# Open output file and extract specific energy absorbed
try:
f = h5py.File(model, 'r')
energy = f['iteration_%05i' % (len(f) - 1)]['specific_energy']
except IOError:
break
# Extract PAH energy
energy = energy[isize, :, :, :]
# Append to lists
energies.append(np.mean(energy) / normalization[isize])
names.append(os.path.basename(model).replace('.rtout', '').replace('energy_%s_' % model_name, '').replace('_', ' ').strip())
# Create list of group numbers
groups = [group(name) for name in names]
# Convert to Numpy arrays and sort
energies, names, groups = np.array(energies), np.array(names), np.array(groups)
key = np.array(zip(groups, energies), dtype=[('groups', int), ('energies', float)])
order = np.argsort(key, order=['groups', 'energies'])
energies, names, groups = energies[order], names[order], groups[order]
# Create pie chart figure
print len(energies)
ax.text(xmin, ymax - isize * dy - isize * ddy + 0.02 / h, labels[isize],
va='bottom', ha='left', size=8, color='black')
make_percent_bar(ax, energies, names,
[xmin, xmax,
def extract(model):
# Check that file is valid
if not os.path.basename(model).startswith('basic_') or not os.path.basename(model).endswith('.rtout'):
raise Exception("Only basic_*.rtout files should be specified")
# Extract model name
model_name = os.path.basename(model).replace('.rtout', '').replace('basic_', '')
m = ModelOutput('models/basic/basic_%s.rtout' % model_name)
for image_set in range(3):
if image_set == 0:
n_x = 1
n_y = 1
image_set_name = 'total'
elif image_set == 1:
n_x = 130
n_y = 1
image_set_name = 'lon'
elif image_set == 2:
n_x = 1
n_y = 40
image_set_name = 'lat'
flux = np.zeros((n_y, n_x, n_groups, n_wav))
print "Direct source emission"
try:
wav, nufnu_all = m.get_image(group=image_set, component='source_emit', units='MJy/sr', source_id='all')
except IOError:
return
for source_id in range(n_sources):
nufnu = nufnu_all[source_id, 0, :, :, :]
spec_type = t_default['Type'][source_id].strip().upper()
group_id = group(spec_type)
flux[:, :, group_id, :] += nufnu
print "Direct dust emission"
wav, nufnu_all = m.get_image(group=image_set, component='dust_emit', units='MJy/sr', dust_id='all')
for dust_id in range(n_dust):
nufnu = nufnu_all[dust_id, 0, :, :, :]
flux[:, :, 5 + dust_id, :] += nufnu
print "Scattered source emission"
wav, nufnu = m.get_image(group=image_set, component='source_scat', units='MJy/sr')
nufnu = nufnu[0, :, :, :]
flux[:, :, 8, :] += nufnu
print "Scattered dust emission"
wav, nufnu = m.get_image(group=image_set, component='dust_scat', units='MJy/sr')
nufnu = nufnu[0, :, :, :]
flux[:, :, 8, :] += nufnu
# Convolve with filters
flux_conv = np.zeros((len(filters), n_y, n_x, n_groups))
for i, filtname in enumerate(filters):
transmission = rebin_filter(filtname, c / (wav * 1.e-4))
flux_conv[i, :, :, :] = np.sum(transmission[np.newaxis, np.newaxis, np.newaxis, :] * flux, axis=3)
pyfits.writeto('models/basic/images_%s_%s.fits' % (image_set_name, model_name), flux, clobber=True)
pyfits.writeto('models/basic/images_%s_%s_conv.fits' % (image_set_name, model_name), flux_conv, clobber=True)
import sys, os path = os.getcwd() + '/tests' sys.path.append(path) from auth import login, register, verifyCode from trips import createTrip from groups import group from users import updateUser # temporary implementation; will be focused on workflows # createTrip() group() updateUser()
