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Dump radial profile information (#48)

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* add radial position path

* pep8

* Add basic fit profile dumping

* pep8

* pep8

* pep8

* pep8

* pep8

* pep8

* Update TODO

* Fix parts is None bug

* Update nb
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Richard Stiskalek 2023-04-27 01:18:30 +02:00 • committed by GitHub
parent 1a115f481d
commit f48eb6dcb0
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29 changed files with 512 additions and 395 deletions
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34
scripts/cluster_knn_auto.py
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@ -23,7 +23,7 @@ import numpy
import yaml
from mpi4py import MPI
from sklearn.neighbors import NearestNeighbors
from TaskmasterMPI import master_process, worker_process
from taskmaster import master_process, worker_process
try:
import csiborgtools
@ -98,7 +98,7 @@ def do_auto(run, cat, ic):
"""Calculate the kNN-CDF single catalgoue autocorrelation."""
_config = config.get(run, None)
if _config is None:
warn("No configuration for run {}.".format(run), UserWarning, stacklevel=1)
warn(f"No configuration for run {run}.", UserWarning, stacklevel=1)
return
rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
@ -106,16 +106,10 @@ def do_auto(run, cat, ic):
knn = NearestNeighbors()
knn.fit(pos)
rs, cdf = knncdf(
knn,
rvs_gen=rvs_gen,
nneighbours=config["nneighbours"],
rmin=config["rmin"],
rmax=config["rmax"],
nsamples=int(config["nsamples"]),
neval=int(config["neval"]),
batch_size=int(config["batch_size"]),
random_state=config["seed"],
)
knn, rvs_gen=rvs_gen, nneighbours=config["nneighbours"],
rmin=config["rmin"], rmax=config["rmax"],
nsamples=int(config["nsamples"]), neval=int(config["neval"]),
batch_size=int(config["batch_size"]), random_state=config["seed"])
joblib.dump(
{"rs": rs, "cdf": cdf, "ndensity": pos.shape[0] / totvol},
@ -127,7 +121,7 @@ def do_cross_rand(run, cat, ic):
"""Calculate the kNN-CDF cross catalogue random correlation."""
_config = config.get(run, None)
if _config is None:
warn("No configuration for run {}.".format(run), UserWarning, stacklevel=1)
warn(f"No configuration for run {run}.", UserWarning, stacklevel=1)
return
rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
@ -140,16 +134,10 @@ def do_cross_rand(run, cat, ic):
knn2.fit(pos2)
rs, cdf0, cdf1, joint_cdf = knncdf.joint(
knn1,
knn2,
rvs_gen=rvs_gen,
nneighbours=int(config["nneighbours"]),
rmin=config["rmin"],
rmax=config["rmax"],
nsamples=int(config["nsamples"]),
neval=int(config["neval"]),
batch_size=int(config["batch_size"]),
random_state=config["seed"],
knn1, knn2, rvs_gen=rvs_gen, nneighbours=int(config["nneighbours"]),
rmin=config["rmin"], rmax=config["rmax"],
nsamples=int(config["nsamples"]), neval=int(config["neval"]),
batch_size=int(config["batch_size"]), random_state=config["seed"],
)
corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
joblib.dump({"rs": rs, "corr": corr}, paths.knnauto_path(run, ic))