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rstiskalek 2024-09-21 17:02:10 +01:00
parent 27f52d7b29
commit 82b71922cc
1 changed files with 29 additions and 6 deletions
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35
scripts/flow/flow_validation.py
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@ -74,8 +74,9 @@ import sys
from os.path import join # noqa from os.path import join # noqa
import csiborgtools # noqa import csiborgtools # noqa
from csiborgtools import fprint # noqa
import jax # noqa import jax # noqa
import numpy as np # noqa
from csiborgtools import fprint # noqa
from h5py import File # noqa from h5py import File # noqa
from numpyro.infer import MCMC, NUTS, init_to_median # noqa from numpyro.infer import MCMC, NUTS, init_to_median # noqa
@ -86,7 +87,8 @@ def print_variables(names, variables):
print(flush=True) print(flush=True)
def get_models(ksim, get_model_kwargs, mag_selection, verbose=True): def get_models(ksim, get_model_kwargs, mag_selection, void_kwargs,
verbose=True):
"""Load the data and create the NumPyro models.""" """Load the data and create the NumPyro models."""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
folder = "/mnt/extraspace/rstiskalek/catalogs/" folder = "/mnt/extraspace/rstiskalek/catalogs/"
@ -125,12 +127,21 @@ def get_models(ksim, get_model_kwargs, mag_selection, verbose=True):
cat, fpath, paths, cat, fpath, paths,
ksmooth=ARGS.ksmooth) ksmooth=ARGS.ksmooth)
models[i] = csiborgtools.flow.get_model( models[i] = csiborgtools.flow.get_model(
loader, mag_selection=mag_selection[i], **get_model_kwargs) loader, mag_selection=mag_selection[i], void_kwargs=void_kwargs,
**get_model_kwargs)
fprint(f"num. radial steps is {len(loader.rdist)}") fprint(f"num. radial steps is {len(loader.rdist)}")
return models return models
def select_void_h(kind):
hs = {"mb": 0.7615, "gauss": 0.7724, "exp": 0.7725}
try:
return hs[kind]
except KeyError:
raise ValueError(f"Unknown void kind: `{kind}`.")
def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, epoch_num): def get_harmonic_evidence(samples, log_posterior, nchains_harmonic, epoch_num):
"""Compute evidence using the `harmonic` package.""" """Compute evidence using the `harmonic` package."""
data, names = csiborgtools.dict_samples_to_array(samples) data, names = csiborgtools.dict_samples_to_array(samples)
@ -339,8 +350,18 @@ if __name__ == "__main__":
if mag_selection and inference_method != "bayes": if mag_selection and inference_method != "bayes":
raise ValueError("Magnitude selection is only supported with `bayes` inference.") # noqa raise ValueError("Magnitude selection is only supported with `bayes` inference.") # noqa
if "IndranilVoid" in ARGS.simname and ARGS.ksim is None: if "IndranilVoid" in ARGS.simname:
raise ValueError("`IndranilVoid` must be run only per specific realization.") # noqa if ARGS.ksim is not None:
raise ValueError(
"`IndranilVoid` does not have multiple realisations.")
kind = ARGS.simname.split("_")[-1]
h = select_void_h(kind)
rdist = np.arange(0, 165, 0.5)
void_kwargs = {"kind": kind, "h": h, "order": 1, "rdist": rdist}
else:
void_kwargs = None
h = 1.
if inference_method != "bayes": if inference_method != "bayes":
mag_selection = [None] * len(ARGS.catalogue) mag_selection = [None] * len(ARGS.catalogue)
@ -360,6 +381,8 @@ if __name__ == "__main__":
"sample_Vmono": sample_Vmono, "sample_Vmono": sample_Vmono,
"sample_beta": sample_beta, "sample_beta": sample_beta,
"sample_h": sample_h, "sample_h": sample_h,
"sample_rLG": "IndranilVoid" in ARGS.simname,
"rLG_min": 0.0, "rLG_max": 500 * h,
} }
print_variables( print_variables(
calibration_hyperparams.keys(), calibration_hyperparams.values()) calibration_hyperparams.keys(), calibration_hyperparams.values())
@ -394,7 +417,7 @@ if __name__ == "__main__":
print(f"{'Current simulation:':<20} {i + 1} ({ksim}) out of {len(ksim_iterator)}.") # noqa print(f"{'Current simulation:':<20} {i + 1} ({ksim}) out of {len(ksim_iterator)}.") # noqa
fname_kwargs["nsim"] = ksim fname_kwargs["nsim"] = ksim
models = get_models(ksim, get_model_kwargs, mag_selection) models = get_models(ksim, get_model_kwargs, mag_selection, void_kwargs)
model_kwargs = { model_kwargs = {
"models": models, "models": models,
"field_calibration_hyperparams": calibration_hyperparams, "field_calibration_hyperparams": calibration_hyperparams,