isort --profile black . (#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 @@ flake8 : passed
 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=kfmNeskzs2o
 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 @@ without needing to store any additional data except the position of the first
 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 @@ https://www.geeksforgeeks.org/segment-tree-efficient-implementation/
 >>> 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 @@ Segment_tree creates a segment tree with a given array and function,
 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 destroyAllWindows, imread, imshow, waitKey
-from cv2 import imread, imshow, waitKey, destroyAllWindows
 
 
 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 @@ Inputs:
 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 cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
-from numpy import array, zeros, ravel, pad, dot, uint8
+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 COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
+from numpy import divide, int8, multiply, ravel, sort, zeros_like
-from numpy import zeros_like, ravel, sort, multiply, divide, int8
 
 
 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 @@ Created on Fri Sep 28 15:22:29 2018
 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 destroyAllWindows, imread, imshow, waitKey
-from cv2 import imread, imshow, waitKey, destroyAllWindows
 
 
 def make_sepia(img, factor: int):

digital_image_processing/test_digital_image_processing.py

@@ -1,21 +1,21 @@
 """
 PyTest's for Digital Image Processing
 """
-
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread
-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 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 @@ if __name__ == "__main__":
     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 @@ Python:
 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 @@ if __name__ == "__main__":
     # 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 @@ class BezierCurve:
             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 @@ while Q is non-empty:
             mark w as explored
             add w to Q (at the end)
 """
-
+from typing import Dict, Set
-from typing import Set, Dict
 
 G = {
     "A": ["B", "C"],

graphs/depth_first_search.py

@@ -11,8 +11,7 @@ Pseudocode:
         if v unexplored:
             DFS(G, v)
 """
-
+from typing import Dict, Set
-from typing import Set, Dict
 
 
 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
 
 

hashes/sha1.py

@@ -23,10 +23,9 @@ state. After the last block is processed the current hash state is returned as
 the final hash.
 Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
 """
-
 import argparse
-import struct
 import hashlib  # hashlib is only used inside the Test class
+import struct
 import unittest
 
 

linear_algebra/src/test_linear_algebra.py

@@ -6,9 +6,16 @@ Created on Mon Feb 26 15:40:07 2018
 
 This file contains the test-suite for the linear algebra library.
 """
-
 import unittest
-from lib import Matrix, Vector, axpy, squareZeroMatrix, unitBasisVector, zeroVector
+
+from lib import (
+    Matrix,
+    Vector,
+    axpy,
+    squareZeroMatrix,
+    unitBasisVector,
+    zeroVector,
+)
 
 
 class Test(unittest.TestCase):

machine_learning/gaussian_naive_bayes.py

@@ -1,10 +1,9 @@
 # Gaussian Naive Bayes Example
-
+from matplotlib import pyplot as plt
-from sklearn.naive_bayes import GaussianNB
-from sklearn.metrics import plot_confusion_matrix
 from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
+from sklearn.naive_bayes import GaussianNB
 
 
 def main():

machine_learning/k_means_clust.py

@@ -52,11 +52,12 @@ Usage:
 
 
 """
+import warnings
+
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 from sklearn.metrics import pairwise_distances
-import warnings
 
 warnings.filterwarnings("ignore")
 
@@ -193,7 +194,7 @@ def kmeans(
 
 # Mock test below
 if False:  # change to true to run this test case.
-    import sklearn.datasets as ds
+    from sklearn import datasets as ds
 
     dataset = ds.load_iris()
     k = 3

machine_learning/k_nearest_neighbours.py

@@ -1,5 +1,6 @@
-import numpy as np
 from collections import Counter
+
+import numpy as np
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 

machine_learning/knn_sklearn.py

@@ -1,5 +1,5 @@
-from sklearn.model_selection import train_test_split
 from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
 
 # Load iris file

machine_learning/linear_discriminant_analysis.py

@@ -41,11 +41,9 @@
 
     Author: @EverLookNeverSee
 """
-
 from math import log
 from os import name, system
-from random import gauss
+from random import gauss, seed
-from random import seed
 
 
 # Make a training dataset drawn from a gaussian distribution

machine_learning/linear_regression.py

@@ -7,8 +7,8 @@ We try to set the weight of these features, over many iterations, so that they b
 fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
 Rating). We try to best fit a line through dataset and estimate the parameters.
 """
-import requests
 import numpy as np
+import requests
 
 
 def collect_dataset():

machine_learning/logistic_regression.py

@@ -14,14 +14,12 @@ 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
+from matplotlib import pyplot as plt
+from sklearn import datasets
 
 # get_ipython().run_line_magic('matplotlib', 'inline')
 
-from sklearn import datasets
-
 
 # In[67]:
 

machine_learning/lstm/lstm_prediction.py

@@ -4,14 +4,12 @@
     * http://colah.github.io/posts/2015-08-Understanding-LSTMs
     * https://en.wikipedia.org/wiki/Long_short-term_memory
 """
-
-from keras.layers import Dense, LSTM
-from keras.models import Sequential
 import numpy as np
 import pandas as pd
+from keras.layers import LSTM, Dense
+from keras.models import Sequential
 from sklearn.preprocessing import MinMaxScaler
 
-
 if __name__ == "__main__":
     """
     First part of building a model is to get the data and prepare

machine_learning/multilayer_perceptron_classifier.py

@@ -1,6 +1,5 @@
 from sklearn.neural_network import MLPClassifier
 
-
 X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
 y = [0, 1, 0, 0]
 

machine_learning/polymonial_regression.py

@@ -1,5 +1,5 @@
-import matplotlib.pyplot as plt
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.linear_model import LinearRegression
 
 # Splitting the dataset into the Training set and Test set

machine_learning/random_forest_classifier.py

@@ -1,10 +1,9 @@
 # Random Forest Classifier Example
-
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import plot_confusion_matrix
-import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
 
 
 def main():

machine_learning/random_forest_regressor.py

@@ -1,10 +1,8 @@
 # Random Forest Regressor Example
-
 from sklearn.datasets import load_boston
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import mean_absolute_error, mean_squared_error
-from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
 
 
 def main():

machine_learning/sequential_minimum_optimization.py

@@ -36,9 +36,9 @@ import os
 import sys
 import urllib.request
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.datasets import make_blobs, make_circles
 from sklearn.preprocessing import StandardScaler
 

machine_learning/support_vector_machines.py

@@ -1,5 +1,5 @@
-from sklearn.datasets import load_iris
 from sklearn import svm
+from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 
 

maths/3n_plus_1.py

@@ -1,4 +1,4 @@
-from typing import Tuple, List
+from typing import List, Tuple
 
 
 def n31(a: int) -> Tuple[List[int], int]:

maths/fibonacci.py

@@ -7,10 +7,10 @@
     reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts.
     <http://www.math.hmc.edu/funfacts>
 """
-import math
 import functools
+import math
 import time
-from decimal import getcontext, Decimal
+from decimal import Decimal, getcontext
 
 getcontext().prec = 100
 

maths/gamma.py

@@ -1,6 +1,7 @@
 import math
-from scipy.integrate import quad
+
 from numpy import inf
+from scipy.integrate import quad
 
 
 def gamma(num: float) -> float:

maths/gaussian.py

@@ -5,7 +5,7 @@ python/black : True
 python : 3.7.3
 
 """
-from numpy import pi, sqrt, exp
+from numpy import exp, pi, sqrt
 
 
 def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:

maths/line_length.py

@@ -1,5 +1,5 @@
-from typing import Callable, Union
 import math as m
+from typing import Callable, Union
 
 
 def line_length(

maths/mobius_function.py

@@ -5,8 +5,8 @@ python/black : True
 flake8 : True
 """
 
-from maths.prime_factors import prime_factors
 from maths.is_square_free import is_square_free
+from maths.prime_factors import prime_factors
 
 
 def mobius(n: int) -> int:

maths/newton_raphson.py

@@ -7,7 +7,6 @@
     limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
     is raised. If iteration limit is reached, try increasing maxiter.
     """
-
 import math as m
 
 
@@ -42,7 +41,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
 
 
 if __name__ == "__main__":
-    import matplotlib.pyplot as plt
+    from matplotlib import pyplot as plt
 
     f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     solution, error, steps = newton_raphson(

maths/prime_numbers.py

@@ -1,5 +1,5 @@
-from typing import Generator
 import math
+from typing import Generator
 
 
 def slow_primes(max: int) -> Generator[int, None, None]:

maths/relu.py

@@ -9,9 +9,9 @@ After through ReLU, the element of the vector always 0 or real number.
 Script inspired from its corresponding Wikipedia article
 https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
 """
+from typing import List
 
 import numpy as np
-from typing import List
 
 
 def relu(vector: List[float]):

maths/volume.py

@@ -3,8 +3,8 @@ Find Volumes of Various Shapes.
 
 Wikipedia reference: https://en.wikipedia.org/wiki/Volume
 """
-from typing import Union
 from math import pi, pow
+from typing import Union
 
 
 def vol_cube(side_length: Union[int, float]) -> float:

maths/zellers_congruence.py

@@ -1,5 +1,5 @@
-import datetime
 import argparse
+import datetime
 
 
 def zeller(date_input: str) -> str:

matrix/tests/test_matrix_operation.py

@@ -6,11 +6,13 @@ the pytest run configuration
 -vv -m mat_ops -p no:cacheprovider
 """
 
+import logging
+
 # standard libraries
 import sys
+
 import numpy as np
 import pytest
-import logging
 
 # Custom/local libraries
 from matrix import matrix_operation as matop

neural_network/back_propagation_neural_network.py

@@ -17,9 +17,8 @@ Github : https://github.com/RiptideBo
 Date: 2017.11.23
 
 """
-
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
 
 
 def sigmoid(x):

neural_network/convolution_neural_network.py

@@ -14,8 +14,9 @@
     - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 import pickle
+
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
 
 
 class CNN:

other/least_recently_used.py

@@ -1,5 +1,5 @@
-from abc import abstractmethod
 import sys
+from abc import abstractmethod
 from collections import deque
 
 

other/sierpinski_triangle.py

@@ -27,8 +27,8 @@ Credits: This code was written by editing the code from
 http://www.riannetrujillo.com/blog/python-fractal/
 
 """
-import turtle
 import sys
+import turtle
 
 PROGNAME = "Sierpinski Triangle"
 

project_euler/problem_07/sol3.py

@@ -5,8 +5,8 @@ By listing the first six prime numbers:
 
 We can see that the 6th prime is 13. What is the Nth prime number?
 """
-import math
 import itertools
+import math
 
 
 def primeCheck(number):

project_euler/problem_42/solution42.py

@@ -15,7 +15,6 @@ words?
 """
 import os
 
-
 # Precomputes a list of the 100 first triangular numbers
 TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
 

scheduling/shortest_job_first.py

@@ -3,9 +3,9 @@ Shortest job remaining first
 Please note arrival time and burst
 Please use spaces to separate times entered.
 """
+from typing import List
 
 import pandas as pd
-from typing import List
 
 
 def calculate_waitingtime(

scripts/validate_filenames.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
-
 import os
+
 from build_directory_md import good_file_paths
 
 filepaths = list(good_file_paths())

searches/hill_climbing.py

@@ -150,7 +150,7 @@ def hill_climbing(
             solution_found = True
 
     if visualization:
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         plt.plot(range(iterations), scores)
         plt.xlabel("Iterations")

searches/simulated_annealing.py

@@ -84,7 +84,7 @@ def simulated_annealing(
             current_state = next_state
 
     if visualization:
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         plt.plot(range(iterations), scores)
         plt.xlabel("Iterations")

searches/tabu_search.py

@@ -24,9 +24,8 @@ python tabu_search.py -f your_file_name.txt -number_of_iterations_of_tabu_search
     -s size_of_tabu_search
 e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
 """
-
-import copy
 import argparse
+import copy
 
 
 def generate_neighbours(path):

sorts/external_sort.py

@@ -3,8 +3,8 @@
 #
 # Sort large text files in a minimum amount of memory
 #
-import os
 import argparse
+import os
 
 
 class FileSplitter:

sorts/random_normal_distribution_quicksort.py

@@ -1,5 +1,6 @@
 from random import randint
 from tempfile import TemporaryFile
+
 import numpy as np
 
 

web_programming/crawl_google_results.py

@@ -1,10 +1,9 @@
 import sys
 import webbrowser
 
+import requests
 from bs4 import BeautifulSoup
 from fake_useragent import UserAgent
-import requests
-
 
 if __name__ == "__main__":
     print("Googling.....")

web_programming/get_imdbtop.py

@@ -1,5 +1,5 @@
-from bs4 import BeautifulSoup
 import requests
+from bs4 import BeautifulSoup
 
 
 def imdb_top(imdb_top_n):