# Return a list of slices corresponding to the unmasked clumps of a 1-D array in Numpy

To return a list of slices corresponding to the unmasked clumps of a 1-D array, use the ma.clump_unmasked() in Python Numpy. A "clump" is defined as a contiguous region of the array. Returns the list of slices, one for each continuous region of unmasked elements in the array.

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

arr = np.array([65, 68, 81, 93, 33, 39, 62, 45, 67])
print("Array...", arr)
print("Array type...", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...",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])
print("Our Masked Array", maskArr)
print("Our Masked Array type...", maskArr.dtype)

Get the dimensions of the Masked Array −

print("Our Masked Array Dimensions...",maskArr.ndim)


Get the shape of the Masked Array 7minus;

print("Our Masked Array Shape...",maskArr.shape)

Get the number of elements of the Masked Array −

print("Elements in the Masked Array...",maskArr.size)


Return a boolean indicating whether the data is contiguous −

print("Check whether the data is contiguous?",maskArr.iscontiguous())

To return a list of slices corresponding to the unmasked clumps of a 1-D array, use the ma.clump_unmasked(). A "clump" is defined as a contiguous region of the array −

print("Result...",np.ma.clump_unmasked(maskArr))


## 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, 62, 45, 67])
print("Array...", arr)
print("Array type...", arr.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",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])
print("Our Masked Array", maskArr)
print("Our Masked Array type...", maskArr.dtype)

# Get the dimensions of the Masked Array
print("Our Masked Array Dimensions...",maskArr.ndim)

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

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

# Return a boolean indicating whether the data is contiguous
print("Check whether the data is contiguous?",maskArr.iscontiguous())

# To return a list of slices corresponding to the unmasked clumps of a 1-D array, use the ma.clump_unmasked() in Python Numpy
# A "clump" is defined as a contiguous region of the array.
print("Result...",np.ma.clump_unmasked(maskArr))

## Output

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

Array type...
int64

Array Dimensions...
1

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

Our Masked Array type...
int64

Our Masked Array Dimensions...
1

Our Masked Array Shape...
(9,)

Elements in the Masked Array...
9

Check whether the data is contiguous?
True

Result...
[slice(2, 3, None), slice(4, 7, None), slice(8, 9, None)]

Updated on: 04-Feb-2022

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