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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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:: 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:: This example sigma clips along one axis:: 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)