isort --profile black . (TheAlgorithms#2181)

* updating DIRECTORY.md

* isort --profile black .

* Black after

* updating DIRECTORY.md

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

.github/workflows/autoblack.yml

@@ -17,7 +17,7 @@ jobs:
         if: failure()
         run: |
           black .
-          isort --profile black --recursive .
+          isort --profile black .
           git config --global user.name github-actions
           git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'          
           git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY

DIRECTORY.md

@@ -253,6 +253,7 @@
   * [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
   * [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
   * [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
+  * [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
   * [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)
@@ -596,6 +597,7 @@
 
 ## Searches
   * [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
+  * [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
   * [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
   * [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
   * [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)

arithmetic_analysis/in_static_equilibrium.py

@@ -6,9 +6,10 @@
 mypy : passed
 """
 
-from numpy import array, cos, sin, radians, cross  # type: ignore
 from typing import List
 
+from numpy import array, cos, cross, radians, sin  # type: ignore
+
 
 def polar_force(
     magnitude: float, angle: float, radian_mode: bool = False

arithmetic_analysis/newton_raphson.py

@@ -2,9 +2,9 @@
 # Author: Syed Haseeb Shah (github.com/QuantumNovice)
 # The Newton-Raphson method (also known as Newton's method) is a way to
 # quickly find a good approximation for the root of a real-valued function
-
 from decimal import Decimal
 from math import *  # noqa: F401, F403
+
 from sympy import diff
 
 

ciphers/hill_cipher.py

@@ -35,8 +35,8 @@
 https://www.youtube.com/watch?v=4RhLNDqcjpA
 
 """
-
 import string
+
 import numpy
 
 

ciphers/playfair_cipher.py

@@ -1,5 +1,5 @@
-import string
 import itertools
+import string
 
 
 def chunker(seq, size):

compression/burrows_wheeler.py

@@ -10,7 +10,7 @@
 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 List, Dict
+from typing import Dict, List
 
 
 def all_rotations(s: str) -> List[str]:

computer_vision/harriscorner.py

@@ -1,5 +1,5 @@
-import numpy as np
 import cv2
+import numpy as np
 
 """
 Harris Corner Detector

data_structures/binary_tree/non_recursive_segment_tree.py

@@ -35,7 +35,7 @@
 >>> st.query(0, 2)
 [1, 2, 3]
 """
-from typing import List, Callable, TypeVar
+from typing import Callable, List, TypeVar
 
 T = TypeVar("T")
 

data_structures/binary_tree/segment_tree_other.py

@@ -3,9 +3,8 @@
 allowing queries to be done later in log(N) time
 function takes 2 values and returns a same type value
 """
-
-from queue import Queue
 from collections.abc import Sequence
+from queue import Queue
 
 
 class SegmentTreeNode(object):

data_structures/hashing/double_hash.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
-
 from hash_table import HashTable
-from number_theory.prime_numbers import next_prime, check_prime
+from number_theory.prime_numbers import check_prime, next_prime
 
 
 class DoubleHash(HashTable):

data_structures/hashing/hash_table_with_linked_list.py

@@ -1,6 +1,7 @@
-from hash_table import HashTable
 from collections import deque
 
+from hash_table import HashTable
+
 
 class HashTableWithLinkedList(HashTable):
     def __init__(self, *args, **kwargs):

digital_image_processing/convert_to_negative.py

@@ -1,8 +1,7 @@
 """
     Implemented an algorithm using opencv to convert a colored image into its negative
 """
-
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 def convert_to_negative(img):

digital_image_processing/dithering/burkes.py

@@ -1,8 +1,8 @@
 """
 Implementation Burke's algorithm (dithering)
 """
-from cv2 import destroyAllWindows, imread, imshow, waitKey
 import numpy as np
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 class Burkes:

digital_image_processing/edge_detection/canny.py

@@ -1,5 +1,6 @@
 import cv2
 import numpy as np
+
 from digital_image_processing.filters.convolve import img_convolve
 from digital_image_processing.filters.sobel_filter import sobel_filter
 

digital_image_processing/filters/bilateral_filter.py

@@ -9,11 +9,11 @@
 Output:
     img:A 2d zero padded image with values in between 0 and 1
 """
+import math
+import sys
 
 import cv2
 import numpy as np
-import math
-import sys
 
 
 def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:

digital_image_processing/filters/convolve.py

@@ -1,8 +1,8 @@
 # @Author  : lightXu
 # @File    : convolve.py
 # @Time    : 2019/7/8 0008 下午 16:13
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import array, zeros, ravel, pad, dot, uint8
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import array, dot, pad, ravel, uint8, zeros
 
 
 def im2col(image, block_size):

digital_image_processing/filters/gaussian_filter.py

@@ -1,10 +1,11 @@
 """
 Implementation of gaussian filter algorithm
 """
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
 from itertools import product
 
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros
+
 
 def gen_gaussian_kernel(k_size, sigma):
     center = k_size // 2

digital_image_processing/filters/median_filter.py

@@ -1,9 +1,8 @@
 """
 Implementation of median filter algorithm
 """
-
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import zeros_like, ravel, sort, multiply, divide, int8
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import divide, int8, multiply, ravel, sort, zeros_like
 
 
 def median_filter(gray_img, mask=3):

digital_image_processing/filters/sobel_filter.py

@@ -2,7 +2,8 @@
 # @File    : sobel_filter.py
 # @Time    : 2019/7/8 0008 下午 16:26
 import numpy as np
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+
 from digital_image_processing.filters.convolve import img_convolve
 
 

digital_image_processing/histogram_equalization/histogram_stretch.py

@@ -6,10 +6,9 @@
 import copy
 import os
 
-import numpy as np
-
 import cv2
-import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import pyplot as plt
 
 
 class contrastStretch:

digital_image_processing/resize/resize.py

@@ -1,6 +1,6 @@
 """ Multiple image resizing techniques """
 import numpy as np
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 class NearestNeighbour:

digital_image_processing/rotation/rotation.py

@@ -1,6 +1,6 @@
-from matplotlib import pyplot as plt
-import numpy as np
 import cv2
+import numpy as np
+from matplotlib import pyplot as plt
 
 
 def get_rotation(

digital_image_processing/sepia.py

@@ -1,8 +1,7 @@
 """
     Implemented an algorithm using opencv to tone an image with sepia technique
 """
-
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 def make_sepia(img, factor: int):

digital_image_processing/test_digital_image_processing.py

@@ -1,21 +1,21 @@
 """
 PyTest's for Digital Image Processing
 """
-
-import digital_image_processing.edge_detection.canny as canny
-import digital_image_processing.filters.gaussian_filter as gg
-import digital_image_processing.filters.median_filter as med
-import digital_image_processing.filters.sobel_filter as sob
-import digital_image_processing.filters.convolve as conv
-import digital_image_processing.change_contrast as cc
-import digital_image_processing.convert_to_negative as cn
-import digital_image_processing.sepia as sp
-import digital_image_processing.dithering.burkes as bs
-import digital_image_processing.resize.resize as rs
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread
 from numpy import array, uint8
 from PIL import Image
 
+from digital_image_processing import change_contrast as cc
+from digital_image_processing import convert_to_negative as cn
+from digital_image_processing import sepia as sp
+from digital_image_processing.dithering import burkes as bs
+from digital_image_processing.edge_detection import canny as canny
+from digital_image_processing.filters import convolve as conv
+from digital_image_processing.filters import gaussian_filter as gg
+from digital_image_processing.filters import median_filter as med
+from digital_image_processing.filters import sobel_filter as sob
+from digital_image_processing.resize import resize as rs
+
 img = imread(r"digital_image_processing/image_data/lena_small.jpg")
 gray = cvtColor(img, COLOR_BGR2GRAY)
 

dynamic_programming/fractional_knapsack.py

@@ -1,5 +1,5 @@
-from itertools import accumulate
 from bisect import bisect
+from itertools import accumulate
 
 
 def fracKnapsack(vl, wt, W, n):

dynamic_programming/max_sub_array.py

@@ -73,9 +73,10 @@ def max_sub_array(nums: List[int]) -> int:
     A random simulation of this algorithm.
     """
     import time
-    import matplotlib.pyplot as plt
     from random import randint
 
+    from matplotlib import pyplot as plt
+
     inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
     tim = []
     for i in inputs:

dynamic_programming/optimal_binary_search_tree.py

@@ -16,9 +16,7 @@
 # frequencies will be placed near the root of the tree while the nodes
 # with low frequencies will be placed near the leaves of the tree thus
 # reducing search time in the most frequent instances.
-
 import sys
-
 from random import randint
 
 

fuzzy_logic/fuzzy_operations.py

@@ -9,7 +9,6 @@
 import numpy as np
 import skfuzzy as fuzz
 
-
 if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
@@ -45,7 +44,7 @@
     # max-product composition
 
     # Plot each set A, set B and each operation result using plot() and subplot().
-    import matplotlib.pyplot as plt
+    from matplotlib import pyplot as plt
 
     plt.figure()
 

geodesy/lamberts_ellipsoidal_distance.py

@@ -1,4 +1,5 @@
 from math import atan, cos, radians, sin, tan
+
 from haversine_distance import haversine_distance
 
 

graphics/bezier_curve.py

@@ -1,7 +1,7 @@
 # https://en.wikipedia.org/wiki/B%C3%A9zier_curve
 # https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
-
 from typing import List, Tuple
+
 from scipy.special import comb
 
 
@@ -78,7 +78,7 @@ def plot_curve(self, step_size: float = 0.01):
             step_size: defines the step(s) at which to evaluate the Bezier curve.
             The smaller the step size, the finer the curve produced.
         """
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         to_plot_x: List[float] = []  # x coordinates of points to plot
         to_plot_y: List[float] = []  # y coordinates of points to plot

graphs/basic_graphs.py

@@ -1,6 +1,5 @@
 from collections import deque
 
-
 if __name__ == "__main__":
     # Accept No. of Nodes and edges
     n, m = map(int, input().split(" "))

graphs/breadth_first_search_2.py

@@ -12,8 +12,7 @@
             mark w as explored
             add w to Q (at the end)
 """
-
-from typing import Set, Dict
+from typing import Dict, Set
 
 G = {
     "A": ["B", "C"],

graphs/depth_first_search.py

@@ -11,8 +11,7 @@
         if v unexplored:
             DFS(G, v)
 """
-
-from typing import Set, Dict
+from typing import Dict, Set
 
 
 def depth_first_search(graph: Dict, start: str) -> Set[int]:

graphs/directed_and_undirected_(weighted)_graph.py

@@ -1,7 +1,7 @@
-from collections import deque
-import random as rand
 import math as math
+import random as rand
 import time
+from collections import deque
 
 # the default weight is 1 if not assigned but all the implementation is weighted
 

graphs/multi_heuristic_astar.py

@@ -1,4 +1,5 @@
 import heapq
+
 import numpy as np
 
 

greedy_method/test_knapsack.py

@@ -1,4 +1,5 @@
 import unittest
+
 import greedy_knapsack as kp