# Return the inner product of two masked Three Dimensional arrays in Numpy

NumpyServer Side ProgrammingProgramming

To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. The out parameter suggests, if both the arrays are scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. out.shape = (*a.shape[:-1], *b.shape[:-1]).

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
import numpy.ma as ma

Create Array 1, with int elements using the numpy.arange() method −

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

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

print("\nMasked Array1...\n",arr1)

Create Array 2, with int elements using the numpy.arange() method −

arr2 = np.arange(24).reshape((2,3,4))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

arr2 = ma.array(arr2)

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

print("\nMasked Array2...\n",arr2)

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

print("\nResult of inner product (3D arrays)...\n",np.ma.inner(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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr1.shape)

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

arr1 = ma.array(arr1)

# Array 2
# Creating another 3D array with int elements using the numpy.arange() method
arr2 = np.arange(24).reshape((2,3,4))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr2.shape)

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

arr2 = ma.array(arr2)

# To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy
print("\nResult of inner product (3D arrays)...\n",np.ma.inner(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

[[[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

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

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

Result of inner product (3D arrays)...
[[[[ 13 21 53]
[ 73 93 69]]

[[ 38 90 214]
[ 302 390 346]]

[[ 62 154 366]
[ 518 670 602]]]

[[[ 86 218 518]
[ 734 950 858]]

[[ 110 282 670]
[ 950 1230 1114]]

[[ 134 346 822]
[1166 1510 1370]]]]