potential_extrapolation_of_hmi_data



In [ ]:
%matplotlib inline

Extrapolating HMI Data

Example of downloading boundary data from VSO, extrapolating using the potential extrapolator and visualising.


In [ ]:
from __future__ import print_function
# General imports
import numpy as np
import sunpy.map as mp
from sunpy.net import vso
from astropy import units as u
from mayavi import mlab
import os

# Module imports
from solarbextrapolation.map3dclasses import Map3D
from solarbextrapolation.extrapolators import PotentialExtrapolator
from solarbextrapolation.visualisation_functions import visualise

# Download the HMI data from VSO

# create a new VSOClient instance
client = vso.VSOClient()

# build our query, this can return one item, or a list of them to DL (matching the filters).
result_hmi = client.query(
    # The following are filters for what data I want.
    vso.attrs.Time(
        (2011, 2, 14, 20, 34, 0), (2011, 2, 14, 21, 0, 0)),  # Time range.
    vso.attrs.Instrument('HMI'),  # Helioseismic and Magnetic Imager.
    vso.attrs.Physobs('LOS_magnetic_field'),  # Physical observables
    vso.attrs.Sample(4000 * u.s)  # Only take a shot every $var seconds.
    # More observables at http://sdac.virtualsolar.org/cgi/show_details?keyword=PHYSOBS
)

result_aia = client.query(
    # The following are filters for what data I want.
    vso.attrs.Time(
        (2011, 2, 14, 20, 34, 0), (2011, 2, 14, 21, 0, 0)),  # Time range.
    vso.attrs.Instrument('AIA'),  # Helioseismic and Magnetic Imager.
    vso.attrs.Physobs('intensity'),  # Physical observables
    vso.attrs.Sample(4000 * u.s)  # Only take a shot every $var seconds.
    # More observables at http://sdac.virtualsolar.org/cgi/show_details?keyword=PHYSOBS
)

# Save the HMI and AIA data to a fits files.
data_hmi = client.get(result_hmi, methods=('URL-FILE_Rice', 'URL-FILE')).wait()
data_aia = client.get(result_aia, methods=('URL-FILE_Rice', 'URL-FILE')).wait()
print('\n' + str(data_hmi))
print(str(data_aia) + '\n')

# Cropping into the active region within the HMI map
str_vol_filepath = data_hmi[0][0:-5] + '_Bxyz.npy'
xrange = u.Quantity([50, 300] * u.arcsec)
yrange = u.Quantity([-350, -100] * u.arcsec)
zrange = u.Quantity([0, 250] * u.arcsec)
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 extrapolation.
map_hmi = mp.Map(data_hmi[0])
map_hmi_cropped = map_hmi.submap(xrange, yrange)
dimensions = u.Quantity([20, 20] * u.pixel)
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(data_hmi[0])

map_boundary_cropped = map_boundary.submap(xrangeextended, yrangeextended)

aPotExt = PotentialExtrapolator(map_hmi_cropped_resampled,
                                filepath=str_vol_filepath,
                                zshape=20,
                                zrange=zrange)
aMap3D = aPotExt.extrapolate()
aPotExt = PotentialExtrapolator(map_hmi_cropped_resampled,
                                filepath=str_vol_filepath,
                                zshape=20,
                                zrange=zrange)
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()