numpy.ma.fix_invalid() function return input with invalid data masked and replaced by a fill value. Where invalid data means values of nan, inf, etc.
Syntax : numpy.ma.fix_invalid(arr, mask = False, copy = True, fill_value = None)
Parameter :
arr : [array_like] Input array.
mask : [sequence, optional] Must be convertible to an array of booleans with the same shape as data. True indicates a masked data.
copy : [bool, optional] Whether to use a copy of a (True) or to fix a in place (False). Default is True.
fill_value : [scalar, optional] Value used for fixing invalid data. Default is None, in which case the arr.fill_value is used.Return : [MaskedArray] The input array with invalid entries fixed.
Code #1 :
# Python program explaining # numpy.ma.fix_invalid() function   # importing numpy as geek import numpy as geek    arr = geek.ma.array([1., -1, geek.nan, geek.inf],                               mask =[1] + [0]*3)   gfg = geek.ma.fix_invalid(arr)   print (gfg) |
Output :
[-- -1.0 -- --]
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Code #2 :
# Python program explaining # numpy.ma.fix_invalid() function   # importing numpy as geek import numpy as geek    arr = geek.ma.array([1., -1, geek.nan,                     geek.inf, -1, geek.nan],                           mask =[1] + [0]*5)   gfg = geek.ma.fix_invalid(arr)   print (gfg) |
Output :
[-- -1.0 -- -- -1.0 --]
