import numpy as np def cv(*args: int) -> np.array: """ Takes n integers, returns a n-dimensional vector column :param *args: values to be integrated into array :type *args: float :returns: n-dimensional vector column :returns type: np.array """ a = np.array(args) return a.reshape(-1, 1) cv(1,2,3)

import numpy as np def cv(input_list: list) -> np.array: """ Takes n integers, returns a n-dimensional vector column :input_list type: list(int) :returns type: np.array """ a = np.array(input_list) return a.reshape(-1, 1) cv([1,4,2])

import numpy as np def transpose(input_array: np.array) -> np.array: """ Returns the transpose of a given array :returns type: np.array """ return np.transpose(input_array) transpose( np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) )

import numpy as np def eqtest( first_array: np.array, second_array: np.array, tolerance: float =0 ) -> bool: """ Takes two arrays, compares them, with a tolerance of {tolerance}. """ if tolerance == 0: return (first_array == second_array).all() max_difference = max(abs(first_array - second_array)) return max_difference < tolerance a=np.array([1,2,3]) b=np.array([0.74,2.3,3.01]) print(eqtest(a,b,0.5)) print(eqtest(a,b))

import numpy as np def has_a_nan(input_array: np.array) -> bool: """ Checks wether a given array contains any NaN """ return np.isnan(input_array).any() print( has_a_nan( np.array( [1, np.NaN] ) ) )

import numpy as np print( np.array([ [0]*10, [1]*10, [5]*10 ]) )

import numpy as np print( np.array( list(range(30, 72, 2)) ) )

import numpy as np def randzeroone(length: int) -> np.array: return np.random.rand(1, length) print( randzeroone(3) )

import numpy as np def randzeroone(length: int) -> np.array: return np.random.rand(1, length) print( randzeroone(15) )

import numpy as np def create_vector() -> list: return np.arange(15, 56) print( create_vector()[1:-1] )

import numpy as np # Quick and dirty a = list() for i in range(8): b = list() for j in range(8): b.append( 1 if (i+j)%2==0 else 0 ) a.append(b) # The numpy way chess = np.zeros(64).reshape(8,8) chess[1::2,1::2] = 1 chess[0::2,0::2] = 1 print(a, chess)

vector = np.random.rand(1, 100) result = -10 + 20 * vector result

init_vector = np.random.rand(1, 100) vector = 20 * init_vector vector[(vector >= 9) & (vector <= 15)] *= -1 vector

def multiply(a: np.array, b: np.array) -> np.array: return a * b a = np.array([1.0, 2.0, 3.0]) b = np.array([2.0, 2.0, 2.0]) multiply(a, b)

5 + 11 * np.random.rand(3, 4)

np.eye(3)

np.ones(4) - np.eye(4)

10 * np.random.rand(3,3,3)

def compute_sums(input_array: np.array) -> list: """ Returns the sum of all elements, the sum of each column and the sum of each row """ return ( np.sum(input_array), np.sum(input_array, axis=0), np.sum(input_array, axis=1) ) a = np.array([[0, 1], [0, 5]]) print(compute_sums(a))

test_vector = np.random.rand(0, 100000) np.save('sizetest.npy', vector) # Result : 0.9 kb import random test_list = [random.random() for i in range(100000)] with open('sizetest.txt', 'w') as f: f.write(str(test_list)) # Result : 1.9 Mb

matrix = np.arange(1, 10).reshape(3, 3) result = matrix[2] result

# Pythonic way def closest_value_finder( input_array: np.array, value_to_find: float ) -> float: """ Returns the closest element from value_to_find of input_array """ closest_value_found = input_array[0] for elem in input_array: if abs(value_to_find - elem) < abs(value_to_find - closest_value_found): closest_value_found = elem return closest_value_found a = list(range(100)) print(closest_value_finder(a, 34.990622))