map_hmi_cropped_resampled = map_hmi_cropped.resample(dimensions, method='linear') # Open the map and create a cropped version for the visualisation. #map_boundary = mp.Map('C:\\git\\solarbextrapolation\\examples\\2011-02-14__20-35-25__02_aia.fits') # For AIA map_boundary = mp.Map( 'C:\\git\\solarbextrapolation\\examples\\2011-02-14__20-35-25__01_hmi.fits' ) # For HMI map_boundary_cropped = map_boundary.submap(xrangeextended, yrangeextended) # Only extrapolate if we don't have a saved version if not os.path.isfile(str_vol_filepath): aPotExt = PotentialExtrapolator(map_hmi_cropped_resampled, filepath=str_vol_filepath, zshape=dimensions[0].value, zrange=zrange) aMap3D = aPotExt.extrapolate() aMap3D = Map3D.load(str_vol_filepath) print('\nextrapolation duration: ' + str(np.round(aMap3D.meta['extrapolator_duration'], 3)) + ' s\n') # Visualise this visualise(aMap3D, boundary=map_boundary_cropped, scale=1.0 * u.Mm, boundary_unit=1.0 * u.arcsec, show_boundary_axes=False, show_volume_axes=True, debug=False) mlab.show()
def text_load_Map3d(text_save_Map3d): aMap3D = Map3D.load(text_save_Map3d) # Compare the returned data array assert (aMap3D.data == np.zeros((2,2,2,2))).all()
################################################################################ # To speed up repeat usage of this script it will save the extrapolation output, # you can use os.path.isfile() to check if the file already exists, assuming it # doesn't you will extrapolate and create it, otherwise you load it. # Only extrapolate if we don't have a saved version str_vol_filepath = data_hmi[0][0:-5] + '_Bxyz.npy' if not os.path.isfile(str_vol_filepath): # Create the potential extrapolator and run the extrapolate method. aPotExt = PotentialExtrapolator(map_hmi_cropped_resampled, filepath=str_vol_filepath, zshape=20, zrange=zrange) aMap3D = aPotExt.extrapolate() # Load the results. aMap3D = Map3D.load(str_vol_filepath) #print '\nextrapolation duration: ' + str(np.round(aMap3D.meta['extrapolator_duration'],3)) + ' s\n' ################################################################################ # For the perposes of visualisation we will want an extended boundary data, not # just that of the extrapolated region, and at the instruments full resolution, # not resampled. xrangeextended = u.Quantity([xrange.value[0] - 50, xrange.value[1] + 50] * xrange.unit) yrangeextended = u.Quantity([yrange.value[0] - 50, yrange.value[1] + 50] * yrange.unit) # Open the map and create a cropped version for the visualisation. map_boundary = mp.Map(data_hmi[0]) map_boundary_cropped = map_boundary.submap(xrangeextended, yrangeextended)