Tighten up psf/black and flake8 (TheAlgorithms#2024)

* Tighten up psf/black and flake8

* Fix some tests

* Fix some E741

* Fix some E741

* updating DIRECTORY.md

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

Author: cclauss

.travis.yml

@@ -4,13 +4,13 @@ language: python
 python: 3.8
 cache: pip
 before_install: pip install --upgrade pip setuptools six
-install: pip install -r requirements.txt
+install: pip install black flake8
 before_script:
-  - black --check . || true
-  - IGNORE=E123,E203,E265,E266,E302,E401,E402,E712,E731,E741,E743,F811,F841,W291,W293,W503
-  - flake8 . --count --ignore=$IGNORE --max-complexity=25 --max-line-length=127 --show-source --statistics
-script:
+  - black --check .
+  - flake8 --ignore=E203,W503 --max-complexity=25 --max-line-length=120 --statistics --count .
   - 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 .
   - pytest --doctest-modules --cov-report=term-missing:skip-covered --cov=. .
 after_success:

DIRECTORY.md

@@ -222,6 +222,7 @@
   * [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
   * [Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py)
   * [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
+  * [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
   * [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
   * [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
   * [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
@@ -242,6 +243,7 @@
   * [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
   * [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
   * [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
+  * [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
   * [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
   * [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
   * [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
@@ -409,6 +411,7 @@
   * [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
   * [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py)
   * [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py)
+  * [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
   * [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
   * [Integeration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/other/integeration_by_simpson_approx.py)
   * [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/other/largest_subarray_sum.py)

arithmetic_analysis/newton_forward_interpolation.py

@@ -2,6 +2,7 @@
 
 import math
 
+
 # for calculating u value
 def ucal(u, p):
     """

backtracking/coloring.py

@@ -18,7 +18,7 @@ def valid_coloring(
 
     >>> neighbours = [0,1,0,1,0]
     >>> colored_vertices = [0, 2, 1, 2, 0]
-    
+
     >>> color = 1
     >>> valid_coloring(neighbours, colored_vertices, color)
     True
@@ -37,11 +37,11 @@ def valid_coloring(
 def util_color(
     graph: List[List[int]], max_colors: int, colored_vertices: List[int], index: int
 ) -> bool:
-    """ 
+    """
     Pseudo-Code
 
     Base Case:
-    1. Check if coloring is complete 
+    1. Check if coloring is complete
         1.1 If complete return True (meaning that we successfully colored graph)
 
     Recursive Step:
@@ -60,7 +60,7 @@ def util_color(
     >>> max_colors = 3
     >>> colored_vertices = [0, 1, 0, 0, 0]
     >>> index = 3
-    
+
     >>> util_color(graph, max_colors, colored_vertices, index)
     True
 
@@ -87,11 +87,11 @@ def util_color(
 
 
 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.
     If True is returned colored_vertices list is filled with correct colorings
-        
+
     >>> graph = [[0, 1, 0, 0, 0],
     ...          [1, 0, 1, 0, 1],
     ...          [0, 1, 0, 1, 0],

backtracking/hamiltonian_cycle.py

@@ -1,9 +1,9 @@
 """
-    A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle 
+    A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
     through a graph that visits each node exactly once.
-    Determining whether such paths and cycles exist in graphs 
+    Determining whether such paths and cycles exist in graphs
     is the 'Hamiltonian path problem', which is NP-complete.
-    
+
     Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
 """
 from typing import List
@@ -18,7 +18,7 @@ def valid_connection(
     2. Next vertex should not be in path
     If both validations succeeds we return true saying that it is possible to connect this vertices
     either we return false
-    
+
     Case 1:Use exact graph as in main function, with initialized values
     >>> graph = [[0, 1, 0, 1, 0],
     ...          [1, 0, 1, 1, 1],
@@ -56,11 +56,11 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int)
     Recursive Step:
     2. Iterate over each vertex
         Check if next vertex is valid for transiting from current vertex
-            2.1 Remember next vertex as next transition 
+            2.1 Remember next vertex as next transition
             2.2 Do recursive call and check if going to this vertex solves problem
             2.3 if next vertex leads to solution return True
             2.4 else backtrack, delete remembered vertex
-            
+
     Case 1: Use exact graph as in main function, with initialized values
     >>> graph = [[0, 1, 0, 1, 0],
     ...          [1, 0, 1, 1, 1],
@@ -111,12 +111,12 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
     Wrapper function to call subroutine called util_hamilton_cycle,
     which will either return array of vertices indicating hamiltonian cycle
     or an empty list indicating that hamiltonian cycle was not found.
-    Case 1: 
-    Following graph consists of 5 edges. 
+    Case 1:
+    Following graph consists of 5 edges.
     If we look closely, we can see that there are multiple Hamiltonian cycles.
-    For example one result is when we iterate like: 
+    For example one result is when we iterate like:
     (0)->(1)->(2)->(4)->(3)->(0)
-    
+
     (0)---(1)---(2)
      |   /   \   |
      |  /     \  |
@@ -130,10 +130,10 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
     ...          [0, 1, 1, 1, 0]]
     >>> hamilton_cycle(graph)
     [0, 1, 2, 4, 3, 0]
-    
-    Case 2: 
+
+    Case 2:
     Same Graph as it was in Case 1, changed starting index from default to 3
-    
+
     (0)---(1)---(2)
      |   /   \   |
      |  /     \  |
@@ -147,11 +147,11 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
     ...          [0, 1, 1, 1, 0]]
     >>> hamilton_cycle(graph, 3)
     [3, 0, 1, 2, 4, 3]
-    
+
     Case 3:
     Following Graph is exactly what it was before, but edge 3-4 is removed.
     Result is that there is no Hamiltonian Cycle anymore.
-    
+
     (0)---(1)---(2)
      |   /   \   |
      |  /     \  |

backtracking/minimax.py

@@ -1,10 +1,10 @@
 import math
 
 """ Minimax helps to achieve maximum score in a game by checking all possible moves
-    depth is current depth in game tree. 
+    depth is current depth in game tree.
     nodeIndex is index of current node in scores[].
     if move is of maximizer return true else false
-    leaves of game tree is stored in scores[]  
+    leaves of game tree is stored in scores[]
     height is maximum height of Game tree
 """
 

backtracking/n_queens.py

@@ -1,9 +1,9 @@
 """
 
- The nqueens problem is of placing N queens on a N * N 
+ The nqueens problem is of placing N queens on a N * N
  chess board such that no queen can attack any other queens placed
  on that chess board.
- This means that one queen cannot have any other queen on its horizontal, vertical and 
+ This means that one queen cannot have any other queen on its horizontal, vertical and
  diagonal lines.
 
 """
@@ -12,7 +12,7 @@
 
 def isSafe(board, row, column):
     """
-    This function returns a boolean value True if it is safe to place a queen there considering 
+    This function returns a boolean value True if it is safe to place a queen there considering
     the current state of the board.
 
     Parameters :
@@ -40,13 +40,13 @@ def isSafe(board, row, column):
 
 def solve(board, row):
     """
-    It creates a state space tree and calls the safe function until it receives a 
-    False Boolean and terminates that branch and backtracks to the next 
+    It creates a state space tree and calls the safe function until it receives a
+    False Boolean and terminates that branch and backtracks to the next
     possible solution branch.
     """
     if row >= len(board):
         """
-        If the row number exceeds N we have board with a successful combination 
+        If the row number exceeds N we have board with a successful combination
         and that combination is appended to the solution list and the board is printed.
 
         """
@@ -56,9 +56,9 @@ def solve(board, row):
         return
     for i in range(len(board)):
         """
-        For every row it iterates through each column to check if it is feasible to place a 
+        For every row it iterates through each column to check if it is feasible to place a
         queen there.
-        If all the combinations for that particular branch are successful the board is 
+        If all the combinations for that particular branch are successful the board is
         reinitialized for the next possible combination.
         """
         if isSafe(board, row, i):

ciphers/decrypt_caesar_with_chi_squared.py

@@ -1,9 +1,12 @@
+#!/usr/bin/env python3
+
+
 def decrypt_caesar_with_chi_squared(
     ciphertext: str,
     cipher_alphabet=None,
     frequencies_dict=None,
     case_sensetive: bool = False,
-) -> list:
+) -> tuple:
     """
     Basic Usage
     ===========
@@ -96,15 +99,19 @@ def decrypt_caesar_with_chi_squared(
     Further Reading
     ================
 
-    * http://practicalcryptography.com/cryptanalysis/text-characterisation/chi-squared-statistic/
+    * http://practicalcryptography.com/cryptanalysis/text-characterisation/chi-squared-
+        statistic/
     * https://en.wikipedia.org/wiki/Letter_frequency
     * https://en.wikipedia.org/wiki/Chi-squared_test
     * https://en.m.wikipedia.org/wiki/Caesar_cipher
 
     Doctests
     ========
-    >>> decrypt_caesar_with_chi_squared('dof pz aol jhlzhy jpwoly zv wvwbshy? pa pz avv lhzf av jyhjr!')
-    (7, 3129.228005747531, 'why is the caesar cipher so popular? it is too easy to crack!')
+    >>> decrypt_caesar_with_chi_squared(
+    ...    'dof pz aol jhlzhy jpwoly zv wvwbshy? pa pz avv lhzf av jyhjr!'
+    ... )  # doctest: +NORMALIZE_WHITESPACE
+    (7, 3129.228005747531,
+     'why is the caesar cipher so popular? it is too easy to crack!')
 
     >>> decrypt_caesar_with_chi_squared('crybd cdbsxq')
     (10, 233.35343938980898, 'short string')
@@ -172,7 +179,7 @@ def decrypt_caesar_with_chi_squared(
                 # Append the character if it isn't in the alphabet
                 decrypted_with_shift += letter
 
-        chi_squared_statistic = 0
+        chi_squared_statistic = 0.0
 
         # Loop through each letter in the decoded message with the shift
         for letter in decrypted_with_shift:
@@ -181,7 +188,8 @@ def decrypt_caesar_with_chi_squared(
                     # Get the amount of times the letter occurs in the message
                     occurrences = decrypted_with_shift.count(letter)
 
-                    # Get the excepcted amount of times the letter should appear based on letter frequencies
+                    # Get the excepcted amount of times the letter should appear based
+                    # on letter frequencies
                     expected = frequencies[letter] * occurrences
 
                     # Complete the chi squared statistic formula
@@ -194,7 +202,8 @@ def decrypt_caesar_with_chi_squared(
                     # Get the amount of times the letter occurs in the message
                     occurrences = decrypted_with_shift.count(letter)
 
-                    # Get the excepcted amount of times the letter should appear based on letter frequencies
+                    # Get the excepcted amount of times the letter should appear based
+                    # on letter frequencies
                     expected = frequencies[letter] * occurrences
 
                     # Complete the chi squared statistic formula
@@ -209,7 +218,8 @@ def decrypt_caesar_with_chi_squared(
             decrypted_with_shift,
         ]
 
-    # Get the most likely cipher by finding the cipher with the smallest chi squared statistic
+    # Get the most likely cipher by finding the cipher with the smallest chi squared
+    # statistic
     most_likely_cipher = min(
         chi_squared_statistic_values, key=chi_squared_statistic_values.get
     )

ciphers/elgamal_key_generator.py

@@ -1,7 +1,9 @@
 import os
 import random
 import sys
-import rabin_miller as rabinMiller, cryptomath_module as cryptoMath
+
+import cryptomath_module as cryptoMath
+import rabin_miller as rabinMiller
 
 min_primitive_root = 3
 

ciphers/mixed_keyword_cypher.py

@@ -25,7 +25,7 @@ def mixed_keyword(key="college", pt="UNIVERSITY"):
     for i in key:
         if i not in temp:
             temp.append(i)
-    l = len(temp)
+    len_temp = len(temp)
     # print(temp)
     alpha = []
     modalpha = []
@@ -40,17 +40,17 @@ def mixed_keyword(key="college", pt="UNIVERSITY"):
     k = 0
     for i in range(r):
         t = []
-        for j in range(l):
+        for j in range(len_temp):
             t.append(temp[k])
             if not (k < 25):
                 break
             k += 1
         modalpha.append(t)
     # print(modalpha)
-    d = dict()
+    d = {}
     j = 0
     k = 0
-    for j in range(l):
+    for j in range(len_temp):
         for i in modalpha:
             if not (len(i) - 1 >= j):
                 break