Set the Python file maximum line length to 88 characters (TheAlgorith…

…ms#2122)

* flake8 --max-line-length=88

* fixup! Format Python code with psf/black push

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

.travis.yml

@@ -10,7 +10,7 @@ notifications:
     on_success: never
 before_script:
   - black --check . || true
-  - flake8 --ignore=E203,W503 --max-complexity=25 --max-line-length=120 --statistics --count .
+  - flake8 --ignore=E203,W503 --max-complexity=25 --max-line-length=88 --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:

arithmetic_analysis/intersection.py

@@ -1,9 +1,11 @@
 import math
 
 
-def intersection(
-    function, x0, x1
-):  # function is the f we want to find its root and x0 and x1 are two random starting points
+def intersection(function, x0, x1):
+    """
+    function is the f we want to find its root
+    x0 and x1 are two random starting points
+    """
     x_n = x0
     x_n1 = x1
     while True:

backtracking/hamiltonian_cycle.py

@@ -16,8 +16,8 @@ def valid_connection(
     Checks whether it is possible to add next into path by validating 2 statements
     1. There should be path between current and next vertex
     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
+    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],
@@ -52,7 +52,8 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int)
     Pseudo-Code
     Base Case:
     1. Chceck if we visited all of vertices
-        1.1 If last visited vertex has path to starting vertex return True either return False
+        1.1 If last visited vertex has path to starting vertex return True either
+            return False
     Recursive Step:
     2. Iterate over each vertex
         Check if next vertex is valid for transiting from current vertex
@@ -74,7 +75,8 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int)
     >>> print(path)
     [0, 1, 2, 4, 3, 0]
 
-    Case 2: Use exact graph as in previous case, but in the properties taken from middle of calculation
+    Case 2: Use exact graph as in previous case, but in the properties taken from
+        middle of calculation
     >>> graph = [[0, 1, 0, 1, 0],
     ...          [1, 0, 1, 1, 1],
     ...          [0, 1, 0, 0, 1],

backtracking/n_queens.py

@@ -12,8 +12,8 @@
 
 def isSafe(board, row, column):
     """
-    This function returns a boolean value True if it is safe to place a queen there considering
-    the current state of the board.
+    This function returns a boolean value True if it is safe to place a queen there
+    considering the current state of the board.
 
     Parameters :
     board(2D matrix) : board
@@ -56,8 +56,8 @@ 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
-        queen there.
+        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
         reinitialized for the next possible combination.
         """

backtracking/sum_of_subsets.py

@@ -1,9 +1,10 @@
 """
-        The sum-of-subsetsproblem states that a set of non-negative integers, and a value M,
-        determine all possible subsets of the given set whose summation sum equal to given M.
+        The sum-of-subsetsproblem states that a set of non-negative integers, and a
+        value M, determine all possible subsets of the given set whose summation sum
+        equal to given M.
 
-        Summation of the chosen numbers must be equal to given number M and one number can
-        be used only once.
+        Summation of the chosen numbers must be equal to given number M and one number
+        can be used only once.
 """
 
 
@@ -21,7 +22,8 @@ def create_state_space_tree(nums, max_sum, num_index, path, result, remaining_nu
         Creates a state space tree to iterate through each branch using DFS.
         It terminates the branching of a node when any of the two conditions
         given below satisfy.
-        This algorithm follows depth-fist-search and backtracks when the node is not branchable.
+        This algorithm follows depth-fist-search and backtracks when the node is not
+        branchable.
 
         """
     if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum:

blockchain/chinese_remainder_theorem.py

@@ -1,8 +1,9 @@
 # Chinese Remainder Theorem:
 # GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
 
-# If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b there exists integer n,
-# such that n = ra (mod a) and n = ra(mod b).  If n1 and n2 are two such integers, then n1=n2(mod ab)
+# If GCD(a,b) = 1, then for any remainder ra modulo a and any remainder rb modulo b
+# there exists integer n, such that n = ra (mod a) and n = ra(mod b).  If n1 and n2 are
+# two such integers, then n1=n2(mod ab)
 
 # Algorithm :
 

blockchain/diophantine_equation.py

@@ -1,5 +1,6 @@
-# Diophantine Equation : Given integers a,b,c ( at least one of a and b != 0), the diophantine equation
-# a*x + b*y = c has a solution (where x and y are integers) iff gcd(a,b) divides c.
+# Diophantine Equation : Given integers a,b,c ( at least one of a and b != 0), the
+# diophantine equation a*x + b*y = c has a solution (where x and y are integers)
+# iff gcd(a,b) divides c.
 
 # GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
 
@@ -29,8 +30,9 @@ def diophantine(a, b, c):
 
 # Finding All solutions of Diophantine Equations:
 
-# Theorem : Let gcd(a,b) = d, a = d*p, b = d*q. If (x0,y0) is a solution of Diophantine Equation a*x + b*y = c.
-# a*x0 + b*y0 = c, then all the solutions have the form a(x0 + t*q) + b(y0 - t*p) = c, where t is an arbitrary integer.
+# Theorem : Let gcd(a,b) = d, a = d*p, b = d*q. If (x0,y0) is a solution of Diophantine
+# Equation a*x + b*y = c.  a*x0 + b*y0 = c, then all the solutions have the form
+# a(x0 + t*q) + b(y0 - t*p) = c, where t is an arbitrary integer.
 
 # n is the number of solution you want, n = 2 by default
 
@@ -75,8 +77,9 @@ def greatest_common_divisor(a, b):
     >>> greatest_common_divisor(7,5)
     1
 
-    Note : In number theory, two integers a and b are said to be relatively prime, mutually prime, or co-prime
-           if the only positive integer (factor) that divides both of them is 1  i.e., gcd(a,b) = 1.
+    Note : In number theory, two integers a and b are said to be relatively prime,
+           mutually prime, or co-prime if the only positive integer (factor) that
+           divides both of them is 1  i.e., gcd(a,b) = 1.
 
     >>> greatest_common_divisor(121, 11)
     11
@@ -91,7 +94,8 @@ def greatest_common_divisor(a, b):
     return b
 
 
-# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x and y, then d = gcd(a,b)
+# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers
+# x and y, then d = gcd(a,b)
 
 
 def extended_gcd(a, b):

blockchain/modular_division.py

@@ -3,8 +3,8 @@
 
 # GCD ( Greatest Common Divisor ) or HCF ( Highest Common Factor )
 
-# Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should return an integer x such that
-#        0≤x≤n−1, and  b/a=x(modn) (that is, b=ax(modn)).
+# Given three integers a, b, and n, such that gcd(a,n)=1 and n>1, the algorithm should
+# return an integer x such that 0≤x≤n−1, and  b/a=x(modn) (that is, b=ax(modn)).
 
 # Theorem:
 # a has a multiplicative inverse modulo n iff gcd(a,n) = 1
@@ -68,7 +68,8 @@ def modular_division2(a, b, n):
     return x
 
 
-# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x and y, then d = gcd(a,b)
+# Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x
+# and y, then d = gcd(a,b)
 
 
 def extended_gcd(a, b):
@@ -123,8 +124,9 @@ def greatest_common_divisor(a, b):
     >>> greatest_common_divisor(7,5)
     1
 
-    Note : In number theory, two integers a and b are said to be relatively prime, mutually prime, or co-prime
-           if the only positive integer (factor) that divides both of them is 1  i.e., gcd(a,b) = 1.
+    Note : In number theory, two integers a and b are said to be relatively prime,
+        mutually prime, or co-prime if the only positive integer (factor) that divides
+        both of them is 1  i.e., gcd(a,b) = 1.
 
     >>> greatest_common_divisor(121, 11)
     11

ciphers/affine_cipher.py

@@ -55,7 +55,8 @@ def check_keys(keyA, keyB, mode):
 
 def encrypt_message(key: int, message: str) -> str:
     """
-    >>> encrypt_message(4545, 'The affine cipher is a type of monoalphabetic substitution cipher.')
+    >>> encrypt_message(4545, 'The affine cipher is a type of monoalphabetic '
+    ...                       'substitution cipher.')
     'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi'
     """
     keyA, keyB = divmod(key, len(SYMBOLS))
@@ -72,7 +73,8 @@ def encrypt_message(key: int, message: str) -> str:
 
 def decrypt_message(key: int, message: str) -> str:
     """
-    >>> decrypt_message(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF{xIp~{HL}Gi')
+    >>> decrypt_message(4545, 'VL}p MM{I}p~{HL}Gp{vp pFsH}pxMpyxIx JHL O}F{~pvuOvF{FuF'
+    ...                       '{xIp~{HL}Gi')
     'The affine cipher is a type of monoalphabetic substitution cipher.'
     """
     keyA, keyB = divmod(key, len(SYMBOLS))

ciphers/base64_cipher.py

@@ -39,7 +39,8 @@ def decode_base64(text):
     'WELCOME to base64 encoding 😁'
     >>> decode_base64('QcOF4ZCD8JCAj/CfpJM=')
     'AÅᐃ𐀏🤓'
-    >>> decode_base64("QUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFB\r\nQUFB")
+    >>> decode_base64("QUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUF"
+    ...               "BQUFBQUFBQUFB\r\nQUFB")
     'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'
     """
     base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

ciphers/elgamal_key_generator.py

@@ -16,7 +16,8 @@ def main():
 
 # I have written my code naively same as definition of primitive root
 # however every time I run this program, memory exceeded...
-# so I used 4.80 Algorithm in Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
+# so I used 4.80 Algorithm in
+# Handbook of Applied Cryptography(CRC Press, ISBN : 0-8493-8523-7, October 1996)
 # and it seems to run nicely!
 def primitiveRoot(p_val):
     print("Generating primitive root of p")

ciphers/hill_cipher.py

@@ -106,7 +106,8 @@ def check_determinant(self) -> None:
         req_l = len(self.key_string)
         if greatest_common_divisor(det, len(self.key_string)) != 1:
             raise ValueError(
-                f"determinant modular {req_l} of encryption key({det}) is not co prime w.r.t {req_l}.\nTry another key."
+                f"determinant modular {req_l} of encryption key({det}) is not co prime "
+                f"w.r.t {req_l}.\nTry another key."
             )
 
     def process_text(self, text: str) -> str:

ciphers/shuffled_shift_cipher.py

@@ -83,27 +83,30 @@ def __make_key_list(self) -> list:
         Shuffling only 26 letters of the english alphabet can generate 26!
         combinations for the shuffled list. In the program we consider, a set of
         97 characters (including letters, digits, punctuation and whitespaces),
-        thereby creating a possibility of 97! combinations (which is a 152 digit number in itself),
-        thus diminishing the possibility of a brute force approach. Moreover,
-        shift keys even introduce a multiple of 26 for a brute force approach
+        thereby creating a possibility of 97! combinations (which is a 152 digit number
+        in itself), thus diminishing the possibility of a brute force approach.
+        Moreover, shift keys even introduce a multiple of 26 for a brute force approach
         for each of the already 97! combinations.
         """
-        # key_list_options contain nearly all printable except few elements from string.whitespace
+        # key_list_options contain nearly all printable except few elements from
+        # string.whitespace
         key_list_options = (
             string.ascii_letters + string.digits + string.punctuation + " \t\n"
         )
 
         keys_l = []
 
-        # creates points known as breakpoints to break the key_list_options at those points and pivot each substring
+        # creates points known as breakpoints to break the key_list_options at those
+        # points and pivot each substring
         breakpoints = sorted(set(self.__passcode))
         temp_list = []
 
         # algorithm for creating a new shuffled list, keys_l, out of key_list_options
         for i in key_list_options:
             temp_list.extend(i)
 
-            # checking breakpoints at which to pivot temporary sublist and add it into keys_l
+            # checking breakpoints at which to pivot temporary sublist and add it into
+            # keys_l
             if i in breakpoints or i == key_list_options[-1]:
                 keys_l.extend(temp_list[::-1])
                 temp_list = []
@@ -131,7 +134,8 @@ def decrypt(self, encoded_message: str) -> str:
         """
         decoded_message = ""
 
-        # decoding shift like Caesar cipher algorithm implementing negative shift or reverse shift or left shift
+        # decoding shift like Caesar cipher algorithm implementing negative shift or
+        # reverse shift or left shift
         for i in encoded_message:
             position = self.__key_list.index(i)
             decoded_message += self.__key_list[
@@ -152,7 +156,8 @@ def encrypt(self, plaintext: str) -> str:
         """
         encoded_message = ""
 
-        # encoding shift like Caesar cipher algorithm implementing positive shift or forward shift or right shift
+        # encoding shift like Caesar cipher algorithm implementing positive shift or
+        # forward shift or right shift
         for i in plaintext:
             position = self.__key_list.index(i)
             encoded_message += self.__key_list[

compression/peak_signal_to_noise_ratio.py

@@ -1,6 +1,8 @@
 """
-        Peak signal-to-noise ratio - PSNR - https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-    Source: https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python/
+Peak signal-to-noise ratio - PSNR
+    https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
+Source:
+https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
 """
 
 import math

conversions/decimal_to_any.py

@@ -66,9 +66,10 @@ def decimal_to_any(num: int, base: int) -> str:
     if base > 36:
         raise ValueError("base must be <= 36")
     # fmt: off
-    ALPHABET_VALUES = {'10': 'A', '11': 'B', '12': 'C', '13': 'D', '14': 'E', '15': 'F', '16': 'G', '17': 'H',
-                       '18': 'I', '19': 'J', '20': 'K', '21': 'L', '22': 'M', '23': 'N', '24': 'O', '25': 'P',
-                       '26': 'Q', '27': 'R', '28': 'S', '29': 'T', '30': 'U', '31': 'V', '32': 'W', '33': 'X',
+    ALPHABET_VALUES = {'10': 'A', '11': 'B', '12': 'C', '13': 'D', '14': 'E', '15': 'F',
+                       '16': 'G', '17': 'H', '18': 'I', '19': 'J', '20': 'K', '21': 'L',
+                       '22': 'M', '23': 'N', '24': 'O', '25': 'P', '26': 'Q', '27': 'R',
+                       '28': 'S', '29': 'T', '30': 'U', '31': 'V', '32': 'W', '33': 'X',
                        '34': 'Y', '35': 'Z'}
     # fmt: on
     new_value = ""

conversions/decimal_to_hexadecimal.py

@@ -23,7 +23,8 @@
 
 def decimal_to_hexadecimal(decimal):
     """
-        take integer decimal value, return hexadecimal representation as str beginning with 0x
+        take integer decimal value, return hexadecimal representation as str beginning
+        with 0x
         >>> decimal_to_hexadecimal(5)
         '0x5'
         >>> decimal_to_hexadecimal(15)

conversions/decimal_to_octal.py

@@ -9,7 +9,8 @@
 def decimal_to_octal(num: int) -> str:
     """Convert a Decimal Number to an Octal Number.
 
-    >>> all(decimal_to_octal(i) == oct(i) for i in (0, 2, 8, 64, 65, 216, 255, 256, 512))
+    >>> all(decimal_to_octal(i) == oct(i) for i
+    ...     in (0, 2, 8, 64, 65, 216, 255, 256, 512))
     True
     """
     octal = 0

data_structures/data_structures/heap/heap_generic.py

@@ -1,5 +1,7 @@
 class Heap:
-    """A generic Heap class, can be used as min or max by passing the key function accordingly.
+    """
+    A generic Heap class, can be used as min or max by passing the key function
+    accordingly.
     """
 
     def __init__(self, key=None):
@@ -9,7 +11,8 @@ def __init__(self, key=None):
         self.pos_map = {}
         # Stores current size of heap.
         self.size = 0
-        # Stores function used to evaluate the score of an item on which basis ordering will be done.
+        # Stores function used to evaluate the score of an item on which basis ordering
+        # will be done.
         self.key = key or (lambda x: x)
 
     def _parent(self, i):
@@ -41,7 +44,10 @@ def _cmp(self, i, j):
         return self.arr[i][1] < self.arr[j][1]
 
     def _get_valid_parent(self, i):
-        """Returns index of valid parent as per desired ordering among given index and both it's children"""
+        """
+        Returns index of valid parent as per desired ordering among given index and
+        both it's children
+        """
         left = self._left(i)
         right = self._right(i)
         valid_parent = i
@@ -87,8 +93,8 @@ def delete_item(self, item):
         self.arr[index] = self.arr[self.size - 1]
         self.pos_map[self.arr[self.size - 1][0]] = index
         self.size -= 1
-        # Make sure heap is right in both up and down direction.
-        # Ideally only one of them will make any change- so no performance loss in calling both.
+        # Make sure heap is right in both up and down direction. Ideally only one
+        # of them will make any change- so no performance loss in calling both.
         if self.size > index:
             self._heapify_up(index)
             self._heapify_down(index)
@@ -109,7 +115,10 @@ def get_top(self):
         return self.arr[0] if self.size else None
 
     def extract_top(self):
-        """Returns top item tuple (Calculated value, item) from heap and removes it as well if present"""
+        """
+        Return top item tuple (Calculated value, item) from heap and removes it as well
+        if present
+        """
         top_item_tuple = self.get_top()
         if top_item_tuple:
             self.delete_item(top_item_tuple[0])