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sCOCA_ML/dataset/production_scripts/w0wa.py
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183
sCOCA_ML/dataset/production_scripts/w0wa.py
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def create_parameters_dict(ID,ts_file,cosmo_params):
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"""
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Create a dictionnary of paramters for a given ID for the sbmy_control program.
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"""
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params=dict(directory="/home/aubin/data/PotentialBCs/ML_dataset/",
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simname=ID,
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mode="alltCOLA",
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ICs_gen="monofonic",
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verbose=1,
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nthreads=128,
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execution="slurm",
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N_particles=768,
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L=480.0,
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ICs="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_DM_delta.h5",
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WriteGravPot=True,
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OutputGravitationalPotentialBase="/home/aubin/data/PotentialBCs/ML_dataset/gravitational_potential/gravpot_"+ID,
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MeshGravPot=768,
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WriteDensity=True,
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OutputDensityBase="/home/aubin/data/PotentialBCs/ML_dataset/density/density_"+ID,
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MeshDensity=768,
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WriteReferenceFrame=True,
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OutputMomentaBase="/home/aubin/data/PotentialBCs/ML_dataset/momenta/momenta_"+ID+"_",
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monofonic_config="/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_"+ID+".conf",
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monofonic_output="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_",
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TimeSteppingFileName=ts_file,
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Omega_m=cosmo_params["Omega_m"],
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Omega_b=cosmo_params["Omega_b"],
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Omega_q=cosmo_params["Omega_q"],
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h=cosmo_params["h"],
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n_s=cosmo_params["n_s"],
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sigma8=cosmo_params["sigma8"],
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Tcmb=cosmo_params["Tcmb"],
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k_p=cosmo_params["k_p"],
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N_ur=cosmo_params["N_ur"],
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m_nu1=cosmo_params["m_nu1"],
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m_nu2=cosmo_params["m_nu2"],
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m_nu3=cosmo_params["m_nu3"],
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w_0=cosmo_params["w_0"],
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w_a=cosmo_params["w_a"],
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fnl=cosmo_params["fnl"],
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gnl=cosmo_params["gnl"],
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)
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return params
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if __name__ == "__main__":
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import numpy as np
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import pandas as pd
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from pysbmy.timestepping import StandardTimeStepping
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from pysbmy.cosmology import full_growthfactors_solver
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from sbmy_control.parameters_monofonic import get_config_from_dict, create_monofonic_config
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# Read the cosmological parameter from the CSV file
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w0wa_set_file = "/home/aubin/PotentialBCs/Planck2018/w0wa_set.csv"
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w0wa_set = pd.read_csv(w0wa_set_file)
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# Extract the IDs from the DataFrame
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IDs = w0wa_set['Run'].tolist()
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# Define the timestepping configurations (TimeStepDistribution, nsteps, name)
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ts_configs=[(0,10,'Ta1'),
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(0,20,'Ta2'),
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(3,10,'TD1'),
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(3,20,'TD2'),]
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# Define the additional cosmological parameters
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cosmo_defaults_add = {
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"Omega_k":0.0,
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"Omega_r": 0.0,
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"Tcmb": 2.7255,
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"k_p": 0.05,
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"N_ur": 2.046,
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"m_nu1": 0.06,
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"m_nu2": 0.0,
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"m_nu3": 0.0,
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"fnl": 0.0,
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"gnl": 0.0,
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"k_max":10.0,
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"tau_reio":0.06,
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"WhichSpectrum":"EH",
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}
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# Iterate over the IDs
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for k, ID in enumerate(IDs):
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if k<6: # Skip the first 6 IDs, already done
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continue
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print(f"Processing ID {k+1}/{len(IDs)}: {ID}")
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# Get the cosmological parameters for the current ID
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cosmo_params = w0wa_set[w0wa_set['Run'] == ID].iloc[0].to_dict()
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# Add the additional cosmological parameters
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cosmo_params.update(cosmo_defaults_add)
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cosmo_params["w_0"] = cosmo_params["w0"]
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cosmo_params["w_a"] = cosmo_params["wa"]
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cosmo_params["w0_fld"] = cosmo_params["w0"]
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cosmo_params["wa_fld"] = cosmo_params["wa"]
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# Create the timestepping
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print(f"> Creating timestepping with configuration {ts_configs[k % len(ts_configs)]} for ID {ID}")
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this_ts_config = ts_configs[k % len(ts_configs)]
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TS = StandardTimeStepping(
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ai= 1/(1+19.0),
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af = 1.,
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integrator=2,
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TimeStepDistribution=this_ts_config[0],
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nsteps=this_ts_config[1],
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cosmo=cosmo_params
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)
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# Write the timestepping
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print(f"> Writing timestepping to file for ID {ID}")
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ts_file = f"/home/aubin/data/PotentialBCs/ML_dataset/params/ts_{ID}_{this_ts_config[2]}.h5"
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TS.write(ts_file)
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# For each force evaluation, get the following values: a, D1, D2, which can be obtained with
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print(f"> Calculating growth factors for ID {ID}")
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a_force = TS.aStart[TS.operations == 2]
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D_array = full_growthfactors_solver(a_force, cosmo_params)
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D1_force = D_array[:, 0]
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D2_force = D_array[:, 2]
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# For each force evaluation, save the following values to a file: ID, nforce, a, D1, D2, h, Omega_m, sigma8
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print(f"> Saving the cosmological parameters and time values for ID {ID}")
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for nforce in range(len(a_force)):
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output_file = f"/home/aubin/data/PotentialBCs/ML_dataset/cosmo_and_time/cosmo_and_time_parameters_{ID}_nforce{nforce}.txt"
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with open(output_file, 'w') as f:
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f.write(f"ID: {ID}\n")
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f.write(f"nforce: {nforce}\n")
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f.write(f"a: {a_force[nforce]}\n")
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f.write(f"D1: {D1_force[nforce]}\n")
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f.write(f"D2: {D2_force[nforce]}\n")
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f.write(f"h: {cosmo_params['h']}\n")
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f.write(f"Omega_m: {cosmo_params['Omega_m']}\n")
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f.write(f"sigma8: {cosmo_params['sigma8']}\n")
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# Create the configuration file for monofonic
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print(f"> Creating the configuration file for monofonic for ID {ID}")
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monofonic_config_file = f"/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_{ID}.conf"
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monofonic_dict = {
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"config" : monofonic_config_file,
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"output" : f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_",
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"gridres": 768,
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"boxlength": 480.0,
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"seed" : np.random.randint(0, 2**32),
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"ParameterSet":None,
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"nthreads":128,
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**cosmo_params,
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}
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create_monofonic_config(monofonic_config_file, get_config_from_dict(monofonic_dict))
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# Run the sbmy_control command with the parameters for the current ID
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print(f"> Running sbmy_control for ID {ID}")
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params = create_parameters_dict(ID,ts_file,cosmo_params)
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import subprocess
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command_args = ["sbmy_control"]
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for key, value in params.items():
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command_args.append(f"--{key}")
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command_args.append(f"{value}")
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subprocess.run(command_args)
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# Remove the initial conditions particles files that are unnecessary
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print(f"> Removing unnecessary initial conditions files for ID {ID}")
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import os
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import glob
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ic_particles_files = glob.glob(f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_particles*")
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for file in ic_particles_files:
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os.remove(file)
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# Remove monofonic slurm script
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monofonic_slurm_script = f"/home/aubin/data/PotentialBCs/ML_dataset/slurm_scripts/monofonic.sh"
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if os.path.isfile(monofonic_slurm_script):
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print(f"> Removing monofonic slurm script for ID {ID}")
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os.remove(monofonic_slurm_script)
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print(f"> Finished processing ID {ID}\n\n\n")
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