def _get_counts(mask, axis, dtype=float):
dtype = _get_dtype(dtype)
if axis is None:
return dtype.type(mask.size - mask.sum())
count = mask.shape[axis] - mask.sum(axis)
try:
return count.astype(dtype)
except AttributeError:
return np.array(count, dtype=dtype)
def _get_counts(mask, axis, dtype=float):
dtype = _get_dtype(dtype)
if axis is None:
return dtype.type(mask.size - mask.sum())
count = mask.shape[axis] - mask.sum(axis)
try:
return count.astype(dtype)
except AttributeError:
return np.array(count, dtype=dtype)
def _get_counts_nanvar(mask, axis, ddof, dtype=float):
dtype = _get_dtype(dtype)
count = _get_counts(mask, axis, dtype=dtype)
d = count - dtype.type(ddof)
# always return NaN, never inf
if np.isscalar(count):
if count <= ddof:
count = np.nan
d = np.nan
else:
mask2 = count <= ddof
if mask2.any():
np.putmask(d, mask2, np.nan)
np.putmask(count, mask2, np.nan)
return count, d
def _get_counts_nanvar(mask, axis, ddof, dtype=float):
dtype = _get_dtype(dtype)
count = _get_counts(mask, axis, dtype=dtype)
d = count - dtype.type(ddof)
# always return NaN, never inf
if np.isscalar(count):
if count <= ddof:
count = np.nan
d = np.nan
else:
mask2 = count <= ddof
if mask2.any():
np.putmask(d, mask2, np.nan)
np.putmask(count, mask2, np.nan)
return count, d
