mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 22:48:02 +00:00
a65e3cb15b
* Add imports * Add field LOS paths * Add basic flow model * Edit script * Add nb * Add nb * Update nb * Add some docs * Add RA reading * Add imoprts * Updates to the flow model * Update script * Bring back A2 * Update imports * Update imports * Add Carrick to ICs * Add Carrick boxsize * Add Carrick and fix minor bugs * Add Carrick box * Update script * Edit imports * Add fixed flow! * Update omega_m and add it * Update nb * Update nb * Update nb * Remove old print statements * Update params * Add thinning of chains * Add import * Add flow validation script * Add submit script * Add ksmooth * Update nb * Update params * Update script * Update string * Move where distributions are defined * Add density bias parameter * Add lognorm mean * Update scripts * Update script
4 KiB
4 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 [ ]: