def add_Z_map(self, **kwargs):
"""Adds metallicity map as numpy array to particle data object, if not stored already in sigame/temp/maps/metallicity/.
"""
# handle default args and kwargs
args = dict(ow=False)
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
for key in args:
exec(key + '=args[key]')
file_location = aux.get_file_location(gal_ob=self.gal_ob, map_type='Z')
if os.path.exists(file_location):
print('Particle data object already has Z map - loading')
if kwargs['ow'] == True:
Z_map = self.get_Z_map(**kwargs)
print('aa')
setattr(
self, 'Z_map',
aux.load_temp_file(gal_ob=self.gal_ob,
map_type='Z')['data'][0])
else:
print(
'Z map not stored for this particle data object, creating it. '
)
Z_map = self.get_Z_map(**kwargs)
setattr(self, 'Z_map', Z_map)
def get_total_sum(self, **kwargs):
"""Returns total value of datacube for all datacube ISM phases in dictionary.
"""
# handle default values and kwargs
args = dict(target='L_CII', all_phases=False, test=False)
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
tot_line_lum = {}
for i, ISM_dc_phase in enumerate(ISM_dc_phases):
dc_i = self.get_dc(ISM_dc_phase=ISM_dc_phase, **kwargs)
if 'L_' in target:
mom0 = dc_i * v_res # Jy * km/s
freq = params[kwargs['target'].replace('L_', 'f_')]
mom0 = aux.Jykm_s_to_L_sun(mom0, freq, self.gal_ob.zred,
self.gal_ob.lum_dist) # Lsun
# L = aux.Jykm_s_to_L_sun(dc_Jy*v_res, freq, self.gal_ob.zred,self.gal_ob.lum_dist)
tot = np.sum(mom0)
else:
tot = np.sum(dc_i)
tot_line_lum[ISM_dc_phase] = tot
if all_phases:
tot_line_lum = sum(tot_line_lum.values())
return tot_line_lum
def plot_map(self, **kwargs):
''' Creates map of surface densities (or sum) of some parameters in sim or ISM data.
'''
# handle default values and kwargs
args = dict(quan='m',
ISM_phase='',
sim_type='',
grid_length=100,
x_res=0.5,
get_sum=False)
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
map_array = self.get_map(**args)
x, y = np.meshgrid(map_array['X'], map_array['Y'])
if quan == 'SFR': lab = getlabel('SFR')
plot.simple_plot(figsize=(8, 8),plot_margin=0.15,xr=[-R_max,R_max],yr=[-R_max,R_max],\
x1=x,y1=y,col1=map_array['Z'],\
colorbar1=True,lab_colorbar1=lab,\
aspect='equal',\
contour_type1='mesh',nlev1=100,xlab='x [kpc]',ylab='y [kpc]',title='G%s' % (self.gal_ob.gal_index+1),\
textfs=9,textcol='white')
def get_dc(self, **kwargs):
"""Returns one datacube as numpy array.
"""
# handle default values and kwargs
args = dict(ISM_dc_phase='', target='')
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
if ISM_dc_phase in ISM_dc_phases:
return self.__get_dc_phase(**args)
else:
return self.__get_dc_summed(**args)
def get_data(self, **kwargs):
"""Returns **rotated** particle data as dataframe.
Parameters
----------
particle_name : str
Name of particles, can be ['gas','star','dm','GMC','dif'], default: 'GMC'
"""
# handle default values and kwargs
args = dict(particle_name='GMC')
args = aux.update_dictionary(args, kwargs)
if args['particle_name'] in ['gas', 'star', 'dm']: args['data'] = 'sim'
if args['particle_name'] in ['GMC', 'dif']: args['data'] = 'ISM'
# get raw data
data = self.get_raw_data(**args)[args['particle_name']]
# get empty arrays for rotated positions and velocities
size = data.shape[0]
X = np.zeros((size, 3))
V = np.zeros_like(X)
# populate X and V with unrotated values
for i, x in enumerate(['x', 'y', 'z']):
X[:, i] = data[x].values
V[:, i] = data['v' + x].values
# rotate X and Y
X = self.__get_rotated_matrix(X)
V = self.__get_rotated_matrix(V)
# update data with rotated values
for i, x in enumerate(['x', 'y', 'z']):
data[x] = X[:, i]
data['v' + x] = V[:, i]
# add radius and speed columns
data['radius'] = self.__get_magnitudes(X)
data['speed'] = self.__get_magnitudes(V)
return data
def print_galaxy_properties(self, **kwargs):
args = dict(gal_index=0)
args = aux.update_dictionary(args, kwargs)
for key, val in args.items():
exec(key + '=val')
print('\nProperties of Galaxy number %s, %s, at redshift %s' %
(gal_index + 1, self.galnames[gal_index], self.zreds[gal_index]))
# Print these properties
print('+%20s+%20s+%15s+' % ((20 * '-'), (20 * '-'), (15 * '-')))
print('|%20s|%20s|%15s|' % ('Property'.center(20), 'Value'.center(20),
'Name in code'.center(15)))
print('+%20s+%20s+%15s+' % ((20 * '-'), (20 * '-'), (15 * '-')))
print('|%20s|%20s|%15s|' % ('Redshift'.center(20), '{:.3f}'.format(
self.zreds[gal_index]), 'zred'.center(15)))
print('|%20s|%20s|%15s|' % ('Radius'.center(20), '{:.3f}'.format(
np.max(self.R_gal[gal_index])), 'R_gal'.center(15)))
print('|%20s|%20s|%15s|' % ('Stellar mass'.center(20), '{:.3e}'.format(
self.M_star[gal_index]), 'M_star'.center(15)))
print('|%20s|%20s|%15s|' % ('ISM mass'.center(20), '{:.3e}'.format(
self.M_gas[gal_index]), 'M_gas'.center(15)))
print('|%20s|%20s|%15s|' %
('Dense gas mass fraction'.center(20), '{:.3e}'.format(
self.f_dense[gal_index] * 100.), 'f_dense'.center(15)))
print('|%20s|%20s|%15s|' % ('DM mass'.center(20), '{:.3e}'.format(
self.M_dm[gal_index]), 'M_dm'.center(15)))
print('|%20s|%20s|%15s|' % ('SFR'.center(20), '{:.3f}'.format(
self.SFR[gal_index]), 'SFR'.center(15)))
print('|%20s|%20s|%15s|' %
('SFR surface density'.center(20), '{:.4f}'.format(
self.SFRsd[gal_index]), 'SFRsd'.center(15)))
print('|%20s|%20s|%15s|' %
('SFR-weighted Z'.center(20), '{:.4f}'.format(
self.Zsfr[gal_index]), 'Zsfr'.center(15)))
print('+%20s+%20s+%15s+' % ((20 * '-'), (20 * '-'), (15 * '-')))
def get_map(self, **kwargs):
''' Creates map of surface densities (or sum) of some parameters in sim or ISM data.
Parameters
----------
quan : str
What gets mapped, default: 'm'
ISM_phase : str
If set, ISM data will be used, default: ''
sim_type : str
If set, sim data will be used, default: ''
get_sum : bool
If True, return map of summed values not divided by area, default: False
'''
# handle default values and kwargs
args = dict(quan='m',
ISM_phase='',
sim_type='',
grid_length=100,
x_res=0.5,
get_sum=False)
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
# pick data to load
if ISM_phase != '':
data = self.get_raw_data(data='ISM')[ISM_phase]
if sim_type != '':
data = self.get_raw_data(data='sim')[sim_type]
# get positional grid
X = np.arange(-x_max_pc / 1000., x_max_pc / 1000., x_res) # kpc
dx = X[-1] - X[-2]
grid = np.matmul(X[:, None], X[None, :])
# create empty map
map_array = np.zeros_like(grid)
for j, y in enumerate(X[:-1]):
for k, x in enumerate(X[:-1]):
# create mask that selects particles in a pixel at position [x,y]
gas1 = data[(data.y >= X[j]) & (data.y < X[j + 1]) &
(data.x >= X[k]) & (data.x < X[k + 1])]
# get total mass of pixel
if get_sum: map_array[j, k] = np.sum(gas1[quan].values)
if not get_sum:
map_array[j, k] = np.sum(gas1[quan].values) / dx**2.
# collect results
results = dict(X=X, Y=X, Z=map_array, title='', plot_style='map')
results = aux.update_dictionary(results, kwargs)
# return plot_it instance
return results
def regions(GR, **kwargs):
'''Creates regions and extracts info from them.
Parameters
----------
size_arcsec : float
Size (diameter) of region in arcsec, default: 16.8 arcsec (Croxall+17 Herschel)
max_regions_per_gal : int
Max number of regions accepted per galaxy, default: 1000
line1 : str
Line ID in line1/line2 ratio, default: 'CII'
line2 : str
Line ID in line1/line2 ratio, default: 'NII_205'
'''
print('\n--- Creating regions! ---')
# handle default values and kwargs
args = dict(size_arcsec=16.6,
max_regions_per_gal=1000,
search_within=0.25,
ISM_phase='GMC',
line1='CII',
line2='NII_205',
extract_from='regions',
units='Wm2')
args = aux.update_dictionary(args, kwargs)
for key in args:
exec(key + '=args[key]')
print('Check if a regions file exist...')
create_regions = False
if os.path.isfile(d_t + 'model_line_ratios_' + line1 + '_' + line2):
if ow:
print(
'Regions file does exist, but overwrite [ow] is on, will create one! '
)
create_regions = True
if not ow:
print(
'Regions file does exist and overwrite [ow] is off, will do nothing. '
)
if not os.path.isfile(d_t + 'model_line_ratios_' + line1 + '_' + line2):
print('Regions file does NOT exist, will create one! ')
create_regions = True
if create_regions:
model_line1 = np.array([])
model_line2 = np.array([])
model_SFRsds_exact = np.array([])
model_SFRsds_CII = np.array([])
f_CII_neus = np.array([])
f_neus = np.array([])
model_F_CII_ergs = np.array([])
model_Zs_exact = np.array([])
model_Zs_OH = np.array([])
gal_indices = np.array([])
for gal_index in range(0, GR.N_gal):
print('\nNow doing galaxy number %s' % (gal_index + 1))
# Initiate galaxy object
gal_ob = gal.galaxy(gal_index=gal_index)
# Add region(s) as attribute
gal_ob.add_attr('regions', **args)
# Evaluate properties and emission from these regions:
results = gal_ob.regions.evaluate_regions(gal_ob,
evaluation_list=[
'Z', 'SFRsd',
'line_lum',
'f_CII_neu', 'f_neu'
],
lines=[line1, line2])
SFRsds_exact = results['SFRsd']['exact']
SFRsds_CII = results['SFRsd']['CII']
F_CII_ergs = results['SFRsd']['F_CII_ergs'] # ergs/s/kpc^2
F_line1 = aux.Jykm_s_to_W_m2(
line1, gal_ob.zred,
results[line1]) / gal_ob.regions.size_sr # W/m/sr
F_line2 = aux.Jykm_s_to_W_m2(
line2, gal_ob.zred,
results[line2]) / gal_ob.regions.size_sr # W/m/sr
f_CII_neu = results['f_CII_neu']
f_neu = results['f_neu']
Zs_exact = results['Z']['exact']
Zs_OH = results['Z']['OH']
# Convert to line flux in W/m^2
region_indices = np.arange(len(F_line1))
# remove regions with no SFRsd or very low line lum:
mask = (SFRsds_exact > 0) & (F_line1 > Herschel_limits[line1])
F_line1 = F_line1[mask]
F_line2 = F_line2[mask]
SFRsds_exact = SFRsds_exact[mask]
SFRsds_CII = SFRsds_CII[mask]
F_CII_ergs = F_CII_ergs[mask]
f_CII_neu = f_CII_neu[mask]
f_neu = f_neu[mask]
region_indices = region_indices[mask]
Zs_exact = Zs_exact[mask]
Zs_OH = Zs_OH[mask]
# Collect results for this galaxy
model_line1 = np.append(model_line1, F_line1)
model_line2 = np.append(model_line2, F_line2)
model_SFRsds_exact = np.append(model_SFRsds_exact, SFRsds_exact)
model_SFRsds_CII = np.append(model_SFRsds_CII, SFRsds_CII)
f_CII_neus = np.append(f_CII_neus, f_CII_neu)
f_neus = np.append(f_neus, f_neu)
model_F_CII_ergs = np.append(model_F_CII_ergs, F_CII_ergs)
model_Zs_exact = np.append(model_Zs_exact, Zs_exact)
model_Zs_OH = np.append(model_Zs_OH, Zs_OH)
gal_indices = np.append(gal_indices,
np.zeros(len(F_line1)) + gal_index)
print('Made %s region(s) in %s ' %
(len(F_line1), GR.galnames[gal_index]))
print('SFR of this galaxy: %s' % gal_ob.SFR)
print('SFR-weighted Z of this galaxy: %s' % gal_ob.Zsfr)
print(
'Median of mass-weighted mean metallicities extracted for this galaxy: %s'
% (np.median(Zs_exact.values)))
# Visualize region(s)
gal_ob.regions.plot_positions(gal_ob,
region_indices,
line='CII',
convolve=True,
units='Wm2_sr')
# Save result:
results = pd.DataFrame(dict(SFRsd_exact=model_SFRsds_exact,SFRsd_CII=model_SFRsds_CII,\
f_CII_neu=f_CII_neus,f_neu=f_neus,line1=model_line1,line2=model_line2,F_CII_ergs=model_F_CII_ergs,\
Z_exact=model_Zs_exact,Z_OH=model_Zs_OH,gal_indices=gal_indices))
results['line_ratio'] = results['line1'] / results['line2']
results.to_pickle(d_t + 'model_line_ratios_' + line1 + '_' + line2)
print('Done! In total: %s region(s) with detectable flux\n' %
(len(results)))