diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ccca6262f..7efad94fc 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -30,16 +30,16 @@ repos:
     rev: v2.4.1
     hooks:
       - id: codespell
-        args: ["-L", "larg"]
         exclude: >
             (?x)^(
                 .*\.txt|
+                .*\.svg|
                 .*\.ipynb
             )$
 
   # Clang format
   - repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v20.1.7
+    rev: v20.1.8
     hooks:
       - id: clang-format
         types_or: [c++, c, cuda]
@@ -55,15 +55,16 @@ repos:
 
   # Ruff, the Python auto-correcting linter/formatter written in Rust
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
+    rev: v0.12.3
     hooks:
       - id: ruff
-        args: ["--fix", "--show-fixes", "--exclude=__init__.py"]
+        args: ["--fix", "--show-fixes"]
       - id: ruff-format
 
-  # Black format Python and notebooks
-  - repo: https://github.com/psf/black
-    rev: 25.1.0
+  # Format docstrings
+  - repo: https://github.com/DanielNoord/pydocstringformatter
+    rev: v0.7.3
     hooks:
-      - id: black-jupyter
+      - id: pydocstringformatter
+        args: ["--style=numpydoc"]
 ...
diff --git a/pyproject.toml b/pyproject.toml
index fed528d4a..1520940ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,82 @@
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+indent-width = 4
+line-length = 88
+target-version = "py39"
+extend-include = ["*.ipynb"]
+
+lint.select = [
+  "ANN", # flake8-annotations
+  "ARG", # flake8-unused-arguments
+  "B",   # flake8-bugbear
+  "C4",  # flake8-comprehensions
+  "C90", # mccabe
+#  "D",   # pydocstyle
+  "DTZ", # flake8-datetimez
+  "E",   # pycodestyle
+  "EM",  # flake8-errmsg
+  "ERA", # eradicate
+  "F",   # Pyflakes
+  "I",   # isort
+  "ICN", # flake8-import-conventions
+  "N",   # pep8-naming
+  "PD",  # pandas-vet
+  "PGH", # pygrep-hooks
+  "PIE", # flake8-pie
+  "PL",  # Pylint
+  "PLC", # Pylint
+  "PLE", # Pylint
+  "PLR", # Pylint
+  "PLW", # Pylint
+  "PT",  # flake8-pytest-style
+  "Q",   # flake8-quotes
+  "RET", # flake8-return
+  "RUF100",  # Ruff-specific
+  "S",   # flake8-bandit
+  "SIM", # flake8-simplify
+#  "T20", # flake8-print
+  "TID", # flake8-tidy-imports
+  "UP",  # pyupgrade
+  "W",   # pycodestyle
+  "YTT",  # flake8-2020
+]
+
+exclude = [
+    "__init__.py",
+    "setup.py",
+]
+
+lint.ignore = [
+    "S301",  # pickle
+    "S311",  # Standard pseudo-random generators
+]
+
+[tool.ruff.lint.pep8-naming]
+extend-ignore-names = [
+    "X", "X1", "X_train", "X_val", "X_test", "X_predict",
+]
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.format]
+docstring-code-format = true
+docstring-code-line-length = 80
+
+[tool.ruff.lint.extend-per-file-ignores]
+"test/**" = ["S101", "PLR2004", "ANN"]
+"python/example_rmux.py" = ["PLR2004"]
+"python/example_maze.py" = ["PLR2004"]
+"python/notebooks/example_cartpole.ipynb" = ["E501", "SIM115"]
+"python/notebooks/example_maze.ipynb" = ["E501", "PLR2004"]
+"python/notebooks/example_regression.ipynb" = ["PLR2004"]
+"python/notebooks/example_rmux.ipynb" = ["PLR2004"]
+"python/notebooks/example_tuning.ipynb" = ["E501", "ANN201"]
+
+[tool.codespell]
+ignore-words-list = [
+    "larg",
+]
diff --git a/python/example_cartpole.py b/python/example_cartpole.py
index c5b37735a..07cae850d 100644
--- a/python/example_cartpole.py
+++ b/python/example_cartpole.py
@@ -117,7 +117,7 @@
 
 
 def replay(replay_size: int = 5000) -> None:
-    """Performs experience replay updates"""
+    """Performs experience replay updates."""
     batch_size: int = min(len(memory), replay_size)
     batch = random.sample(memory, batch_size)
     for state, action, reward, next_state, done in batch:
@@ -133,7 +133,7 @@ def replay(replay_size: int = 5000) -> None:
 
 
 def egreedy_action(state: np.ndarray) -> int:
-    """Selects an action using an epsilon greedy policy"""
+    """Selects an action using an epsilon greedy policy."""
     if np.random.rand() < epsilon:
         return random.randrange(N_ACTIONS)
     prediction_array = xcs.predict(state.reshape(1, -1))[0]
@@ -143,7 +143,7 @@ def egreedy_action(state: np.ndarray) -> int:
 
 
 def episode() -> tuple[float, int]:
-    """Executes a single episode, saving to memory buffer"""
+    """Executes a single episode, saving to memory buffer."""
     episode_score: float = 0
     episode_steps: int = 0
     state: np.ndarray = env.reset()[0]
diff --git a/python/example_maze.py b/python/example_maze.py
index e24b35e90..cfe139af3 100644
--- a/python/example_maze.py
+++ b/python/example_maze.py
@@ -283,7 +283,7 @@ def run_experiment(env: Maze) -> None:
     bar.close()
 
 
-def plot_performance(env: Maze):
+def plot_performance(env: Maze) -> None:
     """Plots learning performance."""
     plt.figure(figsize=(10, 6))
     plt.plot(trials, steps)
diff --git a/python/example_rmux.py b/python/example_rmux.py
index 7336a3d36..e82e19904 100644
--- a/python/example_rmux.py
+++ b/python/example_rmux.py
@@ -101,13 +101,12 @@ def execute(self, act: int) -> tuple[bool, float]:
                     reward += 0.1
             else:
                 reward = self.max_payoff
+        elif self.payoff_map:
+            reward = (pos - self.pos_bits) * 0.2
+            if answer == 1:
+                reward += 0.1
         else:
-            if self.payoff_map:
-                reward = (pos - self.pos_bits) * 0.2
-                if answer == 1:
-                    reward += 0.1
-            else:
-                reward = 0
+            reward = 0
         return correct, reward
 
 
diff --git a/python/notebooks/example_cartpole.ipynb b/python/notebooks/example_cartpole.ipynb
index 75868fed4..c02890d59 100644
--- a/python/notebooks/example_cartpole.ipynb
+++ b/python/notebooks/example_cartpole.ipynb
@@ -33,12 +33,12 @@
     "import random\n",
     "from collections import deque\n",
     "\n",
-    "from matplotlib import rcParams\n",
-    "import matplotlib.pyplot as plt\n",
-    "import imageio\n",
     "import gymnasium as gym\n",
+    "import imageio\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from IPython.display import display, Image\n",
+    "from IPython.display import Image, display\n",
+    "from matplotlib import rcParams\n",
     "from tqdm import tqdm\n",
     "\n",
     "import xcsf\n",
@@ -625,12 +625,12 @@
     }
    ],
    "source": [
-    "annotated_frames = list()\n",
+    "annotated_frames = []\n",
     "\n",
     "if SAVE_GIF:\n",
     "    # add score and episode nr\n",
     "    rcParams[\"font.family\"] = \"monospace\"\n",
-    "    bbox = dict(boxstyle=\"round\", fc=\"0.8\")\n",
+    "    bbox = {\"boxstyle\": \"round\", \"fc\": \"0.8\"}\n",
     "    bar = tqdm(total=len(frames), position=0, leave=True)\n",
     "    for i in range(len(frames)):\n",
     "        fig = plt.figure(dpi=90)\n",
diff --git a/python/notebooks/example_classification.ipynb b/python/notebooks/example_classification.ipynb
index b54b499bf..58075916d 100644
--- a/python/notebooks/example_classification.ipynb
+++ b/python/notebooks/example_classification.ipynb
@@ -21,8 +21,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.datasets import fetch_openml\n",
-    "from sklearn.metrics import ConfusionMatrixDisplay\n",
-    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    classification_report,\n",
+    "    confusion_matrix,\n",
+    ")\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
     "\n",
diff --git a/python/notebooks/example_maze.ipynb b/python/notebooks/example_maze.ipynb
index ff4508b8e..35fde4e94 100644
--- a/python/notebooks/example_maze.ipynb
+++ b/python/notebooks/example_maze.ipynb
@@ -486,7 +486,7 @@
     }
    ],
    "source": [
-    "def plot_performance(env: Maze):\n",
+    "def plot_performance(env: Maze) -> None:\n",
     "    \"\"\"Plots learning performance.\"\"\"\n",
     "    plt.figure(figsize=(10, 6))\n",
     "    plt.plot(trials, steps)\n",
@@ -534,9 +534,10 @@
    ],
    "source": [
     "import io\n",
-    "from PIL import Image\n",
     "from turtle import Screen, Turtle\n",
+    "\n",
     "from IPython.display import display\n",
+    "from PIL import Image\n",
     "\n",
     "GRID_WIDTH: int = maze.x_size\n",
     "GRID_HEIGHT: int = maze.y_size\n",
diff --git a/python/notebooks/example_regression.ipynb b/python/notebooks/example_regression.ipynb
index 8f447a42d..664cf0c00 100644
--- a/python/notebooks/example_regression.ipynb
+++ b/python/notebooks/example_regression.ipynb
@@ -21,7 +21,7 @@
     "import graphviz\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from IPython.display import display, Image\n",
+    "from IPython.display import Image, display\n",
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.ensemble import RandomForestRegressor\n",
     "from sklearn.gaussian_process import GaussianProcessRegressor\n",
diff --git a/python/notebooks/example_rmux.ipynb b/python/notebooks/example_rmux.ipynb
index fe006d0e9..1e86b71a4 100644
--- a/python/notebooks/example_rmux.ipynb
+++ b/python/notebooks/example_rmux.ipynb
@@ -109,13 +109,12 @@
     "                    reward += 0.1\n",
     "            else:\n",
     "                reward = self.max_payoff\n",
+    "        elif self.payoff_map:\n",
+    "            reward = (pos - self.pos_bits) * 0.2\n",
+    "            if answer == 1:\n",
+    "                reward += 0.1\n",
     "        else:\n",
-    "            if self.payoff_map:\n",
-    "                reward = (pos - self.pos_bits) * 0.2\n",
-    "                if answer == 1:\n",
-    "                    reward += 0.1\n",
-    "            else:\n",
-    "                reward = 0\n",
+    "            reward = 0\n",
     "        return correct, reward"
    ]
   },
diff --git a/python/notebooks/example_seeding.ipynb b/python/notebooks/example_seeding.ipynb
index 4c6133335..14f098afa 100644
--- a/python/notebooks/example_seeding.ipynb
+++ b/python/notebooks/example_seeding.ipynb
@@ -14,6 +14,7 @@
    "outputs": [],
    "source": [
     "import json\n",
+    "\n",
     "import numpy as np\n",
     "\n",
     "import xcsf\n",
diff --git a/python/notebooks/example_tuning.ipynb b/python/notebooks/example_tuning.ipynb
index 044d85064..96100488c 100644
--- a/python/notebooks/example_tuning.ipynb
+++ b/python/notebooks/example_tuning.ipynb
@@ -693,12 +693,12 @@
    "source": [
     "# General parameters can be searched in the usual way\n",
     "\n",
-    "parameters = dict(beta=[0.1, 0.5])\n",
+    "parameters = {\"beta\": [0.1, 0.5]}\n",
     "\n",
     "# EA parameters require specifying a dict,\n",
     "# but individual values can still be set because the other values are not changed.\n",
     "\n",
-    "parameters = dict(ea=[{\"lambda\": 2}, {\"lambda\": 10}])\n",
+    "parameters = {\"ea\": [{\"lambda\": 2}, {\"lambda\": 10}]}\n",
     "\n",
     "# However, for actions, conditions, and predictions, the WHOLE dict must be specified\n",
     "# for each value to try in the search. See Optuna below.\n",
@@ -752,7 +752,7 @@
     }
    ],
    "source": [
-    "def objective(trial):\n",
+    "def objective(trial: optuna.trial.Trial) -> float:\n",
     "    \"\"\"Measure a new hyperparameter combination.\"\"\"\n",
     "    # get new parameters to try\n",
     "    e0: float = trial.suggest_float(\"e0\", 0.08, 0.12, step=0.01)\n",
diff --git a/test/python/test_xcsf.py b/test/python/test_xcsf.py
index 15fd1a0f9..78b42f919 100644
--- a/test/python/test_xcsf.py
+++ b/test/python/test_xcsf.py
@@ -20,18 +20,18 @@
 
 from __future__ import annotations
 
-from collections import namedtuple
-
 import json
+import numbers
 import os
 import pickle
-import numbers
+from collections import namedtuple
+from copy import deepcopy
+
 import numpy as np
 import pytest
-from copy import deepcopy
+from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import MinMaxScaler
-from sklearn.datasets import make_regression
 
 import xcsf
 
@@ -99,9 +99,8 @@ def dicts_equal(d1: dict, d2: dict) -> bool:
         if isinstance(value, dict):
             if not isinstance(d2[key], dict) or not dicts_equal(value, d2[key]):
                 return False
-        else:
-            if d2[key] != value:
-                return False
+        elif d2[key] != value:
+            return False
     return True
 
 
@@ -322,13 +321,15 @@ def _compare_dicts(d1, d2, path=""):
     return diffs
 
 
-def _test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start):
-    N = 500
-    DX = 3
-    X = np.random.random((N, DX))
-    y = np.random.randn(N, 1)
+def _test_pop_replace(
+    tmp_path: str, pop_init: bool, clean: bool, fitinbetween: bool, warm_start: bool
+) -> bool:
+    n = 500
+    dx = 3
+    X = np.random.random((n, dx))
+    y = np.random.randn(n, 1)
 
-    xcs = xcsf.XCS(x_dim=DX, pop_size=5, max_trials=1000, pop_init=pop_init)
+    xcs = xcsf.XCS(x_dim=dx, pop_size=5, max_trials=1000, pop_init=pop_init)
     xcs.fit(X, y, verbose=False)
 
     # Initial, “too large” population.
@@ -342,7 +343,7 @@ def _test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start):
     (tmp_path / "pset1.json").write_text(json1)
 
     if fitinbetween:
-        xcs.fit(X, y, warm_start=True, verbose=False)
+        xcs.fit(X, y, warm_start=warm_start, verbose=False)
 
     xcs.json_read(str(tmp_path / "pset1.json"), clean=clean)
 
@@ -354,18 +355,17 @@ def _test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start):
 
     if len(list1) != len(list2):
         return False
-    else:
-        unequal = False
-        for cl1, cl2 in zip(list1, list2):
-            # If there is any difference, …
-            if _compare_dicts(cl1, cl2):
-                unequal = True
-                break
-        return not unequal
+    unequal = False
+    for cl1, cl2 in zip(list1, list2):
+        # If there is any difference, …
+        if _compare_dicts(cl1, cl2):
+            unequal = True
+            break
+    return not unequal
 
 
 @pytest.mark.parametrize(
-    "pop_init,clean,fitinbetween,warm_start",
+    ("pop_init", "clean", "fitinbetween", "warm_start"),
     [
         (False, True, False, False),
         (False, True, True, False),
@@ -375,9 +375,11 @@ def _test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start):
         (True, True, True, True),
     ],
 )
-def test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start):
+def test_pop_replace(
+    tmp_path: str, pop_init: bool, clean: bool, fitinbetween: bool, warm_start: bool
+):
     for seed in range(19):
         np.random.seed(seed)
-        assert _test_pop_replace(
-            tmp_path, pop_init, clean, fitinbetween, warm_start
-        ), f"failed at seed {seed}"
+        assert _test_pop_replace(tmp_path, pop_init, clean, fitinbetween, warm_start), (
+            f"failed at seed {seed}"
+        )
diff --git a/xcsf/utils/viz.py b/xcsf/utils/viz.py
index 28df6c3a7..4356d61b3 100644
--- a/xcsf/utils/viz.py
+++ b/xcsf/utils/viz.py
@@ -69,7 +69,7 @@ def label(self, symbol: str) -> str:
             if start == "feature" and int(end) < len(self.feature_names):
                 return self.feature_names[int(end)]
         elif isinstance(symbol, float):
-            return "%.5f" % symbol
+            return f"{symbol:.5f}"
         return str(symbol)
 
     def read_function(self) -> str:
@@ -132,8 +132,8 @@ def __init__(
         self.gviz = graphviz.Digraph("G", filename=filename + ".gv")
         self.draw()
         label: str = "" if note is None else note
-        label += "\nN = %d\n" % graph["n"]
-        label += "T = %d\n" % graph["t"]
+        label += "\nN = {graph['n']}\n"
+        label += f"T = {graph['t']}\n"
         label += "match node shaded\n"
         self.gviz.attr(label=label)
         self.gviz.view()
@@ -145,7 +145,7 @@ def label(self, symbol: str) -> str:
             if start == "feature" and int(end) < len(self.feature_names):
                 return self.feature_names[int(end)]
         elif isinstance(symbol, float):
-            return "%.5f" % symbol
+            return f"{symbol:.5f}"
         return str(symbol)
 
     def draw(self) -> None:
@@ -157,7 +157,7 @@ def draw(self) -> None:
             for j in range(n_inputs):
                 src = self.connectivity[(i * self.k) + j]
                 if src < self.n_inputs:
-                    feature = "feature_%d" % src
+                    feature = f"feature_{src}"
                     self.gviz.node(feature, label=self.label(feature), shape="square")
                     self.gviz.edge(feature, str(i))
                 else: