# Use potential extrapolator to generate field
aPotExt = PotentialExtrapolator(map_boundary, zshape=arr_grid_shape[2], zrange=zrange)
aMap3D = aPotExt.extrapolate(enable_numba=True)
# The Extrapolations run time is stored in the meta
floSeconds = np.round(aMap3D.meta['extrapolator_duration'],3)
print('\nextrapolation duration: ' + str(floSeconds) + ' s\n')
##############################################################################
# Note that you used enable_numba=True to speed up the computation on systems
# with Anaconda numba installed.
##############################################################################
# You can now get a quick and easy visualisation using the
# solarbextrapolation.example_data_generator.visualise tools:
# Visualise the 3D vector field
fig = visualise(aMap3D,
boundary=map_boundary,
volume_units=[1.0*u.arcsec, 1.0*u.arcsec, 1.0*u.Mm],
show_boundary_axes=False,
boundary_units=[1.0*u.arcsec, 1.0*u.arcsec],
show_volume_axes=True,
debug=False)
mlab.show()
##############################################################################
# Note that the parameters here are simply to decide what boundary ranges
# to display.
self.meta['extrapolator_routine'] = 'Ones Extrapolator'
#arr_4d = np.ones([self.map_boundary_data.data.shape[0], self.map_boundary_data.data.shape[0], self.z, 3])
arr_4d = np.ones(self.shape.tolist() + [3])
return Map3D(arr_4d, self.meta)
###########################################################################
# Instansiate the preprocessor and extrapolate.
aExt = ExtOnes(aPreProMap, zshape=10)
aMap3D = aExt.extrapolate()
###########################################################################
# You can visulise the field using MayaVi.
fig = visualise(aMap3D,
boundary=aPreProMap,
show_boundary_axes=False,
show_volume_axes=False,
debug=False)
mlab.show()
"""
# aPreProData = aMap2D.submap([0,10], [0,10])
# Some checks:
#aPreProData.data # Should be a 2D zeros array.
#aPreProData.meta
#aPreProData.meta['preprocessor_routine']
#aPreProData.meta['preprocessor_start_time']
###########################################################################
# Use potential extrapolator to generate field
aPotExt = PotentialExtrapolator(map_boundary,
zshape=arr_grid_shape[2],
zrange=zrange)
aMap3D = aPotExt.extrapolate(enable_numba=True)
# The Extrapolations run time is stored in the meta
floSeconds = np.round(aMap3D.meta['extrapolator_duration'], 3)
print('\nextrapolation duration: ' + str(floSeconds) + ' s\n')
##############################################################################
# Note that you used enable_numba=True to speed up the computation on systems
# with Anaconda numba installed.
##############################################################################
# You can now get a quick and easy visualisation using the
# solarbextrapolation.example_data_generator.visualise tools:
# Visualise the 3D vector field
fig = visualise(aMap3D,
boundary=map_boundary,
volume_units=[1.0 * u.arcsec, 1.0 * u.arcsec, 1.0 * u.Mm],
show_boundary_axes=False,
boundary_units=[1.0 * u.arcsec, 1.0 * u.arcsec],
show_volume_axes=True,
debug=False)
mlab.show()
##############################################################################
# Note that the parameters here are simply to decide what boundary ranges
# to display.
#arr_4d = np.ones([self.map_boundary_data.data.shape[0], self.map_boundary_data.data.shape[0], self.z, 3])
arr_4d = np.ones(self.shape.tolist() + [3])
return Map3D(arr_4d, self.meta)
###########################################################################
# Instansiate the preprocessor and extrapolate.
aExt = ExtOnes(aPreProMap, zshape=10)
aMap3D = aExt.extrapolate()
###########################################################################
# You can visulise the field using MayaVi.
fig = visualise(aMap3D,
boundary=aPreProMap,
show_boundary_axes=False,
show_volume_axes=False,
debug=False)
mlab.show()
"""
# aPreProData = aMap2D.submap([0,10], [0,10])
# Some checks:
#aPreProData.data # Should be a 2D zeros array.
#aPreProData.meta
#aPreProData.meta['preprocessor_routine']
#aPreProData.meta['preprocessor_start_time']
###########################################################################
# Testing an extrapolator
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()
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)
################################################################################
# You can now get a quick and easy visualisation using the
# solarbextrapolation.example_data_generator.visualise tools:
# Visualise the 3D vector field
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()
