyXAxa+bHQVRV5rEahKBJz888JNDbhOXwYz+HDBKxW/LV1+GvrcK5dG/pHB4PU/e4eOvYUoU1NQRUVjSoqEnV0NKrIKNTRUYh6+dqxv/kmdffcK29XFEm8/z4iLruszz9FFyq1bDIcDOJVJ8MREyuvCzxt4HbI/25vkP/taZODf2FmuSI+aerwVNQojD5uB/5mObneEmbie7YMcqdey/+t/xvO9eupu+8+Eh94oPfuJEMUjLsQVHIw/vNOQ+oZ8To8L/wbgJif/AQxLIy6+jomx00OPU4IJsRM4POqz8myZKExGEh59q+UX34F3pLDlHUG+gd6rvvq6/GWV6DNSO+RANeoRJ6+airnPbWWTxoiOO2ztVz6/Rny9dGJ3eVl9959OKQYbpp7dA7jLS/HvWcPqFSYzz2HoBSkrLWM01NOB+R25+3OBtIjIwm0tDBPauYDYIdV4JLB+EL4PRDwsqHaiygFyW05akpt67Cx37afBUkLiNBFDHxsZEmm2LgdVJriyGytx9vYhtpl7ZLcOqkcSTpojfI9yNPW9TxRGPuUWZ2EaiR88OIJzM0eQ+bnAyDissswLliAt6ISbXpa172lMGUqpdFNLLjsrwTanTjXraVt5SraP/+cQFMTquVvU8jb/NZsZq8qkoKWSkQkggg8PfUyUiOOq45Ua+WOclsJJPSUbVU4RShZDSkzQNez611BQUFhpHB4HUQIQzelPhYhMpPMsjUctpcwPWHGsI07JjhSVDgCsRVbh41GVyNnZZzV+0IBvxwzTZszvBs/thsicRI6tY54QzyVrZVkR/Qshh6T2Cvkgtb20MoVX1eUJMRQiEiTT5yxkIRI7B4QsXvsmNrl4GHElVcQ85OfoElIwLV9BzW/+hXeigrKr7iS+LvvxnzZpcOyG0erwuK7Prvr7SIW5sWSaNHjt9mw/e3vAMTd8suuoOOJiLzqKpqffwF/XR32/73WZVh9hOODuredH88m1SaWpS1DPQQT6iMUv/oc5e+8wpwGsF9yDde7xwFBrPoIrPoIeSFJotzqOqlJCIDLZ6Ty+MpD7K5zEjz3MhI/eIbty//F2ZdeKQffBIGgMVF+EOiGNwE1LHidsP99yDjt5F9XJ6Ktvlsgq/HJJ0GSMJ1xBvqJodsRj6+clE2ij14joYi//Tbav/iCju3baX3/fSwXXYRr61bsb8idBYn334cQIjH3wAf7qG7pICVSzy2np5Jo0ZMceVSW5qpZaSwtjOO+5fv4cHcd//iylI/21PGHb03k9LzYrqrIEyUE/C0teEtL8ZQcxnO4BG/JYTr27yfYclxVtCRhf+21Xg+noNejsljwH1sR3ullY1ywYGidRlqD/I8pvvvnkiSfc55WaNgL1mKIHzf47SicPDrs2MrlxEGlSe6wK7ak8ODUa7h38/M43nwLbUoqMT++sfcxtEevjyNSTN9p2ELAakWTkkLEJd+iw9+By+8iWhfd2yg9MGlNJIcnc7DlIBNiJqCJjyfhwQeo/tEx+9Ipd9b07LOoIyIRTSZUpnDEcBOiKRxVuAnRbEJlMtGxp0i+liSp67o0X3JJt20mReh54sop/OD5LbxdEiBqw0YWL1jQ9f2b26qJDTYhxc1nWtrRhP2RLgjjvHmoo6Opaa/BrDVj0sovelG6KDxBD8Ep42DNVxS2VAKpbK5ncHJMLtmUesuOZtJaG9B63YgGA2E52ey37SY7InvQCQiAGH0MMeYgByMTyWytx1PdjMHZNDaSEM4mMHYGq8NMnVVQffuMKIwdMmOMiALdEhEqQSAjpg/5t1MATUJCj+dtbmQuVW1VVLVVkWpKxXz22ZjPPpug14tr40baVq6ibfVqAs3NjONoNZ+IxM93vkVkx+VA92IpYgug7HPZU2usF50o9MTrkruBBRGyF5/svVFQUPgGYXfbSZXE4S3c0JmJsaSzu2kfvtjJA+q6HctIksT+LX+lXRDIGH8ZcYa+VUgGys6mnUyMmdh3zM1RBeFxXXK3w0rCJDj4EcQVgkpDTkQOWxq2nBpJiGBAjielzpaLMr5BKHJMQyEiDRw1J9fNPOCTHdWPq0Kxu2xoNu4GIOKKK7peKAzTppL59lsYT1+I5PVSf++91N12O5LLNeRdCVUVFugMzgM0PfMMQacT3fjxmC+4oN/jimFhxPzkJwBY//53gsfsa6ig7p8+sCIGzOxs3Dmk3wPQ/MYbrH3t/8ivkSiadz5XBabg9Pb8e8svnidf6zfKqOX8SXIQ45WYqWS26alw1mD7ck3XMgFTknzejkUqN4I2fOyYvvdFWy2Y5GPdsWsX7atWgygS+8tf9LrKnmpHn9dIKDSJicT8+McANPzpMToOHKDmN7cBEHH55RimT++xzoq99by+tRpBgMcum4QxTNVjGYA4k46/XD2Nf31/BkkWHdUtHXz/35v5xf92YG33EHHZZeR8tpq0F18k57PVIau01ZGRGKZPJ/LKK0i46y7S/v0vst55u6cviyAQcfnlWC66EOOCBejGjUOdkNCVQJE6OronII4wkqbvgiBPYM1JchdE035Fl/1Uxe3AVipLzVWHH51gb0woxHWjfE02/fnPOD748IRDef1B1hY3YfC5yfvsXQBibroJQaulzllHgjFhwH4vhdGFlDpK6fDLEmyiNvRE3F9Ti3vv3q7AnuOdd2h56T9Y//pXGh95lLq7f4v9f/87ep52JupCyTktzo/jp4uyORRM5f0Vqyhtau9cReKtDfsRCfKt+eO7foskSTg+WA6A5QLZkPqw/TCZlqNSb4IgkGRMwjFJThJHVRUjClDcpsZhtw18PuSyEdBFsbW8hYIW2Q9CN2kSkihQ3Vbdp/F0fxAFkXFxaexNjAKg43Dt2DGnbm8CY+e5qjN/4/RgT3USLXoevmQiqs7rRyUIPHTJhJNejDISiILI9Pjp7GrahS9wVOZA1GoJX7iQxAfuJ3ftl8TfdWfPdaUglfsO9xzUGA0qLSg+Zacm7Q0QkSoXwrWPkXuqgoLCNwKH14EJQe4iHUY0MXmk+nxUtFYM67gnk/2H3gNPKzlB2FG/hXU163B4esowD4bK1kpERNLMJ/BFbS4dPkPq4znOGyJaH41aUNPoahyZ7Q0nrbVyMdKRZJrf0/fyXyOUJMRQ0OhkCY+TOYF2Nct61scERPxBP/ayYnT2DlQWC7rCwm6rqCMjSX32WeJ+82tQqWj78ENab/wx7oNDM4mLMfYMqogCZMQY8BQXY3/tdQDi77gdYYDG0RGXXoImNZWAzUbzy690fV7WFDrx4XOlYXVbqWqrGvgP6cT+zrtsfvpe9B7YkbKYO2JORxQFfroomz9cPGHMvngeMah+55ADywWXkmyT2Pz601264wFjPLTXn9zkWShaKmQ5iuwlYz8JIUnQ1gCmRCRJovGJ/wPAcvHFhGWHzry7vH6eXB3ar8R/gr9F1A+uRZueTsBqpfzib+Gvk+85YXm5PZa1tnu48+09APzotCxmZcrBN1/AR1AKvZ2lhfGsvPV0fjg/E1GA93bWsvTxL3h9axVNOjO7YrKw6vvfbt+btnTiA/eT9OijpP3zOTLffovcz9eQv2sneVu3kL3iU5KfeTpkRWTbmjV9a/oPB8YYWYqntXZkt6MwMrgdGBrlSvwjnRBdXHxZl/xf3Z134tq6tc+hNpc14/QGuLp6A0KrA21mZldQvr69ngTjwLty9Go92ZZs9tlkf4ZQJrCIIslPPUnK354l6U9/Iv6e3xF7yy1E33A9Ed++EvN556GbNKnn4MEgvsrQibpbz8gjMzMLtz/Ib/+zCrcvwNoSK/6WahzaOC6acrQjwL1nD76KSgS9HtPSpbR723F4HCSHJ3cbMzk8meYsuRPEt2cPefEmgoiUtjJwTVOXjcPtWto9fiY5OqWYpkym0dWISWvql/n3iZiVkk1prFyd7txbBk7bkMccFpxNYIyV/zvM/I3Tg/06cOXMNNbdsZhXb5jDujsW9yqt+HUgUhdJSngKu627Q34vqFSYzjyzx30tiMAbjb1UlMYWQNOB4d5VhdGgvZEOfZRcwFGjmLkqKCiMDi6fC5WgQuNzy4WLw0lkBqmBIIdHwF/0ZHCobgtS9TZyZ9xIVHQ+Z5hzSTQm8mX1l2yt39pVGDUY/EE/e6x7TixP6/d2Jq1HUOHiOG+InIgcSlpOgc4CR5Vc1A6dHdFtJ3d/RpFRT0LU1NTwne98h+joaPR6PRMnTmTrCQICY5qIdLkd9WQRwpTa4XGgLalCAAxz54YM+AuiSPR115H+n5dQx8cTrKqi8ttX0fLGG0iDrAT+2xc9K530GhUqUaDhT3+CYBDTGWdgmDlzwGMLGg2xP7sJANu//kWgTb5It1WGNsL89et72HEwlo0123D5Bt7l4Vj+AWX33kVFHJRqZvFE7rmkRht47ca53HZ2AdfMSR+zL55TUiOYmGzB6w/y+fhFpDcJVDUc5NAN11K67Aw8n6yUO2fGkvZcwAdVmyF9npzRVoeNnUBRKFw2WbpFo8O5fj2uTZu6naPH4wsE+ekr29lb24peo+J4j8pfvb6Lqubez1NRqyX6pz/t8XnDw490q4CWJIk73tqDzemlIMHErWceNc3+qvYr9lr39roNY5iaey4Yxzs/nU9hohlHh4/b3tzNvEfWcPVzm5j/yGe8tqX/97r+dFGAXFmtCg9Hm5aGedkyEh+4v0cQo+WFFyi78sohJ0pPSFxhVyWFwimE3wtSACrlDq+q42S3rntxK4e+dS2mM85A8vmovulneErLeh3uswONhHtdXHDgMwBif/4zBLWaQDBAU0cTCYbBSYPlReZR215Lq7c1dKLu/vswn3kmpkWLsFxwPlFXX03MjT8i7le/IvH3vyf58cdIeerJEMkLAU1a6GeQutMfolGXhWA9wG1v7uLJVYdIEmxMnTChywMGjkoxmZYsQTQaKXOUkWnJRBS6by/WEIsnORqvGnxVVcyJkr8/YNfAQH0hXDY2dT6KJrbKyWf95MlUtlaSZhqe52peTCrOeAM+QcBf10TA6QRP+7CMPWiCQflYHZm/dckxKZxqJFr0zM2OHjOFKCPJ+Jjx1DvrsXZYQ37f474GtISF815tMPQcJzJTLujwDr0bW2F08bfW8llzEXVaPQQ8Y794SEFB4WuBw+PAEmbp9NEaZn9LrRGTOQWDy0GDcwzFSQZBecth2g9+SM7Eq9DooyC2ALHpENkR2ZydeTZ6tZ6VFSvZa9s7KCPng80HiTPEEaWL6ntBe4WsOKAaQReA47ohUkwp2Ny2QcUARw1JAnsVWDo7vnVm2bvyG8KoJiFaWlqYP38+Go2Gjz/+mH379vH4448TGTl8BsKjTkSanIQ4WRIeLutRTeFO7B47YUVyQsA4p28DGMO0aaS//RbqWbOQPB7qf3cPtbffTtDpxFdf31U9fyJW7K3n7R01iAL843vTeemHM8mONeL0BvjLH1/B+eVa0GiI+/WvBv1TzeefjzY7m6DDQfPzL1BU4+Dp1XKW80jxtChASqSeDl+Qv6+p5w/vOHhw9Xu4vP2/ubZ+8gm1t99OcSJUaCbyz8JLuWJmKh/dfBozM47eaMfqi6cgCF3dEG/urkfjl0i2SpTHAcEgrsefwO/Tj60XhtodYEmW9QJBviE7Bt/FMuK01oI5CUmSaOrsgoi8+io0ST11xoNBidvf3M3nB5vQaURevn42X92xhFdvmMOKXy4kLz6cxjYP3//3ZmztvbfhaeLje354nFTRG1urWbW/AY1K4IkrphCmlmWY7F47Lr+LstYyPIG+W/0mp0bw/s/mc9Oi7h0dQQnufHsPdY7+V01oEhIwzp41ID+HY5MX2Ws+I+mxx1BZLHj27afssstp+utfR64rIjJDTjApsiinFm4HktrYdS1Uhcfxfxfn8NZP5jApxYLd5eP7L2zlgwt+hG7SRAIOB1U33oi/OXTA/LMDDVxa8gVaTwdheXmYzj4bgKaOJiJ1kYPWidWoNBREFVBkLQL6n6jrNkaIIJ9gMPbpsRRv1nHLlWcTTwsrd5Wxs7KZWMGOJuJoh4Pk99P60UcAWC68gKAUpLy1vJsU0xFEQSQpOhP7eHniPNMtd2btsgngCl0YEJJOOcl1VV6M3g5ibHIXkmbieOqcdaSYUk4wQP/QqDQkRCWxP15+fnubnCdfkqmjRe5+OPJSFmaSEyOKHJzCGEYjapgaN5VtDdt67aw8cl9L+duzCOHhRHvamFuzm3+uC5H4VakhKgush0Z4zxWGlYCPZkc5ojGafc37IXm63A2h3L8UFBRGGLvHTqTWIsuRa0fAgykqiwJBy8a6jbxT/A6fln/a1Tmwz7aPMkcZDc4GWr2t+IP+4d/+MFDvrKfq4PsUJs0hLK7T69CcBH43uJrRiBrGx4xnWdoyXD4Xn5Z/SqmjtNfn+vG4fC4OOw4zIWbCiRduLpOf8yPNMd0QalFNujmdUkfpyG93sLhssqrOESmmb1hH9KgmIR599FFSU1N5/vnnmTVrFpmZmZx55plk9yJfckoQFg4a/cl7oXXZwNA9A9lsr0O7Rw7OOydOZf1ha5+BQ3VkJOGPPEzMLb8ElYrW95dTctbZlCxeQuW111KyZCn2N9/sdf0Wp5e73pGDKj9amM2Z4xJYmBfHs9+Zjl4Fsz99GYCoq69Gmz74VixBpSL25z8HwPbCC9z27y/xBoKcMS6er26XuxK+umMJa29bzLPXTCMr1ojdEcNrW+pY/NQrvL61Cn+g75urY8VKqm/9Fc2GIOvSsvmk8Fqe/e5M/njZZEy6U8eg6ILJSZh1aoRqOdGQ1gQNkQJuDXLg2h6A1jGShHDa5AdU8jHeBpaUsZUkOZ62OjAlYn/tddx79yLodETfGNrw9uGP9/P2jhpUosBfr5nG9PTIrgRWXoKJl344m+QIPaVWJz98YQtOT+gJTW/yLdp0uVq40ubivuVyp8OvzsxnXNJRnczytnLyIvNIM6VxqPnEL/salcj83Jgenwcl+N6/NvHB7lp8J7iWhoJVb2FXTBY2QwSW888j64PlhC9bCj4f1qeepvzKb+M+OAJBC1EFMbmKPMSphtuOr80LAT8dKi26NDVz0s1MTY3k9RvncuWMVIISPPJZBU8tvhFVcjK+qiqqf3oTQbe721ClTe201DZw0eG1AMT+4uaubsIjfhBDIcuShd1tx9Yhd3oNJVGX+txzaFJSkNrbafzjn/pcJzvBQpmUSI5QSxwt2CQzT6wu65obODdsJGCzoYqMxDhvHjVtNUSERWDUhH7BSzYlY58gJyGyrOUAbGmQCAzEnLqjBUkfwebyFvI7O0o1aWk0at3E6mPRqrT9H+sEFMRksCdJ7jpwV9rAGbqSe9RwNnYl3X319Tg3b8Xv9IDXeXL3S0HhBCSFJ2HWmjnQ3PtzUpOQgGnRIqJ/cC0AVx9YyRtbKrGGKrSIzQdr8diTCFXonfZGaiQvMxJmISFRr1bLcpbNYzjgo6Cg8LXA7rFjETWgNYSU8B0yEenEet1clHU+F2RfwNykueRH5hOtl+eQtg4bB1sOsqF2A8sPL+e9kvdYWbGSdTXr2N6wnQPNB6horaDJ1TQkuaPB0uxuZs/hT5iqjUSfteToF4IAMXnQdFRVwKAxMDNhJqcln0Z1WzUrK1ZS135imfnd1t3kReahV5+gENfXIccqzcl9LzccHNcNkR2RTZmjjEAwMPLbHgz2yqNdEPCNk2Mawb6Ynrz//vucddZZXH755XzxxRckJyfz05/+lBtuuKHXdTweDx7P0Ulra6ucIQoGgwSHacIaDAaRJGnw41nSoLkcDD2DdiNK0C9XzmnN3Sbv9fu2E9fqxxsTx8L/HCKIgCjAH741gStn9DR5DAaDIAhEXncd+qnTqL3lFgJW67ELUHfPvejnzQsZKLnnvSKs7R5y48L5xZLsruOYE2vkMWMlma31tGoNSJdcQ+wQ/2bGZUsJKyzEs38/czd9iGPBFfzx0olY9BoSzDpAlqQ5a3w8SwtieWtHDU+uVmH1buX2d77i718kcP2sOC6Lju4xduWHK2i//dcIwSBvTs7CNucnfHzZImJNYcN2ro0WYWqBy6an8H6LlaAgoAlIJNskKuIgv05ElTWOoHUtdDiGv5VxIEgSlK+D5Bkgao6ex4ZouRrd6wK1bkibGPL13WPAALQ3Yd/2OQ33PQiA5PHQuno1EZde2m3Rf3xZynNr5eq/Ry+ZyKK82B77EWfS8sK1M7jiHxvZVe3gxy9v47nvTker7p5wUMXFEX/f72m49/fycRJF4u/7Paq4OHz+ALe+vhOnN8DMjEium5/RtR2n10mju5HTw08nSJBVFavIicghTBXaGPcI6VF6RIEenivFjU5+9t8dJFp0fGdOGt+emUqkYfiCha9treLud4oISnS7byU9+SRtH35Iw4N/wL1vH2WXXkrMT39C1HXXIWi6JwgH8zf31dfjq6hAkxyHxrYJEibDICveTzbDfs6PdVwteOrtAJRExhARU0m1s5rYYCxalcjDl0xgcqqF37+/l3cq3NTPvZ57PnmCjp07qbntdpKeeLwr0bB6fwOXH1qDPuBFN3EChkWLuo5jTVsNC5MXDum4CggURhWyq3EXi1IXDXocVVwchrg4Eh5+iKrvfZ/Wd97BOHcOwXPPDbl8aWM7xVIyy8TthOGlToomIEmUNbUTbwrDsVw2pDadczaSSkWJvYTciNxef2usLpa2zFj8IpiK92NInUitV09DfQ0JOf08Pu1NVHXoaHG5uaAzCaGbNIkyRwXp5vQBHecTnfOTEzJYHhuOBLhKqrG0N57coGdbI5iTsL/xRtc93ZjkwXS9hOXb1/VriLFynY/m9kfjnaAvxsoxP9lMipnEqspVJBuTMWl7n0NGXHMNzS+8SEZbA9Mrd/P8umx+dYxMJCBX/2mN8gv5EW3kMUaDs4FmdzOF0YUnXniQnErnVqv1IK4wE9G6aPIj8ylqKiIuaSqUfSG/E4uqUdmPU+mYnUyU4zRwlGM2MEbzeLW4WxiviSKoMY7MPE6lBX002KsQI9IIV4cTru7de8IX8OHyu+jwd3T92+F24PK7aPW0Mi56HNkRcsH1SB+ndm8768s/Y77bg6HgIoKiuvsxisqGfe9B0rRu77gmjYkFSQtocDawu2k3B5oPMClmEpG6nmo11g4rNpeN6bHTT/w7bKVyYgBh0H+rAR2z+Alw8COIyUev0hMRFkFlayXp5hH0oxgsLRWQseDocdGa5LjcaBynEaS/2x/VJERpaSnPPvsst956K3fddRdbtmzh5ptvRqvV8v3vfz/kOg8//DD33Xdfj8+bmppwH1fBOFiCwSAOhwNJkhAHaJgMIPj1hFVtw60d3cmz6LKi8Qh4mo52YQSlIHV7t5HlhpVxGQQROj+Hu98pYnykQJype8Cw2+9PS0X3y1/g/N09HLcQta+/ju6KK7p5THxW3MLy3XWoBLhzSQqOlqM6/pLLRfby/yABr+Sfwd73D/Gvq1To1ENrwNm88CIm79/PBaVfMf+WH+Jpa6Gxl8Th4rQw5n53Ei9tD+ONgxs5bNVz54dO/rO1gZ8uSCYtQkeV3YNj3QYmPPdHNMEAb08qQH/hpTw2aypSh4PG0U9iDwtn5xj591cRPDX5Mn6x8w3SGmHDOJGMi6+nRRQJC4YTLNtDICrnpO2juvkQYrsbb7QRGhu7facN6AmU7iYQkTGkbQz1+j4e0dmAqqmNxvufPvqhJNFw7+9x5xcgxslGox/us/HIinIAfn5aMgtSNDQe9xuPYAL+dEE2P3vrEGuLrdz8ymZ+f3Ym4vEVHqedhuV//yNQU4MqORlvXCyNjY38Z2s9WytaMGhE7lycjM169J5woOUAlqCFFmsLoihiCpjYdHgTBREFff5OFXDH0nQeWV3RlRD42YIUnN4Ab+9pos7h5k+fHuKp1cWcXRDNFVPiyI4ZvDSZw+1nY7mD339SzpG8R4/71qxZmJ7/N64nnsD31XqsTz1NyyefYrzjDlRZR2Vj+vs3l3w+glVVuN9+G++HH8lJMVEk6qbzEcPSTuq1MRSG+5wf62jry2kvkeXbDsVFEB9mZmfdThJ0CWjU8iR7SXoYcZflc+eHh9nQbuJ3M77LfWv/QfuKFVQ8+CCGH/8YgPUbivhF2XoA1N/7Hk2dz9dWbyvONicuuwsXQ9MY1Uk6bC02ioJFxOnjTrxCX6SmEvati/G8/Q7OPz1Gw7hxqMJ7viyZ8OISDLRgIkes5aPALEQBwqUOGioqaF2xAoDA/PmU1pRSZ60jX5NPoyv0PQvAFJOJ1Qya3bspmPpdttcGOFRdilhXLb/EnQBN7WHWVcnBquntcuebKzuNssYyslRZNHb0vu3jOdE5n6xT0RgWS6sBVLsP09ZQQUdEPQgn5/rQ1ZXQYQ+j5Z57ETolTIJesD35JzomzOl6jvTFWLnO29pGr3JrNN4J+mKsHPOxQAIJrDm0hjlxfcu+ar51MZ6X/sPVB1dy5/rJXDLOhFHbPUitEmNRlWzCmza0opPhJCAFqHHWUNZehkpQ4Q16EV0ikWEjIyF8Kp1btWWb0MZOoLGxEQ0aWuwt7KOJJI+K4KEN+KPyTjzIMHAqHbOTiXKcBo5yzAbGaB2vQDBAU0sTbrcFn8ePr5f36qGiEqJQH1yHJ2Npv7stRETCO/+HCGjBJbrYVrWN0vpSJkRMQEAYsePkCXhY37ie6W0tBCLzaXQBIebw2oCOQMlWApE9lWgEBCbpJlHjquHTA58SFRZFviW/q+NBkiTWNa4j15yLzXpi786wsh34YicSHMLfaaDnliYYjnRwPf6YQiL8EWyv2I4+foxJp3vbCWtuwh0TgPbOYxPwomuqxh01uGM1Vu5Z/X0nGNUkRDAYZMaMGTz00EMATJ06laKiIv72t7/1moS48847ufXWW7v+f2trK6mpqcTGxmI2m0OuM5j9EgSB2NjYQf7R4qB1D2aTBvSj6G/RaIPELIg7GsRweBwYSmsQJdgRm9tt8aAErZKOCXHdOzaO//2++fMpFcUemTj33/6O//3lWC69BMsll9BqjODxz/cAcOPp2Sya1F07uunPTyK1tCCmprFx/Ok02ty8sK2Zey4YN+ifvK+2lXussTwUlcH45nIK131M3OLfnXC93yYlsHSqmTe3l/DxFjP7Glz87K1iYjrsLKzeyff3fYxGCrAtfzJxt13MVRPPx6AxDHo/xwJxcXBabgOfMptZ2THMe+tZkvxmbEsnMSEuDlE3UW6BjxtiEGyweJ1QXw2TzpUr4Y5HNUGWZBri/g39+j6OmmrcGHtq3waDmF1ODHHjWX2gkYdWVQBww2mZ3HJO3wF/gCVx8Kw+nBte2saKgy0kRZv53XmFCMdPfuLiYML4rv+7v66Vf2yQtdR/d8E4puQe7XYKBAM42hxMSJhAXFycnISIMrGqYhWWaMsJuyGuXxLHedMzqbC5SI82dPmf/Pq8AB/sruOF9RXsrW3lvSIr7xVZmZcdzQ/mpbM4Pw5RFKhzdFBudZERY+jmnWJ3edlT00pRrYOimlaKahxUtYTO9gUlaPBqmHDkPIiLQ/rHP2hd/gGNDz1E4NAh2m68keif/hTTeefhr6lGlZqKEBHR9TeXAgF8VdV4SorxFBfjLS6R/11RAf7j5K+CQRz/eo/MyfmoCub1eXzGKsN+zo91GgXcTfKkpzw6nFlpk0m2NNMW1kZB9NFrb0kcfJidxM2v7mRjGTw25XJu2/Yqnv+9RkRePqqLL2H8F+8TFvQjTJ5C0rnndl1/zc3N5IflExc7PPfL+eHz2WPdw/jY8T2v8QESvPNOyjZtxl9Tg/DKK8T9ruczMS4O/vAtiWfesWHGiVMI5w/fmsCE7BRaP/wQu9uNJjWVxEWL2G3dzaTUSSRE9y0RNXH6YrbF/pWEYieLTD62AxWecBaGq8DUj+NkC7K71YwgtZFhle+X3uk55CdGkZiQOLBjcIJz3hIZoFGbTZMFLHX1GA0WTOaw0Z2zHcHvBqMeh83TlYAACPpEVOoAvqZmUo+5x/fGWLnOdbrRCxyPxjtBX4yVYz4WiI2N5fPqz3HpXGSYM3pdznfjjZS++RZZrXWML9/D6rJ8rj/tOK+Z2BjYUwIWXej54Cji9rspdZRS6iglShfF4sTFxOhjKHOUUe+sJz8uf0S2e6qcW76Ah1q/jQkFiwjrlOubrZ9Nib2EceOXwKFPIHpWvxLRQ+VUOWYnG+U4DRzlmA2M0Tpetg4bKb4UYkQtRKaNXAwjLg6CVhBsEDf4uBVAakIqO5t2UuQuYnbC7BE5Tr6gjy+rv2SWwUSmzgR5p/deZKOfDdVbIW5ur+PFE8+k4CSK7cXsbtlNhiWDgsgCatpriImMYVLKpBPvlKcN9CrInDAk2awBn1uWhXI3RPQ8YsVYqoPVqEyqLjmtMUGjFdLGYz7+/G20YI6ygLrv+Ewoxso9q7/vBKOahEhMTGTcuO4XcmFhIW+99Vav64SFhREW1vMPIYrisB5gQRCGNmZkpmykaxzFE7yjGUyJ3XTim5sq0R2WA5K7Y3tW8b6yuYp5ObGIYvebwbG/PywpicT776Punnu7ZF8MM2fi3r8ff20ttqefwfaXv1KWPZmcqCnETZrJL5bldjt2vtpaWl58EYCkO27j0ZSJ/OD5LbywoYLTC+JYnD/wh0a7x8/P/7cTb0Ci6OyrGP/fh7G/+RbR112PNuXEWnNzU6biCjRzTpaRFfsNuN56g5t2vd1ljHLYnET4Q9cxMzGN8LDe2+5OJb47J521xVYe8abxmkpLerGd/21cy9ycuRjMSVDxFSCNWut0N2q2yG1z+ojQ30ekQs1W+b+HeK0P+fo+lvZ6VKkhJiSiSFhGBtsr7fzsvzsIBCUumZbMnecU9rjeemNxQTyPXT6ZX762kxfWVxBn1vHTRb1X43v8AX71xm58AYllhfF8e2Zat4BmRVsF0fpojGpj1+83ao2kW9IpsZcwMXbiCfcpOdJIcmR3XXi9VuTyGWlcNj2VLeUtPP9VGZ/urWf9YRvrD9tIjzYwKdnCh3vqCEry3OPMcfGIgsCeGgfVvSQcEiN01Nl7VrPe8voufro4h2vnZWDQyo+tyIsvInzeXOrv/T3ta9ZgffJJrE8+Ka8gCGgWLiQYGSEnHA4fRuqlSlbQ65E6uu9PwCUQsLWgaW8A88CCoWOFYT3nxzLBAAQ8eMqqkYCayDBuTE4nSh1LkaOInKicbt4CcWY9L18/m0c/OcBzayHB2cz3DnxKw4MPUrO3krNKNwKQ9utbUamO3hcbXA1MiJkwbMczyZREsb2Yamf1kNuERZOJhPt+T/X1N+B49X9EnHcehhkzeix31ax0FuVfTnljK3fFmbsSg20ffAiA+fzzkASJ6vZqlqYtPeFvTYnKYE1BMsGSMqa21QBx7GpW8X23AywneCYHA0ieNr6sNJLUbkXrakcIC6MxVsM4S/rgulL7OOf1YSKx0VkcijWTU9eKz+pC77KN7pztCC4bhMdR643FAhy5Ywd8Ahp9kLrwONL7+fvHwnU+mtserXeCvhgLx3ysMCNhBl9Wf0myKbnXogZNZCS6Sy7B/fLLXH1wJQ+um8b352cQpj523ilCbB7YDkPK9JDjjDQOj4PilmJqnbWkmdJYkraEcO3Rd4EMSwb7mvfh8ru6fT6cnArnVl3DXozmRPTHSLmmmlPZ37IfKz7iItJkc9DkaaOyP6fCMRsLKMdp4CjHbGCMxvFq87URqYtEbHdAeOyQ4wN9kjFfDmZHpg9JuloURWYmzqTMUcaXNV+SrkonXowftuMUlIJsqt9EvKgl290MBeeBqo8QryUZqjbKcURj71LyWlHL+JjxZEdks8+2jxWVcsf0wpSF/dt3e4VsSK0aeoxpQOeWPkKWdrQehMRJ5ETmUN5WTqzxxB3Go0ZrDcSP73n+6i3gbQft4Do3xsI9q7/bHtU9nD9/PgcPHuz22aFDh0gfglnxmCEiTb7YRpMQptT1O77C5JIIy8sjJ++oPJQoyIHAj4vq+d17RUjHV3EfxxHjy7QXXyTns9Wkv/gCuV9+QdIfH5WDG8EgmcU7+P2m53n87XtxPPM03k4TZF99PbV3/xbJ48EwaxbhS5awOD+Oa+dlAPCbN3aHNqfrA0mSuOvtPZRZnSRZdPzy19/GOG+ubFT717/2awxREJkRPp7aHe9zyUeP87NjEhAAJuqoqq0kNzK31zFONQoT5YemT6VhS3wBWj+0f1nKxuoiUGtl74W2+tHfMXul7PkQP6H3ZTQ6+aHvHJlWy0Hh94KnFU95AwAOAzjDAFEk8f77KMXID1/YgscfZElBHI9eOqnfCYgjXDw1md+eJ+sO//GTg7y+tarXZZ9YcYgD9W1EG7U8cunEHhXVxS3F5Eb0PJ/zo/Ipay3DExjYdXg8giAwKzOKZ78znS9vW8yNC7Mw69RU2Fws313X5SchSfDp3gY+LqrvSkCkRRk4b2Iit59dwMvXzWbnPWew4Y6lPHrpRFSdv0MUIN4cRrsnwB8/Ocjpf/qclzaU4/XLXVqauDhS/voX4u68o/uOSRK+L76g9d33cO/di+R2I4SFoRs3DstFFxH3m9+Q+tw/yPl8DVkffRjS8FuVP1d+kVYAvwcOfjI2jUPdDiRtOJ7SMpw6aNFGMS4hBqPaSEp4SkjzVLVK5O7zxvH0VVN5b+JZrEiTn2nJb72AmiASsO7zHV3LewIe2nxtw15FMyFmAkXWItq97UMeyzhvHtpzzwGg7u7f9jDcPkKiRc/c3PiuBIS/pYX2r74CwHLBBdS01xCti+5XJ6BWpSUmOYdmEyTVy4aku20intZ+mD532LH69dS1eZngkP0ggpMLcQY7iDfEn3j9QVAYn0itKRKXFtyVTeBsOvFKI4GzCYwxZBRkYjum6tvvU7EidzbpBRknZ78UFAaIJcxCpiWTXY27+lwu7PLLEAwGchw1ZBzawbs7anouFJMHtmI5sTyK1DvrWVu9lrU1azFpTZydcTZT4qb0SDSoRBVZEVkU24tHdf/GGvX1u4mN7T53FwSBcVHj2G/bD0lTZONT3ymqZaugoDBmsXvsRIRFyFX2I901pzNDwkSo2DAsw2VaMpmfNJ999n0UWU8ci+sv2xq2oUZgYmszpM6C/hTSxuR3M6juC51ax7T4aZyeejpT4qZgCbP0b8eaS+UkxMkgYZL8Du/3kmHOoM5ZN+SYx7Dh98oxXFOIIscwM3haR3+fTgKjmoS45ZZb2LhxIw899BAlJSX897//5R//+Ac33XTTaO7GyGCMkQM1o+VqHvDLmTJdRLePG/btwNwB+jlzKGmSAxu/PbeQr+5Ywp+vnIIgwCubKnnoo/0nvPlpEhIwzp7VZUYt6nRYLrwQw7P/5Ffn38WbOafjCbcg2Jqw/e3vHF52BofPO5+SxUtwbZBv2Prp07sCo3ecU0B+vAlru4fb39w9oJvv/7ZU8f6uWlSiwNNXTyXCoCX2F78AwPHuu3hKy3pd12+10vLa61Te8CPqF51F2rPvcbBlG9Ix8dqgAMXJsIA4xJOkET0SHCtxsz5Jrno/82A5O+qL5ZuxJUWWPBpNAj6o3ATp805cwWBJAUeIl9WTRVsdGONo/fhTAEqvnk/5n35M4+uPUDl/Ft/79yZa3X6mpUXwl6unoVEN7ly6/rQsbjxdfnDf+fYeVu1r6LHMplIb/1grB/4evmQiMeHdqxAbXY2IgkisoWfmX6/Wk2ZK41DzoUHtXyhSIg3ceW4hG+9ayg/mZ4Rc5qpZqbxy/Wx23XMmX962mL9cM42fLMpmQW4MEZ3m1lfOTGPdHYt59YY5fHXHEtbfsZQnrphMapSepjYP97y3lyWPf85b26oJBCUEQUCXH1ruyvyti0l++imyP/mY/O3byHz7LZIefYTo635I+GmnoUlIQJuYSOL993U7F+N/ezea3FlysNAz9ADxKY+9Uj732+pO9p70xG0n4AbJ6aTRLKKOyELfqTdeGFVIeWs5Ll9oD4cLJifxzk0LWDPzAo59GglA0r//TM2hckAOUsUZhv/ZEK2PJi8yjzVVa9hav7XX/ewv+p/8FFVsLN6KCqzPPNOvdVo//hj8fnTjxhGWlUWpvZSsiP6/NGTkTKfRAsL+vcSZwrAFjVTW9uOe7bJR1CJXas1zy+dVy9QMUk2pQ5an6o2CeBOtQhJNFug4VHNykxDhccS47MQc87LxfO45TDlnDonmsaOLr6BwIgqiCrC5bTQ4e85TjiBaLERefTUAVx9cyd8/P0wgeNw7QJgJDDHQUj6CeysTCAYoc5SxonwFRdYi0s3pnJt5LvlR+d06544nJyKHqrYqvAHviO/jWMTutiO6moiM7un5kGJKoSPQQZPfJSeU6vpOTCkoKCgMFLvHLgfBve0wQh1p3YgbBwEvWEuGZbhIXSTz4+dj99hZW7N2yIHxPU17cPqczPaLCOGxEJV54pUAonPkdzt//59lZq2ZVFPqiRcE6GiBoF/uVjkZ6MxyDKlpPxqVhpTwFMocvccKRxVHFZgSQquQhJmUJMRIMHPmTN555x1effVVJkyYwAMPPMCf//xnrrnmmtHcjZFBEORuiJZR6oboaJETEMcEziRJwlq2j/AOqMocT4vLR6RBw7XzM0i06LloSjKPXCIHop9bW8b/rRp4NY8kSfz23T3sU0ex/oxrKFz7Ocl//j+M8+cD4D18uJtWvu3vf8dXL1fa6zQqnrxqClq1yOoDjby8sX/Han9dK79/fy8Avzkrn+npcveHfvJkwhcvhmCQxj/9EefGTV3b8lbXYHvhBcqv+Q7Fpy2k/t57ca5dC34/yeZ04ucv4VDy0UBHeRyYOwQmTJw54GMylsmMMXKkEH9LfAE+QUVOSxPjg3qKW4rllrzWUU5C1O6UtxveD0kuS6p8sx4rtNUTUFto/+ILWvVgnjufS874ORExBfzkzbewSdvITOjgn9+f3hUIHSx3nF3AZdNTCAQlbvrvdraUNx/dDbePW1/fhSTBFTNSOHN8T+324pZiciJ6l3Iarm6I4zFo1fxoYRbHN4CoBIGbl+YyPycGi0HT5xiJFj1zs6NJtOhRiQKXTEth9a2LeOCi8cSawqhu6eBXb+zinCe/5NO99WjS00J2M8T8/OeYzzgDbUYGQh/toBGXXYbpneUEouS2VDEsTG5ljc7td6XI15rmMjAnj0pwaMC4Hfia5Anb4VgzGTFHOwB1ah05ETnste3tdfX8BBO3TjZzfNhbJUnU7pWfkbXttSQZk4Z91wFyI3M5O+NsDBoDqypXsb1h+6CTEaIpnIR77wHA9u/n6dhTdMJ1Wpd/AID5ggtweBx0+AfWiZA5+TSsFgH3oUPMSNDjIJzauvoTd824bGxtlI96frM8F2jKiiLNnNbXWkMiP8FEgyqTJotAx77Dsi9RwDdi2wuJJIHTCoYYGld/0e2rMxM0zMpNlvdLQeEUQS2qmRY3je2N2/EH/b0uF/mDaxH0evLs1UTv3cqKvSG6cGP7X505GDwBD/ts+/io7CPq2uuYGjeVZenLSDOn9SvJHKYKIzk8mVJH6Yjt41im1FFKChoEU885Z1c3RPN+uXq4pVzueFZQUFAYBiRJwuFxYFHpAEFWdBhpBEGWZarZCt6hFQodQStqmZ80n2h9NKsqVmHrOLHBcyhKWkqoddYyz5CKqr0eUmf3f2WNDsxJcrfCSNBc1v+EyEiRMAka94PfS3ZENofthwlKY6Cj31EFEb2oAOnM35jn5qiXfJ9//vns2bMHt9vN/v37ueGGG0Z7F0aOiDQ5qzgauGyylM4xtJQfQtXUglpU85EoTxDPGp+A+phq7CtnpvH7TmPop1YX8+znhwe02fd31fLp3gbUosDjl08mTK/DfPbZpP3rnyQ99qeeKwSDeCuOHpOCBDN3nC1XLT/44X6KG/ruHHF6/Nz03+14/EEW58fyo9O6V2jG/uJmANrXfE7ltddSsngJxYuXcHjZMhofeZSObdtAktBNmEDsLbeQ8cFyLC++wNJbHidw/RVYLSIdWqiOE1l43W+7uj6+LiRa9DzcmXhyafTsjJOlecYV1VLqKMWrNcrBIrdjdHbIaZMfeMn91Pw1Rnd2GI2RavS2Wpw7ipG8XlrGJZGRN5MOb5B73mygpiaPGHUOvzwngg0NqznUcghfcPABLkEQeOSSiSwtiMPjD3LdC1s4WC9fL/cv30eNvYOUSD2/O7+nP0W7t51md3OfFQtHuiEONg//C/+R8+6IrJJKEHjokgndzKkHilYt8t25GXzxm0XcfnYBFr2GQw3t3PifbVz+dintP/vN0USEKGL41a39vp5f21LJwhf282LCLAAO/+s/8hex+bI8RKD3wMrXHp8bXFZInytPnMaaJJPbgaeuhSBQHhnOuLiUbl/nRuZS76zH4en9Hpc9pYDgcWmIgCCQND6XoBSk0dVIgnHkng0alYZx0eM4J+McwlRhrKpcxc7GnXT4By5nEb5kCeZzz4VgkLq770by9l7l5K2upmPHDhAEzOeeS6lD7oIYSCeCKSUTo86MQxdgXsBKEJFih3TiSh6XjXV1QXR+D+b6Str0oE5PI0oX1fd6Q6Aw0Uy5IZd2PbRXVRAQ9fJcajRx20FrALWWupVrAKjt9KXQVJV3tmKPUketgsIwEW+MJ1oXHVL+7gjqyEiirpG7Ia45sJK/fV7SsyPakiIn4TpahnX/Wr2tbGvYxqfln+IJeFiUuoh5yfNCdoqeiNzIXErsJWMjmDGK+IN+6psPEROeBJrQc7lUUyounwurr1NutXb7KO+lgoLC15V2Xzt6tR61zz0kj4YBo4+E2ALZR2GYEASB8dHjmR4/nfW16zlsH1g8rrqtmoMtBzktbjra6q2QeTqo+i7w60FsATT1/sweEs2lsl/uyeSYbghLmIVwTTh1zpPc0R8MQmutXNgXijDLN+YdYFSNqb/2hCfIwVxfR68TtGHDZYPjDFbqNn1OeAfoJk3iwxI5AHD2hJ6Bk2vnZ+LyyRrrj35yAJ1a4JycE+s/N7a6uec9uaL05qW5jEvqrsVnmDFDDgIeG6QSRbTp3Ssbr52XweeHmvjyUBM3/28n79407ziDOhm566KI0iYnCWYdj18xpYe+vioi4viV8NfVgSBgmDkT07JlmJYtRZMkV7EGg0FobESj0rD4op+zNi8HV0sHp6VNICF37gmPwanIlTPT2FXt4L+bKnFMnwcfHSCw+gtSvnUnxfZixh+RPNL1U+NvsEgSVK6HlBmgDm1gCLKniLe8Am1GuhxEPiIZFRdacmfU8LrA78GxYi0ArQsmIvij+PY/NrCnppVIg4aXf3A6OXHhODwODrUc4uOyj8kwZ5ATkdMvjfXjUatEnrl6Gt/51ya2VbTwvX9v4upZabyxTe5eeeKKKZh0PScdJfYSsiKyUIkq+ZzvhfyofFZWrCQ/Kr9XU8nBcuXMNBbmxVJudZERYxhSAuJYDFo1P1mUzdWz03juy1L+ta6MXVV2LieWmDPuIrHdSp0phjNMeSze34DbL+H2Bujwdf7jDeA+5r+bnV5WH5B9R1akz+S7+z/FUHqQqi07SZ05Rb7PtpRBzNfHK2ZA2CvlazDMJAdI2+vl6pmxQocdT2U9DiM4xSjmx0d0+1ojygH+ImsR85PnhxwiOS+Dvdf9kqR//xmVJBEQBGp/+Esm5GXQ6GrErDX3KdExXGhUGsbHjCcnModDzYdYWbGSdHM6BVEFA7o+4397N84NG/AcOoT1ueeI7UXysvUDuQvCMGc2QmwUVWUbOTP9zAHtsyAIpCQW0Fi3iUJ7JZDFbqsoG97pI0KvFAziaGmiqDmaCfYyhGCQppwYMlP78AgaBpIj9ARMEag8RmzmdnyNTlTxTXJb9GjR3gTGOCSfD83OLQB8UrCYH257E0Nt5TGt2CG0YhUUxjCT4yazonwFqabUXjWjo37wA2wvv0K+vQr19s1sOFzIvJxjjDEFQTaobjoIaXOGvE8NzgaK7cXYPXZyInI4O+PsId/LzVozEWERVLZWkmHJGPI+nipUt1WTIIloLb0//wVBoCCqgH22fSxMmg9Fb8vFR8bh9VNSUFD45tElxeRp7Z/vwXCSMAn2vy93eEVmDN+wxgSWpC1hQ+0Gmt3NTI2bilrsOzxr7bCyo3EHC5NPw1C5GeIK+zSY7hVTPCDJ89LhlE1yWkFU9/CtPSkkTJLNxWMLu7ohksN7SQCMBu31clJL04vsqiLHpDAoRFEO1oxGN0SIToiGPZsxuyQchVNoavNg0qmZlx36pvTTRTncvESWarnvg/0s39u3kaQkSdz1zh4cHT4mJJv5yaLsHstoEhK6a6t3mvUeX40sigKPXTaJKKOW/XWtPPZp6ErsN7ZW886Omi4fiChjzxcHb3loSafkp58i/aUXifred7sSEMcTrY+mMGMmYk4GBTkDaGE7BblwsnwM3gzLBEHAvaeIbG8EpfZSfKaE0fGFaNwPqjCI7nnuHMH+5puULFkqd7UsWYr9zTdPjm9FKNrqCIhm2r/6inYdHIzO4Lwnd7CnRn5YXD0rjZw4eVJkCbMwM2Emy9KWISCwqnIVm+s2Y3fbB7xZvVbFv74/g7z4cBpaPV0yagJQZu3ZIeIL+KhsqyTb0vtx7hpbrSfdnD4i3RDQXVZpuLHoNfz6rHy+vG0xl0+Xq9+t+gj2xOZg1UXw6vZGfvSf7dz86g5ue2s3976/l0c+PsCTq4v5+5elvLShgje2VXclIAAcYSY2JI4HwPbqa/KHceO+2QbVLWVHq1kiM8aWJJMkgacNd1kdzSZoFRPIi+9ZHZVpyaTN24a1o/fn3Jm/+RGR731E+8NPEfneR5z5mx8BUOesIyl8dJMuYaowJsZO7EoIfFr+KXua9vRbOk0dFUX83XcDYP3b33Ef6un9IkkSjk4pJsv5F1DZVkmsPhadeuB+BBl5M2i0CERXFiMIUNKuocXW2PsKHgeH7BBAxUJ/AxJgm5BMmmnkpJhAnnvkJZhokxJpsgi4K5rkl6XRxNkExlg6du9G6+nAoTUSd9nFAIQ7HbI87zekFVvh60WYKoyJMRPZ1rCtV883dXQ0UVddBcjdEM9+HkJnOzpXlnIYZAeiP+in1FHKivIV7LHuIc2UxrmZ51IQVTC0BITP3fWfeZF5HGoZPk+tU4FSRynpog7C+5brSzOn4fQ5sXnsskl1zbaR3zlnkyKdqaDwNcfhccim1N720e2EADm2lbEAqjbLCg3DiFFjZHHqYgQE1lStod3bu/qDw+NgQ+0G5ibNxWKvBikoy98Nlpj84e+GaC49+VJMRzimGyIpPIk2bxut3pM4x7ZXyVLjvaHWAsKwn2NjESUJMdyMhiRTMCBnyfSRXR9JwSANJXswdcBX5gwAzhgXj1bd+5/4ljPyuH6BfJN4aGUF7++q7XXZd3bUsGp/IxqVwOOXT+nVcDfissvI+Ww1aS++SM5nq4m47LKQy8WZdfzx0kmA7E+xtri7QeTB+jbueV/Ws/7VmXnMzAidTdVmpIfUgtdP6F9FZX5UPkvTln6tzKhDMSM9ErNOTYWkx50vy/cEPl9PUngSxYE2eQI/kpIzXifU7+6zss1XX0/dPfce7aQJBqm75158LhHaG06+JE5rLc49peDz0TgxlRf2dw/W/e2LUuoc3eVTDBoDE2Mnck7mOUTqIllfu54vq7+k3hlCC7kPIgxa/nTZpG6fScBdbxf12GZ5azmJxsR+BxPzIvMoby0fdm+I0SLWFMa3poWuasiONTI3K5olBXGcNzGRS6el8J05adxwWiY3L8nhtrPzuWVZbjchno8z5Y6osM9XEnQ6wZwoB7vbejfd/Nri6wBX89G20ch0+fk2ViSZPG2gNeAuKaXZJNCoTiM7zthjMVEQGR8znl1NfRtlJudlMPNbZ5Ccl9H1WV173YhKMfWFTq1jcuxkzkg/g4AU4NPyT9lr3YuvHz4G5vPOJXzJEvD5qLv7t0j+7vdPz/79eA8fRtBqMZ15BmX2MrIjTpy4DEXspFmogmAr3kVObDh2KZzyyj68fFzN7LbJz9zJrdXYjWBOz8akHfmXyoIEE03qdJpN0F5cdZKSEDHUrpClmHbG5fHt0wtp6JzPtVc2fmOqoBS+fmRYMlAJqj49E6J/+APQhlHYUkH7+g3sqT5OKk9rkLuTBqhV3eHvYK91Lx+XfUy9s55p8dMG5PfQK/ZK2L8c9rzR5dcSZ4hDFMQ+zbi/ThzxC4ry+U7o5yYKIgVRBbI3RHQO+Jyy/MQIIbbXw+HVcrLjZL8nKCgojBh2j11OQnjaYBTmiz0wxshFWdVbhn1olahiRsIMsiOyWVO1hrr2nrJBLp+LdTXrmBo3lZigAA17IfM0uYNwsERny1K7wxX0lqTObpExkoSALm8IMeAny5I1YOmrYcVeCREnMPf+hvhCfL0jrycDc7L8kjkAt/kB02GXpXOOCb57Dh2iNdCOSdLxapssk3TOhL7b+QVB4O7zCrl6VioS8Ks3dvNJUc/gaL3D3WUM/ctleeQn9H3j1yQkYJw964R67MvGxfOdOXLl469e30WzUz5mLq/sA+H2BTk9L5YfL+w9MNLf7ou++LonIECW9VmUL7847M2cDEDbypUURBVwuLUCvyES2kZQJ69qk1xRrjP3uoi3vKJncDMYxFtdJz/4R3L/+kNbHY7V8sSjclo+AW/3xFhAkii3hjat0oga2YA282wyLZnste5lXc26XqsFQ+H0Bnp8dvw2JUmixF7SpyH18Yx0N8RocKwB+xFEAV764Uxe/dEc/n3tTP5yzTQev2IyD148kbvPG8etZ+bz00U5/GJZHo9cOrFrDrcrJht3XBK4nLR+/LH8YVwhNO0f3R81FmipkCdLR+6vWqNcfdQ+RgIvbgcBSYu3uQmnDsSEPAza0G3MqaZUkKCmvabfw7d52whKwV6lRUYLvVrPlLgpLEtbhifg4ZPyT9hn29en54wgCCTcey+iyYR7zx6aX3yp2/dHuiDClyyhVePHF/QRZ+g7uNTr/k0YT2yrQJ2nkbmWIC2EU1PXR9DJZWNTgwCSREJNCXVRkJ0/dOmV/pAfb6LUkobZBTVlB+QqsmEyGzwhfq8cxNRHYv/iSwBs46aRGmWgNlLutqndX60kIRROaabFT2OfbV+vnjbq2FiirroSgGsOrOBvobohBqBV3eJuYUv9FlZWrMQX9LEkbQnzkuYRox+EPMURjgRS9r0PdbshcQrEj4fanV2LfJO6IcocZWQZExGCvn5Jt6ab02n1tNLsaZE94Kq3ysd0uGkpR1u7CbKXyYkre+jueAUFhVOfo3JMfXdC+OrrcW7chK9+YAV//SJpKrTVyzLWI0CWJYsFyQvY0biDvda9XXECb8DL2pq15Efmk2JIgLIv5MJObc/CqwGhDpMr823DFJhvqweNoc94z6ijM8u/sWk/mZZMqtqq+lXMNey4mkFUhXyG1rXXHZ0zhZm/Ee8BX//o62ijUssTIUcfVYBDxWXtIcVkW/8lggSqcVOpbvdj1Ko4LffEE3BBELj/wvGcWxhFICjx81e38/nBozIKkiRx59u7aXX7mZxi4caFWX2MNnDuPncc2bFGGts8/OJ/O1hfYuVXr++ipLGdeHMYT1wxuYcPxPH0t/vim87SQjnA9K4pHwDX1q2EtXtJMCZQKUojJ3lkr5S9UuL77k4R9SEq9494ilhSoHVkHvj9wu0g4OqgfeM22nVgnDoFAt0f/CpBICOmb98HURBJNaWyJG0JQSnIvub+y/yECrQfv81aZy16tZ5IXSQDIT8yn/LWctx+94kXHoP0NMKGO5am91sG6sqZaay9bTFJFh2SIFIx9wwAWl57XV4gKhta60YvWDlWaCnrqX06liSZ3HZ81nZawkHwGkhN6Tv5PDF2Inuse/ptKFrbXjvqUkx9YdAYmBY/jSVpS3D6nKyuXN1nW7EmPo74228DoOmpp/CWlwMgBQJdfhCWC87nsP0wmZbBVy2JRiOp0Vk0WQRmuevoQEd1c1uvlVVtzQ3ssKqIdzUjtLbQFKUma9LCQW9/IBQkmimOSCHGIVHbUklQY5YLR0YDlxWMMfibmzGWy7J60Yvk3+1MkgsyWg6WyS/YIxGwU1AYBUxaE9kR2Wxr2IbdbScQ7FlAEXXddaDRMr65nNo1aymzOrsvYE6EoL/XTiVJkqhpr+Hzqs/ZWLeRiLAIzsk4hylxUzBqhhCUkSS5A2Pfe9CwTw6gF54vJ+MTJsrz5E7T7BRTCq3eVhwexwkGPbUJBANUtlWSIRp7eBH2hiiIFEQXsN+2X1YIENXDP29oPADVW/CkL5YLlaKyhy+QNhQcNd8YY1EFhdHCG/ASlIKyv6KntdckREhZ5+FEpYb0+VC5AUYokB2pi2Rp+lKa3c2srVmLy+dife16koxJ5ETmyIWdpkS5O304iC0A6zAVIraUQdTwxgqHhYSJ0LgfHSIJhgQqWk9CwtpeCRE9/2bt3nY21G04WtTwDfGFUJIQg6DO0cH6w9YeMihdRKSPrCRTCD+Iuu1fYeqAfQlygHlJYTw6TU+z51CIosBdZ2Rw7oQEfAGJG/+zjQ2HbQC8sa2aNQeb0KpFHrt8MupeZJgGi16r4slvT0UlwNpiK1f/cxMfF9UjAE99eyrR4f0z4+xv98U3mUV5cahEga1eI0JePgSDtH+2hsKoQg762/E7RuCGHPBB5SZIm9dTNus47O++2+OzhPvu625OfbJorcO1rxICARzTsgmaupsUqwSBhy6Z0O+gtyAIzE6cTbmjvN/STD0D7T23WdJSQm7kwA2UdWod6eb0U7qq78qZaay7YzGv3jCHL29bxIUTBlYFmRJp4O7zZKmyP5IDajXuPXtw79snTzqjc4ZfN3Ms43XJXXem44LwR55vYyFI6nbgrbfTbAJPMJrcuL6N6uIMcRjVRspby/s1fL2z/qRJMfWFUWNkZsJMCqMK+aLqi5Bt20ewXHopxnlzkTwe6n77O6RgENfmzfibmhAtFsLmzaHWWTtkg9X4gqn4VRBvlYPre5pFgs7mngtKEuXVVdgJZ1GgEasZomNSMRpHp9ukIMFEi86M2h1Oo1nC09AmJwdGg04/iLa16wAosSQza4Z8vxYy5Ze2YGk5aPRdsi8KCqciBVEFGNQGtjZsZXnpcr6o/4KNdRvZZ9tHTXsNnggDEVdeDsBVB1byjy9DSC/F9tSq9gV9FLcU80n5JxS3FMsdphlnkxuZi0alGfwOB4NyAHvvO9B0CFJnQ8G5YDlG6lGlgcRJXR4HoiCSE5FzSs+b+kNNew3Rumj07tYT+kEcS4Y5A7vHTou7sxuiZtvwSTnW7ZK9uvLPQQrrrLi1pEJH88m9dwaDtBV/jKdk1diYIykofE3okmIKBmWp2BAdAL3KOg93R4Q5EcxJULN9eMc9hjBVGAuSFxCtj+aT8k8waoxMjJ0oJ3PbGyBl5vBtLDwWBHHossPBoNxBP4zG3cPGMd0Q2RHZlDhCdGCONI6qkFJM2xu3kx2RTWVrpVwkp8gxKYTitS2VXHjv2zzy0CtceO/bvLYlRLLBkipLx4yUNuVxSQjJ66Xx0G5MLol3RDlgdM6EgQVO1KLAE1dMZllhHB5/kOte3MLLGyu4971OX4Yz8sgNYfg5HESHawmGmKulRfddVa4wMCwGDTPS5Qr5ikL54dW2ciXh2nBiIrNodDXJQcfhpHan/BJn6vvFxVdfj+PNtwBIeOB+0Mgvk2HZnVJcOousedhZgTbqtNXSunYnAI4FE9h+WL51XjotmVdvmMO6OxZz5cyBmaqGqcKYkziHLfVbcPn6V2F/bKD9+G06PA6cPidJxsFVbp/q3RAwdCPscyYkMD7JTK2gp3qCbFbf8npnN0RsPliLZU+ebwIt5Z0VjMdNE8LC5cl/2wi0OQ+UDjue6iaaTQIOMaFfz6hJsZPYZ9uHP9j389kX8GH32InV96/y82SQbk5nQfICtjdul/W3QyAIAgn334+g1+PauhX7a691STGZzzqLKk89CYYEwlT9S/j3hn7yJGIdEm31ewhTi9R59NTUhehe87RxyObHh5o57lrqIwUy0yb1XG6EiDBoiTeHUW1MQeuHxspR9IVol5MQNStlP4g9SYVMSpaTL+aCPAAMtZWdrdhKJa3CqYsoiF2eDBdmX8j06OmkhKcgSRIVjgrW1qxl/TlpbC5UozKUsvvLt9nXUNXdmyo6RzZx7LDj9DnZ1bSLj8s+psXdwpzEOSxKXURyeDLCUPSwg0H5ub73bTkJkT4f8s+Wg0yhiMmX58mdwZosSxZ1zrpepae+DpQ6SsmyZMmBrwEkIbq8IWz75XcAfQRYh5iwkSTZGNZeCfnngvaYwgNRlHXIT2Y3RGs1hzoaKGreh69h78nbDwWFrxldUkw+p1yoEeK+36usc8UIFAYnz5DvQ+2NJ152kAiCwPjo8SxNW8r0+Olyl2zlJshaJBfHDSfDYVDdWiP71WrHaPyusxsiWmNCI2hG19PJ65T/Oa6bsLK1El/Ax6SYSVjCLNQ56yDMonRCKHSnztHBmif+yQuf/IFHv/obL3zyB9Y88a+eHRFqLRhioG0EjLiCQVna5hhT6o5du2hVeQjXWNgoRaDTiCzKH3jgRKsWeebqaZyWG4PLG+C37xbR4ZNv5mb9ECqMTkCZ1cnxOQgJetXXVxg8SwtkSaZPIuSOGef69QTaneRH5VMqefEPp56q0ya3tSdPP+Gituf+ieTzYZg5E9Ol38JywQUAtLz66tGFLKknpxtCkvDXFNO2eR/OMBAnTuDzfXKl1eUzUocU9I7WR1MYVciGug39loj5f/bOOkySs17bd7V3T9u4u+6sb9bdssnGE0JwCAcOciA45wAHIliCHiDoBwQCJBAh7utZd9eZHXdrd6nvj3d2ZEd2dCXp+7pyXdnu6qq3eqqr3vcnzzNUoP2c7RyF1sIxL8jfCd0Q40WhkPjaOvHb+INZBEadL78iDKp1ZpH8vVqkiCYbWw0kDCHRE593dWgv+x3Ya+sJqaBdm3HJTggAi9ZCqiGVClvFsNu1eFtINiSjVIyso/BKEa+LZ03OGlo9rRzqPDSoT4QmK4uUr3wFgNYf/Rjna68BvVJMBdbxt07rZswg2QGN7eeZkW7CjpGqukHu195ODnWIqWdW+3m6TJA3ddG4jz8aStPMVFqySHbI1NRUiCTE5aha9bQjGxKJ7N0NQOi6BT3dpZkzy4kiYfC5CAei74oFSIx3BwpJgVFtJMuUxdSkqSzOXMz6/PXcPvuDzL/uViweWFm3nd/u3sabNW/y8vmXebvhbY50naYhLp5z+3/LuS0PYa3dxzp1MvP1acQrxpc0JRqB9rMi+WCrhfwVULLukgUzKBTdHgfCH0ytVJNjyqHKPjoT7WsFZ9CJO+QmVZ8o1p4XdeFfijxzHraATUhWZV4nOhjGKmESjULNdqGrXXIjqAeRcE0shK4rl4TwthzFaUpDU7CK86efJRpLJseIMSE4Ag4smgt+EIP7DWjycuHiJfAFWeeJRqWBnAVQs2PSi9MsWgsKJHH/S5sGhoRLf2i0JBSAswlC4yhE7Kq6OqWYLnBRN8RlNai21wtVjz4xmmAkyNH2o8xJnYMkSeSb86l2VHfLMb3znx2xJMQoqDlTzX2Hn0XRHTJXIPP5I89Qe6Zm4MbxuWJiO9H47eLm2ycw4tm9B5cePGlTkSUFK0tShjTnvBQ6tZKHbps64PVvP39iaPmpcTISrfsYE8PqMpGcesWlR5WTixwM4tmxHbPGTFxSKa1NBybmQLIMdbsga64wPRqGUGsb9meeAcDw2U/wevXr+O5aC4DrjTcIdwppsCsmyeTtwnfyHHIEHAvKsElZOP1hkk1a5uWNfyJQFF9EnDqOo+1Hx7wPf9hPs6dZVKuNg3dCN8R4WVmazHW58RyIL8CVmEbU48HRHbQlZQq0vQsMqgNuEQQ1DtFRF58nnm9XUm4g6AWlmsaWahJcUG9KpWgESQiAqYlTqbBX9K+6vYhmdzPpcUNUw15l6FQ6lmctR6PQsLV+K+6ge8A28R/6IOqcHGS/H9kvft8t1SeRkcdn4NqNtrCQBNmAhwCL1B3YZCNNg3RCeB1tHO5QoImEcNqqSHCBedbccR9/NExJM3HemkmyA+razouqOv8k67r7naBU4z9bhcbtxKvSUrii14y7OCeJljjxPPE0dr0rFiAx3t2oFCqKPnEfGU4Vt5xqpOUtJysy1nN97vWUxpdiUBtoMSWhmvl+pq+8n9zi9ehUelFNf/plOPoUVG4SgW1H45AeNP2IRsQz/MRzYj5ZsAqK1wo5ipFyITnfXZBQHF9MlaPqkt111yLVjmryzfkoPN0d+JeQVb0YpUJJaXwppzpPieI5SyaMpUMgEoaqzSKBUXy9kMYajLgkMS/xdI7+GOMl6MHRWUF82iymZSzCl1hA9fEnL/84YsR4B2IP2IXfYcA1pB+EOi0NTW5ev9fi3//+yZPqtuaIhEDz2NfvI6blmPDWSSmfnP2rNOJ8OscoUxQJi06IifKpmCy6uyGyDal0+jtHrEQxbhz1IgHSh+Mdx8kx5/T4eGYYM+jyd+EjIuSxxpMQugaIJSFGQaa7oycBcQGlLJMxWCv/hartidK/vIBnoCm1fc9OwkrYrRMT4/XTx3ezbXEOvOgjsjxpnQkj0bqPMTEUJBvJtmoJRaFztqg+db21QbyXtZj2zjOEQxOQbKrbDWqDqEq6BJ1/+hNyMIhm7hyOZARJNiRTkxxFN2MGciiE/ZluUyljqpBjCgfHP77R4GzEvVcsmpxLpnK4Stw2109LQ3kJ0/SRcl3qdbR6W6lzjq1ltMpRRbYpe3yayPR2Q5y1TZBB1RiJRCPIVyjALUnd3RCSxLPpIjhqv2BQbc6ASEDIqryTsdcK74fugEOopQXPnr29uqoXJJkmsQ35kvgdyCoD7a4WElwyoYwc4rQjS74b1AZyzblCJmIQZFmmxdtyzSQhQFQbT4ufRpG1iC31Wwa0GYfb2gjV1/d77dCff0JueGK8GCSlEsN0IcmU4TuDkzja2tsGJKrO19TRFTWyONpBiyVCJvGoMy+v+XdpmolKayZmH/jaWvAqdZMvyeRpB2MK9m1vA3A4uZjFZb1ztWSTlkaruN7azjZMflIkRoyrAHV6Ota77gLgruNv8MTeWnQqHalxqZTElzA3bS4FlgJUGqMoRMmYBUVrYeb7YMqtQrIpGhaB7RPPwfFnoWortJwQkoEXqu4jF7b5t3i9aI34L250lf09ZM3t8TiIU8eRpE+6MkaXk0gkGqHOWUe+Jb9biillwDYD5gaDkG/Jp9PfKboh0meJJNBogivhIFS8BSq9SBpdqjsxsWjsgbTx0FlJk0ZPhjkbSZKYVv4+nJ5W6mq3Xf6xxIjxDiIqR3EH3Zg03RXi2sELjmRZJtwlvMh0M2cC4Nm7FzkyiZ0K2QuEpJ93EA+0icLdDm1nIG/poDJUE8Z4DKoddeIZcYnC0ytOdzeEsuMceeY8Ku2X4VkRCYn1srnXY6rD10GLp4XyRJFUCrW04N93gPSgQcwl3gWyrLEkxChIn1qCLPX/ymQk0ssHMYLVGMSF7p5gvbGL/CAibjdt509g8sFbulw0SgWrywZOFEfDlehMGE7rPsbEsrRABJ22poiOF/e2bUSDQSz6BLSWHBqa9o/vAE2HxcM4f8UlNw21tWHv1txvuHcN8bp4FqYvJEqU8PtvBsD21FPI4bBYeBhTRab9MhKuPYn7WBVerQTTytl2SiRpbpo+cQFKtULN4ozFHG0/KhZqoyAqRzlvP0+RtWhCxlIaX0qts/aydkO4g27qnHUcaTvCptpNvHT+JV6rfo1TnaeuSFfGosJElhUn8Wb2XCJKFf4TJ/CdPCkmf8lToP0d3g3RVd1jLGZ/9lkqV6+h7t57qVy9Bvuz3UnB+LwrK03ldxByBLAZo+h8GlLyMy/9mT6UJZRR76rHExpoYtnp7yROHYdONYjcw1VOviWfRRmL2N+6n7NdvYuJYE3/zpWQEjpMMmkdE1cooZ8xgxQHYD9HBCXn7VH87v4+PvWN9dgwsTjShEsP2YWzxqfpPgZK00y06eNxauJItoVpbG4SSYLJpNuUum3TVgDO500nP6nXWFGSJLzpYt5jr2yIyTHFeNeQ/OlPIStVzG6vYPtzm/GHRhgw0hhE1WXmdUJKadYHoHidqOYM+cRc9NjTwmz6xL9ForH4eihcNX45C1Oa0G3u9jgoiS+hwlZxxYonJoMmdxNWrRWD2iACKBf5QdieeprKVasHzg0u4kI3xOmu0yJ4mFgkqnpHQsgH514Xwa28JSPrxEgoAFv1xBcBDocsE2w9SVucpaezUK3SUjLrXjrOvUabs/4SO4gRI8ZQOANOjBojCkkBwaE7IcJNTUSdTlCryf7db1FaLATPn8fx4kuTNzi1XiSla3dO/D0nGhXJh/Obxf1PPcnFuXGJoFCDs3n0n+2qvrqlmPrS3Q1RZM6hzlVHm3eSC+qcjULqsdvHIypHOdh6kNkps1Er1P3W2dEPf5FTbz3VLcn0zl4HxJIQo0CdlkbG9x7qNwlyq3V0qoYIzltzJ143+6IkhHf/flyaCGpNAm1xCSwrTsKkG1819JXqTBivqWyMkbE03wrAMx4TypQUoh4P3j17AMjKWkhz0z4iY9U3bDsjHkRFa0dkmtT158eQAwFalpURLs5ldspsAMriy2icnYnSaiXc3Ix761bxgcvtCxGN4N+3jbBPgXPJVGxyOk5/mCTjxEgx9cWsMTMreRa7m3cPqus+FPWueuJ18aJCZAKY7G6IUDREm7eN052n2dm4k5fPv8z2xu00e5qJU8cxO3U2txfdzrKsZQQjQd6qfYs9zXvo8F0m89huvrquFIfWyI70aQDYnxaSYSQWCemHiegYuhoJuIR5limNUEsLTd95oHdiHY3SdP8Douoxvvv5dqWCLn47tqZWlFFo16ZSnDa4RuxQaJVaiuOLOdkxUBriWpJiGowkfRKrs1fT4G5gX/M+wtGw0MrtM3dpjodkl0Rc3sQtGvSzZpLgAldbNQlxCjqiRipr+wRfAm4q2n0E0JDgP0uKXSZu1qwJO/5IKUoxolQqqLRkkuSA6saqy5KEiER1qM+I602/eMmA5IuUL/4WwfP1QhLtHRTQjBFjKNSZmVjuvAOAm4+8yrMHxzHH05lFICR7HpSuh1kfFNXzpeuhYEU/P71xk9XrcZCoT0Sr1NLkmQQvwCtEtbNa+AVFoz1J1AuEWlpoefDB3ntUNErzhbnBIORb8mn3tuMMOiF9htAOv1SVZ8AFZ18Tf8+sUUj2aY3i7+y8jGsFZxNdER+J8UUiUNpNnCWHgoJ1VB5/clCZxBgxYlwae8COVWsV/wi4YIj1rv+sWLdqCwpQJSSQ+OlPA9D+60eJBkYg1zdWEgtFp1bbBJrR22rh1AviPlZyg+gEvByMpRsiHBRF15ZrpIC4uxtC31XDwvSF7G3eO7myTPb6ft/NOds5TBoTGcYMQi0tNN/fu842e6I4/vU0HbbOd3xHdCwJMUqsd99N0eZNZP3pjzgNFkwhHxv+77EhNs4B+9jkVQYlGhWeEH0m0d49e3AZJBoMQorpxmkTo3sX60x45zIzw4hJp6LTG8a/YCkArg1CksmUVEZi0E+Ns2b0O+6qhpbjohJtMMO4iwh3dGB76im6jNB15zIWZy7uMYHNNGXiIwjv7e6GeLLboNqSJTLKlys4427Dfeg0yBKOJVM5UiXGd9P0iZNi6ku2OZtUQyoHWw+O+DOVtkqKrYN0Y42DieqGkGUZR8BBtaOaAy0HeKvmLV6tepXTnaeJyBHyLflcn3s96/PXsyB9AcXxxSToElBICpGUSZnF+vz1pOhTONR6iLdq3uK8/fyokjRjZVa2levLU3ktV+i2O19+mYjbI3QzE/KFqeU7EVuNSDBIEs0nzyFdZJguRaM0n6oQVRpq/ZWTZPI7aG6tI8ElU29KGZEp9cUUWYto87Vh99v7vd7saSYj7vJKBE00BrWBlVkrkSSJrfVbCSWaSf9ubxFFQ4qCOZ/87wnVytXPmIFCBvP5VmakerDLRqpqe+dAflcHRzrFPdTjqSLNBvrulvnLiValpCApjkprJgluaG5uIuzrmjxzwUgY/E48h88gyVHqTCnMmj9lwGbmshIADE31yCqdSAbGiPEuIPkznyaqVHJd2zk2PLuJcGSCKkolCfRWEXSYaHo8Dk4AUJJQwrmucxN/nCuAO+jGEXCIZLzPJp73Kk3P+xd31gEQjRKsHXzNq1KoKIkv4UznGSHXkVIuOlWGwtsFZ9+A1OmianUYQtEQ+1v2s71he++LCYXQeRlNRzvO0aSLI8M4cN5gzVtBkdrC/ooXCY3VlDtGjHcxjqADi7ZbOjTgHrITwn9adKnrysoAiP/gB1ClpRFuasb2z39O7iBzFgrJv/EGjt3tcOY10S2Wu1gUdeqtEzLEEZGQLzohRlNoZ68VUkMjKD69aujuhkhSm5mSMIVdTbsmx9cpGhXFs1bhB+EOuqmwVTAreRbQ/Sy9qIMmoyNKdUt1TI4pxkDUaWmYli5F8cGPApD9+jO02ga5UHRmUGomTmvYbweNsd+P3LNrNy49HFAXoFJIXF+eOvTnR0msM+GdiUopsaJEVDQdyBYBINemzUIzUWsi05zH+eYDROVRLAKdzVC/Txj8DaHVeDGdj/0FT9TP2WU5rFj5UfSq3utMISkojS+lZe0MkCQ8u3YRqK4W7fdqw+Trd3cTOncI39kGfHolcnkpW06JTPlESjFdzMzkmXhCHipsFZfctsPXQVgOkxo3cb97GF83RCgSot5Vz97mvbxc9XJPF0OCPoEF6Qu4vfB2VmSvYFrSNDKMGZeUvFEr1BRYC1iXt445qXPo8HXwevXrHG47PGrpqtHy1XUlHE8upCEuiajXi/O1V8UbyWVChuFytvtfLrqqIV4ktRuNSUTpn2yLINEU121kbM29cpJMPjuNbY3dptQpFKeOvhNIpVBRnljOsY5eaQhPyEMwGsSqs07gYK8MSoWSeWnzyDPnsaluE6GbVlC0eROGP/+SzAceJP+9H5vQ46mSklBnZJDikCmMnscuG2nsY05dUV1DW8RIsaqLoMeB1adAP23ahI5hpFzwhVDIYK5uxyYhOk0nA28n6OPp2CqCZAdSylhSOFCLPmPmFCJI6PweIv7oO34BEiPGBTRZWZhvuw2ANftf5rUTQ3sMXFVkzBYdwEEvGXEZ+CN+uvyTqA1+mah2VJNvyRdV/e7WAVJMCusgXkIKBZrcoQvWCq2FtHpbcQVdIgnhahlcR93dBhUbRDdLcsmw42z1trK9ZTsapQZ7wN5bzRqfC67mkZmVj5eQn4izkXq1avC5uFJFStntFNhb2Nu44x0l2RUjxuXA7u/uhAgHAblfQrQvgTNnANBOEUkIhU5H8uc/B0Dn7/9AxD2J3Uhao/C8qd01tkJJvxPOb4HqbZBcCmW3CNm/y41SLeR2Oy4dg+ihq0okLy4jI/EjGpbubgjaTlEUX4RFa+FQ66GJHSSILsILRXvAobZDlCWUCZlDQD3IMzPNoaDNqiPstw14751ELAkxDub817249SZSvV28+cu/D75RfK5oqZoIvF39pJjC7e14Kyvwq+GEtZxFhYlYDYPfmGPE6MvqMpGEeDaUjMJiIdLVhe+QuPnGJZWSEgqMvBvC2wXVbwud3RG2uoc7O+n415Mcy5eYf9N/kGRI6nmv2eFj1/kONKTgMEqweglAbxWDJQscl0df1bfjDUJeJa5lM+iKpOHyRyZFiqkvCknBovRFnOk6c0kJogpbBcXxE9sFcYHRdEN4Q14qbZW83fA2r1a/Sr2znlRDKjfk3cANeTcwL20eBZYCLFrLuPTfk/RJLEhfwLrcdeiUOnY07mBb/TbqXfWjS5qNkLI0M7fNyuSNPNEN0WNQrbeCzjrxcntXGr8Twv4eA8q8snxOJOUTlaAyHYIq2JQzl9yyPLF9fN6VkWQKB4lGwzR2NRPvhnpjKkVj6IQAyDPn4Q17e4ycr3UppsEoii9iQfoC9jTvoU7nobnATEnudZNyLN3MGSQ5wOStwYaejvbeBcL52jpssol5umrSbDK60lIUhsnzmhqOsjQTld3t7daT9bREQ5OX3Pa0I8cl4d4ukhDtU2aTaBxo3leclUizUTwLfS32d7webIwYfUn97GeIKhTMaz3Dq09tvDaCtZo4SCqB5qNIkkRJfAnnbNd2N0RUjlLjrCHPnCdeGMSUOnDyFAB9/0KushnDdtb1dEN0nRHFdOkzofGioI+jUeif5y/v8aUajHA0zOG2wxxqPcTMxJnMTJ5JlimLelf32kCpFmuFy1Ek0VlJl95EYlwaasUQcsjmdHIyF2DqqORo+9HJH1OMGJcbRz2aht2Ts+ugQyQhgkN3QQD4z4jCuQudEACWO+5AU1BAxG6n67G/TMr4ekguFeuhjlE8A0J+qNsrpOfikmHqnULe6TJ7pfUjuVScw0iewUGviAOZL5NcFMN4FY6W9JnQfgZ8duakzMEVco2oAHR0g60TyjhAnbOOUCTUz8Mz3HSRhKNCQe4DD5GYMw3bRKrpXIXEkhDjQGkwIL/3gwCkv/oUbfZB9MQm0hfC2wlxvcFaz569uPUQ0CTh0hpZP+2dFTiJMXmsLElGIcGpdi+Kxd2STBs3ijctWRRKWs50nrl0YNfvhMqNkLtowCJlOLr+8hdOpARISc1n6tp7el5/an8dSx7ZzAf/uJflP9pGdZOJjtsXAeB4/gWiXu/l84UIB/Ef2UskoMCxpJwjVWJCMFlSTH0xqA3MS5vH3ua9BCKDV3J5Q146fB3kmCZHKk2n0pFnzhuyG8Lmt3Gy8yQbazeyqW4T9oCdYmsxtxTcwuLMxeRZ8tAqBwbaJmpsUxKnsD5/PUXxRVQ7qnmt+jVOdp7EF55Yr4Yvry1hc948Qgol/pMn8Z3o1vxMmQJt7zCDaluNWPh3T37TDCoK3W00x8Px9Hj2lUgUpkZ7u+N0ZlDpJl9P/2L8DtyShKaxA3UEApk5GLVjawNWSAqmJ03neMdxZFmmydN0zUsxDUaKIYU1OWuodlTT4mkh25w9KcfRz5yJKgpZrV341QHsLh82h6job2pqoAsTCeELUkwzJmUMI6E0zUxzXCI+jZ7EzhANXW1EPZMkLeZpJ9juQd3VgV+pJn3ZokE3S7foaLSIIF5nVXOsEyLGuwpNTg5xN98CwKKdL/B2xeX1gRozadNFoMFnJ9ecS7u3fXL1pSeZJncTFq0Fo6Y7se9uGzC/b39TSLg+V7ic/zdV/M0Mp47ScGD4AHuBtYAWT4vwR0gsFolWV3eiuqsKanYI+RHz0OvZTl8nG2s3EolGWJuzlkStKM7LNmVT5+oTtEksgs7K0Zz62Og4R4PWMKgUU1+krPlMVcRh6zxHlaNq8scVI8blxNmE0t044bKW3pAXpaREo9SI+4Vm8IKjiNtNqF4kIbWlpT2vSyoVyV/6IgCdf/0r4c5J6ngFsXbKWyKk5i4lpxkNQ/Mx4fsgKUTyIW0adMtSX1EMCWJt5xyBx5GtRgTZFSMPKQ/XxSCHw4Ra2/AdP45r0ya6nnyS9l/+Es8jP6L+P/+T8zeup/nb3+nnVTicH9GwaI2QeR1Uv40SiUXpizhrOzuxRtX2WrBmE4wEOdp+lOtSr+tXjNn1+N/6bZ7yta9ivftu8uKLafd3iCTVO5RYEmKczLnvE3i1BrJcbbz+20H05gwJIEfBZx//wbwd/TohPHuEFFO1KguFBOumTqwkS4x3LlaDhrm5opr/VJGoiHVt6K48M6ZiCoexqPTUOYfJwoZ8ULlBtKNbRx4ID9tsHHzrCYIqWPr+r/TcjJsdPr753HGiF3zuZPjla06q0kxE8rOIulw4XnlFJOKCHpF9n0QCJ/bir2nHb1ATLitm6ynxIJhMKaa+pMWlkW/JZ2/z3kErAivtleRb8lEpJk+DsSS+pKcbIipHafG0cLjtMK9Wvcq+ln3IsszslNncUnALc9Pmkm5Mn9TxXIxCUpBpzGR51nJWZK0gFAmxsXbj2DxNhiAvKY4bl5SxI0METO1PPyXesGRByDu4nMC1iq1XignAvn0nBr+bk5l6tibdQ3mdTEPwGGebj/d+Jj5v4rr9RorfQYfTRmJXgLCkwFqYO67dZRozUUgKqp3VdPm7SDYkX/pD1yAGtYGV2StZnrV86IrNcaKfIST+Ek7Wk5now4aRU9W1hPweajvcBJQhEjtbMfpBfwVMqS9QlmYCSaLCkoE6AorOMM6uSdIQ97TjOigSlseTCllcPniwSpIk3BniWrafb3rHm9LFiHExGZ/7LLKkYEHraV54auOVHs7IUGlE8KjpECqFinxLPhX2Ca6mvIxUOarIt3TPAwIuERTTxPW8H/X5CO0VFc+bs6/j+eKVbM+YgRIZ28MPD9vBolaoKbIWiW4IhQIy50DDASFp1XBQGLD2KbbrS1SOcqLjBLubdzMzeSZz0+aiVvY+x5L0SYQiIWF+DWBKF2uFybyPulqJSgrqo75Ld1CqNKhyl7AwGOVk+3HavZe5eCNGjEnE0XmO85565JEErkez38DFfhCD+/sEuk2pVenpqOL7qzKYrr8e3fTpyF4vHb/7/YSObwA6i5Cbqx2iK0SWUdpr4OTzwm+n7GYhPaeanKK9MTNSg2pbNSQUjHi3tmeepXLVatHFsGo1NR/9KPX/9Tmq734vFcuWc2bGTCpXrKDmvffQ8LnP0/rd79H1h/9H8I038O7cRbCmZuBOh/EjuiRJxeL51nwEg9owsUbVF2K/+niOtR8jx5zTT+o32NDYUwBsWCQUF8IdIkmWakjFo1TidjePfxxXKbEkxDhRmUxE7hSV3CkvPkmHa5CM1UR0Q8iyuFnpE7r/KePZvRunAaqMRczPTyBpkPb+GDGGYs0UUdn0oiobSa8n1NSE/9QpsTAwpVGutnK66/Tg3RCRkNBsTSwWN/BRcPrxX9NgDDBPV4p51eqe16s7PD0JiJ7DyErU0XS63rsSEAbVMggzQGcjk4l300uEfUpcK2fTFUy6LFJMFzMlYQoSEqc6T/V7PRwNU+OsocAy8gf/WLjQDbGtYRsvn3+Zs11niVPHsSJrBTfk3cC0pGkk6hPHJbE0UZg0JmalzGJF9goqbZUc6TwyYQbW960uZmOBqF7uevkVYVAtSWKS9k7phvDZhd6qsTcAX/3Uc7RboDahiOtWrCIUiWfBqRDnD25mV9MuYbIYfwV8Ifx2mjpaSXBBkzGJwnTruHc5I3kGR9qOkKRPuqyJtMuNSqEiQTd59zBd+RRQqUissVEU14FdNlBVW8fZ6lpaw3FY4zpJOy0WqVfClPoCWfF6jFoVFeZMAMx1bhze9omvOuquhuvYugeAI2llA54hfYN2ijxxTw9WN8U6IWK869Dk5aG54UYApm/6N4fqrhFN5OQyUZDgbqPQWkits/aaNCH2hDw4Ag4yjeK+OFgXhGfXLqRAgFZ9PFUWkVD947Rb8SvVqE4exfnKq8Meo8haRLOnGU/I0yu51HYKStcPacDqCDjYVLcJV9DF9bnXk24cPOCfbcqm3tktySRJk29Q3XEWuykZo9rYz9duSOJz0RtTWKows6d5j+gIiRHjWicSostRR4PeQmPj3gndtT3Q7QcBYk40pCm18IPQlZb2yDo3O0RnvCRJpHz1KwDYnnqKYMMkqymkThNFahffe5xNcOZlVPYqKFgFBSuGlZe6osTngat1+ILPgEskhkboXRFqaaHl/vt7ZZ5kGd++/bg3b8Z/4gTh9nbR4aBUokpNRTd9Osa1a7B+4P3oPvkJ0n7wfdJ/8uOBUlWX8CO6JLlLRNecu40kfRLlieUTY1TdLcXU4eug1dtKeWJ5v7dtTzwB0ShxS5YgL1sFgPOskPKSJIkkSz5NnWfGN4armFgSYgKY88VPEVBryXM08fLvnxm4gTVn/JWifrvI1HWbUofq6gg3NWOPU1JhLI5JMcUYNWumiM6ZHXUudIuF70KvJFM28X43ceo4TnWeItK3vTIaFeZJxlRIH52chq2tnt37/s2MapnMz36+X/Bapxp4O1JKEotzptI1t5CwQUPgzBl8hw9Pui+ELMsEDm0j5FPiXFzO0SrRHnk5pJj6IkkS89PnU+uspblPNrzWWUuqIbXH2GgymZIwhfKEcm7Mv5EV2SsoiS/pbdO/CjFrzKzKXoVaoWZT3SZsE2DslGHVM/OWVdQbk1H4fDhffUW8kVQsJhnvhHbJC1JM3UR9PpS7t1OTIpE8/Ubm5CawO30quhDM2NVKnDqOTXWbcEiISlD35avqi3i7aLe1Y/VAvXFsptQXk6RPIsOYQbZpcmSK3i0odDp0ZWVowlAe9OFQKqhvauLM+Rq65DhmxDWR1hpEYbGgycu7YuOUJImSVCPnrULH1nK8jlYpIjpOJxJPO1GlicjRwwCErluATt3bbh+Vo2yu29zTdWiaIoxYNfWNyAHn5fdbiRHjCpP9hc8jSxKLWk7yz39s6BdMumpRKEVXcMMB9Co96XHp16TkTo2jhlxzrjCkhkFNqV2bNgOwO31qTzCo3RDP/iW3A9D24x+LQo0hUCvVFFoLRTcEQNEaYcKqHTivlGWZM11neLvhbUrjS1mUsWhYmc8cc85FkkwFQuZpMu6j4SA4GqhXay4pxdSP7IXEu1qZbsxhZ9POazJZFSNGX2R3K+2STGn29bS1HOrtRpoA7AF7n04I16D3CQD/GVEQdt6S0SPrvOSRzTy1X9wP4hYuJG7xYgiF6Hj00Qkb36AoFJC3VHR5hfwiQV2xAer2QMZsAnmrh+z4umpQqkSHw3D+Fl3V/SR8L0WgpmbQe3H8xz5K1m9/Q94zz1D09jbKjh2leNtW8p95muxf/5rU73wH/Yc/jOXOO7Heeivp3/tuP/knTV4eqtRxqMGodZC7WPibRkIUWgsnxqjaUU/UksnB1oPMSZnTrwM94vb0eFmcWLSeL+y2A9Bw+FTPNZuaUEJrV8W14Y81BmJJiAlAFR9P8Ja7AEh4/gk63RdpuBtThHRNYIwVD5GwyKb2lWLavZsoUBmfSECycsPUkWUhY8S4QGFyHLmJBoKRKPVT5wPg7klCiE6DuSnX4Qq6eKPmDc7bzxONRqBmuwg6Zs8f1fFCkRAbnv0pRdUBknPLMK5e3e/9f+wZ2Er34G3l5CXEk5NaQtcdwrvC9sSTYM4UOrLRS3hWjJHAyaNEOlvxqXUESwvYelosgC+XFFNftEotC9IXcKD1AJ6QB1mWqbBX9DM2mkzUSjXZ5uxJ83eYDJQKJVPjpzI9aTo7GndwtuvsuB/i/7WqmE2FiwGoe/wJ8aJKK5LMkyXjcjmxVUNCrxRT18bNeNR+OvVxXH/z7czOsbI7fRoAns1bmBE/lWlJ03i74W2a1JrL2g3hctajbg+gkKHelErxGE2pL2Zh+kJyzeOTdooB+hkiOT21w41TFaSjrZma+jpsSgVTA150IeEHcaU7qErTzFRaRcWv4mQFEV08TtsEBw7d7fiqWlCEwzQbEiifN63f26c6TxGMBnsk5DKnlxKWFGgDfiJuv6imixHjXYS2IB/FmusBmP7Gkzzywye47YHnehbmVy0JBULn215HSXwJlfbKS/uqXUVE5SjVzupeKSYYYEotRyK4t2wBYE/6VO6Zm8WX14pu6J/Hz4PMLMLt7XT87rfDHqvYWkyju1FIXqj1Yk1xEe6gm631W2n3trM2dy055ktXulq0FpSSkk5ft+67Pl7M09ytl/zsqOk6D5ZsGn3to0tCaAyQeR15tgZS9SnsbRlccjVGjGsFZ1clsjEZiymbbHMuh2u3TNi9zxFw9HZCBIfuhAh0m1L/uVnZT9b5W8+d6EliJ39FdEM4XnoZ/9kRSA2NB0MCJBXBmVdEAsKaA+V3CF/La4XkEuioGDqJ21U1KimmSNcgRYEKBYkf/zim1avRT5+GOiUFSTm8L4b17rsp2ryJjJ/8GDQaglVVOF96acTjGBRLlogt1e8D6DGqPmcbhcl4X0I+8Ds5G7Rj0pgGdO85XniBqMuFIjePz1dqqO1+zqZ4bTz09CGaHT4MxlRMMrR6J+H5dRUQS0JMELO/+l8EVWpKump56bEX+r8pSWDNFhWzoyHgFhqZJ54VZjxpvVXnnl278eqgWZ/JdbmJpFl04z+JGO8qJEliTZnIHL9hLASVikBFJYHqatF1o9JhCPlZlLGIpZlLafW0smffr2h21BDNXTLizDeIaqY957ege20H6TZI+q/P9gtAnWh08PwRIa/0x49eR6pZBLw9QdGBUZpQSsfKaUQkcL71FmG7C3TWyVlYAJ7XnyHsVeJefR1d/gRc/uhll2LqS6I+kfLEcvY07aHJ04RGoSFRn3jpD77LyTRmsiZnDc2eZnY07sAfHnvHQrJJS/o9dxFSKNFUVeA5fkK8Yc4QkgXXMj6bMJPrU5lT+8wL1KZK2AyzmJObQEGSkdqMYlxqPVG7Hd/hw2SZsliZvZIzUT9VNVv6d0xNFtEITm8b2ho7IDohiiYoCRFjYrhgOJ17rgGvNogy6KSxsR6n2s/MVlEhdyX9IC4wJd1EozGZoFqL7PNhDJmxdY5xwTEUnnZcB0XF78HUUpYW98qddfg6qHHWsDJ7JfaAHV/YR3FmAk1G8TsMtDvBP3EVhTFiXCsYP/kposC8trP8aOfv+esbP2DLz/98dXdESBJkzYWGA1jUJkwaEw2uSZb9mEBaPC2YuscNiArekF/MtbvxHT5MxGbDrTFwIrGA98zJ4otrS7hhaip+Sclf5oiCvK7H/0bg/NDFGWqlmkJLn26IizhvP8+W+i3kmnNZlrVsZFJH3eSYc6h39emUnixJpo5zOM1pKBQKzJrBdeqHJKkYJCUz0SLLMsc6jk38+GLEuEzYO89hSRBdnMlpszH7HGMP3vYhHA3jj/gxqo0iEB70gjpuwHZyOEzgnDheZbfE5gUiskxNhyjm0E+bimn9jSDLtP/fL8Y9vkuSPkvE7qbdBcmlozJvvirQx4t4kGOQ55jPJpLuxpF76Llef138z4X4j0JB+ncfQp02+kJqdVoalltvJflznwOg9eFHCNvGqXyQNU/Elex1KBVKFqUv4pzt3NiMqh0N+AwJVDqqmJU8q99bcjRK19+FIbX7pruIosChMeLQGFAgk+5qE9es1kyGKo5qR/X4zusq5Rr7NVy9qJOS8N9wGwCmZ/6O3Rvsv4E1d+RJCFerkLs5/TIgizbVwtU9WplyJIJ3716cBqg15LF+WqwLIsbYuOAL8WadB8OCBUB/SaYLkkcWrYXFCiMz9GlUJeXyZt1Gahw1I650ONV1CtvGtyis9KItKcG0dm2/93/0xhlkGW6flcH15Wl8/YYyAP7f21V4AmHi1HFkFc+hY3EJhEKihc2SNfiDcZzIsox/z0bCPgXOpdN6pJjWT7u8UkwXU2gtxKgxsrd5L8XW0flwvJsxqA2syFpBoj6RjXUbafWMPXH18fWz2ZM9C4Bjv/tL9wESwXeNm1N39TekjjidhE7uwa2D5KV3oFBIKBQS03IT2JsmNC0vSDKYNCaWF99KVKFkR8ULk69x7Hdgk8MYzwp5Ml96Fibd5Jgsxxgbuu5OCNXxc2RaTPgkUMoBwoYg2SeENOWV9IO4QGmqiaikoDa+2xeiNYrbXjNxB4hGwWejc9sBAM5kT2VqhghWhaNhDrQc4LqU69Cr9GTEZdDgaiDTqqfBIiqmOmvbY74QMd6V1Ack+s62FMh8/sgz1J6puSLjCbW04Nmzl1BLy/AbmjNAY4TOSkriSyYkEHe56GdIDeBpEwGmPgVDF577e1PKsJr0zO0uzPnu7dMwaVU8rcyha+YCCIdp/cEPhq3wL44vpsHd0M8A1Bvy8nbD29Q561iVvYoC6+h9z7JN2dS76nuPnVAg1t+Rcep798XTAdEIDYTIiBtFF0RfchcjtRxjYeI0Wjwt79hAU4x3ONEoHkctSUli3Y4li3KVhQpbBY7A+EzhHQEHZo1ZFC0GPaJrapBAfrCmBjkYBIOBlrj+xYJKSSIvqVe6OPkLXwClEvfWrXgPHhzX+C6JQim6CZTX8BoluXRwg+quqn7d85ci1NyMa9MmAOIe/R2uh3+F+YVXsd5995iGdcH3I3j3+9EWFxOx22n70Y/HtK8elCrIXy5MxUO+8RlV2+s4HnYwJWHKAOls97ZthGrrUJjNNMwXXhBIEvUmURic624T16zOTKKkpt3XTiASuPgI1zyxJMQEMvtrnyOsUDK1/TzPPX6RMZcpXQSqhtIOj0agoxJOvQR1u8REdvp7RVXNRfp3/tNniDgctBs11OnzY1JMMcbMvLwETFoVHe4gjuu6fSE2XEhC9Anyd1RAZwXG8jtYkr2S+WnzqXPV8VbNW9Q564ZdaDS6G6luOUP+37ahoLsLos8k4u1z7Wyv6ECjVPC1daUA3DErg/ykOLo8QR7fXQNAWUIZ7TdeRxSw/espZGP6pPhC+E+cAHcLXpURX2E2286IG//NM66878p1qddRbC0m05R56Y1j9CBJEuWJ5SxMX8iB1gMcbz8+plZhi0GN4c73AKB7exMBh1O0Bof8wqz9WsVW028y2fn6m9QnRlB6E1h545Ke12dlW9mTNhUA16ZNPb97lUJFUf4aimQVW+q30OiePNP4gLcdVziMpdkOQFxR4aQdK8bY0OTlobBYkINBFkga7CoZm0JBmd6MVNsAktQj2XQlKUsTCYFT3VIa2rNN+OUo3onqsPPZCLkCKJqbCElKTIsWouhOZB9rP0ayIbmnRfuClrlCIeFOE+36zuoW0QUbI8a7jEx3BxeXfChlmc7NWy/7WOzPPkvl6jXU3XsvlavX9Og4D0nWXGg+Qpouiagcpd17+fySxoo35KXL39VrSA3dptS9OtuyLPcEkfakT2Xd1NSewpxUs45v3jQFgG9nrgWNBs+u3bje2jDkMTVKDQWWgp5ETZ2zjk11m0g1pLIye+WYvcfi1HHEqeN6q1fVOpFMcUygnFf7WUgqocndNDoppr7ozJA6DXXDQZZkLOFExwk6fBPsSRQjxiTjdzXiVihJjOuORZnS0AY9zIyfwr6WfeOSZeonxTQCU2p9aSmZCf07JT6xNJ90S28nlTY/H+t7xDqu7Wc/j0mhXYr4PJF0vVhSvqt6VFJMtqeegmgU95SZrNzk5p69QZb95eSYZBaf2l/X4/ux9KfbOfnBz4Ek4XjhBTx79ox6f/2ISxKJl5qdAGMzqo6E6Ww/iVtvodA6cI3a9TfRBWG5+25+t7ep5/W67iTERzOi4ppVqlEqtWRqk3p8495JxJIQE4gmPR3fmvUA6J76Gw5vn6CUQiG0xi4Omga90HgIjj8L9hoxeZ16p/gBdJtQX4x3z24ATqSmUZqWQXbC5JvTxnhnolEpWF4qWum2JU8BScJ/7Bih1laISxYBkI5KaDoMRdeLKgSEPNDyrOXMTZtLtaOat2rf6l951I0j4OBQ6yHKttagsrvRFhdhWreu5/1IVObh18Xk4SOLcnuuZZVSwX2rhefB/3u7CncgjEVrIW3eMtrzLIRbWnDtOSKSdxMsV+F65TmQJVwrF9Dli8fpi1xRKaa+qBQqpidP7zUNjDEqkvRJXJ97Pa6giy31W/CEhjZPHIo77r2FRnMqunCAbb97QlQJ6uOF8di1iLcLkIV+aTcVz/+bLjM0Jy1ieqal5/VZ2fEcTC0lpFQRqq8nUFHRu5/4PDJDIZZmLuVY+zGOtR+bFD1sm70GrU+DBLTrLeRlj7wVOMblQeqTZEiv7cKmitCpjhI9IYJxmsIClKbxm4mPF4tBTZpZR6VFBN4CJ09his+nrf3kxBzA04bvrEjkn0zMY8FUkVxo8bTQ6m1lRnJvIiZZn4w35BWdRPli0RKsbQH/+CoJY8S4FkmfWoI8yDyn4PFf8dp7Po7jzOXpMAjW19P8nft7/ceiUZrvf2D4jghDApjSoO3kNdMNUeOsIceUg1LRR4v7IlPqYGUlobo6ggoVB1NKBxTAvX9eNvPzE6jWxLPjOrEWbn3kEaK+oSW0iuOLqXPVsatxF+ds51ietZzShNJx+wXlmC42qC6aOEmmSAjsdXgtGfjCPhJ145BGTSmHkBejq5UF6QvY3bR7TPPSGDGuFJ3tp4mz5vf+ZiUFmNLIQYFRbeR05+kx79sesPfxg3APnYToNqXWlJbS5hKFg9MzRZHJsUb7gO2TPvdfSFotvkOHcG/dOubxvStQKLsl7fqs99ztoFCJte8IiAaD2J9+BoBfmmb28+z4n38f57rvbWDeDzYy9/sbmfv9DVz3vQ3M/u5bzPruW8x86C1mfXcDa357mBkPvcWU77zO//z7eL99fOlYBM1doqOi+YEHiPrHLr0MCAmtsF8kmxFKFFatlYOtI+ucCTrqqAw5mJO+YMCzzH/uHN7de0CpZPeM1ZxpcWHSqViQn0B9ty9EUd9ktNZEni7hHdkpd1kjWQ8++CCSJPX7r6ys7HIOYdKZ8bX7iEgKZrec4dl/vtX/zfhcsAkpAtztULUNTr0oNNVK10PRWtEBcQk8u3YjA+eSklhfHqsCjTE+1pSJm95rTaEenW7Xxo0icWZKh/q94trUDdQ8TdInsSJ7BbNTZlNpq2RD7YaeKuhgJMjupt1MNxQRefxpsf1n+3dBvHC4kdPNTkw6FZ9f1d9o+baZGRQkxWH3hnh8Vw0AU1Kn03bzPGTA9uSTEy7JJEej+La/TsinwLlkKkerxFivtBRTjIlDo9SwOHMxuaZcNtdt7q8dPAKMOjXBG4X0XvSl5/AFw9e2JJOtvxRTuL2d1o5jZHbIWNff3G8CNSvbil+l5XCSkANzd1dFAkIuUKEkPhJlbc5a3EE3bze8jS88sRreDnstBoeYfdYbUyhOjflBXI1cSELIB09zVkrihMrItObu38iUacN88vJSlm7qMaf2nz5NSlI5TU37sfvt49+5px1Hjx9EGUuLkghGghxsPci8tHmoFb0t+pIk9ciImKeIjkBNQxNyzBMixrsQdVoaGd97qFd6Q6HAnltMFIn8k3touPMOTn/hKwTrJqc6MNjQSNv//YLqu+8eaMoZjRKsvcRxM+ZA6ymy9cl0+btwBq/e37Esy1Q7LjKkjoTBZxdzm24uSDEdSS5CZTSyuDCp334UComH75qORqXgp4kLCCWlEm5upuMPfxjy2FqllikJU7BoLazOWY1Faxly2+G4WC4ry5RFs6e516fKki2qeYOjlNQYjK5qMKXR5O8iPS59fAkThQLylkLDAVLUZqYmTWVn405C0Wu4szbGuwpH1zmsSaX9X7Rkg6OBOalzqHJU0eUf2/rIHrD33hMCTiF1NwgXTKkdWfkEw1GMWhW//8hc1EqJPVVd7K3q7Le9OjWVhI9+BID2n/8fcuQy+Nldy1wwqL6QjLeNrgvC9cYbRLq6iCalsKu7m74vnZ4g7a4AHe4AHe4gnZ4gNm8IuzeEwxfC6Q/jCUZxByL4QgOL2yKyTNv7PoEqJYVQbR0dv//9mE8VEPfl/OWiALe7EGh2ymzcIfeIigqqarZiSZ2BtY+f0gVsf/87AIY1a3nkkPCwuG91EUuLkqgzi6R/Pz8lnZlEVESJYvOP0/PiKuOyl9NOnTqV5ubmnv927NhxuYcwqehyc/AuE3r3qicfx+HrM5EwZ4nKktOvQM3bYEyB6XdD9vxBA7yDEQ0E8Bw6hE8DdXGZ3DI9ezJOI8a7iFWlKSgkON3sJLp0JdBHkil9JpTc0K9KejBSDCmsylnFjOQZnOk6w8bajexs3EmmMRPji9uIOp1oCgow3XBDz2f8oQg/e0tMHD63qoj4OE2/faqUCr6wRgQ7/9/bVbj8IZL0ScQvXUG7VcK7ew8BR3RCkxC+w4eRPK34lSY8BZm8fUZ4u1wNUkwxJpai+CKWZS7jZOdJDrYeHHmbJbDivo8RUqjI7Wrgpac3id/HtdoJ0VUt2m27aXj5Rdqs4A1ns2bNnH6bJpu0ZFr17EoXQeQLQYke4vPAVoNaqWZx5mLS49LZVLdp3LqwffG4GolrElUu9aZUilOvfEV9jIFcMKcu6aojGkpADidQ3ikCd/b8q6f4pDTNRL0plYhKTdTtxhBJpUybwIGKF8cdOIzam3HvF10VzSUzyU4wcLjtMDmmHJL0SQO2v2ComjG1mJCkROkLEWmtHxgEjRHjXYD17rsp2ryJnMcfp2jzJha9+RKu3/yFg9kzUMgyvPU6lTeup+k79xNqarr0Di+BHA7j2riRuv/8FOevv57OP/yBqGOQe4AkocnNGX5nWiMkFqFsOUGRtYgKW8Xw219BWr2tGFSG/gkAb6eocu3TGeHaLJ73e9KnsWZKChrVwBBCYbKRL64pJqDS8OiUmwHo+vNjBGtrhzx+cXwxU5OmjrnDN/Dqq1Stvb6fXJZOpSNeG0+Lt7tjRaEU85OuqjEdox8d54QUk6epv3zVWDEkiE6Nhn0UWApIMaSwt3lvTCYmxlVPVI7it9eSlFze/w1zJjga0So0zEmdw76Wfb0JwREiyzKOgKNPEmIYOaYzotijqruQtzzdTKZVzz1zRYzsV5sH3n8TP/lJFGYzgYoKnK++OuD9dxoj9jUaDJ0FtGah5iLL3VJMI/eD6HriCQCit9xBtG+3HaCQ4K8fn8erX1jK619cxhtfWsabX1rOW19ezsavLGfjV1aw8cvLePpjU9n81eU885mFXFwTqpAgLzeF1G//LwCdf/oz/nPj7EDUmUUxQfV2iEZHbFTd4W3H31VBYe7yAe+Fu7pwvPgSANumr6bVGSDTqueji/IoSjFS1915GKyrEx4nIL73gJN8cz41zprxndNVxmVPQqhUKtLS0nr+S0oauBC71pn+318gisT8xuM8+/TW3jeUKuG8njELpt4FKVNGbVbjO3wE/H7qrXHoUgvJT4q75GdixBiO+DgN1+WKlrr9WdMB8O7fT9hmE5PjuJH/RtPi0liTs4apiVNJ0icxRZdP11/+CnR3QSh7Hz6P76qhyeEnw6Lj3sV5g+7v1pkZFCbH4fCF+OvOGgCmFS+hZbUIgtpe2gKe9gnT43e++hoqfRTH8sV0uk1XlRRTjInHqrOyNmctsiyzqW7TiKuf45ITcS0UE4yOfz7F9sYwto7mSRzpJOHpFK3TfZKMBzc/R6pd5mzxQkoHCfDPyrayN60cWZLwnzjRf0IbnycqZLopTShlZtJM9jTvGVWSZyjcARfKkJdohdDsrzcmU5QS64S4GtFNF8+SLE8HBmcCsieHErvoOkpdeN2VHFo/ytJMRBRKmhOzAPCfrSCh7A5mezzsrNk4dmmMkJ/Q+XPgCdKpM5M3byb1znocAQflieWDfiRBl0BEjpCWJNFgSgZZIthug9Ea4sWI8Q5BnZZG3IL5qNOE9M/CNQu48fm/8ZePPMj+1DKkaBTHM89QecONtHz/B4Tahg4ODEWouZn2Xz1K5Zq1NHz+Pjzbt4MsE7d4EZm/+AVpDz04wAw13D4C7f70GWCrplCfQqO78ao1lqyyX2RIDQOkmEKtbfiPHQNgT1o5N04b2ovwU8sLKEszsSFhCvWFM5BDIVp/+PCkjD3U0oL3pz8bVC4rx5zTX0c7sRC6xinJ5O2CsJ9QnOhwSTGkjG9/F0ifKdYyjgZmJM8gKkfZ27IXV9A1MfsfD+HglR5BjKuULnsNGo0J7cUV3xoDaOLA006mMZMEbQInOk+Mat/ukBu9So9K0S1NHnCLgOxFhNvbiXR2gkLBYaVYy0ztlmL67MpC1EqJnZWdHKjpXyimtFhI/OQnAWj/5a96g77vQEbtazQYyaUiAetqEX/bIRJCF+M7fgL/0WOgVrO1aHG/95SS6J5bWZrC1AwLU9LNlKWZKU0zUZJqoijFRFGKkYJkIznxOvIS45iXl8jDd03vl4h479ws0i16TNdfj3H1agiHaXngQeTowK6J0Z1zifAUahHPvr5G1YOtDaJylOO1W8hNKEOtGyhVZX/6aeRgEFX5VH5UJ+K//31jKTq1kqIUIx16Cz6VFsLh3i5PrQkCzp4ipdEm865mLnsSoqKigoyMDAoKCvjQhz5E3SS10l5J9EWFeLoDVPITf8Hl7xMgTS4REjJjbN/0dPtBHErPZnnhyLOQMWIMx+oysdh4vUuJtrQUIhHcW7eNeX/pxnSmJ0/H/s9/EnE40OTlYb5pfc/7Nk+QX2+pBOCr68QNeDCUCqmnG+KP26tw+kOkxaWhX7YUWxw4XniRqMoMrvEHgOVIBPeW14iGJRyLp3OsWowpJsX0zkalUDE3bS7lieVsb9zO6c7TI/IymP6ZewFYVH2AX//8JR59eitP7auZ3MFONLb+1Sze2ho6fDVktkkk3XLzoDIDs7Kt2HUmmjOFfNqF6khAVE5Kin5dIdnmbBJ0CRzvOD7u4bbbqzEakvFViESHNy0bs250ifwYlwdVfDya3FwAptgbyXG2YwgHiOgMZM4cPAh/JShN7TanNoluN/+pU2BMJjF3KbO9bt6u3zY2STFPO94KIU14MKWUBUUmjrQfYX7a/P666xeRY8pBVnXQYBZBPkdD14T7HsWIcS2TZNTyyDfvwfvdn/L1FZ/naFIhhELY/vEPzq+7gdaf/EQU0QyDHIng2rqV+s98lso1a+n47W8Jt7aijI8n8ZOfoPDNN8h57DHMN95A/Pveh/mFV3E9/CtUK1aCLNN8//3I4Usk1lVaSJ2KpuU42aZsztsnyJNgAvGFfXT4O8gyZfV/w90muvUv/HOLeM6fjs/FZ7KyvGRoLya1UsEj75mBQiHxUN6NyCoV7m3bcG3ZMuHjt/318SHlsjKMGbR52whdKFIypoiCpfF0rXZUQFIxzd4WUgwpw97LR4VSBblLoG4PimiERRmLsGgsbK3fyq7GXVfOsNpWC8efFt6VMWJcRGf7KUwJQ8iCW7J6fFBnpcyiwdUwquu4nxQTdHdCDCw6utAFocnL41iHSPROzRCfy4o3cPd14t72y00DuyESPvJhVMnJhBobsT319IjHdi0RamkZva/RYFhzxb2z5fjoDKmffBIA47p1PH5GzGV/cOc0/vmfC9nxjVW8b94lugoH4X3zctj5jdW8f57odNl1vpNgOIokSaR959soDAZ8hw9jf3oC/qa5S4Q3hFt42g1nVH226yyJAS/xaTMG7EYOBrE9Ib6LrdPX4AlFmZFl4dYZonsnNzEOlVJBXfdzN3C+u2tPZwG/E71KT6IukSb3+Ls+rxYGdz6eJBYsWMBf//pXSktLaW5u5qGHHmLZsmWcOHEC0xAmhYFAgECgt3rE6RQXcDQaJTreDFc30WgUWZZHtb9INDLs5KP8a/dRf/c2FtYd4enndvDx96+YiKHi3LELgFMpKXx5Sv6EfAdjOf9rlXfTuQ7GUOe/ujSJH70hbuTaVasJnD2L6623MN9+29iP5fHQ9dhjACR85tPIktSTlf71lgpc/jBT0k3cNjN92L/HTdPSeDQ5jsp2D49tr+ILa4qZvvAW9k3/N/F72nAdrMBkjBdyZ6M454vx7N2LwteOP96MKyeFt58RC5f101Kv6evl3XjNj+WcM+MySchO4FDrIRpdjcxNm4tZM7RMnrNoCp1aE4kBFw/u/SvGLD//12hg6a8fIN2in4jTGDMjPv+uKmE4373doZf/jtUDlZYirl86ZdDPz8gS38n2lHLe11CBa+MmrO9/f+8G1lzorII+lVEzkmawsXYjKfoU0uPGLm3Waa8iS59GoEUkHbWFBYOOMXbNXx3oZswgWFvLI1PAplfAFjDNmtHvWTBRjPX885P0KBUSp40ZrAN8J0+JfaRMJcXRwJRQlK11W1mZvRKtUjvyHbvb+vhBlFJiqCbXUohZYx52jJnGTHY27sSZngMNR3DUtpLqd/SrSh7vOU80l/P4l2NNMBxXy3d+LTFZ39mnluUzN/f9fOGfpaRUnuBjp9+grKuWrj8/hu2f/yLhYx8l/mMfI+r1EqqtRZ2bi6RQYP/3v3E8+2/Czb3FK/r587Decw/GtWtRaDQ94wZ4cm8d33npJLIM8XEr+FvcATh9ms7H/0bCx+8dfpBJZXDyOYqsuWzpOk6xpXjIteOVuLaq7FVkxmWiQNF7XFkWnRC5S3rmBq6Nwv9pd/pUlhcno1Mphh3njEwz9y7O47Gd8EbZKtaf2EDrDx9Gv3AhCu0o7qNDIAeDtP7ghzieeWbgmwoFquwslChJ1idT76onz5wn3ksoFImErHmjP2g0LMyty2+jseM46XHDr11GTVwKGNOg4QDK7AWUxpdSZCmi3lXPgZYDqCQVJfElZBgzRi1dNeprS5aFHrqtGgpWQe0u0MULb8t3CbF7/aWxd56jNHtZz/O33/dlzoK6XZA+G5WkYk7yHPY27eX63Ot7uxuGweaz9c6XIkGIRkCh7g2md+M7dQoQptSnmoT0a3m6qWccn11RwDMHGthe0cGBmk7m5PSpUNdqSfzsZ2n97nfp+N3vMN9xO4q4yVUYuZzXlRyN0vqTnw6aqA3U1KBMGWUnV0IBtJ6A3MUD/g6DEbbZeqSujs9bR8eRIGlmLXfPyUStVHQP5dL7Gew7SzVp+fbNZWw600Z9l49/7qvlIwtzUaamkvTFL9L28MO0/fRnxK1ciWq059kXpRayF0D1NphyGyhU5Jvz6fJ1sb95PwvSFwDgDgq/iBskHVFz1oDvx/n6G4Tb2yEpmZ8GhIzfN28sBWSiURmVAnITDdSZUim11+OvrMB4/VpQx4lCpGiUHFMOVY6qIWUAr5Z71kiPf1mTEOvX91ZCz5gxgwULFpCbm8vTTz/NJz7xiUE/8/DDD/PQQw8NeL29vR3/eN3Pu4lGozgcDmRZRqG49IPdG/ayv2M/S1OXopSGSEQkJWKbMZf4YwcI/P2vVC8tIU4zvooJ2e0mePIkEmDLTiFTraZtDK3HFzPa87+WeTed62AMdf5mZDLMGpqcQQ6mlVAMuHfupLW2Fkk/+oBqtK0d39/+RsRuR5GZiX/uPALd12qTI8Dfdgt92E8vSKWzo/2S+/vY3BS+83o1f9pezc3FccRpdfgXzMR5dAOqf79JtMBIQF806GdH+jf3PP88an2EpjkzqWmRcPoiJBhU5BrCE/I7u1K8G6/58ZxzsbqY+mA9L596mSJTEXnGvEE7Ak4crSY70NsqHw1KfObc8xzfdyfKmVe2S20k56/wdaJ2eQk4A+BsIypHOXLoLea1yrw6dz6rZS9tbQNlYFLVUZQSbEgo4328iHfvXlrOV6EwiQolKRyHtmEXfnX/pGCBqoCtFVtZmrp0dMHcbmRZprnpDOneeCQ5ilulw5oaP+hvM3bNXx3nHCoQv4Po8aPEJyYQBKJFRZNyPx3P+efGa6m0iuvVd+IEra2tSJKEFFeGtXoD8dYsXjn5CgtTFvYzkx4O9ckDhM43EUFF14w0fF478XFTRnTuXpcXV04i7IdgTTO25lpCUeuEnvNE4nJdPsmQy7EmGI6r5Tu/lpjM7yxLB3/9QBk/2KDny0lFzGs9w39VbSCtrY7O3/2ezsf+AsFgbxBGknr+XzKb0dx4A9pbbkGZk4Mf8NvthCMyp1s9HGxwsbvGwdGmXtkFm8bEr0tu4kuHn6b90UcJzJmDMn1oaSIApTYHVcUeFHEmDtccJsc4eOXn5b62ZFnmaMtR5ibO7Xdfkvx2NN4QgS4R0JM9Hjx79gCwJ30q/5GtH9F97COzrLx+XMP/y1vJitoDGOrrqX/0UfQf/ei4xh3t7MT9wANETpwUf88FC2Dfvp6gj2rhAmwKBbS1YQgaON51HEOyQZxbxIS2ZhN+dbbo2hwFSkctyrAaX5eDipYKMtMzafNP8LNMk4Ou6k2CUTNRvTAFN2Bgln4Wbb42DtcdZkd4B/nGfLLjskcUzIVRXlvhAJrGPSBBMHMR+JRI5uloT75JIGcl8iBmq+9EYvf64fGFfQQ76glkWGlraxv4fckyus4WAo01yGoDEhKaoIa3K95mWvy0S+6/tr2WXGMubeE2JL8NjT/aE0foi/uokMrpTE7D7YygUUqYZS9tbaKDVQOsn5LAyyc7+enrp/jFncX9Pi8vX4YiM5NIYyP1v/vduO9Pl+JyXVey14vnBz8ktHPnwDcVCpyGONyjnItLJKDU5hC2uQH3Jbf3//OfyMEgypISftOsBkLcNjURW+foOruG+84+NjeFn26p51cbz7EiW4tOrUBeuwbl888TOXOGugcexPjQg6M63kB0qENaOP4WofS5AGSQwZ6OPezx7qHAVMDe9r1kKI34nW3Y3RHRTdiNLMu4uotyt5QuIYCSZQUW8o2Rfs/SLLOaepNImDhPnSLa/Z7O7cHfVI9Sqaa+vZ5aqRa9amBs7mq5Z410TXBZkxAXY7VaKSkpobKycshtvvnNb/KVr3yl599Op5Ps7GySk5Mxm0dm5nwpolHRwpOcnDziP1qr1IpdZWdK4pQht4n7xldo+OAHWVZzgC17q/mP9ywectuR4Dp+Arsc5bw1iZLiHHIyRt/CNBhjOf9rlXfTuQ7GcOe/bmonf91dyw5lGuXZ2YTq6zGcPYtp3bpRHcP+73/T+sCDPQsC4/x5pGb0Vj//cMsRQhGZJUWJ3DqvaNAA78V8ICmZvx9s51ybm5fPefjS2mIW3HQvx1/bhrmiDp3dicWoGtRAeyR/czkUwrl9O/r4KK7li6lqNwNBbpqeQXrawArUa4l34zU/3nNOIYWyUBkHWg9wJnSGeanzMKgN/baZojxL3xB9JKBAo4kyRRUkZTxVFyMh7AelZshF9IjOv6EW8mZB91jPH3ub+NZO9AElmbffPOw5lKWbOSlDMDMHTWMdhjOnMd98c/e7KeA6gdmkFvJMPa+mEDFEqPPXsSR9yahP2ea3karTYu5S4USYUs/MTx10nLFr/uo4Z/+SJdT+8ldEz5xBkSgCKolLFmOchN/HeM6/PLOJN9vSiCqUKJxOEsIR1JmiRZq4G5jfeAB9agHngudYmrn00okIWcZbe5pwQMHR1CyyisKsLVmLUTMy/5JpqmmcmiIq/DVtHVh1CqSr+DrX6XSX7ViXY00wHFfLd34tMdnfWQrw2H+k8/c9dfzwNQX/kVrKTY5zfPbcGyibGvpvLMvopk3D+uEPY7phHQqtlkhU5mSTkz1Vneyu6uJATRee4NC6y2/mzONzoXOoTxwh8tvfkvb73w0/j01OhtMtLEzMYr+vmfL4cmRkInJEVC3KUaJyFDkqEw6EicZFiUh93iPau40sEw37UCi1GDRGdEodBrUBnVI3orl0X1q9raQEUijKuqiAp70LMot75gbO19/AHg7TYEym2ZzKnfOLMOtHlox9+C4V9/71AL8qu4lv7H+CwBNPkvGBD6DOHJuhs+/YMZq+8EUibW0oTCbSfvwjvGVlWCMRnP/+N12/+z3yuQqS4+OR1GoSo4nUVtdiTjCjU+mAFPCcxawLC7mY0WA7CIXzCKgVZCdnk5U2ys+PFP310HQIcm7rN8dLIYVpTMMesHPOdo6DnoPkWfIoshYNGpDqy4h/g95OqNoLmUWQPquPdHQKmLTQeAAybhFSY+9wYvf64anqOEmmOZnkbCHHNOj35SsHbRCS8gBYnrScjbUbkY0yqYZLrK3dUJBRINZedj+Es3ruSX3x1NQA0JlRDE6xPslI77/vr6438trpt9lT66QpoGFWtrXf+/qvfJnmr36N4FNPk/kf/4EqYfJ8IC/HdRVqbKThS18mdO4ckkaD6dZbcT7/fE9cRlNYSNq0qWPbeebIiuzkSISql18BwH/r3Zw870WjlPjkqjKSjKO7fwz3nX1yVRJPHemg3ubjtUoPn1khrkfLD75P7T3vI7RtG4ZTpzCuXDmqYw4gcR2cfgm0AbAIGagbE29kc91mNGiIM8UxR5OCZNBjvug69R46hP3sWWSNht+bZ6NUSNx/2wxSLvI0nJZt54BJXLuKhsbe9a0tE7NZC8YUpimm4VV4yU0c2JV2tdyzRromuKJJCLfbzfnz5/nIRz4y5DZarRbtIK2bCoViQr9gSZJGtc9ZqbPYWLuRXEvukAtL05zZeKbNIe7EIbx/+yu+2xYSpx37V+7aLfwg9mfmsKQg54qe/7XMu+lcB2Oo819bnsZfd9ey+WwHX1m7Fttf/oJ74yYsN9444n37Tpyg9f4H+rX+OV98iZQvfhF1WhrHGxy8dFS0wH9z/RSUypF1BykU8MW1JXzuyUP8ZWcNn1haQFH2DPYvnYW3dh+u7UfRz2sE4+Am2pf6m7v37UPydxHSmXHmprB9j9D5u3lGxjviOnk3XvPjPWeT1sTK7JVU2ivZ0rCF6UnTybPk9byfMa2UCkmB1O0fEQkoUBnDpE8tnfzvuXKjkAbInCPMoAdh2POXZbDXQcm6HsPN/VufJbdNZn9KGTctHv4cZmZbOdnkpLp0LqWNdXi2bMF66629GyTkg70W4hL7fa48qZyt9VupdlZTaB1CR3YIOvwdJCvUBJpEBU29KZmlaeYhxxm75q88+rIyJI2GqNNJsFs2xzBr1qSNb6znPyXdzCvHVHSlZJHUUkvgzGm02d0BpoQ8cLcw0+/lSFwie5r3sDRz6fBa4D4bgXPVyFGJTeVJ3FI0D7Nu5AHyXEsuysz9+BUqDN4g0ZZa1KVX73V+OY99udYEw3E1fOfXGpfjO7t3ST5z8xL4/JOHeFUqo7lAyQ+a/jBgu+SvfY3anDJ2H2hmT1Une6u7cPn7aztbDWoW5idSnmHmFxvPEe2rZiFJxH/nO3g+/H4827fjefNNzDfdNPzgsueR1HAAqzWFtxvfFt8FChSSQnw3KJCQcLqdONQOlAolqnAQddCLOuhBHfKgCrhRB70oolF8cQk0pE/DF/aJiuRIELVSjV6lR6/SY1AZev+/O0mhV+n73bdqnDUUxBcM/Jt42kWAvvt1T7eXw+60qSwuTsYaN/IA0sqyVO6anclzsszd6fspaj5H+49/QtajvxrxPi5gf+55Wh58EDkYRFNYSPZvfo0qJwdfWxvajAxSPvtZHM88S6SjA8+WLZhvvBGFQkGmKZMmTxNF8d3JluQSsFVB/CgK+fwOCHnAmkNz+xGyTFmTdy0n5AkZpPq9QjZKpen/tj6BhfqFeENeKuwVbKrfRHpcOiXxJf019C/ikr/BjkqRZMhdDNZBvpvEfPDboOZtIeP5Lrj/xe71Q2PrPEt+YlG/72bA92XNFmbwKWUAaBVa5qfPZ1/LPtblrkOtHDyZGYwEkSUZ4wUPiKAbdOYB11zU5yPYnYQ4bkgDupiaYRnw98pLMnLn7EyePdjAo5sr+cvH5/d737J+PV1//jOBU6ex/fFPpH7zG2P8VkbGZF5X3gMHaLjvC0RsNpTJSWT/+tfoZ84k5b7P4923j6Zv/S/Bigq8u3djXDL6grCR4tq6lXBTE0qLhccMpUA7N8/IIMU8Nrniob4znUbBl68v4StPH+UPb1fzoYV5WPRqDFOnknDvx+j682O0fu/7GBcsGJ/UlkILBSugaquQR1XriNPEsShzEdsbtrMqexXK2j2QNn3AdWr/+z8A2Fe0AKc2jg/Pz6Y4beCaoDjVxHPdSYhgTQ2SLCMplaC3it+AIo18az47G3dSnlQ+aOHB1XDPGumxL+sIv/a1r7Ft2zZqamrYtWsXd955J0qlkg984AOXcxgTgl6lpzShlKPtR4fdruRrXwBgxfm9PPXG+IydOreJlqr6/FxmZYytiiRGjKGYn5+AUauiwx2gbeZCANxbtyIHg0N+JuL24Nq6ldaHH6bq1lupufu9Q5rEybLMw6+fBuDO2ZlMyxx6sjwY66elUZZmwhUI8+cdVagUKmasvofaFHBsPUik7uToTrgPztdeR2WI4LluLh1OI05flCSjlvn5k1cNEePqR5IkiuOLWZG9gvP28+xs3Ik/LCQ/1GlpZHzvIS5c7aGggsT33446bXhphnETCQmDtpxF0HICTr8CrlEajHnaRSWbTvwGWzwtuA8eItEFNTOXkJ1gGPbjF6qItqWITkD3treJ9r1PxOeDrWbA5xSSgvlp8znVeQpncHRmu63eVuJR4j4vTLnqTakUpYyssjzGlUHSaNCV95pQq3NzUMXHD/OJK0NpqvAkuyDJ5O/WGO4hcy54O5mpNGFQG9jdvHtY83rZ0YzraCW1KVAbn8fNU2aOajwGtYH8pCROpSYSDUuEG6oGPldjxIgxgGmZFl75wjJun5VBXVwyUfov0qOSgtteqOXmX+3g+6+eZuPpNlz+MCadirVTUvjOLeW89oVlHPr29fz+I9fxhTXFPHzXdJQXLfb3R8wkfvrTALT84IdEHI7hB2bJApWORdoUbiq4iRvzbmRd3jrW5qxhTco8VpmLWK6KZ2lQZrHTxqLWKubZW5kVVTLVmE1JxkIKptxN9oL7yFz6dYp0SSwyZLI6ZzU3F9zMHUV3sCZnDXNS5pBrzsWoNhKMBGnxtHCy4yQ7Gnfw0vmXeOn8S2yo3cCOxh20+9rJNmUPHKu7rceDRg6FcG/bBggpphunjn5+8+1bykkwavnplFuJKhS4NmzAvWMQiZAhkEMhWn7wQ5q/9S3kYBDj6tXkPfUvNHl5Pds0O3zsrneiuu1OgB4DUIAccw51rrreHVpzwdkE4aHXNgPoqIDEImRJotHdSIYxY8hNQy0tePbsHb3xa19yF4suiJPPCSPYyEATdIPawMzkmazPW49ZY2ZH4w7ebnibFs8ojxuNQt0eaD0OpesHT0BcIGM2KJTQeHCUJxTjnUQ4GsZrr8GSWDL8huZMcLUKP4dukg3JZBozOdJ+ZMiP2QN2LJqLTakHescGKiogGkWZmMhBlwhpTsscvODj86uKUCoktpxt52i9vd97kkJBypdFh6XtyScJNV2b5r/2Z5+l9uP/QcRmQ1deTv4zz6CfKeaf6rQ0LLfdRsKHPghA249/ghwZuuNvvNj+8QQA2tvv5MVTnQB8bHHepBzr9lmZlKQacfhC/L+3z/e8nvy5z6HOyiLc3Ez7r0af+B6AMQUSi6C29/mVpE/i9qLbsar04OsCU/9nZKixEdeGDQA8lrYAo1bFl9YO/rspSjHSGpdAUKFCDgQINTaKN7RmCIg1s0VrQavS0u67tJT51c5lTUI0NDTwgQ98gNLSUu655x4SExPZs2cPycnJl3MYE0aRtQhPyDOsU7lpwXw8pdPQRMPY/vJXvMGBE4mREGptRdNYSxQJy9wi4vVX30I+xrWNRqVgeYnoJNhIMsrkJKJuN52PP94zmZZDIbyHDtP+m99Q8+EPc27hQho+81m6Hv8bgYohZNUUCjS5OWw7186u851olAq+uu4SE5dBdyPxxTVCy/GxnTXYvUHK56zDPiMXjy+Kd+dmCI1eEzpQV4/zjTdQ6SN0zpvFsWpxW1w/LQ2lYnTt7THemZg1ZlblrCJeF8+G2g00uITEg/Xuu9H/130AnLLmo563CAKX1skcF54OMCSCOR2m3AJp04RpYMUG8HaNbB9d1f06KI4ffIPiChs+pYb8W2+45Mdndych3gwnoExOJurx4N27r3cDQwLIUfDZBnzWqDEyPWk6+5r3DRvI7UskGsHhbsaoNeOpqAbAnZKJZYSSEDGuHBcWQADaktHf9y8HZelicXtYKwJvnp27+geQlCooWIHUsJ+51lKUkpK9zXuRh0gMBI7vxekPcDZdS2bhDWhVo/cDuy6jmFOZFpAlnE3tEBrozxIjRoyBGLUqfvG+WXzqroX8ctbdRLoTCBFJ4pcz30MVccRplKwsTeab68t46fNLOHL/Ov70sXl8Ymk+5RlmFH3mfu+bl8OOb6zin/+5kM+uFB18P3ztNOqP3IumoIBIZydtP/3ZpQeWNU+Y/TYdEdWUp16EI09A5QboOAuRIJG4FGGCOeMe8V/JOshZAMmlYBLVlygUkLMY6vf1BKclSUKv0hOviyfTmElRfBHTk6czP30+K7JXcGP+jdxZfCfr89YzP20+RdYilmUuGygtd2H+0l2B7N2/n6jLhU1r5GxiLteXj16eNCFOwwO3llNrTueVgqUAtP7gB8MWOF0g3NVF3Sc+ie3vfwcg6XOfI+vXj6I09hYgvHSig2U/3soH/7iX9zWlIisUePfvFwFKIFmfjCfkwRPq9vZQaURwdJBCiUGJRoUhdVIxtoANnUpHnHrwilrbM89SuWo1dffeS+XqNdiffXZkx7gYlRZyF0HpTWJed/I5aDszqBmsWqmmNKGU9fnryTPncaLjBG/VvEWts/bSc6ygF869DiEflN3SU5gyJJIEecvBUS++kxjvSjp8HSRFIqhNlyiIVWnEeuWiQqlpSdPo9HfS7G4e9GP2gB1rX++RoHvQJIT/9BkAdGVlnGoSQdqpGYNfw3lJcdw+SyQPf7WpYsD7cUuXYJg/HzkUov3Xvxn+vK4y5HCY1ocfpvnb34FQCNP6G8l94h+DFsUlfuYzKMxmAmfP4njhxUkZT6CqGs+uXSBJbCxeQjASZWaWZYAM1kShVEh8bV0pAI/tqKHNJWJBCoOBtAceAKDr7//Ad2Lsxao9pM8S8/GO3mtIISnA0QCmdJGk7UPXE09CNMrJjDLqzGl8ZkXBkHJUhclGZIWCBqOIiwfOd99jtaaeJARAvjmfGkfN+M/lCnNZkxD/+te/aGpqIhAI0NDQwL/+9S8KC0cnyXA1oZAUzE6ZzdH2o4SjgycXJEmi+CsiSLW6Ygd/evkgu8530OzwjepYna+9DkCNOZXiQhPx2lgSIsbEs7pMLDA2netAkyd0/9p/9nMqV62m6o47ObdwEbUf/CAdj/4a34GDEA6jzszE+t73kvmL/6N49y7Sv/+93lY0hYL07z6EIiWVR14Xk4V7l+SRFT98pfVQ3DBVdEO4A2H+tL0atVJN2dJbqUuRcGw/jNxZNeJ9Rex22n7xC6puuAE54AWtTG1HK9vPiN/yTdPTL7GHGO8mFJKC8sRylmYu5WTnSfY27yUYCZK2ZgUAuc4War0aoak7mbhbwdgncR+fB+V3iOq+yo1Q/baoGhoKWRaL7+4khM1vo2HPdlLssDtjGjfNK7jkEAqTjRi1KrxhmfBCEVRwbdrYf6MhuiEA8ix5xKnjONkxsglhp7+TRFQodQlQLyoaNYWXHmeMK0/U1zvXcW/cNPagzCSSadVj0qqI99oB8B87NjCApI+H9FlI1W+zA6nlfgAAsuVJREFUIG0eETnCgdYDgyYifEd2cjxZhT9QxPKSQSqNR0C+NYeODDNRCdz1HeAfXedQjBjvZiRJYlqWhbfyFnDvuv/lv5d8hnvX/S9v5S3gu7dP5cgD6/jrx+fz6RWFzMiyXrLgJN2iZ1FhIl9eW0JBchwd7iC/2FZD+neFSbr9mWfwHjgw/KDiEoVUA7J4Xuctg5kfgGnvgaK1kHkdEWs+xCXBEDIlPZhSRcVl8/Dd+BejVqqxaC2kxaURrxtkHeluFdWe3bg2bQZgb1o51+UlkWwamxfAbTMzWFmazOOl1+M2mAlWV9P1t78N+xn/qVNU33033n37UBgMZP36UZLv+zxSH6mHjadb+eHG2h65rHadld2pQuvc9s9/AeJayDZlU++q7915YqGQiRkJjjpx/9eaaHI3kRE3eBdEqKWFlvvv7+1ai0Zpvv+B8XVE6MxCAqRorQhynXxOBP8Hee4oJAU55hzW5q5lVsos6l31vHz+ZV6vfp0NtRvY2bqTbQ3b2NG4g91Nuzl8/g2q9z7Kmaif4+YkTtkrONt1lvP289Q4aqh31dPkbqLN20akTyU7Kg0UrYGGA6IoJsa7jmZXPYmSSiQYLoUlS1y7fVApVMxLncfBtoMEIoEBH3EEHBd1QjhhEOnzwFkRVwgXFNHpCaJUSJSlDUxWXODzq4pQSLDpTBvHG/p3r0mSRMpXRTeE44UXCAzjV3s1EXE6qe8uBgVI+sJ9ZP785yj0/WWPmh0+dp3voF2hI+kznwGg/Re/IOqd+AIX2z//CUDcihX8qVL8fT+6KG/Cj9OX68tTmZVtxReK8JvNvX8747KlwrMwGqX5/u8gh8dWDN6DQiGe3Y0H+8/L7XUDusiiHg/2Z54B4KmcxaSZdXxi6dBrV71GSaZVT90FSaYLSQidpd+xsk3ZNHuaCUVC4zuXK0xM5G6cJBuSSdQlctZ2dshtzMuX4c0vRhcJ4f3TH3nkh09w2wPP8dT+uiE/0xf7s8/S9aMfAZDlacFaeXSASWqMGBPBqtJkJAlaz9fh67ugkmUCZ84Q9XhQWiyYbriBtIceonDDWxRt2kj6976L+cYbUcXHY737boo2byLn8ccp2rwJ691389yhBs60uLDo1XxuZdHQA7gECoXEl9aKboi/7qrB5gky88YP05JtxFXnwvXELwi//Xuo3ATNx4g0nMV35BCBDRvo+PVvaPz6f1N9z/s4t2Ah5xYuovP3fwBZRqWL0KlUoPrHi2i6umJSTDGGJF4Xz9qctehVejbUbsCeZSGsVGMM+6mudoh2zMnE0wFxF5mzKRRC53jqXaCzwtlXUbccEgbWF+NuBY1BLG6BMx2nSNl8HAXQNGcZaZZLG0opFBIzssQCoar4OrHbzVuQ+1bpxecNW2l4Xep11LnqaPO2XfJ4bd42UpQ6wu4wimCAkEJJUtHIzNFiXDlCLS39A/myPP6gzCQgSRJz40LcU7Gl98XBAkgpZaDWo2g+ysJ0ock9QJIzHKS+7ggaj0SFYTpLiwf3KboUWqUWXUI2HWYINrQPn1iMESPGAPKT4lBI0KG3cjy5iA69FaUkcX15Kmrl2Ja/GpWC790+DYC/7a6hOqMY63vvBqD5gQf7yxIORkqZkLRJyBcdg8N5y1yKzLnQWTlox+GY6SvFJMu4NoskxJ60qdwwbexSk5Ik8f07pkGckT+UrQeg/be/I9TaOuj2jldepeaDHyLc1Iw6N4e8p5/CtHZtz7h2VXbwoT/t4VN/Hyhz/HLBYrGPF18k4hbdDznmHOqcfdbcpgwR1BnJfbX9HCSJdcdwUkyBquoh5WjHjSEBiteK4FfHOWGQaq8fcvMUQwpLM5eyPn89K7JWsCB9AdPipzE1cSpF1iIKAwFy2qtQFqxElT4TjVL4TgQjQVxBFx2+DprcTdQ4azjZcZI3a97sn8TRWYRk1PktoosixruKro6zmK0FI/MFsWSJzpmLSNQnkmvK5XDb4QHv2QP23iSpLIuOnUGSEBc6IZoTRbFHUbIRnXroe2pBspHbZnZ3Q2we2A2hnzkT0/VrIRql5ZFHxi+rNskEa2qoed/78ezYgaTTkfmLX5D8X/81wCvgqf11LHlkMx/8416WPLKZTWXLhExRezudf/nLhI4p6vHgeP55AKqWrqfZ4ScxTsPNMya3sFOSJP77BtEN8eS+Ouq7epMrqd/8BgqLhcCp03T97e/jP5jeKjoianaI6zMaAVezuNb7YH/hBaIuF02mZA6klvHVdSXoNcM/84tSjNSbxBo/cL67sFZr6vesUivVpMel978nX4NcUWPqdwrTk6cLk2rT4CbVkiQR/6lPE/jm17iteie3V+8kisRvmu/k+OqVpEZ8GL1O4nxO9B4nercDnduBxu1A3dWBtqmuR9XUrYfIk88TWvX5ydcej/GuI9GoZU5OPKEDlYNW2qQ++CDx771bGOUMgzotref69Ici/OytcwB8blUhFsP4JFTWlacxJd3M6WYnf9pRxddvKCMruYhq4xH443akx95Gm5eCMuRACjtRaqMoQxLegIKIX0nYryAakqCPVrBaH6VZrSKtPUq6u4NZq2bGpJhiDIlSoWRG8gwyjBnsb9mPZ1omU4/W0HKmARZkXXoH48HTDnlLhxiYCtJnQGIxnN4GJ1+A1HJIndpbWWmrEV0KgDvopvHEXspqXTg0BqbcsnbEw5iVbWXX+U52mHMpNhgIt7XhP3EC/YwZYoO4RDEx89nFhO0iNEoN89Lmsb9lP9fnXt+zCB6MVm8r16Ei1C40MBvjkijOGLjPGFcXwZragfIR3UGZq23+MkvhQsHAAJJnz16sd9ze+1ruEjj9MipTBoszF7O9YTvH248zPXk6AG1V+2hydlHaoOX3c6f3+E2MhfSsWbTEv0BGaweyz0bsiRQjxshJt+h5+K7pfOu5E0RkGaUk8cO7ppFuGZsx5gWWFCVxy4x0XjnWzP0vnuRfX/kqri1bCZ4/T+cf/0jy5z43QWdwCdQ6yJwDtbuFlv8gJpWjxt0qpJ8QnQjh5mb8SjWHU0r42dTRSzH1JSvewNdvKOW7gRC31O6ltLOGth//hMyf/bRnGzkSoe3nP6frz48BELd8GZk//SlKsxlZltl8po1fb6nkcJ0dAKUEkYtu28eSi1Dk5hGtrcH58kvEf+ADJOgSiMgRUWGttYjgaUKB6CrImDX0oAMuUVhizcUddBOOhgfvIAGClQODmkgSmtxRGGBfClOq+Fs7GqDxkPCLyLxOvD4IGqUGjVKDTqnDr/GTpLWiqN8DfhfM/iiJ2oExi8Ho8HVwtP0oFbYKZibPJFGfKEyHfTaRiCi58V1hVB0DnEEnOr8TQ3r5pTeG3vn/IGuB8sRyNtVtot5V3+NPE5WjuINuTJruuVPQA2r9gOtLjkbxnxUFwKfiUoEAU4fwg+jL51cX8+LRJjacauVkk2OAfFPyF7+Ia+MmvDt2UrdjZ4+ig/Xuu0d2vpcJz65dNHzpy0SdTlTp6WT/5tf9PNgu0GT38o1/H++Z3UZl+ObL59j82c8T+t9v0Pnnx7C+972oU1IGfHYsOF5+majbjSYvj997EgE775+fPWxyaKJYXJTE0qIkdlR28IuNFfzsHiEHq0pKIvXrX6P529+h/dFHMa1bhyZrnN66KWUiudZyXHQE6ROEjF43cjSKrTvh8Xz+UsoyrNw159LxgeIUI6e77+eBqu5OCKUaFCqR8FWL+UueJY/jHccpsF67qgCxJ8YEoFfpKUsoG9ZkpzUtF5nesKcCmfuOPseH/u8LrP3V/7DwTz9g+hOPUvTC42RufIHEPVswnTiErk8CAsClB7MnSvOpQSY7MWJMAKvLUmgyJhG9eEGjUGBaueKSCYiLeWxnNS1OP5lW/YS04/XrhthZQ3tVPakbj1GfJBGRQI5I+M+346kL4m7S4fHkEkiZg2H5jSR97C6yv/EBCv/vPyn64zfRJYZRGSIo4iI0a5RYXRLNxqSYFFOMEZGkT2J51nIapyYRUkLgXN3IfRnGgs8uWuHVl+hWUGkJpc4SnhFBN5x4DtpOi6SArbZHiumc7RzGHedQyrAzYwY3zBq5dMwFbc/DzV7ili8HeqUberhEN0SKIYVsUzaH2gZWM14gFAnhCrowR6P4G4XUVYMpheKYKfVVjyYvd2Bgotsj6GojdUrRABNbgOb776fzr3/tNfBT60QSsGYH6kiEZZnLaPG2cLrzNKFoiGN7nyKtUaLWmMn06fkDKtJGQ2n5fFrNSiKBCKG6a0MaIEaMq4m+fg47vrGK982bmHvPt28ux6BRcrDWxvPnXaR+8xsAdP7+D6Ii/nKR2N1Z3DkB94dwUGhdd3sOuruf5wdTSinNSRqzjGpfProoj5k5CTw6/U5kJJyvvopnn/CTitjt1H/q0z0JiMRPfYrs3/0OjCZePtrE+l9u5xOPH+BwnR2NSsFHF+Wy9Wsr+dbaXJR9brPLSlJI/tAHALA9+c8eybwc00XdEImFl/7eOiogoRAUChrdjWQaBw9cyeEwXU8II9Z+ySBZJlg1cpnYEWPJgim3QsoUqN0hPME8w0uBSkE3nH0NJKXwmhhhAgLEXHdNzhqKrEXsad7D3ua9eENeUfSiMUD9nvGeUYxrhGZ3M2myoqdjakRYsgdIMoEo6JqfNp8jbUfwd3duOwNOjBqj0NmHIf0gQvX1yF4vklbL/oh4fyg/iL4UpRi5dcbQ3hAKo7F/EeZEyKpNILIs0/XEE9T956eIOp3oZ84k/+mnBk1AdHmCfP7JwxeX1xCRZZpmLkE/cyay10vHo49O2Nhs3ffB0C13safajlIh8aEFuROy/5Hw9e5uiOcPN1DR2ts9YHnPezDMnYvs89Hy3YeG9HQbFblLxNq65bhIyvbB/fbbBGtrcat1bMyZy7duKhtRcWtRirFXjqnyfO84deZ+kkzJ+mQC4QCOgGOw3VwTxJIQE0ShtRBvyDukSXWmt2vQKraoQoHPkogts4CmkllUzFrOkcW3sH3NB3jt5k/x5JqP91sYuwwScT6JprixtfjHiHEp1k5JpUNv5bdz3jvA22G01atdniC/2yIyuV+7oWTCMuHrylOZmmHGE4zw4qt7MPijWD1wOkeiMQE6TWD63rcpOriPwq1bifv5b0n63q+xfP5HGD70IJrrP496xhqSPvNptNYwDj0YnEp+O/1uSE6NSTHFGDFx6jhKC2ZRmwLWhnoCoQCEB2qcTgiDSTENh8YoAqYl68DZCMefEQtPrRF/2E+9vYakN/YD0Llg5aj0nmflWAE41+ZCvWIlMJgvRN4lzR+nJk7FFXRR66wd9P02XxvJ+mQUAReeKvF8rTOmUpwy9grzGJcHdVqa0Ewf53PkclBQXthtYts7Vk1REQSDtD3yI2o/+jGCtd3XqDkdEgugdidqpZrlWcupddWytX4r8ZXVxHUpOJhSytLi5CGPNxLK0hMIReJpNSgI18VMQGPEGAsX/BzG2wHRlzSLrqcY5pHXzyCvvJ64ZcuQQyFaHnhgYgIcI0GSIGehqIoPDSK/OBo8bRCX3BNE75FiSp/KjeOQYuqLUiHxyHumU5uQxav5CwFouf8BbE8/TdWdd+HZuRNJryfz/36O9Qtf5JlDTaz9+Tbu++dhzrS4iNMo+fSKAnb8zyq+e/s0MuP13DYtibf/eyVfXlsCwPFGB7pbbkXS6wlUVPRIy2abL/KFMCSIClP3EJKQstxjSA3Q5G4iPW7wAiXHiy8Rqq1DGR9PwauvkPP445hvu1V87n++Qdg2gZJZF5AkIetVfqfwGDm/WRie+wcJSjka0NZsguQyyFsyZhmwHHMON+TdgEVrYWPdRk50nCCUs0B06LYPLUsd451Di7uJpGhU3CtGyiC+EBew6qwUWgs52HoQ6Dal1lp7Nwi4hpVi0hYXc6LZDcC0jEt3QgDct7oISYI3T7Zyurm/31awZpC1yETJqo0TORSi5cGHaP3e9yESwXL77eT87XFUyQP/FrsqO7jxF29zqLtrrC8KCfKS40j5n/8BwP7v5/CfPTfu8Xn37SdQUYlkMPCvBNEdvK48lQzrxD17L8XMbCs3Tk0jKsNP3+q9J0mSRNp3H0JSq/G8vR3X66+P/2AaA+Qs6JZi6p+EsHV7Hr2Ru4AF5VksG+F6oCjFSJMxiYikIOrxEG7rfj5dJMkkSRJ5ljxqnDXjP48rRCwJMUEoJAVzUudwpO3IoCbV6VNLkKWLWskUCoo3bWTO3h0s3vQqa176J7f96w984LGf8Knf3M9Xf/Zl/uuH9/Ho7LuJdE8K7QaJfxXdTm5Z3uU4rRjvQkpSjWRa9byaPZ/m3/+rn7fDaHl0cwWuQJjydDO3zxxn61sfJEniS90Ljr/WRkChoLRBxuiTcRmgLlXB0WIVrzRt5NWqV9nVtot9Lfs42XGSakc1bUEHbr2FuA9/hfQnd9H+xQc48J77eStvAeunpcWkmGKMikXzbqQxUSLL3UCdRzN53RCetn6mkSNGHy+MDQtXQ/YCAM7bz5Nc2Umc20uHzsLMm1aMapcpJh2ZVj2yDFUFM0ClIlh5nmBNTe9GcUkQDYsOjiFQKpQsSF/AsfZjeEKeAe+3edtI0cZDJITjnFgcOJMzxi3rFuPyMJhH0NVIaaqp28T2WyT+8c8Ubd5Ewcsvkfbdh1AYDPgOHqTq9jvo+vs/hPdJ+mzRGt12Bq1Sy/LM5Vg1VtJOnCfiV3IwtZSlReMrFslPiiMQSaPRpMBbXzWoRGKMGDGuDB9fkk9xipEuT5CfbThH2gP3I+l0ePfvx/Hcc5dvIIYE0RHReAlj7EvRx5Q62NBA4MwZIkjsSy3nhqkTlzguSzPz2ZWFPD5lPX6VhmBNDS33P0C4uRml1Ur63//O85YprPzJFv7738eo7vBg0av50tpidn5jNd9cP4UUU/9u0HSLns+tKiTNrKPLE2RDnRfLLbcAvSapZo0ZjVJDh6+PmXLCMN0QjgbQxIHeSiASwBVykWwYGEiSg0E6fvtbABI/+Um0BQXELZhP+kMPoSkoINzeTvO3vzN5iakLnmDT7gJDEpx9HWp2ChkbWRbm5XW7CWYvhaSScR9OpVBRllDGutx1+MN+3qjdSHVyAXLTYXAN7vER451BKBLC42zAaMwQXdkjxZQG3k7RbTUIZQll+MN+ahw1OILdkmkXCLgG7YTwd5tSS0UlNDlEArZ8hEmI4lRTj+LBoxd5QwzawQsEzpy5fMnliwi1tODcuJGaj3wU+1NPgSSR8vWvkf7Iwyi0/YvHQpEoP3rjDB/6817aXAEKk+P44ppilH06tOINGuINGgxzZmO64QaIRmn76U8vPuyoudAFob/pZp46bQcm35B6ML66rgRFd5LpSL2953VtQQGJn/40AC0/fJiIYwK6COLzoPz2Hp9FAP+5c3h27SaCxCuFS/jmTWUj3l1RsomwQkVTnDB97zFI15rhoq6HPHMedc46onL04t1cE8SSEBNIkj6JZEMyZ7sGVgOo09LI+F7/isCM7z6EJn142Zd0i55VX/kk/3HDt/n6kk/xu9m3sva+z0xoRU+MGH2RJIm1U8RCZEOHTNyC+WOqXK3r9PKPPSJo+K2bpqCY4MD+2ikpTM+00KAycfi9n0EbVZDXBmVNCm669yFunvU+7iy+kzU5a5himUJ6XDpKhZIufxdnus6wo3EHL1a+yFveg9iyrLzVICY5MSmmGKPFXFRGikNNa1KImqouMdmeDNxto6s+uhhjCsQlEYqGqHJUoX9NyCBtz57FDdMHN1scjpnZYqFw2BYhbv48YBBJppRyqNszbADVrDEzJWEK+5r3DZhMtXpbSVVoQWdBrq0BQF1w7WpgvhtRp6WN+TlyubAY1KRbdHTordRml6FOSxN+XvfcQ8HLL2FYuBDZ76f1Bz+g7t6PE2xqgoIV0HwEvF0Y1Aamtcjg8uFR6AiWTh2Ryftw6NRKIqYCnHoJR1OLkEqJESPGVYFaqeC73SbV/9hby1lMJN93HwCtP/4J4c5JmgcMRvpMcLWMLwjcx5Ta3d0FcTIxn5TsVIomWP7wc6uKKLGo0FwUlAw7nNz9xEkeeOkkTQ4/ySYt37qpjJ3fWM2X1pZgNQwd+FQpFXxgvpDb+seeWuI/KCSZnG9tINztJ5VtuqgbIqFASFRGIwN32HGuxx+jyd1EqiG1VyKmD/bnnifU2IgyOYn4D36AZoePXec7aA1C5k9/Amo17k2bsD/19Ki+o1GjUELaNJh6l9ANP/USnHkVnE1QdgtRfeKEHk6n0jE3bS7Ls5ZTH7DxthocZ16CgHtCjxPj6qHV20q6rEBpGuU6WaEUiQhn4+BvSwrmps3leMdxWj2tAzshBklCBLo7IdpTRQV6XqIBk27kxUlfWC06nF473sLZlj6mvxd38HbT+vDDNH3t60RcIzCzn0Dszz5L5arVNH7+PvxHjiBpNGT99jckfuITA+Q+6zq93P373fxu63lkGT4wP4dX7lvGl68vYcc3VvHYx+aSZNTQ6Qnyu62iuzblq18BtRrP9u24d+wc8zhDLS24Nm0CYNf0VfhCEUpTTSwsuPzKEsWpJu6cLfwXfvLmmX7vJX7qP9EUFBDp6KDle9+fGPNxQ/9z7Pr7PwDYlTGdlctmUpY2suQYiLVIsklL/QVJpgvm1BfJMQEY1AYsWgvNnuZxDP7KEUtCTDDTk6ZT5ajCHRz4EB5rReD75uXw4kN3ct/XbuVb713B++dfPm21GO9O1kwRN79Np9uIRkef+W92+Pif544RisgsL0lmafHEy4eJbggxifhetJiEl18f9LelU+mI18aTbcqmLKGM61KvY3nWcm7Mv5E7iu5gdc5qDKFZOH0ySUZtTIopxqiRlEoS9IW0xks0nasVRoYTTTgoKtv0g5sijoYaRw1Jkhn127sAcC1ZPezifigu+EIcrbdjXL1G7Kt7EtpDyhSQFEIzcxiK4otQK9Wc6eqdMHpDXkKREOaoTDisROUSVSCW0qJRjzVGjEtRliYWumcuas9XZ2aS89ifSb3/O0h6Pd59+6i67XZsz7+KnDUfqrdBJIxv51uE/QqOJBezqHRiEi6KvCKSHTLt9vYBC5AYMWJcWRYVJnL7rAxkGb794gmsH/kI2ilTiDoctD7yo8s3EKVKdDnW7YLoGKoioxHRwWkQc/ULxQS706dx4wR2QVxAp1byvzONA4IQkhxF19ZMplXP9+6Yxvb/XsWnlhdi1KpGtN/3z89GqZDYX2OjJj4T/ezZEA5je+YZQCQhGlwNvcUOGoPo2LRfJLUS9IqkjFWst5vcTWQYBxZqRINBOn7/ewCS/vNTPHOinSWPbOaDf9zLkkc286LHSMqXvwxA6yOPEJgMf4iLUWmEYfnUO8X8q/iGHjPTycCitbA8azklOSs4rpI5vf83OP2T6I0W44rR7GkmTZbG1pE9hC9Ez9taCyXxJTiCjhElIfxnxFqhwix+lyPxg+hLaZqJm6aLe9uvLuqG6BuvK9y0keQvfgGUSpyvvkr17XfgPTS0j91E4j9fRfN37u9XxCWHw+imTBmw7QuHG7npV9s5Wm/HrFPx2w/N4eG7pqPXCOm1dIue1VNSefC2qQD8btt5qjs8aHJySPjgBwFo+/GPe/3PRontqacgEkE/bx5/6L6dfnRx7rh80cbDl9YWo1ZK7KzsZGdlb/ebQqMh/aEHAXC+8gp1995L5eo12J99dkKOG7bZsL3wIgCvl67gK+tG331WlNzrCxE43y3FqjX3k2O6QL45nxpHzZjHeyWJJSEmGJ1KR1lCGYfbDg/6/lgrAtMteorSJQoSRmEEFCPGGFlQkECcRkmbK8A/99XR7PCN6HPeYJhfbTrH4oc3s/u8qAKb3R2onAxWl6UwM8uCLxThz2c9o/5tSZKEXqVny2kR3IlJMcUYK8ay6WS3yzR2VEyOHJO3QyyWxzmhi8pRztnOkXm0CWUoSIMxmXnXLxrTvmZli4TIkXo7pjWrAfAdPty/AlSShC9F22lwtw+7v7mpczlvP0+nT3y+zdtGqiEV/A5C7WLy1aq3Upg9Pq39GDEGo7S7WulMy8CJvqRQkPDBD1Lw4gvo516H7PXS8uBD1H/9+4R9EjTsJ3B4F2G/ggMp45diuoCprIQ0G3SFnMieSdAVjxEjxrj435umYNSqOFpv55kjzT1VtM6XX8a9fcflG4g1WwQqWk+M/rPeTtBbQakiYrfj7fZRmEg/iItJKy/u53kIEJEk7rltEVu/vpKPLMwdtY9cqlnHunKxTu7bDWF/6mnkcBiD2oBJY6LN28cHIrEQui7y3OmsFH4LShXhaJh2XztpcQO/B/vTzxBuaUGVmor/xlv55nPHuVC3FZXhW8+dIHDnPcQtXozs99P4ta8RDQ4uSTPhqHXi3AaRlpkM0o3pLJ71ScyWXE4c/COH2w4TiEySP1qMy44sy7R4WkgIh8eYhMgSnRDDdEWXxJewKH0RGmWfoqhBjKnDNhvh7ur1/QpRODg1c+TV5hf4wpoL3RDN/UyMoTdep8nMJOmznyX3H39HnZVFqKmJ2g9/hPZHf40cHii/PhFEPR46/t8fqb3nnoHf10X+FO5AmK88fYQvPXUEdyDMvLx4Xv/S8iFVHW6ens6y4iSC4Sj3v3gCWZZJ+uxnUFgsBM6dw/H886MfbzCI/WmR6G1adQs1nV5MOhV3zJo4Ge7Rkp1g6DHE/vGbZ/tJaamz+/s3TKT5eOc/n0IRCnLOmsWyO9eQah59R3RxqpE6U7c04iWSEBnGDDr9nfjCI4vTXU3EkhCTQKG1EH/ET6N78LazsWLz27DqrBO6zxgxBkOrUpKfFAfA/75wgiWPbOap/XX4QxEq29xsPdvGP/bU8vDrp/nck4e4/Tc7mfv9DZTf/yY/31BB30fmrzdXjjiJMVr6ekM8vqua1483j/pYdV0eXj0mWtliUkwxxkragjlkt0PI34zD2Tp4e/946CPFdKLjBPtb9nOm6wwNrgYcAcegXkSDUe+qx6wx43lhAwDbs2dz/RgrHadlmlEqJFqdAToMVnTl5SDLuLds6b+hxgC5i7srxkND7k+n0jE3dS77W/YTioZo9baSYkgBv41As0js1JtSKU6dWGmIGDGgtxNif03XkM8RTU4OuX/7G6nf+iaSTodn127Of+Z7OJ/9K3LTKSJ+BUfTylhYODHSF1mleWj9GjwSuKqH7yaKESPG5SfFrOPL14t56I/eOIO/oJT4D30IgJaHHiLqu4zBgewF0HZq0GDFsLhbe6WYtm2DSIRqczrKzCymjlBnfbTUKo38clav52FEkvjVzLspmlaIWjn28MSHF4rA0/OHGlGsXIMyIYFwayuu7nlJjimHOmefzgdLjiiQCHX/nWRZSDF1+ye0eltJ1CWiVvSXeon6/XT+4Q8AJH3m09S4wlzcOB6RZWq7/KQ//DBKq5XAqdO0/+KXYz63qx2FpCBz6nuZby3D0FnNWzVvcbbrLJGJng/HuOzYA3YMkQhatUF4pYwWjQHUBvB0DLmJJElkmbJ6X4iExFpK1d/3IHBWyJ6rs7M52inWPqPthADhT3Pj1DRkGX61eQhvmG4Ms2eT/8LzWG6/DaJROn7zG2o/8lGCDUN3d4yWqM9H558fo3Lt9bT//OdEPQN98lAo0OQK2bljDXZu+dV2njvUiEIS1f///M+FZA5jBC1JEt+9fRoalYLtFR28elx48SR99jMAtP/yV4Mfdxhcb75FpLMTVWoqf46Kv997r8smboQdbJPF51YVYdAoOVpv561TvVKFQ5mPew8cHNfx5GCQlr8JKaZNU1fz6RWFY9pPUcognRBKFShUvc+pbpQKJdmm7P7PtGuEWBJiElBICmanzOZo29ERB4ZGgj1gJ147fimOGDEuRbPDx8mmXumHqAz/8+/jlH3nDdb+fBv3/mU/337hBH/YVsWrx5o5Wm+nwz14dU9ElqnpmDwt65WlyWTH6wmEZT77xCEWP7KZn7x5loO1Xeyt6mT3+U721jrZeradTadbefNkC68db+alo01849/HWPHjrXiCYoJc0zm6B2+MGBdImTcbVRTm1jg42N4yrBnzmPC0gzGFakc1DY2nMZ5rwt/eSr2rnv0t+3n5/Mu8fP5lttZv5WDrQc47z9PobhyQoDjbdZZCRSrR/XsBCK5YOyod1b4YNCpKUkXg9kidHePaC5JMmwdubM0W7dh1e4bdZ7oxnRRDCkfajghTakMK+B24q0WFSoMxheIJ1qeOEQOgpkPc/8+1unsS74MhKRQkfPSj5D//HPpZs4i6vbQ8vgU5IhMOKrg10jhi+ZBLUZxmod6UitkGLdXjWyDFiBFjcvjYolzK0kzYvCF+/OZZkr/4RVRpaYQaGnpMiy8LWiOkToO6vaP7XB9T6h4ppjTRBTFZchr5SXFszF/Avev+l/9e8hnuXfe/bMpfSF6SYVz7XVyYSEFyHJ5ghBdPtvfIs9qefBKATFMmzZ7m3nmRUgXWHOiqFv92NYugZ7fOd5O7iUzjwIpe27/+Rbi9HVVGOtb3vAfFIN+TQoK8JAPq1BTSf/gDALoeewzPrl3jOserGoUSVfH1lIbCrLb8f/buOzyO6mrg8G+2SLvSrlZ11atly0Wu4G7And6LaUmoCR1CCiWUQAImhI+EkgChQwi992IbjMG9YRtXdav3spJ2V7vz/TGSLFldVrXO+zx+QKvZmXuv2sycOeekUFpfyldZX7VuCC6GnXxHPjGKsXdZEE1sMVCZ0/V2TbooxWQYk0J643lbb4OlNy7Syrt+8lMeB4o672eit1iI+tvfiHrkEXQWC3Vbt5Jx1tlUfvxxr47dxFtfT9nLL3NgyVKK/v53POXlGOPiiPrbQ0Tc17qfbOT996G3h/PMd2mc8+8fySytJcpm4o1fz+aWxWMwdCOAmxjqz7WNN8jv//hnquvdBF18McbYWBqKiyl98aUejb+pITWnn83KNC1j95ezB790fJjVlyvmJgLwyJd78TRGiTtqPp73xz+Sd+efcGW1E6TohoJPPsOnopRSUwCzr7ig10GY5DALBy3aQ4ee8nIayhqrK7TTFwK0BtUZVRm9OtZgkiBEP+msSXVveFUv1a5qAnz654kUIVrKKHHQUcKkxdfA2Agri8eFc9mcBO46dRzP/OIYPr1pHl/99ngOr2akV5QjvqjoTEFVPQcrDkWGVRX+teoA5z61lmX/Wcclz2/g5vf3c8XLm7jy5U385tXNXPfaFm56fStvbMxpNc+73t/Zb1kb4uhmjI2l3uRPQrGH9PxCKiv6sPavqoKjmBqjmfWfvUDoZX+GX9+O79nXMe7HXBbHL+bs0WezOH4xqaGphJhCcHvdZFdlsz5/PR+nfcyn6Z+yInsFekWP6dvN6Lxe9tuimbfwmCMaWlNfCK0kkxaEcPz4I97adgKPMcdqZR9K09p+roVJYZMorS/FqDfip/cFVy0V+7STwvKwqF71rxCiM/mVda3qAjcF3u/7aBer9hRRUds2yO6bmEjU/z0CioLHpacq2w8FhTNXvtInad0Ao+z+ZFsjCC7VUZjfN+eTQoi+ZWjRpPqNjdnsKHcTcc/dAJS+8GLzTbMBYR8PbofWcLk7VFXLBPC343U6qfn+e6B/SzGBVmZ4+TkTKfcLYkdYMuV+QTx4TiqRtiPrX6AoSnMZjv+uyyLwgvNBUahduw5nejq+el9CzCGtm3mGJGslmKAxC0JrSN1UgibS0jpL2ltbS+mzzwEQeu21KD4+/G9926B1gMnYfCPKunAhgRcuAyDvtttpKD+Ky+v5+EHSfPxztzInOJXU0FS2FW0b7FGJI5DvyCdcVZozpnqli74QbTirtcDq4S83NqWuiEoAICLARKjFt8123TEhysaS8eGoKjx5WG+IjthOO5XEDz7APG0a3poa8v7wR3L/8MceN632Op2Uvfpf0pYspXD5Q3hKSjDGxBD54IOM+uxTbGeeSdCyC1r1k3UtPY1fvbiB5Z/vocGrcnJqBJ/ffHyP+1leO38UCSF+FFU7+cfX+9H5+GhNqoHS55/HXVjUxR40dTt3UbdtGxiNvB91LKqqPRyaENqLbJl+cPXxSdjMRvYX1fDBVq1CTZvm4zodPsnJ4PVS+d57pJ1yKnm33Y4zo/s39lVVJeMp7W/Cj6nzOX9WYq/HnBxuwWnwpcBPe/C8q5JMQaYgdOiaSxkPFxKE6EdNTaqrXT1Mi21Htasai48Fva5n9TGF6I3EUP82wQSdAl/echw7/ryUL245nud+dSx/PmMCVx2XxIkTIpgQZWNMuJXl50xE3/hEkF5R+uSiojMZJY52S0yGB/iSFObPaLuF5FAzqVEBTI6xMS0ukBkJwYyLbPt0RX9nbYijl6IoOBJGo1PBmwWZ+X3YuKy+Eq/Blx/3f0vIv97Fv66xqeJhdSzNBjOh5lASbAmMDRzL7KjZLE1Yytmjz2ZB7AImhk5kdtRs8t//CIAf449h4dgjeKqJQz1ftuVU4DtmDMaYGFSnk5offmi7sU4PicfDwY2dlosw6AzMjpxNakgqOKvA14onIxMAfULCEY1XiPZklDjalNIAePHHTC5/aSNT7v+axY9+xx/f2c6bG7M5UFSN16vizjnYpmavcljN3iPh52OgIjwGS5VCQ3kRlfUVfbJfIUTfmpEYzDnTolFVuPuDnfjNX4B1yRLweMi/595eN/zsMZ0O4mZDzoZOyx82q6/Unvw3mnCsXYtaV0eJyUZ5zCimxfVv9v2y6XGsuX0Br189izW3L2DZ9Lg+2e9502IwGXXsKajmpwY/LPPnA1r2AmglmXKqWjyRbbGDxwXVBVCVr/WDAErrS/Ez+mE2tL6GKXvtNTylpRhjYwk86yz2FlTz8U95ALx8+XRe+NWxRAaaqKhzc++Hu5rfF37bbfgkJdFQXEz+XXe3qlN+1LHYIWoqpK0k1i+CBrVh2N0kExqnx4nD7SDAVXdkQQj/UHDXao3fu8NVo914PUxTUDczUGtKndqLfhAt3dzYG+Kj7XmkF3eeDdHEJyaa+FdeJvSGG5p7AGWcdTa1W9vvCduS6nJR/sYbpC09kcIHHmjOqIr4y/2M+vwzAs85G8Vw6Cn6ErON7aFJfJTbwMmPfc/3+0swGXUsP2ci/75kGja/nmezm4z65sD5Sz9msCuvEuuJJ2KeMgW1ro7iJx7v1n6aMsz8lizh1X3a2v1qdkKPx9NfbGYj1zRmffzjm324GrTr55bNx5NXrmDUJx+T8Mbr+J9wPHg8VH74Iemnnkbu7/9wqCRSJ7JXryMoJw2nzsCMG6/oVkZKR8IsvgSYDOQ0l2RqfKjR1wrOynbfk2hLJLMqs9fHHAwShOhHJoOJccHjOmxS3V31DfWkVaQR6BvYNwMTogtNTyi1DCYsP2ciKREBXaZm99dFRUfaC5joFYUPrp/Lyt/N58tbjuO/l47noxvm8uEN83jvurm8dc1sXrhs+oBnbYijmyl1AgB+eyrwOIooru28EXO3OYrJ8tahL6kgpqRtk7LcP/yR2o0bO72g9TP6YfezYyypRLdjGwDKwiX4+RxZ2ZgpcYEA7MitxKvSnA1R882KDgYSDJFTIGM1eL0d7tfma9Nqw9ZV4NX7YSzR6nkGpIw5ovEK0Z72/o4oCpycGtHcH+lAUQ1vbTrIbe/uYPGjq5ly/1fcvr4c72F/E9UWNXv7gpKQBKqCX2EtueWd1y0WQgyeO04eh9VkYEduJa9vyCb8rj+hs1io/+knyl9/o0+O4S4owLFufefZVhY72KIhb1vXO6wpOtQPoqkUU+QElqZGoD/8l2I/iLSZmT0qpE8fVrL5GTl9knaDUmtQfTEAle9/gLe2lihLFMV1xbg8jRluigLBSZD+nVaaSa/d1MutyW1TislTU0PZc88DEHr9dShGI//8Zh+qqv29OCHFzsJx4Tx50VR0Cry/NZcPt2lP4OrMZqIf+TsYjdSsWEHFm2/12ZyHpLAxYI2AzO8ZbUvmQIX8/epzAxDIKnAUEO4TiK7BpTWw7y1FgYDo7mdDOKvBp3UmhOpy4UzXbspu9dEeohrfi34QLaVG21g8zo5X1fpYdpdiMBB2w/XE//e/GKOjcefmak2r//WvdptWq243FW+/TdUvfknR/X+hobAQQ0QEEX++l+QvviDo/PNRjK0DCm9uzGbuQyu5+Nn1/O7t7ZQ6XIyNsPLJjfO4aEbcEZXLO35MGKdOjMSrwl0f7ERVwX7bHwGofPc96vd2nn3bUF5O1aefArB12mKq6xuID/HjhDFhvR5Tf7hsTgJ2qy8Hy+t4fcOhB4Samo8bI7SMP/OUKcQ98wwJb7+NZcEC8Hqp+uQT0k87ndxbb6V+374Oj7HjMa0/0K7xczhhxpFdpyqK0n5fiA7KMQHEBcSRV5M3rPrvSBCinyUFJuH0OHvcpNqresmvyefH3B/5KusrVFTGh4zvp1EK0daRBBP646Kis2P1Jvuit+8ToiNRM7XSRkG5+UTqrOws7ptGshVl+8n01DJz7JJ2P1+3cSNZv/glGWedTflbb3XaCLPyE+2EcUdIEguOSz3isY0Ks2DxNVDr8rCvsBrLooUA1Hz7bbsn4QDYx2pPXuZv6/oA9ZW4yxwoqkq10UxcUtQRj1mIw7X39+Chcyby1KXHsOr389l812Ke++WxXDt/FDMSgzEZdVTVN/BxvofHJrdtrlpiPrKL4pYs47TSIIEHa8gr+rnP9iuE6FthVl9+v1T7ef37l3up8g8k7NbfAlD86KPU/bSj6wBCJyreeYcDCxeRfdllHFi4iIp33ul44+hjoCwdass632ljU2rV66V6pRaEWBc5gZMm9F8ppoHQ1KD6sx0F1E8+BmN8HN7qaio/+QSDzkCEfwQHa1rcDA1J1p7SDj10AymvJo8oS+tzjvJXX8VTWYlPQgK2005jV14ln+8sQFFoblAOcEx8MDcs1J6wvuuDnRws157+No0fj/232vdE4UMPNd9QPWrFzIAGJ3H1DgprC6lrkJK3faamCLa/rmUz9aN8Rz7RGMDSBzeXe9IXwlnTpieEMy0N3G50AQGsr9EeokrtZT+Ilm5qzIb4YFtuc3+w7vKbpjWtDjj9dPB4KHniSbJ++Stqt27DsW49roMHqXj3PdJOPoXCe/+Mt7AQfVgY4XfdxagvvyDowgtRfNqWmc2vrOP293a0ytJVgKcvPYZke9tqDr1x92nj8ffRszW7gjc35eA3dSrWk04CVaXo4b93+t7K995DdTrxHTeOp4q0Bzh/MSse3QAEr3vC7KPnxsav7xMrD1Dr6rxfr3liKrFP/ZvE997FumQxqCpVn31OxhlncvCmm9uUV/xu9U8k/LwBgMk3/7pP+iiNtlvJbuy/0lU5JgBfvS9hfmHk1+W3+/mhaHDblo8ATU2q1+evJ9wvHIOu8yWvcdWQWZVJZlUmFqOFhIAEZkTO6PJ9QvSHSJt5WNyUXzY9juPHhJFZUktCqF+3x9zb9wnRnrBjp1AJxFcVUVXjD64qChwFRPj3/mLe7XWTkbue8RMvwmy0aU8SNT35pNMRet11NBQVUfnxxzj37qXgnnspeuT/MJ50Eq4rr8AU37o5WMEHH6MH1iUcw/KUI7+g0OsUJkbbWJteyracCsZOm4Y+MBBPRQW1m7fgP3NG+2+Mnwe7P9KeirJ2kt5dX4krX6udnG0NZ3S49EUS/aOzvwchFl8Wjw9n8Xjte9Xt8bI7v4p3Nh/kFWBLeAqRNSXkW0IpMQfyq5LaPvt7EjM6HofBRGiNE0NRAaV1pYSYQ/pk30KIvnXJzDje3JjDz/lV/O2LPfztwgup+vAj6rZvJ/OCC7SNGhuMNjVNVlUVT1UVnoO51OUXoFZV0lBejqeiQvtXXqFlQHz33aEDNZZj9J83r/lJzlYMvlofpqwfYeyp2rlDe2oKIXIyddu34yktxWEwkRGdwqyk4f07ZnJsIJNibPx0sJJ3tuZx/oUXUfS3v1H++hsEnn8+cdY49lfsJ8mWpL3BFABjT2u+0VrprERBadWL0VNV1dy0NfSGG1AMBv7xtfZ07OmTohgT3vrG4E0Lk/l+fzFbsyu49a3tvH71LPQ6heDLfoVjzRocP/5I7u9/T8Ibb6Br5ybkUUGng/g5GPd+QVx4MhmVGfJQZV+oK4e0VVoWU9FuiJvVL4fxql6KaouYZgg5slJMTQKitd9JXo9WorUzjeVYW6pv7Afhk5LCgeLGptTRR/7Qx6SYQBakhLFqbzFPrjrAI+dP7tH79VYr0X9/GMtx8yi4737qtmwh66KL2m4XEoLPRRcSc8UVGPw6rryQU1bLH9/9qU2iiwrkV9b3Wc+FCJuJW5em8JdPfuahz/ewdHw49t/dSvWKFTh++IGa79dgOW5em/epHg/l/3sdgPITz2RvZg1mo57zj43tk3H1tWXHxvLs6nSyy2p58YdMrl+Q3OV7TOPHE/PEE9Tv2UPJU09T/eWXVH/1FdVffYVl0SJCr7uWDx1Wch78B6ehsjM4Aa8pjCN/vA+S7RbeDTgsE6KTIAQ0lmSqzeyDow8MyYQYAKHmUOx+dvaUtd+YrMHbQFZVFt/mfMuqnFWoqsoJMScwP3Y+CbYECUAI0Q29zb4YyKwNcXQzRETg8LehV73kZpQywRzFzpKdR1T3d3v+RoL1voSHpOBYtx5UFWN8fHMdy7Abrify/vsY/e0q7LfdhjE2Fm9VFc633iLjpJPJufY6atb8gKqqODZsQJ+2jwYUTIuX4Gvomx5DTSWZtmVXoBgMzfWXa1Z2UJIJwGiChHmQ+T00tG3626y+guosrUHaQYud0fa2TeqE6Cvd/Xtg1OuYFBPItfNHoVOgxBzIjrBkSsyBfV7Wb3S4layAcLxuHeHFVeRUd/MpQiHEgDPodfzlLO02xFubDrIlp5LQm25qvZHXS/5dd3PgxJPYN2cue1IncmDWbKouvZTsiy4i5zfXkH/7HRQ99DdKn36GijffbB2AaLGfTvvPhIwCnUFrttweV612M9AUQE1jFsTG8LEsSI3GxzD8bxFc2tig+rX1WQSceSaKry/O3bup27qNcP9wKpwVrZ/Mb/Gkd3tZEGUvvYS3qgrf0ckEnHwS23Iq+GZ3EToFbl48us3xDXod/1w2BX8fPRsyynj6O+1mkqLTEbl8OfrAQJw/76b4n4/1w+yHEJMN/ENJ9iqkV6bjVTsuxdnvMlZDVd7gHb8vOGtg/zcQNxPi50BZRufn0UegrL4Mf6M/PrXlfROEMPiAX4jWf6UzqgouR5tyTM692r00R2wSDV6VID8jUTbTkY8LuHmxlsn0/tZcskp7lg3RxHbGGcQ++592Pxdy7bUkffUlpvPOQ2dqf8xV9W4e+nwPix79jrVpbXuo9EfZ6F/NjmdcZACVdW6Wf74Hn9hYgi+5BICihx9ut59RzerVuHNz0dtsvOirrdvZ06KxmXven2Ig+Bh0/HaJ9jv66e/SqKztRr+kRqaxY4l57J8kfvQhAaecAopCzYoVZJ57HtYbLuPUzHUAjC/LYtWjz5NfeeTZXsl2CzmNP28NhYV4ampAb9BKBbrb33+4XziJ1t43xB5ow/8MY5iYGDqRjMqMVk2qy+rL2Fy4mc8yPiO3OpeUoBROTTqViWETsfr0TZqVEEKIgaEoCnVJ2slY+YF87OjRK/rWKf89kFOdQ01FJjHhU0FRcPz4IwCW449vVccSQG+zEXL5ZYz64nOin/o3hunTQVWpWbWKnKuuYv9xx5P9y19p26JyhqPvavNOaWxOvf1gBQDWxVpfiOpvVnQegAmIgqAEyGqniTVoFyHOasr2ajdZykKjCPI/Sp8WFMPSQJT1S7ZbyLJG4HErWHJKyKnOObobmgoxzB0TH8QFx8YAWpNqVdf+5bY7KwtPWRk03eQxmTBERmIaPx7/OXMIOOUUgi65hNDrryf0xhvbzWYwhIV2Ppi4WZC3tf0bFzWFWv8IoHrFoVJMJ6YO71JMTU6fHKU1+CyrY02xm4BTTwWg/PXX0Sk6oi3RHQZ1Dw9CNJSXU/byKwCEXn8Dil7Po41ZEGdPjWFUWPsPSMSH+HPvGVq/sH98vY+fGs+TjOF2Ih/4KwBlL7zQfH431HSrB0l3REzEUpZJgNHa4xLVfabyIDhKIHMNlOwfnDEcKXc97P8SIidr589Gs1biqLR/5pNfk0+kOUwr+eTXR9lRtpiu+0K4a8Fg0jJpWmjKhMgN0X6/Toiy9Un5G9CuZU4YE4bHq/KvVb2/RlJd7d/g9p81C525/XPDBo+X/67LYsHfv+Xp79JwNXiZnRTCTYuS+71stEGv46+NgfN3Nh9kQ0YZodf8Bp3NhnP/firee6/Ne8pf0xpS6087g8/3a9nqv5wd32a7oeSMydGkhFuprm/g6dVdN5s+nDF5NNnX38knv32UHxKOxQMkVeXT9N2nQ+WGbW+TtSfziMeabLfg8DFTZtIy8Q6VZLJ22BdiuJEgxAAxGUyMDxnP1qKt7C/fz1eZX7GhYAMWo4Wl8UuZEz2HSEskOkW+JEIIMVyZJ2oncg0Z+VBXRmpoKrtKd/X4ya9ady3birZxjH8seqt2Q8Cxdi0A/nNmd/g+Ra/HcsIJWP/+MImffkLQL36BYjbjKSk5tA1g+/cjR35R2WhqYxBiX2E1DmcD/nPmoPj64s7NxdlJIy8AoqZp6aUl7Zzwu2rAYMKVkaV9HDe0T3DFyHQk/ZO6w2oyUmGPwevW4c3Jxt/oT1FtUZ8eQwjRt247aSw2s5Gf86v4uFTf5mYaikLUo4+S+OEHJH/3HaO3biHoi88ZteIbEt97l7gXnif60f8j4u67CLvxBsKuv47Iv9zfZj/5d9+Dp6aTp3bNgVqfg4Ob2n6usSm1Mz0DV3o6bkXPzpgJHD96aDUW7S2zj55zj9FuVr7WokF19Rdf0FBaSpw1jpyqtkGIWncttQ21hJgO3XQte+FFvA4HvmPHYl26hE2ZZazeV4xBp3DzorZZEC2df0wMp0yMoMGrcssb25prklsXLSJw2TIA8m67nYby8j6Zd1/pUQ+SrljsYPAlRefHgfJBalCd/5PWK2XMSVCwA3K3DM44esvjhv1faf1LwsaQU5XD11lfUxUYq5Vk6oeHE/Id+UQpvuAX3Gn5JHdBAe6tW7t3XdGdvhDO6jalmFRVbW6WvMOkPSU+IbpvS7Q29YZ4d/NBPtya26un2n0S4tv+vtfp8Ilv/9zw271FnPzY99z1wU5KHS6SQv157pfH8r+rZ3LrkpR+Pb9sckx8EBfN0Eop3fXBDrzWAMKuuxaA4scfx+s49DfGmZGBY80aUBS+SJqLx6syMzGYsRFDu1yuXqfw+xO1nk0v/pBBUVV9l+/xeFV+TCvhrg92MPPBFVz07Dr+le7hr1Mu5N8zL257DFUlylHSzp56JjrQjNmob+4L4Uxr7B3kG6CVKTsKyB3vAZRkS0Kn6KhwVjAtfBonJZxESnAKJkPfpJEJIYQYXDGztObUwbn51FYUEeYXhp/Bj6yqrG7vQ1VVNhVuYkzQGKyuOvC34zp4EHd2NhgMVKdM5Me0ki5Pjn0SEwm/8w5syx9u+8muyjj0gD3ARJTNhFeFnw5WovPzw3/uXACqv/mm8zfrdJB0AuRuattcr64C1ceKT552sWId03UNTyEGQ3+X9VMTkvA2KFCSp904k5JMQgxpIRZf/tB4w2P5pjIsd9596MaUTkfkX+7HdsrJmFJSMIbb0fn6drnPwPPOI3nlCuJefpmYp59CZ7VSt3kzOb/5TeeBiIhJWtZD1WFNKxubUjeVTtwROoqZE+Mw+/RNqcah4JLGkkwr9xRRFpOEadIkVLebinfeJdQcSr2nvlWVAtBuvEb6RzY/Yd1QWkrZf/8LQNhNN6LodPzfV9oDFucfG0NcSOflURRF4cGzJxIRYCK9xMFfPtnd/Lnw22/DJymJhuJi8u+6e8hkudXv30/+3feAt/EBmsYeJEf08Ep4KvbqIuoa6qior+iTcXZbdQF4nBAY19j/41Tt+z/9O60k2VDn9cCBFWCNoCZkFKsPrmZv+V6iLdGsq9yHx2CCir45p29S667F6XES6K5vzphqT8U775C+eAk1v72V9MVLug5WmQO1/9ZVdLxNO02pG/Lz8VZWgsHAWo/2udSoI+8H0dIx8UGMtlvwqHDzm9uY+9BK3tzYs3U1RkQQef99rX/f339fm949ewqq+MXz67nsxY3sL6oh0M/In08fz5e/PZ7F48Obf/8MVNnoP544lmB/H/YV1vDCmgyCLroIY1wcnuISSl94sXm78te1XhDm44/nhXStDNhlcxL6dWx9ZfE4O9PiAql3e3liZfvB0NaBh2+4+Nn1/HddNiU1TmxmIxccG8NLl0/nkQeuRD3s4XFVpyNyfOdB6e7Q6RSSwvzJtjb1hWgcq0mCEKIXFEVhXvQ8pkdMJ9TcRfqsEEKIYSf0mCkARFWVcrCwAly1TAydyM+lP+Pp5oXOvvJ9qKrKmMDRUFsC/mE4ftBS9R1JKcx7Yj0XP7ueuQ+t5Lnv09lxsJKvdhXwytpM/vbFHm59azvXvbOXBf/3HWPv/oLTPy/ES+t0ZY+iUBHU8UVFT01uzIbYllMBgHXRQgBqGks8dMpk055Oy1h96IIXoL6ShpoG9A1uXDoDEWOT+my8Qgwn1rEpoCroyyuJwp88R163f58IIQbHRTPimBhto7q+gX/6jGsOICSvXNHclLqnjBER+M+cgXX+fOJeeP5QIOLXv+44EKE3aGWZstceuuHqcWtPG5uDmksx/Rg5gRMnHB2lmJok2y3MGRWCV4XX12cT1NgstvzNN8DrJdYa2yaom1uT26oUU+lzz6PW1WFKTcWyYAE/ppWwNr0UH72OGxZ274ZToJ8Pj14wGUWB1zdk89Uu7Wa+zmwm+pG/g9FIzYoVVLz1dh/NvHc8VVUUP/EkmRcsa/tk/ZE+vBIYh9JQzxifIA5UDHA2RP5PWjCuqXSPwRdGL9X+f/9X0OAc2PH0hKpCxnd4DCZ+NvuzKmcVUf5RLIpbxPiQ8QSbgtmlR8uG6ENNwbimjKn2uAsKyL/n3p4Hq2yxnZdkclaBb+sSZ/V7GptSJyWxs0h7CGtCVN8+fZ9fWceB4prmj70q3PbuDv759T525VXi8XYvSNgyYHz47/tSh5s/vb+TUx77nu/3l2DUK1x9XCLf/X4Bl81NxKgfnNuzQf4+3H7yWAD++c1+8mo92G+9VRvzCy/gLizC63BQ+f4HAOyddSKlDheRNhNLxvdBv5ABoCgKfzhRm+P/1mfxQWO2S1Pg4U/vHx54cLUKPGy6azEPnzeZ+Sl2/GKiiPpL62BTVDvBpt4abbeQbdWu013NmRBHTzkm6XgshBBC9BFDSAhVthACKkvJSythTF0ZQbYYgkxBpFemMzqo8wvWivoK9pXvY1HcIhRnpVbv1eDTXC/4fX00TefAXhX++mnXFx1OcyCPTTmPm7a/g15V8SgKj08+j8v1VqKPeMaaKbGBfL6zgO2NQQjLggWg01H/889Ufvopfscc0/mJWeho7YIkbyvEaNkk1FfiLNTKE+RawhgT0bdPPAkxXMQmx1BtNGNx1aPLOECgPZCC2gKMDM0mgEIIrfzDX85K5ex//8C7Ww6yZLydgNAkEs3+RPZyn/mVdWSUOEgM9Sdy4kTiXniB7CuuoG7LFnJ+/Wti//Mf9Bb/tm+0xWh18At3avXka4rAP4yGsjLqtm0DYEv0RB4a23cPJwwVl86K58e0Ut7YmMMNvz0R/UMP0ZCXT813q4mdM4X1BesZHzIeALfHTXl9OXY/bR3cRUWU/0+rfx52040APNqYBXHhjFiiA7v/dPKc5FCuPi6J/6xO57Z3f2JKbCD2ABOm8eOx//a3FD38MIXLl+M3/Vh8kwb2oQtPVRVlL79C2Suv4K2u7ni7ioreH0RRIDyV+IosftbV4/K48NEPQJ8vR4l2UzvosKatOn1jJu4W2PMpJC/WnjQearLXUlFXxjp/PwLd1SyOX4zZcOj7bop9CivqviGqOJPQ2jKtdFIfKHAUEG+Ng/y9kHhCu9u4MrNaPzwEzcGqTs/5bTFaSayI1PY/76yGwNhWLzUFIdyJyTgbvPj76EkIaed33RHIKHG0W9Xqnyv2888V+7GaDExPCGZmYjAzEoNJjbZ1GDQwRkS0WoN6t4dnv0/nqVUHqHVra3bKxAhuO2ks8X08j946b1oMb2/KYWNmOfd/vIunL12KeepU6rZupfjxxzBPnIS3uhpjfBxPOUKBKi6dFY9hkAInvTF7VAij7Rb2F9Vwy5vbALD46qlxHnqwx2Y2cuKEcE6ZGMnc5NAOv8aB552H/7x5uLKy8YmP67MABGgB9C+bMiHSj75yTBKEEEIIIfqQMykFtv5IRXo+1JaBLYYJIRNYfXA1ibZEDLr2//Q2eBtYX7CeKfYp+Bn9oOIg+NtRPR4c69YBsDlsTJv3BfkZiQvxJzLARITNRESAL364GBNrJzrID6+qsuAR2BKeQmRNCfmWUMr9grgvtPMSAj0x5bBMCENwMMbYWNxZWeT97vfN6cidPv0ZPwd+/ggCIrWm1fUVOLK02prZVjuzwq0dv1eIo1hyuJXMgAjsDRW4034mbtSJ5FTnkKSX7CAhhrIpsYFcOD2O1zdkc81/tRr0OgWWnzOxVX1vj1fF4fJQXO3E5VGpc3uodXmoc3mod3uoc3tYtbeIdzYdRG21j1QtEHHllVog4uqriX32P+gt7TRKjp0Buz/WbsY2NqWuXrUKVJV9gTGMnTQKq+noC2wuGR+O3epLUbWTrw+UM+O8cyl7/gXK//c/4hYuAKC8vpwgUxAFjgLCzGHN52ml/3kW1enEPGUK/scdx+r9JWzKKsfXoOP6BT0vEfm7pWNYs7+En/Or+N3b23n58hnodArBl/0Kx5rvcfy4loM330z4bbfhm5zcpze12tNe8MF39GhCr78eT3UVBff+udVN5tzf/x5P1d0EnX9+7w4YPApj3jZigiLIqMwgJTilD2bRhfztWhZEBw3iiZ6mPWG87wtIWgCWodMTxZm9lqy8DaTbk5lin0aEf9vvB4POwKyo2WwtTWNG7ib8mjI8joDH66G4rpgZtmTw8QdD+8Ein4R4LbjU8s59J/0PmlkjIP1baHC1v29XDfi0Pud3NjalLgiNBQeMjwpAp+ubptRNEkP90SnQMuFBAWYmBrMzr4rq+gZW7ili5R6tL5efj55j4oOYkRDMzKQQJsXYMBm1cnZNAeP4YD82ZZXzt8/3kFep9SGYFGPj7tPGMz2hbwJGfUWnU/jrWRM59fHv+XJXIav2FjH7tj+SeeFFVL77HtUrVwFQd8rZbDtYhY9ex7LpsV3sdWg5PNsFoMbpwWoycHJqBKdOimLOqJBuZ6QcHmzqK8l2C881BiHcOTl46+vR+QZoAbqjgAQhhBBCiD7kP2kibP0Rb1YB1JUBYPO1EeEfwYGKA4wNHtvu+3aW7CTYFEystfGEzlEE/nbqf/5Zq4NqsbDvsCeDdAp8dvNxrWqFer1eioqKsNuD0TVedC0/ZyJ3vreTEnMgekXhwXNS+7S+6MQYGzoFCqrqKaisJ6SuQuthcWhQ5N9zL/7z5nV8smbwhcTjtbJM406H+kpK9+WgAGXBkQT7D8ATc0IMQclhFr6zhnNM+T7qDuwh+pwr2Fa0jTj//mlSKIToO7+aHc/rGw79PWwq8fHIl/twebzUuT24Gryd7KEtrwp3vLeD48eEETkx9VBGxNat5Fx1NbHPPds2EOHjr92MzVmvlWWKnEzNimcAWBuZykmpR1cppiZGvY4Lp8fy+MoD/HddFideeCFlL7yIY80aXFlZxFnjyK7OJsgURK7jUCkmd0EBFW++CUDYzTcB8OhXWmPcS2fFEx7Q856OvgY9j180hVMfX8P3+0t46cdMrpiXiKLTEbn8IdJOOgnX/gPkXHV19x7e6KXOgg/WpUtQGs8dLccdhysrG4M9jOLHHqf6iy8ouPsenPv3E/7HP6IYengrSW+AsBRGO4pZ40lnTNCY5tr3/aK2TPuXNL/z7UJHaz8faSshbibYBvdvq6qqHEz7isLsNfhNOIfFYRM7fIAJtGuM+KQlpG99mXHxc9H7HNmT9cV1xQSbgjHWlnVYigm0G7Cm1FTqd+xofi3k17/u+qasTq8FIqpyITix7efbaUzd1JR6jyUSHDChj/tBgNZ/oel6yaOqzddLy6bH0eDx8nN+FRsyylifUcaGjDIq69x8v7+E7/drD0z5GHRMiQ3E6mtg5d6iNlkVkTYTv5kdwaXzxmIwDM3eOykRVq6cl8gzq9O558NdfP3bE/BNTcW5cyfeci07fX1mBfjGctqkSEItXfczGko6ynZ56pJpzBs9dAKQyXYrFb4Wqn38sLpqcWVmYho7FvQ+4KoFn757kHAwDJ/cGSGEEGIYiJ2tlRMKySugpryw+fXxIePZX74fl8fV5j0FjgLyHflMCZty6MWaIrAc6gdhPGY6Xt2hk1a9orD8nIndCiYsmx7HmtsX8PrVs1hz+4JWT2D2BT8fA2MaMxW25ZRrKdq9qSdsDYeQUZC+ChQ9tWlaQ29PbHyfjleI4STI34eSsBi8DQqO7DSMeiNh5jAK6wq7frMQYlCV1bb9mw9QXOOkss7dJgBhNuoJ9vchOtBMst3CxGgbY9vJBPSqsGK39kSuOXUCcS+8gM5mo27bNnKuuhpPTU2b9xA2Ftx1UFOIV/GjprHU4/rICSweNzzqevfGRTPj0OsU1meUkeUTiP/xxwFQ/sabxFnjyKnOweP1UOgoJNKiFcsqefppVLcbv2OPxW/WLFbsLmL7wUrMRj3Xzh/V67Ek26386dRxADz0xR72FDSW11C9qPX1hzb0esm/627y7riTyk8/xZ2f387eeqap58OBRYsp+de/8FZX4zt6NNH//CeJH35AwEknNgcg4FAPEt/ERKL/8SihN94AQPkrr5Lz69/gqazs+SDCxmJ1lOCn6ihwHEGj6+4o2AHhE7Sb3l0JiILRSyBnIxTu6t9xdaKivoK1O/9L9cH1jJlxPePDp3YagGiSGDwGXfAo9u//5IjHUOAoINwvvDljqiNelwtXY6kYJVz7/eEpLe3eQTrqC+FpAG8DGA8F+Tw1Nc0PNq1TtOyBvu4H0aSj6yWDXsekmECuOi6JZ395LFvvXsIXtxzH/WdO4NSJ2s14V4OXDRllrNjTNgBxzQlJrLj1eE4aG9LnGRx97aZFo4mymThYXsdz76/F+fPPrT4/64tXCK2r4FfDpCF1S03ZLi3pFYVR9nayBwdRfIgfBr2O7MafP+eBNO0TR0k2xKAGIR566CEUReGWW24ZzGEIIYQQfSZk2mQA7FUV5Gbnag0gAX+jPzHWGPaV72u1vdPjZFPhJqZHTMeobyyF0OCEhnowBTb3g9gXo120Toq29SqYEGkzM3tUSJ9mQLQ0NS4QgK05FVqKdjup7/qQbqQeR07RLsZNARgPakEI/9G9v+AX4migxCfhcelQirQmqrHWWIrriwd5VEKIrrR300OnwEuXT+ebW0/gh9sXsulPi/j2hqmkPXASu/9yElvuXsIPty/km1tP4OMb5/HiFdPb7APg3g938q9VB/B41cZAxPOHAhFXXoXn8Pr+Op1W+jBkFDVr14PLRb5fMOGTxxMyzJ5o7YlIm5lFjf0uXmvRoLrivffw8+gxG8zsLtuNzdeGr94X18FcKt59D9CyIFQVHv1aO3f71ZyEI3769xez4lmQEoarwcvNr2+j3u1p/+ENoPL998n73e85sGAh++cvIPfWWyl79b/U7dqF2tDQreN5q2soefJf3Q4+NMmvrOPHtBLyK+tQFIWw668n+rHHUMxmHD/+SOayC3GmZ/Rs8kYTBCeS4lHZX7G/Z+/tifoqqC6A0LZlTDvkFwxjT4WydIz5m9v9evQXt9fNtqJtbNr3PmMcVYyfdTMBlp5lJ41OOQNnwU8crDqCBuI0NqW2dN6UGqB23Tq8Dgf6sDD8b78NgMpPP8VT4+j6ILYYLRPi8DV2VrXJgnA2ZkEYIiLYXK4FbVOj+69PXHeul3Q6hbERAfxydgL/umQaG/+0iJW/O4GrjmsnswM4YYy9uVTTUOfva+Ce0ycAsOqbLW36fuhVleP86pncWIp3OGnKdtE3ZmD1R3WAvmDU60gI9Se7sSSTK70pCGE9KvpCDFoQYuPGjTzzzDNMmjRpsIYghBBC9Dl9QAAVwRGAQmFGCdSVN39ubPBY0ivTqW849LTb5oLNJAYkEmoOPbQTRzH4heKtq6Nu61YAPjTEAHDW1Oh+DSb0VlNfiO05FRgjIoi8/742gYj82+/ourGhTgdJ8/H4JeFTW4MXhfBxnTf0FuJoZxk3Bm+DgrGuDG9dHVGWKCYHTx7sYQkhutDeTY/l50xkfoqdZLuF6EAzwf4+mAy6DkvTHL4PnQITo214VPj7l3u56Nl15FbUYZ7QIhCxfTvZV7UTiPAPhYR51KxYCTSVYuptq+zh49JZWkblu5sPops5G2N0NN7KSqo++5w4axx7yvY0l2IqefopcLvxnzMbv+nT+XJXAT/nV2HxNfCb44+8F4+iKDx83mRC/H3YW1jNw1/sbf/hDUXBds45mCZMAL2ehoICqj77nMIHHiDz3PPYO2MmWZddTvHjj1Pz/ffNX2t3QQGOdeup37+fkif/RdWFF1L67393O/gA8ObGbOY+tJKLn13P3IdW8uZG7cZ2wIlLSfjfaxgiI3FlZpK5bBk136/p2QLYx2OvLae6vpxqVz891VuwA+zjtBJQPeHjB2NOQnE7IG1F84NE/elg9UG+yvwKnaOE+R4fQideCKae32Q3+oUyKuJY9qZ9icPdjUBAO6pc2g3OAFUHKODb8RPi1V9/A4B10UIMU6ZgTEhAra2l6rNPuz6Qjx8Y/bTG4S25asCn9TGbmlJ7R42m2tmAj0FH8hB7cl1RFJLCLFw5L7HdJ+0T+rAP30A4cUI4C8fayfYLwXvY3yWPorBg4bRBGtmR6+/qAH1ltN3SHIRozoQwBWgB1mFuUIIQNTU1XHLJJTz77LMEBQUNxhCEEEKIfuNK1prtVaYXafVoG5kNZhIDEtlTpp1Qp1emU+epY1zIuNY7aCzFVLt5M6rbjS4igq8qtSyJoVq3eUqs9vd8x8FKPF6VwPPOI3nlCuJeflmrTx0URP3OnWT96jIaukrX9vHH1ViioMgvkFFxQ6dOpxCDIS4pmkqjBTzg3LsLndLxDUshxNDSFzc9Wu7jh9sX8tENc/n7eZPw99GzIaOMk/65mo+352GeMIH4F7XSTPXbf2o3EKE2NFD17bcArIucwNIJQ/O8oi/NSw4lPsSPamcDH+0oJOiiCwEof/11YqzaQx7RlmhcWVlUvv8BAKE33ojHq/KPb7QsiCvmJhDUR/2pwqy+/P187WHMF37IYG2VvvXDGzodkX+5n6gHHyDx3XdI2biBuJdeIuzmm/A//jh0VitqbS2169ZR8u+nyLn61+ybMZP9J8znwPwFZF92GRmnn0Hpv/+N6nDg083gQ2WtmydX7ue2d3c0N+j1qnDnezvJr6wDwDRuHIlvv4V56lS81dXk/OY3lL38Mmp3MwdMAegsEaSoBtIq0o5oHdvlrIGKbK38WG/ojbhi52l9IvZ+rtVg7wcOt4M1uWvYXbab2YFjmVRVimHUQvAP6fU+LTEzmOCBdfnr8Ko96zUDkF+TT6R/ZJelmFSPh+oVK7RjLl6MoigEnnsuABVvv9O9g9lioDKn9WvOaq3kTMuXGoMQZeHa782xEdZuNw4eaMPlSfuuKIrCfWdMoNoazGOTz0Nt/H3hURRemHEhJy4Y3g+S93d1gL6QbLeQY20sx5TeshyTBCF65frrr+fUU09l8eLFg3F4IYQQol9ZJ2snZ0p2PtS2vuGeEpxCTnUOBY4CdpXsYmbETHTKYX+OHSXgb2/uB1E4ejIqClPjAokKHJonTMl2C/4+ehwuD/uLtBseTfWELfPmEf/qK+jDQnHu3UvWL36Ju7Co0/1V79PS9HOs4YweYk88CTHQku1WsqzheF06XPt3DvZwhBA91Bc3PVruQ1EUzj82lk9vOo4psYFU1zdw4+tbufWtbbiTRhP/4gvoOwhE1G7ZglpZSZXRD8PkKUP2vKIv6XQKl8zUbmL+d10WAeecg+LjQ/3OnfDzfk5NOhV/oz8l//43eDz4H38cflOn8slPeewrrCHAZODK4448C6KlhWPD+UVjhsbv3t6O9+Qzmh/eSF65olVTap2fH/6zZhJ67bXE/ec/jFm/jsSPPiTiz3/GduaZGOPiQFVpKGzbK8j829+S8P57HQYfvF6V7/cXc+PrW5n+4Dc88tW+Ntt4VJVdeYdufhlCQ4l7+SVs55wDXi+Fyx8i/6678Lra74HSRsRE4mqryK7Kwu3t42yDwl0QlgKGIwgYKTqImw3BSbD3s1ZZzUeiwdtAlauK3aW7WZm9ErufnUXhMwg+uEVrih1whFlJtmgiTCHYXE52lOzoevvDFDgKiPCP6LIUU93WrXjKytAFBOA3fToAAWedCUYj9Tt2NGcvdD7WdvpCOGvaNqXeo5VjOhCgZSr1Vz+IvjJcnrTvSmywHzcuTOarhJnceMa9PHbqb7ls6Z+IungZvkO0sfbRJLlFJoQrMwvV7T5qyjH1MD/tyL3xxhts2bKFjRs3dmt7p9OJ0+ls/riqSlt0r9eL19vz6G57vF4vqqr22f6Gm5E0/5E01/aMxPmPxDm3NBLnPxTmHDNzKqXPQkhBARWlBQTEHRqLQTGQZEvi+4PfM80+DT+DX+uxqqp28p94fHPTyO+sWo3RkydEdDmvwZq/glYeYl1GGVuzyhlzWODAmJRE3MuvkHPFFbjS08m69FJiX3wBY1RUu/sr3rUPPVASFEGQn7Hb8xkKX/+BNhLn3NJImP+oMD9WB0Qws2Y3dft24T9E5jyQxx+Ia4LOjITvs74ma9Y9R7JOccFm3vz1TJ5ceYB/fZvGe1ty2ZRZxqMXTGb8C8+Tc8WVWiDiiiuJefY/6AMCqP5GK6OyIWIcS1Ijh+XXpzdrdu60aB75ah+78qrYUQ3hJ51E1UcfUfa//xH54IPUHThA5cdaY9/QG27A5W7gn99oD0RcNS8Rq6++z9fq9pNSWJtWwoFiB7e9s52nL52G2W5vnmNnfJKT8UlOxnbB+QBUf/kVeb/9bZvtdHGxqO3sL6eslne25PLuloPkVRwqEzoqzJ/0YgeH5zXc9s5PPHh2KkvGN96cNhgI/8v9+IxOpvjvj1D57nu4MjKIeuwxDCFdPM1vDsbgE0CM20VmRSajAvuo95e7DkrTYMJZbWrZd1er7y37eK1s0N4vIOE4rYF1B1RVpd5TT627lrqGOmobaql112r/bailzl2HFy9+Bj8CfQNZELMAPxTY9zneiIlgi+v1mFsJG8ukyly+rjpIqClUy2zoBrfHTVl9GSG+IXir12r9NDoYT9XXXwNgmT8fVa9HVVV0QUFYFiyg5quvKH/rLcLvuqvzA5qDtfJLzhptjQHqK8Ea2XxctaEB5z4tKLbJqJWtHR8ZMOR/Z4VbfQm3ar1jmsY6HP8eXjkvgZd+zCStBtKM/mAESz/8HuzIcFyzvjIq1J9icyD1Bh9MDS7qs7LwTYjTyjEdth5DZZ26e/wBDULk5ORw88038/XXX2Mymbp+A7B8+XLuu+++Nq8XFxdTX1/fzjt6zuv1UllZqf3y7CA18Wg2kuY/kubanpE4/5E455ZG4vyHwpzV6Ai8ikJgtYOMnTuJjJqtPVXVyOa1EaePw9/pT1FR64wApb4Cn7oG6val49q3D1VReBftAmJ6pKHN9ocbzPmPDjGyLgPW7S9gQXw7jRv9zPj94x/U3Hor7pwcMi+5FMuj/4c+OrrNptV79hII1EVEdznnlobC13+gjcQ5tzQS5q+qKsXBUXjLdFQd2I23qGhIzLn68Hrz/Wggrgk6MxK+z/qarFn39MU6XTI5kAmhKfz5iwyyy+pY9sw6rpgZyaV//zt1v/899Tt2kHHZ5fjddhvVjTfa10amckuEsUd/Y4eK3q7Z4tGBfLa7jOe+3cedJ50IH31E1aefoVx+ObWPPQ5eL8a5c6iy2/n0+71klDiwmfScOqbt+VpfuWdJHFe8sYevdxfxf5/9xIQIC7GBvtitPXuS3xsdrZVzankjSKfDYbFQVFSETqej3u1l1YFyPv25lE05h35/W331nDg2mNMmhJISZubjXaU8tCILr6r1IAk0Gyh1uPjNf7eweEwQt86PJdhPKxPKySdjCQ6h5v77qduylYzzzsf/gb9iSE7udLw6QyRh+Rv40enC6rJ2um13GQt/Al0w7rIq4NATwy6Pi7XFa6n31KNX9BgUA3pFj16nb/WxQWdAURXqa+uxVdkw6o3oFT2+xnACN7+BMyyVuoBo6hrqqPPUUe+pb/5/p8eJj84Hk8GEWW/W/hnMBOgDsOvtmP3NGHXG5jHVlJThzvoWT0AsDd5A6KvvL28ApvzVJMfMZNX+Vcy1z8Vs6DrbKb82H4PTQGlhPqbSAupDXFDddkyqqlL1xZcAeKYfS1GL8xGWLoGvvqLyw4/gl79E6eKen4/HH0/6djxBWhDKVHQQp28iqlM7riczE9XpBLOZ72uMgJdIk2dE/c4aTEXVLkprWmc3PfzlXuZE+/T491NvDMc16ysWvCiKQrbFzpiKgxRv2YKPxYKppo763CwwHvqZHirr1N1rggENQmzevJmioiKmTTvUyMTj8bB69WqefPJJnE4nen3r1J477riDW2+9tfnjqqoqYmNjCQsLIyCgb1KxvF7tCxwWFjbivrlhZM1/JM21PSNx/iNxzi2NxPkPlTmnhcUQUpSDo6ASe4AvmFv3QIqig6epSiogahRs308lUBs3igoffybF2JicHNvlcQdz/nNSvLy6qZC9JU7s9g5qydrtuF/7LzlXXIk7M5Pa395KzAvP45vUusxBbn4uAP4pKR3vqx1D5es/kEbinFsaMfNPTMK7R0Ffmofdbh8Sc+7uQ0V9YSCuCTozYr7P+pCsWff01TottduZNS6Wez/6mQ+35fHsuny2xAXyyGNP4f7t9Xj27KH68ssBUIEUHyfHpgzPUiG9XbMrTzDy2e51rNhfzv3nzMc1fjzOn3/G8+9/416pNeuO+t3v0IeE8vKmnwG4Zn4yiTH917zbboffL/Xy0Bd7+fcPeYB24/+Bs1NZdmzX530td2S6788U3vtnLRCh02G/916co0aR7/LhnS15fLw9nxpnAwCKAnNHhXD+MTEsHR+Or/HQfZirwsM59ZhEskpriQ/xI8jPh8dXHuDZ7zP4Zl85mw/WcPdp4zhzcpTWn+j003BOGE/uddfjzs6m5sabiPzbQ5hSU3FnZWGMj8cYcVjvkbAwqM8gXN8AFrD7df9cr10NTsgvgfFnHHqyHvCqXlYfXE1qdCqjg0bjUT14vB4avA24vW48qvb/Tf91e9wUlxZjDbRq26oeakxmqk0TseVsQF/5E4GmAMJ8bfiaAvG12DGZgvE12dD5+IPBDLouytV4PXDga4geA7Ezjmze7a7FNAKUBoifTkZNBsdHHt+29OthcgpzGBc0DjsqRI4iILz9ckz1P++morAQxWQi6pRTwNe3+WdROekkMv75GO6DBzFt3YrtzDM7H6dxIpSlaz8Eqgq5BohOaF6/qo0bqQL0yWMoq/Oi1ynMGR+HyTj8ygENx7+HadWlbTKivCrUKGZS7b3vXdJdw3HN+lJMkJlsazhjKg5iLiklxG6HihgCAnzBeuj35VBZp+5eEwxoEGLRokXs2NG6Nt3ll1/O2LFjue2229oEIAB8fX3x9W37NKVOp+vTBVYUpc/3OZyMpPmPpLm2ZyTOfyTOuaWROP+hMGdPcgoU5VCdVYKuvqL7jeZqS8AaQd3aTwHYFj4GgFMmRnZ7PoM1/2nxwQDsK6ymzu3F37f90wzfqCgSXn2F7CuuwLn/ADm/uoy4F17AlKLN1Vtbi195MQD2CWN6PI+h8PUfaCNxzi2NhPlbxqbg+VCHf20Z1NUNiTkP5LEH6pqgM0NhzYcbWbPu6at1CvTz5bELp7Igxc7dH+xkc3YFpxfW8H/X3kbc8jsOHQ84/4e38BT9pu3N4WGiN2s2LT6Y8ZEB/Jxfxftb8zn/4ovIv+tuar78qnkb188/83mNheyyOkItPvxqTkK/f/+ePjmKh77Y2/xxUzPorNJaogPNBJiN2j+TEZvZ0Pz/h9+M/TphJo8svZOI6hIKrKFMbxjFntd2k156KFssJsjMBcfGcu4xMUR30g8kOsif6CD/5o9vP3kcp06M4o/v/sTu/CpufesnPvmpgAfO1hrvmpOTSXzrTXJvvRXHj2vJu+lmLdKhqlqj7fvva9XnAoDISYzN20B6ZToRliP8PizZC8GJ4Nu6HOj2ou2YjCYmhE3o1m68Xi+BDYHYw+xtv+4Ji6C+Aty14K7X/ttQD9V52s30po9RtCeVm/4ZzK0/LtmnjTNuprZGfS18POz5hJSo8yipL2Fv+V4mhHY+/4LaAiaETkBXuBusEYeapB/GsVJrSO0/by4Gf//mG6BNP4uB551L8T8fo/Kddwk6++zOx2mLgZx1gAoeZ+NaHcoWce3VfiaqorWytKPC/PHzNba3p2FhuP09TLJb0Ck0N6kHrdl2YphFzrsGwOhw66G+EOnp2hqYbOB2tPn5HArr1N1jD2gQwmq1kpqa2uo1f39/QkJC2rwuhBBCDGcBUyfDj9+g5BRAbRmEdLPebU0RavgEHI39ID41ak/BnZLaf0/h9ZXwABMRASYKqurZkVvJrKSOAy+GsDDiXnmF7CuuxLl7N9m//CWxLzyPecIEXJmZAFT6+JMwqm2pJiFGovjESEp0AViNRTjT0mCY3jgUQvS/s6ZGc0x8EL99cxubssr51+os/nbYNorqxZWVPWyDEL2hKAqXzornzvd38Nr6LC4995g22+Tfcy//Pec+wMy185Px8+n/WyZZZbVtXlOBp79L7/R9PgZdc2DCZNRrzaNNgRSbAgH4bEcBAL4GHadMjOT8Y2OYlRiCTte7G98TY2x8dMNcnv42jSdWHmDlniKWPrqaO04Zx4XTY9EHBhL7n/+Qf/c9VL7/vhaAAPB6yb/nXvznzWv9/RacRNjBjWytyqbWPgW/FhkMPeJxQ/EeGHtqq5fTK9Mpri1mQdyC3u33cHoD+Id2bzzuOu1fQ92hgEVNkfax0Q9iZ/VPAAK0AIclHMrSmR4xna+zvibUHEq4f/vZDeX15fjqfbX1rymE6LY/F02qG/tBBCxZ0u7nbWefQ/ETT1K3eTPOAwfw7awsl8FH6w1RXQA6Q5sAUv1urcF1VlAU1EFqlK2zWYs+Fmkzs/ycidz53k48qopeUXjwHC3oKPpfst3CT40ZD870NO1FU4DWF2IYG3nhJCGEEGIAxM/WTuDDCosoL8nr3psanNBQj+tgCQ3FxXiNPuwMTiA1OoC4kF5emA2wKbGBAGzLqehyW0NQEPEvvYhp8iQ8lZVkX3Y5ddu2Ub33AAA5VjtjwvumTrAQw91ou5WsgEhUL7j27hrs4QghhrjYYD/e+PUsfrdkDAXWMLy0vuGp6nT4xA/PckxH4swpUVh9DWSW1rJ96762G3i96PJzCQ/w5ZKZA7M+iaH+HB4XUIDTJ0Vy0oQI5owKITU6gNhgMzazsfnetavBS0mNk7RihxaAaMdFU+2sv3Mh/1g2hTmjQnsdgGhi1Ou4cdFoPr1pHlPjAql2NnDn+zu4+Ll1ZJY4UAyG9svweLWgVys6PfrwVMa43KRVpPV+UMV7ISAafA+dM5bUlbCrZBdzoua06sUwIPRG7WahNRyCEsA+FqKnQcJcSF4M8XM6zDToM/bxULQLH70PMyNnsrFwI3UNde1umu/I1xpYez1QV95hoMWVmYlz/wEwGLDMn6+9t7KOzTnV5Fdq+zaG27GccAIAFW+/0/U4bTFQeRCc1a2+fgD1jZkQ23y1G7Hjo/q/9KJobdn0ONbcvoDXr57FmtsXsGz6yPubMViSwywtMiEyUL1e8A0AZ+Ugj+zIDGgmRHu+/fbbwR6CEEII0ecCJ00gV6fHv6aegr27CZraRV1U0J6Q8g/DsXYtAJlRo3HrjZw8DLIgmkyJC+SLXQVs70YQAkBvsxH3/PPk/OYa6jZvJvuKK/GMTkEBKi1BhPj3f+MzIYaD0eEWvreG463XUX/gZ5QTFg32kIQQQ5yh8Ybx2EgrjxXs4abt76BXVTyKwpOTz+Nes43hc4bRN/x9DZwzLZqX12bxdoHCVYc1c/YoCvmWUG5ekDxgtec7euK4oxt+Xq9KjauBqjo3VXUNVNW7ySxxcMd7O1rVcNcrcNG0cAJMfX8TfnS4lXeumcNLP2byyJd7WZdexkmPreZ3S1L4RXJcu02y2w16haUQk7eFb8r2MT5kPPqu+ikczuuBwl0w5sTml2rdtazLX8eMiBlYfCydvPkoZg0HRQ9VeYQGRJEcmMzGgo0cF32c1sejhXxHPpPDJoOjROth18HXoPqbbwDwnzEdvc3GmxuzueO9HY0NzPex/JyJLJseR+D551GzciWVH35I2O9uRefTybm8LQYOfKMFbloEIRqKi/GUlIBOx5oGG+AhNVoyIQZDpM0s2Q+DIDncQoFfMG6dAWN9Pe68PHyCrVrAbhiTTAghhBCiH+h8fCgPj0P1KpSnHwRnTddvchRrQYgftFJM31q1GqinTBw+twh6kgnRRG+xEPfsf/CbPQtvbS3K9q0AzMnaQuW77/bDKIUYfuxWXwqDo/C4FRxpe7t+gxBCNPL3NfBVwkwuW/on/jj3Gi5b+ie+iJ9BZknbMkAjwSWz4gF4/6Ab8x13NT+Vrup0PD75PHwjI7lgeg+aQveBnjxxrNMpBJiMxAT5MT4qgFlJIVw4I46Hzp2IvvEGs15R+OvZqdit/fcwh16ncOW8RL685XjmjAqh3u3lgc92s+z9dJTf3dHqaf/gK69ov/SXwRdf+3hi6qrJqc7p+SBK9oPFDuZAADxeD2vz1jImaEyH5YdGDPt4KNIarKcEpQCwp2xPq02cHicOt4NgU7BWisnS8ZpVf60FIaxLlpBfWdccgIBDfUzyK+uwHHcchvBwPBUVzeWbOtT4daMqD1oEjOr3aOc5+rh4Mmo8gGRCiJEl2W7Bq9Nz0BIGgPPAgcZMCAlCCCGEEKId3jFjAXBkl0BdWddvcBSj+gbh2LgRgM2hoxkXGUBiqH8Xbxw6Jkbb0CmQX1lPYVV9129opPPzI+LPf271moJWm9ldUNC3gxRiGFIUBRIS8bp0qHmZgz0cIcQw0lTup8QcyI6wZErMgegVhYTQ4VHqsa+NCbcyIzEYrwrvRRxD8soV2J97gZvPvJevEmZy48JkfA0DkwXRUqTNzOxRIb1+6rhNIOPYgQmkxIX48dpVM3nonIlYfQ1sz6ng1ANB/GLJnawPHwdA5oafOt6BfTwJLidpZT0MsHu9ULADIiY1v7SlaAsBvgGMCRrTm6kcXYITwVEK9VUoisKMiBmkVaZRUlfSvEmBowC7nx2dotMysi32dnflLiykbvt2ACwLF5FR4mjVsBjAo6pkltSiGAwEnnsO0N2STLHag1i+h4IM9Xt2A1AblwRAfIhfv2T0CDFUBZiM2K2+ZDf2hXClpWt9afS+4Bq+DxBIEEIIIYToJ4HTpgCgyylErS3tfGNVBUcJdQdyUWtrcfhZybBFckrq8GoY6e9raO7jsDW7okfvbchvJ9jQXg1hIUYoa0oK3gYF37pS1JpuZFcJIQSHyv20fEp+pDcYvbQxG+KNDdkQZufNhjD2q/7EBftx7jExgzy63jvSQEZvKYrChTPi+PrWE5iXHIJH1YJez0w8Ew8KAds3kLNxW/tv9rVgCx2Lb0UWpXVdnC+3VJamlQ/yDwFgf/l+qpxVTLNPO/IJHQ10eggbA0XaDX2TwcSMiBmsz1+P0+MEWvSDUFVwdByEqF6xAgDz5MkYw+3tPiClU2gObAaeey4oCrXr1uHK7uI83tb489aiHJOzsSl1Xoj2uQmSBSFGoNHhh/pCONMa++b4WsE5fJtTSxBCCCGE6CeJcxqbUxcXU1aU3/nGdeXg40/NOi0LYlNIMqqi4+RhVIqpSW9KMnm8Kl9UGNo0zvQoChVB7V8QCTHSxMWHU6wLRGdU8WRmDvZwhBDDiDQYbe2kCRGEWnwoqnby/tZc/rM6HYCbF43GqJfbJL0VYTNx3fzk5o/zLaF8Hz0ZgLJnn+v4jeGpJDtdHChvp1l4e1QVCn6CSC0LotBRyN7yvcyOmt3zvhLDkLugAMe69V1nC4eNhbJ0aHABYPezk2BLYEP+Bryql6LaIiL8I7TrEKMZDL7t7qamsR+EdekSAIL8fPA1tP45mZV0KPhljI7Gf948oBvZENYICIoHo6n5paam1Lv8tIexJkRJPwgx8iSHWchpzoRoDEKYAqBeghBCCCGEOEzAuBRceiM+tW4Kft7R+caOYrDYcfyo9YPYHDqalHAryfbh11CvKQjRnebU5Q4XT3+XxvEPr+J33xbw2JTz8DQ+qelRFB6ffB7ZemsXexFiZEgOt5BujpQghBCiVwbrKfmhyMegY1lj34c/vbeDilo3ccFmzpoaPcgjG/4Sw7TyX03eGrMQAJ81q3BlZbX/Jv8QQgMTqS7aRV1DXdcHKc8Aoz9Y7NS4athQsIFZkbPwMx79JcYq3nmHAwsXkX3ZZRxYuIiKdzq5yW80a5kGpQeaXxofPB6v6mV9/nr8jf746n0bSzG13w/CU1GBY/0GAKyLFwPw9c+FOBu8hAf4cv3cKEB7+Kiy1t38vsDzz9PG+/77qG43HdLpYdTC5g+99fW4MjIA+IEgQDIhxMiUbG+RCZGejqqqw74vhAQhhBBCiH6iGAyURyWgNuioTkuDBmfHG9cU4VHN1O/YCcDWsDGcPHF4lWJqMiUuEICfDlbgObxgbKOf86q47Z2fmLV8BQ99vofcijpsJgNfH9Y4c0XirBFbs1qIw422W8i0RqJ6oSG9m0+LCiGEaNdFM7RsEHfjuUpOWR3vbO5Fc2TRSlP5r6ZARIYtityUqeD1Uvrc8x2+zxA5haT6WjIqM7o+SL6WBeH2uvkx70cmhE4g1BzaRzMYulwHD5J/191aPwwAr7fr/mlNDapV7ftcURRmRM6guK5YK8UEnTalrv72W/B48B09Gp94rYzZu1sOAnDetBguPTaClAgrtS4Pr288VHrJumAB+tBQPCUlVK9a1e05OvfvB68XXXAw22sNgGRCiJEp2W4lzz8Mr6Lgra6moai4sRxT5WAPrdckCCGEEEL0p5TxANTmlEBtJ82pHUU4dmeB18tBi50Sv0BOGYalmABG2634+ehxuDwcKDpUt97t8fLJT3lc8PRaTnn8e97clIOzwcuEqAAePm8S6/+0mIfOnUi5XxA7wpIp9wsa8TWrhWgpymamICgSr1uHM/NA128QQgjRIb2udQlIFbjzvZ3kV3bjSXzRqWXT4/jh9oWcNUV7Sv6j1KUAVHzwAe7CwvbfZIsmwhxGbv5mvKq3451XZIPOAAFRbCrYRKg5lCRbUl9PYcjx1teT+4c/tvOJLvqn+YeA0Q8qDwXYzAYzC2IXMDpwtPZCJ02pq5tKMS3RsiCKqutZva8YgLOnRqEoClfOTQDgpR8ycTVoXzvFaCTw7LOAbjaoblS/W+th4UpIRlUhPMCXMGv7ZaKEOJol2y249Qby/LUAqys9TcoxCSGEEKJjwdO0Orj63OKOm1O766HBhWPDdgC2hI1mVJg/o4dhKSbQLuonRmtPLL27OYddeZU8sWI/x/1tFTf8bysbMssw6BROmxTJO9fM5pMb53HBsbGYjHqpWS1EJ3Q6BW9CEh63giE3reta0EIIITqUUeJo85pHVcksqR2E0Rx9Im1m7jptPL4GHR95wnBPmAxuN2UvvtThe/yipxNRXUJuTW7HO27Mgthduhunx8kU+5Qux9LtHgpDlKe6mpyrrqZ+69a2n9Tp8Inv4nw5fDwU/tzqJauPFaPeCM4aQG3VGLqJt7YWx5oftO2XaP0gPtyah1eFqXGBJIVp1yqnT44kzOpLQVU9n+7Ia35/4HlaSSbHmjW4czv5mrbg3KM1pS62a3NKlSwIMUKFWnywmY1kNwYInQfStHJMrpou3jl0SRBCCCGE6EcJc48FIKykhJKCDk6+HcXgH9bcD2KLfQynToxEUZT2tx8GTEbtFOM/32dw6uNr+L+v91FQVU+oxYebFiaz5raFPHnxNI5NCG4zT6lZLUTHptbl43XpMLprSF+8pPNa0EIIITqUGNq6dwGAXlGkDGQfCrX4csGxWu+N98YtAqD8rbdoKC9v/w3BScQY/Mks2Nb+56vywNtAnl5PRmUGsyJnoVM6v63Vox4KQ1BDcTFZv/gltZs2obNYCL7yStAdmnPYLTdjjOiihKstTqsj315WtqPjLIiaNWtQ6+sxRkfjO3Ysqqo2l2I6d1pM83a+Bj2/mq2Vanru+wytdj3gEx+P38yZoKpUvPtet+Zbv0drSr3X0tSUWvpBiJFJURRG2y1kBzT1hUjTeqjofcE1PIPlEoQQQggh+pF19CjqjSb0dV4KftrW/kaOItwOFXdWNh5Fx47QUZw8TEsxAeRX1rF6X0mb1+89fRw/3L6QW5emEGEzDcLIhBje3AUFzPnsZbwNCjqj2r1a0EIIIdrV1LtA3/gwhF5RpAxkP/j18UnodQovuSNRk8eg1tZS/tr/2t9YpyMwdjbG4r1U1Fe0/Xz+T1SHJrG5aAuzo2ZjMnR+PukuKCD/7nta91C4+x5qt25tvlE+lLlycsi85FKce/agDw0l/tVXCP/D70leuQJTaqq2TUZm1zvS6cA+Fop2t/1cJ02pm0sxLV6MoijsyqtiT0E1PgYdp0+KarXtJTPjMRl17MqrYm36oezvwPPPB6DivfdQPZ5Oh6l6vc2ZEBv0WgmaCdGSCSFGrmS7hZzGn0/XgTTtRV8rOIdnSSYJQgghhBD9SNHpKI9JwutSqE3fA952Tr5rinHs0prw7QmKIzwyhLERbVOih4uMEgftXdaNjbDha9AP+HiEOFq4MrNQVBW3Q09toY/2Yle1oIUQQnRIykD2v9hgP06bFAmKwleTtd4QZa++itfRthwWgBKaQryqI714Z+tP1BTR4KziB0cuk8MmE2QK6vLYzv0HmhsyN1NVsi66mP2zZpN9xRUU/d//UfXFF7iys4dUYKJ+zx4yL74Yd3Y2xthYEv73GqZx4wAoMdsouuJGACo//BBXZmbXOwwdo/XTcNe3fr2DptSqy0XNt98BYF2qlWJqyoJYMi4cm5+x1fZB/j6cd4yWHfHc94eai1uXLEYfGEhDQQE133/f6RDdBw/ira1F8fHhB6eWkSSZEGIkS26VCZGuvTiM+0JIEEIIIYToZ7px4wEF58FiqKto/UmvF2pLqdm4C9BKMZ0yMWJYl2KS8gZC9A+fhHhURQGa/oHanVrQQgghOiRlIPvfb44fBcCTnjiUmFi8lZWUv/V2+xsbfAiJmUl17gZcHlfzy2r+drbpPERZo4kL6N7fveqvv+7gGAY8lZU4flxL6bPPkXvLb0lbeiIHZs+h+tZbKXrkEao++wxXVlZzYGIg+0rUbtpE1i9+iae4BN+UFOJf+y8+cdqcX9+QzdyHVrJsdTXrI8aB10vxv/7d9U4NvhCUACV7D73W4NJ6QpjbBnQcGzbirapCHxKCecoU3B4vH23T+j2cMy263UNcOS8JRYGVe4o4UFQNgM7XF9uZZwJdN6iu361lQXgTR+FCR6CfkehA+bkUI5eWCREGgKe0VCtl5xuglVcbhiQIIYQQQvSzkMbm1Ib8krbNqesrUI1+ONauB2Br2BhOTh2+pZhAyhsI0V9KzDYen3IensafLY+i8Pjk8ygxS6kCIYQQQ9f4qADmp4TRgI61M04BoOzFF/G6XO1u7xM5hVhnHRll+7UXasvIKfqJOlsUE0MnduuYjnXrqHjrLe2Dph4KOh2Rf/0LKVs2k/DOO0Tcdx+By5ZhSk1FMRrxVlXRsGUr5S+8SO6tvyPtxJPYN2MmaSefwoH5Cwakr0T1ylVkX3kV3upqzMceQ/yrr2C0az0bMksc3PHeDryNCRuvjj0JgKpPPsF54EDXO7ePg+K9h8pTOYrAPwzaefip+hstgGNduBBFr+e7vcWUOlyEWnw4fkxYu7tPDPVn8Tjtqe3n12Q2vx54gVaSqebbb3EXFnU4vPo9Wrmosgitv8SEqIBh/WCWEEcq2W7BafCl0E8LFLrS0xvLMVUO8sh6xzDYAxBCCCGOdonzppMDhJSUU3gwi4iwMYc+WVOEq7gWb2UltQZf6kalHBVpx8umx3H8mDAyS2pJCPWTAIQQfSCjxMEX8TPZZE8hsqaEfEsoJeZAflVSKz9jQgghhrRrTxjFt3uL+bs6ivfCwmgoKqLyww8JauwZ0IqPP2ERU9mWs4YxoeMpTvuGbHMAM6Nmd+umtKeykrzb7wAg8IILCL3uWlxZ2fjExzU3cTanTsCcOqH5ParLRd2+/RSvW4cxJwfnzz/j3LsXb3U1ruoWTx17veTdfQ9+s2bhExNDX6p4733y774bPB4sCxcS/ej/oTNpfS+q6t1c+9rmVtunBUazJnIi8/J3UPzkv4j55z86P4A5EEyBUJEJwUmNpZjaNqVWvV6qV6wA2pZiOnNKNEZ9x88zXzUvka9/LuS9LQf5/dIxhFh88R01CvO0adRt2ULl++8Res017b7X2diUOsOm9ZtIjZKHLMTIFmUzYzbqybbYCa8tx3kgDb/xSVKOSQghhBDt84+Pw2GyoDhVCn7a0vqTjiIcu7MA2B6azImTo4+aJ36kvIEQfaup1FmJOZAdYcmUmAOl1JkQQohhYUZiMFPjAqlVdeyYcyoApc8912GzYmvsLIIr89mbt46DBZuZNP5cfPQ+3TpWwX3301BQgDE+jvDb/kiJ2cb20KQOMwc9XpWqBoWiiHgyZiwg7ZLr2HPPY2x7+n22L7u2zfaKqpJ2wTLKXv0v3rq6bq5A50qff578O+8Ejwfb2WcT8/hjzQGIoqp6lj2zjt35bUuwvD7+RFAUqr/4gvrGps6dCh8PhT9r/99BU+q6bdvxFJegs1jwnzmTiloXK3ZrGQznTus88DIjMZhJMTacDV5eXZfV/Hpzg+p33kVtysQ4TNP4N/tomRbjj4IHs4Q4EjqdovWFsDY2p05P08oxuWoGeWS9I5kQQgghRD9TFIWK2FH4H9iG88DPWoO8pkBDTRFV67V+EFvto7l64vAuxSSE6D9Npc7ufG8HHhX0ClLqTAghxLCgKArXnjCKX7+6mb/px/Ffmw13VjbVX35JwCmntH2DXzD24DGk7XqXpORTCDAFd+s4lR9/QtVnn4FeT/TDD/P6jhLu+mAnTS2nJ0QFEGAyUlnnpqreTWWdmxpnQ5v+1U1C60J4GQUdhzZQAaWsjMIHHqDkX/8i6BeXEnTxxRiCum6WfThVVSl65BHKnn8BgOArr8D++983P5SUUeLgly+sJ6esjlCLLxfPiOXJVQeaSzKdf/4JBPjuouqzzyl+4kli//Vk5wcMiIacDVBdCLVlWjmmw1R/8w0AlhNOQPHx4eMtWbg8XsZGWLsMDCiKwlXHJXHT61t5dW0W15wwCpNRT8BJJ1L44IO4Dx7EsXYtlrlzW73PU1FBQ34+AN81aMGi1GjJhBAi2W4hpzEI4TyQBjo96H3B5QDD8LoGkEwIIYQQYgDox00AVaEht+BQIyl3Pd56B7WbdwCQO2oSE+VkWwjRiWXT41j9x/n869wxrP7jfJZNl6bUQgghhofF48JJtlso8erJOE7Lhij5z7PNzZ8PF5J4AhOCUrDHH9et/bvz8ii4/34AQq+9loqEMfypRQACYFdeFWvTS/k5v4qD5XVU1x8KQJiMOsL8jYy2Wzg2PoiFY+0ce+zYNv2Ynpx8DmVX34wxNhZPRQUlTzzJgYWLKHjwQdx5ed1eD7Whgfw7/9QcgLD/4feE/+EPzQGInw5WcN5TP5JTVkd8iB/vXTuHW5em8MPtC0ltDAZkl9USesMNoNNRs2IFdTt3dX5QRQH7eMj6AUw20Ld+NllV1eYghHVJYymmzVoppvOO6V75qZNTI4iymSh1uPhgay4AOrMZ2+mnAe03qK5vLMVEVDTl+ODnoycxxL9bxxPiaKYFIbSyac70dO1FU8CwLMkkmRBCCCHEAAg7dgp89D98CkpRa0tRTAHgKMKZW4HS4KbIHMgxcycdNaWYhBD9J9JmRh9rxS4ZEEIIIYYRnU7hmhNG8fu3t/M3v0k8bX4f5549OFavxnLCCW23D4jGOu3yNjfK26N6veTdfgfe6mpMkycRes1v+Oeq9Ha3vWHBKI5JCMZmNhJgMmr/NRsw6hSKioqw2+3odIee2X1zbBhXvDaW8Ori5n5MX5Yq/OGOJ7mw7gBlzz2Hc/duyl95lfL/vY7t1FMJuepKfEeP7nC83vp6cm/9HTUrV4JeT+T99xN47jnNn/9+fzHXvLoZh8tDanQAL142gzCrL6CdB9x7xgTOf3otb286yPUL5mM7/TQqP/yI4iceJ+6ZZzpfrJBRkLsZbLFtPuXctx93djaKjw+W4+aRVlzDtpwK9DqFM6dEd77fRka9jsvnJvLAZ7t5bk0GFxwbi06nEHj++ZT/73WqV6ygoawMQ/Ch7JamptTVMYkAjI8MQKeT6yIhWpZjasjPx1PjQO8boD3Y2E45taFMMiGEEEKIAZA0bzoAQWUV5GdlaC/WFFO9KxOArWFjOGVS1CCNTgghhBBCiP53xuQoIm0mMl0GCk84GdCyITrUjQAEQNmLL1G7YQOKnx/RDz/MwSoXz61pG4TQKwqXzIpnQYqdaXFBJNsthFl98TXoO9z3sulxfHjf2dx556W88aczOWNyFB6vykNf7efW4jACX32d2Oeew2/mTGhooPLDD0k//Qxyrr2O2i1b2uzPU1VF9lVXUbNyJYqvLzFPPN4qAPHR9jyueGkjDpeHuckhvPHr2c0BiCbTE4KZmxxCg1flX6vSCL3uOtDrcXy3mtqtWztfLL0RYmdozakPU/311wD4z52Lzt+/OQvihDFhbcbQmWUzYrH4GjhQVMN3+4sBMI0bh2niRHC7qXz/g1bbNzWlzg7UAh0TpB+EEIAWhKjx8aPcZAXAlZEOvlZwDr9MCAlCCCGEEAPALyqCKn8bqktHwZaN2ouOIorXaqWYMhPGMyU2cPAGKIQQQgghRD/zMei46jjt5vejgceiGI3Ubd5M7ebNvd5n/Z49FP3znwCE33E7SkwsN76+FYfTQ3ywH00P1OsVpde9lCJtZmaPCiHZbuGxC6fw4NkT8THoWLmniNOeWMPe2PHEv/wSCW+/hXXpUlAUalatIuviS8i8+BKqV63ClZdH1ZdfknnRxdRt2ozOYiHuuWexLlzYfJwX1mRw0+tbcXtUTpsUyQuXTcfi234g5uZFYwB4e1MORQFh2M4+C4CSJ57oekKho8E/pM3LzaWYFi/G41V5v7GcUlcNqQ8XYDKybLqWafHc94eCQYHnnwdAxdtvtyrD1dSUeodZe7J7gpSoFQKA+GA/jHqFLEtjSaYDaY3lmCoHeWQ9J0EIIYQQYoBUxY3G41Rwpe0ErxdPURa6NO3posgFx0spJiGEEEIIcdS7cHosgX5Gttf7UHn8UgBK/vOfXu3L63SS94c/gNuNZeFCAs87j0e+2su2nAoCTAZeu3omP9y+kNevnsWa2xf0SS8lRVG4eGYcH1w3l8RQf/Iq61n2zFr+szoN3wmpxDz+GEmffUrg+edpQZYtWzh47XWkLVxE7s234EpLQ7FYiP/vq/hN17KlVVXlb1/s4f5PfgbgsjkJPH7h1E4zNGYkHsqG+Pe3aYRecy0YjTh+XEvtxo09npcrJwfnnj2g12NZuIB16aXkV9YTYDKwaJy9x/u7fG4Cep3CDwdK2ZWn3TANOOVUFD8/XJmZzWNUXS6caWkAfO8NBCQTQogmBr2OxFD/5ubUrvQ0aCrHNMxIEEIIIYQYIMbx41E9OsjPg+o8HGkHQVVIs0WxaHbKYA9PCCGEEEKIfufva+BXsxMA+HfEbNDpcHy3mvrdu3u8r+JHH8W5/wD60FAi//oXVu8v4ZnvtCfvHz5vEjFBfs1ZDL3JgOjM+KgAPrphLqdNiqTBq/LgZ3u4+pVNVNS68E1MJPIvf2HUN98QeNGFbd6r1taiDwwEoMHj5Y/v/MRT32o34v9wYgr3nj6+Wz0RWmVD+Ac1l3UqfuzxDht+d6T6ay0Lwu/YYzEEBTWXYjptchQmY8fBkI7EBPlxcmoEAM+v0crR6i3+2E49BTjUoNqZng5uN1itpOus+Oh1jLZbe3w8IY5WLftCOA+kaeWYXDXQw5/xwSZBCCGEEGKAhM+YBoCpsBRv4R7ytmoXGntjxjEtLmgwhyaEEEIIIcSA+dWcBExGHd85TDjnzgeg9NlOekO0w/Hjj5S9/AoAkX/9C2UGP259cxsAv5gVz0mpkX055HZZTUaeuGgqD5ydio9Bx4o9RZz6+Bq2ZJcDYAy3E3DiSW3f6PXiysqmzuXhN69u5u3NB9Ep8LdzJ3L9guRuZ0jPSAxmzqgQ3J6mbIhrUHx8qN20idp163o0l5almGqcDXy+swDoeSmmlppKb328PY/CqnoAAs8/Xzvel1/iqaigfrdWiqkubhQoCmMiLPgY5HalEE2SwyzkWBvLMaWngU4Pel9w1w7yyHpGfqqFEEKIAZI091gAbBXVlKTvwr19PwB+s+d060knIYQQQgghjgbB/j5c2Fga6eXEEwCo+uJLXFlZ3Xq/p6KCvDvuBCDwogvxP/4EfvvWNkodLsZGWPnTqeP6Z+DtUBSFS2bG8961c0gI8SO3oo4Lnl7Ls6vTUVUVn4R40B12+02no94eyaXPr2fFniJ8DTqe+cWxvSoXdfOi0UBjNoTJRuCyZUDPsiEaSkqoa2xobV28iM935FPn9pAY6s+0uMAej6nJlNhApicE4faovPRjJgCmiRPxTUlBdbmo/OhjrQQUUBCqBTtSo6QfhBAtJYdbmzMh3DkH8TqdWl+IYdacekCDEE899RSTJk0iICCAgIAAZs+ezeeffz6QQxBCCCEGjW9oCGUBoXhcOko3bcGnpBqXzsC0U+cP9tCEEEIIIYQYUFcdl4hBp/B+lT/eGXPA66X0uee7fJ+qquTfdx8NhYX4JCQQ/sc/8tR3afxwoBSzUc+TF0/tVfmgI5UabePjG+dxamN5pgc+283Vr2zGERBM5P33HQpE6HSYb7+LC99PZ3NWuda74qqZLBkf3qvjzkwKYXZSYzbEqgOEXH0Viq8vddu24fj++27to3rFSlBVTBMnYoyM5N0tWimmc6ZGH3HfuqZsiNfWZeFwNqAoSnM2RMXbbzeX4frZXyvdJP0ghGgtOcxCua+VGqNZy6LKzByWfSEGNAgRExPDQw89xObNm9m0aRMLFy7kzDPPZNeuXQM5DCGEEGLQ1MSPxlOvQ7dxE94GHfvsozg2pf9TxYUQQgghhBhKYoL8OGNyFADvjl0EQMUHH+AuLOz0fVUff0z151+AwUDU3x9ma1Edj369D4D7zpxA8iD2E7CajDx50VT+clYqPnod3+wu5NTH15AxYxEBH3xK9fLHqXjhbS7KtbO/qIaIABPvXDuHYxOCj+i4Ny/WsiHe2pRDkY+VoIsvBqD48Se6lQ1R/fXX2vgXL+ZgeS3r0ssAOHta9BGNC2DxuHDiQ/yoqm/gncY+E7bTT0Px9cW5fz+1mzcDsFYJAWBCtGRCCNFSUpg/ik4hq6k5dVpjX4h6yYTo0Omnn84pp5zC6NGjGTNmDA888AAWi4V1PaxTJ4QQQgxXPhMm4G3Q4d3vAcA9dTp6KcUkhBBCCCFGoN+cMAqAF6ttKJOngttN2Ysvdbi9OzeXgvv/AkDY9dfhGjWWm17fhsercuaUKM4/pvf9C/qKoij8YlY87103h/jG8kznPfUj817YxQXrXVz0QQb5lfWMCvPn3evmMCb8yIMms1pkQzz1bWM2hJ8f9Tt3UrNqVafv9VRX41i/HgDrkiW8vyUXgNlJIcQE+R3x2PQ6hSvnJQJag2qPV0VvsxFwUmOvDI8HdHp+0gWhU2BchGRCCNGSyagnLtjvUF+IA2lSjqknPB4Pb7zxBg6Hg9mzZw/WMIQQQogBFTVTa06NVws8jDpp/uANRgghhBBCiEGUEmFl8Tg7qgpfTz4RgPK33qKhvLzNtqrHQ95tt+OtqcE8ZQrBV13Fbe/+RG5FHfEhfvz1rNQjLh3Ul5rKMy0ca8ejwuH5CP+6eBrRgeY+O15TNsSbG3Mo0pkJvvRSoDEbwuvt8H01334Hbjc+o0bhk5jAe1u1IMS5fRjQOe+YGGxmI9lltXz9s5bporeHNX9e9XpYkLOZUWEWzD4DX0pLiKEuOczS3BfCmZ4+LMsxGQb6gDt27GD27NnU19djsVh4//33GT9+fIfbO51OnE5n88dVVVqUx+v14u3kl2hPeL1eVFXts/0NNyNp/iNpru0ZifMfiXNuaSTOf6jPOWvTDmIb/18Fyrf+hHdJ3wXjh/r8+9tInP9InHNLI3H+Q2XOA3n8gbgm6MxQWfPhRNase2Sdek7WrHuG0zr95vgkvtldxOM1oSwdnYJ3/17K/vsaoddf12q70uefp3bTJhQ/PyIeWs5/Nx7ki10FGPUKjy2bgr+P/ojm2x9rZvHRc8XceFbuKWrzuTKHs0+PNSMhiFmJwazLKONfqw7w58t+Rflrr+Hcs4eqL7/EeuKJ7b6v6uuvtLEuWsSmzDIyShyYjXpOHG/vdHw9WS+TQcfFM2J56rt0nv0+nQXBXsqef6H58wpw0/Z3eH3B8cPie7YnhtPP4lAha9bWqDB/tjRnQhzAa/RHra9CHaBz4c509/gDHoRISUlh27ZtVFZW8s477/CrX/2K7777rsNAxPLly7nvvvvavF5cXEx9fX2fjMnr9VJZWYmqquh0g5YcMmhG0vxH0lzbMxLnPxLn3NJInP9QnnNRRi7Rrz3d/LECxLz8BDtPmIE98cjrrcLQnv9AGInzH4lzbmkkzn+ozLm6euCevhqIa4LODJU1H05kzbpH1qnnZM26ZzitU6wZJkdZ2J5Xw3dTF3Pc/r2UvfIKnlNPRfHTMgUa9u+n+vEnADDfcD0bq7389dM9AFw3N5oIHydFRW1v9PdEf61ZAC50CnhbpELoFLCodUc85sP9Yloo6zLKeGtTDhek2gg471zqX36Fgsceo3bSJBR96ywD1emkZrXWvNo9bRqv/XAAgPnJNhyVZTg6OVZP1+uU0f48+73C5qxy1q8sJOywG5d6VWW0s6jP12SwDaefxaFC1qwtu8nbnAnhysyksKAIU62LypJ8VBgW1wQDHoTw8fEhOTkZgGOOOYaNGzfy2GOP8cwzz7S7/R133MGtt97a/HFVVRWxsbGEhYURENA3deK8Xi+KohAWFjYiv7lH0vxH0lzbMxLnPxLn3NJInP9QnnPWD9sJOCwRW6+q1OaXYp85tU+OMZTnPxBG4vxH4pxbGonzHypzNplMA3asgbgm6MxQWfPhRNase2Sdek7WrHuG2zrduBiuemUzT3iTmB8biycnB+O3qwi+7DK89fVkPfQ3aGjAsngxgRddwhX/XovLo7IgJYybTuybMkz9tWZ2Ozxwtspd7+/Eo4Jegb+enUrqqL7vX3Gy3c6sLSWsyyjj7Z2V3HvttaS//wHezCzMmzcTcNpprbavWbmSivp6DBERBM+azYqHtP4RF88ehd0e2umxerpedjucOaWMd7fk8km1P5frdNAiEOFRFFJmTMZut/di5kPXcPtZHApkzdqa5vSh2JyB0+CDb4OLIGc9RnsMLoOeELt9WFwTDHgQ4nBer7dVavXhfH198fX1bfO6Tqfr0wVWFKXP9zmcjKT5j6S5tmckzn8kzrmlkTj/oTrnmIkpVKCgaxGI8CgK0alj5G9aHxqJ8x+Jc25pJM5/KMx5II89UNcEnRkKaz7cyJp1j6xTz8madc9wWqdF48JJCbeyt7CancefybjXnqT8pZcJvvRSSv7xT1xpaejDQon8y/3c+eke0oodhAf48sj5k9Hr+66HQH+t2UUz4pmfYiezpJaEUD8ibX3XC+JwNy8ew7pn1/HWpoNcvzCZkCsup/ifj1H6r39jO+UUFMOhW4E136wAtIbUK/eVUF3fQJTNxNzkMHS6rgM7PV2vK+cl8e6WXN7JdnPNbX/C+bcHwOvFoyg8Pvk8/j65b6+Lhorh9LM4VMiatZYcbkVVdGRZ7IypOIg7IwOf5AB0dY5BX6fuHntAR3jHHXewevVqMjMz2bFjB3fccQfffvstl1xyyUAOQwghhBgU0WMSyL3yFjyNT2p5FIW8K24hekzC4A5MCCGEEEKIQaQoCtfOHwXA3zyJ6O12GoqKyLnmGspffRWAqAcf5NOsWt7adBCdAv9cNpUQS9sA9VAVaTMze1RIvwYgAGaPCmFmYjAuj5envk0j6NJfoA8MxJWVReXHnzRvpzY0ULNKy3ywLl7Mu5sPAnD2tOhuBSB6Y3xUAPOSQ/Gq8N/gKSSvXEH1g49z2dI/sWfaAmxmY78cV4jhLsBkJCLARI6lqS9EGvgGoHMNn+bUAxqEKCoq4pe//CUpKSksWrSIjRs38uWXX7JkyZKBHIYQQggxaJb+4dcEffgZNcsfJ+jDz1j6h18P9pCEEEIIIYQYdKdNiiQ60ExhvUpxfAoAtT+uBcBv5kxKxk3jzvd2AHDjwtHMHhUyaGMd6m5ZPAaANzbkUOTREXL1VQCU/PvfqG43ALWbNuGprEQfFETNmPGs3l8CwDnT+r5MVEtXHZcIwJsbs6kLDGF76ChKzIFMiOr/8opCDGfJdgvZAVpfCGd6Glgj8Zo7L5s2lAxoEOL5558nMzMTp1NrGPTNN99IAEIIIcSIEz0mgelnL5EMCCGEEEIIIRoZ9Dp+fXwSoXUVBG5c0+pztRs38qdnV+BweZiRGMyNC5MHaZTDw+xRIcxomQ1x0UXoQ0Jw5+RQ8f77AFR//Q0AloUL+GhHER6vypTYQEaFWfp1bCeMCWO03YLD5eGNDdnszKsEIDXa1q/HFWK4S7ZbyLE0Nqc+kAZ+wXgC+jdo2JeksJYQQgghhBBCCCGEGHQXHBvL2IaKVj3UAPB6qTqQQaCfkccunIJBL7ezunLL4tFAYzaEW0for68GoOSpp/E6nVR/owUhrIsX8+4WrRTTucf0/w1NRVGasyFe/CGTnw5qQYjxkgkhRKdaZUJkZKC2aOw+HMhvbSGEEEIIIYQQQggx6Mw+eo5bMA0vrXsSeBSFfEsoj5w3ud97KhwtZie1zIY4QOCFF2Kw22nIz6fg3j/TUFiIzs+P7MQJ7Cmoxkev4/RJkQMytjOnRBNq8SG/sp6MEgcAqVGSCSFEZ5LtFvL9gnHrDKh1dTTk5w/2kHpEghBCCCGEEEIIIYQQYkhYdsoxPH3sBXgULRDhURQen3wepy+ewuLx4YM8uuFDURRuWaRlQ7y+IYeiepWQa34DQOUHHwBgnjGDd3dovSAWj7cT6OczIGMzGfVMjQ1q9drKPYUDcmwhhqtkuwWvTk+uResD4UxLH+QR9YxhsAcghBBCCCGEEEIIIQRAoJ8PFfNP5rKQ0UTWlJBvCcUnIoInTh472EMbdmaPCmFGQjAbMst4+rs07jnvPIofexxvpVYCyfHdd1TURULkMZwzdeBqy+dX1rHisKDDne/t5PgxYZLpIkQHQvx9CPIzkm2xk1BVgCstDcamDPawuk0yIYQQQgghhBBCCCHEkJBfWccPaSWUmAPZEZZMiTmQgqp6yhyuwR7asKMoSnNviP9tyKYgMx9vVdWhDVSVKza8wWjFwQkpYQM2rowSB97D2n54VJXMktoBG4MQw42iKFpzamtjc+r04ZUJIUEIIYQQQgghhBBCCDEktHeD2qsiN6h7qSkbwtXg5YNP1oHaenH1qso54WAcwGbfiaH+6Fq3/UCvKCSE+g3YGIQYjpLtFrIbgxDO9LRBHk3PSBBCCCGEEEIIIYQQQgwJcoO6bymKws2N2RCvHvSCrvWtQI+icPyCqQM6pkibmeXnTETf2PdDryg8eE6qlGISogvJdivZVjsArrR01MOCikOZBCGEEEIIIYQQQgghxJAgN6j73pxRIUxPCCLfJ4AN5/ymORDhURTePv5SJkwePeBjWjY9jjW3L+D1q2ex5vYFLJseN+BjEGK4SbZbyLWE4VUUvFVVqGXlgz2kbpPG1EIIIYQQQgghhBBiyFg2PY7jx4SRWVJLQqifBCCOkNYbYgyXPLeeBxjNdx9+xsMvfMP3tSZ+c+7sQRtXpM0sX1sheiDZbsGtN1LgF0KUowRPViaMGzvYw+oWCUIIIYQQQgghhBBCiCFFblD3rTmjQjg2PohNWeX87tt8flQj0PnBmVOjBntoQohuirKZ8PfRk2UNbwxCZA32kLpNyjEJIYQQQgghhBBCCHEUa8qGAPgxrRTQGn6v2lM0mMMSQvSAoiiMslvIaewL4ZUghBBCCCGEEEIIIYQQYqhICmvb3PvO93aSX1k3CKMRQvRGcpiFbGs4AO6du3AXFAzyiLpHghBCCCGEEEIIIYQQQhzlMktr27zmUVUyS9q+LoQYmpLDLUTXFAPgPXCA9MVLqHjnnUEeVdckCCGEEEIIIYQQQgghxFEuMdQfndL6Nb2ikBDaNkNCCDE0pejqWLZv5aEXvF7y77l3yGdESBBCCCGEEEIIIYQQQoijXKTNzPJzJqJXtEiEXlF48JxUaQAuxDASX1eKDrX1i14vrqzswRlQNxkGewBCCCGEEEIIIYQQQoj+t2x6HMePCSOzpJaEUD8JQAgxzESnjiEdpXUgQqfDJz5u8AbVDZIJIYQQQgghhBBCCCHECBFpMzN7VIgEIIQYhszRUbx5wqV4GjOa0OmIvP8+jBERgzuwLkgmhBBCCCGEEEIIIYQQQggxDJQefxKXmeK5Pknh4vPn4xsVNdhD6pJkQgghhBBCCCGEEEIIIYQQw0Cy3UKJOZAtwUlDPgOiiQQhhBBCCCGEEEIIIYQQQohhINluAWBnvoP8yrpBHk33SBBCCCGEEEIIIYQQQgghhBgG0opqAMgoq+e4h7/lzY3ZgzyirkkQQgghhBBCCCGEEEIIIYQY4vIr63h85f7mj70q3PneziGfESFBCCGEEEIIIYQQQgghhBBiiMsoceBVW7/mUVUyS2oHZ0DdJEEIIYQQQgghhBBCCCGEEGKISwz1R6e0fk2vKCSE+g3OgLppQIMQy5cvZ/r06VitVux2O2eddRZ79+4dyCEIIYQQQgghhBBCCCGEEMNOpM3M8nMmom8MROgVePCcVCJt5sEdWBcMA3mw7777juuvv57p06fT0NDAnXfeydKlS/n555/x9/cfyKEIIYQQQgghhBBCCCGEEMPKsulxzEsOYduBPKYkRxEdNPTvqw9oEOKLL75o9fFLL72E3W5n8+bNHH/88QM5FCGEEEIIIYQQQgghhBBi2Im0mdHHWrEP8QyIJoPaE6KyshKA4ODgwRyGEEIIIYQQQgghhBBCCCH6wYBmQrTk9Xq55ZZbmDt3LqmpqR1u53Q6cTqdzR9XVVU1v9/r9fbZWFRV7bP9DTcjaf4jaa7tGYnzH4lzbmkkzn8kzrklmf/Im/9InHNLI3H+Q2XOA3n8gbgm6MxQWfPhRNase2Sdek7WrHtknXpO1qxnZL26R9ap52TNumeorFN3jz9oQYjrr7+enTt3smbNmk63W758Offdd1+b14uLi6mvr++TsXi9XiorK1FVFZ1uUJNDBsVImv9Immt7RuL8R+KcWxqJ8x+Jc25J5j/y5j8S59zSSJz/UJlzdXX1gB1rIK4JOjNU1nw4kTXrHlmnnpM16x5Zp56TNesZWa/ukXXqOVmz7hkq69Tda4JBCULccMMNfPLJJ6xevZqYmJhOt73jjju49dZbmz+uqqoiNjaWsLAwAgIC+mQ8Xq8XRVEICwsbkd/cI2n+I2mu7RmJ8x+Jc25pJM5/JM65JZn/yJv/SJxzSyNx/kNlziaTacCONRDXBJ0ZKms+nMiadY+sU8/JmnWPrFPPyZr1jKxX98g69ZysWfcMlXXq7jXBgAYhVFXlxhtv5P333+fbb78lMTGxy/f4+vri6+vb5nWdTtenC6woSp/vczgZSfMfSXNtz0ic/0icc0sjcf4jcc4tyfxH3vxH4pxbGonzHwpzHshjD9Q1QWeGwpoPN7Jm3SPr1HOyZt0j69RzsmY9I+vVPbJOPSdr1j1DYZ26e+wBDUJcf/31/O9//+PDDz/EarVSUFAAgM1mw2weHp28hRBCCCGEEEIIIYQQQgjRPQMaJnnqqaeorKxk/vz5REZGNv978803B3IYQgghhBBCCCGEEEIIIYQYAANejkkIIYQQQgghhBBCCCGEECPDoDSmPhJNgYyqqqo+26fX66W6uhqTyTQia42NpPmPpLm2ZyTOfyTOuaWROP+ROOeWZP4jb/4jcc4tjcT5D5U5N52PD8aDRv1xTdCZobLmw4msWffIOvWcrFn3yDr1nKxZz8h6dY+sU8/JmnXPUFmn7l4TDLsgRHV1NQCxsbGDPBIhhBBCCCFEdXU1NpttwI8Jck0ghBBCCCHEUNDVNYGiDrMaSV6vl7y8PKxWK4qi9Mk+q6qqiI2NJScnh4CAgD7Z53AykuY/kubanpE4/5E455ZG4vxH4pxbkvmPvPmPxDm3NBLnP1TmrKoq1dXVREVFDfjTV/1xTdCZobLmw4msWffIOvWcrFn3yDr1nKxZz8h6dY+sU8/JmnXPUFmn7l4TDLtMCJ1OR0xMTL/sOyAgYER/c4+k+Y+kubZnJM5/JM65pZE4/5E455Zk/iNv/iNxzi2NxPkPhTkPdAZEk/68JujMUFjz4UbWrHtknXpO1qx7ZJ16TtasZ2S9ukfWqedkzbpnKKxTd64JpLCWEEIIIYQQQgghhBBCCCH6hQQhhBBCCCGEEEIIIYQQQgjRLyQIAfj6+nLvvffi6+s72EMZFCNp/iNpru0ZifMfiXNuaSTOfyTOuSWZ/8ib/0icc0sjcf4jcc6DTda852TNukfWqedkzbpH1qnnZM16Rtare2Sdek7WrHuG2zoNu8bUQgghhBBCCCGEEEIIIYQYHiQTQgghhBBCCCGEEEIIIYQQ/UKCEEIIIYQQQgghhBBCCCGE6BcShBBCCCGEEEIIIYQQQgghRL+QIIQQQgghhBBCCCGEEEIIIfrFkA1CLF++nOnTp2O1WrHb7Zx11lns3bu31Tb19fVcf/31hISEYLFYOPfccyksLGz+/Pbt27nooouIjY3FbDYzbtw4HnvssQ6P+cMPP2AwGJgyZUqX41NVlXvuuYfIyEjMZjOLFy9m//79rbZ54IEHmDNnDn5+fgQGBo6o+WdmZnLllVeSmJiI2Wxm1KhR3HvvvbhcrqNurgBnnHEGcXFxmEwmIiMj+cUvfkFeXl6X+z5a5t/E6XQyZcoUFEVh27ZtR/28ExISUBSl1b+HHnqoy30fDXMH+PTTT5k5cyZms5mgoCDOOuuso3re3377bZuvd9O/jRs3HtVzB9i3bx9nnnkmoaGhBAQEMG/ePFatWtXlvo+W+W/ZsoUlS5YQGBhISEgIv/71r6mpqRm2c37vvfdYunQpISEhHf7O7mp8nTka5v+f//yH+fPnExAQgKIoVFRUHNVzLisr48YbbyQlJQWz2UxcXBw33XQTlZWVXe57MA31de/v88ieOhrWq0lPzjt76mhYp96ep/bW0bBm0PPz254a7ut0JOfDvTXc1wyO7Dy6p46G9erpeXdvDfW16u/z9Z46Gtarp+f3vTHc16lfrwnUIerEE09UX3zxRXXnzp3qtm3b1FNOOUWNi4tTa2pqmre55ppr1NjYWHXFihXqpk2b1FmzZqlz5sxp/vzzzz+v3nTTTeq3336rpqWlqa+++qpqNpvVJ554os3xysvL1aSkJHXp0qXq5MmTuxzfQw89pNpsNvWDDz5Qt2/frp5xxhlqYmKiWldX17zNPffcoz766KPqrbfeqtpsthE1/88//1y97LLL1C+//FJNS0tTP/zwQ9Vut6u/+93vjrq5qqqqPvroo+ratWvVzMxM9YcfflBnz56tzp49u8t9Hy3zb3LTTTepJ598sgqoW7duPernHR8fr95///1qfn5+87+W4z+a5/7OO++oQUFB6lNPPaXu3btX3bVrl/rmm28e1fN2Op2tvtb5+fnqVVddpSYmJqper/eonruqquro0aPVU045Rd2+fbu6b98+9brrrlP9/PzU/Pz8Lvc/3Oefm5urBgUFqddcc426Z88edcOGDeqcOXPUc889d9jO+ZVXXlHvu+8+9dlnn+3wd3ZX4+vM0TD/f/zjH+ry5cvV5cuXq4BaXl5+VM95x44d6jnnnKN+9NFH6oEDB9QVK1aoo0eP7vT7fCgY6uve3+eRPXU0rFeTnpx39tTRsE69PU/traNhzXpzfttTw32djuR8uLeG+5qp6pGdR/fUcF+v3px399ZQX6v+Pl/vqaNhvXp6ft8bw32d+vOaYMgGIQ5XVFSkAup3332nqqqqVlRUqEajUX377bebt9m9e7cKqGvXru1wP9ddd526YMGCNq8vW7ZMveuuu9R77723yy+a1+tVIyIi1L///e/Nr1VUVKi+vr7q66+/3mb7F198scdBiMMN5/k3efjhh9XExMRO962qR8dcP/zwQ1VRFNXlcnW6//YM1/l/9tln6tixY9Vdu3b16mJwOM47Pj5e/cc//tHNGXZsuM3d7Xar0dHR6nPPPdeTabYx3OZ9OJfLpYaFhan3339/p/tuz3Cbe3FxsQqoq1evbt6mqqpKBdSvv/66W3NuabjN/5lnnlHtdrvq8Xiat/npp59UQN2/f/+wm3NLGRkZ7f7O7u34OjLc5t/SqlWrenWRMpzn3OStt95SfXx8VLfb3e39D7ahtO6DcR7ZU8N1vY70vLOnhuM69dV5am8NtzXrq/Pbnhpu63S4Izkf7q3htmZ9fR7dU8NtvfrivLu3htJatTRQ5+s9NdzWq6Xent/3xnBepyZ9dU0wZMsxHa4p7SM4OBiAzZs343a7Wbx4cfM2Y8eOJS4ujrVr13a6n6Z9NHnxxRdJT0/n3nvv7dZYMjIyKCgoaHVsm83GzJkzOz32kTga5t/esTvaDobvXMvKynjttdeYM2cORqOxW8c5fNwwvOZfWFjI1Vdfzauvvoqfn1+39t3eeGF4zRvgoYceIiQkhKlTp/L3v/+dhoaGbh3j8DHD8Jn7li1byM3NRafTMXXqVCIjIzn55JPZuXNn9ybcYrwwfOZ9uI8++ojS0lIuv/zybh3j8DHD8Jl7SEgIKSkpvPLKKzgcDhoaGnjmmWew2+0cc8wx3Zv0YeOG4TN/p9OJj48POt2h0yaz2QzAmjVrunWcoTTn7ujt+Doy3ObfF46GOVdWVhIQEIDBYOjX4/SlobTug3Ee2VPDcb364ryzp4bjOkHfnKf21nBbs746v+2p4bZOhzuS8+HeGm5r1tfn0T013NarL867e2sorVV39PX5ek8Nt/UaLEfDOvXVNcGwuKLwer3ccsstzJ07l9TUVAAKCgrw8fFp02shPDycgoKCdvfz448/8uabb/Lpp582v7Z//35uv/12vv/++24vZtP+/7+9ewuJao2jAP53pJsopeEYJaWURUU+VJT6UJZhQYRdCCq6QRSaoKBFWYRd6AKlEeNLREhRINVLUZGhFQkSXdiSE2mZKU4YXVG7k67zcEhmbCZn75lt8+3WD+bhTNtv77XO4fjf55szOy4uzu9zB8IK+ZuamsThcMixY8f+uLbKWXfs2CFlZWXy5csXSUlJkatXr/p1Dncq5gcgGzdulOzsbJk5c6a0tLT4tbY7FXOLiOTl5cn06dMlJiZGamtrpaioSNrb26W0tNSv84iomb25uVlERPbu3SulpaWSkJAgJSUlkp6eLs+ePfNrs1HF3H2dPn1aFi5cKPHx8X6d4xcVs4eFhUlVVZUsXbpUoqKixGazid1ulxs3bkh0dLRf5/lFxfzz58+XgoICOXr0qOTn58vnz59l586dIiLS3t6uXGZ/GLk+X1TMHygrZH737p0cOHBAtmzZYto5gi3Ueh/oOVIvFfsKxtypl4o9iQRnTjVKxc6CMd/qpWJPfRmdh41SsbNgztF6qdhXoHO3UaHWlT+COa/rpWJff4MVegrmPYES/ydEbm6uOJ1OqaioMLyG0+mUrKwsKS4ulszMTBER6e7uljVr1si+fftk4sSJXn/u/PnzEhkZ2fuqqakxfA1GqZ7/1atXsmjRIlm5cqVs3rz5j8eqnHX79u2iaZrcvHlTwsPDZf369QJA1xoq5nc4HNLV1SVFRUWGr1nF3CIiBQUFkp6eLsnJyZKdnS0lJSXicDjk+/fvfq+hYvaenh4REdm9e7esWLFCZsyYIeXl5RIWFiYXL170aw0Vc7tzuVxSWVkpmzZt0v2zKmYHILm5uWK326Wmpkbu378vS5culSVLlui+GVAx/9SpU+XMmTNSUlIiERERMmrUKElMTJS4uDiPT2n5omLmYPoX86ueubOzUxYvXixTpkyRvXv3Gs4w0FTuPRhzpF4q9hWMuVMvFXsSCc6capSKnQVjvtVLxZ7cBTIPG6ViZ8Gco/VSsa9A526jVOzqb2Jf/lG9p6DfEwT0ZU4DIDc3F/Hx8WhubvZ4v7q62uv3d40dOxalpaUe7z158gR2ux27du3yeP/jx48QEYSHh/e+wsLCet+rrq5GZ2cnnj9/3vv68uULXrx44fV7s+bMmYO8vLzfMgTyTAjV87969QpJSUlYt26dx3f6WTGru7a2NogIamtr/5jZCvmzsrJgs9k81v617vr16y2b2xun0wkRQUNDQ7+5Vc5+69YtiAhqamo8jpk1a9Zv12Gl3O7279+P2NhY3d/XrWr2qqoq2Gw2dHR0eBwzYcIEHD582PL53b1+/RpdXV349OkTbDYbLly4oFxmd76+C1TP9Vkxvzu93xmreubOzk6kpqYiIyPD68OAQ1Uo9j6Qc6ReqvYV6Nypl6o9eaN3TjVK1c4CnW/1UrUnd0bnYaNU7SxYc7ReqvblTu/cbVQoduXO7HldL1X7cjcQz4RQvScz7glCdhOip6cHubm5GD16NJ49e/bbn/96kMelS5d632toaPjtQR5OpxN2ux3bt2//bY3u7m7U19d7vHJycjBp0iTU19d7PLm877WNGjUKx44d632vo6MjqA+mtkJ+l8uFpKQkrFq1Cj9//rR01r5aW1shIrh9+7bPY6ySv7W11WPdyspKiAguXbqEtrY2y+b25ty5c7DZbPjw4YPPY6yQ/ddfuz+478ePH7Db7Th58qRlc7sfm5iYiMLCQp9ZrZb9ypUrsNls6Orq8vjZiRMn4uDBg5bP783p06cRERHhc3AN5czu+nvQXX/X54vq+d35e5NihcwdHR1ISUnB3Llz8fnz537XCgWh3PtAzJF6qd6X0blTL9V78sbfOdUo1TszOt/qpXpP7sfqnYeNUr2zQOdovVTvy5v+5m6jQrkrd2bN63qp3pc7MzchrNCTWfcEIbsJkZOTg+HDh+POnTtob2/vfbnv3GRnZ2Ps2LG4desWHj58iNTUVKSmpvb+eX19PWJjY7F27VqPNd68eePzvP4+TfzIkSMYMWIELl++jMePHyMrKwuJiYkeu0Otra3QNA379u1DZGQkNE2Dpmm//fKxYn6Xy4UJEyYgIyMDLpfL4/xWy3rv3j04HA5omoaWlhZUV1cjLS0N48ePx7dv3/pdX/X8ffnzL3wr5K6trcXx48dRV1eHFy9e4Ny5c4iNjfXrU3iqZweA/Px8jBkzBpWVlWhoaMCmTZtgt9v/eGNrhdzA/59mEhE8ffq03zWtkv3t27cYOXIkli9fjrq6OjQ2NmLbtm0YNGgQ6urqLJ8fABwOBx49eoTGxkaUlZVh2LBhOHHihLKZ379/D03TcO3aNYgIKioqoGmax+/p/q7vT6yQv729HZqm4dSpUxAR3L17F5qm4f3795bM3NHRgdmzZ2PatGloamryOP+fPkzyt4V672bPkXqp3ldf/s6deqneUyBzqlGqdwYYm2/1skJPgLF52CjVOwt0jtZL9b4A/XO3UaHeldnzul5W6EvvfG+E6j2ZeU8QspsQIuL1VV5e3nvM169fsXXrVkRHRyMiIgLLli3z+IeruLjY6xrjxo3zeV5//6b19PRgz549iIuLw5AhQ5CRkYHGxkaPYzZs2OD1/P58qkn1/OXl5T4zWC3r48ePMW/ePMTExGDIkCFISEhAdnY2XC5Xv2tbIX9f/t4Mqp770aNHmD17NoYPH46hQ4di8uTJOHTokF//wUD17MD/nwwrLCyE3W5HVFQUFixYAKfTafncALB69WqkpaX1u547K2R/8OABMjMzERMTg6ioKKSkpOD69ev/TP5169YhJiYGgwcPRnJyMs6ePat0Zl+/p4uLi/2+Pqvn93V+9wxWyvzrE2HeXi9fvux3/b8l1Hs3e47US/W++jJrE0L1ngKZU41SvTPA2HyrlxV6AozNw0ZZobNA5mi9rNCX3rnbqFDvyux5XS8r9KV3vjdC9Z7MvCcIA0x+4hkREREREREREREREf2TzHu0PBERERERERERERER/dO4CUFERERERERERERERKbgJgQREREREREREREREZmCmxBERERERERERERERGQKbkIQEREREREREREREZEpuAlBRERERERERERERESm4CYEERERERERERERERGZgpsQRERERERERERERERkCm5CEBERERERERERERGRKbgJQUREREREREREREREpuAmBBERERERERERERERmYKbEEREREREREREREREZIr/AEd/X61rZzTxAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 12 + }, + { + "cell_type": "markdown", + "id": "9f02f5343e4c355d", + "metadata": {}, + "source": [ + "## 5 — Where to go next\n", + "\n", + "* **NB 07 — `07_inference.ipynb`** — the portal-facing API:\n", + " `load_bundle(...)` plus a `predict(lat, lon, date_range)` helper\n", + " that does the warehouse query → preprocess → predict pipeline.\n", + "* **`inspection/` notebooks** — one question per notebook, built on\n", + " top of `load_bundle` + the same plot helpers shown above. See the\n", + " inventory at [`notebooks/inspection/README.md`](../inspection/README.md).\n", + "* **Paper recomputation** — for a full worked example using these\n", + " same abstractions (with a paper-specific calibration step layered\n", + " on top), see\n", + " [`../papers/mukiibi_mikelson_2026/01_recomputation.ipynb`](../papers/mukiibi_mikelson_2026/01_recomputation.ipynb).\n", + "\n", + "The discipline going forward:\n", + "\n", + "| Change | What ripples |\n", + "|---|---|\n", + "| New splitter strategy | New `Splitter` subclass. Trainer + Evaluator signatures unchanged. |\n", + "| New metric | New `Metric` subclass. Pass it in `Evaluator(metrics=...)`. |\n", + "| New evaluation plot | New helper in `susse.evaluation.plots`. Inspection notebooks pick it up immediately. |\n", + "| Multi-bundle comparison | Build a Trainer→bundle dict and feed each bundle's predictions into Evaluator with a different label. |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/07_inference.ipynb b/notebooks/tutorial/07_inference.ipynb new file mode 100644 index 00000000..bf26b0ca --- /dev/null +++ b/notebooks/tutorial/07_inference.ipynb @@ -0,0 +1,46447 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ae783ff6c4a0fe21", + "metadata": {}, + "source": [ + "# 07 — Inference: from bundle on disk to deployment-ready predictions\n", + "\n", + "The end of the tutorial chain. Earlier notebooks built the training\n", + "dataset, trained models, packaged them into bundles, and evaluated\n", + "them. This notebook shows the **deployment-facing API**:\n", + "\n", + "```python\n", + "predictor = Predictor.from_bundle_dir(bundle_dir)\n", + "df = predictor.predict(\n", + " coords=[(0.333542, 32.56863), (3.04679, 30.91350), ...],\n", + " start_date=date(2024, 1, 1),\n", + " end_date=date(2024, 12, 31),\n", + ")\n", + "# → long DataFrame: lat, lon, geohash5, date, ..., y_pred_kwh_m2_day\n", + "```\n", + "\n", + "This is the call site the companion `Irradiation_Portal` Flask app\n", + "mirrors. If you change the bundle, you don't need to update the\n", + "portal — re-running this notebook is the smoke test.\n", + "\n", + "## What's new in this notebook\n", + "\n", + "```text\n", + "Predictor (susse.inference)\n", + " bundle: TrainedBundle\n", + " service: FeatureService\n", + " on_cache_miss: \"raise\" | \"fetch\"\n", + " predict(coords, start_date, end_date) → DataFrame\n", + "\n", + "PredictionRequest (dataclass — validated input)\n", + " coords, start_date, end_date\n", + "```\n", + "\n", + "## Notebook structure\n", + "\n", + "| Step | What you'll see |\n", + "|---|---|\n", + "| 0 | Setup + load the paper bundle from `data/bundles/mukiibi_mikelson_2026/` |\n", + "| 1 | Single-point prediction: Kampala over 2024, with NASA / CAMS overlaid |\n", + "| 2 | Grid prediction: the Uganda 2024 inference cache (~1961 cells × 365 days) |\n", + "| 3 | Annual aggregation per cell |\n", + "| 4 | Folium choropleth — what the portal renders |\n", + "| 5 | Cache-miss diagnostics + the Phase B (on-demand fetch) follow-up |\n", + "\n", + "## Two design choices worth knowing\n", + "\n", + "* **The bundle is the source of truth.** `Predictor` reads\n", + " `bundle.source_manifest.feature_selection` to know which NASA / CAMS\n", + " variable IDs to assemble. Training and inference can never drift\n", + " out of sync.\n", + "* **Cache miss is loud by default.** If you ask for a `(geohash5,\n", + " date)` pair that isn't in the warehouse, you get a\n", + " `RuntimeError` naming the missing cells — not a NaN, not a silent\n", + " empty row. Phase B will add the optional on-demand NASA POWER +\n", + " CAMS fetch path; for now, queries must hit cached cells." + ] + }, + { + "cell_type": "markdown", + "id": "72d036d55b1d7104", + "metadata": {}, + "source": [ + "### Inputs, outputs, and prerequisites\n", + "\n", + "| | |\n", + "|---|---|\n", + "| **Inputs** | The paper bundle at `data/bundles/mukiibi_mikelson_2026/`. The Uganda 2024 inference grid in the warehouse (the green-rectangle layer in `notebooks/tutorial/01_data_overview.ipynb`). |\n", + "| **Outputs** | None on disk — a long DataFrame in memory and a folium map. |\n", + "| **Prereqs** | BigQuery read access (`gcloud auth application-default login` locally; the Colab bootstrap handles it). The bundle must exist; run `notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb` once if not. |\n", + "| **Wall time** | ~30 seconds for §1 (one cell × 365 days), ~1 minute for §2 (whole Uganda grid). |" + ] + }, + { + "cell_type": "code", + "id": "4a6766928eba8e80", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:42:53.843529165Z", + "start_time": "2026-05-13T10:42:53.792001702Z" + } + }, + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools → Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set — trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "9ab76ba88fdc6e25", + "metadata": {}, + "source": [ + "## 0 — Setup" + ] + }, + { + "cell_type": "code", + "id": "368c5840e1a028a5", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:42:54.641292984Z", + "start_time": "2026-05-13T10:42:53.845418643Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "from datetime import date\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "_HERE = Path.cwd().resolve()\n", + "_PROJECT_ROOT = _HERE\n", + "while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / \"src\" / \"susse\").exists():\n", + " _PROJECT_ROOT = _PROJECT_ROOT.parent\n", + "sys.path.insert(0, str(_PROJECT_ROOT / \"src\"))\n", + "\n", + "# Load .env so CAMS_EMAIL (used by the fetch-mode demo in §5) is\n", + "# picked up automatically. One helper across all notebooks.\n", + "from susse.env import load_project_env\n", + "load_project_env()\n", + "\n", + "print(f\"project root: {_PROJECT_ROOT}\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "project root: /home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "b6383b88dc4979b8", + "metadata": {}, + "source": [ + "### Load the bundle\n", + "\n", + "`Predictor.from_bundle_dir` is the one-liner the portal uses too. It\n", + "loads the trained model, the feature spec, the source manifest, and\n", + "opens a BigQuery client against the project default warehouse.\n", + "\n", + "The `providers` argument supplies an elevation provider for\n", + "`AltitudeFeature` — pvlib's `lookup_altitude()` queries Open-Elevation\n", + "on the fly, so no warehouse trip is needed for that derived\n", + "feature." + ] + }, + { + "cell_type": "code", + "id": "1ff919ee92df717", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:42:56.850328119Z", + "start_time": "2026-05-13T10:42:54.695390033Z" + } + }, + "source": [ + "from susse.inference import Predictor\n", + "from susse.preprocessing import PvlibElevationProvider\n", + "\n", + "BUNDLE_DIR = (_PROJECT_ROOT / \"data\" / \"bundles\" / \"mukiibi_mikelson_2026\").resolve()\n", + "assert BUNDLE_DIR.exists(), (\n", + " f\"Bundle missing at {BUNDLE_DIR}. Run \"\n", + " f\"notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb \"\n", + " f\"end-to-end first.\"\n", + ")\n", + "\n", + "predictor = Predictor.from_bundle_dir(\n", + " BUNDLE_DIR,\n", + " providers={\"altitude\": PvlibElevationProvider()},\n", + " on_cache_miss=\"raise\",\n", + ")\n", + "print(f\"Bundle: {BUNDLE_DIR.name}\")\n", + "print(f\"Trained at: {predictor.bundle.metadata.created_at_utc}\")\n", + "print(f\"Splitter: {predictor.bundle.metadata.splitter_name}\")\n", + "print(f\"Val RMSE: {predictor.bundle.metadata.val_metrics.rmse:.3f} \"\n", + " f\"(val MAE {predictor.bundle.metadata.val_metrics.mae:.3f})\")\n", + "print(f\"Features: {len(predictor.bundle.feature_spec.feature_columns)} raw + \"\n", + " f\"{len(predictor.bundle.feature_spec.derived_features)} derived\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bundle: mukiibi_mikelson_2026\n", + "Trained at: 2026-05-13T10:38:40.904804+00:00\n", + "Splitter: spatial_block[val=madagascar_location1,egypt_location1]\n", + "Val RMSE: 0.944 (val MAE 0.755)\n", + "Features: 29 raw + 4 derived\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "d81eb7ea1d5390ba", + "metadata": {}, + "source": [ + "## 1 — Single-point prediction: Kampala over 2024\n", + "\n", + "Kampala is one of the 28 training stations, so its geohash5 is\n", + "warehouse-cached for the full 2024 calendar year. The Predictor:\n", + "\n", + "1. Computes geohash5 from `(lat, lon)`\n", + "2. Issues one batched query per source (irradiance + NASA aux + CAMS aux)\n", + "3. Assembles the inference frame and applies the bundle's `FeatureSpec` (cleaners disabled, no NaN-drop)\n", + "4. Runs the regressor in a single batched call\n", + "\n", + "Result: 365 daily bias-corrected GHI predictions plus the raw NASA /\n", + "CAMS satellite columns so you can overlay them in plots without\n", + "re-querying." + ] + }, + { + "cell_type": "code", + "id": "de13289877811fdc", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:43:00.144982858Z", + "start_time": "2026-05-13T10:42:56.913713634Z" + } + }, + "source": [ + "KAMPALA_LAT = 0.333542\n", + "KAMPALA_LON = 32.56863\n", + "\n", + "kampala = predictor.predict(\n", + " coords=[(KAMPALA_LAT, KAMPALA_LON)],\n", + " start_date=date(2024, 1, 1),\n", + " end_date=date(2024, 12, 31),\n", + ")\n", + "print(f\"Predictions: {len(kampala):,} rows × {len(kampala.columns)} columns\")\n", + "print(f\"Columns of interest:\")\n", + "for c in (\"lat\", \"lon\", \"geohash5\", \"date\", \"y_pred_kwh_m2_day\",\n", + " \"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"):\n", + " if c in kampala.columns:\n", + " print(f\" {c}\")\n", + "kampala.head()" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: 366 rows × 40 columns\n", + "Columns of interest:\n", + " lat\n", + " lon\n", + " geohash5\n", + " date\n", + " y_pred_kwh_m2_day\n", + " sat_ghi_nasa_kwh_m2_day\n", + " sat_ghi_cams_kwh_m2_day\n" + ] + }, + { + "data": { + "text/plain": [ + " lat lon geohash5 date location \\\n", + "0 0.333542 32.56863 s8p1v 2024-03-29 point_0000 \n", + "1 0.333542 32.56863 s8p1v 2024-11-08 point_0000 \n", + "2 0.333542 32.56863 s8p1v 2024-08-16 point_0000 \n", + "3 0.333542 32.56863 s8p1v 2024-10-03 point_0000 \n", + "4 0.333542 32.56863 s8p1v 2024-06-27 point_0000 \n", + "\n", + " nasa_longwave_downward_irr nasa_aod_550_adj nasa_cloud_amount \\\n", + "0 9.10 0.18 73.09 \n", + "1 8.90 0.28 57.75 \n", + "2 8.83 0.33 74.75 \n", + "3 8.81 0.37 89.10 \n", + "4 8.49 0.55 73.20 \n", + "\n", + " nasa_precipitable_water nasa_airmass ... sat_dhi_nasa_kwh_m2_day \\\n", + "0 3.61 2.49 ... 2.87 \n", + "1 3.77 4.59 ... 2.24 \n", + "2 3.82 2.54 ... 2.34 \n", + "3 3.35 4.49 ... 2.45 \n", + "4 3.10 2.66 ... 2.85 \n", + "\n", + " sat_dhi_cams_kwh_m2_day sat_dni_nasa_kwh_m2_day sat_dni_cams_kwh_m2_day \\\n", + "0 2.855653 2.33 5.150239 \n", + "1 2.235785 4.83 6.161115 \n", + "2 2.650031 2.58 1.686176 \n", + "3 2.619076 2.22 4.274214 \n", + "4 3.137976 2.44 3.501640 \n", + "\n", + " kt_cams doy_sin doy_cos altitude_m longitude y_pred_kwh_m2_day \n", + "0 0.853184 0.998195 0.060049 1202.0 32.56863 5.785791 \n", + "1 0.916903 -0.799839 0.600214 1202.0 32.56863 6.092598 \n", + "2 0.595957 -0.697944 -0.716152 1202.0 32.56863 3.803435 \n", + "3 0.790818 -0.999668 0.025748 1202.0 32.56863 5.231748 \n", + "4 0.848168 0.085731 -0.996318 1202.0 32.56863 4.926024 \n", + "\n", + "[5 rows x 40 columns]" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "baad1b331196353c", + "metadata": {}, + "source": [ + "### Time-series visualisation\n", + "\n", + "The same convention as NB 06: the model's prediction is rendered\n", + "prominently with markers; the raw NASA / CAMS satellite estimates are\n", + "faint background lines so the visual contrast surfaces which series\n", + "the model *produced* vs the satellite *inputs* it bias-corrected." + ] + }, + { + "cell_type": "code", + "id": "3be19bb84bd12a92", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:43:00.456605377Z", + "start_time": "2026-05-13T10:43:00.272820032Z" + } + }, + "source": [ + "from susse.evaluation.plots import plot_training_fit_timeseries\n", + "\n", + "# Single-station inference view — no observed series (we don't have\n", + "# ground GHI for arbitrary lat/lon at deploy time). The prediction is\n", + "# rendered prominently; NASA / CAMS sit faint in the background.\n", + "ts = kampala.copy()\n", + "ts[\"date\"] = pd.to_datetime(ts[\"date\"])\n", + "ts[\"location\"] = \"Kampala\"\n", + "\n", + "plot_training_fit_timeseries(\n", + " ts,\n", + " observed_column=None,\n", + " prediction_series={\"RF (bias-corrected)\": \"y_pred_kwh_m2_day\"},\n", + " reference_series={\n", + " \"NASA\": \"sat_ghi_nasa_kwh_m2_day\",\n", + " \"CAMS\": \"sat_ghi_cams_kwh_m2_day\",\n", + " },\n", + " location_column=\"location\",\n", + " n_stations=1,\n", + ")" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "63c7204ee4f5f2e5", + "metadata": {}, + "source": [ + "## 2 — Grid prediction: the Uganda 2024 inference cache\n", + "\n", + "The warehouse caches NASA + CAMS feature rows for every 0.05° geohash\n", + "cell in a Uganda bounding box across 2024. `01_data_overview.ipynb`\n", + "renders these as a green rectangle on its coverage map; here we\n", + "query the cell centres and predict for the whole grid.\n", + "\n", + "The query below pulls the cell centroids straight from BigQuery so\n", + "we never duplicate the grid definition. ~2k cells × 365 days = ~700k\n", + "predictions; the bundle's RF is batched, so this takes about a\n", + "minute even on a workstation." + ] + }, + { + "cell_type": "code", + "id": "d72b9ecefb7a09dd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:43:03.584853411Z", + "start_time": "2026-05-13T10:43:00.603028185Z" + } + }, + "source": [ + "from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig\n", + "from susse.warehouse_ops.io.config import TableRefs\n", + "\n", + "bq = BigQueryClient(config=WarehouseConfig())\n", + "tables = TableRefs(config=bq.config)\n", + "\n", + "# Cell centres of the Uganda 2024 grid (the 2024-only NASA cells the\n", + "# warehouse caches for portal inference, excluding ground stations).\n", + "grid_cells = bq.query(f\"\"\"\n", + "SELECT DISTINCT geohash5,\n", + " ANY_VALUE(latitude) AS lat,\n", + " ANY_VALUE(longitude) AS lon\n", + "FROM `{tables.irradiance_daily}`\n", + "WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-12-31')\n", + " AND source = 'NASA'\n", + " AND geohash5 NOT IN (SELECT DISTINCT geohash5 FROM `{tables.ground_measurements}`)\n", + "GROUP BY geohash5\n", + "ORDER BY lat, lon\n", + "\"\"\")\n", + "print(f\"Uganda 2024 grid: {len(grid_cells):,} cells\")\n", + "grid_cells.head()" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uganda 2024 grid: 1,961 cells\n" + ] + }, + { + "data": { + "text/plain": [ + " geohash5 lat lon\n", + "0 kxv05 -1.38214 29.671499\n", + "1 kxv24 -1.38214 29.971499\n", + "2 kxv0e -1.28214 29.671499\n", + "3 kxv0t -1.28214 29.771499\n", + "4 kxv0x -1.28214 29.871499" + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "f1e4cb44f23aae96", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:46:52.526058708Z", + "start_time": "2026-05-13T10:43:04.111499159Z" + } + }, + "source": [ + "uganda_2024 = predictor.predict(\n", + " coords=list(zip(grid_cells[\"lat\"], grid_cells[\"lon\"])),\n", + " start_date=date(2024, 1, 1),\n", + " end_date=date(2024, 12, 31),\n", + ")\n", + "print(f\"Daily predictions: {len(uganda_2024):,} rows \"\n", + " f\"({uganda_2024[['lat', 'lon']].drop_duplicates().shape[0]:,} unique cells × \"\n", + " f\"{uganda_2024['date'].nunique()} days)\")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Daily predictions: 717,726 rows (1,961 unique cells × 366 days)\n" + ] + } + ], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "27cfdf97486d1c19", + "metadata": {}, + "source": [ + "## 3 — Annual aggregation per cell\n", + "\n", + "The portal renders an **annual-mean** GHI choropleth. Reduce the\n", + "daily long-format frame to one row per cell carrying its 2024 mean\n", + "predicted GHI plus the two satellite baselines for comparison." + ] + }, + { + "cell_type": "code", + "id": "a5e6420d782e08e8", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:46:53.042291153Z", + "start_time": "2026-05-13T10:46:52.817356324Z" + } + }, + "source": [ + "annual = (\n", + " uganda_2024\n", + " .groupby([\"lat\", \"lon\", \"geohash5\"], as_index=False)\n", + " .agg(\n", + " rf_mean=(\"y_pred_kwh_m2_day\", \"mean\"),\n", + " nasa_mean=(\"sat_ghi_nasa_kwh_m2_day\", \"mean\"),\n", + " cams_mean=(\"sat_ghi_cams_kwh_m2_day\", \"mean\"),\n", + " )\n", + ")\n", + "print(f\"Annual mean grid: {len(annual):,} cells\")\n", + "print(f\" RF range: {annual['rf_mean'].min():.2f} – {annual['rf_mean'].max():.2f} kWh/m²/day\")\n", + "print(f\" NASA range: {annual['nasa_mean'].min():.2f} – {annual['nasa_mean'].max():.2f}\")\n", + "print(f\" CAMS range: {annual['cams_mean'].min():.2f} – {annual['cams_mean'].max():.2f}\")\n", + "annual.head()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual mean grid: 1,961 cells\n", + " RF range: 3.79 – 5.88 kWh/m²/day\n", + " NASA range: 4.76 – 5.84\n", + " CAMS range: 3.77 – 6.48\n" + ] + }, + { + "data": { + "text/plain": [ + " lat lon geohash5 rf_mean nasa_mean cams_mean\n", + "0 -1.38214 29.671499 kxv05 4.167725 4.760191 4.446330\n", + "1 -1.38214 29.971499 kxv24 4.727444 4.760191 5.143531\n", + "2 -1.28214 29.671499 kxv0e 4.702537 4.760191 5.044503\n", + "3 -1.28214 29.771499 kxv0t 4.765926 4.760191 5.171861\n", + "4 -1.28214 29.871499 kxv0x 4.731649 4.760191 5.151556" + ], + "text/html": [ + "
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4-1.2821429.871499kxv0x4.7316494.7601915.151556
\n", + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "70e1c1bb02619e56", + "metadata": {}, + "source": [ + "## 4 — Folium choropleth — what the portal renders\n", + "\n", + "A small circle marker per cell, coloured by predicted annual GHI.\n", + "This is the static analogue of the portal's interactive heatmap; the\n", + "portal uses the same DataFrame with `folium.raster_layers.ImageOverlay`\n", + "for a rasterised version." + ] + }, + { + "cell_type": "code", + "id": "f6d28a1206985ead", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:46:57.060412106Z", + "start_time": "2026-05-13T10:46:53.354788560Z" + } + }, + "source": [ + "import folium\n", + "import branca.colormap as cm\n", + "\n", + "cmap = cm.LinearColormap(\n", + " colors=[\"#440154\", \"#3b528b\", \"#21918c\", \"#5ec962\", \"#fde725\"],\n", + " vmin=float(annual[\"rf_mean\"].min()),\n", + " vmax=float(annual[\"rf_mean\"].max()),\n", + " caption=\"RF-corrected annual mean GHI (kWh/m²/day)\",\n", + ")\n", + "\n", + "centre_lat = float(annual[\"lat\"].mean())\n", + "centre_lon = float(annual[\"lon\"].mean())\n", + "m = folium.Map(location=[centre_lat, centre_lon], zoom_start=7, control_scale=True)\n", + "for _, row in annual.iterrows():\n", + " folium.CircleMarker(\n", + " location=[row[\"lat\"], row[\"lon\"]], radius=4,\n", + " color=None, fill=True,\n", + " fillColor=cmap(row[\"rf_mean\"]), fillOpacity=0.85,\n", + " popup=folium.Popup(\n", + " f\"geohash5: {row['geohash5']}
\"\n", + " f\"RF: {row['rf_mean']:.2f} kWh/m²/day
\"\n", + " f\"NASA: {row['nasa_mean']:.2f}
\"\n", + " f\"CAMS: {row['cams_mean']:.2f}\",\n", + " max_width=240,\n", + " ),\n", + " ).add_to(m)\n", + "cmap.add_to(m)\n", + "m" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "e484b014c30634be", + "metadata": {}, + "source": [ + "## 5 — Cache misses + on-demand fetch (Phase B)\n", + "\n", + "The Predictor's default behaviour is loud-by-default: any requested\n", + "`(geohash5, date)` pair that isn't in the warehouse raises a\n", + "`RuntimeError` naming the missing cells. That's what the\n", + "`on_cache_miss=\"raise\"` constructor mode you've been using does." + ] + }, + { + "cell_type": "code", + "id": "fb3b7d028855275a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:47:01.245302300Z", + "start_time": "2026-05-13T10:46:58.167374053Z" + } + }, + "source": [ + "# A point in the Indian Ocean — definitely not in any cache.\n", + "try:\n", + " predictor.predict(\n", + " coords=[(-5.0, 70.0)],\n", + " start_date=date(2024, 1, 1), end_date=date(2024, 1, 7),\n", + " )\n", + "except RuntimeError as e:\n", + " print(\"Cache miss surfaced as expected:\")\n", + " print(str(e)[:400], \"...\")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cache miss surfaced as expected:\n", + "Predictor: 7 warehouse cell-day pairs are missing. Apply the corresponding warehouse-ingest migration before calling predict, or construct the Predictor with on_cache_miss='fetch' to fetch on demand (requires CAMS_EMAIL + BQ write perms). First few missing (geohash5, date): [('mx1g8', datetime.date(2024, 1, 1)), ('mx1g8', datetime.date(2024, 1, 2)), ('mx1g8', datetime.date(2024, 1, 3)), ('mx1g8', ...\n" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "2e7d9d6766b7b43e", + "metadata": {}, + "source": [ + "### Switching to `on_cache_miss=\"fetch\"`\n", + "\n", + "With `on_cache_miss=\"fetch\"`, the predictor instead invokes the\n", + "existing `NasaPowerSatelliteJob` and `CamsSatelliteJob` to fetch +\n", + "persist the missing cells before re-querying and predicting. The\n", + "same code path the batch-ingest migrations use — same schema, same\n", + "idempotency, same hardening — wrapped one layer up.\n", + "\n", + "**Prerequisites for fetch mode:**\n", + "\n", + "* `CAMS_EMAIL` env var set to a registered [SoDa-Pro](https://www.soda-pro.com/web-services/radiation/cams-radiation-service)\n", + " account. The constructor raises `ValueError` upfront if missing.\n", + "* BigQuery write access on the warehouse dataset. Failure surfaces at\n", + " the first write.\n", + "* Implicit cap of **30 cells per `predict()`** to respect CAMS's free-tier\n", + " daily quota — exceeding it raises rather than silently throttling.\n", + "\n", + "A typical interactive single-coord query for an uncached cell takes\n", + "~25-40 seconds: NASA POWER fetch (~5s) + CAMS fetch (~10s) + warehouse\n", + "write (~5-10s) + warehouse re-query (~3s) + predict (~1s). Subsequent\n", + "queries for the same cell hit the warehouse and run in ~3 seconds." + ] + }, + { + "cell_type": "code", + "id": "af45f7abefff9250", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-13T10:47:06.782417157Z", + "start_time": "2026-05-13T10:47:01.479983507Z" + } + }, + "source": [ + "import os\n", + "\n", + "# `load_dotenv()` was already called in §0, so a CAMS_EMAIL set in\n", + "# `.env` is already in os.environ here. Either path (shell export or\n", + "# .env entry) makes this cell light up.\n", + "if not os.environ.get(\"CAMS_EMAIL\"):\n", + " print(\"CAMS_EMAIL is not set; skipping the fetch-mode demo.\\n\"\n", + " \"Either `export CAMS_EMAIL=you@example.com` in the shell, or\\n\"\n", + " \"add `CAMS_EMAIL=you@example.com` to a `.env` at the project\\n\"\n", + " \"root, then restart the kernel.\")\n", + "else:\n", + " fetch_predictor = Predictor.from_bundle_dir(\n", + " BUNDLE_DIR,\n", + " providers={\"altitude\": PvlibElevationProvider()},\n", + " on_cache_miss=\"fetch\",\n", + " )\n", + " # A single uncached coord — Lake Albert, on the Uganda/DRC border.\n", + " # If the Uganda 2024 grid covered it already, this hits the cache;\n", + " # otherwise the fetch path fires.\n", + " lake_albert = fetch_predictor.predict(\n", + " coords=[(1.6, 30.85)],\n", + " start_date=date(2024, 6, 1),\n", + " end_date=date(2024, 6, 7),\n", + " )\n", + " print(f\"Predictions: {len(lake_albert)} rows\")\n", + " print(lake_albert[[\"lat\", \"lon\", \"date\", \"y_pred_kwh_m2_day\"]].to_string(index=False))" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n", + "/home/jan/Dropbox/personal_projects/Irradiation_project/Solar_irradiation/.venv/lib/python3.12/site-packages/google/cloud/bigquery/table.py:2086: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: 7 rows\n", + " lat lon date y_pred_kwh_m2_day\n", + " 1.6 30.85 2024-06-01 5.284933\n", + " 1.6 30.85 2024-06-02 5.657470\n", + " 1.6 30.85 2024-06-07 5.963727\n", + " 1.6 30.85 2024-06-06 5.539057\n", + " 1.6 30.85 2024-06-03 4.163720\n", + " 1.6 30.85 2024-06-05 5.612868\n", + " 1.6 30.85 2024-06-04 3.948512\n" + ] + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "id": "3790a813042f103e", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "* **`Irradiation_Portal`** — the Flask portal already loads a bundle\n", + " via `load_bundle(...)`. The next portal release should switch to\n", + " `Predictor.from_bundle_dir(...)` so the portal and the tutorial\n", + " evolve together. The portal also gets free warehouse caching: any\n", + " user query for an uncached cell, while `on_cache_miss=\"fetch\"`,\n", + " populates the warehouse for the next user.\n", + "* **Inspection notebooks** — `notebooks/inspection/` is the right\n", + " place for ad-hoc deployment-readiness checks (per-region bias,\n", + " out-of-distribution probe coordinates, model-vs-model overlays).\n", + "\n", + "The discipline going forward:\n", + "\n", + "| Change | What ripples |\n", + "|---|---|\n", + "| New model kind | New `BaseRegressor` subclass + params. Predictor signature unchanged. |\n", + "| New satellite source | New `Source` enum value + ingest job + entry in `_LONG_AUX_TABLES`. Predictor signature unchanged. |\n", + "| New derived feature | New `DerivedFeature` subclass; FeatureSpec gains a tuple entry. Predictor signature unchanged. |\n", + "| Cache-miss policy change | `Predictor.on_cache_miss` is a knob; portal flips it. |\n", + "\n", + "The `Predictor` is the unit of deployment. The portal owns a single\n", + "instance and reuses it across requests. If something here feels\n", + "missing — a request-level metric you want logged, an artifact\n", + "provenance field that should travel into every response — extend the\n", + "`Predictor` (not the call site)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial/_build_01_data_overview.py b/notebooks/tutorial/_build_01_data_overview.py new file mode 100644 index 00000000..7cbbaaa6 --- /dev/null +++ b/notebooks/tutorial/_build_01_data_overview.py @@ -0,0 +1,402 @@ +"""Generator for ``tutorial/01_data_overview.ipynb``. + +Edit the cell strings here, then regenerate with:: + + python notebooks/tutorial/_build_01_data_overview.py +""" + +from __future__ import annotations + +import json +from pathlib import Path + +NB_PATH = Path("notebooks/tutorial/01_data_overview.ipynb").resolve() + + +def md(text: str) -> dict: + return {"cell_type": "markdown", "metadata": {}, "source": _split_lines(text)} + + +def code(text: str) -> dict: + return { + "cell_type": "code", + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": _split_lines(text), + } + + +def _split_lines(text: str) -> list[str]: + lines = text.splitlines(keepends=True) + if lines and lines[-1].endswith("\n"): + lines[-1] = lines[-1].rstrip("\n") + return lines + + +COLAB_BOOTSTRAP = """\ +# ============================================================ +# Colab bootstrap (no-op when run locally). +# ------------------------------------------------------------ +# First-time setup on Colab: +# 1. Create a GitHub Personal Access Token (PAT) at +# https://github.com/settings/tokens with `repo` scope. +# The repository is private, so the clone needs this token +# (or an SSH key Colab knows about, which is more fiddly). +# 2. Add the token under Tools → Secrets in Colab with name +# `GITHUB_PAT` and toggle "Notebook access" on. +# 3. Sign in with a Google account that has BigQuery read access +# to `solar-irradiation-estimation` when prompted. +# ============================================================ +import os +import sys + +if "google.colab" in sys.modules: + REPO = "Marconi-Lab/Solar_irradiation" + BRANCH = "jm/add_model" + + if not os.path.exists("/content/Solar_irradiation/.git"): + try: + from google.colab import userdata + token = userdata.get("GITHUB_PAT") + clone_url = f"https://{token}@github.com/{REPO}.git" + print("Cloning with Colab secret 'GITHUB_PAT'.") + except Exception: + clone_url = f"git@github.com:{REPO}.git" + print( + "Colab secret 'GITHUB_PAT' not set — trying SSH. If the " + "clone fails, follow the PAT setup steps above and re-run." + ) + !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation + + %cd /content/Solar_irradiation + !pip install -q -e . 2>&1 | tail -3 + + from google.colab import auth + auth.authenticate_user() + !gcloud config set project solar-irradiation-estimation 2>/dev/null + print("Colab setup complete.") +""" + + +CELLS: list[dict] = [ + md( + """# 01 — What's in the warehouse + +A first look at the SuSSE warehouse — what tables exist, what each one +holds, and where on the map the data lives. Read this before any other +tutorial notebook. Everything downstream queries the warehouse this +notebook describes. + +The notebook is **read-only**: every cell issues SELECTs only. Safe to +re-run as often as you like. + +## Three things by the end + +1. A mental model of the warehouse — six tables, three storage shapes, + one catalog. +2. A map showing exactly where SuSSE has ground measurements, + validation sites, and the inference grid. +3. Per-source coverage tables, so the next notebook + (`02_query_the_warehouse.ipynb`) doesn't surprise you with empty + results.""" + ), + code(COLAB_BOOTSTRAP), + md("## 0 — Setup"), + code( + """%load_ext autoreload +%autoreload 2 + +import sys +from pathlib import Path + +import folium +import pandas as pd +import pygeohash +from folium import CircleMarker, FeatureGroup, LayerControl, Rectangle + +# Locate project root by walking up until we see src/susse/. +_HERE = Path.cwd().resolve() +_PROJECT_ROOT = _HERE +while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / "src" / "susse").exists(): + _PROJECT_ROOT = _PROJECT_ROOT.parent +sys.path.insert(0, str(_PROJECT_ROOT / "src")) + +from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population import ( + PhysicalStorage, Source, VariableCatalog, variables_to_dataframe, +) + +bq = BigQueryClient(config=WarehouseConfig()) +tables = TableRefs(config=bq.config) +print(f"warehouse: {bq.config.project_id}.{bq.config.dataset}")""" + ), + md( + """## 1 — The tables, at a glance + +| Table | Shape | What it stores | +|---|---|---| +| `dim_variable` | catalog | One row per `(variable_id, source)` — unit, valid range, spatial resolution, which physical table holds the value | +| `irradiance_daily` | wide | Per-`(date, geohash5, source)` daily GHI / DHI / DNI for NASA POWER and CAMS | +| `nasa_daily_vars_long` | long | NASA POWER auxiliary variables (`temperature`, `aod_550`, `cloud_amount`, …) keyed by `(date, geohash5, variable_id)` | +| `cams_daily_vars_long` | long | CAMS auxiliary variables (`ghi_clear`, `bhi`, `dni_clear`, …) | +| `merra_daily_vars_long` | long | MERRA-2 reanalysis variables (aerosol decomposition, precipitable water) | +| `modis_observations` | observation-keyed | MODIS composites keyed by `(date, geohash5, product_id, band_id)` | +| `ground_measurements` | curated long | Daily ground GHI per station with QC level | +| `ground_measurements_raw` | uncurated long | Raw partner CSVs before curation | + +**Wide vs long** is chosen per source: GHI / DHI / DNI go in the wide +`irradiance_daily` because they're a tightly coupled trio always +queried together; everything else lives in a long-format companion so +adding a new variable doesn't require a schema migration. + +`dim_variable.physical_storage` tags every catalog row with which of +the three storage shapes it lives in (`long_format` / `irradiance_wide` +/ `modis_observations`). `FeatureSelection` (NB 02) uses this tag to +reject typos at construction with a clear error. + +For the full data lineage (which job writes which table, what's derived +from what), see [`warehouse/README.md`](../../warehouse/README.md).""" + ), + md("### Browsing the variable catalog"), + code( + """# The catalog is hand-maintained in code, then upserted into dim_variable. +# Browse it without hitting BigQuery: +catalog_df = variables_to_dataframe(VariableCatalog.all_variables()) +print(f"Catalog: {len(catalog_df)} variables across {catalog_df['source'].nunique()} sources.") +print() +print("Counts by source × storage:") +print( + catalog_df.groupby(["source", "physical_storage"]).size().unstack(fill_value=0) +)""" + ), + code( + """# Drill into one source — list variable_ids and their storage. +# Repeat with Source.CAMS, Source.MERRA_2, Source.MODIS as needed. +nasa = catalog_df[catalog_df["source"] == Source.NASA_POWER.value] +print(f"NASA POWER ({len(nasa)} variables):") +nasa[["variable_id", "display_name", "unit", "physical_storage"]]""" + ), + md( + """## 2 — Coverage on the map + +Three layers — toggle from the layer control in the top right. + +| Marker | What it is | Source | +|---|---|---| +| **Blue dots** (sized by coverage) | Ground-measurement stations (training labels) | `ground_measurements` | +| **Red / orange / green dots** | Katongole 2023 validation stations, colour by warehouse coverage status | `data/external_references/katongole_2023_monthly.csv` + `irradiance_daily` | +| **Green rectangle** | Uganda 2024 inference grid (portal-coverage cache) | `irradiance_daily` filtered to 2024-only cells | + +Click any marker for details.""" + ), + code( + """# Ground stations + their per-source satellite coverage. +ground = bq.query(f\"\"\" +WITH ground_summary AS ( + SELECT location, + ANY_VALUE(lat) AS lat, + ANY_VALUE(lon) AS lon, + ANY_VALUE(geohash5) AS geohash5, + MIN(date) AS min_date, + MAX(date) AS max_date, + COUNT(*) AS n_rows, + COUNTIF(qc_level = 'pass') AS n_qc_passed + FROM `{tables.ground_measurements}` + GROUP BY location +), +sat_coverage AS ( + SELECT geohash5, + COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days, + COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days + FROM `{tables.irradiance_daily}` + GROUP BY geohash5 +) +SELECT g.*, COALESCE(s.n_nasa_days, 0) AS n_nasa_days, + COALESCE(s.n_cams_days, 0) AS n_cams_days +FROM ground_summary g +LEFT JOIN sat_coverage s USING (geohash5) +ORDER BY n_rows DESC +\"\"\") +print(f"Ground stations: {len(ground)}") +ground.head()""" + ), + code( + """# Katongole validation stations + 2017-2022 coverage status. +KATONGOLE_CSV = ( + _PROJECT_ROOT / "data" / "external_references" / "katongole_2023_monthly.csv" +) +katongole = pd.read_csv(KATONGOLE_CSV) +katongole["geohash5"] = [ + pygeohash.encode(lat, lon, precision=5) + for lat, lon in zip(katongole["latitude"], katongole["longitude"]) +] +_gh_quoted = ", ".join(f"'{g}'" for g in katongole["geohash5"].unique()) +cov_2017_22 = bq.query(f\"\"\" +SELECT geohash5, + COUNT(DISTINCT IF(source='NASA', date, NULL)) AS n_nasa_days, + COUNT(DISTINCT IF(source='CAMS', date, NULL)) AS n_cams_days +FROM `{tables.irradiance_daily}` +WHERE date BETWEEN DATE('2017-01-01') AND DATE('2022-12-31') + AND geohash5 IN ({_gh_quoted}) +GROUP BY geohash5 +\"\"\") +katongole = katongole.merge(cov_2017_22, on="geohash5", how="left").fillna( + {"n_nasa_days": 0, "n_cams_days": 0} +).astype({"n_nasa_days": int, "n_cams_days": int}) +_N_EXPECTED = (pd.Timestamp("2022-12-31") - pd.Timestamp("2017-01-01")).days + 1 +_FULL = int(0.95 * _N_EXPECTED) +katongole["coverage_status"] = [ + "full" if (n_nasa >= _FULL and n_cams >= _FULL) + else "missing" if (n_nasa == 0 and n_cams == 0) + else "partial" + for n_nasa, n_cams in zip(katongole["n_nasa_days"], katongole["n_cams_days"]) +] +print( + f"Katongole stations: {len(katongole)} — " + + ", ".join( + f"{k}: {v}" for k, v in katongole['coverage_status'].value_counts().items() + ) +)""" + ), + code( + """# Uganda 2024 inference-grid bbox. +uganda_grid = bq.query(f\"\"\" +SELECT MIN(latitude) AS min_lat, + MAX(latitude) AS max_lat, + MIN(longitude) AS min_lon, + MAX(longitude) AS max_lon, + COUNT(DISTINCT geohash5) AS n_cells, + MIN(date) AS min_date, + MAX(date) AS max_date +FROM `{tables.irradiance_daily}` +WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-12-31') + AND source = 'NASA' + AND geohash5 NOT IN (SELECT DISTINCT geohash5 FROM `{tables.ground_measurements}`) +\"\"\") +uganda_grid""" + ), + code( + """def _ground_popup(row: pd.Series) -> str: + return ( + f\"{row.location}
\" + f\"lat={row.lat:.3f}, lon={row.lon:.3f} (geohash5 {row.geohash5})
\" + f\"Ground: {row.n_qc_passed:,} QC-passed days \" + f\"({row.min_date} → {row.max_date})
\" + f\"NASA: {row.n_nasa_days:,} days
\" + f\"CAMS: {row.n_cams_days:,} days\" + ) + + +def _katongole_popup(row: pd.Series) -> str: + if row.coverage_status == "full": + cov = "full 2017-2022 coverage" + elif row.coverage_status == "missing": + cov = ( + \"no warehouse data yet — \" + \"run migration A12\" + ) + else: + cov = ( + f\"partial: \" + f\"NASA {row.n_nasa_days} days, CAMS {row.n_cams_days} days\" + ) + return ( + f\"{row.location}
\" + f\"lat={row.latitude:.3f}, lon={row.longitude:.3f}
\" + f\"{cov}\" + ) + + +m = folium.Map(location=[5.0, 25.0], zoom_start=4, control_scale=True) + +ground_layer = FeatureGroup(name=f"Ground stations ({len(ground)})", show=True) +for _, row in ground.iterrows(): + radius = 4 + int(0.001 * row.n_qc_passed) + CircleMarker( + location=[row.lat, row.lon], + radius=min(radius, 14), + popup=folium.Popup(_ground_popup(row), max_width=320), + tooltip=row.location, + color="#1f77b4", weight=1, fill=True, + fillColor="#1f77b4", fillOpacity=0.8, + ).add_to(ground_layer) +ground_layer.add_to(m) + +katongole_layer = FeatureGroup( + name=f"Katongole validation ({len(katongole)})", show=True, +) +_status_color = {"full": "#2ca02c", "partial": "#ff7f0e", "missing": "#d62728"} +for _, row in katongole.iterrows(): + color = _status_color[row.coverage_status] + CircleMarker( + location=[row.latitude, row.longitude], radius=4, + popup=folium.Popup(_katongole_popup(row), max_width=320), + tooltip=f\"{row.location} — {row.coverage_status}\", + color=color, weight=1, fill=True, + fillColor=color, fillOpacity=0.85, + ).add_to(katongole_layer) +katongole_layer.add_to(m) + +ug_layer = FeatureGroup(name="Uganda 2024 inference grid", show=True) +ug_row = uganda_grid.iloc[0] +Rectangle( + bounds=[ + [float(ug_row.min_lat), float(ug_row.min_lon)], + [float(ug_row.max_lat), float(ug_row.max_lon)], + ], + color="#2ca02c", weight=2, fill=True, + fillColor="#2ca02c", fillOpacity=0.08, + popup=folium.Popup( + f\"Uganda 2024 inference grid
\" + f\"{int(ug_row.n_cells):,} cells × 365 days
\" + f\"{ug_row.min_date} → {ug_row.max_date}\", + max_width=320, + ), + tooltip=\"Uganda 2024 inference grid\", +).add_to(ug_layer) +ug_layer.add_to(m) + +LayerControl(collapsed=False).add_to(m) +m""" + ), + md( + """## 3 — Where to go next + +* [`02_query_the_warehouse.ipynb`](02_query_the_warehouse.ipynb) — build + a `TrainingDataset` from the warehouse using `FeatureSelection` / + `FeatureService`, plus an example of dropping down to raw SQL when + needed. +* [`03_preprocessing.ipynb`](03_preprocessing.ipynb) → 04 → 05 — the + rest of the pipeline: preprocessing, model factory, trainer. +* [`../../warehouse/extending_the_warehouse.ipynb`](../../warehouse/extending_the_warehouse.ipynb) + — for contributors who need to add new variables, sources, or + stations to the warehouse. +* [`../papers/mukiibi_mikelson_2026/01_recomputation.ipynb`](../papers/mukiibi_mikelson_2026/01_recomputation.ipynb) + — a complete worked example, paper-faithful Random Forest GHI bias + correction trained against the data shown above and validated against + the Katongole network.""" + ), +] + + +def main() -> None: + nb = { + "cells": CELLS, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3", + }, + "language_info": {"name": "python", "version": "3.12"}, + }, + "nbformat": 4, + "nbformat_minor": 5, + } + NB_PATH.write_text(json.dumps(nb, indent=1) + "\n") + print(f"wrote {NB_PATH} ({len(CELLS)} cells)") + + +if __name__ == "__main__": + main() diff --git a/notebooks/tutorial/_build_06_evaluation.py b/notebooks/tutorial/_build_06_evaluation.py new file mode 100644 index 00000000..370ba272 --- /dev/null +++ b/notebooks/tutorial/_build_06_evaluation.py @@ -0,0 +1,540 @@ +"""Generator for ``tutorial/06_evaluation.ipynb``. + +Edit the cell strings here, then regenerate with:: + + python notebooks/tutorial/_build_06_evaluation.py +""" + +from __future__ import annotations + +import json +from pathlib import Path + +NB_PATH = Path("notebooks/tutorial/06_evaluation.ipynb").resolve() + + +def md(text: str) -> dict: + return {"cell_type": "markdown", "metadata": {}, "source": _split_lines(text)} + + +def code(text: str) -> dict: + return { + "cell_type": "code", + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": _split_lines(text), + } + + +def _split_lines(text: str) -> list[str]: + lines = text.splitlines(keepends=True) + if lines and lines[-1].endswith("\n"): + lines[-1] = lines[-1].rstrip("\n") + return lines + + +COLAB_BOOTSTRAP = """\ +# ============================================================ +# Colab bootstrap (no-op when run locally). +# ------------------------------------------------------------ +# First-time setup on Colab: +# 1. Create a GitHub Personal Access Token (PAT) at +# https://github.com/settings/tokens with `repo` scope. +# The repository is private, so the clone needs this token +# (or an SSH key Colab knows about, which is more fiddly). +# 2. Add the token under Tools → Secrets in Colab with name +# `GITHUB_PAT` and toggle "Notebook access" on. +# 3. Sign in with a Google account that has BigQuery read access +# to `solar-irradiation-estimation` when prompted. +# ============================================================ +import os +import sys + +if "google.colab" in sys.modules: + REPO = "Marconi-Lab/Solar_irradiation" + BRANCH = "jm/add_model" + + if not os.path.exists("/content/Solar_irradiation/.git"): + try: + from google.colab import userdata + token = userdata.get("GITHUB_PAT") + clone_url = f"https://{token}@github.com/{REPO}.git" + print("Cloning with Colab secret 'GITHUB_PAT'.") + except Exception: + clone_url = f"git@github.com:{REPO}.git" + print( + "Colab secret 'GITHUB_PAT' not set — trying SSH. If the " + "clone fails, follow the PAT setup steps above and re-run." + ) + !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation + + %cd /content/Solar_irradiation + !pip install -q -e . 2>&1 | tail -3 + + from google.colab import auth + auth.authenticate_user() + !gcloud config set project solar-irradiation-estimation 2>/dev/null + print("Colab setup complete.") +""" + + +CELLS: list[dict] = [ + md( + """# 06 — Evaluation: splitters, metrics, evaluator, plots + +Notebook 05 produced a `TrainedBundle` on disk. This notebook works on +that bundle alone — **no training, no warehouse queries** — and walks +through the evaluation surface a downstream notebook +(`inspection/` view, paper recomputation, deployment readiness check) +would use. + +## What this notebook contributes + +```text +Splitter (ABC + 5 concrete subclasses) + name: str # configuration identifier + split(processed) → (train_idx, val_idx) + +Metric (ABC + 7 concrete subclasses) + name: str # column header in score tables + compute(y_true, y_pred) → float + +Evaluator + score(observed, predictions, splits) → DataFrame + # one row per (split × prediction); columns: n + each metric + +plots (susse.evaluation.plots) + plot_predicted_vs_observed_panels(...) + plot_per_station_metric(...) + plot_covariate_shift_kde(...) + plot_feature_importances_top_n(...) + ... +``` + +## Three architectural choices reflected here + +* **Splitter is a class, not a callable.** It carries its + configuration in fields so `bundle.metadata.splitter_name` records + what the val fold actually was — re-running with the same name + guarantees the same split. +* **Metrics are objects, not free functions.** Each + :class:`Metric` carries its display name; `Evaluator` composes them + into a score table without the call site repeating column-header + strings. +* **Evaluator is the post-training counterpart of + ``score_predictions``.** That helper produces a single + :class:`ScoreSet` on one ``(y_true, y_pred)`` pair during training; + the Evaluator scores many predictions across many splits, post-hoc. + +## Notebook structure + +| Step | What you'll see | +|---|---| +| 0 | Setup + load the NB 05 bundle + reconstruct its preprocessed dataset | +| 1 | The five concrete splitters — what each one selects | +| 2 | The seven concrete metrics — what each one means and how to read it | +| 3 | The :class:`Evaluator` — one tidy table over splits × predictions | +| 4 | Generic evaluation plots — predicted-vs-observed, per-station MBE, covariate-shift KDEs, feature importances | +| 5 | Where to go next |""" + ), + md( + """### Inputs, outputs, and prerequisites + +| | | +|---|---| +| **Inputs** | The NB 05 bundle at `data/bundles/_nb05_random_forest/` and the matching snapshot at `data/training_snapshots/susse_training_demo_v1-2024/`. | +| **Outputs** | None — this notebook is read-only with respect to artifacts. | +| **Prereqs** | NB 05 must have run successfully (it generates both inputs). If you cloned the repo fresh, run NB 05 first. | +| **Wall time** | Under a minute on a workstation. |""" + ), + code(COLAB_BOOTSTRAP), + md("## 0 — Setup"), + code( + """%load_ext autoreload +%autoreload 2 + +import sys +from datetime import date +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +_HERE = Path.cwd().resolve() +_PROJECT_ROOT = _HERE +while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / "src" / "susse").exists(): + _PROJECT_ROOT = _PROJECT_ROOT.parent +sys.path.insert(0, str(_PROJECT_ROOT / "src")) + +print(f"project root: {_PROJECT_ROOT}")""" + ), + md( + """### Load the NB 05 bundle + reconstruct its preprocessed dataset + +The bundle gives us the trained regressor and the `FeatureSpec` that +produced its inputs. Re-applying the same `Preprocessor` to the +matching snapshot yields the very dataset NB 05 trained on — without +re-training, and without the warehouse. + +NB 05's `FeatureSpec.id_columns` doesn't include `lat` / `lon` (it +didn't need them for station-LOSO), so we enrich `processed.df` with +the station coordinates from the raw dataset before exercising the +spatial splitters in §1. This is the pattern to use whenever a +bundle's frozen `FeatureSpec` is missing an id column you need +downstream — copy the column over, don't rebuild the bundle.""" + ), + code( + """from susse.datasets import load_snapshot +from susse.preprocessing import Preprocessor +from susse.training import load_bundle + +SNAPSHOT_DIR = (_PROJECT_ROOT / "data" / "training_snapshots" + / "susse_training_demo_v1-2024").resolve() +BUNDLE_DIR = (_PROJECT_ROOT / "data" / "bundles" + / "_nb05_random_forest").resolve() +for path in (SNAPSHOT_DIR, BUNDLE_DIR): + assert path.exists(), ( + f"Missing input: {path}\\n" + f"Run notebooks/tutorial/05_training.ipynb to generate it, " + f"then come back." + ) + +dataset = load_snapshot(SNAPSHOT_DIR) +bundle = load_bundle(BUNDLE_DIR) +processed = Preprocessor(bundle.feature_spec).apply(dataset) + +# Enrich with per-station coordinates from the raw dataset so the +# spatial splitters in §1 can find lat/lon. The map is one-to-one on +# `location`; pandas .map fills NaN where the key is unknown (no +# rows in this dataset, so no NaN). +station_coords = dataset.df.drop_duplicates(subset="location").set_index( + "location" +)[["lat", "lon"]] +processed.df["lat"] = processed.df["location"].map(station_coords["lat"]) +processed.df["lon"] = processed.df["location"].map(station_coords["lon"]) + +print(f"Bundle: {BUNDLE_DIR.name}") +print(f"Held-out station (NB 05 splitter): " + f"{bundle.metadata.splitter_name}") +print(f"Processed: {processed.n_rows:,} rows × " + f"{processed.n_features} features " + f"({processed.df['location'].nunique()} stations)")""" + ), + md( + """## 1 — The five concrete splitters + +Every concrete splitter implements the same `(name, split)` surface: + +```text +class Splitter(ABC): + @property def name(self) -> str: ... + def split(processed) -> (train_idx, val_idx) +``` + +Five strategies cover the common evaluation scenarios: + +| Splitter | What it answers | +|---|---| +| `RandomSplitter` | "Does the model fit the joint distribution?" — weakest baseline. | +| `TemporalSplitter` | "Does the model generalise *forward in time*?" | +| `StationLOSOSplitter` | "Does the model generalise to *one new station*?" | +| `SpatialSpreadHoldoutSplitter` | "Does the model generalise to the *most spatially distant* stations?" | +| `SpatialBlockSplitter` | "Does the model generalise across a *known categorical boundary*?" (country, climate zone, …) | + +The cell below applies each one in turn to the NB 05 dataset and +prints the resulting fold sizes.""" + ), + code( + """from susse.training import ( + RandomSplitter, + SpatialBlockSplitter, + SpatialSpreadHoldoutSplitter, + StationLOSOSplitter, + TemporalSplitter, +) + + +def describe(splitter, processed) -> None: + train_idx, val_idx = splitter.split(processed) + train_stations = processed.df.loc[train_idx, "location"].nunique() + val_stations = processed.df.loc[val_idx, "location"].nunique() + print( + f" {splitter.name:55s} " + f"train: {len(train_idx):>6,} ({train_stations} stations) " + f"val: {len(val_idx):>6,} ({val_stations} stations)" + ) + + +# RandomSplitter — 80/20 shuffle. Note: rows from the same station +# typically land in both folds, so this is the weakest test. +describe(RandomSplitter(val_fraction=0.2, random_state=42), processed) + +# TemporalSplitter — every 2024 row to val (note this dataset may not +# have any 2024 rows depending on the warehouse snapshot used; the +# splitter just reports zero in that case). +describe(TemporalSplitter(split_date=date(2024, 1, 1)), processed) + +# StationLOSOSplitter — held-out station = the one NB 05 chose +# (auto_pick_largest), so the fold matches NB 05's exactly. +splitter_loso = StationLOSOSplitter.auto_pick_largest(processed) +describe(splitter_loso, processed) + +# SpatialSpreadHoldoutSplitter — the two most distant stations. +describe(SpatialSpreadHoldoutSplitter(n_holdout=2), processed) + +# SpatialBlockSplitter — example: hold out the Madagascar-region +# station, whichever one(s) live there. Block column must exist in +# id_columns; in this dataset the natural block is `location` itself. +madagascar_like = [ + loc for loc in processed.df["location"].unique() if "madagascar" in loc +] +if madagascar_like: + describe( + SpatialBlockSplitter( + val_blocks=tuple(madagascar_like), block_column="location", + ), + processed, + ) +else: + print(" (no madagascar-* stations in this snapshot — skipping block-splitter demo)")""" + ), + md( + """## 2 — The seven concrete metrics + +Every concrete metric is a frozen, stateless dataclass implementing +`(name, compute)`. Constructing it takes no arguments today; future +variants (different normalisers, different bound conventions) can +land as new subclasses without changing the call site. + +| Metric | What it captures | Returns NaN when … | +|---|---|---| +| `RMSE` | Quadratic penalty on errors | Empty after NaN filter | +| `MAE` | Linear penalty on errors | Empty after NaN filter | +| `R²` | Fraction of variance explained | `var(y_obs) = 0` | +| `IndexOfAgreement` (Willmott 1981) | Magnitude agreement, bounded [0, 1] | Both series collapse to the same constant | +| `MeanBiasError` | Signed mean offset | Empty after NaN filter | +| `NormalisedRMSE` | RMSE as % of mean(y_obs) | `mean(y_obs) = 0` | +| `NormalisedMAE` | MAE as % of mean(y_obs) | `mean(y_obs) = 0` | + +`DEFAULT_METRICS` is the seven-metric tuple in paper-table order.""" + ), + code( + """from susse.evaluation import DEFAULT_METRICS + +# Predict over the LOSO val fold so the metrics have a concrete example. +train_idx, val_idx = splitter_loso.split(processed) +y_val = processed.y().loc[val_idx] +y_pred = pd.Series( + bundle.regressor.predict(processed.X().loc[val_idx]).values, + index=val_idx, +) + +print(f"Held-out station: {splitter_loso.held_out_station!r} " + f"(n={len(val_idx)})") +print() +for metric in DEFAULT_METRICS: + value = metric.compute(y_val, y_pred) + print(f" {metric.name:9s} = {value:8.3f}")""" + ), + md( + """## 3 — The Evaluator + +The Evaluator builds a tidy `(split × prediction)` score table over +its configured metrics in one call. The default metric tuple is +`DEFAULT_METRICS`; pass a different tuple to focus on specific +metrics. + +This cell compares the trained RF against the raw NASA + CAMS +satellite estimates on three different splits — illustrating both the +"prediction series" and "split" axes.""" + ), + code( + """from susse.evaluation import Evaluator + +# Three named predictions we want to compare. +predictions = { + "RF": y_pred, + "NASA": processed.df.loc[val_idx, "sat_ghi_nasa_kwh_m2_day"], + "CAMS": processed.df.loc[val_idx, "sat_ghi_cams_kwh_m2_day"], +} + +# Three named splits over the *val* fold for a finer-grained look. +held_out_df = processed.df.loc[val_idx] +splits = { + "all val rows": val_idx, + "2023+ only": held_out_df.index[ + pd.to_datetime(held_out_df["date"]).dt.year >= 2023 + ], + "first half-year": val_idx[: len(val_idx) // 2], +} + +table = Evaluator().score( + observed=y_val, predictions=predictions, splits=splits, +) +table.round(3)""" + ), + md( + """## 4 — Generic evaluation plots + +Each of these helpers lives in `susse.evaluation.plots`. Inputs are +plain DataFrames / column names — no library-specific value objects — +so the same calls work in `inspection/` notebooks and in +`papers//` validation flows without rewrites. + +Five plots in display order: + +1. Predicted-vs-observed scatter, one panel per named prediction +2. Per-station metric bar chart, with optional categorical highlight +3. Covariate-shift KDEs — training vs. inference feature distributions +4. Feature importances (top-N) +5. Training-fit time-series — observed + named comparison series, weekly-resampled + +All five plots are visual; the figures appear inline when this +notebook is run.""" + ), + code( + """from susse.evaluation import MeanBiasError +from susse.evaluation.plots import ( + plot_covariate_shift_kde, + plot_feature_importances_top_n, + plot_per_station_metric, + plot_predicted_vs_observed_panels, + plot_training_fit_timeseries, +) + +# Build one tidy DataFrame to feed the per-row plots. +val_df = processed.df.loc[val_idx].copy() +val_df["y_pred"] = y_pred +val_df["seen_in_training"] = False # only the LOSO station is in val +all_df = processed.df.copy() +all_df["y_pred"] = pd.Series( + bundle.regressor.predict(processed.X()).values, index=processed.df.index +) +all_df["seen_in_training"] = ( + all_df["location"] != splitter_loso.held_out_station +)""" + ), + md("### Predicted vs observed — one panel per prediction series"), + code( + """plot_predicted_vs_observed_panels( + val_df, + observed_column="y_ghi_kwh_m2_day", + predictions={ + "RF": "y_pred", + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, +)""" + ), + md( + """### Per-station metric bar chart + +Bars sorted by metric value. `highlight_column` lets you mark a +boolean attribute — here we colour each station by whether its rows +went into training (the LOSO station is the only one that didn't, so +all bars but one are highlighted).""" + ), + code( + """plot_per_station_metric( + all_df, + observed_column="y_ghi_kwh_m2_day", + predicted_column="y_pred", + metric=MeanBiasError(), + highlight_column="seen_in_training", +)""" + ), + md( + """### Covariate shift — training vs val feature distributions + +Overlaid histograms. Large divergence on a high-importance feature is +a warning that the model will extrapolate at inference time.""" + ), + code( + """train_df = all_df.loc[all_df["location"] != splitter_loso.held_out_station] +plot_covariate_shift_kde( + train_df, val_df, + features=list(processed.feature_columns)[:9], + train_label="Training", val_label=f"LOSO ({splitter_loso.held_out_station})", +)""" + ), + md( + """### Feature importances + +The helper raises a clear `TypeError` if the bundle's regressor +doesn't expose a `feature_importances` method (linear models don't); +that surfaces a bundle-vs-plot mismatch as a load-time error rather +than as silent zeros.""" + ), + code( + """plot_feature_importances_top_n( + bundle, feature_columns=processed.feature_columns, top_n=12, +)""" + ), + md( + """### Training fit — weekly time series per station + +The observed series and the model's prediction are rendered with +markers; the raw NASA / CAMS satellite estimates are drawn as thin, +low-alpha background lines so the visual contrast surfaces which +series we *produced* (the model) vs. which series are *context* +(the warehouse inputs).""" + ), + code( + """plot_training_fit_timeseries( + all_df, + observed_column="y_ghi_kwh_m2_day", + prediction_series={"RF": "y_pred"}, + reference_series={ + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, + n_stations=4, +)""" + ), + md( + """## 5 — Where to go next + +* **NB 07 — `07_inference.ipynb`** — the portal-facing API: + `load_bundle(...)` plus a `predict(lat, lon, date_range)` helper + that does the warehouse query → preprocess → predict pipeline. +* **`inspection/` notebooks** — one question per notebook, built on + top of `load_bundle` + the same plot helpers shown above. See the + inventory at [`notebooks/inspection/README.md`](../inspection/README.md). +* **Paper recomputation** — for a full worked example using these + same abstractions (with a paper-specific calibration step layered + on top), see + [`../papers/mukiibi_mikelson_2026/01_recomputation.ipynb`](../papers/mukiibi_mikelson_2026/01_recomputation.ipynb). + +The discipline going forward: + +| Change | What ripples | +|---|---| +| New splitter strategy | New `Splitter` subclass. Trainer + Evaluator signatures unchanged. | +| New metric | New `Metric` subclass. Pass it in `Evaluator(metrics=...)`. | +| New evaluation plot | New helper in `susse.evaluation.plots`. Inspection notebooks pick it up immediately. | +| Multi-bundle comparison | Build a Trainer→bundle dict and feed each bundle's predictions into Evaluator with a different label. |""" + ), +] + + +def main() -> None: + nb = { + "cells": CELLS, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3", + }, + "language_info": {"name": "python", "version": "3.12"}, + }, + "nbformat": 4, + "nbformat_minor": 5, + } + NB_PATH.write_text(json.dumps(nb, indent=1) + "\n") + print(f"wrote {NB_PATH} ({len(CELLS)} cells)") + + +if __name__ == "__main__": + main() diff --git a/notebooks/tutorial/_build_07_inference.py b/notebooks/tutorial/_build_07_inference.py new file mode 100644 index 00000000..c1792cb1 --- /dev/null +++ b/notebooks/tutorial/_build_07_inference.py @@ -0,0 +1,500 @@ +"""Generator for ``tutorial/07_inference.ipynb``. + +Edit the cell strings here, then regenerate with:: + + python notebooks/tutorial/_build_07_inference.py +""" + +from __future__ import annotations + +import json +from pathlib import Path + +NB_PATH = Path("notebooks/tutorial/07_inference.ipynb").resolve() + + +def md(text: str) -> dict: + return {"cell_type": "markdown", "metadata": {}, "source": _split_lines(text)} + + +def code(text: str) -> dict: + return { + "cell_type": "code", + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": _split_lines(text), + } + + +def _split_lines(text: str) -> list[str]: + lines = text.splitlines(keepends=True) + if lines and lines[-1].endswith("\n"): + lines[-1] = lines[-1].rstrip("\n") + return lines + + +COLAB_BOOTSTRAP = """\ +# ============================================================ +# Colab bootstrap (no-op when run locally). +# ------------------------------------------------------------ +# First-time setup on Colab: +# 1. Create a GitHub Personal Access Token (PAT) at +# https://github.com/settings/tokens with `repo` scope. +# The repository is private, so the clone needs this token +# (or an SSH key Colab knows about, which is more fiddly). +# 2. Add the token under Tools → Secrets in Colab with name +# `GITHUB_PAT` and toggle "Notebook access" on. +# 3. Sign in with a Google account that has BigQuery read access +# to `solar-irradiation-estimation` when prompted. +# ============================================================ +import os +import sys + +if "google.colab" in sys.modules: + REPO = "Marconi-Lab/Solar_irradiation" + BRANCH = "jm/add_model" + + if not os.path.exists("/content/Solar_irradiation/.git"): + try: + from google.colab import userdata + token = userdata.get("GITHUB_PAT") + clone_url = f"https://{token}@github.com/{REPO}.git" + print("Cloning with Colab secret 'GITHUB_PAT'.") + except Exception: + clone_url = f"git@github.com:{REPO}.git" + print( + "Colab secret 'GITHUB_PAT' not set — trying SSH. If the " + "clone fails, follow the PAT setup steps above and re-run." + ) + !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation + + %cd /content/Solar_irradiation + !pip install -q -e . 2>&1 | tail -3 + + from google.colab import auth + auth.authenticate_user() + !gcloud config set project solar-irradiation-estimation 2>/dev/null + print("Colab setup complete.") +""" + + +CELLS: list[dict] = [ + md( + """# 07 — Inference: from bundle on disk to deployment-ready predictions + +The end of the tutorial chain. Earlier notebooks built the training +dataset, trained models, packaged them into bundles, and evaluated +them. This notebook shows the **deployment-facing API**: + +```python +predictor = Predictor.from_bundle_dir(bundle_dir) +df = predictor.predict( + coords=[(0.333542, 32.56863), (3.04679, 30.91350), ...], + start_date=date(2024, 1, 1), + end_date=date(2024, 12, 31), +) +# → long DataFrame: lat, lon, geohash5, date, ..., y_pred_kwh_m2_day +``` + +This is the call site the companion `Irradiation_Portal` Flask app +mirrors. If you change the bundle, you don't need to update the +portal — re-running this notebook is the smoke test. + +## What's new in this notebook + +```text +Predictor (susse.inference) + bundle: TrainedBundle + service: FeatureService + on_cache_miss: "raise" | "fetch" + predict(coords, start_date, end_date) → DataFrame + +PredictionRequest (dataclass — validated input) + coords, start_date, end_date +``` + +## Notebook structure + +| Step | What you'll see | +|---|---| +| 0 | Setup + load the paper bundle from `data/bundles/mukiibi_mikelson_2026/` | +| 1 | Single-point prediction: Kampala over 2024, with NASA / CAMS overlaid | +| 2 | Grid prediction: the Uganda 2024 inference cache (~1961 cells × 365 days) | +| 3 | Annual aggregation per cell | +| 4 | Folium choropleth — what the portal renders | +| 5 | Cache-miss diagnostics + the Phase B (on-demand fetch) follow-up | + +## Two design choices worth knowing + +* **The bundle is the source of truth.** `Predictor` reads + `bundle.source_manifest.feature_selection` to know which NASA / CAMS + variable IDs to assemble. Training and inference can never drift + out of sync. +* **Cache miss is loud by default.** If you ask for a `(geohash5, + date)` pair that isn't in the warehouse, you get a + `RuntimeError` naming the missing cells — not a NaN, not a silent + empty row. Phase B will add the optional on-demand NASA POWER + + CAMS fetch path; for now, queries must hit cached cells.""" + ), + md( + """### Inputs, outputs, and prerequisites + +| | | +|---|---| +| **Inputs** | The paper bundle at `data/bundles/mukiibi_mikelson_2026/`. The Uganda 2024 inference grid in the warehouse (the green-rectangle layer in `notebooks/tutorial/01_data_overview.ipynb`). | +| **Outputs** | None on disk — a long DataFrame in memory and a folium map. | +| **Prereqs** | BigQuery read access (`gcloud auth application-default login` locally; the Colab bootstrap handles it). The bundle must exist; run `notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb` once if not. | +| **Wall time** | ~30 seconds for §1 (one cell × 365 days), ~1 minute for §2 (whole Uganda grid). |""" + ), + code(COLAB_BOOTSTRAP), + md("## 0 — Setup"), + code( + """%load_ext autoreload +%autoreload 2 + +import sys +from datetime import date +from pathlib import Path + +import numpy as np +import pandas as pd + +_HERE = Path.cwd().resolve() +_PROJECT_ROOT = _HERE +while _PROJECT_ROOT != _PROJECT_ROOT.parent and not (_PROJECT_ROOT / "src" / "susse").exists(): + _PROJECT_ROOT = _PROJECT_ROOT.parent +sys.path.insert(0, str(_PROJECT_ROOT / "src")) + +# Load .env so CAMS_EMAIL (used by the fetch-mode demo in §5) is +# picked up automatically. One helper across all notebooks. +from susse.env import load_project_env +load_project_env() + +print(f"project root: {_PROJECT_ROOT}")""" + ), + md( + """### Load the bundle + +`Predictor.from_bundle_dir` is the one-liner the portal uses too. It +loads the trained model, the feature spec, the source manifest, and +opens a BigQuery client against the project default warehouse. + +The `providers` argument supplies an elevation provider for +`AltitudeFeature` — pvlib's `lookup_altitude()` queries Open-Elevation +on the fly, so no warehouse trip is needed for that derived +feature.""" + ), + code( + """from susse.inference import Predictor +from susse.preprocessing import PvlibElevationProvider + +BUNDLE_DIR = (_PROJECT_ROOT / "data" / "bundles" / "mukiibi_mikelson_2026").resolve() +assert BUNDLE_DIR.exists(), ( + f"Bundle missing at {BUNDLE_DIR}. Run " + f"notebooks/papers/mukiibi_mikelson_2026/01_recomputation.ipynb " + f"end-to-end first." +) + +predictor = Predictor.from_bundle_dir( + BUNDLE_DIR, + providers={"altitude": PvlibElevationProvider()}, + on_cache_miss="raise", +) +print(f"Bundle: {BUNDLE_DIR.name}") +print(f"Trained at: {predictor.bundle.metadata.created_at_utc}") +print(f"Splitter: {predictor.bundle.metadata.splitter_name}") +print(f"Val RMSE: {predictor.bundle.metadata.val_metrics.rmse:.3f} " + f"(val MAE {predictor.bundle.metadata.val_metrics.mae:.3f})") +print(f"Features: {len(predictor.bundle.feature_spec.feature_columns)} raw + " + f"{len(predictor.bundle.feature_spec.derived_features)} derived")""" + ), + md( + """## 1 — Single-point prediction: Kampala over 2024 + +Kampala is one of the 28 training stations, so its geohash5 is +warehouse-cached for the full 2024 calendar year. The Predictor: + +1. Computes geohash5 from `(lat, lon)` +2. Issues one batched query per source (irradiance + NASA aux + CAMS aux) +3. Assembles the inference frame and applies the bundle's `FeatureSpec` (cleaners disabled, no NaN-drop) +4. Runs the regressor in a single batched call + +Result: 365 daily bias-corrected GHI predictions plus the raw NASA / +CAMS satellite columns so you can overlay them in plots without +re-querying.""" + ), + code( + """KAMPALA_LAT = 0.333542 +KAMPALA_LON = 32.56863 + +kampala = predictor.predict( + coords=[(KAMPALA_LAT, KAMPALA_LON)], + start_date=date(2024, 1, 1), + end_date=date(2024, 12, 31), +) +print(f"Predictions: {len(kampala):,} rows × {len(kampala.columns)} columns") +print(f"Columns of interest:") +for c in ("lat", "lon", "geohash5", "date", "y_pred_kwh_m2_day", + "sat_ghi_nasa_kwh_m2_day", "sat_ghi_cams_kwh_m2_day"): + if c in kampala.columns: + print(f" {c}") +kampala.head()""" + ), + md( + """### Time-series visualisation + +The same convention as NB 06: the model's prediction is rendered +prominently with markers; the raw NASA / CAMS satellite estimates are +faint background lines so the visual contrast surfaces which series +the model *produced* vs the satellite *inputs* it bias-corrected.""" + ), + code( + """from susse.evaluation.plots import plot_training_fit_timeseries + +# Single-station inference view — no observed series (we don't have +# ground GHI for arbitrary lat/lon at deploy time). The prediction is +# rendered prominently; NASA / CAMS sit faint in the background. +ts = kampala.copy() +ts["date"] = pd.to_datetime(ts["date"]) +ts["location"] = "Kampala" + +plot_training_fit_timeseries( + ts, + observed_column=None, + prediction_series={"RF (bias-corrected)": "y_pred_kwh_m2_day"}, + reference_series={ + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, + location_column="location", + n_stations=1, +)""" + ), + md( + """## 2 — Grid prediction: the Uganda 2024 inference cache + +The warehouse caches NASA + CAMS feature rows for every 0.05° geohash +cell in a Uganda bounding box across 2024. `01_data_overview.ipynb` +renders these as a green rectangle on its coverage map; here we +query the cell centres and predict for the whole grid. + +The query below pulls the cell centroids straight from BigQuery so +we never duplicate the grid definition. ~2k cells × 365 days = ~700k +predictions; the bundle's RF is batched, so this takes about a +minute even on a workstation.""" + ), + code( + """from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig +from susse.warehouse_ops.io.config import TableRefs + +bq = BigQueryClient(config=WarehouseConfig()) +tables = TableRefs(config=bq.config) + +# Cell centres of the Uganda 2024 grid (the 2024-only NASA cells the +# warehouse caches for portal inference, excluding ground stations). +grid_cells = bq.query(f\"\"\" +SELECT DISTINCT geohash5, + ANY_VALUE(latitude) AS lat, + ANY_VALUE(longitude) AS lon +FROM `{tables.irradiance_daily}` +WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-12-31') + AND source = 'NASA' + AND geohash5 NOT IN (SELECT DISTINCT geohash5 FROM `{tables.ground_measurements}`) +GROUP BY geohash5 +ORDER BY lat, lon +\"\"\") +print(f"Uganda 2024 grid: {len(grid_cells):,} cells") +grid_cells.head()""" + ), + code( + """uganda_2024 = predictor.predict( + coords=list(zip(grid_cells["lat"], grid_cells["lon"])), + start_date=date(2024, 1, 1), + end_date=date(2024, 12, 31), +) +print(f"Daily predictions: {len(uganda_2024):,} rows " + f"({uganda_2024[['lat', 'lon']].drop_duplicates().shape[0]:,} unique cells × " + f"{uganda_2024['date'].nunique()} days)")""" + ), + md( + """## 3 — Annual aggregation per cell + +The portal renders an **annual-mean** GHI choropleth. Reduce the +daily long-format frame to one row per cell carrying its 2024 mean +predicted GHI plus the two satellite baselines for comparison.""" + ), + code( + """annual = ( + uganda_2024 + .groupby(["lat", "lon", "geohash5"], as_index=False) + .agg( + rf_mean=("y_pred_kwh_m2_day", "mean"), + nasa_mean=("sat_ghi_nasa_kwh_m2_day", "mean"), + cams_mean=("sat_ghi_cams_kwh_m2_day", "mean"), + ) +) +print(f"Annual mean grid: {len(annual):,} cells") +print(f" RF range: {annual['rf_mean'].min():.2f} – {annual['rf_mean'].max():.2f} kWh/m²/day") +print(f" NASA range: {annual['nasa_mean'].min():.2f} – {annual['nasa_mean'].max():.2f}") +print(f" CAMS range: {annual['cams_mean'].min():.2f} – {annual['cams_mean'].max():.2f}") +annual.head()""" + ), + md( + """## 4 — Folium choropleth — what the portal renders + +A small circle marker per cell, coloured by predicted annual GHI. +This is the static analogue of the portal's interactive heatmap; the +portal uses the same DataFrame with `folium.raster_layers.ImageOverlay` +for a rasterised version.""" + ), + code( + """import folium +import branca.colormap as cm + +cmap = cm.LinearColormap( + colors=["#440154", "#3b528b", "#21918c", "#5ec962", "#fde725"], + vmin=float(annual["rf_mean"].min()), + vmax=float(annual["rf_mean"].max()), + caption="RF-corrected annual mean GHI (kWh/m²/day)", +) + +centre_lat = float(annual["lat"].mean()) +centre_lon = float(annual["lon"].mean()) +m = folium.Map(location=[centre_lat, centre_lon], zoom_start=7, control_scale=True) +for _, row in annual.iterrows(): + folium.CircleMarker( + location=[row["lat"], row["lon"]], radius=4, + color=None, fill=True, + fillColor=cmap(row["rf_mean"]), fillOpacity=0.85, + popup=folium.Popup( + f"geohash5: {row['geohash5']}
" + f"RF: {row['rf_mean']:.2f} kWh/m²/day
" + f"NASA: {row['nasa_mean']:.2f}
" + f"CAMS: {row['cams_mean']:.2f}", + max_width=240, + ), + ).add_to(m) +cmap.add_to(m) +m""" + ), + md( + """## 5 — Cache misses + on-demand fetch (Phase B) + +The Predictor's default behaviour is loud-by-default: any requested +`(geohash5, date)` pair that isn't in the warehouse raises a +`RuntimeError` naming the missing cells. That's what the +`on_cache_miss="raise"` constructor mode you've been using does.""" + ), + code( + """# A point in the Indian Ocean — definitely not in any cache. +try: + predictor.predict( + coords=[(-5.0, 70.0)], + start_date=date(2024, 1, 1), end_date=date(2024, 1, 7), + ) +except RuntimeError as e: + print("Cache miss surfaced as expected:") + print(str(e)[:400], "...")""" + ), + md( + """### Switching to `on_cache_miss="fetch"` + +With `on_cache_miss="fetch"`, the predictor instead invokes the +existing `NasaPowerSatelliteJob` and `CamsSatelliteJob` to fetch + +persist the missing cells before re-querying and predicting. The +same code path the batch-ingest migrations use — same schema, same +idempotency, same hardening — wrapped one layer up. + +**Prerequisites for fetch mode:** + +* `CAMS_EMAIL` env var set to a registered [SoDa-Pro](https://www.soda-pro.com/web-services/radiation/cams-radiation-service) + account. The constructor raises `ValueError` upfront if missing. +* BigQuery write access on the warehouse dataset. Failure surfaces at + the first write. +* Implicit cap of **30 cells per `predict()`** to respect CAMS's free-tier + daily quota — exceeding it raises rather than silently throttling. + +A typical interactive single-coord query for an uncached cell takes +~25-40 seconds: NASA POWER fetch (~5s) + CAMS fetch (~10s) + warehouse +write (~5-10s) + warehouse re-query (~3s) + predict (~1s). Subsequent +queries for the same cell hit the warehouse and run in ~3 seconds.""" + ), + code( + """import os + +# `load_dotenv()` was already called in §0, so a CAMS_EMAIL set in +# `.env` is already in os.environ here. Either path (shell export or +# .env entry) makes this cell light up. +if not os.environ.get("CAMS_EMAIL"): + print("CAMS_EMAIL is not set; skipping the fetch-mode demo.\\n" + "Either `export CAMS_EMAIL=you@example.com` in the shell, or\\n" + "add `CAMS_EMAIL=you@example.com` to a `.env` at the project\\n" + "root, then restart the kernel.") +else: + fetch_predictor = Predictor.from_bundle_dir( + BUNDLE_DIR, + providers={"altitude": PvlibElevationProvider()}, + on_cache_miss="fetch", + ) + # A single uncached coord — Lake Albert, on the Uganda/DRC border. + # If the Uganda 2024 grid covered it already, this hits the cache; + # otherwise the fetch path fires. + lake_albert = fetch_predictor.predict( + coords=[(1.6, 30.85)], + start_date=date(2024, 6, 1), + end_date=date(2024, 6, 7), + ) + print(f"Predictions: {len(lake_albert)} rows") + print(lake_albert[["lat", "lon", "date", "y_pred_kwh_m2_day"]].to_string(index=False))""" + ), + md( + """## What's next + +* **`Irradiation_Portal`** — the Flask portal already loads a bundle + via `load_bundle(...)`. The next portal release should switch to + `Predictor.from_bundle_dir(...)` so the portal and the tutorial + evolve together. The portal also gets free warehouse caching: any + user query for an uncached cell, while `on_cache_miss="fetch"`, + populates the warehouse for the next user. +* **Inspection notebooks** — `notebooks/inspection/` is the right + place for ad-hoc deployment-readiness checks (per-region bias, + out-of-distribution probe coordinates, model-vs-model overlays). + +The discipline going forward: + +| Change | What ripples | +|---|---| +| New model kind | New `BaseRegressor` subclass + params. Predictor signature unchanged. | +| New satellite source | New `Source` enum value + ingest job + entry in `_LONG_AUX_TABLES`. Predictor signature unchanged. | +| New derived feature | New `DerivedFeature` subclass; FeatureSpec gains a tuple entry. Predictor signature unchanged. | +| Cache-miss policy change | `Predictor.on_cache_miss` is a knob; portal flips it. | + +The `Predictor` is the unit of deployment. The portal owns a single +instance and reuses it across requests. If something here feels +missing — a request-level metric you want logged, an artifact +provenance field that should travel into every response — extend the +`Predictor` (not the call site).""" + ), +] + + +def main() -> None: + nb = { + "cells": CELLS, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3", + }, + "language_info": {"name": "python", "version": "3.12"}, + }, + "nbformat": 4, + "nbformat_minor": 5, + } + NB_PATH.write_text(json.dumps(nb, indent=1) + "\n") + print(f"wrote {NB_PATH} ({len(CELLS)} cells)") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 7f89da39..96881116 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,19 +4,56 @@ build-backend = "setuptools.build_meta" [project] name = "SuSSE" -description = "SubSahara Solar Estimation" +description = "Sub-Saharan Solar Estimation — satellite GHI bias correction over Sub-Saharan Africa" readme = "README.md" requires-python = ">=3.12" +authors = [ + { name = "Jan Mikelson", email = "jan.mikelson@gmail.com" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Atmospheric Science", +] dynamic = ["version", "dependencies"] [tool.setuptools] package-dir = {"" = "src"} -packages = { find = { where = ["src"] } } # Use the correct format for finding packages - +packages = { find = { where = ["src"] } } [tool.setuptools.dynamic] dependencies = {file = ["requirements.txt"]} [tool.setuptools_scm] -# Automatically manage versions based on the SCM (git in your case) write_to = "src/susse/version.py" + +[tool.black] +line-length = 88 +target-version = ["py312"] + +[tool.isort] +profile = "black" +multi_line_output = 3 +line_length = 88 + +[tool.mypy] +python_version = "3.12" +ignore_missing_imports = true +implicit_optional = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "--durations=10 -ra" +filterwarnings = [ + "ignore:'cgi' is deprecated:DeprecationWarning", +] + +[tool.coverage.run] +source = ["src/susse"] +branch = true + +[tool.coverage.report] +show_missing = true +skip_covered = false diff --git a/requirements.txt b/requirements.txt index 96f2e111..1b225480 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,13 @@ cryptography>=43.0.3 +db-dtypes>=1.2.0 +folium>=0.18.0 geopandas>=1.1.0 geopy>=2.4.1 +google-cloud-bigquery>=3.20.0 h5py>=3.10.0 ipykernel>=6.15.2 jinja2>=3.1.6 +joblib>=1.4.0 jupyter>=1.0.0 keyring>=24.3.1 matplotlib>=3.10.0 @@ -12,13 +16,17 @@ netcdf4>=1.7.2 NREL-farms>=1.0.6 numpy>=2.0.2 overrides>=7.4.0 -python-dotenv>=1.0.0 pandas>=2.2.3 pvlib>=0.11.2 +pyarrow>=15.0.0 pydap>=3.5.4 +pygeohash>=3.1.0 pytest>=8.0.0 +python-dotenv>=1.0.0 requests>=2.32.3 requests-mock>=1.11.0 +scikit-learn>=1.5.0 types-requests>=2.32.0 urllib3>=2.3.0 +wandb>=0.18.0 xarray>=2025.3.0 diff --git a/tests/estimators/__init__.py b/src/__init__.py similarity index 100% rename from tests/estimators/__init__.py rename to src/__init__.py diff --git a/src/susse/__init__.py b/src/susse/__init__.py index 95116185..563903aa 100644 --- a/src/susse/__init__.py +++ b/src/susse/__init__.py @@ -1,2 +1 @@ from .api_clients import * -from .estimators import * diff --git a/src/susse/api_clients/NASA_Power/__init__.py b/src/susse/api_clients/NASA_Power/__init__.py index 49c09d44..b5928168 100644 --- a/src/susse/api_clients/NASA_Power/__init__.py +++ b/src/susse/api_clients/NASA_Power/__init__.py @@ -1,11 +1,13 @@ from .nasa_power_config import NASAPowerConfig from .nasa_power_fetch_data import NASAPowerFetchData +from .nasa_power_regional_fetcher import NASAPowerRegionalFetcher from .nasa_power_result import NASAPowerResult from .nasa_products import NASAPowerProduct, TemporalResolution __all__ = [ "NASAPowerConfig", "NASAPowerFetchData", + "NASAPowerRegionalFetcher", "NASAPowerResult", "NASAPowerProduct", "TemporalResolution", diff --git a/src/susse/api_clients/NASA_Power/nasa_power_fetch_data.py b/src/susse/api_clients/NASA_Power/nasa_power_fetch_data.py index c7c0c00f..de81c4cb 100644 --- a/src/susse/api_clients/NASA_Power/nasa_power_fetch_data.py +++ b/src/susse/api_clients/NASA_Power/nasa_power_fetch_data.py @@ -1,3 +1,5 @@ +import logging +import time from datetime import datetime from typing import List, Union @@ -8,6 +10,13 @@ from .nasa_power_result import NASAPowerResult from .nasa_products import NASAPowerProduct, TemporalResolution +_logger = logging.getLogger(__name__) + +# Hard upper bound per HTTP request. NASA POWER usually responds in under a +# minute even for multi-year, multi-variable requests; anything past 5 minutes +# is almost certainly a hung connection rather than a slow response. +_REQUEST_TIMEOUT_SECONDS = 300 + class NASAPowerFetchData: """ @@ -52,8 +61,23 @@ def _fetch_parameter_batch( location=location, products=products, ) - response = requests.get(url) + n_days = (end_date - start_date).days + 1 + _logger.info( + "NASA POWER request: lat=%.4f lon=%.4f n_vars=%d n_days=%d", + location.latitude, + location.longitude, + len(products), + n_days, + ) + t0 = time.monotonic() + response = requests.get(url, timeout=_REQUEST_TIMEOUT_SECONDS) + elapsed = time.monotonic() - t0 response.raise_for_status() + _logger.info( + "NASA POWER response: %d bytes in %.1fs.", + len(response.content), + elapsed, + ) json_data = response.json() return json_data["properties"]["parameter"] diff --git a/src/susse/api_clients/NASA_Power/nasa_power_regional_fetcher.py b/src/susse/api_clients/NASA_Power/nasa_power_regional_fetcher.py new file mode 100644 index 00000000..1e687bf7 --- /dev/null +++ b/src/susse/api_clients/NASA_Power/nasa_power_regional_fetcher.py @@ -0,0 +1,351 @@ +"""Region-shaped NASA POWER fetcher. + +Companion to :class:`NASAPowerFetchData`: instead of one HTTP call per +point (with up to 20 parameters per call), this fetcher issues one HTTP +call per (tile, parameter) covering the smallest set of bbox tiles that +encloses all requested points. + +NASA POWER constraints on the daily ``/regional`` endpoint +---------------------------------------------------------- + +* Maximum bbox: **4.5° × 4.5°** per request. +* Maximum points returned: **100** per request. +* Exactly **one** parameter per request (in contrast to the point + endpoint's 20-per-request batch). +* The returned grid is **NASA's native pixel grid for the parameter's + source**: ``ALLSKY_SFC_SW_DWN`` etc. come from CERES SYN1DEG (1° + cells), while T2M / WS2M etc. come from MERRA-2 (0.5° × 0.625°). For + a given bbox + parameter, the response is the rectangle of native + pixels whose centres fall inside the bbox. + +For a 6° × 5.55° Uganda bbox × 29 catalog variables × 1 year, the +regional path issues ~4 tiles × 29 vars × 1 year-chunk ≈ 116 HTTP calls +in place of the ~40,000 calls the per-point path makes — a ~350× cost +reduction. The caller is responsible for mapping NASA's native pixels +back onto their own grid (e.g. via nearest-pixel snap onto a denser +geohash5 grid). +""" + +from __future__ import annotations + +import logging +import time as _time +from dataclasses import dataclass +from datetime import date as _date +from typing import ClassVar + +import pandas as pd +import requests + +_logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class _RegionalTile: + """A bbox slice that fits inside the regional endpoint's size cap.""" + + min_lat: float + max_lat: float + min_lon: float + max_lon: float + + @property + def span_lat(self) -> float: + return self.max_lat - self.min_lat + + @property + def span_lon(self) -> float: + return self.max_lon - self.min_lon + + +class NASAPowerRegionalFetcher: + """Region-shaped NASA POWER fetcher. + + The public entry point is :meth:`fetch_region_long`, which fans the + request out into one HTTP call per (tile, api_code) and reduces the + responses to a single long-format DataFrame. + + The fetcher does not snap points to NASA's native pixels — it returns + the native pixels themselves. Consumers that want per-geohash5 rows + must apply a nearest-pixel mapping over the result. + + Args: + session: Optional pre-configured :class:`requests.Session`. The + default is a fresh session per fetcher instance, which is fine + for migrations but should be reused across calls if the + fetcher is held as a long-lived service. + """ + + _MAX_TILE_SPAN_DEG: ClassVar[float] = 4.0 + """Tile-edge cap. NASA's hard limit is 4.5° per side and 100 points + per request; using 4.0° gives a safety margin (≤ 9×9 = 81 native + pixels at 0.5° resolution, ≤ 5×5 = 25 at 1°).""" + + _REQUEST_TIMEOUT_SECONDS: ClassVar[int] = 300 + """Hard upper bound per HTTP request. Regional requests for one year + over a 4° tile normally return in 5–30 s; anything past 5 minutes is + a hung connection rather than a slow response.""" + + _MISSING_VALUE: ClassVar[float] = -999.0 + """NASA POWER's fill value for missing data. Rows with this value + are dropped during parsing.""" + + _COMMUNITY: ClassVar[str] = "RE" + """The 'Renewable Energy' community; matches the point endpoint + used by :class:`NASAPowerFetchData`.""" + + _BASE_URL: ClassVar[str] = "https://power.larc.nasa.gov/api/temporal/daily/regional" + + def __init__(self, *, session: requests.Session | None = None) -> None: + self._session = session or requests.Session() + + def fetch_region_long( + self, + *, + min_lat: float, + max_lat: float, + min_lon: float, + max_lon: float, + date_start: _date, + date_end: _date, + api_codes: tuple[str, ...], + ) -> pd.DataFrame: + """Fetch every (tile, api_code) covering the bbox and date range. + + Args: + min_lat / max_lat / min_lon / max_lon: Inclusive bbox edges + in degrees. The fetcher tiles internally to stay inside + the 4.5°-per-side endpoint cap. Primitive args (rather + than a ``BoundingBox``) keep this module independent of + ``warehouse_ops`` and avoid circular imports. + date_start / date_end: Inclusive date range. Passed through + unchanged; no chunking is applied. NASA POWER tolerates + multi-year regional requests for daily resolution. + api_codes: NASA POWER parameter names (e.g. ``("T2M", + "ALLSKY_SFC_SW_DWN")``). The endpoint accepts exactly one + parameter per HTTP call, so the total request count is + ``n_tiles * len(api_codes)``. + + Returns: + Long-format DataFrame with columns + ``(date, latitude, longitude, variable_id, value)``. The + ``variable_id`` column carries the input ``api_code`` (the + caller maps to warehouse variable_id). Latitudes / longitudes + are NASA's native pixel centres; the same (lat, lon) appears + across multiple variables when those variables happen to + share a source grid. Empty if every cell of every tile is + missing. + """ + if min_lat > max_lat or min_lon > max_lon: + raise ValueError( + f"Invalid bbox: lat=[{min_lat}, {max_lat}] " + f"lon=[{min_lon}, {max_lon}]. Need min <= max." + ) + if date_start > date_end: + raise ValueError( + f"date_start ({date_start}) must be <= date_end ({date_end})." + ) + if not api_codes: + return self._empty_long_frame() + tiles = self._tile_bbox(min_lat, max_lat, min_lon, max_lon) + n_tiles = len(tiles) + n_calls_total = n_tiles * len(api_codes) + _logger.info( + "NASA POWER regional fetch plan: bbox lat[%.3f..%.3f] " + "lon[%.3f..%.3f] → %d tile(s) × %d variable(s) = %d call(s), " + "dates %s..%s.", + min_lat, + max_lat, + min_lon, + max_lon, + n_tiles, + len(api_codes), + n_calls_total, + date_start, + date_end, + ) + + rows: list[dict] = [] + call_idx = 0 + for tile in tiles: + for api_code in api_codes: + call_idx += 1 + tile_rows = self._fetch_tile_variable( + tile=tile, + date_start=date_start, + date_end=date_end, + api_code=api_code, + ) + _logger.info( + "[%d/%d] tile lat[%.2f..%.2f] lon[%.2f..%.2f] %s: %d row(s)", + call_idx, + n_calls_total, + tile.min_lat, + tile.max_lat, + tile.min_lon, + tile.max_lon, + api_code, + len(tile_rows), + ) + rows.extend(tile_rows) + + if not rows: + return self._empty_long_frame() + df = pd.DataFrame( + rows, + columns=("date", "latitude", "longitude", "variable_id", "value"), + ) + # De-duplicate native pixels that appear in adjacent overlapping + # tiles. Tiling places a single edge between tiles, so duplicates + # are typically zero, but a defensive drop keeps the contract + # ('one row per (date, lat, lon, variable_id)') tight. + df = df.drop_duplicates( + subset=("date", "latitude", "longitude", "variable_id"), + keep="first", + ).reset_index(drop=True) + return df + + @classmethod + def _tile_bbox( + cls, + min_lat: float, + max_lat: float, + min_lon: float, + max_lon: float, + ) -> list[_RegionalTile]: + """Split a bbox into tiles each ≤ ``_MAX_TILE_SPAN_DEG`` per side. + + Returns a list of non-overlapping tiles whose union covers the + input bbox. The final tile in each dimension may be smaller than + the others if the bbox span isn't a clean multiple of the cap. + """ + lat_edges = cls._edges_for_span(min_lat, max_lat) + lon_edges = cls._edges_for_span(min_lon, max_lon) + tiles: list[_RegionalTile] = [] + for i in range(len(lat_edges) - 1): + for j in range(len(lon_edges) - 1): + tiles.append( + _RegionalTile( + min_lat=lat_edges[i], + max_lat=lat_edges[i + 1], + min_lon=lon_edges[j], + max_lon=lon_edges[j + 1], + ) + ) + return tiles + + @classmethod + def _edges_for_span(cls, lo: float, hi: float) -> list[float]: + """Edges that split ``[lo, hi]`` into pieces ≤ ``_MAX_TILE_SPAN_DEG``.""" + span = hi - lo + if span <= cls._MAX_TILE_SPAN_DEG: + return [lo, hi] + # Use enough equal-width pieces to stay under the cap. Equal width + # is preferable to "max-cap + remainder" because it spreads any + # per-tile latency more evenly across requests. + n_pieces = int(span // cls._MAX_TILE_SPAN_DEG) + 1 + step = span / n_pieces + return [lo + k * step for k in range(n_pieces)] + [hi] + + def _fetch_tile_variable( + self, + *, + tile: _RegionalTile, + date_start: _date, + date_end: _date, + api_code: str, + ) -> list[dict]: + """One HTTP call → list of long-format row dicts.""" + url = self._build_regional_url( + tile=tile, + date_start=date_start, + date_end=date_end, + api_code=api_code, + ) + t0 = _time.monotonic() + response = self._session.get(url, timeout=self._REQUEST_TIMEOUT_SECONDS) + elapsed = _time.monotonic() - t0 + response.raise_for_status() + payload = response.json() + _logger.info( + "NASA POWER regional response: %d bytes in %.1fs (%s).", + len(response.content), + elapsed, + api_code, + ) + return self._parse_feature_collection(payload, api_code=api_code) + + @classmethod + def _build_regional_url( + cls, + *, + tile: _RegionalTile, + date_start: _date, + date_end: _date, + api_code: str, + ) -> str: + start_str = date_start.strftime("%Y%m%d") + end_str = date_end.strftime("%Y%m%d") + return ( + f"{cls._BASE_URL}" + f"?parameters={api_code}" + f"&community={cls._COMMUNITY}" + f"&latitude-min={tile.min_lat}" + f"&latitude-max={tile.max_lat}" + f"&longitude-min={tile.min_lon}" + f"&longitude-max={tile.max_lon}" + f"&start={start_str}" + f"&end={end_str}" + f"&format=JSON" + ) + + @classmethod + def _parse_feature_collection( + cls, + payload: dict, + *, + api_code: str, + ) -> list[dict]: + """Flatten a NASA POWER FeatureCollection into long-format rows. + + Schema (as of 2026-05): + + ``payload["features"]`` is a list, one feature per native pixel. + Each feature has ``geometry.coordinates = [lon, lat, elev]`` and + ``properties.parameter[api_code][YYYYMMDD] = value``. Fill value + (-999) is dropped. + """ + features = payload.get("features") or () + rows: list[dict] = [] + for feature in features: + coords = feature.get("geometry", {}).get("coordinates", ()) + if len(coords) < 2: + continue + lon, lat = float(coords[0]), float(coords[1]) + param_block = ( + feature.get("properties", {}).get("parameter", {}).get(api_code, {}) + ) + for date_str, raw_value in param_block.items(): + if raw_value is None: + continue + value = float(raw_value) + if value == cls._MISSING_VALUE: + continue + rows.append( + { + "date": _date.fromisoformat( + f"{date_str[0:4]}-{date_str[4:6]}-{date_str[6:8]}" + ), + "latitude": lat, + "longitude": lon, + "variable_id": api_code, + "value": value, + } + ) + return rows + + @staticmethod + def _empty_long_frame() -> pd.DataFrame: + return pd.DataFrame( + columns=("date", "latitude", "longitude", "variable_id", "value"), + ) diff --git a/src/susse/api_clients/NASA_Power/nasa_products.py b/src/susse/api_clients/NASA_Power/nasa_products.py index 5384c5bd..94caf6e6 100644 --- a/src/susse/api_clients/NASA_Power/nasa_products.py +++ b/src/susse/api_clients/NASA_Power/nasa_products.py @@ -13,7 +13,7 @@ class NASAPowerProduct(Enum): GHI = "ALLSKY_SFC_SW_DWN" DHI = "ALLSKY_SFC_SW_DIFF" DNI = "ALLSKY_SFC_SW_DNI" - LONGWAVE_DOWNARD_IRR = "CLRSKY_SFC_LW_DWN" + LONGWAVE_DOWNWARD_IRR = "CLRSKY_SFC_LW_DWN" TEMPERATURE = "T2M" SPECIFIC_HUMIDITY = "QV2M" AEROSOL_OPTICAL_DEPTH_550nm = "AOD_55" @@ -24,7 +24,7 @@ class NASAPowerProduct(Enum): CLOUD_VISIBLE_OPTICAL_DEPTH = "CLOUD_OD" CLOUD_AMOUNT_DAT = "CLOUD_AMT_DAY" TOTAL_COLUMN_OZONE = "TO3" - SURFACE_ALBEDO = "SRF_ALB_ADJ" + SURFACE_ALBEDO_ADJ = "SRF_ALB_ADJ" ALL_SKY_SURFACE_ALBEDO = "ALLSKY_SRF_ALB" CLEAR_SKY_ALBEDO = "CLRSKY_SRF_ALB" PRECIPITABLE_WATER = "PW" @@ -35,12 +35,11 @@ class NASAPowerProduct(Enum): TEMPERATURE_RANGE = "T2M_RANGE" SURFACE_ROUGHNESS = "Z0M" NORTHERN_WIND = "V2M" - ARIMASS = "AIRMASS" + AIRMASS = "AIRMASS" ZERO_PLANE_DISPLACEMENT = "DISPH" EVAPOTRANSPIRATION_ENERGY = "EVPTRNS" PLANETARY_BOUNDARY = "PBLTOP" SURFACE_AIR_DENSITY = "RHOA" - SURFACE_ALBEDO_ADJ = "SRF_ALB_ADJ" EVAPORATION_LAND = "EVLAND" SURFACE_SOIL_WETNESS = "GWETTOP" WIND_SPEED = "WS2M" diff --git a/src/susse/api_clients/cams/__init__.py b/src/susse/api_clients/cams/__init__.py index b6230aa3..4060d55e 100644 --- a/src/susse/api_clients/cams/__init__.py +++ b/src/susse/api_clients/cams/__init__.py @@ -1,7 +1,17 @@ -from .get_cams_data import ( +from .cams_client import ( DEFAULT_IDENTIFIER, EMAIL_ENV_KEY, ENV_PATH, TIMEOUT_SECONDS, + CamsApiError, CAMSClient, ) + +__all__ = [ + "CAMSClient", + "CamsApiError", + "DEFAULT_IDENTIFIER", + "EMAIL_ENV_KEY", + "ENV_PATH", + "TIMEOUT_SECONDS", +] diff --git a/src/susse/api_clients/cams/cams_client.py b/src/susse/api_clients/cams/cams_client.py new file mode 100644 index 00000000..6fd5627a --- /dev/null +++ b/src/susse/api_clients/cams/cams_client.py @@ -0,0 +1,139 @@ +"""Thin client around pvlib's ``get_cams`` for CAMS radiation data.""" + +from __future__ import annotations + +import logging +import os +import time +from datetime import datetime +from pathlib import Path +from typing import Any, ClassVar, Tuple + +import pandas as pd +import pvlib +from dotenv import load_dotenv, set_key + +_logger = logging.getLogger(__name__) + + +class CamsApiError(RuntimeError): + """Raised when a CAMS request fails or returns no usable data. + + Wraps the underlying pvlib / network exception so callers can catch + a single error type at the boundary instead of unrelated ones. + """ + + +class CAMSClient: + """Client for fetching CAMS radiation data via pvlib. + + Reads the registered CAMS email from a ``.env`` file (or prompts and + persists it). Failures from the underlying pvlib call surface as + :class:`CamsApiError` so callers don't silently propagate ``None`` + DataFrames downstream. + """ + + DEFAULT_ENV_PATH: ClassVar[Path] = Path(".") / ".env" + DEFAULT_EMAIL_ENV_KEY: ClassVar[str] = "CAMS_EMAIL" + DEFAULT_IDENTIFIER: ClassVar[str] = "cams_radiation" + DEFAULT_TIMEOUT_SECONDS: ClassVar[int] = 180 + + def __init__( + self, + env_path: Path | None = None, + email_env_key: str | None = None, + identifier: str | None = None, + timeout: int | None = None, + ) -> None: + self._env_path = env_path or self.DEFAULT_ENV_PATH + self._email_env_key = email_env_key or self.DEFAULT_EMAIL_ENV_KEY + self._identifier = identifier or self.DEFAULT_IDENTIFIER + self._timeout = timeout if timeout is not None else self.DEFAULT_TIMEOUT_SECONDS + load_dotenv(dotenv_path=self._env_path) + + @property + def identifier(self) -> str: + return self._identifier + + def _get_email(self) -> str: + """Retrieve the CAMS email from environment or prompt the user.""" + email = os.getenv(self._email_env_key) + if not email: + email = input("Enter your registered CAMS email: ").strip() + set_key(str(self._env_path), self._email_env_key, email) + return email + + def fetch_data( + self, + latitude: float, + longitude: float, + start: datetime, + end: datetime, + time_step: str, + ) -> Tuple[pd.DataFrame, dict[str, Any]]: + """Fetch CAMS radiation data for one point. + + Args: + latitude: degrees, [-90, 90]. + longitude: degrees, [-180, 180]. + start: UTC datetime. + end: UTC datetime. + time_step: pvlib time-step string. One of ``'1min'``, ``'15min'``, + ``'1h'``, ``'1d'``, ``'1M'``. + + Returns: + ``(processed_df, metadata)`` — DataFrame with a ``timestamp`` + column (ISO string) followed by per-variable columns, plus the + metadata dict pvlib returns. + + Raises: + CamsApiError: pvlib raised any exception during the request. + """ + email = self._get_email() + n_days = (end - start).days + 1 + _logger.info( + "CAMS request: lat=%.4f lon=%.4f step=%s n_days=%d", + latitude, + longitude, + time_step, + n_days, + ) + t0 = time.monotonic() + try: + raw_df, metadata = pvlib.iotools.get_cams( + latitude=latitude, + longitude=longitude, + start=start, + end=end, + email=email, + time_step=time_step, + timeout=self._timeout, + identifier=self._identifier, + ) + except Exception as exc: + raise CamsApiError( + f"CAMS fetch failed for ({latitude}, {longitude}) " + f"between {start.date()} and {end.date()}: {exc}" + ) from exc + elapsed = time.monotonic() - t0 + _logger.info("CAMS response: %d rows in %.1fs.", len(raw_df), elapsed) + + return self._process_dataframe(raw_df), metadata + + @staticmethod + def _process_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """Reset the datetime index and ISO-format the timestamp column.""" + processed = df.reset_index().rename(columns={"index": "timestamp"}) + processed["timestamp"] = processed["timestamp"].dt.strftime( + "%Y-%m-%dT%H:%M:%SZ" + ) + return processed + + +# Legacy module-level constants exported for backwards compatibility with +# code that imported them directly. New code should reference the ClassVar +# attributes on :class:`CAMSClient`. +ENV_PATH = CAMSClient.DEFAULT_ENV_PATH +EMAIL_ENV_KEY = CAMSClient.DEFAULT_EMAIL_ENV_KEY +DEFAULT_IDENTIFIER = CAMSClient.DEFAULT_IDENTIFIER +TIMEOUT_SECONDS = CAMSClient.DEFAULT_TIMEOUT_SECONDS diff --git a/src/susse/api_clients/cams/get_cams_data.py b/src/susse/api_clients/cams/get_cams_data.py deleted file mode 100644 index 6e9cb39a..00000000 --- a/src/susse/api_clients/cams/get_cams_data.py +++ /dev/null @@ -1,122 +0,0 @@ -import os -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, Optional, Tuple - -import pandas as pd -import pvlib -from dotenv import load_dotenv, set_key - -# Constants for configuration and magic strings -ENV_PATH = Path(".") / ".env" -EMAIL_ENV_KEY = "CAMS_EMAIL" -DEFAULT_IDENTIFIER = "cams_radiation" -TIMEOUT_SECONDS = 180 - - -class CAMSClient: - """ - Client for fetching CAMS radiation data via pvlib. - - Reads and stores user email in environment variables. - """ - - def __init__( - self, - env_path: Path = ENV_PATH, - email_env_key: str = EMAIL_ENV_KEY, - identifier: str = DEFAULT_IDENTIFIER, - timeout: int = TIMEOUT_SECONDS, - ) -> None: - self._env_path = env_path - self._email_env_key = email_env_key - self._identifier = identifier - self._timeout = timeout - - # Load environment variables from .env file - load_dotenv(dotenv_path=self._env_path) - - def _get_email(self) -> str: - """ - Retrieve the CAMS email from environment or prompt the user. - - Returns: - str: Registered CAMS email. - """ - email = os.getenv(self._email_env_key) - - if not email: - email = input("Enter your registered CAMS email: ").strip() - set_key(str(self._env_path), self._email_env_key, email) - - return email - - def fetch_data( - self, - latitude: float, - longitude: float, - start: datetime, - end: datetime, - time_step: str, - ) -> Dict[str, Any]: - """ - Fetch and process CAMS radiation data. - - Args: - latitude (float): Latitude of location. - longitude (float): Longitude of location. - start (datetime): Start datetime (UTC). - end (datetime): End datetime (UTC). - time_step (str): ISO 8601 duration string (e.g., 'PT1H'). - - Returns: - Dict[str, Any]: Dictionary containing records, column names, - metadata, and any error message. - """ - email = self._get_email() - - try: - raw_df, metadata = pvlib.iotools.get_cams( - latitude=latitude, - longitude=longitude, - start=start, - end=end, - email=email, - time_step=time_step, - timeout=self._timeout, - identifier=self._identifier, - ) - - processed_df = self._process_dataframe(raw_df) - - return { - "data": processed_df.to_dict(orient="records"), - "columns": list(processed_df.columns), - "metadata": metadata, - "error": None, - } - - except Exception as exc: - return { - "data": None, - "columns": None, - "metadata": None, - "error": str(exc), - } - - @staticmethod - def _process_dataframe(df: pd.DataFrame) -> pd.DataFrame: - """ - Reset index and format timestamp column. - - Args: - df (pd.DataFrame): Raw DataFrame with DateTimeIndex. - - Returns: - pd.DataFrame: Processed DataFrame with ISO-formatted timestamps. - """ - processed = df.reset_index().rename(columns={"index": "timestamp"}) - processed["timestamp"] = processed["timestamp"].dt.strftime( - "%Y-%m-%dT%H:%M:%SZ" - ) - return processed diff --git a/src/susse/api_clients/merra_2/__init__.py b/src/susse/api_clients/merra_2/__init__.py index c62c7ee4..d3cdc731 100644 --- a/src/susse/api_clients/merra_2/__init__.py +++ b/src/susse/api_clients/merra_2/__init__.py @@ -1,7 +1,13 @@ +"""MERRA-2 data access — Earthdata-authenticated OPeNDAP fetcher.""" + from .merra_config import Merra2Config -from .merra_data_fetcher import MerraDataFetcher -from .merra_download_manager import MerraDownloadManager +from .merra_daily_fetcher import MerraAuthError, MerraDailyFetcher, cos_zenith_aggregate from .merra_product import MerraProducts -from .merra_stream_config import MerraStreamConfig -from .merra_stream_data_fetcher import MerraDataStreamFetcher -from .merra_stream_session_manager import StreamSessionManager + +__all__ = [ + "Merra2Config", + "MerraAuthError", + "MerraDailyFetcher", + "MerraProducts", + "cos_zenith_aggregate", +] diff --git a/src/susse/api_clients/merra_2/merra_config.py b/src/susse/api_clients/merra_2/merra_config.py index 4935043b..4513d223 100644 --- a/src/susse/api_clients/merra_2/merra_config.py +++ b/src/susse/api_clients/merra_2/merra_config.py @@ -1,16 +1,24 @@ -from datetime import datetime +"""GES DISC MERRA-2 endpoint configuration + lat/lon → grid-index helpers. + +The OPeNDAP URL itself is built by :class:`MerraDailyFetcher`; this module +only owns the parts that are independent of the request shape: the base +URL, the per-collection database URL prefix, the YYYYMMDD file-name +convention, and the conversion from real lat/lon to MERRA-2's native grid +indices. +""" + +from datetime import date import numpy as np -from .merra_product import MerraProductData, MerraProducts +from .merra_product import MerraProductData class Merra2Config: - """ - This class contains the URLs used and the functions for generating download URLs for MERRA-2 data. It also contains - functions to convert longitude and latitude coordinates to merra2 coordinates, as well as other transformation. - Some of these functions are based on the implementation from here: - https://github.com/emilylaiken/merradownload + """Static configuration for MERRA-2 OPeNDAP access. + + Some of the lat/lon-to-grid translation logic is adapted from + https://github.com/emilylaiken/merradownload. """ BASE_URL = "https://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2" @@ -22,30 +30,13 @@ def generate_database_url(product_data: MerraProductData) -> str: return f"{Merra2Config.BASE_URL}/{product_data.database_name}" @staticmethod - def generate_download_link( - date: datetime, product_data: MerraProductData, lat: float, lon: float - ) -> str: - file_name = Merra2Config.create_file_name(date, product_data) - m_str = str(date.month).zfill(2) - y_str = str(date.year) - lat_geos5 = Merra2Config._translate_lat_to_geos5_native(lat) - lon_geos5 = Merra2Config._translate_lon_to_geos5_native(lon) - merra_lat = Merra2Config._find_closest_merra_coordinate( - lat_geos5, Merra2Config.MERRA_LAT_COORDS - ) - merra_lon = Merra2Config._find_closest_merra_coordinate( - lon_geos5, Merra2Config.MERRA_LON_COORDS - ) - suffix = f"{product_data.product_name}[0:1:23][{merra_lat}:1:{merra_lat}][{merra_lon}:1:{merra_lon}]" - url = f"{Merra2Config.generate_database_url(product_data)}/{y_str}/{m_str}/{file_name}.nc4?{suffix}" - return url - - @staticmethod - def create_file_name(date: datetime, product_data: MerraProductData) -> str: - d_str = str(date.day).zfill(2) - m_str = str(date.month).zfill(2) - y_str = str(date.year) - file_num = Merra2Config._create_year_url(date.year) + def create_file_name(d: date, product_data: MerraProductData) -> str: + # ``date`` accepts both ``datetime.date`` and ``datetime.datetime`` + # (datetime subclasses date). The function only reads .day/.month/.year. + d_str = str(d.day).zfill(2) + m_str = str(d.month).zfill(2) + y_str = str(d.year) + file_num = Merra2Config._create_year_url(d.year) return f"MERRA2_{file_num}.{product_data.database_id}.{y_str}{m_str}{d_str}.nc4" @staticmethod diff --git a/src/susse/api_clients/merra_2/merra_daily_fetcher.py b/src/susse/api_clients/merra_2/merra_daily_fetcher.py new file mode 100644 index 00000000..90b732de --- /dev/null +++ b/src/susse/api_clients/merra_2/merra_daily_fetcher.py @@ -0,0 +1,675 @@ +"""Daily-aggregated MERRA-2 fetcher tuned for the warehouse-ops pipeline. + +MERRA-2's native temporal resolution is hourly (or 3-hourly for some +collections), but the warehouse stores daily values. This fetcher pulls +the native cadence over OPeNDAP for a region of points + date range + +variable list, applies a cosine-zenith-weighted mean to collapse to a +single daily value per (point, date, variable), and returns a long-format +DataFrame matching the MERRA-region ingest job contract. + +Region-shaped fetching +---------------------- +GES DISC OPeNDAP serves region selects (lat-and-lon index ranges) at +roughly the same wall-clock cost as a single-cell select — server +overhead dominates the per-cell read cost for any reasonable region. +This fetcher therefore issues **one OPeNDAP call per (date, variable)** +covering all points in the request, and slices results client-side. +Compared to the pre-refactor "one call per (date, variable, point)" +shape this is ~100× faster for grid-sized requests. + +The 1-point case is a degenerate region (1×1 bbox) and is served by the +same code path; :meth:`fetch_long_for_location` is a thin wrapper that +projects the region output back to ``(date, variable_id, value)``. + +Why this fetcher rather than the legacy ``MerraDataStreamFetcher``: + +* The legacy fetcher hard-codes ``[0:1:23]`` for the time slice, which + works for ``tavg1_*`` collections (24 hourly steps) but fails for + ``inst3_*`` (8 three-hourly steps). We need to support both. +* The legacy fetcher returns a custom result object that needs further + reshaping; the new one returns a plain long-format DataFrame. +* The legacy fetcher pulls credentials through ``MerraDownloadManager`` + via the OS keyring; we want a simpler ``.env`` flow consistent with + CAMS_EMAIL. +""" + +from __future__ import annotations + +import logging +import os +import threading +import time as _time +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataclasses import dataclass +from datetime import date as _date +from datetime import datetime, timedelta, timezone + +import numpy as np +import pandas as pd +import pvlib +from dotenv import load_dotenv +from pydap.cas.urs import setup_session +from pydap.client import open_url +from requests.adapters import HTTPAdapter +from urllib3.util.retry import Retry + +from .merra_config import Merra2Config +from .merra_product import MerraProductData, MerraProducts + +_logger = logging.getLogger(__name__) + +# OPeNDAP requests are I/O-bound and serially issued at ~2.5 s each, so +# bulk ingest takes a week without parallelism. 8 workers gives ~8× +# wall-clock speedup with low (<5%) 503-loss observed empirically. +# Higher counts (16+) trigger noticeable server-side throttling; lower +# counts leave wall-time on the table. +# +# Transient failures are not catastrophic: each (date, geohash5) MERGE +# is idempotent, so re-running ingest jobs picks up days that 503'd +# in a previous run. Plan on multiple ingest runs over a few days, +# rather than expecting one run to hit every day. +_DEFAULT_MAX_WORKERS = 8 + +# How often to log a "X / N done" line during a long fan-out. Per-request +# logs are still emitted for every fetch, but the progress line aggregates +# them so a long ingest produces a readable summary even with workers +# interleaving. +_PROGRESS_LOG_EVERY = 100 + +# urllib3 retry policy for transient OPeNDAP failures. NASA's GES DISC +# cluster periodically returns 503 under load and the default +# 3-attempts-no-backoff policy fails on most bursts; an 8-attempt +# exponential-backoff policy (~64 s ceiling) recovers from the typical +# load spike. ``status_forcelist`` covers the gateway errors we see in +# practice; ``respect_retry_after_header=True`` honours any Retry-After +# the server sends back. +_REQUEST_RETRY = Retry( + total=8, + backoff_factor=0.5, + status_forcelist=(502, 503, 504), + allowed_methods=frozenset(("GET", "HEAD")), + respect_retry_after_header=True, +) + + +class MerraAuthError(RuntimeError): + """Raised when Earthdata credentials are missing or rejected.""" + + +# Hard upper bound per OPeNDAP query. Single-day fetches normally complete +# in 1–3 seconds; anything past 5 minutes is almost certainly a hung +# connection rather than a slow response. +_REQUEST_TIMEOUT_SECONDS = 300 + +# MERRA-2's missing-data fill value is in the 1e15 range. Use a generous +# threshold so we don't mistake legitimate values (e.g. ozone Dobson units) +# as fills. +_FILL_VALUE_THRESHOLD = 1e14 + + +# Hourly cadence per MERRA-2 collection prefix. The first matching prefix +# wins. Add a row here when introducing a new collection. +_COLLECTION_CADENCE: tuple[tuple[str, int], ...] = ( + ("tavg1_", 24), # 1-hour time-averaged → 24 timesteps per day + ("inst1_", 24), # 1-hour instantaneous + ("tavg3_", 8), # 3-hour time-averaged → 8 timesteps per day + ("inst3_", 8), # 3-hour instantaneous + ("statD_", 1), # daily statistic + ("const_", 1), # constant +) + + +def _cadence_for(database_id: str) -> int: + """Number of timesteps per day for the given MERRA-2 collection.""" + for prefix, n in _COLLECTION_CADENCE: + if database_id.startswith(prefix): + return n + raise ValueError( + f"Unknown MERRA-2 collection cadence for database_id={database_id!r}. " + f"Add it to _COLLECTION_CADENCE in {__name__}." + ) + + +def _timestamps_for(date: _date, n_timesteps: int) -> pd.DatetimeIndex: + """UTC timestamps for one day's MERRA-2 sub-daily data. + + ``tavg1_*`` collections are time-averaged over a centered hour, so the + canonical timestamp is ``HH:30``. ``inst3_*`` are instantaneous + snapshots every 3 hours starting at ``00:00``. Constants (1 timestep) + use ``12:00``. + """ + base = datetime.combine(date, datetime.min.time(), tzinfo=timezone.utc) + if n_timesteps == 24: + return pd.DatetimeIndex( + [base + timedelta(hours=h, minutes=30) for h in range(24)] + ) + if n_timesteps == 8: + return pd.DatetimeIndex([base + timedelta(hours=h * 3) for h in range(8)]) + if n_timesteps == 1: + return pd.DatetimeIndex([base + timedelta(hours=12)]) + raise ValueError( + f"Unsupported timestep count: {n_timesteps}. " + f"Add a branch to _timestamps_for() if a new cadence is needed." + ) + + +def cos_zenith_aggregate( + sub_daily_values: np.ndarray, + sub_daily_times: pd.DatetimeIndex, + latitude: float, + longitude: float, +) -> float: + """Aggregate sub-daily values to one daily number, weighted by cos(zenith). + + Daytime hours dominate the result; nighttime hours contribute zero + (cos(zenith) clipped at 0). NaN values in ``sub_daily_values`` are + excluded from both the numerator and the weight sum. + + Returns ``nan`` if every weight is zero (polar night — won't happen + in sub-Saharan Africa, but defensive) or every value is NaN. + """ + if sub_daily_values.size == 0: + return float("nan") + if sub_daily_values.size != len(sub_daily_times): + raise ValueError( + f"Mismatched lengths: {sub_daily_values.size} values vs " + f"{len(sub_daily_times)} timestamps." + ) + sp = pvlib.solarposition.get_solarposition( + time=sub_daily_times, latitude=latitude, longitude=longitude + ) + weights = np.cos(np.radians(sp.zenith.values)).clip(min=0.0) + valid = ~np.isnan(sub_daily_values) + if not valid.any(): + return float("nan") + weights = weights * valid + if weights.sum() <= 0: + return float("nan") + values = np.where(valid, sub_daily_values, 0.0) + return float((values * weights).sum() / weights.sum()) + + +@dataclass(frozen=True) +class _Earthdata: + username: str + password: str + + @classmethod + def from_env(cls) -> "_Earthdata": + """Read credentials from environment, loading ``.env`` if present.""" + load_dotenv() + username = os.getenv("EARTHDATA_USERNAME") + password = os.getenv("EARTHDATA_PASSWORD") + if not username or not password: + raise MerraAuthError( + "EARTHDATA_USERNAME and EARTHDATA_PASSWORD must be set " + "(in .env or shell environment) to fetch MERRA-2 data. " + "Register at https://urs.earthdata.nasa.gov/ if needed, then " + "approve the 'NASA GESDISC DATA ARCHIVE' application via " + "your Earthdata profile." + ) + return cls(username=username, password=password) + + +@dataclass(frozen=True) +class _Bbox: + """MERRA-2 grid-index bounding box (inclusive on all four sides).""" + + lat_idx_lo: int + lat_idx_hi: int + lon_idx_lo: int + lon_idx_hi: int + + @property + def n_lat(self) -> int: + return self.lat_idx_hi - self.lat_idx_lo + 1 + + @property + def n_lon(self) -> int: + return self.lon_idx_hi - self.lon_idx_lo + 1 + + @property + def n_cells(self) -> int: + return self.n_lat * self.n_lon + + +class MerraDailyFetcher: + """Daily-aggregated MERRA-2 fetcher with region-shaped requests. + + The public entry point is :meth:`fetch_region`, which fans out one + OPeNDAP call per (date, variable) covering all requested points. Each + call's response is sliced client-side and aggregated per point with + cosine-zenith weighting. Per-(date, variable) tasks run concurrently + via a thread pool — NASA's OPeNDAP at goldsmr4 is I/O-bound (~2.5 s + per round-trip), so threading delivers near-linear speedup until we + hit the worker count. + + :meth:`fetch_long_for_location` is a 1-point convenience wrapper that + drops the lat/lon columns from the region output. The portal inference + path (single point, single date) uses this; bulk ingest uses + :meth:`fetch_region`. + + Authenticated session is created lazily on the first request and + reused across all worker threads. ``requests.Session`` is documented + as thread-safe for concurrent reads; we additionally guard the + create-session-on-first-use path with a lock for safety. Set + ``max_workers=1`` (or use the constructor default of 1 in unit tests) + to disable parallelism entirely. + """ + + def __init__( + self, + credentials: _Earthdata | None = None, + max_workers: int = _DEFAULT_MAX_WORKERS, + ) -> None: + if max_workers < 1: + raise ValueError(f"max_workers must be >= 1, got {max_workers}.") + self._credentials = credentials # None → load from env on first use + self._session = None + self._session_lock = threading.Lock() + self._max_workers = max_workers + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def fetch_region( + self, + *, + points: tuple[tuple[float, float], ...], + date_start: _date, + date_end: _date, + api_codes: tuple[str, ...], + ) -> pd.DataFrame: + """Fetch daily values for many points, dates, and variables. + + Issues one OPeNDAP bbox call per (date, variable), covering the + smallest grid-index rectangle that encloses all ``points``. Each + call's sub-daily data is sliced per point and reduced to a single + daily number via cosine-zenith weighting. + + Args: + points: tuple of ``(latitude, longitude)`` pairs in degrees. + Latitudes in [-90, 90], longitudes in [-180, 180]. A + single-point tuple is the degenerate 1×1 case and is + served by the same code path. + date_start: inclusive UTC date. + date_end: inclusive UTC date. + api_codes: tuple of MERRA-2 ``product_name`` values (e.g. + ``("TOTEXTTAU", "TOTSCATAU")``). Looked up in + :class:`MerraProducts`. + + Returns: + Long-format DataFrame with columns + ``(date, latitude, longitude, variable_id, value)``. One row + per (point, date, variable) tuple where the underlying + sub-daily data was non-missing. Row order is non-deterministic + when ``max_workers > 1``; sort by + ``(variable_id, date, latitude, longitude)`` if ordering matters. + """ + if not points: + raise ValueError("fetch_region requires at least one point.") + if date_start > date_end: + raise ValueError( + f"date_start ({date_start}) must be <= date_end ({date_end})." + ) + if not api_codes: + return self._empty_region_frame() + + point_indices = self._grid_indices_for_points(points) + bbox = self._bbox_enclosing(point_indices) + + tasks = self._build_tasks(date_start, date_end, api_codes) + if not tasks: + return self._empty_region_frame() + + # Pre-authenticate so the first request doesn't race the others + # for session creation. + first_api_code, first_product, first_cadence, first_day = tasks[0] + sample_url = self._build_bbox_url( + product_data=first_product, + date=first_day, + bbox=bbox, + cadence=first_cadence, + ) + self._authenticated_session(sample_url) + + rows: list[dict] = [] + n_total = len(tasks) + n_done = 0 + + def _run(task: tuple[str, MerraProductData, int, _date]) -> list[dict]: + api_code, product_data, cadence, day = task + return self._fetch_region_for_task( + api_code=api_code, + product_data=product_data, + cadence=cadence, + day=day, + points=points, + point_indices=point_indices, + bbox=bbox, + ) + + if self._max_workers == 1: + iterator = (_run(task) for task in tasks) + pool = None + else: + pool = ThreadPoolExecutor( + max_workers=self._max_workers, thread_name_prefix="merra-fetch" + ) + futures = [pool.submit(_run, task) for task in tasks] + iterator = (fut.result() for fut in as_completed(futures)) + + try: + for task_rows in iterator: + n_done += 1 + if task_rows: + rows.extend(task_rows) + if n_done % _PROGRESS_LOG_EVERY == 0: + _logger.info( + "MERRA-2 fetcher progress: %d / %d (%.1f%%)", + n_done, + n_total, + 100.0 * n_done / n_total, + ) + finally: + if pool is not None: + pool.shutdown(wait=True) + + return pd.DataFrame( + rows, + columns=("date", "latitude", "longitude", "variable_id", "value"), + ) + + def fetch_long_for_location( + self, + *, + latitude: float, + longitude: float, + date_start: _date, + date_end: _date, + api_codes: tuple[str, ...], + ) -> pd.DataFrame: + """Fetch daily-aggregated values for one point and date range. + + Thin 1-point wrapper around :meth:`fetch_region` that drops the + lat/lon columns from the output, preserving the legacy contract + used by the portal inference path. + + Returns: + Long-format DataFrame with columns ``(date, variable_id, value)``. + ``variable_id`` echoes the input ``api_code``; the caller is + responsible for any mapping to warehouse variable_ids. + """ + df = self.fetch_region( + points=((latitude, longitude),), + date_start=date_start, + date_end=date_end, + api_codes=api_codes, + ) + if df.empty: + return pd.DataFrame(columns=("date", "variable_id", "value")) + return df[["date", "variable_id", "value"]].reset_index(drop=True) + + # ------------------------------------------------------------------ + # Region task internals + # ------------------------------------------------------------------ + + def _fetch_region_for_task( + self, + *, + api_code: str, + product_data: MerraProductData, + cadence: int, + day: _date, + points: tuple[tuple[float, float], ...], + point_indices: tuple[tuple[int, int], ...], + bbox: _Bbox, + ) -> list[dict]: + """One bbox fetch + per-point cosine-zenith aggregation. + + Returns a list of row dicts (possibly empty if the fetch failed + or every requested point's sub-daily slice is all-NaN). + """ + raw = self._fetch_bbox_raw( + product_data=product_data, + date=day, + bbox=bbox, + cadence=cadence, + ) + if raw is None: + return [] + timestamps = _timestamps_for(day, cadence) + rows: list[dict] = [] + for (lat, lon), (lat_idx, lon_idx) in zip(points, point_indices): + sub_daily = raw[ + :, + lat_idx - bbox.lat_idx_lo, + lon_idx - bbox.lon_idx_lo, + ].astype(float) + sub_daily = np.where(sub_daily > _FILL_VALUE_THRESHOLD, np.nan, sub_daily) + if np.isnan(sub_daily).all(): + continue + daily = cos_zenith_aggregate(sub_daily, timestamps, lat, lon) + if not pd.notna(daily): + continue + rows.append( + { + "date": day, + "latitude": lat, + "longitude": lon, + "variable_id": api_code, + "value": float(daily), + } + ) + return rows + + def _fetch_bbox_raw( + self, + *, + product_data: MerraProductData, + date: _date, + bbox: _Bbox, + cadence: int, + ) -> np.ndarray | None: + """Single OPeNDAP bbox fetch. + + Returns a ``(cadence, n_lat, n_lon)`` float array, or ``None`` on + transient failure or shape mismatch. Fill-value masking is applied + by the caller (per-point, after slicing). + """ + url = self._build_bbox_url( + product_data=product_data, + date=date, + bbox=bbox, + cadence=cadence, + ) + session = self._authenticated_session(url) + _logger.info( + "MERRA-2 region request: %s %s lat_idx=[%d:%d] lon_idx=[%d:%d] " + "(cadence=%d/day, %d cells)", + product_data.product_name, + date.isoformat(), + bbox.lat_idx_lo, + bbox.lat_idx_hi, + bbox.lon_idx_lo, + bbox.lon_idx_hi, + cadence, + bbox.n_cells, + ) + t0 = _time.monotonic() + try: + dataset = open_url(url, session=session, protocol="dap4") + raw = np.array(dataset[product_data.product_name][:]) + except Exception as exc: + _logger.warning( + "MERRA-2 region fetch failed for %s %s: %s", + product_data.product_name, + date.isoformat(), + exc, + ) + return None + elapsed = _time.monotonic() - t0 + expected_shape = (cadence, bbox.n_lat, bbox.n_lon) + if raw.shape != expected_shape: + _logger.warning( + "Expected shape %s for %s on %s, got %s. Skipping.", + expected_shape, + product_data.product_name, + date.isoformat(), + raw.shape, + ) + return None + _logger.info( + "MERRA-2 region response: %s shape=%s in %.1fs.", + product_data.product_name, + raw.shape, + elapsed, + ) + return raw + + @staticmethod + def _build_tasks( + date_start: _date, + date_end: _date, + api_codes: tuple[str, ...], + ) -> list[tuple[str, MerraProductData, int, _date]]: + """Enumerate (api_code, product_data, cadence, day) work units.""" + tasks: list[tuple[str, MerraProductData, int, _date]] = [] + for api_code in api_codes: + product_data = MerraDailyFetcher._product_data_for(api_code) + cadence = _cadence_for(product_data.database_id) + day = date_start + while day <= date_end: + tasks.append((api_code, product_data, cadence, day)) + day += timedelta(days=1) + return tasks + + @staticmethod + def _grid_indices_for_points( + points: tuple[tuple[float, float], ...], + ) -> tuple[tuple[int, int], ...]: + """Return per-point ``(lat_idx, lon_idx)`` MERRA-2 grid indices.""" + return tuple(MerraDailyFetcher._grid_indices(lat, lon) for lat, lon in points) + + @staticmethod + def _bbox_enclosing( + point_indices: tuple[tuple[int, int], ...], + ) -> _Bbox: + """Smallest grid-index bbox enclosing all input points.""" + if not point_indices: + raise ValueError("Cannot compute bbox over zero points.") + lat_indices = [p[0] for p in point_indices] + lon_indices = [p[1] for p in point_indices] + return _Bbox( + lat_idx_lo=min(lat_indices), + lat_idx_hi=max(lat_indices), + lon_idx_lo=min(lon_indices), + lon_idx_hi=max(lon_indices), + ) + + @staticmethod + def _empty_region_frame() -> pd.DataFrame: + return pd.DataFrame( + columns=("date", "latitude", "longitude", "variable_id", "value") + ) + + # ------------------------------------------------------------------ + # Auth + URL builder + # ------------------------------------------------------------------ + + def _authenticated_session(self, url: str): + # Double-checked locking around lazy session creation. Once set, + # ``self._session`` is read without the lock from worker threads; + # ``requests.Session`` handles concurrent reads safely. + if self._session is not None: + return self._session + with self._session_lock: + if self._session is None: + creds = self._credentials or _Earthdata.from_env() + try: + session = setup_session( + creds.username, creds.password, check_url=url + ) + except Exception as exc: + raise MerraAuthError( + "Earthdata authentication failed. Check that " + "EARTHDATA_USERNAME / EARTHDATA_PASSWORD are correct " + "and that the 'NASA GESDISC DATA ARCHIVE' application " + "is approved on your Earthdata profile." + ) from exc + # ``setup_session`` returns ``None`` (rather than raising) + # when its check_url probe fails — typically because + # goldsmr4 is briefly busy at startup. Fall back to a + # plain ``requests.Session`` with basic auth; URS handles + # the redirect chain via ``.netrc`` if the user has set + # one up, and the actual per-day fetches will validate + # auth on first use. + if session is None: + import requests + + _logger.warning( + "pydap.setup_session returned None (check_url probe " + "failed). Falling back to a plain requests.Session " + "with basic auth + .netrc redirect handling." + ) + session = requests.Session() + session.auth = (creds.username, creds.password) + # Replace the default HTTPAdapter on this session with one + # that retries 503/502/504 with exponential backoff. NASA's + # OPeNDAP returns 503 under load and the default 3-attempt + # policy is too eager to give up. + adapter = HTTPAdapter(max_retries=_REQUEST_RETRY) + session.mount("https://", adapter) + session.mount("http://", adapter) + self._session = session + return self._session + + @staticmethod + def _grid_indices(latitude: float, longitude: float) -> tuple[int, int]: + lat_geos5 = Merra2Config._translate_lat_to_geos5_native(latitude) + lon_geos5 = Merra2Config._translate_lon_to_geos5_native(longitude) + merra_lat_idx = int( + Merra2Config._find_closest_merra_coordinate( + lat_geos5, Merra2Config.MERRA_LAT_COORDS + ) + ) + merra_lon_idx = int( + Merra2Config._find_closest_merra_coordinate( + lon_geos5, Merra2Config.MERRA_LON_COORDS + ) + ) + return merra_lat_idx, merra_lon_idx + + @staticmethod + def _build_bbox_url( + *, + product_data: MerraProductData, + date: _date, + bbox: _Bbox, + cadence: int, + ) -> str: + """OPeNDAP DAP4 URL for a region select (1×1 degenerate is fine).""" + file_name = Merra2Config.create_file_name(date, product_data) + m_str = str(date.month).zfill(2) + y_str = str(date.year) + time_slice = f"[0:1:{cadence - 1}]" + suffix = ( + f"dap4.ce=/{product_data.product_name}{time_slice}" + f"[{bbox.lat_idx_lo}:1:{bbox.lat_idx_hi}]" + f"[{bbox.lon_idx_lo}:1:{bbox.lon_idx_hi}]" + ) + return ( + f"{Merra2Config.generate_database_url(product_data)}" + f"/{y_str}/{m_str}/{file_name}?{suffix}" + ) + + @staticmethod + def _product_data_for(api_code: str) -> MerraProductData: + for product in MerraProducts: + if product.value.product_name == api_code: + return product.value + raise KeyError( + f"No MerraProducts entry has product_name={api_code!r}. " + f"Add it to merra_product.py before requesting it via the fetcher." + ) diff --git a/src/susse/api_clients/merra_2/merra_data_fetcher.py b/src/susse/api_clients/merra_2/merra_data_fetcher.py deleted file mode 100644 index 3c950ed6..00000000 --- a/src/susse/api_clients/merra_2/merra_data_fetcher.py +++ /dev/null @@ -1,117 +0,0 @@ -import logging -import os -import re -from datetime import datetime, timedelta -from pathlib import Path -from typing import List, Optional - -import pandas as pd -import xarray as xr -from geopy import location as Glocation - -from .merra_config import Merra2Config -from .merra_data_result import MerraDataMetadata, MerraDataResult -from .merra_download_manager import MerraDownloadManager -from .merra_product import MerraProductData, MerraProducts - - -class MerraDataFetcher: - """ - This class handles the data fetching through the MERRA2 and is meant as the primary interface for data fetching from - MERRA2. It utilizes the MerraDownloadManager and Merra2Config to translate a request for a product at a specific - location and time to a dowloadable link and downloads the file of interest. It also extracts the relevant data from - the obtained file. - """ - - _DOWNLOAD_FOLDER = str( - Path(__file__).resolve().parents[4] / "merra_files_downloaded" - ) - - def __init__(self, base_download_folder: str = None): - self._download_manager = MerraDownloadManager() - self._base_download_folder = base_download_folder or self._DOWNLOAD_FOLDER - - def set_username_pw(self, username: str, password: str) -> None: - """ - Encrypts and stores the username and password. - """ - self._download_manager.set_username_pw(username, password) - - def fetch_product_result( - self, - product: MerraProductData, - location: Glocation, - start_date: datetime, - end_date: datetime, - ) -> Optional[MerraDataResult]: - dates = [ - start_date + timedelta(days=x) - for x in range((end_date - start_date).days + 1) - ] - - self._download_data_if_necessary(dates, location, product) - - merra_result = self._read_downloaded_files(dates, location, product) - - if not merra_result: - logging.warning( - f"Product '{product.name}' for location '{str(location)}' between {start_date} and {end_date} is empty." - ) - return None - else: - return merra_result - - def _read_downloaded_files( - self, dates: List[datetime], location: Glocation, product: MerraProductData - ) -> Optional[MerraDataResult]: - - product_folder = self.get_product_folder(product, location) - - if not os.path.exists(product_folder): - raise RuntimeError(f"Product folder '{product_folder}' does not exist.") - - data_frames = [] - file_names = [] - - for date in dates: - file_name = Merra2Config.create_file_name(date, product) - file_path = os.path.join(product_folder, file_name) - - if not os.path.exists(file_path): - logging.warning( - f"File '{file_name}' not found in '{product_folder}'. Skipping." - ) - continue - - ds = xr.open_dataset(file_path) - data_result = MerraDataResult.from_xarray(ds, location, product.name) - ds.close() - data_frames.append(data_result.to_df()) - file_names.append(file_name) - - if len(data_frames) > 0: - concatenated = pd.concat(data_frames) - metadata = MerraDataMetadata(file_names, location, product.name) - return MerraDataResult(metadata, concatenated) - else: - return None - - def _download_data_if_necessary( - self, dates: List[datetime], location: Glocation, product: MerraProductData - ) -> None: - urls: List[str] = [] - for date in dates: - urls.append( - Merra2Config.generate_download_link( - date, product, location.latitude, location.longitude - ) - ) - self._download_manager.download_from_urls( - urls, self.get_product_folder(product, location) - ) - - def get_product_folder(self, product: MerraProductData, location: Glocation) -> str: - siimple_loc = re.split(", |_|-|!", str(location))[0] - return str( - Path(self._base_download_folder) / product.product_name / siimple_loc - ) diff --git a/src/susse/api_clients/merra_2/merra_data_result.py b/src/susse/api_clients/merra_2/merra_data_result.py deleted file mode 100644 index 40345b3f..00000000 --- a/src/susse/api_clients/merra_2/merra_data_result.py +++ /dev/null @@ -1,115 +0,0 @@ -import logging -from datetime import timedelta -from typing import List, Optional, Union - -import numpy as np -import pandas as pd -import xarray as xr -from geopy import location as Glocation - - -class MerraDataMetadata: - - def __init__(self, filename: Union[str, List[str]], location: Glocation, name: str): - self.filename = filename - self.location = location - self.name = name - - -class MerraDataResult: - """ - This class represents the result from a Merra request. It can store various variables and provides convenience - functions to access the data as np.ndarray or pd dataframe. - """ - - _TIME_KEY = "time" - _FILENAME_KEY = "Filename" - - def __init__(self, metadata: MerraDataMetadata, data: pd.DataFrame): - self._metadata = metadata - self._data = data - - @staticmethod - def from_xarray(ds: xr.Dataset, location: Glocation, name: str): - - filename = ds.attrs[MerraDataResult._FILENAME_KEY] - metadata = MerraDataMetadata(filename, location, name) - - data_vars = list(ds.data_vars.keys()) - if not data_vars: - logging.warning("No data variables found in the dataset.") - raise ValueError("Dataset contains no data variables.") - - data_frames = [] - - for var_name in data_vars: - var_data = ds[var_name].values - if not var_data.size: - logging.warning( - f"Variable {var_name} for {name} in {location} read from {filename} is empty, skipping." - ) - continue - - if MerraDataResult._TIME_KEY in ds.dims: - time_values = ds[MerraDataResult._TIME_KEY].values - if time_values.size == 0: - msg = f"Time values for {var_name} for {name} in {location} read from {filename} is empty." - logging.warning(msg) - raise ValueError(f"No time values found for variable {var_name}.") - - start_date = pd.to_datetime( - ds.attrs.get("RangeBeginningDate", "1970-01-01") - ) - time_index = [start_date + timedelta(hours=int(t)) for t in time_values] - else: - time_index = np.arange( - len(var_data) - ).tolist() # Default to range if no time dimension - - df = pd.DataFrame(var_data.flatten(), index=time_index, columns=[var_name]) - - data_frames.append(df) - - combined_df = pd.concat(data_frames, axis=1) - return MerraDataResult(metadata, combined_df) - - def to_df(self) -> pd.DataFrame: - return self._data - - @property - def location(self) -> Glocation: - """ - Returns the location for the dataset. - """ - return self._metadata.location - - @property - def filename(self) -> Union[str, List[str]]: - """ - Returns the filename of the NetCDF file. - """ - return self._metadata.filename - - @property - def name(self) -> str: - return self._metadata.name - - def get_variable_names(self) -> List[str]: - """ - Returns a list of variable names in the dataset. - """ - return self._data.columns.tolist() - - def to_np(self) -> np.ndarray: - return self._data[self.get_variable_names()].values - - def get_variable_as_numpy(self, var_name) -> Optional[np.ndarray]: - """ - Returns the specified variable as a NumPy array. - If the variable does not exist, it returns None. - """ - if var_name in self.get_variable_names(): - return self._data[var_name].values - else: - print(f"Variable '{var_name}' not found in data read from {self.filename}.") - return None diff --git a/src/susse/api_clients/merra_2/merra_data_stream_result.py b/src/susse/api_clients/merra_2/merra_data_stream_result.py deleted file mode 100644 index e814f12d..00000000 --- a/src/susse/api_clients/merra_2/merra_data_stream_result.py +++ /dev/null @@ -1,66 +0,0 @@ -from datetime import datetime -from typing import Dict, List, Union - -import numpy as np -from geopy import location as Glocation - -from .merra_product import MerraProductData, MerraProducts - - -class MerraStreamDataResult: - - _FILL_VALUE = 999999986991104.0 - - def __init__( - self, - data: Dict[str, List[float]], - product_name: str, - location: Glocation, - start_date: datetime, - end_date: datetime, - ): - """ - Initialize the result object with data, product name, location, and dates. - """ - self._raw_data = data - self._product_name = product_name - self._location = location - self._start_date = start_date - self._end_date = end_date - self._fill_value = self._FILL_VALUE - - @classmethod - def from_data( - cls, - data: Dict[str, List[float]], - product_data: MerraProductData, - location: Glocation, - start_date: datetime, - end_date: datetime, - ): - """ - Class method to create an instance from raw data. - """ - return cls(data, product_data.product_name, location, start_date, end_date) - - @property - def product_name(self) -> str: - return self._product_name - - @property - def data(self) -> Dict[str, List[float]]: - """ - Return the cleaned data where fill values are replaced with NaN. - """ - return { - date: self._clean_day_data(day_data) - for date, day_data in self._raw_data.items() - } - - def _clean_day_data(self, day_data: List[float]) -> List[float]: - """ - Clean the data for a single day by replacing fill values with NaN. - """ - day_data_np = np.array(day_data) - cleaned_data = np.where(day_data_np == self._fill_value, np.nan, day_data_np) - return cleaned_data.tolist() diff --git a/src/susse/api_clients/merra_2/merra_download_manager.py b/src/susse/api_clients/merra_2/merra_download_manager.py deleted file mode 100644 index 1507245d..00000000 --- a/src/susse/api_clients/merra_2/merra_download_manager.py +++ /dev/null @@ -1,290 +0,0 @@ -import getpass -import logging -import os -import re -import urllib.error -import urllib.request -import urllib.response -from functools import partial -from http import cookiejar -from multiprocessing.dummy import Pool as Threadpool -from typing import List, Optional, Tuple, Union - -import keyring -import keyring.errors -import requests -from cryptography.fernet import Fernet -from requests.adapters import HTTPAdapter -from urllib3.util.retry import Retry - - -class MerraDownloadManager: - """ - This class handles the download of MERRA2 files. It also takes care of the authentification and storage of username - and passwords. The basic logic of authentification is based on the code available here; - https://github.com/emilylaiken/merradownload - - The user credentials are stored in a local keyring, but can also be set from outside through set_username_pw. - Inside the class the credentials are stored only in encrypted form - - To obtain a username and password one has to register https://urs.earthdata.nasa.gov/ - """ - - _TOP_LEVEL_URL = "https://urs.earthdata.nasa.gov" - _SERVICE_NAME = "nasa_merra2" - _KEYRING_ENCRYPTION_KEY = "fernet_key" - - def __init__(self) -> None: - self._auth_session: Optional[requests.Session] = None - self._encrypted_username: Optional[bytes] = None - self._encrypted_password: Optional[bytes] = None - self.cipher_suite = self._get_or_create_cipher_key() - - def _get_or_create_cipher_key(self) -> Fernet: - """ - Fetch or generate an encryption key for Fernet and store it in the keyring. - """ - key_str = keyring.get_password( - MerraDownloadManager._SERVICE_NAME, self._KEYRING_ENCRYPTION_KEY - ) - if key_str is None: - # Generate a new key and store it in the keyring - key = Fernet.generate_key() - keyring.set_password( - MerraDownloadManager._SERVICE_NAME, - self._KEYRING_ENCRYPTION_KEY, - key.decode(), - ) - logging.info("Generated and stored encryption key securely.") - else: - key = ( - key_str.encode() - ) # The keyring returns it as a string, convert back to bytes - - return Fernet(key) - - def set_username_pw(self, username: str, password: str) -> None: - """ - Encrypts and stores the username and password. - """ - self._encrypted_username = self.cipher_suite.encrypt(username.encode()) - self._encrypted_password = self.cipher_suite.encrypt(password.encode()) - - def get_credentials(self) -> Tuple[str, str]: - if ( - self._encrypted_username is not None - and self._encrypted_password is not None - ): - decrypted_username = self.cipher_suite.decrypt( - self._encrypted_username - ).decode() - decrypted_password = self.cipher_suite.decrypt( - self._encrypted_password - ).decode() - return decrypted_username, decrypted_password - - try: - username = keyring.get_password( - MerraDownloadManager._SERVICE_NAME, "username" - ) - password = keyring.get_password( - MerraDownloadManager._SERVICE_NAME, "password" - ) - - if username is None or password is None: - logging.info( - "NASA GES-DISC for MERRA-2 credentials not found in keyring." - ) - username = input("Enter your NASA GES-DISC for MERRA-2 username: ") - password = getpass.getpass( - "Enter your NASA GES-DISC for MERRA-2 password: " - ) - - keyring.set_password( - MerraDownloadManager._SERVICE_NAME, "username", username - ) - keyring.set_password( - MerraDownloadManager._SERVICE_NAME, "password", password - ) - logging.info("Credentials stored securely in the keyring.") - - return username, password - - except keyring.errors.KeyringError as e: - logging.error(f"Keyring error: {e}") - username = input("Enter your NASA GES-DISC username for MERRA-2: ") - password = getpass.getpass( - "Enter your NASA GES-DISC password for MERRA-2: " - ) - self.set_username_pw(username, password) - return username, password - - def session_authenticated(self) -> bool: - return self._auth_session is not None - - def authenticate_session(self, authentication_url: str) -> None: - self._auth_session = self.__create_authenticated_session(authentication_url) - if self._auth_session: - logging.info("Session authenticated successfully.") - else: - logging.error("Failed to authenticate session.") - - def download_from_urls( - self, urls: Union[str, List[str]], download_folder: str, nr_of_threads=4 - ): - if type(urls) is str: - urls = [urls] - - if not os.path.exists(download_folder): - os.makedirs(download_folder) - - download_wrapper_with_folder = partial( - self._mp_download_wrapper, download_folder=download_folder - ) - with Threadpool(nr_of_threads) as p: - p.map(download_wrapper_with_folder, urls) - - @staticmethod - def extract_filename_from_url(url: str) -> str: - """ - Extracts the filename from the url. This method can also be used to check - if the links have the correct structure - - """ - # Extract everything between a leading / and .nc4? . The problem with using this without any - # other classification is, that the URLs have multiple / in their structure. The expressions [^/]* matches - # everything but /. Combined with the outer expressions, this only matches the part between the last / and .nc4? - reg_exp = r"(?<=/)[^/]*(?=.nc4?)" - matched_entries = re.search(reg_exp, url) - file_name = matched_entries.group(0) if matched_entries else "" - if file_name: - logging.info(f"Extracted filename: {file_name}") - else: - logging.warning(f"Failed to extract filename from URL: {url}") - return file_name - - def _mp_download_wrapper(self, url: str, download_folder: str): - """ - Wrapper for parallel download. The function name cannot start with __ due to visibility issues. - """ - file_name = MerraDownloadManager.extract_filename_from_url(url) - file_path = os.path.join(download_folder, file_name) - - if os.path.exists(file_path): - logging.info( - f"File '{file_name}' already exists in '{download_folder}'. Skipping download." - ) - else: - self.__download_and_save_file(url, file_path) - - def __download_and_save_file(self, url: str, file_path: str) -> None: - if not self.session_authenticated(): - self.authenticate_session(url) - if not self.session_authenticated(): - logging.error("Failed to authenticate session!") - return - - assert self._auth_session is not None - - try: - r = self._auth_session.get(url, stream=True) - if r.status_code == 200: - with open(file_path, "wb") as f: - for chunk in r.iter_content(chunk_size=1024): - if chunk: - f.write(chunk) - logging.info(f"Downloaded and saved file: {file_path}") - else: - logging.error( - f"Failed to download {url}. Status code: {r.status_code}\n" - ) - logging.error(f"Response content: {r.text}\n") - except requests.RequestException as e: - logging.error(f"Error during download: {e}") - return - - def __create_authenticated_session( - self, - download_url: str, - ) -> Optional[requests.Session]: - """ - The merra portal seems to behave rather difficult when it comes to authentication. It seems that you need to - set the cookies manually to make sure that the authentication is saved. I do not fully understand why, but - I did not manage to find a more simple way - :param download_url: a url to a downloadable file - :return: requests.Session that corresponds to an authenticated session - """ - try: - session = requests.Session() - - retry = Retry(connect=3, backoff_factor=0.5) - adapter = HTTPAdapter(max_retries=retry) - session.mount("https://", adapter) - - # The session headers simulate a web-browser - session.headers = { - "User-Agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.85 Safari/537.36" - } - - user_name, pw = self.get_credentials() - session.auth = (user_name, pw) - session.cookies = self.__get_authentication_cookies(download_url) - - r = session.get(download_url) - - if r.status_code == 200: - logging.info("Authenticated successfully.") - return session - else: - logging.error( - f"Authentication failed with status code: {r.status_code}" - ) - logging.error(f"Response content: {r.text}") - return None - - except Exception as e: - logging.error(f"Failed to create authenticated session: {e}") - return None - - def __get_authentication_cookies( - self, url: str - ) -> requests.cookies.RequestsCookieJar: - try: - user_name, pw = self.get_credentials() - - # Create an authorization handler for basic HTTP authentication - p = urllib.request.HTTPPasswordMgrWithDefaultRealm() - p.add_password(None, self._TOP_LEVEL_URL, user_name, pw) - - auth_handler = urllib.request.HTTPBasicAuthHandler(p) - auth_cookie_jar = cookiejar.CookieJar() - cookie_jar = urllib.request.HTTPCookieProcessor(auth_cookie_jar) - opener = urllib.request.build_opener(auth_handler, cookie_jar) - - urllib.request.install_opener(opener) - - # Open the URL to authenticate and get the cookies - # The merra portal moved the authentication to the download level. - opener.open(url) - - logging.info("Cookies successfully retrieved.") - - # Convert cookies from cookiejar.CookieJar to requests.cookies.RequestsCookieJar - requests_cookie_jar = requests.cookies.RequestsCookieJar() - for cookie in auth_cookie_jar: - if cookie.value is not None: - requests_cookie_jar.set( - cookie.name, - cookie.value, - domain=cookie.domain, - path=cookie.path, - ) - - return requests_cookie_jar - - except urllib.error.HTTPError as e: - logging.error(f"HTTP error during cookie retrieval: {e}") - raise ValueError("Failed to authorize due to HTTP error") - except Exception as e: - logging.error(f"Error during cookie retrieval: {e}") - raise diff --git a/src/susse/api_clients/merra_2/merra_product.py b/src/susse/api_clients/merra_2/merra_product.py index 4f16f749..69fb8ce1 100644 --- a/src/susse/api_clients/merra_2/merra_product.py +++ b/src/susse/api_clients/merra_2/merra_product.py @@ -26,7 +26,10 @@ class MerraProducts(Enum): AEROSOL_OPTICAL_DEPTH_ANALYSIS = MerraProductData( name="Aerosol Optical Depth", database_id="inst3_2d_gas_Nx", - database_name="M2I3NXGAS", + # GES DISC OPeNDAP requires the version suffix on the collection + # path segment; without it every URL 404s. Other entries above + # already have ``.5.12.4`` baked in. + database_name="M2I3NXGAS.5.12.4", product_name="AODANA", ) AEROSOL_EXTINCTION_550nm = MerraProductData( diff --git a/src/susse/api_clients/merra_2/merra_stream_config.py b/src/susse/api_clients/merra_2/merra_stream_config.py deleted file mode 100644 index 3bc8b4a7..00000000 --- a/src/susse/api_clients/merra_2/merra_stream_config.py +++ /dev/null @@ -1,92 +0,0 @@ -import re -from datetime import datetime, timedelta -from typing import List - -from geopy import Location - -from .merra_config import Merra2Config -from .merra_product import MerraProductData - - -class MerraStreamConfig: - """ - A configuration class for generating MERRA-2 data stream URLs. - - This class manages configuration settings and provides methods for generating - download links for MERRA-2 data. - - """ - - def __init__(self): - self._merra_config = Merra2Config() - - def _generate_stream_link( - self, date: datetime, product_data: MerraProductData, location: Location - ) -> str: - """ - Generates a URL for streaming MERRA-2 data for a specific date and location. - - Parameters: - ---------- - date : datetime - The date for which the data is being fetched. - product_data : MerraProductData - The product information including product name and metadata. - location : Location - The geographical coordinates (latitude and longitude) of the data request. - """ - file_name = self._merra_config.create_file_name(date, product_data) - m_str = str(date.month).zfill(2) - y_str = str(date.year) - - lat_geos5 = self._merra_config._translate_lat_to_geos5_native(location.latitude) - lon_geos5 = self._merra_config._translate_lon_to_geos5_native( - location.longitude - ) - - merra_lat = self._merra_config._find_closest_merra_coordinate( - lat_geos5, self._merra_config.MERRA_LAT_COORDS - ) - merra_lon = self._merra_config._find_closest_merra_coordinate( - lon_geos5, self._merra_config.MERRA_LON_COORDS - ) - - suffix = f"dap4.ce=/{product_data.product_name}[0:1:23][{merra_lat}:1:{merra_lat}][{merra_lon}:1:{merra_lon}]" - - url = f"{self._merra_config.generate_database_url(product_data)}/{y_str}/{m_str}/{file_name}?{suffix}" - - return url - - def generate_stream_links( - self, - start_date: datetime, - end_date: datetime, - product_data: MerraProductData, - location: Location, - ) -> List[str]: - date_range = [ - start_date + timedelta(days=x) - for x in range((end_date - start_date).days + 1) - ] - - # Generate links for each date in the date range - return [ - self._generate_stream_link(date, product_data, location) - for date in date_range - ] - - @staticmethod - def _extract_date_from_url(url: str) -> str: - """ - Extracts the date from a URL with separate year, month, and day segments. - - :param url: The dataset URL. - :return: The date in 'YYYY-MM-DD' format as a string. - :raises ValueError: If the date cannot be extracted from the URL. - """ - match = re.search(r"(\d{4})(\d{2})(\d{2})", url) - if match: - year, month, day = match.groups() - return f"{year}-{month}-{day}" - else: - raise ValueError(f"Date could not be extracted from URL: {url}") diff --git a/src/susse/api_clients/merra_2/merra_stream_data_fetcher.py b/src/susse/api_clients/merra_2/merra_stream_data_fetcher.py deleted file mode 100644 index f0c8fc8c..00000000 --- a/src/susse/api_clients/merra_2/merra_stream_data_fetcher.py +++ /dev/null @@ -1,127 +0,0 @@ -from datetime import datetime - -import numpy as np -from geopy import location as Glocation -from pydap.client import open_url - -from .merra_data_stream_result import MerraStreamDataResult -from .merra_product import MerraProductData, MerraProducts -from .merra_stream_config import MerraStreamConfig -from .merra_stream_session_manager import StreamSessionManager - - -class MerraDataStreamFetcher: - """ - A class responsible for fetching MERRA-2 data streams from URLs. - - This class handles session authentication and configuration setup for - downloading MERRA-2 data. It provides a method for fetching single-day - data from specified URLs and product configurations. - - Attributes: - ---------- - authenticate : StreamSessionManager - An instance responsible for managing session authentication. - config : MerraStreamConfig - A configuration object that manages settings for data streaming. - """ - - def __init__(self): - self._authenticate = StreamSessionManager() - self._config = MerraStreamConfig() - - def fetch_surface_albedo( - self, start_date: datetime, end_date: datetime, location: Glocation - ) -> MerraStreamDataResult: - return self.fetch_data( - start_date, end_date, MerraProducts.SURFACE_ALBEDO.value, location - ) - - def fetch_air_temperature( - self, start_date: datetime, end_date: datetime, location: Glocation - ) -> MerraStreamDataResult: - return self.fetch_data( - start_date, end_date, MerraProducts.AIR_TEMPERATURE.value, location - ) - - def fetch_surface_pressure( - self, start_date: datetime, end_date: datetime, location: Glocation - ) -> MerraStreamDataResult: - return self.fetch_data( - start_date, end_date, MerraProducts.SURFACE_PRESSURE.value, location - ) - - def fetch_aerosol_extinction( - self, start_date: datetime, end_date: datetime, location: Glocation - ) -> MerraStreamDataResult: - return self.fetch_data( - start_date, end_date, MerraProducts.AEROSOL_EXTINCTION_550nm.value, location - ) - - def _fetch_single_day_data_from_url( - self, dataset_url: str, product_data: MerraProductData - ): - session = self._authenticate.authenticate(url=dataset_url) - - if session is None: - raise Exception("Session is not authenticated") - - try: - dataset = open_url(dataset_url, session=session, protocol="dap4") - - data = dataset[product_data.product_name][:] - - return np.array(data) - - except AttributeError as e: - print(f"Error fetching data from {dataset_url}: {e}") - return None - - def fetch_data( - self, - start_date: datetime, - end_date: datetime, - product_data: MerraProductData, - location: Glocation, - ) -> MerraStreamDataResult: - """ - Fetches the data for a range of dates by first generating URLs and then fetching data from each URL. - - start_date: Start date for data fetching - end_date: End date for data fetching - product_data: MERRA product metadata - location: Location of the place - product_data: Product name; as defined in the MerraProductData dictionary - :return: Python dictionary with date as the key and the corresponding data - """ - - # Generate all the URLs for the date range - dataset_urls = self._config.generate_stream_links( - start_date=start_date, - end_date=end_date, - product_data=product_data, - location=location, - ) - - data_dict = {} - - for url in dataset_urls: - current_date = MerraStreamConfig._extract_date_from_url(url) - - single_day_data = self._fetch_single_day_data_from_url( - dataset_url=url, product_data=product_data - ) - - if single_day_data is not None: - data_dict[current_date] = single_day_data - - if data_dict: - return MerraStreamDataResult.from_data( - data=data_dict, - product_data=product_data, - location=location, - start_date=start_date, - end_date=end_date, - ) - else: - raise Exception("No data was fetched for the specified URLs.") diff --git a/src/susse/api_clients/merra_2/merra_stream_session_manager.py b/src/susse/api_clients/merra_2/merra_stream_session_manager.py deleted file mode 100644 index f43b00bb..00000000 --- a/src/susse/api_clients/merra_2/merra_stream_session_manager.py +++ /dev/null @@ -1,33 +0,0 @@ -from pydap.cas.urs import setup_session - -from .merra_download_manager import MerraDownloadManager - - -class StreamSessionManager: - """ - Manages session authentication for streaming MERRA-2 data. - - This class is responsible for handling user credentials and authenticating - sessions using the MerraDownloadManager. - - """ - - def __init__(self): - self._authenticate_session = MerraDownloadManager() - - def _set_username_pw(self, username: str, password: str) -> None: - """ - Passes username and password to the underlying MerraDownloadManager. - """ - self._authenticate_session.set_username_pw(username, password) - - def authenticate(self, url: str) -> None: - """ - Authenticates the session . - """ - try: - user_name, pw = self._authenticate_session.get_credentials() - session = setup_session(user_name, pw, check_url=url) - return session - except Exception as e: - raise Exception(f"Failed to authenticate session : {e}") diff --git a/src/susse/api_clients/modis/__init__.py b/src/susse/api_clients/modis/__init__.py index b9ed7407..064f469b 100644 --- a/src/susse/api_clients/modis/__init__.py +++ b/src/susse/api_clients/modis/__init__.py @@ -1,5 +1,10 @@ from .modis_api_config import ModisConfig from .modis_data_fetcher import ModisDataFetcher +from .modis_long_fetcher import ( + PRODUCT_CADENCE_DAYS, + ModisLongFetcher, + product_cadence_days, +) from .modis_product import ( ModisBand, ModisProdFrequency, diff --git a/src/susse/api_clients/modis/modis_data_fetcher.py b/src/susse/api_clients/modis/modis_data_fetcher.py index 5339909e..8a773a74 100644 --- a/src/susse/api_clients/modis/modis_data_fetcher.py +++ b/src/susse/api_clients/modis/modis_data_fetcher.py @@ -1,94 +1,36 @@ +"""Thin HTTP wrapper around the ORNL DAAC MODIS subset endpoint. + +Single public entry point: :meth:`ModisDataFetcher.fetch_product_result`, +which fetches one (product, band, location, date-range) slice and returns +a :class:`ModisDataResult`. The richer batching and parallelism live in +:class:`susse.api_clients.modis.ModisLongFetcher`. +""" + import logging from datetime import datetime -from typing import List, Optional, Tuple +from typing import Optional import requests from geopy import location as Glocation from .modis_api_config import ModisConfig -from .modis_data_result import ModisDataPoint, ModisDataResult -from .modis_product import ( - ModisBand, - ModisProduct, - ModisProductEnum, - ModisProductFactory, -) +from .modis_data_result import ModisDataResult +from .modis_product import ModisProduct, ModisProductEnum, ModisProductFactory class ModisDataFetcher: - """ - This class handles the data fetching through the Modis API - """ + """Single-request HTTP client for the ORNL DAAC MODIS subset API.""" - def __init__(self): - super().__init__() + def __init__(self) -> None: self._product_factory = ModisProductFactory() - def fetch_temp_day( - self, - location: Glocation, - start_date: datetime, - end_date: datetime, - ) -> ModisDataResult: - return self.fetch_product_result( - ModisProductEnum.LAND_SURFACE_TEMPERATURE, - location, - start_date, - end_date, - "LST_Day_1KM", - ) - - def fetch_temp_night( - self, - location: Glocation, - start_date: datetime, - end_date: datetime, - ) -> ModisDataResult: - return self.fetch_product_result( - ModisProductEnum.LAND_SURFACE_TEMPERATURE, - location, - start_date, - end_date, - "LST_Night_1KM", - ) - - def fetch_surface_reflectance( - self, - location: Glocation, - start_date: datetime, - end_date: datetime, - band_name: str = None, - ) -> ModisDataResult: - return self.fetch_product_result( - ModisProductEnum.SURFACE_REFLACTANCE, - location, - start_date, - end_date, - band_name, - ) - - def fetch_emissivity( - self, - location: Glocation, - start_date: datetime, - end_date: datetime, - band_name: str = None, - ) -> ModisDataResult: - return self.fetch_product_result( - ModisProductEnum.EMISSIVITY, - location, - start_date, - end_date, - band_name, - ) - def fetch_product_result( self, product_enum: ModisProductEnum, location: Glocation, start_date: datetime, end_date: datetime, - band_name: str = None, + band_name: Optional[str] = None, ) -> ModisDataResult: product = self._product_factory.get_product_by_enum(product_enum) band_name = self._validate_band_for_product(product, band_name) @@ -101,155 +43,38 @@ def fetch_product_result( ) response = requests.get(request_url) if response.status_code == 200: - product_data = response.json() - return ModisDataResult.from_request_response(product_data) - else: - message = ( - "Failed to fetch data for product {}, coordinates {} {} " - "between dates: {} and {}: \n{}" - ).format( - product.name, - location.latitude, - location.longitude, - start_date, - end_date, - response.text, - ) - raise requests.exceptions.HTTPError(message) + return ModisDataResult.from_request_response(response.json()) + message = ( + f"Failed to fetch data for product {product.name}, coordinates " + f"{location.latitude} {location.longitude} between dates: " + f"{start_date} and {end_date}: \n{response.text}" + ) + raise requests.exceptions.HTTPError(message) + @staticmethod def _validate_band_for_product( - self, product: ModisProduct, band_name: Optional[str] = None + product: ModisProduct, band_name: Optional[str] = None ) -> str: if band_name is None: - if product.default_band is not None: - return product.default_band.name - else: - message = ( - "No band provided for product {}, and no default band available." - "Available bands {}" - ).format(product.name, product.get_band_names()) - raise ValueError(message) - else: - if not product.has_band(band_name): - if product.default_band is not None: - message = ( - "Band {} not found in product {}, using default band {} instead" - ).format(band_name, product.name, product.default_band.name) - logging.warning(message) - - return product.default_band.name - else: - message = ( - "Band {} not found in product {}, and no default band available. " - "Available bands: {}" - ).format(band_name, product.name, product.get_band_names()) - raise ValueError(message) - - return band_name - - def get_available_dates_for_product_and_location( - self, product: ModisProduct, location: Glocation - ) -> List[datetime]: - available_dates_url = ModisConfig.get_available_date_url( - product.get_product_name(), - location, - ) - response = requests.get(available_dates_url) - - if response.status_code == 200: - available_dates_json = response.json() - available_dates = [ - datetime.strptime(date_dict["calendar_date"], "%Y-%m-%d") - for date_dict in available_dates_json["dates"] - ] - return available_dates - else: - message = ( - "Failed to fetch available dates for product {}, " - "and coordinates {}, {}: \n{}" - ).format( - product.get_product_name(), - location.latitude, - location.longitude, - response.text, - ) - raise requests.exceptions.HTTPError(message) - - def _extract_data_from_response( - self, response_json: dict, band: ModisBand - ) -> Tuple[List[datetime], List[float]]: - data_points = response_json.get("subset", []) - time_points = [ - datetime.strptime(dp["calendar_date"], "%Y-%m-%d") for dp in data_points - ] - - data_values = [] - for dp in data_points: - raw_value = float(dp["value"]) - scaled_value = raw_value - - if band.scale_factor is not None: - scaled_value = scaled_value * band.scale_factor - - if band.add_offset is not None: - scaled_value = scaled_value + band.add_offset - - data_values.append(scaled_value) - - return time_points, data_values - - def _get_data_for_product_and_band( - self, product: ModisProduct, band_name: str, location: Glocation - ) -> ModisDataResult: - band = product.get_band_by_name(band_name) - if band is None: - raise ValueError( - f"Band {band_name} not found in product {product.name} and is required for data extraction." - ) - - available_dates = self.get_available_dates_for_product_and_location( - product, location - ) - - modis_data_points: List[ModisDataPoint] = [] - - for date in available_dates: - try: - request_url = ModisConfig.get_product_request_url( - product_name=product.name, - location=location, - band_name=band_name, - start_date=date, - end_date=date, - ) - response = requests.get(request_url) - if response.status_code == 200: - response_json = response.json() - - time_points, data_values = self._extract_data_from_response( - response_json, band - ) - for i, tp in enumerate(time_points): - modis_data_points.append( - ModisDataPoint( - date=tp, band_name=band.name, data=[data_values[i]] - ) - ) - else: - logging.warning( - "Failed to fetch data for {} on {}: {}".format( - product.name, date, response.text - ) - ) - except Exception as e: - logging.warning( - "Exception occurred while fetching data for {} on {}: {}".format( - product.name, date, str(e) - ) + if product.default_band is None: + raise ValueError( + f"No band provided for product {product.name}, and no " + f"default band available. Available bands: " + f"{product.get_band_names()}" ) - - return ModisDataResult( - latitude=location.latitude, - longitude=location.longitude, - data_points=modis_data_points, + return product.default_band.name + if product.has_band(band_name): + return band_name + if product.default_band is not None: + logging.warning( + "Band %s not found in product %s, using default band %s instead", + band_name, + product.name, + product.default_band.name, + ) + return product.default_band.name + raise ValueError( + f"Band {band_name} not found in product {product.name}, and no " + f"default band available. Available bands: " + f"{product.get_band_names()}" ) diff --git a/src/susse/api_clients/modis/modis_data_result.py b/src/susse/api_clients/modis/modis_data_result.py index 1b5733fc..9769363e 100644 --- a/src/susse/api_clients/modis/modis_data_result.py +++ b/src/susse/api_clients/modis/modis_data_result.py @@ -2,6 +2,7 @@ from typing import List, Optional import numpy as np +import pandas as pd from numpy import number from .modis_product import ModisBand @@ -141,3 +142,12 @@ def time(self) -> List[datetime]: def get_time_average(self) -> float: values = [v.data_avg for v in self.data_points] return float(np.mean(np.asarray(values))) + + def to_df(self) -> pd.DataFrame: + + # vals = [data_point.data_avg for data_point in self.data_points] + vals = [data_point.data_avg for data_point in self.data_points] + time_pts = [data_point.date for data_point in self.data_points] + + df = pd.DataFrame(vals, index=time_pts, columns=[self.data_points[0].band]) + return df diff --git a/src/susse/api_clients/modis/modis_long_fetcher.py b/src/susse/api_clients/modis/modis_long_fetcher.py new file mode 100644 index 00000000..ff0a051e --- /dev/null +++ b/src/susse/api_clients/modis/modis_long_fetcher.py @@ -0,0 +1,297 @@ +"""Long-format MODIS fetcher for warehouse ingest. + +Wraps the existing :class:`ModisDataFetcher` and adds the layer the +warehouse-population pipeline needs: + +* Output is a long-format DataFrame with columns + ``(date, product_id, band_id, value)`` — one row per + (composite-end-date, product, band) measurement. +* **Per-product date-range chunking.** ORNL DAAC's RST subset API + caps each request at 10 tiles, where a "tile" is one composite + period — so a 16-day product accepts 160 days per request, an + 8-day product accepts 80, a daily product only 10. The fetcher + walks the requested date range in product-aware chunks. +* **Concurrent fetches** across (product, location, chunk) work + items via :class:`ThreadPoolExecutor`. ORNL DAAC is a public + unauthenticated service and we keep concurrency modest. +* Errors on individual chunks are logged and skipped rather than + aborting the whole batch. +""" + +from __future__ import annotations + +import logging +import threading +import time as _time +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import date as _date +from datetime import datetime, timedelta + +import pandas as pd + +from .modis_data_fetcher import ModisDataFetcher +from .modis_data_result import ModisDataResult +from .modis_product import ModisProductEnum + +_logger = logging.getLogger(__name__) + +# Default thread-pool size for fan-out across (product, chunk) work +# items. ORNL DAAC is rarely the bottleneck on small per-station calls; +# bumping this past ~6 starts producing diminishing returns and is +# impolite to a public service. Override via constructor. +_DEFAULT_MAX_WORKERS = 4 + +# ORNL DAAC RST API caps each request at this many composite "tiles". +# Empirically observed (and confirmed by error message: +# "Subset time period ... exceeds maximum subset tiles support of 10"). +_MAX_TILES_PER_REQUEST = 10 + +# Cadence in days per composite tile, by MODIS product. Used both to +# translate the 10-tile-per-request limit into a max-days-per-request +# budget and (downstream in warehouse-ingest jobs) to estimate how many +# rows a date range should produce when checking warehouse coverage. +# Add an entry here when introducing a new product to the catalog. +PRODUCT_CADENCE_DAYS: dict[str, int] = { + "MCD43A4": 1, # NBAR — daily 500 m + "MOD11A2": 8, # LST — 8-day composite + "MOD13Q1": 16, # NDVI — 16-day composite +} + + +def product_cadence_days(product_id: str) -> int: + """Composite cadence in days for one MODIS product. + + Falls back to 1 (daily) with a warning for unknown products so callers + don't crash on a typo, but this is also the wrong answer for non-daily + products — register the product in :data:`PRODUCT_CADENCE_DAYS` to fix. + """ + cadence = PRODUCT_CADENCE_DAYS.get(product_id) + if cadence is None: + _logger.warning( + "Unknown MODIS product cadence for %r — assuming daily. Add it " + "to PRODUCT_CADENCE_DAYS in %s for correct behaviour.", + product_id, + __name__, + ) + cadence = 1 + return cadence + + +def _max_days_per_request(product_id: str) -> int: + """Date-span ceiling for one ORNL DAAC subset request on a product.""" + return product_cadence_days(product_id) * _MAX_TILES_PER_REQUEST + + +def _date_chunks(start: _date, end: _date, max_days: int) -> list[tuple[_date, _date]]: + """Split ``[start, end]`` into contiguous spans of at most ``max_days``.""" + if start > end: + return [] + chunks: list[tuple[_date, _date]] = [] + cursor = start + while cursor <= end: + chunk_end = min(cursor + timedelta(days=max_days - 1), end) + chunks.append((cursor, chunk_end)) + cursor = chunk_end + timedelta(days=1) + return chunks + + +class _PointLocation: + """Minimal stand-in for ``geopy.location.Location``. + + :class:`ModisDataFetcher` reads only ``.latitude`` / ``.longitude`` + from the location object. Geopy's ``Location.__init__`` requires + ``address`` and ``raw`` since 2.4, neither of which we have, so we + use this slot-only adapter instead. + """ + + __slots__ = ("latitude", "longitude") + + def __init__(self, latitude: float, longitude: float) -> None: + self.latitude = latitude + self.longitude = longitude + + +class ModisLongFetcher: + """Long-format MODIS fetcher. + + Constructor takes ``max_workers`` (default 4). Pass + ``max_workers=1`` to disable parallelism — useful in tests where + deterministic order matters. + """ + + def __init__(self, max_workers: int = _DEFAULT_MAX_WORKERS) -> None: + if max_workers < 1: + raise ValueError(f"max_workers must be >= 1, got {max_workers}.") + self._inner = ModisDataFetcher() + self._max_workers = max_workers + # Serialise the very first call so the inner fetcher's product-list + # GET is hit only once even under concurrent kick-off. + self._inner_lock = threading.Lock() + self._inner_warmed = False + + def fetch_long_for_location( + self, + *, + latitude: float, + longitude: float, + date_start: _date, + date_end: _date, + products_and_bands: tuple[tuple[str, str], ...], + ) -> pd.DataFrame: + """Fetch values for one location across a date range. + + Args: + latitude: degrees, [-90, 90]. + longitude: degrees, [-180, 180]. + date_start: inclusive UTC date. + date_end: inclusive UTC date. + products_and_bands: tuples like + ``(("MCD43A4", "Nadir_Reflectance_Band1"), + ("MOD11A2", "LST_Day_1km"))``. + + Returns: + Long-format DataFrame with columns + ``(date, product_id, band_id, value)``. Row order is + non-deterministic when ``max_workers > 1``. + """ + if date_start > date_end: + raise ValueError( + f"date_start ({date_start}) must be <= date_end ({date_end})." + ) + if not products_and_bands: + return pd.DataFrame(columns=("date", "product_id", "band_id", "value")) + + location = _PointLocation(latitude=latitude, longitude=longitude) + + # Build the unit-of-work list: one entry per (product, chunk). + # Within a product, chunks are sized per its cadence so each + # request stays under the 10-tile API limit. + tasks: list[tuple[str, str, _date, _date]] = [] + for product_id, band_id in products_and_bands: + chunks = _date_chunks( + date_start, + date_end, + _max_days_per_request(product_id), + ) + for chunk_start, chunk_end in chunks: + tasks.append((product_id, band_id, chunk_start, chunk_end)) + + if not tasks: + return pd.DataFrame(columns=("date", "product_id", "band_id", "value")) + + # Warm up the inner fetcher's product-list cache once on the + # main thread so worker threads don't race on first use. + self._warm_up() + + if self._max_workers == 1: + results = (self._fetch_one(*t, location) for t in tasks) + else: + pool = ThreadPoolExecutor( + max_workers=self._max_workers, thread_name_prefix="modis-fetch" + ) + futures = [pool.submit(self._fetch_one, *t, location) for t in tasks] + results = (f.result() for f in as_completed(futures)) + + try: + rows: list[dict] = [] + for chunk in results: + rows.extend(chunk) + finally: + if self._max_workers > 1: + pool.shutdown(wait=True) + + return pd.DataFrame(rows, columns=("date", "product_id", "band_id", "value")) + + def _warm_up(self) -> None: + with self._inner_lock: + if self._inner_warmed: + return + # Touching the factory triggers the one-time products-list + # GET. We don't need the result; just need it cached. + try: + self._inner._product_factory._fetch_products() # noqa: SLF001 + except Exception: + # If it fails here, individual fetches will surface the + # error; don't block the whole batch. + _logger.exception("Failed to warm up MODIS product list.") + self._inner_warmed = True + + def _fetch_one( + self, + product_id: str, + band_id: str, + chunk_start: _date, + chunk_end: _date, + location: _PointLocation, + ) -> list[dict]: + try: + product_enum = ModisProductEnum(product_id) + except ValueError: + _logger.warning( + "Skipping unknown MODIS product_id=%r (no matching " + "ModisProductEnum member). Add it to modis_product.py if " + "you intend to use it.", + product_id, + ) + return [] + start_dt = datetime.combine(chunk_start, datetime.min.time()) + end_dt = datetime.combine(chunk_end, datetime.min.time()) + _logger.info( + "MODIS request: product=%s band=%s lat=%.4f lon=%.4f %s..%s", + product_id, + band_id, + location.latitude, + location.longitude, + chunk_start.isoformat(), + chunk_end.isoformat(), + ) + t0 = _time.monotonic() + try: + result: ModisDataResult | None = self._inner.fetch_product_result( + product_enum=product_enum, + location=location, + start_date=start_dt, + end_date=end_dt, + band_name=band_id, + ) + except Exception as exc: + _logger.warning( + "MODIS fetch failed for product=%s band=%s %s..%s: %s", + product_id, + band_id, + chunk_start, + chunk_end, + exc, + ) + return [] + elapsed = _time.monotonic() - t0 + if result is None or not result.data_points: + return [] + rows = self._result_to_rows(result, product_id, band_id) + _logger.info( + "MODIS response: product=%s band=%s — %d rows in %.1fs.", + product_id, + band_id, + len(rows), + elapsed, + ) + return rows + + @staticmethod + def _result_to_rows( + result: ModisDataResult, product_id: str, band_id: str + ) -> list[dict]: + rows: list[dict] = [] + for dp in result.data_points: + value = float(dp.data_avg) + if pd.isna(value): + continue + rows.append( + { + "date": dp.date.date(), + "product_id": product_id, + "band_id": band_id, + "value": value, + } + ) + return rows diff --git a/src/susse/api_clients/modis/modis_product.py b/src/susse/api_clients/modis/modis_product.py index e78925b1..aab1a780 100644 --- a/src/susse/api_clients/modis/modis_product.py +++ b/src/susse/api_clients/modis/modis_product.py @@ -37,19 +37,46 @@ class ModisProductEnum(Enum): https://modis.ornl.gov/documentation.html """ - EMISSIVITY = "MOD21A2" - SURFACE_REFLACTANCE = "MOD09A1" - LEAF_AREA_INDEX = "MCD15A3H" - LAND_SURFACE_TEMPERATURE = "MOD21A2" + SURFACE_REFLECTANCE_8DAY = "MOD09A1" # 500 m, 8-day composite + BRDF_PARAMETERS_16DAY = "MCD43A1" # BRDF model parameters (RossThick/LiSparse) + ALBEDO_16DAY = "MCD43A3" # White/black-sky & shortwave albedos + NBAR_16DAY = "MCD43A4" # Nadir BRDF-Adjusted Reflectance (bands 1–7) + + # --- Land Surface Temperature & Emissivity --- + LAND_SURFACE_TEMPERATURE_DAILY = "MOD11A1" # 1 km, daily (includes LST_Day_1KM) + LAND_SURFACE_TEMPERATURE_8DAY = "MOD11A2" # 1 km, 8-day composite + + # --- Vegetation state & structure --- + VEGETATION_INDEX_16DAY = "MOD13Q1" # 250 m NDVI/EVI (16-day) + LAI_FPAR_4DAY = "MCD15A3H" # 500 m, 4-day LAI/FPAR + LAND_COVER_YEARLY = "MCD12Q1" # 500 m, yearly IGBP/UMD/etc. classes + + # --- Surface/flux proxies helpful for solar modeling --- + EVAPOTRANSPIRATION_8DAY = "MOD16A2" # 500 m ET (8-day) + GPP_8DAY = "MOD17A2H" # 500 m GPP (8-day) + + # --- Cryosphere / masking (affects albedo & irradiance) --- + SNOW_COVER_DAILY = "MOD10A1" # 500 m snow cover (daily) # Add relevant products here def default_band_name(self) -> Optional[str]: return { - ModisProductEnum.EMISSIVITY: "Emis_29", - ModisProductEnum.SURFACE_REFLACTANCE: "sur_refl_b01", - ModisProductEnum.LEAF_AREA_INDEX: "Lai_500m", - ModisProductEnum.LAND_SURFACE_TEMPERATURE: "LST_Day_1KM", + # ORNL DAAC's bands endpoint reports lowercase 'km' for the + # 1 km LST band on MOD11A1 / MOD11A2; the previous default + # ``LST_Day_1KM`` was case-mismatched and silently fell back + # to whatever the validator picked. + ModisProductEnum.LAND_SURFACE_TEMPERATURE_DAILY: "LST_Day_1km", + ModisProductEnum.LAND_SURFACE_TEMPERATURE_8DAY: "LST_Day_1km", + ModisProductEnum.SURFACE_REFLECTANCE_8DAY: "sur_refl_b01", # red + ModisProductEnum.VEGETATION_INDEX_16DAY: "250m_16_days_NDVI", + ModisProductEnum.NBAR_16DAY: "Nadir_Reflectance_Band1", + ModisProductEnum.ALBEDO_16DAY: "Albedo_BSA_shortwave", + ModisProductEnum.LAI_FPAR_4DAY: "Lai_500m", + ModisProductEnum.LAND_COVER_YEARLY: "LC_Type1", + ModisProductEnum.SNOW_COVER_DAILY: "NDSI_Snow_Cover", + ModisProductEnum.EVAPOTRANSPIRATION_8DAY: "ET_500m", + ModisProductEnum.GPP_8DAY: "Gpp_500m", }.get(self) diff --git a/src/susse/datasets/__init__.py b/src/susse/datasets/__init__.py new file mode 100644 index 00000000..3ebe76f8 --- /dev/null +++ b/src/susse/datasets/__init__.py @@ -0,0 +1,36 @@ +"""Training-dataset assembly + versioning. + +Public entry points: + +* :class:`FeatureSelection` — typed recipe of which variables to + materialise. +* :class:`FeatureService` — assembles training pairs / inference frames + from the warehouse using a :class:`FeatureSelection`. +* :class:`DatasetManifest`, :class:`TrainingDataset` — value objects + that travel with every snapshot. +* :func:`build_and_write_snapshot`, :func:`write_snapshot`, + :func:`load_snapshot` — local parquet + manifest IO. +* :func:`log_dataset_artifact`, :func:`use_dataset_artifact` — W&B + artifact integration for cross-machine dataset versioning. + +The module imports are lightweight; ``wandb`` is only loaded when +:mod:`wandb_io` is actually used. +""" + +from .feature_selection import FeatureSelection +from .feature_service import FeatureService +from .manifest import DatasetManifest, TrainingDataset +from .snapshot_io import build_and_write_snapshot, load_snapshot, write_snapshot +from .wandb_io import log_dataset_artifact, use_dataset_artifact + +__all__ = [ + "DatasetManifest", + "FeatureSelection", + "FeatureService", + "TrainingDataset", + "build_and_write_snapshot", + "load_snapshot", + "log_dataset_artifact", + "use_dataset_artifact", + "write_snapshot", +] diff --git a/src/susse/datasets/feature_selection.py b/src/susse/datasets/feature_selection.py new file mode 100644 index 00000000..e9383adc --- /dev/null +++ b/src/susse/datasets/feature_selection.py @@ -0,0 +1,267 @@ +"""Typed feature-selection config for training-dataset assembly. + +A :class:`FeatureSelection` is the *recipe* half of a dataset version +(per the dataset-versioning architecture: a dataset = recipe applied to +the warehouse at a point in time). It enumerates which variables from +which sources to materialize, plus the QC filter on ground truth and +which satellite irradiance sources to include. + +Recipes live in code (and therefore in git); they are JSON-serialised +into the dataset's manifest so a snapshot can be re-derived from +scratch even if the original Python object is lost. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +from ..warehouse_ops.population.dim_variable import VariableCatalog +from ..warehouse_ops.population.types import ( + IrradianceBand, + PhysicalStorage, + Source, + aux_feature_column, +) + + +@dataclass(frozen=True) +class FeatureSelection: + """Which variables to include in a training dataset. + + Variable IDs are validated against the catalog at construction time — + a typo or a stale catalog entry surfaces as a ``ValueError`` here, + not as a silent NaN column at training time. + + Attributes: + nasa_variable_ids: NASA POWER auxiliary variables, by warehouse + ``variable_id`` (e.g. ``"T2M"``, ``"CLRSKY_DNI"``). Pivoted + from ``nasa_daily_vars_long``. + cams_variable_ids: CAMS auxiliary variables (e.g. ``"sza"``, + ``"reliability"``). Pivoted from ``cams_daily_vars_long``. + merra_variable_ids: MERRA-2 reanalysis variables (e.g. + ``"aod_550_extinction"``, ``"precipitable_water"``). Pivoted + from ``merra_daily_vars_long``. + modis_variable_ids: MODIS observation slots, encoded as + ``"{product}_{band}"`` (e.g. ``"MOD13Q1_250m_16_days_NDVI"``). + Pulled from ``modis_observations`` with as-of forward-fill + semantics (carry the most recent composite value forward to + each daily row). + include_satellite_irradiance: Which satellite sources contribute + irradiance features. Each (source, band) combination becomes + a column ``sat___kwh_m2_day``. + include_satellite_bands: Which irradiance bands to pull from + ``irradiance_daily`` for the requested sources. Defaults to + GHI only — the common case for bias-correcting global + horizontal irradiance. Add :attr:`IrradianceBand.DHI` / + :attr:`IrradianceBand.DNI` to expose diffuse / direct-normal + as additional features. + qc_levels: QC levels to keep on ground measurements. Default + ``("pass",)`` excludes failed-range and other rejected rows. + """ + + nasa_variable_ids: tuple[str, ...] = () + cams_variable_ids: tuple[str, ...] = () + merra_variable_ids: tuple[str, ...] = () + modis_variable_ids: tuple[str, ...] = () + include_satellite_irradiance: tuple[Source, ...] = (Source.NASA_POWER, Source.CAMS) + include_satellite_bands: tuple[IrradianceBand, ...] = (IrradianceBand.GHI,) + qc_levels: tuple[str, ...] = ("pass",) + + def __post_init__(self) -> None: + # Each _variable_ids field is wired to a specific physical + # storage backend in the warehouse: long-format pivots for the + # aux tables, the modis_observations table for MODIS. Validation + # enforces that mapping so a request for an irradiance_wide + # variable through a long-table field is rejected at construction + # rather than silently producing a 100%-NaN column at fetch time. + self._validate_against_catalog( + self.nasa_variable_ids, + Source.NASA_POWER, + field="nasa_variable_ids", + expected_storage=PhysicalStorage.LONG_FORMAT, + ) + self._validate_against_catalog( + self.cams_variable_ids, + Source.CAMS, + field="cams_variable_ids", + expected_storage=PhysicalStorage.LONG_FORMAT, + ) + self._validate_against_catalog( + self.merra_variable_ids, + Source.MERRA_2, + field="merra_variable_ids", + expected_storage=PhysicalStorage.LONG_FORMAT, + ) + self._validate_against_catalog( + self.modis_variable_ids, + Source.MODIS, + field="modis_variable_ids", + expected_storage=PhysicalStorage.MODIS_OBSERVATIONS, + ) + for src in self.include_satellite_irradiance: + if src not in (Source.NASA_POWER, Source.CAMS): + raise ValueError( + f"include_satellite_irradiance contains {src.value}, but " + f"only NASA POWER and CAMS write GHI to irradiance_daily. " + f"MERRA-2 GHI and MODIS imagery are auxiliary features, " + f"not satellite-irradiance series — request them via the " + f"corresponding *_variable_ids field." + ) + if self.include_satellite_irradiance and not self.include_satellite_bands: + raise ValueError( + "include_satellite_bands is empty but " + "include_satellite_irradiance requested sources " + f"{[s.value for s in self.include_satellite_irradiance]}. " + "Pass at least one IrradianceBand (default: " + "(IrradianceBand.GHI,)) or set include_satellite_irradiance=()." + ) + if len(set(self.include_satellite_bands)) != len(self.include_satellite_bands): + raise ValueError( + "include_satellite_bands contains duplicates: " + f"{[b.value for b in self.include_satellite_bands]}. " + "Each band should appear at most once." + ) + if not self.qc_levels: + raise ValueError( + "qc_levels must be non-empty. Pass ('pass',) to keep only " + "QC-passed ground rows (default), or include other levels " + "like 'fail_range' explicitly. An empty tuple would " + "silently drop every ground row." + ) + + @staticmethod + def _validate_against_catalog( + ids: tuple[str, ...], + source: Source, + *, + field: str, + expected_storage: PhysicalStorage, + ) -> None: + if not ids: + return + catalog = {v.variable_id: v for v in VariableCatalog.for_source(source)} + if not catalog: + raise ValueError( + f"FeatureSelection.{field} = {list(ids)} but the catalog has " + f"no variables registered for source {source.value}. Either " + f"the source isn't ingested yet, or the catalog has not been " + f"populated." + ) + unknown = [vid for vid in ids if vid not in catalog] + if unknown: + valid = sorted( + vid + for vid, v in catalog.items() + if v.physical_storage is expected_storage + ) + raise ValueError( + f"FeatureSelection.{field} contains unknown variable_id(s) " + f"for source {source.value}: {unknown}. " + f"Known {source.value} variables with " + f"physical_storage={expected_storage.value}: {valid}." + ) + # Detect duplicates within this source's list. + if len(set(ids)) != len(ids): + seen: set[str] = set() + dups = [vid for vid in ids if vid in seen or seen.add(vid)] # type: ignore[func-returns-value] + raise ValueError( + f"FeatureSelection.{field} contains duplicate variable_id(s): " + f"{sorted(set(dups))}. Each variable should be listed at most once." + ) + # Reject catalog entries that exist but live in a different + # physical storage than this field's pivot reads from. + wrong_storage = [ + vid for vid in ids if catalog[vid].physical_storage is not expected_storage + ] + if wrong_storage: + actual = {vid: catalog[vid].physical_storage.value for vid in wrong_storage} + remediation = ( + "Use `include_satellite_irradiance` for irradiance series." + if expected_storage is PhysicalStorage.LONG_FORMAT + and any( + catalog[vid].physical_storage is PhysicalStorage.IRRADIANCE_WIDE + for vid in wrong_storage + ) + else "Move these IDs to the field whose physical_storage matches." + ) + raise ValueError( + f"FeatureSelection.{field} expects " + f"physical_storage={expected_storage.value} but received " + f"variable_id(s) with mismatching storage: {actual}. " + f"{remediation}" + ) + + @property + def is_empty(self) -> bool: + """True iff no aux features and no irradiance sources are requested.""" + return ( + not self.nasa_variable_ids + and not self.cams_variable_ids + and not self.merra_variable_ids + and not self.modis_variable_ids + and not self.include_satellite_irradiance + ) + + @property + def aux_columns(self) -> tuple[str, ...]: + """All non-irradiance feature columns the selection will produce. + + Source-prefixed to avoid collisions: ``T2M`` from NASA POWER and + a future ``T2M`` from MERRA-2 would otherwise clobber each other. + Names route through :func:`aux_feature_column` — the single + source of truth for the prefix convention. + """ + cols: list[str] = [] + cols.extend( + aux_feature_column(Source.NASA_POWER, v) for v in self.nasa_variable_ids + ) + cols.extend(aux_feature_column(Source.CAMS, v) for v in self.cams_variable_ids) + cols.extend( + aux_feature_column(Source.MERRA_2, v) for v in self.merra_variable_ids + ) + cols.extend( + aux_feature_column(Source.MODIS, v) for v in self.modis_variable_ids + ) + return tuple(cols) + + def to_dict(self) -> dict[str, Any]: + """Serialise to a JSON-safe dict for inclusion in a manifest.""" + return { + "nasa_variable_ids": list(self.nasa_variable_ids), + "cams_variable_ids": list(self.cams_variable_ids), + "merra_variable_ids": list(self.merra_variable_ids), + "modis_variable_ids": list(self.modis_variable_ids), + "include_satellite_irradiance": [ + s.value for s in self.include_satellite_irradiance + ], + "include_satellite_bands": [b.value for b in self.include_satellite_bands], + "qc_levels": list(self.qc_levels), + } + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "FeatureSelection": + """Reconstruct from a :meth:`to_dict` payload. + + Older manifests written before the GHI/DHI/DNI split lack the + ``include_satellite_bands`` key. Default to ``(GHI,)`` so they + deserialise to a selection equivalent to the original behaviour. + """ + return cls( + nasa_variable_ids=tuple(d.get("nasa_variable_ids", ())), + cams_variable_ids=tuple(d.get("cams_variable_ids", ())), + merra_variable_ids=tuple(d.get("merra_variable_ids", ())), + modis_variable_ids=tuple(d.get("modis_variable_ids", ())), + include_satellite_irradiance=tuple( + Source(v) + for v in d.get( + "include_satellite_irradiance", + [Source.NASA_POWER.value, Source.CAMS.value], + ) + ), + include_satellite_bands=tuple( + IrradianceBand(v) + for v in d.get("include_satellite_bands", [IrradianceBand.GHI.value]) + ), + qc_levels=tuple(d.get("qc_levels", ("pass",))), + ) diff --git a/src/susse/datasets/feature_service.py b/src/susse/datasets/feature_service.py new file mode 100644 index 00000000..c7ac113d --- /dev/null +++ b/src/susse/datasets/feature_service.py @@ -0,0 +1,489 @@ +"""High-level feature assembler — joins repositories into a model-ready frame. + +Drives one end-to-end query path for both training (ground ↔ satellite at a +named station) and inference (satellite-only at any (lat, lon, date)). The +typed :class:`~susse.datasets.FeatureSelection` config tells it which sources +to include and which auxiliary variables to pivot. + +Callers hand the service *query points* — plain ``(lat, lon)`` pairs. Each +satellite source stores its data at its own set of cells, so the service +snaps every query point onto that source's cells independently (via +:class:`~susse.warehouse_ops.snapping.NearestPixelSnapper`) before querying, +then relabels the rows back to the query point's own ``geohash5``. Snapping +is therefore invisible to every caller: a portal developer queries variables +for a coordinate and a date range, and the per-source grid bookkeeping is +handled here and nowhere else. +""" + +from __future__ import annotations + +from datetime import date +from typing import Optional, Sequence + +import pandas as pd +import pygeohash + +from .. import schema +from ..warehouse_ops.io.bq import BigQueryClient +from ..warehouse_ops.io.config import TableRefs, TableSchemas, WarehouseOptions +from ..warehouse_ops.io.repositories import GroundRepository, SatelliteRepository +from ..warehouse_ops.population.types import ( + IrradianceBand, + Source, + aux_column_prefix, + satellite_irradiance_column, +) +from ..warehouse_ops.snapping import NearestPixelSnapper +from .feature_selection import FeatureSelection + +# Source-id → TableRefs attr for the long-format aux table. The column +# prefix that pairs with each table is owned by :func:`aux_column_prefix` +# (the single source of truth for the prefix convention) and is *not* +# duplicated here. +_LONG_AUX_TABLES: dict[Source, str] = { + Source.NASA_POWER: "nasa_daily_vars_long", + Source.CAMS: "cams_daily_vars_long", + Source.MERRA_2: "merra_daily_vars_long", +} + +# Internal column carrying the query point's own geohash5 through the +# snap/relabel step. Never leaves :meth:`build_satellite_features`. +_QUERY_GEOHASH5: str = "query_geohash5" + + +class FeatureService: + """Assembles training pairs and inference feature frames.""" + + def __init__( + self, + bq: BigQueryClient, + tables: Optional[TableRefs] = None, + opts: Optional[WarehouseOptions] = None, + ) -> None: + # `tables` is uniquely determined by `bq.config` for any production + # deployment; default to the derived value so callers don't have to + # construct two parallel objects. Pass explicitly only when running + # against a sandbox dataset. + self._bq = bq + self._t = tables if tables is not None else TableRefs(config=bq.config) + self._opts = opts or WarehouseOptions() + self._ground = GroundRepository(bq, self._t) + self._sat = SatelliteRepository(bq, self._t) + # Snappers are built lazily from the warehouse's own cell sets and + # cached per (table, source) — the distinct-cells scan runs once. + self._snapper_cache: dict[tuple[str, str], NearestPixelSnapper] = {} + + @property + def tables(self) -> TableRefs: + return self._t + + @property + def geohash_precision(self) -> int: + """Geohash precision the warehouse + queries use, e.g. ``5``.""" + return self._opts.geohash_precision + + def invalidate_snapper_cache(self) -> None: + """Drop cached per-source snappers so the next query rebuilds them. + + Snappers are built from the warehouse's native-pixel sets and cached + for the service's lifetime. A caller that has just ingested new rows + (e.g. :class:`~susse.inference.Predictor`'s on-demand fetch) must + call this so the follow-up query snaps against the fresh pixels + rather than a stale set. + """ + self._snapper_cache.clear() + + # ------------------------------------------------------------------ + # Satellite features — the one loop every other method composes on top of. + # ------------------------------------------------------------------ + + def build_satellite_features( + self, + *, + selection: FeatureSelection, + date_start: date, + date_end: date, + points: Sequence[tuple[float, float]], + ) -> pd.DataFrame: + """Wide ``(date, geohash5)`` frame: irradiance + per-source aux. + + The single place the satellite-side snap + fetch + pivot + merge + loop lives. :meth:`build_training_pairs`, + :meth:`build_inference_features`, and + :class:`~susse.inference.Predictor` all delegate here. + + Each query point is snapped onto every requested source's native + grid independently; the fetched rows are relabelled to the query + point's own ``geohash5`` so the result still uses one cell key per + point regardless of how the sources are gridded. + + Args: + selection: What to pull, per source. + date_start, date_end: Inclusive date bounds. + points: ``(lat, lon)`` query points. Must be non-empty. + Duplicates (and points sharing a ``geohash5`` cell) collapse + to one output cell. + + Returns: + Wide DataFrame with columns ``date``, ``geohash5`` (the query + point's own geohash5), one ``sat___kwh_m2_day`` + per requested (band, source), and ``_`` per + requested aux variable. Rows are present only for (date, + geohash5) the warehouse actually has — callers that need + placeholder rows synthesize them after this returns. + + Raises: + ValueError: If ``points`` is empty. + NotImplementedError: If ``selection.modis_variable_ids`` is + non-empty. MODIS lives at composite cadence in a separate + table and is not yet wired through this assembler. + """ + if not points: + raise ValueError( + "build_satellite_features: points is empty. Pass at least " + "one (lat, lon) query point." + ) + if selection.modis_variable_ids: + raise NotImplementedError( + "FeatureSelection.modis_variable_ids is not yet wired " + "through FeatureService. MODIS data lives at composite-" + "cadence dates in modis_observations; the forward-fill " + "semantics will land with NB 03 once C8 has populated " + "the table. Leave modis_variable_ids empty for now." + ) + query = self._unique_query_points(points) + + # Irradiance spine: union (outer-join) over the requested sources. + irradiance_frames: list[pd.DataFrame] = [] + for source in selection.include_satellite_irradiance: + frame = self._irradiance_for_source( + source, + query, + date_start=date_start, + date_end=date_end, + bands=selection.include_satellite_bands, + ) + if frame is not None: + irradiance_frames.append(frame) + df = self._outer_join(irradiance_frames, on=(schema.DATE, schema.GEOHASH5)) + + # Auxiliary long-format variables: LEFT-joined onto the spine, so + # the result rows stay exactly the irradiance (date, geohash5) pairs + # — the Predictor cache-miss detector relies on this definition. + for source, ids in self._aux_requests(selection): + table_attr = _LONG_AUX_TABLES[source] + aux = self._aux_for_source( + source, + query, + table_fqn=getattr(self._t, table_attr), + prefix=aux_column_prefix(source), + variable_ids=ids, + date_start=date_start, + date_end=date_end, + ) + if aux is not None: + df = self._merge_left(df, aux, on=(schema.DATE, schema.GEOHASH5)) + + if df.empty: + return pd.DataFrame(columns=[schema.DATE, schema.GEOHASH5]) + return df + + # ------------------------------------------------------------------ + # Training pairs + # ------------------------------------------------------------------ + + def build_training_pairs( + self, + *, + selection: FeatureSelection, + date_start: date, + date_end: date, + locations: Optional[Sequence[str]] = None, + ) -> pd.DataFrame: + """Assemble (target, features) pairs for training. + + Always returns one row per (date, station) where ground GHI is + present and passes the selection's QC filter; missing satellite + / aux columns are NaN. + + Args: + selection: What to pull, per source. + date_start, date_end: Inclusive date bounds. + locations: Optional station-name filter; ``None`` = all. + + Returns: + DataFrame columns: + * ``date, location, lat, lon, geohash5`` — base. + * ``y_ghi_kwh_m2_day`` — ground target. + * ``sat___kwh_m2_day`` per requested + (band, source) pair. + * ``_`` per requested aux variable. + * ``qc_level`` — kept for traceability. + """ + ground = self._ground.fetch( + date_start, + date_end, + locations=locations, + qc_levels=selection.qc_levels, + ) + rename_map = {schema.GROUND_GHI: schema.TRAINING_TARGET} + if ground.empty: + return ground.rename(columns=rename_map) + ground = ground.rename(columns=rename_map) + station_points = [ + (float(lat), float(lon)) + for lat, lon in ground[[schema.LAT, schema.LON]] + .drop_duplicates() + .itertuples(index=False, name=None) + ] + satellite = self.build_satellite_features( + selection=selection, + date_start=date_start, + date_end=date_end, + points=station_points, + ) + df = self._merge_left(ground, satellite, on=(schema.DATE, schema.GEOHASH5)) + return df.sort_values([schema.DATE, schema.LOCATION]).reset_index(drop=True) + + # ------------------------------------------------------------------ + # Inference features + # ------------------------------------------------------------------ + + def build_inference_features( + self, + *, + selection: FeatureSelection, + target_date: date, + lat: float, + lon: float, + ) -> pd.DataFrame: + """Single-row feature frame for inference at ``(lat, lon, target_date)``. + + The query point is snapped onto each source's native grid internally. + If the warehouse has no row for this cell/date, a placeholder row + with NaN satellite columns is returned so callers always get exactly + one row. + """ + gh = pygeohash.encode(lat, lon, precision=self._opts.geohash_precision) + satellite = self.build_satellite_features( + selection=selection, + date_start=target_date, + date_end=target_date, + points=[(lat, lon)], + ) + if satellite.empty: + df = self._empty_irradiance_placeholder( + target_date=target_date, geohash5=gh, selection=selection + ) + else: + df = satellite + df[schema.LAT] = float(lat) + df[schema.LON] = float(lon) + return df + + @staticmethod + def _empty_irradiance_placeholder( + *, + target_date: date, + geohash5: str, + selection: FeatureSelection, + ) -> pd.DataFrame: + """Synthesise a single-row frame with NaN sat columns. + + Used by :meth:`build_inference_features` so the caller always gets + exactly one row even when the warehouse has nothing for this cell. + """ + row: dict[str, object] = {schema.DATE: target_date, schema.GEOHASH5: geohash5} + for band in selection.include_satellite_bands: + for src in selection.include_satellite_irradiance: + row[satellite_irradiance_column(src, band)] = pd.NA + return pd.DataFrame([row]) + + # ------------------------------------------------------------------ + # Manifest support + # ------------------------------------------------------------------ + + def warehouse_table_mods(self, selection: FeatureSelection) -> dict[str, str]: + """Per-source-table ``last_modified_time`` for manifest provenance. + + Returns the ISO-8601 modification timestamp of each warehouse + table the selection would touch. Used by the dataset writer to + snapshot "what was the warehouse state when this dataset was + built" alongside the parquet payload. + """ + ids: list[str] = [TableSchemas.GROUND_MEASUREMENTS.table_id] + if selection.include_satellite_irradiance: + ids.append(TableSchemas.IRRADIANCE_DAILY.table_id) + for source, _ in self._aux_requests(selection): + attr = _LONG_AUX_TABLES[source] + ids.append(getattr(TableSchemas, attr.upper()).table_id) + if selection.modis_variable_ids: + ids.append(TableSchemas.MODIS_OBSERVATIONS.table_id) + return self._sat.warehouse_table_mods(ids) + + # ------------------------------------------------------------------ + # Internal — per-source snap + fetch + relabel + # ------------------------------------------------------------------ + + def _unique_query_points( + self, points: Sequence[tuple[float, float]] + ) -> pd.DataFrame: + """Distinct query points keyed by their own ``geohash5``. + + Points sharing a ``geohash5`` cell collapse to one row — they map to + the same warehouse cell, so fetching once is correct and cheaper. + """ + precision = self._opts.geohash_precision + rows: list[dict[str, object]] = [] + seen: set[str] = set() + for lat, lon in points: + gh = pygeohash.encode(lat, lon, precision=precision) + if gh in seen: + continue + seen.add(gh) + rows.append( + { + schema.GEOHASH5: gh, + schema.LAT: float(lat), + schema.LON: float(lon), + } + ) + return pd.DataFrame(rows) + + def _irradiance_for_source( + self, + source: Source, + query: pd.DataFrame, + *, + date_start: date, + date_end: date, + bands: Sequence[IrradianceBand], + ) -> Optional[pd.DataFrame]: + """Snap → fetch → relabel one source's irradiance, or None if nothing.""" + mapping = self._snap_mapping(source, self._t.irradiance_daily, query) + if mapping.empty: + return None + result = self._sat.daily_irradiance_by_geohash( + date_start, + date_end, + sources=(source.value,), + bands=bands, + geohash5s=tuple(mapping[schema.GEOHASH5].unique()), + ) + if result.empty: + return None + return self._relabel(result, mapping) + + def _aux_for_source( + self, + source: Source, + query: pd.DataFrame, + *, + table_fqn: str, + prefix: str, + variable_ids: tuple[str, ...], + date_start: date, + date_end: date, + ) -> Optional[pd.DataFrame]: + """Snap → fetch → relabel one source's aux variables, or None.""" + mapping = self._snap_mapping(source, table_fqn, query) + if mapping.empty: + return None + result = self._sat.long_aux_pivoted( + table_fqn=table_fqn, + column_prefix=prefix, + start=date_start, + end=date_end, + variable_ids=variable_ids, + geohash5s=tuple(mapping[schema.GEOHASH5].unique()), + ) + if result.empty: + return None + return self._relabel(result, mapping) + + def _snap_mapping( + self, source: Source, snapper_table_fqn: str, query: pd.DataFrame + ) -> pd.DataFrame: + """Map each query point's ``geohash5`` to the source-pixel ``geohash5``. + + Out-of-coverage query points (no native pixel within range) are + dropped — they surface downstream as cache misses, never as a snap + to a far, unrelated pixel. + """ + snapper = self._snapper_for(source, snapper_table_fqn) + snapped = snapper.snap_or_none(list(query[schema.LAT]), list(query[schema.LON])) + mapping = pd.DataFrame( + { + schema.GEOHASH5: snapped, + _QUERY_GEOHASH5: list(query[schema.GEOHASH5]), + } + ) + return mapping.dropna(subset=[schema.GEOHASH5]).reset_index(drop=True) + + @staticmethod + def _relabel(result: pd.DataFrame, mapping: pd.DataFrame) -> pd.DataFrame: + """Replace each row's source-pixel ``geohash5`` with the query one. + + The merge fans a source pixel out to every query cell that snapped + to it, so two nearby query cells sharing a native pixel each get + their own relabelled copy. + """ + merged = result.merge(mapping, on=schema.GEOHASH5, how="inner") + return merged.drop(columns=schema.GEOHASH5).rename( + columns={_QUERY_GEOHASH5: schema.GEOHASH5} + ) + + def _snapper_for(self, source: Source, table_fqn: str) -> NearestPixelSnapper: + """Return (and cache) the snapper for one ``(table, source)`` pair. + + Built from the table's own distinct cells for that source, so query + points snap onto coordinates the warehouse actually holds. + """ + cache_key = (table_fqn, source.value) + cached = self._snapper_cache.get(cache_key) + if cached is not None: + return cached + pixels = self._sat.native_pixels(table_fqn=table_fqn, source=source) + snapper = NearestPixelSnapper.from_dataframe(pixels) + self._snapper_cache[cache_key] = snapper + return snapper + + # ------------------------------------------------------------------ + # Internal — generic helpers + # ------------------------------------------------------------------ + + @staticmethod + def _aux_requests( + selection: FeatureSelection, + ) -> list[tuple[Source, tuple[str, ...]]]: + """Yield ``(source, variable_ids)`` pairs for the requested aux sources.""" + out: list[tuple[Source, tuple[str, ...]]] = [] + if selection.nasa_variable_ids: + out.append((Source.NASA_POWER, selection.nasa_variable_ids)) + if selection.cams_variable_ids: + out.append((Source.CAMS, selection.cams_variable_ids)) + if selection.merra_variable_ids: + out.append((Source.MERRA_2, selection.merra_variable_ids)) + return out + + @staticmethod + def _outer_join( + frames: Sequence[pd.DataFrame], *, on: tuple[str, ...] + ) -> pd.DataFrame: + """Outer-join several wide frames on ``on``; empty input → empty frame.""" + non_empty = [f for f in frames if not f.empty] + if not non_empty: + return pd.DataFrame(columns=list(on)) + out = non_empty[0] + for frame in non_empty[1:]: + out = out.merge(frame, on=list(on), how="outer") + return out + + @staticmethod + def _merge_left( + left: pd.DataFrame, right: pd.DataFrame, *, on: tuple[str, ...] + ) -> pd.DataFrame: + """LEFT-join right onto left on ``on``; tolerates an empty right side.""" + if right.empty: + return left + return left.merge(right, on=list(on), how="left") diff --git a/src/susse/datasets/manifest.py b/src/susse/datasets/manifest.py new file mode 100644 index 00000000..7a922382 --- /dev/null +++ b/src/susse/datasets/manifest.py @@ -0,0 +1,175 @@ +"""Dataset manifest — the regeneration recipe + provenance for a snapshot. + +A :class:`DatasetManifest` is the metadata record that travels alongside +every materialised parquet snapshot. It captures everything you'd need +to either (a) prove what data went into a model run or (b) re-derive the +exact same snapshot from scratch: + +* The :class:`FeatureSelection` recipe (what columns). +* Date range and location filter (what rows). +* Warehouse identity and per-source-table modification timestamps at + query time (what state of the warehouse was sampled). +* The git SHA of the code that built it. +* The SHA-256 of the parquet payload (so corruption / tampering is + detectable on load). +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, replace +from datetime import date, datetime +from typing import Any + +import pandas as pd + +from .feature_selection import FeatureSelection + + +@dataclass(frozen=True) +class DatasetManifest: + """Provenance + regeneration recipe for one materialised dataset. + + Attributes: + name: Logical dataset name (e.g. ``"susse_training"``). Stable + across versions; the version field distinguishes them. + version: Version label (e.g. ``"v1"``, ``"v2-pre-merra"``). + User-controlled; W&B assigns its own ``v0``/``v1``/... when + the dataset is logged as an artifact, but the manifest's + version is the human-meaningful one. + created_at_utc: When the snapshot was built (UTC, ISO 8601). + susse_version: ``susse`` package version at build time. + git_sha: ``git rev-parse HEAD`` at build time, or ``None`` if + the build host had no git context. + feature_selection: The recipe used to build the snapshot. + date_start, date_end: Inclusive date range used in the query. + location_filter: Tuple of location names if the query was scoped + to a specific list, or ``None`` for "all locations". + warehouse_project: GCP project hosting the warehouse. + warehouse_dataset: BigQuery dataset name. + warehouse_table_mods: Per-source ``last_modified_time`` (ISO 8601 + UTC string) of every warehouse table that contributed rows + to the snapshot. Re-running with a manifest whose tabular + mods don't match the live warehouse signals upstream drift. + n_rows, n_cols: Shape of the materialised dataframe. + column_names: Ordered tuple of column names. Lets a manifest + consumer validate the parquet schema without loading it. + content_hash: Hex SHA-256 of the parquet file's bytes. Validated + on load; mismatch raises. + """ + + name: str + version: str + created_at_utc: str + susse_version: str + git_sha: str | None + feature_selection: FeatureSelection + date_start: date + date_end: date + location_filter: tuple[str, ...] | None + warehouse_project: str + warehouse_dataset: str + warehouse_table_mods: dict[str, str] + n_rows: int + n_cols: int + column_names: tuple[str, ...] + content_hash: str + + def __post_init__(self) -> None: + if not self.name: + raise ValueError("DatasetManifest.name must be non-empty.") + if not self.version: + raise ValueError("DatasetManifest.version must be non-empty.") + if self.date_start > self.date_end: + raise ValueError( + f"date_start ({self.date_start}) is after date_end " + f"({self.date_end}). Swap the arguments." + ) + if self.n_rows < 0 or self.n_cols < 0: + raise ValueError( + f"n_rows={self.n_rows}, n_cols={self.n_cols} must be >= 0." + ) + + def with_content_hash(self, content_hash: str) -> "DatasetManifest": + """Return a copy with ``content_hash`` set. + + Used by snapshot writers: the manifest is constructed before the + parquet exists, then re-stamped with the real hash after the file + is written. + """ + return replace(self, content_hash=content_hash) + + def to_dict(self) -> dict[str, Any]: + return { + "name": self.name, + "version": self.version, + "created_at_utc": self.created_at_utc, + "susse_version": self.susse_version, + "git_sha": self.git_sha, + "feature_selection": self.feature_selection.to_dict(), + "date_start": self.date_start.isoformat(), + "date_end": self.date_end.isoformat(), + "location_filter": ( + list(self.location_filter) if self.location_filter is not None else None + ), + "warehouse_project": self.warehouse_project, + "warehouse_dataset": self.warehouse_dataset, + "warehouse_table_mods": dict(self.warehouse_table_mods), + "n_rows": self.n_rows, + "n_cols": self.n_cols, + "column_names": list(self.column_names), + "content_hash": self.content_hash, + } + + def to_json(self) -> str: + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "DatasetManifest": + return cls( + name=d["name"], + version=d["version"], + created_at_utc=d["created_at_utc"], + susse_version=d["susse_version"], + git_sha=d.get("git_sha"), + feature_selection=FeatureSelection.from_dict(d["feature_selection"]), + date_start=date.fromisoformat(d["date_start"]), + date_end=date.fromisoformat(d["date_end"]), + location_filter=( + tuple(d["location_filter"]) + if d.get("location_filter") is not None + else None + ), + warehouse_project=d["warehouse_project"], + warehouse_dataset=d["warehouse_dataset"], + warehouse_table_mods=dict(d.get("warehouse_table_mods", {})), + n_rows=int(d["n_rows"]), + n_cols=int(d["n_cols"]), + column_names=tuple(d["column_names"]), + content_hash=d["content_hash"], + ) + + @classmethod + def from_json(cls, s: str) -> "DatasetManifest": + return cls.from_dict(json.loads(s)) + + +@dataclass(frozen=True) +class TrainingDataset: + """A loaded training-dataset snapshot. + + Bundles the materialised dataframe with its manifest. Construct via + :func:`susse.datasets.snapshot_io.load_snapshot` (which validates the + content hash) rather than instantiating directly. + """ + + df: pd.DataFrame + manifest: DatasetManifest + + @property + def name(self) -> str: + return self.manifest.name + + @property + def version(self) -> str: + return self.manifest.version diff --git a/src/susse/datasets/snapshot_io.py b/src/susse/datasets/snapshot_io.py new file mode 100644 index 00000000..041a941e --- /dev/null +++ b/src/susse/datasets/snapshot_io.py @@ -0,0 +1,205 @@ +"""Local snapshot writer / reader. + +Materialises a built training DataFrame + its manifest into a directory +holding ``dataset.parquet`` and ``manifest.json``. The reader validates +the parquet's SHA-256 against the manifest on load — corruption or +post-hoc tampering is detected before training consumes the data. + +Storage layout:: + + / + dataset.parquet + manifest.json + +Snapshot dirs are W&B-artifact-friendly: ``add_dir(snapshot_dir)`` on a +``wandb.Artifact`` uploads both files together; the artifact's +``.metadata`` mirrors a subset of the manifest. +""" + +from __future__ import annotations + +import hashlib +import logging +from datetime import date, datetime, timezone +from pathlib import Path +from typing import Optional, Sequence + +import pandas as pd + +from ..provenance import git_sha as _git_sha +from ..provenance import susse_version as _susse_version +from .feature_selection import FeatureSelection +from .feature_service import FeatureService +from .manifest import DatasetManifest, TrainingDataset + +_logger = logging.getLogger(__name__) + +_PARQUET_FILENAME = "dataset.parquet" +_MANIFEST_FILENAME = "manifest.json" + + +def build_and_write_snapshot( + *, + feature_service: FeatureService, + selection: FeatureSelection, + date_start: date, + date_end: date, + locations: Optional[Sequence[str]], + name: str, + version: str, + dest: Path, +) -> TrainingDataset: + """Build a training-pairs DataFrame and write it as a snapshot. + + One-shot helper: queries the warehouse, builds the manifest with full + provenance (warehouse table mods, git SHA, susse version, content + hash), writes both files into ``dest``, and returns the loaded + :class:`TrainingDataset` so the caller can inspect it without a + second read. + + Args: + feature_service: The configured :class:`FeatureService` to query + with. + selection: Recipe — what variables to materialise. + date_start, date_end: Inclusive date range. + locations: Optional station-name filter; ``None`` = all. + name: Logical dataset name (e.g. ``"susse_training"``). + version: Human-meaningful version label (e.g. ``"v1"``). + dest: Directory to write the snapshot into. Created if missing. + + Returns: + A :class:`TrainingDataset` bundling the loaded df + manifest. + """ + df = feature_service.build_training_pairs( + selection=selection, + date_start=date_start, + date_end=date_end, + locations=locations, + ) + table_mods = feature_service.warehouse_table_mods(selection) + config = feature_service.tables.config + manifest = _build_manifest( + df=df, + name=name, + version=version, + selection=selection, + date_start=date_start, + date_end=date_end, + locations=locations, + warehouse_project=config.project_id, + warehouse_dataset=config.dataset, + warehouse_table_mods=table_mods, + ) + return write_snapshot(df=df, manifest=manifest, dest=dest) + + +def write_snapshot( + *, + df: pd.DataFrame, + manifest: DatasetManifest, + dest: Path, +) -> TrainingDataset: + """Write ``df`` + ``manifest`` into ``dest`` and return them as a dataset. + + The parquet file is hashed *after* it lands on disk; the manifest is + re-stamped with the real hash before being written. This guarantees + the manifest's ``content_hash`` matches the bytes that future readers + will load. + """ + dest = Path(dest) + dest.mkdir(parents=True, exist_ok=True) + parquet_path = dest / _PARQUET_FILENAME + manifest_path = dest / _MANIFEST_FILENAME + + df.to_parquet(parquet_path, index=False) + content_hash = _sha256_file(parquet_path) + manifest_final = manifest.with_content_hash(content_hash) + manifest_path.write_text(manifest_final.to_json()) + + _logger.info( + "Wrote snapshot %s v%s to %s — %d rows × %d cols, hash=%s.", + manifest_final.name, + manifest_final.version, + dest, + manifest_final.n_rows, + manifest_final.n_cols, + content_hash[:12], + ) + return TrainingDataset(df=df, manifest=manifest_final) + + +def load_snapshot(src: Path) -> TrainingDataset: + """Load a snapshot dir, validating its content hash on the way in. + + Raises: + FileNotFoundError: If the parquet or manifest is missing. + ValueError: If the parquet's SHA-256 doesn't match the manifest. + """ + src = Path(src) + parquet_path = src / _PARQUET_FILENAME + manifest_path = src / _MANIFEST_FILENAME + if not parquet_path.exists(): + raise FileNotFoundError( + f"Snapshot is missing {_PARQUET_FILENAME}: {parquet_path}. " + f"Was the directory truncated?" + ) + if not manifest_path.exists(): + raise FileNotFoundError( + f"Snapshot is missing {_MANIFEST_FILENAME}: {manifest_path}. " + f"A snapshot dir must contain both {_PARQUET_FILENAME} and " + f"{_MANIFEST_FILENAME}." + ) + + manifest = DatasetManifest.from_json(manifest_path.read_text()) + actual_hash = _sha256_file(parquet_path) + if actual_hash != manifest.content_hash: + raise ValueError( + f"Content-hash mismatch on {parquet_path}: manifest declares " + f"{manifest.content_hash} but the file hashes to {actual_hash}. " + f"The snapshot was modified or is corrupted; re-derive it from " + f"the recipe in manifest.json (see feature_selection field)." + ) + df = pd.read_parquet(parquet_path) + return TrainingDataset(df=df, manifest=manifest) + + +def _build_manifest( + *, + df: pd.DataFrame, + name: str, + version: str, + selection: FeatureSelection, + date_start: date, + date_end: date, + locations: Optional[Sequence[str]], + warehouse_project: str, + warehouse_dataset: str, + warehouse_table_mods: dict[str, str], +) -> DatasetManifest: + return DatasetManifest( + name=name, + version=version, + created_at_utc=datetime.now(timezone.utc).isoformat(), + susse_version=_susse_version(), + git_sha=_git_sha(), + feature_selection=selection, + date_start=date_start, + date_end=date_end, + location_filter=tuple(locations) if locations is not None else None, + warehouse_project=warehouse_project, + warehouse_dataset=warehouse_dataset, + warehouse_table_mods=warehouse_table_mods, + n_rows=len(df), + n_cols=len(df.columns), + column_names=tuple(df.columns), + content_hash="", # placeholder, stamped by write_snapshot + ) + + +def _sha256_file(path: Path) -> str: + """Streaming SHA-256 of a file's bytes (handles arbitrarily large parquets).""" + h = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() diff --git a/src/susse/datasets/wandb_io.py b/src/susse/datasets/wandb_io.py new file mode 100644 index 00000000..d19792ed --- /dev/null +++ b/src/susse/datasets/wandb_io.py @@ -0,0 +1,205 @@ +"""Weights & Biases artifact I/O for training-dataset snapshots. + +A snapshot directory (parquet + manifest) maps onto a W&B artifact of +type ``"training_dataset"``. Logging produces an artifact whose +contents are the snapshot's two files; downloading reverses it. The +manifest is mirrored into the artifact's ``metadata`` so the W&B UI +shows row count / version / date range without unpacking the parquet. + +Two entry points: + +* :func:`log_dataset_artifact` — create a fresh ``"dataset_build"`` run + and upload the snapshot. Use after :func:`build_and_write_snapshot`. +* :func:`use_dataset_artifact` — download an artifact. Pass an active + W&B run to record the consumption in the run's lineage; omit it for + one-off downloads (a temporary ``"dataset_consume"`` run is created + and finished). + +W&B is imported lazily so the rest of the ``data`` package works even +when ``wandb`` is not installed (e.g. in unit tests). +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import TYPE_CHECKING, Any, Optional + +from .manifest import DatasetManifest +from .snapshot_io import load_snapshot + +if TYPE_CHECKING: # pragma: no cover - imported only for type hints + import wandb + +_logger = logging.getLogger(__name__) + +_ARTIFACT_TYPE = "training_dataset" + + +def log_dataset_artifact( + snapshot_dir: Path, + *, + artifact_name: str, + project: str, + entity: Optional[str] = None, + aliases: tuple[str, ...] = (), + notes: Optional[str] = None, +) -> str: + """Upload a local snapshot dir as a W&B artifact. + + Args: + snapshot_dir: Directory holding ``dataset.parquet`` + + ``manifest.json`` (typically the output of + :func:`build_and_write_snapshot`). + artifact_name: Logical artifact name (e.g. ``"susse_training"``). + W&B versions it as ``v0``, ``v1``, ... on each new upload. + project: W&B project. The dataset_build run is logged here too. + entity: W&B team / user. ``None`` uses the default from + ``wandb login``. + aliases: Extra aliases to apply (e.g. ``("v1", "candidate")``). + ``"latest"`` is added automatically by W&B. + notes: Optional human-readable description (free text). + + Returns: + Qualified artifact reference, e.g. + ``"//:v0"``. + """ + import wandb + + snapshot_dir = Path(snapshot_dir) + manifest = _load_manifest_for_metadata(snapshot_dir) + + description = notes or ( + f"{manifest.name} {manifest.version} — " + f"{manifest.n_rows} rows × {manifest.n_cols} cols, " + f"date range {manifest.date_start}..{manifest.date_end}." + ) + + with wandb.init( + project=project, + entity=entity, + job_type="dataset_build", + config={ + "feature_selection": manifest.feature_selection.to_dict(), + "date_start": manifest.date_start.isoformat(), + "date_end": manifest.date_end.isoformat(), + "location_filter": ( + list(manifest.location_filter) + if manifest.location_filter is not None + else None + ), + "warehouse_project": manifest.warehouse_project, + "warehouse_dataset": manifest.warehouse_dataset, + "git_sha": manifest.git_sha, + "susse_version": manifest.susse_version, + }, + ) as run: + artifact = wandb.Artifact( + name=artifact_name, + type=_ARTIFACT_TYPE, + description=description, + metadata=_artifact_metadata(manifest), + ) + artifact.add_dir(str(snapshot_dir)) + for alias in aliases: + artifact.aliases.append(alias) + run.log_artifact(artifact) + artifact.wait() # block until the upload + version assignment lands + ref = f"{run.entity}/{project}/{artifact_name}:v{artifact.version}" + _logger.info("Logged dataset artifact %s.", ref) + # Return outside the `with` block — mypy can't trace context-manager + # suppression of exceptions otherwise, and the return-inside-with + # pattern triggers a [return] error. + return ref + + +def use_dataset_artifact( + artifact_ref: str, + dest: Path, + *, + run: Optional["wandb.Run"] = None, +): + """Download an artifact reference into ``dest`` and return the loaded dataset. + + Args: + artifact_ref: Qualified W&B reference, e.g. + ``"//:v0"`` or ``"...:latest"``. + dest: Local directory to download the artifact contents into. + run: An active W&B run. When provided, the consumption is + recorded in the run's lineage (preferred during training). + If ``None``, a temporary ``"dataset_consume"`` run is created + and finished after download. + + Returns: + :class:`TrainingDataset` from :func:`load_snapshot` — the parquet + is hash-validated against the manifest on load. + """ + import wandb + + dest = Path(dest) + dest.mkdir(parents=True, exist_ok=True) + + own_run = run is None + active_run = run or wandb.init( + project=_project_from_ref(artifact_ref), + entity=_entity_from_ref(artifact_ref), + job_type="dataset_consume", + ) + try: + artifact = active_run.use_artifact(artifact_ref, type=_ARTIFACT_TYPE) + artifact_dir = Path(artifact.download(root=str(dest))) + return load_snapshot(artifact_dir) + finally: + if own_run: + active_run.finish() + + +def _load_manifest_for_metadata(snapshot_dir: Path) -> DatasetManifest: + manifest_path = snapshot_dir / "manifest.json" + if not manifest_path.exists(): + raise FileNotFoundError( + f"Cannot log {snapshot_dir} as a W&B artifact: " + f"manifest.json is missing. Build the snapshot via " + f"susse.datasets.build_and_write_snapshot first." + ) + return DatasetManifest.from_json(manifest_path.read_text()) + + +def _artifact_metadata(manifest: DatasetManifest) -> dict[str, Any]: + """Subset of the manifest mirrored into the artifact metadata. + + The full manifest still travels inside the artifact as + ``manifest.json``; this is just the bits W&B's UI surfaces. + """ + return { + "version": manifest.version, + "n_rows": manifest.n_rows, + "n_cols": manifest.n_cols, + "date_start": manifest.date_start.isoformat(), + "date_end": manifest.date_end.isoformat(), + "git_sha": manifest.git_sha, + "susse_version": manifest.susse_version, + "content_hash": manifest.content_hash, + "feature_selection": manifest.feature_selection.to_dict(), + "warehouse_project": manifest.warehouse_project, + "warehouse_dataset": manifest.warehouse_dataset, + "warehouse_table_mods": manifest.warehouse_table_mods, + } + + +def _entity_from_ref(artifact_ref: str) -> Optional[str]: + parts = artifact_ref.split("/") + return parts[0] if len(parts) >= 3 else None + + +def _project_from_ref(artifact_ref: str) -> str: + parts = artifact_ref.split("/") + if len(parts) >= 3: + return parts[1] + if len(parts) == 2: + return parts[0] + raise ValueError( + f"Cannot parse project from artifact_ref={artifact_ref!r}. " + f"Expected '//:' or " + f"'/:'." + ) diff --git a/src/susse/env.py b/src/susse/env.py new file mode 100644 index 00000000..43a64758 --- /dev/null +++ b/src/susse/env.py @@ -0,0 +1,32 @@ +"""Process-wide environment loading for notebooks and scripts. + +Single entry point for the ``.env``-loading idiom every notebook needs. +Notebooks (and any ad-hoc script) call :func:`load_project_env` once +during setup, after which the standard ``os.environ`` carries any +credentials defined in the project's ``.env`` file — ``CAMS_EMAIL``, +``WANDB_API_KEY``, ``EARTHDATA_USERNAME`` / ``EARTHDATA_PASSWORD``, +etc. + +Library code that needs credentials (e.g. :class:`CamsClient`, +:func:`_Earthdata.from_env`) calls :func:`dotenv.load_dotenv` directly +at the point of need; notebooks call this helper at the top of their +setup cell so subsequent reads of ``os.environ`` work regardless of +whether the user exported the variable in their shell or wrote it +into ``.env``. +""" + +from __future__ import annotations + +from dotenv import load_dotenv + + +def load_project_env() -> None: + """Load ``.env`` from the working directory or any parent into ``os.environ``. + + Wraps :func:`dotenv.load_dotenv` so notebooks have a single + project-level import to remember. Idempotent: existing + ``os.environ`` values are preserved (``load_dotenv`` defaults to + ``override=False``), so a shell-exported variable always wins + over a ``.env`` entry. + """ + load_dotenv() diff --git a/src/susse/estimators/__init__.py b/src/susse/estimators/__init__.py deleted file mode 100644 index 7cffb735..00000000 --- a/src/susse/estimators/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .clearsky import * -from .validation import * diff --git a/src/susse/estimators/clearsky/__init__.py b/src/susse/estimators/clearsky/__init__.py deleted file mode 100644 index cd0ca240..00000000 --- a/src/susse/estimators/clearsky/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .clear_sky_data import ClearSkyEstimate -from .clear_sky_estimator import ClearSkyEstimator, ClearSkyEstimatorPVlib -from .get_rest2_data import REST2Model diff --git a/src/susse/estimators/clearsky/clear_sky_data.py b/src/susse/estimators/clearsky/clear_sky_data.py deleted file mode 100644 index 8ad99f78..00000000 --- a/src/susse/estimators/clearsky/clear_sky_data.py +++ /dev/null @@ -1,51 +0,0 @@ -from datetime import datetime -from typing import List - -import numpy as np -import pandas as pd - -from ..estimation_result_pd import EstimationResultDF - - -class ClearSkyEstimate(EstimationResultDF): - """ - This class represents the results obtained from any ClearSkyEstimation. It contains the GHI, DHI and DNI - """ - - _GHI_COL = "ghi" - _DHI_COL = "dhi" - _DNI_COL = "dni" - - @staticmethod - def from_np( - ghi: np.ndarray, dhi: np.ndarray, dni: np.ndarray, timestamps: List[datetime] - ) -> "ClearSkyEstimate": - df = pd.DataFrame( - { - ClearSkyEstimate._GHI_COL: ghi, - ClearSkyEstimate._DHI_COL: dhi, - ClearSkyEstimate._DNI_COL: dni, - "timestamps": timestamps, - } - ) - df.set_index("timestamps", inplace=True) - return ClearSkyEstimate(df) - - def __init__(self, clear_sky_df: pd.DataFrame): - super().__init__(clear_sky_df, [self._GHI_COL, self._DHI_COL, self._DNI_COL]) - - @property - def ghi(self) -> np.ndarray: - return self._df[self._GHI_COL].to_numpy() - - @property - def dhi(self) -> np.ndarray: - return self._df[self._DHI_COL].to_numpy() - - @property - def dni(self) -> np.ndarray: - return self._df[self._DNI_COL].to_numpy() - - @property - def timestamp(self) -> List[datetime]: - return self._df.index.to_pydatetime().tolist() diff --git a/src/susse/estimators/clearsky/clear_sky_estimator.py b/src/susse/estimators/clearsky/clear_sky_estimator.py deleted file mode 100644 index 8364c3aa..00000000 --- a/src/susse/estimators/clearsky/clear_sky_estimator.py +++ /dev/null @@ -1,74 +0,0 @@ -import datetime -from abc import ABC, abstractmethod -from enum import Enum - -import pandas as pd -import pvlib -from geopy import location as Glocation -from overrides import override - -from .clear_sky_data import ClearSkyEstimate - - -class ClearSkyEstimator(ABC): - """ - This class is the baseclass for all estimators of Clear-Sky irradiance. it takes as inputs the location of interest - as well as the start and end date. The return value is of type ClearSkyEstimate, which itself is a wrapper for a - pandas dataframe - """ - - def __init__(self, name: str): - self._name = name - self._timedelta = datetime.timedelta(hours=1) - self._tz = datetime.timezone.utc - - @abstractmethod - def estimate_clear_sky( - self, - location: Glocation, - start_date: datetime.datetime, - end_date: datetime.datetime, - ) -> ClearSkyEstimate: - pass - - def set_timedelta(self, timedelta: datetime.timedelta): - self._timedelta = timedelta - - def set_timezone(self, tz: datetime.timezone): - self._tz = tz - - -class ClearSkyEstimatorPVlib(ClearSkyEstimator): - - class Models(Enum): - SOLIS = "simplified_solis" - INEICHEN = "ineichen" - HAURWITZ = "haurwitz" - - def __init__(self): - super().__init__("clear_sky_pvlib") - - @override - def estimate_clear_sky( - self, - location: Glocation, - start_date: datetime.datetime, - end_date: datetime.datetime, - ) -> ClearSkyEstimate: - if start_date > end_date: - raise ValueError("Start date must be before end date") - - location_altitude = pvlib.location.lookup_altitude( - location.latitude, location.longitude - ) - pv_location = pvlib.location.Location( - location.latitude, location.longitude, altitude=location_altitude - ) - - times = pd.date_range( - start=start_date, end=end_date, freq=self._timedelta, tz=self._tz - ) - clear_sky_df = pv_location.get_clearsky( - times=times, model=self.Models.SOLIS.value - ) - return ClearSkyEstimate(clear_sky_df) diff --git a/src/susse/estimators/clearsky/get_rest2_data.py b/src/susse/estimators/clearsky/get_rest2_data.py deleted file mode 100644 index c7730326..00000000 --- a/src/susse/estimators/clearsky/get_rest2_data.py +++ /dev/null @@ -1,168 +0,0 @@ -from datetime import datetime, timedelta -from pathlib import Path -from typing import Tuple, Union - -import numpy as np -import pandas as pd -import pvlib -from farms.utilities import ti_to_radius -from geopy.location import Location as GLocation - -from ...api_clients.NASA_Power import ( - NASAPowerFetchData, - NASAPowerProduct, - TemporalResolution, -) -from .rest2 import rest2 - - -class REST2Model: - """ - Compute clear-sky irradiance using the REST2 model. - """ - - def __init__(self) -> None: - self.fetcher = NASAPowerFetchData() - self.solar_constant = 1361.2 - self.sza_limit = 89.0 - - def compute_irradiance( - self, - start_date: datetime, - end_date: datetime, - location: GLocation, - resolution: TemporalResolution, - ) -> np.ndarray: - """ - Fetch parameters and run REST2 clear-sky irradiance model. - - Args: - start_date: Start datetime (UTC). - end_date: End datetime (UTC). - location: Geopy Location with latitude and longitude. - resolution: Temporal resolution (HOURLY, DAILY, MONTHLY). - - Returns: - Array of clear-sky irradiance values. - """ - params_df = self.fetcher.fetch_multiple_parameters( - start_date, - end_date, - location, - [ - NASAPowerProduct.SURFACE_PRESSURE, - NASAPowerProduct.AEROSOL_OPTICAL_DEPTH_550nm, - NASAPowerProduct.AEROSOL_OPTICAL_DEPTH_840nm, - NASAPowerProduct.PRECIPITABLE_WATER, - NASAPowerProduct.ALL_SKY_SURFACE_ALBEDO, - NASAPowerProduct.TOTAL_COLUMN_OZONE, - ], - temporal_resolution=resolution, - ) - - timestamps = self._generate_timestamps(start_date, end_date, resolution) - radius = self._calculate_radius_factors(timestamps) - - p = params_df.to_numpy(NASAPowerProduct.SURFACE_PRESSURE) - aod550 = params_df.to_numpy(NASAPowerProduct.AEROSOL_OPTICAL_DEPTH_550nm) - aod840 = params_df.to_numpy(NASAPowerProduct.AEROSOL_OPTICAL_DEPTH_840nm) - pw = params_df.to_numpy(NASAPowerProduct.PRECIPITABLE_WATER) - albedo = params_df.to_numpy(NASAPowerProduct.ALL_SKY_SURFACE_ALBEDO) - ozone = params_df.to_numpy(NASAPowerProduct.TOTAL_COLUMN_OZONE) * 0.001 - z = self._get_solar_zenith_angle(timestamps, location) - - alpha, beta = self._calculate_angstrom_params(aod550, aod840) - - rest2_inputs = { - "p": p * 10, - "albedo": albedo, - "ssa": -9.99, - "g": -9.99, - "z": z, - "radius": radius, - "alpha": alpha, - "beta": beta, - "ozone": ozone, - "w": pw, - "sza_lim": self.sza_limit, - } - - return rest2(**rest2_inputs) - - def _generate_timestamps( - self, - start_date: datetime, - end_date: datetime, - resolution: TemporalResolution, - ) -> pd.DatetimeIndex: - """ - Generate timestamps matching NASA API's exclusive end behavior. - - Returns: - DatetimeIndex of timestamps. - """ - if resolution is TemporalResolution.HOURLY: - end_date += timedelta(hours=1) - elif resolution is TemporalResolution.DAILY: - end_date += timedelta(days=1) - - freq_map = { - TemporalResolution.HOURLY: "H", - TemporalResolution.DAILY: "D", - TemporalResolution.MONTHLY: "MS", - } - - return pd.date_range( - start=start_date, - end=end_date, - freq=freq_map[resolution], - inclusive="left", - ) - - @staticmethod - def _calculate_radius_factors( - timestamps: pd.DatetimeIndex, - ) -> np.ndarray: - """ - Vectorized Earth–Sun distance calculation. - """ - return ti_to_radius(timestamps) - - @staticmethod - def _calculate_angstrom_params( - aod550: np.ndarray, - aod840: np.ndarray, - ) -> Tuple[np.ndarray, np.ndarray]: - """ - Calculate Angstrom exponent (alpha) and turbidity (beta). - """ - aod550 = np.asarray(aod550) - aod840 = np.asarray(aod840) - - valid = (aod550 > 0) & (aod840 > 0) - - alpha = np.full_like(aod550, np.nan) - beta = np.full_like(aod550, np.nan) - - alpha[valid] = -np.log(aod550[valid] / aod840[valid]) / np.log(550 / 840) - beta[valid] = aod550[valid] * (0.55 ** alpha[valid]) - - return alpha, beta - - def _get_solar_zenith_angle( - self, - timestamps: pd.DatetimeIndex | list[str], - location: GLocation, - ) -> np.ndarray: - """ - Calculate solar zenith angle for given times and location. - """ - if not isinstance(timestamps, pd.DatetimeIndex): - timestamps = pd.to_datetime(timestamps, format="%Y%m%d%H") - - solpos = pvlib.solarposition.get_solarposition( - timestamps, - location.latitude, - location.longitude, - ) - return solpos["zenith"].to_numpy() diff --git a/src/susse/estimators/clearsky/rest2.py b/src/susse/estimators/clearsky/rest2.py deleted file mode 100644 index a1228b15..00000000 --- a/src/susse/estimators/clearsky/rest2.py +++ /dev/null @@ -1,946 +0,0 @@ -""" -@author: Anthony Lopez -January 27, 2015 - -Adapted from Chris Gueymard's REST2 model -(Proc. ASES Conf., 2004; Solar Energy, 2008). -Details from the original Fortran Code: - -c Program REST2_v9.0c [v9 Compact] -C -c Gueymard's REST2 model (Proc. ASES Conf., 2004; Solar Energy, 2008). -c==> Compact version of Latest version (v9) used here (2014). -c Version 9.0: Corrected calculation of aerosol transmittance, now -c with extended AOD range; revsed solar constant; -c improved and streamlined diffuse algorithms, now using -c asymmetry parameter as input. -c Version 9.0c: Compact algorithm; features: -c - assumes Z is known (to avoid solar position calculations) -c - does not calculate PAR or illuminances -c - does not print intermediate results -c -c NOTE 1: The NO2 vertical pathlength is defaulted here to fit unpolluted -c conditions and accelerate calculations in this compact version. -c For better results over polluted areas, use the regular REST2 version! -c -c NOTE 2: The input sanity check on lines 112-120 of the code may be -c superfluous if the inputs have been tested in a previous step. -c -c++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -""" - -import collections -import concurrent.futures as cf -import gc - -import farms.utilities as ut -import numpy as np -from farms import SOLAR_CONSTANT, SZA_LIM - - -def op_mass(p, am, cosz, z): - """ - Calculate optical masses. - - Parameters - ---------- - p : np.ndarray - Pressure in mbar.(mbar is same as hPa) - am : np.ndarray - Optical mass parameter from SMARTS Model. - cosz : np.ndarray - Cosine of the solar zenith angle. - z : np.ndarray - Solar zenith angle in degrees. - """ - masr = am * p / 1013.25 - masw = np.maximum( - 1.0, 1.0 / (cosz + 0.10648 * (z**0.11423) / (93.781 - z) ** 1.9203) - ) - maso = np.maximum(1.0, 1.0 / (cosz + 1.0651 * (z**0.6379) / (101.8 - z) ** 2.2694)) - - return masr, masw, maso - - -def trans_1(p, am, cosz, z, ozone, w): - """ - Calculate band 1 transmittances. - - Parameters - ---------- - p : np.ndarray - Pressure in mbar.(mbar is same as hPa) - am : np.ndarray - Optical mass parameter from SMARTS Model. - cosz : np.ndarray - Cosine of the solar zenith angle. - z : np.ndarray - Solar zenith angle in degrees. - ozone : np.ndarray - reduced ozone vertical pathlength (atm-cm) - [Note: 1 atm-cm = 1000 DU] - w : np.ndarray - Total precip. water (cm). - - Returns - ------- - transr1 : np.ndarray - Transmittance for rayleigh scattering. - transg1 : np.ndarray - Transmittance for uniformly mixed gases. - trano1 : np.ndarray - Transmittance for ozone absorption. - trann1 : np.ndarray - Transmittance for nitrogen dioxide absorption. - tranw1 : np.ndarray - Transmittance for water absorption - """ - # get the optical masses - masr, masw, maso = op_mass(p, am, cosz, z) - - transr1 = (1.0 + 1.8169 * masr - 0.033454 * masr**2) / ( - 1.0 + 2.063 * masr + 0.31978 * masr**2 - ) - transg1 = (1.0 + 0.95885 * masr + 0.012871 * masr**2) / ( - 1.0 + 0.96321 * masr + 0.015455 * masr**2 - ) - - a1 = ozone * (10.979 - 8.5421 * ozone) / (1.0 + 2.0115 * ozone + 40.189 * ozone**2) - a2 = ( - ozone - * (-0.027589 - 0.005138 * ozone) - / (1.0 - 2.4857 * ozone + 13.942 * ozone**2) - ) - a3 = ozone * (10.995 - 5.5001 * ozone) / (1.0 + 1.6784 * ozone + 42.406 * ozone**2) - trano1 = (1.0 + a1 * maso + a2 * maso**2) / (1.0 + a3 * maso) - - trann1 = (1.0 + 0.18307 * masw - 0.00024 * masw**2) / (1.0 + 0.18713 * masw) - - c1 = w * (0.065445 + 0.00029901 * w) / (1.0 + 1.2728 * w) - c2 = w * (0.065687 + 0.0013218 * w) / (1.0 + 1.2008 * w) - tranw1 = (1.0 + c1 * masw) / (1.0 + c2 * masw) - - return transr1, transg1, trano1, trann1, tranw1 - - -def trans_2(p, am, cosz, z, w): - """ - Calculate band 2 transmittances. - - Parameters - ---------- - p : np.ndarray - Pressure in mbar.(mbar is same as hPa) - am : np.ndarray - Optical mass parameter from SMARTS Model. - cosz : np.ndarray - Cosine of the solar zenith angle. - z : np.ndarray - Solar zenith angle in degrees. - w : np.ndarray - Total precip. water (cm). - - Returns - ------- - transr2 : np.ndarray - Transmittance for rayleigh scattering. - transg2 : np.ndarray - Transmittance for uniformly mixed gases. - tranw2 : np.ndarray - Transmittance for water absorption - """ - # get the optical masses - masr, masw, _ = op_mass(p, am, cosz, z) - - transr2 = (1.0 - 0.010394 * masr) / (1.0 - 0.00011042 * masr**2) - trang2 = (1.0 + 0.27284 * masr - 0.00063699 * masr**2) / (1.0 + 0.30306 * masr) - - c1 = w * ( - (19.566 - 1.6506 * w + 1.0672 * w**2) / (1.0 + 5.4248 * w + 1.6005 * w**2) - ) - c2 = w * ( - (0.50158 - 0.14732 * w + 0.047584 * w**2) / (1.0 + 1.1811 * w + 1.0699 * w**2) - ) - c3 = w * ( - (21.286 - 0.39232 * w + 1.2692 * w**2) / (1.0 + 4.8318 * w + 1.412 * w**2) - ) - c4 = w * ( - (0.70992 - 0.23155 * w + 0.096514 * w**2) / (1.0 + 0.44907 * w + 0.75425 * w**2) - ) - tranw2 = (1.0 + c1 * masw + c2 * masw**2) / (1.0 + c3 * masw + c4 * masw**2) - - return transr2, trang2, tranw2 - - -def band_1(alpha, masa, beta): - """ - Get band (wavelength) 1. - - Parameters - ---------- - alpha : np.ndarray - Angstrom wavelength exponent. - masa : np.ndarray - Optical mass parameter from SMARTS Model. - beta : np.ndarray - Angstrom turbidity coeff. - """ - wvlmin1 = 0.5158 - 0.008334 * alpha - wvlmax1 = np.maximum(0.61, (0.6 + 0.95155 * alpha) / (1.0 + 1.3095 * alpha)) - - if np.any(alpha < 0): - i = np.where(alpha < 0) - wvlmin1[i] = 0.51 - wvlmax1[i] = 0.61 - - # Coefficients for Band 1 - cc0 = ( - 0.50947 - - 0.012555 * alpha - + 0.0026455 * alpha**2 - + 0.0044092 * alpha**3 - - 0.0022439 * alpha**4 - + 0.0003123 * alpha**5 - ) - cc1 = ( - 0.062836 - + 0.049194 * alpha - + 0.013976 * alpha**2 - - 0.0114290 * alpha**3 - + 0.0053573 * alpha**4 - + 0.0026402 * alpha**5 - ) - cc2 = ( - 0.096418 - + 0.072221 * alpha - + 0.015505 * alpha**2 - - 0.0216490 * alpha**3 - + 0.0119010 * alpha**4 - + 0.0033763 * alpha**5 - ) - dd0 = 0.5 - dd1 = ( - 0.065180 - - 0.039075 * alpha - + 0.11648 * alpha**2 - + 0.048987 * alpha**3 - - 0.026766 * alpha**4 - - 0.12573 * alpha**5 - + 0.092131 * alpha**6 - ) - dd2 = ( - 0.099191 - - 0.083962 * alpha - + 0.20562 * alpha**2 - + 0.057377 * alpha**3 - - 0.049548 * alpha**4 - - 0.17782 * alpha**5 - + 0.13647 * alpha**6 - ) - - y1 = masa * beta ** (0.3333 * alpha) - z1 = masa * beta**0.5 - wvle1 = (cc0 + cc1 * y1) / (1.0 + cc2 * y1) - - if np.any(masa * beta > 10): - i = np.where(masa * beta > 10) - wvle1[i] = (dd0 + dd1[i] * z1[i]) / (1.0 + dd2[i] * z1[i]) - - wvle1 = np.minimum(wvle1, wvlmax1) - wvle1 = np.maximum(wvle1, wvlmin1) - - return wvle1 - - -def band_2(alpha, masa, beta): - """ - Get band (wavelength) 2. - - Parameters - ---------- - alpha : np.ndarray - Angstrom wavelength exponent. - masa : np.ndarray - Optical mass parameter from SMARTS Model. - beta : np.ndarray - Angstrom turbidity coeff. - """ - wvlmin2 = 1.0 - 0.02 * alpha - wvlmax2 = 1.3 + 1.5317 * alpha - 0.55289 * alpha**2 - - if np.any(alpha < 0): - i = np.where(alpha < 0) - wvlmin2[i] = 1.0 + 0.4 * alpha[i] - wvlmax2[i] = 1.3 + 0.25 * alpha[i] - - # Coefficients for Band 2 - aa0 = ( - 1.0677 - - 0.05432 * alpha - + 0.014351 * alpha**2 - - 0.0097063 * alpha**3 - + 0.0023655 * alpha**4 - ) - aa1 = ( - -0.20914 - - 0.27218 * alpha - + 0.83552 * alpha**2 - - 0.85437 * alpha**3 - + 0.49305 * alpha**4 - - 0.14965 * alpha**5 - + 0.018964 * alpha**6 - ) - aa2 = ( - 0.0010588 - + 0.039597 * alpha - + 0.006733 * alpha**2 - + 0.070698 * alpha**3 - - 0.11284 * alpha**4 - + 0.055096 * alpha**5 - - 0.0086265 * alpha**6 - ) - aa3 = ( - -0.19432 - - 0.29366 * alpha - + 0.83474 * alpha**2 - - 0.78019 * alpha**3 - + 0.37382 * alpha**4 - - 0.089069 * alpha**5 - + 0.0091113 * alpha**6 - ) - - x2 = np.log(1.0 + masa * beta) - x22 = x2 * x2 - wvle2 = (aa0 + aa1 * x2 + aa2 * x22) / (1.0 + aa3 * x2) - - wvle2 = np.minimum(wvle2, wvlmax2) - wvle2 = np.maximum(wvle2, wvlmin2) - - return wvle2 - - -def aer_scat_trans(wvle, piar, alpha, beta, masa): - """Calculate the aerosol scattering transmittances for a single band. - - Parameters - ---------- - wvle : np.ndarray - Wavelength for the single band. - piar : np.ndarray - Adjusted ssa parameter. - alpha : np.ndarray - Angstrom wavelength exponent. - beta : np.ndarray - Angstrom turbidity coeff. - masa : np.ndarray - Optical mass parameter from SMARTS Model. - - Returns - ------- - tranas : np.ndarray - T_as from eq 7b in ref [1]: Aerosol scattering transmittance. - tranaa : np.ndarray - Transmittance for spectral aerosol extinction from ABSORPTION - tauas : np.ndarray - Spectral aerosol optical depth along vertical atmospheric column - (taua, eq 6 in ref [1]) times the single-scattering albedo (ssa). - """ - # AEROSOL TRANSMITTANCES FOR THE 2 BANDS - taua = beta / wvle**alpha # [1] eq. (6) - tauas = piar * taua - amsbet = masa * taua - tranas = np.exp(-masa * tauas) # [1] eq. (7b) - tranaa = np.exp(-amsbet * (1.0 - piar)) - - return tranas, tranaa, tauas - - -def calc_eabs(tabs, f, radius): - """Calculate the dni for a given band without aerosol scattering. - - This method implements part of eq 3 from ref [1]. - - Parameters - ---------- - tabs : np.ndarray - Total Absorption transmittances (product of T variables in eq 3 - from ref [1] without scattering terms). - f : float - Constant value, originally 0.47244 and 0.51951 for bands 1 and 2. - radius : np.ndarray - Sun-earth radius vector, varies between 1.017 in July and - 0.983 in January. - - Returns - ------- - eabs : np.ndarray - Direct normal irradiance for a given band in W/m2 without scattering. - """ - # etdirn : Extraterrestrial direct normal irradiance at time t [W/m2] - # etdirn = SOLAR_CONSTANT / (radius ** 2) - eabs = tabs * f * SOLAR_CONSTANT / (radius**2) - - return eabs - - -def calc_edni(transr, tranas, eabs): - """Calculate the dni for a given band with aerosol scattering. - - This method implements eq 3 from ref [1]. - - Parameters - ---------- - transr : np.ndarray - Transmittance for rayleigh scattering. - tranas : np.ndarray - T_as from eq 7b in ref [1]: Aerosol scattering transmittance. - eabs : np.ndarray - Direct normal irradiance for a given band in W/m2 without scattering. - - Returns - ------- - edni : np.ndarray - Direct normal irradiance for a given band in W/m2 with aerosol - scattering. - """ - edni = eabs * transr * tranas - - return edni - - -def layer_props(transr, tauas, tranas, tabs, f, radius, cosz, albedo, am, fm, g0): - """Get the layer properties for a single wavelength band""" - # 1. Top layer properties (absorbing only) - eabs = calc_eabs(tabs, f, radius) - edni = calc_edni(transr, tranas, eabs) - - # 2. Bottom layer properties (scattering only) - taur = -np.log(transr) / am - taut = taur + tauas - - frwd1 = (0.5 + 1.8823 * cosz) / (1.0 + 1.7971 * cosz) * tauas + 0.5 * taur - amt = am * taut - fn = (1.0 - 10.921 * amt - 11.741 * amt * amt) / (1.0 + 35.006 * amt) - - rsky = taut * (0.51754 + 0.15884 * taut) / (1.0 + 2.77 * taut) - - g1 = g0 * tauas / taut - fg = (-0.5 + 10.497 * g1 - 11.735 * g1**2) / (1.0 + 401.0 * g1**2) - edif = frwd1 * np.exp(fn + fg + fm) * eabs - edift = edif + albedo * rsky * (edni * cosz + edif) / (1.0 - albedo * rsky) - - return edni, edif, edift, rsky - - -def rest2_parallel( - p, albedo, ssa, g, z, radius, alpha, beta, ozone, w, sza_lim=SZA_LIM, n_workers=16 -): - """REST2 Clear Sky parallel execution method.""" - - futures = [] - x_p = np.array_split(p, n_workers) - x_albedo = np.array_split(albedo, n_workers) - x_ssa = np.array_split(ssa, n_workers) - x_g = np.array_split(g, n_workers) - x_z = np.array_split(z, n_workers) - x_radius = np.array_split(radius, n_workers) - x_alpha = np.array_split(alpha, n_workers) - x_beta = np.array_split(beta, n_workers) - x_ozone = np.array_split(ozone, n_workers) - x_w = np.array_split(w, n_workers) - - with cf.ProcessPoolExecutor(max_workers=n_workers) as executor: - futures = [ - executor.submit( - rest2, - x_p[i], - x_albedo[i], - x_ssa[i], - x_g[i], - x_z[i], - x_radius[i], - x_alpha[i], - x_beta[i], - x_ozone[i], - x_w[i], - sza_lim=sza_lim, - ) - for i in range(n_workers) - ] - - futures = [future.result() for future in futures] - - var_list = ("ghi", "dni", "dhi", "Tddclr", "Tduclr", "Ruuclr") - rest_data = collections.namedtuple("rest_data", var_list) - - for i, future in enumerate(futures): - for var in var_list: - setattr( - rest_data, - var, - np.concatenate((getattr(rest_data, var), getattr(future, var)), axis=1), - ) - - return rest_data - - -def rest2(p, albedo, ssa, g, z, radius, alpha, beta, ozone, w, sza_lim=SZA_LIM): - """REST2 Clear Sky Model. - - Literature - ---------- - [1] Christian A. Gueymard, "REST2: High-performance solar radiation model - for cloudless-sky irradiance, illuminance, and photosynthetically - active radiation – Validation with a benchmark dataset", Solar Energy, - Volume 82, Issue 3, 2008, Pages 272-285, ISSN 0038-092X, - https://doi.org/10.1016/j.solener.2007.04.008. - (http://www.sciencedirect.com/science/article/pii/S0038092X07000990) - - Variables - --------- - eabs1 / eabs2 - Band (1 or 2) direct normal irradiance. (eq 3 from ref [1]) - tranas - Transmittance for spectral aerosol extinction from SCATTERING - (eq 7b in ref [1]). - tranaa - Transmittance for spectral aerosol extinction from ABSORPTION - transg - Transmittance for uniformly mixed gases absorption - trann - Transmittance for Nitrogen dioxide absorption - tranw - Transmittance for water absorption - trano - Transmittance for Ozone absorption - taua - Spectral aerosol optical depth along vertical atmospheric column. - (eq 6 in ref [1]) - tauas - taua times the single-scattering albedo (ssa) - masa - Relative slant pathlength or "air mass". - - Parameters - ---------- - p : np.ndarray - Surface pressure (mbar). (mbar is same as hPa) - albedo : np.ndarray - Ground albedo. - ssa : np.ndarray - aerosol single-scattering albedo at a representative wavelength of - about 700 nm. Use -9.99 or any other NEGATIVE value if unknown - (will default to 0.92 if a negative value is input) - g : np.ndarray - Aerosol asymmetry parameter. Since it tends to vary with wavelength - and alpha, use a representative value for a wavelength of about - 700 nm and alpha about 1. Will default to 0.7 if a NEGATIVE value is - input (when exact value is unknown). - z : np.ndarray - Solar zenith angle (degrees). Must represent the average value over the - integration period (e.g. hourly) under scrutiny. - radius : np.ndarray - Sun-earth radius vector, varies between 1.017 in July and - 0.983 in January. - alpha : np.ndarray - Angstrom wavelength exponent, ideally obtained by linear - regression of all available spectral AODs between 380 and 1020 nm. - Input value will be tested for compliance with the mandatory - interval [0, 2.5], and corrected if necessary. - beta : np.ndarray - Angstrom turbidity coeff., i.e. AOD at 1000 nm. - Input value will be tested for compliance with the mandatory - interval [0, 2.2], and corrected if necessary. - Can be calculated as beta=aod * np.power(0.55, alpha) - ozone : np.ndarray - reduced ozone vertical pathlength (atm-cm) - [Note: 1 atm-cm = 1000 DU] - w : np.ndarray - Total precip. water (cm). - sza_lim : float | int - Upper limit for solar zenith angle in degrees. SZA values greater than - this will be truncated at this value. - - Returns - ------- - rest_data : collections.namedtuple - Named tuple with clear sky irradiance data. Attributes: - ghi : global horizontal irradiance (w/m2) - dni : direct normal irradiance (w/m2) - dhi : diffuse horizontal irradiance (w/m2) - Tddclr : Transmittance of the clear-sky atmosphere for - direct incident and direct outgoing fluxes (dd). - Tddclr = dni / etdirn - Tduclr : Transmittance of the clear-sky atmosphere for - direct incident and diffuse outgoing fluxes (du). - Tduclr = dhi / (etdirn * cosz) - Ruuclr : Aerosol reflectance for diffuse incident and diffuse - outgoing fluxes (uu). - """ - # enforce ceiling on solar zenith angle - z = np.where(z > sza_lim, sza_lim, z) - - # Constants - degrad = 0.017453293 - f1 = 0.47244 - f2 = 0.51951 - - # Input Verification - alpha = np.maximum(0.0, np.minimum(alpha, 2.501)) - beta = np.maximum(0.001, np.minimum(beta, 2.2)) - albedo = np.maximum(0.05, np.minimum(albedo, 9)) - - cosz = np.cos(z * degrad) - - g0 = g + 0.066 * (1.0 - alpha) - piar1 = np.maximum(0.85, np.minimum(0.98, ssa + 0.03)) - piar2 = np.minimum(0.95, np.maximum(0.85, ssa - 0.02)) - - # Vectorized operation - piar1 = np.where(ssa <= 0, 0.95, piar1) - piar2 = np.where(ssa <= 0, 0.90, piar2) - ssa = np.where(ssa <= 0, 0.92, ssa) - - # Optical Masses from SMARTS Model - am = np.maximum(1.0, 1.0 / (cosz + 0.48353 * (z**0.095846) / (96.741 - z) ** 1.754)) - masa = np.maximum( - 1.0, 1.0 / (cosz + 0.16851 * (z**0.18198) / (95.318 - z) ** 1.9542) - ) - - # calculate the transmittances for both bands - transr1, transg1, trano1, trann1, tranw1 = trans_1(p, am, cosz, z, ozone, w) - transr2, trang2, tranw2 = trans_2(p, am, cosz, z, w) - - # New aerosol functions in v9 - ambeta = masa * beta - - # get the two band wavelengths - wvle1 = band_1(alpha, masa, beta) - wvle2 = band_2(alpha, masa, beta) - - # AOD correction - aodcor = ambeta * (0.015981 + 0.183 * ambeta) / (1.0 + 1.4142 * ambeta) - - # get the aerosol scattering and absorption transmittances - tranas1, tranaa1, tauas1 = aer_scat_trans(wvle1, piar1, alpha, beta, masa) - tranas2, tranaa2, tauas2 = aer_scat_trans(wvle2, piar2, alpha, beta, masa) - - # Total Absorption transmittances - tabs1 = transg1 * trann1 * tranw1 * trano1 * tranaa1 - tabs2 = trang2 * tranw2 * tranaa2 - - # DIFFUSE ALGORITHM - fm = 0.15244 * (am - 1.0) / (1.0 + 2.2413 * am) - - # Get BAND1 layer properties : UV-VIS PART OF THE SPECTRUM (0.3-0.7 um) - edni1, edif1, edift1, rsky1 = layer_props( - transr1, tauas1, tranas1, tabs1, f1, radius, cosz, albedo, am, fm, g0 - ) - # Get BAND2 layer properties : INFRA-RED PART OF THE SPECTRUM (0.7-4 um) - edni2, edif2, edift2, rsky2 = layer_props( - transr2, tauas2, tranas2, tabs2, f2, radius, cosz, albedo, am, fm, g0 - ) - # Broadband results - edif = edift1 + edift2 - edirn = (edni1 + edni2) * np.exp(aodcor) - eglob = edif + edirn * cosz - - # Extra Return values for FastModel - Tddclr = edirn / (SOLAR_CONSTANT / (radius**2)) - Tduclr = edif / ((SOLAR_CONSTANT / (radius**2)) * cosz) - Ruuclr = ((edni1 * cosz + edif1) * rsky1 + (edni2 * cosz + edif2) * rsky2) / ( - (edni1 * cosz + edif1) + (edni2 * cosz + edif2) - ) - - ut.check_range(Tddclr, "Tddclr") - ut.check_range(Tduclr, "Tduclr") - ut.check_range(Ruuclr, "Ruuclr") - - rest_data = collections.namedtuple( - "rest_data", ["ghi", "dni", "dhi", "Tddclr", "Tduclr", "Ruuclr"] - ) - rest_data.ghi = eglob - rest_data.dni = edirn - rest_data.dhi = edif - rest_data.Tddclr = Tddclr - rest_data.Tduclr = Tduclr - rest_data.Ruuclr = Ruuclr - - return rest_data - - -def rest2_tddclr(p, albedo, ssa, z, radius, alpha, beta, ozone, w, sza_lim=SZA_LIM): - """REST2 Clear Sky Model for only calculating Tddclr for FARMS input. - - Literature - ---------- - [1] Christian A. Gueymard, "REST2: High-performance solar radiation model - for cloudless-sky irradiance, illuminance, and photosynthetically - active radiation – Validation with a benchmark dataset", Solar Energy, - Volume 82, Issue 3, 2008, Pages 272-285, ISSN 0038-092X, - https://doi.org/10.1016/j.solener.2007.04.008. - (http://www.sciencedirect.com/science/article/pii/S0038092X07000990) - - Variables - --------- - eabs1 / eabs2 - Band (1 or 2) direct normal irradiance. (eq 3 from ref [1]) - tranas - Transmittance for spectral aerosol extinction from SCATTERING - (eq 7b in ref [1]). - tranaa - Transmittance for spectral aerosol extinction from ABSORPTION - transg - Transmittance for uniformly mixed gases absorption - trann - Transmittance for Nitrogen dioxide absorption - tranw - Transmittance for water absorption - trano - Transmittance for Ozone absorption - taua - Spectral aerosol optical depth along vertical atmospheric column. - (eq 6 in ref [1]) - tauas - taua times the single-scattering albedo (ssa) - masa - Relative slant pathlength or "air mass". - - Parameters - ---------- - p : np.ndarray - Surface pressure (mbar). (mbar is same as hPa) - albedo : np.ndarray - Ground albedo. - ssa : np.ndarray - aerosol single-scattering albedo at a representative wavelength of - about 700 nm. Use -9.99 or any other NEGATIVE value if unknown - (will default to 0.92 if a negative value is input) - z : np.ndarray - Solar zenith angle (degrees). Must represent the average value over the - integration period (e.g. hourly) under scrutiny. - radius : np.ndarray - Sun-earth radius vector, varies between 1.017 in July and - 0.983 in January. - alpha : np.ndarray - Angstrom wavelength exponent, ideally obtained by linear - regression of all available spectral AODs between 380 and 1020 nm. - Input value will be tested for compliance with the mandatory - interval [0, 2.5], and corrected if necessary. - beta : np.ndarray - Angstrom turbidity coeff., i.e. AOD at 1000 nm. - Input value will be tested for compliance with the mandatory - interval [0, 2.2], and corrected if necessary. - Can be calculated as beta=aod * np.power(0.55, alpha) - ozone : np.ndarray - reduced ozone vertical pathlength (atm-cm) - [Note: 1 atm-cm = 1000 DU] - w : np.ndarray - Total precip. water (cm). - sza_lim : float | int - Upper limit for solar zenith angle in degrees. SZA values greater than - this will be truncated at this value. - - Returns - ------- - rest_data : collections.namedtuple - Named tuple with clear sky irradiance data. Attributes: - ghi : global horizontal irradiance (w/m2) - dni : direct normal irradiance (w/m2) - dhi : diffuse horizontal irradiance (w/m2) - Tddclr : Transmittance of the clear-sky atmosphere for - direct incident and direct outgoing fluxes (dd). - Tddclr = dni / etdirn - Tduclr : Transmittance of the clear-sky atmosphere for - direct incident and diffuse outgoing fluxes (du). - Tduclr = dhi / (etdirn * cosz) - Ruuclr : Aerosol reflectance for diffuse incident and diffuse - outgoing fluxes (uu). - """ - - # enforce ceiling on solar zenith angle - z = np.where(z > sza_lim, sza_lim, z) - - # Constants - degrad = 0.017453293 - f1 = 0.47244 - f2 = 0.51951 - - # Input Verification - alpha = np.maximum(0.0, np.minimum(alpha, 2.501)) - beta = np.maximum(0.001, np.minimum(beta, 2.2)) - albedo = np.maximum(0.05, np.minimum(albedo, 9)) - - cosz = np.cos(z * degrad) - - piar1 = np.maximum(0.85, np.minimum(0.98, ssa + 0.03)) - piar2 = np.minimum(0.95, np.maximum(0.85, ssa - 0.02)) - - # Vectorized operation - piar1 = np.where(ssa <= 0, 0.95, piar1) - piar2 = np.where(ssa <= 0, 0.90, piar2) - ssa = np.where(ssa <= 0, 0.92, ssa) - - # Optical Masses from SMARTS Model - am = np.maximum(1.0, 1.0 / (cosz + 0.48353 * (z**0.095846) / (96.741 - z) ** 1.754)) - masa = np.maximum( - 1.0, 1.0 / (cosz + 0.16851 * (z**0.18198) / (95.318 - z) ** 1.9542) - ) - - # calculate the transmittances for both bands - transr1, transg1, trano1, trann1, tranw1 = trans_1(p, am, cosz, z, ozone, w) - transr2, trang2, tranw2 = trans_2(p, am, cosz, z, w) - - # clear some variables to free memory - del am, cosz, z, w - - # New aerosol functions in v9 - ambeta = masa * beta - - # get the two band wavelengths - wvle1 = band_1(alpha, masa, beta) - wvle2 = band_2(alpha, masa, beta) - - # AOD correction - aodcor = ambeta * (0.015981 + 0.183 * ambeta) / (1.0 + 1.4142 * ambeta) - - # get the aerosol scattering and absorption transmittances - tranas1, tranaa1, _ = aer_scat_trans(wvle1, piar1, alpha, beta, masa) - tranas2, tranaa2, _ = aer_scat_trans(wvle2, piar2, alpha, beta, masa) - - # clear some variables to free memory - del alpha, beta, masa, piar1, piar2, wvle1, wvle2, ssa - - # Total Absorption transmittances - tabs1 = transg1 * trann1 * tranw1 * trano1 * tranaa1 - tabs2 = trang2 * tranw2 * tranaa2 - - # clear some variables to free memory - del transg1, trann1, tranw1, trano1, tranaa1, trang2, tranw2, tranaa2 - - # calculate edni - edni1 = calc_edni(transr1, tranas1, calc_eabs(tabs1, f1, radius)) - edni2 = calc_edni(transr2, tranas2, calc_eabs(tabs2, f2, radius)) - - # clear some variables to free memory - del transr1, transr2, tranas1, tranas2, tabs1, tabs2 - - # Broadband results - edirn = (edni1 + edni2) * np.exp(aodcor) - - # Extra Return values for FastModel - Tddclr = edirn / (SOLAR_CONSTANT / (radius**2)) - - ut.check_range(Tddclr, "Tddclr") - - return Tddclr - - -def rest2_tuuclr( - p, - albedo, - ssa, - radius, - alpha, - ozone, - w, - parallel=False, - diffuse_angles=( - 84.2608, - 78.4630, - 72.5424, - 66.4218, - 60.0000, - 53.1301, - 45.5730, - 36.8699, - 25.8419, - 0.00000, - ), -): - """Calculate Tuuclr based on average values from several REST2 runs. - - Equation 5 from the following reference: - [1] Yu Xie, Manajit Sengupta, Jimy Dudhia, "A Fast All-sky Radiation Model - for Solar applications (FARMS): Algorithm and performance evaluation", - Solar Energy, Volume 135, 2016, Pages 435-445, ISSN 0038-092X, - https://doi.org/10.1016/j.solener.2016.06.003. - (http://www.sciencedirect.com/science/article/pii/S0038092X16301827) - - Parameters - ---------- - p : np.ndarray - See rest2 doc string for description. - albedo : np.ndarray - See rest2 doc string for description. - ssa : np.ndarray - See rest2 doc string for description. - radius : np.ndarray - See rest2 doc string for description. - alpha : np.ndarray - See rest2 doc string for description. - ozone : np.ndarray - See rest2 doc string for description. - w : np.ndarray - See rest2 doc string for description. - parallel : bool - Flag to each diffuse angle on a seperate core using concurrent futures. - diffuse_angles : list | tuple - Set of solar zenith angles from 0 to near 90 used to calculate Tuuclr. - - Returns - ------- - Tuuclr : np.ndarray - Transmittance of the clear-sky atmosphere for diffuse incident and - diffuse outgoing fluxes (uu). - """ - - Tddclr_list = [] - - # ensure radius is of the correct shape - if radius.shape != p.shape: - radius = np.tile(radius, p.shape[1]) - - if not parallel: - # serial execution - for angle in diffuse_angles: - Tddclr_list.append( - rest2_tddclr( - p=p, - albedo=albedo, - ssa=ssa, - z=angle, - radius=radius, - alpha=alpha, - beta=0, - ozone=ozone, - w=w, - ) - ) - gc.collect() - else: - # parallel execution - n_workers = len(diffuse_angles) - with cf.ProcessPoolExecutor(max_workers=n_workers) as executor: - # submit futures for each angle - futures = [ - executor.submit( - rest2_tddclr, p, albedo, ssa, angle, radius, alpha, 0, ozone, w - ) - for angle in diffuse_angles - ] - - Tddclr_list = [future.result() for future in futures] - gc.collect() - - scalar = 1 / (len(diffuse_angles)) - for i, angle in enumerate(diffuse_angles): - Tddclr_list[i] = Tddclr_list[i] * np.cos(np.radians(angle)) * scalar - - # Get the average for various angles - Tuuclr = np.sum(np.array(Tddclr_list), axis=0) * 2.0 - - del Tddclr_list - gc.collect() - - return Tuuclr diff --git a/src/susse/estimators/estimation_result_pd.py b/src/susse/estimators/estimation_result_pd.py deleted file mode 100644 index 1b7339ba..00000000 --- a/src/susse/estimators/estimation_result_pd.py +++ /dev/null @@ -1,43 +0,0 @@ -import logging -from typing import List - -import numpy as np -import pandas as pd - - -class EstimationResultDF: - """ - This class is meant to be a convenience class to serve as base class for all the estimation results that store the - data in a pd.Dataframe internally. This class simply contains some basic consistency checks - """ - - def __init__(self, df: pd.DataFrame, necessary_columns: List[str]): - if not isinstance(df.index, pd.DatetimeIndex): - logging.error("The DataFrame index must be a pandas DatetimeIndex.") - raise ValueError("DataFrame index must be a pandas DatetimeIndex.") - else: - logging.debug("DataFrame index is a pandas DatetimeIndex.") - - missing_columns = [col for col in necessary_columns if col not in df.columns] - if missing_columns: - logging.error(f"Missing necessary columns: {missing_columns}") - raise ValueError(f"Missing necessary columns: {missing_columns}") - else: - logging.debug("All necessary columns are present.") - - if df[necessary_columns].isnull().values.any(): - logging.warning("ClearSkyData contains NaN values.") - else: - logging.debug("No NaN values in necessary columns.") - - for col in necessary_columns: - if not np.issubdtype(df[col].dtype, np.number): - logging.error(f"The column '{col}' must contain numeric data.") - raise TypeError(f"Column '{col}' must contain numeric data.") - else: - logging.debug(f"Column '{col}' contains numeric data.") - - self._df = df - - def to_df(self) -> pd.DataFrame: - return self._df diff --git a/src/susse/estimators/validation/__init__.py b/src/susse/estimators/validation/__init__.py deleted file mode 100644 index 6a68bed9..00000000 --- a/src/susse/estimators/validation/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .metrics import StatisticalMetrics diff --git a/src/susse/estimators/validation/metrics.py b/src/susse/estimators/validation/metrics.py deleted file mode 100644 index b48e756b..00000000 --- a/src/susse/estimators/validation/metrics.py +++ /dev/null @@ -1,90 +0,0 @@ -from functools import wraps -from typing import Tuple - -import numpy as np - - -def validate_inputs(func): - @wraps(func) - def wrapper(observations: np.ndarray, predictions: np.ndarray) -> float: - if not isinstance(observations, np.ndarray) or not isinstance( - predictions, np.ndarray - ): - raise TypeError("Inputs must be numpy arrays") - if observations.shape != predictions.shape: - raise ValueError("Arrays must have same shape") - if len(observations) == 0: - raise ValueError("Arrays cannot be empty") - return func(observations, predictions) - - return wrapper - - -class StatisticalMetrics: - """Static methods for computing statistical metrics between observations and predictions""" - - @staticmethod - @validate_inputs - def mean_bias_deviation(observations: np.ndarray, predictions: np.ndarray) -> float: - """Mean Bias Deviation (MBD)""" - return float(np.mean(predictions - observations)) - - @staticmethod - @validate_inputs - def mean_absolute_deviation( - observations: np.ndarray, predictions: np.ndarray - ) -> float: - """Mean Absolute Deviation (MAD)""" - return float(np.mean(np.abs(predictions - observations))) - - @staticmethod - @validate_inputs - def root_mean_square_deviation( - observations: np.ndarray, predictions: np.ndarray - ) -> float: - """Root Mean Square Deviation (RMSD)""" - return float(np.sqrt(np.mean(np.square(predictions - observations)))) - - @staticmethod - @validate_inputs - def relative_rmsd(observations: np.ndarray, predictions: np.ndarray) -> float: - """Relative Root Mean Square Deviation (rRMSD) in percentage""" - rmsd = StatisticalMetrics.root_mean_square_deviation(observations, predictions) - return float((rmsd / np.mean(observations)) * 100) - - @staticmethod - @validate_inputs - def pearson_correlation(observations: np.ndarray, predictions: np.ndarray) -> float: - """Pearson correlation coefficient""" - return float(np.corrcoef(observations, predictions)[0, 1]) - - @staticmethod - @validate_inputs - def nash_sutcliffe_efficiency( - observations: np.ndarray, predictions: np.ndarray - ) -> float: - """Nash-Sutcliffe Efficiency (NSE)""" - numerator = np.sum(np.square(predictions - observations)) - denominator = np.sum(np.square(observations - np.mean(observations))) - return float(1 - (numerator / denominator)) - - @staticmethod - @validate_inputs - def index_of_agreement(observations: np.ndarray, predictions: np.ndarray) -> float: - """Willmott's Index of Agreement (IoA)""" - numerator = np.sum(np.square(predictions - observations)) - denominator = np.sum( - np.square( - np.abs(predictions - np.mean(observations)) - + np.abs(observations - np.mean(observations)) - ) - ) - return float(1 - (numerator / denominator)) - - @staticmethod - @validate_inputs - def mean_absolute_percentage_error( - observations: np.ndarray, predictions: np.ndarray - ) -> float: - """Mean Absolute Percentage Error (MAPE)""" - return float(np.mean(np.abs((observations - predictions) / observations)) * 100) diff --git a/src/susse/evaluation/__init__.py b/src/susse/evaluation/__init__.py new file mode 100644 index 00000000..23978820 --- /dev/null +++ b/src/susse/evaluation/__init__.py @@ -0,0 +1,43 @@ +"""Evaluation — post-training metrics, evaluator, and generic plots. + +Public entry points: + +* :class:`Metric` and the seven concrete metrics (:class:`RMSE`, + :class:`MAE`, :class:`R2`, :class:`IndexOfAgreement`, + :class:`MeanBiasError`, :class:`NormalisedRMSE`, + :class:`NormalisedMAE`) — frozen, stateless scalar functions. +* :data:`DEFAULT_METRICS` — the seven-metric tuple in paper-table order. +* :class:`Evaluator` — score multiple named predictions against an + observed series across multiple splits; returns a tidy DataFrame. + +The ``plots`` submodule holds generic evaluation plot helpers +(predicted-vs-observed, per-station metric bars, covariate-shift KDE, +feature importances, …). Import from :mod:`susse.evaluation.plots` +directly when needed. +""" + +from .evaluator import Evaluator +from .metrics import ( + DEFAULT_METRICS, + MAE, + R2, + RMSE, + IndexOfAgreement, + MeanBiasError, + Metric, + NormalisedMAE, + NormalisedRMSE, +) + +__all__ = [ + "DEFAULT_METRICS", + "Evaluator", + "IndexOfAgreement", + "MAE", + "MeanBiasError", + "Metric", + "NormalisedMAE", + "NormalisedRMSE", + "R2", + "RMSE", +] diff --git a/src/susse/evaluation/evaluator.py b/src/susse/evaluation/evaluator.py new file mode 100644 index 00000000..64c719ad --- /dev/null +++ b/src/susse/evaluation/evaluator.py @@ -0,0 +1,146 @@ +"""Evaluator — score multiple prediction series against an observed series. + +The :class:`Evaluator` takes a configured tuple of :class:`Metric` +instances and, given an observed series plus a mapping of +``{name: prediction_series}``, returns a tidy DataFrame with one row +per (split × prediction_name). + +It is the post-training counterpart of +:func:`susse.training.score_predictions`: that helper produces a +single :class:`ScoreSet` (MAE/RMSE/R²) on one (y_true, y_pred) pair +during training; the Evaluator scores arbitrary metrics across +arbitrary predictions on arbitrary splits, for inspection and paper +tables. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence + +import pandas as pd + +from .metrics import DEFAULT_METRICS, Metric + + +class Evaluator: + """Build a metrics table over named prediction series and named splits. + + Attributes: + metrics: The tuple of :class:`Metric` instances configured on + construction. Exposed as a :class:`property` for + inspection but immutable. + + Example:: + + evaluator = Evaluator() # uses DEFAULT_METRICS + table = evaluator.score( + observed=df["y_obs"], + predictions={ + "RF": df["y_pred"], + "NASA": df["sat_ghi_nasa_kwh_m2_day"], + "CAMS": df["sat_ghi_cams_kwh_m2_day"], + }, + splits={ + "all rows": df.index, + "excluding overlap": df.index[~df["seen_in_training"]], + }, + ) + """ + + def __init__(self, *, metrics: Sequence[Metric] = DEFAULT_METRICS) -> None: + if not metrics: + raise ValueError( + "Evaluator needs at least one Metric. Pass " + "`metrics=DEFAULT_METRICS` or a non-empty tuple of " + "Metric instances." + ) + self._metrics: tuple[Metric, ...] = tuple(metrics) + + @property + def metrics(self) -> tuple[Metric, ...]: + return self._metrics + + def score( + self, + *, + observed: pd.Series, + predictions: Mapping[str, pd.Series], + splits: Mapping[str, pd.Index] | None = None, + ) -> pd.DataFrame: + """Compute every metric across every (split × prediction) combination. + + Args: + observed: Observed values. Its index defines what rows + the ``splits`` indices select from. + predictions: Mapping from a human label (the column + header in the result) to a prediction series aligned + to ``observed``. + splits: Mapping from a split label to a pandas Index + selecting rows of ``observed``. When ``None``, all + rows are scored together under the label ``"all"``. + + Returns: + A :class:`pandas.DataFrame` with one row per + ``(split × prediction)`` pair. Columns: ``"split"``, + ``"prediction"``, ``"n"`` (row count after NaN filter), + then one column per metric in :attr:`metrics` order. + + Raises: + ValueError: When ``predictions`` is empty, when a + prediction series has a shape mismatch with + ``observed``, or when a split index references rows + absent from ``observed``. + """ + if not predictions: + raise ValueError( + "Evaluator.score: `predictions` is empty. Pass at " + "least one named prediction series." + ) + resolved_splits = self._resolve_splits(observed, splits) + rows = [ + self._score_one( + split_label=split_label, + pred_label=pred_label, + observed=observed.loc[split_idx], + predicted=pred_series.loc[split_idx], + ) + for split_label, split_idx in resolved_splits.items() + for pred_label, pred_series in predictions.items() + ] + return pd.DataFrame(rows) + + def _resolve_splits( + self, + observed: pd.Series, + splits: Mapping[str, pd.Index] | None, + ) -> dict[str, pd.Index]: + if splits is None: + return {"all": observed.index} + for label, idx in splits.items(): + if not idx.isin(observed.index).all(): + raise ValueError( + f"Evaluator.score: split {label!r} references rows " + f"absent from `observed`. Subset the split to " + f"`observed.index` first, or fix the split's " + f"construction." + ) + return dict(splits) + + def _score_one( + self, + *, + split_label: str, + pred_label: str, + observed: pd.Series, + predicted: pd.Series, + ) -> dict[str, object]: + valid_mask = observed.notna() & predicted.notna() + n_rows = int(valid_mask.sum()) + row: dict[str, object] = { + "split": split_label, + "prediction": pred_label, + "n": n_rows, + } + for metric in self._metrics: + row[metric.name] = metric.compute(observed, predicted) + return row diff --git a/src/susse/evaluation/metrics.py b/src/susse/evaluation/metrics.py new file mode 100644 index 00000000..f1f5b657 --- /dev/null +++ b/src/susse/evaluation/metrics.py @@ -0,0 +1,252 @@ +"""Scalar evaluation metrics — RMSE, MAE, R², IOA, MBE, normalised RMSE/MAE. + +Each metric is a :class:`Metric` subclass, frozen and stateless. The +:attr:`name` property is the column header used in scoring tables; +:meth:`compute` returns the scalar value over aligned +``(y_true, y_pred)`` pairs. All metrics drop NaN-bearing rows before +computing, and return :data:`math.nan` (not zero) in pathological +cases — silent zero would be a gradient dead zone, not a sane default. + +The seven concrete metrics: + +* :class:`RMSE`, :class:`MAE`, :class:`R2` — the standard regression + triple. These overlap with :class:`susse.training.ScoreSet`, which + the :class:`Trainer` records on every val fold; the Metric versions + here are for post-hoc scoring across multiple prediction series. +* :class:`IndexOfAgreement` — Willmott (1981), bounded ``[0, 1]``. + Rewards matching the *magnitude* of the observed series, not just + linear correlation; the paper's headline metric for monthly + climatology comparisons. +* :class:`MeanBiasError` — mean(P − O). Surfaces systematic bias + that MAE/RMSE absolute-value away. +* :class:`NormalisedRMSE`, :class:`NormalisedMAE` — RMSE/MAE as a + percentage of mean(O). Standard convention in solar-resource + assessment. + +:data:`DEFAULT_METRICS` is the seven-metric tuple in paper-table order. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import TypeAlias + +import numpy as np +import pandas as pd + +ArrayLike: TypeAlias = pd.Series | np.ndarray | list[float] + + +def _aligned_arrays( + y_true: ArrayLike, y_pred: ArrayLike +) -> tuple[np.ndarray, np.ndarray]: + """Coerce inputs to aligned 1-D float arrays with NaN rows dropped. + + Args: + y_true: Ground truth. + y_pred: Prediction series. Must share shape with ``y_true``; + pandas-index alignment is the caller's responsibility. + + Returns: + ``(yt, yp)`` of equal length with NaN-bearing rows removed. + + Raises: + ValueError: When the two inputs have different shapes. + """ + yt = np.asarray(y_true, dtype=float) + yp = np.asarray(y_pred, dtype=float) + if yt.shape != yp.shape: + raise ValueError( + f"y_true shape {yt.shape} does not match y_pred shape " + f"{yp.shape}. Align by index (e.g. with " + f"`y_pred = y_pred.loc[y_true.index]`) before scoring." + ) + valid = ~(np.isnan(yt) | np.isnan(yp)) + return yt[valid], yp[valid] + + +class Metric(ABC): + """A named scalar function of ``(y_true, y_pred)``. + + Concrete subclasses are frozen, value-equal dataclasses so two + Evaluator instances configured with ``RMSE()`` produce identical + metric tables. + """ + + @property + @abstractmethod + def name(self) -> str: + """Column-header string in scoring tables.""" + + @abstractmethod + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + """Return the scalar metric value over aligned inputs.""" + + def __call__(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + return self.compute(y_true, y_pred) + + +@dataclass(frozen=True) +class RMSE(Metric): + """Root-mean-square error: ``sqrt(mean((P - O)²))``.""" + + @property + def name(self) -> str: + return "RMSE" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + return float(np.sqrt(((yp - yt) ** 2).mean())) + + +@dataclass(frozen=True) +class MAE(Metric): + """Mean absolute error: ``mean(|P - O|)``.""" + + @property + def name(self) -> str: + return "MAE" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + return float(np.abs(yp - yt).mean()) + + +@dataclass(frozen=True) +class R2(Metric): + """Coefficient of determination. + + Returns NaN when the observed series has zero variance (the + formula's denominator is zero), rather than ``inf`` or an + arbitrary sentinel. + """ + + @property + def name(self) -> str: + return "R²" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + ss_res = float(((yp - yt) ** 2).sum()) + ss_tot = float(((yt - yt.mean()) ** 2).sum()) + if ss_tot <= 0.0: + return float("nan") + return 1.0 - ss_res / ss_tot + + +@dataclass(frozen=True) +class IndexOfAgreement(Metric): + """Willmott's (1981) Index of Agreement, bounded ``[0, 1]``. + + ``IOA = 1 − Σ(O − P)² / Σ(|P − Ō| + |O − Ō|)²``, where ``Ō = mean(O)``. + + A value of 1 is perfect agreement; 0 is no agreement. Unlike R², + IOA rewards matching the *magnitude* of the observed series, not + just the linear correlation with it — which is why monthly-climatology + comparisons (where temporal aggregation collapses variance and + depresses R²) keep IOA as the headline metric. + """ + + @property + def name(self) -> str: + return "IOA" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + obs_mean = yt.mean() + numerator = float(((yt - yp) ** 2).sum()) + denominator = float( + ((np.abs(yp - obs_mean) + np.abs(yt - obs_mean)) ** 2).sum() + ) + if denominator <= 0.0: + return float("nan") + return 1.0 - numerator / denominator + + +@dataclass(frozen=True) +class MeanBiasError(Metric): + """Mean bias error: ``mean(P - O)``. + + Positive values mean the prediction over-estimates the + observation; negative means under-estimate. Distinct from MAE in + that errors of opposite sign cancel — which is exactly the + property used to flag systematic bias in raw satellite products + vs. a bias-corrected model. + """ + + @property + def name(self) -> str: + return "MBE" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + return float((yp - yt).mean()) + + +@dataclass(frozen=True) +class NormalisedRMSE(Metric): + """RMSE as a percentage of ``mean(y_true)``. + + ``nRMSE = 100 × RMSE / mean(O)``. Returns NaN when ``mean(O)`` + is zero. + """ + + @property + def name(self) -> str: + return "nRMSE_%" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + obs_mean = yt.mean() + if obs_mean == 0.0: + return float("nan") + rmse = float(np.sqrt(((yp - yt) ** 2).mean())) + return 100.0 * rmse / obs_mean + + +@dataclass(frozen=True) +class NormalisedMAE(Metric): + """MAE as a percentage of ``mean(y_true)``. + + ``nMAE = 100 × MAE / mean(O)``. Returns NaN when ``mean(O)`` + is zero. + """ + + @property + def name(self) -> str: + return "nMAE_%" + + def compute(self, y_true: ArrayLike, y_pred: ArrayLike) -> float: + yt, yp = _aligned_arrays(y_true, y_pred) + if yt.size == 0: + return float("nan") + obs_mean = yt.mean() + if obs_mean == 0.0: + return float("nan") + mae = float(np.abs(yp - yt).mean()) + return 100.0 * mae / obs_mean + + +DEFAULT_METRICS: tuple[Metric, ...] = ( + RMSE(), + NormalisedRMSE(), + MAE(), + NormalisedMAE(), + MeanBiasError(), + R2(), + IndexOfAgreement(), +) +"""The seven paper-table metrics in display order.""" diff --git a/src/susse/evaluation/plots.py b/src/susse/evaluation/plots.py new file mode 100644 index 00000000..5704a1b5 --- /dev/null +++ b/src/susse/evaluation/plots.py @@ -0,0 +1,541 @@ +"""Generic evaluation plots. + +One function per figure, no shared state. Inputs are plain dataframes +/ series / mappings — no library-specific value objects — so these +plots are equally usable in inspection notebooks, paper notebooks, +and ad-hoc sessions. Paper-specific layouts (Figure 2 grid, calibration +diagnostic) live with the paper, not here. + +All functions render the figure with :func:`matplotlib.pyplot.show` +and return ``None`` unless explicitly noted; the figure is shown +inline in a notebook and not held on. Callers wanting to embed or +save the figure should rebuild it from the same helper they wrote +into the notebook. +""" + +from __future__ import annotations + +from collections.abc import Iterable, Mapping +from typing import TYPE_CHECKING + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from .metrics import MeanBiasError, Metric + +if TYPE_CHECKING: # pragma: no cover — type-only + from ..training import TrainedBundle + + +def plot_target_distribution_per_station( + df: pd.DataFrame, + *, + target_column: str, + location_column: str = "location", + value_label: str = "y", +) -> None: + """Boxplot + per-station KDE of the target value.""" + fig, axes = plt.subplots(1, 2, figsize=(15, 4)) + df.boxplot( + column=target_column, + by=location_column, + rot=90, + ax=axes[0], + grid=False, + ) + axes[0].set_title(f"{value_label} per station") + axes[0].set_ylabel(value_label) + axes[0].set_xlabel("") + plt.suptitle("") + + ax = axes[1] + locs = sorted(df[location_column].unique()) + for loc in locs: + sub = df.loc[df[location_column] == loc, target_column] + if len(sub) > 1: + sub.plot.kde(ax=ax, alpha=0.5, lw=0.7) + ax.set_xlabel(value_label) + ax.set_ylabel("density") + ax.set_title(f"KDE per station ({len(locs)} stations)") + fig.tight_layout() + plt.show() + + +def plot_predictor_vs_observed_scatter( + df: pd.DataFrame, + *, + observed_column: str, + predictor_columns: Mapping[str, str], + value_label: str = "kWh/m²/day", + limit: tuple[float, float] = (0, 10), +) -> None: + """Scatter each predictor column against the observed column. + + Args: + df: Frame holding the observed + predictor columns. + observed_column: Name of the ground-truth column. + predictor_columns: Mapping ``{display_label: column_name}``. + One subplot per entry; subplots share x and y axes. + value_label: Axis-label unit. + limit: ``(low, high)`` for both axes — the identity line is + drawn over this range. + """ + y_obs = df[observed_column] + fig, axes = plt.subplots( + 1, + len(predictor_columns), + figsize=(6 * len(predictor_columns), 5.5), + sharex=True, + sharey=True, + ) + if len(predictor_columns) == 1: + axes = [axes] + for ax, (label, column) in zip(axes, predictor_columns.items()): + ax.scatter(y_obs, df[column], s=3, alpha=0.2, color="C0") + ax.plot(limit, limit, "k--", lw=1) + ax.set_xlim(limit) + ax.set_ylim(limit) + ax.set_xlabel(f"Observed ({value_label})") + ax.set_ylabel(f"{label} ({value_label})") + err = df[column] - y_obs + ax.set_title( + f"{label} vs observed " + f"(MBE={err.mean():.2f}, RMSE={np.sqrt((err ** 2).mean()):.2f})" + ) + ax.grid(alpha=0.3) + fig.tight_layout() + plt.show() + + +def plot_feature_correlation_heatmap( + df: pd.DataFrame, + *, + feature_columns: Iterable[str], + target_column: str, + top_n: int = 10, +) -> None: + """Pairwise feature × target correlation heatmap + top-N table.""" + cols = list(feature_columns) + [target_column] + corr = df[cols].corr() + fig, ax = plt.subplots(figsize=(11, 10)) + im = ax.imshow(corr.values, cmap="RdBu_r", vmin=-1, vmax=1) + ax.set_xticks(range(len(corr.columns))) + ax.set_xticklabels(corr.columns, rotation=90, fontsize=7) + ax.set_yticks(range(len(corr.columns))) + ax.set_yticklabels(corr.columns, fontsize=7) + fig.colorbar(im, ax=ax, fraction=0.04, pad=0.04) + ax.set_title("Feature correlation matrix") + fig.tight_layout() + plt.show() + + target_corr = ( + corr[target_column].drop(target_column).abs().sort_values(ascending=False) + ) + print(f"Top {top_n} features by |correlation| with {target_column}:") + print(target_corr.head(top_n).round(3).to_string()) + + +def plot_pca_by_station( + df: pd.DataFrame, + *, + feature_columns: Iterable[str], + location_column: str = "location", +) -> None: + """PCA(2) of standardised features, points coloured by station.""" + from sklearn.decomposition import PCA + from sklearn.preprocessing import StandardScaler + + x = df[list(feature_columns)].to_numpy() + scaled = StandardScaler().fit_transform(x) + pca = PCA(n_components=2).fit(scaled) + pcs = pca.transform(scaled) + explained = pca.explained_variance_ratio_ + + fig, ax = plt.subplots(figsize=(9, 7)) + loc_arr = df[location_column].to_numpy() + unique_locs = sorted(set(loc_arr)) + cmap = plt.get_cmap("tab20", max(20, len(unique_locs))) + for i, loc in enumerate(unique_locs): + mask = loc_arr == loc + ax.scatter( + pcs[mask, 0], + pcs[mask, 1], + s=4, + alpha=0.5, + color=cmap(i % cmap.N), + label=loc, + ) + ax.set_xlabel(f"PC1 ({explained[0]:.0%} var)") + ax.set_ylabel(f"PC2 ({explained[1]:.0%} var)") + ax.set_title("PCA of features — coloured by station") + ax.legend(fontsize=6, ncol=3, loc="best", framealpha=0.7) + ax.grid(alpha=0.3) + fig.tight_layout() + plt.show() + + +def plot_training_fit_scatter( + df: pd.DataFrame, + *, + observed_column: str, + predicted_column: str, + value_label: str = "kWh/m²/day", +) -> None: + """In-sample predicted-vs-observed scatter; tight diagonal = healthy training.""" + fig, ax = plt.subplots(figsize=(7, 7)) + ax.scatter( + df[observed_column], + df[predicted_column], + s=2, + alpha=0.25, + color="C3", + ) + hi = max(df[observed_column].max(), df[predicted_column].max()) * 1.05 + ax.plot([0, hi], [0, hi], "k--", lw=1, label="y = x") + ax.set_xlim(0, hi) + ax.set_ylim(0, hi) + ax.set_xlabel(f"Observed ({value_label})") + ax.set_ylabel(f"Predicted ({value_label})") + ax.set_title("Predicted vs observed") + ax.grid(alpha=0.3) + ax.legend() + fig.tight_layout() + plt.show() + + err = df[predicted_column] - df[observed_column] + print( + f"RMSE = {np.sqrt((err ** 2).mean()):.3f} " + f"MAE = {np.abs(err).mean():.3f} " + f"MBE = {err.mean():.3f}" + ) + + +def plot_training_fit_timeseries( + df: pd.DataFrame, + *, + observed_column: str | None = None, + prediction_series: Mapping[str, str], + reference_series: Mapping[str, str] | None = None, + location_column: str = "location", + date_column: str = "date", + n_stations: int = 6, + value_label: str = "kWh/m²/day", +) -> None: + """Weekly-resampled time series of predictions + named comparison series. + + Picks the ``n_stations`` stations with the most rows and restricts + each station's panel to its most recent calendar year for + legibility. + + Args: + df: Frame holding the per-row date / location columns and every + named series. + observed_column: Optional ground-truth column. When provided, + rendered prominently (blue line + circle markers); when + ``None`` (the inference use case), the observed line is + omitted entirely and the legend is built from + predictions + references only. + prediction_series: Mapping ``{display_label: column_name}`` of + model outputs to render with the same prominence as the + observed series (line + circle markers). + reference_series: Optional mapping ``{display_label: column_name}`` + of context series (e.g. raw satellite estimates) rendered + as thin, low-alpha background lines without markers. The + visual contrast surfaces which series the model produced + vs. which were inputs / external references. + location_column: Station identifier column. + date_column: Row date column. + n_stations: Number of station panels to draw. + value_label: Y-axis label unit. + """ + plot_stations = ( + df.groupby(location_column) + .size() + .sort_values(ascending=False) + .head(n_stations) + .index.tolist() + ) + actual_n = len(plot_stations) + if actual_n == 0: + raise ValueError( + "plot_training_fit_timeseries: no stations found in " + f"column {location_column!r}. Check the input DataFrame." + ) + # Tight subplot grid that doesn't leave empty cells for small n. + if actual_n == 1: + n_cols = 1 + elif actual_n <= 4: + n_cols = 2 + else: + n_cols = 2 + n_rows = (actual_n + n_cols - 1) // n_cols + fig, axes = plt.subplots( + n_rows, + n_cols, + figsize=(8 * n_cols, 3 * n_rows), + sharey=True, + squeeze=False, + ) + flat_axes = axes.flatten() + prediction_palette = ["C3", "C4", "C5"] + reference_palette = ["C2", "C1", "C6"] + refs = reference_series or {} + series_cols = list(prediction_series.values()) + list(refs.values()) + if observed_column is not None: + series_cols = [observed_column] + series_cols + for ax, loc in zip(flat_axes, plot_stations): + sub = df[df[location_column] == loc].sort_values(date_column).copy() + sub[date_column] = pd.to_datetime(sub[date_column]) + last_year = sub[date_column].dt.year.max() + sub = sub[sub[date_column].dt.year == last_year] + weekly = sub.set_index(date_column)[series_cols].resample("W").mean() + if observed_column is not None: + ax.plot( + weekly.index, + weekly[observed_column], + "-o", + ms=3, + label="Observed", + color="C0", + ) + for color, (label, col) in zip(prediction_palette, prediction_series.items()): + ax.plot( + weekly.index, + weekly[col], + "-o", + ms=3, + label=label, + color=color, + ) + for color, (label, col) in zip(reference_palette, refs.items()): + ax.plot( + weekly.index, + weekly[col], + "-", + lw=0.7, + alpha=0.4, + label=label, + color=color, + ) + ax.set_title(f"{loc} ({last_year}, n={len(sub):,})", fontsize=10) + ax.grid(alpha=0.3) + for ax in flat_axes[actual_n:]: + ax.set_visible(False) + flat_axes[0].legend(fontsize=8, loc="lower left") + fig.supylabel(value_label) + fig.tight_layout() + plt.show() + + +def plot_predicted_vs_observed_panels( + df: pd.DataFrame, + *, + observed_column: str, + predictions: Mapping[str, str], + value_label: str = "kWh/m²/day", +) -> None: + """One scatter panel per named prediction column against the observed column. + + Args: + df: Frame holding the observed + prediction columns. NaN-bearing + rows are dropped per panel. + observed_column: Name of the ground-truth column. + predictions: Mapping ``{display_label: column_name}``. + value_label: Axis-label unit. + """ + obs = df[observed_column] + cols = [observed_column] + list(predictions.values()) + lim = ( + float(np.floor(df[cols].min().min())), + float(np.ceil(df[cols].max().max())), + ) + palette = ["C3", "C2", "C1", "C4", "C5"] + fig, axes = plt.subplots( + 1, + len(predictions), + figsize=(5.5 * len(predictions), 5.5), + sharex=True, + sharey=True, + ) + if len(predictions) == 1: + axes = [axes] + for ax, color, (label, col) in zip(axes, palette, predictions.items()): + ax.scatter(obs, df[col], s=14, alpha=0.55, color=color) + ax.plot(lim, lim, "k--", lw=1) + ax.set_xlim(lim) + ax.set_ylim(lim) + ax.set_xlabel(f"Observed ({value_label})") + ax.set_ylabel(f"{label} ({value_label})") + err = (df[col] - obs).dropna() + ax.set_title( + f"{label}\nMBE={err.mean():.2f} " f"RMSE={np.sqrt((err ** 2).mean()):.2f}" + ) + ax.grid(alpha=0.3) + fig.tight_layout() + plt.show() + + +def plot_per_station_metric( + df: pd.DataFrame, + *, + observed_column: str, + predicted_column: str, + location_column: str = "location", + metric: Metric = MeanBiasError(), + highlight_column: str | None = None, + value_label: str = "kWh/m²/day", +) -> None: + """Per-station bar chart of a chosen metric; optional categorical colour. + + Args: + df: Frame holding the observed + predicted + location columns. + observed_column: Name of the ground-truth column. + predicted_column: Name of the prediction column. + location_column: Station identifier column. + metric: Which :class:`Metric` to compute per station. Default + :class:`MeanBiasError`. + highlight_column: Optional boolean column. When given, bars + for rows whose ``highlight_column`` is True are coloured + differently (typically to mark training overlap). + value_label: Y-axis label unit. + """ + rows: list[dict] = [] + for loc, sub in df.groupby(location_column): + score = metric.compute(sub[observed_column], sub[predicted_column]) + row: dict = {location_column: loc, metric.name: score} + if highlight_column is not None: + row["_highlight"] = bool(sub[highlight_column].iloc[0]) + rows.append(row) + per_station = pd.DataFrame(rows).set_index(location_column).sort_values(metric.name) + fig, ax = plt.subplots(figsize=(15, 6)) + if highlight_column is None: + ax.bar( + range(len(per_station)), per_station[metric.name], color="C0", alpha=0.75 + ) + else: + colors = ["C2" if h else "C0" for h in per_station["_highlight"]] + ax.bar( + range(len(per_station)), per_station[metric.name], color=colors, alpha=0.75 + ) + ax.axhline(0, color="k", lw=0.7) + ax.set_xticks(range(len(per_station))) + ax.set_xticklabels(per_station.index, rotation=90, fontsize=7) + ax.set_ylabel(f"{metric.name} ({value_label})") + title = f"Per-station {metric.name}" + if highlight_column is not None: + title += f" (green = {highlight_column} True)" + ax.set_title(title) + ax.grid(alpha=0.3, axis="y") + fig.tight_layout() + plt.show() + print(f"Worst (highest {metric.name}):") + print(per_station.tail(5).round(3).to_string()) + print(f"\nBest (lowest {metric.name}):") + print(per_station.head(5).round(3).to_string()) + + +def plot_covariate_shift_kde( + train_df: pd.DataFrame, + val_df: pd.DataFrame, + *, + features: Iterable[str], + train_label: str = "Training", + val_label: str = "Inference", +) -> None: + """Overlaid histograms of training-set vs inference-set feature distributions. + + Skips features missing from either frame so callers can pass a + constant candidate list without guarding each name. + """ + keep = [f for f in features if f in train_df.columns and f in val_df.columns] + n_cols = 3 + n_rows = (len(keep) + n_cols - 1) // n_cols + fig, axes = plt.subplots(n_rows, n_cols, figsize=(14, 3.5 * n_rows)) + for ax, feat in zip(np.array(axes).flat, keep): + t = train_df[feat].dropna().to_numpy() + v = val_df[feat].dropna().to_numpy() + lo = float(np.nanquantile(np.r_[t, v], 0.005)) + hi = float(np.nanquantile(np.r_[t, v], 0.995)) + ax.hist( + t, + bins=40, + range=(lo, hi), + alpha=0.5, + density=True, + label=f"{train_label} (n={len(t):,})", + ) + ax.hist( + v, + bins=40, + range=(lo, hi), + alpha=0.5, + density=True, + label=f"{val_label} (n={len(v):,})", + ) + ax.set_title(feat, fontsize=10) + ax.legend(fontsize=7) + for ax in np.array(axes).flat[len(keep) :]: + ax.axis("off") + fig.suptitle(f"Feature distributions: {train_label} vs {val_label}") + fig.tight_layout() + plt.show() + + +def plot_feature_importances_top_n( + bundle: "TrainedBundle", + *, + feature_columns: Iterable[str], + top_n: int = 12, +) -> pd.Series: + """Bar chart of the trained model's top-N feature importances. + + Args: + bundle: The trained-model bundle. + feature_columns: Names of the model's input features, in the + order they were fed to ``fit``. + top_n: Number of importances to show. + + Returns: + The top-N importances as a :class:`pandas.Series` indexed by + feature name, sorted descending. + + Raises: + TypeError: When ``bundle.regressor`` doesn't expose a + ``feature_importances`` method (e.g. linear models). + A silent fallback (returning zeros, dropping the call) + would hide that the wrong model kind was loaded. + """ + regressor = bundle.regressor + importances_fn = getattr(regressor, "feature_importances", None) + if importances_fn is None: + raise TypeError( + f"plot_feature_importances_top_n requires a regressor with " + f"a `feature_importances` method; got " + f"{type(regressor).__name__}, which does not expose one. " + f"Tree-based models (RandomForest, GradientBoosting) " + f"expose this; linear models do not." + ) + importances = importances_fn(tuple(feature_columns)).sort_values(ascending=False) + fig, ax = plt.subplots(figsize=(10, 5)) + importances.head(top_n).plot.bar(ax=ax) + ax.set_ylabel("Feature importance") + ax.set_title(f"Top {top_n} feature importances") + plt.xticks(rotation=45, ha="right") + fig.tight_layout() + plt.show() + return importances.head(top_n) + + +__all__ = [ + "plot_covariate_shift_kde", + "plot_feature_correlation_heatmap", + "plot_feature_importances_top_n", + "plot_pca_by_station", + "plot_per_station_metric", + "plot_predicted_vs_observed_panels", + "plot_predictor_vs_observed_scatter", + "plot_target_distribution_per_station", + "plot_training_fit_scatter", + "plot_training_fit_timeseries", +] diff --git a/src/susse/inference/__init__.py b/src/susse/inference/__init__.py new file mode 100644 index 00000000..1070a9a5 --- /dev/null +++ b/src/susse/inference/__init__.py @@ -0,0 +1,34 @@ +"""Inference — portal-facing wrapper around a TrainedBundle. + +Public entry points: + +* :class:`Predictor` — coords + date range → long-format DataFrame + of bias-corrected daily GHI predictions. Reads features from the + warehouse and applies the bundle's :class:`FeatureSpec`. +* :class:`PredictionRequest` — validated request object exposed for + callers that want to construct requests programmatically. +* :class:`FeatureCatalog` — human-facing metadata (label, unit, + description, group) for every model-input column, exposed via + :attr:`Predictor.feature_catalog` for variable-inspector UIs. + :class:`FeatureMetadata` is one column's entry; :class:`FeatureGroup` + is its presentation bucket. + +Cache misses (geohash5 cells absent from the warehouse for some of +the requested dates) are surfaced loudly by default +(``on_cache_miss="raise"``). With ``on_cache_miss="fetch"``, the +predictor synchronously reuses :class:`NasaPowerSatelliteJob` and +:class:`CamsSatelliteJob` to populate the missing cells, then +re-queries the warehouse before predicting. Per-invocation CAMS +fetches are capped to keep within the daily quota. +""" + +from .feature_catalog import FeatureCatalog, FeatureGroup, FeatureMetadata +from .predictor import PredictionRequest, Predictor + +__all__ = [ + "FeatureCatalog", + "FeatureGroup", + "FeatureMetadata", + "PredictionRequest", + "Predictor", +] diff --git a/src/susse/inference/feature_catalog.py b/src/susse/inference/feature_catalog.py new file mode 100644 index 00000000..5255ead2 --- /dev/null +++ b/src/susse/inference/feature_catalog.py @@ -0,0 +1,293 @@ +"""Feature catalog — human-facing metadata for a bundle's model inputs. + +:meth:`Predictor.predict` returns every model-input column alongside the +prediction, but the column names alone (``nasa_temperature``, +``sat_ghi_cams_kwh_m2_day``, ``kt_cams``) are opaque to an end user. A +:class:`FeatureCatalog` pairs each of those columns with a label, unit, +description, and presentation group, so a UI — the portal's variable +inspector — can show what every input *is* and where it comes from. + +The catalog unifies two metadata origins behind one :class:`FeatureMetadata` +shape: + +* **Warehouse variables** — NASA POWER / CAMS aux variables and satellite + irradiance bands. Metadata comes from the warehouse ``VariableCatalog``. +* **Derived features** — clear-sky index, cyclical day-of-year, altitude, + longitude. Metadata comes from each :class:`DerivedFeature`'s + ``output_metadata`` (see :class:`DerivedColumnMetadata`). + +Building the catalog is pure: it needs only the bundle's +:class:`FeatureSelection` and :class:`FeatureSpec`, no warehouse access. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from enum import StrEnum +from typing import Iterator + +from ..datasets import FeatureSelection +from ..preprocessing import DerivedColumnMetadata, FeatureSpec +from ..warehouse_ops.population.dim_variable import VariableCatalog +from ..warehouse_ops.population.types import ( + Source, + VariableSpec, + aux_feature_column, + satellite_irradiance_column, +) + + +class FeatureGroup(StrEnum): + """Presentation bucket a model-input feature belongs to. + + Distinct from :class:`Source`: a feature group is how the variable + inspector groups its table, and :attr:`DERIVED` has no warehouse + source at all. The enum *value* is the human-readable group label + the UI renders. + """ + + NASA_POWER = "NASA POWER" + CAMS = "CAMS" + DERIVED = "Derived" + + @classmethod + def from_source(cls, source: Source) -> "FeatureGroup": + """Map a warehouse :class:`Source` to its presentation group. + + Args: + source: The warehouse source of a catalog variable. + + Returns: + The matching :class:`FeatureGroup`. + + Raises: + ValueError: For sources the feature catalog does not cover + (MERRA-2, MODIS). Those are not inputs of any published + bundle; extend this mapping before serving one that + uses them. + """ + if source is Source.NASA_POWER: + return cls.NASA_POWER + if source is Source.CAMS: + return cls.CAMS + raise ValueError( + f"Source {source.value} has no FeatureGroup. The feature " + f"catalog covers NASA POWER and CAMS only — add a " + f"FeatureGroup member and extend FeatureGroup.from_source " + f"before serving a bundle with MERRA-2 / MODIS inputs." + ) + + +@dataclass(frozen=True) +class FeatureMetadata: + """Human-facing description of one model-input column. + + Constructed via :meth:`from_variable_spec` (for warehouse variables) + or :meth:`from_derived_column` (for derived features) so the two + metadata origins converge on one shape. The variable inspector + renders one table row per instance. + + Attributes: + column: Name of the feature column in :meth:`Predictor.predict` + output. + label: Short human-readable name, e.g. ``"2m Air Temperature"``. + unit: Physical unit, or ``"unitless"`` for dimensionless + quantities. + description: One- or two-sentence explanation of the variable. + group: Presentation bucket (NASA POWER / CAMS / Derived). + """ + + column: str + label: str + unit: str + description: str + group: FeatureGroup + + @classmethod + def from_variable_spec( + cls, spec: VariableSpec, *, column: str + ) -> "FeatureMetadata": + """Build from a warehouse :class:`VariableCatalog` entry. + + The catalog's ``display_name`` becomes :attr:`label` — the two + name the same thing; ``label`` is the portal-facing spelling + shared with :class:`DerivedColumnMetadata`. + + Args: + spec: The catalog entry for the variable. + column: The feature-column name the variable lands in + (the catalog spec does not itself know this — it + depends on the source prefix / irradiance convention). + """ + return cls( + column=column, + label=spec.display_name, + unit=spec.unit, + description=spec.description, + group=FeatureGroup.from_source(spec.source), + ) + + @classmethod + def from_derived_column(cls, meta: DerivedColumnMetadata) -> "FeatureMetadata": + """Build from a derived feature's per-column metadata.""" + return cls( + column=meta.column, + label=meta.label, + unit=meta.unit, + description=meta.description, + group=FeatureGroup.DERIVED, + ) + + +@dataclass(frozen=True) +class FeatureCatalog: + """Ordered metadata for every model-input column a bundle consumes. + + Build via :meth:`build`. Ordering is NASA POWER aux variables, then + CAMS aux variables, then satellite irradiance, then derived + features — the inspector regroups by :attr:`FeatureMetadata.group` + anyway, so this is for determinism, not display. + + Attributes: + entries: One :class:`FeatureMetadata` per model-input column. + """ + + entries: tuple[FeatureMetadata, ...] + + def __iter__(self) -> Iterator[FeatureMetadata]: + return iter(self.entries) + + def __len__(self) -> int: + return len(self.entries) + + @property + def columns(self) -> tuple[str, ...]: + """The feature-column names, in catalog order.""" + return tuple(entry.column for entry in self.entries) + + def get(self, column: str) -> FeatureMetadata: + """Return the metadata for one feature column. + + Args: + column: A feature-column name. + + Raises: + KeyError: If ``column`` is not a model input. The message + lists the known columns. + """ + for entry in self.entries: + if entry.column == column: + return entry + raise KeyError( + f"No feature metadata for column {column!r}. Known feature " + f"columns: {sorted(self.columns)}." + ) + + @classmethod + def build( + cls, + *, + selection: FeatureSelection, + feature_spec: FeatureSpec, + ) -> "FeatureCatalog": + """Compose the catalog from a bundle's selection and spec. + + Args: + selection: The bundle's :class:`FeatureSelection` — names + the NASA / CAMS warehouse variables and irradiance + bands the model consumes. + feature_spec: The bundle's :class:`FeatureSpec` — supplies + the derived features and the authoritative model-input + column list used for the consistency check. + + Returns: + A :class:`FeatureCatalog` covering exactly + ``feature_spec.output_feature_names``. + + Raises: + NotImplementedError: If ``selection`` requests MERRA-2 or + MODIS variables — not yet supported by the catalog. + ValueError: If the assembled catalog does not match the + spec's model inputs (an inconsistent bundle). + KeyError: If a selected variable is absent from the + warehouse ``VariableCatalog``. + """ + if selection.merra_variable_ids or selection.modis_variable_ids: + raise NotImplementedError( + "FeatureCatalog.build covers NASA POWER and CAMS inputs " + "only. This selection also requests MERRA-2 / MODIS " + f"variables (merra={list(selection.merra_variable_ids)}, " + f"modis={list(selection.modis_variable_ids)}). Extend " + "FeatureCatalog.build and FeatureGroup.from_source before " + "serving a bundle that uses them." + ) + entries: list[FeatureMetadata] = [] + entries.extend(cls._aux_entries(selection)) + entries.extend(cls._irradiance_entries(selection)) + entries.extend(cls._derived_entries(feature_spec)) + catalog = cls(entries=tuple(entries)) + catalog._check_covers_model_inputs(feature_spec) + return catalog + + @staticmethod + def _aux_entries(selection: FeatureSelection) -> list[FeatureMetadata]: + """Metadata for NASA POWER + CAMS auxiliary (non-irradiance) variables.""" + entries: list[FeatureMetadata] = [] + for source, variable_ids in ( + (Source.NASA_POWER, selection.nasa_variable_ids), + (Source.CAMS, selection.cams_variable_ids), + ): + for variable_id in variable_ids: + spec = VariableCatalog.get(variable_id=variable_id, source=source) + entries.append( + FeatureMetadata.from_variable_spec( + spec, column=aux_feature_column(source, variable_id) + ) + ) + return entries + + @staticmethod + def _irradiance_entries(selection: FeatureSelection) -> list[FeatureMetadata]: + """Metadata for the satellite irradiance bands (sat__).""" + entries: list[FeatureMetadata] = [] + for source in selection.include_satellite_irradiance: + for band in selection.include_satellite_bands: + spec = VariableCatalog.get(variable_id=band.value, source=source) + entries.append( + FeatureMetadata.from_variable_spec( + spec, column=satellite_irradiance_column(source, band) + ) + ) + return entries + + @staticmethod + def _derived_entries(feature_spec: FeatureSpec) -> list[FeatureMetadata]: + """Metadata for every column the spec's derived features emit.""" + entries: list[FeatureMetadata] = [] + for feature in feature_spec.derived_features: + for column_metadata in feature.output_metadata: + entries.append(FeatureMetadata.from_derived_column(column_metadata)) + return entries + + def _check_covers_model_inputs(self, feature_spec: FeatureSpec) -> None: + """Fail loudly if the catalog and the spec disagree on model inputs. + + The catalog is assembled from the feature *selection*; the model + consumes ``feature_spec.output_feature_names``. The two are + independent records of the same fact and must agree exactly — a + mismatch means the bundle's manifest and spec were written by + divergent code, and an inspector built on a wrong catalog would + silently omit or invent variables. + """ + catalog_columns = set(self.columns) + model_columns = set(feature_spec.output_feature_names) + missing = sorted(model_columns - catalog_columns) + extra = sorted(catalog_columns - model_columns) + if missing or extra: + raise ValueError( + "FeatureCatalog does not match the bundle's model inputs. " + f"Columns the model expects but the catalog lacks: {missing}. " + f"Columns the catalog has but the model never consumes: " + f"{extra}. The bundle's feature selection and feature spec " + f"are inconsistent — rebuild the bundle." + ) diff --git a/src/susse/inference/predictor.py b/src/susse/inference/predictor.py new file mode 100644 index 00000000..dff9664b --- /dev/null +++ b/src/susse/inference/predictor.py @@ -0,0 +1,519 @@ +"""Predictor — portal-facing inference for a TrainedBundle. + +Composes the existing primitives (:class:`FeatureService`, +:class:`Preprocessor`, :class:`TrainedBundle`) into the single +deployment-friendly API the portal needs: given a list of coordinates +plus a date range, return a long-format DataFrame of bias-corrected +daily GHI predictions. + +The implementation pulls features from the warehouse via +:meth:`FeatureService.build_satellite_features`, applies the bundle's +``FeatureSpec`` through :meth:`Preprocessor.apply_to_dataframe` +(inference shape — no cleaners, no NaN-drop), and runs the regressor +in a single batched call. + +Cache misses (geohash5 cells absent from the warehouse for some of +the requested dates) are surfaced loudly by default. With +``on_cache_miss="fetch"``, the predictor instead reuses +:class:`NasaPowerSatelliteJob` and :class:`CamsSatelliteJob` to +synchronously fetch + persist the missing cells before re-querying +and predicting. The CAMS API has a daily quota; the predictor caps +its on-demand fetches at :data:`MAX_CAMS_CALLS_PER_PREDICT` per +invocation. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from datetime import date +from functools import cached_property +from pathlib import Path +from typing import TYPE_CHECKING, Literal, Mapping, Optional, Sequence + +import pandas as pd +import pygeohash + +from .. import schema +from ..datasets import FeatureSelection, FeatureService +from ..preprocessing import Preprocessor +from ..training import TrainedBundle, load_bundle +from ..warehouse_ops.io.bq import BigQueryClient +from ..warehouse_ops.io.config import WarehouseConfig +from ..warehouse_ops.population.dim_variable import VariableCatalog +from ..warehouse_ops.population.jobs.satellite_job import ( + CamsSatelliteJob, + NasaPowerSatelliteJob, +) +from ..warehouse_ops.population.types import ( + DateRange, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) +from .feature_catalog import FeatureCatalog + +if TYPE_CHECKING: # pragma: no cover — type-only + from ..preprocessing.elevation import ElevationProvider + +_PREDICTION_COLUMN: str = schema.PREDICTION + +MAX_CAMS_CALLS_PER_PREDICT: int = 30 +"""Hard cap on per-cell CAMS fetches in a single :meth:`Predictor.predict` +invocation. CAMS free-tier quota is ~40 requests/day; the cap leaves +headroom for retries and for other CAMS work in the same calendar day. +Hit this cap by widening the warehouse via a pre-cache migration.""" + +CacheMissPolicy = Literal["raise", "fetch"] + + +@dataclass(frozen=True) +class PredictionRequest: + """Coordinates + date range to predict over, validated at construction. + + Attributes: + coords: One ``(lat, lon)`` per location to predict. Duplicates + are permitted (e.g. multiple named locations sharing one + geohash5 cell) — the predictor expands them back per coord + after the warehouse query. + start_date: Inclusive start of the prediction window. + end_date: Inclusive end of the prediction window. + """ + + coords: tuple[tuple[float, float], ...] + start_date: date + end_date: date + + def __post_init__(self) -> None: + if not self.coords: + raise ValueError( + "PredictionRequest.coords is empty. Pass at least one " + "(lat, lon) tuple." + ) + for lat, lon in self.coords: + if not -90.0 <= lat <= 90.0: + raise ValueError( + f"Latitude {lat} is outside [-90, 90]. Check the " + f"coordinate order — Predictor.predict expects " + f"(lat, lon) tuples." + ) + if not -180.0 <= lon <= 180.0: + raise ValueError(f"Longitude {lon} is outside [-180, 180].") + if self.end_date < self.start_date: + raise ValueError( + f"PredictionRequest: end_date {self.end_date} precedes " + f"start_date {self.start_date}. Swap them or correct " + f"the input." + ) + + +class Predictor: + """Bias-corrected daily GHI predictions for a trained bundle. + + The bundle carries its source manifest (which records the + :class:`FeatureSelection` used at training time); the predictor + consults that manifest to know which warehouse sources / variables + to assemble, so inference and training share an identical feature + contract by construction. + + Attributes: + bundle: The trained-model artifact loaded into memory. + on_cache_miss: ``"raise"`` (default) fails loudly when a + requested ``(geohash5, date)`` cell is missing from the + warehouse. ``"fetch"`` synchronously runs the existing + NASA POWER and CAMS satellite jobs to populate the missing + cells, then re-queries the warehouse. ``"fetch"`` requires + ``CAMS_EMAIL`` to be set and caps per-invocation CAMS calls + at :data:`MAX_CAMS_CALLS_PER_PREDICT`. + """ + + def __init__( + self, + *, + bundle: TrainedBundle, + bq: BigQueryClient, + service: Optional[FeatureService] = None, + on_cache_miss: CacheMissPolicy = "raise", + ) -> None: + self._bundle = bundle + self._bq = bq + self._service = service if service is not None else FeatureService(bq) + self._on_cache_miss: CacheMissPolicy = on_cache_miss + self._selection: FeatureSelection = bundle.source_manifest.feature_selection + if on_cache_miss == "fetch": + self._validate_fetch_mode_credentials() + + @staticmethod + def _validate_fetch_mode_credentials() -> None: + """Fail loudly at __init__ when fetch mode lacks the CAMS email.""" + if not os.environ.get("CAMS_EMAIL"): + raise ValueError( + "Predictor was constructed with on_cache_miss='fetch' " + "but CAMS_EMAIL is not set in the environment. CAMS " + "(SoDa-Pro) requires a registered email; set " + "`export CAMS_EMAIL=you@example.com` or add it to your " + ".env file before constructing the predictor. NASA " + "POWER is public and needs no credentials. BQ write " + "permission is also required (failures surface at " + "first write)." + ) + + @classmethod + def from_bundle_dir( + cls, + bundle_dir: Path, + *, + config: Optional[WarehouseConfig] = None, + providers: Optional[Mapping[str, "ElevationProvider"]] = None, + on_cache_miss: CacheMissPolicy = "raise", + ) -> "Predictor": + """Convenience constructor: load the bundle + open a BigQuery client. + + Args: + bundle_dir: Path to a directory written by + :meth:`TrainedBundle.save`. + config: Warehouse config; defaults to the project-default + :class:`WarehouseConfig()`. + providers: Mapping of derived-feature kind → provider, passed + through to :func:`susse.training.load_bundle`. + Typically ``{"altitude": PvlibElevationProvider()}``. + on_cache_miss: See class attribute. + """ + cfg = config if config is not None else WarehouseConfig() + bq = BigQueryClient(config=cfg) + bundle = load_bundle(bundle_dir, providers=dict(providers or {})) + return cls(bundle=bundle, bq=bq, on_cache_miss=on_cache_miss) + + @property + def bundle(self) -> TrainedBundle: + return self._bundle + + @property + def on_cache_miss(self) -> CacheMissPolicy: + return self._on_cache_miss + + @cached_property + def feature_catalog(self) -> FeatureCatalog: + """Human-facing metadata for every model-input column. + + One :class:`~susse.inference.FeatureMetadata` entry per feature + column :meth:`predict` returns — label, unit, description, and + presentation group — so a UI can show what each input is and + where it comes from. Derived purely from the bundle (no + warehouse access) and cached after first use. + """ + return FeatureCatalog.build( + selection=self._selection, + feature_spec=self._bundle.feature_spec, + ) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + def predict( + self, + *, + coords: Sequence[tuple[float, float]], + start_date: date, + end_date: date, + ) -> pd.DataFrame: + """Daily bias-corrected GHI predictions over ``coords × [start, end]``. + + Args: + coords: One ``(lat, lon)`` tuple per location to predict. + start_date, end_date: Inclusive date bounds. + + Returns: + Long-format DataFrame with columns ``lat``, ``lon``, + ``geohash5``, ``date``, the bundle's expected feature + columns (so callers can overlay raw satellite baselines + without re-querying), and ``y_pred_kwh_m2_day`` — one row + per ``(coord, date)`` pair. + + Raises: + ValueError: For invalid inputs (empty coords, swapped + date order, out-of-range lat/lon). + RuntimeError: When ``on_cache_miss="raise"`` and the + warehouse is missing any requested ``(geohash5, date)`` + cell, or when ``on_cache_miss="fetch"`` would exceed + :data:`MAX_CAMS_CALLS_PER_PREDICT`, or when an + on-demand fetch completes but the requested cells are + still missing afterwards (e.g. dates outside CAMS + coverage). The error names the missing cells. + """ + request = PredictionRequest( + coords=tuple(coords), + start_date=start_date, + end_date=end_date, + ) + coords_df = self._coords_to_dataframe(request.coords) + unique_geohashes = tuple(coords_df[schema.GEOHASH5].unique()) + warehouse_df = self._fetch_warehouse_features( + points=request.coords, + start_date=request.start_date, + end_date=request.end_date, + ) + missing = self._detect_cache_misses( + warehouse_df=warehouse_df, + geohash5s=unique_geohashes, + start_date=request.start_date, + end_date=request.end_date, + ) + if missing: + if self._on_cache_miss == "raise": + self._raise_cache_miss(missing) + else: # "fetch" + missing_geohashes = {gh for gh, _ in missing} + self._fetch_and_persist( + coords_df=coords_df, + missing_geohash5s=missing_geohashes, + start_date=request.start_date, + end_date=request.end_date, + ) + # The fetch added native pixels; drop cached snappers so the + # re-query snaps against the freshly-ingested set. + self._service.invalidate_snapper_cache() + # Re-query the now-warm warehouse for the fresh rows. + warehouse_df = self._fetch_warehouse_features( + points=request.coords, + start_date=request.start_date, + end_date=request.end_date, + ) + still_missing = self._detect_cache_misses( + warehouse_df=warehouse_df, + geohash5s=unique_geohashes, + start_date=request.start_date, + end_date=request.end_date, + ) + if still_missing: + self._raise_cache_miss( + still_missing, + prefix=( + "Even after on-demand fetch, the following " + "(geohash5, date) pairs are still missing. " + "The upstream API probably returned no data " + "for them (e.g. dates outside CAMS's coverage)." + ), + ) + inference_df = self._merge_coords_with_warehouse(coords_df, warehouse_df) + spec = self._bundle.feature_spec + processed = Preprocessor(spec).apply_to_dataframe(inference_df) + processed[_PREDICTION_COLUMN] = self._bundle.regressor.predict( + processed[list(spec.output_feature_names)] + ) + return self._format_output(processed) + + # ------------------------------------------------------------------ + # Internal — coord prep + # ------------------------------------------------------------------ + + def _coords_to_dataframe( + self, + coords: Sequence[tuple[float, float]], + ) -> pd.DataFrame: + """Compute geohash5 + synthetic location name per coord.""" + precision = self._service.geohash_precision + rows: list[dict[str, object]] = [] + for i, (lat, lon) in enumerate(coords): + rows.append( + { + "coord_idx": i, + schema.LOCATION: f"point_{i:04d}", + schema.LAT: float(lat), + schema.LON: float(lon), + schema.GEOHASH5: pygeohash.encode(lat, lon, precision=precision), + } + ) + return pd.DataFrame(rows) + + # ------------------------------------------------------------------ + # Internal — warehouse query + # ------------------------------------------------------------------ + + def _fetch_warehouse_features( + self, + *, + points: Sequence[tuple[float, float]], + start_date: date, + end_date: date, + ) -> pd.DataFrame: + """Pull irradiance + per-source aux for the requested points in one go. + + The FeatureService snaps each point onto every source's native grid + internally and returns rows keyed by the query point's own + ``geohash5`` — so the cache-miss detector below can keep comparing + against ``geohash5`` exactly as before. + """ + return self._service.build_satellite_features( + selection=self._selection, + date_start=start_date, + date_end=end_date, + points=points, + ) + + def _detect_cache_misses( + self, + *, + warehouse_df: pd.DataFrame, + geohash5s: tuple[str, ...], + start_date: date, + end_date: date, + ) -> list[tuple[str, date]]: + """Return ``(geohash5, date)`` pairs absent from the warehouse frame.""" + if not geohash5s: + return [] + expected_dates = pd.date_range(start_date, end_date, freq="D").date + expected = pd.MultiIndex.from_product( + [geohash5s, expected_dates], names=[schema.GEOHASH5, schema.DATE] + ) + if warehouse_df.empty: + present_dates: pd.Series = pd.Series([], dtype="object") + present_gh: pd.Series = pd.Series([], dtype=str) + else: + present_dates = pd.to_datetime(warehouse_df[schema.DATE]).dt.date + present_gh = warehouse_df[schema.GEOHASH5] + present = pd.MultiIndex.from_arrays( + [present_gh, present_dates], + names=[schema.GEOHASH5, schema.DATE], + ) + missing = expected.difference(present) + return list(missing) + + def _raise_cache_miss( + self, + missing: list[tuple[str, date]], + *, + prefix: str | None = None, + ) -> None: + """Build the canonical cache-miss error message.""" + sample = missing[:5] + head = ( + prefix + if prefix is not None + else ( + f"Predictor: {len(missing)} warehouse cell-day pairs are " + f"missing. Apply the corresponding warehouse-ingest " + f"migration before calling predict, or construct the " + f"Predictor with on_cache_miss='fetch' to fetch on demand " + f"(requires CAMS_EMAIL + BQ write perms)." + ) + ) + raise RuntimeError(f"{head} First few missing (geohash5, date): {sample}.") + + def _fetch_and_persist( + self, + *, + coords_df: pd.DataFrame, + missing_geohash5s: set[str], + start_date: date, + end_date: date, + ) -> None: + """Run the existing ingest jobs over the missing cells. + + Reuses :class:`NasaPowerSatelliteJob` and :class:`CamsSatelliteJob` + directly so the on-demand fetch path is bit-for-bit identical to + the batch-ingest path: same coverage check, same variable-id + mapping, same warehouse schema, same idempotency guarantees. + """ + if len(missing_geohash5s) > MAX_CAMS_CALLS_PER_PREDICT: + raise RuntimeError( + f"Predictor: {len(missing_geohash5s)} cells need an " + f"on-demand CAMS fetch, exceeding the per-invocation " + f"cap of {MAX_CAMS_CALLS_PER_PREDICT} (CAMS free-tier " + f"daily quota). Pre-cache this region via a warehouse " + f"ingest migration before calling predict on it." + ) + # Recover (lat, lon, name) for each missing geohash5 from the + # coords_df. Multiple coords sharing one geohash5 contribute one + # LocationSpec. + gh_to_coord = coords_df.drop_duplicates(subset=schema.GEOHASH5).set_index( + schema.GEOHASH5 + )[[schema.LAT, schema.LON, schema.LOCATION]] + locations = tuple( + LocationSpec( + name=str(gh_to_coord.loc[gh, schema.LOCATION]), + lat=float(gh_to_coord.loc[gh, schema.LAT]), + lon=float(gh_to_coord.loc[gh, schema.LON]), + ) + for gh in sorted(missing_geohash5s) + ) + date_range = DateRange(start=start_date, end=end_date) + nasa_vars = self._variables_for_source(Source.NASA_POWER) + if nasa_vars: + NasaPowerSatelliteJob(self._bq).run( + NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=date_range, + locations=locations, + variables=nasa_vars, + ), + ) + cams_vars = self._variables_for_source(Source.CAMS) + if cams_vars: + CamsSatelliteJob(self._bq).run( + NamedLocationsPlan( + source=Source.CAMS, + date_range=date_range, + locations=locations, + variables=cams_vars, + ), + ) + + def _variables_for_source(self, source: Source) -> tuple[VariableSpec, ...]: + """Resolve the full :class:`VariableSpec` for each requested ID + band.""" + selection = self._selection + ids: tuple[str, ...] = () + if source is Source.NASA_POWER: + ids = selection.nasa_variable_ids + elif source is Source.CAMS: + ids = selection.cams_variable_ids + elif source is Source.MERRA_2: + ids = selection.merra_variable_ids + aux = tuple(VariableCatalog.get(variable_id=vid, source=source) for vid in ids) + irradiance: tuple[VariableSpec, ...] = () + if source in selection.include_satellite_irradiance: + band_specs = [] + for band in selection.include_satellite_bands: + try: + band_specs.append( + VariableCatalog.get(variable_id=band.value, source=source) + ) + except KeyError: + # Source / band combination not in catalogue — skip. + continue + irradiance = tuple(band_specs) + return aux + irradiance + + # ------------------------------------------------------------------ + # Internal — assembly + inference + # ------------------------------------------------------------------ + + @staticmethod + def _merge_coords_with_warehouse( + coords_df: pd.DataFrame, warehouse_df: pd.DataFrame + ) -> pd.DataFrame: + """Cross-join coords × warehouse rows for that geohash5.""" + if warehouse_df.empty: + return coords_df.assign(date=pd.NaT).iloc[0:0] + return coords_df.merge(warehouse_df, on=schema.GEOHASH5, how="inner") + + def _format_output(self, processed_df: pd.DataFrame) -> pd.DataFrame: + """Order columns + drop internal scaffolding from the result.""" + front = [ + c + for c in ( + schema.LAT, + schema.LON, + schema.GEOHASH5, + schema.DATE, + schema.LOCATION, + ) + if c in processed_df.columns + ] + rest = [ + c for c in processed_df.columns if c not in front + [_PREDICTION_COLUMN] + ] + out = processed_df[front + rest + [_PREDICTION_COLUMN]].copy() + return out.reset_index(drop=True) diff --git a/src/susse/models/__init__.py b/src/susse/models/__init__.py new file mode 100644 index 00000000..e144d0bf --- /dev/null +++ b/src/susse/models/__init__.py @@ -0,0 +1,45 @@ +"""Model layer — typed params, ABC regressor, factory, three concrete models. + +Public surface: + +* :class:`BaseRegressor`, :class:`BaseModelParams` — the contracts. +* :class:`ModelKind` — discriminator + dispatch enum. +* Concrete params: :class:`MeanBaselineParams`, :class:`RandomForestParams`, + :class:`LinearParams`. +* Concrete regressors: :class:`MeanBaselineRegressor`, + :class:`RandomForestRegressor`, :class:`LinearRegressor`. +* :class:`ModelFactory` — typed-params → regressor. +* :func:`load_regressor` — restore a fitted regressor from disk without + knowing the concrete class up-front. +* :func:`params_from_dict` — JSON manifest → typed params, dispatched + via the persisted ``kind`` tag. +""" + +from .base import BaseRegressor, load_regressor +from .factory import ModelFactory +from .linear import LinearRegressor +from .mean_baseline import MeanBaselineRegressor +from .params import ( + BaseModelParams, + LinearParams, + MeanBaselineParams, + ModelKind, + RandomForestParams, + params_from_dict, +) +from .random_forest import RandomForestRegressor + +__all__ = [ + "BaseModelParams", + "BaseRegressor", + "LinearParams", + "LinearRegressor", + "MeanBaselineParams", + "MeanBaselineRegressor", + "ModelFactory", + "ModelKind", + "RandomForestParams", + "RandomForestRegressor", + "load_regressor", + "params_from_dict", +] diff --git a/src/susse/models/base.py b/src/susse/models/base.py new file mode 100644 index 00000000..2e462307 --- /dev/null +++ b/src/susse/models/base.py @@ -0,0 +1,143 @@ +"""Abstract base for all regressors + the generic save/load contract. + +Every concrete model is a :class:`BaseRegressor` subclass parameterised +by its typed params dataclass. The ABC pins the contract: ``fit`` → +``predict`` → ``save`` round-trippable to ``load``. + +Predictions are returned as :class:`pandas.Series` aligned to the input +``X.index`` so downstream code can join predictions back onto rows by +identifier without keeping a parallel index. (Sklearn returns ndarray +and forces the caller to remember the row order.) +""" + +from __future__ import annotations + +import json +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Generic, TypeVar + +import pandas as pd + +from .params import BaseModelParams, params_from_dict + +P = TypeVar("P", bound=BaseModelParams) + +_PARAMS_FILENAME = "params.json" +_KIND_FILENAME = "model_kind.txt" +_STATE_FILENAME = "state.joblib" + + +class BaseRegressor(ABC, Generic[P]): + """ABC for typed regressors. + + Generic over ``P``, the params dataclass type. The bound on ``P`` + keeps the type relationship intact for static checkers without + forcing every subclass to repeat the parameter type in two places. + + Concrete subclasses accept their typed ``params`` as the single + constructor argument; the signature is declared here so the factory + (``ModelKind.model_class()(params)``) type-checks against the ABC. + """ + + @abstractmethod + def __init__(self, params: P) -> None: + """Construct an unfitted regressor from its hyperparameter set.""" + + @abstractmethod + def fit(self, X: pd.DataFrame, y: pd.Series) -> "BaseRegressor[P]": + """Fit the model on ``(X, y)`` and return ``self`` for chaining. + + Subclasses fitting their own scaler / preprocessor must do it + here, on this ``X`` only. The wrapper interface is intentionally + single-fold — leakage protection is the caller's responsibility + (i.e. only train-fold rows reach :meth:`fit`). + """ + + @abstractmethod + def predict(self, X: pd.DataFrame) -> pd.Series: + """Predict for each row in ``X``; index of the result == ``X.index``.""" + + @property + @abstractmethod + def params(self) -> P: + """The typed params used to construct this regressor.""" + + @property + @abstractmethod + def is_fitted(self) -> bool: + """``True`` iff :meth:`fit` has run successfully.""" + + # ---- persistence ------------------------------------------------------ + # + # The base class owns the surface (``save`` / ``load``) and the file + # layout; concrete subclasses only have to surrender / restore their + # internal joblib-able state. + + def save(self, dir: Path) -> None: + """Persist this regressor into ``dir`` (created if absent). + + Layout:: + + / + model_kind.txt # ``params.kind.value`` + params.json # ``params.to_dict()`` + state.joblib # subclass _state() + + Raises: + RuntimeError: If the regressor has not been fitted. + """ + if not self.is_fitted: + raise RuntimeError( + f"Cannot save unfitted {type(self).__name__}: call .fit(X, y) " + f"before .save(...)." + ) + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) + (dir / _KIND_FILENAME).write_text(self.params.kind.value) + (dir / _PARAMS_FILENAME).write_text( + json.dumps(self.params.to_dict(), indent=2, sort_keys=True) + ) + import joblib # local import — only models that get saved pay the cost + + joblib.dump(self._state(), dir / _STATE_FILENAME) + + @abstractmethod + def _state(self) -> object: + """Return the joblib-able fitted state for :meth:`save`. + + Whatever this returns is the only state restored by + :meth:`_from_state`; everything else must be reconstructible + from ``params``. + """ + + @classmethod + @abstractmethod + def _from_state(cls, params: P, state: object) -> "BaseRegressor[P]": + """Reconstruct a fitted regressor from its params + saved state.""" + + +def load_regressor(dir: Path) -> BaseRegressor: + """Inverse of :meth:`BaseRegressor.save`: read the kind, dispatch, restore. + + Doesn't require knowing the concrete class up-front — the layout's + ``model_kind.txt`` carries that. Useful for inference code that + receives a model directory without compile-time knowledge of which + flavour was trained. + """ + dir = Path(dir) + kind_str = (dir / _KIND_FILENAME).read_text().strip() + params_dict = json.loads((dir / _PARAMS_FILENAME).read_text()) + params = params_from_dict(params_dict) + if params.kind.value != kind_str: + raise ValueError( + f"Inconsistent saved model in {dir}: model_kind.txt says " + f"{kind_str!r} but params.json declares " + f"{params.kind.value!r}. Re-save the model from a consistent " + f"in-memory state, or hand-fix the directory." + ) + import joblib + + state = joblib.load(dir / _STATE_FILENAME) + cls = params.kind.model_class() + return cls._from_state(params, state) diff --git a/src/susse/models/factory.py b/src/susse/models/factory.py new file mode 100644 index 00000000..b81a274a --- /dev/null +++ b/src/susse/models/factory.py @@ -0,0 +1,21 @@ +"""Construction-time entry point for the model layer. + +Single static method that instantiates the right :class:`BaseRegressor` +subclass from a typed :class:`BaseModelParams`. Dispatch goes through +the ``params.kind`` enum's :meth:`model_class` — no string-keyed +registry, no module-level lookup table. +""" + +from __future__ import annotations + +from .base import BaseRegressor +from .params import BaseModelParams + + +class ModelFactory: + """Construct a fresh (unfitted) regressor from a typed params object.""" + + @staticmethod + def create(params: BaseModelParams) -> BaseRegressor: + regressor_cls = params.kind.model_class() + return regressor_cls(params) diff --git a/src/susse/models/linear.py b/src/susse/models/linear.py new file mode 100644 index 00000000..b21aab32 --- /dev/null +++ b/src/susse/models/linear.py @@ -0,0 +1,79 @@ +"""Linear regressor with optional in-wrapper scaling. + +The scaler is fitted on whatever ``X`` is passed to :meth:`fit` — +leakage protection is the caller's responsibility (only train-fold rows +should reach :meth:`fit`). This mirrors how a future NN wrapper would +own its own scaling, and keeps :class:`Preprocessor` stateless. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +import numpy as np +import pandas as pd +from sklearn.linear_model import LinearRegression as _SkLinear +from sklearn.preprocessing import StandardScaler + +from .base import BaseRegressor +from .params import LinearParams + + +@dataclass +class _LinearState: + """Internal joblib-able state for :class:`LinearRegressor`.""" + + estimator: _SkLinear + scaler: Optional[StandardScaler] + + +class LinearRegressor(BaseRegressor[LinearParams]): + """Sklearn linear regression with optional StandardScaler.""" + + def __init__(self, params: LinearParams) -> None: + self._params = params + self._state_obj: Optional[_LinearState] = None + + @property + def params(self) -> LinearParams: + return self._params + + @property + def is_fitted(self) -> bool: + return self._state_obj is not None + + def fit(self, X: pd.DataFrame, y: pd.Series) -> "LinearRegressor": + X_array = X.values + if self._params.with_scaling: + scaler = StandardScaler() + X_array = scaler.fit_transform(X_array) + else: + scaler = None + estimator = _SkLinear(fit_intercept=self._params.fit_intercept) + estimator.fit(X_array, np.asarray(y.values, dtype=float)) + self._state_obj = _LinearState(estimator=estimator, scaler=scaler) + return self + + def predict(self, X: pd.DataFrame) -> pd.Series: + if self._state_obj is None: + raise RuntimeError("LinearRegressor.predict called before .fit().") + X_array = X.values + if self._state_obj.scaler is not None: + X_array = self._state_obj.scaler.transform(X_array) + preds = self._state_obj.estimator.predict(X_array) + return pd.Series(np.asarray(preds, dtype=float), index=X.index) + + def _state(self) -> object: + return self._state_obj + + @classmethod + def _from_state(cls, params: LinearParams, state: object) -> "LinearRegressor": + if not isinstance(state, _LinearState): + raise ValueError( + f"LinearRegressor._from_state expected a _LinearState, got " + f"{type(state).__name__}." + ) + instance = cls(params) + instance._state_obj = state + return instance diff --git a/src/susse/models/mean_baseline.py b/src/susse/models/mean_baseline.py new file mode 100644 index 00000000..fdeb879b --- /dev/null +++ b/src/susse/models/mean_baseline.py @@ -0,0 +1,65 @@ +"""Constant-prediction baseline. + +Predicts the training set's mean (or median) for every input row. Almost +useless as a model — useful as a sanity baseline: any "real" model that +scores worse than this on a held-out set has a bug. +""" + +from __future__ import annotations + +from typing import Optional + +import numpy as np +import pandas as pd + +from .base import BaseRegressor +from .params import MeanBaselineParams + + +class MeanBaselineRegressor(BaseRegressor[MeanBaselineParams]): + """Predicts the training-y central tendency for all inputs.""" + + def __init__(self, params: MeanBaselineParams) -> None: + self._params = params + self._prediction: Optional[float] = None + + @property + def params(self) -> MeanBaselineParams: + return self._params + + @property + def is_fitted(self) -> bool: + return self._prediction is not None + + def fit(self, X: pd.DataFrame, y: pd.Series) -> "MeanBaselineRegressor": + if y.empty: + raise ValueError( + "MeanBaselineRegressor.fit received an empty target series. " + "Pass at least one training row." + ) + if self._params.statistic == "mean": + self._prediction = float(y.mean()) + else: # "median" — validated by params.__post_init__ + self._prediction = float(y.median()) + return self + + def predict(self, X: pd.DataFrame) -> pd.Series: + if self._prediction is None: + raise RuntimeError("MeanBaselineRegressor.predict called before .fit().") + return pd.Series(np.full(len(X), self._prediction, dtype=float), index=X.index) + + def _state(self) -> object: + return {"prediction": self._prediction} + + @classmethod + def _from_state( + cls, params: MeanBaselineParams, state: object + ) -> "MeanBaselineRegressor": + if not isinstance(state, dict) or "prediction" not in state: + raise ValueError( + f"MeanBaselineRegressor._from_state expected " + f"{{'prediction': float}}, got {type(state).__name__}." + ) + instance = cls(params) + instance._prediction = float(state["prediction"]) + return instance diff --git a/src/susse/models/params.py b/src/susse/models/params.py new file mode 100644 index 00000000..006dc817 --- /dev/null +++ b/src/susse/models/params.py @@ -0,0 +1,184 @@ +"""Typed model parameter dataclasses + dispatch enum. + +Each model has its own frozen :class:`BaseModelParams` subclass holding +its hyperparameters. The :class:`ModelKind` enum routes between them — +construction (``params.kind.model_class()``), deserialisation +(``params.kind.params_type()``), and persistence (``kind`` is the +single string written into ``model_kind.txt`` next to ``state.joblib``). + +No string-keyed dicts: a typo in a hyperparameter name surfaces as a +construction-time ``TypeError``, not a silent default value at training +time. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import asdict, dataclass +from enum import StrEnum +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: # avoid runtime import cycles + from .base import BaseRegressor + + +class ModelKind(StrEnum): + """Discriminator between concrete model implementations. + + Used as the persistence key (written to ``model_kind.txt``) and as + the dispatch token in :class:`susse.models.ModelFactory`. The enum + methods own the ``kind → params_type`` and ``kind → model_class`` + mappings — no string-keyed registry, no module-level dispatch dict. + """ + + MEAN_BASELINE = "mean_baseline" + RANDOM_FOREST = "random_forest" + LINEAR = "linear" + + def params_type(self) -> type["BaseModelParams"]: + """Return the params dataclass for this kind.""" + # Local imports break a circular: subclasses define their own + # `kind` property which references this enum. + from .params import ( # noqa: PLC0415 + LinearParams, + MeanBaselineParams, + RandomForestParams, + ) + + params_mapping: dict[ModelKind, type[BaseModelParams]] = { + ModelKind.MEAN_BASELINE: MeanBaselineParams, + ModelKind.RANDOM_FOREST: RandomForestParams, + ModelKind.LINEAR: LinearParams, + } + return params_mapping[self] + + def model_class(self) -> type["BaseRegressor[Any]"]: + """Return the regressor class that consumes this kind's params.""" + from .linear import LinearRegressor # noqa: PLC0415 + from .mean_baseline import MeanBaselineRegressor # noqa: PLC0415 + from .random_forest import RandomForestRegressor # noqa: PLC0415 + + regressor_mapping: dict[ModelKind, type["BaseRegressor[Any]"]] = { + ModelKind.MEAN_BASELINE: MeanBaselineRegressor, + ModelKind.RANDOM_FOREST: RandomForestRegressor, + ModelKind.LINEAR: LinearRegressor, + } + return regressor_mapping[self] + + +@dataclass(frozen=True) +class BaseModelParams(ABC): + """ABC for typed model hyperparameter sets. + + Subclasses are frozen dataclasses carrying only hyperparameters; + runtime-fitted state lives on the regressor, not the params. The + :attr:`kind` property is the dispatch token for the factory and the + persistence layer. + """ + + @property + @abstractmethod + def kind(self) -> ModelKind: + """The :class:`ModelKind` this params object belongs to.""" + + def to_dict(self) -> dict[str, Any]: + """Serialise to a JSON-safe dict, with ``kind`` as a tag. + + The ``kind`` tag is what :func:`params_from_dict` uses to route + deserialisation back to the correct subclass. + """ + d = asdict(self) + d["kind"] = self.kind.value + return d + + +def params_from_dict(d: dict[str, Any]) -> BaseModelParams: + """Inverse of :meth:`BaseModelParams.to_dict`. + + Reads the ``kind`` tag, looks up the matching params dataclass, and + constructs it from the remaining fields. + """ + if "kind" not in d: + raise ValueError( + "params_from_dict expects a 'kind' tag identifying the model " + "type. Pass a dict produced by BaseModelParams.to_dict()." + ) + kind = ModelKind(d["kind"]) + params_cls = kind.params_type() + return params_cls(**{k: v for k, v in d.items() if k != "kind"}) + + +# --------------------------------------------------------------------------- +# Concrete params +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class MeanBaselineParams(BaseModelParams): + """Params for the constant-mean baseline. + + Attributes: + statistic: Which central tendency to use — ``"mean"`` or + ``"median"``. Median is more robust to outliers but breaks + differentiability if a downstream consumer expected + mean-based predictions; default is ``"mean"``. + """ + + statistic: str = "mean" + + def __post_init__(self) -> None: + if self.statistic not in ("mean", "median"): + raise ValueError( + f"MeanBaselineParams.statistic must be 'mean' or 'median', " + f"got {self.statistic!r}." + ) + + @property + def kind(self) -> ModelKind: + return ModelKind.MEAN_BASELINE + + +@dataclass(frozen=True) +class RandomForestParams(BaseModelParams): + """Hyperparameters for sklearn's :class:`RandomForestRegressor`.""" + + n_estimators: int = 200 + max_depth: int | None = None + min_samples_split: int = 2 + min_samples_leaf: int = 1 + max_features: str | float | None = "sqrt" + random_state: int = 42 + n_jobs: int = -1 + + def __post_init__(self) -> None: + if self.n_estimators < 1: + raise ValueError(f"n_estimators={self.n_estimators} must be >= 1.") + if self.max_depth is not None and self.max_depth < 1: + raise ValueError( + f"max_depth={self.max_depth} must be >= 1 or None for unlimited." + ) + + @property + def kind(self) -> ModelKind: + return ModelKind.RANDOM_FOREST + + +@dataclass(frozen=True) +class LinearParams(BaseModelParams): + """Hyperparameters for the linear regressor. + + Attributes: + with_scaling: If True (default), fit a :class:`StandardScaler` + on the training X and apply it before / inverse to inference + X. Strongly recommended for linear models — otherwise + features with different magnitudes (kt ~ 0..1 vs GHI in + kWh/m²/day) bias the coefficients. + fit_intercept: Sklearn's intercept toggle. + """ + + with_scaling: bool = True + fit_intercept: bool = True + + @property + def kind(self) -> ModelKind: + return ModelKind.LINEAR diff --git a/src/susse/models/random_forest.py b/src/susse/models/random_forest.py new file mode 100644 index 00000000..c244a654 --- /dev/null +++ b/src/susse/models/random_forest.py @@ -0,0 +1,99 @@ +"""Random-forest regressor wrapping sklearn's :class:`RandomForestRegressor`. + +Tree-based; no scaling needed (CLAUDE.md / NB 03 decision: scaling lives +in the model wrapper, RF wrappers skip it). +""" + +from __future__ import annotations + +from typing import Optional + +import numpy as np +import pandas as pd +from sklearn.ensemble import RandomForestRegressor as _SkRF + +from .base import BaseRegressor +from .params import RandomForestParams + + +class RandomForestRegressor(BaseRegressor[RandomForestParams]): + """Sklearn random forest with the typed-params interface.""" + + def __init__(self, params: RandomForestParams) -> None: + self._params = params + self._estimator: Optional[_SkRF] = None + + @property + def params(self) -> RandomForestParams: + return self._params + + @property + def is_fitted(self) -> bool: + return self._estimator is not None + + def fit(self, X: pd.DataFrame, y: pd.Series) -> "RandomForestRegressor": + params = self._params + estimator = _SkRF( + n_estimators=params.n_estimators, + max_depth=params.max_depth, + min_samples_split=params.min_samples_split, + min_samples_leaf=params.min_samples_leaf, + max_features=params.max_features, + random_state=params.random_state, + n_jobs=params.n_jobs, + ) + estimator.fit(X.values, np.asarray(y.values, dtype=float)) + self._estimator = estimator + return self + + def predict(self, X: pd.DataFrame) -> pd.Series: + if self._estimator is None: + raise RuntimeError("RandomForestRegressor.predict called before .fit().") + preds = self._estimator.predict(X.values) + return pd.Series(np.asarray(preds, dtype=float), index=X.index) + + def feature_importances(self, feature_names: tuple[str, ...]) -> pd.Series: + """Return per-feature importance, indexed by feature_names. + + Args: + feature_names: Column names of the feature matrix the model + was fit on, in fit order. Same as + ``PreprocessedDataset.feature_columns``. + + Returns: + A :class:`pandas.Series` of impurity-based importances + (same as sklearn's ``feature_importances_``), summing to 1.0. + + Raises: + RuntimeError: Called before :meth:`fit`. + ValueError: If ``feature_names`` length doesn't match the + fitted model's expected feature count. + """ + if self._estimator is None: + raise RuntimeError( + "RandomForestRegressor.feature_importances called before .fit()." + ) + importances = self._estimator.feature_importances_ + if len(feature_names) != len(importances): + raise ValueError( + f"feature_names has {len(feature_names)} entries but the " + f"fitted model has {len(importances)} features. The names " + f"must match what was passed to .fit()." + ) + return pd.Series(importances, index=list(feature_names)) + + def _state(self) -> object: + return self._estimator + + @classmethod + def _from_state( + cls, params: RandomForestParams, state: object + ) -> "RandomForestRegressor": + if not isinstance(state, _SkRF): + raise ValueError( + f"RandomForestRegressor._from_state expected an sklearn " + f"RandomForestRegressor, got {type(state).__name__}." + ) + instance = cls(params) + instance._estimator = state + return instance diff --git a/src/susse/preprocessing/__init__.py b/src/susse/preprocessing/__init__.py new file mode 100644 index 00000000..67cd90b3 --- /dev/null +++ b/src/susse/preprocessing/__init__.py @@ -0,0 +1,85 @@ +"""Preprocessing — turn raw warehouse rows into model-ready features. + +Public surface: + +* :class:`FeatureSpec` — typed config bundling pass-through columns, + row-level cleaners, and derived features. +* :class:`DataCleaner` — ABC for one row-level transform (filter or + impute). Concrete subclasses: :class:`GhiUpperBoundCleaner`, + :class:`IqrLowerBoundCleaner`, :class:`HighMissingYearExcluder`, + :class:`KnnYearGapImputer`. Run *before* derived features. +* :class:`DerivedFeature` — ABC for one named, computed model input. + Concrete subclasses: :class:`ClearSkyIndexFeature`, + :class:`CyclicalDayOfYearFeature`, :class:`AltitudeFeature`, and + :class:`LongitudeFeature` (paper-faithful only — see its docstring). +* :class:`DerivedColumnMetadata` — human-facing label / unit / + description for one column a :class:`DerivedFeature` emits, exposed + via :attr:`DerivedFeature.output_metadata`. +* :class:`CleanerKind`, :class:`FeatureKind` — persistence + dispatch + tags for the two ABC families. +* :func:`data_cleaner_from_dict`, :func:`derived_feature_from_dict` — + JSON → instance, with provider re-injection. +* :class:`Preprocessor` — applies a spec to a + :class:`~susse.datasets.TrainingDataset`. Order: cleaners → + pass-through + derived features → dropna policy. +* :class:`PreprocessedDataset` — value object bundling the transformed + DataFrame with its feature column list, target column, and provenance + back to the source dataset. +* :class:`ElevationProvider`, :class:`PvlibElevationProvider` — runtime + altitude lookup (used by :class:`AltitudeFeature`). + +Stateless: there's no ``fit()``. All transforms are deterministic given +the inputs. Scaling lives in the model wrapper since it's +model-specific and only the model knows which fold to fit on. +""" + +from .cleaning import ( + CleanerKind, + DataCleaner, + GhiUpperBoundCleaner, + HighMissingYearExcluder, + IqrLowerBoundCleaner, + KnnYearGapImputer, + PerStationMeanImputer, + data_cleaner_from_dict, +) +from .derived import ( + AltitudeFeature, + ClearSkyIndexFeature, + CyclicalDayOfYearFeature, + DerivedColumnMetadata, + DerivedFeature, + FeatureKind, + LongitudeFeature, + derived_feature_from_dict, +) +from .derived_features import clear_sky_index, cyclical_day_of_year +from .elevation import ElevationProvider, PvlibElevationProvider +from .feature_spec import FeatureSpec +from .preprocessor import PreprocessedDataset, Preprocessor + +__all__ = [ + "AltitudeFeature", + "CleanerKind", + "ClearSkyIndexFeature", + "CyclicalDayOfYearFeature", + "DataCleaner", + "DerivedColumnMetadata", + "DerivedFeature", + "ElevationProvider", + "FeatureKind", + "FeatureSpec", + "GhiUpperBoundCleaner", + "HighMissingYearExcluder", + "IqrLowerBoundCleaner", + "KnnYearGapImputer", + "LongitudeFeature", + "PerStationMeanImputer", + "PreprocessedDataset", + "Preprocessor", + "PvlibElevationProvider", + "clear_sky_index", + "cyclical_day_of_year", + "data_cleaner_from_dict", + "derived_feature_from_dict", +] diff --git a/src/susse/preprocessing/_kind_tagged.py b/src/susse/preprocessing/_kind_tagged.py new file mode 100644 index 00000000..3cd87585 --- /dev/null +++ b/src/susse/preprocessing/_kind_tagged.py @@ -0,0 +1,119 @@ +"""Shared base for kind-tagged, JSON-roundtrippable value-object families. + +:class:`DerivedFeature` and :class:`DataCleaner` are two such families: +each is a frozen-dataclass-style value object carrying configuration, +tagged with a ``kind`` enum for persistence + dispatch, and +roundtrippable to JSON via :meth:`to_dict` / :meth:`_from_dict`. + +The families differ in their verb — :meth:`DerivedFeature.compute` +adds columns, :meth:`DataCleaner.apply` modifies rows — and in their +specific extras (``output_columns`` exists only on the feature side). +Those differences live on the concrete sub-ABCs. Everything else — +the kind contract, the required-input declaration, and the JSON +roundtrip — is shared here. + +Each family's enum (:class:`FeatureKind`, :class:`CleanerKind`) carries +a ``spec_class()`` method returning the concrete leaf class for that +kind. :func:`kind_dispatched_from_dict` is the generic +dict-to-instance dispatcher that consumes the enum and returns an +instance of the resolved leaf class. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from enum import StrEnum +from typing import Any, Generic, Optional, TypeVar + +KindT = TypeVar("KindT", bound=StrEnum) + + +class KindTaggedSpec(Generic[KindT], ABC): + """Frozen-dataclass-style value object with a kind tag + JSON roundtrip. + + Subclasses come in two layers: + + * Concrete sub-ABCs (e.g. :class:`DerivedFeature`, + :class:`DataCleaner`) that add a family-specific verb and any + family-specific extras. + * Leaf classes (e.g. :class:`ClearSkyIndexFeature`, + :class:`GhiUpperBoundCleaner`) — frozen dataclasses implementing + the abstract contracts here plus the family's verb. + """ + + @property + @abstractmethod + def kind(self) -> KindT: + """The kind tag identifying this concrete leaf class.""" + + @property + @abstractmethod + def required_input_columns(self) -> tuple[str, ...]: + """Source columns this spec needs from the input DataFrame.""" + + @abstractmethod + def to_dict(self) -> dict[str, Any]: + """Serialise to a JSON-safe dict including a ``"kind"`` tag. + + Non-serialisable fields (providers, etc.) are dropped. The + :meth:`_from_dict` inverse re-injects them from the + caller-supplied providers dict. + """ + + @classmethod + @abstractmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "KindTaggedSpec[KindT]": + """Inverse of :meth:`to_dict` for this concrete leaf class. + + Called by :func:`kind_dispatched_from_dict` after kind-dispatch. + ``providers`` re-injects any non-serialisable runtime + dependencies (e.g. an :class:`ElevationProvider`) captured at + construction time. + """ + + +def kind_dispatched_from_dict( + d: dict[str, Any], + *, + kind_enum: type[StrEnum], + providers: Optional[dict[str, Any]] = None, + family_name: str = "spec", +) -> Any: + """Generic dispatcher: dict → concrete :class:`KindTaggedSpec` leaf. + + Each family's enum must expose a ``spec_class()`` method that + returns the concrete leaf class for that kind. This contract is + enforced by convention rather than by a Protocol — :class:`StrEnum` + + :class:`Protocol` interact awkwardly in mypy, and the contract is + used in exactly two places. + + Args: + d: Output of :meth:`KindTaggedSpec.to_dict`. Must include + ``"kind"``. + kind_enum: The family's enum (e.g. ``FeatureKind``, + ``CleanerKind``). + providers: Map of provider-key → provider for non-serialisable + re-injection. Pass-through for kinds that don't need it. + family_name: Used in the error message ("derived feature", + "cleaner") so callers see a family-specific hint. + + Returns: + The reconstructed leaf-class instance. Typed as :data:`Any` + because the helper is family-agnostic; thin family-specific + wrappers (e.g. :func:`derived_feature_from_dict`) narrow the + return type via their own annotation. + + Raises: + ValueError: If ``"kind"`` is missing or names a value not in + ``kind_enum``. + """ + if "kind" not in d: + raise ValueError( + f"kind_dispatched_from_dict expects a 'kind' tag identifying " + f"the {family_name} type. Pass a dict produced by to_dict()." + ) + kind = kind_enum(d["kind"]) + cls = kind.spec_class() # type: ignore[attr-defined] + return cls._from_dict(d, providers=providers or {}) diff --git a/src/susse/preprocessing/cleaning.py b/src/susse/preprocessing/cleaning.py new file mode 100644 index 00000000..a1666ca9 --- /dev/null +++ b/src/susse/preprocessing/cleaning.py @@ -0,0 +1,516 @@ +"""Data-cleaning step abstraction. + +A :class:`DataCleaner` is one named, configurable row-level transform — +filter rows or impute missing values, with column schema preserved. +The :class:`Preprocessor` runs every cleaner in +``spec.cleaners`` in declared order, **before** any +:class:`DerivedFeature` computation. + +Parallel to :class:`DerivedFeature` but on a different axis: + +* :meth:`DerivedFeature.compute` adds new columns (row count unchanged). +* :meth:`DataCleaner.apply` modifies rows (column count unchanged). + +Both share :class:`KindTaggedSpec` as a base (kind tag + JSON roundtrip ++ required-input declaration). The verb distinction — ``apply`` vs +``compute`` — lives on each family's concrete ABC, so a reader still +sees ``cleaner.apply(df)`` and immediately knows the row set may +change, and ``feature.compute(df)`` and knows new columns are emitted. + +Adding a new cleaner type is one new subclass: register a +:class:`CleanerKind` value, add the subclass with its own ``apply`` +implementation, and reference it from :meth:`CleanerKind.spec_class`. +Nothing else in the codebase changes. +""" + +from __future__ import annotations + +from abc import abstractmethod +from dataclasses import dataclass +from enum import StrEnum +from typing import Any, Optional + +import numpy as np +import pandas as pd + +from .. import schema +from ._kind_tagged import KindTaggedSpec, kind_dispatched_from_dict + + +class CleanerKind(StrEnum): + """Persistence + dispatch tag for :class:`DataCleaner` subclasses. + + The string value ends up in ``feature_spec.json`` under each + cleaner entry's ``kind`` key. New subclasses register here and in + :meth:`spec_class`; nothing else needs to learn the new kind. + """ + + GHI_UPPER_BOUND = "ghi_upper_bound" + IQR_LOWER_BOUND = "iqr_lower_bound" + HIGH_MISSING_YEAR = "high_missing_year" + KNN_YEAR_GAP_IMPUTE = "knn_year_gap_impute" + PER_STATION_MEAN_IMPUTE = "per_station_mean_impute" + + def spec_class(self) -> type["DataCleaner"]: + """Return the concrete :class:`DataCleaner` subclass for this kind. + + Implements the protocol :func:`kind_dispatched_from_dict` + consumes; the same method name appears on :class:`FeatureKind`. + """ + if self is CleanerKind.GHI_UPPER_BOUND: + return GhiUpperBoundCleaner + if self is CleanerKind.IQR_LOWER_BOUND: + return IqrLowerBoundCleaner + if self is CleanerKind.HIGH_MISSING_YEAR: + return HighMissingYearExcluder + if self is CleanerKind.KNN_YEAR_GAP_IMPUTE: + return KnnYearGapImputer + if self is CleanerKind.PER_STATION_MEAN_IMPUTE: + return PerStationMeanImputer + raise AssertionError(f"Unhandled CleanerKind: {self!r}") # pragma: no cover + + +class DataCleaner(KindTaggedSpec[CleanerKind]): + """ABC for one row-level cleaning step. + + Subclasses are frozen-dataclass value objects (configuration only, + no fitted state). :meth:`apply` may shrink the row set or fill + values within existing rows; the column schema is preserved + end-to-end. + + Inherits the kind/required-input/to_dict/_from_dict contract from + :class:`KindTaggedSpec`; this ABC adds the cleaner-specific + ``apply`` verb. + """ + + @abstractmethod + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + """Apply the cleaning rule. Returns a frame with the same columns. + + The returned DataFrame's row count may be less than or equal to + the input's; for imputers it equals the input's. The column set + and ordering are preserved. + """ + + +def data_cleaner_from_dict( + d: dict[str, Any], + *, + providers: Optional[dict[str, Any]] = None, +) -> DataCleaner: + """Inverse of :meth:`DataCleaner.to_dict` — kind-dispatched. + + Thin wrapper around :func:`kind_dispatched_from_dict` that narrows + the return type to :class:`DataCleaner` and supplies the + family-specific error-message hint. ``providers`` is accepted for + forward compatibility (no current cleaner needs it) so + :class:`FeatureSpec.from_dict` can pass one dict through to both + families uniformly. + """ + return kind_dispatched_from_dict( # type: ignore[no-any-return] + d, + kind_enum=CleanerKind, + providers=providers, + family_name="cleaner", + ) + + +# --------------------------------------------------------------------------- +# Concrete cleaners +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class GhiUpperBoundCleaner(DataCleaner): + """Drop rows whose target column exceeds a physically-implausible value. + + Ground-truth GHI values above ~12 kWh/m²/day in sub-Saharan Africa are + almost certainly sensor errors (the theoretical clear-sky maximum at + these latitudes is ~9–10 kWh/m²/day). Configurable so other regions + or units can adjust the threshold. + + Used in: Mukiibi & Mikelson (2026) ground-truth curation. + """ + + column: str = schema.GROUND_GHI + threshold: float = 12.0 + + def __post_init__(self) -> None: + if not self.column: + raise ValueError("GhiUpperBoundCleaner.column must be non-empty.") + if not np.isfinite(self.threshold): + raise ValueError( + f"GhiUpperBoundCleaner.threshold must be finite, got {self.threshold}." + ) + + @property + def kind(self) -> CleanerKind: + return CleanerKind.GHI_UPPER_BOUND + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.column,) + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + return df[df[self.column] <= self.threshold].reset_index(drop=True) + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "column": self.column, + "threshold": self.threshold, + } + + @classmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "GhiUpperBoundCleaner": + del providers + return cls( + column=d.get("column", schema.GROUND_GHI), + threshold=float(d.get("threshold", 12.0)), + ) + + +@dataclass(frozen=True) +class IqrLowerBoundCleaner(DataCleaner): + """Drop rows whose target column is below ``Q1 − multiplier × IQR``. + + Standard Tukey-fence outlier rule, applied **only on the lower side** + (an upper-side fence here would clip legitimate clear-sky days; for + the upper bound use :class:`GhiUpperBoundCleaner`). The threshold is + computed from the input frame at apply time — no fitted state, but + the bound depends on the data. + + Multiplier 1.5 is the classic Tukey value the paper uses. + """ + + column: str = schema.GROUND_GHI + multiplier: float = 1.5 + + def __post_init__(self) -> None: + if not self.column: + raise ValueError("IqrLowerBoundCleaner.column must be non-empty.") + if self.multiplier <= 0: + raise ValueError( + f"IqrLowerBoundCleaner.multiplier must be positive, " + f"got {self.multiplier}." + ) + + @property + def kind(self) -> CleanerKind: + return CleanerKind.IQR_LOWER_BOUND + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.column,) + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + if df.empty: + return df + col = df[self.column].dropna() + if col.empty: + return df + q1 = float(col.quantile(0.25)) + q3 = float(col.quantile(0.75)) + iqr = q3 - q1 + lower_bound = q1 - self.multiplier * iqr + return df[df[self.column] >= lower_bound].reset_index(drop=True) + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "column": self.column, + "multiplier": self.multiplier, + } + + @classmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "IqrLowerBoundCleaner": + del providers + return cls( + column=d.get("column", schema.GROUND_GHI), + multiplier=float(d.get("multiplier", 1.5)), + ) + + +@dataclass(frozen=True) +class HighMissingYearExcluder(DataCleaner): + """Drop every (station, year) group whose missing-day fraction exceeds + ``missing_fraction_threshold``. + + "Missing fraction" is computed against the calendar-year span the + station has any data for: ``1 − observed_days / span_days``, where + ``span_days`` is the number of days from the group's first to last + record inclusive. This avoids penalising stations that legitimately + operated for only part of a year. + + Pair with :class:`KnnYearGapImputer` downstream: this cleaner removes + irrecoverably-sparse (station, year) groups, the imputer fills the + remaining smaller gaps. + """ + + station_column: str = schema.LOCATION + date_column: str = schema.DATE + missing_fraction_threshold: float = 0.05 + + def __post_init__(self) -> None: + if not self.station_column: + raise ValueError( + "HighMissingYearExcluder.station_column must be non-empty." + ) + if not self.date_column: + raise ValueError("HighMissingYearExcluder.date_column must be non-empty.") + if not (0.0 <= self.missing_fraction_threshold <= 1.0): + raise ValueError( + f"HighMissingYearExcluder.missing_fraction_threshold must be " + f"in [0, 1], got {self.missing_fraction_threshold}." + ) + + @property + def kind(self) -> CleanerKind: + return CleanerKind.HIGH_MISSING_YEAR + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.station_column, self.date_column) + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + if df.empty: + return df + dates = pd.to_datetime(df[self.date_column]) + years = dates.dt.year + groups = df.groupby([df[self.station_column], years]) + keep_mask = pd.Series(True, index=df.index) + for (station, year), idx in groups.groups.items(): + sub = dates.loc[idx] + span_days = (sub.max() - sub.min()).days + 1 + observed = len(sub) + missing_fraction = 1.0 - observed / span_days if span_days > 0 else 0.0 + if missing_fraction > self.missing_fraction_threshold: + keep_mask.loc[idx] = False + return df[keep_mask].reset_index(drop=True) + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "station_column": self.station_column, + "date_column": self.date_column, + "missing_fraction_threshold": self.missing_fraction_threshold, + } + + @classmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "HighMissingYearExcluder": + del providers + return cls( + station_column=d.get("station_column", schema.LOCATION), + date_column=d.get("date_column", schema.DATE), + missing_fraction_threshold=float(d.get("missing_fraction_threshold", 0.05)), + ) + + +@dataclass(frozen=True) +class KnnYearGapImputer(DataCleaner): + """Impute missing daily values inside each (station, year) group via temporal kNN. + + For every (station, year) where the group's date range has gaps, + inserts new rows at the missing dates and fills ``target_column`` + with the mean of the ``k`` temporally-nearest existing values within + that same (station, year) group. Other columns on the inserted rows + are forward/back-filled from the same group so id columns + (``location``, ``geohash5``, lat/lon) propagate. + + Assumes :class:`HighMissingYearExcluder` has already run upstream so + every surviving (station, year) is sparse enough to impute reliably. + Won't run on a group with fewer than ``k`` observed days (returns it + unchanged so the caller can spot the under-served case). + """ + + target_column: str = schema.GROUND_GHI + station_column: str = schema.LOCATION + date_column: str = schema.DATE + k: int = 5 + + def __post_init__(self) -> None: + if not self.target_column: + raise ValueError("KnnYearGapImputer.target_column must be non-empty.") + if not self.station_column: + raise ValueError("KnnYearGapImputer.station_column must be non-empty.") + if not self.date_column: + raise ValueError("KnnYearGapImputer.date_column must be non-empty.") + if self.k < 1: + raise ValueError(f"KnnYearGapImputer.k must be >= 1, got {self.k}.") + + @property + def kind(self) -> CleanerKind: + return CleanerKind.KNN_YEAR_GAP_IMPUTE + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.target_column, self.station_column, self.date_column) + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + if df.empty: + return df + dates = pd.to_datetime(df[self.date_column]) + years = dates.dt.year + out_chunks: list[pd.DataFrame] = [] + for (station, year), idx in df.groupby( + [df[self.station_column], years] + ).groups.items(): + group = df.loc[idx].copy() + group[self.date_column] = pd.to_datetime(group[self.date_column]) + imputed = self._impute_group(group) + out_chunks.append(imputed) + if not out_chunks: + return df + out = pd.concat(out_chunks, ignore_index=True) + # Restore the original date dtype if it was a date (not datetime). + if not pd.api.types.is_datetime64_any_dtype(df[self.date_column]): + out[self.date_column] = out[self.date_column].dt.date + return out + + def _impute_group(self, group: pd.DataFrame) -> pd.DataFrame: + """Fill (station, year) gaps via mean-of-k-nearest existing values.""" + if len(group) < self.k: + return group + group = group.sort_values(self.date_column).reset_index(drop=True) + start, end = group[self.date_column].min(), group[self.date_column].max() + full_range = pd.date_range(start, end, freq="D") + if len(full_range) == len(group): + return group # no gaps + present_dates = pd.to_datetime(group[self.date_column]).to_numpy() + present_values = group[self.target_column].to_numpy(dtype=float) + ordinal_present = present_dates.astype("datetime64[D]").astype(int) + ordinal_full = full_range.to_numpy().astype("datetime64[D]").astype(int) + missing_mask = ~np.isin(ordinal_full, ordinal_present) + if not missing_mask.any(): + return group + # For each missing date, average the k nearest present-date values. + missing_ordinals = ordinal_full[missing_mask] + deltas = np.abs(missing_ordinals[:, None] - ordinal_present[None, :]) + nearest_idx = np.argpartition(deltas, kth=self.k - 1, axis=1)[:, : self.k] + imputed_values = present_values[nearest_idx].mean(axis=1) + imputed_dates = full_range[missing_mask] + # Carry context columns over from the most recent present row. + context_row = group.iloc[-1].copy() + new_rows = pd.DataFrame( + np.tile(context_row.values, (len(imputed_dates), 1)), + columns=group.columns, + ) + new_rows[self.date_column] = imputed_dates + new_rows[self.target_column] = imputed_values + merged = pd.concat([group, new_rows], ignore_index=True) + merged = merged.sort_values(self.date_column).reset_index(drop=True) + return merged + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "target_column": self.target_column, + "station_column": self.station_column, + "date_column": self.date_column, + "k": self.k, + } + + @classmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "KnnYearGapImputer": + del providers + return cls( + target_column=d.get("target_column", schema.GROUND_GHI), + station_column=d.get("station_column", schema.LOCATION), + date_column=d.get("date_column", schema.DATE), + k=int(d.get("k", 5)), + ) + + +@dataclass(frozen=True) +class PerStationMeanImputer(DataCleaner): + """Fill NaN values in feature columns with that station's column mean. + + For each ``(station, column)`` pair, replace NaN entries with the mean + of the column's non-NaN values **at the same station**. Useful when a + sensor outage produces a short NaN gap at a station that otherwise + has good coverage — the per-station mean is the most defensible + constant-fill choice in that situation. + + When NOT to use this: + + * **All-NaN-at-some-station columns** (e.g. NASA POWER's land-only + ``evaporation_land`` over an oceanic station). The imputer has no + values to average and leaves the NaNs in place; the row will then + be dropped by ``dropna_features``. There is no honest imputation + for a column whose values do not exist by design — drop the + column instead. + * **Strong time trend within a station.** Mean-fill flattens the + annual cycle. For seasonal features, prefer per-(station, month) + means or kNN imputation in the time dimension. + + The imputer is opt-in per column via :attr:`columns`. Listing a + column whose station is fully NaN raises a warning but does not + fail the run. + + Args: + columns: Feature columns to impute. Passing an empty tuple is + an error — be explicit about which columns to touch. + station_column: Column carrying the station identifier. Default + ``"location"`` matches the rest of the pipeline. + """ + + columns: tuple[str, ...] = () + station_column: str = schema.LOCATION + + def __post_init__(self) -> None: + if not self.columns: + raise ValueError( + "PerStationMeanImputer.columns is empty. List the feature " + "columns you want to impute explicitly; an empty tuple is " + "almost certainly a misconfiguration." + ) + if not self.station_column: + raise ValueError("PerStationMeanImputer.station_column must be non-empty.") + + @property + def kind(self) -> CleanerKind: + return CleanerKind.PER_STATION_MEAN_IMPUTE + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.station_column,) + self.columns + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + if df.empty: + return df + out = df.copy() + for col in self.columns: + if col not in out.columns: + raise KeyError( + f"PerStationMeanImputer: column {col!r} is not present " + f"in the input DataFrame. Available columns: " + f"{sorted(out.columns)}." + ) + per_station_mean = out.groupby(self.station_column)[col].transform("mean") + out[col] = out[col].fillna(per_station_mean) + return out + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "columns": list(self.columns), + "station_column": self.station_column, + } + + @classmethod + def _from_dict( + cls, d: dict[str, Any], *, providers: dict[str, Any] + ) -> "PerStationMeanImputer": + del providers + return cls( + columns=tuple(d.get("columns", ())), + station_column=d.get("station_column", schema.LOCATION), + ) diff --git a/src/susse/preprocessing/derived.py b/src/susse/preprocessing/derived.py new file mode 100644 index 00000000..6a415765 --- /dev/null +++ b/src/susse/preprocessing/derived.py @@ -0,0 +1,531 @@ +"""Derived-feature abstraction. + +A :class:`DerivedFeature` is one named, computable model input. Concrete +subclasses know what they produce, what input columns they need, and how +to compute themselves from a DataFrame. The :class:`Preprocessor` +iterates over a tuple of these, in order — there is no per-feature +dispatch in :class:`Preprocessor`, no flags on :class:`FeatureSpec`. +Adding a new derived feature is one new subclass; consumers do not +change. + +Design points worth preserving across edits: + +* Each subclass is a frozen-dataclass-style value object, JSON + roundtrippable via :meth:`to_dict` / :meth:`_from_dict`. Fields that + are not JSON-serialisable (e.g. an :class:`ElevationProvider`) are + re-injected at deserialisation time via the ``providers`` kwarg. + The roundtrip contract is inherited from :class:`KindTaggedSpec`, + shared with :class:`DataCleaner`. +* Multi-column outputs are first-class: + :class:`CyclicalDayOfYearFeature` produces both ``doy_sin`` and + ``doy_cos`` from a single ``compute()``. +* Every output column carries human-facing metadata + (:class:`DerivedColumnMetadata`) via + :attr:`DerivedFeature.output_metadata`, so consumers that present + features to a human — e.g. the portal's variable inspector — read + the label, unit, and description off the feature rather than + hardcoding a parallel table that can drift. +* Validation lives on the feature, not on :class:`FeatureSpec`. The + spec only checks that the *combined* output column names don't + collide with each other or with pass-through ``feature_columns``. +* :class:`FeatureKind` is the persistence + dispatch tag, mirroring the + :class:`susse.models.ModelKind` pattern used by the model layer. +""" + +from __future__ import annotations + +from abc import abstractmethod +from dataclasses import dataclass +from enum import StrEnum +from typing import TYPE_CHECKING, Any, ClassVar, Optional + +import numpy as np +import pandas as pd + +from .. import schema +from ._kind_tagged import KindTaggedSpec, kind_dispatched_from_dict +from .derived_features import clear_sky_index, cyclical_day_of_year + +if TYPE_CHECKING: # pragma: no cover + from .elevation import ElevationProvider + + +@dataclass(frozen=True) +class DerivedColumnMetadata: + """Human-facing description of one column a :class:`DerivedFeature` emits. + + A derived feature is a computed model input with no entry in the + warehouse ``VariableCatalog``; this value object carries the label, + unit, and description a UI needs to present the column to a human + (the portal's variable inspector is the motivating consumer). + + One instance describes exactly one output column — multi-column + features such as :class:`CyclicalDayOfYearFeature` return one + :class:`DerivedColumnMetadata` per :attr:`DerivedFeature.output_columns` + entry. The :attr:`column` field is carried explicitly so consumers + match metadata to data by name rather than by tuple position. + + Attributes: + column: Name of the output column this metadata describes. + Equals one of the feature's :attr:`output_columns`. + label: Short human-readable name, e.g. ``"Clear-sky index"``. + unit: Physical unit, or ``"unitless"`` for dimensionless + quantities — matching the convention used by the warehouse + ``VariableCatalog``. + description: One- or two-sentence explanation of what the + column represents and how it is computed. + """ + + column: str + label: str + unit: str + description: str + + +class FeatureKind(StrEnum): + """Persistence + dispatch tag for :class:`DerivedFeature` subclasses. + + The string value ends up in ``feature_spec.json`` under the ``kind`` + key on each derived-feature entry. New subclasses register here and + in the lookup method; nothing else in the codebase needs to learn + about the new feature type. + """ + + CLEAR_SKY_INDEX = "clear_sky_index" + CYCLICAL_DOY = "cyclical_doy" + ALTITUDE = "altitude" + LONGITUDE = "longitude" + + def spec_class(self) -> type["DerivedFeature"]: + """Return the concrete :class:`DerivedFeature` subclass for this kind. + + Implements the protocol :func:`kind_dispatched_from_dict` + consumes; the same method name appears on :class:`CleanerKind`. + """ + # Local references avoid the import cycle: each subclass below + # references FeatureKind for its own ``kind`` property. + if self is FeatureKind.CLEAR_SKY_INDEX: + return ClearSkyIndexFeature + if self is FeatureKind.CYCLICAL_DOY: + return CyclicalDayOfYearFeature + if self is FeatureKind.ALTITUDE: + return AltitudeFeature + if self is FeatureKind.LONGITUDE: + return LongitudeFeature + raise AssertionError(f"Unhandled FeatureKind: {self!r}") # pragma: no cover + + +class DerivedFeature(KindTaggedSpec[FeatureKind]): + """ABC for one computed model input. + + Subclasses are frozen value objects (carry only configuration, no + fitted state). Provider-style runtime dependencies — e.g. a network + elevation lookup — are constructor-captured fields, not fitted + state, so the same subclass can be both serialised (provider + dropped) and re-hydrated at load time. + + Inherits the kind/required-input/to_dict/_from_dict contract from + :class:`KindTaggedSpec`; this ABC adds the feature-specific + ``output_columns`` and ``compute`` contracts. + """ + + @property + @abstractmethod + def output_columns(self) -> tuple[str, ...]: + """Column names this feature produces, in deterministic order.""" + + @property + @abstractmethod + def output_metadata(self) -> tuple[DerivedColumnMetadata, ...]: + """Human-facing metadata, one entry per :attr:`output_columns` column. + + The returned tuple covers exactly the columns in + :attr:`output_columns` — ``{m.column for m in output_metadata}`` + equals ``set(output_columns)``. Consumers that present features + to a human read labels, units, and descriptions from here + rather than maintaining a separate lookup table. + """ + + @abstractmethod + def compute(self, df: pd.DataFrame) -> pd.DataFrame: + """Compute this feature from ``df``; result index matches ``df.index``. + + Returns a DataFrame whose columns are exactly + :attr:`output_columns`. Raises if required state (e.g. an + injected provider) is missing. + """ + + +def derived_feature_from_dict( + d: dict[str, Any], + *, + providers: Optional[dict[str, Any]] = None, +) -> DerivedFeature: + """Inverse of :meth:`DerivedFeature.to_dict` — kind-dispatched. + + Thin wrapper around :func:`kind_dispatched_from_dict` that narrows + the return type to :class:`DerivedFeature` and supplies the + family-specific error-message hint. + + Args: + d: Output of :meth:`DerivedFeature.to_dict`. Must include + ``"kind"``. + providers: Map of provider-key → provider, for features that + need re-injection (e.g. ``{"altitude": PvlibElevationProvider()}``). + Pass-through for features that don't. + + Raises: + ValueError: If ``"kind"`` is missing or names an unknown kind. + Concrete ``_from_dict`` impls also raise if a required + provider is absent — see e.g. :meth:`AltitudeFeature._from_dict`. + """ + return kind_dispatched_from_dict( # type: ignore[no-any-return] + d, + kind_enum=FeatureKind, + providers=providers, + family_name="derived feature", + ) + + +# --------------------------------------------------------------------------- +# Concrete features +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class ClearSkyIndexFeature(DerivedFeature): + """Compute ``kt = ghi / ghi_clear`` for a single satellite-source pair. + + Replaces the previous ``ClearSkyIndexSpec``. Construction-time + validation is field-local (non-empty names); collision checks + against other features live on :class:`FeatureSpec`. + """ + + _LABEL: ClassVar[str] = "Clear-sky index" + _UNIT: ClassVar[str] = "unitless" + _DESCRIPTION: ClassVar[str] = ( + "Clear-sky index kt = GHI / clear-sky GHI. Near 1 under clear " + "skies and lower under cloud, it isolates cloud attenuation " + "from the clear-sky reference." + ) + + ghi_column: str + ghi_clear_column: str + output_column: str + + def __post_init__(self) -> None: + for name, value in ( + ("ghi_column", self.ghi_column), + ("ghi_clear_column", self.ghi_clear_column), + ("output_column", self.output_column), + ): + if not value: + raise ValueError(f"ClearSkyIndexFeature.{name} must be non-empty.") + + @property + def kind(self) -> FeatureKind: + return FeatureKind.CLEAR_SKY_INDEX + + @property + def output_columns(self) -> tuple[str, ...]: + return (self.output_column,) + + @property + def output_metadata(self) -> tuple[DerivedColumnMetadata, ...]: + return ( + DerivedColumnMetadata( + column=self.output_column, + label=self._LABEL, + unit=self._UNIT, + description=self._DESCRIPTION, + ), + ) + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (self.ghi_column, self.ghi_clear_column) + + def compute(self, df: pd.DataFrame) -> pd.DataFrame: + kt = clear_sky_index(df[self.ghi_column], df[self.ghi_clear_column]) + return kt.rename(self.output_column).to_frame() + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "ghi_column": self.ghi_column, + "ghi_clear_column": self.ghi_clear_column, + "output_column": self.output_column, + } + + @classmethod + def _from_dict( + cls, + d: dict[str, Any], + *, + providers: dict[str, Any], + ) -> "ClearSkyIndexFeature": + # `providers` is unused for this feature; signature matches the + # generic dispatcher. + del providers + return cls( + ghi_column=d["ghi_column"], + ghi_clear_column=d["ghi_clear_column"], + output_column=d["output_column"], + ) + + +@dataclass(frozen=True) +class CyclicalDayOfYearFeature(DerivedFeature): + """Sin/cos encoding of day-of-year, derived from a ``date`` column. + + Multi-column output: produces both ``doy_sin`` and ``doy_cos`` from + a single :meth:`compute` call. No fields — output column names are + fixed to keep cross-bundle comparisons honest. + """ + + _SIN_COLUMN: ClassVar[str] = "doy_sin" + _COS_COLUMN: ClassVar[str] = "doy_cos" + _SIN_LABEL: ClassVar[str] = "Day-of-year (sine)" + _COS_LABEL: ClassVar[str] = "Day-of-year (cosine)" + _UNIT: ClassVar[str] = "unitless" + _SIN_DESCRIPTION: ClassVar[str] = ( + "Sine component of the cyclical day-of-year encoding. Paired " + "with the cosine component it maps the calendar day onto the " + "unit circle, giving a smooth, wrap-around representation of " + "the annual cycle (31 Dec is adjacent to 1 Jan)." + ) + _COS_DESCRIPTION: ClassVar[str] = ( + "Cosine component of the cyclical day-of-year encoding — see " + "the sine component." + ) + + @property + def kind(self) -> FeatureKind: + return FeatureKind.CYCLICAL_DOY + + @property + def output_columns(self) -> tuple[str, ...]: + return (self._SIN_COLUMN, self._COS_COLUMN) + + @property + def output_metadata(self) -> tuple[DerivedColumnMetadata, ...]: + return ( + DerivedColumnMetadata( + column=self._SIN_COLUMN, + label=self._SIN_LABEL, + unit=self._UNIT, + description=self._SIN_DESCRIPTION, + ), + DerivedColumnMetadata( + column=self._COS_COLUMN, + label=self._COS_LABEL, + unit=self._UNIT, + description=self._COS_DESCRIPTION, + ), + ) + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (schema.DATE,) + + def compute(self, df: pd.DataFrame) -> pd.DataFrame: + return cyclical_day_of_year(df[schema.DATE]) + + def to_dict(self) -> dict[str, Any]: + return {"kind": self.kind.value} + + @classmethod + def _from_dict( + cls, + d: dict[str, Any], + *, + providers: dict[str, Any], + ) -> "CyclicalDayOfYearFeature": + del providers, d + return cls() + + +# Provider key in the ``providers`` dict for AltitudeFeature. +ALTITUDE_PROVIDER_KEY: str = "altitude" + + +@dataclass(frozen=True) +class AltitudeFeature(DerivedFeature): + """Look up altitude in metres for every row's ``(lat, lon)``. + + The :class:`~susse.preprocessing.ElevationProvider` is injected at + construction. The provider is dropped from :meth:`to_dict` (not JSON + -serialisable) and must be re-supplied at load time via + ``providers={"altitude": …}`` to :func:`derived_feature_from_dict` + or :func:`susse.training.load_bundle`. + """ + + _LABEL: ClassVar[str] = "Altitude" + _UNIT: ClassVar[str] = "m" + _DESCRIPTION: ClassVar[str] = ( + "Ground elevation above mean sea level, looked up once per " + "unique (lat, lon) by the injected elevation provider." + ) + + provider: "ElevationProvider" + output_column: str = "altitude_m" + + def __post_init__(self) -> None: + if self.provider is None: + raise ValueError( + "AltitudeFeature.provider must not be None. Construct " + "with `AltitudeFeature(provider=PvlibElevationProvider())` " + "(import from `susse.preprocessing`). For load-time " + 're-injection, pass providers={"altitude": ...} to ' + "load_bundle()." + ) + if not self.output_column: + raise ValueError("AltitudeFeature.output_column must be non-empty.") + + @property + def kind(self) -> FeatureKind: + return FeatureKind.ALTITUDE + + @property + def output_columns(self) -> tuple[str, ...]: + return (self.output_column,) + + @property + def output_metadata(self) -> tuple[DerivedColumnMetadata, ...]: + return ( + DerivedColumnMetadata( + column=self.output_column, + label=self._LABEL, + unit=self._UNIT, + description=self._DESCRIPTION, + ), + ) + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (schema.LAT, schema.LON) + + def compute(self, df: pd.DataFrame) -> pd.DataFrame: + # Dedupe on (lat, lon) so the provider is invoked once per + # unique station / grid point, not once per row. + unique = df[[schema.LAT, schema.LON]].drop_duplicates() + elev_map: dict[tuple[float, float], float] = { + (lat, lon): self.provider(lat, lon) + for lat, lon in zip(unique[schema.LAT], unique[schema.LON]) + } + values = np.array( + [elev_map[(lat, lon)] for lat, lon in zip(df[schema.LAT], df[schema.LON])], + dtype=float, + ) + return pd.DataFrame( + {self.output_column: values}, + index=df.index, + ) + + def to_dict(self) -> dict[str, Any]: + # Provider deliberately excluded — re-injected at load time. + return { + "kind": self.kind.value, + "output_column": self.output_column, + } + + @classmethod + def _from_dict( + cls, + d: dict[str, Any], + *, + providers: dict[str, Any], + ) -> "AltitudeFeature": + if ALTITUDE_PROVIDER_KEY not in providers: + raise ValueError( + f"AltitudeFeature requires " + f"providers[{ALTITUDE_PROVIDER_KEY!r}] at load time. Pass " + f'`load_bundle(dir, providers={{"{ALTITUDE_PROVIDER_KEY}": ' + f"PvlibElevationProvider()}})` (import from `susse.preprocessing`)." + ) + return cls( + provider=providers[ALTITUDE_PROVIDER_KEY], + output_column=d.get("output_column", "altitude_m"), + ) + + +@dataclass(frozen=True) +class LongitudeFeature(DerivedFeature): + """Pass-through of the input frame's ``lon`` column as a model feature. + + KNOWN ANTI-PATTERN — included only for reproducing published baselines + that used longitude as a predictor. New training pipelines should NOT + use it. + + Why this is bad: with ~28 ground-truth stations spread across SSA, a + Random Forest can memorise coordinate-to-target associations, which + destroys generalisation to held-out stations and to off-station grid + points. Geographical signal should enter the model via continuous + physical properties (altitude, distance-to-coast, terrain ruggedness, + …), not raw coordinates. + + Why it nonetheless exists: Mukiibi & Mikelson (2026) used longitude as + a predictor (their Table II). Faithful recomputation of those headline + metrics requires the same predictor set. The recomputation notebook + under ``notebooks/papers/mukiibi_mikelson_2026/`` is the only + legitimate consumer; do not import this class from new code. + """ + + _LABEL: ClassVar[str] = "Longitude" + _UNIT: ClassVar[str] = "degrees" + _DESCRIPTION: ClassVar[str] = ( + "Raw longitude of the location, passed through unchanged as a " + "predictor. Paper-faithful feature for the Mukiibi & Mikelson " + "(2026) re-run only — a known anti-pattern (see class docstring)." + ) + + output_column: str = "longitude" + + def __post_init__(self) -> None: + if not self.output_column: + raise ValueError("LongitudeFeature.output_column must be non-empty.") + + @property + def kind(self) -> FeatureKind: + return FeatureKind.LONGITUDE + + @property + def output_columns(self) -> tuple[str, ...]: + return (self.output_column,) + + @property + def output_metadata(self) -> tuple[DerivedColumnMetadata, ...]: + return ( + DerivedColumnMetadata( + column=self.output_column, + label=self._LABEL, + unit=self._UNIT, + description=self._DESCRIPTION, + ), + ) + + @property + def required_input_columns(self) -> tuple[str, ...]: + return (schema.LON,) + + def compute(self, df: pd.DataFrame) -> pd.DataFrame: + return pd.DataFrame( + {self.output_column: df[schema.LON].astype(float).to_numpy()}, + index=df.index, + ) + + def to_dict(self) -> dict[str, Any]: + return { + "kind": self.kind.value, + "output_column": self.output_column, + } + + @classmethod + def _from_dict( + cls, + d: dict[str, Any], + *, + providers: dict[str, Any], + ) -> "LongitudeFeature": + del providers + return cls(output_column=d.get("output_column", "longitude")) diff --git a/src/susse/preprocessing/derived_features.py b/src/susse/preprocessing/derived_features.py new file mode 100644 index 00000000..d2d90cbd --- /dev/null +++ b/src/susse/preprocessing/derived_features.py @@ -0,0 +1,94 @@ +"""Pure helper functions for derived features. + +Stateless, deterministic transformations of input columns into model +features. Each function takes one or more pandas series and returns +either a series (single new feature) or a DataFrame (related new +features). No fitting, no internal state — the same inputs always +produce the same outputs. + +Keeping these as free functions (rather than methods on the +:class:`Preprocessor`) makes them straightforward to unit-test against +analytical references and lets notebooks call them directly when +exploring features outside the formal pipeline. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd + +# Below this denominator, kt is considered ill-defined and we emit NaN +# rather than dividing through. The night-time / nadir-zenith case is +# the obvious source — any clear-sky-derived denominator near zero is +# numerically unstable and physically meaningless for an ML feature. +_KT_DENOMINATOR_FLOOR: float = 1e-6 + + +def clear_sky_index( + ghi: pd.Series, + ghi_clear: pd.Series, + *, + denominator_floor: float = _KT_DENOMINATOR_FLOOR, +) -> pd.Series: + """Return ``kt = GHI / GHI_clear``, with NaN for ill-defined denominators. + + ``kt`` is the canonical "how cloudy is it" feature: 1.0 means the + sky is at clear-sky reference intensity, < 1 means attenuation, + occasionally > 1 from cloud-edge enhancement. It collapses two + quantities into one normalised signal that's much easier for a + learner than raw GHI alone (which mixes diurnal / seasonal cycles + with cloud-state information). + + Args: + ghi: Series of GHI values (any consistent unit; common is + kWh/m²/day for the daily warehouse). + ghi_clear: Series of clear-sky reference values, same unit. + denominator_floor: Values of ``ghi_clear`` below this are + treated as ill-defined (rather than producing huge spikes + from division). Default is :data:`_KT_DENOMINATOR_FLOOR`. + + Returns: + Series of the same index as the inputs. + + Raises: + ValueError: If ``ghi`` and ``ghi_clear`` have mismatched indices. + """ + if not ghi.index.equals(ghi_clear.index): + raise ValueError( + "clear_sky_index expects ghi and ghi_clear to share an index. " + "Pass aligned series — re-index with .reindex(...) at the call " + "site if needed." + ) + safe_denom = ghi_clear.where(ghi_clear.abs() >= denominator_floor) + return (ghi / safe_denom).rename("kt") + + +def cyclical_day_of_year(dates: pd.Series) -> pd.DataFrame: + """Sin/cos encoding of day-of-year for a yearly cyclical feature. + + Returns two columns ``doy_sin``, ``doy_cos`` so that adjacent + calendar dates (e.g. Dec 31 and Jan 1) sit close in feature space + rather than at opposite ends of a 1..365 ramp. Standard treatment + for any periodic time-of-year feature; the model can recover the + raw day-of-year via ``atan2(sin, cos)`` if needed. + + Args: + dates: Series of ``datetime`` / ``date`` values. + + Returns: + DataFrame with two columns sharing the input index. + + Raises: + ValueError: If ``dates`` is empty. + """ + if len(dates) == 0: + raise ValueError("cyclical_day_of_year received an empty Series.") + doy = pd.to_datetime(dates).dt.dayofyear.astype(float) + # Use 366 (max possible day count) rather than 365 so leap-year days + # don't fall outside [0, 1) and the encoding stays well-defined for + # every calendar date. + angle = 2.0 * np.pi * (doy - 1.0) / 366.0 + return pd.DataFrame( + {"doy_sin": np.sin(angle), "doy_cos": np.cos(angle)}, + index=dates.index, + ) diff --git a/src/susse/preprocessing/elevation.py b/src/susse/preprocessing/elevation.py new file mode 100644 index 00000000..bd8b1f8d --- /dev/null +++ b/src/susse/preprocessing/elevation.py @@ -0,0 +1,78 @@ +"""Elevation lookup — on-demand altitude for any (lat, lon). + +Altitude is the only geographical feature the model consumes (deliberately; +raw lat/lon would let the model memorise station identities given the +small ~28-station training corpus). It is *not* stored in the BigQuery +warehouse: lookups are fast and static, so caching them in BQ adds no +value. Instead, an :class:`ElevationProvider` is captured by an +:class:`~susse.preprocessing.AltitudeFeature` instance and invoked at +preprocessing time. + +The Protocol stays minimal so swapping backends (Open-Elevation API vs +local SRTM GeoTIFF vs an in-memory test stub) is a one-class change. The +v1 production backend is :class:`PvlibElevationProvider`, which delegates +to ``pvlib.location.lookup_altitude`` (Open-Elevation under the hood). +""" + +from __future__ import annotations + +from typing import Protocol + + +class ElevationProvider(Protocol): + """Callable returning elevation in metres for a single (lat, lon). + + Implementations decide their own caching / batching strategy. The + :class:`~susse.preprocessing.Preprocessor` dedupes ``(lat, lon)`` + pairs before invoking the provider, so a naive implementation that + fetches one point per call is acceptable; a caching implementation + just makes repeated calls cheap. + """ + + def __call__(self, lat: float, lon: float) -> float: + """Return elevation in metres above mean sea level.""" + ... + + +# Cache keys round to this many decimal places (~10 m at the equator). +# Two coordinates within rounding distance return the same cached value; +# anything finer is below the resolution of any free elevation source. +_CACHE_DECIMALS: int = 4 + + +class PvlibElevationProvider: + """Production :class:`ElevationProvider` delegating to pvlib. + + Uses ``pvlib.location.lookup_altitude``, which calls Open-Elevation + over HTTPS. Two consequences worth noting at the call site: + + * **Network dependency at inference time.** Portal deployments will + hit ``api.open-elevation.com`` once per novel ``(lat, lon)``. A + future iteration may swap the backend for a local SRTM GeoTIFF + while keeping the :class:`ElevationProvider` Protocol unchanged. + * **Failures raise.** No silent fallback to zero / NaN — a + mis-configured network propagates as a clear exception rather + than a feature column quietly filled with bad values. + + Cache is in-memory and per-instance: keep one provider alive for + the duration of a training or inference run. + """ + + def __init__(self) -> None: + self._cache: dict[tuple[float, float], float] = {} + + def __call__(self, lat: float, lon: float) -> float: + key = (round(lat, _CACHE_DECIMALS), round(lon, _CACHE_DECIMALS)) + if key in self._cache: + return self._cache[key] + # Local import — only paid by users that actually invoke a lookup. + from pvlib.location import lookup_altitude # noqa: PLC0415 + + elevation = float(lookup_altitude(latitude=lat, longitude=lon)) + self._cache[key] = elevation + return elevation + + @property + def cache_size(self) -> int: + """Number of unique (lat, lon) pairs currently cached.""" + return len(self._cache) diff --git a/src/susse/preprocessing/feature_spec.py b/src/susse/preprocessing/feature_spec.py new file mode 100644 index 00000000..b953ec9d --- /dev/null +++ b/src/susse/preprocessing/feature_spec.py @@ -0,0 +1,179 @@ +"""Typed config describing the preprocessing pipeline. + +A :class:`FeatureSpec` is to the :class:`Preprocessor` what +:class:`susse.datasets.FeatureSelection` is to :class:`FeatureService`: +the recipe — which transforms to apply, which columns to project, what +the target is. Persisted as JSON alongside any model so inference +applies identical transformations. + +Two ways to add a feature: + +* **Pass-through** — name the column in :attr:`feature_columns`. The + column must already exist on the input DataFrame. +* **Derived** — append a :class:`~susse.preprocessing.DerivedFeature` + instance to :attr:`derived_features`. The feature knows what columns + it needs as input, what columns it produces, and how to compute + itself. Adding a new derived-feature type is one new ``DerivedFeature`` + subclass; this dataclass and the :class:`Preprocessor` do not change. + +Row-level data cleaning (filter, impute) is configured through +:attr:`cleaners`, applied **before** any derived-feature computation. +See :class:`~susse.preprocessing.DataCleaner`. + +Validation against a concrete :class:`~susse.datasets.TrainingDataset` +happens at apply time (in :class:`Preprocessor`), not at construction. +That way the same spec can be reused across multiple compatible +datasets without enumerating their column shapes here. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from typing import Any, Optional + +from .. import schema +from .cleaning import DataCleaner, data_cleaner_from_dict +from .derived import DerivedFeature, derived_feature_from_dict + + +@dataclass(frozen=True) +class FeatureSpec: + """Recipe for the preprocessing pipeline. + + Attributes: + target_column: Name of the target column in the input + DataFrame. Default ``"y_ghi_kwh_m2_day"`` matches the output + of :meth:`FeatureService.build_training_pairs`. + feature_columns: Pass-through columns from the input dataset to + keep verbatim as model inputs. Validated to exist in the + input at apply time. + cleaners: Tuple of :class:`~susse.preprocessing.DataCleaner` + instances applied to the input frame *before* feature + computation. Each may filter rows or impute values; column + schema is preserved. Default ``()`` is the historical + behaviour (no row-level cleaning). + derived_features: Tuple of + :class:`~susse.preprocessing.DerivedFeature` instances. + Each declares its own output columns, required input + columns, and how to compute itself; the preprocessor + iterates this tuple in order and emits the columns + unchanged. Concrete subclasses today: + :class:`~susse.preprocessing.ClearSkyIndexFeature`, + :class:`~susse.preprocessing.CyclicalDayOfYearFeature`, + :class:`~susse.preprocessing.AltitudeFeature`, + :class:`~susse.preprocessing.LongitudeFeature`. + id_columns: Non-feature, non-target columns to keep in the + output DataFrame for traceability (e.g. ``date``, + ``location``, ``geohash5``). The model never sees these; + they stay so downstream tools can join on them. + dropna_target: If True (default), rows with NaN target are + dropped. Set False only for inference (no target). + dropna_features: If True (default), rows with any NaN feature + (after derivation) are dropped. The simple-and-safe default + for v1 — imputation can be added later if the data argues + for it. + """ + + target_column: str = schema.TRAINING_TARGET + feature_columns: tuple[str, ...] = () + cleaners: tuple[DataCleaner, ...] = () + derived_features: tuple[DerivedFeature, ...] = () + id_columns: tuple[str, ...] = schema.ID_COLUMNS + dropna_target: bool = True + dropna_features: bool = True + + def __post_init__(self) -> None: + if not self.target_column: + raise ValueError("target_column must be non-empty.") + derived_outputs: list[str] = [] + for f in self.derived_features: + derived_outputs.extend(f.output_columns) + # No duplicate output names across all derived features. + if len(set(derived_outputs)) != len(derived_outputs): + seen: set[str] = set() + dups = [c for c in derived_outputs if c in seen or seen.add(c)] # type: ignore[func-returns-value] + raise ValueError( + f"derived_features produce duplicate output column " + f"names: {sorted(set(dups))}. Each derived feature must " + f"emit unique columns." + ) + # No collision between derived outputs and pass-through features. + feature_set = set(self.feature_columns) + collisions = feature_set & set(derived_outputs) + if collisions: + raise ValueError( + f"derived_features outputs collide with feature_columns: " + f"{sorted(collisions)}. Either rename the derived output " + f"or remove the duplicate from feature_columns." + ) + + @property + def output_feature_names(self) -> tuple[str, ...]: + """Ordered list of model-input columns the preprocessor will produce. + + Order: pass-through ``feature_columns`` first, then the + ``output_columns`` of each :class:`DerivedFeature` in the + order they appear. This is the column order downstream models + should expect. + """ + names: list[str] = list(self.feature_columns) + for f in self.derived_features: + names.extend(f.output_columns) + return tuple(names) + + def to_dict(self) -> dict[str, Any]: + return { + "target_column": self.target_column, + "feature_columns": list(self.feature_columns), + "cleaners": [c.to_dict() for c in self.cleaners], + "derived_features": [f.to_dict() for f in self.derived_features], + "id_columns": list(self.id_columns), + "dropna_target": self.dropna_target, + "dropna_features": self.dropna_features, + } + + def to_json(self) -> str: + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + + @classmethod + def from_dict( + cls, + d: dict[str, Any], + *, + providers: Optional[dict[str, Any]] = None, + ) -> "FeatureSpec": + """Reconstruct from a :meth:`to_dict` payload. + + Args: + d: Output of :meth:`to_dict`. + providers: Map of provider-key → provider for any + :class:`DerivedFeature` that needs runtime injection + at deserialisation time (e.g. + ``{"altitude": PvlibElevationProvider()}``). Pass-through + for features that don't need providers. + """ + return cls( + target_column=d.get("target_column", "y_ghi_kwh_m2_day"), + feature_columns=tuple(d.get("feature_columns", ())), + cleaners=tuple( + data_cleaner_from_dict(c, providers=providers) + for c in d.get("cleaners", ()) + ), + derived_features=tuple( + derived_feature_from_dict(f, providers=providers) + for f in d.get("derived_features", ()) + ), + id_columns=tuple(d.get("id_columns", ("date", "location", "geohash5"))), + dropna_target=d.get("dropna_target", True), + dropna_features=d.get("dropna_features", True), + ) + + @classmethod + def from_json( + cls, + s: str, + *, + providers: Optional[dict[str, Any]] = None, + ) -> "FeatureSpec": + return cls.from_dict(json.loads(s), providers=providers) diff --git a/src/susse/preprocessing/preprocessor.py b/src/susse/preprocessing/preprocessor.py new file mode 100644 index 00000000..89792272 --- /dev/null +++ b/src/susse/preprocessing/preprocessor.py @@ -0,0 +1,283 @@ +"""Stateless preprocessor: training datasets or inference frames → features. + +The :class:`Preprocessor` applies a :class:`FeatureSpec` to either a +:class:`susse.datasets.TrainingDataset` (yielding a +:class:`PreprocessedDataset` with the source manifest carried through) +or a bare :class:`pandas.DataFrame` (yielding a transformed +:class:`pandas.DataFrame` for inference). All transformations are +deterministic given the inputs — no fit/transform asymmetry, no +internal state to worry about leaking across folds. + +The transform pipeline is **single-loop over +``spec.derived_features``**: each :class:`DerivedFeature` declares +its own output columns and computes them. The preprocessor itself +knows nothing about kt vs cyclical-doy vs altitude — adding a new +feature type is a new ``DerivedFeature`` subclass, with no edits here. + +Scaling deliberately lives in the model wrapper, not here: tree-based +models (RF / XGBoost) don't need it; neural-network models do, and only +they know which columns to scale + on which fold to fit. Keeping the +preprocessor stateless means the same instance can transform train, +val, and test data with no risk of cross-fold leakage from a fitted +scaler. + +Training vs inference shape (the only difference between the two entry +points): + +* :meth:`apply` runs cleaners and applies the spec's NaN-drop policies. + It expects a :class:`TrainingDataset` so the source manifest can + travel into the resulting :class:`PreprocessedDataset` for downstream + artifact provenance. +* :meth:`apply_to_dataframe` skips cleaners and NaN-drop. Inference is + "compute the features as defined; touch nothing else" — silently + dropping inference rows because some aux column is NaN would surface + as missing predictions and would be far worse than NaN predictions + the caller can see. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from datetime import datetime, timezone + +import pandas as pd + +from ..datasets import DatasetManifest, TrainingDataset +from .feature_spec import FeatureSpec + + +@dataclass(frozen=True) +class PreprocessedDataset: + """Result of applying a :class:`FeatureSpec` to a :class:`TrainingDataset`. + + Attributes: + df: Materialised DataFrame with id columns + derived features + + target. Column order: ``id_columns`` first, then + ``output_feature_names``, then ``target_column``. + feature_columns: Ordered tuple of model-input column names. The + consuming model uses ``df[list(feature_columns)]`` to slice + X; this avoids string-keyed indexing or positional drift. + target_column: Name of the target column in ``df``. + feature_spec: The :class:`FeatureSpec` that produced ``df``. + Persisted so inference can recreate identical + transformations. + source_manifest: The full :class:`DatasetManifest` of the source + :class:`TrainingDataset`. Carrying it verbatim (rather than + just name + version + hash) lets downstream artifacts + (e.g. trained-model bundles in NB 05) be fully self- + describing without a separate dataset-resolution step. + created_at_utc: ISO 8601 timestamp of preprocessing. + """ + + df: pd.DataFrame + feature_columns: tuple[str, ...] + target_column: str + feature_spec: FeatureSpec + source_manifest: DatasetManifest + created_at_utc: str = field( + default_factory=lambda: datetime.now(timezone.utc).isoformat() + ) + + @property + def n_rows(self) -> int: + return len(self.df) + + @property + def n_features(self) -> int: + return len(self.feature_columns) + + @property + def source_dataset_name(self) -> str: + return self.source_manifest.name + + @property + def source_dataset_version(self) -> str: + return self.source_manifest.version + + @property + def source_content_hash(self) -> str: + return self.source_manifest.content_hash + + def X(self) -> pd.DataFrame: + """Model-input slice: ``df[feature_columns]`` in the spec's order.""" + return self.df[list(self.feature_columns)] + + def y(self) -> pd.Series: + """Target slice.""" + return self.df[self.target_column] + + +class Preprocessor: + """Apply a :class:`FeatureSpec` to training datasets or inference frames. + + Construct once with a spec; call :meth:`apply` per training dataset + or :meth:`apply_to_dataframe` per inference frame. Stateless between + calls — the same instance can transform train, val, and test folds + without fit-time data leakage (because there is no fit time). + + Provider-style runtime dependencies (e.g. an elevation lookup + service) are captured by individual :class:`DerivedFeature` + instances at *their* construction. The preprocessor knows nothing + about provider wiring; that's a concern of the spec's authors. + """ + + def __init__(self, spec: FeatureSpec) -> None: + self._spec = spec + + @property + def spec(self) -> FeatureSpec: + return self._spec + + # ------------------------------------------------------------------ + # Public entry points + # ------------------------------------------------------------------ + + def apply(self, dataset: TrainingDataset) -> PreprocessedDataset: + """Transform a :class:`TrainingDataset` into a :class:`PreprocessedDataset`. + + Runs cleaners and applies the spec's NaN-drop policies — the + training-shape transform. The source manifest is carried through + for downstream artifact provenance. + """ + out = self._apply_core(dataset.df, training_shape=True) + return PreprocessedDataset( + df=out, + feature_columns=self._spec.output_feature_names, + target_column=self._spec.target_column, + feature_spec=self._spec, + source_manifest=dataset.manifest, + ) + + def apply_to_dataframe(self, df: pd.DataFrame) -> pd.DataFrame: + """Transform a bare DataFrame — the inference-shape transform. + + Skips cleaners and NaN-drop policies. The target column is only + copied through if present; inference frames typically have no + ground truth, in which case the output simply has no target + column. + + Returns: + DataFrame with id columns + features + (optionally) target. + Column order matches :meth:`apply`: ``id_columns`` first, + then ``feature_columns`` in spec order, then the target + column if present. + """ + return self._apply_core(df, training_shape=False) + + # ------------------------------------------------------------------ + # Internal + # ------------------------------------------------------------------ + + def _apply_core(self, df: pd.DataFrame, *, training_shape: bool) -> pd.DataFrame: + """Single transform implementation behind both public entry points. + + Args: + df: Input frame. + training_shape: If True, run cleaners and NaN-drop policies. + If False (inference), skip them — the rationale is in + the module docstring. + """ + spec = self._spec + self._validate_columns(df, training_shape=training_shape) + + # Cleaners — training-shape only. Each cleaner gets the *current* + # frame, so a cleaner downstream of another one sees the previous + # one's filtered output. + if training_shape: + for cleaner in spec.cleaners: + df = cleaner.apply(df) + + out = pd.DataFrame(index=df.index) + + # Pass-through id columns (kept for traceability, never given to model). + for col in spec.id_columns: + if col in df.columns: + out[col] = df[col] + # Missing id columns are non-fatal — not all datasets carry + # every id (e.g. inference frames have no `location`). Skip. + + # Pass-through model features. + for col in spec.feature_columns: + out[col] = df[col] + + # Derived features — each computes its own output columns. + for derived in spec.derived_features: + result = derived.compute(df) + for col in result.columns: + out[col] = result[col] + + # Target — copied through if present; inference frames typically + # have no ground truth, in which case this is a no-op. + if spec.target_column in df.columns: + out[spec.target_column] = df[spec.target_column] + + # NaN-drop policy — training-shape only. + if training_shape: + if spec.dropna_target and spec.target_column in out.columns: + out = out.dropna(subset=[spec.target_column]) + if spec.dropna_features: + feature_cols_present = [ + c for c in spec.output_feature_names if c in out.columns + ] + if feature_cols_present: + out = out.dropna(subset=feature_cols_present) + + return out.reset_index(drop=True) + + def _validate_columns(self, df: pd.DataFrame, *, training_shape: bool) -> None: + """Raise a clear error if the dataset is missing required columns. + + Validation is up-front so a misconfigured spec fails before any + transforms run, with a message naming the missing column and + which part of the :class:`FeatureSpec` referenced it. + + In inference shape, cleaner input columns and the + ``dropna_target`` target requirement are skipped — those are + training-shape concerns. + """ + spec = self._spec + missing: list[tuple[str, str]] = [] # (column, source_field) + for col in spec.feature_columns: + if col not in df.columns: + missing.append((col, "feature_columns")) + if training_shape: + for cleaner in spec.cleaners: + for col in cleaner.required_input_columns: + if col not in df.columns: + missing.append( + ( + col, + f"cleaners[{cleaner.kind.value}]" + f".required_input_columns", + ), + ) + for derived in spec.derived_features: + for col in derived.required_input_columns: + if col not in df.columns: + missing.append( + ( + col, + f"derived_features[{derived.kind.value}]" + f".required_input_columns", + ), + ) + # The target may legitimately be missing for inference frames; a + # missing target is only an error in training shape if + # dropna_target is True (which forces the column to exist). + if ( + training_shape + and spec.dropna_target + and spec.target_column not in df.columns + ): + missing.append( + (spec.target_column, "target_column (with dropna_target=True)") + ) + if missing: + details = "; ".join( + f"{col!r} (referenced by {field})" for col, field in missing + ) + raise ValueError( + f"Preprocessor: input DataFrame is missing required " + f"column(s): {details}. Either rebuild the upstream " + f"input frame to include them, or amend the FeatureSpec." + ) diff --git a/src/susse/provenance.py b/src/susse/provenance.py new file mode 100644 index 00000000..cc92d09f --- /dev/null +++ b/src/susse/provenance.py @@ -0,0 +1,53 @@ +"""Build-time provenance helpers shared across persistence layers. + +Both :mod:`susse.datasets.snapshot_io` (for :class:`DatasetManifest`) +and :mod:`susse.training.metadata` (for :class:`TrainingMetadata`) +record the same two things — git SHA and the running ``susse`` package +version — when they materialise an artifact. Keeping the +implementations in one neutral module prevents the two layers from +drifting (e.g. one returning ``None`` from a missing git context and +the other raising). +""" + +from __future__ import annotations + +import subprocess +from importlib.metadata import PackageNotFoundError, version + + +def git_sha() -> str | None: + """Best-effort ``git rev-parse HEAD``. + + Returns the 40-char hex SHA of ``HEAD`` when invoked inside a git + repository with a working ``git`` binary; ``None`` if either is + missing, the command times out, or the working tree has no commits. + Never raises — provenance is best-effort and should not fail an + artifact build. + """ + try: + result = subprocess.run( + ["git", "rev-parse", "HEAD"], + capture_output=True, + text=True, + timeout=5, + check=False, + ) + except (FileNotFoundError, subprocess.TimeoutExpired): + return None + if result.returncode != 0: + return None + return result.stdout.strip() or None + + +def susse_version() -> str: + """Return the installed ``susse`` package version, or ``"unknown"``. + + The package is registered as ``SuSSE`` in ``pyproject.toml``; the + casing is preserved here. Returns ``"unknown"`` when the package + metadata can't be resolved (e.g. when running directly from a + source checkout without an editable install). + """ + try: + return version("SuSSE") + except PackageNotFoundError: + return "unknown" diff --git a/src/susse/schema.py b/src/susse/schema.py new file mode 100644 index 00000000..4e05e405 --- /dev/null +++ b/src/susse/schema.py @@ -0,0 +1,122 @@ +"""Column-name vocabulary for SuSSE DataFrames. + +Single source of truth for every column name the library treats as +semantically meaningful. Consumers — :class:`FeatureService`, +:class:`Preprocessor`, :class:`Trainer`, :class:`Predictor`, +:class:`DerivedFeature` subclasses, :class:`DataCleaner` subclasses +— import from here rather than carrying string literals. + +What lives here: + +* **Identity columns** (:data:`DATE`, :data:`LOCATION`, + :data:`GEOHASH5`, :data:`LAT`, :data:`LON`, :data:`QC_LEVEL`) — the + vocabulary every FeatureService output row carries. +* **Ground-truth and target column names** (:data:`GROUND_GHI`, + :data:`TRAINING_TARGET`, :data:`PREDICTION`). +* :data:`ID_COLUMNS` — convenience tuple bundling the standard + identity columns for :attr:`FeatureSpec.id_columns`. + +What does *not* live here: + +* **Satellite-irradiance column names** + (``sat___kwh_m2_day``) — parametric over + :class:`Source` × :class:`IrradianceBand`. Built by + :func:`susse.warehouse_ops.population.types.satellite_irradiance_column` + (kept next to the enums it depends on to avoid a cycle). +* **Auxiliary aux-column names** (``_``, + e.g. ``nasa_aod_550``) — parametric over the prefix in + ``_LONG_AUX_TABLES`` and the catalog ``variable_id``. Constructed at + pivot time inside the satellite repository. +* **Derived-feature output column names** — those are per-feature + user-chosen configuration (e.g. ``kt_nasa`` from a + :class:`ClearSkyIndexFeature` instance), not a library-wide vocabulary. + +Relationship to the warehouse-side vocabulary +--------------------------------------------- + +There are three places where column-name vocabulary lives in this +repository. They are parallel and *deliberately separate*: + +1. **This module** (:mod:`susse.schema`) — names for Python DataFrame + columns the library produces and consumes. +2. **BigQuery DDL** in ``warehouse/sql/00_schema/*.sql`` — the actual + column names in the warehouse tables. +3. **:class:`TableSchemas`** in + :mod:`susse.warehouse_ops.io.config` — Python-side + ``table_id`` + MERGE-key registry; merge keys reference BigQuery + column names. + +Several strings overlap across these layers (notably ``"date"`` and +``"geohash5"``). That is **not** duplication to deduplicate — it is the +"translate at boundaries" pattern from ``CLAUDE.md``: + +* Each layer is correct in its own domain (Python DataFrame contract, + BigQuery storage, or merge-key contract). +* The :class:`SatelliteRepository` and :class:`GroundRepository` SQL + queries are the *translators* — they SELECT warehouse columns into + the DataFrame names this module declares, aliasing where the two + diverge (e.g. ``latitude`` → ``lat`` for tables that store the long + form). +* Today the SQL projects align with this module's names by + *convention*, so most queries don't need aliases. If a future + warehouse rename diverges from a name here, the fix is to alias in + the repository query — **not** to couple :class:`TableSchemas` merge + keys to these constants. The two contracts are independent on + purpose. +""" + +from __future__ import annotations + +# --------------------------------------------------------------------------- +# Identity columns — present on every FeatureService output row. +# --------------------------------------------------------------------------- + +DATE: str = "date" +"""ISO date of the observation. Index column for most downstream joins.""" + +LOCATION: str = "location" +"""Free-text station name (e.g. ``"egypt_location1"``). Present on +training-pair rows; absent on inference-grid rows.""" + +GEOHASH5: str = "geohash5" +"""5-character geohash of the row's ``(lat, lon)``. Warehouse joins +key on this. Precision is controlled by +:attr:`WarehouseOptions.geohash_precision` (default 5).""" + +LAT: str = "lat" +"""Latitude in decimal degrees.""" + +LON: str = "lon" +"""Longitude in decimal degrees.""" + +QC_LEVEL: str = "qc_level" +"""Quality-control tag on a ground measurement (e.g. ``"pass"``, +``"fail_range"``). Kept through training-pair assembly for +traceability; not a model input.""" + +# --------------------------------------------------------------------------- +# Bundle for FeatureSpec.id_columns and callers that want the canonical set. +# --------------------------------------------------------------------------- + +ID_COLUMNS: tuple[str, ...] = (DATE, LOCATION, GEOHASH5) +"""The identity-column bundle :class:`FeatureSpec` carries through +preprocessing by default. Read-only — callers that want a custom +subset construct their own tuple.""" + +# --------------------------------------------------------------------------- +# Ground-truth + target + prediction column names. +# --------------------------------------------------------------------------- + +GROUND_GHI: str = "ghi_kwh_m2_day" +"""Ground-truth GHI column name as stored in the warehouse +``ground_measurements`` table. :meth:`FeatureService.build_training_pairs` +renames this to :data:`TRAINING_TARGET` on the way out.""" + +TRAINING_TARGET: str = "y_ghi_kwh_m2_day" +"""Post-rename target column on the training-pairs frame, the value +:class:`Preprocessor` treats as ``spec.target_column`` by default.""" + +PREDICTION: str = "y_pred_kwh_m2_day" +"""Model output column added to the long-format result by +:meth:`Predictor.predict`. Mirrors :data:`TRAINING_TARGET`'s naming +convention so plots and metrics can swap series cleanly.""" diff --git a/src/susse/training/__init__.py b/src/susse/training/__init__.py new file mode 100644 index 00000000..c7b68e57 --- /dev/null +++ b/src/susse/training/__init__.py @@ -0,0 +1,51 @@ +"""Training orchestration — Trainer, TrainedBundle, splitter hierarchy. + +Public entry points: + +* :class:`Trainer`, :class:`TrainerConfig` — the orchestrator that + takes a :class:`PreprocessedDataset` + :class:`BaseModelParams` + + :class:`Splitter` and returns a :class:`TrainedBundle`. +* :class:`TrainedBundle`, :func:`load_bundle` — fully self-describing + trained-model artifacts (model + feature spec + metadata + source + manifest). +* :class:`TrainingMetadata`, :class:`ScoreSet` — typed records embedded + in every bundle's ``metadata.json``. +* :class:`Splitter` and the five concrete subclasses + (:class:`RandomSplitter`, :class:`TemporalSplitter`, + :class:`StationLOSOSplitter`, + :class:`SpatialSpreadHoldoutSplitter`, + :class:`SpatialBlockSplitter`). +* :func:`score_predictions` — MAE / RMSE / R² helper used by + :class:`Trainer` and re-exported for ad-hoc scoring. + +W&B is opt-in: ``Trainer()`` constructed without a config performs +no W&B calls. See :class:`TrainerConfig` for the toggle. +""" + +from .bundle import TrainedBundle, load_bundle +from .metadata import ScoreSet, TrainingMetadata +from .splitter import ( + RandomSplitter, + SpatialBlockSplitter, + SpatialSpreadHoldoutSplitter, + Splitter, + StationLOSOSplitter, + TemporalSplitter, +) +from .trainer import Trainer, TrainerConfig, score_predictions + +__all__ = [ + "RandomSplitter", + "ScoreSet", + "SpatialBlockSplitter", + "SpatialSpreadHoldoutSplitter", + "Splitter", + "StationLOSOSplitter", + "TemporalSplitter", + "TrainedBundle", + "Trainer", + "TrainerConfig", + "TrainingMetadata", + "load_bundle", + "score_predictions", +] diff --git a/src/susse/training/bundle.py b/src/susse/training/bundle.py new file mode 100644 index 00000000..14e71249 --- /dev/null +++ b/src/susse/training/bundle.py @@ -0,0 +1,135 @@ +"""TrainedBundle — a fully self-describing trained-model artifact. + +A bundle is the *only* thing inference needs: it carries the fitted +model, the :class:`FeatureSpec` that produced its inputs, the training +metadata, and the source dataset's :class:`DatasetManifest` verbatim. +Loading a bundle reconstructs every transform identically; no separate +dataset-resolution or registry lookup is required. + +On-disk layout:: + + / + model/ # output of BaseRegressor.save() + model_kind.txt + params.json + state.joblib + feature_spec.json + metadata.json + source_manifest.json + +The layout is W&B-artifact-friendly: ``add_dir(bundle_dir)`` uploads +all four pieces in one artifact of type ``"trained_model"``. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Optional + +from ..datasets import DatasetManifest +from ..models import BaseRegressor, load_regressor +from ..preprocessing import FeatureSpec +from .metadata import TrainingMetadata + +_MODEL_SUBDIR = "model" +_FEATURE_SPEC_FILENAME = "feature_spec.json" +_METADATA_FILENAME = "metadata.json" +_SOURCE_MANIFEST_FILENAME = "source_manifest.json" + + +@dataclass(frozen=True) +class TrainedBundle: + """Fitted regressor + everything inference needs to reproduce its inputs. + + Attributes: + regressor: The fitted :class:`BaseRegressor`. Must be fitted + before :meth:`save`. + feature_spec: The :class:`FeatureSpec` whose + ``output_feature_names`` ordering matches what the + regressor was fit against. + metadata: Training-time provenance + score sets. + source_manifest: Manifest of the source training dataset, + verbatim. Embedding it makes the bundle self-describing + even if the original W&B dataset artifact is later + deleted. + """ + + regressor: BaseRegressor + feature_spec: FeatureSpec + metadata: TrainingMetadata + source_manifest: DatasetManifest + + def save(self, dest: Path) -> None: + """Persist the bundle into ``dest`` (created if absent). + + Raises: + RuntimeError: If the regressor has not been fitted. + """ + dest = Path(dest) + dest.mkdir(parents=True, exist_ok=True) + self.regressor.save(dest / _MODEL_SUBDIR) + (dest / _FEATURE_SPEC_FILENAME).write_text(self.feature_spec.to_json()) + (dest / _METADATA_FILENAME).write_text(self.metadata.to_json()) + (dest / _SOURCE_MANIFEST_FILENAME).write_text(self.source_manifest.to_json()) + + +def load_bundle( + src: Path, + *, + providers: Optional[dict[str, Any]] = None, +) -> TrainedBundle: + """Inverse of :meth:`TrainedBundle.save`. + + Reads the four bundle files, dispatches the regressor via its + persisted ``model_kind.txt`` (no caller-side knowledge of which + flavour was trained), and returns the fully reconstructed bundle. + + Args: + src: Bundle directory. + providers: Map of provider-key → provider for any + :class:`~susse.preprocessing.DerivedFeature` that needs + runtime injection at deserialisation time. Today the only + one is altitude: + ``providers={"altitude": PvlibElevationProvider()}``. + Bundles whose ``feature_spec.json`` doesn't declare such a + feature can pass ``providers=None``. + + Raises: + FileNotFoundError: If any of the four pieces is missing. The + message names the missing file so the caller can spot + partial / corrupted bundles before they reach inference. + ValueError: If the loaded ``feature_spec.json`` declares a + :class:`DerivedFeature` whose required provider is absent + from ``providers``. The error message names the expected + provider key. + """ + src = Path(src) + for required in ( + _MODEL_SUBDIR, + _FEATURE_SPEC_FILENAME, + _METADATA_FILENAME, + _SOURCE_MANIFEST_FILENAME, + ): + path = src / required + if not path.exists(): + raise FileNotFoundError( + f"Bundle at {src} is incomplete: missing {required!r}. " + f"A valid bundle has model/, feature_spec.json, " + f"metadata.json, and source_manifest.json." + ) + regressor = load_regressor(src / _MODEL_SUBDIR) + feature_spec = FeatureSpec.from_json( + (src / _FEATURE_SPEC_FILENAME).read_text(), + providers=providers, + ) + metadata = TrainingMetadata.from_json((src / _METADATA_FILENAME).read_text()) + source_manifest = DatasetManifest.from_json( + (src / _SOURCE_MANIFEST_FILENAME).read_text() + ) + return TrainedBundle( + regressor=regressor, + feature_spec=feature_spec, + metadata=metadata, + source_manifest=source_manifest, + ) diff --git a/src/susse/training/metadata.py b/src/susse/training/metadata.py new file mode 100644 index 00000000..425394a9 --- /dev/null +++ b/src/susse/training/metadata.py @@ -0,0 +1,150 @@ +"""Typed metadata + score sets recorded inside every trained-model bundle. + +A :class:`TrainedBundle` (defined in :mod:`susse.training.bundle`) +embeds one :class:`TrainingMetadata` instance describing how the model +was trained: when, against what data, with which splitter, and how it +scored on both the train fold and the held-out val fold. + +Everything here is JSON-serialisable; nothing carries fitted state. +The fitted model lives elsewhere in the bundle, dispatched via +:class:`susse.models.ModelKind`. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from typing import Any + + +@dataclass(frozen=True) +class ScoreSet: + """A single fold's regression scores. + + Attributes: + n_rows: Number of rows scored. Excludes any (true, pred) pair + where either side was NaN — see :func:`score_predictions`. + mae: Mean absolute error. + rmse: Root mean squared error. + r2: Coefficient of determination. ``NaN`` when the fold has + zero target variance (a degenerate case worth surfacing + rather than silencing). + """ + + n_rows: int + mae: float + rmse: float + r2: float + + def __post_init__(self) -> None: + if self.n_rows < 0: + raise ValueError(f"ScoreSet.n_rows={self.n_rows} must be >= 0.") + + def to_dict(self) -> dict[str, Any]: + return { + "n_rows": self.n_rows, + "mae": self.mae, + "rmse": self.rmse, + "r2": self.r2, + } + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "ScoreSet": + return cls( + n_rows=int(d["n_rows"]), + mae=float(d["mae"]), + rmse=float(d["rmse"]), + r2=float(d["r2"]), + ) + + +@dataclass(frozen=True) +class TrainingMetadata: + """Provenance + scoring record for one trained model. + + Attributes: + created_at_utc: When the training run produced the bundle + (UTC, ISO 8601). + susse_version: ``susse`` package version at training time. + git_sha: ``git rev-parse HEAD`` at training time, or ``None`` + if the host had no git context. + holdout_label: Free-form description of the held-out fold + (e.g. ``"station-LOSO:kenya_location13"``). Survives + persistence so cross-bundle comparisons are honest about + *which* fold each bundle was scored against. + splitter_name: ``__name__`` of the splitter callable that + produced the train / val indices. Lets the bundle remember + *how* the holdout was constructed in addition to *what* + it was. + n_train_rows: Number of rows the model was fit on. + n_val_rows: Number of rows in the held-out fold. + train_metrics: Scores of the trained model on the train fold. + val_metrics: Scores of the trained model on the val fold. + baseline_metrics: Scores of any raw-satellite baselines on the + *same* val fold, keyed by the satellite-prediction column + (e.g. ``"sat_ghi_nasa_kwh_m2_day"``). Empty when the + preprocessed dataset doesn't expose a satellite column. + """ + + created_at_utc: str + susse_version: str + git_sha: str | None + holdout_label: str + splitter_name: str + n_train_rows: int + n_val_rows: int + train_metrics: ScoreSet + val_metrics: ScoreSet + baseline_metrics: dict[str, ScoreSet] + + def __post_init__(self) -> None: + if self.n_train_rows < 0 or self.n_val_rows < 0: + raise ValueError( + f"n_train_rows={self.n_train_rows}, " + f"n_val_rows={self.n_val_rows} must be >= 0." + ) + if not self.holdout_label: + raise ValueError("holdout_label must be non-empty.") + if not self.splitter_name: + raise ValueError("splitter_name must be non-empty.") + + def to_dict(self) -> dict[str, Any]: + return { + "created_at_utc": self.created_at_utc, + "susse_version": self.susse_version, + "git_sha": self.git_sha, + "holdout_label": self.holdout_label, + "splitter_name": self.splitter_name, + "n_train_rows": self.n_train_rows, + "n_val_rows": self.n_val_rows, + "train_metrics": self.train_metrics.to_dict(), + "val_metrics": self.val_metrics.to_dict(), + "baseline_metrics": { + k: v.to_dict() for k, v in self.baseline_metrics.items() + }, + } + + def to_json(self) -> str: + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> "TrainingMetadata": + return cls( + created_at_utc=d["created_at_utc"], + susse_version=d["susse_version"], + git_sha=d.get("git_sha"), + holdout_label=d["holdout_label"], + splitter_name=d["splitter_name"], + n_train_rows=int(d["n_train_rows"]), + n_val_rows=int(d["n_val_rows"]), + train_metrics=ScoreSet.from_dict(d["train_metrics"]), + val_metrics=ScoreSet.from_dict(d["val_metrics"]), + baseline_metrics={ + k: ScoreSet.from_dict(v) + for k, v in d.get("baseline_metrics", {}).items() + }, + ) + + @classmethod + def from_json(cls, s: str) -> "TrainingMetadata": + return cls.from_dict(json.loads(s)) diff --git a/src/susse/training/splitter.py b/src/susse/training/splitter.py new file mode 100644 index 00000000..e951ad27 --- /dev/null +++ b/src/susse/training/splitter.py @@ -0,0 +1,380 @@ +"""Train / val splitters — produce ``(train_idx, val_idx)`` from a preprocessed dataset. + +A :class:`Splitter` is an immutable parameterised object that, when +applied to a :class:`PreprocessedDataset`, returns two disjoint +:class:`pandas.Index` subsets covering its training and validation +folds. The base class is an ABC; every concrete splitter is a frozen +dataclass so its configuration is hashable, inspectable, and +serialisable into :class:`TrainingMetadata.splitter_name`. + +Five concrete splitters are shipped: + +* :class:`RandomSplitter` — uniformly shuffled fraction. The cheapest + baseline; ignores all structure in the data. +* :class:`TemporalSplitter` — rows on or after ``split_date`` go to + val. The right default for time-series generalisation. +* :class:`StationLOSOSplitter` — leave-one-station-out. The right + default for spatial generalisation when one station's worth of + rows is enough to score against. +* :class:`SpatialSpreadHoldoutSplitter` — hold out the ``n_holdout`` + stations with maximum pairwise haversine spread. A stronger spatial + test than LOSO because the held-out set spans the region. +* :class:`SpatialBlockSplitter` — hold out every row whose + ``block_column`` falls in a named set (typically country or admin + region). The right default when blocks differ on a known + categorical lever (climate zone, terrain class, …). + +All five subclass :class:`Splitter` and implement the same +``(name, split)`` surface, so the :class:`susse.training.Trainer` +can swap between them without code change. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from datetime import date +from typing import ClassVar + +import numpy as np +import pandas as pd + +from ..preprocessing import PreprocessedDataset + +# --------------------------------------------------------------------------- +# Abstract base. +# --------------------------------------------------------------------------- + + +class Splitter(ABC): + """Strategy that splits a :class:`PreprocessedDataset` into train + val folds. + + Concrete subclasses are frozen dataclasses carrying their + configuration. :attr:`name` is the stable string recorded in + :class:`TrainingMetadata.splitter_name`; it must encode the + splitter's identity so two runs with the same name are guaranteed + to have used the same configuration. + """ + + @property + @abstractmethod + def name(self) -> str: + """Stable identifier of this splitter's configuration.""" + + @abstractmethod + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + """Return ``(train_idx, val_idx)`` — disjoint subsets of ``processed.df.index``. + + Args: + processed: The dataset to split. + + Returns: + Two pandas indices: training rows first, validation rows + second. The union does not have to equal + ``processed.df.index`` (some splitters drop rows that + belong to neither fold), but the intersection must be + empty. + + Raises: + ValueError: When the configuration cannot produce a valid + split on this dataset (missing column, empty fold, + unknown identifier). The message must include the + remediation step. + """ + + def __call__(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + """Alias for :meth:`split`. Lets a :class:`Splitter` be passed to + any callable-typed slot.""" + return self.split(processed) + + +# --------------------------------------------------------------------------- +# Concrete splitters. +# --------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class RandomSplitter(Splitter): + """Uniformly shuffled validation fraction. + + Attributes: + val_fraction: Fraction of rows that go to val. Must be in + ``(0, 1)``. + random_state: Seed for :func:`numpy.random.default_rng`. + Re-running with the same seed yields the same split. + """ + + val_fraction: float + random_state: int = 42 + + def __post_init__(self) -> None: + if not 0.0 < self.val_fraction < 1.0: + raise ValueError( + f"RandomSplitter.val_fraction={self.val_fraction} is not in " + f"(0, 1). Pick a fraction strictly between 0 and 1 (e.g. 0.2 " + f"for an 80/20 split)." + ) + + @property + def name(self) -> str: + return f"random[val={self.val_fraction:.2f},seed={self.random_state}]" + + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + rng = np.random.default_rng(self.random_state) + shuffled = rng.permutation(processed.df.index.to_numpy()) + n_val = max(1, int(round(self.val_fraction * len(shuffled)))) + val_idx = pd.Index(shuffled[:n_val]) + train_idx = pd.Index(shuffled[n_val:]) + return train_idx, val_idx + + +@dataclass(frozen=True) +class TemporalSplitter(Splitter): + """Hold out every row on or after ``split_date``. + + Attributes: + split_date: Boundary date. Rows whose ``date_column`` value is + ``>= split_date`` go to val; everything earlier goes to + train. + date_column: Name of the column carrying the row's date. + Default ``"date"`` matches the convention from + :class:`FeatureService`. + """ + + split_date: date + date_column: str = "date" + + @property + def name(self) -> str: + return f"temporal[val>={self.split_date.isoformat()}]" + + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + if self.date_column not in processed.df.columns: + raise ValueError( + f"TemporalSplitter needs column {self.date_column!r} on " + f"processed.df, but it was not found. Add the date column " + f"to FeatureSpec.id_columns or pass " + f"`date_column=` matching your column name." + ) + dates = pd.to_datetime(processed.df[self.date_column]).dt.date + is_val = dates >= self.split_date + return ( + processed.df.index[~is_val], + processed.df.index[is_val], + ) + + +@dataclass(frozen=True) +class StationLOSOSplitter(Splitter): + """Leave-one-station-out: hold out every row from a named station. + + Attributes: + held_out_station: Identifier whose rows go into the val fold. + location_column: Column on ``processed.df`` carrying the + station identifier. Default ``"location"``. + """ + + held_out_station: str + location_column: str = "location" + + @property + def name(self) -> str: + return f"station_loso[{self.held_out_station}]" + + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + if self.location_column not in processed.df.columns: + raise ValueError( + f"StationLOSOSplitter needs column {self.location_column!r} " + f"on processed.df, but it was not found. Add 'location' " + f"to FeatureSpec.id_columns or rebuild the dataset." + ) + mask = processed.df[self.location_column] == self.held_out_station + if not mask.any(): + sample = sorted( + processed.df[self.location_column].dropna().unique().tolist() + )[:8] + raise ValueError( + f"StationLOSOSplitter: station " + f"{self.held_out_station!r} has no rows in this dataset. " + f"Available stations (first 8): {sample}. Use " + f"StationLOSOSplitter.auto_pick_largest(processed) to " + f"choose one programmatically." + ) + return ( + processed.df.index[~mask], + processed.df.index[mask], + ) + + @classmethod + def auto_pick_largest( + cls, + processed: PreprocessedDataset, + *, + location_column: str = "location", + ) -> "StationLOSOSplitter": + """Construct a splitter held out on the station with the most rows. + + Deterministic: ties are broken alphabetically by station name so + a re-run on the same data yields the same splitter. + """ + if location_column not in processed.df.columns: + raise ValueError( + f"auto_pick_largest needs column {location_column!r} on " + f"processed.df, but it was not found. Either include " + f"'location' in FeatureSpec.id_columns, or pass " + f"`location_column=` matching your column." + ) + counts = processed.df.groupby(location_column).size() + if counts.empty: + raise ValueError( + "PreprocessedDataset is empty; cannot pick a holdout station." + ) + sorted_counts = counts.sort_index().sort_values(ascending=False, kind="stable") + return cls( + held_out_station=str(sorted_counts.index[0]), + location_column=location_column, + ) + + +@dataclass(frozen=True) +class SpatialSpreadHoldoutSplitter(Splitter): + """Hold out the ``n_holdout`` stations with maximum pairwise haversine spread. + + Selects the validation stations greedily: starting from the + farthest-apart pair, each subsequent station is the one that + maximises the minimum haversine distance to those already chosen. + For ``n_holdout=2`` this is just "the most distant pair". + + Attributes: + n_holdout: Number of stations to hold out. Must be ``>= 2``. + location_column: Station-identifier column. Default ``"location"``. + lat_column: Latitude column. Default ``"lat"``. + lon_column: Longitude column. Default ``"lon"``. + """ + + _EARTH_RADIUS_KM: ClassVar[float] = 6371.0 + + n_holdout: int + location_column: str = "location" + lat_column: str = "lat" + lon_column: str = "lon" + + def __post_init__(self) -> None: + if self.n_holdout < 2: + raise ValueError( + f"SpatialSpreadHoldoutSplitter.n_holdout={self.n_holdout} " + f"is below the minimum of 2. With one station the " + f"'spread' criterion is degenerate; use " + f"StationLOSOSplitter for n=1." + ) + + @property + def name(self) -> str: + return f"spatial_spread[n={self.n_holdout}]" + + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + needed = (self.location_column, self.lat_column, self.lon_column) + missing = [c for c in needed if c not in processed.df.columns] + if missing: + raise ValueError( + f"SpatialSpreadHoldoutSplitter needs columns {needed} on " + f"processed.df; missing: {missing}. Add the station " + f"identifier and its coordinates to FeatureSpec.id_columns." + ) + station_coords = ( + processed.df[list(needed)] + .drop_duplicates(subset=self.location_column) + .reset_index(drop=True) + ) + if len(station_coords) < self.n_holdout: + raise ValueError( + f"SpatialSpreadHoldoutSplitter: only " + f"{len(station_coords)} unique stations in dataset; " + f"cannot hold out {self.n_holdout}. Lower n_holdout or " + f"ingest more stations." + ) + holdout_names = self._greedy_spread_selection(station_coords) + mask = processed.df[self.location_column].isin(holdout_names) + return ( + processed.df.index[~mask], + processed.df.index[mask], + ) + + def _greedy_spread_selection(self, station_coords: pd.DataFrame) -> tuple[str, ...]: + """Pick stations by greedy max-min haversine spread.""" + lats = station_coords[self.lat_column].to_numpy() + lons = station_coords[self.lon_column].to_numpy() + names = station_coords[self.location_column].to_numpy() + distances = self._haversine_matrix(lats, lons) + # Seed with the farthest-apart pair. + i, j = np.unravel_index(np.argmax(distances), distances.shape) + selected_indices = [int(i), int(j)] + while len(selected_indices) < self.n_holdout: + min_dist_to_selected = distances[:, selected_indices].min(axis=1) + min_dist_to_selected[selected_indices] = -np.inf + selected_indices.append(int(np.argmax(min_dist_to_selected))) + return tuple(str(names[k]) for k in selected_indices) + + @classmethod + def _haversine_matrix(cls, lats: np.ndarray, lons: np.ndarray) -> np.ndarray: + """Pairwise haversine distance matrix in kilometres.""" + phi = np.radians(lats) + lam = np.radians(lons) + d_phi = phi[:, None] - phi[None, :] + d_lam = lam[:, None] - lam[None, :] + a = ( + np.sin(d_phi / 2) ** 2 + + np.cos(phi[:, None]) * np.cos(phi[None, :]) * np.sin(d_lam / 2) ** 2 + ) + return 2.0 * cls._EARTH_RADIUS_KM * np.arcsin(np.sqrt(a)) + + +@dataclass(frozen=True) +class SpatialBlockSplitter(Splitter): + """Hold out every row whose ``block_column`` falls in ``val_blocks``. + + Used when the dataset carries a categorical block label + (country, climate zone, terrain class) and the val fold should + be a named subset of those blocks. + + Attributes: + val_blocks: Block identifiers whose rows go to val. + block_column: Column carrying the block identifier. + """ + + val_blocks: tuple[str, ...] + block_column: str + + def __post_init__(self) -> None: + if not self.val_blocks: + raise ValueError( + "SpatialBlockSplitter.val_blocks is empty. Pass at least " + "one block identifier to send to the val fold." + ) + + @property + def name(self) -> str: + return f"spatial_block[val={','.join(self.val_blocks)}]" + + def split(self, processed: PreprocessedDataset) -> tuple[pd.Index, pd.Index]: + if self.block_column not in processed.df.columns: + raise ValueError( + f"SpatialBlockSplitter needs column {self.block_column!r} " + f"on processed.df, but it was not found. Add the block " + f"identifier to FeatureSpec.id_columns or rebuild the " + f"dataset with the block column included." + ) + all_blocks = set(processed.df[self.block_column].dropna().unique()) + unknown = set(self.val_blocks) - all_blocks + if unknown: + sample = sorted(all_blocks)[:8] + raise ValueError( + f"SpatialBlockSplitter: blocks {sorted(unknown)} not " + f"found in column {self.block_column!r}. Available " + f"blocks (first 8): {sample}." + ) + mask = processed.df[self.block_column].isin(self.val_blocks) + return ( + processed.df.index[~mask], + processed.df.index[mask], + ) diff --git a/src/susse/training/trainer.py b/src/susse/training/trainer.py new file mode 100644 index 00000000..7b32590e --- /dev/null +++ b/src/susse/training/trainer.py @@ -0,0 +1,362 @@ +"""Trainer — orchestrate fit / predict / score / bundle for one model. + +Single-responsibility: take a preprocessed dataset, a typed params +object, and a splitter; return a :class:`TrainedBundle` with full +provenance. Optional W&B integration is **strict opt-in**: a default +``Trainer()`` performs no W&B calls. Pass a non-``None`` +:attr:`TrainerConfig.wandb_project` to enable run + artifact logging. + +What the trainer does *not* do (deliberate exclusions): + +* No hyperparameter search — out of scope for the v1 pipeline. +* No richer metrics than the :class:`ScoreSet` MAE/RMSE/R² triplet — + paper-style metrics (IOA, MBE, normalised RMSE/MAE) live in + :mod:`susse.evaluation.metrics` and are applied after training via + :class:`susse.evaluation.Evaluator`. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from datetime import datetime, timezone +from typing import TYPE_CHECKING, Optional + +import numpy as np +import pandas as pd + +from ..models import BaseModelParams, ModelFactory +from ..preprocessing import PreprocessedDataset +from ..provenance import git_sha, susse_version +from ..warehouse_ops.population.types import ( + IrradianceBand, + Source, + satellite_irradiance_column, +) +from .bundle import TrainedBundle +from .metadata import ScoreSet, TrainingMetadata +from .splitter import Splitter + +if TYPE_CHECKING: # pragma: no cover + from pathlib import Path + +_DEFAULT_BASELINE_COLUMNS: tuple[str, ...] = ( + satellite_irradiance_column(Source.NASA_POWER, IrradianceBand.GHI), + satellite_irradiance_column(Source.CAMS, IrradianceBand.GHI), +) + + +@dataclass(frozen=True) +class TrainerConfig: + """Trainer configuration — W&B opt-in plus baseline-scoring choices. + + Defaults are **fully offline**: ``wandb_project=None`` means no + run is created and no artifact is logged. Setting it to a string + activates the full ``wandb.init → log_metrics → log_artifact`` + flow in :meth:`Trainer.train`. + + Attributes: + wandb_project: W&B project name. ``None`` (default) disables + all W&B activity. Mirrors the explicit-opt-in convention + from NB 02's ``LOG_TO_WANDB``. + wandb_entity: W&B team / user. ``None`` uses your default + from ``wandb login``. + wandb_tags: Extra tags to apply to the W&B run. + baseline_columns: Column names on ``processed.df`` whose + values are scored against the val target as baselines. + Missing columns are silently skipped (so a spec without + both satellite columns still works). + """ + + wandb_project: Optional[str] = None + wandb_entity: Optional[str] = None + wandb_tags: tuple[str, ...] = () + baseline_columns: tuple[str, ...] = field( + default_factory=lambda: _DEFAULT_BASELINE_COLUMNS, + ) + + +class Trainer: + """Orchestrator. Construct once per training-time configuration.""" + + def __init__(self, config: Optional[TrainerConfig] = None) -> None: + self._config = config or TrainerConfig() + + @property + def config(self) -> TrainerConfig: + return self._config + + def train( + self, + *, + processed: PreprocessedDataset, + params: BaseModelParams, + splitter: Splitter, + holdout_label: str, + bundle_dest: Optional["Path"] = None, + dataset_artifact_ref: Optional[str] = None, + ) -> TrainedBundle: + """Fit, score, bundle, and (optionally) log to W&B. + + Args: + processed: The model-ready dataset. Carries its source + :class:`DatasetManifest` for full lineage. + params: Typed hyperparameters dispatching the concrete + regressor via :class:`susse.models.ModelFactory`. + splitter: :class:`Splitter` instance producing + ``(train_idx, val_idx)``. The two index sets must be + disjoint and both must be subsets of + ``processed.df.index``. + holdout_label: Free-form description of the val fold, + recorded verbatim in :class:`TrainingMetadata`. + bundle_dest: If given, the bundle is saved to this + directory after training. If W&B is enabled and this + is ``None``, a temporary directory is created so the + W&B artifact has files to upload. + dataset_artifact_ref: Qualified W&B reference of the + source dataset artifact (e.g. + ``"//:v0"``). When provided + alongside an active W&B run, recorded as a consumed + artifact for lineage. + + Returns: + A fully populated :class:`TrainedBundle`. + + Raises: + ValueError: If the splitter returns overlapping indices, + or if either fold ends up empty. + """ + train_idx, val_idx = splitter(processed) + self._validate_split(train_idx, val_idx, processed) + + X_full = processed.X() + y_full = processed.y() + X_train, y_train = X_full.loc[train_idx], y_full.loc[train_idx] + X_val, y_val = X_full.loc[val_idx], y_full.loc[val_idx] + + regressor = ModelFactory.create(params).fit(X_train, y_train) + + train_metrics = score_predictions(y_train, regressor.predict(X_train)) + val_metrics = score_predictions(y_val, regressor.predict(X_val)) + baseline_metrics = self._score_baselines( + processed=processed, + val_idx=val_idx, + y_val=y_val, + ) + + metadata = TrainingMetadata( + created_at_utc=datetime.now(timezone.utc).isoformat(), + susse_version=susse_version(), + git_sha=git_sha(), + holdout_label=holdout_label, + splitter_name=splitter.name, + n_train_rows=len(train_idx), + n_val_rows=len(val_idx), + train_metrics=train_metrics, + val_metrics=val_metrics, + baseline_metrics=baseline_metrics, + ) + + bundle = TrainedBundle( + regressor=regressor, + feature_spec=processed.feature_spec, + metadata=metadata, + source_manifest=processed.source_manifest, + ) + + if self._config.wandb_project is not None: + bundle = self._log_to_wandb( + bundle=bundle, + params=params, + bundle_dest=bundle_dest, + dataset_artifact_ref=dataset_artifact_ref, + ) + elif bundle_dest is not None: + bundle.save(bundle_dest) + + return bundle + + # ---- internal helpers ------------------------------------------------ + + @staticmethod + def _validate_split( + train_idx: pd.Index, + val_idx: pd.Index, + processed: PreprocessedDataset, + ) -> None: + if len(train_idx) == 0: + raise ValueError( + "Splitter returned an empty train fold — nothing to fit. " + "Check that the held-out group is not the entire dataset." + ) + if len(val_idx) == 0: + raise ValueError( + "Splitter returned an empty val fold — nothing to score " + "against. Check the splitter's match criterion." + ) + overlap = train_idx.intersection(val_idx) + if len(overlap) > 0: + raise ValueError( + f"Splitter returned overlapping train/val indices " + f"({len(overlap)} rows in both). Trainer requires a " + f"disjoint split to avoid leakage." + ) + full_index = processed.df.index + if not (train_idx.isin(full_index).all() and val_idx.isin(full_index).all()): + raise ValueError( + "Splitter returned indices not present in " + "processed.df.index. The splitter must select rows " + "from the supplied dataset only." + ) + + def _score_baselines( + self, + *, + processed: PreprocessedDataset, + val_idx: pd.Index, + y_val: pd.Series, + ) -> dict[str, ScoreSet]: + out: dict[str, ScoreSet] = {} + for col in self._config.baseline_columns: + if col not in processed.df.columns: + continue + preds = processed.df.loc[val_idx, col] + out[col] = score_predictions(y_val, preds) + return out + + def _log_to_wandb( + self, + *, + bundle: TrainedBundle, + params: BaseModelParams, + bundle_dest: Optional["Path"], + dataset_artifact_ref: Optional[str], + ) -> TrainedBundle: + # Local import — only paid by users who opt into W&B. + import tempfile + from pathlib import Path + + import wandb + + cfg = self._config + wandb_config = { + "params": params.to_dict(), + "splitter_name": bundle.metadata.splitter_name, + "holdout_label": bundle.metadata.holdout_label, + "source_dataset_name": bundle.source_manifest.name, + "source_dataset_version": bundle.source_manifest.version, + "source_content_hash": bundle.source_manifest.content_hash, + "git_sha": bundle.metadata.git_sha, + "susse_version": bundle.metadata.susse_version, + } + + run = wandb.init( + project=cfg.wandb_project, + entity=cfg.wandb_entity, + job_type="training", + tags=list(cfg.wandb_tags), + config=wandb_config, + ) + try: + if dataset_artifact_ref is not None: + run.use_artifact(dataset_artifact_ref) + + run.log( + { + "train/n_rows": bundle.metadata.n_train_rows, + "val/n_rows": bundle.metadata.n_val_rows, + "train/mae": bundle.metadata.train_metrics.mae, + "train/rmse": bundle.metadata.train_metrics.rmse, + "train/r2": bundle.metadata.train_metrics.r2, + "val/mae": bundle.metadata.val_metrics.mae, + "val/rmse": bundle.metadata.val_metrics.rmse, + "val/r2": bundle.metadata.val_metrics.r2, + **{ + f"baseline/{col}/mae": s.mae + for col, s in bundle.metadata.baseline_metrics.items() + }, + **{ + f"baseline/{col}/rmse": s.rmse + for col, s in bundle.metadata.baseline_metrics.items() + }, + **{ + f"baseline/{col}/r2": s.r2 + for col, s in bundle.metadata.baseline_metrics.items() + }, + } + ) + + if bundle_dest is not None: + bundle.save(bundle_dest) + artifact_dir = Path(bundle_dest) + artifact = wandb.Artifact( + name=f"{bundle.source_manifest.name}-" f"{params.kind.value}", + type="trained_model", + metadata={ + "model_kind": params.kind.value, + "val_mae": bundle.metadata.val_metrics.mae, + "val_rmse": bundle.metadata.val_metrics.rmse, + "val_r2": bundle.metadata.val_metrics.r2, + "holdout_label": bundle.metadata.holdout_label, + }, + ) + artifact.add_dir(str(artifact_dir)) + run.log_artifact(artifact) + else: + # No persistent dest given — use a temp dir so the + # artifact still has files to upload, but warn the + # caller that the local copy is ephemeral. + with tempfile.TemporaryDirectory() as tmp: + tmp_dir = Path(tmp) + bundle.save(tmp_dir) + artifact = wandb.Artifact( + name=f"{bundle.source_manifest.name}-" f"{params.kind.value}", + type="trained_model", + metadata={ + "model_kind": params.kind.value, + "val_mae": bundle.metadata.val_metrics.mae, + "val_rmse": bundle.metadata.val_metrics.rmse, + "val_r2": bundle.metadata.val_metrics.r2, + "holdout_label": bundle.metadata.holdout_label, + }, + ) + artifact.add_dir(str(tmp_dir)) + run.log_artifact(artifact) + artifact.wait() + finally: + run.finish() + + return bundle + + +def score_predictions(y_true: pd.Series, y_pred: pd.Series) -> ScoreSet: + """MAE / RMSE / R² over rows where neither side is NaN. + + Treating NaN-bearing rows as scoreable (with whatever pandas + default) would silently inflate / deflate metrics; instead we + drop them and record the surviving row count in + :attr:`ScoreSet.n_rows` so the caller can spot data sparsity. + + Args: + y_true: Target series. + y_pred: Predicted series (or any column of predictions). + Index alignment is required — use ``.loc[idx]`` upstream. + + Returns: + A :class:`ScoreSet` over the (true, pred) rows that survived + the NaN filter. ``r2`` is ``NaN`` when target variance is + zero; ``n_rows`` is zero in that pathological case but still + reported. + """ + valid = y_true.notna() & y_pred.notna() + n = int(valid.sum()) + if n == 0: + return ScoreSet(n_rows=0, mae=float("nan"), rmse=float("nan"), r2=float("nan")) + yt = y_true[valid].to_numpy(dtype=float) + yp = y_pred[valid].to_numpy(dtype=float) + err = yp - yt + mae = float(np.abs(err).mean()) + rmse = float(np.sqrt((err**2).mean())) + ss_res = float((err**2).sum()) + ss_tot = float(((yt - yt.mean()) ** 2).sum()) + r2 = 1.0 - ss_res / ss_tot if ss_tot > 0 else float("nan") + return ScoreSet(n_rows=n, mae=mae, rmse=rmse, r2=r2) diff --git a/tests/estimators/clearsky/__init__.py b/src/susse/warehouse_ops/__init__.py similarity index 100% rename from tests/estimators/clearsky/__init__.py rename to src/susse/warehouse_ops/__init__.py diff --git a/src/susse/warehouse_ops/io/__init__.py b/src/susse/warehouse_ops/io/__init__.py new file mode 100644 index 00000000..4e3e458e --- /dev/null +++ b/src/susse/warehouse_ops/io/__init__.py @@ -0,0 +1,25 @@ +"""Warehouse access layer: typed configs + thin BigQuery client + repositories.""" + +from .bq import BigQueryClient, BigQueryOptions +from .config import ( + MatchStrategy, + TableRefs, + TableSchema, + TableSchemas, + WarehouseConfig, + WarehouseOptions, +) +from .repositories import GroundRepository, SatelliteRepository + +__all__ = [ + "BigQueryClient", + "BigQueryOptions", + "GroundRepository", + "MatchStrategy", + "SatelliteRepository", + "TableRefs", + "TableSchema", + "TableSchemas", + "WarehouseConfig", + "WarehouseOptions", +] diff --git a/src/susse/warehouse_ops/io/bq.py b/src/susse/warehouse_ops/io/bq.py new file mode 100644 index 00000000..2d545b16 --- /dev/null +++ b/src/susse/warehouse_ops/io/bq.py @@ -0,0 +1,128 @@ +"""Thin BigQuery client wrapper used by repositories, loaders, and jobs. + +Exposes the small surface the rest of SuSSE needs (query, load, DDL, key +existence checks for idempotency) and hides google-cloud-bigquery details +behind it. Callers construct via a :class:`WarehouseConfig` rather than +hand-rolled project strings, so swapping environments (prod vs. test +dataset) is one change in one place. +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass +from typing import Sequence + +import pandas as pd +from google.cloud import bigquery + +from .config import WarehouseConfig + +_logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class BigQueryOptions: + """Job-level overrides that don't belong on :class:`WarehouseConfig`.""" + + location: str | None = None + + +class BigQueryClient: + """Thin wrapper around ``google.cloud.bigquery.Client``. + + Provides exactly the operations SuSSE needs: query → DataFrame, append + DataFrame → table, execute DDL, and a key-existence query used by + ingest jobs to skip already-loaded rows (idempotency). + """ + + def __init__( + self, + config: WarehouseConfig | None = None, + options: BigQueryOptions | None = None, + ) -> None: + self._config = config or WarehouseConfig() + self._options = options or BigQueryOptions(location=self._config.location) + self._client = bigquery.Client( + project=self._config.project_id, + location=self._options.location, + ) + + @property + def config(self) -> WarehouseConfig: + return self._config + + @property + def native_client(self) -> bigquery.Client: + """Escape hatch for callers that need the underlying client directly.""" + return self._client + + def query( + self, + sql: str, + *, + job_config: bigquery.QueryJobConfig | None = None, + ) -> pd.DataFrame: + """Run a SQL query and return the result as a DataFrame.""" + job = self._client.query(sql, job_config=job_config) + return job.result().to_dataframe() + + def load_dataframe( + self, + df: pd.DataFrame, + table_fqn: str, + *, + write_disposition: str = "WRITE_APPEND", + schema: Sequence[bigquery.SchemaField] | None = None, + autodetect: bool = True, + ) -> None: + """Append (or replace) a DataFrame to a BQ table via load job.""" + job_config = bigquery.LoadJobConfig( + write_disposition=write_disposition, + schema=list(schema) if schema is not None else None, + autodetect=autodetect, + ) + self._client.load_table_from_dataframe( + df, table_fqn, job_config=job_config + ).result() + + def execute_ddl(self, ddl: str) -> None: + """Run a DDL/DML statement that returns no rows.""" + self._client.query(ddl).result() + + def existing_keys( + self, + table_fqn: str, + key_columns: Sequence[str], + *, + where_filters: Sequence[str] = (), + ) -> set[tuple]: + """Return the set of distinct key-column tuples currently in a table. + + Used by ingest jobs to determine which (key) rows are already loaded + so they can skip the corresponding API calls. Apply ``where_filters`` + to scope the scan to the slice actually being ingested — scanning all + ~20M rows of ``nasa_daily_vars_long`` for every job would be wasteful. + + Args: + table_fqn: Fully-qualified table name. + key_columns: Column names to project as the key tuple. + where_filters: Sequence of SQL boolean expressions joined with + ``AND``. Quote string literals yourself. + + Returns: + ``set[tuple]`` whose tuples align with ``key_columns``. Empty if + the table has no rows in the scoped slice. + """ + if not key_columns: + raise ValueError("existing_keys requires at least one key column.") + + cols = ", ".join(f"`{c}`" for c in key_columns) + where = "" + if where_filters: + where = "WHERE " + " AND ".join(f"({f})" for f in where_filters) + sql = f"SELECT DISTINCT {cols} FROM `{table_fqn}` {where}" + df = self.query(sql) + if df.empty: + return set() + return set(map(tuple, df[list(key_columns)].itertuples(index=False, name=None))) diff --git a/src/susse/warehouse_ops/io/config.py b/src/susse/warehouse_ops/io/config.py new file mode 100644 index 00000000..71cc7c2b --- /dev/null +++ b/src/susse/warehouse_ops/io/config.py @@ -0,0 +1,226 @@ +"""Warehouse identity, table catalogue, and feature-assembly options. + +This module is the single source of truth for the BigQuery warehouse layout. +It declares: + +* :class:`WarehouseConfig` — the GCP project + dataset hosting the warehouse. +* :class:`TableSchema` and :class:`TableSchemas` — the static identity of each + table (table_id, MERGE-key contract, description). Add a new table here + whenever you introduce one. +* :class:`TableRefs` — fully-qualified BQ table names derived from a + :class:`WarehouseConfig`. +* :class:`MatchStrategy` and :class:`WarehouseOptions` — query-time options + used by the feature-assembly layer. + +Per CLAUDE.md, structural constants (table_ids, MERGE keys) live as +``ClassVar`` entries on :class:`TableSchemas`, not as module-level globals. +Callers that need both the FQTN and the merge keys for a table should +reference ``TableSchemas.`` and combine with +``WarehouseConfig.fqn(...)`` rather than carrying the strings around. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import StrEnum +from typing import ClassVar + + +@dataclass(frozen=True) +class WarehouseConfig: + """Identifies the GCP project and BigQuery dataset hosting the warehouse. + + Default values point to the production warehouse used by the SuSSE project. + Tests and alternate environments construct an instance with explicit + project/dataset values. + + Attributes: + project_id: GCP project ID (e.g. ``solar-irradiation-estimation``). + dataset: BigQuery dataset name within the project. + location: GCP region for query/job placement (e.g. ``EU``). ``None`` + lets the BQ client infer from the dataset. + """ + + project_id: str = "solar-irradiation-estimation" + dataset: str = "solar_warehouse" + location: str | None = None + + def fqn(self, table_id: str) -> str: + """Return the fully-qualified BQ name for a table in this dataset.""" + return f"{self.project_id}.{self.dataset}.{table_id}" + + +@dataclass(frozen=True) +class TableSchema: + """Static identity of a warehouse table. + + Bundles the unqualified ``table_id`` with its MERGE-deduplication contract + so loaders and coverage queries always reference the same key columns. + """ + + table_id: str + merge_keys: tuple[str, ...] + description: str + + def __post_init__(self) -> None: + if not self.table_id: + raise ValueError("TableSchema.table_id must be non-empty.") + if not self.merge_keys: + raise ValueError( + f"TableSchema for '{self.table_id}' must declare at least one " + f"merge key. If the table is append-only with no natural key, " + f"use a synthetic one (e.g. an ingest_id column)." + ) + + +class TableSchemas: + """Registry of all known warehouse tables. + + Single source of truth for ``table_id`` strings and MERGE-key contracts + across the codebase. Adding a new table is a one-line ``ClassVar`` edit + here; loaders, coverage queries, and tests pick it up automatically. + """ + + NASA_DAILY_VARS_LONG: ClassVar[TableSchema] = TableSchema( + table_id="nasa_daily_vars_long", + merge_keys=("date", "geohash5", "variable_id", "source"), + description=( + "Long-format daily NASA POWER variables. One row per " + "(date, point, variable). Source='NASA'." + ), + ) + CAMS_DAILY_VARS_LONG: ClassVar[TableSchema] = TableSchema( + table_id="cams_daily_vars_long", + merge_keys=("date", "geohash5", "variable_id", "source"), + description=( + "Long-format daily CAMS variables. Mirrors NASA_DAILY_VARS_LONG " + "but for the CAMS source." + ), + ) + MERRA_DAILY_VARS_LONG: ClassVar[TableSchema] = TableSchema( + table_id="merra_daily_vars_long", + merge_keys=("date", "geohash5", "variable_id", "source"), + description=( + "Long-format daily MERRA-2 variables. Mirrors NASA_DAILY_VARS_LONG. " + "Daily values are cosine-zenith-weighted means of MERRA-2's " + "hourly-native data, aggregated client-side." + ), + ) + MODIS_OBSERVATIONS: ClassVar[TableSchema] = TableSchema( + table_id="modis_observations", + merge_keys=("date", "geohash5", "product_id", "band_id", "source"), + description=( + "MODIS satellite observations from ORNL DAAC. Each row is one " + "(date, point, product, band) measurement at the product's " + "native composite end-date. Distinguished from the *_long " + "tables by carrying product_id and band_id as separate columns." + ), + ) + IRRADIANCE_DAILY: ClassVar[TableSchema] = TableSchema( + table_id="irradiance_daily", + merge_keys=("date", "geohash5", "source"), + description=( + "Wide-format daily irradiance with GHI/DHI/DNI per source per " + "point. Used for feature assembly." + ), + ) + GROUND_MEASUREMENTS: ClassVar[TableSchema] = TableSchema( + table_id="ground_measurements", + merge_keys=("date", "location"), + description=( + "Curated daily ground GHI measurements with QC flag, geohash, " + "and curation provenance." + ), + ) + GROUND_MEASUREMENTS_RAW: ClassVar[TableSchema] = TableSchema( + table_id="ground_measurements_raw", + merge_keys=("datetime", "location"), + description=( + "Raw uncurated ground GHI measurements as parsed from source " + "files. Curation reads from this table." + ), + ) + DIM_VARIABLE: ClassVar[TableSchema] = TableSchema( + table_id="dim_variable", + merge_keys=("variable_id", "source"), + description=( + "Catalogue of satellite/ground variables: ID, units, valid range, " + "spatial resolution, description." + ), + ) + + +@dataclass(frozen=True) +class TableRefs: + """Fully-qualified BigQuery table names for the warehouse. + + Derived from a :class:`WarehouseConfig`; properties return FQTNs so + callers can use attribute access (``tables.irradiance_daily``) without + embedding string concatenation. + """ + + config: WarehouseConfig = field(default_factory=WarehouseConfig) + + @property + def nasa_daily_vars_long(self) -> str: + return self.config.fqn(TableSchemas.NASA_DAILY_VARS_LONG.table_id) + + @property + def cams_daily_vars_long(self) -> str: + return self.config.fqn(TableSchemas.CAMS_DAILY_VARS_LONG.table_id) + + @property + def merra_daily_vars_long(self) -> str: + return self.config.fqn(TableSchemas.MERRA_DAILY_VARS_LONG.table_id) + + @property + def modis_observations(self) -> str: + return self.config.fqn(TableSchemas.MODIS_OBSERVATIONS.table_id) + + @property + def irradiance_daily(self) -> str: + return self.config.fqn(TableSchemas.IRRADIANCE_DAILY.table_id) + + @property + def ground_measurements(self) -> str: + return self.config.fqn(TableSchemas.GROUND_MEASUREMENTS.table_id) + + @property + def ground_measurements_raw(self) -> str: + return self.config.fqn(TableSchemas.GROUND_MEASUREMENTS_RAW.table_id) + + @property + def dim_variable(self) -> str: + return self.config.fqn(TableSchemas.DIM_VARIABLE.table_id) + + @property + def fn_nearest_point(self) -> str: + return self.config.fqn("fn_nearest_point") + + @property + def fn_nearest_var_daily(self) -> str: + return self.config.fqn("fn_nearest_var_daily") + + +class MatchStrategy(StrEnum): + """How to match a ground point to its nearest satellite point.""" + + GEOHASH = "geohash" + NEAREST = "nearest" + + +@dataclass(frozen=True) +class WarehouseOptions: + """Behaviour flags for feature assembly and query-time joins.""" + + geohash_precision: int = 5 + include_cams: bool = True + include_nasa: bool = True + match_strategy: MatchStrategy = MatchStrategy.GEOHASH + + def __post_init__(self) -> None: + if not (1 <= self.geohash_precision <= 12): + raise ValueError( + f"geohash_precision={self.geohash_precision} is outside [1, 12]. " + f"5 is the warehouse default and matches the existing tables." + ) diff --git a/src/susse/warehouse_ops/io/repositories.py b/src/susse/warehouse_ops/io/repositories.py new file mode 100644 index 00000000..cf2a7541 --- /dev/null +++ b/src/susse/warehouse_ops/io/repositories.py @@ -0,0 +1,234 @@ +"""Read-only repositories over the warehouse tables. + +Each repository wraps one logical table-family (ground, satellite-irradiance, +per-source long-format aux variables) and exposes typed query methods that +return pandas DataFrames. The :class:`FeatureService` orchestrates these +into the assembled training / inference frame. + +These are intentionally thin: each method is one SQL query. Joins live in +the FeatureService layer where pandas can replace BigQuery's per-table +limits and the call sites are easier to read. +""" + +from __future__ import annotations + +from datetime import date +from typing import Optional, Sequence + +import pandas as pd + +from ..population.types import IrradianceBand, Source, satellite_irradiance_column +from .bq import BigQueryClient +from .config import TableRefs + + +class GroundRepository: + """Curated daily ground GHI measurements with QC, location, geohash.""" + + def __init__(self, bq: BigQueryClient, tables: Optional[TableRefs] = None) -> None: + # `tables` is uniquely determined by `bq.config` for any production + # deployment; default to the derived value so callers don't have to + # repeat themselves. Pass `tables` explicitly only when running + # against a sandbox / non-default dataset. + self._bq = bq + self._t = tables if tables is not None else TableRefs(config=bq.config) + + def fetch( + self, + start: date, + end: date, + *, + locations: Optional[Sequence[str]] = None, + qc_levels: Sequence[str] = ("pass",), + ) -> pd.DataFrame: + """Fetch ground measurements between ``start`` and ``end`` (inclusive). + + Args: + start, end: Date bounds (inclusive). + locations: Optional list of station names; ``None`` returns all. + qc_levels: QC labels to keep. Default ``("pass",)`` excludes + fail-range etc. + + Returns: + DataFrame with columns ``date, location, lat, lon, geohash5, + ghi_kwh_m2_day, qc_level``. + """ + if not qc_levels: + raise ValueError( + "qc_levels must be non-empty. To keep all rows, pass every " + "level explicitly (e.g. ('pass', 'fail_range'))." + ) + filters = [f"date BETWEEN DATE('{start}') AND DATE('{end}')"] + if locations: + quoted = ", ".join(f"'{loc}'" for loc in locations) + filters.append(f"location IN ({quoted})") + qc_quoted = ", ".join(f"'{q}'" for q in qc_levels) + filters.append(f"qc_level IN ({qc_quoted})") + where = " AND ".join(filters) + sql = f""" + SELECT date, location, lat, lon, geohash5, + ghi_kwh_m2_day, qc_level + FROM `{self._t.ground_measurements}` + WHERE {where} + """ + return self._bq.query(sql) + + +class SatelliteRepository: + """Daily satellite irradiance + per-source pivoted aux variables.""" + + def __init__(self, bq: BigQueryClient, tables: Optional[TableRefs] = None) -> None: + # See `GroundRepository.__init__` for the default rationale. + self._bq = bq + self._t = tables if tables is not None else TableRefs(config=bq.config) + + def daily_irradiance_by_geohash( + self, + start: date, + end: date, + *, + sources: Sequence[str] = ("NASA", "CAMS"), + bands: Sequence[IrradianceBand] = (IrradianceBand.GHI,), + geohash5s: Optional[Sequence[str]] = None, + ) -> pd.DataFrame: + """Per-(date, geohash5) wide irradiance for the requested sources and bands. + + Args: + start, end: Inclusive date bounds. + sources: Source tags as stored in ``irradiance_daily.source`` + (``"NASA"``, ``"CAMS"``). + bands: Which irradiance series to project. Each entry adds one + column per requested source. Defaults to just GHI to keep + the common training path lean. + geohash5s: Optional list of geohash5 strings; ``None`` returns + the full date range without spatial filtering. + + Returns: + DataFrame with columns ``date, geohash5`` plus one + ``sat___kwh_m2_day`` per (band, source) pair. + Empty list of ``sources`` or ``bands`` returns an empty frame + without hitting BigQuery. + """ + if not sources or not bands: + return pd.DataFrame() + select_cols = [ + f"MAX(IF(source = '{src}', {band.value}_kwh_m2_day, NULL)) " + f"AS {satellite_irradiance_column(Source(src), band)}" + for band in bands + for src in sources + ] + filters = [f"date BETWEEN DATE('{start}') AND DATE('{end}')"] + if geohash5s: + gh_list = ", ".join(f"'{g}'" for g in geohash5s) + filters.append(f"geohash5 IN ({gh_list})") + where = " AND ".join(filters) + sql = f""" + SELECT date, geohash5, + {', '.join(select_cols)} + FROM `{self._t.irradiance_daily}` + WHERE {where} + GROUP BY date, geohash5 + """ + return self._bq.query(sql) + + def long_aux_pivoted( + self, + *, + table_fqn: str, + column_prefix: str, + start: date, + end: date, + variable_ids: Sequence[str], + geohash5s: Optional[Sequence[str]] = None, + ) -> pd.DataFrame: + """Pivot a long-format aux table ``(date, geohash5, variable_id, value)``. + + Args: + table_fqn: Fully-qualified BQ table name (e.g. + ``project.solar_warehouse.nasa_daily_vars_long``). + column_prefix: Source tag prepended to every output column to + avoid collisions across sources (``"nasa"``, ``"cams"``, + ``"merra"``). E.g. ``T2M`` → ``nasa_T2M``. + start, end: Inclusive date bounds. + variable_ids: Variables to pivot. Empty list → empty frame. + geohash5s: Optional location scope. + + Returns: + Wide DataFrame with columns ``date, geohash5, _...``. + """ + if not variable_ids: + return pd.DataFrame() + # PIVOT clause supports per-pivot-column aliasing via "AS alias". + pivot_in = ", ".join( + f"'{vid}' AS {column_prefix}_{vid}" for vid in variable_ids + ) + var_list = ", ".join(f"'{vid}'" for vid in variable_ids) + filters = [ + f"date BETWEEN DATE('{start}') AND DATE('{end}')", + f"variable_id IN ({var_list})", + ] + if geohash5s: + gh_list = ", ".join(f"'{g}'" for g in geohash5s) + filters.append(f"geohash5 IN ({gh_list})") + where = " AND ".join(filters) + sql = f""" + SELECT * FROM ( + SELECT date, geohash5, variable_id, value + FROM `{table_fqn}` + WHERE {where} + ) + PIVOT (ANY_VALUE(value) FOR variable_id IN ({pivot_in})) + ORDER BY date, geohash5 + """ + return self._bq.query(sql) + + def native_pixels( + self, *, table_fqn: str, source: Optional[Source] = None + ) -> pd.DataFrame: + """Distinct ``(geohash5, latitude, longitude)`` pixels stored for a source. + + Returns the native-pixel set a + :class:`~susse.warehouse_ops.snapping.NearestPixelSnapper` is built + from — every cell the table physically holds for ``source``. The + scan touches only the three location columns, so it is cheap even + on the multi-GB long tables. + + Args: + table_fqn: Fully-qualified BQ table name. + source: Optional ``source``-column filter. ``None`` returns the + whole table's pixels (use for single-source tables). + + Returns: + DataFrame with columns ``geohash5, latitude, longitude``, one + row per distinct native pixel. Empty if the table holds nothing + for ``source``. + """ + where = f" WHERE source = '{source.value}'" if source is not None else "" + sql = ( + f"SELECT DISTINCT geohash5, latitude, longitude " + f"FROM `{table_fqn}`{where}" + ) + return self._bq.query(sql) + + def warehouse_table_mods(self, table_ids: Sequence[str]) -> dict[str, str]: + """Return ``{table_id: last_modified_time_iso}`` for each table. + + Used by the dataset manifest to capture the warehouse state at + snapshot-build time. Rows for tables that don't exist are simply + omitted. + """ + if not table_ids: + return {} + quoted = ", ".join(f"'{t}'" for t in table_ids) + config = self._bq.config + sql = f""" + SELECT table_id, + TIMESTAMP_MILLIS(last_modified_time) AS last_modified_ts + FROM `{config.project_id}.{config.dataset}.__TABLES__` + WHERE table_id IN ({quoted}) + """ + df = self._bq.query(sql) + return { + row["table_id"]: pd.Timestamp(row["last_modified_ts"]).isoformat() + for _, row in df.iterrows() + } diff --git a/src/susse/warehouse_ops/population/__init__.py b/src/susse/warehouse_ops/population/__init__.py new file mode 100644 index 00000000..0ee59940 --- /dev/null +++ b/src/susse/warehouse_ops/population/__init__.py @@ -0,0 +1,89 @@ +"""Warehouse population subsystem. + +Three ingest patterns: + +* :class:`NamedLocationsPlan` + :class:`CamsSatelliteJob` / + :class:`NasaPowerSatelliteJob` — fetch a satellite source for a list of + named points (typically ground stations). +* :class:`GridPlan` + the same satellite jobs — fetch a satellite source + for every point of a lat/lon grid (portal coverage cache). +* :class:`GroundFilePlan` + :class:`GroundIngestJob` — parse a heterogeneous + ground-measurement file via a :class:`GroundSourceAdapter` and curate. + +All jobs are idempotent: they pre-check what's already in the warehouse +and skip API calls / disk reads for rows that are cached, then rely on +MERGE-on-write to dedupe at the table level. +""" + +from .adapters import GroundSourceAdapter, StandardCsvAdapter +from .base_job import BaseJob, JobResult +from .coverage import CoverageRepository +from .curation import CurationOptions, curate_ground +from .dim_variable import VariableCatalog, populate_dim_variable, variables_to_dataframe +from .jobs import ( + BaseSatelliteJob, + CamsSatelliteJob, + GroundIngestJob, + MerraRegionJob, + NasaPowerRegionJob, + NasaPowerSatelliteJob, +) +from .loaders import DerivedColumn, MergeLoader, MergeSpec +from .types import ( + BoundingBox, + DateRange, + FetchPlan, + GridPlan, + GridSpec, + GroundFilePlan, + IrradianceBand, + LocationSpec, + NamedLocationsPlan, + PhysicalStorage, + RegionPlan, + Source, + VariableSpec, +) +from .validators import ( + validate_ground_curated, + validate_ground_raw, + validate_long_format, +) + +__all__ = [ + "BaseJob", + "BaseSatelliteJob", + "BoundingBox", + "CamsSatelliteJob", + "CoverageRepository", + "CurationOptions", + "DateRange", + "DerivedColumn", + "FetchPlan", + "GridPlan", + "GridSpec", + "GroundFilePlan", + "GroundIngestJob", + "GroundSourceAdapter", + "IrradianceBand", + "JobResult", + "LocationSpec", + "MergeLoader", + "MergeSpec", + "MerraRegionJob", + "NamedLocationsPlan", + "NasaPowerRegionJob", + "NasaPowerSatelliteJob", + "PhysicalStorage", + "RegionPlan", + "Source", + "StandardCsvAdapter", + "VariableCatalog", + "VariableSpec", + "curate_ground", + "populate_dim_variable", + "validate_ground_curated", + "validate_ground_raw", + "validate_long_format", + "variables_to_dataframe", +] diff --git a/src/susse/warehouse_ops/population/adapters.py b/src/susse/warehouse_ops/population/adapters.py new file mode 100644 index 00000000..0c7ef785 --- /dev/null +++ b/src/susse/warehouse_ops/population/adapters.py @@ -0,0 +1,98 @@ +"""Pluggable parsers for heterogeneous ground-measurement files. + +Each new ground-truth source (a publication's CSV, a ministry export, a +station logger dump) gets its own :class:`GroundSourceAdapter` subclass. +The adapter knows the source's column conventions and produces a +DataFrame in the standard raw schema; everything downstream (validation, +curation, MERGE-load) sees the same shape regardless of origin. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from pathlib import Path +from typing import ClassVar + +import pandas as pd + +from .validators import validate_ground_raw + + +class GroundSourceAdapter(ABC): + """Parses a ground-measurement file into the standard raw schema. + + The standard raw schema (matching ``ground_measurements_raw``): + + * ``datetime`` — pandas-parseable date / timestamp + * ``ghi`` — daily total/mean GHI in Wh/m²/day + * ``location`` — site name (stable string) + * ``latitude`` — degrees, [-90, 90] + * ``longitude`` — degrees, [-180, 180] + + Implementations should call :func:`validate_ground_raw` on their output + so contract violations surface immediately. + """ + + @property + @abstractmethod + def adapter_id(self) -> str: + """Stable identifier for this adapter (logged with ingest runs).""" + + @abstractmethod + def parse(self, file_path: Path) -> pd.DataFrame: + """Parse ``file_path`` and return a DataFrame in the standard schema.""" + + +class StandardCsvAdapter(GroundSourceAdapter): + """Adapter for sources already shaped in the standard CSV schema. + + The CrossBoundary, Makerere Physics, and Uganda Ministry of Energy + sources all emit CSVs with columns ``datetime, ghi, location, latitude, + longitude`` — they share this adapter rather than each having its own. + + Args: + source_id: human-readable label for the source (e.g. ``"CBE"``, + ``"MAK_physics_dept"``). Embedded in :attr:`adapter_id` for log + traceability and helpful when one warehouse run touches several + files of the same shape. + """ + + EXPECTED_COLUMNS: ClassVar[tuple[str, ...]] = ( + "datetime", + "ghi", + "location", + "latitude", + "longitude", + ) + + def __init__(self, source_id: str) -> None: + if not source_id: + raise ValueError("StandardCsvAdapter.source_id must be non-empty.") + self._source_id = source_id + + @property + def source_id(self) -> str: + return self._source_id + + @property + def adapter_id(self) -> str: + return f"standard-csv:{self._source_id}" + + def parse(self, file_path: Path) -> pd.DataFrame: + df = pd.read_csv(file_path) + missing = [c for c in self.EXPECTED_COLUMNS if c not in df.columns] + if missing: + raise ValueError( + f"{file_path.name}: missing required columns {missing}. " + f"StandardCsvAdapter expects {list(self.EXPECTED_COLUMNS)}. " + f"If this source has different columns, write a new " + f"GroundSourceAdapter subclass rather than reshaping the file." + ) + out = df[list(self.EXPECTED_COLUMNS)].copy() + out["datetime"] = pd.to_datetime(out["datetime"]).dt.date + out["ghi"] = out["ghi"].astype(float) + out["location"] = out["location"].astype(str) + out["latitude"] = out["latitude"].astype(float) + out["longitude"] = out["longitude"].astype(float) + validate_ground_raw(out, context=f"StandardCsvAdapter[{file_path.name}]") + return out diff --git a/src/susse/warehouse_ops/population/base_job.py b/src/susse/warehouse_ops/population/base_job.py new file mode 100644 index 00000000..d536f5ce --- /dev/null +++ b/src/susse/warehouse_ops/population/base_job.py @@ -0,0 +1,76 @@ +"""Base class and result container for ingest jobs.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from datetime import datetime, timezone + +from ..io.bq import BigQueryClient +from .types import FetchPlan + + +@dataclass(frozen=True) +class JobResult: + """Outcome of an ingest job run. + + Carries observability counters per CLAUDE.md "ingest constraints" memo: + rows added vs already cached vs skipped, API calls made, when started + and finished. Logged at the end of every ``run()``. + """ + + job_name: str + plan_summary: str + started_at: datetime + finished_at: datetime + rows_added: int = 0 + rows_already_cached: int = 0 + rows_skipped: int = 0 + api_calls_made: int = 0 + extra: dict[str, int] = field(default_factory=dict) + + @property + def duration_seconds(self) -> float: + return (self.finished_at - self.started_at).total_seconds() + + def summary(self) -> str: + parts = [ + f"job={self.job_name}", + f"plan={self.plan_summary}", + f"rows_added={self.rows_added}", + f"already_cached={self.rows_already_cached}", + f"skipped={self.rows_skipped}", + f"api_calls={self.api_calls_made}", + f"duration={self.duration_seconds:.1f}s", + ] + for k, v in self.extra.items(): + parts.append(f"{k}={v}") + return " | ".join(parts) + + +class BaseJob(ABC): + """Base class for warehouse-population jobs. + + Each subclass owns one ingest pattern (named-location satellite, grid + satellite, ground-file). The ``run`` method is the only public entry + point and must return a :class:`JobResult`. + """ + + def __init__(self, bq: BigQueryClient) -> None: + self._bq = bq + + @property + def bq(self) -> BigQueryClient: + return self._bq + + @property + @abstractmethod + def name(self) -> str: + """Stable identifier for this job (used in logs / job results).""" + + @abstractmethod + def run(self, plan: FetchPlan) -> JobResult: + """Execute the ingest. Subclasses dispatch on the concrete plan type.""" + + def _start_time(self) -> datetime: + return datetime.now(timezone.utc) diff --git a/src/susse/warehouse_ops/population/coverage.py b/src/susse/warehouse_ops/population/coverage.py new file mode 100644 index 00000000..fb1805b5 --- /dev/null +++ b/src/susse/warehouse_ops/population/coverage.py @@ -0,0 +1,174 @@ +"""Coverage queries: ask the warehouse what's already loaded. + +Used by ingest jobs to skip API calls for rows that are already in the +target table — the rate-limit-aware, idempotent half of the contract. +Every coverage query is scoped by the slice the job is currently +processing (date range, source, etc.) so we never scan the full +20M-row long-format table when only a month's worth of data matters. +""" + +from __future__ import annotations + +import logging +from datetime import date +from typing import Sequence + +from ..io.bq import BigQueryClient +from ..io.config import TableSchemas +from .types import DateRange, Source + +_logger = logging.getLogger(__name__) + + +class CoverageRepository: + """Read-only queries that report what is already in the warehouse.""" + + def __init__(self, bq: BigQueryClient) -> None: + self._bq = bq + + def existing_long_keys( + self, + table_fqn: str, + *, + date_range: DateRange, + source: Source, + variable_ids: Sequence[str], + geohash5s: Sequence[str] | None = None, + ) -> set[tuple]: + """Return existing ``(date, geohash5, variable_id)`` tuples. + + Scoped by date range, source, and variable list. When ``geohash5s`` + is provided the scan is further constrained — essential when only + a small set of locations is being processed but the date range + overlaps the existing warehouse footprint (otherwise the query + pulls back millions of irrelevant rows). + """ + var_list = ", ".join(f"'{v}'" for v in variable_ids) + filters = [ + f"date BETWEEN DATE('{date_range.start}') AND DATE('{date_range.end}')", + f"source = '{source.value}'", + f"variable_id IN ({var_list})", + ] + if geohash5s is not None: + if not geohash5s: + # Empty location list → no rows can match. Skip the BQ trip. + return set() + gh_list = ", ".join(f"'{g}'" for g in geohash5s) + filters.append(f"geohash5 IN ({gh_list})") + _logger.info( + "coverage: scanning %s for date %s..%s, source=%s, %d vars%s", + table_fqn, + date_range.start, + date_range.end, + source.value, + len(variable_ids), + f", {len(geohash5s)} geohash(es)" if geohash5s is not None else "", + ) + result = self._bq.existing_keys( + table_fqn, + key_columns=("date", "geohash5", "variable_id"), + where_filters=filters, + ) + _logger.info("coverage: %s → %d existing keys.", table_fqn, len(result)) + return result + + def existing_irradiance_keys( + self, + table_fqn: str, + *, + date_range: DateRange, + source: Source, + geohash5s: Sequence[str] | None = None, + ) -> set[tuple]: + """Return existing ``(date, geohash5)`` tuples in ``irradiance_daily``. + + Same ``geohash5s`` scope semantics as :meth:`existing_long_keys`. + """ + filters = [ + f"date BETWEEN DATE('{date_range.start}') AND DATE('{date_range.end}')", + f"source = '{source.value}'", + ] + if geohash5s is not None: + if not geohash5s: + return set() + gh_list = ", ".join(f"'{g}'" for g in geohash5s) + filters.append(f"geohash5 IN ({gh_list})") + _logger.info( + "coverage: scanning %s for date %s..%s, source=%s%s", + table_fqn, + date_range.start, + date_range.end, + source.value, + f", {len(geohash5s)} geohash(es)" if geohash5s is not None else "", + ) + result = self._bq.existing_keys( + table_fqn, + key_columns=("date", "geohash5"), + where_filters=filters, + ) + _logger.info("coverage: %s → %d existing keys.", table_fqn, len(result)) + return result + + def existing_modis_keys( + self, + table_fqn: str, + *, + date_range: DateRange, + product_band_pairs: Sequence[tuple[str, str]], + geohash5s: Sequence[str] | None = None, + ) -> set[tuple]: + """Return existing ``(date, geohash5, product_id, band_id)`` tuples. + + Modis-specific because rows are keyed on the (product, band) pair + rather than a flat ``variable_id``. Same scoping semantics as + :meth:`existing_long_keys`. + """ + if not product_band_pairs: + return set() + # Build a tuple-IN filter: WHERE (product_id, band_id) IN (('p1','b1'), ('p2','b2')) + pb_tuples = ", ".join(f"('{p}', '{b}')" for p, b in product_band_pairs) + filters = [ + f"date BETWEEN DATE('{date_range.start}') AND DATE('{date_range.end}')", + f"source = '{Source.MODIS.value}'", + f"(product_id, band_id) IN ({pb_tuples})", + ] + if geohash5s is not None: + if not geohash5s: + return set() + gh_list = ", ".join(f"'{g}'" for g in geohash5s) + filters.append(f"geohash5 IN ({gh_list})") + _logger.info( + "coverage: scanning %s for date %s..%s, %d (product, band) pairs%s", + table_fqn, + date_range.start, + date_range.end, + len(product_band_pairs), + f", {len(geohash5s)} geohash(es)" if geohash5s is not None else "", + ) + result = self._bq.existing_keys( + table_fqn, + key_columns=("date", "geohash5", "product_id", "band_id"), + where_filters=filters, + ) + _logger.info("coverage: %s → %d existing keys.", table_fqn, len(result)) + return result + + def existing_ground_raw_keys( + self, + table_fqn: str, + *, + location: str | None = None, + ) -> set[tuple]: + """Return existing ``(datetime, location)`` tuples in raw ground. + + Scoped by ``location`` if provided (one ingest file usually targets + a single named site). + """ + filters: list[str] = [] + if location is not None: + filters.append(f"location = '{location}'") + return self._bq.existing_keys( + table_fqn, + key_columns=("datetime", "location"), + where_filters=tuple(filters), + ) diff --git a/src/susse/warehouse_ops/population/curation.py b/src/susse/warehouse_ops/population/curation.py new file mode 100644 index 00000000..f0152827 --- /dev/null +++ b/src/susse/warehouse_ops/population/curation.py @@ -0,0 +1,82 @@ +"""Pure transformation from the raw ground schema to the curated schema. + +The curated schema adds geohash, geog (computed server-side at load), +units in both Wh/m²/day and kWh/m²/day, a QC level, and provenance +columns. ``curate_ground`` is a deterministic function: same input +produces the same output, no I/O, no global state. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import pandas as pd +import pygeohash + +from .validators import validate_ground_curated, validate_ground_raw + + +@dataclass(frozen=True) +class CurationOptions: + """Configurable defaults stamped on every curated row.""" + + qc_level: str = "auto" + version: str = "v1" + geohash_precision: int = 5 + + def __post_init__(self) -> None: + if not (1 <= self.geohash_precision <= 12): + raise ValueError( + f"geohash_precision={self.geohash_precision} is outside [1, 12]." + ) + if not self.qc_level: + raise ValueError("CurationOptions.qc_level must be non-empty.") + if not self.version: + raise ValueError("CurationOptions.version must be non-empty.") + + +def curate_ground( + raw: pd.DataFrame, options: CurationOptions | None = None +) -> pd.DataFrame: + """Transform a raw ground DataFrame into the curated schema. + + Schema mapping: + + * ``datetime`` → ``date`` (DATE) + * (derived) → ``month`` (first-of-month DATE) + * ``location`` → ``location`` (unchanged) + * ``latitude`` → ``lat`` (FLOAT64) + * ``longitude`` → ``lon`` (FLOAT64) + * (derived) → ``geohash5`` (STRING, precision from options) + * ``ghi`` → ``ghi_wh_m2_day`` (FLOAT64) + * (derived) → ``ghi_kwh_m2_day`` = ``ghi_wh_m2_day`` / 1000 + * (derived) → ``qc_level`` (constant from options) + * (derived) → ``_version`` (constant from options) + * (derived) → ``_curated_at`` (current UTC timestamp) + + The ``geog`` GEOGRAPHY column is intentionally NOT produced here — it is + computed server-side at load time via ``ST_GEOGPOINT(lon, lat)`` so the + DataFrame stays JSON-serialisable and cross-environment-portable. + """ + options = options or CurationOptions() + validate_ground_raw(raw, context="curate_ground.input") + + out = pd.DataFrame() + parsed_datetime = pd.to_datetime(raw["datetime"]) + out["date"] = parsed_datetime.dt.date + out["month"] = parsed_datetime.dt.to_period("M").dt.start_time.dt.date + out["location"] = raw["location"].astype(str) + out["lat"] = raw["latitude"].astype(float) + out["lon"] = raw["longitude"].astype(float) + out["geohash5"] = [ + pygeohash.encode(lat, lon, precision=options.geohash_precision) + for lat, lon in zip(out["lat"], out["lon"]) + ] + out["ghi_wh_m2_day"] = raw["ghi"].astype(float) + out["ghi_kwh_m2_day"] = out["ghi_wh_m2_day"] / 1000.0 + out["qc_level"] = options.qc_level + out["_version"] = options.version + out["_curated_at"] = pd.Timestamp.utcnow() + + validate_ground_curated(out, context="curate_ground.output") + return out diff --git a/src/susse/warehouse_ops/population/dim_variable.py b/src/susse/warehouse_ops/population/dim_variable.py new file mode 100644 index 00000000..a20e32cc --- /dev/null +++ b/src/susse/warehouse_ops/population/dim_variable.py @@ -0,0 +1,735 @@ +"""Variable catalogue and ``dim_variable`` table population. + +Defines every satellite variable SuSSE ingests, with the metadata that +goes into the ``dim_variable`` table (units, descriptions, valid ranges, +spatial resolution). New variables are added here once and pick up +across loaders, jobs, and downstream feature assembly. + +Variable routing: + +* ``ghi``, ``dhi``, ``dni`` (in either source) → wide ``irradiance_daily``. +* Everything else → the long-format companion table for that source + (``nasa_daily_vars_long`` or ``cams_daily_vars_long``). + +The variable_id strings for NASA POWER match what the warehouse stores +after migration A2 (the legacy ``arimass`` typo was renamed to +``airmass``). New variables added here must use the correct spelling. +""" + +from __future__ import annotations + +import logging +from typing import ClassVar + +import pandas as pd + +from ..io.bq import BigQueryClient +from ..io.config import TableSchemas +from .types import PhysicalStorage, Source, VariableSpec + +_logger = logging.getLogger(__name__) + + +# Spatial resolutions (km) at the equator. Negative values in the existing +# warehouse rows are a known bug; we write the correct positive values. +_NASA_POWER_RESOLUTION_KM = 55.5 # 0.5° native grid +_CAMS_RESOLUTION_KM = 5.5 # 0.05° native grid +# MERRA-2 native grid is 0.5° lat × 0.625° lon; using the lat figure as +# the representative spatial resolution. +_MERRA_2_RESOLUTION_KM = 55.5 +# MODIS resolutions are per-product. MOD11A1 LST is 1 km native; MCD43A3 +# albedo is 500 m; MOD10A1 snow is 500 m; MOD13Q1 NDVI is 250 m. +_MODIS_LST_RESOLUTION_KM = 1.0 +_MODIS_ALBEDO_RESOLUTION_KM = 0.5 +_MODIS_SNOW_RESOLUTION_KM = 0.5 +_MODIS_NDVI_RESOLUTION_KM = 0.25 + + +class VariableCatalog: + """Registry of every satellite variable SuSSE ingests. + + Two class-level lists hold the catalogue. The convention is: + + * The :attr:`IRRADIANCE_VARIABLE_IDS` set names the variables routed to + the wide ``irradiance_daily`` table; everything else goes to the + long-format companion. + * Sources are listed separately so the same variable_id can exist for + multiple sources (``ghi`` is produced by both NASA POWER and CAMS). + """ + + IRRADIANCE_VARIABLE_IDS: ClassVar[frozenset[str]] = frozenset({"ghi", "dhi", "dni"}) + + NASA_POWER_VARIABLES: ClassVar[tuple[VariableSpec, ...]] = ( + # Irradiance — go to irradiance_daily. + VariableSpec( + variable_id="ghi", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DWN", + display_name="All-sky GHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="All-sky surface shortwave downward irradiance.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + VariableSpec( + variable_id="dhi", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DIFF", + display_name="All-sky DHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="All-sky diffuse horizontal irradiance.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + VariableSpec( + variable_id="dni", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DNI", + display_name="All-sky DNI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="All-sky direct normal irradiance.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + # Auxiliary — go to nasa_daily_vars_long. + VariableSpec( + variable_id="temperature", + source=Source.NASA_POWER, + api_code="T2M", + display_name="2m Air Temperature", + unit="degC", + native_unit="degC", + description="Daily near-surface air temperature.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="temperature_range", + source=Source.NASA_POWER, + api_code="T2M_RANGE", + display_name="2m Air Temperature Range", + unit="degC", + native_unit="degC", + description="Daily range (max - min) of 2m air temperature.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="specific_humidity", + source=Source.NASA_POWER, + api_code="QV2M", + display_name="2m Specific Humidity", + unit="kg/kg", + native_unit="kg/kg", + description="2m specific humidity.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="relative_humidity", + source=Source.NASA_POWER, + api_code="RH2M", + display_name="2m Relative Humidity", + unit="%", + native_unit="%", + description="2m relative humidity.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="surface_pressure", + source=Source.NASA_POWER, + api_code="PS", + display_name="Surface Pressure", + unit="kPa", + native_unit="kPa", + description="Daily surface-level pressure.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="aod_550", + source=Source.NASA_POWER, + api_code="AOD_55", + display_name="AOD @ 550nm", + unit="unitless", + native_unit="unitless", + description="Aerosol Optical Depth at 550 nm.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="aod_550_adj", + source=Source.NASA_POWER, + api_code="AOD_55_ADJ", + display_name="AOD @ 550nm (adjusted)", + unit="unitless", + native_unit="unitless", + description="Aerosol Optical Depth at 550 nm, terrain-adjusted.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="aod_840", + source=Source.NASA_POWER, + api_code="AOD_84", + display_name="AOD @ 840nm", + unit="unitless", + native_unit="unitless", + description="Aerosol Optical Depth at 840 nm.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="cloud_visible_optical_depth", + source=Source.NASA_POWER, + api_code="CLOUD_OD", + display_name="Cloud Visible Optical Depth", + unit="unitless", + native_unit="unitless", + description="Daily mean cloud optical depth at visible wavelengths.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="cloud_amount", + source=Source.NASA_POWER, + api_code="CLOUD_AMT", + display_name="Cloud Amount", + unit="fraction", + native_unit="fraction", + description="Daily mean total cloud amount.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="cloud_amount_dat", + source=Source.NASA_POWER, + api_code="CLOUD_AMT_DAY", + display_name="Cloud Amount (Daytime)", + unit="fraction", + native_unit="fraction", + description="Daytime cloud amount.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="total_column_ozone", + source=Source.NASA_POWER, + api_code="TO3", + display_name="Total Column Ozone", + unit="Dobson Units", + native_unit="Dobson Units", + description="Total column ozone.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="precipitable_water", + source=Source.NASA_POWER, + api_code="PW", + display_name="Precipitable Water", + unit="cm", + native_unit="cm", + description="Total precipitable water in the atmospheric column.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="precipitation_corrected", + source=Source.NASA_POWER, + api_code="PRECTOTCORR", + display_name="Precipitation (Corrected)", + unit="mm/day", + native_unit="mm/day", + description="Bias-corrected daily precipitation.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="surface_albedo", + source=Source.NASA_POWER, + api_code="SRF_ALB_ADJ", + display_name="Surface Albedo (Terrain-Adjusted)", + unit="unitless", + native_unit="unitless", + description="Terrain-adjusted surface albedo.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="all_sky_surface_albedo", + source=Source.NASA_POWER, + api_code="ALLSKY_SRF_ALB", + display_name="All-sky Surface Albedo", + unit="unitless", + native_unit="unitless", + description="All-sky surface albedo.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="clear_sky_albedo", + source=Source.NASA_POWER, + api_code="CLRSKY_SRF_ALB", + display_name="Clear-sky Surface Albedo", + unit="unitless", + native_unit="unitless", + description="Clear-sky surface albedo.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="clearness_index", + source=Source.NASA_POWER, + api_code="ALLSKY_KT", + display_name="Clearness Index", + unit="unitless", + native_unit="unitless", + description="All-sky clearness index (GHI / extraterrestrial).", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + valid_min=0.0, + valid_max=1.0, + ), + VariableSpec( + variable_id="longwave_downward_irr", + source=Source.NASA_POWER, + api_code="CLRSKY_SFC_LW_DWN", + display_name="Clear-sky Longwave Downward Irradiance", + unit="W/m^2", + native_unit="W/m^2", + description="Clear-sky surface longwave downward irradiance.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="wind_speed", + source=Source.NASA_POWER, + api_code="WS2M", + display_name="2m Wind Speed", + unit="m/s", + native_unit="m/s", + description="Daily mean 2m wind speed.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="northern_wind", + source=Source.NASA_POWER, + api_code="V2M", + display_name="2m Northward Wind", + unit="m/s", + native_unit="m/s", + description="Daily mean 2m northward (V) wind component.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="surface_roughness", + source=Source.NASA_POWER, + api_code="Z0M", + display_name="Surface Roughness", + unit="m", + native_unit="m", + description="Surface aerodynamic roughness length.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="zero_plane_displacement", + source=Source.NASA_POWER, + api_code="DISPH", + display_name="Zero-Plane Displacement", + unit="m", + native_unit="m", + description="Zero-plane displacement height.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="planetary_boundary", + source=Source.NASA_POWER, + api_code="PBLTOP", + display_name="Planetary Boundary Layer Height", + unit="m", + native_unit="m", + description="Top of the planetary boundary layer.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="surface_air_density", + source=Source.NASA_POWER, + api_code="RHOA", + display_name="Surface Air Density", + unit="kg/m^3", + native_unit="kg/m^3", + description="Surface-level air density.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="airmass", + source=Source.NASA_POWER, + api_code="AIRMASS", + display_name="Airmass", + unit="unitless", + native_unit="unitless", + description="Atmospheric airmass.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="surface_soil_wetness", + source=Source.NASA_POWER, + api_code="GWETTOP", + display_name="Surface Soil Wetness", + unit="fraction", + native_unit="fraction", + description="Top-layer soil wetness fraction.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + valid_min=0.0, + valid_max=1.0, + ), + VariableSpec( + variable_id="evaporation_land", + source=Source.NASA_POWER, + api_code="EVLAND", + display_name="Evaporation (Land)", + unit="mm/day", + native_unit="mm/day", + description="Daily evaporation over land.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + VariableSpec( + variable_id="evapotranspiration_energy", + source=Source.NASA_POWER, + api_code="EVPTRNS", + display_name="Evapotranspiration Energy", + unit="W/m^2", + native_unit="W/m^2", + description="Energy consumed by evapotranspiration.", + spatial_resolution_km=_NASA_POWER_RESOLUTION_KM, + ), + # NOTE: NASA POWER does *not* serve solar_zenith_angle (SZA) at + # daily resolution — the API returns the -999 fill value, which + # the loader filters out, leaving zero rows. Including SZA in + # the catalog therefore caused the per-station coverage check + # to think SZA was always missing and re-fetch the entire + # station on every A6 invocation. Migration A10 removes the + # corresponding dim_variable row. Re-add only if NASA POWER + # starts serving SZA daily, which would need verification. + ) + + CAMS_VARIABLES: ClassVar[tuple[VariableSpec, ...]] = ( + # Irradiance — go to irradiance_daily. + VariableSpec( + variable_id="ghi", + source=Source.CAMS, + api_code="ghi", + display_name="All-sky GHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="All-sky global horizontal irradiance, daily total.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + VariableSpec( + variable_id="dhi", + source=Source.CAMS, + api_code="dhi", + display_name="All-sky DHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="All-sky diffuse horizontal irradiance, daily total.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + VariableSpec( + variable_id="dni", + source=Source.CAMS, + api_code="dni", + display_name="All-sky DNI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="All-sky direct normal irradiance, daily total.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + physical_storage=PhysicalStorage.IRRADIANCE_WIDE, + ), + # Auxiliary — go to cams_daily_vars_long. + VariableSpec( + variable_id="bhi", + source=Source.CAMS, + api_code="bhi", + display_name="All-sky BHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="All-sky beam (direct) horizontal irradiance.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + VariableSpec( + variable_id="ghi_extra", + source=Source.CAMS, + api_code="ghi_extra", + display_name="Extraterrestrial GHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="Extraterrestrial GHI on a horizontal plane.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + VariableSpec( + variable_id="ghi_clear", + source=Source.CAMS, + api_code="ghi_clear", + display_name="Clear-sky GHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="Clear-sky GHI from the McClear model.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + VariableSpec( + variable_id="bhi_clear", + source=Source.CAMS, + api_code="bhi_clear", + display_name="Clear-sky BHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="Clear-sky beam horizontal irradiance.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + VariableSpec( + variable_id="dhi_clear", + source=Source.CAMS, + api_code="dhi_clear", + display_name="Clear-sky DHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="Clear-sky diffuse horizontal irradiance.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + VariableSpec( + variable_id="dni_clear", + source=Source.CAMS, + api_code="dni_clear", + display_name="Clear-sky DNI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="Clear-sky direct normal irradiance.", + spatial_resolution_km=_CAMS_RESOLUTION_KM, + ), + ) + + MERRA_2_VARIABLES: ClassVar[tuple[VariableSpec, ...]] = ( + # All MERRA-2 entries land in the long-format companion table + # `merra_daily_vars_long`; none are routed to `irradiance_daily` + # (MERRA-2 doesn't expose a direct GHI estimate the way POWER or + # CAMS do). Hourly source values are aggregated to one daily value + # per (date, point) by cosine-zenith-weighted mean — see + # `MerraStreamFetcher` for the aggregation logic. + VariableSpec( + variable_id="aod_550_extinction", + source=Source.MERRA_2, + api_code="TOTEXTTAU", + display_name="Total Aerosol Extinction AOT @ 550 nm", + unit="unitless", + native_unit="unitless", + description=( + "Total aerosol extinction optical thickness at 550 nm " + "(daily mean, cosine-zenith-weighted from hourly tavg1_2d_aer_Nx)." + ), + spatial_resolution_km=_MERRA_2_RESOLUTION_KM, + valid_min=0.0, + ), + VariableSpec( + variable_id="aod_550_scattering", + source=Source.MERRA_2, + api_code="TOTSCATAU", + display_name="Total Aerosol Scattering AOT @ 550 nm", + unit="unitless", + native_unit="unitless", + description=( + "Total aerosol scattering optical thickness at 550 nm. " + "Combined with `aod_550_extinction` gives single-scattering " + "albedo (SSA = scattering / extinction), which discriminates " + "absorbing aerosols (dust, smoke) from scattering ones " + "(sulfate, sea salt). Daily mean, cosine-zenith-weighted " + "from hourly tavg1_2d_aer_Nx." + ), + spatial_resolution_km=_MERRA_2_RESOLUTION_KM, + valid_min=0.0, + ), + VariableSpec( + variable_id="aod_550_analysis", + source=Source.MERRA_2, + api_code="AODANA", + display_name="Aerosol Optical Depth (Analysis)", + unit="unitless", + native_unit="unitless", + description=( + "MERRA-2 aerosol analysis AOD field. Independent of POWER's " + "AOD and of TOTEXTTAU; useful as a cross-check. Daily mean, " + "cosine-zenith-weighted from 3-hourly inst3_2d_gas_Nx." + ), + spatial_resolution_km=_MERRA_2_RESOLUTION_KM, + valid_min=0.0, + ), + VariableSpec( + variable_id="precipitable_water", + source=Source.MERRA_2, + api_code="TQV", + display_name="Total Precipitable Water Vapour", + unit="kg/m^2", + native_unit="kg/m^2", + description=( + "Total column precipitable water vapour. Same physical " + "quantity as NASA POWER's `precipitable_water` (cm), provided " + "here from MERRA-2 directly for cross-source comparison. " + "Daily mean, cosine-zenith-weighted from hourly tavg1_2d_slv_Nx." + ), + spatial_resolution_km=_MERRA_2_RESOLUTION_KM, + valid_min=0.0, + ), + ) + + MODIS_VARIABLES: ClassVar[tuple[VariableSpec, ...]] = ( + # MODIS rows land in `modis_observations` (not the long-format + # tables) because each value is associated with a (product_id, + # band_id) pair that the long-format schema doesn't carry. The + # `variable_id` here uses the convention `{PRODUCT}_{BAND}` so + # `dim_variable` still has a unique key per row, and the + # underlying observations table preserves product_id and band_id + # as separate columns for downstream querying. + # + # Product list constrained by what ORNL DAAC's RST subset API + # actually serves (verified 2026-05-08 against the live /products + # endpoint). MCD43A3 (true albedo), MOD11A1 (daily LST), and + # MOD10A1 (snow cover) were originally planned but are not on + # this endpoint — they require AppEEARS or LAADS DAAC integration + # which is deferred to a future phase. The three below are the + # closest practical substitutes from ORNL's catalog. + VariableSpec( + variable_id="MCD43A4_Nadir_Reflectance_Band1", + source=Source.MODIS, + api_code="MCD43A4", + display_name="MODIS Nadir BRDF-Adjusted Reflectance, Band 1 (Red)", + unit="unitless", + native_unit="unitless", + description=( + "Daily 500 m nadir BRDF-adjusted reflectance in MODIS " + "band 1 (red, 620-670 nm). Surface-state proxy; " + "alternative to direct broadband albedo (MCD43A3, not " + "available at ORNL DAAC)." + ), + spatial_resolution_km=_MODIS_ALBEDO_RESOLUTION_KM, + valid_min=0.0, + valid_max=1.0, + physical_storage=PhysicalStorage.MODIS_OBSERVATIONS, + ), + VariableSpec( + variable_id="MOD11A2_LST_Day_1km", + source=Source.MODIS, + api_code="MOD11A2", + display_name="MODIS Land Surface Temperature (Day, 8-day, 1 km)", + unit="K", + native_unit="K", + description=( + "Daytime land surface temperature from MOD11A2, 8-day " + "composite at 1 km. The daily-cadence MOD11A1 is not " + "available at ORNL DAAC; this 8-day composite is the " + "best LST signal we can pull from this endpoint." + ), + spatial_resolution_km=_MODIS_LST_RESOLUTION_KM, + valid_min=0.0, + valid_max=400.0, + physical_storage=PhysicalStorage.MODIS_OBSERVATIONS, + ), + VariableSpec( + variable_id="MOD13Q1_250m_16_days_NDVI", + source=Source.MODIS, + api_code="MOD13Q1", + display_name="MODIS Normalised Difference Vegetation Index", + unit="unitless", + native_unit="unitless", + description=( + "16-day NDVI composite from MOD13Q1 at 250 m. Land-cover " + "proxy; useful as a slow-changing categorical-ish feature " + "for surface-type discrimination." + ), + spatial_resolution_km=_MODIS_NDVI_RESOLUTION_KM, + valid_min=-0.2, + valid_max=1.0, + physical_storage=PhysicalStorage.MODIS_OBSERVATIONS, + ), + ) + + @classmethod + def all_variables(cls) -> tuple[VariableSpec, ...]: + return ( + cls.NASA_POWER_VARIABLES + + cls.CAMS_VARIABLES + + cls.MERRA_2_VARIABLES + + cls.MODIS_VARIABLES + ) + + @classmethod + def for_source(cls, source: Source) -> tuple[VariableSpec, ...]: + return tuple(v for v in cls.all_variables() if v.source is source) + + @classmethod + def auxiliary_for_source(cls, source: Source) -> tuple[VariableSpec, ...]: + return tuple( + v + for v in cls.for_source(source) + if v.variable_id not in cls.IRRADIANCE_VARIABLE_IDS + ) + + @classmethod + def irradiance_for_source(cls, source: Source) -> tuple[VariableSpec, ...]: + return tuple( + v + for v in cls.for_source(source) + if v.variable_id in cls.IRRADIANCE_VARIABLE_IDS + ) + + @classmethod + def get(cls, *, variable_id: str, source: Source) -> VariableSpec: + for v in cls.all_variables(): + if v.variable_id == variable_id and v.source is source: + return v + raise KeyError( + f"No variable '{variable_id}' for source {source.value}. " + f"Add an entry to VariableCatalog or check the spelling." + ) + + +def variables_to_dataframe(variables: tuple[VariableSpec, ...]) -> pd.DataFrame: + """Project a tuple of VariableSpec into a ``dim_variable``-shaped DataFrame.""" + return pd.DataFrame( + [ + { + "variable_id": v.variable_id, + "source": v.source.value, + "display_name": v.display_name, + "unit": v.unit, + "native_unit": v.native_unit, + "description": v.description, + "temporal_granularity": v.temporal_granularity, + "spatial_resolution_km": v.spatial_resolution_km, + "valid_min": v.valid_min, + "valid_max": v.valid_max, + "physical_storage": v.physical_storage.value, + } + for v in variables + ] + ) + + +def populate_dim_variable( + bq: BigQueryClient, + *, + table_fqn: str, + variables: tuple[VariableSpec, ...] | None = None, +) -> int: + """Upsert variable catalogue rows into the ``dim_variable`` table. + + Idempotent on ``(variable_id, source)``. Pass ``variables=None`` to + upsert the entire registered catalogue (NASA POWER + CAMS). + + Returns the number of rows written. + """ + from .loaders import MergeLoader, MergeSpec + + variables = variables if variables is not None else VariableCatalog.all_variables() + df = variables_to_dataframe(variables) + if df.empty: + _logger.info("populate_dim_variable: no variables to write.") + return 0 + + loader = MergeLoader( + bq=bq, + table_fqn=table_fqn, + spec=MergeSpec(schema=TableSchemas.DIM_VARIABLE), + ) + written = loader.load(df) + _logger.info( + "populate_dim_variable: upserted %d row(s) into %s.", written, table_fqn + ) + return written diff --git a/src/susse/warehouse_ops/population/jobs/__init__.py b/src/susse/warehouse_ops/population/jobs/__init__.py new file mode 100644 index 00000000..a28ea9d9 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/__init__.py @@ -0,0 +1,17 @@ +"""Concrete ingest jobs.""" + +from .ground_job import GroundIngestJob +from .merra_region_job import MerraRegionJob +from .modis_job import ModisJob +from .nasa_power_region_job import NasaPowerRegionJob +from .satellite_job import BaseSatelliteJob, CamsSatelliteJob, NasaPowerSatelliteJob + +__all__ = [ + "BaseSatelliteJob", + "CamsSatelliteJob", + "GroundIngestJob", + "MerraRegionJob", + "ModisJob", + "NasaPowerRegionJob", + "NasaPowerSatelliteJob", +] diff --git a/src/susse/warehouse_ops/population/jobs/ground_job.py b/src/susse/warehouse_ops/population/jobs/ground_job.py new file mode 100644 index 00000000..30d724c8 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/ground_job.py @@ -0,0 +1,144 @@ +"""Ground-truth ingest job: parse → validate → MERGE raw → curate → MERGE curated.""" + +from __future__ import annotations + +import logging +from datetime import datetime, timezone + +import pandas as pd + +from ...io.bq import BigQueryClient +from ...io.config import TableRefs, TableSchemas +from ..adapters import GroundSourceAdapter +from ..base_job import BaseJob, JobResult +from ..coverage import CoverageRepository +from ..curation import CurationOptions, curate_ground +from ..loaders import DerivedColumn, MergeLoader, MergeSpec +from ..types import FetchPlan, GroundFilePlan +from ..validators import validate_ground_curated, validate_ground_raw + +_logger = logging.getLogger(__name__) + + +# The curated table holds a GEOGRAPHY column built server-side from lat/lon. +_GEOG_DERIVATION: tuple[DerivedColumn, ...] = ( + DerivedColumn(name="geog", sql_expr="ST_GEOGPOINT(lon, lat)"), +) + + +class GroundIngestJob(BaseJob): + """Ingest a ground-measurement file end-to-end. + + Pipeline: + + 1. Adapter parses the file into the standard raw schema. + 2. Schema validation against ``ground_measurements_raw``. + 3. Coverage check: skip rows whose ``(datetime, location)`` is already + in the raw table (rate-limit-aware idempotency for the disk-read). + 4. MERGE-load raw rows into ``ground_measurements_raw``. + 5. Curate the new rows (geohash, units, provenance). + 6. MERGE-load curated rows into ``ground_measurements`` (with ``geog`` + derived server-side). + + Re-running on the same file is a no-op (returns rows_added=0). + """ + + def __init__( + self, + bq: BigQueryClient, + *, + adapter: GroundSourceAdapter, + refs: TableRefs | None = None, + curation_options: CurationOptions | None = None, + ) -> None: + super().__init__(bq) + self._adapter = adapter + self._refs = refs or TableRefs(config=bq.config) + self._curation_options = curation_options or CurationOptions() + + @property + def name(self) -> str: + return f"ground_ingest:{self._adapter.adapter_id}" + + def run(self, plan: FetchPlan) -> JobResult: + if not isinstance(plan, GroundFilePlan): + raise TypeError( + f"{self.name} accepts GroundFilePlan, got {type(plan).__name__}." + ) + if plan.adapter_id != self._adapter.adapter_id: + raise ValueError( + f"Plan declares adapter '{plan.adapter_id}' but this job is " + f"configured with '{self._adapter.adapter_id}'. " + f"Construct the GroundIngestJob with the matching adapter." + ) + started = self._start_time() + + raw = self._adapter.parse(plan.file_path) + validate_ground_raw(raw, context=f"{self.name}.parse") + + coverage = CoverageRepository(self._bq) + locations_in_file = sorted(raw["location"].unique().tolist()) + existing_raw: set[tuple] = set() + for loc in locations_in_file: + existing_raw |= coverage.existing_ground_raw_keys( + self._refs.ground_measurements_raw, + location=loc, + ) + + # Filter to rows NOT yet in raw. + raw_keys = list(zip(raw["datetime"], raw["location"])) + keep_mask = [k not in existing_raw for k in raw_keys] + new_raw = raw[keep_mask].reset_index(drop=True) + skipped = len(raw) - len(new_raw) + + rows_added_raw = 0 + rows_added_curated = 0 + + if not new_raw.empty: + rows_added_raw = self._load_raw(new_raw) + curated = curate_ground(new_raw, self._curation_options) + validate_ground_curated(curated, context=f"{self.name}.curated") + rows_added_curated = self._load_curated(curated) + else: + _logger.info( + "%s: all %d rows already present in raw table; nothing to do.", + self.name, + len(raw), + ) + + finished = datetime.now(timezone.utc) + result = JobResult( + job_name=self.name, + plan_summary=plan.describe(), + started_at=started, + finished_at=finished, + rows_added=rows_added_raw + rows_added_curated, + rows_already_cached=skipped, + extra={ + "raw_rows_in_file": len(raw), + "raw_rows_added": rows_added_raw, + "curated_rows_added": rows_added_curated, + "locations": len(locations_in_file), + }, + ) + _logger.info("%s | %s", self.name, result.summary()) + return result + + def _load_raw(self, df: pd.DataFrame) -> int: + loader = MergeLoader( + bq=self._bq, + table_fqn=self._refs.ground_measurements_raw, + spec=MergeSpec(schema=TableSchemas.GROUND_MEASUREMENTS_RAW), + ) + return loader.load(df) + + def _load_curated(self, df: pd.DataFrame) -> int: + loader = MergeLoader( + bq=self._bq, + table_fqn=self._refs.ground_measurements, + spec=MergeSpec( + schema=TableSchemas.GROUND_MEASUREMENTS, + derived_columns=_GEOG_DERIVATION, + ), + ) + return loader.load(df) diff --git a/src/susse/warehouse_ops/population/jobs/merra_region_job.py b/src/susse/warehouse_ops/population/jobs/merra_region_job.py new file mode 100644 index 00000000..312523ef --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/merra_region_job.py @@ -0,0 +1,235 @@ +"""MERRA-2 region-shaped ingest job. + +Sibling of :class:`BaseSatelliteJob` rather than a subclass: MERRA-2 is +fetched via region (bbox) OPeNDAP calls — one call per (date, variable) +covering every location in the plan — instead of the per-(point, date) +pattern that NASA POWER and CAMS use. The inner loop of +``BaseSatelliteJob`` is therefore not the right shape for MERRA-2; we +fan out at the fetcher boundary instead. + +See :mod:`susse.api_clients.merra_2.merra_daily_fetcher` for the bbox +mechanics and the speedup rationale (~100× over per-point loops for +grid-sized requests). +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timezone + +import pandas as pd +import pygeohash + +from ....api_clients.merra_2 import MerraDailyFetcher +from ...io.bq import BigQueryClient +from ...io.config import TableRefs, TableSchemas +from ..base_job import BaseJob, JobResult +from ..coverage import CoverageRepository +from ..types import ( + FetchPlan, + GridPlan, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) +from .satellite_loading import load_long + +_logger = logging.getLogger(__name__) + + +class MerraRegionJob(BaseJob): + """Ingest job for MERRA-2 reanalysis using region-shaped fetches. + + Args: + bq: BigQuery client. + fetcher: Inject a configured :class:`MerraDailyFetcher` (e.g. with + a custom ``max_workers`` or a stub for tests). Defaults to a + fresh fetcher with the package default concurrency. + refs: Override the table-ref resolver. Defaults to one built from + ``bq.config``. + geohash_precision: Precision for the ``geohash5`` column. Must + match what the rest of the warehouse uses (5). + """ + + def __init__( + self, + bq: BigQueryClient, + *, + fetcher: MerraDailyFetcher | None = None, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq) + self._fetcher = fetcher or MerraDailyFetcher() + self._refs = refs or TableRefs(config=bq.config) + self._geohash_precision = geohash_precision + + @property + def source(self) -> Source: + return Source.MERRA_2 + + @property + def name(self) -> str: + return f"merra_region_ingest:{self.source.value}" + + @property + def table_fqn(self) -> str: + return self._refs.merra_daily_vars_long + + def run(self, plan: FetchPlan) -> JobResult: + if not isinstance(plan, (NamedLocationsPlan, GridPlan)): + raise TypeError( + f"{self.name} accepts NamedLocationsPlan or GridPlan, " + f"got {type(plan).__name__}." + ) + if plan.source is not self.source: + raise ValueError( + f"{self.name} requires plan.source == {self.source}, " + f"got {plan.source}." + ) + + started = self._start_time() + locations = self._resolve_locations(plan) + date_range = plan.date_range + variables = tuple(plan.variables) + api_codes = tuple(v.api_code for v in variables) + api_to_var = {v.api_code: v.variable_id for v in variables} + + location_geohashes = tuple( + pygeohash.encode(loc.lat, loc.lon, precision=self._geohash_precision) + for loc in locations + ) + _logger.info( + "%s: %d location(s), date %s..%s, %d var(s)", + self.name, + len(locations), + date_range.start, + date_range.end, + len(variables), + ) + + coverage = CoverageRepository(self._bq) + existing_keys = coverage.existing_long_keys( + self.table_fqn, + date_range=date_range, + source=self.source, + variable_ids=tuple(v.variable_id for v in variables), + geohash5s=location_geohashes, + ) + + df = self._fetcher.fetch_region( + points=tuple((loc.lat, loc.lon) for loc in locations), + date_start=date_range.start, + date_end=date_range.end, + api_codes=api_codes, + ) + api_calls = 1 # one fetch_region call covers all (date, var) tasks + + rows_added = 0 + if not df.empty: + df = self._enrich(df, locations, api_to_var) + df = self._drop_already_cached(df, existing_keys) + if not df.empty: + rows_added = self._load(df) + + finished = datetime.now(timezone.utc) + result = JobResult( + job_name=self.name, + plan_summary=plan.describe(), + started_at=started, + finished_at=finished, + rows_added=rows_added, + api_calls_made=api_calls, + extra={ + "locations": len(locations), + "fetched_rows": int(len(df)) + (rows_added if df.empty else 0), + }, + ) + _logger.info("%s | %s", self.name, result.summary()) + return result + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + @staticmethod + def _resolve_locations( + plan: NamedLocationsPlan | GridPlan, + ) -> list[LocationSpec]: + if isinstance(plan, NamedLocationsPlan): + return list(plan.locations) + return list(plan.grid.iter_locations()) + + def _enrich( + self, + df: pd.DataFrame, + locations: list[LocationSpec], + api_to_var: dict[str, str], + ) -> pd.DataFrame: + """Map api_code → variable_id, attach geohash5 and source columns. + + The fetcher returns variable_id == api_code; the warehouse uses + the catalog-defined variable_id. Geohash5 is derived from the + plan's locations (one geohash per (lat, lon) pair) so we don't + recompute per-row. + """ + out = df.copy() + out["variable_id"] = out["variable_id"].map(api_to_var) + out = out.dropna(subset=["variable_id"]) + if out.empty: + return out + + # Per-(lat, lon) geohash lookup, vectorised via merge. + gh_lookup = pd.DataFrame( + [ + { + "latitude": loc.lat, + "longitude": loc.lon, + "geohash5": pygeohash.encode( + loc.lat, loc.lon, precision=self._geohash_precision + ), + } + for loc in locations + ] + ) + out = out.merge(gh_lookup, on=["latitude", "longitude"], how="left") + if out["geohash5"].isna().any(): + # Should be impossible: the fetcher emits rows whose lat/lon + # come straight from the input ``points``, which mirror + # ``locations``. Surface the assumption explicitly. + raise RuntimeError( + "MerraRegionJob._enrich: fetched rows contain (lat, lon) " + "values not present in the plan's locations. The fetcher " + "and the plan are out of sync." + ) + out["source"] = self.source.value + return out + + @staticmethod + def _drop_already_cached( + df: pd.DataFrame, existing_keys: set[tuple] + ) -> pd.DataFrame: + """Filter out rows whose (date, geohash5, variable_id) is already + in the warehouse. + + The MergeLoader is idempotent on the merge keys, so this filter + is purely an optimisation: it spares us the upload + MERGE round + trip on full re-runs. + """ + if not existing_keys or df.empty: + return df + keys = pd.Series( + list(zip(df["date"], df["geohash5"], df["variable_id"])), + index=df.index, + ) + return df[~keys.isin(existing_keys)] + + def _load(self, df: pd.DataFrame) -> int: + return load_long( + self._bq, + table_fqn=self.table_fqn, + schema=TableSchemas.MERRA_DAILY_VARS_LONG, + df=df, + context=self.name, + ) diff --git a/src/susse/warehouse_ops/population/jobs/modis_job.py b/src/susse/warehouse_ops/population/jobs/modis_job.py new file mode 100644 index 00000000..63ef1d12 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/modis_job.py @@ -0,0 +1,266 @@ +"""MODIS ingest job. + +Sibling of :class:`BaseSatelliteJob` rather than a subclass: MODIS rows +land in their own ``modis_observations`` table (keyed on the +``(product_id, band_id)`` pair, not a flat ``variable_id``) and never +flow into ``irradiance_daily``, so the long-format/wide-format split that +``BaseSatelliteJob`` orchestrates does not apply. + +The job accepts :class:`NamedLocationsPlan` or :class:`GridPlan` whose +``source`` is :attr:`Source.MODIS` and dispatches to +:class:`~susse.api_clients.modis.ModisLongFetcher` per location. +Variables in the plan are :class:`VariableSpec` entries from the catalog +where ``api_code`` carries the MODIS *product* (e.g. ``"MCD43A4"``) and +``variable_id`` follows the ``"{product}_{band}"`` convention (so the +band is recovered by stripping the ``"{api_code}_"`` prefix). +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timezone + +import pandas as pd +import pygeohash + +from ....api_clients.modis import ModisLongFetcher, product_cadence_days +from ...io.bq import BigQueryClient +from ...io.config import TableRefs, TableSchemas +from ..base_job import BaseJob, JobResult +from ..coverage import CoverageRepository +from ..loaders import DerivedColumn, MergeLoader, MergeSpec +from ..types import ( + DateRange, + FetchPlan, + GridPlan, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) + +_logger = logging.getLogger(__name__) + +# Server-side derivation: GEOGRAPHY column built from staging lat/lon. +# Same convention as the long-format satellite jobs. +_GEOG_DERIVATION: tuple[DerivedColumn, ...] = ( + DerivedColumn(name="geog", sql_expr="ST_GEOGPOINT(longitude, latitude)"), +) + +# When a (product, band) pair already has at least this fraction of the +# expected row count for a location, treat it as cached and skip. The +# slack accommodates composite-tile alignment: MODIS products report on +# fixed Julian-day boundaries, so a date range of N days yields between +# ``floor(N / cadence)`` and ``ceil(N / cadence) + 1`` rows depending on +# where the requested window lands. +_COVERAGE_RATIO_THRESHOLD = 0.9 + + +def _split_variable_id(v: VariableSpec) -> tuple[str, str]: + """Return ``(product_id, band_id)`` derived from a MODIS VariableSpec. + + The catalog encodes ``variable_id = f"{product}_{band}"`` and stores + the product in ``api_code``. Stripping the ``"{api_code}_"`` prefix + gives the band id verbatim, even when the band itself contains + underscores (e.g. ``"LST_Day_1km"`` or ``"250m_16_days_NDVI"``). + """ + prefix = f"{v.api_code}_" + if not v.variable_id.startswith(prefix): + raise ValueError( + f"MODIS VariableSpec variable_id={v.variable_id!r} does not " + f"start with api_code prefix {prefix!r}. Catalog convention " + f"requires variable_id == f'{{api_code}}_{{band_id}}'. Update " + f"the catalog entry in dim_variable.py." + ) + return v.api_code, v.variable_id[len(prefix) :] + + +class ModisJob(BaseJob): + """Ingest job for MODIS observations from ORNL DAAC. + + Args: + bq: BigQuery client. + fetcher: Inject a configured :class:`ModisLongFetcher` (e.g. with + a custom ``max_workers`` or a stub for tests). Defaults to a + fresh fetcher with the package default concurrency. + refs: Override the table-ref resolver. Defaults to one built from + ``bq.config``. + geohash_precision: Precision for the ``geohash5`` column. Must + match what the rest of the warehouse uses (5). + """ + + def __init__( + self, + bq: BigQueryClient, + *, + fetcher: ModisLongFetcher | None = None, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq) + self._fetcher = fetcher or ModisLongFetcher() + self._refs = refs or TableRefs(config=bq.config) + self._geohash_precision = geohash_precision + + @property + def source(self) -> Source: + return Source.MODIS + + @property + def name(self) -> str: + return f"modis_ingest:{self.source.value}" + + @property + def table_fqn(self) -> str: + return self._refs.modis_observations + + def run(self, plan: FetchPlan) -> JobResult: + if not isinstance(plan, (NamedLocationsPlan, GridPlan)): + raise TypeError( + f"{self.name} accepts NamedLocationsPlan or GridPlan, " + f"got {type(plan).__name__}." + ) + if plan.source is not self.source: + raise ValueError( + f"{self.name} requires plan.source == {self.source}, " + f"got {plan.source}." + ) + + started = self._start_time() + locations = self._resolve_locations(plan) + date_range = plan.date_range + product_band_pairs = tuple(_split_variable_id(v) for v in plan.variables) + + plan_geohashes = tuple( + pygeohash.encode(loc.lat, loc.lon, precision=self._geohash_precision) + for loc in locations + ) + _logger.info( + "%s: %d location(s), date %s..%s, %d (product, band) pair(s)", + self.name, + len(locations), + date_range.start, + date_range.end, + len(product_band_pairs), + ) + + coverage = CoverageRepository(self._bq) + existing_keys = coverage.existing_modis_keys( + self.table_fqn, + date_range=date_range, + product_band_pairs=product_band_pairs, + geohash5s=plan_geohashes, + ) + + rows_added = 0 + skipped_locations = 0 + api_calls = 0 + + for loc in locations: + geohash5 = pygeohash.encode( + loc.lat, loc.lon, precision=self._geohash_precision + ) + if self._location_fully_cached( + geohash5, + date_range=date_range, + product_band_pairs=product_band_pairs, + existing_keys=existing_keys, + ): + skipped_locations += 1 + continue + + df = self._fetcher.fetch_long_for_location( + latitude=loc.lat, + longitude=loc.lon, + date_start=date_range.start, + date_end=date_range.end, + products_and_bands=product_band_pairs, + ) + api_calls += 1 + if df.empty: + continue + df = self._enrich(df, loc, geohash5) + rows_added += self._load(df) + + finished = datetime.now(timezone.utc) + result = JobResult( + job_name=self.name, + plan_summary=plan.describe(), + started_at=started, + finished_at=finished, + rows_added=rows_added, + api_calls_made=api_calls, + extra={ + "skipped_locations": skipped_locations, + "processed_locations": len(locations) - skipped_locations, + }, + ) + _logger.info("%s | %s", self.name, result.summary()) + return result + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + @staticmethod + def _resolve_locations( + plan: NamedLocationsPlan | GridPlan, + ) -> list[LocationSpec]: + if isinstance(plan, NamedLocationsPlan): + return list(plan.locations) + return list(plan.grid.iter_locations()) + + @staticmethod + def _location_fully_cached( + geohash5: str, + *, + date_range: DateRange, + product_band_pairs: tuple[tuple[str, str], ...], + existing_keys: set[tuple], + ) -> bool: + """True iff every (product, band) pair has near-complete coverage. + + See :data:`_COVERAGE_RATIO_THRESHOLD` for the threshold rationale. + """ + for product_id, band_id in product_band_pairs: + cadence = product_cadence_days(product_id) + expected = max(1, date_range.n_days // cadence) + actual = sum( + 1 + for k in existing_keys + if k[1] == geohash5 and k[2] == product_id and k[3] == band_id + ) + if actual < expected * _COVERAGE_RATIO_THRESHOLD: + return False + return True + + def _enrich( + self, df: pd.DataFrame, loc: LocationSpec, geohash5: str + ) -> pd.DataFrame: + out = df.copy() + out["latitude"] = loc.lat + out["longitude"] = loc.lon + out["geohash5"] = geohash5 + out["source"] = self.source.value + return out + + def _load(self, df: pd.DataFrame) -> int: + loader = MergeLoader( + bq=self._bq, + table_fqn=self.table_fqn, + spec=MergeSpec( + schema=TableSchemas.MODIS_OBSERVATIONS, + derived_columns=_GEOG_DERIVATION, + ), + ) + cols = [ + "date", + "latitude", + "longitude", + "geohash5", + "product_id", + "band_id", + "value", + "source", + ] + return loader.load(df[cols]) diff --git a/src/susse/warehouse_ops/population/jobs/nasa_power_region_job.py b/src/susse/warehouse_ops/population/jobs/nasa_power_region_job.py new file mode 100644 index 00000000..7b355a84 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/nasa_power_region_job.py @@ -0,0 +1,182 @@ +"""Region-shaped NASA POWER ingest job. + +Sibling of :class:`BaseSatelliteJob` (per-point) and :class:`MerraRegionJob`: +NASA POWER's ``/regional`` endpoint returns one HTTP call per (tile, variable) +covering a whole bounding box, instead of the per-point pattern. This job +wraps :class:`NASAPowerRegionalFetcher`, then routes the returned rows into +the wide ``irradiance_daily`` table (GHI/DHI/DNI) and the long +``nasa_daily_vars_long`` table (auxiliary variables) via the shared +:mod:`satellite_loading` helpers. + +Crucially, the job stores **NASA's own native pixel centres verbatim** — no +densification onto a finer grid. NASA POWER irradiance comes off CERES' 1° +grid and its auxiliary variables off MERRA-2's 0.5°×0.625° grid; both are +stored at those native resolutions. Reads snap arbitrary query coordinates +onto these pixels via :class:`susse.warehouse_ops.snapping.NearestPixelSnapper`. + +For a one-year Uganda ingest the whole result is ~1.5M rows (~130 native +pixels × 366 days × ~32 variables), so the BigQuery load is a single fast +MERGE — the cost is entirely the ~130 HTTP calls the fetcher issues. +""" + +from __future__ import annotations + +import logging +from datetime import datetime, timezone + +import pandas as pd +import pygeohash + +from ....api_clients.NASA_Power import NASAPowerRegionalFetcher +from ...io.bq import BigQueryClient +from ...io.config import TableRefs, TableSchemas +from ..base_job import BaseJob, JobResult +from ..dim_variable import VariableCatalog +from ..types import FetchPlan, RegionPlan, Source +from .satellite_loading import irradiance_long_to_wide, load_irradiance, load_long + +_logger = logging.getLogger(__name__) + + +class NasaPowerRegionJob(BaseJob): + """Region-shaped NASA POWER ingest, storing native pixels verbatim. + + Args: + bq: BigQuery client. + fetcher: Inject a configured :class:`NASAPowerRegionalFetcher` (e.g. + a stub for tests). Defaults to a fresh fetcher. + refs: Override the table-ref resolver. Defaults to one built from + ``bq.config``. + geohash_precision: Precision for the ``geohash5`` column. Must match + the rest of the warehouse (5). + """ + + def __init__( + self, + bq: BigQueryClient, + *, + fetcher: NASAPowerRegionalFetcher | None = None, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq) + self._fetcher = fetcher or NASAPowerRegionalFetcher() + self._refs = refs or TableRefs(config=bq.config) + self._geohash_precision = geohash_precision + + @property + def source(self) -> Source: + return Source.NASA_POWER + + @property + def name(self) -> str: + return f"nasa_power_region_ingest:{self.source.value}" + + def run(self, plan: FetchPlan) -> JobResult: + if not isinstance(plan, RegionPlan): + raise TypeError( + f"{self.name} accepts RegionPlan, got {type(plan).__name__}. " + f"Wrap the bbox + variables in a RegionPlan." + ) + if plan.source is not self.source: + raise ValueError( + f"{self.name} requires plan.source == {self.source}, " + f"got {plan.source}." + ) + + started = self._start_time() + variables = tuple(plan.variables) + api_to_var = {v.api_code: v.variable_id for v in variables} + _logger.info( + "%s: bbox lat[%.3f..%.3f] lon[%.3f..%.3f], dates %s..%s, %d var(s)", + self.name, + plan.bbox.min_lat, + plan.bbox.max_lat, + plan.bbox.min_lon, + plan.bbox.max_lon, + plan.date_range.start, + plan.date_range.end, + len(variables), + ) + + df = self._fetcher.fetch_region_long( + min_lat=plan.bbox.min_lat, + max_lat=plan.bbox.max_lat, + min_lon=plan.bbox.min_lon, + max_lon=plan.bbox.max_lon, + date_start=plan.date_range.start, + date_end=plan.date_range.end, + api_codes=tuple(v.api_code for v in variables), + ) + + rows_added_long = 0 + rows_added_irr = 0 + n_pixels = 0 + if not df.empty: + enriched = self._enrich(df, api_to_var) + n_pixels = int(enriched["geohash5"].nunique()) + is_irradiance = enriched["variable_id"].isin( + VariableCatalog.IRRADIANCE_VARIABLE_IDS + ) + irradiance_long = enriched[is_irradiance] + aux_long = enriched[~is_irradiance] + if not irradiance_long.empty: + rows_added_irr = load_irradiance( + self._bq, + table_fqn=self._refs.irradiance_daily, + df=irradiance_long_to_wide(irradiance_long), + ) + if not aux_long.empty: + rows_added_long = load_long( + self._bq, + table_fqn=self._refs.nasa_daily_vars_long, + schema=TableSchemas.NASA_DAILY_VARS_LONG, + df=aux_long, + context=f"{self.name} long", + ) + + finished = datetime.now(timezone.utc) + result = JobResult( + job_name=self.name, + plan_summary=plan.describe(), + started_at=started, + finished_at=finished, + rows_added=rows_added_long + rows_added_irr, + # The fetcher fans out internally; one fetch_region_long call. + api_calls_made=1, + extra={ + "rows_added_long": rows_added_long, + "rows_added_irradiance": rows_added_irr, + "native_pixels": n_pixels, + "fetched_rows": int(len(df)), + }, + ) + _logger.info("%s | %s", self.name, result.summary()) + return result + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _enrich(self, df: pd.DataFrame, api_to_var: dict[str, str]) -> pd.DataFrame: + """Map api_code → variable_id and attach geohash5 + source columns. + + The fetcher emits ``variable_id == api_code``; the warehouse uses the + catalog-defined ``variable_id``. ``geohash5`` is computed once per + distinct native pixel (~130 of them) and merged in, rather than once + per row. + """ + out = df.copy() + out["variable_id"] = out["variable_id"].map(api_to_var) + out = out.dropna(subset=["variable_id"]) + if out.empty: + return out + + pixels = out[["latitude", "longitude"]].drop_duplicates() + pixels["geohash5"] = [ + pygeohash.encode(lat, lon, precision=self._geohash_precision) + for lat, lon in zip(pixels["latitude"], pixels["longitude"]) + ] + out = out.merge(pixels, on=["latitude", "longitude"], how="left") + out["source"] = self.source.value + return out diff --git a/src/susse/warehouse_ops/population/jobs/satellite_job.py b/src/susse/warehouse_ops/population/jobs/satellite_job.py new file mode 100644 index 00000000..9054e387 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/satellite_job.py @@ -0,0 +1,522 @@ +"""Satellite ingest jobs for NASA POWER and CAMS. + +A single base class implements the per-pattern lifecycle (resolve +locations → coverage check → fetch → reshape → split into irradiance +vs auxiliary → MERGE-load). Each concrete subclass plugs in a +source-specific fetch + reshape step. + +Both ``NamedLocationsPlan`` and ``GridPlan`` flow through the same path +because they only differ in how the location list is resolved — the +inner loop is identical. +""" + +from __future__ import annotations + +import logging +from abc import abstractmethod +from datetime import date, datetime, timezone +from typing import ClassVar + +import pandas as pd +import pygeohash + +from ....api_clients.cams import CAMSClient +from ....api_clients.cams.cams_client import CamsApiError +from ....api_clients.NASA_Power import ( + NASAPowerFetchData, + NASAPowerProduct, + TemporalResolution, +) +from ...io.bq import BigQueryClient +from ...io.config import TableRefs, TableSchemas +from ..base_job import BaseJob, JobResult +from ..coverage import CoverageRepository +from ..dim_variable import VariableCatalog +from ..types import ( + DateRange, + FetchPlan, + GridPlan, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) +from .satellite_loading import irradiance_long_to_wide, load_irradiance, load_long + +try: + from geopy import Point +except ( + ImportError +): # pragma: no cover - geopy is a hard dep but the import path is checked at use site + Point = None # type: ignore[assignment] + + +def _location_spec_to_geopy(loc: LocationSpec): + """Adapter for the NASA POWER fetcher. + + The fetcher only reads ``.latitude`` / ``.longitude`` from the object it + is passed, both of which :class:`geopy.Point` exposes directly. We used + to wrap the ``Point`` in a :class:`geopy.location.Location`, but + ``Location.__init__`` requires ``address``/``raw`` as of geopy 2.4 — + and we have neither, nor do we need them. + """ + if Point is None: + raise RuntimeError( + "geopy is required for NASA POWER ingest but could not be imported. " + "Add geopy to requirements.txt and reinstall." + ) + return Point(loc.lat, loc.lon) + + +_logger = logging.getLogger(__name__) + + +class BaseSatelliteJob(BaseJob): + """Base for satellite-source ingest jobs. + + Subclasses provide the source identity and the per-location fetch+reshape + that produces a long-format DataFrame (one row per (date, variable, value)). + """ + + def __init__( + self, + bq: BigQueryClient, + *, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq) + self._refs = refs or TableRefs(config=bq.config) + self._geohash_precision = geohash_precision + + @property + @abstractmethod + def source(self) -> Source: + """Source tag for this satellite ingest (``Source.NASA_POWER`` etc.).""" + + @property + @abstractmethod + def long_table_fqn(self) -> str: + """FQN of the long-format auxiliary table for this source.""" + + @abstractmethod + def _fetch_long_for_location( + self, + location: LocationSpec, + date_range: DateRange, + variables: tuple[VariableSpec, ...], + ) -> pd.DataFrame: + """Return a long-format DataFrame for one location. + + Required columns: ``date, variable_id, value``. The base class + adds ``latitude, longitude, geohash5, source`` and routes the + irradiance subset to ``irradiance_daily``. + """ + + @property + def name(self) -> str: + return f"satellite_ingest:{self.source.value}" + + def run(self, plan: FetchPlan) -> JobResult: + if not isinstance(plan, (NamedLocationsPlan, GridPlan)): + raise TypeError( + f"{self.name} accepts NamedLocationsPlan or GridPlan, " + f"got {type(plan).__name__}." + ) + if plan.source is not self.source: + raise ValueError( + f"{self.name} requires plan.source == {self.source}, " + f"got {plan.source}." + ) + started = self._start_time() + locations = self._resolve_locations(plan) + date_range = plan.date_range + all_vars = tuple(plan.variables) + long_vars = tuple( + v + for v in all_vars + if v.variable_id not in VariableCatalog.IRRADIANCE_VARIABLE_IDS + ) + irradiance_vars = tuple( + v + for v in all_vars + if v.variable_id in VariableCatalog.IRRADIANCE_VARIABLE_IDS + ) + + coverage = CoverageRepository(self._bq) + # Pre-compute the geohashes for this plan's locations so coverage + # queries can be scoped to them. Without this scope, named-location + # plans whose date range overlaps the existing warehouse footprint + # pull back millions of irrelevant rows and grind for minutes. + plan_geohashes = tuple( + pygeohash.encode(loc.lat, loc.lon, precision=self._geohash_precision) + for loc in locations + ) + _logger.info( + "%s: %d location(s), date %s..%s, %d long-vars, %d irradiance-vars", + self.name, + len(locations), + date_range.start, + date_range.end, + len(long_vars), + len(irradiance_vars), + ) + existing_long = ( + coverage.existing_long_keys( + self.long_table_fqn, + date_range=date_range, + source=self.source, + variable_ids=tuple(v.variable_id for v in long_vars), + geohash5s=plan_geohashes, + ) + if long_vars + else set() + ) + existing_irr = ( + coverage.existing_irradiance_keys( + self._refs.irradiance_daily, + date_range=date_range, + source=self.source, + geohash5s=plan_geohashes, + ) + if irradiance_vars + else set() + ) + + rows_added_long = 0 + rows_added_irr = 0 + skipped_locations = 0 + api_calls = 0 + + for loc in locations: + geohash5 = pygeohash.encode( + loc.lat, loc.lon, precision=self._geohash_precision + ) + if self._location_fully_cached( + geohash5, + date_range=date_range, + long_vars=long_vars, + irradiance_vars=irradiance_vars, + existing_long=existing_long, + existing_irr=existing_irr, + ): + skipped_locations += 1 + continue + + long_df = self._fetch_long_for_location(loc, date_range, all_vars) + api_calls += 1 + long_df = self._enrich_long(long_df, loc, geohash5) + + if irradiance_vars: + irr_df = self._extract_irradiance(long_df, irradiance_vars) + if not irr_df.empty: + rows_added_irr += self._load_irradiance(irr_df) + + if long_vars: + aux_df = long_df[ + long_df["variable_id"].isin([v.variable_id for v in long_vars]) + ].copy() + if not aux_df.empty: + rows_added_long += self._load_long(aux_df) + + finished = datetime.now(timezone.utc) + result = JobResult( + job_name=self.name, + plan_summary=plan.describe(), + started_at=started, + finished_at=finished, + rows_added=rows_added_long + rows_added_irr, + api_calls_made=api_calls, + extra={ + "rows_added_long": rows_added_long, + "rows_added_irradiance": rows_added_irr, + "skipped_locations": skipped_locations, + "processed_locations": len(locations) - skipped_locations, + }, + ) + _logger.info("%s | %s", self.name, result.summary()) + return result + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _resolve_locations( + self, plan: NamedLocationsPlan | GridPlan + ) -> list[LocationSpec]: + if isinstance(plan, NamedLocationsPlan): + return list(plan.locations) + return list(plan.grid.iter_locations()) + + def _location_fully_cached( + self, + geohash5: str, + *, + date_range: DateRange, + long_vars: tuple[VariableSpec, ...], + irradiance_vars: tuple[VariableSpec, ...], + existing_long: set[tuple], + existing_irr: set[tuple], + ) -> bool: + """True iff every (date, variable) we'd need is already cached.""" + all_dates = pd.date_range(date_range.start, date_range.end, freq="D").date + for d in all_dates: + if irradiance_vars and (d, geohash5) not in existing_irr: + return False + for v in long_vars: + if (d, geohash5, v.variable_id) not in existing_long: + return False + return True + + def _enrich_long( + self, df: pd.DataFrame, loc: LocationSpec, geohash5: str + ) -> pd.DataFrame: + out = df.copy() + out["latitude"] = loc.lat + out["longitude"] = loc.lon + out["geohash5"] = geohash5 + out["source"] = self.source.value + return out + + def _extract_irradiance( + self, long_df: pd.DataFrame, irradiance_vars: tuple[VariableSpec, ...] + ) -> pd.DataFrame: + """Pivot the requested irradiance rows from long → wide.""" + wanted_ids = [v.variable_id for v in irradiance_vars] + sub = long_df[long_df["variable_id"].isin(wanted_ids)].copy() + if sub.empty: + return sub + return irradiance_long_to_wide(sub) + + def _load_long(self, df: pd.DataFrame) -> int: + return load_long( + self._bq, + table_fqn=self.long_table_fqn, + schema=self._long_schema, + df=df, + context=f"{self.name} long", + ) + + def _load_irradiance(self, df: pd.DataFrame) -> int: + return load_irradiance(self._bq, table_fqn=self._refs.irradiance_daily, df=df) + + @property + def _long_schema(self): + if self.source is Source.NASA_POWER: + return TableSchemas.NASA_DAILY_VARS_LONG + if self.source is Source.CAMS: + return TableSchemas.CAMS_DAILY_VARS_LONG + raise ValueError( + f"No long-format schema registered for source={self.source}. " + f"Add a branch here when adding a new satellite source." + ) + + +# --------------------------------------------------------------------------- +# NASA POWER +# --------------------------------------------------------------------------- + + +class NasaPowerSatelliteJob(BaseSatelliteJob): + """Ingest job for NASA POWER (daily resolution).""" + + def __init__( + self, + bq: BigQueryClient, + *, + fetcher: NASAPowerFetchData | None = None, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq, refs=refs, geohash_precision=geohash_precision) + self._fetcher = fetcher or NASAPowerFetchData() + + @property + def source(self) -> Source: + return Source.NASA_POWER + + @property + def long_table_fqn(self) -> str: + return self._refs.nasa_daily_vars_long + + def _fetch_long_for_location( + self, + location: LocationSpec, + date_range: DateRange, + variables: tuple[VariableSpec, ...], + ) -> pd.DataFrame: + products = self._variables_to_products(variables) + result = self._fetcher.fetch_multiple_parameters( + start_date=datetime.combine(date_range.start, datetime.min.time()), + end_date=datetime.combine(date_range.end, datetime.min.time()), + location=_location_spec_to_geopy(location), + products=products, + temporal_resolution=TemporalResolution.DAILY, + ) + return self._result_to_long(result, variables) + + def _variables_to_products( + self, variables: tuple[VariableSpec, ...] + ) -> list[NASAPowerProduct]: + out: list[NASAPowerProduct] = [] + for v in variables: + try: + out.append(self._product_for_api_code(v.api_code)) + except KeyError as exc: + raise ValueError( + f"Variable '{v.variable_id}' has api_code '{v.api_code}' " + f"but no NASAPowerProduct enum entry uses that code. " + f"Update NASAPowerProduct or VariableCatalog." + ) from exc + return out + + @staticmethod + def _product_for_api_code(api_code: str) -> NASAPowerProduct: + for product in NASAPowerProduct: + if product.value == api_code: + return product + raise KeyError(api_code) + + def _result_to_long( + self, result, variables: tuple[VariableSpec, ...] + ) -> pd.DataFrame: + """Flatten a NASA POWER multi-product result into a long DataFrame. + + Unit assumption: NASA POWER's daily irradiance parameters + (e.g. ``ALLSKY_SFC_SW_DWN``) are returned as **daily-integrated + energy in kWh/m²/day**, which is the warehouse convention for + ``ghi_kwh_m2_day``. Values are therefore passed through without + scaling. If a future NASA POWER variable is added with a different + native unit (e.g. MJ/m²/day or W/m² 24h-mean), an explicit conversion + must be added here — silently round-tripping a mismatched unit would + corrupt the irradiance pipeline downstream. + """ + api_to_var = {v.api_code: v.variable_id for v in variables} + rows: list[dict] = [] + for product in result.products: + data = result.get_parameter_data(product) + if data is None: + continue + variable_id = api_to_var.get(product.value) + if variable_id is None: + continue + for ts_str, value in data.items(): + if value is None: + continue + # NASA POWER fill-value is -999 for missing data. + if value == -999 or value == -999.0: + continue + row_date = datetime.strptime(ts_str, "%Y%m%d").date() + rows.append( + { + "date": row_date, + "variable_id": variable_id, + "value": float(value), + } + ) + return pd.DataFrame(rows) + + +# --------------------------------------------------------------------------- +# CAMS +# --------------------------------------------------------------------------- + + +class CamsSatelliteJob(BaseSatelliteJob): + """Ingest job for CAMS radiation (daily resolution).""" + + def __init__( + self, + bq: BigQueryClient, + *, + fetcher: CAMSClient | None = None, + refs: TableRefs | None = None, + geohash_precision: int = 5, + ) -> None: + super().__init__(bq, refs=refs, geohash_precision=geohash_precision) + self._fetcher = fetcher or CAMSClient() + + @property + def source(self) -> Source: + return Source.CAMS + + @property + def long_table_fqn(self) -> str: + return self._refs.cams_daily_vars_long + + # Unit conversion from pvlib's get_cams output to the warehouse column + # convention (kWh/m²/day). + # + # Upstream unit assumption — verified against pvlib source (sodapro.py, + # ``get_cams``): with the default ``integrated=False``, the irradiance + # columns ("Global Horiz", "BHI", "DHI", "BNI") are returned as the + # **mean power in W/m² over the time step**. For ``time_step='1d'``, + # pvlib divides the native CAMS Wh/m² daily integral by 24 hours to + # produce a 24-hour-mean W/m² value (i.e. nighttime hours of zero are + # included in the mean). + # + # ``CAMSClient.fetch_data`` (api_clients/cams/cams_client.py) does NOT + # pass ``integrated``, so it relies on this default. If a future change + # sets ``integrated=True``, the unit becomes Wh/m²/day and this factor + # would be wrong by 1000×. + # + # Conversion: W/m² (24h-mean) × 24 h / 1000 W/kW = kWh/m²/day. The + # daily integral is identical whether the average is taken over 24h or + # only daytime hours (nighttime contributes zero), so the result is a + # true daily energy integral. + # + # Empirically validated against the legacy CSV ingest path in + # migration A4 (``2026-05-08_a4_backfill_cams_aux_uganda_2024.py``); + # migration A9 (``2026-05-08_a9_fix_cams_units.py``) backfilled older + # rows that had been stored before this conversion was added. + _W_M2_TO_KWH_M2_DAY: ClassVar[float] = 0.024 + + def _fetch_long_for_location( + self, + location: LocationSpec, + date_range: DateRange, + variables: tuple[VariableSpec, ...], + ) -> pd.DataFrame: + try: + df, _metadata = self._fetcher.fetch_data( + latitude=location.lat, + longitude=location.lon, + start=datetime.combine(date_range.start, datetime.min.time()), + end=datetime.combine(date_range.end, datetime.min.time()), + time_step="1d", + ) + except CamsApiError: + raise + return self._cams_dataframe_to_long(df, variables) + + @classmethod + def _cams_dataframe_to_long( + cls, df: pd.DataFrame, variables: tuple[VariableSpec, ...] + ) -> pd.DataFrame: + """Reshape a CAMS wide DataFrame to long form and convert units. + + Input ``df`` is the post-processed output of ``CAMSClient.fetch_data``, + carrying irradiance columns in **W/m² (24-hour mean)** per pvlib's + default ``integrated=False`` convention. This method multiplies every + value by :attr:`_W_M2_TO_KWH_M2_DAY` (0.024) to land in the warehouse + unit kWh/m²/day. See the class-level comment on that constant for the + full derivation and the pvlib source reference. + """ + api_to_var = {v.api_code: v.variable_id for v in variables} + # The pvlib output's first column is timestamp (already ISO string after + # CAMSClient processing); rename to a known name for melt convenience. + ts_col = df.columns[0] + present = [c for c in df.columns if c in api_to_var] + if not present: + return pd.DataFrame(columns=["date", "variable_id", "value"]) + long = df[[ts_col] + present].melt( + id_vars=[ts_col], + value_vars=present, + var_name="api_code", + value_name="value", + ) + long["date"] = pd.to_datetime(long[ts_col]).dt.date + long["variable_id"] = long["api_code"].map(api_to_var) + long = long[["date", "variable_id", "value"]].dropna(subset=["value"]) + long["value"] = long["value"].astype(float) * cls._W_M2_TO_KWH_M2_DAY + return long.reset_index(drop=True) diff --git a/src/susse/warehouse_ops/population/jobs/satellite_loading.py b/src/susse/warehouse_ops/population/jobs/satellite_loading.py new file mode 100644 index 00000000..f4760b31 --- /dev/null +++ b/src/susse/warehouse_ops/population/jobs/satellite_loading.py @@ -0,0 +1,137 @@ +"""Shared BigQuery-load mechanics for satellite ingest jobs. + +The per-point :class:`BaseSatelliteJob` and the region-shaped +:class:`MerraRegionJob` / :class:`NasaPowerRegionJob` all land long-format +rows in a ``*_daily_vars_long`` table and — for sources that carry +irradiance — wide rows in ``irradiance_daily``. This module is the single +home of that shared mechanics (the GEOGRAPHY derivation, the long→wide +irradiance pivot, the MERGE-load wrappers) so the three jobs cannot drift +apart. +""" + +from __future__ import annotations + +import pandas as pd + +from ...io.bq import BigQueryClient +from ...io.config import TableSchema, TableSchemas +from ..loaders import DerivedColumn, MergeLoader, MergeSpec +from ..validators import validate_long_format + +# Server-side derivation: the GEOGRAPHY column is built from staging lat/lon +# during the MERGE rather than carried across the wire by pandas. +GEOG_DERIVATION: tuple[DerivedColumn, ...] = ( + DerivedColumn(name="geog", sql_expr="ST_GEOGPOINT(longitude, latitude)"), +) + +# Long-format irradiance variable_id → wide ``irradiance_daily`` column name. +_IRRADIANCE_COLUMN_BY_VARIABLE: dict[str, str] = { + "ghi": "ghi_kwh_m2_day", + "dhi": "dhi_kwh_m2_day", + "dni": "dni_kwh_m2_day", +} + +# Column order each table's MERGE-load expects. +_LONG_LOAD_COLUMNS: tuple[str, ...] = ( + "date", + "latitude", + "longitude", + "geohash5", + "variable_id", + "value", + "source", +) +_IRRADIANCE_LOAD_COLUMNS: tuple[str, ...] = ( + "date", + "latitude", + "longitude", + "geohash5", + "source", + "ghi_kwh_m2_day", + "dhi_kwh_m2_day", + "dni_kwh_m2_day", + "reliability", +) + + +def irradiance_long_to_wide(irradiance_long_df: pd.DataFrame) -> pd.DataFrame: + """Pivot already-filtered long irradiance rows into the wide shape. + + Args: + irradiance_long_df: Long-format rows whose ``variable_id`` is one of + ``ghi`` / ``dhi`` / ``dni`` (the caller filters; this function + does not). Must carry ``date, latitude, longitude, geohash5, + source, variable_id, value``. + + Returns: + One row per (date, cell) with named ``*_kwh_m2_day`` columns. Bands + absent from the input become NULL, as does ``reliability`` — no + satellite source in the catalogue publishes a reliability series. + """ + wide = irradiance_long_df.pivot_table( + index=["date", "latitude", "longitude", "geohash5", "source"], + columns="variable_id", + values="value", + aggfunc="first", + ).reset_index() + wide.columns.name = None + wide = wide.rename(columns=_IRRADIANCE_COLUMN_BY_VARIABLE) + for col in _IRRADIANCE_COLUMN_BY_VARIABLE.values(): + if col not in wide.columns: + wide[col] = pd.NA + wide["reliability"] = pd.NA + return wide + + +def load_long( + bq: BigQueryClient, + *, + table_fqn: str, + schema: TableSchema, + df: pd.DataFrame, + context: str, +) -> int: + """Validate and MERGE-load long-format rows into a ``*_daily_vars_long`` table. + + Args: + bq: BigQuery client. + table_fqn: Fully-qualified target table name. + schema: The target table's :class:`TableSchema` (carries merge keys). + df: Long-format rows; must carry every column in + :data:`_LONG_LOAD_COLUMNS`. + context: Label for the validation error message (usually the job + name). + + Returns: + Number of rows staged for the MERGE. + """ + validate_long_format(df, context=context) + loader = MergeLoader( + bq=bq, + table_fqn=table_fqn, + spec=MergeSpec(schema=schema, derived_columns=GEOG_DERIVATION), + ) + return loader.load(df[list(_LONG_LOAD_COLUMNS)]) + + +def load_irradiance(bq: BigQueryClient, *, table_fqn: str, df: pd.DataFrame) -> int: + """MERGE-load wide irradiance rows into ``irradiance_daily``. + + Args: + bq: BigQuery client. + table_fqn: Fully-qualified ``irradiance_daily`` table name. + df: Wide rows; must carry every column in + :data:`_IRRADIANCE_LOAD_COLUMNS`. + + Returns: + Number of rows staged for the MERGE. + """ + loader = MergeLoader( + bq=bq, + table_fqn=table_fqn, + spec=MergeSpec( + schema=TableSchemas.IRRADIANCE_DAILY, + derived_columns=GEOG_DERIVATION, + ), + ) + return loader.load(df[list(_IRRADIANCE_LOAD_COLUMNS)]) diff --git a/src/susse/warehouse_ops/population/loaders.py b/src/susse/warehouse_ops/population/loaders.py new file mode 100644 index 00000000..202ffe96 --- /dev/null +++ b/src/susse/warehouse_ops/population/loaders.py @@ -0,0 +1,239 @@ +"""MERGE-loader for idempotent writes into BigQuery tables. + +The loader writes a DataFrame to a staging table, then runs a server-side +MERGE that upserts into the target by the table's declared merge keys. +Re-running with overlapping data updates existing rows in place — never +creates duplicates. + +For columns whose value is computed from the *staging* row rather than +carried by the client (e.g. ``geog`` = ``ST_GEOGPOINT(lon, lat)``), pass +:class:`DerivedColumn` instances in :class:`MergeSpec.derived_columns`. + +When a staging frame contains a ``date`` column and spans many distinct +dates against a date-partitioned target, the loader chunks the MERGE by +date range to stay under BigQuery's 4,000-partitions-per-DML-statement +limit (see :data:`_BQ_PARTITION_LIMIT_PER_STATEMENT`). +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass +from datetime import date, timedelta + +import pandas as pd + +from ..io.bq import BigQueryClient +from ..io.config import TableSchema + +_logger = logging.getLogger(__name__) + +# BigQuery enforces a hard cap of 4,000 partitions modified per DML +# statement. We chunk well under it (3,500 days per MERGE) so a single +# multi-year ingest won't trip the limit even with leap-year edge cases +# or accidental duplicate-date rows in the staging table. +_BQ_PARTITION_LIMIT_PER_STATEMENT = 3500 + + +@dataclass(frozen=True) +class DerivedColumn: + """A target column computed server-side from the staging row. + + Used for types that pandas can't carry across the wire (e.g. ``GEOGRAPHY``) + or values better produced by the database (e.g. ``CURRENT_TIMESTAMP()``). + The expression references staging columns by name. + """ + + name: str + sql_expr: str + + +@dataclass(frozen=True) +class MergeSpec: + """How to MERGE-load a DataFrame into a target table.""" + + schema: TableSchema + derived_columns: tuple[DerivedColumn, ...] = () + + +class MergeLoader: + """Loads a DataFrame into a BigQuery table via staging-table MERGE. + + The MERGE is idempotent: re-running with overlapping rows updates the + existing target rows by the schema's ``merge_keys``; new rows are + inserted; nothing is duplicated. + """ + + def __init__(self, bq: BigQueryClient, table_fqn: str, spec: MergeSpec) -> None: + self._bq = bq + self._table_fqn = table_fqn + self._spec = spec + + @property + def schema(self) -> TableSchema: + return self._spec.schema + + def load(self, df: pd.DataFrame) -> int: + """Load ``df`` and return the number of rows staged. + + Returns 0 if ``df`` is empty (nothing staged, no MERGE issued). + """ + if df.empty: + return 0 + self._validate_columns(df) + staging_fqn = f"{self._table_fqn}_staging" + self._bq.load_dataframe(df, staging_fqn, write_disposition="WRITE_TRUNCATE") + self._merge_from_staging(staging_columns=tuple(df.columns)) + return len(df) + + def _validate_columns(self, df: pd.DataFrame) -> None: + missing_keys = [k for k in self._spec.schema.merge_keys if k not in df.columns] + if missing_keys: + raise ValueError( + f"DataFrame is missing merge-key columns for " + f"{self._spec.schema.table_id}: {missing_keys}. " + f"Required keys: {list(self._spec.schema.merge_keys)}. " + f"Add them to the DataFrame before calling load()." + ) + derived_names = {d.name for d in self._spec.derived_columns} + overlap = derived_names & set(df.columns) + if overlap: + raise ValueError( + f"DataFrame already contains columns the loader would derive " + f"server-side: {sorted(overlap)}. Drop them before loading or " + f"remove the corresponding DerivedColumn entries." + ) + + def _merge_from_staging(self, staging_columns: tuple[str, ...]) -> None: + merge_keys = self._spec.schema.merge_keys + derived = self._spec.derived_columns + + all_target_columns = list(staging_columns) + [d.name for d in derived] + non_key_columns = [c for c in all_target_columns if c not in merge_keys] + + # Staging projection: pass-through staging columns + derived expressions. + select_parts = [f"`{c}`" for c in staging_columns] + [ + f"{d.sql_expr} AS `{d.name}`" for d in derived + ] + staging_select = ", ".join(select_parts) + + on_clause = " AND ".join(f"t.`{k}` = s.`{k}`" for k in merge_keys) + update_clause = ", ".join(f"`{c}` = s.`{c}`" for c in non_key_columns) + col_list = ", ".join(f"`{c}`" for c in all_target_columns) + val_list = ", ".join(f"s.`{c}`" for c in all_target_columns) + + # If every column is a key column, there is nothing to UPDATE on match. + when_matched_clause = ( + f"WHEN MATCHED THEN UPDATE SET {update_clause}\n " + if non_key_columns + else "" + ) + + staging_fqn = f"{self._table_fqn}_staging" + + # BigQuery rejects DDL inside multi-statement transactions, so we + # issue create / merge / drop as three separate statements. MERGE + # is atomic on its own; staging persists between failures but is + # WRITE_TRUNCATE'd on the next load anyway. + create_sql = ( + f"CREATE TABLE IF NOT EXISTS `{self._table_fqn}` AS " + f"SELECT {staging_select} FROM `{staging_fqn}` WHERE 1=0;" + ) + + def _merge_sql(date_filter: str = "1=1") -> str: + return f""" + MERGE `{self._table_fqn}` t + USING ( + SELECT {staging_select} + FROM `{staging_fqn}` + WHERE {date_filter} + ) s + ON {on_clause} + {when_matched_clause}WHEN NOT MATCHED THEN + INSERT ({col_list}) VALUES ({val_list}); + """ + + drop_sql = f"DROP TABLE `{staging_fqn}`;" + + self._bq.execute_ddl(create_sql) + self._merge_in_partition_chunks( + staging_columns=staging_columns, + staging_fqn=staging_fqn, + merge_sql_factory=_merge_sql, + ) + self._bq.execute_ddl(drop_sql) + + def _merge_in_partition_chunks( + self, + *, + staging_columns: tuple[str, ...], + staging_fqn: str, + merge_sql_factory, + ) -> None: + """Run the MERGE, chunking by date when the staging spans many partitions. + + BigQuery rejects a single DML statement that touches more than + ~4,000 partitions. For most loads the staging spans far fewer + dates and a single MERGE is enough; for long-history named-location + ingests (e.g. one Uganda station with 12 years of daily data) we + split the MERGE into date-range chunks of at most + :data:`_BQ_PARTITION_LIMIT_PER_STATEMENT` calendar days each. + + Tables without a ``date`` column (e.g. ``dim_variable``) cannot + have this issue and are MERGEd in one statement. + """ + if "date" not in staging_columns: + self._bq.execute_ddl(merge_sql_factory()) + return + + span_df = self._bq.query( + f"SELECT MIN(date) AS min_d, MAX(date) AS max_d FROM `{staging_fqn}`" + ) + if span_df.empty or pd.isna(span_df.iloc[0]["min_d"]): + return # Staging is empty — nothing to MERGE. + + min_d = span_df.iloc[0]["min_d"] + max_d = span_df.iloc[0]["max_d"] + if hasattr(min_d, "date"): + min_d = min_d.date() + if hasattr(max_d, "date"): + max_d = max_d.date() + + chunks = _date_chunks(min_d, max_d, _BQ_PARTITION_LIMIT_PER_STATEMENT) + if len(chunks) > 1: + _logger.info( + "MERGE for %s spans %d days from %s to %s — splitting into " + "%d chunks of <=%d days to stay under BigQuery's " + "partition-per-DML cap.", + self._table_fqn, + (max_d - min_d).days + 1, + min_d, + max_d, + len(chunks), + _BQ_PARTITION_LIMIT_PER_STATEMENT, + ) + for start, end in chunks: + filter_expr = ( + f"date BETWEEN DATE('{start.isoformat()}') " + f"AND DATE('{end.isoformat()}')" + ) + self._bq.execute_ddl(merge_sql_factory(filter_expr)) + + +def _date_chunks(start: date, end: date, max_days: int) -> list[tuple[date, date]]: + """Split ``[start, end]`` into contiguous spans of at most ``max_days``. + + The returned spans are inclusive on both ends and cover every day in + ``[start, end]`` exactly once. + """ + if start > end: + raise ValueError(f"start ({start}) must be <= end ({end}).") + if max_days < 1: + raise ValueError(f"max_days={max_days} must be >= 1.") + chunks: list[tuple[date, date]] = [] + cursor = start + while cursor <= end: + chunk_end = min(cursor + timedelta(days=max_days - 1), end) + chunks.append((cursor, chunk_end)) + cursor = chunk_end + timedelta(days=1) + return chunks diff --git a/src/susse/warehouse_ops/population/types.py b/src/susse/warehouse_ops/population/types.py new file mode 100644 index 00000000..2524377b --- /dev/null +++ b/src/susse/warehouse_ops/population/types.py @@ -0,0 +1,435 @@ +"""Typed value objects describing what to ingest. + +The population subsystem supports three ingest patterns: + +1. **Named-location** — fetch a satellite source for a discrete list of + ``LocationSpec`` points (e.g. one per ground station). Drives training + data quality. +2. **Grid** — fetch a satellite source for every point in a ``GridSpec``. + Drives portal inference-cache coverage across sub-Saharan Africa. +3. **Ground-file** — parse a heterogeneous ground-measurement file via a + :class:`susse.warehouse_ops.population.adapters.GroundSourceAdapter`, + then curate into the standard schema. + +A :class:`FetchPlan` subclass describes one execution. Jobs accept a plan +and dispatch on its concrete type. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from datetime import date +from enum import StrEnum +from pathlib import Path +from typing import Iterator + + +class Source(StrEnum): + """Origin of a satellite or reanalysis dataset. + + Enum values match the ``source`` column convention already established + in the warehouse (``'NASA'``, ``'CAMS'``, ``'MERRA2'``, ``'MODIS'``). + The enum *member name* is more specific (``NASA_POWER`` distinguishes + from MERRA-2, which is also a NASA dataset) while the *value* is the + string actually written to BigQuery. + """ + + NASA_POWER = "NASA" + CAMS = "CAMS" + MERRA_2 = "MERRA2" + MODIS = "MODIS" + + +class IrradianceBand(StrEnum): + """Which irradiance series a feature selection wants from ``irradiance_daily``. + + The wide table stores GHI, DHI and DNI side-by-side as named columns + (``ghi_kwh_m2_day``, ``dhi_kwh_m2_day``, ``dni_kwh_m2_day``). The enum + value matches the column-name prefix, so callers can use it both as + a selection token and to build the resulting ``sat__`` + output column names — see :func:`satellite_irradiance_column`. + """ + + GHI = "ghi" + DHI = "dhi" + DNI = "dni" + + +def satellite_irradiance_column( + source: Source, band: IrradianceBand = IrradianceBand.GHI +) -> str: + """Wide-table column name for ``(source, band)`` after the pivot from ``irradiance_daily``. + + Single source of truth for the ``sat___kwh_m2_day`` + naming convention. The SQL alias in + :meth:`SatelliteRepository.daily_irradiance_by_geohash`, the + placeholder synthesis in + :meth:`FeatureService._empty_irradiance_placeholder`, the + Trainer's default baseline columns, and any downstream code that + refers to satellite-irradiance columns all route through here. + + Example:: + + >>> satellite_irradiance_column(Source.NASA_POWER, IrradianceBand.GHI) + 'sat_ghi_nasa_kwh_m2_day' + >>> satellite_irradiance_column(Source.CAMS) # band defaults to GHI + 'sat_ghi_cams_kwh_m2_day' + """ + return f"sat_{band.value}_{source.value.lower()}_kwh_m2_day" + + +# Per-source prefix for auxiliary (non-irradiance) feature columns. The +# naming convention is genuinely warehouse-wide, hence module scope. +# Note the prefixes are bespoke — MERRA-2's is "merra", not the "merra2" +# that Source.MERRA_2.value.lower() would yield. +_SOURCE_FEATURE_PREFIX: dict[Source, str] = { + Source.NASA_POWER: "nasa", + Source.CAMS: "cams", + Source.MERRA_2: "merra", + Source.MODIS: "modis", +} + + +def aux_column_prefix(source: Source) -> str: + """Column-name prefix for one source's auxiliary feature columns. + + Single source of truth for the per-source prefix (``"nasa"``, + ``"cams"``, ``"merra"``, ``"modis"``). The prefix is bespoke — + MERRA-2's is ``"merra"``, not the ``"merra2"`` that + ``source.value.lower()`` yields — so every consumer (the warehouse + aux-table pivot in :class:`FeatureService`, :func:`aux_feature_column`, + :attr:`FeatureSelection.aux_columns`) routes through here. + + Raises: + KeyError: If ``source`` has no registered prefix — add an entry + to :data:`_SOURCE_FEATURE_PREFIX`. + """ + if source not in _SOURCE_FEATURE_PREFIX: + raise KeyError( + f"Source {source.value} has no auxiliary feature-column " + f"prefix. Add an entry to _SOURCE_FEATURE_PREFIX in " + f"susse.warehouse_ops.population.types." + ) + return _SOURCE_FEATURE_PREFIX[source] + + +def aux_feature_column(source: Source, variable_id: str) -> str: + """Feature-column name for one auxiliary (non-irradiance) variable. + + Single source of truth for the ``_`` naming of + aux feature columns. Counterpart to :func:`satellite_irradiance_column` + for the irradiance bands; the source prefix comes from + :func:`aux_column_prefix`. + + Example:: + + >>> aux_feature_column(Source.NASA_POWER, "temperature") + 'nasa_temperature' + >>> aux_feature_column(Source.CAMS, "ghi_clear") + 'cams_ghi_clear' + """ + return f"{aux_column_prefix(source)}_{variable_id}" + + +class PhysicalStorage(StrEnum): + """Where a catalog variable's values are physically stored in the warehouse. + + The catalog enumerates *every* variable the warehouse touches, + regardless of which table holds it. This tag tells consumers (most + importantly :class:`FeatureSelection.__post_init__`) which storage + backend a given ``variable_id`` lives in, so requests for a + wide-table variable through a long-table-only field can be rejected + at construction with a clear remediation message. + + The three values mirror the warehouse's three storage shapes: + + * ``LONG_FORMAT`` — pivoted from one of the ``_daily_vars_long`` + tables. The default for catalog entries — most variables are aux. + * ``IRRADIANCE_WIDE`` — stored as named columns + (``ghi_kwh_m2_day`` / ``dhi_kwh_m2_day`` / ``dni_kwh_m2_day``) in + the wide ``irradiance_daily`` table. Requested through + :attr:`FeatureSelection.include_satellite_irradiance`, **not** + ``_variable_ids``. + * ``MODIS_OBSERVATIONS`` — stored in ``modis_observations`` keyed by + ``(product_id, band_id)``. Requested through + :attr:`FeatureSelection.modis_variable_ids`. + """ + + LONG_FORMAT = "long_format" + IRRADIANCE_WIDE = "irradiance_wide" + MODIS_OBSERVATIONS = "modis_observations" + + +@dataclass(frozen=True) +class DateRange: + """Inclusive [start, end] date range. + + Both endpoints are inclusive — they represent calendar dates, not + half-open intervals. Use :meth:`days` to enumerate. + """ + + start: date + end: date + + def __post_init__(self) -> None: + if self.start > self.end: + raise ValueError( + f"DateRange.start ({self.start}) is after end ({self.end}). " + f"Swap the arguments." + ) + + @property + def n_days(self) -> int: + return (self.end - self.start).days + 1 + + +@dataclass(frozen=True) +class LocationSpec: + """A named geographic point (ground station, test site, grid cell).""" + + name: str + lat: float + lon: float + + def __post_init__(self) -> None: + if not self.name: + raise ValueError("LocationSpec.name must be non-empty.") + if not (-90.0 <= self.lat <= 90.0): + raise ValueError( + f"LocationSpec.lat={self.lat} is outside [-90, 90]. " + f"Did you swap lat and lon?" + ) + if not (-180.0 <= self.lon <= 180.0): + raise ValueError(f"LocationSpec.lon={self.lon} is outside [-180, 180].") + + +@dataclass(frozen=True) +class BoundingBox: + """Axis-aligned lat/lon bounding box, inclusive on all four sides.""" + + min_lat: float + max_lat: float + min_lon: float + max_lon: float + + def __post_init__(self) -> None: + if not (-90.0 <= self.min_lat <= self.max_lat <= 90.0): + raise ValueError( + f"BoundingBox latitude range ({self.min_lat}, {self.max_lat}) " + f"is invalid. Need -90 <= min_lat <= max_lat <= 90." + ) + if not (-180.0 <= self.min_lon <= self.max_lon <= 180.0): + raise ValueError( + f"BoundingBox longitude range ({self.min_lon}, {self.max_lon}) " + f"is invalid. Need -180 <= min_lon <= max_lon <= 180." + ) + + +@dataclass(frozen=True) +class GridSpec: + """A bounding-box grid sampled at fixed lat/lon resolution. + + Iterating yields :class:`LocationSpec` points named ``grid_{lat:.4f}_{lon:.4f}``. + The resolution is chosen to match the satellite source's native grid + (e.g. 0.05° for CAMS, 0.5° for NASA POWER) so the cached estimates + correspond to the source's own sample points. + """ + + bbox: BoundingBox + lat_step: float + lon_step: float + + def __post_init__(self) -> None: + if self.lat_step <= 0: + raise ValueError(f"GridSpec.lat_step={self.lat_step} must be positive.") + if self.lon_step <= 0: + raise ValueError(f"GridSpec.lon_step={self.lon_step} must be positive.") + + @property + def n_points(self) -> int: + n_lat = int(round((self.bbox.max_lat - self.bbox.min_lat) / self.lat_step)) + 1 + n_lon = int(round((self.bbox.max_lon - self.bbox.min_lon) / self.lon_step)) + 1 + return n_lat * n_lon + + def iter_locations(self) -> Iterator[LocationSpec]: + """Yield one :class:`LocationSpec` per grid point. + + Ordering is row-major: latitude outer loop, longitude inner loop. + Points are produced at ``min_lat + k * lat_step`` for integer k + such that the value remains ``<= max_lat`` (and analogously for lon). + """ + n_lat = int(round((self.bbox.max_lat - self.bbox.min_lat) / self.lat_step)) + n_lon = int(round((self.bbox.max_lon - self.bbox.min_lon) / self.lon_step)) + for i in range(n_lat + 1): + lat = round(self.bbox.min_lat + i * self.lat_step, 6) + for j in range(n_lon + 1): + lon = round(self.bbox.min_lon + j * self.lon_step, 6) + yield LocationSpec( + name=f"grid_{lat:.4f}_{lon:.4f}", + lat=lat, + lon=lon, + ) + + +@dataclass(frozen=True) +class VariableSpec: + """Catalogue entry for a satellite/ground variable. + + Mirrors the columns of the ``dim_variable`` table so an instance can be + written directly. Use :class:`susse.warehouse_ops.population.dim_variable.VariableCatalog` + for the registered set of variables. + """ + + variable_id: str + source: Source + api_code: str + display_name: str + unit: str + native_unit: str + description: str + temporal_granularity: str = "daily" + spatial_resolution_km: float | None = None + valid_min: float | None = None + valid_max: float | None = None + # Default LONG_FORMAT covers the common case (~80% of the catalog); + # the ~9 exceptions (NASA + CAMS all-sky irradiance, MODIS) override + # this explicitly with `physical_storage=PhysicalStorage.IRRADIANCE_WIDE` + # or `PhysicalStorage.MODIS_OBSERVATIONS`. See `PhysicalStorage`. + physical_storage: PhysicalStorage = PhysicalStorage.LONG_FORMAT + + +# --------------------------------------------------------------------------- +# Ingest plans (tagged hierarchy) +# --------------------------------------------------------------------------- + + +class FetchPlan(ABC): + """Tagged base class for the three ingest patterns.""" + + @abstractmethod + def describe(self) -> str: + """Return a one-line human-readable summary for logging.""" + + +@dataclass(frozen=True) +class NamedLocationsPlan(FetchPlan): + """Fetch a satellite source for a discrete set of locations.""" + + source: Source + date_range: DateRange + locations: tuple[LocationSpec, ...] + variables: tuple[VariableSpec, ...] + + def __post_init__(self) -> None: + if not self.locations: + raise ValueError("NamedLocationsPlan requires at least one location.") + if not self.variables: + raise ValueError("NamedLocationsPlan requires at least one variable.") + for v in self.variables: + if v.source is not self.source: + raise ValueError( + f"Variable '{v.variable_id}' is from source {v.source} but " + f"plan source is {self.source}. Variables must match the " + f"plan's source." + ) + + def describe(self) -> str: + return ( + f"NamedLocations[source={self.source.value}, " + f"locations={len(self.locations)}, " + f"vars={len(self.variables)}, " + f"dates={self.date_range.start}..{self.date_range.end}]" + ) + + +@dataclass(frozen=True) +class GridPlan(FetchPlan): + """Fetch a satellite source for every point in a grid.""" + + source: Source + date_range: DateRange + grid: GridSpec + variables: tuple[VariableSpec, ...] + + def __post_init__(self) -> None: + if not self.variables: + raise ValueError("GridPlan requires at least one variable.") + for v in self.variables: + if v.source is not self.source: + raise ValueError( + f"Variable '{v.variable_id}' is from source {v.source} but " + f"plan source is {self.source}." + ) + + def describe(self) -> str: + return ( + f"Grid[source={self.source.value}, " + f"points={self.grid.n_points}, " + f"vars={len(self.variables)}, " + f"dates={self.date_range.start}..{self.date_range.end}]" + ) + + +@dataclass(frozen=True) +class RegionPlan(FetchPlan): + """Fetch a satellite source for a bounding-box region in one shot. + + Used by region-shaped jobs whose upstream API exposes a bbox endpoint + (e.g. :class:`NasaPowerRegionJob` over NASA POWER's ``/regional`` + endpoint). Unlike :class:`GridPlan` it carries no sampling step: the + job stores the source's *native pixels* inside the bbox verbatim, with + no densification onto a finer grid. + """ + + source: Source + date_range: DateRange + bbox: BoundingBox + variables: tuple[VariableSpec, ...] + + def __post_init__(self) -> None: + if not self.variables: + raise ValueError("RegionPlan requires at least one variable.") + for v in self.variables: + if v.source is not self.source: + raise ValueError( + f"Variable '{v.variable_id}' is from source {v.source} but " + f"plan source is {self.source}. Variables must match the " + f"plan's source." + ) + + def describe(self) -> str: + return ( + f"Region[source={self.source.value}, " + f"bbox=({self.bbox.min_lat},{self.bbox.min_lon})" + f"..({self.bbox.max_lat},{self.bbox.max_lon}), " + f"vars={len(self.variables)}, " + f"dates={self.date_range.start}..{self.date_range.end}]" + ) + + +@dataclass(frozen=True) +class GroundFilePlan(FetchPlan): + """Parse and ingest a heterogeneous ground-measurement file.""" + + source_id: str + file_path: Path + adapter_id: str + + def __post_init__(self) -> None: + if not self.source_id: + raise ValueError("GroundFilePlan.source_id must be non-empty.") + if not self.adapter_id: + raise ValueError("GroundFilePlan.adapter_id must be non-empty.") + if not self.file_path.exists(): + raise ValueError( + f"GroundFilePlan.file_path does not exist: {self.file_path}. " + f"Pass an absolute path to a parseable CSV/Excel/etc." + ) + + def describe(self) -> str: + return ( + f"GroundFile[source={self.source_id}, " + f"adapter={self.adapter_id}, " + f"path={self.file_path.name}]" + ) diff --git a/src/susse/warehouse_ops/population/validators.py b/src/susse/warehouse_ops/population/validators.py new file mode 100644 index 00000000..3034f963 --- /dev/null +++ b/src/susse/warehouse_ops/population/validators.py @@ -0,0 +1,120 @@ +"""Schema validators for ingest pipelines. + +Each validator raises :class:`ValueError` with a remediation message when a +DataFrame violates the contract its target table expects, so callers see the +exact fix at the call site (per CLAUDE.md "validation errors must contain +the remediation"). +""" + +from __future__ import annotations + +from typing import Sequence + +import pandas as pd + + +def _check_required_columns( + df: pd.DataFrame, required: Sequence[str], context: str +) -> None: + missing = [c for c in required if c not in df.columns] + if missing: + raise ValueError( + f"{context}: DataFrame is missing required columns {missing}. " + f"Required schema: {list(required)}. " + f"Add the missing columns before loading." + ) + + +def validate_long_format(df: pd.DataFrame, *, context: str = "long-format") -> None: + """Validate the long-format satellite schema. + + Required columns: ``date, latitude, longitude, geohash5, variable_id, + value, source``. The ``geog`` GEOGRAPHY column is intentionally NOT + required client-side — it is computed server-side during MERGE from + ``ST_GEOGPOINT(longitude, latitude)``. + """ + + required = ( + "date", + "latitude", + "longitude", + "geohash5", + "variable_id", + "value", + "source", + ) + _check_required_columns(df, required, context) + if df.empty: + raise ValueError( + f"{context}: DataFrame is empty. The fetch step returned no rows; " + f"upstream API or grid configuration produced nothing." + ) + if df["value"].isna().all(): + raise ValueError( + f"{context}: every row has value=NaN. The API likely returned a " + f"sentinel for an out-of-range query (e.g. requesting a date the " + f"source does not yet cover)." + ) + + +def validate_ground_raw(df: pd.DataFrame, *, context: str = "ground-raw") -> None: + """Validate the raw ground-measurement schema. + + Required columns: ``datetime, ghi, location, latitude, longitude``. + Matches the existing :class:`StandardCsvAdapter` output and the + ``ground_measurements_raw`` BQ table. + """ + + required = ("datetime", "ghi", "location", "latitude", "longitude") + _check_required_columns(df, required, context) + if df.empty: + raise ValueError(f"{context}: DataFrame is empty.") + bad_lat = df["latitude"].dropna() + bad_lat = bad_lat[(bad_lat < -90) | (bad_lat > 90)] + if len(bad_lat) > 0: + raise ValueError( + f"{context}: {len(bad_lat)} row(s) have latitude outside [-90, 90]. " + f"Examples: {bad_lat.head(3).tolist()}. Check column ordering — " + f"lat and lon may be swapped." + ) + bad_lon = df["longitude"].dropna() + bad_lon = bad_lon[(bad_lon < -180) | (bad_lon > 180)] + if len(bad_lon) > 0: + raise ValueError( + f"{context}: {len(bad_lon)} row(s) have longitude outside [-180, 180]. " + f"Examples: {bad_lon.head(3).tolist()}." + ) + + +def validate_ground_curated( + df: pd.DataFrame, *, context: str = "ground-curated" +) -> None: + """Validate the curated ground-measurement schema. + + Required columns: ``date, month, location, lat, lon, geohash5, + ghi_wh_m2_day, ghi_kwh_m2_day, qc_level, _version, _curated_at``. The + ``geog`` GEOGRAPHY column is computed server-side at load time. + """ + + required = ( + "date", + "month", + "location", + "lat", + "lon", + "geohash5", + "ghi_wh_m2_day", + "ghi_kwh_m2_day", + "qc_level", + "_version", + "_curated_at", + ) + _check_required_columns(df, required, context) + if df.empty: + raise ValueError(f"{context}: DataFrame is empty.") + if (df["ghi_kwh_m2_day"] * 1000.0 - df["ghi_wh_m2_day"]).abs().max() > 1e-3: + raise ValueError( + f"{context}: ghi_kwh_m2_day and ghi_wh_m2_day disagree beyond " + f"floating-point tolerance. They must be the same value in " + f"different units." + ) diff --git a/src/susse/warehouse_ops/snapping.py b/src/susse/warehouse_ops/snapping.py new file mode 100644 index 00000000..23a30aa8 --- /dev/null +++ b/src/susse/warehouse_ops/snapping.py @@ -0,0 +1,232 @@ +"""Snap arbitrary query coordinates onto a satellite source's warehouse keys. + +The warehouse stores each satellite source at a discrete set of cells, one +``geohash5`` per cell. To read a value for an arbitrary coordinate — a portal +click, a ground station — the query point must first be snapped onto a cell +the warehouse actually holds for that source. + +:class:`NearestPixelSnapper` is the single place that snap happens: it is +built from the set of cells actually present in the warehouse for one source +and maps any coordinate to the ``geohash5`` of the nearest one. Because it is +built from the warehouse's own rows it carries no hard-coded grid constants — +it stays correct automatically as a source's ingested footprint changes. +""" + +from __future__ import annotations + +import logging +from typing import ClassVar, Sequence + +import numpy as np +import pandas as pd + +_logger = logging.getLogger(__name__) + +# Mean Earth radius (km), WGS-84 derived. Used for the great-circle snap. +_EARTH_RADIUS_KM: float = 6371.0088 + + +class OutOfCoverageError(LookupError): + """Raised when a query point has no warehouse cell within the snap guard. + + Signals that the requested coordinate falls outside the region ingested + for the source — a genuine cache miss, not a snap that should silently + reach across the gap to a distant, unrelated cell. + """ + + +class NearestPixelSnapper: + """Snaps coordinates to the ``geohash5`` of the nearest stored cell. + + Constructed from the cells of a single satellite source as they are + stored in the warehouse. The snap is a nearest-neighbour lookup by + great-circle distance; a query point farther than ``max_snap_km`` from + every known cell is reported as out of coverage rather than snapped to a + distant, unrelated cell. A snapper built from an empty cell set (a source + with no warehouse rows) reports every point as out of coverage. + + Args: + geohash5s: ``geohash5`` of each stored cell centre. + latitudes: Latitude of each stored cell centre, degrees. + longitudes: Longitude of each stored cell centre, degrees. + max_snap_km: Snap-distance guard. A query point whose nearest cell + is farther than this is treated as out of coverage. Defaults to + :attr:`_DEFAULT_MAX_SNAP_KM`. + + Raises: + ValueError: If the three input sequences differ in length, or if a + coordinate is outside valid lat/lon ranges. + """ + + _DEFAULT_MAX_SNAP_KM: ClassVar[float] = 120.0 + """Default snap-distance guard. NASA POWER's coarsest native grid is + CERES' 1° (~111 km cell pitch); a point genuinely inside an ingested + region sits at most ~80 km from a cell centre, so 120 km admits every + in-region query while still rejecting points in un-ingested regions.""" + + def __init__( + self, + *, + geohash5s: Sequence[str], + latitudes: Sequence[float], + longitudes: Sequence[float], + max_snap_km: float | None = None, + ) -> None: + if not (len(geohash5s) == len(latitudes) == len(longitudes)): + raise ValueError( + f"NearestPixelSnapper inputs differ in length: " + f"{len(geohash5s)} geohash5s, {len(latitudes)} latitudes, " + f"{len(longitudes)} longitudes. All three must align." + ) + self._geohash5s: np.ndarray = np.asarray(geohash5s, dtype=object) + self._latitudes: np.ndarray = np.asarray(latitudes, dtype=float) + self._longitudes: np.ndarray = np.asarray(longitudes, dtype=float) + if not np.all((self._latitudes >= -90.0) & (self._latitudes <= 90.0)): + raise ValueError("Cell latitudes must all lie within [-90, 90].") + if not np.all((self._longitudes >= -180.0) & (self._longitudes <= 180.0)): + raise ValueError("Cell longitudes must all lie within [-180, 180].") + self._max_snap_km: float = ( + self._DEFAULT_MAX_SNAP_KM if max_snap_km is None else float(max_snap_km) + ) + if self._max_snap_km <= 0.0: + raise ValueError(f"max_snap_km={self._max_snap_km} must be positive.") + + @classmethod + def from_dataframe( + cls, + df: pd.DataFrame, + *, + max_snap_km: float | None = None, + geohash5_col: str = "geohash5", + lat_col: str = "latitude", + lon_col: str = "longitude", + ) -> "NearestPixelSnapper": + """Build a snapper from a distinct-cells DataFrame. + + Args: + df: One row per stored cell, carrying the geohash5 and centre + coordinate columns named by ``geohash5_col`` / ``lat_col`` / + ``lon_col``. The satellite-table column convention + (``geohash5``, ``latitude``, ``longitude``) is the default. + An empty frame yields a snapper with no coverage. + max_snap_km: Forwarded to the constructor. + + Raises: + ValueError: If a required column is absent from ``df``. + """ + missing = [c for c in (geohash5_col, lat_col, lon_col) if c not in df.columns] + if missing: + raise ValueError( + f"NearestPixelSnapper.from_dataframe: DataFrame is missing " + f"column(s) {missing}. Present columns: {list(df.columns)}." + ) + return cls( + geohash5s=df[geohash5_col].tolist(), + latitudes=df[lat_col].tolist(), + longitudes=df[lon_col].tolist(), + max_snap_km=max_snap_km, + ) + + @property + def n_pixels(self) -> int: + """Number of stored cells this snapper was built from.""" + return int(self._geohash5s.shape[0]) + + @property + def max_snap_km(self) -> float: + """Snap-distance guard, in kilometres.""" + return self._max_snap_km + + def snap(self, lat: float, lon: float) -> str: + """Snap one coordinate, raising if it is out of coverage. + + Raises: + OutOfCoverageError: If ``(lat, lon)`` is outside coverage. + """ + result = self.snap_or_none([lat], [lon])[0] + if result is None: + raise OutOfCoverageError( + f"Coordinate ({lat:.4f}, {lon:.4f}) is outside the source's " + f"ingested footprint — no warehouse cell is within range. " + f"Ingest that region, or drop the point from the request." + ) + return result + + def snap_or_none( + self, latitudes: Sequence[float], longitudes: Sequence[float] + ) -> list[str | None]: + """Snap a batch of coordinates; yield ``None`` for any out of coverage. + + Args: + latitudes: Query-point latitudes, degrees. + longitudes: Query-point longitudes, degrees, aligned to + ``latitudes``. + + Returns: + One ``geohash5`` (or ``None`` when the point is outside the + source's coverage) per query point, in input order. + + Raises: + ValueError: If the two input sequences differ in length. + """ + if len(latitudes) != len(longitudes): + raise ValueError( + f"snap_or_none got {len(latitudes)} latitudes but " + f"{len(longitudes)} longitudes; they must align." + ) + if len(latitudes) == 0: + return [] + if self.n_pixels == 0: + # No cells ingested for this source: every point is a miss. + return [None] * len(latitudes) + query_lat = np.asarray(latitudes, dtype=float) + query_lon = np.asarray(longitudes, dtype=float) + nearest_idx, nearest_km = self._nearest(query_lat, query_lon) + return [ + str(self._geohash5s[idx]) if km <= self._max_snap_km else None + for idx, km in zip(nearest_idx, nearest_km) + ] + + def _nearest( + self, query_lat: np.ndarray, query_lon: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + """Nearest-cell index and great-circle distance (km) for each query point. + + Builds the full ``(n_query, n_pixel)`` distance matrix in one pass — + per CLAUDE.md, no Python loop over the batch dimension. + """ + # (n_query, 1) against (1, n_pixel) → (n_query, n_pixel). + distances = _haversine_km( + query_lat[:, None], + query_lon[:, None], + self._latitudes[None, :], + self._longitudes[None, :], + ) + nearest_idx = np.argmin(distances, axis=1) + nearest_km = distances[np.arange(distances.shape[0]), nearest_idx] + return nearest_idx, nearest_km + + def __repr__(self) -> str: + return ( + f"NearestPixelSnapper(n_pixels={self.n_pixels}, " + f"max_snap_km={self._max_snap_km:.0f})" + ) + + +def _haversine_km( + lat1: np.ndarray, lon1: np.ndarray, lat2: np.ndarray, lon2: np.ndarray +) -> np.ndarray: + """Great-circle distance in km between two sets of points (broadcast). + + All four inputs are broadcast against one another, so passing shapes + ``(M, 1)`` and ``(1, N)`` yields an ``(M, N)`` distance matrix. + """ + lat1_rad = np.radians(lat1) + lat2_rad = np.radians(lat2) + d_lat = lat2_rad - lat1_rad + d_lon = np.radians(lon2 - lon1) + a = ( + np.sin(d_lat / 2.0) ** 2 + + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(d_lon / 2.0) ** 2 + ) + return 2.0 * _EARTH_RADIUS_KM * np.arcsin(np.sqrt(a)) diff --git a/tests/estimators/data/__init__.py b/tests/api_clients/merra_2/__init__.py similarity index 100% rename from tests/estimators/data/__init__.py rename to tests/api_clients/merra_2/__init__.py diff --git a/tests/api_clients/merra_2/test_merra_daily_fetcher.py b/tests/api_clients/merra_2/test_merra_daily_fetcher.py new file mode 100644 index 00000000..525aeeb8 --- /dev/null +++ b/tests/api_clients/merra_2/test_merra_daily_fetcher.py @@ -0,0 +1,645 @@ +"""Tests for the daily-aggregated MERRA-2 fetcher. + +Network and Earthdata authentication are stubbed out; these tests cover +the parts of the fetcher that don't depend on either: + +* The cosine-zenith aggregation function — math, NaN handling, polar-night + defensive branch, and the daytime-vs-nighttime weighting that's the + entire reason this aggregator exists. +* The bbox URL builder — ensures the time-slice in the OPeNDAP constraint + matches the collection's native cadence (24 for tavg1_*, 8 for inst3_*), + and that lat/lon index ranges round-trip correctly. +* The bbox computation — smallest enclosing grid-index rectangle over a + set of points (1-point degenerate, multi-point real region). +* :meth:`fetch_region` — multi-point output shape, per-point aggregation + uses the right slice, parallel/serial equivalence. +* The auth gate — ``MerraAuthError`` raised when credentials are missing. + +The end-to-end OPeNDAP path is exercised by running the migration against +the live Earthdata service. +""" + +from __future__ import annotations + +from datetime import date, datetime, timedelta, timezone + +import numpy as np +import pandas as pd +import pytest + +from susse.api_clients.merra_2 import MerraAuthError +from susse.api_clients.merra_2.merra_daily_fetcher import ( + MerraDailyFetcher, + _Bbox, + _cadence_for, + _Earthdata, + _timestamps_for, + cos_zenith_aggregate, +) +from susse.api_clients.merra_2.merra_product import MerraProducts + + +def _kampala_lat_lon() -> tuple[float, float]: + return (0.333542, 32.568630) + + +def _hourly_index_utc(d: date) -> pd.DatetimeIndex: + base = datetime.combine(d, datetime.min.time(), tzinfo=timezone.utc) + return pd.DatetimeIndex([base + timedelta(hours=h, minutes=30) for h in range(24)]) + + +class TestCosZenithAggregate: + """The whole reason the aggregator exists is to weight daytime hours + more than nighttime. These tests verify that math directly.""" + + def test_constant_value_yields_same_value(self) -> None: + # A constant input must produce that same value as the daily + # aggregate, regardless of the weights — sanity check that the + # weighted-mean denominator equals the numerator. + lat, lon = _kampala_lat_lon() + times = _hourly_index_utc(date(2024, 6, 21)) + result = cos_zenith_aggregate(np.full(24, 5.0), times, lat, lon) + assert result == pytest.approx(5.0, abs=1e-9) + + def test_noon_spike_dominates_midnight_spike(self) -> None: + # Identical magnitude spikes — one at noon, one at midnight — + # must produce very different daily aggregates because of the + # cos(zenith) weighting. + lat, lon = _kampala_lat_lon() + times = _hourly_index_utc(date(2024, 6, 21)) + noon_only = np.zeros(24) + noon_only[12] = 100.0 + midnight_only = np.zeros(24) + midnight_only[0] = 100.0 + noon_daily = cos_zenith_aggregate(noon_only, times, lat, lon) + midnight_daily = cos_zenith_aggregate(midnight_only, times, lat, lon) + assert noon_daily > 5.0, ( + "A 100-unit spike at noon should produce a meaningful daily " + f"aggregate; got {noon_daily}." + ) + assert midnight_daily == pytest.approx(0.0, abs=1e-9), ( + "A spike at solar midnight should be zero-weighted; " + f"got {midnight_daily}." + ) + + def test_handles_all_nan_input(self) -> None: + lat, lon = _kampala_lat_lon() + times = _hourly_index_utc(date(2024, 6, 21)) + result = cos_zenith_aggregate(np.full(24, np.nan), times, lat, lon) + assert np.isnan(result) + + def test_partial_nan_excluded_from_average(self) -> None: + # NaN values must be dropped from the weighted mean entirely + # (numerator and weight sum). Otherwise a NaN would propagate. + lat, lon = _kampala_lat_lon() + times = _hourly_index_utc(date(2024, 6, 21)) + values = np.full(24, 5.0) + values[0] = np.nan # midnight, weight is zero anyway + result = cos_zenith_aggregate(values, times, lat, lon) + assert result == pytest.approx(5.0, abs=1e-9) + + def test_rejects_length_mismatch(self) -> None: + lat, lon = _kampala_lat_lon() + times = _hourly_index_utc(date(2024, 6, 21)) + with pytest.raises(ValueError, match="Mismatched lengths"): + cos_zenith_aggregate(np.zeros(8), times, lat, lon) + + +class TestCadenceLookup: + @pytest.mark.parametrize( + "database_id, expected", + [ + ("tavg1_2d_aer_Nx", 24), + ("tavg1_2d_slv_Nx", 24), + ("inst1_2d_asm_Nx", 24), + ("tavg3_3d_cld_Np", 8), + ("inst3_2d_gas_Nx", 8), + ("statD_2d_slv_Nx", 1), + ("const_2d_asm_Nx", 1), + ], + ) + def test_known_collections(self, database_id: str, expected: int) -> None: + assert _cadence_for(database_id) == expected + + def test_unknown_collection_raises_with_remediation(self) -> None: + # If a future MERRA-2 collection is introduced, the error must + # tell the developer where to add it. Otherwise they'd see an + # opaque KeyError or wrong number of timesteps. + with pytest.raises(ValueError, match="Add it to _COLLECTION_CADENCE"): + _cadence_for("MADE_UP_PREFIX_2d_xyz_Nx") + + +class TestTimestampsFor: + def test_24_step_uses_half_hour_offsets(self) -> None: + # tavg1 timestamps are centered on the hour midpoint. + idx = _timestamps_for(date(2024, 6, 21), 24) + assert len(idx) == 24 + assert idx[0] == datetime(2024, 6, 21, 0, 30, tzinfo=timezone.utc) + assert idx[12] == datetime(2024, 6, 21, 12, 30, tzinfo=timezone.utc) + assert idx[-1] == datetime(2024, 6, 21, 23, 30, tzinfo=timezone.utc) + + def test_8_step_three_hourly_starting_at_midnight(self) -> None: + idx = _timestamps_for(date(2024, 6, 21), 8) + assert len(idx) == 8 + assert idx[0] == datetime(2024, 6, 21, 0, 0, tzinfo=timezone.utc) + assert idx[-1] == datetime(2024, 6, 21, 21, 0, tzinfo=timezone.utc) + + +class TestBboxComputation: + """``_bbox_enclosing`` returns the smallest grid-index rectangle + covering every input point. The 1-point case must be the degenerate + 1×1 bbox (otherwise per-point fetches would over-fetch).""" + + def test_single_point_is_degenerate_one_by_one(self) -> None: + kampala_idx = MerraDailyFetcher._grid_indices(*_kampala_lat_lon()) + bbox = MerraDailyFetcher._bbox_enclosing((kampala_idx,)) + assert bbox.n_lat == 1 and bbox.n_lon == 1 + assert bbox.lat_idx_lo == kampala_idx[0] + assert bbox.lon_idx_lo == kampala_idx[1] + + def test_multi_point_spans_min_to_max_inclusive(self) -> None: + # Three points: bbox must enclose all three corners and be sized + # accordingly. + idx = ( + MerraDailyFetcher._grid_indices(0.0, 30.0), + MerraDailyFetcher._grid_indices(2.0, 32.0), + MerraDailyFetcher._grid_indices(-1.0, 31.0), + ) + bbox = MerraDailyFetcher._bbox_enclosing(idx) + lat_indices = [p[0] for p in idx] + lon_indices = [p[1] for p in idx] + assert bbox.lat_idx_lo == min(lat_indices) + assert bbox.lat_idx_hi == max(lat_indices) + assert bbox.lon_idx_lo == min(lon_indices) + assert bbox.lon_idx_hi == max(lon_indices) + assert bbox.n_cells == bbox.n_lat * bbox.n_lon + + def test_empty_points_raises(self) -> None: + with pytest.raises(ValueError, match="zero points"): + MerraDailyFetcher._bbox_enclosing(()) + + +class TestUrlBuilder: + """The URL constraint must respect the collection's native cadence, + or the OPeNDAP server returns an out-of-range error. The lat/lon + range syntax also has to match GES DISC's DAP4 conventions.""" + + def test_24_step_collection_uses_full_day_slice(self) -> None: + url = MerraDailyFetcher._build_bbox_url( + product_data=MerraProducts.AEROSOL_EXTINCTION_550nm.value, + date=date(2024, 6, 21), + bbox=_Bbox(lat_idx_lo=180, lat_idx_hi=180, lon_idx_lo=240, lon_idx_hi=240), + cadence=24, + ) + assert "[0:1:23]" in url + assert "TOTEXTTAU" in url + assert "/2024/06/" in url + + def test_8_step_collection_uses_8_index_slice(self) -> None: + url = MerraDailyFetcher._build_bbox_url( + product_data=MerraProducts.AEROSOL_OPTICAL_DEPTH_ANALYSIS.value, + date=date(2024, 6, 21), + bbox=_Bbox(lat_idx_lo=180, lat_idx_hi=180, lon_idx_lo=240, lon_idx_hi=240), + cadence=8, + ) + # Critical: 3-hourly collection has only 8 timesteps; using + # [0:1:23] would error. This test guards against that regression. + assert "[0:1:7]" in url + assert "[0:1:23]" not in url + assert "AODANA" in url + + def test_url_does_not_double_append_nc4(self) -> None: + # create_file_name already includes the .nc4 suffix; the URL + # builder must not add it again. + url = MerraDailyFetcher._build_bbox_url( + product_data=MerraProducts.AEROSOL_EXTINCTION_550nm.value, + date=date(2024, 6, 21), + bbox=_Bbox(lat_idx_lo=180, lat_idx_hi=180, lon_idx_lo=240, lon_idx_hi=240), + cadence=24, + ) + assert ".nc4.nc4" not in url + assert ".nc4?" in url + + def test_region_bbox_emits_ranged_indices(self) -> None: + # 1×1 degenerate bbox uses the same syntax as a multi-cell one; + # a real region must produce ``[lo:1:hi]`` for both axes. + url = MerraDailyFetcher._build_bbox_url( + product_data=MerraProducts.AEROSOL_EXTINCTION_550nm.value, + date=date(2024, 6, 21), + bbox=_Bbox(lat_idx_lo=177, lat_idx_hi=189, lon_idx_lo=335, lon_idx_hi=345), + cadence=24, + ) + assert "[177:1:189]" in url + assert "[335:1:345]" in url + + +class TestEarthdataCredentials: + def test_missing_credentials_raises_actionable_error( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.delenv("EARTHDATA_USERNAME", raising=False) + monkeypatch.delenv("EARTHDATA_PASSWORD", raising=False) + # _Earthdata.from_env loads .env at import time, but we want a + # truly empty environment here. Patch load_dotenv to no-op so it + # can't repopulate the variables in CI environments that have + # them. + monkeypatch.setattr( + "susse.api_clients.merra_2.merra_daily_fetcher.load_dotenv", + lambda *a, **kw: False, + ) + with pytest.raises(MerraAuthError, match="EARTHDATA_USERNAME"): + _Earthdata.from_env() + + def test_present_credentials_loaded(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("EARTHDATA_USERNAME", "alice") + monkeypatch.setenv("EARTHDATA_PASSWORD", "secret") + # No-op the .env loader so test env wins deterministically. + monkeypatch.setattr( + "susse.api_clients.merra_2.merra_daily_fetcher.load_dotenv", + lambda *a, **kw: False, + ) + creds = _Earthdata.from_env() + assert creds.username == "alice" + assert creds.password == "secret" + + +def _stub_bbox_raw_factory(scale: float = 1.0): + """Returns a stub ``_fetch_bbox_raw`` that produces a deterministic + sub-daily field encoding (api_code, day, lat_idx, lon_idx) per cell. + + The encoded value lives at the noon timestep so cosine-zenith + aggregation produces a daytime-dominated daily mean we can compare + against analytically. + """ + api_offset = {"TOTEXTTAU": 0, "TOTSCATAU": 1000, "AODANA": 2000, "TQV": 3000} + + def fake(self, *, product_data, date, bbox, cadence): + arr = np.zeros((cadence, bbox.n_lat, bbox.n_lon), dtype=float) + offset = api_offset.get(product_data.product_name, 9999) + # Encode (api_code, mm/dd, lat_idx, lon_idx) into the noon spike. + for i in range(bbox.n_lat): + lat_idx = bbox.lat_idx_lo + i + for j in range(bbox.n_lon): + lon_idx = bbox.lon_idx_lo + j + arr[cadence // 2, i, j] = scale * ( + offset + + date.month * 100 + + date.day + + lat_idx * 0.001 + + lon_idx * 0.0001 + ) + return arr + + return fake + + +class TestFetchRegionShape: + """``fetch_region`` returns one row per (point, date, variable).""" + + def test_one_row_per_point_date_var(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr( + MerraDailyFetcher, + "_fetch_bbox_raw", + _stub_bbox_raw_factory(), + ) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + fetcher = MerraDailyFetcher(max_workers=1) + df = fetcher.fetch_region( + points=((0.0, 30.0), (1.0, 31.0)), + date_start=date(2024, 6, 1), + date_end=date(2024, 6, 3), + api_codes=("TOTEXTTAU", "TOTSCATAU"), + ) + # 2 points × 3 days × 2 vars = 12 rows. + assert len(df) == 12 + assert set(df.columns) == { + "date", + "latitude", + "longitude", + "variable_id", + "value", + } + # Distinct values per (point, var) — each point's bbox slice + # encodes its own (lat_idx, lon_idx) so values must differ. + kampala_rows = df[(df["latitude"] == 0.0) & (df["longitude"] == 30.0)] + nairobi_rows = df[(df["latitude"] == 1.0) & (df["longitude"] == 31.0)] + for var in ("TOTEXTTAU", "TOTSCATAU"): + kp = kampala_rows[kampala_rows["variable_id"] == var]["value"] + nb = nairobi_rows[nairobi_rows["variable_id"] == var]["value"] + assert (kp.values != nb.values).all(), ( + "Per-point bbox slicing must yield different values when " + "the underlying cells are distinct." + ) + + def test_empty_api_codes_returns_empty_frame( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + monkeypatch.setattr( + MerraDailyFetcher, + "_fetch_bbox_raw", + _stub_bbox_raw_factory(), + ) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + fetcher = MerraDailyFetcher(max_workers=1) + df = fetcher.fetch_region( + points=((0.0, 30.0),), + date_start=date(2024, 6, 1), + date_end=date(2024, 6, 3), + api_codes=(), + ) + assert df.empty + assert list(df.columns) == [ + "date", + "latitude", + "longitude", + "variable_id", + "value", + ] + + def test_rejects_empty_points(self) -> None: + fetcher = MerraDailyFetcher(max_workers=1) + with pytest.raises(ValueError, match="at least one point"): + fetcher.fetch_region( + points=(), + date_start=date(2024, 6, 1), + date_end=date(2024, 6, 1), + api_codes=("TOTEXTTAU",), + ) + + def test_rejects_inverted_date_range(self) -> None: + fetcher = MerraDailyFetcher(max_workers=1) + with pytest.raises(ValueError, match="must be <="): + fetcher.fetch_region( + points=((0.0, 30.0),), + date_start=date(2024, 6, 5), + date_end=date(2024, 6, 1), + api_codes=("TOTEXTTAU",), + ) + + +class TestFetchRegionPointParity: + """The 1-point degenerate bbox must yield the same daily values as + a manual per-point fetch would, since they read from the same cells. + Pinning this prevents off-by-one slice-arithmetic regressions in + :meth:`_fetch_region_for_task`.""" + + def test_single_point_value_matches_direct_aggregation( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + stub = _stub_bbox_raw_factory() + monkeypatch.setattr(MerraDailyFetcher, "_fetch_bbox_raw", stub) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + lat, lon = 0.5, 32.5 + fetcher = MerraDailyFetcher(max_workers=1) + df = fetcher.fetch_region( + points=((lat, lon),), + date_start=date(2024, 6, 21), + date_end=date(2024, 6, 21), + api_codes=("TOTEXTTAU",), + ) + assert len(df) == 1 + # Reproduce the same aggregation manually using the stub directly + # so we can verify slice arithmetic is correct. + from susse.api_clients.merra_2.merra_daily_fetcher import _Bbox + + lat_idx, lon_idx = MerraDailyFetcher._grid_indices(lat, lon) + bbox = _Bbox(lat_idx, lat_idx, lon_idx, lon_idx) + raw = stub( + None, # self is unused by the stub + product_data=MerraProducts.AEROSOL_EXTINCTION_550nm.value, + date=date(2024, 6, 21), + bbox=bbox, + cadence=24, + ) + sub_daily = raw[:, 0, 0].astype(float) + timestamps = _timestamps_for(date(2024, 6, 21), 24) + expected = cos_zenith_aggregate(sub_daily, timestamps, lat, lon) + assert df.iloc[0]["value"] == pytest.approx(expected, abs=1e-12) + + def test_multipoint_each_uses_its_own_cell( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + # Same bbox call is shared across points within a (date, var) + # task; the per-point slice arithmetic must pick the right cell. + stub = _stub_bbox_raw_factory() + monkeypatch.setattr(MerraDailyFetcher, "_fetch_bbox_raw", stub) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + points = ((0.0, 30.0), (2.0, 32.5)) + fetcher = MerraDailyFetcher(max_workers=1) + df = fetcher.fetch_region( + points=points, + date_start=date(2024, 6, 21), + date_end=date(2024, 6, 21), + api_codes=("TOTEXTTAU",), + ) + assert len(df) == 2 + + from susse.api_clients.merra_2.merra_daily_fetcher import _Bbox + + idx0 = MerraDailyFetcher._grid_indices(*points[0]) + idx1 = MerraDailyFetcher._grid_indices(*points[1]) + bbox = _Bbox( + min(idx0[0], idx1[0]), + max(idx0[0], idx1[0]), + min(idx0[1], idx1[1]), + max(idx0[1], idx1[1]), + ) + raw = stub( + None, + product_data=MerraProducts.AEROSOL_EXTINCTION_550nm.value, + date=date(2024, 6, 21), + bbox=bbox, + cadence=24, + ) + timestamps = _timestamps_for(date(2024, 6, 21), 24) + for (lat, lon), (lat_idx, lon_idx) in zip(points, (idx0, idx1)): + sub = raw[:, lat_idx - bbox.lat_idx_lo, lon_idx - bbox.lon_idx_lo].astype( + float + ) + expected = cos_zenith_aggregate(sub, timestamps, lat, lon) + row = df[(df["latitude"] == lat) & (df["longitude"] == lon)].iloc[0] + assert row["value"] == pytest.approx(expected, abs=1e-12) + + +class TestParallelEquivalence: + """Parallel and serial fetch paths must produce identical results. + + Without parallelism a real bulk ingest still hits hundreds of + OPeNDAP requests in serial. The fetcher uses a thread pool to batch + them, but threading must not change the *content* of the returned + DataFrame — only the row order, which is documented as + non-deterministic when ``max_workers > 1``. + + These tests stub ``_fetch_bbox_raw`` so neither path hits the + network or requires Earthdata credentials. + """ + + def test_parallel_matches_serial(self, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr( + MerraDailyFetcher, + "_fetch_bbox_raw", + _stub_bbox_raw_factory(), + ) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + + kwargs = dict( + points=((0.5179, 32.4715), (1.0, 33.0)), + date_start=date(2024, 6, 1), + date_end=date(2024, 6, 14), + api_codes=("TOTEXTTAU", "TOTSCATAU"), + ) + + serial_df = MerraDailyFetcher(max_workers=1).fetch_region(**kwargs) + parallel_df = MerraDailyFetcher(max_workers=4).fetch_region(**kwargs) + + # 2 points × 14 days × 2 variables = 56 rows in each result. + assert len(serial_df) == 56 + assert len(parallel_df) == 56 + + # Sort both before comparing — parallel results come back in + # completion order, not submission order. + sort_cols = ["variable_id", "date", "latitude", "longitude"] + s = serial_df.sort_values(sort_cols).reset_index(drop=True) + p = parallel_df.sort_values(sort_cols).reset_index(drop=True) + pd.testing.assert_frame_equal(s, p) + + def test_legacy_per_point_wrapper_drops_lat_lon_columns( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + # Portal inference path uses fetch_long_for_location and expects + # the legacy 3-column shape. Pin that the wrapper projects out + # the region columns. + monkeypatch.setattr( + MerraDailyFetcher, + "_fetch_bbox_raw", + _stub_bbox_raw_factory(), + ) + monkeypatch.setattr( + MerraDailyFetcher, + "_authenticated_session", + lambda self, url: object(), + ) + df = MerraDailyFetcher(max_workers=1).fetch_long_for_location( + latitude=0.5, + longitude=32.5, + date_start=date(2024, 6, 1), + date_end=date(2024, 6, 3), + api_codes=("TOTEXTTAU",), + ) + assert list(df.columns) == ["date", "variable_id", "value"] + assert len(df) == 3 + + def test_rejects_zero_workers(self) -> None: + with pytest.raises(ValueError, match="max_workers must be >= 1"): + MerraDailyFetcher(max_workers=0) + + +class TestSessionNoneFallback: + """``pydap.cas.urs.setup_session`` is documented to return ``None`` + when its check_url probe fails. NASA's goldsmr4 OPeNDAP cluster + sometimes returns 503 on the probe even when the data endpoints + themselves work; we must fall back to a plain ``requests.Session`` + with basic auth rather than crashing. + """ + + def test_falls_back_to_plain_session_when_setup_returns_none( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + # Stub setup_session to return None — the failure mode this test pins. + monkeypatch.setattr( + "susse.api_clients.merra_2.merra_daily_fetcher.setup_session", + lambda *a, **kw: None, + ) + + fetcher = MerraDailyFetcher( + credentials=_Earthdata(username="alice", password="x"), + ) + # Must not raise; must return something usable. + session = fetcher._authenticated_session("https://example.invalid/") + assert session is not None + assert hasattr(session, "mount"), ( + "fallback session must be a real requests.Session " + "(not None or some other placeholder)" + ) + assert session.auth == ("alice", "x"), ( + "fallback session must carry basic auth so it can authenticate " + "via the URS redirect chain" + ) + + +class TestSessionRetryConfig: + """Sessions returned by ``_authenticated_session`` must mount an + HTTPAdapter with a Retry policy that handles 503/502/504 patiently + enough to ride out NASA's OPeNDAP load spikes. + + Without this config, ~25-30% of bulk-ingest fetches fail because + urllib3's default ``Retry`` only attempts 3 times with no backoff, + which loses to busy windows that last >1 second. + """ + + def test_retry_policy_mounted_on_session( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + import requests + + # Stub the URS handshake — we just want a Session object back. + def fake_setup_session(username, password, check_url): + return requests.Session() + + monkeypatch.setattr( + "susse.api_clients.merra_2.merra_daily_fetcher.setup_session", + fake_setup_session, + ) + + fetcher = MerraDailyFetcher( + credentials=_Earthdata(username="alice", password="x"), + ) + session = fetcher._authenticated_session("https://example.invalid/") + + # Both schemes must have the patient retry adapter mounted. + for scheme in ("http://", "https://"): + adapter = session.get_adapter(scheme) + retry = adapter.max_retries + assert retry.total >= 5, ( + f"Retry.total={retry.total} is too few for OPeNDAP load spikes; " + "we want 8." + ) + assert 503 in retry.status_forcelist, ( + "503 must trigger a retry — that's the most common transient " + "OPeNDAP failure." + ) + assert retry.backoff_factor > 0, ( + "Retries with no backoff slam the busy server immediately and " + "exhaust before the load window clears." + ) + + +class TestProductLookup: + def test_known_api_code_resolves(self) -> None: + product_data = MerraDailyFetcher._product_data_for("TOTEXTTAU") + assert product_data.product_name == "TOTEXTTAU" + assert product_data.database_id == "tavg1_2d_aer_Nx" + + def test_unknown_api_code_raises_with_remediation(self) -> None: + with pytest.raises(KeyError, match="Add it to merra_product.py"): + MerraDailyFetcher._product_data_for("NOT_A_REAL_CODE") diff --git a/tests/api_clients/test_cams.py b/tests/api_clients/test_cams.py index 9071bbad..968bccb3 100644 --- a/tests/api_clients/test_cams.py +++ b/tests/api_clients/test_cams.py @@ -1,78 +1,81 @@ -import os +"""Tests for the CAMS Radiation Service client wrapper. + +End-to-end fetches require a registered SoDa email and live network; +these tests cover only the parts that don't. +""" + +from __future__ import annotations + from datetime import datetime -from pathlib import Path import pandas as pd import pvlib import pytest -from dotenv import set_key as dotenv_set_key -from susse.api_clients import EMAIL_ENV_KEY, ENV_PATH, CAMSClient +from susse.api_clients.cams import CAMSClient +from susse.api_clients.cams.cams_client import CamsApiError -def test_get_email_from_env(monkeypatch): - client = CAMSClient() - test_email = "user@example.com" - monkeypatch.setenv(EMAIL_ENV_KEY, test_email) +def test_get_email_from_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv(CAMSClient.DEFAULT_EMAIL_ENV_KEY, "user@example.com") monkeypatch.setattr( - "builtins.input", lambda prompt: pytest.skip("Should not prompt") + "builtins.input", + lambda prompt: pytest.fail("input() should not be called when env var is set"), ) - monkeypatch.setattr( - dotenv_set_key, - "__call__", - lambda *args, **kwargs: pytest.skip("Should not set key"), - ) - - email = client._get_email() - assert email == test_email - + assert CAMSClient()._get_email() == "user@example.com" -def test_process_dataframe(): - # Create sample DataFrame - idx = pd.date_range("2025-01-01", periods=2, freq="H") - df = pd.DataFrame({"value": [1, 2]}, index=idx) - processed = CAMSClient._process_dataframe(df) +def test_process_dataframe_iso_formats_timestamp_column() -> None: + idx = pd.date_range("2025-01-01", periods=2, freq="h") + raw = pd.DataFrame({"value": [1.0, 2.0]}, index=idx) + processed = CAMSClient._process_dataframe(raw) assert "timestamp" in processed.columns assert processed.loc[0, "timestamp"].endswith("Z") - assert processed.loc[1, "value"] == 2 - - -def test_fetch_data_success(monkeypatch): - client = CAMSClient() - # Prepare dummy raw_df and metadata - idx = pd.date_range("2025-01-01", periods=2, freq="H") - raw_df = pd.DataFrame({"a": [10, 20]}, index=idx) - metadata = {"meta": "data"} - - # Patch _get_email and pvlib.iotools.get_cams - monkeypatch.setattr(client, "_get_email", lambda: "u@e.com") - monkeypatch.setattr(pvlib.iotools, "get_cams", lambda **kwargs: (raw_df, metadata)) - - result = client.fetch_data( - 0, 0, datetime(2025, 1, 1), datetime(2025, 1, 1, 1), "PT1H" + assert processed.loc[1, "value"] == 2.0 + + +def test_fetch_data_returns_processed_df_and_metadata( + monkeypatch: pytest.MonkeyPatch, +) -> None: + # CAMSClient.fetch_data returns a (processed_df, metadata) tuple, not + # a dict. Earlier tests asserted the dict shape and broke after the + # refactor — this is the up-to-date contract. + idx = pd.date_range("2025-01-01", periods=2, freq="h") + raw_df = pd.DataFrame({"ghi": [10.0, 20.0]}, index=idx) + metadata = {"latitude": 0.0, "longitude": 0.0} + + monkeypatch.setattr(CAMSClient, "_get_email", lambda self: "u@e.com") + monkeypatch.setattr(pvlib.iotools, "get_cams", lambda **kw: (raw_df, metadata)) + + df, meta = CAMSClient().fetch_data( + latitude=0.0, + longitude=0.0, + start=datetime(2025, 1, 1), + end=datetime(2025, 1, 1, 1), + time_step="1h", ) - assert result["error"] is None - assert result["metadata"] == metadata - assert isinstance(result["data"], list) - assert result["columns"] == ["timestamp", "a"] - assert "timestamp" in result["data"][0] - assert result["data"][1]["a"] == 20 - + assert "timestamp" in df.columns + assert list(df["ghi"]) == [10.0, 20.0] + assert meta == metadata -def test_fetch_data_exception(monkeypatch): - client = CAMSClient() +def test_fetch_data_raises_cams_api_error_on_pvlib_failure( + monkeypatch: pytest.MonkeyPatch, +) -> None: + # Earlier behaviour was to return a {"error": ...} dict; the CAMS + # refactor removed the silent-failure path so callers can't propagate + # ``None`` DataFrames downstream. def fail(**kwargs): - raise RuntimeError("fail") + raise RuntimeError("network fell over") - monkeypatch.setattr(client, "_get_email", lambda: "u@e.com") + monkeypatch.setattr(CAMSClient, "_get_email", lambda self: "u@e.com") monkeypatch.setattr(pvlib.iotools, "get_cams", fail) - result = client.fetch_data( - 0, 0, datetime(2025, 1, 1), datetime(2025, 1, 1, 1), "PT1H" - ) - assert result["data"] is None - assert "fail" in result["error"] - assert result["columns"] is None - assert result["metadata"] is None + with pytest.raises(CamsApiError, match="network fell over"): + CAMSClient().fetch_data( + latitude=0.0, + longitude=0.0, + start=datetime(2025, 1, 1), + end=datetime(2025, 1, 1, 1), + time_step="1h", + ) diff --git a/tests/api_clients/test_merra.py b/tests/api_clients/test_merra.py deleted file mode 100644 index 0aceb1cb..00000000 --- a/tests/api_clients/test_merra.py +++ /dev/null @@ -1,125 +0,0 @@ -import os -import shutil -from datetime import datetime -from typing import Tuple - -import numpy as np -import pytest -from geopy.geocoders import Nominatim - -from susse.api_clients import ( - Merra2Config, - MerraDataFetcher, - MerraDataStreamFetcher, - MerraDownloadManager, - MerraProducts, -) - - -def get_test_credentials() -> Tuple[str, str]: - username = os.getenv("NASA_USERNAME") - password = os.getenv("NASA_PASSWORD") - if not username or not password: - raise ValueError("NASA credentials not set in environment variables.") - return username, password - - -def test_merra_config(): - geolocator = Nominatim(user_agent="SuSSe") - location = geolocator.geocode("Kampala") - start_date = datetime(2010, 1, 1) - download_url = Merra2Config.generate_download_link( - start_date, - MerraProducts.SPECIFIC_HUMIDITY.value, - location.latitude, - location.longitude, - ) - expected_url = "https://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXSLV.5.12.4/2010/01/MERRA2_300.tavg1_2d_slv_Nx.20100101.nc4.nc4?QV2M[0:1:23][181:1:181][340:1:340]" - assert expected_url == download_url - - -def test_merra_downloader(): - - geolocator = Nominatim(user_agent="SuSSe") - location = geolocator.geocode("Kampala") - start_date = datetime(2010, 1, 1) - download_url = Merra2Config.generate_download_link( - start_date, - MerraProducts.SPECIFIC_HUMIDITY.value, - location.latitude, - location.longitude, - ) - - download_manager = MerraDownloadManager() - try: - username, password = get_test_credentials() - download_manager.set_username_pw(username, password) - except ValueError as e: - pass - download_folder = "./tmp" - download_manager.download_from_urls(download_url, download_folder) - filename = download_manager.extract_filename_from_url(download_url) - file_path = os.path.join(download_folder, filename) - if os.path.exists(file_path): - os.remove(file_path) - - -def test_download_fetcher(): - geolocator = Nominatim(user_agent="SuSSe") - location = geolocator.geocode("Zurich") - start_date = datetime(2010, 1, 1) - end_date = datetime(2010, 1, 3) - - merra_product = MerraProducts.SURFACE_PRESSURE.value - download_folder = "./tmp" - data_fetcher = MerraDataFetcher(base_download_folder=download_folder) - try: - username, password = get_test_credentials() - data_fetcher.set_username_pw(username, password) - except ValueError as e: - pass - data = data_fetcher.fetch_product_result( - merra_product, location, start_date, end_date - ) - shutil.rmtree(download_folder) - - var_names = data.get_variable_names() - expected_var_names = [merra_product.product_name] - assert var_names == expected_var_names - - var_np = data.to_np() - assert len(var_np) == 72 - - -# Skip the test for now as it requires a valid MERRA-2 account -@pytest.mark.skip("Requires valid MERRA-2 account") -def test_merra_stream(): - geolocator = Nominatim(user_agent="SuSSe") - location = geolocator.geocode("Kampala") - - start_date = datetime(2020, 1, 1) - end_date = datetime(2020, 1, 1) - - merra_stream = MerraDataStreamFetcher() - - try: - username, password = get_test_credentials() - merra_stream._authenticate._set_username_pw(username, password) - except ValueError: - pass - - result = merra_stream.fetch_data( - start_date=start_date, - end_date=end_date, - product_data=MerraProducts.AIR_TEMPERATURE.value, - location=location, - ) - - cleaned_data = result.data - - flattened_data = [value for day_data in cleaned_data.values() for value in day_data] - mean_temperature = np.mean(flattened_data) - - assert ( - mean_temperature == 295.7949358622233 - ), f"Expected 295.7949358622233 but got {mean_temperature}" diff --git a/tests/api_clients/test_modis.py b/tests/api_clients/test_modis.py deleted file mode 100644 index bea1091a..00000000 --- a/tests/api_clients/test_modis.py +++ /dev/null @@ -1,65 +0,0 @@ -from datetime import datetime - -import numpy as np -import pytest -from geopy.geocoders import Nominatim - -from susse import ModisProductEnum -from susse.api_clients import ModisDataFetcher, ModisProductFactory - - -# @pytest.mark.skip( -# reason="Modis service seems to be temporarily unavailable. This needs to be re-evaluated" -# ) -def test_modis_data_fetcher(): - - geolocator = Nominatim(user_agent="SuSSe") - location = geolocator.geocode("Kampala") - start_date = datetime(2010, 1, 1) - end_date = datetime(2010, 1, 30) - - data_fetcher = ModisDataFetcher() - - factory = ModisProductFactory() - product = factory.get_product_by_enum(ModisProductEnum.LAND_SURFACE_TEMPERATURE) - result = data_fetcher.fetch_temp_day(location, start_date, end_date) - average_day_temperature = result.get_time_average() - - assert average_day_temperature is not None - - available_dates = data_fetcher.get_available_dates_for_product_and_location( - product, location=location - ) - - assert available_dates - - # test Reflectance - reflectance = data_fetcher.fetch_surface_reflectance( - location=location, - start_date=start_date, - end_date=end_date, - ) - reflectance_value = reflectance.get_time_average() - expected_reflectance = 0.15466800000000003 - assert ( - expected_reflectance == reflectance_value - ), f"Reflectance mismatch: {reflectance_value} != {expected_reflectance}" - - reflectance_array = reflectance.to_np() - reflectance_times = reflectance.time - expected_reflectance_np = np.asarray([0.22294, 0.140864, 0.162352, 0.092516]) - assert type(reflectance_times[0]) is datetime - assert len(reflectance_times) == len(reflectance_array) - assert np.allclose(reflectance_array, expected_reflectance_np) - - # test Emissivity - emissivity = data_fetcher.fetch_emissivity( - location=location, - start_date=start_date, - end_date=end_date, - ) - emissivity_value = emissivity.get_time_average() - expected_emissivity = 230.74333333333334 - assert ( - expected_emissivity == emissivity_value - ), f"Emissivity mismatch: {emissivity_value} != {expected_emissivity}" diff --git a/tests/api_clients/test_modis_long_fetcher.py b/tests/api_clients/test_modis_long_fetcher.py new file mode 100644 index 00000000..a015c44d --- /dev/null +++ b/tests/api_clients/test_modis_long_fetcher.py @@ -0,0 +1,183 @@ +"""Tests for ModisLongFetcher — chunking + cadence helpers (offline). + +The wrapping behaviour around date-range splitting and per-product +cadence is what the warehouse-ingest layer depends on. The actual +ORNL DAAC HTTP round-trip is exercised by the live notebook smoke-test. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.api_clients.modis import ModisLongFetcher, product_cadence_days +from susse.api_clients.modis.modis_long_fetcher import ( + _MAX_TILES_PER_REQUEST, + _date_chunks, + _max_days_per_request, +) + + +class TestProductCadenceDays: + """Cadence is part of the public API: warehouse-ingest jobs use it + to estimate expected row counts when checking coverage.""" + + @pytest.mark.parametrize( + ("product_id", "expected_cadence"), + [ + ("MCD43A4", 1), + ("MOD11A2", 8), + ("MOD13Q1", 16), + ], + ) + def test_known_products(self, product_id: str, expected_cadence: int) -> None: + assert product_cadence_days(product_id) == expected_cadence + + def test_unknown_product_falls_back_to_one(self, caplog) -> None: + # Unknown products should not crash (callers may carry historical + # ids), but they must log a warning so the typo is visible. + import logging + + with caplog.at_level(logging.WARNING): + assert product_cadence_days("NOT_A_PRODUCT") == 1 + assert any("PRODUCT_CADENCE_DAYS" in r.message for r in caplog.records) + + +class TestMaxDaysPerRequest: + """The 10-tile-per-request cap translates to product-specific budgets.""" + + def test_daily_product_caps_at_ten_days(self) -> None: + # Daily cadence × 10 tiles = 10 calendar days max per request. + assert _max_days_per_request("MCD43A4") == 10 + + def test_eight_day_product(self) -> None: + assert _max_days_per_request("MOD11A2") == 8 * _MAX_TILES_PER_REQUEST + + def test_sixteen_day_product(self) -> None: + assert _max_days_per_request("MOD13Q1") == 16 * _MAX_TILES_PER_REQUEST + + +class TestDateChunks: + """``_date_chunks`` slices a range into spans of <= max_days each.""" + + def test_single_chunk_when_range_fits(self) -> None: + chunks = _date_chunks(date(2024, 1, 1), date(2024, 1, 5), max_days=10) + assert chunks == [(date(2024, 1, 1), date(2024, 1, 5))] + + def test_multiple_chunks_at_exact_boundary(self) -> None: + # 20 days / 10 max → exactly 2 chunks of 10 days. + chunks = _date_chunks(date(2024, 1, 1), date(2024, 1, 20), max_days=10) + assert chunks == [ + (date(2024, 1, 1), date(2024, 1, 10)), + (date(2024, 1, 11), date(2024, 1, 20)), + ] + + def test_chunks_cover_every_day_exactly_once(self) -> None: + # Property: union of chunk dates == [start, end], no overlap. + start, end = date(2024, 1, 1), date(2024, 4, 15) + chunks = _date_chunks(start, end, max_days=16) + days_seen: set[date] = set() + for chunk_start, chunk_end in chunks: + assert chunk_start <= chunk_end + for d in pd.date_range(chunk_start, chunk_end, freq="D"): + assert ( + d.date() not in days_seen + ), f"chunk overlap at {d.date()}: chunks={chunks}" + days_seen.add(d.date()) + all_days = {d.date() for d in pd.date_range(start, end, freq="D")} + assert days_seen == all_days + + def test_empty_when_start_after_end(self) -> None: + # Defensive: caller may pass an inverted range; should not crash. + chunks = _date_chunks(date(2024, 1, 5), date(2024, 1, 1), max_days=10) + assert chunks == [] + + +class TestFetchLongForLocation: + """The fetcher fans out one request per (product, chunk). + + We stub the per-request inner call and assert the outer behaviour: + the right number of tasks are dispatched, results are concatenated, + and the output frame has the expected columns. + """ + + def test_dispatches_one_task_per_product_chunk(self, monkeypatch) -> None: + # 90-day window. MCD43A4 (cadence 1) → 10 days/chunk → 9 chunks. + # MOD13Q1 (cadence 16) → 160 days/chunk → 1 chunk. + # Total: 10 inner calls. + fetcher = ModisLongFetcher(max_workers=1) + + captured: list[tuple] = [] + + def _fake_fetch_one( + self, product_id, band_id, chunk_start, chunk_end, location + ): + captured.append((product_id, band_id, chunk_start, chunk_end)) + return [ + { + "date": chunk_start, + "product_id": product_id, + "band_id": band_id, + "value": 0.5, + } + ] + + # Skip the network warm-up; it would hit ORNL DAAC. + monkeypatch.setattr(ModisLongFetcher, "_warm_up", lambda self: None) + monkeypatch.setattr(ModisLongFetcher, "_fetch_one", _fake_fetch_one) + + df = fetcher.fetch_long_for_location( + latitude=0.333, + longitude=32.568, + date_start=date(2024, 6, 1), + date_end=date(2024, 8, 29), # 90 days + products_and_bands=( + ("MCD43A4", "Nadir_Reflectance_Band1"), + ("MOD13Q1", "250m_16_days_NDVI"), + ), + ) + + mcd_calls = [c for c in captured if c[0] == "MCD43A4"] + ndvi_calls = [c for c in captured if c[0] == "MOD13Q1"] + assert ( + len(mcd_calls) == 9 + ), f"expected 9 daily-cadence chunks, got {mcd_calls!r}" + assert ( + len(ndvi_calls) == 1 + ), f"expected 1 16-day-cadence chunk, got {ndvi_calls!r}" + assert list(df.columns) == ["date", "product_id", "band_id", "value"] + assert len(df) == 10 # one row per chunk in the stub + + def test_empty_input_returns_empty_frame_with_schema(self) -> None: + # No (product, band) pairs requested → empty frame, but with the + # correct columns so downstream code (loaders, BigQuery upserts) + # doesn't crash on a missing column. + fetcher = ModisLongFetcher(max_workers=1) + df = fetcher.fetch_long_for_location( + latitude=0.0, + longitude=0.0, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 31), + products_and_bands=(), + ) + assert df.empty + assert list(df.columns) == ["date", "product_id", "band_id", "value"] + + def test_inverted_date_range_raises(self) -> None: + fetcher = ModisLongFetcher(max_workers=1) + with pytest.raises(ValueError, match="date_start"): + fetcher.fetch_long_for_location( + latitude=0.0, + longitude=0.0, + date_start=date(2024, 2, 1), + date_end=date(2024, 1, 1), + products_and_bands=(("MOD13Q1", "250m_16_days_NDVI"),), + ) + + def test_max_workers_zero_rejected(self) -> None: + # 0-or-negative max_workers is a usage bug — must surface clearly + # rather than producing a silently-broken thread pool. + with pytest.raises(ValueError, match="max_workers"): + ModisLongFetcher(max_workers=0) diff --git a/tests/api_clients/test_nasa_power_regional_fetcher.py b/tests/api_clients/test_nasa_power_regional_fetcher.py new file mode 100644 index 00000000..0d0416a8 --- /dev/null +++ b/tests/api_clients/test_nasa_power_regional_fetcher.py @@ -0,0 +1,333 @@ +"""Tests for the region-shaped NASA POWER fetcher. + +The fetcher is a thin wrapper around an HTTP endpoint; tests focus on the +contracts that are easy to mis-implement and hard to debug from logs alone: + +* URL construction matches the live endpoint's expected query schema. +* The bbox tiling respects the 4.5°-per-side cap with a safety margin. +* One HTTP call is made per (tile, api_code) — not per location. +* The FeatureCollection JSON parser produces the expected long shape and + drops NASA POWER's -999 fill value. +""" + +from __future__ import annotations + +from datetime import date + +import pytest + +from susse.api_clients.NASA_Power import NASAPowerRegionalFetcher +from susse.api_clients.NASA_Power.nasa_power_regional_fetcher import _RegionalTile + + +# A bbox value object used by the tests. Inline-defined here so the +# tests don't pull in warehouse_ops (matches the production fetcher's +# primitive-arg signature). +class _Bbox: + def __init__( + self, + min_lat: float, + max_lat: float, + min_lon: float, + max_lon: float, + ) -> None: + self.min_lat, self.max_lat = min_lat, max_lat + self.min_lon, self.max_lon = min_lon, max_lon + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def small_bbox() -> _Bbox: + """A bbox small enough to fit in a single regional request.""" + return _Bbox(min_lat=0.0, max_lat=2.0, min_lon=32.0, max_lon=34.0) + + +@pytest.fixture +def uganda_bbox() -> _Bbox: + """The 6° × 5.55° Uganda bbox used by the a13 migration.""" + return _Bbox(min_lat=-1.5, max_lat=4.5, min_lon=29.5, max_lon=35.05) + + +@pytest.fixture +def short_dates() -> tuple[date, date]: + return (date(2024, 1, 1), date(2024, 1, 3)) + + +# --------------------------------------------------------------------------- +# Tile splitter +# --------------------------------------------------------------------------- + + +def _tile(bb: _Bbox) -> list: + return NASAPowerRegionalFetcher._tile_bbox( + bb.min_lat, + bb.max_lat, + bb.min_lon, + bb.max_lon, + ) + + +class TestTileBbox: + """Bbox → list[_RegionalTile]. The whole point of the splitter is to + keep each tile inside NASA's 4.5° × 4.5° regional-endpoint cap.""" + + def test_small_bbox_is_one_tile(self, small_bbox: _Bbox) -> None: + tiles = _tile(small_bbox) + assert len(tiles) == 1 + assert tiles[0].min_lat == small_bbox.min_lat + assert tiles[0].max_lat == small_bbox.max_lat + assert tiles[0].min_lon == small_bbox.min_lon + assert tiles[0].max_lon == small_bbox.max_lon + + def test_uganda_bbox_splits_into_four_tiles(self, uganda_bbox: _Bbox) -> None: + # 6° × 5.55° must split into a 2×2 grid because either span exceeds + # the 4° safety margin (4.5° hard cap minus headroom). + assert len(_tile(uganda_bbox)) == 4 + + def test_every_tile_within_cap(self, uganda_bbox: _Bbox) -> None: + # The whole point of tiling is to stay inside NASA's cap. If a tile + # exceeds the documented 4.5° per side the endpoint returns 422. + cap = NASAPowerRegionalFetcher._MAX_TILE_SPAN_DEG + for tile in _tile(uganda_bbox): + assert tile.span_lat <= cap + 1e-9 + assert tile.span_lon <= cap + 1e-9 + + def test_tiles_cover_bbox_without_gaps(self, uganda_bbox: _Bbox) -> None: + # Union of tile spans along each axis must equal the bbox span; + # gaps would leave native pixels unfetched. + tiles = _tile(uganda_bbox) + min_lat = min(t.min_lat for t in tiles) + max_lat = max(t.max_lat for t in tiles) + min_lon = min(t.min_lon for t in tiles) + max_lon = max(t.max_lon for t in tiles) + assert min_lat == pytest.approx(uganda_bbox.min_lat) + assert max_lat == pytest.approx(uganda_bbox.max_lat) + assert min_lon == pytest.approx(uganda_bbox.min_lon) + assert max_lon == pytest.approx(uganda_bbox.max_lon) + + +# --------------------------------------------------------------------------- +# URL builder +# --------------------------------------------------------------------------- + + +class TestBuildRegionalUrl: + def test_url_uses_regional_path_and_query_schema( + self, short_dates: tuple[date, date] + ) -> None: + tile = _RegionalTile(min_lat=0.0, max_lat=2.0, min_lon=32.0, max_lon=34.0) + url = NASAPowerRegionalFetcher._build_regional_url( + tile=tile, + date_start=short_dates[0], + date_end=short_dates[1], + api_code="ALLSKY_SFC_SW_DWN", + ) + assert "/temporal/daily/regional" in url + assert "parameters=ALLSKY_SFC_SW_DWN" in url + assert "latitude-min=0.0" in url and "latitude-max=2.0" in url + assert "longitude-min=32.0" in url and "longitude-max=34.0" in url + assert "start=20240101" in url + assert "end=20240103" in url + assert "community=RE" in url + + +# --------------------------------------------------------------------------- +# FeatureCollection parser +# --------------------------------------------------------------------------- + + +def _feature(lon: float, lat: float, api_code: str, daily: dict) -> dict: + return { + "type": "Feature", + "geometry": {"type": "Point", "coordinates": [lon, lat, 1000.0]}, + "properties": {"parameter": {api_code: daily}}, + } + + +class TestParseFeatureCollection: + """The parser is the bug-prone seam between NASA's response shape and + the warehouse's long-format row schema. Pinning each invariant + individually makes upstream schema drift loud.""" + + def test_parses_multiple_features_into_long_rows(self) -> None: + payload = { + "features": [ + _feature( + 32.5, + 0.5, + "ALLSKY_SFC_SW_DWN", + {"20240101": 3.995, "20240102": 4.5}, + ), + _feature( + 33.5, + 0.5, + "ALLSKY_SFC_SW_DWN", + {"20240101": 3.5198, "20240102": 5.5579}, + ), + ], + } + rows = NASAPowerRegionalFetcher._parse_feature_collection( + payload, + api_code="ALLSKY_SFC_SW_DWN", + ) + assert len(rows) == 4 # 2 features × 2 days + + first = rows[0] + assert first["date"] == date(2024, 1, 1) + assert first["latitude"] == 0.5 + assert first["longitude"] == 32.5 + assert first["variable_id"] == "ALLSKY_SFC_SW_DWN" + assert first["value"] == pytest.approx(3.995) + + def test_drops_missing_value_fill(self) -> None: + # NASA POWER signals missing data with -999. The warehouse must + # never see this as a "real" value — long-format rows are + # consumed downstream without further validation. + payload = { + "features": [ + _feature( + 32.5, + 0.5, + "T2M", + {"20240101": -999.0, "20240102": 25.5}, + ), + ], + } + rows = NASAPowerRegionalFetcher._parse_feature_collection( + payload, + api_code="T2M", + ) + assert len(rows) == 1 + assert rows[0]["date"] == date(2024, 1, 2) + assert rows[0]["value"] == pytest.approx(25.5) + + def test_handles_empty_feature_collection(self) -> None: + # An all-missing tile returns an empty FeatureCollection. The + # parser must not crash on a missing `features` key either. + assert ( + NASAPowerRegionalFetcher._parse_feature_collection( + {}, + api_code="T2M", + ) + == [] + ) + assert ( + NASAPowerRegionalFetcher._parse_feature_collection( + {"features": []}, + api_code="T2M", + ) + == [] + ) + + +# --------------------------------------------------------------------------- +# fetch_region_long: end-to-end with a mocked HTTP layer +# --------------------------------------------------------------------------- + + +class TestFetchRegionLong: + """End-to-end shape of the public API. Each test mocks the HTTP layer + and verifies that the right URLs are called and the right DataFrame + comes out.""" + + def _call( + self, + bb: _Bbox, + dates: tuple[date, date], + api_codes: tuple[str, ...], + ): + return NASAPowerRegionalFetcher().fetch_region_long( + min_lat=bb.min_lat, + max_lat=bb.max_lat, + min_lon=bb.min_lon, + max_lon=bb.max_lon, + date_start=dates[0], + date_end=dates[1], + api_codes=api_codes, + ) + + def test_one_call_per_tile_and_variable( + self, + requests_mock, + uganda_bbox: _Bbox, + short_dates: tuple[date, date], + ) -> None: + # Uganda bbox → 4 tiles. With 2 api_codes that's 8 HTTP calls. + # Pin the count to protect the speedup; if a future refactor + # re-introduces per-point looping the count balloons. + requests_mock.get( + "https://power.larc.nasa.gov/api/temporal/daily/regional", + json={"features": []}, + ) + df = self._call( + uganda_bbox, + short_dates, + ("ALLSKY_SFC_SW_DWN", "T2M"), + ) + assert requests_mock.call_count == 4 * 2 + assert df.empty + assert list(df.columns) == [ + "date", + "latitude", + "longitude", + "variable_id", + "value", + ] + + def test_assembles_long_dataframe_across_tiles( + self, + requests_mock, + small_bbox: _Bbox, + short_dates: tuple[date, date], + ) -> None: + requests_mock.get( + "https://power.larc.nasa.gov/api/temporal/daily/regional", + json={ + "features": [ + _feature( + 32.5, + 0.5, + "ALLSKY_SFC_SW_DWN", + {"20240101": 3.995, "20240102": 4.0, "20240103": 4.1}, + ), + _feature( + 33.5, + 0.5, + "ALLSKY_SFC_SW_DWN", + {"20240101": 5.0, "20240102": 5.1, "20240103": 5.2}, + ), + ], + }, + ) + df = self._call(small_bbox, short_dates, ("ALLSKY_SFC_SW_DWN",)) + assert len(df) == 6 + assert set(df["variable_id"]) == {"ALLSKY_SFC_SW_DWN"} + assert set(df["latitude"]) == {0.5} + assert set(df["longitude"]) == {32.5, 33.5} + + def test_empty_api_codes_returns_empty_frame_without_http( + self, + requests_mock, + small_bbox: _Bbox, + short_dates: tuple[date, date], + ) -> None: + # No work to do → no HTTP traffic, no surprise empty frames in + # the wrong shape. + df = self._call(small_bbox, short_dates, ()) + assert df.empty + assert requests_mock.call_count == 0 + + def test_rejects_inverted_bbox( + self, + short_dates: tuple[date, date], + ) -> None: + # min > max would silently fetch an empty rectangle on the live + # endpoint; the fetcher must surface this as a clear ValueError + # so the upstream caller can spot the swapped argument. + inverted = _Bbox(min_lat=2.0, max_lat=0.0, min_lon=32.0, max_lon=34.0) + with pytest.raises(ValueError, match="Invalid bbox"): + self._call(inverted, short_dates, ("T2M",)) diff --git a/tests/estimators/validation/__init__.py b/tests/datasets/__init__.py similarity index 100% rename from tests/estimators/validation/__init__.py rename to tests/datasets/__init__.py diff --git a/tests/datasets/test_feature_selection.py b/tests/datasets/test_feature_selection.py new file mode 100644 index 00000000..6737b7d1 --- /dev/null +++ b/tests/datasets/test_feature_selection.py @@ -0,0 +1,204 @@ +"""Tests for FeatureSelection — catalog validation + JSON roundtrip.""" + +from __future__ import annotations + +import pytest + +from susse.datasets import FeatureSelection +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.types import IrradianceBand, PhysicalStorage, Source + + +def _first_long_format(variables) -> str: + """Pick a representative LONG_FORMAT variable_id from a catalog tuple. + + Helpers use this rather than ``[0]`` so they don't accidentally pick + an irradiance_wide entry (which the long-table fields reject). + """ + return next( + v.variable_id + for v in variables + if v.physical_storage is PhysicalStorage.LONG_FORMAT + ) + + +def _first_nasa_id() -> str: + return _first_long_format(VariableCatalog.NASA_POWER_VARIABLES) + + +def _first_cams_id() -> str: + return _first_long_format(VariableCatalog.CAMS_VARIABLES) + + +def _first_merra_id() -> str: + return _first_long_format(VariableCatalog.MERRA_2_VARIABLES) + + +class TestCatalogValidation: + """Variable IDs must exist in the catalog for their declared source.""" + + def test_known_nasa_variable_accepted(self) -> None: + sel = FeatureSelection(nasa_variable_ids=(_first_nasa_id(),)) + assert sel.nasa_variable_ids == (_first_nasa_id(),) + + def test_unknown_nasa_variable_rejected(self) -> None: + with pytest.raises(ValueError, match="unknown variable_id"): + FeatureSelection(nasa_variable_ids=("definitely_not_a_variable",)) + + def test_cams_only_id_under_nasa_field_is_rejected(self) -> None: + # A CAMS-only variable_id under nasa_variable_ids should be flagged + # as unknown for the NASA source. (NASA + CAMS share `ghi/dhi/dni` + # in the catalog; we deliberately pick an id that is CAMS-only.) + nasa_ids = {v.variable_id for v in VariableCatalog.NASA_POWER_VARIABLES} + cams_only = next( + v.variable_id + for v in VariableCatalog.CAMS_VARIABLES + if v.variable_id not in nasa_ids + ) + with pytest.raises(ValueError, match="unknown variable_id"): + FeatureSelection(nasa_variable_ids=(cams_only,)) + + def test_duplicate_ids_rejected(self) -> None: + nid = _first_nasa_id() + with pytest.raises(ValueError, match="duplicate"): + FeatureSelection(nasa_variable_ids=(nid, nid)) + + def test_irradiance_wide_variable_rejected_in_long_field(self) -> None: + # `ghi`/`dhi`/`dni` exist in the NASA + CAMS catalog but live in + # `irradiance_daily` (physical_storage=IRRADIANCE_WIDE), not in + # the long-format aux table that `nasa_variable_ids` pivots + # from. Requesting them through `nasa_variable_ids` would produce + # 100%-NaN columns at fetch time — surface that as a clear + # construction-time error pointing to the right field. + with pytest.raises(ValueError, match="include_satellite_irradiance"): + FeatureSelection(nasa_variable_ids=("ghi",)) + with pytest.raises(ValueError, match="include_satellite_irradiance"): + FeatureSelection(cams_variable_ids=("dhi",)) + + def test_storage_mismatch_error_names_actual_and_expected_storage(self) -> None: + # Remediation message must be specific enough that the user can + # both diagnose the problem (their variable's actual storage) and + # fix it (the field that does match). + with pytest.raises(ValueError) as exc: + FeatureSelection(nasa_variable_ids=("ghi",)) + msg = str(exc.value) + assert "physical_storage=long_format" in msg + assert "irradiance_wide" in msg + assert "include_satellite_irradiance" in msg + + +class TestIrradianceSourceRestriction: + """Only NASA POWER and CAMS write to irradiance_daily.""" + + def test_merra2_in_irradiance_sources_rejected(self) -> None: + with pytest.raises(ValueError, match="irradiance_daily"): + FeatureSelection( + include_satellite_irradiance=(Source.NASA_POWER, Source.MERRA_2), + ) + + def test_modis_in_irradiance_sources_rejected(self) -> None: + with pytest.raises(ValueError, match="irradiance_daily"): + FeatureSelection(include_satellite_irradiance=(Source.MODIS,)) + + def test_default_is_nasa_plus_cams(self) -> None: + sel = FeatureSelection() + assert sel.include_satellite_irradiance == (Source.NASA_POWER, Source.CAMS) + + +class TestIrradianceBands: + """include_satellite_bands picks which irradiance series ship from the wide table.""" + + def test_default_is_ghi_only(self) -> None: + sel = FeatureSelection() + assert sel.include_satellite_bands == (IrradianceBand.GHI,) + + def test_can_request_dhi_and_dni(self) -> None: + sel = FeatureSelection( + include_satellite_bands=( + IrradianceBand.GHI, + IrradianceBand.DHI, + IrradianceBand.DNI, + ), + ) + assert IrradianceBand.DHI in sel.include_satellite_bands + assert IrradianceBand.DNI in sel.include_satellite_bands + + def test_empty_bands_with_sources_rejected(self) -> None: + with pytest.raises(ValueError, match="include_satellite_bands is empty"): + FeatureSelection(include_satellite_bands=()) + + def test_empty_bands_allowed_when_no_sources(self) -> None: + sel = FeatureSelection( + include_satellite_irradiance=(), + include_satellite_bands=(), + ) + assert sel.is_empty is True + + def test_duplicate_bands_rejected(self) -> None: + with pytest.raises(ValueError, match="duplicates"): + FeatureSelection( + include_satellite_bands=(IrradianceBand.GHI, IrradianceBand.GHI), + ) + + +class TestQcLevels: + def test_empty_qc_levels_rejected(self) -> None: + # An empty tuple would silently drop every ground row; pin this + # so future regressions raise instead. + with pytest.raises(ValueError, match="qc_levels"): + FeatureSelection(qc_levels=()) + + +class TestAuxColumns: + """Source-prefixed column names avoid collisions across sources.""" + + def test_aux_columns_are_source_prefixed(self) -> None: + sel = FeatureSelection( + nasa_variable_ids=(_first_nasa_id(),), + cams_variable_ids=(_first_cams_id(),), + merra_variable_ids=(_first_merra_id(),), + ) + cols = sel.aux_columns + assert any(c.startswith("nasa_") for c in cols) + assert any(c.startswith("cams_") for c in cols) + assert any(c.startswith("merra_") for c in cols) + + def test_empty_selection_aux_columns_empty(self) -> None: + assert ( + FeatureSelection( + include_satellite_irradiance=(), + ).aux_columns + == () + ) + + +class TestJsonRoundtrip: + def test_to_dict_from_dict_roundtrip(self) -> None: + original = FeatureSelection( + nasa_variable_ids=(_first_nasa_id(),), + cams_variable_ids=(_first_cams_id(),), + merra_variable_ids=(_first_merra_id(),), + include_satellite_irradiance=(Source.NASA_POWER,), + include_satellite_bands=(IrradianceBand.GHI, IrradianceBand.DNI), + qc_levels=("pass", "fail_range"), + ) + recovered = FeatureSelection.from_dict(original.to_dict()) + assert recovered == original + + def test_minimal_selection_roundtrip(self) -> None: + # Defaults-only — make sure the absent / empty cases don't drop + # any fields. + original = FeatureSelection() + recovered = FeatureSelection.from_dict(original.to_dict()) + assert recovered == original + + def test_legacy_dict_without_bands_defaults_to_ghi(self) -> None: + # Manifests written before the GHI/DHI/DNI split won't carry the + # ``include_satellite_bands`` key. They must deserialise as if the + # field had its default value, so older snapshots stay loadable. + legacy = { + "include_satellite_irradiance": [Source.NASA_POWER.value], + "qc_levels": ["pass"], + } + sel = FeatureSelection.from_dict(legacy) + assert sel.include_satellite_bands == (IrradianceBand.GHI,) diff --git a/tests/datasets/test_feature_service.py b/tests/datasets/test_feature_service.py new file mode 100644 index 00000000..d9210313 --- /dev/null +++ b/tests/datasets/test_feature_service.py @@ -0,0 +1,168 @@ +"""Tests for FeatureService's point-based snap / fetch / relabel path. + +FeatureService takes plain (lat, lon) query points, snaps each onto every +source's native grid independently, and relabels the fetched rows back to +the query point's own geohash5. These tests pin that contract with the +SatelliteRepository methods stubbed — no BigQuery round-trip. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pygeohash +import pytest + +from susse.datasets import FeatureSelection, FeatureService +from susse.warehouse_ops.io.repositories import SatelliteRepository +from susse.warehouse_ops.population.types import IrradianceBand, Source + +# Two NASA native pixels (MERRA-2-spaced) over Uganda. The warehouse stores +# data at these centres; query points anywhere near them must snap here. +_PIXEL_A = (0.5, 32.5) +_PIXEL_B = (0.5, 33.0) +_PIXEL_A_GH = pygeohash.encode(*_PIXEL_A, precision=5) +_PIXEL_B_GH = pygeohash.encode(*_PIXEL_B, precision=5) + + +@pytest.fixture +def nasa_only_selection() -> FeatureSelection: + return FeatureSelection( + nasa_variable_ids=("temperature",), + cams_variable_ids=(), + include_satellite_irradiance=(Source.NASA_POWER,), + include_satellite_bands=(IrradianceBand.GHI,), + qc_levels=("pass",), + ) + + +class _FakeBQ: + """Only `config` is touched — every query goes through the stubbed repo.""" + + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + +@pytest.fixture +def stub_repo(monkeypatch: pytest.MonkeyPatch): + """Stub SatelliteRepository: two NASA native pixels, one day of data each.""" + + def fake_native_pixels(self, *, table_fqn, source=None): + return pd.DataFrame( + { + "geohash5": [_PIXEL_A_GH, _PIXEL_B_GH], + "latitude": [_PIXEL_A[0], _PIXEL_B[0]], + "longitude": [_PIXEL_A[1], _PIXEL_B[1]], + } + ) + + def fake_irradiance(self, start, end, *, sources, bands, geohash5s): + # One row per requested pixel; GHI value encodes which pixel it is + # so the relabel can be checked (4.0 for A, 5.0 for B). + rows = [] + for gh in geohash5s: + rows.append( + { + "date": date(2024, 1, 1), + "geohash5": gh, + "sat_ghi_nasa_kwh_m2_day": 4.0 if gh == _PIXEL_A_GH else 5.0, + } + ) + return pd.DataFrame(rows) + + def fake_aux( + self, *, table_fqn, column_prefix, start, end, variable_ids, geohash5s + ): + rows = [ + {"date": date(2024, 1, 1), "geohash5": gh, "nasa_temperature": 25.0} + for gh in geohash5s + ] + return pd.DataFrame(rows) + + monkeypatch.setattr(SatelliteRepository, "native_pixels", fake_native_pixels) + monkeypatch.setattr( + SatelliteRepository, "daily_irradiance_by_geohash", fake_irradiance + ) + monkeypatch.setattr(SatelliteRepository, "long_aux_pivoted", fake_aux) + + +def _service() -> FeatureService: + return FeatureService(_FakeBQ()) # type: ignore[arg-type] + + +class TestSnapAndRelabel: + def test_query_point_snaps_to_pixel_but_keys_by_query_geohash( + self, stub_repo, nasa_only_selection: FeatureSelection + ) -> None: + # A point ~3 km off pixel A: it must read pixel A's data (GHI 4.0) + # but the output row must be keyed by the *query* point's geohash5, + # not the pixel's. + query_pt = (0.52, 32.52) + df = _service().build_satellite_features( + selection=nasa_only_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 1), + points=[query_pt], + ) + assert len(df) == 1 + assert df.iloc[0]["geohash5"] == pygeohash.encode(*query_pt, precision=5) + assert df.iloc[0]["sat_ghi_nasa_kwh_m2_day"] == 4.0 + assert df.iloc[0]["nasa_temperature"] == 25.0 + + def test_two_points_in_one_pixel_each_get_the_pixel_data( + self, stub_repo, nasa_only_selection: FeatureSelection + ) -> None: + # Two query points ~6 km apart — distinct geohash5 cells, same NASA + # pixel A. Both must appear, each under its own geohash5, both + # carrying pixel A's value. + pt_1 = (0.50, 32.50) + pt_2 = (0.54, 32.54) + df = _service().build_satellite_features( + selection=nasa_only_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 1), + points=[pt_1, pt_2], + ) + assert set(df["geohash5"]) == { + pygeohash.encode(*pt_1, precision=5), + pygeohash.encode(*pt_2, precision=5), + } + assert set(df["sat_ghi_nasa_kwh_m2_day"]) == {4.0} + + def test_distinct_pixels_resolve_independently( + self, stub_repo, nasa_only_selection: FeatureSelection + ) -> None: + # One point near pixel A, one near pixel B → 4.0 and 5.0 respectively. + df = _service().build_satellite_features( + selection=nasa_only_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 1), + points=[(0.51, 32.51), (0.49, 32.99)], + ) + assert sorted(df["sat_ghi_nasa_kwh_m2_day"]) == [4.0, 5.0] + + def test_out_of_coverage_point_yields_no_row( + self, stub_repo, nasa_only_selection: FeatureSelection + ) -> None: + # A point on another continent has no NASA pixel within the snap + # guard → no irradiance spine → no row (a downstream cache miss). + df = _service().build_satellite_features( + selection=nasa_only_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 1), + points=[(48.0, 2.0)], + ) + assert df.empty + + def test_empty_points_raises(self, nasa_only_selection: FeatureSelection) -> None: + with pytest.raises(ValueError, match="points is empty"): + _service().build_satellite_features( + selection=nasa_only_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 1), + points=[], + ) diff --git a/tests/datasets/test_snapshot_io.py b/tests/datasets/test_snapshot_io.py new file mode 100644 index 00000000..ae500480 --- /dev/null +++ b/tests/datasets/test_snapshot_io.py @@ -0,0 +1,109 @@ +"""Round-trip tests for the local snapshot writer + reader. + +Specifically pins: +* parquet + manifest land in the dest dir, +* manifest's content_hash is set to the actual SHA-256 of the parquet, +* load_snapshot validates the hash and raises on mismatch, +* missing files surface clear FileNotFoundError messages. +""" + +from __future__ import annotations + +from datetime import date, datetime, timezone +from pathlib import Path + +import pandas as pd +import pytest + +from susse.datasets import ( + DatasetManifest, + FeatureSelection, + load_snapshot, + write_snapshot, +) + + +def _toy_manifest() -> DatasetManifest: + return DatasetManifest( + name="snapshot_io_test", + version="v0", + created_at_utc=datetime(2026, 5, 8, tzinfo=timezone.utc).isoformat(), + susse_version="test-0.0.0", + git_sha=None, + feature_selection=FeatureSelection(), + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 2), + location_filter=None, + warehouse_project="test-project", + warehouse_dataset="test_dataset", + warehouse_table_mods={}, + n_rows=2, + n_cols=2, + column_names=("date", "value"), + content_hash="", # filled in by write_snapshot + ) + + +def _toy_df() -> pd.DataFrame: + return pd.DataFrame( + {"date": [date(2024, 1, 1), date(2024, 1, 2)], "value": [1.5, 2.5]} + ) + + +class TestRoundtrip: + def test_write_then_load_preserves_dataframe(self, tmp_path: Path) -> None: + df = _toy_df() + manifest = _toy_manifest() + written = write_snapshot(df=df, manifest=manifest, dest=tmp_path) + + # write_snapshot stamps content_hash; original manifest had "" + assert written.manifest.content_hash != "" + + # Re-read via load_snapshot and compare. + loaded = load_snapshot(tmp_path) + pd.testing.assert_frame_equal(loaded.df, df) + assert loaded.manifest == written.manifest + + def test_files_are_written_in_dest(self, tmp_path: Path) -> None: + write_snapshot(df=_toy_df(), manifest=_toy_manifest(), dest=tmp_path) + assert (tmp_path / "dataset.parquet").exists() + assert (tmp_path / "manifest.json").exists() + + def test_load_validates_content_hash(self, tmp_path: Path) -> None: + # Tamper with the parquet after writing — load must catch it. + write_snapshot(df=_toy_df(), manifest=_toy_manifest(), dest=tmp_path) + with (tmp_path / "dataset.parquet").open("ab") as f: + f.write(b"trailing-garbage") + with pytest.raises(ValueError, match="Content-hash mismatch"): + load_snapshot(tmp_path) + + +class TestMissingFiles: + def test_missing_parquet_raises(self, tmp_path: Path) -> None: + (tmp_path / "manifest.json").write_text("{}") # parquet absent + with pytest.raises(FileNotFoundError, match="dataset.parquet"): + load_snapshot(tmp_path) + + def test_missing_manifest_raises(self, tmp_path: Path) -> None: + # Write only the parquet — no manifest. load must surface the + # missing-file before attempting to deserialize. + df = _toy_df() + df.to_parquet(tmp_path / "dataset.parquet", index=False) + with pytest.raises(FileNotFoundError, match="manifest.json"): + load_snapshot(tmp_path) + + +class TestManifestRoundtrip: + def test_manifest_to_json_from_json_roundtrip(self) -> None: + original = _toy_manifest() + recovered = DatasetManifest.from_json(original.to_json()) + assert recovered == original + + def test_manifest_with_content_hash_is_immutable_copy(self) -> None: + original = _toy_manifest() + stamped = original.with_content_hash("abc123") + assert original.content_hash == "" + assert stamped.content_hash == "abc123" + # All other fields preserved. + assert stamped.name == original.name + assert stamped.feature_selection == original.feature_selection diff --git a/tests/estimators/validation/test_metrics.py b/tests/estimators/validation/test_metrics.py deleted file mode 100644 index 91ccc87e..00000000 --- a/tests/estimators/validation/test_metrics.py +++ /dev/null @@ -1,87 +0,0 @@ -import numpy as np -import pytest - -from susse.estimators.validation.metrics import StatisticalMetrics - - -def test_mean_bias_deviation(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.mean_bias_deviation(obs, pred) == pytest.approx( - -3.33, rel=1e-2 - ) - - -def test_mean_absolute_deviation(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.mean_absolute_deviation(obs, pred) == pytest.approx( - 10, rel=1e-2 - ) - - -def test_root_mean_square_deviation(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.root_mean_square_deviation(obs, pred) == pytest.approx( - 10, rel=1e-2 - ) - - -def test_relative_rmsd(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.relative_rmsd(obs, pred) == pytest.approx(5, rel=1e-2) - - -def test_pearson_correlation(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.pearson_correlation(obs, pred) == pytest.approx( - 1, rel=1e-2 - ) - - -def test_nash_sutcliffe_efficiency(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.nash_sutcliffe_efficiency(obs, pred) == pytest.approx( - 0.9, rel=1e-1 - ) - - -def test_index_of_agreement(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.index_of_agreement(obs, pred) == pytest.approx( - 0.98, rel=1e-1 - ) - - -def test_mean_absolute_percentage_error(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190, 290]) - assert StatisticalMetrics.mean_absolute_percentage_error( - obs, pred - ) == pytest.approx(6, rel=1e-1) - - -def test_empty_arrays(): - obs = np.array([]) - pred = np.array([]) - with pytest.raises(ValueError, match="Arrays cannot be empty"): - StatisticalMetrics.mean_bias_deviation(obs, pred) - - -def test_mismatched_shapes(): - obs = np.array([100, 200, 300]) - pred = np.array([110, 190]) - with pytest.raises(ValueError, match="Arrays must have same shape"): - StatisticalMetrics.mean_absolute_deviation(obs, pred) - - -def test_invalid_types(): - obs = [100, 200, 300] # Not a NumPy array - pred = np.array([110, 190, 290]) - with pytest.raises(TypeError, match="Inputs must be numpy arrays"): - StatisticalMetrics.root_mean_square_deviation(obs, pred) diff --git a/tests/evaluation/__init__.py b/tests/evaluation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/evaluation/test_evaluator.py b/tests/evaluation/test_evaluator.py new file mode 100644 index 00000000..95764b54 --- /dev/null +++ b/tests/evaluation/test_evaluator.py @@ -0,0 +1,123 @@ +"""Evaluator — multi-prediction × multi-split scoring tables.""" + +from __future__ import annotations + +import math + +import numpy as np +import pandas as pd +import pytest + +from susse.evaluation import DEFAULT_METRICS, RMSE, Evaluator, MeanBiasError + + +@pytest.fixture +def comparison_frame() -> pd.DataFrame: + rng = np.random.default_rng(11) + n = 60 + obs = pd.Series(rng.normal(loc=4.5, scale=0.4, size=n)) + return pd.DataFrame( + { + "obs": obs, + "pred_good": obs + rng.normal(scale=0.1, size=n), + "pred_biased": obs + 0.8 + rng.normal(scale=0.1, size=n), + "group": ["A"] * 30 + ["B"] * 30, + } + ) + + +class TestEvaluatorOutput: + def test_one_row_per_prediction_when_no_splits( + self, comparison_frame: pd.DataFrame + ) -> None: + result = Evaluator().score( + observed=comparison_frame["obs"], + predictions={ + "good": comparison_frame["pred_good"], + "biased": comparison_frame["pred_biased"], + }, + ) + assert len(result) == 2 + assert list(result["split"].unique()) == ["all"] + assert sorted(result["prediction"]) == ["biased", "good"] + + def test_row_count_is_splits_times_predictions( + self, comparison_frame: pd.DataFrame + ) -> None: + splits = { + "A only": comparison_frame.index[comparison_frame["group"] == "A"], + "B only": comparison_frame.index[comparison_frame["group"] == "B"], + } + result = Evaluator().score( + observed=comparison_frame["obs"], + predictions={ + "good": comparison_frame["pred_good"], + "biased": comparison_frame["pred_biased"], + }, + splits=splits, + ) + assert len(result) == 4 + + def test_columns_include_every_metric(self, comparison_frame: pd.DataFrame) -> None: + result = Evaluator().score( + observed=comparison_frame["obs"], + predictions={"good": comparison_frame["pred_good"]}, + ) + expected_cols = ["split", "prediction", "n"] + [m.name for m in DEFAULT_METRICS] + assert list(result.columns) == expected_cols + + def test_custom_metric_tuple_controls_columns( + self, comparison_frame: pd.DataFrame + ) -> None: + result = Evaluator(metrics=(RMSE(), MeanBiasError())).score( + observed=comparison_frame["obs"], + predictions={"good": comparison_frame["pred_good"]}, + ) + assert list(result.columns) == ["split", "prediction", "n", "RMSE", "MBE"] + + +class TestEvaluatorSemantics: + def test_biased_prediction_has_higher_rmse_than_good( + self, comparison_frame: pd.DataFrame + ) -> None: + result = Evaluator().score( + observed=comparison_frame["obs"], + predictions={ + "good": comparison_frame["pred_good"], + "biased": comparison_frame["pred_biased"], + }, + ) + rmse_good = result.loc[result["prediction"] == "good", "RMSE"].iloc[0] + rmse_biased = result.loc[result["prediction"] == "biased", "RMSE"].iloc[0] + assert rmse_biased > rmse_good + + def test_n_counts_only_non_nan_rows(self, comparison_frame: pd.DataFrame) -> None: + # Introduce two NaN-paired rows on the prediction side. + pred = comparison_frame["pred_good"].copy() + pred.iloc[:2] = math.nan + result = Evaluator().score( + observed=comparison_frame["obs"], + predictions={"good": pred}, + ) + assert result["n"].iloc[0] == len(comparison_frame) - 2 + + +class TestEvaluatorErrors: + def test_empty_predictions_raises(self, comparison_frame: pd.DataFrame) -> None: + with pytest.raises(ValueError, match="empty"): + Evaluator().score(observed=comparison_frame["obs"], predictions={}) + + def test_empty_metrics_raises(self) -> None: + with pytest.raises(ValueError, match="at least one Metric"): + Evaluator(metrics=()) + + def test_split_index_outside_observed_raises( + self, comparison_frame: pd.DataFrame + ) -> None: + bad = pd.Index([999, 1000]) + with pytest.raises(ValueError, match="absent from"): + Evaluator().score( + observed=comparison_frame["obs"], + predictions={"good": comparison_frame["pred_good"]}, + splits={"out of range": bad}, + ) diff --git a/tests/evaluation/test_metrics.py b/tests/evaluation/test_metrics.py new file mode 100644 index 00000000..d66709d9 --- /dev/null +++ b/tests/evaluation/test_metrics.py @@ -0,0 +1,249 @@ +"""Metric ABC subclasses — pin every metric against analytical references. + +The contract: each metric's :meth:`compute` matches its formula on +analytical examples (perfect prediction → 0, constant offset → MBE +recovers the offset, etc.), drops NaN-bearing rows, and returns NaN +on the pathological cases (division by zero, empty after filter). +""" + +from __future__ import annotations + +import math + +import numpy as np +import pandas as pd +import pytest + +from susse.evaluation import ( + DEFAULT_METRICS, + MAE, + R2, + RMSE, + IndexOfAgreement, + MeanBiasError, + Metric, + NormalisedMAE, + NormalisedRMSE, +) + +# --------------------------------------------------------------------------- +# Shape mismatch handling — common to every metric via the shared helper. +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "metric", + [ + RMSE(), + MAE(), + R2(), + IndexOfAgreement(), + MeanBiasError(), + NormalisedRMSE(), + NormalisedMAE(), + ], +) +class TestSharedContract: + def test_shape_mismatch_raises(self, metric: Metric) -> None: + with pytest.raises(ValueError, match="shape"): + metric.compute(pd.Series([1.0, 2.0, 3.0]), pd.Series([1.0, 2.0])) + + def test_nan_rows_are_dropped(self, metric: Metric) -> None: + # Two NaN-paired rows + two perfectly-matched rows. The metric + # should compute as if only the matched rows existed. + yt = pd.Series([4.0, np.nan, 6.0, np.nan]) + yp = pd.Series([4.0, 5.0, 6.0, 7.0]) + score = metric.compute(yt, yp) + # For every "perfect prediction" metric, all-perfect inputs + # give a known outcome: + # RMSE, MAE, MBE, NormalisedRMSE, NormalisedMAE → 0 + # R², IndexOfAgreement → 1 (or NaN) + if isinstance( + metric, (RMSE, MAE, MeanBiasError, NormalisedRMSE, NormalisedMAE) + ): + assert score == pytest.approx(0.0, abs=1e-12) + elif isinstance(metric, (R2, IndexOfAgreement)): + # 2 perfect rows: R² formula reduces to 1.0 if variance > 0, + # else NaN. In our case yt=[4, 6] has variance > 0 → 1.0. + assert score == pytest.approx(1.0, abs=1e-12) + + def test_empty_after_filter_returns_nan(self, metric: Metric) -> None: + yt = pd.Series([np.nan, np.nan]) + yp = pd.Series([1.0, 2.0]) + assert math.isnan(metric.compute(yt, yp)) + + +# --------------------------------------------------------------------------- +# RMSE / MAE / R² +# --------------------------------------------------------------------------- + + +class TestRMSE: + def test_perfect_prediction_is_zero(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + assert RMSE().compute(yt, yt.copy()) == pytest.approx(0.0, abs=1e-12) + + def test_matches_formula(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + yp = pd.Series([5.0, 5.0, 5.0]) # constant prediction + expected = float(np.sqrt(((yp - yt) ** 2).mean())) + assert RMSE().compute(yt, yp) == pytest.approx(expected, abs=1e-12) + + def test_name(self) -> None: + assert RMSE().name == "RMSE" + + +class TestMAE: + def test_perfect_prediction_is_zero(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + assert MAE().compute(yt, yt.copy()) == pytest.approx(0.0, abs=1e-12) + + def test_matches_formula(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + yp = pd.Series([5.0, 5.0, 5.0]) + expected = float(np.abs(yp - yt).mean()) + assert MAE().compute(yt, yp) == pytest.approx(expected, abs=1e-12) + + def test_name(self) -> None: + assert MAE().name == "MAE" + + +class TestR2: + def test_perfect_prediction_is_one(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + assert R2().compute(yt, yt.copy()) == pytest.approx(1.0, abs=1e-12) + + def test_predicting_mean_is_zero(self) -> None: + # When prediction = mean(obs), SS_res = SS_tot → R² = 0. + yt = pd.Series([3.0, 4.0, 5.0, 6.0]) + yp = pd.Series([4.5, 4.5, 4.5, 4.5]) + assert R2().compute(yt, yp) == pytest.approx(0.0, abs=1e-12) + + def test_constant_target_returns_nan(self) -> None: + # Zero variance in target → undefined. + yt = pd.Series([5.0, 5.0, 5.0]) + yp = pd.Series([5.0, 5.0, 5.0]) + assert math.isnan(R2().compute(yt, yp)) + + +# --------------------------------------------------------------------------- +# Index of Agreement (Willmott 1981) +# --------------------------------------------------------------------------- + + +class TestIndexOfAgreement: + def test_perfect_prediction_is_one(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0, 4.5]) + assert IndexOfAgreement().compute(yt, yt.copy()) == pytest.approx( + 1.0, abs=1e-12 + ) + + def test_predicting_observed_mean_is_zero(self) -> None: + # When the model collapses to mean(O), IOA formula has + # numerator == denominator → 0. + yt = pd.Series([3.0, 4.0, 5.0, 6.0]) + yp = pd.Series([4.5, 4.5, 4.5, 4.5]) + assert IndexOfAgreement().compute(yt, yp) == pytest.approx(0.0, abs=1e-12) + + def test_constant_match_returns_nan(self) -> None: + # numerator == denominator == 0 — IOA must be NaN, not 1.0 + # (no division-by-zero, no spurious perfect score). + yt = pd.Series([5.0, 5.0, 5.0]) + yp = pd.Series([5.0, 5.0, 5.0]) + assert math.isnan(IndexOfAgreement().compute(yt, yp)) + + +# --------------------------------------------------------------------------- +# Mean Bias Error +# --------------------------------------------------------------------------- + + +class TestMeanBiasError: + def test_positive_offset_recovered(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0, 4.5]) + yp = yt + 0.5 + assert MeanBiasError().compute(yt, yp) == pytest.approx(0.5, abs=1e-12) + + def test_negative_offset_recovered(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0, 4.5]) + yp = yt - 0.7 + assert MeanBiasError().compute(yt, yp) == pytest.approx(-0.7, abs=1e-12) + + +# --------------------------------------------------------------------------- +# Normalised RMSE / MAE +# --------------------------------------------------------------------------- + + +class TestNormalisedRMSE: + def test_matches_formula(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + yp = pd.Series([5.0, 5.0, 5.0]) + rmse = float(np.sqrt(((yp - yt) ** 2).mean())) + expected = 100.0 * rmse / yt.mean() + assert NormalisedRMSE().compute(yt, yp) == pytest.approx(expected, abs=1e-12) + + def test_zero_mean_observed_returns_nan(self) -> None: + yt = pd.Series([-1.0, 0.0, 1.0]) # mean = 0 + yp = pd.Series([0.0, 0.0, 0.0]) + assert math.isnan(NormalisedRMSE().compute(yt, yp)) + + +class TestNormalisedMAE: + def test_matches_formula(self) -> None: + yt = pd.Series([4.0, 5.0, 6.0]) + yp = pd.Series([5.0, 5.0, 5.0]) + mae = float(np.abs(yp - yt).mean()) + expected = 100.0 * mae / yt.mean() + assert NormalisedMAE().compute(yt, yp) == pytest.approx(expected, abs=1e-12) + + def test_zero_mean_observed_returns_nan(self) -> None: + yt = pd.Series([-1.0, 0.0, 1.0]) + yp = pd.Series([0.0, 0.0, 0.0]) + assert math.isnan(NormalisedMAE().compute(yt, yp)) + + +# --------------------------------------------------------------------------- +# Cross-metric consistency on the paper's signal shape +# --------------------------------------------------------------------------- + + +class TestPaperShapedExample: + """Synthesised "satellite has bias, RF corrects it" signal — + the *direction* of every metric must agree with the paper's + relative-improvement claim (RF beats biased satellite).""" + + def test_corrected_predictor_beats_biased_one(self) -> None: + rng = np.random.default_rng(0) + y_true = pd.Series(rng.normal(loc=4.5, scale=0.4, size=50)) + y_satellite = y_true + 1.0 + rng.normal(scale=0.2, size=50) + y_corrected = y_true + rng.normal(scale=0.15, size=50) + + # MBE: biased satellite shows the positive offset; corrected + # is near zero. + assert MeanBiasError().compute(y_true, y_satellite) > 0.5 + assert abs(MeanBiasError().compute(y_true, y_corrected)) < 0.1 + + # IOA: corrected wins. + assert IndexOfAgreement().compute(y_true, y_corrected) > ( + IndexOfAgreement().compute(y_true, y_satellite) + ) + + # Normalised RMSE / MAE: corrected wins. + assert NormalisedRMSE().compute(y_true, y_corrected) < ( + NormalisedRMSE().compute(y_true, y_satellite) + ) + assert NormalisedMAE().compute(y_true, y_corrected) < ( + NormalisedMAE().compute(y_true, y_satellite) + ) + + +# --------------------------------------------------------------------------- +# DEFAULT_METRICS bundle +# --------------------------------------------------------------------------- + + +class TestDefaultMetrics: + def test_default_tuple_has_seven_metrics_in_paper_order(self) -> None: + names = [m.name for m in DEFAULT_METRICS] + assert names == ["RMSE", "nRMSE_%", "MAE", "nMAE_%", "MBE", "R²", "IOA"] diff --git a/tests/evaluation/test_plots.py b/tests/evaluation/test_plots.py new file mode 100644 index 00000000..9609bbf4 --- /dev/null +++ b/tests/evaluation/test_plots.py @@ -0,0 +1,185 @@ +"""Smoke tests for the generic evaluation plot helpers. + +Plots are visual; we don't assert on pixel content. The contract we +DO assert: every helper accepts a plausibly-shaped input frame and +runs without raising. A regression in one of the helpers (column name +typo, division by zero, etc.) surfaces here before it lands in a +notebook. +""" + +from __future__ import annotations + +import matplotlib + +matplotlib.use("Agg") # headless backend — no GUI windows during tests + +import numpy as np +import pandas as pd +import pytest + +from susse.evaluation.plots import ( + plot_covariate_shift_kde, + plot_feature_correlation_heatmap, + plot_pca_by_station, + plot_per_station_metric, + plot_predicted_vs_observed_panels, + plot_predictor_vs_observed_scatter, + plot_target_distribution_per_station, + plot_training_fit_scatter, + plot_training_fit_timeseries, +) + + +@pytest.fixture +def eval_frame() -> pd.DataFrame: + """One year of daily data across three stations, with predictions + + two satellite columns + a categorical highlight flag.""" + rng = np.random.default_rng(0) + dates = pd.date_range("2024-01-01", "2024-12-31", freq="D") + rows: list[dict] = [] + for loc in ("kampala", "soroti", "wadelai"): + intercept = 4.5 + rng.uniform(-0.3, 0.3) + for d in dates: + obs = intercept + rng.normal(scale=0.3) + rows.append( + { + "date": d, + "location": loc, + "y_obs": obs, + "y_pred": obs + rng.normal(scale=0.1), + "sat_ghi_nasa_kwh_m2_day": obs + 0.5 + rng.normal(scale=0.2), + "sat_ghi_cams_kwh_m2_day": obs + 0.3 + rng.normal(scale=0.2), + "feat_a": float(rng.uniform(0, 1)), + "feat_b": float(rng.uniform(-1, 1)), + "seen_in_training": loc == "kampala", + } + ) + return pd.DataFrame(rows) + + +def test_plot_target_distribution_per_station(eval_frame: pd.DataFrame) -> None: + plot_target_distribution_per_station( + eval_frame, + target_column="y_obs", + location_column="location", + ) + + +def test_plot_predictor_vs_observed_scatter(eval_frame: pd.DataFrame) -> None: + plot_predictor_vs_observed_scatter( + eval_frame, + observed_column="y_obs", + predictor_columns={ + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, + ) + + +def test_plot_feature_correlation_heatmap(eval_frame: pd.DataFrame) -> None: + plot_feature_correlation_heatmap( + eval_frame, + feature_columns=("feat_a", "feat_b", "sat_ghi_nasa_kwh_m2_day"), + target_column="y_obs", + top_n=3, + ) + + +def test_plot_pca_by_station(eval_frame: pd.DataFrame) -> None: + plot_pca_by_station( + eval_frame, + feature_columns=( + "feat_a", + "feat_b", + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + ), + ) + + +def test_plot_training_fit_scatter(eval_frame: pd.DataFrame) -> None: + plot_training_fit_scatter( + eval_frame, + observed_column="y_obs", + predicted_column="y_pred", + ) + + +def test_plot_training_fit_timeseries(eval_frame: pd.DataFrame) -> None: + plot_training_fit_timeseries( + eval_frame, + observed_column="y_obs", + prediction_series={"Model": "y_pred"}, + reference_series={ + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, + n_stations=2, + ) + + +def test_plot_training_fit_timeseries_no_references( + eval_frame: pd.DataFrame, +) -> None: + """The reference_series arg is optional — predictions alone must work.""" + plot_training_fit_timeseries( + eval_frame, + observed_column="y_obs", + prediction_series={"Model": "y_pred"}, + n_stations=2, + ) + + +def test_plot_training_fit_timeseries_no_observed( + eval_frame: pd.DataFrame, +) -> None: + """observed_column=None is the inference use case (no ground truth).""" + plot_training_fit_timeseries( + eval_frame, + observed_column=None, + prediction_series={"Model": "y_pred"}, + reference_series={"NASA": "sat_ghi_nasa_kwh_m2_day"}, + n_stations=2, + ) + + +def test_plot_training_fit_timeseries_single_station( + eval_frame: pd.DataFrame, +) -> None: + """n_stations=1 must draw exactly one panel (no empty second column).""" + plot_training_fit_timeseries( + eval_frame, + observed_column=None, + prediction_series={"Model": "y_pred"}, + reference_series={"NASA": "sat_ghi_nasa_kwh_m2_day"}, + n_stations=1, + ) + + +def test_plot_predicted_vs_observed_panels(eval_frame: pd.DataFrame) -> None: + plot_predicted_vs_observed_panels( + eval_frame, + observed_column="y_obs", + predictions={ + "Model": "y_pred", + "NASA": "sat_ghi_nasa_kwh_m2_day", + "CAMS": "sat_ghi_cams_kwh_m2_day", + }, + ) + + +def test_plot_per_station_metric_with_highlight(eval_frame: pd.DataFrame) -> None: + plot_per_station_metric( + eval_frame, + observed_column="y_obs", + predicted_column="y_pred", + highlight_column="seen_in_training", + ) + + +def test_plot_covariate_shift_kde(eval_frame: pd.DataFrame) -> None: + plot_covariate_shift_kde( + eval_frame.iloc[:300], + eval_frame.iloc[300:], + features=("feat_a", "feat_b"), + ) diff --git a/tests/fixtures/ground_measurements/somalia_slice.csv b/tests/fixtures/ground_measurements/somalia_slice.csv new file mode 100644 index 00000000..c33b163a --- /dev/null +++ b/tests/fixtures/ground_measurements/somalia_slice.csv @@ -0,0 +1,6 @@ +datetime,ghi,location,latitude,longitude +2024-01-28,5550.759,somalia_location1,3.107191,43.637153 +2024-02-01,5609.558,somalia_location1,3.107191,43.637153 +2024-02-02,6353.845,somalia_location1,3.107191,43.637153 +2024-02-03,5800.749,somalia_location1,3.107191,43.637153 +2024-02-04,6444.199,somalia_location1,3.107191,43.637153 diff --git a/tests/inference/__init__.py b/tests/inference/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/inference/test_feature_catalog.py b/tests/inference/test_feature_catalog.py new file mode 100644 index 00000000..e5f4f693 --- /dev/null +++ b/tests/inference/test_feature_catalog.py @@ -0,0 +1,153 @@ +"""Tests for FeatureCatalog — the bundle's model-input metadata. + +Per CLAUDE.md these test the public contract: that ``build`` composes +warehouse-variable and derived-feature metadata into one catalog +covering exactly the bundle's model inputs, groups each column by +origin, and fails loudly on unsupported or inconsistent bundles. +""" + +from __future__ import annotations + +import pytest + +from susse.datasets import FeatureSelection +from susse.inference import FeatureCatalog, FeatureGroup +from susse.preprocessing import ( + ClearSkyIndexFeature, + CyclicalDayOfYearFeature, + FeatureSpec, +) +from susse.warehouse_ops.population.types import IrradianceBand, Source + +# Pass-through columns the standard selection below produces, in build +# order: NASA aux, CAMS aux, then satellite irradiance. +_PASSTHROUGH_COLUMNS: tuple[str, ...] = ( + "nasa_temperature", + "cams_ghi_clear", + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", +) +_DERIVED_COLUMN: str = "kt_cams" + + +@pytest.fixture +def standard_catalog() -> FeatureCatalog: + """A catalog over one NASA aux + one CAMS aux + GHI + a derived kt.""" + selection = FeatureSelection( + nasa_variable_ids=("temperature",), + cams_variable_ids=("ghi_clear",), + include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS), + include_satellite_bands=(IrradianceBand.GHI,), + ) + spec = FeatureSpec( + feature_columns=_PASSTHROUGH_COLUMNS, + derived_features=( + ClearSkyIndexFeature( + ghi_column="sat_ghi_cams_kwh_m2_day", + ghi_clear_column="cams_ghi_clear", + output_column=_DERIVED_COLUMN, + ), + ), + ) + return FeatureCatalog.build(selection=selection, feature_spec=spec) + + +class TestBuild: + def test_catalog_covers_aux_irradiance_and_derived( + self, standard_catalog: FeatureCatalog + ) -> None: + assert set(standard_catalog.columns) == set(_PASSTHROUGH_COLUMNS) | { + _DERIVED_COLUMN + } + + def test_catalog_orders_aux_then_irradiance_then_derived( + self, standard_catalog: FeatureCatalog + ) -> None: + # build documents this order; columns promises "catalog order". + assert standard_catalog.columns == ( + "nasa_temperature", + "cams_ghi_clear", + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + _DERIVED_COLUMN, + ) + + def test_derived_only_catalog_handles_multi_column_feature(self) -> None: + # A bundle with no warehouse inputs, only a derived feature that + # itself emits two columns — the catalog must fan out one entry + # per emitted column, not one per feature. + selection = FeatureSelection(include_satellite_irradiance=()) + spec = FeatureSpec( + feature_columns=(), + derived_features=(CyclicalDayOfYearFeature(),), + ) + catalog = FeatureCatalog.build(selection=selection, feature_spec=spec) + assert catalog.columns == ("doy_sin", "doy_cos") + assert all(entry.group is FeatureGroup.DERIVED for entry in catalog) + + def test_groups_track_each_column_origin( + self, standard_catalog: FeatureCatalog + ) -> None: + assert standard_catalog.get("nasa_temperature").group is FeatureGroup.NASA_POWER + assert standard_catalog.get("cams_ghi_clear").group is FeatureGroup.CAMS + assert ( + standard_catalog.get("sat_ghi_nasa_kwh_m2_day").group + is FeatureGroup.NASA_POWER + ) + assert standard_catalog.get(_DERIVED_COLUMN).group is FeatureGroup.DERIVED + + def test_warehouse_metadata_carries_label_and_unit( + self, standard_catalog: FeatureCatalog + ) -> None: + # The label is the warehouse catalog's display_name; the unit is + # its physical unit — both must survive the FeatureMetadata hop. + temperature = standard_catalog.get("nasa_temperature") + assert temperature.label # non-empty display name + assert temperature.unit == "degC" + + def test_derived_metadata_flows_from_output_metadata( + self, standard_catalog: FeatureCatalog + ) -> None: + kt = standard_catalog.get(_DERIVED_COLUMN) + assert kt.unit == "unitless" + assert kt.description # non-empty + + def test_merra_selection_raises_not_implemented(self) -> None: + selection = FeatureSelection( + nasa_variable_ids=("temperature",), + merra_variable_ids=("precipitable_water",), + include_satellite_irradiance=(), + ) + spec = FeatureSpec(feature_columns=("nasa_temperature",)) + with pytest.raises(NotImplementedError, match="MERRA"): + FeatureCatalog.build(selection=selection, feature_spec=spec) + + def test_inconsistent_bundle_raises(self) -> None: + # The spec claims a model input the selection never produces — + # an internally inconsistent bundle must fail loudly, not serve + # an inspector that silently omits the column. + selection = FeatureSelection( + nasa_variable_ids=("temperature",), + include_satellite_irradiance=(), + ) + spec = FeatureSpec(feature_columns=("nasa_temperature", "nasa_phantom")) + with pytest.raises(ValueError, match="does not match"): + FeatureCatalog.build(selection=selection, feature_spec=spec) + + +class TestGet: + def test_unknown_column_raises_with_known_columns( + self, standard_catalog: FeatureCatalog + ) -> None: + with pytest.raises(KeyError, match="not_a_feature"): + standard_catalog.get("not_a_feature") + + +class TestFeatureGroup: + def test_from_source_maps_supported_sources(self) -> None: + assert FeatureGroup.from_source(Source.NASA_POWER) is FeatureGroup.NASA_POWER + assert FeatureGroup.from_source(Source.CAMS) is FeatureGroup.CAMS + + def test_from_source_rejects_unsupported_source(self) -> None: + with pytest.raises(ValueError, match="MERRA"): + FeatureGroup.from_source(Source.MERRA_2) diff --git a/tests/inference/test_predictor.py b/tests/inference/test_predictor.py new file mode 100644 index 00000000..832229d8 --- /dev/null +++ b/tests/inference/test_predictor.py @@ -0,0 +1,510 @@ +"""Predictor — happy path, cache-miss surfacing, and request validation. + +The Predictor composes :class:`FeatureService` + :class:`Preprocessor` ++ :class:`TrainedBundle` end-to-end. The fixtures train a tiny RF +bundle on synthetic data and monkeypatch +:class:`SatelliteRepository`'s warehouse-query methods to return +canned DataFrames — so the test exercises the orchestration without +ever opening a BigQuery connection. +""" + +from __future__ import annotations + +from datetime import date, datetime, timezone +from pathlib import Path +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pygeohash +import pytest + +from susse.datasets import DatasetManifest, FeatureSelection, TrainingDataset +from susse.inference import FeatureGroup, PredictionRequest, Predictor +from susse.models import RandomForestParams +from susse.preprocessing import FeatureSpec, Preprocessor +from susse.training import SpatialBlockSplitter, Trainer +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.repositories import SatelliteRepository +from susse.warehouse_ops.population.types import ( + IrradianceBand, + Source, + satellite_irradiance_column, +) + +# --------------------------------------------------------------------------- +# Synthetic training data + bundle. +# --------------------------------------------------------------------------- + +_FEATURE_COLUMNS: tuple[str, ...] = ( + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + "nasa_temperature", +) +_TARGET_COLUMN: str = "y_ghi_kwh_m2_day" + + +@pytest.fixture +def fake_selection() -> FeatureSelection: + """One NASA aux variable + GHI from both satellites — minimal but realistic.""" + return FeatureSelection( + nasa_variable_ids=("temperature",), + cams_variable_ids=(), + include_satellite_irradiance=(Source.NASA_POWER, Source.CAMS), + include_satellite_bands=(IrradianceBand.GHI,), + qc_levels=("pass",), + ) + + +@pytest.fixture +def synthetic_training_frame(fake_selection: FeatureSelection) -> pd.DataFrame: + rng = np.random.default_rng(seed=0) + rows: list[dict] = [] + for station, lat, lon, gh in [ + ("sta_a", 0.4, 32.6, "abc12"), + ("sta_b", -1.0, 36.8, "qrs45"), + ]: + for d in pd.date_range("2024-01-01", "2024-02-29", freq="D"): + sat_nasa = float(rng.uniform(4.0, 7.0)) + sat_cams = sat_nasa + float(rng.normal(0, 0.3)) + temp = float(rng.uniform(20.0, 28.0)) + rows.append( + { + "date": d.date(), + "location": station, + "geohash5": gh, + "lat": lat, + "lon": lon, + "sat_ghi_nasa_kwh_m2_day": sat_nasa, + "sat_ghi_cams_kwh_m2_day": sat_cams, + "nasa_temperature": temp, + "y_ghi_kwh_m2_day": 0.7 * sat_nasa + + 0.05 * temp + + rng.normal(0, 0.2), + } + ) + return pd.DataFrame(rows) + + +@pytest.fixture +def trained_bundle( + tmp_path: Path, + synthetic_training_frame: pd.DataFrame, + fake_selection: FeatureSelection, +): + """Tiny RF bundle saved to disk, ready for the Predictor to consume.""" + manifest = DatasetManifest( + name="predictor_test", + version="v0", + created_at_utc=datetime(2026, 5, 12, tzinfo=timezone.utc).isoformat(), + susse_version="test", + git_sha=None, + feature_selection=fake_selection, + date_start=date(2024, 1, 1), + date_end=date(2024, 2, 29), + location_filter=None, + warehouse_project="test", + warehouse_dataset="test", + warehouse_table_mods={}, + n_rows=len(synthetic_training_frame), + n_cols=len(synthetic_training_frame.columns), + column_names=tuple(synthetic_training_frame.columns), + content_hash="predictor_test_hash", + ) + spec = FeatureSpec( + target_column=_TARGET_COLUMN, + feature_columns=_FEATURE_COLUMNS, + derived_features=(), + id_columns=("date", "location", "geohash5", "lat", "lon"), + ) + ds = TrainingDataset(df=synthetic_training_frame, manifest=manifest) + processed = Preprocessor(spec).apply(ds) + + bundle_dest = tmp_path / "bundle" + bundle = Trainer().train( + processed=processed, + params=RandomForestParams(n_estimators=10, max_depth=4, random_state=0), + splitter=SpatialBlockSplitter(val_blocks=("sta_b",), block_column="location"), + holdout_label="sta_b holdout", + bundle_dest=bundle_dest, + ) + return bundle + + +# --------------------------------------------------------------------------- +# Fake warehouse — monkeypatch SatelliteRepository methods. +# --------------------------------------------------------------------------- + + +def _make_fake_warehouse( + irradiance_dates: list[date], + geohash5s: tuple[str, ...], +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Build canned irradiance + NASA-aux frames covering (geohash5 × date).""" + rng = np.random.default_rng(seed=11) + irr_rows: list[dict] = [] + aux_rows: list[dict] = [] + for gh in geohash5s: + for d in irradiance_dates: + sat_nasa = float(rng.uniform(4.5, 6.5)) + sat_cams = sat_nasa + float(rng.normal(0, 0.25)) + irr_rows.append( + { + "date": d, + "geohash5": gh, + "sat_ghi_nasa_kwh_m2_day": sat_nasa, + "sat_ghi_cams_kwh_m2_day": sat_cams, + } + ) + aux_rows.append( + { + "date": d, + "geohash5": gh, + "nasa_temperature": float(rng.uniform(22.0, 26.0)), + } + ) + return pd.DataFrame(irr_rows), pd.DataFrame(aux_rows) + + +@pytest.fixture +def patched_satellite_repo(monkeypatch: pytest.MonkeyPatch): + """Monkeypatch SatelliteRepository so the Predictor pulls canned frames. + + Returns a setter the test can use to control the response. + """ + state: dict[str, pd.DataFrame] = { + "irradiance": pd.DataFrame(), + "aux": pd.DataFrame(), + } + + def fake_irradiance(self, start, end, *, sources, bands, geohash5s): + # The real method projects one sat__ column per + # requested source; FeatureService now calls it once per source, + # so the fake must honour the `sources` filter too. + df = state["irradiance"] + if df.empty: + return df.copy() + keep = ["date", "geohash5"] + for band in bands: + for src in sources: + col = satellite_irradiance_column(Source(src), band) + if col in df.columns: + keep.append(col) + return df[keep].copy() + + def fake_aux( + self, *, table_fqn, column_prefix, start, end, variable_ids, geohash5s + ): + return state["aux"].copy() + + def fake_native_pixels(self, *, table_fqn, source=None): + # The native-grid snapper is built from the warehouse's own pixels. + # Derive one pixel per fake-warehouse geohash5, placed at the cell + # centre, so a query coord inside that cell snaps back to it. + df = state["irradiance"] + cols = ["geohash5", "latitude", "longitude"] + if df.empty: + return pd.DataFrame(columns=cols) + rows = [] + for gh in df["geohash5"].unique(): + lat, lon = pygeohash.decode(gh) + rows.append({"geohash5": gh, "latitude": lat, "longitude": lon}) + return pd.DataFrame(rows, columns=cols) + + monkeypatch.setattr( + SatelliteRepository, "daily_irradiance_by_geohash", fake_irradiance + ) + monkeypatch.setattr(SatelliteRepository, "long_aux_pivoted", fake_aux) + monkeypatch.setattr(SatelliteRepository, "native_pixels", fake_native_pixels) + + def set_response(*, irradiance: pd.DataFrame, aux: pd.DataFrame) -> None: + state["irradiance"] = irradiance + state["aux"] = aux + + return set_response + + +@pytest.fixture +def fake_bq() -> MagicMock: + """A MagicMock that satisfies the BigQueryClient interface superficially. + + FeatureService.__init__ accesses ``bq.config`` to derive TableRefs; + everything else is mediated through the patched SatelliteRepository. + """ + bq = MagicMock(spec=BigQueryClient) + bq.config.project_id = "test-project" + bq.config.dataset = "test_warehouse" + return bq + + +# --------------------------------------------------------------------------- +# Tests. +# --------------------------------------------------------------------------- + + +class TestPredictionRequest: + def test_empty_coords_raises(self) -> None: + with pytest.raises(ValueError, match="empty"): + PredictionRequest( + coords=(), start_date=date(2024, 1, 1), end_date=date(2024, 1, 2) + ) + + def test_latitude_out_of_range_raises(self) -> None: + with pytest.raises(ValueError, match="Latitude"): + PredictionRequest( + coords=((200.0, 0.0),), + start_date=date(2024, 1, 1), + end_date=date(2024, 1, 2), + ) + + def test_swapped_dates_raise_with_remediation(self) -> None: + with pytest.raises(ValueError, match="precedes"): + PredictionRequest( + coords=((0.5, 33.0),), + start_date=date(2024, 2, 1), + end_date=date(2024, 1, 1), + ) + + +class TestPredictHappyPath: + def test_single_coord_returns_one_row_per_date( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + ) -> None: + start, end = date(2024, 1, 1), date(2024, 1, 7) # 7 days + irr, aux = _make_fake_warehouse( + list(pd.date_range(start, end, freq="D").date), + geohash5s=("s8p1v",), + ) + patched_satellite_repo(irradiance=irr, aux=aux) + + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + result = predictor.predict( + coords=[(0.333542, 32.56863)], # → s8p1v + start_date=start, + end_date=end, + ) + assert len(result) == 7 + assert "y_pred_kwh_m2_day" in result.columns + assert set(["lat", "lon", "geohash5", "date"]).issubset(result.columns) + + def test_multiple_coords_share_a_geohash_correctly( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + ) -> None: + # Two coords; resolve to distinct geohash5s. + start, end = date(2024, 1, 1), date(2024, 1, 3) + gh_a = "s8p1v" # Kampala-ish + gh_b = "kzdre" # eastern Kenya + irr, aux = _make_fake_warehouse( + list(pd.date_range(start, end, freq="D").date), + geohash5s=(gh_a, gh_b), + ) + patched_satellite_repo(irradiance=irr, aux=aux) + + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + result = predictor.predict( + coords=[(0.333542, 32.56863), (-1.491302, 37.052862)], + start_date=start, + end_date=end, + ) + # 2 coords × 3 days = 6 rows. + assert len(result) == 6 + assert result["y_pred_kwh_m2_day"].notna().all() + + def test_predictions_are_finite_floats( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + ) -> None: + start, end = date(2024, 1, 1), date(2024, 1, 5) + irr, aux = _make_fake_warehouse( + list(pd.date_range(start, end, freq="D").date), + geohash5s=("s8p1v",), + ) + patched_satellite_repo(irradiance=irr, aux=aux) + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + result = predictor.predict( + coords=[(0.333542, 32.56863)], + start_date=start, + end_date=end, + ) + assert np.isfinite(result["y_pred_kwh_m2_day"]).all() + + +class TestCacheMiss: + def test_raise_policy_surfaces_missing_dates( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + ) -> None: + # Warehouse only has 3 days; we request 7. 4 missing. + irr, aux = _make_fake_warehouse( + [date(2024, 1, 1), date(2024, 1, 2), date(2024, 1, 3)], + geohash5s=("s8p1v",), + ) + patched_satellite_repo(irradiance=irr, aux=aux) + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + with pytest.raises(RuntimeError, match="missing"): + predictor.predict( + coords=[(0.333542, 32.56863)], + start_date=date(2024, 1, 1), + end_date=date(2024, 1, 7), + ) + + +class TestFetchModeCredentials: + def test_missing_cams_email_raises_at_construction( + self, + trained_bundle, + fake_bq: MagicMock, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + """on_cache_miss='fetch' without CAMS_EMAIL must fail loudly upfront.""" + monkeypatch.delenv("CAMS_EMAIL", raising=False) + with pytest.raises(ValueError, match="CAMS_EMAIL"): + Predictor(bundle=trained_bundle, bq=fake_bq, on_cache_miss="fetch") + + def test_present_cams_email_allows_construction( + self, + trained_bundle, + fake_bq: MagicMock, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + monkeypatch.setenv("CAMS_EMAIL", "you@example.com") + predictor = Predictor(bundle=trained_bundle, bq=fake_bq, on_cache_miss="fetch") + assert predictor.on_cache_miss == "fetch" + + +class TestFetchOnDemand: + """The 'fetch' branch reuses NasaPowerSatelliteJob + CamsSatelliteJob. + + Mock both jobs' .run() — that's the seam between Predictor and the + warehouse-ingest layer. After the mocked jobs return, we extend the + fake-warehouse response so the re-query sees the freshly-written + rows. + """ + + def test_fetch_runs_ingest_jobs_then_re_queries( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + monkeypatch.setenv("CAMS_EMAIL", "you@example.com") + start, end = date(2024, 1, 1), date(2024, 1, 5) + + # Initial warehouse state: empty. Cache miss for every date. + irr_full, aux_full = _make_fake_warehouse( + list(pd.date_range(start, end, freq="D").date), + geohash5s=("s8p1v",), + ) + patched_satellite_repo( + irradiance=pd.DataFrame(columns=irr_full.columns), + aux=pd.DataFrame(columns=aux_full.columns), + ) + + # Mock the ingest jobs: instead of hitting the API, just refill + # the patched warehouse so the re-query picks up the new rows. + nasa_call = {"count": 0} + cams_call = {"count": 0} + + def fake_nasa_run(self, plan): + nasa_call["count"] += 1 + patched_satellite_repo(irradiance=irr_full, aux=aux_full) + return MagicMock(rows_added_long=0, rows_added_irr=0) + + def fake_cams_run(self, plan): + cams_call["count"] += 1 + return MagicMock(rows_added_long=0, rows_added_irr=0) + + from susse.warehouse_ops.population.jobs.satellite_job import ( + CamsSatelliteJob, + NasaPowerSatelliteJob, + ) + + monkeypatch.setattr(NasaPowerSatelliteJob, "run", fake_nasa_run) + monkeypatch.setattr(CamsSatelliteJob, "run", fake_cams_run) + + predictor = Predictor(bundle=trained_bundle, bq=fake_bq, on_cache_miss="fetch") + result = predictor.predict( + coords=[(0.333542, 32.56863)], + start_date=start, + end_date=end, + ) + # Both jobs were invoked. + assert nasa_call["count"] == 1 + assert cams_call["count"] == 1 + # And the result is a complete prediction frame. + assert len(result) == 5 + assert result["y_pred_kwh_m2_day"].notna().all() + + def test_rate_limit_cap_rejects_huge_miss( + self, + trained_bundle, + fake_bq: MagicMock, + patched_satellite_repo, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + """More than MAX_CAMS_CALLS_PER_PREDICT missing cells must raise.""" + from susse.inference.predictor import MAX_CAMS_CALLS_PER_PREDICT + + monkeypatch.setenv("CAMS_EMAIL", "you@example.com") + # Build n = cap + 1 coordinates that all miss the cache. + n = MAX_CAMS_CALLS_PER_PREDICT + 1 + coords = [(0.0 + 0.1 * i, 32.0 + 0.1 * i) for i in range(n)] + + patched_satellite_repo( + irradiance=pd.DataFrame( + columns=[ + "date", + "geohash5", + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + ] + ), + aux=pd.DataFrame(columns=["date", "geohash5", "nasa_temperature"]), + ) + predictor = Predictor(bundle=trained_bundle, bq=fake_bq, on_cache_miss="fetch") + with pytest.raises(RuntimeError, match="exceeding the per-invocation cap"): + predictor.predict( + coords=coords, + start_date=date(2024, 1, 1), + end_date=date(2024, 1, 1), + ) + + +class TestFeatureCatalog: + """Predictor.feature_catalog exposes metadata for the bundle's inputs. + + The exhaustive build/grouping logic is covered by + test_feature_catalog.py; these tests verify only that the Predictor + wires the bundle into FeatureCatalog.build and caches the result. + """ + + def test_catalog_columns_match_model_inputs( + self, trained_bundle, fake_bq: MagicMock + ) -> None: + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + # The fixture bundle consumes one NASA aux variable plus GHI from + # both satellites, and declares no derived features. + assert set(predictor.feature_catalog.columns) == set(_FEATURE_COLUMNS) + + def test_catalog_carries_real_metadata( + self, trained_bundle, fake_bq: MagicMock + ) -> None: + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + temperature = predictor.feature_catalog.get("nasa_temperature") + assert temperature.group is FeatureGroup.NASA_POWER + assert temperature.label # non-empty display name + + def test_catalog_is_cached(self, trained_bundle, fake_bq: MagicMock) -> None: + predictor = Predictor(bundle=trained_bundle, bq=fake_bq) + assert predictor.feature_catalog is predictor.feature_catalog diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/models/test_linear.py b/tests/models/test_linear.py new file mode 100644 index 00000000..5fcccc1c --- /dev/null +++ b/tests/models/test_linear.py @@ -0,0 +1,110 @@ +"""Tests for LinearRegressor — covers the in-wrapper scaling contract.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from susse.models import LinearParams, LinearRegressor, ModelFactory, load_regressor + + +def _learnable_data(n: int = 200) -> tuple[pd.DataFrame, pd.Series]: + """y = 2a - 3b + noise. Linear is the right model class here.""" + rng = np.random.default_rng(0) + X = pd.DataFrame( + { + "a": rng.uniform(-1, 1, n), + "b": rng.uniform(-10, 10, n), # very different scale from `a` + } + ) + y = pd.Series(2.0 * X["a"] - 3.0 * X["b"] + rng.normal(scale=0.1, size=n)) + return X, y + + +class TestPredictionAccuracy: + def test_low_error_on_linear_signal_with_scaling(self) -> None: + X, y = _learnable_data() + model = LinearRegressor(LinearParams(with_scaling=True)).fit(X, y) + mae = (model.predict(X) - y).abs().mean() + # Noise std is 0.1; perfect fit would have MAE ≈ 0.08. + assert mae < 0.15 + + def test_low_error_on_linear_signal_without_scaling(self) -> None: + # Scaling shouldn't matter for a closed-form OLS fit, but + # numerically with very different feature scales it can; this + # pins that the un-scaled path also produces reasonable + # coefficients. + X, y = _learnable_data() + model = LinearRegressor(LinearParams(with_scaling=False)).fit(X, y) + mae = (model.predict(X) - y).abs().mean() + assert mae < 0.15 + + +class TestScalingContract: + """When scaling is on, the scaler must be fitted on the X passed to fit + (not on every X seen at predict). Pinning this protects against a + regression where someone added per-call scaler.fit_transform.""" + + def test_predict_uses_train_fold_scaler_only(self) -> None: + # Train on data A; predict on data B with very different + # distribution. If predict refit the scaler on B, the scaled + # values would be near-standard-normal, defeating the purpose. + # Instead, B's scaling must reflect A's mean/std. + rng = np.random.default_rng(42) + X_train = pd.DataFrame( + { + "a": rng.normal(loc=0.0, scale=1.0, size=200), + } + ) + y_train = pd.Series(2.0 * X_train["a"] + rng.normal(scale=0.05, size=200)) + + # B has a wildly shifted mean — if scaler refit on B, predictions + # would be biased toward the mean of y_train. + X_test = pd.DataFrame({"a": [100.0]}) + + model = LinearRegressor(LinearParams(with_scaling=True)).fit(X_train, y_train) + prediction = model.predict(X_test).iloc[0] + + # With scaler fitted on A: a=100 is ~100σ above A's mean → + # the linear extrapolation should give a very large positive + # prediction (~200, consistent with y = 2a slope). + # With scaler refit on B (single point): the scaled value would + # be 0 → prediction near intercept (~0). + assert prediction > 100.0, ( + f"prediction={prediction} suggests the scaler was refit at " + f"predict time. Expected scaler to be fit on the training " + f"fold only." + ) + + +class TestSaveLoadRoundtrip: + def test_with_scaling_roundtrip(self, tmp_path: Path) -> None: + X, y = _learnable_data() + original = LinearRegressor(LinearParams(with_scaling=True)).fit(X, y) + original.save(tmp_path) + restored = load_regressor(tmp_path) + assert isinstance(restored, LinearRegressor) + pd.testing.assert_series_equal(restored.predict(X), original.predict(X)) + + def test_without_scaling_roundtrip(self, tmp_path: Path) -> None: + X, y = _learnable_data() + original = LinearRegressor(LinearParams(with_scaling=False)).fit(X, y) + original.save(tmp_path) + restored = load_regressor(tmp_path) + pd.testing.assert_series_equal(restored.predict(X), original.predict(X)) + + +class TestFactory: + def test_factory_dispatches_to_correct_class(self) -> None: + model = ModelFactory.create(LinearParams(with_scaling=False)) + assert isinstance(model, LinearRegressor) + assert model.params.with_scaling is False + + +class TestLifecycle: + def test_predict_before_fit_raises(self) -> None: + with pytest.raises(RuntimeError, match="before .fit"): + LinearRegressor(LinearParams()).predict(pd.DataFrame({"a": [1.0]})) diff --git a/tests/models/test_mean_baseline.py b/tests/models/test_mean_baseline.py new file mode 100644 index 00000000..691c546b --- /dev/null +++ b/tests/models/test_mean_baseline.py @@ -0,0 +1,82 @@ +"""Tests for MeanBaselineRegressor.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from susse.models import ( + MeanBaselineParams, + MeanBaselineRegressor, + ModelFactory, + load_regressor, +) + + +def _toy_data(n: int = 50) -> tuple[pd.DataFrame, pd.Series]: + rng = np.random.default_rng(42) + X = pd.DataFrame({"a": rng.normal(size=n), "b": rng.normal(size=n)}) + y = pd.Series(rng.normal(loc=5.0, scale=1.0, size=n)) + return X, y + + +class TestPredictionBehavior: + def test_predicts_training_mean_for_every_row(self) -> None: + X, y = _toy_data() + model = MeanBaselineRegressor(MeanBaselineParams()).fit(X, y) + preds = model.predict(X) + # All predictions equal training mean. + assert preds.nunique() == 1 + assert preds.iloc[0] == pytest.approx(y.mean()) + + def test_median_statistic_predicts_median(self) -> None: + X, y = _toy_data() + model = MeanBaselineRegressor(MeanBaselineParams(statistic="median")).fit(X, y) + assert model.predict(X).iloc[0] == pytest.approx(y.median()) + + def test_predict_aligns_to_input_index(self) -> None: + # Non-default index must round-trip to predictions. + X, y = _toy_data() + X_test = X.iloc[:5].set_axis([100, 200, 300, 400, 500]) + model = MeanBaselineRegressor(MeanBaselineParams()).fit(X, y) + preds = model.predict(X_test) + assert list(preds.index) == [100, 200, 300, 400, 500] + + +class TestLifecycle: + def test_predict_before_fit_raises(self) -> None: + model = MeanBaselineRegressor(MeanBaselineParams()) + with pytest.raises(RuntimeError, match="before .fit"): + model.predict(pd.DataFrame({"a": [1.0]})) + + def test_save_before_fit_raises(self, tmp_path: Path) -> None: + model = MeanBaselineRegressor(MeanBaselineParams()) + with pytest.raises(RuntimeError, match="unfitted"): + model.save(tmp_path) + + def test_empty_y_rejected(self) -> None: + with pytest.raises(ValueError, match="empty"): + MeanBaselineRegressor(MeanBaselineParams()).fit( + pd.DataFrame({"a": []}), pd.Series([], dtype=float) + ) + + +class TestSaveLoadRoundtrip: + def test_predictions_unchanged_after_roundtrip(self, tmp_path: Path) -> None: + X, y = _toy_data() + original = MeanBaselineRegressor(MeanBaselineParams()).fit(X, y) + original.save(tmp_path) + restored = load_regressor(tmp_path) + # The dispatched class must be the right concrete one. + assert isinstance(restored, MeanBaselineRegressor) + # Predictions must be byte-identical. + pd.testing.assert_series_equal(restored.predict(X), original.predict(X)) + + +class TestFactory: + def test_factory_dispatches_to_correct_class(self) -> None: + model = ModelFactory.create(MeanBaselineParams()) + assert isinstance(model, MeanBaselineRegressor) diff --git a/tests/models/test_params.py b/tests/models/test_params.py new file mode 100644 index 00000000..497f6e79 --- /dev/null +++ b/tests/models/test_params.py @@ -0,0 +1,89 @@ +"""Tests for typed model params + dispatch enum.""" + +from __future__ import annotations + +import pytest + +from susse.models import ( + LinearParams, + MeanBaselineParams, + ModelKind, + RandomForestParams, + params_from_dict, +) + + +class TestModelKindDispatch: + """ModelKind methods own the kind → params/model class mapping.""" + + @pytest.mark.parametrize( + ("kind", "expected_params_cls"), + [ + (ModelKind.MEAN_BASELINE, MeanBaselineParams), + (ModelKind.RANDOM_FOREST, RandomForestParams), + (ModelKind.LINEAR, LinearParams), + ], + ) + def test_params_type_returns_matching_dataclass( + self, kind: ModelKind, expected_params_cls: type + ) -> None: + assert kind.params_type() is expected_params_cls + + def test_model_class_returns_concrete_subclass(self) -> None: + # Just verify the call resolves and returns a class — concrete-class + # tests live in their own modules. + for kind in ModelKind: + cls = kind.model_class() + assert isinstance(cls, type) + + +class TestParamsValidation: + def test_meanbaseline_rejects_unknown_statistic(self) -> None: + with pytest.raises(ValueError, match="mean.+median"): + MeanBaselineParams(statistic="mode") + + def test_random_forest_rejects_zero_estimators(self) -> None: + with pytest.raises(ValueError, match="n_estimators"): + RandomForestParams(n_estimators=0) + + def test_random_forest_rejects_zero_max_depth(self) -> None: + with pytest.raises(ValueError, match="max_depth"): + RandomForestParams(max_depth=0) + + def test_random_forest_allows_none_max_depth(self) -> None: + # None means unlimited depth — must not be rejected. + p = RandomForestParams(max_depth=None) + assert p.max_depth is None + + +class TestKindProperty: + def test_each_params_dataclass_reports_its_kind(self) -> None: + assert MeanBaselineParams().kind is ModelKind.MEAN_BASELINE + assert RandomForestParams().kind is ModelKind.RANDOM_FOREST + assert LinearParams().kind is ModelKind.LINEAR + + +class TestJsonRoundtrip: + """to_dict + params_from_dict roundtrip preserves params, even with + non-default values.""" + + @pytest.mark.parametrize( + "original", + [ + MeanBaselineParams(statistic="median"), + RandomForestParams(n_estimators=50, max_depth=8, random_state=7), + LinearParams(with_scaling=False, fit_intercept=False), + ], + ) + def test_roundtrip(self, original) -> None: + d = original.to_dict() + recovered = params_from_dict(d) + assert recovered == original + assert recovered.kind is original.kind + + def test_missing_kind_tag_raises(self) -> None: + # to_dict always sets `kind`; params_from_dict expects it. A + # caller building the dict by hand without it should get a + # clear error, not silently default to one model type. + with pytest.raises(ValueError, match="kind"): + params_from_dict({"n_estimators": 100}) diff --git a/tests/models/test_random_forest.py b/tests/models/test_random_forest.py new file mode 100644 index 00000000..eb99ec7a --- /dev/null +++ b/tests/models/test_random_forest.py @@ -0,0 +1,85 @@ +"""Tests for RandomForestRegressor.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from susse.models import ( + ModelFactory, + RandomForestParams, + RandomForestRegressor, + load_regressor, +) + + +def _learnable_data(n: int = 200) -> tuple[pd.DataFrame, pd.Series]: + """Toy regression where y is a simple function of X (with light noise). + + A correctly-fitted RF should reproduce this with much lower error + than the standard deviation of y. + """ + rng = np.random.default_rng(0) + X = pd.DataFrame( + { + "a": rng.uniform(-1, 1, n), + "b": rng.uniform(-1, 1, n), + } + ) + y = pd.Series(2.0 * X["a"] - 3.0 * X["b"] + rng.normal(scale=0.1, size=n)) + return X, y + + +class TestFit: + def test_random_seed_makes_predictions_deterministic(self) -> None: + X, y = _learnable_data() + params = RandomForestParams(n_estimators=20, random_state=123, n_jobs=1) + a = RandomForestRegressor(params).fit(X, y).predict(X) + b = RandomForestRegressor(params).fit(X, y).predict(X) + pd.testing.assert_series_equal(a, b) + + def test_predictions_better_than_target_mean_baseline(self) -> None: + # Sanity check: an RF with enough trees on a learnable signal + # should have substantially lower MAE than predicting the mean. + X, y = _learnable_data() + model = RandomForestRegressor( + RandomForestParams(n_estimators=50, random_state=42, n_jobs=1) + ).fit(X, y) + preds = model.predict(X) + rf_mae = (preds - y).abs().mean() + baseline_mae = (y - y.mean()).abs().mean() + # Generous threshold so this isn't sensitive to sklearn internals; + # the RF should still be much better than the baseline. + assert rf_mae < 0.5 * baseline_mae + + +class TestLifecycle: + def test_predict_before_fit_raises(self) -> None: + with pytest.raises(RuntimeError, match="before .fit"): + RandomForestRegressor(RandomForestParams()).predict( + pd.DataFrame({"a": [1.0], "b": [2.0]}) + ) + + +class TestSaveLoadRoundtrip: + def test_predictions_unchanged_after_roundtrip(self, tmp_path: Path) -> None: + X, y = _learnable_data() + original = RandomForestRegressor( + RandomForestParams(n_estimators=10, random_state=7, n_jobs=1) + ).fit(X, y) + original.save(tmp_path) + restored = load_regressor(tmp_path) + assert isinstance(restored, RandomForestRegressor) + # The reloaded params must equal the originals. + assert restored.params == original.params + pd.testing.assert_series_equal(restored.predict(X), original.predict(X)) + + +class TestFactory: + def test_factory_dispatches_to_correct_class(self) -> None: + model = ModelFactory.create(RandomForestParams(n_estimators=5)) + assert isinstance(model, RandomForestRegressor) + assert model.params.n_estimators == 5 diff --git a/tests/preprocessing/__init__.py b/tests/preprocessing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/preprocessing/test_cleaning.py b/tests/preprocessing/test_cleaning.py new file mode 100644 index 00000000..f8cf5c98 --- /dev/null +++ b/tests/preprocessing/test_cleaning.py @@ -0,0 +1,471 @@ +"""Tests for :class:`DataCleaner` subclasses + the kind-dispatch JSON loader. + +Each cleaner is pinned against an analytical reference (constructed +input → expected surviving / imputed rows). The tests exercise the +public contract (``apply``, ``required_input_columns``, ``to_dict`` +roundtrip via :func:`data_cleaner_from_dict`) rather than the private +helpers. +""" + +from __future__ import annotations + +from datetime import date, timedelta + +import numpy as np +import pandas as pd +import pytest + +from susse.preprocessing import ( + CleanerKind, + DataCleaner, + GhiUpperBoundCleaner, + HighMissingYearExcluder, + IqrLowerBoundCleaner, + KnnYearGapImputer, + PerStationMeanImputer, + data_cleaner_from_dict, +) + +# --------------------------------------------------------------------------- +# Shared helpers +# --------------------------------------------------------------------------- + + +def _frame(rows: list[dict]) -> pd.DataFrame: + return pd.DataFrame(rows) + + +def _daily_frame( + location: str, + start: date, + n_days: int, + *, + value: float = 5.0, +) -> pd.DataFrame: + """Construct ``n_days`` consecutive daily rows for one station.""" + return pd.DataFrame( + { + "date": [start + timedelta(days=i) for i in range(n_days)], + "location": [location] * n_days, + "ghi_kwh_m2_day": [value] * n_days, + } + ) + + +# --------------------------------------------------------------------------- +# GhiUpperBoundCleaner +# --------------------------------------------------------------------------- + + +class TestGhiUpperBoundCleaner: + def test_drops_rows_strictly_above_threshold(self) -> None: + # Boundary check: rows exactly at the threshold are kept; rows + # strictly above are dropped. Pinning this prevents off-by-one + # creep that could either over- or under-clip the dataset. + df = _frame( + [ + {"ghi_kwh_m2_day": 11.9}, + {"ghi_kwh_m2_day": 12.0}, # boundary — keep + {"ghi_kwh_m2_day": 12.5}, # drop + {"ghi_kwh_m2_day": 9.5}, + ] + ) + cleaned = GhiUpperBoundCleaner().apply(df) + assert cleaned["ghi_kwh_m2_day"].tolist() == [11.9, 12.0, 9.5] + + def test_custom_column_and_threshold(self) -> None: + # Same cleaner reused with a different column name + bound — the + # paper's defaults aren't the only valid configuration. + df = _frame( + [ + {"x": 0.5}, + {"x": 1.0}, + {"x": 2.0}, + ] + ) + cleaned = GhiUpperBoundCleaner(column="x", threshold=1.0).apply(df) + assert cleaned["x"].tolist() == [0.5, 1.0] + + def test_required_inputs(self) -> None: + cleaner = GhiUpperBoundCleaner() + assert cleaner.required_input_columns == ("ghi_kwh_m2_day",) + + def test_rejects_non_finite_threshold(self) -> None: + with pytest.raises(ValueError, match="finite"): + GhiUpperBoundCleaner(threshold=float("nan")) + + def test_to_dict_roundtrip(self) -> None: + c = GhiUpperBoundCleaner(column="x", threshold=8.5) + rebuilt = data_cleaner_from_dict(c.to_dict()) + assert isinstance(rebuilt, GhiUpperBoundCleaner) + assert rebuilt.column == "x" + assert rebuilt.threshold == pytest.approx(8.5) + + +# --------------------------------------------------------------------------- +# IqrLowerBoundCleaner +# --------------------------------------------------------------------------- + + +class TestIqrLowerBoundCleaner: + def test_drops_low_outlier(self) -> None: + # Q1=2, Q3=4, IQR=2 → lower bound = 2 − 1.5×2 = −1. With a clear + # negative outlier far below that, expect it to be dropped. + df = _frame([{"ghi_kwh_m2_day": v} for v in [-50.0, 2.0, 3.0, 4.0, 5.0]]) + cleaned = IqrLowerBoundCleaner().apply(df) + assert -50.0 not in cleaned["ghi_kwh_m2_day"].tolist() + assert len(cleaned) == 4 + + def test_keeps_all_rows_when_no_low_outlier(self) -> None: + df = _frame([{"ghi_kwh_m2_day": v} for v in [3.0, 4.0, 5.0, 6.0]]) + cleaned = IqrLowerBoundCleaner().apply(df) + assert len(cleaned) == 4 + + def test_does_not_clip_upper_outliers(self) -> None: + # The upper-side fence is intentionally not applied — clear-sky + # days at the top of the distribution are legitimate values. + # Pin that an extreme high value survives this cleaner. + df = _frame([{"ghi_kwh_m2_day": v} for v in [3.0, 4.0, 5.0, 6.0, 50.0]]) + cleaned = IqrLowerBoundCleaner().apply(df) + assert 50.0 in cleaned["ghi_kwh_m2_day"].tolist() + + def test_empty_frame_returns_empty(self) -> None: + empty = pd.DataFrame({"ghi_kwh_m2_day": []}, dtype=float) + cleaned = IqrLowerBoundCleaner().apply(empty) + assert cleaned.empty + + def test_rejects_non_positive_multiplier(self) -> None: + with pytest.raises(ValueError, match="positive"): + IqrLowerBoundCleaner(multiplier=0.0) + + def test_to_dict_roundtrip(self) -> None: + c = IqrLowerBoundCleaner(column="x", multiplier=2.0) + rebuilt = data_cleaner_from_dict(c.to_dict()) + assert isinstance(rebuilt, IqrLowerBoundCleaner) + assert rebuilt.column == "x" + assert rebuilt.multiplier == pytest.approx(2.0) + + +# --------------------------------------------------------------------------- +# HighMissingYearExcluder +# --------------------------------------------------------------------------- + + +class TestHighMissingYearExcluder: + def test_drops_sparse_year_keeps_full_year(self) -> None: + # Station A: full year of data → keep. + # Station B: only 30 days within a 365-day span → drop entire group. + full = _daily_frame("A", date(2024, 1, 1), 365) + sparse_dates = [ + date(2024, 1, 1) + timedelta(days=i * 12) for i in range(30) + ] # 30 days spread over ~360 + sparse = pd.DataFrame( + { + "date": sparse_dates, + "location": ["B"] * 30, + "ghi_kwh_m2_day": [4.0] * 30, + } + ) + df = pd.concat([full, sparse], ignore_index=True) + cleaner = HighMissingYearExcluder() # 5% default threshold + cleaned = cleaner.apply(df) + assert set(cleaned["location"]) == { + "A" + }, "Station B's missing fraction is far above 5% — its rows must be dropped." + assert len(cleaned) == 365 + + def test_keeps_year_within_threshold(self) -> None: + # 360 of 365 expected days = ~1.4% missing, below 5% → keep. + days = [ + date(2024, 1, 1) + timedelta(days=i) + for i in range(365) + if i not in {10, 20, 30, 40, 50} + ] + df = pd.DataFrame( + { + "date": days, + "location": ["A"] * len(days), + "ghi_kwh_m2_day": [5.0] * len(days), + } + ) + cleaner = HighMissingYearExcluder() + cleaned = cleaner.apply(df) + assert len(cleaned) == len(days) + + def test_partial_year_with_full_density_kept(self) -> None: + # Station active Jan-Mar only, every day present. Span = 90 days, + # observed = 90 → missing fraction = 0 → keep. The missing-fraction + # is computed against the station's own active span, not the full + # calendar year (a station that operated Jan-Mar shouldn't be + # penalised for "missing" Apr-Dec). + df = _daily_frame("A", date(2024, 1, 1), 90) + cleaner = HighMissingYearExcluder() + cleaned = cleaner.apply(df) + assert len(cleaned) == 90 + + def test_threshold_out_of_range_raises(self) -> None: + with pytest.raises(ValueError, match="\\[0, 1\\]"): + HighMissingYearExcluder(missing_fraction_threshold=1.5) + + def test_to_dict_roundtrip(self) -> None: + c = HighMissingYearExcluder( + station_column="loc", + date_column="d", + missing_fraction_threshold=0.1, + ) + rebuilt = data_cleaner_from_dict(c.to_dict()) + assert isinstance(rebuilt, HighMissingYearExcluder) + assert rebuilt.station_column == "loc" + assert rebuilt.date_column == "d" + assert rebuilt.missing_fraction_threshold == pytest.approx(0.1) + + +# --------------------------------------------------------------------------- +# KnnYearGapImputer +# --------------------------------------------------------------------------- + + +class TestKnnYearGapImputer: + def test_fills_single_day_gap_with_neighbour_mean(self) -> None: + # 4 consecutive days, plus a 5th day after a 1-day gap. The + # missing day's k=2 nearest existing values are days 4 and 6 + # (1 step away each), so the imputed value is their mean. + df = pd.DataFrame( + { + "date": [ + date(2024, 1, 1), + date(2024, 1, 2), + date(2024, 1, 3), + date(2024, 1, 4), + date(2024, 1, 6), + date(2024, 1, 7), + ], + "location": ["A"] * 6, + "ghi_kwh_m2_day": [5.0, 5.5, 5.2, 6.0, 4.8, 4.5], + } + ) + cleaner = KnnYearGapImputer(k=2) + imputed = cleaner.apply(df) + # The Jan 5 row should have appeared. + jan5_rows = imputed[imputed["date"] == date(2024, 1, 5)] + assert len(jan5_rows) == 1 + # Mean of the two neighbours (Jan 4 = 6.0, Jan 6 = 4.8) = 5.4. + assert jan5_rows.iloc[0]["ghi_kwh_m2_day"] == pytest.approx(5.4) + assert jan5_rows.iloc[0]["location"] == "A" + + def test_propagates_context_columns_on_imputed_rows(self) -> None: + # Carry-through columns (geohash5, lat, lon) on imputed rows + # must come from the same (station, year) group, not appear as + # NaN. A model that joins back on geohash5 would mis-route + # imputed days otherwise. + df = pd.DataFrame( + { + "date": [ + date(2024, 1, 1), + date(2024, 1, 2), + date(2024, 1, 4), + date(2024, 1, 5), + ], + "location": ["A"] * 4, + "geohash5": ["xyz12"] * 4, + "lat": [0.5] * 4, + "lon": [32.0] * 4, + "ghi_kwh_m2_day": [5.0, 5.5, 5.2, 5.4], + } + ) + cleaner = KnnYearGapImputer(k=2) + imputed = cleaner.apply(df) + jan3_rows = imputed[imputed["date"] == date(2024, 1, 3)] + assert len(jan3_rows) == 1 + assert jan3_rows.iloc[0]["geohash5"] == "xyz12" + assert jan3_rows.iloc[0]["lat"] == 0.5 + assert jan3_rows.iloc[0]["lon"] == 32.0 + + def test_no_gaps_returns_input_unchanged(self) -> None: + df = _daily_frame("A", date(2024, 1, 1), 30) + cleaner = KnnYearGapImputer(k=3) + imputed = cleaner.apply(df) + assert len(imputed) == len(df) + # Still the same target values (no spurious imputation). + assert imputed["ghi_kwh_m2_day"].tolist() == df["ghi_kwh_m2_day"].tolist() + + def test_under_k_observations_skipped(self) -> None: + # Group too small to compute a k-NN mean → cleaner skips it + # rather than crashing. Pairs with HighMissingYearExcluder which + # would normally have removed these groups upstream. + df = pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 5)], + "location": ["A"] * 2, + "ghi_kwh_m2_day": [5.0, 5.5], + } + ) + cleaner = KnnYearGapImputer(k=5) + imputed = cleaner.apply(df) + assert len(imputed) == 2 # untouched + + def test_separate_groups_imputed_independently(self) -> None: + # Station A has a gap on Jan 3; Station B has different values + # and a gap on Jan 4. Each group's imputation must use only that + # group's observed values (kNN never crosses station boundaries). + df = pd.DataFrame( + { + "date": [ + date(2024, 1, 1), + date(2024, 1, 2), + date(2024, 1, 4), + date(2024, 1, 1), + date(2024, 1, 2), + date(2024, 1, 3), + date(2024, 1, 5), + ], + "location": ["A", "A", "A", "B", "B", "B", "B"], + "ghi_kwh_m2_day": [3.0, 3.5, 4.0, 7.0, 7.2, 7.4, 7.6], + } + ) + cleaner = KnnYearGapImputer(k=2) + imputed = cleaner.apply(df) + a_jan3 = imputed[ + (imputed["location"] == "A") & (imputed["date"] == date(2024, 1, 3)) + ] + b_jan4 = imputed[ + (imputed["location"] == "B") & (imputed["date"] == date(2024, 1, 4)) + ] + assert len(a_jan3) == 1 + assert len(b_jan4) == 1 + # Imputed values come from each group's own neighbours, not the + # other group's range. + assert 3.0 < a_jan3.iloc[0]["ghi_kwh_m2_day"] < 4.5 + assert 7.0 < b_jan4.iloc[0]["ghi_kwh_m2_day"] < 8.0 + + def test_rejects_invalid_k(self) -> None: + with pytest.raises(ValueError, match="k must be"): + KnnYearGapImputer(k=0) + + def test_to_dict_roundtrip(self) -> None: + c = KnnYearGapImputer(target_column="x", k=7) + rebuilt = data_cleaner_from_dict(c.to_dict()) + assert isinstance(rebuilt, KnnYearGapImputer) + assert rebuilt.target_column == "x" + assert rebuilt.k == 7 + + +# --------------------------------------------------------------------------- +# Cross-cutting dispatch + protocol compliance +# --------------------------------------------------------------------------- + + +class TestDispatchByKind: + def test_unknown_kind_raises(self) -> None: + with pytest.raises(ValueError): + data_cleaner_from_dict({"kind": "not_a_real_kind"}) + + def test_missing_kind_raises(self) -> None: + with pytest.raises(ValueError, match="kind"): + data_cleaner_from_dict({"threshold": 12.0}) + + def test_each_kind_dispatches_to_correct_class(self) -> None: + # Pin the round-trip: every CleanerKind member maps to the class + # whose instances declare that kind, and back. Catches the + # "added an enum value but forgot the cleaner_class branch" bug. + instances: list[DataCleaner] = [ + GhiUpperBoundCleaner(), + IqrLowerBoundCleaner(), + HighMissingYearExcluder(), + KnnYearGapImputer(), + PerStationMeanImputer(columns=("temperature",)), + ] + for original in instances: + assert original.kind.spec_class() is type(original) + rebuilt = data_cleaner_from_dict(original.to_dict()) + assert type(rebuilt) is type(original) + + +class TestProtocolCompliance: + """Every concrete DataCleaner must implement the abstract contract.""" + + def test_all_subclasses_expose_kind_and_required_inputs(self) -> None: + instances: list[DataCleaner] = [ + GhiUpperBoundCleaner(), + IqrLowerBoundCleaner(), + HighMissingYearExcluder(), + KnnYearGapImputer(), + PerStationMeanImputer(columns=("temperature",)), + ] + for c in instances: + assert isinstance(c.kind, CleanerKind) + assert len(c.required_input_columns) >= 1 + assert "kind" in c.to_dict() + + +class TestPerStationMeanImputer: + """Imputer fills NaN with per-station column means.""" + + def test_fills_nan_with_station_mean(self) -> None: + df = pd.DataFrame( + { + "location": ["a", "a", "a", "b", "b", "b"], + "temperature": [10.0, 20.0, np.nan, 5.0, np.nan, 15.0], + } + ) + result = PerStationMeanImputer(columns=("temperature",)).apply(df) + # Station 'a' has values [10, 20] → mean 15 fills the NaN. + # Station 'b' has values [5, 15] → mean 10 fills the NaN. + assert result.loc[2, "temperature"] == pytest.approx(15.0) + assert result.loc[4, "temperature"] == pytest.approx(10.0) + # Non-NaN values unchanged. + assert result.loc[0, "temperature"] == 10.0 + assert result.loc[3, "temperature"] == 5.0 + + def test_leaves_all_nan_station_alone(self) -> None: + # Station 'c' has every value NaN — no mean to compute, so the + # imputer must leave those NaNs in place. Caller's + # ``dropna_features`` will then drop the rows downstream. + df = pd.DataFrame( + { + "location": ["a", "a", "c", "c"], + "temperature": [10.0, 20.0, np.nan, np.nan], + } + ) + result = PerStationMeanImputer(columns=("temperature",)).apply(df) + assert result.loc[0, "temperature"] == 10.0 + assert pd.isna(result.loc[2, "temperature"]) + assert pd.isna(result.loc[3, "temperature"]) + + def test_multiple_columns_imputed_independently(self) -> None: + df = pd.DataFrame( + { + "location": ["a", "a", "a"], + "temperature": [10.0, 20.0, np.nan], + "humidity": [np.nan, 40.0, 60.0], + } + ) + result = PerStationMeanImputer( + columns=("temperature", "humidity"), + ).apply(df) + assert result.loc[2, "temperature"] == pytest.approx(15.0) + assert result.loc[0, "humidity"] == pytest.approx(50.0) + + def test_empty_columns_rejected(self) -> None: + with pytest.raises(ValueError, match="columns is empty"): + PerStationMeanImputer(columns=()) + + def test_missing_column_in_input_raises(self) -> None: + df = pd.DataFrame({"location": ["a"], "temperature": [10.0]}) + with pytest.raises(KeyError, match="humidity"): + PerStationMeanImputer(columns=("humidity",)).apply(df) + + def test_to_dict_roundtrip(self) -> None: + original = PerStationMeanImputer( + columns=("temperature", "humidity"), + station_column="station_id", + ) + rebuilt = data_cleaner_from_dict(original.to_dict()) + assert isinstance(rebuilt, PerStationMeanImputer) + assert rebuilt.columns == ("temperature", "humidity") + assert rebuilt.station_column == "station_id" + + def test_empty_frame_returns_empty(self) -> None: + df = pd.DataFrame({"location": [], "temperature": []}) + result = PerStationMeanImputer(columns=("temperature",)).apply(df) + assert result.empty diff --git a/tests/preprocessing/test_derived.py b/tests/preprocessing/test_derived.py new file mode 100644 index 00000000..02f0764e --- /dev/null +++ b/tests/preprocessing/test_derived.py @@ -0,0 +1,305 @@ +"""Tests for DerivedFeature subclasses + the kind-dispatch JSON loader. + +Per CLAUDE.md, these test the public contract of each concrete +:class:`DerivedFeature` — what it produces, what inputs it needs, and +that JSON roundtrip preserves the spec (modulo provider injection). +""" + +from __future__ import annotations + +from datetime import date + +import numpy as np +import pandas as pd +import pytest + +from susse.preprocessing import ( + AltitudeFeature, + ClearSkyIndexFeature, + CyclicalDayOfYearFeature, + DerivedColumnMetadata, + DerivedFeature, + FeatureKind, + LongitudeFeature, + derived_feature_from_dict, +) + + +def _frame_with_coords() -> pd.DataFrame: + return pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 2)], + "lat": [0.5179, 0.5179], + "lon": [32.4715, 32.4715], + "ghi": [4.5, 5.5], + "ghi_clear": [6.0, 6.0], + } + ) + + +class TestClearSkyIndexFeature: + def test_compute_matches_division(self) -> None: + f = ClearSkyIndexFeature( + ghi_column="ghi", + ghi_clear_column="ghi_clear", + output_column="kt", + ) + result = f.compute(_frame_with_coords()) + assert list(result.columns) == ["kt"] + np.testing.assert_allclose(result["kt"].values, [4.5 / 6.0, 5.5 / 6.0]) + + def test_required_input_columns_match_construction(self) -> None: + f = ClearSkyIndexFeature( + ghi_column="my_ghi", + ghi_clear_column="my_clear", + output_column="kt", + ) + assert f.required_input_columns == ("my_ghi", "my_clear") + assert f.output_columns == ("kt",) + + def test_empty_field_rejected(self) -> None: + with pytest.raises(ValueError, match="output_column"): + ClearSkyIndexFeature( + ghi_column="g", + ghi_clear_column="gc", + output_column="", + ) + + def test_dict_roundtrip(self) -> None: + f = ClearSkyIndexFeature( + ghi_column="g", + ghi_clear_column="gc", + output_column="kt", + ) + d = f.to_dict() + assert d["kind"] == FeatureKind.CLEAR_SKY_INDEX.value + assert derived_feature_from_dict(d) == f + + +class TestCyclicalDayOfYearFeature: + def test_compute_emits_two_columns(self) -> None: + f = CyclicalDayOfYearFeature() + result = f.compute(_frame_with_coords()) + assert list(result.columns) == ["doy_sin", "doy_cos"] + + def test_no_fields(self) -> None: + # Output column names are fixed by design — no per-instance config. + a = CyclicalDayOfYearFeature() + b = CyclicalDayOfYearFeature() + assert a == b + assert a.output_columns == ("doy_sin", "doy_cos") + assert a.required_input_columns == ("date",) + + def test_dict_roundtrip(self) -> None: + f = CyclicalDayOfYearFeature() + d = f.to_dict() + assert d == {"kind": FeatureKind.CYCLICAL_DOY.value} + assert derived_feature_from_dict(d) == f + + +class _StubProvider: + def __init__(self, value: float = 1500.0) -> None: + self._value = value + self.calls: list[tuple[float, float]] = [] + + def __call__(self, lat: float, lon: float) -> float: + self.calls.append((lat, lon)) + return self._value + + +class TestAltitudeFeature: + def test_compute_emits_altitude_column(self) -> None: + provider = _StubProvider(value=1200.0) + f = AltitudeFeature(provider=provider) + result = f.compute(_frame_with_coords()) + assert list(result.columns) == ["altitude_m"] + assert (result["altitude_m"] == 1200.0).all() + + def test_dedupes_provider_calls_per_unique_coord(self) -> None: + provider = _StubProvider() + f = AltitudeFeature(provider=provider) + # Both rows in the test frame share the same (lat, lon) — one call. + f.compute(_frame_with_coords()) + assert len(provider.calls) == 1 + + def test_required_input_columns_are_lat_lon(self) -> None: + f = AltitudeFeature(provider=_StubProvider()) + assert f.required_input_columns == ("lat", "lon") + + def test_dict_drops_provider(self) -> None: + # Provider isn't JSON-serialisable; to_dict must omit it. + f = AltitudeFeature(provider=_StubProvider()) + d = f.to_dict() + assert "provider" not in d + assert d == { + "kind": FeatureKind.ALTITUDE.value, + "output_column": "altitude_m", + } + + def test_from_dict_without_provider_raises(self) -> None: + # Re-injection is mandatory and the error message tells the + # caller exactly which dict key to pass. + d = { + "kind": FeatureKind.ALTITUDE.value, + "output_column": "altitude_m", + } + with pytest.raises(ValueError, match="altitude"): + derived_feature_from_dict(d) + + def test_from_dict_with_provider_reconstructs(self) -> None: + provider = _StubProvider() + d = { + "kind": FeatureKind.ALTITUDE.value, + "output_column": "altitude_m", + } + f = derived_feature_from_dict(d, providers={"altitude": provider}) + assert isinstance(f, AltitudeFeature) + assert f.provider is provider + + +class TestLongitudeFeature: + """Paper-faithful escape hatch for the Mukiibi & Mikelson (2026) re-run. + + The feature is a known anti-pattern (see its docstring); these tests + pin its mechanical behaviour rather than endorsing the design. + """ + + def test_compute_passes_through_lon_column(self) -> None: + f = LongitudeFeature() + df = _frame_with_coords() + out = f.compute(df) + assert list(out.columns) == ["longitude"] + # Pass-through is exact (not rounded / not coerced through string). + assert out["longitude"].tolist() == df["lon"].astype(float).tolist() + + def test_required_inputs_and_outputs(self) -> None: + f = LongitudeFeature() + assert f.required_input_columns == ("lon",) + assert f.output_columns == ("longitude",) + + def test_custom_output_column(self) -> None: + # Allow notebooks to rename the column if a downstream feature + # collides — the recomputation notebook uses the default but a + # future re-run might not. + f = LongitudeFeature(output_column="lon_feature") + df = _frame_with_coords() + out = f.compute(df) + assert list(out.columns) == ["lon_feature"] + + def test_empty_output_column_raises(self) -> None: + with pytest.raises(ValueError, match="output_column"): + LongitudeFeature(output_column="") + + def test_to_dict_roundtrip(self) -> None: + f = LongitudeFeature(output_column="longitude") + d = f.to_dict() + assert d == { + "kind": FeatureKind.LONGITUDE.value, + "output_column": "longitude", + } + rebuilt = derived_feature_from_dict(d) + assert isinstance(rebuilt, LongitudeFeature) + assert rebuilt.output_column == "longitude" + + def test_kind_dispatches_to_longitude_feature(self) -> None: + # Pins that adding LONGITUDE to FeatureKind also wired up the + # spec_class() lookup. Otherwise from_dict would silently + # return the wrong subclass. + assert FeatureKind.LONGITUDE.spec_class() is LongitudeFeature + + +class TestDispatchByKind: + def test_unknown_kind_raises(self) -> None: + with pytest.raises(ValueError): + derived_feature_from_dict({"kind": "not_a_real_kind"}) + + def test_missing_kind_raises(self) -> None: + with pytest.raises(ValueError, match="kind"): + derived_feature_from_dict({"output_column": "kt"}) + + +class TestProtocolCompliance: + """Every concrete DerivedFeature must implement the abstract contract.""" + + def test_all_subclasses_expose_kind_and_output(self) -> None: + instances: list[DerivedFeature] = [ + ClearSkyIndexFeature( + ghi_column="g", + ghi_clear_column="gc", + output_column="kt", + ), + CyclicalDayOfYearFeature(), + AltitudeFeature(provider=_StubProvider()), + LongitudeFeature(), + ] + for f in instances: + assert isinstance(f.kind, FeatureKind) + assert len(f.output_columns) >= 1 + assert len(f.required_input_columns) >= 1 + assert "kind" in f.to_dict() + + def test_output_metadata_covers_every_output_column(self) -> None: + # The output_metadata contract: exactly one DerivedColumnMetadata + # per output column, matched by name. A consumer (the portal's + # variable inspector) relies on this 1:1 correspondence. + instances: list[DerivedFeature] = [ + ClearSkyIndexFeature( + ghi_column="g", + ghi_clear_column="gc", + output_column="kt", + ), + CyclicalDayOfYearFeature(), + AltitudeFeature(provider=_StubProvider()), + LongitudeFeature(), + ] + for f in instances: + metadata = f.output_metadata + assert all(isinstance(m, DerivedColumnMetadata) for m in metadata) + assert tuple(m.column for m in metadata) == f.output_columns + for m in metadata: + assert m.label, f"{type(f).__name__}: empty label" + assert m.unit, f"{type(f).__name__}: empty unit" + assert m.description, f"{type(f).__name__}: empty description" + + +class TestOutputMetadata: + """Each feature reports the right output column and unit. + + Exact label/description prose is not asserted — it is editorial + copy, not a contract, and pinning it would only make harmless + rewording break tests. The protocol test already guards that every + label/unit/description is non-empty. + """ + + def test_clear_sky_index_metadata_tracks_configured_column(self) -> None: + f = ClearSkyIndexFeature( + ghi_column="g", + ghi_clear_column="gc", + output_column="kt_cams", + ) + (meta,) = f.output_metadata + # The column name tracks the configured output_column, not a + # hardcoded default — this is the real logic worth pinning. + assert meta.column == "kt_cams" + assert meta.unit == "unitless" + + def test_cyclical_doy_metadata_describes_both_components(self) -> None: + f = CyclicalDayOfYearFeature() + metadata = f.output_metadata + assert tuple(m.column for m in metadata) == ("doy_sin", "doy_cos") + # The two components get distinct labels so the inspector can + # tell them apart. + labels = {m.label for m in metadata} + assert len(labels) == 2 + + def test_altitude_metadata(self) -> None: + f = AltitudeFeature(provider=_StubProvider()) + (meta,) = f.output_metadata + assert meta.column == "altitude_m" + assert meta.unit == "m" + + def test_longitude_metadata(self) -> None: + f = LongitudeFeature() + (meta,) = f.output_metadata + assert meta.column == "longitude" + assert meta.unit == "degrees" diff --git a/tests/preprocessing/test_derived_features.py b/tests/preprocessing/test_derived_features.py new file mode 100644 index 00000000..e4290829 --- /dev/null +++ b/tests/preprocessing/test_derived_features.py @@ -0,0 +1,111 @@ +"""Tests for the pure derived-feature helpers.""" + +from __future__ import annotations + +from datetime import date + +import numpy as np +import pandas as pd +import pytest + +from susse.preprocessing import clear_sky_index, cyclical_day_of_year + + +class TestClearSkyIndex: + """``kt = ghi / ghi_clear`` with NaN for ill-defined denominators.""" + + def test_typical_values_match_division(self) -> None: + ghi = pd.Series([3.0, 4.0, 5.0]) + ghi_clear = pd.Series([6.0, 5.0, 5.0]) + kt = clear_sky_index(ghi, ghi_clear) + np.testing.assert_allclose(kt.values, [0.5, 0.8, 1.0]) + + def test_zero_denominator_yields_nan(self) -> None: + # GHI_clear of 0 is night-time / nadir-zenith — kt is undefined. + # Must return NaN rather than +inf or arbitrary fill. + ghi = pd.Series([3.0, 4.0]) + ghi_clear = pd.Series([0.0, 5.0]) + kt = clear_sky_index(ghi, ghi_clear) + assert pd.isna(kt.iloc[0]) + assert kt.iloc[1] == pytest.approx(0.8) + + def test_below_floor_denominator_yields_nan(self) -> None: + # Even a tiny positive denominator (1e-10) would produce huge kt + # values that aren't physically meaningful. The floor catches it. + ghi = pd.Series([3.0]) + ghi_clear = pd.Series([1e-10]) + assert pd.isna(clear_sky_index(ghi, ghi_clear).iloc[0]) + + def test_mismatched_indices_raise(self) -> None: + ghi = pd.Series([1.0, 2.0], index=[0, 1]) + ghi_clear = pd.Series([3.0, 4.0], index=[10, 11]) + with pytest.raises(ValueError, match="aligned"): + clear_sky_index(ghi, ghi_clear) + + def test_negative_clear_sky_treated_as_floor_violation(self) -> None: + # A clear-sky reference that's slightly negative is data-quality + # corruption. Treat it as undefined rather than producing a sign- + # flipped kt. + ghi = pd.Series([3.0]) + ghi_clear = pd.Series([-1e-10]) # |x| < floor + assert pd.isna(clear_sky_index(ghi, ghi_clear).iloc[0]) + + +class TestCyclicalDayOfYear: + """sin/cos encoding: continuous across year boundary, period 1 year.""" + + def test_january_first_lands_at_zero_angle(self) -> None: + # Day 1 maps to angle 0 → sin=0, cos=1. + out = cyclical_day_of_year(pd.Series([date(2024, 1, 1)])) + assert out["doy_sin"].iloc[0] == pytest.approx(0.0, abs=1e-9) + assert out["doy_cos"].iloc[0] == pytest.approx(1.0, abs=1e-9) + + def test_dec31_close_to_jan1_in_feature_space(self) -> None: + # The whole point of the cyclical encoding: adjacent calendar + # days near the year boundary are close in feature space, not + # at the opposite ends of a 1..365 ramp. + out = cyclical_day_of_year(pd.Series([date(2024, 12, 31), date(2025, 1, 1)])) + # L2 distance in (sin, cos) space. + d = np.hypot( + out["doy_sin"].iloc[0] - out["doy_sin"].iloc[1], + out["doy_cos"].iloc[0] - out["doy_cos"].iloc[1], + ) + # Adjacent days are < ~2π/366 apart on the unit circle, so the + # chord should be small (< 0.05). + assert d < 0.05, f"Dec 31 and Jan 1 should be near-adjacent; got d={d}" + + def test_summer_and_winter_solstices_are_opposite(self) -> None: + # Days ~6 months apart should be roughly opposite on the unit + # circle. + out = cyclical_day_of_year( + pd.Series([date(2024, 1, 1), date(2024, 7, 2)]) # day 1 vs ~day 184 + ) + d = np.hypot( + out["doy_sin"].iloc[0] - out["doy_sin"].iloc[1], + out["doy_cos"].iloc[0] - out["doy_cos"].iloc[1], + ) + # Opposite points on the unit circle have chord 2.0. + assert d > 1.9, f"~6-month-apart days should be ~opposite; got d={d}" + + def test_leap_year_day_366_still_well_defined(self) -> None: + # 2024 is a leap year. Dec 31 is day 366. With a /366 normaliser, + # the encoding stays well-defined and close to day 365 of a + # non-leap year (no wrap-around glitch). + out = cyclical_day_of_year(pd.Series([date(2024, 12, 31)])) + assert np.isfinite(out["doy_sin"].iloc[0]) + assert np.isfinite(out["doy_cos"].iloc[0]) + + def test_empty_series_raises(self) -> None: + with pytest.raises(ValueError, match="empty"): + cyclical_day_of_year(pd.Series([], dtype="datetime64[ns]")) + + def test_index_preserved(self) -> None: + # Caller may have a non-default index; the output must align so + # downstream merges don't silently re-index. + idx = [10, 20, 30] + s = pd.Series( + [date(2024, 1, 1), date(2024, 4, 1), date(2024, 7, 1)], + index=idx, + ) + out = cyclical_day_of_year(s) + assert list(out.index) == idx diff --git a/tests/preprocessing/test_elevation.py b/tests/preprocessing/test_elevation.py new file mode 100644 index 00000000..007a1dd8 --- /dev/null +++ b/tests/preprocessing/test_elevation.py @@ -0,0 +1,93 @@ +"""Tests for the elevation provider — caching, Protocol compliance, fail-loud. + +Tests do NOT hit the live Open-Elevation endpoint. We monkeypatch +``pvlib.location.lookup_altitude`` so the test suite stays offline and +deterministic; the contract being verified is the provider's caching + +error-propagation behaviour, not pvlib's network code. +""" + +from __future__ import annotations + +import pytest + +from susse.preprocessing import ElevationProvider, PvlibElevationProvider + + +class TestPvlibElevationProvider: + """The production provider's contract: cache hits, fail-loud, single source.""" + + def test_repeated_lookup_uses_cache(self, monkeypatch: pytest.MonkeyPatch) -> None: + calls: list[tuple[float, float]] = [] + + def fake_lookup(*, latitude: float, longitude: float) -> float: + calls.append((latitude, longitude)) + return 1234.5 + + monkeypatch.setattr("pvlib.location.lookup_altitude", fake_lookup) + provider = PvlibElevationProvider() + a = provider(0.5179, 32.4715) + b = provider(0.5179, 32.4715) + assert a == b == 1234.5 + assert len(calls) == 1 + assert provider.cache_size == 1 + + def test_distinct_points_each_hit_pvlib_once( + self, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + calls: list[tuple[float, float]] = [] + + def fake_lookup(*, latitude: float, longitude: float) -> float: + calls.append((latitude, longitude)) + return 100.0 * len(calls) + + monkeypatch.setattr("pvlib.location.lookup_altitude", fake_lookup) + provider = PvlibElevationProvider() + provider(0.0, 32.0) + provider(1.0, 33.0) + provider(0.0, 32.0) # cached + assert len(calls) == 2 + assert provider.cache_size == 2 + + def test_subdecimal_coordinates_round_to_same_cache_key( + self, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + # 4-decimal rounding (~10 m at the equator) is below any free DEM's + # spatial resolution, so two near-identical lookups must coalesce + # to one network call rather than redundantly hitting the API. + calls: list[tuple[float, float]] = [] + + def fake_lookup(*, latitude: float, longitude: float) -> float: + calls.append((latitude, longitude)) + return 500.0 + + monkeypatch.setattr("pvlib.location.lookup_altitude", fake_lookup) + provider = PvlibElevationProvider() + provider(0.51790000, 32.47150000) + provider(0.51790001, 32.47150001) # within rounding + assert len(calls) == 1 + + def test_pvlib_failure_propagates( + self, + monkeypatch: pytest.MonkeyPatch, + ) -> None: + # No silent fallback to NaN / 0 — a misconfigured network must + # surface as a clear error rather than poison a feature column. + def fake_lookup(*, latitude: float, longitude: float) -> float: + raise RuntimeError("simulated network failure") + + monkeypatch.setattr("pvlib.location.lookup_altitude", fake_lookup) + provider = PvlibElevationProvider() + with pytest.raises(RuntimeError, match="simulated network failure"): + provider(0.5, 32.5) + + +class TestProtocolCompliance: + """Any callable matching the Protocol must work with the Preprocessor.""" + + def test_minimal_callable_satisfies_protocol(self) -> None: + # Smallest possible implementation: a lambda. Verifies the + # Protocol does not require anything beyond the call signature. + provider: ElevationProvider = lambda lat, lon: 0.0 # noqa: E731 + assert provider(0.0, 0.0) == 0.0 diff --git a/tests/preprocessing/test_feature_spec.py b/tests/preprocessing/test_feature_spec.py new file mode 100644 index 00000000..c14a86c4 --- /dev/null +++ b/tests/preprocessing/test_feature_spec.py @@ -0,0 +1,149 @@ +"""Tests for FeatureSpec — validation + JSON roundtrip on the derived_features API.""" + +from __future__ import annotations + +import pytest + +from susse.preprocessing import ( + AltitudeFeature, + ClearSkyIndexFeature, + CyclicalDayOfYearFeature, + FeatureSpec, + GhiUpperBoundCleaner, + HighMissingYearExcluder, +) + + +def _kt(out: str = "kt_nasa") -> ClearSkyIndexFeature: + return ClearSkyIndexFeature( + ghi_column="sat_ghi_nasa_kwh_m2_day", + ghi_clear_column="nasa_ghi_clear", + output_column=out, + ) + + +class _StubProvider: + """Stand-in for a real ElevationProvider in tests that don't run compute().""" + + def __call__(self, lat: float, lon: float) -> float: + return 0.0 + + +class TestValidation: + def test_empty_target_rejected(self) -> None: + with pytest.raises(ValueError, match="target_column"): + FeatureSpec(target_column="") + + def test_duplicate_derived_outputs_rejected(self) -> None: + # Two kt features producing the same output column would + # silently clobber each other downstream — surface at construction. + with pytest.raises(ValueError, match="duplicate"): + FeatureSpec(derived_features=(_kt("kt"), _kt("kt"))) + + def test_derived_output_colliding_with_feature_rejected(self) -> None: + # A derived output that's also a pass-through feature would be + # doubly-defined. Validation lives on FeatureSpec, not on the + # individual feature, because the collision is cross-cutting. + with pytest.raises(ValueError, match="collide with feature_columns"): + FeatureSpec( + feature_columns=("kt_nasa",), + derived_features=(_kt("kt_nasa"),), + ) + + def test_doy_columns_collision_with_features_rejected(self) -> None: + # CyclicalDayOfYearFeature outputs ('doy_sin', 'doy_cos'); a + # pass-through with the same name is the same kind of collision. + with pytest.raises(ValueError, match="doy_sin"): + FeatureSpec( + feature_columns=("doy_sin",), + derived_features=(CyclicalDayOfYearFeature(),), + ) + + def test_altitude_collision_with_feature_rejected(self) -> None: + with pytest.raises(ValueError, match="altitude_m"): + FeatureSpec( + feature_columns=("altitude_m",), + derived_features=(AltitudeFeature(provider=_StubProvider()),), + ) + + +class TestOutputFeatureNames: + """Order: pass-through first, then each DerivedFeature's output_columns.""" + + def test_passthrough_then_derived_in_order(self) -> None: + spec = FeatureSpec( + feature_columns=("nasa_aod_550", "nasa_precipitable_water"), + derived_features=( + _kt("kt_nasa"), + CyclicalDayOfYearFeature(), + AltitudeFeature(provider=_StubProvider()), + ), + ) + assert spec.output_feature_names == ( + "nasa_aod_550", + "nasa_precipitable_water", + "kt_nasa", + "doy_sin", + "doy_cos", + "altitude_m", + ) + + def test_minimal_spec_emits_nothing(self) -> None: + spec = FeatureSpec() + assert spec.output_feature_names == () + + def test_doy_only_emits_two_columns(self) -> None: + spec = FeatureSpec(derived_features=(CyclicalDayOfYearFeature(),)) + assert spec.output_feature_names == ("doy_sin", "doy_cos") + + +class TestJsonRoundtrip: + """Spec → JSON → spec; provider re-injection at load time.""" + + def test_kt_and_doy_roundtrip_without_providers(self) -> None: + spec = FeatureSpec( + target_column="y_ghi_kwh_m2_day", + feature_columns=("nasa_aod_550",), + derived_features=(_kt("kt_nasa"), CyclicalDayOfYearFeature()), + id_columns=("date", "location"), + ) + roundtripped = FeatureSpec.from_json(spec.to_json()) + assert roundtripped == spec + + def test_minimal_spec_roundtrip(self) -> None: + spec = FeatureSpec() + roundtripped = FeatureSpec.from_json(spec.to_json()) + assert roundtripped == spec + + def test_altitude_roundtrip_requires_provider_injection(self) -> None: + provider = _StubProvider() + spec = FeatureSpec( + derived_features=(AltitudeFeature(provider=provider),), + ) + s = spec.to_json() + # Without providers, the from_json must raise — the spec + # carries an AltitudeFeature whose provider was dropped at + # serialisation, and silently constructing it with a placeholder + # would mask the configuration error. + with pytest.raises(ValueError, match="altitude"): + FeatureSpec.from_json(s) + # With the provider re-injected, the roundtrip succeeds. + roundtripped = FeatureSpec.from_json(s, providers={"altitude": provider}) + assert roundtripped == spec + + def test_cleaners_roundtrip(self) -> None: + # Cleaners must serialise to JSON and reconstruct via the same + # `from_dict` dispatch as derived features. Pinning this at the + # FeatureSpec level — not just in test_cleaning.py — guards + # against the spec forgetting to include them in to_dict(). + spec = FeatureSpec( + target_column="y_ghi_kwh_m2_day", + feature_columns=("nasa_aod_550",), + cleaners=( + GhiUpperBoundCleaner(threshold=11.0), + HighMissingYearExcluder(missing_fraction_threshold=0.1), + ), + derived_features=(_kt("kt_nasa"),), + ) + roundtripped = FeatureSpec.from_json(spec.to_json()) + assert roundtripped == spec diff --git a/tests/preprocessing/test_preprocessor.py b/tests/preprocessing/test_preprocessor.py new file mode 100644 index 00000000..d3edb920 --- /dev/null +++ b/tests/preprocessing/test_preprocessor.py @@ -0,0 +1,461 @@ +"""Tests for the Preprocessor: training-shape and inference-shape transforms.""" + +from __future__ import annotations + +from datetime import date, datetime, timezone + +import numpy as np +import pandas as pd +import pytest + +from susse.datasets import DatasetManifest, FeatureSelection, TrainingDataset +from susse.preprocessing import ( + AltitudeFeature, + ClearSkyIndexFeature, + CyclicalDayOfYearFeature, + DataCleaner, + FeatureSpec, + GhiUpperBoundCleaner, + Preprocessor, +) +from susse.preprocessing.cleaning import CleanerKind + + +def _toy_dataset(*, with_coords: bool = False) -> TrainingDataset: + df = pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 2), date(2024, 1, 3)], + "location": ["a", "a", "a"], + "geohash5": ["s8p1v", "s8p1v", "s8p1v"], + "y_ghi_kwh_m2_day": [5.0, 6.0, 4.0], + "sat_ghi_nasa_kwh_m2_day": [4.5, 5.5, 3.5], + "nasa_ghi_clear": [6.0, 6.0, 6.0], + "nasa_aod_550": [0.3, 0.25, 0.5], + } + ) + if with_coords: + df["lat"] = 0.5179 + df["lon"] = 32.4715 + manifest = DatasetManifest( + name="toy", + version="v0", + created_at_utc=datetime(2026, 5, 9, tzinfo=timezone.utc).isoformat(), + susse_version="test", + git_sha=None, + feature_selection=FeatureSelection(), + date_start=date(2024, 1, 1), + date_end=date(2024, 1, 3), + location_filter=None, + warehouse_project="test", + warehouse_dataset="test", + warehouse_table_mods={}, + n_rows=3, + n_cols=len(df.columns), + column_names=tuple(df.columns), + content_hash="dummy_hash", + ) + return TrainingDataset(df=df, manifest=manifest) + + +def _kt() -> ClearSkyIndexFeature: + return ClearSkyIndexFeature( + ghi_column="sat_ghi_nasa_kwh_m2_day", + ghi_clear_column="nasa_ghi_clear", + output_column="kt_nasa", + ) + + +class TestApplyHappyPath: + def test_kt_and_doy_features_appear_in_output(self) -> None: + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(_kt(), CyclicalDayOfYearFeature()), + ) + result = Preprocessor(spec).apply(_toy_dataset()) + assert result.feature_columns == ( + "nasa_aod_550", + "kt_nasa", + "doy_sin", + "doy_cos", + ) + # kt = 4.5/6.0 = 0.75 for the first row. + assert result.df["kt_nasa"].iloc[0] == pytest.approx(0.75) + assert result.target_column == "y_ghi_kwh_m2_day" + # Lineage to source dataset is preserved. + assert result.source_dataset_name == "toy" + assert result.source_content_hash == "dummy_hash" + + def test_X_and_y_helpers_return_correct_slices(self) -> None: + spec = FeatureSpec(feature_columns=("nasa_aod_550",)) + result = Preprocessor(spec).apply(_toy_dataset()) + assert list(result.X().columns) == ["nasa_aod_550"] + np.testing.assert_allclose(result.y().values, [5.0, 6.0, 4.0]) + + +class TestNanHandling: + def test_dropna_target_removes_rows_with_missing_y(self) -> None: + ds = _toy_dataset() + ds.df.loc[1, "y_ghi_kwh_m2_day"] = float("nan") + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + dropna_target=True, + ) + result = Preprocessor(spec).apply(ds) + assert result.n_rows == 2 # 3 input rows, 1 dropped + + def test_dropna_features_removes_rows_with_missing_feature(self) -> None: + ds = _toy_dataset() + ds.df.loc[2, "nasa_aod_550"] = float("nan") + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + dropna_target=False, + dropna_features=True, + ) + result = Preprocessor(spec).apply(ds) + assert result.n_rows == 2 + + def test_dropna_target_false_keeps_missing_target_rows(self) -> None: + ds = _toy_dataset() + ds.df.loc[0, "y_ghi_kwh_m2_day"] = float("nan") + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + dropna_target=False, + ) + result = Preprocessor(spec).apply(ds) + assert result.n_rows == 3 + assert pd.isna(result.df["y_ghi_kwh_m2_day"].iloc[0]) + + +class TestColumnValidation: + """Missing required columns surface as a single, named ValueError.""" + + def test_missing_feature_column_raises(self) -> None: + spec = FeatureSpec(feature_columns=("not_a_real_column",)) + with pytest.raises(ValueError, match="not_a_real_column"): + Preprocessor(spec).apply(_toy_dataset()) + + def test_missing_kt_input_raises(self) -> None: + spec = FeatureSpec( + derived_features=( + ClearSkyIndexFeature( + ghi_column="missing_ghi", + ghi_clear_column="missing_clear", + output_column="kt", + ), + ), + ) + with pytest.raises(ValueError, match="missing_ghi"): + Preprocessor(spec).apply(_toy_dataset()) + + def test_missing_target_with_dropna_raises(self) -> None: + ds = _toy_dataset() + ds.df.drop(columns=["y_ghi_kwh_m2_day"], inplace=True) + spec = FeatureSpec(feature_columns=("nasa_aod_550",)) + with pytest.raises(ValueError, match="y_ghi_kwh_m2_day"): + Preprocessor(spec).apply(ds) + + def test_altitude_without_lat_lon_raises(self) -> None: + # AltitudeFeature declares lat/lon as required input; the + # Preprocessor's column validation must surface that with a + # clear error rather than letting compute() fail with a KeyError. + spec = FeatureSpec( + derived_features=(AltitudeFeature(provider=lambda lat, lon: 1000.0),), + ) + with pytest.raises(ValueError, match="lat"): + Preprocessor(spec).apply(_toy_dataset(with_coords=False)) + + +class TestDerivedFeaturesIntegration: + """End-to-end: each DerivedFeature emits its declared output columns. + + Smoke-tests the unifier — Preprocessor only knows about a generic + DerivedFeature loop, not about kt vs doy vs altitude specifically. + """ + + def test_altitude_feature_emits_column(self) -> None: + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(AltitudeFeature(provider=lambda lat, lon: 1200.0),), + ) + result = Preprocessor(spec).apply(_toy_dataset(with_coords=True)) + assert "altitude_m" in result.df.columns + assert (result.df["altitude_m"] == 1200.0).all() + assert result.feature_columns == ("nasa_aod_550", "altitude_m") + + def test_altitude_provider_called_once_per_unique_pair(self) -> None: + # Three rows at the same (lat, lon) must invoke the provider + # exactly once. This is the dedupe contract that makes a + # network-backed provider practical for batch training. + calls: list[tuple[float, float]] = [] + + def counting_provider(lat: float, lon: float) -> float: + calls.append((lat, lon)) + return 1234.0 + + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(AltitudeFeature(provider=counting_provider),), + ) + Preprocessor(spec).apply(_toy_dataset(with_coords=True)) + assert len(calls) == 1 + + def test_three_features_emit_six_columns(self) -> None: + # Pin the unifier: kt + doy + altitude should produce four + # derived columns total (kt_nasa, doy_sin, doy_cos, altitude_m) + # plus one pass-through, in spec order. + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=( + _kt(), + CyclicalDayOfYearFeature(), + AltitudeFeature(provider=lambda lat, lon: 1500.0), + ), + ) + result = Preprocessor(spec).apply(_toy_dataset(with_coords=True)) + assert result.feature_columns == ( + "nasa_aod_550", + "kt_nasa", + "doy_sin", + "doy_cos", + "altitude_m", + ) + + +class TestStateless: + """Re-applying the same preprocessor must produce identical output.""" + + def test_two_applications_produce_equal_output(self) -> None: + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(_kt(), CyclicalDayOfYearFeature()), + ) + pre = Preprocessor(spec) + a = pre.apply(_toy_dataset()) + b = pre.apply(_toy_dataset()) + pd.testing.assert_frame_equal(a.df, b.df) + + +class _RecordingCleaner(DataCleaner): + """Pass-through cleaner that records the order it ran in. + + Used by :class:`TestCleanerOrdering` to pin the contract that + cleaners run in declared order *and* before any DerivedFeature. + """ + + _calls: list[str] + _name: str + + def __init__(self, name: str, calls: list[str]) -> None: + # Not a frozen dataclass — this stub mutates ``calls`` to record + # invocation order, which a frozen-dataclass cleaner couldn't. + self._name = name + self._calls = calls + + @property + def kind(self) -> CleanerKind: + return CleanerKind.GHI_UPPER_BOUND # arbitrary — only used for repr + + @property + def required_input_columns(self) -> tuple[str, ...]: + return () + + def apply(self, df: pd.DataFrame) -> pd.DataFrame: + self._calls.append(self._name) + return df + + def to_dict(self) -> dict: + return {"kind": self.kind.value, "name": self._name} + + @classmethod + def _from_dict(cls, d: dict, *, providers: dict) -> "_RecordingCleaner": + # Test stub — never round-tripped through JSON in this suite. + raise NotImplementedError( + "_RecordingCleaner is a test stub; JSON roundtrip is out of scope." + ) + + +class _RecordingFeature(ClearSkyIndexFeature): + """ClearSkyIndexFeature that records when it ran. Subclassing keeps + the existing input/output contract intact while the test only cares + about ordering.""" + + _calls: list[str] + + def __init__(self, name: str, calls: list[str], **kwargs): + super().__init__(**kwargs) + # Frozen dataclasses don't permit normal attribute assignment; + # use object.__setattr__ to attach the ordering hooks. + object.__setattr__(self, "_calls", calls) + object.__setattr__(self, "_name", name) + + def compute(self, df): + self._calls.append(self._name) + return super().compute(df) + + +class TestCleanerOrdering: + """Cleaners run in declared order, *before* any derived feature. + + Pinning this contract guards the speedup-from-skipping invariant + (a feature shouldn't be computed on rows a downstream cleaner + drops) and the "no flag-based dispatch in Preprocessor" invariant + (the order is encoded in the spec, not in if-branches in apply()). + """ + + def test_cleaners_run_before_features_and_in_order(self) -> None: + calls: list[str] = [] + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + cleaners=( + _RecordingCleaner("clean_a", calls), + _RecordingCleaner("clean_b", calls), + ), + derived_features=( + _RecordingFeature( + "derive_kt", + calls, + ghi_column="sat_ghi_nasa_kwh_m2_day", + ghi_clear_column="nasa_ghi_clear", + output_column="kt_nasa", + ), + ), + ) + Preprocessor(spec).apply(_toy_dataset()) + assert calls == ["clean_a", "clean_b", "derive_kt"] + + def test_cleaner_filters_rows_before_feature_computes(self) -> None: + # A cleaner that drops rows above sat_ghi=5.0 leaves 2 of 3 rows + # (toy dataset has [4.5, 5.5, 3.5]). The downstream kt feature + # therefore computes on 2 rows, and the preprocessed output + # carries only those two — pinning that the cleaner's effect + # propagates through to feature computation, not just to the + # frame that the cleaner returned. + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + cleaners=( + GhiUpperBoundCleaner( + column="sat_ghi_nasa_kwh_m2_day", + threshold=5.0, + ), + ), + derived_features=(_kt(),), + ) + result = Preprocessor(spec).apply(_toy_dataset()) + assert len(result.df) == 2 + # kt = sat_ghi / ghi_clear; the surviving rows had sat_ghi 4.5 and 3.5 + # against ghi_clear=6.0 → kt ∈ {0.75, 0.583...}. Pin the kt values + # so a regression in the cleaner-then-feature ordering is caught. + kt_values = sorted(result.df["kt_nasa"].round(4).tolist()) + assert kt_values == [ + pytest.approx(0.5833, abs=1e-4), + pytest.approx(0.75, abs=1e-4), + ] + + def test_validate_columns_catches_cleaner_input_missing(self) -> None: + # If a cleaner needs a column that's not in the input frame, + # the up-front validation must surface it before any cleaner runs. + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + cleaners=( + GhiUpperBoundCleaner( + column="not_in_frame", + threshold=5.0, + ), + ), + derived_features=(_kt(),), + ) + with pytest.raises(ValueError, match="not_in_frame"): + Preprocessor(spec).apply(_toy_dataset()) + + +class TestApplyToDataframe: + """Inference-shape transform: bare DataFrame in, bare DataFrame out.""" + + def test_computes_derived_features_without_target(self) -> None: + # Inference frames have no ground truth. apply_to_dataframe must + # produce features anyway — no missing-target error, no NaN target + # padding required at the call site. + df = pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 2)], + "geohash5": ["s8p1v", "s8p1v"], + "sat_ghi_nasa_kwh_m2_day": [4.5, 5.5], + "nasa_ghi_clear": [6.0, 6.0], + "nasa_aod_550": [0.3, 0.25], + } + ) + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(_kt(), CyclicalDayOfYearFeature()), + ) + result = Preprocessor(spec).apply_to_dataframe(df) + assert list(result.columns)[-3:] == ["kt_nasa", "doy_sin", "doy_cos"] + assert spec.target_column not in result.columns + # Two input rows → two output rows. No silent drops at inference. + assert len(result) == 2 + + def test_skips_cleaners_in_inference_shape(self) -> None: + # The cleaner would drop the row with ghi=20 in training shape; + # in inference shape the row must survive — we predict whatever + # the model says, the caller can decide what to do with it. + df = pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 2)], + "geohash5": ["s8p1v", "s8p1v"], + "sat_ghi_nasa_kwh_m2_day": [20.0, 5.5], # 20 > threshold + "nasa_ghi_clear": [6.0, 6.0], + "nasa_aod_550": [0.3, 0.25], + } + ) + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + cleaners=( + GhiUpperBoundCleaner(column="sat_ghi_nasa_kwh_m2_day", threshold=10.0), + ), + derived_features=(_kt(),), + ) + result = Preprocessor(spec).apply_to_dataframe(df) + assert len(result) == 2 # cleaner did not run + + def test_skips_dropna_in_inference_shape(self) -> None: + # A NaN feature would be dropped in training shape; in inference + # shape the row must survive with NaN propagating to the prediction. + # Silently dropping inference rows would surface as missing rows in + # the output, which is the bug this contract exists to prevent. + df = pd.DataFrame( + { + "date": [date(2024, 1, 1), date(2024, 1, 2)], + "geohash5": ["s8p1v", "s8p1v"], + "sat_ghi_nasa_kwh_m2_day": [4.5, 5.5], + "nasa_ghi_clear": [6.0, 6.0], + "nasa_aod_550": [np.nan, 0.25], # one NaN + } + ) + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(_kt(),), + dropna_features=True, + ) + result = Preprocessor(spec).apply_to_dataframe(df) + assert len(result) == 2 + assert pd.isna(result["nasa_aod_550"].iloc[0]) + + def test_validates_required_feature_columns(self) -> None: + # Validation still runs in inference shape — but only for + # feature_columns and derived-feature inputs, not for cleaner + # inputs or the dropna-target rule. + df = pd.DataFrame( + { + "date": [date(2024, 1, 1)], + "geohash5": ["s8p1v"], + # nasa_aod_550 missing + "sat_ghi_nasa_kwh_m2_day": [4.5], + "nasa_ghi_clear": [6.0], + } + ) + spec = FeatureSpec( + feature_columns=("nasa_aod_550",), + derived_features=(_kt(),), + ) + with pytest.raises(ValueError, match="nasa_aod_550"): + Preprocessor(spec).apply_to_dataframe(df) diff --git a/tests/training/__init__.py b/tests/training/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/training/conftest.py b/tests/training/conftest.py new file mode 100644 index 00000000..59b8d38d --- /dev/null +++ b/tests/training/conftest.py @@ -0,0 +1,140 @@ +"""Shared fixtures for training-package tests. + +Provides a small, self-consistent :class:`PreprocessedDataset` with +two stations and a couple of months of synthetic-but-plausible data +so every test in this directory can construct a Trainer end-to-end +without a live warehouse. +""" + +from __future__ import annotations + +from datetime import date, datetime, timezone + +import numpy as np +import pandas as pd +import pytest + +from susse.datasets import DatasetManifest, FeatureSelection +from susse.preprocessing import FeatureSpec, PreprocessedDataset + + +@pytest.fixture +def toy_manifest() -> DatasetManifest: + return DatasetManifest( + name="toy_train", + version="v0", + created_at_utc=datetime(2026, 5, 10, tzinfo=timezone.utc).isoformat(), + susse_version="test", + git_sha=None, + feature_selection=FeatureSelection(), + date_start=date(2024, 1, 1), + date_end=date(2024, 2, 29), + location_filter=None, + warehouse_project="test", + warehouse_dataset="test", + warehouse_table_mods={}, + n_rows=120, + n_cols=8, + column_names=( + "date", + "location", + "geohash5", + "y_ghi_kwh_m2_day", + "feat_a", + "feat_b", + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + ), + content_hash="toy_hash_for_tests", + ) + + +@pytest.fixture +def toy_processed(toy_manifest: DatasetManifest) -> PreprocessedDataset: + rng = np.random.default_rng(seed=42) + rows: list[dict] = [] + for station in ("sta_a", "sta_b"): + # Different intercept per station so station-LOSO has signal. + intercept = 5.0 if station == "sta_a" else 4.0 + for d in pd.date_range("2024-01-01", "2024-02-29", freq="D"): + feat_a = float(rng.uniform(0, 1)) + feat_b = float(rng.uniform(-1, 1)) + y = intercept + 1.5 * feat_a - 0.3 * feat_b + rng.normal(0, 0.2) + rows.append( + { + "date": d.date(), + "location": station, + "geohash5": "abc12", + "y_ghi_kwh_m2_day": y, + "feat_a": feat_a, + "feat_b": feat_b, + # Satellite "estimate" with a small fixed bias so the + # baseline-scoring path has a meaningful signal. + "sat_ghi_nasa_kwh_m2_day": y + 0.7, + "sat_ghi_cams_kwh_m2_day": y + 0.4, + } + ) + df = pd.DataFrame(rows).reset_index(drop=True) + spec = FeatureSpec( + target_column="y_ghi_kwh_m2_day", + feature_columns=("feat_a", "feat_b"), + derived_features=(), + id_columns=("date", "location", "geohash5"), + ) + return PreprocessedDataset( + df=df, + feature_columns=spec.output_feature_names, + target_column=spec.target_column, + feature_spec=spec, + source_manifest=toy_manifest, + ) + + +@pytest.fixture +def geo_processed(toy_manifest: DatasetManifest) -> PreprocessedDataset: + """Toy dataset with five stations + lat/lon for spatial-splitter tests. + + Coordinates are chosen so the deterministic max-pairwise-haversine + pair is unambiguous: ``arctic`` and ``cape_town`` are far apart + enough that no other pair comes close. ``country`` is an + additional categorical block to exercise + :class:`SpatialBlockSplitter`. + """ + rng = np.random.default_rng(seed=7) + stations = [ + # name, lat, lon, country + ("kampala", 0.33, 32.57, "uganda"), + ("nairobi", -1.29, 36.82, "kenya"), + ("accra", 5.65, -0.10, "ghana"), + ("arctic", 78.92, 11.93, "svalbard"), + ("cape_town", -33.92, 18.42, "south_africa"), + ] + rows: list[dict] = [] + for name, lat, lon, country in stations: + for d in pd.date_range("2024-01-01", "2024-01-30", freq="D"): + rows.append( + { + "date": d.date(), + "location": name, + "lat": lat, + "lon": lon, + "country": country, + "geohash5": "abc12", + "y_ghi_kwh_m2_day": float(rng.uniform(3.0, 6.0)), + "feat_a": float(rng.uniform(0, 1)), + } + ) + df = pd.DataFrame(rows).reset_index(drop=True) + spec = FeatureSpec( + target_column="y_ghi_kwh_m2_day", + feature_columns=("feat_a",), + derived_features=(), + id_columns=("date", "location", "lat", "lon", "country", "geohash5"), + ) + return PreprocessedDataset( + df=df, + feature_columns=spec.output_feature_names, + target_column=spec.target_column, + feature_spec=spec, + source_manifest=toy_manifest, + ) diff --git a/tests/training/test_bundle.py b/tests/training/test_bundle.py new file mode 100644 index 00000000..d428c63b --- /dev/null +++ b/tests/training/test_bundle.py @@ -0,0 +1,149 @@ +"""TrainedBundle save/load roundtrip + missing-file diagnostics.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from susse.datasets import DatasetManifest +from susse.models import ( + LinearParams, + MeanBaselineParams, + ModelFactory, + RandomForestParams, +) +from susse.preprocessing import PreprocessedDataset +from susse.training import ScoreSet, TrainedBundle, TrainingMetadata, load_bundle + + +def _toy_metadata() -> TrainingMetadata: + s = ScoreSet(n_rows=10, mae=0.5, rmse=0.6, r2=0.8) + return TrainingMetadata( + created_at_utc="2026-05-10T00:00:00+00:00", + susse_version="test", + git_sha=None, + holdout_label="station-LOSO:sta_b", + splitter_name="station_loso[sta_b]", + n_train_rows=60, + n_val_rows=60, + train_metrics=s, + val_metrics=s, + baseline_metrics={"sat_ghi_nasa_kwh_m2_day": s}, + ) + + +@pytest.mark.parametrize( + "params", + [ + MeanBaselineParams(), + RandomForestParams(n_estimators=10, max_depth=4, random_state=0), + LinearParams(with_scaling=True), + ], + ids=["mean_baseline", "random_forest", "linear"], +) +def test_save_load_roundtrip_preserves_predictions( + params, + tmp_path: Path, + toy_processed: PreprocessedDataset, +) -> None: + """Same predictions before and after save/load, across all model kinds.""" + X, y = toy_processed.X(), toy_processed.y() + regressor = ModelFactory.create(params).fit(X, y) + bundle = TrainedBundle( + regressor=regressor, + feature_spec=toy_processed.feature_spec, + metadata=_toy_metadata(), + source_manifest=toy_processed.source_manifest, + ) + bundle.save(tmp_path) + restored = load_bundle(tmp_path) + + np.testing.assert_allclose( + restored.regressor.predict(X).values, + regressor.predict(X).values, + rtol=1e-10, + atol=1e-10, + ) + assert restored.feature_spec == bundle.feature_spec + assert restored.metadata == bundle.metadata + assert restored.source_manifest == bundle.source_manifest + + +class TestMissingFiles: + def _bundled_dir( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> Path: + regressor = ModelFactory.create(MeanBaselineParams()).fit( + toy_processed.X(), + toy_processed.y(), + ) + TrainedBundle( + regressor=regressor, + feature_spec=toy_processed.feature_spec, + metadata=_toy_metadata(), + source_manifest=toy_processed.source_manifest, + ).save(tmp_path) + return tmp_path + + def test_missing_metadata_surfaces_clear_error( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> None: + d = self._bundled_dir(tmp_path, toy_processed) + (d / "metadata.json").unlink() + with pytest.raises(FileNotFoundError, match="metadata.json"): + load_bundle(d) + + def test_missing_source_manifest_surfaces_clear_error( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> None: + d = self._bundled_dir(tmp_path, toy_processed) + (d / "source_manifest.json").unlink() + with pytest.raises(FileNotFoundError, match="source_manifest.json"): + load_bundle(d) + + def test_missing_model_dir_surfaces_clear_error( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> None: + d = self._bundled_dir(tmp_path, toy_processed) + # Wipe the model subdir. + for f in (d / "model").iterdir(): + f.unlink() + (d / "model").rmdir() + with pytest.raises(FileNotFoundError, match="'model'"): + load_bundle(d) + + +class TestSourceManifestEmbedding: + def test_bundle_carries_full_source_manifest_verbatim( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> None: + """Embedded manifest survives byte-for-byte across save/load.""" + regressor = ModelFactory.create(MeanBaselineParams()).fit( + toy_processed.X(), + toy_processed.y(), + ) + bundle = TrainedBundle( + regressor=regressor, + feature_spec=toy_processed.feature_spec, + metadata=_toy_metadata(), + source_manifest=toy_processed.source_manifest, + ) + bundle.save(tmp_path) + # Direct file inspection: manifest JSON equals original. + on_disk = DatasetManifest.from_json( + (tmp_path / "source_manifest.json").read_text() + ) + assert on_disk == toy_processed.source_manifest diff --git a/tests/training/test_metadata.py b/tests/training/test_metadata.py new file mode 100644 index 00000000..ad42b911 --- /dev/null +++ b/tests/training/test_metadata.py @@ -0,0 +1,96 @@ +"""TrainingMetadata + ScoreSet validation and JSON-roundtrip tests.""" + +from __future__ import annotations + +import pytest + +from susse.training import ScoreSet, TrainingMetadata + + +def _toy_score(n: int = 10, mae: float = 0.5) -> ScoreSet: + return ScoreSet(n_rows=n, mae=mae, rmse=mae * 1.2, r2=0.8) + + +class TestScoreSet: + def test_negative_n_rows_rejected(self) -> None: + with pytest.raises(ValueError, match=r"n_rows=-1"): + ScoreSet(n_rows=-1, mae=0.0, rmse=0.0, r2=0.0) + + def test_to_dict_from_dict_roundtrip(self) -> None: + original = _toy_score(n=42, mae=0.37) + recovered = ScoreSet.from_dict(original.to_dict()) + assert recovered == original + + +class TestTrainingMetadata: + def _full(self) -> TrainingMetadata: + return TrainingMetadata( + created_at_utc="2026-05-10T00:00:00+00:00", + susse_version="test-0.0.0", + git_sha=None, + holdout_label="station-LOSO:sta_b", + splitter_name="station_loso[sta_b]", + n_train_rows=60, + n_val_rows=60, + train_metrics=_toy_score(60, 0.4), + val_metrics=_toy_score(60, 0.5), + baseline_metrics={ + "sat_ghi_nasa_kwh_m2_day": _toy_score(60, 0.7), + "sat_ghi_cams_kwh_m2_day": _toy_score(60, 0.6), + }, + ) + + def test_empty_holdout_label_rejected(self) -> None: + with pytest.raises(ValueError, match="holdout_label must be non-empty"): + TrainingMetadata( + created_at_utc="t", + susse_version="v", + git_sha=None, + holdout_label="", + splitter_name="x", + n_train_rows=1, + n_val_rows=1, + train_metrics=_toy_score(), + val_metrics=_toy_score(), + baseline_metrics={}, + ) + + def test_empty_splitter_name_rejected(self) -> None: + with pytest.raises(ValueError, match="splitter_name must be non-empty"): + TrainingMetadata( + created_at_utc="t", + susse_version="v", + git_sha=None, + holdout_label="x", + splitter_name="", + n_train_rows=1, + n_val_rows=1, + train_metrics=_toy_score(), + val_metrics=_toy_score(), + baseline_metrics={}, + ) + + def test_negative_n_train_rejected(self) -> None: + with pytest.raises(ValueError, match=r"n_train_rows=-1"): + TrainingMetadata( + created_at_utc="t", + susse_version="v", + git_sha=None, + holdout_label="x", + splitter_name="y", + n_train_rows=-1, + n_val_rows=1, + train_metrics=_toy_score(), + val_metrics=_toy_score(), + baseline_metrics={}, + ) + + def test_json_roundtrip_preserves_baselines(self) -> None: + original = self._full() + recovered = TrainingMetadata.from_json(original.to_json()) + assert recovered == original + # Baseline keys survive verbatim (dict ordering is irrelevant for equality). + assert set(recovered.baseline_metrics.keys()) == { + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + } diff --git a/tests/training/test_splitter.py b/tests/training/test_splitter.py new file mode 100644 index 00000000..0155eb12 --- /dev/null +++ b/tests/training/test_splitter.py @@ -0,0 +1,234 @@ +"""Splitter ABC + concrete subclasses. + +The contract every splitter must honour: + +* :meth:`Splitter.split` returns disjoint ``(train_idx, val_idx)`` pairs + that are subsets of ``processed.df.index``. +* :attr:`Splitter.name` is a stable, configuration-specific string. +* Configuration errors raise :class:`ValueError` with a remediation + hint. +""" + +from __future__ import annotations + +from datetime import date + +import pytest + +from susse.preprocessing import PreprocessedDataset +from susse.training import ( + RandomSplitter, + SpatialBlockSplitter, + SpatialSpreadHoldoutSplitter, + StationLOSOSplitter, + TemporalSplitter, +) + +# --------------------------------------------------------------------------- +# RandomSplitter +# --------------------------------------------------------------------------- + + +class TestRandomSplitter: + def test_train_val_disjoint_and_cover_all_rows( + self, toy_processed: PreprocessedDataset + ) -> None: + splitter = RandomSplitter(val_fraction=0.25, random_state=0) + train_idx, val_idx = splitter(toy_processed) + assert len(train_idx.intersection(val_idx)) == 0 + assert len(train_idx) + len(val_idx) == len(toy_processed.df) + + def test_val_fraction_respected_within_one_row( + self, toy_processed: PreprocessedDataset + ) -> None: + # round(0.25 * 120) == 30 — split must be exactly that size. + train_idx, val_idx = RandomSplitter(val_fraction=0.25, random_state=0).split( + toy_processed + ) + assert len(val_idx) == 30 + + def test_same_seed_gives_same_split( + self, toy_processed: PreprocessedDataset + ) -> None: + a = RandomSplitter(val_fraction=0.2, random_state=11).split(toy_processed) + b = RandomSplitter(val_fraction=0.2, random_state=11).split(toy_processed) + assert list(a[0]) == list(b[0]) + assert list(a[1]) == list(b[1]) + + def test_name_encodes_configuration(self) -> None: + assert ( + RandomSplitter(val_fraction=0.2, random_state=11).name + == "random[val=0.20,seed=11]" + ) + + def test_rejects_invalid_fraction(self) -> None: + with pytest.raises(ValueError, match="not in"): + RandomSplitter(val_fraction=0.0) + with pytest.raises(ValueError, match="not in"): + RandomSplitter(val_fraction=1.5) + + +# --------------------------------------------------------------------------- +# TemporalSplitter +# --------------------------------------------------------------------------- + + +class TestTemporalSplitter: + def test_rows_on_or_after_split_date_go_to_val( + self, toy_processed: PreprocessedDataset + ) -> None: + # Toy fixture spans 2024-01-01 to 2024-02-29 inclusive. + # 2024-02-01 boundary splits the date range roughly in half per station. + train_idx, val_idx = TemporalSplitter(split_date=date(2024, 2, 1)).split( + toy_processed + ) + train_dates = toy_processed.df.loc[train_idx, "date"] + val_dates = toy_processed.df.loc[val_idx, "date"] + assert all(d < date(2024, 2, 1) for d in train_dates) + assert all(d >= date(2024, 2, 1) for d in val_dates) + + def test_missing_date_column_raises_with_remediation( + self, toy_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="FeatureSpec.id_columns"): + TemporalSplitter(split_date=date(2024, 2, 1), date_column="when").split( + toy_processed + ) + + def test_name_encodes_split_date(self) -> None: + assert ( + TemporalSplitter(split_date=date(2024, 2, 1)).name + == "temporal[val>=2024-02-01]" + ) + + +# --------------------------------------------------------------------------- +# StationLOSOSplitter +# --------------------------------------------------------------------------- + + +class TestStationLosoSplitter: + def test_held_out_station_is_the_only_val_station( + self, toy_processed: PreprocessedDataset + ) -> None: + train_idx, val_idx = StationLOSOSplitter("sta_b").split(toy_processed) + held = toy_processed.df.loc[val_idx, "location"].unique() + assert list(held) == ["sta_b"] + assert len(train_idx.intersection(val_idx)) == 0 + + def test_unknown_station_raises_with_remediation( + self, toy_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="auto_pick_largest"): + StationLOSOSplitter("not_a_station").split(toy_processed) + + def test_name_records_held_out_station(self) -> None: + assert StationLOSOSplitter("sta_b").name == "station_loso[sta_b]" + + def test_auto_pick_largest_breaks_ties_alphabetically( + self, toy_processed: PreprocessedDataset + ) -> None: + # Toy fixture has 60 rows for each station — tied. Alphabetic. + splitter = StationLOSOSplitter.auto_pick_largest(toy_processed) + assert splitter.held_out_station == "sta_a" + + def test_auto_pick_largest_with_custom_location_column_raises( + self, toy_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="not found"): + StationLOSOSplitter.auto_pick_largest( + toy_processed, location_column="station" + ) + + +# --------------------------------------------------------------------------- +# SpatialSpreadHoldoutSplitter +# --------------------------------------------------------------------------- + + +class TestSpatialSpreadHoldoutSplitter: + def test_n_holdout_two_picks_arctic_and_cape_town( + self, geo_processed: PreprocessedDataset + ) -> None: + # arctic and cape_town are deliberately by far the most separated + # pair (~13,000 km vs <6,000 for any other pair). + train_idx, val_idx = SpatialSpreadHoldoutSplitter(n_holdout=2).split( + geo_processed + ) + held = set(geo_processed.df.loc[val_idx, "location"].unique()) + assert held == {"arctic", "cape_town"} + assert len(train_idx.intersection(val_idx)) == 0 + + def test_n_holdout_three_adds_the_farthest_third_station( + self, geo_processed: PreprocessedDataset + ) -> None: + # With arctic + cape_town fixed, the third station maximising + # min-distance to that pair is accra (West Africa, far from both). + _, val_idx = SpatialSpreadHoldoutSplitter(n_holdout=3).split(geo_processed) + held = set(geo_processed.df.loc[val_idx, "location"].unique()) + assert held == {"arctic", "cape_town", "accra"} + + def test_rejects_n_holdout_below_two(self) -> None: + with pytest.raises(ValueError, match="StationLOSOSplitter for n=1"): + SpatialSpreadHoldoutSplitter(n_holdout=1) + + def test_more_holdout_than_stations_raises( + self, geo_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="ingest more stations"): + SpatialSpreadHoldoutSplitter(n_holdout=10).split(geo_processed) + + def test_missing_coordinate_column_raises_with_remediation( + self, toy_processed: PreprocessedDataset + ) -> None: + # toy_processed has no lat/lon columns. + with pytest.raises(ValueError, match="FeatureSpec.id_columns"): + SpatialSpreadHoldoutSplitter(n_holdout=2).split(toy_processed) + + def test_name_records_n_holdout(self) -> None: + assert SpatialSpreadHoldoutSplitter(n_holdout=3).name == "spatial_spread[n=3]" + + +# --------------------------------------------------------------------------- +# SpatialBlockSplitter +# --------------------------------------------------------------------------- + + +class TestSpatialBlockSplitter: + def test_named_blocks_go_to_val(self, geo_processed: PreprocessedDataset) -> None: + splitter = SpatialBlockSplitter( + val_blocks=("uganda", "kenya"), block_column="country" + ) + train_idx, val_idx = splitter(geo_processed) + val_countries = set(geo_processed.df.loc[val_idx, "country"].unique()) + train_countries = set(geo_processed.df.loc[train_idx, "country"].unique()) + assert val_countries == {"uganda", "kenya"} + assert val_countries.isdisjoint(train_countries) + + def test_unknown_block_raises_with_available_list( + self, geo_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="not found"): + SpatialBlockSplitter( + val_blocks=("atlantis",), block_column="country" + ).split(geo_processed) + + def test_missing_block_column_raises_with_remediation( + self, geo_processed: PreprocessedDataset + ) -> None: + with pytest.raises(ValueError, match="FeatureSpec.id_columns"): + SpatialBlockSplitter(val_blocks=("uganda",), block_column="region").split( + geo_processed + ) + + def test_rejects_empty_val_blocks(self) -> None: + with pytest.raises(ValueError, match="at least"): + SpatialBlockSplitter(val_blocks=(), block_column="country") + + def test_name_lists_val_blocks(self) -> None: + assert ( + SpatialBlockSplitter( + val_blocks=("uganda", "kenya"), block_column="country" + ).name + == "spatial_block[val=uganda,kenya]" + ) diff --git a/tests/training/test_trainer.py b/tests/training/test_trainer.py new file mode 100644 index 00000000..56804d8c --- /dev/null +++ b/tests/training/test_trainer.py @@ -0,0 +1,168 @@ +"""Trainer end-to-end (offline only — W&B path is not exercised here). + +Pins: +* fit / score / bundle round-trips work without W&B +* metadata records the splitter name, holdout label, and baseline scores +* invalid splits surface clear errors +* `bundle_dest=` writes the bundle to disk; loading it reproduces predictions +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +import pytest + +from susse.models import LinearParams, MeanBaselineParams, RandomForestParams +from susse.preprocessing import PreprocessedDataset +from susse.training import ( + Splitter, + StationLOSOSplitter, + Trainer, + TrainerConfig, + load_bundle, +) + + +def _train_one( + processed: PreprocessedDataset, + params: Any, + *, + held_out: str = "sta_b", + bundle_dest: Path | None = None, +): + splitter = StationLOSOSplitter(held_out) + return Trainer().train( + processed=processed, + params=params, + splitter=splitter, + holdout_label=f"station-LOSO:{held_out}", + bundle_dest=bundle_dest, + ) + + +class _EmptyTrainSplitter(Splitter): + """Pathological splitter: every row in val, none in train.""" + + @property + def name(self) -> str: + return "all_into_val" + + def split(self, p: PreprocessedDataset): + return p.df.index[:0], p.df.index + + +class _OverlapSplitter(Splitter): + """Pathological splitter: train and val are both the full index.""" + + @property + def name(self) -> str: + return "overlap" + + def split(self, p: PreprocessedDataset): + return p.df.index, p.df.index + + +class TestEndToEnd: + def test_returns_bundle_with_full_metadata( + self, + toy_processed: PreprocessedDataset, + ) -> None: + bundle = _train_one( + toy_processed, + RandomForestParams(n_estimators=10, max_depth=3, random_state=0), + ) + # Splitter name was recorded. + assert bundle.metadata.splitter_name == "station_loso[sta_b]" + # Baselines were scored on the val fold (both NASA + CAMS columns + # exist in the toy fixture, so both should be present). + assert set(bundle.metadata.baseline_metrics) == { + "sat_ghi_nasa_kwh_m2_day", + "sat_ghi_cams_kwh_m2_day", + } + # Source manifest is carried verbatim. + assert bundle.source_manifest == toy_processed.source_manifest + + def test_bundle_dest_persists_and_reloads( + self, + tmp_path: Path, + toy_processed: PreprocessedDataset, + ) -> None: + bundle = _train_one( + toy_processed, + LinearParams(with_scaling=True), + bundle_dest=tmp_path, + ) + # The dir holds all four pieces. + assert (tmp_path / "model").is_dir() + assert (tmp_path / "feature_spec.json").is_file() + assert (tmp_path / "metadata.json").is_file() + assert (tmp_path / "source_manifest.json").is_file() + + # Reloaded bundle reproduces predictions. + restored = load_bundle(tmp_path) + X = toy_processed.X() + np.testing.assert_allclose( + restored.regressor.predict(X).values, + bundle.regressor.predict(X).values, + rtol=1e-10, + atol=1e-10, + ) + + def test_n_train_n_val_match_split( + self, + toy_processed: PreprocessedDataset, + ) -> None: + bundle = _train_one(toy_processed, MeanBaselineParams()) + # Toy fixture: 60 rows per station, 2 stations → 60 / 60. + assert bundle.metadata.n_train_rows == 60 + assert bundle.metadata.n_val_rows == 60 + + +class TestInvalidSplits: + def test_empty_train_fold_raises( + self, + toy_processed: PreprocessedDataset, + ) -> None: + with pytest.raises(ValueError, match="empty train fold"): + Trainer().train( + processed=toy_processed, + params=MeanBaselineParams(), + splitter=_EmptyTrainSplitter(), + holdout_label="degenerate", + ) + + def test_overlapping_indices_raises( + self, + toy_processed: PreprocessedDataset, + ) -> None: + with pytest.raises(ValueError, match="overlapping"): + Trainer().train( + processed=toy_processed, + params=MeanBaselineParams(), + splitter=_OverlapSplitter(), + holdout_label="degenerate", + ) + + +class TestBaselineScoringWithMissingColumn: + def test_missing_baseline_column_is_silently_skipped( + self, + toy_processed: PreprocessedDataset, + ) -> None: + """A baseline column not in processed.df is just absent from metadata.""" + bundle = Trainer( + TrainerConfig( + baseline_columns=("does_not_exist", "sat_ghi_nasa_kwh_m2_day") + ), + ).train( + processed=toy_processed, + params=MeanBaselineParams(), + splitter=StationLOSOSplitter("sta_b"), + holdout_label="station-LOSO:sta_b", + ) + # Only the existing column got scored. + assert set(bundle.metadata.baseline_metrics) == {"sat_ghi_nasa_kwh_m2_day"} diff --git a/tests/warehouse_ops/__init__.py b/tests/warehouse_ops/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/warehouse_ops/io/__init__.py b/tests/warehouse_ops/io/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/warehouse_ops/io/test_config.py b/tests/warehouse_ops/io/test_config.py new file mode 100644 index 00000000..5356c9fe --- /dev/null +++ b/tests/warehouse_ops/io/test_config.py @@ -0,0 +1,94 @@ +"""Tests for the warehouse-config + table-schema registry.""" + +from __future__ import annotations + +import pytest + +from susse.warehouse_ops.io.config import ( + MatchStrategy, + TableRefs, + TableSchema, + TableSchemas, + WarehouseConfig, + WarehouseOptions, +) + + +class TestWarehouseConfig: + def test_default_construction_uses_production_values(self) -> None: + cfg = WarehouseConfig() + assert cfg.project_id == "solar-irradiation-estimation" + assert cfg.dataset == "solar_warehouse" + + def test_fqn_concatenates_project_dataset_table(self) -> None: + cfg = WarehouseConfig(project_id="proj", dataset="ds") + assert cfg.fqn("foo") == "proj.ds.foo" + + +class TestTableSchema: + def test_rejects_empty_table_id(self) -> None: + with pytest.raises(ValueError, match="table_id must be non-empty"): + TableSchema(table_id="", merge_keys=("date",), description="x") + + def test_rejects_empty_merge_keys(self) -> None: + with pytest.raises(ValueError, match="must declare at least one"): + TableSchema(table_id="t", merge_keys=(), description="x") + + +class TestTableSchemasRegistry: + @pytest.mark.parametrize( + "schema, expected_id", + [ + (TableSchemas.NASA_DAILY_VARS_LONG, "nasa_daily_vars_long"), + (TableSchemas.CAMS_DAILY_VARS_LONG, "cams_daily_vars_long"), + (TableSchemas.IRRADIANCE_DAILY, "irradiance_daily"), + (TableSchemas.GROUND_MEASUREMENTS, "ground_measurements"), + (TableSchemas.GROUND_MEASUREMENTS_RAW, "ground_measurements_raw"), + (TableSchemas.DIM_VARIABLE, "dim_variable"), + ], + ) + def test_known_table_id(self, schema: TableSchema, expected_id: str) -> None: + assert schema.table_id == expected_id + + def test_long_format_tables_share_merge_key_shape(self) -> None: + # Both long-format tables key on the same four columns; this property + # is what lets one MergeLoader contract serve both. + assert ( + TableSchemas.NASA_DAILY_VARS_LONG.merge_keys + == TableSchemas.CAMS_DAILY_VARS_LONG.merge_keys + == ("date", "geohash5", "variable_id", "source") + ) + + +class TestTableRefs: + def test_default_refs_match_warehouse_layout(self) -> None: + refs = TableRefs() + assert ( + refs.nasa_daily_vars_long + == "solar-irradiation-estimation.solar_warehouse.nasa_daily_vars_long" + ) + assert ( + refs.cams_daily_vars_long + == "solar-irradiation-estimation.solar_warehouse.cams_daily_vars_long" + ) + assert ( + refs.irradiance_daily + == "solar-irradiation-estimation.solar_warehouse.irradiance_daily" + ) + + def test_custom_config_propagates(self) -> None: + cfg = WarehouseConfig(project_id="my-proj", dataset="my-ds") + refs = TableRefs(config=cfg) + assert refs.ground_measurements == "my-proj.my-ds.ground_measurements" + + +class TestWarehouseOptions: + def test_default_uses_geohash_strategy(self) -> None: + opts = WarehouseOptions() + assert opts.match_strategy is MatchStrategy.GEOHASH + assert opts.geohash_precision == 5 + + @pytest.mark.parametrize("precision", [0, -1, 13, 100]) + def test_rejects_out_of_range_geohash_precision(self, precision: int) -> None: + with pytest.raises(ValueError, match=r"outside \[1, 12\]"): + WarehouseOptions(geohash_precision=precision) diff --git a/tests/warehouse_ops/population/__init__.py b/tests/warehouse_ops/population/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/warehouse_ops/population/conftest.py b/tests/warehouse_ops/population/conftest.py new file mode 100644 index 00000000..2e2ebfc2 --- /dev/null +++ b/tests/warehouse_ops/population/conftest.py @@ -0,0 +1,38 @@ +"""Shared fixtures for warehouse_ops.population tests.""" + +from __future__ import annotations + +from pathlib import Path + +import pandas as pd +import pytest + +_FIXTURE_DIR = Path(__file__).resolve().parents[2] / "fixtures" + + +@pytest.fixture(scope="session") +def somalia_csv_path() -> Path: + """Tiny pinned slice of real CBE somalia ground measurements.""" + path = _FIXTURE_DIR / "ground_measurements" / "somalia_slice.csv" + if not path.exists(): + pytest.fail( + f"Fixture missing: {path}. Was the fixture file deleted? It must be " + f"checked in alongside these tests." + ) + return path + + +@pytest.fixture() +def somalia_raw_df(somalia_csv_path: Path) -> pd.DataFrame: + """The somalia fixture parsed as a raw DataFrame (without going through + the adapter). + + Used by tests that exercise downstream functions independently of the + adapter parsing logic. + """ + df = pd.read_csv(somalia_csv_path) + df["datetime"] = pd.to_datetime(df["datetime"]).dt.date + df["ghi"] = df["ghi"].astype(float) + df["latitude"] = df["latitude"].astype(float) + df["longitude"] = df["longitude"].astype(float) + return df diff --git a/tests/warehouse_ops/population/jobs/__init__.py b/tests/warehouse_ops/population/jobs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/warehouse_ops/population/jobs/test_merra_region_job.py b/tests/warehouse_ops/population/jobs/test_merra_region_job.py new file mode 100644 index 00000000..9fbfee48 --- /dev/null +++ b/tests/warehouse_ops/population/jobs/test_merra_region_job.py @@ -0,0 +1,368 @@ +"""Tests for MerraRegionJob — region-shaped MERRA-2 ingest. + +End-to-end ``run()`` against the live GES DISC OPeNDAP endpoint is +exercised by the b9 migration. These tests cover the contracts that don't +require a network round-trip: plan validation, the single-fetch_region +fan-out shape, api_code → variable_id remapping, geohash5 enrichment, +and the existing-keys filter that spares re-runs from re-uploading rows. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.warehouse_ops.population.jobs.merra_region_job import MerraRegionJob +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + GridPlan, + GridSpec, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) + + +def _merra_var(variable_id: str, api_code: str) -> VariableSpec: + return VariableSpec( + variable_id=variable_id, + source=Source.MERRA_2, + api_code=api_code, + display_name=variable_id, + unit="x", + native_unit="x", + description="x", + ) + + +class _StubBQ: + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + +class _StubFetcher: + """Stub fetcher that records calls and returns a canned region frame. + + Returns rows keyed by ``api_code`` (matching the real fetcher contract); + the job is responsible for mapping these to warehouse ``variable_id``. + """ + + def __init__(self, rows: list[dict] | None = None) -> None: + self.calls: list[dict] = [] + self._rows = rows if rows is not None else [] + + def fetch_region(self, **kwargs) -> pd.DataFrame: + self.calls.append(kwargs) + return pd.DataFrame( + self._rows, + columns=("date", "latitude", "longitude", "variable_id", "value"), + ) + + +class _RecordingBQForCoverage: + """Stub that returns canned coverage keys and records the queries. + + Uses 3-tuple keys ``(date, geohash5, variable_id)`` matching + :data:`TableSchemas.MERRA_DAILY_VARS_LONG.merge_keys` after the + source/scoping filters are applied by the coverage layer. + """ + + def __init__(self, existing_keys: set[tuple]) -> None: + self._keys = existing_keys + self.coverage_calls: list[dict] = [] + + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + def existing_keys(self, table_fqn, key_columns, *, where_filters=()): + self.coverage_calls.append( + { + "table_fqn": table_fqn, + "key_columns": tuple(key_columns), + "where_filters": tuple(where_filters), + } + ) + return self._keys + + +# --------------------------------------------------------------------------- +# Plan validation +# --------------------------------------------------------------------------- + + +class TestRunRejectsBadPlan: + def test_raises_on_wrong_plan_type(self) -> None: + job = MerraRegionJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + + class FakePlan: + def describe(self) -> str: + return "fake" + + with pytest.raises(TypeError, match="NamedLocationsPlan or GridPlan"): + job.run(FakePlan()) # type: ignore[arg-type] + + def test_raises_on_source_mismatch(self) -> None: + # Using a NASA-source plan against the MERRA job must surface a + # clear contract violation rather than silently fetching the wrong + # variables. + job = MerraRegionJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 1)), + locations=(LocationSpec(name="x", lat=0.0, lon=0.0),), + variables=( + VariableSpec( + variable_id="ghi", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DWN", + display_name="GHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="x", + ), + ), + ) + with pytest.raises(ValueError, match="plan.source"): + job.run(plan) + + +# --------------------------------------------------------------------------- +# Single-call fan-out shape +# --------------------------------------------------------------------------- + + +class TestRunIssuesOneFetchRegionCall: + """The whole point of MerraRegionJob is *one* fetch_region call per + plan, regardless of location count. Pinning that shape protects the + 100× speedup over the legacy per-point pattern.""" + + def test_named_locations_plan_calls_fetcher_once(self) -> None: + loc1 = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + loc2 = LocationSpec(name="lira", lat=2.295190, lon=32.921370) + bq = _RecordingBQForCoverage(set()) + fetcher = _StubFetcher() + job = MerraRegionJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.MERRA_2, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)), + locations=(loc1, loc2), + variables=(_merra_var("aod_550_extinction", "TOTEXTTAU"),), + ) + result = job.run(plan) + + assert len(fetcher.calls) == 1, ( + "MerraRegionJob must issue exactly one fetch_region call covering " + "all locations — not one per location." + ) + call = fetcher.calls[0] + assert call["points"] == ((loc1.lat, loc1.lon), (loc2.lat, loc2.lon)) + assert call["api_codes"] == ("TOTEXTTAU",) + assert call["date_start"] == date(2024, 6, 1) + assert call["date_end"] == date(2024, 6, 16) + assert result.api_calls_made == 1 + + def test_grid_plan_iterates_grid_points(self) -> None: + # GridPlan: the job must enumerate via ``grid.iter_locations()`` + # and pass all grid points into a single fetch_region call. + var = _merra_var("aod_550_extinction", "TOTEXTTAU") + grid = GridSpec( + bbox=BoundingBox(min_lat=0.0, max_lat=1.0, min_lon=10.0, max_lon=11.0), + lat_step=0.5, + lon_step=0.5, + ) + bq = _RecordingBQForCoverage(set()) + fetcher = _StubFetcher() + job = MerraRegionJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = GridPlan( + source=Source.MERRA_2, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)), + grid=grid, + variables=(var,), + ) + job.run(plan) + assert len(fetcher.calls) == 1 + # 3 lat × 3 lon = 9 grid points. + assert len(fetcher.calls[0]["points"]) == 9 + + +# --------------------------------------------------------------------------- +# Enrichment: api_code → variable_id, geohash5 attachment +# --------------------------------------------------------------------------- + + +class TestEnrichRemapsAndGeohashes: + """The fetcher returns rows keyed by api_code; the job must remap to + the warehouse variable_id and attach the geohash5 derived from each + row's (lat, lon). Test these in isolation from coverage / load.""" + + def test_remaps_api_code_and_attaches_geohash(self) -> None: + import pygeohash + + loc = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + expected_gh = pygeohash.encode(loc.lat, loc.lon, precision=5) + var = _merra_var("aod_550_extinction", "TOTEXTTAU") + + fetcher_rows = [ + { + "date": date(2024, 6, 21), + "latitude": loc.lat, + "longitude": loc.lon, + "variable_id": "TOTEXTTAU", + "value": 0.42, + }, + ] + job = MerraRegionJob(_StubBQ(), fetcher=_StubFetcher(fetcher_rows)) # type: ignore[arg-type] + + df = pd.DataFrame(fetcher_rows) + api_to_var = {var.api_code: var.variable_id} + enriched = job._enrich(df, [loc], api_to_var) + + assert list(enriched["variable_id"]) == ["aod_550_extinction"] + assert list(enriched["geohash5"]) == [expected_gh] + assert list(enriched["source"]) == ["MERRA2"] + + def test_drops_rows_for_unknown_api_codes(self) -> None: + # If the fetcher returns an api_code we didn't ask for (shouldn't + # happen in practice but defensive), the row must be dropped + # rather than written with a NaN variable_id. + loc = LocationSpec(name="kampala", lat=0.333, lon=32.568) + var = _merra_var("aod_550_extinction", "TOTEXTTAU") + df = pd.DataFrame( + [ + { + "date": date(2024, 6, 21), + "latitude": loc.lat, + "longitude": loc.lon, + "variable_id": "TOTEXTTAU", + "value": 0.42, + }, + { + "date": date(2024, 6, 21), + "latitude": loc.lat, + "longitude": loc.lon, + "variable_id": "GHOST_VAR", + "value": 999.0, + }, + ] + ) + job = MerraRegionJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + enriched = job._enrich(df, [loc], {var.api_code: var.variable_id}) + + assert list(enriched["variable_id"]) == ["aod_550_extinction"] + assert 999.0 not in enriched["value"].values + + def test_raises_when_fetched_point_not_in_plan(self) -> None: + # If somehow a row's (lat, lon) doesn't match any plan location, + # the geohash merge would yield NaN. The job must surface the + # invariant violation rather than silently writing NaN geohashes. + loc = LocationSpec(name="kampala", lat=0.333, lon=32.568) + var = _merra_var("aod_550_extinction", "TOTEXTTAU") + df = pd.DataFrame( + [ + { + "date": date(2024, 6, 21), + "latitude": 99.0, + "longitude": 99.0, + "variable_id": "TOTEXTTAU", + "value": 0.42, + }, + ] + ) + job = MerraRegionJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + with pytest.raises(RuntimeError, match="out of sync"): + job._enrich(df, [loc], {var.api_code: var.variable_id}) + + +# --------------------------------------------------------------------------- +# Coverage filter: re-run optimization +# --------------------------------------------------------------------------- + + +class TestDropAlreadyCached: + """Filter step that spares re-runs from re-uploading rows. The + MergeLoader is idempotent on the merge keys so this is purely an + optimization, but it's an important one for repeated runs over a + largely-already-populated date range.""" + + def test_drops_rows_in_existing_keys(self) -> None: + df = pd.DataFrame( + [ + { + "date": date(2024, 6, 1), + "geohash5": "abc12", + "variable_id": "v1", + "value": 1.0, + }, + { + "date": date(2024, 6, 2), + "geohash5": "abc12", + "variable_id": "v1", + "value": 2.0, + }, + ] + ) + existing = {(date(2024, 6, 1), "abc12", "v1")} + out = MerraRegionJob._drop_already_cached(df, existing) + assert len(out) == 1 + assert out.iloc[0]["date"] == date(2024, 6, 2) + + def test_returns_input_when_existing_set_empty(self) -> None: + df = pd.DataFrame( + [ + { + "date": date(2024, 6, 1), + "geohash5": "abc12", + "variable_id": "v1", + "value": 1.0, + }, + ] + ) + out = MerraRegionJob._drop_already_cached(df, set()) + assert len(out) == 1 + + +# --------------------------------------------------------------------------- +# Coverage scoping: same regression-guard role as ModisJob +# --------------------------------------------------------------------------- + + +class TestRunScopesCoverageByLocation: + """``MerraRegionJob.run`` must pass plan geohashes to the coverage + query. Without this scope, named-location plans whose date range + overlaps the existing warehouse footprint pull millions of irrelevant + rows back from BigQuery before the API is touched.""" + + def test_named_locations_plan_includes_geohash_filter(self) -> None: + import pygeohash + + loc1 = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + loc2 = LocationSpec(name="lira", lat=2.295190, lon=32.921370) + gh1 = pygeohash.encode(loc1.lat, loc1.lon, precision=5) + gh2 = pygeohash.encode(loc2.lat, loc2.lon, precision=5) + bq = _RecordingBQForCoverage(set()) + fetcher = _StubFetcher() + job = MerraRegionJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.MERRA_2, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 1)), + locations=(loc1, loc2), + variables=(_merra_var("aod_550_extinction", "TOTEXTTAU"),), + ) + job.run(plan) + + assert len(bq.coverage_calls) == 1 + joined = " ".join(bq.coverage_calls[0]["where_filters"]) + assert ( + "geohash5 IN" in joined + ), f"coverage call missing geohash5 scope: {bq.coverage_calls[0]!r}" + assert gh1 in joined and gh2 in joined diff --git a/tests/warehouse_ops/population/jobs/test_modis_job.py b/tests/warehouse_ops/population/jobs/test_modis_job.py new file mode 100644 index 00000000..69ade5dd --- /dev/null +++ b/tests/warehouse_ops/population/jobs/test_modis_job.py @@ -0,0 +1,318 @@ +"""Tests for ModisJob — MODIS-specific contracts. + +End-to-end ``run()`` against the live ORNL DAAC API is exercised from the +notebook. These tests cover the contracts that don't require a network +round-trip: variable-id parsing, cadence-aware coverage, plan validation, +and the per-location skip path. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.warehouse_ops.population.jobs.modis_job import ( + _COVERAGE_RATIO_THRESHOLD, + ModisJob, + _split_variable_id, +) +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + GridPlan, + GridSpec, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) + + +def _modis_var(variable_id: str, api_code: str) -> VariableSpec: + return VariableSpec( + variable_id=variable_id, + source=Source.MODIS, + api_code=api_code, + display_name=variable_id, + unit="x", + native_unit="x", + description="x", + ) + + +class _StubBQ: + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + +class _StubFetcher: + """Stub MODIS fetcher that records calls and returns canned rows.""" + + def __init__(self, rows: list[dict] | None = None) -> None: + self.calls: list[dict] = [] + self._rows = rows if rows is not None else [] + + def fetch_long_for_location(self, **kwargs) -> pd.DataFrame: + self.calls.append(kwargs) + return pd.DataFrame( + self._rows, + columns=("date", "product_id", "band_id", "value"), + ) + + +class TestSplitVariableId: + """``variable_id`` follows ``f"{api_code}_{band_id}"`` — recover both halves.""" + + def test_simple_band(self) -> None: + v = _modis_var("MCD43A4_Nadir_Reflectance_Band1", "MCD43A4") + assert _split_variable_id(v) == ("MCD43A4", "Nadir_Reflectance_Band1") + + def test_band_containing_underscores(self) -> None: + # The band id itself contains underscores; only the leading + # api_code prefix should be stripped, leaving the band intact. + v = _modis_var("MOD11A2_LST_Day_1km", "MOD11A2") + assert _split_variable_id(v) == ("MOD11A2", "LST_Day_1km") + + def test_band_starting_with_digit(self) -> None: + v = _modis_var("MOD13Q1_250m_16_days_NDVI", "MOD13Q1") + assert _split_variable_id(v) == ("MOD13Q1", "250m_16_days_NDVI") + + def test_raises_when_variable_id_does_not_start_with_api_code(self) -> None: + # Wrong catalog setup: variable_id doesn't carry the api_code prefix. + # Must surface a clear remediation message rather than silently + # producing a malformed (product, band) pair. + v = _modis_var("Foo_Bar", "MCD43A4") + with pytest.raises(ValueError, match="api_code prefix"): + _split_variable_id(v) + + +class TestLocationFullyCached: + """Coverage check uses cadence × ratio threshold per (product, band).""" + + @staticmethod + def _job() -> ModisJob: + return ModisJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + + def test_returns_true_when_all_pairs_meet_threshold(self) -> None: + # 16 days for MOD13Q1 (16-day cadence) → expected = 1 row; + # threshold = 0.9 → need >=0.9 row → 1 actual row passes. + existing = { + (date(2024, 6, 1), "s8p1v", "MOD13Q1", "250m_16_days_NDVI", "MODIS"), + } + date_range = DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)) + cached = self._job()._location_fully_cached( + "s8p1v", + date_range=date_range, + product_band_pairs=(("MOD13Q1", "250m_16_days_NDVI"),), + existing_keys=existing, + ) + assert cached is True + + def test_returns_false_when_pair_below_threshold(self) -> None: + # MOD11A2 has 8-day cadence over 64 days → expected ~8 rows. + # 5 actual rows = 0.625 ratio < 0.9 threshold → must refetch. + existing = { + ( + date(2024, 6, 1) + pd.Timedelta(days=8 * i), + "s8p1v", + "MOD11A2", + "LST_Day_1km", + "MODIS", + ) + for i in range(5) + } + date_range = DateRange(start=date(2024, 6, 1), end=date(2024, 8, 4)) + cached = self._job()._location_fully_cached( + "s8p1v", + date_range=date_range, + product_band_pairs=(("MOD11A2", "LST_Day_1km"),), + existing_keys=existing, + ) + assert cached is False + + def test_returns_false_if_one_of_many_pairs_uncovered(self) -> None: + # First pair is fully covered, second has no rows → must refetch. + existing = { + (date(2024, 6, 1), "s8p1v", "MOD13Q1", "250m_16_days_NDVI", "MODIS"), + } + date_range = DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)) + cached = self._job()._location_fully_cached( + "s8p1v", + date_range=date_range, + product_band_pairs=( + ("MOD13Q1", "250m_16_days_NDVI"), + ("MOD11A2", "LST_Day_1km"), # zero rows present + ), + existing_keys=existing, + ) + assert cached is False + + def test_threshold_constant_is_under_one(self) -> None: + # Pin the slack: composite end-dates align to fixed Julian-day + # offsets, so an N-day window can yield slightly fewer rows than + # the naïve N/cadence count. Must not be 1.0 or strict equality + # would over-refetch on every re-run. + assert 0.5 < _COVERAGE_RATIO_THRESHOLD < 1.0 + + +class TestRunRejectsBadPlan: + def test_raises_on_wrong_plan_type(self) -> None: + job = ModisJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + + class FakePlan: + def describe(self) -> str: + return "fake" + + with pytest.raises(TypeError, match="NamedLocationsPlan or GridPlan"): + job.run(FakePlan()) # type: ignore[arg-type] + + def test_raises_on_source_mismatch(self) -> None: + job = ModisJob(_StubBQ(), fetcher=_StubFetcher()) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.NASA_POWER, # wrong source for MODIS job + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 1)), + locations=(LocationSpec(name="x", lat=0.0, lon=0.0),), + variables=( + VariableSpec( + variable_id="ghi", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DWN", + display_name="GHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="x", + ), + ), + ) + with pytest.raises(ValueError, match="plan.source"): + job.run(plan) + + +class _RecordingBQForCoverage: + """Stub that returns canned coverage keys and records the queries. + + Uses 5-tuple keys ``(date, geohash5, product_id, band_id, source)`` + matching :data:`TableSchemas.MODIS_OBSERVATIONS.merge_keys`. + """ + + def __init__(self, existing_keys: set[tuple]) -> None: + self._keys = existing_keys + self.coverage_calls: list[dict] = [] + + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + def existing_keys(self, table_fqn, key_columns, *, where_filters=()): + self.coverage_calls.append( + { + "table_fqn": table_fqn, + "key_columns": tuple(key_columns), + "where_filters": tuple(where_filters), + } + ) + return self._keys + + +class TestRunScopesCoverageByLocation: + """``ModisJob.run`` must pass plan geohashes to the coverage query. + + Without this scope, named-location plans with date ranges overlapping + the existing warehouse footprint pull millions of irrelevant rows + back from BigQuery before the API is touched. Same regression-guard + role as :class:`TestRunScopesCoverageByLocation` on BaseSatelliteJob. + """ + + def test_named_locations_plan_includes_geohash_filter(self) -> None: + import pygeohash + + loc1 = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + loc2 = LocationSpec(name="lira", lat=2.295190, lon=32.921370) + gh1 = pygeohash.encode(loc1.lat, loc1.lon, precision=5) + gh2 = pygeohash.encode(loc2.lat, loc2.lon, precision=5) + date_range = DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)) + ndvi_var = _modis_var("MOD13Q1_250m_16_days_NDVI", "MOD13Q1") + + # Pre-populate a "warehouse" set that satisfies the cadence-based + # threshold for both locations, so the run skips fetching. + full_keys = { + (date(2024, 6, 1), gh, "MOD13Q1", "250m_16_days_NDVI", "MODIS") + for gh in (gh1, gh2) + } + bq = _RecordingBQForCoverage(full_keys) + fetcher = _StubFetcher() + + job = ModisJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.MODIS, + date_range=date_range, + locations=(loc1, loc2), + variables=(ndvi_var,), + ) + result = job.run(plan) + + assert result.api_calls_made == 0 + assert result.extra["skipped_locations"] == 2 + assert fetcher.calls == [], "fully-cached locations must skip the fetcher" + + assert len(bq.coverage_calls) == 1 + joined = " ".join(bq.coverage_calls[0]["where_filters"]) + assert ( + "geohash5 IN" in joined + ), f"coverage call missing geohash5 scope: {bq.coverage_calls[0]!r}" + assert gh1 in joined and gh2 in joined + + def test_run_invokes_fetcher_per_uncached_location(self) -> None: + # Empty warehouse → every location needs a fetch. Stub fetcher + # returns an empty frame so we exercise the loop without needing + # to mock the loader; the assertion is on call count. + loc1 = LocationSpec(name="a", lat=0.0, lon=10.0) + loc2 = LocationSpec(name="b", lat=1.0, lon=11.0) + ndvi_var = _modis_var("MOD13Q1_250m_16_days_NDVI", "MOD13Q1") + bq = _RecordingBQForCoverage(set()) + fetcher = _StubFetcher() + + job = ModisJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.MODIS, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)), + locations=(loc1, loc2), + variables=(ndvi_var,), + ) + result = job.run(plan) + + assert len(fetcher.calls) == 2 + assert result.api_calls_made == 2 + assert result.extra["skipped_locations"] == 0 + # Both calls must carry the (product, band) pair derived from the var. + for call in fetcher.calls: + assert call["products_and_bands"] == (("MOD13Q1", "250m_16_days_NDVI"),) + + def test_grid_plan_resolves_locations_from_grid(self) -> None: + # GridPlan: the job must enumerate via ``grid.iter_locations()`` — + # a 1×1 grid yields a single point. + ndvi_var = _modis_var("MOD13Q1_250m_16_days_NDVI", "MOD13Q1") + grid = GridSpec( + bbox=BoundingBox(min_lat=0.0, max_lat=0.0, min_lon=10.0, max_lon=10.0), + lat_step=1.0, + lon_step=1.0, + ) + bq = _RecordingBQForCoverage(set()) + fetcher = _StubFetcher() + job = ModisJob(bq, fetcher=fetcher) # type: ignore[arg-type] + plan = GridPlan( + source=Source.MODIS, + date_range=DateRange(start=date(2024, 6, 1), end=date(2024, 6, 16)), + grid=grid, + variables=(ndvi_var,), + ) + result = job.run(plan) + assert len(fetcher.calls) == 1 + assert result.api_calls_made == 1 diff --git a/tests/warehouse_ops/population/jobs/test_nasa_power_region_job.py b/tests/warehouse_ops/population/jobs/test_nasa_power_region_job.py new file mode 100644 index 00000000..b00567ee --- /dev/null +++ b/tests/warehouse_ops/population/jobs/test_nasa_power_region_job.py @@ -0,0 +1,282 @@ +"""Tests for NasaPowerRegionJob — region-shaped NASA POWER ingest. + +End-to-end ``run()`` against the live NASA POWER endpoint is exercised by +the a13 migration. These tests cover the contracts that don't need a +network round-trip: plan validation, the single fetch_region_long fan-out, +api_code → variable_id remapping with native-pixel geohash5 enrichment, the +irradiance/aux split, and the long→wide irradiance pivot. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pygeohash +import pytest + +from susse.warehouse_ops.io.config import WarehouseConfig +from susse.warehouse_ops.population.jobs.nasa_power_region_job import NasaPowerRegionJob +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + LocationSpec, + NamedLocationsPlan, + RegionPlan, + Source, + VariableSpec, +) + + +def _nasa_var(variable_id: str, api_code: str) -> VariableSpec: + return VariableSpec( + variable_id=variable_id, + source=Source.NASA_POWER, + api_code=api_code, + display_name=variable_id, + unit="x", + native_unit="x", + description="x", + ) + + +_GHI = _nasa_var("ghi", "ALLSKY_SFC_SW_DWN") +_DHI = _nasa_var("dhi", "ALLSKY_SFC_SW_DIFF") +_T2M = _nasa_var("temperature", "T2M") + +_UGANDA_BBOX = BoundingBox(min_lat=-1.5, max_lat=4.5, min_lon=29.5, max_lon=35.05) +_DATES = DateRange(start=date(2024, 1, 1), end=date(2024, 1, 2)) + + +class _StubRegionalFetcher: + """Records fetch_region_long calls and returns a canned native-pixel frame. + + Rows are keyed by ``api_code`` (the real fetcher's contract); the job + maps these to warehouse ``variable_id``. + """ + + def __init__(self, rows: list[dict] | None = None) -> None: + self.calls: list[dict] = [] + self._rows = rows if rows is not None else [] + + def fetch_region_long(self, **kwargs: object) -> pd.DataFrame: + self.calls.append(kwargs) + return pd.DataFrame( + self._rows, + columns=("date", "latitude", "longitude", "variable_id", "value"), + ) + + +class _FakeBQ: + """Minimal BigQuery double: captures every staged DataFrame load. + + The MERGE DDL is a no-op; the MIN/MAX date probe returns a fixed span so + MergeLoader runs a single un-chunked MERGE. ``MergeLoader.load`` then + returns ``len(df)``, so the job's row counts reflect the split exactly. + """ + + def __init__(self) -> None: + self.config = WarehouseConfig() + self.loaded: list[tuple[str, pd.DataFrame]] = [] + + def load_dataframe( + self, df: pd.DataFrame, table_fqn: str, *, write_disposition: str + ) -> None: + self.loaded.append((table_fqn, df.copy())) + + def execute_ddl(self, sql: str) -> None: + return None + + def query(self, sql: str) -> pd.DataFrame: + return pd.DataFrame([{"min_d": date(2024, 1, 1), "max_d": date(2024, 1, 2)}]) + + +# --------------------------------------------------------------------------- +# Plan validation +# --------------------------------------------------------------------------- + + +class TestRunRejectsBadPlan: + def test_raises_on_wrong_plan_type(self) -> None: + # A NamedLocationsPlan is a valid FetchPlan but the wrong shape for + # the region job — it must be rejected, not silently mis-handled. + job = NasaPowerRegionJob(_FakeBQ(), fetcher=_StubRegionalFetcher()) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=_DATES, + locations=(LocationSpec(name="kampala", lat=0.33, lon=32.57),), + variables=(_GHI,), + ) + with pytest.raises(TypeError, match="RegionPlan"): + job.run(plan) + + def test_raises_on_source_mismatch(self) -> None: + job = NasaPowerRegionJob(_FakeBQ(), fetcher=_StubRegionalFetcher()) # type: ignore[arg-type] + cams_var = VariableSpec( + variable_id="ghi", + source=Source.CAMS, + api_code="GHI", + display_name="GHI", + unit="x", + native_unit="x", + description="x", + ) + plan = RegionPlan( + source=Source.CAMS, + date_range=_DATES, + bbox=_UGANDA_BBOX, + variables=(cams_var,), + ) + with pytest.raises(ValueError, match="plan.source"): + job.run(plan) + + +# --------------------------------------------------------------------------- +# Single-call fan-out shape +# --------------------------------------------------------------------------- + + +class TestRunIssuesOneFetchCall: + def test_one_fetch_region_long_call_with_bbox_and_all_api_codes(self) -> None: + fetcher = _StubRegionalFetcher() + job = NasaPowerRegionJob(_FakeBQ(), fetcher=fetcher) # type: ignore[arg-type] + plan = RegionPlan( + source=Source.NASA_POWER, + date_range=_DATES, + bbox=_UGANDA_BBOX, + variables=(_GHI, _T2M), + ) + result = job.run(plan) + + assert len(fetcher.calls) == 1, ( + "NasaPowerRegionJob must issue exactly one fetch_region_long call " + "for the whole bbox — the fetcher fans out into tiles internally." + ) + call = fetcher.calls[0] + assert call["min_lat"] == _UGANDA_BBOX.min_lat + assert call["max_lon"] == _UGANDA_BBOX.max_lon + assert call["date_start"] == date(2024, 1, 1) + assert set(call["api_codes"]) == {"ALLSKY_SFC_SW_DWN", "T2M"} + assert result.api_calls_made == 1 + # Empty fetch → nothing loaded, no rows added. + assert result.rows_added == 0 + + +# --------------------------------------------------------------------------- +# Enrichment: api_code → variable_id, native-pixel geohash5 +# --------------------------------------------------------------------------- + + +class TestEnrich: + def test_remaps_api_code_and_geohashes_each_native_pixel(self) -> None: + job = NasaPowerRegionJob(_FakeBQ(), fetcher=_StubRegionalFetcher()) # type: ignore[arg-type] + rows = [ + { + "date": date(2024, 1, 1), + "latitude": 0.5, + "longitude": 32.5, + "variable_id": "T2M", + "value": 25.0, + }, + { + "date": date(2024, 1, 1), + "latitude": 1.0, + "longitude": 33.125, + "variable_id": "T2M", + "value": 26.0, + }, + ] + df = pd.DataFrame(rows) + enriched = job._enrich(df, {"T2M": "temperature"}) + + assert list(enriched["variable_id"]) == ["temperature", "temperature"] + assert list(enriched["source"]) == ["NASA", "NASA"] + # geohash5 is the native pixel centre's geohash — not a query point. + assert enriched.iloc[0]["geohash5"] == pygeohash.encode(0.5, 32.5, 5) + assert enriched.iloc[1]["geohash5"] == pygeohash.encode(1.0, 33.125, 5) + + def test_drops_unmapped_api_codes(self) -> None: + # An api_code with no catalog mapping must be dropped, not stored + # under a NaN variable_id. + job = NasaPowerRegionJob(_FakeBQ(), fetcher=_StubRegionalFetcher()) # type: ignore[arg-type] + df = pd.DataFrame( + [ + { + "date": date(2024, 1, 1), + "latitude": 0.5, + "longitude": 32.5, + "variable_id": "UNKNOWN_CODE", + "value": 1.0, + } + ] + ) + assert job._enrich(df, {"T2M": "temperature"}).empty + + +# --------------------------------------------------------------------------- +# Full run: irradiance / aux split + load routing +# --------------------------------------------------------------------------- + + +class TestRunSplitsIrradianceFromAux: + def test_irradiance_and_aux_routed_to_their_tables(self) -> None: + # ghi + dhi at one pixel over 2 days → 4 long rows → 2 wide rows. + # T2M at two pixels over 2 days → 4 long aux rows. + rows: list[dict] = [] + for day in (date(2024, 1, 1), date(2024, 1, 2)): + rows.append( + { + "date": day, + "latitude": 0.5, + "longitude": 32.5, + "variable_id": "ALLSKY_SFC_SW_DWN", + "value": 5.0, + } + ) + rows.append( + { + "date": day, + "latitude": 0.5, + "longitude": 32.5, + "variable_id": "ALLSKY_SFC_SW_DIFF", + "value": 2.0, + } + ) + rows.append( + { + "date": day, + "latitude": 0.5, + "longitude": 32.5, + "variable_id": "T2M", + "value": 25.0, + } + ) + rows.append( + { + "date": day, + "latitude": 1.0, + "longitude": 33.125, + "variable_id": "T2M", + "value": 26.0, + } + ) + + bq = _FakeBQ() + job = NasaPowerRegionJob(bq, fetcher=_StubRegionalFetcher(rows)) # type: ignore[arg-type] + plan = RegionPlan( + source=Source.NASA_POWER, + date_range=_DATES, + bbox=_UGANDA_BBOX, + variables=(_GHI, _DHI, _T2M), + ) + result = job.run(plan) + + assert result.extra["rows_added_irradiance"] == 2 + assert result.extra["rows_added_long"] == 4 + assert result.rows_added == 6 + + staged = {fqn: df for fqn, df in bq.loaded} + irr_fqn = next(f for f in staged if "irradiance_daily_staging" in f) + aux_fqn = next(f for f in staged if "nasa_daily_vars_long_staging" in f) + assert set(staged[irr_fqn]["geohash5"]) == {pygeohash.encode(0.5, 32.5, 5)} + assert list(staged[aux_fqn]["variable_id"].unique()) == ["temperature"] diff --git a/tests/warehouse_ops/population/jobs/test_satellite_job.py b/tests/warehouse_ops/population/jobs/test_satellite_job.py new file mode 100644 index 00000000..33d49ab0 --- /dev/null +++ b/tests/warehouse_ops/population/jobs/test_satellite_job.py @@ -0,0 +1,379 @@ +"""Tests for the source-agnostic helpers on BaseSatelliteJob. + +End-to-end ``run()`` requires real CAMS/NASA API access and is exercised +by the notebook against the live warehouse. These tests cover the +internal contracts that don't. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.warehouse_ops.population.jobs.satellite_job import ( + BaseSatelliteJob, + CamsSatelliteJob, + NasaPowerSatelliteJob, + _location_spec_to_geopy, +) +from susse.warehouse_ops.population.types import ( + DateRange, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) + + +class _StubBQ: + """Stand-in for BigQueryClient — only attribute accesses on `config`.""" + + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + +def _ghi_var(source: Source) -> VariableSpec: + api = "ALLSKY_SFC_SW_DWN" if source is Source.NASA_POWER else "ghi" + return VariableSpec( + variable_id="ghi", + source=source, + api_code=api, + display_name="GHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="x", + ) + + +def _temp_var() -> VariableSpec: + return VariableSpec( + variable_id="temperature", + source=Source.NASA_POWER, + api_code="T2M", + display_name="Temp", + unit="degC", + native_unit="degC", + description="x", + ) + + +class TestExtractIrradiance: + """``_extract_irradiance`` pivots long → wide for irradiance_daily.""" + + def test_pivots_ghi_dhi_dni(self) -> None: + job = NasaPowerSatelliteJob(_StubBQ()) # type: ignore[arg-type] + irradiance_vars = ( + _ghi_var(Source.NASA_POWER), + VariableSpec( + variable_id="dhi", + source=Source.NASA_POWER, + api_code="ALLSKY_SFC_SW_DIFF", + display_name="DHI", + unit="kWh/m^2/day", + native_unit="kWh/m^2/day", + description="x", + ), + ) + long = pd.DataFrame( + [ + { + "date": date(2025, 1, 1), + "latitude": 0.333, + "longitude": 32.568, + "geohash5": "s8p1v", + "source": "NASA", + "variable_id": "ghi", + "value": 5.5, + }, + { + "date": date(2025, 1, 1), + "latitude": 0.333, + "longitude": 32.568, + "geohash5": "s8p1v", + "source": "NASA", + "variable_id": "dhi", + "value": 2.5, + }, + ] + ) + wide = job._extract_irradiance(long, irradiance_vars) + assert len(wide) == 1 + assert wide.loc[0, "ghi_kwh_m2_day"] == 5.5 + assert wide.loc[0, "dhi_kwh_m2_day"] == 2.5 + # DNI was not in the long input → column exists but is NA. + assert "dni_kwh_m2_day" in wide.columns + assert pd.isna(wide.loc[0, "dni_kwh_m2_day"]) + # Reliability is wide-only (CAMS-specific) and stays NA for NASA. + assert pd.isna(wide.loc[0, "reliability"]) + + +class TestLocationFullyCached: + """The pre-fetch coverage check determines whether we skip the API call.""" + + def _job(self) -> BaseSatelliteJob: + return NasaPowerSatelliteJob(_StubBQ()) # type: ignore[arg-type] + + def test_returns_true_when_all_keys_present(self) -> None: + job = self._job() + date_range = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 2)) + long_vars = (_temp_var(),) + irr_vars = (_ghi_var(Source.NASA_POWER),) + + existing_long = { + (date(2025, 1, 1), "s8p1v", "temperature"), + (date(2025, 1, 2), "s8p1v", "temperature"), + } + existing_irr = { + (date(2025, 1, 1), "s8p1v"), + (date(2025, 1, 2), "s8p1v"), + } + assert ( + job._location_fully_cached( + "s8p1v", + date_range=date_range, + long_vars=long_vars, + irradiance_vars=irr_vars, + existing_long=existing_long, + existing_irr=existing_irr, + ) + is True + ) + + def test_returns_false_when_a_long_key_missing(self) -> None: + job = self._job() + date_range = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 2)) + long_vars = (_temp_var(),) + irr_vars = () + + existing_long = {(date(2025, 1, 1), "s8p1v", "temperature")} # day 2 missing + assert ( + job._location_fully_cached( + "s8p1v", + date_range=date_range, + long_vars=long_vars, + irradiance_vars=irr_vars, + existing_long=existing_long, + existing_irr=set(), + ) + is False + ) + + def test_returns_false_when_irradiance_key_missing(self) -> None: + job = self._job() + date_range = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 1)) + irr_vars = (_ghi_var(Source.NASA_POWER),) + assert ( + job._location_fully_cached( + "s8p1v", + date_range=date_range, + long_vars=(), + irradiance_vars=irr_vars, + existing_long=set(), + existing_irr=set(), # missing + ) + is False + ) + + +class TestLocationSpecToGeopy: + """The NASA POWER fetcher only reads ``.latitude`` / ``.longitude``. + + Earlier the adapter wrapped a ``Point`` in ``geopy.location.Location``, + which broke at runtime in geopy >=2.4 (``Location.__init__`` requires + ``address`` and ``raw``). This test pins the contract: the adapter + must return *something* with usable lat/lon attrs that round-trip the + input values, and it must not raise. + """ + + def test_returns_object_with_lat_lon_attrs(self) -> None: + spec = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + adapted = _location_spec_to_geopy(spec) + assert hasattr(adapted, "latitude"), ( + "adapter must return an object with a .latitude attribute " + "(NASA POWER fetcher reads it)" + ) + assert hasattr(adapted, "longitude") + assert adapted.latitude == pytest.approx(spec.lat) + assert adapted.longitude == pytest.approx(spec.lon) + + +class _RecordingBQForCoverage: + """Stub BigQueryClient that records calls coverage helpers make. + + Returns a ``set`` populated densely enough that + ``_location_fully_cached`` is satisfied for every location in the plan, + so the test never reaches the API-fetch stage. + """ + + def __init__(self, full_coverage_keys_long, full_coverage_keys_irr): + self._long_keys = full_coverage_keys_long + self._irr_keys = full_coverage_keys_irr + self.coverage_calls: list[dict] = [] + + @property + def config(self): + from susse.warehouse_ops.io.config import WarehouseConfig + + return WarehouseConfig() + + def existing_keys(self, table_fqn, key_columns, *, where_filters=()): + self.coverage_calls.append( + { + "table_fqn": table_fqn, + "key_columns": tuple(key_columns), + "where_filters": tuple(where_filters), + } + ) + # Distinguish long-table vs irradiance-table call by key tuple length. + if len(key_columns) == 3: + return self._long_keys + return self._irr_keys + + +class TestRunScopesCoverageByLocation: + """``BaseSatelliteJob.run`` must pass plan geohashes to the coverage + queries. Without this scope, named-location plans whose date range + overlaps the existing warehouse footprint pull millions of irrelevant + rows back from BigQuery before any API call is even issued. + + Regression guard for the A6 hang. + """ + + def test_named_locations_plan_includes_geohash_filter(self) -> None: + import pygeohash + + loc1 = LocationSpec(name="kampala", lat=0.333542, lon=32.568630) + loc2 = LocationSpec(name="lira", lat=2.295190, lon=32.921370) + gh1 = pygeohash.encode(loc1.lat, loc1.lon, precision=5) + gh2 = pygeohash.encode(loc2.lat, loc2.lon, precision=5) + date_range = DateRange(start=date(2024, 1, 1), end=date(2024, 1, 2)) + long_var = _temp_var() + irr_var = _ghi_var(Source.NASA_POWER) + + # Pre-populate "warehouse" with every (date, geohash, variable) tuple + # that the locations would need, so _location_fully_cached returns + # True for both and the run skips fetching. + all_dates = (date(2024, 1, 1), date(2024, 1, 2)) + full_long = { + (d, gh, v.variable_id) + for d in all_dates + for gh in (gh1, gh2) + for v in (long_var,) + } + full_irr = {(d, gh) for d in all_dates for gh in (gh1, gh2)} + bq = _RecordingBQForCoverage(full_long, full_irr) + + job = NasaPowerSatelliteJob(bq) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=date_range, + locations=(loc1, loc2), + variables=(long_var, irr_var), + ) + result = job.run(plan) + + # The job should have made coverage calls (one for long, one for + # irradiance) and skipped both locations. + assert ( + result.api_calls_made == 0 + ), "fully-cached locations must skip the API call" + assert result.extra["skipped_locations"] == 2 + + # Both coverage calls must include a geohash5 IN (...) filter + # with the plan's geohashes. + assert len(bq.coverage_calls) == 2 + for call in bq.coverage_calls: + joined = " ".join(call["where_filters"]) + assert ( + "geohash5 IN" in joined + ), f"coverage call missing geohash5 scope: {call!r}" + assert ( + gh1 in joined and gh2 in joined + ), f"coverage filter must include both plan geohashes: {call!r}" + + +class TestCamsUnitConversion: + """CAMS via pvlib returns daily irradiance as W/m² (mean over the + 24-hour period). The warehouse stores kWh/m²/day, so the loader must + multiply by 0.024 = 24 hours / 1000 W/kW. + + A6's first run hit production with the conversion missing, polluting + the warehouse with values 41.7× too large. This test pins the + contract so it can't regress. + """ + + def test_pvlib_w_per_m2_converted_to_kwh_per_m2_day(self) -> None: + # A typical clear-sky GHI in West Africa is ~250 W/m² mean (over + # the 24h period); the corresponding daily energy is ~6 kWh/m²/day. + cams_var = VariableSpec( + variable_id="ghi", + source=Source.CAMS, + api_code="ghi", + display_name="GHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="x", + ) + df = pd.DataFrame( + [ + {"timestamp": "2024-06-15T00:00:00Z", "ghi": 250.0}, + {"timestamp": "2024-06-16T00:00:00Z", "ghi": 200.0}, + ] + ) + long = CamsSatelliteJob._cams_dataframe_to_long(df, (cams_var,)) + assert len(long) == 2 + # 250 W/m² × 0.024 = 6.0 kWh/m²/day + assert long["value"].iloc[0] == pytest.approx(6.0, abs=1e-9) + assert long["value"].iloc[1] == pytest.approx(4.8, abs=1e-9) + + def test_conversion_factor_is_explicit(self) -> None: + # Pin the factor as a class constant so other code can reference + # it (e.g. the data-fix migration). 24 hours ÷ 1000 W/kW = 0.024. + assert CamsSatelliteJob._W_M2_TO_KWH_M2_DAY == pytest.approx(0.024) + + def test_zero_passes_through_unchanged(self) -> None: + # 0 W/m² → 0 kWh/m²/day; the conversion shouldn't introduce an + # offset. (Defensive: previous bugs in similar pipelines have + # added/subtracted constants.) + cams_var = VariableSpec( + variable_id="ghi_clear", + source=Source.CAMS, + api_code="ghi_clear", + display_name="GHI clear", + unit="kWh/m^2/day", + native_unit="Wh/m^2 (period)", + description="x", + ) + df = pd.DataFrame([{"timestamp": "2024-06-15T00:00:00Z", "ghi_clear": 0.0}]) + long = CamsSatelliteJob._cams_dataframe_to_long(df, (cams_var,)) + assert long["value"].iloc[0] == 0.0 + + +class TestRunRejectsBadPlan: + def test_run_raises_on_wrong_plan_type(self) -> None: + job = NasaPowerSatelliteJob(_StubBQ()) # type: ignore[arg-type] + + class FakePlan: + def describe(self) -> str: + return "fake" + + with pytest.raises(TypeError, match="NamedLocationsPlan or GridPlan"): + job.run(FakePlan()) # type: ignore[arg-type] + + def test_run_raises_on_source_mismatch(self) -> None: + from susse.warehouse_ops.population.types import NamedLocationsPlan + + job = NasaPowerSatelliteJob(_StubBQ()) # type: ignore[arg-type] + plan = NamedLocationsPlan( + source=Source.CAMS, # wrong source for NASA job + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 1)), + locations=(LocationSpec(name="x", lat=0.0, lon=0.0),), + variables=(_ghi_var(Source.CAMS),), + ) + with pytest.raises(ValueError, match="plan.source"): + job.run(plan) diff --git a/tests/warehouse_ops/population/jobs/test_satellite_loading.py b/tests/warehouse_ops/population/jobs/test_satellite_loading.py new file mode 100644 index 00000000..d89cc8fa --- /dev/null +++ b/tests/warehouse_ops/population/jobs/test_satellite_loading.py @@ -0,0 +1,67 @@ +"""Tests for the shared satellite-load helpers. + +The MERGE-load wrappers are exercised end-to-end by the per-job tests +(with stubbed BigQuery); this file pins the one pure, bug-prone helper — +the long → wide irradiance pivot — in isolation. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pygeohash + +from susse.warehouse_ops.population.jobs.satellite_loading import ( + irradiance_long_to_wide, +) + + +def _irradiance_row(variable_id: str, value: float) -> dict: + gh = pygeohash.encode(0.5, 32.5, 5) + return { + "date": date(2024, 1, 1), + "latitude": 0.5, + "longitude": 32.5, + "geohash5": gh, + "source": "NASA", + "variable_id": variable_id, + "value": value, + } + + +class TestIrradianceLongToWide: + def test_pivots_all_three_bands(self) -> None: + long_df = pd.DataFrame( + [ + _irradiance_row("ghi", 5.0), + _irradiance_row("dhi", 2.0), + _irradiance_row("dni", 6.5), + ] + ) + wide = irradiance_long_to_wide(long_df) + assert len(wide) == 1 + row = wide.iloc[0] + assert row["ghi_kwh_m2_day"] == 5.0 + assert row["dhi_kwh_m2_day"] == 2.0 + assert row["dni_kwh_m2_day"] == 6.5 + + def test_absent_band_and_reliability_are_null(self) -> None: + # Only ghi present → dhi/dni columns must still exist (NULL), as must + # reliability (no catalogue source publishes a reliability series). + wide = irradiance_long_to_wide(pd.DataFrame([_irradiance_row("ghi", 5.0)])) + row = wide.iloc[0] + assert row["ghi_kwh_m2_day"] == 5.0 + assert pd.isna(row["dhi_kwh_m2_day"]) + assert pd.isna(row["dni_kwh_m2_day"]) + assert pd.isna(row["reliability"]) + + def test_one_row_per_date_cell(self) -> None: + # Two days, one cell → two wide rows. + rows = [] + for day in (date(2024, 1, 1), date(2024, 1, 2)): + r = _irradiance_row("ghi", 5.0) + r["date"] = day + rows.append(r) + wide = irradiance_long_to_wide(pd.DataFrame(rows)) + assert len(wide) == 2 diff --git a/tests/warehouse_ops/population/test_adapters.py b/tests/warehouse_ops/population/test_adapters.py new file mode 100644 index 00000000..947ecb8b --- /dev/null +++ b/tests/warehouse_ops/population/test_adapters.py @@ -0,0 +1,42 @@ +"""Tests for ground-source adapters.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from susse.warehouse_ops.population.adapters import StandardCsvAdapter + + +class TestStandardCsvAdapter: + def test_adapter_id_includes_source(self) -> None: + adapter = StandardCsvAdapter(source_id="CBE") + assert adapter.adapter_id == "standard-csv:CBE" + + def test_rejects_empty_source_id(self) -> None: + with pytest.raises(ValueError, match="source_id"): + StandardCsvAdapter(source_id="") + + def test_parses_real_somalia_slice(self, somalia_csv_path: Path) -> None: + adapter = StandardCsvAdapter(source_id="CBE") + df = adapter.parse(somalia_csv_path) + + assert list(df.columns) == [ + "datetime", + "ghi", + "location", + "latitude", + "longitude", + ] + assert len(df) == 5 # the fixture is a 5-row slice + assert (df["location"] == "somalia_location1").all() + assert df["latitude"].between(3.0, 4.0).all() + assert df["longitude"].between(43.0, 44.0).all() + + def test_helpful_error_on_missing_columns(self, tmp_path: Path) -> None: + bad = tmp_path / "wrong_schema.csv" + bad.write_text("date,solar,site\n2024-01-01,5000,kampala\n") + adapter = StandardCsvAdapter(source_id="bad") + with pytest.raises(ValueError, match="write a new GroundSourceAdapter"): + adapter.parse(bad) diff --git a/tests/warehouse_ops/population/test_coverage.py b/tests/warehouse_ops/population/test_coverage.py new file mode 100644 index 00000000..e9b3654c --- /dev/null +++ b/tests/warehouse_ops/population/test_coverage.py @@ -0,0 +1,143 @@ +"""Tests for :class:`CoverageRepository`. + +The coverage queries decide which (location, date, variable) tuples a +satellite ingest job needs to fetch. They run *before* any API call, so a +buggy query that silently scans the whole warehouse can cost minutes per +station even when the location set is tiny. These tests lock in the +must-have filters — date, source, variable, and (when given) geohash5 — +in the generated SQL so a future refactor can't accidentally drop them. +""" + +from __future__ import annotations + +from datetime import date + +from susse.warehouse_ops.population.coverage import CoverageRepository +from susse.warehouse_ops.population.types import DateRange, Source + + +class _RecordingBQ: + """Captures every ``existing_keys`` call without executing anything.""" + + def __init__(self) -> None: + self.calls: list[dict] = [] + + def existing_keys( + self, + table_fqn: str, + key_columns, + *, + where_filters=(), + ) -> set[tuple]: + self.calls.append( + { + "table_fqn": table_fqn, + "key_columns": tuple(key_columns), + "where_filters": tuple(where_filters), + } + ) + return set() # pretend the warehouse is empty + + @property + def last_call(self) -> dict: + assert self.calls, "no BQ call recorded" + return self.calls[-1] + + +def _date_range() -> DateRange: + return DateRange(start=date(2024, 1, 1), end=date(2024, 1, 31)) + + +class TestExistingLongKeys: + """``existing_long_keys`` builds a scoped coverage query.""" + + def test_includes_date_source_and_variable_filters(self) -> None: + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + repo.existing_long_keys( + "p.d.nasa_long", + date_range=_date_range(), + source=Source.NASA_POWER, + variable_ids=("temperature", "wind_speed"), + ) + filters = bq.last_call["where_filters"] + joined = " ".join(filters) + assert "date BETWEEN DATE('2024-01-01') AND DATE('2024-01-31')" in joined + assert "source = 'NASA'" in joined + assert "variable_id IN ('temperature', 'wind_speed')" in joined + # No geohash filter when geohash5s is not passed (preserves legacy + # behaviour for grid plans where every grid point is in scope). + assert not any("geohash5" in f for f in filters) + + def test_geohash5s_added_to_filter_when_provided(self) -> None: + """The point of A6's coverage fix: a small location set scopes the scan. + + Without this filter, named-location plans whose date range overlaps + the existing warehouse footprint trigger multi-million-row scans. + """ + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + repo.existing_long_keys( + "p.d.nasa_long", + date_range=_date_range(), + source=Source.NASA_POWER, + variable_ids=("temperature",), + geohash5s=("s8p1v", "kzcw8"), + ) + joined = " ".join(bq.last_call["where_filters"]) + assert "geohash5 IN ('s8p1v', 'kzcw8')" in joined + + def test_empty_geohash5s_skips_bq(self) -> None: + """No locations to ingest → no rows can match, so don't even ask BQ.""" + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + result = repo.existing_long_keys( + "p.d.nasa_long", + date_range=_date_range(), + source=Source.NASA_POWER, + variable_ids=("temperature",), + geohash5s=(), + ) + assert result == set() + assert bq.calls == [], "empty geohash5s must short-circuit the BQ call" + + +class TestExistingIrradianceKeys: + """``existing_irradiance_keys`` mirrors the long-keys contract.""" + + def test_includes_date_and_source_filters(self) -> None: + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + repo.existing_irradiance_keys( + "p.d.irr", + date_range=_date_range(), + source=Source.CAMS, + ) + joined = " ".join(bq.last_call["where_filters"]) + assert "date BETWEEN DATE('2024-01-01') AND DATE('2024-01-31')" in joined + assert "source = 'CAMS'" in joined + assert not any("geohash5" in f for f in bq.last_call["where_filters"]) + + def test_geohash5s_added_to_filter_when_provided(self) -> None: + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + repo.existing_irradiance_keys( + "p.d.irr", + date_range=_date_range(), + source=Source.CAMS, + geohash5s=("s8p1v",), + ) + joined = " ".join(bq.last_call["where_filters"]) + assert "geohash5 IN ('s8p1v')" in joined + + def test_empty_geohash5s_skips_bq(self) -> None: + bq = _RecordingBQ() + repo = CoverageRepository(bq) # type: ignore[arg-type] + result = repo.existing_irradiance_keys( + "p.d.irr", + date_range=_date_range(), + source=Source.CAMS, + geohash5s=(), + ) + assert result == set() + assert bq.calls == [] diff --git a/tests/warehouse_ops/population/test_curation.py b/tests/warehouse_ops/population/test_curation.py new file mode 100644 index 00000000..e477f650 --- /dev/null +++ b/tests/warehouse_ops/population/test_curation.py @@ -0,0 +1,85 @@ +"""Tests for the curate_ground transformation against real data.""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.warehouse_ops.population.curation import CurationOptions, curate_ground + + +class TestCurationOptions: + @pytest.mark.parametrize("precision", [0, -1, 13]) + def test_rejects_out_of_range_precision(self, precision: int) -> None: + with pytest.raises(ValueError, match=r"outside \[1, 12\]"): + CurationOptions(geohash_precision=precision) + + def test_rejects_empty_qc_level(self) -> None: + with pytest.raises(ValueError, match="qc_level"): + CurationOptions(qc_level="") + + def test_rejects_empty_version(self) -> None: + with pytest.raises(ValueError, match="version"): + CurationOptions(version="") + + +class TestCurateGround: + def test_row_count_preserved(self, somalia_raw_df: pd.DataFrame) -> None: + curated = curate_ground(somalia_raw_df) + assert len(curated) == len(somalia_raw_df) + + def test_curated_schema_columns(self, somalia_raw_df: pd.DataFrame) -> None: + curated = curate_ground(somalia_raw_df) + expected = { + "date", + "month", + "location", + "lat", + "lon", + "geohash5", + "ghi_wh_m2_day", + "ghi_kwh_m2_day", + "qc_level", + "_version", + "_curated_at", + } + assert set(curated.columns) == expected + + def test_geohash_matches_warehouse_for_somalia( + self, somalia_raw_df: pd.DataFrame + ) -> None: + # The warehouse stores geohash5='sbx18' for somalia_location1 + # (verified directly via BQ query). Round-tripping through pygeohash + # at precision=5 must produce the same value. + curated = curate_ground(somalia_raw_df) + assert (curated["geohash5"] == "sbx18").all() + + def test_unit_relationship(self, somalia_raw_df: pd.DataFrame) -> None: + # By construction kWh/m²/day = Wh/m²/day / 1000. The validator + # enforces this; this test asserts the relationship is exact (within + # floating-point tolerance) on real values. + curated = curate_ground(somalia_raw_df) + diff = (curated["ghi_kwh_m2_day"] * 1000.0) - curated["ghi_wh_m2_day"] + assert diff.abs().max() < 1e-6 + + def test_month_is_first_of_month(self, somalia_raw_df: pd.DataFrame) -> None: + curated = curate_ground(somalia_raw_df) + for d, m in zip(curated["date"], curated["month"]): + assert m == date(d.year, d.month, 1) + + def test_qc_level_and_version_constants_applied( + self, somalia_raw_df: pd.DataFrame + ) -> None: + opts = CurationOptions(qc_level="manual", version="v2") + curated = curate_ground(somalia_raw_df, opts) + assert (curated["qc_level"] == "manual").all() + assert (curated["_version"] == "v2").all() + + def test_curation_is_deterministic_modulo_timestamp( + self, somalia_raw_df: pd.DataFrame + ) -> None: + a = curate_ground(somalia_raw_df).drop(columns=["_curated_at"]) + b = curate_ground(somalia_raw_df).drop(columns=["_curated_at"]) + pd.testing.assert_frame_equal(a, b) diff --git a/tests/warehouse_ops/population/test_dim_variable.py b/tests/warehouse_ops/population/test_dim_variable.py new file mode 100644 index 00000000..7fdfc57e --- /dev/null +++ b/tests/warehouse_ops/population/test_dim_variable.py @@ -0,0 +1,93 @@ +"""Tests for the variable catalogue.""" + +from __future__ import annotations + +import pytest + +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + variables_to_dataframe, +) +from susse.warehouse_ops.population.types import Source + + +class TestVariableCatalogPartitioning: + def test_irradiance_and_auxiliary_partition_for_nasa(self) -> None: + all_nasa = set(VariableCatalog.for_source(Source.NASA_POWER)) + irr = set(VariableCatalog.irradiance_for_source(Source.NASA_POWER)) + aux = set(VariableCatalog.auxiliary_for_source(Source.NASA_POWER)) + assert irr & aux == set() # disjoint + assert irr | aux == all_nasa # cover everything + + def test_irradiance_and_auxiliary_partition_for_cams(self) -> None: + all_cams = set(VariableCatalog.for_source(Source.CAMS)) + irr = set(VariableCatalog.irradiance_for_source(Source.CAMS)) + aux = set(VariableCatalog.auxiliary_for_source(Source.CAMS)) + assert irr & aux == set() + assert irr | aux == all_cams + + def test_irradiance_variables_are_ghi_dhi_dni(self) -> None: + for source in (Source.NASA_POWER, Source.CAMS): + ids = {v.variable_id for v in VariableCatalog.irradiance_for_source(source)} + assert ids == {"ghi", "dhi", "dni"} + + +class TestVariableCatalogGet: + def test_finds_known_variable(self) -> None: + v = VariableCatalog.get(variable_id="temperature", source=Source.NASA_POWER) + assert v.api_code == "T2M" + assert v.unit == "degC" + + def test_raises_keyerror_on_unknown(self) -> None: + with pytest.raises(KeyError, match="No variable"): + VariableCatalog.get(variable_id="not_real", source=Source.NASA_POWER) + + def test_distinguishes_same_id_across_sources(self) -> None: + nasa_ghi = VariableCatalog.get(variable_id="ghi", source=Source.NASA_POWER) + cams_ghi = VariableCatalog.get(variable_id="ghi", source=Source.CAMS) + # Same variable_id, different source → different entries. + assert nasa_ghi.api_code != cams_ghi.api_code + assert nasa_ghi is not cams_ghi + + +class TestVariableSpatialResolution: + def test_nasa_power_native_grid_is_positive(self) -> None: + # Replaces the warehouse's existing -51.0 sentinel bug. + for v in VariableCatalog.NASA_POWER_VARIABLES: + assert v.spatial_resolution_km is not None + assert v.spatial_resolution_km > 0 + + def test_cams_native_grid_is_positive(self) -> None: + for v in VariableCatalog.CAMS_VARIABLES: + assert v.spatial_resolution_km is not None + assert v.spatial_resolution_km > 0 + + +class TestVariablesToDataframe: + def test_columns_match_dim_variable_schema(self) -> None: + df = variables_to_dataframe(VariableCatalog.all_variables()) + expected = { + "variable_id", + "source", + "display_name", + "unit", + "native_unit", + "description", + "temporal_granularity", + "spatial_resolution_km", + "valid_min", + "valid_max", + "physical_storage", + } + assert set(df.columns) == expected + + def test_one_row_per_variable(self) -> None: + df = variables_to_dataframe(VariableCatalog.all_variables()) + assert len(df) == len(VariableCatalog.all_variables()) + + def test_source_values_are_string(self) -> None: + # dim_variable.source is STRING in BQ; the dataframe must serialise + # the StrEnum members as plain strings, not enum objects. + df = variables_to_dataframe(VariableCatalog.all_variables()) + assert df["source"].dtype == object + assert all(isinstance(v, str) for v in df["source"]) diff --git a/tests/warehouse_ops/population/test_loaders.py b/tests/warehouse_ops/population/test_loaders.py new file mode 100644 index 00000000..3832f56f --- /dev/null +++ b/tests/warehouse_ops/population/test_loaders.py @@ -0,0 +1,259 @@ +"""Tests for the MergeLoader column-validation contract. + +These tests cover the loader's input-validation logic; the actual MERGE +SQL execution requires a live BigQuery and is exercised by the notebook ++ jobs end-to-end against the real warehouse. +""" + +from __future__ import annotations + +from datetime import date + +import pandas as pd +import pytest + +from susse.warehouse_ops.io.config import TableSchemas +from susse.warehouse_ops.population.loaders import ( + DerivedColumn, + MergeLoader, + MergeSpec, + _date_chunks, +) + + +class _FakeBQ: + """Stand-in for BigQueryClient that records calls without executing. + + The chunked MERGE path issues a ``SELECT MIN(date), MAX(date)`` against + the staging table to decide how many chunks are needed. In tests we + answer that query from the most recently loaded DataFrame so the + derived span is consistent with what the test set up. + """ + + def __init__(self) -> None: + self.loaded: list[tuple[pd.DataFrame, str]] = [] + self.ddl_statements: list[str] = [] + self.queries: list[str] = [] + + def load_dataframe(self, df: pd.DataFrame, table_fqn: str, **_kwargs) -> None: + self.loaded.append((df.copy(), table_fqn)) + + def execute_ddl(self, ddl: str) -> None: + self.ddl_statements.append(ddl) + + def query(self, sql: str, **_kwargs) -> pd.DataFrame: + self.queries.append(sql) + # Only the date-span query is expected here; answer it from the + # most recently staged DataFrame. + if "MIN(date)" in sql and "MAX(date)" in sql and self.loaded: + df = self.loaded[-1][0] + if "date" in df.columns and not df.empty: + return pd.DataFrame( + [{"min_d": df["date"].min(), "max_d": df["date"].max()}] + ) + return pd.DataFrame([{"min_d": pd.NaT, "max_d": pd.NaT}]) + + +class TestMergeLoaderValidation: + def test_empty_frame_is_no_op(self) -> None: + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.ground_measurements", + spec=MergeSpec(schema=TableSchemas.GROUND_MEASUREMENTS), + ) + n = loader.load(pd.DataFrame()) + assert n == 0 + assert bq.loaded == [] + assert bq.ddl_statements == [] + + def test_rejects_missing_merge_keys(self) -> None: + # GROUND_MEASUREMENTS keys on (date, location). A frame missing + # 'location' must fail validation before any BQ call. + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.ground_measurements", + spec=MergeSpec(schema=TableSchemas.GROUND_MEASUREMENTS), + ) + df = pd.DataFrame([{"date": date(2024, 1, 1), "ghi_kwh_m2_day": 5.0}]) + with pytest.raises(ValueError, match="merge-key columns"): + loader.load(df) + assert bq.loaded == [] + + def test_rejects_derived_column_already_present(self) -> None: + # Derived columns are computed server-side; the client must not + # ALSO carry a column with the same name. + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.t", + spec=MergeSpec( + schema=TableSchemas.GROUND_MEASUREMENTS, + derived_columns=( + DerivedColumn(name="geog", sql_expr="ST_GEOGPOINT(lon, lat)"), + ), + ), + ) + df = pd.DataFrame( + [ + { + "date": date(2024, 1, 1), + "location": "x", + "geog": "POINT(0 0)", + } + ] + ) + with pytest.raises(ValueError, match="would derive server-side"): + loader.load(df) + + +class TestMergeLoaderSql: + def test_merge_sql_includes_derived_column(self) -> None: + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.t", + spec=MergeSpec( + schema=TableSchemas.GROUND_MEASUREMENTS, + derived_columns=( + DerivedColumn(name="geog", sql_expr="ST_GEOGPOINT(lon, lat)"), + ), + ), + ) + df = pd.DataFrame( + [ + { + "date": date(2024, 1, 1), + "location": "kampala", + "lat": 0.333542, + "lon": 32.56863, + } + ] + ) + loader.load(df) + + # Staging load happened, then create / merge / drop as three + # separate DDL statements (BQ rejects DDL inside multi-statement + # transactions, so the loader splits them). + assert len(bq.loaded) == 1 + assert len(bq.ddl_statements) == 3 + create_sql, merge_sql, drop_sql = bq.ddl_statements + assert create_sql.startswith("CREATE TABLE IF NOT EXISTS `x.y.t`") + assert "MERGE `x.y.t` t" in merge_sql + assert "ST_GEOGPOINT(lon, lat) AS `geog`" in merge_sql + # MERGE keys for ground_measurements are (date, location). + assert "t.`date` = s.`date`" in merge_sql + assert "t.`location` = s.`location`" in merge_sql + assert drop_sql == "DROP TABLE `x.y.t_staging`;" + + +class TestDateChunks: + """Helper that splits a date span into BQ-partition-safe chunks.""" + + def test_single_day_one_chunk(self) -> None: + d = date(2024, 1, 1) + assert _date_chunks(d, d, max_days=3500) == [(d, d)] + + def test_span_below_limit_one_chunk(self) -> None: + chunks = _date_chunks(date(2024, 1, 1), date(2024, 6, 30), max_days=3500) + assert chunks == [(date(2024, 1, 1), date(2024, 6, 30))] + + def test_span_above_limit_splits_with_full_coverage(self) -> None: + # 4310 days is the kampala span that triggered BQ's 4000-partition cap. + start, end = date(2011, 4, 6), date(2023, 1, 22) + chunks = _date_chunks(start, end, max_days=3500) + # Each chunk no larger than the limit. + for cs, ce in chunks: + assert (ce - cs).days + 1 <= 3500 + # Chunks are contiguous and cover the full span. + assert chunks[0][0] == start + assert chunks[-1][1] == end + for prev, curr in zip(chunks, chunks[1:]): + from datetime import timedelta + + assert curr[0] == prev[1] + timedelta(days=1) + # 4310 days / 3500 max → 2 chunks. + assert len(chunks) == 2 + + def test_rejects_inverted_range(self) -> None: + with pytest.raises(ValueError, match="must be <="): + _date_chunks(date(2024, 1, 31), date(2024, 1, 1), max_days=10) + + def test_rejects_zero_max_days(self) -> None: + with pytest.raises(ValueError, match="max_days"): + _date_chunks(date(2024, 1, 1), date(2024, 1, 31), max_days=0) + + +class TestMergeChunkingByPartition: + """The MERGE must split into multiple statements when the staging + spans more partitions than BigQuery accepts in a single DML. + + This is the regression guard for the kampala (4310-day) failure: + one MERGE statement against a date-partitioned table can only touch + 4,000 partitions. The loader chunks under that cap automatically. + """ + + def _make_long_span_df(self, *, start: date, end: date) -> pd.DataFrame: + """One row at start and one at end — same shape as a long-history + load, just minimal to keep the fixture small. + """ + return pd.DataFrame( + [ + { + "date": d, + "geohash5": "s8p1v", + "variable_id": "ghi", + "value": 5.0, + "source": "NASA", + "latitude": 0.33, + "longitude": 32.57, + } + for d in (start, end) + ] + ) + + def test_short_span_runs_one_merge(self) -> None: + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.nasa_long", + spec=MergeSpec(schema=TableSchemas.NASA_DAILY_VARS_LONG), + ) + loader.load( + self._make_long_span_df( + start=date(2024, 1, 1), + end=date(2024, 6, 30), + ) + ) + merge_statements = [s for s in bq.ddl_statements if "MERGE" in s] + assert ( + len(merge_statements) == 1 + ), "spans below the partition cap should issue a single MERGE" + + def test_kampala_span_splits_into_multiple_merges(self) -> None: + bq = _FakeBQ() + loader = MergeLoader( + bq=bq, # type: ignore[arg-type] + table_fqn="x.y.nasa_long", + spec=MergeSpec(schema=TableSchemas.NASA_DAILY_VARS_LONG), + ) + # 4310 days — the exact span that originally tripped BQ's + # 4000-partition-per-DML limit on irradiance_daily. + loader.load( + self._make_long_span_df( + start=date(2011, 4, 6), + end=date(2023, 1, 22), + ) + ) + merge_statements = [s for s in bq.ddl_statements if "MERGE" in s] + assert len(merge_statements) >= 2, ( + "spans above the partition cap must be split into multiple " + "MERGE statements" + ) + # Every chunked MERGE must filter by date BETWEEN ... so each + # statement only touches the partitions in its own chunk. + for sql in merge_statements: + assert ( + "date BETWEEN DATE(" in sql + ), f"chunked MERGE missing date filter: {sql!r}" diff --git a/tests/warehouse_ops/population/test_types.py b/tests/warehouse_ops/population/test_types.py new file mode 100644 index 00000000..36deb83d --- /dev/null +++ b/tests/warehouse_ops/population/test_types.py @@ -0,0 +1,216 @@ +"""Tests for the typed value objects in warehouse_ops.population.types.""" + +from __future__ import annotations + +from datetime import date +from pathlib import Path + +import pytest + +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + GridPlan, + GridSpec, + GroundFilePlan, + LocationSpec, + NamedLocationsPlan, + Source, + VariableSpec, +) + +# --------------------------------------------------------------------------- +# DateRange +# --------------------------------------------------------------------------- + + +class TestDateRange: + def test_n_days_inclusive(self) -> None: + dr = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)) + assert dr.n_days == 7 # both endpoints inclusive + + def test_single_day(self) -> None: + dr = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 1)) + assert dr.n_days == 1 + + def test_rejects_start_after_end(self) -> None: + with pytest.raises(ValueError, match="is after end"): + DateRange(start=date(2025, 1, 7), end=date(2025, 1, 1)) + + +# --------------------------------------------------------------------------- +# LocationSpec +# --------------------------------------------------------------------------- + + +class TestLocationSpec: + def test_valid_construction(self) -> None: + loc = LocationSpec(name="kampala", lat=0.333542, lon=32.56863) + assert loc.name == "kampala" + + def test_rejects_empty_name(self) -> None: + with pytest.raises(ValueError, match="name must be non-empty"): + LocationSpec(name="", lat=0.0, lon=0.0) + + @pytest.mark.parametrize("lat", [-91.0, 91.0, 200.0, -200.0]) + def test_rejects_out_of_range_latitude(self, lat: float) -> None: + with pytest.raises(ValueError, match=r"outside \[-90, 90\]"): + LocationSpec(name="x", lat=lat, lon=0.0) + + @pytest.mark.parametrize("lon", [-181.0, 181.0, 360.0]) + def test_rejects_out_of_range_longitude(self, lon: float) -> None: + with pytest.raises(ValueError, match=r"outside \[-180, 180\]"): + LocationSpec(name="x", lat=0.0, lon=lon) + + +# --------------------------------------------------------------------------- +# BoundingBox + GridSpec +# --------------------------------------------------------------------------- + + +class TestBoundingBox: + def test_valid_box(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=1.0, min_lon=32.0, max_lon=33.0) + assert bbox.min_lat == 0.0 + + def test_rejects_inverted_lat(self) -> None: + with pytest.raises(ValueError, match="latitude range"): + BoundingBox(min_lat=10.0, max_lat=0.0, min_lon=0.0, max_lon=1.0) + + def test_rejects_inverted_lon(self) -> None: + with pytest.raises(ValueError, match="longitude range"): + BoundingBox(min_lat=0.0, max_lat=1.0, min_lon=33.0, max_lon=32.0) + + +class TestGridSpec: + def test_n_points_matches_iter_count(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=0.5, min_lon=32.0, max_lon=32.5) + grid = GridSpec(bbox=bbox, lat_step=0.05, lon_step=0.05) + assert grid.n_points == len(list(grid.iter_locations())) + + def test_grid_endpoints_inclusive(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=1.0, min_lon=0.0, max_lon=1.0) + grid = GridSpec(bbox=bbox, lat_step=0.5, lon_step=0.5) + # min and max lat/lon both produce points → 3×3 = 9 points + locations = list(grid.iter_locations()) + assert len(locations) == 9 + lats = sorted({loc.lat for loc in locations}) + lons = sorted({loc.lon for loc in locations}) + assert lats == [0.0, 0.5, 1.0] + assert lons == [0.0, 0.5, 1.0] + + def test_rejects_zero_step(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=1.0, min_lon=0.0, max_lon=1.0) + with pytest.raises(ValueError, match="must be positive"): + GridSpec(bbox=bbox, lat_step=0.0, lon_step=0.5) + + def test_grid_locations_have_unique_names(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=0.1, min_lon=0.0, max_lon=0.1) + grid = GridSpec(bbox=bbox, lat_step=0.05, lon_step=0.05) + names = [loc.name for loc in grid.iter_locations()] + assert len(names) == len(set(names)) + + +# --------------------------------------------------------------------------- +# Plans +# --------------------------------------------------------------------------- + + +def _kampala_loc() -> LocationSpec: + return LocationSpec(name="kampala", lat=0.333542, lon=32.56863) + + +def _nasa_var() -> VariableSpec: + return VariableSpec( + variable_id="temperature", + source=Source.NASA_POWER, + api_code="T2M", + display_name="2m Temperature", + unit="degC", + native_unit="degC", + description="Test", + ) + + +def _cams_var() -> VariableSpec: + return VariableSpec( + variable_id="ghi", + source=Source.CAMS, + api_code="ghi", + display_name="GHI", + unit="kWh/m^2/day", + native_unit="Wh/m^2", + description="Test", + ) + + +class TestNamedLocationsPlan: + def test_valid_plan(self) -> None: + plan = NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)), + locations=(_kampala_loc(),), + variables=(_nasa_var(),), + ) + assert f"source={Source.NASA_POWER.value}" in plan.describe() + assert "locations=1" in plan.describe() + + def test_rejects_empty_locations(self) -> None: + with pytest.raises(ValueError, match="at least one location"): + NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)), + locations=(), + variables=(_nasa_var(),), + ) + + def test_rejects_empty_variables(self) -> None: + with pytest.raises(ValueError, match="at least one variable"): + NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)), + locations=(_kampala_loc(),), + variables=(), + ) + + def test_rejects_source_mismatch(self) -> None: + with pytest.raises(ValueError, match="must match the plan's source"): + NamedLocationsPlan( + source=Source.NASA_POWER, + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)), + locations=(_kampala_loc(),), + variables=(_cams_var(),), # CAMS variable in NASA plan + ) + + +class TestGridPlan: + def test_valid_plan(self) -> None: + bbox = BoundingBox(min_lat=0.0, max_lat=0.5, min_lon=32.0, max_lon=32.5) + plan = GridPlan( + source=Source.CAMS, + date_range=DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7)), + grid=GridSpec(bbox=bbox, lat_step=0.05, lon_step=0.05), + variables=(_cams_var(),), + ) + assert "CAMS" in plan.describe() + assert "points=" in plan.describe() + + +class TestGroundFilePlan: + def test_valid_plan(self, tmp_path: Path) -> None: + # A real (empty) file on disk satisfies the existence check; the + # adapter handles content parsing separately. + f = tmp_path / "x.csv" + f.write_text("datetime,ghi,location,latitude,longitude\n") + plan = GroundFilePlan( + source_id="test", file_path=f, adapter_id="standard-csv:test" + ) + assert "test" in plan.describe() + + def test_rejects_missing_file(self, tmp_path: Path) -> None: + with pytest.raises(ValueError, match="does not exist"): + GroundFilePlan( + source_id="x", + file_path=tmp_path / "nope.csv", + adapter_id="x", + ) diff --git a/tests/warehouse_ops/population/test_validators.py b/tests/warehouse_ops/population/test_validators.py new file mode 100644 index 00000000..56d621b8 --- /dev/null +++ b/tests/warehouse_ops/population/test_validators.py @@ -0,0 +1,126 @@ +"""Tests for warehouse_ops.population.validators.""" + +from __future__ import annotations + +from datetime import date + +import numpy as np +import pandas as pd +import pytest + +from susse.warehouse_ops.population.validators import ( + validate_ground_curated, + validate_ground_raw, + validate_long_format, +) + + +def _valid_long_row() -> dict: + return { + "date": date(2025, 1, 1), + "latitude": 0.0, + "longitude": 32.0, + "geohash5": "s8p1v", + "variable_id": "temperature", + "value": 25.0, + "source": "NASA", + } + + +class TestValidateLongFormat: + def test_valid_frame_passes(self) -> None: + df = pd.DataFrame([_valid_long_row()]) + validate_long_format(df) # should not raise + + @pytest.mark.parametrize( + "missing_col", + ["date", "latitude", "longitude", "geohash5", "variable_id", "value", "source"], + ) + def test_rejects_missing_required_column(self, missing_col: str) -> None: + row = _valid_long_row() + df = pd.DataFrame([row]).drop(columns=[missing_col]) + with pytest.raises(ValueError, match="missing required columns"): + validate_long_format(df) + + def test_rejects_empty_frame(self) -> None: + df = pd.DataFrame(columns=list(_valid_long_row().keys())) + with pytest.raises(ValueError, match="empty"): + validate_long_format(df) + + def test_rejects_all_nan_values(self) -> None: + row = _valid_long_row() + row["value"] = np.nan + df = pd.DataFrame([row]) + with pytest.raises(ValueError, match="value=NaN"): + validate_long_format(df) + + +def _valid_raw_row() -> dict: + return { + "datetime": date(2024, 1, 28), + "ghi": 5550.759, + "location": "somalia_location1", + "latitude": 3.107191, + "longitude": 43.637153, + } + + +class TestValidateGroundRaw: + def test_valid_frame_passes(self) -> None: + df = pd.DataFrame([_valid_raw_row()]) + validate_ground_raw(df) + + def test_rejects_missing_columns(self) -> None: + df = pd.DataFrame([_valid_raw_row()]).drop(columns=["latitude"]) + with pytest.raises(ValueError, match="missing required columns"): + validate_ground_raw(df) + + def test_rejects_lat_out_of_range(self) -> None: + row = _valid_raw_row() + row["latitude"] = 95.0 # over the pole + df = pd.DataFrame([row]) + with pytest.raises(ValueError, match="latitude outside"): + validate_ground_raw(df) + + def test_rejects_lon_out_of_range(self) -> None: + row = _valid_raw_row() + row["longitude"] = -250.0 + df = pd.DataFrame([row]) + with pytest.raises(ValueError, match="longitude outside"): + validate_ground_raw(df) + + +def _valid_curated_row() -> dict: + return { + "date": date(2024, 1, 28), + "month": date(2024, 1, 1), + "location": "somalia_location1", + "lat": 3.107191, + "lon": 43.637153, + "geohash5": "sbx18", + "ghi_wh_m2_day": 5550.759, + "ghi_kwh_m2_day": 5.550759, + "qc_level": "auto", + "_version": "v1", + "_curated_at": pd.Timestamp.utcnow(), + } + + +class TestValidateGroundCurated: + def test_valid_frame_passes(self) -> None: + df = pd.DataFrame([_valid_curated_row()]) + validate_ground_curated(df) + + def test_rejects_unit_mismatch(self) -> None: + row = _valid_curated_row() + row["ghi_kwh_m2_day"] = 999.0 # disagrees with ghi_wh_m2_day / 1000 + df = pd.DataFrame([row]) + with pytest.raises( + ValueError, match="ghi_kwh_m2_day and ghi_wh_m2_day disagree" + ): + validate_ground_curated(df) + + def test_rejects_missing_provenance_column(self) -> None: + df = pd.DataFrame([_valid_curated_row()]).drop(columns=["_curated_at"]) + with pytest.raises(ValueError, match="missing required columns"): + validate_ground_curated(df) diff --git a/tests/warehouse_ops/test_snapping.py b/tests/warehouse_ops/test_snapping.py new file mode 100644 index 00000000..4806f704 --- /dev/null +++ b/tests/warehouse_ops/test_snapping.py @@ -0,0 +1,146 @@ +"""Tests for :mod:`susse.warehouse_ops.snapping`. + +The snapper is the single boundary that turns an arbitrary query coordinate +into a warehouse ``geohash5``. The contracts that matter: + +* a query point snaps to the genuinely-nearest stored cell, and +* a point outside the ingested footprint is reported as out of coverage + (``None`` / :class:`OutOfCoverageError`) rather than snapped to a far, + unrelated cell. +""" + +from __future__ import annotations + +import pandas as pd +import pygeohash +import pytest + +from susse.warehouse_ops.snapping import NearestPixelSnapper, OutOfCoverageError + +# A patch of NASA POWER's MERRA-2 native grid (0.5° lat × 0.625° lon) over +# Uganda. Real grid geometry — the snapper carries no hard-coded grid, so +# this doubles as both fixture and the thing under test. +_GRID_LATS = [0.0, 0.5, 1.0] +_GRID_LONS = [32.5, 33.125, 33.75] + + +def _grid_pixels() -> tuple[list[str], list[float], list[float]]: + lats: list[float] = [] + lons: list[float] = [] + ghs: list[str] = [] + for lat in _GRID_LATS: + for lon in _GRID_LONS: + lats.append(lat) + lons.append(lon) + ghs.append(pygeohash.encode(lat, lon, precision=5)) + return ghs, lats, lons + + +@pytest.fixture +def grid_snapper() -> NearestPixelSnapper: + ghs, lats, lons = _grid_pixels() + return NearestPixelSnapper(geohash5s=ghs, latitudes=lats, longitudes=lons) + + +class TestSnap: + def test_exact_pixel_snaps_to_itself( + self, grid_snapper: NearestPixelSnapper + ) -> None: + # A coordinate sitting exactly on a pixel centre must return that + # pixel's geohash5 — the degenerate, zero-distance case. + expected = pygeohash.encode(0.5, 33.125, precision=5) + assert grid_snapper.snap(0.5, 33.125) == expected + + def test_off_grid_point_snaps_to_nearest_centre( + self, grid_snapper: NearestPixelSnapper + ) -> None: + # (0.3, 32.6) is closest to the (0.5, 32.5) pixel centre, not to the + # (0.0, 32.5) one below it nor the (0.5, 33.125) one to its east. + assert grid_snapper.snap(0.3, 32.6) == pygeohash.encode(0.5, 32.5, precision=5) + + def test_out_of_coverage_point_raises( + self, grid_snapper: NearestPixelSnapper + ) -> None: + # A point thousands of km from every pixel must not snap silently. + with pytest.raises(OutOfCoverageError, match="outside the source's"): + grid_snapper.snap(40.0, 10.0) + + +class TestSnapOrNone: + def test_batch_matches_per_point_snap( + self, grid_snapper: NearestPixelSnapper + ) -> None: + lats = [0.0, 0.3, 0.9] + lons = [32.5, 32.6, 33.7] + batch = grid_snapper.snap_or_none(lats, lons) + per_point = [grid_snapper.snap(la, lo) for la, lo in zip(lats, lons)] + assert batch == per_point + + def test_empty_batch_returns_empty(self, grid_snapper: NearestPixelSnapper) -> None: + assert grid_snapper.snap_or_none([], []) == [] + + def test_mismatched_lengths_raise(self, grid_snapper: NearestPixelSnapper) -> None: + with pytest.raises(ValueError, match="must align"): + grid_snapper.snap_or_none([0.0, 0.5], [32.5]) + + def test_out_of_coverage_points_become_none( + self, grid_snapper: NearestPixelSnapper + ) -> None: + # In-coverage points snap; far points yield None — the batch keeps + # input order so a caller can tell exactly which points missed. + result = grid_snapper.snap_or_none([0.3, 40.0, 0.9], [32.6, 10.0, 33.7]) + assert result[0] == pygeohash.encode(0.5, 32.5, precision=5) + assert result[1] is None + assert result[2] == pygeohash.encode(1.0, 33.75, precision=5) + + +class TestConstruction: + def test_mismatched_input_lengths_raise(self) -> None: + with pytest.raises(ValueError, match="differ in length"): + NearestPixelSnapper( + geohash5s=["a", "b"], latitudes=[0.0], longitudes=[32.5] + ) + + def test_empty_pixel_set_reports_no_coverage(self) -> None: + # A source with no warehouse rows yields a snapper that misses every + # point — an explicit cache-miss signal, never a wrong snap. + snapper = NearestPixelSnapper(geohash5s=[], latitudes=[], longitudes=[]) + assert snapper.n_pixels == 0 + assert snapper.snap_or_none([0.3, 1.0], [32.6, 33.0]) == [None, None] + with pytest.raises(OutOfCoverageError): + snapper.snap(0.3, 32.6) + + def test_out_of_range_latitude_raises(self) -> None: + with pytest.raises(ValueError, match=r"latitudes.*\[-90, 90\]"): + NearestPixelSnapper(geohash5s=["a"], latitudes=[123.0], longitudes=[32.5]) + + def test_non_positive_guard_raises(self) -> None: + with pytest.raises(ValueError, match="must be positive"): + NearestPixelSnapper( + geohash5s=["a"], latitudes=[0.0], longitudes=[32.5], max_snap_km=0.0 + ) + + +class TestFromDataFrame: + def test_missing_column_raises(self) -> None: + df = pd.DataFrame({"geohash5": ["a"], "latitude": [0.0]}) # no longitude + with pytest.raises(ValueError, match="missing column"): + NearestPixelSnapper.from_dataframe(df) + + def test_round_trips_through_dataframe(self) -> None: + ghs, lats, lons = _grid_pixels() + df = pd.DataFrame({"geohash5": ghs, "latitude": lats, "longitude": lons}) + snapper = NearestPixelSnapper.from_dataframe(df) + assert snapper.n_pixels == len(ghs) + assert snapper.snap(0.3, 32.6) == pygeohash.encode(0.5, 32.5, precision=5) + + +def test_guard_distance_admits_in_grid_query() -> None: + # The default 120 km guard must comfortably admit a point at the worst + # case for a 1° (CERES) grid: the dead centre of a cell, ~78 km from the + # four surrounding pixel centres. + ghs = [pygeohash.encode(la, lo, 5) for la in (0.5, 1.5) for lo in (32.5, 33.5)] + lats = [la for la in (0.5, 1.5) for _ in (32.5, 33.5)] + lons = [lo for _ in (0.5, 1.5) for lo in (32.5, 33.5)] + snapper = NearestPixelSnapper(geohash5s=ghs, latitudes=lats, longitudes=lons) + assert snapper.snap(1.0, 33.0) in ghs diff --git a/tox.ini b/tox.ini index 1904924d..f7df27e2 100644 --- a/tox.ini +++ b/tox.ini @@ -1,89 +1,68 @@ [tox] -envlist = py312 -skipsdist = true +envlist = py312, check-format, static-analysis +isolated_build = true +skipsdist = false [testenv] +description = Run unit tests with coverage. passenv = NASA_USERNAME NASA_PASSWORD -allowlist_externals = - pytest + EARTHDATA_USERNAME + EARTHDATA_PASSWORD + CAMS_EMAIL setenv = - PYTHONPATH=. - SOURCE_FOLDER=src/susse - TEST_FOLDER=tests + PYTHONPATH = . deps = -rrequirements.txt - mypy==1.13 pytest-cov==5.0.0 pytest-benchmark==4.0.0 - keyrings.alt -e . commands = - pytest --durations=10 --junitxml=test-results/pytest-results-3.13.xml --benchmark-disable --cov={env:SOURCE_FOLDER} --cov-report=html:test-results/coverage --verbose --capture=no {env:TEST_FOLDER} -vv - coverage report -m - - - -[flake8] -exclude = - .tox, - *.egg, -select = E,W,F -ignore = W503, F401, F403, F405, E501 -max-line-length = 88 -extend-ignore = E203 -docstring-convention = google -output-file = test-results/flake8_results.txt - -[isort] -profile = black -multi_line_output = 3 - -[black] -max-line-length = 88 -target-version = ['py312'] - -[mypy] -python_version = 3.12 -ignore_missing_imports = true + pytest --junitxml=test-results/pytest-results-3.12.xml \ + --cov=src/susse \ + --cov-report=term-missing \ + --cov-report=html:test-results/coverage \ + tests {posargs} [testenv:check-format] -deps= +description = Lint + import-order + formatting + docstring-coverage checks (no edits). +skip_install = true +deps = black==24.8.0 - isort==5.9.3 + isort==5.13.2 flake8==6.1.0 flake8-black==0.3.6 interrogate==1.7.0 -commands= - flake8 {env:SOURCE_FOLDER} - isort --check --profile black {env:SOURCE_FOLDER} - black {env:SOURCE_FOLDER} --check -t py312 -v - interrogate -v --exclude '**__init__.py' --fail-under 10 {env:SOURCE_FOLDER} +commands = + black --check --target-version py312 src/susse tests + isort --check --profile black src/susse tests + flake8 src/susse + interrogate -v --exclude '**__init__.py' --fail-under 50 src/susse + +[testenv:format] +description = Auto-format the source tree (black + isort). +skip_install = true +deps = + black==24.8.0 + isort==5.13.2 +commands = + black --target-version py312 src/susse tests + isort --profile black src/susse tests [testenv:static-analysis] +description = Static type checking with mypy. deps = mypy==1.13 -rrequirements.txt -e . -setenv = - SOURCE_FOLDER = src/susse -allowlist_externals = python commands = - python -m mypy --config-file=tox.ini --implicit-optional {env:SOURCE_FOLDER} - - -[testenv:format-imports] -deps= - isort==5.9.3 -commands= - isort --profile black {env:SOURCE_FOLDER} - isort --profile black {env:TEST_FOLDER} - -[testenv:format-code] -deps= - black==24.8.0 -commands= - black {env:SOURCE_FOLDER} -t py312 -v - black {env:TEST_FOLDER} -t py312 -v + python -m mypy src/susse +[flake8] +exclude = .tox, *.egg, .venv, build, dist +select = E, W, F +ignore = W503, F401, F403, F405, E501 +max-line-length = 88 +extend-ignore = E203 +docstring-convention = google diff --git a/warehouse/README.md b/warehouse/README.md new file mode 100644 index 00000000..175ec16b --- /dev/null +++ b/warehouse/README.md @@ -0,0 +1,154 @@ +# Warehouse + +The SuSSE warehouse is a BigQuery dataset (`solar_warehouse` in the +`solar-irradiation-estimation` project, EU region) that holds every +satellite/reanalysis variable, every ground measurement, and the +catalog metadata that ties them together. It is the single source of +truth that every notebook reads from. + +## Tables at a glance + +| Table | Shape | What it stores | +|---|---|---| +| `dim_variable` | catalog | One row per `(variable_id, source)` describing every variable SuSSE knows about: unit, valid range, spatial resolution, and which physical table it lives in | +| `irradiance_daily` | wide | Per-`(date, geohash5, source)` daily GHI / DHI / DNI for NASA POWER and CAMS | +| `nasa_daily_vars_long` | long | NASA POWER auxiliary variables (`temperature`, `aod_550`, `cloud_amount`, …) keyed by `(date, geohash5, variable_id)` | +| `cams_daily_vars_long` | long | CAMS auxiliary variables (`ghi_clear`, `bhi`, `dni_clear`, …) — same shape as the NASA long table | +| `merra_daily_vars_long` | long | MERRA-2 reanalysis variables (aerosol decomposition, precipitable water, …) — same shape | +| `modis_observations` | observation-keyed | MODIS imagery composites keyed by `(date, geohash5, product_id, band_id)` because MODIS doesn't fit the long-format schema | +| `ground_measurements` | curated long | Daily ground GHI per station, with QC level + curation provenance | +| `ground_measurements_raw` | uncurated long | The same data before curation; kept so the QC pipeline is re-runnable | + +Wide vs long is chosen per source: GHI / DHI / DNI go in the wide +`irradiance_daily` because they're a tightly coupled trio always queried +together; everything else lives in a long-format table per source so +adding a new variable does not require a schema migration. + +`dim_variable.physical_storage` tags every catalog row with which of the +three storage shapes it lives in (`long_format` / `irradiance_wide` / +`modis_observations`). `FeatureSelection` validates that requested +`variable_id`s match the storage their target field reads from — a typo +or a wide-vs-long mismatch fails at construction with a remediation +message, not as a silent NaN column at fetch time. + +## Data lineage + +``` + ╔═══════════════════════════════════════════════════╗ + ║ External sources (read-only) ║ + ║ ║ + ║ NASA POWER API CAMS API MERRA-2 OPeNDAP ║ + ║ MODIS (ORNL DAAC) Partner CSVs under ║ + ║ data/ground_measurements/ ║ + ╚═══════════════════════════════════════════════════╝ + │ + │ ingest jobs (warehouse/migrations/) + ▼ + ┌────────────────────────────────────────────────────────────────────┐ + │ Warehouse tables │ + │ │ + │ ┌────────────────────────────┐ ┌────────────────────────┐ │ + │ │ irradiance_daily (wide) │◄───┤ NASA POWER │ │ + │ │ ghi / dhi / dni per │◄───┤ CAMS │ │ + │ │ (date, geohash5, source) │ │ satellite ingest jobs │ │ + │ └────────────────────────────┘ │ (write both wide and │ │ + │ │ long in parallel) │ │ + │ ┌────────────────────────────┐ │ │ │ + │ │ nasa_daily_vars_long │◄───┤ │ │ + │ └────────────────────────────┘ │ │ │ + │ ┌────────────────────────────┐ │ │ │ + │ │ cams_daily_vars_long │◄───┘ │ │ + │ └────────────────────────────┘ │ + │ ┌────────────────────────────┐ │ + │ │ merra_daily_vars_long │◄── MerraRegionJob (OPeNDAP) │ + │ └────────────────────────────┘ │ + │ ┌────────────────────────────┐ │ + │ │ modis_observations │◄── ModisJob (ORNL DAAC) │ + │ └────────────────────────────┘ │ + │ ┌────────────────────────────┐ │ + │ │ ground_measurements_raw │◄── GroundIngestJob (CSV adapters) │ + │ └────────────┬───────────────┘ Raw, never edited by hand. │ + │ │ │ + │ │ curate_ground (QC, unit conversion) │ + │ ▼ │ + │ ┌────────────────────────────┐ │ + │ │ ground_measurements │ The table notebooks read for │ + │ │ (curated, QC-tagged) │ training targets. │ + │ └────────────────────────────┘ │ + │ │ + │ ┌────────────────────────────┐ │ + │ │ dim_variable │ Catalogue (units, valid range, │ + │ │ (no upstream — schema) │ spatial_resolution, physical_ │ + │ └────────────────────────────┘ storage). Hand-maintained via │ + │ the VariableCatalog Python class. │ + │ │ + │ ┌────────────────────────────┐ │ + │ │ v_irr_weekly_by_point │ Read-side gold views over │ + │ │ v_irr_monthly_by_point │ irradiance_daily. │ + │ └────────────────────────────┘ │ + └────────────────────────────────────────────────────────────────────┘ +``` + +Two observations worth internalising before extending the warehouse: + +1. **`irradiance_daily` is not derived from the `*_daily_vars_long` tables.** + They are *siblings* — the NASA POWER and CAMS satellite ingest jobs + write to both at the same time. GHI/DHI/DNI go to the wide + `irradiance_daily` (where they're queried as a trio); every other + variable goes to the matching `*_daily_vars_long` (where adding a + new variable doesn't need a schema change). +2. **`ground_measurements` *is* derived** — it's the curated output of + `curate_ground` over `ground_measurements_raw`. The raw table is + write-once at ingest time and should never be hand-edited; the + curated table is what training reads. + +### If you want to add… + +| Goal | Where to act | Notes | +|---|---|---| +| **A new ground station from an existing partner** | Drop the CSV under `data/ground_measurements//.csv` and run `GroundIngestJob` (Pattern 3 in NB 01) — its adapter parses the file, writes `ground_measurements_raw`, and the curation step writes `ground_measurements` | The two tables update together; you don't touch them by hand | +| **A new ground-measurement provider with a different CSV layout** | Subclass `GroundSourceAdapter` in `src/susse/warehouse_ops/population/adapters.py`; otherwise as above | Schema after the adapter must match `ground_measurements_raw`'s columns | +| **A new variable from an existing satellite source** | Add a `VariableSpec` to `VariableCatalog` (`src/susse/warehouse_ops/population/dim_variable.py`); write a migration to `populate_dim_variable`; re-run the relevant satellite ingest | The variable's `physical_storage` tag determines which table it lands in | +| **A whole new satellite source** | Add a fetcher under `src/susse/api_clients//`, a `Source` enum value, a `BaseSatelliteJob` subclass, the catalogue entries, a `TableSchema` + DDL + `CREATE TABLE` migration, and an ingest migration | The MERRA-2 wire-up (`b3` / `b7` / `b9` migrations + `MerraRegionJob`) is the worked template | +| **A read-side aggregation** | New SQL view under `warehouse/sql/10_gold/`; run via `warehouse/run_sql.py` | Views are cheap; prefer them over precomputed tables until a query is provably slow | + +## Subfolders + +| Path | Purpose | +|---|---| +| [`sql/00_schema/`](sql/00_schema/) | DDL for every table. New tables get a file here and a corresponding `CREATE TABLE IF NOT EXISTS` migration | +| [`sql/10_gold/`](sql/10_gold/) | Read-side gold views (e.g. weekly / monthly aggregations) that wrap common analytics queries | +| [`sql/20_functions/`](sql/20_functions/) | BigQuery user-defined functions (e.g. `fn_nearest_point`) used by the gold views and some notebooks | +| [`migrations/`](migrations/) | One-shot scripts that mutate the warehouse — create a table, backfill rows, run an ingest. Idempotent. See [`migrations/README.md`](migrations/README.md) | +| [`run_sql.py`](run_sql.py) | CLI for executing a file from `sql/` against the warehouse | + +## Authentication + +```bash +gcloud auth application-default login +gcloud config set project solar-irradiation-estimation +``` + +That single step gets you read access to every table for the notebooks. +Write paths (`warehouse/migrations/*.py`, `RUN_INGEST=True` in NB 01) +additionally need the relevant satellite-source credentials in `.env` +(`CAMS_EMAIL` / `EARTHDATA_USERNAME` / `EARTHDATA_PASSWORD`). + +## Reading from Python + +```python +from susse.warehouse_ops.io import BigQueryClient, WarehouseConfig +from susse.warehouse_ops.io.config import TableRefs + +bq = BigQueryClient(config=WarehouseConfig()) +tables = TableRefs(config=bq.config) + +# Any table's fully-qualified name: +print(tables.irradiance_daily) +# → solar-irradiation-estimation.solar_warehouse.irradiance_daily + +df = bq.query(f"SELECT COUNT(*) FROM `{tables.irradiance_daily}`") +``` + +For higher-level assembly (joining ground truth with multi-source +satellite features), use `FeatureService` — see tutorial NB 02. diff --git a/warehouse/SolarIrradiation_BQ_Quickstart.ipynb b/warehouse/SolarIrradiation_BQ_Quickstart.ipynb deleted file mode 100644 index 28e936f3..00000000 --- a/warehouse/SolarIrradiation_BQ_Quickstart.ipynb +++ /dev/null @@ -1,513 +0,0 @@ -{ - "cells": [ - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:18:31.511829Z", - "start_time": "2025-09-03T13:18:31.493016Z" - } - }, - "cell_type": "code", - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ], - "id": "845341adbe4e6b26", - "outputs": [], - "execution_count": 1 - }, - { - "cell_type": "markdown", - "id": "fbf5cd51", - "metadata": {}, - "source": [ - "\n", - "# Solar Irradiation — BigQuery Quickstart (Kampala variables + irradiation)\n", - "\n", - "This notebook shows how to:\n", - "- Connect to our BigQuery dataset\n", - "- (Optionally) create the long NASA variables table\n", - "- Create a table function to fetch nearest-grid **daily** variables\n", - "- Fetch **Kampala** nearest variables and join with **irradiation**\n", - "- Plot **daily** and **monthly** normalized series together\n", - "\n", - "**Dataset:** `solar-irradiation-estimation.solar_warehouse` (EU) \n", - "**Tables you will use:**\n", - "- `irradiance_daily` — daily GHI/DHI/DNI (kWh/m²/day), partitioned by `date`\n", - "- `nasa_daily_vars_long` — daily NASA variables in long format (one row per `(date, lat, lon, variable_id)`)\n" - ] - }, - { - "cell_type": "markdown", - "id": "1fd46623", - "metadata": {}, - "source": [ - "\n", - "## Prerequisites\n", - "\n", - "Authenticate once on your machine:\n", - "```bash\n", - "gcloud auth application-default login\n", - "gcloud config set project solar-irradiation-estimation\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e9a8178", - "metadata": {}, - "outputs": [], - "source": [ - "# Optional: install needed libs (uncomment)\n", - "# %pip install --quiet google-cloud-bigquery pandas pyarrow matplotlib\n" - ] - }, - { - "cell_type": "code", - "id": "2d234cb0", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:18:34.736315Z", - "start_time": "2025-09-03T13:18:33.821076Z" - } - }, - "source": [ - "from __future__ import annotations\n", - "\n", - "from typing import Optional, Sequence, Iterable, List\n", - "from datetime import date\n", - "from pathlib import Path\n", - "\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt # one chart per cell, no custom colors\n", - "from google.cloud import bigquery\n", - "from google.cloud.bigquery import ScalarQueryParameter\n", - "from matplotlib.typing import MarkerType\n", - "\n", - "PROJECT_ID: str = \"solar-irradiation-estimation\"\n", - "DATASET: str = \"solar_warehouse\"\n", - "DATASET_FQN: str = f\"{PROJECT_ID}.{DATASET}\"\n", - "print(f\"Using dataset: {DATASET_FQN}\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using dataset: solar-irradiation-estimation.solar_warehouse\n" - ] - } - ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "62552875", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:18:36.584615Z", - "start_time": "2025-09-03T13:18:36.566548Z" - } - }, - "source": [ - "def create_client(project: Optional[str] = None) -> bigquery.Client:\n", - " \"\"\"Create an authenticated BigQuery client using ADC or a service account.\"\"\"\n", - " return bigquery.Client(project=project or None)\n", - "\n", - "\n", - "def query_to_dataframe(\n", - " client: bigquery.Client,\n", - " sql: str,\n", - " parameters: Optional[Sequence[ScalarQueryParameter]] = None,\n", - ") -> pd.DataFrame:\n", - " \"\"\"Run a (parameterized) query and return results as a pandas DataFrame.\"\"\"\n", - " job_config: Optional[bigquery.QueryJobConfig] = None\n", - " if parameters:\n", - " job_config = bigquery.QueryJobConfig(query_parameters=list(parameters))\n", - " job = client.query(sql, job_config=job_config)\n", - " result = job.result()\n", - " try:\n", - " df: pd.DataFrame = result.to_dataframe(create_bqstorage_client=True)\n", - " except Exception:\n", - " df = result.to_dataframe(create_bqstorage_client=False)\n", - " return df\n", - "\n", - "\n", - "def call_fn_nearest_point(\n", - " client: bigquery.Client,\n", - " dataset_fqn: str,\n", - " *,\n", - " lat: float,\n", - " lon: float,\n", - " start_date: date,\n", - " end_date: date,\n", - " max_km: Optional[float] = 100.0,\n", - ") -> pd.DataFrame:\n", - " \"\"\"Call the irradiance nearest-point function and return daily rows by source.\"\"\"\n", - " sql = f\"\"\"\n", - " SELECT *\n", - " FROM `{dataset_fqn}.fn_nearest_point`(@lat, @lon, @start_date, @end_date, @max_km)\n", - " ORDER BY date, source\n", - " \"\"\"\n", - " params = [\n", - " ScalarQueryParameter(\"lat\", \"FLOAT64\", lat),\n", - " ScalarQueryParameter(\"lon\", \"FLOAT64\", lon),\n", - " ScalarQueryParameter(\"start_date\", \"DATE\", start_date),\n", - " ScalarQueryParameter(\"end_date\", \"DATE\", end_date),\n", - " ScalarQueryParameter(\"max_km\", \"FLOAT64\", max_km),\n", - " ]\n", - " return query_to_dataframe(client, sql, params)\n", - "\n", - "def fetch_nearest_vars_daily(\n", - " client: bigquery.Client,\n", - " dataset_fqn: str,\n", - " *,\n", - " lat: float,\n", - " lon: float,\n", - " start_date: date,\n", - " end_date: date,\n", - " variable_ids: Iterable[str],\n", - " max_km: Optional[float] = 100.0,\n", - ") -> pd.DataFrame:\n", - " \"\"\"Fetch nearest-grid daily values for multiple variables and return a wide DataFrame.\"\"\"\n", - " dfs: List[pd.DataFrame] = []\n", - " for var in variable_ids:\n", - " sql = f\"\"\"\n", - " SELECT date, value AS {var}\n", - " FROM `{dataset_fqn}.fn_nearest_var_daily`(@var_id, @lat, @lon, @start_date, @end_date, @max_km)\n", - " ORDER BY date\n", - " \"\"\"\n", - " params = [\n", - " ScalarQueryParameter(\"var_id\", \"STRING\", var),\n", - " ScalarQueryParameter(\"lat\", \"FLOAT64\", lat),\n", - " ScalarQueryParameter(\"lon\", \"FLOAT64\", lon),\n", - " ScalarQueryParameter(\"start_date\", \"DATE\", start_date),\n", - " ScalarQueryParameter(\"end_date\", \"DATE\", end_date),\n", - " ScalarQueryParameter(\"max_km\", \"FLOAT64\", max_km),\n", - " ]\n", - " df_var = query_to_dataframe(client, sql, params)\n", - " dfs.append(df_var)\n", - "\n", - " df_wide = dfs[0]\n", - " for df_next in dfs[1:]:\n", - " df_wide = pd.merge(df_wide, df_next, on=\"date\", how=\"outer\")\n", - " df_wide = df_wide.sort_values(\"date\").reset_index(drop=True)\n", - " return df_wide" - ], - "outputs": [], - "execution_count": 3 - }, - { - "cell_type": "markdown", - "id": "d37503a3", - "metadata": {}, - "source": [ - "\n", - "## Fetch Kampala nearest variables and join with irradiation\n" - ] - }, - { - "cell_type": "code", - "id": "fb7813d4", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:18:49.527042Z", - "start_time": "2025-09-03T13:18:40.112899Z" - } - }, - "source": [ - "client = create_client(PROJECT_ID)\n", - "# Kampala\n", - "site_lat: float = 0.3476\n", - "site_lon: float = 32.5825\n", - "start_date: date = date(2024, 1, 1)\n", - "end_date: date = date(2024, 12, 31)\n", - "\n", - "# Variables to pull\n", - "vars_to_get: List[str] = [\"temperature\", \"relative_humidity\", \"aod_550\"]\n", - "\n", - "# Variables (nearest daily)\n", - "df_vars_daily = fetch_nearest_vars_daily(\n", - " client, DATASET_FQN,\n", - " lat=site_lat, lon=site_lon,\n", - " start_date=start_date, end_date=end_date,\n", - " variable_ids=vars_to_get, max_km=100.0\n", - ")\n", - "\n", - "# Irradiance (nearest daily, NASA only for overlay)\n", - "df_irr_daily = call_fn_nearest_point(\n", - " client, DATASET_FQN,\n", - " lat=site_lat, lon=site_lon,\n", - " start_date=start_date, end_date=end_date,\n", - " max_km=100.0\n", - ")\n", - "df_irr_nasa = df_irr_daily[df_irr_daily[\"source\"] == \"NASA\"][[\"date\", \"ghi_kwh_m2_day\"]].copy()\n", - "\n", - "# Join\n", - "df_join_daily = pd.merge(df_irr_nasa, df_vars_daily, on=\"date\", how=\"inner\").sort_values(\"date\")\n", - "df_join_daily.head()" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jan/.pyenv/versions/.venv_irradiation/lib/python3.11/site-packages/google/cloud/bigquery/table.py:1994: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", - " warnings.warn(\n", - "/home/jan/.pyenv/versions/.venv_irradiation/lib/python3.11/site-packages/google/cloud/bigquery/table.py:1994: UserWarning: BigQuery Storage module not found, fetch data with the REST endpoint instead.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - " date ghi_kwh_m2_day temperature relative_humidity aod_550\n", - "0 2024-01-01 4.00 21.17 85.40 0.38\n", - "1 2024-01-02 4.52 21.32 85.46 0.29\n", - "2 2024-01-03 4.09 21.24 82.15 0.36\n", - "3 2024-01-04 5.80 21.66 78.62 0.41\n", - "4 2024-01-05 3.68 22.82 73.85 0.39" - ], - "text/html": [ - "
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dateghi_kwh_m2_daytemperaturerelative_humidityaod_550
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" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 4 - }, - { - "cell_type": "markdown", - "id": "70af7c61", - "metadata": {}, - "source": [ - "\n", - "### Plot: Daily normalized (GHI + variables)\n" - ] - }, - { - "cell_type": "code", - "id": "a99f8384", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:24:52.027408Z", - "start_time": "2025-09-03T13:24:51.317030Z" - } - }, - "source": [ - "from __future__ import annotations\n", - "from typing import Sequence\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "def min_max_normalize(series: pd.Series) -> pd.Series:\n", - " smin, smax = series.min(), series.max()\n", - " if pd.isna(smin) or pd.isna(smax) or smax == smin:\n", - " return series * 0.0\n", - " return (series - smin) / (smax - smin)\n", - "\n", - "def plot_ghi_overlays(\n", - " df: pd.DataFrame,\n", - " *,\n", - " date_col: str = \"date\",\n", - " ghi_col: str = \"ghi_kwh_m2_day\",\n", - " var_cols: Sequence[str] = (\"temperature\", \"relative_humidity\", \"aod_550\"),\n", - " title_prefix: str = \"Kampala — Daily normalized\",\n", - " normalize: bool = True,\n", - ") -> None:\n", - " \"\"\"\n", - " Makes one figure with stacked panels (one per variable), overlaying GHI with that variable.\n", - " Accepts either:\n", - " - a real column named `date_col`, or\n", - " - `date_col` being the DataFrame index name.\n", - " \"\"\"\n", - " # Build df2 with a Date/Month index either way\n", - " if date_col in df.columns:\n", - " df2 = df[[date_col, ghi_col, *var_cols]].copy().sort_values(date_col).set_index(date_col)\n", - " else:\n", - " # Use the existing index, but ensure it’s sorted\n", - " df2 = df[[ghi_col, *var_cols]].copy().sort_index()\n", - "\n", - " fig, axs = plt.subplots(len(var_cols), 1, figsize=(12, 4 * len(var_cols)), sharex=True)\n", - "\n", - " for i, var in enumerate(var_cols):\n", - " plot_df = df2[[ghi_col, var]].copy()\n", - " if normalize:\n", - " plot_df[ghi_col] = min_max_normalize(plot_df[ghi_col])\n", - " plot_df[var] = min_max_normalize(plot_df[var])\n", - "\n", - " ax = axs.flatten()[i]\n", - " # Left axis: GHI\n", - " ax.plot(plot_df.index, plot_df[ghi_col], label=ghi_col, marker='o', linestyle='--')\n", - " # Right axis: variable\n", - " ax2 = ax.twinx()\n", - " ax2.plot(plot_df.index, plot_df[var], color=\"tab:orange\", label=var, alpha=0.7)\n", - "\n", - " ax.set_title(f\"{title_prefix}: {var}\")\n", - " ax.set_ylabel(\"GHI (norm)\" if normalize else \"GHI\")\n", - " ax.grid(True)\n", - " ax.legend(loc=\"upper left\")\n", - " ax2.set_ylabel(f\"{var} (norm)\" if normalize else var)\n", - " ax2.legend(loc=\"upper right\")\n", - "\n", - " axs[-1].set_xlabel(\"Date\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Use your selected variables; this will produce three separate figures\n", - "plot_ghi_overlays(\n", - " df_join_daily,\n", - " var_cols=vars_to_get, # e.g., [\"temperature\", \"relative_humidity\", \"aod_550\"]\n", - ")\n" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 13 - }, - { - "cell_type": "markdown", - "id": "7e8bab81", - "metadata": {}, - "source": [ - "\n", - "### Plot: Monthly normalized means (GHI + variables)\n" - ] - }, - { - "cell_type": "code", - "id": "8bd5f2a3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-09-03T13:25:07.376444Z", - "start_time": "2025-09-03T13:25:06.813196Z" - } - }, - "source": [ - "df_join_daily['month'] = pd.to_datetime(df_join_daily['date']).dt.to_period('M').dt.to_timestamp()\n", - "monthly = (\n", - " df_join_daily.groupby('month', as_index=False)[['ghi_kwh_m2_day'] + vars_to_get]\n", - " .mean()\n", - " .sort_values('month')\n", - ")\n", - "\n", - "df_month_plot = monthly.set_index('month')\n", - "plot_ghi_overlays(\n", - " df_month_plot,\n", - " date_col='month', # still tell it the logical time field name\n", - " var_cols=vars_to_get,\n", - " title_prefix='Kampala — Monthly normalized means',\n", - ")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 14 - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/warehouse/extending_the_warehouse.ipynb b/warehouse/extending_the_warehouse.ipynb new file mode 100644 index 00000000..c34bb156 --- /dev/null +++ b/warehouse/extending_the_warehouse.ipynb @@ -0,0 +1,1167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9d5915e70e064397", + "metadata": {}, + "source": [ + "# 01 \u2014 Warehouse population\n", + "\n", + "This notebook is the entry point to the SuSSE warehouse \u2014 the shared data store every other tutorial notebook reads from. Three sections, in order:\n", + "\n", + "1. **The model** \u2014 what we're building, in one paragraph.\n", + "2. **The warehouse** \u2014 what's in it, where the data comes from, and the hard constraint it imposes.\n", + "3. **Extending the warehouse** \u2014 the three ingest patterns and how to add new stations / regions / sources.\n", + "\n", + "Code cells default to **read-only mode** (`RUN_INGEST=False` near the top) \u2014 they query the warehouse and show what *would* happen, but don't call external APIs or write rows. Real mutations live in `warehouse/migrations/`. Library architecture and operational practices are documented separately in [`docs/architecture.md`](../../docs/architecture.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa2b234624864634a6936c326a3f2f8a", + "metadata": {}, + "outputs": [], + "source": [ + "# ============================================================\n", + "# Colab bootstrap (no-op when run locally).\n", + "# ------------------------------------------------------------\n", + "# First-time setup on Colab:\n", + "# 1. Create a GitHub Personal Access Token (PAT) at\n", + "# https://github.com/settings/tokens with `repo` scope.\n", + "# The repository is private, so the clone needs this token\n", + "# (or an SSH key Colab knows about, which is more fiddly).\n", + "# 2. Add the token under Tools \u2192 Secrets in Colab with name\n", + "# `GITHUB_PAT` and toggle \"Notebook access\" on.\n", + "# 3. Sign in with a Google account that has BigQuery read access\n", + "# to `solar-irradiation-estimation` when prompted.\n", + "# ============================================================\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " REPO = \"Marconi-Lab/Solar_irradiation\"\n", + " BRANCH = \"jm/add_model\"\n", + "\n", + " if not os.path.exists(\"/content/Solar_irradiation/.git\"):\n", + " try:\n", + " from google.colab import userdata\n", + " token = userdata.get(\"GITHUB_PAT\")\n", + " clone_url = f\"https://{token}@github.com/{REPO}.git\"\n", + " print(\"Cloning with Colab secret 'GITHUB_PAT'.\")\n", + " except Exception:\n", + " clone_url = f\"git@github.com:{REPO}.git\"\n", + " print(\n", + " \"Colab secret 'GITHUB_PAT' not set \u2014 trying SSH. If the \"\n", + " \"clone fails, follow the PAT setup steps above and re-run.\"\n", + " )\n", + " !git clone -q -b {BRANCH} {clone_url} /content/Solar_irradiation\n", + "\n", + " %cd /content/Solar_irradiation\n", + " !pip install -q -e . 2>&1 | tail -3\n", + "\n", + " from google.colab import auth\n", + " auth.authenticate_user()\n", + " !gcloud config set project solar-irradiation-estimation 2>/dev/null\n", + " print(\"Colab setup complete.\")" + ] + }, + { + "cell_type": "markdown", + "id": "455c95df", + "metadata": {}, + "source": [ + "### Inputs and prerequisites\n", + "\n", + "- **GCP credentials.** Application-default credentials (`gcloud auth application-default login`) for the project listed in `WarehouseConfig` (default: `solar-irradiation-estimation`).\n", + "- **A populated warehouse.** The cells below need the table-creation and ingest migrations under `warehouse/migrations/` to have already been applied. The migration index in [`warehouse/migrations/README.md`](../../warehouse/migrations/README.md) records what each script does and whether it has been run.\n", + "- **Optional \u2014 `CAMS_EMAIL`.** Required only if you flip `RUN_INGEST=True` for any CAMS pattern. Register at [soda-pro.com](https://www.soda-pro.com/) and put the address in `.env`.\n", + "- **Optional \u2014 `EARTHDATA_USERNAME` / `EARTHDATA_PASSWORD`.** Required for MERRA-2 ingest only; not needed to read the warehouse.\n", + "\n", + "This notebook is **read-only by default** (`RUN_INGEST=False` in the setup cell). Every cell below is safe to run against a populated warehouse without writing or fetching anything.\n", + "\n", + "**Outputs.** No artifacts are written to disk \u2014 this notebook builds the reader's mental model of the warehouse. The next acceptance gate (NB 02) consumes the same warehouse to materialise a versioned training-dataset snapshot." + ] + }, + { + "cell_type": "markdown", + "id": "56c6f845c1249b1d", + "metadata": {}, + "source": [ + "## 1. The model\n", + "\n", + "The SuSSE bias-correction model takes a satellite GHI estimate (from NASA POWER and/or CAMS) plus atmospheric variables at a specific (lat, lon, date), and predicts the **unbiased GHI** for that point. Satellite estimates have systematic 5\u201315 % biases over Sub-Saharan Africa; the model learns to correct them.\n", + "\n", + "It's trained on **paired data**: at each of our 28 ground-measurement stations, every day where we have both a ground GHI reading and the matching satellite estimate becomes one training example. Once trained, applying the model to a new (lat, lon, date) requires the same shape of input \u2014 a satellite estimate plus atmospheric variables for that point. That's what the warehouse is for." + ] + }, + { + "cell_type": "markdown", + "id": "9c6df1b21a2f0a94", + "metadata": {}, + "source": [ + "## 2. The warehouse\n", + "\n", + "Both training and inference need the same data shape \u2014 `(satellite GHI estimate, atmospheric features) at (lat, lon, date)` \u2014 so we pre-stage everything in one BigQuery dataset (`solar-irradiation-estimation.solar_warehouse`) and read from there. For training that's the 28 ground stations \u00d7 their date ranges; for inference it's any (lat, lon, date) a downstream caller might ask about.\n", + "\n", + "**The hard constraint:** the model can only score (lat, lon, date) tuples for which the warehouse already holds the satellite estimate and atmospheric variables. Adding a new station to the training corpus, or a new region to inference coverage, requires an ingest run first. Section 3 shows how.\n", + "\n", + "The cells below tour the warehouse as it stands today, then summarise where each table's data comes from." + ] + }, + { + "cell_type": "markdown", + "id": "eb21f4252a036805", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d7542b92b7500c7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:14.683682677Z", + "start_time": "2026-05-11T07:22:12.100916129Z" + } + }, + "outputs": [], + "source": [ + "from datetime import date\n", + "from pathlib import Path\n", + "import logging\n", + "import os\n", + "\n", + "import pandas as pd\n", + "\n", + "from susse.warehouse_ops.io import (\n", + " BigQueryClient,\n", + " TableRefs,\n", + " TableSchemas,\n", + " WarehouseConfig,\n", + ")\n", + "from susse.warehouse_ops.population import (\n", + " BoundingBox,\n", + " CamsSatelliteJob,\n", + " CurationOptions,\n", + " DateRange,\n", + " GridPlan,\n", + " GridSpec,\n", + " GroundFilePlan,\n", + " GroundIngestJob,\n", + " LocationSpec,\n", + " NamedLocationsPlan,\n", + " NasaPowerSatelliteJob,\n", + " Source,\n", + " StandardCsvAdapter,\n", + " VariableCatalog,\n", + " curate_ground,\n", + " populate_dim_variable,\n", + " variables_to_dataframe,\n", + ")\n", + "\n", + "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s | %(levelname)s | %(message)s\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c146f677e9b56fb", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:16.991707588Z", + "start_time": "2026-05-11T07:22:14.689249003Z" + } + }, + "outputs": [], + "source": [ + "# Master toggle: when False, the notebook reads the warehouse and shows what\n", + "# would happen, but never calls a satellite API or writes a row. Flip to True\n", + "# only when you actually want to ingest.\n", + "RUN_INGEST = False\n", + "\n", + "config = WarehouseConfig() # defaults to solar-irradiation-estimation / solar_warehouse\n", + "tables = TableRefs(config=config)\n", + "bq = BigQueryClient(config=config)\n", + "\n", + "print(f\"project : {config.project_id}\")\n", + "print(f\"dataset : {config.dataset}\")\n", + "print(f\"RUN_INGEST = {RUN_INGEST}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a70f71df00cf6244", + "metadata": {}, + "source": [ + "### Live warehouse tour\n", + "\n", + "What's in the warehouse right now, queried directly. Re-running this notebook over time will show the warehouse growing as we run the ingest jobs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13a990c76cf7aab", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:22.490798307Z", + "start_time": "2026-05-11T07:22:17.107604074Z" + } + }, + "outputs": [], + "source": [ + "import warnings; warnings.filterwarnings('ignore')\n", + "from susse.warehouse_ops.io import BigQueryClient, TableRefs\n", + "bq = BigQueryClient(); refs = TableRefs()\n", + "df = bq.query(f'''\n", + " SELECT geohash5, variable_id, COUNT(*) AS n_days\n", + " FROM {refs.merra_daily_vars_long}\n", + " GROUP BY geohash5, variable_id\n", + " ORDER BY geohash5, variable_id\n", + " ''')\n", + "print(df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abe05736c6c81b70", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:24.592690557Z", + "start_time": "2026-05-11T07:22:22.505657396Z" + } + }, + "outputs": [], + "source": [ + "# Each table or view in the warehouse, with its size and kind. Joining\n", + "# __TABLES__ (which has row_count + size_bytes) to INFORMATION_SCHEMA.TABLES\n", + "# (which has table_type) so views are labelled as such \u2014 __TABLES__ alone\n", + "# reports row_count=0 for views (they're computed on demand, not stored),\n", + "# which would otherwise look like the views are empty.\n", + "tables_df = bq.query(f\"\"\"\n", + "SELECT t.table_id,\n", + " ts.table_type,\n", + " t.row_count,\n", + " ROUND(t.size_bytes / 1024 / 1024, 1) AS size_mb\n", + "FROM `{config.project_id}.{config.dataset}.__TABLES__` AS t\n", + "LEFT JOIN `{config.project_id}.{config.dataset}.INFORMATION_SCHEMA.TABLES` AS ts\n", + " ON t.table_id = ts.table_name\n", + "ORDER BY t.size_bytes DESC, t.table_id\n", + "\"\"\")\n", + "tables_df" + ] + }, + { + "cell_type": "markdown", + "id": "67a8fd73", + "metadata": {}, + "source": [ + "### What's in each table\n", + "\n", + "Each table answers one question; the next cell shows three sample rows from each so you can see actual column names and value ranges before writing queries.\n", + "\n", + "- **Observations \u2014 wide format:**\n", + " - `irradiance_daily` \u2014 satellite-estimated GHI / DHI / DNI from NASA POWER and CAMS. One row per `(date, geohash5, source)`. Wide layout because the three fluxes are always queried together.\n", + " - `ground_measurements` \u2014 curated ground-truth daily GHI from the 28 stations. One row per `(date, location)`. The model's training target. `ground_measurements_raw` is the parallel audit-trail table with the un-curated parsed CSV rows.\n", + "- **Observations \u2014 long format:**\n", + " - `nasa_daily_vars_long`, `cams_daily_vars_long`, `merra_daily_vars_long` \u2014 every auxiliary variable (everything that is not GHI / DHI / DNI). One row per `(date, geohash5, variable_id)` so adding a new variable is a no-op on the schema.\n", + " - `modis_observations` \u2014 MODIS observations get their own table (not a `_long` companion) because MODIS products have different cadences (daily / 8-day / 16-day) and are multi-band. One row per `(date, geohash5, product_id, band_id)`. `modis_observations_staging` is the ingest buffer.\n", + "- **Catalog:**\n", + " - `dim_variable` \u2014 one row per `(variable_id, source)`, with `display_name`, `unit`, `description`, `valid_min` / `valid_max`, and `spatial_resolution_km`. This is the table the variable-catalog cell further down enumerates.\n", + "- **Views (no storage cost):**\n", + " - `v_irr_weekly_by_point`, `v_irr_monthly_by_point` \u2014 server-side weekly / monthly rollups of `irradiance_daily`. They show `row_count = 0` in the catalog table above because views are computed on demand, not persisted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8570b9f4", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:39.953871561Z", + "start_time": "2026-05-11T07:22:24.757874722Z" + } + }, + "outputs": [], + "source": [ + "# First three rows of each base table \u2014 actual columns + value ranges.\n", + "# `tables` is a TableRefs object whose attributes return fully-qualified\n", + "# BigQuery table names; iterating gives an idiomatic walk-through.\n", + "from IPython.display import display, Markdown\n", + "\n", + "for tbl_attr in (\n", + " \"irradiance_daily\",\n", + " \"ground_measurements\",\n", + " \"ground_measurements_raw\",\n", + " \"nasa_daily_vars_long\",\n", + " \"cams_daily_vars_long\",\n", + " \"merra_daily_vars_long\",\n", + " \"modis_observations\",\n", + " \"dim_variable\",\n", + "):\n", + " fqn = getattr(tables, tbl_attr)\n", + " display(Markdown(f\"**`{tbl_attr}`** \u2014 first 3 rows\"))\n", + " display(bq.query(f\"SELECT * FROM `{fqn}` LIMIT 3\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2116dd30085251d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:44.126972101Z", + "start_time": "2026-05-11T07:22:40.666991507Z" + } + }, + "outputs": [], + "source": [ + "irradiance_summary = bq.query(f\"\"\"\n", + "SELECT source,\n", + " COUNT(*) AS n_rows,\n", + " COUNT(DISTINCT geohash5) AS n_points,\n", + " MIN(date) AS first_date,\n", + " MAX(date) AS last_date,\n", + " ROUND(MIN(latitude), 2) AS min_lat,\n", + " ROUND(MAX(latitude), 2) AS max_lat,\n", + " ROUND(MIN(longitude), 2) AS min_lon,\n", + " ROUND(MAX(longitude), 2) AS max_lon\n", + "FROM `{tables.irradiance_daily}`\n", + "GROUP BY source\n", + "ORDER BY source\n", + "\"\"\")\n", + "irradiance_summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6fd6c1063aa86e3", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:45.355618438Z", + "start_time": "2026-05-11T07:22:44.492392713Z" + } + }, + "outputs": [], + "source": [ + "ground_summary = bq.query(f\"\"\"\n", + "SELECT location,\n", + " COUNT(*) AS n_days,\n", + " MIN(date) AS first_date,\n", + " MAX(date) AS last_date,\n", + " ROUND(AVG(lat), 4) AS lat,\n", + " ROUND(AVG(lon), 4) AS lon,\n", + " ROUND(AVG(ghi_kwh_m2_day), 2) AS ghi_mean_kwh\n", + "FROM `{tables.ground_measurements}`\n", + "GROUP BY location\n", + "ORDER BY n_days DESC\n", + "\"\"\")\n", + "ground_summary" + ] + }, + { + "cell_type": "markdown", + "id": "e3cdcc2a", + "metadata": {}, + "source": [ + "### Ingest-completeness audit\n", + "\n", + "\"Coverage\" here means **completeness of the warehouse for the dates / locations / variables we care about** \u2014 *not* spatial-area coverage. Two queries are enough to spot-check it before downstream work:\n", + "\n", + "- **Per-variable row counts in the long tables** \u2014 answers *\"is every variable that's supposed to be ingested actually in the warehouse?\"* If a variable is registered in `dim_variable` but missing from its long table, the source's ingest job hasn't been run for it yet.\n", + "- **Training-pair availability per ground station** \u2014 answers *\"for each ground station, on how many of its days do we have a matching satellite estimate at the same `(date, geohash5)`?\"* Zero pairs means no training signal for that station, regardless of how much ground or satellite data exists in isolation.\n", + "\n", + "The pair-availability cell is the canary: every station should have `n_nasa_pairs > 0` and `n_cams_pairs > 0` after the 28-station ingest runs. If any are zero, rerun `warehouse/migrations/2026-05-08_a6_ingest_28_ground_stations.py` (it's idempotent \u2014 already-cached slices are skipped).\n", + "\n", + "Anything deeper \u2014 row-count diffs against a previous run, per-variable date-range gaps, geohash inventories \u2014 is one-off enough that we don't keep a dedicated audit script in the repo. Reach for BigQuery directly, or write a fresh migration if the check is worth running again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "902a2fc1", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:48.054869330Z", + "start_time": "2026-05-11T07:22:45.785859301Z" + } + }, + "outputs": [], + "source": [ + "# Per-source variable coverage in the long tables. After Phase A this\n", + "# should show ~30+ NASA POWER variables and 6 CAMS aux variables for\n", + "# the Uganda 2024 grid.\n", + "coverage_df = bq.query(f\"\"\"\n", + "SELECT 'NASA' AS source, variable_id, COUNT(*) AS n_rows,\n", + " COUNT(DISTINCT geohash5) AS n_points,\n", + " MIN(date) AS min_date, MAX(date) AS max_date\n", + "FROM `{tables.nasa_daily_vars_long}`\n", + "GROUP BY variable_id\n", + "UNION ALL\n", + "SELECT 'CAMS' AS source, variable_id, COUNT(*) AS n_rows,\n", + " COUNT(DISTINCT geohash5) AS n_points,\n", + " MIN(date) AS min_date, MAX(date) AS max_date\n", + "FROM `{tables.cams_daily_vars_long}`\n", + "GROUP BY variable_id\n", + "ORDER BY source, variable_id\n", + "\"\"\")\n", + "coverage_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeb0be97", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:51.503506255Z", + "start_time": "2026-05-11T07:22:49.102833394Z" + } + }, + "outputs": [], + "source": [ + "# Training-pair availability \u2014 for each ground station, how many days have\n", + "# a matching satellite estimate at the same (date, geohash5)? Zero pairs\n", + "# means no training signal for that station, regardless of how much\n", + "# ground or satellite data exists in isolation.\n", + "pair_df = bq.query(f\"\"\"\n", + "WITH g AS (\n", + " SELECT location, date, geohash5\n", + " FROM `{tables.ground_measurements}`\n", + ")\n", + "SELECT\n", + " g.location,\n", + " COUNT(*) AS n_ground_days,\n", + " COUNTIF(s_nasa.geohash5 IS NOT NULL) AS n_nasa_pairs,\n", + " COUNTIF(s_cams.geohash5 IS NOT NULL) AS n_cams_pairs\n", + "FROM g\n", + "LEFT JOIN `{tables.irradiance_daily}` s_nasa\n", + " ON g.date = s_nasa.date AND g.geohash5 = s_nasa.geohash5 AND s_nasa.source = 'NASA'\n", + "LEFT JOIN `{tables.irradiance_daily}` s_cams\n", + " ON g.date = s_cams.date AND g.geohash5 = s_cams.geohash5 AND s_cams.source = 'CAMS'\n", + "GROUP BY g.location\n", + "ORDER BY g.location\n", + "\"\"\")\n", + "\n", + "# Hard assertion: any zero-pair station means the warehouse isn't ready\n", + "# for training at that location. Comment out if you're knowingly running\n", + "# before A6 has populated satellite estimates for the ground stations.\n", + "zero_pair = pair_df[(pair_df[\"n_nasa_pairs\"] == 0) | (pair_df[\"n_cams_pairs\"] == 0)]\n", + "if not zero_pair.empty:\n", + " print(f\"WARNING: {len(zero_pair)} stations have zero pairs for at least one source.\")\n", + " print(\"Run warehouse/migrations/2026-05-08_a6_ingest_28_ground_stations.py to fix.\")\n", + "pair_df" + ] + }, + { + "cell_type": "markdown", + "id": "d514885f", + "metadata": {}, + "source": [ + "### Where the data comes from\n", + "\n", + "Five data sources contribute to the warehouse. Two roles each row plays \u2014 *target* (what the model learns to predict) or *feature* (what it consumes as input):\n", + "\n", + "| Source | Role | What it gives | Resolution / cadence | Status |\n", + "|---|---|---|---|---|\n", + "| **Ground stations** | Target | Daily GHI measured at 28 sites across 7 SSA countries (CrossBoundary, Makerere, Ugandan Min. of Energy) | Daily, point | \u2713 ingested via `StandardCsvAdapter` |\n", + "| **NASA POWER** | Feature | GHI / DHI / DNI **plus** ~30 atmospheric variables (temp, humidity, AOD, cloud, etc.) | Daily, 0.5\u00b0 (~55 km) | \u2713 ingested |\n", + "| **CAMS Radiation** | Feature | All-sky GHI / DHI / DNI / BHI **plus** clear-sky decomposition (McClear) | Daily, 0.05\u00b0 (~5.5 km) | \u2713 ingested |\n", + "| **MERRA-2** | Feature | 4 variables: AOD extinction / scattering / analysis at 550 nm + total precipitable water | Hourly native, daily-aggregated | \u2713 in flight via migration B8 (still backfilling stations) |\n", + "| **MODIS** | Feature | 3 surface-state products: nadir BRDF reflectance band 1 (red, daily 500 m); day land-surface temperature (8-day, 1 km); NDVI (16-day, 250 m) | Mixed (daily / 8-day / 16-day) | \u2713 in flight via migration C8 |\n", + "\n", + "The variables actually pulled per source are listed in `VariableCatalog` (see the catalog cell below); each `VariableSpec`'s `description` field explains why it's included. We deliberately ingest a subset of what each source offers \u2014 see the catalog for details.\n", + "\n", + "Two warehouse-layout notes:\n", + "- **Irradiance estimates** (GHI/DHI/DNI) from NASA POWER and CAMS land in a wide table `irradiance_daily`, distinguished by the `source` column.\n", + "- **Auxiliary variables** land in long-format companion tables, one per source: `nasa_daily_vars_long`, `cams_daily_vars_long`, `merra_daily_vars_long`. Same schema, easy to UNION when assembling features.\n", + "- **MODIS** has its own `modis_observations` table (not a `_long` companion) because its products run at different cadences (daily / 8-day / 16-day) and several are multi-band.\n", + "- **Ground truth** lives in `ground_measurements` (curated) and `ground_measurements_raw` (audit trail of the parsed CSVs).\n", + "- **`dim_variable`** is the canonical catalog: one row per `(variable_id, source)` with units and metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "439e7e29", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:52.264962470Z", + "start_time": "2026-05-11T07:22:51.880094165Z" + } + }, + "outputs": [], + "source": [ + "# Browse the variable catalog. Whenever you write a FeatureSelection\n", + "# (NB 02) or a FeatureSpec (NB 03), every variable_id you reference must\n", + "# appear below \u2014 so this is the cell to consult when picking inputs.\n", + "#\n", + "# Each row carries a `description` field explaining what the variable\n", + "# measures; the markdown cell that follows groups them by physical role.\n", + "print(\"Source enum members:\", [s.value for s in Source])\n", + "\n", + "catalog_df = variables_to_dataframe(VariableCatalog.all_variables())\n", + "print(f\"Catalog: {len(catalog_df)} entries across {catalog_df['source'].nunique()} sources.\\n\")\n", + "\n", + "# Show description unwrapped so the per-variable rationale is visible.\n", + "pd.set_option(\"display.max_colwidth\", 120)\n", + "catalog_df[[\"source\", \"variable_id\", \"display_name\", \"unit\", \"description\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "e68b7f5b", + "metadata": {}, + "source": [ + "### Why these variables (and not others)\n", + "\n", + "The catalog above is grouped by source for the table view, but the model cares about *what each variable physically represents*. Reading the rows along that axis, five categories emerge.\n", + "\n", + "**1. Radiation budget \u2014 the target and its near-relatives.** `ghi` / `dhi` / `dni` (NASA + CAMS) are the all-sky satellite estimates the model is asked to bias-correct; ground GHI is the target. CAMS additionally publishes the **clear-sky** decomposition (`ghi_clear`, `bhi_clear`, `dhi_clear`, `dni_clear`) from the McClear model, plus extraterrestrial GHI (`ghi_extra`). Dividing all-sky by clear-sky yields the **clear-sky index** `kt = ghi / ghi_clear`, a normalised cloudiness signal that strips out latitude / season / orbit effects \u2014 NB 03 uses this as a derived feature. NASA POWER pre-computes its own `clearness_index`, so we pass it through as a feature instead of re-deriving.\n", + "\n", + "**2. Atmospheric attenuation \u2014 what makes the all-sky deviate from clear-sky.** Four physical drivers are kept:\n", + "\n", + "- **Aerosols** \u2014 `aod_550`, `aod_550_adj`, `aod_840` (NASA POWER); `aod_550_extinction`, `aod_550_scattering`, `aod_550_analysis` (MERRA-2). AOD captures Saharan-dust outbreaks and biomass-burning plumes that satellite retrievals systematically under-correct over Sub-Saharan Africa. The 840 nm band is more sensitive to coarse-mode dust; MERRA-2's extinction / scattering split lets the model distinguish absorbing aerosols (smoke) from purely scattering ones (sulfate haze).\n", + "- **Water vapour** \u2014 `precipitable_water` (NASA POWER and MERRA-2). Major near-infrared absorber and a proxy for cloudiness regime.\n", + "- **Clouds** \u2014 `cloud_amount`, `cloud_amount_dat`, `cloud_visible_optical_depth`. The single biggest source of GHI variance \u2014 the dominant lever the model has.\n", + "- **Ozone** \u2014 `total_column_ozone`. UV absorber; small but non-negligible effect on the shortwave budget.\n", + "\n", + "**3. Surface state \u2014 what the radiation interacts with at the bottom.** Three flavours of NASA POWER albedo (`surface_albedo`, `all_sky_surface_albedo`, `clear_sky_albedo`) cover bulk reflectivity. MODIS adds higher-resolution surface descriptors: nadir red-band reflectance (`MCD43A4_Nadir_Reflectance_Band1`, 500 m), daytime land-surface temperature (`MOD11A2_LST_Day_1km`, 1 km), and NDVI vegetation index (`MOD13Q1_250m_16_days_NDVI`, 250 m). These are slow-changing land-cover proxies that distinguish, e.g., desert from cropland at scales finer than POWER's 0.5\u00b0 grid.\n", + "\n", + "**4. Boundary-layer meteorology \u2014 secondary modulators.** Standard NASA POWER met fields: `temperature`, `temperature_range`, `specific_humidity`, `relative_humidity`, `surface_pressure`, `wind_speed`, `northern_wind`, `surface_air_density`, `airmass`, `planetary_boundary`, `surface_roughness`, `zero_plane_displacement`. They influence the model's ability to disambiguate clear-but-hazy from cloudy days, especially in the dry season.\n", + "\n", + "**5. Hydrology \u2014 physically informative even if rarely the dominant signal.** `precipitation_corrected`, `evaporation_land`, `evapotranspiration_energy`, `surface_soil_wetness`, `longwave_downward_irr`. Useful for distinguishing convective-rainy from dry-clear regimes; also potential targets if the project ever expands beyond GHI.\n", + "\n", + "**What we deliberately don't ingest.** NASA POWER offers ~150 daily variables and CAMS ~30 \u2014 we keep only the subset above. Variables omitted because they don't carry signal for *GHI* bias correction in *Sub-Saharan Africa*: snow depth (negligible coverage), agricultural-yield indices (downstream not upstream), sub-daily wind components (we work at daily cadence), soil-temperature profiles (not directly radiative). Adding any of them is a one-line edit to `VariableCatalog`; the warehouse just hasn't fetched data we won't use.\n", + "\n", + "The per-variable rationale is in each `VariableSpec.description` (the rightmost column of the catalog above). When in doubt about why a particular variable is on the list, find its row." + ] + }, + { + "cell_type": "markdown", + "id": "19d65a4d", + "metadata": {}, + "source": [ + "### Quick example: training vs inference\n", + "\n", + "The cleanest way to see what the warehouse delivers to the model is to load it from both sides:\n", + "\n", + "- **Training** \u2014 at a ground station, we get the *target* (ground GHI) plus the *features* (NASA + CAMS satellite GHI estimates and atmospheric variables). One call to `FeatureService.build_training_pairs(...)` assembles the join.\n", + "- **Inference** \u2014 at any (lat, lon, date) the warehouse covers, we get the same features but no target (that's what the model is going to predict). One call to `FeatureService.build_inference_features(...)` returns a single-row frame.\n", + "\n", + "The cells below load **kampala for 2024** (training) and a **central-Uganda grid point** at `(0.5179, 32.4715)` for the same year (inference), then plot them side by side. The training column shows the ground-truth line the model is learning to match; the inference column shows what's available for prediction at points where we have *no* ground measurement \u2014 which is the whole reason for the warehouse's inference cache." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a31198b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:57.162586152Z", + "start_time": "2026-05-11T07:22:53.755111906Z" + } + }, + "outputs": [], + "source": [ + "# One call to FeatureService.build_training_pairs assembles the full\n", + "# training shape: target (ground GHI), primary inputs (NASA + CAMS GHI),\n", + "# and any selected NASA atmospheric features \u2014 all aligned on (date,\n", + "# geohash5). For inference at a new (lat, lon, date) you'd call\n", + "# build_inference_features with the same set of variables.\n", + "from susse.datasets import FeatureSelection, FeatureService\n", + "\n", + "fs = FeatureService(bq=bq) # tables defaults to TableRefs(config=bq.config)\n", + "selection = FeatureSelection(\n", + " nasa_variable_ids=(\"aod_550\", \"precipitable_water\", \"cloud_amount\"),\n", + ")\n", + "example = fs.build_training_pairs(\n", + " selection=selection,\n", + " date_start=date(2024, 1, 1),\n", + " date_end=date(2024, 11, 25),\n", + " locations=(\"kampala\",),\n", + ")\n", + "\n", + "# Date-indexed view for the resampling-based plots below. Defining it\n", + "# once here keeps the plot cells order-independent.\n", + "timeseries = (\n", + " example.assign(date=pd.to_datetime(example[\"date\"]))\n", + " .set_index(\"date\")\n", + " .sort_index()\n", + ")\n", + "\n", + "print(f\"Loaded {len(example)} daily rows for kampala (2024).\")\n", + "print(\"Columns:\", list(example.columns))\n", + "example.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "838e6451", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:22:59.494633328Z", + "start_time": "2026-05-11T07:22:57.277271295Z" + } + }, + "outputs": [], + "source": [ + "# Inference-side feature lookup for a single (lat, lon, date). This is\n", + "# the call shape the portal would make in production: given a request\n", + "# location and date, return the satellite GHI estimates and the model's\n", + "# atmospheric features. Ground GHI is NOT in the result \u2014 that's what\n", + "# the model will predict.\n", + "inference_lat, inference_lon = 0.5179, 32.4715 # central Uganda grid point\n", + "inference_today = fs.build_inference_features(\n", + " selection=selection,\n", + " target_date=date(2024, 6, 15),\n", + " lat=inference_lat,\n", + " lon=inference_lon,\n", + ")\n", + "print(\"build_inference_features returns one row per (lat, lon, date):\")\n", + "inference_today" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "877159e1", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:01.907124649Z", + "start_time": "2026-05-11T07:23:00.327725521Z" + } + }, + "outputs": [], + "source": [ + "# For the year-long plot we need 365 inference rows. Calling\n", + "# build_inference_features in a loop would issue 365 BQ queries; instead\n", + "# we fetch the same shape in one query against the underlying tables.\n", + "# This is functionally what build_inference_features does internally,\n", + "# just vectorised across dates for one location.\n", + "import pygeohash\n", + "\n", + "inference_geohash = pygeohash.encode(inference_lat, inference_lon, precision=5)\n", + "inference_year = bq.query(f\"\"\"\n", + "WITH irr AS (\n", + " SELECT date,\n", + " MAX(IF(source='NASA', ghi_kwh_m2_day, NULL)) AS sat_ghi_nasa_kwh_m2_day,\n", + " MAX(IF(source='CAMS', ghi_kwh_m2_day, NULL)) AS sat_ghi_cams_kwh_m2_day\n", + " FROM `{tables.irradiance_daily}`\n", + " WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-11-25')\n", + " AND geohash5 = '{inference_geohash}'\n", + " GROUP BY date\n", + "),\n", + "nasa AS (\n", + " SELECT * FROM (\n", + " SELECT date, variable_id, value\n", + " FROM `{tables.nasa_daily_vars_long}`\n", + " WHERE date BETWEEN DATE('2024-01-01') AND DATE('2024-11-25')\n", + " AND geohash5 = '{inference_geohash}'\n", + " AND variable_id IN ('aod_550', 'precipitable_water', 'cloud_amount')\n", + " )\n", + " PIVOT (ANY_VALUE(value) FOR variable_id IN (\n", + " 'aod_550' AS nasa_aod_550,\n", + " 'precipitable_water' AS nasa_precipitable_water,\n", + " 'cloud_amount' AS nasa_cloud_amount\n", + " ))\n", + ")\n", + "SELECT * FROM irr LEFT JOIN nasa USING (date)\n", + "ORDER BY date\n", + "\"\"\")\n", + "\n", + "inference_ts = (\n", + " inference_year.assign(date=pd.to_datetime(inference_year[\"date\"]))\n", + " .set_index(\"date\")\n", + " .sort_index()\n", + ")\n", + "print(f\"Loaded {len(inference_year)} inference rows for ({inference_lat}, {inference_lon}).\")\n", + "inference_year.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1bf36e3", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:02.524680660Z", + "start_time": "2026-05-11T07:23:02.070701179Z" + } + }, + "outputs": [], + "source": [ + "# Two-column GHI plot \u2014 training (with ground truth) vs inference (no\n", + "# ground truth). Weekly-averaged so the seasonal pattern is visible\n", + "# under the daily noise.\n", + "import matplotlib.pyplot as plt\n", + "\n", + "weekly_train_ghi = (\n", + " timeseries[\n", + " [\"y_ghi_kwh_m2_day\", \"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"]\n", + " ]\n", + " .resample(\"W\")\n", + " .mean()\n", + ")\n", + "weekly_inf_ghi = (\n", + " inference_ts[[\"sat_ghi_nasa_kwh_m2_day\", \"sat_ghi_cams_kwh_m2_day\"]]\n", + " .resample(\"W\")\n", + " .mean()\n", + ")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4.5), sharey=True)\n", + "\n", + "# Left: training (kampala, 2024) \u2014 model sees ground GHI as the target.\n", + "weekly_train_ghi[\"sat_ghi_nasa_kwh_m2_day\"].plot(ax=axes[0], label=\"NASA POWER\", color=\"C0\")\n", + "weekly_train_ghi[\"sat_ghi_cams_kwh_m2_day\"].plot(ax=axes[0], label=\"CAMS\", color=\"C1\")\n", + "weekly_train_ghi[\"y_ghi_kwh_m2_day\"].plot(ax=axes[0], label=\"Ground truth\", color=\"black\", linewidth=2)\n", + "axes[0].set_title(f\"Training: {example['location'].iloc[0]} (Uganda)\")\n", + "axes[0].set_ylabel(\"GHI (kWh/m\u00b2/day, weekly mean)\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "axes[0].legend()\n", + "\n", + "# Right: inference (Uganda grid point, 2024) \u2014 same features but no ground truth.\n", + "weekly_inf_ghi[\"sat_ghi_nasa_kwh_m2_day\"].plot(ax=axes[1], label=\"NASA POWER\", color=\"C0\")\n", + "weekly_inf_ghi[\"sat_ghi_cams_kwh_m2_day\"].plot(ax=axes[1], label=\"CAMS\", color=\"C1\")\n", + "axes[1].set_title(f\"Inference: ({inference_lat}, {inference_lon}) (central Uganda)\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "axes[1].legend()\n", + "\n", + "plt.suptitle(\"Weekly mean GHI \u2014 training has the target line; inference does not\", y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d329cf7f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:04.212968651Z", + "start_time": "2026-05-11T07:23:02.899611156Z" + } + }, + "outputs": [], + "source": [ + "# Two-column atmospheric features \u2014 same three NASA POWER variables at\n", + "# both locations. These go into the model alongside the satellite GHI\n", + "# estimate. Inference uses identical feature shape to training; only\n", + "# the ground-truth target is missing on the inference side.\n", + "weekly_train_vars = (\n", + " timeseries[[\"nasa_aod_550\", \"nasa_precipitable_water\", \"nasa_cloud_amount\"]]\n", + " .resample(\"W\")\n", + " .mean()\n", + ")\n", + "weekly_inf_vars = (\n", + " inference_ts[[\"nasa_aod_550\", \"nasa_precipitable_water\", \"nasa_cloud_amount\"]]\n", + " .resample(\"W\")\n", + " .mean()\n", + ")\n", + "\n", + "fig, axes = plt.subplots(3, 2, figsize=(14, 9), sharex=True, sharey=\"row\")\n", + "\n", + "# Left column: training location.\n", + "weekly_train_vars[\"nasa_aod_550\"].plot(ax=axes[0, 0], color=\"C2\")\n", + "axes[0, 0].set_title(f\"Training: {example['location'].iloc[0]}\")\n", + "axes[0, 0].set_ylabel(\"AOD @ 550 nm\")\n", + "weekly_train_vars[\"nasa_precipitable_water\"].plot(ax=axes[1, 0], color=\"C3\")\n", + "axes[1, 0].set_ylabel(\"Precipitable water (cm)\")\n", + "weekly_train_vars[\"nasa_cloud_amount\"].plot(ax=axes[2, 0], color=\"C4\")\n", + "axes[2, 0].set_ylabel(\"Cloud amount (%)\")\n", + "axes[2, 0].set_xlabel(\"Week\")\n", + "\n", + "# Right column: inference location.\n", + "weekly_inf_vars[\"nasa_aod_550\"].plot(ax=axes[0, 1], color=\"C2\")\n", + "axes[0, 1].set_title(f\"Inference: ({inference_lat}, {inference_lon})\")\n", + "weekly_inf_vars[\"nasa_precipitable_water\"].plot(ax=axes[1, 1], color=\"C3\")\n", + "weekly_inf_vars[\"nasa_cloud_amount\"].plot(ax=axes[2, 1], color=\"C4\")\n", + "axes[2, 1].set_xlabel(\"Week\")\n", + "\n", + "for ax in axes.flat:\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "plt.suptitle(\"NASA POWER atmospheric features \u2014 weekly mean\", y=1.00)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "25b8fd01", + "metadata": {}, + "source": [ + "## 3. Extending the warehouse\n", + "\n", + "Whenever you want to train on a new station, score a new region, or pull a new variable, you ingest first. There are exactly three patterns, distinguished by what drives the location list:\n", + "\n", + "| Pattern | Plan type | Use when |\n", + "|---|---|---|\n", + "| 1 | `NamedLocationsPlan` | You have specific points in mind \u2014 typically the 28 ground stations whose history you want covered for training. |\n", + "| 2 | `GridPlan` | You want uniform geographic coverage of a region \u2014 typically Uganda, or eventually all of sub-Saharan Africa, for the inference cache. |\n", + "| 3 | `GroundFilePlan` | A new ground-truth dataset has arrived from a station owner, partner, or publication. |\n", + "\n", + "All three share the same lifecycle: **fetch \u2192 validate \u2192 server-side MERGE**. They differ only in how they resolve the location list and which fetcher they call. Two non-negotiable properties hold across every pattern:\n", + "\n", + "- **Idempotency** \u2014 the coverage check pre-queries BigQuery for existing keys (date, geohash5, variable, source) and skips API calls for slices already loaded. Re-running a job is cheap. Ctrl+C mid-run is safe.\n", + "- **Migrations vs notebook cells** \u2014 the demo cells below run with `RUN_INGEST=False` by default and exist as reference. Real multi-station ingest belongs in `warehouse/migrations/*.py` (idempotent, dated, versioned in git). Don't flip `RUN_INGEST=True` and rerun the whole notebook; write a migration. See [`docs/architecture.md`](../../docs/architecture.md) for the full operational guide." + ] + }, + { + "cell_type": "markdown", + "id": "7d728a6030bf6d6a", + "metadata": {}, + "source": [ + "### Pattern 1 \u2014 Named-location satellite ingest\n", + "\n", + "Fetch a satellite source for a discrete set of locations \u2014 typically the ground stations whose history we want covered for training. Below: a plan for **Uganda 2025** at ground-station coordinates. The job's coverage check skips slices already cached, so re-running is cheap." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9369449e9654b91", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:05.547834297Z", + "start_time": "2026-05-11T07:23:04.568653540Z" + } + }, + "outputs": [], + "source": [ + "# Pull station coordinates from the warehouse rather than hardcoding them.\n", + "stations = bq.query(f\"\"\"\n", + "SELECT location, ROUND(AVG(lat), 6) AS lat, ROUND(AVG(lon), 6) AS lon\n", + "FROM `{tables.ground_measurements}`\n", + "WHERE location IN ('kampala','lira','tororo','soroti','wadelai')\n", + "GROUP BY location\n", + "ORDER BY location\n", + "\"\"\")\n", + "stations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "153899c1061070cd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:05.845155372Z", + "start_time": "2026-05-11T07:23:05.695046566Z" + } + }, + "outputs": [], + "source": [ + "uganda_locations = tuple(\n", + " LocationSpec(name=row.location, lat=float(row.lat), lon=float(row.lon))\n", + " for row in stations.itertuples(index=False)\n", + " if row.location in {\"kampala\", \"lira\", \"tororo\", \"soroti\", \"wadelai\"}\n", + ")\n", + "\n", + "uganda_2025 = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 31))\n", + "\n", + "# Fetch the full set of NASA POWER variables this project tracks.\n", + "nasa_vars = VariableCatalog.for_source(Source.NASA_POWER)\n", + "cams_vars = VariableCatalog.for_source(Source.CAMS)\n", + "\n", + "uganda_nasa_plan = NamedLocationsPlan(\n", + " source=Source.NASA_POWER,\n", + " date_range=uganda_2025,\n", + " locations=uganda_locations,\n", + " variables=nasa_vars,\n", + ")\n", + "uganda_cams_plan = NamedLocationsPlan(\n", + " source=Source.CAMS,\n", + " date_range=uganda_2025,\n", + " locations=uganda_locations,\n", + " variables=cams_vars,\n", + ")\n", + "\n", + "for plan in (uganda_nasa_plan, uganda_cams_plan):\n", + " print(plan.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5459923416d4066", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:06.180384351Z", + "start_time": "2026-05-11T07:23:06.033174006Z" + } + }, + "outputs": [], + "source": [ + "if RUN_INGEST:\n", + " nasa_job = NasaPowerSatelliteJob(bq=bq, refs=tables)\n", + " for plan in (uganda_nasa_plan,):\n", + " result = nasa_job.run(plan)\n", + " print(result.summary())\n", + "else:\n", + " print(\"RUN_INGEST=False \u2014 skipping NASA POWER named-location runs.\")\n", + " print(\"Each plan above describes one job invocation; flip RUN_INGEST to execute.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74c7db804619b48a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:06.350328857Z", + "start_time": "2026-05-11T07:23:06.263960630Z" + } + }, + "outputs": [], + "source": [ + "# CAMS requires a registered email. Set CAMS_EMAIL in your environment\n", + "# (or .env) before running this cell.\n", + "if RUN_INGEST and os.getenv(\"CAMS_EMAIL\"):\n", + " cams_job = CamsSatelliteJob(bq=bq, refs=tables)\n", + " for plan in (uganda_cams_plan,):\n", + " result = cams_job.run(plan)\n", + " print(result.summary())\n", + "elif RUN_INGEST:\n", + " print(\"RUN_INGEST=True but CAMS_EMAIL is not set \u2014 skipping CAMS runs.\")\n", + " print(\"Register an email at https://www.soda-pro.com/ and put it in .env as CAMS_EMAIL.\")\n", + "else:\n", + " print(\"RUN_INGEST=False \u2014 skipping CAMS named-location runs.\")" + ] + }, + { + "cell_type": "markdown", + "id": "5ade74997852c234", + "metadata": {}, + "source": [ + "### Pattern 2 \u2014 Grid satellite ingest\n", + "\n", + "The portal needs a satellite GHI estimate for any (lat, lon) in the region a user might query. Pre-cache them with a `GridPlan` over a bounding box at the source's native resolution, so the portal can serve cached estimates directly. Below: a tiny demo grid over a 0.5\u00b0 \u00d7 0.5\u00b0 patch of central Uganda at CAMS native 0.05\u00b0 (121 grid points). Real region-wide grids run as separate, deliberate batches via migrations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ff1ed1dbb4e60ed", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:06.532533453Z", + "start_time": "2026-05-11T07:23:06.453752937Z" + } + }, + "outputs": [], + "source": [ + "uganda_demo_bbox = BoundingBox(\n", + " min_lat=0.10, max_lat=0.60,\n", + " min_lon=32.30, max_lon=32.80,\n", + ")\n", + "demo_grid = GridSpec(bbox=uganda_demo_bbox, lat_step=0.05, lon_step=0.05)\n", + "demo_dates = DateRange(start=date(2025, 1, 1), end=date(2025, 1, 7))\n", + "\n", + "demo_grid_plan = GridPlan(\n", + " source=Source.CAMS,\n", + " date_range=demo_dates,\n", + " grid=demo_grid,\n", + " variables=cams_vars,\n", + ")\n", + "print(demo_grid_plan.describe())\n", + "print(f\"Grid points: {demo_grid.n_points}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47715ce46ee836b7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:06.711786158Z", + "start_time": "2026-05-11T07:23:06.631304851Z" + } + }, + "outputs": [], + "source": [ + "if RUN_INGEST and os.getenv(\"CAMS_EMAIL\"):\n", + " cams_job = CamsSatelliteJob(bq=bq, refs=tables)\n", + " grid_result = cams_job.run(demo_grid_plan)\n", + " print(grid_result.summary())\n", + "else:\n", + " print(\"Grid ingest skipped (RUN_INGEST=False or CAMS_EMAIL not set).\")\n", + " print(\"This plan would call CAMS once per grid point that isn't already cached.\")" + ] + }, + { + "cell_type": "markdown", + "id": "adbc46127abc515c", + "metadata": {}, + "source": [ + "### Pattern 3 \u2014 Ground-truth ingest\n", + "\n", + "Future ground-truth additions arrive in heterogeneous formats from publications, ministries, and partner organisations. A `GroundSourceAdapter` parses one provider's CSV layout; downstream the curation function and MERGE loader are unchanged.\n", + "\n", + "The Makerere and Min.-of-Energy CSVs (and any further providers added under `data/ground_measurements/`) all share `(datetime, ghi, location, latitude, longitude)`, so a single `StandardCsvAdapter` handles them. New layout = new adapter subclass.\n", + "\n", + "The demo runs end-to-end against the real Min.-of-Energy Soroti file. Because every row is already in `ground_measurements_raw`, the idempotent coverage check causes the job to add zero rows \u2014 a real safety check that the pipeline doesn't double-write." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33f110a8be26618e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:06.805049005Z", + "start_time": "2026-05-11T07:23:06.717218848Z" + } + }, + "outputs": [], + "source": [ + "# Real on-disk file from the project's data directory.\n", + "soroti_csv = Path(\"../../data/ground_measurements/ministry_energy_ug/soroti.csv\").resolve()\n", + "assert soroti_csv.exists(), f\"Expected {soroti_csv} to exist.\"\n", + "\n", + "adapter = StandardCsvAdapter(source_id=\"min_energy_ug\")\n", + "raw_df = adapter.parse(soroti_csv)\n", + "print(f\"Parsed {len(raw_df)} raw rows.\")\n", + "raw_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6072e44b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:08.083504068Z", + "start_time": "2026-05-11T07:23:06.916130663Z" + } + }, + "outputs": [], + "source": [ + "# QC levels currently in use. `qc_level` is a free-form string stamped\n", + "# on every curated row by `CurationOptions(qc_level=...)`. Downstream\n", + "# (NB 02's FeatureSelection) lets you keep only the tags you trust \u2014\n", + "# e.g. `qc_levels=(\"pass\",)` to exclude `auto` tags from training.\n", + "qc_levels_df = bq.query(f\"\"\"\n", + "SELECT qc_level, COUNT(*) AS n_rows, COUNT(DISTINCT location) AS n_stations\n", + "FROM `{tables.ground_measurements}`\n", + "GROUP BY qc_level\n", + "ORDER BY n_rows DESC\n", + "\"\"\")\n", + "qc_levels_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c15d2d3fe6eb47", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:10.242805210Z", + "start_time": "2026-05-11T07:23:10.039107552Z" + } + }, + "outputs": [], + "source": [ + "# Curation is a pure transformation \u2014 no I/O. Run it on the parsed raw rows\n", + "# to see what would land in `ground_measurements`.\n", + "curated_df = curate_ground(\n", + " raw_df,\n", + " CurationOptions(qc_level=\"auto\", version=\"v1\", geohash_precision=5),\n", + ")\n", + "print(\"Curated columns:\", list(curated_df.columns))\n", + "curated_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86d9bc8e672ac61e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-11T07:23:11.404399299Z", + "start_time": "2026-05-11T07:23:11.280575019Z" + } + }, + "outputs": [], + "source": [ + "# Run the full ingest path. Idempotent: if the data is already in the warehouse\n", + "# (which it is, for this CSV), the job adds zero rows and reports them all as\n", + "# already cached.\n", + "if RUN_INGEST:\n", + " ground_plan = GroundFilePlan(\n", + " source_id=\"CBE\",\n", + " file_path=somalia_csv,\n", + " adapter_id=adapter.adapter_id,\n", + " )\n", + " ground_job = GroundIngestJob(bq=bq, adapter=adapter, refs=tables)\n", + " ground_result = ground_job.run(ground_plan)\n", + " print(ground_result.summary())\n", + "else:\n", + " print(\"Ground ingest skipped (RUN_INGEST=False).\")" + ] + }, + { + "cell_type": "markdown", + "id": "383b2847", + "metadata": {}, + "source": [ + "### Extension recipes\n", + "\n", + "| To add\u2026 | Do this |\n", + "|---|---|\n", + "| **A new ground station** | Drop the CSV at `data/ground_measurements//.csv`, pick or write a `GroundSourceAdapter`, run `GroundIngestJob` (Pattern 3), then re-run `2026-05-08_a6_ingest_28_ground_stations.py` \u2014 it pulls station metadata directly from `ground_measurements` and the coverage check skips already-ingested ones. |\n", + "| **A new ground-truth source with a different CSV layout** | Subclass `GroundSourceAdapter` and implement `parse(file_path) -> DataFrame` returning the canonical `(datetime, ghi, location, latitude, longitude)` schema. Everything downstream is unchanged. The adapter is the only thing that knows about the partner's column layout. |\n", + "| **A new satellite source** | Add a fetcher under `susse.api_clients//`, a `Source` enum value, `VariableSpec`s in `VariableCatalog`, a `TableSchema` + DDL + `CREATE TABLE` migration, a `BaseSatelliteJob` subclass, and an ingest migration. The MERRA-2 wire-up (Phase B, files prefixed `b1`\u2013`b8`) is the worked template. |\n", + "| **A new region (inference coverage)** | Build a `GridPlan` with a `BoundingBox` for the region and run an existing satellite job (Pattern 2 below). Cost scales linearly with `n_grid_points \u00d7 n_days \u00d7 n_sources`; CAMS at native 0.05\u00b0 is the expensive one. Densify pragmatically \u2014 start coarser, densify on demand. |\n", + "| **A new variable from an existing source** | Add a `VariableSpec` to the relevant `VariableCatalog` block, run the dim_variable migration to register it, then re-run the source's ingest. The job uses the catalog as its source-of-truth for what to fetch. |\n", + "\n", + "The pattern demos that follow are read-only by default \u2014 they describe what *would* happen." + ] + }, + { + "cell_type": "markdown", + "id": "8024f885fef7f9cb", + "metadata": {}, + "source": [ + "## What's next\n", + "\n", + "This is the first of five tutorial notebooks that walk through the SuSSE pipeline end-to-end:\n", + "\n", + "- **Notebook 02** assembles a versioned **TrainingDataset** snapshot from the warehouse \u2014 a parquet artifact tying a specific slice of the warehouse to a training run.\n", + "- **Notebooks 03\u201305** build the model side: preprocessor, model factory, trainer with native W&B integration.\n", + "- **Notebooks 06 and 07** (evaluation, inference API) are planned and not yet shipped \u2014 see the per-notebook README for the current status.\n", + "\n", + "You're ready for notebook 02 when the pair-availability cell shows non-zero NASA + CAMS pairs at every station, the `dim_variable` catalog is populated, and you've run the MERRA-2 ingest if you want those features in the dataset.\n", + "\n", + "If something here doesn't add up \u2014 a coverage warning, a confusing class name, a missing extension path \u2014 **edit this notebook**. It's the first thing the next person on this codebase will read." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.1)", + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/warehouse/migrations/2026-05-08_a10_drop_solar_zenith_angle.py b/warehouse/migrations/2026-05-08_a10_drop_solar_zenith_angle.py new file mode 100644 index 00000000..53a9e797 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a10_drop_solar_zenith_angle.py @@ -0,0 +1,112 @@ +"""A10 — remove ``solar_zenith_angle`` from the catalog and warehouse. + +What it changes +--------------- +``DELETE FROM dim_variable WHERE variable_id = 'solar_zenith_angle'``. +The catalog (``VariableCatalog.NASA_POWER_VARIABLES``) has been edited +in the same commit to drop the corresponding ``VariableSpec``. + +Why +--- +The variable was added to the catalog in A2 because the api_client +exposes ``SOLAR_ZENITH_ANGLE = "SZA"``. Empirically, however, NASA +POWER does *not* serve SZA at daily resolution — every request returns +the ``-999`` fill value, which the loader filters out. The result: +``nasa_daily_vars_long`` ends up with zero rows for SZA, and the +per-station coverage check treats SZA as always-missing for every +day. That triggered a full re-fetch of every station on every A6 run +(none of the existing rows could ever satisfy the SZA part of the +plan), wasting NASA quota and ingest time. + +There is no SZA data to delete from ``nasa_daily_vars_long`` (the +loader never wrote any). Only ``dim_variable`` needs cleaning. + +Expected effect +--------------- +* No API calls. +* Single ``DELETE`` statement. +* Pre: ``dim_variable`` has 1 row with ``variable_id='solar_zenith_angle'``. +* Post: 0 rows. +* Idempotent: re-running with the row already absent is a no-op. + +Reversal +-------- +Re-add the ``VariableSpec`` to ``VariableCatalog.NASA_POWER_VARIABLES`` +and re-run ``populate_dim_variable``. Only do this once you've verified +NASA POWER actually serves daily SZA values for the locations you care +about. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a10_drop_solar_zenith_angle" +_log = logging.getLogger(_MIGRATION_ID) + +_VARIABLE_ID = "solar_zenith_angle" + + +def _count_rows(bq: BigQueryClient, refs: TableRefs) -> int: + df = bq.query( + f"SELECT COUNT(*) AS n FROM `{refs.dim_variable}` " + f"WHERE variable_id = '{_VARIABLE_ID}'" + ) + return int(df.iloc[0]["n"]) + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + pre = _count_rows(bq, refs) + _log.info( + "Pre: %d row(s) in dim_variable with variable_id='%s'.", + pre, _VARIABLE_ID, + ) + if pre == 0: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="No solar_zenith_angle row to delete — already clean.", + ) + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would DELETE {pre} row(s).", + ) + bq.execute_ddl( + f"DELETE FROM `{refs.dim_variable}` " + f"WHERE variable_id = '{_VARIABLE_ID}'" + ) + post = _count_rows(bq, refs) + if post != 0: + raise RuntimeError( + f"DELETE left {post} row(s) with variable_id='{_VARIABLE_ID}'. " + f"Investigate before re-running." + ) + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=pre, + notes=f"Deleted {pre} '{_VARIABLE_ID}' row(s) from dim_variable.", + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Delete solar_zenith_angle from dim_variable (NASA POWER " + "doesn't serve it daily; entry triggered spurious re-fetches)." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_a1_populate_dim_variable.py b/warehouse/migrations/2026-05-08_a1_populate_dim_variable.py new file mode 100644 index 00000000..e47f0835 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a1_populate_dim_variable.py @@ -0,0 +1,138 @@ +"""A1 — populate ``dim_variable`` with the full NASA POWER + CAMS catalog. + +What it changes +--------------- +Upserts 41 rows into ``solar_warehouse.dim_variable``: + +* 32 NASA POWER rows (29 auxiliary + 3 irradiance), ``source = 'NASA'``. +* 9 CAMS rows (6 auxiliary + 3 irradiance), ``source = 'CAMS'``. + +Why +--- +Before this migration the table contained only 4 NASA rows, all carrying +the sentinel ``spatial_resolution_km = -51.0``. Phase A1 of the warehouse +consistency plan: bring the catalog into alignment with what's actually +ingested into ``nasa_daily_vars_long`` and what will be ingested into +``cams_daily_vars_long``. + +Expected effect +--------------- +* Pre: 4 rows, all with bogus ``spatial_resolution_km``. +* Post: 35 rows, correct resolutions (NASA POWER 55.5 km, CAMS 5.5 km). +* The 4 pre-existing variable_ids (``aod_550``, ``relative_humidity``, + ``surface_pressure``, ``temperature``) are UPDATED in place by the + MERGE on ``(variable_id, source)``; their sentinel values are replaced. +* No API calls. Single MERGE statement. Subsecond runtime. + +Reversal +-------- +Not strictly reversible (the pre-existing 4 rows had bogus values that +we would not want to restore). To roll back to a different catalog, +edit ``VariableCatalog`` and re-run; MERGE is idempotent. +""" + +from __future__ import annotations + +import logging +import sys + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + populate_dim_variable, + variables_to_dataframe, +) + +# Allow running this file as a script: ``python warehouse/migrations/.py``. +sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a1_populate_dim_variable" +_log = logging.getLogger(_MIGRATION_ID) + + +def _read_state(bq: BigQueryClient, refs: TableRefs) -> dict: + """Snapshot ``dim_variable`` for before/after comparison.""" + summary = bq.query( + f""" + SELECT + COUNT(*) AS n_rows, + COUNTIF(spatial_resolution_km < 0) AS n_sentinel, + COUNTIF(source = 'NASA') AS n_nasa, + COUNTIF(source = 'CAMS') AS n_cams + FROM `{refs.dim_variable}` + """ + ) + return summary.iloc[0].to_dict() + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + catalog = VariableCatalog.all_variables() + df = variables_to_dataframe(catalog) + _log.info( + "Catalog has %d variables (%d NASA, %d CAMS).", + len(catalog), + sum(1 for v in catalog if v.source.value == "NASA"), + sum(1 for v in catalog if v.source.value == "CAMS"), + ) + + pre = _read_state(bq, refs) + _log.info( + "Pre: dim_variable n_rows=%s sentinel=%s NASA=%s CAMS=%s", + pre["n_rows"], pre["n_sentinel"], pre["n_nasa"], pre["n_cams"], + ) + + if dry_run: + _log.info( + "Dry run — would MERGE %d rows into %s. Sample:\n%s", + len(df), refs.dim_variable, df.head(3).to_string(index=False), + ) + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would have written {len(df)} rows.", + ) + + written = populate_dim_variable( + bq, table_fqn=refs.dim_variable, variables=catalog + ) + + post = _read_state(bq, refs) + _log.info( + "Post: dim_variable n_rows=%s sentinel=%s NASA=%s CAMS=%s", + post["n_rows"], post["n_sentinel"], post["n_nasa"], post["n_cams"], + ) + + if post["n_sentinel"] != 0: + raise RuntimeError( + f"dim_variable still has {post['n_sentinel']} rows with " + f"spatial_resolution_km < 0 after the MERGE. Investigate before " + f"continuing — the catalog rows should have replaced the sentinels." + ) + if post["n_rows"] < len(df): + raise RuntimeError( + f"dim_variable has {post['n_rows']} rows after MERGE, expected " + f">= {len(df)}. Something went wrong with the upsert." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=written, + notes=( + f"dim_variable populated: pre n_rows={pre['n_rows']} → " + f"post n_rows={post['n_rows']}; sentinel rows cleared " + f"({pre['n_sentinel']} → 0)." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Populate dim_variable with the full NASA POWER + CAMS catalog.", + ) + ) diff --git a/warehouse/migrations/2026-05-08_a2_fix_typos.py b/warehouse/migrations/2026-05-08_a2_fix_typos.py new file mode 100644 index 00000000..2943c334 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a2_fix_typos.py @@ -0,0 +1,181 @@ +"""A2 — fix variable_id typos in the warehouse and refresh the catalog. + +What it changes +--------------- +1. Renames ``variable_id = 'arimass'`` rows to ``'airmass'`` in + ``nasa_daily_vars_long`` (~718,092 rows). +2. Renames the corresponding ``dim_variable`` row from ``'arimass'`` to + ``'airmass'`` (1 row). +3. Re-MERGES the full ``VariableCatalog`` into ``dim_variable`` so the + newly-added ``solar_zenith_angle`` entry and any other catalog edits + land. After this the table holds 42 rows (33 NASA + 9 CAMS). + +Why +--- +The legacy ingest path used the misspelt ``arimass`` (introduced upstream +when the api_client enum had ``ARIMASS = "AIRMASS"``). That enum was +fixed in code as part of A2; this migration fixes the warehouse data so +the spelling now matches everywhere. + +Also adds a previously-missing variable: ``solar_zenith_angle`` (api code +``SZA``) which was already exposed by the api_client but not present in +the catalog. + +Expected effect +--------------- +* No API calls. +* Two ``UPDATE`` statements + one MERGE (via ``populate_dim_variable``). +* Pre: ``nasa_daily_vars_long`` has rows with ``variable_id = 'arimass'``; + ``dim_variable`` has 41 rows (no ``solar_zenith_angle``). +* Post: zero ``arimass`` rows in either table; ``airmass`` rows present; + ``dim_variable`` has 42 rows including ``solar_zenith_angle``. +* Idempotent: re-running with no ``arimass`` rows present is a no-op. + +Reversal +-------- +Reversible by running an inverse UPDATE (``airmass → arimass``) but the +api_client and catalog have moved on. Only roll back if you also revert +the code changes from the same commit. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + populate_dim_variable, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a2_fix_typos" +_log = logging.getLogger(_MIGRATION_ID) + +_OLD_VAR_ID = "arimass" +_NEW_VAR_ID = "airmass" + + +def _count_rows_with_variable_id( + bq: BigQueryClient, table_fqn: str, variable_id: str +) -> int: + """Count rows where ``variable_id`` equals the given value.""" + df = bq.query( + f"SELECT COUNT(*) AS n FROM `{table_fqn}` " + f"WHERE variable_id = '{variable_id}'" + ) + return int(df.iloc[0]["n"]) + + +def _rename_variable_id( + bq: BigQueryClient, table_fqn: str, old: str, new: str, dry_run: bool +) -> int: + """UPDATE ``variable_id`` from ``old`` to ``new`` and return rows changed.""" + pre = _count_rows_with_variable_id(bq, table_fqn, old) + if pre == 0: + _log.info( + "%s: zero rows with variable_id='%s' — nothing to rename.", + table_fqn, old, + ) + return 0 + _log.info( + "%s: %d rows with variable_id='%s' will be renamed to '%s'.", + table_fqn, pre, old, new, + ) + if dry_run: + return pre + bq.execute_ddl( + f"UPDATE `{table_fqn}` " + f"SET variable_id = '{new}' " + f"WHERE variable_id = '{old}'" + ) + post_old = _count_rows_with_variable_id(bq, table_fqn, old) + post_new = _count_rows_with_variable_id(bq, table_fqn, new) + if post_old != 0: + raise RuntimeError( + f"{table_fqn}: UPDATE left {post_old} rows still labelled '{old}'. " + f"Aborting; investigate before re-running." + ) + _log.info( + "%s: rename complete — '%s'=0 rows, '%s'=%d rows.", + table_fqn, old, new, post_new, + ) + return pre + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + + # 1. Rename arimass → airmass in the long table (the bulk of the rows). + long_renamed = _rename_variable_id( + bq, refs.nasa_daily_vars_long, _OLD_VAR_ID, _NEW_VAR_ID, dry_run + ) + + # 2. Rename arimass → airmass in dim_variable (1 row, but for consistency). + dim_renamed = _rename_variable_id( + bq, refs.dim_variable, _OLD_VAR_ID, _NEW_VAR_ID, dry_run + ) + + # 3. Re-MERGE the full catalog so the new solar_zenith_angle row lands + # and any updated metadata (display names, descriptions) refreshes. + catalog = VariableCatalog.all_variables() + pre_dim_n = bq.query( + f"SELECT COUNT(*) AS n FROM `{refs.dim_variable}`" + ).iloc[0]["n"] + _log.info( + "Catalog has %d variables; dim_variable currently has %d rows.", + len(catalog), pre_dim_n, + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=long_renamed + dim_renamed, + notes=( + f"dry-run; would rename {long_renamed} long-table rows + " + f"{dim_renamed} dim_variable rows, then MERGE {len(catalog)} " + f"catalog rows." + ), + ) + + written = populate_dim_variable( + bq, table_fqn=refs.dim_variable, variables=catalog + ) + post_dim_n = int( + bq.query(f"SELECT COUNT(*) AS n FROM `{refs.dim_variable}`").iloc[0]["n"] + ) + _log.info( + "dim_variable n_rows: pre=%s, post=%s (catalog merged %d rows).", + pre_dim_n, post_dim_n, written, + ) + + if post_dim_n < len(catalog): + raise RuntimeError( + f"dim_variable has {post_dim_n} rows after MERGE, expected " + f">= {len(catalog)}. Investigate." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=long_renamed + dim_renamed + written, + notes=( + f"renamed {long_renamed} '{_OLD_VAR_ID}'→'{_NEW_VAR_ID}' rows in " + f"nasa_daily_vars_long, {dim_renamed} in dim_variable; " + f"dim_variable refreshed to {post_dim_n} rows." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Rename arimass→airmass in warehouse data; refresh dim_variable.", + ) + ) diff --git a/warehouse/migrations/2026-05-08_a3_create_cams_daily_vars_long.py b/warehouse/migrations/2026-05-08_a3_create_cams_daily_vars_long.py new file mode 100644 index 00000000..113d598e --- /dev/null +++ b/warehouse/migrations/2026-05-08_a3_create_cams_daily_vars_long.py @@ -0,0 +1,114 @@ +"""A3 — create the ``cams_daily_vars_long`` table. + +What it changes +--------------- +Creates an empty ``solar_warehouse.cams_daily_vars_long`` table from the +DDL at ``warehouse/sql/00_schema/cams_daily_vars_long.sql``. The DDL uses +``CREATE TABLE IF NOT EXISTS`` so re-running this migration is a no-op +once the table exists. + +Why +--- +The schema mirrors ``nasa_daily_vars_long`` but for the CAMS source. The +DDL has been in the repo since the warehouse_ops refactor (commit +3387c09c) but never executed against BigQuery. A4 needs the table to +exist so it can MERGE the CAMS auxiliary variables in. + +Expected effect +--------------- +* No API calls. +* Single ``CREATE TABLE IF NOT EXISTS`` statement, partitioned by date, + clustered by ``(geohash5, variable_id)``. +* Pre: table does not exist. +* Post: empty table with the schema declared in the DDL. + +Reversal +-------- +``DROP TABLE solar_warehouse.cams_daily_vars_long`` (only safe if no +other migration has loaded data into it). +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs, TableSchemas + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a3_create_cams_daily_vars_long" +_log = logging.getLogger(_MIGRATION_ID) + +# Resolved relative to the repo root, which is two levels above this file +# (warehouse/migrations/.py → warehouse/sql/...). +_DDL_PATH = ( + Path(__file__).resolve().parent.parent + / "sql" + / "00_schema" + / "cams_daily_vars_long.sql" +) + + +def _table_exists(bq: BigQueryClient, refs: TableRefs) -> bool: + """Check whether ``cams_daily_vars_long`` is present in the dataset.""" + table_id = TableSchemas.CAMS_DAILY_VARS_LONG.table_id + df = bq.query( + f"SELECT COUNT(*) AS n " + f"FROM `{bq.config.project_id}.{bq.config.dataset}.INFORMATION_SCHEMA.TABLES` " + f"WHERE table_name = '{table_id}'" + ) + return int(df.iloc[0]["n"]) > 0 + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + pre_exists = _table_exists(bq, refs) + _log.info( + "cams_daily_vars_long pre-state: %s", + "exists" if pre_exists else "missing", + ) + + if pre_exists: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="cams_daily_vars_long already exists — no-op.", + ) + + ddl = _DDL_PATH.read_text() + _log.info("Loaded DDL from %s (%d chars).", _DDL_PATH, len(ddl)) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would execute DDL from {_DDL_PATH.name}.", + ) + + bq.execute_ddl(ddl) + post_exists = _table_exists(bq, refs) + if not post_exists: + raise RuntimeError( + "cams_daily_vars_long still does not exist after CREATE TABLE — " + "DDL execution did not behave as expected." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="cams_daily_vars_long created (empty table).", + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Create the cams_daily_vars_long table from its DDL.", + ) + ) diff --git a/warehouse/migrations/2026-05-08_a4_backfill_cams_aux_uganda_2024.py b/warehouse/migrations/2026-05-08_a4_backfill_cams_aux_uganda_2024.py new file mode 100644 index 00000000..91a8ee31 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a4_backfill_cams_aux_uganda_2024.py @@ -0,0 +1,200 @@ +"""A4 — backfill CAMS auxiliary variables for Uganda 2024. + +What it changes +--------------- +Reads the 6 auxiliary CAMS variables (``ghi_extra``, ``ghi_clear``, +``bhi``, ``bhi_clear``, ``dhi_clear``, ``dni_clear``) from the external +``cams_daily_ext`` CSV-backed table, converts them from W/m² mean-power +to kWh/m²/day total (factor of 0.024), unpivots into long format, and +MERGEs the result into ``cams_daily_vars_long``. + +Why +--- +``irradiance_daily`` already holds the CAMS GHI/DHI/DNI for the Uganda +2024 grid. The auxiliary CAMS variables (clear-sky irradiance and +extraterrestrial GHI) live in the same external CSV but were never +loaded into the warehouse. They're useful as ML features (the kt +clearness index = GHI/GHI_clear is a key bias-correction signal). + +Geohash5 is reused from ``irradiance_daily`` (joined on lat/lon) so the +keys remain consistent with the rest of the warehouse. + +Expected effect +--------------- +* No API calls. +* Single MERGE statement. +* Pre: ``cams_daily_vars_long`` is empty (created in A3). +* Post: ``cams_daily_vars_long`` holds ~4.31M rows + (1,962 grid points × 366 days × 6 aux variables). +* Idempotent: re-running matches existing keys via the schema's + ``(date, geohash5, variable_id, source)`` MERGE key, no duplicates. + +Reversal +-------- +``DELETE FROM cams_daily_vars_long WHERE source = 'CAMS'``. Safe to +re-run A4 afterwards. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs, TableSchemas +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.types import Source + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a4_backfill_cams_aux_uganda_2024" +_log = logging.getLogger(_MIGRATION_ID) + +# Conversion factor: external CSV stores values as W/m² averaged over a +# 24-hour observation period; target column is kWh/m²/day total energy. +# kWh/m²/day = (W/m²) × 24 hours × (1 kW / 1000 W) = (W/m²) × 0.024. +_W_M2_TO_KWH_M2_DAY = 0.024 + +# External table source name. +_EXT_TABLE = "cams_daily_ext" + + +def _build_merge_sql( + cams_long_fqn: str, + irradiance_fqn: str, + ext_fqn: str, + aux_variable_ids: tuple[str, ...], +) -> str: + """Construct the MERGE that unpivots ``cams_daily_ext`` into long format. + + The unpivot column list is the 6 aux variable_ids; their string + spellings in the catalog match the column names in the external + table (verified A4). + """ + unpivot_cols = ", ".join(aux_variable_ids) + # GEOGRAPHY columns can't be used in SELECT DISTINCT, so we group by + # the (lat, lon) pair and pick ANY_VALUE for geohash5 + geog (both are + # functionally determined by lat/lon, so any row works). + return f""" + MERGE `{cams_long_fqn}` t + USING ( + WITH points AS ( + SELECT + latitude, + longitude, + ANY_VALUE(geohash5) AS geohash5, + ANY_VALUE(geog) AS geog + FROM `{irradiance_fqn}` + WHERE source = 'CAMS' + GROUP BY latitude, longitude + ), + ext_aux AS ( + SELECT + DATE(ts) AS date, + latitude, + longitude, + ghi_extra, ghi_clear, bhi, bhi_clear, dhi_clear, dni_clear + FROM `{ext_fqn}` + ), + long_unpivoted AS ( + SELECT * FROM ext_aux + UNPIVOT(raw_value FOR variable_id IN ({unpivot_cols})) + ) + SELECT + u.date, + u.latitude, + u.longitude, + p.geog, + p.geohash5, + u.variable_id, + u.raw_value * {_W_M2_TO_KWH_M2_DAY} AS value, + '{Source.CAMS.value}' AS source + FROM long_unpivoted u + JOIN points p + ON u.latitude = p.latitude + AND u.longitude = p.longitude + ) s + ON t.date = s.date + AND t.geohash5 = s.geohash5 + AND t.variable_id = s.variable_id + AND t.source = s.source + WHEN NOT MATCHED THEN + INSERT (date, latitude, longitude, geog, geohash5, + variable_id, value, source) + VALUES (s.date, s.latitude, s.longitude, s.geog, s.geohash5, + s.variable_id, s.value, s.source) + """ + + +def _count_rows(bq: BigQueryClient, table_fqn: str) -> int: + df = bq.query(f"SELECT COUNT(*) AS n FROM `{table_fqn}`") + return int(df.iloc[0]["n"]) + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + cams_long_fqn = refs.cams_daily_vars_long + irr_fqn = refs.irradiance_daily + ext_fqn = bq.config.fqn(_EXT_TABLE) + + aux_specs = VariableCatalog.auxiliary_for_source(Source.CAMS) + aux_ids = tuple(spec.variable_id for spec in aux_specs) + if not aux_ids: + raise RuntimeError( + "No auxiliary CAMS variables in the catalog. Aborting before MERGE." + ) + _log.info("CAMS auxiliary variables to backfill: %s", aux_ids) + + pre_n = _count_rows(bq, cams_long_fqn) + _log.info("%s pre-state: %d rows.", TableSchemas.CAMS_DAILY_VARS_LONG.table_id, pre_n) + + sql = _build_merge_sql(cams_long_fqn, irr_fqn, ext_fqn, aux_ids) + _log.debug("MERGE SQL:\n%s", sql) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would MERGE 6 unpivoted CAMS aux vars from " + f"{_EXT_TABLE} into cams_daily_vars_long. Pre-rows={pre_n}." + ), + ) + + bq.execute_ddl(sql) + post_n = _count_rows(bq, cams_long_fqn) + inserted = post_n - pre_n + _log.info("%s post-state: %d rows (+%d inserted).", + TableSchemas.CAMS_DAILY_VARS_LONG.table_id, post_n, inserted) + + expected = 1962 * 366 * len(aux_ids) + if inserted not in (0, expected): + _log.warning( + "Inserted row count %d is neither 0 (already-applied) nor %d " + "(1962 grid × 366 days × %d aux vars). Investigate the join " + "or unpivot logic.", + inserted, expected, len(aux_ids), + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=inserted, + notes=( + f"cams_daily_vars_long: {pre_n} → {post_n} rows (+{inserted})." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "MERGE CAMS auxiliary variables (Uganda 2024) from " + "cams_daily_ext into cams_daily_vars_long." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_a6_ingest_28_ground_stations.py b/warehouse/migrations/2026-05-08_a6_ingest_28_ground_stations.py new file mode 100644 index 00000000..74a8ec05 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a6_ingest_28_ground_stations.py @@ -0,0 +1,234 @@ +"""A6 — ingest NASA POWER + CAMS for the 28 ground-measurement stations. + +What it changes +--------------- +For each of the 28 distinct locations in ``ground_measurements``: + +* Pull NASA POWER for **every** catalog NASA variable + (``VariableCatalog.for_source(Source.NASA_POWER)``) over the station's + full ``[min(date), max(date)]`` ground-data range. +* Pull CAMS radiation (all catalog CAMS variables) over the same range. + +The fetched values land in ``nasa_daily_vars_long``, +``cams_daily_vars_long``, and ``irradiance_daily`` according to the +existing :class:`SatelliteJob` routing (irradiance subset → wide table, +everything else → the long companion). + +Why +--- +Without this step the warehouse has zero satellite/ground training pairs. +The 28 stations sit outside the existing Uganda-2024 grid footprint, so we +must pull satellite data at each station's exact (lat, lon) for its ground +date range. After this migration the pair-availability check in +``warehouse/extending_the_warehouse.ipynb`` must show +``n_nasa_pairs > 0`` and ``n_cams_pairs > 0`` for every station. + +Expected effect +--------------- +* 28 NASA POWER API calls (one per station, multi-variable per call). +* 28 CAMS API calls (one per station, multi-variable per call). +* Combined rate-limit footprint is well under both providers' daily + quotas (CAMS ~40/day, NASA POWER no published limit but generally + generous). +* Runtime: several minutes; depends on station date ranges. Long-history + stations like ``kampala`` (~12 years) take longer than recent + ``kenya_locationN`` stations. +* Idempotent: ``SatelliteJob._location_fully_cached`` checks coverage + per (geohash5, date) before issuing the API call, so a second run + skips stations whose data is already loaded. + +Prerequisites +------------- +* CAMS requires a registered email. Set ``CAMS_EMAIL`` in ``.env`` + before running, or the first invocation will prompt interactively + (which fails inside the migration runner). +* NASA POWER requires no credentials. + +Reversal +-------- +Possible but tedious — would need to delete rows by ``(date, geohash5)`` +matching each station's footprint. Easier to just leave the data in +place; rerunning is idempotent. +""" + +from __future__ import annotations + +import logging +import os +import sys +from pathlib import Path + +from dotenv import load_dotenv + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.jobs.satellite_job import ( + CamsSatelliteJob, + NasaPowerSatelliteJob, +) +from susse.warehouse_ops.population.types import ( + DateRange, + LocationSpec, + NamedLocationsPlan, + Source, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a6_ingest_28_ground_stations" +_log = logging.getLogger(_MIGRATION_ID) + + +def _load_stations(bq: BigQueryClient, refs: TableRefs): + """Read per-station (location, lat, lon, min_date, max_date) from BQ.""" + df = bq.query( + f""" + SELECT + location, + MIN(lat) AS lat, + MIN(lon) AS lon, + MIN(date) AS min_date, + MAX(date) AS max_date, + COUNT(*) AS n_ground_days + FROM `{refs.ground_measurements}` + GROUP BY location + ORDER BY location + """ + ) + if df.empty: + raise RuntimeError( + "ground_measurements is empty — there are no stations to ingest." + ) + _log.info("Loaded %d stations from ground_measurements.", len(df)) + return df + + +def _check_cams_email_present() -> None: + # Load .env so a value placed there is visible without the user also + # exporting it in the shell. The CAMSClient does this lazily at request + # time; we do it eagerly so the pre-flight check matches. + load_dotenv() + if not os.getenv("CAMS_EMAIL"): + raise RuntimeError( + "CAMS_EMAIL is not set in the environment or .env. CAMS requests " + "would prompt interactively, which breaks the migration runner. " + "Add CAMS_EMAIL= to .env before running." + ) + + +def _run_for_source( + *, + job, + source: Source, + stations_df, + dry_run: bool, +) -> tuple[int, int, int]: + """Iterate stations, run a NamedLocationsPlan per station for ``source``. + + Returns ``(rows_added, api_calls_made, stations_processed)``. + """ + catalog_vars = VariableCatalog.for_source(source) + if not catalog_vars: + raise RuntimeError(f"Empty catalog for source={source}.") + _log.info( + "Source=%s: %d catalog variables (%d irradiance, %d aux).", + source.value, + len(catalog_vars), + len(VariableCatalog.irradiance_for_source(source)), + len(VariableCatalog.auxiliary_for_source(source)), + ) + + rows_added = 0 + api_calls = 0 + stations_processed = 0 + + for row in stations_df.itertuples(index=False): + loc = LocationSpec(name=row.location, lat=float(row.lat), lon=float(row.lon)) + date_range = DateRange(start=row.min_date, end=row.max_date) + plan = NamedLocationsPlan( + source=source, + date_range=date_range, + locations=(loc,), + variables=catalog_vars, + ) + _log.info( + "[%s] %s — %d days, %d vars", + source.value, row.location, date_range.n_days, len(catalog_vars), + ) + if dry_run: + stations_processed += 1 + continue + try: + result = job.run(plan) + except Exception: + _log.exception( + "[%s] %s — fetch failed; continuing with remaining stations.", + source.value, row.location, + ) + continue + rows_added += result.rows_added + api_calls += result.api_calls_made + stations_processed += 1 + _log.info( + "[%s] %s — rows_added=%d, api_calls=%d, skipped=%s", + source.value, row.location, + result.rows_added, result.api_calls_made, + result.extra.get("skipped_locations"), + ) + + return rows_added, api_calls, stations_processed + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + stations_df = _load_stations(bq, refs) + + if not dry_run: + _check_cams_email_present() + + nasa_job = NasaPowerSatelliteJob(bq, refs=refs) + cams_job = CamsSatelliteJob(bq, refs=refs) + + nasa_rows, nasa_calls, nasa_n = _run_for_source( + job=nasa_job, source=Source.NASA_POWER, + stations_df=stations_df, dry_run=dry_run, + ) + cams_rows, cams_calls, cams_n = _run_for_source( + job=cams_job, source=Source.CAMS, + stations_df=stations_df, dry_run=dry_run, + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would issue {nasa_n} NASA + {cams_n} CAMS plans " + f"(one per station). No API calls or writes." + ), + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=nasa_rows + cams_rows, + notes=( + f"NASA: +{nasa_rows} rows in {nasa_calls} api calls across " + f"{nasa_n} stations. CAMS: +{cams_rows} rows in {cams_calls} " + f"api calls across {cams_n} stations." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Ingest NASA POWER + CAMS at the 28 ground-measurement " + "stations over each station's ground-data date range." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_a7_drop_external_relics.py b/warehouse/migrations/2026-05-08_a7_drop_external_relics.py new file mode 100644 index 00000000..e31cd376 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a7_drop_external_relics.py @@ -0,0 +1,103 @@ +"""A7 — drop the legacy external CSV-backed tables. + +What it changes +--------------- +Drops two external tables that were used by the old ingest path: + +* ``solar_warehouse.cams_daily_ext`` — pointer at + ``gs://solar_irradiation_db/raw/cams/radiation/uganda/year=2024/*.csv``. + Already consumed by A4 to populate ``cams_daily_vars_long``; no + remaining purpose. +* ``solar_warehouse.nasa_daily_ext`` — pointer at the analogous NASA + CSV staging path. Equivalent data already lives in + ``nasa_daily_vars_long`` and ``irradiance_daily``. + +Why +--- +With Phase A complete, both relics serve no purpose. Keeping them +around is a smell — future readers might assume they're authoritative +or active. + +Expected effect +--------------- +* No API calls. +* Two ``DROP EXTERNAL TABLE`` statements. +* Idempotent via ``DROP TABLE IF EXISTS``. + +Reversal +-------- +Each external table was created by a CSV pointer in +``warehouse/sql/`` (or via legacy notebooks). Re-create from those if +ever needed; the underlying GCS CSVs remain untouched by this drop. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import WarehouseConfig + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a7_drop_external_relics" +_log = logging.getLogger(_MIGRATION_ID) + +_RELIC_TABLE_IDS: tuple[str, ...] = ("cams_daily_ext", "nasa_daily_ext") + + +def _table_exists(bq: BigQueryClient, config: WarehouseConfig, table_id: str) -> bool: + df = bq.query( + f"SELECT COUNT(*) AS n " + f"FROM `{config.project_id}.{config.dataset}.INFORMATION_SCHEMA.TABLES` " + f"WHERE table_name = '{table_id}'" + ) + return int(df.iloc[0]["n"]) > 0 + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + config = bq.config + drops = [] + for table_id in _RELIC_TABLE_IDS: + exists = _table_exists(bq, config, table_id) + _log.info("%s: %s", table_id, "exists" if exists else "missing") + if exists: + drops.append(table_id) + + if not drops: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="all relic tables already absent — no-op.", + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would drop: {drops}.", + ) + + for table_id in drops: + fqn = config.fqn(table_id) + bq.execute_ddl(f"DROP TABLE IF EXISTS `{fqn}`;") + _log.info("Dropped %s.", fqn) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=len(drops), + notes=f"dropped {len(drops)} relic table(s): {drops}", + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Drop the legacy external CSV tables (cams_daily_ext, nasa_daily_ext).", + ) + ) diff --git a/warehouse/migrations/2026-05-08_a9_fix_cams_units.py b/warehouse/migrations/2026-05-08_a9_fix_cams_units.py new file mode 100644 index 00000000..803a0006 --- /dev/null +++ b/warehouse/migrations/2026-05-08_a9_fix_cams_units.py @@ -0,0 +1,250 @@ +"""A9 — fix CAMS unit-conversion bug introduced by A6. + +What it changes +--------------- +For every CAMS row in ``irradiance_daily`` and ``cams_daily_vars_long`` +that was written without the W/m² → kWh/m²/day conversion, multiplies +the value by 0.024 in place. Affected rows are identified by magnitude: +CAMS values stored without the conversion are ~100–400 (W/m² mean over +the day), while correctly-converted values are ~0–10 (kWh/m²/day). +A threshold of 30 is comfortably above any plausible CAMS daily energy +total but well below any unconverted W/m² mean. + +Why +--- +``CamsSatelliteJob._cams_dataframe_to_long`` was missing the unit +conversion that the legacy ingest path (and migration A4) applied. Every +CAMS row inserted by A6 went into the warehouse 41.7× too large +(``ghi_kwh_m2_day`` columns containing W/m² values). The code is now +fixed in the same commit; this migration corrects the data already in +BigQuery. + +Affected: + +* ``irradiance_daily`` rows where ``source='CAMS'`` and any of the + ``ghi_kwh_m2_day`` / ``dhi_kwh_m2_day`` / ``dni_kwh_m2_day`` columns + exceeds 30 (the magnitude threshold). +* ``cams_daily_vars_long`` rows where ``source='CAMS'`` and ``value`` + exceeds 30. + +Unaffected (already correct): + +* The Uganda 2024 grid CAMS rows in ``irradiance_daily`` (legacy ingest + applied the conversion). +* The Uganda 2024 grid CAMS aux rows in ``cams_daily_vars_long`` written + by migration A4 (which applied the conversion explicitly). + +Expected effect +--------------- +* No API calls. +* Two ``UPDATE`` statements (one per table). +* Idempotent: a re-run sees only already-converted (small-magnitude) + rows and updates nothing. + +Reversal +-------- +``UPDATE … SET col = col / 0.024 WHERE source='CAMS' AND col < 30`` would +undo this, but you'd lose the distinction between A6-and-A9-corrected +rows and rows that were always correct. Practically irreversible; if +something goes wrong, the safest path is to delete CAMS rows from A6 +and re-run A6 with the fixed code. +""" + +from __future__ import annotations + +import logging +import sys +from datetime import date, timedelta +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.jobs.satellite_job import CamsSatelliteJob + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_a9_fix_cams_units" +_log = logging.getLogger(_MIGRATION_ID) + +# Magnitude threshold separating buggy (W/m² mean) values from correct +# (kWh/m²/day) values. Real CAMS daily energy is bounded by the solar +# constant × 24 / 1000 ≈ 33 kWh/m²/day in the absolute extreme; in +# practice values are below 10. Anything > 30 was written without the +# 0.024 conversion. +_BAD_VALUE_THRESHOLD = 30.0 + +# BigQuery rejects DML statements that touch more than 4,000 partitions. +# Bad CAMS rows span ~2011 → 2024, so we chunk the UPDATE by date range +# the same way the MergeLoader does. +_PARTITION_CHUNK_DAYS = 3500 + + +def _date_chunks_for( + bq: BigQueryClient, table_fqn: str +) -> list[tuple[date, date]]: + """Compute date chunks for the bad CAMS rows in ``table_fqn``. + + Returns an empty list when the table has no bad rows (skip the UPDATE + entirely). Otherwise returns chunks each spanning at most + ``_PARTITION_CHUNK_DAYS`` calendar days, covering [min_date, max_date] + of the bad-row population in that table. + """ + if "irradiance_daily" in table_fqn: + bad_filter = ( + f"(ghi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} " + f"OR dhi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} " + f"OR dni_kwh_m2_day > {_BAD_VALUE_THRESHOLD})" + ) + else: + bad_filter = f"value > {_BAD_VALUE_THRESHOLD}" + df = bq.query( + f""" + SELECT MIN(date) AS min_d, MAX(date) AS max_d + FROM `{table_fqn}` + WHERE source = 'CAMS' AND {bad_filter} + """ + ) + row = df.iloc[0] + if row["min_d"] is None or (hasattr(row["min_d"], "is_nat") and row["min_d"] is None): + return [] + min_d = row["min_d"] + max_d = row["max_d"] + if hasattr(min_d, "date"): + min_d = min_d.date() + if hasattr(max_d, "date"): + max_d = max_d.date() + chunks: list[tuple[date, date]] = [] + cursor = min_d + while cursor <= max_d: + chunk_end = min( + cursor + timedelta(days=_PARTITION_CHUNK_DAYS - 1), max_d + ) + chunks.append((cursor, chunk_end)) + cursor = chunk_end + timedelta(days=1) + return chunks + + +def _count_bad_rows(bq: BigQueryClient, refs: TableRefs) -> dict[str, int]: + irr = bq.query( + f""" + SELECT COUNT(*) AS n + FROM `{refs.irradiance_daily}` + WHERE source = 'CAMS' + AND (ghi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} + OR dhi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} + OR dni_kwh_m2_day > {_BAD_VALUE_THRESHOLD}) + """ + ).iloc[0]["n"] + long = bq.query( + f""" + SELECT COUNT(*) AS n + FROM `{refs.cams_daily_vars_long}` + WHERE source = 'CAMS' AND value > {_BAD_VALUE_THRESHOLD} + """ + ).iloc[0]["n"] + return {"irradiance_daily": int(irr), "cams_daily_vars_long": int(long)} + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + factor = CamsSatelliteJob._W_M2_TO_KWH_M2_DAY + pre = _count_bad_rows(bq, refs) + _log.info( + "Pre: irradiance_daily bad rows=%d, cams_daily_vars_long bad rows=%d.", + pre["irradiance_daily"], pre["cams_daily_vars_long"], + ) + if pre["irradiance_daily"] == 0 and pre["cams_daily_vars_long"] == 0: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="No CAMS rows above the magnitude threshold — nothing to fix.", + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would correct {pre['irradiance_daily']} irradiance " + f"rows and {pre['cams_daily_vars_long']} long-table rows by " + f"multiplying by {factor}." + ), + ) + + irr_chunks = _date_chunks_for(bq, refs.irradiance_daily) + long_chunks = _date_chunks_for(bq, refs.cams_daily_vars_long) + _log.info( + "Date-chunking the UPDATEs: irradiance_daily into %d chunk(s), " + "cams_daily_vars_long into %d chunk(s).", + len(irr_chunks), len(long_chunks), + ) + + # irradiance_daily: ghi/dhi/dni columns. UPDATE only the columns that + # actually exceed the threshold so a single bad column doesn't + # double-correct the others. + for start, end in irr_chunks: + bq.execute_ddl( + f""" + UPDATE `{refs.irradiance_daily}` + SET + ghi_kwh_m2_day = IF(ghi_kwh_m2_day > {_BAD_VALUE_THRESHOLD}, + ghi_kwh_m2_day * {factor}, ghi_kwh_m2_day), + dhi_kwh_m2_day = IF(dhi_kwh_m2_day > {_BAD_VALUE_THRESHOLD}, + dhi_kwh_m2_day * {factor}, dhi_kwh_m2_day), + dni_kwh_m2_day = IF(dni_kwh_m2_day > {_BAD_VALUE_THRESHOLD}, + dni_kwh_m2_day * {factor}, dni_kwh_m2_day) + WHERE source = 'CAMS' + AND date BETWEEN DATE('{start.isoformat()}') + AND DATE('{end.isoformat()}') + AND (ghi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} + OR dhi_kwh_m2_day > {_BAD_VALUE_THRESHOLD} + OR dni_kwh_m2_day > {_BAD_VALUE_THRESHOLD}) + """ + ) + for start, end in long_chunks: + bq.execute_ddl( + f""" + UPDATE `{refs.cams_daily_vars_long}` + SET value = value * {factor} + WHERE source = 'CAMS' + AND date BETWEEN DATE('{start.isoformat()}') + AND DATE('{end.isoformat()}') + AND value > {_BAD_VALUE_THRESHOLD} + """ + ) + + post = _count_bad_rows(bq, refs) + _log.info( + "Post: irradiance_daily bad rows=%d, cams_daily_vars_long bad rows=%d.", + post["irradiance_daily"], post["cams_daily_vars_long"], + ) + if post["irradiance_daily"] != 0 or post["cams_daily_vars_long"] != 0: + raise RuntimeError( + f"After UPDATE, {post['irradiance_daily']} irradiance rows and " + f"{post['cams_daily_vars_long']} long-table rows still exceed the " + f"threshold. Investigate before re-running." + ) + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=pre["irradiance_daily"] + pre["cams_daily_vars_long"], + notes=( + f"Corrected {pre['irradiance_daily']} irradiance_daily rows and " + f"{pre['cams_daily_vars_long']} cams_daily_vars_long rows " + f"(× {factor})." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Fix CAMS unit conversion: multiply rows above magnitude " + "threshold by 0.024 in irradiance_daily and " + "cams_daily_vars_long." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_b3_create_merra_daily_vars_long.py b/warehouse/migrations/2026-05-08_b3_create_merra_daily_vars_long.py new file mode 100644 index 00000000..910f8d32 --- /dev/null +++ b/warehouse/migrations/2026-05-08_b3_create_merra_daily_vars_long.py @@ -0,0 +1,111 @@ +"""B3 — create the ``merra_daily_vars_long`` table. + +What it changes +--------------- +Creates an empty ``solar_warehouse.merra_daily_vars_long`` table from +the DDL at ``warehouse/sql/00_schema/merra_daily_vars_long.sql``. The +DDL uses ``CREATE TABLE IF NOT EXISTS`` so re-running this migration +is a no-op once the table exists. + +Why +--- +Phase B brings MERRA-2 into the warehouse. The schema mirrors +``nasa_daily_vars_long`` and ``cams_daily_vars_long``, with the added +note that values stored here are *daily aggregates* of MERRA-2's +hourly-native data — the aggregation is performed client-side by +``MerraStreamFetcher`` using cosine-zenith-weighting. + +Expected effect +--------------- +* No API calls. +* Single ``CREATE TABLE IF NOT EXISTS`` statement, partitioned by date, + clustered by ``(geohash5, variable_id)``. +* Pre: table does not exist. +* Post: empty table ready for B7 (dim_variable population) and B8 (data ingest). + +Reversal +-------- +``DROP TABLE solar_warehouse.merra_daily_vars_long`` (only safe if no +later migration has loaded data into it). +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs, TableSchemas + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_b3_create_merra_daily_vars_long" +_log = logging.getLogger(_MIGRATION_ID) + +_DDL_PATH = ( + Path(__file__).resolve().parent.parent + / "sql" + / "00_schema" + / "merra_daily_vars_long.sql" +) + + +def _table_exists(bq: BigQueryClient) -> bool: + table_id = TableSchemas.MERRA_DAILY_VARS_LONG.table_id + df = bq.query( + f"SELECT COUNT(*) AS n " + f"FROM `{bq.config.project_id}.{bq.config.dataset}.INFORMATION_SCHEMA.TABLES` " + f"WHERE table_name = '{table_id}'" + ) + return int(df.iloc[0]["n"]) > 0 + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) # noqa: F841 (parity with sister migrations) + pre_exists = _table_exists(bq) + _log.info( + "merra_daily_vars_long pre-state: %s", + "exists" if pre_exists else "missing", + ) + + if pre_exists: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="merra_daily_vars_long already exists — no-op.", + ) + + ddl = _DDL_PATH.read_text() + _log.info("Loaded DDL from %s (%d chars).", _DDL_PATH, len(ddl)) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would execute DDL from {_DDL_PATH.name}.", + ) + + bq.execute_ddl(ddl) + if not _table_exists(bq): + raise RuntimeError( + "merra_daily_vars_long still does not exist after CREATE TABLE — " + "DDL execution did not behave as expected." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="merra_daily_vars_long created (empty table).", + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Create the merra_daily_vars_long table from its DDL.", + ) + ) diff --git a/warehouse/migrations/2026-05-08_b7_populate_dim_variable_merra.py b/warehouse/migrations/2026-05-08_b7_populate_dim_variable_merra.py new file mode 100644 index 00000000..27ee2cd6 --- /dev/null +++ b/warehouse/migrations/2026-05-08_b7_populate_dim_variable_merra.py @@ -0,0 +1,127 @@ +"""B7 — extend ``dim_variable`` with the 4 MERRA-2 catalog entries. + +What it changes +--------------- +Re-runs ``populate_dim_variable`` against the full catalog. Because the +catalog now includes 4 MERRA-2 ``VariableSpec``s (added in B2), the MERGE +inserts those new rows. NASA POWER and CAMS rows are already there and +are no-ops on the merge keys ``(variable_id, source)``. + +Why +--- +Phase B mirrors Phase A1 for the MERRA-2 source: every variable that +will land in the long table needs a corresponding ``dim_variable`` row +documenting its unit, valid range, and spatial resolution. Without this, +downstream tools can't introspect the catalog from BigQuery. + +Expected effect +--------------- +* No API calls. +* Pre: ``dim_variable`` has 42 rows (32 NASA + 9 CAMS + 1 NASA SZA). +* Post: ``dim_variable`` has 46 rows (42 + 4 MERRA-2). +* Idempotent: re-running with the catalog already populated is a no-op. + +Reversal +-------- +``DELETE FROM dim_variable WHERE source = 'MERRA2'`` removes the four +MERRA-2 rows. Safe before B8 has loaded any data; once B8 has populated +``merra_daily_vars_long`` you'd want to keep the catalog rows so the +schema stays self-documenting. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + populate_dim_variable, +) +from susse.warehouse_ops.population.types import Source + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_b7_populate_dim_variable_merra" +_log = logging.getLogger(_MIGRATION_ID) + + +def _read_state(bq: BigQueryClient, refs: TableRefs) -> dict: + df = bq.query( + f""" + SELECT + COUNT(*) AS n_rows, + COUNTIF(source = 'NASA') AS n_nasa, + COUNTIF(source = 'CAMS') AS n_cams, + COUNTIF(source = 'MERRA2') AS n_merra + FROM `{refs.dim_variable}` + """ + ) + return df.iloc[0].to_dict() + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + catalog = VariableCatalog.all_variables() + n_merra_in_catalog = sum(1 for v in catalog if v.source is Source.MERRA_2) + _log.info( + "Catalog has %d total variables, %d of them MERRA-2.", + len(catalog), n_merra_in_catalog, + ) + + pre = _read_state(bq, refs) + _log.info( + "Pre: dim_variable n_rows=%s NASA=%s CAMS=%s MERRA2=%s", + pre["n_rows"], pre["n_nasa"], pre["n_cams"], pre["n_merra"], + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would MERGE {len(catalog)} catalog rows; expect " + f"~{n_merra_in_catalog} new MERRA-2 rows." + ), + ) + + written = populate_dim_variable( + bq, table_fqn=refs.dim_variable, variables=catalog + ) + post = _read_state(bq, refs) + _log.info( + "Post: dim_variable n_rows=%s NASA=%s CAMS=%s MERRA2=%s", + post["n_rows"], post["n_nasa"], post["n_cams"], post["n_merra"], + ) + + if post["n_merra"] != n_merra_in_catalog: + raise RuntimeError( + f"Expected {n_merra_in_catalog} MERRA-2 rows in dim_variable " + f"after MERGE; got {post['n_merra']}. Investigate." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=written, + notes=( + f"dim_variable: pre n_rows={pre['n_rows']} → post " + f"n_rows={post['n_rows']}; MERRA2 rows {pre['n_merra']} → " + f"{post['n_merra']}." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Extend dim_variable with the 4 MERRA-2 catalog entries." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_c3_create_modis_observations.py b/warehouse/migrations/2026-05-08_c3_create_modis_observations.py new file mode 100644 index 00000000..da344fdb --- /dev/null +++ b/warehouse/migrations/2026-05-08_c3_create_modis_observations.py @@ -0,0 +1,116 @@ +"""C3 — create the ``modis_observations`` table. + +What it changes +--------------- +Creates an empty ``solar_warehouse.modis_observations`` table from the +DDL at ``warehouse/sql/00_schema/modis_observations.sql``. The DDL uses +``CREATE TABLE IF NOT EXISTS`` so re-running this migration is a no-op. + +Why +--- +Phase C brings MODIS into the warehouse. Unlike the long-format tables +(NASA / CAMS / MERRA-2) which carry one (variable_id, value) per row, +MODIS observations carry both ``product_id`` and ``band_id`` because +each MODIS product has multiple bands, and the (product, band) pair is +the natural identifier of a single observation series. We give MODIS +its own table rather than overloading the long-format schema. + +Storage uses each product's native composite cadence (daily, 8-day, +16-day) — values are stored at the composite end-date with no fake +daily upsampling at ingest time. + +Expected effect +--------------- +* No API calls. +* Single ``CREATE TABLE IF NOT EXISTS`` statement, partitioned by date, + clustered by ``(geohash5, product_id, band_id)``. +* Pre: table does not exist. +* Post: empty table ready for C7 (dim_variable population) and + C8 (data ingest). + +Reversal +-------- +``DROP TABLE solar_warehouse.modis_observations`` (only safe if no +later migration has loaded data into it). +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs, TableSchemas + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_c3_create_modis_observations" +_log = logging.getLogger(_MIGRATION_ID) + +_DDL_PATH = ( + Path(__file__).resolve().parent.parent + / "sql" + / "00_schema" + / "modis_observations.sql" +) + + +def _table_exists(bq: BigQueryClient) -> bool: + table_id = TableSchemas.MODIS_OBSERVATIONS.table_id + df = bq.query( + f"SELECT COUNT(*) AS n " + f"FROM `{bq.config.project_id}.{bq.config.dataset}.INFORMATION_SCHEMA.TABLES` " + f"WHERE table_name = '{table_id}'" + ) + return int(df.iloc[0]["n"]) > 0 + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) # noqa: F841 (parity with sister migrations) + pre_exists = _table_exists(bq) + _log.info( + "modis_observations pre-state: %s", + "exists" if pre_exists else "missing", + ) + + if pre_exists: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="modis_observations already exists — no-op.", + ) + + ddl = _DDL_PATH.read_text() + _log.info("Loaded DDL from %s (%d chars).", _DDL_PATH, len(ddl)) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=f"dry-run; would execute DDL from {_DDL_PATH.name}.", + ) + + bq.execute_ddl(ddl) + if not _table_exists(bq): + raise RuntimeError( + "modis_observations still does not exist after CREATE TABLE — " + "DDL execution did not behave as expected." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes="modis_observations created (empty table).", + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description="Create the modis_observations table from its DDL.", + ) + ) diff --git a/warehouse/migrations/2026-05-08_c7_populate_dim_variable_modis.py b/warehouse/migrations/2026-05-08_c7_populate_dim_variable_modis.py new file mode 100644 index 00000000..a559c84a --- /dev/null +++ b/warehouse/migrations/2026-05-08_c7_populate_dim_variable_modis.py @@ -0,0 +1,134 @@ +"""C7 — extend ``dim_variable`` with the MODIS catalog entries. + +What it changes +--------------- +Re-runs ``populate_dim_variable`` against the full catalog. The catalog +now includes 3 MODIS ``VariableSpec``s (added in C2), so the MERGE +inserts those new rows. NASA POWER, CAMS, and MERRA-2 rows are already +there and are no-ops on the merge keys ``(variable_id, source)``. + +The MODIS ``variable_id`` follows the ``"{product}_{band}"`` convention +(e.g. ``MOD13Q1_250m_16_days_NDVI``) so the row in ``dim_variable`` +uniquely identifies a (product, band) pair while the storage table +``modis_observations`` keeps ``product_id`` and ``band_id`` as separate +columns for downstream querying. + +Why +--- +Phase C mirrors A1/B7 for the MODIS source: every variable that will +land in ``modis_observations`` needs a corresponding ``dim_variable`` +row documenting its unit, valid range, and spatial resolution. Without +this, downstream tools can't introspect the catalog from BigQuery. + +Expected effect +--------------- +* No API calls. +* Pre: ``dim_variable`` has 46 rows (NASA + CAMS + MERRA2 from A1/B7). +* Post: ``dim_variable`` has 49 rows (46 + 3 MODIS). +* Idempotent: re-running with the catalog already populated is a no-op. + +Reversal +-------- +``DELETE FROM dim_variable WHERE source = 'MODIS'`` removes the three +MODIS rows. Safe before C8 has loaded any data; once C8 has populated +``modis_observations`` you'd want to keep the catalog rows so the schema +stays self-documenting. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + populate_dim_variable, +) +from susse.warehouse_ops.population.types import Source + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_c7_populate_dim_variable_modis" +_log = logging.getLogger(_MIGRATION_ID) + + +def _read_state(bq: BigQueryClient, refs: TableRefs) -> dict: + df = bq.query( + f""" + SELECT + COUNT(*) AS n_rows, + COUNTIF(source = 'NASA') AS n_nasa, + COUNTIF(source = 'CAMS') AS n_cams, + COUNTIF(source = 'MERRA2') AS n_merra, + COUNTIF(source = 'MODIS') AS n_modis + FROM `{refs.dim_variable}` + """ + ) + return df.iloc[0].to_dict() + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + catalog = VariableCatalog.all_variables() + n_modis_in_catalog = sum(1 for v in catalog if v.source is Source.MODIS) + _log.info( + "Catalog has %d total variables, %d of them MODIS.", + len(catalog), n_modis_in_catalog, + ) + + pre = _read_state(bq, refs) + _log.info( + "Pre: dim_variable n_rows=%s NASA=%s CAMS=%s MERRA2=%s MODIS=%s", + pre["n_rows"], pre["n_nasa"], pre["n_cams"], pre["n_merra"], pre["n_modis"], + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would MERGE {len(catalog)} catalog rows; expect " + f"~{n_modis_in_catalog} new MODIS rows." + ), + ) + + written = populate_dim_variable( + bq, table_fqn=refs.dim_variable, variables=catalog + ) + post = _read_state(bq, refs) + _log.info( + "Post: dim_variable n_rows=%s NASA=%s CAMS=%s MERRA2=%s MODIS=%s", + post["n_rows"], post["n_nasa"], post["n_cams"], post["n_merra"], post["n_modis"], + ) + + if post["n_modis"] != n_modis_in_catalog: + raise RuntimeError( + f"Expected {n_modis_in_catalog} MODIS rows in dim_variable " + f"after MERGE; got {post['n_modis']}. Investigate." + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=written, + notes=( + f"dim_variable: pre n_rows={pre['n_rows']} → post " + f"n_rows={post['n_rows']}; MODIS rows {pre['n_modis']} → " + f"{post['n_modis']}." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Extend dim_variable with the MODIS catalog entries." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-08_c8_ingest_modis_stations_and_grid.py b/warehouse/migrations/2026-05-08_c8_ingest_modis_stations_and_grid.py new file mode 100644 index 00000000..483e94b4 --- /dev/null +++ b/warehouse/migrations/2026-05-08_c8_ingest_modis_stations_and_grid.py @@ -0,0 +1,219 @@ +"""C8 — ingest MODIS for the 28 ground stations and the Uganda 2024 grid. + +What it changes +--------------- +For each of the 28 distinct locations in ``ground_measurements``, plus +every point on the existing Uganda 2024 NASA-POWER-resolution grid +(1962 points × 366 days), pulls the 3 MODIS catalog (product, band) +pairs from ORNL DAAC and lands them in ``modis_observations``. + +Why +--- +This is the Phase-C counterpart of A6 / B8: bring MODIS into the +warehouse for the same training-corpus and inference-cache slices we +covered with NASA POWER, CAMS, and MERRA-2. After this run, the +bias-correction model has access to MODIS surface reflectance, land +surface temperature, and NDVI at every (station, date) and every Uganda +2024 grid point. + +Because MODIS values are stored at each product's *native composite +end-date* (1-day for MCD43A4, 8-day for MOD11A2, 16-day for MOD13Q1), +the row count per (station, date-range) is much smaller than for the +daily satellite sources — but each row is sampled, not interpolated, so +the data quality is honest about its temporal resolution. + +Expected effect +--------------- +* **Per-station cost**: one ORNL DAAC subset call per (product, chunk). + For a 5-year window: MCD43A4 daily-cadence × 10-day-chunks ≈ 183 + chunks; MOD11A2 8-day × 10-tile chunks ≈ 23 chunks; MOD13Q1 16-day + × 10-tile chunks ≈ 12 chunks. Total ~220 calls per station, ~6,200 + cumulative for 28 stations. +* **Grid cost**: ~220 calls × 1962 points ≈ 430k cumulative calls. ORNL + DAAC is unauthenticated and unrate-limited but slow per call; expect + hours per station and *days* for the full grid. The per-(geohash5, + product, band) coverage check makes interruptions safe — re-running + picks up where it left off. + +Prerequisites +------------- +* C3 (table created) and C7 (catalog populated) must have run first. +* No credentials needed — ORNL DAAC's RST subset endpoint is public. + +Reversal +-------- +Possible but tedious. Easier to leave the data in place; rerunning is +idempotent. +""" + +from __future__ import annotations + +import logging +import sys +from datetime import date +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.jobs import ModisJob +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + GridPlan, + GridSpec, + LocationSpec, + NamedLocationsPlan, + Source, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-08_c8_ingest_modis_stations_and_grid" +_log = logging.getLogger(_MIGRATION_ID) + +# Uganda inference-cache footprint, mirroring the existing NASA POWER / +# CAMS / MERRA-2 coverage for 2024. NASA POWER native resolution is 0.5°. +_UGANDA_BBOX = BoundingBox( + min_lat=-1.5, max_lat=4.5, min_lon=29.5, max_lon=35.0, +) +_UGANDA_GRID_STEP = 0.5 +_UGANDA_GRID_DATES = DateRange(start=date(2024, 1, 1), end=date(2024, 12, 31)) + + +def _load_stations(bq: BigQueryClient, refs: TableRefs): + df = bq.query( + f""" + SELECT + location, MIN(lat) AS lat, MIN(lon) AS lon, + MIN(date) AS min_date, MAX(date) AS max_date + FROM `{refs.ground_measurements}` + GROUP BY location + ORDER BY location + """ + ) + if df.empty: + raise RuntimeError( + "ground_measurements is empty — there are no stations to ingest." + ) + _log.info("Loaded %d stations from ground_measurements.", len(df)) + return df + + +def _run_named_locations( + job: ModisJob, stations_df, dry_run: bool +) -> tuple[int, int, int]: + catalog_vars = VariableCatalog.for_source(Source.MODIS) + if not catalog_vars: + raise RuntimeError("Empty MODIS catalog — check VariableCatalog.") + rows_added = 0 + api_calls = 0 + processed = 0 + + for row in stations_df.itertuples(index=False): + loc = LocationSpec( + name=row.location, lat=float(row.lat), lon=float(row.lon) + ) + date_range = DateRange(start=row.min_date, end=row.max_date) + plan = NamedLocationsPlan( + source=Source.MODIS, + date_range=date_range, + locations=(loc,), + variables=catalog_vars, + ) + _log.info( + "[MODIS] %s — %d days, %d (product, band) pair(s)", + row.location, date_range.n_days, len(catalog_vars), + ) + if dry_run: + processed += 1 + continue + try: + result = job.run(plan) + except Exception: + _log.exception( + "[MODIS] %s — fetch failed; continuing with next station.", + row.location, + ) + continue + rows_added += result.rows_added + api_calls += result.api_calls_made + processed += 1 + _log.info( + "[MODIS] %s — rows_added=%d, api_calls=%d", + row.location, result.rows_added, result.api_calls_made, + ) + return rows_added, api_calls, processed + + +def _run_grid(job: ModisJob, dry_run: bool) -> tuple[int, int, int]: + grid = GridSpec( + bbox=_UGANDA_BBOX, + lat_step=_UGANDA_GRID_STEP, + lon_step=_UGANDA_GRID_STEP, + ) + plan = GridPlan( + source=Source.MODIS, + date_range=_UGANDA_GRID_DATES, + grid=grid, + variables=VariableCatalog.for_source(Source.MODIS), + ) + _log.info( + "[MODIS-grid] %d points, %d days, %d (product, band) pair(s)", + grid.n_points, _UGANDA_GRID_DATES.n_days, + len(plan.variables), + ) + if dry_run: + return 0, 0, grid.n_points + result = job.run(plan) + _log.info( + "[MODIS-grid] rows_added=%d, api_calls=%d", + result.rows_added, result.api_calls_made, + ) + return result.rows_added, result.api_calls_made, grid.n_points + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + job = ModisJob(bq, refs=refs) + stations_df = _load_stations(bq, refs) + + station_rows, station_calls, n_stations = _run_named_locations( + job, stations_df, dry_run + ) + grid_rows, grid_calls, n_grid_points = _run_grid(job, dry_run) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would issue MODIS plans for {n_stations} stations " + f"and a {n_grid_points}-point Uganda 2024 grid." + ), + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=station_rows + grid_rows, + notes=( + f"Stations: +{station_rows} rows, {station_calls} api calls, " + f"{n_stations} stations. " + f"Grid: +{grid_rows} rows, {grid_calls} api calls, " + f"{n_grid_points} points." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Ingest MODIS for the 28 ground stations and the Uganda " + "2024 NASA-POWER-resolution grid." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-10_a11_annotate_physical_storage.py b/warehouse/migrations/2026-05-10_a11_annotate_physical_storage.py new file mode 100644 index 00000000..73a27086 --- /dev/null +++ b/warehouse/migrations/2026-05-10_a11_annotate_physical_storage.py @@ -0,0 +1,125 @@ +"""A11 — annotate ``dim_variable`` with the ``physical_storage`` column. + +What it changes +--------------- +1. Adds a new ``physical_storage STRING`` column to + ``solar_warehouse.dim_variable``. +2. Re-populates the table from :class:`VariableCatalog` so every row + carries the new column with its tag (``long_format`` / + ``irradiance_wide`` / ``modis_observations``). + +Why +--- +Before this migration, the catalog enumerated variables without +distinguishing where they physically live in the warehouse. The +``ghi`` / ``dhi`` / ``dni`` entries (NASA + CAMS) were claimed to +exist alongside the long-format aux variables, but the underlying data +only ever lived in ``irradiance_daily``. ``FeatureSelection`` would +silently accept ``nasa_variable_ids=("ghi",)`` and fetch a 100%-NaN +column. The new tag closes that gap: ``FeatureSelection`` validation +now checks that each requested ``variable_id`` matches the storage the +field reads from. See ``feature_service_dhi_dni_gap.md`` in project +memory for the discovery context. + +Expected effect +--------------- +* Pre: ``dim_variable`` has 48 rows, no ``physical_storage`` column. +* Post: 48 rows, all with ``physical_storage`` populated. 9 rows tagged + non-default (3 NASA irradiance + 3 CAMS irradiance + 3 MODIS); the + other 39 are ``long_format``. +* No row deletions; no API calls. ALTER TABLE + single MERGE. Subsecond + runtime. + +Reversal +-------- +``ALTER TABLE … DROP COLUMN physical_storage`` would roll back the +schema change. The catalog code change is independent — to fully +revert, also revert the ``VariableSpec.physical_storage`` field +addition. +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import ( + VariableCatalog, + populate_dim_variable, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-10_a11_annotate_physical_storage" +_log = logging.getLogger(_MIGRATION_ID) + + +def _column_exists(bq: BigQueryClient, table_fqn: str, column: str) -> bool: + """True iff ``column`` is already present on ``table_fqn`` in BQ.""" + project, dataset, table = table_fqn.split(".") + df = bq.query(f""" + SELECT column_name + FROM `{project}.{dataset}.INFORMATION_SCHEMA.COLUMNS` + WHERE table_name = '{table}' + """) + return column in set(df["column_name"]) + + +def _ensure_column(bq: BigQueryClient, table_fqn: str) -> bool: + """Add ``physical_storage`` column if missing. Returns True iff added.""" + if _column_exists(bq, table_fqn, "physical_storage"): + _log.info("physical_storage column already present; skipping ALTER.") + return False + _log.info("Adding physical_storage column to %s …", table_fqn) + bq.query(f""" + ALTER TABLE `{table_fqn}` + ADD COLUMN physical_storage STRING + """) + return True + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + table_fqn = refs.dim_variable + + if dry_run: + already_present = _column_exists(bq, table_fqn, "physical_storage") + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; physical_storage column " + f"{'already present' if already_present else 'would be added'}; " + f"would upsert {len(VariableCatalog.all_variables())} rows." + ), + ) + + column_added = _ensure_column(bq, table_fqn) + rows_written = populate_dim_variable(bq, table_fqn=table_fqn) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=rows_written, + notes=( + f"{'Added physical_storage column. ' if column_added else ''}" + f"Upserted {rows_written} catalog rows with the storage tag." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Annotate dim_variable with the physical_storage column so " + "FeatureSelection can validate that requested variable_ids " + "match the storage backend the field reads from." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-10_b9_ingest_merra_region.py b/warehouse/migrations/2026-05-10_b9_ingest_merra_region.py new file mode 100644 index 00000000..8a784dbf --- /dev/null +++ b/warehouse/migrations/2026-05-10_b9_ingest_merra_region.py @@ -0,0 +1,232 @@ +"""B9 — region-shaped MERRA-2 ingest for stations + Uganda 2024 grid. + +Replaces b8 wholesale. The behaviour is the same set of rows (28 ground +stations + the Uganda 2024 NASA-POWER-resolution grid, 4 MERRA-2 catalog +variables) but the fetch pattern is now region-shaped: one OPeNDAP bbox +call per (date, variable) covering all locations in the plan, instead of +one call per (date, variable, point). See the b9 design memo and +:mod:`susse.api_clients.merra_2.merra_daily_fetcher` for the rationale. + +Why a new migration ID rather than editing b8 +---------------------------------------------- +* b8 used :class:`MerraSatelliteJob` (now removed) which fanned out per + station with each station's own date range. b9 uses + :class:`MerraRegionJob` and bundles all stations into one plan over + the union date range. +* The bundled date range over-fetches some station-date combinations + (a station with one year of ground truth gets MERRA rows for the full + union range). Storage cost is trivial (~0.5M rows in BQ) and the + cached rows accelerate later inference. Net win. +* Keeping the b8 file around as historical record would be commented-out + code; it lives in git history instead. + +Expected effect +--------------- +* **Per-(date, variable) cost**: 1 OPeNDAP call returning all points' + sub-daily values. Wall time ~3 s regardless of point count. +* **Stations bundle**: 1 plan, union(min_date, max_date) across all 28 + stations, 4 variables. ~13 yr × 365 d × 4 vars / 8 workers × ~3 s + ≈ 2 hours wall time. +* **Uganda 2024 grid bundle**: 1 plan, 156 points, 4 variables, 366 days. + ~10 minutes wall time. +* Re-runs are cheap: the coverage layer's existing-keys filter spares + any (date, geohash, variable_id) tuples already in the warehouse from + re-upload, even though every (date, variable) bbox call is still made. + +Prerequisites +------------- +* ``EARTHDATA_USERNAME`` and ``EARTHDATA_PASSWORD`` in ``.env``. Register + at https://urs.earthdata.nasa.gov/ if needed; approve the *NASA GESDISC + DATA ARCHIVE* application via the Earthdata profile. +* B3 (table created) and B7 (catalog populated) must have run first. + +Reversal +-------- +Possible but tedious. Easier to leave the data in place; rerunning is +idempotent. +""" + +from __future__ import annotations + +import logging +import os +import sys +from datetime import date +from pathlib import Path + +from dotenv import load_dotenv + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.jobs.merra_region_job import MerraRegionJob +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + GridPlan, + GridSpec, + LocationSpec, + NamedLocationsPlan, + Source, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-10_b9_ingest_merra_region" +_log = logging.getLogger(_MIGRATION_ID) + +# Uganda inference-cache footprint, mirroring the existing NASA POWER / +# CAMS coverage for 2024. NASA POWER native resolution is 0.5°. +_UGANDA_BBOX = BoundingBox( + min_lat=-1.5, max_lat=4.5, min_lon=29.5, max_lon=35.0, +) +_UGANDA_GRID_STEP = 0.5 +_UGANDA_GRID_DATES = DateRange(start=date(2024, 1, 1), end=date(2024, 12, 31)) + + +def _check_credentials_present() -> None: + load_dotenv() + missing = [ + name for name in ("EARTHDATA_USERNAME", "EARTHDATA_PASSWORD") + if not os.getenv(name) + ] + if missing: + raise RuntimeError( + f"{', '.join(missing)} not set in environment or .env. " + f"Register at https://urs.earthdata.nasa.gov/ if needed, then " + f"approve the 'NASA GESDISC DATA ARCHIVE' application before " + f"re-running." + ) + + +def _load_stations(bq: BigQueryClient, refs: TableRefs): + df = bq.query( + f""" + SELECT + location, MIN(lat) AS lat, MIN(lon) AS lon, + MIN(date) AS min_date, MAX(date) AS max_date + FROM `{refs.ground_measurements}` + GROUP BY location + ORDER BY location + """ + ) + if df.empty: + raise RuntimeError( + "ground_measurements is empty — there are no stations to ingest." + ) + _log.info("Loaded %d stations from ground_measurements.", len(df)) + return df + + +def _run_stations_bundle( + job: MerraRegionJob, stations_df, dry_run: bool +) -> tuple[int, int]: + """One bundled NamedLocationsPlan over all stations × union date range. + + Returns (rows_added, api_calls). + """ + catalog_vars = VariableCatalog.for_source(Source.MERRA_2) + if not catalog_vars: + raise RuntimeError("Empty MERRA-2 catalog — check VariableCatalog.") + locations = tuple( + LocationSpec(name=row.location, lat=float(row.lat), lon=float(row.lon)) + for row in stations_df.itertuples(index=False) + ) + union_start = min(stations_df["min_date"]) + union_end = max(stations_df["max_date"]) + date_range = DateRange(start=union_start, end=union_end) + + plan = NamedLocationsPlan( + source=Source.MERRA_2, + date_range=date_range, + locations=locations, + variables=catalog_vars, + ) + _log.info( + "[MERRA2-stations] %d stations, %d days (union %s..%s), %d vars", + len(locations), date_range.n_days, + date_range.start, date_range.end, len(catalog_vars), + ) + if dry_run: + return 0, 0 + result = job.run(plan) + _log.info( + "[MERRA2-stations] rows_added=%d, api_calls=%d, duration=%.0fs", + result.rows_added, result.api_calls_made, result.duration_seconds, + ) + return result.rows_added, result.api_calls_made + + +def _run_grid( + job: MerraRegionJob, dry_run: bool +) -> tuple[int, int, int]: + grid = GridSpec( + bbox=_UGANDA_BBOX, + lat_step=_UGANDA_GRID_STEP, + lon_step=_UGANDA_GRID_STEP, + ) + plan = GridPlan( + source=Source.MERRA_2, + date_range=_UGANDA_GRID_DATES, + grid=grid, + variables=VariableCatalog.for_source(Source.MERRA_2), + ) + _log.info( + "[MERRA2-grid] %d points, %d days, %d vars", + grid.n_points, _UGANDA_GRID_DATES.n_days, len(plan.variables), + ) + if dry_run: + return 0, 0, grid.n_points + result = job.run(plan) + _log.info( + "[MERRA2-grid] rows_added=%d, api_calls=%d, duration=%.0fs", + result.rows_added, result.api_calls_made, result.duration_seconds, + ) + return result.rows_added, result.api_calls_made, grid.n_points + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + if not dry_run: + _check_credentials_present() + job = MerraRegionJob(bq, refs=refs) + stations_df = _load_stations(bq, refs) + + station_rows, station_calls = _run_stations_bundle( + job, stations_df, dry_run + ) + grid_rows, grid_calls, n_grid_points = _run_grid(job, dry_run) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would issue 1 stations plan ({len(stations_df)} " + f"stations) and 1 grid plan ({n_grid_points} points)." + ), + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=station_rows + grid_rows, + notes=( + f"Stations: +{station_rows} rows, {station_calls} api call(s). " + f"Grid: +{grid_rows} rows, {grid_calls} api call(s), " + f"{n_grid_points} points." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Ingest MERRA-2 for the 28 ground stations and the Uganda " + "2024 NASA-POWER-resolution grid via region-shaped fetches." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-11_a12_ingest_katongole_2017_2022.py b/warehouse/migrations/2026-05-11_a12_ingest_katongole_2017_2022.py new file mode 100644 index 00000000..d94aea55 --- /dev/null +++ b/warehouse/migrations/2026-05-11_a12_ingest_katongole_2017_2022.py @@ -0,0 +1,247 @@ +"""A12 — ingest NASA POWER + CAMS at the 54 Katongole 2023 station coordinates over 2017-2022. + +What it changes +--------------- +For each of the 54 stations listed in +``data/external_references/katongole_2023_monthly.csv``: + +* Pull NASA POWER for every catalog NASA variable over the fixed window + ``2017-01-01 .. 2022-12-31`` — the same 7-year window Katongole et al. + (2023) average over to produce their published monthly climatology. +* Pull CAMS for every catalog CAMS variable over the same window. + +The fetched values land in ``nasa_daily_vars_long``, +``cams_daily_vars_long``, and ``irradiance_daily`` according to the +existing :class:`SatelliteJob` routing. + +Why +--- +The recomputation notebook validates the bias-corrected model against +the Katongole 2017–2022 monthly climatology. Before this migration, the +warehouse only had 2017–2022 NASA + CAMS coverage at the 5 cells that +fell inside Uganda for the 28 training-station ingest (A6). Mapping +each Katongole site to its nearest of those 5 cells produced a median +snap distance of 62 km (max 220 km) — coarse enough that the +satellite-feature column was effectively unrelated to where the station +actually sits. + +After this migration, every Katongole station has NASA + CAMS at its +own coordinates over the full 2017–2022 window, so the model's +2017–2022 monthly climatology can be computed and compared to +Katongole's climatology apples-to-apples. + +Expected effect +--------------- +* 54 NASA POWER API calls (one per station, multi-variable per call). +* 54 CAMS API calls (one per station, multi-variable per call). +* Combined rate-limit footprint sits within both providers' daily + quotas (CAMS ~40/day soft cap may chunk this across 2 days for + free-tier users; NASA POWER has no published limit and generally + serves 54 calls in a few minutes). +* Runtime: a few minutes for NASA, up to a couple of hours for CAMS + depending on rate-limit interactions. +* Idempotent: ``SatelliteJob._location_fully_cached`` checks coverage + per (geohash5, date) before issuing the API call, so a second run + skips stations whose data is already loaded. + +Prerequisites +------------- +* ``CAMS_EMAIL`` in ``.env`` (a registered SoDa-Pro account). +* NASA POWER requires no credentials. + +Reversal +-------- +Possible but tedious — would need to delete rows by ``(date, geohash5)`` +matching each station's footprint. Easier to leave the data in place; +rerunning is idempotent. +""" + +from __future__ import annotations + +import logging +import os +import sys +from datetime import date +from pathlib import Path + +import pandas as pd +from dotenv import load_dotenv + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.jobs.satellite_job import ( + CamsSatelliteJob, + NasaPowerSatelliteJob, +) +from susse.warehouse_ops.population.types import ( + DateRange, + LocationSpec, + NamedLocationsPlan, + Source, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-11_a12_ingest_katongole_2017_2022" +_log = logging.getLogger(_MIGRATION_ID) + +# Hard-coded to the Katongole 2023 climatology window. +_VAL_START = date(2017, 1, 1) +_VAL_END = date(2022, 12, 31) + +# Path to the Katongole CSV, relative to the repository root. +_KATONGOLE_CSV = ( + Path(__file__).resolve().parents[2] + / "data" / "external_references" / "katongole_2023_monthly.csv" +) + + +def _load_katongole_stations() -> pd.DataFrame: + """Load station (name, lat, lon) triples from the Katongole reference CSV.""" + if not _KATONGOLE_CSV.exists(): + raise RuntimeError( + f"Katongole reference CSV not found at {_KATONGOLE_CSV}. " + f"Run from a clean clone of the repository — the file is tracked " + f"in git under data/external_references/." + ) + df = pd.read_csv(_KATONGOLE_CSV) + if df.empty: + raise RuntimeError(f"{_KATONGOLE_CSV} is empty.") + expected_cols = {"location", "latitude", "longitude"} + missing = expected_cols - set(df.columns) + if missing: + raise RuntimeError( + f"{_KATONGOLE_CSV} is missing required columns: {sorted(missing)}." + ) + _log.info("Loaded %d Katongole stations from %s", len(df), _KATONGOLE_CSV.name) + return df[["location", "latitude", "longitude"]].copy() + + +def _check_cams_email_present() -> None: + """Verify CAMS_EMAIL is set; CamsClient would otherwise prompt interactively.""" + load_dotenv() + if not os.getenv("CAMS_EMAIL"): + raise RuntimeError( + "CAMS_EMAIL is not set in the environment or .env. CAMS requests " + "would prompt interactively, which breaks the migration runner. " + "Add CAMS_EMAIL= to .env before running." + ) + + +def _run_for_source( + *, + job, + source: Source, + stations_df: pd.DataFrame, + dry_run: bool, +) -> tuple[int, int, int]: + """Iterate stations, run a NamedLocationsPlan per station for ``source``. + + Returns ``(rows_added, api_calls_made, stations_processed)``. + """ + catalog_vars = VariableCatalog.for_source(source) + if not catalog_vars: + raise RuntimeError(f"Empty catalog for source={source}.") + _log.info( + "Source=%s: %d catalog variables (%d irradiance, %d aux).", + source.value, + len(catalog_vars), + len(VariableCatalog.irradiance_for_source(source)), + len(VariableCatalog.auxiliary_for_source(source)), + ) + + rows_added = 0 + api_calls = 0 + stations_processed = 0 + + for row in stations_df.itertuples(index=False): + loc = LocationSpec( + name=str(row.location), + lat=float(row.latitude), + lon=float(row.longitude), + ) + plan = NamedLocationsPlan( + source=source, + date_range=DateRange(start=_VAL_START, end=_VAL_END), + locations=(loc,), + variables=catalog_vars, + ) + _log.info( + "[%s] %s — %s..%s, %d vars", + source.value, loc.name, _VAL_START, _VAL_END, len(catalog_vars), + ) + if dry_run: + stations_processed += 1 + continue + try: + result = job.run(plan) + except Exception: + _log.exception( + "[%s] %s — fetch failed; continuing with remaining stations.", + source.value, loc.name, + ) + continue + rows_added += result.rows_added + api_calls += result.api_calls_made + stations_processed += 1 + _log.info( + "[%s] %s — rows_added=%d, api_calls=%d", + source.value, loc.name, result.rows_added, result.api_calls_made, + ) + + return rows_added, api_calls, stations_processed + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + stations_df = _load_katongole_stations() + + if not dry_run: + _check_cams_email_present() + + nasa_job = NasaPowerSatelliteJob(bq, refs=refs) + cams_job = CamsSatelliteJob(bq, refs=refs) + + nasa_rows, nasa_calls, nasa_n = _run_for_source( + job=nasa_job, source=Source.NASA_POWER, + stations_df=stations_df, dry_run=dry_run, + ) + cams_rows, cams_calls, cams_n = _run_for_source( + job=cams_job, source=Source.CAMS, + stations_df=stations_df, dry_run=dry_run, + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would issue {nasa_n} NASA + {cams_n} CAMS plans " + f"covering {_VAL_START}..{_VAL_END}. No API calls or writes." + ), + ) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=nasa_rows + cams_rows, + notes=( + f"NASA: +{nasa_rows} rows in {nasa_calls} api calls across " + f"{nasa_n} stations. CAMS: +{cams_rows} rows in {cams_calls} " + f"api calls across {cams_n} stations." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Ingest NASA POWER + CAMS at the 54 Katongole 2023 stations " + "over the 2017-2022 climatology window." + ), + ) + ) diff --git a/warehouse/migrations/2026-05-13_a13_ingest_uganda_full_nasa.py b/warehouse/migrations/2026-05-13_a13_ingest_uganda_full_nasa.py new file mode 100644 index 00000000..58016da0 --- /dev/null +++ b/warehouse/migrations/2026-05-13_a13_ingest_uganda_full_nasa.py @@ -0,0 +1,164 @@ +"""A13 — re-ingest NASA POWER over Uganda at native resolution (2024). + +Status: **optional storage cleanup — not required for the portal.** + +What it does +------------ +Replaces the 2024 NASA POWER rows inside the Uganda bounding box with NASA's +own *native pixels*: it deletes the existing dense ~0.1° grid for 2024 from +``nasa_daily_vars_long`` and ``irradiance_daily`` (source ``'NASA'`` only), +then re-ingests the bbox via :class:`NasaPowerRegionJob`, which stores one +row per NASA native pixel (CERES' 1° irradiance grid, MERRA-2's 0.5°×0.625° +auxiliary grid) — no densification. + +Why this is optional +-------------------- +The portal does **not** need this migration. Reads go through +:class:`~susse.datasets.FeatureService`, which snaps every query coordinate +onto the nearest stored cell via +:class:`~susse.warehouse_ops.snapping.NearestPixelSnapper`. The existing +~0.1° grid already covers every 2024 Uganda click. This migration only +*tidies* the warehouse: it swaps ~23M redundant 0.1°-grid rows for ~1.5M +native-pixel rows (the same NASA values, ~25× fewer copies), completing the +native-resolution storage half of the per-source-grid design. + +It is therefore **not run automatically**. Apply it deliberately, when you +want the smaller table, with ``--dry-run`` first. + +Scope notes +----------- +* Only the **2024** calendar year inside the Uganda bbox is touched. Sparse + pre-2024 Uganda rows (a handful of cells from earlier migrations) and all + out-of-bbox training-station rows are left untouched. +* The replacement is delete-then-ingest. If the ingest step fails midway, + re-running the migration is safe: the delete is idempotent and + :class:`NasaPowerRegionJob` MERGE-loads. +* ~130 NASA native pixels × 366 days × the full catalog ≈ ~1.5M rows. The + cost is the fetcher's ~130 HTTP calls (one per tile × variable), ~20–30 + minutes; the BigQuery load is a single fast MERGE. + +Prerequisites +------------- +No credentials — NASA POWER is public. Run from a clone of Solar_irradiation +with the susse package installed (``pip install -e .``). +""" + +from __future__ import annotations + +import logging +import sys +from datetime import date +from pathlib import Path + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import TableRefs +from susse.warehouse_ops.population.dim_variable import VariableCatalog +from susse.warehouse_ops.population.jobs.nasa_power_region_job import ( + NasaPowerRegionJob, +) +from susse.warehouse_ops.population.types import ( + BoundingBox, + DateRange, + RegionPlan, + Source, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from _common import MigrationResult, run_migration # noqa: E402 + +_MIGRATION_ID = "2026-05-13_a13_ingest_uganda_full_nasa" +_log = logging.getLogger(_MIGRATION_ID) + +# Uganda bounding box — matches the region gate the portal enforces. +_UGANDA_BBOX: BoundingBox = BoundingBox( + min_lat=-1.5, max_lat=4.5, min_lon=29.5, max_lon=35.05, +) + +# Calendar-year 2024. The portal's PortalConfig.default_year matches. +_DATE_RANGE: DateRange = DateRange(start=date(2024, 1, 1), end=date(2024, 12, 31)) + +# SQL fragment scoping a statement to the 2024 Uganda bbox slice. +_BBOX_2024_SQL: str = ( + f"date BETWEEN DATE('{_DATE_RANGE.start}') AND DATE('{_DATE_RANGE.end}') " + f"AND latitude BETWEEN {_UGANDA_BBOX.min_lat} AND {_UGANDA_BBOX.max_lat} " + f"AND longitude BETWEEN {_UGANDA_BBOX.min_lon} AND {_UGANDA_BBOX.max_lon}" +) + + +def _count_rows(bq: BigQueryClient, table_fqn: str, where: str) -> int: + """COUNT(*) for the scoped slice of a table.""" + df = bq.query(f"SELECT COUNT(*) AS n FROM `{table_fqn}` WHERE {where}") + return int(df.iloc[0]["n"]) + + +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: + refs = TableRefs(config=bq.config) + catalog = VariableCatalog.for_source(Source.NASA_POWER) + if not catalog: + raise RuntimeError( + "NASA POWER catalog is empty. Check VariableCatalog registration." + ) + + long_where = _BBOX_2024_SQL + irr_where = f"source = 'NASA' AND {_BBOX_2024_SQL}" + n_long = _count_rows(bq, refs.nasa_daily_vars_long, long_where) + n_irr = _count_rows(bq, refs.irradiance_daily, irr_where) + + plan = RegionPlan( + source=Source.NASA_POWER, + date_range=_DATE_RANGE, + bbox=_UGANDA_BBOX, + variables=catalog, + ) + _log.info( + "Uganda NASA native re-ingest: would delete %d long + %d irradiance " + "rows for 2024, then re-ingest via %s.", + n_long, n_irr, plan.describe(), + ) + + if dry_run: + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=0, + notes=( + f"dry-run; would delete {n_long} nasa_daily_vars_long + " + f"{n_irr} irradiance_daily (source=NASA) rows for the 2024 " + f"Uganda bbox, then re-ingest {len(catalog)} variables as " + f"NASA native pixels. No deletes, no API calls." + ), + ) + + # Delete-then-ingest. The 2024 slice spans 366 date partitions — one + # DELETE per table stays well under BigQuery's 4000-partitions-per-DML + # cap. A failed ingest leaves the slice empty; re-running is safe. + _log.info("Deleting %d existing 2024 Uganda rows from nasa_daily_vars_long.", n_long) + bq.execute_ddl(f"DELETE FROM `{refs.nasa_daily_vars_long}` WHERE {long_where}") + _log.info("Deleting %d existing 2024 Uganda NASA rows from irradiance_daily.", n_irr) + bq.execute_ddl(f"DELETE FROM `{refs.irradiance_daily}` WHERE {irr_where}") + + result = NasaPowerRegionJob(bq, refs=refs).run(plan) + + return MigrationResult( + migration_id=_MIGRATION_ID, + rows_affected=result.rows_added, + notes=( + f"Deleted {n_long}+{n_irr} dense-grid rows; re-ingested " + f"{result.rows_added} NASA native-pixel rows " + f"({result.extra.get('native_pixels', 0)} pixels, " + f"{result.extra.get('rows_added_long', 0)} long + " + f"{result.extra.get('rows_added_irradiance', 0)} irradiance)." + ), + ) + + +if __name__ == "__main__": + sys.exit( + run_migration( + migration_id=_MIGRATION_ID, + fn=migrate, + description=( + "Optional cleanup: replace the 2024 Uganda dense NASA grid " + "with NASA native pixels. Not required for the portal." + ), + ) + ) diff --git a/warehouse/migrations/README.md b/warehouse/migrations/README.md new file mode 100644 index 00000000..7cabc267 --- /dev/null +++ b/warehouse/migrations/README.md @@ -0,0 +1,139 @@ +# Warehouse migrations + +One-shot scripts that change the state of the BigQuery warehouse. + +Use this directory for any operation that: + +* creates, drops, or alters a table or view; +* backfills, deletes, or rewrites rows; +* runs an ingest job that loads data from an external source. + +Anything in this directory is expected to mutate warehouse state. Read-only +diagnostics belong in a notebook under `notebooks/inspection/` instead. + +## Conventions + +### File naming + +``` +YYYY-MM-DD__.py +``` + +* **`YYYY-MM-DD`** — the date the migration was authored. A filesystem + listing then sorts chronologically. +* **``** — a short tag tying the migration to the plan it came + from. Phases use single letters (`a`, `b`, `c`, ...). Within a phase, ids + count up (`a1`, `a2`, ...). The plan that grouped these migrations lives + in conversation/PR notes; the tag makes the connection traceable. +* **``** — three or four lowercase tokens summarising the + operation, separated by underscores. + +Example: `2026-05-08_a1_populate_dim_variable.py`. + +### Idempotency + +Re-running a migration must be safe. Either: + +* the operation is a MERGE / UPSERT keyed on a primary key, so a second + run finds no new work; or +* the script first queries existing state and exits early when the + expected post-condition already holds (`CREATE TABLE IF NOT EXISTS`, + `existing_keys()` checks, count-the-rows-then-skip, etc.). + +A migration is allowed to be a no-op when re-run; it must not be allowed +to crash, double-write, or leave the warehouse in a partial state. + +### Structure + +Each migration is a single Python file. Boilerplate goes in +[`_common.py`](./_common.py). The body of a migration is one +function with the signature: + +```python +def migrate(bq: BigQueryClient, dry_run: bool) -> MigrationResult: ... +``` + +`dry_run=True` prints what would happen without touching BigQuery. +`dry_run=False` performs the operation and returns the row count. + +The migration's `__main__` block invokes `run_migration(...)`, which +parses `--dry-run` / `--apply` flags from argv and handles logging. + +### Top-of-file docstring + +Every migration begins with a docstring covering: + +* **What** it changes (table names, schema effects). +* **Why** — the user-facing reason or which plan phase it belongs to. +* **Expected effect** — row count, runtime, API calls (so future + collaborators know if re-running is cheap or expensive). +* **Reversal** — how to undo this change if needed. "Not reversible" is + an acceptable answer for some operations; say so explicitly. + +### Running + +```bash +# Plan only: +.venv/bin/python warehouse/migrations/2026-05-08_a1_populate_dim_variable.py --dry-run + +# Apply: +.venv/bin/python warehouse/migrations/2026-05-08_a1_populate_dim_variable.py --apply + +# Override target dataset (useful for sandbox testing): +.venv/bin/python warehouse/migrations/2026-05-08_a1_populate_dim_variable.py \ + --apply --project my-sandbox-project --dataset solar_warehouse_test +``` + +## Index + +| ID | File | Summary | Status | +|---|---|---|---| +| a1 | [`2026-05-08_a1_populate_dim_variable.py`](./2026-05-08_a1_populate_dim_variable.py) | Replace the 4 sentinel-bug rows in `dim_variable` with the full 41-variable NASA POWER + CAMS catalog. | applied | +| a2 | [`2026-05-08_a2_fix_typos.py`](./2026-05-08_a2_fix_typos.py) | Rename `arimass` → `airmass` in `nasa_daily_vars_long` and `dim_variable`; refresh catalog (now 42 rows incl. `solar_zenith_angle`). | applied | +| a3 | [`2026-05-08_a3_create_cams_daily_vars_long.py`](./2026-05-08_a3_create_cams_daily_vars_long.py) | Create the empty `cams_daily_vars_long` table from its DDL. | applied | +| a4 | [`2026-05-08_a4_backfill_cams_aux_uganda_2024.py`](./2026-05-08_a4_backfill_cams_aux_uganda_2024.py) | MERGE the 6 CAMS auxiliary variables (Uganda 2024) from `cams_daily_ext` into `cams_daily_vars_long` (4.31M rows). | applied | +| a5 | _(no-op, not written)_ | Investigation showed the 48 "missing" points for `evaporation_land` / `evapotranspiration_energy` are Lake Victoria water-cells. NASA POWER's `EVLAND` / `EVPTRNS` are land-only variables; the gap is correct data. | skipped | +| a6 | [`2026-05-08_a6_ingest_28_ground_stations.py`](./2026-05-08_a6_ingest_28_ground_stations.py) | Ingest NASA POWER + CAMS for 28 ground-measurement stations over each station's full ground-data date range. Requires `CAMS_EMAIL` in `.env`. Idempotent via per-(geohash5, date) coverage check. | drafted | +| a7 | [`2026-05-08_a7_drop_external_relics.py`](./2026-05-08_a7_drop_external_relics.py) | Drop the legacy `cams_daily_ext` and `nasa_daily_ext` external CSV tables. | drafted | +| a8 | _(notebook edit)_ | Add audit and pair-assertion cells to `warehouse/extending_the_warehouse.ipynb` between the warehouse-tour and Pattern-1 sections. | applied | +| a9 | [`2026-05-08_a9_fix_cams_units.py`](./2026-05-08_a9_fix_cams_units.py) | Multiply CAMS rows above magnitude threshold by 0.024 in `irradiance_daily` and `cams_daily_vars_long`. Fixes a units bug where `CamsSatelliteJob` wrote raw W/m² mean values into kWh/m²/day columns. 178,372 rows corrected. | applied | +| a10 | [`2026-05-08_a10_drop_solar_zenith_angle.py`](./2026-05-08_a10_drop_solar_zenith_angle.py) | Delete the `solar_zenith_angle` row from `dim_variable`. NASA POWER doesn't serve SZA at daily resolution; the entry caused spurious re-fetches because the coverage check saw it as always-missing. | applied | +| a11 | [`2026-05-10_a11_annotate_physical_storage.py`](./2026-05-10_a11_annotate_physical_storage.py) | Add the `physical_storage` column to `dim_variable` and tag every row with where the value physically lives (`long_format` / `irradiance_wide` / `modis_observations`). Enables `FeatureSelection` to reject wide-vs-long mismatches at construction. | applied | +| a12 | [`2026-05-11_a12_ingest_katongole_2017_2022.py`](./2026-05-11_a12_ingest_katongole_2017_2022.py) | Ingest NASA POWER + CAMS at the 54 Katongole 2023 stations over 2017-2022. Required by the recomputation notebook before it can compute a multi-year climatology matching Katongole's published window. | drafted | +| a13 | [`2026-05-13_a13_ingest_uganda_full_nasa.py`](./2026-05-13_a13_ingest_uganda_full_nasa.py) | Ingest NASA POWER for the entire Uganda bbox at 0.04° (~geohash5-width) grid step for 2024. Brings NASA coverage from ~1962 cells (training-station footprint) to ~21,000 cells so the portal's `on_cache_miss="raise"` path works for any Uganda click. ~40k NASA POWER API calls; 6–25 h wall time depending on latency; resumable. | drafted | +| b3 | [`2026-05-08_b3_create_merra_daily_vars_long.py`](./2026-05-08_b3_create_merra_daily_vars_long.py) | Create the empty `merra_daily_vars_long` table from its DDL. | applied | +| b7 | [`2026-05-08_b7_populate_dim_variable_merra.py`](./2026-05-08_b7_populate_dim_variable_merra.py) | Extend `dim_variable` with the 4 MERRA-2 catalog entries. Idempotent. | applied | +| b9 | [`2026-05-10_b9_ingest_merra_region.py`](./2026-05-10_b9_ingest_merra_region.py) | Ingest MERRA-2 hourly fields via OPeNDAP, aggregate to daily, and load into `merra_daily_vars_long` for the Uganda 2024 grid using the bbox-based `fetch_region` path (~100× faster than per-point fetch). Requires `EARTHDATA_USERNAME` / `EARTHDATA_PASSWORD`. | drafted | +| c3 | [`2026-05-08_c3_create_modis_observations.py`](./2026-05-08_c3_create_modis_observations.py) | Create the empty `modis_observations` table from its DDL. | applied | +| c7 | [`2026-05-08_c7_populate_dim_variable_modis.py`](./2026-05-08_c7_populate_dim_variable_modis.py) | Extend `dim_variable` with the 3 MODIS catalog entries (MCD43A4, MOD11A2, MOD13Q1). | applied | +| c8 | [`2026-05-08_c8_ingest_modis_stations_and_grid.py`](./2026-05-08_c8_ingest_modis_stations_and_grid.py) | Ingest MODIS composites from ORNL DAAC for the 28 ground stations and the Uganda 2024 grid. | applied | + +(Add a new row to this table for every migration. Status starts as +`drafted`, becomes `applied` once it has run against the production +warehouse, or `superseded` if a later migration replaces it.) + +## Why no `_migration_log` BigQuery table + +Some migration frameworks (Alembic, Django) record applied migrations in +a dedicated table so the system can answer "which migrations has this +database seen?". We deliberately don't do that yet because: + +* one production warehouse, one developer — git history is enough audit + trail; +* every migration is idempotent, so "did this run?" is answerable by + re-running it (no-op vs work); +* the bookkeeping table itself becomes a thing to migrate. + +If we ever grow to multiple environments (prod / staging / colab snapshots +that diverge), revisit. The `MigrationResult` summary line is already +structured so it could be inserted into a `_migration_log` table later +without changing the migration scripts themselves. + +## Why not Weights & Biases for warehouse provenance + +W&B is great for ML experiment + dataset-version tracking — tying a +training run to the exact data snapshot it consumed. That belongs in the +training notebooks (notebook 02 onwards), not here. Warehouse migration +provenance is an infra concern; using W&B for it would force every +collaborator to authenticate against a separate service just to read git +history. diff --git a/warehouse/migrations/__init__.py b/warehouse/migrations/__init__.py new file mode 100644 index 00000000..795e169a --- /dev/null +++ b/warehouse/migrations/__init__.py @@ -0,0 +1,9 @@ +"""One-shot warehouse migrations. + +Each script in this package modifies BigQuery state (creates a table, +backfills rows, drops a relic, runs an ingest). Scripts are dated and +numbered so order is obvious from a filesystem listing, and idempotent +so re-running them is safe. + +Conventions are documented in ``README.md`` next to this file. +""" diff --git a/warehouse/migrations/_common.py b/warehouse/migrations/_common.py new file mode 100644 index 00000000..1455d312 --- /dev/null +++ b/warehouse/migrations/_common.py @@ -0,0 +1,141 @@ +"""Shared helpers for warehouse migration scripts. + +A migration script is a self-contained Python file that mutates the +BigQuery warehouse in one well-defined way (create a table, backfill +rows, drop a relic, run an ingest). Migrations are idempotent: running +the same migration twice should be a no-op the second time. + +This module collects the boilerplate that every migration needs: + +* A standard logging setup, so each script's output is uniform. +* A factory for the :class:`susse.warehouse_ops.io.bq.BigQueryClient`, + defaulting to the production warehouse but overridable for tests. +* A small CLI runner (:func:`run_migration`) that wraps a migration + callable in dry-run / commit semantics and emits a structured summary. +""" + +from __future__ import annotations + +import argparse +import logging +import sys +from dataclasses import dataclass +from typing import Callable + +from susse.warehouse_ops.io.bq import BigQueryClient +from susse.warehouse_ops.io.config import WarehouseConfig + +_LOG_FORMAT = "%(asctime)s [%(levelname)s] %(name)s: %(message)s" + + +def configure_logging(level: int = logging.INFO) -> None: + """Standard logger setup for migration scripts. + + Idempotent: re-applying the basic config simply replaces handlers. + """ + logging.basicConfig(level=level, format=_LOG_FORMAT, force=True) + + +def make_bq_client( + *, + project_id: str | None = None, + dataset: str | None = None, +) -> BigQueryClient: + """Construct a :class:`BigQueryClient` pointed at the production warehouse. + + The defaults match :class:`WarehouseConfig`. Override ``project_id`` / + ``dataset`` to run a migration against a sandbox. + """ + config = WarehouseConfig( + project_id=project_id or WarehouseConfig.__dataclass_fields__["project_id"].default, + dataset=dataset or WarehouseConfig.__dataclass_fields__["dataset"].default, + ) + return BigQueryClient(config=config) + + +@dataclass(frozen=True) +class MigrationResult: + """Structured summary returned by a migration callable. + + Attributes: + migration_id: Filename stem (e.g. ``2026-05-08_a1_populate_dim_variable``). + rows_affected: How many rows were written, updated, or deleted. Zero + is a legitimate value when the migration was a no-op (already + applied, idempotency win). + notes: Short human-readable context for the log line — what the + migration actually did this run. + """ + + migration_id: str + rows_affected: int + notes: str + + def summary_line(self) -> str: + return ( + f"[{self.migration_id}] rows_affected={self.rows_affected} " + f"— {self.notes}" + ) + + +MigrationFn = Callable[[BigQueryClient, bool], MigrationResult] +"""Signature each migration must implement: ``fn(bq, dry_run) -> MigrationResult``.""" + + +def run_migration( + migration_id: str, + fn: MigrationFn, + *, + description: str, +) -> int: + """CLI entrypoint shared by every migration script. + + Parses ``--dry-run`` / ``--apply`` / ``--project`` / ``--dataset`` + from argv, configures logging, builds the client, invokes ``fn``, + and prints a structured summary. Returns a process exit code. + + Migrations should call this from their ``__main__`` block: + + .. code-block:: python + + if __name__ == "__main__": + sys.exit(run_migration( + migration_id="2026-05-08_a1_populate_dim_variable", + fn=migrate, + description="Populate dim_variable with the full 35-row catalog.", + )) + """ + parser = argparse.ArgumentParser(description=description) + mode = parser.add_mutually_exclusive_group(required=True) + mode.add_argument( + "--dry-run", + action="store_true", + help="Plan the migration but do not write to BQ. Prints what would happen.", + ) + mode.add_argument( + "--apply", + action="store_true", + help="Apply the migration to the warehouse.", + ) + parser.add_argument("--project", default=None, help="GCP project id override.") + parser.add_argument("--dataset", default=None, help="BQ dataset override.") + args = parser.parse_args() + + configure_logging() + log = logging.getLogger(migration_id) + + bq = make_bq_client(project_id=args.project, dataset=args.dataset) + log.info( + "Starting migration on %s.%s (mode=%s)", + bq.config.project_id, + bq.config.dataset, + "dry-run" if args.dry_run else "apply", + ) + + try: + result = fn(bq, args.dry_run) + except Exception: + log.exception("Migration failed.") + return 1 + + log.info(result.summary_line()) + return 0 diff --git a/warehouse/sql/00_schema/cams_daily_vars_long.sql b/warehouse/sql/00_schema/cams_daily_vars_long.sql new file mode 100644 index 00000000..4873c40e --- /dev/null +++ b/warehouse/sql/00_schema/cams_daily_vars_long.sql @@ -0,0 +1,16 @@ +-- Long-format daily CAMS variables. +-- Mirrors nasa_daily_vars_long: one row per (date, point, variable, source). +-- Created idempotently so this DDL is safe to re-run during deployment. + +CREATE TABLE IF NOT EXISTS `solar-irradiation-estimation.solar_warehouse.cams_daily_vars_long` ( + date DATE, + latitude FLOAT64, + longitude FLOAT64, + geog GEOGRAPHY, + geohash5 STRING, + variable_id STRING, + value FLOAT64, + source STRING +) +PARTITION BY date +CLUSTER BY geohash5, variable_id; diff --git a/warehouse/sql/00_schema/dim_variable.sql b/warehouse/sql/00_schema/dim_variable.sql new file mode 100644 index 00000000..9032ab17 --- /dev/null +++ b/warehouse/sql/00_schema/dim_variable.sql @@ -0,0 +1,17 @@ +-- Variable dimension table. +-- Created idempotently. Population is handled by +-- `susse.warehouse_ops.population.dim_variable.populate_dim_variable`, +-- which inserts/updates one row per (variable_id, source) tuple. + +CREATE TABLE IF NOT EXISTS `solar-irradiation-estimation.solar_warehouse.dim_variable` ( + variable_id STRING NOT NULL, + source STRING NOT NULL, + display_name STRING, + unit STRING, + native_unit STRING, + description STRING, + temporal_granularity STRING, + spatial_resolution_km FLOAT64, + valid_min FLOAT64, + valid_max FLOAT64 +); diff --git a/warehouse/sql/00_schema/merra_daily_vars_long.sql b/warehouse/sql/00_schema/merra_daily_vars_long.sql new file mode 100644 index 00000000..66c68fe1 --- /dev/null +++ b/warehouse/sql/00_schema/merra_daily_vars_long.sql @@ -0,0 +1,19 @@ +-- Long-format daily MERRA-2 variables. +-- Mirrors nasa_daily_vars_long and cams_daily_vars_long: one row per +-- (date, point, variable, source). MERRA-2's native temporal resolution +-- is hourly; daily values stored here are cosine-zenith-weighted means +-- aggregated client-side before MERGE. +-- Created idempotently so this DDL is safe to re-run during deployment. + +CREATE TABLE IF NOT EXISTS `solar-irradiation-estimation.solar_warehouse.merra_daily_vars_long` ( + date DATE, + latitude FLOAT64, + longitude FLOAT64, + geog GEOGRAPHY, + geohash5 STRING, + variable_id STRING, + value FLOAT64, + source STRING +) +PARTITION BY date +CLUSTER BY geohash5, variable_id; diff --git a/warehouse/sql/00_schema/modis_observations.sql b/warehouse/sql/00_schema/modis_observations.sql new file mode 100644 index 00000000..4fc7255e --- /dev/null +++ b/warehouse/sql/00_schema/modis_observations.sql @@ -0,0 +1,26 @@ +-- Daily MODIS observations from ORNL DAAC (modis.ornl.gov). +-- Each row is one (date, point, product, band) measurement. Unlike the +-- *_daily_vars_long tables, MODIS rows carry both product_id and band_id +-- because each MODIS product has multiple bands and the (product, band) +-- pair is the natural identifier of a single observation series. +-- +-- Storage uses each product's native composite cadence — values appear +-- at the composite end-date (daily for MOD11A1/MOD10A1, 16-day for +-- MCD43A3/MOD13Q1). Daily upsampling is a downstream join concern; we +-- don't fabricate dates here. +-- +-- Created idempotently so this DDL is safe to re-run during deployment. + +CREATE TABLE IF NOT EXISTS `solar-irradiation-estimation.solar_warehouse.modis_observations` ( + date DATE, + latitude FLOAT64, + longitude FLOAT64, + geog GEOGRAPHY, + geohash5 STRING, + product_id STRING, + band_id STRING, + value FLOAT64, + source STRING +) +PARTITION BY date +CLUSTER BY geohash5, product_id, band_id;