csiborgtools/scripts_plots/borg_acl.ipynb
Richard Stiskalek 9e4b34f579
Overlap fixing and more (#107)
* Update README

* Update density field reader

* Update name of SDSSxALFAFA

* Fix quick bug

* Add little fixes

* Update README

* Put back fit_init

* Add paths to initial snapshots

* Add export

* Remove some choices

* Edit README

* Add Jens' comments

* Organize imports

* Rename snapshot

* Add additional print statement

* Add paths to initial snapshots

* Add masses to the initial files

* Add normalization

* Edit README

* Update README

* Fix bug in CSiBORG1 so that does not read fof_00001

* Edit README

* Edit README

* Overwrite comments

* Add paths to init lag

* Fix Quijote path

* Add lagpatch

* Edit submits

* Update README

* Fix numpy int problem

* Update README

* Add a flag to keep the snapshots open when fitting

* Add a flag to keep snapshots open

* Comment out some path issue

* Keep snapshots open

* Access directly snasphot

* Add lagpatch for CSiBORG2

* Add treatment of x-z coordinates flipping

* Add radial velocity field loader

* Update README

* Add lagpatch to Quijote

* Fix typo

* Add setter

* Fix typo

* Update README

* Add output halo cat as ASCII

* Add import

* Add halo plot

* Update README

* Add evaluating field at radial distanfe

* Add field shell evaluation

* Add enclosed mass computation

* Add BORG2 import

* Add BORG boxsize

* Add BORG paths

* Edit run

* Add BORG2 overdensity field

* Add bulk flow clauclation

* Update README

* Add new plots

* Add nbs

* Edit paper

* Update plotting

* Fix overlap paths to contain simname

* Add normalization of positions

* Add default paths to CSiBORG1

* Add overlap path simname

* Fix little things

* Add CSiBORG2 catalogue

* Update README

* Add import

* Add TNG density field constructor

* Add TNG density

* Add draft of calculating BORG ACL

* Fix bug

* Add ACL of enclosed density

* Add nmean acl

* Add galaxy bias calculation

* Add BORG acl notebook

* Add enclosed mass calculation

* Add TNG300-1 dir

* Add TNG300 and BORG1 dir

* Update nb
2024-01-30 16:14:07 +00:00

50 KiB

In [1]:
# Copyright (C) 2024 Richard Stiskalek
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
from os.path import join

import numpy as np
import matplotlib.pyplot as plt
from h5py import File

%matplotlib inline

Supernovae data

In [2]:
a2dir = "/Users/richard/Data/PV/A2_paper_data/A2"

LOSS data set

In [3]:
names = ["z_CMB", "mB", "x1", "c", "e_mB", "e_x1", "e_c", "RA", "DEC"]
dtype = [(n, np.float32) for n in names]
data = np.genfromtxt(join(a2dir, "loss.csv"), delimiter=",", skip_header=1,
                     usecols=[5 + n for n in range(len(names))])

loss_data = np.empty(len(data), dtype=dtype)
for i, n in enumerate(names):
    loss_data[n] = data[:, i]

Foundation data set

In [4]:
names = ["z_CMB", "RA", "DEC", "x1", "mB", "c", "peak", "e_peak", "e_x1", "e_mB", "e_c"]
dtype = [(n, np.float32) for n in names]
data = np.genfromtxt(join(a2dir, "foundation.csv"), delimiter=",", skip_header=1,
                     usecols=[3 + n for n in range(len(names))])

foundation_data = np.empty(len(data), dtype=dtype)
for i, n in enumerate(names):
    foundation_data[n] = data[:, i]

Write output as HDF5 file

In [5]:
outdir = "/Users/richard/Downloads"
fname = "PV_compilation_Supranta2019.hdf5"

with File(join(outdir, fname), 'w') as f:
    # Write LOSS
    grp = f.create_group("LOSS")
    for name in loss_data.dtype.names:
        grp.create_dataset(name, data=loss_data[name])

    # Write Foundation
    grp = f.create_group("Foundation")
    for name in foundation_data.dtype.names:
        grp.create_dataset(name, data=foundation_data[name])
In [ ]: