Downscale existing array to resolution requested by cloud detector

        :param hr_array: High-resolution data array to be downscaled
        :param meta_info: Meta-info of eopatch
        :param interp: Interpolation method to be used in downscaling. Default is `'linear'`
        :param smooth: Apply Gaussian smoothing in spatial directions before downscaling. Sigma of kernel is estimated
                        by rescaling factor. Default is `True`
        :return: Down-scaled array

def _downscaling(self, hr_array, meta_info, interp='linear', smooth=True):
        """ Downscale existing array to resolution requested by cloud detector

        :param hr_array: High-resolution data array to be downscaled
        :param meta_info: Meta-info of eopatch
        :param interp: Interpolation method to be used in downscaling. Default is `'linear'`
        :param smooth: Apply Gaussian smoothing in spatial directions before downscaling. Sigma of kernel is estimated
                        by rescaling factor. Default is `True`
        :return: Down-scaled array
        """
        # Run cloud mask on full resolution
        if (self.cm_size_y is None) and (self.cm_size_x is None):
            return hr_array, None

        # Rescaling factor in spatial (height, width) dimensions
        rescale = self._get_rescale_factors(hr_array.shape[1:3], meta_info)

        if smooth:
            sigma = (0,) + tuple(int(1/x) for x in rescale) + (0,)
            hr_array = scipy.ndimage.gaussian_filter(hr_array, sigma)

        lr_array = scipy.ndimage.interpolation.zoom(hr_array, (1.0,) + rescale + (1.0,),
                                                    order=INTERP_METHODS.index(interp), mode='nearest')

        return lr_array, rescale