Return the outer product of two masked Three-Dimensional Numpy arrays

Numpy Server Side Programming Programming

To return the outer product of two 3D masked arrays, use the ma.outer() method in Python Numpy. The first parameter is the input vector. Input is flattened if not already 1-dimensional. The second parameter is the second input vector. Input is flattened if not already 1-dimensional.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

Steps

At first, import the required library −

import numpy as np

Create Array1, a 3D array with int elements using the numpy.arange() method −

arr1 = np.arange(24).reshape((2,3,4))
print("Array1...
", arr1)
print("
Array type...
", arr1.dtype)

Create masked array1 −

arr1 = ma.array(arr1)

Mask Array1 −

arr1[0, 0, 1] = ma.masked

Display Masked Array 1 −

print("
Masked Array1...
",arr1)

Creating another 3D array2 with int elements using the numpy.arange() method −

arr2 = np.arange(24).reshape((2,3,4))
print("
Array2...
", arr2)
print("
Array type...
", arr2.dtype)

Create masked array2 −

arr2 = ma.array(arr2)

Mask Array2 −

arr2[0, 1, 2] = ma.masked
arr2[1, 2, 2] = ma.masked

Display Masked Array 2 −

print("
Masked Array2...
",arr2)

To return the outer product of two masked arrays, use the ma.outer() method in Python Numpy

print("
Result of outer product (3D arrays)...
",np.ma.outer(arr1, arr2))

Example

import numpy as np
import numpy.ma as ma

# Array 1
# Creating a 3D array with int elements using the numpy.arange() method
arr1 = np.arange(24).reshape((2,3,4))
print("Array1...
", arr1)
print("
Array type...
", arr1.dtype)

# Get the dimensions of the Array
print("
Array Dimensions...
",arr1.ndim)

# Get the shape of the Array
print("
Our Array Shape...
",arr1.shape)

# Get the number of elements of the Array
print("
Elements in the Array...
",arr1.size)

# Create a masked array
arr1 = ma.array(arr1)

# Mask Array1
arr1[0, 0, 1] = ma.masked

# Display Masked Array 1
print("
Masked Array1...
",arr1)

# Array 2
# Creating another 3D array with int elements using the numpy.arange() method
arr2 = np.arange(24).reshape((2,3,4))
print("
Array2...
", arr2)
print("
Array type...
", arr2.dtype)

# Get the dimensions of the Array
print("
Array Dimensions...
",arr2.ndim)

# Get the shape of the Array
print("
Our Array Shape...
",arr2.shape)

# Get the number of elements of the Array
print("
Elements in the Array...
",arr2.size)

# Create a masked array
arr2 = ma.array(arr2)

# Mask Array2
arr2[0, 1, 2] = ma.masked
arr2[1, 2, 2] = ma.masked

# Display Masked Array 2
print("
Masked Array2...
",arr2)

# To return the outer product of two masked arrays, use the ma.outer() method in Python Numpy
print("
Result of outer product (3D arrays)...
",np.ma.outer(arr1, arr2))

Output

Array1...
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]

Array type...
int64

Array Dimensions...
3

Our Array Shape...
(2, 3, 4)

Elements in the Array...
24

Masked Array1...
[[[0 -- 2 3]
[4 5 6 7]
[8 9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]

Array2...
[[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 22 23]]]

Array type...
int64

Array Dimensions...
3

Our Array Shape...
(2, 3, 4)

Elements in the Array...
24

Masked Array2...
[[[0 1 2 3]
[4 5 -- 7]
[8 9 10 11]]

[[12 13 14 15]
[16 17 18 19]
[20 21 -- 23]]]

Result of outer product (3D arrays)...
[[0 0 0 0 0 0 -- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -- 0]
[-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --]
[0 2 4 6 8 10 -- 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 -- 46]
[0 3 6 9 12 15 -- 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 -- 69]
[0 4 8 12 16 20 -- 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 -- 92]
[0 5 10 15 20 25 -- 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 --
115]
[0 6 12 18 24 30 -- 42 48 54 60 66 72 78 84 90 96 102 108 114 120 126 --
138]
[0 7 14 21 28 35 -- 49 56 63 70 77 84 91 98 105 112 119 126 133 140 147
-- 161]
[0 8 16 24 32 40 -- 56 64 72 80 88 96 104 112 120 128 136 144 152 160
168 -- 184]
[0 9 18 27 36 45 -- 63 72 81 90 99 108 117 126 135 144 153 162 171 180
189 -- 207]
[0 10 20 30 40 50 -- 70 80 90 100 110 120 130 140 150 160 170 180 190
200 210 -- 230]
[0 11 22 33 44 55 -- 77 88 99 110 121 132 143 154 165 176 187 198 209
220 231 -- 253]
[0 12 24 36 48 60 -- 84 96 108 120 132 144 156 168 180 192 204 216 228
240 252 -- 276]
[0 13 26 39 52 65 -- 91 104 117 130 143 156 169 182 195 208 221 234 247
260 273 -- 299]
[0 14 28 42 56 70 -- 98 112 126 140 154 168 182 196 210 224 238 252 266
280 294 -- 322]
[0 15 30 45 60 75 -- 105 120 135 150 165 180 195 210 225 240 255 270 285
300 315 -- 345]
[0 16 32 48 64 80 -- 112 128 144 160 176 192 208 224 240 256 272 288 304
320 336 -- 368]
[0 17 34 51 68 85 -- 119 136 153 170 187 204 221 238 255 272 289 306 323
340 357 -- 391]
[0 18 36 54 72 90 -- 126 144 162 180 198 216 234 252 270 288 306 324 342
360 378 -- 414]
[0 19 38 57 76 95 -- 133 152 171 190 209 228 247 266 285 304 323 342 361
380 399 -- 437]
[0 20 40 60 80 100 -- 140 160 180 200 220 240 260 280 300 320 340 360
380 400 420 -- 460]
[0 21 42 63 84 105 -- 147 168 189 210 231 252 273 294 315 336 357 378
399 420 441 -- 483]
[0 22 44 66 88 110 -- 154 176 198 220 242 264 286 308 330 352 374 396
418 440 462 -- 506]
[0 23 46 69 92 115 -- 161 184 207 230 253 276 299 322 345 368 391 414
437 460 483 -- 529]]

AmitDiwan

Updated on: 22-Feb-2022

202 Views

Kickstart Your Career

Get certified by completing the course

Get Started