def Fit_Simulation_Phase_Diagram(self, sim_id):
print(f' Fitting Simulation: {sim_id}')
sim_dir = self.Get_Simulation_Directory(sim_id)
input_dir = sim_dir + 'analysis_files/'
fit_dir = input_dir + 'fit_mcmc/'
create_directory(fit_dir)
files = [
f for f in listdir(input_dir)
if (isfile(join(input_dir, f)) and (f.find('_analysis') > 0))
]
indices = ['{0:03}'.format(int(file.split('_')[0])) for file in files]
indices.sort()
n_files = len(files)
print(f' N_Analysis_Files: {n_files}')
for n_file in indices:
n_file = int(n_file)
fit_file = fit_dir + f'fit_{n_file}.pkl'
file_path = Path(fit_file)
if file_path.is_file():
print(f' Skiping File: {n_file} ')
continue
data = load_analysis_data(n_file, input_dir)
values_to_fit = get_density_temperature_values_to_fit(
data['phase_diagram'],
delta_min=-1,
delta_max=1,
n_samples_line=50,
fraction_enclosed=0.70)
fit_values = fit_thermal_parameters_mcmc(n_file, values_to_fit,
fit_dir)
from load_data import load_analysis_data
from phase_diagram_functions import fit_thermal_parameters_mcmc, get_density_temperature_values_to_fit
# data_dir = '/home/bruno/Desktop/data/'
# data_dir = '/home/brunoq/Desktop/ssd_0/data/'
data_dir = '/raid/bruno/data/'
sim_name = '1024_50Mpc'
sim_name = '2048_100Mpc'
input_dir = data_dir + f'cosmo_sims/rescaled_P19/{sim_name}/analysis_files/'
output_dir = data_dir + f'cosmo_sims/rescaled_P19/figures/phase_diagram_{sim_name}/fit/'
create_directory(output_dir)
n_file = 55
for n_file in range(3, 56):
data = load_analysis_data(n_file, input_dir)
z = data['cosmology']['current_z']
data_pd = data['phase_diagram']
fit_mcmc = Load_Pickle_Directory(input_dir + f'fit_mcmc/fit_{n_file}.pkl')
values_to_fit = get_density_temperature_values_to_fit(
data['phase_diagram'],
delta_min=-1.5,
delta_max=3,
n_samples_line=50,
fraction_enclosed=0.67)
dens = values_to_fit['density']
temp = values_to_fit['temperature']
temp_sigma_p = values_to_fit['temperature_sigma_p']
temp_sigma_m = values_to_fit['temperature_sigma_l']
rank = comm.Get_rank()
n_procs = comm.Get_size()
else:
rank = 0
n_procs = 1
data_dir = '/raid/bruno/data/'
input_dir = data_dir + 'cosmo_sims/sim_grid/512_p19/analysis_files/'
output_dir = input_dir + 'figures/'
if rank == 0: create_directory(output_dir)
if use_mpi: comm.Barrier()
n_snapshot = 3
data = load_analysis_data(n_snapshot,
input_dir,
phase_diagram=False,
lya_statistics=True,
load_skewer=True)
cosmology = data['cosmology']
box = data['box']
skewer = data['lya_statistics']['skewer']
# skewer['HI_density'] *= 1e-5
HI_density = skewer['HI_density']
tau_cholla = skewer['optical_depth']
vel_Hubble_cholla = skewer['vel_Hubble']
temperature = skewer['temperature']
velocity = skewer['velocity']
F_cholla = skewer['transmitted_flux']
tau_data = compute_optical_depth(cosmology,
def Load_Simulation_Power_Spectum_Data(self,
sim_id,
indices,
load_normalized_ps=False,
ps_norm=None):
sim_dir = self.Get_Simulation_Directory(sim_id)
input_dir = sim_dir + 'analysis_files/'
indices.sort()
sim_data = {}
z_vals = []
data_ps_mean = []
data_kvals = []
data_kmin, data_kmax = [], []
for n_file in indices:
n_file = int(n_file)
data = load_analysis_data(n_file,
input_dir,
phase_diagram=False,
lya_statistics=True,
load_skewer=False,
load_fit=False)
z = data['cosmology']['current_z']
k_vals = data['lya_statistics']['power_spectrum']['k_vals']
ps_mean = data['lya_statistics']['power_spectrum']['ps_mean']
z_vals.append(z)
data_kvals.append(k_vals)
data_ps_mean.append(ps_mean)
data_kmin.append(k_vals.min())
data_kmax.append(k_vals.max())
z_vals = np.array(z_vals)
data_kmin, data_kmax = np.array(data_kmin), np.array(data_kmax)
data_ps = {
'z': z_vals,
'k_min': data_kmin,
'k_max': data_kmax,
'k_vals': data_kvals,
'ps_mean': data_ps_mean
}
self.Grid[sim_id]['analysis']['power_spectrum'] = data_ps
if load_normalized_ps:
root_dir = self.root_dir
sim_name = self.Grid[sim_id]['key']
ps_dir = root_dir + f'flux_power_spectrum_files/{sim_name}/'
normalizations = ['Simulation', 'Becker']
types = ['F_mean', 'tau_eff']
normalization = ps_norm['normalization']
type = ps_norm['type']
# type = types[0]
# normalization = 'Simulation'
# for normalization in normalizations:
ps_key = f'power_spectrum_norm_{normalization}_{type}'
z_vals, data_ps_mean, data_kvals, data_kmax, data_kmin = [], [], [], [], []
for n_file in indices:
file_name = ps_dir + f'flux_ps_{n_file}.h5'
file = h5.File(file_name, 'r')
current_z = file.attrs['current_z']
# print( file.keys() )
k_vals = file['k_vals'][...]
ps_data = file[normalization]
tau_eff = ps_data.attrs['tau_eff']
ps_mean = ps_data[type][...]
z_vals.append(current_z)
data_kvals.append(k_vals)
data_ps_mean.append(ps_mean)
data_kmax.append(k_vals.max())
data_kmin.append(k_vals.min())
z_vals = np.array(z_vals)
data_kmin, data_kmax = np.array(data_kmin), np.array(data_kmax)
data_ps = {
'z': z_vals,
'k_min': data_kmin,
'k_max': data_kmax,
'k_vals': data_kvals,
'ps_mean': data_ps_mean,
'normalization_key': ps_key
}
self.Grid[sim_id]['analysis'][
'power_spectrum_normalized'] = data_ps
def Load_Simulation_Analysis_Data(self,
sim_id,
load_fit=True,
mcmc_fit_dir=None):
str = f' Loading Simulation Analysis: {sim_id}'
print_line_flush(str)
sim_dir = self.Get_Simulation_Directory(sim_id)
input_dir = sim_dir + 'analysis_files/'
files = [
f for f in listdir(input_dir)
if (isfile(join(input_dir, f)) and (f.find('_analysis') > 0))
]
indices = ['{0:03}'.format(int(file.split('_')[0])) for file in files]
indices.sort()
n_files = len(files)
sim_data = {}
sim_data['z'] = []
sim_data['T0'] = []
sim_data['gamma'] = []
sim_data['F_mean'] = []
sim_data['tau'] = []
sim_data['tau_HeII'] = []
sim_data['ps_mean'] = []
sim_data['ps_kvals'] = []
z_power_spectrum = []
data_ps_mean = []
data_kvals = []
data_kmin, data_kmax = [], []
ps_available_indices = []
for n_file in indices:
n_file = int(n_file)
data = load_analysis_data(n_file,
input_dir,
phase_diagram=False,
lya_statistics=True,
load_skewer=False,
load_fit=load_fit,
mcmc_fit_dir=mcmc_fit_dir)
z = data['cosmology']['current_z']
if load_fit:
T0 = data['phase_diagram']['fit']['T0']
gamma = data['phase_diagram']['fit']['gamma']
F_mean = data['lya_statistics']['Flux_mean']
tau = data['lya_statistics']['tau']
tau_HeII = data['lya_statistics']['tau_HeII']
k_vals = data['lya_statistics']['power_spectrum']['k_vals']
ps_mean = data['lya_statistics']['power_spectrum']['ps_mean']
if ps_mean is not None and z < 5.5:
ps_available_indices.append(n_file)
# z_power_spectrum.append( z )
# data_ps_mean.append( ps_mean )
# data_kvals.append( k_vals )
# data_kmin.append( k_vals.min() )
# data_kmax.append( k_vals.max() )
sim_data['ps_kvals'].append(k_vals)
sim_data['ps_mean'].append(ps_mean)
sim_data['z'].append(z)
if load_fit:
sim_data['T0'].append(T0)
sim_data['gamma'].append(gamma)
sim_data['F_mean'].append(F_mean)
sim_data['tau'].append(tau)
sim_data['tau_HeII'].append(tau_HeII)
sim_data['z'] = np.array(sim_data['z'])
if load_fit:
sim_data['T0'] = np.array(sim_data['T0'])
sim_data['gamma'] = np.array(sim_data['gamma'])
sim_data['F_mean'] = np.array(sim_data['F_mean'])
sim_data['tau'] = np.array(sim_data['tau'])
sim_data['tau_HeII'] = np.array(sim_data['tau_HeII'])
sim_data['ps_available_indices'] = ps_available_indices
# z_power_spectrum = np.array( z_power_spectrum )
# data_kmin, data_kmax = np.array( data_kmin ), np.array( data_kmax )
# data_ps = { 'z':z_power_spectrum, 'k_min':data_kmin, 'k_max':data_kmax, 'k_vals':data_kvals, 'ps_mean':data_ps_mean, 'available_indices':ps_available_indices }
# sim_data['power_spectrum'] = data_ps
self.Grid[sim_id]['analysis'] = sim_data