numpy.ma.fix_invalid — NumPy v1.11 Manual (original) (raw)

numpy.ma.fix_invalid(a, mask=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:	a : array_like Input array, a (subclass of) ndarray. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as data. True indicates a masked (i.e. invalid) 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 a.fill_value is used.
Returns:	b : MaskedArray The input array with invalid entries fixed.

Parameters:

a : array_like Input array, a (subclass of) ndarray. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as data. True indicates a masked (i.e. invalid) 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 a.fill_value is used.

Returns:

b : MaskedArray The input array with invalid entries fixed.

Notes

A copy is performed by default.

Examples

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([ 1.00000000e+00, -1.00000000e+00, 1.00000000e+20, 1.00000000e+20]) x.data array([ 1., -1., NaN, Inf])