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Python NumPy (Page: 5)
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 NumPy creating;
 NumPy array reshaping;
 Images with NumPy;
 NumPy copy;
 NumPy mask;
 Geometry;
Mask in NymPy is a simple way to refer few elements from the big NumPy array. Results will be given in the mask format.
Mask for onedimentional array
Onedimentional mask
import numpy as np
x1 = np.arange(10)**3
m1 = [2, 5, 8]
print(x1) # [ 0 1 8 27 64 125 216 343 512 729]
print(x1[m1]) # [ 8 125 512]
x1[m1] = 111
print(x1) # [ 0 1 111 27 64 111 216 343 111 729]
Twodimentional mask
Multidimentional mask should be created as NumPy array
import numpy as np
x2 = np.arange(10)**3
m2 = np.array([[1, 4], [0, 9]])
print(x1[m2]) # [[ 1 64] [ 0 729]]
x1[m2] = 222
print(x1) # [222 222 8 27 222 125 216 343 512 222]
Mask for twodimentional array
We can make a mask for twodimensional arrays as well.
Onedimensional mask  row
x = np.arange(9).reshape(3,3)
print("x = ", x) # [[0 1 2] [3 4 5] [6 7 8]]
# select 1 row
r0 = np.array([[0]])
print("x[r0] = ", x[r0]) # [[[0 1 2]]]
# select 2 rows
r1 = [0, 2]
print("x[r1] = ", x[r1]) # [[0 1 2] [6 7 8]]
# select 2 sets of rows
r2 = np.array([[0, 2], [2, 1]])
print("x[r2] = ", x[r2]) # [[[0 1 2] [6 7 8]] [[6 7 8] [3 4 5]]]
Twodimensional mask  intersection of row and columns
Mask can be created as intersection of rows and columns
# select 1 intersection of 1 row 1 column
r0 = np.array([[0]])
c0 = np.array([[1]])
print("x[r0, c0] = ", x[r0, c0]) # [[1]]
# 2 row 1 column => 2 intersections
r1 = np.array([[0, 2]])
c1 = np.array([[2]])
print("x[r1, c1] = ", x[r1, c1]) # [[2 8]]
# Select four corners
r2 = np.array([[0, 0], [2, 2]])
c2 = np.array([[0, 2], [0, 2]])
print("x[r2, c2] = ", x[r2, c2]) # [[0 2] [6 8]]
x[r2, c2] = 10 # change values to these elements
print(x) # [[10 1 10] [ 3 4 5] [10 7 10]]
Boolean masks
It is possible to generate boolean masks for NumPy array. This mask will have True and Fals elements, which can be used as 1 and 0 in mathematical calculations, respectively. To create this boolean mask, it is nesessary to apply conditions to NumPy array.
x = np.arange(12).reshape(4, 3)
# [[ 0 1 2]
# [ 3 4 5]
# [ 6 7 8]
# [ 9 10 11]]
idx_b = x > 7
print(idx_b)
# [[False False False]
# [False False False]
# [False False True]
# [ True True True]]
print(x[idx_b]) # [ 8 9 10 11]
# shorter writing
print(x[x>8]) # [ 9 10 11]
# use math equations over boolean
print(np.sum(x<5, axis = 0)) # [2 2 1]
print(np.sum(x<5, axis = 1)) # [3 2 0 0]
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Published: 20211004 11:48:19 Updated: 20211114 08:41:55
