Find contiguous unmasked data in a masked array along the given axis in Numpy

To find contiguous unmasked data in a masked array along the given axis, use the numpy.ma.notmasked_contiguous in Python Numpy. The method returns a list of slices (start and end indexes) of unmasked indexes in the array. If the input is 2d and axis is specified, the result is a list of lists

The axis is the axis along which to perform the operation. If None (default), applies to a flattened version of the array, and this is the same as flatnotmasked_contiguous.

Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...<br>",arr.ndim)

Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])
print("\nOur Masked Array<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...<br>",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...<br>",maskArr.size)

Return a boolean indicating whether the data is contiguous −

print("\nCheck whether the data is contiguous?<br>",maskArr.iscontiguous())

To find contiguous unmasked data in a masked array along the given axis, use the numpy.ma.notmasked_contiguous:

print("\nResult...<br>",np.ma.notmasked_contiguous(maskArr, axis = 0))

Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...<br>",arr.ndim)

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])
print("\nOur Masked Array<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

# Get the shape of the Masked Array
print("\nOur Masked Array Shape...<br>",maskArr.shape)

# Get the number of elements of the Masked Array
print("\nElements in the Masked Array...<br>",maskArr.size)

# Return a boolean indicating whether the data is contiguous
print("\nCheck whether the data is contiguous?<br>",maskArr.iscontiguous())

# To find contiguous unmasked data in a masked array along the given axis, use the numpy.ma.notmasked_contiguous in Python Numpy
print("\nResult...<br>",np.ma.notmasked_contiguous(maskArr, axis = 0))

Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

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

Elements in the Masked Array...
12

Check whether the data is contiguous?
True
Result...
[[slice(2, 4, None)], [slice(1, 2, None)], [slice(0, 4, None)]]