def cluster_initial_models(Mclus=10**4,
write_csv=False,
output_folder='output_cluster',
exec_name='zams'):
'''
Generates the initial model (cluster a t=0). Gives the overall parameters
(we don't care about profiles in this part).
D. Vallés
Parameters:
Mclus: mass of the cluster one wants to generate
write_csv: (default: False) whether one wants to save the values as a csv.
output_folder: where to save the outputs
exec_name: name of the executable file for the simulation
'''
## We pick the masses in ZAMS
#we generate a large number of sampled masses from the Salpeter IMF
print(' Generating the IMF...')
random_masses = np.array(
list(metropolis(salpeter_xi, 10**6, uniform_proposal)))
#and we start picking masses until we have 10**4
print('Done! \n Filling our ZAMS...')
masses = [np.random.choice(random_masses)]
while sum(masses) < Mclus:
masses.append(np.random.choice(random_masses))
#we deal the best we can do with the last one:
#our criterion for choosing if we keep or not the last one is the following:
#p = (mass we were missing before adding the last one)/(last mass)
p = (Mclus - sum(masses)) / masses[-1] + 1
if np.random.rand() > p:
masses.pop()
print('Done! \n Generating the model guesses...')
models = several_stars_guess(masses)
failed = []
parameters = []
os.system('mkdir ' + output_folder)
os.system('cp ' + exec_name + ' ./' + output_folder)
os.chdir(output_folder)
print('Done! \n Starting the siulations...')
for idx, model in enumerate(models):
print('ZAMS: Starting star no. {:04d} of {:04d}'.format(
idx, len(models)))
gm.write_pars_file(filename='custom_pars.pars',
mass=model[0],
x=0.7381,
y=0.2485,
p=model[1],
T=model[2],
R=model[3],
L=model[4],
output_filename='custom_output.dat')
gm.launch_model(parameters_filename='custom_pars.pars',
exec_name=exec_name)
if not gm.has_succeeded(output_filename='custom_output.dat'):
failed.append(idx)
print('Failed!! :C')
else:
parameters.append(rd.read_params('custom_output.dat'))
totaleps = integrate_nuclear_production('custom_output.dat',
model[0])
Hburningrate = how_much_hydrogen_burnt(totaleps)
parameters[-1].append(Hburningrate)
EstimatedlifetimeZAMS = estimate_lifetime(0.7381, model[0],
Hburningrate)
parameters[-1].append(EstimatedlifetimeZAMS)
print(np.array(parameters).shape)
print('The following have failed: {}.'.format(failed))
missingmass = sum([masses[i] for i in failed])
if missingmass > Mclus / 10**3:
print('We re-run for the missing mass:')
parameters.extend(cluster_initial_models(Mclus=missingmass))
os.system('rm -r ./' + output_folder)
print(np.array(parameters).shape)
os.system('rm ./' + exec_name)
os.system('rm ./' + 'custom_pars.pars')
os.system('rm ./' + 'custom_output.dat')
if write_csv:
values = np.array(parameters)
column_names = [
'm', 'x', 'y', 'p', 'Tc', 'R', 'Lergs', 'Teff', 'Lsununits',
'Hburningrate', 'EstimatedlifetimeZAMS'
]
pd.DataFrame(values).to_csv('initial.csv',
sep=',',
index=False,
header=column_names)
os.chdir('..')
return parameters
def time_evolution(parameters,
timestep,
itnum,
write_csv=False,
output_folder='output_cluster',
exec_name='zams'):
'''
Work in progress!!!
Returns the time-evolved models, considering the change in composition
due to nuclear burning.
D. Vallés
'''
#we update the compositions
print('Starting it. {}. Updating compositions...'.format(itnum))
for idx in range(len(parameters)):
M, x, y, Hburningrate = parameters[idx][0], parameters[idx][
1], parameters[idx][2], parameters[idx][9]
parameters[idx][1], parameters[idx][2] = update_composition(
x, y, M, Hburningrate, timestep)
failed = []
newparameters = []
os.system('mkdir ' + output_folder)
os.system('cp ' + exec_name + ' ./' + output_folder)
os.chdir(output_folder)
print('Done! \n Starting the siulations...')
for idx, model in enumerate(parameters):
print('It. num. {}: Starting star no. {:04d} of {:04d}'.format(
itnum, idx, len(parameters)))
gm.write_pars_file(filename='custom_pars.pars',
mass=model[0],
x=model[1],
y=model[2],
p=model[3],
T=model[4],
R=model[5],
L=model[8],
output_filename='custom_output.dat')
gm.launch_model(parameters_filename='custom_pars.pars',
exec_name=exec_name)
if not gm.has_succeeded(output_filename='custom_output.dat'):
failed.append(idx)
print('Failed!! :C')
else:
newparameters.append(rd.read_params('custom_output.dat'))
totaleps = integrate_nuclear_production('custom_output.dat',
newparameters[-1][0])
Hburningrate = how_much_hydrogen_burnt(totaleps)
newparameters[-1].append(Hburningrate)
EstimatedlifetimeZAMS = estimate_lifetime(newparameters[-1][1],
newparameters[-1][0],
Hburningrate)
parameters[-1].append(EstimatedlifetimeZAMS)
print(np.array(newparameters).shape)
print('The following have failed: {}.'.format(failed))
os.system('rm ./' + exec_name)
os.system('rm ./' + 'custom_pars.pars')
os.system('rm ./' + 'custom_output.dat')
if write_csv:
values = np.array(newparameters)
column_names = [
'm', 'x', 'y', 'p', 'Tc', 'R', 'Lergs', 'Teff', 'Lsununits',
'Hburningrate', 'EstimatedlifetimeZAMS'
]
pd.DataFrame(values).to_csv('iteration_{}.csv'.format(itnum),
sep=',',
index=False,
header=column_names)
os.chdir('..')
return newparameters
def full_sim(output_dir, M_list, x, y):
'''
make a stellar evolution model
extract the profile and the parameter
save all outputs in output_dir (*.csv, dat*)
Arguments:
output_dir: name of the dir which will be saved in ./
M_list: list of mass guesses for the simulation
x, y: metallicity (H and He) portions
M. Batzer
'''
#interpolation for initial values
lista_guess = cluster.several_stars_guess(M_list)
#create or check existence of output_dir
try:
os.mkdir(output_dir)
print("Directory ", output_dir, " created ")
except FileExistsError:
print("Directory ", output_dir, " already exists")
#copy zams.f file in directory and make it executable
sh.copyfile('./' + 'zams.f', output_dir + 'zams.f')
os.system('gfortran -o ' + output_dir + 'zams ' + output_dir + 'zams.f')
#write *.pars files in ./output_dir
i = 0
for guess in lista_guess:
gm.write_pars_file('pars_{:04}.pars'.format(i), output_dir, guess[0],
x, y, guess[1], guess[2], guess[3], guess[4],
'output_{:04}.dat'.format(i))
i = i + 1
#launch model with *.pars files and save in ./
for pars in os.listdir(output_dir):
if pars.endswith('.pars'):
#print(dirName + pars)
gm.launch_model(output_dir + pars, output_dir + 'zams')
#move *.dat files in outout_dir
for output in os.listdir('./'):
if output.endswith('.dat'):
os.replace(output, output_dir + output)
#check succes of the model
for file in os.listdir(output_dir):
if file.endswith('.dat'):
if gm.has_succeeded(file, output_dir) == False:
print(file, 'with mass', round(M_list[int(file[7:11])], 3),
gm.has_succeeded(file, output_dir))
#read profiles from *.dat file
for file in os.listdir(output_dir):
if file.endswith('.dat'):
if gm.has_succeeded(file, output_dir) == True:
rd.read_output(file, output_dir)
#create csv of profile
for file in os.listdir(output_dir):
if file.endswith('.dat'):
if gm.has_succeeded(file, output_dir) == True:
rd.write_csv(file, output_dir)
#read the parameters from *.dat file
a_lista = []
for file in os.listdir(output_dir):
if file.endswith('.dat'):
if gm.has_succeeded(file, output_dir) == True:
a_n = rd.read_params(file, output_dir)
a_lista.append(a_n)
para_all = np.array(a_lista)
para_all = para_all[para_all[:, 0].argsort()]
#Create csv of parameter
pd.DataFrame(para_all).to_csv(
output_dir + 'para_all.csv',
sep=",",
index=False,
header=['m_sun', 'x', 'y', 'Pc', 'Tc', 'R', 'L', 'Teff', 'L/L_sun'])
return para_all