Migrate to uv

.github/workflows/python-package.yml

@@ -15,45 +15,29 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
-        poetry-version: ["1.3"]
+        python-version: ["3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v6
         with:
           submodules: true
 
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v6
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Python Poetry Action
-        uses: abatilo/actions-poetry@v4
-        with:
-          poetry-version: ${{ matrix.poetry-version }}
-
-      - name: Configure Poetry to use matrix Python version
-        run: |
-          poetry env use python${{ matrix.python-version }}
-
       - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-          poetry install --extras "cli fast solvents"
+        run: uv sync --all-extras
 
       - name: Lint with Ruff
-        run: |
-          poetry run ruff check --output-format=github .
+        run: uv run ruff check --output-format=github .
 
-      - name: Lint with Black
-        run: |
-          poetry run black --check . 
+      - name: Format check with Ruff
+        run: uv run ruff format --check .
 
       - name: Test with pytest
-        run: |
-          poetry run pytest --cov=. --cov-report=xml
+        run: uv run pytest --cov=. --cov-report=xml
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v5

.python-version

@@ -1 +1 @@
-3.10.13
+3.12

CONTRIBUTING.md

@@ -11,13 +11,20 @@ forked repository.
 > from [Open Source Guides](https://opensource.guide/) They may even have a
 > translation for your native language!
 
-After cloning your fork, we recommend using [Poetry](https://python-poetry.org/)
-for managing your contributions:
+We use [`uv`](https://docs.astral.sh/uv/) to develop the project, so first make sure it's installed.
+
+After cloning your fork, run:
 
 ```console
-$ git clone git@github.com:your-username/overreact.git # your-username is your GitHub username
+$ git clone --recurse-submodules git@github.com:your-username/overreact.git # your-username is your GitHub username
 $ cd overreact
-$ poetry install -E cli -E fast -E solvents # all optional features
+$ uv sync --all-extras
+```
+
+Before submitting any pull requests, make sure all tests pass with:
+
+```console
+$ uv run pytest
 ```
 
 ## Recommended practices
@@ -54,7 +61,8 @@ questions, the discussions are a better place to ask questions 😄.)
 - Include tests if your patch solves a bug, and explain clearly
   under which circumstances the bug happens. Make sure the test fails without
   your patch.
-- Use [Black](https://black.readthedocs.io/) to auto-format your code.
+- Use [ruff format](https://docs.astral.sh/ruff/formatter/) to auto-format your code.
+- Use [ruff check](https://docs.astral.sh/ruff/linter/) to check for code quality issues.
 - Use
   [Numpydoc documentation strings](https://numpydoc.readthedocs.io/en/latest/format.html)
   to document your code.

README.md

@@ -121,11 +121,10 @@ output files thanks to [`cclib`](https://cclib.github.io/) (see the
 
 ## Installation
 
-**overreact** is a Python package, so you can easily install it with
-[`pip`](https://pypi.org/project/pip/):
+The preferred method of installation is through [`uv`](https://docs.astral.sh/uv/)
 
 ```console
-$ pip install "overreact[cli,fast]"
+$ uv tool install "overreact[cli,fast]"
 ```
 
 See the

overreact/_cli.py

@@ -386,7 +386,9 @@ def _yield_thermochemistry(self):
                 temperature=self.temperature,
                 pressure=self.pressure,
             ),
-        ), "reaction free energies do not match reaction enthalpies and reaction entropies"
+        ), (
+            "reaction free energies do not match reaction enthalpies and reaction entropies"
+        )
 
         delta_activation_mass = rx.get_delta(scheme.B, molecular_masses)
         delta_activation_energies = rx.get_delta(scheme.B, energies)
@@ -413,7 +415,9 @@ def _yield_thermochemistry(self):
                 temperature=self.temperature,
                 pressure=self.pressure,
             ),
-        ), "activation free energies do not match activation enthalpies and activation entropies"
+        ), (
+            "activation free energies do not match activation enthalpies and activation entropies"
+        )
 
         circ_table = Table(
             Column("no", justify="right"),

overreact/_datasets.py

@@ -2,33 +2,22 @@
 
 from __future__ import annotations
 
-import os
+from pathlib import Path
 
 import overreact as rx
 
-data_path = os.path.normpath(
-    os.path.join(os.path.dirname(__file__), "../data/"),
-)
-
+data_path = Path(__file__).parent.parent / "data"
 
 logfiles = {}
-for name in os.listdir(data_path):
-    walk_dir = os.path.join(data_path, name)
-    if os.path.isdir(walk_dir):
+for walk_dir in data_path.iterdir():
+    if walk_dir.is_dir():
+        name = walk_dir.name
         logfiles[name] = rx.io._LazyDict()
         logfiles[name]._function = rx.io.read_logfile
-        for root, _, files in os.walk(walk_dir):
-            for filename in files:
-                if filename.endswith(".out"):
-                    logfiles[name][
-                        f"{filename[:-4]}@{os.path.relpath(root, walk_dir)}".replace(
-                            "@.",
-                            "",
-                        )
-                    ] = os.path.join(
-                        root,
-                        filename,
-                    )
+        for filepath in walk_dir.rglob("*.out"):
+            rel = filepath.parent.relative_to(walk_dir)
+            key = f"{filepath.stem}@{rel}".replace("@.", "")
+            logfiles[name][key] = str(filepath)
 
 
 if __name__ == "__main__":

overreact/_misc.py

@@ -57,7 +57,7 @@
     >>> Np = 3 # point number for central derivative
     >>> weights = _central_diff_weights(Np) # weights for first derivative
     >>> vals = [f(x + (i - Np/2) * h) for i in range(Np)]
-    >>> sum(w * v for (w, v) in zip(weights, vals))/h
+    >>> float(sum(w * v for (w, v) in zip(weights, vals))/h)
     11.79999999999998
 
     This value is close to the analytical solution:
@@ -118,7 +118,7 @@
     --------
     >>> def f(x):
     ...     return x**3 + x**2
-    >>> _derivative(f, 1.0, dx=1e-6)
+    >>> float(_derivative(f, 1.0, dx=1e-6))
     4.9999999999217337
     """
     first_deriv_weight_map = {
@@ -196,7 +196,7 @@
     """
     if isinstance(obj, np.ndarray):
         return (tuple(obj.shape), tuple(obj.ravel()))
     elif isinstance(obj, (list, set)):
         return tuple(make_hashable(item) for item in obj)
     else:
         return obj
@@ -235,7 +235,7 @@
                     shape, flat_data = arg
                     if (
                         isinstance(shape, tuple)
                         and all(isinstance(dim, (int, np.integer)) for dim in shape)
                         and isinstance(flat_data, tuple)
                     ):
                         if len(flat_data) == 0 or any(dim <= 0 for dim in shape):
@@ -841,7 +841,7 @@
                 *args,
                 **kwargs,
             )
-            for xp, yp in zip(x0, y0)
+            for xp, yp in zip(x0, y0, strict=False)
         ],
         axis=0,
     )
@@ -873,7 +873,7 @@
     ((2, 2), (2, -2))
     """
     # we don't touch some types, and this includes namedtuples
     if isinstance(a, (int, float, str, rx.Scheme)):
         return a
 
     with contextlib.suppress(AttributeError):
@@ -932,24 +932,24 @@
     estimates done with Halton sequences. Compare the following estimates of
     the integral of x between 0 and 1, which is exactly 0.5:
 
-    >>> np.mean(halton(100, cranley_patterson=False))
+    >>> float(np.mean(halton(100, cranley_patterson=False)))
     0.489921875
     >>> I = [np.mean(halton(100)) for i in range(1000)]
-    >>> np.mean(I), np.var(I) < 0.00004
+    >>> float(np.mean(I)), bool(np.var(I) < 0.00004)
     (0.500, True)
 
     Now the integral of x**2 between 0 and 1, which is exactly 1/3:
 
-    >>> np.mean(halton(100, cranley_patterson=False)**2)
+    >>> float(np.mean(halton(100, cranley_patterson=False)**2))
     0.3222149658203125
     >>> I = [np.mean(halton(100)**2) for i in range(1000)]
-    >>> np.mean(I), np.var(I) < 0.00004
+    >>> float(np.mean(I)), bool(np.var(I) < 0.00004)
     (0.333, True)
 
     >>> x = halton(1500)
-    >>> np.mean(x)  # estimate of the integral of x between 0 and 1
+    >>> float(np.mean(x))  # estimate of the integral of x between 0 and 1
     0.50
-    >>> np.mean(x**2)  # estimate of the integral of x**2 between 0 and 1
+    >>> float(np.mean(x**2))  # estimate of the integral of x**2 between 0 and 1
     0.33
     """
     actual_dim = 1 if dim is None else dim