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Find contiguous unmasked data in a masked array along axis 1 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...
", 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], [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 find contiguous unmasked data in a masked array along the given axis, use the numpy.ma.notmasked_contiguous −
print("
Result...
",np.ma.notmasked_contiguous(maskArr, axis = 1))
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...
", 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], [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 find contiguous unmasked data in a masked array along the given axis, use the numpy.ma.notmasked_contiguous in Python Numpy print("
Result...
",np.ma.notmasked_contiguous(maskArr, axis = 1))
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, 3, None)], [slice(1, 3, None)], [slice(0, 1, None), slice(2, 3, None)], [slice(0, 1, None), slice(2, 3, None)]]