numpy.ma.fix_invalid#

ma.fix_invalid(a, mask=np.False_, copy=True, fill_value=None)[source]#

Return input with invalid data masked and replaced by a fill value.

Invalid data means values of nan, inf, etc.

Parameters:

aarray_like: Input array, a (subclass of) ndarray.
masksequence, optional: Mask. Must be convertible to an array of booleans with the same shape as data. True indicates a masked (i.e. invalid) data.
copybool, optional: Whether to use a copy of a (True) or to fix a in place (False). Default is True.
fill_valuescalar, optional: Value used for fixing invalid data. Default is None, in which case the a.fill_value is used.

Returns:

bMaskedArray: The input array with invalid entries fixed.

Notes

A copy is performed by default.

Examples

>>> import numpy as np
>>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3)
>>> x
masked_array(data=[--, -1.0, nan, inf],
             mask=[ True, False, False, False],
       fill_value=1e+20)
>>> np.ma.fix_invalid(x)
masked_array(data=[--, -1.0, --, --],
             mask=[ True, False,  True,  True],
       fill_value=1e+20)

>>> fixed = np.ma.fix_invalid(x)
>>> fixed.data
array([ 1.e+00, -1.e+00,  1.e+20,  1.e+20])
>>> x.data
array([ 1., -1., nan, inf])