Find the indices of the first and last unmasked values in Numpy

NumpyServer Side ProgrammingProgramming

To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy. Returns the indices of first and last non-masked value in the array. Returns None if all values are masked.

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], [73, 88, 51], [62, 45, 67]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",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\n", maskArr)
print("\nOur Masked Array type...\n", maskArr.dtype)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...\n",maskArr.size)

Return a boolean indicating whether the data is contiguous −

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

Find contiguous unmasked data in a masked array −

print("\nContiguous unmasked data...\n",np.ma.flatnotmasked_contiguous(maskArr))

To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy −

print("\nResult...\n",np.ma.flatnotmasked_edges(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], [73, 88, 51], [62, 45, 67]])
print("Array...\n", arr)
print("\nArray type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...\n",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\n", maskArr)
print("\nOur Masked Array type...\n", maskArr.dtype)

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

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

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

# Find contiguous unmasked data in a masked array
print("\nContiguous unmasked data...\n",np.ma.flatnotmasked_contiguous(maskArr))

# To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy
print("\nResult...\n",np.ma.flatnotmasked_edges(maskArr))

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 Shape...
(4, 3)

Elements in the Masked Array...
12

Check whether the data is contiguous?
True

Contiguous unmasked data...
[slice(2, 3, None), slice(4, 7, None), slice(8, 10, None), slice(11, 12, None)]

Result...
[ 2 11]
raja
Updated on 04-Feb-2022 10:03:21

Advertisements