def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
"""
is_new = not isinstance(a, np.ndarray)
if is_new:
a = np.array(a)
if not issubclass(a.dtype.type, np.inexact):
return a, None
if not is_new:
# need copy
a = np.array(a, subok=True)
mask = np.isnan(a)
np.copyto(a, val, where=mask)
return a, mask
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