# Output directories and filename
odir = os.path.expanduser(output)
otypes_dir = {}
otypes_filename = {}
# Morphological radii
sradii = ''
for r in radii:
for v in r:
sradii = sradii + str(v) + '_'
sradii = sradii[0: -1]
# Load in the wave params
params = swave_params.waves(lon_start=-180 * u.degree + 0.0 * u.degree)[example]
# Unraveling params are different compared to the wave definition params
params_unravel = copy.deepcopy(params)
# Sum over many of the original bins used to create the wave in an attempt to
# beat down transform artifacts
params_unravel['lon_bin'] = unraveling_factor * params['lon_bin']
params_unravel['lat_bin'] = unraveling_factor * params['lat_bin']
# Move zero location of longitudinal reconstruction relative to the
# wavefront
# params_unravel['lon_min'] = params_unravel['lon_min']
# params_unravel['lon_max'] = params_unravel['lon_max']
# Storage for the results
str(ntrials) + '_' + str(max_steps) + '_' + str(temporal_summing) + '_' + str(spatial_summing.value),
sradii,
position_choice + '_' + error_choice,
aware_utils.convert_dict_to_single_string(ransac_kwargs)]:
idir = os.path.join(idir, loc)
filename = filename + loc + '.'
filename = filename[0: -1]
if not(os.path.exists(idir)):
os.makedirs(idir)
otypes_dir[ot] = idir
otypes_filename[ot] = filename + '.' + str(great_circle_points)
# Load in the wave params
if not sws.observational:
params = swave_params.waves()[example]
#
# Load the results
#
if not os.path.exists(otypes_dir['dat']):
os.makedirs(otypes_dir['dat'])
filepath = os.path.join(otypes_dir['dat'], otypes_filename['dat'] + '.pkl')
print('\nLoading ' + filepath + '\n')
f = open(filepath, 'rb')
results = pickle.load(f)
f.close()
# How many arcs?
nlon = len(results[0])
angles = ((np.linspace(0, 2*np.pi, nlon+1))[0:-1] * u.rad).to(u.deg)
print(' - special designation = %s' % special_designation)
print(' - position choice = %s' % position_choice)
print(' - error choice = %s' % error_choice)
print(' - along wavefront sampling = %i' % along_wavefront_sampling)
print(' - perpendicular to wavefront sampling = %i' % perpendicular_to_wavefront_sampling)
print(' - RANSAC parameters = %s' % str(ransac_kwargs))
print(' - starting trial %i out of %i\n' % (i + 1, n_random))
if not observational:
print('\nSimulating %s ' % wave_name)
if not use_saved:
# Simulate the wave and return a dictionary
print(" - Creating test waves.")
# Load in the wave params
simulated_wave_parameters = swave_params.waves()[wave_name]
# Transform parameters used to convert HPC image data to HG data.
# The HPC data is transformed to HG using the location below as the
# "pole" around which the data is transformed
transform_hpc2hg_parameters['epi_lon'] = -simulated_wave_parameters['epi_lon']
transform_hpc2hg_parameters['epi_lat'] = -simulated_wave_parameters['epi_lat']
# Simulate the waves
euv_wave_data = wave2d.simulate(simulated_wave_parameters,
max_steps, verbose=True,
output=['finalmaps','raw', 'transformed', 'noise'],
use_transform2=use_transform2)
if save_test_waves:
print(" - Saving test waves.")
file_path = os.path.join(otypes_dir['dat'], otypes_filename['dat'] + '.pkl')
# Output directories and filename
odir = os.path.expanduser(output)
otypes_dir = {}
otypes_filename = {}
# Morphological radii
sradii = ''
for r in radii:
for v in r:
sradii = sradii + str(v) + '_'
sradii = sradii[0: -1]
# Load in the simulated wave params
simulated_wave_parameters = swave_params.waves(lon_start=-180 * u.degree + 0.0 * u.degree)[example]
# Unraveling parameters used to convert HPC image data to HG data
unraveling_hpc2hg_parameters = {'lon_bin': 1.0*u.degree,
'lat_bin': 1.0*u.degree,
'epi_lon': 0.0*u.degree,
'epi_lat': 0.0*u.degree,
'lon_num': 200*u.pixel,
'lat_num': 300*u.pixel}
# Unraveling parameters used to convert HG image data to HPC data
unraveling_hg2hpc_parameters = {'epi_lon': simulated_wave_parameters['epi_lon'],
'epi_lat': simulated_wave_parameters['epi_lat'],
'xnum': 800*u.pixel,
'ynum': 800*u.pixel}
