B q\y @sddlZddlZddlmZddlmZddlmZyddl Z dZ ddl m Z Wne k rhdZ YnXdd d gZd d Zdd dZdddZd ddZGdddZedddd!dd Zedddd"dd ZdS)#N) isiterable)deprecated_renamed_argument)AstropyUserWarningT)QuantityF SigmaClip sigma_clipsigma_clipped_statscs`t}tfddt|jD7tt|j}t||}|d|j|d}|S)z 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 c3s|]}|kr|VqdS)N).0i)axisr ;lib/python3.7/site-packages/astropy/stats/sigma_clipping.py "sz)_move_tuple_axes_first..)N)lentuplerangendimnpZmoveaxisreshapeshape)arrayr ZnaxisZ destinationZ array_newr )r r _move_tuple_axes_firsts  rcCsJt|trt||d}d}t|tr8|tj||dStj||dSdS)z3Bottleneck nanmean function that handle tuple axis.)r rN) isinstancerrr__array_wrap__ bottlenecknanmean)rr r r r _nanmean1s    rcCsJt|trt||d}d}t|tr8|tj||dStj||dSdS)z5Bottleneck nanmedian function that handle tuple axis.)r rN)rrrrrr nanmedian)rr r r r _nanmedian>s    rcCsNt|trt||d}d}t|tr:|tj|||dStj|||dSdS)z2Bottleneck nanstd function that handle tuple axis.)r r)r ddofN)rrrrrrnanstd)rr r r r r _nanstdKs     r"c@sneZdZdZeddddd d Zd d ZddZddZddZ dddZ d ddZ d!ddZ d"ddZ dS)#raJ 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 `_ 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. itersmaxitersz3.1@NmedianstdcCsB||_|p ||_|p||_|p"tj|_|||_|||_ dS)N) sigma sigma_lower sigma_upperrinfr$_parse_cenfunccenfunc_parse_stdfuncstdfunc)selfr)r*r+r$r.r0r r r __init__s     zSigmaClip.__init__cCs d|j|j|j|j|j|jS)Nz^SigmaClip(sigma={0}, sigma_lower={1}, sigma_upper={2}, maxiters={3}, cenfunc={4}, stdfunc={5}))formatr)r*r+r$r.r0)r1r r r __repr__s zSigmaClip.__repr__c CsRd|jjdg}ddddddg}x$|D]}|d |t||q(Wd |S) N<>r)r*r+r$r.r0z {0}: {1} ) __class____name__appendr3getattrjoin)r1linesZattrsattrr r r __str__s   zSigmaClip.__str__cCsPt|trL|dkr$trt}qLtj}n(|dkr>tr6t}qLtj}ntd ||S)Nr'meanz{} is an invalid cenfunc.) rstrHAS_BOTTLENECKrrrrr ValueErrorr3)r1r.r r r r-s zSigmaClip._parse_cenfunccCs4t|tr0|dkr td|tr*t}ntj}|S)Nr(z{} is an invalid stdfunc.)rrArCr3rBr"rr!)r1r0r r r r/s zSigmaClip._parse_stdfuncc CsjtXtjdtd|j||d|_|j||d}|j||j|_|j||j 7_WdQRXdS)Nignore)category)r ) warningscatch_warnings simplefilterRuntimeWarningr. _max_valuer0r* _min_valuer+)r1datar r(r r r _compute_bounds s  zSigmaClip._compute_boundsTFc Cs|}t|tjjr$|j|j}t|}t|rN||}t dt d}d}xT|dkr||j kr|d7}|j } |j|dd|||jk||jk@}| |j }qXW||_|rtjj||d}tjdd(|jt||jk||jkO_WdQRX|r||j|jfS|SdS) z Sigma clip the data when ``axis`` is None. In this simple case, we remove clipped elements from the flattened array during each iteration. zTInput data contains invalid values (NaNs or infs), which were automatically clipped.rN)r )copyrD)invalid)Zravelrrma MaskedArrayrLmaskisfiniteanyrFwarnrr$sizerMrKrJ _niterationsmasked_invaliderrstate logical_or) r1rLmasked return_boundsrO filtered_dataZ good_masknchanged iterationrWr r r _sigmaclip_noaxiss4    zSigmaClip._sigmaclip_noaxisc s|tt}t|r6tj|<tdtt tj j rbtj t tjtspftfddDtfddtjD}d}d} x|dkrZ| |jkrZ| d7} tt} |jdt|js|j||_|j||_tjdd  tj|jk|jkB<Wd QRX| tt}qW| |_|r|r|tj nNtjdd <tj j |d d } tj jt| |jk| |jk| d d Wd QRX|rވ|j|jfSSd S) z Sigma clip the data when ``axis`` is specified. In this case, we replace clipped values with NaNs as placeholder values. zTInput data contains invalid values (NaNs or infs), which were automatically clipped.c3s$|]}|dkrj|n|VqdS)rN)r)r n)r^r r rbsz0SigmaClip._sigmaclip_withaxis..c3s"|]\}}|krdn|VqdS)rNNr )r ZdimrW)r r r rfsrNr)r rD)rPNF)rO)ZastypefloatrrTrUnanrFrVrrrQrRrYZfilledrr enumeraterr$Z count_nonzeroZisnanrMZisscalarrKrrJrZrXZ masked_wherer[) r1rLr r\r]rOZbad_maskZmshaper_r`Zn_nanoutr )r r^r _sigmaclip_withaxisFsL      $ zSigmaClip._sigmaclip_withaxiscCsft|}|jdkr|St|tjjr4|jr4|S|dkrN|j||||dS|j |||||dSdS)a 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. rN)r\r]rO)r r\r]rO) rZ asanyarrayrWrrQrRrSallrarg)r1rLr r\r]rOr r r __call__s8   zSigmaClip.__call__)r%NNr&r'r()N)TFT)NTFT)NTFT)r9 __module__ __qualname____doc__rr2r4r?r-r/rMrargrir r r r rYsw    1 Gr#r$z3.1r&r'r(c Cs&t||||||d} | |||| | dS)a8 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 `_ 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. )r)r*r+r$r.r0)r r\r]rO)r) rLr)r*r+r$r.r0r r\r]rOsigclipr r r rs * @c Cs|dk rtj||}|dk r,tj||}t||||||d} | || dddd} tr~t| | d} t| | d}t| | | d}n,tj | | d} tj | | d}tj | | | d}| ||fS)aN 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 N)r)r*r+r$r.r0F)r r\r]rO)r )r r ) rrQrRZ masked_valuesrrBrrr"rrr!)rLrSZ mask_valuer)r*r+r$r.r0Zstd_ddofr rnZ data_clippedr@r'r(r r r rs"Z    )N)N)Nr) rmNNr&r'r(NTFT) NNroNNr&r'r(rN)rFZnumpyrZ astropy.utilsrZastropy.utils.decoratorsrZastropy.utils.exceptionsrrrBZ astropy.unitsr ImportError__all__rrrr"rrrr r r r s:        /