csiborgtools/notebooks/field_sample.ipynb

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 [ ]: