# Licensed under a 3-clause BSD style license - see LICENSE.rst
import warnings
import numpy as np
from astropy.utils import isiterable
from astropy.utils.decorators import deprecated_renamed_argument
from astropy.utils.exceptions import AstropyUserWarning
try:
import bottleneck # pylint: disable=W0611
HAS_BOTTLENECK = True
except ImportError:
HAS_BOTTLENECK = False
__all__ = ['SigmaClip', 'sigma_clip', 'sigma_clipped_stats']
def _move_tuple_axes_first(array, axis):
"""
Bottleneck can only take integer axis, not tuple, so this function
takes all the axes to be operated on and combines them into the
first dimension of the array so that we can then use axis=0
"""
# Figure out how many axes we are operating over
naxis = len(axis)
# Add remaining axes to the axis tuple
axis += tuple(i for i in range(array.ndim) if i not in axis)
# The new position of each axis is just in order
destination = tuple(range(array.ndim))
# Reorder the array so that the axes being operated on are at the beginning
array_new = np.moveaxis(array, axis, destination)
# Figure out the size of the product of the dimensions being operated on
first = np.prod(array_new.shape[:naxis])
# Collapse the dimensions being operated on into a single dimension so that
# we can then use axis=0 with the bottleneck functions
array_new = array_new.reshape((first,) + array_new.shape[naxis:])
return array_new
def _nanmean(array, axis=None):
"""Bottleneck nanmean function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmean(array, axis=axis)
def _nanmedian(array, axis=None):
"""Bottleneck nanmedian function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanmedian(array, axis=axis)
def _nanstd(array, axis=None, ddof=0):
"""Bottleneck nanstd function that handle tuple axis."""
if isinstance(axis, tuple):
array = _move_tuple_axes_first(array, axis=axis)
axis = 0
return bottleneck.nanstd(array, axis=axis, ddof=ddof)
[docs]class SigmaClip:
"""
Class to perform sigma clipping.
The data will be iterated over, each time rejecting values that are
less or more than a specified number of standard deviations from a
center value.
Clipped (rejected) pixels are those where::
data < cenfunc(data [,axis=int]) - (sigma_lower * stdfunc(data [,axis=int]))
data > cenfunc(data [,axis=int]) + (sigma_upper * stdfunc(data [,axis=int]))
Invalid data values (i.e. NaN or inf) are automatically clipped.
For a functional interface to sigma clipping, see
:func:`sigma_clip`.
.. note::
`scipy.stats.sigmaclip
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sigmaclip.html>`_
provides a subset of the functionality in this class. Also, its
input data cannot be a masked array and it does not handle data
that contains invalid values (i.e. NaN or inf). Also note that
it uses the mean as the centering function.
If your data is a `~numpy.ndarray` with no invalid values and
you want to use the mean as the centering function with
``axis=None`` and iterate to convergence, then
`scipy.stats.sigmaclip` is ~25-30% faster than the equivalent
settings here (``s = SigmaClip(cenfunc='mean', maxiters=None);
s(data, axis=None)``).
Parameters
----------
sigma : float, optional
The number of standard deviations to use for both the lower and
upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is
3.
sigma_lower : float or `None`, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or `None`, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or `None`, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute the
center value for the clipping. If set to ``'median'`` or
``'mean'`` then having the optional `bottleneck`_ package
installed will result in the best performance. If using a
callable function/object and the ``axis`` keyword is used, then
it must be callable that can ignore NaNs (e.g. `numpy.nanmean`)
and has an ``axis`` keyword to return an array with axis
dimension(s) removed. The default is ``'median'``.
.. _bottleneck: https://github.com/kwgoodman/bottleneck
stdfunc : {'std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If set to ``'std'``
then having the optional `bottleneck`_ package installed will
result in the best performance. If using a callable
function/object and the ``axis`` keyword is used, then it must
be callable that can ignore NaNs (e.g. `numpy.nanstd`) and has
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
See Also
--------
sigma_clip, sigma_clipped_stats
Examples
--------
This example uses a data array of random variates from a Gaussian
distribution. We clip all points that are more than 2 sample
standard deviations from the median. The result is a masked array,
where the mask is `True` for clipped data::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=2, maxiters=5)
>>> filtered_data = sigclip(randvar)
This example clips all points that are more than 3 sigma relative to
the sample *mean*, clips until convergence, returns an unmasked
`~numpy.ndarray`, and modifies the data in-place::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import randn
>>> from numpy import mean
>>> randvar = randn(10000)
>>> sigclip = SigmaClip(sigma=3, maxiters=None, cenfunc='mean')
>>> filtered_data = sigclip(randvar, masked=False, copy=False)
This example sigma clips along one axis::
>>> from astropy.stats import SigmaClip
>>> from numpy.random import normal
>>> from numpy import arange, diag, ones
>>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))
>>> sigclip = SigmaClip(sigma=2.3)
>>> filtered_data = sigclip(data, axis=0)
Note that along the other axis, no points would be clipped, as the
standard deviation is higher.
"""
@deprecated_renamed_argument('iters', 'maxiters', '3.1')
def __init__(self, sigma=3., sigma_lower=None, sigma_upper=None,
maxiters=5, cenfunc='median', stdfunc='std'):
self.sigma = sigma
self.sigma_lower = sigma_lower or sigma
self.sigma_upper = sigma_upper or sigma
self.maxiters = maxiters or np.inf
self.cenfunc = self._parse_cenfunc(cenfunc)
self.stdfunc = self._parse_stdfunc(stdfunc)
def __repr__(self):
return ('SigmaClip(sigma={0}, sigma_lower={1}, sigma_upper={2}, '
'maxiters={3}, cenfunc={4}, stdfunc={5})'
.format(self.sigma, self.sigma_lower, self.sigma_upper,
self.maxiters, self.cenfunc, self.stdfunc))
def __str__(self):
lines = ['<' + self.__class__.__name__ + '>']
attrs = ['sigma', 'sigma_lower', 'sigma_upper', 'maxiters', 'cenfunc',
'stdfunc']
for attr in attrs:
lines.append(' {0}: {1}'.format(attr, getattr(self, attr)))
return '\n'.join(lines)
def _parse_cenfunc(self, cenfunc):
if isinstance(cenfunc, str):
if cenfunc == 'median':
if HAS_BOTTLENECK:
cenfunc = _nanmedian
else:
cenfunc = np.nanmedian # pragma: no cover
elif cenfunc == 'mean':
if HAS_BOTTLENECK:
cenfunc = _nanmean
else:
cenfunc = np.nanmean # pragma: no cover
else:
raise ValueError('{} is an invalid cenfunc.'.format(cenfunc))
return cenfunc
def _parse_stdfunc(self, stdfunc):
if isinstance(stdfunc, str):
if stdfunc != 'std':
raise ValueError('{} is an invalid stdfunc.'.format(stdfunc))
if HAS_BOTTLENECK:
stdfunc = _nanstd
else:
stdfunc = np.nanstd # pragma: no cover
return stdfunc
def _compute_bounds(self, data, axis=None):
# ignore RuntimeWarning if the array (or along an axis) has only
# NaNs
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
self._max_value = self.cenfunc(data, axis=axis)
std = self.stdfunc(data, axis=axis)
self._min_value = self._max_value - (std * self.sigma_lower)
self._max_value += std * self.sigma_upper
def _sigmaclip_noaxis(self, data, masked=True, return_bounds=False,
copy=True):
"""
Sigma clip the data when ``axis`` is None.
In this simple case, we remove clipped elements from the
flattened array during each iteration.
"""
filtered_data = data.ravel()
# remove masked values and convert to ndarray
if isinstance(filtered_data, np.ma.MaskedArray):
filtered_data = filtered_data.data[~filtered_data.mask]
# remove invalid values
good_mask = np.isfinite(filtered_data)
if np.any(~good_mask):
filtered_data = filtered_data[good_mask]
warnings.warn('Input data contains invalid values (NaNs or '
'infs), which were automatically clipped.',
AstropyUserWarning)
nchanged = 1
iteration = 0
while nchanged != 0 and (iteration < self.maxiters):
iteration += 1
size = filtered_data.size
self._compute_bounds(filtered_data, axis=None)
filtered_data = filtered_data[(filtered_data >= self._min_value) &
(filtered_data <= self._max_value)]
nchanged = size - filtered_data.size
self._niterations = iteration
if masked:
# return a masked array and optional bounds
filtered_data = np.ma.masked_invalid(data, copy=copy)
# update the mask in place, ignoring RuntimeWarnings for
# comparisons with NaN data values
with np.errstate(invalid='ignore'):
filtered_data.mask |= np.logical_or(data < self._min_value,
data > self._max_value)
if return_bounds:
return filtered_data, self._min_value, self._max_value
else:
return filtered_data
def _sigmaclip_withaxis(self, data, axis=None, masked=True,
return_bounds=False, copy=True):
"""
Sigma clip the data when ``axis`` is specified.
In this case, we replace clipped values with NaNs as placeholder
values.
"""
# float array type is needed to insert nans into the array
filtered_data = data.astype(float) # also makes a copy
# remove invalid values
bad_mask = ~np.isfinite(filtered_data)
if np.any(bad_mask):
filtered_data[bad_mask] = np.nan
warnings.warn('Input data contains invalid values (NaNs or '
'infs), which were automatically clipped.',
AstropyUserWarning)
# remove masked values and convert to plain ndarray
if isinstance(filtered_data, np.ma.MaskedArray):
filtered_data = np.ma.masked_invalid(filtered_data).astype(float)
filtered_data = filtered_data.filled(np.nan)
# convert negative axis/axes
if not isiterable(axis):
axis = (axis,)
axis = tuple(filtered_data.ndim + n if n < 0 else n for n in axis)
# define the shape of min/max arrays so that they can be broadcast
# with the data
mshape = tuple(1 if dim in axis else size
for dim, size in enumerate(filtered_data.shape))
nchanged = 1
iteration = 0
while nchanged != 0 and (iteration < self.maxiters):
iteration += 1
n_nan = np.count_nonzero(np.isnan(filtered_data))
self._compute_bounds(filtered_data, axis=axis)
if not np.isscalar(self._min_value):
self._min_value = self._min_value.reshape(mshape)
self._max_value = self._max_value.reshape(mshape)
with np.errstate(invalid='ignore'):
filtered_data[(filtered_data < self._min_value) |
(filtered_data > self._max_value)] = np.nan
nchanged = n_nan - np.count_nonzero(np.isnan(filtered_data))
self._niterations = iteration
if masked:
# create an output masked array
if copy:
filtered_data = np.ma.masked_invalid(filtered_data)
else:
# ignore RuntimeWarnings for comparisons with NaN data values
with np.errstate(invalid='ignore'):
out = np.ma.masked_invalid(data, copy=False)
filtered_data = np.ma.masked_where(np.logical_or(
out < self._min_value, out > self._max_value),
out, copy=False)
if return_bounds:
return filtered_data, self._min_value, self._max_value
else:
return filtered_data
[docs] def __call__(self, data, axis=None, masked=True, return_bounds=False,
copy=True):
"""
Perform sigma clipping on the provided data.
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
The data to be sigma clipped.
axis : `None` or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If `None`,
then the flattened data will be used. ``axis`` is passed
to the ``cenfunc`` and ``stdfunc``. The default is `None`.
masked : bool, optional
If `True`, then a `~numpy.ma.MaskedArray` is returned, where
the mask is `True` for clipped values. If `False`, then a
`~numpy.ndarray` and the minimum and maximum clipping
thresholds are returned. The default is `True`.
return_bounds : bool, optional
If `True`, then the minimum and maximum clipping bounds are
also returned.
copy : bool, optional
If `True`, then the ``data`` array will be copied. If
`False` and ``masked=True``, then the returned masked array
data will contain the same array as the input ``data`` (if
``data`` is a `~numpy.ndarray` or `~numpy.ma.MaskedArray`).
The default is `True`.
Returns
-------
result : flexible
If ``masked=True``, then a `~numpy.ma.MaskedArray` is
returned, where the mask is `True` for clipped values. If
``masked=False``, then a `~numpy.ndarray` is returned.
If ``return_bounds=True``, then in addition to the (masked)
array above, the minimum and maximum clipping bounds are
returned.
If ``masked=False`` and ``axis=None``, then the output array
is a flattened 1D `~numpy.ndarray` where the clipped values
have been removed. If ``return_bounds=True`` then the
returned minimum and maximum thresholds are scalars.
If ``masked=False`` and ``axis`` is specified, then the
output `~numpy.ndarray` will have the same shape as the
input ``data`` and contain ``np.nan`` where values were
clipped. If ``return_bounds=True`` then the returned
minimum and maximum clipping thresholds will be be
`~numpy.ndarray`\\s.
"""
data = np.asanyarray(data)
if data.size == 0:
return data
if isinstance(data, np.ma.MaskedArray) and data.mask.all():
return data
# These two cases are treated separately because when
# ``axis=None`` we can simply remove clipped values from the
# array. This is not possible when ``axis`` is specified, so
# instead we replace clipped values with NaNs as a placeholder
# value.
if axis is None:
return self._sigmaclip_noaxis(data, masked=masked,
return_bounds=return_bounds,
copy=copy)
else:
return self._sigmaclip_withaxis(data, axis=axis, masked=masked,
return_bounds=return_bounds,
copy=copy)
[docs]@deprecated_renamed_argument('iters', 'maxiters', '3.1')
def sigma_clip(data, sigma=3, sigma_lower=None, sigma_upper=None, maxiters=5,
cenfunc='median', stdfunc='std', axis=None, masked=True,
return_bounds=False, copy=True):
"""
Perform sigma-clipping on the provided data.
The data will be iterated over, each time rejecting values that are
less or more than a specified number of standard deviations from a
center value.
Clipped (rejected) pixels are those where::
data < cenfunc(data [,axis=int]) - (sigma_lower * stdfunc(data [,axis=int]))
data > cenfunc(data [,axis=int]) + (sigma_upper * stdfunc(data [,axis=int]))
Invalid data values (i.e. NaN or inf) are automatically clipped.
For an object-oriented interface to sigma clipping, see
:class:`SigmaClip`.
.. note::
`scipy.stats.sigmaclip
<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sigmaclip.html>`_
provides a subset of the functionality in this class. Also, its
input data cannot be a masked array and it does not handle data
that contains invalid values (i.e. NaN or inf). Also note that
it uses the mean as the centering function.
If your data is a `~numpy.ndarray` with no invalid values and
you want to use the mean as the centering function with
``axis=None`` and iterate to convergence, then
`scipy.stats.sigmaclip` is ~25-30% faster than the equivalent
settings here (``sigma_clip(data, cenfunc='mean', maxiters=None,
axis=None)``).
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
The data to be sigma clipped.
sigma : float, optional
The number of standard deviations to use for both the lower and
upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is
3.
sigma_lower : float or `None`, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or `None`, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or `None`, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute the
center value for the clipping. If set to ``'median'`` or
``'mean'`` then having the optional `bottleneck`_ package
installed will result in the best performance. If using a
callable function/object and the ``axis`` keyword is used, then
it must be callable that can ignore NaNs (e.g. `numpy.nanmean`)
and has an ``axis`` keyword to return an array with axis
dimension(s) removed. The default is ``'median'``.
.. _bottleneck: https://github.com/kwgoodman/bottleneck
stdfunc : {'std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If set to ``'std'``
then having the optional `bottleneck`_ package installed will
result in the best performance. If using a callable
function/object and the ``axis`` keyword is used, then it must
be callable that can ignore NaNs (e.g. `numpy.nanstd`) and has
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
axis : `None` or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If `None`,
then the flattened data will be used. ``axis`` is passed to the
``cenfunc`` and ``stdfunc``. The default is `None`.
masked : bool, optional
If `True`, then a `~numpy.ma.MaskedArray` is returned, where the
mask is `True` for clipped values. If `False`, then a
`~numpy.ndarray` and the minimum and maximum clipping thresholds
are returned. The default is `True`.
return_bounds : bool, optional
If `True`, then the minimum and maximum clipping bounds are also
returned.
copy : bool, optional
If `True`, then the ``data`` array will be copied. If `False`
and ``masked=True``, then the returned masked array data will
contain the same array as the input ``data`` (if ``data`` is a
`~numpy.ndarray` or `~numpy.ma.MaskedArray`). The default is
`True`.
Returns
-------
result : flexible
If ``masked=True``, then a `~numpy.ma.MaskedArray` is returned,
where the mask is `True` for clipped values. If
``masked=False``, then a `~numpy.ndarray` is returned.
If ``return_bounds=True``, then in addition to the (masked)
array above, the minimum and maximum clipping bounds are
returned.
If ``masked=False`` and ``axis=None``, then the output array is
a flattened 1D `~numpy.ndarray` where the clipped values have
been removed. If ``return_bounds=True`` then the returned
minimum and maximum thresholds are scalars.
If ``masked=False`` and ``axis`` is specified, then the output
`~numpy.ndarray` will have the same shape as the input ``data``
and contain ``np.nan`` where values were clipped. If
``return_bounds=True`` then the returned minimum and maximum
clipping thresholds will be be `~numpy.ndarray`\\s.
See Also
--------
SigmaClip, sigma_clipped_stats
Examples
--------
This example uses a data array of random variates from a Gaussian
distribution. We clip all points that are more than 2 sample
standard deviations from the median. The result is a masked array,
where the mask is `True` for clipped data::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import randn
>>> randvar = randn(10000)
>>> filtered_data = sigma_clip(randvar, sigma=2, maxiters=5)
This example clips all points that are more than 3 sigma relative to
the sample *mean*, clips until convergence, returns an unmasked
`~numpy.ndarray`, and does not copy the data::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import randn
>>> from numpy import mean
>>> randvar = randn(10000)
>>> filtered_data = sigma_clip(randvar, sigma=3, maxiters=None,
... cenfunc=mean, masked=False, copy=False)
This example sigma clips along one axis::
>>> from astropy.stats import sigma_clip
>>> from numpy.random import normal
>>> from numpy import arange, diag, ones
>>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))
>>> filtered_data = sigma_clip(data, sigma=2.3, axis=0)
Note that along the other axis, no points would be clipped, as the
standard deviation is higher.
"""
sigclip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower,
sigma_upper=sigma_upper, maxiters=maxiters,
cenfunc=cenfunc, stdfunc=stdfunc)
return sigclip(data, axis=axis, masked=masked,
return_bounds=return_bounds, copy=copy)
[docs]@deprecated_renamed_argument('iters', 'maxiters', '3.1')
def sigma_clipped_stats(data, mask=None, mask_value=None, sigma=3.0,
sigma_lower=None, sigma_upper=None, maxiters=5,
cenfunc='median', stdfunc='std', std_ddof=0,
axis=None):
"""
Calculate sigma-clipped statistics on the provided data.
Parameters
----------
data : array-like or `~numpy.ma.MaskedArray`
Data array or object that can be converted to an array.
mask : `numpy.ndarray` (bool), optional
A boolean mask with the same shape as ``data``, where a `True`
value indicates the corresponding element of ``data`` is masked.
Masked pixels are excluded when computing the statistics.
mask_value : float, optional
A data value (e.g., ``0.0``) that is ignored when computing the
statistics. ``mask_value`` will be masked in addition to any
input ``mask``.
sigma : float, optional
The number of standard deviations to use for both the lower and
upper clipping limit. These limits are overridden by
``sigma_lower`` and ``sigma_upper``, if input. The default is
3.
sigma_lower : float or `None`, optional
The number of standard deviations to use as the lower bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
sigma_upper : float or `None`, optional
The number of standard deviations to use as the upper bound for
the clipping limit. If `None` then the value of ``sigma`` is
used. The default is `None`.
maxiters : int or `None`, optional
The maximum number of sigma-clipping iterations to perform or
`None` to clip until convergence is achieved (i.e., iterate
until the last iteration clips nothing). If convergence is
achieved prior to ``maxiters`` iterations, the clipping
iterations will stop. The default is 5.
cenfunc : {'median', 'mean'} or callable, optional
The statistic or callable function/object used to compute the
center value for the clipping. If set to ``'median'`` or
``'mean'`` then having the optional `bottleneck`_ package
installed will result in the best performance. If using a
callable function/object and the ``axis`` keyword is used, then
it must be callable that can ignore NaNs (e.g. `numpy.nanmean`)
and has an ``axis`` keyword to return an array with axis
dimension(s) removed. The default is ``'median'``.
.. _bottleneck: https://github.com/kwgoodman/bottleneck
stdfunc : {'std'} or callable, optional
The statistic or callable function/object used to compute the
standard deviation about the center value. If set to ``'std'``
then having the optional `bottleneck`_ package installed will
result in the best performance. If using a callable
function/object and the ``axis`` keyword is used, then it must
be callable that can ignore NaNs (e.g. `numpy.nanstd`) and has
an ``axis`` keyword to return an array with axis dimension(s)
removed. The default is ``'std'``.
std_ddof : int, optional
The delta degrees of freedom for the standard deviation
calculation. The divisor used in the calculation is ``N -
std_ddof``, where ``N`` represents the number of elements. The
default is 0.
axis : `None` or int or tuple of int, optional
The axis or axes along which to sigma clip the data. If `None`,
then the flattened data will be used. ``axis`` is passed
to the ``cenfunc`` and ``stdfunc``. The default is `None`.
Returns
-------
mean, median, stddev : float
The mean, median, and standard deviation of the sigma-clipped
data.
See Also
--------
SigmaClip, sigma_clip
"""
if mask is not None:
data = np.ma.MaskedArray(data, mask)
if mask_value is not None:
data = np.ma.masked_values(data, mask_value)
sigclip = SigmaClip(sigma=sigma, sigma_lower=sigma_lower,
sigma_upper=sigma_upper, maxiters=maxiters,
cenfunc=cenfunc, stdfunc=stdfunc)
data_clipped = sigclip(data, axis=axis, masked=False, return_bounds=False,
copy=False)
if HAS_BOTTLENECK:
mean = _nanmean(data_clipped, axis=axis)
median = _nanmedian(data_clipped, axis=axis)
std = _nanstd(data_clipped, ddof=std_ddof, axis=axis)
else: # pragma: no cover
mean = np.nanmean(data_clipped, axis=axis)
median = np.nanmedian(data_clipped, axis=axis)
std = np.nanstd(data_clipped, ddof=std_ddof, axis=axis)
return mean, median, std