Merge branch 'master' of https://github.com/MaximSmolskiy/Python

Author: MaximSmolskiy

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.7
+    rev: v0.5.7
     hooks:
       - id: ruff
       - id: ruff-format
@@ -29,7 +29,7 @@ repos:
           - tomli
 
   - repo: https://github.com/tox-dev/pyproject-fmt
-    rev: "2.1.3"
+    rev: "2.2.1"
     hooks:
       - id: pyproject-fmt
 
@@ -47,10 +47,11 @@ repos:
       - id: validate-pyproject
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.10.0
+    rev: v1.11.1
     hooks:
       - id: mypy
         args:
+          - --explicit-package-bases
           - --ignore-missing-imports
           - --install-types # See mirrors-mypy README.md
           - --non-interactive

DIRECTORY.md

@@ -540,8 +540,7 @@
   * [Lu Decomposition](linear_algebra/lu_decomposition.py)
   * Src
     * [Conjugate Gradient](linear_algebra/src/conjugate_gradient.py)
-    * Gaussian Elimination Pivoting
-      * [Gaussian Elimination Pivoting](linear_algebra/src/gaussian_elimination_pivoting/gaussian_elimination_pivoting.py)
+    * [Gaussian Elimination Pivoting](linear_algebra/src/gaussian_elimination_pivoting.py)
     * [Lib](linear_algebra/src/lib.py)
     * [Polynom For Points](linear_algebra/src/polynom_for_points.py)
     * [Power Iteration](linear_algebra/src/power_iteration.py)
@@ -863,6 +862,7 @@
   * [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
   * [Photoelectric Effect](physics/photoelectric_effect.py)
   * [Potential Energy](physics/potential_energy.py)
+  * [Rainfall Intensity](physics/rainfall_intensity.py)
   * [Reynolds Number](physics/reynolds_number.py)
   * [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
   * [Shear Stress](physics/shear_stress.py)
@@ -1259,6 +1259,7 @@
   * [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
   * [Capitalize](strings/capitalize.py)
   * [Check Anagrams](strings/check_anagrams.py)
+  * [Count Vowels](strings/count_vowels.py)
   * [Credit Card Validator](strings/credit_card_validator.py)
   * [Damerau Levenshtein Distance](strings/damerau_levenshtein_distance.py)
   * [Detecting English Programmatically](strings/detecting_english_programmatically.py)

backtracking/knight_tour.py

@@ -24,10 +24,10 @@ def get_valid_pos(position: tuple[int, int], n: int) -> list[tuple[int, int]]:
     ]
     permissible_positions = []
 
-    for position in positions:
-        y_test, x_test = position
+    for inner_position in positions:
+        y_test, x_test = inner_position
         if 0 <= y_test < n and 0 <= x_test < n:
-            permissible_positions.append(position)
+            permissible_positions.append(inner_position)
 
     return permissible_positions
 

computer_vision/haralick_descriptors.py

@@ -141,7 +141,7 @@ def transform(
 
     center_x, center_y = (x // 2 for x in kernel.shape)
 
-    # Use padded image when applying convolotion
+    # Use padded image when applying convolution
     # to not go out of bounds of the original the image
     transformed = np.zeros(image.shape, dtype=np.uint8)
     padded = np.pad(image, 1, "constant", constant_values=constant)

conversions/weight_conversion.py

@@ -297,6 +297,12 @@ def weight_conversion(from_type: str, to_type: str, value: float) -> float:
     1.660540199e-23
     >>> weight_conversion("atomic-mass-unit","atomic-mass-unit",2)
     1.999999998903455
+    >>> weight_conversion("slug", "kilogram", 1)
+    Traceback (most recent call last):
+    ...
+    ValueError: Invalid 'from_type' or 'to_type' value: 'slug', 'kilogram'
+    Supported values are: kilogram, gram, milligram, metric-ton, long-ton, short-ton, \
+pound, stone, ounce, carrat, atomic-mass-unit
     """
     if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
         msg = (

data_structures/binary_tree/is_sorted.py

@@ -80,9 +80,9 @@ def is_sorted(self) -> bool:
         """
         if self.left and (self.data < self.left.data or not self.left.is_sorted):
             return False
-        if self.right and (self.data > self.right.data or not self.right.is_sorted):
-            return False
-        return True
+        return not (
+            self.right and (self.data > self.right.data or not self.right.is_sorted)
+        )
 
 
 if __name__ == "__main__":

data_structures/binary_tree/red_black_tree.py

@@ -1,8 +1,3 @@
-"""
-psf/black : true
-ruff : passed
-"""
-
 from __future__ import annotations
 
 from collections.abc import Iterator
@@ -321,9 +316,7 @@ def check_coloring(self) -> bool:
             return False
         if self.left and not self.left.check_coloring():
             return False
-        if self.right and not self.right.check_coloring():
-            return False
-        return True
+        return not (self.right and not self.right.check_coloring())
 
     def black_height(self) -> int | None:
         """Returns the number of black nodes from this node to the
@@ -561,9 +554,7 @@ def test_rotations() -> bool:
     right_rot.right.right = RedBlackTree(10, parent=right_rot.right)
     right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right)
     right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right)
-    if tree != right_rot:
-        return False
-    return True
+    return tree == right_rot
 
 
 def test_insertion_speed() -> bool:
@@ -606,13 +597,11 @@ def test_insert_and_search() -> bool:
     tree.insert(12)
     tree.insert(10)
     tree.insert(11)
-    if 5 in tree or -6 in tree or -10 in tree or 13 in tree:
+    if any(i in tree for i in (5, -6, -10, 13)):
         # Found something not in there
         return False
-    if not (11 in tree and 12 in tree and -8 in tree and 0 in tree):
-        # Didn't find something in there
-        return False
-    return True
+    # Find all these things in there
+    return all(i in tree for i in (11, 12, -8, 0))
 
 
 def test_insert_delete() -> bool:
@@ -634,9 +623,7 @@ def test_insert_delete() -> bool:
     tree = tree.remove(9)
     if not tree.check_color_properties():
         return False
-    if list(tree.inorder_traverse()) != [-8, 0, 4, 8, 10, 11, 12]:
-        return False
-    return True
+    return list(tree.inorder_traverse()) == [-8, 0, 4, 8, 10, 11, 12]
 
 
 def test_floor_ceil() -> bool:
@@ -664,9 +651,7 @@ def test_min_max() -> bool:
     tree.insert(24)
     tree.insert(20)
     tree.insert(22)
-    if tree.get_max() != 22 or tree.get_min() != -16:
-        return False
-    return True
+    return not (tree.get_max() != 22 or tree.get_min() != -16)
 
 
 def test_tree_traversal() -> bool:
@@ -682,9 +667,7 @@ def test_tree_traversal() -> bool:
         return False
     if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]:
         return False
-    if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]:
-        return False
-    return True
+    return list(tree.postorder_traverse()) == [-16, 8, 20, 24, 22, 16, 0]
 
 
 def test_tree_chaining() -> bool:
@@ -695,9 +678,7 @@ def test_tree_chaining() -> bool:
         return False
     if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]:
         return False
-    if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]:
-        return False
-    return True
+    return list(tree.postorder_traverse()) == [-16, 8, 20, 24, 22, 16, 0]
 
 
 def print_results(msg: str, passes: bool) -> None:

linear_algebra/src/gaussian_elimination_pivoting/__init__.py renamed to docs/source/__init__.py

@@ -156,9 +156,11 @@ def remove_vertex(self, vertex: T) -> None:
         self.vertex_to_index.pop(vertex)
 
         # decrement indices for vertices shifted by the deleted vertex in the adj matrix
-        for vertex in self.vertex_to_index:
-            if self.vertex_to_index[vertex] >= start_index:
-                self.vertex_to_index[vertex] = self.vertex_to_index[vertex] - 1
+        for inner_vertex in self.vertex_to_index:
+            if self.vertex_to_index[inner_vertex] >= start_index:
+                self.vertex_to_index[inner_vertex] = (
+                    self.vertex_to_index[inner_vertex] - 1
+                )
 
     def contains_vertex(self, vertex: T) -> bool:
         """

graphs/graph_adjacency_matrix.py

@@ -79,7 +79,7 @@ def key(start: TPos, i: int, goal: TPos, g_function: dict[TPos, float]):
 
 
 def do_something(back_pointer, goal, start):
-    grid = np.chararray((n, n))
+    grid = np.char.chararray((n, n))
     for i in range(n):
         for j in range(n):
             grid[i][j] = "*"
@@ -123,9 +123,7 @@ def do_something(back_pointer, goal, start):
 def valid(p: TPos):
     if p[0] < 0 or p[0] > n - 1:
         return False
-    if p[1] < 0 or p[1] > n - 1:
-        return False
-    return True
+    return not (p[1] < 0 or p[1] > n - 1)
 
 
 def expand_state(

graphs/multi_heuristic_astar.py

@@ -38,7 +38,7 @@ def find_components(
     reversed_graph: dict[int, list[int]], vert: int, visited: list[bool]
 ) -> list[int]:
     """
-    Use depth first search to find strongliy connected
+    Use depth first search to find strongly connected
     vertices. Now graph is reversed
     >>> find_components({0: [1], 1: [2], 2: [0]}, 0, 5 * [False])
     [0, 1, 2]

graphs/strongly_connected_components.py

@@ -103,4 +103,4 @@ def create_graph(n: int, edges: list[tuple[int, int]]) -> list[list[int]]:
     edges = list(zip(source, target))
     g = create_graph(n_vertices, edges)
 
-    assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
+    assert tarjan(g) == [[5], [6], [4], [3, 2, 1, 0]]

graphs/tarjans_scc.py

@@ -82,8 +82,8 @@ def reformat_hex(i: int) -> bytes:
 
     hex_rep = format(i, "08x")[-8:]
     little_endian_hex = b""
-    for i in [3, 2, 1, 0]:
-        little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
+    for j in [3, 2, 1, 0]:
+        little_endian_hex += hex_rep[2 * j : 2 * j + 2].encode("utf-8")
     return little_endian_hex
 
 

hashes/md5.py

@@ -1,15 +1,5 @@
 import numpy as np
 
-matrix = np.array(
-    [
-        [5.0, -5.0, -3.0, 4.0, -11.0],
-        [1.0, -4.0, 6.0, -4.0, -10.0],
-        [-2.0, -5.0, 4.0, -5.0, -12.0],
-        [-3.0, -3.0, 5.0, -5.0, 8.0],
-    ],
-    dtype=float,
-)
-
 
 def solve_linear_system(matrix: np.ndarray) -> np.ndarray:
     """
@@ -87,15 +77,18 @@ def solve_linear_system(matrix: np.ndarray) -> np.ndarray:
 
 if __name__ == "__main__":
     from doctest import testmod
-    from pathlib import Path
 
     testmod()
-    file_path = Path(__file__).parent / "matrix.txt"
-    try:
-        matrix = np.loadtxt(file_path)
-    except FileNotFoundError:
-        print(f"Error: {file_path} not found.  Using default matrix instead.")
-
-    # Example usage:
-    print(f"Matrix:\n{matrix}")
-    print(f"{solve_linear_system(matrix) = }")
+
+    example_matrix = np.array(
+        [
+            [5.0, -5.0, -3.0, 4.0, -11.0],
+            [1.0, -4.0, 6.0, -4.0, -10.0],
+            [-2.0, -5.0, 4.0, -5.0, -12.0],
+            [-3.0, -3.0, 5.0, -5.0, 8.0],
+        ],
+        dtype=float,
+    )
+
+    print(f"Matrix:\n{example_matrix}")
+    print(f"{solve_linear_system(example_matrix) = }")

linear_algebra/src/gaussian_elimination_pivoting/gaussian_elimination_pivoting.py renamed to linear_algebra/src/gaussian_elimination_pivoting.py

@@ -4,7 +4,7 @@
 
 Purpose : You have one function f(x) which takes float integer and returns
 float you have to integrate the function in limits a to b.
-The approximation proposed by Thomas Simpsons in 1743 is one way to calculate
+The approximation proposed by Thomas Simpson in 1743 is one way to calculate
 integration.
 
 ( read article : https://cp-algorithms.com/num_methods/simpson-integration.html )

linear_algebra/src/gaussian_elimination_pivoting/matrix.txt

@@ -76,9 +76,9 @@ def get_3d_vectors_cross(ab: Vector3d, ac: Vector3d) -> Vector3d:
 
 def is_zero_vector(vector: Vector3d, accuracy: int) -> bool:
     """
-    Check if vector is equal to (0, 0, 0) of not.
+    Check if vector is equal to (0, 0, 0) or not.
 
-    Sine the algorithm is very accurate, we will never get a zero vector,
+    Since the algorithm is very accurate, we will never get a zero vector,
     so we need to round the vector axis,
     because we want a result that is either True or False.
     In other applications, we can return a float that represents the collinearity ratio.
@@ -97,9 +97,9 @@ def are_collinear(a: Point3d, b: Point3d, c: Point3d, accuracy: int = 10) -> boo
     """
     Check if three points are collinear or not.
 
-    1- Create tow vectors AB and AC.
-    2- Get the cross vector of the tow vectors.
-    3- Calcolate the length of the cross vector.
+    1- Create two vectors AB and AC.
+    2- Get the cross vector of the two vectors.
+    3- Calculate the length of the cross vector.
     4- If the length is zero then the points are collinear, else they are not.
 
     The use of the accuracy parameter is explained in is_zero_vector docstring.

maths/numerical_analysis/integration_by_simpson_approx.py

@@ -84,7 +84,6 @@ def __dft(self, which):
         # Corner case
         if len(dft) <= 1:
             return dft[0]
-        #
         next_ncol = self.c_max_length // 2
         while next_ncol > 0:
             new_dft = [[] for i in range(next_ncol)]

maths/points_are_collinear_3d.py

@@ -1,7 +1,7 @@
 """
  - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - CNN - Convolution Neural Network For Photo Recognizing
-Goal - - Recognize Handing Writing Word Photo
+Goal - - Recognize Handwriting Word Photo
 Detail: Total 5 layers neural network
         * Convolution layer
         * Pooling layer
@@ -135,7 +135,7 @@ def convolute(self, data, convs, w_convs, thre_convs, conv_step):
             )
             data_featuremap.append(featuremap)
 
-        # expanding the data slice to One dimenssion
+        # expanding the data slice to one dimension
         focus1_list = []
         for each_focus in data_focus:
             focus1_list.extend(self.Expand_Mat(each_focus))
@@ -304,7 +304,7 @@ def draw_error():
             plt.grid(True, alpha=0.5)
             plt.show()
 
-        print("------------------Training Complished---------------------")
+        print("------------------Training Complete---------------------")
         print((" - - Training epoch: ", rp, f"     - - Mse: {mse:.6f}"))
         if draw_e:
             draw_error()
@@ -353,5 +353,5 @@ def convolution(self, data):
 
 if __name__ == "__main__":
     """
-    I will put the example on other file
+    I will put the example in another file
     """

maths/radix2_fft.py

@@ -1 +0,0 @@
-#

neural_network/convolution_neural_network.py

@@ -1 +0,0 @@
-#

project_euler/problem_034/__init__.py

@@ -1 +0,0 @@
-#