photutils/extern/sigma_clipping.py
def perform_clip(_filtered_data, _kwargs):
        """
        Perform sigma clip by comparing the data to the minimum and
        maximum values (median + sig * standard deviation). Use
        sigma_lower and sigma_upper to get the correct limits. Data
        values less or greater than the minimum / maximum values will
        have True set in the mask array.
        """

        max_value = cenfunc(_filtered_data, **_kwargs)
        std = stdfunc(_filtered_data, **_kwargs)
        min_value = max_value - std * sigma_lower
        max_value += std * sigma_upper
        if axis is not None:
            if axis != 0:
                min_value = np.expand_dims(min_value, axis=axis)
                max_value = np.expand_dims(max_value, axis=axis)
        _filtered_data.mask |= _filtered_data > max_value
        _filtered_data.mask |= _filtered_data < min_value

        Perform sigma clip by comparing the data to the minimum and
        maximum values (median + sig * standard deviation). Use
        sigma_lower and sigma_upper to get the correct limits. Data
        values less or greater than the minimum / maximum values will
        have True set in the mask array.
        
Perform sigma clip by comparing the data to the minimum and
maximum values (median + sig * standard deviation). Use
sigma_lower and sigma_upper to get the correct limits. Data
values less or greater than the minimum / maximum values will
have True set in the mask array.
Perform sigma clip by comparing the data to the minimum and maximum values ( median + sig * standard deviation ) . Use sigma_lower and sigma_upper to get the correct limits . Data values less or greater than the minimum / maximum values will have True set in the mask array .
def perform_clip(_filtered_data, _kwargs):
        max_value = cenfunc(_filtered_data, **_kwargs)
        std = stdfunc(_filtered_data, **_kwargs)
        min_value = max_value - std * sigma_lower
        max_value += std * sigma_upper
        if axis is not None:
            if axis != 0:
                min_value = np.expand_dims(min_value, axis=axis)
                max_value = np.expand_dims(max_value, axis=axis)
        _filtered_data.mask |= _filtered_data > max_value
        _filtered_data.mask |= _filtered_data < min_value
def perform_clip ( _filtered_data , _kwargs ) : max_value = cenfunc ( _filtered_data , ** _kwargs ) std = stdfunc ( _filtered_data , ** _kwargs ) min_value = max_value - std * sigma_lower max_value += std * sigma_upper if axis is not None : if axis != 0 : min_value = np . expand_dims ( min_value , axis = axis ) max_value = np . expand_dims ( max_value , axis = axis ) _filtered_data . mask |= _filtered_data > max_value _filtered_data . mask |= _filtered_data < min_value
Perform sigma clip by comparing the data to the minimum and
maximum values (median + sig * standard deviation).
Perform sigma clip by comparing the data to the minimum and maximum values ( median + sig * standard deviation ) .
"""
        Perform sigma clip by comparing the data to the minimum and
        maximum values (median + sig * standard deviation). Use
        sigma_lower and sigma_upper to get the correct limits. Data
        values less or greater than the minimum / maximum values will
        have True set in the mask array.
        """
_filtered_data
_kwargs