from __future__ import annotations

.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 @@
 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 @@
 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 @@
 https://mathworld.wolfram.com/ElementaryCellularAutomaton.html
 """
 
-from typing import List
+from __future__ import annotations
 
 from PIL import Image
 
@@ -15,7 +15,7 @@
 # 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 @@
 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 @@
 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 @@ def right(self, idx: int) -> int:
         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 @@
 >>> 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 @@ def __str__(self):
         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 @@
 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 @@ def random_level(self) -> int:
 
         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)