Source code for sunpy.physics.solar_rotation

"""
This module provides routines for applying solar rotation functions to
map sequences.
"""
import copy

import numpy as np
from scipy.ndimage import shift

import astropy.units as u

import sunpy.map
from sunpy.physics.differential_rotation import solar_rotate_coordinate
from sunpy.util.decorators import deprecated

__author__ = 'J. Ireland'

__all__ = ['calculate_solar_rotate_shift', 'mapsequence_solar_derotate']


[docs]@deprecated(since='4.0', alternative='`sunkit_image.coalignment.calculate_solar_rotate_shift`') def calculate_solar_rotate_shift(mc, layer_index=0, **kwargs): """ Calculate the shift that must be applied to each map contained in a mapsequence in order to compensate for solar rotation. The center of the map is used to calculate the position of each mapsequence layer. Shifts are calculated relative to a specified layer in the mapsequence. When using this functionality, it is a good idea to check that the shifts that were applied to were reasonable and expected. One way of checking this is to animate the original mapsequence, animate the derotated mapsequence, and compare the differences you see to the calculated shifts. An example use is as follows. If you select data from the SDO cutout service, it is common to not use the solar tracking implemented by this service. This is because (at time of writing) the solar tracking implemented by that service moves the image by single pixels at a time. This is not optimal for many use cases, as it introduces artificial jumps in the data. So with solar tracking not chosen, the selected area is like a window through which you can see the Sun rotating underneath. Parameters ---------- mc : `sunpy.map.MapSequence` The input mapsequence. layer_index : int The index layer. Shifts are calculated relative to the time of this layer. ``**kwargs`` These keywords are passed to the function `sunpy.physics.differential_rotation.solar_rotate_coordinate`. Returns ------- x, y : `~astropy.units.Quantity`, ~astropy.units.Quantity` The shifts relative to the index layer that can be applied to the input mapsequence in order to compensate for solar rotation. The shifts are given in arcseconds as understood in helioprojective coordinates systems. """ # Size of the data nt = len(mc.maps) # Storage for the shifts in arcseconds xshift_arcseconds = np.zeros(nt) * u.arcsec yshift_arcseconds = np.zeros_like(xshift_arcseconds) # Layer that rotate_to_this_layer = mc.maps[layer_index] # Calculate the rotations and the shifts for i, m in enumerate(mc): # Skip the reference layer if i == layer_index: continue # Calculate the rotation of the center of the map 'm' at its # observation time to the observation time of the reference layer # indicated by "layer_index". new_coordinate = solar_rotate_coordinate(m.center, observer=rotate_to_this_layer.observer_coordinate, **kwargs) # Calculate the shift in arcseconds xshift_arcseconds[i] = new_coordinate.Tx - rotate_to_this_layer.center.Tx yshift_arcseconds[i] = new_coordinate.Ty - rotate_to_this_layer.center.Ty return {"x": xshift_arcseconds, "y": yshift_arcseconds}
[docs]@deprecated(since='4.0', alternative='`sunkit_image.coalignment.mapsequence_coalign_by_rotation`') def mapsequence_solar_derotate(mc, layer_index=0, clip=True, shift=None, **kwargs): """ Move the layers in a mapsequence according to the input shifts. If an input shift is not given, the shifts due to solar rotation relative to an index layer is calculated and applied. When using this functionality, it is a good idea to check that the shifts that were applied to were reasonable and expected. One way of checking this is to animate the original mapsequence, animate the derotated mapsequence, and compare the differences you see to the calculated shifts. Parameters ---------- mc : `sunpy.map.MapSequence` A mapsequence of shape (ny, nx, nt), where nt is the number of layers in the mapsequence. layer_index : int Solar derotation shifts of all maps in the mapsequence are assumed to be relative to the layer in the mapsequence indexed by layer_index. clip : bool If True, then clip off x, y edges in the datasequence that are potentially affected by edges effects. ``**kwargs`` These keywords are passed to the function `sunpy.physics.solar_rotation.calculate_solar_rotate_shift`. Returns ------- output : `sunpy.map.MapSequence` The results of the shifts applied to the input mapsequence. Examples -------- >>> import sunpy.data.sample # doctest: +REMOTE_DATA >>> from sunpy.physics.solar_rotation import mapsequence_solar_derotate >>> map1 = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE) # doctest: +REMOTE_DATA >>> map2 = sunpy.map.Map(sunpy.data.sample.EIT_195_IMAGE) # doctest: +REMOTE_DATA >>> mc = sunpy.map.Map([map1, map2], sequence=True) # doctest: +REMOTE_DATA >>> derotated_mc = mapsequence_solar_derotate(mc) # doctest: +SKIP >>> derotated_mc = mapsequence_solar_derotate(mc, layer_index=-1) # doctest: +SKIP >>> derotated_mc = mapsequence_solar_derotate(mc, clip=False) # doctest: +SKIP """ # Size of the data nt = len(mc.maps) # Storage for the pixel shifts and the shifts in arcseconds xshift_keep = np.zeros(nt) * u.pix yshift_keep = np.zeros_like(xshift_keep) # If no shifts are passed in, calculate them. Otherwise, # use the shifts passed in. if shift is None: shift = calculate_solar_rotate_shift(mc, layer_index=layer_index, **kwargs) xshift_arcseconds = shift['x'] yshift_arcseconds = shift['y'] # Calculate the pixel shifts for i, m in enumerate(mc): xshift_keep[i] = xshift_arcseconds[i] / m.scale[0] yshift_keep[i] = yshift_arcseconds[i] / m.scale[1] # Apply the pixel shifts and return the mapsequence return _apply_shifts(mc, yshift_keep, xshift_keep, clip=clip)
def _apply_shifts(mc, yshift: u.pix, xshift: u.pix, clip=True, **kwargs): """ Apply a set of pixel shifts to a `~sunpy.map.MapSequence`, and return a new `~sunpy.map.MapSequence`. Parameters ---------- mc : `sunpy.map.MapSequence` A `~sunpy.map.MapSequence` of shape ``(ny, nx, nt)``, where ``nt`` is the number of layers in the `~sunpy.map.MapSequence`. ``ny`` is the number of pixels in the "y" direction, ``nx`` is the number of pixels in the "x" direction. yshift : `~astropy.units.Quantity` An array of pixel shifts in the y-direction for an image. xshift : `~astropy.units.Quantity` An array of pixel shifts in the x-direction for an image. clip : `bool`, optional If `True` (default), then clip off "x", "y" edges of the maps in the sequence that are potentially affected by edges effects. Notes ----- All other keywords are passed to `scipy.ndimage.shift`. Returns ------- `sunpy.map.MapSequence` A `~sunpy.map.MapSequence` of the same shape as the input. All layers in the `~sunpy.map.MapSequence` have been shifted according the input shifts. """ # New mapsequence will be constructed from this list new_mc = [] # Calculate the clipping if clip: yclips, xclips = _calculate_clipping(-yshift, -xshift) # Shift the data and construct the mapsequence for i, m in enumerate(mc): shifted_data = shift(copy.deepcopy(m.data), [yshift[i].value, xshift[i].value], **kwargs) new_meta = copy.deepcopy(m.meta) # Clip if required. Use the submap function to return the appropriate # portion of the data. if clip: shifted_data = _clip_edges(shifted_data, yclips, xclips) new_meta['naxis1'] = shifted_data.shape[1] new_meta['naxis2'] = shifted_data.shape[0] # Add one to go from zero-based to one-based indexing new_meta['crpix1'] = m.reference_pixel.x.value + 1 + xshift[i].value - xshift[0].value new_meta['crpix2'] = m.reference_pixel.y.value + 1 + yshift[i].value - yshift[0].value new_map = sunpy.map.Map(shifted_data, new_meta) # Append to the list new_mc.append(new_map) return sunpy.map.Map(new_mc, sequence=True) def _clip_edges(data, yclips: u.pix, xclips: u.pix): """ Clips off the "y" and "x" edges of a 2D array according to a list of pixel values. This function is useful for removing data at the edge of 2d images that may be affected by shifts from solar de- rotation and layer co- registration, leaving an image unaffected by edge effects. Parameters ---------- data : `numpy.ndarray` A numpy array of shape ``(ny, nx)``. yclips : `astropy.units.Quantity` The amount to clip in the y-direction of the data. Has units of pixels, and values should be whole non-negative numbers. xclips : `astropy.units.Quantity` The amount to clip in the x-direction of the data. Has units of pixels, and values should be whole non-negative numbers. Returns ------- `numpy.ndarray` A 2D image with edges clipped off according to ``yclips`` and ``xclips`` arrays. """ ny = data.shape[0] nx = data.shape[1] # The purpose of the int below is to ensure integer type since by default # astropy quantities are converted to floats. return data[int(yclips[0].value): ny - int(yclips[1].value), int(xclips[0].value): nx - int(xclips[1].value)] def _calculate_clipping(y: u.pix, x: u.pix): """ Return the upper and lower clipping values for the "y" and "x" directions. Parameters ---------- y : `astropy.units.Quantity` An array of pixel shifts in the y-direction for an image. x : `astropy.units.Quantity` An array of pixel shifts in the x-direction for an image. Returns ------- `tuple` The tuple is of the form ``([y0, y1], [x0, x1])``. The number of (integer) pixels that need to be clipped off at each edge in an image. The first element in the tuple is a list that gives the number of pixels to clip in the y-direction. The first element in that list is the number of rows to clip at the lower edge of the image in y. The clipped image has "clipping[0][0]" rows removed from its lower edge when compared to the original image. The second element in that list is the number of rows to clip at the upper edge of the image in y. The clipped image has "clipping[0][1]" rows removed from its upper edge when compared to the original image. The second element in the "clipping" tuple applies similarly to the x-direction (image columns). The parameters ``y0, y1, x0, x1`` have the type `~astropy.units.Quantity`. """ return ([_lower_clip(y.value), _upper_clip(y.value)] * u.pix, [_lower_clip(x.value), _upper_clip(x.value)] * u.pix) def _upper_clip(z): """ Find smallest integer bigger than all the positive entries in the input array. """ zupper = 0 zcond = z >= 0 if np.any(zcond): zupper = int(np.max(np.ceil(z[zcond]))) return zupper def _lower_clip(z): """ Find smallest positive integer bigger than the absolute values of the negative entries in the input array. """ zlower = 0 zcond = z <= 0 if np.any(zcond): zlower = int(np.max(np.ceil(-z[zcond]))) return zlower