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)
