ready for help

    if (ref_clip is None) or (moving_clip is None):

        return None, None, None

    # Just takes 2d arrays, no idea about affines

    matcher_shift_x, matcher_shift_y, metrics, corrmap = func(moving_clip, ref_clip, **kwargs)

    if matcher_shift_x is None:

        return None, None, None

    # Apply the shift to the center of the moving roi to the center of the reference ROI in index space. One pixel == one index (unitless).

    # All this does is adjust from the upper left of the maximum correlation to the origin of the 2d array.

    new_affine_transformed_center_x = moving_roi.center[0] - matcher_shift_x  #Center is indices.

    new_affine_transformed_center_y = moving_roi.center[1] - matcher_shift_y

    @property

    def center(self):

        return (self.x, self.y)

        ie = self.image_extent

        return ((ie[1] - ie[0])-1)/2. + 0.5, ((ie[3]-ie[2])-1)/2. + 0.5

    @property

    def x(self):

        """

        pixels = self.image_extent

        if (np.asarray(pixels) - self.buffer < 0).any():

            raise IndexError('Image coordinates plus read buffer are outside of the available data. Please select a smaller ROI and/or a smaller read buffer.')

        if isinstance(self.data, np.ndarray):

            data = self.data[pixels[2]-self.buffer:pixels[3]+1+self.buffer, 

                             pixels[0]-self.buffer:pixels[1]+1+self.buffer]

        else:

            # Have to reformat to [xstart, ystart, xnumberpixels, ynumberpixels]

            # TODO: I think this will result in an incorrect obj.center when the passed data is a GeoDataset

            '''pixels = [pixels[0]-self.buffer, 

            pixels = [pixels[0]-self.buffer, 

                      pixels[2]-self.buffer, 

                      pixels[1]-pixels[0]+(self.buffer*2)+1, 

                      pixels[3]-pixels[2]+(self.buffer*2)+1]'''

                      pixels[3]-pixels[2]+(self.buffer*2)+1]

            pixels = map(floor, [self.x-size_x, self.y-size_y, size_x*2+1, size_y*2+1])

            data = self.data.read_array(pixels=pixels, dtype=dtype)

            return data