Numpy MaskedArray.atleast_1d() function | Python
numpy.MaskedArray.atleast_1d()
function is used to convert inputs to masked arrays with at least one dimension.Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
Syntax : numpy.ma.atleast_1d(*arys)
Parameters:
arys:[ array_like] One or more input arrays.
Return : [ ndarray] An array, or list of arrays, each with arr.ndim >= 1
Code #1 :
import numpy as geek
import numpy.ma as ma
in_arr1 = geek.array([[ 1 , 2 ], [ 3 , - 1 ], [ 5 , - 3 ]])
print ( "1st Input array : " , in_arr1)
in_arr2 = geek.array( 2 )
print ( "2nd Input array : " , in_arr2)
mask_arr1 = ma.masked_array(in_arr1, mask = [[ 1 , 0 ], [ 0 , 1 ], [ 0 , 0 ]])
print ( "1st Masked array : " , mask_arr1)
mask_arr2 = ma.masked_array(in_arr2, mask = 0 )
print ( "2nd Masked array : " , mask_arr2)
out_arr = ma.atleast_1d(mask_arr1, mask_arr2)
print ( "Output masked array : " , out_arr)
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Output:
1st Input array : [[ 1 2]
[ 3 -1]
[ 5 -3]]
2nd Input array : 2
1st Masked array : [[-- 2]
[3 --]
[5 -3]]
2nd Masked array : 2
Output masked array : [masked_array(
data=[[--, 2],
[3, --],
[5, -3]],
mask=[[ True, False],
[False, True],
[False, False]],
fill_value=999999), masked_array(data=[2],
mask=[False],
fill_value=999999)]
Code #2 :
import numpy as geek
import numpy.ma as ma
in_arr = geek.array([[[ 2e8 , 3e - 5 ]], [[ - 45.0 , 2e5 ]]])
print ( "Input array : " , in_arr)
mask_arr = ma.masked_array(in_arr, mask = [[[ 1 , 0 ]], [[ 0 , 0 ]]])
print ( "3D Masked array : " , mask_arr)
out_arr = ma.atleast_1d(mask_arr)
print ( "Output masked array : " , out_arr)
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Output:
Input array : [[[ 2.0e+08 3.0e-05]]
[[-4.5e+01 2.0e+05]]]
3D Masked array : [[[-- 3e-05]]
[[-45.0 200000.0]]]
Output masked array : [[[-- 3e-05]]
[[-45.0 200000.0]]]
Last Updated :
13 Oct, 2019
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