masked_invalid(a, copy=True)
This function is a shortcut to masked_where
, with :None:None:`condition` = ~(np.isfinite(a)). Any pre-existing mask is conserved. Only applies to arrays with a dtype where NaNs or infs make sense (i.e. floating point types), but accepts any array_like object.
Mask an array where invalid values occur (NaNs or infs).
masked_where
Mask where a condition is met.
>>> import numpy.ma as maThis example is valid syntax, but we were not able to check execution
... a = np.arange(5, dtype=float)
... a[2] = np.NaN
... a[3] = np.PINF
... a array([ 0., 1., nan, inf, 4.])
>>> ma.masked_invalid(a) masked_array(data=[0.0, 1.0, --, --, 4.0], mask=[False, False, True, True, False], fill_value=1e+20)See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.masked_where
dask.array.ma.masked_invalid
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