Exemple #1
0
    def resample(self, dimensions, method='linear'):
        """Returns a new Map that has been resampled up or down

        Arbitrary resampling of the Map to new dimension sizes.

        Uses the same parameters and creates the same co-ordinate lookup points
        as IDL''s congrid routine, which apparently originally came from a
        VAX/VMS routine of the same name.

        Parameters
        ----------
        dimensions : tuple
            Dimensions that new Map should have.
            Note: the first argument corresponds to the 'x' axis and the second
            argument corresponds to the 'y' axis.
        method : {'neighbor' | 'nearest' | 'linear' | 'spline'}
            Method to use for resampling interpolation.
                * neighbor - Closest value from original data
                * nearest and linear - Uses n x 1-D interpolations using
                  scipy.interpolate.interp1d
                * spline - Uses ndimage.map_coordinates

        Returns
        -------
        out : Map
            A new Map which has been resampled to the desired dimensions.

        References
        ----------
        | http://www.scipy.org/Cookbook/Rebinning (Original source, 2011/11/19)
        """

        # Note: because the underlying ndarray is transposed in sense when
        #   compared to the Map, the ndarray is transposed, resampled, then
        #   transposed back
        # Note: "center" defaults to True in this function because data
        #   coordinates in a Map are at pixel centers

        # Make a copy of the original data and perform resample
        new_data = sunpy_image_resample(self.data.copy().T, dimensions,
                                    method, center=True)
        new_data = new_data.T

        # Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
        # respectively
        scale_factor_x = (float(self.shape[1]) / dimensions[0])
        scale_factor_y = (float(self.shape[0]) / dimensions[1])

        new_map = deepcopy(self)
        # Update image scale and number of pixels
        new_meta = self.meta.copy()

        # Update metadata
        new_meta['cdelt1'] *= scale_factor_x
        new_meta['cdelt2'] *= scale_factor_y
        if 'CD1_1' in new_meta:
            new_meta['CD1_1'] *= scale_factor_x
            new_meta['CD2_1'] *= scale_factor_x
            new_meta['CD1_2'] *= scale_factor_y
            new_meta['CD2_2'] *= scale_factor_y
        new_meta['crpix1'] = (dimensions[0] + 1) / 2.
        new_meta['crpix2'] = (dimensions[1] + 1) / 2.
        new_meta['crval1'] = self.center['x']
        new_meta['crval2'] = self.center['y']

        # Create new map instance
        new_map.data = new_data
        new_map.meta = new_meta
        return new_map
Exemple #2
0
    def resample(self, dimensions, method='linear'):
        """Returns a new Map that has been resampled up or down

        Arbitrary resampling of the Map to new dimension sizes.

        Uses the same parameters and creates the same co-ordinate lookup points
        as IDL''s congrid routine, which apparently originally came from a
        VAX/VMS routine of the same name.

        Parameters
        ----------
        dimensions : tuple
            Dimensions that new Map should have.
            Note: the first argument corresponds to the 'x' axis and the second
            argument corresponds to the 'y' axis.
        method : {'neighbor' | 'nearest' | 'linear' | 'spline'}
            Method to use for resampling interpolation.
                * neighbor - Closest value from original data
                * nearest and linear - Uses n x 1-D interpolations using
                  scipy.interpolate.interp1d
                * spline - Uses ndimage.map_coordinates

        Returns
        -------
        out : Map
            A new Map which has been resampled to the desired dimensions.

        References
        ----------
        | http://www.scipy.org/Cookbook/Rebinning (Original source, 2011/11/19)
        """

        # Note: because the underlying ndarray is transposed in sense when
        #   compared to the Map, the ndarray is transposed, resampled, then
        #   transposed back
        # Note: "center" defaults to True in this function because data
        #   coordinates in a Map are at pixel centers

        # Make a copy of the original data and perform resample
        new_data = sunpy_image_resample(self.data.copy().T,
                                        dimensions,
                                        method,
                                        center=True)
        new_data = new_data.T

        # Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
        # respectively
        scale_factor_x = (float(self.shape[1]) / dimensions[0])
        scale_factor_y = (float(self.shape[0]) / dimensions[1])

        new_map = deepcopy(self)
        # Update image scale and number of pixels
        new_meta = self.meta.copy()

        # Update metadata
        new_meta['cdelt1'] *= scale_factor_x
        new_meta['cdelt2'] *= scale_factor_y
        new_meta['crpix1'] = (dimensions[0] + 1) / 2.
        new_meta['crpix2'] = (dimensions[1] + 1) / 2.
        new_meta['crval1'] = self.center['x']
        new_meta['crval2'] = self.center['y']

        # Create new map instance
        new_map.data = new_data
        new_map.meta = new_meta
        return new_map
Exemple #3
0
    def resample(self, dimensions, method="linear"):
        """Returns a new Map that has been resampled up or down

        Arbitrary resampling of the Map to new dimension sizes.

        Uses the same parameters and creates the same co-ordinate lookup points
        as IDL''s congrid routine, which apparently originally came from a
        VAX/VMS routine of the same name.

        Parameters
        ----------
        dimensions : `~astropy.units.Quantity`
            Pixel dimensions that new Map should have.
            Note: the first argument corresponds to the 'x' axis and the second
            argument corresponds to the 'y' axis.
        method : {'neighbor' | 'nearest' | 'linear' | 'spline'}
            Method to use for resampling interpolation.
                * neighbor - Closest value from original data
                * nearest and linear - Uses n x 1-D interpolations using
                  scipy.interpolate.interp1d
                * spline - Uses ndimage.map_coordinates

        Returns
        -------
        out : `~sunpy.map.GenericMap` or subclass
            A new Map which has been resampled to the desired dimensions.

        References
        ----------
        * `Rebinning <http://www.scipy.org/Cookbook/Rebinning>`_ (Original source, 2011/11/19)
        """

        # Note: because the underlying ndarray is transposed in sense when
        #   compared to the Map, the ndarray is transposed, resampled, then
        #   transposed back
        # Note: "center" defaults to True in this function because data
        #   coordinates in a Map are at pixel centers

        # Make a copy of the original data and perform resample
        new_data = sunpy_image_resample(self.data.copy().T, dimensions, method, center=True)
        new_data = new_data.T

        scale_factor_x = float(self.dimensions[0] / dimensions[0])
        scale_factor_y = float(self.dimensions[1] / dimensions[1])

        new_map = deepcopy(self)
        # Update image scale and number of pixels
        new_meta = self.meta.copy()

        # Update metadata
        new_meta["cdelt1"] *= scale_factor_x
        new_meta["cdelt2"] *= scale_factor_y
        if "CD1_1" in new_meta:
            new_meta["CD1_1"] *= scale_factor_x
            new_meta["CD2_1"] *= scale_factor_x
            new_meta["CD1_2"] *= scale_factor_y
            new_meta["CD2_2"] *= scale_factor_y
        new_meta["crpix1"] = (dimensions[0].value + 1) / 2.0
        new_meta["crpix2"] = (dimensions[1].value + 1) / 2.0
        new_meta["crval1"] = self.center.x.value
        new_meta["crval2"] = self.center.y.value

        # Create new map instance
        new_map.data = new_data
        new_map.meta = new_meta
        return new_map