def get_log_spirals(subject_id,
gal=None,
angle=None,
pic_array=None,
bar_length=0):
drawn_arms = gu.get_drawn_arms(subject_id, gu.classifications)
if gal is None or angle is None:
gal, angle = gu.get_galaxy_and_angle(subject_id)
if pic_array is None:
pic_array, deprojected_image = gu.get_image(gal, subject_id, angle)
path_to_subject = './lib/distances/subject-{}.npy'.format(subject_id)
distances = gu.get_distances(subject_id)
if distances is None or distances.shape[0] != len(
drawn_arms) or not os.path.exists(path_to_subject):
# print('\t- Calculating distances')
distances = metric.calculate_distance_matrix(drawn_arms)
np.save('./lib/distances/subject-{}.npy'.format(subject_id), distances)
p = Pipeline(drawn_arms,
phi=angle,
ba=gal['PETRO_BA90'],
image_size=pic_array.shape[0],
distances=distances)
arms = p.get_arms(clean_points=True, bar_length=bar_length)
# print('Identified {} spiral arms'.format(len(arms)))
return [arm.reprojected_log_spiral for arm in arms]
def get_gal_pa(subject_id):
try:
p = Pipeline.load('lib/pipelines/{}.json'.format(subject_id))
except FileNotFoundError:
drawn_arms = gu.get_drawn_arms(subject_id, gu.classifications)
gal, angle = gu.get_galaxy_and_angle(subject_id)
pic_array, deprojected_image = gu.get_image(gal, subject_id, angle)
p = Pipeline(drawn_arms,
phi=angle,
ba=gal['PETRO_BA90'],
image_size=pic_array.shape[0])
arms = (Arm.load(os.path.join('lib/spiral_arms', f))
for f in os.listdir('lib/spiral_arms')
if re.match('^{}-[0-9]+.pickle$'.format(subject_id), f))
arms = [arm for arm in arms if not arm.FLAGGED_AS_BAD]
pa = np.zeros(len(arms))
sigma_pa = np.zeros(pa.shape)
length = np.zeros(pa.shape)
for i, arm in enumerate(arms):
pa[i] = arm.pa
length[i] = arm.length
sigma_pa[i] = arm.sigma_pa
if len(arms) == 0:
return (np.nan, np.nan,
np.stack((np.tile(subject_id, len(pa)), pa, sigma_pa, length),
axis=1))
combined_pa = (pa * length).sum() / length.sum()
combined_sigma_pa = np.sqrt((length**2 * sigma_pa**2).sum()) / length.sum()
return (
combined_pa,
combined_sigma_pa,
np.stack((np.tile(subject_id, len(pa)), pa, sigma_pa, length), axis=1),
)
def make_combined_arms():
bar = Bar('Obtaining combined spirals',
max=len(dr8ids), suffix='%(percent).1f%% - %(eta)ds')
for i in range(len(dr8ids)):
original_id = ss_ids[i]
validation_id = validation_ids[i]
gal, angle = gu.get_galaxy_and_angle(original_id)
original_drawn_arms = gu.get_drawn_arms(original_id)
validation_drawn_arms = gu.get_drawn_arms(validation_id)
drawn_arms = np.array(
list(original_drawn_arms) + list(validation_drawn_arms),
)
p = Pipeline(drawn_arms, phi=angle, ba=gal['PETRO_BA90'],
image_size=512, distances=None, parallel=True)
arms = p.get_arms()
for j, arm in enumerate(arms):
arm.save('lib/duplicate_spiral_arms/{}-{}'.format(dr8ids[i], j))
bar.next()
bar.finish()
def get_spiral_arms(subject_id, should_recreate=True):
if (
(not os.path.exists('lib/pipelines/{}.json'.format(subject_id)))
or should_recreate
):
gal, angle = gu.get_galaxy_and_angle(subject_id)
drawn_arms = gu.get_drawn_arms(subject_id, gu.classifications)
p = Pipeline(drawn_arms, phi=angle, ba=gal['PETRO_BA90'],
image_size=512, parallel=True)
p.save('lib/pipelines/{}.json'.format(subject_id))
arms = p.get_arms()
for i, arm in enumerate(arms):
arm.save('lib/spiral_arms/{}-{}'.format(subject_id, i))
return arms
else:
arm_files = [
os.path.join('lib/spiral_arms', i)
for i in os.listdir('lib/spiral_arms')
if str(subject_id) in i
]
return [Arm.load(a) for a in arm_files]
res['validation_bulge_ba'] = v_bulge['axRatio']
res['original_bulge_reff'] = o_bulge['rEff']
res['validation_bulge_reff'] = v_bulge['rEff']
if both_have_bars:
o_bar = ash.sanitize_param_dict(original_components['bar'])
v_bar = ash.sanitize_param_dict(validation_components['bar'])
res['original_bar_ba'] = o_bar['axRatio']
res['validation_bar_ba'] = v_bar['axRatio']
res['original_bar_reff'] = o_bar['rEff']
res['validation_bar_reff'] = v_bar['rEff']
original_drawn_arms = gu.get_drawn_arms(original_id)
validation_drawn_arms = gu.get_drawn_arms(validation_id)
try:
original_p = Pipeline.load(
'lib/pipelines/{}.json'.format(original_id))
original_arms = (
Arm.load(os.path.join('lib/spiral_arms', i))
for i in os.listdir('lib/spiral_arms')
if re.match('^{}-[0-9]+.pickle$'.format(original_id), i))
original_arms = [
arm for arm in original_arms if not arm.FLAGGED_AS_BAD
]
except IOError as e:
print(e)
original_p = Pipeline(original_drawn_arms,
phi=angle,
ba=gal['PETRO_BA90'],
image_size=512,
parallel=True)
original_arms = original_p.get_arms()
def make_comparison(dr8id, ss_id, val_id):
comp_file = 'model-variances/{}_components.pickle'.format(dr8id)
pa_file = 'model-variances/{}_pa.pickle'.format(dr8id)
if os.path.isfile(comp_file) and os.path.isfile(pa_file):
return
all_cls = gu.classifications.query(
'(subject_ids == {}) or (subject_ids == {})'.format(ss_id, val_id))
all_models = all_cls['annotations'].apply(json.loads).apply(
ash.remove_scaling).apply(pa.parse_annotation)
all_geoms = pd.DataFrame(all_models.apply(gas.get_geoms).values.tolist(),
columns=('disk', 'bulge', 'bar'))
ss = ShuffleSplit(n_splits=20, test_size=0.5, random_state=0)
split_models = []
pas = []
gal, angle = gu.get_galaxy_and_angle(ss_id)
bar = Bar(str(dr8id), max=ss.n_splits, suffix='%(percent).1f%% - %(eta)ds')
for i, (train_index, _) in enumerate(ss.split(all_geoms)):
models = all_models.iloc[train_index]
drawn_arms = get_drawn_arms((ss_id, val_id), all_cls.iloc[train_index])
if len(drawn_arms) > 1:
p = Pipeline(drawn_arms,
phi=angle,
ba=gal['PETRO_BA90'],
image_size=512,
parallel=True)
pas.append(p.get_pitch_angle(p.get_arms()))
else:
pas.append((np.nan, np.nan))
geoms = all_geoms.iloc[train_index]
labels = list(map(np.array, gas.cluster_components(geoms)))
comps = gas.get_aggregate_components(geoms, models, labels)
aggregate_disk, aggregate_bulge, aggregate_bar = comps
split_models.append({
'disk': aggregate_disk if aggregate_disk else None,
'bulge': aggregate_bulge if aggregate_bulge else None,
'bar': aggregate_bar if aggregate_bar else None,
})
bar.next()
bar.finish()
splits_df = []
for model in split_models:
model_comps = {}
for key in ('disk', 'bulge', 'bar'):
if model.get(key, None) is None:
model[key] = {}
mu = model[key].get('mu', (np.nan, np.nan))
model_comps['{}-mux'.format(key)] = mu[0]
model_comps['{}-muy'.format(key)] = mu[1]
for param in ('roll', 'rEff', 'axRatio', 'i0', 'n', 'c'):
model_comps['{}-{}'.format(key, param)] = (model[key].get(
param, np.nan))
splits_df.append(model_comps)
splits_df = pd.DataFrame(splits_df)
pas = pd.DataFrame(pas,
columns=('pa', 'sigma_pa'),
index=pd.Series(range(len(pas)), name='split_index'))
splits_df.to_pickle(comp_file)
pas.to_pickle(pa_file)
def main(mangaid, subject_id):
gal, angle = gu.get_galaxy_and_angle(subject_id)
unit_converter = convert_arcsec_to_km(gal)
df = read_file(mangaid)
invalid_mask = df.values == -9999.0
mask = np.any(invalid_mask, axis=1)
df.iloc[mask] = np.nan
df = df.dropna()
df['R-arcsec'] = df['R']
df['R'] = unit_converter(df['R'])
scale = 4 * float(gal['PETRO_THETA'])
zoo_coords_r = df['R-arcsec'].values / scale
keys = (
'GAS_IC-V',
'GAS___-V',
'BTH_IC-V',
'BTH___-V',
)
labels = (
r'$H_\alpha$ velocity, fixed center & inclination',
r'$H_\alpha$ velocity, varying center & inclination',
r'$H_\alpha$ and stellar velocity, fixed center & inclination',
r'$H_\alpha$ and stellar velocity, varying centre and inclination',
)
drawn_arms = gu.get_drawn_arms(subject_id, gu.classifications)
arm_pipeline = Pipeline(drawn_arms,
phi=angle,
ba=gal['PETRO_BA90'],
image_size=512,
parallel=True)
arms = arm_pipeline.get_arms()
gzb_pa, gzb_sigma_pa = arm_pipeline.get_pitch_angle(arms)
arm_details = [{
'pa':
arm.pa,
'sigma_pa':
arm.sigma_pa,
'min_r':
unit_converter(
np.linalg.norm(arm.log_spiral - (256, 256), axis=1).min() *
float(gal['PETRO_THETA']) * 4 / 512),
'max_r':
unit_converter(
np.linalg.norm(arm.log_spiral - (256, 256), axis=1).max() *
float(gal['PETRO_THETA']) * 4 / 512)
} for arm in arms]
min_r = min(a['min_r'] for a in arm_details)
max_r = max(a['max_r'] for a in arm_details)
fitted = {}
fig, ax = plt.subplots(figsize=(8, 6))
sa_pas = []
sa_pa_datas = []
for i, (key, label) in enumerate(zip(keys, labels)):
f = tanh_model(df['R'].values, df[key].values)
p = least_squares(f, (160, 1E-17), x_scale=(10, 1E-17))['x']
fitted[key] = f(p) + df[key].values
# Calculate shear from analytic solve of dln(Ω)/dln(R)
shear = shear_from_tanh(p[1], df['R'].values)
omega = df[key] / (2 * np.pi * df['R'])
shear_data = get_shear(omega[:-1], df['R'].values[:-1])
plt.plot(df['R'], shear, c='C{}'.format(i % 10), label=label)
plt.plot(np.stack((df['R'][:-1], df['R'][1:])).mean(axis=0),
shear_data,
'--',
c='C{}'.format(i % 10))
sa_pa = np.rad2deg(get_predicted_pa(shear))
sa_pa_data = np.rad2deg(get_predicted_pa(shear_data))
sa_pas.append(sa_pa)
sa_pa_datas.append(sa_pa_data)
print('For key: {}'.format(key))
msk = (df['R'] > min_r) & (df['R'] < max_r)
print('\tRotation-predicted: {:.4f}°'.format(sa_pa[msk].mean()))
print('\tGZB measured PA: {:.4f} ± {:.4f}°'.format(
gzb_pa, gzb_sigma_pa))
plt.plot([], [], 'k-', label=r'Analytic differentiation')
plt.plot([], [], 'k--', label='Numerical differentiation')
plt.xlabel('Distance from galaxy centre [km]')
plt.ylabel(r'Shear rate, $\Gamma$')
plt.legend()
plt.savefig('{}_shear.pdf'.format(mangaid), bbox_inches='tight')
plt.close()
np.save('pavr', np.stack((zoo_coords_r, sa_pas[0]), axis=1))
imshow_kwargs = {
'cmap': 'gray',
'origin': 'lower',
'extent': [-0.5 * scale, 0.5 * scale] * 2,
}
pic_array, _ = gu.get_image(gal, subject_id, angle)
fig, ax = plt.subplots(ncols=1, figsize=(5, 5))
plt.imshow(pic_array, **imshow_kwargs)
for i, arm in enumerate(arms):
varying_arm_t = fit_varying_pa(arm, zoo_coords_r,
np.stack(sa_pas).mean(axis=0))
t_predict = np.linspace(varying_arm_t.min(), varying_arm_t.max(), 100)
f = interp1d(varying_arm_t, zoo_coords_r)
varying_arm = xy_from_r_theta(f(t_predict), t_predict)
log_spiral = xy_from_r_theta(*np.flipud(arm.polar_logsp))
plt.plot(*arm.deprojected_coords.T * scale, '.', markersize=1, alpha=1)
plt.plot(*log_spiral * scale, c='r', linewidth=3, alpha=0.8)
plt.plot(*varying_arm * scale, c='g', linewidth=3, alpha=0.8)
# plots for legend
plt.plot([], [],
c='g',
linewidth=3,
alpha=0.8,
label='Swing-amplified spiral')
plt.plot([], [], c='r', linewidth=3, alpha=0.8, label='Logarithmic spiral')
plt.axis('equal')
plt.xlabel('Arcseconds from galaxy centre')
plt.ylabel('Arcseconds from galaxy centre')
plt.xlim(-25, 25)
plt.ylim(-25, 25)
plt.legend()
plt.savefig('{}_varying-pa.pdf'.format(mangaid), bbox_inches='tight')
plt.close()
return
fig, ax = plt.subplots(figsize=(8, 6))
for sa_pa, label in zip(sa_pas, labels):
plt.plot(df['R'], sa_pa, label=label)
for row in arm_details:
plt.hlines(row['pa'], row['min_r'], row['max_r'])
plt.fill_between(
np.linspace(row['min_r'], row['max_r'], 2),
row['pa'] - row['sigma_pa'],
row['pa'] + row['sigma_pa'],
color='k',
alpha=0.2,
)
plt.legend()
plt.xlabel('Distance from galaxy centre [km]')
plt.ylabel('Pitch angle [degrees]')
plt.savefig('{}_pa.pdf'.format(mangaid), bbox_inches='tight')
plt.close()
fig, ax = plt.subplots(figsize=(8, 6))
# df.plot('R', keys, label=labels, ax=ax)
for i, key in enumerate(keys):
plt.fill_between(
df['R'].values,
df[key].values - df[key + 'e'].values,
df[key].values + df[key + 'e'].values,
color='C{}'.format(i % 10),
alpha=0.1,
)
plt.plot(df['R'].values, df[key].values, '--', c='C{}'.format(i % 10))
plt.plot(df['R'].values, fitted[key], c='C{}'.format(i % 10))
for i, label in enumerate(labels):
plt.plot([], [], c='C{}'.format(i % 10), label=label)
plt.plot([], [], 'k-', label=r'$A\tanh(bR)$ model')
plt.plot([], [], 'k--', label='Data')
plt.legend()
plt.xlabel('Distance from galaxy centre [km]')
plt.ylabel(r'Rotational velocity [$\mathrm{km}\mathrm{s}^{-1}$]')
plt.savefig('{}_rotational-velocity_2.pdf'.format(mangaid),
bbox_inches='tight')
plt.close()