From 29e8571e922d37868c40c3cb7322bf2a42e95888 Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 14 Sep 2026 23:01:37 -0400 Subject: [PATCH 1/5] Move packaging from Poetry to uv and formatting from black to ruff pyproject.toml: the Poetry groups become PEP 735 dependency groups (dev, test, docs) with the same bounds, black is swapped for ruff in dev, and the package builds with uv_build. default-groups is empty so that, as with Poetry's optional groups, a plain `uv sync` installs only the runtime dependencies. [tool.ruff] keeps black's line length and, like black, leaves notebooks and Markdown alone. uv.lock replaces poetry.lock. Each package is pinned at the newer of its poetry.lock and requirements.txt versions, which had drifted apart on main. requirements.txt, still read by Read the Docs, is now exported from the lock. The tests, lint, mypy and notebooks workflows install uv with astral-sh/setup-uv and run `uv sync --locked --group ...` and `uv run`. pre-commit runs ruff-format, uv-lock and uv-export in place of black and poetry-check, and the large-file check skips the generated uv.lock. dependabot tracks the uv ecosystem. The devcontainer, docs, campaign scripts and two comments now point at uv. dem_rerun.py and compare_qubit_order_bb.py are reformatted here because they also carry text edits; the remaining ruff reformat follows in its own commit. Part of #437. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01S6mbxc9eJDQMVx7tjvYwud --- .devcontainer/Dockerfile | 7 +- .devcontainer/devcontainer-lock.json | 8 +- .devcontainer/devcontainer.json | 8 +- .github/CONTRIBUTING.md | 2 +- .github/dependabot.yml | 4 +- .github/workflows/lint.yml | 13 +- .github/workflows/mypy.yml | 13 +- .github/workflows/notebooks.yml | 14 +- .github/workflows/tests.yml | 11 +- .pre-commit-config.yaml | 19 +- README.md | 12 +- docs/source/README.rst | 14 +- docs/source/contributing.rst | 4 +- docs/source/getting_started.rst | 4 +- docs/source/testing.rst | 4 +- examples/decoding/dem_campaign/README.md | 3 +- examples/decoding/dem_campaign/dem_rerun.py | 12 +- .../decoding/quantum_bivariate_bicycle.sh | 2 +- examples/decoding/quantum_csp.sh | 2 +- examples/decoding/quantum_csp_chi_scan.sh | 2 +- .../decoding/quantum_hypergraph_product.sh | 2 +- examples/decoding/quantum_surface.sh | 2 +- .../plotting/compare_qubit_order_bb.py | 8 +- poetry.lock | 4630 ----------------- pyproject.toml | 88 +- requirements.txt | 650 +-- uv.lock | 3583 +++++++++++++ 27 files changed, 3986 insertions(+), 5135 deletions(-) delete mode 100644 poetry.lock create mode 100644 uv.lock diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index c003551c..ca0f8cf4 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -26,7 +26,7 @@ RUN DEBIAN_FRONTEND=noninteractive \ libffi-dev \ liblzma-dev -# Python and poetry installation +# Python and uv installation USER $USER ARG HOME="/home/$USER" ARG PYTHON_VERSION=3.11 @@ -41,6 +41,5 @@ RUN echo "linux installed" \ && pyenv install ${PYTHON_VERSION} \ && echo "python installed" \ && pyenv global ${PYTHON_VERSION} \ - && curl -sSL https://install.python-poetry.org | python3 - \ - && echo "poetry installed" \ - && poetry config virtualenvs.in-project true \ No newline at end of file + && curl -LsSf https://astral.sh/uv/install.sh | sh \ + && echo "uv installed" \ No newline at end of file diff --git a/.devcontainer/devcontainer-lock.json b/.devcontainer/devcontainer-lock.json index 8b2c6675..219acb6b 100644 --- a/.devcontainer/devcontainer-lock.json +++ b/.devcontainer/devcontainer-lock.json @@ -1,9 +1,3 @@ { - "features": { - "ghcr.io/devcontainers-extra/features/poetry:2": { - "version": "2.0.18", - "resolved": "ghcr.io/devcontainers-extra/features/poetry@sha256:7b5f2c66d25d8b3cc5d601af6b81b720c77dd315f24d5e6f84b342cccc3c9228", - "integrity": "sha256:7b5f2c66d25d8b3cc5d601af6b81b720c77dd315f24d5e6f84b342cccc3c9228" - } - } + "features": {} } diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index b7880797..06bce788 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,5 +1,5 @@ { - "name": "poetry3-poetry-pyenv-mdopt", + "name": "mdopt", "build": { "dockerfile": "Dockerfile" }, @@ -11,7 +11,7 @@ // "forwardPorts": [], // πŸ‘‡ Use 'postCreateCommand' to run commands after the container is created. - "postCreateCommand": "poetry install --with dev,test,docs", + "postCreateCommand": "uv sync --group dev --group test --group docs", // πŸ‘‡ Configure tool-specific properties. "customizations": { @@ -23,9 +23,7 @@ ] } }, - "features": { - "ghcr.io/devcontainers-extra/features/poetry:2": {} - } + "features": {} // πŸ‘‡ Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root. // "remoteUser": "root" diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index eef0620b..b632a3c0 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -11,7 +11,7 @@ Additionally, make sure any new code is properly accompanied by working through - [ ] All other tests still pass (when running ``pytest`` in the root directory) - [ ] Any functions added have [``numpy`` style docstrings](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html) - [ ] If suitable, funtionality is documented in ``docs`` or an example added in ``examples`` - - [ ] The code is linted with ``black``. + - [ ] The code is formatted with ``ruff format``. # Contributor Covenant Code of Conduct diff --git a/.github/dependabot.yml b/.github/dependabot.yml index b4ee19fc..b6cccd3f 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -7,8 +7,8 @@ updates: schedule: interval: "daily" - # Maintain dependencies for Poetry - - package-ecosystem: "pip" + # Maintain dependencies in pyproject.toml and uv.lock + - package-ecosystem: "uv" directory: "/" schedule: interval: "daily" diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 2b85b822..7f7d91f5 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -8,16 +8,13 @@ jobs: steps: - uses: actions/checkout@v7 - - name: Set up Python 3.11 - uses: actions/setup-python@v7 + - name: Set up uv and Python 3.11 + uses: astral-sh/setup-uv@v10.1.0 with: - python-version: '3.11' - - - name: Set up Poetry - run: pip install poetry + python-version: '3.11' - name: Install dependencies - run: poetry install --with dev + run: uv sync --locked --group dev - name: Run linter - run: poetry run pylint mdopt --fail-under=9 + run: uv run pylint mdopt --fail-under=9 diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index faafd681..f7e10ac5 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -8,16 +8,13 @@ jobs: steps: - uses: actions/checkout@v7 - - name: Set up Python 3.11 - uses: actions/setup-python@v7 + - name: Set up uv and Python 3.11 + uses: astral-sh/setup-uv@v10.1.0 with: - python-version: '3.11' - - - name: Set up Poetry - run: pip install poetry + python-version: '3.11' - name: Install dependencies - run: poetry install --with dev + run: uv sync --locked --group dev - name: Run mypy - run: poetry run mypy mdopt + run: uv run mypy mdopt diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml index 75c5af80..8971b42f 100644 --- a/.github/workflows/notebooks.yml +++ b/.github/workflows/notebooks.yml @@ -21,7 +21,7 @@ on: - "mdopt/**" - "scripts/run_notebooks.py" - "pyproject.toml" - - "poetry.lock" + - "uv.lock" - ".github/workflows/notebooks.yml" pull_request: paths: @@ -29,7 +29,7 @@ on: - "mdopt/**" - "scripts/run_notebooks.py" - "pyproject.toml" - - "poetry.lock" + - "uv.lock" - ".github/workflows/notebooks.yml" workflow_dispatch: @@ -51,7 +51,8 @@ jobs: steps: - uses: actions/checkout@v7 - - uses: actions/setup-python@v7 + - name: Set up uv and Python 3.11 + uses: astral-sh/setup-uv@v10.1.0 with: python-version: "3.11" @@ -65,15 +66,12 @@ jobs: texlive-latex-base texlive-latex-extra texlive-fonts-recommended \ cm-super dvipng ghostscript - - name: Set up Poetry - run: pip install poetry - # The dev group carries ipykernel, nbclient, nbformat and matplotlib. - name: Install package and notebook dependencies - run: poetry install --with dev + run: uv sync --locked --group dev - name: Execute notebooks env: MDOPT_NB_FAST: "1" MPLBACKEND: Agg - run: poetry run python scripts/run_notebooks.py + run: uv run python scripts/run_notebooks.py diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 036bafab..c809ce4c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -12,20 +12,17 @@ jobs: steps: - uses: actions/checkout@v7 - - name: Set up Python 3.11 - uses: actions/setup-python@v7 + - name: Set up uv and Python 3.11 + uses: astral-sh/setup-uv@v10.1.0 with: python-version: '3.11' - - name: Set up Poetry - run: pip install poetry - - name: Install dependencies - run: poetry install --with test + run: uv sync --locked --group test - name: Generate report run: | - poetry run pytest --cov=./ --cov-report=xml -W ignore::DeprecationWarning + uv run pytest --cov=./ --cov-report=xml -W ignore::DeprecationWarning - name: Upload Coverage to Codecov uses: codecov/codecov-action@v7 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0fabc8aa..47d64240 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,6 +3,8 @@ repos: rev: v6.0.0 hooks: - id: check-added-large-files + # uv.lock is generated and larger than the 500 KB default. + exclude: ^uv\.lock$ - id: check-docstring-first - id: check-merge-conflict - id: check-toml @@ -10,11 +12,10 @@ repos: - id: detect-private-key - id: trailing-whitespace - - repo: https://github.com/psf/black-pre-commit-mirror - rev: 26.5.1 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.16.7 hooks: - - id: black - entry: black . + - id: ruff-format - repo: local hooks: @@ -38,7 +39,11 @@ repos: - id: mypy exclude: '(^examples|^docs|^mdopt/examples)/.*' - - repo: https://github.com/python-poetry/poetry - rev: 2.4.1 + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.12.13 hooks: - - id: poetry-check \ No newline at end of file + # Fails when uv.lock is out of date with pyproject.toml. + - id: uv-lock + # Keeps requirements.txt (read by readthedocs) in step with uv.lock. + - id: uv-export + args: ["--frozen", "--no-default-groups", "--no-emit-project", "--output-file=requirements.txt", "--quiet"] diff --git a/README.md b/README.md index 398bcc12..9aa60652 100644 --- a/README.md +++ b/README.md @@ -26,12 +26,14 @@ To install the current release, use the package manager [pip](https://pip.pypa.i pip install mdopt ``` -Otherwise, you can clone the repository and use [poetry](https://python-poetry.org/). +Otherwise, you can clone the repository and use [uv](https://docs.astral.sh/uv/). ```bash -poetry install +uv sync ``` +`uv sync --group dev --group test` also installs the development and test tools. + ### A note on NumPy's BLAS on Apple silicon On Apple silicon running macOS 14 or later, pip installs the NumPy and @@ -190,11 +192,11 @@ Developers may find the following guidelines useful: ``` - **Coding style.** - The project follows the [Black](https://black.readthedocs.io/en/stable/) code style. - Please run Black before submitting a pull request: + The code is formatted with [Ruff](https://docs.astral.sh/ruff/formatter/), whose style matches Black's. + Please run the formatter before submitting a pull request: ```bash - black . + uv run --group dev ruff format . ``` - **Pre-commit hooks.** diff --git a/docs/source/README.rst b/docs/source/README.rst index ed033bfb..729a1b19 100644 --- a/docs/source/README.rst +++ b/docs/source/README.rst @@ -23,11 +23,13 @@ To install the current release, use the package manager pip install mdopt Otherwise, you can clone the repository and use -`poetry `__. +`uv `__. .. code:: bash - poetry install + uv sync + +``uv sync --group dev --group test`` also installs the development and test tools. Minimal example --------------- @@ -199,13 +201,13 @@ Developers may find the following guidelines useful: ./generate_docs.sh -- **Coding style.** The project follows the - `Black `__ code style. Please - run Black before submitting a pull request: +- **Coding style.** The code is formatted with + `Ruff `__, whose style matches + Black's. Please run the formatter before submitting a pull request: .. code:: bash - black . + uv run --group dev ruff format . - **Pre-commit hooks.** `Pre-commit `__ hooks are configured to enforce consistent style automatically. To enable diff --git a/docs/source/contributing.rst b/docs/source/contributing.rst index 5d6cb3f8..85f52c1d 100644 --- a/docs/source/contributing.rst +++ b/docs/source/contributing.rst @@ -4,11 +4,11 @@ Contributing We welcome contributions of all sizes! Before opening a pull request, please: 1. Run the test suite (see :doc:`testing`). -2. Follow the **Black** code style: +2. Format the code with **Ruff**: .. code-block:: bash - black . + uv run --group dev ruff format . 3. Install pre-commit hooks: diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index 195277ec..4b0da6af 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -10,13 +10,13 @@ You can install the package from PyPI: pip install mdopt -Or, alternatively, from source using Poetry: +Or, alternatively, from source using uv: .. code-block:: bash git clone https://github.com/quicophy/mdopt.git cd mdopt - poetry install + uv sync Minimal example --------------------- diff --git a/docs/source/testing.rst b/docs/source/testing.rst index 98a063e4..35d68e85 100644 --- a/docs/source/testing.rst +++ b/docs/source/testing.rst @@ -6,8 +6,8 @@ We use ``pytest`` for tests. From the project root: .. code-block:: bash pytest - # or, if installed with poetry: - poetry run pytest + # or, from a uv checkout: + uv run --group test pytest Tips ---- diff --git a/examples/decoding/dem_campaign/README.md b/examples/decoding/dem_campaign/README.md index 30ffe2e6..6d24c422 100644 --- a/examples/decoding/dem_campaign/README.md +++ b/examples/decoding/dem_campaign/README.md @@ -20,7 +20,8 @@ from its last record. ## Running -From the repository root, inside the Poetry environment: +From the repository root, inside the uv environment (`uv sync --group test`, which +provides pymatching, then `source .venv/bin/activate`): ```bash cd examples/decoding/dem_campaign diff --git a/examples/decoding/dem_campaign/dem_rerun.py b/examples/decoding/dem_campaign/dem_rerun.py index 040b4e83..d75d663c 100644 --- a/examples/decoding/dem_campaign/dem_rerun.py +++ b/examples/decoding/dem_campaign/dem_rerun.py @@ -32,8 +32,8 @@ try: from beliefmatching import BeliefMatching except ImportError: # pragma: no cover - optional third decoder - # beliefmatching's metadata pins a numpy range Poetry cannot reconcile - # with ours, so it is not a declared dependency; `pip install + # beliefmatching's metadata pins numpy<=2.2.6, which uv.lock cannot + # reconcile with our numpy>=2.3, so it is not a declared dependency; `pip install # beliefmatching` works. Without it the harness still runs and simply # records no belief-matching column. BeliefMatching = None @@ -163,7 +163,7 @@ def run(tag, circuit, shots, seed, ladder=(16, 32, 64), escalate=128): if n % 200 == 0: z = (obs_fail - pred_fail) / np.sqrt(max(pred_fail, 1.0)) print( - f"[{tag}] {i+1}/{shots} calib: predicted {pred_fail:.1f} " + f"[{tag}] {i + 1}/{shots} calib: predicted {pred_fail:.1f} " f"observed {int(obs_fail)} z={z:+.2f} escalations={escalations}", flush=True, ) @@ -185,7 +185,7 @@ def run(tag, circuit, shots, seed, ladder=(16, 32, 64), escalate=128): if scored: f = sum(r[dec] != r["truth"] for r in scored) print( - f"[{tag}] {dec}: fails={f}/{len(scored)} ({f/len(scored):.4f})", + f"[{tag}] {dec}: fails={f}/{len(scored)} ({f / len(scored):.4f})", flush=True, ) for chi in ladder: @@ -196,7 +196,7 @@ def run(tag, circuit, shots, seed, ladder=(16, 32, 64), escalate=128): f = sum(r["flips"][str(chi)] != r["truth"] for r in scored) print( f"[{tag}] ladder LER(chi={chi}): {f}/{len(scored)} " - f"({f/len(scored):.4f})", + f"({f / len(scored):.4f})", flush=True, ) # Recomputed from every loaded row so a resumed run's summary covers the @@ -205,7 +205,7 @@ def run(tag, circuit, shots, seed, ladder=(16, 32, 64), escalate=128): of = sum(r["map"] != r["truth"] for r in rows) print( f"[{tag}] final calibration: predicted {pf:.1f} observed {of} " - f"z={(of-pf)/np.sqrt(max(pf,1)):+.2f}", + f"z={(of - pf) / np.sqrt(max(pf, 1)):+.2f}", flush=True, ) print(f"[{tag}] complete ({n})", flush=True) diff --git a/examples/decoding/quantum_bivariate_bicycle.sh b/examples/decoding/quantum_bivariate_bicycle.sh index 6086a725..15b42812 100755 --- a/examples/decoding/quantum_bivariate_bicycle.sh +++ b/examples/decoding/quantum_bivariate_bicycle.sh @@ -93,7 +93,7 @@ for seed in "${seeds[@]}"; do for cut in "${cuts[@]}"; do echo "Running ${num_experiments} experiments for orders ${order_x}, ${order_y}, polynomials "${poly_a}", "${poly_b}", bond dimension ${bond_dim}, error rate ${error_rate}, error model ${error_model}, and seed ${seed}." # Run the Python script with the specified arguments - poetry run python -m mdopt.examples.decoding.quantum_bivariate_bicycle --order_x ${order_x} --order_y ${order_y} --poly_a "${poly_a}" --poly_b "${poly_b}" --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} + uv run python -m mdopt.examples.decoding.quantum_bivariate_bicycle --order_x ${order_x} --order_y ${order_y} --poly_a "${poly_a}" --poly_b "${poly_b}" --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} (( current_iteration++ )) show_progress "$current_iteration" "$total_iterations" done diff --git a/examples/decoding/quantum_csp.sh b/examples/decoding/quantum_csp.sh index 0b4a0c2a..0b679258 100755 --- a/examples/decoding/quantum_csp.sh +++ b/examples/decoding/quantum_csp.sh @@ -37,7 +37,7 @@ for seed in "${seeds[@]}"; do # Print current configuration echo "Running for ${num_qubits} qubits, batch ${batch}, code id ${code_id}, bond dimension ${bond_dim}, error rate ${error_rate}, error model ${error_model}, and seed ${seed}." # Run the Python script - poetry run python -m mdopt.examples.decoding.quantum_csp --num_qubits ${num_qubits} --batch ${batch} --code_id ${code_id} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} + uv run python -m mdopt.examples.decoding.quantum_csp --num_qubits ${num_qubits} --batch ${batch} --code_id ${code_id} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} done done done diff --git a/examples/decoding/quantum_csp_chi_scan.sh b/examples/decoding/quantum_csp_chi_scan.sh index 5e6b8b6f..6148e0de 100644 --- a/examples/decoding/quantum_csp_chi_scan.sh +++ b/examples/decoding/quantum_csp_chi_scan.sh @@ -107,7 +107,7 @@ run_one_job() { echo "[$((IDX+1))/${TOTAL}] Starting: N=${N}, chi=${CHI}, code_id=${CODE_ID}" - poetry run python -m mdopt.examples.decoding.quantum_csp \ + uv run python -m mdopt.examples.decoding.quantum_csp \ --num_qubits "$N" \ --batch "$BATCH" \ --code_id "$CODE_ID" \ diff --git a/examples/decoding/quantum_hypergraph_product.sh b/examples/decoding/quantum_hypergraph_product.sh index 73a49afc..5f30ec2b 100755 --- a/examples/decoding/quantum_hypergraph_product.sh +++ b/examples/decoding/quantum_hypergraph_product.sh @@ -33,7 +33,7 @@ for seed in "${seeds[@]}"; do # Print current configuration echo "Running for system size ${system_size}, bond dimension ${bond_dim}, error rate ${error_rate}, error model ${error_model}, and seed ${seed}." # Run the Python script - poetry run python -m mdopt.examples.decoding.quantum_hypergraph_product --system_size ${system_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} + uv run python -m mdopt.examples.decoding.quantum_hypergraph_product --system_size ${system_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} done done done diff --git a/examples/decoding/quantum_surface.sh b/examples/decoding/quantum_surface.sh index e2bde1a0..f9f3b2a9 100755 --- a/examples/decoding/quantum_surface.sh +++ b/examples/decoding/quantum_surface.sh @@ -33,7 +33,7 @@ for seed in "${seeds[@]}"; do # Print current configuration echo "Running for lattice size ${lattice_size}, bond dimension ${bond_dim}, error rate ${error_rate}, error model ${error_model}, and seed ${seed}." # Run the Python script - poetry run python -m pyinstrument -r html -o profile.html -m mdopt.examples.decoding.quantum_surface --lattice_size ${lattice_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} + uv run python -m pyinstrument -r html -o profile.html -m mdopt.examples.decoding.quantum_surface --lattice_size ${lattice_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} done done done diff --git a/mdopt/examples/decoding/plotting/compare_qubit_order_bb.py b/mdopt/examples/decoding/plotting/compare_qubit_order_bb.py index 368e4855..a11c8a8c 100644 --- a/mdopt/examples/decoding/plotting/compare_qubit_order_bb.py +++ b/mdopt/examples/decoding/plotting/compare_qubit_order_bb.py @@ -7,7 +7,7 @@ - Logical error rate (avg over trials). Usage: - poetry run python examples/decoding/compare_qubit_order_bb.py + uv run python mdopt/examples/decoding/plotting/compare_qubit_order_bb.py """ # --- asset paths (the code lives in the package; the data does not) --- @@ -149,8 +149,8 @@ def main(): print(f" Natural: max_span={bw_nat:4d}, total_span={ts_nat:6d}") print(f" Optimised: max_span={bw_opt:4d}, total_span={ts_opt:6d}") print( - f" Reduction: max_span {100*(bw_nat-bw_opt)/bw_nat:.1f}%," - f" total_span {100*(ts_nat-ts_opt)/ts_nat:.1f}%" + f" Reduction: max_span {100 * (bw_nat - bw_opt) / bw_nat:.1f}%," + f" total_span {100 * (ts_nat - ts_opt) / ts_nat:.1f}%" ) # ── Decoding trials ──────────────────────────────────────────────────────── @@ -177,7 +177,7 @@ def main(): f" Optimised: LER={ler_opt:.6f} Β± {sem_opt:.6f}, avg time/decode={t_opt:.3f}s" ) if ler_nat > 0 and ler_opt > 0: - print(f" LER ratio (nat/opt): {ler_nat/ler_opt:.2f}Γ—") + print(f" LER ratio (nat/opt): {ler_nat / ler_opt:.2f}Γ—") if __name__ == "__main__": diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 3d82c815..00000000 --- a/poetry.lock +++ /dev/null @@ -1,4630 +0,0 @@ -# This file is automatically @generated by Poetry 2.4.3 and should not be changed by hand. - 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Keep that: `uv sync` installs the package alone, and +# contributors ask for groups explicitly (`uv sync --group dev --group test`). +default-groups = [] -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +[tool.uv.build-backend] +# Flat layout: the package lives at ./mdopt, not under src/. +module-root = "" -[tool.poetry.requires-plugins] -poetry-plugin-export = ">=1.8" +[tool.ruff] +line-length = 88 +target-version = "py311" +# Parity with black, which formatted Python files only: notebooks (whose +# outputs are regenerated and synced into the docs) and the Python blocks in +# Markdown files are left as they are. +extend-exclude = ["*.ipynb", "*.md"] +# Apply the excludes even to files named explicitly, as pre-commit does. +force-exclude = true diff --git a/requirements.txt b/requirements.txt index 3076c17f..afbde2bc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,13 @@ -cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ +# This file was autogenerated by uv via the following command: +# uv export --frozen --no-default-groups --no-emit-project --output-file=requirements.txt +cffi==2.1.1 \ --hash=sha256:046bfc24911b37851ee1b51aab8bffe713d89c68c6a057b09484ce9fd5f69b4e \ --hash=sha256:06c72bb76605a4b0cd0aad6930b69d4baf7dd5d806cfc409b824191099700e66 \ --hash=sha256:0beceaabe56af686895136a2de78db54ecd8e4046b236b8fd6d6cb61389e9bf2 \ --hash=sha256:154852545011f779917b11c78db2358d095da62a9a172b78ad0a583ee5adc0d0 \ - --hash=sha256:194cffa889098ced9976c3fc6340305e43f6303657d298da55366907c05c22d6 \ --hash=sha256:19ee6127ee34de7d83ce3d371ebc5ed91addbdcc39f9ab15ce4eb35a4e534971 \ --hash=sha256:1a18a57b58cfb21fc28d72e876acf10eaed67a1ed96226f92af4df681d571c4c \ --hash=sha256:1aa5645c30469b09530c4ebca77ebf8f17618293c58f8549cb1a543a50236e7d \ - --hash=sha256:1dea0e4d7d4f11f619fe8c1d76caf49e24405b4b5743c0e3be16a500ecd930c9 \ --hash=sha256:208f941bb9d18e768138677f0a6d2ce01f590df56043dda1df1535ac57c88517 \ --hash=sha256:210019b6c7cf07f081b4c54635c8cf744377001350e29cc0f81c4377b4797735 \ --hash=sha256:246fa40ce8645a614ff682e0b70f37134e460eaf93a775e0cbe3cca585a67a80 \ @@ -23,9 +23,7 @@ cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:34e261f78cb6ceaaa36f42f2613f4380d94d9c759a9c73c769ee6e0247364632 \ --hash=sha256:363e05fa78e15116c3c32c210ee36884fd6b9afa6d440e47112c3bd511d64cb6 \ --hash=sha256:398aff33cee2767e3e781d2554c54bd0dff386bb437581e0d8011fde1a942ec1 \ - --hash=sha256:3d22a20b1fb1632cc72c22f95f7b0d2961c3e1c235f245ba4c606c4771035659 \ --hash=sha256:42a494cee34437f05546455144f2b5d9ac09b1face62bcfce597d2e521066688 \ - --hash=sha256:42e2f76b9455f5a9a844f770bf3e200ed3da0e15f5df3db9c31fe80b04b3d004 \ --hash=sha256:42f6930c31dc7f50732c9ae793c2786c7b6b044195967bbdde40bb9be81c4cc0 \ --hash=sha256:456a61fa52d579ebf9df2e9552ead5129855dbaff6c1e5a9b1bc408809bdc062 \ --hash=sha256:471cee653ae88de62096552e6d24ccb4a5adb8c8c9f10b5054d0122c15bf2779 \ @@ -36,8 +34,6 @@ cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:507a24c282e0f42f8ed737cf048572cbf580468da5555764a8331735e9c736b6 \ --hash=sha256:51b31d1c98274844cfd7838ce00bfc27c7423a4dc00fc0772fc3331c2cc90676 \ --hash=sha256:58acb8ab8e295e6c5ea12f888cbb13cf21511ef2a3303a23f4325c29d17fe5c1 \ - --hash=sha256:5a59cc1c4442bc3d5c703bf720b51138d0bfc173618807c9ee2490a7541dd3d9 \ - --hash=sha256:5bb4e7ea95dcd6a014a6fef62e62467d67d8e582326443f3d68e71d6320a9fcf \ --hash=sha256:5c58fe613dc5e5336357eff555824a314d8e43282600435c8d1cb6a7a2fedd13 \ --hash=sha256:5e7cecbaadb83884793e05828cee59b210b24583b9c7425d0ba6a754fe22eb4e \ --hash=sha256:616f097f2fe415bc92a247f02e11f634e1f9e9a83d327e3c915c15089c87869e \ @@ -48,20 +44,16 @@ cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:6a8dddef476fab96d066d578fc88526767b836ab5ab21754e1d5bf3879c31c7c \ --hash=sha256:6e192623c49c94421616a5778fba35cf0d5a8d000650c1967ef4448ee5cdd990 \ --hash=sha256:7225e4514edb64eb6740324353e0da0711954fd8d7da4576755b1c6e09b697cd \ - --hash=sha256:75f80557d1389eddbd0de2681f6a390a0c5338c31ddaa821381c203fc3fd50d9 \ --hash=sha256:770de9db11e84213beec501cfcaa013b019820ca881e03344dea5844f7876d94 \ --hash=sha256:7750c6449dff7864bb9bb27ddfb0267756189201a3afc911d82b3caacd70dfc3 \ --hash=sha256:7bde5e4cc5c10140859842b9d383af292b22639a4dffb725314baf45968cef80 \ - --hash=sha256:7ce713ace7c0e4520535b42b77eaa742c16dab813978064913e5a3cf82973b41 \ --hash=sha256:7da0c5eff80f0197f3b3d1232ec5a682a9325f4ae9016a78f5f5ca35f9ced1f5 \ --hash=sha256:7dbb61fe3a7699468030f71bbe5f8a0e326a151daa91beb11a6fc1f980c55e1c \ --hash=sha256:811bd1e21d32de12efca32393a0ab3f5133b54fce9bd44b8bd77ab07da14bf6a \ --hash=sha256:8ef53b2de9bcb9197d31854256575d59dbac0cba72ac627bb291ef5eceb74be4 \ --hash=sha256:937c0052c05a31ca1daf18de3158eed4dbfcb9cc107adbea227728d647be701e \ --hash=sha256:9d2055050ea716bd38b7f7f1579c275386646b4894c155a3e2f3cd62ed41b7c6 \ - --hash=sha256:9f8d177621de5cb38ee3e731eda45d421db093ec0739f46a5594babda7987a98 \ --hash=sha256:a2d7755bef5a12ed488f4ef1f1b69ee9191d7396083b755a5d2295f6edb4768b \ - --hash=sha256:a48d62ab9d6f4f98c983223a547af44be6ca3691074c31cecced6facd3ba2dc1 \ --hash=sha256:a4f00aa42f75d6e4595e8866e748cc1705adc0cddfeb2ca86d0d03993d63ba03 \ --hash=sha256:a6e721d4b0e45d5b65e87534470e67b18dcd092c83f68fba09f152b9cbc061af \ --hash=sha256:a730a083190634c65cca36ba5f489531576ebd79bcd5c8e172130f6453127231 \ @@ -72,13 +64,11 @@ cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:aeae0e330c9f6acd681f647d46cefd30c29f93e3392882e792e82080c9691399 \ --hash=sha256:b0431303acaea1089ad4b3e9ce4e6518193def1118d4073ca848635ee4ea2e96 \ --hash=sha256:b5bdfd1c873d4e093aabc0ca84c4ca6dbc4f752afb5c86f146d9742580c9da2e \ - --hash=sha256:baed1e86cc735622097354b9d1281406caf42ff42a886d29faa8e8d1630333be \ --hash=sha256:c1453022f490d2459a11819d83ad1d586e9ff65a12ac3e705ffebd46d3685dcf \ --hash=sha256:c26608d2222fb1e94487e4a387d85f13eb55d5ed725cb25a0c589ac4ee60e7bc \ --hash=sha256:c7659f22557c5a0bc4855cd635f55edec690cc008a40768527762cb9fb263455 \ --hash=sha256:c8c69575568085ba0b1b10c0249d779a214aea6f6522e949a0fc9fb0fcb449d0 \ --hash=sha256:c8d2c9fd1f2d16f780d15127abb050d13d1a76c03a4bd87d7e4980e45e511e12 \ - --hash=sha256:ca82be1a1d406ecfe1d25dc16cb33488e5a16bf4438c9fb590484ea29d92478b \ --hash=sha256:cc572dace3f60ef98d7b12ff411d20f5362feb31a0439eab0085bbfd349982d7 \ --hash=sha256:d18e5ac0f2f03f4f518d3e23db0f0cad7faa1da8620e9c09461d443bbf6e6692 \ --hash=sha256:d28630f5854ab07ab1fd4aba756de52326c82e6be15d414b12793f1975048b54 \ @@ -99,68 +89,54 @@ cffi==2.1.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:f8ec5e643a9a937f64e1999eb9f75d072263751912dc5cd06d3c85f8f44be7c3 \ --hash=sha256:fb92203a88b3d3053034db775110081c49d28be6551923805e039924093761e4 \ --hash=sha256:fcd22650c908d7b7da162bbfaab594a1227a15d1643a98c68b122ac642fa2264 -clarabel==0.11.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via clarabel +clarabel==0.11.1 \ --hash=sha256:557d5148a4377ae1980b65d00605ae870a8f34f95f0f6a41e04aa6d3edf67148 \ --hash=sha256:8963687ee250d27310d139eea5a6816f9c3ae31f33691b56579ca4f0f0b64b63 \ --hash=sha256:c39160e4222040f051f2a0598691c4f9126b4d17f5b9e7678f76c71d611e12d8 \ --hash=sha256:c8c41aaa6f3f8c0f3bd9d86c3e568dcaee079562c075bd2ec9fb3a80287380ef \ --hash=sha256:e4837b9d0db01e98239f04b1e3526a6cf568529d3c19a8b3f591befdc467f9bb \ --hash=sha256:e7c41c47f0e59aeab99aefff9e58af4a8753ee5269bbeecbd5526fc6f41b9598 -click==8.1.8 ; python_version >= "3.10" and python_version < "4.0" \ + # via cvxpy +click==8.1.8 \ --hash=sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2 \ --hash=sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a -cloudpickle==3.1.2 ; python_version >= "3.10" and python_version < "4.0" \ + # via qecsim +cloudpickle==3.1.2 \ --hash=sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414 \ --hash=sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a -colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32") \ + # via joblib +colorama==0.4.6 ; sys_platform == 'win32' \ --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 -contourpy==1.2.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # click + # pytest + # tqdm +contourpy==1.2.1 \ --hash=sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2 \ - --hash=sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9 \ - --hash=sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9 \ --hash=sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4 \ --hash=sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce \ --hash=sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7 \ - --hash=sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f \ --hash=sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922 \ --hash=sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4 \ --hash=sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e \ - --hash=sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b \ - --hash=sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619 \ --hash=sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205 \ - --hash=sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480 \ --hash=sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965 \ --hash=sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c \ - --hash=sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd \ --hash=sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5 \ --hash=sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f \ --hash=sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc \ --hash=sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec \ - --hash=sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd \ --hash=sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b \ - --hash=sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9 \ - --hash=sha256:a12a813949e5066148712a0626895c26b2578874e4cc63160bb007e6df3436fe \ --hash=sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce \ - --hash=sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609 \ - --hash=sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8 \ --hash=sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0 \ - --hash=sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f \ --hash=sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8 \ - --hash=sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b \ - --hash=sha256:bd3db01f59fdcbce5b22afad19e390260d6d0222f35a1023d9adc5690a889364 \ - --hash=sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040 \ --hash=sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f \ - --hash=sha256:d31a63bc6e6d87f77d71e1abbd7387ab817a66733734883d1fc0021ed9bfa083 \ --hash=sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df \ - --hash=sha256:ddcb8581510311e13421b1f544403c16e901c4e8f09083c881fab2be80ee31ba \ - --hash=sha256:e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445 \ - --hash=sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da \ - --hash=sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3 \ - --hash=sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72 \ - --hash=sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02 \ - --hash=sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985 -cvxpy==1.9.2 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72 + # via matplotlib +cvxpy==1.9.2 \ --hash=sha256:074897ae8f2378174099c082feef5a93b44f4c8dc9b3e960397c31fa3cebf722 \ --hash=sha256:1cc56d04515201a3e2f58a16ea59812a0126dd1419f0726f8322e6dc227a9062 \ --hash=sha256:27d0d08dfc1484eed29cbb98616409a677be41a81e58c91c2b954b8c19968dfa \ @@ -186,16 +162,16 @@ cvxpy==1.9.2 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:f649df44a798420c5a2fec75e073c067ffda92fb00a6d64d2221bef275d2d065 \ --hash=sha256:fa6e86a6ecd19a63751b470a3ed44d5fba6663ab9e05d6040bb954e80a4f7551 \ --hash=sha256:fb6aa08b89aedfee2a3c62902551df67d5c6b8684038e019fbe8e5a472142845 -cycler==0.12.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via qldpc +cycler==0.12.1 \ --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c -diskcache==5.6.3 ; python_version >= "3.10" and python_version < "4.0" \ + # via matplotlib +diskcache==5.6.3 \ --hash=sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc \ --hash=sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19 -exceptiongroup==1.3.1 ; python_version >= "3.10" and python_version < "3.11" \ - --hash=sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219 \ - --hash=sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598 -fonttools==4.65.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via qldpc +fonttools==4.65.0 \ --hash=sha256:04f73dd01005752a6e75cf4a8dc6b70dc724d1d4bc34cc89522153f4a2f07680 \ --hash=sha256:05595385ae99f4b9626cebb973bf171b8fe38a8f40708e6e42abba0ed7537778 \ --hash=sha256:06273c71e692caf5989c0437ca50875a5e49e216ddf653228fe9bb35bdc82c0f \ @@ -212,7 +188,6 @@ fonttools==4.65.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:52ea9d2a8385075770db74d5e5718fa80b2222bb4fc62856a377dd2865ca8848 \ --hash=sha256:580eb68ff7bd6954a7a76afddd864bfc66eaaf5f5c20dd6ead9186d0055a4ffe \ --hash=sha256:5a977e3645dbffaee924209828aa702a215f7ff68bc08010740c10c723787e62 \ - --hash=sha256:5c1700a60e4ff23a0425d5a64abf43d092e6b55071354825781faf255904dcb4 \ --hash=sha256:6031e77b3fb8c765055ba2b8bd8dcb17030f3bf2484c448b472fdedf4460ba80 \ --hash=sha256:6275863dad195ee34b6e0ca3fc61c74096bc37e5d6fb8e049f4d68d65865a2b7 \ --hash=sha256:62c5e42c79449def957adf8a9a65a43018efa7e2a6bc6baa3afe955e0d5fb2ab \ @@ -235,19 +210,15 @@ fonttools==4.65.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:8b7bb52817a24731d2e4f4df0e71fdde05e6c806c8f8f1517b015d142fdacfa5 \ --hash=sha256:8e44a34d91b3c793879767eb115867ced74d2eb94974e64e72fe9e2eea71cf1a \ --hash=sha256:924d06e6130429168318db71c40174a765ad016fc4b56ca811287e3d7373b3a6 \ - --hash=sha256:93a73af2075036d36d7fbf856779c56a1b3b86ffcdae6abede7596604c42c156 \ --hash=sha256:9db2cb95847c18eef74a4ef0fe257a893ae3f4b0395f4866e2f426ab07f3d804 \ --hash=sha256:9f201796c8e24e657be77c16fa664e798a46122144217f90838982937a964f0a \ --hash=sha256:a3991732c87b3f054a2a8cf86dd0d602833fa8cb37c911503173771646e1013d \ - --hash=sha256:a6c8d184e523580a7c55d21cde37176a3c91cb539cf06c2aa36ffc634fd75296 \ --hash=sha256:aa50dd7b9baf75e2bbd43401fc0d237f7a94a8ad2e0c57ea97160fc631af5eb0 \ --hash=sha256:aecc899fdbf9ecbf728f8977977e2e1043ee4d70c257124c8fa4cbcf796fcd83 \ - --hash=sha256:aff640a4fcb021fa83f9879d5bfa115b6931522dae991a24faa75888bd6aeff6 \ --hash=sha256:b11d8a4a0c3ca74bbd4c105b7ef82501945c939e6096d9934ec7d288cdf5aaa9 \ --hash=sha256:b30e953de049bf43fc0a63c7d0c44d205c923e4bbf24716aae1518c0e65f977c \ --hash=sha256:bbd9faf777a9deb6790df4f2b0be611857c45fe86605e840d7154a028d828af7 \ --hash=sha256:be9b9a95ed0af03375e99020e921c4bc6b41fad10e053dea7acad370521a3c46 \ - --hash=sha256:c130be2232e3caf8d2b476854ea78421ec1642917ff5ab695284bac31bbb072b \ --hash=sha256:c779d838815b91889c95ed64c9be5950ad5a683279f91aeb23384cb757ddc6a3 \ --hash=sha256:ca2b02d74e9ad7e21a1d11e4701425800a4b0c63cf90486e60258262feccbcbf \ --hash=sha256:d0d25027ade65ec46b13c0436e51bcb7c5171a4ea255a5e7a8d0d1d3ab4cffd7 \ @@ -256,32 +227,24 @@ fonttools==4.65.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:d95b34dd68fbfc0e4a1740c421597656117f979ed8dc85de66e08f9f9981806e \ --hash=sha256:dc87a9f846bec83c3795804f62b4632716d46e3522869a3dd9cd44a5d245b006 \ --hash=sha256:e2c21772fcf70325189707b19f346812690bb1b0bd7e207e6ac205244806b303 \ - --hash=sha256:e3944e0bdba42effb71959e43d91b599326b02b59c78310d5675e8a75525e7d8 \ - --hash=sha256:e5ceccaf2e57d83b753a2b5db5d94aa0a8071886d4afebd2d520c9683e6bef0e \ --hash=sha256:e844a45c9e5ced6536f184cf1a65b5d65e8f7e711993b413e10500a8223622e5 \ - --hash=sha256:fb53892b570f7f1f0055e75fc4de32673e32f749c4c8a606b63d5c436650e634 \ --hash=sha256:fcf39949f56911348514b466714efa9118bec3d2be249e1c487263f7cda6edab \ --hash=sha256:ffc918702661f1d74d2fbb2f5551036b64f6d2d743139e105289b694bcd16f54 -galois==0.4.11 ; python_version >= "3.10" and python_version < "4.0" \ + # via matplotlib +galois==0.4.11 \ --hash=sha256:d2e12a2b7fd44b108cc15d7de27dabcb8a98a556ea2c69c94fe695139629d9aa \ --hash=sha256:f5055546c4b39d1e36decae9d4f21f3d66d9c6c7c9aec39189cb5d2284e9022f -highspy==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via qldpc +highspy==1.15.1 \ --hash=sha256:00e1c13912501e96893136a1805b56b74cb4868fa04c1c2eacc5c0454304e08e \ --hash=sha256:01c6585e83938ecf4139248b074b2ee736816d63716a20dc608b1d2fc9637b66 \ - --hash=sha256:064f4778ee2a0a22e11220dfc6e6237c332c3062708616391372b86553679d80 \ --hash=sha256:070c1ce9238b9e8b4c273253647ab0dbafc1839c195a52c7ef1eeb7ef6976f05 \ --hash=sha256:0b5be1c777d0b57b6dc26e1d9754923e642a17c6313bcdf5186473644b214f0b \ --hash=sha256:138506088c7f6106cbb58d1cd0ef14793dfb47477fd83a7ae0db5b104d1cf969 \ - --hash=sha256:16688ab89afba436d2178d30b49bf4bf1620427d57f7cbfed914a3474e010db9 \ --hash=sha256:193b9751d3705bc948552b138800af0ad8af17a5b801d5940d7db7ff1ffc4f10 \ --hash=sha256:20ed2fbf1cb64bf3044ee6632364b7e2653d93e6901e2b19fd3d5df10702e8c5 \ --hash=sha256:238b2ee88b974b21c7e9ef198139502a7d87451939cae143dce789bbda121182 \ - --hash=sha256:3797f2046caa212cfc6b095b057cb6d63e847f4ec6acd9c8e1f791a81f01fa15 \ --hash=sha256:383cd3f28cce0753dec8e949719b10864e068c53a485624fcab4c6b585496dd7 \ - --hash=sha256:3aedd87892b39e070e011ba30fcdf6cf3724652430d72d33fd05a421b5dce4c6 \ - --hash=sha256:3b5ea8e1bd0b1768f779231e6b54612f0a889bb9eef897844e649f7180e1b15e \ - --hash=sha256:3cd22d9cf5affcc414782f3a30e564cdfadfe140a0d55e2f58542b1f2172ae5a \ - --hash=sha256:41e52e62366fc56086c45840ecbf31c530f46d0fdd722eec87d39cf9df9215fe \ --hash=sha256:45eb9f022f9083ef2e56d66f972d5fd40e6634f4497194b1f3f215ca0e8ea958 \ --hash=sha256:46fe314b918257361c54170852bc561c78d0f84d94e2ad263859d818127e6e76 \ --hash=sha256:4715fcfbcff50fdbcc288499116f7e5722a9f9d2647087d54317febb94ec2b32 \ @@ -290,7 +253,6 @@ highspy==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:565cf6a6e7c84e36c101b118a3c5fd09bc14aeece599bba12625e79b5ab0cecb \ --hash=sha256:5de2dddc554442f3572bb4a36116278bee79568fbd726a697251d2606b79a5a1 \ --hash=sha256:605d3204e41a465f9ce2f254571a90e8781605451a5e6a548f6b4be8988afb4f \ - --hash=sha256:62785dd5bb0df337c150ba7b53e555ee21a29fdad6d86f72aabaa1615fdd7874 \ --hash=sha256:6298b6ef691e83544d395d45fa4e856874c44b32936d85c36564f7697d27bb0b \ --hash=sha256:6a6a2f21ee31a9205a928fbbc3f8c054893c1aec34f6a7c56588317e2800e673 \ --hash=sha256:6cc7008b82094b2a2377338398b38f5b6c306397bd23282e55dec46a101a2dac \ @@ -298,11 +260,8 @@ highspy==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:74c1eb71d3c0fa0c190492d9c0c67266d1dd6b4244c93b53e95a687504db309d \ --hash=sha256:780c021441f548711818833d3a986fcb253849734aa00c3bf83d342c38b03629 \ --hash=sha256:787c92d5ff274256ba8848ab174cfc65d5af696f51bffe87423c85b2ea25c3fe \ - --hash=sha256:78bd23d371f633056a31e88da13d40606837db46d634626a8fcab6a1168a7370 \ --hash=sha256:818256db731339605a7b2c31cabfcbf820fe50402ff5e9b7aa8410ead06e8735 \ - --hash=sha256:81c869e9c1245e1930d7aa0cb726a3ed27367afe528655235033d461bd75f5b4 \ --hash=sha256:864258c59aeaea9d3bd7ccdd10c03258e2be764e2cf1e21f829fd1f8d8c15d57 \ - --hash=sha256:8a2f1f95baa6151c10c59d838044c138fc485210fad70e6c51cc43332f728f8c \ --hash=sha256:8c548165270608a40147a7ea6d985fd62a65fabf0f075b3c0c59ea910b724223 \ --hash=sha256:9499d631edeb9642fc08dee59ca6c5815be1764c13a336c58ab7ba063011aa24 \ --hash=sha256:9730647160a6481426729f46d9989a0507d05f3cf96f9fb180f4ab9891bea67b \ @@ -313,49 +272,40 @@ highspy==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:a24329c328942b37a6a318ecf163d07dd387974f071b98b4498725eaea80f06f \ --hash=sha256:a781dc8432568ea990fcdcc8d6e4365e67aa4848ca1f99275db096645b27cae3 \ --hash=sha256:a7b11dc80781052a6e7c163b5c2696fe9e06c72927cfdb48f67f7e8c77096f4f \ - --hash=sha256:aa3a97459f9350335b6448b8e83bf73467ab5a80b32f207a52c8fd9c928116bb \ - --hash=sha256:b517da9c7ee97773b55ff6a23148152be5a9366d2fe2628243e571233821b752 \ --hash=sha256:b6dcc545235c0765b48fc736122b105e174d907622d20986ac653c5b2a04911f \ - --hash=sha256:b72d0e7b43a623404d2ba49075110883285f3174845eceff209c501f9b21b0db \ - --hash=sha256:bb0d891973210511b6cc369ed9440fda12c58b0ab60a95972d348504cc6f9cf0 \ --hash=sha256:bbb22b7ceed298c0b75237186eb4671915b1c41c07f966e527643af10493671e \ --hash=sha256:cb8b8298a74786e1cbc1a9e102b7749e2bbd9c41826ffd4a1d7ba738232646ff \ - --hash=sha256:cdb93d7a8dfce49b0661b87cc113d5efd9b63b2c2abf7877b7ff508038f317c0 \ --hash=sha256:dd9ee8e139e7260ec1306a48e30f1bd7937d9cfb8cb201d25da10e1099e5129b \ - --hash=sha256:e11bcf5efdd15447e5490d7b1830043c754e26445ab896b8aae23ae7ff047437 \ - --hash=sha256:ede82b16a610b07ab16a1ac361d68f924b86f634d0f0d27bd6c94aa9df05732b \ - --hash=sha256:ef048fa722cdeb80062d271b8ba211cd6650ab73419762d80da7642bbd4a8420 \ - --hash=sha256:fc6997138d0cffe3ffb5c81dc750b9f272e301a1c6e9d284e212a90e4c188dfe \ - --hash=sha256:ff1fcca9cbef41de4c506774a7ac77c8bb5289d2ab268c4ad980262553397ff7 -iniconfig==2.3.0 ; python_version >= "3.10" and python_version < "4.0" \ + --hash=sha256:ef048fa722cdeb80062d271b8ba211cd6650ab73419762d80da7642bbd4a8420 + # via cvxpy +iniconfig==2.3.0 \ --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \ --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 -jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0" \ + # via pytest +jinja2==3.1.6 \ --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 -joblib==1.6.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via osqp +joblib==1.6.0 \ --hash=sha256:2ccc96785b12046c08fd6d55839c12857831b54a3c1673ffadd2f04bfc4eda03 \ --hash=sha256:3dbbf9f6e4b592a2357b854608e980fe6390d131d7a82f011a377ef2ebef7aba -kiwisolver==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via osqp +kiwisolver==1.5.1 \ --hash=sha256:007a5553dfc4f4e8d184f588a0200e2cd4b63a59cc8796df3c39909e679dc7a0 \ --hash=sha256:0324cd2567259b7a095f6cf18a52b0ffc6f3de9e69528ff1bc0e7a37bd43ff1a \ --hash=sha256:0627b9bceb9c3cdcf12b8a18655eedfed2692b038df27423383c120d0b7dc2d6 \ --hash=sha256:06a6917674de9e0fe3f66f5430787f59a9f2ddb64af9b714eaec547e29ef5c19 \ --hash=sha256:072bdb15a3c19a5b5dbc8f8fb1f4e1884bf4f3507eeb4cc6334401274d37a5c0 \ - --hash=sha256:0a4faea5c6db201c6a21391d2ac926ea97acf7dacdbc3c417189e1adb1a00837 \ --hash=sha256:0ba9527afc80ae3d7814ed98b6572d02bf85eaf48065678342c5f0c6dab7a8c7 \ --hash=sha256:0d8924877ce22e17326a99a418c3c82037da078df3c6a260b13eca677444e6e7 \ --hash=sha256:0ebdef3eae5336568147c39a55be6a2036ffde53faa9ca2d978989ae7c2da12c \ --hash=sha256:1209042a623ddfda5497e4066c7b77651dde8e1d3a9dd97599dc7e97f3b9b78c \ - --hash=sha256:16895f553ee6620a827d2da56b871f835fb70b9216cca5d188e885caf6e3bd23 \ --hash=sha256:17851e5dad4484be0cbccbde3b15331deae036de9aebd45eed964487802b172f \ --hash=sha256:1798e83840c3f627246104c4d8a9639c60fa068adf9ce92b61791781fa8a68c1 \ --hash=sha256:18170a77ddfecf40ec60d0928268dc95880c881864e015a8f34094ed18b9b9ad \ --hash=sha256:186884a58486651e3c217b6acea0a53eaa9498fdd472057c46f2f0fb5c25aad5 \ --hash=sha256:18a0cfb124546a4c2e6087c5f3029c7f44b37c85b142e0ced71f73a7599ac208 \ - --hash=sha256:1983f0974a750a6f6556f368ba11105d1d8369c735b944747c9f12ae5aea7aae \ --hash=sha256:1a7587dc335f2c0f5bd577fd0540bd16c66006bdb60f759a1059f025e6c4f071 \ - --hash=sha256:1acc7e5b7ef05e9da8bb70cd6c7c4513090213d2e1ad9720f599f0bf6c52aec5 \ --hash=sha256:1d852545c4d0e35a72728d072cbaa59e2fa7dd84bdf01e068d670dd0ceb58eb6 \ --hash=sha256:1ed0f5e49d0ceff8b72190824d9e59c062fbbc02c231b853112c78474b3f5ec2 \ --hash=sha256:1fff05e239575b1481b6ed1a782f6fad616efbf1f0b1f44e6e85c4dfe426e483 \ @@ -392,16 +342,13 @@ kiwisolver==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:5daa1f19e097050b9c4d9a78fcc9263cb96c9dfae08037ddc1b7c4ad1889f2a2 \ --hash=sha256:61e9a64c7635095a6bfe483e2ff055d437c59bd45f3617a228b37277f0185d62 \ --hash=sha256:63fb7294b768f444eb4b068965f2662f28c2fd4161e23bd60fcf3ff27b74c046 \ - --hash=sha256:685929988b208a911f1285e2f8ed54210b0d681a3dc0f03e00d599d291986e7e \ --hash=sha256:6a797a1cefc8b9c93170db580337e1fe3d011ad18b1299943231279406342048 \ - --hash=sha256:6b92f60017dda7d877fdc546438b5e28f31c523264f49cf5a48c1d0ce1a0dfbc \ --hash=sha256:70ed9a45c7484d2b30cdacf60d220f494a1763b9fec1ad03285c6553fa0889f2 \ --hash=sha256:719a35fa1156db3640555f95ebb94f60a444e64d1c69626b0edef5df78eba225 \ --hash=sha256:74ad5c3dad54a4641b4c28cd15ded70899d04459c6c7aeacafea716be97cce6d \ --hash=sha256:74ea337e0ec3f6f342a36a4f1b5cd94dd9affddcd28ba9aae2905af932ee8c6b \ --hash=sha256:75d9b1cf8258462dbdc1eeda718c96ea7f079324c09067f6daabfcf37712b7fe \ --hash=sha256:77a4c8187a5948d7f8795adb765a3c7b553d07d86d88e43038fc32fc1fb9a3f3 \ - --hash=sha256:7824b5e8bdbf0bccb4ccd37bbb115849a1dc45437fb4de8351385ed07c437ee0 \ --hash=sha256:7d38b0c279c3032e8c9cc013b405c6df8e1668dbf15465779aa7f15f61201812 \ --hash=sha256:7e9c01d3dd7ceba4d1d436cc021d40d592466e40b9bc7f5d83dc4e98a5c9cd8c \ --hash=sha256:7fd82debf43c6acd0a94359d232f6bb516ee13f269a7993736a9ac9f988bb5d9 \ @@ -414,22 +361,18 @@ kiwisolver==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:8a708a47ade1fe19e8371d5da076bac0dd4b0a5a7985ad6c637f7f7e361b6baa \ --hash=sha256:8af9b142ad719ae3a911ebf616bc4b78b32bbab84d6a40d3ad2f129670509957 \ --hash=sha256:8bf4df63592c2a66b4f8edc5df2544998c288aa02f96ce0acd880cd1de8c8127 \ - --hash=sha256:8de6f2a4ce7e7bd27d23dd94abf0ccafe0e0e5cc9c764b0577191f2c25f08f26 \ --hash=sha256:8f8fddb8e323bd6eee4e54e69a39243beab22689070f4c66b472c4cc88bb89d8 \ --hash=sha256:8fca690b00c4c48f6c2a547b0160ed511357093a4e4c9b47e0fadf3128066d89 \ --hash=sha256:9506e892bcc3b409831d363c6f53e5985e1c8d1f6f6b0256d00358684ff85378 \ --hash=sha256:958254518717542d02d0688d0d20cbf771da5e415e6f49543f92481c850a4540 \ --hash=sha256:95a02752aa032eef4aed01cda6d9b687c669bd0396bf4519eef8bba22a286720 \ - --hash=sha256:96c30002424670b5e1e46495c2b8cbffef39cf77c1d79e76462029d50339785b \ --hash=sha256:98b208a7cc42c803445ef551d6753cc42a5ea13e9cab1ee66cd8b9cb70195330 \ --hash=sha256:9b3092d8992a1d69b7a59c3e39f35e1b9be327a17f68a7c35fc17329e337d6f2 \ --hash=sha256:9e51c119992ea8820706871c30a4642ec76de20ae82f9b50b9a45517d8e9f810 \ - --hash=sha256:a5716a33bfabb2c6ce27b6cf03253467b3804f83e215f4d202685cf93c6c9874 \ --hash=sha256:a5a00665d1a0e26763a7338d7e911d4598fbc1d50dd0d6b7919b7dc6c5d6569f \ --hash=sha256:a5ca5aebae78a0bc13c1943af4af615d4966c5b650b05d5aa83b50e427196fee \ --hash=sha256:a7b85b2cc6ea45e5f7e8c9a30bc9fabd47cda09106cbb4b967335c3e6c43b69d \ --hash=sha256:a83ee7107df13abe42a54a6654670eef9bb39425cf2e27f65e0007465e1286ab \ - --hash=sha256:aa7d00b1700966d2917e54d278aba86897890ca9276dd8b76cf6446b6c181b92 \ --hash=sha256:ab620eb663952455271ac37f9aaad86b73c969c02f11f53cea405b38e96a4300 \ --hash=sha256:ad8b9671348d7c8716715652ae11f85ed0eb99e265a2df2ca490577d69860b2c \ --hash=sha256:aefe930d113798330e9462f7874542977869c0613cba3262e2de3a8d5dee8f3a \ @@ -439,12 +382,10 @@ kiwisolver==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:b5664603a253efd3a75716d793d1d3a6a82723b61dc6db767b2460bbbeec4c0f \ --hash=sha256:b69602970994a2ed8bbfa78c2f0394a7435226c6040489702d9f0a0ad0c07052 \ --hash=sha256:b6ae6a0328f0bc035741820fdeecdcd67bf4694eee03972e843663107122f450 \ - --hash=sha256:bad20d4c69c851c982a1e3606f4c293edfd5a87885786c50082412240c4b1ffd \ --hash=sha256:bb7c99f0673c03017a3ee01e54a5c2617a05468b11eabe513b0080e063ed95b1 \ --hash=sha256:bebb89489b279b2f5661bbbb2abcc87bcd4a46607bb4a5c966f04f1db6b8df9a \ --hash=sha256:bfd1de989b3330420e29de39352f5c049905c9e3ee67233a50d550e3d652c148 \ --hash=sha256:c2306e8bb53601979fcb3fa09cc65e031876d9ae01eff2fcbcd7a84ef94d5bc1 \ - --hash=sha256:c3a4e41e3096bf1f0f1b76e2ffd6d828d6547f574f702d59bdbef7acfa59db9c \ --hash=sha256:c6834b92dd2428e2dd85ef3d85f723d3c12f20aaf43a2ddd4f944ca25d833408 \ --hash=sha256:c90d3022d8a94778939cda8638c6c8da8fa757b8958dad7ec868ce29c87681b8 \ --hash=sha256:ca307d6c259e5c98d3cb9ade55342b47a6839762caf2536f3d7b46ee660cc82e \ @@ -461,46 +402,45 @@ kiwisolver==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:da3275833be0edbaf4830fae08bae3dc7219f40ce0c37eaa6c25825957e06612 \ --hash=sha256:dc1a26b8e53395a01c2c611e58602fa47461f136fba7cd5542e6db6d64be1839 \ --hash=sha256:dc23390afe9f4ef9ac3bcc72a03a56eebbde03f4c571a32cb38f859cff9a6524 \ - --hash=sha256:e05c2f7925f1d88778e53cb44f14e0223204a3bdd09a41664750363acfb1f2ef \ --hash=sha256:e12dfea7f5fc2a34a9080efbf79c4c44eb380ec5b9c6fea09407e08f0d1e941d \ --hash=sha256:e4e4523d6f336708d732516e6cfca7796cf3d96c9474eb5aecf6165f2f1fefc3 \ - --hash=sha256:e4e49f7e1a4e7191bdf9dc67a974db714501b1fc52c24324103d06a86abd5c08 \ --hash=sha256:e68e151428b5384f766cd25739bf77c7e4a3dc93b5ded7a12118d9fbfdf78ab6 \ --hash=sha256:e8e4d953faaded9ec7ede36824e9814082d22d4c7b1eafbfa079ecba8cd0d076 \ --hash=sha256:ee9df1f0d77b9c6e94f4ac0fec533fbddd5ea3a327807f18d7b069ae019ded80 \ - --hash=sha256:f0a887b6565bbfe80efde2b7f6e8890d7d9bbdb11bdb17028a3690c32fe0621f \ - --hash=sha256:f0f4a42db92d6ec7677ab9d12830a2a8ec145a9c6d15db2b593466bc875c78d7 \ --hash=sha256:f1303ef2eec81262a4b708c3e858afe58d7c75ad91c1c05266eda7673369859a \ - --hash=sha256:f1d56ec54d257d05e0b50f5780d967540cd07beeaf9e5f645b26d50cce79f4d8 \ --hash=sha256:f4167e87b397f273dc2356fcf1eaf50a6bac51e6105f45103ef7129c8efb0255 \ --hash=sha256:f76fc85bd054c806960f917ec0f329e24e436f1712267d90588e4c39890caa63 \ --hash=sha256:f942903fde7363d1d879057ec5de01310efda2597161784d752fa9953a01a71a \ --hash=sha256:f9b1c4900736e489a812c529100de4b8fb617d4db075e931e213c57424b83d9b \ --hash=sha256:fc271a6f0a2126958f4090e5507b9da5848927dae331f8f763bd4aa642b3d2cd \ --hash=sha256:febcce10f2bcdbb80b4ea919238a6a4ac13dbc4c7cadbe8d5d75c3682f8b5404 -ldpc==2.3.6 ; python_version >= "3.10" and python_version < "4.0" \ - --hash=sha256:053331b35f2d928308caf0c7a3b3d62b8fbb1ac2d487a48434776b116a5547ef \ - --hash=sha256:065e9dc09cedd60ce6bdab22ed219adf9059dcda7bcb573ded243245f51e6aaa \ - --hash=sha256:0785f5e6c7836baecf3bb22b4f2d8d6591daf01352b495c799e6b9458bcc0ffd \ - --hash=sha256:0bb1c48e174593f5fbf2081d7cc0e9e2b5dc9a44c5132d2c06c94b3118979a05 \ - --hash=sha256:231caaafc6e2cb02d22825008e83f3dd338db5656d39e749a99ad24335fb0c7d \ - --hash=sha256:23d4166f7a42e19cab31826088009840f34d8ea1904db18c23a8263537c42cef \ - --hash=sha256:3472d9a651e25e9c7c641c502a1f6d7043555fc6ef2c2b71eb2a0eb1d1f822e1 \ - --hash=sha256:50609e3bb421e96e9a07ea4e381d0da805b311ce284881d191bb35d727d6c336 \ - --hash=sha256:602e429559bef0dcc1665a355e6ba3508b04c7b829ae3fa085e8f442d69bbbac \ - --hash=sha256:72533a6e77fd9e634df9ad223f32fffc9f7d2b1eb437f07a70b2afddb66a7e36 \ - --hash=sha256:789c5348e574b1bfd5164f0237eef99c024dcd0d19c0ac527cf1e11e5f3cb0a3 \ - --hash=sha256:80d7fc40841944431f9a8d1384c3b05ec1b3676bf907da6563d385ef5653d473 \ - --hash=sha256:b7287998fe0f31d430d43f8e4f8686ab9a289c375ea1626f29085e7868343720 \ - --hash=sha256:b73e57fc7ad46710c9f44142f4a0ee4c33d25e8880c5cbde0e52bc14854fef32 \ - --hash=sha256:d2b734bbc61c5688860da2658819e9d4d3cc0cabe5857afc4b695255034aae56 \ - --hash=sha256:d72cb69f856efe97cd13184fe568758215b8ce645bd1cff8aa1a32f0f52064f1 \ - --hash=sha256:dcd6a9f8d3ff9c6ea603f82c7188395910081226669e1c92347bbe27c5843db4 \ - --hash=sha256:e06b6baccb9e8af169a30c786ceebe2ddefba30f2b0e34bea8d728fc7e605cc8 \ - --hash=sha256:e090a83f7c0bdc1f199d673e9dcc568d0211d9d17b69c754d43324c30f98d16a \ - --hash=sha256:f42fcc5422a1b58a7e28e40d8a6e6148bd4b592af4b5dd5823d71dabcbf67172 \ - --hash=sha256:f6d908e25cbdb3baf40386ddc41b6fbae9b5a73d7ea636f511f1e188151b6bcb -llvmlite==0.49.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via matplotlib +ldpc==2.4.1 \ + --hash=sha256:035ceb48f9d8c0d2ab37eee67f87692a935ba586ebc380a0f7ad7bac3192a872 \ + --hash=sha256:07ecf26a3ffe5ffddd6bbe5d6c4c764d11753664379b566a86e136a4a6f9dd59 \ + --hash=sha256:08ebc2e6cde49b1c0645a0eab8e2b4a843935858daeb3fd86598119c9c3a6d99 \ + --hash=sha256:1af8949d463273bfec595411dbf98192510a80255844bac464a05b909939fc80 \ + --hash=sha256:1d7965b6c3dc53ac0bc852e3769516a4b4a2756f26c6a683ea675c1e1771da42 \ + --hash=sha256:35426040ccbf633683009de0528eee06b4fa944ffdf13e6f89167770bc4fe10d \ + --hash=sha256:37028d9d045130b101f4fcedae3b7a4c04802633bf2b09927703d3a0604c8554 \ + --hash=sha256:4010c9ac9eff008c058dfa9f225325d5aed8b7a0b55ec0975a737fc7352aa301 \ + --hash=sha256:569e9b6b13aa0165a49c6108eec3fda573054f62bb04927ff934c06ba5420c8e \ + --hash=sha256:5790180b77c588a72d2bf828b4e38e6f85c5e9dae967420c6b2ac7c317b3bd83 \ + --hash=sha256:602445197320d9dc9ce4a2af6e0661d8d303ef2cddea24b99bee6b51415a343e \ + --hash=sha256:609f71596553e9c1d916bc154674dd6c9b90062de8a53ff0a197e2d2e9422480 \ + --hash=sha256:6701b042f8d505f205c7fe39820566513f1191ed04413e0968bb54bfbc6f0086 \ + --hash=sha256:729376026af4e46e550c333152f073dcf42224589380d309f38923e00736ceba \ + --hash=sha256:73c59007646cb2256711240e42a857dfda3c65c099c4b8c45d99857d8737e803 \ + --hash=sha256:9a77bcfd3570bc3cda0c429ba9e5b3a4404c422228d3b6fcc1b0e7dee6493cdf \ + --hash=sha256:9b4d3630f4410700a099a8a3fefd030684294ed624e3285f415c6997b0a9f63f \ + --hash=sha256:a15d944db7434a7435df512666b7b60e7c243809fa160f94bb9123009656c4dd \ + --hash=sha256:b49b8b14413ec0d4c68c4f5a9bcaff0232363aed71652a75ed6af1234c58031b \ + --hash=sha256:c5a5359842a5be1bd026a887e4cdd77e8ba01c5ad70088fd50180715b60eac21 \ + --hash=sha256:fae91c5a279cb60c0c1d78ae343e241124e4de1a6dcca881cda688a9ea7a404c + # via + # mdopt + # qldpc +llvmlite==0.49.0 \ --hash=sha256:00f16db782f4a13c78c5804aedc434e46794a77e89999a168f9401106270e50a \ --hash=sha256:039fa4054a06f537fb39248d4472284ca96be311a142ec09e69f95630ab469cc \ --hash=sha256:20496a5c9fdb8179fb9300e7d19f6782555d98aeeb4a322264aa7fd99f980618 \ @@ -512,50 +452,38 @@ llvmlite==0.49.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:6a5b06c1b5fc4ae4c9b169b065f42b719448ef1f873687ef224ef69969b75ec3 \ --hash=sha256:6acba646d88abbc87d5c113a3d62c1fbf8b8fee11c6493f516803e30f21ae870 \ --hash=sha256:80a84683d04516bb51da1bbeebddaf2c2f558809c93078a8f91807909ae331f8 \ - --hash=sha256:854941c2267fd4fc5b2ce02b8af8ecdffa79fb7784591d3a89370322039ea09f \ --hash=sha256:95d1071023ed858b79f6971954fd7cc1f5dbcbab987718a4ccbe1411e47d0b81 \ --hash=sha256:a1b414dc6b164738ec39dd8987cea73829057b7dd92fc6d91b52838385fc1dd2 \ --hash=sha256:a8c0fc9d624bdc30a3d2db11eb2fb98f80fb209d20b37604eda516cd9b699cf4 \ --hash=sha256:b095f15fb12c4d90495df5b1a3772b4732cc408398b204a787dbedd370e09c69 \ - --hash=sha256:b352c14353330c879e339b8f8d7491d565fe94242697714a24e80bd757202384 \ --hash=sha256:b541c8fac3450db7574d1f53cf9dff83f285bfed9d69bf81fe71fc2a7d4f97fe \ --hash=sha256:be637e465010bc9c50f070468f7f1cf5385e92fee364d192dd5e6cea790ecba9 \ --hash=sha256:d3dee64784201b64c13a8df62c48a4f4218858faaa65889866bb29bdc243c038 \ --hash=sha256:d5555ea1d63928481cbf7fcb1d67452b216c7e5b393a4eb7aa1401e67f2a4fc4 \ - --hash=sha256:da7b64474ac15ca595efa2644d5c6836638ccf70709fad3aba3fc56a55966928 \ --hash=sha256:ddc7aecd4f56397ed6e8f120ec5dcd5a1a8f0e6032ca4af413462792d4dca2e3 \ --hash=sha256:e32adb84fdaae28aeb86fdb6253084ee707ee157289a2e98fe3caf48a62bee82 \ - --hash=sha256:ee81e96c15a6f870918f1eb60c913551c16aa23defb4f5f1acfa660d6a0aaac2 \ --hash=sha256:f3f2ff0aeb17d34fcce9f79b99baac441cfd3efa41b83e233ca4530a72381f72 -markupsafe==3.0.3 ; python_version >= "3.10" and python_version < "4.0" \ - --hash=sha256:0303439a41979d9e74d18ff5e2dd8c43ed6c6001fd40e5bf2e43f7bd9bbc523f \ + # via numba +markupsafe==3.0.3 \ --hash=sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a \ --hash=sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf \ --hash=sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19 \ --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ - --hash=sha256:0f4b68347f8c5eab4a13419215bdfd7f8c9b19f2b25520968adfad23eb0ce60c \ --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \ --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \ --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \ --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \ --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \ - --hash=sha256:15d939a21d546304880945ca1ecb8a039db6b4dc49b2c5a400387cdae6a62e26 \ - --hash=sha256:177b5253b2834fe3678cb4a5f0059808258584c559193998be2601324fdeafb1 \ --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \ --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \ - --hash=sha256:1ba88449deb3de88bd40044603fafffb7bc2b055d626a330323a9ed736661695 \ --hash=sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad \ --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ - --hash=sha256:2a15a08b17dd94c53a1da0438822d70ebcd13f8c3a95abe3a9ef9f11a94830aa \ - --hash=sha256:2f981d352f04553a7171b8e44369f2af4055f888dfb147d55e42d29e29e74559 \ --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ - --hash=sha256:3537e01efc9d4dccdf77221fb1cb3b8e1a38d5428920e0657ce299b20324d758 \ --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ - --hash=sha256:38664109c14ffc9e7437e86b4dceb442b0096dfe3541d7864d9cbe1da4cf36c8 \ --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ --hash=sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c \ --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ @@ -564,7 +492,6 @@ markupsafe==3.0.3 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ - --hash=sha256:591ae9f2a647529ca990bc681daebdd52c8791ff06c2bfa05b65163e28102ef2 \ --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ --hash=sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50 \ @@ -573,18 +500,15 @@ markupsafe==3.0.3 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ --hash=sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115 \ - --hash=sha256:7c3fb7d25180895632e5d3148dbdc29ea38ccb7fd210aa27acbd1201a1902c6e \ --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ - --hash=sha256:949b8d66bc381ee8b007cd945914c721d9aba8e27f71959d750a46f7c282b20b \ --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ - --hash=sha256:a320721ab5a1aba0a233739394eb907f8c8da5c98c9181d1161e77a0c8e36f2d \ --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ @@ -592,32 +516,24 @@ markupsafe==3.0.3 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ - --hash=sha256:c0c0b3ade1c0b13b936d7970b1d37a57acde9199dc2aecc4c336773e1d86049c \ --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ - --hash=sha256:c4ffb7ebf07cfe8931028e3e4c85f0357459a3f9f9490886198848f4fa002ec8 \ --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ - --hash=sha256:d2ee202e79d8ed691ceebae8e0486bd9a2cd4794cec4824e1c99b6f5009502f6 \ --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ --hash=sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01 \ - --hash=sha256:df2449253ef108a379b8b5d6b43f4b1a8e81a061d6537becd5582fba5f9196d7 \ - --hash=sha256:e1c1493fb6e50ab01d20a22826e57520f1284df32f2d8601fdd90b6304601419 \ --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ - --hash=sha256:e2103a929dfa2fcaf9bb4e7c091983a49c9ac3b19c9061b6d5427dd7d14d81a1 \ --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ - --hash=sha256:e8fc20152abba6b83724d7ff268c249fa196d8259ff481f3b1476383f8f24e42 \ --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ - --hash=sha256:f42d0984e947b8adf7dd6dde396e720934d12c506ce84eea8476409563607591 \ - --hash=sha256:f71a396b3bf33ecaa1626c255855702aca4d3d9fea5e051b41ac59a9c1c41edc \ --hash=sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a \ --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 -matplotlib==3.11.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via jinja2 +matplotlib==3.11.1 \ --hash=sha256:0c1f44890d435c1b4ef52f701ad5828cb450ea97bcc83918fda6be74965d6cd2 \ --hash=sha256:11664c551345553db92e61cae6cf1376f138f8c47cafdf13b64b18f3e3e9e464 \ --hash=sha256:1524e2bdd48a93557aa47ddcfe9c225dfdd57d5a01a5c49128c20f0632980ee1 \ @@ -664,19 +580,32 @@ matplotlib==3.11.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:e15ef41507f3d525f46154ac9e3ae785dacde9f20e593a25de8986267892ef74 \ --hash=sha256:e4b9ac2f1f607ecda2af90a5232beee2af7582fce1cc30c4b6a1b012dc21ee99 \ --hash=sha256:f2912f647f3fbe1ccf085f91e213936f9101bead81a5e670565b1f1b3712f4fb -matrex==0.0.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # pymatching + # seaborn + # sinter +matrex==0.0.1 \ --hash=sha256:48a3c9fc69ef893d1dfbd6248b3e156bdc7cf554096b0d3c2f9e2da07bbba899 \ --hash=sha256:7cf243fe0c726c928aaf5c30622292bb21189e5f91b8a3c79e5ce60e08fb0460 -more-itertools==10.8.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +more-itertools==10.8.0 \ --hash=sha256:52d4362373dcf7c52546bc4af9a86ee7c4579df9a8dc268be0a2f949d376cc9b \ --hash=sha256:f638ddf8a1a0d134181275fb5d58b086ead7c6a72429ad725c67503f13ba30bd -mpmath==1.3.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +mpmath==1.3.0 \ --hash=sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f \ --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c -networkx==3.6.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # qecsim + # sympy +networkx==3.6.1 \ --hash=sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509 \ --hash=sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762 -numba==0.67.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # pymatching + # qecsim + # qldpc +numba==0.67.0 \ --hash=sha256:00c964a5b94d3ae82d83ac162cd610755875b98dadb779fdde06e6bfcdbca47e \ --hash=sha256:3fa3d1b27f96f2c0d54513d953d7197886aa1eaa7d2439a0eedc44d993fb181a \ --hash=sha256:4a2ed006635bbd0fe45681ed49f3b4f4bad1abf0c233bcc5842c9e3a34cabd61 \ @@ -684,118 +613,140 @@ numba==0.67.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:50e2b72406c18cda5dd7431b0082cb85ea94e06c64c33607248fc8bef92cfb81 \ --hash=sha256:5269245a675abdd3e2c35ec6bb2f250355effa9032514d8f2354f0d2d10854bd \ --hash=sha256:6004d8d5f28d4028687fb2d972d629295b13685943bd2ed5cd8810c3b848e219 \ - --hash=sha256:694c81c6560b2b47e5fc1dc39c29175b907adf862d9af0af801453400a022a61 \ --hash=sha256:76d3335aaeffb9dc88309420890e73497a00be08a7530441bc2b58ffe025bfa5 \ --hash=sha256:77e1c7173fee57a0d84e006c7e70346689d6cb3e7db503489bae58646b4eff7b \ --hash=sha256:7930748ce8355d2a5a28602abab056a61fdc676d17377f27d17993905428171f \ --hash=sha256:83ab968b0e0fa744eba03351282dd8000796e6ec8e4518f47bd3ed86c0a20c7b \ --hash=sha256:88f6e0f5cb6c545e158b6ef0496c01b6d6958a7ccc6634a1576a94bbbab29ff2 \ - --hash=sha256:8c0e88acd4341ddf40779db3c0228b9188aca7fcab5f5f3ce9949a1fc71e9a02 \ --hash=sha256:8c80c847301dc33dc8f84a97a952004023d9a05578ae4512b087176264cc1960 \ --hash=sha256:9c4953387c77864b596d8296e2cfbdef82b0eea4166ab4864b05d226c51143e0 \ --hash=sha256:aa5f002f665bec321b950dacaa26ee009e1d720f6ac9d9856eed5efe1caa03a6 \ --hash=sha256:b68ad5125fe245339cc8dcc036081fc1ea482c5063387b9612a76ccd83dc91cd \ --hash=sha256:cd75aa535b33fa05d9d930b1ae8af9f97a2881e96d72dfb38ec9b78284d9f851 \ --hash=sha256:cfba1ac34f0363fb1a250a10e97240780d11e05227892f7286b26fbfd0ad58ce \ - --hash=sha256:d6c8e9ba3f9602471e8c6f563ffcce8db8046741f0bafb782a052e41dc6b6861 \ --hash=sha256:e7a7b0121466f1e9a8a074b0545fe90e16389623abf979b5d7c299dca1294d7e \ - --hash=sha256:ed333e0af4386294e7f03e550e01411856b6935e717d859225e0a7338c6b6795 \ --hash=sha256:f074a8e23db78490f11a3930c940be758316c10ac5985be83d2f298dc080acf7 \ --hash=sha256:f63d43db06b4756424d6d2484737c902e0ae944a0eec3e8b0b4de2c695b15caa \ --hash=sha256:f99f880ff25f418a67f9a1d00d0ddfbc63430f627b523e515085a592a7567f4b -numpy==2.2.6 ; python_version >= "3.10" and python_version < "4.0" \ - --hash=sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff \ - --hash=sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47 \ - --hash=sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84 \ - --hash=sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d \ - --hash=sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6 \ - --hash=sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f \ - --hash=sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b \ - --hash=sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49 \ - --hash=sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163 \ - --hash=sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571 \ - --hash=sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42 \ - --hash=sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff \ - --hash=sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491 \ - --hash=sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4 \ - --hash=sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566 \ - --hash=sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf \ - --hash=sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40 \ - --hash=sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd \ - --hash=sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06 \ - --hash=sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282 \ - --hash=sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680 \ - --hash=sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db \ - --hash=sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3 \ - --hash=sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90 \ - --hash=sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1 \ - --hash=sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289 \ - --hash=sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab \ - --hash=sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c \ - --hash=sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d \ - --hash=sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb \ - --hash=sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d \ - --hash=sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a \ - --hash=sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf \ - --hash=sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1 \ - --hash=sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2 \ - --hash=sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a \ - --hash=sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543 \ - --hash=sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00 \ - --hash=sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c \ - --hash=sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f \ - --hash=sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd \ - --hash=sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868 \ - --hash=sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303 \ - --hash=sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83 \ - --hash=sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3 \ - --hash=sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d \ - --hash=sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87 \ - --hash=sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa \ - --hash=sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f \ - --hash=sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae \ - --hash=sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda \ - --hash=sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915 \ - --hash=sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249 \ - --hash=sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de \ - --hash=sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8 -opt-einsum==3.4.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via galois +numpy==2.4.6 \ + --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ + --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ + --hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \ + --hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \ + --hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \ + --hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \ + --hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \ + --hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \ + --hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \ + --hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \ + --hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \ + --hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \ + --hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \ + --hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \ + --hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \ + --hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \ + --hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \ + --hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \ + --hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \ + --hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \ + --hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \ + --hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \ + --hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \ + --hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \ + --hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \ + 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--hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \ + --hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \ + --hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \ + --hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \ + --hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \ + --hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \ + --hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \ + --hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \ + --hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \ + --hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \ + --hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \ + --hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \ + --hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \ + --hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \ + --hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \ + --hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \ + --hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \ + --hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \ + --hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \ + --hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \ + --hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \ + --hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \ + --hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \ + --hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \ + --hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \ + --hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \ + --hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \ + --hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \ + --hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \ + --hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \ + --hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \ + --hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \ + --hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \ + --hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \ + --hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20 + # via + # clarabel + # contourpy + # cvxpy + # galois + # highspy + # ldpc + # matplotlib + # mdopt + # numba + # osqp + # pandas + # pymatching + # qdldl + # qecsim + # qldpc + # scipy + # scs + # seaborn + # sinter + # sparsediffpy + # stim +opt-einsum==3.4.0 \ --hash=sha256:69bb92469f86a1565195ece4ac0323943e83477171b91d24c35afe028a90d7cd \ --hash=sha256:96ca72f1b886d148241348783498194c577fa30a8faac108586b14f1ba4473ac -osqp==1.1.3 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +osqp==1.1.3 \ --hash=sha256:0a168153f6058075d017e713d20eb4214c5daeb957b497c3c31340d0e63e1288 \ - --hash=sha256:0f30aee462d990a29ae9718e6b967db0c6527e0570b3d92db6b46fc6632d0c16 \ --hash=sha256:1547e515c16feb1ab2889b64bcd4eecbbc59c40af4faf67258047a199780b98a \ - --hash=sha256:18aff2450a88e2b4deccd21ba02560eed4131d27ed308e8e293abf6c9b07953b \ --hash=sha256:2b59e8094fe29d928d568cad0156a42daa44257ce142fb7808400016a62dc28c \ --hash=sha256:2d3ee63e8c65ef89fce979c068d05bfc3ed92b1bbc4246fbacc663f86cbe02b2 \ - --hash=sha256:32c0a2325ec1b3bc56c79ca7aa2011dd492fadff4c72b10dfdebea1356e9bd67 \ --hash=sha256:3efaf5fa72b45bec9b67dcaf5ae58a4e2d1bd5d8053b249c0ecd5b8b60c1bc0e \ --hash=sha256:3fefecf59a4d84a63ff6fb34e24987f12d61c740e78cf7fb7864f427a3d1c74e \ - --hash=sha256:41234c3c842a1bdad65a15c3160834527e1238bcde8b50a69a235f6da59ae99d \ --hash=sha256:48f53ef5ec89e6ce99ffa955bc6ea0cf2eec09ea3d40905f0c9fadc939609907 \ --hash=sha256:4bdcbe47c2c37bf7296971a40811a883e793923699394a73a9d677ab7a9093e8 \ - --hash=sha256:6b804957493c70bc6bc5508b59c9f1cab0db6de42e21df9b70b82ee83711159a \ --hash=sha256:6ceef7fb4f332892b6e0bbc17323d5c9e028c3f9db726b62a15d876d0f81cc06 \ - --hash=sha256:78ce2e56ab87aa41ceedcb8e1ed3f02af197d693b0996c1768075b3f183bbcd5 \ - --hash=sha256:7f4ca31c0aa1714580b91404b8dfefef1bf9f4f346b2fa16a455f4ea25f1b631 \ --hash=sha256:97df452d5e3b000b075fcce1c9f628289411501a2c0ea9a94acea281ed57bbd3 \ - --hash=sha256:98e606622d677221d4c31f71804546a5ed561192e60753b434caa6c6a8828f56 \ --hash=sha256:9b9fe3daa15313d281233babeb102007062933241a855f6b399becd03ec5f1e3 \ - --hash=sha256:9f48db2468efe5deeaf6433645ea0cadc46c591a3c82301d38cbfe5c8a8599e0 \ --hash=sha256:a8ae5b09be77b987421216dae535b4c007a14f6c034a844ad6eb4244289a881d \ --hash=sha256:a9c46b4adf2b1f76a40ef8f72426be5aa014c94efca20ac5acf8afa52e352afa \ --hash=sha256:b2b12ea5bdcf3750703c1fc1ea5d678f3f0fdddc6513c9352439f8075ca07bf4 \ - --hash=sha256:b9ad65d8948b7309911adf8bc4db156fbf462cca45a802eb7e95214efbed5c21 \ --hash=sha256:bcca27693d0506c6816f989cf38e6352ed74ac8eaada9babc5487456d0cb0bf8 \ - --hash=sha256:c2b79b59039f0659ce60282ffe41b6e6d58dbee61ab8cf5c25cad3b386e85bf8 \ - --hash=sha256:c941ec3f055ee93a7bb6051ea84001d384c6b7f636eb5b34d9611cedb7ac60e9 \ - --hash=sha256:cb3372ed1d6fb225e1e9957836c93941042aa0c81eee7b2c4ec1b0853922ac6a \ - --hash=sha256:d6f47334756a0c0b4b6d9d8f48e18e30422a7cfc1a71030c66c82adb34a8c606 \ --hash=sha256:e1d6cf30cdccf07baecb7232dc5fe22a1421ea3278a88a40580df3563a419d64 \ --hash=sha256:e44ad08234cfbd6d9f2a823118547e683b038676887e096533935b9fcc15fd3c \ --hash=sha256:e5620abbdb259190da6f0e5421b9fdfd9386b46690210ce82aea6102cc85c67f \ - --hash=sha256:e63289c5c82e19accf0d492302ad16b251373fc959e24568903849b84578be5f \ --hash=sha256:e730047c4cba86ad97ca73c03ea4c3cca765b22fa79c3fbefff11b3cce317742 \ --hash=sha256:e7e9bd3939e47e726673218a7b04b9784ecbf9ef0b8bc69107d20cc659836ac1 \ --hash=sha256:e80447b95d7b7dec3d13cc9e5a67ef4c5eaac3affee60fd83d1ce461e9270816 \ @@ -804,27 +755,21 @@ osqp==1.1.3 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:f6e6b384c356681b93c8698304b8910bd4a198e95664ab533cf5a422c126cd40 \ --hash=sha256:fb3734923e7606d8a5c14e41e1e710352aa6696e9f3f94e770def85d4633abf4 \ --hash=sha256:ff6cf15a5404b28a4d9aaada72ae73c6b179120892321ec9b70f52ecb7f34261 -packaging==26.3 ; python_version >= "3.10" and python_version < "4.0" \ + # via cvxpy +packaging==26.3 \ --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \ --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c -pandas==2.3.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # matplotlib + # pytest +pandas==2.3.1 \ --hash=sha256:025e92411c16cbe5bb2a4abc99732a6b132f439b8aab23a59fa593eb00704232 \ - --hash=sha256:09e3b1587f0f3b0913e21e8b32c3119174551deb4a4eba4a89bc7377947977e7 \ --hash=sha256:0a95b9ac964fe83ce317827f80304d37388ea77616b1425f0ae41c9d2d0d7bb2 \ - --hash=sha256:0f951fbb702dacd390561e0ea45cdd8ecfa7fb56935eb3dd78e306c19104b9b0 \ - --hash=sha256:1b916a627919a247d865aed068eb65eb91a344b13f5b57ab9f610b7716c92de1 \ --hash=sha256:1c78cf43c8fde236342a1cb2c34bcff89564a7bfed7e474ed2fffa6aed03a956 \ - --hash=sha256:1d12f618d80379fde6af007f65f0c25bd3e40251dbd1636480dfffce2cf1e6da \ - --hash=sha256:22c2e866f7209ebc3a8f08d75766566aae02bcc91d196935a1d9e59c7b990ac9 \ - --hash=sha256:2323294c73ed50f612f67e2bf3ae45aea04dce5690778e08a09391897f35ff88 \ --hash=sha256:2b0540963d83431f5ce8870ea02a7430adca100cec8a050f0811f8e31035541b \ --hash=sha256:2ba6aff74075311fc88504b1db890187a3cd0f887a5b10f5525f8e2ef55bfdb9 \ - --hash=sha256:2eb789ae0274672acbd3c575b0598d213345660120a257b47b5dafdc618aec83 \ --hash=sha256:2f4d6feeba91744872a600e6edbbd5b033005b431d5ae8379abee5bcfa479fab \ - --hash=sha256:342e59589cc454aaff7484d75b816a433350b3d7964d7847327edda4d532a2e3 \ --hash=sha256:3462c3735fe19f2638f2c3a40bd94ec2dc5ba13abbb032dd2fa1f540a075509d \ - --hash=sha256:3583d348546201aff730c8c47e49bc159833f971c2899d6097bce68b9112a4f1 \ - --hash=sha256:4645f770f98d656f11c69e81aeb21c6fca076a44bed3dcbb9396a4311bc7f6d8 \ --hash=sha256:4d544806b485ddf29e52d75b1f559142514e60ef58a832f74fb38e48d757b299 \ --hash=sha256:56a342b231e8862c96bdb6ab97170e203ce511f4d0429589c8ede1ee8ece48b8 \ --hash=sha256:5db9637dbc24b631ff3707269ae4559bce4b7fd75c1c4d7e13f40edc42df4444 \ @@ -840,17 +785,14 @@ pandas==2.3.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:9b7ff55f31c4fcb3e316e8f7fa194566b286d6ac430afec0d461163312c5841e \ --hash=sha256:ac942bfd0aca577bef61f2bc8da8147c4ef6879965ef883d8e8d5d2dc3e744b8 \ --hash=sha256:b3cd4273d3cb3707b6fffd217204c52ed92859533e31dc03b7c5008aa933aaab \ - --hash=sha256:b4b0de34dc8499c2db34000ef8baad684cfa4cbd836ecee05f323ebfba348c7d \ --hash=sha256:ca7ed14832bce68baef331f4d7f294411bed8efd032f8109d690df45e00c4679 \ - --hash=sha256:cd05b72ec02ebfb993569b4931b2e16fbb4d6ad6ce80224a3ee838387d83a191 \ - --hash=sha256:dd71c47a911da120d72ef173aeac0bf5241423f9bfea57320110a978457e069e \ --hash=sha256:e5635178b387bd2ba4ac040f82bc2ef6e6b500483975c4ebacd34bec945fda12 \ --hash=sha256:e6723a27ad7b244c0c79d8e7007092d7c8f0f11305770e2f4cd778b3ad5f9f85 \ --hash=sha256:ec6c851509364c59a5344458ab935e6451b31b818be467eb24b0fe89bd05b6b9 \ --hash=sha256:fe37e757f462d31a9cd7580236a82f353f5713a80e059a29753cf938c6775d96 \ - --hash=sha256:fe67dc676818c186d5a3d5425250e40f179c2a89145df477dd82945eaea89e97 \ --hash=sha256:fe7317f578c6a153912bd2292f02e40c1d8f253e93c599e82620c7f69755c74f -pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via seaborn +pillow==12.3.0 \ --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ @@ -870,13 +812,10 @@ pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd \ --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ - --hash=sha256:25b9b82bb22e6e2b3cd07b39c68b7b862001226cb3dff7130d1cb914121b39ed \ --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ - --hash=sha256:300557495eb45ebb8aec96c2da9c4be642fbf7cd937278b4013ba894ea8eb0eb \ --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ --hash=sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6 \ - --hash=sha256:37dc8f7bbb66efe481bb60defacef820c950c24713fb44962ed6aa2a50966de1 \ --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ @@ -884,19 +823,15 @@ pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ --hash=sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c \ - --hash=sha256:514435a37670e3e5e08f3945b68718b6ed329bb84367777e16f9f4dfe1e61a0f \ --hash=sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64 \ - --hash=sha256:5594fc43d548a7ed94949d139aa1341b270f1863f11cfd37f5a6c8b778a6b67f \ --hash=sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a \ --hash=sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827 \ --hash=sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17 \ --hash=sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4 \ - --hash=sha256:6c0016e7b354317c4e9e525b937ac8596c38d2d232b419529b9cd7a1cd46e39a \ --hash=sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701 \ --hash=sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e \ --hash=sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91 \ --hash=sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66 \ - --hash=sha256:85f998ea1848bc6757289e739cfbdda3a04adfd58b02fc018ce54d754a5ce468 \ --hash=sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217 \ --hash=sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658 \ --hash=sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418 \ @@ -915,7 +850,6 @@ pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ --hash=sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd \ - --hash=sha256:bcc33feacfaefce60c12fd500a277533bdc02b10a19f7f6d348763d8140bbba7 \ --hash=sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c \ --hash=sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777 \ --hash=sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35 \ @@ -930,7 +864,6 @@ pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ --hash=sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26 \ - --hash=sha256:f0606c8bf2cdefea14a43530f7657cbbb7ecf1c4222512492ef4a4434a9501ec \ --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ @@ -938,59 +871,64 @@ pillow==12.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 -platformdirs==4.11.7 ; python_version >= "3.10" and python_version < "4.0" \ + # via matplotlib +platformdirs==4.11.7 \ --hash=sha256:4f41487eeeeeb07f3a6625e61d9bc0ae6809f92d3386dbd74392fbb76108104d \ --hash=sha256:8a02cb259042c79d1cd0450facc2fe6dc9d303ae7901afbe33bf8ea0b188cef6 -pluggy==1.6.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via qldpc +pluggy==1.6.0 \ --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \ --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 -pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" \ + # via pytest +pycparser==2.22 ; implementation_name != 'PyPy' \ --hash=sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6 \ --hash=sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc -pymatching==2.4.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via cffi +pygments==2.20.0 \ + --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \ + --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 + # via pytest +pymatching==2.4.0 \ --hash=sha256:0c30dafa506b086f50d15530debd7b7b39228597d22c52d5ccd114bcbb0a2326 \ --hash=sha256:0e071c90c108daf1c6f8cefcac9ad4989a1a1694fe7433ee413cef7b65f4207e \ --hash=sha256:133168b1fec946bfc992a9e5abbdbbc7eebc672b04d27cfd060ea153eb6cf576 \ --hash=sha256:15e6d73153713a8f383f44ba4497d478fb4c4d765fbdd30f9fc1e1d47af75760 \ --hash=sha256:1a1b831f8ebf47f5e2a865d1bcc250da208c57ff881a37177ee548248da447c2 \ - --hash=sha256:1fb6717d7b42876d46e07708510bbe30044293200a776deb20b7532b94ba55da \ - --hash=sha256:4683b652ba3983bc26405107382d666d8ccf68f53ea50fc5fb274251871177ec \ - --hash=sha256:51a3f98ca3a0b94df33d2142e55be819fe7b5060498231277282e1c517c099bd \ --hash=sha256:5c5ce4ed245d3670ea7d62b09f8a29b105533b032a3d21fdf6df04c9b6441ff1 \ - --hash=sha256:633f52c4486e86bb4c6fe1acbb9e87a5a9d4c3a6176bb7417ff21ce196f38072 \ --hash=sha256:6450797ca5001449fcf5b43a68181a783f4ca50650f880a02ef5bf91b13aa359 \ --hash=sha256:70869b2a4a8fab2f1e54cc0b68fd050b7556133b5ce8c2d2d5d4f4d1319c14d3 \ - --hash=sha256:8778d0a06b606b169f8517279f01d4c0f9bf1725085f7a3d1ddcb24a53952fdd \ --hash=sha256:881f3410975b5a2d393561d44fa4513c878653d0e718e55930b08f52e9a04fc5 \ - --hash=sha256:9e9486118f37f20c4631e9471218a8c05df8f1e254b6b95cda6b319211218c17 \ --hash=sha256:a14022ecf4e810152db998d14429d430630433b9ed1edc2abe53494753b0a0ca \ --hash=sha256:a49db8f20d01523c9e12fc8207a5831ed261b53fb8e629c78e45b0081fcd5199 \ --hash=sha256:ac25684a41ab859cbb0f2ea34d86485aa89d4d22f97df684f9579c167c7d560a \ --hash=sha256:af03cdd7389e7b9f93069f1dd1bb9931c3836a2bfc0d01952993f394b0bfa10d \ - --hash=sha256:c51ee0ffa161f35ae6b887a0bebdf85e3fa456139e56d5e070823ef0434f692f \ - --hash=sha256:d8889c54d8daaea52ec80b35aa43f812eb944632e57531f14c06253c033a22dc \ --hash=sha256:df8c9229c5265d71f871fba3fbdbd27cda71ca81969275eefeb64688ee2f59f4 \ --hash=sha256:eb4428581a2578a8cc77f695ab267c184f444d9de5cb25301fe9f3f893214ea4 \ --hash=sha256:f2390db2679b5009509b6ea1b0838187966c2d8fd1ab8050e5d9b6c908a3ea7e \ - --hash=sha256:f24dad954a5425e53d587e5cb88010c172061f59650ba2cb900acc4b3d28a6ef \ - --hash=sha256:fabeaf35310805e0e4b36c8650e80152a02d854a802ec05b84655e82c9e45cbf \ - --hash=sha256:fd8699227cda0d9c95152ee67ce2e5119fc845ff7951bfd8ed34dab9628733c1 \ --hash=sha256:fe3baa498155a227f5ce1ee6fe07f40524d9fb9961ac299a2050e69f7de50dba -pyparsing==3.3.2 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # ldpc + # qldpc +pyparsing==3.3.2 \ --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \ --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc -pytest==9.1.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via matplotlib +pytest==9.1.1 \ --hash=sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313 \ --hash=sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c -python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "4.0" \ + # via ldpc +python-dateutil==2.9.0.post0 \ --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -pytz==2025.2 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # matplotlib + # pandas +pytz==2025.2 \ --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \ --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 -qdldl==0.1.9.post1 ; python_version >= "3.10" and python_version < "4.0" \ + # via pandas +qdldl==0.1.9.post1 \ --hash=sha256:00331a5e45cc60bf6f75f027ae2a2c137295de0b5e1a0af358a37aaa07427476 \ - --hash=sha256:04c8e83875ae609776697493b8fe57674a87e75dff4f4f57f05da26b063e25c3 \ --hash=sha256:0cb2e82b4f40cb18638da47adf491a016d76d0d82f25377c4c330453a8785f94 \ --hash=sha256:0d69a8d011f47f287c5e7668b92b9e3574798b9687bc751af6f89c15037aa26e \ --hash=sha256:0db9f197fb51c6fa96ff1964707e2ba9a89b7b0d91c305e6e0b668e1bcb66fbf \ @@ -1004,16 +942,9 @@ qdldl==0.1.9.post1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:25da2bcd44dd272435db3b4208b47943837503f43db4d24c02e5e15b7d5ddf33 \ --hash=sha256:27b02a730c39b2dba205bb5c08dca5deb994f655f6eeacee687ac006c50b3366 \ --hash=sha256:31296314679c6ccfad8760704dbc9e25b5e49fd442f803638a2aaa1886724f7d \ - --hash=sha256:33b69d72d05616261a3f5427798d19d832427d1fae2fa4c05f1c14ff88de626a \ - --hash=sha256:3c4bebeb1a27b889092a5fc213bab94c3b05b282334351a6430e2b569c770190 \ --hash=sha256:496bebf154c002fb7a36443b9a26cc0f7251e8e9251d3742590300cd2edec7a8 \ - --hash=sha256:532006e40ba63893ce3c32fd1245df21b3a8b93601f94680ee00df6652939050 \ --hash=sha256:547f73046ef615827317fbedb21b5e1b11f5082d304e8d775e9e17d6c447a598 \ --hash=sha256:576edaf8b847d087806e3d2a860a06d52a435aa3192b21bbc39e69a55187805d \ - --hash=sha256:80838c66f944547280e0849fec3bf67d6e0770633c3fd375263f51e10055cad4 \ - --hash=sha256:86b9e72efec255fe7d777002d8a788a928c202c1defb845bb72ad9db9bc8205f \ - --hash=sha256:89dd7df36066f55efb6f96b5aead21fe828db8f1e02030024660ea58cbe1e1fd \ - --hash=sha256:90a2e52aab2cce319d95bd4ef045c63ec9e78be4178d5f619af07ab1f9cce04f \ --hash=sha256:98a13e234ea335484cf76441b95638d0f9ecdd43242dd5a073f77c758977d980 \ --hash=sha256:98a1da222112bd46fe14e2854aa43361203d488451ba300d08f8c9a43c5093e3 \ --hash=sha256:9a2aed01dbdc705bcab71270ce20753a016bd7b112ea7e06d1166d897e7483cb \ @@ -1021,12 +952,8 @@ qdldl==0.1.9.post1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:a2c005c9365dea6389a9feacf636028453790cace114b54e40fd302d6f4bf91c \ --hash=sha256:a90b12436906ce2703795511332969ff1bc50b38cd4b6fcc67ae0e9e7e7901da \ --hash=sha256:aa0e9721d272467c95a9748e6acea8911a12041ef3d8b20176aff829388ce57d \ - --hash=sha256:accb19fb72478ddd59f668eb515e95bfca0fb470762a9c89fde3b947e2304dea \ - --hash=sha256:b5f2cdd882281d71da67a1d9b98617c5e8045e0a3e9b07747263feafbf979a12 \ --hash=sha256:ba3e19399553821b5ceee0c082fdb4453d00a38bd420b76dddec92ce2a5a065c \ --hash=sha256:bc0f30e521da345d25e4fa5308fda4964e25beacd10e8de9f08d084a600a42e2 \ - --hash=sha256:bc31de23656bc420753a081da041871a6b02afe2f57df0def3e40f324042141d \ - --hash=sha256:d3623224fe65becd31c3ca6bcecfb5c26da69678bdf03857c83b71f58940e284 \ --hash=sha256:d3d4ac1885bc99d214044671121b9e95f96fb9caa63e2a1d6ac30bae9f8266a8 \ --hash=sha256:da2016d541c26cefc79bca4d8b5bebfa00f35db19704abb20efbd1c08df3b4c7 \ --hash=sha256:dbfb46f7290917146b076449fb3fee1eefdf018e710c7033c11739ae1723da07 \ @@ -1037,22 +964,23 @@ qdldl==0.1.9.post1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:e8c0148de01346722ef07ce9434485b97b8f3aa7042cac5c341b58a728fe7588 \ --hash=sha256:ee5b35c1dd419cbf388664d26d410c89a3e0c7e055e25223b6d28bd920349c8a \ --hash=sha256:f5a9bcda38dd19f75d72e47558f5132a99e5443238e5153a984c6f552bc4f4ac \ - --hash=sha256:f80a04d76bc89af89bbf2ce4c5c634a22ef7a3f7101357d9868e1057e3e07066 \ - --hash=sha256:fada1975e65de10949f7b71fa2b53fe717251f3c95fcb7c354f04e7d237367ce \ - --hash=sha256:fbd6ca88867514f3f2061a99abdd6cc8bbce76c8fe699586f81486e2958160d8 \ --hash=sha256:fcc2184cac1e502ed624efe7f00c1c215b95cfd324a8cacf7f0053c00fa524f5 -qecsim==1.0b9 ; python_version >= "3.10" and python_version < "4.0" \ + # via cvxpy +qecsim==1.0b9 \ --hash=sha256:71a53803ae382953a792618a212e6a4dbc0e0d60de7df039e1c32118b52e445b \ --hash=sha256:ca13b6c74cd801f2aefcc14baf1217086f5cef446402376eeb378aec6ce885da -qecstruct==0.2.9 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +qecstruct==0.2.9 \ --hash=sha256:833b6fced75a03f174350f2af0e6b47fad75e9cc2e305ae4acdb4d0fac00912a \ --hash=sha256:9ea8264e163a4854961a58e20f9624359c84bcd706574879e6faadf694b1dcfc \ --hash=sha256:bc462f8e1f6ca5157be215361f04a048de3ffa8358e90499125df43f1387bd6b \ --hash=sha256:e8428680d0d8fd75d74466b7b01d6ebcfc81364b216f6a66177add50622b7b26 -qldpc==0.1.3 ; python_version >= "3.10" and python_version < "4.0" \ - --hash=sha256:418ab571549178c2a11340a137330bb7b073c57cc50d57e2d60a7f25b1f27806 \ - --hash=sha256:72094c4e5d574ca4c86417d52faa7aa041f838f46fa4df843af115a94ccb0570 -scipy==1.17.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +qldpc==0.1.4 \ + --hash=sha256:2fc6b8ebb17d67d532ef9c5912e497837e27259c34c37d6697c88936592e01e1 \ + --hash=sha256:f0354757998bf44195ae98de5d8329a7ef09548264e3a568a5d5f3cd93d25242 + # via mdopt +scipy==1.17.1 \ --hash=sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0 \ --hash=sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458 \ --hash=sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118 \ @@ -1114,26 +1042,32 @@ scipy==1.17.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea \ --hash=sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293 \ --hash=sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76 -scs==3.3.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # clarabel + # cvxpy + # ldpc + # mdopt + # osqp + # pymatching + # qdldl + # qecsim + # qldpc + # scs + # sinter +scs==3.3.1 \ --hash=sha256:0107d0b282fa783e956f1d68320f4d5ac6796186816e0996984b4d240b9704a6 \ --hash=sha256:0495b1fb68f04456d497490c3fad62e8e12d69b4902275636078eca6a590bde9 \ --hash=sha256:077be0e95ca17e6e623fa4eefe4df9b2f9840875cfd65b2da58cfe72f274b2aa \ --hash=sha256:0bd0271f32335d109f00842b5f9ce90544af412c564603927273dc6a9326fc7a \ - --hash=sha256:1b5d1abed5b259fda1b2409275f6ae5f706832cbe05c231231aaf0c820a68be8 \ - --hash=sha256:22e7250907632a8de56b7e72c1c8aed0f1098e67a4c2b03dabf19b7489d74745 \ --hash=sha256:29eb5b976997de28aaf0592f9abd8678b2af699980895b79ad22c8363a2a2e88 \ --hash=sha256:2d268e22f871dadf96c622bbe8a301e20007474e3afa4783806074492214156b \ --hash=sha256:34093ab7c5ae455ee2a6e5c0c0ae990be63a3fb57e7766a3563014041fcaceb2 \ --hash=sha256:34fb479df550539c29bea9ad9dff57ba870300485b0b1ea081b4a7bea870d19b \ --hash=sha256:388a32d8fc5094a76a77ad4d9b843c3b03015387046a2e85f03fd6de7268df9b \ --hash=sha256:38e8eeb2b8f43f3109569862d398ca787ab4e4a50e16d1148206ed1cecd1840f \ - --hash=sha256:47612abf2bb6b7136be81d766abce2d6aeb5124761767747c8f37d4fdbdbaf09 \ --hash=sha256:491bf3734ea064056c67f5932bd735d0f36d6600763d5e2eecac0940212da7dc \ - --hash=sha256:4bd542948eeafb5103c1cefd68817efa4db419f37e289b4eda4842dba58de5f7 \ --hash=sha256:4d743b1544fc0feb27843c94dfaf577cd594c3910cb1d150d2f286c786ffe763 \ --hash=sha256:566d64908cf38a7baa03fe554df0d0f96b9798bb71d8f5d8f4e782489f05a15d \ - --hash=sha256:5e135ceb100df91b6f96815d2b7b8ff4195143cf3c7067dbdbc304f354eaf507 \ - --hash=sha256:6267319bb5a040d8f5a062799a8c36b5f86e97c806f11c475048171301e43e02 \ --hash=sha256:681437635f89563a9c8d4bb19beccd29e90c1f719846ff1b5ada04c1fccb122f \ --hash=sha256:78e34594d751614b7403756d6916703211a590fb9bfe2c9a7faaf4c3272a1f04 \ --hash=sha256:7b8e140fdf41d79c15777ace14f92a38989d17e0b3356ba571d229acbc797a9e \ @@ -1143,36 +1077,37 @@ scs==3.3.1 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:93cf91abcfb5ad4c412b5e826ccdd6e10552c2e9a3ca768e605be947f8db28da \ --hash=sha256:98cb9677e2e8b33ce7850ca4c3df58e4f6f89143ed11822699e63c293c53181d \ --hash=sha256:b09747d1151e42f258f23c50ee6177b11602a758a1630a618b2e71b2ba7c048d \ - --hash=sha256:b1ae098f85afae2fac0ce69b0bd0ae17bd55288f57d0a37cee95b997aebc26ed \ --hash=sha256:bd687819d850b0d21f90276b43064703fdedfd86bf91e4008e9a833e6bd0e680 \ --hash=sha256:c1d7343e7e4e86a72d3f9b6efbfbce11cff9821f3164ac3122f47eecb11f19d7 \ --hash=sha256:c8ace1a3d784fdb9810e056f0ac7115efd74740663d188438cf5c83f36b6e338 \ --hash=sha256:cc8479a3c31394d4ca453007b9c13222bf5a270e28cb675661579b2357fa8ef6 \ --hash=sha256:cccb8d20780aca568aa1448239bf71d60a69b41b0470e972ad8dab0f328808dd \ - --hash=sha256:d30c97c2207bf5397f27db86e089edf3783239e5ed07326bb40d848ca096e7a2 \ --hash=sha256:d34c3ac05c264e380b93e327026b2b5d9cfa2cb7f3c4e28689df346fa89d446e \ --hash=sha256:d73628563095fc898ef835ffb180f1ee5d8379c871dad56d8f0f579be2e05067 \ --hash=sha256:dd9d43c1f2f189a84e84c26721e2c5129abf921a6a8acc48c154abae993357b1 \ --hash=sha256:e488cbb471807b8cad9d9f4c56407d90221996a29e3bdcf8bd6668c01dfcc1e8 \ - --hash=sha256:e62e2791aa7c147396edc38f0806a98564db0e03fa561b927e426dc96660c130 \ --hash=sha256:e8c01e6a943a31be436c55573ce0316494169d9737b4890d96463615693f3cd5 \ --hash=sha256:eacba22bdb4605ea07f138648292f5f7131a48527de7df8dce790b57c2e4cf55 \ --hash=sha256:eaf4d85c71516139c472a7aa7dbf14bbf3e3ebbc1cf7f548e0d71b6108f30b55 \ --hash=sha256:ee4a4555930cac82b988b66a81993e87d674d358d22d909ec31ba7cbc0709f8a \ - --hash=sha256:f95c78da2dedcbcee4ab998bacbee8f19af6803193dfab18c5d0debe0a7765f8 \ --hash=sha256:fc10932f33c97d9a490be30eb8720e2012931e014bf9259980dcd23e57db6dba -seaborn==0.13.2 ; python_version >= "3.10" and python_version < "4.0" \ + # via cvxpy +seaborn==0.13.2 \ --hash=sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987 \ --hash=sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7 -setuptools==84.0.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +setuptools==84.0.0 \ --hash=sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670 \ --hash=sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73 -sinter==1.16.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via osqp +sinter==1.16.0 \ --hash=sha256:fd0f7bff97cb951345893f53b347d76f99b53977590054abc3d057150f82037b -six==1.17.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via ldpc +six==1.17.0 \ --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 -sparsediffpy==0.3.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via python-dateutil +sparsediffpy==0.3.0 \ --hash=sha256:22848d97852554c8814cd5a0bffc4f4f41930aa7155f2193e99332e8a9cf6f6d \ --hash=sha256:2c13dd751963611419273d7cf99c9f7e0c7793c7edba2941a57847cc62f3aa18 \ --hash=sha256:31560dc28732401d922cb910d997adabed72f26338dbf03409e1a3c6639d1908 \ @@ -1194,17 +1129,15 @@ sparsediffpy==0.3.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:cc49c774a44776afd728aca4ee1f062756416ee25366acbf32ad6416bdfb2630 \ --hash=sha256:d9a6af38f1bb24eccfd9cc089263e7d652b0c8e97e19385258e6ee83c40f7d68 \ --hash=sha256:fdd9115db63ee228d09e1917365b263a16811645c6d32ee7dce50ada09b3d5a5 -stim==1.16.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via cvxpy +stim==1.16.0 \ --hash=sha256:1822adb4920f4616b253792fe1893ad07a9bea8cbc45b2f4cdf7c75a8083cd00 \ --hash=sha256:1890bda08adfd6daa875d1d5e41039c5ae8c58faabc7cd1813f8d8de9f1536a2 \ --hash=sha256:1e78fe10ab4c28af9ceb57ac8ed895f3efcde9607d851ef931c53d1f6f80bc8e \ --hash=sha256:1fa60a13ef921d6e6aea420e2fe7551d71a4a07b513d86da89e39259dd3739f9 \ --hash=sha256:3e2883216fb4f57411531c99dfa1838c0d8201c619ccd01b197b9afd54d326cf \ - --hash=sha256:41c02d52af847e0ab886c35751e8f7e17e2e0d055e390ece93a50a12dcf87e56 \ --hash=sha256:55231457b5897b0ef798059aae01d9e8247fc8b2089e8a27f9f4bb13b0342af2 \ - --hash=sha256:609666b77197d45705e1579a9840123a53cebe614e8bf1d496c0df24f797e653 \ --hash=sha256:66dbc6532169df84d127ceede353b15445e845a94aa008c5d0a4062b51cc2b02 \ - --hash=sha256:84b777678cc12e808b3d6747d57e07eed951f8ec18047a936dfa25dbc950a2e2 \ --hash=sha256:85b9c544c65377a42c6b5c9a57616a5ed5580e1cabb286a604800a045c7a12fb \ --hash=sha256:883b24c3c95fa69818e931e8b60daf4fc8195d159d321e7548d8706dbac16558 \ --hash=sha256:8d44f858f3d5d1107e674886facbb011ed85438f0888278d6aa3c1f42d9e5a5f \ @@ -1212,70 +1145,35 @@ stim==1.16.0 ; python_version >= "3.10" and python_version < "4.0" \ --hash=sha256:b367f90826e796417932ea46c0e53d80e3c5bebac0c6633c0aa07a8fcaa4298b \ --hash=sha256:b8580dfbe5ff27594dd15a83b24262630b82cf089d1463d29dbee90ffbc6898b \ --hash=sha256:bd2d28093818f981f8dedb2be160b62d818279b81748d72dc4fca6cdd160291d \ - --hash=sha256:d467b5599de7e171651a1ff7fac353dd64d78df75d9c63e7d870d576eb0fd64b \ --hash=sha256:ed3aa70f82690946ba6f70abb9dad448d49ab9499fa5161de5f286da87c30ab1 \ --hash=sha256:f22a009bad1e7816aeb70bdeafb6789e4840710da070f69cde91e2ac6cf1e800 \ --hash=sha256:f94040cbeeff786e7fb8860249e54d9ba3f777b5b098d21f1eccd072d3a3c32a -sympy==1.14.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # ldpc + # mdopt + # qldpc + # sinter +sympy==1.14.0 \ --hash=sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517 \ --hash=sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5 -threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # mdopt + # qldpc +threadpoolctl==3.6.0 \ --hash=sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb \ --hash=sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e -tomli==2.4.1 ; python_version >= "3.10" and python_version < "3.11" \ - --hash=sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853 \ - --hash=sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe \ - --hash=sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5 \ - --hash=sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d \ - --hash=sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd \ - --hash=sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26 \ - --hash=sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54 \ - --hash=sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6 \ - --hash=sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c \ - --hash=sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a \ - --hash=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \ - --hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \ - --hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \ - --hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \ - --hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \ - --hash=sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9 \ - --hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \ - --hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \ - --hash=sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e \ - --hash=sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c \ - --hash=sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41 \ - --hash=sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f \ - --hash=sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085 \ - --hash=sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15 \ - --hash=sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7 \ - --hash=sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c \ - --hash=sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36 \ - --hash=sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076 \ - --hash=sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \ - --hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \ - --hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \ - --hash=sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece \ - --hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \ - --hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \ - --hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \ - --hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \ - --hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \ - --hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \ - --hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \ - --hash=sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc \ - --hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \ - --hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \ - --hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \ - --hash=sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30 \ - --hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \ - --hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 \ - --hash=sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049 -tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0" \ + # via mdopt +tqdm==4.67.1 \ --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \ --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2 -typing-extensions==4.16.0 ; python_version >= "3.10" and python_version < "4.0" \ + # via + # ldpc + # mdopt +typing-extensions==4.16.0 \ --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ 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hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6", size = 1164730, upload-time = "2024-08-22T12:18:22.534Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/21/02/88b65cc394961a60c43c70517066b6b679738caf78506a5da7b88ffcb643/widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71", size = 2335872, upload-time = "2024-08-22T12:18:19.491Z" }, +] From 0a8a1245503135b24c7f73e1aaab52f5356d0cf7 Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 14 Sep 2026 23:01:50 -0400 Subject: [PATCH 2/5] Reformat Python sources with ruff format Mechanical: each of these 24 files is exactly ruff 0.16.7's output of its previous version, under the [tool.ruff] settings from the previous commit, and parses to the same syntax tree as before. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01S6mbxc9eJDQMVx7tjvYwud --- examples/decoding/dem_campaign/nishimori.py | 4 +-- mdopt/examples/decoding/classical_ldpc.py | 2 +- mdopt/examples/decoding/classical_ldpc_bp.py | 4 +-- mdopt/examples/decoding/data_handling.py | 6 ++-- .../plotting/aggregate_qubit_order_bb.py | 2 +- .../plotting/aggregate_vert_horiz_bb.py | 11 +++--- .../plotting/aggregate_vert_horiz_surface.py | 7 ++-- .../aggregate_vert_horiz_surface_variants.py | 4 +-- .../examples/decoding/plotting/augment_bp.py | 2 +- .../decoding/plotting/gen_qubit_order_bb.py | 4 +-- .../plotting/gen_vert_horiz_surface.py | 4 +-- .../gen_vert_horiz_surface_highchi.py | 2 +- .../decoding/quantum_bivariate_bicycle.py | 4 +-- mdopt/examples/decoding/quantum_csp.py | 2 +- .../decoding/quantum_hypergraph_product.py | 2 +- mdopt/examples/decoding/quantum_surface.py | 2 +- mdopt/mps/canonical.py | 4 +-- tests/decoding/test_decoders.py | 34 +++++++++---------- tests/decoding/test_dem.py | 18 +++++----- tests/decoding/test_surface_ler_regression.py | 12 +++---- tests/mps/test_canonical.py | 12 +++---- tests/mps/test_explicit.py | 6 ++-- tests/mps/test_utils.py | 12 +++---- tests/optimiser/test_utils.py | 6 ++-- 24 files changed, 82 insertions(+), 84 deletions(-) diff --git a/examples/decoding/dem_campaign/nishimori.py b/examples/decoding/dem_campaign/nishimori.py index 34860a23..c7fa7783 100644 --- a/examples/decoding/dem_campaign/nishimori.py +++ b/examples/decoding/dem_campaign/nishimori.py @@ -82,14 +82,14 @@ def run(distance, p, shots, chi=128, seed=0): if (i + 1) % 500 == 0: sink.flush() print( - f"[{tag}] {i+1}/{shots} rss={_rss_gb():.2f}GB", + f"[{tag}] {i + 1}/{shots} rss={_rss_gb():.2f}GB", flush=True, ) rows = [json.loads(l) for l in open(path)] m = sum(r["map"] != r["truth"] for r in rows) w = sum(r["mwpm"] != r["truth"] for r in rows) print( - f"[{tag}] n={len(rows)} MAP {m/len(rows):.4f} MWPM {w/len(rows):.4f}", + f"[{tag}] n={len(rows)} MAP {m / len(rows):.4f} MWPM {w / len(rows):.4f}", flush=True, ) diff --git a/mdopt/examples/decoding/classical_ldpc.py b/mdopt/examples/decoding/classical_ldpc.py index c5e95b22..73c0c629 100755 --- a/mdopt/examples/decoding/classical_ldpc.py +++ b/mdopt/examples/decoding/classical_ldpc.py @@ -282,7 +282,7 @@ def save_experiment_data( pickle.dump(data, pickle_file) logging.info( f"Saved experiment data for {path} with " - f"{np.mean(data['failures'])*100:.2f}% failure rate." + f"{np.mean(data['failures']) * 100:.2f}% failure rate." ) diff --git a/mdopt/examples/decoding/classical_ldpc_bp.py b/mdopt/examples/decoding/classical_ldpc_bp.py index 8f1746b9..bb7756af 100755 --- a/mdopt/examples/decoding/classical_ldpc_bp.py +++ b/mdopt/examples/decoding/classical_ldpc_bp.py @@ -48,7 +48,7 @@ def main(): print(f"H shape: {m} x {n}") print( - f"Estimated code params: n={n_}, kβ‰ˆ{k_est} (Rβ‰ˆ{k_est/n_:.3f}, lower bound {rate_lb:.2f}), d_estβ‰ˆ{d_est}" + f"Estimated code params: n={n_}, kβ‰ˆ{k_est} (Rβ‰ˆ{k_est / n_:.3f}, lower bound {rate_lb:.2f}), d_estβ‰ˆ{d_est}" ) col_w = np.asarray(h.sum(axis=0)).ravel() @@ -128,7 +128,7 @@ def main(): plt.xlabel("Physical bit-flip rate $p$ (BSC)") plt.ylabel("Logical error rate") plt.title( - f"Gallager (3,4) LDPC β€” n={n_}, kβ‰ˆ{k_est} (Rβ‰ˆ{k_est/n_:.2f})," + f"Gallager (3,4) LDPC β€” n={n_}, kβ‰ˆ{k_est} (Rβ‰ˆ{k_est / n_:.2f})," f" max_iter={max_iters}, {bp_method}" ) plt.grid(True, which="both") diff --git a/mdopt/examples/decoding/data_handling.py b/mdopt/examples/decoding/data_handling.py index 12f2ba7c..c2cd622c 100644 --- a/mdopt/examples/decoding/data_handling.py +++ b/mdopt/examples/decoding/data_handling.py @@ -470,7 +470,7 @@ def check_error_consistency( total_inconsistencies += num_inconsistent total_checked += num_total print( - f"lattice_size={lattice_size}, chi_max={chi_max}: Inconsistent ({num_inconsistent}/{num_total}), {num_inconsistent/num_total*100:.2f}%" + f"lattice_size={lattice_size}, chi_max={chi_max}: Inconsistent ({num_inconsistent}/{num_total}), {num_inconsistent / num_total * 100:.2f}%" ) if total_inconsistencies == 0: @@ -879,7 +879,7 @@ def objective_function(params): } print(f"Lattice size: {lattice_size}, Bond dimension: {chi_max}") - print(f" Estimated threshold (p_th): {p_th*100:.5f}%") + print(f" Estimated threshold (p_th): {p_th * 100:.5f}%") print(f" Scaling exponent (nu): {nu:.5f}") # Skip plotting if no valid results exist @@ -927,7 +927,7 @@ def objective_function(params): plt.plot( fit_p, fit_y, - label=f"Fit L={lattice_size}, p_th={p_th*100:.2f}%, nu={nu:.2f}", + label=f"Fit L={lattice_size}, p_th={p_th * 100:.2f}%, nu={nu:.2f}", linewidth=3, color=cmap(norm(index)), # Ensure fit curve matches data color ) diff --git a/mdopt/examples/decoding/plotting/aggregate_qubit_order_bb.py b/mdopt/examples/decoding/plotting/aggregate_qubit_order_bb.py index e7beffb2..e3b9f441 100644 --- a/mdopt/examples/decoding/plotting/aggregate_qubit_order_bb.py +++ b/mdopt/examples/decoding/plotting/aggregate_qubit_order_bb.py @@ -160,7 +160,7 @@ def main(): f"{o['ler']:.5f}Β±{o['ler_sem']:.5f} | " f"{n['avg_time_s']:>7.3f}Β±{n['avg_time_sem']:>5.3f} | " f"{o['avg_time_s']:>7.3f}Β±{o['avg_time_sem']:>5.3f} | " - f"{100*s['mean']:>6.2f}Β±{100*s['sem']:.2f}" + f"{100 * s['mean']:>6.2f}Β±{100 * s['sem']:.2f}" ) # ── Figure ───────────────────────────────────────────────────────────────── diff --git a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_bb.py b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_bb.py index 18c58be6..9beeaf56 100644 --- a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_bb.py +++ b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_bb.py @@ -99,9 +99,9 @@ def per_seed_speedup(agg, chi, p, baseline, target): """Per-seed (1 - t_target / t_baseline). Returns mean Β± SEM and array.""" b = agg[(chi, p, *baseline)] t = agg[(chi, p, *target)] - assert ( - b["seeds"] == t["seeds"] - ).all(), f"seed mismatch baseline={baseline} target={target} at ({chi},{p})" + assert (b["seeds"] == t["seeds"]).all(), ( + f"seed mismatch baseline={baseline} target={target} at ({chi},{p})" + ) per = 1.0 - t["seed_times"] / b["seed_times"] return float(per.mean()), float(per.std(ddof=1) / np.sqrt(len(per))), per @@ -146,7 +146,7 @@ def main(): f"\nSpeedup % vs baseline (Naive vert + Natural horiz), mean across " f"p values (Β± std):" ) - print(f"{'chi':>4} | {'vert only':>14} | {'horiz only':>14} | " f"{'both':>14}") + print(f"{'chi':>4} | {'vert only':>14} | {'horiz only':>14} | {'both':>14}") for chi in CHI_LIST: row = [f"{chi:>4}"] for label in ("vert", "horiz", "both"): @@ -156,8 +156,7 @@ def main(): print(f"\nDecode time at chi={PLOT_CHI} (s):") print( - f"{'p':>7} | {'baseline':>10} | {'+vert':>10} | {'+horiz':>10} | " - f"{'+both':>10}" + f"{'p':>7} | {'baseline':>10} | {'+vert':>10} | {'+horiz':>10} | {'+both':>10}" ) for p in P_LIST: b = agg[(PLOT_CHI, p, *BASELINE)]["avg_time_s"] diff --git a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface.py b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface.py index fb46a0cd..30a3831e 100644 --- a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface.py +++ b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface.py @@ -87,8 +87,8 @@ def main(): print(f" bandwidth Natural : max_span={bw_n[0]}, total_span={bw_n[1]}") print(f" Optimised: max_span={bw_o[0]}, total_span={bw_o[1]}") print( - f" reduction max {100*(bw_n[0]-bw_o[0])/bw_n[0]:.1f}%, " - f"total {100*(bw_n[1]-bw_o[1])/bw_n[1]:.1f}%" + f" reduction max {100 * (bw_n[0] - bw_o[0]) / bw_n[0]:.1f}%, " + f"total {100 * (bw_n[1] - bw_o[1]) / bw_n[1]:.1f}%" ) rng = np.random.default_rng(BOOT_SEED) @@ -100,8 +100,7 @@ def main(): print(f"\nDecoding-time reduction (%) β€” paired, bootstrap SEM (N_boot={N_BOOT}):") print( - f"{'chi':>4} {'p':>6} | {'vert only':>14} | {'horiz only':>14} | " - f"{'both':>14}" + f"{'chi':>4} {'p':>6} | {'vert only':>14} | {'horiz only':>14} | {'both':>14}" ) for c in chis: for p in ps: diff --git a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface_variants.py b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface_variants.py index 44ab350d..82145c86 100644 --- a/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface_variants.py +++ b/mdopt/examples/decoding/plotting/aggregate_vert_horiz_surface_variants.py @@ -316,9 +316,9 @@ def ler_or_ub(cell): ax.grid(True, ls=":", linewidth=0.6) for ax in list(axes_flat)[n_chi:]: ax.set_visible(False) - for ax in (axes[-1] if n_rows > 1 else axes): + for ax in axes[-1] if n_rows > 1 else axes: ax.set_xlabel(r"Physical error rate $p$") - for ax in (axes[:, 0] if n_rows > 1 else [axes[0]]): + for ax in axes[:, 0] if n_rows > 1 else [axes[0]]: ax.set_ylabel(r"Avg.\ decoding time per shot (s)") (axes[0, 0] if n_rows > 1 else axes[0]).legend( loc="lower right", framealpha=0.9, fontsize=7 diff --git a/mdopt/examples/decoding/plotting/augment_bp.py b/mdopt/examples/decoding/plotting/augment_bp.py index d898610c..ef05aefc 100644 --- a/mdopt/examples/decoding/plotting/augment_bp.py +++ b/mdopt/examples/decoding/plotting/augment_bp.py @@ -150,7 +150,7 @@ def main(): reps_per_np[(N, TARGET_P)] = target_total elapsed = time.perf_counter() - t0 - print(f"N={N}: wall {elapsed/60:.1f} min") + print(f"N={N}: wall {elapsed / 60:.1f} min") # Persist after each N so a partial run is recoverable. with open(CACHE_FILE, "wb") as f: diff --git a/mdopt/examples/decoding/plotting/gen_qubit_order_bb.py b/mdopt/examples/decoding/plotting/gen_qubit_order_bb.py index eab6c8d0..1715c0ed 100644 --- a/mdopt/examples/decoding/plotting/gen_qubit_order_bb.py +++ b/mdopt/examples/decoding/plotting/gen_qubit_order_bb.py @@ -133,8 +133,8 @@ def main(): print(f" Natural: max_span={max_nat:4d}, total_span={sum_nat:6d}") print(f" Optimised: max_span={max_opt:4d}, total_span={sum_opt:6d}") print( - f" Reduction: max {100*(max_nat-max_opt)/max_nat:.1f}%, " - f"total {100*(sum_nat-sum_opt)/sum_nat:.1f}%" + f" Reduction: max {100 * (max_nat - max_opt) / max_nat:.1f}%, " + f"total {100 * (sum_nat - sum_opt) / sum_nat:.1f}%" ) # ── Generate shared errors per p ─────────────────────────────────────────── diff --git a/mdopt/examples/decoding/plotting/gen_vert_horiz_surface.py b/mdopt/examples/decoding/plotting/gen_vert_horiz_surface.py index a5ea2169..6c98fa73 100644 --- a/mdopt/examples/decoding/plotting/gen_vert_horiz_surface.py +++ b/mdopt/examples/decoding/plotting/gen_vert_horiz_surface.py @@ -127,8 +127,8 @@ def main(): print(f" Natural: max_span={bw_nat[0]:4d}, total_span={bw_nat[1]:6d}") print(f" Optimised: max_span={bw_opt[0]:4d}, total_span={bw_opt[1]:6d}") print( - f" Reduction: max {100*(bw_nat[0]-bw_opt[0])/bw_nat[0]:.1f}%, " - f"total {100*(bw_nat[1]-bw_opt[1])/bw_nat[1]:.1f}%" + f" Reduction: max {100 * (bw_nat[0] - bw_opt[0]) / bw_nat[0]:.1f}%, " + f"total {100 * (bw_nat[1] - bw_opt[1]) / bw_nat[1]:.1f}%" ) # Pre-generate errors per p, shared across all (chi, ordering, vertical). diff --git a/mdopt/examples/decoding/plotting/gen_vert_horiz_surface_highchi.py b/mdopt/examples/decoding/plotting/gen_vert_horiz_surface_highchi.py index 1a6c9acc..8700276a 100644 --- a/mdopt/examples/decoding/plotting/gen_vert_horiz_surface_highchi.py +++ b/mdopt/examples/decoding/plotting/gen_vert_horiz_surface_highchi.py @@ -193,7 +193,7 @@ def run_chi_group(chi, errors_per_p, n_qubits): }, fh, ) - print(f"[chi={chi}] saved {pkl} (wall {elapsed/60:.1f} min)") + print(f"[chi={chi}] saved {pkl} (wall {elapsed / 60:.1f} min)") def main(): diff --git a/mdopt/examples/decoding/quantum_bivariate_bicycle.py b/mdopt/examples/decoding/quantum_bivariate_bicycle.py index a9b091d3..85010647 100644 --- a/mdopt/examples/decoding/quantum_bivariate_bicycle.py +++ b/mdopt/examples/decoding/quantum_bivariate_bicycle.py @@ -342,12 +342,12 @@ def save_experiment_data( order_tag = ( "" if qubit_order_strategy == "Natural" else f"_order{qubit_order_strategy}" ) - file_key = f"latticesize{order_x*order_y}_bonddim{chi_max}_errorrate{error_rate}_errormodel{error_model}_bias_prob{bias_prob}_numexperiments{num_experiments}_tolerance{tolerance}_cut{cut}_seed{seed}{order_tag}.pkl" + file_key = f"latticesize{order_x * order_y}_bonddim{chi_max}_errorrate{error_rate}_errormodel{error_model}_bias_prob{bias_prob}_numexperiments{num_experiments}_tolerance{tolerance}_cut{cut}_seed{seed}{order_tag}.pkl" with open(file_key, "wb") as pickle_file: pickle.dump(data, pickle_file) logging.info( f"Saved data for {file_key} with " - f"{np.nanmean(data['failures'])*100:.2f}Β±{sem(data['failures'], nan_policy='omit')*100:.2f}% failure rate." + f"{np.nanmean(data['failures']) * 100:.2f}Β±{sem(data['failures'], nan_policy='omit') * 100:.2f}% failure rate." ) diff --git a/mdopt/examples/decoding/quantum_csp.py b/mdopt/examples/decoding/quantum_csp.py index 5a5a66e8..da9137cb 100644 --- a/mdopt/examples/decoding/quantum_csp.py +++ b/mdopt/examples/decoding/quantum_csp.py @@ -450,7 +450,7 @@ def save_experiment_data( pickle.dump(data, pickle_file) logging.info( f"Saved data for {file_key} with " - f"{np.nanmean(data['failures'])*100:.2f}Β±{sem(data['failures'], nan_policy='omit')*100:.2f}% failure rate." + f"{np.nanmean(data['failures']) * 100:.2f}Β±{sem(data['failures'], nan_policy='omit') * 100:.2f}% failure rate." ) diff --git a/mdopt/examples/decoding/quantum_hypergraph_product.py b/mdopt/examples/decoding/quantum_hypergraph_product.py index 062088b6..8788ba12 100644 --- a/mdopt/examples/decoding/quantum_hypergraph_product.py +++ b/mdopt/examples/decoding/quantum_hypergraph_product.py @@ -350,7 +350,7 @@ def save_experiment_data( pickle.dump(data, pickle_file) logging.info( f"Saved data for {file_key} with " - f"{np.nanmean(data['failures'])*100:.2f}Β±{sem(data['failures'], nan_policy='omit')*100:.2f}% failure rate." + f"{np.nanmean(data['failures']) * 100:.2f}Β±{sem(data['failures'], nan_policy='omit') * 100:.2f}% failure rate." ) diff --git a/mdopt/examples/decoding/quantum_surface.py b/mdopt/examples/decoding/quantum_surface.py index dbf98d7f..71a22efe 100644 --- a/mdopt/examples/decoding/quantum_surface.py +++ b/mdopt/examples/decoding/quantum_surface.py @@ -328,7 +328,7 @@ def save_experiment_data( pickle.dump(data, pickle_file) logging.info( f"Saved data for {file_key} with " - f"{np.nanmean(data['failures'])*100:.2f}Β±{sem(data['failures'], nan_policy='omit')*100:.2f}% failure rate." + f"{np.nanmean(data['failures']) * 100:.2f}Β±{sem(data['failures'], nan_policy='omit') * 100:.2f}% failure rate." ) diff --git a/mdopt/mps/canonical.py b/mdopt/mps/canonical.py index c2d5a6d0..ac2241b8 100644 --- a/mdopt/mps/canonical.py +++ b/mdopt/mps/canonical.py @@ -102,7 +102,7 @@ def __init__( if orth_centre and orth_centre not in range(self.num_sites): raise ValueError( f"The orthogonality centre index must reside anywhere from site 0 " - f"to {self.num_sites-1}, the one given is at position {orth_centre}." + f"to {self.num_sites - 1}, the one given is at position {orth_centre}." ) for i, tensor in enumerate(tensors): @@ -381,7 +381,7 @@ def move_orth_centre( if final_pos not in range(self.num_sites): raise ValueError( "The final position of the orthogonality centre should be" - f"from 0 to {self.num_sites-1}, given {final_pos}." + f"from 0 to {self.num_sites - 1}, given {final_pos}." ) singular_values: List[np.ndarray] = [] diff --git a/tests/decoding/test_decoders.py b/tests/decoding/test_decoders.py index bd6ad3cf..d2f1d5b1 100644 --- a/tests/decoding/test_decoders.py +++ b/tests/decoding/test_decoders.py @@ -308,9 +308,9 @@ def test_dense_and_dmrg_readout_agree_when_the_map_class_is_unique(): num_runs=2, dense_readout_max_sites=0, ) - assert ( - int(round(float(overlap))) == dense_success - ), f"readouts disagree on {error} (degenerate: {degenerate})" + assert int(round(float(overlap))) == dense_success, ( + f"readouts disagree on {error} (degenerate: {degenerate})" + ) compared += 1 assert compared >= 5, "too few shots to be a meaningful check" @@ -351,9 +351,9 @@ def test_qubit_reordering_leaves_the_posterior_invariant(bias): ) probs = np.asarray(dense_out, dtype=float) ** 2 posteriors.append(probs / probs.sum()) - assert np.allclose( - posteriors[0], posteriors[1], atol=1e-9 - ), f"reordering changed the posterior for {error}" + assert np.allclose(posteriors[0], posteriors[1], atol=1e-9), ( + f"reordering changed the posterior for {error}" + ) compared += 1 assert compared >= 3 @@ -399,9 +399,9 @@ def test_dmrg_readout_finds_the_true_maximum_on_decoder_posteriors(): amplitudes = np.abs(np.asarray(logical_mps.dense(flatten=True), dtype=float)) found = abs(inner_product(engine.mps, logical_mps)) - assert found == pytest.approx( - amplitudes.max(), rel=1e-6 - ), f"DMRG stopped below the true maximum for {error}" + assert found == pytest.approx(amplitudes.max(), rel=1e-6), ( + f"DMRG stopped below the true maximum for {error}" + ) checked += 1 assert checked >= 5 @@ -525,9 +525,9 @@ def warnings_for(chi_max): patch.object(ExplicitMPS, "dense", return_value=readout), ): decode_css(code, errors[0], chi_max=4, silent=False) - assert any( - "Negative logical amplitude" in r.message for r in caplog.records - ), "a posterior with a negative amplitude must be flagged" + assert any("Negative logical amplitude" in r.message for r in caplog.records), ( + "a posterior with a negative amplitude must be flagged" + ) def test_max_product_readout_is_optimal_and_certified(): @@ -571,9 +571,9 @@ def test_max_product_readout_is_optimal_and_certified(): assert amplitude == pytest.approx(true_max, rel=1e-9) assert amplitude <= bound * (1 + 1e-9), "a witness cannot exceed the bound" - assert amplitude >= bound * ( - 1 - 1e-9 - ), "bracket should close on a converged run" + assert amplitude >= bound * (1 - 1e-9), ( + "bracket should close on a converged run" + ) checked += 1 assert checked >= 5 @@ -783,8 +783,8 @@ def kernel(matrix): if bias == "Depolarising": weight = lambda dx, dz: (1 - prob) if (dx == 0 and dz == 0) else prob / 3 else: - weight = lambda dx, dz: ((1 - prob) if dx == 0 else prob) * ( - (1 - prob) if dz == 0 else prob + weight = lambda dx, dz: ( + ((1 - prob) if dx == 0 else prob) * ((1 - prob) if dz == 0 else prob) ) def reference(error): diff --git a/tests/decoding/test_dem.py b/tests/decoding/test_dem.py index ce340be1..e0873c08 100644 --- a/tests/decoding/test_dem.py +++ b/tests/decoding/test_dem.py @@ -204,9 +204,9 @@ def test_two_observable_bit_ordering_matches_the_enumeration(): for syndrome in ([0, 0], [1, 0], [0, 1], [1, 1]): masses, flips = decode_dem(problem, np.array(syndrome)) exact = _exact_class_masses(problem, np.array(syndrome)) - assert np.allclose( - masses / masses.sum(), exact / exact.sum(), atol=1e-9 - ), syndrome + assert np.allclose(masses / masses.sum(), exact / exact.sum(), atol=1e-9), ( + syndrome + ) assert np.array_equal( flips, [(int(np.argmax(exact)) >> j) & 1 for j in range(2)], @@ -343,9 +343,9 @@ def test_untouched_observable_is_deterministically_unflipped(): for syndrome in ([0, 0], [1, 0], [0, 1], [1, 1]): masses, flips = decode_dem(problem, np.array(syndrome)) exact = _exact_class_masses(problem, np.array(syndrome)) - assert np.allclose( - masses / masses.sum(), exact / exact.sum(), atol=1e-9 - ), syndrome + assert np.allclose(masses / masses.sum(), exact / exact.sum(), atol=1e-9), ( + syndrome + ) assert flips[1] == 0, syndrome # No mass may sit on any class with the untouched bit set. norm = masses / masses.sum() @@ -485,9 +485,9 @@ def test_unsorted_rows_decode_like_sorted_rows(): exact = _exact_class_masses(sorted_problem, np.array(syndrome)) for problem in (sorted_problem, shuffled_problem): masses, flips = decode_dem(problem, np.array(syndrome)) - assert np.allclose( - masses / masses.sum(), exact / exact.sum(), atol=1e-9 - ), syndrome + assert np.allclose(masses / masses.sum(), exact / exact.sum(), atol=1e-9), ( + syndrome + ) assert flips[0] == int(np.argmax(exact)), syndrome diff --git a/tests/decoding/test_surface_ler_regression.py b/tests/decoding/test_surface_ler_regression.py index f4007983..d41da91b 100644 --- a/tests/decoding/test_surface_ler_regression.py +++ b/tests/decoding/test_surface_ler_regression.py @@ -65,9 +65,9 @@ def test_surface_5_truncated_ler_does_not_increase(): pinned = "1111111111111111011011101111111111011111" verdicts = _verdicts(_surface_code(5), 0.08) failures = verdicts.count("0") - assert failures <= pinned.count( - "0" - ), f"{failures} failures, pinned {pinned.count('0')}: {verdicts}" - assert ( - verdicts == pinned - ), f"verdict pattern changed:\n got {verdicts}\n pinned {pinned}" + assert failures <= pinned.count("0"), ( + f"{failures} failures, pinned {pinned.count('0')}: {verdicts}" + ) + assert verdicts == pinned, ( + f"verdict pattern changed:\n got {verdicts}\n pinned {pinned}" + ) diff --git a/tests/mps/test_canonical.py b/tests/mps/test_canonical.py index 0aa0f0c4..d3e023d6 100644 --- a/tests/mps/test_canonical.py +++ b/tests/mps/test_canonical.py @@ -469,12 +469,12 @@ def test_canonical_explicit_near_zero_singular_values(): mps_explicit = mps_canonical.explicit() for i, gamma in enumerate(mps_explicit.tensors): - assert np.all( - np.isfinite(gamma) - ), f"Ξ“[{i}] contains NaN or Inf for eps={eps}, form={form}" - assert np.all( - np.isfinite(mps_explicit.dense()) - ), f"dense() contains NaN or Inf for eps={eps}, form={form}" + assert np.all(np.isfinite(gamma)), ( + f"Ξ“[{i}] contains NaN or Inf for eps={eps}, form={form}" + ) + assert np.all(np.isfinite(mps_explicit.dense())), ( + f"dense() contains NaN or Inf for eps={eps}, form={form}" + ) # float32 path psi32 = psi.astype(np.complex64) diff --git a/tests/mps/test_explicit.py b/tests/mps/test_explicit.py index c42d9735..094beabf 100644 --- a/tests/mps/test_explicit.py +++ b/tests/mps/test_explicit.py @@ -702,9 +702,9 @@ def test_explicit_compress_bond_accepts_a_list_spectrum(): truncation cut raises. Coercing the type here keeps both constructors usable. """ mps = create_simple_product_state(4, which="+", form="Explicit") - assert isinstance( - mps.singular_values[1], list - ), "constructor no longer yields lists" + assert isinstance(mps.singular_values[1], list), ( + "constructor no longer yields lists" + ) compressed, truncation_error = mps.compress_bond( bond=1, chi_max=8, cut=1e-16, renormalise=True, return_truncation_error=True diff --git a/tests/mps/test_utils.py b/tests/mps/test_utils.py index d937b459..fc5fbbaf 100644 --- a/tests/mps/test_utils.py +++ b/tests/mps/test_utils.py @@ -164,12 +164,12 @@ def test_mps_from_dense_near_zero_singular_values(): mps = mps_from_dense(psi, form="Explicit") for i, gamma in enumerate(mps.tensors): - assert np.all( - np.isfinite(gamma) - ), f"Ξ“[{i}] contains NaN or Inf for eps={eps}" - assert np.all( - np.isfinite(mps.dense()) - ), f"dense() contains NaN or Inf for eps={eps}" + assert np.all(np.isfinite(gamma)), ( + f"Ξ“[{i}] contains NaN or Inf for eps={eps}" + ) + assert np.all(np.isfinite(mps.dense())), ( + f"dense() contains NaN or Inf for eps={eps}" + ) # float32 path psi32 = psi.astype(np.complex64) diff --git a/tests/optimiser/test_utils.py b/tests/optimiser/test_utils.py index 3ff0813c..ee5562bf 100644 --- a/tests/optimiser/test_utils.py +++ b/tests/optimiser/test_utils.py @@ -145,9 +145,9 @@ def test_random_constraints(): assert key in result assert isinstance(result[key], list) - assert ( - len(result["all_constrained_bits"]) <= constraint_size - ), f"Number of constrained bits exceeds the maximum constraint_size={constraint_size}" + assert len(result["all_constrained_bits"]) <= constraint_size, ( + f"Number of constrained bits exceeds the maximum constraint_size={constraint_size}" + ) assert len(result["xor_left_sites"]) == 1 assert len(result["xor_right_sites"]) == 1 assert result["xor_left_sites"][0] < result["xor_right_sites"][0] From de7feb58d3de9ba9af33c65777b7c258442b44d7 Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 14 Sep 2026 23:05:21 -0400 Subject: [PATCH 3/5] Document the OpenBLAS wheels for uv environments A NumPy or SciPy wheel swapped in by hand, as the README's pip commands do, lasts only until the next `uv run`, which reinstalls the locked Accelerate build. `uv sync --python-platform aarch64-apple-darwin` targets macOS 13, selects the OpenBLAS wheels, and keeps them across later `uv run` and `uv sync` calls. Checked in a scratch copy of the project with all dependency groups: nothing builds from source and the backend tests pass. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01S6mbxc9eJDQMVx7tjvYwud --- README.md | 10 ++++++++++ mdopt/backend/array.py | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/README.md b/README.md index 9aa60652..c63f0bef 100644 --- a/README.md +++ b/README.md @@ -63,6 +63,16 @@ pip download "scipy==$(python -c 'import scipy; print(scipy.__version__)')" \ pip install --force-reinstall --no-deps /tmp/openblas-wheels/*.whl ``` +In a uv checkout, a reinstall like this lasts only until the next `uv run` +or `uv sync`, which put the locked Accelerate wheels back. Sync against a +macOS 13 target instead (adding your usual `--group` flags). It selects the +same OpenBLAS wheels, and later `uv run` and `uv sync` calls keep them. Run +it again whenever the lock moves NumPy or SciPy to a new version: + +```bash +uv sync --python-platform aarch64-apple-darwin +``` + Set `MDOPT_ALLOW_ACCELERATE=1` to silence the warning if you must keep Accelerate. Use `OMP_NUM_THREADS=1` (or `OPENBLAS_NUM_THREADS=1`) for the per-process BLAS of worker pools. diff --git a/mdopt/backend/array.py b/mdopt/backend/array.py index 1a9e944f..bf21938d 100644 --- a/mdopt/backend/array.py +++ b/mdopt/backend/array.py @@ -91,6 +91,10 @@ def _warn_if_accelerate(numpy_module) -> None: --only-binary=:all: --no-deps -d /tmp/numpy-openblas pip install --force-reinstall --no-deps /tmp/numpy-openblas/*.whl + In a uv project environment the next ``uv run`` reinstalls the locked + wheels; ``uv sync --python-platform aarch64-apple-darwin`` targets macOS 13 + and so selects the OpenBLAS NumPy and SciPy wheels instead. + Set MDOPT_ALLOW_ACCELERATE=1 to silence the warning. """ if os.getenv("MDOPT_ALLOW_ACCELERATE") == "1": From 01fa051a58fb9dc26deec560c55783b8b0525425 Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 14 Sep 2026 23:16:46 -0400 Subject: [PATCH 4/5] Allow the diskcache advisory in dependency review diskcache 5.6.3 (GHSA-w8v5-vhqr-4h9v) has no fixed release, and qldpc, which mdopt's BB-code helpers import, loads it at module level, so the package cannot be dropped. Main already locked this version; the check only flagged it here because uv.lock is a new manifest. Allow exactly this advisory so that any other vulnerable dependency still fails. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01S6mbxc9eJDQMVx7tjvYwud --- .github/workflows/dependency-review.yml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.github/workflows/dependency-review.yml b/.github/workflows/dependency-review.yml index 70e7f49e..0e5f2c45 100644 --- a/.github/workflows/dependency-review.yml +++ b/.github/workflows/dependency-review.yml @@ -12,3 +12,10 @@ jobs: uses: actions/checkout@v7 - name: 'Dependency Review' uses: actions/dependency-review-action@v5 + with: + # diskcache <= 5.6.3 unpickles cache files, so anyone who can write + # to the cache directory can run code (CVE-2025-69872). No fixed + # release exists. qldpc imports it at module level (qldpc.cache, + # loaded by qldpc.codes), so it cannot be dropped. Remove this entry + # once diskcache ships a fix or qldpc stops requiring it. + allow-ghsas: GHSA-w8v5-vhqr-4h9v From 91a009764a5e67a38422f02ffad0a6b8381fe5dc Mon Sep 17 00:00:00 2001 From: meandmytram Date: Mon, 14 Sep 2026 23:37:26 -0400 Subject: [PATCH 5/5] Request uv groups and tools explicitly in campaign instructions quantum_surface.sh profiles with pyinstrument, which lives in the dev group, and default-groups is empty, so the script now runs `uv run --group dev`. uv environments are not seeded with pip, so the DEM campaign README and dem_rerun.py now install the optional beliefmatching decoder with `uv pip install --no-deps beliefmatching`. Its numpy<=2.2.6 pin is what keeps it out of the lock; in a scratch uv environment 0.2.0 decoded a distance-3 surface-code memory experiment normally on numpy 2.4.6. `uv run` and `uv sync --inexact` keep it, and a plain `uv sync` removes it, which the README now says. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01S6mbxc9eJDQMVx7tjvYwud --- examples/decoding/dem_campaign/README.md | 17 +++++++++++++---- examples/decoding/dem_campaign/dem_rerun.py | 7 ++++--- examples/decoding/quantum_surface.sh | 2 +- 3 files changed, 18 insertions(+), 8 deletions(-) diff --git a/examples/decoding/dem_campaign/README.md b/examples/decoding/dem_campaign/README.md index 6d24c422..e29ed265 100644 --- a/examples/decoding/dem_campaign/README.md +++ b/examples/decoding/dem_campaign/README.md @@ -38,10 +38,19 @@ REPRO_D=3 REPRO_SHOTS=2000 python repro_tnd3d.py ``` `dem_rerun.py` and `repro_tnd3d.py` also decode every shot with belief -matching when the optional `beliefmatching` package is importable -(`pip install beliefmatching`; it is not a declared dependency because its -numpy pin cannot be reconciled with ours). Without it the `bm` column is -simply omitted. +matching when the optional `beliefmatching` package is importable. It is not +a declared dependency: its metadata pins `numpy<=2.2.6`, below mdopt's +`numpy>=2.3`, so uv cannot lock it. Its other dependencies (stim, ldpc, sinter +and pymatching) are already present after `uv sync --group test`, so install +it on its own: + +```bash +uv pip install --no-deps beliefmatching +``` + +Despite the pin, beliefmatching 0.2.0 decodes normally on NumPy 2.4. `uv run` +and `uv sync --inexact` keep it, but a plain `uv sync` removes it because it +is not in the lock. Without it the `bm` column is simply omitted. Close the laptop lid and the run sleeps. Use `caffeinate -is -w ` on macOS, or run on a machine that does not suspend. diff --git a/examples/decoding/dem_campaign/dem_rerun.py b/examples/decoding/dem_campaign/dem_rerun.py index d75d663c..66dea107 100644 --- a/examples/decoding/dem_campaign/dem_rerun.py +++ b/examples/decoding/dem_campaign/dem_rerun.py @@ -33,9 +33,10 @@ from beliefmatching import BeliefMatching except ImportError: # pragma: no cover - optional third decoder # beliefmatching's metadata pins numpy<=2.2.6, which uv.lock cannot - # reconcile with our numpy>=2.3, so it is not a declared dependency; `pip install - # beliefmatching` works. Without it the harness still runs and simply - # records no belief-matching column. + # reconcile with our numpy>=2.3, so it is not a declared dependency. + # `uv pip install --no-deps beliefmatching` installs it, and it decodes + # normally on newer numpy (see the campaign README). Without it the + # harness still runs and simply records no belief-matching column. BeliefMatching = None from mdopt.decoding.dem import decode_dem, dem_to_problem diff --git a/examples/decoding/quantum_surface.sh b/examples/decoding/quantum_surface.sh index f9f3b2a9..21e2c090 100755 --- a/examples/decoding/quantum_surface.sh +++ b/examples/decoding/quantum_surface.sh @@ -33,7 +33,7 @@ for seed in "${seeds[@]}"; do # Print current configuration echo "Running for lattice size ${lattice_size}, bond dimension ${bond_dim}, error rate ${error_rate}, error model ${error_model}, and seed ${seed}." # Run the Python script - uv run python -m pyinstrument -r html -o profile.html -m mdopt.examples.decoding.quantum_surface --lattice_size ${lattice_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} + uv run --group dev python -m pyinstrument -r html -o profile.html -m mdopt.examples.decoding.quantum_surface --lattice_size ${lattice_size} --bond_dim ${bond_dim} --error_rate ${error_rate} --num_experiments ${num_experiments} --bias_prob ${bias_prob} --error_model "${error_model}" --seed ${seed} --num_processes ${num_processes} --silent ${silent} --tolerance ${tolerance} --cut ${cut} done done done