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Correct fitting script for both clumps and halos (#46)

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* Minor typos

* fix minor bugs

* pep8

* formatting

* pep8

* Fix minor bugs

* New path & pep8

* add splitt

* Updates

* Improve calculation within radius

* pep8

* pep8

* get the script working

* Add matter overdensity

* Add m200m to the script

* Fix looping bug

* add parents support

* add import

* Optionally concatenate velocities

* Make optional masking

* Ignore the error message

* Start reading in raw data

* Fix cat reading

* Additional units conversions

* Add clump reading

* Fix indexing

* Remove old comment

* Remove old comment

* set npart to 0 instead of overflow from NaN

* fix docs

* rm boring stuff

* Remove old stuff

* Remove old stuff

* Remove old comment

* Update nb
This commit is contained in:
Richard Stiskalek 2023-04-19 16:39:35 +02:00 • committed by GitHub
parent c2fde1566b
commit 39b3498621
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17 changed files with 709 additions and 357 deletions
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69
scripts/cluster_knn_auto.py
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@ -29,6 +29,7 @@ try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
@ -43,23 +44,13 @@ nproc = comm.Get_size()
parser = ArgumentParser()
parser.add_argument("--runs", type=str, nargs="+")
args = parser.parse_args()
with open('../scripts/knn_auto.yml', 'r') as file:
with open("../scripts/knn_auto.yml", "r") as file:
config = yaml.safe_load(file)
Rmax = 155 / 0.705 # Mpc (h = 0.705) high resolution region radius
totvol = 4 * numpy.pi * Rmax**3 / 3
minmass = 1e12
ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,
8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,
8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,
9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,
9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
9820, 9844]
paths = csiborgtools.read.CSiBORGPaths(**csiborgtools.paths_glamdring)
ics = paths.get_ics(False)
knncdf = csiborgtools.clustering.kNN_CDF()
###############################################################################
@ -75,9 +66,9 @@ def read_single(selection, cat):
psel = selection["primary"]
pmin, pmax = psel.get("min", None), psel.get("max", None)
if pmin is not None:
mmask &= (cat[psel["name"]] >= pmin)
mmask &= cat[psel["name"]] >= pmin
if pmax is not None:
mmask &= (cat[psel["name"]] < pmax)
mmask &= cat[psel["name"]] < pmax
pos = pos[mmask, ...]
# Secondary selection
@ -92,12 +83,13 @@ def read_single(selection, cat):
if ssel.get("marked", True):
x = cat[psel["name"]][mmask]
prop = csiborgtools.clustering.normalised_marks(
x, prop, nbins=config["nbins_marks"])
x, prop, nbins=config["nbins_marks"]
)
if smin is not None:
smask &= (prop >= smin)
smask &= prop >= smin
if smax is not None:
smask &= (prop < smax)
smask &= prop < smax
return pos[smask, ...]
@ -106,8 +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("No configuration for run {}.".format(run), UserWarning, stacklevel=1)
return
rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
@ -115,21 +106,28 @@ 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},
paths.knnauto_path(run, ic))
joblib.dump(
{"rs": rs, "cdf": cdf, "ndensity": pos.shape[0] / totvol},
paths.knnauto_path(run, ic),
)
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("No configuration for run {}.".format(run), UserWarning, stacklevel=1)
return
rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
@ -142,10 +140,17 @@ 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))
@ -180,4 +185,4 @@ comm.Barrier()
if rank == 0:
print("{}: all finished.".format(datetime.now()))
quit() # Force quit the script
quit() # Force quit the script