extrapolate_datasets



In [ ]:
%matplotlib inline

Extrapolation Example

This is a full example


In [ ]:
import matplotlib
matplotlib.use('Agg')

import numpy as np
import sunpy.map as mp
from astropy import units as u
from mayavi import mlab

# Module Imports
from solarbextrapolation.classes import *
from solarbextrapolation.potential_field_extrapolator import *
from solarbextrapolation.utilities import *
from solarbextrapolation.example_data_generator import *
from solarbextrapolation.visualisation_functions import *

# 2015
str_folder = "C://fits//"
str_map_filepath = str_folder + "2015-01-04__19-41-12__02_aia.fits"
str_map_filepath = str_folder + "2015-01-04__19-41-12__01_hmi.fits"
str_vol_filepath = str_folder + "2015-01-04__19-41-12__03_Bxyz.npy"
str_ext_filepath = str_folder + "2015-01-04__19-41-12__04_extrapolator.ext"
lis_cropping = [[-200, 200], [-250, 150], [0, 400], 'data']
xrange = u.Quantity([-200, 200] * u.arcsec)
yrange = u.Quantity([-250, 150] * u.arcsec)
zrange = u.Quantity([0, 400] * u.arcsec)

# 2014
str_map_filepath = str_folder + "2014-01-06__07-28-36__02_aia.fits"
str_map_filepath = str_folder + "2014-01-06__07-28-36__01_hmi.fits"
str_vol_filepath = str_folder + "2014-01-06__07-28-36__03_Bxyz.npy"
str_ext_filepath = str_folder + "2014-01-06__07-28-36__04_extrapolator.ext"
lis_cropping = [[-550, -200], [-245, 105], [0, 300], 'data']
xrange = u.Quantity([-550, -200] * u.arcsec)
yrange = u.Quantity([-245, 105] * u.arcsec)
zrange = u.Quantity([0, 300] * u.arcsec)

# 2011
str_map_filepath = str_folder + "2011-02-14__20-35-25__02_aia"
str_map_filepath = str_folder + "2011-02-14__20-35-25__01_hmi.fits"
str_vol_filepath = str_folder + "2011-02-14__20-35-25__03_Bxyz.npy"
str_ext_filepath = str_folder + "2011-02-14__20-35-25__04_extrapolator.ext"
lis_cropping = [[50, 300], [-350, -100], [0, 250], 'data']
xrange = u.Quantity([50, 300] * u.arcsec)
yrange = u.Quantity([-350, -100] * u.arcsec)
zrange = u.Quantity([0, 250] * u.arcsec)

# Open the map and create a cropped version for the visualisation.
aMap2D = mp.Map(str_map_filepath)
#aMap2D_cropped = aMap2D.submap(lis_cropping[0] * u.arcsec, lis_cropping[1] * u.arcsec)
aMap2D_cropped = aMap2D.submap(xrange, yrange)
dimensions = u.Quantity([30, 30] * u.pixel)
aMap2D_cropped_resampled = aMap2D_cropped.resample(dimensions, method='linear')
aMap2D_visulisation = aMap2D.submap(
    [lis_cropping[0][0] - 50, lis_cropping[0][1] + 50] * u.arcsec,
    [lis_cropping[1][0] - 50, lis_cropping[1][1] + 50] * u.arcsec)

# Only extrapolate if we don't have a saved version
if not os.path.isfile(str_vol_filepath):
    aPotExt = PotentialExtrapolator(aMap2D_cropped_resampled,
                                    filepath=str_vol_filepath,
                                    zshape=30,
                                    zrange=zrange)
    aMap3D = aPotExt.extrapolate()
aMap3D = Map3D.load(str_vol_filepath)

# Visualise this
visualise(aMap3D,
          boundary=aMap2D_visulisation,
          scale=1.0 * u.Mm,
          boundary_unit=1.0 * u.arcsec,
          show_boundary_axes=False,
          show_volume_axes=True,
          debug=False)
mlab.show()