Merge branch 'master' into fix-join

Author: MaximSmolskiy

.github/workflows/build.yml

@@ -10,21 +10,18 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+          cache-dependency-glob: uv.lock
       - uses: actions/setup-python@v5
         with:
           python-version: 3.13
           allow-prereleases: true
-      - uses: actions/cache@v4
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip setuptools wheel
-          python -m pip install pytest-cov -r requirements.txt
+      - run: uv sync --group=test
       - name: Run tests
         # TODO: #8818 Re-enable quantum tests
-        run: pytest
+        run: uv run pytest
           --ignore=computer_vision/cnn_classification.py
           --ignore=docs/conf.py
           --ignore=dynamic_programming/k_means_clustering_tensorflow.py

.github/workflows/project_euler.yml

@@ -15,25 +15,21 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.x
-      - name: Install pytest and pytest-cov
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install --upgrade numpy pytest pytest-cov
-      - run: pytest --doctest-modules --cov-report=term-missing:skip-covered --cov=project_euler/ project_euler/
+      - run: uv sync --group=euler-validate --group=test
+      - run: uv run pytest --doctest-modules --cov-report=term-missing:skip-covered --cov=project_euler/ project_euler/
   validate-solutions:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.x
-      - name: Install pytest and requests
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install --upgrade numpy pytest requests
-      - run: pytest scripts/validate_solutions.py
+      - run: uv sync --group=euler-validate --group=test
+      - run: uv run pytest scripts/validate_solutions.py
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/ruff.yml

@@ -12,5 +12,5 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - run: pip install --user ruff
-      - run: ruff check --output-format=github .
+      - uses: astral-sh/setup-uv@v5
+      - run: uvx ruff check --output-format=github .

.github/workflows/sphinx.yml

@@ -26,14 +26,14 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
       - uses: actions/setup-python@v5
         with:
           python-version: 3.13
           allow-prereleases: true
-      - run: pip install --upgrade pip
-      - run: pip install myst-parser sphinx-autoapi sphinx-pyproject
+      - run: uv sync --group=docs
       - uses: actions/configure-pages@v5
-      - run: sphinx-build -c docs . docs/_build/html
+      - run: uv run sphinx-build -c docs . docs/_build/html
       - uses: actions/upload-pages-artifact@v3
         with:
           path: docs/_build/html

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.1
+    rev: v0.8.3
     hooks:
       - id: ruff
       - id: ruff-format

audio_filters/iir_filter.py

@@ -10,13 +10,17 @@ class IIRFilter:
 
     Implementation details:
     Based on the 2nd-order function from
-     https://en.wikipedia.org/wiki/Digital_biquad_filter,
+    https://en.wikipedia.org/wiki/Digital_biquad_filter,
     this generalized N-order function was made.
 
     Using the following transfer function
-    H(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+...+b_{k}z^{-k}}{a_{0}+a_{1}z^{-1}+a_{2}z^{-2}+...+a_{k}z^{-k}}
+        .. math:: H(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+...+b_{k}z^{-k}}
+                  {a_{0}+a_{1}z^{-1}+a_{2}z^{-2}+...+a_{k}z^{-k}}
+
     we can rewrite this to
-    y[n]={\frac{1}{a_{0}}}\left(\left(b_{0}x[n]+b_{1}x[n-1]+b_{2}x[n-2]+...+b_{k}x[n-k]\right)-\left(a_{1}y[n-1]+a_{2}y[n-2]+...+a_{k}y[n-k]\right)\right)
+        .. math:: y[n]={\frac{1}{a_{0}}}
+                  \left(\left(b_{0}x[n]+b_{1}x[n-1]+b_{2}x[n-2]+...+b_{k}x[n-k]\right)-
+                  \left(a_{1}y[n-1]+a_{2}y[n-2]+...+a_{k}y[n-k]\right)\right)
     """
 
     def __init__(self, order: int) -> None:
@@ -34,17 +38,19 @@ def __init__(self, order: int) -> None:
 
     def set_coefficients(self, a_coeffs: list[float], b_coeffs: list[float]) -> None:
         """
-        Set the coefficients for the IIR filter. These should both be of size order + 1.
-        a_0 may be left out, and it will use 1.0 as default value.
+        Set the coefficients for the IIR filter.
+        These should both be of size `order` + 1.
+        :math:`a_0` may be left out, and it will use 1.0 as default value.
 
         This method works well with scipy's filter design functions
-            >>> # Make a 2nd-order 1000Hz butterworth lowpass filter
-            >>> import scipy.signal
-            >>> b_coeffs, a_coeffs = scipy.signal.butter(2, 1000,
-            ...                                          btype='lowpass',
-            ...                                          fs=48000)
-            >>> filt = IIRFilter(2)
-            >>> filt.set_coefficients(a_coeffs, b_coeffs)
+
+        >>> # Make a 2nd-order 1000Hz butterworth lowpass filter
+        >>> import scipy.signal
+        >>> b_coeffs, a_coeffs = scipy.signal.butter(2, 1000,
+        ...                                          btype='lowpass',
+        ...                                          fs=48000)
+        >>> filt = IIRFilter(2)
+        >>> filt.set_coefficients(a_coeffs, b_coeffs)
         """
         if len(a_coeffs) < self.order:
             a_coeffs = [1.0, *a_coeffs]
@@ -68,7 +74,7 @@ def set_coefficients(self, a_coeffs: list[float], b_coeffs: list[float]) -> None
 
     def process(self, sample: float) -> float:
         """
-        Calculate y[n]
+        Calculate :math:`y[n]`
 
         >>> filt = IIRFilter(2)
         >>> filt.process(0)

cellular_automata/wa_tor.py

@@ -1,9 +1,9 @@
 """
 Wa-Tor algorithm (1984)
 
-@ https://en.wikipedia.org/wiki/Wa-Tor
-@ https://beltoforion.de/en/wator/
-@ https://beltoforion.de/en/wator/images/wator_medium.webm
+| @ https://en.wikipedia.org/wiki/Wa-Tor
+| @ https://beltoforion.de/en/wator/
+| @ https://beltoforion.de/en/wator/images/wator_medium.webm
 
 This solution aims to completely remove any systematic approach
 to the Wa-Tor planet, and utilise fully random methods.
@@ -97,8 +97,8 @@ class WaTor:
 
     :attr time_passed: A function that is called every time
         time passes (a chronon) in order to visually display
-        the new Wa-Tor planet. The time_passed function can block
-        using time.sleep to slow the algorithm progression.
+        the new Wa-Tor planet. The `time_passed` function can block
+        using ``time.sleep`` to slow the algorithm progression.
 
     >>> wt = WaTor(10, 15)
     >>> wt.width
@@ -216,7 +216,7 @@ def get_surrounding_prey(self, entity: Entity) -> list[Entity]:
         """
         Returns all the prey entities around (N, S, E, W) a predator entity.
 
-        Subtly different to the try_to_move_to_unoccupied square.
+        Subtly different to the `move_and_reproduce`.
 
         >>> wt = WaTor(WIDTH, HEIGHT)
         >>> wt.set_planet([
@@ -260,7 +260,7 @@ def move_and_reproduce(
         """
         Attempts to move to an unoccupied neighbouring square
         in either of the four directions (North, South, East, West).
-        If the move was successful and the remaining_reproduction time is
+        If the move was successful and the `remaining_reproduction_time` is
         equal to 0, then a new prey or predator can also be created
         in the previous square.
 
@@ -351,12 +351,12 @@ def perform_prey_actions(
         Performs the actions for a prey entity
 
         For prey the rules are:
-          1. At each chronon, a prey moves randomly to one of the adjacent unoccupied
-            squares. If there are no free squares, no movement takes place.
-          2. Once a prey has survived a certain number of chronons it may reproduce.
-            This is done as it moves to a neighbouring square,
-            leaving behind a new prey in its old position.
-            Its reproduction time is also reset to zero.
+            1. At each chronon, a prey moves randomly to one of the adjacent unoccupied
+               squares. If there are no free squares, no movement takes place.
+            2. Once a prey has survived a certain number of chronons it may reproduce.
+               This is done as it moves to a neighbouring square,
+               leaving behind a new prey in its old position.
+               Its reproduction time is also reset to zero.
 
         >>> wt = WaTor(WIDTH, HEIGHT)
         >>> reproducable_entity = Entity(True, coords=(0, 1))
@@ -382,15 +382,15 @@ def perform_predator_actions(
         :param occupied_by_prey_coords: Move to this location if there is prey there
 
         For predators the rules are:
-          1. At each chronon, a predator moves randomly to an adjacent square occupied
-            by a prey. If there is none, the predator moves to a random adjacent
-            unoccupied square. If there are no free squares, no movement takes place.
-          2. At each chronon, each predator is deprived of a unit of energy.
-          3. Upon reaching zero energy, a predator dies.
-          4. If a predator moves to a square occupied by a prey,
-            it eats the prey and earns a certain amount of energy.
-          5. Once a predator has survived a certain number of chronons
-          it may reproduce in exactly the same way as the prey.
+            1. At each chronon, a predator moves randomly to an adjacent square occupied
+               by a prey. If there is none, the predator moves to a random adjacent
+               unoccupied square. If there are no free squares, no movement takes place.
+            2. At each chronon, each predator is deprived of a unit of energy.
+            3. Upon reaching zero energy, a predator dies.
+            4. If a predator moves to a square occupied by a prey,
+               it eats the prey and earns a certain amount of energy.
+            5. Once a predator has survived a certain number of chronons
+               it may reproduce in exactly the same way as the prey.
 
         >>> wt = WaTor(WIDTH, HEIGHT)
         >>> wt.set_planet([[Entity(True, coords=(0, 0)), Entity(False, coords=(0, 1))]])
@@ -430,7 +430,7 @@ def perform_predator_actions(
 
     def run(self, *, iteration_count: int) -> None:
         """
-        Emulate time passing by looping iteration_count times
+        Emulate time passing by looping `iteration_count` times
 
         >>> wt = WaTor(WIDTH, HEIGHT)
         >>> wt.run(iteration_count=PREDATOR_INITIAL_ENERGY_VALUE - 1)
@@ -484,11 +484,9 @@ def visualise(wt: WaTor, iter_number: int, *, colour: bool = True) -> None:
     an ascii code in terminal to clear and re-print
     the Wa-Tor planet at intervals.
 
-    Uses ascii colour codes to colourfully display
-    the predators and prey.
-
-    (0x60f197) Prey = #
-    (0xfffff) Predator = x
+    Uses ascii colour codes to colourfully display the predators and prey:
+        * (0x60f197) Prey = ``#``
+        * (0xfffff) Predator = ``x``
 
     >>> wt = WaTor(30, 30)
     >>> wt.set_planet([

computer_vision/README.md

@@ -8,4 +8,3 @@ Image processing and computer vision are a little different from each other. Ima
 While computer vision comes from modelling image processing using the techniques of machine learning, computer vision applies machine learning to recognize patterns for interpretation of images (much like the process of visual reasoning of human vision).
 
 * <https://en.wikipedia.org/wiki/Computer_vision>
-* <https://www.datarobot.com/blog/introduction-to-computer-vision-what-it-is-and-how-it-works/>

data_structures/binary_tree/mirror_binary_tree.py

@@ -56,6 +56,8 @@ def mirror(self) -> Node:
 def make_tree_seven() -> Node:
     r"""
     Return a binary tree with 7 nodes that looks like this:
+    ::
+
            1
          /   \
         2     3
@@ -81,13 +83,15 @@ def make_tree_seven() -> Node:
 def make_tree_nine() -> Node:
     r"""
     Return a binary tree with 9 nodes that looks like this:
-          1
-         /  \
-        2    3
-       / \    \
-      4   5    6
-     / \   \
-    7   8   9
+    ::
+
+            1
+           / \
+          2   3
+         / \   \
+        4   5   6
+       / \   \
+      7   8   9
 
     >>> tree_nine = make_tree_nine()
     >>> len(tree_nine)
@@ -117,23 +121,25 @@ def main() -> None:
     >>> tuple(tree.mirror())
     (6, 3, 1, 9, 5, 2, 8, 4, 7)
 
-    nine_tree:
-          1
-         /  \
-        2    3
-       / \    \
-      4   5    6
-     / \   \
-    7   8   9
-
-    The mirrored tree looks like this:
+    nine_tree::
+
+            1
+           / \
+          2   3
+         / \   \
+        4   5   6
+       / \   \
+      7   8   9
+
+    The mirrored tree looks like this::
+
           1
-         /  \
-        3    2
-       /    / \
-      6    5   4
-          /   / \
-         9   8   7
+         / \
+        3   2
+       /   / \
+      6   5   4
+         /   / \
+        9   8   7
     """
     trees = {"zero": Node(0), "seven": make_tree_seven(), "nine": make_tree_nine()}
     for name, tree in trees.items():

electronics/electric_power.py

@@ -23,20 +23,22 @@ def electric_power(voltage: float, current: float, power: float) -> tuple:
     >>> electric_power(voltage=2, current=4, power=2)
     Traceback (most recent call last):
         ...
-    ValueError: Only one argument must be 0
+    ValueError: Exactly one argument must be 0
     >>> electric_power(voltage=0, current=0, power=2)
     Traceback (most recent call last):
         ...
-    ValueError: Only one argument must be 0
+    ValueError: Exactly one argument must be 0
     >>> electric_power(voltage=0, current=2, power=-4)
     Traceback (most recent call last):
         ...
     ValueError: Power cannot be negative in any electrical/electronics system
     >>> electric_power(voltage=2.2, current=2.2, power=0)
     Result(name='power', value=4.84)
+    >>> electric_power(current=0, power=6, voltage=2)
+    Result(name='current', value=3.0)
     """
     if (voltage, current, power).count(0) != 1:
-        raise ValueError("Only one argument must be 0")
+        raise ValueError("Exactly one argument must be 0")
     elif power < 0:
         raise ValueError(
             "Power cannot be negative in any electrical/electronics system"
@@ -48,7 +50,7 @@ def electric_power(voltage: float, current: float, power: float) -> tuple:
     elif power == 0:
         return Result("power", float(round(abs(voltage * current), 2)))
     else:
-        raise ValueError("Exactly one argument must be 0")
+        raise AssertionError
 
 
 if __name__ == "__main__":

geodesy/haversine_distance.py

@@ -21,10 +21,11 @@ def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> fl
     computation like Haversine can be handy for shorter range distances.
 
     Args:
-        lat1, lon1: latitude and longitude of coordinate 1
-        lat2, lon2: latitude and longitude of coordinate 2
+        * `lat1`, `lon1`: latitude and longitude of coordinate 1
+        * `lat2`, `lon2`: latitude and longitude of coordinate 2
     Returns:
         geographical distance between two points in metres
+
     >>> from collections import namedtuple
     >>> point_2d = namedtuple("point_2d", "lat lon")
     >>> SAN_FRANCISCO = point_2d(37.774856, -122.424227)