numpy.ma.mask_or() function | Python
Last Updated :
22 Apr, 2020
numpy.ma.mask_or()
function combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False).
Syntax : numpy.ma.mask_or(m1, m2, copy = False, shrink = True)
Parameters :
m1, m2 : [ array_like] Input masks.
copy : [bool, optional] If copy is False and one of the inputs is nomask, return a view of the other input mask. Defaults to False
shrink : [bool, optional] Whether to shrink the output to nomask if all its values are False. Defaults to True.
Return : The result masks values that are masked in either m1 or m2.
Code #1 :
import numpy as geek
import numpy.ma as ma
m1 = geek.ma.make_mask([ 1 , 1 , 0 , 1 ])
m2 = geek.ma.make_mask([ 1 , 0 , 0 , 0 ])
gfg = geek.ma.mask_or(m1, m2)
print (gfg)
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Output :
[ True True False True]
Code #2 :
import numpy as geek
import numpy.ma as ma
m1 = geek.ma.make_mask([ 1 , 0 , 0 , 0 ])
m2 = geek.ma.make_mask([ 1 , 1 , 0 , 1 ])
gfg = geek.ma.mask_or(m1, m2)
print (gfg)
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Output :
[ True True False True]
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