Create codespell.yml

.github/workflows/codespell.yml

@@ -9,4 +9,6 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v1
       - run: pip install codespell flake8
-      - run: codespell -L ans --skip="./.*,./other/dictionary.txt,./other/words,./project_euler/problem_22/p022_names.txt,*.jpg,*.png,*.jpeg,*.gif"
+      - run: |
+          SKIP="./.*,./other/dictionary.txt,./other/words,./project_euler/problem_22/p022_names.txt,*.bak,*.gif,*.jpeg,*.jpg,*.json,*.png,*.pyc"
+          codespell -L ans,fo,hist,iff,secant,tim --skip=$SKIP

DIRECTORY.md

@@ -177,14 +177,14 @@
   * [Longest Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py)
   * [Matrix Chain Order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py)
   * [Max Sub Array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py)
-  * [Max Sum Contigous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contigous_subsequence.py)
+  * [Max Sum Contiguous Subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sum_contiguous_subsequence.py)
   * [Minimum Partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py)
   * [Rod Cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py)
   * [Subset Generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py)
   * [Sum Of Subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py)
 
 ## File Transfer
-  * [Recieve File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/recieve_file.py)
+  * [Receive File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/receive_file.py)
   * [Send File](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py)
 
 ## Fuzzy Logic
@@ -219,7 +219,7 @@
   * [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)
   * [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
-  * [Multi Hueristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_hueristic_astar.py)
+  * [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
   * [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
   * [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
   * [Scc Kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py)
@@ -511,6 +511,7 @@
   * [Quick Select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py)
   * [Sentinel Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py)
   * [Simple-Binary-Search](https://github.com/TheAlgorithms/Python/blob/master/searches/simple-binary-search.py)
+  * [Simulated Annealing](https://github.com/TheAlgorithms/Python/blob/master/searches/simulated_annealing.py)
   * [Tabu Search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py)
   * [Ternary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py)
 
@@ -567,7 +568,7 @@
   * [Reverse Words](https://github.com/TheAlgorithms/Python/blob/master/strings/reverse_words.py)
   * [Split](https://github.com/TheAlgorithms/Python/blob/master/strings/split.py)
   * [Upper](https://github.com/TheAlgorithms/Python/blob/master/strings/upper.py)
-  * [Word Occurence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurence.py)
+  * [Word Occurrence](https://github.com/TheAlgorithms/Python/blob/master/strings/word_occurrence.py)
 
 ## Traversals
   * [Binary Tree Traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py)

backtracking/n_queens.py

@@ -40,8 +40,8 @@ def isSafe(board, row, column):
 
 def solve(board, row):
     """
-    It creates a state space tree and calls the safe function untill it receives a 
-    False Boolean and terminates that brach 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 
     poosible solution branch.
     """
     if row >= len(board):
@@ -58,7 +58,7 @@ def solve(board, row):
         """
         For every row it iterates through each column to check if it is feesible to place a 
         queen there.
-        If all the combinations for that particaular branch are successfull 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):
@@ -70,7 +70,7 @@ def solve(board, row):
 
 def printboard(board):
     """
-    Prints the boards that have a successfull combination.
+    Prints the boards that have a successful combination.
     """
     for i in range(len(board)):
         for j in range(len(board)):

ciphers/hill_cipher.py

@@ -3,15 +3,15 @@
 Hill Cipher:
 The below defined class 'HillCipher' implements the Hill Cipher algorithm.
 The Hill Cipher is an algorithm that implements modern linear algebra techniques
-In this algortihm, you have an encryption key matrix. This is what will be used
+In this algorithm, you have an encryption key matrix. This is what will be used
 in encoding and decoding your text.
 
-Algortihm:
+Algorithm:
 Let the order of the encryption key be N (as it is a square matrix).
 Your text is divided into batches of length N and converted to numerical vectors
 by a simple mapping starting with A=0 and so on.
 
-The key is then mulitplied with the newly created batch vector to obtain the
+The key is then multiplied with the newly created batch vector to obtain the
 encoded vector. After each multiplication modular 36 calculations are performed
 on the vectors so as to bring the numbers between 0 and 36 and then mapped with
 their corresponding alphanumerics.

ciphers/rsa_cipher.py

@@ -6,7 +6,7 @@
 
 def main():
     filename = "encrypted_file.txt"
-    response = input(r"Encrypte\Decrypt [e\d]: ")
+    response = input(r"Encrypt\Decrypt [e\d]: ")
 
     if response.lower().startswith("e"):
         mode = "encrypt"

ciphers/rsa_key_generator.py

@@ -42,12 +42,12 @@ def makeKeyFiles(name, keySize):
 
     publicKey, privateKey = generateKey(keySize)
     print("\nWriting public key to file %s_pubkey.txt..." % name)
-    with open("%s_pubkey.txt" % name, "w") as fo:
-        fo.write("{},{},{}".format(keySize, publicKey[0], publicKey[1]))
+    with open("%s_pubkey.txt" % name, "w") as out_file:
+        out_file.write("{},{},{}".format(keySize, publicKey[0], publicKey[1]))
 
     print("Writing private key to file %s_privkey.txt..." % name)
-    with open("%s_privkey.txt" % name, "w") as fo:
-        fo.write("{},{},{}".format(keySize, privateKey[0], privateKey[1]))
+    with open("%s_privkey.txt" % name, "w") as out_file:
+        out_file.write("{},{},{}".format(keySize, privateKey[0], privateKey[1]))
 
 
 if __name__ == "__main__":

compression/burrows_wheeler.py

@@ -157,11 +157,11 @@ def reverse_bwt(bwt_string: str, idx_original_string: int) -> str:
     entry_msg = "Provide a string that I will generate its BWT transform: "
     s = input(entry_msg).strip()
     result = bwt_transform(s)
-    bwt_output_msg = "Burrows Wheeler tranform for string '{}' results in '{}'"
+    bwt_output_msg = "Burrows Wheeler transform for string '{}' results in '{}'"
     print(bwt_output_msg.format(s, result["bwt_string"]))
     original_string = reverse_bwt(result["bwt_string"], result["idx_original_string"])
     fmt = (
-        "Reversing Burrows Wheeler tranform for entry '{}' we get original"
+        "Reversing Burrows Wheeler transform for entry '{}' we get original"
         " string '{}'"
     )
     print(fmt.format(result["bwt_string"], original_string))

compression/huffman.py

@@ -21,7 +21,7 @@ def __init__(self, freq, left, right):
 def parse_file(file_path):
     """
     Read the file and build a dict of all letters and their
-    frequences, then convert the dict into a list of Letters.
+    frequencies, then convert the dict into a list of Letters.
     """
     chars = {}
     with open(file_path) as f:

compression/peak_signal_to_noise_ratio.py

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

conversions/decimal_to_octal.py

@@ -24,7 +24,7 @@ def decimal_to_octal(num: int) -> str:
 
 
 def main():
-    """Print octal equivelents of decimal numbers."""
+    """Print octal equivalents of decimal numbers."""
     print("\n2 in octal is:")
     print(decimal_to_octal(2))  # = 2
     print("\n8 in octal is:")

data_structures/binary_tree/binary_search_tree.py

@@ -15,7 +15,7 @@ def __repr__(self):
 
         if self.left is None and self.right is None:
             return str(self.value)
-        return pformat({"%s" % (self.value): (self.left, self.right)}, indent=1,)
+        return pformat({"%s" % (self.value): (self.left, self.right)}, indent=1)
 
 
 class BinarySearchTree:

data_structures/binary_tree/lowest_common_ancestor.py

@@ -11,7 +11,7 @@ def swap(a, b):
     return a, b
 
 
-# creating sparse table which saves each nodes 2^ith parent
+# creating sparse table which saves each nodes 2^i-th parent
 def creatSparse(max_node, parent):
     j = 1
     while (1 << j) < max_node:

data_structures/binary_tree/number_of_possible_binary_trees.py

@@ -41,7 +41,7 @@ def binomial_coefficient(n: int, k: int) -> int:
 
 def catalan_number(node_count: int) -> int:
     """
-    We can find Catalan number many ways but here we use Binomial Coefficent because it
+    We can find Catalan number many ways but here we use Binomial Coefficient because it
     does the job in O(n)
 
     return the Catalan number of n using 2nCn/(n+1).

data_structures/binary_tree/red_black_tree.py

@@ -12,7 +12,7 @@ class RedBlackTree:
     less strict, so it will perform faster for writing/deleting nodes
     and slower for reading in the average case, though, because they're
     both balanced binary search trees, both will get the same asymptotic
-    perfomance.
+    performance.
     To read more about them, https://en.wikipedia.org/wiki/Red–black_tree
     Unless otherwise specified, all asymptotic runtimes are specified in
     terms of the size of the tree.
@@ -37,7 +37,7 @@ def __init__(self, label=None, color=0, parent=None, left=None, right=None):
     def rotate_left(self):
         """Rotate the subtree rooted at this node to the left and
         returns the new root to this subtree.
-        Perfoming one rotation can be done in O(1).
+        Performing one rotation can be done in O(1).
         """
         parent = self.parent
         right = self.right
@@ -656,7 +656,7 @@ def test_tree_traversal():
 
 
 def test_tree_chaining():
-    """Tests the three different tree chaning functions."""
+    """Tests the three different tree chaining functions."""
     tree = RedBlackTree(0)
     tree = tree.insert(-16).insert(16).insert(8).insert(24).insert(20).insert(22)
     if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]:

data_structures/binary_tree/treap.py

@@ -21,7 +21,7 @@ def __repr__(self):
             return f"'{self.value}: {self.prior:.5}'"
         else:
             return pformat(
-                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1,
+                {f"{self.value}: {self.prior:.5}": (self.left, self.right)}, indent=1
             )
 
     def __str__(self):
@@ -161,7 +161,7 @@ def main():
     """After each command, program prints treap"""
     root = None
     print(
-        "enter numbers to creat a tree, + value to add value into treap, - value to erase all nodes with value. 'q' to quit. "
+        "enter numbers to create a tree, + value to add value into treap, - value to erase all nodes with value. 'q' to quit. "
     )
 
     args = input()

data_structures/heap/binomial_heap.py

@@ -49,7 +49,7 @@ class BinomialHeap:
     r"""
     Min-oriented priority queue implemented with the Binomial Heap data
     structure implemented with the BinomialHeap class. It supports:
-        - Insert element in a heap with n elemnts: Guaranteed logn, amoratized 1
+        - Insert element in a heap with n elements: Guaranteed logn, amoratized 1
         - Merge (meld) heaps of size m and n: O(logn + logm)
         - Delete Min: O(logn)
         - Peek (return min without deleting it): O(1)

data_structures/linked_list/deque_doubly.py

@@ -3,7 +3,7 @@
 Operations:
     1. insertion in the front -> O(1)
     2. insertion in the end -> O(1)
-    3. remove fron the front -> O(1)
+    3. remove from the front -> O(1)
     4. remove from the end -> O(1)
 """
 

data_structures/linked_list/doubly_linked_list.py

@@ -3,7 +3,7 @@
 - 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.
- - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficent"""
+ - 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

data_structures/linked_list/singly_linked_list.py

@@ -79,7 +79,7 @@ def __repr__(self):  # String representation/visualization of a Linked Lists
         # END represents end of the LinkedList
         return string_repr + "END"
 
-    # Indexing Support. Used to get a node at particaular position
+    # Indexing Support. Used to get a node at particular position
     def __getitem__(self, index):
         current = self.head
 

data_structures/stacks/postfix_evaluation.py

@@ -54,7 +54,7 @@ def Solve(Postfix):
 
             Stack.append(
                 str(Opr[x](int(A), int(B)))
-            )  # evaluate the 2 values poped from stack & push result to stack
+            )  # evaluate the 2 values popped from stack & push result to stack
             print(
                 x.rjust(8),
                 ("push(" + A + x + B + ")").ljust(12),

data_structures/stacks/stock_span_problem.py

@@ -21,7 +21,7 @@ def calculateSpan(price, S):
     # Calculate span values for rest of the elements
     for i in range(1, n):
 
-        # Pop elements from stack whlie stack is not
+        # Pop elements from stack while stack is not
         # empty and top of stack is smaller than price[i]
         while len(st) > 0 and price[st[0]] <= price[i]:
             st.pop()

digital_image_processing/index_calculation.py

@@ -541,7 +541,7 @@ def NDRE(self):
 # instantiating the class with the values
 #cl = indexCalculation(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
 
-# how set the values after instantiate the class cl, (for update the data or when dont 
+# how set the values after instantiate the class cl, (for update the data or when don't
 # instantiating the class with the values)
 cl.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
 
@@ -551,8 +551,8 @@ def NDRE(self):
                                      redEdge=redEdge, nir=nir).astype(np.float64)
 indexValue_form2    = cl.CCCI()
 
-# calculating the index with the values directly -- you can set just the values preferred -- 
-# note: the *calculation* fuction performs the function *setMatrices*
+# calculating the index with the values directly -- you can set just the values
+# preferred  note: the *calculation* function performs the function *setMatrices*
 indexValue_form3    = cl.calculation("CCCI", red=red, green=green, blue=blue,
                                      redEdge=redEdge, nir=nir).astype(np.float64)
 

digital_image_processing/test_digital_image_processing.py

@@ -42,7 +42,7 @@ def test_gen_gaussian_kernel():
 # canny.py
 def test_canny():
     canny_img = imread("digital_image_processing/image_data/lena_small.jpg", 0)
-    # assert ambiguos array for all == True
+    # assert ambiguous array for all == True
     assert canny_img.all()
     canny_array = canny.canny(canny_img)
     # assert canny array for at least one True

dynamic_programming/bitmask.py

@@ -42,7 +42,7 @@ def CountWaysUtil(self, mask, taskno):
         if self.dp[mask][taskno] != -1:
             return self.dp[mask][taskno]
 
-        # Number of ways when we dont this task in the arrangement
+        # Number of ways when we don't this task in the arrangement
         total_ways_util = self.CountWaysUtil(mask, taskno + 1)
 
         # now assign the tasks one by one to all possible persons and recursively assign for the remaining tasks.

dynamic_programming/knapsack.py

@@ -49,9 +49,9 @@ def knapsack_with_example_solution(W: int, wt: list, val: list):
 
     W: int, the total maximum weight for the given knapsack problem.
     wt: list, the vector of weights for all items where wt[i] is the weight
-    of the ith item.
+    of the i-th item.
     val: list, the vector of values for all items where val[i] is the value
-    of te ith item
+    of the i-th item
 
     Returns
     -------

dynamic_programming/longest_sub_array.py

@@ -1,5 +1,5 @@
 """
-Auther  : Yvonne
+Author  : Yvonne
 
 This is a pure Python implementation of Dynamic Programming solution to the longest_sub_array problem.
 

dynamic_programming/subset_generation.py

@@ -1,10 +1,10 @@
-# python program to print all subset combination of n element in given set of r element .
+# Python program to print all subset combinations of n element in given set of r element.
 # arr[]  ---> Input Array
 # data[] ---> Temporary array to store current combination
 # start & end ---> Staring and Ending indexes in arr[]
 # index  ---> Current index in data[]
 # r ---> Size of a combination to be printed
-def combinationUtil(arr, n, r, index, data, i):
+def combination_util(arr, n, r, index, data, i):
     # Current combination is ready to be printed,
     # print it
     if index == r:
@@ -15,29 +15,26 @@ def combinationUtil(arr, n, r, index, data, i):
     #  When no more elements are there to put in data[]
     if i >= n:
         return
-    # current is included, put next at next
-    # location
+    # current is included, put next at next location
     data[index] = arr[i]
-    combinationUtil(arr, n, r, index + 1, data, i + 1)
+    combination_util(arr, n, r, index + 1, data, i + 1)
     # current is excluded, replace it with
     # next (Note that i+1 is passed, but
     # index is not changed)
-    combinationUtil(arr, n, r, index, data, i + 1)
+    combination_util(arr, n, r, index, data, i + 1)
     # The main function that prints all combinations
     # of size r in arr[] of size n. This function
     # mainly uses combinationUtil()
 
 
-def printcombination(arr, n, r):
-    # A temporary array to store all combination
-    # one by one
+def print_combination(arr, n, r):
+    # A temporary array to store all combination one by one
     data = [0] * r
-    # Print all combination using temprary
-    # array 'data[]'
-    combinationUtil(arr, n, r, 0, data, 0)
+    # Print all combination using temporary array 'data[]'
+    combination_util(arr, n, r, 0, data, 0)
 
 
 # Driver function to check for above function
 arr = [10, 20, 30, 40, 50]
-printcombination(arr, len(arr), 3)
+print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu