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Add CF4 and more improvements (#141)
* Update params counting * Update imports * Add CF4 group * Update submit * Update submit * Many updates * Many more updates * Add CF4 TFR * Add CF4 TF * Fix RA bug in CF4 TF * Add CF4 quality cut * Start sampling alpha * Update scripts * Some comments * Update script * Add option to have magnitude selection. * Add calibration dipoles
This commit is contained in:
Richard Stiskalek
GitHub
parent
d578c71b83
commit
d13246a394
7 changed files with 420 additions and 255 deletions
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@ -15,6 +15,7 @@
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MPI script to interpolate the density and velocity fields along the line of
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sight.
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"""
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import sys
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from argparse import ArgumentParser
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from datetime import datetime
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from gc import collect
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@ -32,7 +33,8 @@ from mpi4py import MPI
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from numba import jit
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from taskmaster import work_delegation # noqa
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from utils import get_nsims
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sys.path.append("../")
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from utils import get_nsims # noqa
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###############################################################################
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# I/O functions #
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@ -84,8 +86,18 @@ def get_los(catalogue_name, simname, comm):
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with File(fname, 'r') as f:
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RA = f["RA"][:]
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dec = f["DEC"][:]
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elif catalogue_name == "CF4_GroupAll":
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fname = "/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_GroupAll.hdf5" # noqa
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with File(fname, 'r') as f:
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RA = f["RA"][:]
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dec = f["DE"][:]
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elif catalogue_name == "CF4_TFR":
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fname = "/mnt/extraspace/rstiskalek/catalogs/PV/CF4/CF4_TF-distances.hdf5" # noqa
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with File(fname, 'r') as f:
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RA = f["RA"][:] * 360 / 24 # Convert to degrees from hours.
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dec = f["DE"][:]
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else:
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raise ValueError(f"Unknown field name: `{catalogue_name}`.")
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raise ValueError(f"Unknown catalogue name: `{catalogue_name}`.")
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if comm.Get_rank() == 0:
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print(f"The dataset contains {len(RA)} objects.")
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@ -10,8 +10,8 @@ MAS="SPH"
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grid=1024
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for simname in "CF4"; do
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for catalogue in "Foundation"; do
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for simname in "Carrick2015"; do
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for catalogue in "CF4_TFR"; do
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pythoncm="$env $file --catalogue $catalogue --nsims $nsims --simname $simname --MAS $MAS --grid $grid"
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if [ $on_login -eq 1 ]; then
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echo $pythoncm
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@ -100,6 +100,10 @@ def get_models(get_model_kwargs, verbose=True):
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"Pantheon+_groups", "Pantheon+_groups_zSN",
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"Pantheon+_zSN"]:
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fpath = join(folder, "PV_compilation.hdf5")
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elif "CF4_TFR" in cat:
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fpath = join(folder, "PV/CF4/CF4_TF-distances.hdf5")
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elif cat in ["CF4_GroupAll"]:
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fpath = join(folder, "PV/CF4/CF4_GroupAll.hdf5")
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else:
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raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.")
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@ -139,11 +143,7 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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samples = mcmc.get_samples()
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log_posterior = -mcmc.get_extra_fields()["potential_energy"]
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log_likelihood = samples.pop("ll_values")
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if log_likelihood is None:
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raise ValueError("The samples must contain the log likelihood values under the key `ll_values`.") # noqa
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BIC, AIC = csiborgtools.BIC_AIC(samples, log_likelihood, ndata)
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BIC, AIC = csiborgtools.BIC_AIC(samples, log_posterior, ndata)
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print(f"{'BIC':<20} {BIC}")
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print(f"{'AIC':<20} {AIC}")
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mcmc.print_summary()
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@ -174,7 +174,6 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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grp.create_dataset(key, data=value)
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# Write log likelihood and posterior
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f.create_dataset("log_likelihood", data=log_likelihood)
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f.create_dataset("log_posterior", data=log_posterior)
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# Write goodness of fit
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@ -207,10 +206,9 @@ def run_model(model, nsteps, nburn, model_kwargs, out_folder, sample_beta,
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# Command line interface #
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###############################################################################
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def get_distmod_hyperparams(catalogue):
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def get_distmod_hyperparams(catalogue, sample_alpha):
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alpha_min = -1.0
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alpha_max = 3.0
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sample_alpha = True
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if catalogue in ["LOSS", "Foundation", "Pantheon+", "Pantheon+_groups", "Pantheon+_zSN"]: # noqa
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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@ -220,12 +218,24 @@ def get_distmod_hyperparams(catalogue):
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"alpha_min": alpha_min, "alpha_max": alpha_max,
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"sample_alpha": sample_alpha
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}
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elif catalogue in ["SFI_gals", "2MTF"]:
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elif catalogue in ["SFI_gals", "2MTF"] or "CF4_TFR" in catalogue:
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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"a_mean": -21., "a_std": 5.0,
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"b_mean": -5.95, "b_std": 3.0,
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"b_mean": -5.95, "b_std": 4.0,
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"c_mean": 0., "c_std": 20.0,
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"sample_curvature": False,
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"a_dipole_mean": 0., "a_dipole_std": 1.0,
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"sample_a_dipole": True,
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"alpha_min": alpha_min, "alpha_max": alpha_max,
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"sample_alpha": sample_alpha
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"sample_alpha": sample_alpha,
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}
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elif catalogue in ["CF4_GroupAll"]:
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return {"e_mu_min": 0.001, "e_mu_max": 1.0,
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"dmu_min": -3.0, "dmu_max": 3.0,
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"dmu_dipole_mean": 0., "dmu_dipole_std": 1.0,
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"sample_dmu_dipole": True,
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"alpha_min": alpha_min, "alpha_max": alpha_max,
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"sample_alpha": sample_alpha,
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}
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else:
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raise ValueError(f"Unsupported catalogue: `{ARGS.catalogue}`.")
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@ -241,37 +251,46 @@ if __name__ == "__main__":
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# Fixed user parameters #
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###########################################################################
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nsteps = 5000
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nburn = 1500
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nsteps = 1500
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nburn = 1000
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zcmb_min = 0
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zcmb_max = 0.05
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calculate_evidence = False
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nchains_harmonic = 10
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num_epochs = 30
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inference_method = "mike"
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maxmag_selection = None
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sample_alpha = True
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sample_beta = True
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sample_Vmono = False
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if nsteps % nchains_harmonic != 0:
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raise ValueError(
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"The number of steps must be divisible by the number of chains.")
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main_params = {"nsteps": nsteps, "nburn": nburn, "zcmb_max": zcmb_max,
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main_params = {"nsteps": nsteps, "nburn": nburn,
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"zcmb_min": zcmb_min,
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"zcmb_max": zcmb_max,
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"maxmag_selection": maxmag_selection,
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"calculate_evidence": calculate_evidence,
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"nchains_harmonic": nchains_harmonic,
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"num_epochs": num_epochs}
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"num_epochs": num_epochs,
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"inference_method": inference_method}
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print_variables(main_params.keys(), main_params.values())
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calibration_hyperparams = {"Vext_min": -1000, "Vext_max": 1000,
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"Vmono_min": -1000, "Vmono_max": 1000,
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"beta_min": -1.0, "beta_max": 3.0,
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"sigma_v_min": 1.0, "sigma_v_max": 750.,
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"sample_Vmono": False,
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"sample_beta": True,
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"sample_sigma_v_ext": False,
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"sample_Vmono": sample_Vmono,
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"sample_beta": sample_beta,
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}
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print_variables(
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calibration_hyperparams.keys(), calibration_hyperparams.values())
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distmod_hyperparams_per_catalogue = []
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for cat in ARGS.catalogue:
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x = get_distmod_hyperparams(cat)
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x = get_distmod_hyperparams(cat, sample_alpha)
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print(f"\n{cat} hyperparameters:")
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print_variables(x.keys(), x.values())
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distmod_hyperparams_per_catalogue.append(x)
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@ -280,12 +299,14 @@ if __name__ == "__main__":
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*distmod_hyperparams_per_catalogue)
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###########################################################################
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get_model_kwargs = {"zcmb_max": zcmb_max}
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get_model_kwargs = {"zcmb_min": zcmb_min, "zcmb_max": zcmb_max,
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"maxmag_selection": maxmag_selection}
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models = get_models(get_model_kwargs, )
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model_kwargs = {
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"models": models,
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"field_calibration_hyperparams": calibration_hyperparams,
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"distmod_hyperparams_per_model": distmod_hyperparams_per_catalogue,
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"inference_method": inference_method,
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}
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model = csiborgtools.flow.PV_validation_model
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@ -39,7 +39,8 @@ fi
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# for simname in "Lilow2024" "CF4" "CF4gp" "csiborg1" "csiborg2_main" "csiborg2X"; do
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for simname in "Carrick2015"; do
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for catalogue in "LOSS,2MTF,SFI_gals"; do
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for catalogue in "CF4_GroupAll"; do
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# for catalogue in "CF4_TFR_i"; do
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# for ksim in 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20; do
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for ksim in "none"; do
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pythoncm="$env $file --catalogue $catalogue --simname $simname --ksim $ksim --ksmooth $ksmooth --ndevice $ndevice --device $device"
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