Wrap lines that go beyond GitHub Editor (#1925)

* Wrap lines that go beyond GiHub Editor

* flake8 --count --select=E501 --max-line-length=127

* updating DIRECTORY.md

* Update strassen_matrix_multiplication.py

* fixup! Format Python code with psf/black push

* Update decision_tree.py

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

.travis.yml

@@ -7,7 +7,7 @@ before_install: pip install --upgrade pip setuptools six
 install: pip install -r requirements.txt
 before_script:
   - black --check . || true
-  - flake8 . --count --select=E101,E722,E9,F4,F63,F7,F82,W191 --show-source --statistics
+  - flake8 . --count --select=E101,E501,E722,E9,F4,F63,F7,F82,W191 --max-line-length=127 --show-source --statistics
   - flake8 . --count --exit-zero --max-line-length=127 --statistics
 script:
   - scripts/validate_filenames.py  # no uppercase, no spaces, in a directory

DIRECTORY.md

@@ -14,6 +14,7 @@
   * [All Combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py)
   * [All Permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py)
   * [All Subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py)
+  * [Coloring](https://github.com/TheAlgorithms/Python/blob/master/backtracking/coloring.py)
   * [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
   * [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
   * [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
@@ -31,6 +32,7 @@
   * [One Dimensional](https://github.com/TheAlgorithms/Python/blob/master/cellular_automata/one_dimensional.py)
 
 ## Ciphers
+  * [A1Z26](https://github.com/TheAlgorithms/Python/blob/master/ciphers/a1z26.py)
   * [Affine Cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py)
   * [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py)
   * [Base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py)
@@ -138,6 +140,8 @@
 ## Digital Image Processing
   * [Change Contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py)
   * [Convert To Negative](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convert_to_negative.py)
+  * Dithering
+    * [Burkes](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/dithering/burkes.py)
   * Edge Detection
     * [Canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py)
   * Filters

ciphers/rsa_cipher.py

@@ -1,4 +1,7 @@
-import sys, rsa_key_generator as rkg, os
+import os
+import sys
+
+import rsa_key_generator as rkg
 
 DEFAULT_BLOCK_SIZE = 128
 BYTE_SIZE = 256
@@ -92,7 +95,9 @@ def encryptAndWriteToFile(
     keySize, n, e = readKeyFile(keyFilename)
     if keySize < blockSize * 8:
         sys.exit(
-            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Either decrease the block size or use different keys."
+            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher "
+            "requires the block size to be equal to or greater than the key size. "
+            "Either decrease the block size or use different keys."
             % (blockSize * 8, keySize)
         )
 
@@ -117,7 +122,9 @@ def readFromFileAndDecrypt(messageFilename, keyFilename):
 
     if keySize < blockSize * 8:
         sys.exit(
-            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher requires the block size to be equal to or greater than the key size. Did you specify the correct key file and encrypted file?"
+            "ERROR: Block size is %s bits and key size is %s bits. The RSA cipher "
+            "requires the block size to be equal to or greater than the key size. "
+            "Did you specify the correct key file and encrypted file?"
             % (blockSize * 8, keySize)
         )
 

ciphers/rsa_key_generator.py

@@ -1,5 +1,9 @@
-import random, sys, os
+import os
-import rabin_miller as rabinMiller, cryptomath_module as cryptoMath
+import random
+import sys
+
+import cryptomath_module as cryptoMath
+import rabin_miller as rabinMiller
 
 
 def main():
@@ -35,7 +39,8 @@ def makeKeyFiles(name, keySize):
     ):
         print("\nWARNING:")
         print(
-            '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \nUse a different name or delete these files and re-run this program.'
+            '"%s_pubkey.txt" or "%s_privkey.txt" already exists. \n'
+            "Use a different name or delete these files and re-run this program."
             % (name, name)
         )
         sys.exit()

data_structures/linked_list/doubly_linked_list.py

@@ -1,9 +1,12 @@
 """
-- A linked list is similar to an array, it holds values. However, links in a linked list do not have indexes.
+- A linked list is similar to an array, it holds values. However, links in a linked
+    list do not have indexes.
 - This is an example of a double ended, doubly linked list.
 - Each link references the next link and the previous one.
-- A Doubly Linked List (DLL) contains an extra pointer, typically called previous pointer, together with next pointer and data which are there in singly linked list.
+- A Doubly Linked List (DLL) contains an extra pointer, typically called previous
- - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficient"""
+    pointer, together with next pointer and data which are there in singly linked list.
+ - Advantages over SLL - IT can be traversed in both forward and backward direction.,
+     Delete operation is more efficient"""
 
 
 class LinkedList:  # making main class named linked list
@@ -13,7 +16,7 @@ class LinkedList:  # making main class named linked list
 
     def insertHead(self, x):
         newLink = Link(x)  # Create a new link with a value attached to it
-        if self.isEmpty() == True:  # Set the first element added to be the tail
+        if self.isEmpty():  # Set the first element added to be the tail
             self.tail = newLink
         else:
             self.head.previous = newLink  # newLink <-- currenthead(head)
@@ -23,7 +26,9 @@ class LinkedList:  # making main class named linked list
     def deleteHead(self):
         temp = self.head
         self.head = self.head.next  # oldHead <--> 2ndElement(head)
-        self.head.previous = None  # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed
+        # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be
+        # removed
+        self.head.previous = None
         if self.head is None:
             self.tail = None  # if empty linked list
         return temp

divide_and_conquer/strassen_matrix_multiplication.py

@@ -31,12 +31,19 @@ def matrix_subtraction(matrix_a: List, matrix_b: 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.
+    Given an even length matrix, returns the top_left, top_right, bot_left, bot_right
+    quadrant.
 
     >>> split_matrix([[4,3,2,4],[2,3,1,1],[6,5,4,3],[8,4,1,6]])
     ([[4, 3], [2, 3]], [[2, 4], [1, 1]], [[6, 5], [8, 4]], [[4, 3], [1, 6]])
-    >>> split_matrix([[4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],[4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]])
+    >>> split_matrix([
-    ([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]])
+    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6],
+    ...     [4,3,2,4,4,3,2,4],[2,3,1,1,2,3,1,1],[6,5,4,3,6,5,4,3],[8,4,1,6,8,4,1,6]
+    ... ])  # doctest: +NORMALIZE_WHITESPACE
+    ([[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4],
+      [2, 3, 1, 1], [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1],
+      [6, 5, 4, 3], [8, 4, 1, 6]], [[4, 3, 2, 4], [2, 3, 1, 1], [6, 5, 4, 3],
+      [8, 4, 1, 6]])
     """
     if len(a) % 2 != 0 or len(a[0]) % 2 != 0:
         raise Exception("Odd matrices are not supported!")
@@ -66,8 +73,8 @@ def print_matrix(matrix: List) -> None:
 
 def actual_strassen(matrix_a: List, matrix_b: List) -> List:
     """
-    Recursive function to calculate the product of two matrices, using the Strassen Algorithm.
+    Recursive function to calculate the product of two matrices, using the Strassen
-    It only supports even length matrices.
+    Algorithm.  It only supports even length matrices.
     """
     if matrix_dimensions(matrix_a) == (2, 2):
         return default_matrix_multiplication(matrix_a, matrix_b)
@@ -106,7 +113,8 @@ def strassen(matrix1: List, matrix2: List) -> List:
     """
     if matrix_dimensions(matrix1)[1] != matrix_dimensions(matrix2)[0]:
         raise Exception(
-            f"Unable to multiply these matrices, please check the dimensions. \nMatrix A:{matrix1} \nMatrix B:{matrix2}"
+            f"Unable to multiply these matrices, please check the dimensions. \n"
+            f"Matrix A:{matrix1} \nMatrix B:{matrix2}"
         )
     dimension1 = matrix_dimensions(matrix1)
     dimension2 = matrix_dimensions(matrix2)
@@ -119,7 +127,8 @@ def strassen(matrix1: List, matrix2: List) -> List:
     new_matrix1 = matrix1
     new_matrix2 = matrix2
 
-    # Adding zeros to the matrices so that the arrays dimensions are the same and also power of 2
+    # Adding zeros to the matrices so that the arrays dimensions are the same and also
+    # power of 2
     for i in range(0, maxim):
         if i < dimension1[0]:
             for j in range(dimension1[1], maxim):

dynamic_programming/bitmask.py

@@ -4,10 +4,9 @@ This is a Python implementation for questions involving task assignments between
 Here Bitmasking and DP are used for solving this.
 
 Question :-
-We have N tasks and M people. Each person in M can do only certain of these tasks. Also a person can do only one task and a task is performed only by one person.
+We have N tasks and M people. Each person in M can do only certain of these tasks. Also
+a person can do only one task and a task is performed only by one person.
 Find the total no of ways in which the tasks can be distributed.
-
-
 """
 from collections import defaultdict
 
@@ -25,7 +24,8 @@ class AssignmentUsingBitmask:
 
         self.task = defaultdict(list)  # stores the list of persons for each task
 
-        # final_mask is used to check if all persons are included by setting all bits to 1
+        # final_mask is used to check if all persons are included by setting all bits
+        # to 1
         self.final_mask = (1 << len(task_performed)) - 1
 
     def CountWaysUtil(self, mask, task_no):
@@ -45,7 +45,8 @@ class AssignmentUsingBitmask:
         # Number of ways when we don't this task in the arrangement
         total_ways_util = self.CountWaysUtil(mask, task_no + 1)
 
-        # now assign the tasks one by one to all possible persons and recursively assign for the remaining tasks.
+        # now assign the tasks one by one to all possible persons and recursively
+        # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
 
@@ -53,7 +54,8 @@ class AssignmentUsingBitmask:
                 if mask & (1 << p):
                     continue
 
-                # assign this task to p and change the mask value. And recursively assign tasks with the new mask value.
+                # assign this task to p and change the mask value. And recursively
+                # assign tasks with the new mask value.
                 total_ways_util += self.CountWaysUtil(mask | (1 << p), task_no + 1)
 
         # save the value.
@@ -85,6 +87,7 @@ if __name__ == "__main__":
     )
     """
     For the particular example the tasks can be distributed as
-    (1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4), (3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
+    (1,2,3), (1,2,4), (1,5,3), (1,5,4), (3,1,4),
+    (3,2,4), (3,5,4), (4,1,3), (4,2,3), (4,5,3)
     total 10
     """

dynamic_programming/edit_distance.py

@@ -2,10 +2,12 @@
 Author  : Turfa Auliarachman
 Date    : October 12, 2016
 
-This is a pure Python implementation of Dynamic Programming solution to the edit distance problem.
+This is a pure Python implementation of Dynamic Programming solution to the edit
+distance problem.
 
 The problem is :
-Given two strings A and B. Find the minimum number of operations to string B such that A = B. The permitted operations are removal,  insertion, and substitution.
+Given two strings A and B. Find the minimum number of operations to string B such that
+A = B. The permitted operations are removal,  insertion, and substitution.
 """
 
 

machine_learning/decision_tree.py

@@ -20,15 +20,21 @@ class Decision_Tree:
         mean_squared_error:
         @param labels: a one dimensional numpy array
         @param prediction: a floating point value
-        return value: mean_squared_error calculates the error if prediction is used to estimate the labels
+        return value: mean_squared_error calculates the error if prediction is used to
+            estimate the labels
         >>> tester = Decision_Tree()
         >>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
         >>> test_prediction = np.float(6)
-        >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
+        >>> tester.mean_squared_error(test_labels, test_prediction) == (
+        ...     Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
+        ...         test_prediction))
+        True
         >>> test_labels = np.array([1,2,3])
         >>> test_prediction = np.float(2)
-        >>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
+        >>> tester.mean_squared_error(test_labels, test_prediction) == (
-
+        ...     Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
+        ...         test_prediction))
+        True
         """
         if labels.ndim != 1:
             print("Error: Input labels must be one dimensional")
@@ -46,7 +52,8 @@ class Decision_Tree:
         """
 
         """
-        this section is to check that the inputs conform to our dimensionality constraints
+        this section is to check that the inputs conform to our dimensionality
+        constraints
         """
         if X.ndim != 1:
             print("Error: Input data set must be one dimensional")
@@ -72,7 +79,8 @@ class Decision_Tree:
         """
         loop over all possible splits for the decision tree. find the best split.
         if no split exists that is less than 2 * error for the entire array
-        then the data set is not split and the average for the entire array is used as the predictor
+        then the data set is not split and the average for the entire array is used as
+        the predictor
         """
         for i in range(len(X)):
             if len(X[:i]) < self.min_leaf_size:
@@ -147,9 +155,10 @@ class Test_Decision_Tree:
 
 def main():
     """
-    In this demonstration we're generating a sample data set from the sin function in numpy.
+    In this demonstration we're generating a sample data set from the sin function in
-    We then train a decision tree on the data set and use the decision tree to predict the
+    numpy.  We then train a decision tree on the data set and use the decision tree to
-    label of 10 different test values. Then the mean squared error over this test is displayed.
+    predict the label of 10 different test values. Then the mean squared error over
+    this test is displayed.
     """
     X = np.arange(-1.0, 1.0, 0.005)
     y = np.sin(X)

machine_learning/logistic_regression.py

@@ -1,6 +1,6 @@
 #!/usr/bin/python
 
-## Logistic Regression from scratch
+# Logistic Regression from scratch
 
 # In[62]:
 
@@ -8,8 +8,12 @@
 
 # importing all the required libraries
 
-""" Implementing logistic regression for classification problem
+"""
-     Helpful resources : 1.Coursera ML course    2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac"""
+Implementing logistic regression for classification problem
+Helpful resources:
+Coursera ML course
+https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
+"""
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -21,7 +25,8 @@ from sklearn import datasets
 
 # In[67]:
 
-# sigmoid function or logistic function is used as a hypothesis function in classification problems
+# sigmoid function or logistic function is used as a hypothesis function in
+# classification problems
 
 
 def sigmoid_function(z):

machine_learning/support_vector_machines.py

@@ -3,6 +3,7 @@ from sklearn import svm
 from sklearn.model_selection import train_test_split
 import doctest
 
+
 # different functions implementing different types of SVM's
 def NuSVC(train_x, train_y):
     svc_NuSVC = svm.NuSVC()
@@ -17,8 +18,11 @@ def Linearsvc(train_x, train_y):
 
 
 def SVC(train_x, train_y):
-    # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)
+    # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
-    # various parameters like "kernel","gamma","C" can effectively tuned for a given machine learning model.
+    # probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
+    # max_iter=-1, random_state=None)
+    # various parameters like "kernel","gamma","C" can effectively tuned for a given
+    # machine learning model.
     SVC = svm.SVC(gamma="auto")
     SVC.fit(train_x, train_y)
     return SVC
@@ -27,8 +31,8 @@ def SVC(train_x, train_y):
 def test(X_new):
     """
     3 test cases to be passed
-    an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm)
+    an array containing the sepal length (cm), sepal width (cm), petal length (cm),
-    based on which  the target name will be predicted
+    petal width (cm) based on which  the target name will be predicted
     >>> test([1,2,1,4])
     'virginica'
     >>> test([5, 2, 4, 1])

maths/pi_monte_carlo_estimation.py

@@ -23,9 +23,11 @@ class Point:
 
 def estimate_pi(number_of_simulations: int) -> float:
     """
-    Generates an estimate of the mathematical constant PI (see https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview).
+    Generates an estimate of the mathematical constant PI.
+    See https://en.wikipedia.org/wiki/Monte_Carlo_method#Overview
 
-    The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
+    The estimate is generated by Monte Carlo simulations. Let U be uniformly drawn from
+    the unit square [0, 1) x [0, 1). The probability that U lies in the unit circle is:
 
         P[U in unit circle] = 1/4 PI
 
@@ -33,10 +35,12 @@ def estimate_pi(number_of_simulations: int) -> float:
 
         PI = 4 * P[U in unit circle]
 
-    We can get an estimate of the probability P[U in unit circle] (see https://en.wikipedia.org/wiki/Empirical_probability) by:
+    We can get an estimate of the probability P[U in unit circle].
+    See https://en.wikipedia.org/wiki/Empirical_probability by:
 
         1. Draw a point uniformly from the unit square.
-        2. Repeat the first step n times and count the number of points in the unit circle, which is called m.
+        2. Repeat the first step n times and count the number of points in the unit
+            circle, which is called m.
         3. An estimate of P[U in unit circle] is m/n
     """
     if number_of_simulations < 1:

maths/zellers_congruence.py

@@ -147,7 +147,10 @@ if __name__ == "__main__":
 
     doctest.testmod()
     parser = argparse.ArgumentParser(
-        description="Find out what day of the week nearly any date is or was. Enter date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
+        description=(
+            "Find out what day of the week nearly any date is or was. Enter "
+            "date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
+        )
     )
     parser.add_argument(
         "date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"

matrix/matrix_class.py

@@ -3,7 +3,8 @@
 
 class Matrix:
     """
-    Matrix object generated from a 2D array where each element is an array representing a row.
+    Matrix object generated from a 2D array where each element is an array representing
+    a row.
     Rows can contain type int or float.
     Common operations and information available.
     >>> rows = [
@@ -33,7 +34,8 @@ class Matrix:
     >>> matrix.is_invertable()
     False
 
-    Identity, Minors, Cofactors and Adjugate are returned as Matrices.  Inverse can be a Matrix or Nonetype
+    Identity, Minors, Cofactors and Adjugate are returned as Matrices.  Inverse can be
+    a Matrix or Nonetype
     >>> print(matrix.identity())
     [[1. 0. 0.]
      [0. 1. 0.]
@@ -46,7 +48,8 @@ class Matrix:
     [[-3. 6. -3.]
      [6. -12. 6.]
      [-3. 6. -3.]]
-    >>> print(matrix.adjugate()) # won't be apparent due to the nature of the cofactor matrix
+    >>>  # won't be apparent due to the nature of the cofactor matrix
+    >>> print(matrix.adjugate())
     [[-3. 6. -3.]
      [6. -12. 6.]
      [-3. 6. -3.]]
@@ -57,7 +60,8 @@ class Matrix:
     >>> matrix.determinant()
     0
 
-    Negation, scalar multiplication, addition, subtraction, multiplication and exponentiation are available and all return a Matrix
+    Negation, scalar multiplication, addition, subtraction, multiplication and
+    exponentiation are available and all return a Matrix
     >>> print(-matrix)
     [[-1. -2. -3.]
      [-4. -5. -6.]
@@ -102,8 +106,9 @@ class Matrix:
 
     def __init__(self, rows):
         error = TypeError(
-            "Matrices must be formed from a list of zero or more lists containing at least "
+            "Matrices must be formed from a list of zero or more lists containing at "
-            "one and the same number of values, each of which must be of type int or float."
+            "least one and the same number of values, each of which must be of type "
+            "int or float."
         )
         if len(rows) != 0:
             cols = len(rows[0])
@@ -159,10 +164,8 @@ class Matrix:
             )
         else:
             return sum(
-                [
+                self.rows[0][column] * self.cofactors().rows[0][column]
-                    self.rows[0][column] * self.cofactors().rows[0][column]
+                for column in range(self.num_columns)
-                    for column in range(self.num_columns)
-                ]
             )
 
     def is_invertable(self):
@@ -346,7 +349,7 @@ class Matrix:
 
     @classmethod
     def dot_product(cls, row, column):
-        return sum([row[i] * column[i] for i in range(len(row))])
+        return sum(row[i] * column[i] for i in range(len(row)))
 
 
 if __name__ == "__main__":

other/password_generator.py

@@ -30,7 +30,8 @@ def alternative_password_generator(ctbi, i):
     i = i - len(ctbi)
     quotient = int(i / 3)
     remainder = i % 3
-    # chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) + random_number(digits, i / 3) + random_characters(punctuation, i / 3)
+    # chars = ctbi + random_letters(ascii_letters, i / 3 + remainder) +
+    #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
     chars = (
         ctbi
         + random(ascii_letters, quotient + remainder)

other/sierpinski_triangle.py

@@ -5,11 +5,14 @@
 Simple example of Fractal generation using recursive function.
 
 What is Sierpinski Triangle?
->>The Sierpinski triangle (also with the original orthography Sierpinski), also called the Sierpinski gasket or the Sierpinski Sieve, 
+>>The Sierpinski triangle (also with the original orthography Sierpinski), also called
-is a fractal and attractive fixed set with the overall shape of an equilateral triangle, subdivided recursively into smaller 
+the Sierpinski gasket or the Sierpinski Sieve, is a fractal and attractive fixed set
-equilateral triangles. Originally constructed as a curve, this is one of the basic examples of self-similar sets, i.e., 
+with the overall shape of an equilateral triangle, subdivided recursively into smaller
-it is a mathematically generated pattern that can be reproducible at any magnification or reduction. It is named after 
+equilateral triangles. Originally constructed as a curve, this is one of the basic
-the Polish mathematician Wacław Sierpinski, but appeared as a decorative pattern many centuries prior to the work of Sierpinski.
+examples of self-similar sets, i.e., it is a mathematically generated pattern that can
+be reproducible at any magnification or reduction. It is named after the Polish
+mathematician Wacław Sierpinski, but appeared as a decorative pattern many centuries
+prior to the work of Sierpinski.
 
 Requirements(pip):
   - turtle
@@ -20,7 +23,8 @@ Python:
 Usage:
   - $python sierpinski_triangle.py <int:depth_for_fractal>
 
-Credits: This code was written by editing the code from http://www.riannetrujillo.com/blog/python-fractal/
+Credits: This code was written by editing the code from
+http://www.riannetrujillo.com/blog/python-fractal/
 
 """
 import turtle

project_euler/problem_99/sol1.py

@@ -1,11 +1,14 @@
 """
 Problem:
 
-Comparing two numbers written in index form like 2'11 and 3'7 is not difficult, as any calculator would confirm that 2^11 = 2048 < 3^7 = 2187.
+Comparing two numbers written in index form like 2'11 and 3'7 is not difficult, as any
+calculator would confirm that 2^11 = 2048 < 3^7 = 2187.
 
-However, confirming that 632382^518061 > 519432^525806 would be much more difficult, as both numbers contain over three million digits.
+However, confirming that 632382^518061 > 519432^525806 would be much more difficult, as
+both numbers contain over three million digits.
 
-Using base_exp.txt, a 22K text file containing one thousand lines with a base/exponent pair on each line, determine which line number has the greatest numerical value.
+Using base_exp.txt, a 22K text file containing one thousand lines with a base/exponent
+pair on each line, determine which line number has the greatest numerical value.
 
 NOTE: The first two lines in the file represent the numbers in the example given above.
 """

searches/binary_search.py

@@ -14,15 +14,18 @@ import bisect
 
 def bisect_left(sorted_collection, item, lo=0, hi=None):
     """
-    Locates the first element in a sorted array that is larger or equal to a given value.
+    Locates the first element in a sorted array that is larger or equal to a given
+    value.
 
-    It has the same interface as https://docs.python.org/3/library/bisect.html#bisect.bisect_left .
+    It has the same interface as
+    https://docs.python.org/3/library/bisect.html#bisect.bisect_left .
 
     :param sorted_collection: some ascending sorted collection with comparable items
     :param item: item to bisect
     :param lo: lowest index to consider (as in sorted_collection[lo:hi])
     :param hi: past the highest index to consider (as in sorted_collection[lo:hi])
-    :return: index i such that all values in sorted_collection[lo:i] are < item and all values in sorted_collection[i:hi] are >= item.
+    :return: index i such that all values in sorted_collection[lo:i] are < item and all
+        values in sorted_collection[i:hi] are >= item.
 
     Examples:
     >>> bisect_left([0, 5, 7, 10, 15], 0)
@@ -57,13 +60,15 @@ def bisect_right(sorted_collection, item, lo=0, hi=None):
     """
     Locates the first element in a sorted array that is larger than a given value.
 
-    It has the same interface as https://docs.python.org/3/library/bisect.html#bisect.bisect_right .
+    It has the same interface as
+    https://docs.python.org/3/library/bisect.html#bisect.bisect_right .
 
     :param sorted_collection: some ascending sorted collection with comparable items
     :param item: item to bisect
     :param lo: lowest index to consider (as in sorted_collection[lo:hi])
     :param hi: past the highest index to consider (as in sorted_collection[lo:hi])
-    :return: index i such that all values in sorted_collection[lo:i] are <= item and all values in sorted_collection[i:hi] are > item.
+    :return: index i such that all values in sorted_collection[lo:i] are <= item and
+        all values in sorted_collection[i:hi] are > item.
 
     Examples:
     >>> bisect_right([0, 5, 7, 10, 15], 0)
@@ -98,7 +103,8 @@ def insort_left(sorted_collection, item, lo=0, hi=None):
     """
     Inserts a given value into a sorted array before other values with the same value.
 
-    It has the same interface as https://docs.python.org/3/library/bisect.html#bisect.insort_left .
+    It has the same interface as
+    https://docs.python.org/3/library/bisect.html#bisect.insort_left .
 
     :param sorted_collection: some ascending sorted collection with comparable items
     :param item: item to insert
@@ -138,7 +144,8 @@ def insort_right(sorted_collection, item, lo=0, hi=None):
     """
     Inserts a given value into a sorted array after other values with the same value.
 
-    It has the same interface as https://docs.python.org/3/library/bisect.html#bisect.insort_right .
+    It has the same interface as
+    https://docs.python.org/3/library/bisect.html#bisect.insort_right .
 
     :param sorted_collection: some ascending sorted collection with comparable items
     :param item: item to insert

web_programming/slack_message.py

@@ -14,5 +14,6 @@ def send_slack_message(message_body: str, slack_url: str) -> None:
 
 
 if __name__ == "main":
-    # Set the slack url to the one provided by Slack when you create the webhook at https://my.slack.com/services/new/incoming-webhook/
+    # Set the slack url to the one provided by Slack when you create the webhook at
+    # https://my.slack.com/services/new/incoming-webhook/
     send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")