diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 1ef20fd..e065f28 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -49,7 +49,7 @@ jobs: uv venv .test-venv source .test-venv/bin/activate uv pip install dist/*.whl --reinstall - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples pytest -v - uses: actions/upload-artifact@v5 @@ -104,7 +104,7 @@ jobs: uv pip install dist/*.whl --reinstall # Install dev dependencies from pyproject.toml using pip interface - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples # Run tests directly pytest -v @@ -132,7 +132,7 @@ jobs: sudo apt-get install -y llvm-18-dev libclang-18-dev libzstd-dev libpolly-18-dev patchelf - name: Generate stubs - run: cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs + run: cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs env: LLVM_SYS_181_PREFIX: /usr/lib/llvm-18 LIBCLANG_PATH: /usr/lib/llvm-18/lib diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..d2399a7 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,59 @@ +name: Documentation + +on: + push: + branches: ["main"] + tags: + - '*' + pull_request: + workflow_dispatch: + +# Prevent concurrent deployments +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +# Default permissions: read-only +permissions: + contents: read + +jobs: + build-and-deploy: + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for git-revision-date plugin + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Cache uv dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/uv + key: ${{ runner.os }}-uv-docs-deploy-${{ hashFiles('.github/workflows/docs.yml') }} + restore-keys: | + ${{ runner.os }}-uv-docs-deploy- + + - name: Install dependencies + run: uv pip install --system --group docs + + - name: Build documentation + run: mkdocs build --strict + + - name: Deploy to GitHub Pages + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/') + uses: peaceiris/actions-gh-pages@v4 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./site + cname: false # Set to your custom domain if you have one diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f91c182..2bad492 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -50,7 +50,7 @@ jobs: uv venv .test-venv source .test-venv/bin/activate uv pip install dist/*.whl --reinstall - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples pytest -v - uses: actions/upload-artifact@v5 @@ -106,7 +106,7 @@ jobs: uv pip install dist/*.whl --reinstall # Install dev dependencies from pyproject.toml using pip interface - uv pip install --group dev --group examples + uv pip install --group dev --group docs --group examples # Run tests directly pytest -v @@ -134,7 +134,7 @@ jobs: sudo apt-get install -y llvm-18-dev libclang-18-dev libzstd-dev libpolly-18-dev patchelf - name: Generate stubs - run: cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs + run: cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs env: LLVM_SYS_181_PREFIX: /usr/lib/llvm-18 LIBCLANG_PATH: /usr/lib/llvm-18/lib diff --git a/.gitignore b/.gitignore index 195e23d..ecb7737 100644 --- a/.gitignore +++ b/.gitignore @@ -65,6 +65,9 @@ coverage.xml # Sphinx documentation docs/_build/ +# MkDocs documentation +site/ + # PyCharm .idea/ diff --git a/Cargo.lock b/Cargo.lock index a20b4fc..142e7d1 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -153,7 +153,7 @@ dependencies = [ "regex", "rustc-hash 2.1.1", "shlex", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -179,9 +179,9 @@ dependencies = [ [[package]] name = "bumpalo" -version = "3.19.0" +version = "3.19.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "46c5e41b57b8bba42a04676d81cb89e9ee8e859a1a66f80a5a72e1cb76b34d43" +checksum = "5dd9dc738b7a8311c7ade152424974d8115f2cdad61e8dab8dac9f2362298510" dependencies = [ "allocator-api2", ] @@ -203,7 +203,7 @@ checksum = "f9abbd1bc6865053c427f7198e6af43bfdedc55ab791faed4fbd361d789575ff" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -226,9 +226,9 @@ checksum = "37b2a672a2cb129a2e41c10b1224bb368f9f37a2b16b612598138befd7b37eb5" [[package]] name = "cc" -version = "1.2.48" +version = "1.2.53" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c481bdbf0ed3b892f6f806287d72acd515b352a4ec27a208489b8c1bc839633a" +checksum = "755d2fce177175ffca841e9a06afdb2c4ab0f593d53b4dee48147dfaade85932" dependencies = [ "find-msvc-tools", "shlex", @@ -257,42 +257,15 @@ checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724" [[package]] name = "chrono" -version = "0.4.42" +version = "0.4.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "145052bdd345b87320e369255277e3fb5152762ad123a901ef5c262dd38fe8d2" +checksum = "fac4744fb15ae8337dc853fee7fb3f4e48c0fbaa23d0afe49c447b4fab126118" dependencies = [ "iana-time-zone", "js-sys", "num-traits", "wasm-bindgen", - "windows-link 0.2.1", -] - -[[package]] -name = "chronopt" -version = "0.2.0" -dependencies = [ - "criterion", - "diffsol", - "nalgebra", - "proptest", - "rand 0.9.2", - "rand_distr", - "rayon", -] - -[[package]] -name = "chronopt-py" -version = "0.2.0" -dependencies = [ - "chronopt", - "clap 4.5.53", - "nalgebra", - "numpy", - "pyo3", - "pyo3-build-config", - "pyo3-stub-gen", - "pyo3-stub-gen-derive", + "windows-link", ] [[package]] @@ -319,9 +292,9 @@ dependencies = [ [[package]] name = "clap" -version = "4.5.53" +version = "4.5.54" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c9e340e012a1bf4935f5282ed1436d1489548e8f72308207ea5df0e23d2d03f8" +checksum = "c6e6ff9dcd79cff5cd969a17a545d79e84ab086e444102a591e288a8aa3ce394" dependencies = [ "clap_builder", "clap_derive", @@ -329,9 +302,9 @@ dependencies = [ [[package]] name = "clap_builder" -version = "4.5.53" +version = "4.5.54" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d76b5d13eaa18c901fd2f7fca939fefe3a0727a953561fefdf3b2922b8569d00" +checksum = "fa42cf4d2b7a41bc8f663a7cab4031ebafa1bf3875705bfaf8466dc60ab52c00" dependencies = [ "anstream", "anstyle", @@ -348,20 +321,20 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "clap_lex" -version = "0.7.6" +version = "0.7.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a1d728cc89cf3aee9ff92b05e62b19ee65a02b5702cff7d5a377e32c6ae29d8d" +checksum = "c3e64b0cc0439b12df2fa678eae89a1c56a529fd067a9115f7827f1fffd22b32" [[package]] name = "cmake" -version = "0.1.54" +version = "0.1.57" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e7caa3f9de89ddbe2c607f4101924c5abec803763ae9534e4f4d7d8f84aa81f0" +checksum = "75443c44cd6b379beb8c5b45d85d0773baf31cce901fe7bb252f4eff3008ef7d" dependencies = [ "cc", ] @@ -728,11 +701,38 @@ version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "930c7171c8df9fb1782bdf9b918ed9ed2d33d1d22300abb754f9085bc48bf8e8" +[[package]] +name = "diffid" +version = "0.2.0" +dependencies = [ + "criterion", + "diffsol", + "nalgebra", + "proptest", + "rand 0.9.2", + "rand_distr", + "rayon", +] + +[[package]] +name = "diffid-py" +version = "0.2.0" +dependencies = [ + "clap 4.5.54", + "diffid", + "nalgebra", + "numpy", + "pyo3", + "pyo3-build-config", + "pyo3-stub-gen", + "pyo3-stub-gen-derive", +] + [[package]] name = "diffsl" -version = "0.6.1" +version = "0.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8734b13a8f6961387db25fcfaa25ebfff86916397a10447c9ed65855bb7760d" +checksum = "72660d2c9a38eff333fa73aaf4ff2db9e564e7e193be3b5bdbd0abdd0da265d2" dependencies = [ "aliasable", "anyhow", @@ -749,6 +749,7 @@ dependencies = [ "lazy_static", "libc", "llvm-sys", + "log", "mmap-rs", "ndarray", "num-traits", @@ -764,18 +765,20 @@ dependencies = [ [[package]] name = "diffsol" -version = "0.8.1" +version = "0.10.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c9a4db7fb66c4839af9d320584a6b48621d192cd52dbbacc5a5f819bd7c4601f" +checksum = "3a2ba5b9cf0c9a7badb8f44cdad0058e286f8a69f9b56e899d79ecccb8917b48" dependencies = [ "diffsl", "faer", "faer-traits", + "log", "nalgebra", "nalgebra-sparse", "num-traits", "petgraph", "serde", + "serde_json", "thiserror 2.0.17", ] @@ -820,7 +823,7 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -849,7 +852,7 @@ checksum = "3bf679796c0322556351f287a51b49e48f7c4986e727b5dd78c972d30e2e16cc" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -860,7 +863,7 @@ checksum = "44f23cf4b44bfce11a86ace86f8a73ffdec849c9fd00a386a53d278bd9e81fb3" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -904,7 +907,7 @@ checksum = "2cc4b8cd876795d3b19ddfd59b03faa303c0b8adb9af6e188e81fc647c485bb9" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -933,9 +936,9 @@ checksum = "2acce4a10f12dc2fb14a218589d4f1f62ef011b2d0cc4b3cb1bba8e94da14649" [[package]] name = "find-msvc-tools" -version = "0.1.5" +version = "0.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3a3076410a55c90011c298b04d0cfa770b00fa04e1e3c97d3f6c9de105a03844" +checksum = "8591b0bcc8a98a64310a2fae1bb3e9b8564dd10e381e6e28010fde8e8e8568db" [[package]] name = "fixedbitset" @@ -945,9 +948,9 @@ checksum = "1d674e81391d1e1ab681a28d99df07927c6d4aa5b027d7da16ba32d1d21ecd99" [[package]] name = "flate2" -version = "1.1.5" +version = "1.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bfe33edd8e85a12a67454e37f8c75e730830d83e313556ab9ebf9ee7fbeb3bfb" +checksum = "b375d6465b98090a5f25b1c7703f3859783755aa9a80433b36e0379a3ec2f369" dependencies = [ "crc32fast", "miniz_oxide", @@ -1086,16 +1089,17 @@ checksum = "5881e4c3c2433fe4905bb19cfd2b5d49d4248274862b68c27c33d9ba4e13f9ec" [[package]] name = "generator" -version = "0.8.7" +version = "0.8.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "605183a538e3e2a9c1038635cc5c2d194e2ee8fd0d1b66b8349fad7dbacce5a2" +checksum = "52f04ae4152da20c76fe800fa48659201d5cf627c5149ca0b707b69d7eef6cf9" dependencies = [ "cc", "cfg-if", "libc", "log", "rustversion", - "windows 0.61.3", + "windows-link", + "windows-result", ] [[package]] @@ -1119,9 +1123,9 @@ dependencies = [ [[package]] name = "getrandom" -version = "0.2.16" +version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "335ff9f135e4384c8150d6f27c6daed433577f86b4750418338c01a1a2528592" +checksum = "ff2abc00be7fca6ebc474524697ae276ad847ad0a6b3faa4bcb027e9a4614ad0" dependencies = [ "cfg-if", "libc", @@ -1243,9 +1247,9 @@ checksum = "8babf46d4c1c9d92deac9f7be466f76dfc4482b6452fc5024b5e8daf6ffeb3ee" [[package]] name = "glam" -version = "0.30.9" +version = "0.30.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bd47b05dddf0005d850e5644cae7f2b14ac3df487979dbfff3b56f20b1a6ae46" +checksum = "19fc433e8437a212d1b6f1e68c7824af3aed907da60afa994e7f542d18d12aa9" [[package]] name = "glob" @@ -1320,7 +1324,7 @@ dependencies = [ "js-sys", "log", "wasm-bindgen", - "windows-core 0.62.2", + "windows-core", ] [[package]] @@ -1334,9 +1338,9 @@ dependencies = [ [[package]] name = "indexmap" -version = "2.12.1" +version = "2.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0ad4bb2b565bca0645f4d68c5c9af97fba094e9791da685bf83cb5f3ce74acf2" +checksum = "7714e70437a7dc3ac8eb7e6f8df75fd8eb422675fc7678aff7364301092b1017" dependencies = [ "equivalent", "hashbrown 0.16.1", @@ -1372,7 +1376,7 @@ checksum = "ad9a7dd586b00f2b20e0b9ae3c6faa351fbfd56d15d63bbce35b13bece682eda" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1404,7 +1408,7 @@ dependencies = [ "heck", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1451,15 +1455,15 @@ dependencies = [ [[package]] name = "itoa" -version = "1.0.15" +version = "1.0.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4a5f13b858c8d314ee3e8f639011f7ccefe71f97f96e50151fb991f267928e2c" +checksum = "92ecc6618181def0457392ccd0ee51198e065e016d1d527a7ac1b6dc7c1f09d2" [[package]] name = "js-sys" -version = "0.3.83" +version = "0.3.85" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "464a3709c7f55f1f721e5389aa6ea4e3bc6aba669353300af094b29ffbdde1d8" +checksum = "8c942ebf8e95485ca0d52d97da7c5a2c387d0e7f0ba4c35e93bfcaee045955b3" dependencies = [ "once_cell", "wasm-bindgen", @@ -1479,9 +1483,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe" [[package]] name = "libc" -version = "0.2.178" +version = "0.2.180" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "37c93d8daa9d8a012fd8ab92f088405fb202ea0b6ab73ee2482ae66af4f42091" +checksum = "bcc35a38544a891a5f7c865aca548a982ccb3b8650a5b06d0fd33a10283c56fc" [[package]] name = "libloading" @@ -1490,7 +1494,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d7c4b02199fee7c5d21a5ae7d8cfa79a6ef5bb2fc834d6e9058e89c825efdc55" dependencies = [ "cfg-if", - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -1611,7 +1615,7 @@ dependencies = [ "sysctl", "thiserror 2.0.17", "widestring", - "windows 0.48.0", + "windows", ] [[package]] @@ -1636,7 +1640,7 @@ dependencies = [ "glam 0.27.0", "glam 0.28.0", "glam 0.29.3", - "glam 0.30.9", + "glam 0.30.10", "matrixmultiply", "nalgebra-macros", "num-complex", @@ -1654,7 +1658,7 @@ checksum = "973e7178a678cfd059ccec50887658d482ce16b0aa9da3888ddeab5cd5eb4889" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -1741,9 +1745,9 @@ dependencies = [ [[package]] name = "ndarray" -version = "0.17.1" +version = "0.17.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c7c9125e8f6f10c9da3aad044cc918cf8784fa34de857b1aa68038eb05a50a9" +checksum = "520080814a7a6b4a6e9070823bb24b4531daac8c4627e08ba5de8c5ef2f2752d" dependencies = [ "matrixmultiply", "num-complex", @@ -1922,9 +1926,9 @@ checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" [[package]] name = "pest" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cbcfd20a6d4eeba40179f05735784ad32bdaef05ce8e8af05f180d45bb3e7e22" +checksum = "2c9eb05c21a464ea704b53158d358a31e6425db2f63a1a7312268b05fe2b75f7" dependencies = [ "memchr", "ucd-trie", @@ -1932,9 +1936,9 @@ dependencies = [ [[package]] name = "pest_derive" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51f72981ade67b1ca6adc26ec221be9f463f2b5839c7508998daa17c23d94d7f" +checksum = "68f9dbced329c441fa79d80472764b1a2c7e57123553b8519b36663a2fb234ed" dependencies = [ "pest", "pest_generator", @@ -1942,22 +1946,22 @@ dependencies = [ [[package]] name = "pest_generator" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dee9efd8cdb50d719a80088b76f81aec7c41ed6d522ee750178f83883d271625" +checksum = "3bb96d5051a78f44f43c8f712d8e810adb0ebf923fc9ed2655a7f66f63ba8ee5" dependencies = [ "pest", "pest_meta", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "pest_meta" -version = "2.8.4" +version = "2.8.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bf1d70880e76bdc13ba52eafa6239ce793d85c8e43896507e43dd8984ff05b82" +checksum = "602113b5b5e8621770cfd490cfd90b9f84ab29bd2b0e49ad83eb6d186cef2365" dependencies = [ "pest", "sha2", @@ -2049,9 +2053,9 @@ dependencies = [ [[package]] name = "portable-atomic" -version = "1.11.1" +version = "1.13.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f84267b20a16ea918e43c6a88433c2d54fa145c92a811b5b047ccbe153674483" +checksum = "f89776e4d69bb58bc6993e99ffa1d11f228b839984854c7daeb5d37f87cbe950" [[package]] name = "portable-atomic-util" @@ -2078,7 +2082,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b" dependencies = [ "proc-macro2", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2099,9 +2103,9 @@ dependencies = [ [[package]] name = "proc-macro2" -version = "1.0.103" +version = "1.0.105" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ee95bc4ef87b8d5ba32e8b7714ccc834865276eab0aed5c9958d00ec45f49e8" +checksum = "535d180e0ecab6268a3e718bb9fd44db66bbbc256257165fc699dadf70d16fe7" dependencies = [ "unicode-ident", ] @@ -2193,7 +2197,7 @@ dependencies = [ "proc-macro2", "pyo3-macros-backend", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2206,7 +2210,7 @@ dependencies = [ "proc-macro2", "pyo3-build-config", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2243,7 +2247,7 @@ dependencies = [ "proc-macro2", "quote", "rustpython-parser", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2260,9 +2264,9 @@ dependencies = [ [[package]] name = "quote" -version = "1.0.42" +version = "1.0.43" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a338cc41d27e6cc6dce6cefc13a0729dfbb81c262b1f519331575dd80ef3067f" +checksum = "dc74d9a594b72ae6656596548f56f667211f8a97b3d4c3d467150794690dc40a" dependencies = [ "proc-macro2", ] @@ -2291,7 +2295,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6db2770f06117d490610c7488547d543617b21bfa07796d7a12f6f1bd53850d1" dependencies = [ "rand_chacha 0.9.0", - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2311,7 +2315,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d3022b5f1df60f26e1ffddd6c66e8aa15de382ae63b3a0c1bfc0e4d3e3f325cb" dependencies = [ "ppv-lite86", - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2320,14 +2324,14 @@ version = "0.6.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c" dependencies = [ - "getrandom 0.2.16", + "getrandom 0.2.17", ] [[package]] name = "rand_core" -version = "0.9.3" +version = "0.9.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "99d9a13982dcf210057a8a78572b2217b667c3beacbf3a0d8b454f6f82837d38" +checksum = "76afc826de14238e6e8c374ddcc1fa19e374fd8dd986b0d2af0d02377261d83c" dependencies = [ "getrandom 0.3.4", ] @@ -2348,7 +2352,7 @@ version = "0.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "513962919efc330f829edb2535844d1b912b0fbe2ca165d613e4e8788bb05a5a" dependencies = [ - "rand_core 0.9.3", + "rand_core 0.9.5", ] [[package]] @@ -2394,9 +2398,9 @@ checksum = "03251193000f4bd3b042892be858ee50e8b3719f2b08e5833ac4353724632430" [[package]] name = "regalloc2" -version = "0.13.3" +version = "0.13.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4e249c660440317032a71ddac302f25f1d5dff387667bcc3978d1f77aa31ac34" +checksum = "08effbc1fa53aaebff69521a5c05640523fab037b34a4a2c109506bc938246fa" dependencies = [ "allocator-api2", "bumpalo", @@ -2539,9 +2543,9 @@ dependencies = [ [[package]] name = "ryu" -version = "1.0.20" +version = "1.0.22" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "28d3b2b1366ec20994f1fd18c3c594f05c5dd4bc44d8bb0c1c632c8d6829481f" +checksum = "a50f4cf475b65d88e057964e0e9bb1f0aa9bbb2036dc65c64596b42932536984" [[package]] name = "safe_arch" @@ -2616,27 +2620,27 @@ checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] name = "serde_json" -version = "1.0.145" +version = "1.0.149" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "402a6f66d8c709116cf22f558eab210f5a50187f702eb4d7e5ef38d9a7f1c79c" +checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86" dependencies = [ "itoa", "memchr", - "ryu", "serde", "serde_core", + "zmij", ] [[package]] name = "serde_spanned" -version = "1.0.3" +version = "1.0.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e24345aa0fe688594e73770a5f6d1b216508b4f93484c0026d521acd30134392" +checksum = "f8bbf91e5a4d6315eee45e704372590b30e260ee83af6639d64557f51b067776" dependencies = [ "serde_core", ] @@ -2682,9 +2686,9 @@ dependencies = [ [[package]] name = "simd-adler32" -version = "0.3.7" +version = "0.3.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d66dc143e6b11c1eddc06d5c423cfc97062865baf299914ab64caa38182078fe" +checksum = "e320a6c5ad31d271ad523dcf3ad13e2767ad8b1cb8f047f75a8aeaf8da139da2" [[package]] name = "siphasher" @@ -2700,9 +2704,9 @@ checksum = "67b1b7a3b5fe4f1376887184045fcf45c69e92af734b7aaddc05fb777b6fbd03" [[package]] name = "spindle" -version = "0.2.5" +version = "0.2.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f794dedb367e82477aa6bbf83ea9bbce9bc074b3caacaa82fc4ba398ec9b701d" +checksum = "673aaca3d8aa5387a6eba861fbf984af5348d9df5d940c25c6366b19556fdf64" dependencies = [ "atomic-wait", "crossbeam", @@ -2742,9 +2746,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.111" +version = "2.0.114" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "390cc9a294ab71bdb1aa2e99d13be9c753cd2d7bd6560c77118597410c4d2e87" +checksum = "d4d107df263a3013ef9b1879b0df87d706ff80f65a86ea879bd9c31f9b307c2a" dependencies = [ "proc-macro2", "quote", @@ -2767,9 +2771,9 @@ dependencies = [ [[package]] name = "target-lexicon" -version = "0.13.3" +version = "0.13.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df7f62577c25e07834649fc3b39fafdc597c0a3527dc1c60129201ccfcbaa50c" +checksum = "b1dd07eb858a2067e2f3c7155d54e929265c264e6f37efe3ee7a8d1b5a1dd0ba" [[package]] name = "textwrap" @@ -2806,7 +2810,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2817,7 +2821,7 @@ checksum = "3ff15c8ecd7de3849db632e14d18d2571fa09dfc5ed93479bc4485c7a517c913" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -2850,9 +2854,9 @@ dependencies = [ [[package]] name = "toml" -version = "0.9.8" +version = "0.9.11+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0dc8b1fb61449e27716ec0e1bdf0f6b8f3e8f6b05391e8497b8b6d7804ea6d8" +checksum = "f3afc9a848309fe1aaffaed6e1546a7a14de1f935dc9d89d32afd9a44bab7c46" dependencies = [ "indexmap", "serde_core", @@ -2865,33 +2869,33 @@ dependencies = [ [[package]] name = "toml_datetime" -version = "0.7.3" +version = "0.7.5+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f2cdb639ebbc97961c51720f858597f7f24c4fc295327923af55b74c3c724533" +checksum = "92e1cfed4a3038bc5a127e35a2d360f145e1f4b971b551a2ba5fd7aedf7e1347" dependencies = [ "serde_core", ] [[package]] name = "toml_parser" -version = "1.0.4" +version = "1.0.6+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0cbe268d35bdb4bb5a56a2de88d0ad0eb70af5384a99d648cd4b3d04039800e" +checksum = "a3198b4b0a8e11f09dd03e133c0280504d0801269e9afa46362ffde1cbeebf44" dependencies = [ "winnow", ] [[package]] name = "toml_writer" -version = "1.0.4" +version = "1.0.6+spec-1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df8b2b54733674ad286d16267dcfc7a71ed5c776e4ac7aa3c3e2561f7c637bf2" +checksum = "ab16f14aed21ee8bfd8ec22513f7287cd4a91aa92e44edfe2c17ddd004e92607" [[package]] name = "tracing" -version = "0.1.43" +version = "0.1.44" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2d15d90a0b5c19378952d479dc858407149d7bb45a14de0142f6c534b16fc647" +checksum = "63e71662fa4b2a2c3a26f570f037eb95bb1f85397f3cd8076caed2f026a6d100" dependencies = [ "pin-project-lite", "tracing-core", @@ -2899,9 +2903,9 @@ dependencies = [ [[package]] name = "tracing-core" -version = "0.1.35" +version = "0.1.36" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a04e24fab5c89c6a36eb8558c9656f30d81de51dfa4d3b45f26b21d61fa0a6c" +checksum = "db97caf9d906fbde555dd62fa95ddba9eecfd14cb388e4f491a66d74cd5fb79a" dependencies = [ "once_cell", "valuable", @@ -3100,18 +3104,18 @@ checksum = "ccf3ec651a847eb01de73ccad15eb7d99f80485de043efb2f370cd654f4ea44b" [[package]] name = "wasip2" -version = "1.0.1+wasi-0.2.4" +version = "1.0.2+wasi-0.2.9" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0562428422c63773dad2c345a1882263bbf4d65cf3f42e90921f787ef5ad58e7" +checksum = "9517f9239f02c069db75e65f174b3da828fe5f5b945c4dd26bd25d89c03ebcf5" dependencies = [ "wit-bindgen", ] [[package]] name = "wasm-bindgen" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0d759f433fa64a2d763d1340820e46e111a7a5ab75f993d1852d70b03dbb80fd" +checksum = "64024a30ec1e37399cf85a7ffefebdb72205ca1c972291c51512360d90bd8566" dependencies = [ "cfg-if", "once_cell", @@ -3122,9 +3126,9 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "48cb0d2638f8baedbc542ed444afc0644a29166f1595371af4fecf8ce1e7eeb3" +checksum = "008b239d9c740232e71bd39e8ef6429d27097518b6b30bdf9086833bd5b6d608" dependencies = [ "quote", "wasm-bindgen-macro-support", @@ -3132,22 +3136,22 @@ dependencies = [ [[package]] name = "wasm-bindgen-macro-support" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cefb59d5cd5f92d9dcf80e4683949f15ca4b511f4ac0a6e14d4e1ac60c6ecd40" +checksum = "5256bae2d58f54820e6490f9839c49780dff84c65aeab9e772f15d5f0e913a55" dependencies = [ "bumpalo", "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", "wasm-bindgen-shared", ] [[package]] name = "wasm-bindgen-shared" -version = "0.2.106" +version = "0.2.108" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cbc538057e648b67f72a982e708d485b2efa771e1ac05fec311f9f63e5800db4" +checksum = "1f01b580c9ac74c8d8f0c0e4afb04eeef2acf145458e52c03845ee9cd23e3d12" dependencies = [ "unicode-ident", ] @@ -3175,9 +3179,9 @@ dependencies = [ [[package]] name = "web-sys" -version = "0.3.83" +version = "0.3.85" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9b32828d774c412041098d182a8b38b16ea816958e07cf40eec2bc080ae137ac" +checksum = "312e32e551d92129218ea9a2452120f4aabc03529ef03e4d0d82fb2780608598" dependencies = [ "js-sys", "wasm-bindgen", @@ -3239,41 +3243,6 @@ dependencies = [ "windows-targets 0.48.5", ] -[[package]] -name = "windows" -version = "0.61.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9babd3a767a4c1aef6900409f85f5d53ce2544ccdfaa86dad48c91782c6d6893" -dependencies = [ - "windows-collections", - "windows-core 0.61.2", - "windows-future", - "windows-link 0.1.3", - "windows-numerics", -] - -[[package]] -name = "windows-collections" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3beeceb5e5cfd9eb1d76b381630e82c4241ccd0d27f1a39ed41b2760b255c5e8" -dependencies = [ - "windows-core 0.61.2", -] - -[[package]] -name = "windows-core" -version = "0.61.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0fdd3ddb90610c7638aa2b3a3ab2904fb9e5cdbecc643ddb3647212781c4ae3" -dependencies = [ - "windows-implement", - "windows-interface", - "windows-link 0.1.3", - "windows-result 0.3.4", - "windows-strings 0.4.2", -] - [[package]] name = "windows-core" version = "0.62.2" @@ -3282,20 +3251,9 @@ checksum = "b8e83a14d34d0623b51dce9581199302a221863196a1dde71a7663a4c2be9deb" dependencies = [ "windows-implement", "windows-interface", - "windows-link 0.2.1", - "windows-result 0.4.1", - "windows-strings 0.5.1", -] - -[[package]] -name = "windows-future" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fc6a41e98427b19fe4b73c550f060b59fa592d7d686537eebf9385621bfbad8e" -dependencies = [ - "windows-core 0.61.2", - "windows-link 0.1.3", - "windows-threading", + "windows-link", + "windows-result", + "windows-strings", ] [[package]] @@ -3306,7 +3264,7 @@ checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] [[package]] @@ -3317,56 +3275,22 @@ checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] -[[package]] -name = "windows-link" -version = "0.1.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5e6ad25900d524eaabdbbb96d20b4311e1e7ae1699af4fb28c17ae66c80d798a" - [[package]] name = "windows-link" version = "0.2.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5" -[[package]] -name = "windows-numerics" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9150af68066c4c5c07ddc0ce30421554771e528bde427614c61038bc2c92c2b1" -dependencies = [ - "windows-core 0.61.2", - "windows-link 0.1.3", -] - -[[package]] -name = "windows-result" -version = "0.3.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "56f42bd332cc6c8eac5af113fc0c1fd6a8fd2aa08a0119358686e5160d0586c6" -dependencies = [ - "windows-link 0.1.3", -] - [[package]] name = "windows-result" version = "0.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7781fa89eaf60850ac3d2da7af8e5242a5ea78d1a11c49bf2910bb5a73853eb5" dependencies = [ - "windows-link 0.2.1", -] - -[[package]] -name = "windows-strings" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "56e6c93f3a0c3b36176cb1327a4958a0353d5d166c2a35cb268ace15e91d3b57" -dependencies = [ - "windows-link 0.1.3", + "windows-link", ] [[package]] @@ -3375,7 +3299,7 @@ version = "0.5.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7837d08f69c77cf6b07689544538e017c1bfcf57e34b4c0ff58e6c2cd3b37091" dependencies = [ - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -3417,7 +3341,7 @@ version = "0.61.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc" dependencies = [ - "windows-link 0.2.1", + "windows-link", ] [[package]] @@ -3457,7 +3381,7 @@ version = "0.53.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "4945f9f551b88e0d65f3db0bc25c33b8acea4d9e41163edf90dcd0b19f9069f3" dependencies = [ - "windows-link 0.2.1", + "windows-link", "windows_aarch64_gnullvm 0.53.1", "windows_aarch64_msvc 0.53.1", "windows_i686_gnu 0.53.1", @@ -3468,15 +3392,6 @@ dependencies = [ "windows_x86_64_msvc 0.53.1", ] -[[package]] -name = "windows-threading" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b66463ad2e0ea3bbf808b7f1d371311c80e115c0b71d60efc142cafbcfb057a6" -dependencies = [ - "windows-link 0.1.3", -] - [[package]] name = "windows_aarch64_gnullvm" version = "0.42.2" @@ -3665,26 +3580,32 @@ checksum = "5a5364e9d77fcdeeaa6062ced926ee3381faa2ee02d3eb83a5c27a8825540829" [[package]] name = "wit-bindgen" -version = "0.46.0" +version = "0.51.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f17a85883d4e6d00e8a97c586de764dabcc06133f7f1d55dce5cdc070ad7fe59" +checksum = "d7249219f66ced02969388cf2bb044a09756a083d0fab1e566056b04d9fbcaa5" [[package]] name = "zerocopy" -version = "0.8.31" +version = "0.8.33" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fd74ec98b9250adb3ca554bdde269adf631549f51d8a8f8f0a10b50f1cb298c3" +checksum = "668f5168d10b9ee831de31933dc111a459c97ec93225beb307aed970d1372dfd" dependencies = [ "zerocopy-derive", ] [[package]] name = "zerocopy-derive" -version = "0.8.31" +version = "0.8.33" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8a8d209fdf45cf5138cbb5a506f6b52522a25afccc534d1475dad8e31105c6a" +checksum = "2c7962b26b0a8685668b671ee4b54d007a67d4eaf05fda79ac0ecf41e32270f1" dependencies = [ "proc-macro2", "quote", - "syn 2.0.111", + "syn 2.0.114", ] + +[[package]] +name = "zmij" +version = "1.0.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bd8f3f50b848df28f887acb68e41201b5aea6bc8a8dacc00fb40635ff9a72fea" diff --git a/README.md b/README.md index 9de9a41..e8a135d 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,18 @@
-# chronopt -[![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fbradyplanden%2Fchronopt%2Fmain%2Fpyproject.toml&label=Python)](https://pypi.org/project/chronopt/) -[![License](https://img.shields.io/github/license/bradyplanden/chronopt?color=blue)](https://github.com/bradyplanden/chronopt/blob/main/LICENSE) -[![Releases](https://img.shields.io/github/v/release/bradyplanden/chronopt?color=gold)](https://github.com/bradyplanden/chronopt/releases) +# diffid +[![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fbradyplanden%2Fdiffid%2Fmain%2Fpyproject.toml&label=Python)](https://pypi.org/project/diffid/) +[![License](https://img.shields.io/github/license/bradyplanden/diffid?color=blue)](https://github.com/bradyplanden/diffid/blob/main/LICENSE) +[![Releases](https://img.shields.io/github/v/release/bradyplanden/diffid?color=gold)](https://github.com/bradyplanden/diffid/releases) -**chron**os-**opt**imum is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. +**diff**erential **id**entification offers a different paradigm for a parameter inference library. Conventional Python-based inference libraries are constructed via python bindings to a high-performance forward model with the optimisation algorithms implemented in Python. Diffid offers an alternative, where the Python layer acts purely as a declarative configuration interface, while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below,
+

+ Diffid architecture describing the two optimisation approaches. +

+ ## Project goals - Speed and numerical accuracy through a Rust core. - Modular components with informative diagnostics. @@ -20,24 +24,18 @@ - Customisable likelihood/cost metrics and Monte-Carlo sampling for posterior exploration. - Flexible integration with state-of-the-art differential solvers, such as [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) - -## Why Chronopt? -- Optimisation based workflow run the forward simulation thousands of times, a performance improvement on the process can produce results hour or days earlier -- The Rust core provides a high-performance inference loop with fewer runtime errors. -- Quickly integrated into Python workflows, and later use the rust crate directly for even higher performance. - ## Installation -Chronopt targets Python >= 3.11. Windows builds are currently marked experimental. +Diffid targets Python >= 3.11. Windows builds are currently marked experimental. ```bash -pip install chronopt +pip install diffid # Or with uv -uv pip install chronopt +uv pip install diffid # Optional extras -pip install "chronopt[plotting]" +pip install "diffid[plotting]" ``` ## Quickstart @@ -45,7 +43,7 @@ pip install "chronopt[plotting]" ### ScalarProblem ```python import numpy as np -import chronopt as chron +import diffid def rosenbrock(x): @@ -54,8 +52,8 @@ def rosenbrock(x): builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.5) .with_parameter("y", -1.5) ) @@ -71,13 +69,12 @@ print(f"Success: {result.success}") ```python import numpy as np -import chronopt as chron +import diffid # Example diffsol ODE (logistic growth) dsl = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -87,7 +84,7 @@ observations = np.exp(-1.3 * t) data = np.column_stack((t, observations)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("k", 1.0) @@ -95,7 +92,7 @@ builder = ( ) problem = builder.build() -optimiser = chron.CMAES().with_max_iter(1000) +optimiser = diffid.CMAES().with_max_iter(1000) result = optimiser.run(problem, [0.5,0.5]) print(result.x) @@ -118,13 +115,13 @@ uv run maturin develop Regenerate `.pyi` stubs after changing the bindings: ```bash -uv run cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +uv run cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs ``` Without `uv`, invoke the generator directly: ```bash -cargo run -p chronopt-py --no-default-features --features stubgen --bin generate_stubs +cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs ``` ### Pre-commit hooks diff --git a/docs/algorithms/index.md b/docs/algorithms/index.md new file mode 100644 index 0000000..de856a3 --- /dev/null +++ b/docs/algorithms/index.md @@ -0,0 +1,128 @@ +# Algorithms + +Detailed documentation for each optimisation and sampling algorithm in Diffid. + +## Optimisers + +Gradient-free and gradient-based algorithms for finding optimal parameters. + +
+ +- :material-triangle:{ .lg .middle } __Nelder-Mead__ + + --- + + Simplex-based gradient-free optimiser for local search. + + [:octicons-arrow-right-24: Details](optimisers/nelder-mead.md) + +- :material-chart-scatter-plot:{ .lg .middle } __CMA-ES__ + + --- + + Covariance Matrix Adaptation Evolution Strategy for global optimisation. + + [:octicons-arrow-right-24: Details](optimisers/cmaes.md) + +- :material-alpha-a:{ .lg .middle } __Adam__ + + --- + + Adaptive Moment Estimation for gradient-based optimisation. + + [:octicons-arrow-right-24: Details](optimisers/adam.md) + +
+ +## Samplers + +MCMC and nested sampling for uncertainty quantification and model comparison. + +
+ +- :material-chart-timeline-variant:{ .lg .middle } __Metropolis-Hastings__ + + --- + + MCMC sampling for posterior exploration. + + [:octicons-arrow-right-24: Details](samplers/metropolis-hastings.md) + +- :material-layers-triple:{ .lg .middle } __Dynamic Nested Sampling__ + + --- + + Evidence calculation for Bayesian model comparison. + + [:octicons-arrow-right-24: Details](samplers/dynamic-nested-sampling.md) + +
+ +## Algorithm Comparison + +| Algorithm | Type | Gradients | Best For | Parallelisable | +|-----------|------|-----------|----------|----------------| +| Nelder-Mead | Local | No | < 10 params, noisy | No | +| CMA-ES | Global | No | 10-100+ params | Yes | +| Adam | Local | Yes | Smooth objectives | No | +| Metropolis-Hastings | MCMC | No | Uncertainty | No | +| Nested Sampling | Evidence | No | Model comparison | Yes | + +## Choosing an Algorithm + +```mermaid +graph TD + A[Start] --> B{Need uncertainty?} + B -->|No| C{Gradients available?} + B -->|Yes| D{Need evidence?} + C -->|Yes| E[Adam] + C -->|No| F{Problem size?} + D -->|Yes| G[Nested Sampling] + D -->|No| H[Metropolis-Hastings] + F -->|< 10 params| I[Nelder-Mead] + F -->|> 10 params| J[CMA-ES] +``` + +## Performance Characteristics + +### Convergence Speed + +Fast → Slow: + +1. **Adam** (with gradients) +2. **Nelder-Mead** (small problems) +3. **CMA-ES** (large problems) +4. **MCMC samplers** (many evaluations) +5. **Nested sampling** (most evaluations) + +### Robustness to Local Minima + +Least → Most robust: + +1. **Adam** (gradient descent) +2. **Nelder-Mead** (local search) +3. **CMA-ES** (global search) +4. **MCMC** (explores posterior) +5. **Nested sampling** (explores full space) + +## Implementation Details + +All algorithms are implemented in Rust for performance: + +- **Zero-copy**: Efficient memory usage +- **Parallel**: Where applicable (CMA-ES, nested sampling) +- **Numerically stable**: Careful floating-point handling +- **Well-tested**: Comprehensive test suite + +Source code: [rust/src/optimisers/](https://github.com/bradyplanden/diffid/tree/main/rust/src/optimisers) + +## References + +Each algorithm page includes references to original papers and implementation details. + +## See Also + +- [Choosing an Optimiser](../guides/choosing-optimiser.md) +- [Tuning Optimisers](../guides/tuning-optimisers.md) +- [API Reference](../api-reference/index.md) +- [Tutorials](../tutorials/index.md) diff --git a/docs/algorithms/optimisers/adam.md b/docs/algorithms/optimisers/adam.md new file mode 100644 index 0000000..49f1052 --- /dev/null +++ b/docs/algorithms/optimisers/adam.md @@ -0,0 +1,142 @@ +# Adam Algorithm + +Adaptive Moment Estimation (Adam) is a first-order gradient-based optimiser that maintains adaptive learning rates for each parameter using estimates of first and second moments of the gradients. + +## Algorithm Overview + +Adam combines the benefits of AdaGrad (adapting to sparse gradients) and RMSprop (adapting to non-stationary objectives) by tracking exponential moving averages of both the gradient and squared gradient. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Local, gradient-based | +| Parallelisable | No | +| Function evaluations | 1 per iteration | +| Best for | Smooth, differentiable objectives | + +## Mathematical Foundation + +At each iteration $t$, Adam updates parameters $\theta$ using: + +**Gradient computation:** + +$$ +g_t = \nabla_\theta f(\theta_{t-1}) +$$ + +**Biased moment estimates:** +$$ +m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t +$$ +$$ +v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2 +$$ + +**Bias correction:** +$$ +\hat{m}_t = \frac{m_t}{1 - \beta_1^t} +$$ +$$ +\hat{v}_t = \frac{v_t}{1 - \beta_2^t} +$$ + +**Parameter update:** +$$ +\theta_t = \theta_{t-1} - \alpha \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon} +$$ + +Where $\alpha$ is the learning rate (step size). + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum iterations | +| `step_size` | 0.01 | Learning rate $\alpha$ | +| `beta1` | 0.9 | First moment decay rate $\beta_1$ | +| `beta2` | 0.999 | Second moment decay rate $\beta_2$ | +| `eps` | 1e-8 | Numerical stability constant $\epsilon$ | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `gradient_threshold` | None | Gradient norm convergence tolerance | +| `patience` | None | Timeout in seconds | + +## Convergence Criteria + +The algorithm terminates when any condition is met: + +1. **Iteration limit**: `iteration >= max_iter` +2. **Gradient norm**: $\|g_t\| <$ `gradient_threshold` +3. **Objective change**: Change in objective below `threshold` +4. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Learning rate** (`step_size`) is the most critical parameter: + +- Start with a conservative step-size (i.e, 1e-3) +- Try orders of magnitude: (1e-2 to 1e-4) +- Too large: oscillation, divergence, or overshooting +- Too small: slow convergence + +**Beta parameters** rarely need adjustment: + +- $\beta_1 = 0.9$: controls momentum (gradient smoothing) +- $\beta_2 = 0.999$: controls adaptive scaling (variance smoothing) +- Lower $\beta_1$ for less momentum, more responsiveness +- Lower $\beta_2$ for faster adaptation to gradient scale changes + +**Epsilon** almost never needs tuning: + +- Prevents division by zero when gradients are near zero +- Default 1e-8 works for most cases + +## When to Use + +**Strengths:** + +- Fast convergence on smooth objectives +- Per-parameter adaptive learning rates +- Handles sparse gradients well +- Works with noisy gradients (e.g., mini-batches) +- Low memory overhead + +**Limitations:** + +- Requires gradient computation (automatic differentiation or numerical) +- Converges to local minima (no global search) +- Sensitive to learning rate choice +- May oscillate near optima + +## Example + +```python +import diffid as chron + +optimiser = ( + diffid.Adam() + .with_max_iter(5000) + .with_step_size(0.001) + .with_betas(0.9, 0.999) + .with_threshold(1e-8) +) + +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +## Implementation Notes + +- Supports automatic numerical gradient computation via central differences +- Bias correction is essential for early iterations +- Tracks both current position and best-found position + +## References + +1. Kingma, D.P. and Ba, J. (2015). "Adam: A Method for Stochastic Optimization". *ICLR 2015*. arXiv:1412.6980. +2. Reddi, S.J. et al. (2018). "On the Convergence of Adam and Beyond". *ICLR 2018*. + +## See Also + +- [API Reference](../../api-reference/python/optimisers.md#adam) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) +- [Tuning Optimisers](../../guides/tuning-optimisers.md) diff --git a/docs/algorithms/optimisers/cmaes.md b/docs/algorithms/optimisers/cmaes.md new file mode 100644 index 0000000..3945716 --- /dev/null +++ b/docs/algorithms/optimisers/cmaes.md @@ -0,0 +1,141 @@ +# CMA-ES Algorithm + +Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic, derivative-free algorithm for global optimisation. It maintains a multivariate Gaussian distribution that adapts its covariance structure to the objective landscape. + +## Algorithm Overview + +CMA-ES samples a population of candidate solutions from a multivariate normal distribution, ranks them by fitness, and updates the distribution parameters to bias future samples towards better regions. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Global, gradient-free | +| Parallelisable | Yes (population evaluation) | +| Function evaluations | $\lambda$ per generation | +| Best dimensions | 10-100+ parameters | + +## Algorithm + +The algorithm samples offspring from: + +$$ +x_k \sim m + \sigma \cdot \mathcal{N}(0, C) +$$ + +Where: + +- $m$ is the distribution mean (current best estimate) +- $\sigma$ is the global step size +- $C$ is the covariance matrix + +## Algorithm Steps + +1. **Sample**: Generate $\lambda$ offspring from $\mathcal{N}(m, \sigma^2 C)$ +2. **Evaluate**: Compute fitness for all offspring (parallelisable) +3. **Select**: Rank offspring; select best $\mu$ as parents +4. **Update mean**: $m \leftarrow \sum_{i=1}^{\mu} w_i x_{i:\lambda}$ +5. **Update evolution paths**: + - Conjugate path: $p_\sigma \leftarrow (1-c_\sigma) p_\sigma + \sqrt{c_\sigma(2-c_\sigma)\mu_\text{eff}} \cdot C^{-1/2}(m - m_\text{old})/\sigma$ + - Covariance path: $p_c \leftarrow (1-c_c) p_c + \sqrt{c_c(2-c_c)\mu_\text{eff}} \cdot (m - m_\text{old})/\sigma$ +6. **Update covariance**: Rank-one + rank-$\mu$ update +7. **Update step size**: Based on evolution path length vs expected length +8. **Repeat**: Until convergence + +### Strategy Parameters + +The algorithm automatically computes these from dimension $n$: + +| Parameter | Formula | Purpose | +|-----------|---------|---------| +| $\lambda$ | $\max(4, \lfloor 4 + 3\ln(n) \rfloor)$ | Population size | +| $\mu$ | $\lfloor \lambda / 2 \rfloor$ | Parent count | +| $\mu_\text{eff}$ | $1 / \sum w_i^2$ | Effective parent count | +| $c_\sigma$ | $(μ_\text{eff} + 2) / (n + μ_\text{eff} + 5)$ | Step size learning rate | +| $c_c$ | $(4 + μ_\text{eff}/n) / (n + 4 + 2μ_\text{eff}/n)$ | Covariance path learning rate | + + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum generations | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `step_size` | 0.5 | Initial search radius $\sigma$ | +| `population_size` | Auto | Offspring per generation $\lambda$ | +| `seed` | None | Random seed for reproducibility | +| `patience` | None | Timeout in seconds | + +## Convergence Criteria + +The algorithm terminates when any condition is met: + +1. **Iteration limit**: `generation >= max_iter` +2. **Function tolerance**: Best objective value below `threshold` +3. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Step size** ($\sigma$) is the most important parameter: + +- Set to ~1/3 of the expected distance to the optimum +- Too large: slow convergence, overshooting +- Too small: premature convergence, stuck in local optima + +**Population size** affects exploration vs exploitation: + +- Default formula works well for most problems +- Increase for highly multi-modal landscapes +- Decrease for faster convergence on simpler problems +- Match to available parallel compute resources + +## When to Use + +**Strengths:** + +- Global search capability +- Scales well to high dimensions (10-100+ parameters) +- Self-adapting covariance learns problem structure +- Parallelisable population evaluation +- Robust to local minima + +**Limitations:** + +- More evaluations than gradient methods +- Stochastic results (use seed for reproducibility) +- Memory scales as $O(n^2)$ for covariance matrix +- Overkill for simple, low-dimensional problems + +## Example + +```python +import diffid as chron + +optimiser = ( + diffid.CMAES() + .with_max_iter(500) + .with_step_size(0.3) + .with_population_size(20) + .with_seed(42) +) + +result = optimiser.run(problem, initial_guess=[0.5] * n_params) +``` + +## Implementation Notes + +- Full covariance matrix (not diagonal approximation) +- Lazy eigendecomposition for efficient sampling +- Bounds enforced via clamping after each sample + +## References + +1. Hansen, N. (2016). "The CMA Evolution Strategy: A Tutorial". arXiv:1604.00772. +2. Hansen, N. and Ostermeier, A. (2001). "Completely Derandomized Self-Adaptation in Evolution Strategies". *Evolutionary Computation*, 9(2), 159-195. +3. Hansen, N. et al. (2003). "Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)". *Evolutionary Computation*, 11(1), 1-18. + +## See Also + +- [API Reference](../../api-reference/python/optimisers.md#cma-es) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) +- [Parallel Execution](../../guides/parallel-execution.md) diff --git a/docs/algorithms/optimisers/nelder-mead.md b/docs/algorithms/optimisers/nelder-mead.md new file mode 100644 index 0000000..11fbc78 --- /dev/null +++ b/docs/algorithms/optimisers/nelder-mead.md @@ -0,0 +1,112 @@ +# Nelder-Mead Algorithm + +The Nelder-Mead algorithm (downhill simplex method) is a gradient-free local optimisation algorithm that uses a geometric simplex to navigate the parameter space. + +## Algorithm Overview + +Nelder-Mead maintains a **simplex** of $n+1$ vertices in $n$-dimensional space. At each iteration, it transforms the simplex through reflection, expansion, contraction, or shrinking operations to move towards lower objective values. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Local, gradient-free | +| Parallelisable | No | +| Function evaluations | Sequential | +| Best dimensions | 2-10 parameters | + +## Algorithm Steps + +1. **Initialise**: Create simplex from initial point using step size +2. **Order**: Sort vertices by objective value: $f(x_1) \leq f(x_2) \leq \cdots \leq f(x_{n+1})$ +3. **Reflect**: Compute reflection point $x_r = \bar{x} + \alpha(\bar{x} - x_{n+1})$ +4. **Transform**: Based on $f(x_r)$: + - If $f(x_1) \leq f(x_r) < f(x_n)$: accept reflection + - If $f(x_r) < f(x_1)$: try expansion $x_e = \bar{x} + \gamma(x_r - \bar{x})$ + - If $f(x_r) \geq f(x_n)$: try contraction $x_c = \bar{x} + \rho(x_{n+1} - \bar{x})$ +5. **Shrink**: If contraction fails, shrink towards best vertex +6. **Repeat**: Until convergence criteria met + +Where $\bar{x}$ is the centroid of all vertices except the worst. + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `max_iter` | 1000 | Maximum iterations | +| `threshold` | 1e-6 | Objective convergence tolerance | +| `step_size` | 0.1 | Initial simplex size | +| `position_tolerance` | 1e-6 | Parameter space convergence tolerance | +| `patience` | None | Timeout in seconds | + +### Simplex Coefficients + +| Coefficient | Symbol | Default | Purpose | +|-------------|--------|---------|---------| +| Reflection | $\alpha$ | 1.0 | Scale of reflection step | +| Expansion | $\gamma$ | 2.0 | Scale of expansion step | +| Contraction | $\rho$ | 0.5 | Scale of contraction step | +| Shrinking | $\sigma$ | 0.5 | Scale of shrink operation | + +## Convergence Criteria + +The algorithm terminates when any of the following conditions is met: + +1. **Iteration limit**: `iteration >= max_iter` +2. **Function tolerance**: Change in objective value below `threshold` +3. **Position tolerance**: Simplex diameter below `position_tolerance` +4. **Patience**: Elapsed time exceeds `patience` seconds + +## Tuning Guidance + +**Step size** controls the initial simplex scale: + +- Set to ~10-50% of the expected parameter range +- Larger values explore more broadly +- Smaller values for local refinement near a known solution + +**Thresholds** control precision vs speed: + +- Tighter tolerances (1e-8) for high precision +- Looser tolerances (1e-4) for quick estimates + +## When to Use + +**Strengths:** + +- No gradient computation required +- Robust to moderate noise +- Simple and reliable for small problems +- Low memory footprint + +**Limitations:** + +- Convergence slows significantly beyond 10 parameters +- Can converge to local minima +- Not parallelisable + +## Example + +```python +import diffid as chron + +optimiser = ( + diffid.NelderMead() + .with_max_iter(5000) + .with_step_size(0.1) + .with_threshold(1e-8) +) + +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +## References + +1. Nelder, J.A. and Mead, R. (1965). "A Simplex Method for Function Minimization". *The Computer Journal*, 7(4), 308-313. +2. Lagarias, J.C. et al. (1998). "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions". *SIAM Journal on Optimization*, 9(1), 112-147. + +## See Also + +- [API Reference](../../api-reference/python/optimisers.md#nelder-mead) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) +- [Tuning Optimisers](../../guides/tuning-optimisers.md) diff --git a/docs/algorithms/samplers/dynamic-nested-sampling.md b/docs/algorithms/samplers/dynamic-nested-sampling.md new file mode 100644 index 0000000..95e171d --- /dev/null +++ b/docs/algorithms/samplers/dynamic-nested-sampling.md @@ -0,0 +1,168 @@ +# Dynamic Nested Sampling Algorithm + +Dynamic Nested Sampling is a Bayesian inference algorithm that computes the model evidence (marginal likelihood) while simultaneously generating posterior samples. It is particularly suited for model comparison via Bayes factors. + +## Algorithm Overview + +Nested sampling transforms the multi-dimensional evidence integral into a one-dimensional integral over prior volume. It maintains a set of "live points" that progressively shrink the prior volume while tracking the likelihood threshold. The "dynamic" aspect allows the algorithm to allocate more live points in regions that contribute most to either the evidence or posterior, improving efficiency. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | Evidence sampler | +| Parallelisable | Yes (batch proposals) | +| Output | Evidence + posterior samples | +| Best for | Model comparison | + +## Mathematical Foundation + +The model evidence (marginal likelihood) is: + +$$ +\mathcal{Z} = \int \mathcal{L}(\theta) \pi(\theta) \, d\theta +$$ + +Nested sampling transforms this by defining the prior volume: + +$$ +X(\lambda) = \int_{\mathcal{L}(\theta) > \lambda} \pi(\theta) \, d\theta +$$ + +The evidence becomes a one-dimensional integral: + +$$ +\mathcal{Z} = \int_0^1 \mathcal{L}(X) \, dX +$$ + +This is approximated by iteratively shrinking the prior volume: + +$$ +\mathcal{Z} \approx \sum_{i=1}^{N} \mathcal{L}_i \, \Delta X_i +$$ + +Where $\Delta X_i = X_{i-1} - X_i$ and the shrinkage ratio is estimated statistically. + +## Algorithm Steps + +1. **Initialise**: Sample $K$ live points uniformly from the prior +2. **Iterate**: + - Find lowest-likelihood live point $\mathcal{L}^*$ + - Record point as "dead point" with prior volume estimate + - Replace with new point sampled uniformly from prior with $\mathcal{L} > \mathcal{L}^*$ +3. **Terminate**: When remaining evidence contribution is negligible +4. **Compute**: Sum contributions to estimate $\mathcal{Z}$ and uncertainty +5. **Return**: Log-evidence, evidence error, and posterior samples + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `live_points` | 64 | Number of live points $K$ | +| `expansion_factor` | 0.5 | Live set expansion aggressiveness | +| `termination_tol` | 1e-3 | Evidence convergence tolerance | +| `mcmc_batch_size` | 8 | Proposals generated per iteration | +| `mcmc_step_size` | 0.01 | MCMC proposal step size | +| `seed` | None | Random seed for reproducibility | + +## Tuning Guidance + +**Live points** control accuracy vs cost: + +- More live points = better evidence estimate but more evaluations +- Minimum recommended: $25 × n_{\text{params}}$ +- For accurate posteriors: $>50 × n_{\text{params}}$ + +**Termination tolerance** affects precision: + +- Smaller values = more accurate evidence but more iterations +- Default 1e-3 sufficient for most model comparisons + +**MCMC parameters** affect replacement efficiency: + +- `mcmc_batch_size`: larger batches for parallel evaluation +- `mcmc_step_size`: tune for ~20-50% acceptance in constrained sampling + +## Evidence Interpretation + +The log-evidence can be used for model comparison via Bayes factors: + +$$ +\ln B_{12} = \ln \mathcal{Z}_1 - \ln \mathcal{Z}_2 +$$ + +| $\ln B_{12}$ | $B_{12}$ | Evidence strength | +|--------------|----------|-------------------| +| < 1 | < 3 | Inconclusive | +| 1-2.5 | 3-12 | Positive | +| 2.5-5 | 12-150 | Strong | +| > 5 | > 150 | Decisive | + +## When to Use + +**Strengths:** + +- Computes model evidence directly +- Handles multi-modal posteriors well +- Provides posterior samples as byproduct +- Robust to complex likelihood landscapes +- Parallelisable + +**Limitations:** + +- More expensive than MCMC for posterior-only inference +- Constrained sampling can be challenging in high dimensions +- Evidence accuracy depends on live point count + +## Cost Metric Requirement + +Nested sampling requires a **negative log-likelihood** cost metric: + +```python +problem = ( + diffid.ScalarProblemBuilder() + .with_objective(model_fn) + .with_cost_metric(diffid.CostMetric.GaussianNLL) # Required + .build() +) +``` + +The objective function should return negative log-likelihood; the algorithm internally negates to work with log-likelihood. + +## Example + +```python +import diffid as chron + +sampler = ( + diffid.DynamicNestedSampling() + .with_live_points(128) + .with_termination_tol(1e-3) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess=[1.0, 2.0]) + +# Access results +log_evidence = result.log_evidence +evidence_error = result.evidence_error +samples = result.samples +``` + +## Implementation Notes + +- Uses MCMC for constrained prior sampling +- Batch evaluation for parallel efficiency +- Automatic termination based on remaining evidence contribution + +## References + +1. Skilling, J. (2006). "Nested Sampling for General Bayesian Computation". *Bayesian Analysis*, 1(4), 833-860. +2. Higson, E. et al. (2019). "Dynamic Nested Sampling: An Improved Algorithm for Parameter Estimation and Evidence Calculation". *Statistics and Computing*, 29, 891-913. +3. Speagle, J.S. (2020). "dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences". *Monthly Notices of the Royal Astronomical Society*, 493(3), 3132-3158. + +## See Also + +- [API Reference](../../api-reference/python/samplers.md) +- [Choosing a Sampler](../../guides/choosing-sampler.md) +- [Cost Metrics](../../guides/cost-metrics.md) diff --git a/docs/algorithms/samplers/metropolis-hastings.md b/docs/algorithms/samplers/metropolis-hastings.md new file mode 100644 index 0000000..594cd18 --- /dev/null +++ b/docs/algorithms/samplers/metropolis-hastings.md @@ -0,0 +1,143 @@ +# Metropolis-Hastings Algorithm + +Metropolis-Hastings is a Markov Chain Monte Carlo (MCMC) algorithm for sampling from posterior distributions. It enables uncertainty quantification by generating samples that characterise the probability distribution over parameters. + +## Algorithm Overview + +The algorithm constructs a Markov chain whose stationary distribution is the target posterior. By proposing random moves and accepting/rejecting them based on the likelihood ratio, it explores the parameter space in proportion to posterior probability. + +### Key Properties + +| Property | Value | +|----------|-------| +| Type | MCMC sampler | +| Parallelisable | Yes (across chains) | +| Output | Posterior samples | +| Best for | Uncertainty quantification | + +## Mathematical Foundation + +Given a target distribution $\pi(\theta)$ (the posterior), the algorithm: + +1. Proposes a new state from proposal distribution $q(\theta' | \theta)$ +2. Computes acceptance probability: + +$$ +\alpha = \min\left(1, \frac{\pi(\theta') q(\theta | \theta')}{\pi(\theta) q(\theta' | \theta)}\right) +$$ + +For the symmetric random walk proposal used here ($q(\theta' | \theta) = q(\theta | \theta')$): + +$$ +\alpha = \min\left(1, \frac{\pi(\theta')}{\pi(\theta)}\right) = \min\left(1, \exp(\log\pi(\theta') - \log\pi(\theta))\right) +$$ + +## Algorithm Steps + +1. **Initialise**: Start at initial point $\theta_0$ +2. **Propose**: Generate candidate $\theta' = \theta_t + \sigma \cdot \mathcal{N}(0, I)$ +3. **Evaluate**: Compute log-likelihood $\log\mathcal{L}(\theta')$ +4. **Accept/Reject**: + - Draw $u \sim \text{Uniform}(0, 1)$ + - If $u < \exp(\log\mathcal{L}(\theta') - \log\mathcal{L}(\theta_t))$: accept $\theta_{t+1} = \theta'$ + - Else: reject $\theta_{t+1} = \theta_t$ +5. **Repeat**: For specified number of iterations +6. **Return**: Chain of samples $\{\theta_0, \theta_1, \ldots, \theta_T\}$ + +## Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `iterations` | 1000 | MCMC iterations per chain | +| `num_chains` | 1 | Number of independent chains | +| `step_size` | 0.1 | Proposal distribution width $\sigma$ | +| `seed` | None | Random seed for reproducibility | + +## Tuning Guidance + +**Step size** controls exploration efficiency: + +- Target acceptance rate: 20-50% (optimal ~23% for high dimensions) +- Too large: low acceptance rate, chain gets stuck +- Too small: high acceptance rate but slow mixing +- Tune by monitoring acceptance ratio + +**Number of chains** aids convergence diagnostics: + +- Multiple chains from different starting points detect convergence issues +- Enables Gelman-Rubin $\hat{R}$ diagnostic +- More chains = more robust inference but higher cost + +**Iterations** should be sufficient for: + +- Burn-in period (discard initial samples) +- Effective sample size for reliable estimates + +## Convergence Diagnostics + +After sampling, assess convergence: + +- **Trace plots**: Visual inspection for stationarity +- **Acceptance rate**: Should be 20-50% +- **Autocorrelation**: Lower is better; high autocorrelation means inefficient sampling +- **$\hat{R}$ statistic**: Should be < 1.1 for all parameters (requires multiple chains) + +## When to Use + +**Strengths:** + +- Full posterior characterisation +- Uncertainty quantification (credible intervals) +- Parameter correlation analysis +- Simple and robust + +**Limitations:** + +- Many function evaluations required +- Can be slow for high-dimensional problems +- Requires careful tuning for efficiency +- Does not compute model evidence (use nested sampling) + +## Cost Metric Requirement + +Metropolis-Hastings requires a **negative log-likelihood** cost metric: + +```python +problem = ( + diffid.ScalarProblemBuilder() + .with_objective(model_fn) + .with_cost_metric(diffid.CostMetric.GaussianNLL) # Required + .build() +) +``` + +## Example + +```python +import diffid as chron + +sampler = ( + diffid.MetropolisHastings() + .with_iterations(5000) + .with_num_chains(4) + .with_step_size(0.05) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess=[1.0, 2.0]) + +# Access samples +samples = result.samples # Shape: (n_chains * iterations, n_params) +``` + +## References + +1. Metropolis, N. et al. (1953). "Equation of State Calculations by Fast Computing Machines". *Journal of Chemical Physics*, 21(6), 1087-1092. +2. Hastings, W.K. (1970). "Monte Carlo Sampling Methods Using Markov Chains and Their Applications". *Biometrika*, 57(1), 97-109. +3. Gelman, A. et al. (2013). *Bayesian Data Analysis*. 3rd ed. Chapman & Hall/CRC. + +## See Also + +- [API Reference](../../api-reference/python/samplers.md) +- [Choosing a Sampler](../../guides/choosing-sampler.md) +- [Cost Metrics](../../guides/cost-metrics.md) diff --git a/docs/api-reference/index.md b/docs/api-reference/index.md new file mode 100644 index 0000000..3469e43 --- /dev/null +++ b/docs/api-reference/index.md @@ -0,0 +1,135 @@ +# API Reference + +Complete API documentation for Diffid's Python and Rust interfaces. + +## Python API + +Diffid provides a comprehensive Python API with full type hints and automatic documentation. + +
+ +- :material-hammer-wrench:{ .lg .middle } __Builders__ + + --- + + Problem builders for different use cases: scalar functions, ODE fitting, and custom solvers. + + [:octicons-arrow-right-24: Builders](python/builders.md) + +- :material-tune:{ .lg .middle } __Optimisers__ + + --- + + Gradient-free (Nelder-Mead, CMA-ES) and gradient-based (Adam) optimisation algorithms. + + [:octicons-arrow-right-24: Optimisers](python/optimisers.md) + +- :material-chart-bell-curve:{ .lg .middle } __Samplers__ + + --- + + MCMC and nested sampling for posterior exploration and model comparison. + + [:octicons-arrow-right-24: Samplers](python/samplers.md) + +- :material-function-variant:{ .lg .middle } __Cost Metrics__ + + --- + + Metrics for comparing model predictions to observations (SSE, RMSE, GaussianNLL). + + [:octicons-arrow-right-24: Cost Metrics](python/cost-metrics.md) + +- :material-chart-line:{ .lg .middle } __Results__ + + --- + + Result objects returned by optimisers and samplers with diagnostics. + + [:octicons-arrow-right-24: Results](python/results.md) + +
+ +## Rust API + +The Rust core provides high-performance implementations of all algorithms. + +[:octicons-arrow-right-24: Rust Documentation on docs.rs](https://docs.rs/diffid/latest/diffid/) + +## Module Structure + +``` +diffid/ +├── ScalarBuilder # Direct function optimisation +├── DiffsolBuilder # ODE fitting with DiffSL/Diffsol +├── VectorBuilder # Custom solver integration +├── Problem # Built problem instance +├── Optimisers +│ ├── NelderMead # Simplex gradient-free optimiser +│ ├── CMAES # Covariance matrix adaptation +│ └── Adam # Adaptive moment estimation +├── Samplers +│ ├── MetropolisHastings # MCMC sampling +│ └── DynamicNestedSampling # Evidence calculation +├── CostMetric # Cost/likelihood metrics +└── OptimisationResults # Result container +``` + +## Type Hints + +All Python functions include comprehensive type hints: + +```python +from diffid import ScalarBuilder, CMAES, OptimisationResults +import numpy.typing as npt + +def optimize_function( + func: Callable[[npt.NDArray[np.float64]], npt.NDArray[np.float64]], + initial_params: dict[str, float] +) -> OptimisationResults: + builder = ScalarBuilder().with_objective(func) + for name, value in initial_params.items(): + builder = builder.with_parameter(name, value) + + problem = builder.build() + optimiser = CMAES().with_max_iter(1000) + return optimiser.run(problem, list(initial_params.values())) +``` + +Type stubs are automatically generated from the Rust implementation and are available in IDE completions. + +## Conventions + +### Parameter Order + +All builders maintain parameter order based on the sequence of `.with_parameter()` calls: + +```python +builder = ( + diffid.ScalarBuilder() + .with_parameter("x", 1.0) # Index 0 + .with_parameter("y", 2.0) # Index 1 +) + +result = problem.optimise() +print(result.x) # [optimal_x, optimal_y] +``` + +### Return Types + +- **Optimisers** return `OptimisationResults` with `.x`, `.value`, `.success`, etc. +- **Samplers** return sampling-specific results with `.samples`, `.log_likelihood`, etc. +- All results include diagnostic information + +### Array Conventions + +- **Input**: Python callables accept NumPy arrays +- **Output**: Python callables must return NumPy arrays +- **Data**: Data arrays are `(n_timepoints, n_variables + 1)` with time in first column + +## Next Steps + +- [Builders API](python/builders.md) - Start building problems +- [Optimisers API](python/optimisers.md) - Configure optimisation algorithms +- [User Guides](../guides/index.md) - Learn when to use each component +- [Tutorials](../tutorials/index.md) - Interactive examples diff --git a/docs/api-reference/python/builders.md b/docs/api-reference/python/builders.md new file mode 100644 index 0000000..2bd4f05 --- /dev/null +++ b/docs/api-reference/python/builders.md @@ -0,0 +1,250 @@ +# Builders + +Builders provide a fluent API for constructing optimisation problems. Choose the appropriate builder based on your problem type. + +## Overview + +| Builder | Use Case | Example | +|---------|----------|---------| +| `ScalarBuilder` | Direct function optimisation | Rosenbrock, Rastrigin | +| `DiffsolBuilder` | ODE fitting with DiffSL | Parameter identification | +| `VectorBuilder` | Custom solver integration | JAX, Julia, external simulators | + +## ScalarBuilder + +::: diffid.ScalarBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_objective + - with_parameter + - with_cost_metric + - build + +### Example Usage + +```python +import numpy as np +import diffid as chron + +def rosenbrock(x): + return np.asarray([(1 - x[0])**2 + 100*(x[1] - x[0]**2)**2]) + +builder = ( + diffid.ScalarBuilder() + .with_objective(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5) +) +problem = builder.build() +result = problem.optimise() +``` + +### When to Use + +- You have a Python function to minimise directly +- No differential equations involved +- Simple parameter optimisation or test functions + +--- + +## DiffsolBuilder + +::: diffid.DiffsolBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_diffsl + - with_data + - with_parameter + - with_backend + - with_cost_metric + - with_max_threads + - build + +### Example Usage + +```python +import numpy as np +import diffid as chron + +dsl = """ +in { r = 1, k = 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) + +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") +) +problem = builder.build() +optimiser = diffid.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 0.5]) +``` + +### Backend Options + +- **`"dense"`** (default): For systems with < 100 variables, dense Jacobian +- **`"sparse"`**: For large systems (> 100 variables), sparse Jacobian + +### DiffSL Syntax + +DiffSL is a domain-specific language for ODEs: + +``` +in_i { param1 = default1, param2 = default 2 } # Parameters to fit with defaults +u_i { state1 = init1 } # Initial conditions +F_i { derivative_expr } # dy/dt expressions +out_i { state1, state2 } # Optional: output variables +``` + +### When to Use + +- Fitting ODE parameters to time-series data +- Using Diffid's built-in high-performance solver +- Models expressible in DiffSL syntax + +--- + +## VectorBuilder + +::: diffid.VectorBuilder + options: + show_root_heading: true + show_source: false + members: + - __new__ + - with_objective + - with_data + - with_parameter + - with_cost_metric + - build + +### Example Usage + +```python +import numpy as np +import diffid as chron + +def custom_solver(params): + """Your custom ODE solver (e.g., using JAX/Diffrax).""" + # Solve ODE with params + # Return predictions at observation times + return predictions # NumPy array + +# Observed data +t = np.linspace(0, 10, 100) +observations = ... # Your experimental data +data = np.column_stack((t, observations)) + +builder = ( + diffid.VectorBuilder() + .with_objective(custom_solver) + .with_data(data) + .with_parameter("alpha", 1.0) + .with_parameter("beta", 0.5) +) +problem = builder.build() +result = problem.optimise() +``` + +### Callable Requirements + +Your callable must: + +1. Accept a NumPy array of parameters +2. Return a NumPy array of predictions +3. Predictions must match observation times in the data + +### When to Use + +- Need specific solver features (stiff solvers, event detection, etc.) +- Using JAX/Diffrax for automatic differentiation +- Using Julia/DifferentialEquations.jl via diffeqpy +- Custom forward models beyond ODEs (PDEs, agent-based models, etc.) + +See the [Custom Solvers Guide](../../guides/custom-solvers.md) for examples with Diffrax and DifferentialEquations.jl. + +--- + +## Problem + +::: diffid.Problem + options: + show_root_heading: true + show_source: false + +The `Problem` class is created by calling `.build()` on a builder. It represents a fully configured optimisation problem. + +### Common Methods + +```python +# Optimise with default settings (Nelder-Mead) +result = problem.optimise() + +# Evaluate objective at specific parameters +value = problem.evaluate(params) +``` + +--- + +## Common Patterns + +### Chaining Methods + +Builders use a fluent interface - chain methods in any order: + +```python +builder = ( + diffid.ScalarBuilder() + .with_objective(func) + .with_parameter("x", 1.0) + .with_parameter("y", 2.0) + .with_cost_metric(diffid.RMSE()) +) +``` + +### Reusing Builders + +Builders are immutable - each method returns a new builder: + +```python +base = diffid.ScalarBuilder().with_objective(func) + +problem1 = base.with_parameter("x", 1.0).build() +problem2 = base.with_parameter("x", 2.0).build() # Different initial guess +``` + +### Parameter Order Matters + +Parameters are indexed in the order they're added: + +```python +builder = ( + diffid.ScalarBuilder() + .with_parameter("y", 2.0) # Index 0 + .with_parameter("x", 1.0) # Index 1 +) + +result = problem.optimise() +print(result.x) # [optimal_y, optimal_x] +``` + +## See Also + +- [Getting Started: Core Concepts](../../getting-started/concepts.md) +- [Cost Metrics](cost-metrics.md) +- [Optimisers](optimisers.md) +- [Custom Solvers Guide](../../guides/custom-solvers.md) diff --git a/docs/api-reference/python/cost-metrics.md b/docs/api-reference/python/cost-metrics.md new file mode 100644 index 0000000..e5ba79b --- /dev/null +++ b/docs/api-reference/python/cost-metrics.md @@ -0,0 +1,226 @@ +# Cost Metrics + +Cost metrics define how model predictions are compared to observations. They determine the objective function that optimisers minimise. + +## Overview + +| Metric | Formula | Use Case | +|--------|---------|----------| +| `SSE` | $\sum (y_i - \hat{y}_i)^2$ | Standard least squares | +| `RMSE` | $\sqrt{\frac{1}{n}\sum (y_i - \hat{y}_i)^2}$ | Normalised error | +| `GaussianNLL` | $-\log p(data \mid params)$ | Bayesian inference | + +## CostMetric + +::: diffid.CostMetric + options: + show_root_heading: true + show_source: false + +--- + +## SSE (Sum of Squared Errors) + +The default cost metric. Minimises the sum of squared residuals. + +$$\text{SSE} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ + +### Example Usage + +```python +import diffid as chron + +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + # SSE is used by default, but can be explicit: + # .with_cost_metric(diffid.SSE()) +) +``` + +### When to Use + +**Advantages:** + +- Standard least squares approach +- Well-understood statistical properties +- Efficient to compute + +**Limitations:** + +- Not normalised (sensitive to number of data points) +- Sensitive to outliers (squared errors magnify them) +- Assumes Gaussian errors with constant variance + +**Typical Use Cases:** + +- Standard parameter fitting +- When absolute error scale matters +- Comparing models with same number of points + +--- + +## RMSE (Root Mean Squared Error) + +Normalised version of SSE. Divides by number of points and takes square root. + +$$\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$ + +### Example Usage + +```python +import diffid as chron + +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(diffid.RMSE()) +) +``` + +### When to Use + +**Advantages:** + +- Normalised (independent of data size) +- Same units as observations +- Better for comparing models with different data sizes + +**Limitations:** + +- Still sensitive to outliers +- Assumes Gaussian errors + +**Typical Use Cases:** + +- Comparing models with different numbers of observations +- Reporting error in interpretable units +- Cross-validation and model selection + +--- + +## GaussianNLL (Gaussian Negative Log-Likelihood) + +Probabilistic cost metric for Bayesian inference. Assumes Gaussian observation noise. + +$$\text{GaussianNLL} = -\log p(data \mid params) = \frac{n}{2}\log(2\pi\sigma^2) + \frac{1}{2\sigma^2}\sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ + +where $\sigma^2$ is estimated from residuals. + +### Example Usage + +```python +import diffid as chron + +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(diffid.GaussianNLL()) +) + +# Use with MCMC sampling for Bayesian inference +sampler = diffid.MetropolisHastings().with_max_iter(10000) +result = sampler.run(problem, initial_guess) +``` + +### When to Use + +**Advantages:** + +- Proper probabilistic interpretation +- Required for Bayesian inference (MCMC, nested sampling) +- Automatically accounts for noise level +- Enables uncertainty quantification + +**Limitations:** + +- Assumes Gaussian observation noise +- More computationally expensive than SSE +- Requires understanding of likelihood + +**Typical Use Cases:** + +- MCMC sampling for uncertainty quantification +- Model comparison with Bayes factors +- When probabilistic interpretation is needed +- Nested sampling for evidence calculation + +--- + +## Choosing a Cost Metric + +```mermaid +graph TD + A[Start] --> B{Using samplers?} + B -->|Yes| C[GaussianNLL] + B -->|No| D{Comparing models?} + D -->|Different data sizes| E[RMSE] + D -->|Same data size| F[SSE] + D -->|No comparison| F +``` + +### Decision Guide + +**Use SSE when:** + +- Standard least squares fitting +- Single model, fixed data +- Speed is critical + +**Use RMSE when:** + +- Comparing models with different data sizes +- Want interpretable error in original units +- Cross-validation or model selection + +**Use GaussianNLL when:** + +- MCMC sampling (Metropolis-Hastings) +- Nested sampling (model evidence) +- Need confidence intervals +- Bayesian inference required + +For more details, see the [Cost Metrics Guide](../../guides/cost-metrics.md). + +--- + +## Custom Cost Metrics + +Currently, custom cost metrics must be implemented in Rust. Python-level custom metrics are planned for a future release. + +To implement a custom cost metric: + +1. Add implementation in `rust/src/cost/` +2. Expose through Python bindings +3. Rebuild the package + +See the [Development Guide](../../development/architecture.md) for details on extending Diffid. + +--- + +## Mathematical Background + +### Relationship to Maximum Likelihood + +Minimising SSE is equivalent to maximum likelihood estimation under Gaussian noise with known variance: + +$$\hat{\theta} = \arg\min_\theta \sum (y_i - f(x_i; \theta))^2 = \arg\max_\theta p(data \mid \theta)$$ + +### Weighted Least Squares + +For heteroscedastic data (varying noise), weighted metrics can be important. Contact the developers if you need this feature. + +--- + +## See Also + +- [Cost Metrics Guide](../../guides/cost-metrics.md) +- [Builders](builders.md) +- [Samplers](samplers.md) (for Bayesian inference) +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/parameter_uncertainty.ipynb) diff --git a/docs/api-reference/python/optimisers.md b/docs/api-reference/python/optimisers.md new file mode 100644 index 0000000..40e8af6 --- /dev/null +++ b/docs/api-reference/python/optimisers.md @@ -0,0 +1,299 @@ +# Optimisers + +Optimisation algorithms for finding parameter values that minimise the objective function. + +## Overview + +| Optimiser | Type | Best For | Parameters | +|-----------|------|----------|------------| +| `NelderMead` | Gradient-free | Local search, < 10 params, noisy | 2-10 | +| `CMAES` | Gradient-free | Global search, 10-100+ params | 1-100+ | +| `Adam` | Gradient-based | Smooth objectives, fast convergence | Any | + +## Nelder-Mead + +::: diffid.NelderMead + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import diffid as chron + +# Create optimiser with custom settings +optimiser = ( + diffid.NelderMead() + .with_max_iter(5000) + .with_step_size(0.1) + .with_threshold(1e-6) +) + +# Run optimisation +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +### When to Use + +**Advantages:** + +- No gradient computation required +- Robust to noisy objectives +- Simple and reliable for small problems +- Default choice for quick exploration + +**Limitations:** + +- Slow convergence for > 10 parameters +- Can get stuck in local minima +- Performance degrades with dimensionality + +**Typical Use Cases:** + +- Initial parameter exploration +- Noisy experimental data +- Small-scale problems (< 10 parameters) + +### Parameter Tuning + +- **`step_size`**: Initial simplex size (default: 1.0) + - Larger values explore more globally + - Smaller values for local refinement + - Start with 10-50% of parameter range + +- **`threshold`**: Convergence tolerance (default: 1e-6) + - Smaller for higher precision + - Larger for faster termination + +- **`max_iter`**: Maximum iterations (default: 1000) + - Rule of thumb: `100 * n_parameters` minimum + +See the [Nelder-Mead Algorithm Guide](../../algorithms/optimisers/nelder-mead.md) for more details. + +--- + +## CMA-ES + +::: diffid.CMAES + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import diffid as chron + +# Create CMA-ES optimiser +optimiser = ( + diffid.CMAES() + .with_max_iter(1000) + .with_step_size(0.5) + .with_population_size(20) + .with_seed(42) # For reproducibility +) + +result = optimiser.run(problem, initial_guess=[0.5, 0.5]) +``` + +### When to Use + +**Advantages:** + +- Global optimisation (avoids local minima) +- Scales to high dimensions (10-100+ parameters) +- Parallelisable (evaluates population in parallel) +- Self-adapting (no gradient tuning needed) + +**Limitations:** + +- More function evaluations than gradient methods +- Requires population-sized memory +- Stochastic (results vary between runs) + +**Typical Use Cases:** + +- Global parameter search +- High-dimensional problems (> 10 parameters) +- Multi-modal landscapes +- Parallel hardware available + +### Parameter Tuning + +- **`step_size`**: Initial search radius (default: 1.0) + - Start with ~1/3 of expected parameter range + - Too large: slow convergence + - Too small: premature convergence + +- **`population_size`**: Offspring per generation (default: `4 + floor(3*ln(n_params))`) + - Larger populations explore more but cost more + - Typical range: 10-100 + - Must match available parallelism + +- **`max_iter`**: Maximum generations (default: 1000) + - Each iteration evaluates `population_size` candidates + - Total evaluations = `max_iter * population_size` + +- **`threshold`**: Objective value threshold (default: 1e-6) + - Stop when best value < threshold + +- **`seed`**: Random seed for reproducibility + - Omit for non-deterministic runs + - Set for reproducible benchmarks + +See the [CMA-ES Algorithm Guide](../../algorithms/optimisers/cmaes.md) for more details. + +--- + +## Adam + +::: diffid.Adam + options: + show_root_heading: true + show_source: false + +### Example Usage + +```python +import diffid as chron + +# Create Adam optimiser +optimiser = ( + diffid.Adam() + .with_max_iter(5000) + .with_step_size(0.01) # Learning rate + .with_betas(0.9, 0.999) + .with_threshold(1e-6) +) + +result = optimiser.run(problem, initial_guess=[1.0, 2.0]) +``` + +### When to Use + +**Advantages:** + +- Fast convergence on smooth objectives +- Adaptive learning rate +- Well-suited for large-scale problems +- Efficient (uses gradients) + +**Limitations:** + +- Requires automatic differentiation +- Can get stuck in local minima +- Sensitive to learning rate tuning + +**Typical Use Cases:** + +- Smooth, differentiable objectives +- Large-scale problems +- When gradients are available or cheap to compute + +### Parameter Tuning + +- **`step_size`**: Learning rate (default: 0.001) + - Most critical parameter + - Too large: oscillation or divergence + - Too small: slow convergence + - Try: 0.1, 0.01, 0.001, 0.0001 + +- **`betas`**: Momentum decay rates (default: (0.9, 0.999)) + - `beta1`: First moment (mean) decay + - `beta2`: Second moment (variance) decay + - Rarely need tuning, defaults work well + +- **`eps`**: Numerical stability constant (default: 1e-8) + - Prevents division by zero + - Almost never needs tuning + +- **`threshold`**: Gradient norm threshold (default: 1e-6) + - Stop when gradient is small + - Smaller for higher precision + +See the [Adam Algorithm Guide](../../algorithms/optimisers/adam.md) for more details. + +--- + +## Common Patterns + +### Running Optimisation + +All optimisers have a `.run()` method: + +```python +result = optimiser.run(problem, initial_guess) +``` + +For the default optimiser (Nelder-Mead): + +```python +result = problem.optimise() # Uses Nelder-Mead with defaults +``` + +### Configuring Stopping Criteria + +```python +optimiser = ( + diffid.CMAES() + .with_max_iter(10000) # Maximum iterations + .with_threshold(1e-8) # Objective threshold + .with_patience(300.0) # Patience in seconds +) +``` + +The optimiser stops when: +1. `max_iter` iterations reached, OR +2. Objective value < `threshold`, OR +3. `patience` seconds elapsed without improvement + +### Reproducibility + +For stochastic optimisers (CMA-ES), set a seed: + +```python +optimiser = diffid.CMAES().with_seed(42) +result1 = optimiser.run(problem, [0.0, 0.0]) +result2 = optimiser.run(problem, [0.0, 0.0]) +# result1 == result2 (same random sequence) +``` + +### Warm Starts + +Run multiple optimisations with different starting points: + +```python +import numpy as np + +initial_guesses = [ + [1.0, 1.0], + [-1.0, -1.0], + [0.0, 2.0], +] + +results = [optimiser.run(problem, guess) for guess in initial_guesses] +best_result = min(results, key=lambda r: r.value) +``` + +## Choosing an Optimiser + +```mermaid +graph TD + A[Start] --> B{Gradients available?} + B -->|Yes| C[Adam] + B -->|No| D{Problem size?} + D -->|< 10 params| E[Nelder-Mead] + D -->|> 10 params| F[CMA-ES] + D -->|Need global search| F +``` + +For detailed guidance, see: +- [Choosing an Optimiser Guide](../../guides/choosing-optimiser.md) +- [Tuning Optimisers Guide](../../guides/tuning-optimisers.md) + +## See Also + +- [Algorithm Guides](../../algorithms/index.md) +- [Results](results.md) +- [Tutorials](../../tutorials/index.md) diff --git a/docs/api-reference/python/results.md b/docs/api-reference/python/results.md new file mode 100644 index 0000000..00ce41d --- /dev/null +++ b/docs/api-reference/python/results.md @@ -0,0 +1,313 @@ +# Results + +Result objects returned by optimisers and samplers containing optimal parameters, diagnostics, and metadata. + +## OptimisationResults + +::: diffid.OptimisationResults + options: + show_root_heading: true + show_source: false + +All optimisers return an `OptimisationResults` object with the following attributes: + +### Example Usage + +```python +import diffid as chron + +problem = builder.build() +result = problem.optimise() + +# Access results +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +print(f"Evaluations: {result.evaluations}") +print(f"Message: {result.message}") +``` + +--- + +## Attributes + +### `x` + +**Type:** `numpy.ndarray` + +Optimal parameter values found by the optimiser. + +```python +result.x # e.g., array([1.0, 2.5, 0.8]) +``` + +Parameters are ordered according to `.with_parameter()` calls: + +```python +builder = ( + diffid.ScalarBuilder() + .with_parameter("alpha", 1.0) # result.x[0] + .with_parameter("beta", 2.0) # result.x[1] +) +``` + +--- + +### `value` + +**Type:** `float` + +Objective function value at the optimum (`result.x`). + +```python +result.value # e.g., 1.234e-08 +``` + +For minimisation problems, lower values are better. + +--- + +### `success` + +**Type:** `bool` + +Indicates whether optimisation terminated successfully. + +```python +if result.success: + print("Optimisation converged") +else: + print(f"Failed: {result.message}") +``` + +**`True`** when: + +- Convergence criteria met +- Threshold reached +- Normal termination + +**`False`** when: + +- Maximum iterations exceeded without convergence +- Numerical errors occurred +- User-requested termination + +--- + +### `iterations` + +**Type:** `int` + +Number of iterations performed. + +```python +result.iterations # e.g., 157 +``` + +- **Nelder-Mead**: Number of simplex iterations +- **CMA-ES**: Number of generations +- **Adam**: Number of gradient steps + +--- + +### `evaluations` + +**Type:** `int` + +Total number of objective function evaluations. + +```python +result.evaluations # e.g., 314 +``` + +Note that `evaluations` can be much larger than `iterations`: + +- **CMA-ES**: `evaluations ≈ iterations × population_size` +- **Nelder-Mead**: `evaluations ≈ iterations × (n_params + 1)` +- **Adam**: `evaluations ≈ iterations` (one per step) + +--- + +### `message` + +**Type:** `str` + +Human-readable termination message. + +```python +result.message +# e.g., "Converged successfully" +# e.g., "Maximum iterations reached" +# e.g., "Threshold achieved" +``` + +Use this for debugging and logging. + +--- + +## Common Patterns + +### Checking Success + +```python +result = problem.optimise() + +if result.success: + print(f"Found optimum: {result.x}") + print(f"Objective: {result.value:.3e}") +else: + print(f"Optimisation failed: {result.message}") + print(f"Best found: {result.x} (value: {result.value:.3e})") +``` + +Even when `success=False`, `result.x` contains the best parameters found. + +--- + +### Extracting Parameters by Name + +Results don't store parameter names. Track them manually if needed: + +```python +param_names = ["alpha", "beta", "gamma"] + +builder = diffid.ScalarBuilder().with_objective(func) +for name in param_names: + builder = builder.with_parameter(name, 1.0) + +problem = builder.build() +result = problem.optimise() + +# Create a dictionary +params = dict(zip(param_names, result.x)) +print(params) # {'alpha': 1.05, 'beta': 2.31, 'gamma': 0.87} +``` + +--- + +### Comparing Multiple Runs + +```python +initial_guesses = [[1, 1], [-1, -1], [0, 2]] + +results = [ + optimiser.run(problem, guess) + for guess in initial_guesses +] + +# Find best result +best = min(results, key=lambda r: r.value) +print(f"Best from {len(results)} runs: {best.x}, value={best.value:.3e}") +``` + +--- + +### Assessing Convergence + +```python +result = optimiser.run(problem, initial_guess) + +# Check various indicators +converged = ( + result.success + and result.value < 1e-6 + and result.iterations < max_iters +) + +if not converged: + print(f"Warning: May not have converged") + print(f" Iterations: {result.iterations}") + print(f" Value: {result.value:.3e}") + print(f" Message: {result.message}") +``` + +--- + +## Sampler Results (Future) + +When samplers are fully implemented, they will return different result types: + +### MCMC Results + +```python +result = mcmc_sampler.run(problem, initial_guess) + +result.samples # (n_samples, n_params) array +result.log_likelihood # Log-likelihood values +result.acceptance_rate # For diagnostics +``` + +### Nested Sampling Results + +```python +result = nested_sampler.run(problem, initial_guess) + +result.log_evidence # Log marginal likelihood +result.evidence_error # Uncertainty in evidence +result.samples # Posterior samples +result.weights # Sample weights +``` + +--- + +## Diagnostics + +### Visualising Convergence + +```python +import matplotlib.pyplot as plt + +# Run optimisation with tracking (custom wrapper) +values = [] + +def tracked_func(x): + val = original_func(x) + values.append(val[0]) + return val + +# ... run optimisation ... + +plt.plot(values) +plt.xlabel('Evaluation') +plt.ylabel('Objective Value') +plt.yscale('log') +plt.title('Optimisation Convergence') +plt.show() +``` + +### Checking Parameter Bounds + +```python +result = problem.optimise() + +# Check if parameters are in reasonable range +for i, val in enumerate(result.x): + if abs(val) > 1e3: + print(f"Warning: Parameter {i} has large magnitude: {val}") +``` + +--- + +## Performance Metrics + +```python +import time + +start = time.time() +result = optimiser.run(problem, initial_guess) +elapsed = time.time() - start + +print(f"Total time: {elapsed:.2f}s") +print(f"Evaluations: {result.evaluations}") +print(f"Time per evaluation: {elapsed/result.evaluations*1000:.2f}ms") +``` + +--- + +## See Also + +- [Optimisers](optimisers.md) +- [Samplers](samplers.md) +- [Choosing an Optimiser](../../guides/choosing-optimiser.md) +- [Troubleshooting](../../guides/troubleshooting.md) diff --git a/docs/api-reference/python/samplers.md b/docs/api-reference/python/samplers.md new file mode 100644 index 0000000..ead0b94 --- /dev/null +++ b/docs/api-reference/python/samplers.md @@ -0,0 +1,202 @@ +# Samplers + +MCMC and nested sampling algorithms for posterior exploration and model comparison. + +## Overview + +| Sampler | Type | Use Case | +|---------|------|----------| +| `MetropolisHastings` | MCMC | Posterior exploration, uncertainty quantification | +| `DynamicNestedSampling` | Nested | Model evidence, Bayes factors | + +Samplers complement optimisers by providing full posterior distributions rather than point estimates. + +--- + +## Metropolis-Hastings + +MCMC sampling for exploring parameter posterior distributions. + +```python +import diffid as chron + +# Will be available in a future release +sampler = ( + diffid.MetropolisHastings() + .with_max_iter(10000) + .with_step_size(0.1) + .with_burn_in(1000) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess) + +# Access samples +print(result.samples.shape) # (n_samples, n_params) +print(result.acceptance_rate) # Target: 0.2-0.4 +``` + +### When to Use + +**Advantages:** + +- Provides full posterior distribution +- Quantifies parameter uncertainty +- Captures correlations between parameters +- Enables credible intervals + +**Limitations:** + +- Requires many function evaluations +- Need to assess convergence +- Requires likelihood (GaussianNLL cost metric) + +**Typical Use Cases:** + +- Uncertainty quantification +- Confidence intervals +- Posterior predictive distributions +- Parameter correlations + +--- + +## Dynamic Nested Sampling + +Nested sampling for calculating model evidence (marginal likelihood) for model comparison. + +```python +import diffid as chron + +# Will be available in a future release +sampler = ( + diffid.DynamicNestedSampling() + .with_max_iter(5000) + .with_n_live_points(500) + .with_seed(42) +) + +result = sampler.run(problem, initial_guess) + +# Access evidence +print(f"Log evidence: {result.log_evidence:.2f}") +print(f"Error: {result.evidence_error:.2f}") + +# Posterior samples also available +print(result.samples.shape) +``` + +### When to Use + +**Advantages:** + +- Calculates marginal likelihood (evidence) +- Enables model comparison via Bayes factors +- Provides posterior samples as byproduct +- Efficient for multi-modal posteriors + +**Limitations:** + +- More expensive than MCMC +- Requires careful tuning of live points +- Needs likelihood (GaussianNLL cost metric) + +**Typical Use Cases:** + +- Model comparison +- Bayes factors +- Evidence calculation +- Multi-modal posteriors + +--- + +## Optimisers vs Samplers + +| | Optimisers | Samplers | +|---|------------|----------| +| **Output** | Single best parameters | Distribution of parameters | +| **Use case** | Point estimate | Uncertainty quantification | +| **Cost metric** | SSE, RMSE, GaussianNLL | GaussianNLL (required) | +| **Computational cost** | Lower | Higher | +| **Uncertainty** | None | Full posterior | + +### When to Use Which + +```mermaid +graph TD + A[Start] --> B{Need uncertainty?} + B -->|No| C[Use Optimiser] + B -->|Yes| D{Need model comparison?} + D -->|No| E[Use MCMC] + D -->|Yes| F[Use Nested Sampling] +``` + +--- + +## Cost Metric Requirement + +!!! warning + Samplers require a probabilistic cost metric (typically `GaussianNLL`): + +```python +# Required for samplers +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(diffid.GaussianNLL()) # Required! +) +``` + +SSE and RMSE cannot be used with samplers as they lack probabilistic interpretation. + +--- + +## Workflow Example + +Typical workflow: optimise first, then sample for uncertainty: + +```python +import diffid as chron + +# 1. Build problem with GaussianNLL +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(diffid.GaussianNLL()) +) +problem = builder.build() + +# 2. Find MAP estimate with optimiser +optimiser = diffid.CMAES().with_max_iter(1000) +opt_result = optimiser.run(problem, [1.0]) + +print(f"MAP estimate: {opt_result.x}") + +# 3. Sample around MAP for uncertainty (future API) +# sampler = diffid.MetropolisHastings().with_max_iter(10000) +# sample_result = sampler.run(problem, opt_result.x) +# print(f"Posterior mean: {sample_result.samples.mean(axis=0)}") +# print(f"Posterior std: {sample_result.samples.std(axis=0)}") +``` + +--- + +## Tutorials + +The followings present sampler functionality: + +- [Parameter Uncertainty Tutorial](../../tutorials/notebooks/parameter_uncertainty.ipynb) +- [Model Comparison Tutorial](../../tutorials/notebooks/model_comparison.ipynb) +- [Choosing a Sampler Guide](../../guides/choosing-sampler.md) + +--- + +## See Also + +- [Optimisers](optimisers.md) - For point estimates +- [Cost Metrics](cost-metrics.md) - GaussianNLL required +- [Choosing a Sampler Guide](../../guides/choosing-sampler.md) (future) +- [Algorithm Guides](../../algorithms/index.md) diff --git a/docs/api-reference/rust/index.md b/docs/api-reference/rust/index.md new file mode 100644 index 0000000..09d9e1a --- /dev/null +++ b/docs/api-reference/rust/index.md @@ -0,0 +1,203 @@ +# Rust API Documentation + +Diffid's Rust core provides high-performance implementations of all optimisation and sampling algorithms. + +## Official Documentation + +The complete Rust API documentation is hosted on docs.rs: + +**[:octicons-arrow-right-24: Diffid Rust Documentation on docs.rs](https://docs.rs/diffid/latest/diffid/)** + +## When to Use the Rust API + +Consider using the Rust crate directly when: + +- **Maximum performance** is critical +- Building **Rust-native applications** +- Need **zero-copy** data handling +- Deploying to **embedded systems** or **constrained environments** +- Building **custom tooling** around Diffid + +For most users, the Python API provides excellent performance with easier integration. + +## Crate Structure + +``` +diffid/ +├── builders/ # Problem builders (ScalarBuilder, DiffsolBuilder, etc.) +├── optimisers/ # Optimisation algorithms +│ ├── nelder_mead/ # Nelder-Mead simplex +│ ├── cmaes/ # CMA-ES evolution strategy +│ └── adam/ # Adam gradient descent +├── sampler/ # MCMC and nested sampling +├── cost/ # Cost metrics (SSE, RMSE, GaussianNLL) +├── problem/ # Problem types and evaluation +└── common/ # Shared types and utilities +``` + +## Quick Example + +```rust +use diffid::prelude::*; +use ndarray::array; + +// Define objective function +fn rosenbrock(x: &[f64]) -> f64 { + (1.0 - x[0]).powi(2) + 100.0 * (x[1] - x[0].powi(2)).powi(2) +} + +fn main() { + // Build problem + let builder = ScalarBuilder::new() + .with_objective(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5); + + let problem = builder.build(); + + // Run optimisation + let result = problem.optimise(); + + println!("Optimal parameters: {:?}", result.x); + println!("Objective value: {:.3e}", result.value); + println!("Success: {}", result.success); +} +``` + +## Adding Diffid to Your Project + +Add to your `Cargo.toml`: + +```toml +[dependencies] +diffid = "0.2" +ndarray = "0.15" +``` + +For ODE support with DiffSL: + +```toml +[dependencies] +diffid = { version = "0.2", features = ["diffsol"] } +``` + +## Key Rust Features + +### Zero-Copy Performance + +The Rust API avoids unnecessary allocations and copies: + +```rust +// Efficient in-place evaluation +let mut output = vec![0.0; n]; +problem.evaluate_into(¶ms, &mut output)?; +``` + +### Type Safety + +Strong typing catches errors at compile time: + +```rust +// Compiler ensures correct types +let builder: ScalarBuilder = ScalarBuilder::new() + .with_objective(objective) + .with_parameter("x", 1.0); + +let problem: Problem = builder.build(); +``` + +### Parallel Execution + +Native Rayon parallelism: + +```rust +use rayon::prelude::*; + +let results: Vec<_> = initial_guesses + .par_iter() + .map(|guess| optimiser.run(&problem, guess)) + .collect(); +``` + +## Documentation Generation + +Generate local documentation: + +```bash +cd rust +cargo doc --open --no-deps +``` + +This builds and opens the full API documentation in your browser. + +## Contributing to Rust Core + +See the [Contributing Guide](../../development/contributing.md) and [Architecture](../../development/architecture.md) docs for: + +- Code organization and patterns +- Adding new optimisers or samplers +- Implementing custom cost metrics +- Testing strategies +- PyO3 binding guidelines + +## Module Documentation + +Key modules (click through on docs.rs for full details): + +### `builders` + +Problem construction with fluent API. + +```rust +pub use diffid::builders::{ScalarBuilder, DiffsolBuilder, VectorBuilder}; +``` + +### `optimisers` + +Optimisation algorithms. + +```rust +pub use diffid::optimisers::{NelderMead, CMAES, Adam}; +``` + +### `cost` + +Cost metrics for objective functions. + +```rust +pub use diffid::cost::{CostMetric, SSE, RMSE, GaussianNLL}; +``` + +### `problem` + +Problem types and evaluation. + +```rust +pub use diffid::problem::{Problem, ScalarProblem, VectorProblem}; +``` + +## Performance Tips + +1. **Use release builds**: `cargo build --release` (10-100x faster than debug) +2. **Profile first**: Use `cargo flamegraph` to identify bottlenecks +3. **Parallel backends**: Enable Rayon for CMA-ES and Diffsol +4. **Sparse matrices**: Use sparse backend for large ODE systems +5. **Avoid allocations**: Reuse buffers in hot loops + +## Examples + +The repository includes Rust examples: + +```bash +cd rust +cargo run --example rosenbrock +cargo run --example ode_fitting +``` + +Browse examples on GitHub: [rust/examples/](https://github.com/bradyplanden/diffid/tree/main/rust/examples) + +## See Also + +- [Python API Reference](../index.md) +- [Architecture](../../development/architecture.md) +- [Contributing](../../development/contributing.md) diff --git a/docs/assets/diffid.drawio b/docs/assets/diffid.drawio new file mode 100644 index 0000000..4f7bc83 --- /dev/null +++ b/docs/assets/diffid.drawio @@ -0,0 +1,188 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/assets/diffid.svg b/docs/assets/diffid.svg new file mode 100644 index 0000000..b64369b --- /dev/null +++ b/docs/assets/diffid.svg @@ -0,0 +1,4 @@ + + + +
Conventional Approach
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(for i in range(n):)
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builder = DiffsolBuilder()
  .with_diffsl(model_code)
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problem = builder.build()
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result = problem.optimise()
Single call,
returns results
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FFI Boundary (crossed once)
Rust Core (compiled, fast)
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Optimisers
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 \ No newline at end of file diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..90cb31c --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1 @@ +--8<-- "CHANGELOG.md" \ No newline at end of file diff --git a/docs/development/architecture.md b/docs/development/architecture.md new file mode 100644 index 0000000..6c9f9df --- /dev/null +++ b/docs/development/architecture.md @@ -0,0 +1,18 @@ +# Architecture + +## High-Level Overview + +
+ ![Paradigm Comparison](../assets/diffid.svg){ width="100%" } +
+ +## Key Design Patterns + +- **Builder Pattern**: Fluent API for problem construction +- **Zero-Copy**: Efficient data handling between Python and Rust +- **Trait-Based**: Extensible algorithm interfaces + +## See Also + +- [Contributing](contributing.md) +- [Rust API](../api-reference/rust/index.md) diff --git a/docs/development/contributing.md b/docs/development/contributing.md new file mode 100644 index 0000000..c28c44e --- /dev/null +++ b/docs/development/contributing.md @@ -0,0 +1,35 @@ +# Contributing to Diffid + +!!! info "Coming Soon" + Detailed contributing guidelines are being written. + +## Quick Start + +```bash +# Fork and clone +git clone https://github.com/YOUR_USERNAME/diffid.git +cd diffid + +# Set up environment +uv sync +uv run maturin develop + +# Run tests +uv run pytest -v +cargo test + +# Make changes and submit PR +``` + +## Areas to Contribute + +- Bug fixes +- New algorithms +- Documentation +- Examples +- Tests + +## See Also + +- [Architecture](architecture.md) +- [GitHub Issues](https://github.com/bradyplanden/diffid/issues) diff --git a/docs/development/index.md b/docs/development/index.md new file mode 100644 index 0000000..72422d6 --- /dev/null +++ b/docs/development/index.md @@ -0,0 +1,235 @@ +# Development + +Resources for contributors and developers working with Diffid. + +## Getting Started with Development + +
+ +- :material-source-pull:{ .lg .middle } __Contributing__ + + --- + + Guidelines for contributing code, documentation, and bug reports. + + [:octicons-arrow-right-24: Contributing Guide](contributing.md) + +- :material-file-tree:{ .lg .middle } __Architecture__ + + --- + + Understanding Diffid's Rust core and PyO3 bindings design. + + [:octicons-arrow-right-24: Architecture Overview](architecture.md) + +
+ +## Quick Setup + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/diffid.git +cd diffid + +# Create Python environment +uv sync + +# Build Rust extension with Python bindings +uv run maturin develop + +# Run tests +uv run pytest -v # Python tests +cargo test # Rust tests +``` + +## Development Workflow + +### 1. Make Changes + +Edit Rust source in `rust/src/` or Python bindings in `python/`. + +### 2. Rebuild + +```bash +uv run maturin develop # For Python binding changes +``` + +### 3. Test + +```bash +# Python tests +uv run pytest -v + +# Rust tests +cargo test + +# Both +cargo test && uv run pytest -v +``` + +### 4. Update Stubs + +If you modified Python bindings: + +```bash +uv run cargo run -p diffid-py --no-default-features --features stubgen --bin generate_stubs +``` + +### 5. Format and Lint + +```bash +# Rust +cargo fmt +cargo clippy + +# Python +uv run ruff check . +uv run ruff format . +``` + +## Project Structure + +``` +diffid/ +├── rust/ # Rust core implementation +│ ├── src/ +│ │ ├── builders/ # Problem builders +│ │ ├── optimisers/ # Optimisation algorithms +│ │ ├── sampler/ # MCMC and nested sampling +│ │ ├── cost/ # Cost metrics +│ │ └── problem/ # Problem types +│ ├── Cargo.toml +│ └── tests/ # Rust tests +├── python/ # Python bindings +│ ├── src/diffid/ +│ │ ├── __init__.py +│ │ └── _diffid.pyi # Generated type stubs +│ └── diffid/ # PyO3 bindings source +├── examples/ # Example scripts +├── tests/ # Python tests +├── docs/ # Documentation (this site) +├── pyproject.toml # Python package config +└── README.md +``` + +## Key Technologies + +- **Rust**: Core algorithms, high performance +- **PyO3**: Python bindings with minimal overhead +- **Maturin**: Build system for Rust Python extensions +- **uv**: Fast Python package management +- **MkDocs Material**: Documentation site + +## Testing Strategy + +### Unit Tests + +Rust unit tests alongside code: + +```rust +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_feature() { + // ... + } +} +``` + +### Integration Tests + +Python integration tests in `tests/`: + +```python +def test_optimisation(): + builder = diffid.ScalarBuilder().with_objective(func) + # ... + assert result.success +``` + +### Continuous Integration + +GitHub Actions runs: +- Rust tests and clippy +- Python tests on multiple versions +- Type checking with mypy +- Linting with ruff +- Documentation builds + +## Documentation + +### Rust Docs + +```bash +cargo doc --open --no-deps +``` + +### Python Docs + +Edit markdown files in `docs/`: + +```bash +mkdocs serve # Live preview +mkdocs build # Build static site +``` + +### Docstrings + +Use NumPy-style docstrings: + +```python +def example(x, y): + """ + Brief description. + + Parameters + ---------- + x : float + Description of x. + y : float + Description of y. + + Returns + ------- + float + Description of return value. + """ +``` + +## Code Style + +### Rust + +- Follow [Rust API Guidelines](https://rust-lang.github.io/api-guidelines/) +- Use `cargo fmt` (automatic formatting) +- Pass `cargo clippy` (linting) + +### Python + +- Follow PEP 8 +- Use type hints +- Format with `ruff format` + +## Performance Profiling + +### Rust + +```bash +cargo install flamegraph +cargo flamegraph --example your_example +``` + +### Python + +```bash +uv pip install py-spy +py-spy record --native -- python examples/your_example.py +``` + +## See Also + +- [Architecture](architecture.md) - System design +- [Contributing](contributing.md) - Contribution guidelines +- [API Reference](../api-reference/index.md) - API documentation diff --git a/docs/examples/gallery.md b/docs/examples/gallery.md new file mode 100644 index 0000000..4d2a036 --- /dev/null +++ b/docs/examples/gallery.md @@ -0,0 +1,131 @@ +# Examples Gallery + +Visual gallery of Diffid applications and use cases. + +!!! info "Gallery Under Construction" + This gallery is being populated with examples. Check the [examples directory](https://github.com/bradyplanden/diffid/tree/main/examples) for current code. + +## Available Examples + +### Scalar Optimisation + +#### Rosenbrock Function +Classic 2D optimisation test problem. + +**Files:** + +- [python_problem.py](https://github.com/bradyplanden/diffid/blob/main/examples/python_problem.py) +- [python_contour.py](https://github.com/bradyplanden/diffid/blob/main/examples/python_contour.py) + +**Topics:** ScalarBuilder, contour plots, optimiser comparison + +--- + +### ODE Parameter Fitting + +#### Logistic Growth +Single-variable ODE with DiffSL. + +**File:** [logistic_growth.py](https://github.com/bradyplanden/diffid/blob/main/examples/logistic_growth.py) + +**Topics:** DiffsolBuilder, DiffSL syntax, data fitting + +--- + +#### Bouncy Ball +Physics-based model with event handling. + +**Files:** + +- [bouncy_ball.py](https://github.com/bradyplanden/diffid/blob/main/examples/bouncy_ball.py) +- [bouncy_ball_sampling.py](https://github.com/bradyplanden/diffid/blob/main/examples/bouncy_ball_sampling.py) + +**Topics:** Event detection, parameter uncertainty, MCMC + +--- + +### Model Comparison + +#### Bicycle Model +Comparing different bicycle dynamics formulations. + +**Files:** + +- [bicycle_model_diffsol.py](https://github.com/bradyplanden/diffid/blob/main/examples/bicycle_model_diffsol.py) +- [bicycle_model_evidence.py](https://github.com/bradyplanden/diffid/blob/main/examples/bicycle_model_evidence.py) + +**Topics:** Model selection, evidence calculation, Bayes factors + +--- + +### Multi-Backend ODE Solving + +#### Predator-Prey Models +Lotka-Volterra equations with multiple solver backends. + +**Files:** + +- [predator_prey_diffsol.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffsol.py) +- [predator_prey_diffrax.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) + +**Topics:** VectorBuilder, JAX/Diffrax, Julia/DifferentialEquations.jl, performance comparison + +--- + +## Running Examples + +Clone the repository: + +```bash +git clone https://github.com/bradyplanden/diffid.git +cd diffid +``` + +Install dependencies: + +```bash +pip install diffid matplotlib +``` + +Run an example: + +```bash +python examples/python_problem.py +``` + +For ODE examples: + +```bash +python examples/logistic_growth.py +``` + +For multi-backend examples (requires additional dependencies): + +```bash +# For Diffrax (JAX) +pip install jax diffrax + +# For DifferentialEquations.jl (Julia) +pip install diffeqpy +# Then follow Julia setup instructions + +python examples/predator_prey/predator_prey_diffrax.py +``` + +## Contributing Examples + +Have an interesting use case? We'd love to include it! + +1. Fork the repository +2. Add your example to `examples/` +3. Include a brief comment header explaining the example +4. Open a pull request + +See the [Contributing Guide](../development/contributing.md) for details. + +## See Also + +- [Tutorials](../tutorials/index.md) - Interactive Jupyter notebooks +- [Getting Started](../getting-started/index.md) - Core concepts +- [API Reference](../api-reference/index.md) - Complete API docs diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md new file mode 100644 index 0000000..78624ee --- /dev/null +++ b/docs/getting-started/concepts.md @@ -0,0 +1,304 @@ +# Core Concepts + +This guide explains the fundamental concepts and patterns in Diffid. + +## The Builder Pattern + +Diffid uses the **builder pattern** for constructing problems. This provides a fluent, chainable API for configuration: + +```python +builder = ( + diffid.ScalarBuilder() + .with_objective(my_function) + .with_parameter("x", 1.0) + .with_parameter("y", 2.0) +) +problem = builder.build() +``` + +**Benefits:** + +- **Clear and readable**: Method names clearly describe what's being configured +- **Flexible**: Add components in any order +- **Type-safe**: Catch errors early with proper type hints +- **Chainable**: Fluent interface for concise code + +## Problem Types + +Diffid provides different builders for different problem types: + +### ScalarBuilder + +For **direct function optimisation** where you have a Python callable: + +```python +def objective(x): + return np.asarray([x[0]**2 + x[1]**2]) + +problem = ( + diffid.ScalarBuilder() + .with_objective(objective) + .with_parameter("x", 0.0) + .with_parameter("y", 0.0) + .build() +) +``` + +**Use when:** + +- You have a direct Python function to minimise +- No differential equations involved +- Simple parameter optimisation + +### DiffsolBuilder + +For **ODE parameter fitting** using the built-in DiffSL/Diffsol solver: + +```python +dsl = """ +in_i {r = 1, k = 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +problem = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") + .build() +) +``` + +**Use when:** + +- Fitting ODE parameters to time-series data +- Using DiffSL for model definition +- Need high-performance multi-threaded solving + +### VectorBuilder + +For **custom ODE solvers** (Diffrax, DifferentialEquations.jl, etc.): + +```python +def solve_ode(params): + # Your custom ODE solver + # Returns predictions at observation times + return predictions + +problem = ( + diffid.VectorBuilder() + .with_objective(solve_ode) + .with_data(data) + .with_parameter("alpha", 1.0) + .with_parameter("beta", 0.5) + .build() +) +``` + +**Use when:** + +- Need a specific solver (JAX/Diffrax, Julia/DifferentialEquations.jl) +- Complex ODEs not supported by DiffSL +- Custom forward models beyond ODEs + +See the [Custom Solvers Guide](../guides/custom-solvers.md) for examples. + +## Parameters + +Parameters are the decision variables you want to optimise: + +```python +builder = ( + diffid.ScalarBuilder() + .with_parameter("x", initial_value=1.0) # Name and initial guess + .with_parameter("y", initial_value=-1.0) +) +``` + +**Important:** + +- Parameters must have unique names +- Initial values are required +- Order matters: results will be returned in the same order + +## Optimisers vs Samplers + +Diffid provides two types of algorithms: + +### Optimisers: Finding the Best Solution + +**Goal**: Find parameter values that minimise the objective function. + +**Algorithms**: + +- **Nelder-Mead**: Gradient-free, local search +- **CMA-ES**: Gradient-free, global search +- **Adam**: Gradient-based (automatic differentiation) + +**Usage:** + +```python +# Default optimiser (Nelder-Mead) +result = problem.optimise() + +# Specific optimiser +optimiser = diffid.CMAES().with_max_iter(1000) +result = optimiser.run(problem, initial_guess) +``` + +**Returns:** A single best solution + +### Samplers: Exploring Uncertainty + +**Goal**: Sample from the posterior distribution to quantify parameter uncertainty. + +**Algorithms**: + +- **Metropolis-Hastings**: MCMC sampling for posterior exploration +- **Dynamic Nested Sampling**: Evidence calculation for model comparison + +**Usage:** + +```python +sampler = diffid.MetropolisHastings().with_max_iter(10000) +result = sampler.run(problem, initial_guess) + +# Result contains samples, not a single optimum +print(result.samples.shape) # (n_samples, n_parameters) +``` + +**Returns:** A collection of samples from the posterior + +**When to use:** + +| Use Optimisers When | Use Samplers When | +|---------------------|-------------------| +| You want the single best fit | You want to quantify uncertainty | +| Point estimates are sufficient | You need confidence intervals | +| Computation budget is limited | You need full posterior distributions | +| | Comparing multiple models (Bayes factors) | + +See [Choosing an Optimiser](../guides/choosing-optimiser.md) and [Choosing a Sampler](../guides/choosing-sampler.md) for detailed guidance. + +## The Ask/Tell Pattern + +For advanced use cases, Diffid supports the **ask/tell pattern** for manual control of the optimisation loop: + +```python +optimiser = diffid.CMAES().with_max_iter(1000) + +# Ask for candidates +candidates = optimiser.ask(n_candidates=10) + +# Evaluate them (potentially in parallel or on remote machines) +evaluations = [problem.evaluate(c) for c in candidates] + +# Tell the optimiser the results +optimiser.tell(candidates, evaluations) + +# Repeat until convergence +while not optimiser.should_stop(): + candidates = optimiser.ask() + evaluations = [problem.evaluate(c) for c in candidates] + optimiser.tell(candidates, evaluations) + +result = optimiser.get_result() +``` + +**Use cases:** + +- Distributed optimisation across multiple machines +- Custom evaluation pipelines +- Hybrid optimisation strategies +- Integration with external simulators + +## Cost Metrics + +Cost metrics define how model predictions are compared to observations: + +```python +from diffid import SSE, RMSE, GaussianNLL + +# Sum of squared errors (default) +builder = builder.with_cost_metric(SSE()) + +# Root mean squared error (normalised) +builder = builder.with_cost_metric(RMSE()) + +# Gaussian negative log-likelihood (for probabilistic inference) +builder = builder.with_cost_metric(GaussianNLL()) +``` + +**Common metrics:** + +- **SSE** (Sum of Squared Errors): Standard least squares, sensitive to outliers +- **RMSE** (Root Mean Squared Error): Normalised by number of points +- **GaussianNLL**: For Bayesian inference and sampling + +See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. + +## Results + +All optimisers and samplers return result objects with standard attributes: + +### Optimiser Results + +```python +result = problem.optimise() + +print(result.x) # Optimal parameters (NumPy array) +print(result.value) # Objective value at optimum (float) +print(result.success) # Whether optimisation succeeded (bool) +print(result.iterations) # Number of iterations (int) +print(result.evaluations) # Number of function evaluations (int) +print(result.message) # Termination message (str) +``` + +### Sampler Results + +```python +result = sampler.run(problem, initial_guess) + +print(result.samples) # MCMC samples (NumPy array, shape: (n_samples, n_params)) +print(result.log_likelihood) # Log-likelihood values +print(result.acceptance_rate) # Acceptance rate (for diagnostics) +``` + +For nested sampling: + +```python +result = dns_sampler.run(problem, initial_guess) + +print(result.log_evidence) # Log marginal likelihood +print(result.evidence_error) # Uncertainty in evidence +print(result.samples) # Posterior samples +``` + +## Parallelisation + +Diffid automatically parallelises where possible: + +- **DiffsolBuilder**: Multi-threaded ODE solving +- **CMA-ES**: Parallel candidate evaluation +- **Dynamic Nested Sampling**: Parallel live point evaluation + +Control parallelism: + +```python +# Limit threads for ODE solving +builder = builder.with_max_threads(4) + +# Population size for CMA-ES (larger = more parallel work) +optimiser = diffid.CMAES().with_population_size(20) +``` + +See the [Parallel Execution Guide](../guides/parallel-execution.md) for details. + +## Next Steps + +- **[Tutorials](../tutorials/index.md)**: Interactive Jupyter notebooks +- **[Choosing an Optimiser](../guides/choosing-optimiser.md)**: Learn when to use each algorithm +- **[API Reference](../api-reference/index.md)**: Browse complete API documentation +- **[Examples Gallery](../examples/gallery.md)**: Visual gallery of applications diff --git a/docs/getting-started/first-ode-fit.md b/docs/getting-started/first-ode-fit.md new file mode 100644 index 0000000..e16aac1 --- /dev/null +++ b/docs/getting-started/first-ode-fit.md @@ -0,0 +1,231 @@ +# First ODE Fit + +This tutorial demonstrates how to fit ordinary differential equations (ODEs) to data using Diffid's DiffSL integration with the Diffsol solver. + +## The Problem: Logistic Growth + +We'll fit a logistic growth model to synthetic population data. The logistic growth equation is: + +$$\frac{dy}{dt} = r \cdot y \cdot \left(1 - \frac{y}{k}\right)$$ + +where: $r$ is the growth rate, $k$ is the carrying capacity, and $y$ is the population. + +## Complete Example + +```python +import numpy as np +import diffid as chron + +# Define the ODE model in DiffSL syntax +dsl = """ +in_i { r = 1, k = 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +# Generate synthetic data from the logistic model with noise +from scipy.integrate import odeint + +def logistic(y, t, r, k): + return r * y * (1 - y / k) + +t = np.linspace(0.0, 10.0, 51) +y_true = odeint(logistic, 0.1, t, args=(0.8, 2.0)).flatten() # True: r=0.8, k=2.0 +np.random.seed(42) +observations = y_true + 0.05 * np.random.randn(len(t)) +data = np.column_stack((t, observations)) + +# Build the problem +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("r", 0.5) # Initial guess for growth rate + .with_parameter("k", 1.0) # Initial guess for carrying capacity + .with_backend("dense") +) +problem = builder.build() + +# Run optimisation with CMA-ES +optimiser = diffid.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 1.0]) + +# Display results +print(f"Fitted parameters:") +print(f" r (growth rate) = {result.x[0]:.4f}") +print(f" k (capacity) = {result.x[1]:.4f}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +``` + +## Understanding DiffSL Syntax + +DiffSL is a domain-specific language for defining differential equations. Let's break down the syntax: + +``` +in_i { r = 1, k = 1 } # Input parameter tensor with defaults +u_i { y = 0.1 } # Initial conditions +F_i { (r * y) * (1 - (y / k)) } # Right-hand side of dy/dt = ... +``` + +## Data Format + +Diffid expects data as a 2D NumPy array where: + +- **First column**: Time points +- **Remaining columns**: Observed values for each variable + +```python +# Example: 51 time points, 1 state variable +t = np.linspace(0.0, 10.0, 51) +observations = ... # Your observed data +data = np.column_stack((t, observations)) # Shape: (51, 2) +``` + +For multi-variable systems: + +```python +# Example: 2 state variables +data = np.column_stack((t, obs_var1, obs_var2)) # Shape: (n, 3) +``` + +## Visualising Results + +```python +import matplotlib.pyplot as plt + +# Generate fitted curve using optimised parameters +t_dense = np.linspace(0.0, 10.0, 200) +y_fitted = odeint(logistic, 0.1, t_dense, args=(result.x[0], result.x[1])).flatten() + +# Plot +plt.figure(figsize=(10, 6)) +plt.plot(data[:, 0], data[:, 1], 'o', label='Observed data', alpha=0.6) +plt.plot(t_dense, y_fitted, '-', label='Fitted model', linewidth=2) +plt.xlabel('Time') +plt.ylabel('y') +plt.title('Logistic Growth Model Fit') +plt.legend() +plt.grid(True, alpha=0.3) +plt.show() +``` + +![Logistic growth model fit](logistic_fit.png) + +## Common ODE Patterns + +### Multiple State Variables + +```python +dsl = """ +in_i { alpha = 1, beta = 1 } +u_i { + x = 1.0 + y = 0.5 +} +F_i { + alpha * x - beta * x * y, + -beta * y + alpha * x * y +} +out_i { x, y } +""" +``` + +### With Algebraic Variables + +```python +dsl = """ +in_i {k1 = 1, k2 = 1 } +u_i { A = 1.0 } +dudt_i { -k1 * A } +F_i { + -k1 * A, + k1 * A - k2 * B +} +out_i { A, B } +""" +``` + +### Time-Dependent Forcing + +```python +dsl = """ +in_i { k = 1 } +u_i { y = 0.0 } +F_i { k * sin(t) - y } +""" +``` + +## Cost Metrics + +By default, Diffid uses sum of squared errors (SSE). You can specify different cost metrics as shown below. See the [Cost Metrics Guide](../guides/cost-metrics.md) for more details. + +```python +from diffid import GaussianNLL, RMSE + +# Use Gaussian negative log-likelihood +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_cost_metric(GaussianNLL()) # For probabilistic inference +) + +# Or use root mean squared error +builder = builder.with_cost_metric(RMSE()) # Normalised by number of points +``` + +## Optimiser Selection + +Different optimisers work better for different problems. For a detailed comparison, see [Choosing an Optimiser](../guides/choosing-optimiser.md). + +### Nelder-Mead (Default) + +```python +result = problem.optimise() # Uses Nelder-Mead +``` + +**Best for**: Small problems (< 10 parameters), noisy objectives + +### CMA-ES + +```python +optimiser = diffid.CMAES().with_max_iter(1000).with_step_size(0.5) +result = optimiser.run(problem, initial_guess) +``` + +**Best for**: Global optimisation, 10-100 parameters, parallelisable + +### Adam + +```python +optimiser = diffid.Adam().with_max_iter(1000).with_step_size(0.01) +result = optimiser.run(problem, initial_guess) +``` + +**Best for**: Smooth problems, fast convergence on well-behaved objectives + +## Troubleshooting + +### Poor Fit Quality + +1. **Check initial conditions**: Ensure they're physically reasonable +2. **Try different optimisers**: CMA-ES is often more robust than Nelder-Mead +3. **Increase iterations**: Use `.with_max_iter(10000)` +4. **Check data scale**: Normalise data if variables have very different magnitudes + +### Solver Errors + +1. **Stiff equations**: Try changing the solver tolerance +2. **Numerical instability**: Check for divide-by-zero or exp overflow in your ODE +3. **Backend mismatch**: Try switching between `dense` and `sparse` + +For more help, see the [Troubleshooting Guide](../guides/troubleshooting.md). + +## Next Steps + +- **[Core Concepts](concepts.md)**: Understand builders, problems, and the ask/tell pattern +- **[ODE Fitting Tutorial](../tutorials/notebooks/ode_fitting_diffsol.ipynb)**: Interactive notebook with more examples +- **[Custom Solvers](../guides/custom-solvers.md)**: Integrate Diffrax or DifferentialEquations.jl +- **[Parameter Uncertainty](../tutorials/notebooks/parameter_uncertainty.ipynb)**: Use MCMC sampling to quantify uncertainty diff --git a/docs/getting-started/index.md b/docs/getting-started/index.md new file mode 100644 index 0000000..e3280eb --- /dev/null +++ b/docs/getting-started/index.md @@ -0,0 +1,39 @@ +# Getting Started with Diffid + +Welcome to Diffid! This section will help you get up and running with time-series inference and optimisation. + +## Learning Path + +We recommend following this sequence: + +1. **[Installation](installation.md)** - Platform-specific installation instructions and troubleshooting +2. **[5-Minute Quickstart](quickstart.md)** - Simple scalar optimisation with the Rosenbrock function +3. **[First ODE Fit](first-ode-fit.md)** - Fitting differential equations to data with DiffSL +4. **[Core Concepts](concepts.md)** - Understanding builders, problems, and the ask/tell pattern + +## Prerequisites + +- Python >= 3.11 +- Basic understanding of Python programming +- Familiarity with NumPy arrays (helpful but not required) +- For ODE fitting: basic knowledge of differential equations + +## What You'll Learn + +By the end of this section, you will be able to: + +- Install Diffid on your platform +- Create and solve scalar optimisation problems +- Fit differential equations to experimental data +- Understand the builder pattern and problem types +- Choose between optimisers and samplers for your use case + +## Need Help? + +If you encounter issues: + +1. Check the [Troubleshooting](../guides/troubleshooting.md) guide +2. Browse the [examples gallery](../examples/gallery.md) +3. Open an issue on [GitHub](https://github.com/bradyplanden/diffid/issues) + +Ready to begin? Start with [Installation](installation.md). diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md new file mode 100644 index 0000000..59e8ff3 --- /dev/null +++ b/docs/getting-started/installation.md @@ -0,0 +1,112 @@ +# Installation + +Diffid is available as a Python package with pre-built wheels for most platforms. + +## Installation Methods + +=== "uv" + + [uv](https://docs.astral.sh/uv/) is a fast Python package installer and resolver. + + ```bash + uv pip install diffid + ``` + +=== "pip" + + ```bash + pip install diffid + ``` + +### Optional Dependencies + +Diffid has optional plotting support via matplotlib: + +=== "uv" + + ```bash + uv pip install "diffid[plotting]" + ``` + +=== "pip" + + ```bash + pip install "diffid[plotting]" + ``` + +## Verifying Installation + +After installation, verify that Diffid is working correctly: + +```python +import diffid +import numpy as np + +# Simple test +def test_func(x): + return np.asarray([(x[0] - 1.0) ** 2]) + +builder = diffid.ScalarBuilder().with_objective(test_func).with_parameter("x", 0.0) +problem = builder.build() +result = problem.optimise() + +print(f"Success: {result.success}") +print(f"Optimal x: {result.x[0]:.3f}") +``` + +Expected output: +``` +Success: True +Optimal x: 1.000 +``` + +## Platform-Specific Notes + +### Linux + +Pre-built wheels are available for x86_64 and aarch64 architectures. No additional setup required. + +### macOS + +Pre-built wheels are available for both Intel (x86_64) and Apple Silicon (arm64) Macs. + +If you're using Apple Silicon and encounter issues, ensure you're using a native arm64 Python installation rather than running under Rosetta. + +### Windows + +Windows builds are marked experimental. Pre-built wheels are available but don't currently support diffsol gradients due to LLVM integration issues. + +If you encounter issues consider using [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) + +## Building from Source + +If you need to build from source (for development or if pre-built wheels aren't available): + +### Prerequisites + +- [Rust](https://rustup.rs/) >= 1.70 +- Python >= 3.11 +- [uv](https://docs.astral.sh/uv/) (recommended) + +### Build Steps + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/diffid.git +cd diffid + +# Create Python environment +uv sync + +# Build Rust extension with Python bindings +uv run maturin develop + +# Run tests +uv run pytest -v +``` + +For additional troubleshooting, see the [Troubleshooting Guide](../guides/troubleshooting.md). + +## Next Steps + +Now that Diffid is installed, proceed to the [5-Minute Quickstart](quickstart.md) to run your first optimisation. diff --git a/docs/getting-started/logistic_fit.png b/docs/getting-started/logistic_fit.png new file mode 100644 index 0000000..bc18c80 Binary files /dev/null and b/docs/getting-started/logistic_fit.png differ diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md new file mode 100644 index 0000000..155330b --- /dev/null +++ b/docs/getting-started/quickstart.md @@ -0,0 +1,151 @@ +# 5-Minute Quickstart + +This quickstart guide demonstrates scalar optimisation using the classic Rosenbrock function. + +## The Problem + +The Rosenbrock function is defined as: + +$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$ + +The global minimum is at $(x, y) = (1, 1)$ with $f(1, 1) = 0$. + +## Basic Example + +```python +import numpy as np +import diffid + + +def rosenbrock(x): + """The Rosenbrock function - a classic optimisation test problem.""" + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + + +# Build the problem +builder = ( + diffid.ScalarBuilder() + .with_objective(rosenbrock) + .with_parameter("x", 1.5) # Initial guess + .with_parameter("y", -1.5) # Initial guess +) +problem = builder.build() + +# Run optimisation (uses Nelder-Mead by default) +result = problem.optimise() + +# Display results +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +``` + +**Output:** +``` +Optimal parameters: [1.0, 1.0] +Objective value: 0.000e+00 +Success: True +Iterations: 157 +``` + +## Understanding the Code + +1. **Define the objective function**: The function must accept a NumPy array and return a NumPy array + +2. **Create a builder**: `ScalarBuilder()` is used for scalar optimisation problems where you directly evaluate a function + +3. **Add parameters**: Use `with_parameter(name, initial_value)` to define decision variables + +4. **Build the problem**: Call `build()` to create an optimisable problem instance + +5. **Run optimisation**: Call `optimise()` to run the default optimiser (Nelder-Mead) + +## Using Different Optimisers + +You can specify which optimiser to use: + +### CMA-ES + +```python +# Use CMA-ES for global search +optimiser = diffid.CMAES().with_max_iter(1000).with_step_size(0.5) +result = optimiser.run(problem, [1.5, -1.5]) + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +``` + +### Adam (Gradient-Based) + +```python +# Use Adam optimiser +optimiser = diffid.Adam().with_max_iter(1000).with_step_size(0.01) +result = optimiser.run(problem, [1.5, -1.5]) + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +``` + +## Visualising the Optimisation + +If you installed the `plotting` extra, you can visualise the optimisation landscape: + +```python +import numpy as np +import matplotlib.pyplot as plt +import diffid + + +def rosenbrock(x): + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + + +# Create a grid for plotting +x = np.linspace(-2, 2, 200) +y = np.linspace(-1, 3, 200) +X, Y = np.meshgrid(x, y) +Z = np.zeros_like(X) + +for i in range(X.shape[0]): + for j in range(X.shape[1]): + Z[i, j] = rosenbrock([X[i, j], Y[i, j]])[0] + +# Plot contours +plt.figure(figsize=(10, 8)) +levels = np.logspace(-1, 3.5, 20) +plt.contour(X, Y, Z, levels=levels, cmap='viridis') +plt.colorbar(label='f(x, y)') + +# Mark the optimum +plt.plot(1.0, 1.0, 'r*', markersize=20, label='Global minimum') + +# Run optimisation and plot path +builder = ( + diffid.ScalarBuilder() + .with_objective(rosenbrock) + .with_parameter("x", -1.5) + .with_parameter("y", -0.5) +) +problem = builder.build() +result = problem.optimise() + +plt.plot(result.x[0], result.x[1], 'go', markersize=10, label='Found optimum') +plt.xlabel('x') +plt.ylabel('y') +plt.title('Rosenbrock Function Optimisation') +plt.legend() +plt.grid(True, alpha=0.3) +plt.show() +``` + +![Rosenbrock contour plot](rosenbrock_contour.png) + +## Next Steps + +- **[First ODE Fit](first-ode-fit.md)**: Learn how to fit differential equations to data +- **[Core Concepts](concepts.md)**: Understand the builder pattern and problem types +- **[Choosing an Optimiser](../guides/choosing-optimiser.md)**: Learn when to use each optimiser +- **[Tutorials](../tutorials/index.md)**: Explore interactive Jupyter notebooks diff --git a/docs/getting-started/rosenbrock_contour.png b/docs/getting-started/rosenbrock_contour.png new file mode 100644 index 0000000..85f218f Binary files /dev/null and b/docs/getting-started/rosenbrock_contour.png differ diff --git a/docs/guides/choosing-optimiser.md b/docs/guides/choosing-optimiser.md new file mode 100644 index 0000000..e664dfc --- /dev/null +++ b/docs/guides/choosing-optimiser.md @@ -0,0 +1,32 @@ +# Choosing an Optimiser + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed optimiser selection guidance. + +## Quick Reference + +| Problem Characteristics | Recommended Optimiser | +|-------------------------|----------------------| +| < 10 parameters, noisy | Nelder-Mead | +| 10-100+ parameters | CMA-ES | +| Smooth, gradients available | Adam | +| Need global search | CMA-ES | +| Fast local refinement | Nelder-Mead | + +## Decision Tree + +```mermaid +graph TD + A[Start] --> B{Gradients available?} + B -->|Yes| C[Adam] + B -->|No| D{Problem size?} + D -->|< 10 params| E[Nelder-Mead] + D -->|> 10 params| F[CMA-ES] + D -->|Need global| F +``` + +## See Also + +- [Optimisers API](../api-reference/python/optimisers.md) +- [Tuning Optimisers](tuning-optimisers.md) +- [Algorithm Details](../algorithms/index.md) diff --git a/docs/guides/choosing-sampler.md b/docs/guides/choosing-sampler.md new file mode 100644 index 0000000..c673a16 --- /dev/null +++ b/docs/guides/choosing-sampler.md @@ -0,0 +1,30 @@ +# Choosing a Sampler + +!!! info "Coming Soon" + This guide is being written. Sampler Python bindings are also in development. + +## Quick Reference + +| Use Case | Sampler | +|----------|---------| +| Uncertainty quantification | Metropolis-Hastings | +| Model comparison | Dynamic Nested Sampling | +| Just posterior samples | Metropolis-Hastings | +| Need evidence/Bayes factors | Dynamic Nested Sampling | + +## When to Sample vs Optimize + +Use **optimisers** when: +- Point estimate is sufficient +- Speed is critical +- Don't need uncertainty + +Use **samplers** when: +- Need confidence intervals +- Comparing models +- Want full posterior + +## See Also + +- [Samplers API](../api-reference/python/samplers.md) +- [Optimisers vs Samplers](../getting-started/concepts.md#optimisers-vs-samplers) diff --git a/docs/guides/cost-metrics.md b/docs/guides/cost-metrics.md new file mode 100644 index 0000000..69bc899 --- /dev/null +++ b/docs/guides/cost-metrics.md @@ -0,0 +1,23 @@ +# Cost Metrics Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed cost metric guidance. + +## Quick Reference + +| Metric | Use Case | +|--------|----------| +| SSE | Standard least squares | +| RMSE | Normalised error, model comparison | +| GaussianNLL | Bayesian inference, sampling | + +## Choosing a Metric + +- **SSE**: Default for most optimisation +- **RMSE**: When comparing models with different data sizes +- **GaussianNLL**: Required for samplers (MCMC, nested sampling) + +## See Also + +- [Cost Metrics API](../api-reference/python/cost-metrics.md) +- [Samplers](../api-reference/python/samplers.md) diff --git a/docs/guides/custom-solvers.md b/docs/guides/custom-solvers.md new file mode 100644 index 0000000..0f04ba2 --- /dev/null +++ b/docs/guides/custom-solvers.md @@ -0,0 +1,38 @@ +# Custom Solvers Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for Diffrax and DifferentialEquations.jl integration examples. + +## Overview + +Use `VectorBuilder` to integrate custom ODE solvers: +- JAX/Diffrax +- Julia/DifferentialEquations.jl +- Custom Python solvers + +## Basic Pattern + +```python +def custom_solver(params): + # Your solver here + # Return predictions at observation times + return predictions + +builder = ( + diffid.VectorBuilder() + .with_objective(custom_solver) + .with_data(data) + .with_parameter("alpha", 1.0) +) +``` + +## Examples + +See the predator-prey examples: +- [predator_prey_diffrax.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffrax.py) +- [predator_prey_diffeqpy.py](https://github.com/bradyplanden/diffid/blob/main/examples/predator_prey/predator_prey_diffeqpy.py) + +## See Also + +- [VectorBuilder API](../api-reference/python/builders.md#vectorbuilder) +- [Examples Gallery](../examples/gallery.md) diff --git a/docs/guides/diffsol-backend.md b/docs/guides/diffsol-backend.md new file mode 100644 index 0000000..05db37c --- /dev/null +++ b/docs/guides/diffsol-backend.md @@ -0,0 +1,27 @@ +# DiffSL Backend Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for detailed backend selection guidance. + +## Quick Reference + +| Backend | Best For | +|---------|----------| +| `"dense"` | < 100 state variables | +| `"sparse"` | > 100 state variables, sparse Jacobian | + +## Usage + +```python +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_backend("dense") # or "sparse" +) +``` + +## See Also + +- [DiffsolBuilder API](../api-reference/python/builders.md#diffsolbuilder) +- [First ODE Fit](../getting-started/first-ode-fit.md) diff --git a/docs/guides/index.md b/docs/guides/index.md new file mode 100644 index 0000000..bb6772b --- /dev/null +++ b/docs/guides/index.md @@ -0,0 +1,136 @@ +# User Guides + +In-depth guides for making the most of Diffid's optimisation and sampling capabilities. + +## Algorithm Selection + +
+ +- :material-tune:{ .lg .middle } __Choosing an Optimiser__ + + --- + + Decision trees and guidelines for selecting the right optimisation algorithm. + + [:octicons-arrow-right-24: Choosing an Optimiser](choosing-optimiser.md) + +- :material-speedometer:{ .lg .middle } __Tuning Optimisers__ + + --- + + Parameter tuning strategies and troubleshooting for each algorithm. + + [:octicons-arrow-right-24: Tuning Guide](tuning-optimisers.md) + +- :material-chart-bell-curve:{ .lg .middle } __Choosing a Sampler__ + + --- + + When to use MCMC vs nested sampling for uncertainty quantification. + + [:octicons-arrow-right-24: Choosing a Sampler](choosing-sampler.md) + +
+ +## Problem Configuration + +
+ +- :material-function-variant:{ .lg .middle } __Cost Metrics__ + + --- + + Understanding SSE, RMSE, and GaussianNLL for different use cases. + + [:octicons-arrow-right-24: Cost Metrics Guide](cost-metrics.md) + +- :material-script:{ .lg .middle } __DiffSL Backend__ + + --- + + Choosing between dense and sparse solvers for ODE systems. + + [:octicons-arrow-right-24: DiffSL Backend Guide](diffsol-backend.md) + +- :material-connection:{ .lg .middle } __Custom Solvers__ + + --- + + Integrating Diffrax, DifferentialEquations.jl, and other external solvers. + + [:octicons-arrow-right-24: Custom Solvers](custom-solvers.md) + +
+ +## Performance + +
+ +- :material-lightning-bolt:{ .lg .middle } __Parallel Execution__ + + --- + + Thread safety, parallelisation strategies, and performance optimisation. + + [:octicons-arrow-right-24: Parallel Execution](parallel-execution.md) + +
+ +## Troubleshooting + +
+ +- :material-help-circle:{ .lg .middle } __Troubleshooting__ + + --- + + Common errors, solutions, and debugging strategies. + + [:octicons-arrow-right-24: Troubleshooting Guide](troubleshooting.md) + +
+ +## Quick Reference + +### When to Use Each Component + +| Component | Use Case | +|-----------|----------| +| **ScalarBuilder** | Direct function optimisation | +| **DiffsolBuilder** | ODE fitting with DiffSL | +| **VectorBuilder** | Custom solvers (JAX, Julia) | +| **Nelder-Mead** | Local search, < 10 parameters | +| **CMA-ES** | Global search, 10-100+ parameters | +| **Adam** | Gradient-based, smooth objectives | +| **SSE** | Standard least squares | +| **RMSE** | Normalised error | +| **GaussianNLL** | Bayesian inference | + +### Typical Workflows + +**Simple Optimisation:** +``` +ScalarBuilder → optimise() → Result +``` + +**ODE Fitting:** +``` +DiffsolBuilder → CMAES → Result → Visualise +``` + +**Uncertainty Quantification:** +``` +DiffsolBuilder + GaussianNLL → Optimise → MCMC → Posterior +``` + +**Model Comparison:** +``` +Multiple models + GaussianNLL → Nested Sampling → Evidence → Bayes factors +``` + +## See Also + +- [Getting Started](../getting-started/index.md) - Core concepts and installation +- [Tutorials](../tutorials/index.md) - Interactive notebooks +- [API Reference](../api-reference/index.md) - Complete API documentation +- [Algorithms](../algorithms/index.md) - Algorithm-specific documentation diff --git a/docs/guides/parallel-execution.md b/docs/guides/parallel-execution.md new file mode 100644 index 0000000..0e12ee0 --- /dev/null +++ b/docs/guides/parallel-execution.md @@ -0,0 +1,25 @@ +# Parallel Execution Guide + +!!! info "Coming Soon" + This guide is being written. Check back soon for parallelisation strategies. + +## Quick Tips + +- **DiffsolBuilder**: Automatically multi-threaded +- **CMA-ES**: Parallel population evaluation +- **Dynamic Nested Sampling**: Parallel live point evaluation + +## Controlling Threads + +```python +# Limit threads for ODE solving +builder = builder.with_max_threads(4) + +# Population size for CMA-ES +optimiser = diffid.CMAES().with_population_size(20) +``` + +## See Also + +- [DiffsolBuilder API](../api-reference/python/builders.md#diffsolbuilder) +- [CMA-ES API](../api-reference/python/optimisers.md#cma-es) diff --git a/docs/guides/troubleshooting.md b/docs/guides/troubleshooting.md new file mode 100644 index 0000000..818c973 --- /dev/null +++ b/docs/guides/troubleshooting.md @@ -0,0 +1,32 @@ +# Troubleshooting Guide + +### Import Error + +```python +ImportError: No module named 'diffid' +``` + +**Solution**: Install Diffid: `pip install diffid` + +### Poor Fit Quality + +**Solutions:** + +1. Try different optimisers +2. Increase iterations, i.e. `.with_max_iter(10000)` +3. Try different initial conditions +4. Normalise data if scales vary widely + +### Slow Performance + +**Solutions:** + +1. Use CMA-ES for parallelisation +2. Reduce the number of data points +3. Use sparse backend for large ODE systems + +## See Also + +- [Installation](../getting-started/installation.md) +- [Tuning Optimisers](tuning-optimisers.md) +- [GitHub Issues](https://github.com/bradyplanden/diffid/issues) diff --git a/docs/guides/tuning-optimisers.md b/docs/guides/tuning-optimisers.md new file mode 100644 index 0000000..e59a331 --- /dev/null +++ b/docs/guides/tuning-optimisers.md @@ -0,0 +1,29 @@ +# Tuning Optimisers + +!!! info "Coming Soon" + This guide is being written. Check back soon for parameter tuning strategies. + +## Quick Tips + +### Nelder-Mead + +- **step_size**: Start with 10-50% of parameter range +- **threshold**: Use 1e-6 for standard precision +- **max_iter**: Use `100 * n_parameters` as minimum + +### CMA-ES + +- **step_size**: Start with ~1/3 of expected parameter range +- **population_size**: Default formula works well, increase for more exploration +- **max_iter**: Each iteration evaluates `population_size` candidates + +### Adam + +- **step_size**: Most critical - try 0.1, 0.01, 0.001, 0.0001 +- **betas**: Defaults (0.9, 0.999) usually work well + +## See Also + +- [Optimisers API](../api-reference/python/optimisers.md) +- [Choosing an Optimiser](choosing-optimiser.md) +- [Troubleshooting](troubleshooting.md) diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..33690df --- /dev/null +++ b/docs/index.md @@ -0,0 +1,145 @@ +# Diffid + +differential identification is a Rust-first toolkit for time-series inference and optimisation with ergonomic Python bindings. It couples high-performance solvers with a highly customisable builder API for identification and optimisation of differential systems. + +## Why Diffid? + +Diffid offers a different paradigm for a parameter inference library. Conventionally, Python-based inference libraries are constructed via python bindings to a high-performance forward model with the inference algorithms implemented in Python. Alongside this approach, Diffid introduces an alternative, where the Python layer acts purely as a declarative configuration interface, while all computationally intensive work (the optimisation / sampling loop, gradient calculations, etc.) happens entirely within the Rust runtime without crossing the FFI boundary repeatedly. This is architecture is presented visually below, + +
+ +
+ ![Paradigm Comparison](assets/diffid.svg){ width="100%" } +
Conventional vs configuration approach: the optimisation loop moves from Python to Rust
+
+ + +## Core Capabilities + +
+ +- __Optimisation Algorithms__ + + --- + + Gradient-free (Nelder-Mead, CMA-ES) and gradient-based (Adam) optimisers with configurable convergence criteria + +- __High-Performance ODE Fitting__ + + --- + + Multi-threaded differential equation fitting via [DiffSL](https://github.com/martinjrobins/diffsl) with dense or sparse [Diffsol](https://github.com/martinjrobins/diffsol) backends + +- __Uncertainty Quantification__ + + --- + + Customisable likelihood/cost metrics and Monte-Carlo sampling for posterior exploration + +- __Flexible Integration__ + + --- + + Integration with state-of-the-art differential solvers: [Diffrax](https://github.com/patrick-kidger/diffrax), [DifferentialEquations.jl](https://github.com/SciML/diffeqpy) + +
+ +## Installation + +Diffid targets Python >= 3.11. Windows builds are currently marked experimental. + +=== "pip" + + ```bash + pip install diffid + + # Optional extras for plotting + pip install "diffid[plotting]" + ``` + +=== "uv" + + ```bash + uv pip install diffid + + # Optional extras for plotting + uv pip install "diffid[plotting]" + ``` + +## Example: Scalar Optimisation + +```python +import numpy as np +import diffid + +def rosenbrock(x): + value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 + return np.asarray([value]) + +builder = ( + diffid.ScalarBuilder() + .with_objective(rosenbrock) + .with_parameter("x", 1.5) + .with_parameter("y", -1.5) +) +problem = builder.build() +result = problem.optimise() + +print(f"Optimal parameters: {result.x}") +print(f"Objective value: {result.value:.3e}") +print(f"Success: {result.success}") +``` + +## Example: ODE Fitting + +```python +import numpy as np +import diffid + +# Logistic growth model in DiffSL +dsl = """ +in_i {r = 1, k = 1 } +u_i { y = 0.1 } +F_i { (r * y) * (1 - (y / k)) } +""" + +t = np.linspace(0.0, 5.0, 51) +observations = np.exp(-1.3 * t) +data = np.column_stack((t, observations)) + +builder = ( + diffid.DiffsolBuilder() + .with_diffsl(dsl) + .with_data(data) + .with_parameter("k", 1.0) + .with_backend("dense") +) +problem = builder.build() + +optimiser = diffid.CMAES().with_max_iter(1000) +result = optimiser.run(problem, [0.5, 0.5]) + +print(result.x) +``` + +## Next Steps + +
+ +- [:material-school:{ .lg .middle } __Tutorials__](tutorials/index.md) + + Interactive Jupyter notebooks for hands-on learning + +- [:material-book-multiple:{ .lg .middle } __User Guides__](guides/index.md) + + In-depth guides on choosing and tuning algorithms + +- [:material-flask:{ .lg .middle } __Examples Gallery__](examples/gallery.md) + + Visual gallery of example applications + +- [:material-code-braces:{ .lg .middle } __Development__](development/index.md) + + Contributing, architecture, and building from source + +
diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 0000000..084e0b8 --- /dev/null +++ b/docs/javascripts/mathjax.js @@ -0,0 +1,12 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"], ["$", "$"]], + displayMath: [["\\[", "\\]"], ["$$", "$$"]], + processEscapes: true, + processEnvironments: true + } +}; + +document$.subscribe(() => { + MathJax.typesetPromise() +}) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md new file mode 100644 index 0000000..9358645 --- /dev/null +++ b/docs/tutorials/index.md @@ -0,0 +1,155 @@ +# Tutorials + +## Learning Paths + +Follow these progressive learning paths based on your experience level and goals. + +### Beginner Track + +Perfect for those new to Diffid or optimisation: + +
+ +- :material-tune:{ .lg .middle } __Optimisation Basics__ + + --- + + Learn scalar optimisation with the Rosenbrock function. Compare Nelder-Mead, CMA-ES, and Adam optimisers. + + [:octicons-arrow-right-24: Optimisation Basics](notebooks/optimisation_basics.ipynb) + +- :material-function-variant:{ .lg .middle } __ODE Fitting with DiffSL__ + + --- + + Fit a logistic growth model to data using DiffSL and Diffsol. + + [:octicons-arrow-right-24: ODE Fitting with DiffSL](notebooks/ode_fitting_diffsol.ipynb) + + +
+ +### Intermediate Track + +Building on the basics with real-world applications: + +
+ +- :material-chart-bell-curve:{ .lg .middle } __Parameter Uncertainty__ + + --- + + Go from optimisation to MCMC sampling. Quantify parameter uncertainty with confidence intervals. + + [:octicons-arrow-right-24: Parameter Uncertainty](notebooks/parameter_uncertainty.ipynb) + +- :material-scale-balance:{ .lg .middle } __Model Comparison__ + + --- + + Use Dynamic Nested Sampling to calculate model evidence and Bayes factors. + + [:octicons-arrow-right-24: Model Comparison](notebooks/model_comparison.ipynb) + +
+ +### Advanced Track + +Complex problems and advanced techniques: + +
+ +- :material-connection:{ .lg .middle } __Multi-Backend ODE Solving__ + + --- + + Compare Diffsol, JAX/Diffrax, and Julia/DifferentialEquations.jl for predator-prey models. + + [:octicons-arrow-right-24: Multi-Backend ODE Solving](notebooks/advanced_predator_prey.ipynb) + +
+ +## Quick Reference + +| Tutorial | Difficulty | Key Concepts | Prerequisites | +|--------------------------|----------|---------------------------|---------------| +| Optimisation Basics | Beginner | ScalarBuilder, Optimisers | None | +| ODE Fitting | Beginner | DiffsolBuilder, DiffSL | Optimisation Basics | +| Parameter Uncertainty | Intermediate | MCMC, uncertainty | Optimisation Basics, ODE Fitting | +| Model Comparison | Intermediate | Nested sampling, evidence | Optimisation Basics, ODE Fitting, Parameter Uncertainty | +| Multi-Backend | Advanced | VectorBuilder, JAX, Julia | Optimisation Basics, ODE Fitting | + + +## Running the Notebooks + +### Installation + +Install Diffid with Jupyter and plotting support: + +=== "pip" + + ```bash + pip install diffid jupyter matplotlib + ``` + +=== "uv" + + ```bash + uv pip install diffid jupyter matplotlib + ``` + +### Clone and Run + +```bash +# Clone the repository +git clone https://github.com/bradyplanden/diffid.git +cd diffid/docs/tutorials/notebooks + +# Launch Jupyter +jupyter notebook +``` + +## Alternative: Python Scripts + +Prefer scripts to notebooks? Check out the [examples directory](https://github.com/bradyplanden/diffid/tree/main/examples): + +- `python_problem.py` - Basic scalar optimisation +- `logistic_growth.py` - ODE fitting +- `bouncy_ball.py` / `bouncy_ball_sampling.py` - Optimisation and MCMC +- `bicycle_model_evidence.py` - Model comparison +- `predator_prey/` - Multi-backend comparisons + +## Contributing + +Found an issue or want to improve a tutorial? + +1. Fork the [repository](https://github.com/bradyplanden/diffid) +2. Edit notebooks in `docs/tutorials/notebooks/` +3. Test your changes locally +4. Submit a pull request + +See the [Contributing Guide](../development/contributing.md) for details. + +## Next Steps + +After completing the tutorials: + +
+ +- [:material-book-multiple:{ .lg .middle } __User Guides__](../guides/index.md) + + In-depth guides on choosing and tuning algorithms + +- [:material-code-tags:{ .lg .middle } __API Reference__](../api-reference/index.md) + + Complete API documentation + +- [:material-flask:{ .lg .middle } __Examples Gallery__](../examples/gallery.md) + + More applications and use cases + +- [:material-github:{ .lg .middle } __GitHub Repository__](https://github.com/bradyplanden/diffid) + + Source code and development + +
diff --git a/docs/tutorials/notebooks/advanced_cost_functions.ipynb b/docs/tutorials/notebooks/advanced_cost_functions.ipynb new file mode 100644 index 0000000..777201a --- /dev/null +++ b/docs/tutorials/notebooks/advanced_cost_functions.ipynb @@ -0,0 +1,1102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Advanced Cost Functions\n\n**Learning Objectives:**\n- Understand built-in cost metrics (SSE, RMSE, GaussianNLL)\n- Implement custom cost functions\n- Apply weighted fitting for heteroscedastic data\n- Use regularisation to prevent over-fitting\n- Combine multiple objectives\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic statistics\n\n**Runtime:** ~15 minutes" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The **cost function** (or loss function) quantifies how well model predictions match observations. Diffid provides built-in metrics, but real-world problems often require:\n", + "\n", + "- **Weighted fitting** when measurement errors vary\n", + "- **Custom metrics** for domain-specific requirements\n", + "- **Regularisation** to prevent over-fitting\n", + "- **Multi-objective** combinations\n", + "\n", + "This tutorial demonstrates advanced cost function techniques." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:04.282907Z", + "start_time": "2026-01-11T10:46:04.000312Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:11.244941Z", + "iopub.status.busy": "2026-01-10T22:23:11.244567Z", + "iopub.status.idle": "2026-01-10T22:23:11.951631Z", + "shell.execute_reply": "2026-01-10T22:23:11.951005Z" + } + }, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "from functools import partial\n", + "\n", + "import diffid\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "np.random.seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built-in Cost Metrics\n", + "\n", + "Diffid provides three standard metrics:\n", + "\n", + "### 1. Sum of Squared Errors (SSE)\n", + "\n", + "$$\n", + "\\text{SSE} = \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2\n", + "$$\n", + "\n", + "- Default metric\n", + "- Penalises large errors heavily\n", + "- Assumes constant variance\n", + "\n", + "### 2. Root Mean Squared Error (RMSE)\n", + "\n", + "$$\n", + "\\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum_{i=1}^{n}(y_i - \\hat{y}_i)^2}\n", + "$$\n", + "- Normalised by sample size\n", + "- Same units as data\n", + "- Better for comparing across datasets\n", + "\n", + "### 3. Gaussian Negative Log-Likelihood (GaussianNLL)\n", + "\n", + "$$\n", + "\\text{NLL} = \\frac{n}{2}\\log(2\\pi\\sigma^2) + \\frac{1}{2\\sigma^2}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2\n", + "$$\n", + "- Statistically principled\n", + "- Enables Bayesian inference\n", + "- Can estimate noise parameter $\\sigma$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:05.280410Z", + "start_time": "2026-01-11T10:46:04.327254Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:11.954892Z", + "iopub.status.busy": "2026-01-10T22:23:11.954600Z", + "iopub.status.idle": "2026-01-10T22:23:12.139229Z", + "shell.execute_reply": "2026-01-10T22:23:12.138722Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate simple test data\n", + "x_data = np.linspace(0, 10, 50)\n", + "y_true = 2.5 * x_data + 1.0\n", + "y_observed = y_true + np.random.normal(0, 2.0, len(x_data))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_data, y_observed, \"o\", label=\"Observed\", alpha=0.6, markersize=6)\n", + "plt.plot(x_data, y_true, \"--\", label=\"True\", linewidth=2)\n", + "plt.xlabel(\"x\", fontsize=12)\n", + "plt.ylabel(\"y\", fontsize=12)\n", + "plt.title(\"Linear Model Test Data\", fontsize=14, fontweight=\"bold\")\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:05.876850Z", + "start_time": "2026-01-11T10:46:05.688696Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.142024Z", + "iopub.status.busy": "2026-01-10T22:23:12.141801Z", + "iopub.status.idle": "2026-01-10T22:23:12.156991Z", + "shell.execute_reply": "2026-01-10T22:23:12.156108Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "COST METRIC COMPARISON\n", + "======================================================================\n", + "True parameters: [2.5, 1.0]\n", + "----------------------------------------------------------------------\n", + "Metric Slope Intercept Cost Value\n", + "----------------------------------------------------------------------\n", + "SSE 2.3840 1.1288 1.650e+02 \n", + "RMSE 2.3840 1.1288 1.668e+02 \n", + "GaussianNLL 2.3840 1.1288 2.953e+02 \n", + "\n", + "💡 Note: All metrics produce similar parameter estimates!\n", + " The cost values differ in scale, but optima are equivalent.\n" + ] + } + ], + "source": [ + "# Compare built-in metrics\n", + "def linear_model(params):\n", + " \"\"\"Simple linear model: y = slope * x + intercept\"\"\"\n", + " slope, intercept = params\n", + " return slope * x_data + intercept\n", + "\n", + "\n", + "# Define problem\n", + "builder = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(linear_model)\n", + " .with_data(y_observed)\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + ")\n", + "\n", + "# Test each metric\n", + "metrics = {\n", + " \"SSE\": diffid.SSE(),\n", + " \"RMSE\": diffid.RMSE(),\n", + " \"GaussianNLL\": diffid.GaussianNLL(),\n", + "}\n", + "\n", + "results = {}\n", + "for name, metric in metrics.items():\n", + " result = builder.with_cost(metric).build().optimise()\n", + " results[name] = result\n", + "\n", + "# Display comparison\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"COST METRIC COMPARISON\")\n", + "print(\"=\" * 70)\n", + "print(\"True parameters: [2.5, 1.0]\")\n", + "print(\"-\" * 70)\n", + "print(f\"{'Metric':<15} {'Slope':<12} {'Intercept':<12} {'Cost Value'}\")\n", + "print(\"-\" * 70)\n", + "\n", + "for name, result in results.items():\n", + " print(f\"{name:<15} {result.x[0]:<12.4f} {result.x[1]:<12.4f} {result.value:<12.3e}\")\n", + "\n", + "print(\"\\n💡 Note: All metrics produce similar parameter estimates!\")\n", + "print(\" The cost values differ in scale, but optima are equivalent.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weighted Fitting: Heteroscedastic Data\n", + "\n", + "Real data often has **non-uniform measurement errors** (heteroscedasticity). Points with larger errors should contribute less to the cost function.\n", + "\n", + "### Weighted Least Squares\n", + "$$\\text{WSSE} = \\sum_{i=1}^{n} w_i (y_i - \\hat{y}_i)^2$$\n", + "\n", + "where $w_i = 1/\\sigma_i^2$ (inverse variance weighting)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:06.607936Z", + "start_time": "2026-01-11T10:46:05.889635Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.159555Z", + "iopub.status.busy": "2026-01-10T22:23:12.159356Z", + "iopub.status.idle": "2026-01-10T22:23:12.308578Z", + "shell.execute_reply": "2026-01-10T22:23:12.307933Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error range: [0.50, 3.50]\n", + "Notice: Uncertainty increases from left to right!\n" + ] + } + ], + "source": [ + "# Generate heteroscedastic data (error increases with x)\n", + "x_hetero = np.linspace(0, 10, 50)\n", + "y_true_hetero = 2.0 * x_hetero + 3.0\n", + "\n", + "# Error increases linearly with x\n", + "error_std = 0.5 + 0.3 * x_hetero\n", + "y_hetero = y_true_hetero + np.random.normal(0, error_std)\n", + "\n", + "# Visualize heteroscedastic data\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Error bars showing measurement uncertainty\n", + "ax.errorbar(\n", + " x_hetero,\n", + " y_hetero,\n", + " yerr=error_std,\n", + " fmt=\"o\",\n", + " alpha=0.6,\n", + " label=\"Observed (with error bars)\",\n", + " capsize=3,\n", + " markersize=6,\n", + ")\n", + "ax.plot(x_hetero, y_true_hetero, \"r--\", linewidth=2, label=\"True\")\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=12)\n", + "ax.set_ylabel(\"y\", fontsize=12)\n", + "ax.set_title(\n", + " \"Heteroscedastic Data (Error Increases with x)\", fontsize=14, fontweight=\"bold\"\n", + ")\n", + "ax.legend(fontsize=11)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Error range: [{error_std.min():.2f}, {error_std.max():.2f}]\")\n", + "print(\"Notice: Uncertainty increases from left to right!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:06.775918Z", + "start_time": "2026-01-11T10:46:06.667958Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.311675Z", + "iopub.status.busy": "2026-01-10T22:23:12.311373Z", + "iopub.status.idle": "2026-01-10T22:23:12.323897Z", + "shell.execute_reply": "2026-01-10T22:23:12.323183Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "WEIGHTED vs UNWEIGHTED FITTING\n", + "======================================================================\n", + "True parameters: [2.0, 3.0]\n", + "Unweighted fit: [1.93530391 3.27981493]\n", + "Weighted fit: [1.97635486 3.10714606]\n", + "----------------------------------------------------------------------\n", + "Unweighted error: 0.2872\n", + "Weighted error: 0.1097\n", + "\n", + "💡 Weighted fit is more accurate!\n", + " It down-weights noisy (high-x) points appropriately.\n" + ] + } + ], + "source": [ + "# Custom weighted SSE cost function\n", + "class WeightedSSE:\n", + " \"\"\"Weighted sum of squared errors.\"\"\"\n", + "\n", + " def __init__(self, weights):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " weights : array_like\n", + " Weight for each data point (typically 1/sigma^2)\n", + " \"\"\"\n", + " self.weights = np.asarray(weights)\n", + "\n", + " def __call__(self, predicted, observed):\n", + " \"\"\"Compute weighted SSE.\"\"\"\n", + " residuals = observed - predicted\n", + " return np.sum(self.weights * residuals**2)\n", + "\n", + "\n", + "# Weights: inverse variance (1/sigma^2)\n", + "weights = 1.0 / error_std**2\n", + "\n", + "\n", + "def linear_model_hetero(params):\n", + " slope, intercept = params\n", + " return slope * x_hetero + intercept\n", + "\n", + "\n", + "# Construct custom cost\n", + "weighted_sse = WeightedSSE(weights)\n", + "\n", + "# Fit WITHOUT weights (standard SSE)\n", + "result_unweighted = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(linear_model_hetero)\n", + " .with_data(y_hetero)\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + " .with_cost(diffid.SSE()) # Standard SSE\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "# Fit with weights\n", + "result_weighted = (\n", + " diffid.ScalarBuilder()\n", + " .with_objective(lambda x: weighted_sse(linear_model_hetero(x), y_hetero))\n", + " .with_parameter(\"slope\", 1.0)\n", + " .with_parameter(\"intercept\", 0.0)\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"WEIGHTED vs UNWEIGHTED FITTING\")\n", + "print(\"=\" * 70)\n", + "print(\"True parameters: [2.0, 3.0]\")\n", + "print(f\"Unweighted fit: {result_unweighted.x}\")\n", + "print(f\"Weighted fit: {result_weighted.x}\")\n", + "print(\"-\" * 70)\n", + "print(f\"Unweighted error: {np.linalg.norm(result_unweighted.x - [2.0, 3.0]):.4f}\")\n", + "print(f\"Weighted error: {np.linalg.norm(result_weighted.x - [2.0, 3.0]):.4f}\")\n", + "print(\"\\n💡 Weighted fit is more accurate!\")\n", + "print(\" It down-weights noisy (high-x) points appropriately.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:07.700277Z", + "start_time": "2026-01-11T10:46:06.788131Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.326233Z", + "iopub.status.busy": "2026-01-10T22:23:12.326024Z", + "iopub.status.idle": "2026-01-10T22:23:12.499606Z", + "shell.execute_reply": "2026-01-10T22:23:12.499014Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize comparison\n", + "x_plot = np.linspace(0, 10, 200)\n", + "y_true_plot = 2.0 * x_plot + 3.0\n", + "y_unweighted = result_unweighted.x[0] * x_plot + result_unweighted.x[1]\n", + "y_weighted = result_weighted.x[0] * x_plot + result_weighted.x[1]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "\n", + "# Data with error bars\n", + "ax.errorbar(\n", + " x_hetero,\n", + " y_hetero,\n", + " yerr=error_std,\n", + " fmt=\"o\",\n", + " alpha=0.5,\n", + " label=\"Data (with uncertainties)\",\n", + " capsize=3,\n", + " markersize=6,\n", + " color=\"gray\",\n", + ")\n", + "\n", + "# Fits\n", + "ax.plot(x_plot, y_true_plot, \"k--\", linewidth=3, label=\"True\", alpha=0.8)\n", + "ax.plot(x_plot, y_unweighted, linewidth=2.5, label=\"Unweighted fit\", color=\"#ff7f0e\")\n", + "ax.plot(x_plot, y_weighted, linewidth=2.5, label=\"Weighted fit\", color=\"#2ca02c\")\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=13)\n", + "ax.set_ylabel(\"y\", fontsize=13)\n", + "ax.set_title(\"Weighted vs Unweighted Fitting\", fontsize=15, fontweight=\"bold\")\n", + "ax.legend(fontsize=12, loc=\"upper left\")\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Highlight the difference in high-error region\n", + "ax.axvspan(7, 10, alpha=0.1, color=\"red\", label=\"High uncertainty region\")\n", + "ax.text(\n", + " 8.5,\n", + " 5,\n", + " \"High uncertainty\\nregion\",\n", + " ha=\"center\",\n", + " fontsize=10,\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularisation: Preventing Over-fitting\n", + "\n", + "When fitting complex models with many parameters, **regularisation** prevents over-fitting by penalising large parameter values.\n", + "\n", + "### L2 Regularisation (Ridge)\n", + "$$\\text{Cost} = \\text{Data Term} + \\lambda \\sum_{i=1}^{p} \\theta_i^2$$\n", + "\n", + "### L1 Regularisation (Lasso) \n", + "$$\\text{Cost} = \\text{Data Term} + \\lambda \\sum_{i=1}^{p} |\\theta_i|$$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:08.500949Z", + "start_time": "2026-01-11T10:46:07.742793Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.502354Z", + "iopub.status.busy": "2026-01-10T22:23:12.502153Z", + "iopub.status.idle": "2026-01-10T22:23:12.657074Z", + "shell.execute_reply": "2026-01-10T22:23:12.656367Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate data for polynomial fitting\n", + "np.random.seed(123)\n", + "x_poly = np.linspace(-1, 1, 20)\n", + "y_true_poly = 2 * x_poly - 3 * x_poly**2 # True: quadratic\n", + "y_poly = y_true_poly + np.random.normal(0, 0.3, len(x_poly))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_poly, y_poly, \"o\", label=\"Observed\", alpha=0.7, markersize=8)\n", + "plt.plot(x_poly, y_true_poly, \"r--\", linewidth=2, label=\"True (quadratic)\")\n", + "plt.xlabel(\"x\", fontsize=12)\n", + "plt.ylabel(\"y\", fontsize=12)\n", + "plt.title(\"Polynomial Fitting Test Data\", fontsize=14, fontweight=\"bold\")\n", + "plt.legend(fontsize=11)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:09.033038Z", + "start_time": "2026-01-11T10:46:08.563801Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.659574Z", + "iopub.status.busy": "2026-01-10T22:23:12.659370Z", + "iopub.status.idle": "2026-01-10T22:23:12.739142Z", + "shell.execute_reply": "2026-01-10T22:23:12.738443Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "POLYNOMIAL FITTING (Degree 8)\n", + "======================================================================\n", + "Unregularised coefficients:\n", + " [ 1.76916563e-03 1.96793262e+00 -4.26606389e+00 1.70512990e+00\n", + " 3.98781374e+00 -2.59931773e+00 -3.98125430e-01 1.08590028e+00\n", + " -2.43719499e+00]\n", + " Max |coef|: 4.27\n", + " SSE: 1.8069\n" + ] + } + ], + "source": [ + "# Fit high-degree polynomial (degree 8) - prone to over-fitting\n", + "degree = 8\n", + "\n", + "\n", + "def polynomial_model(params):\n", + " \"\"\"Polynomial model: sum of coefficients * x^i\"\"\"\n", + " y_pred = np.zeros_like(x_poly)\n", + " for i, coef in enumerate(params):\n", + " y_pred += coef * x_poly**i\n", + " return y_pred\n", + "\n", + "\n", + "# Custom cost with L2 regularisation\n", + "class RegularisedSSE:\n", + " \"\"\"SSE with L2 regularisation (Ridge).\"\"\"\n", + "\n", + " def __init__(self, lambda_reg=0.0):\n", + " self.lambda_reg = lambda_reg\n", + "\n", + " def __call__(self, predicted, observed, params=None):\n", + " # Data term\n", + " sse = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Regularisation term (note: params must be passed separately)\n", + " if params is not None and self.lambda_reg > 0:\n", + " reg_term = self.lambda_reg * np.sum(params**2)\n", + " return sse + reg_term\n", + "\n", + " return sse\n", + "\n", + "\n", + "# Note: Diffid's built-in costs don't support parameter access yet,\n", + "# so we'll compare by manually adding regularisation to parameter update\n", + "\n", + "# Unregularised fit\n", + "initial_params = np.zeros(degree + 1)\n", + "initial_params[0] = np.mean(y_poly)\n", + "\n", + "builder_poly = diffid.VectorBuilder().with_objective(polynomial_model).with_data(y_poly)\n", + "\n", + "for i in range(degree + 1):\n", + " builder_poly = builder_poly.with_parameter(f\"c{i}\", initial_params[i])\n", + "\n", + "result_unreg = (\n", + " builder_poly.with_cost(diffid.SSE())\n", + " .with_optimiser(diffid.NelderMead().with_max_iter(2000))\n", + " .build()\n", + " .optimise()\n", + ")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"POLYNOMIAL FITTING (Degree 8)\")\n", + "print(\"=\" * 70)\n", + "print(\"Unregularised coefficients:\")\n", + "print(f\" {result_unreg.x}\")\n", + "print(f\" Max |coef|: {np.max(np.abs(result_unreg.x)):.2f}\")\n", + "print(f\" SSE: {result_unreg.value:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualise Overfitting\n", + "\n", + "To visualise model overfitting, an extrapolation region is plotted.\n", + "This provides insight into whether the model accurately captures the system dynamics and not the underlying noise model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:11.447302Z", + "start_time": "2026-01-11T10:46:09.035951Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:12.741769Z", + "iopub.status.busy": "2026-01-10T22:23:12.741459Z", + "iopub.status.idle": "2026-01-10T22:23:13.202027Z", + "shell.execute_reply": "2026-01-10T22:23:13.198370Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "⚠️ Over-fitting Alert!\n", + " The high-degree polynomial fits training data well but\n", + " extrapolates poorly. Regularisation would help!\n" + ] + } + ], + "source": [ + "# Visualize fits\n", + "x_dense = np.linspace(-1, 1, 200)\n", + "\n", + "\n", + "def eval_poly(x, coeffs):\n", + " y = np.zeros_like(x)\n", + " for i, c in enumerate(coeffs):\n", + " y += c * x**i\n", + " return y\n", + "\n", + "\n", + "y_true_dense = 2 * x_dense - 3 * x_dense**2\n", + "y_unreg_dense = eval_poly(x_dense, result_unreg.x)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", + "\n", + "# Left: Training data fit\n", + "ax1.plot(\n", + " x_poly, y_poly, \"o\", label=\"Training data\", alpha=0.7, markersize=8, color=\"gray\"\n", + ")\n", + "ax1.plot(\n", + " x_dense, y_true_dense, \"k--\", linewidth=2.5, label=\"True (quadratic)\", alpha=0.8\n", + ")\n", + "ax1.plot(x_dense, y_unreg_dense, linewidth=2.5, label=\"Degree-8 fit\", color=\"#d62728\")\n", + "ax1.set_xlabel(\"x\", fontsize=12)\n", + "ax1.set_ylabel(\"y\", fontsize=12)\n", + "ax1.set_title(\"Training Data: Over-fitting\", fontsize=14, fontweight=\"bold\")\n", + "ax1.legend(fontsize=11)\n", + "ax1.grid(True, alpha=0.3)\n", + "ax1.set_ylim(-4, 3)\n", + "\n", + "# Right: Extrapolation (shows over-fitting clearly)\n", + "x_extrap = np.linspace(-1.5, 1.5, 200)\n", + "y_true_extrap = 2 * x_extrap - 3 * x_extrap**2\n", + "y_unreg_extrap = eval_poly(x_extrap, result_unreg.x)\n", + "\n", + "ax2.plot(\n", + " x_poly, y_poly, \"o\", label=\"Training data\", alpha=0.7, markersize=8, color=\"gray\"\n", + ")\n", + "ax2.plot(x_extrap, y_true_extrap, \"k--\", linewidth=2.5, label=\"True\", alpha=0.8)\n", + "ax2.plot(x_extrap, y_unreg_extrap, linewidth=2.5, label=\"Degree-8 fit\", color=\"#d62728\")\n", + "ax2.axvline(x=-1, color=\"red\", linestyle=\":\", alpha=0.5)\n", + "ax2.axvline(x=1, color=\"red\", linestyle=\":\", alpha=0.5)\n", + "ax2.set_xlabel(\"x\", fontsize=12)\n", + "ax2.set_ylabel(\"y\", fontsize=12)\n", + "ax2.set_title(\"Extrapolation: Catastrophic Failure\", fontsize=14, fontweight=\"bold\")\n", + "ax2.legend(fontsize=11)\n", + "ax2.grid(True, alpha=0.3)\n", + "ax2.set_ylim(-10, 5)\n", + "ax2.text(-1.25, -8, \"Extrapolation\\nregion\", fontsize=10, ha=\"center\")\n", + "ax2.text(1.25, -8, \"Extrapolation\\nregion\", fontsize=10, ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n⚠️ Over-fitting Alert!\")\n", + "print(\" The high-degree polynomial fits training data well but\")\n", + "print(\" extrapolates poorly. Regularisation would help!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-Objective Cost Functions\n", + "\n", + "Sometimes we want to balance **multiple objectives** simultaneously:\n", + "\n", + "$$\\text{Cost} = w_1 \\cdot \\text{Fit Quality} + w_2 \\cdot \\text{Smoothness} + w_3 \\cdot \\text{Physical Constraints}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:14.310435Z", + "start_time": "2026-01-11T10:46:11.575479Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:13.206075Z", + "iopub.status.busy": "2026-01-10T22:23:13.205389Z", + "iopub.status.idle": "2026-01-10T22:23:13.701531Z", + "shell.execute_reply": "2026-01-10T22:23:13.700970Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smoothness weight= 0.0: SSE=1.807\n", + "Smoothness weight= 0.1: SSE=1.816\n", + "Smoothness weight= 1.0: SSE=1.976\n", + "Smoothness weight= 10.0: SSE=2.267\n" + ] + } + ], + "source": [ + "# Example: Fit with smoothness penalty\n", + "class MultiObjectiveCost:\n", + " \"\"\"Combined data fit + smoothness penalty.\"\"\"\n", + "\n", + " def __init__(self, weight_fit=1.0, weight_smooth=0.1):\n", + " self.weight_fit = weight_fit\n", + " self.weight_smooth = weight_smooth\n", + "\n", + " def __call__(self, predicted, observed):\n", + " # Data fit term (SSE)\n", + " fit_cost = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Smoothness term (penalise large second derivatives)\n", + " second_deriv = np.diff(predicted, n=2)\n", + " smooth_cost = np.sum(second_deriv**2)\n", + "\n", + " return self.weight_fit * fit_cost + self.weight_smooth * smooth_cost\n", + "\n", + "\n", + "# Fit with different smoothness weights\n", + "weights_smooth = [0.0, 0.1, 1.0, 10.0]\n", + "results_multi = {}\n", + "\n", + "\n", + "# Cost wrapper\n", + "def wrapper(x, y_poly, cost_func):\n", + " return cost_func(polynomial_model(x), y_poly)\n", + "\n", + "\n", + "for w_smooth in weights_smooth:\n", + " # Construct custom cost\n", + " multi_cost = MultiObjectiveCost(weight_fit=1.0, weight_smooth=w_smooth)\n", + " result = diffid.ScalarBuilder().with_objective(\n", + " partial(wrapper, y_poly=y_poly, cost_func=multi_cost)\n", + " )\n", + "\n", + " for i in range(degree + 1):\n", + " result = result.with_parameter(f\"c{i}\", initial_params[i])\n", + "\n", + " result = (\n", + " result.with_optimiser(diffid.NelderMead().with_max_iter(2000))\n", + " .build()\n", + " .optimise()\n", + " )\n", + "\n", + " results_multi[w_smooth] = result\n", + " print(\n", + " f\"Smoothness weight={w_smooth:5.1f}: SSE={np.sum((eval_poly(x_poly, result.x) - y_poly) ** 2):.3f}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:17.318731Z", + "start_time": "2026-01-11T10:46:14.462850Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:13.703941Z", + "iopub.status.busy": "2026-01-10T22:23:13.703698Z", + "iopub.status.idle": "2026-01-10T22:23:14.285027Z", + "shell.execute_reply": "2026-01-10T22:23:14.284354Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 Smoothness Penalty Effect:\n", + " λ=0.0: No penalty → over-fitting\n", + " λ=0.1: Slight smoothing\n", + " λ=1.0: Balanced fit\n", + " λ=10.0: Too smooth → under-fitting\n" + ] + } + ], + "source": [ + "# Visualize effect of smoothness weight\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "axes = axes.flatten()\n", + "\n", + "for idx, (w_smooth, result) in enumerate(results_multi.items()):\n", + " ax = axes[idx]\n", + "\n", + " y_fit = eval_poly(x_dense, result.x)\n", + "\n", + " ax.plot(x_poly, y_poly, \"o\", label=\"Data\", alpha=0.6, markersize=7, color=\"gray\")\n", + " ax.plot(x_dense, y_true_dense, \"k--\", linewidth=2, label=\"True\", alpha=0.7)\n", + " ax.plot(x_dense, y_fit, linewidth=2.5, label=f\"Fit (λ={w_smooth})\", color=\"#2ca02c\")\n", + "\n", + " ax.set_xlabel(\"x\", fontsize=11)\n", + " ax.set_ylabel(\"y\", fontsize=11)\n", + " ax.set_title(f\"Smoothness Weight λ = {w_smooth}\", fontsize=13, fontweight=\"bold\")\n", + " ax.legend(fontsize=10)\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_ylim(-4, 3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n💡 Smoothness Penalty Effect:\")\n", + "print(\" λ=0.0: No penalty → over-fitting\")\n", + "print(\" λ=0.1: Slight smoothing\")\n", + "print(\" λ=1.0: Balanced fit\")\n", + "print(\" λ=10.0: Too smooth → under-fitting\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Physical Constraints as Cost Penalties\n", + "\n", + "In scientific applications, we often have **physical constraints**:\n", + "- Parameters must be positive (e.g., rate constants)\n", + "- Conservation laws (e.g., mass balance)\n", + "- Monotonicity requirements\n", + "\n", + "These can be enforced via penalty terms:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-11T10:46:17.544721Z", + "start_time": "2026-01-11T10:46:17.432337Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:14.288666Z", + "iopub.status.busy": "2026-01-10T22:23:14.288443Z", + "iopub.status.idle": "2026-01-10T22:23:14.293720Z", + "shell.execute_reply": "2026-01-10T22:23:14.293151Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example constraint penalties:\n", + " • Sum constraint: forces Σθᵢ ≈ target\n", + " • Positivity: penalises θᵢ < 0\n", + " • Bounds: penalises θᵢ outside [a, b]\n", + " • Monotonicity: penalises θᵢ₊₁ < θᵢ\n", + "\n", + "💡 Tip: Start with large penalty weights, then tune.\n" + ] + } + ], + "source": [ + "class ConstrainedCost:\n", + " \"\"\"Cost with soft constraints via penalties.\"\"\"\n", + "\n", + " def __init__(self, penalty_weight=1000.0):\n", + " self.penalty_weight = penalty_weight\n", + "\n", + " def __call__(self, predicted, observed, params=None):\n", + " # Data fit term\n", + " sse = np.sum((observed - predicted) ** 2)\n", + "\n", + " # Example constraint: parameters should sum to a target value\n", + " if params is not None:\n", + " # Soft constraint: sum(params) ≈ 0\n", + " constraint_violation = (np.sum(params) - 0.0) ** 2\n", + "\n", + " # Positivity constraint: penalise negative parameters\n", + " negative_penalty = np.sum(np.minimum(0, params) ** 2)\n", + "\n", + " penalty = self.penalty_weight * (constraint_violation + negative_penalty)\n", + " return sse + penalty\n", + "\n", + " return sse\n", + "\n", + "\n", + "print(\"Example constraint penalties:\")\n", + "print(\" • Sum constraint: forces Σθᵢ ≈ target\")\n", + "print(\" • Positivity: penalises θᵢ < 0\")\n", + "print(\" • Bounds: penalises θᵢ outside [a, b]\")\n", + "print(\" • Monotonicity: penalises θᵢ₊₁ < θᵢ\")\n", + "print(\"\\n💡 Tip: Start with large penalty weights, then tune.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "### Choosing a Cost Function\n", + "\n", + "1. **Default (SSE)**: Good starting point for most problems\n", + "2. **RMSE**: When comparing across datasets with different sizes\n", + "3. **GaussianNLL**: For Bayesian inference and uncertainty quantification\n", + "4. **Weighted**: When measurement errors vary (heteroscedastic data)\n", + "5. **Custom**: For domain-specific requirements\n", + "\n", + "### Regularisation Guidelines\n", + "\n", + "- **When to use**: High-dimensional problems, limited data, polynomial fitting\n", + "- **L2 (Ridge)**: Shrinks all coefficients smoothly\n", + "- **L1 (Lasso)**: Promotes sparsity (some coefficients → 0)\n", + "- **Tuning λ**: Cross-validation or information criteria (AIC, BIC)\n", + "\n", + "### Multi-Objective Balancing\n", + "\n", + "- **Start simple**: Fit data first, add penalties incrementally\n", + "- **Scale matters**: Normalise objectives to similar magnitudes\n", + "- **Weight tuning**: Use logarithmic search (0.001, 0.01, 0.1, 1, 10, ...)\n", + "- **Validation**: Check if constraints are actually satisfied" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary: Cost Function Design Pattern\n", + "\n", + "```python\n", + "class CustomCost:\n", + " def __init__(self, **hyperparameters):\n", + " # Store configuration\n", + " pass\n", + " \n", + " def __call__(self, predicted, observed, params=None):\n", + " # 1. Data fidelity term\n", + " fit_cost = compute_fit(predicted, observed)\n", + " \n", + " # 2. Regularisation term (optional)\n", + " reg_cost = compute_regularisation(params)\n", + " \n", + " # 3. Physical constraints (optional)\n", + " constraint_cost = compute_penalties(params)\n", + " \n", + " # 4. Combine with weights\n", + " return w1*fit_cost + w2*reg_cost + w3*constraint_cost\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Key Takeaways\n\n1. **Built-in metrics** (SSE, RMSE, GaussianNLL) cover most use cases\n2. **Weighted fitting** essential for heteroscedastic data (varying errors)\n3. **Regularisation** prevents over-fitting in high-dimensional problems\n4. **Multi-objective costs** balance competing goals (fit vs smoothness)\n5. **Constraint penalties** enforce physical requirements\n6. **Custom costs** are easy to implement via `__call__` interface\n7. **Scale and normalisation** critical for multi-term objectives\n\n## Next Steps\n\n- [Guide: Cost Metrics](../../guides/cost-metrics.md) - Detailed metric selection\n- [Parameter Uncertainty](parameter_uncertainty.ipynb) - Bayesian inference with GaussianNLL\n- [API Reference: Cost Metrics](../../api-reference/python/cost-metrics.md) - Complete API" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Implement L1 Regularisation**: Create a `LassoSSE` class and compare with L2\n", + "\n", + "2. **Cross-Validation**: Implement k-fold cross-validation to tune regularisation strength\n", + "\n", + "3. **Robust Fitting**: Implement Huber loss (robust to outliers):\n", + " $$L_\\delta(r) = \\begin{cases} \\frac{1}{2}r^2 & \\text{for } |r| \\leq \\delta \\\\ \\delta(|r| - \\frac{1}{2}\\delta) & \\text{otherwise} \\end{cases}$$\n", + "\n", + "4. **Time-Series Smoothness**: Add a penalty on parameter changes over time for dynamic fitting\n", + "\n", + "5. **Information Criteria**: Compute AIC and BIC for model selection:\n", + " - AIC = $2k - 2\\ln(\\mathcal{L})$\n", + " - BIC = $\\ln(n)k - 2\\ln(\\mathcal{L})$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/advanced_predator_prey.ipynb b/docs/tutorials/notebooks/advanced_predator_prey.ipynb new file mode 100644 index 0000000..dc38320 --- /dev/null +++ b/docs/tutorials/notebooks/advanced_predator_prey.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Multi-Backend ODE Solving\n\n**Learning Objectives:**\n- Use VectorBuilder for custom ODE solvers\n- Compare Diffsol, Diffrax (JAX), and DifferentialEquations.jl\n- Understand performance trade-offs\n- Integrate external solvers with Diffid\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic JAX or Julia knowledge (optional)\n\n**Runtime:** ~20 minutes" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Diffid's **DiffsolBuilder** provides high-performance ODE solving for most cases. But sometimes you need:\n", + "\n", + "- **JAX/Diffrax**: Automatic differentiation, GPU acceleration\n", + "- **Julia/DifferentialEquations.jl**: Specialized solvers, stiff equations\n", + "- **Custom simulators**: Agent-based models, PDEs, hybrid systems\n", + "\n", + "**VectorBuilder** lets you integrate any Python-callable forward model with Diffid's optimisers.\n", + "\n", + "## The Lotka-Volterra Model\n", + "\n", + "The predator-prey equations:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= \\alpha x - \\beta x y \\\\\n", + "\\frac{dy}{dt} &= \\delta x y - \\gamma y\n", + "\\end{aligned}$$\n", + "\n", + "where:\n", + "- $x$ is prey population\n", + "- $y$ is predator population\n", + "- $\\alpha, \\beta, \\gamma, \\delta$ are interaction rates\n", + "\n", + "This tutorial demonstrates parameter fitting with three different solver backends.\n", + "\n", + "## Coming Soon\n", + "\n", + "This advanced tutorial is under development. It will cover:\n", + "\n", + "### Backend 1: Diffsol (Built-in)\n", + "```python\n", + "builder = (\n", + " diffid.DiffsolBuilder()\n", + " .with_diffsl(lotka_volterra_dsl)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + " # ... more parameters\n", + ")\n", + "```\n", + "\n", + "### Backend 2: JAX/Diffrax\n", + "```python\n", + "import jax\n", + "from diffrax import diffeqsolve, ODETerm, Tsit5\n", + "\n", + "def diffrax_solver(params):\n", + " # Your Diffrax integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(diffrax_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "### Backend 3: Julia/DifferentialEquations.jl\n", + "```python\n", + "from diffeqpy import de\n", + "\n", + "def diffeqpy_solver(params):\n", + " # Your Julia integration\n", + " return predictions\n", + "\n", + "builder = (\n", + " diffid.VectorBuilder()\n", + " .with_objective(diffeqpy_solver)\n", + " .with_data(data)\n", + " .with_parameter(\"alpha\", 1.0)\n", + ")\n", + "```\n", + "\n", + "## Performance Comparison\n", + "\n", + "The tutorial will benchmark all three backends:\n", + "\n", + "- **Accuracy**: Parameter recovery quality\n", + "- **Speed**: Time per function evaluation\n", + "- **Ease of use**: Setup complexity\n", + "- **Special features**: Gradients, GPU, stiff solvers\n", + "\n", + "## When to Use Each Backend\n", + "\n", + "| Backend | Best For |\n", + "|---------|----------|\n", + "| **Diffsol** | General purpose, fast, built-in |\n", + "| **JAX/Diffrax** | Gradients, GPU, neural ODEs |\n", + "| **Julia/DiffEq** | Stiff systems, specialized solvers, DAEs |\n", + "| **Custom** | Non-ODE models, complex physics |\n", + "\n", + "## Example Data\n", + "\n", + "The predator-prey examples directory contains:\n", + "- `generate_data_diffrax.py`: Creates synthetic data\n", + "- `predator_prey_diffsol.py`: Diffsol backend\n", + "- `predator_prey_diffrax.py`: JAX/Diffrax backend\n", + "- `predator_prey_diffeqpy.py`: Julia backend\n", + "\n", + "Run these scripts directly to see the backends in action!\n", + "\n", + "## Installation\n", + "\n", + "For JAX/Diffrax:\n", + "```bash\n", + "pip install jax diffrax\n", + "```\n", + "\n", + "For Julia/DifferentialEquations.jl:\n", + "```bash\n", + "pip install diffeqpy\n", + "python -c \"from diffeqpy import de; de.install()\"\n", + "```\n", + "\n", + "## Key Takeaways\n", + "\n", + "1. **VectorBuilder** integrates any Python callable\n", + "2. **Diffsol** is the default - fast and easy\n", + "3. **JAX/Diffrax** for gradients and GPU\n", + "4. **Julia/DiffEq** for specialized solvers\n", + "5. All backends work with Diffid's optimisers\n", + "\n", + "## Next Steps\n", + "\n", + "- [Custom Solvers Guide](../../guides/custom-solvers.md) - Detailed integration guide\n", + "- [VectorBuilder API](../../api-reference/python/builders.md#vectorbuilder)\n", + "- [Examples Gallery](../../examples/gallery.md) - More backend examples" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/model_comparison.ipynb b/docs/tutorials/notebooks/model_comparison.ipynb new file mode 100644 index 0000000..36bc5d4 --- /dev/null +++ b/docs/tutorials/notebooks/model_comparison.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Model Comparison\n\n**Learning Objectives:**\n- Use Dynamic Nested Sampling for model evidence\n- Calculate Bayes factors for model comparison\n- Interpret evidence values\n- Compare bicycle dynamics models\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, Parameter Uncertainty, Bayesian model comparison basics\n\n**Runtime:** ~20 minutes\n\n**Note:** This tutorial requires the Dynamic Nested Sampling feature. Coming soon!" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Introduction\n\nWhen you have multiple models, **which one is best?**\n\n**Dynamic Nested Sampling** calculates the **model evidence** (marginal likelihood), allowing rigorous Bayesian model comparison via **Bayes factors**.\n\n$$\\text{Bayes Factor} = \\frac{p(\\text{Data}|\\text{Model}_1)}{p(\\text{Data}|\\text{Model}_2)} = \\frac{Z_1}{Z_2}$$\n\nThis tutorial will demonstrate model comparison using bicycle dynamics models.\n\n## The Problem: Bicycle Model\n\nA bicycle traveling at constant velocity $v$ with steer angle $\\delta$ follows curved motion determined by wheelbase $L$:\n\n$$\\begin{aligned}\n\\frac{dx}{dt} &= v \\cos(\\theta) \\\\\n\\frac{dy}{dt} &= v \\sin(\\theta) \\\\\n\\frac{d\\theta}{dt} &= \\frac{v}{L} \\tan(\\delta)\n\\end{aligned}$$\n\n**Task:** Estimate wheelbase $L$ and compare different model formulations.\n\n## Coming Soon\n\nThis tutorial is under development. Check back soon for:\n\n- Setting up multiple model variants\n- Running Dynamic Nested Sampling\n- Calculating log evidence for each model\n- Interpreting Bayes factors\n- Making model selection decisions\n\n## Preview: Model Evidence Interpretation\n\n| $\\log(Z_1 / Z_2)$ | Bayes Factor | Evidence for Model 1 |\n|-------------------|--------------|----------------------|\n| < 0 | < 1 | Negative (prefer Model 2) |\n| 0-1 | 1-3 | Barely worth mentioning |\n| 1-2.5 | 3-12 | Positive |\n| 2.5-5 | 12-150 | Strong |\n| > 5 | > 150 | Very strong |\n\n## References\n\n- Skilling, J. (2006). Nested sampling for general Bayesian computation.\n- Higson, E. et al. (2019). Dynamic nested sampling.\n\n## Next Steps\n\n- [Predator-Prey Models](advanced_predator_prey.ipynb) - Multi-backend comparison\n- [Choosing a Sampler](../../guides/choosing-sampler.md)\n- [API Reference: Samplers](../../api-reference/python/samplers.md)" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb new file mode 100644 index 0000000..47d9ae7 --- /dev/null +++ b/docs/tutorials/notebooks/ode_fitting_diffsol.ipynb @@ -0,0 +1,516 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# ODE Fitting with DiffSL\n\n**Objectives:**\n- Understand the DiffsolBuilder API\n- Learn DiffSL syntax for defining ODEs\n- Fit parameters of a logistic growth model\n- Visualise model fits and residuals" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The logistic growth model describes population dynamics with limited resources:\n", + "\n", + "$$\\frac{dy}{dt} = r \\cdot y \\cdot \\left(1 - \\frac{y}{k}\\right)$$\n", + "\n", + "where:\n", + "- $y$ is the population\n", + "- $r$ is the growth rate\n", + "- $k$ is the carrying capacity (maximum population)\n", + "\n", + "This tutorial demonstrates parameter fitting using experimental or simulated data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:29.582370Z", + "iopub.status.busy": "2026-01-10T22:21:29.582083Z", + "iopub.status.idle": "2026-01-10T22:21:30.043052Z", + "shell.execute_reply": "2026-01-10T22:21:30.042273Z" + } + }, + "outputs": [], + "source": "# Import plotting utilities\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom diffid.plotting import ode_fit" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the ODE in DiffSL\n", + "\n", + "DiffSL (Differential Specification Language) is a concise syntax for ODEs:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.045734Z", + "iopub.status.busy": "2026-01-10T22:21:30.045475Z", + "iopub.status.idle": "2026-01-10T22:21:30.050602Z", + "shell.execute_reply": "2026-01-10T22:21:30.049845Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DiffSL Model Definition:\n", + "==================================================\n", + "\n", + "in_i {r = 1, k = 1 }\n", + "u_i { y = 0.1 }\n", + "F_i { (r * y) * (1 - (y / k)) }\n", + "\n", + "\n", + "Explanation:\n", + " in_i {r = 1, k = 1 } → Parameters to fit\n", + " u_i { y = 0.1 } → Initial condition: y(0) = 0.1\n", + " F_i { ... } → Right-hand side: dy/dt = ...\n" + ] + } + ], + "source": [ + "dsl_model = \"\"\"\n", + "in_i {r = 1, k = 1 }\n", + "u_i { y = 0.1 }\n", + "F_i { (r * y) * (1 - (y / k)) }\n", + "\"\"\"\n", + "\n", + "print(\"DiffSL Model Definition:\")\n", + "print(\"=\" * 50)\n", + "print(dsl_model)\n", + "print(\"\\nExplanation:\")\n", + "print(\" in_i {r = 1, k = 1 } → Parameters to fit\")\n", + "print(\" u_i { y = 0.1 } → Initial condition: y(0) = 0.1\")\n", + "print(\" F_i { ... } → Right-hand side: dy/dt = ...\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Synthetic Data\n", + "\n", + "For this tutorial, we'll create synthetic data from the logistic function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.052995Z", + "iopub.status.busy": "2026-01-10T22:21:30.052808Z", + "iopub.status.idle": "2026-01-10T22:21:30.079769Z", + "shell.execute_reply": "2026-01-10T22:21:30.079161Z" + } + }, + "outputs": [], + "source": "# Time points\nt_span = np.linspace(0, 4, 100)\n\n# True logistic growth solution with known parameters\n# y(t) = y0 * exp(r*t) / (1 + y0 * (exp(r*t) - 1) / k)\ny0 = 0.1\nr_true = 1.0\nk_true = 1.0\n\n# Generate clean data\nexp_rt = np.exp(r_true * t_span)\ny_data = y0 * exp_rt / (1 + y0 * (exp_rt - 1) / k_true)\n\n# Add some noise\nnp.random.seed(42)\nnoise_level = 0.01\ny_noisy = y_data + np.random.normal(0, noise_level, size=y_data.shape)\n\n# Diffid expects data as [time, observation] columns\ndata = np.column_stack((t_span, y_noisy))\n\nprint(f\"Generated {len(t_span)} data points\")\nprint(f\"Time range: [{t_span[0]:.2f}, {t_span[-1]:.2f}]\")\nprint(f\"Value range: [{y_noisy.min():.3f}, {y_noisy.max():.3f}]\")\nprint(f\"Noise level: {noise_level}\")\nprint(f\"\\nTrue parameters: r = {r_true}, k = {k_true}\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.082238Z", + "iopub.status.busy": "2026-01-10T22:21:30.081984Z", + "iopub.status.idle": "2026-01-10T22:21:30.230233Z", + "shell.execute_reply": "2026-01-10T22:21:30.229694Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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uXbqo0dHRIk25Q4cO6p133qkWFBTUuM8zzzwj0kmpQFt1ggvwVYeWU5qvH0oxps9JSUlRNRpNjUX8jnUMYvv27erIkSNFWq5erxfF0C688EJ1wYIFVbZ78803RfE3Sns/VhE/SqN9+OGHRQo0HZOOTQXQ6LOOhj9luKaJ0taDU7/pmtOxKT2Ziq3VVMTvpZdeEmnEVEiwf//+6rJly0SRwnPPPfewa+5PYS4uLhZp9nR8ShOOi4tTBwwYoH788cdVjk1F7Cg9OSYmplZF/H777Tf17LPPFtvTcamo48svv6zWBioqeP/994vChVQAj8ZD94LuGxUmDMafCk4p4tWhtPgpU6YE7kt2dvZhRfyCCyXScejaBjN06FCxfMmSJYftQ6nYdI46na7GIn7VoRRsuj/HIrhoIj3rsbGx4nhjxoxR//jjjxr3eeuttwJFJOle0rn4r00wmzZtEkU26boGF/Gj52n06NFqcnKy+N0eNmyY2JbOpabUcSYy4d5SDHMQKi7mLypXm2/cTMNBMSeU8UKF5MhlxTAMczxwzA3DHIyRoPgQcgmwsGlcKG6meswEucsoPqN680+GYZj6wDE3TERDMSdU1I8CkNetW4cvv/yyuU8p7KHO7GQho5L7FCdBhddIWFLMEy1jGIY5XljcMBENBclSYCMFXFJ/JgqEZRoXCuim9GDKrCJrDQXnUrDz008/HfKdzBmGaRlwzA3DMAzDMGEFx9wwDMMwDBNWsLhhGIZhGCas0EViymlubq4oZtVcZckZhmEYhqkblGVJzY+pqCMV4TwaESduSNhQMCPDMAzDMKEHVQGnbvZHI+LEjb/hIF2c2NjYBrcKUfYNFSM7lqoMRcJ9fJEwRh5f6MP3MLQJ9/vXmGOsqKgQxgn/e/xoRJy48buiSNg0hrihAmV03HB8aMN9fJEwRh5f6MP3MLQJ9/vXFGOsTUhJeF5ZhmEYhmEiFhY3DMMwDMOEFSxuGIZhGIYJKyIu5qa2yLIMr9dbZz8j7UO+xnD0pYb7+MJxjHq9HlqttrlPg2EYpklhcVNDHn1+fj7KysrqtS+9HCkPPxxr6IT7+MJ1jNQ3Kz09PWzGwzAMcyxY3FTDL2xSU1NhsVjq9EKgF6PP54NOpwvLF0m4jy/cxkhjcTgcKCwsFPMZGRnNfUoMwzBNAoubaq4ov7BJSkqK6BdjJI4vHMdoNpvFvyRw6LkOhzExDMMci9APKmhA/DE2ZLFhmHDB/zzXNYaMYRgmVGFxUwP87ZYJJ/h5Zhgm0mC3FMMwDMMwx41XVrBufzk27C9HwYEypCW50D0rDj2z4qDXNq0thcVNI93g9bkV+De3Ana3D1FGHbplxjbLDa4POTk5uPvuu8XUHAwePBh9+vTBzJkzEQ7s2rULbdu2xd9//y3GxTAMEy5C5t+D77kKlxdbCqyocPrgkxXIPh+0e+z4Zl0e2iZHYeTAHHTNaNiWR0eDxU0DszHPig//3IddB+zwyapwCVCQ6tdrcxv1Bt9www149913MW3aNEyYMCGw/IsvvsCll14qzqG2/Pnnn4iKimrwc4wE6D5QUDpddz/U6C0vLw/JycnNem4MwzANIWZ2FNmwdl85nF4ZRp0Et0/B/lInZFVFYpQBXdNjYNLoREKD06dgW6ENMxdvwbghncQX/aaAxU0D8m9eBV75aTtKnV5kJVhg1h8qnkYPQWPfYJPJhOnTp+OWW25BQkJCvY9DnVwjCRJ+lClHGVKNARXRozozDMMwoci/uRWYu2IXdhbbUeH0IrfcJb68m3QSYs06eGRVeCXiDBL0rhKcvOsDpFiA33LuEu/B9inR2F5kw9wVuzH1ku5N4sFo+T6SEFK1c5fvRonDg/YpUVWEDeG/wSV2j7jBtH1DM3ToUPESJevN0fj000/RvXt3GI1G4YJ6/vnnq6ynZX6XEL34H3vsMbRu3VqIpzZt2uCuu+4S66ZOnYoePXocdnxyvTz66KNH/Pyff/4Z/fv3F59PtVfI0kTp18HQ/NixYxEXFycsHnS8YOvTq6++io4dO4pzSktLwxVXXBFYR0X46BqQK4i+OfTu3RsLFiwIrF+6dKmwqH333Xfo27evOI85c+aIZZs2bapyHi+88ALat28vfiYB9J///Cdw3M6dO+PFF18MbEvXiaxnX375pTgWTfRZ5Jain9esWVPra0CuObrODzzwABITE8V9peP7Cb4vdIzMzMzAfWEYhjke6P30155SvE9i5H8bcN8na7BmbxlSYozCSqOXJKTGGBFt0ol3WqHVjTSpDLd738P/5Ntxg/wJhlq/QJSnSByP/v7RF/6dxTas31+OpoAtNw0EmevIFZUVbz5idkr1G3xC6/pbV45kIXjqqadwzTXXiBddq1atDttm9erVuOqqq8SLccSIEfj9999x++23i7o+5FKpSQjRC37evHno1q0b9u/fj/Xr14t1N954I6ZMmSLcWCeddJJYRnEla9euxWeffVbjOdL+559/vvis9957T4iJMWPGCJES/PImkUBCYuXKlVi1ahVuvvlm8SKnbWmexjd37lwMGjQIJSUl+PXXXwP7krB5//33MXv2bCGAfvnlF1x33XXCInXGGWcEtiNB8dxzz6Fdu3bC0vXmm2/igw8+wOTJkwPb0DxdT79oomv6ySefiOtF147Oi8QJXdP77rsPGzduREVFBd5++22xDwmT3Nzcel+D8ePH448//sDy5cvF9qeccgrOPvvsKveFhCoVn/znn39q9ZwwDMPUxkrj8SnYW+IUngezXhJWG6dHQYy5sg6Ylt5pUgmu0nyOEZ4fYcKhchMSFKSU/gV7+rmBL/heWcWG3IoGf/fVBIubBnwgvD4FpmoWm+o09g2m+BqynNAL+q233jps/YwZMzBkyJCAZaVTp074999/8eyzz9Yobvbs2SOsBmQVIrcNWQgGDhwo1tGLftiwYeJF7hc39DMJCBIMNUEWF4pBeeWVV8QvR5cuXcTL/8EHH8SkSZMC/ZxoG3p50zZkIVm3bp2YJxFA50QxQRdeeCFiYmKENemEE04Q+7ndbiHwFi9eHDhPOpfffvsNr7/+ehVxQ5YnEgp+rr32WnFefnGzZcsWIQZJKPn7NJGY80MWHBIdH3/8sRA30dHRwqJD53A0N1Rtr0GvXr0C50IijbZfsmSJOOfg+0LnRcKPLEEMwzDH8x57cckWYY2hL+IUKLyv1ImkaAPoK3uxzS0ET5RRi1QU4WrPJzhPXgy99pDV2QM9vtGfgzd9F8CiaYuOQceXNBo4PFWt9I0Fu6UaCHoIJKl21V8b+wZT3A196ycrQnVoGX37D4bmt27dKtwu1bnyyivhdDqFQCBhQYGywe4TWvbRRx+JRpMejwcffvihsOgcCfp8Eh3B1i36fJvNhn379gWWnXzyyVW2oX3850gvdxI0dE7XX3+9sK5QmwFi27Zt4mfahsSGfyILyfbt26ucS79+/arM/9///Z9wIZGlhKDjnnjiiUJ8+Jk1a5ZwZZEViI77xhtvCKFRF2p7DUjcBEMWIn8rher35fPPPz/MtccwDFOn0IoVu4SwoRAK+iJe6vBAUVXoJAlaSYJBJyFdLcKdzll4z34LLvYuhB6Vf3fsqhHvaS7GtVFv4hXTzSjQJMOnVA2/oGNZDE1jU2Fx00BQurei1C4jqbFv8Omnny4sKhMnTjzuY5GFYfPmzcLaQFYJcgeR9cNf7faiiy4SMR/0cv3f//4nlgfHvzQGZK3566+/hKiiFz5ZOyiuhrKUSCAQ33zzjYhx8U9knQqOuyGqZ4SRJeSss84Srh6ChBpZc/zQcnI9kbvs+++/F8cdPXq0EHWNAVlkgiExRK6xmu4LuRbpvnMVYoZh6htaQa4ostj4v3h5fSo0wmYDJCvFeEh5E4v19+AqLD4kamDGbGU4hvhewovSSJRIiWI5vQ1JFPkh15Zeq0F3zpYKLSj76eu1ElxeGRbjkYVLU93gp59+WrinyKUTTNeuXbFs2bIqy2ie3FMUs1MT9PIkEUNuIMrE6tmzp3ATkVWDXFWjRo0S7iiDwSCsH/5+RjVBn0/xIhQQ6/8Fos8nwRIcI+S3nvhZsWKFcM34z5E+l1wyNJHrhjpf//jjj8JiQ2KLrCnBLqjaQvE15B6if3fs2CHGE3ydKMaHhISf6tYgugY1WcDqcw2Ohf++0HTHHXcIC5P/vjAMw9TVJUUZUMHJMHqdBolqCW5xfYULvQthoJiagwZnG8z43HARFugvxg67AU6fjDghaQBZUUGOjISoyi9o9Lduf6kDHVJj0CMrDk0Bx9w0EFSgLycpCtsKreiQGl1jUHFT3mASIGR1eOmll6osv/fee0V8zOOPPy4CiilmhGI5yAJQE++88454WQ8YMEC8TMmaQf+SW8jPTTfdJF7YRHXhVB0SBpSJdeedd4psKLI+kDihwFl/rAlB4oSWkZgiK83LL78cyOr6+uuvhfAgSwUFAn/77bfCokFCjgQCWVfuuecesezUU09FeXm5OK/Y2FghxI7GZZddJs6RpjPPPFPEGPkhcUXurUWLFol4GwpopmBq+jk404zW07go6Jiyvep7DY5G8H2h3lEUF1T9vjAMw9QltCL4vWXxleMm57sYLH8Bk3zIOk2i5h35XCzQXQStsbJ2V4zRJ7KoKAmYXFEOKl5r0CHRYhBf6Om9R/VvRg5s02SFbFncNBB0w+jGzfxhC7YX2Wusc9PUN5gCZufPn19lGX2rpwBYcuWQwCG3Dm1XUzAxQRYRsgLRi5deppT6/dVXX1Xpmk4vfX/WEr1sj0ZWVpYQI/fff79wJVE2Ebl5HnnkkSrbjRw5UsSUUJAsWWvGjRsnMpP850TZWJRZRLE+9PnkoqKsIYLGRTExlDVFIoi2p3E/9NBDx7xmJI4uuOAC4cKi9PBgSGhRNhiJQvojcPXVVwuhQinlfij+hdK/KZ6HXGQ//fSTEDz1uQZHo/p9ITFLbsH6dLNnGIaJMurEF3CD7MCpxR/jtKKPYFLsgQvjhBFfGC7EXOlibLMaYFC0SFIU6CSNiNeh1HDKnqKYHa0GMJm1orabXieJL/T03mvKCsUatS6la8MAStOlb9P0bZ6+yQdDL8qdO3eKb+KUlltX6FKu21uKj/7ch50H7CIrioKHKcaGXFFtk6Ob/AY3JDQ+Cloll1CwwqflJDDoRU8v21DmSGMMZYKfa3KbUVByampqra1EoQRZ68J5fJEwRh5f07ZNiDrYHkj2OPHvVy/iMvs8xMhlh7bX6PGZdC7mYDhcpiThlSp3emExaIW1xumRhYDJiDMhxqRDnNmATqlRkHxupCbFCy8FTQ3xhf5o7+/qsOWmgemaEYMpl3QXqd40UVYUBQ9TjE1D3eCWRFFRkQi0pTorFFzLMAzDtPwaNr6D7YGgyChbsRTX2OfiJG9+YFsZWqxOPB9LUkdjty8evgIrnC6fEET0TiNLDVUmNhu06N0qTnx597/nyHLT3AKcxU0jQAKGatg0RaGi5oYeXqogTCnRx9PygWEYhjl+C8yRGjT/W62GjVknoaNtJc7New2Zrm1Vtl1mOh1LM2+GLbrSpZ5ggOjuvbXAiuRoAwa2T6oiZqp/nj+rszlhccMcFxHm1WQYhmmRFpiaGjR7DwqgdfvK8dlf+1Dm9KJjajRyvNtx4d5Z6GBbXeW4f0gn4B3zKBRGdYa7TIbBViHq2/hDK/pkJ4RMaAWLG4ZhGIYJEQ6zwByhQfNFvTKxbHuxEEBlDi/2lzmRpSnB8F3zcL76M6SDadvEFqk9XtRch+XoAa9NRabOC51WEoVpe7WKPaqVpqXC4oZhGIZhQrCKcPWkB3+D5nX7y/D0wk1IjzWhVaIFOp8dV2jm4zr1fzDhUFp3oTYds6Rr8J0yEGaTHgmShHKnBzFmPVolWESGL7VfuPzEbOHyCiVY3DAMwzBMiFYRrg5FCtjcssho6pYWhVOtC3FW0WwkqqWBbSoQjf9qLsf78jmQNEbEmvWB41FFYn8xPxJK24tsmEvdwS/pHjJWG4LFDcMwDMOEaBXhYKgF0I5iGw7YPThB3oB7d89FZ3VHYL0XOnypvwDvG69CvscEq92DZINUtbQHVOgo3elgyxcSUjuLbVi/vzykkmRY3DAMwzBMCFYRDoaaXFI2k65iL55V5+I87R+VDZ4OsgQD8Jb5BhTossS8T/EKK483KLNJVhRRm40qC/shIUU126i0CYsbhmEYhmEaNN6GYm0KKpziZ7KuJFgMouo9uaC27ivECPdnGKn5HCZNZWNjYpuUg2cxCr/6usHolpCgrcyuogwo4lDCqyrEU6zZgISoQ+KGIMFDNdtCCbbcMC0GaltA/ZxKS0tFe4H6smvXLlGNl1olUPNQhmGYUK5hU+HyYkuBFftLXSixe2F3ydBIwB6NA9EGCYO8v2Oy521koDhwjGI1Dv/VX4slprOhaCQYbW5RUZiORTVxSLAQ9A9ZbOhzjHotOqVFB9b5ISFEhftCidA6W6ZGjtUmgJoyUh+mcIR6YpWVleGLL74ILMvOzkZeXp4oLsgwDBPKNWwq+xI6IasqEix6xFv0cHsV0eqglbwHd9hfxwCsD+zvgxbvysPwpnQlLOZEESBMxFkqLTxGnSRaJnh8PlEbx+OT4dBohMWGhE18kEuKoM+nGjeUCh5KsLgJA+hF7ocaZVJTTOo07Sc6OjrwMz3M1GiReieFK9RoMz09vblPg2EY5rhq2Bi1ElbtLhFZSnEGCQ6PLL7MRsGJG+yf4Br1a+ggB/Zfqe2DZ9RR+FfJFPtayOV08LsvFePTShqkxZmQZDFgc74VOklGjImsNTFIjDYeZrGh9wWlg1PjS6pxE0qETl4Xc0ToRe6fqKkYPfz++U2bNolO19S5um/fvjAajfjtt9+ExWP48OFVjnP33Xdj8ODBVUpoU2dtcvGYzWbh4vn000+PeideffVV0USTGo+mpaXhiiuuCKxzu9246667RMsGWn/qqafizz//POKxyNpU3a00c+bMQJdtWv/uu+/iyy+/FGOmiVxb5Jain9esWRPY7+effxYdxmn81Al9woQJokGmHxo3ndsDDzwgzpu2CVdrF8MwLb+GDQXyljg8sLp8sBi10Gm1iDXpMEReho994zBS/TIgbPaqKbjVOx63KA+hNKo9TmgdL1xPtK8vKGCYrDhk9aFKxV0zY/HE8B7okhEr5sllVd1iQ2ngFNNDVYlDKQ2cCN+v70wV6GX+3HPPoV27drXuAUXC5v3338fs2bOFYCGBQKKIRFOwCPKzatUqIRDmzp2LQYMGoaSkBL/++mtgPQkHEkckSNq0aYNnnnkGw4YNw7Zt25CYmFjnO3bfffdh48aNolPs22+/LZbRcXJzc6tst3//fpx//vni3N977z0h+MaMGSMEVrCAofO65557hPgj0UWNQE855RScffbZdT43hmGY461hQxlQFO+ikyRkKbm4yzUb/ZQ1VTp2v6+9DC95LoRXZ0R2nBm9suOFBSbGpBfZUyRwFNUnhA0FBTs8WvRqFR9oo5CTHCXq2FC6N2VF0b7+dgtksQmVdgstTtzMmjULzz77rOgq3bt3b7z88sviG/aRoG/ur732Gvbs2SNiKsgyQC9helE1Gq+fAdgKa7WpTuTeHT0GptZEpwK3/Nwgh5o6dWqdXtJkZXnqqaewePFiDBw4UCwjCw6JFWqSWZO4oXsSFRWFCy+8UFiLSMCccMIJYp3dbhf37Z133sF5550nlr355pv44Ycf8NZbb+H++++v85jI3UYWJTrXo7mhyJpEcTivvPKK+KPRpUsXIYAefPBB4cLzd63t1auXiE8ii07Xrl3Fs7lkyRIWNwzDNEsNG69PhUH14Xr3Z7jG8wkMOJQFtdp4En7MuQ9bvMnQ7y2DJKsiINjvWkqwGNCvTaKw/pBIojgbEja3ntEel53YKmCJocrDVKCP6thQujcJIAoeDrV2Cy1K3FB8yPjx44VlYMCAAUK40Dd5ihch10V1PvzwQ2GBmDNnjrAMbNmyRXwbpxfWjBkzGu9ESdhYq1oDaqKBJE2j0K9fvzptT9YUh8Nx2Ivd4/EEBEt1aFsSNGQdOvfcc8V06aWXwmKxYPv27fB6vcIS4kev1wshS9aXxoSOTwItOPCazsNms2Hfvn1o3bp1QNwEQ66pwsLaiVqGYZjaup/W59mQt9MlYmj83bwrnN7DkkNOlNfgBvkVtJEPvX8KNMl4RvMfbI85HV2NcUjUqyK4uKDCjeo6RJI0SI42IinKIFxMZLEJFjZ+aJ5q2IRSHZsWLW5IkJB7gMz/BImcb775RogXEjHV+f3338VL6ZprrhHzFHtx9dVX448//mjcEyULSi04WDXgoGez6T63NpBFJRiyVlTv6E3iww+9+Am6H1lZlUWfaHuyalQ/lh+y1vz1118i7uX7778XVhFy+xwtruZoHOscGxoSW8GIWhBB/mqGYZjjSeveUWTDP/vKYXO4EGU2iiBf+hv31T/7UeH0iQaXtH2CpgJjHG9hkO2HwHFkSFigvwTvGf8PBS4tcnRSQMC0TooSsTpknaFYGXO1ZpoUFByqsTMhJ27IArB69WpMnDixysts6NChWL58eY37kLWGYkBWrlwpvvHv2LED3377La6//vojfg65LGjyQ/EZBL20qr+4aJ4eNP8U4OaltR4XvXzpJVn1lXwcVHu5H3tz9Yj/Bo+JXHrr16+vsowCcMW5q6pwy1Dw7e7du3H66acfPr4jnBdlKg0ZMkRMJG4ovodcO2SRMxgMIp7FbymhY5HwGTduXJXz8/9M50juSrov/m80/iBh/7Z0LpT9FXw+1Y9DbqjPPvusynHoPEiMkXA70jWrfrxQxX8d/M+8/+dwJNzHFwljDKfxbcyjtO7d2FXsEGnYeRUuIV6MWsCrSiL1unI7K4psbpGePcSzBPfjPcTDGjjOGnTBy+ZbsUvXFrJCcTGySAun9wNdK5vLi/5tExFn1mNPiQNenyJED7Vj0OskEaB8/cmt0Tktukmua2Pdw7ocr9nETXFxsXgpUWZKMDRPAZ81QRYb2o+ybPxWhFtvvRUPPfTQET+H4nGmTJly2PKioiK4XK4qy+hlSxePjhucSVNb/GnWtak901j4b77//P3nU31MZ5xxhggwpkDck08+Wbj8SOxQdhJtR7EsFFxLbkO/O6m8vBzLli0TGVkjR4487LPJyrNz505xf0jULFy4UJxP+/bthVC65ZZbRFAx7U8xMM8//7xwfY0aNUp8ZvVzpePQfXr66adx2WWXCWsQZX3FxsYGxkJCiZZv2LABSUlJ4tj+df7j3HzzzXjxxRcxduxY3HbbbcKdSRYlElXBL3yaaKz+8/Cvq8+z0JKg86dxHDhwQIhPuo80Vn+sUThB4wzn8UXCGMNlfFuLHHhrRR5KnT6kxeiRV+aBFipizVr4ZAVlDjdW7ar84k3fn3oYCzEOr+OUoJo1VljwkuY6fKE5C4pbglnxwkE1bow6mDUKSq125FV4kGDW4bo+iWibaMamQge2FDmEFcds0KJTigVdUi3QaV0oLKz6zgu1e2i1HhJ8LT6guC6Qu4OCXClAlGJ0KC6EXlCPP/44Hn300Rr3IcsQvaCDLTf0Yk1JSREvyWBI7NDFoxowx1MHprp7oynxP0j+86eXmX8+eEyUPfTII48IYUjjJtcgWcBI4Pi3e/LJJ4XYpIBvEgVUNZjEz8MPP1zj9SFxQXFTdD/omJRhRaKJAsWJ6dOni4edPouuM8UBkQCie1HTufbs2VME9ZJApft++eWX49577xWByP7PJ8FEQc4UU0OutB9//DGQKu4/DsUBkfAiYUWfSRlVN954o7As+Y/jTyX33zv6l64lTaFeE4jOn8ZB94esZzROuuah/OI4En7rXLiOLxLGGA7jI+vMN8s2wC5r0DUzXjSydPqAaLMBOvG3RobRoEFeuRtayLjD8gNu9HwIk+QJHGOJ9lS8ZrwJuz3RMOolaDUalDi8oJ6W0SY99lt9wirTNStBWGX8GU2ZGcBZCM97WJfEIY3aTDZ3cktRoOmCBQuq1Fuhb/FUcZZql1TntNNOE1YGetn6ITcVfTOnF1ttLiKJG/p2T6qyJnFDlgfKCqpP9pXfmkQvk+ay3DQm4T6+cB1j8HNN4oaCpClgP1RfHMf6oxrO44uEMYbD+P7aU4pnFm5CepxZxL9sLbRiV7EdcWaq/qtC9snwqkCSbSue1L6OnppDnbtzkYzp0hgs1/YTKdn0hnb7ZLRJtCAzwYLOaTGINetadEaT0kj38Gjv7+o021dS+iNLReUoHsMvbuiC0Dy5D2qCXBjVL5T/236ox0UwDMMw4ZnWTSndwWkmetWLa92f4Abt59BrDrrBocEX+gsxUxkh3Pq9Y00ihdvnU0U696kdU3DfsM4tTsi0VJrV3k7uIrLUkKuAAoTJpUH1UPzZUxTXQQGf5JYgLrroIpFhRanIfrcUuaNouV/kMAzDMExzQk0ogy2/ep0G6sE0k07yVjzgfhFt1b2B+iE70AovWO7Cv9oucDg9SNVJIoWbJmJboQ1J0QYWNqEibkaMGCECRin2gbJiKJ6DYjD8QcZUFC7YUkMxIvTA0L9UdZb8eSRsKDaEYRiGYVoCVLsm2JtABfXyUYbRro9wtfdzaFGZ+OFVtXhDvQTv66+ERRslunNTEb7Eas0rQ7Erd3PT7FeLXFBHckNRAHEwFAdBFWRpYhiGYZiWCBXl+3ptbqDmTC9swyPK42ij7Atss1nTFg/4bsUmtEaKnmI8VWHxoe7cCVGGkO/KjUgXNy0Rjt9hwgl+nhmmaQv2rdtXLlKx1+8uxD36z3Be+XxIfmsNdHhHeyU+Nl6OvV7q+URZrhAViql9AtW+8bdQCOWu3M0Ni5sg/GnAFLhMdV4YJhyg57m5SxQwTKhWFSZrir9FQs+jZCbRttTVm5pfUjBxa8923Ol4Dp2wJ7DNbmMnPK69A3+5MuC2ySKVm7xXpQ6vqCDcLSMW8QddUpFaWbihYHETBAUlUy0Xfz8hSlWvSzpwOKYRR9L4wm2MNBYSNvQ803NNz3c4VH1lmMakukihvwP0u0RuprbJURg5MOewLtm0z4tLtogWCNnxBpxX+hHOqnhb1LAhvNDidVyB7y3XiL8tKVoPYiwm9M6Og0mvxZYCq2i/UGh1o9jmCYuu3M0Ni5tq+LtL16dhor/cNAVBh/qLMRLHF65jJGFztK7pDMMcLlKyEiyH9WiirKWZi7dg3JBOwpLjt/KQGKJ9BsSWYMTuJ5DtPNQMONfUHv9NvB9LytKRoNXj4j6ZaGVRcFqPNjDqdYeaaYZZV+7mhsVNNeiFRt2gqfhQXZs0+kvcUyXYUC0+FcnjC8cxkiuKyyQwzLEJFinUi6n6lxsSOrScumtTv6ipl3QXwoPcVzuLbLhKXYRLtr4Gg1rZUkGGFj+nXosfU0dDlvQ4IU5GfrkTvVrFIdPoqSJawrErd3PD4uYI0Auhri8FejHSy4SqG4fDizHSxhcpY2QY5nCESCm2C4vNkay2tJzW7yy2Yc2eUmi1Ej77eRXuLngSJyt/B7YrNrTCx60fxV5L9yriyCurwjqT2bbuFfCZusHihmEYhol4qlcVPhK0njp8T1+4GQM9y3B/6UzEwRZY/6X+PPzYaiwslsOzmygLitxOTOPD4oZhGIaJeKpXFa6OolS2Qdhf5kBB8QE8on0Pl+HHwPpiTSKeMYzFT3IvGPM86JnlCWQ+BY7BxfiaDBY3DMMwTMRTvapwMNTjaavIaPKijWsTPpNeRg4KAuuXYABmmu+AQxuPWKhiuy0FNvTLSQjUrKlajO9Q92+mcWBxwzAMwyDSg4m1kgYH7G4498kwG7SiZQLVmCEXFGUyeT1e3IgvMEY7DzpNZUkFJ0x4VjMa8+QzYHTRPpWp4ySUrC4vSu0eJEUbqxTjI3FTeqC4uYcc9rC4YRiGYRDpdW0oC8rq8qHI6oFRp8EeSUK0UQuPrCLWU4Qn8RJOkNcHml3+K3XCNPN45EoZMNrccPsUVLi8QtjoJAmK6hNuLItRx8X4mgEWNwzDMExEUr2uTWKUUVhp3F4FRp0klg/0rcTT2tmBoGFZ1WCu/gp8aLoasqbyFRpnqbTw0D7UdoGEjcurIK/MBZ1UtRgfF9JsGljcMAzDMBFHTXVtKBOKCudRfI3b6cCDytu4Wvt9YJ98JOEhzV3YbT6hyrG0kiTcWmlxJiRFGUWMTl6ZE33bJGD0KTlcjK8ZYHHDMAzDRBxHqmtDsTbnpVsxYvckZGt2BJb/YToFH6Tei/V5KsyKIlxPwWigAXU3SY42HnRNaYSw4cJ8zQOLG4ZhGCbiqLGujarixNLvcMn+GTCoLrHIBQOel0bj77hL0D42BjGlJaIPVIxJU0UUqVCh01b2oeJO3s0PixuGYRgm4jp+/7q1CAUVLhEgTNaadJMPl+XOwAlliwLb5urbYLx6N7Yq2chWAEnSoGNajIjLoeBji1ErLDgy9aOjLCmDVgQmcyfv5ofFDcMwDBNxHb9zy1wim0nUn8FG3Cw/h2w1N7DtysSL8FX6XSja64C7wg1/KygSQv64HBI4suKD2yeL7t4URMydvFsGLG4YhmGYiMuMijHp8c8eHy6XfsLdnjdhPFhYz6mx4IvsB7A2fqiYb52kEftQFhQJIXJjkcDp1yYReRUu7C62IynagHO6pWNI11QOHm4hsLhhGIZhIi4zKlrjxhN4Bed4lga224wcPGGZgMy47iBDDcXP2FxeDGiXhDiTHrtL7KL5JbmgqJUCVRwe1CE5kObNtBxY3DAMwzARlRmV4tqFa3c/gjTfrsA2X+jOxauGG1Hm0cJsr1p8b9yQjuiQGi1ibairNzW/tBh0otowuaj0fp8V02JgccMwDMNETGZU79IfcOn+Z2BUnGKdS2PGc8bb8ZU8CIpHhcvrw6b8CmTGmw+Ln6G0bk7tDg1Y3DAMwzBhlQlFYoa6fFO9mW6ZsaKRpR5eXLz/eQw88Hlg+zxTe3zY5nFY9dno7fCI4nv55S50y4jFLWe0Z6tMCMPihmEYhgmrTCiy0pD7iWJmvl6bi3hvPh4reRKd5a2B7VclnI+vssbDK5lEfA0V36OJGoOf2jGFLTQhDosbhmEYJqwyoYIL87UuXYFr86YgVrWKea/GIETNqoQLgKAifIRIC9dqRCwNE9qwuGEYhmHCKhOK0KgKziici7ML/gsJqli2D2lY0O5JFER1Oew4XFk4vGBxwzAMw4RVjyiTbMWVe59Et4rfAtutMZ+Mu1y3ILokGZ0MlfVqgi02/swoCiDm7KfQh8UNwzAME7JWm0Xr85Fb5hTVgam3U3ddLu4onIxkzz6xjQINFqf9B0tTRyJqX7lI4c4vdx5Wr4YrC4cXLG4YhmGYkA0gXrat+GAFYQVnKb/jQWUWLKhseunQxuKjVpOwQnsCSovsol1CdoIZZ3VNg9uriLYJXK8mPGFxwzAMw4RUmveOIhtW7DggImniLQa43B7cI32Iq32fBrbfrGmHV5MnY31JPKyuMmGhcXllbMq3osLlQ9vkKIwcmMOVhcMUFjcMwzBMi4JSuf/eU4qN+TZRr6bC5cWWAisqnD4hcvaWOA/2eZKQpLXjRXkGBsr/BPb/XjcYk3w3oSJPD4vBK+rdUDQOuaG6pMeK6sPbCm2YuXgLxg3pJGrhMOEFixuGYRimxbAxrwJv/LQHBXYFPkU9GOzrhKyqIuA3I84kMripWWU7ZRcedz6FVigU+8qQ8KrxJnyuOx82rxtenwKjWQ+dpBGF/GLNBiREGYTIocyq7UU2zF2xG1Mv6c5BxGEGN8RgGIZhWlC9mm3YVeJCepxJCBByJVH2UqJFD4ebWiNY4fEpOFNegVedEwLCpliNw23ayVigOx9uWRXCSNIATp8ihI1Rr0WntGghbAjKrKIMq53FNtEzigkv2HLDMAzDtJh6NdQCISfBKFK1i+0eEQRsMWqhkyTEmiUUlNtxOz7B7a5DbRQ2S+0xTr0PVn0avB4Zdo9PuLYIEkJpsSYhbCg+Jxj6DMqaomaY3DMqvGgRlptZs2YhJycHJpMJAwYMwMqVK4+47eDBg4Xirj5dcMEFTXrODMMwTP1EzF97SvH+it14/eft4l+apxgbUa8m3hyoV0NChwKBSdgQFtWJWdLzuF06JGx+0A3G3ZanUaxNQVqcCb2z4xFv0cOok2DSa0V2VL+chMOEjR+y5FCXbya8aHbLzfz58zF+/HjMnj1bCJuZM2di2LBh2Lx5M1JTUw/b/rPPPoPH4wnMHzhwAL1798aVV17ZxGfOMAzDNFT/J3IjOdwy2iZp4TyoNbw+FRoRCgxkKHl4wvkEcrBXzMvQ4A3jaCzQXyLaKGjghaJU9ojKiDPD6ZGFKMpOtARcUTVB4onSwZnwotnv6IwZMzBmzBiMHj1azJPI+eabbzBnzhxMmDDhsO0TExOrzM+bNw8Wi4XFDcMwTIj2f6Kg4VW7DsDmltE60QzTQS2i12mgQkUf31pMdk5HLCr7Q5WrUbhXvRs79P0DVh7ajor4EfFmvXA3xZi0IoD4SHAvqfClWcUNWWBWr16NiRMnBpZJkoShQ4di+fLltTrGW2+9hf/7v/9DVFRUjevdbreY/FRUVIh/FUURU0NCx6NvIQ193JZCuI8vEsbI4wt9QvEekivqveU7K/s/JUdVChJqv30Qs05CeowJGx1WbCmwoUeaSYwxwazHScoi3OucAx1kse1uqRXuVO7HHikTepdXpHX707wTLHo4PT7hzoo16RBN64I+p6ZeUhS03C0jpsmuZyjev5Yyxrocr1nFTXFxMWRZRlpaWpXlNL9p06Zj7k+xOevXrxcC50hMmzYNU6ZMOWx5UVERXK7KKpYNeeHLy8vFTSWRFm6E+/giYYw8vtAnFO/h+jwbtuSVITXacMS/uxadCqNWgzK7C0UVKtLhw8gDr+E05evANsulE/GY/m4c8JnQLt6EAw4P7E4PHF4FJr2E/BK7sPZkx5twWfcM/LC5FBtzy5ARa4BJd+hauXwK8io8SDDrcEGnaJQeKEZTEYr3r6WM0WqttNyFhFvqeCBR07NnT/Tv3/+I25BViGJ6gi032dnZSElJQWxsbIPfUPpGQscOx4c23McXCWPk8YU+oXgP83a6oJF0SIip2cJOZBhN2G/1Ib/CBdVjw+35L6CjfXVg/XuaS/C2cSRsXhVxFj06ZsShlVfG1kIb+aQwsH0i2iZHo3tmrJgofbxPe4rx2YNdB+zwOnyQJA0URYVeJ6FrVgKuP7l1k1coDsX711LGSElHISFukpOTodVqUVBQUGU5zaenpx91X7vdLuJtpk6detTtjEajmKpDF7wxHiy6oY117JZAuI8vEsbI4wt9Qu0eOjxy5bkeJbBX0mrQMS0GUbZdmF4+HW2QK5b7NHrMTbkX79hPFm4tCquheJ1tRXbR8LJPdoLo5F2TSOmeFY/Hh8eKOjaU7k1ZUS2hl1So3b+WMsa6HKtZxY3BYEDfvn2xZMkSDB8+PKD4aH7s2LFH3feTTz4RsTTXXXddE50twzAMUx+o/QG5KI5FP3ktRuBhxMAu5ss1cXg2YRI2arohLRbolBaDzmkxiDXrai1SaB3VsOE6NpFFs7ulyGU0atQo9OvXT7iXKBWcrDL+7KmRI0ciKytLxM5Ud0mRIEpKSmqmM2cYhmGO1NySekKRqKG+TVRAjzKZKvtBHcqSCuakA1/ikv0zoD0YOOyM74wfe81Ea10aurQAawsTWjS7uBkxYoQI7p00aRLy8/PRp08fLFy4MBBkvGfPnsNMUVQD57fffsP333/fTGfNMAzD1LaGTZtEC+LNhkB2kj99m9CoMs7LexWnFc8PLHO2Hgzj1e/hUnMcX2QmNMUNQS6oI7mhli5detiyzp0718rEyTAMwzR/DZsdxZXxMWR1oWaV/m0MsgMj9k5Ft4rfAtsX97wJvoHjkWqM4VvHhLa4YRiGYUK7J5SoYVPNKkMYtRLizHpsLbDBqNMixqTDtgIrMjQleKh8Ctr6tovtZGhReNqTSDvzFhQWVjbDZJj6wuKGYRiGqTcUYyN6QiVYDhM2VExva4FVNL/0KYoQQBqNCZ3UnXjMNhVJygGxnc8QA1z5LjI6Dgnr4nZM08HihmEYhjkulxTF2FQPFCZhQynYbq9ysKu3HuVOD87Q/I17K56GUXWK7TwxrWEYuQBI6cx3gWkwOOycYRiGqTeUFVXdYkOF8shiQ8KG3FD+rt5XKQvxYNmUgLDZrO+KZ7JnwZvYke8A06Cw5YZhGIZp0Bo2JQ6PcEWRxUYUc1Nl3OJ+B1coXwa2WRt3JuamT8TeMgVr9pRCq5WEFcjm8kLxODGgkx69shM49ZupFyxuGIZhmHpDdWwo3Tu4hg25pBRVFRYbo+rGRNfzOM23IrDP0pTr8H36zdBpJJQXlmL6ws2iDk5lCjng9njwy04b2qZEYeTAnCZvkcCEPixuGIZhmHpnSvnEpGLVrhKkxRqRGGWEx6dAAw3ilHI84Xwc3ZQtYnsZEr7Iuherki4JiKC8chdijDr0zUmsFEeqCqfTCegM2FZow8zFWzBuSCchohimtrC4YRiGYY6raB/1bLK5fSjL98KkcwAaINO3Hy/L05Cl5ovtHTDhvxlTkJd0SpW4HBJGqbHGwwKSaZ5Sy6kuztwVuzH1ku7somJqDYsbhmEY5riK9lE37tZJlWnf5U4vOro2YJb2WSTCJrYv1iTi9VZPw5bQrUpcDm1r0klIijq8uTFB8Tp0/J3FNpF5xf2hmNrC2VIMwzBMvYv2+S0uCRYD+rVJxH8S1+Fd3RNI1FQKm736tnij0xtVhA1RaveIOJ04iwEJUYYjfh4d3yuroqs3w9QWttwwDMMwDVK079SSBbig8CVIqMyeWoGeeC7qYbSSUmAO2o5ETX6FU7iZqKmmVO041aH15PpimNrC4oZhGIY5rqJ9GlXBuXmv4vTieYFlfyWciyfVm2E0mpFf7hTWFxIplEVFfabS48xwuGXEW45stfFD+1gM/Lpiag8/LQzDMEy9i/ZpFQ+u3PskepcvCSz7MXUUfki7CaYiO87qkoKT2yUJtxJZX0ikdM+MFe6tGT9sqZJCXhO0nsQQ7cMwtYXFDcMwDFOvon1G2Ybrd01Ee/vfYl6BhC+zxmNl0vDKeVVFjEkvAoGrBwOTuGmbHCXSvWtquEnQ5+wvdaBDagx6ZMXxXWJqDQcUMwzDMLWG6s1QwT29owC3bL8jIGw8GhPm5kwLCJtjWVwo3oYK9CVGGUS6N20fDM3Tclo/cmAbTgNn6gRbbhiGYZjDIMsKBQ9TjA25okx6LQw6CU6PjCTHTtyeNwnpapHY1qaNx3ttp2OvpXudLC5UeZgK9FEdG0r3rozLAVxuD6LMitifhA1XKGbqCosbhmGYCKe6kKlwebGlwIoKpw+yogorSkGFS1Qe7qvdihflpxELq9g3T5OK2VnPwmNpL+ZpWxI2tbW4kCWICvRRHRuKy7G7vZDdTpzcOQs9W8WzxYapFyxuGIZhIpjgSsOUBVUpTpyQVVUIlFbxZuRXuEQ7hfMMazDF8xxM8Ih9t0jtcLMyAWV7zWhlK4NRrxWuqLpaXEgA+eNyFEVBYWEhUlPjIR3sJs4wdYXFDcMwTIRSvdKwUSth1e4SITbiDBLsLi/+3uuGUSfhav0vuNc9C1ooYt/V2t74vOPT6OjWY0uBTTTJvLh3Jnq1ihOuqGNZbBimMWFxwzAME4FUrzRM2UrFNjesLh8sRm1lR2+9Klok3Kz5HHcrh2rYLNGehkc1d6CHx4DkGCOiTHpRy4aEDbdIYFoCLK0ZhmEikJoqDVOXbkrfJmFDeLwyHtW9h7s1h4TNZ/qLMM18LzzQif5QBLdIYFoabLlhGIaJQGqqNOz1qSK2htCpXkxVXsR50m+B9W8aRmKe4XLqaCm2o/39cIsEpiXB4oZhGCYCqV5pmNDrNFChwqS6MNk5Df3Vyho2PlXCE9Kt+NV4bmBb2o7q3fjhFglMS4LFDcMwTASleVOFYUq/pro1/krDfqizdzkK8IzjKXRXtohlLlWPu+VxWKU/Gf6KNbKiCEtN4sG+UNwigWlpsLhhGIaJkDRvstSQoPl6bS5iTDp4ZKVKb6e2+jI8ojyCNso+MW9DFG7DA1ihdEJqIPtJFSIp1mxAQpSBWyQwLRIWNwzDMBGQ5u0XMIqiIq/Chc15VpQ5vSiyukWLhM76Aty0czwSlAKxXTHicb/5Max3ZUKvU+GWFUg+GS6vLOrZdEqLhtun1KlgH8M0FSxuGIZhIiDN258NtbXAKtK9SeTQdgdsbpTvWIUxmqcQjwqxXaE+E3fpJmGDMxEUVpMRZxIp4TRRC4Z4sx6FVne9CvYxTFPA4oZhGCZC0rypxYHbqwTq2ESbdOjgWIMX1emIglNst0PbFk/GPQFFn4iTMwzonBaDWLMORp0WJr0El1eB2yfDYtAJiw8X7GNaIixuGIZhwjzNm6w0ZLEhYUOxNn7Bc6ryJx5Vn4ERXjG/VuqK6XFTcM6JnbnSMBPSsLhhGIYJ8zRvKrbnrzzsX36290fc73op0E5hU8wgzM2cDNmqcqVhJuRhccMwDBNmad/UXTu3zClq0VB6N8XeBFcevtTzP4x1vxnY7xfTmViUMxk6jQ7eMqvYn9soMKEMixuGYZgwS/suc3hR4fLCWeiDTivBpygQJW1UFdd75uEGz0eB/eZL5+PHjHuQqKl8HXClYSYcYHHDMAwTZmnfbRKjhBWnwumDWS/hgN0Hr8+HiZp3cZX8dWC/NzVXYp7lOvSLNgWWcaVhJhxo9qIEs2bNQk5ODkwmEwYMGICVK1cedfuysjLccccdyMjIgNFoRKdOnfDtt9822fkyDMO05LRvCiKWJA06psXAqJfg8CiIM2jwlPRGFWEzQ7oB75quQaf0GGGtIbjSMBMuNKvlZv78+Rg/fjxmz54thM3MmTMxbNgwbN68GampqYdt7/F4cPbZZ4t1CxYsQFZWFnbv3o34+PhmOX+GYZiWmPZNUKwNpWnvyi/BA/bncZb2D7FchgZPam/Hz5Zh6JkWjfiDLRSocjEV5KO6NbQfw4QyzSpuZsyYgTFjxmD06NFinkTON998gzlz5mDChAmHbU/LS0pK8Pvvv0Ov14tlZPVhGIaJVGrq7u0n1SRjPKajk/qnmPeoWjykvQdFWeegX5y5isWGKw0z4USziRuywqxevRoTJ04MLJMkCUOHDsXy5ctr3Oerr77CwIEDhVvqyy+/REpKCq655ho8+OCD0GoP/8Um3G63mPxUVFRW4FQURUwNCR2Pvv009HFbCuE+vkgYI48v/O6hzeWF0CjVmmAaZRtu2PUAchzrxLxHY8TUmIewzdQPJp+CbQVW4b6iGjh6nSRcWtef3Bqd06Kb9fnnZzT0URrp72hdjtds4qa4uBiyLCMtLa3KcprftGlTjfvs2LEDP/74I6699loRZ7Nt2zbcfvvt8Hq9mDx5co37TJs2DVOmTDlseVFREVwuFxr6wpeXl4ubSkIt3Aj38UXCGHl84XcPFY8Tbo8HTmdlhWEiSi7Df3IfQrZ7m5h3Sha8kfEk/nG0wzntY9E1LQpbihxwemSYDVp0SrGgS6oFOq0LhYUN+3exrvAzGvoojfR31Gq1hme2FF0wird54403hKWmb9++2L9/P5599tkjihuyDFFcT7DlJjs7W1h9YmNjG/z8yOdNxw7XF2M4jy8SxsjjC797OKCTHr/stAE6g3BNxXqLcOPe+5Hm3i22t2njMKftDOzUtUeU6sIpXVuhT3Y8zkLLhJ/R0EdppL+jlHjU4sVNcnKyECgFBZUdaP3QfHp6eo37UIYUxdoEu6C6du2K/Px84eYyGCoD44KhjCqaqkMXvDFeXnRDG+vYLYFwH18kjJHHF173sFd2AtqmRGFboQ394iowZufdSPTkie3K9Sl4q+0LKDS2wf4imwgW7tkqvsU/2/yMhj6aRvg7WpdjNdsTTkKELC9LliypovZonuJqauKUU04Rrqhgv9uWLVuE6KlJ2DAMw4Q7eq2EkQNz0FWfj5u23h4QNgcMmXi9/Szs0WZje5ENiVEG0b2btmeYcKdZn3JyF7355pt49913sXHjRtx2222w2+2B7KmRI0dWCTim9ZQtNW7cOCFqKLPqqaeeEgHGDMMwkUpX7MKkovuQpBwQ83u1rTEx7ln8URqD/HKnsNjcPbQTumY0rCueYVoqzRpzM2LECBHYO2nSJOFa6tOnDxYuXBgIMt6zZ08VMxTFyixatAj33HMPevXqJerckNChbCmGYZiIZN8q4P3LoHOVi1lHUnes6P0q+iEGFoMO3TNjRd0attgwkUSzBxSPHTtWTDWxdOnSw5aRy2rFihVNcGYMwzAtG9/2X6D56P+g9dnFfGF8H+Sd+y6Gt2vNYoaJaJpd3DAMwzB1Z+/Kr5C5aAx0qkfMrzX0wXTDZMg/5aHtuorKOBx2QzERCkeWMQzDhBgla75G1sL/QH9Q2GyMGYRPOj2P1ukpSI8zi8ypmYu3iOrFDBOJsLhhGIYJIeR/PkbnFfdDB5+YXxt3Jj5o8yR8UmXJC6p1Q9WGqZHm3BW7RWNNhok02C3FMAzTAiFRQk0xyfpid/tg0mvRJe8L9F/3GDSobLXwe/TZ+F/2Q4BU2WsvuMYINdLcWWzD+v3lOKF1QjONgmGaBxY3DMMwLQwSNHNX7BLdvqkpJjW2PKPsc4xS3gpss0BzDp51jUH07gp0SosJdPf2QxYcr6xiQ24Fixsm4mBxwzAM08KEzYtLtgi3EllfXF4ZPXe9gzuU9wLbvC2fj7eiboJZK6HC6RUWnp5ZcYcJHOr67fBUuq8YJpLgmBuGYZgW5Ioiiw0JG4qbMUoanLznTdzhOyRs/qu5HE/I16LC5RNdvWPNeri9MrYU2KBU6wxO81TrhmEiDX7qGYZhmjmeJsqoQ7fMWPhkRbiiyGKjATB43yyc65kf2O8tw3V4W7oMWq8bbp8Mj1eBUa8V+1tdXpTaPUiKrgwsJleWXqsRRfwYJtJgccMwDNOM8TQU/KuqKr5emwufosLhltE2ScKFuS/ilPIFgf1mGf+DzwyXwKgqMEgSXD4FTq9PiButJEFRfShxVIobOt7+Uodou0DViRkm0mBxwzAM00zxNBT0SyiKirwKFzbtLYPH68VYxys4xb0wsN8LxtvwteE88TOJoVijFh5ZgcurwKco0EkSNNAEgo9J2HCjTCaSYXHDMAzTDPE0JFKIUocHWwussLp8kGUfpmpm43z3L2KdAg0ew21YZji3yrEMOgkWgxY6rQSnRxZWGwo8LrG7YdJLwmJDHcC5QjETqbC4YRiGaQIoxiYQTxMkbKgOjdurIMag4kl5Fs5WfhPrfJDwKMbiU98gJHt8MAcFBvtUFQadFr1axYljFVrdKLW7cV6PDJzbI50bZTIRD4sbhmGYJnJJkdso2BVFFhsSNglGFY+6n8NpSmVTYI+qxcPau7HachqkMpew9mToJUgaCVS/z+GRkRBlFPE1JJPKHB6c0iEF9w3rzA0zGYZTwRmGYZoGyoryW2wICv4lV1S8QcYU13Sc5jsobKDDWHk8vvX1h6xCxM5QA4VSuxcen4wKlxdGnYSOqdFw+xRsL7JxfA3DVIMtNwzDME2Q9k2VgnPLnFChIsFiENYYveLCU+7ncJL8t9jWDQMeNT+Mla6uokBfZTyNCq0G8CoKyp1eYZmhgOIiqxt6nZfjaximocRNQUEB7rvvPixZsgSFhYUi7TAYWZbrc1iGYZiwTfsuc3iF1cVZ6BOBwDrZgZnK0zgJG8S2ThjxiPlRrJZ6wKCTA/E0FJeTX+5Ct4wYDOmaJgTPgdJypCbFi9gamkjwMAxznOLmhhtuwJ49e/Doo48iIyOjiqmVYRiGOTztu01ilLDiVDh9SNS58YTnSfTTbBKXyg4zHjJPxnpdV9idXsSaDSKehtonJIu6NcDpnVIx4qTWUBRFfKlMTU2FJLGoYZgGEze//fYbfv31V/Tp06c+uzMMw0Rk2nfHtBjs3JeLZ5xT0UuzRSwrV6MwMeox/KvpKIQNFeXrlBYthA3BlYYZponETXZ29mGuKIZhGObIad9EpsGFCerjyEGlsClVozHSOxF7HK1h1MvCYkPCxt8AkysNM0z9qJdNc+bMmZgwYQJ27dpVz49lGIaJnLRvwuIrw007xiHHvVnMW6U4PBz3FLZK7aHVSuiaEYN+OQkBYUMWG86EYpgmtNyMGDECDocD7du3h8VigV6vr7K+pKSknqfDMAwTfmnfUb5SIWzSXTvEvFWXiP+2m4k4Uzu015WJBpeU1r2t0CbcUZQhRcu40jDDNKG4IcsNwzAMc+y07xyDFWMK7keae7fYplyXjP+2ewnFptZi3qTX4uLemSI7ivZzeHywGHSimzdnQjFME4qbUaNG1fPjGIZhIiftO6agAHepU5CGPLFNmT5VCJsDxlZVgoVJ2JzQOkFMDMM0YxE/qmXzxRdfYOPGjWK+e/fuuPjii6HVHvIxMwzDRGrad4I3H8/aJ6MVCsQ2uZpUvJr5AuSDwoaDhRmmhYmbbdu24fzzz8f+/fvRuXNnsWzatGkii+qbb74RsTgMwzCRmvad4MnFA+5HkHRQ2OzTpONWaTKcJTHoF6uK+Jr9pQ5um8AwLUnc3HXXXULArFixAomJiWLZgQMHcN1114l1JHAYhmHCMZ6GrDQUMBxl1KFbZix8slIl7TvJvQ837bgL8b5Csd9eTSZu1T6GPDUB3goX/tlbKjKiOFiYYVqYuPn555+rCBsiKSkJTz/9NE455ZSGPD+GYZgWFU9DKd4kYsit9PXaXPgUFQ63jLbJWiS79+Cm7Xchzlcs9isw5uDdnBeQ6Y2F2eFBXpkTndJiMfqUHA4WZpiWJm6MRiOsVuthy202GwyGyhoNDMMw4RhPE1y7hgKCV+06AJtbxoCYIozJvQ+xvgNiXb6pHd5qOxN2fSKSjRBtFDTQoEdWLAcOM0xLLOJ34YUX4uabb8Yff/whvr3QRJacW2+9VQQVMwzDhGM8TbCwIYxaCdFGPVq5d2HsnrsDwibX1AFvtnsJNv0h6zZB9WsozZthmBYobl566SURczNw4ECYTCYxkTuqQ4cOePHFFxv+LBmGYZpB2CxYvQ8rdhyAwyNjW5ENxTa3ECgEdetetbsEseWb8Z5uKpJQLpZvRDvcrnsMuR5LleNxjyiGaTrq9RUiPj4eX375JbZu3YpNmyq72nbt2lWIG4ZhmHCJsSFhU2h1w6KXRUG+PRoHYkw6pMeasLvEgRzPNsxSpyBOYxP7rUd7TLBMQYHLhOL95eiZFSeChzntm2GaluOyj3bs2FFMDMMw4RhjQy4kEjax5soWM7KioJwCg8tdOEG7E6+pjyMGdrHub6UjbsVDMGmiEWvWoMLpxZYCm4ixoWrFiVEGjBzYBnptvQzmDMM0hrgZP348Hn/8cURFRYmfj8aMGTPqcg4MwzAtMsaGXFFksfGjlSQY9VrkuDbhNc00xGgcYvk6bTfcgQch66Lg9MjCdUXeq4IKJ8x6CT1bxQth0zUjthlHxzCRQ63Fzd9//w2v1xv4uSGZNWsWnn32WeTn56N37954+eWX0b9//xq3feeddzB69OjDsrdcLleDnhPDMJEH1bEJrlmTYDEIV5RPUaCTKi0unb0b8ZLuKURrnGJ+jbYHJhofhs9rQJ9WcWI/isfx+VSUODw4pUMy7hvWmS02DNMSxc1PP/1U48/Hy/z584UlaPbs2RgwYIBoyjls2DBs3rwZqampNe4TGxsr1vsJ7r7LMAxzPC4pqmPjz4pKtBhEjE2F04cYkwa95A14Sn4cFk3ll6mVmh6YYn4UxS6tcF0lRRtFV29K+yaoy3dStIGFDcM0MfVy/t5444011rmx2+1iXV0gF9aYMWOENaZbt25C5FgsFsyZM+eI+5CYSU9PD0xpaWn1GQbDMEzAHfXXnlL8urUIBRUubC20iswoaICOaTEw6iV0cvyNac4psKBS2Pym9MJdmIBCl1a4qjqlRQthEwynfjNMCImbd999F05npUk2GFr23nvv1fo4Ho8Hq1evxtChQw+dkCSJ+eXLlx9xPyoW2KZNG9HL6pJLLsGGDRvqMQqGYZhKa82kL9fjmYWbsDHPKlxJu4rt+GdvGVbtKiF9gysStmGm8hTMcItLtlTpgzHe8bCrBsSaDYGsqGA49ZthQiRbqqKiIlC0jyw3VN8muEv4t99+e0RXUk0UFxeL/apbXmjen2JeHWrUSVadXr16oby8HM899xwGDRokBE6rVpXddoNxu91iCh4DoSiKmBoSOh5dm4Y+bksh3McXCWPk8VVlYx5lRm0TMTJZ8WbEGHX4Z58Ms0ErRA1lPFl2/4Q7fE9Dj8qYwz8NA/CA9y4oihZd0qLRLuWgxeZg/RvCn/pNQcndMmIa9HniexjahPv9a8wx1uV4urrWtyGXEE2dOnU6bD0tnzJlChoTKhxIkx8SNlRj5/XXXxfZXNWhbuU1nVNRUVGDByHThSfBRTeVLFDhRriPLxLGyOM7BMXWvPHTHhSUuZCTYAR8HpglFWYdYHN6EG3Q4izpLzzueQZ6+MQ+a6MG4aO0hxBf5IXWJcPt9aLc5oBJd+hZcfkU5FV4kGDW4YJO0Sg9UNlniu8hP6OR8DvYmGOsKRymQcQNBRLTyZ511ln49NNPqzTOpJ5S5CrKzMys9fGSk5Oh1WpRUFBQZTnNUyxNbdDr9TjhhBOwbdu2GtdPnDixSuo6WW7InZWSkiICkxv6hpLAo2OH40Mb7uOLhDHy+A7x955SFNgVtE2NrdJWoWumDuv3l6OfZwWeUGYEhM2qqNPxYatJ2FvuRU6KGRf1ysCy7Qew64AdXocPkqSBoqjQ6yR0zUrA9Se3bpTUb76HoU2437/GHGOwt6hBxc0ZZ5wh/t25c6cQCMd70iSI+vbtiyVLlmD48OGBi0LzY8eOrdUxyK21bt06nH/++TWupzRxmqpD594YDxbd0MY6dksg3McXCWPk8VWyMd8mOnqbq/V6Sogy4vr4tfhP/vPQQRbLvlMH4hnlLqRZfeiQGhOoWXNuzwwhhDbkVsDh8Ymif90zYxu94zffw9Am3O9fY42xLseqV4VistAQDocDe/bsEYHBwVA8TG0hq8qoUaPQr18/UduGUsEp68pfy2bkyJHIysoS7iVi6tSpOPnkk0Wrh7KyMlEfZ/fu3bjpppvqMxSGYSIUu9tXYxmJnmVLMCL/cWgPCptfTGfiadyBrpkJuOWM9lWEC/17QusE7vLNMC2Meokbilch8fHdd98d0ZpSW0aMGCGON2nSJFHEr0+fPli4cGEgyJjEU7BaKy0tFanjtG1CQoKw/Pz+++8ijZxhGKa2RBl1ws0eTO/S73HV3icgoTJwcVXC+VjY6kEkFzpwascUFjEME87i5u677xZWkz/++AODBw/G559/LuJknnjiCTz//PN1Ph65oI7khlq6dGmV+RdeeEFMDMMwx0O3zFh8vTZXpGxTzM0Jpd/hir3TAsJmZeJF+CLrfjh8KvRajXA3MQwTxuLmxx9/FF3ByZVEVhVyU5199tkiQJfcRxdccEHDnynDMEwDQrVp2iZHiSrCI7Q/47L90yEd7CP1R+Il+DLrXijQYH+pXcTZkDuKYZjQoF6RPhQT469nQ64hcisRPXv2xF9//dWwZ8gwDNMIFYmpj1RSlBGnlv0PV+x/OiBsfk+6HF9k3ScsNtuLbNzNm2EixXJDhfSot1NOTo5odEk1Zuhnap2QkZHR8GfJMAzTgBWJqfM3Ncgcav0KN7leDaz7QHMhPlJugLHQJlxRwZlRDMOEubgZN24c8vLyxM+TJ0/Gueeeiw8++ECkdlPXboZhmJYqbF5csgUldg+uVr/FZdZDwubrmKvwhu9qGBUVZ7RLwtCuqY2e0s0wTAsSN9ddd13gZ8pWolRsapfQunVrUZiPYRimJbqiyGJDwmY0vsL5Ba8F1v2YOhLL0saAap+TK4q2YWHDMBEmbqpDXbxPPPHEhjgUwzBMo8TXLFqfj2XbDmAMPsP5jkMNfhenjsaStBup6pjoJ5WVYMHOYpsozkc1bBiGCWNxE9zC4FjMmDGjvufDMAxzXFDPKGqtQBWIdxTZsHZfuUj3rnB4MML5Ia7XLAhs+2XijViRfmOV/Skt3CurouowixuGCXNx8/fff9dqu5oqfjIMwzQF1OVbNMO0K6hweZFb7hJix6TV4Fb1I9yk+Syw7UvSdfjYeRF6OjyItxiqHIe6fFM7BYZhwlzcUNNMhmGYlh0svE10+W6TEoMimxt6SUKiWYsxnndwtfeLwLavGv+DLw0Xw+30YkuBDf1yEoSg8aOoqugTxTBMaMJpAAzDhE2wcKnDg5wEI5weGVYXNbKUcIfnrSrC5kl1NBYYLiY7s2jBYHV5UWo/1B+PXFhckZhhQpt6fTU588wzj+p+ogrGDMMwTQUFDFPdmqx4M+DzoNThharIGO/9Ly7xHuqB95g6Bh94z0KqR4bJoINWkqCoPpQ4PEiKNopeU/tLHVyRmGEiUdxQc8tgvF4v1qxZg/Xr14sO3wzDME3tkqLYGgoGdvoAn8+Hh5U3cIm8WKynNgrPme7EYs2Z0FS4UO7yQaeToJMkUI4U7UsWGxI2iVEGUbiP69swTISJmyM1rnzsscdgs9mO95wYhmHqhN3tC1iTNaqMu+0zMVhdIuZlSHjGNA6L9WfCSN3ADTpoJI1wXZHVxuWVUWJ3w6SXuCIxw4QJDRoxR8X9+vfvj+eee64hD8swDHNUKHaGXEqS6sP1BdNxomupWO6DhGmme7FUf1pgW61Wg9ZJFtFXqtDqRqndjfN6ZODcHulcuI9hwoQGDShevnw5TCZTQx6SYRjmmHTLjIVR8uGqXZNxoq1S2Hihw0TpPvykOzWwnawoIisqOcqIpCgDDFoNTumQgvuGdRY1bdgVxTARbLm57LLLqszTNybqNbVq1So8+uijDXVuDMMwh1UapvgackORtYZETc+sOPRMM+Fh29PoZlsmtvVp9Hgj/TEsL+sGN2VNGbXQSRqxX6zZALNByx2/GSaMqZe4iYuLqzIvSZLoFD516lScc845DXVuDMNEMMFiJrjSsFEniSwn+lL19dpcdEzU4cHyJ9DNWils3DBgTqsnsS9hIHpEebC1wIpyp1fE2Oh1Egw6DQoqXBxfwzBhTL3Ezdtvv93wZ8IwDHMQEjRUt4bSuyucQZWGdRLiLHp0SosRVYVltw0jttyLaM8/Yj9FZ8abqY/hN7knvAVW4YKKM+th0muFtaZ3qzi0TY5G98xYjq9hmDDmuAKKyQ21ceNG8XO3bt1Eh3CGYZjjrzS8RXTmzog3o8h6sNKwRScqB5PYIYvOSRk63JX/MNp61or93JIZmqs/wXBTBwzyGfFvnlW0UKBKwyxmGCayqJe42bdvH66++mosW7YM8fHxYllZWRkGDRqEefPmoVWrVg19ngzDRFClYRI27VOiccDuqaw0bNSKVG+tRoNYsx6qoxQ373oKbZUtYj+nFI0nE5/AZbruyNR60CcjHie2SWzu4TAME0rZUjfddJMo3EdWm5KSEjHRz4qiiHUMwzDHVWk4wSLEDLVTIGsNFdvzE6taMVudiq4HhY1VisWkhGn4ydYaby/bhfV5NiGSGIaJXOplufn555/x+++/iyBiP/Tzyy+/jNNOO1RPgmEYpr6VhgmvTxUVhP0kKKV4xjkJ7ZTdYv4A4jBWmoyt5RlweX34a08p9pdYsXiHHaMGtUXXjFi+AQwTgdTLcpOdnS0sN9WRZRmZmZkNcV4Mw0R4pWFCr9NAhSp+TlYOYIbzoYCwKVATMEqZjFxjO8SZDaLCcEacGanRBmwvsmPm4i1CLDEME3nUS9w8++yzuPPOO0VAsR/6edy4cVydmGGY46407CfBYqgsuufLwwuOiWit7BfLc5GMa7yTkKfPFi4rf3G+hCi9yKhqnxwl4nbmrtjNLiqGiUDqJW5uuOEG0ShzwIABMBqNYqKf//rrL9x4441ITEwMTAzDMLWFivLptBpRz4ZItBjQxVCIF50PI1PNF8v2adJxnW8y9knpMOvJs64Ki0+MSS+2J8j6Q3E7O4ttWL+/nG8Aw0QY9Yq5mTlzZsOfCcMwEQ9VG26bHIVthTaRLZXu2YWHXA8jDiXi2uzWtMKt0qPYo8TCYtCKPlGUGm7Ua9EpLVpYb/xQ3I5XVrEht0K0VmAYJnKol7gZNWpUw58JwzARD/V2GjkwR8TLyLlrMKbsEUTLlZaX7VIObtU8il1Oi4jCkQ529qZ2CiRsqKgfglxaBIkdqnXDMExkUe8ifhQ8/MUXXwSK+HXv3h0XX3wxtNrKLAeGYZj69o06zbwLl+6ciCjFJtZv13XElPjHYVCikWH0weWVRWdvckMlRFXG5dQEpZFTET+GYSKLev3Wb9u2Deeffz72798fSAefNm2ayKL65ptv0L59+4Y+T4ZhIqDVAqWBd/euw4TSyTCrTrF+X0xv/HLCSzjHkiAqDZMQmvHDFqTHmQMp4zVBcTt6rUbswzBMZFEvcXPXXXcJAbNixYpA0PCBAwdw3XXXiXUkcBiGYerSaoECgHu5VuH6XY9Ar3rE+nWG3ngj+Unc1qWdCDYmSNwEx+UEp477oYyr/aUO0RyzR1bVRr8Mw4Q/Un2L+D3zzDNVsqGSkpLw9NNPi3UMwzB1bbXQ17EMI3dNCAibTTGDML/j88h3aqukdPvjchKjqJ6NLZBZ5cflU7C92C7WjxzYRmzPMExkUS/LDaV+W63Ww5bbbDYYDJWpmAzDMLVttXBC2Q+4Yu+T0KJSqKyLG4z52ZMhS3pkJegDKd3+rCeqPDxuSCchemgdZUVR3A21gFEVHzplJGDUoByuUMwwEUq9xM2FF16Im2++GW+99Rb69+8vlv3xxx+49dZbRVAxwzDM0SArzKL1+cgtc+L0im9wpe0VSAcrEf+VcC4+bTUBikZ31JRuclNNvaS7ED20jrKiaNsMk4zTerSBUdTAYRgmEqnXb/9LL70kCvlRF3CdrvIQPp9PCJsXX3yxoc+RYZgwDCBetq0YF9g/x+2a9wLrvtSfh0WJ9yLuoLA5Vko3uZxI8PhFD1luCgsL2RXFMBFOnZzR9Idj+vTpuOCCC0Sm1PDhw/HJJ59gwYIF2Lx5Mz7//HPExdU9eG/WrFnIycmByWQSlY5XrlxZq/3mzZsnggnpPBiGCZ0A4m0FVtyKT/FgkLD5SD8cTyg3Ym2uFWWOyrgbP5zSzTBMo4mbJ598Eg899BCio6ORlZWFb7/9VtS6ueiii9ChQwfUh/nz52P8+PGYPHmyaN/Qu3dvDBs2THz7Ohq7du3Cfffdx13IGSbUAohtbtwpv4drHO8H1r1tuAb/NY5GrMUAt1fGlgKbEDQEp3QzDNOo4ua9997Dq6++ikWLFglR87///Q8ffPCBsOjUlxkzZmDMmDEYPXo0unXrhtmzZ8NisWDOnDlHLSB47bXXYsqUKWjXrl29P5thmKYNIN5VVIF73K/i9OJ5geUvSKMw1zCCGkJRVyjRPNPq8qLU7gmkdLdNjuaUboZhGifmZs+ePaJ4n5+hQ4cKt1Bubi5atWqFuuLxeLB69WpMnDgxsEySJHHc5cuXH3G/qVOnIjU1Ff/5z3/w66+/HvUz3G63mPxUVFSIf0mQHY8oqwmRqaGqDX7clkK4jy8Sxtic4/t3bzHuKHkGg9yV5SIUaPBhyj341Hoq3C4vLEYddJIGWkkjrDYFFS6UOjyiM/h1A7Kh1VSefyTfv0gYI48v9FEa6Rmty/HqJG4oaJjiYoLR6/Xwer2oD8XFxcIKk5aWVmU5zW/atKnGfX777TeRpUVdyWsDVU4mC091ioqK4HK50NAXvry8XNxUEmnhRriPLxLG2Gzj87lx8sq70MH9u5iVocX7aQ/g75gz0dHgw44SJ+xODxS10oDj9CootjrRLzsWV/RORJLWhcLCY/++hvv9i4Qx8vhCH6WRntGaStA0iLihE6UsKapz44cEAqWAR0VFBZZ99tlnaAxoYNdffz3efPNNJCcn12ofsgpRTE+w5YbaRKSkpCA2NrbBbyhZsujY4fpHJ5zHFwljbIrxUWwNpWf/m2cVfaLitG5cuGE80stXVK7XGPBh66nYFHsKzJTqbQZS46NR4vCg1OGFT1ZEYb9ze6Tj3nM61SnzKdzvXySMkccX+iiN9IxWN640mLipqRs4tVyoLyRQqNFmQUFBleU0n56eftj227dvF4HEFMBc3UxFKemUsVW9rxUJsWAx5ocueGP8YaAb2ljHbgmE+/giYYyNOb7qfaJi1Qo8VPIIYnxbxXo7TJgWOwkF2pNA9c39DS8lrQbJMSYxUQCxyeDEeT0z6lWrJtzvXySMkccX+mga4Rmty7Hq9Jfj7bffRkNC1Yz79u2LJUuWBNK5SazQ/NixYw/bvkuXLli3bl2VZY888oiw6FB9HbLIMAzTMvpEpaEEo7Y/gEzfbrG+TI3GTfKD2FDSDtH2UsSa9eiUFoN4y6Gq5twTimGYhqDZS3iSy4gsQv369RPVjmfOnAm73S6yp4iRI0eKtHOKnSGTVI8eParsHx8fL/6tvpxhmObrE5XkycUN2+9Gii9PrC/WJOBByxRsd6ZDI6uQVRVldo/IoOqZFScEDllsKDOKe0IxDBPy4mbEiBEiuHfSpEnIz89Hnz59sHDhwkCQMWVohavplWHCQdSQQKFWCsu2HUBClB6m0k24Jf9BxPpKxDa5mjQ8YJmKPCkDcRYF5U5voKUCpXuv2lWK7EQzDDpJdPGmZpfUO4phGCZkxQ1BLqia3FDE0qVLj7rvO++800hnxTBMbeNrcstcOGB3o4N7PcbL0xALu9hmpyYbD1qm4oCUJOa1kiRSvdPiTEiKMqLQ6kap3Y2TchJFAHGPrDhuncAwTHiIG4ZhQstSs2RjAX74twBun4I2SRbEW/To7VqFZ+RnYUZlXal/lPZ4yDIJLulQs0tCA+reDSRHG8W0pcCK7ERLlaaYDMMwxwOLG4Zh6mSp2V5kw44iO1xeBUadBpvyrDhLXobHlRehhyy2/QM9McY3HnCZkaxXhaDxo0KFjiryHaMpJsMwTH1hccMwzFGtNCRqdhTZsGLHAVC3J7K2kCBJijZAJ0m4wP0t7lZehyTWAr/oBuJBdRzcEqCRFXhIBOm1Yp2sKGLfxKAMKW6KyTBMQ8PihmGYo8bTeHwK9pY4RTaTWS+hyOoWNWxijDpc656PGz0fBPb7RDkTs41jIckSNB43VEWFyycfFDeqKOoXazYgIapS3HBTTIZhGgMWNwzDHLVeDQmSfaVOYakhZ1KR1QWfLONW6R1cKX8T2O997XBM9Y2AxaEg1iyJwGCnLENRVGGxoeOQyOmUFi2sN1zThmGYxoLFDcMwR6xXQ1VG95U6hOuIXFBEtB54SH0Vl8jLAvvNNo7GJ4ZLYVFcIp6G4nEorIbia+wenxAzcRaDEDZc04ZhmMaGxQ3DMAEoxoZcUWSxIWFDeH2HAoJNqgtPqM9goPYvMe+DhBmmsVikHyrm9VotMuNNSI01iTTvgnKn6OpN6d9Ux6bY5hHL9VoN17RhGKbRYHHDMEwVlxTF01CRPT96nUZYYKhP1JOOqeimbBHL3aoeE7T3YK3+1MC2tJ1eJyEpyoAyhwdndknDoxd2xeZ8KzbkVoisKItBh+6ZsVzThmGYRoPFDcMwASguxm+x8UOWFzd2Y6b9CbRR94llVlhwi3w//lG7IVlRhMvKnwkVZdCKdHF/GwUSM1TDhuvYMAzTVLC4YRgmkPZN1pXcMqewwJCoIYHSRdqPJ+WHkaoeEFfqgCYBE8yP4V9nBswUNOyRISs+uH0yTHqtiLfhNgoMwzQnLG4YJsIJTvsuc3hR4fLCWeiDTithgH4rbnU9iSjVKrbdg3Tcb5qCAk0aDDoZvVrFwSOr2F1sF9lU53RLx5CuqexyYhimWWFxwzARTPW07zaJUcKKU+H0YYhmFSY7nocJHrHtbmMnjNc9jL2eaNjdbuFuov0oUHhQh2RueMkwTIuBxQ3DRCg1pX0THdNi0Hb3J7jP8zq0UMSy1dre+KL9M8hSjHAUWJEcbcDA9klomxzNwcEMw7Q4WNwwTIRSU9o3VBWXWz/A2d63Att9qzkVD/tuR1quG/EWFX2yE9hKwzBMi4bFDcNEKNXTviXVh4v3z8CAkq8C2yyKuxJvm26ApdyNTmmxGH1KDsfTMAzT4mFxwzARSnDat15x4v92P4Zu1kNVh7/JuAO/pVyNDjSjsaFHViynczMMExKwuGGYCCXKqBP9naJ8pRi560G0dvwrlvs0Onza6iGsSTgnsC137mYYJpSobBbDMEzE0S0zFllqHm7ZemtA2LikKLyT81wVYcOduxmGCTXYcsMwEUovzQ48UXIvYnylYr5cl4x32j6HfLNwRAm4czfDMKEIixuGiUDkjd9C+vwmxPicYn6nJhsvpU6DwdQmYM4li83+UkegjYJey4ZehmFCAxY3DBOmrRQoG4qChim2hlxQPbPioNUAthXvIHXNdEgHa9is0/XAXep9OJBrgKGwAOmxJhj1Wu7czTBMyMLihmHCtJUCpXlTNhS5lr5em4t2SWbc6HoPHbYeqmHzT9wQfJL9MPpDj7wKl2ij4JEVnNE5FUO5jQLDMCEKixuGCdNWCv76NYTX7cD5Wyahg/vnwLKlKdfi+/RboGok4YrKijcjM84kOnrTMXpkxbErimGYkITFDcOEcSsFwuIrx3V7J6Kte62YlyHhq4xxWJly+WHHof1IGO0stmH9/nKua8MwTEjCEYIME66tFAAkuffhtm23oK2jUtg4YcT90n341nzBEY9FFh+vrGJDbkWTnDvDMExDw5YbhgmDYOF1+8qrtFIgWtvXYeSuCYiSy8V8qSYB46QHsV5th3Z2L5KjTUf8HEmjgcPja5IxMQzDNDQsbhgmDIKFnR4ZsqIGtu1ZtgRX7n0SetUj5vfrc3C39iFscMRBAwXlTq+oOkwipia4IjHDMKEMixuGCfFgYapH8/fuUpQ7PchOMONS20c4p+C/gfWrtb1xv3ovDrhN8PpkIVzyyl1YtasEndJiEG8xVPksrkjMMEyowzE3DBNiwcLBwoag+Y5p0ZBULy7Z/UQVYfO1dgjuwEOQjbFIijLCqJdAxpoooxYVTq9wcZU5Kq07wRWJ2yZHi2wphmGYUIQtNwwTosHCwWQbXRgrPYU+vg2BZW8aRuIN+WLEmPUH91OhlzSQtZJwYVG8jsPtw5YCG/rlJMDtU7giMcMwYQGLG4YJAZdU9WDhYJJdezBq1wNIVvaJeTcMeD15At4v7w2LSSuEjawoBwOQ9eiQrEOJS4HV5YOsqiiscOGfvaXCPdUhNUa0WuiaEdvEo2QYhmk4WNwwTAuHRMmRLDbtKv7ENXseRZRiE/MHEI/JUY9gvbMD7B43VJXsNT4ROBxrNqBjahRMGhntUk0ocXpR6vAgr8yJTmmxGH1KDhfuYxgmLGBxwzAtrP9T9QaVtI5iYarTI28B/q/oJWgP9ojaita4TX0APmSJvlCxJj3SYk3QaTVItBiQEGUQQXZOpxOSpEFytFFMGmjQIyuWC/YxDBM2tIiA4lmzZiEnJwcmkwkDBgzAypUrj7jtZ599hn79+iE+Ph5RUVHo06cP5s6d26TnyzDHAwmaSV+uxzMLN+GLv/dj8cZC8S/N0/KNeVWL55HoIYFCWUyEpPpw9u4ZuLZoZkDYLNeehLss01FhyECbpCh4fCqcHh8y4kzomBqDpGgjp30zDBMxNLu4mT9/PsaPH4/Jkyfjr7/+Qu/evTFs2DAUFhbWuH1iYiIefvhhLF++HGvXrsXo0aPFtGjRoiY/d4apb0r3tkIb0uPM6JgWgw6p0eJfmqflMxdvEdv5IWtO2+QoEexr9FVg5M4HcFb5Z4H18/WXYpJ5Ioo9esSY9AePGw1ZBf7NqxCp30eC074ZhglHml3czJgxA2PGjBECpVu3bpg9ezYsFgvmzJlT4/aDBw/GpZdeiq5du6J9+/YYN24cevXqhd9++63Jz51hGjqlm5bT+rkrdovtCXJTjRyYgy76AozZfDM62yotmz5o8ZxxLF4zjEKZS4FRr0UnSgnXaETad2KUQRyrxOau8Xw47ZthmHClWWNuPB4PVq9ejYkTJwaWSZKEoUOHCsvMsaA/zj/++CM2b96M6dOn17iN2+0Wk5+KispvxIqiiKkhoePROTX0cVsK4T6+xh7j2r2l2FlkF923RXhwDRYVWk7rqXHlun1l6JMdL5Z3tv2JxwruglaufH5L1RhM0N6L1XIPSIqMWLMeHVOjEW/Wi+NKGqBregxW7DiALQVWEbdD4onGRp9KLqv95S4kWAy4bkA2tJrKsYc6/IyGPuF+D8N9fI05xrocr1nFTXFxMWRZRlpaWpXlNL9p06Yj7ldeXo6srCwhWrRaLV599VWcffbZNW47bdo0TJky5bDlRUVFcLlcaOgLT+dGN5VEWrgR7uNr7DH+saUIDhLaPgnOGto2kfuozOlDucuHfKsHr/24CSN6p6Bv0eeIX/E0JLUy5ibfkIO7cD/slixkSRLiTDrEm3WQNLIIFvZj0gBp0XroJA32FFeIZpgkejxeH8wGPbITTLiidyKStC4UFjbs70Jzwc9o6BPu9zDcx9eYY7RareGdLRUTE4M1a9bAZrNhyZIlImanXbt2wmVVHbIK0fpgy012djZSUlIQGxvb4DeUUnbp2OH40Ib7+Bp7jJLBBqPBALPZfNg6SsneWmgTtWcURYXLp2BLXjnkvBeQ4Pk+sF1Z9hAs6TAF8oYK9EqLOeZnRpt9uKh3Jnq1ihNdvm0uLxSPEwM6ZaJnq/jDMrNCHX5GQ59wv4fhPr7GHCMlHYWEuElOThaWl4KCgirLaT49Pf2I+9HF6tChg/iZsqU2btwoLDQ1iRuj0Simmo7RGA8W3dDGOnZLINzH15hjjDbpKz1R1WrWkLBZn1sBt1eBxaiFjqwx1nzM8DyH3tgS2O5j4xX43Dca0btd8CoqnD7liIX9AsHCOgm9s+NFmveJbRLFHx0K1k9NTQzbe8jPaOgT7vcw3MfXWGOsy7Ga9coaDAb07dtXWF/80B9fmh84cGCtj0P7BMfVMExLpHpKN0FWmq0FViFsYkw6IWxyXBvxgTohIGyo4vAj0ji8bhiJ1LioyiBhu1vsV1P9G4KDhRmGiWSa3S1FLqNRo0aJ2jX9+/fHzJkzYbfbRfYUMXLkSBFfQ5YZgv6lbSlTigTNt99+K+rcvPbaa808EoY5Ov6Ubkr3pqwo+mZT4vAIVxRZbGh+iOdH3OuZBaPGK/Yp1CRjkvkhbNK0g8PlhdMji7o1Lq+McqdXHKtV4uFdwiltnLKlqJVCuLmeGIZhWry4GTFihAjunTRpEvLz84WbaeHChYEg4z179lQxRZHwuf3227Fv3z4Ru9ClSxe8//774jgM05Lxp3RTHZvtRTbRCJNcUhRIbNQouNn5X1zh+7oyZYoaZmq7YoppAkqlBJB0UVSfEENUkK9jWiy2F1qREKVHfrnzYLCwRhyLqhNzjyiGYSKZZhc3xNixY8VUE0uXLq0y/8QTT4iJYUIRakg5bkgnUceG0r2pr5PZW4an5BfQTz3U0ftr/Tl42XgLfBp9YBm1SaAGmgRZagw6LU7tkBIIFnZ4fLAYdOieGcs9ohiGiWhahLhhmEiLvZl6SXes31+OxUsWYeTuh5GmFot1XujwrPQfLDFdcNh+VKGGYnb8kKXG7ZNFsDBNDMMwTCUsbhimmVxUJ5R8h9777oaEymD4Cl0Sno59GN9XtEZcte1lRRFihhpg+iEXFFlqGIZhmKrwX0aGaeIO4DF6BWfveQEpmz4IpCtu1nfFgvZPocgTC8laBp+iiMypSlSxX6y5srM3wT2hGIZhjgyLG4ZpREjQUD+pncV2ES+TpBTj3rInkOLdHNimtNt1eMl1LQrLgIx4rUgJr3D6EGOqDBAmYRPcN8qf5k1Bwz2yqtt4GIZhGBY3DNPIHcCpLg1lRnVz/4Or90xGjK9UrPdAjw9T7kb/U+/CHUAgyNigk+ClYntWN0w6CXEWgxA28RYDp3kzDMPUAhY3DNMIbqi/95TimYWbkV/hQlqMAacVzMWlJXMgobLxW6k+HXPbPIFfbVnYvGK3CDD2BxlT5hOJnH/2lot6NiR2im0eIXY4zZthGObYsLhhmEZwQ63dV47dBxxIkpy40/UKBqt/BrbZGn0S5rV+DA5dHLL0shAyJGr8WU/+zCcSSX6xw2neDMMwtYfFDcM0ghtKp5XQS7cbzyrPI0vND2wzR3cVVqWNQZzOHKhXQwX4SMBUT+cWGVWc5s0wDFNnWNwwzHFkPkUZdaJuTZf0GGGxIWHTPjkKrXYtwE3ybJjgEftVIAbTzPdgsbcXYgud6JdjEsHBBP1LlhmGYRimYWBxwzD1zHyiXlBUf+ajlQpkRUWZw4vWsSou2TUDA20/BPbbJHXAFPMEFEqpiJIUWF1elNor2ygQXK+GYRimYWFxwzD1yHwidxL1haLO3NTAssLlRVtlH6Z5XkR77Avs94XuXMw2jYH3YBsFrSRV6RHF9WoYhmEaHhY3DFMLV1TA5XSwmzcJGwr2dXsV4Zo6T16KicqbsBysNmyHCY8qt+An5VQkQOfvhVmlRxTXq2EYhmkcWNwwzDGgGBtyRZHFhoSNoqjCYkPCJsXow13uV3GuuiTQzXubJgePWx7AWkcy3D5FWHVIAPkrDqsH/6PO4IlRBowc2EYEDzMMwzANA4sbhqmFS4osLeSKIsilZHX50EO/F485n0Mb5ZAb6hPlTMzU3wSTFIM4iyJcVkadBKdHFu4oVYVodinLKjpkxAhhQ53CGYZhmIaDxQ3DHAPKiiKLjZ9SuxuXyN/jPu/bMB7MhnLAhKkYgwW+QYjV6WA6GF+jlTRIizMhKcoogojzK5xomxKFB8/tgj7Z8WyxYRiGaQRY3DDMMSCXEsXHECbZirtLn8Kpym+B9VultnjC/AC2K+mQvC54ZDXQ+JLiaxSl8hhlDg+6Z8bh7qGd2FrDMAzTiLC4YZigwOG1e0vxx5YiSAYbok16UcOG+jrptBqklf+DUblTkeAtCFyzz/UX4HXjaHg1BuhQGVxsMWgDbihqn1Bid8Okl0SjS3ZDMQzDND4sbhgmuIZNkR0OtxtGg0HEx3y9Nhc5CUZc7ZqPC3PfhfZgb6gKROEZ41gsN5xy8PpVdu9OjDLixNbxKHN6RS8ocmGd1yMD5/ZIFx28OXCYYRim8WFxw0Q8VWrYxJsBnwSz2QxoNDA68nDllvvR3bsucJ22mXvhUekubHMnIEZVRRE+EjZGvfaglUdCUpRBuKFO6ZCC+4Z1ZlHDMAzThLC4YSKaw2rYAHAe7ITQo3QJhu9/FlGKTczLkPB1/HWYZxqBMrcCr9MlrDMmnYQ4i0EIm3iLQRTm21/q4DRvhmGYZoLFDRPRVK9hQ74oo+LARbuexyDrosB2+UjGBPUOFGn6IdNiRN+2sXB6ffhnb7mIqzHoJBTbPELs6LUajq9hGIZpRljcMBFN9Ro22Y4NuHz3FKTJeYFtftKdhpmm27DfZUCWThI9pLYWWjFuSCdMOK+rqFRMXb2p+aXFoEP3zFiOr2EYhmlGWNwwEd3d+9etRSiocEELH65zz8cFZR9COhg0bIcZL5luxWLdYBF/o4FXpHaT+4qqC89dsRtTL+mOE1oniIlhGIZpGbC4YSK6u3dumQuxjl2Y5HwF3bE9sM16qTOeNt+LPCk9sIxaJlBKOLmvyI21s9gmrDYsbBiGYVoWLG6YiM2MahVvwkXub3C543WYDlYa9qpavKxcji9NV0EnGQL7yYoCSaNBoqVyGbmxvLIq3FEsbhiGYVoWLG6YkHQnkUih9GsqmkeF9nrWooZMcGbUiXF2XLHvUXS0rQqs36PJwkTNnfjD2xoWl4JkvSrcUP4aNrFmAxKiDgkeEjsUZ8MwDMO0LFjcMCHpTqIgYHIPUVsEKrTXNjkKIwfmiLYGRxJAPlnBziIbhmt+weVbX4JJsQeOvUB7PmbiOjigh6TxwSMr8HgV4YYKrmFDgsYP1behAGKGYRimZcF/mZnQK7SXYAlkNxFUV2ZboQ0zF2/BRb0ysWx7cY0CKNp7AOPKXsJA7x+Bfcv0qfi01UT8qe0NY4EVdrtHiBbZp6LM6RHihSw2/ho2wZ9JKd+UGcUwDMO0LFjcMKFXaC/IekKQ0KHl6/aX4emFm5Aea0KrxCABpKroemARLimaiThUFuQjViech68z74JLGwPKderXJhHFNhf+3lMKm0dGnFkvGl2SKyrYYkNiiYr0Ua8oaqnAMAzDtCxY3DChV2ivBqgPlM0to9zpRdf0mICwIWvN8P3PoXvFr4FtDyAOb8aNw/aEM5AoGeCP1JEkDVJjTOidGY2/99tEGwWLUVdF2HD1YYZhmJYPixsm5ArtBaMoKkocHuwtcaDY6hHp2vtKnaL1wQnli3Fx7kxY5IrA9v+TB2KqbxS8FUkwOMoQY9KhU1pMFZeTUSchO9GC9DgT8sudIiuKBA65q7j6MMMwTMuHxQ3T4qGA3posNqUOD7YWWGF1+WD3+OD2VRbf85Tuw3DrVAyU/6xirXnBcAu+9PSDU5YRJwEWgxYVTq+wDFG2FQkccjnlVXjQPSsBky7qhs35Vq4+zDAME2KwuGFaPJTtRKKjurChAnpurwKLUQufokKRZVyl+RH3az5AtOwMbLtIcwpes9wKqxSHOK0s9nF5FUSbgFizXgicLQU29MiKRW6ZEwlmHa4/ubUIJubqwwzDMKHH0QuDNBGzZs1CTk4OTCYTBgwYgJUrVx5x2zfffBOnnXYaEhISxDR06NCjbs+EPpTGTSnZFO/id0WRxYZECrmVdJKE1sjHO9ITmKL9L6JRKWyKEI+x8ng8ablfCBvCqNPCYpBgNmjh9FTG6CgqUFDhxPZCmwhM/s/JGSKlnGEYhglNml3czJ8/H+PHj8fkyZPx119/oXfv3hg2bBgKCwtr3H7p0qW4+uqr8dNPP2H58uXIzs7GOeecg/379zf5uTNNA7mMqI4NZSiRBYdibMgVRRYbHWT8n3sB5svjMUD6N7DPd7ohuEJ6AQvlk6DIapVKwwadFn2y49E7Ox45yVHITrAgMcqIUzokY8rF3dAxxcK3lmEYJoRpdnEzY8YMjBkzBqNHj0a3bt0we/ZsWCwWzJkzp8btP/jgA9x+++3o06cPunTpgv/+979QFAVLlixp8nNnmgaqPEwF+ihImBpWFlrdIri3u7odrzruxRjPezAebJ+QixQ8YHoMz5nHwaqJEctcvkqLj7/ScIxJj6RoI5KjjeiYGoOumbFIizUhKdpwzCrHDMMwTMunWf+SezwerF69WriWAickSWKerDK1weFwwOv1IjExsRHPlGluyE00bkgnUVvGZSvDOO9beNlxPzooO8V6GRK+i74Uo0wv4idfT/gO9oIiKFyHLDYUW1NTpWGCqw0zDMOED80aUFxcXAxZlpGWllZlOc1v2rSpVsd48MEHkZmZWUUgBeN2u8Xkp6KiMi2YrD00NSR0PHKbNPRxWwrNPb4uaVGY2nE7nJvuR6zmkNtyr6E9vmj1AHKjuqE9ZVAV2oTbyuuThWhxenzQShoRPNwxNRrxZn2l4qlWbbhbRkyzj7Gx4fGFPnwPQ5twv3+NOca6HC+ks6WefvppzJs3T8ThUDByTUybNg1Tpkw5bHlRURFcLleDX/jy8nJxU8kCFW405/gk637E/vY4TLt/gv7gMo/GgEWJ1+On+MuhaHSA0wmTBuieakKZ04cimwe7SxVEG7XonGxCgoX6RslwOg9lUtFYdpW6kZNoQqrOLWK9+B6GLuH+OxgJY+TxhT5KIz2jVqs1NMRNcnIytFotCgoKqiyn+fT09KPu+9xzzwlxs3jxYvTq1euI202cOFEELAdbbigIOSUlBbGxsQ1+Q6keCx07XP/oNNT4qKUCpXL/m2c91Nwyo7KdQZW4F9kL/PEaND9Ph8brCCzeHD0AL5luQUx6BxhrqIFjMatwKnacnhILWaE+UV5IesNhPako9TstPgo3n9kBmRmxfA9DnHD/HYyEMfL4Qh+lkZ7RIxkxWpy4MRgM6Nu3rwgGHj58uFjmDw4eO3bsEfd75pln8OSTT2LRokXo16/fUT/DaDSKqTp0wRvjDwPd0MY6dkugIcZ3pO7e36zTVOnujZ2/At/eBxQFuSij04HznoaScBZ8S7Zi+8G2DNVFC2VWUQDyuKGdhAdq7ord2Flsq1W1Yb6HoU24379IGCOPL/TRNMIzWpdjNbtbiqwqo0aNEiKlf//+mDlzJux2u8ieIkaOHImsrCzhXiKmT5+OSZMm4cMPPxS1cfLz88Xy6OhoMTGh292bqgyv2VOGPbt/xATtB+hZ8n1gnQoNNrf+P7hOn4jubbPRVSuJAOPaipapl3QXlqINuRVweHyiQB919D7MUsQwDMOEPM0ubkaMGCHiX0iwkFChFO+FCxcGgoz37NlTRa299tprIsvqiiuuqHIcqpPz2GOPNfn5Mw3T3ZsqDu/IL8V5jv/hJnk+YjSH4mLWowPejB2L/e7O0P2Yi7bJ5cK6Q8X9aita6GeuNswwDBMZNLu4IcgFdSQ3FAULB7Nr164mOiumqbp7k7Ax7vkVsz1voh32AQdXlSEab+ivxwLlTBi8evRMNYpU7m2FNsxcvEVYbkjgsGhhGIZhgmF7PNOs3b1jXXm4ZtfDeNEzuVLYUNwVNPhYPhOXSy9ikek8xFiMcHtl0f+JOnaT1YesP+SSImsQwzAMw7Q4yw0Ted299YoTZxR+gNOKPoRBrawuTGzQdMJk3yisU9ohWqNHvFiqEdlUVpcXpXaPqC5M1h+KtSGXFFluGIZhGMYPixumySCBAkVGn9JFODd/NuK8RYF1pZo4vGm8AZ94T4VVlUEaKDjDWytJUFSf6CtF4oasPxRETLE2LG4YhmGYYFjcME0CuY9Sy//BY8VPoLuy+dBy6DBfcz4+jroadk0UZK8HVDuYhI1Jd8h9RWigEW4tP5QdRUHEDMMwDBMMixumUYQMBQ9TjI1wRZXtRp8tL+Ice9Xg8F80/fCCNAo7lXQkaSprEUka6gOlwmLQwqCvGhKmQoVOe8icw/2gGIZhmJpgccM0KMEF+gxeGy62foSLXV/CgEMWlp2aVnheMxr/GE8UcTQOrwyLxweDToJPViBJGkQbdcJS44caX5KlJtFiqNIPitK+GYZhGCYYFjdMgxfoq7DZcRV+wLCS9xAllwfWlyAWc/RX45+US5Br9cLt8lUW3lMUFFrdovdTYpQR0bICt1eBSa2sXkw2G7IAxZoNSIgyiGrGVIGYCvVRPRuGYRiGCYbFDVNr9xK5ijJMPpyWlAxjtTLYtO37y3egS/EPGOl8D0me3MA6N/T41HAx5hkuR57LgFirDye2ThD9nqjGDWVA5ZU7RcBxz1ZxcPsq+05RZ2+TXoLLK4v6Np3SosU6f2sFqkDM1YUZhmGY6rC4YWrd/0m0sVd8WLzDjlGD2lbpx7Rz1Xe4cdNj6ODdUuU432lOxXuWG1AopYr5KKMiXFHlTi+So41iItbsLUWUQYeCCpfIgooz61HgdYntyF0Vb9YL686R+kExDMMwjB8WN0zt+z+pKkqtdmwvsh+qEIztwOIp6LTjpyrH2BbdF7N0o/CbLQuxkv6IKd1+oo16nNUlBSe3Swq0UjDqtActNwrcPpn7QTEMwzC1gsUNU+v+T4SJKgQnm+DM3wzf/CeA8qqiJs/UHt9l3I6t0f2xt8gG1WY/7ApXT+n2Zz7FmPTcSoFhGIY5bljcMLXq/+Qn0ZuP8w/Mw4mlCyHhUOuDUmMmXlGvxN/RQyE5dUjQeIRriQKGfYoCXVCMTvWUbs58YhiGYRoSFjfMMfs/EbHeIpxZ8C76lXwNXVBat12fiMUpozDHeTq2l3hhLHFDI7mxR+NAtFEr4mUcbhkxJo0QS9VTujnziWEYhmloWNxEaOYTZSZRR+2eWXGBjCPaZkuBFUU2N3xKpXUlx2DFJfZPcHLJl9AH9YByaqPxseEyfKq/EEYlBm3SzLD5ylHh9CHGpBNuJsp2IkFDhfnoZ4tRC0dQSjdZbDjziWEYhmloWNyEoUipTeYTWUy+XpuLtslRGDkwh2KFxTYrdhxAsc0Ns6sY1ytf4HL1e5hwSNS4NGYsS7kKvySNwDdbHYiSdBh4MDanY1pMIIWbhEysWY8KynbSa6HVaEQcD3mjzHoJ2wptnPnEMAzDNAosbkKI2oiU6unRR8p8UhQVeRUu/L7tAH7fWgytVoJRL6F7rBM3OT7E5fKiqqIGBvwSNxw/JVyBYk0idu53CBGTYNbjgN0j6s4kWAyiqN7WAqtYR9YbEk1lDg/aJFrQKT0JndNiEGvWceYTwzAM02iwuAn19GyKe/H4sGZPGf7Zu0akUrdLiRbWnC7pMTVmPlHhPL8AkWUF5S4vMlCCW4zf4NLSxTDAGzi2GwZ8pT8PbykXwuZJglSgwuUtQ5nLC0UBSp1elO8tE66oTmkxQuD0a5MoUr3pc3w+Vfx8ascU3DesMxfdYxiGYRodFjchnp4dLFRI5BRZPchONOPrtZIQHAUVbiF2goUNuY6ovQG5jlK8RbhS9wku1SyFQZWrWGo+1ZyDBabLUaFLgtPjQ7HVA6MWMGi1MOu0ogdUnNkggoTJ/UTuMnKPxVsMVQr0kQsqKdrAwoZhGIZpEljchHB6dnWhQu0RHB5ZiAqLUYe/d5ei3OlBVrxZWHrIFUVCiLbvqd+Pa9yfYrDvV+ikQyndThixQDMMnxiHw6ZPgo2sOx437G5ZNLXUazSIjzXApNeKasL+wnz++JotBTb0y0kQGVF+uHs3wzAM05SwuAnR9OxgoUIWGr/ooeq/xXY3kgBRX4bEzp+7StAnOx5UNq+N41/ciM9xqnNllc+wqWbMl87D15bLUIposV+vjChx3L0lDri9TmGpSYvWo1+bBJQ4vCiyuoNq2GhEcDO1VqBeUf7qw1zDhmEYhmlqWNyEQNYTra9eUI/iWPxZScHrZFnFrmIH9pU4hZtKUYEKhxvmXYtxg/oV+uLfKscpQwzelc/FB+owKPp4xEkGaA+KJGps2TE1RliITIbKcyIh5a9TQz9Xpn5X1rCp3lqBa9gwDMMwzQGLmxDIeiLhQ+uDIcFB7p7gyr/Uf4kEjUGnRUy0AWZJxkD1F9yo+R86qfuq7F+oScYnhuH4DEOw364RVYNTdNoaWyR4fZVZT1qKsTFVPjJkxame+k3n4t+Pa9gwDMMwzQWLmxaU9USCgIJvA00pMysFDv1LwofW+7cnwUFCwg+JH0q5JhHSyujE5d6vcan3ayRLJVU+f5uaifc0F2NF1FDIGgNUleJtXOJYep2mxhYJ1Inb5ZORHmtCvPnQI3N46rcPLq+MErtbNLzk7t0MwzBMc8DipgU1pSThQsu3F9kwd8VuTL2ku3BRkauKLDokfPz7kRAhARJstUnz5uI/+oW41LMUZrirHHu12gUf6YfjG1cvKNAgxaeFTqsIlxcFA9OZ2FwyLEay2iDgeiJBRdsYtBJaJ5pFteFgglO/C61ulNrdOK9HBs7tkS6Ez5EKCzIMwzBMY8HipgU1pSRoOa3fWWwTLh8SCLRfUpQRq52lWL7jANokWQ41pZRlnKisw0XOrzBYvxqS5pDgIRHzm3YA5igXYqupGzw+BQp8QmSVOtyIMupFK4ROadFCJgVSyt0+UWSPKhUbXF70ahWPNk4vDtjdiNUdLlbIRZUUZRCWo1M6cD0bhmEYpnlhcdMCmlJWh9Z7ZRWLNxbg41V7A7E5ZAUhqw8V7IuWPLhQ/Q3/5/kGnTR7K80tQencC/VD8anhImx2JyM22oBBreNFgPD+Mgd2FNmh12rRNSMG6XFkjancuftBF1NytAED2yehbXI0umfGCoFFVqMXftiCXaV2tNUbYTYcenQ4voZhGIZpSbC4aSJqakpJLh1qW+AXF5Te7a/sS+nX+0ocSIwmy0qMsKQQca796Jn3KU61foc4WKuImgIk4ivDhfjacC7KVYuwwBj1WmGZ0WmlQGE9isuJMujg9ilCtNDnU3Ayxdb0yU7AyIFtDmvjQPPjhnTAGz9tRn6FS4iv4P04voZhGIZpKbC4acLsKH9TSodbFvEyezSOQNuCYLcQVfwtd3pFdhKJB6/Xh4tjt2Co9St0sf4OKSjWhthl6YF5mvMxz9YHWp8BGoViZuSAy4kqBgdbWcilde/ZnYTg2ZBbAYen0g3lt9IcKU6GBM79Z7ZGoc+If/Ostd6PYRiGYZoSFjdNmB2VnRglKv2aDZVp0/62Bat3lwoDDNWkoZRqt7cy9qat2Y1LNItwgW0hWtvyqxzXp9Hj75jB+NxwIS694CIMAbBq4WZRNTg11ihidBKCrEJEcN2ZPq0ThBg5oXVCncZDFqc+GfE4sU1ig10jhmEYhmlIWNw0YXYUuYNIXPgL31HRO7Lc5JZVtjHIiDeBbB+t3evxkPYHnOtdAQN8VY5Zpk/FH0nD8WfiRbDrEoSra3OBDded3AaPD+8h0sjp86j9QrCw4bgYhmEYJlJgcdOA1YY37C9HwYEypCW5RHAu9WIKzo4irVG98J2/UF48yjHcuRAXyYvRTrOvSiwNsUrqjY8156Ci1dlIiLEElpOAIffQobiYTiKNnLKtOC6GYRiGiURY3DRgtWGvT4Hs80HabcO8P/cKAUNF7SiA2B88HFz4zub0oLv3H1yqWYJh0p8wyFWtNBWIwUL9EHxjGIZ9UpZogpnjlJEQgyM2paSCf1QfhwRUXeJpGIZhGCZcYHHTkNWGdRLyS6zYU+EVAcE0URyNR1ZEETx/8HBbXQkuN3+LE5zfIFlTANHMKYgtpp74wDsYvxtPg6I11dgS4WhNKf2xNHWNp2EYhmGYcIDFTQNWG6bqvJsKHfBRqrWx8tKWObwixTvB5MUA+8+4aMfP6KusPSzjqRSx+NUyFNtaXY4CQ2us2l1yMDansvdU9ZYI3JSSYRiGYWqm2X0Us2bNQk5ODkwmEwYMGICVK1cecdsNGzbg8ssvF9vTC3/mzJloKdWGScBsLbQJKw1ZaCgbyqTVYKC0EZOVWfjccQOmKi/hJOWfgLChCsLrzCdhgjQe50uv4+v0O1BkahNoSmnUS8K15VMUkVkV3BKBWjSQm4tq0rCriWEYhmFaiOVm/vz5GD9+PGbPni2EDYmVYcOGYfPmzUhNTT1se4fDgXbt2uHKK6/EPffcg5ZUbZiK74kgYb0WbZR9GOL7BUO8S5GhKzxs331Iw6qEC7Ap7QKUaFPw02baRoN4i/6ITSmrt0TgonkMwzAM0wLFzYwZMzBmzBiMHj1azJPI+eabbzBnzhxMmDDhsO1POukkMRE1rW9KSGwE94dSrfkY4fsBF+JXdFa3H7a9VTXjZ/2p+EE/BMs87ZFjjEYrjQX7D9jRMS1aiBu/JcgvmEjgHK0lAltsGIZhGKYFiRuPx4PVq1dj4sSJgWWSJGHo0KFYvnx5g32O2+0Wk5+Kigrxr6IoYqovFoO2cn8qXkNiq3QyOqjbqmwjQ8JqbW98Lp+GX3QD4NWYoHhUuHwy8sqc0EkaEa9z/cmtxfZzV+zBrgOVWVfkmiJXl14noXereLFN9ZYIx3P+9YE+j2J9mvpzm5JwHyOPL/ThexjahPv9a8wx1uV4zSZuiouLIcsy0tLSqiyn+U2bNjXY50ybNg1Tpkw5bHlRURFcrsriefUhw+SDqvhQarXDpJPwi/40dPBViptNmvb4QXcGftKegkLEwwUZXVKjIGmAcpcPBVYV3dLMGNEnFV1SLdBpK89j7Mkp2FQYhS1FDjg9lZWMO6VYAtsUFtb/fBvqwSovLxcPLQnRcCTcx8jjC334HoY24X7/GnOMVqu11tuGfbYUWYYorifYcpOdnY2UlBTExla1hNSF05KSsXiHHduL7GifbMK/ycPw1r4KLNGdjgJjTmURPhVwu7yIsxiRlRRTGRDslWE2uXDbWZ3RJzv+sONmZgBnoeU+sOSKo2sXzr+U4TxGHl/ow/cwtAn3+9eYY6TEoxYvbpKTk6HValFQUFBlOc2np6c32OcYjUYxVYcu+PFcdKMkYdSgtqLdwfZiOzLi0/Fh9EiU2d2IA5njENSVO0Z8lkjfLnOKYOCereJD8sGmB/Z4r11LJ9zHyOMLffgehjbhfv8aa4x1OVazXVmDwYC+fftiyZIlVdQezQ8cOBChgL/dAYmVwgqXKNRH1YgLrW7YXD7RlbtnVpzoys3p2wzDMAzTNDSrW4rcRaNGjUK/fv3Qv39/kQput9sD2VMjR45EVlaWiJvxByH/+++/gZ/379+PNWvWIDo6Gh06dGiWMQS3OxAtD3YXYVupV7RdMOgkFNs8QuxQJWFO32YYhmGYMBc3I0aMEIG9kyZNQn5+Pvr06YOFCxcGgoz37NlTxQyVm5uLE044ITD/3HPPiemMM87A0qVL0Vz42x30bhWHwrYmJCQl4988K/d2YhiGYZhmoNkDiseOHSummqguWKgyMcWttHS4txPDMAzDNB/hG83EMAzDMExEwuKGYRiGYZiwgsUNwzAMwzBhBYsbhmEYhmHCChY3DMMwDMOEFSxuGIZhGIYJK1jcMAzDMAwTVrC4YRiGYRgmrGBxwzAMwzBMWNHsFYqbGn+F44qKigY/NjX+tFqtoi17OHZ7DffxRcIYeXyhD9/D0Cbc719jjtH/3q5Np4KIEzd0wYns7OzmPhWGYRiGYerxHo+LizvqNho1FJo1NbCipAacMTEx0Gg0DXpsUpUkmvbu3YvY2FiEG+E+vkgYI48v9OF7GNqE+/1rzDGSXCFhk5mZeUyLUMRZbuiCtGrVqlE/g25muD60kTC+SBgjjy/04XsY2oT7/WusMR7LYuMnPB1+DMMwDMNELCxuGIZhGIYJK1jcNCBGoxGTJ08W/4Yj4T6+SBgjjy/04XsY2oT7/WspY4y4gGKGYRiGYcIbttwwDMMwDBNWsLhhGIZhGCasYHHDMAzDMExYweKGYRiGYZiwgsVNHZk1axZycnJEz4wBAwZg5cqVR93+k08+QZcuXcT2PXv2xLfffotwGd8777wjqjwHT7RfS+WXX37BRRddJKpb0rl+8cUXx9xn6dKlOPHEE0XUf4cOHcSYw2V8NLbq94+m/Px8tESmTZuGk046SVQXT01NxfDhw7F58+Zj7hdKv4P1GWMo/R6+9tpr6NWrV6C428CBA/Hdd9+Fzf2r6/hC6d7VxNNPPy3O+e6770ZLu4csburA/PnzMX78eJHi9tdff6F3794YNmwYCgsLa9z+999/x9VXX43//Oc/+Pvvv8UfKprWr1+PcBgfQb/AeXl5gWn37t1oqdjtdjEmEnC1YefOnbjgggtw5plnYs2aNeIX+KabbsKiRYsQDuPzQy/P4HtIL9WWyM8//4w77rgDK1aswA8//ACv14tzzjlHjPtIhNrvYH3GGEq/h1Qdnl6Iq1evxqpVq3DWWWfhkksuwYYNG8Li/tV1fKF076rz559/4vXXXxdi7mg02z2kVHCmdvTv31+94447AvOyLKuZmZnqtGnTatz+qquuUi+44IIqywYMGKDecsstYTG+t99+W42Li1NDEXr0P//886Nu88ADD6jdu3evsmzEiBHqsGHD1HAY308//SS2Ky0tVUORwsJCcf4///zzEbcJtd/B+owxlH8PiYSEBPW///1vWN6/Y40vVO+d1WpVO3bsqP7www/qGWecoY4bN+6I2zbXPWTLTS3xeDxCjQ8dOrRKnyqaX758eY370PLg7QmyhBxp+1AbH2Gz2dCmTRvRJO1Y31BCjVC6f8dDnz59kJGRgbPPPhvLli1DqFBeXi7+TUxMDNt7WJsxhurvoSzLmDdvnrBKkfsm3O5fbcYXqvfujjvuEFbt6vemJd1DFje1pLi4WDysaWlpVZbT/JFiFGh5XbYPtfF17twZc+bMwZdffon3339fdFwfNGgQ9u3bh3DgSPePOt46nU6EOiRoZs+ejU8//VRM9Md18ODBwiXZ0qFnjdyEp5xyCnr06HHE7ULpd7C+Ywy138N169YhOjpaxLHdeuut+Pzzz9GtW7ewuX91GV+o3TuCBBv9jaD4sNrQXPcw4rqCMw0HfRsJ/kZCv5Rdu3YVftjHH3+cL3ULh/6w0hR8/7Zv344XXngBc+fORUv/5kg++99++w3hSm3HGGq/h/TMUQwbWaUWLFiAUaNGiVijIwmAUKMu4wu1e7d3716MGzdOxIO19MBnFje1JDk5GVqtFgUFBVWW03x6enqN+9DyumwfauOrjl6vxwknnIBt27YhHDjS/aMAQLPZjHCkf//+LV4wjB07Fl9//bXIDqMAzqMRSr+D9R1jqP0eGgwGkXlI9O3bVwSmvvjii+KFHg73ry7jC7V7t3r1apFgQhmkfsjiT8/pK6+8ArfbLd4jLeEesluqDg8sPahLliwJLCMTIs0fyZ9Ky4O3J0jxHs3/Gkrjqw495GSSJXdHOBBK96+hoG+cLfX+UZw0vfTJzP/jjz+ibdu2YXcP6zPGUP89pL8z9FIMh/tX1/GF2r0bMmSIOD/6O+Gf+vXrh2uvvVb8XF3YNOs9bNRw5TBj3rx5qtFoVN955x3133//VW+++WY1Pj5ezc/PF+uvv/56dcKECYHtly1bpup0OvW5555TN27cqE6ePFnV6/XqunXr1HAY35QpU9RFixap27dvV1evXq3+3//9n2oymdQNGzaoLTXC/++//xYTPfozZswQP+/evVusp7HRGP3s2LFDtVgs6v333y/u36xZs1StVqsuXLhQDYfxvfDCC+oXX3yhbt26VTyTlPEgSZK6ePFitSVy2223icySpUuXqnl5eYHJ4XAEtgn138H6jDGUfg/pvCnza+fOneratWvFvEajUb///vuwuH91HV8o3bsjUT1bqqXcQxY3deTll19WW7durRoMBpE6vWLFiio3edSoUVW2//jjj9VOnTqJ7Smt+JtvvlHDZXx33313YNu0tDT1/PPPV//66y+1peJPfa4++cdE/9IYq+/Tp08fMcZ27dqJ1M1wGd/06dPV9u3biz+miYmJ6uDBg9Uff/xRbanUNDaagu9JqP8O1meMofR7eOONN6pt2rQR55qSkqIOGTIk8OIPh/tX1/GF0r2rrbhpKfdQQ/9rXNsQwzAMwzBM08ExNwzDMAzDhBUsbhiGYRiGCStY3DAMwzAME1awuGEYhmEYJqxgccMwDMMwTFjB4oZhGIZhmLCCxQ3DMAzDMGEFixuGYUKKG264AcOHD2/u02AYpgXDjTMZhmkxaDSao66fPHmyaELItUcZhjkaLG4Yhmkx5OXlBX6eP38+Jk2ahM2bNweWRUdHi4lhGOZosFuKYZgWQ3p6emCKi4sTlpzgZSRsqrulBg8ejDvvvBN33303EhISkJaWhjfffBN2ux2jR49GTEwMOnTogO+++67KZ61fvx7nnXeeOCbtc/3116O4uLgZRs0wTEPD4oZhmJDn3XffRXJyMlauXCmEzm233YYrr7wSgwYNwl9//YVzzjlHiBeHwyG2Lysrw1lnnYUTTjgBq1atwsKFC1FQUICrrrqquYfCMEwDwOKGYZiQp3fv3njkkUfQsWNHTJw4ESaTSYidMWPGiGXk3jpw4ADWrl0rtn/llVeEsHnqqafQpUsX8fOcOXPw008/YcuWLc09HIZhjhOOuWEYJuTp1atX4GetVoukpCT07NkzsIzcTkRhYaH4959//hFCpqb4ne3bt6NTp05Nct4MwzQOLG4Yhgl59Hp9lXmK1Qle5s/CUhRF/Guz2XDRRRdh+vTphx0rIyOj0c+XYZjGhcUNwzARx4knnohPP/0UOTk50On4zyDDhBscc8MwTMRxxx13oKSkBFdffTX+/PNP4YpatGiRyK6SZbm5T49hmOOExQ3DMBFHZmYmli1bJoQMZVJRfA6lksfHx0OS+M8iw4Q6GpVLfTIMwzAME0bwVxSGYRiGYcIKFjcMwzAMw4QVLG4YhmEYhgkrWNwwDMMwDBNWsLhhGIZhGCasYHHDMAzDMExYweKGYRiGYZiwgsUNwzAMwzBhBYsbhmEYhmHCChY3DMMwDMOEFSxuGIZhGOb/260DGgAAAIRB9k9tjw9SQIrcAAArOQAHDjyHLzrCAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ode_fit(\n", + " t_span,\n", + " y_noisy,\n", + " t_span,\n", + " y_data,\n", + " title=\"Synthetic Logistic Growth Data\",\n", + " xlabel=\"Time\",\n", + " ylabel=\"Population\",\n", + " show=False,\n", + ")\n", + "\n", + "# Update legend\n", + "ax.legend([\"Noisy observations\", \"True solution\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build the Optimisation Problem\n", + "\n", + "Use `DiffsolBuilder` for ODE parameter fitting:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:30.232622Z", + "iopub.status.busy": "2026-01-10T22:21:30.232409Z", + "iopub.status.idle": "2026-01-10T22:21:30.236659Z", + "shell.execute_reply": "2026-01-10T22:21:30.236189Z" + } + }, + "outputs": [], + "source": "# Create builder\nbuilder = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_tolerances(1e-6, 1e-8) # Relative and absolute tolerances\n .with_parameter(\"r\", 10.0) # Initial guess (deliberately wrong)\n .with_parameter(\"k\", 10.0) # Initial guess (deliberately wrong)\n .with_parallel(True) # Enable parallel evaluation\n)\n\nproblem = builder.build()\n\nprint(\"Problem built successfully!\")\nprint(\"\\nInitial parameter guesses: r = 50.0, k = 50.0\")\nprint(f\"(Far from true values: r = {r_true}, k = {k_true})\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimise with CMA-ES\n", + "\n", + "CMA-ES works well for ODE parameter fitting:" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "# Create optimiser\noptimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12)\n\n# Run optimisation\nresult = optimiser.run(problem, problem.initial_values())\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"OPTIMISATION RESULTS\")\nprint(\"=\" * 60)\nprint(f\"Success: {result.success}\")\nprint(\"\\nFitted parameters:\")\nprint(f\" r = {result.x[0]:.6f} (true: {r_true})\")\nprint(f\" k = {result.x[1]:.6f} (true: {k_true})\")\nprint(\"\\nOptimisation details:\")\nprint(f\" Final cost: {result.value:.3e}\")\nprint(f\" Iterations: {result.iterations}\")\nprint(f\" Function evaluations: {result.evaluations}\")\nprint(f\" Time: {result.time.microseconds / 1e3:.3f} milliseconds\")\nprint(f\" Message: {result.message}\")\n\n# Calculate parameter errors\nr_error = abs(result.x[0] - r_true) / r_true * 100\nk_error = abs(result.x[1] - k_true) / k_true * 100\nprint(\"\\nParameter errors:\")\nprint(f\" r: {r_error:.3f}%\")\nprint(f\" k: {k_error:.3f}%\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise the Fit\n", + "\n", + "Let's plot the fitted model against the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:34.764024Z", + "iopub.status.busy": "2026-01-10T22:21:34.763803Z", + "iopub.status.idle": "2026-01-10T22:21:35.052762Z", + "shell.execute_reply": "2026-01-10T22:21:35.052223Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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lXAJuvQbdLpdLXZteP7DtFcdGnzgu+sRx0SeOiz5xXPSJ46JPHBd9CrSiGOZgS4z1ffVERERERERErRiDbiIiIiIiIqJmwqCbiIiIiIiIqJlwTfcxXJPg8Xha7Lm9Xq9aE6H39RDtjYyNz+dTPzk2RERERERtD4PuY0CC7Z07d6rAqqX6x8lzSw859hHXl+DYlJWVoVu3brBarS19SUREREREdBQx6D4GQdX+/fthMplUOfmWyGbKNUg21Ww2M+jWGRkbmYWQnZ2tPiedOnXiGBERERERtSEMupuZBLsVFRXo2LEjwsLC0BIYdOuXjI18GZKYmIh9+/apz4vFYmnpyyIiIiIioqOEC3ybmd/vVz85bZgaEwy0g58XIiIiIiJqGxh0HyNcS038fBARERERtT8MuomIiIiIiIiaCYNuanZdunTBM888c8ze6fnz56uZBUVFRcfsOYmIiIiIiOrDoLuVCAQ07Mgtw+q9Reqn3G9Ol19+uQpcH3/88Rrbv/jii0OeKr9s2TJce+210LOxY8fi1ltvbenLICIiIiKiNobVy1uBdZnF+GxFBrbllMHtDcBmMSI90YkpQ1PRPyWq2Z7Xbrfj3//+N6677jrExMQc9nkSEhKO6nUREREREVEbTzjmlWNPVjnKjOXoFu+E0XhoiT89Yaa7FQTcz83ZirUZxYh2WNElPlz9lPuyXR5vLhMmTEBSUhIee+yxRvf77LPP0K9fP9hsNjWV/L///W+D08ulRdaDDz6o+lHL/tJK7eabb1aPPfzww+jfv3+d8w8ePBj33Xdfg88/a9Ys9OzZEw6HA6eccgp27dpV4/H8/HxcfPHFSElJUW3bBgwYgA8++KBGVn/BggV49tlnVRZfbnIOqSR+1VVXoWvXrurcvXr1UvsQEREREVHzWJdZjEdmbsBDX23Acz9nqJ9yvznjnubGoFvn3/BIhrug3KMy2067GSajQf2U+7J9xorMZptqbjKZ8Oijj+L5559HRkZGvfssX74cf/zjH3HRRRdh7dq1KqCWAPmtt95qMEB/+umn8corr2Dr1q1quroEweLKK6/Exo0b1XT0oJUrV2LNmjW44oor6j3f3r17MXnyZJxzzjlYtWoVrr76atx999019nG5XBg2bBhmzpyJdevWqanul156KZYuXaoel0B61KhRuOaaa7B//351S0tLQyAQQGpqKj755BNs2LAB999/P/7+97/j448/Puz3lIiIiIiovQs0sHQ2lHDcW4T+hu241v8xuptzj0nCsTlxermO7covV1PKk6McddZRy33ZvjWnVO3XLcHZLNdw/vnnq0zzAw88gNdff73O40899RTGjx8fykRLxlkC1CeeeEJlkGvbs2ePyp5LFl16U0vGe8SIEeoxCXAnTpyIN998E8cdd5zaJn8++eST0a1bt3qv7+WXX0b37t1D2XXJRkvwL9PigyTDfccdd4Tu33TTTfjuu+9U8CzPHRUVpfqoSxZcrq36lw4PPfRQ6L5kvBctWqSOky8aiIiIiIioLgmiJUYpdfkQYTejS1x4aHp4Q0tnJw+Iw7KfPsHA/O8A41bkBTw4vagAmj0KrsSL1P6ScOybHNnqppoz6NYx+ZDKB9ERZar3cYfVhOySgNqvOUkAO27cuBqBa5Bkps8777wa28aMGaOmk8v0bAlcq/vDH/6gHpMg+vTTT8eZZ56pstRmc9VHUbLNkvGWYN5oNOL9999XmfGGyPOPHDmyxjbJWlcn1yEZewmWMzMz4fF44Ha7VZB9MC+++CLeeOMN9WVBZWWlOla+hCAiIiIiokOrRyUkYy0zdiWBmBhWiO5FC9Fj7c/wrFuHlVEOaPZgQG3CLosZfUoX4pfEi49JwrG5MOjWMflWSD6klR6/mlJem2yXx2W/5nTSSSepDPQ999xTb/b6UMi07c2bN+PHH3/EDz/8gBtuuEFlxWVNtWS+JQCXtd6ff/65yj57vV5ccMEFR/Sccn6ZQi7BvkxlDw8PV5XKJYBuzIcffqi+aJAsugTyERER6lxLliw5oushIiIiImqrWezqQbUkDys9fjU9PKOgAg6LCY7ijTjT8jMGZa7CsPLNofOWGA3QDFVJsQAMCBhjMCP2VHhjLjimCcfmwKBbx+QDLN8KyYc03easMcVcCpLtL67EwNRotV9zk9ZhkuGV6dvV9enTBwsXLqyxTe7LNPPaWe4gKUomwbXc/vKXv6B3795qSvjQoUNVxnvatGlqWrkE3bJWXPZviDz/V199VWPb4sWL61yPZOOnTp2q7sta7S1btqBv376hfeS5JCNe+7jRo0erLwaCtm/f3si7RERERETUPrPYMu27ej2qYOwSYTOgZ9gGePNnAYaNyLS6sUIzwmmsxLBq59YQDVtlKtzh/WCNORV2YxdkuNxwWBzHNOHYHFrfFbcj8o2RfIAzCytDa7vlGx75wEnAHRtuxeShKcdkTYNkiC+55BI899xzNbb/9a9/VeuvH3nkEVx44YVqzfMLL7yAl156qd7zSIE1CW5lSrhM737vvfdUUN25c+fQPlIMTYJpUTugr+36669Xmeg777xTHSeF3WoXcevRowc+/fRT/Prrr6r1mUxdz87OrhF0S4V1yWBL1XKn04nY2Fh13DvvvKPWf8t67nfffVcVeZM/ExERERG1J41lsSVekbglGLNYNTeSSxbAVDEPJf6t2GL2Q3MYatTyXmOz4gRvN2yKPBEbI8dgt7Unftmaj1ijFYPio1E9wjnWCcejjdXLdU76cN88vgcGpEahqNKDXXnl6qd84GR7c/bprk1aekmWuDrJTstaaZmKLe2+pMK37NfQNPTo6Gi89tprat33wIED1TTzr7/+GnFxcaF9JNiVDLNkwGuv165NCrFJRXSpgj5o0CBMnz5drd+u7t5771XXKVPkx44dq4qlTZo0qcY+Mo1cMvMSiEtfcVnDLf3JpTK6fJkg1yGtx6pnvYmIiIiI2oOmdFWas3ILjiv5ERNy74Z1/0VY75qONcbN2GUJQDuQ9ZYa5T5EItx6HCpj78ZzPd7CD0nXICOsLyp9QMdoB2KdVhW8l7l88Ac09VPuH8uE49Fm0ORrAzoiJSUlqgJ2cXExIiMj67Sr2rlzp8qO2u32Zlk7cTAyxD6fT03drl0FXY/keiXwlgD39ttvR1sWHBu5SZb9SD8ndHTIl0s5OTlITExUBf1IHzgu+sRx0SeOiz5xXPSJ43Jw0tbrga/WI9phrVFrKtyTh16lP6Nf4U/oUbESFviw2WLB87HRv7+/MMJsjIfV0A8riofDYe+NAR1j6iydlcBaEovnD+mIGSszsS27DKWVLkQ47OjRIUIF3Mcy4XikcWB1nF7eSkiA3dqq9B2O3NxclTXPyspqsDc3EREREREdO9W7KkW4MxFT+g287qXINOYhuawcfStdoX3TvV5YNTMM5o6wWofBEzYBZmMStueWY0CqAxVef6NLZyWw7tcxCjvyyrBnXw46dUxEt3hnq8xwBzHoJl2RzGJ8fDxeffVVtf6aiIiIiIhalrNyF47z/h9M+1dhn7EQ+2QmoIokjVhts+GEShfyTIko73o6Pi4fgj3+zkh2xsJkNcHj8WN3cbkKqq85qZs6X7AYm1Qjl+JokuGunslWCcf4cDgD4UiMb/oMX71i0E26wtUOREREREQtrzJ7PdavehOr9i7ABk8BPOaahdBEwODAvrB+uNN4PiK6HId7z+6LM/aXoOIgQXXf5MjDXjrbGjHoJiIiIiIiIiBvG7D+c3VbVLYDMyIOLG+ttv46gDBEGNOBsPEoM47A5hIvYqOtmDYsVQXOElgfLKg2tpOls0EMuomIiIiIiNqJ2gWaE/27sWHNm+i041ckZa0P7TfIaAwF3U5bFAZ2GIrYxLOwaH837MithLtcstiBOlns9hhUHwyDbiIiIiIionbSa1u1/tq3HomuWTAaViHLXAq/wYDx5RU4v9q+cR2H4dy0AejSexK6p50Ak9Gktp92BF2V2isG3URERERERDpwJG2CD2bNprWYPfd5WHzLAHMJ9luD5636udJuw6SoXjD0mwz0PQ+I7oTT6jkPs9iHjkE3ERERERGRTrLQUoBM2nNJAbL0RCemDE09pP7U1QP3KJQgsPtTzNv6OdZW7oNP1mZbZK/fA3mzwQmTrwdMiechMOVPqn+2On5vETPZRwmDbiIiIiIiohYOuJ+bsxUF5Z6q/tVRVf2r12YUI7OwEjeP79GkwFvO8/WyzYjZ8wNGlM1DqmcFNlpNWBkTVaMYmgXhiDL3gTf8DPhtQ1Du9mNbqQc/bszG4p0FRxz4U02/13snaqKxY8fi1ltvPWbv11tvvYXo6Ohj9nyN2bVrl/r2b9WqVbp9v4iIiIio9ZDMtFpnXe5RAa7TbobJaFA/5b5sn7EiU+3XEM3rxoJ5L+Kbz8/DaRsm4fqC/2CoZxnM8KO3xwNHIACjZkO8cRCiI++CLfEduOP+gYB9qPrd1mE1oaDMgzcW7lKBfrTDii7x4eqn3JcvBCSgp8PDTDfV6/LLL8fbb79dZ/vWrVsxY8YMWCxqXorSpUsXFVRWDywlUJb7RUVFfIeJiIiIiBogU7klsywZbgmAq5P7sn1rTqnar3pFcM3vR8aWr/Hb+vexPHcNiuADTMBvYSYM81TtU2RJxOqo8Sjx9sGayt4Y3ikBToel2uTyKhVuHworPTAYgf4do0LXoQJ/m1NdnwT+0gqMRdMOHYNuatDpp5+ON998s8a2hIQEmExVlQuJiIiIiOjIyNprmcotU8rrI1no7JKA2k+y3WvXzcWmjW9jc+Fy5GjuOvtvtobj57hTsD56IvaE9YdmMMJS6YVhbyF2FpTXCKqFplWtARddY8MPKfCnpuH0cmqQzWZDUlJSjZsE3NWnS8ufd+/ejdtuu039hZTb/PnzccUVV6C4uDi07cEHH1T7u91u3HHHHUhJSUF4eDhGjhyp9q9OsuSdOnVCWFgYzj//fOTn5zdpyvfHH3+ME088EQ6HA8cddxy2bNmCZcuWYfjw4XA6nTjjjDOQm5sbOi4QCODhhx9Gamqqeq2DBw/G7Nmza5x76dKlGDJkCOx2uzrPypUr6zz/unXr1LnlOTp06IBLL70UeXl5/GQRERER0UFJlXJZOy1ruOsj2xONRchb8jgemT4Ery28Dj8X/Foj4DbAiAh/RyTa/wRr4tuYlXIndocPVAG3CLOZEeOwItxqVlnrMpcP/oCmfsr9cHk8zAqHzdxg4C9fDEjgT4eOQTcdEZlqLkGrBK/79+9Xt9GjR+OZZ55BZGRkaJsE2uLGG2/EokWL8OGHH2LNmjX4wx/+oDLqMm1dLFmyBFdddZXaT9ZNn3LKKfjnP//ZpGt54IEHcO+992LFihUwm83405/+hL/97W949tln8fPPP2Pbtm24//77Q/vL9v/+97948skn1bVMnDgR5557buhaysrKcPbZZ6Nv375Yvny5+uIg+DqCZPr8uHHjVGD+22+/qaA9Ozsbf/zjH/nJIiIiIqKDkrZgsnZ7f3GlyjoHWQIuDCz8HlfuugPP7fsT+m18FbmGstDjsqfTH4dow5mYOOIzZDieQK7tfBiMYfUG7rFOK64c0wUDUqNQVOnBrrxy9XNgajSuHNMVseHWRgN/+WJAviCgQ8d3rSW8cjJQlnNMn9Isfy2dHYDrFjT5mG+++UZlb4Mkm/vJJ5/U2Cc2NlZlvyMiIlQmPCgqqmraSvVte/bsUdPV5WfHjh3VNgliJVCV7Y8++qgKhCUIl2BZ9OzZE7/++mudDHR95FwSOItbbrkFF198MebMmYMxY8aobRLMSxY9SILtu+66CxdddJG6/+9//xvz5s1TXxi8+OKLeP/991U2/PXXX1eZ7n79+iEjIwN//vOfQ+d44YUXVMAt1x70xhtvIC0tTWXa5fqJiIiIiBrrwy3VwaVK+bbsYgywLoTV+yOGlW7BaRW/Fy9L8gNpXi+KzLGIsA5DRcT58JnTsCmnDJZMI9ITnFibWazWYNeePi4BvQTXp/ZNUrfa1yAW78xXRdMaOz64Lx0aBt0tQQLu0n3H7OmCf2W0OiUTGidZ5pdffjl0X6aDH4m1a9fC7/fXCURlynlcXJz688aNG9WU8upGjRrVpKB74MCBoT/LNG8xYMCAGttycqq+7CgpKcG+fftCAXmQ3F+9enXoWuScEnBXv5bqZF8J1Kt/ORG0fft2Bt1ERERE7UhDQfXB+nB3wBaMdbyINWVLkeutqoLmcxhxakXV7/K5pg5YHnUqtLgJgCMdpQeeTx6T9dbbcsswdWQnZBZVhoqyyZRwyVBLwCxZ7MlDU0LXUt+67FDg34Tj6dAw6G4JzsRj+nRVk1S0Q35eCbLT09OP2nXIdG3JistU7drF2OoLWg9V9YrqwW/nam+TzPXRJK/pnHPOUVny2pKTk4/qcxERERGRfjUWVIvafbi9FTlw7XwdH+1ZjkLjgYx2tZi20GJDwaCLUJgyGf9Y7kTnhAjVSqyhQmtJUQ7Vzzt4DbJNrkEy1BIwH6zPtjx+JMdTwxh0t4RDmOJ9VGgafD6fWufcHKxWq8pgH2ybTMOWbZJtloJn9enTp49a113d4sWLj/o1y3pzmeK+cOFCnHzyyaHtcn/EiBGha3n33XfhcrlC2e7a1zJ06FB89tlnqm1ac72/RERERKT/gLt2UC1ZYpmunVFQAYfVrB7rkeBAavlCFBZ/hhwtA6WWmr23JaTuE56KET3OwcAhV8Nqj0Jxbhmsa9er80kLr8bWW0sGW9p6NZRtPxgJrI/keKofC6nREZOA86effkJmZmaoardskyywrKmWbRUVFWqq9SWXXILLLrtMFWDbuXOnqg7+2GOPYebMmeq4m2++WU0ll/XWUtBM1kw3ZWr54bjzzjtVhvqjjz7C5s2bcffdd6vibbIeXEghNsmOX3PNNdiwYQNmzZqlrqu6v/zlLygoKFDrx6VSukwp/+6771T19tpfOhARERFR25xSLtlhCaolsy2BsWSkVY9rVSDNhX07N+Aq7/u4a/MfceWue5Gv7UHA8HvAHeF3YHzH0/HPP87CDZfNx/BRf1UBd2OF1qqvt+6RGBFaby0BsgTfg9Ki1c9DDZiP9HhqJ0G3FMGSoE+yk9KSSgK7xkjhrF69eqlWU1IAS9pfSXaTmkYql0vbru7du6s+3kIqmF9//fW48MIL1bb//Oc/arsUTJOg+69//at6zydNmqSCVWkRJo4//ni89tprqqDaoEGD8P3336uK5M1BAvzbb79dXYus/Zbg/quvvkKPHj1CU96//vprtRZdsvT/+Mc/6kwjD2bLJcA+7bTT1HmknVp0dDSMxjb514uIiIiIqpGscHAddPUCZAZ/MaKK38AU/5341HMDzih4F9HeHDg0DYNdbtg1E1INvZEUfge2maaj29B/IiqubhHeYKE1WVddX7svrrfWP4NW++uSVk6ylhLUTZ8+XQXcElBLxW3JZCYm1l3TLBWqr7zySlVxWgJFqTh9+eWXq4rWTz31VJOeU4pySbVu6Ust05ark+BdMrpdu3atUZDrWNKqTS+v3eyeWlZwbOQmX1y05OeEfidr/2UZhPw/g1+e6AfHRZ84LvrEcdEnjkvbHJfVe4vwr5kb0SU+HEZoCK+cD5TPQm5gJwKGAKyahsdy82DTgACM2BJxPBbGTsB254nQTA4VPEvrrofO7VdvgbPG1oxLhrutrrcOtILfxxqLA6trc4tQJVCW6cAyvVdI8C1TlyWolunDtUk7KqlYLVOJhWTIZapw7XXFREREREREtcm650hjNsLzv0B5YAWyUVH1wIFcl8dgwFxbR2QbT0Vmp0kos1bNDD3Udlxcb916tamg2+PxqMrY99xzT2ibfCsyYcIELFq0qN5jJLv93nvvqSnoUkBrx44dau3upZde2uDzSIsruVX/hiP4bUzt6thyX/4yBW8tJfjcbWxiQ5tQfWzq+wzRsRf8e8ux0BeOiz5xXPSJ46JPHJeWfv817CqoQKnLiwi7BV1iw9TU7eC4+Hx+7Ckqr/N4w+fzYe2a97Bo40ew+Xcg90DPoCCbZkAHdEOmfzxmpY5DpV9DUaEHyZHe39txlVS14zp/iHS9kd89Dv67epe4sGr3mnZMaxRoBb+PNfXa2lTQLQW7ZG1tsEdzkNzftGlTvcdIhluOO+GEE0JTfWUt8t///vcGn0cKfz300EN1tufm5tZZC+71etVgBKcQtwR5XcGiXpxeri/BsQl+TvLz82u0OaOWIWMh04RkfPQ6nak94rjoE8dFnzgu+sRxaTmbcyowa0M+dha44PEHYDUZ0TXWjjP7xqFHvB3Ld+Tg5wV7savAXefxXolVQW5A05BR5IanKAud932DpN1f4AtrOfJNplCnL4OmISEQBatpDPZYz8bCChuiHWZcOTBWPV51DeWh50iPs+OMPnFIMLvVVGpqXX9fSkuDHdPbUdB9OObPn49HH30UL730kloDvm3bNlW9+pFHHsF9991X7zGSSZcCXNUz3VKATQqG1bemWwZD1lO3dEspBnP6Jv8ziYuL45punfxPXr6gkr/Tev2ffHvEcdEnjos+cVz0iePSMtbvK8Z7q/aiULXzCg9lmbcXVuK9Vfk4s18SPt9Qhgq/od7Hbx4XA5/Phbk/vY6x+37BKNcSmFCV4RwVHoZvnOGIgRn9YoYiA+didVlXuH1+2GDC0C5OTB7aEf06Vq25HtO3c73Zdmqdf1+aWoupTQXd8fHxMJlMyM7OrrFd7iclJdV7jATWMpX86quvVvel+nR5eTmuvfZaVa26vgG22WzqVpvsW3t/uS8fluCtJci3Q8HnZqZbX2qPTX2fIWoZHA994rjoE8dFnzgu+sRxObZk6vWMlftQWO5FemJE6Pcup8OIdLsZW7NL8dovO2ExBNCnYwwMB34PCz6+L2sVPvnmPyjS1iMADy71FoQCbhGr9cKUHufg5JNvgNHiqJrC3kiPazm9XAe1jb8vTb2uNhV0W61WDBs2TPWGllZUwW9I5P6NN95Y7zHSP7r2myWBu+D6ZyIiIiKittfOS8j9SIcV23ML0bfD748HAi6ElX0Ft2suIozZKKi2ZPpXhx2nuBz4LfZs/BZzBpYVRWJgZTRONtlr9LgmarNBt5Bp39OmTcPw4cNVYTRpGSaZ62A1c2knlpKSotZli3POOUdVPJc+zMHp5ZL9lu3B4JuIiIiIiFofyThLey1HVP2/15uMgC+gwWQ0wODZCmvZxyj0rUU5POrxYJhu1jSkasnYFXcm/hN5BgKGqjAqOcqHrTmlKrhnsE3tJui+8MILVUGz+++/H1lZWRg8eDBmz54dKq62Z8+eGpnte++9V32rJT8zMzPVmgEJuP/1r3+14KsgIiIiIqIjJVO8pZ+1rNF22uuGPn6/huMNG9Cz8gvs9meGguygWJ8Z7oq+QOJU5Ef1qHO8rP/OLgmo4J6o3QTdQqaSNzSdXAqnVSfFzR544AF1IyIiIiKitkPWVKcnOrE2oxjpNmdoCrndX4ohBbMxJGcG0rAXKypseMMaGcpqd9KSYAw7E3NKRiNfC2CYNQb1TRqXYF6CegnuiRrCTwe1WfIFyymnnILCwkJER0cf9nl27dqFrl27YuXKlWrmBBERERG1DrLGesrQVGQWVmJrTgm62hbD4v0W5xdswkB3WWi/gW43OnkMsJqHojT6Euw0pGF/cSWSoi3ommjGvqJKVVit+rpwqf8k+wxMjVbBPVFDGHRTHQercC6zAh588ME2+c5dfvnlKCoqwhdffBHaJu3g9u/fr6rjExEREVHrkhbtwUlxn2J5xXdwuUvhArDYbsRAd9XjWqdR2NLhHHi8o7A23wt3aQA2i0cF05OHpqh9npuzNVSQLdhSTALu2HCr2odtv6gxDLqpDgkwgz766CO1Pn7z5s2hbU6ns8Y3fH6/v8V7kDcnKajXUMs5IiIiItIf+R11e8av+Gn5S1iVvQKBgLfG4/utdniG/wHW466GltAbsTk5uDs+AXsKK+tt93Xz+B74bEWGCrxlDbdMKQ8G5f1TqnpwEzVEnw3PqEVJgBm8RUVFqcx38P6mTZsQERGBb7/9VrVnk37lv/zyi8oQB9u0Bd16660YO3Zs6L60b5Oq8TJV2+FwYNCgQfj0008bvZaXXnoJPXr0UI3npRjeBRdcEHrM7Xbj5ptvRmJionr8hBNOwLJlyxo8l2Tna08Pl+r2Xbp0CT3+9ttv48svvwz1VZcp6jK9XP68atWq0HELFixQ1fHl9ScnJ+Puu++Gz/d7AQ153XJtf/vb3xAbG6veu7Y6O4CIiIhID6RH9qb9efhkzlN4+J2xePqbaVixf0ko4DZoGgaYo/Hn/lfh/qtWwHr2U0CHvqHjg+2+BqVFq5/Vs9cSWN93Vl88dG4//OOsPurnvWf1YcBNTdJ205PUrCTIfPLJJ9GtWzfExMQ06RgJuN977z1Mnz5dBdI//fQTpk6dqirGn3zyyXX2/+2331Tg+u6772L06NEoKCjAzz//HHpcAtrPPvtMBcqdO3fGf/7zH0ycOFG1fZNA91Ddcccd2LhxI0pKSvDmm2+qbXKeffv21dhPqtyfeeaZ6ouGd955R30Rcc0116jAv3pgLdclLeyWLFmCRYsWqf3HjBmDU0899ZCvjYiIiKi1B8TSVqu+LPLRsGHXPuyc+zoScj7GgqhKtS149nBNw+iYvjhh+F8Qlz5R1lIe1nOwBzcdLgbdLWTOnjmYu2fuQfdLi0jD9YOur7Ft+urp2Fu696DHjus0DuM7jUdzePjhhw8peJSs9KOPPooff/wRo0aNUtskYJcs+SuvvFJv0C3t3cLDw3H22Wer7LoE1tJPXUjv9ZdffhlvvfUWzjjjDLXttddeww8//IDXX38dd9555yG/Jpk2Lxl4udbGppNL9l3Web/wwgsqA967d28VmN91111qKn6wJd3AgQNDVfHlSwbZf86cOQy6iYiIqF1Zl1kcmpotPbNlarZUFJcCZ0cyNVumkJdkr4H3l3fQZcMH6BsoV9sXhEVjt8WCJK8JTtMITJhwD/r3+j2jTXSsMehuIS6fC8Xu4oPuF2Orm0Uu85Q16Vh5juYyfPjwQ9pfss8VFRV1Ak6PxxMKpGuTfSXQluD89NNPV7fzzz8fYWFh2L59O7xer8ocB1ksFjXlW7LVzUnOL18cVC84J9dRVlaGjIwMdOrUKRR0VyfT0HNycpr12oiIiIj0FnBLEbKCck9VEbKoqiJk0sJLKorLWulDDbwrPOVYsup1LNj0KaxFe3BPQWGN/tojfakIc05EZsLZWJXrQskWoG8PjcXOqMUw6G4hdrMdUbaD/w/GaXXWu60px8pzNBfJQFcn2V35trE6CYqDJCAVM2fOREpKVRXIIFkXXR/Jbq9YsUKtq/7+++9VFlmmbze2brsxB7vGo02+BKhOgnRZ105ERETUXqaUS4ZbAm7JbAcTFk67WfXMlsz3jBWZ6Jsc2aSAOKtoJxYseRpLMn6Cx3Og3ZfFjG0WCzr7DFgdfRp+jZ+CLEeP0DHJUQZszSlVU9tlnTZRS2DQ3UJk2vfhTv2uPd1cD2Rd9rp162psk8JjwcCzb9++KriWKeP1TSVviFRFnzBhgrrJVG3ptz137ly1dttqtWLhwoUqGx4MoCUglwJuDV1jVlaWCryD/9OvXhxNyDmlGntj+vTpo9aSVz+PXId8SZCamtrk10ZERETUlkmgG2yzVbslrdyX7QcLiOX3rQ2752He8pexMW8tDIHfC9eK7poV34adj4LkS+G21p0hKu29pNq4rCUnaikMuumoGDduHJ544glVWEymXkvBNAnCg1PHJSCVQmW33XabyvZKpfHi4mIVrEZGRmLatGl1zvnNN99gx44dOOmkk1SxtlmzZqlje/XqpTLtf/7zn9XabSl2JlO6pZCaTGG/6qqr6r1GqSiem5ur9pMq6LNnz1ZV2OX5g6SS+XfffadapMXFxanq7bXdcMMNqur5TTfdhBtvvFHtK18ISNG04HpuIiIiovZOAl1Zwy1TyuvTaECsaVi4+k3MWf8uskv2qunjwbDdqmnoHojHyH6XI7bvxXjgm82IDlhRX9guU9llDbkUbyNqKYwQ6KiQzPN9992nKoofd9xxKC0txWWXXVZjn0ceeUTtI1XMJVssa7Rlurm0EKuPZLVnzJihAnrZX6qef/DBB+jXr596/PHHH8eUKVNw6aWXYujQoWrduATMDVVTl3NIEbQXX3xRtStbunSp+iKgOqlCLkG9rFmXzLh8KVCbTI+XLwDkeDnP9ddfrwL9e++99wjeQSIiIqK2RQJdCXgl8MVBAmKZir4jtwyrd+cie+F70F4bj03z7kfOgYBbRPs1DAj0RKz9IcwzPY1X9x6HMi/U1PX9xZV1lhHKfdneIzFCVUsnaikGrfankw6ZtJiSjKhkbqtnTYXL5cLOnTtVYCktpVqCDLH0kJap2rWn9lDLCo6N3KQfeEt+Tuh3MqNCit5JD3jOXtAPjos+cVz0ieOiT+1tXCSQfmTmBlU0rfqa7uDvQDL1fGBqNM4f0hEfLJ6HAXvn47TSbxAXyFP77LCY8VRsDFK9RsRZT0B29KUot8bXe/zzc7f9XqzNWlWsTQLu2HDrQYu1tbdxaS0CrWBcGosDq+M8CyIiIiIiOuqkOJq0BZMq5cG13dUD4pgwMzo4fsNr37wNr7YX3d1FiAu4fz8eaejomojshMkoDgtrcE14uM2sAutgWzKZsi4ZdAnIJw9NOaK2ZERHA4NuIiIiIiI64qy2FEST9dkyXVymc0vQLQFv7YDYbPagV+Q8+L0/Ys3WLDV9XFZ9zw0LwzCXB5siT8DCuD/g84IuKKj04WSb/aBrwgelRasq6PVdA1FLY9BNRERERERH1Is7GFRL4TTJMst0cslyS9AtNwmIV2XswrI1L2FT9lwUlxSrY4MhcZJPQ6J5GJ7qeSUK7J2qtgVc2F9aiNwyN5KiHActkiYBNtuCkR4x6CYiIiIiosMOuJ+bs/X39dRRVdPHZR23TCuXLHeH8EJ8tfBfWLVvMTRfZY3ju3mMiLKdgozES7HVUnMaeLzTCpvJiKxiFzpE2uusCZcp6jKFnEXSSO8YdBMRERER0WFNKZcMtwTc1QulOe1mpNucyNu/B4Vfv4MOJV9hldME7cDjFk3DCEssBqRfiie3D0VEWDiclrphicsbQMdoB8JspnrXhEuRNFmzzSnkpHcMuo8RFoknfj6IiIioLZH108FgOBhwBzQfwlzrcEb+dxhSOBsWzau2D7ZEYKvVipMj0nHi8bfB2eN0BDSga9mB6ua2utXNJbAe0ilGVSefsTKTRdKo1WLQ3cwsFov6H0hubq7q+9wSLbvYMky/ZGy8Xi+KiorUZ0M+L0REREStgRQskzXcMqXcF6hAoOIrVLh+hNmTg6EFBaFAI2C04A+dz4Bj1E2wJA8MHS81zhqrbh7MZMua8H4do1gkjVotBt3NzGQyITU1FRkZGaoPc0sFdtLnTvrbsU+3vgTHRnqoy+dEPi9ERERErYEUMDOZS+Aqehdu/yJYAqWqCnmxyYTldhsGesyYG34m+k++C5279qj3HPVVN6+v3ReLpFFrxqD7GHA6nejRo4fKaLYECery8/MRFxen28by7VVwbJKSkpjlJiIiolbTDmx/yW78tOIpRPrnIRCoRHCunkHT0NtnwZaYi/GC72z06JSCMzqnN3r+YHVztvuitopB9zEiGcyWymJKYCfTlu12O4NunQmODTPcRERE1BragVnMO9HBOAMu30YY/e5QsC3F0fp6wmEIn4J1UWcgo8SH2IimFzpjJpvaMgbdRERERETUaDuwHuEuTHB9jqyKmVjkMCI4dzIsEMDxlmQU2y/GPM8wuHyAzaXVmR5O1J4x6CYiIiIiojpTyj9Zvhso2YXbtFk4buc3sGpu5BuNWGyPRUwggF7oislnPgRHtxPV/ifXmoLOVl5EVRh0ExERERFRiNvvxlfL/od9+97ABRU7MKaiPPRYdMCAU/0Dsdo5FbMDqRgV0Q/dOD2cqFEMuomIiIiICOXecvy09h3MW/8+ysv2wwZgQZgd4yrKoRnsWBp3Ln6JvxDF1g7wBzS486oy20TUOAbdRERERETtrPp49anfxa4izF35Kn7e8jk8FblqW/DRZL8B3yZegtXxF6PCHB06RnppS2svOR8RNY5/S4iIiIiI2kn1cQmU0xOdmDI0FckxXvy4/EUs2v4NfJWFoWOk7dcwzQqHdSK+DpyNzA6JMBh+D9I1TcP+4kpVLE0CeCJqHINuIiIiIqI2Xn08OcoBR5RJZajX7S1EbMZs7LT8Dx5vSWh/s6ZhFMIwfsg1iB9+LdbluDFnzlYVsKvjrVXHS8AdG970dmBE7R2DbiIiIiKiNjilXDLcEnBLZlsy1QbNjzGV8zC2+F0kuXfgc2c45oSHwappONEYhXEjbkbU4KmAqSpE6J9ix83je4Qy5dklVZlytgMjOjQMuomIiIiI2hhZwx3MUFcGMmAo/QB/zV6BNHdGaJ9xFZUwOlMx4YQ7EN7vAsAY7L79O+mz3Tc5stE14UTUOAbdRERERERtjATIZd5diMQMuEvWqR7b68zlSHNXPb7b0Q/v2y/EmWdehvBOMY2eSwLsbgnOY3PhRG0Qg24iIiIiojZSndxpM8Gr7cSsZU8gActh8HhgPbDfapsNPdAL8zpcjjWmgShyeXGhw3LQ6uZEdGQYdBMRERERtfLq5FtzSlHu3YpYw6ewaFth1n4PtuP8fvQNdEJe9DV43Tmoqvp4Tplam13u9uGRmRvqrW4uU8uJ6Mgx6CYiIiIi0qnGstDB6uSF5XsQY34bTmyBOeANHZvg8yHd3wWrjZfil/ihVdXHXb5Q9fFBaVF4fu62OtXN12YUI7OwUhVRY+BNdOQYdBMRERER6TSobqjHthQ3+/K3bRid8yHOcH+KJ6KNcB0ohJbk86GTpytcHf+CgSdOwLaVmSqzXb36+KQhHfH5yswa1c2F025Gus2pnnPGikz1PJxqTnRkGHQTEREREbWAxoJqUV+P7TUZRcjI34m/Rq7FjeumIypQpPYdWxGGNbJm29ALmTHXYpmxG4oqPJhgM+O+s/rWCeyrVzcPBtxBcl+2y5R12Y9F1IiODINuIiIiIqJjLDg1vL6p3RkFFXBYzTWy0LIOWzNtRrz1fVgrtiB1dxacmqbOFYABHc0jsTXucqxwdFfbHAFNZbYl0K6v+rhsl0Bfnrc+MhU9eDwRHZm6zfjagBdffBFdunSB3W7HyJEjsXTp0gb3HTt2rPofWe3bWWeddUyvmYiIiIjaz5RyyXAHg2qZ0m0yGqqmdic6sb/Yhd92FyA50q72L/WuR2HRffAVPgSLZwM0gw9zw8NUsL3CeQqe6/k2Pun8CHIOBNxCAnjJnEtmuz6yXR6X/epzsOOJqOna3N+ijz76CLfffjumT5+uAu5nnnkGEydOxObNm5GYmFhn/xkzZsDj8YTu5+fnY9CgQfjDH/5wjK+ciIiIiNqDXQUVjU7tjgmzYndBOUp8m+Ep+hAG7xZYtN8LpKV6fShDfzze+TqscCWrNdjVz6KqkxdXqrXbMpW8PrJdAnzJrKvjq11HU44nonac6X7qqadwzTXX4IorrkDfvn1V8B0WFoY33nij3v1jY2ORlJQUuv3www9qfwbdRERERNSUrPWO3DKs3lukfsr9gyl1eaumdlvrTu2WgNdk3Ylesc/DVvEwLJ71MB8IuNO8Xoz3psMf8SS+jXkEw44braqQSwBf5vLBH9DUT7kv2ycPTWmwCJpsl7Xjh3s8EbXTTLdkrJcvX4577rkntM1oNGLChAlYtGhRk87x+uuv46KLLkJ4eMPf6rndbnULKikpUT8DgYC66Y1ck/wPXI/X1t5xbPSJ46JPHBd94rjoE8fl2Fi/r1hV+N6WUw63zw+b2YT0xHAVrPbrGNXguDitJtjMRlS6fWpKeZAp4EVU8Zso8M1FksVTI7PdC72wK/Y6LLF3x7bcMgxIcWJ870R0jLaHriG7pOoaBqREYfLQjuibHNHo73/y+E3juh/28W0F/77oU6AVxDBNvbY2FXTn5eXB7/ejQ4cONbbL/U2bNh30eFn7vW7dOhV4N+axxx7DQw89VGd7bm4uXC4X9PhhKC4uVh9a+RKC9INjo08cF33iuOgTx0WfOC7Nb3NOBV5fvA/FLh8SnVZEhxnh8vmxYlcetmcV4arjO6JXYli94xIRGUBHpxGbckrQJcYOEwIYXvojJha8h3B/Dh6Ij0W50YiOXh86u7tga8RVmBvWAy5PADkFhYh2mDG2iwN5eblIMAPXHBeHjCInytx+OG0mpEbbYDS4kZOTc9DXcaTHtwX8+6JPgVYQw5SWlra/oPtISbA9YMAAjBgxotH9JJMu68arZ7rT0tKQkJCAyMhI6PEDK+t05Pr0+oFtrzg2+sRx0SeOiz5xXPSJ49Lc76+GV5ZuRIXfgD4dY37vcQ0gLlJTmej5uyoxpm/nGtOzq4/L1BPC8O8582Ep+hF/K5mHRG9maL8ppWXwJAxF4vH34IO9cdgumfSKqiz00C4JKgtdO5OeVDPndMiO9PjWjH9f9CnQCmIYKdzd7oLu+Ph4mEwmZGdn19gu92W9dmPKy8vx4Ycf4uGHHz7o89hsNnWrTT4Mev1AyAdWz9fXnnFs9Injok8cF33iuOgTx6X57Movw7bcciRHhcFQ63erqh7XYSrw3lNYWadVlzy+t3QPft74LCK88+E3lcHmLwg9vi1yJOJO+Tu6Dxmr7t83SKvTY5vrrI8+/n3RJ4POY5imXlebCrqtViuGDRuGOXPmYNKkSaFvSOT+jTfe2Oixn3zyiVqnPXXq1GN0tURERETUGh1uj+vM0gzMWP4EtmQvhMFdAjnaazDgJ4cdYyMHoeT4v6HbgFNqBNX19dgmotalTQXdQqZ9T5s2DcOHD1fTxKVlmGSxpZq5uOyyy5CSkqLWZdeeWi6BelxcXAtdORERERG1BtV7XFcvhNZQj+us8izM+u0FrNjxLeAqDrX3ivH7cYYjDSMnPwRT91MQcYxfBxEdG20u6L7wwgtVQbP7778fWVlZGDx4MGbPnh0qrrZnz5460wCkh/cvv/yC77//voWumoiIiIhai6b2uI4Ic+HdX5/F0q1fQqv4fQp5lN+PidYOGHXKA7D0Ol3m0LbQKyGiY6HNBd1CppI3NJ18/vz5dbb16tVL/Q+SiIiIiOhggj2uMwsrVU/r5CiHmlIuGW4JuKXH9cVdSrH2izuxpGRz6DhnIIDxiMJJJ98LW//JDLaJ2ok2GXQTERERETWlCvnhFinrnxKFm8f3wGcrMlTgLWu4ZUr5yQlluNT1PqI/+wJdoGFufCzcBgMmGCJw0ug7UdzhFFiSkhlwE7UjDLqJiIiIqN1Zl1kcCpilKJoEzDJlXDLYElA3hezXNzkSm3Py8cv2L+HcMwsXbZgPQ6CqgJoFwDVeOxJOuB1hQy9HwGAC2knvayL6HYNuIiIiImp3Afdzc7aioNxTNTU8qmpquKzRlinjksFuSuDt9Xvx845v8f2Kl1FWuB3GgB8TDAEkyIOOWODEv6LzcVcBFkfVAYFAs782ItIfBt1ERERE1K6mlEuGWwJuyWwHi6BJFXIpiiaZ7xkrMlUGW9Q3/dwf8GPp3gWY9dtzKMzbBBzIbEuFoK1hUUgYdj1w/A2AveocRNS+MegmIiIionZDguhg8bPqVceF3JftW3NK8cOGLCzeWVBj+nn3hHAM6pqH9TteRVb2KsDvCR07zO3DWT2nIPHkvwPhbEFLRL9j0E1ERERE7YZkrSWIlinl9ZEq5DtyPXhj4S74A1po+nm+axc273sbmfs2waa5Q/v3c3txdufTkDbuQSAq5Ri+EiJqLRh0ExEREVG7IdPEJWsta7hlSnltFW4fCis9MBiB/h2jILnwXqWLEVv4Er63l4X26+bx4twOI5E+4V9AfI9j/CqIqDVh0E1ERERE7Yasy5a13FI0TdZwV59irmlVLcTUfjFh6FSxHqdnTUe38lWQVdsrzTGwaEBfTyqGnf440vqfcEhtycJtJoRpsvKbiNoTBt1ERERE1G5IITRpCyZVyoNru2VKuWS+9xdXwm5zI948G51LluGGnFU1fmmeUtkBCxOvxfuuvuga1Qdph9qWzGxER6cRU0+wY0BqTLO/ViLSBwbdRERERNSuSDswaQsWDIizSwKwWLzoELMEfvc3MFVkYo+mIdNsQorPj1xrGr5Lvg7rI09GmdsPm9+jpqkfclsytw+bckrw3NxtuGV8zyb3Ayei1o1BNxERERG1SdWndldv+SUk4JW2YNtyi7Fw13dYs/1NeIp2wKxV9dK2ANhui8WSDpdjeexZCBjMavq5ZMMHpkarcx1OW7IuMXZklnlCbcmC10NEbReDbiIiIiJqc+pM7bYYVQAsU8sl4JYAevn+X/H10mdQkLMOCHhV0TSTpmG0R0PAcBZeCT8fseExcGhVWWoJuGPDrZg8NKXRYPmgbckiq9qSyX7dEpzH4N0gopbEoJuIiIiI2pR6p3Z7/Kp4mqzlPn+EEat3vIiMzKWArzJ03HBp/9VjCuLH/gPriszYUm36uQTtkuGWgPtg08Kb0pYsu9St9iOito9BNxERERG1GY1N7ZZq5faMX4BvX0eGvTh0TG+3B+elnoK08Q8DMZ3Vtv7hUNO/G5qefiRtyWS7PH6wdeFE1DbwbzoRERERtRn1Te2WqeRJrh04I+tl1XNbmnatNlVVD58U3Re9Jz0OJA+scy4JsA9n+vfB2pLtL5V14TGNrgsnoraDQTcRERERtRnVp3b7NBcKXd+jS+l83Jy1DEYVbkOt3b4CSUg8458w9Jhw7NqSuX3YU+hCh+jwg64LJ6K2g0E3EREREbUZMmXbagkgq+InGLwzEO7Ziyxo2G0xoavXhwJzIj6OvBynXXQTDImRx7QtmfTp7tMhHJeMSWe7MKJ2hEE3EREREbUJMnU737MOFuO/YS3fAguqCpVZNA27rE5siPsT3gtMRJ+0RHSJj2j26wm2JQuuCw+3mRDmL0NSB/bnJmpPGHQTERERUau3tWALPl/2NPbsXYhwb4XaZtA0DHX70MEyDrOTLsf2MgtinQdv+XU0VV8XHggEkJNTfkyel4j0g0E3EREREemyCnlTKodnl2fjixUvYe32WUBlYWh7L7cHfUyDMCvsKnyrdYDNIy2/IprU8ouI6Ghi0E1EREREuuuzHVwLLUXRpL2WVAOX4mTVA+ZA/k7s+PYOrC1dHdqW7PNhUng39D3739BShqPbYbT8IiI6mhh0ExEREZFuMtkScD83Z6vqs62qfkeZVF9rab8l1cClOFn/WA253z6KmLVv4HjNi0Ux0cg3GXGSJxw9R9yHrmP+CBgMqkr54bT8IiI6mhh0ExEREZEuMtnnD0nB5yszVcAt94P9rZ12M7pbw7E+bxk+/O45PJj9ExLcv08lv6AMWBh9Md6wn46oHWG4uWsJp5ATkW4w6CYiIiKiY6axTPaWrFJUePxIjQkLBdyixLcH5RUfIBmrUOR2YZtWht4AvAYrFsb/AfMTL4Xb5ERXTVOB/IwVmapqOKeSE5EeMOgmIiIiomM2pVwy3PVlstNtTqzeW4SCCi96JFZNCXcFClFU+RlQ+TOc/pLQedbZrCi3nYgfU/6MImtyaLucTwL5rTmlauo6p5YTkR4w6CYiIiKiY0ICYclES2BcPZMt5H5SlAP7S1zIKiuCxfoLvJUzEeHNUWuzRUevD4MrEzDXcjeWdhoBUz1F0RxWE7JLAmqtOBGRHjDoJiIiIqJjQgJhWcMtU8rrExtugj1sNSrds+B0ZcKBgNoe6Q/gZLcda4xXY06X05FT6ka0x68y5LXJVHVZIy7F2YiI9MDY0hdARERERO2DBMISEEtgXJtBCyC58GMMDH8THbS9MCIAi6ZhXIUfgwwX4E3HS9gaNwHTxnRBegcn9hdXQtO0GueQ+7K9R2KEqoZORKQH/AqQiIiIiI5ay6/GyD6ylluKpska7uAU887lq3HWvheQVrkRL0VHYYPNiuGVHkRqJ+AbxzR4bdHomxiByUNTVFVyo8Gg2ocFp6rLlHIJ5CXgjg23qv1YRI2I9IJBNxEREREdlZZfU4amNtqqSwJh2UcC5s052YgLW44b8xdhcMn80D5TSstwVvxJSJvyb+wypKB7PUG9PIf06w5eg6zhlmsYmBodCsyJiPSCQTcRERERHZWWXxJMSzDcWNDbKykMI3tvwU/rXoO1IgMGd1HoMVdsb3Q46zGg+zh1v1sj1yHPIW3BDjXbTkR0rDHoJiIiIqKj0vKreo9sUT0g7hwbhlU5y/HlsqdRkLUKEX6P2ue78DD0NUbCNO5e2IddBhjrL7JWHwmw2RaMiPSOQTcRERERHZWWX8Ee2T9syMLinQWh6eew5CLc+iXs7uUwecuq9tc0nOD24sy+U2E5+W7AXhWoExG1NQy6iYiIiOiotPySgmY7cj14Y+Eu+AMa4iN9MFpnI1D5I8LLCkL79XJ7MCVpNDqe9hgQ25XvPhG1aQy6iYiIiOiQW37V1yO7wu1DYaUHBiPQP9GFzLJHEenJgAFVrb0SfT6c5InDiec/A1O3E/muE1G7wD7dRERERNQkwZZfDfXIlunn0m/7YtMC/GPrtTi5ZJsKuMMCAZxZDnQ1X4U3op7H7oghfMeJqN1gppuIiIiImtSDu3rLr9o9sveUZGKIYR+m+aYjPWe7Os/5pUY4AwZE2c/E4s5XoNLggCuv6rxERO0Fg24iIiIianIP7to9sveVlALmOYg2z8M5RTuQ7neHzrUv4iTsSb4BhdaO6n6ly6fOJ4E8EVF70Sanl7/44ovo0qUL7HY7Ro4ciaVLlza6f1FREf7yl78gOTkZNpsNPXv2xKxZs47Z9RIRERHpqQe39NyOdljRJT5c/ZT7sl0eFxJ4331GD0wcugdJkU8iyfcZIrx5+NIZDrcB2GXuile7Pof3O/8zFHDL9HOZlt4jMUJlzomI2os29zXjRx99hNtvvx3Tp09XAfczzzyDiRMnYvPmzUhMTKyzv8fjwamnnqoe+/TTT5GSkoLdu3cjOjq6Ra6fiIiISM89uPskRWB9/lrMWPoU8vYvh8HnUvs5AgGM85uRPfpfeGzfcciv8CPZ4gtNP5eAOzbcislDU9Q0dSKi9qLNBd1PPfUUrrnmGlxxxRXqvgTfM2fOxBtvvIG77767zv6yvaCgAL/++issFovaJllyIiIiovakKT241+fsxr/nvI19mXMAV1HVY5qG0S4Pzu5zMSLG/h1wROOmalPUs0uqpqgPTI1WAbdkyYmI2pM2FXRL1nr58uW45557QtuMRiMmTJiARYsW1XvMV199hVGjRqnp5V9++SUSEhLwpz/9CXfddRdMpvp7UBIRERG1px7cMjW8RPsWRt/XyNyei2BI3sPjwQVxQ5DyxyeB+B6h/SWw7pscWW8xNiKi9qZNBd15eXnw+/3o0KFDje1yf9OmTfUes2PHDsydOxeXXHKJWse9bds23HDDDfB6vXjggQfqPcbtdqtbUElJifoZCATUTW/kmuQfSz1eW3vHsdEnjos+cVz0iePSdsYl3GaCzWxEpdtXowe3KeDFqPzPkFX5IVbaq8oBxfr9ON8Yg4ETH4Wh52lQz1LPc3WJC6t2T66nZpux9oZ/X/SJ46JPgVYQwzT12tpU0H24b5Ss53711VdVZnvYsGHIzMzEE0880WDQ/dhjj+Ghhx6qsz03NxcuV9W6Jr29xuLiYvWhlcw/6QfHRp84LvrEcdEnjkvbGZcwTUNHpxGbckrQJcautvWtWIbz86Yj0ZuBYqMR26wxONGjYWSfq+AdcBlyTVYgJ6eZX03bwb8v+sRx0adAK4hhSktL21/QHR8frwLn7OzsGtvlflJSUr3HSMVyWctdfSp5nz59kJWVpaarW63WOsfI9HUp1lY9052WlqampkdGRkKPH1hZiyXXp9cPbHvFsdEnjos+cVz0iePStsZl6gl2/HfOCuyq+AAXVq7Cn4pWhR6LCGi4NWY8Es75JxCe0ExX3rbx74s+cVz0KdAKYhjpltXugm4JkCVTPWfOHEyaNCk0WHL/xhtvrPeYMWPG4P3331f7BQdzy5YtKhivL+AW0lZMbrXJ8Xr9QMgHVs/X155xbPSJ46JPHBd94ri0jXHx+r3YWzEXYeaXER7YjdUWPyYZDCoDvid8IDynPor0wSc2+3W3dfz7ok8cF30y6DyGaep1tamgW0gGetq0aRg+fDhGjBihWoaVl5eHqplfdtllqi2YTBEXf/7zn/HCCy/glltuwU033YStW7fi0Ucfxc0339zCr4SIiIio+cnUzVXZK1QLsMKslTD6PWq7lDzb6+yA6OPvRerxl8Bo0ucvvUREetfmgu4LL7xQra2+//771RTxwYMHY/bs2aHianv27KnxjYRMC//uu+9w2223YeDAgSoglwBcqpcTERERtWWZZZn4dPkL2Lrje8BVrLYZNQ0nu7w4Y8AVCDv5b4A1vKUvk4ioVWtzQbeQqeQNTSefP39+nW3SMmzx4sXH4MqIiIiIWl65txwzN36En9e+A60kI7S9t9uDCxJHIOn0/wCx3Vr0GomI2oo2GXQTERERUQP8Xsyecxd+2v0jEPCpTfF+PyYbYzHgrH/D0GMC3zoioqOIQTcRERFRe7FjAfDt3zAxbzOWxMfCZzBgolvDuJG3wTLyz4DJ0tJXSETU5jDoJiIiImrDit3F2Ld/BfosfRPY8KXa5gRwZXEJrJ3OgunEB2FK6wIYpXQaEREdbQy6iYiIiNogf8CPH3fMwuyVL8OUtwX35+Qi4sBjGWF98YHzemws6wnbjzlIT6zAlKGp6J8S1cJXTUTU9jDoJiIiImpjNuStxwdL/4Oi3FUw+CrVttnOMJzvc+A95xWYaRiLpOhwdLGaUOnxY21GMTILK3Hz+B4MvImIjjIG3URERERtRF5lHj5b9SrWbPkShop81WvboGkY4/LgjN5/wpOeC/BbVgDpiU4YDFXTyZ12M9JtTmzLKcOMFZnomxwJI6eaExEdNQy6iYiIiFqZQEDDrvxylLp8iLCb0THagh93fI0fV74KX+FOGKCp/bp5vDgnvDe6T3kGu4xpWP/VeiRHOUIBd5Dcl+1bc0rVebslyKpvIiI6Ghh0ExEREbUi6zKL8dmKDJWZdnsDgDkPNsubiHBvgtHvUftE+f0YW27Batuf8TDGIn1pAANSitT+jihTved1WE3ILgmoQJ6IiI4eBt1ERERErSjgfm7OVhSUe1RmuqttJ07b9xTeC9+HMqMRRk3DSZUeeAKn44u4ixEeFYdob0Ct2d6SVQqvP6DWcMuU8tpku81iVJlzIiI6evh/VSIiIqJWMqVcMtz55W4MjANOzX4ex+d/DiMCOC9gw3K7Hd1cPfBN+A2ITE6HxeWCyWgIrdneml0Kty+A/cWV6n71KeaapqntA1Oj0SUuvCVfJhFRm8Ogm4iIiKgF12NLkNuUwmU788qwKmcZnJYvcPXWlUjxFoUeSw8kYJH1evy9pBeGdIhGVD1rtjtGhyGjsEJls2VqumTKZUq5ZLgl4I4Nt2Ly0BQWUSMiOsoYdBMRERG10HpsCYClkvjBemRnlmXird/+i2jvHIRr5fjR4cc0L+Ax2DG3wzT8En8hssoD8GUXwFwr4A6SANtiMuKCoalYk1msrkHWcMs1SIZbAm726SYiOvoYdBMRERG1wHpsKWh2sB7ZFd4KzNz0MX5a+xYCxRkITvx2GQ1YGXUKvku+EcXWDmqbP+CC2WSAT6uqXN7Qmu1BadE4b3DKYWXbiYjo0DHoJiIiIjpG67El4D5Yj+zgVPLFmb/it22vwFOwAYaAT/Xcjvf5cVK5E9uT/4GPI4bXWJNdUulBxyiHCqQ7RNQMvGuv2ZYAm23BiIiODQbdRERERM1MssrBddSN9cj+YUMWfty2Advz3kW0bwXCtAoVbFs1DRNdAfTt/Wc8mnsScisCSLb4aqzJjnPacNbAZMxcsx/bcssQYzPAatNQ6eWabSKilsSgm4iIiKiZSfb5YD2yd+R68MlPy6EZHkNHZIceG+JyoVNgGNLOfQJpPXriL9XWhde3Jrt7ghOfLd+L9RmFKPaUw2Yxcc02EVELYtBNRERE1Mxk3bQExw31yHa53BhX+gWu83+ATTYf3oqKRJLPh7HuGKxLfACvlHfHwE0+3NtdU4G1TENvaE22PN67gxMrtu6FJTwSUQ4r12wTEbUgBt1EREREzUyCYlnLLUXTqvfILgvsR9fyXbhmz0vo5t+ptg1zAW6DBzkx0/Btx8kIGMxINvvU9HMJtGUt9sHWZMvjnWLsSEyMhtFo5PgSEbUgBt1EREREzUyCYGkLJlXKZVp4QiRQjh8QqJiNgWUZ6OYvCe27IuYMzE/6M8ossTWmn8tUcslsExFR68Kgm4iIiOgoVyqvb+q3TPu+cVx3TF/yNfIK/oc43y6Y4Mdquw2bKyywoRNmJN2G3JjBDbb7kvMREVHrwv9zExERER3FXtzBImdSOE0CZZlWLllup7MQszc/C0PxPCT6ytT+tkAAZ3mAruP/icdzRmNNZhnSNa1GhfPa7b6IiKh10U3QnZ2djTvuuANz5sxBTk6O+gemOr/f32LXRkRERNSUgPu5OVtVL25pASaVyiVDvSojC+vyPkKsaR4s5ftVCzBxXKULk7qeiahT/wU4EzA5sxgZRVtDrcWqtwOLDbeq6uTBYmlERNR66Cbovvzyy7Fnzx7cd999SE5OrtPDkoiIiEjPU8olwy0Bt2S2g7/HeM2bEGZ7BxGuHTCjKoGQ7PPhj9aO6PGHZ4C0EaFzyPTzm8f3aLQdGBERtT66Cbp/+eUX/Pzzzxg8uO46JiIiIiI9kzXcwQx1MODuXL4ap+Y8iRlhv08lP8MDjB3zd5iHXwUY6/bsPlg7MCIian10E3SnpaXVmVJORERE1BpIgCxruGVKudNbgNOzXsawwm8hv9lsNkUiLKAh1jcCSZOegLln90bPdbB2YERE1LropnHjM888g7vvvhu7du1q6UshIiIiOiThNiM8lrXwFTyG2zb/SQXcQvLTZ7k6IC/qcXwcdxfCYjrwnSUiamd0k+m+8MILUVFRge7duyMsLAwWi6XG4wUFBS12bUREREQN2V2yG5+tfQbxvrnwBMqxzeLBQDdQaXLiu6TrsCTmHGzNlerjEaw+TkTUDpn1lOkmIiIiai19uHPKSrB8/+fYuPNjoCQT9gOP77BYUG6bgO87Xo+8QCT257L6OBFRe6aboHvatGktfQlEREREB20L9unyvViVuxgm/6dI8O8MVSVP8vkwydgBS1L+ic8qusJdJNXHPaw+TkTUzukm6A724v7iiy+wceNGdb9fv34499xzYTLVre5JREREdKwD7n//+AtK3J8gXlsJR6BcbbdqGk4t96H/wNuQdupN6AsjTmH1cSIi0lvQvW3bNpx55pnIzMxEr1691LbHHntMVTWfOXOmWutNREREdDSmhh9qOy457pPFP8HrehRpgdzQ9mEuF7oZj8c7jiuwzNMV98LI6uNERKTPoPvmm29WgfXixYsRGxurtuXn52Pq1KnqMQm8iYiIiI4kU/3ZigzVT1vae9ksRqQnOjFlaKrqj92ggB/5C17Fnev/hQ8iDFhtt6mp5Ke4Y7C2w4OYHT4IYS4ftuaUqoCe7b6IiEiXQfeCBQtqBNwiLi4Ojz/+OMaMGdOi10ZEREStP+B+bs5WFJR7kBzlUP20Kz1+rM0oRmZhJW4e36NO4J1dno3Ewr0wzLoDCftWqm1TSo1I9ZngjpqKWR2nIGCo+lXKYTUhuySgMuhERES6DLptNhtKS0vrbC8rK4PVam2RayIiIqLWT6aGS4ZbAm7JbBsMVdPJnXYz0m1OlfmesSITfZMj1dTwCm8FZm7+GD+teRtX7F6PoW536FzbwsdjZepfUGqJr/EcEsBL5lymrBMREVWnm38Zzj77bFx77bV4/fXXMWLECLVtyZIluP7661UxNSIiIqLDIVO+JbCWDHcw4A6S+7JdpobvzCtDrnc9Plr0JDz562EIePFZRDj6ejywxffGWzE3YmZxN6Sbnah+Fk3TsL9Y+nBHsw83ERHpN+h+7rnnVNuwUaNGwWKxqG0+n08F3M8++2xLXx4RERG10kJp8mdZwy1TyusjU8MzSnPxwi/3oiRvARz+MhVUS1Xy0a4Aco/7O9Im3objsiqwaM7WUAAvx0mGWwLu2HArJg9NaVJRNiIial90E3RHR0fjyy+/xNatW7Fp0ya1rU+fPkhPT2/pSyMiIqJWXCjt+K5x6s8SIMuU8ur8mgd7K36AFV/Dk50Nx4Htg11udNeG4H37NfgyLxk3Z1WoNd+y9jv4HLKGW84rGW4JuBstxkZERO2WboLuoB49eqgbERER0dEolJZRUIGYMCv2FVWqNdzBKeb5vo0oL/8fIl2bYUFVAbR4nx8T3E5sTLwX3zuHIU7Taqz5lsBafh5O2zEiImqfWjTovv322/HII48gPDxc/bkxTz311DG7LiIiImpbhdLCrGbEhFtDU8O7BnYirvAJ7DIXqn0tmoZTKrwwOC/C7OSL4Dda6qz5DrYDkwCbbcGIiKhVBN0rV66E1+sN/floefHFF/HEE08gKysLgwYNwvPPPx8qzlbbW2+9hSuuuKJOJXWXy3XUroeIiIhatlBaQYUHU0d2woptezFk27M4texLlBmBh+NikO71opu7H35L+yvK7El1zs92YERE1GqD7nnz5tX75yPx0Ucfqaz59OnTMXLkSDzzzDOYOHEiNm/ejMTExHqPiYyMVI8H1f4Hm4iIiPTrYIXS7BYjCku3QMv4EXdvexuGsmy1PSoA3BOIgvfER/C3VQmIhhXOeo5nOzAiIjoSRujElVdeWW+f7vLycvVYU8k09GuuuUZlr/v27auC77CwMLzxxhsNHiNBdlJSUujWoUOHw34dREREdGzJuupgobTaXIFC7C3/P8Thaczf/joqy3OqHjDbgXH3Iv7Pi9FhyFlqWrpUIZf2X6inHViPxAi2AyMiotZdSO3tt9/G448/joiIiBrbKysr8c477zQaNAd5PB4sX74c99xzT2ib0WjEhAkTsGjRogaPKysrQ+fOnREIBDB06FA8+uij6NevX4P7u91udQsqKSlRP+V4uemNXJP80qDHa2vvODb6xHHRJ46LPh3rcVEtwQoqUOryIsJuQZfYMHSKcSA9IRxrM4uRbq1a0x3QfMjx/ARPxeeI82aqFmBlRiN+s9twYqdx0CY+BkR3OnBWDZOHdERGYQW25ZQiObJaO7CSqnZg5w9JVvvJ87cG/PuiTxwXfeK46FOgFcQwTb22Fg+6JWCVN1Nukum22+2hx/x+P2bNmtXgtPDa8vLy1DG1M9VyP9iGrLZevXqpgH7gwIEoLi7Gk08+idGjR2P9+vVITU2t95jHHnsMDz30UJ3tubm5ulwLLh8GeW3yHsuXEKQfHBt94rjoE8dFn47luGzOqcCsDfnYWeCCxx+A1WRE11g7zuwbh7FdwrA9qwgb9xUiLDwDmvYponybEa5V1Y6J9vsxyR+Gbic/guwupwAeADkHst4AEszA1MFxB85fHjp/epwdZ/SJQ4LZjZxq++sd/77oE8dFnzgu+hRoBTFMfTO1dRl0S39u+UZabj179qzzuGyvL8A9WkaNGqVuQRJwS3/wV155RVVWr49k0qtXW5cvDtLS0pCQkKDWh+vxAyvvo1yfXj+w7RXHRp84LvrEcWnf47J+XzHeW7UXhaolWHgoE729sBLvrcrHzePSce14Da8vfhH+svmIDFTNQjNqGsa5PDh90LWwnnA7YAl24q5LvuMf07dznUx6a2wHxr8v+sRx0SeOiz4FWkEMUz1hrOugWwqoybcX48aNw2effYbY2NjQY1arVU377tixY5POFR8fD5PJhOzsqgIpQXJf1mo3hcViwZAhQ7Bt27YG95Hq5nKrTT4Mev1AyAdWz9fXnnFs9Injok8cl/Y5LjKle8bKfSgs9yI9MeL3lmAOI9LtZlW5/K3FP8DvfwNhJdthQNUU8J4eD/4QOxTJFz0FxHZr0nPJS5DnaAv490WfOC76xHHRJ4POY5imXleLB90nn3yy+rlz506VLT6SN1SC9GHDhmHOnDmYNGlS6BsSuX/jjTc26RwyPX3t2rU488wzD/s6iIiI6Bi1BAMw0fQbTt30PF6M8cNnNCLK78f5WjiGnfo0DH3Olt/aOBxERNRiWjzoDpKMtqioqMCePXtUUbTqZM11U8i072nTpmH48OGqN7e0DJMK6MFe3JdddhlSUlLUumzx8MMP4/jjj0d6ejqKiopUf+/du3fj6quvPuqvkYiIiI5OS7CA5ke8Jwvn7nsavUoXq23nldmRY7HizIFXwn7SXYA1jG83ERG1ON0E3VKETALjb7/9tsEMdFNceOGF6lz3338/srKyMHjwYMyePTtUXE0C+urZ9MLCQtViTPaNiYlRmfJff/1VtRsjIiIifbUEC7MZkOtdBEPZ57h73yrEBH7vJjI0fgTCznsaiE9v0eslIiLSZdB96623qkzzkiVLMHbsWHz++edqLfY///lP/Pe//z2kc8lU8oamk8+fP7/G/aefflrdiIiISJ+6xIWrPtrLMtchyj4D4a51sGpuzHJacEmJG/nGOMztcjumXHIDYNLnuj8iImq/dBN0z507F19++aWaFi6ZaJlufuqpp6pq4DIV/KyzzmrpSyQiIqIWUOwpgi3sC8Qav0BkZaHaZtA0WDQDPg+bgrmJV+C68QNhZMBNREQ6pJugW9ZdB/txyzRvmSIuLcQGDBiAFStWtPTlERER0THmC/iwYNcPmLn8BXjytyJSC6jtXT1eDHal4PuoWxGe0h/XDU1B/5Qojg8REemSboLuXr16YfPmzejSpQsGDRqk+mTLn6dPn47k5OSWvjwiIiI6hrYUbsHHS55CVsavgLdCbQsPBDDJZ0XXYQ8gu9PZuM5hUVPPW2MfbSIiaj90E3Tfcsst2L9/v/rzAw88gNNPPx3/+9//VBuwt956q6Uvj4iIiJqZ9OOW9mCl2Vvx7vKb4KrICk0lP8Hlwdl9/oTwU/4B2CORxNEgIqJWQjdB99SpU0N/lgri0rZr06ZN6NSpE+Lj41v02oiIiKh5rcssxhe/7UDPHW/jvOL3MdluwPtREejs9eKPET3RecqzQAd2FiEiotZHN0F3bWFhYRg6dGhLXwYRERE1s283r8SaeQtwZe6r6OjPVNtGuQCfwYktzutQeuoNQIdojgMREbVKLRp033777U3e96mnnmrWayEiIqJjq9hdjM/XvoV5K9/HuIpMdPSXqu0BGLEobjLmdLgK6/OBfSv3oW/HKK7dJiKiVqlFg+6VK1c2aT+DgQVSiIiI2sqa7eJKNzYX/YplW16FK28zIrQAljnsOLmiErD2wZcptyPL0UMdkxzlw9acUnVctwRnS78EIiKi1hV0z5s3ryWfnoiIiJqjEJrLhwi7uUZlcVmz/dmKDKzL2QKf/2PEBdbCrrkgj4YFAphQYcDSpL9hVczp0AzG0DkdVhOySwLqnERERK2Rbtd0ExERUeuxfl8xZqzch205ZXB7A7BZjEhPdGLK0FT1+FM/rkauayaiDfMR5c8PHTey0g2H70T8mHw9TOExdc5b6fGrc0kQT0RE1Brp5l+wU045pdFp5HPnzj2m10NERERNszmnAu+t2ovCci+SoxxwRJlUsLw2oxgZBRXwmDagwvUO0gJ7YURAHZPq9WGMNwlfmG/ARlNXhJWZkB6m1fhdQNM07C+uxMDUaJU1JyIiao10E3QPHjy4xn2v14tVq1Zh3bp1mDZtWotdFxERETU+pXzWhnwUlnuQnhgRCpqddjPSbU6Ydi3A8b6X8V2kR213HJhKXhxzPb6NOQMedwC2wgqVzZYsuQrarVVBuwTcseFWTB6awiJqRETUaukm6H766afr3f7ggw+irKzsmF8PERERHdyuggrsLHAhOSq8RpY60pODs/Y/j4Gl86AB2GGPQqxfQ0fLKfil83VwmSPVfg6rARaTERcMTcWazGIVeMsabgnCJcMtAXf/lCgOBRERtVq6CbobMnXqVIwYMQJPPvlkS18KERER1VLq8sLjD6jstKYFkOddjqTS73D7/gWwBSrVPhKKTyxPxved/4YNjp71rtkelBaN8wanNFiIjYiIqLXSfdC9aNEi2O32lr4MIiIiqkeE3QKryYg89154/B/B4VqFokAldpp96O0BSk3ReNYwFUucp6GfPUYF4A2t2ZYAm23BiIiordFN0D158uQa99U/xPv347fffsN9993XYtdFREREDYtz+mB3zoS7fA5iAoWh7dssVhREnINXjBchOjYRSV4/12wTEVG7pJugOyqq5noto9GIXr164eGHH8Zpp53WYtdFREREdfkDfvy8Zy6+/u05RFRshkGrVpXck4Rf427HMneqKoR2zUnd1GPSp5trtomIqL3RTdD95ptvtvQlEBERURNsK9yGj5f+F/v2LgS8FWrKuFQlP63SgB3WK/FC+HhYNTMGpkbUKITWNzmSa7aJiKjd0U3QHSTTyTdu3Kj+3LdvXwwbNqylL4mIiKhdtP5qShGz3Xt/xTPz7wDKckLbjq9049yeF8A57n50L7fg+AbOwTXbRETUHukm6M7IyMDFF1+MhQsXIjo6Wm0rKirC6NGj8eGHHyI1NbWlL5GIiKhNBtXrMotDU7/d3qp2XemJTkwZmvp7uy6vC/j1OXT6+b/o5bRhs82KNK8XfwhPR+SJ98HZ9yS1NKxbWMu+TiIiIr3RTdB99dVXw+v1qiy3rOUWmzdvxhVXXKEemz17dktfIhERUavUWFAtnpuzFQXlHiRHOeCIMqk2XmszipFZWIkLR4VhfGA3MPtuoHCXmkr+x1IvthgSMObkR4ABf0RObm5Lv0QiIiLd0k3QvWDBAvz666+hgFvIn59//nmceOKJLXptRERErTngbiioziiogMNqVo9JEG4wVGW+nXYzOlk1bMv/H96d/ws65O5Cf4+n6oQGEzocdx06jL0bsEchEKgqoEZEREQ6D7rT0tJUprs2v9+Pjh07tsg1ERERtfYp5ZLhri+oTrc5VUBeUOHBsLSY0GMBzY8876/wlH+K1MBeGAMaPo10oldeASxdTgTOfAJI7NPCr4yIiKj1MEInnnjiCdx0002qkFqQ/PmWW27Bk08+2aLXRkRE1BrJGm6ZUi4Z7mBQHST3Y8KsKHP54NM0ta3YtxP7Sv4Fc9ELiPLsgREawgMBjPPZYJ7yOjDtawbcRERErTXTffnll6OiogIjR46E2Vx1WT6fT/35yiuvVLeggoKCFrxSIiKi1kGKpskabplSXh/JeItCVz4qtdkwVC5AlL9YbTNoGkZXemAxnIa0Cx+FIaVDVTG2vLIaxdiIiIiolQTdzzzzTEtfAhERUZsigbEUTZM13MEAu/Z0t8jopdB83yHcm6WKpIluHi+G+7vhE+ufkdClPyYnJzZYjG3ykI5I0M1vE0RERPqjm38mp02b1tKXQERE1KZIJloCYymaJmu4a0wxD/jRO2cmepnewxatqhhapD+AcS4bdkTchOf8wxHrtGHy0BRs2F/ScDG2wgpMHRyHxMSWe51ERER6ppugO1g07YsvvlBtw0S/fv1w7rnnwmSqf1ocERERNUz6cEtbMGn9FVzb7bCaEFeyEZP2P41e3o3IMxnxaFwsRlV64TechTfDL4TR7MDAjhEq4O6bHIlHZm5osBjbtpxSfLsxH2P6doZRN5ViiIiI9EM3Qfe2bdtw5plnIjMzM9Q27LHHHlNVzWfOnInu3bu39CUSERG1Ov1TonDz+B74ePkOrMuZhdOzluLqkgWqSJqI9wfwz5jjYL/ocezyx6NPtfXaErTvyC1rtBhbcqQDO/LLsaugAumJES30KomIiPRLN0H3zTffrALrxYsXIzY2Vm3Lz8/H1KlT1WMSeBMREdGh0TQNlYaN8Pn/i2T/WuyxuuE1aLBJzB3XAzjj3whLH6/27XYYxdgkc+7xB1Dqqtv2k4iIiHQUdC9YsKBGwC3i4uLw+OOPY8yYMS16bURERK3R3tK9+GTp09ixex7gLlWF0kqNRuwMi0Lv0XcAI68HzNYjKsYm260mIyLslmZ8JURERK2XboJum82G0tLSOtvLyspgtTb+CwERERH9rtxbjq/XvYuF6/8HrXR/aPsAtxuTU8YhYerjQGTyERdjkyz6/pJKpMfZ0SU2jENARESk56D77LPPxrXXXovXX38dI0aMUNuWLFmC66+/XhVTIyIiooZJD+0deaX4de88rNj6GrxFW2HQ/OqxRJ8PU6zJ6Df5aaDT8UelGJtkuPcXVyI23Ioz+sSp/YiIiEjHQfdzzz2Hyy+/HKNHj4bZXHVZPp9PBdzPPvtsS18eERGRbqke2sv3Invfy/BiPmyaW00lt2oazvAYcMqY+2AedjlgNB1RMbZgn+7skqo+3QNTo3H+kGQkmN1H/TURERG1FS0edAcCATzxxBP46quv4PF4MGnSJNWzW6av9enTB+np6S19iURERLoOuD/6di4m57yArr4VeDg+Fh6DAcMr3Yg3nIjUcx+DuXvnI34eCbylfdiu/HJVXC1Y4RzQkJOTc1ReCxERUVvU4kH3v/71Lzz44IOYMGECHA4HZs2ahaioKLzxxhstfWlERES65fV7sb9gG0q+ehYPZH8MM6qmkl9YUgqXtSdWJt6Ob0qTMHBDOe7tqh2V6d9yjm4JzjrT2omIiEjHQfc777yDl156Cdddd526/+OPP+Kss87C//3f/8FoNLb05REREemKFC9bk7MaM5Y8Afe+Fbg/Zz/MB3puF1kSsT3xL1gbNU6aaCPZ6MPWnFKVna4dLBMREVE7Cbr37NmDM888M3RfMt4ytXzfvn1ITU1t0WsjIiLSk6zyLHzy23PYvOM7wFWsts0OD8M55V78lHAJFiReAq/RHtpfCp7J+muZDk5EREQto8VTyVIszW7//RcEYbFY4PV6D/ucL774Irp06aLOO3LkSCxdurRJx3344Ycq4Jd15URERHpR4a3Ap2vewMMz/ojNGz4OBdw9PR7YMQD/7PI2fky6qkbALaTCuBQ8k/XXRERE1DLMepgmJ1XLpU93kMvlUq3CwsOlQEuVGTNmNOl8H330EW6//XZMnz5dBdzPPPMMJk6ciM2bNyMxMbHB43bt2oU77rgDJ5544hG+IiIioqMjoAXw694F+OTXp+At3ALjgXXbMX4/xrkjkXbCM/hfTleslx7aTq1uD+3iSlVhvKrgGREREbXLoFsqldc2derUwz7fU089hWuuuQZXXHGFui/B98yZM1VhtrvvvrveY/x+Py655BI89NBD+Pnnn1FUVHTYz09ERHQ0ZJXtx1u/PIQ9exfC6KtUU9MsmoaTKgJwOS7Gy44zEL3ZgbMGRjfaQ3vy0BT20CYiImrPQfebb7551M4lLceWL1+Oe+65J7RNirHJOvFFixY1eNzDDz+ssuBXXXWVCroPxu12q1tQSUlJqP2Z3PRGrkkyHnq8tvaOY6NPHBd9alfjkrsJYbPvQn7Z+lBR0SEuNzqZT8TitOtRYY5GN03DttwyrNpThL+M7Y4vVmViW045skv8sJlNGJAShclDO6JvckSzvmftalxaEY6LPnFc9Injok+BVvDvS1OvrcWD7qMpLy9PZa07dOhQY7vc37RpU73H/PLLL3j99dexatWqJj/PY489prLiteXm5qqp8Xr8MBQXF6sPLSvC6wvHRp84LvrU1sdFvS5PCZzLnkfY+vcRqflxRpgDS+12DHN3xIrYm7DW1g2QkifeSnVMjM2A9RkFOKNHOK45Lg4ZRU6Uuf1w2kxIjbbBaHA3ew/ttj4urRXHRZ84LvrEcdGnQCv496W0tLT9Bd2H8yZdeumleO211xAfH9/k4ySTLuvGq2e609LSkJCQgMjISOjxAyvr/OT69PqBba84NvrEcdGntjou8svEyuzf8P1vz+GmTb8ivKIg9NjxxlisCEzDD50nwmQywlHrWKtNQ7GnHJbwSCR1iEZSze+cj4m2Oi6tHcdFnzgu+sRx0adAK/j3pXZB8HYRdEvgbDKZkJ2dXWO73E9KSqqz//bt21UBtXPOOafOFAGz2ayKr3Xv3r3OcVL0rXrhtyD5MOj1AyEfWD1fX3vGsdEnjos+tbVx2VuyF58s/S927J4PeMowy+TBhfKAJQw44XZk97wcq2ftQLRPg9P8e5G0oEqvVCc3IcphbdH3pK2NS1vBcdEnjos+cVz0yaDzf1+ael1tKui2Wq0YNmwY5syZE2r7JUG03L/xxhvr7N+7d2+sXbu2xrZ7771XZcCfffZZlb0mIiI6UoGAhl355apftrTvio3wY+a6t7B4w0fQyn+f/l1gMiLQ/wIYT30YiEpB54CG9MQcrJXq5DYnq5MTERG1Qm0q6BYy7Vsqog8fPhwjRoxQLcPKy8tD1cwvu+wypKSkqHXZMh2gf//+NY6Pjo5WP2tvJyIiOhzrMovx2YoMVV3c5fXAb16GCMPXcHoyYICm9kn0+XCWJQURYx7Cri7Ho0tEuKpWbjQaMGVoKquTExERtWJtLui+8MILVUGz+++/H1lZWRg8eDBmz54dKq62Z88e3U5PICKithdwPzdnK/LL3XBGZCDM8D4cni0wa1INDbAHAjjNZ0FJ1LV41nciXMsB25r1SE90qmC7f0qUut08vkcocM8uCcBmMar+29IOTB4nIiIi/WpzQbeQqeT1TScX8+fPb/TYt956q5muioiI2tuUcgmUC8o9GBe2CZvLnkNAK1ePGTQNIyu9cFrOwFdRU5FVaUJylB1JB3psy3Ry6b0twXYw8O6bHFljinqXuHD23yYiImoF2mTQTURE1JJrtiUglvsl+7bg3vI3MDj7J/zisOPDyAikezzojwH4Oe4G/JBlR6wB6N/x9/XaTrtZrd+WrPaMFZkq2JZp5nLrluDkwBIREbUyDLqJiIiO0ppttzcAq0VDVOQaXFm5Fo/v/wgW1VgbGFXpgs+cgk3xt2JOxBAUVXhQ5spD9/jwGgXShNxPjnJga06pCt4ZbBMREbVeDLqJiIiOcM22TCFPirTDG74DFWXvwJe3BRsqyzDsQMBdYo7D90nXYEXMGdAMJrWtzOVTP512S73ndlhNav22ZM+JiIio9WLQTUREdBjTx0VwzXbH+AqUVbwOY9kyRAUq1GNL7TacVubBL45JWN35SnjNVccITdNQWOFRU8nNtbLcQbK2WwqmyfMRERFR68V/yYmIiA5h+rgEwlJd/PiucdiUkwWH4weUFM6B018YOqaHx4Ne/gG4L/oqlId1hDs/gOQon8peSzC9v7hSFU6TaeP7iiqRbjezBzcREVEbxaCbiIjatfqy2FK0rPb0cVlj7YiqCprXZORj5f5v4Ah8jUhPFoK56ni/H2M8cdgWfwvmhg1CUV45Lh6aijWZxfW2+xJyfnlMnb9aUB4bblX7BK+FiIiIWicG3URE1G41lMWWHtlSNTw4fVy2BYudRVk1BCxPwe/dCDP8oX7bJ7nMKI++GvOSZd22EZUunzrfoLRonDc4pcHAnj24iYiI2jYG3URE1C41lMUO9siWwDuYgVYBt6ahb8lPOHP/S9hhzMPbUZEH+m37EOk4F791vhReoyO0Zluy1ZLRDgbYDVUgZw9uIiKito1BNxERtcsp5fVlsav3yP5mzX6V/TZGliG2fDumZL2JbuWr1H6xAHabKxHwD8PciGuRZ4hFsscCh1U7rOnh7MFNRETUdjHoJiKidkemetfIYtfTIzujuAiaYR4KC3/AgPIMdCsvCe2zM3wwtsfegHVaF0wd2QmLdxbUu2ZbsthERETUvjHoJiKidkfWVksWW6aU1xbQ/HBhKRyGDxCtZcLo17DKbsP2CjOiDEn4NvkGrI84AdtyyzEwNQKn9k1St4bWbBMREVH7xqCbiIjaHQmMJSMtU8FlSnlwHXaJfwsqy96FzbMFsZpXbTdrGkZVaFgSfTXWJvwRZT4j9ueW15k+3tCabSIiImrfGHQTEVG7awkmN1nLLUXTZA13RSAHFeXvweBejvBAZejYYW4vTkg5DzPMF2NdgRHuAg+njxMREdEhYdBNRETtsiWY3KRK+d6c92ExfQ+n//c12908XpwaPRwDLnwSiO2KOxvp5U1ERETUGAbdRETULluC/XVkGP5tfBF7imbitehIdUy8z4/RvkR0O+lfSB82IXQuVhcnIiKiw8Wgm4iI2k1LsDCbAT1iK3Dc7jfR/cMvYNY8iAYw2OVGJ3MU+g+9C0nHXwKjydjSL4GIiIjaCAbdRETU5luCqSJpvo2oLH8PA8o2Y3JFdmhfgz0aVx//N+C4qwGzrUWvm4iIiNoeBt1ERNSmW4KV+TNRUf4uTK7VCNdc2GEBNlgt6OEBCgdcgcQz/w44Ylr6komIiKiNYtBNRERtjhQ7M1iKkVvyLoy+X+Hwl4UeS/d4sMsxGq/FXYWbThqPRAdbfREREVHzYdBNRERtSomnBEt3vYmwwP8Q7isIbU/0+XCcLwVb427GG+VpGNgxWlUhJyIiImpODLqJiKjNWLxtFj5e+iQ8xXsQDKcj/QGMdDuRG3s9ZlmPx/4SF2LDrZg8NIVtv4iIiKjZMegmIqLWz1UCLHwWMb9NhyfSoTZZNQ3jfFa4Ii7FZ/axcLkNsAW8GJgarQLu/ilRLX3VRERE1A4w6CYiolZJKpJXuAoRvvoj4KcngIp89ALQ32ZEjMGKMwZdhcjRtyJgdmBofrkqriZrvWVKufTdJiIiIjoWGHQTEVGrszV/M75c8iSwdzH+un8PQiG00YLrev4JhpP/BoTHVW0C0C2BxdKIiIioZTDoJiKiViOrYj9mzHsCG3bPBdylattqmxWD3R4UdjsXpaPvRmq3vjAwk01EREQ6waCbiIh0r8BVgG+Wv4zFmz+H0V0U2p7k8yEvrB/ujb4aWyq6w7agDOkbN2DK0FSu2SYiIiJdYNBNREQtJhDQsKuR9dZlnjJ8v+4d/LT+ffjKstRUcRHt9+NUUwf8Zr0C0wMDkBzlQBerCZUeP9ZmFCOzsBI3j+/BwJuIiIhaHINuIiJqEesyi/HZigxsyymD2xuAzWJEeqIzlKX+bfu3+GDJE3AX7wkdExYI4DRDBE486V48vqsP1maWqGMMhqpA3Wk3I93mVOecsSITfZMjWTSNiIiIWhSDbiIiapGA+7k5W1FQ7lFZakfU71nq4rws/D3yWyRs+R/c0VXdti2ahpP9FozsfSU6jL0Fu4q82LZsvTo2GHAHyX3ZvjWnVGXRWUSNiIiIWhKDbiIiOuZTyiXDLQF3MEutaQGEmwtwnfYNxuz5AGFaJeIBDHOZYDfaccbAKxA56hbkFJUDJgtKXZUqOy7Ben0cVhOySwJq2joRERFRS2LQTUREx5Rkn2X6t2SjRYl3Ezzl7yG1YhMm5GWF2n8FTHZc3u8KGE64DQiLRSAQAFCuHpP13zIdXbLjMqW8Ntkuj8t+RERERC2Jv40QEVGzFUKrj+wrWWq/czcKit+HwbMONs2DXBPwm92GoS4f5jomouN5D6Bf7971nkOeR7LkMh1d1nBXn2KuaRr2F1diYGq02o+IiIioJTHoJiKiZimE1pASXwbsxufhLVwDm+YKbe/s9SLXMQL/SroOW/2JeCgutUZgX1zpgbfchfh4DWazUT2PVCkPZs1lSrlkuCXgjg23YvLQFBZRIyIiohbHoJuIiI5qIbSG2nXtK83EN8uexepdPyDSX1qj1/agQFfsjL0BP4X1UkH0wNQIlaWuGdj7gYAf/baWYcqwNHV+eZ7g47KGWwJ/yXBLwN1Y4E9ERER0rDDoJiKiIy6EdrB2XQuWv4xP17wOzVUUWrMd5/ejnysRGTF/xs9RQ6uy1DlloSz1hv0lNQP7SCMKS8uxNrMYmUWuUGAvz3OoU9yJiIiIjhUG3UREdNiF0A7arsu9GZj3T/TatQCIi5EdEOX343RbEjoO/hvey+2BbbnlcOeV18hSSyD9yMwNNQN7TUO41YS4SLs6pnpgz7ZgREREpFcMuomI6JAEC6E11K7LbHEhonAZ4r7+D7DnR7UtCcDYikpEOZNw0th/wNpvsgrA72ugENuO3LKmB/YJTo4gERER6RaDbiIiOiQNtevyaZUoq/wWropv0VnLRviegtBjbmcqThz5V8SPugxG8+/HNJSlPlhgzz7cRERE1FoY0Qa9+OKL6NKlC+x2O0aOHImlS5c2uO+MGTMwfPhwREdHIzw8HIMHD8a77757TK+XiKg1Cbbrkirh0p7Lr7lRUvENCgpuhbH0XYT785BnMmGxw44SSwLejLkZl4e/jL9u6YdHvt2siqMdSmBfH/bhJiIiotaizWW6P/roI9x+++2YPn26CrifeeYZTJw4EZs3b0ZiYmKd/WNjY/GPf/wDvXv3htVqxTfffIMrrrhC7SvHERG1px7aTSHnkHZdewtKsC33K0QaZ8Phz4LjwOMmTcNIrwlbw6biRdM5iI+OQpr14NXNq2MfbiIiImor2lzQ/dRTT+Gaa65RgbOQ4HvmzJl44403cPfdd9fZf+zYsTXu33LLLXj77bfxyy+/MOgmonbXQ7spPH4PsktmI8L4CmzaXhj8mtpu1DQMcwMj0y/FDP/ZWLG/6dXNGwrsa/ThthhR7vEjo1QqnNvYh5uIiIhahTY1vdzj8WD58uWYMGFCaJvRaFT3Fy1adNDjZZrknDlzVFb8pJNOauarJSJq3h7aklWOdljRJT5c/ZT7sr0p07sbVLIPX35xKT5fcC+8ZXtggAaDpmG4R8NNaRfi0utXwHr8HdiY729SEbTGBPtwD0iNQlGlR+1f7PJjQEr0QTPlRERERHrRpjLdeXl58Pv96NChQ43tcn/Tpk0NHldcXIyUlBS43W6YTCa89NJLOPXUUxvcX/aTW1BJSYn6GQgE1E1v5JrkCwU9Xlt7x7HRp9Y8LqqH9vK9KCh3Iz2hWpbZZkJ6Qji25Zbhs+UZ6N3BeWhTzUuzYFj4DLD8LZwCL36Oi4Xkt4f4gDN6XYgOJ9wBOKLVrsW5RXB7/aqvtrT5qk0y1tklfhRXeg76HvdNjkDvM3pjV0EFSio88FaUYEh6KsxmU6scn7aoNf99acs4LvrEcdEnjos+BVrBvy9NvbY2FXQfroiICKxatQplZWUq0y1rwrt161Zn6nnQY489hoceeqjO9tzcXLhcLujxwyBfLMiHVjL/pB8cG31qzeOyp9CF9RmFiLGb6v3/UYzNgPUZBVixdS86xdgbPZcv4MPKffMQteMHnLh5Ngz+qi8b4wH8ocKL5O6TEDHkz9DsMcgp9QClOepxb7kLCPhRWFqu+mrXJlPE5XFveQlycjxNel1S3zzMGkBxpQt5ebmtblzastb896Ut47joE8dFnzgu+hRoBf++lJaWtr+gOz4+XmWqs7Oza2yX+0lJ0iW2fjKI6enp6s9SvXzjxo0qsG4o6L7nnntUYF49052WloaEhARERkZCjx9YyXbJ9en1A9tecWz0qTWPy35PEWA0ISYiHKZ6MtlWm4ZiTzks4ZGIj49SGeRSlxcRdgu6xIap7Lc34MWSHbPx/crpKCzcjmifF6P8blhkGY4lDDjuaowZdRMQLuF3XfHxGvptLcPazGLERdprTDGXfzhlTfaA1FgM7ZF2SNn21jwubRnHRZ84LvrEcdEnjos+BVrBv/vSLavdBd1SfXzYsGEqWz1p0qTQYMn9G2+8scnnkWOqTx+vzWazqVtt8mHQ6wdCPrB6vr72jGOjT611XKIcVtgsJlR6AzV6aAdVev3q8ZwSN/61elONQmvdEuzolbQBG3a+g6LC7YAWgITExSYT1jmc6NrzT4gcfweMkR3U9obIWzZlWBoyi1zYllteVQTtQPVyaTMmRdCmDKuaIt5exqWt47joE8dFnzgu+sRx0SeDzv/db+p1tamgW0gGetq0aar39ogRI1TLsPLy8lA188suu0yt35ZMtpCfsm/37t1VoD1r1izVp/vll19u4VdCRHTomtJqKyXagU9XZKKw3KMCYlukhqKKX7B732fIzdwHo1qtXaWP24cIbRRedl6CyoIEpC/Iw5Sh9oMWMQsWQQtWUM8uqQrsB6ZGq6rjLIJGRERE7UWbC7ovvPBCtbb6/vvvR1ZWlpouPnv27FBxtT179tT4RkIC8htuuAEZGRlwOByqX/d7772nzkNE1NrU22qrWpY5JsyiQmoJuCU4L3cvREnRx7D6MmCvFmz39fgRhdGYZ74YjtgURFtNsB1Cn20hj0tbsOboFU5ERETUWhg0SX3QEZE13VFRUWqhv17XdOfk5CAxMVG3UzPaK46NPrWFcamvT3ePxAiM7BqL95bsRldzIc4o+RB7PHPxc5g1dFxvt19ltnckXo31JfYafbaF/JMh55SM9b1n9TmmAXRbGJe2iOOiTxwXfeK46BPHRZ8CreDf/abGgW0u001ERPVnmZOjzdi6eT2m5T2Nsa4fYNZ86Gs0YpEjFumeAJIsJ2FD0hX4LtcMU5EBaTEH77PdLUHqihMRERFRQxh0ExG1UZKFlqDY5XPhp42f4JUf3sS4zE2YWFke2scOG07VTsHKjpch0xyDMpcPJmO56vct09LrI9tljbYE80RERETUOAbdRERtVIW3Aj9t+ABz172LipIMtW1umB1jK8rhQxiWxF+AhQl/RIU5ukahte4JEcgpcal14PVWQPdIBXSjyp4TERERUeP4GxMRURtT6inF/DVvY8HGD+EqywptN2gaegeM2Dv4RjxfcCoyXVYk+xxwGLVq7bysmDa6Mz5fmdloBXRZ0y1F0YiIiIiocQy6iYjaiGJ3MX5c+SoWbp4BT0VujWB7uN+Eif2nImnUrYA9EtdUK7RWXzsvo8HQYAV0CcxlP1YhJyIiIjo4Bt1ERK2dpsG3/Uc89su9KKvMD202aRpGBiw4deAVSBj5F8DmbHI7L/bZJiIiIjo6GHQTEbVSAb8fOcu/RMSyZxGeuwqjnOH4ITwMFk3DaIRhwpBrETP8GsBib7TQWkPYZ5uIiIjoyDHoJiJqZfYUbceMBf/FeZsWomvlrtD2ceUV8DkScOrImxE55DLAZDni5zpYYE5EREREjWPQTUSkU9K2Kzj922kzwaftxPfLnsHGfUth9Luw3liOrgf2zbR2x4eOP2KL8xT0Su6N/kch4CYiIiKiI8egm4hIh9YdKHS2NacULu96xGAGTNgBs+aF8cA+q+w29NO64afES7EpYjQ0APk5ZZixIlOt12ahMyIiIqKWx6CbiEiHAfezP25CUcVixJlmIkLbDRP8ocfj/H709KWhOOZKvNpxKHCgpZf8VyqNS6AuGXJOCyciIiJqeQy6iYh0NqX8y1++gb9iOhKQBaNf8tdVknx+JLm6Yb77j6jsdTxMpmDO+3fS2ktagMmUdCIiIiJqeQy6iYiaeT127XZcDcpcjoq5T+L2Hd/igfhYVBirgurOXj/STEOwN+4qLHQnYt3eQljK3EiKctQ5hfTSlp7b8pxERERE1PL4WxkRUTOtx96WUwa3N6CC4PREJ6YMTVVtuKoH5RnFOXBnzsSEHbNh2P0LgnXCT6qoxE6rAwnWE7AjcRpWWxPV9nhLADaTEVnFLnSItMNwYGq50DQN+4srMTA1WgX5RERERNTyGHQTER3lgPu5OVtRUO5R66sdUSaVfV6bUYzMwkrcPL6H2u/dpUtRmP0OAlgFZ6ACI/LyURWOA0XGWPgcU1DQYTL2mSJqnN/lDaBjtANhNpMK6tVzWKueQwLu2HArJg9NYRE1IiIiIp1g0E1EdJRI9loy3BJwS2Y7mIV22s1ItzlVgbPn586EseJj+ANbYIVHPe4zGPBTmAPHuWPhG3kj3iofiVX7XEg3OlVxtNqZ7CGdYnD+kI6YsTJTBd6yhluy6ZLhloA7mE0nIiIiopbHoJuI6CiR6eLB7HP1ad8BzQ+XexESjTOgle1RlchNBx4LCwTQ3x+L7NgbcLPrOPSvjMP5wzpiT/G2RjPZElj36xh16OvGiYiIiOiYYtBNRHSUSPAra7hlSnmQv/w7lFZ+AaM/KxRoi3ifHz3QFcVRl2FjeFXbrw4un8qGh9vMahp6cF14Q5lsCbDZFoyIiIhI3xh0ExEdJZJtluC40u3DUN9ynJD7Efb71+LjyN/XZad5/eiAgciPuwrrHF0bbPc1KC0afZMjmckmIiIiauUYdBMRHS2GDEw0zsTJOz5Hmn+P2tQZwCxnOFJ8RsB7PL71TkL3Tt3gdFgO2u6LmWwiIiKi1o9BNxHREQhoAazZswBzl7+MHXnrcHxZCdL8paHHSy0d0d9wPr61TEBYdATSrWbsK6pEut3Mdl9ERERE7QCDbiKiegQ0DTvyylHu9tdbpMztd2PRho8xf907yCveDWgBtX2Z3YZzyspQFDYQn9vOwyLzSFitFvRKjFDrsYW0FGO7LyIiIqL2gUE3EVEt6/cV471fMrCvLAC3r6qImbQAmzI0FcnRHvy0cjp+3T4LlRW5NY5L8vkxLnEYws65ExEpI3BufjlOqaeyeFOKpBERERFR28Cgm4iomnWZxXhu7jbkFJWjU1wkHDazWmu9ac9uvLLvIfiNG2H0VdZ4z3r5DRjf9XT0GfM3GKLTQtsbqiwugTWLpBERERG1Dwy6iYgOCAQ0lYEuLPegS4wdYXYzYrz7MargMwwrmIknYyzIMVf9b9OsaRiKcPTrfAFiB1+LzskdYDiEHtkskkZERETUPjDoJiI6YFd+uZryHe/0w+H6Fpfkrkbfkl9gRNV67bEVdswOD8cQSyrKHBfgR89wfLPXAFvWbqQn5qvp55weTkRERETVMegmIjpgW85GhLufgeZdhzKtEjEVBaGA22uwwmwfh0LP6fjU0hP+cg3JUQ4kWU1q+vnajGJkFlaq9doMvImIiIgoiEE3EbVrvoAPK7d/iwWrXsfOgk0wG3yAVvXY/DAHzqmwYVHcZCyLOxdZXidW7y1ErMWH/h2jQi2/nHYz0m1OlSWfsSJTrdeuXumciIiIiNovBt1E1C7Xbq/bvw8r1r+FDftmobwyGxIiB8PksEAAPb0xcEVdgf90OgN+owWapmFXVrF6vGtseI0e20LuS+Z7a06pmqbeUBE1IiIiImpfGHQTUbuyYedezJz3MLa5F8ECl9oWDJ87+gMYHjUUv7rOwdf+zuhkj4QDZlS6fNhfXIlwm1klwaWieX0cVpNqAVbq8h3DV0REREREesagm4jah+z1KJj3Irpt/gxnWgJ4OaaqH7ZB09DDY4bNdBJOPP0u9O2RjuSMQlT+sg37yrzILnWH+miP7BqL95bsVmu4ZUp5bbJd9pW+3EREREREgr8ZEtExn9ot068lGyzBaZe48GZb/5xTkomfV7yI3juWoN+e3xB7YHtfD9DF60W0IRmB8EnYlXgatuS6kL3Jg3u7a+jXMQq3nJyKCpMT5W5/6DrF4p35qmiarOGuPsVcpp9LNlyC8+C+REREREQMuonomFmXWaz6YEvBMbc3oLLC6YnOQ2611Vjg7g/4sW73XPy86nVsyl0D+D3Y5/ag34Fj3QY7VsaeDlPMJGwPSw+dMznKEFqP3SUuDEaDAd3i5bzGGs8t1ypVyuU1yBpumVIuGW4JuGPDrZg8NIVF1IiIiIgohEE3ER2zgPu5OVtRUO6pClajDq/VVkOB+8T+ThTs/wgLt3+N4rL9NY7ZYbUgL7obvvSfjl2p58JncR72emy5RrnW4DXIMcHp5xJws10YEREREVXHoJuImp1kpiVIlYBbAuTDbbVVO3C3RxpRXrEaOXtm4M3MrbBo7hr7x/kDODG2L0YNvwnZ0aMx/+sNiPZb4bTgiNZjS2At13qspskTERERUevFoJuImp0Ep8Hp2IfbaqtG4J4Qjq4Va9Az91N8at0Ag1GD5UBvbSmM1h82nND9LPQ9/lYYolLU9i4BTQX8TVuPfeBkjZAAm23BiIiIiOhgGHQTUbOTbLBMBZcp5fWpPbW7vjXbO/PKsHf/FvwpsBQnbJ2JRPdute/amChssVoR6Q8gzZeMccddhx4jL8auQjfWlPgQ4SkLZaGbuh5bnp+IiIiI6Ghg0E1EzU4CZ5m63ZRWW7XXbJvNLnS1z4PXMx/Jnkycn5cX6qstTq40wWkdiazoizGvLA5JjjR8+O2WBou1cT02ERERER1LDLqJqNlJprkpU7vL3T48P3cb8svdSArbhXDtS5T4NqC41KX2rTAZsMlqQR+PFzvCB2Np7LlYFzUWfqMVZS4fvP4KfLoiQwXbjRVr43psIiIiIjpWavbCaSNefPFFdOnSBXa7HSNHjsTSpUsb3Pe1117DiSeeiJiYGHWbMGFCo/sT0aELTu2WKdySgZYA2R/Q1E+5L9snDemID3/bBEPp+0g1/g2oeBAV3hUwa1UBt+jkMeAX2wQ82fN/eK37C1gdc5oKuCVw31dUAbcvoAJuCfAlo24yGqqKtSU61VpwKdYmU8eD67EHpUWrnyyARkRERETNpc0F3R999BFuv/12PPDAA1ixYgUGDRqEiRMnIicnp97958+fj4svvhjz5s3DokWLkJaWhtNOOw2ZmZnH/NqJ2rLg1O4BqVEoqvRgV165+ikZ7r+ODMOixTcif/+VsBi/gj+QDcOBYmZhgQAG+BLQ03Aplplew/yoW7G4OLZO4G63mGAzG5tUrI2IiIiI6Fhpc9PLn3rqKVxzzTW44oor1P3p06dj5syZeOONN3D33XfX2f9///tfjfv/93//h88++wxz5szBZZdddsyum6g9qD61u6y8Ah32z0Xito9h+GAuVkSEwxzmCO3bxWtEjGUY8mMuwi57NxVgG/LKccHQVKzJLK7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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fit quality:\n", + " RMSE: 0.009066\n", + " Max residual: 0.025379\n", + " Mean residual: -0.000468\n" + ] + } + ], + "source": [ + "# Generate predictions with fitted parameters\n", + "r_fit, k_fit = result.x\n", + "exp_rt_fit = np.exp(r_fit * t_span)\n", + "y_fit = y0 * exp_rt_fit / (1 + y0 * (exp_rt_fit - 1) / k_fit)\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(2, 1, figsize=(10, 10))\n", + "\n", + "# Top: Data and fits\n", + "ax1 = axes[0]\n", + "ax1.plot(t_span, y_noisy, \"o\", label=\"Noisy data\", alpha=0.6, markersize=6)\n", + "ax1.plot(t_span, y_fit, \"-\", label=\"Fitted model\", linewidth=2)\n", + "ax1.plot(t_span, y_data, \"--\", label=\"True solution\", linewidth=2, alpha=0.7)\n", + "ax1.set_xlabel(\"Time\")\n", + "ax1.set_ylabel(\"Population\")\n", + "ax1.set_title(\"Logistic Growth Model Fit\")\n", + "ax1.legend()\n", + "ax1.grid(True, alpha=0.3)\n", + "\n", + "# Bottom: Residuals\n", + "ax2 = axes[1]\n", + "residuals = y_noisy - y_fit\n", + "ax2.plot(t_span, residuals, \"o-\", alpha=0.6)\n", + "ax2.axhline(0, color=\"r\", linestyle=\"--\", linewidth=2, alpha=0.7)\n", + "ax2.set_xlabel(\"Time\")\n", + "ax2.set_ylabel(\"Residuals\")\n", + "ax2.set_title(\"Fitting Residuals\")\n", + "ax2.grid(True, alpha=0.3)\n", + "\n", + "# Add statistics\n", + "rmse = np.sqrt(np.mean(residuals**2))\n", + "ax2.text(\n", + " 0.02,\n", + " 0.98,\n", + " f\"RMSE = {rmse:.4f}\",\n", + " transform=ax2.transAxes,\n", + " verticalalignment=\"top\",\n", + " bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\nFit quality:\")\n", + "print(f\" RMSE: {rmse:.6f}\")\n", + "print(f\" Max residual: {np.abs(residuals).max():.6f}\")\n", + "print(f\" Mean residual: {np.mean(residuals):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Different Initial Guesses\n", + "\n", + "Let's see how different starting points affect convergence:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:35.056134Z", + "iopub.status.busy": "2026-01-10T22:21:35.055933Z", + "iopub.status.idle": "2026-01-10T22:21:42.610049Z", + "shell.execute_reply": "2026-01-10T22:21:42.609184Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "======================================================================\n", + "INITIAL GUESS SENSITIVITY\n", + "======================================================================\n", + "Initial [r, k] Final [r, k] Iterations Success\n", + "----------------------------------------------------------------------\n", + "[0.5, 0.5] [0.9967, 1.0029] 73 True\n", + "[2.0, 2.0] [0.9967, 1.0029] 77 True\n", + "[10.0, 10.0] [0.9967, 1.0029] 183 True\n", + "[100.0, 100.0] [0.5947, 23.7961] 410 True\n", + "\n", + "True values: [r, k] = [1.0, 1.0]\n" + ] + } + ], + "source": [ + "# Try several initial guesses\n", + "initial_guesses = [\n", + " [0.5, 0.5],\n", + " [2.0, 2.0],\n", + " [10.0, 10.0],\n", + " [100.0, 100.0],\n", + "]\n", + "\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"INITIAL GUESS SENSITIVITY\")\n", + "print(\"=\" * 70)\n", + "print(f\"{'Initial [r, k]':<20} {'Final [r, k]':<30} {'Iterations':<12} {'Success'}\")\n", + "print(\"-\" * 70)\n", + "\n", + "for guess in initial_guesses:\n", + " result = optimiser.run(problem, guess)\n", + " final_str = f\"[{result.x[0]:.4f}, {result.x[1]:.4f}]\"\n", + " guess_str = f\"{guess}\"\n", + " print(f\"{guess_str:<20} {final_str:<30} {result.iterations:<12} {result.success}\")\n", + "\n", + "print(f\"\\nTrue values: [r, k] = [{r_true}, {k_true}]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Optimisers\n", + "\n", + "Let's compare different optimization algorithms:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:42.612961Z", + "iopub.status.busy": "2026-01-10T22:21:42.612739Z", + "iopub.status.idle": "2026-01-10T22:21:46.725883Z", + "shell.execute_reply": "2026-01-10T22:21:46.725244Z" + } + }, + "outputs": [], + "source": "optimisers = {\n \"Nelder-Mead\": diffid.NelderMead().with_max_iter(1000),\n \"CMA-ES\": diffid.CMAES().with_max_iter(500),\n \"Adam\": diffid.Adam().with_max_iter(1000).with_step_size(0.01),\n}\n\ninitial = [2.0, 2.0]\n\nprint(\"\\n\" + \"=\" * 80)\nprint(\"OPTIMISER COMPARISON\")\nprint(\"=\" * 80)\nprint(\n f\"{'Algorithm':<15} {'r (fitted)':<15} {'k (fitted)':<15} {'Cost':<15} {'Iters':<8} {'Time (s)'}\"\n)\nprint(\"-\" * 80)\n\nfor name, opt in optimisers.items():\n result = opt.run(problem, initial)\n print(\n f\"{name:<15} {result.x[0]:<15.6f} {result.x[1]:<15.6f} \"\n f\"{result.value:<15.3e} {result.iterations:<8} {result.time.microseconds / 1e6:.3f}\"\n )\n\nprint(f\"\\nTrue values: {r_true:<15.6f} {k_true:<15.6f}\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding DiffSL Syntax\n", + "\n", + "Let's explore more complex DiffSL examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:46.728570Z", + "iopub.status.busy": "2026-01-10T22:21:46.728356Z", + "iopub.status.idle": "2026-01-10T22:21:46.732842Z", + "shell.execute_reply": "2026-01-10T22:21:46.732181Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example: Predator-Prey Model (Lotka-Volterra)\n", + "==================================================\n", + "\n", + "in_i { alpha = 1, beta = 0.1, gamma = 1.5, delta = 0.075 }\n", + "u_i {\n", + " prey = 10.0\n", + " predator = 5.0\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator\n", + "}\n", + "out_i { prey, predator }\n", + "\n", + "\n", + "Example: Sequential Reactions (A → B → C)\n", + "==================================================\n", + "\n", + "in_i { k1 = 0.5, k2 = 0.3 }\n", + "u_i { A = 1.0 }\n", + "F_i {\n", + " -k1 * A,\n", + " k1 * A - k2 * B\n", + "}\n", + "out_i { A, B }\n", + "\n" + ] + } + ], + "source": [ + "# Example 1: Multiple state variables (Lotka-Volterra)\n", + "predator_prey = \"\"\"\n", + "in_i { alpha = 1, beta = 0.1, gamma = 1.5, delta = 0.075 }\n", + "u_i {\n", + " prey = 10.0\n", + " predator = 5.0\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator\n", + "}\n", + "out_i { prey, predator }\n", + "\"\"\"\n", + "\n", + "print(\"Example: Predator-Prey Model (Lotka-Volterra)\")\n", + "print(\"=\" * 50)\n", + "print(predator_prey)\n", + "\n", + "# Example 2: With algebraic equations\n", + "with_algebra = \"\"\"\n", + "in_i { k1 = 0.5, k2 = 0.3 }\n", + "u_i { A = 1.0 }\n", + "F_i {\n", + " -k1 * A,\n", + " k1 * A - k2 * B\n", + "}\n", + "out_i { A, B }\n", + "\"\"\"\n", + "\n", + "print(\"\\nExample: Sequential Reactions (A → B → C)\")\n", + "print(\"=\" * 50)\n", + "print(with_algebra)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Key Takeaways\n\n1. **DiffsolBuilder** is used for ODE parameter fitting\n2. **DiffSL** provides concise ODE syntax: `in`, `u_i`, `F_i`\n3. Data format: `[time, observation(s)]` in columns\n4. **CMA-ES** typically works well for ODE fitting\n5. Parallel evaluation improves performance\n6. Always check residuals to assess fit quality\n\n## Next Steps\n\n- [Parameter Uncertainty](parameter_uncertainty.ipynb) - Quantify parameter uncertainty with MCMC\n- [DiffSL Backend Guide](../../guides/diffsol-backend.md) - Dense vs sparse solvers\n- [Custom Solvers](../../guides/custom-solvers.md) - Using JAX/Diffrax or Julia" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Add Noise**: Increase `noise_level` to 0.05 and see how it affects fitting\n", + "\n", + "2. **Different Model**: Implement exponential decay: $\\frac{dy}{dt} = -k \\cdot y$\n", + "\n", + "3. **Multiple Variables**: Try fitting the predator-prey model with synthetic data\n", + "\n", + "4. **Sparse Data**: Reduce the number of data points (e.g., 20 instead of 100) and observe the impact" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/optimisation_basics.ipynb b/docs/tutorials/notebooks/optimisation_basics.ipynb new file mode 100644 index 0000000..b52fc68 --- /dev/null +++ b/docs/tutorials/notebooks/optimisation_basics.ipynb @@ -0,0 +1,440 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Optimisation Basics\n\n**Objectives:**\n- Understand the ScalarBuilder API\n- Optimise the classic Rosenbrock function\n- Visualise optimisation landscapes with contour plots\n- Compare different optimisers" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The Rosenbrock function is a classic test problem in optimisation. It's defined as:\n", + "\n", + "$$f(x, y) = (1 - x)^2 + 100(y - x^2)^2$$\n", + "\n", + "The global minimum is at $(1, 1)$ with $f(1, 1) = 0$. Despite being simple to state, it's challenging for optimisers because of its narrow, curved valley." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:26.832609Z", + "iopub.status.busy": "2026-01-10T22:21:26.832243Z", + "iopub.status.idle": "2026-01-10T22:21:27.524510Z", + "shell.execute_reply": "2026-01-10T22:21:27.523489Z" + } + }, + "outputs": [], + "source": [ + "# Import plotting utilities\n", + "import diffid\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from diffid.plotting import contour_2d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Define the Objective Function\n\nIn Diffid, objective functions must:\n1. Accept a NumPy array as input\n2. Return a NumPy array as output (even for scalar values)" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:27.527692Z", + "iopub.status.busy": "2026-01-10T22:21:27.527458Z", + "iopub.status.idle": "2026-01-10T22:21:27.531149Z", + "shell.execute_reply": "2026-01-10T22:21:27.530296Z" + } + }, + "outputs": [], + "source": [ + "def rosenbrock(x):\n", + " \"\"\"The Rosenbrock function.\"\"\"\n", + " value = (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n", + " return np.array([value], dtype=float)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build the Problem\n", + "\n", + "Use `ScalarBuilder` for direct function optimisation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:27.533691Z", + "iopub.status.busy": "2026-01-10T22:21:27.533430Z", + "iopub.status.idle": "2026-01-10T22:21:27.538107Z", + "shell.execute_reply": "2026-01-10T22:21:27.537575Z" + } + }, + "outputs": [], + "source": [ + "builder = (\n", + " diffid.ScalarBuilder()\n", + " .with_objective(rosenbrock)\n", + " .with_parameter(\"x\", 10.0) # Initial guess\n", + " .with_parameter(\"y\", 10.0) # Initial guess\n", + ")\n", + "problem = builder.build()\n", + "\n", + "print(\"Problem built successfully!\")\n", + "print(\"Number of parameters: 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Optimise with Default Settings\n\nThe problem class is constructed as the core object in diffid, as such\n an `optimise()` method if provided for fast optimisation. This uses Nelder-Mead by default:" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:27.574708Z", + "iopub.status.busy": "2026-01-10T22:21:27.574501Z", + "iopub.status.idle": "2026-01-10T22:21:27.583798Z", + "shell.execute_reply": "2026-01-10T22:21:27.583240Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "OPTIMISATION RESULTS\n", + "==================================================\n", + "Success: True\n", + "Optimal parameters: [1.00082688 1.0017059 ]\n", + "Objective value: 9.484e-07\n", + "Iterations: 159\n", + "Function evaluations: 342\n", + "Message: Function tolerance met\n" + ] + } + ], + "source": [ + "result = problem.optimise() # Uses initial guesses provided\n", + "\n", + "print(\"\\n\" + \"=\" * 50)\n", + "print(\"OPTIMISATION RESULTS\")\n", + "print(\"=\" * 50)\n", + "print(f\"Success: {result.success}\")\n", + "print(f\"Optimal parameters: {result.x}\")\n", + "print(f\"Objective value: {result.value:.3e}\")\n", + "print(f\"Iterations: {result.iterations}\")\n", + "print(f\"Function evaluations: {result.evaluations}\")\n", + "print(f\"Message: {result.message}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Visualise the Optimisation Landscape\n\nLet's create a contour plot to see the function's shape:" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:27.586141Z", + "iopub.status.busy": "2026-01-10T22:21:27.585957Z", + "iopub.status.idle": "2026-01-10T22:21:27.968383Z", + "shell.execute_reply": "2026-01-10T22:21:27.967837Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance from true optimum: 1.896e-03\n" + ] + } + ], + "source": [ + "# Create contour plot\n", + "fig, ax = contour_2d(\n", + " rosenbrock,\n", + " xlim=(-2, 2),\n", + " ylim=(-1, 3),\n", + " levels=np.logspace(-1, 3.5, 20),\n", + " optimum=(1.0, 1.0),\n", + " found=(result.x[0], result.x[1]),\n", + " title=\"Rosenbrock Function Landscape\",\n", + " show=False,\n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "print(f\"Distance from true optimum: {np.linalg.norm(result.x - [1.0, 1.0]):.3e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare Different Optimisers\n", + "\n", + "Let's compare Nelder-Mead, CMA-ES, and Adam:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:27.973637Z", + "iopub.status.busy": "2026-01-10T22:21:27.973424Z", + "iopub.status.idle": "2026-01-10T22:21:28.085544Z", + "shell.execute_reply": "2026-01-10T22:21:28.084924Z" + } + }, + "outputs": [], + "source": [ + "# Define optimisers\n", + "optimisers = {\n", + " \"Nelder-Mead\": diffid.NelderMead().with_max_iter(1000),\n", + " \"CMA-ES\": diffid.CMAES().with_max_iter(300).with_step_size(0.5),\n", + " \"Adam\": diffid.Adam()\n", + " .with_max_iter(2000)\n", + " .with_step_size(0.25)\n", + " .with_threshold(1e-12),\n", + "}\n", + "\n", + "# Test starting point\n", + "initial_guess = [-1.0, -3.0]\n", + "\n", + "# Run all optimisers\n", + "results = {}\n", + "for name, optimiser in optimisers.items():\n", + " result = optimiser.run(problem, initial_guess)\n", + " results[name] = result\n", + "\n", + "# Display comparison\n", + "print(\"\\n\" + \"=\" * 70)\n", + "print(\"OPTIMISER COMPARISON\")\n", + "print(\"=\" * 70)\n", + "print(\n", + " f\"{'Optimiser':<15} {'Success':<10} {'Final Value':<15} {'Iterations':<12} {'Evaluations'}\"\n", + ")\n", + "print(\"-\" * 70)\n", + "\n", + "for name, result in results.items():\n", + " print(\n", + " f\"{name:<15} {str(result.success):<10} {result.value:<15.3e} \"\n", + " f\"{result.iterations:<12} {result.evaluations}\"\n", + " )\n", + "\n", + "print(\"\\nFinal parameters:\")\n", + "for name, result in results.items():\n", + " print(f\"{name:<15} x = {result.x}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise All Results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:28.088177Z", + "iopub.status.busy": "2026-01-10T22:21:28.087996Z", + "iopub.status.idle": "2026-01-10T22:21:28.441449Z", + "shell.execute_reply": "2026-01-10T22:21:28.440265Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create contour plot with all optimisers' results\n", + "x = np.linspace(-2, 2, 200)\n", + "y = np.linspace(-5, 3, 200)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z = np.zeros_like(X)\n", + "\n", + "for i in range(X.shape[0]):\n", + " for j in range(X.shape[1]):\n", + " Z[i, j] = rosenbrock([X[i, j], Y[i, j]])[0]\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 9))\n", + "\n", + "# Plot contours\n", + "levels = np.logspace(-1, 3.5, 20)\n", + "cs = ax.contour(X, Y, Z, levels=levels, cmap=\"viridis\", alpha=0.6)\n", + "ax.clabel(cs, inline=True, fontsize=8)\n", + "\n", + "# Plot true optimum\n", + "ax.plot(1.0, 1.0, \"r*\", markersize=20, label=\"True minimum\", zorder=5)\n", + "\n", + "# Plot starting point\n", + "ax.plot(\n", + " initial_guess[0],\n", + " initial_guess[1],\n", + " \"kx\",\n", + " markersize=15,\n", + " markeredgewidth=3,\n", + " label=\"Starting point\",\n", + " zorder=5,\n", + ")\n", + "\n", + "# Plot optimiser results\n", + "colors = {\"Nelder-Mead\": \"blue\", \"CMA-ES\": \"green\", \"Adam\": \"orange\"}\n", + "markers = {\"Nelder-Mead\": \"o\", \"CMA-ES\": \"s\", \"Adam\": \"^\"}\n", + "\n", + "for name, result in results.items():\n", + " ax.plot(\n", + " result.x[0],\n", + " result.x[1],\n", + " marker=markers[name],\n", + " color=colors[name],\n", + " markersize=12,\n", + " label=name,\n", + " zorder=5,\n", + " markeredgecolor=\"black\",\n", + " markeredgewidth=1.5,\n", + " )\n", + "\n", + "ax.set_xlabel(\"x\", fontsize=12)\n", + "ax.set_ylabel(\"y\", fontsize=12)\n", + "ax.set_title(\"Optimiser Comparison on Rosenbrock Function\", fontsize=14)\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend(loc=\"upper left\", fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect of Starting Point\n", + "\n", + "Let's see how starting position affects convergence:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:28.447521Z", + "iopub.status.busy": "2026-01-10T22:21:28.447189Z", + "iopub.status.idle": "2026-01-10T22:21:28.461367Z", + "shell.execute_reply": "2026-01-10T22:21:28.460670Z" + } + }, + "outputs": [], + "source": [ + "# Try multiple starting points\n", + "starting_points = [\n", + " [-1.5, -0.5],\n", + " [1.5, 1.5],\n", + " [0.0, 2.0],\n", + " [-1.0, 1.0],\n", + "]\n", + "\n", + "optimiser = diffid.NelderMead().with_max_iter(1000)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"STARTING POINT SENSITIVITY\")\n", + "print(\"=\" * 60)\n", + "\n", + "for i, start in enumerate(starting_points, 1):\n", + " result = optimiser.run(problem, start)\n", + " error = np.linalg.norm(result.x - [1.0, 1.0])\n", + "\n", + " print(f\"\\nStart {i}: {start}\")\n", + " print(f\" Final: {result.x}\")\n", + " print(f\" Iterations: {result.iterations}\")\n", + " print(f\" Error: {error:.3e}\")\n", + " print(f\" Success: {result.success}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Key Takeaways\n\n1. **ScalarBuilder** is used for direct function optimisation\n2. **Nelder-Mead** is provided as the default optimiser - good for small problems\n3. **CMA-ES** is more robust for global search but requires more evaluations\n4. **Adam** can be fast on smooth problems but may struggle on complex landscapes\n5. Starting point can significantly affect convergence speed\n\n## Next Steps\n\n- [ODE Fitting with DiffSL](ode_fitting_diffsol.ipynb) - Learn how to fit differential equations\n- [Choosing an Optimiser](../../guides/choosing-optimiser.md) - Detailed optimiser selection guide\n- [API Reference: Optimisers](../../api-reference/python/optimisers.md) - Complete API documentation" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "Try these challenges:\n", + "\n", + "1. **Rastrigin Function**: Implement and optimise:\n", + " $$f(x, y) = 20 + x^2 + y^2 - 10(\\cos(2\\pi x) + \\cos(2\\pi y))$$\n", + " \n", + "2. **Parameter Tuning**: Experiment with CMA-ES `step_size` parameter (try 0.1, 0.5, 1.0, 2.0)\n", + "\n", + "3. **Constraint Handling**: How would you restrict the search to $x, y \\in [-2, 2]$?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/parallel_optimisation.ipynb b/docs/tutorials/notebooks/parallel_optimisation.ipynb new file mode 100644 index 0000000..b56d358 --- /dev/null +++ b/docs/tutorials/notebooks/parallel_optimisation.ipynb @@ -0,0 +1,495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Parallel Optimisation\n\n**Learning Objectives:**\n- Understand which optimisers support parallelism\n- Configure population-based optimisers for parallel execution\n- Benchmark scaling performance\n- Identify performance bottlenecks\n- Apply best practices for thread-safe optimisation\n\n**Prerequisites:** Optimisation Basics, ODE Fitting, basic understanding of parallelism\n\n**Runtime:** ~10 minutes" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Introduction\n\nDiffid supports **parallel evaluation** for population-based optimisers (CMA-ES, Dynamic Nested Sampling). However, the effectiveness depends on the evaluation cost and backend used.\n\n### Key Insights\n\n1. **DiffsolBuilder** with `.with_parallel(True)` uses Rust's rayon for parallel ODE integration\n2. **Fast evaluations** (< 1ms) may see limited speedup due to efficient solver caching\n3. **Python callables** cannot be parallelised with threads (GIL), but **multiprocessing** works\n\nThis tutorial covers:\n- Parallel ODE fitting with `DiffsolBuilder`\n- Using `multiprocessing` for expensive Python callables\n- Understanding when parallelism helps" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:18.792193Z", + "start_time": "2026-01-22T19:00:17.788924Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:06.718164Z", + "iopub.status.busy": "2026-01-10T22:22:06.717720Z", + "iopub.status.idle": "2026-01-10T22:22:07.287201Z", + "shell.execute_reply": "2026-01-10T22:22:07.286361Z" + } + }, + "outputs": [], + "source": "import multiprocessing\nimport time\n\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.integrate import solve_ivp\n\n# Detect available cores\nn_cores = multiprocessing.cpu_count()\nprint(f\"Available CPU cores: {n_cores}\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Test Problem: Lotka-Volterra ODE Fitting\n\nWe'll use the predator-prey model from Tutorial 6. ODE integration is computationally expensive, making it ideal for demonstrating parallel speedup.\n\n$$\\frac{dx}{dt} = \\alpha x - \\beta xy \\quad \\text{(prey)}$$\n$$\\frac{dy}{dt} = \\delta xy - \\gamma y \\quad \\text{(predator)}$$\n\nThe DiffSL model definition and synthetic data generation:" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:18.910392Z", + "start_time": "2026-01-22T19:00:18.812730Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:07.289542Z", + "iopub.status.busy": "2026-01-10T22:22:07.289285Z", + "iopub.status.idle": "2026-01-10T22:22:07.350768Z", + "shell.execute_reply": "2026-01-10T22:22:07.349658Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data points: 500\n", + "Time span: [0, 100]\n", + "Parameters to fit: 4\n", + "True parameters: [1.1, 0.4, 0.1, 0.4]\n" + ] + } + ], + "source": [ + "# DiffSL model for Lotka-Volterra\n", + "model_str = \"\"\"\n", + "in_i { alpha = 1.0, beta = 0.5, delta = 0.1, gamma = 0.5 }\n", + "x0 { 10.0 } y0 { 5.0 }\n", + "u_i {\n", + " prey = x0,\n", + " predator = y0,\n", + "}\n", + "F_i {\n", + " alpha * prey - beta * prey * predator,\n", + " delta * prey * predator - gamma * predator,\n", + "}\n", + "\"\"\"\n", + "\n", + "# True parameters for data generation\n", + "true_params = {\"alpha\": 1.1, \"beta\": 0.4, \"delta\": 0.1, \"gamma\": 0.4}\n", + "\n", + "# Generate synthetic data using scipy\n", + "# Longer time span = more expensive integration\n", + "np.random.seed(42)\n", + "t_data = np.linspace(0, 100, 500) # Long integration with many points\n", + "\n", + "\n", + "def lotka_volterra(t, state, alpha, beta, delta, gamma):\n", + " x, y = state\n", + " return [alpha * x - beta * x * y, delta * x * y - gamma * y]\n", + "\n", + "\n", + "sol = solve_ivp(\n", + " lotka_volterra,\n", + " [t_data[0], t_data[-1]],\n", + " [10.0, 5.0],\n", + " args=(\n", + " true_params[\"alpha\"],\n", + " true_params[\"beta\"],\n", + " true_params[\"delta\"],\n", + " true_params[\"gamma\"],\n", + " ),\n", + " t_eval=t_data,\n", + " method=\"RK45\",\n", + ")\n", + "\n", + "y_true = sol.y.T\n", + "y_observed = y_true + np.random.normal(0, 0.3, y_true.shape)\n", + "\n", + "# Combine for DiffsolBuilder (time in first column)\n", + "data = np.column_stack((t_data, y_observed))\n", + "\n", + "print(f\"Data points: {len(t_data)}\")\n", + "print(\"Time span: [0, 100]\")\n", + "print(\"Parameters to fit: 4\")\n", + "print(f\"True parameters: {list(true_params.values())}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Sequential vs Parallel: DiffsolBuilder\n\nThe key difference is the `.with_parallel()` method on `DiffsolBuilder`. When enabled, population-based optimisers evaluate multiple parameter sets concurrently.\n\nLet's compare sequential and parallel CMA-ES on our ODE fitting problem:" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.020454Z", + "start_time": "2026-01-22T19:00:18.953488Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:07.355771Z", + "iopub.status.busy": "2026-01-10T22:22:07.355407Z", + "iopub.status.idle": "2026-01-10T22:22:08.805007Z", + "shell.execute_reply": "2026-01-10T22:22:08.804066Z" + } + }, + "outputs": [], + "source": "# Sequential execution (parallel=False)\nprint(\"Running CMA-ES (sequential)...\")\n\nstart = time.time()\n\nresult_seq = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(False) # Sequential evaluation\n .with_optimiser(diffid.CMAES().with_max_iter(100).with_step_size(0.3))\n .build()\n .optimise()\n)\n\ntime_seq = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"CMA-ES (Sequential)\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_seq.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_seq.value:.3f}\")\nprint(f\"Evaluations: {result_seq.evaluations}\")\nprint(f\"Time: {time_seq:.2f}s\")\nprint(f\"Time per eval: {time_seq / result_seq.evaluations * 1000:.2f}ms\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Parallel Execution\n\nNow let's enable parallelism with `.with_parallel(True)`:" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.380944Z", + "start_time": "2026-01-22T19:00:20.205935Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:08.808619Z", + "iopub.status.busy": "2026-01-10T22:22:08.808319Z", + "iopub.status.idle": "2026-01-10T22:22:12.220604Z", + "shell.execute_reply": "2026-01-10T22:22:12.217981Z" + } + }, + "outputs": [], + "source": "# Parallel execution (parallel=True)\nprint(\"Running CMA-ES (parallel)...\")\n\nstart = time.time()\n\nresult_par = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True) # Parallel evaluation!\n .with_optimiser(diffid.CMAES().with_max_iter(100).with_step_size(0.3))\n .build()\n .optimise()\n)\n\ntime_par = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"CMA-ES (Parallel)\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_par.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_par.value:.3f}\")\nprint(f\"Evaluations: {result_par.evaluations}\")\nprint(f\"Time: {time_par:.2f}s\")\nprint(f\"Time per eval: {time_par / result_par.evaluations * 1000:.2f}ms\")\n\nspeedup = time_seq / time_par\nprint(f\"\\n🚀 Speedup: {speedup:.2f}x (with {n_cores} cores)\")" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.695642Z", + "start_time": "2026-01-22T19:00:20.397778Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:12.225212Z", + "iopub.status.busy": "2026-01-10T22:22:12.224831Z", + "iopub.status.idle": "2026-01-10T22:22:19.045346Z", + "shell.execute_reply": "2026-01-10T22:22:19.043950Z" + } + }, + "outputs": [], + "source": "# Parallel with larger population (more parallel work per generation)\nprint(\"Running CMA-ES (parallel, large population)...\")\n\nstart = time.time()\n\nresult_par_large = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True)\n .with_optimiser(\n diffid.CMAES()\n .with_max_iter(50)\n .with_step_size(0.3)\n .with_population_size(2 * n_cores) # Match population to available cores\n )\n .build()\n .optimise()\n)\n\ntime_par_large = time.time() - start\n\nprint(\"\\n\" + \"=\" * 60)\nprint(f\"CMA-ES (Parallel, Population={2 * n_cores})\")\nprint(\"=\" * 60)\nprint(f\"Solution: {result_par_large.x}\")\nprint(f\"True params: {list(true_params.values())}\")\nprint(f\"Final SSE: {result_par_large.value:.3f}\")\nprint(f\"Evaluations: {result_par_large.evaluations}\")\nprint(f\"Time: {time_par_large:.2f}s\")\nprint(f\"Time per eval: {time_par_large / result_par_large.evaluations * 1000:.2f}ms\")\n\nspeedup_large = time_seq / time_par_large\nprint(f\"\\n🚀 Speedup: {speedup_large:.2f}x\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Parallelising Python Callables with Multiprocessing\n\nPython's GIL prevents thread-based parallelism for Python code. However, you can use **multiprocessing** to parallelise expensive Python functions by wrapping them appropriately.\n\nHere's how to create a parallel-capable objective function:" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:20.944100Z", + "start_time": "2026-01-22T19:00:20.709478Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:19.049873Z", + "iopub.status.busy": "2026-01-10T22:22:19.049477Z", + "iopub.status.idle": "2026-01-10T22:22:30.396419Z", + "shell.execute_reply": "2026-01-10T22:22:30.395476Z" + } + }, + "outputs": [], + "source": "# Define an expensive objective function\ndef expensive_objective(params):\n \"\"\"\n An expensive objective function that simulates computation.\n In practice, this could be a complex simulation, ML model, etc.\n \"\"\"\n alpha, beta, delta, gamma = params\n\n # Simulate expensive computation (ODE integration with scipy)\n sol = solve_ivp(\n lotka_volterra,\n [0, 100],\n [10.0, 5.0],\n args=(alpha, beta, delta, gamma),\n t_eval=t_data,\n method=\"RK45\",\n )\n\n if not sol.success:\n return 1e10 # Return large value for failed integrations\n\n # Compute SSE\n y_pred = sol.y.T\n sse = np.sum((y_pred - y_observed) ** 2)\n return sse\n\n\n# Test single evaluation time\nn_evals = 20\ntest_params = [[0.8 + i * 0.02, 0.3, 0.05, 0.3] for i in range(n_evals)]\n\nprint(f\"Testing {n_evals} sequential evaluations...\")\n\nstart = time.time()\nseq_results = [expensive_objective(p) for p in test_params]\ntime_seq_python = time.time() - start\nprint(\n f\"Sequential: {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n)\n\n# Note: Multiprocessing in notebooks has pickling limitations\n# In a regular Python script, you would use:\nprint(\"\"\"\n💡 To parallelise in a script, use multiprocessing:\n\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_batch_parallel(params_list, n_workers=8):\n with ProcessPoolExecutor(max_workers=n_workers) as executor:\n return list(executor.map(expensive_objective, params_list))\n\n# This provides near-linear speedup for expensive functions\n\"\"\")" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:21.289092Z", + "start_time": "2026-01-22T19:00:20.946379Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:30.400184Z", + "iopub.status.busy": "2026-01-10T22:22:30.399842Z", + "iopub.status.idle": "2026-01-10T22:22:30.693763Z", + "shell.execute_reply": "2026-01-10T22:22:30.693093Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 Single Python evaluation: 9.8ms\n", + " With multiprocessing, you could achieve near-linear speedup.\n" + ] + } + ], + "source": [ + "# Visualize DiffsolBuilder parallel performance\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Time comparison for DiffsolBuilder\n", + "methods = [\"Sequential\", \"Parallel\", f\"Parallel\\n(pop={2 * n_cores})\"]\n", + "times = [time_seq, time_par, time_par_large]\n", + "colors = [\"#d62728\", \"#2ca02c\", \"#1f77b4\"]\n", + "\n", + "bars = ax1.bar(methods, times, color=colors, alpha=0.8, edgecolor=\"black\")\n", + "ax1.set_ylabel(\"Time (s)\", fontsize=12)\n", + "ax1.set_title(\"DiffsolBuilder: Optimisation Time\", fontsize=14, fontweight=\"bold\")\n", + "ax1.grid(True, axis=\"y\", alpha=0.3)\n", + "\n", + "for bar, t in zip(bars, times, strict=False):\n", + " ax1.text(\n", + " bar.get_x() + bar.get_width() / 2,\n", + " bar.get_height(),\n", + " f\"{t:.2f}s\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=11,\n", + " fontweight=\"bold\",\n", + " )\n", + "\n", + "# Single evaluation time for Python callable\n", + "ax2.bar(\n", + " [\"Sequential\\nPython\"],\n", + " [time_seq_python],\n", + " color=\"#9467bd\",\n", + " alpha=0.8,\n", + " edgecolor=\"black\",\n", + ")\n", + "ax2.set_ylabel(\"Time (s)\", fontsize=12)\n", + "ax2.set_title(f\"Python Callable: {n_evals} evaluations\", fontsize=14, fontweight=\"bold\")\n", + "ax2.grid(True, axis=\"y\", alpha=0.3)\n", + "ax2.text(\n", + " 0,\n", + " time_seq_python,\n", + " f\"{time_seq_python:.2f}s\",\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " fontsize=11,\n", + " fontweight=\"bold\",\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"\\n💡 Single Python evaluation: {time_seq_python / n_evals * 1000:.1f}ms\")\n", + "print(\" With multiprocessing, you could achieve near-linear speedup.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Understanding Parallel Performance\n\n### Why DiffsolBuilder Speedups May Be Modest\n\nDiffsolBuilder uses **solver caching** - each thread maintains cached ODE solver state that gets reused across evaluations. This makes sequential evaluation very fast, reducing the relative benefit of parallelism.\n\nParallelism helps more when:\n- Evaluations are expensive (>10ms each)\n- Large populations are used\n- Cache reuse is limited (e.g., parameters vary widely)\n\n### Multiprocessing vs Threading\n\n| Approach | Use Case | Overhead |\n|----------|----------|----------|\n| `.with_parallel(True)` | DiffsolBuilder only | Low (shared memory) |\n| `multiprocessing` | Any Python callable | Higher (process creation) |\n| `threading` | I/O-bound tasks | GIL blocks CPU work |\n\nFor expensive Python simulations, multiprocessing provides the best parallelism." + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.060489Z", + "start_time": "2026-01-22T19:00:21.315395Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:22:30.696868Z", + "iopub.status.busy": "2026-01-10T22:22:30.696562Z", + "iopub.status.idle": "2026-01-10T22:23:09.262187Z", + "shell.execute_reply": "2026-01-10T22:23:09.260026Z" + } + }, + "outputs": [], + "source": "# Test DiffsolBuilder scaling with data size\ndata_sizes = [100, 200, 500]\nscaling_results = []\n\nprint(\"Testing DiffsolBuilder scaling with data size...\\n\")\n\nfor n_points in data_sizes:\n # Generate data with different sizes\n t_test = np.linspace(0, 100, n_points)\n sol_test = solve_ivp(\n lotka_volterra,\n [t_test[0], t_test[-1]],\n [10.0, 5.0],\n args=(\n true_params[\"alpha\"],\n true_params[\"beta\"],\n true_params[\"delta\"],\n true_params[\"gamma\"],\n ),\n t_eval=t_test,\n method=\"RK45\",\n )\n y_test = sol_test.y.T + np.random.normal(0, 0.3, (n_points, 2))\n data_test = np.column_stack((t_test, y_test))\n\n # Sequential\n start = time.time()\n _ = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data_test)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(False)\n .with_optimiser(diffid.CMAES().with_max_iter(30).with_step_size(0.3))\n .build()\n .optimise()\n )\n t_seq_scale = time.time() - start\n\n # Parallel\n start = time.time()\n _ = (\n diffid.DiffsolBuilder()\n .with_diffsl(model_str)\n .with_data(data_test)\n .with_parameter(\"alpha\", 0.8)\n .with_parameter(\"beta\", 0.3)\n .with_parameter(\"delta\", 0.05)\n .with_parameter(\"gamma\", 0.3)\n .with_cost(diffid.SSE())\n .with_parallel(True)\n .with_optimiser(diffid.CMAES().with_max_iter(30).with_step_size(0.3))\n .build()\n .optimise()\n )\n t_par_scale = time.time() - start\n\n sp = t_seq_scale / t_par_scale\n scaling_results.append(\n {\n \"n_points\": n_points,\n \"time_seq\": t_seq_scale,\n \"time_par\": t_par_scale,\n \"speedup\": sp,\n }\n )\n print(\n f\"N={n_points:3d}: Sequential={t_seq_scale:.2f}s, Parallel={t_par_scale:.2f}s, Speedup={sp:.2f}x\"\n )" + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.316526Z", + "start_time": "2026-01-22T19:00:23.100926Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:09.267174Z", + "iopub.status.busy": "2026-01-10T22:23:09.266754Z", + "iopub.status.idle": "2026-01-10T22:23:09.644278Z", + "shell.execute_reply": "2026-01-10T22:23:09.643629Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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9bc781FNPJbtos+3fvz/F9tzXeV6A+UMDGB2pXZcBAwbI1Vdf7QpU9aKtV69efm3HWwCcEfYI6spzlGHdP1/flXt+XYyvC2xtxpyZdKoevaGizVM1AFm5cqUrf7VZuD9zd/sKnH3ta9++faVfv36pBtypbddfmZHfaaVNvLW5vm5Pm9rq/Oeeo+PryPu+eJYnz9+VXfbSc2zpeY97Wfdn//wNKv39br2N4u3+mZ7758sXX3zhetypUycTdGlg6H4zyz1g1XOeNvFX2v3jhhtukMcee0w++OADczNJb1jYTfsz+3yv7GnDMuN36It2UblYwJ3W7er3pc339QaA3hDRG3x6I8n9plDNmjXlpptu8nubekNBbxrpTRL7+9fuN+7lIC1lW4P/okWL+p0ewKVFTTeAS0YvVuz+3Ernb03LlFAZoReT2rdW++PqPujcqlrrpX0i9eJL+9ja/Sjdp+vyNhWS+zrti5hR27dvNzXx0dHRKWonPWuN3WsyqlatKplJ+z7a7H3x54JO88vOE61JmzJlis/P8NW/WPtzZ5T2ldcylRrtA2n3Pde81QBFL3y11lBrmOyAJDW+9lX7bbsHF9o3Vccw0BpeDf41IM8MntPJvfDCC5esdkvLqeaXLhrA6VzEdn/T1MqjZ3ny/F3ZZc/92LTWVVtX+GLXyqYnPzznTb7Y/rlz/03q79Gdv79Jz8/z/Ez332JqtD+9ttjR4FbHDNDA273Ps2etqNagah9pnV9db6Lo+7T1j5ZVrf3X35CWU52WMaP0nOHewkTzTVvyZNbv0Bu9YeAecGsrJm2xoDdc9XP+/PNPVyul9NBtaB9vXbRPeIsWLcw5L63fv+aNBu16w9WuTdeA3bMfv2fZ1jEAUus/znzgQOAi6AZwSejFndYqe15AXAr23LFaQ6lNw3VxD5q1VkkvALXZpV70aGBoN6PVGwV6UWrX6mmtqXvTz4wMUmXTQYO0BsVmX4x6u/jWC2W9gaA1bJk9F7k2KbebmOtfvYC0m5hrQOmrZl3zQC/a7abGn3zySYrm8VqroxfaWT1vrfuNA71A1Tmn7Rs/2hois7ateWkP0qTlTwdwyiyeZU4DYQ0APGm5TU9LDHc6D7GWM60J3bhxY4rXNUjyJ1DU8qNN7+2Bvrp165bsdfs36f7b08/WgaE8gwz9fWh+agBlvyet+aEtIfR8YDcx79y5swwePNjVxNxz/9y5H6c2gdcmwlqWoqKiTK2xP7S1jXZNsX8P+thuWu6eH/7QwFpbHrgPPKkOHjxoAll3ev7S71FvCmmTeps289ZuESoz5rPXc4WWGfebrHr+18EZM/I7dO+ioDybU+t3oQPD2bS5/bZt21z/BulAhmmlraC0tcD333+foum4liN3/t4s0XOkPTibblMH7tNA3pOWcf3N2Mekx68tpLz9G6JlMSu6JQHwD0E3AEe0bdvWBJJ6IapNCfW5e980He1a+xReChpUa79urRnTEW31Il4v1LSWwr0Zp33BpBeL2nxQA0W9wNfaU/fRy+2gWC+WNG1a6UjBOtqs0jzSWh6t+XCvvXK/+NbaFft1vfDT2kW9wLL7ZmYWbaqqtUL6WXqRZx+3NkPV0ct90YtArWHT92mgrvms+6nNJPUiWJtPaj91rYlz71+aFdwDRw3A9KJcgzYtC75GVk/Ltu3mtFrbpoGfXqxrVwZteppZtNm8lgFtGmz/lrR20C4regGurSU0yNQaS/fylJ7gqU+fPmbR5seaV9o9Qn8D2pfa3gelI9b7oiOCa4Cp+a2PNci16U0ku4ZvxIgRpnuFBlMamGlLAe23rJ+p5UePSWtj9bvT4FJ/s+nND22GbI8ir+VW90+DVM/98+Q+4rWWbx2LQM8rmh9p6a+vtbn6m7NHL7dpYJVaDb8nbab90ksvmd+se+Cu/dLtmxy2N99809T06ojbmqcamJcuXVp69uyZ5sDR2zlNy4ve4NTyrzdNbNpf3Q7qM/I7tJun23SUen2ffs96btFm4Hoj1b658vzzz5tWBZo3mse+ZmBIjQbqeoNYa6O137v+DvSxHp976xbNa3/+TdORy93Pp1ru9OaE+w0KvRHx2WefmWOxz8tKx1XQG6J6zNq03W6xoDdKtMzo7wBA4GIEO/KAMkAZyHAZ8By11xcdiVVHl/YcIdfJ0ct1fuiLWbx4sRUSEnJJ5ulOjY4u7DmP85gxY7ym9ZzfOzPm6X799de9fpaOPn3gwAGf83Tr6NWpzdPtbT/c88Rze/4sF5un29uiowHv3r3b6755ji7tXgZTyzN7ueqqq7zmgY4wPWnSJJ8jGafnO9S5glObl9pbvqZn9HJ/5kdWW7duNaMw+zquGTNmmFGePcXGxlrVq1dP9r5OnTqlmAfdG/fvJz35oaNiL1y40Gs6HYXfV9nSEdU3btzo12/S1+jlOrq45pk37qON+7to/nrynGtel5kzZ6aaP3rO0xkH/PlMf89pmpeeZSO9v0MdYT4mJsbr+xo0aGDS9OvXz+vrq1atspYuXZrsc/w5Ts/98WXgwIF+jV7uz/bc0+fNm/ei83Sn5XhYyAPKgGRJHjCQGgDH6J1/rRHQWg7tW6ejv2rtlDaBvFR9uZXWFmizT60V0dHHdUAw3TetTdBaK60N0XmC3eeFnjRpkql50FHGtYm11ozroiOfa9NArb3XPnkZpfuh+6P9ArUGRJuZevYZfvDBB02NnM4Pq7XrWkv0n//8x9QuZzatSXnkkUdMPulnaasArTnUvoep9bvWfNI8+fjjj83+aVqtsdOaXu3HOXTo0HQ17XSiLGhtmjat1e9fm0drLaU2j09L7aI3WoOqtXT6V2uhtMZQa/B8jdieEVpDqc2wtVZYay31uZYlbe6qrTq01k9bUGR0PnAd1V/3X38jWoumTbK1n6l+lualzv+sfaj1t2I3HfZGmw9rTbTWFOo+at7od6Dl3bPZuo4JoLXN2oJCa7G1yax+njYb1xq+N954w+yT+3z26ckPfV33Sbdn/7b0+HRAOP3N+aLp9Hyhv3/NAz0vaD7ccsstfue37p/WUGptu7YI0fKiNebp/c48+25r9wZvI5HrtrUpudau2sesn60tDbT8ay24vjc9tLZZ80LLgZ5XP/30U9PCRedo9ywb6f0davN/7fusI3776r+s3+ejjz5qypWm18/Wc7bui2dzcH/PiTrDhH5XmjfaQkDzTBdtuq7N5nXWBruJf2bTPNXzij06vZ5T9dyqeab/HmlLEJ3jO7WBDAFkPR1289Jd+QIAgFzB/caaDqjlazqx3EIDY3tgMb0RpYEaACB3oKYbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxCn24AAAAAABxCTTcAAAAAAA4h6AYAAAAAwCGhksOULl3azOkJAAAAAICTIiMjZc+ePbkn6NaAOyYmJqt3AwAAAACQS1x22WWpBt45Kui2a7j1oKntBgAAAAA4Wcutlb4Xiz1zVNBt04Mm6AYAAAAAZDUGUgMAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAIDcE3cHBwTJ06FDZunWrnDx5UjZv3izPP/98Vu8WAAAAAADpEioBpH///tK7d2/p3r27rF27Vho2bCijRo2So0ePyogRI7J69wAAAAAAyL5Bd/PmzWXKlCny448/muc7duyQu+66Sxo3bpzGLUWISJzb85CkQ00UkQSPdOq0l7TnRORsOtOGi0iQiJwREcutUUFYBtOGJb12Nuk1SXpveBrTWkmf55k2ISmP0prWV/6EJuVRZqe18z0tadPy3WeknHj7PjOjnNj5ntFy4uv7TG858ZU/gVROLtV3zzki8L77QCwnnCMCr5xwjuA6IpDKCeeI8zhHpL2cEGtk3XWEf6xAWQYOHGht27bNqlq1qnl++eWXW/v27bPuvvtur+nDw8OtyMhI11K6dGlLRUbOtIKDC1shISFmCQrqaolMs4KC+rrW6SIyyawPDi7pWhccfEtS2v8kSxsU9E1S2gpuadslpX3BI+3nSWmru6W9Nint/zzSjkxKW88tbfOktMM80g5PStvELW39pLTveaR9LSltS7e0tZPSfuKRdkhS2jZuaaskpR3tkXZgUtoObmnLJKUd55H2qaS0nd3SFjfrRH7wSPto0jbucUsbmZRWtxHulvaBpLQPuKUNd0sb6Zb2nqS0j3p89z8kpS3uto3OSWmf8ti3cUlpy7il7ZCUdqBH2tFJaau4pW2TlHaIR9pPktLWdkvbMintax5p30tKW98tbZOktMM90g5LStvcLW29pLQjPdL+LynttW5pqyel/dwj7QtJadu5pa2QlPYbj7T/SUp7i1vakknf0SSPtH2TttHVLW1h1/eZPO3DSWnvc0ubz+27z+eW9r6ktA97fPd2Ws4RnCM4R3CO4BzBdQTXEVxHcB1BrDEt3bFGVNSDSfFnZKpxbkDVdL/22msSFRUlGzZskMTERAkJCZFBgwbJt99+6zX9wIEDZciQISnWFy9eTJo3byNhYSfN8z17rpTdu6OlePFaUrFiO1e6v/4qKefOhUm9etdJRMRRs27fvgayc2e0FC1aQypXvpB2+fLSkpCQT+rWbS158x406w4cuFK2b4+WwoWrStWqF9KuXFlGTp8uJLVqtZQCBaqadbGxdWTr1mgpWLCyVK9+Ie3q1eUlPr6Y1KjRQqKiypp1hw9Xl3/+iZYCBU5LrVoX0q5dW0FOnIiWatWaS6FCxc26o0crysaN0ZIvnyV16lxIu359JYmLi5YqVZpKkSJRZl1cXBlZvz5a8uQJlcsvv5B206YqcuRItFSq1EiKFTt/B+fEiRKydm20hIfnlSuuuJB28+bqcuhQtJQv30BKlDh/Z+3UqSKyalW0hIaekvr1L6TdurWmxMZGS9my9aVUqfN3hM6ciZS//46WoKBz0qjRhbTbt9eWAwei5bLLrpDLLjv/XSQkRMjy5dHmccOGbSU4+HxN7M6ddWXfvmgpWbKulCt3fhvnzgXLX3+dT1u//g0SGnr+82JirpCYmGiJjq4tFSpc+LylS0uIZQXLFVdcL+Hh51tF7N1bX3btipZixWpKpUru330pSUjII5dffq3kyXPIrNu/v4Hs2BEtRYpUlypVLqT9++/LzDHWrt1K8uevkfTdX26++0KFqki1ahfSrlpV1uRdzZpXS2RkRbPu0KFasnlztERGxkvNmhfSrllTQU6ejJbq1a+SggVLm3VHjlSVTZuiJX/+BKld+0LadesqyvHj0VK1ajMpXLiIWXfsWHnZsCFa8uYNlrp1L6TduLGyHD2q331jKVYsn1l3/HgpWbcuWiIiwqVevQtp//mnqhw+HC0VKjSU6Ojzd/Xi44vL6tX63RdI9t1v2VJD/v03WsqVayAlS56/23/6dEFZuTJagoPPSsOGF9Ju21ZLDh6MljJlrpTSpU+YdWfP5pMVK85/n40bX0i7Y0cd2b8/WkqXridlypz/LhITw2TZsvNpGzS4UUJCzt8J3r27nuzZEy0lStSR8uUvbGPJkvNpr7yScwTnCM4RnCM4R3AdwXUE1xFcR9iINc6lOdY4cqSu+CPIrU1CluvatasMGzZM/vOf/5g+3VdccYW888478vTTT8uYMWNSpA8PD5eICLuaXyQyMlJiYmKkUKGSEhcXK0F6dHqA1oXmPkFBF5r7WNaFJgIp056ToKCz6Ux7oclHUND57NUAz24KnP60F5oCa9B6ft2F5uX+p7UkKOiMl7QJEhSUmI60+v/z+RMUdKG5hWVdaMaR0bTe8z0tadPy3WeknHj7PjOjnFxoXp6xcuLr+0xvOfH1fQZSOblU3z3niMD77gOxnHCOCLxywjmC64hAKiecIzhHEGtIwPz7cPG0BQoUkiNHYk3FcVyce/fmAA66d+7caWq7R44c6VqnNd3dunWTmjVrXvT9GnQfO3bsogcNAAAAAEBG+Bt/BtSUYfny5ZNz5+zBnM7TZuY6lRgAAAAAANlNQPXpnjZtmqnZ1hpvbV5+5ZVXmqblX3zxRVbvGgAAAAAAaRZQzcsLFCggL730ktx6660SHR0te/bskbFjx8rQoUPl7Fn3YfK9o3k5AAAAAOBS8Df+DKigO6MIugEAAAAAl0K27NMNAAAAAEBOQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAACQG4Lubdu2iWVZKZb3338/q3cNAAAAAIA0C5UA0qhRIwkJCXE9r1OnjsybN08mTpyYpfsFAAAAAEC2D7pjY2OTPR8wYIBs3rxZFixYkGX7BAAAAABAjmhe7i4sLEy6desmX3zxRVbvCgAAAAAA2b+m290tt9wihQoVki+//NJnmvDwcImIiHA9j4yMNH+1ibp7M3UAAAAAADKTvzFnwAbdDzzwgMycOVP27t3rM83AgQNlyJAhKda3adNG4uPjHd5DAAAAAEBulTdvXr/SBYmIJQGmXLlysnXrVrnttttk6tSpaarpjomJkcKFC0tcXNwl2lsAAAAAQG4TGRkphw8flqioqFTjz4Cs6e7Zs6ccOHBAZsyYkWq6M2fOmMVTYmKiWQAAAAAAcIK/MWfADaQWFBRkgu7Ro0cTOAMAAAAAsrWAC7qvv/56KV++PKOWAwAAAACyvYBrXj537lxT2w0AAAAAQHYXcDXdAAAAAADkFATdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAAAgtwTdpUuXlq+++kpiY2Pl5MmTsmrVKmnQoEFW7xYAAAAAAGkWKgGkUKFCsmjRIpk/f760a9dODh48KFWrVpXDhw9n9a4BAAAAAJC9g+7+/fvLrl275P7773et2759e5buEwAAAAAAOSLo7tixo8yePVsmTJggrVq1kpiYGBk5cqR89tlnXtOHh4dLRESE63lkZKT5GxISYhYAAAAAAJzgb8wZUEF3pUqVpHfv3vL222/LK6+8Io0aNZL33ntPzpw5I2PGjEmRfuDAgTJkyJAU69u0aSPx8fGXaK8BAAAAALlN3rx5/UoXJCKWBIjTp0/LX3/9JVdddZVr3bvvvmuC7+bNm/tV062144ULF5a4uLhLtt8AAAAAgNwlMjLSjD8WFRWVavwZUDXde/fulXXr1iVbt379euncubPX9FoDrounxMREswAAAAAA4AR/Y86AmjJMRy6vXr16snXVqlWTHTt2ZNk+AQAAAACQXgEVdA8fPlyaNm1q+mpXrlxZ7rrrLnn44Yflgw8+yOpdAwAAAAAgzQKqT7e6+eab5dVXXzXzc2/bts0MquZr9HJvbeqPHTt20Tb1AAAAAABkhL/xZ8AF3RlB0A0AAAAACKT4M6CalwMAAAAAkJMQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAABB0AwAAAACQvVDTDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAAkBuC7sGDB4tlWcmW9evXZ/VuAQAAAACQLqESYNasWSPXX3+963lCQkKW7g8AAAAAADkm6NYge//+/Vm9GwAAAAAA5Lygu2rVqhITEyOnTp2SP/74QwYOHCi7du3ymjY8PFwiIiJczyMjI83fkJAQswAAAAAA4AR/Y86ACrr//PNP6dGjh2zcuFFKlSpl+nj/9ttvUqdOHTl+/HiK9BqQDxkyJMX6Nm3aSHx8/CXaawAAAABAbpM3b16/0gWJiCUBqmDBgrJjxw55+umn5YsvvvCrpltryQsXLixxcXGXeG8BAAAAALlFZGSkHD58WKKiolKNPwOqptvT0aNHZdOmTVKlShWvr585c8YsnhITE80CAAAAAIAT/I05A2rKME/58+eXypUry969e7N6VwAAAAAASLOACrqHDRsmLVu2lPLly0uzZs1k8uTJ5u7B2LFjs3rXAAAAAABIs4BqXl6mTBkTYBctWlQOHjwoCxculKZNm0psbGxW7xoAAAAAANk76L7rrruyehcAAAAAAMiZzcsBAAAAAMhJCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAACPeguXry4WQAAAAAAQCYE3TVr1pSJEyfKsWPHZO/evWbRx7qudu3aGdk0AAAAAADZXmh639iiRQuZOXOmBAcHy5QpU2TTpk1mffXq1aVjx47Srl07adu2rSxcuDAz9xcAAAAAgGwjSESs9Lxx6dKlUqRIEWnVqpXs3r072WtlypSRX3/9VWJjY6Vx48ZyqURGRpqa9qioKImLi7tknwsAAAAAyF0i/Yw/0928XJuPjxw5MkXArXTdhx9+SBNzAAAAAECulu6ge8eOHRIREeHz9fDwcNm1a1d6Nw8AAAAAQO4NuocOHSp9+/aVevXqpXjtiiuukD59+siQIUMyun8AAAAAAOS+gdSaNm0q+/fvl2XLlsnvv/8umzdvNuurVq0qzZo1kzVr1pi/utgsy5Inn3wyc/YcAAAAAC4iX758UqxYMQkK0uGsgIvTuFXHJzt58qRk6UBqiYmJ6dr50NB0x/kXxUBqAAAAAJQG2T179pRrrrmGDEG6/PLLLzJq1CgTx2Yk/kx3BBwSEpLetwIAAACAozTg1pmWxo8fLxs2bJCEhARyHH7RiuIaNWrIHXfcYZ5/8cUXGdteht4NAAAAAAEmf/78poZbA+4ZM2Zk9e4gG9qyZYv527VrVxk3blyGmpqneyA1AAAAAAhERYsWNX+1hhtIL7v86JgAWVLTrX26fbVtT/YBDvbhBgAAAABP9qBpNClHRtjlJ6OD8IVmZMowz6Bb+3lXqFBBbrnlFtm4caNMnz49QzsHAAAAAEB2lu6g+8UXX/T5WsmSJWXx4sWyadOm9G4eAAAAAHK9wYMHy5AhQ+TXX381A8O5Gz58uKnwrFixYq7Jp8mTJ0uhQoWkdevWkl040qd737598tFHH8kLL7zgxOYBAAAAIFdp2bJliqAb2YNjA6mdOHEiV91xAQAAAAAnHD9+XP78808qNbMpR4Lu2rVrS9++fWleDgAAAACZ4KWXXpLrrrtOmjVrlmq6cuXKycSJE+XIkSMmWJ81a5bUqVPHr3nN16xZY6bGio2Nld9++00aNmzoel3H8+rfv7+8/vrrcuDAATl27JiMGjVKChQokGw7BQsWlA8++ED27Nkjp06dkr/++kvatGmT4vNuuukm0yVZP0+3N3LkSMmXL1+yNDpX9i+//CLx8fGyefNmue+++1JsR/dh9erVKfZB97d79+6uddu2bZMRI0bIs88+K7t37zaVxD/88IPpGh2wfbq3bt3qdfRybV+vB6mZp/0LAAAAAAAZo/ONL1++3PTxbtu2rdc0GgBrkHru3Dnp1auXCXoHDRpk+oNffvnlJtj05uqrr5YvvvhChg0bJj/++KMJfhs3bmxiO3d9+vQx+6DBrLZqfu211yRPnjxy1113mdfDwsJk7ty5UqJECfO5MTEx0q1bN7Pv9evXN0G96ty5s5lDXQNmPZ5SpUqZbRUuXNi1rYiICJkzZ44Jju+9917XYN5RUVHyzz//pCsPb731VtmxY4f07t3bfJbeQPj++++lefPmEpBB94IFC1IE3fr88OHDZiJxnUBcHwMAAABAoEhtSmONZ3RqZKfTptf//vc/EyQ2atRIli5d6rW2unz58qblsT3HtMZtO3fulCeffNLU8nqjAfa///4r/fr1c63T4NvT6dOnTcWqBvVKa6A/++wzM9Cbzl51zz33yBVXXCH16tWT9evXmzRz5syRqlWrmqbxXbt2NevefPNNE3Q/9NBDrm3v3bvXfKbW6K9bt0569OghpUuXNrXdWsutVqxYYT4nvUF3ZGSktGvXztTSq127dsnPP/8sN9xwg9nPgAu69QsFAAAAgOykS5cuPl/TJtEapNpuu+02My2yN9ok+qeffnI979ixo6md9ebQoUMye/ZsyYyRu7Up9X//+1/p0KGD1xprrU22A26lFaFa+9yiRQuf29Xa66JFi5qa52+++UYWLVpkAmpP06ZNcwXcatKkSaaGXIN2DYY1eNX901ms3PNt7ty5psZbVatWzUwzrTcB3NNovuu2tUm7Bt1NmjQxx2IH3Eord1euXCnpNX/+fFfAbT/Xmw36WU4G3Y4NpAYAAAAAyFwvv/yytG/fXq688soUr2mT6f3796dYr+uKFCnic5safGpQrDXkenNA+3SPHj3abM/zRoO7uLg4E5xr83BVrFgx04w8ISEh2fLCCy9I2bJlXWmU9qd2T6Pb0dYCdjrdpufn2ceSXt62p+vs/c/ymu70TP+lzSi0CQQAAAAABAIdZMwXz+6z2pTb37RTp071O21GTJgwwTTn1vhM+yd71qhXr149xXu0j7W+lhqt4dZFa7w7depk5gA/e/asPPjgg6400dHRKZpr582b1zQNtz9fa6IfeOABn59zKGk/HnvsMTMiu7fWBkq3qQG8t2Nxr63Wfuvh4eHJ0njeLPC1//Y6e/+zPOjWL9ZX4QkKCkqxXtcRdAMAAAAIJFqrmtVpM0JjLK3t1ppoHTTN3cKFC+X22283Tbi1ibfSwdCuv/56+eSTT/zavja31ibjOrp4zZo1k72mTdqffvppVxNz/Sx9bPcvnzdvnnmfBs6+AtkNGzaYvtSVKlUyI5b7smTJEjNaeeXKlU2zcqWPtb+4jqxu08HhypQpI/nz5zeDrilt5u5N69atzUBsdtCuz/Umg7fgP7NZ6VlKly5trVixwvrqq6+sBg0aWAUKFDBLw4YNra+//tpavny5VapUqXRtO71LZGSkpfTvpfxcFvKAMkAZoAxQBigDlAHKAGWAMhA4ZaB8+fLWmDFjzN+s3peMLoMHD7bi4uKSrQsODrb++ecfE/ts27bNtV7jsa1bt5rXunbtanXq1MlasmSJdejQIatMmTI+P2PIkCHWiBEjrM6dO1tXX3219eijj1rHjx+3hg4d6kqjdu/ebU2dOtVq27at1bt3b+vYsWPW+PHjXWnCw8OtpUuXWhs3brQeeughq1WrVmYfdPuvvPKKK93tt99unTlzxvrwww+t9u3bW61bt7Z69OhhTZw40apatapJkydPHvN569evN+l1WbdunVk3f/5817Zq1qxpJSYmmv24/vrrrSeeeMJavXq12d/u3bu70mk+6XsXLVpkdejQwbr33nutPXv2WIsXL053OUpD/Jm+L3/y5MnWhAkTfL6uGfb9999f0gJJ0J31JwUW8oAyQBmgDFAGKAOUAcoAZSCry0BOD7p1uf/++1ME3bqUK1fOmjRpknX06FETOM+ePduqU6dOqp9x8803W3PnzrX2799vxcfHm6BdPzckJMSVRvXv39968803rdjYWBNwjx49OkXAqc/feusta/v27dbp06etmJgYa/r06dZNN92ULJ0GyBo867HpooHysGHDrKioKFeaWrVqWQsWLLBOnTplbdmyxQTmGoe6B926dOvWzdq0aZN14sQJc7yXX36516BbbyzoMeg+nTx50poyZUqqFcVZHnTrl9irVy+fr+trmuZSFkiC7qw/KbCQB5QBygBlgDJAGaAMUAYoA1ldBnJS0B0oi3rmmWeyfD8knYsddKflPZkVdKd79HLtsN6sWTOfr+sE45oGAAAAAIDcKt1Bt45sp5Ofv/vuu1KlShUzcJou+vi9996Tu+++26QBAAAAACC38nv0ck/9+/c3c6w9/vjjZrh3ewS74OBgE3yPHTvWpAEAAAAAZG+eM1ZlNxUrVsx+QbfO2aZDuA8bNkxuvvlmKVeunFmvc8XNnDlTVq1alZn7CQAAAABAtpPuoNu2evVqswAAAAAAgEwOups0aWImFY+OjjaTm2/evFny5s0rNWrUMBOy2xOUAwAAAACQ26R7ILWwsDD57rvvZNGiRfLyyy9L3759pWzZsuY17d89Z84ceeKJJzJzXwEAAAAAyB1B90svvSTt27eX3r17S/Xq1ZN1rD99+rRMnDhROnXqlFn7CQAAAABA7gm677rrLvnwww/l008/lUOHDqV4ff369VKpUqWM7h8AAAAAALkv6NY+3KkNoJaYmCj58uVL7+YBAAAAAMi9QfeuXbvMYGm+XHXVVWZQNQAAAABA+gwePFji4uIumm748OGybdu2LN0H9cYbb8iECRNSxIY///yzaSF98OBB+fHHH6VevXpp2gftzrxhwwa5++67JdcE3d9++6088sgj0rRpU9c6y7LM3wcffFDuuOMOGTNmTObsJQAAAAAgoJUqVUoee+wxee2111zrqlWrZgbZ1lmttIvyAw88IEWKFJGffvpJSpQo4fe2NdbU7b744osSEhIiuWLKMB2xXAPuX3/91fTf1kzQuyuagWXKlDF3L/Q5AAAAACDn00rZf/75R5YvX+5ad+utt5pa6i5dusipU6fMulWrVpla+TZt2sjXX3/t9/bHjx8vI0aMMAN6T5kyRXJ8TffZs2elbdu20rNnT9m6daup6o+IiDAZ2KNHD+nQoYOZOiy9+vfv7wrkAQAAAAAXapQ16NTa4927d8t//vMfr1lz2WWXyVdffWWadJ88eVIWLFgg9evXT5bm3nvvld9++03+/fdf0/x7/vz50qhRo3Rl9X333SeTJk1KMdX06dOnXQG3Onr0qPlrz4DVoEEDOXPmjKkld3/f33//bfbNThcfHy8zZsyQ7t27546abts333xjlszUsGFDc5dk5cqVmbpdAAAAAMjuNODW1sU6ffORI0dkwIABUrZsWUlISHClKVSokCxcuFCOHz8uffr0MYGu/tW+1VWrVjWBuKpQoYLpFrxlyxYJDw83TcC1NfPll19uaq39VblyZalYsaIsWrQo2fpx48aZCtX//e9/8vbbb5uK2ldffVV27tzpqq1etmyZvPLKK6Y/+Ny5c2XTpk1mimrdpvb9trsxq99//12GDh1qAnH39YHOysgSHh5uNW3a1OrYsaNVtGjRDG1Ll/z581sbN260rrvuOmv+/PnW8OHD/X5vZGSkpfRvRveDhTygDFAGKAOUAcoAZYAyQBmgDGTPMlC+fHlrzJgx5m/K1yOSFvd1IUnrQtOQNiwDaf1fBg8ebMXFxbme33jjjSbmad26tWtdVFSUdfToUWvbtm2udUOGDLEOHz5sFS9ePFnstn37duv111/3+llBQUFWSEiItX79euvll1/2uQ/eljvvvNPsl7eYsHHjxtbevXst29atW62qVasmS6Ofu3TpUuvPP/+0WrZsaSUkJFgPPfRQim21atXKbKNWrVpZXI78jz/T3bxc6Z2SvXv3mrsZ33//vbkboooWLWrunGjT87T64IMPTJMB7VgPAAAAAJlrYtIS5bbutqR1vTzSfp20vrjbupuT1vX1SPt50vqybuuuS1rXL9P2vkmTJqZ2W5uB244dOybz5s1Llu6GG24wabTJuA48potO66xNzN2bj+uMVBrL7du3z3QP1tpyXacDoKW1ybtuX5upu6tatap89913ZjC166+/3vTH3rFjh8ycOdNMQ23T92pT97p168rs2bNl1qxZ8umnn6b4nNjYWNfn5fjm5dpv+5133jHNBTQDv/jiC9drmtHabOHOO++UUaNG+b3Nrl27mj4G/vYh0OYP2jzBFhkZaf7ahQoAAABA7pOTYwENNu2m4e7279+f7HmxYsWkWbNmyZqc2+ypnQsUKGBiOd3e008/bYJh7Xv92WefSZ48edK0X5pex/3y9Morr5iA3r0f9i+//GKalz/xxBMyaNAg13odJ2zFihXSvHlzUxnrjfYPV3nz5pVLxVd86W85S3fQ/cwzz5g2+Pfcc48ZsdyTtsvv29fz7o9v2ifh3XffNSPY2Rl5MQMHDpQhQ4akWK/b0E72AAAAAHIfDTg1KNNKuYIFCyZ7zbIeTHoUJEFB51+zLG1lu0DrW13rzq+3B/Y645Z2oYgsFpFzHmmfTnp02i3tXzqhcoq0aWEHv/ZxHD582NQQex6XxlPBwcGu9dqHW/tH66xTnnTQMk3XunVr0xdc+3GvWbPG9XrhwoVNoGxvy3MfvNH4S9PpvrnHc3Xq1JGlS5emeK+OXq416u7rtaV048aNzb7ogNo6Crr7AGz2cSr9jNT2JzNo+dFy1LJlS1cNuzt/A/90B91VqlSR9957z+fr2oxBm5n7S0es03na3IeXDw0NNQf4+OOPmxptz9HQtQO+dsZ3z5SYmBhTuPydvB0AAABAzlK+fHm59tprTUxgj5SdXdlBp30cOsiZVj5qC2G7iXlUVJS0atXKxGB2Om2i3a1bNxPw6sjl3tjxlbZUtt+nteOaf6tXr3at89wHb3SkcaUVslpj7R5c165dO9l7NW6rVKmSaRJvr9fnOniaDqb20Ucfmc/XAeKeeuqpFDdUlMaNTn+3Ohid3kzQPNdWAJ7sltaOBd3aj8A+YG9q1apl7o74S/tw610Qd9o0Xb+w119/3ev0Y3qHRhdP2h9AFwAAAAC5T06OBbSvs7Yq1hmkdFRwjcs0CNd+3e60clJbJWsfbm1RrM25ixcvbvqE79mzx3QVXrx4sbkxoU25X3vtNTPF2IsvvmimIUurJUuWmOblWpnqHnR/9NFHpoW07q+Okq614dpqWitVtRm70pHIR48ebZq9a0tm3Y42PdcuzPpebY7uPtPVunXrUvQdd5Kv+NLfcpbugdR+/PFHefjhh71W6WvA/dBDD8nUqVP93p4OZb927dpki847p5mpjwEAAAAAIp06dTKB98cff2yCWo27POfH1lrvpk2bmhporcTUvtvaZFunCPvzzz9NmgMHDkiXLl1Mk3ANbp988kkzdbPd5zsttDZdB0dr165dsvVTp041n6EtpcePH28GR9PaY23abn9Ov379zLheOs+33S9cg3Ddpy+//DJZjbJu3/NYs4N0DZ9eqlQpa+fOndauXbuskSNHmiHdv/zyS+urr76yTp48aW3ZsiXDU4gxZZizQ+CzkAeUAcoAZYAyQBmgDFAGKAM5sQxcbKonFmfyoH379taxY8esvHnzOrL9WrVqWWfPnrUqVKgQEOXI8SnDdKowbTqgzRt01HFtEqBDvHfo0EHGjh1r7qpktMpf7354tuEHAAAAAASe6dOny6ZNm+TBB+3B6jKXNkvXJurbt2+X7CTdfbqVDi2vzch10f7dOlqerrMsDegBAAAAALlJr169pF69epm+3aCgINMcXYPu7CZDQbdnJmiwTcANAAAAALnTX3/9ZZbMZlmWmb0qO0p383JVs2ZNmThxohkpT5ub66KPdZ0OCw8AAAAAQG6W7pruFi1amNHptEm5jiqnbfdV9erVpWPHjmZUubZt28rChTp5PAAAAAAAuU+6g24dbl6HmNdJ2D3ncStTpoyZQFznhmvcuHFm7CcAAAAA+MXu8hoammm9aZELhSaVn4x2oU5383JtPj5y5EivE6frug8//JAm5gAAAAAuOXsWpRo1apD7SDe7/MTGxmZNTfeOHTskIiLC5+vh4eGya9eu9G4eAAAAANLlxIkT8ssvv8gdd9xhnm/YsEESEhLITfhdw60Bt5YfLUcnT57M2PbS+8ahQ4eaJuYzZsyQlStXJnvtiiuukD59+siTTz6ZoZ0DAAAAgPQYNWqU+du1a1cyEOmiAbddjjIiSJuop+eN7777rlxzzTWmCfnvv/9u5kxTVatWlWbNmsmaNWtkwYIFyd6jbeGdDMQjIyPN6OlRUVESFxfn2OcAAAAAyB7y5csnxYoVM1McA/7QuFWblF+shtvf+DPdQXdiYmK6dt7JwQwIugEAAAAAl4K/8We6I+CQkJD0vhUAAAAAgFwh06qddX7uLl26SKlSpcxABV9++SVNvAEAAAAAuVqagu7HHntM+vbtK82bN3cNw6/at28vEydONCOW2zRd06ZNk6UDAAAAACA3SdM83R07dpQtW7YkC6S1mflnn31m+nj37NlT6tatKwMGDJDy5cvLoEGDnNhnAAAAAAByXtBdq1YtWbx4cbJ1rVu3luLFi5vpw8aMGSPr1q2TYcOGyYQJE+Smm27K7P0FAAAAACBnBt1FixaVXbt2JVt33XXXmVHJJ0+enGz9okWLpFy5cpmzlwAAAAAA5PSge//+/VKyZMlk666++mozf9nKlSuTrT9z5oxZAAAAAADIrdIUdP/111/SvXt3KVCggKu5eePGjWX27Nkp5u2uUaOG7N69O3P3FgAAAACAnDp6+YsvvihLly6Vf/75R9auXSsNGjQwTctfffXVFGlvvfVW+fnnnzNzXwEAAAAAyLk13WvWrJFrr71Wli1bJqVLlzaDqulgacuXL0+WrlWrVqbJuU4jBgAAAABAbhUkIpbkEJGRkXLs2DGJioqSuLi4rN4dAAAAAEAO5W/8maaabgAAAAAA4D+CbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAB7aabbpI5c+ZITEyMnDp1Sk6cOCFr1qyR//3vf5I/f/6Lvj8sLEyGDRsmixYtkvj4eLEsyyw33nij1/RNmjSR2bNny9GjR+X48eOycOFCadeunQNHBgAAgNzCyilLZGSkpfRvVu8LC3lAGcicMvDSSy9Zvvz4448XfX/BggW9vvfGG29Mkfbqq6+2Tp06lSJtYmKideedd/Kd8rumDFAGKAOUAcoAZYAyQBmw0hp/UtMNIKAtWbJEbrnlFilVqpTkzZtXOnbsaGq8ldZAFy5cONX3nz17Vt5//325++675cMPP0w17UcffSQRERFy5MgRadiwoVSuXFl27NghwcHBMmLECMmTJ0+mHhsAAAByPoJuAAFt2rRpMmXKFNm3b58JtvX52rVrkwXVqTl58qT06dNHxo4dK/v37/eZ7sorr5RatWqZx+PGjZNly5bJ1q1bTSCuihUrJm3btpV8+fLJP//8Y5qo6187ENebAXbT9UGDBmXS0QMAACC7I+gGkG1ogNupUyepXbu2ef7VV1+ZfteZoX79+q7HGzZs8PpY02gQ3717d0lMTJQqVarI4MGDJSoqSkaOHGnSLF68WF599dVM2ScAAABkfwTdAAJeiRIlTA2yDoT2ww8/mOB7/Pjxcv/992faZxQvXtz1+NixY14fR0dHm7+///67vPnmm+bxM888IxMnTpTLLrvMBOT33XefnDt3LtP2CwAAANkbQTeAbKlr164yatQoxz8nKCjI9VgDf9sLL7wgq1atMqOj33DDDWZd//79TZNzAAAAwEbQDSDgaV9sDX61P/U111wju3btMuu7deuWrFl4Rhw8eND1uGDBgq7HkZGRXtNoX3K7v7fSZu5ffvllpuwLAAAAcg6CbgDZhjYvX7BggUyaNMm1rmrVqpmy7eXLl7seV69e3fW4Ro0aXtPowGran9tWoEABMx84AAAA4I6gG0BA+/TTT03ttga5Op1X8+bNpXPnzq7XdYRx27Zt20wT8Pnz5yfbRtGiRc2iNeU2HfxM1+lftWLFClm3bp15fOedd0qDBg3MlGG9evUy62JjY2XWrFmu93/88cemr/nRo0ddg6hpWrupOQAAAGDLdZOTs5AHlIHsUwZS88MPPyRLu23bNrN+/vz5fm/DPW3Lli2tU6dOpUiTmJho3Xnnna503bp1c7320EMPWaGhodaKFSvM8127dlkFCxbM8nxjIQ8oA5QBygBlgDJAGaAMSEDEn9R0Awho77zzjpkz+99//5WEhAQ5fPiwLFq0yMy9ffvtt2fqZ/3666/SqlUrmTNnjhm1/MSJE+az2rdvb+buVjpK+XvvvWce//zzz6YmXverZ8+epp93mTJl5P3338/U/QIAAED2FZQUfecIOuCRXihrc9G4uLis3h0AAAAAQA7lb/xJTTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgkFCnNgwA/gqKCJLgsNxxD/Dc2XNinc4xk0YAAAAgOwXdvXr1kt69e0uFChXM87Vr18rQoUNl1qxZWb1rABykAXfxjsUlrHBYjs7ns4fPysGpByXxdGJW7woAAAByY9C9e/duGTBggPzzzz8SFBQk3bt3lylTpsiVV14p69aty+rdA+AgDbjDi4WTxwAAAMhRAironj59erLnzz//vKn5btq0KUE3AAAAACDbCaig211wcLB06dJF8ufPL3/88YfXNOHh4RIREeF6HhkZaf6GhISYBUD2YH6zweeXnMwco56bcvZhAgAA5AohfsacARd016lTxwTZefLkkePHj8utt94q69ev95p24MCBMmTIkBTr27RpI/Hx8ZdgbwFkhpC8IVKkVhEJjQq4U1KmSohOkEM3HJLEePp0AwAAZHd58+b1K12QiATUMLphYWFSrlw5KViwoNx+++3y4IMPSqtWrbwG3t5qumNiYqRw4cISFxd3ifccQHqFFAiR0veVzvF9us/EnpE9Y/ZI4nGCbgAAgOxO48/Dhw9LVFRUqvFnwFUrnT17VrZs2WIeL1++XBo1aiRPPPGEGdnc05kzZ8ziKTEx0SwAsolEkcRziWbJycwxcn4CAADIEfyNOQN+Ylzt2+1emw0AAAAAQHYRUDXdr7zyisycOVN27txpqurvvvtuueaaa+TGG2/M6l0DAAAAACB7B93R0dEyZswYKVWqlBw9elRWrVplAu558+Zl9a4BAAAAAJC9g24dNA0AAAAAgJwi4Pt0AwAAAACQXRF0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAA5Iage8CAAbJkyRI5duyY7N+/XyZPnizVqlXL6t0CAAAAACD7B92tWrWSDz74QJo2bSpt2rSRsLAwmTNnjuTLly+rdw0AAAAAgDQLlQDSrl27ZM979OghBw8elAYNGshvv/2WZfsFAAAAAEC2r+n2VLBgQfP30KFDWb0rAAAAAABk75pud0FBQfLOO+/IwoULZe3atV7ThIeHS0REhOt5ZGSk+RsSEmIWANmD+c0Gn19yMnOMem7K2YcJAACQK/gbcwZs0K19u+vUqSMtWrTwmWbgwIEyZMiQFOu1P3h8fLzDewggs4TkDZEitYpIaFTAnpIyRUJ0ghy64ZAkxidm9a4AAAAgg/LmzetXuiARsSTAjBgxQjp16iQtW7aU7du3+0znraY7JiZGChcuLHFxcZdobwFkVEiBECl9X2kJLxaeozPzTOwZ2TNmjyQeJ+gGAADI7jT+PHz4sERFRaUaf4YGYsB96623yjXXXJNqwK3OnDljFk+JiYlmAZBNJIoknks0S05mjpHzEwAAQI7gb8wZGmhNyu+++25Ty613CkqUKGHWHz16VE6dOpXVuwcAAAAAQPYdvfzRRx+VQoUKyYIFC2Tfvn2upWvXrlm9awAAAAAApFlooI1YDgAAAABAThFQNd0AAAAAAOQkBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAAMAhBN0AAAAAADiEoBsAAAAAAIcQdAMAAAAA4BCCbgAAAAAAHELQDQAAAACAQwi6AQAAAABwCEE3AAAAAAAOIegGAAAAACA3BN1XX321TJ06VWJiYsSyLOnUqVNW7xIAAAAAZAulS5eWUaNGyb59+yQ+Pl7Wrl0rTz75pAQFBaX6vvnz55v4y9ei2/R05513yq+//ipHjhyREydOyObNm+WLL75w8Oiyr1AJIPnz55eVK1eaL2vy5MlZvTsAAAAAkC0UL15cfv/9dylfvrxrXa1atWT48OFSrVo1efTRR9O97ePHjyd7/vbbb8tTTz2VbF3lypXN8tBDD0liYmK6PysnCqige9asWWbJqNDQULN40rs07gXAW5rMSBsSEuLzbpJTaVVCQkJApQ0ODjZLIKfV/NV8DpS0mreax76cO3fOLIGS9mK/DX/TBocES5AkL1fBqTTEsZL+8yet2Q85F1BpOUck5RnnCL/ygXOEc+eeS3ltwHXEeVxHcB3h1G+Zc4TI0KFDXQH3/fffL9OmTTM11O3bt5fevXvLV199JUuXLvV6nmrdunWK3+fIkSNNAK3Gjx/vWt+uXTtXwP3TTz/Js88+a2q5L7vsMunQoYP5N+1SxDCBch2R7YLutAoPD5eIiAjX88jISPP3tttuM80pPO3du9c0gbB17tzZZ3Bx8OBB+fnnn13Ptam7+2e5O3TokMydO9f1XAu21tp7c+zYMZk5c2ayQhsVFeU1rTbTmD59uut5mzZtpEiRIl7Tnj59Wn744QfX82uvvdbc7fJGfwSTJk1yPW/ZsqWUKlVKfHH/kTVv3lzKli3rM61u1/6RNW7cWCpWrOgzre6v7rdq0KCBVKlSxWdaPWmcPHnSPK5Xr57UqFHDZ1rNX81nVadOHaldu7bPtPq96fendJu67dSa3Rw4cMA81n3VffZFy5mWN1WhQgVp0qSJz7R6R3LXrl3mseat5rEvf/75p2zfvt081u9Mvztfli1bZk6AKjo62pxMfdEWJhs2bDCPtYxpWfNFmymtWbPGPNayq2XYF92mblvly5fPnIi9CQoNkti8sbIteJt5HmaFSXPxnQ/7ZJ9sDNpoHgdbwXK1XO0z7UE5KOuC1rmet7Ja+Uz7r/wra4LOH5tqYbWQEPF+jjgiR2Rl0PljU82t5hImYV7TxkmcLA9aLiHBIeac0659O84RnCM4R6ThHKH0fKbnNaX/Ht9yyy0+027btk2WLFliHutv7vbbb/eZVs+/eh623XHHHT7Tch1xHtcRF3AdERjXESq3nyM0UL333ntd+TZmzBjz+JdffjHp1X//+18TePsTa+TNm9e1Pc2vEiVKuI5Fm6srvTYfN26cqU3XRe3Zs8fEYhoTfPPNN9K1a1c5c+aMPP/886YLsW7/jTfeMNtfvXq1vPbaa9k61kgtznCntxEuVBcFEL3zoj+WKVOm+EwzePBgGTJkSIr1AwYMcGWwOy1YdiCkKlWq5PPuhAbtWjBs+oX6+tGcOnVKdu/e7Xqud5jCwrxffGuh27lzp+t5uXLlzM0Db86ePSs7duxwPS9TpozkyZPHa1otfPqDsOmdJi3Mvu4Ebt261fVcgzdfNwmUHbipkiVLSoECBXym3bJli6vWVgM9XzcUlO6v/aPRGwQFCxb0mVYDTfuuUtGiRaVw4cI+02r+aj4r/WH7ulFhn0jtslKoUCEpVqyYz7RaHuybObqvvm5q2Ccc+4erN4P0ROWL9rmxm+xo3moe+7J//36Ji4tz/eOj/XZ80ZP50aNHzWMtC1omfImNjTX9cZSe8FM72emJ375RoWVXy7Avhw8fln///dd1d1pvQHgTFBIkCaUT5FTJU+efJwZJ4f2+v+PTeU/LicInzj85J1Jkn+/v+EyeM3K8yIUmUUX2+E57NuKsxBWNu5B2bxGfZ8iE8AQ5Vuz8CVcV3ldYgs55vzObGJYoR4sflYRjCXLo50NSJroM5wjOEZwj0nCOUHo+0/Oa0n+PU7vQ0osh+yapXohqc0df9Pyr52FbahdlXEecx3XEBVxHBMZ1hMrt5wi9hh00aJB5vHjxYnn55ZfN4+rVq8ubb75pHuv1/4gRI/yKNa666irXzQgNihctWmSu3fV49Lmm0+tdzRttuq7fj8YBOj6XrtPP0uva999/31y763v1sx988EEToOt1sgbf+r1l51hDr8U//vhjsx/2NXqOC7q91XRr4dVC5+2gdZvuTVRSa0LrVFrl2dzC37RayFNrbpFb0mq6izXFzOq0Wh7cm4xndVp1sSbjWZk2pECIlLynpIQVS/oHxPKjeXmQ5Vdasx9Bbs3AraxLeyb2jOwZs0ckZUOcZDhHpMwHzhG5+xwRKP+GB0JaxTkiZT5wjuAcEQi/z6xMq60q7Vp2rc3u2bOneazBuV1ptGnTJqlbt65fvyNtNaAtRvVmit4Q0CBZ02rw6n5jwdsNGK2BtisZ27Zt66pN1xpiu2VDjx49ZOzYsanuQ3aINTTY1ptIFwu6s3Xzcr3DYN9l8Ge9p7R08Cdt4OQD0icQvjtfaUPOhEjCuQQJPnfhBJcoF9mu2+3C7JI28VyiyYPEM4H7XeSEtEifQPjuSEs+XIrygPTh9xnY+aC11r7GlLBp4J5ajGS/p0WLFibgVtpM3W7FoDwDV61df/fdd6Vfv36m+bpWfj7yyCOm5bGaMWOGqQnWdXbA/d1338no0aP9PraL7W9mp00L9/7d2WbKMAAAAABA2thN65V7M2p7zCvPNKlxH+VcB1PzbPLvXuuuTca1Wb02I7d5jpH07rvvJnv+wQcfSG4TUEG39ivWL8n+orQpgz5OrV8IAAAAAORm2odaA2K7H7fNfUCw5cuXX3Q72nxcB0JTOtDbxo3nB651719uD5rni+eA1iPc+pErncLM1/hXOVVABd0NGzaUv//+2yz2F6KPdfh7AAAAAEBK2qTc7iOtgbb2mdam3s8995wrjY4mbg8wpul1Zh5PDzzwgGvMLM9abtu3337retynTx9Tcfr444+71umI6e6vX3fddebx66+/bmrJtVLV22DYOVnADqSWHtp8QkcjvFhHdgCBRQdSK929tIQX8z6Sf05hBlIbvUcSj9P3EAAAZC6tpdZ5uO25ut19+OGHrmbjGnTrSPAaHLtPKav9tXV0cG1trINT63a89YXWWXF0GlvPQdnUqlWrpGnTpqa2W0c1X7FihZlxRwP1e+65xzRDf+yxx0xfaO07rtvJDfFnQNV0AwAAAADSTvts6zzXOkiZTomm0+KuW7dOnnrqKRPoXowOdGZPtfbpp5/6HHxMA+pWrVqZAFqDcx3ETUcr177bLVu2NK/r6N+6HxpwHzx4UPr27Wve279/fzM9l04xpoO0+ZriOKehphtAlqOmGwAAANkNNd0AAAAAAGQxmpcDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcEioUxsGAAAAAGSeoIggCQ7LHfWm586eE+u0JTkBQXcAK126tLz88stmzryCBQvK1q1bzZx5OgeeZflXACMiImTt2rVSuXJl83zcuHFy1113pUh35513yqOPPiqXX365hIWFyd69e+XXX3+V+++/P9OPCwAAAEDaacBdvGNxCSsclqOz7+zhs3Jw6kFJPO19rvDshqA7QBUvXlx+//13KV++vGtdrVq1ZPjw4VKtWjUTIPtjwIABroDbl7ffflueeuqpZOv0Pbo89NBDkpiYMwo7AAAAkN1pwB1eLDyrdwNpkDvaJmRDQ4YMcQXcWtusQfi0adPM8969e0ujRo0uuo1KlSpJ//795fjx4z7TaC26HXDPmzdP6tatK3nz5pUqVarI008/LefOncu0YwIAAACA3IagOwAFBQXJ3XffbR5v2LBBRo0aJbGxsfLKK6+40txzzz0X3c6IESNMAP3SSy/5TPPEE0+Yv0ePHpUuXbrImjVr5NSpU7JlyxZTq243Yx87dqx5rK9pjbsqU6aMeZ+unzNnToaPGwAAAAByGoLuAKQ11IUKFXIF3Tb3x/Xr1091G7fccovcdNNNpvZ6/PjxXtMEBwdLixYtzOOdO3fKhx9+KAcPHpRjx46ZWvUaNWq40mpz9j179pg+4tqvXG8MfPTRRxIVFSWHDx+Wnj17Zvi4AQAAACCnIegOQNqU3KYBsLfH0dHRPt+vtdtaS3369Gl57LHHfKYrWrSo5M+f3zzWZuU6mFqxYsUkMjJS2rdvL7/99ptcdtll5nUNrB944AHzuHnz5iYov/nmm83zPn36SExMTIaOGQAAAAByIoLubERrl22pjV7+/PPPS4UKFeStt96STZs2+UwXGpp8HL1BgwZJgQIFZOjQoea5BuAaUNtmzZolH3/8sXlsB9zfffedfPPNNxk4KgAAAADIuQi6A5A28bbpVGE2rYH2lsazlvuZZ56RI0eOyPTp06VevXquPthKm63rujx58pjaa/eB0rQP+IkTJ+T99993rdO07nS6MncffPBBuo8TAAAAAHI6gu4ApPNxa0Csqlev7lrv3sd6+fLlXt8bHh5u+l1rcK1Tjv3999/y448/ul5v27atWafb1UHR3PuJexMfH5/suQbm7rQZu87rDQAAAABIiaA7AGnTcR0t3A60e/ToYZp6P/fcc640dpPubdu2mfTz589P12d9++23rsfalFz7eD/++OOudb/88kuy16+77jrz+PXXXze15FoTrtObAQAAAABSIugOUBrI7tixwzzWKcO0OXmHDh3Mcx1lfOnSpV7fp1N4ad9v90X7d9vGjRtn1q1cudI8f/vtt2X16tXm8csvv2zm9P7vf/9rnq9atcqMVK6qVasmr732mitQHzBggNkP1a9fP2nSpImDuQEAAAAA2RNBd4DSIFtHCR89erQcOHDAjES+bt06eeqpp1IdkTyttPl4q1atTD9uHYH87NmzZvow7bvdsmVL87pOLab7kS9fPrNfffv2Ne/t37+/bN++3QzINmbMGNOfHAAAAABwgQ6H7XsY7GxGBxrTabV07ui4uLis3h0AfgopECKlu5eW8GLhOTrPzsSekT2j90ji8cSs3hUAAJANcc2UPeNParoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAh4Q6tWGkLigiSILDcsc9j3Nnz4l1OscMkg8AAAAAfiPoziIacBfvWFzCCodJTnb28Fk5OPWgJJ5miiQAAAAAuQ9BdxbSgDunz0sMAAAAALlZ7mjfDAAAAABAFiDoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAADkpqD70UcflW3btkl8fLwsXrxYGjVqlNW7BAAAAABA9g+677jjDnn77bflxRdflPr168vKlStl9uzZUrx48azeNQAAAAAAsnfQ/fTTT8unn34qX375paxfv1569eolJ0+elPvvvz+rdw0AAAAAgOwbdIeFhUmDBg1k3rx5rnWWZZnnzZo1y9J9AwAAAAAgrUIlgBQrVkxCQ0Nl//79ydbr8xo1aqRIHx4eLhEREa7nkZGR5m+hQoUkJCREAllIgRDJdzqfhJ0Mk5ws9HSo+T4SQxOzelcQwPg9AAAAcM2U3WIIO/7MVkF3Wg0cOFCGDBmSYv3OnTuzZH8AAAAAALlLZGSkxMXFZY+gOzY2VhISEqREiRLJ1uvzffv2pUj/6quvmkHX3BUpUkQOHTrk+L5m18IQExMjl112WaqFAsgN+D0AAABwvZQZ15R79uxJNU1ABd1nz56VZcuWyXXXXSdTpkwx64KCgszz999/P0X6M2fOmMUdweTFaR6RTwC/BwAAAH8QP/jmT1wVUEG30prr0aNHy19//SVLliyRJ598UvLnzy+jRo3K6l0DAAAAACBNAi7onjBhgpmTe+jQoVKyZEn5+++/pW3btnLgwIGs3jUAAAAAALJ30K0++OADsyBznT592gw8p3+B3I7fAwAAANdLl0KQToV9ST4JAAAAAIBcJjirdwAAAAAAgJyKoBsAAAAAAIcQdAMAAAAA4BCC7mzu6quvlqlTp0pMTIxYliWdOnVKkebFF180E7afPHlS5s6dK1WqVEn2euHCheXrr7+Wo0ePyuHDh+Wzzz4z07QB2c2AAQPMVIPHjh2T/fv3y+TJk6VatWrJ0kRERMj7778vsbGxZl7FSZMmSXR0dLI0ZcuWlenTp8uJEyfMdt544w0JCQm5xEcDAACQ+QYPHmziBvdl/fr1rte5Vsp8BN3ZnAbHK1eulMcee8zr6/369ZO+fftKr169pEmTJiaImD17tvkx2b755hupXbu2tGnTRtq3by8tW7aUTz755BIeBZA5WrVqZWY+aNq0qSnPYWFhMmfOHMmXL58rzfDhw6VDhw7SpUsXk7506dLy/fffu14PDg6WGTNmSHh4uDRv3ly6d+8uPXr0MNMYAgAA5ARr1qwx0zPbS4sWLVyvca3kDB29nCUH5IHq1KlTsnV79uyxnnnmGdfzqKgoKz4+3uratat5XqNGDfO+Bg0auNLceOONVmJiolWqVKksPyYW8iAjZaBYsWKmfF999dWu8n/69Gmrc+fOrjTVq1c3aZo0aWKet23b1kpISLCio6NdaR555BHryJEjVlhYGGWSMkkZoAxQBigDlAHKQLYuA4MHD7ZWrFjh9TWulcSRPKemOwerWLGilCpVSubNm+dap81u//zzT2nWrJl5rn+1SfmyZctcaTT9uXPnTM04kJ0VLFjQ/D106JD526BBA1OD7f6b2Lhxo+zYsSPZb2L16tVy4MABVxptHaLb0hYhAAAA2V3VqlVN99QtW7aYbqbatU5xreQMgu4cTJuKKO2T6k6f26/pX/fgQiUmJpogxU4DZEdBQUHyzjvvyMKFC2Xt2rVmnZbp06dPm/ELUvtNePvN2K8BAABkZ1oBp13n2rZtK7179zYVdb/99psUKFCAayWHhDq1YQDIStq3u06dOsn6KAEAAOR2s2bNcj3W1n0ahGurvzvuuEPi4+OzdN9yKmq6c7B9+/aZvyVKlEi2Xp/br+lfz5GbdZTmIkWKuNIA2c2IESPMoICtW7c2TadsWqZ1EEG72bmv34S334z9GgAAQE6iLQA3bdpkZjjiWskZBN052LZt22Tv3r1y3XXXudZFRkaavtp//PGHea5/dcqw+vXru9Jce+21ZgRnvesFZMeA+9ZbbzXlePv27cle07ELzpw5k+w3oVOKlS9fPtlvom7dulK8eHFXGh0JXf9BWrdu3SU8EgAAgEszG1LlypVN3MC1knOyfAQ9lvTnQf78+a169eqZRT355JPmcdmyZc3r/fr1sw4dOmR16NDBqlOnjjV58mRry5YtVkREhGsbP/74o7Vs2TKrUaNGVvPmza2NGzda33zzDd8LZTPblYEPPvjAOnz4sNWyZUurRIkSriVPnjyuNCNHjrS2b99uXXPNNVb9+vWtRYsWmcU1umRwsLVq1Spr1qxZ1uWXX27dcMMN1v79+62XX345y4+PhTygDFAGKAOUAcoAZSCjZWDYsGHmWql8+fJWs2bNrDlz5lgHDhwws75wrSRO/cYouNk5D1q1amV5M2rUKFeaF1980dq7d6+ZKmzu3LlW1apVk22jcOHCJsg+duyYmRbp888/N8F8Vh8bC3mQ1jLgS/fu3V1p9IbT+++/b/3777/W8ePHre+++84E5u7bKVeunDVjxgzrxIkT5h8h/ccpJCSEMkmZpAxQBigDlAHKAGUg25eBsWPHWjExMdapU6esXbt2meeVKlXiWkmcy/OgpAcAAAAAACCT0acbAAAAAACHEHQDAAAAAOAQgm4AAAAAABxC0A0AAAAAgEMIugEAAAAAcAhBNwAAAAAADiHoBgAAAADAIQTdAAAAAAA4hKAbAIA0aNWqlViWJZ07d84W+RYdHS0TJ06U2NhYs99PPPFEVu9SwBo8eLDJIwAAMhNBNwAg4HTv3t0EP/Hx8VK6dOkUr8+fP19Wr16dJfuW3QwfPlxuvPFGefXVV6Vbt24ya9Ysn2k1z+3l7Nmz8u+//8pff/0l77zzjtSsWTPd+5A3b14T0OoNi8xUvnz5ZPuckJAgO3bskO+//17q1asnl1KzZs3MMRYsWPCSfi4AIPARdAMAAlaePHlkwIABWb0b2dq1114rU6ZMkbfeeku++eYb2bhxY6rp58yZY4Lznj17yn//+19Zvny5uQmycuVKeeqpp9K1D/ny5ZMhQ4bINddcI0749ttvzT7ff//95rEe8+LFi9MceP/vf/8zZS49mjdvbo6xUKFC6Xo/ACDnCs3qHQAAwJcVK1bIQw89ZGpp9+7dm6sySgPVkydPZkrz8iNHjvidftOmTSY4d6c3PqZNmyZvv/22bNiwQWbOnCmBRG8MuO/zokWLzP727t1bevXq5fd2EhMTzQIAQGaiphsAELBeeeUVCQkJuWhtt93MWGtkPel6bfbr2W+3atWq8tVXX5mA9MCBAzJ06FDzepkyZeSHH36Qo0ePmkD/6aef9vqZul8vv/yySXP8+HFTm6zv9dS4cWMTpOrnnDhxQn755RdTK+rO3idtwq3B46FDh2ThwoWpHnPFihVlwoQJpgm4bvePP/6Qm266KUUT/eDgYHn88cddTbDTQ/fnzjvvNE3OBw0a5FofFhYmL774ommCrsen+fDrr78mq9HW70b7kyutCbb3w/5O6tatK6NGjZItW7aY7gSan59//rkUKVJE0uvnn3925ZHt9ttvN/upNzIOHjxovnvPrgve+nTr8xEjRkinTp1Ml4ZTp07JmjVrTJN99/e9+eab5vH27dtdx6jHrq6//nr57bff5PDhwxIXF2duXGjZAQDkDtR0AwAC1rZt22TMmDGmtvu1117L1Nru8ePHy/r1601Af/PNN8sLL7xggstHHnnEBG39+/eXe+65xzTLXrp0qQma3GnwqYHV66+/bmqTn3zySZk3b55cccUVJjBTrVu3NgH3smXLTHB67tw502xbt3/11Veb7brTAc/++ecfee655yQoKMjnvuvn/f7776Y2/L333jOBtwbZU6dONcGl3jTQ4FebXH/99demybjmY0bs2rVLFixYYI4pMjLSBI9RUVHy4IMPytixY+XTTz816x944AGZPXu2udmgTdI1wNXa5o8++sj0tdZFrVq1yvxt06aNVKpUyQTe+/btk9q1a8vDDz9s/jZt2jRd+1q5cmXzV/NFad58+eWXsmTJEhk4cKCUKFHCDCh31VVXyZVXXmlusKSmRYsWctttt8nIkSPNcfft21e+++47KVeunCkzekzVqlWTu+++25QD+yaDHnutWrVk+vTp5ni1uf7p06elSpUq5rMBALmH3tJlIQ8oA5QBygBlIGDKQPfu3S3VoEEDq2LFitaZM2esd955x/X6/PnzrdWrV7uely9f3qTX93luSw0ePNj1XB+rjz76yLUuODjY2rlzp5WYmGj169fPtb5gwYLWiRMnrFGjRrnWtWrVyrx/165dVoECBVzrb7/9drO+T58+rnUbN260Zs6cmWx/8uTJY23ZssWaPXt2in365ptv/Mqft99+26S/6qqrXOvy589vtrt161YrKCgo2fGPGDHCr+1eLO3w4cNNmrp167ryLSwsLFkazbO9e/dan332mWtd0aJFU3wP7vnhua5r164mfYsWLVLdX/t7f+GFF8xnREdHWy1btrSWLVtm1t96661WaGiotW/fPmvVqlVWRESE67033XSTSTNkyJAU34Nnnpw6dcqqVKmSa50ev3rsscdc65555hmzTvfJ/f1PPPGEWa/7l9W/KxbygDJAGaAMSJbkAc3LAQABX9utTYG19rNkyZKZtt3PPvvM9VhroLXpsTbF1qbNNq0B1YHHtCbWk9Yca3Nq26RJk2TPnj2uJt5a4621nzqwV9GiRV1L/vz55aeffpKWLVumqM3W2mB/6Gf8+eefpu+yTZuYf/LJJ6ZJtdauOsE+Xq3RtvNNm5wrPZbChQtLaGioycv69ev7tU27VYCKiIgweaSDoCl/t6FdA7R2ef/+/aY2Xmu6+/XrJ5MnT5aGDRuamm2tpdZaZtuPP/5oWjpoK4eL0RYMW7dudT3XZuZaNryVC092f3ptnp5a6wUAQM5F0A0ACHg6qrQGc5k5kvnOnTuTPdcgSvsU202S3ddrMOlJm4F72rx5s1SoUME81j7jdnCuAaH7os3ldZRsz+ml9AaDP7SvsLdRyDWItF93QoECBcxfbWJtu++++0wzcg2etam1Hl/79u39njpL81anJNOm5boNfb/2i1b+buPjjz82/aZ11HIN1LX5/bBhw5Llhbf80r7V/uSVZ1lR2j/bW7nw1o1B++frzRy9KaBN8bt06UIADgC5CH26AQABT4NR7Zustd3at9uTrwHCtObaF2+jVPsauTo9NZT2Zz/77LPy999/e03jXlOuNOgPZHXq1DFzYds3B7TP++jRo02Nsga5OiCd5qH2m7b7VV+MDganA8vp+zWfNE8077RfeGrfn+cNEG094JSMlAu9kaCtGrQvvNaqt23b1gxKp/t7ww03mNYCAICcjaAbAJBtart1YDAd4MxbraPynCPZqRpf95psdzpAlj1AmI7GrY4dO5bpAeGOHTukevXqKdbXqFHD9XpmK1u2rLRq1cqMkm7fLNBB2/Q4dZAxdzponD83RfT70hpqHWDspZdeSpaPmcXOC82v+fPnJ3tN12VWXqU2Mry+poPn6fLMM8+YmxI6Mr8G4k7eLAAABAaalwMAsgXtU6u13Tq6uGffbm3urCNFa42iu0cffdSx/dFm1XZzazsA1Smo7DmsdcRybW6uNd3aj9tTsWLF0v3Z2h+5SZMmyUb31pHMtSWA1kKvW7dOMpM2o9Zm0fY0aZ41wO41vjpqebNmzZK9355v3POmiLf3Kx0BPLNo/3Jt1q0jqIeHh7vWa42z9n2fMWNGpnyO9qn3dozemqDbLR+0DzsAIOejphsAkG1owHfvvfeaGl2dK9lzYDStQdSpqzTQ0gBcBzJzij2Xtk51pQN1aaCozZz18+3aTZ1OS4PwtWvXmnQxMTFy2WWXmRpOrQHv2LFjuj5bm9jfddddZts6ZZjui06LpYOode7cOd3zcSvNM202roGwTglWr1490wdZbzDonOXa7NumU2Hp52nzcg1e9fM1uNWg3/2GhDax1jzo2rWrbNq0yeyvfn+6Tgc+00HPdM5vzR9tcu0+v3ZGaXN4bR2hU4bpZ+nNA3vKML1BMXz48Ez5HL3JYpfRcePGmQHmpk2bZmrxtSxq/mituvY315tBOgXbxeZiBwDkHAwdTx5QBigDlAHKQMBOGeb5mk7fpdynDLOnnvr000+tw4cPW0ePHrXGjRtnFStWzOeUYZ5TOOl24+LiUnye5/Rk9pRhOq3Vyy+/bKaj0mnFpk2bZpUtWzbF++vVq2dNmjTJOnjwoBUfH29t27bN7Fvr1q0vuk+pLTqV2oQJE6xDhw5ZJ0+etBYvXmymwfJMl9Ypw2wJCQlm2zr9lk4VVrNmTa/vGTBggDkmPTZNq/ugeanr3NM1bdrUWrp0qZl+y/07KV26tPXdd9+Zz9Lvbvz48VbJkiV9TjHmbcowna7rYsfWpUsXs3+6n7GxsdZXX31lPts9ja8pw7zlnx6f+1RyugwaNMhMJad5Z08fpt/z5MmTrd27d5tj1786NVyVKlWy/HfGQh5QBigDlAG5JHkQlPQAAAAAAABkMvp0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAA4h6AYAAAAAwCEE3QAAAAAAOISgGwAAAAAAhxB0AwAAAADgEIJuAAAAAAAcQtANAAAAAIBDCLoBAAAAAHAIQTcAAAAAAOKM/wNSGcjFdiFjRAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "💡 DiffsolBuilder speedup is modest due to efficient solver caching.\n", + " For more parallelism, use multiprocessing with Python callables.\n" + ] + } + ], + "source": [ + "# Visualize DiffsolBuilder scaling\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "n_pts = [r[\"n_points\"] for r in scaling_results]\n", + "speedups_scale = [r[\"speedup\"] for r in scaling_results]\n", + "\n", + "ax.bar(n_pts, speedups_scale, color=\"#2ca02c\", alpha=0.8, edgecolor=\"black\", width=30)\n", + "ax.axhline(y=1.0, color=\"gray\", linestyle=\"--\", alpha=0.7, label=\"No speedup\")\n", + "ax.axhline(\n", + " y=n_cores, color=\"blue\", linestyle=\":\", alpha=0.7, label=f\"Ideal ({n_cores}x)\"\n", + ")\n", + "\n", + "ax.set_xlabel(\"Number of Data Points\", fontsize=12)\n", + "ax.set_ylabel(\"Speedup\", fontsize=12)\n", + "ax.set_title(\n", + " \"DiffsolBuilder Parallel Speedup vs Data Size\", fontsize=14, fontweight=\"bold\"\n", + ")\n", + "ax.set_xticks(n_pts)\n", + "ax.grid(True, axis=\"y\", alpha=0.3)\n", + "ax.legend(fontsize=11)\n", + "\n", + "for x, y in zip(n_pts, speedups_scale, strict=False):\n", + " ax.text(x, y + 0.1, f\"{y:.2f}x\", ha=\"center\", fontsize=11, fontweight=\"bold\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"\\n💡 DiffsolBuilder speedup is modest due to efficient solver caching.\")\n", + "print(\" For more parallelism, use multiprocessing with Python callables.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## When to Use Each Approach\n\n### DiffsolBuilder with `.with_parallel(True)`\n\nBest for:\n- ODE fitting problems where evaluations are moderately expensive\n- When you want automatic parallelism without code changes\n- Lower overhead than multiprocessing\n\n```python\nproblem = (\n diffid.DiffsolBuilder()\n .with_diffsl(model)\n .with_data(data)\n .with_parallel(True) # Enable rayon parallelism\n .build()\n)\n```\n\n### Multiprocessing for Python Callables\n\nBest for:\n- Expensive Python simulations (>10ms per evaluation)\n- Complex custom objective functions\n- When DiffsolBuilder isn't applicable\n\n```python\nfrom concurrent.futures import ProcessPoolExecutor\n\ndef evaluate_parallel(params_list):\n with ProcessPoolExecutor(max_workers=n_cores) as executor:\n return list(executor.map(expensive_objective, params_list))\n```" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Best Practices Summary\n\n| Scenario | Recommended Approach | Expected Speedup |\n|----------|---------------------|------------------|\n| DiffsolBuilder ODE fitting | `.with_parallel(True)` | 1-2x (limited by caching) |\n| Expensive Python callable | `multiprocessing` | Near-linear with cores |\n| Fast Python callable (<1ms) | Sequential | N/A (overhead dominates) |\n| I/O-bound operations | `threading` | Varies |\n\n### Tips for Maximum Speedup\n\n1. **Profile first**: Measure single evaluation time to assess parallel benefit\n2. **Use multiprocessing for Python**: Bypass the GIL for CPU-bound work\n3. **Match workers to cores**: `n_workers = multiprocessing.cpu_count()`\n4. **Batch evaluations**: Amortise process creation overhead\n5. **Avoid shared state**: Ensure objective functions are stateless" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Performance Summary" + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2026-01-22T19:00:23.357601Z", + "start_time": "2026-01-22T19:00:23.331285Z" + }, + "execution": { + "iopub.execute_input": "2026-01-10T22:23:09.647536Z", + "iopub.status.busy": "2026-01-10T22:23:09.647312Z", + "iopub.status.idle": "2026-01-10T22:23:09.653024Z", + "shell.execute_reply": "2026-01-10T22:23:09.652554Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "PERFORMANCE SUMMARY\n", + "======================================================================\n", + "\n", + "📊 DiffsolBuilder (rayon parallelism):\n", + " Sequential: 0.98s\n", + " Parallel: 0.14s (6.90x speedup)\n", + " Note: Limited speedup due to efficient solver caching\n", + "\n", + "📊 Python Callables:\n", + " Sequential (20 evals): 0.20s (9.8ms/eval)\n", + " For parallelism, use multiprocessing in scripts\n", + "\n", + "🔧 System: 8 CPU cores\n" + ] + } + ], + "source": [ + "# Final summary\n", + "print(\"=\" * 70)\n", + "print(\"PERFORMANCE SUMMARY\")\n", + "print(\"=\" * 70)\n", + "\n", + "print(\"\\n📊 DiffsolBuilder (rayon parallelism):\")\n", + "print(f\" Sequential: {time_seq:.2f}s\")\n", + "print(f\" Parallel: {time_par:.2f}s ({time_seq / time_par:.2f}x speedup)\")\n", + "print(\" Note: Limited speedup due to efficient solver caching\")\n", + "\n", + "print(\"\\n📊 Python Callables:\")\n", + "print(\n", + " f\" Sequential ({n_evals} evals): {time_seq_python:.2f}s ({time_seq_python / n_evals * 1000:.1f}ms/eval)\"\n", + ")\n", + "print(\" For parallelism, use multiprocessing in scripts\")\n", + "\n", + "print(f\"\\n🔧 System: {n_cores} CPU cores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Key Takeaways\n\n1. **DiffsolBuilder** supports parallel evaluation with `.with_parallel(True)`, but speedup is limited by efficient solver caching\n\n2. **Python callables** can achieve excellent parallelism using `multiprocessing.ProcessPoolExecutor`\n\n3. **Threading doesn't work** for CPU-bound Python code due to the GIL\n\n4. **Profile first** - measure single evaluation time to determine if parallelism will help\n\n5. **Multiprocessing is best** for expensive Python simulations (>10ms per evaluation)\n\n## Next Steps\n\n- [Advanced Cost Functions](advanced_cost_functions.ipynb) - Custom objective functions\n- [API Reference: DiffsolBuilder](../../api-reference/python/builders.md#diffsolbuilder) - Complete API\n- [API Reference: CMA-ES](../../api-reference/python/optimisers.md#cmaes) - Population configuration" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Exercises\n\n1. **Multiprocessing Integration**: Modify the `expensive_objective` function to use a different ODE system and measure the parallel speedup\n\n2. **Process Pool Reuse**: Create a persistent `ProcessPoolExecutor` and measure the overhead reduction from reusing it across multiple batches\n\n3. **Hybrid Approach**: Combine DiffsolBuilder's parallel evaluation with multiprocessing by running multiple independent optimisation problems in parallel\n\n4. **Scaling Study**: Measure how multiprocessing speedup changes with:\n - Number of workers (1, 2, 4, 8, ...)\n - Evaluation cost (1ms, 10ms, 100ms, 1s)\n - Batch size (10, 50, 100, 500)" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/tutorials/notebooks/parameter_uncertainty.ipynb b/docs/tutorials/notebooks/parameter_uncertainty.ipynb new file mode 100644 index 0000000..dc267be --- /dev/null +++ b/docs/tutorials/notebooks/parameter_uncertainty.ipynb @@ -0,0 +1,671 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Parameter Uncertainty\n\n**Learning Objectives:**\n- Go from optimisation to uncertainty quantification\n- Use MCMC sampling to explore parameter distributions\n- Interpret MCMC diagnostics and traces\n- Calculate confidence intervals" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Optimisation gives us a **single best** parameter estimate. But how **certain** are we about these values?\n", + "\n", + "MCMC (Markov Chain Monte Carlo) sampling explores the full **posterior distribution**, letting us:\n", + "- Quantify parameter uncertainty\n", + "- Calculate confidence intervals\n", + "- Detect parameter correlations\n", + "- Make probabilistic predictions\n", + "\n", + "We'll use a bouncy ball physics model as our example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.226019Z", + "iopub.status.busy": "2026-01-10T22:21:48.225767Z", + "iopub.status.idle": "2026-01-10T22:21:48.858444Z", + "shell.execute_reply": "2026-01-10T22:21:48.857896Z" + } + }, + "outputs": [], + "source": "# Import plotting utilities\nimport diffid\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom diffid.plotting import parameter_distributions, parameter_traces\n\nnp.random.seed(42) # For reproducibility" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Physics Problem: Falling Ball\n", + "\n", + "A ball falls from height $h$ with gravitational acceleration $g$:\n", + "\n", + "$$\\begin{aligned}\n", + "\\frac{dx}{dt} &= v \\\\\n", + "\\frac{dv}{dt} &= -g\n", + "\\end{aligned}$$\n", + "\n", + "where:\n", + "- $x$ is height\n", + "- $v$ is velocity \n", + "- $g$ is gravitational acceleration (Earth: ~9.81 m/s²)\n", + "- $h$ is initial height\n", + "\n", + "**Task**: Estimate $g$ and $h$ from noisy observations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Synthetic Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.861095Z", + "iopub.status.busy": "2026-01-10T22:21:48.860829Z", + "iopub.status.idle": "2026-01-10T22:21:48.866723Z", + "shell.execute_reply": "2026-01-10T22:21:48.866205Z" + } + }, + "outputs": [], + "source": "def ball_states(t, g, h):\n \"\"\"Analytical solution for ball trajectory.\"\"\"\n height = h - 0.5 * g * t**2\n height = np.maximum(height, 0.0) # Can't go below ground\n velocity = -g * t\n return height, velocity\n\n\n# True parameters\ng_true = 9.81 # m/s²\nh_true = 10.0 # meters\n\n# Time to hit ground: t = sqrt(2h/g)\nt_stop = np.sqrt(2.0 * h_true / g_true)\nt_final = 0.7 * t_stop # Stop before hitting ground\nt_span = np.linspace(0.0, t_final, 61)\n\n# Generate clean data\nheight, velocity = ball_states(t_span, g_true, h_true)\n\n# Add measurement noise\nnoise_std = 0.1\nheight_noisy = height + np.random.normal(0, noise_std, len(t_span))\nvelocity_noisy = velocity + np.random.normal(0, noise_std, len(t_span))\n\n# Format for Diffid: [time, height, velocity]\ndata = np.column_stack((t_span, height_noisy, velocity_noisy))\n\nprint(f\"Generated {len(t_span)} observations\")\nprint(f\"Time span: [0, {t_final:.3f}] seconds\")\nprint(f\"Noise level: σ = {noise_std}\")\nprint(\"\\nTrue parameters:\")\nprint(f\" g = {g_true} m/s²\")\nprint(f\" h = {h_true} m\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:48.869022Z", + "iopub.status.busy": "2026-01-10T22:21:48.868803Z", + "iopub.status.idle": "2026-01-10T22:21:49.144073Z", + "shell.execute_reply": "2026-01-10T22:21:49.143541Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Height\n", + "axes[0].plot(t_span, height, \"--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "axes[0].plot(t_span, height_noisy, \"o\", label=\"Observed\", alpha=0.6)\n", + "axes[0].set_xlabel(\"Time (s)\")\n", + "axes[0].set_ylabel(\"Height (m)\")\n", + "axes[0].set_title(\"Ball Height\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Velocity\n", + "axes[1].plot(t_span, velocity, \"--\", label=\"True\", linewidth=2, alpha=0.7)\n", + "axes[1].plot(t_span, velocity_noisy, \"o\", label=\"Observed\", alpha=0.6)\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Velocity (m/s)\")\n", + "axes[1].set_title(\"Ball Velocity\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the ODE Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:49.146902Z", + "iopub.status.busy": "2026-01-10T22:21:49.146594Z", + "iopub.status.idle": "2026-01-10T22:21:49.150528Z", + "shell.execute_reply": "2026-01-10T22:21:49.149899Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DiffSL Model:\n", + "\n", + "in_i { g = 1, h = 1 }\n", + "u_i {x = h, v = 0}\n", + "F_i {v, -g}\n", + "stop {x}\n", + "\n", + "\n", + "Explanation:\n", + " x = height, v = velocity\n", + " dx/dt = v (velocity determines height change)\n", + " dv/dt = -g (gravity accelerates downward)\n", + " stop {x} (terminate when height reaches zero)\n" + ] + } + ], + "source": [ + "# DiffSL model for falling ball\n", + "dsl_model = \"\"\"\n", + "in_i {g = 1, h = 1 }\n", + "u_i {x = h, v = 0}\n", + "F_i {v, -g}\n", + "stop {x}\n", + "\"\"\"\n", + "\n", + "print(\"DiffSL Model:\")\n", + "print(dsl_model)\n", + "print(\"\\nExplanation:\")\n", + "print(\" x = height, v = velocity\")\n", + "print(\" dx/dt = v (velocity determines height change)\")\n", + "print(\" dv/dt = -g (gravity accelerates downward)\")\n", + "print(\" stop {x} (terminate when height reaches zero)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Optimisation\n", + "\n", + "First, find the maximum a posteriori (MAP) estimate:" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "# Build problem\n", + "builder = (\n", + " diffid.DiffsolBuilder()\n", + " .with_diffsl(dsl_model)\n", + " .with_data(data)\n", + " .with_parameter(\"g\", 5.0) # Initial guess\n", + " .with_parameter(\"h\", 5.0) # Initial guess\n", + " .with_cost(diffid.RMSE(2.0)) # 2 observables (height + velocity)\n", + ")\n", + "\n", + "problem = builder.build()\n", + "\n", + "# Optimise\n", + "optimiser = diffid.Adam().with_step_size(0.05).with_max_iter(1500)\n", + "opt_result = optimiser.run(problem, [5.0, 5.0])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"OPTIMISATION RESULTS (MAP Estimate)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Success: {opt_result.success}\")\n", + "print(\"\\nFitted parameters:\")\n", + "print(f\" g = {opt_result.x[0]:.4f} m/s² (true: {g_true})\")\n", + "print(f\" h = {opt_result.x[1]:.4f} m (true: {h_true})\")\n", + "print(f\"\\nCost: {opt_result.value:.6f}\")\n", + "print(f\"Iterations: {opt_result.iterations}\")\n", + "\n", + "g_map, h_map = opt_result.x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: MCMC Sampling\n", + "\n", + "Now explore the full posterior distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:49.638310Z", + "iopub.status.busy": "2026-01-10T22:21:49.638105Z", + "iopub.status.idle": "2026-01-10T22:21:58.286731Z", + "shell.execute_reply": "2026-01-10T22:21:58.286103Z" + } + }, + "outputs": [], + "source": "# Rebuild problem with GaussianNLL (required for sampling)\nbuilder_sampling = (\n diffid.DiffsolBuilder()\n .with_diffsl(dsl_model)\n .with_data(data)\n .with_parameter(\"g\", g_map) # Start from MAP\n .with_parameter(\"h\", h_map)\n # .with_parallel(True)\n .with_cost(diffid.GaussianNLL(variance=noise_std**2))\n)\n\nproblem_sampling = builder_sampling.build()\n\n# Setup MCMC sampler\nsampler = (\n diffid.MetropolisHastings()\n .with_num_chains(10)\n .with_iterations(1000)\n .with_step_size(0.035)\n)\n\nprint(\"\\n\" + \"=\" * 60)\nprint(\"MCMC SAMPLING\")\nprint(\"=\" * 60)\nprint(f\"\\nStarting from MAP estimate: g = {g_map:.4f}, h = {h_map:.4f}\")\nprint(\"\\nRunning MCMC... (this may take a minute)\")\n\n# Run sampling\nmcmc_result = sampler.run(problem_sampling, [g_map, h_map])\n\nprint(\"\\nSampling complete!\")\nprint(f\"Samples shape: {mcmc_result.samples.shape}\")\nprint(f\"Acceptance rate: {mcmc_result.acceptance_rate}\")\nprint(\"Target acceptance: 0.20 - 0.40\")\n\nif np.any(mcmc_result.acceptance_rate < 0.15) or np.any(\n mcmc_result.acceptance_rate > 0.50\n):\n print(\"⚠️ Warning: Acceptance rate is outside optimal range\")\n print(\" Consider adjusting step_size\")\nelse:\n print(\"✓ Acceptance rate looks good!\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze MCMC Results\n", + "\n", + "### Parameter Traces\n", + "\n", + "Trace plots show how parameters evolved during sampling:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:58.289229Z", + "iopub.status.busy": "2026-01-10T22:21:58.289039Z", + "iopub.status.idle": "2026-01-10T22:21:58.887409Z", + "shell.execute_reply": "2026-01-10T22:21:58.886603Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "What to look for in traces:\n", + " - Good mixing (wiggly, no trends)\n", + " - Stationary (mean stays constant)\n", + " - No long excursions\n", + " - Rapid exploration of parameter space\n" + ] + } + ], + "source": [ + "# Plot traces\n", + "fig, axes = parameter_traces(\n", + " mcmc_result.samples,\n", + " param_names=[\"g (m/s²)\", \"h (m)\"],\n", + " true_values=[g_true, h_true],\n", + " show=False,\n", + ")\n", + "\n", + "plt.show()\n", + "\n", + "print(\"\\nWhat to look for in traces:\")\n", + "print(\" - Good mixing (wiggly, no trends)\")\n", + "print(\" - Stationary (mean stays constant)\")\n", + "print(\" - No long excursions\")\n", + "print(\" - Rapid exploration of parameter space\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Distributions\n", + "\n", + "Histograms show the posterior distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:58.893041Z", + "iopub.status.busy": "2026-01-10T22:21:58.892726Z", + "iopub.status.idle": "2026-01-10T22:21:59.407317Z", + "shell.execute_reply": "2026-01-10T22:21:59.406395Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot distributions\n", + "fig, axes = parameter_distributions(\n", + " mcmc_result.samples,\n", + " param_names=[\"g (m/s²)\", \"h (m)\"],\n", + " true_values=[g_true, h_true],\n", + " show=False,\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.411973Z", + "iopub.status.busy": "2026-01-10T22:21:59.411102Z", + "iopub.status.idle": "2026-01-10T22:21:59.453567Z", + "shell.execute_reply": "2026-01-10T22:21:59.452263Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "POSTERIOR STATISTICS (after burn-in)\n", + "============================================================\n", + "Samples used: 8,008\n", + "\n", + "Gravitational acceleration (g):\n", + " True value: 9.8100 m/s²\n", + " MAP: 9.8035 m/s²\n", + " Posterior: 9.8061 ± 0.0210 m/s²\n", + " 95% CI: [9.7679, 9.8473]\n", + "\n", + "Initial height (h):\n", + " True value: 10.0000 m\n", + " MAP: 9.9841 m\n", + " Posterior: 9.9834 ± 0.0134 m\n", + " 95% CI: [9.9569, 10.0096]\n", + "\n", + "True values in 95% CI:\n", + " g: ✓\n", + " h: ✓\n" + ] + } + ], + "source": [ + "# Burn-in: discard first 20% of samples\n", + "burn_in = int(0.2 * len(mcmc_result.samples))\n", + "samples_burned = mcmc_result.samples[burn_in:]\n", + "\n", + "# Calculate statistics\n", + "g_samples = samples_burned[:, 0]\n", + "h_samples = samples_burned[:, 1]\n", + "\n", + "# Means and standard deviations\n", + "g_mean = np.mean(g_samples)\n", + "g_std = np.std(g_samples)\n", + "h_mean = np.mean(h_samples)\n", + "h_std = np.std(h_samples)\n", + "\n", + "# 95% credible intervals\n", + "g_ci = np.percentile(g_samples, [2.5, 97.5])\n", + "h_ci = np.percentile(h_samples, [2.5, 97.5])\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"POSTERIOR STATISTICS (after burn-in)\")\n", + "print(\"=\" * 60)\n", + "print(f\"Samples used: {len(samples_burned):,}\")\n", + "print(\"\\nGravitational acceleration (g):\")\n", + "print(f\" True value: {g_true:.4f} m/s²\")\n", + "print(f\" MAP: {g_map:.4f} m/s²\")\n", + "print(f\" Posterior: {g_mean:.4f} ± {g_std:.4f} m/s²\")\n", + "print(f\" 95% CI: [{g_ci[0]:.4f}, {g_ci[1]:.4f}]\")\n", + "print(\"\\nInitial height (h):\")\n", + "print(f\" True value: {h_true:.4f} m\")\n", + "print(f\" MAP: {h_map:.4f} m\")\n", + "print(f\" Posterior: {h_mean:.4f} ± {h_std:.4f} m\")\n", + "print(f\" 95% CI: [{h_ci[0]:.4f}, {h_ci[1]:.4f}]\")\n", + "\n", + "# Check if true values are in credible intervals\n", + "g_in_ci = g_ci[0] <= g_true <= g_ci[1]\n", + "h_in_ci = h_ci[0] <= h_true <= h_ci[1]\n", + "\n", + "print(\"\\nTrue values in 95% CI:\")\n", + "print(f\" g: {'✓' if g_in_ci else '✗'}\")\n", + "print(f\" h: {'✓' if h_in_ci else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter Correlations" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.468223Z", + "iopub.status.busy": "2026-01-10T22:21:59.467853Z", + "iopub.status.idle": "2026-01-10T22:21:59.813369Z", + "shell.execute_reply": "2026-01-10T22:21:59.812640Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Parameter correlation: 0.2667\n", + " Low correlation - parameters are nearly independent\n" + ] + } + ], + "source": [ + "# Scatter plot of joint distribution\n", + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "\n", + "# Hexbin plot for large sample sizes\n", + "hb = ax.hexbin(g_samples, h_samples, gridsize=50, cmap=\"Blues\", mincnt=1)\n", + "ax.plot(g_true, h_true, \"r*\", markersize=20, label=\"True values\", zorder=5)\n", + "ax.plot(g_map, h_map, \"go\", markersize=12, label=\"MAP estimate\", zorder=5)\n", + "ax.plot(g_mean, h_mean, \"mo\", markersize=12, label=\"Posterior mean\", zorder=5)\n", + "\n", + "ax.set_xlabel(\"g (m/s²)\", fontsize=12)\n", + "ax.set_ylabel(\"h (m)\", fontsize=12)\n", + "ax.set_title(\"Joint Posterior Distribution\", fontsize=14)\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.colorbar(hb, ax=ax, label=\"Sample density\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Calculate correlation\n", + "correlation = np.corrcoef(g_samples, h_samples)[0, 1]\n", + "print(f\"\\nParameter correlation: {correlation:.4f}\")\n", + "\n", + "if abs(correlation) > 0.7:\n", + " print(\" High correlation - parameters are not independently identifiable\")\n", + "elif abs(correlation) > 0.3:\n", + " print(\" Moderate correlation\")\n", + "else:\n", + " print(\" Low correlation - parameters are nearly independent\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior Predictive Distribution\n", + "\n", + "Use samples to make probabilistic predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-10T22:21:59.816483Z", + "iopub.status.busy": "2026-01-10T22:21:59.816233Z", + "iopub.status.idle": "2026-01-10T22:22:00.197985Z", + "shell.execute_reply": "2026-01-10T22:22:00.197208Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shaded region shows the 95% credible interval for predictions\n", + "This accounts for both parameter uncertainty and observation noise\n" + ] + } + ], + "source": [ + "# Take random subset of samples for predictions\n", + "n_pred_samples = 200\n", + "sample_indices = np.random.choice(len(samples_burned), n_pred_samples, replace=False)\n", + "\n", + "# Generate predictions for each sample\n", + "predictions_height = []\n", + "predictions_velocity = []\n", + "\n", + "for idx in sample_indices:\n", + " g_sample, h_sample = samples_burned[idx]\n", + " h_pred, v_pred = ball_states(t_span, g_sample, h_sample)\n", + " predictions_height.append(h_pred)\n", + " predictions_velocity.append(v_pred)\n", + "\n", + "predictions_height = np.array(predictions_height)\n", + "predictions_velocity = np.array(predictions_velocity)\n", + "\n", + "# Calculate percentiles\n", + "h_median = np.median(predictions_height, axis=0)\n", + "h_lower = np.percentile(predictions_height, 2.5, axis=0)\n", + "h_upper = np.percentile(predictions_height, 97.5, axis=0)\n", + "\n", + "v_median = np.median(predictions_velocity, axis=0)\n", + "v_lower = np.percentile(predictions_velocity, 2.5, axis=0)\n", + "v_upper = np.percentile(predictions_velocity, 97.5, axis=0)\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Height\n", + "axes[0].fill_between(t_span, h_lower, h_upper, alpha=0.3, label=\"95% CI\")\n", + "axes[0].plot(t_span, h_median, \"-\", linewidth=2, label=\"Median prediction\")\n", + "axes[0].plot(t_span, height, \"--\", linewidth=2, label=\"True\", alpha=0.7)\n", + "axes[0].plot(t_span, height_noisy, \"o\", label=\"Observed\", alpha=0.4, markersize=4)\n", + "axes[0].set_xlabel(\"Time (s)\")\n", + "axes[0].set_ylabel(\"Height (m)\")\n", + "axes[0].set_title(\"Posterior Predictive: Height\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Velocity\n", + "axes[1].fill_between(t_span, v_lower, v_upper, alpha=0.3, label=\"95% CI\")\n", + "axes[1].plot(t_span, v_median, \"-\", linewidth=2, label=\"Median prediction\")\n", + "axes[1].plot(t_span, velocity, \"--\", linewidth=2, label=\"True\", alpha=0.7)\n", + "axes[1].plot(t_span, velocity_noisy, \"o\", label=\"Observed\", alpha=0.4, markersize=4)\n", + "axes[1].set_xlabel(\"Time (s)\")\n", + "axes[1].set_ylabel(\"Velocity (m/s)\")\n", + "axes[1].set_title(\"Posterior Predictive: Velocity\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"The shaded region shows the 95% credible interval for predictions\")\n", + "print(\"This accounts for both parameter uncertainty and observation noise\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Key Takeaways\n\n1. **Optimisation** gives point estimates; **MCMC** quantifies uncertainty\n2. **GaussianNLL** cost metric is required for sampling\n3. **Acceptance rate** should be 20-40% for efficient exploration\n4. **Burn-in** period discards initial non-stationary samples\n5. **Credible intervals** provide uncertainty bounds\n6. **Posterior predictive** distributions account for parameter uncertainty\n\n## MCMC Diagnostics Checklist\n\n✓ Acceptance rate in [0.2, 0.4] \n✓ Trace plots show good mixing \n✓ No trends in traces \n✓ Distributions look reasonable \n✓ True values in credible intervals \n\n## Next Steps\n\n- [Model Comparison](model_comparison.ipynb) - Use nested sampling for Bayes factors\n- [Choosing a Sampler](../../guides/choosing-sampler.md) - MCMC vs Nested Sampling\n- [API Reference: Samplers](../../api-reference/python/samplers.md)" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "1. **Step Size**: Try different `step_size` values (0.1, 0.5, 1.0) and observe acceptance rates\n", + "\n", + "2. **More Data**: Increase the number of observations and see how uncertainty decreases\n", + "\n", + "3. **More Noise**: Increase `noise_std` to 0.5 and observe wider credible intervals\n", + "\n", + "4. **Longer Chains**: Run with 10,000 iterations per chain for better convergence" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/bicycle_model_diffsol.py b/examples/bicycle_model_diffsol.py index 94329e8..a03f2a7 100644 --- a/examples/bicycle_model_diffsol.py +++ b/examples/bicycle_model_diffsol.py @@ -1,6 +1,6 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np TRUE_L = 2.5 # wheelbase @@ -8,8 +8,8 @@ DELTA = 0.05 # steer angle dsl = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { x = 0.0, y = 0.0, @@ -36,10 +36,10 @@ stacked_data = np.column_stack((t_span, x_obs, y_obs, psi_obs)) -optimiser = chron.CMAES().with_max_iter(500).with_threshold(1e-10) +optimiser = diffid.CMAES().with_max_iter(500).with_threshold(1e-10) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) diff --git a/examples/bicycle_model_evidence.py b/examples/bicycle_model_evidence.py index 5e6ce10..00998bd 100644 --- a/examples/bicycle_model_evidence.py +++ b/examples/bicycle_model_evidence.py @@ -1,6 +1,6 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np TRUE_L = 2.5 # wheelbase @@ -8,8 +8,8 @@ DELTA = 0.05 # steer angle dsl = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { x = 0.0, y = 0.0, @@ -36,11 +36,11 @@ stacked_data = np.column_stack((t_span, x_obs, y_obs, psi_obs)) -optimiser = chron.CMAES().with_max_iter(500).with_threshold(1e-10) -cost = chron.GaussianNLL(0.05) +optimiser = diffid.CMAES().with_max_iter(500).with_threshold(1e-10) +cost = diffid.GaussianNLL(0.05) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -52,7 +52,7 @@ results = problem.optimise() print(results) -sampler = chron.DynamicNestedSampler().with_live_points(128) +sampler = diffid.DynamicNestedSampler().with_live_points(128) samples = sampler.run(problem, initial=results.x) print("time :", samples.time) diff --git a/examples/bouncy_ball.py b/examples/bouncy_ball.py index 5eaa9d7..bc8b98e 100644 --- a/examples/bouncy_ball.py +++ b/examples/bouncy_ball.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -11,8 +11,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} @@ -31,14 +30,14 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # Configure the problem initial_values = [4.0, 4.0] builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", initial_values[0]) .with_parameter("h", initial_values[1]) - .with_optimiser(chron.Adam().with_step_size(0.05).with_max_iter(1500)) - # .with_cost(chron.GaussianNLL(variance=0.01)) - .with_cost(chron.RMSE(2.0)) + .with_optimiser(diffid.Adam().with_step_size(0.05).with_max_iter(1500)) + # .with_cost(diffid.GaussianNLL(variance=0.01)) + .with_cost(diffid.RMSE(2.0)) ) problem = builder.build() diff --git a/examples/bouncy_ball_sampling.py b/examples/bouncy_ball_sampling.py index 475300e..48cc9ef 100644 --- a/examples/bouncy_ball_sampling.py +++ b/examples/bouncy_ball_sampling.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -11,8 +11,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} @@ -31,20 +30,20 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr # Configure the problem initial_values = [4.0, 4.0] builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", initial_values[0]) .with_parameter("h", initial_values[1]) .with_parallel(True) - .with_cost(chron.GaussianNLL(variance=0.01)) + .with_cost(diffid.GaussianNLL(variance=0.01)) ) problem = builder.build() # Setup sampler sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(100) .with_iterations(1000) .with_step_size(0.25) diff --git a/examples/logistic_growth.py b/examples/logistic_growth.py index dafcb7d..f6b5bf8 100644 --- a/examples/logistic_growth.py +++ b/examples/logistic_growth.py @@ -1,10 +1,9 @@ -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -16,11 +15,11 @@ # Create an optimiser -optimiser = chron.CMAES().with_max_iter(1000).with_threshold(1e-12) +optimiser = diffid.CMAES().with_max_iter(1000).with_threshold(1e-12) # Simple API builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(1e-6, 1e-8) @@ -36,9 +35,9 @@ print(f"result: {results}") -# For now, just print the optimization result since Hamiltonian sampler is not implemented yet +# For now, just print the optimisation result since Hamiltonian sampler is not implemented yet print(f"Optimal parameters: {results.x}") print(f"Optimal cost: {results.value}") -print(f"Optimization success: {results.success}") +print(f"Optimisation success: {results.success}") print(f"Iterations: {results.iterations}") print(f"Optimisation time: {results.time}") diff --git a/examples/model_evidence.py b/examples/model_evidence.py index 10fb988..4ac136a 100644 --- a/examples/model_evidence.py +++ b/examples/model_evidence.py @@ -2,7 +2,7 @@ from __future__ import annotations -import chronopt as chron +import diffid def rosenbrock(x: list[float]) -> float: @@ -11,17 +11,17 @@ def rosenbrock(x: list[float]) -> float: builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", initial_value=1.2) .with_parameter("y", initial_value=1.4) - .with_optimiser(chron.NelderMead().with_max_iter(2000)) + .with_optimiser(diffid.NelderMead().with_max_iter(2000)) ) problem = builder.build() optimised = problem.optimise() -sampler = chron.DynamicNestedSampler().with_live_points(256).with_seed(1234) +sampler = diffid.DynamicNestedSampler().with_live_points(256).with_seed(1234) samples = sampler.run(problem, initial=optimised.x) print("time :", samples.time) diff --git a/examples/model_evidence_diffsol.py b/examples/model_evidence_diffsol.py index c143520..955f9c6 100644 --- a/examples/model_evidence_diffsol.py +++ b/examples/model_evidence_diffsol.py @@ -2,13 +2,12 @@ from __future__ import annotations -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -19,7 +18,7 @@ stacked_data = np.column_stack((t_span, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("r", initial_value=1.2) @@ -30,7 +29,7 @@ optimised = problem.optimise() -sampler = chron.DynamicNestedSampler().with_live_points(256).with_seed(1234) +sampler = diffid.DynamicNestedSampler().with_live_points(256).with_seed(1234) samples = sampler.run(problem, initial=optimised.x) print("time :", samples.time) diff --git a/examples/predator_prey/README.md b/examples/predator_prey/README.md index 767e29a..5388abb 100644 --- a/examples/predator_prey/README.md +++ b/examples/predator_prey/README.md @@ -1,7 +1,7 @@ # Predator-Prey Parameter Identification Examples This directory showcases how to recover the parameters of the Lotka–Volterra -predator-prey model using Chronopt with several ODE solver backends. All +predator-prey model using Diffid with several ODE solver backends. All examples share a synthetic dataset so that the optimisation results can be compared side by side. @@ -12,7 +12,7 @@ compared side by side. - `predator_prey_diffrax.py` – fits the model using the JAX/Diffrax simulator. - `predator_prey_diffeqpy.py` – fits the model using the Julia DifferentialEquations.jl stack via diffeqpy. -- `predator_prey_diffsol.py` – fits the model using Chronopt's Diffsol backend. +- `predator_prey_diffsol.py` – fits the model using Diffid's Diffsol backend. - `synthetic_data.npz` – cached dataset generated by the script above (re-created on demand). @@ -24,7 +24,7 @@ compared side by side. ```bash # Base requirements for all examples - pip install chronopt numpy + pip install diffid numpy # Diffrax example pip install jax diffrax @@ -35,7 +35,7 @@ compared side by side. ``` The Diffsol example only needs the base requirements because the solver is - provided by Chronopt. + provided by Diffid. ## Generating the dataset @@ -69,10 +69,10 @@ performance across solver backends. platform has compatible JAX wheels. - Diffeqpy: the `de.install()` command downloads a Julia runtime if one is not already configured. This can take a few minutes. -- Diffsol: Chronopt bundles the solver; no extra setup is required beyond the +- Diffsol: Diffid bundles the solver; no extra setup is required beyond the base dependencies. ## Further reading -Check the top-level project README for more background on Chronopt and links to +Check the top-level project README for more background on Diffid and links to additional examples. \ No newline at end of file diff --git a/examples/predator_prey/predator_prey_diffeqpy.py b/examples/predator_prey/predator_prey_diffeqpy.py index 70c7cc4..22b28f7 100644 --- a/examples/predator_prey/predator_prey_diffeqpy.py +++ b/examples/predator_prey/predator_prey_diffeqpy.py @@ -1,12 +1,12 @@ -"""Predator-prey parameter identification using DifferentialEquations.jl and Chronopt. +"""Predator-prey parameter identification using DifferentialEquations.jl and diffid. Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in Julia via diffeqpy 2. Generating synthetic noisy observations -3. Recovering parameters using Chronopt optimization +3. Recovering parameters using diffid optimisation Prerequisites: - pip install diffeqpy chronopt numpy + pip install diffeqpy diffid numpy python -c "from diffeqpy import de; de.install()" """ @@ -15,7 +15,7 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import numpy as np from diffeqpy import de @@ -67,15 +67,15 @@ def simulate(params): # Parameter identification result = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(simulate) .with_data(observed) .with_parameter("alpha", 0.8) .with_parameter("beta", 0.3) .with_parameter("delta", 0.05) .with_parameter("gamma", 0.6) - .with_cost(chron.SSE()) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_cost(diffid.SSE()) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) .build() .optimise() ) @@ -89,3 +89,6 @@ def simulate(params): print( f"Relative error: {np.linalg.norm(result.x - TRUE_PARAMS) / np.linalg.norm(TRUE_PARAMS):.2%}" ) +print(f"Success: {result.success}") +print(f"Iterations: {result.iterations}") +print(f"Time: {result.time}s") diff --git a/examples/predator_prey/predator_prey_diffrax.py b/examples/predator_prey/predator_prey_diffrax.py index 51e4ab0..53e0d99 100644 --- a/examples/predator_prey/predator_prey_diffrax.py +++ b/examples/predator_prey/predator_prey_diffrax.py @@ -1,12 +1,12 @@ -"""Predator-prey parameter identification using JAX/Diffrax and Chronopt. +"""Predator-prey parameter identification using JAX/Diffrax and diffid. Demonstrates parameter estimation for the Lotka-Volterra model by: 1. Defining the predator-prey ODE in JAX 2. Generating synthetic noisy observations -3. Recovering parameters using Chronopt optimization +3. Recovering parameters using diffid optimisation Prerequisites: - pip install chronopt diffrax jax numpy + pip install diffid diffrax jax numpy """ from __future__ import annotations @@ -14,7 +14,7 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import diffrax as dfx import jax.numpy as jnp import numpy as np @@ -67,7 +67,7 @@ def simulate_jax(params): def simulate(params): - """NumPy wrapper for Chronopt compatibility.""" + """NumPy wrapper for diffid compatibility.""" return np.asarray(simulate_jax(jnp.asarray(params))) @@ -89,15 +89,15 @@ def simulate(params): # Parameter identification result = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(simulate) .with_data(observed) .with_parameter("alpha", 1.3) .with_parameter("beta", 0.3) .with_parameter("delta", 0.05) .with_parameter("gamma", 0.6) - .with_cost(chron.SSE()) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_cost(diffid.SSE()) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) .build() .optimise() ) diff --git a/examples/predator_prey/predator_prey_diffsol.py b/examples/predator_prey/predator_prey_diffsol.py index 5dd0d2c..9c57caf 100644 --- a/examples/predator_prey/predator_prey_diffsol.py +++ b/examples/predator_prey/predator_prey_diffsol.py @@ -1,13 +1,13 @@ import importlib.util import pathlib -import chronopt as chron +import diffid import numpy as np # Example diffsol ODE (logistic growth) ode = """ -in = [a, b, c, d ] -a { 2.0/3.0 } b { 4.0/3.0 } c { 1.0 } d { 1.0 } x0 { 10.0 } y0 { 5.0 } +in_i { a = 2.0/3.0, b = 4.0/3.0, c = 1.0, d = 1.0 } +x0 { 10.0 } y0 { 5.0 } u_i { y1 = x0, y2 = y0, @@ -32,7 +32,7 @@ # Simple API builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ode) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -42,7 +42,7 @@ .with_parameter("d", 0.6) .with_parallel(True) .with_optimiser( - chron.NelderMead().with_max_iter(1000) + diffid.NelderMead().with_max_iter(1000) ) # Override default optimiser ) problem = builder.build() @@ -54,7 +54,7 @@ print(results) print(f"Optimal parameters: {results.x}") print(f"Optimal cost: {results.value}") -print(f"Optimization success: {results.success}") +print(f"Optimisation success: {results.success}") print(f"Iterations: {results.iterations}") print(f"Optimisation time: {results.time}") print(f"Eval per ms: {results.evaluations / (results.time.total_seconds() * 1000)}") diff --git a/examples/python_contour.png b/examples/python_contour.png new file mode 100644 index 0000000..24ab72e Binary files /dev/null and b/examples/python_contour.png differ diff --git a/examples/python_contour.py b/examples/python_contour.py index 896d6bb..8144922 100644 --- a/examples/python_contour.py +++ b/examples/python_contour.py @@ -2,7 +2,7 @@ from pathlib import Path -import chronopt as chron +import diffid import matplotlib.pyplot as plt import numpy as np @@ -15,19 +15,19 @@ def rosenbrock(x: np.ndarray) -> float: # Setup builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) problem = builder.build() # Optimise -optimiser = chron.NelderMead().with_max_iter(500).with_threshold(1e-8) +optimiser = diffid.NelderMead().with_max_iter(500).with_threshold(1e-8) result = optimiser.run(problem, [-1.5, 1.5]) # Plot -contour_set = chron.plotting.contour( +contour_set = diffid.plotting.contour( problem, x_bounds=(-2.0, 2.0), y_bounds=(-1.0, 3.0), diff --git a/examples/python_problem.py b/examples/python_problem.py index 257560b..4de56e3 100644 --- a/examples/python_problem.py +++ b/examples/python_problem.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -10,11 +10,11 @@ def rosenbrock(x): # Simple API builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) - .with_optimiser(chron.NelderMead().with_max_iter(1000)) + .with_optimiser(diffid.NelderMead().with_max_iter(1000)) ) problem = builder.build() result = problem.optimise(initial=[10.0, 10.0]) diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..eb0f145 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,198 @@ +site_name: Diffid +site_description: High-performance time-series inference and optimisation toolkit with Rust core and ergonomic Python bindings +site_author: Brady Planden +site_url: https://bradyplanden.github.io/diffid/ +repo_name: bradyplanden/diffid +repo_url: https://github.com/bradyplanden/diffid +edit_uri: edit/main/docs/ + +theme: + name: material + palette: + # Light mode + - media: "(prefers-color-scheme: light)" + scheme: default + primary: blue + accent: indigo + toggle: + icon: material/brightness-7 + name: Switch to dark mode + # Dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: blue + accent: indigo + toggle: + icon: material/brightness-4 + name: Switch to light mode + font: + text: Roboto + code: Roboto Mono + features: + - navigation.instant + - navigation.tracking + - navigation.tabs + - navigation.tabs.sticky + - navigation.sections + - navigation.expand + - navigation.path + - navigation.indexes + - navigation.top + - navigation.footer + - search.suggest + - search.highlight + - search.share + - toc.follow + - content.code.copy + - content.code.annotate + - content.tabs.link + +plugins: + - search: + separator: '[\s\-,:!=\[\]()"/]+|(?!\b)(?=[A-Z][a-z])|\.(?!\d)|&[lg]t;' + - git-revision-date-localized: + enable_creation_date: true + type: date + fallback_to_build_date: true + strict: false + - minify: + minify_html: true + - mkdocstrings: + handlers: + python: + paths: [python/src] + options: + docstring_style: numpy + docstring_section_style: table + members_order: source + show_source: false + show_root_heading: true + show_root_full_path: false + show_symbol_type_heading: true + show_symbol_type_toc: true + signature_crossrefs: true + separate_signature: true + line_length: 80 + merge_init_into_class: true + docstring_options: + ignore_init_summary: true + - mkdocs-jupyter: + include_source: true + execute: false + allow_errors: false + kernel_name: python3 + +markdown_extensions: + - abbr + - admonition + - attr_list + - def_list + - footnotes + - md_in_html + - toc: + permalink: true + toc_depth: 3 + - pymdownx.arithmatex: + generic: true + - pymdownx.betterem: + smart_enable: all + - pymdownx.caret + - pymdownx.details + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.keys + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true + - pymdownx.tasklist: + custom_checkbox: true + - pymdownx.tilde + - pymdownx.snippets + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + +extra: + social: + - icon: fontawesome/brands/github + link: https://github.com/bradyplanden/diffid + - icon: fontawesome/brands/python + link: https://pypi.org/project/diffid/ + version: + provider: mike + default: latest + +extra_javascript: + - javascripts/mathjax.js + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + +nav: + - Home: index.md + - Getting Started: + - getting-started/index.md + - Installation: getting-started/installation.md + - 5-Minute Quickstart: getting-started/quickstart.md + - First ODE Fit: getting-started/first-ode-fit.md + - Core Concepts: getting-started/concepts.md + - Tutorials: + - tutorials/index.md + - Notebooks: + - Optimisation Basics: tutorials/notebooks/optimisation_basics.ipynb + - ODE Fitting with DiffSL: tutorials/notebooks/ode_fitting_diffsol.ipynb + - Parameter Uncertainty: tutorials/notebooks/parameter_uncertainty.ipynb + - Model Comparison: tutorials/notebooks/model_comparison.ipynb + - Predator-Prey Models: tutorials/notebooks/advanced_predator_prey.ipynb + - Parallel Optimisation: tutorials/notebooks/parallel_optimisation.ipynb + - Advanced Cost Functions: tutorials/notebooks/advanced_cost_functions.ipynb + - User Guides: + - guides/index.md + - Choosing an Optimiser: guides/choosing-optimiser.md + - Tuning Optimisers: guides/tuning-optimisers.md + - Choosing a Sampler: guides/choosing-sampler.md + - Cost Metrics: guides/cost-metrics.md + - DiffSL Backend: guides/diffsol-backend.md + - Parallel Execution: guides/parallel-execution.md + - Custom Solvers: guides/custom-solvers.md + - Troubleshooting: guides/troubleshooting.md + - Algorithms: + - algorithms/index.md + - Optimisers: + - Nelder-Mead: algorithms/optimisers/nelder-mead.md + - CMA-ES: algorithms/optimisers/cmaes.md + - Adam: algorithms/optimisers/adam.md + - Samplers: + - Metropolis-Hastings: algorithms/samplers/metropolis-hastings.md + - Dynamic Nested Sampling: algorithms/samplers/dynamic-nested-sampling.md + - API Reference: + - api-reference/index.md + - Python: + - Builders: api-reference/python/builders.md + - Optimisers: api-reference/python/optimisers.md + - Samplers: api-reference/python/samplers.md + - Cost Metrics: api-reference/python/cost-metrics.md + - Results: api-reference/python/results.md + - Rust: + - api-reference/rust/index.md + - Examples: + - examples/gallery.md + - Development: + - development/index.md + - Contributing: development/contributing.md + - Architecture: development/architecture.md + - Changelog: changelog.md + +strict: true # Fail on warnings + +watch: + - python/src/diffid + - docs diff --git a/pyproject.toml b/pyproject.toml index 1b68c77..ecde2dd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["maturin>=1.9,<2.0"] build-backend = "maturin" [project] -name = "chronopt" +name = "diffid" requires-python = ">=3.11" dynamic = ["version"] license = { file = "LICENSE" } @@ -31,7 +31,7 @@ diffeqpy = [ [tool.maturin] manifest-path = "python/Cargo.toml" -module-name = "chronopt._chronopt" +module-name = "diffid._diffid" features = ["extension-module"] python-source = "python/src" sdist-include = ["LICENSE", "README.md"] @@ -41,6 +41,17 @@ dev = [ "pytest>=8.3.5", "scipy>=1.16.2", ] +docs = [ + "mkdocs>=1.5.0", + "mkdocs-material>=9.5.0", + "mkdocstrings[python]>=0.24.0", + "mkdocs-git-revision-date-localized-plugin>=1.2.0", + "mkdocs-minify-plugin>=0.8.0", + "mkdocs-jupyter>=0.24.0", + "pymdown-extensions>=10.7", + "matplotlib>=3.8", + "nbconvert>=7.0", +] examples = [ # macOS x86_64 + Python < 3.14: diffrax with pinned jax/jaxlib "diffrax>=0.7.0; python_version < '3.14' and sys_platform == 'darwin' and platform_machine == 'x86_64'", diff --git a/python/Cargo.toml b/python/Cargo.toml index 32f36cd..f7f26fb 100644 --- a/python/Cargo.toml +++ b/python/Cargo.toml @@ -1,12 +1,12 @@ [package] -name = "chronopt-py" +name = "diffid-py" version.workspace = true edition.workspace = true license.workspace = true readme = "../README.md" [package.metadata.maturin] -name = "chronopt" +name = "diffid" python-source = "src" artifact = "dist" @@ -16,7 +16,7 @@ extension-module = ["pyo3/extension-module"] stubgen = ["clap", "pyo3-stub-gen", "pyo3-stub-gen-derive"] [lib] -name = "_chronopt" +name = "_diffid" crate-type = ["cdylib", "rlib"] [dependencies] pyo3 = { workspace = true, default-features = false, features = ["macros"] } @@ -27,10 +27,10 @@ pyo3-stub-gen-derive = { version = "0.17.2", optional = true } clap = { version = "4.5", optional = true, features = ["derive"] } [target.'cfg(not(windows))'.dependencies] -chronopt_core = { package = "chronopt", path = "../rust" } +diffid_core = { package = "diffid", path = "../rust" } [target.'cfg(windows)'.dependencies] -chronopt_core = { package = "chronopt", path = "../rust", default-features = false, features = ["cranelift-backend"] } +diffid_core = { package = "diffid", path = "../rust", default-features = false, features = ["cranelift-backend"] } [build-dependencies] pyo3-build-config = "0.27.1" diff --git a/python/src/bin/generate_stubs.rs b/python/src/bin/generate_stubs.rs index 254f01f..fe6dc72 100644 --- a/python/src/bin/generate_stubs.rs +++ b/python/src/bin/generate_stubs.rs @@ -4,7 +4,7 @@ use std::fs; use std::io::Write; use std::path::{Path, PathBuf}; -use chronopt::{stub_info, stub_info_from}; +use _diffid::{stub_info, stub_info_from}; use clap::Parser; use pyo3_stub_gen::Result; @@ -38,7 +38,7 @@ fn main() -> Result<()> { } fn post_process_sampler_stub() -> Result<()> { - let sampler_stub_path = resolve_workspace_root()?.join("python/src/chronopt/sampler.pyi"); + let sampler_stub_path = resolve_workspace_root()?.join("python/src/diffid/sampler.pyi"); let contents = fs::read_to_string(&sampler_stub_path)?; let mut lines: Vec<&str> = contents.lines().collect(); diff --git a/python/src/builders.rs b/python/src/builders.rs index 116d06d..b95c9ec 100644 --- a/python/src/builders.rs +++ b/python/src/builders.rs @@ -6,11 +6,11 @@ use pyo3::types::PyDict; use std::collections::HashMap; use std::sync::Arc; -use chronopt_core::builders::{ +use diffid_core::builders::{ DiffsolBackend, DiffsolProblemBuilder, ScalarProblemBuilder, VectorProblemBuilder, }; -use chronopt_core::common::Unbounded; -use chronopt_core::problem::{NoFunction, NoGradient}; +use diffid_core::common::Unbounded; +use diffid_core::problem::{NoFunction, NoGradient}; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -245,7 +245,7 @@ impl PyScalarBuilder { } /// Attach the objective function callable executed during optimisation. - fn with_callable(mut slf: PyRefMut<'_, Self>, obj: Py) -> PyResult> { + fn with_objective(mut slf: PyRefMut<'_, Self>, obj: Py) -> PyResult> { Python::attach(|py| { if !obj.bind(py).is_callable() { return Err(PyTypeError::new_err("Object must be callable")); @@ -333,7 +333,7 @@ impl PyScalarBuilder { .push((name.clone(), initial_value, bounds)); // Convert Option<(f64, f64)> to ParameterRange - let range: chronopt_core::problem::ParameterRange = + let range: diffid_core::problem::ParameterRange = bounds.map(|b| b.into()).unwrap_or_else(|| Unbounded.into()); slf.state = match std::mem::replace( diff --git a/python/src/chronopt/_chronopt.pyi b/python/src/chronopt/_chronopt.pyi deleted file mode 100644 index fff8acb..0000000 --- a/python/src/chronopt/_chronopt.pyi +++ /dev/null @@ -1,441 +0,0 @@ -# This file is automatically generated by pyo3_stub_gen -# ruff: noqa: E501, F401 - -import builtins -import datetime -import typing - -import numpy -import numpy.typing - -@typing.final -class Adam: - r""" - Adaptive Moment Estimation (Adam) gradient-based optimiser. - """ - def __new__(cls) -> Adam: - r""" - Create an Adam optimiser with library defaults. - """ - def with_max_iter(self, max_iter: builtins.int) -> Adam: - r""" - Limit the maximum number of optimisation iterations. - """ - def with_threshold(self, threshold: builtins.float) -> Adam: - r""" - Set the stopping threshold on the gradient norm. - """ - def with_step_size(self, step_size: builtins.float) -> Adam: - r""" - Configure the base learning rate / step size. - """ - def with_betas(self, beta1: builtins.float, beta2: builtins.float) -> Adam: - r""" - Override the exponential decay rates for the first and second moments. - """ - def with_eps(self, eps: builtins.float) -> Adam: - r""" - Override the numerical stability constant added to the denominator. - """ - def with_patience(self, patience_seconds: builtins.float) -> Adam: - r""" - Abort the run once the patience window has elapsed. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem using Adam starting from the provided point. - """ - -@typing.final -class CMAES: - r""" - Covariance Matrix Adaptation Evolution Strategy optimiser. - """ - def __new__(cls) -> CMAES: - r""" - Create a CMA-ES optimiser with library defaults. - """ - def with_max_iter(self, max_iter: builtins.int) -> CMAES: - r""" - Limit the number of iterations/generations before termination. - """ - def with_threshold(self, threshold: builtins.float) -> CMAES: - r""" - Set the stopping threshold on the best objective value. - """ - def with_step_size(self, step_size: builtins.float) -> CMAES: - r""" - Set the initial global step-size (standard deviation). - """ - def with_patience(self, patience_seconds: builtins.float) -> CMAES: - r""" - Abort the run if no improvement occurs for the given wall-clock duration. - """ - def with_population_size(self, population_size: builtins.int) -> CMAES: - r""" - Specify the number of offspring evaluated per generation. - """ - def with_seed(self, seed: builtins.int) -> CMAES: - r""" - Initialise the internal RNG for reproducible runs. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem starting from the provided mean vector. - """ - -@typing.final -class CostMetric: - @property - def name(self) -> builtins.str: - r""" - Name of the cost metric. - """ - def __repr__(self) -> builtins.str: ... - -@typing.final -class DiffsolBuilder: - r""" - Differential equation solver builder. - """ - def __new__(cls) -> DiffsolBuilder: - r""" - Create an empty differential solver builder. - """ - def __copy__(self) -> DiffsolBuilder: ... - def __deepcopy__(self, _memo: dict) -> DiffsolBuilder: ... - def with_diffsl(self, dsl: builtins.str) -> DiffsolBuilder: - r""" - Register the DiffSL program describing the system dynamics. - """ - def remove_diffsl(self) -> DiffsolBuilder: - r""" - Remove any registered DiffSL program. - """ - def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> DiffsolBuilder: - r""" - Attach observed data used to fit the differential equation. - - The first column must contain the time samples (t_span) and the remaining - columns the observed trajectories. - """ - def remove_data(self) -> DiffsolBuilder: - r""" - Remove any previously attached data along with its time span. - """ - def with_backend(self, backend: builtins.str) -> DiffsolBuilder: - r""" - Choose whether to use dense or sparse diffusion solvers. - """ - def with_parallel(self, parallel: builtins.bool | None = None) -> DiffsolBuilder: - r""" - Opt into parallel proposal generation when supported by the backend. - """ - def with_config( - self, config: typing.Mapping[builtins.str, builtins.float] - ) -> DiffsolBuilder: ... - def with_rtol(self, rtol: builtins.float) -> DiffsolBuilder: - r""" - Adjust the relative integration tolerance. - """ - def with_atol(self, atol: builtins.float) -> DiffsolBuilder: - r""" - Adjust the absolute integration tolerance. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> DiffsolBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def clear_parameters(self) -> DiffsolBuilder: - r""" - Remove previously provided parameter defaults. - """ - def with_cost(self, cost: CostMetric) -> DiffsolBuilder: - r""" - Select the error metric used to compare simulated and observed data. - """ - def remove_cost(self) -> DiffsolBuilder: - r""" - Reset the cost metric to the default sum of squared errors. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> DiffsolBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def build(self) -> Problem: - r""" - Create a `Problem` representing the differential solver model. - """ - -@typing.final -class NelderMead: - r""" - Classic simplex-based direct search optimiser. - """ - def __new__(cls) -> NelderMead: - r""" - Create a Nelder-Mead optimiser with default coefficients. - """ - def with_max_iter(self, max_iter: builtins.int) -> NelderMead: - r""" - Limit the number of simplex iterations. - """ - def with_threshold(self, threshold: builtins.float) -> NelderMead: - r""" - Set the stopping threshold on simplex size or objective reduction. - """ - def with_position_tolerance(self, tolerance: builtins.float) -> NelderMead: - r""" - Stop once simplex vertices fall within the supplied positional tolerance. - """ - def with_max_evaluations(self, max_evaluations: builtins.int) -> NelderMead: - r""" - Abort after evaluating the objective `max_evaluations` times. - """ - def with_coefficients( - self, - alpha: builtins.float, - gamma: builtins.float, - rho: builtins.float, - sigma: builtins.float, - ) -> NelderMead: - r""" - Override the reflection, expansion, contraction, and shrink coefficients. - """ - def with_patience(self, patience_seconds: builtins.float) -> NelderMead: - r""" - Abort if the objective fails to improve within the allotted time. - """ - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> OptimisationResults: - r""" - Optimise the given problem starting from the provided initial simplex centre. - """ - -@typing.final -class OptimisationResults: - r""" - Container for optimiser outputs and diagnostic metadata. - """ - @property - def x(self) -> builtins.list[builtins.float]: - r""" - Decision vector corresponding to the best-found objective value. - """ - @property - def fun(self) -> builtins.float: - r""" - Objective value evaluated at `x`. - """ - @property - def nit(self) -> builtins.int: - r""" - Number of iterations performed by the optimiser. - """ - @property - def evaluations(self) -> builtins.int: - r""" - Total number of objective function evaluations. - """ - @property - def time(self) -> datetime.timedelta: - r""" - Total number of objective function evaluations. - """ - @property - def success(self) -> builtins.bool: - r""" - Whether the run satisfied its convergence criteria. - """ - @property - def message(self) -> builtins.str: - r""" - Human-readable status message summarising the termination state. - """ - @property - def termination_reason(self) -> builtins.str: - r""" - Structured termination flag describing why the run ended. - """ - @property - def final_simplex(self) -> builtins.list[builtins.list[builtins.float]]: - r""" - Simplex vertices at termination, when provided by the optimiser. - """ - @property - def final_simplex_values(self) -> builtins.list[builtins.float]: - r""" - Objective values corresponding to `final_simplex`. - """ - @property - def covariance( - self, - ) -> builtins.list[builtins.list[builtins.float]] | None: - r""" - Estimated covariance of the search distribution, if available. - """ - def __repr__(self) -> builtins.str: - r""" - Render a concise summary of the optimisation outcome. - """ - -@typing.final -class Problem: - r""" - Executable optimisation problem wrapping the Chronopt core implementation. - """ - def evaluate(self, x: typing.Sequence[builtins.float]) -> builtins.float: - r""" - Evaluate the configured objective function at `x`. - """ - def evaluate_gradient( - self, x: typing.Sequence[builtins.float] - ) -> builtins.list[builtins.float] | None: - r""" - Evaluate the gradient of the objective function at `x` if available. - """ - def optimise( - self, - initial: typing.Sequence[builtins.float] | None = None, - optimiser: NelderMead | CMAES | Adam | None = None, - ) -> OptimisationResults: - r""" - Solve the problem starting from `initial` using the supplied optimiser. - """ - def get_config(self, key: builtins.str) -> builtins.float | None: - r""" - Return the numeric configuration value stored under `key` if present. - """ - def dimension(self) -> builtins.int: - r""" - Return the number of parameters the problem expects. - """ - def parameters( - self, - ) -> builtins.list[ - tuple[ - builtins.str, - builtins.float, - tuple[builtins.float, builtins.float] | None, - ] - ]: ... - def default_parameters(self) -> builtins.list[builtins.float]: - r""" - Return the default parameter vector implied by the builder. - """ - def config(self) -> builtins.dict[builtins.str, builtins.float]: - r""" - Return a copy of the problem configuration dictionary. - """ - -@typing.final -class ScalarBuilder: - r""" - High-level builder for optimisation `Problem` instances exposed to Python. - """ - def __new__(cls) -> ScalarBuilder: - r""" - Create an empty builder with no objective, parameters, or default optimiser. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> ScalarBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def with_callable(self, obj: typing.Any) -> ScalarBuilder: - r""" - Attach the objective function callable executed during optimisation. - """ - def with_gradient(self, obj: typing.Any) -> ScalarBuilder: - r""" - Attach the gradient callable returning derivatives of the objective. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> ScalarBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def build(self) -> Problem: - r""" - Finalize the builder into an executable `Problem`. - """ - -@typing.final -class VectorBuilder: - r""" - Time-series problem builder for vector-valued objectives. - """ - def __new__(cls) -> VectorBuilder: - r""" - Create an empty vector problem builder. - """ - def with_objective(self, objective: typing.Any) -> VectorBuilder: - r""" - Register a callable that produces predictions matching the data shape. - - The callable should accept a parameter vector and return a numpy array - of the same shape as the observed data. - """ - def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> VectorBuilder: - r""" - Attach observed data used to fit the model. - - The data should be a 1D numpy array. The shape will be inferred - from the data length. - """ - def with_config(self, key: builtins.str, value: builtins.float) -> VectorBuilder: - r""" - Stores an optimisation configuration value keyed by name. - """ - def with_parameter( - self, - name: builtins.str, - initial_value: builtins.float, - bounds: tuple[builtins.float, builtins.float] | None = None, - ) -> VectorBuilder: - r""" - Register a named optimisation variable in the order it appears in vectors. - """ - def clear_parameters(self) -> VectorBuilder: - r""" - Remove previously provided parameter defaults. - """ - def with_cost(self, cost: CostMetric) -> VectorBuilder: - r""" - Select the error metric used to compare predictions and observed data. - """ - def remove_cost(self) -> VectorBuilder: - r""" - Reset the cost metric to the default sum of squared errors. - """ - def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> VectorBuilder: - r""" - Configure the default optimiser used when `Problem.optimise` omits one. - """ - def build(self) -> Problem: - r""" - Create a `Problem` representing the vector optimisation model. - """ - -def GaussianNLL( - variance: builtins.float, weight: builtins.float = 1.0 -) -> CostMetric: ... -def RMSE(weight: builtins.float = 1.0) -> CostMetric: ... -def SSE(weight: builtins.float = 1.0) -> CostMetric: ... -def builder_factory_py() -> ScalarBuilder: - r""" - Return a convenience factory for creating `Builder` instances. - """ diff --git a/python/src/chronopt/plotting/__init__.py b/python/src/chronopt/plotting/__init__.py deleted file mode 100644 index 4d450b1..0000000 --- a/python/src/chronopt/plotting/__init__.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Plotting utilities for Chronopt. - -This module currently provides a convenience helper to visualise two-dimensional -objective functions via contour plots. The implementation only depends on -``numpy`` at import time and lazily imports ``matplotlib`` when required so that -plotting remains an optional dependency of the project. -""" - -from __future__ import annotations - -from collections.abc import Callable, Iterable, Sequence -from typing import TYPE_CHECKING, Any, Tuple, Union - -import numpy as np - -if TYPE_CHECKING: # pragma: no cover - type checking only - from chronopt import Problem - -__all__ = ["contour"] - -ObjectiveLike = Union[Callable[[Sequence[float]], float], Callable[[np.ndarray], float]] -Bounds = tuple[float, float] - - -def _evaluate(objective: ObjectiveLike | Problem, point: np.ndarray) -> float: - if hasattr(objective, "evaluate"): - return float(objective.evaluate(point.tolist())) - return float(objective(point)) - - -def contour( - objective: ObjectiveLike | Problem, - x_bounds: Bounds, - y_bounds: Bounds, - *, - grid_size: int = 100, - levels: int | Iterable[float] = 10, - ax: Any | None = None, - cmap: str = "viridis", - show: bool = True, - **contour_kwargs: Any, -) -> Any: - """Render a contour plot of a two-dimensional objective. - - Parameters - ---------- - objective: - A callable mapping a two-dimensional input to a scalar value, or a - :class:`chronopt.Problem` instance whose ``evaluate`` method will be - invoked. - x_bounds, y_bounds: - Inclusive ranges ``(min, max)`` spanning the region to sample along each - axis. - grid_size: - Number of sample points per axis used to generate the evaluation grid. - levels: - Either the number of contour levels, or an explicit iterable of level - values passed through to :func:`matplotlib.pyplot.contour`. - ax: - Optional existing matplotlib axes to draw on. If omitted, a new figure - and axes are created. - cmap: - Name of the matplotlib colormap to use for the contour lines. - show: - Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. - **contour_kwargs: - Additional keyword arguments forwarded to ``Axes.contour``. - - Returns - ------- - matplotlib.contour.QuadContourSet - The contour set created by matplotlib. - - Raises - ------ - ValueError - If ``grid_size`` is not greater than one or if the provided bounds are - invalid. - ModuleNotFoundError - If ``matplotlib`` is not installed in the current environment. - """ - - if grid_size <= 1: - raise ValueError("grid_size must be greater than 1") - - x_min, x_max = x_bounds - y_min, y_max = y_bounds - if x_min >= x_max or y_min >= y_max: - raise ValueError("Bounds must satisfy min < max along both axes") - - try: - import matplotlib.pyplot as plt - except ModuleNotFoundError as exc: # pragma: no cover - import guard - raise ModuleNotFoundError( - "matplotlib is required for plotting; install it via 'pip install chronopt[plotting]'" - ) from exc - - xs = np.linspace(x_min, x_max, grid_size) - ys = np.linspace(y_min, y_max, grid_size) - grid_x, grid_y = np.meshgrid(xs, ys) - - stacked = np.stack([grid_x, grid_y], axis=-1) - flattened = stacked.reshape(-1, 2) - evaluated = np.array([_evaluate(objective, point) for point in flattened]).reshape( - grid_size, grid_size - ) - - if ax is None: - _, ax = plt.subplots() - - contour_set = ax.contour(grid_x, grid_y, evaluated, levels=levels, cmap=cmap, **contour_kwargs) - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_title("Objective contour") - ax.clabel(contour_set, inline=True, fontsize=8) - - if show: - plt.show() - - return contour_set diff --git a/python/src/chronopt/sampler.pyi b/python/src/chronopt/sampler.pyi deleted file mode 100644 index e5b66d2..0000000 --- a/python/src/chronopt/sampler.pyi +++ /dev/null @@ -1,83 +0,0 @@ -# This file is automatically generated by pyo3_stub_gen -# ruff: noqa: E501, F401 - -import builtins -import datetime -import typing - -from chronopt._chronopt import Problem - -@typing.final -class DynamicNestedSampler: - r""" - Dynamic nested sampler binding exposing DNS configuration knobs. - """ - def __new__(cls) -> DynamicNestedSampler: ... - def with_live_points(self, live_points: builtins.int) -> DynamicNestedSampler: ... - def with_expansion_factor( - self, expansion_factor: builtins.float - ) -> DynamicNestedSampler: ... - def with_termination_tolerance( - self, tolerance: builtins.float - ) -> DynamicNestedSampler: ... - def with_seed(self, seed: builtins.int) -> DynamicNestedSampler: ... - def run( - self, - problem: Problem, - initial: typing.Sequence[builtins.float] | None = None, - ) -> NestedSamples: ... - -@typing.final -class MetropolisHastings: - r""" - Basic Metropolis-Hastings sampler binding mirroring the optimiser API. - """ - def __new__(cls) -> MetropolisHastings: ... - def with_num_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... - def set_number_of_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... - def with_iterations(self, iterations: builtins.int) -> MetropolisHastings: ... - def with_num_steps(self, steps: builtins.int) -> MetropolisHastings: ... - def with_step_size(self, step_size: builtins.float) -> MetropolisHastings: ... - def with_seed(self, seed: builtins.int) -> MetropolisHastings: ... - def run( - self, problem: Problem, initial: typing.Sequence[builtins.float] - ) -> Samples: ... - -@typing.final -class NestedSamples: - r""" - Nested sampling results including evidence estimates. - """ - @property - def posterior( - self, - ) -> builtins.list[ - tuple[builtins.list[builtins.float], builtins.float, builtins.float] - ]: ... - @property - def mean(self) -> builtins.list[builtins.float]: ... - @property - def draws(self) -> builtins.int: ... - @property - def log_evidence(self) -> builtins.float: ... - @property - def information(self) -> builtins.float: ... - @property - def time(self) -> datetime.timedelta: ... - def to_samples(self) -> Samples: ... - def __repr__(self) -> builtins.str: ... - -@typing.final -class Samples: - r""" - Container for sampler draws and diagnostics. - """ - @property - def chains(self) -> builtins.list[builtins.list[builtins.list[builtins.float]]]: ... - @property - def mean_x(self) -> builtins.list[builtins.float]: ... - @property - def draws(self) -> builtins.int: ... - @property - def time(self) -> datetime.timedelta: ... - def __repr__(self) -> builtins.str: ... diff --git a/python/src/chronopt/__init__.py b/python/src/diffid/__init__.py similarity index 87% rename from python/src/chronopt/__init__.py rename to python/src/diffid/__init__.py index ebb5280..8ca3963 100644 --- a/python/src/chronopt/__init__.py +++ b/python/src/diffid/__init__.py @@ -1,22 +1,22 @@ -"""Chronopt public Python API.""" +"""Diffid public Python API.""" from __future__ import annotations # Error hierarchy -from chronopt.errors import ( +from diffid.errors import ( AlreadyTerminated, BuildError, - ChronoptError, + DiffidError, EvaluationError, ResultCountMismatch, TellError, ) # Plotting module -from chronopt import plotting +from diffid import plotting # Core bindings - optimisers -from chronopt._chronopt import ( +from diffid._diffid import ( Adam, AdamState, CMAES, @@ -26,7 +26,7 @@ ) # Core bindings - samplers -from chronopt._chronopt import ( +from diffid._diffid import ( DynamicNestedSampler, DynamicNestedSamplerState, MetropolisHastings, @@ -36,14 +36,14 @@ ) # Core bindings - builders -from chronopt._chronopt import ( +from diffid._diffid import ( DiffsolBuilder, ScalarBuilder, VectorBuilder, ) # Core bindings - results and problems -from chronopt._chronopt import ( +from diffid._diffid import ( CostMetric, Done, Evaluate, @@ -52,9 +52,9 @@ ) # Cost metric factory functions -from chronopt._chronopt import RMSE as _RMSE -from chronopt._chronopt import SSE as _SSE -from chronopt._chronopt import GaussianNLL as _GaussianNLL +from diffid._diffid import RMSE as _RMSE +from diffid._diffid import SSE as _SSE +from diffid._diffid import GaussianNLL as _GaussianNLL def SSE(weight: float = 1.0) -> CostMetric: @@ -116,7 +116,7 @@ def GaussianNLL(variance: float = 1.0, weight: float = 1.0) -> CostMetric: # Modules "plotting", # Errors - "ChronoptError", + "DiffidError", "EvaluationError", "BuildError", "TellError", diff --git a/python/src/diffid/_diffid.pyi b/python/src/diffid/_diffid.pyi new file mode 100644 index 0000000..99d2b73 --- /dev/null +++ b/python/src/diffid/_diffid.pyi @@ -0,0 +1,956 @@ +# This file is automatically generated by pyo3_stub_gen +# ruff: noqa: E501, F401 + +import builtins +import datetime +import typing + +import numpy +import numpy.typing + +from diffid.sampler import DynamicNestedSampler, MetropolisHastings + +@typing.final +class Adam: + r""" + Adaptive Moment Estimation (Adam) gradient-based optimiser. + """ + def __new__(cls) -> Adam: + r""" + Create an Adam optimiser with library defaults. + """ + def with_max_iter(self, max_iter: builtins.int) -> Adam: + r""" + Limit the maximum number of optimisation iterations. + """ + def with_threshold(self, threshold: builtins.float) -> Adam: + r""" + Set the stopping threshold on the gradient norm. + """ + def with_step_size(self, step_size: builtins.float) -> Adam: + r""" + Configure the base learning rate / step size. + """ + def with_betas(self, beta1: builtins.float, beta2: builtins.float) -> Adam: + r""" + Override the exponential decay rates for the first and second moments. + """ + def with_eps(self, eps: builtins.float) -> Adam: + r""" + Override the numerical stability constant added to the denominator. + """ + def with_patience(self, patience: typing.Any) -> Adam: + r""" + Abort the run once the patience window has elapsed. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem using Adam starting from the provided point. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> AdamState: + r""" + Initialize ask-tell optimization state. + + Returns an AdamState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial parameter vector + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + AdamState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.Adam() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate_with_gradient(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class AdamState: + r""" + Ask-tell state for incremental Adam optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.Adam().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... # Adam requires gradient information + ... values = [(f(pt), grad_f(pt)) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + + Examples + -------- + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... print(f"Need to evaluate {len(result.points)} points") + >>> elif isinstance(result, diffid.Done): + ... print(f"Optimisation complete: {result.result}") + """ + def tell( + self, result: tuple[builtins.float, typing.Sequence[builtins.float]] + ) -> None: + r""" + Provide evaluation results (value and gradient) for the requested points. + + Parameters + ---------- + result : tuple[float, list[float]] + Tuple of (value, gradient) where gradient is a list of partial derivatives. + Adam requires gradient information. + + Raises + ------ + TellError + If called after optimization has terminated or if result format is invalid + EvaluationError + If the evaluation failed or contained invalid values + + Examples + -------- + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... point = result.points[0] + ... value = objective(point) + ... gradient = compute_gradient(point) + ... state.tell((value, gradient)) + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value found so far. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def current_position(self) -> builtins.list[builtins.float]: + r""" + Get the current parameter position. + + Returns + ------- + list[float] + Current parameter vector + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class CMAES: + r""" + Covariance Matrix Adaptation Evolution Strategy optimiser. + """ + def __new__(cls) -> CMAES: + r""" + Create a CMA-ES optimiser with library defaults. + """ + def with_max_iter(self, max_iter: builtins.int) -> CMAES: + r""" + Limit the number of iterations/generations before termination. + """ + def with_threshold(self, threshold: builtins.float) -> CMAES: + r""" + Set the stopping threshold on the best objective value. + """ + def with_step_size(self, step_size: builtins.float) -> CMAES: + r""" + Set the initial global step-size (standard deviation). + """ + def with_patience(self, patience: typing.Any) -> CMAES: + r""" + Abort the run if no improvement occurs for the given wall-clock duration. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def with_population_size(self, population_size: builtins.int) -> CMAES: + r""" + Specify the number of offspring evaluated per generation. + """ + def with_seed(self, seed: builtins.int) -> CMAES: + r""" + Initialise the internal RNG for reproducible runs. + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem starting from the provided mean vector. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> CMAESState: + r""" + Initialize ask-tell optimization state. + + Returns a CMAESState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial mean vector for the search distribution + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + CMAESState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.CMAES() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class CMAESState: + r""" + Ask-tell state for incremental CMA-ES optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get a population of points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.CMAES().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... values = [f(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + + Notes + ----- + CMA-ES evaluates a population of points each iteration. The number + of points returned depends on the population_size setting. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the requested population of points. + + Parameters + ---------- + results : list[float] + List of objective function values corresponding to the points + from the last ask() call. Must match the number of points. + + Raises + ------ + TellError + If called after optimization has terminated or if wrong number + of results provided + EvaluationError + If evaluations failed or contained invalid values + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration (generation) count. + + Returns + ------- + int + Number of generations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value found so far. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def mean(self) -> builtins.list[builtins.float]: + r""" + Get the current mean of the search distribution. + + Returns + ------- + list[float] + Current mean vector + """ + def sigma(self) -> builtins.float: + r""" + Get the current step size (sigma). + + Returns + ------- + float + Current global step size + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class CostMetric: + @property + def name(self) -> builtins.str: + r""" + Name of the cost metric. + """ + def __repr__(self) -> builtins.str: ... + +@typing.final +class DiffsolBuilder: + r""" + Differential equation solver builder. + """ + def __new__(cls) -> DiffsolBuilder: + r""" + Create an empty differential solver builder. + """ + def __copy__(self) -> DiffsolBuilder: ... + def __deepcopy__(self, _memo: dict) -> DiffsolBuilder: ... + def with_diffsl(self, dsl: builtins.str) -> DiffsolBuilder: + r""" + Register the DiffSL program describing the system dynamics. + """ + def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> DiffsolBuilder: + r""" + Attach observed data used to fit the differential equation. + + The first column must contain the time samples (t_span) and the remaining + columns the observed trajectories. + """ + def remove_data(self) -> DiffsolBuilder: + r""" + Remove any previously attached data along with its time span. + """ + def with_backend(self, backend: builtins.str) -> DiffsolBuilder: + r""" + Choose whether to use dense or sparse diffusion solvers. + """ + def with_parallel(self, parallel: builtins.bool | None = None) -> DiffsolBuilder: + r""" + Opt into parallel proposal generation when supported by the backend. + """ + def with_config( + self, config: typing.Mapping[builtins.str, builtins.float] + ) -> DiffsolBuilder: ... + def with_tolerances( + self, rtol: builtins.float, atol: builtins.float + ) -> DiffsolBuilder: + r""" + Adjust the relative and absolute integration tolerances. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> DiffsolBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def clear_parameters(self) -> DiffsolBuilder: + r""" + Clear all previously registered parameters while preserving other configuration. + """ + def with_cost(self, cost: CostMetric) -> DiffsolBuilder: + r""" + Select the error metric used to compare simulated and observed data. + """ + def remove_cost(self) -> DiffsolBuilder: + r""" + Reset the cost metric to the default sum of squared errors. + """ + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> DiffsolBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def build(self) -> Problem: + r""" + Create a `Problem` representing the differential solver model. + """ + +@typing.final +class Done: + r""" + Optimisation/sampling is complete with final results. + + This is returned by `ask()` when the algorithm has terminated. + Access the results via the `result` attribute. + + Examples + -------- + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Optimisation complete: {result.result}") + ... break + """ + @property + def result(self) -> typing.Any: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class Evaluate: + r""" + Request to evaluate objective function at specific points. + + This is returned by `ask()` when the optimiser/sampler needs function + evaluations. Call `tell()` with the results after evaluation. + + Examples + -------- + >>> state = optimiser.init(problem, initial=[1.0, 2.0]) + >>> result = state.ask() + >>> if isinstance(result, diffid.Evaluate): + ... values = [problem.evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + @property + def points(self) -> builtins.list[builtins.list[builtins.float]]: ... + def __new__( + cls, points: typing.Sequence[typing.Sequence[builtins.float]] + ) -> Evaluate: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NelderMead: + r""" + Classic simplex-based direct search optimiser. + """ + def __new__(cls) -> NelderMead: + r""" + Create a Nelder-Mead optimiser with default coefficients. + """ + def with_step_size(self, step_size: builtins.float) -> NelderMead: + r""" + Set the initial global step-size (standard deviation). + """ + def with_max_iter(self, max_iter: builtins.int) -> NelderMead: + r""" + Limit the number of simplex iterations. + """ + def with_threshold(self, threshold: builtins.float) -> NelderMead: + r""" + Set the stopping threshold on simplex size or objective reduction. + """ + def with_position_tolerance(self, tolerance: builtins.float) -> NelderMead: + r""" + Stop once simplex vertices fall within the supplied positional tolerance. + """ + def with_max_evaluations(self, max_evaluations: builtins.int) -> NelderMead: + r""" + Abort after evaluating the objective `max_evaluations` times. + """ + def with_coefficients( + self, + alpha: builtins.float, + gamma: builtins.float, + rho: builtins.float, + sigma: builtins.float, + ) -> NelderMead: + r""" + Override the reflection, expansion, contraction, and shrink coefficients. + """ + def with_patience(self, patience: typing.Any) -> NelderMead: + r""" + Abort if the objective fails to improve within the allotted time. + + Parameters + ---------- + patience : float or timedelta + Either seconds (float) or a timedelta object + """ + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> OptimisationResults: + r""" + Optimise the given problem starting from the provided initial simplex centre. + """ + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> NelderMeadState: + r""" + Initialize ask-tell optimization state. + + Returns a NelderMeadState object that can be used for incremental optimization + via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial parameter vector (simplex center) + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + NelderMeadState + State object for ask-tell optimization + + Examples + -------- + >>> optimiser = diffid.NelderMead() + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class NelderMeadState: + r""" + Ask-tell state for incremental Nelder-Mead optimization. + + This state object allows step-by-step control over the optimization process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> optimiser = diffid.NelderMead().with_max_iter(100) + >>> state = optimiser.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Final result: {result.result}") + ... break + ... values = [f(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or optimization complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion. + """ + def tell(self, result: builtins.float) -> None: + r""" + Provide evaluation result (scalar value) for the requested point. + + Parameters + ---------- + result : float + Scalar objective function value + + Raises + ------ + TellError + If called after optimization has terminated + EvaluationError + If the evaluation failed or contained invalid values + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def evaluations(self) -> builtins.int: + r""" + Get the total number of function evaluations. + + Returns + ------- + int + Number of function evaluations performed + """ + def best( + self, + ) -> tuple[builtins.list[builtins.float], builtins.float] | None: + r""" + Get the current best point and value from the simplex. + + Returns + ------- + tuple[list[float], float] | None + (best_point, best_value) or None if no valid evaluations yet + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NestedSamplesIterator: + r""" + Iterator for NestedSamples posterior + """ + def __iter__(self) -> NestedSamplesIterator: ... + def __next__( + self, + ) -> ( + tuple[builtins.list[builtins.float], builtins.float, builtins.float] | None + ): ... + +@typing.final +class OptimisationResults: + r""" + Container for optimiser outputs and diagnostic metadata. + """ + @property + def x(self) -> numpy.typing.NDArray[numpy.float64]: + r""" + Decision vector corresponding to the best-found objective value. + + Returns + ------- + numpy.ndarray + Best parameter vector as a NumPy array + """ + @property + def value(self) -> builtins.float: + r""" + Objective value evaluated at `x`. + """ + @property + def iterations(self) -> builtins.int: + r""" + Number of iterations performed by the optimiser. + """ + @property + def evaluations(self) -> builtins.int: + r""" + Total number of objective function evaluations. + """ + @property + def time(self) -> datetime.timedelta: + r""" + Total number of objective function evaluations. + """ + @property + def success(self) -> builtins.bool: + r""" + Whether the run satisfied its convergence criteria. + """ + @property + def message(self) -> builtins.str: + r""" + Human-readable status message summarising the termination state. + """ + @property + def termination_reason(self) -> builtins.str: + r""" + Structured termination flag describing why the run ended. + """ + @property + def final_simplex(self) -> builtins.list[builtins.list[builtins.float]]: + r""" + Simplex vertices at termination, when provided by the optimiser. + """ + @property + def final_simplex_values(self) -> builtins.list[builtins.float]: + r""" + Objective values corresponding to `final_simplex`. + """ + @property + def covariance( + self, + ) -> builtins.list[builtins.list[builtins.float]] | None: + r""" + Estimated covariance of the search distribution, if available. + """ + def __repr__(self) -> builtins.str: + r""" + Render a concise summary of the optimisation outcome. + """ + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the result. + """ + def __bool__(self) -> builtins.bool: + r""" + Return truthiness based on optimization success. + + Allows using `if result:` instead of `if result.success:`. + """ + +@typing.final +class Problem: + r""" + Executable optimisation problem wrapping the Diffid core implementation. + """ + def evaluate(self, x: typing.Sequence[builtins.float]) -> builtins.float: + r""" + Evaluate the configured objective function at `x`. + """ + def evaluate_gradient( + self, x: typing.Sequence[builtins.float] + ) -> builtins.list[builtins.float] | None: + r""" + Evaluate the gradient of the objective function at `x` if available. + """ + def optimise( + self, + initial: typing.Sequence[builtins.float] | None = None, + optimiser: NelderMead | CMAES | Adam | None = None, + ) -> OptimisationResults: + r""" + Solve the problem starting from `initial` using the supplied optimiser. + """ + def sample( + self, + initial: typing.Sequence[builtins.float] | None = None, + sampler: MetropolisHastings | DynamicNestedSampler | None = None, + ) -> typing.Any: + r""" + Sample from the problem starting from `initial` using the supplied sampler. + """ + def get_config(self, _key: builtins.str) -> builtins.float | None: + r""" + Return the numeric configuration value stored under `key` if present. + """ + def dimension(self) -> builtins.int: + r""" + Return the number of parameters the problem expects. + """ + def bounds(self) -> builtins.list[tuple[builtins.float, builtins.float]]: + r""" + Return the parameter bounds for the problem as a list of (lower, upper) tuples. + """ + def parameters( + self, + ) -> builtins.list[ + tuple[ + builtins.str, + builtins.float, + tuple[builtins.float, builtins.float] | None, + ] + ]: ... + def initial_values(self) -> builtins.list[builtins.float]: ... + def default_parameters(self) -> builtins.list[builtins.float]: + r""" + Return the default parameter vector implied by the builder. + """ + def config(self) -> builtins.dict[builtins.str, builtins.float]: + r""" + Return a copy of the problem configuration dictionary. + """ + def __call__(self, x: typing.Sequence[builtins.float]) -> builtins.float: + r""" + Call the problem as a function (shorthand for evaluate). + + Allows using `problem(x)` instead of `problem.evaluate(x)`. + """ + def __repr__(self) -> builtins.str: + r""" + Return a detailed string representation of the problem. + """ + def __str__(self) -> builtins.str: + r""" + Return a concise string representation of the problem. + """ + +@typing.final +class SamplesIterator: + r""" + Iterator for Samples chains + """ + def __iter__(self) -> SamplesIterator: ... + def __next__( + self, + ) -> builtins.list[builtins.list[builtins.float]] | None: ... + +@typing.final +class ScalarBuilder: + r""" + High-level builder for optimisation `Problem` instances exposed to Python. + """ + def __new__(cls) -> ScalarBuilder: + r""" + Create an empty builder with no objective, parameters, or default optimiser. + """ + def __copy__(self) -> ScalarBuilder: ... + def __deepcopy__(self, _memo: dict) -> ScalarBuilder: ... + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> ScalarBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def with_objective(self, obj: typing.Any) -> ScalarBuilder: + r""" + Attach the objective function callable executed during optimisation. + """ + def with_gradient(self, obj: typing.Any) -> ScalarBuilder: + r""" + Attach the gradient callable returning derivatives of the objective. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> ScalarBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def build(self) -> Problem: + r""" + Finalize the builder into an executable `Problem`. + """ + +@typing.final +class VectorBuilder: + r""" + Time-series problem builder for vector-valued objectives. + """ + def __new__(cls) -> VectorBuilder: + r""" + Create an empty vector problem builder. + """ + def __copy__(self) -> VectorBuilder: ... + def __deepcopy__(self, _memo: dict) -> VectorBuilder: ... + def with_objective(self, objective: typing.Any) -> VectorBuilder: + r""" + Register a callable that produces predictions matching the data shape. + + The callable should accept a parameter vector and return a numpy array + of the same shape as the observed data. + """ + def with_data(self, data: numpy.typing.NDArray[numpy.float64]) -> VectorBuilder: + r""" + Attach observed data used to fit the model. + + The data should be a 1D numpy array. The shape will be inferred + from the data length. + """ + def with_parameter( + self, + name: builtins.str, + initial_value: builtins.float, + bounds: tuple[builtins.float, builtins.float] | None = None, + ) -> VectorBuilder: + r""" + Register a named optimisation variable in the order it appears in vectors. + """ + def with_cost(self, cost: CostMetric) -> VectorBuilder: + r""" + Select the error metric used to compare predictions and observed data. + """ + def remove_cost(self) -> VectorBuilder: + r""" + Reset the cost metric to the default sum of squared errors. + """ + def with_optimiser(self, optimiser: NelderMead | CMAES | Adam) -> VectorBuilder: + r""" + Configure the default optimiser used when `Problem.optimise` omits one. + """ + def with_config(self, key: builtins.str, value: builtins.float) -> VectorBuilder: + r""" + Attach an arbitrary configuration value to the problem. + """ + def build(self) -> Problem: + r""" + Create a `Problem` representing the vector optimisation model. + """ + +def GaussianNLL( + variance: builtins.float, weight: builtins.float = 1.0 +) -> CostMetric: ... +def RMSE(weight: builtins.float = 1.0) -> CostMetric: ... +def SSE(weight: builtins.float = 1.0) -> CostMetric: ... +def builder_factory_py() -> ScalarBuilder: + r""" + Return a convenience factory for creating `Builder` instances. + """ diff --git a/python/src/chronopt/errors.py b/python/src/diffid/errors.py similarity index 90% rename from python/src/chronopt/errors.py rename to python/src/diffid/errors.py index 6946247..e22c963 100644 --- a/python/src/chronopt/errors.py +++ b/python/src/diffid/errors.py @@ -1,30 +1,30 @@ -"""Custom exception hierarchy for Chronopt. +"""Custom exception hierarchy for Diffid. -This module defines the exception hierarchy used throughout the chronopt library, +This module defines the exception hierarchy used throughout the diffid library, providing clear and actionable error messages for different failure modes. """ from __future__ import annotations -class ChronoptError(Exception): - """Base exception class for all Chronopt errors. +class DiffidError(Exception): + """Base exception class for all Diffid errors. - All custom exceptions in the chronopt library inherit from this class, - making it easy to catch any chronopt-specific error. + All custom exceptions in the diffid library inherit from this class, + making it easy to catch any diffid-specific error. Examples -------- >>> try: ... optimiser.run(problem, initial=[1.0, 2.0]) - ... except ChronoptError as e: - ... print(f"Chronopt error occurred: {e}") + ... except DiffidError as e: + ... print(f"Diffid error occurred: {e}") """ pass -class EvaluationError(ChronoptError): +class EvaluationError(DiffidError): """Raised when objective function evaluation fails. This exception is raised when the objective function (or callback) throws @@ -65,7 +65,7 @@ def __init__( self.original_error = original_error -class BuildError(ChronoptError): +class BuildError(DiffidError): """Raised when problem or optimiser construction fails. This exception is raised during the build phase when invalid parameters @@ -84,7 +84,7 @@ class BuildError(ChronoptError): pass -class TellError(ChronoptError): +class TellError(DiffidError): """Base exception for errors during the 'tell' phase of ask-tell interface. This exception is raised when providing results back to an optimiser or @@ -161,7 +161,7 @@ def __init__(self): __all__ = [ - "ChronoptError", + "DiffidError", "EvaluationError", "BuildError", "TellError", diff --git a/python/src/diffid/plotting/__init__.py b/python/src/diffid/plotting/__init__.py new file mode 100644 index 0000000..8335511 --- /dev/null +++ b/python/src/diffid/plotting/__init__.py @@ -0,0 +1,627 @@ +"""Plotting utilities for Diffid. + +This module provides convenience helpers for visualising optimisation and sampling +results. The implementation only depends on ``numpy`` at import time and lazily +imports ``matplotlib`` when required so that plotting remains an optional +dependency of the project. +""" + +from __future__ import annotations + +from collections.abc import Callable, Iterable, Sequence +from typing import TYPE_CHECKING, Any, Tuple, Union + +import numpy as np + +if TYPE_CHECKING: # pragma: no cover - type checking only + from diffid import Problem + +__all__ = [ + "contour", + "contour_2d", + "ode_fit", + "convergence", + "parameter_traces", + "parameter_distributions", + "compare_models", +] + +ObjectiveLike = Union[Callable[[Sequence[float]], float], Callable[[np.ndarray], float]] +Bounds = tuple[float, float] + + +def _setup_plotting() -> None: + """Configure matplotlib for nice-looking plots.""" + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError: + return + + plt.rcParams.setdefault("figure.figsize", (10, 6)) + plt.rcParams.setdefault("font.size", 11) + plt.rcParams.setdefault("axes.labelsize", 12) + plt.rcParams.setdefault("axes.titlesize", 14) + plt.rcParams.setdefault("legend.fontsize", 10) + plt.rcParams.setdefault("xtick.labelsize", 10) + plt.rcParams.setdefault("ytick.labelsize", 10) + + +def _evaluate(objective: ObjectiveLike | Problem, point: np.ndarray) -> float: + if hasattr(objective, "evaluate"): + return float(objective.evaluate(point.tolist())) + return float(objective(point)) + + +def contour( + objective: ObjectiveLike | Problem, + x_bounds: Bounds, + y_bounds: Bounds, + *, + grid_size: int = 100, + levels: int | Iterable[float] = 10, + ax: Any | None = None, + cmap: str = "viridis", + show: bool = True, + **contour_kwargs: Any, +) -> Any: + """Render a contour plot of a two-dimensional objective. + + Parameters + ---------- + objective: + A callable mapping a two-dimensional input to a scalar value, or a + :class:`diffid.Problem` instance whose ``evaluate`` method will be + invoked. + x_bounds, y_bounds: + Inclusive ranges ``(min, max)`` spanning the region to sample along each + axis. + grid_size: + Number of sample points per axis used to generate the evaluation grid. + levels: + Either the number of contour levels, or an explicit iterable of level + values passed through to :func:`matplotlib.pyplot.contour`. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + cmap: + Name of the matplotlib colormap to use for the contour lines. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + **contour_kwargs: + Additional keyword arguments forwarded to ``Axes.contour``. + + Returns + ------- + matplotlib.contour.QuadContourSet + The contour set created by matplotlib. + + Raises + ------ + ValueError + If ``grid_size`` is not greater than one or if the provided bounds are + invalid. + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + + if grid_size <= 1: + raise ValueError("grid_size must be greater than 1") + + x_min, x_max = x_bounds + y_min, y_max = y_bounds + if x_min >= x_max or y_min >= y_max: + raise ValueError("Bounds must satisfy min < max along both axes") + + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: # pragma: no cover - import guard + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + xs = np.linspace(x_min, x_max, grid_size) + ys = np.linspace(y_min, y_max, grid_size) + grid_x, grid_y = np.meshgrid(xs, ys) + + stacked = np.stack([grid_x, grid_y], axis=-1) + flattened = stacked.reshape(-1, 2) + evaluated = np.array([_evaluate(objective, point) for point in flattened]).reshape( + grid_size, grid_size + ) + + if ax is None: + _, ax = plt.subplots() + + contour_set = ax.contour(grid_x, grid_y, evaluated, levels=levels, cmap=cmap, **contour_kwargs) + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title("Objective contour") + ax.clabel(contour_set, inline=True, fontsize=8) + + if show: + plt.show() + + return contour_set + + +def contour_2d( + func: Callable, + xlim: tuple[float, float] = (-2, 2), + ylim: tuple[float, float] = (-1, 3), + *, + levels: np.ndarray | None = None, + optimum: tuple[float, float] | None = None, + found: tuple[float, float] | None = None, + title: str = "Optimisation Landscape", + show: bool = True, +) -> tuple[Any, Any]: + """Plot 2D function contours with optional optimum markers. + + This is a convenience wrapper for notebook usage that provides a simplified + interface for plotting objective functions with marked optima. + + Parameters + ---------- + func: + Function that takes [x, y] and returns scalar value. + xlim: + x-axis limits (min, max). + ylim: + y-axis limits (min, max). + levels: + Contour levels to plot (optional). + optimum: + True optimum location (x, y) to mark (optional). + found: + Found optimum location (x, y) to mark (optional). + title: + Plot title. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + x = np.linspace(xlim[0], xlim[1], 200) + y = np.linspace(ylim[0], ylim[1], 200) + X, Y = np.meshgrid(x, y) + Z = np.zeros_like(X) + + for i in range(X.shape[0]): + for j in range(X.shape[1]): + result = func([X[i, j], Y[i, j]]) + # Handle both scalar and array-like returns + Z[i, j] = result[0] if hasattr(result, "__getitem__") else result + + fig, ax = plt.subplots(figsize=(10, 8)) + + if levels is None: + levels = np.logspace(-1, 3.5, 20) + + cs = ax.contour(X, Y, Z, levels=levels, cmap="viridis") + ax.clabel(cs, inline=True, fontsize=8) + + if optimum: + ax.plot( + optimum[0], + optimum[1], + "r*", + markersize=20, + label="Global minimum", + zorder=5, + ) + + if found: + ax.plot( + found[0], found[1], "go", markersize=10, label="Found optimum", zorder=5 + ) + + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title(title) + ax.grid(True, alpha=0.3) + ax.legend() + + if show: + plt.show() + + return fig, ax + + +def ode_fit( + t_data: np.ndarray, + y_data: np.ndarray, + t_pred: np.ndarray | None = None, + y_pred: np.ndarray | None = None, + *, + title: str = "ODE Fit", + xlabel: str = "Time", + ylabel: str = "State Variable", + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot ODE data and fitted model. + + Parameters + ---------- + t_data: + Time points for observed data. + y_data: + Observed data values. + t_pred: + Time points for predictions (optional). + y_pred: + Predicted values (optional). + title: + Plot title. + xlabel: + x-axis label. + ylabel: + y-axis label. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot(t_data, y_data, "o", label="Observed data", alpha=0.6, markersize=8) + + if t_pred is not None and y_pred is not None: + ax.plot(t_pred, y_pred, "-", label="Fitted model", linewidth=2) + + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + if show: + plt.show() + + return fig, ax + + +def convergence( + values: np.ndarray, + *, + title: str = "Optimisation Convergence", + log_scale: bool = True, + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot optimisation convergence history. + + Parameters + ---------- + values: + Objective function values over iterations. + title: + Plot title. + log_scale: + Use log scale for y-axis. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot(values, linewidth=2) + ax.set_xlabel("Iteration") + ax.set_ylabel("Objective Value") + ax.set_title(title) + ax.grid(True, alpha=0.3) + + if log_scale: + ax.set_yscale("log") + + if show: + plt.show() + + return fig, ax + + +def parameter_traces( + samples: np.ndarray, + param_names: Sequence[str], + true_values: Sequence[float] | None = None, + *, + show: bool = True, +) -> tuple[Any, Any]: + """Plot MCMC parameter traces. + + Parameters + ---------- + samples: + MCMC samples with shape (n_samples, n_params). + param_names: + Parameter names. + true_values: + True parameter values to mark (optional). + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, list[Axes]] + The matplotlib figure and list of axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + ValueError + If the number of parameter names doesn't match sample dimensions. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + n_params = samples.shape[1] + if len(param_names) != n_params: + raise ValueError( + f"Number of parameter names ({len(param_names)}) must match " + f"sample dimensions ({n_params})" + ) + + fig, axes = plt.subplots(n_params, 1, figsize=(10, 3 * n_params)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.plot(samples[:, i], alpha=0.7, linewidth=0.5) + ax.set_ylabel(name) + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axhline( + true_values[i], + color="r", + linestyle="--", + label=f"True value: {true_values[i]:.3f}", + ) + ax.legend() + + if i == n_params - 1: + ax.set_xlabel("Sample") + + fig.suptitle("MCMC Parameter Traces") + plt.tight_layout() + + if show: + plt.show() + + return fig, axes + + +def parameter_distributions( + samples: np.ndarray, + param_names: Sequence[str], + true_values: Sequence[float] | None = None, + *, + bins: int = 30, + show: bool = True, +) -> tuple[Any, Any]: + """Plot posterior parameter distributions. + + Parameters + ---------- + samples: + MCMC samples with shape (n_samples, n_params). + param_names: + Parameter names. + true_values: + True parameter values to mark (optional). + bins: + Number of histogram bins. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, list[Axes]] + The matplotlib figure and list of axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + ValueError + If the number of parameter names doesn't match sample dimensions. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + n_params = samples.shape[1] + if len(param_names) != n_params: + raise ValueError( + f"Number of parameter names ({len(param_names)}) must match " + f"sample dimensions ({n_params})" + ) + + fig, axes = plt.subplots(1, n_params, figsize=(4 * n_params, 4)) + + if n_params == 1: + axes = [axes] + + for i, (ax, name) in enumerate(zip(axes, param_names)): + ax.hist(samples[:, i], bins=bins, density=True, alpha=0.7, edgecolor="black") + ax.set_xlabel(name) + ax.set_ylabel("Density") + ax.grid(True, alpha=0.3) + + if true_values and i < len(true_values): + ax.axvline( + true_values[i], + color="r", + linestyle="--", + linewidth=2, + label=f"True: {true_values[i]:.3f}", + ) + + # Add mean and std + mean = np.mean(samples[:, i]) + std = np.std(samples[:, i]) + ax.axvline( + mean, + color="blue", + linestyle=":", + linewidth=2, + alpha=0.7, + label=f"Mean: {mean:.3f}", + ) + ax.set_title(f"{name}\n(σ = {std:.3f})") + ax.legend() + + fig.suptitle("Parameter Posterior Distributions") + plt.tight_layout() + + if show: + plt.show() + + return fig, axes + + +def compare_models( + t_data: np.ndarray, + y_data: np.ndarray, + predictions: dict[str, tuple[np.ndarray, np.ndarray]], + *, + title: str = "Model Comparison", + ax: Any | None = None, + show: bool = True, +) -> tuple[Any, Any]: + """Plot multiple model predictions against data. + + Parameters + ---------- + t_data: + Time points for observed data. + y_data: + Observed data. + predictions: + Dictionary mapping model names to (t_pred, y_pred) tuples. + title: + Plot title. + ax: + Optional existing matplotlib axes to draw on. If omitted, a new figure + and axes are created. + show: + Whether to call :func:`matplotlib.pyplot.show` after drawing the plot. + + Returns + ------- + tuple[Figure, Axes] + The matplotlib figure and axes objects. + + Raises + ------ + ModuleNotFoundError + If ``matplotlib`` is not installed in the current environment. + """ + try: + import matplotlib.pyplot as plt + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "matplotlib is required for plotting; install it via 'pip install diffid[plotting]'" + ) from exc + + _setup_plotting() + + if ax is None: + fig, ax = plt.subplots() + else: + fig = ax.get_figure() + + ax.plot( + t_data, y_data, "ko", label="Observed data", alpha=0.6, markersize=8, zorder=5 + ) + + for name, (t_pred, y_pred) in predictions.items(): + ax.plot(t_pred, y_pred, "-", label=name, linewidth=2, alpha=0.8) + + ax.set_xlabel("Time") + ax.set_ylabel("State Variable") + ax.set_title(title) + ax.legend() + ax.grid(True, alpha=0.3) + + if show: + plt.show() + + return fig, ax diff --git a/python/src/chronopt/plotting/__init__.pyi b/python/src/diffid/plotting/__init__.pyi similarity index 100% rename from python/src/chronopt/plotting/__init__.pyi rename to python/src/diffid/plotting/__init__.pyi diff --git a/python/src/chronopt/py.typed b/python/src/diffid/py.typed similarity index 100% rename from python/src/chronopt/py.typed rename to python/src/diffid/py.typed diff --git a/python/src/diffid/sampler.pyi b/python/src/diffid/sampler.pyi new file mode 100644 index 0000000..6054e40 --- /dev/null +++ b/python/src/diffid/sampler.pyi @@ -0,0 +1,353 @@ +# This file is automatically generated by pyo3_stub_gen +# ruff: noqa: E501, F401 + +import builtins +import datetime +import typing + +import numpy +import numpy.typing + +from diffid._diffid import NestedSamplesIterator, Problem, SamplesIterator + +@typing.final +class DynamicNestedSampler: + r""" + Dynamic nested sampler binding exposing DNS configuration knobs. + """ + def __new__(cls) -> DynamicNestedSampler: ... + def with_live_points(self, live_points: builtins.int) -> DynamicNestedSampler: ... + def with_expansion_factor( + self, expansion_factor: builtins.float + ) -> DynamicNestedSampler: ... + def with_termination_tolerance( + self, tolerance: builtins.float + ) -> DynamicNestedSampler: ... + def with_seed(self, seed: builtins.int) -> DynamicNestedSampler: ... + def run( + self, + problem: Problem, + initial: typing.Sequence[builtins.float] | None = None, + ) -> NestedSamples: ... + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> DynamicNestedSamplerState: + r""" + Initialize ask-tell sampling state. + + Returns a DynamicNestedSamplerState object that can be used for incremental + sampling via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial point for the sampler + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + DynamicNestedSamplerState + State object for ask-tell sampling + + Examples + -------- + >>> sampler = diffid.DynamicNestedSampler() + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class DynamicNestedSamplerState: + r""" + Ask-tell state for incremental Dynamic Nested Sampling. + + This state object allows step-by-step control over the sampling process. + Use `ask()` to get points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> sampler = diffid.DynamicNestedSampler() + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Sampling complete: {result.result}") + ... break + ... values = [negative_log_likelihood(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate points or sampling complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion with NestedSamples. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the requested points. + + Parameters + ---------- + results : list[float] + Negative log-likelihood values for each requested point. + + Raises + ------ + TellError + If called after sampling has terminated or if wrong number + of results provided + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def num_live_points(self) -> builtins.int: + r""" + Get the number of live points. + + Returns + ------- + int + Current number of live points in the sampler + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class MetropolisHastings: + r""" + Basic Metropolis-Hastings sampler binding mirroring the optimiser API. + """ + def __new__(cls) -> MetropolisHastings: ... + def with_num_chains(self, num_chains: builtins.int) -> MetropolisHastings: ... + def with_iterations(self, iterations: builtins.int) -> MetropolisHastings: ... + def with_step_size(self, step_size: builtins.float) -> MetropolisHastings: ... + def with_seed(self, seed: builtins.int) -> MetropolisHastings: ... + def run( + self, problem: Problem, initial: typing.Sequence[builtins.float] + ) -> Samples: ... + def init( + self, + initial: typing.Sequence[builtins.float], + bounds: typing.Sequence[tuple[builtins.float, builtins.float]] | None = None, + ) -> MetropolisHastingsState: + r""" + Initialize ask-tell sampling state. + + Returns a MetropolisHastingsState object that can be used for incremental + sampling via the ask-tell interface. + + Parameters + ---------- + initial : list[float] + Initial point for all chains + bounds : list[tuple[float, float]], optional + Parameter bounds as [(lower, upper), ...]. If None, unbounded. + + Returns + ------- + MetropolisHastingsState + State object for ask-tell sampling + + Examples + -------- + >>> sampler = diffid.MetropolisHastings().with_num_chains(4) + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... break + ... values = [evaluate(pt) for pt in result.points] + ... state.tell(values) + """ + +@typing.final +class MetropolisHastingsState: + r""" + Ask-tell state for incremental Metropolis-Hastings MCMC sampling. + + This state object allows step-by-step control over the sampling process. + Use `ask()` to get proposal points to evaluate, and `tell()` to provide results. + + Examples + -------- + >>> sampler = diffid.MetropolisHastings().with_num_chains(4) + >>> state = sampler.init(initial=[1.0, 2.0]) + >>> while True: + ... result = state.ask() + ... if isinstance(result, diffid.Done): + ... print(f"Sampling complete: {result.result}") + ... break + ... values = [negative_log_likelihood(pt) for pt in result.points] + ... state.tell(values) + """ + def ask(self) -> typing.Any: + r""" + Get the next action: evaluate proposal points or sampling complete. + + Returns + ------- + Evaluate | Done + Either Evaluate(points) requiring function evaluations, + or Done(result) indicating completion with Samples. + + Notes + ----- + Returns one proposal point per chain. + """ + def tell(self, results: typing.Sequence[builtins.float]) -> None: + r""" + Provide evaluation results for the proposed points. + + Parameters + ---------- + results : list[float] + Negative log-likelihood values for each proposal point. + Must match the number of chains. + + Raises + ------ + TellError + If called after sampling has terminated or if wrong number + of results provided + """ + def iterations(self) -> builtins.int: + r""" + Get the current iteration count. + + Returns + ------- + int + Number of iterations completed + """ + def num_chains(self) -> builtins.int: + r""" + Get the number of chains being run. + + Returns + ------- + int + Number of parallel MCMC chains + """ + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: ... + +@typing.final +class NestedSamples: + r""" + Nested sampling results including evidence estimates. + """ + @property + def posterior( + self, + ) -> builtins.list[ + tuple[builtins.list[builtins.float], builtins.float, builtins.float] + ]: ... + @property + def mean(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def draws(self) -> builtins.int: ... + @property + def log_evidence(self) -> builtins.float: ... + @property + def information(self) -> builtins.float: ... + @property + def time(self) -> datetime.timedelta: ... + def to_samples(self) -> Samples: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the nested samples. + """ + def __len__(self) -> builtins.int: + r""" + Return the number of posterior samples. + """ + def __iter__(self) -> NestedSamplesIterator: + r""" + Iterate over posterior samples. + + Yields tuples of (position, log_likelihood, log_weight). + """ + def __getitem__( + self, idx: builtins.int + ) -> tuple[builtins.list[builtins.float], builtins.float, builtins.float]: + r""" + Get a specific posterior sample by index. + + Parameters + ---------- + idx : int + Sample index (0 to num_samples - 1) + + Returns + ------- + tuple[list[float], float, float] + Tuple of (position, log_likelihood, log_weight) + """ + +@typing.final +class Samples: + r""" + Container for sampler draws and diagnostics. + """ + @property + def chains(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def samples(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def mean_x(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def acceptance_rate(self) -> numpy.typing.NDArray[numpy.float64]: ... + @property + def draws(self) -> builtins.int: ... + @property + def time(self) -> datetime.timedelta: ... + def __repr__(self) -> builtins.str: ... + def __str__(self) -> builtins.str: + r""" + Return a human-readable summary of the samples. + """ + def __len__(self) -> builtins.int: + r""" + Return the number of chains. + """ + def __iter__(self) -> SamplesIterator: + r""" + Iterate over chains. + + Yields each chain as a list of samples. + """ + def __getitem__( + self, idx: builtins.int + ) -> builtins.list[builtins.list[builtins.float]]: + r""" + Get a specific chain by index. + + Parameters + ---------- + idx : int + Chain index (0 to num_chains - 1) + + Returns + ------- + list[list[float]] + The requested chain + """ diff --git a/python/src/errors.rs b/python/src/errors.rs index 157da4a..a56ad97 100644 --- a/python/src/errors.rs +++ b/python/src/errors.rs @@ -2,11 +2,11 @@ use pyo3::exceptions::PyValueError; use pyo3::prelude::*; use pyo3::types::PyModule; -use chronopt_core::errors::{EvaluationError as CoreEvaluationError, TellError as CoreTellError}; +use diffid_core::errors::{EvaluationError as CoreEvaluationError, TellError as CoreTellError}; -/// Get the custom exception class from chronopt.errors module +/// Get the custom exception class from diffid.errors module fn get_exception_class<'py>(py: Python<'py>, name: &str) -> PyResult> { - let errors_module = PyModule::import(py, "chronopt.errors")?; + let errors_module = PyModule::import(py, "diffid.errors")?; errors_module.getattr(name) } @@ -71,11 +71,11 @@ pub fn tell_error_to_py(err: CoreTellError) -> PyErr { Ok(exc_class) => match exc_class.call0() { Ok(exc_instance) => PyErr::from_value(exc_instance.into()), Err(_) => PyValueError::new_err( - "Cannot provide results to an already terminated optimization", + "Cannot provide results to an already terminated optimisation", ), }, Err(_) => PyValueError::new_err( - "Cannot provide results to an already terminated optimization", + "Cannot provide results to an already terminated optimisation", ), } } diff --git a/python/src/lib.rs b/python/src/lib.rs index 05d9775..d2452f5 100644 --- a/python/src/lib.rs +++ b/python/src/lib.rs @@ -10,9 +10,9 @@ use std::env; #[cfg(feature = "stubgen")] use std::path::PathBuf; -use chronopt_core::cost::{CostMetric, GaussianNll, RootMeanSquaredError, SumSquaredError}; -use chronopt_core::prelude::*; -use chronopt_core::sampler::SamplingResults; +use diffid_core::cost::{CostMetric, GaussianNll, RootMeanSquaredError, SumSquaredError}; +use diffid_core::prelude::*; +use diffid_core::sampler::SamplingResults; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pyfunction, gen_stub_pymethods}; @@ -39,7 +39,7 @@ use samplers::Sampler; type ParameterSpecEntry = (String, f64, Option<(f64, f64)>); // Import objective types for the problem enum -use chronopt_core::problem::{DiffsolObjective, ScalarObjective, VectorObjective}; +use diffid_core::problem::{DiffsolObjective, ScalarObjective, VectorObjective}; // Enum to hold different Problem types internally pub(crate) enum DynProblem { @@ -57,7 +57,7 @@ pub(crate) enum DynProblem { } impl DynProblem { - fn evaluate(&self, x: &[f64]) -> Result { + fn evaluate(&self, x: &[f64]) -> Result { match self { Self::Scalar(p) => p.evaluate(x), Self::ScalarWithGradient(p) => p.evaluate(x), @@ -93,6 +93,15 @@ impl DynProblem { } } + fn initial_values(&self) -> Vec { + match self { + Self::Scalar(p) => p.initial_values(), + Self::ScalarWithGradient(p) => p.initial_values(), + Self::Vector(p) => p.initial_values(), + Self::Diffsol(p) => p.initial_values(), + } + } + fn optimise( &self, initial: Option>, @@ -201,7 +210,7 @@ fn gaussian_nll(variance: f64, weight: f64) -> PyResult { } // Problem -/// Executable optimisation problem wrapping the Chronopt core implementation. +/// Executable optimisation problem wrapping the Diffid core implementation. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass(name = "Problem")] pub struct PyProblem { @@ -218,7 +227,7 @@ impl PyProblem { /// Evaluate the configured objective function at `x`. fn evaluate(&self, x: Vec) -> PyResult { self.inner.evaluate(&x).map_err(|e| { - crate::errors::evaluation_error_to_py(chronopt_core::errors::EvaluationError::message( + crate::errors::evaluation_error_to_py(diffid_core::errors::EvaluationError::message( format!("{}", e), )) }) @@ -230,7 +239,7 @@ impl PyProblem { DynProblem::ScalarWithGradient(p) => match p.evaluate_with_gradient(&x) { Ok((_val, grad_opt)) => Ok(grad_opt), Err(e) => Err(crate::errors::evaluation_error_to_py( - chronopt_core::errors::EvaluationError::message(format!("{}", e)), + diffid_core::errors::EvaluationError::message(format!("{}", e)), )), }, _ => Ok(None), @@ -312,6 +321,10 @@ impl PyProblem { self.parameter_specs.clone() } + fn initial_values(&self) -> Vec { + self.inner.initial_values() + } + /// Return the default parameter vector implied by the builder. #[pyo3(name = "default_parameters")] fn default_parameters_py(&self) -> Vec { @@ -386,7 +399,7 @@ fn builder_factory_py() -> PyScalarBuilder { } #[pymodule] -fn _chronopt(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { +fn _diffid(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { // Main classes m.add_class::()?; m.add_class::()?; @@ -448,11 +461,11 @@ fn _chronopt(py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_submodule(&sampler_module)?; m.setattr("sampler", &sampler_module)?; - // Register submodules for `import chronopt.builder` and `chronopt.cost` + // Register submodules for `import diffid.builder` and `diffid.cost` let sys_modules = py.import("sys")?.getattr("modules")?; - sys_modules.set_item("chronopt.builder", &builder_module)?; - sys_modules.set_item("chronopt.cost", &cost_module)?; - sys_modules.set_item("chronopt.sampler", &sampler_module)?; + sys_modules.set_item("diffid.builder", &builder_module)?; + sys_modules.set_item("diffid.cost", &cost_module)?; + sys_modules.set_item("diffid.sampler", &sampler_module)?; // Factory function m.add_function(wrap_pyfunction!(builder_factory_py, m)?)?; diff --git a/python/src/optimisers.rs b/python/src/optimisers.rs index 5043383..fdffb45 100644 --- a/python/src/optimisers.rs +++ b/python/src/optimisers.rs @@ -2,9 +2,9 @@ use pyo3::exceptions::PyTypeError; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::common::{AskResult, Bounds}; -use chronopt_core::optimisers::{AdamState, CMAESState, NelderMeadState}; -use chronopt_core::prelude::*; +use diffid_core::common::{AskResult, Bounds}; +use diffid_core::optimisers::{AdamState, CMAESState, NelderMeadState}; +use diffid_core::prelude::*; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -28,9 +28,9 @@ pub(crate) enum Optimiser { #[cfg(feature = "stubgen")] #[allow(dead_code)] pub(crate) fn optimiser_type_info() -> TypeInfo { - TypeInfo::unqualified("chronopt._chronopt.NelderMead") - | TypeInfo::unqualified("chronopt._chronopt.CMAES") - | TypeInfo::unqualified("chronopt._chronopt.Adam") + TypeInfo::unqualified("diffid._diffid.NelderMead") + | TypeInfo::unqualified("diffid._diffid.CMAES") + | TypeInfo::unqualified("diffid._diffid.Adam") } impl FromPyObject<'_, '_> for Optimiser { @@ -53,7 +53,7 @@ impl FromPyObject<'_, '_> for Optimiser { impl Optimiser { /// Convert to the core Optimiser enum - pub(crate) fn to_core(&self) -> chronopt_core::optimisers::Optimiser { + pub(crate) fn to_core(&self) -> diffid_core::optimisers::Optimiser { match self { Optimiser::NelderMead(nm) => nm.clone().into(), Optimiser::Cmaes(cma) => cma.clone().into(), @@ -159,9 +159,9 @@ impl PyNelderMead { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns a NelderMeadState object that can be used for incremental optimization + /// Returns a NelderMeadState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -174,15 +174,15 @@ impl PyNelderMead { /// Returns /// ------- /// NelderMeadState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- - /// >>> optimiser = chronopt.NelderMead() + /// >>> optimiser = diffid.NelderMead() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -286,9 +286,9 @@ impl PyCMAES { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns a CMAESState object that can be used for incremental optimization + /// Returns a CMAESState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -301,15 +301,15 @@ impl PyCMAES { /// Returns /// ------- /// CMAESState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- - /// >>> optimiser = chronopt.CMAES() + /// >>> optimiser = diffid.CMAES() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -404,9 +404,9 @@ impl PyAdam { PyOptimisationResults { inner: result } } - /// Initialize ask-tell optimization state. + /// Initialize ask-tell optimisation state. /// - /// Returns an AdamState object that can be used for incremental optimization + /// Returns an AdamState object that can be used for incremental optimisation /// via the ask-tell interface. /// /// Parameters @@ -419,15 +419,15 @@ impl PyAdam { /// Returns /// ------- /// AdamState - /// State object for ask-tell optimization + /// State object for ask-tell optimisation /// /// Examples /// -------- - /// >>> optimiser = chronopt.Adam() + /// >>> optimiser = diffid.Adam() /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate_with_gradient(pt) for pt in result.points] /// ... state.tell(values) @@ -443,18 +443,18 @@ impl PyAdam { } // Adam State -/// Ask-tell state for incremental Adam optimization. +/// Ask-tell state for incremental Adam optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get points to evaluate, and `tell()` to provide results. /// /// Examples /// -------- -/// >>> optimiser = chronopt.Adam().with_max_iter(100) +/// >>> optimiser = diffid.Adam().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... # Adam requires gradient information @@ -469,7 +469,7 @@ pub struct PyAdamState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyAdamState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -480,10 +480,10 @@ impl PyAdamState { /// Examples /// -------- /// >>> result = state.ask() - /// >>> if isinstance(result, chronopt.Evaluate): + /// >>> if isinstance(result, diffid.Evaluate): /// ... print(f"Need to evaluate {len(result.points)} points") - /// >>> elif isinstance(result, chronopt.Done): - /// ... print(f"Optimization complete: {result.result}") + /// >>> elif isinstance(result, diffid.Done): + /// ... print(f"optimisation complete: {result.result}") fn ask(&self, py: Python<'_>) -> Py { match self.inner.ask() { AskResult::Evaluate(points) => Py::new(py, PyEvaluate { points }).unwrap().into_any(), @@ -504,14 +504,14 @@ impl PyAdamState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated or if result format is invalid + /// If called after optimisation has terminated or if result format is invalid /// EvaluationError /// If the evaluation failed or contained invalid values /// /// Examples /// -------- /// >>> result = state.ask() - /// >>> if isinstance(result, chronopt.Evaluate): + /// >>> if isinstance(result, diffid.Evaluate): /// ... point = result.points[0] /// ... value = objective(point) /// ... gradient = compute_gradient(point) @@ -581,18 +581,18 @@ impl PyAdamState { } // Nelder-Mead State -/// Ask-tell state for incremental Nelder-Mead optimization. +/// Ask-tell state for incremental Nelder-Mead optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get points to evaluate, and `tell()` to provide results. /// /// Examples /// -------- -/// >>> optimiser = chronopt.NelderMead().with_max_iter(100) +/// >>> optimiser = diffid.NelderMead().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... values = [f(pt) for pt in result.points] @@ -606,7 +606,7 @@ pub struct PyNelderMeadState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyNelderMeadState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -632,7 +632,7 @@ impl PyNelderMeadState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated + /// If called after optimisation has terminated /// EvaluationError /// If the evaluation failed or contained invalid values fn tell(&mut self, result: f64) -> PyResult<()> { @@ -692,18 +692,18 @@ impl PyNelderMeadState { } // CMAES State -/// Ask-tell state for incremental CMA-ES optimization. +/// Ask-tell state for incremental CMA-ES optimisation. /// -/// This state object allows step-by-step control over the optimization process. +/// This state object allows step-by-step control over the optimisation process. /// Use `ask()` to get a population of points to evaluate, and `tell()` to provide results. /// /// Examples /// -------- -/// >>> optimiser = chronopt.CMAES().with_max_iter(100) +/// >>> optimiser = diffid.CMAES().with_max_iter(100) /// >>> state = optimiser.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Final result: {result.result}") /// ... break /// ... values = [f(pt) for pt in result.points] @@ -717,7 +717,7 @@ pub struct PyCMAESState { #[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl PyCMAESState { - /// Get the next action: evaluate points or optimization complete. + /// Get the next action: evaluate points or optimisation complete. /// /// Returns /// ------- @@ -749,7 +749,7 @@ impl PyCMAESState { /// Raises /// ------ /// TellError - /// If called after optimization has terminated or if wrong number + /// If called after optimisation has terminated or if wrong number /// of results provided /// EvaluationError /// If evaluations failed or contained invalid values diff --git a/python/src/results.rs b/python/src/results.rs index 6ae8bdd..38deada 100644 --- a/python/src/results.rs +++ b/python/src/results.rs @@ -2,7 +2,7 @@ use numpy::{PyArray1, ToPyArray}; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::prelude::OptimisationResults; +use diffid_core::prelude::OptimisationResults; #[cfg(feature = "stubgen")] use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods}; @@ -18,7 +18,7 @@ use crate::{PyNestedSamples, PySamples}; /// -------- /// >>> state = optimiser.init(problem, initial=[1.0, 2.0]) /// >>> result = state.ask() -/// >>> if isinstance(result, chronopt.Evaluate): +/// >>> if isinstance(result, diffid.Evaluate): /// ... values = [problem.evaluate(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] @@ -46,7 +46,7 @@ impl PyEvaluate { } } -/// Optimization/sampling is complete with final results. +/// Optimisation/sampling is complete with final results. /// /// This is returned by `ask()` when the algorithm has terminated. /// Access the results via the `result` attribute. @@ -55,8 +55,8 @@ impl PyEvaluate { /// -------- /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): -/// ... print(f"Optimization complete: {result.result}") +/// ... if isinstance(result, diffid.Done): +/// ... print(f"Optimisation complete: {result.result}") /// ... break #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass(name = "Done")] @@ -212,7 +212,7 @@ impl PyOptimisationResults { } } - /// Return truthiness based on optimization success. + /// Return truthiness based on optimisation success. /// /// Allows using `if result:` instead of `if result.success:`. fn __bool__(&self) -> bool { diff --git a/python/src/samplers.rs b/python/src/samplers.rs index a2034e8..1bfc918 100644 --- a/python/src/samplers.rs +++ b/python/src/samplers.rs @@ -1,10 +1,10 @@ -use numpy::{PyArray1, ToPyArray}; +use numpy::{PyArray1, PyArray2, PyArray3, ToPyArray}; use pyo3::exceptions::PyTypeError; use pyo3::prelude::*; use std::time::Duration; -use chronopt_core::common::{AskResult, Bounds}; -use chronopt_core::sampler::{ +use diffid_core::common::{AskResult, Bounds}; +use diffid_core::sampler::{ DynamicNestedSampler as CoreDynamicNestedSampler, DynamicNestedSamplerState as CoreDynamicNestedSamplerState, MetropolisHastings as CoreMetropolisHastings, @@ -34,8 +34,8 @@ pub(crate) enum Sampler { #[cfg(feature = "stubgen")] #[allow(dead_code)] pub(crate) fn sampler_type_info() -> TypeInfo { - TypeInfo::unqualified("chronopt._chronopt.MetropolisHastings") - | TypeInfo::unqualified("chronopt._chronopt.DynamicNestedSampler") + TypeInfo::unqualified("diffid._diffid.MetropolisHastings") + | TypeInfo::unqualified("diffid._diffid.DynamicNestedSampler") } impl FromPyObject<'_, '_> for Sampler { @@ -70,7 +70,7 @@ impl Sampler { /// Container for sampler draws and diagnostics. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "Samples")] +#[pyclass(module = "diffid.sampler", name = "Samples")] pub struct PySamples { pub(crate) inner: CoreSamples, } @@ -79,8 +79,36 @@ pub struct PySamples { #[pymethods] impl PySamples { #[getter] - fn chains(&self) -> Vec>> { - self.inner.chains().to_vec() + fn chains<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray3> { + use numpy::ndarray::Array3; + + let rust_chains = self.inner.chains(); + + if rust_chains.is_empty() { + let empty_array = Array3::::zeros((0, 0, 0)); + return empty_array.to_pyarray(py); + } + + let n_chains = rust_chains.len(); + let n_iterations = rust_chains[0].len(); + let n_params = rust_chains[0][0].len(); + + // Create ndarray from nested vec + let mut array = Array3::::zeros((n_chains, n_iterations, n_params)); + for (i, chain) in rust_chains.iter().enumerate() { + for (j, sample) in chain.iter().enumerate() { + for (k, &value) in sample.iter().enumerate() { + array[[i, j, k]] = value; + } + } + } + + array.to_pyarray(py) + } + + #[getter] + fn samples<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray2> { + PyArray2::from_vec2(py, &self.inner.samples()).expect("Valid Array2") } #[getter] @@ -88,6 +116,12 @@ impl PySamples { self.inner.mean_x().to_pyarray(py) } + #[getter] + fn acceptance_rate<'py>(&self, py: Python<'py>) -> Bound<'py, PyArray1> { + let rates = self.inner.acceptance_rates(); + rates.to_pyarray(py) + } + #[getter] fn draws(&self) -> usize { self.inner.draws() @@ -164,12 +198,14 @@ impl PySamples { } /// Iterator for Samples chains +#[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass] struct SamplesIterator { chains: Vec>>, index: usize, } +#[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl SamplesIterator { fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> { @@ -189,7 +225,7 @@ impl SamplesIterator { /// Nested sampling results including evidence estimates. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "NestedSamples")] +#[pyclass(module = "diffid.sampler", name = "NestedSamples")] #[derive(Clone)] pub struct PyNestedSamples { pub(crate) inner: CoreNestedSamples, @@ -331,12 +367,14 @@ impl PyNestedSamples { } /// Iterator for NestedSamples posterior +#[cfg_attr(feature = "stubgen", gen_stub_pyclass)] #[pyclass] struct NestedSamplesIterator { samples: Vec<(Vec, f64, f64)>, index: usize, } +#[cfg_attr(feature = "stubgen", gen_stub_pymethods)] #[pymethods] impl NestedSamplesIterator { fn __iter__(slf: PyRef<'_, Self>) -> PyRef<'_, Self> { @@ -356,7 +394,7 @@ impl NestedSamplesIterator { /// Basic Metropolis-Hastings sampler binding mirroring the optimiser API. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "MetropolisHastings")] +#[pyclass(module = "diffid.sampler", name = "MetropolisHastings")] #[derive(Clone)] pub struct PyMetropolisHastings { pub(crate) inner: CoreMetropolisHastings, @@ -419,11 +457,11 @@ impl PyMetropolisHastings { /// /// Examples /// -------- - /// >>> sampler = chronopt.MetropolisHastings().with_num_chains(4) + /// >>> sampler = diffid.MetropolisHastings().with_num_chains(4) /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -444,7 +482,7 @@ impl PyMetropolisHastings { /// Dynamic nested sampler binding exposing DNS configuration knobs. #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "DynamicNestedSampler")] +#[pyclass(module = "diffid.sampler", name = "DynamicNestedSampler")] #[derive(Clone)] pub struct PyDynamicNestedSampler { pub(crate) inner: CoreDynamicNestedSampler, @@ -515,11 +553,11 @@ impl PyDynamicNestedSampler { /// /// Examples /// -------- - /// >>> sampler = chronopt.DynamicNestedSampler() + /// >>> sampler = diffid.DynamicNestedSampler() /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() - /// ... if isinstance(result, chronopt.Done): + /// ... if isinstance(result, diffid.Done): /// ... break /// ... values = [evaluate(pt) for pt in result.points] /// ... state.tell(values) @@ -545,17 +583,17 @@ impl PyDynamicNestedSampler { /// /// Examples /// -------- -/// >>> sampler = chronopt.MetropolisHastings().with_num_chains(4) +/// >>> sampler = diffid.MetropolisHastings().with_num_chains(4) /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Sampling complete: {result.result}") /// ... break /// ... values = [negative_log_likelihood(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "MetropolisHastingsState")] +#[pyclass(module = "diffid.sampler", name = "MetropolisHastingsState")] pub struct PyMetropolisHastingsState { inner: CoreMetropolisHastingsState, } @@ -647,17 +685,17 @@ impl PyMetropolisHastingsState { /// /// Examples /// -------- -/// >>> sampler = chronopt.DynamicNestedSampler() +/// >>> sampler = diffid.DynamicNestedSampler() /// >>> state = sampler.init(initial=[1.0, 2.0]) /// >>> while True: /// ... result = state.ask() -/// ... if isinstance(result, chronopt.Done): +/// ... if isinstance(result, diffid.Done): /// ... print(f"Sampling complete: {result.result}") /// ... break /// ... values = [negative_log_likelihood(pt) for pt in result.points] /// ... state.tell(values) #[cfg_attr(feature = "stubgen", gen_stub_pyclass)] -#[pyclass(module = "chronopt.sampler", name = "DynamicNestedSamplerState")] +#[pyclass(module = "diffid.sampler", name = "DynamicNestedSamplerState")] pub struct PyDynamicNestedSamplerState { inner: CoreDynamicNestedSamplerState, } diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 7d4c18d..c27fb3a 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -1,19 +1,19 @@ [package] -name = "chronopt" +name = "diffid" version.workspace = true edition.workspace = true license.workspace = true description = "Time-series optimisation and inference toolkit with differential-system support" -repository = "https://github.com/BradyPlanden/chronopt" -homepage = "https://github.com/BradyPlanden/chronopt" -documentation = "https://docs.rs/chronopt" +repository = "https://github.com/BradyPlanden/diffid" +homepage = "https://github.com/BradyPlanden/diffid" +documentation = "https://docs.rs/diffid" readme = "../README.md" keywords = ["optimisation", "time-series", "differential-equations", "sampler", "inference"] categories = ["algorithms", "science", "simulation"] include = ["src/**", "benches/**", "Cargo.toml", "../README.md", "../LICENSE"] [dependencies] -diffsol = { version = "0.8.0" } +diffsol = { version = "0.10.1" } nalgebra.workspace = true rand = { version = "0.9.2", features = ["std"] } rand_distr = "0.5.1" diff --git a/rust/benches/diffsol_benches.rs b/rust/benches/diffsol_benches.rs index ea76050..7a8b94f 100644 --- a/rust/benches/diffsol_benches.rs +++ b/rust/benches/diffsol_benches.rs @@ -1,13 +1,13 @@ -use chronopt::builders::DiffsolBackend; -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion}; +use diffid::builders::DiffsolBackend; +use diffid::prelude::*; use nalgebra::DMatrix; use std::time::Duration; macro_rules! build_logistic_problem { ($backend:expr, $parallel:expr) => {{ let dsl = r#" -in = [r, k] +in_i { r = 1, k = 1 } r { 1 } k { 1 } u_i { y = 0.1 } diff --git a/rust/benches/optimisers_benches.rs b/rust/benches/optimisers_benches.rs index 1f4e56e..03375e3 100644 --- a/rust/benches/optimisers_benches.rs +++ b/rust/benches/optimisers_benches.rs @@ -1,6 +1,6 @@ -use chronopt::common::Bounds; -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, Criterion}; +use diffid::common::Bounds; +use diffid::prelude::*; use std::time::Duration; fn bench_nelder_mead_quadratic(c: &mut Criterion) { diff --git a/rust/benches/samplers_benches.rs b/rust/benches/samplers_benches.rs index fd30d3a..8d74010 100644 --- a/rust/benches/samplers_benches.rs +++ b/rust/benches/samplers_benches.rs @@ -1,5 +1,5 @@ -use chronopt::prelude::*; use criterion::{black_box, criterion_group, criterion_main, Criterion}; +use diffid::prelude::*; use std::time::Duration; fn bench_metropolis_hastings_gaussian(c: &mut Criterion) { diff --git a/rust/src/builders/mod.rs b/rust/src/builders/mod.rs index 878e4d6..b80faab 100644 --- a/rust/src/builders/mod.rs +++ b/rust/src/builders/mod.rs @@ -57,8 +57,7 @@ mod tests { #[test] fn test_diffsol_builder() { let dsl = r#" -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1} u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/rust/src/common.rs b/rust/src/common.rs index 5346843..874fc89 100644 --- a/rust/src/common.rs +++ b/rust/src/common.rs @@ -24,7 +24,7 @@ pub enum AskResult { #[derive(Debug, Clone)] pub struct Unbounded; -/// Represents parameter bounds for optimization and sampling algorithms. +/// Represents parameter bounds for optimisation and sampling algorithms. /// /// Each dimension has a lower and upper bound represented as a `RangeInclusive`. /// Bounds can be finite (e.g., `[0.0, 1.0]`) or infinite (e.g., `[-∞, ∞]`). @@ -32,7 +32,7 @@ pub struct Unbounded; /// # Examples /// /// ``` -/// use chronopt::common::Bounds; +/// use diffid::common::Bounds; /// /// // Create bounds for a two-dimensional parameter-space /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0)]); @@ -56,7 +56,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-10.0, 10.0)]); /// ``` @@ -69,7 +69,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0), (0.0, 100.0)]); /// assert_eq!(bounds.dimension(), 3); @@ -89,7 +89,7 @@ impl Bounds { /// /// # Examples /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let initial = [0.0]; /// let bounds = Bounds::unbounded_like(&initial); @@ -123,7 +123,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds = Bounds::new(vec![(0.0, 1.0), (-5.0, 5.0)]); /// let mut pos = vec![1.5, -10.0]; @@ -156,7 +156,7 @@ impl Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// use rand::SeedableRng; /// use rand::prelude::StdRng; /// @@ -205,7 +205,7 @@ impl From> for Bounds { /// # Examples /// /// ``` - /// use chronopt::common::Bounds; + /// use diffid::common::Bounds; /// /// let bounds: Bounds = vec![(0.0, 1.0), (-5.0, 5.0)].into(); /// assert_eq!(bounds.dimension(), 2); diff --git a/rust/src/lib.rs b/rust/src/lib.rs index d6ede1d..e57bdc5 100644 --- a/rust/src/lib.rs +++ b/rust/src/lib.rs @@ -8,7 +8,7 @@ pub mod problem; pub mod sampler; mod types; -// Convenience re-exports so users can `use chronopt::prelude::*;` +// Convenience re-exports so users can `use diffid::prelude::*;` pub mod prelude { pub use crate::builders::{ DiffsolConfig, DiffsolProblemBuilder, ScalarProblemBuilder, VectorProblemBuilder, diff --git a/rust/src/optimisers/adam.rs b/rust/src/optimisers/adam.rs index 0d1b2a3..27812a4 100644 --- a/rust/src/optimisers/adam.rs +++ b/rust/src/optimisers/adam.rs @@ -80,7 +80,7 @@ impl Adam { self.gradient_threshold.unwrap_or(self.threshold) } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (AdamState, Point) { @@ -211,7 +211,7 @@ impl AdamState { } } - /// Get the next point to evaluate, or the final result if optimization is complete + /// Get the next point to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { AdamPhase::Terminated(reason) => AskResult::Done(self.build_results(reason.clone())), @@ -400,7 +400,7 @@ impl AdamState { // Convenience wrapper impl Adam { - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// The closure should return `(value, gradient)` for a given point pub fn run( @@ -438,7 +438,7 @@ impl Adam { } } - /// Run optimization with numerical gradient approximation + /// Run optimisation with numerical gradient approximation /// /// Uses central differences to approximate the gradient pub fn run_with_numerical_gradient( @@ -729,7 +729,7 @@ mod tests { let mut positions = vec![first_point[0]]; - // Collect positions during optimization + // Collect positions during optimisation for _ in 0..5 { state.tell(result).expect("tell should succeed"); match state.ask() { diff --git a/rust/src/optimisers/cmaes.rs b/rust/src/optimisers/cmaes.rs index 06c0c58..c3ae1c0 100644 --- a/rust/src/optimisers/cmaes.rs +++ b/rust/src/optimisers/cmaes.rs @@ -77,7 +77,7 @@ impl CMAES { }) } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (CMAESState, Point) { @@ -274,7 +274,7 @@ impl CMAESState { } } - /// Get the next point(s) to evaluate, or the final result if optimization is complete + /// Get the next point(s) to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { CMAESPhase::Terminated(reason) => AskResult::Done(self.build_results(reason.clone())), @@ -311,6 +311,8 @@ impl CMAESState { .collect(); // Take ownership of current phase + // Placeholder MaxIters is set, will be replaced + // in handle methods below let phase = std::mem::replace( &mut self.phase, CMAESPhase::Terminated(TerminationReason::MaxIterationsReached), @@ -661,7 +663,7 @@ impl CMAESState { } impl CMAES { - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// This is a convenience wrapper around the ask/tell interface pub fn run( @@ -700,23 +702,20 @@ impl CMAES { } } - /// Run optimization with parallel evaluation - #[allow(dead_code)] - #[cfg(feature = "rayon")] - pub fn run_parallel( + pub fn run_batch( &self, + objective: F, initial: Point, bounds: Bounds, - objective: F, ) -> OptimisationResults where - F: Fn(&[f64]) -> Result + Sync, + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, { - use rayon::prelude::*; - let (mut state, first_point) = self.init(initial, bounds); - let mut results = vec![objective(&first_point)]; + let mut results = objective(&vec![first_point]); loop { if state.tell(results).is_err() { @@ -725,7 +724,7 @@ impl CMAES { match state.ask() { AskResult::Evaluate(points) => { - results = points.par_iter().map(|p| objective(p)).collect(); + results = objective(&points); } AskResult::Done(opt_results) => { return opt_results; @@ -771,7 +770,7 @@ mod tests { current_results = points.iter().map(|p| sphere(p)).collect(); } AskResult::Done(results) => { - println!("Optimization complete!"); + println!("optimisation complete!"); println!("Best value: {}", results.value); println!("Iterations: {}", results.iterations); println!("Evaluations: {}", results.evaluations); @@ -890,7 +889,7 @@ mod tests { #[test] fn d_sigma_matches_hansen_2016_formula() { - // Test case 1: Standard parameters from 10-dimensional optimization + // Test case 1: Standard parameters from 10-dimensional optimisation let mu_eff = 4.5; let dim_f = 10.0; let c_sigma = 0.3; @@ -1516,9 +1515,9 @@ mod tests { } }; - let optimizer2 = CMAES::new().with_max_iter(50).with_seed(seed); + let optimiser2 = CMAES::new().with_max_iter(50).with_seed(seed); - let (mut state2, first_point2) = optimizer2.init(vec![2.0, -1.0], Bounds::unbounded(2)); + let (mut state2, first_point2) = optimiser2.init(vec![2.0, -1.0], Bounds::unbounded(2)); let mut results2 = vec![sphere_cmaes(&first_point2)]; let result2 = loop { diff --git a/rust/src/optimisers/mod.rs b/rust/src/optimisers/mod.rs index d8268d0..a071ae5 100644 --- a/rust/src/optimisers/mod.rs +++ b/rust/src/optimisers/mod.rs @@ -48,12 +48,12 @@ impl ScalarOptimiser { /// * `bounds` - Optional parameter bounds /// /// # Returns - /// Optimization results including best point, value, and diagnostics + /// Optimisation results including best point, value, and diagnostics /// /// # Example /// ``` - /// use chronopt::common::Bounds; - /// use chronopt::optimisers::{ScalarOptimiser, NelderMead}; + /// use diffid::common::Bounds; + /// use diffid::optimisers::{ScalarOptimiser, NelderMead}; /// /// let optimiser = ScalarOptimiser::from(NelderMead::new()); /// let result = optimiser.run( @@ -73,6 +73,52 @@ impl ScalarOptimiser { ScalarOptimiser::CMAES(cm) => cm.run(objective, initial, bounds), } } + + /// Run the optimiser with batched candidates + /// + /// # Arguments + /// * `objective` - Function that batch evaluates the objective + /// * `initial` - Initial point (will be auto-expanded if needed) + /// * `bounds` - Optional parameter bounds + /// + /// # Returns + /// Optimisation results including best point, value, and diagnostics + /// + /// # Example + /// ``` + /// use diffid::common::Bounds; + /// use diffid::optimisers::{ScalarOptimiser, NelderMead}; + /// + /// let optimiser = ScalarOptimiser::from(NelderMead::new()); + /// let result = optimiser.run_batch( + /// |xs| xs.iter().map(|x| x[0].powi(2) + x[1].powi(2)).collect(), + /// vec![1.0, 2.0], + /// Bounds::unbounded(2) + /// ); + /// ``` + pub fn run_batch( + &self, + objective: F, + initial: Point, + bounds: Bounds, + ) -> OptimisationResults + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + match self { + ScalarOptimiser::CMAES(cm) => cm.run_batch(objective, initial, bounds), + ScalarOptimiser::NelderMead(nm) => nm.run( + |x| { + let result = objective(&vec![x.to_vec()]); + result.into_iter().next().unwrap() // ToDO: This needs proper error integration + }, + initial, + bounds, + ), + } + } } impl GradientOptimiser { @@ -87,12 +133,12 @@ impl GradientOptimiser { /// * `bounds` - Optional parameter bounds /// /// # Returns - /// Optimization results including best point, value, and diagnostics + /// Optimisation results including best point, value, and diagnostics /// /// # Example /// ``` - /// use chronopt::common::Bounds; - /// use chronopt::optimisers::{GradientOptimiser, Adam}; + /// use diffid::common::Bounds; + /// use diffid::optimisers::{GradientOptimiser, Adam}; /// /// let optimiser = GradientOptimiser::from(Adam::new()); /// let result = optimiser.run( diff --git a/rust/src/optimisers/nelder_mead.rs b/rust/src/optimisers/nelder_mead.rs index 051aac2..ce52862 100644 --- a/rust/src/optimisers/nelder_mead.rs +++ b/rust/src/optimisers/nelder_mead.rs @@ -75,7 +75,7 @@ impl NelderMead { self } - /// Initialize the optimization state + /// Initialize the optimisation state /// /// Returns the state and the first point to evaluate pub fn init(&self, initial: Point, bounds: Bounds) -> (NelderMeadState, Vec) { @@ -98,7 +98,7 @@ impl NelderMead { (state, vec![initial_point]) } - /// Run optimization using a closure for evaluation + /// Run optimisation using a closure for evaluation /// /// This is a convenience wrapper around the ask/tell interface pub fn run( @@ -202,7 +202,7 @@ pub struct NelderMeadState { } impl NelderMeadState { - /// Get the next point to evaluate, or the final result if optimization is complete + /// Get the next point to evaluate, or the final result if optimisation is complete pub fn ask(&self) -> AskResult { match &self.phase { NelderMeadPhase::Terminated(reason) => { @@ -721,7 +721,7 @@ mod tests { current_value = rosenbrock(&point[0]); } AskResult::Done(results) => { - println!("Optimization complete!"); + println!("optimisation complete!"); println!("Best value: {}", results.value); println!("Iterations: {}", results.iterations); println!("Evaluations: {}", results.evaluations); diff --git a/rust/src/problem/diffsol_problem.rs b/rust/src/problem/diffsol_problem.rs index 7ff7847..8339c43 100644 --- a/rust/src/problem/diffsol_problem.rs +++ b/rust/src/problem/diffsol_problem.rs @@ -270,6 +270,8 @@ impl Objective for DiffsolObjective { }) } + /// Population based evaluation supporting + /// both sequential and parallel configurations. fn evaluate_population(&self, params: &[Vec]) -> Vec> { if self.config.parallel { params.par_iter().map(|x| self.evaluate(x)).collect() @@ -278,6 +280,12 @@ impl Objective for DiffsolObjective { } } + /// Support parallel evaluation if config.parallel + /// is set by user. + fn supports_parallel_evaluation(&self) -> bool { + self.config.parallel + } + fn has_gradient(&self) -> bool { // Check the backend config matches!(self.config.backend, DiffsolBackend::Dense) @@ -300,9 +308,7 @@ mod tests { #[allow(dead_code)] fn build_logistic_problem(backend: DiffsolBackend) -> DiffsolObjective { let dsl = r#" -in = [r, k] -r { 1 } -k { 1 } +in_i {r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/rust/src/problem/mod.rs b/rust/src/problem/mod.rs index 7f42eae..2e74c8b 100644 --- a/rust/src/problem/mod.rs +++ b/rust/src/problem/mod.rs @@ -139,6 +139,13 @@ impl ParameterSet { .collect(), } } + + pub fn initial_values(&self) -> Vec { + if self.0.is_empty() { + return vec![]; + } + self.0.iter().map(|spec| spec.initial_value).collect() + } } #[derive(Debug, Clone, PartialEq)] @@ -209,9 +216,18 @@ pub trait Objective: Send + Sync { Ok((self.evaluate(x)?, self.gradient(x))) } + /// Evaluates a batch of candidates provided as `xs` + /// Default is sequential evaluation, certain objectives + /// support parallel evaluation. fn evaluate_population(&self, xs: &[Vec]) -> Vec> { xs.iter().map(|x| self.evaluate(x)).collect() } + + /// Boolean flag to denote whether the objective can support + /// parallel evaluations. + fn supports_parallel_evaluation(&self) -> bool { + false + } } pub struct ScalarObjective { @@ -363,6 +379,10 @@ impl Problem { self.parameters.bounds() } + pub fn initial_values(&self) -> Vec { + self.parameters.initial_values() + } + /// A convenience function for optimisation of the problem pub fn optimise( &self, @@ -381,8 +401,15 @@ impl Problem { match opt { Optimiser::Scalar(scalar_opt) => { - // Only need objective values - scalar_opt.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + if self.objective.supports_parallel_evaluation() { + scalar_opt.run_batch( + |xs| self.evaluate_population(xs), + x0, + self.parameters.bounds(), + ) + } else { + scalar_opt.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + } } Optimiser::Gradient(grad_opt) => { // Needs objective + gradient values @@ -422,7 +449,15 @@ impl Problem { match sampler { Sampler::Scalar(scalar_sampler) => { - scalar_sampler.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + if self.objective.supports_parallel_evaluation() { + scalar_sampler.run_batch( + |xs| self.evaluate_population(xs), + x0, + self.parameters.bounds(), + ) + } else { + scalar_sampler.run(|x| self.evaluate(x), x0, self.parameters.bounds()) + } } Sampler::Gradient(_grad_sampler) => { unimplemented!("Gradient samplers not yet implemented") diff --git a/rust/src/sampler/dynamic_nested/mod.rs b/rust/src/sampler/dynamic_nested/mod.rs index f9bea96..845a5ac 100644 --- a/rust/src/sampler/dynamic_nested/mod.rs +++ b/rust/src/sampler/dynamic_nested/mod.rs @@ -5,7 +5,7 @@ //! **IMPORTANT**: The objective function must return **negative log-likelihood** (-log L). //! Internally, values are negated to obtain log-likelihood for nested sampling calculations. //! -//! This convention aligns with optimization where lower values are better, while nested +//! This convention aligns with optimisation where lower values are better, while nested //! sampling requires higher log-likelihood values. use rand::prelude::StdRng; @@ -590,6 +590,48 @@ impl DynamicNestedSampler { } } } + + /// Run Dynamic Nested Sampling with automatic evaluation loop + /// + /// Internally uses the ask/tell interface. For external control, + /// use `init()`, `ask()`, and `tell()` directly. + /// + /// # Arguments + /// * `objective` - Function to evaluate. **Must return negative log-likelihood** (-log L). + /// The sampler will negate this internally to obtain log-likelihood for + /// nested sampling calculations. + /// * `initial` - Initial point (currently unused, reserved for future) + /// * `bounds` - Parameter bounds + pub fn run_batch(&self, objective: F, initial: Point, bounds: Bounds) -> NestedSamples + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + let (mut state, first_batch) = self.init(initial, bounds); + let mut results = objective(&first_batch); + + loop { + // Call ask and break if an error is encountered + if state.tell(results).is_err() { + break; + } + + match state.ask() { + AskResult::Evaluate(points) => { + results = objective(&points); + } + AskResult::Done(SamplingResults::Nested(samples)) => return samples, + _ => unreachable!("DynamicNestedSampler always returns Nested results"), + } + } + + // Final ask call for if tell returned an error + match state.ask() { + AskResult::Done(SamplingResults::Nested(samples)) => samples, + _ => panic!("Unexpected state after tell error"), + } + } } impl Default for DynamicNestedSampler { diff --git a/rust/src/sampler/dynamic_nested/results.rs b/rust/src/sampler/dynamic_nested/results.rs index 9b54484..87f6620 100644 --- a/rust/src/sampler/dynamic_nested/results.rs +++ b/rust/src/sampler/dynamic_nested/results.rs @@ -151,7 +151,17 @@ impl NestedSamples { .iter() .map(|sample| sample.position.clone()) .collect::>()]; - Samples::new(chains, self.mean.clone(), self.draws, self.time) + + // DynamicNested doesn't use MCMC so no acceptance data + let acceptance_data = vec![Vec::new(); chains.len()]; + + Samples::new( + chains, + self.mean.clone(), + self.draws, + self.time, + acceptance_data, + ) } } diff --git a/rust/src/sampler/metropolis_hastings.rs b/rust/src/sampler/metropolis_hastings.rs index ef801e0..eda9acb 100644 --- a/rust/src/sampler/metropolis_hastings.rs +++ b/rust/src/sampler/metropolis_hastings.rs @@ -84,6 +84,48 @@ impl MetropolisHastings { } } } + + /// Run Metropolis-Hastings MCMC with batch sampling + /// + /// This method evaluates the problem's objective function to sample + /// from the posterior distribution using a random walk Metropolis algorithm. + /// + /// Internally uses the ask/tell interface for consistency. For external + /// control of the evaluation loop, use `init()`, `ask()`, and `tell()` directly. + pub fn run_batch(&self, objective: F, initial: Point, bounds: Bounds) -> Samples + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + let initial_point = initial; + let mut state = self.init(initial_point.clone(), bounds); // ToDo: performance improvement, remove clone + + let mut results = objective(&vec![initial_point]); + + loop { + // Call ask and break if an error is encountered + if state.tell(results).is_err() { + break; + } + + match state.ask() { + AskResult::Evaluate(points) => { + results = objective(&points); + } + AskResult::Done(SamplingResults::MCMC(samples)) => { + return samples; + } + _ => unreachable!("MetropolisHastings always returns MCMC results"), + } + } + + // Final ask call for if tell returned an error + match state.ask() { + AskResult::Done(SamplingResults::MCMC(samples)) => samples, + _ => panic!("Unexpected state after tell error"), + } + } } /// State for ask/tell interface of Metropolis-Hastings sampler @@ -104,6 +146,7 @@ struct ChainState { proposal: Vec, samples: Vec>, rng: StdRng, + acceptances: Vec, // Track accept/reject per iteration } /// Phase tracking for MCMC sampling @@ -167,6 +210,7 @@ impl MetropolisHastings { proposal, samples: vec![initial_point.clone()], rng, + acceptances: Vec::new(), } }) .collect(); @@ -278,6 +322,8 @@ impl MetropolisHastingsState { chain.current_log_likelihood = proposal_log_likelihood; } + // Track acceptance for diagnostics + chain.acceptances.push(accept); chain.samples.push(chain.current.clone()); // Generate next proposal @@ -341,6 +387,19 @@ impl MetropolisHastingsState { } } - Samples::new(chains, mean_x, total_samples, self.start_time.elapsed()) + // Extract acceptance data from all chains + let acceptance_data: Vec> = self + .chains + .iter() + .map(|chain| chain.acceptances.clone()) + .collect(); + + Samples::new( + chains, + mean_x, + total_samples, + self.start_time.elapsed(), + acceptance_data, + ) } } diff --git a/rust/src/sampler/mod.rs b/rust/src/sampler/mod.rs index d84cda1..b9e40b2 100644 --- a/rust/src/sampler/mod.rs +++ b/rust/src/sampler/mod.rs @@ -42,12 +42,12 @@ impl ScalarSampler { /// Run the sampler with a scalar objective function /// /// # Arguments - /// * `problem` - Problem defining objective and parameter specs + /// * `objective` - Function that evaluates the objective at a single point /// * `initial` - Initial point for sampling /// * `bounds` - Parameter bounds /// /// # Returns - /// Sampling results (type depends on sampler algorithm) + /// Sampling results pub fn run(&self, objective: F, initial: Point, bounds: Bounds) -> SamplingResults where F: FnMut(&[f64]) -> R, @@ -65,6 +65,38 @@ impl ScalarSampler { } } } + + /// Run the sampler with a batched scalar objective function + /// + /// # Arguments + /// * `objective` - Function that evaluates the objective with a batch of parameter candidates + /// * `initial` - Initial point for sampling + /// * `bounds` - Parameter bounds + /// + /// # Returns + /// Sampling results + pub fn run_batch( + &self, + objective: F, + initial: Point, + bounds: Bounds, + ) -> SamplingResults + where + F: Fn(&[Vec]) -> Vec, + R: TryInto, + E: Into, + { + match self { + ScalarSampler::MetropolisHastings(mh) => { + // Delegates to individual sampler's run() method + SamplingResults::MCMC(mh.run_batch(objective, initial, bounds)) + } + ScalarSampler::DynamicNested(dns) => { + // Delegates to individual sampler's run() method + SamplingResults::Nested(dns.run_batch(objective, initial, bounds)) + } + } + } } impl GradientSampler { @@ -239,15 +271,23 @@ pub struct Samples { mean_x: Vec, draws: usize, time: Duration, + acceptance_data: Vec>, // Per-chain acceptance history } impl Samples { - pub fn new(chains: Vec>>, mean_x: Vec, draws: usize, time: Duration) -> Self { + pub fn new( + chains: Vec>>, + mean_x: Vec, + draws: usize, + time: Duration, + acceptance_data: Vec>, + ) -> Self { Self { chains, mean_x, draws, time, + acceptance_data, } } @@ -255,6 +295,10 @@ impl Samples { &self.chains } + pub fn samples(&self) -> Vec> { + self.chains.iter().flatten().cloned().collect() + } + pub fn mean_x(&self) -> &[f64] { &self.mean_x } @@ -266,6 +310,23 @@ impl Samples { pub fn time(&self) -> Duration { self.time } + + /// Get the acceptance rate for each chain + /// + /// Returns a vector of acceptance rates (proportion of accepted proposals) + /// for each MCMC chain. Each value is between 0.0 and 1.0. + pub fn acceptance_rates(&self) -> Vec { + self.acceptance_data + .iter() + .map(|chain_accepts| { + if chain_accepts.is_empty() { + 0.0 + } else { + chain_accepts.iter().filter(|&&a| a).count() as f64 / chain_accepts.len() as f64 + } + }) + .collect() + } } /// Unified result type for all samplers diff --git a/rust/tests/diffsol_optimisation.rs b/rust/tests/diffsol_optimisation.rs index ce4e965..6003559 100644 --- a/rust/tests/diffsol_optimisation.rs +++ b/rust/tests/diffsol_optimisation.rs @@ -1,11 +1,10 @@ -use chronopt::prelude::*; +use diffid::prelude::*; use nalgebra::DMatrix; #[test] fn diffsol_builder_supports_end_to_end_optimisation() { let dsl = r#" -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.1 } F_i { a * y } "#; diff --git a/rust/tests/dynamic_nested.rs b/rust/tests/dynamic_nested.rs index 535391e..e584042 100644 --- a/rust/tests/dynamic_nested.rs +++ b/rust/tests/dynamic_nested.rs @@ -1,5 +1,5 @@ -use chronopt::builders::DiffsolBackend; -use chronopt::prelude::*; +use diffid::builders::DiffsolBackend; +use diffid::prelude::*; use nalgebra::DMatrix; #[test] @@ -48,9 +48,7 @@ fn build_logistic_objective( parallel: bool, ) -> (impl Fn(&[f64]) -> f64, Bounds) { let dsl = r#" -in = [r, k] -r { 1 } -k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } "#; diff --git a/rust/tests/dynamic_nested_advanced.rs b/rust/tests/dynamic_nested_advanced.rs index 7de0c13..d130439 100644 --- a/rust/tests/dynamic_nested_advanced.rs +++ b/rust/tests/dynamic_nested_advanced.rs @@ -1,5 +1,5 @@ -use chronopt::prelude::*; -use chronopt::problem::ParameterRange; +use diffid::prelude::*; +use diffid::problem::ParameterRange; /// Test evidence calculation against known analytical result. /// For a Gaussian N(x | 0, σ²) with uniform prior U(a, b), diff --git a/tests/integration/test_diffsol_dynamic_nested_parallel.py b/tests/integration/test_diffsol_dynamic_nested_parallel.py index 32b10fa..cf5d431 100644 --- a/tests/integration/test_diffsol_dynamic_nested_parallel.py +++ b/tests/integration/test_diffsol_dynamic_nested_parallel.py @@ -1,12 +1,10 @@ -import chronopt as chron +import diffid import numpy as np def _logistic_dsl() -> str: return """ -in = [r, k] -r { 1 } -k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -18,10 +16,10 @@ def _logistic_data(n: int = 40) -> np.ndarray: return np.column_stack((t_span, y)) -def _build_diffsol_problem(parallel: bool) -> chron.Problem: +def _build_diffsol_problem(parallel: bool) -> diffid.Problem: data = _logistic_data(40) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(_logistic_dsl()) .with_data(data) .with_parameter("r", 1.0, bounds=(0.1, 3.0)) @@ -40,7 +38,7 @@ def test_dynamic_nested_diffsol_parallel_vs_sequential(): initial = [1.0, 1.0] sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.3) .with_termination_tolerance(1e-3) @@ -68,14 +66,14 @@ def test_dynamic_nested_diffsol_parallel_vs_sequential(): def test_dynamic_nested_sampler_parallel_fallback_for_non_parallel_problems(): # Scalar problem does not support parallel evaluation; sampler should still work problem = ( - chron.ScalarBuilder() - .with_callable(lambda x: 0.5 * (x[0] - 0.5) ** 2) + diffid.ScalarBuilder() + .with_objective(lambda x: 0.5 * (x[0] - 0.5) ** 2) .with_parameter("x", 0.5, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(24) .with_expansion_factor(0.2) .with_termination_tolerance(1e-3) diff --git a/tests/integration/test_diffsol_sampling.py b/tests/integration/test_diffsol_sampling.py index 256dd66..00fad37 100644 --- a/tests/integration/test_diffsol_sampling.py +++ b/tests/integration/test_diffsol_sampling.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -12,8 +12,7 @@ def ball_states(t: np.ndarray, g: float, h: float) -> tuple[np.ndarray, np.ndarr def test_diffsol_sampling_tracks_bouncy_ball_parameters(): # DiffSL program for a falling (bouncy) ball terminated when the height reaches zero. dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} @@ -28,12 +27,12 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): data = np.column_stack((t_span, height, velocity)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", g_true) .with_parameter("h", h_true) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() @@ -42,7 +41,7 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): initial_cost = problem.evaluate(initial_guess) sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(2) .with_iterations(250) .with_step_size(0.25) @@ -70,8 +69,7 @@ def test_diffsol_sampling_tracks_bouncy_ball_parameters(): def test_diffsol_dynamic_nested_sampler_produces_evidence(): dsl = """ -in = [g, h] -g { 1 } h { 1 } +in_i { g = 2.5, h = 1 } u_i {x = h, v = 0} F_i {v, -g} stop {x} @@ -86,19 +84,19 @@ def test_diffsol_dynamic_nested_sampler_produces_evidence(): data = np.column_stack((t_span, height, velocity)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(dsl) .with_data(data) .with_parameter("g", g_true) .with_parameter("h", h_true) .with_tolerances(rtol=1e-6, atol=1e-6) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.15) .with_termination_tolerance(1e-3) diff --git a/tests/integration/test_mathematical_suite.py b/tests/integration/test_mathematical_suite.py index 5016bd3..515d924 100644 --- a/tests/integration/test_mathematical_suite.py +++ b/tests/integration/test_mathematical_suite.py @@ -4,7 +4,7 @@ from functools import partial -import chronopt as chron +import diffid import numpy as np import pytest @@ -59,9 +59,9 @@ def ridge(x: list[float], alpha: float = 1.0) -> np.ndarray: return np.asarray([value], dtype=float) -def make_nelder_mead() -> chron.NelderMead: +def make_nelder_mead() -> diffid.NelderMead: return ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(800) .with_step_size(0.2) .with_threshold(1e-8) @@ -69,9 +69,9 @@ def make_nelder_mead() -> chron.NelderMead: ) -def make_cmaes() -> chron.CMAES: +def make_cmaes() -> diffid.CMAES: return ( - chron.CMAES() + diffid.CMAES() .with_max_iter(1500) .with_threshold(1e-8) .with_step_size(0.8) @@ -161,7 +161,7 @@ def test_python_objectives_converge( ): """Ensure optimisation reaches known minima for several analytic functions.""" - builder = chron.ScalarBuilder().with_callable(objective) + builder = diffid.ScalarBuilder().with_objective(objective) for idx in range(dimension): builder = builder.with_parameter(f"x{idx}", 1.0) @@ -176,8 +176,7 @@ def test_python_objectives_converge( _LOGISTIC_DSL = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -197,7 +196,7 @@ def test_diffsol_logistic_convergence(): stacked_data = np.column_stack((time_points, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(_LOGISTIC_DSL) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) @@ -208,7 +207,7 @@ def test_diffsol_logistic_convergence(): problem = builder.build() optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(400) .with_threshold(1e-7) .with_position_tolerance(1e-6) diff --git a/tests/test_docs.py b/tests/test_docs.py new file mode 100644 index 0000000..3ac02b0 --- /dev/null +++ b/tests/test_docs.py @@ -0,0 +1,70 @@ +"""Documentation quality tests. + +Validates that: +1. Documentation builds without errors +2. Notebooks are valid and well-structured +""" + +import json +import pathlib +import subprocess + +import pytest + +# Project root +ROOT = pathlib.Path(__file__).parent.parent +DOCS_DIR = ROOT / "docs" +MKDOCS_YML = ROOT / "mkdocs.yml" + + +class TestDocumentationBuild: + """Test that documentation builds successfully.""" + + def test_mkdocs_build_succeeds(self): + """Test that mkdocs builds without errors. + + mkdocs strict mode validates: + - All internal links resolve + - No broken references + - Proper navigation structure + """ + result = subprocess.run( + ["mkdocs", "build", "--strict"], + cwd=ROOT, + capture_output=True, + text=True, + ) + + assert result.returncode == 0, ( + f"mkdocs build failed:\n{result.stdout}\n{result.stderr}" + ) + + +class TestNotebookStructure: + """Test that notebooks are valid and well-structured.""" + + def test_all_notebooks_are_valid_json(self): + """Verify all notebooks are valid JSON with proper structure.""" + notebooks = list((DOCS_DIR / "tutorials" / "notebooks").glob("*.ipynb")) + + assert len(notebooks) > 0, "No notebooks found" + + for notebook_path in notebooks: + with open(notebook_path, encoding="utf-8") as f: + nb_data = json.load(f) + + # Validate basic notebook structure + assert "cells" in nb_data, f"{notebook_path.name} missing cells" + assert "metadata" in nb_data, f"{notebook_path.name} missing metadata" + assert len(nb_data["cells"]) > 0, f"{notebook_path.name} has no cells" + + +def test_readme_not_in_docs(): + """Ensure README doesn't conflict with index.md.""" + readme = DOCS_DIR / "README.md" + assert not readme.exists(), "docs/README.md conflicts with index.md" + + +if __name__ == "__main__": + # Run tests with pytest + pytest.main([__file__, "-v"]) diff --git a/tests/unit/optimisers/test_adam.py b/tests/unit/optimisers/test_adam.py index f62754b..c937133 100644 --- a/tests/unit/optimisers/test_adam.py +++ b/tests/unit/optimisers/test_adam.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -16,8 +16,8 @@ def quadratic_grad(x): def build_quadratic_problem_with_gradient(): return ( - chron.ScalarBuilder() - .with_callable(quadratic) + diffid.ScalarBuilder() + .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) .with_parameter("y", 1.0) @@ -28,7 +28,9 @@ def build_quadratic_problem_with_gradient(): def test_adam_direct_run_minimises_quadratic(): problem = build_quadratic_problem_with_gradient() - optimiser = chron.Adam().with_step_size(0.1).with_max_iter(500).with_threshold(1e-8) + optimiser = ( + diffid.Adam().with_step_size(0.1).with_max_iter(500).with_threshold(1e-8) + ) result = optimiser.run(problem, [5.0, -4.0]) @@ -39,14 +41,16 @@ def test_adam_direct_run_minimises_quadratic(): def test_python_builder_optimise_with_adam_default(): builder = ( - chron.ScalarBuilder() - .with_callable(quadratic) + diffid.ScalarBuilder() + .with_objective(quadratic) .with_gradient(quadratic_grad) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) - optimiser = chron.Adam().with_step_size(0.1).with_max_iter(400).with_threshold(1e-8) + optimiser = ( + diffid.Adam().with_step_size(0.1).with_max_iter(400).with_threshold(1e-8) + ) builder.with_optimiser(optimiser) problem = builder.build() @@ -60,15 +64,15 @@ def test_python_builder_optimise_with_adam_default(): def test_adam_falls_back_with_numerical_grad(): builder = ( - chron.ScalarBuilder() - .with_callable(quadratic) + diffid.ScalarBuilder() + .with_objective(quadratic) .with_parameter("x", 0.0) .with_parameter("y", 0.0) ) problem = builder.build() - optimiser = chron.Adam().with_max_iter(10) + optimiser = diffid.Adam().with_max_iter(10) result = optimiser.run(problem, [0.0, 0.0]) assert result.iterations == 10 diff --git a/tests/unit/optimisers/test_cmaes.py b/tests/unit/optimisers/test_cmaes.py index 1d972e3..1f0baa2 100644 --- a/tests/unit/optimisers/test_cmaes.py +++ b/tests/unit/optimisers/test_cmaes.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -10,8 +10,8 @@ def rosenbrock(x): def build_rosenbrock_problem(): return ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) .build() @@ -22,7 +22,7 @@ def test_cmaes_direct_run_minimises_rosenbrock(): problem = build_rosenbrock_problem() optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(400) .with_threshold(1e-8) .with_step_size(0.6) @@ -38,14 +38,14 @@ def test_cmaes_direct_run_minimises_rosenbrock(): def test_python_builder_optimise_with_cmaes_default(): builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) optimiser = ( - chron.CMAES() + diffid.CMAES() .with_max_iter(300) .with_threshold(1e-8) .with_step_size(0.5) @@ -63,7 +63,7 @@ def test_python_builder_optimise_with_cmaes_default(): def test_set_optimiser_rejects_unknown_type(): - builder = chron.ScalarBuilder() + builder = diffid.ScalarBuilder() with pytest.raises(TypeError): builder.with_optimiser(object()) @@ -72,7 +72,7 @@ def test_set_optimiser_rejects_unknown_type(): def test_cmaes_result_covariance_available(): problem = build_rosenbrock_problem() - optimiser = chron.CMAES().with_max_iter(50).with_seed(123) + optimiser = diffid.CMAES().with_max_iter(50).with_seed(123) result = optimiser.run(problem, [1.5, -1.5]) diff --git a/tests/unit/test_diffsol.py b/tests/unit/test_diffsol.py index 348b2f6..404162d 100644 --- a/tests/unit/test_diffsol.py +++ b/tests/unit/test_diffsol.py @@ -1,6 +1,6 @@ import copy -import chronopt as chron +import diffid import numpy as np import pytest @@ -9,8 +9,7 @@ def test_diffsol_builder(): """Test basic Diffsol builder functionality""" # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -23,14 +22,14 @@ def test_diffsol_builder(): # Build the problem builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) .with_parameter("r", 1.0, None) .with_parameter("k", 1.0, None) - .with_cost(chron.SSE()) - .with_cost(chron.RMSE()) + .with_cost(diffid.SSE()) + .with_cost(diffid.RMSE()) ) problem = builder.build() @@ -45,7 +44,7 @@ def test_diffsol_builder(): # Test that we can optimise the problem optimiser = ( - chron.NelderMead().with_max_iter(500).with_threshold(1e-7).with_patience(10) + diffid.NelderMead().with_max_iter(500).with_threshold(1e-7).with_patience(10) ) result = optimiser.run(problem, x0) assert result.success @@ -54,8 +53,7 @@ def test_diffsol_builder(): def test_diffsol_builder_remove_methods(): ds = """ -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.0 } F_i { a * y } """ @@ -64,10 +62,10 @@ def test_diffsol_builder_remove_methods(): data = t_span**2 stacked_data = np.column_stack((t_span, data)) - metric = chron.RMSE() + metric = diffid.RMSE() builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("a", 1.0, None) @@ -96,7 +94,7 @@ def test_diffsol_builder_remove_methods(): # Change cost builder = builder.remove_cost() - builder = builder.with_cost(chron.SSE()) + builder = builder.with_cost(diffid.SSE()) problem_5 = builder.build() # Check that problems are different @@ -111,8 +109,7 @@ def test_diffsol_builder_remove_methods(): def test_problem_optimise_defaults_to_builder_params(): ds = """ -in = [a] -a { 1 } +in_i { a = 1 } u_i { y = 0.1 } F_i { a * y } """ @@ -123,7 +120,7 @@ def test_problem_optimise_defaults_to_builder_params(): stacked_data = np.column_stack((t_span, data)) problem = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_tolerances(rtol=1e-4, atol=1e-4) .with_data(stacked_data) @@ -131,7 +128,7 @@ def test_problem_optimise_defaults_to_builder_params(): .build() ) - optimiser = chron.NelderMead().with_max_iter(0) + optimiser = diffid.NelderMead().with_max_iter(0) result = problem.optimise(optimiser=optimiser) @@ -146,8 +143,7 @@ def test_diffsol_cost_metrics(variance: float) -> None: """Ensure selectable cost metrics produce consistent values.""" ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -158,7 +154,7 @@ def test_diffsol_cost_metrics(variance: float) -> None: def build_problem(cost_metric=None): builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-6) @@ -170,9 +166,9 @@ def build_problem(cost_metric=None): return builder.build() sse_problem = build_problem() - sse_problem_explicit = build_problem(chron.SSE()) - rmse_problem = build_problem(chron.RMSE()) - gaussian_problem = build_problem(chron.GaussianNLL(variance)) + sse_problem_explicit = build_problem(diffid.SSE()) + rmse_problem = build_problem(diffid.RMSE()) + gaussian_problem = build_problem(diffid.GaussianNLL(variance)) test_params = [0.8, 1.2] sse_cost = sse_problem.evaluate(test_params) @@ -193,13 +189,13 @@ def build_problem(cost_metric=None): assert pytest.approx(expected_gaussian, rel=1e-6, abs=1e-9) == gaussian_cost with pytest.raises(ValueError): - chron.GaussianNLL(0.0) + diffid.GaussianNLL(0.0) def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: ds = """ -in = [L] -L { 2.5 } v { 5.0 } delta { 0.05 } +in_i { L = 2.5 } +v { 5.0 } delta { 0.05 } u_i { y = 0.0, psi = 0.0, @@ -221,7 +217,7 @@ def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: stacked_data = np.column_stack((t_span, y_true, psi_true)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_tolerances(rtol=1e-6, atol=1e-8) @@ -231,7 +227,7 @@ def test_diffsol_bicycle_model_neldermead_recovers_wheelbase() -> None: problem = builder.build() optimiser = ( - chron.NelderMead() + diffid.NelderMead() .with_max_iter(500) .with_threshold(1e-10) .with_position_tolerance(1e-8) diff --git a/tests/unit/test_dynamic_nested_sampler.py b/tests/unit/test_dynamic_nested_sampler.py index de58757..50d4819 100644 --- a/tests/unit/test_dynamic_nested_sampler.py +++ b/tests/unit/test_dynamic_nested_sampler.py @@ -4,7 +4,7 @@ import math -import chronopt as chron +import diffid import numpy as np import pytest from scipy.stats import norm @@ -36,14 +36,14 @@ def gaussian_nll(x: list[float]) -> float: return 0.5 * (diff / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) problem = ( - chron.ScalarBuilder() - .with_callable(gaussian_nll) + diffid.ScalarBuilder() + .with_objective(gaussian_nll) .with_parameter("x", mu, bounds=(prior_lower, prior_upper)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_termination_tolerance(1e-5) @@ -84,14 +84,14 @@ def exponential_nll(x: list[float]) -> float: return lambda_param * x[0] problem = ( - chron.ScalarBuilder() - .with_callable(exponential_nll) + diffid.ScalarBuilder() + .with_objective(exponential_nll) .with_parameter("x", 1.0, bounds=(0.0, x_max)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.01) .with_termination_tolerance(1e-6) @@ -123,14 +123,14 @@ def bimodal_nll(x: list[float]) -> float: return -(log_sum - np.log(2) - np.log(sigma) - 0.5 * np.log(2 * np.pi)) problem = ( - chron.ScalarBuilder() - .with_callable(bimodal_nll) + diffid.ScalarBuilder() + .with_objective(bimodal_nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_termination_tolerance(1e-4) @@ -163,14 +163,14 @@ def pathological_nll(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(pathological_nll) + diffid.ScalarBuilder() + .with_objective(pathological_nll) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.2) .with_seed(456) @@ -195,7 +195,7 @@ def high_dim_quadratic(x: list[float]) -> float: """Simple quadratic in high dimensions.""" return 0.5 * sum(xi**2 for xi in x) - problem = chron.ScalarBuilder().with_callable(high_dim_quadratic) + problem = diffid.ScalarBuilder().with_objective(high_dim_quadratic) for i in range(dimension): problem = problem.with_parameter(f"x{i}", 0.0, bounds=(-3.0, 3.0)) @@ -203,7 +203,7 @@ def high_dim_quadratic(x: list[float]) -> float: problem = problem.build() sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.15) .with_termination_tolerance(1e-4) @@ -227,14 +227,14 @@ def sharp_peak(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(sharp_peak) + diffid.ScalarBuilder() + .with_objective(sharp_peak) .with_parameter("x", 0.0, bounds=(-1.0, 1.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.05) # Small expansion for narrow peak .with_termination_tolerance(1e-3) @@ -257,14 +257,14 @@ def large_offset(x: list[float]) -> float: return 100.0 + 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(large_offset) + diffid.ScalarBuilder() + .with_objective(large_offset) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(111) @@ -285,14 +285,14 @@ def simple_quadratic(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(simple_quadratic) + diffid.ScalarBuilder() + .with_objective(simple_quadratic) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(555) @@ -320,14 +320,14 @@ def quadratic(x: list[float]) -> float: return 0.5 * (x[0] - 1.0) ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(quadratic) + diffid.ScalarBuilder() + .with_objective(quadratic) .with_parameter("x", 1.0, bounds=(-3.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(777) @@ -359,14 +359,14 @@ def narrow_gaussian(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) problem = ( - chron.ScalarBuilder() - .with_callable(narrow_gaussian) + diffid.ScalarBuilder() + .with_objective(narrow_gaussian) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.1) .with_termination_tolerance(1e-3) @@ -388,21 +388,21 @@ def simple_problem(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(simple_problem) + diffid.ScalarBuilder() + .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler1 = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(12345) ) sampler2 = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(12345) @@ -430,14 +430,14 @@ def multimodal(x: list[float]) -> float: ) problem = ( - chron.ScalarBuilder() - .with_callable(multimodal) + diffid.ScalarBuilder() + .with_objective(multimodal) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.5) # Allow significant expansion .with_seed(999) @@ -458,14 +458,14 @@ def bounded_quadratic(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(bounded_quadratic) + diffid.ScalarBuilder() + .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(lower, upper)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(444) @@ -486,14 +486,14 @@ def simple_problem(x: list[float]) -> float: return 0.5 * x[0] ** 2 problem = ( - chron.ScalarBuilder() - .with_callable(simple_problem) + diffid.ScalarBuilder() + .with_objective(simple_problem) .with_parameter("x", 0.0, bounds=(-5.0, 5.0)) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(64) .with_expansion_factor(0.2) .with_seed(666) @@ -513,15 +513,15 @@ def nll(x: list[float]) -> float: return 0.5 * (x[0] / sigma) ** 2 + np.log(sigma) + 0.5 * np.log(2 * np.pi) return ( - chron.ScalarBuilder() - .with_callable(nll) + diffid.ScalarBuilder() + .with_objective(nll) .with_parameter("x", 0.0, bounds=(-10.0, 10.0)) .build() ) def sampler_config(seed): return ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(128) .with_expansion_factor(0.2) .with_seed(seed) diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py index 302cf3b..f857506 100644 --- a/tests/unit/test_optimisation.py +++ b/tests/unit/test_optimisation.py @@ -1,11 +1,11 @@ -import chronopt as chron +import diffid import numpy as np def test_builder_exposes_config_and_parameters(): builder = ( - chron.ScalarBuilder() - .with_callable(lambda x: np.asarray([float(x[0]) ** 2])) + diffid.ScalarBuilder() + .with_objective(lambda x: np.asarray([float(x[0]) ** 2])) .with_parameter("x", 3.5, bounds=(0.0, 10.0)) ) @@ -30,8 +30,8 @@ def bounded_quadratic(x): def test_python_builder_rosenbrock(): builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.2, None) .with_parameter("y", -1.2, None) ) @@ -39,7 +39,7 @@ def test_python_builder_rosenbrock(): # Create the optimisation optimiser = ( - chron.NelderMead().with_max_iter(500).with_threshold(1e-6).with_step_size(0.15) + diffid.NelderMead().with_max_iter(500).with_threshold(1e-6).with_step_size(0.15) ) results = optimiser.run(problem, [1.5, -1.5]) @@ -51,14 +51,14 @@ def test_python_builder_rosenbrock(): def test_python_builder_bounds_respected(): builder = ( - chron.ScalarBuilder() - .with_callable(bounded_quadratic) + diffid.ScalarBuilder() + .with_objective(bounded_quadratic) .with_parameter("x", 0.0, bounds=(0.0, 1.0)) .with_parameter("y", 0.0, bounds=(0.0, 2.0)) ) problem = builder.build() - optimiser = chron.NelderMead().with_max_iter(200).with_threshold(1e-8) + optimiser = diffid.NelderMead().with_max_iter(200).with_threshold(1e-8) results = optimiser.run(problem, [0.5, 1.0]) assert results.success diff --git a/tests/unit/test_optimisation_api.py b/tests/unit/test_optimisation_api.py index 6fbf309..9f7db58 100644 --- a/tests/unit/test_optimisation_api.py +++ b/tests/unit/test_optimisation_api.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np @@ -6,8 +6,7 @@ def _test_optimisation_api(): """Test basic Diffsol builder functionality""" # Example diffsol ODE (logistic growth) ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -20,7 +19,7 @@ def _test_optimisation_api(): # Build the problem builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_config({"rtol": 1e-6}) @@ -38,8 +37,7 @@ def _test_optimisation_api(): def test_diffsol_builder_allows_multiple_builds(): ds = """ -in = [r, k] -r { 1 } k { 1 } +in_i { r = 1, k = 1 } u_i { y = 0.1 } F_i { (r * y) * (1 - (y / k)) } """ @@ -49,7 +47,7 @@ def test_diffsol_builder_allows_multiple_builds(): stacked_data = np.column_stack((t_span, data)) builder = ( - chron.DiffsolBuilder() + diffid.DiffsolBuilder() .with_diffsl(ds) .with_data(stacked_data) .with_parameter("r", 1.0) @@ -72,8 +70,8 @@ def rosenbrock(x): return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2 builder = ( - chron.ScalarBuilder() - .with_callable(rosenbrock) + diffid.ScalarBuilder() + .with_objective(rosenbrock) .with_parameter("x", 1.0) .with_parameter("y", 1.0) ) @@ -94,7 +92,7 @@ def exponential_model(params): return y0 * np.exp(rate * t_span) builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(exponential_model) .with_data(data) .with_parameter("rate", 1.0) @@ -112,17 +110,17 @@ def test_all_builders_support_copy(): import copy # ScalarBuilder - scalar_builder = chron.ScalarBuilder().with_callable(lambda x: x[0] ** 2) + scalar_builder = diffid.ScalarBuilder().with_objective(lambda x: x[0] ** 2) scalar_copy = copy.copy(scalar_builder) copy.deepcopy(scalar_builder) # Test deepcopy # DiffsolBuilder - diffsol_builder = chron.DiffsolBuilder().with_diffsl("in = [a]") + diffsol_builder = diffid.DiffsolBuilder().with_diffsl("in { a }") diffsol_copy = copy.copy(diffsol_builder) copy.deepcopy(diffsol_builder) # Test deepcopy # VectorBuilder - vector_builder = chron.VectorBuilder().with_objective(lambda x: [x[0]]) + vector_builder = diffid.VectorBuilder().with_objective(lambda x: [x[0]]) vector_copy = copy.copy(vector_builder) copy.deepcopy(vector_builder) # Test deepcopy diff --git a/tests/unit/test_python_autodiff.py b/tests/unit/test_python_autodiff.py index ad5a64f..fb05e15 100644 --- a/tests/unit/test_python_autodiff.py +++ b/tests/unit/test_python_autodiff.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -24,10 +24,10 @@ def _quadratic_gradient(x): def quadratic_problem(): - """Creates a 3D quadratic optimization problem using ScalarBuilder""" + """Creates a 3D quadratic optimisation problem using ScalarBuilder""" return ( - chron.ScalarBuilder() - .with_callable(_quadratic_objective) + diffid.ScalarBuilder() + .with_objective(_quadratic_objective) .with_gradient(_quadratic_gradient) .with_parameter("x1", 1.0) .with_parameter("x2", 2.0) diff --git a/tests/unit/test_samplers.py b/tests/unit/test_samplers.py index 414261d..dba5fca 100644 --- a/tests/unit/test_samplers.py +++ b/tests/unit/test_samplers.py @@ -1,6 +1,6 @@ import math -import chronopt as chron +import diffid import numpy as np import pytest @@ -12,14 +12,14 @@ def quadratic_potential(x: np.ndarray) -> np.ndarray: def test_metropolis_hastings_runs_and_returns_samples(): problem = ( - chron.ScalarBuilder() - .with_callable(quadratic_potential) + diffid.ScalarBuilder() + .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) sampler = ( - chron.MetropolisHastings() + diffid.MetropolisHastings() .with_num_chains(3) .with_iterations(400) .with_step_size(0.4) @@ -44,14 +44,14 @@ def test_metropolis_hastings_runs_and_returns_samples(): def test_dynamic_nested_sampler_runs_on_scalar_problem(): problem = ( - chron.ScalarBuilder() - .with_callable(quadratic_potential) + diffid.ScalarBuilder() + .with_objective(quadratic_potential) .with_parameter("x", 0.5) .build() ) sampler = ( - chron.DynamicNestedSampler() + diffid.DynamicNestedSampler() .with_live_points(32) .with_expansion_factor(0.1) .with_seed(99) @@ -67,20 +67,20 @@ def test_dynamic_nested_sampler_runs_on_scalar_problem(): def test_dynamic_nested_invalid_live_points_are_clamped(): problem = ( - chron.ScalarBuilder() - .with_callable(quadratic_potential) + diffid.ScalarBuilder() + .with_objective(quadratic_potential) .with_parameter("x", 1.0) .build() ) - sampler = chron.DynamicNestedSampler().with_live_points(1) + sampler = diffid.DynamicNestedSampler().with_live_points(1) nested = sampler.run(problem) assert nested.draws >= 0 def test_dynamic_nested_requires_problem_instance(): - sampler = chron.DynamicNestedSampler() + sampler = diffid.DynamicNestedSampler() with pytest.raises(TypeError): sampler.run(object()) # type: ignore[arg-type] diff --git a/tests/unit/test_vector.py b/tests/unit/test_vector.py index 8629500..24a13c9 100644 --- a/tests/unit/test_vector.py +++ b/tests/unit/test_vector.py @@ -1,4 +1,4 @@ -import chronopt as chron +import diffid import numpy as np import pytest @@ -17,12 +17,12 @@ def exponential_model(params): # Build the problem builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(exponential_model) .with_data(data) .with_parameter("rate", 1.0, None) .with_parameter("y0", 1.0, None) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem = builder.build() @@ -34,8 +34,8 @@ def exponential_model(params): assert np.isfinite(cost), f"Cost should be finite, got {cost}" assert cost >= 0, f"Cost should be non-negative, got {cost}" - # Test optimization - optimiser = chron.NelderMead().with_max_iter(1000).with_threshold(1e-8) + # Test optimisation + optimiser = diffid.NelderMead().with_max_iter(1000).with_threshold(1e-8) result = problem.optimise(x0, optimiser) assert result.success @@ -55,7 +55,7 @@ def model(params): return params[0] * np.ones(n_points) + params[1] builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("scale", 1.0) @@ -80,22 +80,22 @@ def sinusoid(params): return amp * np.sin(freq * t + phase) problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(sinusoid) .with_data(data) .with_parameter("amplitude", 2.0, (0.0, 5.0)) .with_parameter("frequency", 1.0, (0.1, 3.0)) .with_parameter("phase", 0.0, (-np.pi, np.pi)) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) .build() ) x0 = [2.0, 1.0, 0.0] - optimiser = chron.NelderMead().with_max_iter(2000).with_threshold(1e-9) + optimiser = diffid.NelderMead().with_max_iter(2000).with_threshold(1e-9) result = problem.optimise(x0, optimiser) # Note: Sinusoidal fitting can be challenging due to local minima - # We just verify the optimization runs and produces reasonable results + # We just verify the optimisation runs and produces reasonable results assert ( result.success or result.iterations >= 1000 ) # Either converged or tried hard enough @@ -114,7 +114,7 @@ def quadratic(params): def build_problem(cost_metric=None): builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(quadratic) .with_data(data) .with_parameter("scale", 1.5) @@ -124,9 +124,9 @@ def build_problem(cost_metric=None): return builder.build() sse_problem = build_problem() - sse_problem_explicit = build_problem(chron.SSE()) - rmse_problem = build_problem(chron.RMSE()) - gaussian_problem = build_problem(chron.GaussianNLL(1.0)) + sse_problem_explicit = build_problem(diffid.SSE()) + rmse_problem = build_problem(diffid.RMSE()) + gaussian_problem = build_problem(diffid.GaussianNLL(1.0)) test_params = [1.5] sse_cost = sse_problem.evaluate(test_params) @@ -157,21 +157,21 @@ def model(params): # Build with SSE builder1 = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("a", 1.0) - .with_cost(chron.SSE()) + .with_cost(diffid.SSE()) ) problem1 = builder1.build() # Build with RMSE builder2 = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("a", 1.0) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) ) problem2 = builder2.build() @@ -188,10 +188,10 @@ def test_vector_builder_with_default_optimiser(): def linear(params): return params[0] * np.arange(4) + params[1] - optimiser = chron.NelderMead().with_max_iter(100) + optimiser = diffid.NelderMead().with_max_iter(100) problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(linear) .with_data(data) .with_parameter("slope", 0.5) @@ -214,7 +214,7 @@ def wrong_size(params): return params[0] * np.ones(5) # Wrong size! problem = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(wrong_size) .with_data(data) .with_parameter("a", 1.0) @@ -222,7 +222,7 @@ def wrong_size(params): ) with pytest.raises( - chron.errors.EvaluationError, + diffid.errors.EvaluationError, match="Evaluation failed: Evaluation failed:: expected 3 elements, got 5", ): problem.evaluate([1.0]) @@ -237,16 +237,16 @@ def model(params): # Build two problems with same configuration builder = ( - chron.VectorBuilder() + diffid.VectorBuilder() .with_objective(model) .with_data(data) .with_parameter("scale", 1.0) - .with_cost(chron.RMSE()) + .with_cost(diffid.RMSE()) ) problem1 = builder.build() builder.remove_cost() - builder.with_cost(chron.SSE()) + 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