Return the inner product of two masked arrays in Numpy

To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

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, a 3x3 array with int elements using the numpy.arange() method −

arr1 = np.arange(9).reshape((3,3))
print("Array1...
", arr1)
print("
Array type...
", arr1.dtype)

Create masked array1 −

arr1 = ma.array(arr1)

Mask Array1 −

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

Display Masked Array 1 −

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

Create Array 2, a 3x3 array with int elements using the numpy.arange() method −

arr2 = np.arange(9).reshape((3,3))
print("
Array2...
", arr2)
print("
Array type...
", arr2.dtype)

Create masked array2 −

arr2 = ma.array(arr2)

Mask Array2 −

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

Display Masked Array 2 −

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

To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes:

print("
Result of inner product...
",np.ma.inner(arr1, arr2))

Example

# Python ma.MaskedArray - Return the inner product of two masked arrays

import numpy as np
import numpy.ma as ma

# Array 1
# Creating a 3x3 array with int elements using the numpy.arange() method
arr1 = np.arange(9).reshape((3,3))
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, 1] = ma.masked
arr1[1, 1] = ma.masked

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

# Array 2
# Creating another 3x3 array with int elements using the numpy.arange() method
arr2 = np.arange(9).reshape((3,3))
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[2, 1] = ma.masked
arr2[2, 2] = ma.masked

# Display Masked Array 2
print("
Masked Array2...
",arr2)
# To return the inner product of two masked arrays, use the ma.inner() method in Python Numpy
# Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.
print("
Result of inner product...
",np.ma.inner(arr1, arr2))

Output

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

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

Masked Array1...
[[0 -- 2]
[3 -- 5]
[6 7 8]]

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

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

Masked Array2...
[[0 1 2]
[3 4 5]
[6 -- --]]

Result of inner product...
[[ 4 10 0]
[10 34 18]
[23 86 36]]