diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..87273c7 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,64 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.11", "3.12", "3.13"] + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Check package version matches __init__ + run: | + python - <<'PY' + from pathlib import Path + import re + import sys + import tomllib + + pyproject_version = tomllib.loads(Path("pyproject.toml").read_text())["project"]["version"] + init_text = Path("rings/__init__.py").read_text() + init_version = re.search(r'^__version__ = "([^"]+)"$', init_text, re.MULTILINE) + + if init_version is None: + sys.exit("Could not find __version__ in rings/__init__.py") + + if pyproject_version != init_version.group(1): + sys.exit( + f"Version mismatch: pyproject.toml has {pyproject_version}, " + f"rings/__init__.py has {init_version.group(1)}" + ) + PY + + + - name: Install dependencies + run: uv sync --dev --python ${{ matrix.python-version }} + + - name: Run Python tests + run: uv run pytest -v + + - name: Run Ruff lint checks + if: matrix.python-version == '3.13' + run: uv run ruff check rings examples tests + + - name: Run Ruff formatting checks + if: matrix.python-version == '3.13' + run: uv run ruff format --check rings examples tests diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml index dab338a..f224d21 100644 --- a/.github/workflows/pages.yml +++ b/.github/workflows/pages.yml @@ -4,6 +4,9 @@ on: push: branches: - main + pull_request: + branches: + - main # security: restrict permissions for CI jobs. permissions: @@ -15,13 +18,22 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v5 with: python-version: "3.13" - # Install dependencies and documentation tools - - run: python -m pip install -r requirements.txt - - run: sphinx-build -M html docs/source docs/build + - name: Install dependencies + run: uv sync --dev --group docs --python 3.13 + + - name: Build documentation + run: uv run sphinx-build -M html docs/source docs/build - uses: actions/upload-pages-artifact@v3 with: @@ -30,6 +42,7 @@ jobs: # Deploy the artifact to GitHub pages. # This is a separate job so that only actions/deploy-pages has the necessary permissions. deploy: + if: github.event_name == 'push' && github.ref == 'refs/heads/main' needs: build runs-on: ubuntu-latest permissions: diff --git a/.gitignore b/.gitignore index 0a19790..84d3a4e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,8 @@ +# Dataset files +data/ +checkpoints/ + + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -7,6 +12,7 @@ __pycache__/ *.so # Distribution / packaging +uv.lock .Python build/ develop-eggs/ diff --git a/README.md b/README.md index 97a281b..413ca5b 100644 --- a/README.md +++ b/README.md @@ -1,181 +1,113 @@ # RINGS -This is the official repository for our ICML paper: +**[No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets](https://arxiv.org/abs/2502.02379)** โ€” ICML 2025. -**[No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets](https://arxiv.org/abs/2502.02379)** - -which introduces **RINGS**: a perturbation framework for attributed graphs, designed to enable more principled evaluations of graph-learning benchmarks from first principles. - ---- - -## ๐Ÿšง Repository Status - -This repository is **under active development**. -Weโ€™re making it public early to invite feedback, discussion, and **transparency** as we move from research prototypes to a stable, user-friendly package. - -In the coming weeks, weโ€™ll release updates, architectural notes, and implementation details via a series of pull requests. You're welcome to follow along, open issues, or suggest improvements! - ---- - -## ๐Ÿš€ Current MVP Release - -This initial Minimum Viable Product (MVP) includes: - -- A set of **graph perturbation transformations** for manipulating node features and graph structure -- The **SeparabilityFunctor**, which enables statistical comparisons between distributions -- The **ComplementarityFunctor**, which computes mode complementarity between node features and graph structure -- Example scripts demonstrating usage with PyTorch Geometric datasets and toy performance distributions +RINGS is a perturbation framework for attributed graphs that lets you evaluate graph-learning datasets and models from first principles: apply structured perturbations, train as usual, and compare performance distributions with statistically rigorous tests. --- -## ๐Ÿ’ Framework Overview +## Install -We are developing a community-friendly implementation of the **RINGS** framework introduced in the paper. Our goal is to make it easy for the graph-learning community to: - -- Apply dataset perturbations tailored to graph-learning datasets -- Conduct more rigorous and insightful evaluations of both datasets and models -- Promote better dataset practices and evaluation hygiene across the field - -If you have feedback on the paper or suggestions for how this package could better integrate with popular frameworks, please feel free to reach out to the authors. - ---- - -## ๐Ÿ“ฆ Installation - -RINGS uses [uv](https://github.com/astral-sh/uv) as the package manager, which provides faster dependency resolution and installation. - -### Prerequisites - -Install `uv` if you donโ€™t have it already: +Install from [PyPI](https://pypi.org/project/rings-evaluation/): ```bash -pip install uv +pip install rings-evaluation ``` -### Installing RINGS +Requires Python 3.11+. -Clone the repository and install dependencies using `uv`: +### From source -```bash -# Clone the repository -git clone https://github.com/aidos-lab/rings.git -cd rings +To contribute or run the examples in this repo: -# Install dependencies -uv sync - -# Activate environment -source .venv/bin/activate +```bash +pip install uv +git clone https://github.com/aidos-lab/rings.git && cd rings +uv sync && source .venv/bin/activate ``` --- -## ๐Ÿ”‘ Key Components - -### Mode Perturbations - -RINGS provides several perturbation transforms that can be applied to graph datasets: - -**Node Feature Perturbations:** +## Quickstart -- `Original`: Keeps node features unchanged (baseline) -- `EmptyFeatures`: Replaces node features with zero vectors -- `RandomFeatures`: Replaces node features with random values -- `CompleteFeatures`: Assigns unique one-hot vectors to nodes +> Drop RINGS evaluations into your GNN pipeline. -**Graph Structure Perturbations:** +Keep your training loop. Wrap it with `SeparabilityStudy` to iterate perturbation ร— seed, record one scalar per run from *your* evaluator, and get a pairwise separability table back. -- `EmptyGraph`: Removes all edges from the graph -- `CompleteGraph`: Connects every pair of nodes -- `RandomGraph`: Generates a random graph structure -- `RandomConnectedGraph`: Generates a random graph that is guaranteed to be connected +```python +from rings import Original, EmptyGraph, RandomFeatures, CompleteFeatures +from rings.integrations import SeparabilityStudy -### SeparabilityFunctor +study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", # or "wilcoxon" + alpha=0.05, +) -The `SeparabilityFunctor` computes statistically rigorous comparisons between multiple distributions to determine if they differ significantly. This is useful for: +for name, transform, seed in study.runs(): + perturbed = study.apply(base_dataset, transform) + score = train_and_eval(perturbed, seed=seed) # your code + study.record(name, score) -- Evaluating whether different graph perturbations produce statistically distinct model performances -- Identifying which perturbations most impact model behavior -- Making rigorous, statistically valid claims about distribution separability - -It employs statistical tests with permutation testing and built-in correction for multiple hypotheses (Bonferroni correction). - -Available comparators include: - -- `KSComparator`: Kolmogorovโ€“Smirnov test for comparing distributions -- `WilcoxonComparator`: Wilcoxon signed-rank test for paired comparisons - -### ComplementarityFunctor - -The `ComplementarityFunctor` measures the alignment between node features and graph structure by comparing their induced metric spaces. It can help you understand: - -- Whether node features and graph structure contain complementary information -- How different perturbations affect this complementarity -- The distribution of information content across modalities in graph datasets - ---- - -## ๐Ÿ” Example Usage - -The repository includes example scripts that demonstrate how to use RINGS to analyze graph datasets. - -### Performance Separability - -Measure distances between performance distributions to test whether the original dataset statistically outperforms perturbed versions. - -```bash -# Run a basic separability analysis with the default KS comparator -python -m examples.separability --comparator ks --alpha 0.05 +results = study.evaluate(n_permutations=1000) +# DataFrame: mode1, mode2, score, pvalue_adjusted, significant ``` -```bash -# Use the Wilcoxon test comparator -python -m examples.separability --comparator wilcoxon --alpha 0.01 -``` +**PyTorch Lightning** โ€” attach `SeparabilityCallback` to your `Trainer` and it records the logged `test_acc` automatically: -```bash -# Get help and see all available options -python -m examples.separability --help -``` +```python +import pytorch_lightning as pl +from rings.integrations import SeparabilityStudy, SeparabilityCallback -The script analyzes and compares distributions from synthetic data, showing how to determine if differences are statistically significant. +for name, transform, seed in study.runs(): + pl.seed_everything(seed, workers=True) + dm = make_datamodule(study.apply(base_dataset, transform), seed=seed) + trainer = pl.Trainer( + max_epochs=20, + callbacks=[SeparabilityCallback(study, perturbation_name=name)], + ) + trainer.fit(model, datamodule=dm) + trainer.test(model, datamodule=dm) ---- +results = study.evaluate() +``` -### Mode Complementarity +**Custom evaluator** (GraphBench, OGB, anything that returns a scalar): just pass the number to `study.record(name, score)`. -Assess the complementarity of geometric information in the metric spaces of node features and graph structure. +### Runnable examples ```bash -# Run the example on the MUTAG dataset with original (unperturbed) graphs -python -m examples.complementarity --dataset MUTAG --perturbation original +uv run -m examples.integrations.pyg +uv run --with lightning -m examples.integrations.lightning +uv run --with graphbench-lib -m examples.integrations.graphbench ``` -```bash -# Try different perturbations -python -m examples.complementarity --dataset MUTAG --perturbation random-features -``` +--- -```bash -# Get help and see all available options -python -m examples.complementarity --help -``` +## Learn more -The script outputs complementarity statistics that measure how well node features align with graph structure in the dataset. +- **[Perturbations](https://aidos-lab.github.io/rings/perturbations.html)** โ€” node-feature and graph-structure transforms (`Original`, `EmptyGraph`, `RandomFeatures`, `CompleteFeatures`, `RandomConnectedGraph`, โ€ฆ) +- **[SeparabilityFunctor](https://aidos-lab.github.io/rings/separability/functor.html)** โ€” pairwise distribution tests (KS, Wilcoxon) with permutation p-values and Bonferroni correction +- **[ComplementarityFunctor](https://aidos-lab.github.io/rings/complementarity/functor.html)** โ€” metric-space alignment between node features and graph structure +- **[Integrations API](https://aidos-lab.github.io/rings/integrations.html)** โ€” full reference for `SeparabilityStudy` and `SeparabilityCallback` +- **`examples/`** โ€” end-to-end scripts for separability, complementarity, and the three integration recipes -## ๐Ÿ“š Citation +--- -If you use RINGS in your research, please cite our paper: +## Citation ```bibtex -@inproceedings{ -coupette2025metric, -title={No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets}, -author={Corinna Coupette and Jeremy Wayland and Emily Simons and Bastian Rieck}, -booktitle={Forty-second International Conference on Machine Learning}, -year={2025}, -url={https://openreview.net/forum?id=XbmBNwrfG5} +@inproceedings{coupette2025metric, + title = {No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets}, + author = {Corinna Coupette and Jeremy Wayland and Emily Simons and Bastian Rieck}, + booktitle = {Forty-second International Conference on Machine Learning}, + year = {2025}, + url = {https://openreview.net/forum?id=XbmBNwrfG5} } ``` - -Stay tuned for more examples and documentation! diff --git a/docs/source/complementarity.rst b/docs/source/complementarity.rst new file mode 100644 index 0000000..320ae1f --- /dev/null +++ b/docs/source/complementarity.rst @@ -0,0 +1,58 @@ +๐Ÿ”‘ Mode Complementarity +======================== + +The ``rings.complementarity`` module measures the alignment between node features and graph structure by comparing their induced metric spaces. It pairs **graph metrics** (diffusion, heat-kernel, resistance, shortest-path) with **matrix-norm comparators** and a **functor** that handles disconnected graphs component-wise. + +| + +.. image:: _static/complementarity-overview.svg + :width: 600 + :alt: Mode complementarity overview + :align: center + +| + +Functor +------- + +.. image:: _static/complementarity-functor.svg + :width: 500 + :alt: Functor + :align: center + +| + +.. automodule:: rings.complementarity.functor + :members: + +Comparators +----------- + +.. image:: _static/complementarity-comparator.svg + :width: 500 + :alt: Comparator + :align: center + +| + +.. automodule:: rings.complementarity.comparator + :members: + +Metrics +------- + +.. image:: _static/complementarity-metrics.svg + :width: 500 + :alt: Metrics + :align: center + +| + +.. automodule:: rings.complementarity.metrics + :members: + +Utilities +--------- + +.. automodule:: rings.complementarity.utils + :members: diff --git a/docs/source/complementarity/comparator.rst b/docs/source/complementarity/comparator.rst deleted file mode 100644 index 371ffc2..0000000 --- a/docs/source/complementarity/comparator.rst +++ /dev/null @@ -1,14 +0,0 @@ -complementarity.comparator.py -=================================== - -| - -.. image:: ../_static/complementarity-comparator.svg - :width: 500 - :alt: Code diagram highlighting comparator file - :align: center - -| - -.. automodule:: rings.complementarity.comparator - :members: diff --git a/docs/source/complementarity/functor.rst b/docs/source/complementarity/functor.rst deleted file mode 100644 index bea8dc2..0000000 --- a/docs/source/complementarity/functor.rst +++ /dev/null @@ -1,15 +0,0 @@ -complementarity.functor.py -=================================== - - -| - -.. image:: ../_static/complementarity-functor.svg - :width: 500 - :alt: Code diagram highlighting functor file - :align: center - -| - -.. automodule:: rings.complementarity.functor - :members: \ No newline at end of file diff --git a/docs/source/complementarity/metrics.rst b/docs/source/complementarity/metrics.rst deleted file mode 100644 index ed7ef5d..0000000 --- a/docs/source/complementarity/metrics.rst +++ /dev/null @@ -1,14 +0,0 @@ -complementarity.metrics.py -=================================== - -| - -.. image:: ../_static/complementarity-metrics.svg - :width: 500 - :alt: Code diagram highlighting metrics file - :align: center - -| - -.. automodule:: rings.complementarity.metrics - :members: \ No newline at end of file diff --git a/docs/source/complementarity/utils.rst b/docs/source/complementarity/utils.rst deleted file mode 100644 index ed86f3c..0000000 --- a/docs/source/complementarity/utils.rst +++ /dev/null @@ -1,5 +0,0 @@ -complementarity.utils.py -=================================== - -.. automodule:: rings.complementarity.utils - :members: \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 50f90b6..cf76788 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -45,6 +45,16 @@ html_static_path = ["_static"] +html_sidebars = { + "**": [ + "sidebar/scroll-start.html", + "sidebar/brand.html", + "sidebar/search.html", + "sidebar/navigation.html", + "sidebar/scroll-end.html", + ] +} + # Specifies how to actually find the sources of the modules. Ensures # that people can jump to files in the repository directly. diff --git a/docs/source/examples/complementarity.rst b/docs/source/examples/complementarity.rst deleted file mode 100644 index 031d6d1..0000000 --- a/docs/source/examples/complementarity.rst +++ /dev/null @@ -1,5 +0,0 @@ -examples.complementarity.py -=================================== - -.. automodule:: examples.complementarity - :members: \ No newline at end of file diff --git a/docs/source/examples/separability.rst b/docs/source/examples/separability.rst deleted file mode 100644 index 2c931db..0000000 --- a/docs/source/examples/separability.rst +++ /dev/null @@ -1,5 +0,0 @@ -examples.separability.py -=================================== - -.. automodule:: examples.separability - :members: \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index f103a4e..e9213b1 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,19 +1,5 @@ -.. RINGS: Relevant Information in Node Features and Graph Structure documentation master file, created by - sphinx-quickstart on Mon Jun 30 10:20:28 2025. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -RINGS: Relevant Information in Node Features and Graph Structure documentation -============================================================================== - -.. note:: - ๐Ÿšง This project is under active development. ๐Ÿšง - - We've made it public early to invite feedback, discussion, and transparency as we transition from research prototypes to a stable, user-friendly package. - - In the coming weeks, weโ€™ll be releasing updates, architectural notes, and implementation details via a series of pull requests. You're welcome to follow along, open issues, or suggest improvements! - -| +RINGS: Relevant Information in Node Features and Graph Structure +================================================================== .. image:: _static/rings-logo.svg :width: 150 @@ -21,209 +7,138 @@ RINGS: Relevant Information in Node Features and Graph Structure documentation :alt: RINGS logo :align: left +Official implementation of the ICML 2025 paper `No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets `__. RINGS is a perturbation framework for attributed graphs that lets you evaluate graph-learning datasets and models from first principles: apply structured perturbations, train as usual, and compare performance distributions with statistically rigorous tests. Source on `GitHub `__. -Welcome to the official repository for the 2025 ICML paper, `No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets `__, which introduces RINGS: a perturbation framework for attributed graphs, designed to facilitate more principled evaluations of graph learning benchmarks from first principles. - -The full repo is available `here `__. | -๐Ÿ’ Framework Overview ----------------------- - -We are developing a community-friendly implementation of the **RINGS** framework introduced in the paper. Our goal is to make it easy for the graph-learning community to: - -- Apply dataset perturbations tailored to graph-learning datasets -- Conduct more rigorous and insightful evaluations of both datasets and models -- Promote better dataset practices and evaluation hygiene across the field - -If you have feedback on the paper or suggestions for how this package could better integrate with popular frameworks, please feel free to reach out to the authors. - ---- - -๐Ÿ“ฆ Installation ----------------------- - -RINGS uses `uv `_ as the package manager, which provides faster dependency resolution and installation. - -Prerequisites -^^^^^^^^^^^^^^^ - -Install ``uv`` if you donโ€™t have it already:: - - pip install uv - -Installing RINGS -^^^^^^^^^^^^^^^^^^^^ - -Clone the repository and install dependencies using ``uv``:: - - # Clone the repository - git clone https://github.com/aidos-lab/rings.git - cd rings - - # Install dependencies - uv sync - - # Activate environment - source .venv/bin/activate - ---- - -๐Ÿ”‘ Key Components ----------------------- - -Mode Perturbations -^^^^^^^^^^^^^^^^^^^^^^ - -RINGS provides several perturbation transforms that can be applied to graph datasets: +Install +------- -**Node Feature Perturbations:** +.. code-block:: bash -- ``Original``: Keeps node features unchanged (baseline) -- ``EmptyFeatures``: Replaces node features with zero vectors -- ``RandomFeatures``: Replaces node features with random values -- ``CompleteFeatures``: Assigns unique one-hot vectors to nodes + pip install rings-evaluation -**Graph Structure Perturbations:** +Requires Python 3.11+. Package on `PyPI `__. -- ``EmptyGraph``: Removes all edges from the graph -- ``CompleteGraph``: Connects every pair of nodes -- ``RandomGraph``: Generates a random graph structure -- ``RandomConnectedGraph``: Generates a random graph that is guaranteed to be connected +From source +~~~~~~~~~~~ -SeparabilityFunctor -^^^^^^^^^^^^^^^^^^^^^^^ +To contribute or run the examples in this repo: -The ``SeparabilityFunctor`` computes statistically rigorous comparisons between multiple distributions to determine if they differ significantly. This is useful for: +.. code-block:: bash -- Evaluating whether different graph perturbations produce statistically distinct model performances -- Identifying which perturbations most impact model behavior -- Making rigorous, statistically valid claims about distribution separability + pip install uv + git clone https://github.com/aidos-lab/rings.git && cd rings + uv sync && source .venv/bin/activate -It employs statistical tests with permutation testing and built-in correction for multiple hypotheses (Bonferroni correction). +Quickstart +---------------------------------------------- -Available comparators include: - -- ``KSComparator``: Kolmogorovโ€“Smirnov test for comparing distributions -- ``WilcoxonComparator``: Wilcoxon signed-rank test for paired comparisons - -ComplementarityFunctor -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The ``ComplementarityFunctor`` measures the alignment between node features and graph structure by comparing their induced metric spaces. It can help you understand: - -- Whether node features and graph structure contain complementary information -- How different perturbations affect this complementarity -- The distribution of information content across modalities in graph datasets - ---- - -๐Ÿ” Example Usage ----------------------- - -The repository includes example scripts that demonstrate how to use RINGS to analyze graph datasets. - -Performance Separability -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Measure distances between performance distributions to test whether the original dataset statistically outperforms perturbed versions. - -Run a basic separability analysis with the default KS comparator:: - - python -m examples.separability --comparator ks --alpha 0.05 - -Use the Wilcoxon test comparator:: - - python -m examples.separability --comparator wilcoxon --alpha 0.01 +.. note:: + RINGS can be integrated into your GNN training pipeline. -Get help and see all available options:: - python -m examples.separability --help +Keep your training loop. Wrap it with ``SeparabilityStudy`` to iterate perturbation ร— seed, record one scalar per run from *your* evaluator, and get a pairwise separability table back. -The script analyzes and compares distributions from synthetic data, showing how to determine if differences are statistically significant. +.. code-block:: python -Mode Complementarity -^^^^^^^^^^^^^^^^^^^^^^^^^ + from rings import Original, EmptyGraph, RandomFeatures, CompleteFeatures + from rings.integrations import SeparabilityStudy -Assess the complementarity of geometric information in the metric spaces of node features and graph structure. + study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", # or "wilcoxon" + alpha=0.05, + ) -Run the example on the MUTAG dataset with original (unperturbed) graphs:: + for name, transform, seed in study.runs(): + perturbed = study.apply(base_dataset, transform) + score = train_and_eval(perturbed, seed=seed) # your code + study.record(name, score) - python -m examples.complementarity --dataset MUTAG --perturbation original + results = study.evaluate(n_permutations=1000) + # DataFrame: mode1, mode2, score, pvalue_adjusted, significant -Try different perturbations:: +**PyTorch Lightning** โ€” attach ``SeparabilityCallback`` to your ``Trainer`` and it records the logged ``test_acc`` automatically: - python -m examples.complementarity --dataset MUTAG --perturbation random-features +.. code-block:: python -Get help and see all available options:: + import pytorch_lightning as pl + from rings.integrations import SeparabilityStudy, SeparabilityCallback - python -m examples.complementarity --help + for name, transform, seed in study.runs(): + pl.seed_everything(seed, workers=True) + dm = make_datamodule(study.apply(base_dataset, transform), seed=seed) + trainer = pl.Trainer( + max_epochs=20, + callbacks=[SeparabilityCallback(study, perturbation_name=name)], + ) + trainer.fit(model, datamodule=dm) + trainer.test(model, datamodule=dm) -The script outputs complementarity statistics that measure how well node features align with graph structure in the dataset. + results = study.evaluate() +**Custom evaluator** (GraphBench, OGB, anything that returns a scalar): just pass the number to ``study.record(name, score)``. +Runnable examples:: -๐Ÿ” Table of Contents ---------------------- + uv run -m examples.integrations.pyg + uv run --with lightning -m examples.integrations.lightning + uv run --with graphbench-lib -m examples.integrations.graphbench -.. toctree:: - :maxdepth: 2 - :caption: RINGS +| - perturbations - utils +Reference +--------- .. toctree:: - :maxdepth: 2 - :caption: ๐Ÿ”‘ Performance Separability + :maxdepth: 1 + :caption: ๐Ÿ”Œ Quickstart - separability/comparator - separability/functor + integrations .. toctree:: - :maxdepth: 2 - :caption: ๐Ÿ”‘ Mode Complementarity + :maxdepth: 1 + :caption: ๐Ÿ“– Core Concepts - complementarity/comparator - complementarity/functor - complementarity/metrics - complementarity/utils + perturbations + separability + complementarity .. toctree:: - :maxdepth: 2 - :caption: ๐Ÿ” Example Scripts - - examples/complementarity - examples/separability + :maxdepth: 1 + :caption: ๐Ÿ›  Utilities + utils -๐Ÿ“š Citation -------------- -If you use RINGS in your research, please cite our paper: +Citation +-------- .. code-block:: bibtex - @inproceedings{ - coupette2025metric, - title={No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets}, - author={Corinna Coupette and Jeremy Wayland and Emily Simons and Bastian Rieck}, - booktitle={Forty-second International Conference on Machine Learning}, - year={2025}, - url={https://openreview.net/forum?id=XbmBNwrfG5} + @inproceedings{coupette2025metric, + title = {No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets}, + author = {Corinna Coupette and Jeremy Wayland and Emily Simons and Bastian Rieck}, + booktitle = {Forty-second International Conference on Machine Learning}, + year = {2025}, + url = {https://openreview.net/forum?id=XbmBNwrfG5} } - - +| .. image:: _static/aidos_logo.png :width: 120 :height: 120 - :alt: SCOTT logo + :alt: AIDOS Lab logo :align: left | -| **Interested in more of our work?** -| -| See what we are working on at `AIDOS Lab `_ or check out our `GitHub `_. +| **Interested in more of our work?** See `AIDOS Lab `_ or our `GitHub `_. diff --git a/docs/source/integrations.rst b/docs/source/integrations.rst new file mode 100644 index 0000000..91e5a49 --- /dev/null +++ b/docs/source/integrations.rst @@ -0,0 +1,91 @@ +๐Ÿ”Œ Integrations +================ + +RINGS can be easily integrated into your GNN training pipeline. + +The ``rings.integrations`` module ships two small utilities: + +- :class:`~rings.integrations.study.SeparabilityStudy` โ€” a collector that iterates perturbation ร— seed, applies transforms PyG-idiomatically, records scalar scores from *your* evaluator, and returns a pairwise separability DataFrame. Use it with plain PyG, Lightning, or any other framework. +- :class:`~rings.integrations.lightning.SeparabilityCallback` โ€” a PyTorch Lightning specific callback that records a logged test metric into a study automatically at the end of ``trainer.test()``. + +Plain PyG +--------- + +.. code-block:: python + + from rings import Original, EmptyGraph, RandomFeatures, CompleteFeatures + from rings.integrations import SeparabilityStudy + + study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", # or "wilcoxon" + alpha=0.05, + ) + + for name, transform, seed in study.runs(): + perturbed = study.apply(base_dataset, transform) + score = train_and_eval(perturbed, seed=seed) # your code + study.record(name, score) + + results = study.evaluate(n_permutations=1000) + +Lightning +--------- + +.. code-block:: python + + import pytorch_lightning as pl + from rings.integrations import SeparabilityStudy, SeparabilityCallback + + for name, transform, seed in study.runs(): + pl.seed_everything(seed, workers=True) + dm = make_datamodule(study.apply(base_dataset, transform), seed=seed) + trainer = pl.Trainer( + max_epochs=20, + callbacks=[SeparabilityCallback(study, perturbation_name=name)], + ) + trainer.fit(model, datamodule=dm) + trainer.test(model, datamodule=dm) + + results = study.evaluate() + +Your ``LightningModule.test_step`` must call ``self.log("test_acc", acc)`` (or whatever ``metric_key`` you pass to ``SeparabilityCallback``). + +Custom evaluators +----------------- + +``study.record(name, score)`` accepts any scalar โ€” plug in `GraphBench `_, OGB evaluators, or your own metric. See ``examples/integrations/graphbench.py``. + +Runnable recipes +---------------- + +.. code-block:: bash + + uv run -m examples.integrations.pyg + uv run --with lightning -m examples.integrations.lightning + uv run --with graphbench-lib -m examples.integrations.graphbench + +API reference +------------- + +SeparabilityStudy +^^^^^^^^^^^^^^^^^ + +.. automodule:: rings.integrations.study + :members: + :undoc-members: + :show-inheritance: + +SeparabilityCallback +^^^^^^^^^^^^^^^^^^^^ + +.. automodule:: rings.integrations.lightning + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/separability.rst b/docs/source/separability.rst new file mode 100644 index 0000000..cb10e60 --- /dev/null +++ b/docs/source/separability.rst @@ -0,0 +1,39 @@ +๐Ÿ”‘ Performance Separability +============================ + +The ``rings.separability`` module tests whether performance distributions from different perturbations are statistically distinguishable. It pairs a **comparator** (the statistical test) with a **functor** (the orchestration: pairwise comparison, permutation testing, Bonferroni correction). + +| + +.. image:: _static/separability-overview.svg + :width: 600 + :alt: Performance separability overview + :align: center + +| + +Comparators +----------- + +.. image:: _static/separability-comparator.svg + :width: 500 + :alt: Comparator + :align: center + +| + +.. automodule:: rings.separability.comparator + :members: + +Functor +------- + +.. image:: _static/separability-functor.svg + :width: 500 + :alt: Functor + :align: center + +| + +.. automodule:: rings.separability.functor + :members: diff --git a/docs/source/separability/comparator.rst b/docs/source/separability/comparator.rst deleted file mode 100644 index 9ca49a4..0000000 --- a/docs/source/separability/comparator.rst +++ /dev/null @@ -1,14 +0,0 @@ -separability.comparator.py -=================================== - -| - -.. image:: ../_static/separability-comparator.svg - :width: 500 - :alt: Code diagram highlighting comparator file - :align: center - -| - -.. automodule:: rings.separability.comparator - :members: diff --git a/docs/source/separability/functor.rst b/docs/source/separability/functor.rst deleted file mode 100644 index 3d3962f..0000000 --- a/docs/source/separability/functor.rst +++ /dev/null @@ -1,14 +0,0 @@ -separability.functor.py -=================================== - -| - -.. image:: ../_static/separability-functor.svg - :width: 500 - :alt: Code diagram highlighting functor file - :align: center - -| - -.. automodule:: rings.separability.functor - :members: \ No newline at end of file diff --git a/examples/complementarity.py b/examples/complementarity.py index 7873989..07c4120 100644 --- a/examples/complementarity.py +++ b/examples/complementarity.py @@ -21,7 +21,14 @@ from torch_geometric.loader import DataLoader from rings.complementarity import ComplementarityFunctor, MatrixNormComparator -from rings.perturbations import * +from rings.perturbations import ( + CompleteGraph, + EmptyFeatures, + EmptyGraph, + Original, + RandomFeatures, + RandomGraph, +) def get_available_perturbations(): @@ -238,7 +245,7 @@ def main(): # Print results print("\n" + "=" * 60) - print(f"๐Ÿ“Š Results Summary".center(60)) + print("๐Ÿ“Š Results Summary".center(60)) print("=" * 60) print(f"๐Ÿ“ Dataset: {args.dataset}") print(f"๐Ÿงช Perturbation: {args.perturbation}") diff --git a/examples/integrations/graphbench.py b/examples/integrations/graphbench.py new file mode 100644 index 0000000..3a6a3c5 --- /dev/null +++ b/examples/integrations/graphbench.py @@ -0,0 +1,126 @@ +""" +RINGS separability for a GraphBench-evaluated PyG training pipeline. + +This example shows the canonical recipe when you want GraphBench metrics on a +standard graph-classification benchmark (MUTAG) while keeping the same GCN training +loop as the other integration examples: + +1. Load MUTAG with PyG's ``TUDataset`` (GraphBench does not ship TU benchmarks). +2. Loop over perturbations x seeds via ``SeparabilityStudy.runs()``. +3. Apply each perturbation to the base dataset and train a GCN with an ordinary + PyG loop (same architecture and hyperparameters as ``lightning.py``). +4. Score the test set with ``graphbench.Evaluator`` (``algoreas_classification`` โ†’ ACC). +5. Call ``study.evaluate()`` for the pairwise separability table. + +Usage: + uv run --with graphbench-lib python -m examples.integrations.graphbench +""" + +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.datasets import TUDataset +from torch_geometric.loader import DataLoader +from torch_geometric.nn import GCNConv, global_mean_pool + +import graphbench + +from rings import CompleteFeatures, EmptyGraph, Original, RandomFeatures +from rings.integrations import SeparabilityStudy + + +class SimpleGCN(torch.nn.Module): + def __init__(self, hidden_channels, num_node_features, num_classes): + super().__init__() + self.conv1 = GCNConv(num_node_features, hidden_channels) + self.conv2 = GCNConv(hidden_channels, hidden_channels) + self.lin = Linear(hidden_channels, num_classes) + + def forward(self, x, edge_index, batch): + x = self.conv1(x, edge_index).relu() + x = self.conv2(x, edge_index) + x = global_mean_pool(x, batch) + x = F.dropout(x, p=0.5, training=self.training) + return self.lin(x) + + +def train_and_eval(dataset, seed, evaluator, epochs=20): + """Train a GCN on MUTAG and return GraphBench ACC on the held-out split.""" + torch.manual_seed(seed) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + n = len(dataset) + split = int(0.8 * n) + perm = torch.randperm(n, generator=torch.Generator().manual_seed(seed)).tolist() + train_ds = dataset[perm[:split]] + test_ds = dataset[perm[split:]] + + train_loader = DataLoader(train_ds, batch_size=32, shuffle=True) + test_loader = DataLoader(test_ds, batch_size=32) + + model = SimpleGCN( + hidden_channels=64, + num_node_features=dataset.num_node_features, + num_classes=dataset.num_classes, + ).to(device) + optim = torch.optim.Adam(model.parameters(), lr=0.01) + loss_fn = torch.nn.CrossEntropyLoss() + + model.train() + for _ in range(epochs): + for data in train_loader: + data = data.to(device) + optim.zero_grad() + out = model(data.x, data.edge_index, data.batch) + loss_fn(out, data.y).backward() + optim.step() + + model.eval() + preds, labels = [], [] + with torch.no_grad(): + for data in test_loader: + data = data.to(device) + pred = model(data.x, data.edge_index, data.batch).argmax(dim=1) + preds.append(pred.cpu()) + labels.append(data.y.cpu()) + + y_pred = torch.cat(preds).unsqueeze(1) + y_true = torch.cat(labels).unsqueeze(1) + metrics = evaluator.evaluate(y_pred, y_true) + # algoreas_classification reports ACC and F1; keep ACC to mirror lightning's test_acc. + return metrics[0] if isinstance(metrics, list) else metrics + + +def main(): + base_dataset = TUDataset(root="data/TUDataset", name="MUTAG") + max_nodes = max(g.num_nodes for g in base_dataset) + evaluator = graphbench.Evaluator("algoreas_classification") + + study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", + alpha=0.05, + ) + + for name, transform, seed in study.runs(): + perturbed = study.apply(base_dataset, transform) + score = train_and_eval(perturbed, seed=seed, evaluator=evaluator) + study.record(name, score) + print(f" [{name} seed={seed}] test_acc={score:.3f}") + + print("\nSeparability:") + print( + study.evaluate(n_permutations=1000)[ + ["mode1", "mode2", "score", "pvalue_adjusted", "significant"] + ] + ) + + +if __name__ == "__main__": + main() diff --git a/examples/integrations/lightning.py b/examples/integrations/lightning.py new file mode 100644 index 0000000..d9ccda4 --- /dev/null +++ b/examples/integrations/lightning.py @@ -0,0 +1,134 @@ +""" +RINGS separability for a PyTorch Lightning training pipeline. + +This example shows the canonical recipe when you're using Lightning: + +1. Wrap your existing GCN as a ``LightningModule``. +2. Loop over perturbations x seeds via ``SeparabilityStudy.runs()``. +3. Apply each perturbation to your base dataset, wrap it in + ``torch_geometric.data.lightning.LightningDataset``, and run + ``Trainer.fit(...) / Trainer.test(...)`` exactly as you normally would. +4. Attach ``SeparabilityCallback`` to the test ``Trainer`` so the logged + ``test_acc`` is recorded into the study automatically. +5. Call ``study.evaluate()`` for the pairwise separability table. + +Usage: + uv run --with lightning -m examples.integrations.lightning +""" + +import logging +import os +import warnings + +# Quiet Lightning before importing it: drop info-level chatter ("GPU available...", +# "Seed set...", "LOCAL_RANK...", "Trainer.fit stopped..."), the litlogger tip, +# and the usual deprecation warnings. Lightning emits these through several +# logger namespaces (and some via print), so we disable INFO-and-below globally +# while the script runs and restore it before exiting `main`. +os.environ.setdefault("PYTHONWARNINGS", "ignore") +warnings.filterwarnings("ignore") +logging.disable(logging.WARNING) + +import pytorch_lightning as pl # noqa: E402 +import torch # noqa: E402 +import torch.nn.functional as F # noqa: E402 +from torch.nn import Linear # noqa: E402 +from torch_geometric.data.lightning import LightningDataset # noqa: E402 +from torch_geometric.datasets import TUDataset # noqa: E402 +from torch_geometric.nn import GCNConv, global_mean_pool # noqa: E402 + +from rings import CompleteFeatures, EmptyGraph, Original, RandomFeatures # noqa: E402 +from rings.integrations import SeparabilityCallback, SeparabilityStudy # noqa: E402 + + +class GCNClassifier(pl.LightningModule): + def __init__(self, num_node_features, num_classes, hidden_channels=64, lr=0.01): + super().__init__() + self.save_hyperparameters() + self.conv1 = GCNConv(num_node_features, hidden_channels) + self.conv2 = GCNConv(hidden_channels, hidden_channels) + self.lin = Linear(hidden_channels, num_classes) + self.lr = lr + + def forward(self, data): + x = self.conv1(data.x, data.edge_index).relu() + x = self.conv2(x, data.edge_index) + x = global_mean_pool(x, data.batch) + x = F.dropout(x, p=0.5, training=self.training) + return self.lin(x) + + def training_step(self, batch, _): + logits = self(batch) + loss = F.cross_entropy(logits, batch.y) + self.log("train_loss", loss, batch_size=batch.num_graphs) + return loss + + def test_step(self, batch, _): + pred = self(batch).argmax(dim=1) + acc = (pred == batch.y).float().mean() + self.log("test_acc", acc, batch_size=batch.num_graphs) + + def configure_optimizers(self): + return torch.optim.Adam(self.parameters(), lr=self.lr) + + +def make_datamodule(dataset, seed: int, batch_size: int = 32) -> LightningDataset: + n = len(dataset) + split = int(0.8 * n) + perm = torch.randperm(n, generator=torch.Generator().manual_seed(seed)).tolist() + train_ds = dataset[perm[:split]] + test_ds = dataset[perm[split:]] + return LightningDataset( + train_dataset=train_ds, + test_dataset=test_ds, + batch_size=batch_size, + ) + + +def main(): + base_dataset = TUDataset(root="data/TUDataset", name="MUTAG") + max_nodes = max(g.num_nodes for g in base_dataset) + + study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", + alpha=0.05, + ) + + for name, transform, seed in study.runs(): + pl.seed_everything(seed, workers=True) + perturbed = study.apply(base_dataset, transform) + dm = make_datamodule(perturbed, seed=seed) + + model = GCNClassifier( + num_node_features=base_dataset.num_node_features, + num_classes=base_dataset.num_classes, + ) + trainer = pl.Trainer( + max_epochs=20, + enable_progress_bar=False, + enable_model_summary=False, + enable_checkpointing=False, + logger=False, + callbacks=[SeparabilityCallback(study, perturbation_name=name)], + ) + trainer.fit(model, datamodule=dm) + trainer.test(model, datamodule=dm, verbose=False) + print(f" [{name} seed={seed}] test_acc={study.scores[name][-1]:.3f}") + + print("\nSeparability:") + print( + study.evaluate(n_permutations=1000)[ + ["mode1", "mode2", "score", "pvalue_adjusted", "significant"] + ] + ) + + +if __name__ == "__main__": + main() diff --git a/examples/integrations/pyg.py b/examples/integrations/pyg.py new file mode 100644 index 0000000..7f19c0a --- /dev/null +++ b/examples/integrations/pyg.py @@ -0,0 +1,111 @@ +""" +Drop-in RINGS separability analysis for an existing PyG training pipeline. + +This script shows the canonical recipe: keep your own training/eval code, wrap it +with ``SeparabilityStudy`` to iterate perturbation x seed, record one scalar per +run, then call ``evaluate()`` to get a pairwise separability table. + +Usage: + uv run -m examples.integrations.pyg +""" + +import torch +import torch.nn.functional as F +from torch.nn import Linear +from torch_geometric.datasets import TUDataset +from torch_geometric.loader import DataLoader +from torch_geometric.nn import GCNConv, global_mean_pool + +from rings import CompleteFeatures, EmptyGraph, Original, RandomFeatures +from rings.integrations import SeparabilityStudy + + +class SimpleGCN(torch.nn.Module): + def __init__(self, hidden_channels, num_node_features, num_classes): + super().__init__() + self.conv1 = GCNConv(num_node_features, hidden_channels) + self.conv2 = GCNConv(hidden_channels, hidden_channels) + self.lin = Linear(hidden_channels, num_classes) + + def forward(self, x, edge_index, batch): + x = self.conv1(x, edge_index).relu() + x = self.conv2(x, edge_index) + x = global_mean_pool(x, batch) + x = F.dropout(x, p=0.5, training=self.training) + return self.lin(x) + + +def train_and_eval(dataset, seed, epochs=20): + """A perfectly ordinary PyG training + evaluation loop. RINGS does not touch it.""" + torch.manual_seed(seed) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + n = len(dataset) + split = int(0.8 * n) + perm = torch.randperm(n, generator=torch.Generator().manual_seed(seed)).tolist() + train_ds = dataset[perm[:split]] + test_ds = dataset[perm[split:]] + + train_loader = DataLoader(train_ds, batch_size=32, shuffle=True) + test_loader = DataLoader(test_ds, batch_size=32) + + model = SimpleGCN( + hidden_channels=64, + num_node_features=dataset.num_node_features, + num_classes=dataset.num_classes, + ).to(device) + optim = torch.optim.Adam(model.parameters(), lr=0.01) + loss_fn = torch.nn.CrossEntropyLoss() + + model.train() + for _ in range(epochs): + for data in train_loader: + data = data.to(device) + optim.zero_grad() + out = model(data.x, data.edge_index, data.batch) + loss_fn(out, data.y).backward() + optim.step() + + model.eval() + correct = total = 0 + with torch.no_grad(): + for data in test_loader: + data = data.to(device) + pred = model(data.x, data.edge_index, data.batch).argmax(dim=1) + correct += (pred == data.y).sum().item() + total += data.y.size(0) + return correct / max(total, 1) + + +def main(): + dataset = TUDataset(root="data/TUDataset", name="MUTAG") + max_nodes = max(g.num_nodes for g in dataset) + + study = SeparabilityStudy( + perturbations={ + "Original": Original(), + "EmptyGraph": EmptyGraph(), + "RandomFeatures": RandomFeatures(shuffle=True), + "CompleteFeatures": CompleteFeatures(max_nodes=max_nodes), + }, + num_seeds=5, + comparator="ks", + alpha=0.05, + ) + + for name, transform, seed in study.runs(): + perturbed = study.apply(dataset, transform) + score = train_and_eval(perturbed, seed=seed) + print(f" [{name} seed={seed}] accuracy={score:.3f}") + study.record(name, score) + + print("\nSeparability:") + print( + study.evaluate(n_permutations=1000)[ + ["mode1", "mode2", "score", "pvalue_adjusted", "significant"] + ] + ) + + +if __name__ == "__main__": + main() diff --git a/examples/separability.py b/examples/separability.py index 9fd8252..95d26fb 100644 --- a/examples/separability.py +++ b/examples/separability.py @@ -92,14 +92,14 @@ def basic_comparator_example(seed=42): s2 = np.random.normal(0.5, 1, 100) # From distribution 2 print("๐Ÿ“ˆ Comparing two normal distributions:") - print(f" - Distribution 1: ฮผ = 0, ฯƒ = 1, n = 100") - print(f" - Distribution 2: ฮผ = 0.5, ฯƒ = 1, n = 100") + print(" - Distribution 1: ฮผ = 0, ฯƒ = 1, n = 100") + print(" - Distribution 2: ฮผ = 0.5, ฯƒ = 1, n = 100") print("-" * 60) # Using KS Comparator ks = KSComparator() ks_result = ks(s1, s2, n_hypotheses=5) - print(f"๐Ÿงช KS Test Results:") + print("๐Ÿงช KS Test Results:") print(f" - Statistic: {ks_result['score']:.4f}") print(f" - P-value: {ks_result['pvalue']:.4f}") print(f" - Adjusted: {ks_result['pvalue_adjusted']:.4f}") @@ -108,7 +108,7 @@ def basic_comparator_example(seed=42): # Using Wilcoxon Comparator wc = WilcoxonComparator() wc_result = wc(s1, s2, n_hypotheses=5) - print(f"\n๐Ÿงฎ Wilcoxon Test Results:") + print("\n๐Ÿงฎ Wilcoxon Test Results:") print(f" - Statistic: {wc_result['score']:.4f}") print(f" - P-value: {wc_result['pvalue']:.4f}") print(f" - Adjusted: {wc_result['pvalue_adjusted']:.4f}") @@ -195,12 +195,12 @@ def print_results_summary(results_df, results_list, comparator_name): total_comparisons = len(results_list) print("\n" + "=" * 60) - print(f"๐Ÿ“Š Results Summary".center(60)) + print("๐Ÿ“Š Results Summary".center(60)) print("=" * 60) print(f"๐Ÿงช Comparator: {comparator_name.upper()}") print(f"๐Ÿ“ˆ Total Comparisons: {total_comparisons}") print( - f"โš ๏ธ Significant Diffs: {significant_count} ({significant_count/total_comparisons*100:.1f}%)" + f"โš ๏ธ Significant Diffs: {significant_count} ({significant_count / total_comparisons * 100:.1f}%)" ) print("-" * 60) diff --git a/pyproject.toml b/pyproject.toml index e65990d..f93c240 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,9 +1,31 @@ [project] -name = "rings" +name = "rings-evaluation" version = "0.1.0" -description = "Add your description here" +description = "RINGS: Relevant Information in Node Features and Graph Structure. An evaluation framework for graph-learning based on first principles." readme = "README.md" -requires-python = ">=3.13" +license = "BSD-3-Clause" +license-files = ["LICENSE"] +requires-python = ">=3.11" +authors = [ + { name = "Jeremy Wayland", email = "jeremy.don.wayland@gmail.com" }, + { name = "Corinna Coupette", email = "corinna.coupette@aalto.fi" }, + { name = "Emily Simons", email = "emsimons4@gmail.com" }, + { name = "Bastian Rieck", email = "bastian.grossenbacher@unifr.ch" }, +] +keywords = [ + "graph-learning", + "graph-neural-networks", + "dataset-evaluation", + "perturbation", + "separability", + "complementarity", +] +classifiers = [ + "Intended Audience :: Science/Research", + "License :: OSI Approved :: BSD License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", +] dependencies = [ "networkx>=3.4.2", "pot>=0.9.5", @@ -14,11 +36,35 @@ dependencies = [ "torchvision>=0.22.0", ] +[project.urls] +Documentation = "https://aidos.group/rings/" +Repository = "https://github.com/aidos-lab/rings" +Issues = "https://github.com/aidos-lab/rings/issues" +Paper = "https://proceedings.mlr.press/v267/coupette25a.html" + +[tool.hatch.build.targets.wheel] +packages = ["rings-evaluation"] + +[tool.hatch.build.targets.sdist] +include = [ + "/rings", + "/README.md", + "/LICENSE", +] + +[build-system] +requires = ["hatchling>=1.27.0"] +build-backend = "hatchling.build" + [dependency-groups] dev = [ - "furo>=2024.8.6", "ipykernel>=6.29.5", + "lightning>=2.6.1", "pytest>=8.4.1", "pytest-cov>=6.2.1", + "ruff>=0.15.13", +] +docs = [ + "furo>=2024.8.6", "sphinx>=8.2.3", ] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 367b202..0000000 --- a/requirements.txt +++ /dev/null @@ -1,70 +0,0 @@ -aiohappyeyeballs==2.6.1 -aiohttp==3.11.18 -aiosignal==1.3.2 -alabaster==1.0.0 -attrs==25.3.0 -babel==2.17.0 -beautifulsoup4==4.13.4 -certifi==2025.4.26 -charset-normalizer==3.4.1 -contourpy==1.3.2 -coverage==7.9.1 -cycler==0.12.1 -docutils==0.21.2 -filelock==3.18.0 -fonttools==4.57.0 -frozenlist==1.6.0 -fsspec==2025.3.2 -furo==2024.8.6 -idna==3.10 -imagesize==1.4.1 -iniconfig==2.1.0 -jinja2==3.1.6 -joblib==1.5.1 -kiwisolver==1.4.8 -markupsafe==3.0.2 -matplotlib==3.10.1 -mpmath==1.3.0 -multidict==6.4.3 -networkx==3.4.2 -numpy==2.2.5 -packaging==25.0 -pandas==2.2.3 -pillow==11.2.1 -pluggy==1.6.0 -pot==0.9.5 -propcache==0.3.1 -psutil==7.0.0 -pygments==2.19.2 -pyparsing==3.2.3 -pytest==8.4.1 -pytest-cov==6.2.1 -python-dateutil==2.9.0.post0 -pytz==2025.2 -requests==2.32.3 -roman-numerals-py==3.1.0 -scikit-learn==1.7.0 -scipy==1.15.2 -seaborn==0.13.2 -setuptools==80.0.0 -six==1.17.0 -snowballstemmer==3.0.1 -soupsieve==2.7 -sphinx==8.2.3 -sphinx-basic-ng==1.0.0b2 -sphinxcontrib-applehelp==2.0.0 -sphinxcontrib-devhelp==2.0.0 -sphinxcontrib-htmlhelp==2.1.0 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==2.0.0 -sphinxcontrib-serializinghtml==2.0.0 -sympy==1.14.0 -threadpoolctl==3.6.0 -torch==2.7.0 -torch-geometric==2.6.1 -torchvision==0.22.0 -tqdm==4.67.1 -typing-extensions==4.13.2 -tzdata==2025.2 -urllib3==2.4.0 -yarl==1.20.0 diff --git a/rings/__init__.py b/rings/__init__.py index 3b1a093..a9d08be 100644 --- a/rings/__init__.py +++ b/rings/__init__.py @@ -6,7 +6,9 @@ CompleteFeatures, CompleteGraph, ) +from rings import integrations +__version__ = "0.1.0" __all__ = [ "Original", @@ -15,4 +17,5 @@ "RandomFeatures", "CompleteFeatures", "CompleteGraph", + "integrations", ] diff --git a/rings/complementarity/comparator.py b/rings/complementarity/comparator.py index 060ae0c..a47c86c 100644 --- a/rings/complementarity/comparator.py +++ b/rings/complementarity/comparator.py @@ -1,7 +1,6 @@ """This module contains classes that compare metric spaces (i.e. pairwise distance matrices).""" import numpy as np -from typing import Dict, Optional class MatrixNormComparator: diff --git a/rings/complementarity/functor.py b/rings/complementarity/functor.py index 11e27b6..dc21217 100644 --- a/rings/complementarity/functor.py +++ b/rings/complementarity/functor.py @@ -172,9 +172,7 @@ def forward( for i in range(batch_size) ) else: - outputs = [ - self._process_single(batch[i]) for i in range(batch_size) - ] + outputs = [self._process_single(batch[i]) for i in range(batch_size)] # Convert to DataFrame if requested if as_dataframe: @@ -263,9 +261,7 @@ def _preprocess_graph(self, data, edge_attr=None) -> nx.Graph: nx.set_edge_attributes(G, attributes, "weight") else: # Always set all edge weights to 1.0 if not using edge information - nx.set_edge_attributes( - G, {edge: 1.0 for edge in G.edges()}, "weight" - ) + nx.set_edge_attributes(G, {edge: 1.0 for edge in G.edges()}, "weight") return G @@ -359,9 +355,7 @@ def _lift_metrics(self, G, X, empty_graph: bool): # Lift graphs for each component D_G = [ - lift_graph( - G.subgraph(C), metric=self.graph_metric, **self.kwargs - ) + lift_graph(G.subgraph(C), metric=self.graph_metric, **self.kwargs) for C in components ] @@ -434,9 +428,7 @@ def _compute_scores(self, D_X, D_G): List of complementarity scores for each component. """ # Compute complementarity scores for each component - return [ - self.comparator(d_x, d_g)["score"] for d_x, d_g in zip(D_X, D_G) - ] + return [self.comparator(d_x, d_g)["score"] for d_x, d_g in zip(D_X, D_G)] def _aggregate(self, scores, sizes): """ diff --git a/rings/integrations/__init__.py b/rings/integrations/__init__.py new file mode 100644 index 0000000..e14abfa --- /dev/null +++ b/rings/integrations/__init__.py @@ -0,0 +1,11 @@ +from rings.integrations.study import SeparabilityStudy + +__all__ = ["SeparabilityStudy", "SeparabilityCallback"] + + +def __getattr__(name): + if name == "SeparabilityCallback": + from rings.integrations.lightning import SeparabilityCallback + + return SeparabilityCallback + raise AttributeError(f"module 'rings.integrations' has no attribute {name!r}") diff --git a/rings/integrations/lightning.py b/rings/integrations/lightning.py new file mode 100644 index 0000000..b80e956 --- /dev/null +++ b/rings/integrations/lightning.py @@ -0,0 +1,86 @@ +"""PyTorch Lightning callback for recording test-time metrics into a SeparabilityStudy.""" + +from typing import TYPE_CHECKING + +from rings.integrations.study import SeparabilityStudy + +try: + import pytorch_lightning as _pl +except ImportError: + try: + import lightning.pytorch as _pl + except ImportError: + _pl = None + +if TYPE_CHECKING: + import pytorch_lightning as _pl_typing # noqa: F401 + +_HAS_LIGHTNING = _pl is not None +_Callback = _pl.Callback if _HAS_LIGHTNING else object + + +class SeparabilityCallback(_Callback): + """ + Record a Lightning test metric into a :class:`SeparabilityStudy` once per ``trainer.test()`` call. + + Attach one of these per perturbation run. On ``on_test_end`` it reads + ``trainer.callback_metrics[metric_key]`` and appends the scalar value to the + study under ``perturbation_name``. After looping over all perturbation x seed + combinations, call ``study.evaluate()`` to get the separability DataFrame. + + Parameters + ---------- + study : SeparabilityStudy + The study to record into. + perturbation_name : str + Which perturbation this run corresponds to. Must already be a key in + ``study.perturbations``. + metric_key : str, default="test_acc" + Key under which the test metric is logged in ``trainer.callback_metrics``. + The user's ``LightningModule.test_step`` (or ``test_epoch_end``) must call + ``self.log(metric_key, value)`` for this to work. + + Examples + -------- + >>> from rings.integrations import SeparabilityStudy, SeparabilityCallback + >>> study = SeparabilityStudy(perturbations={"Original": Original(), ...}) + >>> for name, transform, seed in study.runs(): + ... dm = build_data_module(study.apply(base_dataset, transform), seed) + ... trainer = pl.Trainer(callbacks=[SeparabilityCallback(study, name)]) + ... trainer.fit(model, dm) + ... trainer.test(model, dm) + >>> results = study.evaluate() + """ + + def __init__( + self, + study: SeparabilityStudy, + perturbation_name: str, + metric_key: str = "test_acc", + ): + if not _HAS_LIGHTNING: + raise ImportError( + "pytorch-lightning is required for SeparabilityCallback. " + "Install it with 'pip install rings[lightning]' or 'pip install pytorch-lightning'." + ) + if perturbation_name not in study.perturbations: + raise KeyError( + f"'{perturbation_name}' is not a registered perturbation in the study. " + f"Known: {list(study.perturbations)}" + ) + super().__init__() + self.study = study + self.perturbation_name = perturbation_name + self.metric_key = metric_key + + def on_test_end(self, trainer, pl_module) -> None: + metrics = trainer.callback_metrics + if self.metric_key not in metrics: + raise KeyError( + f"Metric '{self.metric_key}' was not logged during testing. " + f"Available keys: {list(metrics)}. " + f"Ensure your LightningModule calls `self.log('{self.metric_key}', ...)`." + ) + value = metrics[self.metric_key] + score = float(value.item() if hasattr(value, "item") else value) + self.study.record(self.perturbation_name, score) diff --git a/rings/integrations/study.py b/rings/integrations/study.py new file mode 100644 index 0000000..290fe5a --- /dev/null +++ b/rings/integrations/study.py @@ -0,0 +1,147 @@ +"""Lightweight collector for running RINGS separability studies inside an existing pipeline. + +``SeparabilityStudy`` is intentionally framework-agnostic: it does not own the training +loop, the dataset loader, or the evaluator. The user drives those โ€” the study just +holds the perturbation set, hands out ``(name, transform, seed)`` triples to iterate +over, applies a transform to a PyG ``Data`` or ``Dataset``, records scalar scores, +and runs ``SeparabilityFunctor`` over the collected distributions. +""" + +from collections import defaultdict +from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union + +import numpy as np + +from rings.separability.comparator import KSComparator, WilcoxonComparator +from rings.separability.functor import SeparabilityFunctor + + +class SeparabilityStudy: + """ + Collect per-perturbation, per-seed scores from a user-driven training loop and + compute pairwise separability across perturbations. + + Parameters + ---------- + perturbations : Dict[str, Callable] + Mapping of perturbation name to a PyG ``BaseTransform`` (e.g. ``Original()``, + ``EmptyGraph()``). + num_seeds : int, default=5 + Number of seeds to iterate per perturbation. The seed values yielded are + ``range(num_seeds)``; the user is responsible for using them to seed any + framework RNGs inside their loop. + comparator : str or Callable, default="ks" + Either ``"ks"`` / ``"wilcoxon"`` or a comparator instance passed straight + to ``SeparabilityFunctor``. + alpha : float, default=0.01 + Family-wise significance level for the separability test. + n_jobs : int, default=1 + Forwarded to ``SeparabilityFunctor`` for parallel pairwise comparison. + + Examples + -------- + >>> from rings import Original, EmptyGraph + >>> from rings.integrations import SeparabilityStudy + >>> study = SeparabilityStudy( + ... perturbations={"Original": Original(), "EmptyGraph": EmptyGraph()}, + ... num_seeds=5, + ... ) + >>> for name, transform, seed in study.runs(): + ... dataset = study.apply(base_dataset, transform) + ... score = my_train_and_eval(dataset, seed=seed) + ... study.record(name, score) + >>> results = study.evaluate() + """ + + def __init__( + self, + perturbations: Dict[str, Callable], + num_seeds: int = 5, + comparator: Union[str, Callable] = "ks", + alpha: float = 0.01, + n_jobs: int = 1, + ): + if not perturbations: + raise ValueError("`perturbations` must contain at least one entry.") + + self.perturbations = perturbations + self.num_seeds = num_seeds + self.alpha = alpha + self.n_jobs = n_jobs + self.comparator = self._resolve_comparator(comparator) + self._scores: Dict[str, List[float]] = defaultdict(list) + + @staticmethod + def _resolve_comparator(comparator: Union[str, Callable]) -> Callable: + if not isinstance(comparator, str): + return comparator + key = comparator.lower() + if key == "ks": + return KSComparator() + if key == "wilcoxon": + return WilcoxonComparator() + raise ValueError( + f"Unknown comparator '{comparator}'. Use 'ks', 'wilcoxon', or a comparator instance." + ) + + def runs(self) -> Iterator[Tuple[str, Callable, int]]: + """Yield ``(perturbation_name, transform, seed)`` for every perturbation ร— seed.""" + for name, transform in self.perturbations.items(): + for seed in range(self.num_seeds): + yield name, transform, seed + + @staticmethod + def apply(data: Any, transform: Callable) -> Any: + """Apply ``transform`` to a single ``Data`` object or to a PyG ``Dataset``. + + For a ``Dataset``, this sets ``dataset.transform`` so that the transform is + applied lazily on each ``__getitem__`` call โ€” the PyG-idiomatic pattern. For + a single ``Data`` object, the transform is called directly and the result + is returned. Other inputs are passed straight through ``transform(data)`` + as a fallback. + """ + from torch_geometric.data import Data, Dataset + + if isinstance(data, Dataset): + data.transform = transform + return data + if isinstance(data, Data): + return transform(data) + return transform(data) + + def record(self, name: str, score: float) -> None: + """Record a scalar score for ``name``.""" + if name not in self.perturbations: + raise KeyError( + f"'{name}' is not a registered perturbation. Known: {list(self.perturbations)}" + ) + self._scores[name].append(float(score)) + + @property + def scores(self) -> Dict[str, np.ndarray]: + """Recorded scores keyed by perturbation name.""" + return {name: np.asarray(vals) for name, vals in self._scores.items()} + + def evaluate( + self, + n_permutations: int = 10_000, + random_state: Optional[int] = 42, + as_dataframe: bool = True, + ): + """Run pairwise separability on the recorded distributions.""" + if not self._scores: + raise RuntimeError( + "No scores recorded. Call `record(name, score)` inside your training loop " + "before calling `evaluate()`." + ) + functor = SeparabilityFunctor( + comparator=self.comparator, + n_jobs=self.n_jobs, + alpha=self.alpha, + ) + return functor.forward( + distributions=self.scores, + n_permutations=n_permutations, + random_state=random_state, + as_dataframe=as_dataframe, + ) diff --git a/rings/perturbations.py b/rings/perturbations.py index 677f0c3..e013188 100644 --- a/rings/perturbations.py +++ b/rings/perturbations.py @@ -29,7 +29,7 @@ class Original(BaseTransform): >>> assert transformed_data == data """ - def __call__(self, data): + def forward(self, data): """ Return the original, unmodified data object. @@ -77,7 +77,7 @@ class EmptyFeatures(BaseTransform): >>> assert transformed_data.x.size(1) == 1 """ - def __call__(self, data): + def forward(self, data): """ Assign zero vectors as features to each node. @@ -137,7 +137,7 @@ def __init__(self, max_nodes): """ self.max_nodes = max_nodes - def __call__(self, data): + def forward(self, data): """ Apply the transform to assign one-hot encoded node IDs as features. @@ -230,7 +230,7 @@ def __init__(self, shuffle=False, fixed_dimension=None): current_seed = torch.initial_seed() self.generator = torch.Generator().manual_seed(current_seed) - def __call__(self, data): + def forward(self, data): """ Apply the transform to assign or shuffle node features. @@ -324,7 +324,7 @@ class EmptyGraph(BaseTransform): >>> assert torch.equal(transformed_data.x, data.x) """ - def __call__(self, data): + def forward(self, data): """ Remove all edges from the graph. @@ -381,7 +381,7 @@ class CompleteGraph(BaseTransform): >>> assert edges == expected_edges """ - def __call__(self, data): + def forward(self, data): """ Convert the graph into a complete graph. @@ -475,7 +475,7 @@ def __init__(self, p=None, shuffle=False): self.shuffle = shuffle self.generator = torch.Generator().manual_seed(torch.initial_seed()) - def __call__(self, data): + def forward(self, data): """ Replace the graph structure with a random graph. @@ -538,9 +538,7 @@ def _randomize_graph(self, data): ) # Ensure no self-loops - row, col = ensure_no_self_loops( - row, col, num_nodes, generator=self.generator - ) + row, col = ensure_no_self_loops(row, col, num_nodes, generator=self.generator) # Create sparse adjacency matrix edge_index = torch.stack([row, col], dim=0).to(data.edge_index.device) @@ -573,9 +571,7 @@ def _set_num_edges(data, N, p): If p is None, the same number of edges as in the original graph is used. Otherwise, the number of edges is computed as p * N * (N-1) / 2. """ - num_edges = ( - data.edge_index.size(1) if p is None else int(p * N * (N - 1) / 2) - ) + num_edges = data.edge_index.size(1) if p is None else int(p * N * (N - 1) / 2) return num_edges @@ -640,7 +636,7 @@ def __init__(self, p=None, shuffle=False): self.shuffle = shuffle self.generator = torch.Generator().manual_seed(torch.initial_seed()) - def __call__(self, data): + def forward(self, data): """ Generate a random connected graph structure. @@ -654,9 +650,7 @@ def __call__(self, data): torch_geometric.data.Data Transformed graph with connected structure. """ - transform = ( - self._shuffle if self.shuffle else self._randomize_connected_graph - ) + transform = self._shuffle if self.shuffle else self._randomize_connected_graph data = transform(data) while not is_connected(data): data = transform(data) @@ -715,14 +709,10 @@ def _randomize_connected_graph(self, data): # Avoid self-loops and duplicate edges if u != v: - edge_set.add( - (min(u, v), max(u, v)) - ) # Use sorted edges for consistency + edge_set.add((min(u, v), max(u, v))) # Use sorted edges for consistency # Convert edge_set to a PyTorch edge_index on CPU - edge_index = torch.tensor( - list(edge_set), dtype=torch.long, device="cpu" - ).t() + edge_index = torch.tensor(list(edge_set), dtype=torch.long, device="cpu").t() # Transfer the edge_index to the same device as the input data data.edge_index = edge_index.to(data.edge_index.device) @@ -786,8 +776,6 @@ def _set_num_additional_edges(data, N, p, l_tree): If p is None, it tries to match the original graph's edge count. Otherwise, it uses p to determine the total number of edges. """ - num_edges = ( - data.edge_index.size(1) if p is None else int(p * N * (N - 1) / 2) - ) + num_edges = data.edge_index.size(1) if p is None else int(p * N * (N - 1) / 2) num_new_edges = num_edges - l_tree return num_new_edges if num_new_edges > 0 else 0 diff --git a/rings/separability/comparator.py b/rings/separability/comparator.py index df76973..99fd763 100644 --- a/rings/separability/comparator.py +++ b/rings/separability/comparator.py @@ -116,7 +116,6 @@ def __call__( # Prepare for permutation test combined = np.concatenate((x, y)) n1 = len(x) - n2 = len(y) # Run permutation test permuted_statistics = np.zeros(n_permutations) diff --git a/rings/separability/functor.py b/rings/separability/functor.py index aa2f199..9883f19 100644 --- a/rings/separability/functor.py +++ b/rings/separability/functor.py @@ -176,9 +176,7 @@ def forward( array_data = np.array(distribution) # Check if data is numeric if not np.issubdtype(array_data.dtype, np.number): - raise Exception( - f"Distribution '{mode}' contains non-numeric data" - ) + raise Exception(f"Distribution '{mode}' contains non-numeric data") except (ValueError, TypeError) as e: raise Exception(f"Invalid data in distribution '{mode}': {e}") diff --git a/rings/utils.py b/rings/utils.py index ced5761..b054d6d 100644 --- a/rings/utils.py +++ b/rings/utils.py @@ -56,9 +56,7 @@ class Shuffle(BaseTransform): >>> assert not t2_data.edge_index.equal(data.edge_index) """ - def __init__( - self, shuffle_edges=False, shuffle_features=False, generator=None - ): + def __init__(self, shuffle_edges=False, shuffle_features=False, generator=None): """ Initialize the Shuffle transform. @@ -75,7 +73,7 @@ def __init__( self.shuffle_features = shuffle_features self.generator = generator - def __call__(self, data): + def forward(self, data): """ Apply the shuffle transformation to the graph. @@ -176,9 +174,7 @@ def _shuffle_edges(self, data): ) assert shuffled_target_nodes.size() == target_nodes.size() # Update edge_index with the shuffled edges - data.edge_index = torch.stack( - [source_nodes, shuffled_target_nodes], dim=0 - ) + data.edge_index = torch.stack([source_nodes, shuffled_target_nodes], dim=0) return data @@ -285,9 +281,7 @@ def ensure_no_self_loops(source_nodes, target_nodes, num_nodes, generator): device=target_nodes.device, generator=generator, ) - valid_targets_mask = ( - random_target_nodes != source_nodes[self_loop_mask] - ) + valid_targets_mask = random_target_nodes != source_nodes[self_loop_mask] # Replace only target nodes to keep the source nodes intact target_nodes[self_loop_mask] = random_target_nodes diff --git a/tests/complementarity/conftest.py b/tests/complementarity/conftest.py index 474a719..fb58a9f 100644 --- a/tests/complementarity/conftest.py +++ b/tests/complementarity/conftest.py @@ -1,7 +1,6 @@ import pytest import numpy as np from unittest.mock import MagicMock -from rings.complementarity.comparator import L11MatrixNormComparator from rings.complementarity.functor import ComplementarityFunctor @@ -21,9 +20,7 @@ def ensure_float_weights(weights_dict): # Helper function to check weight values consistently -def check_weights_approx( - weights_dict, expected_value, message=None, tolerance=1e-6 -): +def check_weights_approx(weights_dict, expected_value, message=None, tolerance=1e-6): """Helper function to check weights consistently, handling both float and list weights.""" weights_dict = ensure_float_weights(weights_dict) for weight in weights_dict.values(): diff --git a/tests/complementarity/test_functor.py b/tests/complementarity/test_functor.py index 4b84b10..35e814e 100644 --- a/tests/complementarity/test_functor.py +++ b/tests/complementarity/test_functor.py @@ -4,7 +4,7 @@ import networkx as nx import pandas as pd import warnings -from unittest.mock import MagicMock, patch, call +from unittest.mock import MagicMock, patch from torch_geometric.data import Data from rings.complementarity.functor import ComplementarityFunctor @@ -13,10 +13,7 @@ class TestComplementarityFunctor: - - def test_init( - self, mock_feature_metric, mock_graph_metric, mock_comparator - ): + def test_init(self, mock_feature_metric, mock_graph_metric, mock_comparator): """Test initialization of ComplementarityFunctor.""" functor = ComplementarityFunctor( feature_metric=mock_feature_metric, @@ -63,9 +60,7 @@ def test_init_with_custom_edge_attr( ) assert functor.use_edge_information is True - assert ( - functor.edge_attr == "custom_edge_weight" - ) # Custom edge attribute name + assert functor.edge_attr == "custom_edge_weight" # Custom edge attribute name mock_comparator.assert_called_once_with(n_jobs=1) @patch("rings.complementarity.functor.to_networkx") @@ -205,13 +200,11 @@ def test_forward_with_edge_attr(self, mock_to_networkx, functor): with patch.object(nx, "is_connected", return_value=True): with patch.object( nx, "get_node_attributes", return_value={0: [1, 2], 1: [3, 4]} - ) as mock_get_node: + ): with patch.object( nx, "get_edge_attributes", return_value={(0, 1): [0.5, 0.5]} ) as mock_get_edge: - with patch.object( - nx, "set_edge_attributes" - ) as mock_set_edge: + with patch.object(nx, "set_edge_attributes") as mock_set_edge: # Apply patching for internal methods with patch.object( functor, @@ -219,9 +212,7 @@ def test_forward_with_edge_attr(self, mock_to_networkx, functor): return_value={"complementarity": 0.5}, ): # Run forward with as_dataframe=False to get tensor outputs - result = functor.forward( - [test_data], as_dataframe=False - ) + functor.forward([test_data], as_dataframe=False) # Check edge attribute processing (inside context manager) mock_to_networkx.assert_called_with( @@ -266,9 +257,7 @@ def test_complementarity_connected_graph(self, functor): patch.object( functor, "_compute_scores", return_value=[0.5] ) as mock_compute_scores, - patch.object( - functor, "_aggregate", return_value=0.5 - ) as mock_aggregate, + patch.object(functor, "_aggregate", return_value=0.5) as mock_aggregate, ): # Call the function result = functor._compute_complementarity(G) @@ -323,7 +312,7 @@ def test_complementarity_return_metric_spaces(self, functor): [np.array([[0, 1], [1, 0]])], [2], ), - ) as mock_lift_metrics, + ), patch.object( functor, "_normalize_metrics", @@ -331,18 +320,11 @@ def test_complementarity_return_metric_spaces(self, functor): [np.array([[0, 2], [2, 0]])], [np.array([[0, 1], [1, 0]])], ), - ) as mock_normalize_metrics, - patch.object( - functor, "_compute_scores", return_value=[0.5] - ) as mock_compute_scores, - patch.object( - functor, "_aggregate", return_value=0.5 - ) as mock_aggregate, + ), + patch.object(functor, "_compute_scores", return_value=[0.5]), + patch.object(functor, "_aggregate", return_value=0.5), ): - - result = functor._compute_complementarity( - G, return_metric_spaces=True - ) + result = functor._compute_complementarity(G, return_metric_spaces=True) # Check result contains metric spaces assert "complementarity" in result @@ -392,9 +374,7 @@ def test_lift_metrics_disconnected_graph(self, functor): G = nx.Graph() G.add_nodes_from([0, 1, 2, 3]) G.add_edges_from([(0, 1), (2, 3)]) - nx.set_node_attributes( - G, {0: [1, 2], 1: [3, 4], 2: [5, 6], 3: [7, 8]}, "x" - ) + nx.set_node_attributes(G, {0: [1, 2], 1: [3, 4], 2: [5, 6], 3: [7, 8]}, "x") X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) # Mock the lift functions @@ -538,8 +518,8 @@ def test_preprocess_graph(self, mock_to_networkx, functor): nx, "get_edge_attributes", return_value={(0, 1): [0.5, 0.5], (1, 0): [0.5, 0.5]}, - ) as mock_get_edge1: - with patch.object(nx, "set_edge_attributes") as mock_set_edge1: + ): + with patch.object(nx, "set_edge_attributes"): # Test without edge information functor.use_edge_information = False functor._preprocess_graph(test_data, None) @@ -605,9 +585,7 @@ def test_process_single(self, functor): result = functor._process_single(test_data) # Check that methods were called and correct result returned - mock_preprocess.assert_called_once_with( - test_data, functor.edge_attr - ) + mock_preprocess.assert_called_once_with(test_data, functor.edge_attr) mock_compute_complementarity.assert_called_once() assert result["complementarity"] == 0.5 @@ -625,9 +603,7 @@ def test_lift_metrics_with_edge_weights(self, functor): functor.edge_attr = "edge_attr" # Test with mock for lift_graph to verify weight param is passed - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: mock_lift_graph.return_value = np.array([[0, 1], [1, 0]]) # Also mock other required functions @@ -662,9 +638,7 @@ def test_lift_metrics_without_edge_weights(self, functor): functor.edge_attr = None # Ensure edge attribute is not set # Test with mock for lift_graph to verify weight param is not passed - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: mock_lift_graph.return_value = np.array([[0, 1], [1, 0]]) # Also mock other required functions @@ -721,9 +695,7 @@ def test_weighted_vs_unweighted_preprocessing(self): G_weighted, weighted_functor.edge_attr ) - assert ( - processed_G.number_of_edges() == 7 - ) # All edges should be included + assert processed_G.number_of_edges() == 7 # All edges should be included weights = nx.get_edge_attributes(processed_G, "weight") assert weights, "Edge attribute 'weight' should not be empty" @@ -736,9 +708,9 @@ def test_weighted_vs_unweighted_preprocessing(self): normalize_diameters=True, ) - assert ( - unweighted_functor.edge_attr is None - ), "Edge attribute should be empty for unweighted functor" + assert unweighted_functor.edge_attr is None, ( + "Edge attribute should be empty for unweighted functor" + ) def test_correct_weight_parameter_passing(self, functor): """Test that the weight parameter is correctly passed through from functor to the graph metric.""" @@ -755,12 +727,8 @@ def test_correct_weight_parameter_passing(self, functor): functor.edge_attr = "edge_attr" # Mock the lift_graph function to check if weight is passed - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: - mock_lift_graph.return_value = np.array( - [[0, 1, 6], [1, 0, 5], [6, 5, 0]] - ) + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: + mock_lift_graph.return_value = np.array([[0, 1, 6], [1, 0, 5], [6, 5, 0]]) # Also mock other required functions for _lift_metrics with ( @@ -858,9 +826,7 @@ def test_disconnected_graph_weighted_aggregation_complex(self, functor): result = functor._aggregate(scores, sizes) assert abs(result - expected) < 1e-10 - assert ( - result < 0.12 - ) # Should be close to 0.1 due to large component weight + assert result < 0.12 # Should be close to 0.1 due to large component weight def test_disconnected_graph_edge_cases(self, functor): """Test edge cases in disconnected graph processing.""" @@ -995,9 +961,7 @@ def test_disconnected_graph_metric_consistency(self, functor): @patch("rings.complementarity.functor.to_networkx") @patch.object(ComplementarityFunctor, "_process_single") - def test_forward_as_dataframe( - self, mock_process_single, mock_to_networkx, functor - ): + def test_forward_as_dataframe(self, mock_process_single, mock_to_networkx, functor): """Test forward method with as_dataframe=True.""" # Setup mock mock_process_single.return_value = { @@ -1014,15 +978,13 @@ def test_forward_as_dataframe( result = functor.forward([test_data], as_dataframe=True) # Check results - assert isinstance( - result, pd.DataFrame - ), "Result should be a pandas DataFrame" - assert ( - "complementarity" in result.columns - ), "Result should have 'complementarity' column" - assert ( - "other_metric" in result.columns - ), "Result should have other metric columns" + assert isinstance(result, pd.DataFrame), "Result should be a pandas DataFrame" + assert "complementarity" in result.columns, ( + "Result should have 'complementarity' column" + ) + assert "other_metric" in result.columns, ( + "Result should have other metric columns" + ) assert len(result) == 1, "DataFrame should have one row for one graph" assert result["complementarity"].iloc[0] == 0.5 @@ -1030,13 +992,9 @@ def test_weighted_graph_metric_space_computation(self): """Test detailed metric space computation for weighted graphs.""" # Create a weighted graph where edge weights should affect shortest path calculations x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) # Heavy weight between 1-2, light weight between 0-1 - edge_attr = torch.tensor( - [[1.0], [1.0], [10.0], [10.0]], dtype=torch.float - ) + edge_attr = torch.tensor([[1.0], [1.0], [10.0], [10.0]], dtype=torch.float) weighted_data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr) # Create weighted functor @@ -1050,9 +1008,7 @@ def test_weighted_graph_metric_space_computation(self): ) # Test with real to_networkx function - processed_graph = weighted_functor._preprocess_graph( - weighted_data, "edge_attr" - ) + processed_graph = weighted_functor._preprocess_graph(weighted_data, "edge_attr") assert isinstance(processed_graph, nx.Graph) weights = nx.get_edge_attributes(processed_graph, "weight") @@ -1064,9 +1020,7 @@ def test_weighted_graph_metric_space_computation(self): assert weight > 0 # Should have positive weights # Test that lift_graph is called with the weighted graph - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: mock_lift_graph.return_value = np.array( [[0, 1, 11], [1, 0, 10], [11, 10, 0]] ) @@ -1094,25 +1048,17 @@ def test_weighted_vs_unweighted_metric_differences(self): """Test that weighted and unweighted graphs produce different metric spaces.""" # Create identical graph structure with different edge weights x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) # Version 1: Uniform weights uniform_edge_attr = torch.tensor( [[1.0], [1.0], [1.0], [1.0]], dtype=torch.float ) - uniform_data = Data( - x=x, edge_index=edge_index, edge_attr=uniform_edge_attr - ) + uniform_data = Data(x=x, edge_index=edge_index, edge_attr=uniform_edge_attr) # Version 2: Non-uniform weights - varied_edge_attr = torch.tensor( - [[1.0], [1.0], [5.0], [5.0]], dtype=torch.float - ) - varied_data = Data( - x=x, edge_index=edge_index, edge_attr=varied_edge_attr - ) + varied_edge_attr = torch.tensor([[1.0], [1.0], [5.0], [5.0]], dtype=torch.float) + varied_data = Data(x=x, edge_index=edge_index, edge_attr=varied_edge_attr) # Create weighted functors weighted_functor = ComplementarityFunctor( @@ -1126,15 +1072,11 @@ def test_weighted_vs_unweighted_metric_differences(self): # Test with real to_networkx function # Process the uniform graph - uniform_graph = weighted_functor._preprocess_graph( - uniform_data, "edge_attr" - ) + uniform_graph = weighted_functor._preprocess_graph(uniform_data, "edge_attr") uniform_weights = nx.get_edge_attributes(uniform_graph, "weight") # Process the varied graph - varied_graph = weighted_functor._preprocess_graph( - varied_data, "edge_attr" - ) + varied_graph = weighted_functor._preprocess_graph(varied_data, "edge_attr") varied_weights = nx.get_edge_attributes(varied_graph, "weight") # Get unique edge weights from both graphs @@ -1142,9 +1084,9 @@ def test_weighted_vs_unweighted_metric_differences(self): varied_weight_set = set(varied_weights.values()) # Assert the graphs have different weight values - assert ( - uniform_weight_set != varied_weight_set - ), f"Uniform: {uniform_weight_set}, Varied: {varied_weight_set}" + assert uniform_weight_set != varied_weight_set, ( + f"Uniform: {uniform_weight_set}, Varied: {varied_weight_set}" + ) # And the uniform graph should have only one unique weight value assert len(uniform_weight_set) == 1 @@ -1153,9 +1095,7 @@ def test_unweighted_graph_metric_consistency(self): """Test that unweighted graphs always use unit weights.""" # Create graph with edge attributes that should be ignored x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) edge_attr = torch.tensor( [[100.0], [100.0], [0.1], [0.1]], dtype=torch.float ) # Extreme values @@ -1172,9 +1112,7 @@ def test_unweighted_graph_metric_consistency(self): ) # Process graph - should ignore edge attributes - processed_graph = unweighted_functor._preprocess_graph( - data_with_attrs, None - ) + processed_graph = unweighted_functor._preprocess_graph(data_with_attrs, None) weights = nx.get_edge_attributes(processed_graph, "weight") # All weights should be 1.0 regardless of edge attributes @@ -1201,9 +1139,7 @@ def test_weighted_graph_pathological_cases(self): ) # Test with real to_networkx function - processed_graph = weighted_functor._preprocess_graph( - zero_data, "edge_attr" - ) + processed_graph = weighted_functor._preprocess_graph(zero_data, "edge_attr") weights = nx.get_edge_attributes(processed_graph, "weight") # Zero-magnitude edge attributes should result in zero weights @@ -1214,13 +1150,9 @@ def test_weighted_graph_pathological_cases(self): multidim_edge_attr = torch.tensor( [[3.0, 4.0], [3.0, 4.0]], dtype=torch.float ) # Norm = 5.0 - multidim_data = Data( - x=x, edge_index=edge_index, edge_attr=multidim_edge_attr - ) + multidim_data = Data(x=x, edge_index=edge_index, edge_attr=multidim_edge_attr) - processed_graph = weighted_functor._preprocess_graph( - multidim_data, "edge_attr" - ) + processed_graph = weighted_functor._preprocess_graph(multidim_data, "edge_attr") weights = nx.get_edge_attributes(processed_graph, "weight") # Multi-dimensional attributes should be converted to norms @@ -1232,17 +1164,13 @@ def test_metric_space_properties_weighted_vs_unweighted(self): """Test that weighted and unweighted graphs preserve metric space properties.""" # Create a simple path graph: 0 -- 1 -- 2 x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) # Weighted version with large weight on second edge heavy_edge_attr = torch.tensor( [[1.0], [1.0], [10.0], [10.0]], dtype=torch.float ) - weighted_data = Data( - x=x, edge_index=edge_index, edge_attr=heavy_edge_attr - ) + weighted_data = Data(x=x, edge_index=edge_index, edge_attr=heavy_edge_attr) # Test both functors weighted_functor = ComplementarityFunctor( @@ -1271,9 +1199,7 @@ def test_metric_space_properties_weighted_vs_unweighted(self): unweighted_distances = np.array([[0, 1, 2], [1, 0, 1], [2, 1, 0]]) # Use a sequence of patched returns to get the behavior we want - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: # Set up the return values for the calls mock_lift_graph.side_effect = [ weighted_distances, @@ -1309,9 +1235,9 @@ def test_metric_space_properties_weighted_vs_unweighted(self): unweighted_D_G = unweighted_result[1][0] # Graph distances # Distance from node 0 to node 2 should be different - assert ( - weighted_D_G[0, 2] > unweighted_D_G[0, 2] - ), f"Weighted: {weighted_D_G[0, 2]}, Unweighted: {unweighted_D_G[0, 2]}" + assert weighted_D_G[0, 2] > unweighted_D_G[0, 2], ( + f"Weighted: {weighted_D_G[0, 2]}, Unweighted: {unweighted_D_G[0, 2]}" + ) # Weighted should have larger distance due to heavy edge assert weighted_D_G[0, 2] > 2 * unweighted_D_G[0, 2] # Restate to be extra clear @@ -1375,9 +1301,7 @@ def test_unweighted_graph_distance_properties(self): ] ) - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: mock_lift_graph.return_value = expected_distances with patch( @@ -1387,13 +1311,9 @@ def test_unweighted_graph_distance_properties(self): [[0, 1, 2, 3], [1, 0, 1, 2], [2, 1, 0, 1], [3, 2, 1, 0]] ) - processed_graph = unweighted_functor._preprocess_graph( - cycle_data, None - ) + processed_graph = unweighted_functor._preprocess_graph(cycle_data, None) X = np.array([[0.0], [1.0], [2.0], [3.0]]) - result = unweighted_functor._lift_metrics( - processed_graph, X, empty_graph=False - ) + unweighted_functor._lift_metrics(processed_graph, X, empty_graph=False) # Verify lift_graph was called with unweighted graph mock_lift_graph.assert_called_once() @@ -1430,9 +1350,7 @@ def test_unweighted_disconnected_components(self): ) # Mock components and lift functions - with patch( - "rings.complementarity.functor.nx.is_connected", return_value=False - ): + with patch("rings.complementarity.functor.nx.is_connected", return_value=False): with patch( "rings.complementarity.functor.nx.connected_components", return_value=[{0, 1, 2}, {3, 4, 5}], @@ -1441,9 +1359,7 @@ def test_unweighted_disconnected_components(self): "rings.complementarity.functor.lift_graph" ) as mock_lift_graph: # Each triangle should have distances: 0->1=1, 0->2=1, 1->2=1 - triangle_distances = np.array( - [[0, 1, 1], [1, 0, 1], [1, 1, 0]] - ) + triangle_distances = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]) mock_lift_graph.side_effect = [ triangle_distances, triangle_distances, @@ -1460,9 +1376,7 @@ def test_unweighted_disconnected_components(self): processed_graph = unweighted_functor._preprocess_graph( disconnected_data, None ) - X = np.array( - [[0.0], [1.0], [2.0], [10.0], [11.0], [12.0]] - ) + X = np.array([[0.0], [1.0], [2.0], [10.0], [11.0], [12.0]]) D_X, D_G, sizes = unweighted_functor._lift_metrics( processed_graph, X, empty_graph=False ) @@ -1477,9 +1391,7 @@ def test_unweighted_disconnected_components(self): for call_args in mock_lift_graph.call_args_list: args, _ = call_args called_graph = args[0] - weights = nx.get_edge_attributes( - called_graph, "weight" - ) + weights = nx.get_edge_attributes(called_graph, "weight") check_weights_approx(weights, 1.0) def test_unweighted_graph_consistency_across_topologies(self): @@ -1526,26 +1438,22 @@ def test_unweighted_graph_consistency_across_topologies(self): processed_graph = unweighted_functor._preprocess_graph(data, None) # Verify correct number of edges - assert ( - processed_graph.number_of_edges() == case["expected_edges"] - ), f"Failed for {case['name']}" + assert processed_graph.number_of_edges() == case["expected_edges"], ( + f"Failed for {case['name']}" + ) # Verify all weights are 1.0 weights = nx.get_edge_attributes(processed_graph, "weight") - assert ( - len(weights) == case["expected_edges"] - ), f"Missing weights for {case['name']}" # Undirected edges stored once + assert len(weights) == case["expected_edges"], ( + f"Missing weights for {case['name']}" + ) # Undirected edges stored once - check_weights_approx( - weights, 1.0, f"Wrong weight for {case['name']}" - ) + check_weights_approx(weights, 1.0, f"Wrong weight for {case['name']}") def test_unweighted_graph_feature_independence(self): """Test that unweighted graph processing is independent of node features.""" # Same graph structure with different node features - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) # Case 1: Simple features x1 = torch.tensor([[1.0], [2.0], [3.0]], dtype=torch.float) @@ -1572,14 +1480,8 @@ def test_unweighted_graph_feature_independence(self): processed_graph2 = unweighted_functor._preprocess_graph(data2, None) # Graph structures should be identical (same edges, same weights) - assert ( - processed_graph1.number_of_edges() - == processed_graph2.number_of_edges() - ) - assert ( - processed_graph1.number_of_nodes() - == processed_graph2.number_of_nodes() - ) + assert processed_graph1.number_of_edges() == processed_graph2.number_of_edges() + assert processed_graph1.number_of_nodes() == processed_graph2.number_of_nodes() weights1 = nx.get_edge_attributes(processed_graph1, "weight") weights2 = nx.get_edge_attributes(processed_graph2, "weight") @@ -1593,9 +1495,7 @@ def test_unweighted_graph_feature_independence(self): def test_metric_space_triangle_inequality_preservation(self): """Test that metric spaces preserve triangle inequality property.""" # Create a triangle graph where we can verify triangle inequality - x = torch.tensor( - [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]], dtype=torch.float - ) + x = torch.tensor([[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]], dtype=torch.float) edge_index = torch.tensor( [[0, 1, 2, 1, 2, 0], [1, 0, 0, 2, 1, 2]], dtype=torch.long ) @@ -1624,9 +1524,7 @@ def test_metric_space_triangle_inequality_preservation(self): ) # Mock lift functions to return valid distance matrices - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: # Return a valid metric (triangle inequality preserved) if use_edge_info: # Weighted: different path costs @@ -1682,9 +1580,7 @@ def test_metric_space_triangle_inequality_preservation(self): def test_metric_space_symmetry_property(self): """Test that metric spaces preserve symmetry property.""" x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) path_data = Data(x=x, edge_index=edge_index) functor = ComplementarityFunctor( @@ -1696,13 +1592,9 @@ def test_metric_space_symmetry_property(self): normalize_diameters=False, ) - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: # Symmetric distance matrix - mock_lift_graph.return_value = np.array( - [[0, 1, 2], [1, 0, 1], [2, 1, 0]] - ) + mock_lift_graph.return_value = np.array([[0, 1, 2], [1, 0, 1], [2, 1, 0]]) with patch( "rings.complementarity.functor.lift_attributes" @@ -1724,15 +1616,9 @@ def test_metric_space_symmetry_property(self): for i in range(3): for j in range(3): + assert abs(graph_distances[i, j] - graph_distances[j, i]) < 1e-6 assert ( - abs(graph_distances[i, j] - graph_distances[j, i]) - < 1e-6 - ) - assert ( - abs( - feature_distances[i, j] - - feature_distances[j, i] - ) + abs(feature_distances[i, j] - feature_distances[j, i]) < 1e-6 ) @@ -1751,9 +1637,7 @@ def test_metric_space_non_negativity_property(self): normalize_diameters=False, ) - with patch( - "rings.complementarity.functor.lift_graph" - ) as mock_lift_graph: + with patch("rings.complementarity.functor.lift_graph") as mock_lift_graph: mock_lift_graph.return_value = np.array([[0, 1], [1, 0]]) with patch( @@ -1798,9 +1682,7 @@ def test_edge_case_empty_edge_attributes(self): ) # Should use unit weights when no edge attributes - processed_graph = weighted_functor._preprocess_graph( - data_no_attrs, None - ) + processed_graph = weighted_functor._preprocess_graph(data_no_attrs, None) weights = nx.get_edge_attributes(processed_graph, "weight") check_weights_approx(weights, 1.0) @@ -1838,9 +1720,7 @@ def test_edge_case_extremely_large_weights(self): # Extremely large edge weights large_edge_attr = torch.tensor([[1e10], [1e10]], dtype=torch.float) - large_weight_data = Data( - x=x, edge_index=edge_index, edge_attr=large_edge_attr - ) + large_weight_data = Data(x=x, edge_index=edge_index, edge_attr=large_edge_attr) weighted_functor = ComplementarityFunctor( feature_metric="euclidean", @@ -1864,14 +1744,10 @@ def test_edge_case_extremely_large_weights(self): def test_edge_case_mixed_positive_zero_weights(self): """Test handling of mixed positive and zero edge weights.""" x = torch.tensor([[0.0], [1.0], [2.0]], dtype=torch.float) - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) # Mixed weights: one zero, one positive - mixed_edge_attr = torch.tensor( - [[0.0], [0.0], [5.0], [5.0]], dtype=torch.float - ) + mixed_edge_attr = torch.tensor([[0.0], [0.0], [5.0], [5.0]], dtype=torch.float) mixed_data = Data(x=x, edge_index=edge_index, edge_attr=mixed_edge_attr) weighted_functor = ComplementarityFunctor( @@ -1884,18 +1760,16 @@ def test_edge_case_mixed_positive_zero_weights(self): ) # Test with real to_networkx function - processed_graph = weighted_functor._preprocess_graph( - mixed_data, "edge_attr" - ) + processed_graph = weighted_functor._preprocess_graph(mixed_data, "edge_attr") # Get weights weights = nx.get_edge_attributes(processed_graph, "weight") # Should have both zero and positive weights weight_values = list(weights.values()) - assert any( - abs(w) < 1e-6 for w in weight_values - ), f"Expected some zero weights but got {weight_values}" - assert any( - abs(w - 5.0) < 1e-6 for w in weight_values - ), f"Expected some 5.0 weights but got {weight_values}" + assert any(abs(w) < 1e-6 for w in weight_values), ( + f"Expected some zero weights but got {weight_values}" + ) + assert any(abs(w - 5.0) < 1e-6 for w in weight_values), ( + f"Expected some 5.0 weights but got {weight_values}" + ) diff --git a/tests/complementarity/test_metrics.py b/tests/complementarity/test_metrics.py index 4b84cfc..d6cfe34 100644 --- a/tests/complementarity/test_metrics.py +++ b/tests/complementarity/test_metrics.py @@ -2,7 +2,7 @@ import numpy as np import networkx as nx import sklearn.metrics -from unittest.mock import patch, MagicMock +from unittest.mock import patch from rings.complementarity.metrics import ( lift_attributes, @@ -25,9 +25,7 @@ def test_lift_attributes_with_standard_metric(self): result = lift_attributes(X, metric="euclidean", n_jobs=1) # Calculate expected result manually - expected = sklearn.metrics.pairwise.pairwise_distances( - X, metric="euclidean" - ) + expected = sklearn.metrics.pairwise.pairwise_distances(X, metric="euclidean") np.testing.assert_array_almost_equal(result, expected) @@ -86,9 +84,7 @@ def test_standard_feature_metrics(self): X = np.array([[1, 2], [3, 4], [5, 6]]) result = standard_feature_metrics(X, metric="euclidean") - expected = sklearn.metrics.pairwise.pairwise_distances( - X, metric="euclidean" - ) + expected = sklearn.metrics.pairwise.pairwise_distances(X, metric="euclidean") np.testing.assert_array_almost_equal(result, expected) @@ -155,9 +151,7 @@ def test_shortest_path_distance(self): result = shortest_path_distance(G) # Expected distances for a path graph with 4 nodes (0-1-2-3) - expected = np.array( - [[0, 1, 2, 3], [1, 0, 1, 2], [2, 1, 0, 1], [3, 2, 1, 0]] - ) + expected = np.array([[0, 1, 2, 3], [1, 0, 1, 2], [2, 1, 0, 1], [3, 2, 1, 0]]) np.testing.assert_array_equal(result, expected) @@ -168,9 +162,7 @@ def test_shortest_path_distance_weighted(self): result = shortest_path_distance(G, weight="weight") - expected = np.array( - [[0, 1, 3, 6], [1, 0, 2, 5], [3, 2, 0, 3], [6, 5, 3, 0]] - ) + expected = np.array([[0, 1, 3, 6], [1, 0, 2, 5], [3, 2, 0, 3], [6, 5, 3, 0]]) np.testing.assert_array_equal(result, expected) diff --git a/tests/complementarity/test_utils.py b/tests/complementarity/test_utils.py index 29f8c97..2538444 100644 --- a/tests/complementarity/test_utils.py +++ b/tests/complementarity/test_utils.py @@ -1,4 +1,3 @@ -import pytest import numpy as np from rings.complementarity.utils import maybe_normalize_diameter diff --git a/tests/separability/test_comparator.py b/tests/separability/test_comparator.py index 0320bc8..a172d98 100644 --- a/tests/separability/test_comparator.py +++ b/tests/separability/test_comparator.py @@ -9,9 +9,7 @@ def test_ks_comparator_detects_difference(self): x = np.random.normal(0, 1, 100) y = np.random.normal(5, 1, 100) comparator = KSComparator() - result = comparator( - x, y, n_permutations=500, alpha=0.05, random_state=123 - ) + result = comparator(x, y, n_permutations=500, alpha=0.05, random_state=123) assert result["significant"] is True assert result["method"] == "KS" assert 0 <= result["pvalue"] <= 1 @@ -26,9 +24,7 @@ def test_ks_comparator_bonferroni_correction(self): result = comparator( x, y, n_permutations=200, n_hypotheses=n_hypotheses, random_state=42 ) - assert result["pvalue_adjusted"] == min( - 1.0, result["pvalue"] * n_hypotheses - ) + assert result["pvalue_adjusted"] == min(1.0, result["pvalue"] * n_hypotheses) assert result["method"] == "KS" def test_ks_comparator_empty_input(self): @@ -46,9 +42,7 @@ def test_wilcoxon_comparator_identical_samples(self): x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) y = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) comparator = WilcoxonComparator() - result = comparator( - x, y, n_permutations=100, alpha=0.05, random_state=123 - ) + result = comparator(x, y, n_permutations=100, alpha=0.05, random_state=123) assert result["significant"] is False assert result["method"] == "Wilcoxon" assert 0 <= result["pvalue"] <= 1 diff --git a/tests/separability/test_functor.py b/tests/separability/test_functor.py index 932d317..63ad9f5 100644 --- a/tests/separability/test_functor.py +++ b/tests/separability/test_functor.py @@ -1,7 +1,6 @@ import pytest import numpy as np import pandas as pd -import sys from rings.separability.functor import SeparabilityFunctor @@ -30,7 +29,6 @@ def __call__(self, s1, s2, alpha=None, n_hypotheses=None, **kwargs): class TestSeparabilityFunctor: - def test_forward_multiple_distributions_returns_dataframe(self): comparator = DummyComparator() functor = SeparabilityFunctor(comparator=comparator) diff --git a/tests/test_lightning_callback.py b/tests/test_lightning_callback.py new file mode 100644 index 0000000..fe40aa2 --- /dev/null +++ b/tests/test_lightning_callback.py @@ -0,0 +1,66 @@ +import pytest + +pytest.importorskip("pytorch_lightning") + +import torch + +from rings import EmptyGraph, Original +from rings.integrations import SeparabilityCallback, SeparabilityStudy + + +class _StubTrainer: + def __init__(self, metrics): + self.callback_metrics = metrics + + +class TestSeparabilityCallback: + def test_records_metric_into_study(self): + study = SeparabilityStudy(perturbations={"Original": Original()}) + cb = SeparabilityCallback(study, "Original", metric_key="test_acc") + trainer = _StubTrainer({"test_acc": torch.tensor(0.87)}) + + cb.on_test_end(trainer, pl_module=None) + + assert study.scores["Original"].tolist() == [pytest.approx(0.87, rel=1e-5)] + + def test_unknown_perturbation_raises(self): + study = SeparabilityStudy(perturbations={"Original": Original()}) + with pytest.raises(KeyError): + SeparabilityCallback(study, "NotRegistered") + + def test_missing_metric_raises(self): + study = SeparabilityStudy(perturbations={"Original": Original()}) + cb = SeparabilityCallback(study, "Original", metric_key="test_acc") + trainer = _StubTrainer({"other_metric": torch.tensor(0.5)}) + + with pytest.raises(KeyError, match="test_acc"): + cb.on_test_end(trainer, pl_module=None) + + def test_accumulates_across_calls(self): + study = SeparabilityStudy( + perturbations={"A": Original(), "B": EmptyGraph()}, num_seeds=2 + ) + cb_a = SeparabilityCallback(study, "A") + cb_b = SeparabilityCallback(study, "B") + + cb_a.on_test_end(_StubTrainer({"test_acc": torch.tensor(0.9)}), None) + cb_a.on_test_end(_StubTrainer({"test_acc": torch.tensor(0.85)}), None) + cb_b.on_test_end(_StubTrainer({"test_acc": torch.tensor(0.5)}), None) + cb_b.on_test_end(_StubTrainer({"test_acc": torch.tensor(0.55)}), None) + + assert study.scores["A"].tolist() == [ + pytest.approx(0.9, rel=1e-5), + pytest.approx(0.85, rel=1e-5), + ] + assert study.scores["B"].tolist() == [ + pytest.approx(0.5, rel=1e-5), + pytest.approx(0.55, rel=1e-5), + ] + + def test_accepts_plain_float_metric(self): + study = SeparabilityStudy(perturbations={"Original": Original()}) + cb = SeparabilityCallback(study, "Original") + trainer = _StubTrainer({"test_acc": 0.42}) + + cb.on_test_end(trainer, pl_module=None) + assert study.scores["Original"].tolist() == [pytest.approx(0.42, rel=1e-5)] diff --git a/tests/test_perturbations.py b/tests/test_perturbations.py index cc6566b..cafb4c4 100644 --- a/tests/test_perturbations.py +++ b/tests/test_perturbations.py @@ -1,4 +1,3 @@ -import pytest import torch from torch_geometric.data import Data @@ -34,9 +33,7 @@ def setup_method(self): self.path_edge_index = torch.tensor( [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]], dtype=torch.long ) - self.path_data = Data( - x=self.x, edge_index=self.path_edge_index, num_nodes=4 - ) + self.path_data = Data(x=self.x, edge_index=self.path_edge_index, num_nodes=4) # Set fixed seed for reproducibility torch.manual_seed(42) @@ -185,15 +182,10 @@ def test_random_graph_transform_shuffle(self): transformed_data = transform(self.data.clone()) # Check that the number of edges remains the same - assert ( - transformed_data.edge_index.shape[1] - == self.data.edge_index.shape[1] - ) + assert transformed_data.edge_index.shape[1] == self.data.edge_index.shape[1] # Check that the edges were actually shuffled - assert not torch.equal( - transformed_data.edge_index, self.data.edge_index - ) + assert not torch.equal(transformed_data.edge_index, self.data.edge_index) # Check that node features remain unchanged assert torch.equal(transformed_data.x, self.data.x) @@ -205,15 +197,6 @@ def test_random_graph_transform_p_value(self): transform = RandomGraph(p=p) transformed_data = transform(self.data.clone()) - # Expected number of edges with p=0.5 - n = self.data.num_nodes - expected_num_edges = ( - int(p * n * (n - 1) / 2) * 2 - ) # Bidirectional, but estimate might be slightly off - - # Check edge count is reasonable (considering randomness) - actual_edges = transformed_data.edge_index.shape[1] - # Check that node features remain unchanged assert torch.equal(transformed_data.x, self.data.x) @@ -223,9 +206,7 @@ def test_random_graph_transform_p_value(self): def test_random_graph_transform_documentation_example(self): """Test RandomGraph transform using the example from documentation.""" # Create a simple graph as shown in documentation - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) data = Data(edge_index=edge_index, num_nodes=4) # Example from documentation: Random graph with same number of edges @@ -247,15 +228,10 @@ def test_random_connected_graph_transform_shuffle(self): transformed_data = transform(self.data.clone()) # Check that the number of edges remains the same - assert ( - transformed_data.edge_index.shape[1] - == self.data.edge_index.shape[1] - ) + assert transformed_data.edge_index.shape[1] == self.data.edge_index.shape[1] # Check that the edges were actually shuffled - assert not torch.equal( - transformed_data.edge_index, self.data.edge_index - ) + assert not torch.equal(transformed_data.edge_index, self.data.edge_index) # Check that node features remain unchanged assert torch.equal(transformed_data.x, self.data.x) @@ -272,9 +248,7 @@ def test_random_connected_graph_transform_p_value(self): # Expected number of edges with p=0.5 but need at least n-1 for connected n = self.data.num_nodes - min_edges_for_connected = ( - n - 1 - ) # Minimum edges needed for a connected graph + min_edges_for_connected = n - 1 # Minimum edges needed for a connected graph # Check that we have at least the minimum required edges assert transformed_data.edge_index.shape[1] >= min_edges_for_connected @@ -308,9 +282,7 @@ def test_connectivity_guarantees(self): # Test multiple times to ensure consistency for _ in range(5): - transform = RandomConnectedGraph( - p=0.1 - ) # Low probability to test edge case + transform = RandomConnectedGraph(p=0.1) # Low probability to test edge case result = transform(disconnected_data.clone()) assert is_connected(result) diff --git a/tests/test_study.py b/tests/test_study.py new file mode 100644 index 0000000..7a74dcb --- /dev/null +++ b/tests/test_study.py @@ -0,0 +1,99 @@ +import numpy as np +import pytest +import torch +from torch_geometric.data import Data + +from rings import EmptyGraph, Original +from rings.integrations import SeparabilityStudy +from rings.separability.comparator import KSComparator, WilcoxonComparator + + +def _toy_data(): + edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long) + x = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + return Data(x=x, edge_index=edge_index) + + +class TestSeparabilityStudy: + def test_init_empty_perturbations_raises(self): + with pytest.raises(ValueError): + SeparabilityStudy(perturbations={}) + + def test_comparator_string_shortcuts(self): + s = SeparabilityStudy(perturbations={"Original": Original()}, comparator="ks") + assert isinstance(s.comparator, KSComparator) + s = SeparabilityStudy( + perturbations={"Original": Original()}, comparator="wilcoxon" + ) + assert isinstance(s.comparator, WilcoxonComparator) + + def test_comparator_unknown_string_raises(self): + with pytest.raises(ValueError): + SeparabilityStudy( + perturbations={"Original": Original()}, comparator="bogus" + ) + + def test_runs_cartesian_product(self): + study = SeparabilityStudy( + perturbations={"A": Original(), "B": EmptyGraph()}, num_seeds=3 + ) + triples = list(study.runs()) + assert len(triples) == 6 + names = [t[0] for t in triples] + assert names.count("A") == 3 and names.count("B") == 3 + seeds = sorted({t[2] for t in triples}) + assert seeds == [0, 1, 2] + + def test_apply_on_single_data_returns_transformed(self): + data = _toy_data() + out = SeparabilityStudy.apply(data, EmptyGraph()) + # EmptyGraph should strip edges + assert out.edge_index.shape[1] == 0 + + def test_apply_on_dataset_sets_transform_attribute(self): + # Use a real PyG Dataset (TUDataset cached avoidance: build minimal fake via subclass). + from torch_geometric.data import InMemoryDataset + + class TinyDataset(InMemoryDataset): + def __init__(self): + super().__init__(root=None) + self._data_list = [_toy_data(), _toy_data()] + + def len(self): + return len(self._data_list) + + def get(self, idx): + return self._data_list[idx] + + ds = TinyDataset() + transform = EmptyGraph() + out = SeparabilityStudy.apply(ds, transform) + assert out is ds + assert ds.transform is transform + + def test_record_unknown_name_raises(self): + study = SeparabilityStudy(perturbations={"A": Original()}) + with pytest.raises(KeyError): + study.record("B", 0.5) + + def test_evaluate_without_scores_raises(self): + study = SeparabilityStudy(perturbations={"A": Original()}) + with pytest.raises(RuntimeError): + study.evaluate() + + def test_record_and_evaluate_produces_dataframe(self): + study = SeparabilityStudy( + perturbations={"A": Original(), "B": EmptyGraph()}, + num_seeds=10, + alpha=0.05, + ) + rng = np.random.default_rng(0) + for _ in range(10): + study.record("A", float(rng.normal(0.9, 0.02))) + study.record("B", float(rng.normal(0.5, 0.02))) + + results = study.evaluate(n_permutations=200) + assert len(results) == 1 + for col in ("mode1", "mode2", "significant"): + assert col in results.columns + assert bool(results.iloc[0]["significant"]) is True diff --git a/tests/test_utils.py b/tests/test_utils.py index 6553b47..9d52ffd 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,16 +1,13 @@ -import pytest import torch from torch_geometric.data import Data -from rings.utils import Shuffle, is_connected +from rings.utils import Shuffle class TestShuffleTransform: def setup_method(self): # Create a simple graph for testing - self.edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + self.edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) self.x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=torch.float) self.data = Data(x=self.x, edge_index=self.edge_index, num_nodes=3) @@ -58,14 +55,10 @@ def test_shuffle_edges(self): assert transformed_data.edge_index.shape == self.data.edge_index.shape # Verify that edges were actually shuffled - assert not torch.equal( - transformed_data.edge_index[1], self.data.edge_index[1] - ) + assert not torch.equal(transformed_data.edge_index[1], self.data.edge_index[1]) # Source nodes should remain the same - assert torch.equal( - transformed_data.edge_index[0], self.data.edge_index[0] - ) + assert torch.equal(transformed_data.edge_index[0], self.data.edge_index[0]) def test_no_self_loops(self): # Test that no self-loops are created during edge shuffling @@ -89,9 +82,7 @@ def test_combined_transform(self): transformed_data = transform(self.data.clone()) # Verify both edges and features were modified - assert not torch.equal( - transformed_data.edge_index, self.data.edge_index - ) + assert not torch.equal(transformed_data.edge_index, self.data.edge_index) assert not torch.equal(transformed_data.x, self.data.x) def test_no_transformation(self): @@ -105,15 +96,11 @@ def test_no_transformation(self): def test_with_no_features(self): # Test behavior when data has no features - edge_index = torch.tensor( - [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long - ) + edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long) data_no_features = Data(edge_index=edge_index, num_nodes=3) transform = Shuffle(shuffle_edges=True, shuffle_features=True) transformed_data = transform(data_no_features.clone()) # Only edges should be shuffled, no error should be raised - assert not torch.equal( - transformed_data.edge_index, data_no_features.edge_index - ) + assert not torch.equal(transformed_data.edge_index, data_no_features.edge_index) diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 3e35c0a..0000000 --- a/uv.lock +++ /dev/null @@ -1,1709 +0,0 @@ -version = 1 -revision = 2 -requires-python = ">=3.13" - -[[package]] -name = "aiohappyeyeballs" -version = "2.6.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" }, -] - -[[package]] -name = "aiohttp" -version = "3.11.18" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "aiohappyeyeballs" }, - { name = "aiosignal" }, - { name = "attrs" }, - { name = "frozenlist" }, - { name = "multidict" }, - { name = "propcache" }, - { name = "yarl" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/63/e7/fa1a8c00e2c54b05dc8cb5d1439f627f7c267874e3f7bb047146116020f9/aiohttp-3.11.18.tar.gz", hash = "sha256:ae856e1138612b7e412db63b7708735cff4d38d0399f6a5435d3dac2669f558a", size = 7678653, upload-time = "2025-04-21T09:43:09.191Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0a/18/be8b5dd6b9cf1b2172301dbed28e8e5e878ee687c21947a6c81d6ceaa15d/aiohttp-3.11.18-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:474215ec618974054cf5dc465497ae9708543cbfc312c65212325d4212525811", size = 699833, upload-time = "2025-04-21T09:42:00.298Z" }, - { url = "https://files.pythonhosted.org/packages/0d/84/ecdc68e293110e6f6f6d7b57786a77555a85f70edd2b180fb1fafaff361a/aiohttp-3.11.18-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6ced70adf03920d4e67c373fd692123e34d3ac81dfa1c27e45904a628567d804", size = 462774, upload-time = "2025-04-21T09:42:02.015Z" }, - { url = "https://files.pythonhosted.org/packages/d7/85/f07718cca55884dad83cc2433746384d267ee970e91f0dcc75c6d5544079/aiohttp-3.11.18-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2d9f6c0152f8d71361905aaf9ed979259537981f47ad099c8b3d81e0319814bd", size = 454429, upload-time = "2025-04-21T09:42:03.728Z" }, - { url = "https://files.pythonhosted.org/packages/82/02/7f669c3d4d39810db8842c4e572ce4fe3b3a9b82945fdd64affea4c6947e/aiohttp-3.11.18-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a35197013ed929c0aed5c9096de1fc5a9d336914d73ab3f9df14741668c0616c", size = 1670283, upload-time = "2025-04-21T09:42:06.053Z" }, - { url = "https://files.pythonhosted.org/packages/ec/79/b82a12f67009b377b6c07a26bdd1b81dab7409fc2902d669dbfa79e5ac02/aiohttp-3.11.18-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:540b8a1f3a424f1af63e0af2d2853a759242a1769f9f1ab053996a392bd70118", size = 1717231, upload-time = "2025-04-21T09:42:07.953Z" }, - { url = "https://files.pythonhosted.org/packages/a6/38/d5a1f28c3904a840642b9a12c286ff41fc66dfa28b87e204b1f242dbd5e6/aiohttp-3.11.18-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f9e6710ebebfce2ba21cee6d91e7452d1125100f41b906fb5af3da8c78b764c1", size = 1769621, upload-time = "2025-04-21T09:42:09.855Z" }, - { url = "https://files.pythonhosted.org/packages/53/2d/deb3749ba293e716b5714dda06e257f123c5b8679072346b1eb28b766a0b/aiohttp-3.11.18-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8af2ef3b4b652ff109f98087242e2ab974b2b2b496304063585e3d78de0b000", size = 1678667, upload-time = "2025-04-21T09:42:11.741Z" }, - { url = "https://files.pythonhosted.org/packages/b8/a8/04b6e11683a54e104b984bd19a9790eb1ae5f50968b601bb202d0406f0ff/aiohttp-3.11.18-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:28c3f975e5ae3dbcbe95b7e3dcd30e51da561a0a0f2cfbcdea30fc1308d72137", size = 1601592, upload-time = "2025-04-21T09:42:14.137Z" }, - { url = "https://files.pythonhosted.org/packages/5e/9d/c33305ae8370b789423623f0e073d09ac775cd9c831ac0f11338b81c16e0/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c28875e316c7b4c3e745172d882d8a5c835b11018e33432d281211af35794a93", size = 1621679, upload-time = "2025-04-21T09:42:16.056Z" }, - { url = "https://files.pythonhosted.org/packages/56/45/8e9a27fff0538173d47ba60362823358f7a5f1653c6c30c613469f94150e/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:13cd38515568ae230e1ef6919e2e33da5d0f46862943fcda74e7e915096815f3", size = 1656878, upload-time = "2025-04-21T09:42:18.368Z" }, - { url = "https://files.pythonhosted.org/packages/84/5b/8c5378f10d7a5a46b10cb9161a3aac3eeae6dba54ec0f627fc4ddc4f2e72/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:0e2a92101efb9f4c2942252c69c63ddb26d20f46f540c239ccfa5af865197bb8", size = 1620509, upload-time = "2025-04-21T09:42:20.141Z" }, - { url = "https://files.pythonhosted.org/packages/9e/2f/99dee7bd91c62c5ff0aa3c55f4ae7e1bc99c6affef780d7777c60c5b3735/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:e6d3e32b8753c8d45ac550b11a1090dd66d110d4ef805ffe60fa61495360b3b2", size = 1680263, upload-time = "2025-04-21T09:42:21.993Z" }, - { url = "https://files.pythonhosted.org/packages/03/0a/378745e4ff88acb83e2d5c884a4fe993a6e9f04600a4560ce0e9b19936e3/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ea4cf2488156e0f281f93cc2fd365025efcba3e2d217cbe3df2840f8c73db261", size = 1715014, upload-time = "2025-04-21T09:42:23.87Z" }, - { url = "https://files.pythonhosted.org/packages/f6/0b/b5524b3bb4b01e91bc4323aad0c2fcaebdf2f1b4d2eb22743948ba364958/aiohttp-3.11.18-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9d4df95ad522c53f2b9ebc07f12ccd2cb15550941e11a5bbc5ddca2ca56316d7", size = 1666614, upload-time = "2025-04-21T09:42:25.764Z" }, - { url = "https://files.pythonhosted.org/packages/c7/b7/3d7b036d5a4ed5a4c704e0754afe2eef24a824dfab08e6efbffb0f6dd36a/aiohttp-3.11.18-cp313-cp313-win32.whl", hash = "sha256:cdd1bbaf1e61f0d94aced116d6e95fe25942f7a5f42382195fd9501089db5d78", size = 411358, upload-time = "2025-04-21T09:42:27.558Z" }, - { url = "https://files.pythonhosted.org/packages/1e/3c/143831b32cd23b5263a995b2a1794e10aa42f8a895aae5074c20fda36c07/aiohttp-3.11.18-cp313-cp313-win_amd64.whl", hash = "sha256:bdd619c27e44382cf642223f11cfd4d795161362a5a1fc1fa3940397bc89db01", size = 437658, upload-time = "2025-04-21T09:42:29.209Z" }, -] - -[[package]] -name = "aiosignal" -version = "1.3.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "frozenlist" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/ba/b5/6d55e80f6d8a08ce22b982eafa278d823b541c925f11ee774b0b9c43473d/aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54", size = 19424, upload-time = "2024-12-13T17:10:40.86Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/6a/bc7e17a3e87a2985d3e8f4da4cd0f481060eb78fb08596c42be62c90a4d9/aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5", size = 7597, upload-time = "2024-12-13T17:10:38.469Z" }, -] - -[[package]] -name = "alabaster" -version = "1.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a6/f8/d9c74d0daf3f742840fd818d69cfae176fa332022fd44e3469487d5a9420/alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e", size = 24210, upload-time = "2024-07-26T18:15:03.762Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z" }, -] - -[[package]] -name = "appnope" -version = "0.1.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, -] - -[[package]] -name = "asttokens" -version = "3.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918, upload-time = "2024-11-30T04:30:10.946Z" }, -] - -[[package]] -name = "attrs" -version = "25.3.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/1367933a8532ee6ff8d63537de4f1177af4bff9f3e829baf7331f595bb24/attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b", size = 812032, upload-time = "2025-03-13T11:10:22.779Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" }, -] - -[[package]] -name = "babel" -version = "2.17.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852, upload-time = "2025-02-01T15:17:41.026Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" }, -] - -[[package]] -name = "beautifulsoup4" -version = "4.13.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "soupsieve" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/d8/e4/0c4c39e18fd76d6a628d4dd8da40543d136ce2d1752bd6eeeab0791f4d6b/beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195", size = 621067, upload-time = "2025-04-15T17:05:13.836Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285, upload-time = "2025-04-15T17:05:12.221Z" }, -] - -[[package]] -name = "certifi" -version = "2025.4.26" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" }, -] - -[[package]] -name = "cffi" -version = "1.17.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "pycparser" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621, upload-time = "2024-09-04T20:45:21.852Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989, upload-time = "2024-09-04T20:44:28.956Z" }, - { url = "https://files.pythonhosted.org/packages/8b/f1/672d303ddf17c24fc83afd712316fda78dc6fce1cd53011b839483e1ecc8/cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2", size = 178802, upload-time = "2024-09-04T20:44:30.289Z" }, - { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792, upload-time = "2024-09-04T20:44:32.01Z" }, - { url = "https://files.pythonhosted.org/packages/75/b2/fbaec7c4455c604e29388d55599b99ebcc250a60050610fadde58932b7ee/cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683", size = 478893, upload-time = "2024-09-04T20:44:33.606Z" }, - { url = "https://files.pythonhosted.org/packages/4f/b7/6e4a2162178bf1935c336d4da8a9352cccab4d3a5d7914065490f08c0690/cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5", size = 485810, upload-time = "2024-09-04T20:44:35.191Z" }, - { url = "https://files.pythonhosted.org/packages/c7/8a/1d0e4a9c26e54746dc08c2c6c037889124d4f59dffd853a659fa545f1b40/cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4", size = 471200, upload-time = "2024-09-04T20:44:36.743Z" }, - { url = "https://files.pythonhosted.org/packages/26/9f/1aab65a6c0db35f43c4d1b4f580e8df53914310afc10ae0397d29d697af4/cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd", size = 479447, upload-time = "2024-09-04T20:44:38.492Z" }, - { url = "https://files.pythonhosted.org/packages/5f/e4/fb8b3dd8dc0e98edf1135ff067ae070bb32ef9d509d6cb0f538cd6f7483f/cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed", size = 484358, upload-time = "2024-09-04T20:44:40.046Z" }, - { url = "https://files.pythonhosted.org/packages/f1/47/d7145bf2dc04684935d57d67dff9d6d795b2ba2796806bb109864be3a151/cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9", size = 488469, upload-time = "2024-09-04T20:44:41.616Z" }, - { url = "https://files.pythonhosted.org/packages/bf/ee/f94057fa6426481d663b88637a9a10e859e492c73d0384514a17d78ee205/cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d", size = 172475, upload-time = "2024-09-04T20:44:43.733Z" }, - { url = "https://files.pythonhosted.org/packages/7c/fc/6a8cb64e5f0324877d503c854da15d76c1e50eb722e320b15345c4d0c6de/cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a", size = 182009, upload-time = "2024-09-04T20:44:45.309Z" }, -] - -[[package]] -name = "charset-normalizer" -version = "3.4.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/16/b0/572805e227f01586461c80e0fd25d65a2115599cc9dad142fee4b747c357/charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3", size = 123188, upload-time = "2024-12-24T18:12:35.43Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/38/94/ce8e6f63d18049672c76d07d119304e1e2d7c6098f0841b51c666e9f44a0/charset_normalizer-3.4.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:aabfa34badd18f1da5ec1bc2715cadc8dca465868a4e73a0173466b688f29dda", size = 195698, upload-time = "2024-12-24T18:11:05.834Z" }, - { url = "https://files.pythonhosted.org/packages/24/2e/dfdd9770664aae179a96561cc6952ff08f9a8cd09a908f259a9dfa063568/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22e14b5d70560b8dd51ec22863f370d1e595ac3d024cb8ad7d308b4cd95f8313", size = 140162, upload-time = "2024-12-24T18:11:07.064Z" }, - { url = "https://files.pythonhosted.org/packages/24/4e/f646b9093cff8fc86f2d60af2de4dc17c759de9d554f130b140ea4738ca6/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8436c508b408b82d87dc5f62496973a1805cd46727c34440b0d29d8a2f50a6c9", size = 150263, upload-time = "2024-12-24T18:11:08.374Z" }, - { url = "https://files.pythonhosted.org/packages/5e/67/2937f8d548c3ef6e2f9aab0f6e21001056f692d43282b165e7c56023e6dd/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2d074908e1aecee37a7635990b2c6d504cd4766c7bc9fc86d63f9c09af3fa11b", size = 142966, upload-time = "2024-12-24T18:11:09.831Z" }, - { url = "https://files.pythonhosted.org/packages/52/ed/b7f4f07de100bdb95c1756d3a4d17b90c1a3c53715c1a476f8738058e0fa/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:955f8851919303c92343d2f66165294848d57e9bba6cf6e3625485a70a038d11", size = 144992, upload-time = "2024-12-24T18:11:12.03Z" }, - { url = "https://files.pythonhosted.org/packages/96/2c/d49710a6dbcd3776265f4c923bb73ebe83933dfbaa841c5da850fe0fd20b/charset_normalizer-3.4.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:44ecbf16649486d4aebafeaa7ec4c9fed8b88101f4dd612dcaf65d5e815f837f", size = 147162, upload-time = "2024-12-24T18:11:13.372Z" }, - { url = "https://files.pythonhosted.org/packages/b4/41/35ff1f9a6bd380303dea55e44c4933b4cc3c4850988927d4082ada230273/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:0924e81d3d5e70f8126529951dac65c1010cdf117bb75eb02dd12339b57749dd", size = 140972, upload-time = "2024-12-24T18:11:14.628Z" }, - { url = "https://files.pythonhosted.org/packages/fb/43/c6a0b685fe6910d08ba971f62cd9c3e862a85770395ba5d9cad4fede33ab/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:2967f74ad52c3b98de4c3b32e1a44e32975e008a9cd2a8cc8966d6a5218c5cb2", size = 149095, upload-time = "2024-12-24T18:11:17.672Z" }, - { url = "https://files.pythonhosted.org/packages/4c/ff/a9a504662452e2d2878512115638966e75633519ec11f25fca3d2049a94a/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c75cb2a3e389853835e84a2d8fb2b81a10645b503eca9bcb98df6b5a43eb8886", size = 152668, upload-time = "2024-12-24T18:11:18.989Z" }, - { url = "https://files.pythonhosted.org/packages/6c/71/189996b6d9a4b932564701628af5cee6716733e9165af1d5e1b285c530ed/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:09b26ae6b1abf0d27570633b2b078a2a20419c99d66fb2823173d73f188ce601", size = 150073, upload-time = "2024-12-24T18:11:21.507Z" }, - { url = "https://files.pythonhosted.org/packages/e4/93/946a86ce20790e11312c87c75ba68d5f6ad2208cfb52b2d6a2c32840d922/charset_normalizer-3.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fa88b843d6e211393a37219e6a1c1df99d35e8fd90446f1118f4216e307e48cd", size = 145732, upload-time = "2024-12-24T18:11:22.774Z" }, - { url = "https://files.pythonhosted.org/packages/cd/e5/131d2fb1b0dddafc37be4f3a2fa79aa4c037368be9423061dccadfd90091/charset_normalizer-3.4.1-cp313-cp313-win32.whl", hash = "sha256:eb8178fe3dba6450a3e024e95ac49ed3400e506fd4e9e5c32d30adda88cbd407", size = 95391, upload-time = "2024-12-24T18:11:24.139Z" }, - { url = "https://files.pythonhosted.org/packages/27/f2/4f9a69cc7712b9b5ad8fdb87039fd89abba997ad5cbe690d1835d40405b0/charset_normalizer-3.4.1-cp313-cp313-win_amd64.whl", hash = "sha256:b1ac5992a838106edb89654e0aebfc24f5848ae2547d22c2c3f66454daa11971", size = 102702, upload-time = "2024-12-24T18:11:26.535Z" }, - { url = "https://files.pythonhosted.org/packages/0e/f6/65ecc6878a89bb1c23a086ea335ad4bf21a588990c3f535a227b9eea9108/charset_normalizer-3.4.1-py3-none-any.whl", hash = "sha256:d98b1668f06378c6dbefec3b92299716b931cd4e6061f3c875a71ced1780ab85", size = 49767, upload-time = "2024-12-24T18:12:32.852Z" }, -] - -[[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 = "comm" -version = "0.2.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210, upload-time = "2024-03-12T16:53:41.133Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180, upload-time = "2024-03-12T16:53:39.226Z" }, -] - -[[package]] -name = "contourpy" -version = "1.3.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2e/61/5673f7e364b31e4e7ef6f61a4b5121c5f170f941895912f773d95270f3a2/contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb", size = 271630, upload-time = "2025-04-15T17:38:19.142Z" }, - { url = "https://files.pythonhosted.org/packages/ff/66/a40badddd1223822c95798c55292844b7e871e50f6bfd9f158cb25e0bd39/contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08", size = 255670, upload-time = "2025-04-15T17:38:23.688Z" }, - { url = "https://files.pythonhosted.org/packages/1e/c7/cf9fdee8200805c9bc3b148f49cb9482a4e3ea2719e772602a425c9b09f8/contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c", size = 306694, upload-time = "2025-04-15T17:38:28.238Z" }, - { url = "https://files.pythonhosted.org/packages/dd/e7/ccb9bec80e1ba121efbffad7f38021021cda5be87532ec16fd96533bb2e0/contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f", size = 345986, upload-time = "2025-04-15T17:38:33.502Z" }, - { url = "https://files.pythonhosted.org/packages/dc/49/ca13bb2da90391fa4219fdb23b078d6065ada886658ac7818e5441448b78/contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85", size = 318060, upload-time = "2025-04-15T17:38:38.672Z" }, - { url = "https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841", size = 322747, upload-time = "2025-04-15T17:38:43.712Z" }, - { url = "https://files.pythonhosted.org/packages/72/30/669b8eb48e0a01c660ead3752a25b44fdb2e5ebc13a55782f639170772f9/contourpy-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c66c4906cdbc50e9cba65978823e6e00b45682eb09adbb78c9775b74eb222422", size = 1308895, upload-time = "2025-04-15T17:39:00.224Z" }, - { url = "https://files.pythonhosted.org/packages/05/5a/b569f4250decee6e8d54498be7bdf29021a4c256e77fe8138c8319ef8eb3/contourpy-1.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8b7fc0cd78ba2f4695fd0a6ad81a19e7e3ab825c31b577f384aa9d7817dc3bef", size = 1379098, upload-time = "2025-04-15T17:43:29.649Z" }, - { url = "https://files.pythonhosted.org/packages/19/ba/b227c3886d120e60e41b28740ac3617b2f2b971b9f601c835661194579f1/contourpy-1.3.2-cp313-cp313-win32.whl", hash = "sha256:15ce6ab60957ca74cff444fe66d9045c1fd3e92c8936894ebd1f3eef2fff075f", size = 178535, upload-time = "2025-04-15T17:44:44.532Z" }, - { url = "https://files.pythonhosted.org/packages/12/6e/2fed56cd47ca739b43e892707ae9a13790a486a3173be063681ca67d2262/contourpy-1.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e1578f7eafce927b168752ed7e22646dad6cd9bca673c60bff55889fa236ebf9", size = 223096, upload-time = "2025-04-15T17:44:48.194Z" }, - { url = "https://files.pythonhosted.org/packages/54/4c/e76fe2a03014a7c767d79ea35c86a747e9325537a8b7627e0e5b3ba266b4/contourpy-1.3.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0475b1f6604896bc7c53bb070e355e9321e1bc0d381735421a2d2068ec56531f", size = 285090, upload-time = "2025-04-15T17:43:34.084Z" }, - { url = "https://files.pythonhosted.org/packages/7b/e2/5aba47debd55d668e00baf9651b721e7733975dc9fc27264a62b0dd26eb8/contourpy-1.3.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c85bb486e9be652314bb5b9e2e3b0d1b2e643d5eec4992c0fbe8ac71775da739", size = 268643, upload-time = "2025-04-15T17:43:38.626Z" }, - { url = "https://files.pythonhosted.org/packages/a1/37/cd45f1f051fe6230f751cc5cdd2728bb3a203f5619510ef11e732109593c/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:745b57db7758f3ffc05a10254edd3182a2a83402a89c00957a8e8a22f5582823", size = 310443, upload-time = "2025-04-15T17:43:44.522Z" }, - { url = "https://files.pythonhosted.org/packages/8b/a2/36ea6140c306c9ff6dd38e3bcec80b3b018474ef4d17eb68ceecd26675f4/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:970e9173dbd7eba9b4e01aab19215a48ee5dd3f43cef736eebde064a171f89a5", size = 349865, upload-time = "2025-04-15T17:43:49.545Z" }, - { url = "https://files.pythonhosted.org/packages/95/b7/2fc76bc539693180488f7b6cc518da7acbbb9e3b931fd9280504128bf956/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6c4639a9c22230276b7bffb6a850dfc8258a2521305e1faefe804d006b2e532", size = 321162, upload-time = "2025-04-15T17:43:54.203Z" }, - { url = "https://files.pythonhosted.org/packages/f4/10/76d4f778458b0aa83f96e59d65ece72a060bacb20cfbee46cf6cd5ceba41/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc829960f34ba36aad4302e78eabf3ef16a3a100863f0d4eeddf30e8a485a03b", size = 327355, upload-time = "2025-04-15T17:44:01.025Z" }, - { url = "https://files.pythonhosted.org/packages/43/a3/10cf483ea683f9f8ab096c24bad3cce20e0d1dd9a4baa0e2093c1c962d9d/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d32530b534e986374fc19eaa77fcb87e8a99e5431499949b828312bdcd20ac52", size = 1307935, upload-time = "2025-04-15T17:44:17.322Z" }, - { url = "https://files.pythonhosted.org/packages/78/73/69dd9a024444489e22d86108e7b913f3528f56cfc312b5c5727a44188471/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e298e7e70cf4eb179cc1077be1c725b5fd131ebc81181bf0c03525c8abc297fd", size = 1372168, upload-time = "2025-04-15T17:44:33.43Z" }, - { url = "https://files.pythonhosted.org/packages/0f/1b/96d586ccf1b1a9d2004dd519b25fbf104a11589abfd05484ff12199cca21/contourpy-1.3.2-cp313-cp313t-win32.whl", hash = "sha256:d0e589ae0d55204991450bb5c23f571c64fe43adaa53f93fc902a84c96f52fe1", size = 189550, upload-time = "2025-04-15T17:44:37.092Z" }, - { url = "https://files.pythonhosted.org/packages/b0/e6/6000d0094e8a5e32ad62591c8609e269febb6e4db83a1c75ff8868b42731/contourpy-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:78e9253c3de756b3f6a5174d024c4835acd59eb3f8e2ca13e775dbffe1558f69", size = 238214, upload-time = "2025-04-15T17:44:40.827Z" }, -] - -[[package]] -name = "coverage" -version = "7.9.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e7/e0/98670a80884f64578f0c22cd70c5e81a6e07b08167721c7487b4d70a7ca0/coverage-7.9.1.tar.gz", hash = "sha256:6cf43c78c4282708a28e466316935ec7489a9c487518a77fa68f716c67909cec", size = 813650, upload-time = "2025-06-13T13:02:28.627Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d0/a7/a027970c991ca90f24e968999f7d509332daf6b8c3533d68633930aaebac/coverage-7.9.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:31324f18d5969feef7344a932c32428a2d1a3e50b15a6404e97cba1cc9b2c631", size = 212358, upload-time = "2025-06-13T13:01:30.909Z" }, - { url = "https://files.pythonhosted.org/packages/f2/48/6aaed3651ae83b231556750280682528fea8ac7f1232834573472d83e459/coverage-7.9.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0c804506d624e8a20fb3108764c52e0eef664e29d21692afa375e0dd98dc384f", size = 212620, upload-time = "2025-06-13T13:01:32.256Z" }, - { url = "https://files.pythonhosted.org/packages/6c/2a/f4b613f3b44d8b9f144847c89151992b2b6b79cbc506dee89ad0c35f209d/coverage-7.9.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ef64c27bc40189f36fcc50c3fb8f16ccda73b6a0b80d9bd6e6ce4cffcd810bbd", size = 245788, upload-time = "2025-06-13T13:01:33.948Z" }, - { url = "https://files.pythonhosted.org/packages/04/d2/de4fdc03af5e4e035ef420ed26a703c6ad3d7a07aff2e959eb84e3b19ca8/coverage-7.9.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4fe2348cc6ec372e25adec0219ee2334a68d2f5222e0cba9c0d613394e12d86", size = 243001, upload-time = "2025-06-13T13:01:35.285Z" }, - { url = "https://files.pythonhosted.org/packages/f5/e8/eed18aa5583b0423ab7f04e34659e51101135c41cd1dcb33ac1d7013a6d6/coverage-7.9.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:34ed2186fe52fcc24d4561041979a0dec69adae7bce2ae8d1c49eace13e55c43", size = 244985, upload-time = "2025-06-13T13:01:36.712Z" }, - { url = "https://files.pythonhosted.org/packages/17/f8/ae9e5cce8885728c934eaa58ebfa8281d488ef2afa81c3dbc8ee9e6d80db/coverage-7.9.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:25308bd3d00d5eedd5ae7d4357161f4df743e3c0240fa773ee1b0f75e6c7c0f1", size = 245152, upload-time = "2025-06-13T13:01:39.303Z" }, - { url = "https://files.pythonhosted.org/packages/5a/c8/272c01ae792bb3af9b30fac14d71d63371db227980682836ec388e2c57c0/coverage-7.9.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:73e9439310f65d55a5a1e0564b48e34f5369bee943d72c88378f2d576f5a5751", size = 243123, upload-time = "2025-06-13T13:01:40.727Z" }, - { url = "https://files.pythonhosted.org/packages/8c/d0/2819a1e3086143c094ab446e3bdf07138527a7b88cb235c488e78150ba7a/coverage-7.9.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:37ab6be0859141b53aa89412a82454b482c81cf750de4f29223d52268a86de67", size = 244506, upload-time = "2025-06-13T13:01:42.184Z" }, - { url = "https://files.pythonhosted.org/packages/8b/4e/9f6117b89152df7b6112f65c7a4ed1f2f5ec8e60c4be8f351d91e7acc848/coverage-7.9.1-cp313-cp313-win32.whl", hash = "sha256:64bdd969456e2d02a8b08aa047a92d269c7ac1f47e0c977675d550c9a0863643", size = 214766, upload-time = "2025-06-13T13:01:44.482Z" }, - { url = "https://files.pythonhosted.org/packages/27/0f/4b59f7c93b52c2c4ce7387c5a4e135e49891bb3b7408dcc98fe44033bbe0/coverage-7.9.1-cp313-cp313-win_amd64.whl", hash = "sha256:be9e3f68ca9edb897c2184ad0eee815c635565dbe7a0e7e814dc1f7cbab92c0a", size = 215568, upload-time = "2025-06-13T13:01:45.772Z" }, - { url = "https://files.pythonhosted.org/packages/09/1e/9679826336f8c67b9c39a359352882b24a8a7aee48d4c9cad08d38d7510f/coverage-7.9.1-cp313-cp313-win_arm64.whl", hash = "sha256:1c503289ffef1d5105d91bbb4d62cbe4b14bec4d13ca225f9c73cde9bb46207d", size = 213939, upload-time = "2025-06-13T13:01:47.087Z" }, - { url = "https://files.pythonhosted.org/packages/bb/5b/5c6b4e7a407359a2e3b27bf9c8a7b658127975def62077d441b93a30dbe8/coverage-7.9.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0b3496922cb5f4215bf5caaef4cf12364a26b0be82e9ed6d050f3352cf2d7ef0", size = 213079, upload-time = "2025-06-13T13:01:48.554Z" }, - { url = "https://files.pythonhosted.org/packages/a2/22/1e2e07279fd2fd97ae26c01cc2186e2258850e9ec125ae87184225662e89/coverage-7.9.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:9565c3ab1c93310569ec0d86b017f128f027cab0b622b7af288696d7ed43a16d", size = 213299, upload-time = "2025-06-13T13:01:49.997Z" }, - { url = "https://files.pythonhosted.org/packages/14/c0/4c5125a4b69d66b8c85986d3321520f628756cf524af810baab0790c7647/coverage-7.9.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2241ad5dbf79ae1d9c08fe52b36d03ca122fb9ac6bca0f34439e99f8327ac89f", size = 256535, upload-time = "2025-06-13T13:01:51.314Z" }, - { url = "https://files.pythonhosted.org/packages/81/8b/e36a04889dda9960be4263e95e777e7b46f1bb4fc32202612c130a20c4da/coverage-7.9.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3bb5838701ca68b10ebc0937dbd0eb81974bac54447c55cd58dea5bca8451029", size = 252756, upload-time = "2025-06-13T13:01:54.403Z" }, - { url = "https://files.pythonhosted.org/packages/98/82/be04eff8083a09a4622ecd0e1f31a2c563dbea3ed848069e7b0445043a70/coverage-7.9.1-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b30a25f814591a8c0c5372c11ac8967f669b97444c47fd794926e175c4047ece", size = 254912, upload-time = "2025-06-13T13:01:56.769Z" }, - { url = "https://files.pythonhosted.org/packages/0f/25/c26610a2c7f018508a5ab958e5b3202d900422cf7cdca7670b6b8ca4e8df/coverage-7.9.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:2d04b16a6062516df97969f1ae7efd0de9c31eb6ebdceaa0d213b21c0ca1a683", size = 256144, upload-time = "2025-06-13T13:01:58.19Z" }, - { url = "https://files.pythonhosted.org/packages/c5/8b/fb9425c4684066c79e863f1e6e7ecebb49e3a64d9f7f7860ef1688c56f4a/coverage-7.9.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:7931b9e249edefb07cd6ae10c702788546341d5fe44db5b6108a25da4dca513f", size = 254257, upload-time = "2025-06-13T13:01:59.645Z" }, - { url = "https://files.pythonhosted.org/packages/93/df/27b882f54157fc1131e0e215b0da3b8d608d9b8ef79a045280118a8f98fe/coverage-7.9.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:52e92b01041151bf607ee858e5a56c62d4b70f4dac85b8c8cb7fb8a351ab2c10", size = 255094, upload-time = "2025-06-13T13:02:01.37Z" }, - { url = "https://files.pythonhosted.org/packages/41/5f/cad1c3dbed8b3ee9e16fa832afe365b4e3eeab1fb6edb65ebbf745eabc92/coverage-7.9.1-cp313-cp313t-win32.whl", hash = "sha256:684e2110ed84fd1ca5f40e89aa44adf1729dc85444004111aa01866507adf363", size = 215437, upload-time = "2025-06-13T13:02:02.905Z" }, - { url = "https://files.pythonhosted.org/packages/99/4d/fad293bf081c0e43331ca745ff63673badc20afea2104b431cdd8c278b4c/coverage-7.9.1-cp313-cp313t-win_amd64.whl", hash = "sha256:437c576979e4db840539674e68c84b3cda82bc824dd138d56bead1435f1cb5d7", size = 216605, upload-time = "2025-06-13T13:02:05.638Z" }, - { url = "https://files.pythonhosted.org/packages/1f/56/4ee027d5965fc7fc126d7ec1187529cc30cc7d740846e1ecb5e92d31b224/coverage-7.9.1-cp313-cp313t-win_arm64.whl", hash = "sha256:18a0912944d70aaf5f399e350445738a1a20b50fbea788f640751c2ed9208b6c", size = 214392, upload-time = "2025-06-13T13:02:07.642Z" }, - { url = "https://files.pythonhosted.org/packages/08/b8/7ddd1e8ba9701dea08ce22029917140e6f66a859427406579fd8d0ca7274/coverage-7.9.1-py3-none-any.whl", hash = "sha256:66b974b145aa189516b6bf2d8423e888b742517d37872f6ee4c5be0073bd9a3c", size = 204000, upload-time = "2025-06-13T13:02:27.173Z" }, -] - -[[package]] -name = "cycler" -version = "0.12.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, -] - -[[package]] -name = "debugpy" -version = "1.8.14" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/bd/75/087fe07d40f490a78782ff3b0a30e3968936854105487decdb33446d4b0e/debugpy-1.8.14.tar.gz", hash = "sha256:7cd287184318416850aa8b60ac90105837bb1e59531898c07569d197d2ed5322", size = 1641444, upload-time = "2025-04-10T19:46:10.981Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4d/e4/395c792b243f2367d84202dc33689aa3d910fb9826a7491ba20fc9e261f5/debugpy-1.8.14-cp313-cp313-macosx_14_0_universal2.whl", hash = "sha256:329a15d0660ee09fec6786acdb6e0443d595f64f5d096fc3e3ccf09a4259033f", size = 2485676, upload-time = "2025-04-10T19:46:32.96Z" }, - { url = "https://files.pythonhosted.org/packages/ba/f1/6f2ee3f991327ad9e4c2f8b82611a467052a0fb0e247390192580e89f7ff/debugpy-1.8.14-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f920c7f9af409d90f5fd26e313e119d908b0dd2952c2393cd3247a462331f15", size = 4217514, upload-time = "2025-04-10T19:46:34.336Z" }, - { url = "https://files.pythonhosted.org/packages/79/28/b9d146f8f2dc535c236ee09ad3e5ac899adb39d7a19b49f03ac95d216beb/debugpy-1.8.14-cp313-cp313-win32.whl", hash = "sha256:3784ec6e8600c66cbdd4ca2726c72d8ca781e94bce2f396cc606d458146f8f4e", size = 5254756, upload-time = "2025-04-10T19:46:36.199Z" }, - { url = "https://files.pythonhosted.org/packages/e0/62/a7b4a57013eac4ccaef6977966e6bec5c63906dd25a86e35f155952e29a1/debugpy-1.8.14-cp313-cp313-win_amd64.whl", hash = "sha256:684eaf43c95a3ec39a96f1f5195a7ff3d4144e4a18d69bb66beeb1a6de605d6e", size = 5297119, upload-time = "2025-04-10T19:46:38.141Z" }, - { url = "https://files.pythonhosted.org/packages/97/1a/481f33c37ee3ac8040d3d51fc4c4e4e7e61cb08b8bc8971d6032acc2279f/debugpy-1.8.14-py2.py3-none-any.whl", hash = "sha256:5cd9a579d553b6cb9759a7908a41988ee6280b961f24f63336835d9418216a20", size = 5256230, upload-time = "2025-04-10T19:46:54.077Z" }, -] - -[[package]] -name = "decorator" -version = "5.2.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" }, -] - -[[package]] -name = "docutils" -version = "0.21.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ae/ed/aefcc8cd0ba62a0560c3c18c33925362d46c6075480bfa4df87b28e169a9/docutils-0.21.2.tar.gz", hash = "sha256:3a6b18732edf182daa3cd12775bbb338cf5691468f91eeeb109deff6ebfa986f", size = 2204444, upload-time = "2024-04-23T18:57:18.24Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl", hash = "sha256:dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2", size = 587408, upload-time = "2024-04-23T18:57:14.835Z" }, -] - -[[package]] -name = "executing" -version = "2.2.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755", size = 978693, upload-time = "2025-01-22T15:41:29.403Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702, upload-time = "2025-01-22T15:41:25.929Z" }, -] - -[[package]] -name = "filelock" -version = "3.18.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0a/10/c23352565a6544bdc5353e0b15fc1c563352101f30e24bf500207a54df9a/filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2", size = 18075, upload-time = "2025-03-14T07:11:40.47Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4d/36/2a115987e2d8c300a974597416d9de88f2444426de9571f4b59b2cca3acc/filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de", size = 16215, upload-time = "2025-03-14T07:11:39.145Z" }, -] - -[[package]] -name = "fonttools" -version = "4.57.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/03/2d/a9a0b6e3a0cf6bd502e64fc16d894269011930cabfc89aee20d1635b1441/fonttools-4.57.0.tar.gz", hash = "sha256:727ece10e065be2f9dd239d15dd5d60a66e17eac11aea47d447f9f03fdbc42de", size = 3492448, upload-time = "2025-04-03T11:07:13.898Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e9/2f/11439f3af51e4bb75ac9598c29f8601aa501902dcedf034bdc41f47dd799/fonttools-4.57.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:408ce299696012d503b714778d89aa476f032414ae57e57b42e4b92363e0b8ef", size = 2739175, upload-time = "2025-04-03T11:06:19.583Z" }, - { url = "https://files.pythonhosted.org/packages/25/52/677b55a4c0972dc3820c8dba20a29c358197a78229daa2ea219fdb19e5d5/fonttools-4.57.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:bbceffc80aa02d9e8b99f2a7491ed8c4a783b2fc4020119dc405ca14fb5c758c", size = 2276583, upload-time = "2025-04-03T11:06:21.753Z" }, - { url = "https://files.pythonhosted.org/packages/64/79/184555f8fa77b827b9460a4acdbbc0b5952bb6915332b84c615c3a236826/fonttools-4.57.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f022601f3ee9e1f6658ed6d184ce27fa5216cee5b82d279e0f0bde5deebece72", size = 4766437, upload-time = "2025-04-03T11:06:23.521Z" }, - { url = "https://files.pythonhosted.org/packages/f8/ad/c25116352f456c0d1287545a7aa24e98987b6d99c5b0456c4bd14321f20f/fonttools-4.57.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dea5893b58d4637ffa925536462ba626f8a1b9ffbe2f5c272cdf2c6ebadb817", size = 4838431, upload-time = "2025-04-03T11:06:25.423Z" }, - { url = "https://files.pythonhosted.org/packages/53/ae/398b2a833897297797a44f519c9af911c2136eb7aa27d3f1352c6d1129fa/fonttools-4.57.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:dff02c5c8423a657c550b48231d0a48d7e2b2e131088e55983cfe74ccc2c7cc9", size = 4951011, upload-time = "2025-04-03T11:06:27.41Z" }, - { url = "https://files.pythonhosted.org/packages/b7/5d/7cb31c4bc9ffb9a2bbe8b08f8f53bad94aeb158efad75da645b40b62cb73/fonttools-4.57.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:767604f244dc17c68d3e2dbf98e038d11a18abc078f2d0f84b6c24571d9c0b13", size = 5205679, upload-time = "2025-04-03T11:06:29.804Z" }, - { url = "https://files.pythonhosted.org/packages/4c/e4/6934513ec2c4d3d69ca1bc3bd34d5c69dafcbf68c15388dd3bb062daf345/fonttools-4.57.0-cp313-cp313-win32.whl", hash = "sha256:8e2e12d0d862f43d51e5afb8b9751c77e6bec7d2dc00aad80641364e9df5b199", size = 2144833, upload-time = "2025-04-03T11:06:31.737Z" }, - { url = "https://files.pythonhosted.org/packages/c4/0d/2177b7fdd23d017bcfb702fd41e47d4573766b9114da2fddbac20dcc4957/fonttools-4.57.0-cp313-cp313-win_amd64.whl", hash = "sha256:f1d6bc9c23356908db712d282acb3eebd4ae5ec6d8b696aa40342b1d84f8e9e3", size = 2190799, upload-time = "2025-04-03T11:06:34.784Z" }, - { url = "https://files.pythonhosted.org/packages/90/27/45f8957c3132917f91aaa56b700bcfc2396be1253f685bd5c68529b6f610/fonttools-4.57.0-py3-none-any.whl", hash = "sha256:3122c604a675513c68bd24c6a8f9091f1c2376d18e8f5fe5a101746c81b3e98f", size = 1093605, upload-time = "2025-04-03T11:07:11.341Z" }, -] - -[[package]] -name = "frozenlist" -version = "1.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ee/f4/d744cba2da59b5c1d88823cf9e8a6c74e4659e2b27604ed973be2a0bf5ab/frozenlist-1.6.0.tar.gz", hash = "sha256:b99655c32c1c8e06d111e7f41c06c29a5318cb1835df23a45518e02a47c63b68", size = 42831, upload-time = "2025-04-17T22:38:53.099Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6f/e5/04c7090c514d96ca00887932417f04343ab94904a56ab7f57861bf63652d/frozenlist-1.6.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1d7fb014fe0fbfee3efd6a94fc635aeaa68e5e1720fe9e57357f2e2c6e1a647e", size = 158182, upload-time = "2025-04-17T22:37:16.837Z" }, - { url = "https://files.pythonhosted.org/packages/e9/8f/60d0555c61eec855783a6356268314d204137f5e0c53b59ae2fc28938c99/frozenlist-1.6.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01bcaa305a0fdad12745502bfd16a1c75b14558dabae226852f9159364573117", size = 122838, upload-time = "2025-04-17T22:37:18.352Z" }, - { url = "https://files.pythonhosted.org/packages/5a/a7/d0ec890e3665b4b3b7c05dc80e477ed8dc2e2e77719368e78e2cd9fec9c8/frozenlist-1.6.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8b314faa3051a6d45da196a2c495e922f987dc848e967d8cfeaee8a0328b1cd4", size = 120980, upload-time = "2025-04-17T22:37:19.857Z" }, - { url = "https://files.pythonhosted.org/packages/cc/19/9b355a5e7a8eba903a008579964192c3e427444752f20b2144b10bb336df/frozenlist-1.6.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da62fecac21a3ee10463d153549d8db87549a5e77eefb8c91ac84bb42bb1e4e3", size = 305463, upload-time = "2025-04-17T22:37:21.328Z" }, - { url = "https://files.pythonhosted.org/packages/9c/8d/5b4c758c2550131d66935ef2fa700ada2461c08866aef4229ae1554b93ca/frozenlist-1.6.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d1eb89bf3454e2132e046f9599fbcf0a4483ed43b40f545551a39316d0201cd1", size = 297985, upload-time = "2025-04-17T22:37:23.55Z" }, - { url = "https://files.pythonhosted.org/packages/48/2c/537ec09e032b5865715726b2d1d9813e6589b571d34d01550c7aeaad7e53/frozenlist-1.6.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d18689b40cb3936acd971f663ccb8e2589c45db5e2c5f07e0ec6207664029a9c", size = 311188, upload-time = "2025-04-17T22:37:25.221Z" }, - { url = "https://files.pythonhosted.org/packages/31/2f/1aa74b33f74d54817055de9a4961eff798f066cdc6f67591905d4fc82a84/frozenlist-1.6.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e67ddb0749ed066b1a03fba812e2dcae791dd50e5da03be50b6a14d0c1a9ee45", size = 311874, upload-time = "2025-04-17T22:37:26.791Z" }, - { url = "https://files.pythonhosted.org/packages/bf/f0/cfec18838f13ebf4b37cfebc8649db5ea71a1b25dacd691444a10729776c/frozenlist-1.6.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fc5e64626e6682638d6e44398c9baf1d6ce6bc236d40b4b57255c9d3f9761f1f", size = 291897, upload-time = "2025-04-17T22:37:28.958Z" }, - { url = "https://files.pythonhosted.org/packages/ea/a5/deb39325cbbea6cd0a46db8ccd76150ae2fcbe60d63243d9df4a0b8c3205/frozenlist-1.6.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:437cfd39564744ae32ad5929e55b18ebd88817f9180e4cc05e7d53b75f79ce85", size = 305799, upload-time = "2025-04-17T22:37:30.889Z" }, - { url = "https://files.pythonhosted.org/packages/78/22/6ddec55c5243a59f605e4280f10cee8c95a449f81e40117163383829c241/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:62dd7df78e74d924952e2feb7357d826af8d2f307557a779d14ddf94d7311be8", size = 302804, upload-time = "2025-04-17T22:37:32.489Z" }, - { url = "https://files.pythonhosted.org/packages/5d/b7/d9ca9bab87f28855063c4d202936800219e39db9e46f9fb004d521152623/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:a66781d7e4cddcbbcfd64de3d41a61d6bdde370fc2e38623f30b2bd539e84a9f", size = 316404, upload-time = "2025-04-17T22:37:34.59Z" }, - { url = "https://files.pythonhosted.org/packages/a6/3a/1255305db7874d0b9eddb4fe4a27469e1fb63720f1fc6d325a5118492d18/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:482fe06e9a3fffbcd41950f9d890034b4a54395c60b5e61fae875d37a699813f", size = 295572, upload-time = "2025-04-17T22:37:36.337Z" }, - { url = "https://files.pythonhosted.org/packages/2a/f2/8d38eeee39a0e3a91b75867cc102159ecccf441deb6ddf67be96d3410b84/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:e4f9373c500dfc02feea39f7a56e4f543e670212102cc2eeb51d3a99c7ffbde6", size = 307601, upload-time = "2025-04-17T22:37:37.923Z" }, - { url = "https://files.pythonhosted.org/packages/38/04/80ec8e6b92f61ef085422d7b196822820404f940950dde5b2e367bede8bc/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e69bb81de06827147b7bfbaeb284d85219fa92d9f097e32cc73675f279d70188", size = 314232, upload-time = "2025-04-17T22:37:39.669Z" }, - { url = "https://files.pythonhosted.org/packages/3a/58/93b41fb23e75f38f453ae92a2f987274c64637c450285577bd81c599b715/frozenlist-1.6.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7613d9977d2ab4a9141dde4a149f4357e4065949674c5649f920fec86ecb393e", size = 308187, upload-time = "2025-04-17T22:37:41.662Z" }, - { url = "https://files.pythonhosted.org/packages/6a/a2/e64df5c5aa36ab3dee5a40d254f3e471bb0603c225f81664267281c46a2d/frozenlist-1.6.0-cp313-cp313-win32.whl", hash = "sha256:4def87ef6d90429f777c9d9de3961679abf938cb6b7b63d4a7eb8a268babfce4", size = 114772, upload-time = "2025-04-17T22:37:43.132Z" }, - { url = "https://files.pythonhosted.org/packages/a0/77/fead27441e749b2d574bb73d693530d59d520d4b9e9679b8e3cb779d37f2/frozenlist-1.6.0-cp313-cp313-win_amd64.whl", hash = "sha256:37a8a52c3dfff01515e9bbbee0e6063181362f9de3db2ccf9bc96189b557cbfd", size = 119847, upload-time = "2025-04-17T22:37:45.118Z" }, - { url = "https://files.pythonhosted.org/packages/df/bd/cc6d934991c1e5d9cafda83dfdc52f987c7b28343686aef2e58a9cf89f20/frozenlist-1.6.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:46138f5a0773d064ff663d273b309b696293d7a7c00a0994c5c13a5078134b64", size = 174937, upload-time = "2025-04-17T22:37:46.635Z" }, - { url = "https://files.pythonhosted.org/packages/f2/a2/daf945f335abdbfdd5993e9dc348ef4507436936ab3c26d7cfe72f4843bf/frozenlist-1.6.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:f88bc0a2b9c2a835cb888b32246c27cdab5740059fb3688852bf91e915399b91", size = 136029, upload-time = "2025-04-17T22:37:48.192Z" }, - { url = "https://files.pythonhosted.org/packages/51/65/4c3145f237a31247c3429e1c94c384d053f69b52110a0d04bfc8afc55fb2/frozenlist-1.6.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:777704c1d7655b802c7850255639672e90e81ad6fa42b99ce5ed3fbf45e338dd", size = 134831, upload-time = "2025-04-17T22:37:50.485Z" }, - { url = "https://files.pythonhosted.org/packages/77/38/03d316507d8dea84dfb99bdd515ea245628af964b2bf57759e3c9205cc5e/frozenlist-1.6.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85ef8d41764c7de0dcdaf64f733a27352248493a85a80661f3c678acd27e31f2", size = 392981, upload-time = "2025-04-17T22:37:52.558Z" }, - { url = "https://files.pythonhosted.org/packages/37/02/46285ef9828f318ba400a51d5bb616ded38db8466836a9cfa39f3903260b/frozenlist-1.6.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:da5cb36623f2b846fb25009d9d9215322318ff1c63403075f812b3b2876c8506", size = 371999, upload-time = "2025-04-17T22:37:54.092Z" }, - { url = "https://files.pythonhosted.org/packages/0d/64/1212fea37a112c3c5c05bfb5f0a81af4836ce349e69be75af93f99644da9/frozenlist-1.6.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cbb56587a16cf0fb8acd19e90ff9924979ac1431baea8681712716a8337577b0", size = 392200, upload-time = "2025-04-17T22:37:55.951Z" }, - { url = "https://files.pythonhosted.org/packages/81/ce/9a6ea1763e3366e44a5208f76bf37c76c5da570772375e4d0be85180e588/frozenlist-1.6.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6154c3ba59cda3f954c6333025369e42c3acd0c6e8b6ce31eb5c5b8116c07e0", size = 390134, upload-time = "2025-04-17T22:37:57.633Z" }, - { url = "https://files.pythonhosted.org/packages/bc/36/939738b0b495b2c6d0c39ba51563e453232813042a8d908b8f9544296c29/frozenlist-1.6.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2e8246877afa3f1ae5c979fe85f567d220f86a50dc6c493b9b7d8191181ae01e", size = 365208, upload-time = "2025-04-17T22:37:59.742Z" }, - { url = "https://files.pythonhosted.org/packages/b4/8b/939e62e93c63409949c25220d1ba8e88e3960f8ef6a8d9ede8f94b459d27/frozenlist-1.6.0-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b0f6cce16306d2e117cf9db71ab3a9e8878a28176aeaf0dbe35248d97b28d0c", size = 385548, upload-time = "2025-04-17T22:38:01.416Z" }, - { url = "https://files.pythonhosted.org/packages/62/38/22d2873c90102e06a7c5a3a5b82ca47e393c6079413e8a75c72bff067fa8/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:1b8e8cd8032ba266f91136d7105706ad57770f3522eac4a111d77ac126a25a9b", size = 391123, upload-time = "2025-04-17T22:38:03.049Z" }, - { url = "https://files.pythonhosted.org/packages/44/78/63aaaf533ee0701549500f6d819be092c6065cb5c577edb70c09df74d5d0/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:e2ada1d8515d3ea5378c018a5f6d14b4994d4036591a52ceaf1a1549dec8e1ad", size = 394199, upload-time = "2025-04-17T22:38:04.776Z" }, - { url = "https://files.pythonhosted.org/packages/54/45/71a6b48981d429e8fbcc08454dc99c4c2639865a646d549812883e9c9dd3/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:cdb2c7f071e4026c19a3e32b93a09e59b12000751fc9b0b7758da899e657d215", size = 373854, upload-time = "2025-04-17T22:38:06.576Z" }, - { url = "https://files.pythonhosted.org/packages/3f/f3/dbf2a5e11736ea81a66e37288bf9f881143a7822b288a992579ba1b4204d/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:03572933a1969a6d6ab509d509e5af82ef80d4a5d4e1e9f2e1cdd22c77a3f4d2", size = 395412, upload-time = "2025-04-17T22:38:08.197Z" }, - { url = "https://files.pythonhosted.org/packages/b3/f1/c63166806b331f05104d8ea385c4acd511598568b1f3e4e8297ca54f2676/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:77effc978947548b676c54bbd6a08992759ea6f410d4987d69feea9cd0919911", size = 394936, upload-time = "2025-04-17T22:38:10.056Z" }, - { url = "https://files.pythonhosted.org/packages/ef/ea/4f3e69e179a430473eaa1a75ff986526571215fefc6b9281cdc1f09a4eb8/frozenlist-1.6.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a2bda8be77660ad4089caf2223fdbd6db1858462c4b85b67fbfa22102021e497", size = 391459, upload-time = "2025-04-17T22:38:11.826Z" }, - { url = "https://files.pythonhosted.org/packages/d3/c3/0fc2c97dea550df9afd072a37c1e95421652e3206bbeaa02378b24c2b480/frozenlist-1.6.0-cp313-cp313t-win32.whl", hash = "sha256:a4d96dc5bcdbd834ec6b0f91027817214216b5b30316494d2b1aebffb87c534f", size = 128797, upload-time = "2025-04-17T22:38:14.013Z" }, - { url = "https://files.pythonhosted.org/packages/ae/f5/79c9320c5656b1965634fe4be9c82b12a3305bdbc58ad9cb941131107b20/frozenlist-1.6.0-cp313-cp313t-win_amd64.whl", hash = "sha256:e18036cb4caa17ea151fd5f3d70be9d354c99eb8cf817a3ccde8a7873b074348", size = 134709, upload-time = "2025-04-17T22:38:15.551Z" }, - { url = "https://files.pythonhosted.org/packages/71/3e/b04a0adda73bd52b390d730071c0d577073d3d26740ee1bad25c3ad0f37b/frozenlist-1.6.0-py3-none-any.whl", hash = "sha256:535eec9987adb04701266b92745d6cdcef2e77669299359c3009c3404dd5d191", size = 12404, upload-time = "2025-04-17T22:38:51.668Z" }, -] - -[[package]] -name = "fsspec" -version = "2025.3.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/45/d8/8425e6ba5fcec61a1d16e41b1b71d2bf9344f1fe48012c2b48b9620feae5/fsspec-2025.3.2.tar.gz", hash = "sha256:e52c77ef398680bbd6a98c0e628fbc469491282981209907bbc8aea76a04fdc6", size = 299281, upload-time = "2025-03-31T15:27:08.524Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/44/4b/e0cfc1a6f17e990f3e64b7d941ddc4acdc7b19d6edd51abf495f32b1a9e4/fsspec-2025.3.2-py3-none-any.whl", hash = "sha256:2daf8dc3d1dfa65b6aa37748d112773a7a08416f6c70d96b264c96476ecaf711", size = 194435, upload-time = "2025-03-31T15:27:07.028Z" }, -] - -[[package]] -name = "furo" -version = "2024.8.6" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "beautifulsoup4" }, - { name = "pygments" }, - { name = "sphinx" }, - { name = "sphinx-basic-ng" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/a0/e2/d351d69a9a9e4badb4a5be062c2d0e87bd9e6c23b5e57337fef14bef34c8/furo-2024.8.6.tar.gz", hash = "sha256:b63e4cee8abfc3136d3bc03a3d45a76a850bada4d6374d24c1716b0e01394a01", size = 1661506, upload-time = "2024-08-06T08:07:57.567Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/27/48/e791a7ed487dbb9729ef32bb5d1af16693d8925f4366befef54119b2e576/furo-2024.8.6-py3-none-any.whl", hash = "sha256:6cd97c58b47813d3619e63e9081169880fbe331f0ca883c871ff1f3f11814f5c", size = 341333, upload-time = "2024-08-06T08:07:54.44Z" }, -] - -[[package]] -name = "idna" -version = "3.10" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f1/70/7703c29685631f5a7590aa73f1f1d3fa9a380e654b86af429e0934a32f7d/idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9", size = 190490, upload-time = "2024-09-15T18:07:39.745Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" }, -] - -[[package]] -name = "imagesize" -version = "1.4.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a7/84/62473fb57d61e31fef6e36d64a179c8781605429fd927b5dd608c997be31/imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a", size = 1280026, upload-time = "2022-07-01T12:21:05.687Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b", size = 8769, upload-time = "2022-07-01T12:21:02.467Z" }, -] - -[[package]] -name = "iniconfig" -version = "2.1.0" -source = { registry = "https://pypi.org/simple" } -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 = "ipykernel" -version = "6.29.5" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "appnope", marker = "sys_platform == 'darwin'" }, - { name = "comm" }, - { name = "debugpy" }, - { name = "ipython" }, - { name = "jupyter-client" }, - { name = "jupyter-core" }, - { name = "matplotlib-inline" }, - { name = "nest-asyncio" }, - { name = "packaging" }, - { name = "psutil" }, - { name = "pyzmq" }, - { name = "tornado" }, - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/e9/5c/67594cb0c7055dc50814b21731c22a601101ea3b1b50a9a1b090e11f5d0f/ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215", size = 163367, upload-time = "2024-07-01T14:07:22.543Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5", size = 117173, upload-time = "2024-07-01T14:07:19.603Z" }, -] - -[[package]] -name = "ipython" -version = "9.4.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, - { name = "decorator" }, - { name = "ipython-pygments-lexers" }, - { name = "jedi" }, - { name = "matplotlib-inline" }, - { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, - { name = "prompt-toolkit" }, - { name = "pygments" }, - { name = "stack-data" }, - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/54/80/406f9e3bde1c1fd9bf5a0be9d090f8ae623e401b7670d8f6fdf2ab679891/ipython-9.4.0.tar.gz", hash = "sha256:c033c6d4e7914c3d9768aabe76bbe87ba1dc66a92a05db6bfa1125d81f2ee270", size = 4385338, upload-time = "2025-07-01T11:11:30.606Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/63/f8/0031ee2b906a15a33d6bfc12dd09c3dfa966b3cb5b284ecfb7549e6ac3c4/ipython-9.4.0-py3-none-any.whl", hash = "sha256:25850f025a446d9b359e8d296ba175a36aedd32e83ca9b5060430fe16801f066", size = 611021, upload-time = "2025-07-01T11:11:27.85Z" }, -] - -[[package]] -name = "ipython-pygments-lexers" -version = "1.1.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "pygments" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, -] - -[[package]] -name = "jedi" -version = "0.19.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "parso" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" }, -] - -[[package]] -name = "jinja2" -version = "3.1.6" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "markupsafe" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, -] - -[[package]] -name = "joblib" -version = "1.5.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/dc/fe/0f5a938c54105553436dbff7a61dc4fed4b1b2c98852f8833beaf4d5968f/joblib-1.5.1.tar.gz", hash = "sha256:f4f86e351f39fe3d0d32a9f2c3d8af1ee4cec285aafcb27003dda5205576b444", size = 330475, upload-time = "2025-05-23T12:04:37.097Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7d/4f/1195bbac8e0c2acc5f740661631d8d750dc38d4a32b23ee5df3cde6f4e0d/joblib-1.5.1-py3-none-any.whl", hash = "sha256:4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a", size = 307746, upload-time = "2025-05-23T12:04:35.124Z" }, -] - -[[package]] -name = "jupyter-client" -version = "8.6.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "jupyter-core" }, - { name = "python-dateutil" }, - { name = "pyzmq" }, - { name = "tornado" }, - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019, upload-time = "2024-09-17T10:44:17.613Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105, upload-time = "2024-09-17T10:44:15.218Z" }, -] - -[[package]] -name = "jupyter-core" -version = "5.8.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "platformdirs" }, - { name = "pywin32", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'win32'" }, - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/99/1b/72906d554acfeb588332eaaa6f61577705e9ec752ddb486f302dafa292d9/jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941", size = 88923, upload-time = "2025-05-27T07:38:16.655Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2f/57/6bffd4b20b88da3800c5d691e0337761576ee688eb01299eae865689d2df/jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0", size = 28880, upload-time = "2025-05-27T07:38:15.137Z" }, -] - -[[package]] -name = "kiwisolver" -version = "1.4.8" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538, upload-time = "2024-12-24T18:30:51.519Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/79/b3/e62464a652f4f8cd9006e13d07abad844a47df1e6537f73ddfbf1bc997ec/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09", size = 124156, upload-time = "2024-12-24T18:29:45.368Z" }, - { url = "https://files.pythonhosted.org/packages/8d/2d/f13d06998b546a2ad4f48607a146e045bbe48030774de29f90bdc573df15/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1", size = 66555, upload-time = "2024-12-24T18:29:46.37Z" }, - { url = "https://files.pythonhosted.org/packages/59/e3/b8bd14b0a54998a9fd1e8da591c60998dc003618cb19a3f94cb233ec1511/kiwisolver-1.4.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c", size = 65071, upload-time = "2024-12-24T18:29:47.333Z" }, - { url = "https://files.pythonhosted.org/packages/f0/1c/6c86f6d85ffe4d0ce04228d976f00674f1df5dc893bf2dd4f1928748f187/kiwisolver-1.4.8-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b", size = 1378053, upload-time = "2024-12-24T18:29:49.636Z" }, - { url = "https://files.pythonhosted.org/packages/4e/b9/1c6e9f6dcb103ac5cf87cb695845f5fa71379021500153566d8a8a9fc291/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47", size = 1472278, upload-time = "2024-12-24T18:29:51.164Z" }, - { url = "https://files.pythonhosted.org/packages/ee/81/aca1eb176de671f8bda479b11acdc42c132b61a2ac861c883907dde6debb/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16", size = 1478139, upload-time = "2024-12-24T18:29:52.594Z" }, - { url = "https://files.pythonhosted.org/packages/49/f4/e081522473671c97b2687d380e9e4c26f748a86363ce5af48b4a28e48d06/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc", size = 1413517, upload-time = "2024-12-24T18:29:53.941Z" }, - { url = "https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246", size = 1474952, upload-time = "2024-12-24T18:29:56.523Z" }, - { url = "https://files.pythonhosted.org/packages/82/13/13fa685ae167bee5d94b415991c4fc7bb0a1b6ebea6e753a87044b209678/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794", size = 2269132, upload-time = "2024-12-24T18:29:57.989Z" }, - { url = "https://files.pythonhosted.org/packages/ef/92/bb7c9395489b99a6cb41d502d3686bac692586db2045adc19e45ee64ed23/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b", size = 2425997, upload-time = "2024-12-24T18:29:59.393Z" }, - { url = "https://files.pythonhosted.org/packages/ed/12/87f0e9271e2b63d35d0d8524954145837dd1a6c15b62a2d8c1ebe0f182b4/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3", size = 2376060, upload-time = "2024-12-24T18:30:01.338Z" }, - { url = "https://files.pythonhosted.org/packages/02/6e/c8af39288edbce8bf0fa35dee427b082758a4b71e9c91ef18fa667782138/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957", size = 2520471, upload-time = "2024-12-24T18:30:04.574Z" }, - { url = "https://files.pythonhosted.org/packages/13/78/df381bc7b26e535c91469f77f16adcd073beb3e2dd25042efd064af82323/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb", size = 2338793, upload-time = "2024-12-24T18:30:06.25Z" }, - { url = "https://files.pythonhosted.org/packages/d0/dc/c1abe38c37c071d0fc71c9a474fd0b9ede05d42f5a458d584619cfd2371a/kiwisolver-1.4.8-cp313-cp313-win_amd64.whl", hash = "sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2", size = 71855, upload-time = "2024-12-24T18:30:07.535Z" }, - { url = "https://files.pythonhosted.org/packages/a0/b6/21529d595b126ac298fdd90b705d87d4c5693de60023e0efcb4f387ed99e/kiwisolver-1.4.8-cp313-cp313-win_arm64.whl", hash = "sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30", size = 65430, upload-time = "2024-12-24T18:30:08.504Z" }, - { url = "https://files.pythonhosted.org/packages/34/bd/b89380b7298e3af9b39f49334e3e2a4af0e04819789f04b43d560516c0c8/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c", size = 126294, upload-time = "2024-12-24T18:30:09.508Z" }, - { url = "https://files.pythonhosted.org/packages/83/41/5857dc72e5e4148eaac5aa76e0703e594e4465f8ab7ec0fc60e3a9bb8fea/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc", size = 67736, upload-time = "2024-12-24T18:30:11.039Z" }, - { url = "https://files.pythonhosted.org/packages/e1/d1/be059b8db56ac270489fb0b3297fd1e53d195ba76e9bbb30e5401fa6b759/kiwisolver-1.4.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712", size = 66194, upload-time = "2024-12-24T18:30:14.886Z" }, - { url = "https://files.pythonhosted.org/packages/e1/83/4b73975f149819eb7dcf9299ed467eba068ecb16439a98990dcb12e63fdd/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e", size = 1465942, upload-time = "2024-12-24T18:30:18.927Z" }, - { url = "https://files.pythonhosted.org/packages/c7/2c/30a5cdde5102958e602c07466bce058b9d7cb48734aa7a4327261ac8e002/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880", size = 1595341, upload-time = "2024-12-24T18:30:22.102Z" }, - { url = "https://files.pythonhosted.org/packages/ff/9b/1e71db1c000385aa069704f5990574b8244cce854ecd83119c19e83c9586/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062", size = 1598455, upload-time = "2024-12-24T18:30:24.947Z" }, - { url = "https://files.pythonhosted.org/packages/85/92/c8fec52ddf06231b31cbb779af77e99b8253cd96bd135250b9498144c78b/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7", size = 1522138, upload-time = "2024-12-24T18:30:26.286Z" }, - { url = "https://files.pythonhosted.org/packages/0b/51/9eb7e2cd07a15d8bdd976f6190c0164f92ce1904e5c0c79198c4972926b7/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed", size = 1582857, upload-time = "2024-12-24T18:30:28.86Z" }, - { url = "https://files.pythonhosted.org/packages/0f/95/c5a00387a5405e68ba32cc64af65ce881a39b98d73cc394b24143bebc5b8/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d", size = 2293129, upload-time = "2024-12-24T18:30:30.34Z" }, - { url = "https://files.pythonhosted.org/packages/44/83/eeb7af7d706b8347548313fa3a3a15931f404533cc54fe01f39e830dd231/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165", size = 2421538, upload-time = "2024-12-24T18:30:33.334Z" }, - { url = "https://files.pythonhosted.org/packages/05/f9/27e94c1b3eb29e6933b6986ffc5fa1177d2cd1f0c8efc5f02c91c9ac61de/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6", size = 2390661, upload-time = "2024-12-24T18:30:34.939Z" }, - { url = "https://files.pythonhosted.org/packages/d9/d4/3c9735faa36ac591a4afcc2980d2691000506050b7a7e80bcfe44048daa7/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90", size = 2546710, upload-time = "2024-12-24T18:30:37.281Z" }, - { url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213, upload-time = "2024-12-24T18:30:40.019Z" }, -] - -[[package]] -name = "markupsafe" -version = "3.0.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b2/97/5d42485e71dfc078108a86d6de8fa46db44a1a9295e89c5d6d4a06e23a62/markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0", size = 20537, upload-time = "2024-10-18T15:21:54.129Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/83/0e/67eb10a7ecc77a0c2bbe2b0235765b98d164d81600746914bebada795e97/MarkupSafe-3.0.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ba9527cdd4c926ed0760bc301f6728ef34d841f405abf9d4f959c478421e4efd", size = 14274, upload-time = "2024-10-18T15:21:24.577Z" }, - { url = "https://files.pythonhosted.org/packages/2b/6d/9409f3684d3335375d04e5f05744dfe7e9f120062c9857df4ab490a1031a/MarkupSafe-3.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f8b3d067f2e40fe93e1ccdd6b2e1d16c43140e76f02fb1319a05cf2b79d99430", size = 12352, upload-time = "2024-10-18T15:21:25.382Z" }, - { url = "https://files.pythonhosted.org/packages/d2/f5/6eadfcd3885ea85fe2a7c128315cc1bb7241e1987443d78c8fe712d03091/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:569511d3b58c8791ab4c2e1285575265991e6d8f8700c7be0e88f86cb0672094", size = 24122, upload-time = "2024-10-18T15:21:26.199Z" }, - { url = "https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396", size = 23085, upload-time = "2024-10-18T15:21:27.029Z" }, - { url = "https://files.pythonhosted.org/packages/c2/cf/c9d56af24d56ea04daae7ac0940232d31d5a8354f2b457c6d856b2057d69/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3818cb119498c0678015754eba762e0d61e5b52d34c8b13d770f0719f7b1d79", size = 22978, upload-time = "2024-10-18T15:21:27.846Z" }, - { url = "https://files.pythonhosted.org/packages/2a/9f/8619835cd6a711d6272d62abb78c033bda638fdc54c4e7f4272cf1c0962b/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cdb82a876c47801bb54a690c5ae105a46b392ac6099881cdfb9f6e95e4014c6a", size = 24208, upload-time = "2024-10-18T15:21:28.744Z" }, - { url = "https://files.pythonhosted.org/packages/f9/bf/176950a1792b2cd2102b8ffeb5133e1ed984547b75db47c25a67d3359f77/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:cabc348d87e913db6ab4aa100f01b08f481097838bdddf7c7a84b7575b7309ca", size = 23357, upload-time = "2024-10-18T15:21:29.545Z" }, - { url = "https://files.pythonhosted.org/packages/ce/4f/9a02c1d335caabe5c4efb90e1b6e8ee944aa245c1aaaab8e8a618987d816/MarkupSafe-3.0.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:444dcda765c8a838eaae23112db52f1efaf750daddb2d9ca300bcae1039adc5c", size = 23344, upload-time = "2024-10-18T15:21:30.366Z" }, - { url = "https://files.pythonhosted.org/packages/ee/55/c271b57db36f748f0e04a759ace9f8f759ccf22b4960c270c78a394f58be/MarkupSafe-3.0.2-cp313-cp313-win32.whl", hash = "sha256:bcf3e58998965654fdaff38e58584d8937aa3096ab5354d493c77d1fdd66d7a1", size = 15101, upload-time = "2024-10-18T15:21:31.207Z" }, - { url = "https://files.pythonhosted.org/packages/29/88/07df22d2dd4df40aba9f3e402e6dc1b8ee86297dddbad4872bd5e7b0094f/MarkupSafe-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:e6a2a455bd412959b57a172ce6328d2dd1f01cb2135efda2e4576e8a23fa3b0f", size = 15603, upload-time = "2024-10-18T15:21:32.032Z" }, - { url = "https://files.pythonhosted.org/packages/62/6a/8b89d24db2d32d433dffcd6a8779159da109842434f1dd2f6e71f32f738c/MarkupSafe-3.0.2-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:b5a6b3ada725cea8a5e634536b1b01c30bcdcd7f9c6fff4151548d5bf6b3a36c", size = 14510, upload-time = "2024-10-18T15:21:33.625Z" }, - { url = "https://files.pythonhosted.org/packages/7a/06/a10f955f70a2e5a9bf78d11a161029d278eeacbd35ef806c3fd17b13060d/MarkupSafe-3.0.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a904af0a6162c73e3edcb969eeeb53a63ceeb5d8cf642fade7d39e7963a22ddb", size = 12486, upload-time = "2024-10-18T15:21:34.611Z" }, - { url = "https://files.pythonhosted.org/packages/34/cf/65d4a571869a1a9078198ca28f39fba5fbb910f952f9dbc5220afff9f5e6/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4aa4e5faecf353ed117801a068ebab7b7e09ffb6e1d5e412dc852e0da018126c", size = 25480, upload-time = "2024-10-18T15:21:35.398Z" }, - { url = "https://files.pythonhosted.org/packages/0c/e3/90e9651924c430b885468b56b3d597cabf6d72be4b24a0acd1fa0e12af67/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0ef13eaeee5b615fb07c9a7dadb38eac06a0608b41570d8ade51c56539e509d", size = 23914, upload-time = "2024-10-18T15:21:36.231Z" }, - { url = "https://files.pythonhosted.org/packages/66/8c/6c7cf61f95d63bb866db39085150df1f2a5bd3335298f14a66b48e92659c/MarkupSafe-3.0.2-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d16a81a06776313e817c951135cf7340a3e91e8c1ff2fac444cfd75fffa04afe", size = 23796, upload-time = "2024-10-18T15:21:37.073Z" }, - { url = "https://files.pythonhosted.org/packages/bb/35/cbe9238ec3f47ac9a7c8b3df7a808e7cb50fe149dc7039f5f454b3fba218/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6381026f158fdb7c72a168278597a5e3a5222e83ea18f543112b2662a9b699c5", size = 25473, upload-time = "2024-10-18T15:21:37.932Z" }, - { url = "https://files.pythonhosted.org/packages/e6/32/7621a4382488aa283cc05e8984a9c219abad3bca087be9ec77e89939ded9/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:3d79d162e7be8f996986c064d1c7c817f6df3a77fe3d6859f6f9e7be4b8c213a", size = 24114, upload-time = "2024-10-18T15:21:39.799Z" }, - { url = "https://files.pythonhosted.org/packages/0d/80/0985960e4b89922cb5a0bac0ed39c5b96cbc1a536a99f30e8c220a996ed9/MarkupSafe-3.0.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:131a3c7689c85f5ad20f9f6fb1b866f402c445b220c19fe4308c0b147ccd2ad9", size = 24098, upload-time = "2024-10-18T15:21:40.813Z" }, - { url = "https://files.pythonhosted.org/packages/82/78/fedb03c7d5380df2427038ec8d973587e90561b2d90cd472ce9254cf348b/MarkupSafe-3.0.2-cp313-cp313t-win32.whl", hash = "sha256:ba8062ed2cf21c07a9e295d5b8a2a5ce678b913b45fdf68c32d95d6c1291e0b6", size = 15208, upload-time = "2024-10-18T15:21:41.814Z" }, - { url = "https://files.pythonhosted.org/packages/4f/65/6079a46068dfceaeabb5dcad6d674f5f5c61a6fa5673746f42a9f4c233b3/MarkupSafe-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:e444a31f8db13eb18ada366ab3cf45fd4b31e4db1236a4448f68778c1d1a5a2f", size = 15739, upload-time = "2024-10-18T15:21:42.784Z" }, -] - -[[package]] -name = "matplotlib" -version = "3.10.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "contourpy" }, - { name = "cycler" }, - { name = "fonttools" }, - { name = "kiwisolver" }, - { name = "numpy" }, - { name = "packaging" }, - { name = "pillow" }, - { name = "pyparsing" }, - { name = "python-dateutil" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/2f/08/b89867ecea2e305f408fbb417139a8dd941ecf7b23a2e02157c36da546f0/matplotlib-3.10.1.tar.gz", hash = "sha256:e8d2d0e3881b129268585bf4765ad3ee73a4591d77b9a18c214ac7e3a79fb2ba", size = 36743335, upload-time = "2025-02-27T19:19:51.038Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/60/73/6770ff5e5523d00f3bc584acb6031e29ee5c8adc2336b16cd1d003675fe0/matplotlib-3.10.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c42eee41e1b60fd83ee3292ed83a97a5f2a8239b10c26715d8a6172226988d7b", size = 8176112, upload-time = "2025-02-27T19:19:07.59Z" }, - { url = "https://files.pythonhosted.org/packages/08/97/b0ca5da0ed54a3f6599c3ab568bdda65269bc27c21a2c97868c1625e4554/matplotlib-3.10.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4f0647b17b667ae745c13721602b540f7aadb2a32c5b96e924cd4fea5dcb90f1", size = 8046931, upload-time = "2025-02-27T19:19:10.515Z" }, - { url = "https://files.pythonhosted.org/packages/df/9a/1acbdc3b165d4ce2dcd2b1a6d4ffb46a7220ceee960c922c3d50d8514067/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa3854b5f9473564ef40a41bc922be978fab217776e9ae1545c9b3a5cf2092a3", size = 8453422, upload-time = "2025-02-27T19:19:12.738Z" }, - { url = "https://files.pythonhosted.org/packages/51/d0/2bc4368abf766203e548dc7ab57cf7e9c621f1a3c72b516cc7715347b179/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e496c01441be4c7d5f96d4e40f7fca06e20dcb40e44c8daa2e740e1757ad9e6", size = 8596819, upload-time = "2025-02-27T19:19:15.306Z" }, - { url = "https://files.pythonhosted.org/packages/ab/1b/8b350f8a1746c37ab69dda7d7528d1fc696efb06db6ade9727b7887be16d/matplotlib-3.10.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5d45d3f5245be5b469843450617dcad9af75ca50568acf59997bed9311131a0b", size = 9402782, upload-time = "2025-02-27T19:19:17.841Z" }, - { url = "https://files.pythonhosted.org/packages/89/06/f570373d24d93503988ba8d04f213a372fa1ce48381c5eb15da985728498/matplotlib-3.10.1-cp313-cp313-win_amd64.whl", hash = "sha256:8e8e25b1209161d20dfe93037c8a7f7ca796ec9aa326e6e4588d8c4a5dd1e473", size = 8063812, upload-time = "2025-02-27T19:19:20.888Z" }, - { url = "https://files.pythonhosted.org/packages/fc/e0/8c811a925b5a7ad75135f0e5af46408b78af88bbb02a1df775100ef9bfef/matplotlib-3.10.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:19b06241ad89c3ae9469e07d77efa87041eac65d78df4fcf9cac318028009b01", size = 8214021, upload-time = "2025-02-27T19:19:23.412Z" }, - { url = "https://files.pythonhosted.org/packages/4a/34/319ec2139f68ba26da9d00fce2ff9f27679fb799a6c8e7358539801fd629/matplotlib-3.10.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:01e63101ebb3014e6e9f80d9cf9ee361a8599ddca2c3e166c563628b39305dbb", size = 8090782, upload-time = "2025-02-27T19:19:28.33Z" }, - { url = "https://files.pythonhosted.org/packages/77/ea/9812124ab9a99df5b2eec1110e9b2edc0b8f77039abf4c56e0a376e84a29/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f06bad951eea6422ac4e8bdebcf3a70c59ea0a03338c5d2b109f57b64eb3972", size = 8478901, upload-time = "2025-02-27T19:19:31.536Z" }, - { url = "https://files.pythonhosted.org/packages/c9/db/b05bf463689134789b06dea85828f8ebe506fa1e37593f723b65b86c9582/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3dfb036f34873b46978f55e240cff7a239f6c4409eac62d8145bad3fc6ba5a3", size = 8613864, upload-time = "2025-02-27T19:19:34.233Z" }, - { url = "https://files.pythonhosted.org/packages/c2/04/41ccec4409f3023a7576df3b5c025f1a8c8b81fbfe922ecfd837ac36e081/matplotlib-3.10.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dc6ab14a7ab3b4d813b88ba957fc05c79493a037f54e246162033591e770de6f", size = 9409487, upload-time = "2025-02-27T19:19:36.924Z" }, - { url = "https://files.pythonhosted.org/packages/ac/c2/0d5aae823bdcc42cc99327ecdd4d28585e15ccd5218c453b7bcd827f3421/matplotlib-3.10.1-cp313-cp313t-win_amd64.whl", hash = "sha256:bc411ebd5889a78dabbc457b3fa153203e22248bfa6eedc6797be5df0164dbf9", size = 8134832, upload-time = "2025-02-27T19:19:39.431Z" }, -] - -[[package]] -name = "matplotlib-inline" -version = "0.1.7" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "traitlets" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/99/5b/a36a337438a14116b16480db471ad061c36c3694df7c2084a0da7ba538b7/matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90", size = 8159, upload-time = "2024-04-15T13:44:44.803Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899, upload-time = "2024-04-15T13:44:43.265Z" }, -] - -[[package]] -name = "mpmath" -version = "1.3.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, -] - -[[package]] -name = "multidict" -version = "6.4.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/da/2c/e367dfb4c6538614a0c9453e510d75d66099edf1c4e69da1b5ce691a1931/multidict-6.4.3.tar.gz", hash = "sha256:3ada0b058c9f213c5f95ba301f922d402ac234f1111a7d8fd70f1b99f3c281ec", size = 89372, upload-time = "2025-04-10T22:20:17.956Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6c/4b/86fd786d03915c6f49998cf10cd5fe6b6ac9e9a071cb40885d2e080fb90d/multidict-6.4.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:7a76534263d03ae0cfa721fea40fd2b5b9d17a6f85e98025931d41dc49504474", size = 63831, upload-time = "2025-04-10T22:18:48.748Z" }, - { url = "https://files.pythonhosted.org/packages/45/05/9b51fdf7aef2563340a93be0a663acba2c428c4daeaf3960d92d53a4a930/multidict-6.4.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:805031c2f599eee62ac579843555ed1ce389ae00c7e9f74c2a1b45e0564a88dd", size = 37888, upload-time = "2025-04-10T22:18:50.021Z" }, - { url = "https://files.pythonhosted.org/packages/0b/43/53fc25394386c911822419b522181227ca450cf57fea76e6188772a1bd91/multidict-6.4.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c56c179839d5dcf51d565132185409d1d5dd8e614ba501eb79023a6cab25576b", size = 36852, upload-time = "2025-04-10T22:18:51.246Z" }, - { url = "https://files.pythonhosted.org/packages/8a/68/7b99c751e822467c94a235b810a2fd4047d4ecb91caef6b5c60116991c4b/multidict-6.4.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c64f4ddb3886dd8ab71b68a7431ad4aa01a8fa5be5b11543b29674f29ca0ba3", size = 223644, upload-time = "2025-04-10T22:18:52.965Z" }, - { url = "https://files.pythonhosted.org/packages/80/1b/d458d791e4dd0f7e92596667784fbf99e5c8ba040affe1ca04f06b93ae92/multidict-6.4.3-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3002a856367c0b41cad6784f5b8d3ab008eda194ed7864aaa58f65312e2abcac", size = 230446, upload-time = "2025-04-10T22:18:54.509Z" }, - { url = "https://files.pythonhosted.org/packages/e2/46/9793378d988905491a7806d8987862dc5a0bae8a622dd896c4008c7b226b/multidict-6.4.3-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3d75e621e7d887d539d6e1d789f0c64271c250276c333480a9e1de089611f790", size = 231070, upload-time = "2025-04-10T22:18:56.019Z" }, - { url = "https://files.pythonhosted.org/packages/a7/b8/b127d3e1f8dd2a5bf286b47b24567ae6363017292dc6dec44656e6246498/multidict-6.4.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:995015cf4a3c0d72cbf453b10a999b92c5629eaf3a0c3e1efb4b5c1f602253bb", size = 229956, upload-time = "2025-04-10T22:18:59.146Z" }, - { url = "https://files.pythonhosted.org/packages/0c/93/f70a4c35b103fcfe1443059a2bb7f66e5c35f2aea7804105ff214f566009/multidict-6.4.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2b0fabae7939d09d7d16a711468c385272fa1b9b7fb0d37e51143585d8e72e0", size = 222599, upload-time = "2025-04-10T22:19:00.657Z" }, - { url = "https://files.pythonhosted.org/packages/63/8c/e28e0eb2fe34921d6aa32bfc4ac75b09570b4d6818cc95d25499fe08dc1d/multidict-6.4.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:61ed4d82f8a1e67eb9eb04f8587970d78fe7cddb4e4d6230b77eda23d27938f9", size = 216136, upload-time = "2025-04-10T22:19:02.244Z" }, - { url = "https://files.pythonhosted.org/packages/72/f5/fbc81f866585b05f89f99d108be5d6ad170e3b6c4d0723d1a2f6ba5fa918/multidict-6.4.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:062428944a8dc69df9fdc5d5fc6279421e5f9c75a9ee3f586f274ba7b05ab3c8", size = 228139, upload-time = "2025-04-10T22:19:04.151Z" }, - { url = "https://files.pythonhosted.org/packages/bb/ba/7d196bad6b85af2307d81f6979c36ed9665f49626f66d883d6c64d156f78/multidict-6.4.3-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:b90e27b4674e6c405ad6c64e515a505c6d113b832df52fdacb6b1ffd1fa9a1d1", size = 226251, upload-time = "2025-04-10T22:19:06.117Z" }, - { url = "https://files.pythonhosted.org/packages/cc/e2/fae46a370dce79d08b672422a33df721ec8b80105e0ea8d87215ff6b090d/multidict-6.4.3-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:7d50d4abf6729921e9613d98344b74241572b751c6b37feed75fb0c37bd5a817", size = 221868, upload-time = "2025-04-10T22:19:07.981Z" }, - { url = "https://files.pythonhosted.org/packages/26/20/bbc9a3dec19d5492f54a167f08546656e7aef75d181d3d82541463450e88/multidict-6.4.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:43fe10524fb0a0514be3954be53258e61d87341008ce4914f8e8b92bee6f875d", size = 233106, upload-time = "2025-04-10T22:19:09.5Z" }, - { url = "https://files.pythonhosted.org/packages/ee/8d/f30ae8f5ff7a2461177f4d8eb0d8f69f27fb6cfe276b54ec4fd5a282d918/multidict-6.4.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:236966ca6c472ea4e2d3f02f6673ebfd36ba3f23159c323f5a496869bc8e47c9", size = 230163, upload-time = "2025-04-10T22:19:11Z" }, - { url = "https://files.pythonhosted.org/packages/15/e9/2833f3c218d3c2179f3093f766940ded6b81a49d2e2f9c46ab240d23dfec/multidict-6.4.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:422a5ec315018e606473ba1f5431e064cf8b2a7468019233dcf8082fabad64c8", size = 225906, upload-time = "2025-04-10T22:19:12.875Z" }, - { url = "https://files.pythonhosted.org/packages/f1/31/6edab296ac369fd286b845fa5dd4c409e63bc4655ed8c9510fcb477e9ae9/multidict-6.4.3-cp313-cp313-win32.whl", hash = "sha256:f901a5aace8e8c25d78960dcc24c870c8d356660d3b49b93a78bf38eb682aac3", size = 35238, upload-time = "2025-04-10T22:19:14.41Z" }, - { url = "https://files.pythonhosted.org/packages/23/57/2c0167a1bffa30d9a1383c3dab99d8caae985defc8636934b5668830d2ef/multidict-6.4.3-cp313-cp313-win_amd64.whl", hash = "sha256:1c152c49e42277bc9a2f7b78bd5fa10b13e88d1b0328221e7aef89d5c60a99a5", size = 38799, upload-time = "2025-04-10T22:19:15.869Z" }, - { url = "https://files.pythonhosted.org/packages/c9/13/2ead63b9ab0d2b3080819268acb297bd66e238070aa8d42af12b08cbee1c/multidict-6.4.3-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:be8751869e28b9c0d368d94f5afcb4234db66fe8496144547b4b6d6a0645cfc6", size = 68642, upload-time = "2025-04-10T22:19:17.527Z" }, - { url = "https://files.pythonhosted.org/packages/85/45/f1a751e1eede30c23951e2ae274ce8fad738e8a3d5714be73e0a41b27b16/multidict-6.4.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0d4b31f8a68dccbcd2c0ea04f0e014f1defc6b78f0eb8b35f2265e8716a6df0c", size = 40028, upload-time = "2025-04-10T22:19:19.465Z" }, - { url = "https://files.pythonhosted.org/packages/a7/29/fcc53e886a2cc5595cc4560df333cb9630257bda65003a7eb4e4e0d8f9c1/multidict-6.4.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:032efeab3049e37eef2ff91271884303becc9e54d740b492a93b7e7266e23756", size = 39424, upload-time = "2025-04-10T22:19:20.762Z" }, - { url = "https://files.pythonhosted.org/packages/f6/f0/056c81119d8b88703971f937b371795cab1407cd3c751482de5bfe1a04a9/multidict-6.4.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9e78006af1a7c8a8007e4f56629d7252668344442f66982368ac06522445e375", size = 226178, upload-time = "2025-04-10T22:19:22.17Z" }, - { url = "https://files.pythonhosted.org/packages/a3/79/3b7e5fea0aa80583d3a69c9d98b7913dfd4fbc341fb10bb2fb48d35a9c21/multidict-6.4.3-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:daeac9dd30cda8703c417e4fddccd7c4dc0c73421a0b54a7da2713be125846be", size = 222617, upload-time = "2025-04-10T22:19:23.773Z" }, - { url = "https://files.pythonhosted.org/packages/06/db/3ed012b163e376fc461e1d6a67de69b408339bc31dc83d39ae9ec3bf9578/multidict-6.4.3-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1f6f90700881438953eae443a9c6f8a509808bc3b185246992c4233ccee37fea", size = 227919, upload-time = "2025-04-10T22:19:25.35Z" }, - { url = "https://files.pythonhosted.org/packages/b1/db/0433c104bca380989bc04d3b841fc83e95ce0c89f680e9ea4251118b52b6/multidict-6.4.3-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f84627997008390dd15762128dcf73c3365f4ec0106739cde6c20a07ed198ec8", size = 226097, upload-time = "2025-04-10T22:19:27.183Z" }, - { url = "https://files.pythonhosted.org/packages/c2/95/910db2618175724dd254b7ae635b6cd8d2947a8b76b0376de7b96d814dab/multidict-6.4.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3307b48cd156153b117c0ea54890a3bdbf858a5b296ddd40dc3852e5f16e9b02", size = 220706, upload-time = "2025-04-10T22:19:28.882Z" }, - { url = "https://files.pythonhosted.org/packages/d1/af/aa176c6f5f1d901aac957d5258d5e22897fe13948d1e69063ae3d5d0ca01/multidict-6.4.3-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ead46b0fa1dcf5af503a46e9f1c2e80b5d95c6011526352fa5f42ea201526124", size = 211728, upload-time = "2025-04-10T22:19:30.481Z" }, - { url = "https://files.pythonhosted.org/packages/e7/42/d51cc5fc1527c3717d7f85137d6c79bb7a93cd214c26f1fc57523774dbb5/multidict-6.4.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:1748cb2743bedc339d63eb1bca314061568793acd603a6e37b09a326334c9f44", size = 226276, upload-time = "2025-04-10T22:19:32.454Z" }, - { url = "https://files.pythonhosted.org/packages/28/6b/d836dea45e0b8432343ba4acf9a8ecaa245da4c0960fb7ab45088a5e568a/multidict-6.4.3-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:acc9fa606f76fc111b4569348cc23a771cb52c61516dcc6bcef46d612edb483b", size = 212069, upload-time = "2025-04-10T22:19:34.17Z" }, - { url = "https://files.pythonhosted.org/packages/55/34/0ee1a7adb3560e18ee9289c6e5f7db54edc312b13e5c8263e88ea373d12c/multidict-6.4.3-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:31469d5832b5885adeb70982e531ce86f8c992334edd2f2254a10fa3182ac504", size = 217858, upload-time = "2025-04-10T22:19:35.879Z" }, - { url = "https://files.pythonhosted.org/packages/04/08/586d652c2f5acefe0cf4e658eedb4d71d4ba6dfd4f189bd81b400fc1bc6b/multidict-6.4.3-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:ba46b51b6e51b4ef7bfb84b82f5db0dc5e300fb222a8a13b8cd4111898a869cf", size = 226988, upload-time = "2025-04-10T22:19:37.434Z" }, - { url = "https://files.pythonhosted.org/packages/82/e3/cc59c7e2bc49d7f906fb4ffb6d9c3a3cf21b9f2dd9c96d05bef89c2b1fd1/multidict-6.4.3-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:389cfefb599edf3fcfd5f64c0410da686f90f5f5e2c4d84e14f6797a5a337af4", size = 220435, upload-time = "2025-04-10T22:19:39.005Z" }, - { url = "https://files.pythonhosted.org/packages/e0/32/5c3a556118aca9981d883f38c4b1bfae646f3627157f70f4068e5a648955/multidict-6.4.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:64bc2bbc5fba7b9db5c2c8d750824f41c6994e3882e6d73c903c2afa78d091e4", size = 221494, upload-time = "2025-04-10T22:19:41.447Z" }, - { url = "https://files.pythonhosted.org/packages/b9/3b/1599631f59024b75c4d6e3069f4502409970a336647502aaf6b62fb7ac98/multidict-6.4.3-cp313-cp313t-win32.whl", hash = "sha256:0ecdc12ea44bab2807d6b4a7e5eef25109ab1c82a8240d86d3c1fc9f3b72efd5", size = 41775, upload-time = "2025-04-10T22:19:43.707Z" }, - { url = "https://files.pythonhosted.org/packages/e8/4e/09301668d675d02ca8e8e1a3e6be046619e30403f5ada2ed5b080ae28d02/multidict-6.4.3-cp313-cp313t-win_amd64.whl", hash = "sha256:7146a8742ea71b5d7d955bffcef58a9e6e04efba704b52a460134fefd10a8208", size = 45946, upload-time = "2025-04-10T22:19:45.071Z" }, - { url = "https://files.pythonhosted.org/packages/96/10/7d526c8974f017f1e7ca584c71ee62a638e9334d8d33f27d7cdfc9ae79e4/multidict-6.4.3-py3-none-any.whl", hash = "sha256:59fe01ee8e2a1e8ceb3f6dbb216b09c8d9f4ef1c22c4fc825d045a147fa2ebc9", size = 10400, upload-time = "2025-04-10T22:20:16.445Z" }, -] - -[[package]] -name = "nest-asyncio" -version = "1.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" }, -] - -[[package]] -name = "networkx" -version = "3.4.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/fd/1d/06475e1cd5264c0b870ea2cc6fdb3e37177c1e565c43f56ff17a10e3937f/networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1", size = 2151368, upload-time = "2024-10-21T12:39:38.695Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f", size = 1723263, upload-time = "2024-10-21T12:39:36.247Z" }, -] - -[[package]] -name = "numpy" -version = "2.2.5" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/dc/b2/ce4b867d8cd9c0ee84938ae1e6a6f7926ebf928c9090d036fc3c6a04f946/numpy-2.2.5.tar.gz", hash = "sha256:a9c0d994680cd991b1cb772e8b297340085466a6fe964bc9d4e80f5e2f43c291", size = 20273920, upload-time = "2025-04-19T23:27:42.561Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e2/a0/0aa7f0f4509a2e07bd7a509042967c2fab635690d4f48c6c7b3afd4f448c/numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:059b51b658f4414fff78c6d7b1b4e18283ab5fa56d270ff212d5ba0c561846f4", size = 20935102, upload-time = "2025-04-19T22:41:16.234Z" }, - { url = "https://files.pythonhosted.org/packages/7e/e4/a6a9f4537542912ec513185396fce52cdd45bdcf3e9d921ab02a93ca5aa9/numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:47f9ed103af0bc63182609044b0490747e03bd20a67e391192dde119bf43d52f", size = 14191709, upload-time = "2025-04-19T22:41:38.472Z" }, - { url = "https://files.pythonhosted.org/packages/be/65/72f3186b6050bbfe9c43cb81f9df59ae63603491d36179cf7a7c8d216758/numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:261a1ef047751bb02f29dfe337230b5882b54521ca121fc7f62668133cb119c9", size = 5149173, upload-time = "2025-04-19T22:41:47.823Z" }, - { url = "https://files.pythonhosted.org/packages/e5/e9/83e7a9432378dde5802651307ae5e9ea07bb72b416728202218cd4da2801/numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:4520caa3807c1ceb005d125a75e715567806fed67e315cea619d5ec6e75a4191", size = 6684502, upload-time = "2025-04-19T22:41:58.689Z" }, - { url = "https://files.pythonhosted.org/packages/ea/27/b80da6c762394c8ee516b74c1f686fcd16c8f23b14de57ba0cad7349d1d2/numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d14b17b9be5f9c9301f43d2e2a4886a33b53f4e6fdf9ca2f4cc60aeeee76372", size = 14084417, upload-time = "2025-04-19T22:42:19.897Z" }, - { url = "https://files.pythonhosted.org/packages/aa/fc/ebfd32c3e124e6a1043e19c0ab0769818aa69050ce5589b63d05ff185526/numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ba321813a00e508d5421104464510cc962a6f791aa2fca1c97b1e65027da80d", size = 16133807, upload-time = "2025-04-19T22:42:44.433Z" }, - { url = "https://files.pythonhosted.org/packages/bf/9b/4cc171a0acbe4666f7775cfd21d4eb6bb1d36d3a0431f48a73e9212d2278/numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4cbdef3ddf777423060c6f81b5694bad2dc9675f110c4b2a60dc0181543fac7", size = 15575611, upload-time = "2025-04-19T22:43:09.928Z" }, - { url = "https://files.pythonhosted.org/packages/a3/45/40f4135341850df48f8edcf949cf47b523c404b712774f8855a64c96ef29/numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:54088a5a147ab71a8e7fdfd8c3601972751ded0739c6b696ad9cb0343e21ab73", size = 17895747, upload-time = "2025-04-19T22:43:36.983Z" }, - { url = "https://files.pythonhosted.org/packages/f8/4c/b32a17a46f0ffbde8cc82df6d3daeaf4f552e346df143e1b188a701a8f09/numpy-2.2.5-cp313-cp313-win32.whl", hash = "sha256:c8b82a55ef86a2d8e81b63da85e55f5537d2157165be1cb2ce7cfa57b6aef38b", size = 6309594, upload-time = "2025-04-19T22:47:10.523Z" }, - { url = "https://files.pythonhosted.org/packages/13/ae/72e6276feb9ef06787365b05915bfdb057d01fceb4a43cb80978e518d79b/numpy-2.2.5-cp313-cp313-win_amd64.whl", hash = "sha256:d8882a829fd779f0f43998e931c466802a77ca1ee0fe25a3abe50278616b1471", size = 12638356, upload-time = "2025-04-19T22:47:30.253Z" }, - { url = "https://files.pythonhosted.org/packages/79/56/be8b85a9f2adb688e7ded6324e20149a03541d2b3297c3ffc1a73f46dedb/numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:e8b025c351b9f0e8b5436cf28a07fa4ac0204d67b38f01433ac7f9b870fa38c6", size = 20963778, upload-time = "2025-04-19T22:44:09.251Z" }, - { url = "https://files.pythonhosted.org/packages/ff/77/19c5e62d55bff507a18c3cdff82e94fe174957bad25860a991cac719d3ab/numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8dfa94b6a4374e7851bbb6f35e6ded2120b752b063e6acdd3157e4d2bb922eba", size = 14207279, upload-time = "2025-04-19T22:44:31.383Z" }, - { url = "https://files.pythonhosted.org/packages/75/22/aa11f22dc11ff4ffe4e849d9b63bbe8d4ac6d5fae85ddaa67dfe43be3e76/numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:97c8425d4e26437e65e1d189d22dff4a079b747ff9c2788057bfb8114ce1e133", size = 5199247, upload-time = "2025-04-19T22:44:40.361Z" }, - { url = "https://files.pythonhosted.org/packages/4f/6c/12d5e760fc62c08eded0394f62039f5a9857f758312bf01632a81d841459/numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:352d330048c055ea6db701130abc48a21bec690a8d38f8284e00fab256dc1376", size = 6711087, upload-time = "2025-04-19T22:44:51.188Z" }, - { url = "https://files.pythonhosted.org/packages/ef/94/ece8280cf4218b2bee5cec9567629e61e51b4be501e5c6840ceb593db945/numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b4c0773b6ada798f51f0f8e30c054d32304ccc6e9c5d93d46cb26f3d385ab19", size = 14059964, upload-time = "2025-04-19T22:45:12.451Z" }, - { url = "https://files.pythonhosted.org/packages/39/41/c5377dac0514aaeec69115830a39d905b1882819c8e65d97fc60e177e19e/numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:55f09e00d4dccd76b179c0f18a44f041e5332fd0e022886ba1c0bbf3ea4a18d0", size = 16121214, upload-time = "2025-04-19T22:45:37.734Z" }, - { url = "https://files.pythonhosted.org/packages/db/54/3b9f89a943257bc8e187145c6bc0eb8e3d615655f7b14e9b490b053e8149/numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:02f226baeefa68f7d579e213d0f3493496397d8f1cff5e2b222af274c86a552a", size = 15575788, upload-time = "2025-04-19T22:46:01.908Z" }, - { url = "https://files.pythonhosted.org/packages/b1/c4/2e407e85df35b29f79945751b8f8e671057a13a376497d7fb2151ba0d290/numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c26843fd58f65da9491165072da2cccc372530681de481ef670dcc8e27cfb066", size = 17893672, upload-time = "2025-04-19T22:46:28.585Z" }, - { url = "https://files.pythonhosted.org/packages/29/7e/d0b44e129d038dba453f00d0e29ebd6eaf2f06055d72b95b9947998aca14/numpy-2.2.5-cp313-cp313t-win32.whl", hash = "sha256:1a161c2c79ab30fe4501d5a2bbfe8b162490757cf90b7f05be8b80bc02f7bb8e", size = 6377102, upload-time = "2025-04-19T22:46:39.949Z" }, - { url = "https://files.pythonhosted.org/packages/63/be/b85e4aa4bf42c6502851b971f1c326d583fcc68227385f92089cf50a7b45/numpy-2.2.5-cp313-cp313t-win_amd64.whl", hash = "sha256:d403c84991b5ad291d3809bace5e85f4bbf44a04bdc9a88ed2bb1807b3360bb8", size = 12750096, upload-time = "2025-04-19T22:47:00.147Z" }, -] - -[[package]] -name = "nvidia-cublas-cu12" -version = "12.6.4.1" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/af/eb/ff4b8c503fa1f1796679dce648854d58751982426e4e4b37d6fce49d259c/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08ed2686e9875d01b58e3cb379c6896df8e76c75e0d4a7f7dace3d7b6d9ef8eb", size = 393138322, upload-time = "2024-11-20T17:40:25.65Z" }, -] - -[[package]] -name = "nvidia-cuda-cupti-cu12" -version = "12.6.80" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/49/60/7b6497946d74bcf1de852a21824d63baad12cd417db4195fc1bfe59db953/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6768bad6cab4f19e8292125e5f1ac8aa7d1718704012a0e3272a6f61c4bce132", size = 8917980, upload-time = "2024-11-20T17:36:04.019Z" }, - { url = "https://files.pythonhosted.org/packages/a5/24/120ee57b218d9952c379d1e026c4479c9ece9997a4fb46303611ee48f038/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a3eff6cdfcc6a4c35db968a06fcadb061cbc7d6dde548609a941ff8701b98b73", size = 8917972, upload-time = "2024-10-01T16:58:06.036Z" }, -] - -[[package]] -name = "nvidia-cuda-nvrtc-cu12" -version = "12.6.77" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/75/2e/46030320b5a80661e88039f59060d1790298b4718944a65a7f2aeda3d9e9/nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:35b0cc6ee3a9636d5409133e79273ce1f3fd087abb0532d2d2e8fff1fe9efc53", size = 23650380, upload-time = "2024-10-01T17:00:14.643Z" }, -] - -[[package]] -name = "nvidia-cuda-runtime-cu12" -version = "12.6.77" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e1/23/e717c5ac26d26cf39a27fbc076240fad2e3b817e5889d671b67f4f9f49c5/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ba3b56a4f896141e25e19ab287cd71e52a6a0f4b29d0d31609f60e3b4d5219b7", size = 897690, upload-time = "2024-11-20T17:35:30.697Z" }, - { url = "https://files.pythonhosted.org/packages/f0/62/65c05e161eeddbafeca24dc461f47de550d9fa8a7e04eb213e32b55cfd99/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a84d15d5e1da416dd4774cb42edf5e954a3e60cc945698dc1d5be02321c44dc8", size = 897678, upload-time = "2024-10-01T16:57:33.821Z" }, -] - -[[package]] -name = "nvidia-cudnn-cu12" -version = "9.5.1.17" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "nvidia-cublas-cu12" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/2a/78/4535c9c7f859a64781e43c969a3a7e84c54634e319a996d43ef32ce46f83/nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:30ac3869f6db17d170e0e556dd6cc5eee02647abc31ca856634d5a40f82c15b2", size = 570988386, upload-time = "2024-10-25T19:54:26.39Z" }, -] - -[[package]] -name = "nvidia-cufft-cu12" -version = "11.3.0.4" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/8f/16/73727675941ab8e6ffd86ca3a4b7b47065edcca7a997920b831f8147c99d/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ccba62eb9cef5559abd5e0d54ceed2d9934030f51163df018532142a8ec533e5", size = 200221632, upload-time = "2024-11-20T17:41:32.357Z" }, - { url = "https://files.pythonhosted.org/packages/60/de/99ec247a07ea40c969d904fc14f3a356b3e2a704121675b75c366b694ee1/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.whl", hash = "sha256:768160ac89f6f7b459bee747e8d175dbf53619cfe74b2a5636264163138013ca", size = 200221622, upload-time = "2024-10-01T17:03:58.79Z" }, -] - -[[package]] -name = "nvidia-cufile-cu12" -version = "1.11.1.6" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b2/66/cc9876340ac68ae71b15c743ddb13f8b30d5244af344ec8322b449e35426/nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc23469d1c7e52ce6c1d55253273d32c565dd22068647f3aa59b3c6b005bf159", size = 1142103, upload-time = "2024-11-20T17:42:11.83Z" }, -] - -[[package]] -name = "nvidia-curand-cu12" -version = "10.3.7.77" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/73/1b/44a01c4e70933637c93e6e1a8063d1e998b50213a6b65ac5a9169c47e98e/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a42cd1344297f70b9e39a1e4f467a4e1c10f1da54ff7a85c12197f6c652c8bdf", size = 56279010, upload-time = "2024-11-20T17:42:50.958Z" }, - { url = "https://files.pythonhosted.org/packages/4a/aa/2c7ff0b5ee02eaef890c0ce7d4f74bc30901871c5e45dee1ae6d0083cd80/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:99f1a32f1ac2bd134897fc7a203f779303261268a65762a623bf30cc9fe79117", size = 56279000, upload-time = "2024-10-01T17:04:45.274Z" }, -] - -[[package]] -name = "nvidia-cusolver-cu12" -version = "11.7.1.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "nvidia-cublas-cu12" }, - { name = "nvidia-cusparse-cu12" }, - { name = "nvidia-nvjitlink-cu12" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/f0/6e/c2cf12c9ff8b872e92b4a5740701e51ff17689c4d726fca91875b07f655d/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e9e49843a7707e42022babb9bcfa33c29857a93b88020c4e4434656a655b698c", size = 158229790, upload-time = "2024-11-20T17:43:43.211Z" }, - { url = "https://files.pythonhosted.org/packages/9f/81/baba53585da791d043c10084cf9553e074548408e04ae884cfe9193bd484/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6cf28f17f64107a0c4d7802be5ff5537b2130bfc112f25d5a30df227058ca0e6", size = 158229780, upload-time = "2024-10-01T17:05:39.875Z" }, -] - -[[package]] -name = "nvidia-cusparse-cu12" -version = "12.5.4.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/06/1e/b8b7c2f4099a37b96af5c9bb158632ea9e5d9d27d7391d7eb8fc45236674/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7556d9eca156e18184b94947ade0fba5bb47d69cec46bf8660fd2c71a4b48b73", size = 216561367, upload-time = "2024-11-20T17:44:54.824Z" }, - { url = "https://files.pythonhosted.org/packages/43/ac/64c4316ba163e8217a99680c7605f779accffc6a4bcd0c778c12948d3707/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:23749a6571191a215cb74d1cdbff4a86e7b19f1200c071b3fcf844a5bea23a2f", size = 216561357, upload-time = "2024-10-01T17:06:29.861Z" }, -] - -[[package]] -name = "nvidia-cusparselt-cu12" -version = "0.6.3" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/3b/9a/72ef35b399b0e183bc2e8f6f558036922d453c4d8237dab26c666a04244b/nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:e5c8a26c36445dd2e6812f1177978a24e2d37cacce7e090f297a688d1ec44f46", size = 156785796, upload-time = "2024-10-15T21:29:17.709Z" }, -] - -[[package]] -name = "nvidia-nccl-cu12" -version = "2.26.2" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/67/ca/f42388aed0fddd64ade7493dbba36e1f534d4e6fdbdd355c6a90030ae028/nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:694cf3879a206553cc9d7dbda76b13efaf610fdb70a50cba303de1b0d1530ac6", size = 201319755, upload-time = "2025-03-13T00:29:55.296Z" }, -] - -[[package]] -name = "nvidia-nvjitlink-cu12" -version = "12.6.85" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9d/d7/c5383e47c7e9bf1c99d5bd2a8c935af2b6d705ad831a7ec5c97db4d82f4f/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a", size = 19744971, upload-time = "2024-11-20T17:46:53.366Z" }, -] - -[[package]] -name = "nvidia-nvtx-cu12" -version = "12.6.77" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/56/9a/fff8376f8e3d084cd1530e1ef7b879bb7d6d265620c95c1b322725c694f4/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b90bed3df379fa79afbd21be8e04a0314336b8ae16768b58f2d34cb1d04cd7d2", size = 89276, upload-time = "2024-11-20T17:38:27.621Z" }, - { url = "https://files.pythonhosted.org/packages/9e/4e/0d0c945463719429b7bd21dece907ad0bde437a2ff12b9b12fee94722ab0/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6574241a3ec5fdc9334353ab8c479fe75841dbe8f4532a8fc97ce63503330ba1", size = 89265, upload-time = "2024-10-01T17:00:38.172Z" }, -] - -[[package]] -name = "packaging" -version = "25.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" }, -] - -[[package]] -name = "pandas" -version = "2.2.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, - { name = "python-dateutil" }, - { name = "pytz" }, - { name = "tzdata" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/9c/d6/9f8431bacc2e19dca897724cd097b1bb224a6ad5433784a44b587c7c13af/pandas-2.2.3.tar.gz", hash = "sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667", size = 4399213, upload-time = "2024-09-20T13:10:04.827Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/64/22/3b8f4e0ed70644e85cfdcd57454686b9057c6c38d2f74fe4b8bc2527214a/pandas-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015", size = 12477643, upload-time = "2024-09-20T13:09:25.522Z" }, - { url = "https://files.pythonhosted.org/packages/e4/93/b3f5d1838500e22c8d793625da672f3eec046b1a99257666c94446969282/pandas-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28", size = 11281573, upload-time = "2024-09-20T13:09:28.012Z" }, - { url = "https://files.pythonhosted.org/packages/f5/94/6c79b07f0e5aab1dcfa35a75f4817f5c4f677931d4234afcd75f0e6a66ca/pandas-2.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0", size = 15196085, upload-time = "2024-09-20T19:02:10.451Z" }, - { url = "https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24", size = 12711809, upload-time = "2024-09-20T13:09:30.814Z" }, - { url = "https://files.pythonhosted.org/packages/ee/7c/c6dbdb0cb2a4344cacfb8de1c5808ca885b2e4dcfde8008266608f9372af/pandas-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659", size = 16356316, upload-time = "2024-09-20T19:02:13.825Z" }, - { url = "https://files.pythonhosted.org/packages/57/b7/8b757e7d92023b832869fa8881a992696a0bfe2e26f72c9ae9f255988d42/pandas-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb", size = 14022055, upload-time = "2024-09-20T13:09:33.462Z" }, - { url = "https://files.pythonhosted.org/packages/3b/bc/4b18e2b8c002572c5a441a64826252ce5da2aa738855747247a971988043/pandas-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d", size = 11481175, upload-time = "2024-09-20T13:09:35.871Z" }, - { url = "https://files.pythonhosted.org/packages/76/a3/a5d88146815e972d40d19247b2c162e88213ef51c7c25993942c39dbf41d/pandas-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468", size = 12615650, upload-time = "2024-09-20T13:09:38.685Z" }, - { url = "https://files.pythonhosted.org/packages/9c/8c/f0fd18f6140ddafc0c24122c8a964e48294acc579d47def376fef12bcb4a/pandas-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18", size = 11290177, upload-time = "2024-09-20T13:09:41.141Z" }, - { url = "https://files.pythonhosted.org/packages/ed/f9/e995754eab9c0f14c6777401f7eece0943840b7a9fc932221c19d1abee9f/pandas-2.2.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2", size = 14651526, upload-time = "2024-09-20T19:02:16.905Z" }, - { url = "https://files.pythonhosted.org/packages/25/b0/98d6ae2e1abac4f35230aa756005e8654649d305df9a28b16b9ae4353bff/pandas-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4", size = 11871013, upload-time = "2024-09-20T13:09:44.39Z" }, - { url = "https://files.pythonhosted.org/packages/cc/57/0f72a10f9db6a4628744c8e8f0df4e6e21de01212c7c981d31e50ffc8328/pandas-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d", size = 15711620, upload-time = "2024-09-20T19:02:20.639Z" }, - { url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436, upload-time = "2024-09-20T13:09:48.112Z" }, -] - -[[package]] -name = "parso" -version = "0.8.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/66/94/68e2e17afaa9169cf6412ab0f28623903be73d1b32e208d9e8e541bb086d/parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d", size = 400609, upload-time = "2024-04-05T09:43:55.897Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650, upload-time = "2024-04-05T09:43:53.299Z" }, -] - -[[package]] -name = "pexpect" -version = "4.9.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "ptyprocess" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, -] - -[[package]] -name = "pillow" -version = "11.2.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/af/cb/bb5c01fcd2a69335b86c22142b2bccfc3464087efb7fd382eee5ffc7fdf7/pillow-11.2.1.tar.gz", hash = "sha256:a64dd61998416367b7ef979b73d3a85853ba9bec4c2925f74e588879a58716b6", size = 47026707, upload-time = "2025-04-12T17:50:03.289Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/36/9c/447528ee3776e7ab8897fe33697a7ff3f0475bb490c5ac1456a03dc57956/pillow-11.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:fdec757fea0b793056419bca3e9932eb2b0ceec90ef4813ea4c1e072c389eb28", size = 3190098, upload-time = "2025-04-12T17:48:23.915Z" }, - { url = "https://files.pythonhosted.org/packages/b5/09/29d5cd052f7566a63e5b506fac9c60526e9ecc553825551333e1e18a4858/pillow-11.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b0e130705d568e2f43a17bcbe74d90958e8a16263868a12c3e0d9c8162690830", size = 3030166, upload-time = "2025-04-12T17:48:25.738Z" }, - { url = "https://files.pythonhosted.org/packages/71/5d/446ee132ad35e7600652133f9c2840b4799bbd8e4adba881284860da0a36/pillow-11.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bdb5e09068332578214cadd9c05e3d64d99e0e87591be22a324bdbc18925be0", size = 4408674, upload-time = "2025-04-12T17:48:27.908Z" }, - { url = "https://files.pythonhosted.org/packages/69/5f/cbe509c0ddf91cc3a03bbacf40e5c2339c4912d16458fcb797bb47bcb269/pillow-11.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d189ba1bebfbc0c0e529159631ec72bb9e9bc041f01ec6d3233d6d82eb823bc1", size = 4496005, upload-time = "2025-04-12T17:48:29.888Z" }, - { url = "https://files.pythonhosted.org/packages/f9/b3/dd4338d8fb8a5f312021f2977fb8198a1184893f9b00b02b75d565c33b51/pillow-11.2.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:191955c55d8a712fab8934a42bfefbf99dd0b5875078240943f913bb66d46d9f", size = 4518707, upload-time = "2025-04-12T17:48:31.874Z" }, - { url = "https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155", size = 4610008, upload-time = "2025-04-12T17:48:34.422Z" }, - { url = "https://files.pythonhosted.org/packages/72/d1/924ce51bea494cb6e7959522d69d7b1c7e74f6821d84c63c3dc430cbbf3b/pillow-11.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:750f96efe0597382660d8b53e90dd1dd44568a8edb51cb7f9d5d918b80d4de14", size = 4585420, upload-time = "2025-04-12T17:48:37.641Z" }, - { url = "https://files.pythonhosted.org/packages/43/ab/8f81312d255d713b99ca37479a4cb4b0f48195e530cdc1611990eb8fd04b/pillow-11.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fe15238d3798788d00716637b3d4e7bb6bde18b26e5d08335a96e88564a36b6b", size = 4667655, upload-time = "2025-04-12T17:48:39.652Z" }, - { url = "https://files.pythonhosted.org/packages/94/86/8f2e9d2dc3d308dfd137a07fe1cc478df0a23d42a6c4093b087e738e4827/pillow-11.2.1-cp313-cp313-win32.whl", hash = "sha256:3fe735ced9a607fee4f481423a9c36701a39719252a9bb251679635f99d0f7d2", size = 2332329, upload-time = "2025-04-12T17:48:41.765Z" }, - { url = "https://files.pythonhosted.org/packages/6d/ec/1179083b8d6067a613e4d595359b5fdea65d0a3b7ad623fee906e1b3c4d2/pillow-11.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:74ee3d7ecb3f3c05459ba95eed5efa28d6092d751ce9bf20e3e253a4e497e691", size = 2676388, upload-time = "2025-04-12T17:48:43.625Z" }, - { url = "https://files.pythonhosted.org/packages/23/f1/2fc1e1e294de897df39fa8622d829b8828ddad938b0eaea256d65b84dd72/pillow-11.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:5119225c622403afb4b44bad4c1ca6c1f98eed79db8d3bc6e4e160fc6339d66c", size = 2414950, upload-time = "2025-04-12T17:48:45.475Z" }, - { url = "https://files.pythonhosted.org/packages/c4/3e/c328c48b3f0ead7bab765a84b4977acb29f101d10e4ef57a5e3400447c03/pillow-11.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8ce2e8411c7aaef53e6bb29fe98f28cd4fbd9a1d9be2eeea434331aac0536b22", size = 3192759, upload-time = "2025-04-12T17:48:47.866Z" }, - { url = "https://files.pythonhosted.org/packages/18/0e/1c68532d833fc8b9f404d3a642991441d9058eccd5606eab31617f29b6d4/pillow-11.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:9ee66787e095127116d91dea2143db65c7bb1e232f617aa5957c0d9d2a3f23a7", size = 3033284, upload-time = "2025-04-12T17:48:50.189Z" }, - { url = "https://files.pythonhosted.org/packages/b7/cb/6faf3fb1e7705fd2db74e070f3bf6f88693601b0ed8e81049a8266de4754/pillow-11.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9622e3b6c1d8b551b6e6f21873bdcc55762b4b2126633014cea1803368a9aa16", size = 4445826, upload-time = "2025-04-12T17:48:52.346Z" }, - { url = "https://files.pythonhosted.org/packages/07/94/8be03d50b70ca47fb434a358919d6a8d6580f282bbb7af7e4aa40103461d/pillow-11.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63b5dff3a68f371ea06025a1a6966c9a1e1ee452fc8020c2cd0ea41b83e9037b", size = 4527329, upload-time = "2025-04-12T17:48:54.403Z" }, - { url = "https://files.pythonhosted.org/packages/fd/a4/bfe78777076dc405e3bd2080bc32da5ab3945b5a25dc5d8acaa9de64a162/pillow-11.2.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:31df6e2d3d8fc99f993fd253e97fae451a8db2e7207acf97859732273e108406", size = 4549049, upload-time = "2025-04-12T17:48:56.383Z" }, - { url = "https://files.pythonhosted.org/packages/65/4d/eaf9068dc687c24979e977ce5677e253624bd8b616b286f543f0c1b91662/pillow-11.2.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:062b7a42d672c45a70fa1f8b43d1d38ff76b63421cbbe7f88146b39e8a558d91", size = 4635408, upload-time = "2025-04-12T17:48:58.782Z" }, - { url = "https://files.pythonhosted.org/packages/1d/26/0fd443365d9c63bc79feb219f97d935cd4b93af28353cba78d8e77b61719/pillow-11.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4eb92eca2711ef8be42fd3f67533765d9fd043b8c80db204f16c8ea62ee1a751", size = 4614863, upload-time = "2025-04-12T17:49:00.709Z" }, - { url = "https://files.pythonhosted.org/packages/49/65/dca4d2506be482c2c6641cacdba5c602bc76d8ceb618fd37de855653a419/pillow-11.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f91ebf30830a48c825590aede79376cb40f110b387c17ee9bd59932c961044f9", size = 4692938, upload-time = "2025-04-12T17:49:02.946Z" }, - { url = "https://files.pythonhosted.org/packages/b3/92/1ca0c3f09233bd7decf8f7105a1c4e3162fb9142128c74adad0fb361b7eb/pillow-11.2.1-cp313-cp313t-win32.whl", hash = "sha256:e0b55f27f584ed623221cfe995c912c61606be8513bfa0e07d2c674b4516d9dd", size = 2335774, upload-time = "2025-04-12T17:49:04.889Z" }, - { url = "https://files.pythonhosted.org/packages/a5/ac/77525347cb43b83ae905ffe257bbe2cc6fd23acb9796639a1f56aa59d191/pillow-11.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:36d6b82164c39ce5482f649b437382c0fb2395eabc1e2b1702a6deb8ad647d6e", size = 2681895, upload-time = "2025-04-12T17:49:06.635Z" }, - { url = "https://files.pythonhosted.org/packages/67/32/32dc030cfa91ca0fc52baebbba2e009bb001122a1daa8b6a79ad830b38d3/pillow-11.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:225c832a13326e34f212d2072982bb1adb210e0cc0b153e688743018c94a2681", size = 2417234, upload-time = "2025-04-12T17:49:08.399Z" }, -] - -[[package]] -name = "platformdirs" -version = "4.3.8" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/fe/8b/3c73abc9c759ecd3f1f7ceff6685840859e8070c4d947c93fae71f6a0bf2/platformdirs-4.3.8.tar.gz", hash = "sha256:3d512d96e16bcb959a814c9f348431070822a6496326a4be0911c40b5a74c2bc", size = 21362, upload-time = "2025-05-07T22:47:42.121Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fe/39/979e8e21520d4e47a0bbe349e2713c0aac6f3d853d0e5b34d76206c439aa/platformdirs-4.3.8-py3-none-any.whl", hash = "sha256:ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4", size = 18567, upload-time = "2025-05-07T22:47:40.376Z" }, -] - -[[package]] -name = "pluggy" -version = "1.6.0" -source = { registry = "https://pypi.org/simple" } -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 = "pot" -version = "0.9.5" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, - { name = "scipy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/c1/40/3e0c8dd88328d944f9d82b30cafd2a1c911bddff0b8bccc8dc9dd5e45b7c/pot-0.9.5.tar.gz", hash = "sha256:9644ee7ff51c3cffa3c2632b9dd9dff4f3520266f9fb771450935ffb646d6042", size = 440808, upload-time = "2024-11-07T10:05:05.567Z" } - -[[package]] -name = "prompt-toolkit" -version = "3.0.51" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "wcwidth" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/bb/6e/9d084c929dfe9e3bfe0c6a47e31f78a25c54627d64a66e884a8bf5474f1c/prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed", size = 428940, upload-time = "2025-04-15T09:18:47.731Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ce/4f/5249960887b1fbe561d9ff265496d170b55a735b76724f10ef19f9e40716/prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07", size = 387810, upload-time = "2025-04-15T09:18:44.753Z" }, -] - -[[package]] -name = "propcache" -version = "0.3.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/07/c8/fdc6686a986feae3541ea23dcaa661bd93972d3940460646c6bb96e21c40/propcache-0.3.1.tar.gz", hash = "sha256:40d980c33765359098837527e18eddefc9a24cea5b45e078a7f3bb5b032c6ecf", size = 43651, upload-time = "2025-03-26T03:06:12.05Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/58/60/f645cc8b570f99be3cf46714170c2de4b4c9d6b827b912811eff1eb8a412/propcache-0.3.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f1528ec4374617a7a753f90f20e2f551121bb558fcb35926f99e3c42367164b8", size = 77865, upload-time = "2025-03-26T03:04:53.406Z" }, - { url = "https://files.pythonhosted.org/packages/6f/d4/c1adbf3901537582e65cf90fd9c26fde1298fde5a2c593f987112c0d0798/propcache-0.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:dc1915ec523b3b494933b5424980831b636fe483d7d543f7afb7b3bf00f0c10f", size = 45452, upload-time = "2025-03-26T03:04:54.624Z" }, - { url = "https://files.pythonhosted.org/packages/d1/b5/fe752b2e63f49f727c6c1c224175d21b7d1727ce1d4873ef1c24c9216830/propcache-0.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a110205022d077da24e60b3df8bcee73971be9575dec5573dd17ae5d81751111", size = 44800, upload-time = "2025-03-26T03:04:55.844Z" }, - { url = "https://files.pythonhosted.org/packages/62/37/fc357e345bc1971e21f76597028b059c3d795c5ca7690d7a8d9a03c9708a/propcache-0.3.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d249609e547c04d190e820d0d4c8ca03ed4582bcf8e4e160a6969ddfb57b62e5", size = 225804, upload-time = "2025-03-26T03:04:57.158Z" }, - { url = "https://files.pythonhosted.org/packages/0d/f1/16e12c33e3dbe7f8b737809bad05719cff1dccb8df4dafbcff5575002c0e/propcache-0.3.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5ced33d827625d0a589e831126ccb4f5c29dfdf6766cac441d23995a65825dcb", size = 230650, upload-time = "2025-03-26T03:04:58.61Z" }, - { url = "https://files.pythonhosted.org/packages/3e/a2/018b9f2ed876bf5091e60153f727e8f9073d97573f790ff7cdf6bc1d1fb8/propcache-0.3.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4114c4ada8f3181af20808bedb250da6bae56660e4b8dfd9cd95d4549c0962f7", size = 234235, upload-time = "2025-03-26T03:05:00.599Z" }, - { url = "https://files.pythonhosted.org/packages/45/5f/3faee66fc930dfb5da509e34c6ac7128870631c0e3582987fad161fcb4b1/propcache-0.3.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:975af16f406ce48f1333ec5e912fe11064605d5c5b3f6746969077cc3adeb120", size = 228249, upload-time = "2025-03-26T03:05:02.11Z" }, - { url = "https://files.pythonhosted.org/packages/62/1e/a0d5ebda5da7ff34d2f5259a3e171a94be83c41eb1e7cd21a2105a84a02e/propcache-0.3.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a34aa3a1abc50740be6ac0ab9d594e274f59960d3ad253cd318af76b996dd654", size = 214964, upload-time = "2025-03-26T03:05:03.599Z" }, - { url = "https://files.pythonhosted.org/packages/db/a0/d72da3f61ceab126e9be1f3bc7844b4e98c6e61c985097474668e7e52152/propcache-0.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:9cec3239c85ed15bfaded997773fdad9fb5662b0a7cbc854a43f291eb183179e", size = 222501, upload-time = "2025-03-26T03:05:05.107Z" }, - { url = "https://files.pythonhosted.org/packages/18/6d/a008e07ad7b905011253adbbd97e5b5375c33f0b961355ca0a30377504ac/propcache-0.3.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:05543250deac8e61084234d5fc54f8ebd254e8f2b39a16b1dce48904f45b744b", size = 217917, upload-time = "2025-03-26T03:05:06.59Z" }, - { url = "https://files.pythonhosted.org/packages/98/37/02c9343ffe59e590e0e56dc5c97d0da2b8b19fa747ebacf158310f97a79a/propcache-0.3.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5cb5918253912e088edbf023788de539219718d3b10aef334476b62d2b53de53", size = 217089, upload-time = "2025-03-26T03:05:08.1Z" }, - { url = "https://files.pythonhosted.org/packages/53/1b/d3406629a2c8a5666d4674c50f757a77be119b113eedd47b0375afdf1b42/propcache-0.3.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f3bbecd2f34d0e6d3c543fdb3b15d6b60dd69970c2b4c822379e5ec8f6f621d5", size = 228102, upload-time = "2025-03-26T03:05:09.982Z" }, - { url = "https://files.pythonhosted.org/packages/cd/a7/3664756cf50ce739e5f3abd48febc0be1a713b1f389a502ca819791a6b69/propcache-0.3.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:aca63103895c7d960a5b9b044a83f544b233c95e0dcff114389d64d762017af7", size = 230122, upload-time = "2025-03-26T03:05:11.408Z" }, - { url = "https://files.pythonhosted.org/packages/35/36/0bbabaacdcc26dac4f8139625e930f4311864251276033a52fd52ff2a274/propcache-0.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5a0a9898fdb99bf11786265468571e628ba60af80dc3f6eb89a3545540c6b0ef", size = 226818, upload-time = "2025-03-26T03:05:12.909Z" }, - { url = "https://files.pythonhosted.org/packages/cc/27/4e0ef21084b53bd35d4dae1634b6d0bad35e9c58ed4f032511acca9d4d26/propcache-0.3.1-cp313-cp313-win32.whl", hash = "sha256:3a02a28095b5e63128bcae98eb59025924f121f048a62393db682f049bf4ac24", size = 40112, upload-time = "2025-03-26T03:05:14.289Z" }, - { url = "https://files.pythonhosted.org/packages/a6/2c/a54614d61895ba6dd7ac8f107e2b2a0347259ab29cbf2ecc7b94fa38c4dc/propcache-0.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:813fbb8b6aea2fc9659815e585e548fe706d6f663fa73dff59a1677d4595a037", size = 44034, upload-time = "2025-03-26T03:05:15.616Z" }, - { url = "https://files.pythonhosted.org/packages/5a/a8/0a4fd2f664fc6acc66438370905124ce62e84e2e860f2557015ee4a61c7e/propcache-0.3.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:a444192f20f5ce8a5e52761a031b90f5ea6288b1eef42ad4c7e64fef33540b8f", size = 82613, upload-time = "2025-03-26T03:05:16.913Z" }, - { url = "https://files.pythonhosted.org/packages/4d/e5/5ef30eb2cd81576256d7b6caaa0ce33cd1d2c2c92c8903cccb1af1a4ff2f/propcache-0.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0fbe94666e62ebe36cd652f5fc012abfbc2342de99b523f8267a678e4dfdee3c", size = 47763, upload-time = "2025-03-26T03:05:18.607Z" }, - { url = "https://files.pythonhosted.org/packages/87/9a/87091ceb048efeba4d28e903c0b15bcc84b7c0bf27dc0261e62335d9b7b8/propcache-0.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:f011f104db880f4e2166bcdcf7f58250f7a465bc6b068dc84c824a3d4a5c94dc", size = 47175, upload-time = "2025-03-26T03:05:19.85Z" }, - { url = "https://files.pythonhosted.org/packages/3e/2f/854e653c96ad1161f96194c6678a41bbb38c7947d17768e8811a77635a08/propcache-0.3.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e584b6d388aeb0001d6d5c2bd86b26304adde6d9bb9bfa9c4889805021b96de", size = 292265, upload-time = "2025-03-26T03:05:21.654Z" }, - { url = "https://files.pythonhosted.org/packages/40/8d/090955e13ed06bc3496ba4a9fb26c62e209ac41973cb0d6222de20c6868f/propcache-0.3.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8a17583515a04358b034e241f952f1715243482fc2c2945fd99a1b03a0bd77d6", size = 294412, upload-time = "2025-03-26T03:05:23.147Z" }, - { url = "https://files.pythonhosted.org/packages/39/e6/d51601342e53cc7582449e6a3c14a0479fab2f0750c1f4d22302e34219c6/propcache-0.3.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5aed8d8308215089c0734a2af4f2e95eeb360660184ad3912686c181e500b2e7", size = 294290, upload-time = "2025-03-26T03:05:24.577Z" }, - { url = "https://files.pythonhosted.org/packages/3b/4d/be5f1a90abc1881884aa5878989a1acdafd379a91d9c7e5e12cef37ec0d7/propcache-0.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8e309ff9a0503ef70dc9a0ebd3e69cf7b3894c9ae2ae81fc10943c37762458", size = 282926, upload-time = "2025-03-26T03:05:26.459Z" }, - { url = "https://files.pythonhosted.org/packages/57/2b/8f61b998c7ea93a2b7eca79e53f3e903db1787fca9373af9e2cf8dc22f9d/propcache-0.3.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b655032b202028a582d27aeedc2e813299f82cb232f969f87a4fde491a233f11", size = 267808, upload-time = "2025-03-26T03:05:28.188Z" }, - { url = "https://files.pythonhosted.org/packages/11/1c/311326c3dfce59c58a6098388ba984b0e5fb0381ef2279ec458ef99bd547/propcache-0.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9f64d91b751df77931336b5ff7bafbe8845c5770b06630e27acd5dbb71e1931c", size = 290916, upload-time = "2025-03-26T03:05:29.757Z" }, - { url = "https://files.pythonhosted.org/packages/4b/74/91939924b0385e54dc48eb2e4edd1e4903ffd053cf1916ebc5347ac227f7/propcache-0.3.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:19a06db789a4bd896ee91ebc50d059e23b3639c25d58eb35be3ca1cbe967c3bf", size = 262661, upload-time = "2025-03-26T03:05:31.472Z" }, - { url = "https://files.pythonhosted.org/packages/c2/d7/e6079af45136ad325c5337f5dd9ef97ab5dc349e0ff362fe5c5db95e2454/propcache-0.3.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:bef100c88d8692864651b5f98e871fb090bd65c8a41a1cb0ff2322db39c96c27", size = 264384, upload-time = "2025-03-26T03:05:32.984Z" }, - { url = "https://files.pythonhosted.org/packages/b7/d5/ba91702207ac61ae6f1c2da81c5d0d6bf6ce89e08a2b4d44e411c0bbe867/propcache-0.3.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:87380fb1f3089d2a0b8b00f006ed12bd41bd858fabfa7330c954c70f50ed8757", size = 291420, upload-time = "2025-03-26T03:05:34.496Z" }, - { url = "https://files.pythonhosted.org/packages/58/70/2117780ed7edcd7ba6b8134cb7802aada90b894a9810ec56b7bb6018bee7/propcache-0.3.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:e474fc718e73ba5ec5180358aa07f6aded0ff5f2abe700e3115c37d75c947e18", size = 290880, upload-time = "2025-03-26T03:05:36.256Z" }, - { url = "https://files.pythonhosted.org/packages/4a/1f/ecd9ce27710021ae623631c0146719280a929d895a095f6d85efb6a0be2e/propcache-0.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:17d1c688a443355234f3c031349da69444be052613483f3e4158eef751abcd8a", size = 287407, upload-time = "2025-03-26T03:05:37.799Z" }, - { url = "https://files.pythonhosted.org/packages/3e/66/2e90547d6b60180fb29e23dc87bd8c116517d4255240ec6d3f7dc23d1926/propcache-0.3.1-cp313-cp313t-win32.whl", hash = "sha256:359e81a949a7619802eb601d66d37072b79b79c2505e6d3fd8b945538411400d", size = 42573, upload-time = "2025-03-26T03:05:39.193Z" }, - { url = "https://files.pythonhosted.org/packages/cb/8f/50ad8599399d1861b4d2b6b45271f0ef6af1b09b0a2386a46dbaf19c9535/propcache-0.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:e7fb9a84c9abbf2b2683fa3e7b0d7da4d8ecf139a1c635732a8bda29c5214b0e", size = 46757, upload-time = "2025-03-26T03:05:40.811Z" }, - { url = "https://files.pythonhosted.org/packages/b8/d3/c3cb8f1d6ae3b37f83e1de806713a9b3642c5895f0215a62e1a4bd6e5e34/propcache-0.3.1-py3-none-any.whl", hash = "sha256:9a8ecf38de50a7f518c21568c80f985e776397b902f1ce0b01f799aba1608b40", size = 12376, upload-time = "2025-03-26T03:06:10.5Z" }, -] - -[[package]] -name = "psutil" -version = "7.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003, upload-time = "2025-02-13T21:54:07.946Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051, upload-time = "2025-02-13T21:54:12.36Z" }, - { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535, upload-time = "2025-02-13T21:54:16.07Z" }, - { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004, upload-time = "2025-02-13T21:54:18.662Z" }, - { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986, upload-time = "2025-02-13T21:54:21.811Z" }, - { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544, upload-time = "2025-02-13T21:54:24.68Z" }, - { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053, upload-time = "2025-02-13T21:54:34.31Z" }, - { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885, upload-time = "2025-02-13T21:54:37.486Z" }, -] - -[[package]] -name = "ptyprocess" -version = "0.7.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, -] - -[[package]] -name = "pure-eval" -version = "0.2.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, -] - -[[package]] -name = "pycparser" -version = "2.22" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/1d/b2/31537cf4b1ca988837256c910a668b553fceb8f069bedc4b1c826024b52c/pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6", size = 172736, upload-time = "2024-03-30T13:22:22.564Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc", size = 117552, upload-time = "2024-03-30T13:22:20.476Z" }, -] - -[[package]] -name = "pygments" -version = "2.19.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" }, -] - -[[package]] -name = "pyparsing" -version = "3.2.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/bb/22/f1129e69d94ffff626bdb5c835506b3a5b4f3d070f17ea295e12c2c6f60f/pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be", size = 1088608, upload-time = "2025-03-25T05:01:28.114Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf", size = 111120, upload-time = "2025-03-25T05:01:24.908Z" }, -] - -[[package]] -name = "pytest" -version = "8.4.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, - { name = "iniconfig" }, - { name = "packaging" }, - { name = "pluggy" }, - { name = "pygments" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/08/ba/45911d754e8eba3d5a841a5ce61a65a685ff1798421ac054f85aa8747dfb/pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c", size = 1517714, upload-time = "2025-06-18T05:48:06.109Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7", size = 365474, upload-time = "2025-06-18T05:48:03.955Z" }, -] - -[[package]] -name = "pytest-cov" -version = "6.2.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "coverage" }, - { name = "pluggy" }, - { name = "pytest" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/18/99/668cade231f434aaa59bbfbf49469068d2ddd945000621d3d165d2e7dd7b/pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2", size = 69432, upload-time = "2025-06-12T10:47:47.684Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5", size = 24644, upload-time = "2025-06-12T10:47:45.932Z" }, -] - -[[package]] -name = "python-dateutil" -version = "2.9.0.post0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "six" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, -] - -[[package]] -name = "pytz" -version = "2025.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884, upload-time = "2025-03-25T02:25:00.538Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" }, -] - -[[package]] -name = "pywin32" -version = "310" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1c/09/9c1b978ffc4ae53999e89c19c77ba882d9fce476729f23ef55211ea1c034/pywin32-310-cp313-cp313-win32.whl", hash = "sha256:5d241a659c496ada3253cd01cfaa779b048e90ce4b2b38cd44168ad555ce74ab", size = 8794384, upload-time = "2025-03-17T00:56:04.383Z" }, - { url = "https://files.pythonhosted.org/packages/45/3c/b4640f740ffebadd5d34df35fecba0e1cfef8fde9f3e594df91c28ad9b50/pywin32-310-cp313-cp313-win_amd64.whl", hash = "sha256:667827eb3a90208ddbdcc9e860c81bde63a135710e21e4cb3348968e4bd5249e", size = 9503039, upload-time = "2025-03-17T00:56:06.207Z" }, - { url = "https://files.pythonhosted.org/packages/b4/f4/f785020090fb050e7fb6d34b780f2231f302609dc964672f72bfaeb59a28/pywin32-310-cp313-cp313-win_arm64.whl", hash = "sha256:e308f831de771482b7cf692a1f308f8fca701b2d8f9dde6cc440c7da17e47b33", size = 8458152, upload-time = "2025-03-17T00:56:07.819Z" }, -] - -[[package]] -name = "pyzmq" -version = "27.0.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "cffi", marker = "implementation_name == 'pypy'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/f1/06/50a4e9648b3e8b992bef8eb632e457307553a89d294103213cfd47b3da69/pyzmq-27.0.0.tar.gz", hash = "sha256:b1f08eeb9ce1510e6939b6e5dcd46a17765e2333daae78ecf4606808442e52cf", size = 280478, upload-time = "2025-06-13T14:09:07.087Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/93/a7/9ad68f55b8834ede477842214feba6a4c786d936c022a67625497aacf61d/pyzmq-27.0.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:cbabc59dcfaac66655c040dfcb8118f133fb5dde185e5fc152628354c1598e52", size = 1305438, upload-time = "2025-06-13T14:07:31.676Z" }, - { url = "https://files.pythonhosted.org/packages/ba/ee/26aa0f98665a22bc90ebe12dced1de5f3eaca05363b717f6fb229b3421b3/pyzmq-27.0.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:cb0ac5179cba4b2f94f1aa208fbb77b62c4c9bf24dd446278b8b602cf85fcda3", size = 895095, upload-time = "2025-06-13T14:07:33.104Z" }, - { url = "https://files.pythonhosted.org/packages/cf/85/c57e7ab216ecd8aa4cc7e3b83b06cc4e9cf45c87b0afc095f10cd5ce87c1/pyzmq-27.0.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53a48f0228eab6cbf69fde3aa3c03cbe04e50e623ef92ae395fce47ef8a76152", size = 651826, upload-time = "2025-06-13T14:07:34.831Z" }, - { url = "https://files.pythonhosted.org/packages/69/9a/9ea7e230feda9400fb0ae0d61d7d6ddda635e718d941c44eeab22a179d34/pyzmq-27.0.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:111db5f395e09f7e775f759d598f43cb815fc58e0147623c4816486e1a39dc22", size = 839750, upload-time = "2025-06-13T14:07:36.553Z" }, - { url = "https://files.pythonhosted.org/packages/08/66/4cebfbe71f3dfbd417011daca267539f62ed0fbc68105357b68bbb1a25b7/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c8878011653dcdc27cc2c57e04ff96f0471e797f5c19ac3d7813a245bcb24371", size = 1641357, upload-time = "2025-06-13T14:07:38.21Z" }, - { url = "https://files.pythonhosted.org/packages/ac/f6/b0f62578c08d2471c791287149cb8c2aaea414ae98c6e995c7dbe008adfb/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:c0ed2c1f335ba55b5fdc964622254917d6b782311c50e138863eda409fbb3b6d", size = 2020281, upload-time = "2025-06-13T14:07:39.599Z" }, - { url = "https://files.pythonhosted.org/packages/37/b9/4f670b15c7498495da9159edc374ec09c88a86d9cd5a47d892f69df23450/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:e918d70862d4cfd4b1c187310015646a14e1f5917922ab45b29f28f345eeb6be", size = 1877110, upload-time = "2025-06-13T14:07:41.027Z" }, - { url = "https://files.pythonhosted.org/packages/66/31/9dee25c226295b740609f0d46db2fe972b23b6f5cf786360980524a3ba92/pyzmq-27.0.0-cp312-abi3-win32.whl", hash = "sha256:88b4e43cab04c3c0f0d55df3b1eef62df2b629a1a369b5289a58f6fa8b07c4f4", size = 559297, upload-time = "2025-06-13T14:07:42.533Z" }, - { url = "https://files.pythonhosted.org/packages/9b/12/52da5509800f7ff2d287b2f2b4e636e7ea0f001181cba6964ff6c1537778/pyzmq-27.0.0-cp312-abi3-win_amd64.whl", hash = "sha256:dce4199bf5f648a902ce37e7b3afa286f305cd2ef7a8b6ec907470ccb6c8b371", size = 619203, upload-time = "2025-06-13T14:07:43.843Z" }, - { url = "https://files.pythonhosted.org/packages/93/6d/7f2e53b19d1edb1eb4f09ec7c3a1f945ca0aac272099eab757d15699202b/pyzmq-27.0.0-cp312-abi3-win_arm64.whl", hash = "sha256:56e46bbb85d52c1072b3f809cc1ce77251d560bc036d3a312b96db1afe76db2e", size = 551927, upload-time = "2025-06-13T14:07:45.51Z" }, - { url = "https://files.pythonhosted.org/packages/19/62/876b27c4ff777db4ceba1c69ea90d3c825bb4f8d5e7cd987ce5802e33c55/pyzmq-27.0.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:c36ad534c0c29b4afa088dc53543c525b23c0797e01b69fef59b1a9c0e38b688", size = 1340826, upload-time = "2025-06-13T14:07:46.881Z" }, - { url = "https://files.pythonhosted.org/packages/43/69/58ef8f4f59d3bcd505260c73bee87b008850f45edca40ddaba54273c35f4/pyzmq-27.0.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:67855c14173aec36395d7777aaba3cc527b393821f30143fd20b98e1ff31fd38", size = 897283, upload-time = "2025-06-13T14:07:49.562Z" }, - { url = "https://files.pythonhosted.org/packages/43/15/93a0d0396700a60475ad3c5d42c5f1c308d3570bc94626b86c71ef9953e0/pyzmq-27.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8617c7d43cd8ccdb62aebe984bfed77ca8f036e6c3e46dd3dddda64b10f0ab7a", size = 660567, upload-time = "2025-06-13T14:07:51.364Z" }, - { url = "https://files.pythonhosted.org/packages/0e/b3/fe055513e498ca32f64509abae19b9c9eb4d7c829e02bd8997dd51b029eb/pyzmq-27.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:67bfbcbd0a04c575e8103a6061d03e393d9f80ffdb9beb3189261e9e9bc5d5e9", size = 847681, upload-time = "2025-06-13T14:07:52.77Z" }, - { url = "https://files.pythonhosted.org/packages/b6/4f/ff15300b00b5b602191f3df06bbc8dd4164e805fdd65bb77ffbb9c5facdc/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5cd11d46d7b7e5958121b3eaf4cd8638eff3a720ec527692132f05a57f14341d", size = 1650148, upload-time = "2025-06-13T14:07:54.178Z" }, - { url = "https://files.pythonhosted.org/packages/c4/6f/84bdfff2a224a6f26a24249a342e5906993c50b0761e311e81b39aef52a7/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:b801c2e40c5aa6072c2f4876de8dccd100af6d9918d4d0d7aa54a1d982fd4f44", size = 2023768, upload-time = "2025-06-13T14:07:55.714Z" }, - { url = "https://files.pythonhosted.org/packages/64/39/dc2db178c26a42228c5ac94a9cc595030458aa64c8d796a7727947afbf55/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:20d5cb29e8c5f76a127c75b6e7a77e846bc4b655c373baa098c26a61b7ecd0ef", size = 1885199, upload-time = "2025-06-13T14:07:57.166Z" }, - { url = "https://files.pythonhosted.org/packages/c7/21/dae7b06a1f8cdee5d8e7a63d99c5d129c401acc40410bef2cbf42025e26f/pyzmq-27.0.0-cp313-cp313t-win32.whl", hash = "sha256:a20528da85c7ac7a19b7384e8c3f8fa707841fd85afc4ed56eda59d93e3d98ad", size = 575439, upload-time = "2025-06-13T14:07:58.959Z" }, - { url = "https://files.pythonhosted.org/packages/eb/bc/1709dc55f0970cf4cb8259e435e6773f9946f41a045c2cb90e870b7072da/pyzmq-27.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:d8229f2efece6a660ee211d74d91dbc2a76b95544d46c74c615e491900dc107f", size = 639933, upload-time = "2025-06-13T14:08:00.777Z" }, -] - -[[package]] -name = "requests" -version = "2.32.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi" }, - { name = "charset-normalizer" }, - { name = "idna" }, - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/63/70/2bf7780ad2d390a8d301ad0b550f1581eadbd9a20f896afe06353c2a2913/requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760", size = 131218, upload-time = "2024-05-29T15:37:49.536Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6", size = 64928, upload-time = "2024-05-29T15:37:47.027Z" }, -] - -[[package]] -name = "rings" -version = "0.1.0" -source = { virtual = "." } -dependencies = [ - { name = "networkx" }, - { name = "pot" }, - { name = "scikit-learn" }, - { name = "seaborn" }, - { name = "torch" }, - { name = "torch-geometric" }, - { name = "torchvision" }, -] - -[package.dev-dependencies] -dev = [ - { name = "furo" }, - { name = "ipykernel" }, - { name = "pytest" }, - { name = "pytest-cov" }, - { name = "sphinx" }, -] - -[package.metadata] -requires-dist = [ - { name = "networkx", specifier = ">=3.4.2" }, - { name = "pot", specifier = ">=0.9.5" }, - { name = "scikit-learn", specifier = ">=1.7.0" }, - { name = "seaborn", specifier = ">=0.13.2" }, - { name = "torch", specifier = ">=2.7.0" }, - { name = "torch-geometric", specifier = ">=2.6.1" }, - { name = "torchvision", specifier = ">=0.22.0" }, -] - -[package.metadata.requires-dev] -dev = [ - { name = "furo", specifier = ">=2024.8.6" }, - { name = "ipykernel", specifier = ">=6.29.5" }, - { name = "pytest", specifier = ">=8.4.1" }, - { name = "pytest-cov", specifier = ">=6.2.1" }, - { name = "sphinx", specifier = ">=8.2.3" }, -] - -[[package]] -name = "roman-numerals-py" -version = "3.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/30/76/48fd56d17c5bdbdf65609abbc67288728a98ed4c02919428d4f52d23b24b/roman_numerals_py-3.1.0.tar.gz", hash = "sha256:be4bf804f083a4ce001b5eb7e3c0862479d10f94c936f6c4e5f250aa5ff5bd2d", size = 9017, upload-time = "2025-02-22T07:34:54.333Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl", hash = "sha256:9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c", size = 7742, upload-time = "2025-02-22T07:34:52.422Z" }, -] - -[[package]] -name = "scikit-learn" -version = "1.7.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "joblib" }, - { name = "numpy" }, - { name = "scipy" }, - { name = "threadpoolctl" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/df/3b/29fa87e76b1d7b3b77cc1fcbe82e6e6b8cd704410705b008822de530277c/scikit_learn-1.7.0.tar.gz", hash = "sha256:c01e869b15aec88e2cdb73d27f15bdbe03bce8e2fb43afbe77c45d399e73a5a3", size = 7178217, upload-time = "2025-06-05T22:02:46.703Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9a/c3/a85dcccdaf1e807e6f067fa95788a6485b0491d9ea44fd4c812050d04f45/scikit_learn-1.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5b7974f1f32bc586c90145df51130e02267e4b7e77cab76165c76cf43faca0d9", size = 11559841, upload-time = "2025-06-05T22:02:23.308Z" }, - { url = "https://files.pythonhosted.org/packages/d8/57/eea0de1562cc52d3196eae51a68c5736a31949a465f0b6bb3579b2d80282/scikit_learn-1.7.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:014e07a23fe02e65f9392898143c542a50b6001dbe89cb867e19688e468d049b", size = 10616463, upload-time = "2025-06-05T22:02:26.068Z" }, - { url = "https://files.pythonhosted.org/packages/10/a4/39717ca669296dfc3a62928393168da88ac9d8cbec88b6321ffa62c6776f/scikit_learn-1.7.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e7e7ced20582d3a5516fb6f405fd1d254e1f5ce712bfef2589f51326af6346e8", size = 11766512, upload-time = "2025-06-05T22:02:28.689Z" }, - { url = "https://files.pythonhosted.org/packages/d5/cd/a19722241d5f7b51e08351e1e82453e0057aeb7621b17805f31fcb57bb6c/scikit_learn-1.7.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1babf2511e6ffd695da7a983b4e4d6de45dce39577b26b721610711081850906", size = 12461075, upload-time = "2025-06-05T22:02:31.233Z" }, - { url = "https://files.pythonhosted.org/packages/f3/bc/282514272815c827a9acacbe5b99f4f1a4bc5961053719d319480aee0812/scikit_learn-1.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:5abd2acff939d5bd4701283f009b01496832d50ddafa83c90125a4e41c33e314", size = 10652517, upload-time = "2025-06-05T22:02:34.139Z" }, - { url = "https://files.pythonhosted.org/packages/ea/78/7357d12b2e4c6674175f9a09a3ba10498cde8340e622715bcc71e532981d/scikit_learn-1.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:e39d95a929b112047c25b775035c8c234c5ca67e681ce60d12413afb501129f7", size = 12111822, upload-time = "2025-06-05T22:02:36.904Z" }, - { url = "https://files.pythonhosted.org/packages/d0/0c/9c3715393343f04232f9d81fe540eb3831d0b4ec351135a145855295110f/scikit_learn-1.7.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:0521cb460426c56fee7e07f9365b0f45ec8ca7b2d696534ac98bfb85e7ae4775", size = 11325286, upload-time = "2025-06-05T22:02:39.739Z" }, - { url = "https://files.pythonhosted.org/packages/64/e0/42282ad3dd70b7c1a5f65c412ac3841f6543502a8d6263cae7b466612dc9/scikit_learn-1.7.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:317ca9f83acbde2883bd6bb27116a741bfcb371369706b4f9973cf30e9a03b0d", size = 12380865, upload-time = "2025-06-05T22:02:42.137Z" }, - { url = "https://files.pythonhosted.org/packages/4e/d0/3ef4ab2c6be4aa910445cd09c5ef0b44512e3de2cfb2112a88bb647d2cf7/scikit_learn-1.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:126c09740a6f016e815ab985b21e3a0656835414521c81fc1a8da78b679bdb75", size = 11549609, upload-time = "2025-06-05T22:02:44.483Z" }, -] - -[[package]] -name = "scipy" -version = "1.15.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/b7/b9/31ba9cd990e626574baf93fbc1ac61cf9ed54faafd04c479117517661637/scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec", size = 59417316, upload-time = "2025-02-17T00:42:24.791Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/53/40/09319f6e0f276ea2754196185f95cd191cb852288440ce035d5c3a931ea2/scipy-1.15.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01edfac9f0798ad6b46d9c4c9ca0e0ad23dbf0b1eb70e96adb9fa7f525eff0bf", size = 38717587, upload-time = "2025-02-17T00:32:53.196Z" }, - { url = "https://files.pythonhosted.org/packages/fe/c3/2854f40ecd19585d65afaef601e5e1f8dbf6758b2f95b5ea93d38655a2c6/scipy-1.15.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:08b57a9336b8e79b305a143c3655cc5bdbe6d5ece3378578888d2afbb51c4e37", size = 30100266, upload-time = "2025-02-17T00:32:59.318Z" }, - { url = "https://files.pythonhosted.org/packages/dd/b1/f9fe6e3c828cb5930b5fe74cb479de5f3d66d682fa8adb77249acaf545b8/scipy-1.15.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:54c462098484e7466362a9f1672d20888f724911a74c22ae35b61f9c5919183d", size = 22373768, upload-time = "2025-02-17T00:33:04.091Z" }, - { url = "https://files.pythonhosted.org/packages/15/9d/a60db8c795700414c3f681908a2b911e031e024d93214f2d23c6dae174ab/scipy-1.15.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:cf72ff559a53a6a6d77bd8eefd12a17995ffa44ad86c77a5df96f533d4e6c6bb", size = 25154719, upload-time = "2025-02-17T00:33:08.909Z" }, - { url = "https://files.pythonhosted.org/packages/37/3b/9bda92a85cd93f19f9ed90ade84aa1e51657e29988317fabdd44544f1dd4/scipy-1.15.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9de9d1416b3d9e7df9923ab23cd2fe714244af10b763975bea9e4f2e81cebd27", size = 35163195, upload-time = "2025-02-17T00:33:15.352Z" }, - { url = "https://files.pythonhosted.org/packages/03/5a/fc34bf1aa14dc7c0e701691fa8685f3faec80e57d816615e3625f28feb43/scipy-1.15.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb530e4794fc8ea76a4a21ccb67dea33e5e0e60f07fc38a49e821e1eae3b71a0", size = 37255404, upload-time = "2025-02-17T00:33:22.21Z" }, - { url = "https://files.pythonhosted.org/packages/4a/71/472eac45440cee134c8a180dbe4c01b3ec247e0338b7c759e6cd71f199a7/scipy-1.15.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5ea7ed46d437fc52350b028b1d44e002646e28f3e8ddc714011aaf87330f2f32", size = 36860011, upload-time = "2025-02-17T00:33:29.446Z" }, - { url = "https://files.pythonhosted.org/packages/01/b3/21f890f4f42daf20e4d3aaa18182dddb9192771cd47445aaae2e318f6738/scipy-1.15.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:11e7ad32cf184b74380f43d3c0a706f49358b904fa7d5345f16ddf993609184d", size = 39657406, upload-time = "2025-02-17T00:33:39.019Z" }, - { url = "https://files.pythonhosted.org/packages/0d/76/77cf2ac1f2a9cc00c073d49e1e16244e389dd88e2490c91d84e1e3e4d126/scipy-1.15.2-cp313-cp313-win_amd64.whl", hash = "sha256:a5080a79dfb9b78b768cebf3c9dcbc7b665c5875793569f48bf0e2b1d7f68f6f", size = 40961243, upload-time = "2025-02-17T00:34:51.024Z" }, - { url = "https://files.pythonhosted.org/packages/4c/4b/a57f8ddcf48e129e6054fa9899a2a86d1fc6b07a0e15c7eebff7ca94533f/scipy-1.15.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:447ce30cee6a9d5d1379087c9e474628dab3db4a67484be1b7dc3196bfb2fac9", size = 38870286, upload-time = "2025-02-17T00:33:47.62Z" }, - { url = "https://files.pythonhosted.org/packages/0c/43/c304d69a56c91ad5f188c0714f6a97b9c1fed93128c691148621274a3a68/scipy-1.15.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:c90ebe8aaa4397eaefa8455a8182b164a6cc1d59ad53f79943f266d99f68687f", size = 30141634, upload-time = "2025-02-17T00:33:54.131Z" }, - { url = "https://files.pythonhosted.org/packages/44/1a/6c21b45d2548eb73be9b9bff421aaaa7e85e22c1f9b3bc44b23485dfce0a/scipy-1.15.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:def751dd08243934c884a3221156d63e15234a3155cf25978b0a668409d45eb6", size = 22415179, upload-time = "2025-02-17T00:33:59.948Z" }, - { url = "https://files.pythonhosted.org/packages/74/4b/aefac4bba80ef815b64f55da06f62f92be5d03b467f2ce3668071799429a/scipy-1.15.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:302093e7dfb120e55515936cb55618ee0b895f8bcaf18ff81eca086c17bd80af", size = 25126412, upload-time = "2025-02-17T00:34:06.328Z" }, - { url = "https://files.pythonhosted.org/packages/b1/53/1cbb148e6e8f1660aacd9f0a9dfa2b05e9ff1cb54b4386fe868477972ac2/scipy-1.15.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd5b77413e1855351cdde594eca99c1f4a588c2d63711388b6a1f1c01f62274", size = 34952867, upload-time = "2025-02-17T00:34:12.928Z" }, - { url = "https://files.pythonhosted.org/packages/2c/23/e0eb7f31a9c13cf2dca083828b97992dd22f8184c6ce4fec5deec0c81fcf/scipy-1.15.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d0194c37037707b2afa7a2f2a924cf7bac3dc292d51b6a925e5fcb89bc5c776", size = 36890009, upload-time = "2025-02-17T00:34:19.55Z" }, - { url = "https://files.pythonhosted.org/packages/03/f3/e699e19cabe96bbac5189c04aaa970718f0105cff03d458dc5e2b6bd1e8c/scipy-1.15.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:bae43364d600fdc3ac327db99659dcb79e6e7ecd279a75fe1266669d9a652828", size = 36545159, upload-time = "2025-02-17T00:34:26.724Z" }, - { url = "https://files.pythonhosted.org/packages/af/f5/ab3838e56fe5cc22383d6fcf2336e48c8fe33e944b9037fbf6cbdf5a11f8/scipy-1.15.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f031846580d9acccd0044efd1a90e6f4df3a6e12b4b6bd694a7bc03a89892b28", size = 39136566, upload-time = "2025-02-17T00:34:34.512Z" }, - { url = "https://files.pythonhosted.org/packages/0a/c8/b3f566db71461cabd4b2d5b39bcc24a7e1c119535c8361f81426be39bb47/scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db", size = 40477705, upload-time = "2025-02-17T00:34:43.619Z" }, -] - -[[package]] -name = "seaborn" -version = "0.13.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "matplotlib" }, - { name = "numpy" }, - { name = "pandas" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" }, -] - -[[package]] -name = "setuptools" -version = "80.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/44/80/97e25f0f1e4067677806084b7382a6ff9979f3d15119375c475c288db9d7/setuptools-80.0.0.tar.gz", hash = "sha256:c40a5b3729d58dd749c0f08f1a07d134fb8a0a3d7f87dc33e7c5e1f762138650", size = 1354221, upload-time = "2025-04-27T17:21:10.806Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/23/63/5517029d6696ddf2bd378d46f63f479be001c31b462303170a1da57650cb/setuptools-80.0.0-py3-none-any.whl", hash = "sha256:a38f898dcd6e5380f4da4381a87ec90bd0a7eec23d204a5552e80ee3cab6bd27", size = 1240907, upload-time = "2025-04-27T17:21:09.175Z" }, -] - -[[package]] -name = "six" -version = "1.17.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, -] - -[[package]] -name = "snowballstemmer" -version = "3.0.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/75/a7/9810d872919697c9d01295633f5d574fb416d47e535f258272ca1f01f447/snowballstemmer-3.0.1.tar.gz", hash = "sha256:6d5eeeec8e9f84d4d56b847692bacf79bc2c8e90c7f80ca4444ff8b6f2e52895", size = 105575, upload-time = "2025-05-09T16:34:51.843Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c8/78/3565d011c61f5a43488987ee32b6f3f656e7f107ac2782dd57bdd7d91d9a/snowballstemmer-3.0.1-py3-none-any.whl", hash = "sha256:6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064", size = 103274, upload-time = "2025-05-09T16:34:50.371Z" }, -] - -[[package]] -name = "soupsieve" -version = "2.7" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3f/f4/4a80cd6ef364b2e8b65b15816a843c0980f7a5a2b4dc701fc574952aa19f/soupsieve-2.7.tar.gz", hash = "sha256:ad282f9b6926286d2ead4750552c8a6142bc4c783fd66b0293547c8fe6ae126a", size = 103418, upload-time = "2025-04-20T18:50:08.518Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/9c/0e6afc12c269578be5c0c1c9f4b49a8d32770a080260c333ac04cc1c832d/soupsieve-2.7-py3-none-any.whl", hash = "sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4", size = 36677, upload-time = "2025-04-20T18:50:07.196Z" }, -] - -[[package]] -name = "sphinx" -version = "8.2.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "alabaster" }, - { name = "babel" }, - { name = "colorama", marker = "sys_platform == 'win32'" }, - { name = "docutils" }, - { name = "imagesize" }, - { name = "jinja2" }, - { name = "packaging" }, - { name = "pygments" }, - { name = "requests" }, - { name = "roman-numerals-py" }, - { name = "snowballstemmer" }, - { name = "sphinxcontrib-applehelp" }, - { name = "sphinxcontrib-devhelp" }, - { name = "sphinxcontrib-htmlhelp" }, - { name = "sphinxcontrib-jsmath" }, - { name = "sphinxcontrib-qthelp" }, - { name = "sphinxcontrib-serializinghtml" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/38/ad/4360e50ed56cb483667b8e6dadf2d3fda62359593faabbe749a27c4eaca6/sphinx-8.2.3.tar.gz", hash = "sha256:398ad29dee7f63a75888314e9424d40f52ce5a6a87ae88e7071e80af296ec348", size = 8321876, upload-time = "2025-03-02T22:31:59.658Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl", hash = "sha256:4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3", size = 3589741, upload-time = "2025-03-02T22:31:56.836Z" }, -] - -[[package]] -name = "sphinx-basic-ng" -version = "1.0.0b2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "sphinx" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/98/0b/a866924ded68efec7a1759587a4e478aec7559d8165fac8b2ad1c0e774d6/sphinx_basic_ng-1.0.0b2.tar.gz", hash = "sha256:9ec55a47c90c8c002b5960c57492ec3021f5193cb26cebc2dc4ea226848651c9", size = 20736, upload-time = "2023-07-08T18:40:54.166Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/3c/dd/018ce05c532a22007ac58d4f45232514cd9d6dd0ee1dc374e309db830983/sphinx_basic_ng-1.0.0b2-py3-none-any.whl", hash = "sha256:eb09aedbabfb650607e9b4b68c9d240b90b1e1be221d6ad71d61c52e29f7932b", size = 22496, upload-time = "2023-07-08T18:40:52.659Z" }, -] - -[[package]] -name = "sphinxcontrib-applehelp" -version = "2.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ba/6e/b837e84a1a704953c62ef8776d45c3e8d759876b4a84fe14eba2859106fe/sphinxcontrib_applehelp-2.0.0.tar.gz", hash = "sha256:2f29ef331735ce958efa4734873f084941970894c6090408b079c61b2e1c06d1", size = 20053, upload-time = "2024-07-29T01:09:00.465Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5d/85/9ebeae2f76e9e77b952f4b274c27238156eae7979c5421fba91a28f4970d/sphinxcontrib_applehelp-2.0.0-py3-none-any.whl", hash = "sha256:4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5", size = 119300, upload-time = "2024-07-29T01:08:58.99Z" }, -] - -[[package]] -name = "sphinxcontrib-devhelp" -version = "2.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f6/d2/5beee64d3e4e747f316bae86b55943f51e82bb86ecd325883ef65741e7da/sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad", size = 12967, upload-time = "2024-07-29T01:09:23.417Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/35/7a/987e583882f985fe4d7323774889ec58049171828b58c2217e7f79cdf44e/sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2", size = 82530, upload-time = "2024-07-29T01:09:21.945Z" }, -] - -[[package]] -name = "sphinxcontrib-htmlhelp" -version = "2.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/43/93/983afd9aa001e5201eab16b5a444ed5b9b0a7a010541e0ddfbbfd0b2470c/sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9", size = 22617, upload-time = "2024-07-29T01:09:37.889Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0a/7b/18a8c0bcec9182c05a0b3ec2a776bba4ead82750a55ff798e8d406dae604/sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8", size = 98705, upload-time = "2024-07-29T01:09:36.407Z" }, -] - -[[package]] -name = "sphinxcontrib-jsmath" -version = "1.0.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b2/e8/9ed3830aeed71f17c026a07a5097edcf44b692850ef215b161b8ad875729/sphinxcontrib-jsmath-1.0.1.tar.gz", hash = "sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8", size = 5787, upload-time = "2019-01-21T16:10:16.347Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl", hash = "sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178", size = 5071, upload-time = "2019-01-21T16:10:14.333Z" }, -] - -[[package]] -name = "sphinxcontrib-qthelp" -version = "2.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/68/bc/9104308fc285eb3e0b31b67688235db556cd5b0ef31d96f30e45f2e51cae/sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab", size = 17165, upload-time = "2024-07-29T01:09:56.435Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb", size = 88743, upload-time = "2024-07-29T01:09:54.885Z" }, -] - -[[package]] -name = "sphinxcontrib-serializinghtml" -version = "2.0.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3b/44/6716b257b0aa6bfd51a1b31665d1c205fb12cb5ad56de752dfa15657de2f/sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d", size = 16080, upload-time = "2024-07-29T01:10:09.332Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331", size = 92072, upload-time = "2024-07-29T01:10:08.203Z" }, -] - -[[package]] -name = "stack-data" -version = "0.6.3" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "asttokens" }, - { name = "executing" }, - { name = "pure-eval" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, -] - -[[package]] -name = "sympy" -version = "1.14.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "mpmath" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, -] - -[[package]] -name = "threadpoolctl" -version = "3.6.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" }, -] - -[[package]] -name = "torch" -version = "2.7.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "filelock" }, - { name = "fsspec" }, - { name = "jinja2" }, - { name = "networkx" }, - { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "setuptools" }, - { name = "sympy" }, - { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "typing-extensions" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/14/24/720ea9a66c29151b315ea6ba6f404650834af57a26b2a04af23ec246b2d5/torch-2.7.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:868ccdc11798535b5727509480cd1d86d74220cfdc42842c4617338c1109a205", size = 99015553, upload-time = "2025-04-23T14:34:41.075Z" }, - { url = "https://files.pythonhosted.org/packages/4b/27/285a8cf12bd7cd71f9f211a968516b07dcffed3ef0be585c6e823675ab91/torch-2.7.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:9b52347118116cf3dff2ab5a3c3dd97c719eb924ac658ca2a7335652076df708", size = 865046389, upload-time = "2025-04-23T14:32:01.16Z" }, - { url = "https://files.pythonhosted.org/packages/74/c8/2ab2b6eadc45554af8768ae99668c5a8a8552e2012c7238ded7e9e4395e1/torch-2.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:434cf3b378340efc87c758f250e884f34460624c0523fe5c9b518d205c91dd1b", size = 212490304, upload-time = "2025-04-23T14:33:57.108Z" }, - { url = "https://files.pythonhosted.org/packages/28/fd/74ba6fde80e2b9eef4237fe668ffae302c76f0e4221759949a632ca13afa/torch-2.7.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:edad98dddd82220465b106506bb91ee5ce32bd075cddbcf2b443dfaa2cbd83bf", size = 68856166, upload-time = "2025-04-23T14:34:04.012Z" }, - { url = "https://files.pythonhosted.org/packages/cb/b4/8df3f9fe6bdf59e56a0e538592c308d18638eb5f5dc4b08d02abb173c9f0/torch-2.7.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2a885fc25afefb6e6eb18a7d1e8bfa01cc153e92271d980a49243b250d5ab6d9", size = 99091348, upload-time = "2025-04-23T14:33:48.975Z" }, - { url = "https://files.pythonhosted.org/packages/9d/f5/0bd30e9da04c3036614aa1b935a9f7e505a9e4f1f731b15e165faf8a4c74/torch-2.7.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:176300ff5bc11a5f5b0784e40bde9e10a35c4ae9609beed96b4aeb46a27f5fae", size = 865104023, upload-time = "2025-04-23T14:30:40.537Z" }, - { url = "https://files.pythonhosted.org/packages/d1/b7/2235d0c3012c596df1c8d39a3f4afc1ee1b6e318d469eda4c8bb68566448/torch-2.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:d0ca446a93f474985d81dc866fcc8dccefb9460a29a456f79d99c29a78a66993", size = 212750916, upload-time = "2025-04-23T14:32:22.91Z" }, - { url = "https://files.pythonhosted.org/packages/90/48/7e6477cf40d48cc0a61fa0d41ee9582b9a316b12772fcac17bc1a40178e7/torch-2.7.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:27f5007bdf45f7bb7af7f11d1828d5c2487e030690afb3d89a651fd7036a390e", size = 68575074, upload-time = "2025-04-23T14:32:38.136Z" }, -] - -[[package]] -name = "torch-geometric" -version = "2.6.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "aiohttp" }, - { name = "fsspec" }, - { name = "jinja2" }, - { name = "numpy" }, - { name = "psutil" }, - { name = "pyparsing" }, - { name = "requests" }, - { name = "tqdm" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/e8/81/e1b015494cb9e0bf4c47cc8426e49736120248733be0e22072a5628ae9ed/torch_geometric-2.6.1.tar.gz", hash = "sha256:1f18f9d0fc4d2239d526221e4f22606a4a3895b5d965a9856d27610a3df662c6", size = 771490, upload-time = "2024-09-26T08:11:30.25Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/03/9f/157e913626c1acfb3b19ce000b1a6e4e4fb177c0bc0ea0c67ca5bd714b5a/torch_geometric-2.6.1-py3-none-any.whl", hash = "sha256:8faeb353f9655f7dbec44c5e0b44c721773bdfb279994da96b9b8b12fd30f427", size = 1135632, upload-time = "2024-09-26T08:11:27.194Z" }, -] - -[[package]] -name = "torchvision" -version = "0.22.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "numpy" }, - { name = "pillow" }, - { name = "torch" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/e1/2a/9b34685599dcb341d12fc2730055155623db7a619d2415a8d31f17050952/torchvision-0.22.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ece17995857dd328485c9c027c0b20ffc52db232e30c84ff6c95ab77201112c5", size = 1947823, upload-time = "2025-04-23T14:41:39.956Z" }, - { url = "https://files.pythonhosted.org/packages/77/77/88f64879483d66daf84f1d1c4d5c31ebb08e640411139042a258d5f7dbfe/torchvision-0.22.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:471c6dd75bb984c6ebe4f60322894a290bf3d4b195e769d80754f3689cd7f238", size = 2471592, upload-time = "2025-04-23T14:41:54.991Z" }, - { url = "https://files.pythonhosted.org/packages/f7/82/2f813eaae7c1fae1f9d9e7829578f5a91f39ef48d6c1c588a8900533dd3d/torchvision-0.22.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:2b839ac0610a38f56bef115ee5b9eaca5f9c2da3c3569a68cc62dbcc179c157f", size = 7446333, upload-time = "2025-04-23T14:41:36.603Z" }, - { url = "https://files.pythonhosted.org/packages/58/19/ca7a4f8907a56351dfe6ae0a708f4e6b3569b5c61d282e3e7f61cf42a4ce/torchvision-0.22.0-cp313-cp313-win_amd64.whl", hash = "sha256:4ada1c08b2f761443cd65b7c7b4aec9e2fc28f75b0d4e1b1ebc9d3953ebccc4d", size = 1716693, upload-time = "2025-04-23T14:41:41.031Z" }, - { url = "https://files.pythonhosted.org/packages/6f/a7/f43e9c8d13118b4ffbaebea664c9338ab20fa115a908125afd2238ff16e7/torchvision-0.22.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cdc96daa4658b47ce9384154c86ed1e70cba9d972a19f5de6e33f8f94a626790", size = 2137621, upload-time = "2025-04-23T14:41:51.427Z" }, - { url = "https://files.pythonhosted.org/packages/6a/9a/2b59f5758ba7e3f23bc84e16947493bbce97392ec6d18efba7bdf0a3b10e/torchvision-0.22.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:753d3c84eeadd5979a33b3b73a25ecd0aa4af44d6b45ed2c70d44f5e0ac68312", size = 2476555, upload-time = "2025-04-23T14:41:38.357Z" }, - { url = "https://files.pythonhosted.org/packages/7d/40/a7bc2ab9b1e56d10a7fd9ae83191bb425fa308caa23d148f1c568006e02c/torchvision-0.22.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:b30e3ed29e4a61f7499bca50f57d8ebd23dfc52b14608efa17a534a55ee59a03", size = 7617924, upload-time = "2025-04-23T14:41:42.709Z" }, - { url = "https://files.pythonhosted.org/packages/c1/7b/30d423bdb2546250d719d7821aaf9058cc093d165565b245b159c788a9dd/torchvision-0.22.0-cp313-cp313t-win_amd64.whl", hash = "sha256:e5d680162694fac4c8a374954e261ddfb4eb0ce103287b0f693e4e9c579ef957", size = 1638621, upload-time = "2025-04-23T14:41:46.06Z" }, -] - -[[package]] -name = "tornado" -version = "6.5.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/51/89/c72771c81d25d53fe33e3dca61c233b665b2780f21820ba6fd2c6793c12b/tornado-6.5.1.tar.gz", hash = "sha256:84ceece391e8eb9b2b95578db65e920d2a61070260594819589609ba9bc6308c", size = 509934, upload-time = "2025-05-22T18:15:38.788Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/77/89/f4532dee6843c9e0ebc4e28d4be04c67f54f60813e4bf73d595fe7567452/tornado-6.5.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:d50065ba7fd11d3bd41bcad0825227cc9a95154bad83239357094c36708001f7", size = 441948, upload-time = "2025-05-22T18:15:20.862Z" }, - { url = "https://files.pythonhosted.org/packages/15/9a/557406b62cffa395d18772e0cdcf03bed2fff03b374677348eef9f6a3792/tornado-6.5.1-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:9e9ca370f717997cb85606d074b0e5b247282cf5e2e1611568b8821afe0342d6", size = 440112, upload-time = "2025-05-22T18:15:22.591Z" }, - { url = "https://files.pythonhosted.org/packages/55/82/7721b7319013a3cf881f4dffa4f60ceff07b31b394e459984e7a36dc99ec/tornado-6.5.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b77e9dfa7ed69754a54c89d82ef746398be82f749df69c4d3abe75c4d1ff4888", size = 443672, upload-time = "2025-05-22T18:15:24.027Z" }, - { url = "https://files.pythonhosted.org/packages/7d/42/d11c4376e7d101171b94e03cef0cbce43e823ed6567ceda571f54cf6e3ce/tornado-6.5.1-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:253b76040ee3bab8bcf7ba9feb136436a3787208717a1fb9f2c16b744fba7331", size = 443019, upload-time = "2025-05-22T18:15:25.735Z" }, - { url = "https://files.pythonhosted.org/packages/7d/f7/0c48ba992d875521ac761e6e04b0a1750f8150ae42ea26df1852d6a98942/tornado-6.5.1-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:308473f4cc5a76227157cdf904de33ac268af770b2c5f05ca6c1161d82fdd95e", size = 443252, upload-time = "2025-05-22T18:15:27.499Z" }, - { url = "https://files.pythonhosted.org/packages/89/46/d8d7413d11987e316df4ad42e16023cd62666a3c0dfa1518ffa30b8df06c/tornado-6.5.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:caec6314ce8a81cf69bd89909f4b633b9f523834dc1a352021775d45e51d9401", size = 443930, upload-time = "2025-05-22T18:15:29.299Z" }, - { url = "https://files.pythonhosted.org/packages/78/b2/f8049221c96a06df89bed68260e8ca94beca5ea532ffc63b1175ad31f9cc/tornado-6.5.1-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:13ce6e3396c24e2808774741331638ee6c2f50b114b97a55c5b442df65fd9692", size = 443351, upload-time = "2025-05-22T18:15:31.038Z" }, - { url = "https://files.pythonhosted.org/packages/76/ff/6a0079e65b326cc222a54720a748e04a4db246870c4da54ece4577bfa702/tornado-6.5.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:5cae6145f4cdf5ab24744526cc0f55a17d76f02c98f4cff9daa08ae9a217448a", size = 443328, upload-time = "2025-05-22T18:15:32.426Z" }, - { url = "https://files.pythonhosted.org/packages/49/18/e3f902a1d21f14035b5bc6246a8c0f51e0eef562ace3a2cea403c1fb7021/tornado-6.5.1-cp39-abi3-win32.whl", hash = "sha256:e0a36e1bc684dca10b1aa75a31df8bdfed656831489bc1e6a6ebed05dc1ec365", size = 444396, upload-time = "2025-05-22T18:15:34.205Z" }, - { url = "https://files.pythonhosted.org/packages/7b/09/6526e32bf1049ee7de3bebba81572673b19a2a8541f795d887e92af1a8bc/tornado-6.5.1-cp39-abi3-win_amd64.whl", hash = "sha256:908e7d64567cecd4c2b458075589a775063453aeb1d2a1853eedb806922f568b", size = 444840, upload-time = "2025-05-22T18:15:36.1Z" }, - { url = "https://files.pythonhosted.org/packages/55/a7/535c44c7bea4578e48281d83c615219f3ab19e6abc67625ef637c73987be/tornado-6.5.1-cp39-abi3-win_arm64.whl", hash = "sha256:02420a0eb7bf617257b9935e2b754d1b63897525d8a289c9d65690d580b4dcf7", size = 443596, upload-time = "2025-05-22T18:15:37.433Z" }, -] - -[[package]] -name = "tqdm" -version = "4.67.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" }, -] - -[[package]] -name = "traitlets" -version = "5.14.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" }, -] - -[[package]] -name = "triton" -version = "3.3.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "setuptools" }, -] -wheels = [ - { url = "https://files.pythonhosted.org/packages/7d/74/4bf2702b65e93accaa20397b74da46fb7a0356452c1bb94dbabaf0582930/triton-3.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47bc87ad66fa4ef17968299acacecaab71ce40a238890acc6ad197c3abe2b8f1", size = 156516468, upload-time = "2025-04-09T20:27:48.196Z" }, - { url = "https://files.pythonhosted.org/packages/0a/93/f28a696fa750b9b608baa236f8225dd3290e5aff27433b06143adc025961/triton-3.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce4700fc14032af1e049005ae94ba908e71cd6c2df682239aed08e49bc71b742", size = 156580729, upload-time = "2025-04-09T20:27:55.424Z" }, -] - -[[package]] -name = "typing-extensions" -version = "4.13.2" -source = { registry = "https://pypi.org/simple" } -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 = "tzdata" -version = "2025.2" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" }, -] - -[[package]] -name = "urllib3" -version = "2.4.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/8a/78/16493d9c386d8e60e442a35feac5e00f0913c0f4b7c217c11e8ec2ff53e0/urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466", size = 390672, upload-time = "2025-04-10T15:23:39.232Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813", size = 128680, upload-time = "2025-04-10T15:23:37.377Z" }, -] - -[[package]] -name = "wcwidth" -version = "0.2.13" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5", size = 101301, upload-time = "2024-01-06T02:10:57.829Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166, upload-time = "2024-01-06T02:10:55.763Z" }, -] - -[[package]] -name = "yarl" -version = "1.20.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "idna" }, - { name = "multidict" }, - { name = "propcache" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/62/51/c0edba5219027f6eab262e139f73e2417b0f4efffa23bf562f6e18f76ca5/yarl-1.20.0.tar.gz", hash = "sha256:686d51e51ee5dfe62dec86e4866ee0e9ed66df700d55c828a615640adc885307", size = 185258, upload-time = "2025-04-17T00:45:14.661Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0f/6f/514c9bff2900c22a4f10e06297714dbaf98707143b37ff0bcba65a956221/yarl-1.20.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2137810a20b933b1b1b7e5cf06a64c3ed3b4747b0e5d79c9447c00db0e2f752f", size = 145030, upload-time = "2025-04-17T00:43:15.083Z" }, - { url = "https://files.pythonhosted.org/packages/4e/9d/f88da3fa319b8c9c813389bfb3463e8d777c62654c7168e580a13fadff05/yarl-1.20.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:447c5eadd750db8389804030d15f43d30435ed47af1313303ed82a62388176d3", size = 96894, upload-time = "2025-04-17T00:43:17.372Z" }, - { url = "https://files.pythonhosted.org/packages/cd/57/92e83538580a6968b2451d6c89c5579938a7309d4785748e8ad42ddafdce/yarl-1.20.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:42fbe577272c203528d402eec8bf4b2d14fd49ecfec92272334270b850e9cd7d", size = 94457, upload-time = "2025-04-17T00:43:19.431Z" }, - { url = "https://files.pythonhosted.org/packages/e9/ee/7ee43bd4cf82dddd5da97fcaddb6fa541ab81f3ed564c42f146c83ae17ce/yarl-1.20.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18e321617de4ab170226cd15006a565d0fa0d908f11f724a2c9142d6b2812ab0", size = 343070, upload-time = "2025-04-17T00:43:21.426Z" }, - { url = "https://files.pythonhosted.org/packages/4a/12/b5eccd1109e2097bcc494ba7dc5de156e41cf8309fab437ebb7c2b296ce3/yarl-1.20.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4345f58719825bba29895011e8e3b545e6e00257abb984f9f27fe923afca2501", size = 337739, upload-time = "2025-04-17T00:43:23.634Z" }, - { url = "https://files.pythonhosted.org/packages/7d/6b/0eade8e49af9fc2585552f63c76fa59ef469c724cc05b29519b19aa3a6d5/yarl-1.20.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5d9b980d7234614bc4674468ab173ed77d678349c860c3af83b1fffb6a837ddc", size = 351338, upload-time = "2025-04-17T00:43:25.695Z" }, - { url = "https://files.pythonhosted.org/packages/45/cb/aaaa75d30087b5183c7b8a07b4fb16ae0682dd149a1719b3a28f54061754/yarl-1.20.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af4baa8a445977831cbaa91a9a84cc09debb10bc8391f128da2f7bd070fc351d", size = 353636, upload-time = "2025-04-17T00:43:27.876Z" }, - { url = "https://files.pythonhosted.org/packages/98/9d/d9cb39ec68a91ba6e66fa86d97003f58570327d6713833edf7ad6ce9dde5/yarl-1.20.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:123393db7420e71d6ce40d24885a9e65eb1edefc7a5228db2d62bcab3386a5c0", size = 348061, upload-time = "2025-04-17T00:43:29.788Z" }, - { url = "https://files.pythonhosted.org/packages/72/6b/103940aae893d0cc770b4c36ce80e2ed86fcb863d48ea80a752b8bda9303/yarl-1.20.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ab47acc9332f3de1b39e9b702d9c916af7f02656b2a86a474d9db4e53ef8fd7a", size = 334150, upload-time = "2025-04-17T00:43:31.742Z" }, - { url = "https://files.pythonhosted.org/packages/ef/b2/986bd82aa222c3e6b211a69c9081ba46484cffa9fab2a5235e8d18ca7a27/yarl-1.20.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4a34c52ed158f89876cba9c600b2c964dfc1ca52ba7b3ab6deb722d1d8be6df2", size = 362207, upload-time = "2025-04-17T00:43:34.099Z" }, - { url = "https://files.pythonhosted.org/packages/14/7c/63f5922437b873795d9422cbe7eb2509d4b540c37ae5548a4bb68fd2c546/yarl-1.20.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:04d8cfb12714158abf2618f792c77bc5c3d8c5f37353e79509608be4f18705c9", size = 361277, upload-time = "2025-04-17T00:43:36.202Z" }, - { url = "https://files.pythonhosted.org/packages/81/83/450938cccf732466953406570bdb42c62b5ffb0ac7ac75a1f267773ab5c8/yarl-1.20.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:7dc63ad0d541c38b6ae2255aaa794434293964677d5c1ec5d0116b0e308031f5", size = 364990, upload-time = "2025-04-17T00:43:38.551Z" }, - { url = "https://files.pythonhosted.org/packages/b4/de/af47d3a47e4a833693b9ec8e87debb20f09d9fdc9139b207b09a3e6cbd5a/yarl-1.20.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f9d02b591a64e4e6ca18c5e3d925f11b559c763b950184a64cf47d74d7e41877", size = 374684, upload-time = "2025-04-17T00:43:40.481Z" }, - { url = "https://files.pythonhosted.org/packages/62/0b/078bcc2d539f1faffdc7d32cb29a2d7caa65f1a6f7e40795d8485db21851/yarl-1.20.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:95fc9876f917cac7f757df80a5dda9de59d423568460fe75d128c813b9af558e", size = 382599, upload-time = "2025-04-17T00:43:42.463Z" }, - { url = "https://files.pythonhosted.org/packages/74/a9/4fdb1a7899f1fb47fd1371e7ba9e94bff73439ce87099d5dd26d285fffe0/yarl-1.20.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:bb769ae5760cd1c6a712135ee7915f9d43f11d9ef769cb3f75a23e398a92d384", size = 378573, upload-time = "2025-04-17T00:43:44.797Z" }, - { url = "https://files.pythonhosted.org/packages/fd/be/29f5156b7a319e4d2e5b51ce622b4dfb3aa8d8204cd2a8a339340fbfad40/yarl-1.20.0-cp313-cp313-win32.whl", hash = "sha256:70e0c580a0292c7414a1cead1e076c9786f685c1fc4757573d2967689b370e62", size = 86051, upload-time = "2025-04-17T00:43:47.076Z" }, - { url = "https://files.pythonhosted.org/packages/52/56/05fa52c32c301da77ec0b5f63d2d9605946fe29defacb2a7ebd473c23b81/yarl-1.20.0-cp313-cp313-win_amd64.whl", hash = "sha256:4c43030e4b0af775a85be1fa0433119b1565673266a70bf87ef68a9d5ba3174c", size = 92742, upload-time = "2025-04-17T00:43:49.193Z" }, - { url = "https://files.pythonhosted.org/packages/d4/2f/422546794196519152fc2e2f475f0e1d4d094a11995c81a465faf5673ffd/yarl-1.20.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:b6c4c3d0d6a0ae9b281e492b1465c72de433b782e6b5001c8e7249e085b69051", size = 163575, upload-time = "2025-04-17T00:43:51.533Z" }, - { url = "https://files.pythonhosted.org/packages/90/fc/67c64ddab6c0b4a169d03c637fb2d2a212b536e1989dec8e7e2c92211b7f/yarl-1.20.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8681700f4e4df891eafa4f69a439a6e7d480d64e52bf460918f58e443bd3da7d", size = 106121, upload-time = "2025-04-17T00:43:53.506Z" }, - { url = "https://files.pythonhosted.org/packages/6d/00/29366b9eba7b6f6baed7d749f12add209b987c4cfbfa418404dbadc0f97c/yarl-1.20.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:84aeb556cb06c00652dbf87c17838eb6d92cfd317799a8092cee0e570ee11229", size = 103815, upload-time = "2025-04-17T00:43:55.41Z" }, - { url = "https://files.pythonhosted.org/packages/28/f4/a2a4c967c8323c03689383dff73396281ced3b35d0ed140580825c826af7/yarl-1.20.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f166eafa78810ddb383e930d62e623d288fb04ec566d1b4790099ae0f31485f1", size = 408231, upload-time = "2025-04-17T00:43:57.825Z" }, - { url = "https://files.pythonhosted.org/packages/0f/a1/66f7ffc0915877d726b70cc7a896ac30b6ac5d1d2760613603b022173635/yarl-1.20.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:5d3d6d14754aefc7a458261027a562f024d4f6b8a798adb472277f675857b1eb", size = 390221, upload-time = "2025-04-17T00:44:00.526Z" }, - { url = "https://files.pythonhosted.org/packages/41/15/cc248f0504610283271615e85bf38bc014224122498c2016d13a3a1b8426/yarl-1.20.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2a8f64df8ed5d04c51260dbae3cc82e5649834eebea9eadfd829837b8093eb00", size = 411400, upload-time = "2025-04-17T00:44:02.853Z" }, - { url = "https://files.pythonhosted.org/packages/5c/af/f0823d7e092bfb97d24fce6c7269d67fcd1aefade97d0a8189c4452e4d5e/yarl-1.20.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4d9949eaf05b4d30e93e4034a7790634bbb41b8be2d07edd26754f2e38e491de", size = 411714, upload-time = "2025-04-17T00:44:04.904Z" }, - { url = "https://files.pythonhosted.org/packages/83/70/be418329eae64b9f1b20ecdaac75d53aef098797d4c2299d82ae6f8e4663/yarl-1.20.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c366b254082d21cc4f08f522ac201d0d83a8b8447ab562732931d31d80eb2a5", size = 404279, upload-time = "2025-04-17T00:44:07.721Z" }, - { url = "https://files.pythonhosted.org/packages/19/f5/52e02f0075f65b4914eb890eea1ba97e6fd91dd821cc33a623aa707b2f67/yarl-1.20.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:91bc450c80a2e9685b10e34e41aef3d44ddf99b3a498717938926d05ca493f6a", size = 384044, upload-time = "2025-04-17T00:44:09.708Z" }, - { url = "https://files.pythonhosted.org/packages/6a/36/b0fa25226b03d3f769c68d46170b3e92b00ab3853d73127273ba22474697/yarl-1.20.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9c2aa4387de4bc3a5fe158080757748d16567119bef215bec643716b4fbf53f9", size = 416236, upload-time = "2025-04-17T00:44:11.734Z" }, - { url = "https://files.pythonhosted.org/packages/cb/3a/54c828dd35f6831dfdd5a79e6c6b4302ae2c5feca24232a83cb75132b205/yarl-1.20.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:d2cbca6760a541189cf87ee54ff891e1d9ea6406079c66341008f7ef6ab61145", size = 402034, upload-time = "2025-04-17T00:44:13.975Z" }, - { url = "https://files.pythonhosted.org/packages/10/97/c7bf5fba488f7e049f9ad69c1b8fdfe3daa2e8916b3d321aa049e361a55a/yarl-1.20.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:798a5074e656f06b9fad1a162be5a32da45237ce19d07884d0b67a0aa9d5fdda", size = 407943, upload-time = "2025-04-17T00:44:16.052Z" }, - { url = "https://files.pythonhosted.org/packages/fd/a4/022d2555c1e8fcff08ad7f0f43e4df3aba34f135bff04dd35d5526ce54ab/yarl-1.20.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:f106e75c454288472dbe615accef8248c686958c2e7dd3b8d8ee2669770d020f", size = 423058, upload-time = "2025-04-17T00:44:18.547Z" }, - { url = "https://files.pythonhosted.org/packages/4c/f6/0873a05563e5df29ccf35345a6ae0ac9e66588b41fdb7043a65848f03139/yarl-1.20.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:3b60a86551669c23dc5445010534d2c5d8a4e012163218fc9114e857c0586fdd", size = 423792, upload-time = "2025-04-17T00:44:20.639Z" }, - { url = "https://files.pythonhosted.org/packages/9e/35/43fbbd082708fa42e923f314c24f8277a28483d219e049552e5007a9aaca/yarl-1.20.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:3e429857e341d5e8e15806118e0294f8073ba9c4580637e59ab7b238afca836f", size = 422242, upload-time = "2025-04-17T00:44:22.851Z" }, - { url = "https://files.pythonhosted.org/packages/ed/f7/f0f2500cf0c469beb2050b522c7815c575811627e6d3eb9ec7550ddd0bfe/yarl-1.20.0-cp313-cp313t-win32.whl", hash = "sha256:65a4053580fe88a63e8e4056b427224cd01edfb5f951498bfefca4052f0ce0ac", size = 93816, upload-time = "2025-04-17T00:44:25.491Z" }, - { url = "https://files.pythonhosted.org/packages/3f/93/f73b61353b2a699d489e782c3f5998b59f974ec3156a2050a52dfd7e8946/yarl-1.20.0-cp313-cp313t-win_amd64.whl", hash = "sha256:53b2da3a6ca0a541c1ae799c349788d480e5144cac47dba0266c7cb6c76151fe", size = 101093, upload-time = "2025-04-17T00:44:27.418Z" }, - { url = "https://files.pythonhosted.org/packages/ea/1f/70c57b3d7278e94ed22d85e09685d3f0a38ebdd8c5c73b65ba4c0d0fe002/yarl-1.20.0-py3-none-any.whl", hash = "sha256:5d0fe6af927a47a230f31e6004621fd0959eaa915fc62acfafa67ff7229a3124", size = 46124, upload-time = "2025-04-17T00:45:12.199Z" }, -]