numpy.ma.fix_invalid()

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:

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.

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])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ma.fix_invalid.html

doc_NumPy

2025-01-10 15:47:30

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