From c5f464d031fd33655e34a8177fa70f5c1c5dbef2 Mon Sep 17 00:00:00 2001 From: Mayeul Aubin Date: Thu, 5 Jun 2025 15:16:06 +0200 Subject: [PATCH] dataset production --- sCOCA_ML/__init__.py | 0 sCOCA_ML/dataset/__init__.py | 0 .../production_scripts/DESI_w0wa_CDM.ipynb | 921 ++ .../ML_dataset_generation_w0wa.py | 183 + sCOCA_ML/dataset/production_scripts/Planck.py | 185 + .../read_planck_chains.ipynb | 8567 +++++++++++++++++ 6 files changed, 9856 insertions(+) create mode 100644 sCOCA_ML/__init__.py create mode 100644 sCOCA_ML/dataset/__init__.py create mode 100644 sCOCA_ML/dataset/production_scripts/DESI_w0wa_CDM.ipynb create mode 100644 sCOCA_ML/dataset/production_scripts/ML_dataset_generation_w0wa.py create mode 100644 sCOCA_ML/dataset/production_scripts/Planck.py create mode 100644 sCOCA_ML/dataset/production_scripts/read_planck_chains.ipynb diff --git a/sCOCA_ML/__init__.py b/sCOCA_ML/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/sCOCA_ML/dataset/__init__.py b/sCOCA_ML/dataset/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/sCOCA_ML/dataset/production_scripts/DESI_w0wa_CDM.ipynb b/sCOCA_ML/dataset/production_scripts/DESI_w0wa_CDM.ipynb new file mode 100644 index 0000000..5b11609 --- /dev/null +++ b/sCOCA_ML/dataset/production_scripts/DESI_w0wa_CDM.ipynb @@ -0,0 +1,921 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7dcb16bc", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b580fb3f", + "metadata": {}, + "outputs": [], + "source": [ + "## Since we don't have access to the DESI MCMC chains, we will use their table of results and approximate the posterior as Gaussians with no correlations.\n", + "## This is a very crude approximation, but it will allow us to probe a bit the impact of w0wa on our simulations.\n", + "\n", + "# Let's define the cosmological parameters, their mean and standard deviation (from the DESI paper)\n", + "DESI_cosmo_table = [\n", + " ['Omega_m', 0.3142, 0.0063],\n", + " ['sigma8', 0.8163, 0.0083],\n", + " ['H0', 67.48, 0.6200],\n", + " ['w0', -0.761, 0.0650],\n", + " ['wa', -0.96, 0.3000],\n", + "]\n", + "\n", + "Planck2018_additional_cosmo_table = [\n", + " ['Omega_bh^2', 0.02242, 0.00014],\n", + " ['n_s', 0.9665, 0.0038],\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4105ff31", + "metadata": {}, + "outputs": [], + "source": [ + "## Now let's sample N cosmological parameters sets from the DESI and Planck tables, assuming uncorrelated Gaussians posterior distributions.\n", + "N=30\n", + "\n", + "# For each parameter, create an array of samples\n", + "samples = {}\n", + "for param, mean, std in DESI_cosmo_table + Planck2018_additional_cosmo_table:\n", + " samples[param] = np.random.normal(loc=mean, scale=std, size=N)\n", + "\n", + "# Convert the samples to a DataFrame\n", + "cosmo_samples = pd.DataFrame(samples)\n", + "\n", + "# Change H0 in h:=H0/100\n", + "cosmo_samples['h'] = cosmo_samples['H0'] / 100.0\n", + "cosmo_samples = cosmo_samples.drop(columns=['H0'])\n", + "\n", + "# Change Omega_bh^2 in Omega_b\n", + "cosmo_samples['Omega_b'] = cosmo_samples['Omega_bh^2'] / cosmo_samples['h']**2\n", + "cosmo_samples = cosmo_samples.drop(columns=['Omega_bh^2'])\n", + "\n", + "# Define Omega_q as Omega_q = 1 - Omega_m\n", + "cosmo_samples['Omega_q'] = 1.0 - cosmo_samples['Omega_m']\n", + "\n", + "# Round values to 4 decimal places\n", + "cosmo_samples = cosmo_samples.round(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6006ce1d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Omega_m", + "rawType": "float64", + "type": "float" + }, + { + "name": "sigma8", + "rawType": "float64", + "type": "float" + }, + { + "name": "w0", + "rawType": "float64", + "type": "float" + }, + { + "name": "wa", + "rawType": "float64", + "type": "float" + }, + { + "name": "n_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "h", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_b", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_q", + "rawType": "float64", + "type": "float" + } + ], + "ref": "c3749bad-2115-4fab-9dd2-407301414639", + "rows": [ + [ + "0", + "0.3072", + "0.8143", + "-0.7615", + "-0.7624", + "0.9679", + "0.6789", + "0.049", + "0.6928" + ], + [ + "1", + "0.3139", + "0.8227", + "-0.9124", + "-0.7684", + "0.9639", + "0.6778", + "0.0489", + "0.6861" + ], + [ + "2", + "0.3179", + "0.8266", + "-0.7549", + "-0.7952", + "0.9679", + "0.6593", + "0.0524", + "0.6821" + ], + [ + "3", + "0.3142", + "0.8238", + "-0.7042", + "-1.0894", + "0.9634", + "0.6762", + "0.0491", + "0.6858" + ], + [ + "4", + "0.3136", + "0.809", + "-0.7589", + "-0.5622", + "0.969", + "0.6703", + "0.0502", + "0.6864" + ], + [ + "5", + "0.3185", + "0.8256", + "-0.7325", + "-1.1989", + "0.9661", + "0.661", + "0.0517", + "0.6815" + ], + [ + "6", + "0.3124", + "0.8109", + "-0.7643", + "-1.1421", + "0.9676", + "0.6756", + "0.0495", + "0.6876" + ], + [ + "7", + "0.3228", + "0.8133", + "-0.8212", + "-0.6498", + "0.9634", + "0.6738", + "0.0494", + "0.6772" + ], + [ + "8", + "0.3208", + "0.8174", + "-0.5928", + "-1.1095", + "0.9681", + "0.6791", + "0.0483", + "0.6792" + ], + [ + "9", + "0.3212", + "0.8384", + "-0.7382", + "-1.0966", + "0.9685", + "0.6716", + "0.0496", + "0.6788" + ], + [ + "10", + "0.3157", + "0.8079", + "-0.834", + "-0.713", + "0.9633", + "0.6745", + "0.0491", + "0.6843" + ], + [ + "11", + "0.3043", + "0.812", + "-0.841", + "-1.2495", + "0.9626", + "0.677", + "0.0492", + "0.6957" + ], + [ + "12", + "0.316", + "0.8309", + "-0.7108", + "-0.9241", + "0.9589", + "0.687", + "0.0479", + "0.684" + ], + [ + "13", + "0.3069", + "0.8263", + "-0.6679", + "-1.1336", + "0.9663", + "0.6668", + "0.0507", + "0.6931" + ], + [ + "14", + "0.3224", + "0.8179", + "-0.7521", + "-1.185", + "0.9661", + "0.6803", + "0.0488", + "0.6776" + ], + [ + "15", + "0.3233", + "0.8196", + "-0.8316", + "-1.0697", + "0.9616", + "0.6703", + "0.0502", + "0.6767" + ], + [ + "16", + "0.3179", + "0.8065", + "-0.7125", + "-1.1323", + "0.9696", + "0.67", + "0.0504", + "0.6821" + ], + [ + "17", + "0.3125", + "0.8175", + "-0.6896", + "-1.2742", + "0.9649", + "0.6796", + "0.048", + "0.6875" + ], + [ + "18", + "0.3115", + "0.824", + "-0.6955", + "-1.0946", + "0.9679", + "0.6767", + "0.0494", + "0.6885" + ], + [ + "19", + "0.3121", + "0.8188", + "-0.7677", + "-1.0277", + "0.9569", + "0.679", + "0.0487", + "0.6879" + ], + [ + "20", + "0.3169", + "0.8271", + "-0.8419", + "-0.9485", + "0.9646", + "0.6723", + "0.0492", + "0.6831" + ], + [ + "21", + "0.3084", + "0.8191", + "-0.858", + "-0.9611", + "0.9674", + "0.6761", + "0.0491", + "0.6916" + ], + [ + "22", + "0.3106", + "0.8203", + "-0.7033", + "-0.9152", + "0.9709", + "0.679", + "0.0482", + "0.6894" + ], + [ + "23", + "0.3144", + "0.8214", + "-0.7278", + "-0.8972", + "0.9688", + "0.6778", + "0.0489", + "0.6856" + ], + [ + "24", + "0.3113", + "0.8223", + "-0.635", + "-1.2027", + "0.9619", + "0.6723", + "0.0498", + "0.6887" + ], + [ + "25", + "0.3111", + "0.8139", + "-0.6984", + "-0.7764", + "0.9661", + "0.6641", + "0.0512", + "0.6889" + ], + [ + "26", + "0.3144", + "0.8079", + "-0.7536", + "-1.1702", + "0.9608", + "0.6621", + "0.0507", + "0.6856" + ], + [ + "27", + "0.3146", + "0.8191", + "-0.6247", + "-1.0546", + "0.9707", + "0.6775", + "0.0488", + "0.6854" + ], + [ + "28", + "0.3134", + "0.809", + "-0.7978", + "-1.154", + "0.9678", + "0.6727", + "0.0498", + "0.6866" + ], + [ + "29", + "0.3173", + "0.8119", + "-0.7385", + "-1.0606", + "0.9642", + "0.6732", + "0.0493", + "0.6827" + ] + ], + "shape": { + "columns": 8, + "rows": 30 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " Omega_m sigma8 w0 wa n_s h Omega_b Omega_q\n", + "0 0.3072 0.8143 -0.7615 -0.7624 0.9679 0.6789 0.0490 0.6928\n", + "1 0.3139 0.8227 -0.9124 -0.7684 0.9639 0.6778 0.0489 0.6861\n", + "2 0.3179 0.8266 -0.7549 -0.7952 0.9679 0.6593 0.0524 0.6821\n", + "3 0.3142 0.8238 -0.7042 -1.0894 0.9634 0.6762 0.0491 0.6858\n", + "4 0.3136 0.8090 -0.7589 -0.5622 0.9690 0.6703 0.0502 0.6864\n", + "5 0.3185 0.8256 -0.7325 -1.1989 0.9661 0.6610 0.0517 0.6815\n", + "6 0.3124 0.8109 -0.7643 -1.1421 0.9676 0.6756 0.0495 0.6876\n", + "7 0.3228 0.8133 -0.8212 -0.6498 0.9634 0.6738 0.0494 0.6772\n", + "8 0.3208 0.8174 -0.5928 -1.1095 0.9681 0.6791 0.0483 0.6792\n", + "9 0.3212 0.8384 -0.7382 -1.0966 0.9685 0.6716 0.0496 0.6788\n", + "10 0.3157 0.8079 -0.8340 -0.7130 0.9633 0.6745 0.0491 0.6843\n", + "11 0.3043 0.8120 -0.8410 -1.2495 0.9626 0.6770 0.0492 0.6957\n", + "12 0.3160 0.8309 -0.7108 -0.9241 0.9589 0.6870 0.0479 0.6840\n", + "13 0.3069 0.8263 -0.6679 -1.1336 0.9663 0.6668 0.0507 0.6931\n", + "14 0.3224 0.8179 -0.7521 -1.1850 0.9661 0.6803 0.0488 0.6776\n", + "15 0.3233 0.8196 -0.8316 -1.0697 0.9616 0.6703 0.0502 0.6767\n", + "16 0.3179 0.8065 -0.7125 -1.1323 0.9696 0.6700 0.0504 0.6821\n", + "17 0.3125 0.8175 -0.6896 -1.2742 0.9649 0.6796 0.0480 0.6875\n", + "18 0.3115 0.8240 -0.6955 -1.0946 0.9679 0.6767 0.0494 0.6885\n", + "19 0.3121 0.8188 -0.7677 -1.0277 0.9569 0.6790 0.0487 0.6879\n", + "20 0.3169 0.8271 -0.8419 -0.9485 0.9646 0.6723 0.0492 0.6831\n", + "21 0.3084 0.8191 -0.8580 -0.9611 0.9674 0.6761 0.0491 0.6916\n", + "22 0.3106 0.8203 -0.7033 -0.9152 0.9709 0.6790 0.0482 0.6894\n", + "23 0.3144 0.8214 -0.7278 -0.8972 0.9688 0.6778 0.0489 0.6856\n", + "24 0.3113 0.8223 -0.6350 -1.2027 0.9619 0.6723 0.0498 0.6887\n", + "25 0.3111 0.8139 -0.6984 -0.7764 0.9661 0.6641 0.0512 0.6889\n", + "26 0.3144 0.8079 -0.7536 -1.1702 0.9608 0.6621 0.0507 0.6856\n", + "27 0.3146 0.8191 -0.6247 -1.0546 0.9707 0.6775 0.0488 0.6854\n", + "28 0.3134 0.8090 -0.7978 -1.1540 0.9678 0.6727 0.0498 0.6866\n", + "29 0.3173 0.8119 -0.7385 -1.0606 0.9642 0.6732 0.0493 0.6827" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosmo_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "51e830b5", + "metadata": {}, + "outputs": [], + "source": [ + "## Reorder the columns: h, Omega_m, Omega_b, Omega_q, n_s, sigma8, A_s, w0, wa\n", + "ordered_columns = ['h', 'Omega_m', 'Omega_b', 'Omega_q', 'n_s', 'sigma8','w0', 'wa']\n", + "cosmo_samples = cosmo_samples[ordered_columns]\n", + "## Rename index to 'Run' and set it to 'train_LCDM_{index+1}'\n", + "cosmo_samples.index = [f'train_w0wa_{i+1}' for i in range(len(cosmo_samples))]\n", + "cosmo_samples.index.name = 'Run'\n", + "## Save the sampled chain to a CSV file\n", + "output_file = \"/home/aubin/PotentialBCs/Planck2018/w0wa_set.csv\"\n", + "cosmo_samples.to_csv(output_file, index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "febf6a0e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sCOCA_ML/dataset/production_scripts/ML_dataset_generation_w0wa.py b/sCOCA_ML/dataset/production_scripts/ML_dataset_generation_w0wa.py new file mode 100644 index 0000000..88e7d88 --- /dev/null +++ b/sCOCA_ML/dataset/production_scripts/ML_dataset_generation_w0wa.py @@ -0,0 +1,183 @@ + + +def create_parameters_dict(ID,ts_file,cosmo_params): + """ + Create a dictionnary of paramters for a given ID for the sbmy_control program. + """ + + params=dict(directory="/home/aubin/data/PotentialBCs/ML_dataset/", + simname=ID, + mode="alltCOLA", + ICs_gen="monofonic", + verbose=1, + nthreads=128, + execution="slurm", + N_particles=768, + L=480.0, + ICs="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_DM_delta.h5", + WriteGravPot=True, + OutputGravitationalPotentialBase="/home/aubin/data/PotentialBCs/ML_dataset/gravitational_potential/gravpot_"+ID, + MeshGravPot=768, + WriteDensity=True, + OutputDensityBase="/home/aubin/data/PotentialBCs/ML_dataset/density/density_"+ID, + MeshDensity=768, + WriteReferenceFrame=True, + OutputMomentaBase="/home/aubin/data/PotentialBCs/ML_dataset/momenta/momenta_"+ID+"_", + monofonic_config="/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_"+ID+".conf", + monofonic_output="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_", + TimeSteppingFileName=ts_file, + Omega_m=cosmo_params["Omega_m"], + Omega_b=cosmo_params["Omega_b"], + Omega_q=cosmo_params["Omega_q"], + h=cosmo_params["h"], + n_s=cosmo_params["n_s"], + sigma8=cosmo_params["sigma8"], + Tcmb=cosmo_params["Tcmb"], + k_p=cosmo_params["k_p"], + N_ur=cosmo_params["N_ur"], + m_nu1=cosmo_params["m_nu1"], + m_nu2=cosmo_params["m_nu2"], + m_nu3=cosmo_params["m_nu3"], + w_0=cosmo_params["w_0"], + w_a=cosmo_params["w_a"], + fnl=cosmo_params["fnl"], + gnl=cosmo_params["gnl"], + ) + + return params + + +if __name__ == "__main__": + import numpy as np + import pandas as pd + from pysbmy.timestepping import StandardTimeStepping + from pysbmy.cosmology import full_growthfactors_solver + from sbmy_control.parameters_monofonic import get_config_from_dict, create_monofonic_config + + # Read the cosmological parameter from the CSV file + w0wa_set_file = "/home/aubin/PotentialBCs/Planck2018/w0wa_set.csv" + w0wa_set = pd.read_csv(w0wa_set_file) + + # Extract the IDs from the DataFrame + IDs = w0wa_set['Run'].tolist() + + # Define the timestepping configurations (TimeStepDistribution, nsteps, name) + ts_configs=[(0,10,'Ta1'), + (0,20,'Ta2'), + (3,10,'TD1'), + (3,20,'TD2'),] + + # Define the additional cosmological parameters + cosmo_defaults_add = { + "Omega_k":0.0, + "Omega_r": 0.0, + "Tcmb": 2.7255, + "k_p": 0.05, + "N_ur": 2.046, + "m_nu1": 0.06, + "m_nu2": 0.0, + "m_nu3": 0.0, + "fnl": 0.0, + "gnl": 0.0, + "k_max":10.0, + "tau_reio":0.06, + "WhichSpectrum":"EH", + } + + # Iterate over the IDs + for k, ID in enumerate(IDs): + if k<6: # Skip the first 6 IDs, already done + continue + print(f"Processing ID {k+1}/{len(IDs)}: {ID}") + + # Get the cosmological parameters for the current ID + cosmo_params = w0wa_set[w0wa_set['Run'] == ID].iloc[0].to_dict() + # Add the additional cosmological parameters + cosmo_params.update(cosmo_defaults_add) + + cosmo_params["w_0"] = cosmo_params["w0"] + cosmo_params["w_a"] = cosmo_params["wa"] + cosmo_params["w0_fld"] = cosmo_params["w0"] + cosmo_params["wa_fld"] = cosmo_params["wa"] + + # Create the timestepping + print(f"> Creating timestepping with configuration {ts_configs[k % len(ts_configs)]} for ID {ID}") + this_ts_config = ts_configs[k % len(ts_configs)] + TS = StandardTimeStepping( + ai= 1/(1+19.0), + af = 1., + integrator=2, + TimeStepDistribution=this_ts_config[0], + nsteps=this_ts_config[1], + cosmo=cosmo_params + ) + + # Write the timestepping + print(f"> Writing timestepping to file for ID {ID}") + ts_file = f"/home/aubin/data/PotentialBCs/ML_dataset/params/ts_{ID}_{this_ts_config[2]}.h5" + TS.write(ts_file) + + # For each force evaluation, get the following values: a, D1, D2, which can be obtained with + print(f"> Calculating growth factors for ID {ID}") + a_force = TS.aStart[TS.operations == 2] + D_array = full_growthfactors_solver(a_force, cosmo_params) + D1_force = D_array[:, 0] + D2_force = D_array[:, 2] + + # For each force evaluation, save the following values to a file: ID, nforce, a, D1, D2, h, Omega_m, sigma8 + print(f"> Saving the cosmological parameters and time values for ID {ID}") + for nforce in range(len(a_force)): + output_file = f"/home/aubin/data/PotentialBCs/ML_dataset/cosmo_and_time/cosmo_and_time_parameters_{ID}_nforce{nforce}.txt" + with open(output_file, 'w') as f: + f.write(f"ID: {ID}\n") + f.write(f"nforce: {nforce}\n") + f.write(f"a: {a_force[nforce]}\n") + f.write(f"D1: {D1_force[nforce]}\n") + f.write(f"D2: {D2_force[nforce]}\n") + f.write(f"h: {cosmo_params['h']}\n") + f.write(f"Omega_m: {cosmo_params['Omega_m']}\n") + f.write(f"sigma8: {cosmo_params['sigma8']}\n") + + # Create the configuration file for monofonic + print(f"> Creating the configuration file for monofonic for ID {ID}") + monofonic_config_file = f"/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_{ID}.conf" + monofonic_dict = { + "config" : monofonic_config_file, + "output" : f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_", + "gridres": 768, + "boxlength": 480.0, + "seed" : np.random.randint(0, 2**32), + "ParameterSet":None, + "nthreads":128, + **cosmo_params, + } + create_monofonic_config(monofonic_config_file, get_config_from_dict(monofonic_dict)) + + # Run the sbmy_control command with the parameters for the current ID + print(f"> Running sbmy_control for ID {ID}") + params = create_parameters_dict(ID,ts_file,cosmo_params) + + import subprocess + + command_args = ["sbmy_control"] + for key, value in params.items(): + command_args.append(f"--{key}") + command_args.append(f"{value}") + + subprocess.run(command_args) + + # Remove the initial conditions particles files that are unnecessary + print(f"> Removing unnecessary initial conditions files for ID {ID}") + import os + import glob + ic_particles_files = glob.glob(f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_particles*") + for file in ic_particles_files: + os.remove(file) + + # Remove monofonic slurm script + monofonic_slurm_script = f"/home/aubin/data/PotentialBCs/ML_dataset/slurm_scripts/monofonic.sh" + if os.path.isfile(monofonic_slurm_script): + print(f"> Removing monofonic slurm script for ID {ID}") + os.remove(monofonic_slurm_script) + + print(f"> Finished processing ID {ID}\n\n\n") diff --git a/sCOCA_ML/dataset/production_scripts/Planck.py b/sCOCA_ML/dataset/production_scripts/Planck.py new file mode 100644 index 0000000..2700e07 --- /dev/null +++ b/sCOCA_ML/dataset/production_scripts/Planck.py @@ -0,0 +1,185 @@ + + +def create_parameters_dict(ID,ts_file,cosmo_params): + """ + Create a dictionnary of paramters for a given ID for the sbmy_control program. + """ + + params=dict(directory="/home/aubin/data/PotentialBCs/ML_dataset/", + simname=ID, + mode="alltCOLA", + ICs_gen="monofonic", + verbose=1, + nthreads=128, + execution="slurm", + N_particles=768, + L=480.0, + ICs="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_DM_delta.h5", + WriteGravPot=True, + OutputGravitationalPotentialBase="/home/aubin/data/PotentialBCs/ML_dataset/gravitational_potential/gravpot_"+ID, + MeshGravPot=768, + WriteDensity=True, + OutputDensityBase="/home/aubin/data/PotentialBCs/ML_dataset/density/density_"+ID, + MeshDensity=768, + WriteReferenceFrame=True, + OutputMomentaBase="/home/aubin/data/PotentialBCs/ML_dataset/momenta/momenta_"+ID+"_", + monofonic_config="/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_"+ID+".conf", + monofonic_output="/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_"+ID+"_", + TimeSteppingFileName=ts_file, + Omega_m=cosmo_params["Omega_m"], + Omega_b=cosmo_params["Omega_b"], + Omega_q=cosmo_params["Omega_q"], + h=cosmo_params["h"], + n_s=cosmo_params["n_s"], + sigma8=cosmo_params["sigma8"], + A_s=cosmo_params["A_s"], + Tcmb=cosmo_params["Tcmb"], + k_p=cosmo_params["k_p"], + N_ur=cosmo_params["N_ur"], + m_nu1=cosmo_params["m_nu1"], + m_nu2=cosmo_params["m_nu2"], + m_nu3=cosmo_params["m_nu3"], + w_0=cosmo_params["w_0"], + w_a=cosmo_params["w_a"], + fnl=cosmo_params["fnl"], + gnl=cosmo_params["gnl"], + ) + + return params + + +if __name__ == "__main__": + import numpy as np + import pandas as pd + from pysbmy.timestepping import StandardTimeStepping + from pysbmy.cosmology import full_growthfactors_solver + from sbmy_control.parameters_monofonic import get_config_from_dict, create_monofonic_config + + # Read the cosmological parameter from the CSV file + planck_set_file = "/home/aubin/PotentialBCs/Planck2018/Planck_set.csv" + planck_set = pd.read_csv(planck_set_file) + + # Extract the IDs from the DataFrame + IDs = planck_set['Run'].tolist() + + # Define the timestepping configurations (TimeStepDistribution, nsteps, name) + ts_configs=[(0,10,'Ta1'), + (0,20,'Ta2'), + (3,10,'TD1'), + (3,20,'TD2'),] + + # Define the additional cosmological parameters + cosmo_defaults_add = { + "Omega_k":0.0, + "Omega_r": 0.0, + "Tcmb": 2.7255, + "k_p": 0.05, + "N_ur": 2.046, + "m_nu1": 0.06, + "m_nu2": 0.0, + "m_nu3": 0.0, + "w_0": -1.0, + "w_a": 0.0, + "fnl": 0.0, + "gnl": 0.0, + "k_max":10.0, + "tau_reio":0.06, + "WhichSpectrum":"EH", + "w0_fld":-1.0, + "wa_fld":0.0, + } + + # Iterate over the IDs + for k, ID in enumerate(IDs): + print(f"Processing ID {k+1}/{len(IDs)}: {ID}") + if k < 4: + continue # Already done for the first 4 IDs + + # Get the cosmological parameters for the current ID + cosmo_params = planck_set[planck_set['Run'] == ID].iloc[0].to_dict() + # Add the additional cosmological parameters + cosmo_params.update(cosmo_defaults_add) + # Multiply A_s by 1e-9 to convert it to the correct units + cosmo_params["A_s"] *= 1e-9 + + # Create the timestepping + print(f"> Creating timestepping with configuration {ts_configs[k % len(ts_configs)]} for ID {ID}") + this_ts_config = ts_configs[k % len(ts_configs)] + TS = StandardTimeStepping( + ai= 1/(1+19.0), + af = 1., + integrator=2, + TimeStepDistribution=this_ts_config[0], + nsteps=this_ts_config[1], + cosmo=cosmo_params + ) + + # Write the timestepping + print(f"> Writing timestepping to file for ID {ID}") + ts_file = f"/home/aubin/data/PotentialBCs/ML_dataset/params/ts_{ID}_{this_ts_config[2]}.h5" + TS.write(ts_file) + + # For each force evaluation, get the following values: a, D1, D2, which can be obtained with + print(f"> Calculating growth factors for ID {ID}") + a_force = TS.aStart[TS.operations == 2] + D_array = full_growthfactors_solver(a_force, cosmo_params) + D1_force = D_array[:, 0] + D2_force = D_array[:, 2] + + # For each force evaluation, save the following values to a file: ID, nforce, a, D1, D2, h, Omega_m, sigma8 + print(f"> Saving the cosmological parameters and time values for ID {ID}") + for nforce in range(len(a_force)): + output_file = f"/home/aubin/data/PotentialBCs/ML_dataset/cosmo_and_time/cosmo_and_time_parameters_{ID}_nforce{nforce}.txt" + with open(output_file, 'w') as f: + f.write(f"ID: {ID}\n") + f.write(f"nforce: {nforce}\n") + f.write(f"a: {a_force[nforce]}\n") + f.write(f"D1: {D1_force[nforce]}\n") + f.write(f"D2: {D2_force[nforce]}\n") + f.write(f"h: {cosmo_params['h']}\n") + f.write(f"Omega_m: {cosmo_params['Omega_m']}\n") + f.write(f"sigma8: {cosmo_params['sigma8']}\n") + + # Create the configuration file for monofonic + print(f"> Creating the configuration file for monofonic for ID {ID}") + monofonic_config_file = f"/home/aubin/data/PotentialBCs/ML_dataset/monofonic_config/monofonic_config_{ID}.conf" + monofonic_dict = { + "config" : monofonic_config_file, + "output" : f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_", + "gridres": 768, + "boxlength": 480.0, + "seed" : np.random.randint(0, 2**32), + "ParameterSet":None, + "nthreads":128, + **cosmo_params, + } + create_monofonic_config(monofonic_config_file, get_config_from_dict(monofonic_dict)) + + # Run the sbmy_control command with the parameters for the current ID + print(f"> Running sbmy_control for ID {ID}") + params = create_parameters_dict(ID,ts_file,cosmo_params) + + import subprocess + + command_args = ["sbmy_control"] + for key, value in params.items(): + command_args.append(f"--{key}") + command_args.append(f"{value}") + + subprocess.run(command_args) + + # Remove the initial conditions particles files that are unnecessary + print(f"> Removing unnecessary initial conditions files for ID {ID}") + import os + import glob + ic_particles_files = glob.glob(f"/home/aubin/data/PotentialBCs/ML_dataset/initial_conditions/ICs_{ID}_particles*") + for file in ic_particles_files: + os.remove(file) + + # Remove monofonic slurm script + monofonic_slurm_script = f"/home/aubin/data/PotentialBCs/ML_dataset/slurm_scripts/monofonic.sh" + if os.path.isfile(monofonic_slurm_script): + print(f"> Removing monofonic slurm script for ID {ID}") + os.remove(monofonic_slurm_script) + + print(f"> Finished processing ID {ID}\n\n\n") \ No newline at end of file diff --git a/sCOCA_ML/dataset/production_scripts/read_planck_chains.ipynb b/sCOCA_ML/dataset/production_scripts/read_planck_chains.ipynb new file mode 100644 index 0000000..84c6ab3 --- /dev/null +++ b/sCOCA_ML/dataset/production_scripts/read_planck_chains.ipynb @@ -0,0 +1,8567 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6d803aa0", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bc23d10c", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the parameters names file : each line is a parameter name with name and latex name\n", + "def read_parameters_names(file_path):\n", + " parameters = []\n", + " with open(file_path, 'r') as file:\n", + " for line in file:\n", + " line = line.strip()\n", + " if line: # Skip empty lines\n", + " parts = line.split('\\t')\n", + " parameters.append([parts[0], ' '.join(parts[1:])]) # First part is the name, rest is the latex name\n", + " return parameters\n", + "\n", + "parameter_names_file = \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing.paramnames\"\n", + "\n", + "parameters = read_parameters_names(parameter_names_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0d1f3a2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['omegabh2', '\\\\Omega_b h^2'],\n", + " ['omegach2', '\\\\Omega_c h^2'],\n", + " ['theta', '100\\\\theta_{MC}'],\n", + " ['tau', '\\\\tau'],\n", + " ['logA', '{\\\\rm{ln}}(10^{10} A_s)'],\n", + " ['ns', 'n_s'],\n", + " ['calPlanck', 'y_{\\\\rm cal}'],\n", + " ['acib217', 'A^{CIB}_{217}'],\n", + " ['xi', '\\\\xi^{tSZ-CIB}'],\n", + " ['asz143', 'A^{tSZ}_{143}'],\n", + " ['aps100', 'A^{PS}_{100}'],\n", + " ['aps143', 'A^{PS}_{143}'],\n", + " ['aps143217', 'A^{PS}_{143\\\\times217}'],\n", + " ['aps217', 'A^{PS}_{217}'],\n", + " ['aksz', 'A^{kSZ}'],\n", + " ['kgal100', 'A^{{\\\\rm dust}TT}_{100}'],\n", + " ['kgal143', 'A^{{\\\\rm dust}TT}_{143}'],\n", + " ['kgal143217', 'A^{{\\\\rm dust}TT}_{143\\\\times217}'],\n", + " ['kgal217', 'A^{{\\\\rm dust}TT}_{217}'],\n", + " ['galfTE100', 'A^{{\\\\rm dust}TE}_{100}'],\n", + " ['galfTE100143', 'A^{{\\\\rm dust}TE}_{100\\\\times143}'],\n", + " ['galfTE100217', 'A^{{\\\\rm dust}TE}_{100\\\\times217}'],\n", + " ['galfTE143', 'A^{{\\\\rm dust}TE}_{143}'],\n", + " ['galfTE143217', 'A^{{\\\\rm dust}TE}_{143\\\\times217}'],\n", + " ['galfTE217', 'A^{{\\\\rm dust}TE}_{217}'],\n", + " ['cal0', 'c_{100}'],\n", + " ['cal2', 'c_{217}'],\n", + " ['H0*', 'H_0'],\n", + " ['omegal*', '\\\\Omega_\\\\Lambda'],\n", + " ['omegam*', '\\\\Omega_m'],\n", + " ['omegamh2*', '\\\\Omega_m h^2'],\n", + " ['omeganuh2*', '\\\\Omega_\\\\nu h^2'],\n", + " ['omegamh3*', '\\\\Omega_m h^3'],\n", + " ['sigma8*', '\\\\sigma_8'],\n", + " ['S8*', 'S_8'],\n", + " ['s8omegamp5*', '\\\\sigma_8 \\\\Omega_m^{0.5}'],\n", + " ['s8omegamp25*', '\\\\sigma_8 \\\\Omega_m^{0.25}'],\n", + " ['s8h5*', '\\\\sigma_8/h^{0.5}'],\n", + " ['rdragh*', 'r_{\\\\rm drag} h'],\n", + " ['rmsdeflect*', '\\\\langle d^2\\\\rangle^{1/2}'],\n", + " ['zrei*', 'z_{\\\\rm re}'],\n", + " ['A*', '10^9 A_s'],\n", + " ['clamp*', '10^9 A_s e^{-2\\\\tau}'],\n", + " ['DL40*', 'D_{40}'],\n", + " ['DL220*', 'D_{220}'],\n", + " ['DL810*', 'D_{810}'],\n", + " ['DL1420*', 'D_{1420}'],\n", + " ['DL2000*', 'D_{2000}'],\n", + " ['ns02*', 'n_{s,0.002}'],\n", + " ['yheused*', 'Y_P'],\n", + " ['YpBBN*', 'Y_P^{\\\\rm{BBN}}'],\n", + " ['DHBBN*', '10^5D/H'],\n", + " ['age*', '{\\\\rm{Age}}/{\\\\rm{Gyr}}'],\n", + " ['zstar*', 'z_*'],\n", + " ['rstar*', 'r_*'],\n", + " ['thetastar*', '100\\\\theta_*'],\n", + " ['DAstar*', 'D_{\\\\rm{M}}(z_*)/{\\\\rm{Gpc}}'],\n", + " ['zdrag*', 'z_{\\\\rm{drag}}'],\n", + " ['rdrag*', 'r_{\\\\rm{drag}}'],\n", + " ['kd*', 'k_{\\\\rm D}'],\n", + " ['thetad*', '100\\\\theta_{\\\\rm{D}}'],\n", + " ['zeq*', 'z_{\\\\rm{eq}}'],\n", + " ['keq*', 'k_{\\\\rm{eq}}'],\n", + " ['thetaeq*', '100\\\\theta_{\\\\rm{eq}}'],\n", + " ['thetarseq*', '100\\\\theta_{\\\\rm{s,eq}}'],\n", + " ['Hubble015*', 'H(0.15)'],\n", + " ['DM015*', 'D_{\\\\rm{M}}(0.15)'],\n", + " ['Hubble038*', 'H(0.38)'],\n", + " ['DM038*', 'D_{\\\\rm{M}}(0.38)'],\n", + " ['Hubble051*', 'H(0.51)'],\n", + " ['DM051*', 'D_{\\\\rm{M}}(0.51)'],\n", + " ['Hubble061*', 'H(0.61)'],\n", + " ['DM061*', 'D_{\\\\rm{M}}(0.61)'],\n", + " ['Hubble233*', 'H(2.33)'],\n", + " ['DM233*', 'D_{\\\\rm{M}}(2.33)'],\n", + " ['fsigma8z015*', 'f\\\\sigma_8(0.15)'],\n", + " ['sigma8z015*', '\\\\sigma_8(0.15)'],\n", + " ['fsigma8z038*', 'f\\\\sigma_8(0.38)'],\n", + " ['sigma8z038*', '\\\\sigma_8(0.38)'],\n", + " ['fsigma8z051*', 'f\\\\sigma_8(0.51)'],\n", + " ['sigma8z051*', '\\\\sigma_8(0.51)'],\n", + " ['fsigma8z061*', 'f\\\\sigma_8(0.61)'],\n", + " ['sigma8z061*', '\\\\sigma_8(0.61)'],\n", + " ['fsigma8z233*', 'f\\\\sigma_8(2.33)'],\n", + " ['sigma8z233*', '\\\\sigma_8(2.33)'],\n", + " ['f2000_143*', 'f_{2000}^{143}'],\n", + " ['f2000_x*', 'f_{2000}^{143\\\\times217}'],\n", + " ['f2000_217*', 'f_{2000}^{217}'],\n", + " ['chi2_lensing*', '\\\\chi^2_{\\\\rm lensing}'],\n", + " ['chi2_simall*', '\\\\chi^2_{\\\\rm simall}'],\n", + " ['chi2_lowl*', '\\\\chi^2_{\\\\rm lowl}'],\n", + " ['chi2_plik*', '\\\\chi^2_{\\\\rm plik}'],\n", + " ['chi2_prior*', '\\\\chi^2_{\\\\rm prior}'],\n", + " ['chi2_CMB*', '\\\\chi^2_{\\\\rm CMB}']]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bf8cf9a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2299662/3662306240.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n" + ] + } + ], + "source": [ + "## Read a chain file\n", + "# a chain file is a text file with one entry per line, each entry is composed of len(parameters) values separated by a 4-spaces\n", + "def read_chain_file(file_path):\n", + " # Read the chain file into a DataFrame\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n", + " \n", + " # Rename the columns based on the parameters names\n", + " df.columns = [\"weight\", \"-loglike\",]+[param[0] for param in parameters]\n", + " \n", + " return df\n", + "\n", + "chain_file = \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing_1.txt\"\n", + "chain_df = read_chain_file(chain_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e0293331", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2299662/3662306240.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n", + "/tmp/ipykernel_2299662/3662306240.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n", + "/tmp/ipykernel_2299662/3662306240.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n", + "/tmp/ipykernel_2299662/3662306240.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n", + " df = pd.read_csv(file_path, delim_whitespace=True, header=None)\n" + ] + } + ], + "source": [ + "## Concatenate the 4 chains from the 4 files\n", + "\n", + "chain_files = [\n", + " \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing_1.txt\",\n", + " \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing_2.txt\",\n", + " \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing_3.txt\",\n", + " \"/home/aubin/PotentialBCs/Planck2018/base_plikHM_TTTEEE_lowl_lowE_lensing_4.txt\"\n", + "]\n", + "\n", + "def concatenate_chain_files(chain_files):\n", + " # Read and concatenate all chain files into a single DataFrame\n", + " chain_dfs = [read_chain_file(file) for file in chain_files]\n", + " concatenated_df = pd.concat(chain_dfs, ignore_index=True)\n", + " \n", + " return concatenated_df\n", + "chain_df = concatenate_chain_files(chain_files)" + ] + }, + { + "cell_type": "code", + "execution_count": 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weight-loglikeomegabh2omegach2thetataulogAnscalPlanckacib217...sigma8z233*f2000_143*f2000_x*f2000_217*chi2_lensing*chi2_simall*chi2_lowl*chi2_plik*chi2_prior*chi2_CMB*
01.01397.7550.0222360.1190221.0408600.0577323.0447840.9728230.99942145.26123...0.30764028.0858629.72710105.00979.080994396.756721.860472363.2084.6042922790.906
11.01401.9020.0222120.1190421.0409300.0600193.0501580.9710520.99946233.66526...0.30834526.9085829.55360106.24658.902029397.506822.268782361.34413.7815402790.022
22.01394.9040.0221880.1190341.0408940.0603753.0507330.9712150.99946238.38740...0.30842428.6616930.64146106.76118.909747397.634522.232372356.4514.5804362785.227
31.01398.8940.0221390.1194901.0410480.0610473.0539690.9719591.00013746.48444...0.30924029.8324031.17108106.47969.093967397.891922.090512364.5134.1992662793.589
41.01400.8560.0220640.1205951.0408850.0579953.0487510.9689570.99923346.08949...0.30805529.0305331.05737106.33729.498806397.056322.551372365.8536.7523612794.959
..................................................................
252203.01399.5390.0225730.1190871.0413480.0631163.0630140.9669811.00104358.59281...0.31011627.0147730.58379105.06148.728878398.691423.581982357.38610.6895302788.389
252219.01398.5700.0226690.1190861.0413340.0652103.0700970.9693881.00266548.06052...0.31143524.1063430.59411105.24768.978655399.504423.141522355.5889.9276162787.213
252221.01397.6010.0227620.1182951.0412120.0635123.0631850.9710121.00119848.12786...0.31027323.9404329.76514104.93128.516112398.562722.838922356.1299.1562972786.047
252231.01401.6180.0227530.1185521.0411550.0634443.0639930.9708401.00129340.10526...0.31035824.3381129.17152106.76158.616866398.561322.880172365.8267.3522872795.884
252242.01411.5480.0227560.1185691.0411610.0637203.0639560.9710581.00103646.49693...0.31039127.6964728.16482102.18768.623035398.686422.831832373.30619.6493402803.447
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25225 rows × 96 columns

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" + ], + "text/plain": [ + " weight -loglike omegabh2 omegach2 theta tau logA \\\n", + "0 1.0 1397.755 0.022236 0.119022 1.040860 0.057732 3.044784 \n", + "1 1.0 1401.902 0.022212 0.119042 1.040930 0.060019 3.050158 \n", + "2 2.0 1394.904 0.022188 0.119034 1.040894 0.060375 3.050733 \n", + "3 1.0 1398.894 0.022139 0.119490 1.041048 0.061047 3.053969 \n", + "4 1.0 1400.856 0.022064 0.120595 1.040885 0.057995 3.048751 \n", + "... ... ... ... ... ... ... ... \n", + "25220 3.0 1399.539 0.022573 0.119087 1.041348 0.063116 3.063014 \n", + "25221 9.0 1398.570 0.022669 0.119086 1.041334 0.065210 3.070097 \n", + "25222 1.0 1397.601 0.022762 0.118295 1.041212 0.063512 3.063185 \n", + "25223 1.0 1401.618 0.022753 0.118552 1.041155 0.063444 3.063993 \n", + "25224 2.0 1411.548 0.022756 0.118569 1.041161 0.063720 3.063956 \n", + "\n", + " ns calPlanck acib217 ... sigma8z233* f2000_143* f2000_x* \\\n", + "0 0.972823 0.999421 45.26123 ... 0.307640 28.08586 29.72710 \n", + "1 0.971052 0.999462 33.66526 ... 0.308345 26.90858 29.55360 \n", + "2 0.971215 0.999462 38.38740 ... 0.308424 28.66169 30.64146 \n", + "3 0.971959 1.000137 46.48444 ... 0.309240 29.83240 31.17108 \n", + "4 0.968957 0.999233 46.08949 ... 0.308055 29.03053 31.05737 \n", + "... ... ... ... ... ... ... ... \n", + "25220 0.966981 1.001043 58.59281 ... 0.310116 27.01477 30.58379 \n", + "25221 0.969388 1.002665 48.06052 ... 0.311435 24.10634 30.59411 \n", + "25222 0.971012 1.001198 48.12786 ... 0.310273 23.94043 29.76514 \n", + "25223 0.970840 1.001293 40.10526 ... 0.310358 24.33811 29.17152 \n", + "25224 0.971058 1.001036 46.49693 ... 0.310391 27.69647 28.16482 \n", + "\n", + " f2000_217* chi2_lensing* chi2_simall* chi2_lowl* chi2_plik* \\\n", + "0 105.0097 9.080994 396.7567 21.86047 2363.208 \n", + "1 106.2465 8.902029 397.5068 22.26878 2361.344 \n", + "2 106.7611 8.909747 397.6345 22.23237 2356.451 \n", + "3 106.4796 9.093967 397.8919 22.09051 2364.513 \n", + "4 106.3372 9.498806 397.0563 22.55137 2365.853 \n", + "... ... ... ... ... ... \n", + "25220 105.0614 8.728878 398.6914 23.58198 2357.386 \n", + "25221 105.2476 8.978655 399.5044 23.14152 2355.588 \n", + "25222 104.9312 8.516112 398.5627 22.83892 2356.129 \n", + "25223 106.7615 8.616866 398.5613 22.88017 2365.826 \n", + "25224 102.1876 8.623035 398.6864 22.83183 2373.306 \n", + "\n", + " chi2_prior* chi2_CMB* \n", + "0 4.604292 2790.906 \n", + "1 13.781540 2790.022 \n", + "2 4.580436 2785.227 \n", + "3 4.199266 2793.589 \n", + "4 6.752361 2794.959 \n", + "... ... ... \n", + "25220 10.689530 2788.389 \n", + "25221 9.927616 2787.213 \n", + "25222 9.156297 2786.047 \n", + "25223 7.352287 2795.884 \n", + "25224 19.649340 2803.447 \n", + "\n", + "[25225 rows x 96 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain_df" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "41c0bb86", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2299662/3568995478.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " reduced_chain_df['omegab*'] = reduced_chain_df['omegabh2'] / (reduced_chain_df['H0*']/ 100)**2\n" + ] + } + ], + "source": [ + "reduced_chain_df = chain_df[['weight', 'H0*', 'omegam*', 'omegal*', 'A*', 'ns', 'sigma8*','omegabh2']]\n", + "reduced_chain_df['omegab*'] = reduced_chain_df['omegabh2'] / (reduced_chain_df['H0*']/ 100)**2\n", + "reduced_chain_df = reduced_chain_df.drop(columns=['omegabh2'])\n", + "reduced_chain_df['h'] = reduced_chain_df['H0*'] / 100\n", + "reduced_chain_df = reduced_chain_df.drop(columns=['H0*'])\n", + "reduced_chain_df = reduced_chain_df.rename(columns={\n", + " 'omegam*': 'Omega_m',\n", + " 'omegal*': 'Omega_q',\n", + " 'A*': 'A_s',\n", + " 'ns': 'n_s',\n", + " 'sigma8*': 'sigma8',\n", + " 'omegab*': 'Omega_b'\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "09021330", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "weight", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_m", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_q", + "rawType": "float64", + "type": "float" + }, + { + "name": "A_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "n_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "sigma8", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_b", + "rawType": "float64", + "type": "float" + }, + { + "name": "h", + "rawType": "float64", + "type": "float" + } + ], + "ref": 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weightOmega_mOmega_qA_sn_ssigma8Omega_bh
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11.00.3108590.6891412.1118670.9710520.8129020.0486600.675629
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31.00.3133430.6866572.1199330.9719590.8166640.0487580.673834
41.00.3203630.6796372.1088990.9689570.8174260.0493240.668817
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" + ], + "text/plain": [ + " weight Omega_m Omega_q A_s n_s sigma8 Omega_b \\\n", + "0 1.0 0.310835 0.689165 2.100550 0.972823 0.811030 0.048708 \n", + "1 1.0 0.310859 0.689141 2.111867 0.971052 0.812902 0.048660 \n", + "2 2.0 0.311059 0.688941 2.113083 0.971215 0.813223 0.048650 \n", + "3 1.0 0.313343 0.686657 2.119933 0.971959 0.816664 0.048758 \n", + "4 1.0 0.320363 0.679637 2.108899 0.968957 0.817426 0.049324 \n", + "... ... ... ... ... ... ... ... \n", + "25220 3.0 0.307865 0.692135 2.139194 0.966981 0.815879 0.048835 \n", + "25221 9.0 0.307409 0.692591 2.154398 0.969388 0.819089 0.048938 \n", + "25222 1.0 0.303057 0.696943 2.139559 0.971012 0.813521 0.048681 \n", + "25223 1.0 0.304663 0.695337 2.141289 0.970840 0.814673 0.048834 \n", + "25224 2.0 0.304707 0.695293 2.141210 0.971058 0.814786 0.048840 \n", + "\n", + " h \n", + "0 0.675665 \n", + "1 0.675629 \n", + "2 0.675336 \n", + "3 0.673834 \n", + "4 0.668817 \n", + "... ... \n", + "25220 0.679877 \n", + "25221 0.680609 \n", + "25222 0.683795 \n", + "25223 0.682587 \n", + "25224 0.682587 \n", + "\n", + "[25225 rows x 8 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reduced_chain_df" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1dcb1420", + "metadata": {}, + "outputs": [], + "source": [ + "## Randomly sample the chain based on the weights and return N samples\n", + "def sample_chain(chain_df, N):\n", + " # Normalize the weights\n", + " weights = chain_df['weight']\n", + " normalized_weights = weights / np.sum(weights)\n", + " \n", + " # Sample indices based on the normalized weights\n", + " sampled_indices = np.random.choice(chain_df.index, size=N, p=normalized_weights)\n", + " \n", + " # Return the sampled rows\n", + " return chain_df.loc[sampled_indices]\n", + "\n", + "# Sample 70 rows from the chain\n", + "N_samples = 70\n", + "sampled_chain_df = sample_chain(reduced_chain_df, N_samples)\n", + "# Remove 'weight' column\n", + "sampled_chain_df = sampled_chain_df.drop(columns=['weight'])\n", + "# Reset the index of the sampled DataFrame\n", + "sampled_chain_df = sampled_chain_df.reset_index(drop=True)\n", + "# Round values to 4 decimal places\n", + "sampled_chain_df = sampled_chain_df.round(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "041ac464", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Omega_m", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_q", + "rawType": "float64", + "type": "float" + }, + { + "name": "A_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "n_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "sigma8", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_b", + "rawType": "float64", + "type": "float" + }, + { + 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the parameters w0 and wa with constant values: w0 = -1 and wa = 0\n", + "sampled_chain_df['w0'] = -1.0\n", + "sampled_chain_df['wa'] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4e3ea9fb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Omega_m", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_q", + "rawType": "float64", + "type": "float" + }, + { + "name": "A_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "n_s", + "rawType": "float64", + "type": "float" + }, + { + "name": "sigma8", + "rawType": "float64", + "type": "float" + }, + { + "name": "Omega_b", + "rawType": "float64", + "type": "float" + }, + { + "name": "h", + "rawType": "float64", + "type": "float" + }, + { + "name": "w0", + "rawType": "float64", + "type": "float" + }, + { + "name": "wa", + 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Omega_mOmega_qA_sn_ssigma8Omega_bhw0wa
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40.31030.68972.13730.97130.81700.04890.6767-1.00.0
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650.32430.67572.12050.96240.81960.05010.6674-1.00.0
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670.31920.68082.08560.96470.81030.04990.6713-1.00.0
680.31580.68422.10050.95880.81090.04930.6738-1.00.0
690.30840.69162.09790.96560.80740.04890.6790-1.00.0
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70 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Omega_m Omega_q A_s n_s sigma8 Omega_b h w0 wa\n", + "0 0.3118 0.6882 2.0890 0.9707 0.8099 0.0488 0.6760 -1.0 0.0\n", + "1 0.3185 0.6815 2.0811 0.9626 0.8082 0.0497 0.6712 -1.0 0.0\n", + "2 0.3201 0.6799 2.0625 0.9629 0.8066 0.0497 0.6705 -1.0 0.0\n", + "3 0.3201 0.6799 2.1389 0.9614 0.8204 0.0494 0.6690 -1.0 0.0\n", + "4 0.3103 0.6897 2.1373 0.9713 0.8170 0.0489 0.6767 -1.0 0.0\n", + ".. ... ... ... ... ... ... ... ... ...\n", + "65 0.3243 0.6757 2.1205 0.9624 0.8196 0.0501 0.6674 -1.0 0.0\n", + "66 0.3129 0.6871 2.1271 0.9666 0.8153 0.0490 0.6746 -1.0 0.0\n", + "67 0.3192 0.6808 2.0856 0.9647 0.8103 0.0499 0.6713 -1.0 0.0\n", + "68 0.3158 0.6842 2.1005 0.9588 0.8109 0.0493 0.6738 -1.0 0.0\n", + "69 0.3084 0.6916 2.0979 0.9656 0.8074 0.0489 0.6790 -1.0 0.0\n", + "\n", + "[70 rows x 9 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sampled_chain_df" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "78ba4073", + "metadata": {}, + "outputs": [], + "source": [ + "## Reorder the columns: h, Omega_m, Omega_b, Omega_q, n_s, sigma8, A_s, w0, wa\n", + "ordered_columns = ['h', 'Omega_m', 'Omega_b', 'Omega_q', 'n_s', 'sigma8', 'A_s', 'w0', 'wa']\n", + "sampled_chain_df = sampled_chain_df[ordered_columns]\n", + "## Rename index to 'Run' and set it to 'train_LCDM_{index+1}'\n", + "sampled_chain_df.index = [f'train_LCDM_{i+1}' for i in range(len(sampled_chain_df))]\n", + "sampled_chain_df.index.name = 'Run'\n", + "## Save the sampled chain to a CSV file\n", + "output_file = \"/home/aubin/PotentialBCs/Planck2018/sample_chain.csv\"\n", + "sampled_chain_df.to_csv(output_file, index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dd903e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}