How to compensate for differential rotation in the default sunpy image. This is based upon a combination of pr/452 and pr/482 my branch for which can be found here: https://github.com/Cadair/sunpy/tree/map_refactor_diff_rot

gistfile1.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 29 21:31:58 2013

@author: stuart
"""

import numpy as np
from datetime import timedelta

import matplotlib.pyplot as plt
plt.ion()

from skimage import transform
from skimage.util import img_as_float

import sunpy
import sunpy.wcs as wcs
import sunpy.coords.util as cu
from sunpy.time import parse_time

data = sunpy.Map(sunpy.AIA_171_IMAGE)

#Scikit-image needs float arrays between 0 and 1
def to_norm(arr):
    arr = img_as_float(arr)
    if arr.min() < 0:
        arr += arr.min()
    arr /= arr.max()
    return arr

def un_norm(arr, ori):
    arr *= ori.max()
    if ori.min() < 0:
        arr -= ori.min()
    
    return arr   

#Define a function that returns a new list of coordinates for each input coord.
def warp_sun2(xy, data):
    x,y = xy.T
    
    #Define some tuples
    scale = [data.scale['x'], data.scale['y']]
    ref_pix = [data.reference_pixel['x'], data.reference_pixel['y']]
    ref_coord = [data.reference_coordinate['x'], data.reference_coordinate['y']]

    #Calculate the hpc coords
    hpc_coords = wcs.convert_pixel_to_data(data.shape, scale,
                                           ref_pix, ref_coord)
    
    #Do the diff rot
    rotted = cu.rot_hpc(hpc_coords[1], hpc_coords[0], data.date,
                parse_time(data.date)- timedelta(days=2))
    
    #Go back to pixel coords
    x2,y2 = wcs.convert_data_to_pixel(rotted[0], rotted[1], scale, ref_pix,
                                      ref_coord)
    
    #Restack the data to make it correct output form
    xy2 = np.column_stack([x2.flat,y2.flat])
    
    #Remove NaNs
    mask = np.isnan(xy2)
    xy2[mask] = 0.0

    return xy2
    

#Do the warp:
out = transform.warp(to_norm(data.data), inverse_map=warp_sun2, map_args={'data':data})
#de norm
out = un_norm(out, data.data)
plt.figure()

plt.subplot(121)
plt.imshow(data.data, cmap=data.cmap, origin='lower', norm=data.norm())

plt.subplot(122)
plt.imshow(out, cmap=data.cmap, origin='lower',norm=data.norm())

An implementation of this is being discussed in sunpy/sunpy#1530