def sw_f(a,
axis=None,
masked=False,
weights=None,
N=None,
sum_of_squares=False):
"""Return the sum of weights for an array.
:Parameters:
a: numpy array_like
Input array
axis: `int`, optional
Axis along which to operate. By default, flattened input
is used.
masked: `bool`, optional
weights: numpy array-like, optional
Weights associated with values of the array. By default the
statistics are unweighted.
N: `numpy.ndarray`
Sample size
sum_of_squares: delta degrees of freedom
:Returns:
2-`tuple` of `numpy.ndarray`
The sample size and the sum of weights.
"""
if N is None:
(N, ) = sample_size_f(a, axis=axis, masked=masked)
if weights is not None:
# A weights array has been provided
weights = double_precision(weights)
if weights.ndim < a.ndim:
weights = broadcast_array(weights, a.shape)
if masked:
weights = numpy_ma_array(weights, mask=a.mask, copy=False)
if sum_of_squares:
weights = weights * weights
sw = weights.sum(axis=axis)
if not numpy_ndim(sw):
sw = numpy_asanyarray(sw)
else:
# The sum of weights is equal to the sample size (i.e. an
# unweighted sample)
sw = N.copy()
return asanyarray(N, sw)
def sw_f(a,
axis=None,
masked=False,
weights=None,
N=None,
sum_of_squares=False):
'''TODO
'''
if N is None:
N, = sample_size_f(a, axis=axis, masked=masked)
if weights is not None:
# A weights array has been provided
weights = double_precision(weights)
if weights.ndim < a.ndim:
weights = broadcast_array(weights, a.shape)
if masked:
weights = numpy_ma_array(weights, mask=a.mask, copy=False)
if sum_of_squares:
weights = weights * weights
sw = weights.sum(axis=axis)
if not numpy_ndim(sw):
sw = numpy_asanyarray(sw)
else:
# The sum of weights is equal to the sample size (i.e. an
# unweighted sample)
sw = N.copy()
return asanyarray(N, sw)
def mask_where_too_few_values(Nmin, N, x):
"""Mask elements of N and x where N is strictly less than Nmin.
:Parameters:
Nmin: `int`
N: `numpy.ndarray`
x: `numpy.ndarray`
:Returns:
(`numpy.ndarray`, `numpy.ndarray`)
A tuple containing *N* and *x*, both masked where *N* is
strictly less than *Nmin*.
"""
if N.min() < Nmin:
mask = N < Nmin
N = numpy_ma_array(N, mask=mask, copy=False, shrink=False)
x = numpy_ma_array(x, mask=mask, copy=False, shrink=True)
return asanyarray(N, x)
def psum(x, y):
"""Add two arrays element-wise.
If either or both of the arrays are masked then the output array is
masked only where both input arrays are masked.
:Parameters:
x: numpy array-like
*Might be updated in place*.
y: numpy array-like
Will not be updated in place.
:Returns:
`numpy.ndarray`
**Examples:**
>>> c = psum(a, b)
"""
if numpy_ma_isMA(x):
if numpy_ma_isMA(y):
# x and y are both masked
x_mask = x.mask
x = x.filled(0)
x += y.filled(0)
x = numpy_ma_array(x, mask=x_mask & y.mask, copy=False)
else:
# Only x is masked
x = x.filled(0)
x += y
elif numpy_ma_isMA(y):
# Only y is masked
x += y.filled(0)
else:
# x and y are both unmasked
x += y
return x
def __getitem__(self, indices):
'''x.__getitem__(indices) <==> x[indices]
Returns a numpy array.
'''
array = numpy_load(self._partition_file)
indices = parse_indices(array.shape, indices)
array = get_subspace(array, indices)
if self._get_component('_masked_as_record'):
# Convert a record array to a masked array
array = numpy_ma_array(array['_data'], mask=array['_mask'],
copy=False)
array.shrink_mask()
# Return the numpy array
return array
