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
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
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
