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pre-commit autoupdate 2025-09-11 (#12963)

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* pre-commit autoupdate 2025-09-11

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Christian Clauss
2025-09-13 00:56:14 +02:00
committed by GitHub
parent 18c853d301
commit 63180d7e24
16 changed files with 33 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.pre-commit-config.yaml
View File

@@ -16,7 +16,7 @@ repos:
- id: auto-walrus
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.12.12
rev: v0.13.0
hooks:
- id: ruff-check
- id: ruff-format

2
data_structures/arrays/sudoku_solver.py
View File

@@ -149,7 +149,7 @@ def search(values):
if all(len(values[s]) == 1 for s in squares):
return values ## Solved!
## Chose the unfilled square s with the fewest possibilities
n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
_n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
return some(search(assign(values.copy(), s, d)) for d in values[s])

4
data_structures/trie/radix_tree.py
View File

@@ -115,7 +115,7 @@ class RadixNode:
if not incoming_node:
return False
else:
matching_string, remaining_prefix, remaining_word = incoming_node.match(
_matching_string, remaining_prefix, remaining_word = incoming_node.match(
word
)
# If there is remaining prefix, the word can't be on the tree
@@ -144,7 +144,7 @@ class RadixNode:
if not incoming_node:
return False
else:
matching_string, remaining_prefix, remaining_word = incoming_node.match(
_matching_string, remaining_prefix, remaining_word = incoming_node.match(
word
)
# If there is remaining prefix, the word can't be on the tree

10
graphs/graph_adjacency_list.py
View File

@@ -448,7 +448,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
(
undirected_graph,
directed_graph,
random_vertices,
_random_vertices,
random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
@@ -502,7 +502,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for vertex in random_vertices:
@@ -516,7 +516,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for i in range(101):
@@ -530,7 +530,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
(
undirected_graph,
directed_graph,
random_vertices,
_random_vertices,
random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
@@ -569,7 +569,7 @@ class TestGraphAdjacencyList(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for vertex in random_vertices:

10
graphs/graph_adjacency_matrix.py
View File

@@ -469,7 +469,7 @@ class TestGraphMatrix(unittest.TestCase):
(
undirected_graph,
directed_graph,
random_vertices,
_random_vertices,
random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
@@ -523,7 +523,7 @@ class TestGraphMatrix(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for vertex in random_vertices:
@@ -537,7 +537,7 @@ class TestGraphMatrix(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for i in range(101):
@@ -551,7 +551,7 @@ class TestGraphMatrix(unittest.TestCase):
(
undirected_graph,
directed_graph,
random_vertices,
_random_vertices,
random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
@@ -590,7 +590,7 @@ class TestGraphMatrix(unittest.TestCase):
undirected_graph,
directed_graph,
random_vertices,
random_edges,
_random_edges,
) = self.__generate_graphs(20, 0, 100, 4)
for vertex in random_vertices:

12
knapsack/tests/test_greedy_knapsack.py
View File

@@ -28,7 +28,7 @@ class TestClass(unittest.TestCase):
# profit = [10, 20, 30, 40, 50, 60]
# weight = [2, 4, 6, 8, 10, 12]
# max_weight = -15
pytest.raises(ValueError, match="max_weight must greater than zero.")
pytest.raises(ValueError, match=r"max_weight must greater than zero.")
def test_negative_profit_value(self):
"""
@@ -38,7 +38,7 @@ class TestClass(unittest.TestCase):
# profit = [10, -20, 30, 40, 50, 60]
# weight = [2, 4, 6, 8, 10, 12]
# max_weight = 15
pytest.raises(ValueError, match="Weight can not be negative.")
pytest.raises(ValueError, match=r"Weight can not be negative.")
def test_negative_weight_value(self):
"""
@@ -48,7 +48,7 @@ class TestClass(unittest.TestCase):
# profit = [10, 20, 30, 40, 50, 60]
# weight = [2, -4, 6, -8, 10, 12]
# max_weight = 15
pytest.raises(ValueError, match="Profit can not be negative.")
pytest.raises(ValueError, match=r"Profit can not be negative.")
def test_null_max_weight(self):
"""
@@ -58,7 +58,7 @@ class TestClass(unittest.TestCase):
# profit = [10, 20, 30, 40, 50, 60]
# weight = [2, 4, 6, 8, 10, 12]
# max_weight = null
pytest.raises(ValueError, match="max_weight must greater than zero.")
pytest.raises(ValueError, match=r"max_weight must greater than zero.")
def test_unequal_list_length(self):
"""
@@ -68,7 +68,9 @@ class TestClass(unittest.TestCase):
# profit = [10, 20, 30, 40, 50]
# weight = [2, 4, 6, 8, 10, 12]
# max_weight = 100
pytest.raises(IndexError, match="The length of profit and weight must be same.")
pytest.raises(
IndexError, match=r"The length of profit and weight must be same."
)
if __name__ == "__main__":

2
linear_algebra/gaussian_elimination.py
View File

@@ -33,7 +33,7 @@ def retroactive_resolution(
[ 0.5]])
"""
rows, columns = np.shape(coefficients)
rows, _columns = np.shape(coefficients)
x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
for row in reversed(range(rows)):

4
linear_algebra/jacobi_iteration_method.py
View File

@@ -112,7 +112,7 @@ def jacobi_iteration_method(
(coefficient_matrix, constant_matrix), axis=1
)
rows, cols = table.shape
rows, _cols = table.shape
strictly_diagonally_dominant(table)
@@ -149,7 +149,7 @@ def jacobi_iteration_method(
# Here we get 'i_col' - these are the column numbers, for each row
# without diagonal elements, except for the last column.
i_row, i_col = np.where(masks)
_i_row, i_col = np.where(masks)
ind = i_col.reshape(-1, rows - 1)
#'i_col' is converted to a two-dimensional list 'ind', which will be

2
machine_learning/polynomial_regression.py
View File

@@ -93,7 +93,7 @@ class PolynomialRegression:
...
ValueError: Data must have dimensions N x 1
"""
rows, *remaining = data.shape
_rows, *remaining = data.shape
if remaining:
raise ValueError("Data must have dimensions N x 1")

2
machine_learning/principle_component_analysis.py
View File

@@ -65,7 +65,7 @@ def main() -> None:
"""
Driver function to execute PCA and display results.
"""
data_x, data_y = collect_dataset()
data_x, _data_y = collect_dataset()
# Number of principal components to retain
n_components = 2

2
maths/chinese_remainder_theorem.py
View File

@@ -65,7 +65,7 @@ def invert_modulo(a: int, n: int) -> int:
1
"""
(b, x) = extended_euclid(a, n)
(b, _x) = extended_euclid(a, n)
if b < 0:
b = (b % n + n) % n
return b

4
maths/modular_division.py
View File

@@ -31,7 +31,7 @@ def modular_division(a: int, b: int, n: int) -> int:
assert n > 1
assert a > 0
assert greatest_common_divisor(a, n) == 1
(d, t, s) = extended_gcd(n, a) # Implemented below
(_d, _t, s) = extended_gcd(n, a) # Implemented below
x = (b * s) % n
return x
@@ -47,7 +47,7 @@ def invert_modulo(a: int, n: int) -> int:
1
"""
(b, x) = extended_euclid(a, n) # Implemented below
(b, _x) = extended_euclid(a, n) # Implemented below
if b < 0:
b = (b % n + n) % n
return b

4
neural_network/convolution_neural_network.py
View File

@@ -317,7 +317,7 @@ class CNN:
print((" - - Shape: Test_Data ", np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(
_data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,
@@ -339,7 +339,7 @@ class CNN:
def convolution(self, data):
# return the data of image after convoluting process so we can check it out
data_test = np.asmatrix(data)
data_focus1, data_conved1 = self.convolute(
_data_focus1, data_conved1 = self.convolute(
data_test,
self.conv1,
self.w_conv1,

2
project_euler/problem_551/sol1.py
View File

@@ -185,7 +185,7 @@ def solution(n: int = 10**15) -> int:
i = 1
dn = 0
while True:
diff, terms_jumped = next_term(digits, 20, i + dn, n)
_diff, terms_jumped = next_term(digits, 20, i + dn, n)
dn += terms_jumped
if dn == n - i:
break

1
pyproject.toml
View File

@@ -124,7 +124,6 @@ lint.ignore = [
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes -- FIX ME
"SIM905", # Consider using a list literal instead of `str.split` -- DO NOT FIX
"SLF001", # Private member accessed: `_Iterator` -- FIX ME
"UP038", # Use `X | Y` in `{}` call instead of `(X, Y)` -- DO NOT FIX
]
lint.per-file-ignores."data_structures/hashing/tests/test_hash_map.py" = [

2
scheduling/multi_level_feedback_queue.py
View File

@@ -255,7 +255,7 @@ class MLFQ:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1):
finished, self.ready_queue = self.round_robin(
_finished, self.ready_queue = self.round_robin(
self.ready_queue, self.time_slices[i]
)
# the last queue has first_come_first_served algorithm