csiborgtools/scripts_independent/A2_to_hdf5.ipynb

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    "# Copyright (C) 2024 Richard Stiskalek\n",
    "# This program is free software; you can redistribute it and/or modify it\n",
    "# under the terms of the GNU General Public License as published by the\n",
    "# Free Software Foundation; either version 3 of the License, or (at your\n",
    "# option) any later version.\n",
    "# This program is distributed in the hope that it will be useful, but\n",
    "# WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General\n",
    "# Public License for more details.\n",
    "#\n",
    "# You should have received a copy of the GNU General Public License along\n",
    "# with this program; if not, write to the Free Software Foundation, Inc.,\n",
    "# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n",
    "from os.path import join\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from h5py import File\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supernovae data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2dir = \"/Users/richard/Data/PV/A2_paper_data/A2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOSS data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"z_CMB\", \"mB\", \"x1\", \"c\", \"e_mB\", \"e_x1\", \"e_c\", \"RA\", \"DEC\"]\n",
    "dtype = [(n, np.float32) for n in names]\n",
    "data = np.genfromtxt(join(a2dir, \"loss.csv\"), delimiter=\",\", skip_header=1,\n",
    "                     usecols=[5 + n for n in range(len(names))])\n",
    "\n",
    "loss_data = np.empty(len(data), dtype=dtype)\n",
    "for i, n in enumerate(names):\n",
    "    loss_data[n] = data[:, i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Foundation data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"z_CMB\", \"RA\", \"DEC\", \"x1\", \"mB\", \"c\", \"peak\", \"e_peak\", \"e_x1\", \"e_mB\", \"e_c\"]\n",
    "dtype = [(n, np.float32) for n in names]\n",
    "data = np.genfromtxt(join(a2dir, \"foundation.csv\"), delimiter=\",\", skip_header=1,\n",
    "                     usecols=[3 + n for n in range(len(names))])\n",
    "\n",
    "foundation_data = np.empty(len(data), dtype=dtype)\n",
    "for i, n in enumerate(names):\n",
    "    foundation_data[n] = data[:, i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write output as HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdir = \"/Users/richard/Downloads\"\n",
    "fname = \"PV_compilation_Supranta2019.hdf5\"\n",
    "\n",
    "with File(join(outdir, fname), 'w') as f:\n",
    "    # Write LOSS\n",
    "    grp = f.create_group(\"LOSS\")\n",
    "    for name in loss_data.dtype.names:\n",
    "        grp.create_dataset(name, data=loss_data[name])\n",
    "\n",
    "    # Write Foundation\n",
    "    grp = f.create_group(\"Foundation\")\n",
    "    for name in foundation_data.dtype.names:\n",
    "        grp.create_dataset(name, data=foundation_data[name])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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