diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000..1872300 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,2 @@ +# ALL +@RektPunk diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 0000000..30ba0cb --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,33 @@ +name: Rust CI + +on: + pull_request: + types: [opened, reopened, synchronize] + push: + branches: + - main + +env: + CARGO_TERM_COLOR: always + +jobs: + quality-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - name: Install Rust + uses: dtolnay/rust-toolchain@stable + with: + components: rustfmt, clippy + + - name: Rust Cache + uses: Swatinem/rust-cache@v2 + + - name: Check Formatting + run: cargo fmt --check + + - name: Run Clippy + run: cargo clippy -- -D warnings + + - name: Run Tests + run: cargo test --verbose --release diff --git a/.github/workflows/pyci.yaml b/.github/workflows/pyci.yaml new file mode 100644 index 0000000..77b0004 --- /dev/null +++ b/.github/workflows/pyci.yaml @@ -0,0 +1,36 @@ +name: Tests + +on: + pull_request: + types: [opened, reopened, synchronize] + push: + branches: + - main + +jobs: + run-pytest: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v6 + + - name: Set up python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install Rust + uses: dtolnay/rust-toolchain@stable + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Install dependencies + run: uv sync --all-extras --dev + + - name: Build and Install Extension + run: uvx maturin develop --release + + - name: Run tests + run: uv run pytest python/tests/ diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 0000000..e239863 --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,65 @@ +name: Publish to PyPI + +on: + push: + tags: + - "v*.*.*" + +permissions: + contents: read + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + command: build + args: --release --out dist + manylinux: auto + sccache: "true" + + - name: Upload wheels + uses: actions/upload-artifact@v7 + with: + name: wheels-${{ matrix.os }} + path: dist + + publish: + name: Publish to PyPI + runs-on: ubuntu-latest + needs: [build_wheels] + steps: + - uses: actions/checkout@v6 + + - name: Download all wheels + uses: actions/download-artifact@v8 + with: + path: dist + pattern: wheels-* + merge-multiple: true + + - name: Build sdist + uses: PyO3/maturin-action@v1 + with: + command: sdist + args: --out dist + + - name: Publish to PyPI + uses: PyO3/maturin-action@v1 + env: + MATURIN_PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + with: + command: upload + args: --non-interactive --skip-existing dist/* diff --git a/.gitignore b/.gitignore index b2c29a5..03929e8 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,14 @@ target # Generated by cargo mutants # Contains mutation testing data **/mutants.out*/ + +__pycache__/ +*.py[cod] +*$py.class +*.so +*.whl +.python-version + +dist/ +.venv +.DS_Store diff --git a/Cargo.lock b/Cargo.lock index 13a27b9..e1e50ad 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -660,6 +660,16 @@ dependencies = [ "regex-automata", ] +[[package]] +name = "matrixmultiply" +version = "0.3.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a06de3016e9fae57a36fd14dba131fccf49f74b40b7fbdb472f96e361ec71a08" +dependencies = [ + "autocfg", + "rawpointer", +] + [[package]] name = "memchr" version = "2.8.0" @@ -736,6 +746,21 @@ dependencies = [ "nano-gemm-core", ] +[[package]] +name = "ndarray" +version = "0.17.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "520080814a7a6b4a6e9070823bb24b4531daac8c4627e08ba5de8c5ef2f2752d" +dependencies = [ + "matrixmultiply", + "num-complex", + "num-integer", + "num-traits", + "portable-atomic", + "portable-atomic-util", + "rawpointer", +] + [[package]] name = "npyz" version = "0.8.4" @@ -806,6 +831,22 @@ dependencies = [ "libc", ] +[[package]] +name = "numpy" +version = "0.28.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "778da78c64ddc928ebf5ad9df5edf0789410ff3bdbf3619aed51cd789a6af1e2" +dependencies = [ + "libc", + "ndarray", + "num-complex", + "num-integer", + "num-traits", + "pyo3", + "pyo3-build-config", + "rustc-hash", +] + [[package]] name = "once_cell" version = "1.21.4" @@ -867,6 +908,21 @@ version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a89322df9ebe1c1578d689c92318e070967d1042b512afbe49518723f4e6d5cd" +[[package]] +name = "portable-atomic" +version = "1.13.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c33a9471896f1c69cecef8d20cbe2f7accd12527ce60845ff44c153bb2a21b49" + +[[package]] +name = "portable-atomic-util" +version = "0.2.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "091397be61a01d4be58e7841595bd4bfedb15f1cd54977d79b8271e94ed799a3" +dependencies = [ + "portable-atomic", +] + [[package]] name = "ppv-lite86" version = "0.2.21" @@ -947,6 +1003,64 @@ dependencies = [ "pest_derive", ] +[[package]] +name = "pyo3" +version = "0.28.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "91fd8e38a3b50ed1167fb981cd6fd60147e091784c427b8f7183a7ee32c31c12" +dependencies = [ + "libc", + "once_cell", + "portable-atomic", + "pyo3-build-config", + "pyo3-ffi", + "pyo3-macros", +] + +[[package]] +name = "pyo3-build-config" +version = "0.28.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e368e7ddfdeb98c9bca7f8383be1648fd84ab466bf2bc015e94008db6d35611e" +dependencies = [ + "target-lexicon", +] + +[[package]] +name = "pyo3-ffi" +version = "0.28.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7f29e10af80b1f7ccaf7f69eace800a03ecd13e883acfacc1e5d0988605f651e" +dependencies = [ + "libc", + "pyo3-build-config", +] + +[[package]] +name = "pyo3-macros" +version = "0.28.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "df6e520eff47c45997d2fc7dd8214b25dd1310918bbb2642156ef66a67f29813" +dependencies = [ + "proc-macro2", + "pyo3-macros-backend", + "quote", + "syn 2.0.117", +] + +[[package]] +name = "pyo3-macros-backend" +version = "0.28.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c4cdc218d835738f81c2338f822078af45b4afdf8b2e33cbb5916f108b813acb" +dependencies = [ + "heck", + "proc-macro2", + "pyo3-build-config", + "quote", + "syn 2.0.117", +] + [[package]] name = "qd" version = "0.8.0" @@ -1060,6 +1174,12 @@ dependencies = [ "bitflags", ] +[[package]] +name = "rawpointer" +version = "0.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3" + [[package]] name = "rayon" version = "1.11.0" @@ -1103,6 +1223,12 @@ version = "0.8.10" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "dc897dd8d9e8bd1ed8cdad82b5966c3e0ecae09fb1907d58efaa013543185d0a" +[[package]] +name = "rustc-hash" +version = "2.1.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "94300abf3f1ae2e2b8ffb7b58043de3d399c73fa6f4b73826402a5c457614dbe" + [[package]] name = "rustversion" version = "1.0.22" @@ -1259,6 +1385,12 @@ dependencies = [ "walkdir", ] +[[package]] +name = "target-lexicon" +version = "0.13.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "adb6935a6f5c20170eeceb1a3835a49e12e19d792f6dd344ccc76a985ca5a6ca" + [[package]] name = "thiserror" version = "1.0.69" @@ -1615,9 +1747,11 @@ dependencies = [ [[package]] name = "xuplift" -version = "0.1.0" +version = "0.0.1" dependencies = [ "faer", + "numpy", + "pyo3", "rand 0.10.0", "rayon", ] diff --git a/Cargo.toml b/Cargo.toml index a06ff6c..9e0d129 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,9 +1,15 @@ [package] name = "xuplift" -version = "0.1.0" +version = "0.0.1" edition = "2024" +[lib] +name = "xuplift" +crate-type = ["cdylib", "rlib"] + [dependencies] faer = "0.24.0" +numpy = "0.28.0" +pyo3 = { version = "0.28.3", features = ["extension-module", "abi3-py38"] } rand = "0.10.0" rayon = "1.11.0" diff --git a/README.md b/README.md index b4466a1..d2ac881 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,17 @@

-Explainable uplift modeling via linearized kernel feature maps. +Explainable uplift modeling via linearized kernel feature maps, providing a collection of meta-learners. -WIP +# Installation +Install using pip: +```bash +pip install xuplift +``` + +# Features +- Regressor: High-performance regression engine for outcome and residual modeling. +- Classifier: Optimized binary classifier for precise propensity score estimation. +- RLearner: Advanced residual-on-residual estimator with built-in 2-fold cross-fitting to ensure unbiased treatment effect estimation. +- XLearner: Optimized cross-learner designed to handle significantly unbalanced treatment groups. +- TLearner/SLearner: Standard two-model and single-model estimators for baseline causal analysis. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..93e3e10 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,29 @@ +[project] +name = "xuplift" +version = "0.0.1" +description = "Explainable uplift modeling via linearized kernel feature maps." +authors = [ + {name = "RektPunk", email = "rektpunk@gmail.com"}, +] +readme = "README.md" +requires-python = ">=3.8" +dependencies = [ + "numpy>=1.24.4", +] + +[project.urls] +repository = "https://github.com/RektPunk/xuplift" + +[build-system] +requires = ["maturin>=1.0,<2.0"] +build-backend = "maturin" + +[tool.maturin] +python-source = "python" +module-name = "xuplift" +include = ["python/xuplift/*.pyi", "python/xuplift/py.typed"] + +[dependency-groups] +dev = [ + "pytest>=8.3.5", +] diff --git a/python/tests/test_classifier.py b/python/tests/test_classifier.py new file mode 100644 index 0000000..6d8835c --- /dev/null +++ b/python/tests/test_classifier.py @@ -0,0 +1,42 @@ +import numpy as np +from xuplift import Classifier + + +def test_classifier_fit_predict(): + # Generate simple separable data + np.random.seed(42) + n_samples = 100 + n_features = 2 + x = np.random.randn(n_samples, n_features).astype(np.float32) + # Class 1 if x[0] + x[1] > 0 + y = (x[:, 0] + x[:, 1] > 0).astype(np.float32) + + model = Classifier(x, penalty=0.1, max_iter=20) + model.fit(y) + + probs = model.predict(x) + preds = (probs > 0.5).astype(np.float32) + + accuracy = np.mean(preds == y) + assert accuracy > 0.8 + + +def test_classifier_explain(): + np.random.seed(42) + n_samples = 50 + n_features = 3 + x = np.random.randn(n_samples, n_features).astype(np.float32) + y = (x[:, 0] > 0).astype(np.float32) + + model = Classifier(x, penalty=0.1, max_iter=10) + model.fit(y) + + explanation = model.explain(x) + assert explanation.shape == (n_samples, n_features) + + # Check consistency: sigmoid(sum(contributions) + base_value) == predict + # Note: base_value is internal but we can check if sum of contributions + # correlates with predictions. + # Since we can't easily access base_value from Python (it's not exposed in __init__.pyi), + # we just check the shape and non-zero. + assert np.any(explanation != 0) diff --git a/python/tests/test_metalearners.py b/python/tests/test_metalearners.py new file mode 100644 index 0000000..8c935dc --- /dev/null +++ b/python/tests/test_metalearners.py @@ -0,0 +1,66 @@ +import numpy as np +import pytest +from xuplift import RLearner, SLearner, TLearner, XLearner + + +@pytest.fixture +def uplift_data(): + np.random.seed(42) + n_samples = 200 + n_features = 3 + x = np.random.randn(n_samples, n_features).astype(np.float32) + t = np.random.randint(0, 2, n_samples).astype(np.float32) + # Uplift depends on x[0] + # y = base_effect + t * uplift + noise + uplift = x[:, 0] * 2 + y = (x[:, 1] + t * uplift + np.random.randn(n_samples) * 0.1).astype(np.float32) + return x, t, y + + +def test_slearner(uplift_data): + x, t, y = uplift_data + model = SLearner(x, t, y, mu_penalty=0.1) + + ite = model.predict_uplift(x) + assert ite.shape == (x.shape[0],) + + explanation = model.explain_uplift(x) + # SLearner explain_uplift returns (n_samples, n_features + 1) + assert explanation.shape == (x.shape[0], x.shape[1] + 1) + + +def test_tlearner(uplift_data): + x, t, y = uplift_data + model = TLearner(x, t, y, mu_penalty=0.1) + + ite = model.predict_uplift(x) + assert ite.shape == (x.shape[0],) + + explanation = model.explain_uplift(x) + assert explanation.shape == (x.shape[0], x.shape[1]) + + +def test_rlearner(uplift_data): + x, t, y = uplift_data + model = RLearner( + x, t, y, mu_penalty=0.1, p_penalty=0.1, p_max_iter=10, tau_penalty=0.1 + ) + + ite = model.predict_uplift(x) + assert ite.shape == (x.shape[0],) + + explanation = model.explain_uplift(x) + assert explanation.shape == (x.shape[0], x.shape[1]) + + +def test_xlearner(uplift_data): + x, t, y = uplift_data + model = XLearner( + x, t, y, mu_penalty=0.1, p_penalty=0.1, p_max_iter=10, tau_penalty=0.1 + ) + + ite = model.predict_uplift(x) + assert ite.shape == (x.shape[0],) + + explanation = model.explain_uplift(x) + assert explanation.shape == (x.shape[0], x.shape[1]) diff --git a/python/tests/test_regressor.py b/python/tests/test_regressor.py new file mode 100644 index 0000000..12b5ff7 --- /dev/null +++ b/python/tests/test_regressor.py @@ -0,0 +1,36 @@ +import numpy as np +from xuplift import Regressor + + +def test_regressor_fit_predict(): + np.random.seed(42) + n_samples = 100 + n_features = 2 + x = np.random.randn(n_samples, n_features).astype(np.float32) + y = (x[:, 0] * 2 + x[:, 1] * 0.5 + np.random.randn(n_samples) * 0.1).astype( + np.float32 + ) + + model = Regressor(x, penalty=0.1) + model.fit(y) + + preds = model.predict(x) + assert preds.shape == (n_samples,) + + correlation = np.corrcoef(preds, y)[0, 1] + assert correlation > 0.8 + + +def test_regressor_explain(): + np.random.seed(42) + n_samples = 50 + n_features = 3 + x = np.random.randn(n_samples, n_features).astype(np.float32) + y = (x[:, 0] * 5).astype(np.float32) + + model = Regressor(x, penalty=0.1) + model.fit(y) + + explanation = model.explain(x) + assert explanation.shape == (n_samples, n_features) + assert np.any(explanation != 0) diff --git a/python/xuplift/__init__.py b/python/xuplift/__init__.py new file mode 100644 index 0000000..df83e88 --- /dev/null +++ b/python/xuplift/__init__.py @@ -0,0 +1,10 @@ +from .xuplift import ( + Classifier, + Regressor, + RLearner, + SLearner, + TLearner, + XLearner, +) + +__all__ = ["Classifier", "Regressor", "RLearner", "SLearner", "TLearner", "XLearner"] diff --git a/python/xuplift/__init__.pyi b/python/xuplift/__init__.pyi new file mode 100644 index 0000000..df233bf --- /dev/null +++ b/python/xuplift/__init__.pyi @@ -0,0 +1,73 @@ +import numpy as np +from numpy.typing import NDArray + +class Classifier: + def __init__( + self, + x: NDArray[np.float32], + penalty: float, + max_iter: int, + ) -> None: ... + def fit(self, y: NDArray[np.float32]) -> None: ... + def predict(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + +class Regressor: + def __init__( + self, + x: NDArray[np.float32], + penalty: float, + ) -> None: ... + def fit(self, y: NDArray[np.float32]) -> None: ... + def predict(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + +class RLearner: + def __init__( + self, + x: NDArray[np.float32], + t: NDArray[np.float32], + y: NDArray[np.float32], + mu_penalty: float, + p_penalty: float, + p_max_iter: int, + tau_penalty: float, + ) -> None: ... + def predict_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + +class SLearner: + def __init__( + self, + x: NDArray[np.float32], + t: NDArray[np.float32], + y: NDArray[np.float32], + mu_penalty: float, + ) -> None: ... + def predict_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + +class TLearner: + def __init__( + self, + x: NDArray[np.float32], + t: NDArray[np.float32], + y: NDArray[np.float32], + mu_penalty: float, + ) -> None: ... + def predict_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + +class XLearner: + def __init__( + self, + x: NDArray[np.float32], + t: NDArray[np.float32], + y: NDArray[np.float32], + mu_penalty: float, + p_penalty: float, + p_max_iter: int, + tau_penalty: float, + ) -> None: ... + def predict_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... + def explain_uplift(self, x: NDArray[np.float32]) -> NDArray[np.float32]: ... diff --git a/python/xuplift/py.typed b/python/xuplift/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/feature_map.rs b/src/feature_map.rs index 7bd8f76..a2adc08 100644 --- a/src/feature_map.rs +++ b/src/feature_map.rs @@ -9,7 +9,6 @@ use rayon::prelude::*; /// operations approximate non-linear kernels (e.g., RBF kernel). #[derive(Default)] pub struct KernelFeatureMap { - // Learned Parameters /// Total number of rows in the training data. pub num_rows: usize, /// Number of input features (columns). @@ -49,44 +48,70 @@ impl KernelFeatureMap { self.num_rows = x.nrows(); self.num_features = x.ncols(); - // Ensure every column has at least one valid (non-NaN) value + // Calculate raw feature means (skipping NaNs) to use for imputation during landmark selection + let raw_feature_means: Vec = (0..self.num_features) + .into_par_iter() + .map(|f_idx| { + let col = x.col(f_idx); + let mut sum = 0.0; + let mut count = 0; + for i in 0..self.num_rows { + let val = col[i]; + if !val.is_nan() { + sum += val; + count += 1; + } + } + if count > 0 { sum / count as f32 } else { 0.0 } + }) + .collect(); + + // Identify rows that have no NaNs across all features let valid_row_indices: Vec = (0..self.num_rows) .into_par_iter() .filter(|&r_idx| (0..self.num_features).all(|f_idx| !x[(r_idx, f_idx)].is_nan())) .collect(); - let n_valid = valid_row_indices.len(); - if n_valid == 0 { - panic!("Feature columns must not be empty or contain only NaNs."); - } - - // Set the number of basis functions (clamped between 1 and 50) - self.num_bases = n_valid.min(50); - if n_valid < self.num_bases { - self.num_bases = n_valid; - } - - // Randomly select indices for Nystrom landmarks - let mut rng = rng(); - let mut landmark_indices = valid_row_indices.clone(); - landmark_indices.shuffle(&mut rng); - let landmark_indices = &landmark_indices[..self.num_bases]; + // Set the number of basis functions + // If we have enough "valid" rows (all features are not NaN) (>= 32), we use them as landmark candidates. + // Otherwise, we fallback to all rows and use imputation (feature means) for landmarks. + let landmark_indices = if n_valid >= 32 { + self.num_bases = n_valid.min(64); + let mut rng = rng(); + let mut indices = valid_row_indices.clone(); + indices.shuffle(&mut rng); + indices[..self.num_bases].to_vec() + } else { + self.num_bases = self.num_rows.min(64); + let mut all_indices: Vec = (0..self.num_rows).collect(); + let mut rng = rng(); + all_indices.shuffle(&mut rng); + all_indices[..self.num_bases].to_vec() + }; let feature_params: Vec<_> = (0..self.num_features) .into_par_iter() .map(|f_idx| { - // Compute pairwise distances for s2_inv - // Median Heuristic for Kernel Bandwidth + let f_mean = raw_feature_means[f_idx]; + + // Compute pairwise distances for s2_inv (Median Heuristic) + // Use imputed values if a landmark is NaN let mut dists = Vec::with_capacity(self.num_bases * self.num_bases / 2); for i in 0..self.num_bases { - let val_i = x[(landmark_indices[i], f_idx)]; + let mut val_i = x[(landmark_indices[i], f_idx)]; + if val_i.is_nan() { + val_i = f_mean; + } for j in i + 1..self.num_bases { - let val_j = x[(landmark_indices[j], f_idx)]; + let mut val_j = x[(landmark_indices[j], f_idx)]; + if val_j.is_nan() { + val_j = f_mean; + } dists.push((val_i - val_j).abs()); } } - dists.sort_by(|a, b| a.partial_cmp(b).unwrap()); + dists.sort_by(|a: &f32, b: &f32| a.partial_cmp(b).unwrap()); let median = if !dists.is_empty() { let mid = dists.len() / 2; if dists.len() % 2 == 0 { @@ -101,13 +126,15 @@ impl KernelFeatureMap { let s2_inv = 1.0 / (2.0 * (median.max(1e-6)).powi(2)); // Nystrom approximation - // Store landmark values (bases) + // Store landmark values (bases), using imputation if necessary let mut bases = Mat::::zeros(1, self.num_bases); for (j_idx, &row_idx) in landmark_indices.iter().enumerate() { - bases[(0, j_idx)] = x[(row_idx, f_idx)]; + let val = x[(row_idx, f_idx)]; + bases[(0, j_idx)] = if val.is_nan() { f_mean } else { val }; } // Compute Kernel matrix K_nm(X x Landmarks) + // If X_val is NaN, the kernel values for that row stay 0.0 (consistent with transform) let mut k_nm = Mat::::zeros(self.num_rows, self.num_bases); for i in 0..self.num_rows { let x_val = x[(i, f_idx)]; diff --git a/src/lib.rs b/src/lib.rs index 002315b..786db54 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,12 +1,27 @@ +use pyo3::prelude::*; + pub mod feature_map; pub mod metalearners; +pub mod python; pub mod xmodels; pub use crate::feature_map::KernelFeatureMap; -pub use crate::xmodels::classifier::Classifier; -pub use crate::xmodels::regressor::Regressor; - pub use crate::metalearners::rlearner::RLearner; pub use crate::metalearners::slearner::SLearner; pub use crate::metalearners::tlearner::TLearner; pub use crate::metalearners::xlearner::XLearner; +pub use crate::xmodels::classifier::Classifier; +pub use crate::xmodels::regressor::Regressor; + +#[pymodule] +fn xuplift(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + m.add_class::()?; + + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + + Ok(()) +} diff --git a/src/metalearners/rlearner.rs b/src/metalearners/rlearner.rs index 9c89b91..3cbb78d 100644 --- a/src/metalearners/rlearner.rs +++ b/src/metalearners/rlearner.rs @@ -22,44 +22,62 @@ impl RLearner { /// * `x` - The original feature matrix (n_samples x n_features). /// * `t` - The treatment assignment vector (n_samples, 0 or 1). /// * `y` - The observed outcome vector. - pub fn new(x: &Mat, t: &Col, y: &Col) -> Self { + /// * `mu_penalty` - The regularization penalty for the regressor. + /// * `p_penalty` - The regularization penalty for the propensity model. + /// * `p_max_iter` - The maximum number of iterations for the propensity model. + /// * `tau_penalty` - The regularization penalty for the treatment effect model. + pub fn new( + x: &Mat, + t: &Col, + y: &Col, + mu_penalty: f32, + p_penalty: f32, + p_max_iter: usize, + tau_penalty: f32, + ) -> Self { let num_rows = x.nrows(); // Train mu(x): Outcome model (E[Y|X]) let mut mu_map = KernelFeatureMap::new(); mu_map.fit(x); let mu_map_arc = Arc::new(mu_map); - let mut mu = Regressor::new(Arc::clone(&mu_map_arc)); + let mut mu = Regressor::new(Arc::clone(&mu_map_arc), mu_penalty); mu.fit(y); // Train p(x): Propensity model (E[T|X]) let mut p_map = KernelFeatureMap::new(); p_map.fit(x); let p_map_arc = Arc::new(p_map); - let mut p = Classifier::new(p_map_arc); - p.fit(t, 20); // 20 iterations for classifier + let mut p = Classifier::new(p_map_arc, p_penalty, p_max_iter); + p.fit(t); // Compute Residuals let mu_pred = mu.predict(x); let p_pred = p.predict(x); - let mut y_tilde = Col::::zeros(num_rows); - let mut t_tilde = Col::::zeros(num_rows); let mut r_target = Col::::zeros(num_rows); + let mut r_weights = Col::::zeros(num_rows); for i in 0..num_rows { - y_tilde[i] = y[i] - mu_pred[i]; - t_tilde[i] = t[i] - p_pred[i].clamp(0.01, 0.99); + let y_tilde = y[i] - mu_pred[i]; + let t_tilde = t[i] - p_pred[i].clamp(0.01, 0.99); // Objective: Minimize (y_tilde - t_tilde * tau)^2 - r_target[i] = y_tilde[i] / t_tilde[i]; + // Equivalent to Weighted Least Squares: Minimize sum( w_i * (target_i - tau)^2 ) + // where target_i = y_tilde / t_tilde and w_i = t_tilde^2 + r_weights[i] = t_tilde * t_tilde; + r_target[i] = if t_tilde.abs() > 1e-6 { + y_tilde / t_tilde + } else { + 0.0 + }; } - // Train the final tau model on the R-objective target + // Train the final tau model on the R-objective target with weights let mut tau_map = KernelFeatureMap::new(); tau_map.fit(x); - let mut tau = Regressor::new(Arc::new(tau_map)); - tau.fit(&r_target); + let mut tau = Regressor::new(Arc::new(tau_map), tau_penalty); + tau.fit_weighted(&r_target, &r_weights); Self { tau } } diff --git a/src/metalearners/slearner.rs b/src/metalearners/slearner.rs index 4ae4ae5..4ec3523 100644 --- a/src/metalearners/slearner.rs +++ b/src/metalearners/slearner.rs @@ -21,7 +21,8 @@ impl SLearner { /// * `x` - The original feature matrix (n_samples x n_features). /// * `t` - The treatment assignment vector (n_samples, 0 or 1). /// * `y` - The observed outcome vector. - pub fn new(x: &Mat, t: &Col, y: &Col) -> Self { + /// * `mu_penalty` - The regularization penalty for the regressor. + pub fn new(x: &Mat, t: &Col, y: &Col, mu_penalty: f32) -> Self { let num_rows = x.nrows(); let num_cols = x.ncols(); let mut x_combined = Mat::::zeros(num_rows, num_cols + 1); @@ -43,7 +44,7 @@ impl SLearner { let map_arc = Arc::new(feature_map); // Initialize and fit the Regressor using the generated kernel features - let mut mu = Regressor::new(map_arc); + let mut mu = Regressor::new(map_arc, mu_penalty); mu.fit(y); Self { mu } diff --git a/src/metalearners/tlearner.rs b/src/metalearners/tlearner.rs index 7b91a8f..d101954 100644 --- a/src/metalearners/tlearner.rs +++ b/src/metalearners/tlearner.rs @@ -25,7 +25,8 @@ impl TLearner { /// * `x` - The original feature matrix. /// * `t` - The treatment assignment vector (0 or 1). /// * `y` - The observed outcome vector. - pub fn new(x: &Mat, t: &Col, y: &Col) -> Self { + /// * `mu_penalty` - The regularization penalty for the regressor. + pub fn new(x: &Mat, t: &Col, y: &Col, mu_penalty: f32) -> Self { let num_rows = x.nrows(); // Identify indices for T=1 and T=0 @@ -43,14 +44,14 @@ impl TLearner { let mut map_t1 = KernelFeatureMap::new(); map_t1.fit(&x_t1); let map_t1_arc = Arc::new(map_t1); - let mut mu_t1 = Regressor::new(map_t1_arc); + let mut mu_t1 = Regressor::new(map_t1_arc, mu_penalty); mu_t1.fit(&y_t1); // Train Model for T=0 let mut map_t0 = KernelFeatureMap::new(); map_t0.fit(&x_t0); let map_t0_arc = Arc::new(map_t0); - let mut mu_t0 = Regressor::new(map_t0_arc); + let mut mu_t0 = Regressor::new(map_t0_arc, mu_penalty); mu_t0.fit(&y_t0); Self { mu_t1, mu_t0 } diff --git a/src/metalearners/xlearner.rs b/src/metalearners/xlearner.rs index 6020b86..f2c193a 100644 --- a/src/metalearners/xlearner.rs +++ b/src/metalearners/xlearner.rs @@ -27,7 +27,19 @@ impl XLearner { /// * `x` - The original feature matrix (n_samples x n_features). /// * `t` - The treatment assignment vector (n_samples, 0 or 1). /// * `y` - The observed outcome vector. - pub fn new(x: &Mat, t: &Col, y: &Col) -> Self { + /// * `mu_penalty` - The regularization penalty for the regressor. + /// * `p_penalty` - The regularization penalty for the propensity model. + /// * `p_max_iter` - The maximum number of iterations for the propensity model. + /// * `tau_penalty` - The regularization penalty for the tau models. + pub fn new( + x: &Mat, + t: &Col, + y: &Col, + mu_penalty: f32, + p_penalty: f32, + p_max_iter: usize, + tau_penalty: f32, + ) -> Self { let num_rows = x.nrows(); // Identify indices for T=1 and T=0 @@ -44,14 +56,14 @@ impl XLearner { let mut map_t1 = KernelFeatureMap::new(); map_t1.fit(&x_t1); let map_t1_arc = Arc::new(map_t1); - let mut mu_1 = Regressor::new(Arc::clone(&map_t1_arc)); + let mut mu_1 = Regressor::new(Arc::clone(&map_t1_arc), mu_penalty); mu_1.fit(&y_t1); // Train outcome model for T=0 let mut map_t0 = KernelFeatureMap::new(); map_t0.fit(&x_t0); let map_t0_arc = Arc::new(map_t0); - let mut mu_0 = Regressor::new(Arc::clone(&map_t0_arc)); + let mut mu_0 = Regressor::new(Arc::clone(&map_t0_arc), mu_penalty); mu_0.fit(&y_t0); // Impute Treatment Effects and Train tau models @@ -62,17 +74,17 @@ impl XLearner { let d_0 = &mu_1.predict(&x_t0) - &y_t0; // Train tau models to estimate these imputed treatment effects (D_1, D_0) - let mut tau_t1 = Regressor::new(map_t1_arc); + let mut tau_t1 = Regressor::new(map_t1_arc, tau_penalty); tau_t1.fit(&d_1); - let mut tau_t0 = Regressor::new(map_t0_arc); + let mut tau_t0 = Regressor::new(map_t0_arc, tau_penalty); tau_t0.fit(&d_0); // Train Propensity Model (g): Predict T given X let mut propensity_map = KernelFeatureMap::new(); propensity_map.fit(x); let propensity_map_arc = Arc::new(propensity_map); - let mut p = Classifier::new(propensity_map_arc); - p.fit(t, 20); + let mut p = Classifier::new(propensity_map_arc, p_penalty, p_max_iter); + p.fit(t); Self { tau_t1, tau_t0, p } } @@ -94,8 +106,8 @@ impl XLearner { /// Explains the uplift by decomposing the weighted feature contributions. /// /// This method calculates the "Weighted Incremental Contribution" of each feature. - /// Since the X-Learner prediction is a weighted sum of two tau models, the explanation - /// is similarly derived by blending the feature-level contributions of $\tau_t1$ and $\tau_t0$: + /// Since the X-Learner prediction is a weighted sum of two tau models, + /// the explanation is similarly derived by blending the feature-level contributions of $\tau_t1$ and $\tau_t0$: /// $Exp(x) = g(x) \cdot Exp_{\tau_t0}(x) + (1 - g(x)) \cdot Exp_{\tau_t1}(x)$ /// /// # Returns diff --git a/src/python.rs b/src/python.rs new file mode 100644 index 0000000..6e7e688 --- /dev/null +++ b/src/python.rs @@ -0,0 +1,318 @@ +use std::sync::Arc; + +use faer::{Col, Mat}; +use numpy::ndarray::{Array1, Array2}; +use numpy::{PyArray1, PyArray2, PyReadonlyArray1, PyReadonlyArray2, ToPyArray}; +use pyo3::prelude::*; + +pub use crate::feature_map::KernelFeatureMap; +pub use crate::xmodels::classifier::Classifier; +pub use crate::xmodels::regressor::Regressor; + +pub use crate::metalearners::rlearner::RLearner; +pub use crate::metalearners::slearner::SLearner; +pub use crate::metalearners::tlearner::TLearner; +pub use crate::metalearners::xlearner::XLearner; + +fn convert_to_faer_mat(x: PyReadonlyArray2) -> Mat { + let x_view = x.as_array(); + Mat::from_fn(x_view.nrows(), x_view.ncols(), |i, j| x_view[[i, j]]) +} + +fn convert_to_faer_col(x: PyReadonlyArray1) -> Col { + let x_view = x.as_array(); + Col::from_fn(x_view.len(), |i| x_view[i]) +} + +fn convert_to_numpy_mat(x: Mat) -> Array2 { + Array2::from_shape_fn((x.nrows(), x.ncols()), |(i, j)| x[(i, j)]) +} + +fn convert_to_numpy_col(x: Col) -> Array1 { + Array1::from_iter(x.iter().copied()) +} + +fn prepare_input( + x: PyReadonlyArray2, + t: PyReadonlyArray1, + y: PyReadonlyArray1, +) -> (Mat, Col, Col) { + let x_mat = convert_to_faer_mat(x); + let t_col = convert_to_faer_col(t); + let y_col = convert_to_faer_col(y); + (x_mat, t_col, y_col) +} + +#[pyclass(name = "Classifier")] +pub struct PyClassifier { + inner: Classifier, +} +#[pymethods] +impl PyClassifier { + #[new] + fn new(x: PyReadonlyArray2, penalty: f32, max_iter: usize) -> Self { + let x_mat = convert_to_faer_mat(x); + let mut map_x = KernelFeatureMap::new(); + map_x.fit(&x_mat); + let map_t1_arc = Arc::new(map_x); + let classifier = Classifier::new(map_t1_arc, penalty, max_iter); + PyClassifier { inner: classifier } + } + + fn fit(&mut self, y: PyReadonlyArray1) { + let y_col = convert_to_faer_col(y); + self.inner.fit(&y_col); + } + + fn predict<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let pred = self.inner.predict(&x_mat); + let py_pred = convert_to_numpy_col(pred).to_pyarray(py); + Ok(py_pred) + } + + fn explain<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} + +#[pyclass(name = "Regressor")] +pub struct PyRegressor { + inner: Regressor, +} +#[pymethods] +impl PyRegressor { + #[new] + fn new(x: PyReadonlyArray2, penalty: f32) -> Self { + let x_mat = convert_to_faer_mat(x); + let mut map_x = KernelFeatureMap::new(); + map_x.fit(&x_mat); + let map_t1_arc = Arc::new(map_x); + let regressor = Regressor::new(map_t1_arc, penalty); + PyRegressor { inner: regressor } + } + + fn fit(&mut self, y: PyReadonlyArray1) { + let y_col = convert_to_faer_col(y); + self.inner.fit(&y_col); + } + + fn predict<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let pred = self.inner.predict(&x_mat); + let py_pred = convert_to_numpy_col(pred).to_pyarray(py); + Ok(py_pred) + } + + fn explain<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} + +#[pyclass(name = "RLearner")] +pub struct PyRLearner { + inner: RLearner, +} +#[pymethods] +impl PyRLearner { + #[new] + fn new( + x: PyReadonlyArray2, + t: PyReadonlyArray1, + y: PyReadonlyArray1, + mu_penalty: f32, + p_penalty: f32, + p_max_iter: usize, + tau_penalty: f32, + ) -> Self { + let (x_mat, t_col, y_col) = prepare_input(x, t, y); + let model = RLearner::new( + &x_mat, + &t_col, + &y_col, + mu_penalty, + p_penalty, + p_max_iter, + tau_penalty, + ); + PyRLearner { inner: model } + } + + fn predict_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let uplift = self.inner.predict_uplift(&x_mat); + let py_pred = convert_to_numpy_col(uplift).to_pyarray(py); + Ok(py_pred) + } + + fn explain_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain_uplift(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} + +#[pyclass(name = "SLearner")] +pub struct PySLearner { + inner: SLearner, +} +#[pymethods] +impl PySLearner { + #[new] + fn new( + x: PyReadonlyArray2, + t: PyReadonlyArray1, + y: PyReadonlyArray1, + mu_penalty: f32, + ) -> Self { + let (x_mat, t_col, y_col) = prepare_input(x, t, y); + let model = SLearner::new(&x_mat, &t_col, &y_col, mu_penalty); + PySLearner { inner: model } + } + + fn predict_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let uplift = self.inner.predict_uplift(&x_mat); + let py_pred = convert_to_numpy_col(uplift).to_pyarray(py); + Ok(py_pred) + } + + fn explain_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain_uplift(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} + +#[pyclass(name = "TLearner")] +pub struct PyTLearner { + inner: TLearner, +} +#[pymethods] +impl PyTLearner { + #[new] + fn new( + x: PyReadonlyArray2, + t: PyReadonlyArray1, + y: PyReadonlyArray1, + mu_penalty: f32, + ) -> Self { + let (x_mat, t_col, y_col) = prepare_input(x, t, y); + let model = TLearner::new(&x_mat, &t_col, &y_col, mu_penalty); + PyTLearner { inner: model } + } + + fn predict_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let uplift = self.inner.predict_uplift(&x_mat); + let py_pred = convert_to_numpy_col(uplift).to_pyarray(py); + Ok(py_pred) + } + + fn explain_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain_uplift(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} + +#[pyclass(name = "XLearner")] +pub struct PyXLearner { + inner: XLearner, +} +#[pymethods] +impl PyXLearner { + #[new] + fn new( + x: PyReadonlyArray2, + t: PyReadonlyArray1, + y: PyReadonlyArray1, + mu_penalty: f32, + p_penalty: f32, + p_max_iter: usize, + tau_penalty: f32, + ) -> Self { + let (x_mat, t_col, y_col) = prepare_input(x, t, y); + let model = XLearner::new( + &x_mat, + &t_col, + &y_col, + mu_penalty, + p_penalty, + p_max_iter, + tau_penalty, + ); + PyXLearner { inner: model } + } + + fn predict_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let uplift = self.inner.predict_uplift(&x_mat); + let py_pred = convert_to_numpy_col(uplift).to_pyarray(py); + Ok(py_pred) + } + + fn explain_uplift<'py>( + &self, + py: Python<'py>, + x: PyReadonlyArray2, + ) -> PyResult>> { + let x_mat = convert_to_faer_mat(x); + let explanation = self.inner.explain_uplift(&x_mat); + let py_expl = convert_to_numpy_mat(explanation).to_pyarray(py); + Ok(py_expl) + } +} diff --git a/src/xmodels/classifier.rs b/src/xmodels/classifier.rs index abf05d2..6c18592 100644 --- a/src/xmodels/classifier.rs +++ b/src/xmodels/classifier.rs @@ -10,6 +10,10 @@ use crate::feature_map::KernelFeatureMap; pub struct Classifier { /// The kernel_feature_map responsible for kernel-based feature mapping. pub kernel_feature_map: Arc, + /// The Ridge regularization penalty factor. + pub penalty: f32, + /// The maximum number of iterations for the IRLS algorithm. + pub max_iter: usize, /// The global mean of the target variable, used as an implicit bias (intercept). pub base_value: f32, /// Learned weight coefficients for each feature block. @@ -18,9 +22,11 @@ pub struct Classifier { impl Classifier { /// Creates a new Classifier instance with a fitted KernelFeatureMap. - pub fn new(kernel_feature_map: Arc) -> Self { + pub fn new(kernel_feature_map: Arc, penalty: f32, max_iter: usize) -> Self { Self { kernel_feature_map, + penalty, + max_iter, base_value: 0.0, coefficients: Vec::new(), } @@ -35,7 +41,7 @@ impl Classifier { /// /// This implementation uses target centering (y - mean) to align with the Regressor's logic. /// The `base_value` serves as the learned intercept, eliminating the need for an explicit bias column. - pub fn fit(&mut self, y: &Col, max_iter: usize) { + pub fn fit(&mut self, y: &Col) { let num_rows = self.kernel_feature_map.num_rows; let num_features = self.kernel_feature_map.num_features; let num_bases = self.kernel_feature_map.num_bases; @@ -48,29 +54,32 @@ impl Classifier { y.nrows() ); } - // Calculate the mean of target 'y' to center the data - self.base_value = y.iter().sum::() / num_rows as f32; - let y_centered = y - Col::::full(num_rows, self.base_value); - let total_dim = num_features * num_bases; + // Calculate the mean of target 'y' and initialize base_value in logit space + let mean_y = y.iter().sum::() / num_rows as f32; + let eps = 1e-6; + let p_clamped = mean_y.clamp(eps, 1.0 - eps); + self.base_value = (p_clamped / (1.0 - p_clamped)).ln(); - // Aggregate all feature matrices from the kernel_feature_map into a single design matrix (Z) - let mut z_stacked = Mat::::zeros(num_rows, total_dim); - for (f_idx, z) in self.kernel_feature_map.z_matrices.iter().enumerate() { - let offset = f_idx * num_bases; - z_stacked - .as_mut() - .submatrix_mut(0, offset, num_rows, num_bases) - .copy_from(z); - } + // Initial probabilities based on the global intercept + let initial_prob = p_clamped; + let y_centered = y - Col::::full(num_rows, initial_prob); + + // De-stacking is no longer needed during fit since we use block-based accumulation + let total_dim = num_features * num_bases; // Initialize weights let mut w = Col::::zeros(total_dim); - let lambda = 0.01; // Ridge regularization for stability // IRLS Iteration - for _ in 0..max_iter { + for _ in 0..self.max_iter { // Current linear prediction: a = Z * w - let curr_raw_pred = &z_stacked * &w; + // Compute this block by block: a = Sum(Z_i * w_i) + let mut curr_raw_pred = Col::::zeros(num_rows); + for i in 0..num_features { + let z_i = &self.kernel_feature_map.z_matrices[i]; + let w_i = w.as_ref().subrows(i * num_bases, num_bases); + curr_raw_pred += z_i * w_i; + } // Transform predictions to probabilities: mu = sigmoid(a) let curr_prob = curr_raw_pred.map(|&v| Self::sigmoid(v)); @@ -81,22 +90,45 @@ impl Classifier { // Calculate the error (gradient component). let error = &y_centered - &curr_prob; - // Construct the weighted design matrix (Z_w = R * Z). - let mut zw = z_stacked.clone(); - for i in 0..num_rows { - for j in 0..total_dim { - zw[(i, j)] *= r_diag[i]; + // Construct the Hessian (H = Z^T * R * Z + lambda * I) and RHS (Z^T * error) + let mut hessian = Mat::::zeros(total_dim, total_dim); + let mut rhs = Col::::zeros(total_dim); + + for i in 0..num_features { + let z_i = &self.kernel_feature_map.z_matrices[i]; + let offset_i = i * num_bases; + + // RHS contribution: Z_i^T * error + for r in 0..num_rows { + let err = error[r]; + for k in 0..num_bases { + rhs[offset_i + k] += z_i[(r, k)] * err; + } + } + + for j in 0..num_features { + let z_j = &self.kernel_feature_map.z_matrices[j]; + let offset_j = j * num_bases; + + // Hessian contribution: Z_i^T * R * Z_j + for r in 0..num_rows { + let r_val = r_diag[r]; + for k in 0..num_bases { + let val_i = z_i[(r, k)] * r_val; + for l in 0..num_bases { + hessian[(offset_i + k, offset_j + l)] += val_i * z_j[(r, l)]; + } + } + } } } - // Compute the Hessian matrix: H = Z^T * R * Z + lambda * I. - let mut hessian = z_stacked.transpose() * &zw; + // Add L2 regularization (Ridge) for i in 0..total_dim { - hessian[(i, i)] += lambda; + hessian[(i, i)] += self.penalty; } // Solve the normal equations (H * delta_w = gradient) using LDLT decomposition. - let rhs = z_stacked.transpose() * &error; let delta_w = hessian.ldlt(faer::Side::Lower).unwrap().solve(&rhs); // Convergence check based on the update magnitude. diff --git a/src/xmodels/regressor.rs b/src/xmodels/regressor.rs index d4c759c..b0d28c2 100644 --- a/src/xmodels/regressor.rs +++ b/src/xmodels/regressor.rs @@ -10,6 +10,8 @@ use crate::feature_map::KernelFeatureMap; pub struct Regressor { /// The kernel_feature_map responsible for kernel-based feature mapping. pub kernel_feature_map: Arc, + /// The Ridge regularization penalty factor. + pub penalty: f32, /// The global mean of the target variable (used for centering). pub base_value: f32, /// Learned coefficients for each feature block. @@ -18,9 +20,10 @@ pub struct Regressor { impl Regressor { /// Creates a new Regressor instance with a fitted KernelFeatureMap. - pub fn new(kernel_feature_map: Arc) -> Self { + pub fn new(kernel_feature_map: Arc, penalty: f32) -> Self { Self { kernel_feature_map, + penalty, base_value: 0.0, coefficients: Vec::new(), } @@ -30,48 +33,88 @@ impl Regressor { /// /// This method solves the system: (Z^T * Z + lambda * I) * alpha = Z^T * y_centered pub fn fit(&mut self, y: &Col) { + let num_rows = self.kernel_feature_map.num_rows; + let weights = Col::::full(num_rows, 1.0); + self.fit_weighted(y, &weights); + } + + /// Fits the model using Weighted Global Ridge Regression. + /// + /// This method solves the system: (Z^T * W * Z + lambda * I) * alpha = Z^T * W * y_centered + /// where W is a diagonal weight matrix. + pub fn fit_weighted(&mut self, y: &Col, weights: &Col) { let num_rows = self.kernel_feature_map.num_rows; let num_features = self.kernel_feature_map.num_features; let num_bases = self.kernel_feature_map.num_bases; - // Validate that the number of rows in the feature map matches the number of target values - if num_rows != y.nrows() { + if num_rows != y.nrows() || num_rows != weights.nrows() { panic!( - "Mismatched dimensions: The number of rows in the feature map ({}) must match the number of target values ({}).", + "Mismatched dimensions: The number of rows in the feature map ({}) must match the number of target values ({}) and weights ({}).", num_rows, - y.nrows() + y.nrows(), + weights.nrows() ); } - // Calculate the mean of target 'y' to center the data - self.base_value = y.iter().sum::() / num_rows as f32; - let y_centered = y - Col::::full(num_rows, self.base_value); + let total_weight: f32 = weights.iter().sum(); + self.base_value = if total_weight > 1e-6 { + weights + .iter() + .zip(y.iter()) + .map(|(&w, &yi)| w * yi) + .sum::() + / total_weight + } else { + y.iter().sum::() / num_rows as f32 + }; - // Aggregate all feature matrices from the kernel_feature_map into a single design matrix (Z) + let y_centered = y - Col::::full(num_rows, self.base_value); let total_dim = num_features * num_bases; - let mut z_stacked = Mat::::zeros(num_rows, total_dim); - for (f_idx, z) in self.kernel_feature_map.z_matrices.iter().enumerate() { - let offset = f_idx * num_bases; - z_stacked - .as_mut() - .submatrix_mut(0, offset, num_rows, num_bases) - .copy_from(z); + + // Initialize the Hessian (LHS) and Gradient (RHS) for the normal equations + let mut ridge_lhs = Mat::::zeros(total_dim, total_dim); + let mut rhs = Col::::zeros(total_dim); + + // Block-based accumulation to save memory: + // We compute ridge_lhs = Z^T * W * Z and rhs = Z^T * W * y_centered + // by iterating over feature blocks Z_i and Z_j. + for i in 0..num_features { + let z_i = &self.kernel_feature_map.z_matrices[i]; + let offset_i = i * num_bases; + + // Compute RHS contribution: Z_i^T * W * y_centered + for r in 0..num_rows { + let w_y = weights[r] * y_centered[r]; + for k in 0..num_bases { + rhs[offset_i + k] += z_i[(r, k)] * w_y; + } + } + + for j in 0..num_features { + let z_j = &self.kernel_feature_map.z_matrices[j]; + let offset_j = j * num_bases; + + // Accumulate Z_i^T * W * Z_j into the global Hessian matrix + for r in 0..num_rows { + let w = weights[r]; + for k in 0..num_bases { + let val_i = z_i[(r, k)] * w; + for l in 0..num_bases { + ridge_lhs[(offset_i + k, offset_j + l)] += val_i * z_j[(r, l)]; + } + } + } + } } - // Construct and solve the Normal Equation: (Z^T * Z + lambda * I) - let lhs = z_stacked.transpose() * &z_stacked; - let mut ridge_lhs = lhs; - // Add L2 regularization (Ridge) to the diagonal for numerical stability - let lambda = 0.01; + // Add L2 regularization (Ridge) to the diagonal for i in 0..total_dim { - ridge_lhs[(i, i)] += lambda; // Ridge regularization + ridge_lhs[(i, i)] += self.penalty; } - let rhs = z_stacked.transpose() * &y_centered; // Solve the linear system using LDLT decomposition let alpha_total = ridge_lhs.ldlt(faer::Side::Lower).unwrap().solve(&rhs); - // Partition the global coefficient vector back into per-feature blocks self.coefficients = (0..num_features) .into_par_iter() .map(|f_idx| { @@ -80,7 +123,6 @@ impl Regressor { }) .collect(); } - /// Predicts target values for the given input matrix X. /// /// It maps X to the kernel space and calculates the weighted sum of contributions. diff --git a/tests/classifier_test.rs b/tests/classifier_test.rs index 6380b28..95e8171 100644 --- a/tests/classifier_test.rs +++ b/tests/classifier_test.rs @@ -12,6 +12,7 @@ fn test_gaussian_classification() { let mut rng = rand::rng(); let n_samples = 500; let n_features = 2; + let penalty = 0.01; let mut x = Mat::::zeros(n_samples, n_features); let mut y = Col::::zeros(n_samples); @@ -45,8 +46,8 @@ fn test_gaussian_classification() { // 3. Setup and Fit Classifier (IRLS) // Train a Logistic Regression model using Iteratively Reweighted Least Squares (IRLS). - let mut model = Classifier::new(map_arc); - model.fit(&y, 20); // Perform 20 iterations for convergence + let mut model = Classifier::new(map_arc, penalty, 20); + model.fit(&y); // Perform 20 iterations for convergence // 4. Verify Accuracy // Ensure that the model can linearly separate the kernel-mapped Gaussian blobs. diff --git a/tests/regressior_test.rs b/tests/regressior_test.rs index 58dfaf9..d4a8f8e 100644 --- a/tests/regressior_test.rs +++ b/tests/regressior_test.rs @@ -13,6 +13,7 @@ fn test_regression() { let mut x = Mat::::zeros(n_samples, n_features); let mut y = Col::::zeros(n_samples); + let penalty = 0.01; // 1. Generate Synthetic Multi-variable Data // Rule: y = 2.0*x0 - 1.5*x1 + 0.5*x2 + 5.0 (base_value) @@ -39,7 +40,7 @@ fn test_regression() { // 3. Setup and Fit Regressor // Initialize the Regressor with the fitted map and solve for coefficients. - let mut model = Regressor::new(map_arc); + let mut model = Regressor::new(map_arc, penalty); model.fit(&y); // 4. Verify Prediction Accuracy (MAE) diff --git a/tests/rlearner_test.rs b/tests/rlearner_test.rs index 9034a37..7f9b3b2 100644 --- a/tests/rlearner_test.rs +++ b/tests/rlearner_test.rs @@ -35,7 +35,7 @@ fn test_rlearner() { // 2. Model Training // R-Learner trains: m(x) [Outcome], e(x) [Propensity], and tau(x) [Residual-on-Residual] - let rlearner = RLearner::new(&x, &t, &y); + let rlearner = RLearner::new(&x, &t, &y, 0.1, 0.1, 20, 0.1); // 3. Uplift Estimation // In R-Learner, the tau model directly estimates the treatment effect. @@ -55,7 +55,7 @@ fn test_rlearner() { // Verify if the average estimated uplift is close to 3.0 assert!( - (avg_uplift - 5.0).abs() < 0.5, + (avg_uplift - 5.0).abs() < 0.1, "R-Learner estimation is too far from ground truth. Got: {:.4}", avg_uplift ); diff --git a/tests/slearner_test.rs b/tests/slearner_test.rs index 8a71690..4226204 100644 --- a/tests/slearner_test.rs +++ b/tests/slearner_test.rs @@ -32,7 +32,7 @@ fn test_slearner() { // 2. Model Training // Initialize SLearner which internally handles feature augmentation and kernel mapping. - let slearner = SLearner::new(&x, &t, &y); + let slearner = SLearner::new(&x, &t, &y, 0.01); // 3. Uplift Estimation // Estimate Individual Treatment Effect (ITE) using the counterfactual approach: diff --git a/tests/tlearner_test.rs b/tests/tlearner_test.rs index 0496615..24f5461 100644 --- a/tests/tlearner_test.rs +++ b/tests/tlearner_test.rs @@ -34,7 +34,7 @@ fn test_tlearner() { // 2. Model Training // Initialize TLearner which splits data into T=1 and T=0 and trains two regressors. - let tlearner = TLearner::new(&x, &t, &y); + let tlearner = TLearner::new(&x, &t, &y, 0.01); // 3. Uplift Estimation // Estimate Individual Treatment Effect (ITE) by subtracting diff --git a/tests/xlearner_test.rs b/tests/xlearner_test.rs index defe10b..51301d9 100644 --- a/tests/xlearner_test.rs +++ b/tests/xlearner_test.rs @@ -35,7 +35,7 @@ fn test_xlearner() { // 2. Model Training // X-Learner internally trains 5 models: // Stage 1: mu_1, mu_0 | Stage 2: tau_1, tau_0 | Stage 3: p (propensity) - let xlearner = XLearner::new(&x, &t, &y); + let xlearner = XLearner::new(&x, &t, &y, 0.1, 0.1, 20, 0.1); // 3. Uplift Estimation // The estimate uses the weighted average: g(x)*tau_0 + (1-g(x))*tau_1 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..c6ef07e --- /dev/null +++ b/uv.lock @@ -0,0 +1,507 @@ +version = 1 +revision = 3 +requires-python = ">=3.8" +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version == '3.10.*'", + "python_full_version == '3.9.*'", + "python_full_version < '3.9'", +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, +] + +[[package]] +name = "exceptiongroup" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions", version = "4.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "typing-extensions", version = "4.15.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.9' and python_full_version < '3.11'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740, upload-time = "2025-11-21T23:01:53.443Z" }, +] + +[[package]] +name = "iniconfig" +version = "2.1.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", + "python_full_version < '3.9'", +] +sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7", size = 4793, upload-time = "2025-03-19T20:09:59.721Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" }, +] + +[[package]] +name = "iniconfig" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, +] + +[[package]] +name = "numpy" +version = "1.24.4" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.9'", +] +sdist = { url = "https://files.pythonhosted.org/packages/a4/9b/027bec52c633f6556dba6b722d9a0befb40498b9ceddd29cbe67a45a127c/numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463", size = 10911229, upload-time = "2023-06-26T13:39:33.218Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6b/80/6cdfb3e275d95155a34659163b83c09e3a3ff9f1456880bec6cc63d71083/numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64", size = 19789140, upload-time = "2023-06-26T13:22:33.184Z" }, + { url = "https://files.pythonhosted.org/packages/64/5f/3f01d753e2175cfade1013eea08db99ba1ee4bdb147ebcf3623b75d12aa7/numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1", size = 13854297, upload-time = "2023-06-26T13:22:59.541Z" }, + { url = "https://files.pythonhosted.org/packages/5a/b3/2f9c21d799fa07053ffa151faccdceeb69beec5a010576b8991f614021f7/numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4", size = 13995611, upload-time = "2023-06-26T13:23:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/10/be/ae5bf4737cb79ba437879915791f6f26d92583c738d7d960ad94e5c36adf/numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6", size = 17282357, upload-time = "2023-06-26T13:23:51.446Z" }, + { url = "https://files.pythonhosted.org/packages/c0/64/908c1087be6285f40e4b3e79454552a701664a079321cff519d8c7051d06/numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc", size = 12429222, upload-time = "2023-06-26T13:24:13.849Z" }, + { url = "https://files.pythonhosted.org/packages/22/55/3d5a7c1142e0d9329ad27cece17933b0e2ab4e54ddc5c1861fbfeb3f7693/numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e", size = 14841514, upload-time = "2023-06-26T13:24:38.129Z" }, + { url = "https://files.pythonhosted.org/packages/a9/cc/5ed2280a27e5dab12994c884f1f4d8c3bd4d885d02ae9e52a9d213a6a5e2/numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810", size = 19775508, upload-time = "2023-06-26T13:25:08.882Z" }, + { url = "https://files.pythonhosted.org/packages/c0/bc/77635c657a3668cf652806210b8662e1aff84b818a55ba88257abf6637a8/numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254", size = 13840033, upload-time = "2023-06-26T13:25:33.417Z" }, + { url = "https://files.pythonhosted.org/packages/a7/4c/96cdaa34f54c05e97c1c50f39f98d608f96f0677a6589e64e53104e22904/numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7", size = 13991951, upload-time = "2023-06-26T13:25:55.725Z" }, + { url = "https://files.pythonhosted.org/packages/22/97/dfb1a31bb46686f09e68ea6ac5c63fdee0d22d7b23b8f3f7ea07712869ef/numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5", size = 17278923, upload-time = "2023-06-26T13:26:25.658Z" }, + { url = "https://files.pythonhosted.org/packages/35/e2/76a11e54139654a324d107da1d98f99e7aa2a7ef97cfd7c631fba7dbde71/numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d", size = 12422446, upload-time = "2023-06-26T13:26:49.302Z" }, + { url = "https://files.pythonhosted.org/packages/d8/ec/ebef2f7d7c28503f958f0f8b992e7ce606fb74f9e891199329d5f5f87404/numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694", size = 14834466, upload-time = "2023-06-26T13:27:16.029Z" }, + { url = "https://files.pythonhosted.org/packages/11/10/943cfb579f1a02909ff96464c69893b1d25be3731b5d3652c2e0cf1281ea/numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61", size = 19780722, upload-time = "2023-06-26T13:27:49.573Z" }, + { url = "https://files.pythonhosted.org/packages/a7/ae/f53b7b265fdc701e663fbb322a8e9d4b14d9cb7b2385f45ddfabfc4327e4/numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f", size = 13843102, upload-time = "2023-06-26T13:28:12.288Z" }, + { url = "https://files.pythonhosted.org/packages/25/6f/2586a50ad72e8dbb1d8381f837008a0321a3516dfd7cb57fc8cf7e4bb06b/numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e", size = 14039616, upload-time = "2023-06-26T13:28:35.659Z" }, + { url = "https://files.pythonhosted.org/packages/98/5d/5738903efe0ecb73e51eb44feafba32bdba2081263d40c5043568ff60faf/numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc", size = 17316263, upload-time = "2023-06-26T13:29:09.272Z" }, + { url = "https://files.pythonhosted.org/packages/d1/57/8d328f0b91c733aa9aa7ee540dbc49b58796c862b4fbcb1146c701e888da/numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2", size = 12455660, upload-time = "2023-06-26T13:29:33.434Z" }, + { url = "https://files.pythonhosted.org/packages/69/65/0d47953afa0ad569d12de5f65d964321c208492064c38fe3b0b9744f8d44/numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706", size = 14868112, upload-time = "2023-06-26T13:29:58.385Z" }, + { url = "https://files.pythonhosted.org/packages/9a/cd/d5b0402b801c8a8b56b04c1e85c6165efab298d2f0ab741c2406516ede3a/numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400", size = 19816549, upload-time = "2023-06-26T13:30:36.976Z" }, + { url = "https://files.pythonhosted.org/packages/14/27/638aaa446f39113a3ed38b37a66243e21b38110d021bfcb940c383e120f2/numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f", size = 13879950, upload-time = "2023-06-26T13:31:01.787Z" }, + { url = "https://files.pythonhosted.org/packages/8f/27/91894916e50627476cff1a4e4363ab6179d01077d71b9afed41d9e1f18bf/numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9", size = 14030228, upload-time = "2023-06-26T13:31:26.696Z" }, + { url = "https://files.pythonhosted.org/packages/7a/7c/d7b2a0417af6428440c0ad7cb9799073e507b1a465f827d058b826236964/numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d", size = 17311170, upload-time = "2023-06-26T13:31:56.615Z" }, + { url = "https://files.pythonhosted.org/packages/18/9d/e02ace5d7dfccee796c37b995c63322674daf88ae2f4a4724c5dd0afcc91/numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835", size = 12454918, upload-time = "2023-06-26T13:32:16.8Z" }, + { url = "https://files.pythonhosted.org/packages/63/38/6cc19d6b8bfa1d1a459daf2b3fe325453153ca7019976274b6f33d8b5663/numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8", size = 14867441, upload-time = "2023-06-26T13:32:40.521Z" }, + { url = "https://files.pythonhosted.org/packages/a4/fd/8dff40e25e937c94257455c237b9b6bf5a30d42dd1cc11555533be099492/numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef", size = 19156590, upload-time = "2023-06-26T13:33:10.36Z" }, + { url = "https://files.pythonhosted.org/packages/42/e7/4bf953c6e05df90c6d351af69966384fed8e988d0e8c54dad7103b59f3ba/numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a", size = 16705744, upload-time = "2023-06-26T13:33:36.703Z" }, + { url = "https://files.pythonhosted.org/packages/fc/dd/9106005eb477d022b60b3817ed5937a43dad8fd1f20b0610ea8a32fcb407/numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2", size = 14734290, upload-time = "2023-06-26T13:34:05.409Z" }, +] + +[[package]] +name = "numpy" +version = "2.0.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/a9/75/10dd1f8116a8b796cb2c737b674e02d02e80454bda953fa7e65d8c12b016/numpy-2.0.2.tar.gz", hash = "sha256:883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78", size = 18902015, upload-time = "2024-08-26T20:19:40.945Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/21/91/3495b3237510f79f5d81f2508f9f13fea78ebfdf07538fc7444badda173d/numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece", size = 21165245, upload-time = "2024-08-26T20:04:14.625Z" }, + { url = "https://files.pythonhosted.org/packages/05/33/26178c7d437a87082d11019292dce6d3fe6f0e9026b7b2309cbf3e489b1d/numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04", size = 13738540, upload-time = "2024-08-26T20:04:36.784Z" }, + { url = "https://files.pythonhosted.org/packages/ec/31/cc46e13bf07644efc7a4bf68df2df5fb2a1a88d0cd0da9ddc84dc0033e51/numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66", size = 5300623, upload-time = "2024-08-26T20:04:46.491Z" }, + { url = "https://files.pythonhosted.org/packages/6e/16/7bfcebf27bb4f9d7ec67332ffebee4d1bf085c84246552d52dbb548600e7/numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b", size = 6901774, upload-time = "2024-08-26T20:04:58.173Z" }, + { url = "https://files.pythonhosted.org/packages/f9/a3/561c531c0e8bf082c5bef509d00d56f82e0ea7e1e3e3a7fc8fa78742a6e5/numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2da5960c3cf0df7eafefd806d4e612c5e19358de82cb3c343631188991566ccd", size = 13907081, upload-time = "2024-08-26T20:05:19.098Z" }, + { url = "https://files.pythonhosted.org/packages/fa/66/f7177ab331876200ac7563a580140643d1179c8b4b6a6b0fc9838de2a9b8/numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:496f71341824ed9f3d2fd36cf3ac57ae2e0165c143b55c3a035ee219413f3318", size = 19523451, upload-time = "2024-08-26T20:05:47.479Z" }, + { url = "https://files.pythonhosted.org/packages/25/7f/0b209498009ad6453e4efc2c65bcdf0ae08a182b2b7877d7ab38a92dc542/numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a61ec659f68ae254e4d237816e33171497e978140353c0c2038d46e63282d0c8", size = 19927572, upload-time = "2024-08-26T20:06:17.137Z" }, + { url = "https://files.pythonhosted.org/packages/3e/df/2619393b1e1b565cd2d4c4403bdd979621e2c4dea1f8532754b2598ed63b/numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d731a1c6116ba289c1e9ee714b08a8ff882944d4ad631fd411106a30f083c326", size = 14400722, upload-time = "2024-08-26T20:06:39.16Z" }, + { url = "https://files.pythonhosted.org/packages/22/ad/77e921b9f256d5da36424ffb711ae79ca3f451ff8489eeca544d0701d74a/numpy-2.0.2-cp310-cp310-win32.whl", hash = "sha256:984d96121c9f9616cd33fbd0618b7f08e0cfc9600a7ee1d6fd9b239186d19d97", size = 6472170, upload-time = "2024-08-26T20:06:50.361Z" }, + { url = "https://files.pythonhosted.org/packages/10/05/3442317535028bc29cf0c0dd4c191a4481e8376e9f0db6bcf29703cadae6/numpy-2.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:c7b0be4ef08607dd04da4092faee0b86607f111d5ae68036f16cc787e250a131", size = 15905558, upload-time = "2024-08-26T20:07:13.881Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cf/034500fb83041aa0286e0fb16e7c76e5c8b67c0711bb6e9e9737a717d5fe/numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:49ca4decb342d66018b01932139c0961a8f9ddc7589611158cb3c27cbcf76448", size = 21169137, upload-time = "2024-08-26T20:07:45.345Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d9/32de45561811a4b87fbdee23b5797394e3d1504b4a7cf40c10199848893e/numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:11a76c372d1d37437857280aa142086476136a8c0f373b2e648ab2c8f18fb195", size = 13703552, upload-time = "2024-08-26T20:08:06.666Z" }, + { url = "https://files.pythonhosted.org/packages/c1/ca/2f384720020c7b244d22508cb7ab23d95f179fcfff33c31a6eeba8d6c512/numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:807ec44583fd708a21d4a11d94aedf2f4f3c3719035c76a2bbe1fe8e217bdc57", size = 5298957, upload-time = "2024-08-26T20:08:15.83Z" }, + { url = "https://files.pythonhosted.org/packages/0e/78/a3e4f9fb6aa4e6fdca0c5428e8ba039408514388cf62d89651aade838269/numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8cafab480740e22f8d833acefed5cc87ce276f4ece12fdaa2e8903db2f82897a", size = 6905573, upload-time = "2024-08-26T20:08:27.185Z" }, + { url = "https://files.pythonhosted.org/packages/a0/72/cfc3a1beb2caf4efc9d0b38a15fe34025230da27e1c08cc2eb9bfb1c7231/numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a15f476a45e6e5a3a79d8a14e62161d27ad897381fecfa4a09ed5322f2085669", size = 13914330, upload-time = "2024-08-26T20:08:48.058Z" }, + { url = "https://files.pythonhosted.org/packages/ba/a8/c17acf65a931ce551fee11b72e8de63bf7e8a6f0e21add4c937c83563538/numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13e689d772146140a252c3a28501da66dfecd77490b498b168b501835041f951", size = 19534895, upload-time = "2024-08-26T20:09:16.536Z" }, + { url = "https://files.pythonhosted.org/packages/ba/86/8767f3d54f6ae0165749f84648da9dcc8cd78ab65d415494962c86fac80f/numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9ea91dfb7c3d1c56a0e55657c0afb38cf1eeae4544c208dc465c3c9f3a7c09f9", size = 19937253, upload-time = "2024-08-26T20:09:46.263Z" }, + { url = "https://files.pythonhosted.org/packages/df/87/f76450e6e1c14e5bb1eae6836478b1028e096fd02e85c1c37674606ab752/numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c1c9307701fec8f3f7a1e6711f9089c06e6284b3afbbcd259f7791282d660a15", size = 14414074, upload-time = "2024-08-26T20:10:08.483Z" }, + { url = "https://files.pythonhosted.org/packages/5c/ca/0f0f328e1e59f73754f06e1adfb909de43726d4f24c6a3f8805f34f2b0fa/numpy-2.0.2-cp311-cp311-win32.whl", hash = "sha256:a392a68bd329eafac5817e5aefeb39038c48b671afd242710b451e76090e81f4", size = 6470640, upload-time = "2024-08-26T20:10:19.732Z" }, + { url = "https://files.pythonhosted.org/packages/eb/57/3a3f14d3a759dcf9bf6e9eda905794726b758819df4663f217d658a58695/numpy-2.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:286cd40ce2b7d652a6f22efdfc6d1edf879440e53e76a75955bc0c826c7e64dc", size = 15910230, upload-time = "2024-08-26T20:10:43.413Z" }, + { url = "https://files.pythonhosted.org/packages/45/40/2e117be60ec50d98fa08c2f8c48e09b3edea93cfcabd5a9ff6925d54b1c2/numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:df55d490dea7934f330006d0f81e8551ba6010a5bf035a249ef61a94f21c500b", size = 20895803, upload-time = "2024-08-26T20:11:13.916Z" }, + { url = "https://files.pythonhosted.org/packages/46/92/1b8b8dee833f53cef3e0a3f69b2374467789e0bb7399689582314df02651/numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8df823f570d9adf0978347d1f926b2a867d5608f434a7cff7f7908c6570dcf5e", size = 13471835, upload-time = "2024-08-26T20:11:34.779Z" }, + { url = "https://files.pythonhosted.org/packages/7f/19/e2793bde475f1edaea6945be141aef6c8b4c669b90c90a300a8954d08f0a/numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:9a92ae5c14811e390f3767053ff54eaee3bf84576d99a2456391401323f4ec2c", size = 5038499, upload-time = "2024-08-26T20:11:43.902Z" }, + { url = "https://files.pythonhosted.org/packages/e3/ff/ddf6dac2ff0dd50a7327bcdba45cb0264d0e96bb44d33324853f781a8f3c/numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:a842d573724391493a97a62ebbb8e731f8a5dcc5d285dfc99141ca15a3302d0c", size = 6633497, upload-time = "2024-08-26T20:11:55.09Z" }, + { url = "https://files.pythonhosted.org/packages/72/21/67f36eac8e2d2cd652a2e69595a54128297cdcb1ff3931cfc87838874bd4/numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c05e238064fc0610c840d1cf6a13bf63d7e391717d247f1bf0318172e759e692", size = 13621158, upload-time = "2024-08-26T20:12:14.95Z" }, + { url = "https://files.pythonhosted.org/packages/39/68/e9f1126d757653496dbc096cb429014347a36b228f5a991dae2c6b6cfd40/numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a", size = 19236173, upload-time = "2024-08-26T20:12:44.049Z" }, + { url = "https://files.pythonhosted.org/packages/d1/e9/1f5333281e4ebf483ba1c888b1d61ba7e78d7e910fdd8e6499667041cc35/numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:96a55f64139912d61de9137f11bf39a55ec8faec288c75a54f93dfd39f7eb40c", size = 19634174, upload-time = "2024-08-26T20:13:13.634Z" }, + { url = "https://files.pythonhosted.org/packages/71/af/a469674070c8d8408384e3012e064299f7a2de540738a8e414dcfd639996/numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ec9852fb39354b5a45a80bdab5ac02dd02b15f44b3804e9f00c556bf24b4bded", size = 14099701, upload-time = "2024-08-26T20:13:34.851Z" }, + { url = "https://files.pythonhosted.org/packages/d0/3d/08ea9f239d0e0e939b6ca52ad403c84a2bce1bde301a8eb4888c1c1543f1/numpy-2.0.2-cp312-cp312-win32.whl", hash = "sha256:671bec6496f83202ed2d3c8fdc486a8fc86942f2e69ff0e986140339a63bcbe5", size = 6174313, upload-time = "2024-08-26T20:13:45.653Z" }, + { url = "https://files.pythonhosted.org/packages/b2/b5/4ac39baebf1fdb2e72585c8352c56d063b6126be9fc95bd2bb5ef5770c20/numpy-2.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:cfd41e13fdc257aa5778496b8caa5e856dc4896d4ccf01841daee1d96465467a", size = 15606179, upload-time = "2024-08-26T20:14:08.786Z" }, + { url = "https://files.pythonhosted.org/packages/43/c1/41c8f6df3162b0c6ffd4437d729115704bd43363de0090c7f913cfbc2d89/numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9059e10581ce4093f735ed23f3b9d283b9d517ff46009ddd485f1747eb22653c", size = 21169942, upload-time = "2024-08-26T20:14:40.108Z" }, + { url = "https://files.pythonhosted.org/packages/39/bc/fd298f308dcd232b56a4031fd6ddf11c43f9917fbc937e53762f7b5a3bb1/numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:423e89b23490805d2a5a96fe40ec507407b8ee786d66f7328be214f9679df6dd", size = 13711512, upload-time = "2024-08-26T20:15:00.985Z" }, + { url = "https://files.pythonhosted.org/packages/96/ff/06d1aa3eeb1c614eda245c1ba4fb88c483bee6520d361641331872ac4b82/numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl", hash = "sha256:2b2955fa6f11907cf7a70dab0d0755159bca87755e831e47932367fc8f2f2d0b", size = 5306976, upload-time = "2024-08-26T20:15:10.876Z" }, + { url = "https://files.pythonhosted.org/packages/2d/98/121996dcfb10a6087a05e54453e28e58694a7db62c5a5a29cee14c6e047b/numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl", hash = "sha256:97032a27bd9d8988b9a97a8c4d2c9f2c15a81f61e2f21404d7e8ef00cb5be729", size = 6906494, upload-time = "2024-08-26T20:15:22.055Z" }, + { url = "https://files.pythonhosted.org/packages/15/31/9dffc70da6b9bbf7968f6551967fc21156207366272c2a40b4ed6008dc9b/numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e795a8be3ddbac43274f18588329c72939870a16cae810c2b73461c40718ab1", size = 13912596, upload-time = "2024-08-26T20:15:42.452Z" }, + { url = "https://files.pythonhosted.org/packages/b9/14/78635daab4b07c0930c919d451b8bf8c164774e6a3413aed04a6d95758ce/numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b258c385842546006213344c50655ff1555a9338e2e5e02a0756dc3e803dd", size = 19526099, upload-time = "2024-08-26T20:16:11.048Z" }, + { url = "https://files.pythonhosted.org/packages/26/4c/0eeca4614003077f68bfe7aac8b7496f04221865b3a5e7cb230c9d055afd/numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fec9451a7789926bcf7c2b8d187292c9f93ea30284802a0ab3f5be8ab36865d", size = 19932823, upload-time = "2024-08-26T20:16:40.171Z" }, + { url = "https://files.pythonhosted.org/packages/f1/46/ea25b98b13dccaebddf1a803f8c748680d972e00507cd9bc6dcdb5aa2ac1/numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:9189427407d88ff25ecf8f12469d4d39d35bee1db5d39fc5c168c6f088a6956d", size = 14404424, upload-time = "2024-08-26T20:17:02.604Z" }, + { url = "https://files.pythonhosted.org/packages/c8/a6/177dd88d95ecf07e722d21008b1b40e681a929eb9e329684d449c36586b2/numpy-2.0.2-cp39-cp39-win32.whl", hash = "sha256:905d16e0c60200656500c95b6b8dca5d109e23cb24abc701d41c02d74c6b3afa", size = 6476809, upload-time = "2024-08-26T20:17:13.553Z" }, + { url = "https://files.pythonhosted.org/packages/ea/2b/7fc9f4e7ae5b507c1a3a21f0f15ed03e794c1242ea8a242ac158beb56034/numpy-2.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:a3f4ab0caa7f053f6797fcd4e1e25caee367db3112ef2b6ef82d749530768c73", size = 15911314, upload-time = "2024-08-26T20:17:36.72Z" }, + { url = "https://files.pythonhosted.org/packages/8f/3b/df5a870ac6a3be3a86856ce195ef42eec7ae50d2a202be1f5a4b3b340e14/numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:7f0a0c6f12e07fa94133c8a67404322845220c06a9e80e85999afe727f7438b8", size = 21025288, upload-time = "2024-08-26T20:18:07.732Z" }, + { url = "https://files.pythonhosted.org/packages/2c/97/51af92f18d6f6f2d9ad8b482a99fb74e142d71372da5d834b3a2747a446e/numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl", hash = "sha256:312950fdd060354350ed123c0e25a71327d3711584beaef30cdaa93320c392d4", size = 6762793, upload-time = "2024-08-26T20:18:19.125Z" }, + { url = "https://files.pythonhosted.org/packages/12/46/de1fbd0c1b5ccaa7f9a005b66761533e2f6a3e560096682683a223631fe9/numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:26df23238872200f63518dd2aa984cfca675d82469535dc7162dc2ee52d9dd5c", size = 19334885, upload-time = "2024-08-26T20:18:47.237Z" }, + { url = "https://files.pythonhosted.org/packages/cc/dc/d330a6faefd92b446ec0f0dfea4c3207bb1fef3c4771d19cf4543efd2c78/numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a46288ec55ebbd58947d31d72be2c63cbf839f0a63b49cb755022310792a3385", size = 15828784, upload-time = "2024-08-26T20:19:11.19Z" }, +] + +[[package]] +name = "numpy" +version = "2.2.6" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9a/3e/ed6db5be21ce87955c0cbd3009f2803f59fa08df21b5df06862e2d8e2bdd/numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb", size = 21165245, upload-time = "2025-05-17T21:27:58.555Z" }, + { url = "https://files.pythonhosted.org/packages/22/c2/4b9221495b2a132cc9d2eb862e21d42a009f5a60e45fc44b00118c174bff/numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90", size = 14360048, upload-time = "2025-05-17T21:28:21.406Z" }, + { url = "https://files.pythonhosted.org/packages/fd/77/dc2fcfc66943c6410e2bf598062f5959372735ffda175b39906d54f02349/numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163", size = 5340542, upload-time = "2025-05-17T21:28:30.931Z" }, + { url = "https://files.pythonhosted.org/packages/7a/4f/1cb5fdc353a5f5cc7feb692db9b8ec2c3d6405453f982435efc52561df58/numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf", size = 6878301, upload-time = "2025-05-17T21:28:41.613Z" }, + { url = "https://files.pythonhosted.org/packages/eb/17/96a3acd228cec142fcb8723bd3cc39c2a474f7dcf0a5d16731980bcafa95/numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83", size = 14297320, upload-time = "2025-05-17T21:29:02.78Z" }, + { url = "https://files.pythonhosted.org/packages/b4/63/3de6a34ad7ad6646ac7d2f55ebc6ad439dbbf9c4370017c50cf403fb19b5/numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915", size = 16801050, upload-time = "2025-05-17T21:29:27.675Z" }, + { url = "https://files.pythonhosted.org/packages/07/b6/89d837eddef52b3d0cec5c6ba0456c1bf1b9ef6a6672fc2b7873c3ec4e2e/numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680", size = 15807034, upload-time = "2025-05-17T21:29:51.102Z" }, + { url = "https://files.pythonhosted.org/packages/01/c8/dc6ae86e3c61cfec1f178e5c9f7858584049b6093f843bca541f94120920/numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289", size = 18614185, upload-time = "2025-05-17T21:30:18.703Z" }, + { url = "https://files.pythonhosted.org/packages/5b/c5/0064b1b7e7c89137b471ccec1fd2282fceaae0ab3a9550f2568782d80357/numpy-2.2.6-cp310-cp310-win32.whl", hash = "sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d", size = 6527149, upload-time = "2025-05-17T21:30:29.788Z" }, + { url = "https://files.pythonhosted.org/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl", hash = "sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3", size = 12904620, upload-time = "2025-05-17T21:30:48.994Z" }, + { url = "https://files.pythonhosted.org/packages/da/a8/4f83e2aa666a9fbf56d6118faaaf5f1974d456b1823fda0a176eff722839/numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae", size = 21176963, upload-time = "2025-05-17T21:31:19.36Z" }, + { url = "https://files.pythonhosted.org/packages/b3/2b/64e1affc7972decb74c9e29e5649fac940514910960ba25cd9af4488b66c/numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a", size = 14406743, upload-time = "2025-05-17T21:31:41.087Z" }, + { url = "https://files.pythonhosted.org/packages/4a/9f/0121e375000b5e50ffdd8b25bf78d8e1a5aa4cca3f185d41265198c7b834/numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42", size = 5352616, upload-time = "2025-05-17T21:31:50.072Z" }, + { url = "https://files.pythonhosted.org/packages/31/0d/b48c405c91693635fbe2dcd7bc84a33a602add5f63286e024d3b6741411c/numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491", size = 6889579, upload-time = "2025-05-17T21:32:01.712Z" }, + { url = "https://files.pythonhosted.org/packages/52/b8/7f0554d49b565d0171eab6e99001846882000883998e7b7d9f0d98b1f934/numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a", size = 14312005, upload-time = "2025-05-17T21:32:23.332Z" }, + { url = "https://files.pythonhosted.org/packages/b3/dd/2238b898e51bd6d389b7389ffb20d7f4c10066d80351187ec8e303a5a475/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf", size = 16821570, upload-time = "2025-05-17T21:32:47.991Z" }, + { url = "https://files.pythonhosted.org/packages/83/6c/44d0325722cf644f191042bf47eedad61c1e6df2432ed65cbe28509d404e/numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1", size = 15818548, upload-time = "2025-05-17T21:33:11.728Z" }, + { url = "https://files.pythonhosted.org/packages/ae/9d/81e8216030ce66be25279098789b665d49ff19eef08bfa8cb96d4957f422/numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab", size = 18620521, upload-time = "2025-05-17T21:33:39.139Z" }, + { url = "https://files.pythonhosted.org/packages/6a/fd/e19617b9530b031db51b0926eed5345ce8ddc669bb3bc0044b23e275ebe8/numpy-2.2.6-cp311-cp311-win32.whl", hash = "sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47", size = 6525866, upload-time = "2025-05-17T21:33:50.273Z" }, + { url = "https://files.pythonhosted.org/packages/31/0a/f354fb7176b81747d870f7991dc763e157a934c717b67b58456bc63da3df/numpy-2.2.6-cp311-cp311-win_amd64.whl", hash = "sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303", size = 12907455, upload-time = "2025-05-17T21:34:09.135Z" }, + { url = "https://files.pythonhosted.org/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" }, + { url = "https://files.pythonhosted.org/packages/66/ee/560deadcdde6c2f90200450d5938f63a34b37e27ebff162810f716f6a230/numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c", size = 14119362, upload-time = "2025-05-17T21:35:01.241Z" }, + { url = "https://files.pythonhosted.org/packages/3c/65/4baa99f1c53b30adf0acd9a5519078871ddde8d2339dc5a7fde80d9d87da/numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3", size = 5084103, upload-time = "2025-05-17T21:35:10.622Z" }, + { url = "https://files.pythonhosted.org/packages/cc/89/e5a34c071a0570cc40c9a54eb472d113eea6d002e9ae12bb3a8407fb912e/numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282", size = 6625382, upload-time = "2025-05-17T21:35:21.414Z" }, + { url = "https://files.pythonhosted.org/packages/f8/35/8c80729f1ff76b3921d5c9487c7ac3de9b2a103b1cd05e905b3090513510/numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87", size = 14018462, upload-time = "2025-05-17T21:35:42.174Z" }, + { url = "https://files.pythonhosted.org/packages/8c/3d/1e1db36cfd41f895d266b103df00ca5b3cbe965184df824dec5c08c6b803/numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249", size = 16527618, upload-time = "2025-05-17T21:36:06.711Z" }, + { url = "https://files.pythonhosted.org/packages/61/c6/03ed30992602c85aa3cd95b9070a514f8b3c33e31124694438d88809ae36/numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49", size = 15505511, upload-time = "2025-05-17T21:36:29.965Z" }, + { url = "https://files.pythonhosted.org/packages/b7/25/5761d832a81df431e260719ec45de696414266613c9ee268394dd5ad8236/numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de", size = 18313783, upload-time = "2025-05-17T21:36:56.883Z" }, + { url = "https://files.pythonhosted.org/packages/57/0a/72d5a3527c5ebffcd47bde9162c39fae1f90138c961e5296491ce778e682/numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4", size = 6246506, upload-time = "2025-05-17T21:37:07.368Z" }, + { url = "https://files.pythonhosted.org/packages/36/fa/8c9210162ca1b88529ab76b41ba02d433fd54fecaf6feb70ef9f124683f1/numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2", size = 12614190, upload-time = "2025-05-17T21:37:26.213Z" }, + { url = "https://files.pythonhosted.org/packages/f9/5c/6657823f4f594f72b5471f1db1ab12e26e890bb2e41897522d134d2a3e81/numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84", size = 20867828, upload-time = "2025-05-17T21:37:56.699Z" }, + { url = "https://files.pythonhosted.org/packages/dc/9e/14520dc3dadf3c803473bd07e9b2bd1b69bc583cb2497b47000fed2fa92f/numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b", size = 14143006, upload-time = "2025-05-17T21:38:18.291Z" }, + { url = "https://files.pythonhosted.org/packages/4f/06/7e96c57d90bebdce9918412087fc22ca9851cceaf5567a45c1f404480e9e/numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d", size = 5076765, upload-time = "2025-05-17T21:38:27.319Z" }, + { url = "https://files.pythonhosted.org/packages/73/ed/63d920c23b4289fdac96ddbdd6132e9427790977d5457cd132f18e76eae0/numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566", size = 6617736, upload-time = "2025-05-17T21:38:38.141Z" }, + { url = "https://files.pythonhosted.org/packages/85/c5/e19c8f99d83fd377ec8c7e0cf627a8049746da54afc24ef0a0cb73d5dfb5/numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f", size = 14010719, upload-time = "2025-05-17T21:38:58.433Z" }, + { url = "https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f", size = 16526072, upload-time = "2025-05-17T21:39:22.638Z" }, + { url = "https://files.pythonhosted.org/packages/b2/6c/04b5f47f4f32f7c2b0e7260442a8cbcf8168b0e1a41ff1495da42f42a14f/numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868", size = 15503213, upload-time = "2025-05-17T21:39:45.865Z" }, + { url = "https://files.pythonhosted.org/packages/17/0a/5cd92e352c1307640d5b6fec1b2ffb06cd0dabe7d7b8227f97933d378422/numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d", size = 18316632, upload-time = "2025-05-17T21:40:13.331Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3b/5cba2b1d88760ef86596ad0f3d484b1cbff7c115ae2429678465057c5155/numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd", size = 6244532, upload-time = "2025-05-17T21:43:46.099Z" }, + { url = "https://files.pythonhosted.org/packages/cb/3b/d58c12eafcb298d4e6d0d40216866ab15f59e55d148a5658bb3132311fcf/numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c", size = 12610885, upload-time = "2025-05-17T21:44:05.145Z" }, + { url = "https://files.pythonhosted.org/packages/6b/9e/4bf918b818e516322db999ac25d00c75788ddfd2d2ade4fa66f1f38097e1/numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6", size = 20963467, upload-time = "2025-05-17T21:40:44Z" }, + { url = "https://files.pythonhosted.org/packages/61/66/d2de6b291507517ff2e438e13ff7b1e2cdbdb7cb40b3ed475377aece69f9/numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda", size = 14225144, upload-time = "2025-05-17T21:41:05.695Z" }, + { url = "https://files.pythonhosted.org/packages/e4/25/480387655407ead912e28ba3a820bc69af9adf13bcbe40b299d454ec011f/numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40", size = 5200217, upload-time = "2025-05-17T21:41:15.903Z" }, + { url = "https://files.pythonhosted.org/packages/aa/4a/6e313b5108f53dcbf3aca0c0f3e9c92f4c10ce57a0a721851f9785872895/numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8", size = 6712014, upload-time = "2025-05-17T21:41:27.321Z" }, + { url = "https://files.pythonhosted.org/packages/b7/30/172c2d5c4be71fdf476e9de553443cf8e25feddbe185e0bd88b096915bcc/numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f", size = 14077935, upload-time = "2025-05-17T21:41:49.738Z" }, + { url = "https://files.pythonhosted.org/packages/12/fb/9e743f8d4e4d3c710902cf87af3512082ae3d43b945d5d16563f26ec251d/numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa", size = 16600122, upload-time = "2025-05-17T21:42:14.046Z" }, + { url = "https://files.pythonhosted.org/packages/12/75/ee20da0e58d3a66f204f38916757e01e33a9737d0b22373b3eb5a27358f9/numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571", size = 15586143, upload-time = "2025-05-17T21:42:37.464Z" }, + { url = "https://files.pythonhosted.org/packages/76/95/bef5b37f29fc5e739947e9ce5179ad402875633308504a52d188302319c8/numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1", size = 18385260, upload-time = "2025-05-17T21:43:05.189Z" }, + { url = "https://files.pythonhosted.org/packages/09/04/f2f83279d287407cf36a7a8053a5abe7be3622a4363337338f2585e4afda/numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff", size = 6377225, upload-time = "2025-05-17T21:43:16.254Z" }, + { url = "https://files.pythonhosted.org/packages/67/0e/35082d13c09c02c011cf21570543d202ad929d961c02a147493cb0c2bdf5/numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06", size = 12771374, upload-time = "2025-05-17T21:43:35.479Z" }, + { url = "https://files.pythonhosted.org/packages/9e/3b/d94a75f4dbf1ef5d321523ecac21ef23a3cd2ac8b78ae2aac40873590229/numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d", size = 21040391, upload-time = "2025-05-17T21:44:35.948Z" }, + { url = "https://files.pythonhosted.org/packages/17/f4/09b2fa1b58f0fb4f7c7963a1649c64c4d315752240377ed74d9cd878f7b5/numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db", size = 6786754, upload-time = "2025-05-17T21:44:47.446Z" }, + { url = "https://files.pythonhosted.org/packages/af/30/feba75f143bdc868a1cc3f44ccfa6c4b9ec522b36458e738cd00f67b573f/numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543", size = 16643476, upload-time = "2025-05-17T21:45:11.871Z" }, + { url = "https://files.pythonhosted.org/packages/37/48/ac2a9584402fb6c0cd5b5d1a91dcf176b15760130dd386bbafdbfe3640bf/numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00", size = 12812666, upload-time = "2025-05-17T21:45:31.426Z" }, +] + +[[package]] +name = "numpy" +version = "2.4.4" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.11'", +] +sdist = { url = "https://files.pythonhosted.org/packages/d7/9f/b8cef5bffa569759033adda9481211426f12f53299629b410340795c2514/numpy-2.4.4.tar.gz", hash = "sha256:2d390634c5182175533585cc89f3608a4682ccb173cc9bb940b2881c8d6f8fa0", size = 20731587, upload-time = "2026-03-29T13:22:01.298Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ef/c6/4218570d8c8ecc9704b5157a3348e486e84ef4be0ed3e38218ab473c83d2/numpy-2.4.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f983334aea213c99992053ede6168500e5f086ce74fbc4acc3f2b00f5762e9db", size = 16976799, upload-time = "2026-03-29T13:18:15.438Z" }, + { url = "https://files.pythonhosted.org/packages/dd/92/b4d922c4a5f5dab9ed44e6153908a5c665b71acf183a83b93b690996e39b/numpy-2.4.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:72944b19f2324114e9dc86a159787333b77874143efcf89a5167ef83cfee8af0", size = 14971552, upload-time = "2026-03-29T13:18:18.606Z" }, + { url = "https://files.pythonhosted.org/packages/8a/dc/df98c095978fa6ee7b9a9387d1d58cbb3d232d0e69ad169a4ce784bde4fd/numpy-2.4.4-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:86b6f55f5a352b48d7fbfd2dbc3d5b780b2d79f4d3c121f33eb6efb22e9a2015", size = 5476566, upload-time = "2026-03-29T13:18:21.532Z" }, + { url = "https://files.pythonhosted.org/packages/28/34/b3fdcec6e725409223dd27356bdf5a3c2cc2282e428218ecc9cb7acc9763/numpy-2.4.4-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:ba1f4fc670ed79f876f70082eff4f9583c15fb9a4b89d6188412de4d18ae2f40", size = 6806482, upload-time = "2026-03-29T13:18:23.634Z" }, + { url = "https://files.pythonhosted.org/packages/68/62/63417c13aa35d57bee1337c67446761dc25ea6543130cf868eace6e8157b/numpy-2.4.4-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a87ec22c87be071b6bdbd27920b129b94f2fc964358ce38f3822635a3e2e03d", size = 15973376, upload-time = "2026-03-29T13:18:26.677Z" }, + { url = "https://files.pythonhosted.org/packages/cf/c5/9fcb7e0e69cef59cf10c746b84f7d58b08bc66a6b7d459783c5a4f6101a6/numpy-2.4.4-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:df3775294accfdd75f32c74ae39fcba920c9a378a2fc18a12b6820aa8c1fb502", size = 16925137, upload-time = "2026-03-29T13:18:30.14Z" }, + { url = "https://files.pythonhosted.org/packages/7e/43/80020edacb3f84b9efdd1591120a4296462c23fd8db0dde1666f6ef66f13/numpy-2.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0d4e437e295f18ec29bc79daf55e8a47a9113df44d66f702f02a293d93a2d6dd", size = 17329414, upload-time = "2026-03-29T13:18:33.733Z" }, + { url = "https://files.pythonhosted.org/packages/fd/06/af0658593b18a5f73532d377188b964f239eb0894e664a6c12f484472f97/numpy-2.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6aa3236c78803afbcb255045fbef97a9e25a1f6c9888357d205ddc42f4d6eba5", size = 18658397, upload-time = "2026-03-29T13:18:37.511Z" }, + { url = "https://files.pythonhosted.org/packages/e6/ce/13a09ed65f5d0ce5c7dd0669250374c6e379910f97af2c08c57b0608eee4/numpy-2.4.4-cp311-cp311-win32.whl", hash = "sha256:30caa73029a225b2d40d9fae193e008e24b2026b7ee1a867b7ee8d96ca1a448e", size = 6239499, upload-time = "2026-03-29T13:18:40.372Z" }, + { url = "https://files.pythonhosted.org/packages/bd/63/05d193dbb4b5eec1eca73822d80da98b511f8328ad4ae3ca4caf0f4db91d/numpy-2.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:6bbe4eb67390b0a0265a2c25458f6b90a409d5d069f1041e6aff1e27e3d9a79e", size = 12614257, upload-time = "2026-03-29T13:18:42.95Z" }, + { url = "https://files.pythonhosted.org/packages/87/c5/8168052f080c26fa984c413305012be54741c9d0d74abd7fbeeccae3889f/numpy-2.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:fcfe2045fd2e8f3cb0ce9d4ba6dba6333b8fa05bb8a4939c908cd43322d14c7e", size = 10486775, upload-time = "2026-03-29T13:18:45.835Z" }, + { url = "https://files.pythonhosted.org/packages/28/05/32396bec30fb2263770ee910142f49c1476d08e8ad41abf8403806b520ce/numpy-2.4.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15716cfef24d3a9762e3acdf87e27f58dc823d1348f765bbea6bef8c639bfa1b", size = 16689272, upload-time = "2026-03-29T13:18:49.223Z" }, + { url = "https://files.pythonhosted.org/packages/c5/f3/a983d28637bfcd763a9c7aafdb6d5c0ebf3d487d1e1459ffdb57e2f01117/numpy-2.4.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23cbfd4c17357c81021f21540da84ee282b9c8fba38a03b7b9d09ba6b951421e", size = 14699573, upload-time = "2026-03-29T13:18:52.629Z" }, + { url = "https://files.pythonhosted.org/packages/9b/fd/e5ecca1e78c05106d98028114f5c00d3eddb41207686b2b7de3e477b0e22/numpy-2.4.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:8b3b60bb7cba2c8c81837661c488637eee696f59a877788a396d33150c35d842", size = 5204782, upload-time = "2026-03-29T13:18:55.579Z" }, + { url = "https://files.pythonhosted.org/packages/de/2f/702a4594413c1a8632092beae8aba00f1d67947389369b3777aed783fdca/numpy-2.4.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:e4a010c27ff6f210ff4c6ef34394cd61470d01014439b192ec22552ee867f2a8", size = 6552038, upload-time = "2026-03-29T13:18:57.769Z" }, + { url = "https://files.pythonhosted.org/packages/7f/37/eed308a8f56cba4d1fdf467a4fc67ef4ff4bf1c888f5fc980481890104b1/numpy-2.4.4-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f9e75681b59ddaa5e659898085ae0eaea229d054f2ac0c7e563a62205a700121", size = 15670666, upload-time = "2026-03-29T13:19:00.341Z" }, + { url = "https://files.pythonhosted.org/packages/0a/0d/0e3ecece05b7a7e87ab9fb587855548da437a061326fff64a223b6dcb78a/numpy-2.4.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:81f4a14bee47aec54f883e0cad2d73986640c1590eb9bfaaba7ad17394481e6e", size = 16645480, upload-time = "2026-03-29T13:19:03.63Z" }, + { url = "https://files.pythonhosted.org/packages/34/49/f2312c154b82a286758ee2f1743336d50651f8b5195db18cdb63675ff649/numpy-2.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:62d6b0f03b694173f9fcb1fb317f7222fd0b0b103e784c6549f5e53a27718c44", size = 17020036, upload-time = "2026-03-29T13:19:07.428Z" }, + { url = "https://files.pythonhosted.org/packages/7b/e9/736d17bd77f1b0ec4f9901aaec129c00d59f5d84d5e79bba540ef12c2330/numpy-2.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fbc356aae7adf9e6336d336b9c8111d390a05df88f1805573ebb0807bd06fd1d", size = 18368643, upload-time = "2026-03-29T13:19:10.775Z" }, + { url = "https://files.pythonhosted.org/packages/63/f6/d417977c5f519b17c8a5c3bc9e8304b0908b0e21136fe43bf628a1343914/numpy-2.4.4-cp312-cp312-win32.whl", hash = "sha256:0d35aea54ad1d420c812bfa0385c71cd7cc5bcf7c65fed95fc2cd02fe8c79827", size = 5961117, upload-time = "2026-03-29T13:19:13.464Z" }, + { url = "https://files.pythonhosted.org/packages/2d/5b/e1deebf88ff431b01b7406ca3583ab2bbb90972bbe1c568732e49c844f7e/numpy-2.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:b5f0362dc928a6ecd9db58868fca5e48485205e3855957bdedea308f8672ea4a", size = 12320584, upload-time = "2026-03-29T13:19:16.155Z" }, + { url = "https://files.pythonhosted.org/packages/58/89/e4e856ac82a68c3ed64486a544977d0e7bdd18b8da75b78a577ca31c4395/numpy-2.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:846300f379b5b12cc769334464656bc882e0735d27d9726568bc932fdc49d5ec", size = 10221450, upload-time = "2026-03-29T13:19:18.994Z" }, + { url = "https://files.pythonhosted.org/packages/14/1d/d0a583ce4fefcc3308806a749a536c201ed6b5ad6e1322e227ee4848979d/numpy-2.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:08f2e31ed5e6f04b118e49821397f12767934cfdd12a1ce86a058f91e004ee50", size = 16684933, upload-time = "2026-03-29T13:19:22.47Z" }, + { url = "https://files.pythonhosted.org/packages/c1/62/2b7a48fbb745d344742c0277f01286dead15f3f68e4f359fbfcf7b48f70f/numpy-2.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e823b8b6edc81e747526f70f71a9c0a07ac4e7ad13020aa736bb7c9d67196115", size = 14694532, upload-time = "2026-03-29T13:19:25.581Z" }, + { url = "https://files.pythonhosted.org/packages/e5/87/499737bfba066b4a3bebff24a8f1c5b2dee410b209bc6668c9be692580f0/numpy-2.4.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4a19d9dba1a76618dd86b164d608566f393f8ec6ac7c44f0cc879011c45e65af", size = 5199661, upload-time = "2026-03-29T13:19:28.31Z" }, + { url = "https://files.pythonhosted.org/packages/cd/da/464d551604320d1491bc345efed99b4b7034143a85787aab78d5691d5a0e/numpy-2.4.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d2a8490669bfe99a233298348acc2d824d496dee0e66e31b66a6022c2ad74a5c", size = 6547539, upload-time = "2026-03-29T13:19:30.97Z" }, + { url = "https://files.pythonhosted.org/packages/7d/90/8d23e3b0dafd024bf31bdec225b3bb5c2dbfa6912f8a53b8659f21216cbf/numpy-2.4.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45dbed2ab436a9e826e302fcdcbe9133f9b0006e5af7168afb8963a6520da103", size = 15668806, upload-time = "2026-03-29T13:19:33.887Z" }, + { url = "https://files.pythonhosted.org/packages/d1/73/a9d864e42a01896bb5974475438f16086be9ba1f0d19d0bb7a07427c4a8b/numpy-2.4.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c901b15172510173f5cb310eae652908340f8dede90fff9e3bf6c0d8dfd92f83", size = 16632682, upload-time = "2026-03-29T13:19:37.336Z" }, + { url = "https://files.pythonhosted.org/packages/34/fb/14570d65c3bde4e202a031210475ae9cde9b7686a2e7dc97ee67d2833b35/numpy-2.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:99d838547ace2c4aace6c4f76e879ddfe02bb58a80c1549928477862b7a6d6ed", size = 17019810, upload-time = "2026-03-29T13:19:40.963Z" }, + { url = "https://files.pythonhosted.org/packages/8a/77/2ba9d87081fd41f6d640c83f26fb7351e536b7ce6dd9061b6af5904e8e46/numpy-2.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0aec54fd785890ecca25a6003fd9a5aed47ad607bbac5cd64f836ad8666f4959", size = 18357394, upload-time = "2026-03-29T13:19:44.859Z" }, + { url = "https://files.pythonhosted.org/packages/a2/23/52666c9a41708b0853fa3b1a12c90da38c507a3074883823126d4e9d5b30/numpy-2.4.4-cp313-cp313-win32.whl", hash = "sha256:07077278157d02f65c43b1b26a3886bce886f95d20aabd11f87932750dfb14ed", size = 5959556, upload-time = "2026-03-29T13:19:47.661Z" }, + { url = "https://files.pythonhosted.org/packages/57/fb/48649b4971cde70d817cf97a2a2fdc0b4d8308569f1dd2f2611959d2e0cf/numpy-2.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:5c70f1cc1c4efbe316a572e2d8b9b9cc44e89b95f79ca3331553fbb63716e2bf", size = 12317311, upload-time = "2026-03-29T13:19:50.67Z" }, + { url = "https://files.pythonhosted.org/packages/ba/d8/11490cddd564eb4de97b4579ef6bfe6a736cc07e94c1598590ae25415e01/numpy-2.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:ef4059d6e5152fa1a39f888e344c73fdc926e1b2dd58c771d67b0acfbf2aa67d", size = 10222060, upload-time = "2026-03-29T13:19:54.229Z" }, + { url = "https://files.pythonhosted.org/packages/99/5d/dab4339177a905aad3e2221c915b35202f1ec30d750dd2e5e9d9a72b804b/numpy-2.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4bbc7f303d125971f60ec0aaad5e12c62d0d2c925f0ab1273debd0e4ba37aba5", size = 14822302, upload-time = "2026-03-29T13:19:57.585Z" }, + { url = "https://files.pythonhosted.org/packages/eb/e4/0564a65e7d3d97562ed6f9b0fd0fb0a6f559ee444092f105938b50043876/numpy-2.4.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:4d6d57903571f86180eb98f8f0c839fa9ebbfb031356d87f1361be91e433f5b7", size = 5327407, upload-time = "2026-03-29T13:20:00.601Z" }, + { url = "https://files.pythonhosted.org/packages/29/8d/35a3a6ce5ad371afa58b4700f1c820f8f279948cca32524e0a695b0ded83/numpy-2.4.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:4636de7fd195197b7535f231b5de9e4b36d2c440b6e566d2e4e4746e6af0ca93", size = 6647631, upload-time = "2026-03-29T13:20:02.855Z" }, + { url = "https://files.pythonhosted.org/packages/f4/da/477731acbd5a58a946c736edfdabb2ac5b34c3d08d1ba1a7b437fa0884df/numpy-2.4.4-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ad2e2ef14e0b04e544ea2fa0a36463f847f113d314aa02e5b402fdf910ef309e", size = 15727691, upload-time = "2026-03-29T13:20:06.004Z" }, + { url = "https://files.pythonhosted.org/packages/e6/db/338535d9b152beabeb511579598418ba0212ce77cf9718edd70262cc4370/numpy-2.4.4-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a285b3b96f951841799528cd1f4f01cd70e7e0204b4abebac9463eecfcf2a40", size = 16681241, upload-time = "2026-03-29T13:20:09.417Z" }, + { url = "https://files.pythonhosted.org/packages/e2/a9/ad248e8f58beb7a0219b413c9c7d8151c5d285f7f946c3e26695bdbbe2df/numpy-2.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f8474c4241bc18b750be2abea9d7a9ec84f46ef861dbacf86a4f6e043401f79e", size = 17085767, upload-time = "2026-03-29T13:20:13.126Z" }, + { url = "https://files.pythonhosted.org/packages/b5/1a/3b88ccd3694681356f70da841630e4725a7264d6a885c8d442a697e1146b/numpy-2.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4e874c976154687c1f71715b034739b45c7711bec81db01914770373d125e392", size = 18403169, upload-time = "2026-03-29T13:20:17.096Z" }, + { url = "https://files.pythonhosted.org/packages/c2/c9/fcfd5d0639222c6eac7f304829b04892ef51c96a75d479214d77e3ce6e33/numpy-2.4.4-cp313-cp313t-win32.whl", hash = "sha256:9c585a1790d5436a5374bac930dad6ed244c046ed91b2b2a3634eb2971d21008", size = 6083477, upload-time = "2026-03-29T13:20:20.195Z" }, + { url = "https://files.pythonhosted.org/packages/d5/e3/3938a61d1c538aaec8ed6fd6323f57b0c2d2d2219512434c5c878db76553/numpy-2.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:93e15038125dc1e5345d9b5b68aa7f996ec33b98118d18c6ca0d0b7d6198b7e8", size = 12457487, upload-time = "2026-03-29T13:20:22.946Z" }, + { url = "https://files.pythonhosted.org/packages/97/6a/7e345032cc60501721ef94e0e30b60f6b0bd601f9174ebd36389a2b86d40/numpy-2.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:0dfd3f9d3adbe2920b68b5cd3d51444e13a10792ec7154cd0a2f6e74d4ab3233", size = 10292002, upload-time = "2026-03-29T13:20:25.909Z" }, + { url = "https://files.pythonhosted.org/packages/6e/06/c54062f85f673dd5c04cbe2f14c3acb8c8b95e3384869bb8cc9bff8cb9df/numpy-2.4.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f169b9a863d34f5d11b8698ead99febeaa17a13ca044961aa8e2662a6c7766a0", size = 16684353, upload-time = "2026-03-29T13:20:29.504Z" }, + { url = "https://files.pythonhosted.org/packages/4c/39/8a320264a84404c74cc7e79715de85d6130fa07a0898f67fb5cd5bd79908/numpy-2.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2483e4584a1cb3092da4470b38866634bafb223cbcd551ee047633fd2584599a", size = 14704914, upload-time = "2026-03-29T13:20:33.547Z" }, + { url = "https://files.pythonhosted.org/packages/91/fb/287076b2614e1d1044235f50f03748f31fa287e3dbe6abeb35cdfa351eca/numpy-2.4.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:2d19e6e2095506d1736b7d80595e0f252d76b89f5e715c35e06e937679ea7d7a", size = 5210005, upload-time = "2026-03-29T13:20:36.45Z" }, + { url = "https://files.pythonhosted.org/packages/63/eb/fcc338595309910de6ecabfcef2419a9ce24399680bfb149421fa2df1280/numpy-2.4.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:6a246d5914aa1c820c9443ddcee9c02bec3e203b0c080349533fae17727dfd1b", size = 6544974, upload-time = "2026-03-29T13:20:39.014Z" }, + { url = "https://files.pythonhosted.org/packages/44/5d/e7e9044032a716cdfaa3fba27a8e874bf1c5f1912a1ddd4ed071bf8a14a6/numpy-2.4.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989824e9faf85f96ec9c7761cd8d29c531ad857bfa1daa930cba85baaecf1a9a", size = 15684591, upload-time = "2026-03-29T13:20:42.146Z" }, + { url = "https://files.pythonhosted.org/packages/98/7c/21252050676612625449b4807d6b695b9ce8a7c9e1c197ee6216c8a65c7c/numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27a8d92cd10f1382a67d7cf4db7ce18341b66438bdd9f691d7b0e48d104c2a9d", size = 16637700, upload-time = "2026-03-29T13:20:46.204Z" }, + { url = "https://files.pythonhosted.org/packages/b1/29/56d2bbef9465db24ef25393383d761a1af4f446a1df9b8cded4fe3a5a5d7/numpy-2.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e44319a2953c738205bf3354537979eaa3998ed673395b964c1176083dd46252", size = 17035781, upload-time = "2026-03-29T13:20:50.242Z" }, + { url = "https://files.pythonhosted.org/packages/e3/2b/a35a6d7589d21f44cea7d0a98de5ddcbb3d421b2622a5c96b1edf18707c3/numpy-2.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e892aff75639bbef0d2a2cfd55535510df26ff92f63c92cd84ef8d4ba5a5557f", size = 18362959, upload-time = "2026-03-29T13:20:54.019Z" }, + { url = "https://files.pythonhosted.org/packages/64/c9/d52ec581f2390e0f5f85cbfd80fb83d965fc15e9f0e1aec2195faa142cde/numpy-2.4.4-cp314-cp314-win32.whl", hash = "sha256:1378871da56ca8943c2ba674530924bb8ca40cd228358a3b5f302ad60cf875fc", size = 6008768, upload-time = "2026-03-29T13:20:56.912Z" }, + { url = "https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74", size = 12449181, upload-time = "2026-03-29T13:20:59.548Z" }, + { url = "https://files.pythonhosted.org/packages/70/2e/14cda6f4d8e396c612d1bf97f22958e92148801d7e4f110cabebdc0eef4b/numpy-2.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:2c194dd721e54ecad9ad387c1d35e63dce5c4450c6dc7dd5611283dda239aabb", size = 10496035, upload-time = "2026-03-29T13:21:02.524Z" }, + { url = "https://files.pythonhosted.org/packages/b1/e8/8fed8c8d848d7ecea092dc3469643f9d10bc3a134a815a3b033da1d2039b/numpy-2.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2aa0613a5177c264ff5921051a5719d20095ea586ca88cc802c5c218d1c67d3e", size = 14824958, upload-time = "2026-03-29T13:21:05.671Z" }, + { url = "https://files.pythonhosted.org/packages/05/1a/d8007a5138c179c2bf33ef44503e83d70434d2642877ee8fbb230e7c0548/numpy-2.4.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:42c16925aa5a02362f986765f9ebabf20de75cdefdca827d14315c568dcab113", size = 5330020, upload-time = "2026-03-29T13:21:08.635Z" }, + { url = "https://files.pythonhosted.org/packages/99/64/ffb99ac6ae93faf117bcbd5c7ba48a7f45364a33e8e458545d3633615dda/numpy-2.4.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:874f200b2a981c647340f841730fc3a2b54c9d940566a3c4149099591e2c4c3d", size = 6650758, upload-time = "2026-03-29T13:21:10.949Z" }, + { url = "https://files.pythonhosted.org/packages/6e/6e/795cc078b78a384052e73b2f6281ff7a700e9bf53bcce2ee579d4f6dd879/numpy-2.4.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9b39d38a9bd2ae1becd7eac1303d031c5c110ad31f2b319c6e7d98b135c934d", size = 15729948, upload-time = "2026-03-29T13:21:14.047Z" }, + { url = "https://files.pythonhosted.org/packages/5f/86/2acbda8cc2af5f3d7bfc791192863b9e3e19674da7b5e533fded124d1299/numpy-2.4.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b268594bccac7d7cf5844c7732e3f20c50921d94e36d7ec9b79e9857694b1b2f", size = 16679325, upload-time = "2026-03-29T13:21:17.561Z" }, + { url = "https://files.pythonhosted.org/packages/bc/59/cafd83018f4aa55e0ac6fa92aa066c0a1877b77a615ceff1711c260ffae8/numpy-2.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ac6b31e35612a26483e20750126d30d0941f949426974cace8e6b5c58a3657b0", size = 17084883, upload-time = "2026-03-29T13:21:21.106Z" }, + { url = "https://files.pythonhosted.org/packages/f0/85/a42548db84e65ece46ab2caea3d3f78b416a47af387fcbb47ec28e660dc2/numpy-2.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8e3ed142f2728df44263aaf5fb1f5b0b99f4070c553a0d7f033be65338329150", size = 18403474, upload-time = "2026-03-29T13:21:24.828Z" }, + { url = "https://files.pythonhosted.org/packages/ed/ad/483d9e262f4b831000062e5d8a45e342166ec8aaa1195264982bca267e62/numpy-2.4.4-cp314-cp314t-win32.whl", hash = "sha256:dddbbd259598d7240b18c9d87c56a9d2fb3b02fe266f49a7c101532e78c1d871", size = 6155500, upload-time = "2026-03-29T13:21:28.205Z" }, + { url = "https://files.pythonhosted.org/packages/c7/03/2fc4e14c7bd4ff2964b74ba90ecb8552540b6315f201df70f137faa5c589/numpy-2.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:a7164afb23be6e37ad90b2f10426149fd75aee07ca55653d2aa41e66c4ef697e", size = 12637755, upload-time = "2026-03-29T13:21:31.107Z" }, + { url = "https://files.pythonhosted.org/packages/58/78/548fb8e07b1a341746bfbecb32f2c268470f45fa028aacdbd10d9bc73aab/numpy-2.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:ba203255017337d39f89bdd58417f03c4426f12beed0440cfd933cb15f8669c7", size = 10566643, upload-time = "2026-03-29T13:21:34.339Z" }, + { url = "https://files.pythonhosted.org/packages/6b/33/8fae8f964a4f63ed528264ddf25d2b683d0b663e3cba26961eb838a7c1bd/numpy-2.4.4-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:58c8b5929fcb8287cbd6f0a3fae19c6e03a5c48402ae792962ac465224a629a4", size = 16854491, upload-time = "2026-03-29T13:21:38.03Z" }, + { url = "https://files.pythonhosted.org/packages/bc/d0/1aabee441380b981cf8cdda3ae7a46aa827d1b5a8cce84d14598bc94d6d9/numpy-2.4.4-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:eea7ac5d2dce4189771cedb559c738a71512768210dc4e4753b107a2048b3d0e", size = 14895830, upload-time = "2026-03-29T13:21:41.509Z" }, + { url = "https://files.pythonhosted.org/packages/a5/b8/aafb0d1065416894fccf4df6b49ef22b8db045187949545bced89c034b8e/numpy-2.4.4-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:51fc224f7ca4d92656d5a5eb315f12eb5fe2c97a66249aa7b5f562528a3be38c", size = 5400927, upload-time = "2026-03-29T13:21:44.747Z" }, + { url = "https://files.pythonhosted.org/packages/d6/77/063baa20b08b431038c7f9ff5435540c7b7265c78cf56012a483019ca72d/numpy-2.4.4-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:28a650663f7314afc3e6ec620f44f333c386aad9f6fc472030865dc0ebb26ee3", size = 6715557, upload-time = "2026-03-29T13:21:47.406Z" }, + { url = "https://files.pythonhosted.org/packages/c7/a8/379542d45a14f149444c5c4c4e7714707239ce9cc1de8c2803958889da14/numpy-2.4.4-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:19710a9ca9992d7174e9c52f643d4272dcd1558c5f7af7f6f8190f633bd651a7", size = 15804253, upload-time = "2026-03-29T13:21:50.753Z" }, + { url = "https://files.pythonhosted.org/packages/a2/c8/f0a45426d6d21e7ea3310a15cf90c43a14d9232c31a837702dba437f3373/numpy-2.4.4-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9b2aec6af35c113b05695ebb5749a787acd63cafc83086a05771d1e1cd1e555f", size = 16753552, upload-time = "2026-03-29T13:21:54.344Z" }, + { url = "https://files.pythonhosted.org/packages/04/74/f4c001f4714c3ad9ce037e18cf2b9c64871a84951eaa0baf683a9ca9301c/numpy-2.4.4-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f2cf083b324a467e1ab358c105f6cad5ea950f50524668a80c486ff1db24e119", size = 12509075, upload-time = "2026-03-29T13:21:57.644Z" }, +] + +[[package]] +name = "packaging" +version = "26.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134, upload-time = "2026-04-24T20:15:23.917Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195, upload-time = "2026-04-24T20:15:22.081Z" }, +] + +[[package]] +name = "pluggy" +version = "1.5.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.9'", +] +sdist = { url = "https://files.pythonhosted.org/packages/96/2d/02d4312c973c6050a18b314a5ad0b3210edb65a906f868e31c111dede4a6/pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1", size = 67955, upload-time = "2024-04-20T21:34:42.531Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556, upload-time = "2024-04-20T21:34:40.434Z" }, +] + +[[package]] +name = "pluggy" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version == '3.10.*'", + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, +] + +[[package]] +name = "pygments" +version = "2.20.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991, upload-time = "2026-03-29T13:29:33.898Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151, upload-time = "2026-03-29T13:29:30.038Z" }, +] + +[[package]] +name = "pytest" +version = "8.3.5" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.9'", +] +dependencies = [ + { name = "colorama", marker = "python_full_version < '3.9' and sys_platform == 'win32'" }, + { name = "exceptiongroup", marker = "python_full_version < '3.9'" }, + { name = "iniconfig", version = "2.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "packaging", marker = "python_full_version < '3.9'" }, + { name = "pluggy", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "tomli", marker = "python_full_version < '3.9'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ae/3c/c9d525a414d506893f0cd8a8d0de7706446213181570cdbd766691164e40/pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845", size = 1450891, upload-time = "2025-03-02T12:54:54.503Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/30/3d/64ad57c803f1fa1e963a7946b6e0fea4a70df53c1a7fed304586539c2bac/pytest-8.3.5-py3-none-any.whl", hash = "sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820", size = 343634, upload-time = "2025-03-02T12:54:52.069Z" }, +] + +[[package]] +name = "pytest" +version = "8.4.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +dependencies = [ + { name = "colorama", marker = "python_full_version == '3.9.*' and sys_platform == 'win32'" }, + { name = "exceptiongroup", marker = "python_full_version == '3.9.*'" }, + { name = "iniconfig", version = "2.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "packaging", marker = "python_full_version == '3.9.*'" }, + { name = "pluggy", version = "1.6.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "pygments", marker = "python_full_version == '3.9.*'" }, + { name = "tomli", marker = "python_full_version == '3.9.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a3/5c/00a0e072241553e1a7496d638deababa67c5058571567b92a7eaa258397c/pytest-8.4.2.tar.gz", hash = "sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01", size = 1519618, upload-time = "2025-09-04T14:34:22.711Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a8/a4/20da314d277121d6534b3a980b29035dcd51e6744bd79075a6ce8fa4eb8d/pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79", size = 365750, upload-time = "2025-09-04T14:34:20.226Z" }, +] + +[[package]] +name = "pytest" +version = "9.0.3" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version == '3.10.*'", +] +dependencies = [ + { name = "colorama", marker = "python_full_version >= '3.10' and sys_platform == 'win32'" }, + { name = "exceptiongroup", marker = "python_full_version == '3.10.*'" }, + { name = "iniconfig", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, + { name = "packaging", marker = "python_full_version >= '3.10'" }, + { name = "pluggy", version = "1.6.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, + { name = "pygments", marker = "python_full_version >= '3.10'" }, + { name = "tomli", marker = "python_full_version == '3.10.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, +] + +[[package]] +name = "tomli" +version = "2.4.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/22/de/48c59722572767841493b26183a0d1cc411d54fd759c5607c4590b6563a6/tomli-2.4.1.tar.gz", hash = "sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f", size = 17543, upload-time = "2026-03-25T20:22:03.828Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/11/db3d5885d8528263d8adc260bb2d28ebf1270b96e98f0e0268d32b8d9900/tomli-2.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30", size = 154704, upload-time = "2026-03-25T20:21:10.473Z" }, + { url = "https://files.pythonhosted.org/packages/6d/f7/675db52c7e46064a9aa928885a9b20f4124ecb9bc2e1ce74c9106648d202/tomli-2.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a", size = 149454, upload-time = "2026-03-25T20:21:12.036Z" }, + { url = "https://files.pythonhosted.org/packages/61/71/81c50943cf953efa35bce7646caab3cf457a7d8c030b27cfb40d7235f9ee/tomli-2.4.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076", size = 237561, upload-time = "2026-03-25T20:21:13.098Z" }, + { url = "https://files.pythonhosted.org/packages/48/c1/f41d9cb618acccca7df82aaf682f9b49013c9397212cb9f53219e3abac37/tomli-2.4.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9", size = 243824, upload-time = "2026-03-25T20:21:14.569Z" }, + { url = "https://files.pythonhosted.org/packages/22/e4/5a816ecdd1f8ca51fb756ef684b90f2780afc52fc67f987e3c61d800a46d/tomli-2.4.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c", size = 242227, upload-time = "2026-03-25T20:21:15.712Z" }, + { url = "https://files.pythonhosted.org/packages/6b/49/2b2a0ef529aa6eec245d25f0c703e020a73955ad7edf73e7f54ddc608aa5/tomli-2.4.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc", size = 247859, upload-time = "2026-03-25T20:21:17.001Z" }, + { url = "https://files.pythonhosted.org/packages/83/bd/6c1a630eaca337e1e78c5903104f831bda934c426f9231429396ce3c3467/tomli-2.4.1-cp311-cp311-win32.whl", hash = "sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049", size = 97204, upload-time = "2026-03-25T20:21:18.079Z" }, + { url = "https://files.pythonhosted.org/packages/42/59/71461df1a885647e10b6bb7802d0b8e66480c61f3f43079e0dcd315b3954/tomli-2.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e", size = 108084, upload-time = "2026-03-25T20:21:18.978Z" }, + { url = "https://files.pythonhosted.org/packages/b8/83/dceca96142499c069475b790e7913b1044c1a4337e700751f48ed723f883/tomli-2.4.1-cp311-cp311-win_arm64.whl", hash = "sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece", size = 95285, upload-time = "2026-03-25T20:21:20.309Z" }, + { url = "https://files.pythonhosted.org/packages/c1/ba/42f134a3fe2b370f555f44b1d72feebb94debcab01676bf918d0cb70e9aa/tomli-2.4.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a", size = 155924, upload-time = "2026-03-25T20:21:21.626Z" }, + { url = "https://files.pythonhosted.org/packages/dc/c7/62d7a17c26487ade21c5422b646110f2162f1fcc95980ef7f63e73c68f14/tomli-2.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085", size = 150018, upload-time = "2026-03-25T20:21:23.002Z" }, + { url = "https://files.pythonhosted.org/packages/5c/05/79d13d7c15f13bdef410bdd49a6485b1c37d28968314eabee452c22a7fda/tomli-2.4.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9", size = 244948, upload-time = "2026-03-25T20:21:24.04Z" }, + { url = "https://files.pythonhosted.org/packages/10/90/d62ce007a1c80d0b2c93e02cab211224756240884751b94ca72df8a875ca/tomli-2.4.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5", size = 253341, upload-time = "2026-03-25T20:21:25.177Z" }, + { url = "https://files.pythonhosted.org/packages/1a/7e/caf6496d60152ad4ed09282c1885cca4eea150bfd007da84aea07bcc0a3e/tomli-2.4.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585", size = 248159, upload-time = "2026-03-25T20:21:26.364Z" }, + { url = "https://files.pythonhosted.org/packages/99/e7/c6f69c3120de34bbd882c6fba7975f3d7a746e9218e56ab46a1bc4b42552/tomli-2.4.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1", size = 253290, upload-time = "2026-03-25T20:21:27.46Z" }, + { url = "https://files.pythonhosted.org/packages/d6/2f/4a3c322f22c5c66c4b836ec58211641a4067364f5dcdd7b974b4c5da300c/tomli-2.4.1-cp312-cp312-win32.whl", hash = "sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917", size = 98141, upload-time = "2026-03-25T20:21:28.492Z" }, + { url = "https://files.pythonhosted.org/packages/24/22/4daacd05391b92c55759d55eaee21e1dfaea86ce5c571f10083360adf534/tomli-2.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9", size = 108847, upload-time = "2026-03-25T20:21:29.386Z" }, + { url = "https://files.pythonhosted.org/packages/68/fd/70e768887666ddd9e9f5d85129e84910f2db2796f9096aa02b721a53098d/tomli-2.4.1-cp312-cp312-win_arm64.whl", hash = "sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257", size = 95088, upload-time = "2026-03-25T20:21:30.677Z" }, + { url = "https://files.pythonhosted.org/packages/07/06/b823a7e818c756d9a7123ba2cda7d07bc2dd32835648d1a7b7b7a05d848d/tomli-2.4.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54", size = 155866, upload-time = "2026-03-25T20:21:31.65Z" }, + { url = "https://files.pythonhosted.org/packages/14/6f/12645cf7f08e1a20c7eb8c297c6f11d31c1b50f316a7e7e1e1de6e2e7b7e/tomli-2.4.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a", size = 149887, upload-time = "2026-03-25T20:21:33.028Z" }, + { url = "https://files.pythonhosted.org/packages/5c/e0/90637574e5e7212c09099c67ad349b04ec4d6020324539297b634a0192b0/tomli-2.4.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897", size = 243704, upload-time = "2026-03-25T20:21:34.51Z" }, + { url = "https://files.pythonhosted.org/packages/10/8f/d3ddb16c5a4befdf31a23307f72828686ab2096f068eaf56631e136c1fdd/tomli-2.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f", size = 251628, upload-time = "2026-03-25T20:21:36.012Z" }, + { url = "https://files.pythonhosted.org/packages/e3/f1/dbeeb9116715abee2485bf0a12d07a8f31af94d71608c171c45f64c0469d/tomli-2.4.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d", size = 247180, upload-time = "2026-03-25T20:21:37.136Z" }, + { url = "https://files.pythonhosted.org/packages/d3/74/16336ffd19ed4da28a70959f92f506233bd7cfc2332b20bdb01591e8b1d1/tomli-2.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5", size = 251674, upload-time = "2026-03-25T20:21:38.298Z" }, + { url = "https://files.pythonhosted.org/packages/16/f9/229fa3434c590ddf6c0aa9af64d3af4b752540686cace29e6281e3458469/tomli-2.4.1-cp313-cp313-win32.whl", hash = "sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd", size = 97976, upload-time = "2026-03-25T20:21:39.316Z" }, + { url = "https://files.pythonhosted.org/packages/6a/1e/71dfd96bcc1c775420cb8befe7a9d35f2e5b1309798f009dca17b7708c1e/tomli-2.4.1-cp313-cp313-win_amd64.whl", hash = "sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36", size = 108755, upload-time = "2026-03-25T20:21:40.248Z" }, + { url = "https://files.pythonhosted.org/packages/83/7a/d34f422a021d62420b78f5c538e5b102f62bea616d1d75a13f0a88acb04a/tomli-2.4.1-cp313-cp313-win_arm64.whl", hash = "sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd", size = 95265, upload-time = "2026-03-25T20:21:41.219Z" }, + { url = "https://files.pythonhosted.org/packages/3c/fb/9a5c8d27dbab540869f7c1f8eb0abb3244189ce780ba9cd73f3770662072/tomli-2.4.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf", size = 155726, upload-time = "2026-03-25T20:21:42.23Z" }, + { url = "https://files.pythonhosted.org/packages/62/05/d2f816630cc771ad836af54f5001f47a6f611d2d39535364f148b6a92d6b/tomli-2.4.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac", size = 149859, upload-time = "2026-03-25T20:21:43.386Z" }, + { url = "https://files.pythonhosted.org/packages/ce/48/66341bdb858ad9bd0ceab5a86f90eddab127cf8b046418009f2125630ecb/tomli-2.4.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662", size = 244713, upload-time = "2026-03-25T20:21:44.474Z" }, + { url = "https://files.pythonhosted.org/packages/df/6d/c5fad00d82b3c7a3ab6189bd4b10e60466f22cfe8a08a9394185c8a8111c/tomli-2.4.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853", size = 252084, upload-time = "2026-03-25T20:21:45.62Z" }, + { url = "https://files.pythonhosted.org/packages/00/71/3a69e86f3eafe8c7a59d008d245888051005bd657760e96d5fbfb0b740c2/tomli-2.4.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15", size = 247973, upload-time = "2026-03-25T20:21:46.937Z" }, + { url = "https://files.pythonhosted.org/packages/67/50/361e986652847fec4bd5e4a0208752fbe64689c603c7ae5ea7cb16b1c0ca/tomli-2.4.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba", size = 256223, upload-time = "2026-03-25T20:21:48.467Z" }, + { url = "https://files.pythonhosted.org/packages/8c/9a/b4173689a9203472e5467217e0154b00e260621caa227b6fa01feab16998/tomli-2.4.1-cp314-cp314-win32.whl", hash = "sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6", size = 98973, upload-time = "2026-03-25T20:21:49.526Z" }, + { url = "https://files.pythonhosted.org/packages/14/58/640ac93bf230cd27d002462c9af0d837779f8773bc03dee06b5835208214/tomli-2.4.1-cp314-cp314-win_amd64.whl", hash = "sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7", size = 109082, upload-time = "2026-03-25T20:21:50.506Z" }, + { url = "https://files.pythonhosted.org/packages/d5/2f/702d5e05b227401c1068f0d386d79a589bb12bf64c3d2c72ce0631e3bc49/tomli-2.4.1-cp314-cp314-win_arm64.whl", hash = "sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232", size = 96490, upload-time = "2026-03-25T20:21:51.474Z" }, + { url = "https://files.pythonhosted.org/packages/45/4b/b877b05c8ba62927d9865dd980e34a755de541eb65fffba52b4cc495d4d2/tomli-2.4.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4", size = 164263, upload-time = "2026-03-25T20:21:52.543Z" }, + { url = "https://files.pythonhosted.org/packages/24/79/6ab420d37a270b89f7195dec5448f79400d9e9c1826df982f3f8e97b24fd/tomli-2.4.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c", size = 160736, upload-time = "2026-03-25T20:21:53.674Z" }, + { url = "https://files.pythonhosted.org/packages/02/e0/3630057d8eb170310785723ed5adcdfb7d50cb7e6455f85ba8a3deed642b/tomli-2.4.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d", size = 270717, upload-time = "2026-03-25T20:21:55.129Z" }, + { url = "https://files.pythonhosted.org/packages/7a/b4/1613716072e544d1a7891f548d8f9ec6ce2faf42ca65acae01d76ea06bb0/tomli-2.4.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41", size = 278461, upload-time = "2026-03-25T20:21:56.228Z" }, + { url = "https://files.pythonhosted.org/packages/05/38/30f541baf6a3f6df77b3df16b01ba319221389e2da59427e221ef417ac0c/tomli-2.4.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c", size = 274855, upload-time = "2026-03-25T20:21:57.653Z" }, + { url = "https://files.pythonhosted.org/packages/77/a3/ec9dd4fd2c38e98de34223b995a3b34813e6bdadf86c75314c928350ed14/tomli-2.4.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f", size = 283144, upload-time = "2026-03-25T20:21:59.089Z" }, + { url = "https://files.pythonhosted.org/packages/ef/be/605a6261cac79fba2ec0c9827e986e00323a1945700969b8ee0b30d85453/tomli-2.4.1-cp314-cp314t-win32.whl", hash = "sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8", size = 108683, upload-time = "2026-03-25T20:22:00.214Z" }, + { url = "https://files.pythonhosted.org/packages/12/64/da524626d3b9cc40c168a13da8335fe1c51be12c0a63685cc6db7308daae/tomli-2.4.1-cp314-cp314t-win_amd64.whl", hash = "sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26", size = 121196, upload-time = "2026-03-25T20:22:01.169Z" }, + { url = "https://files.pythonhosted.org/packages/5a/cd/e80b62269fc78fc36c9af5a6b89c835baa8af28ff5ad28c7028d60860320/tomli-2.4.1-cp314-cp314t-win_arm64.whl", hash = "sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396", size = 100393, upload-time = "2026-03-25T20:22:02.137Z" }, + { url = "https://files.pythonhosted.org/packages/7b/61/cceae43728b7de99d9b847560c262873a1f6c98202171fd5ed62640b494b/tomli-2.4.1-py3-none-any.whl", hash = "sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe", size = 14583, upload-time = "2026-03-25T20:22:03.012Z" }, +] + +[[package]] +name = "typing-extensions" +version = "4.13.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.9'", +] +sdist = { url = "https://files.pythonhosted.org/packages/f6/37/23083fcd6e35492953e8d2aaaa68b860eb422b34627b13f2ce3eb6106061/typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef", size = 106967, upload-time = "2025-04-10T14:19:05.416Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8b/54/b1ae86c0973cc6f0210b53d508ca3641fb6d0c56823f288d108bc7ab3cc8/typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c", size = 45806, upload-time = "2025-04-10T14:19:03.967Z" }, +] + +[[package]] +name = "typing-extensions" +version = "4.15.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.10.*'", + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, +] + +[[package]] +name = "xuplift" +version = "0.0.1" +source = { editable = "." } +dependencies = [ + { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, +] + +[package.dev-dependencies] +dev = [ + { name = "pytest", version = "8.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "pytest", version = "8.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "pytest", version = "9.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, +] + +[package.metadata] +requires-dist = [{ name = "numpy", specifier = ">=1.24.4" }] + +[package.metadata.requires-dev] +dev = [{ name = "pytest", specifier = ">=8.3.5" }]