borg_velocity/tests/SN_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de9cdd42-f9bb-4dd6-80ba-d63d0dbc3ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import linecache\n",
    "\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72780ead-06f0-4fbb-b12e-602b203286a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04197595 0.17233 0.0350472\n",
      "-0.0372089 1.2312531999999994\n",
      "-0.0186623 0.07995692199999997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n",
      "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n",
      "/tmp/ipykernel_4414/3947829372.py:5: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  df = pd.read_csv(fname, sep=\"\\s+\", skipinitialspace=True, skiprows=7, names=columns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151c2e3a4bf0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fname = '/data101/bartlett/fsigma8/PV_data/Foundation_DR1/Foundation_DR1.FITRES.TEXT'\n",
    "\n",
    "# Get header\n",
    "columns = ['SN'] + linecache.getline(fname, 6).strip().split()[1:]\n",
    "df = pd.read_csv(fname, sep=\"\\s+\", skipinitialspace=True, skiprows=7, names=columns)\n",
    "\n",
    "zCMB = df['zCMB']\n",
    "m = df['mB']\n",
    "m_err = df['mBERR']\n",
    "\n",
    "x1 = df['x1']\n",
    "hyper_stretch_mu = np.median(x1)\n",
    "hyper_stretch_sigma = (np.percentile(x1, 84) - np.percentile(x1, 16)) / 2\n",
    "\n",
    "c = df['c']\n",
    "hyper_c_mu = np.median(c)\n",
    "hyper_c_sigma = (np.percentile(c, 84) - np.percentile(c, 16)) / 2\n",
    "\n",
    "sigma_m = np.median(df['mBERR'])\n",
    "sigma_stretch = np.median(df['x1ERR'])\n",
    "sigma_c = np.median(df['cERR'])\n",
    "print(sigma_m, sigma_stretch, sigma_c)\n",
    "\n",
    "print(hyper_stretch_mu, hyper_stretch_sigma)\n",
    "print(hyper_c_mu, hyper_c_sigma)\n",
    "    \n",
    "plt.figure()\n",
    "speed_of_light = 299792  # km/s\n",
    "mock_z = np.load('sn_z.npy')\n",
    "mock_z /= speed_of_light\n",
    "plt.hist(mock_z, bins=10, label='mock', histtype='step', density=True)\n",
    "plt.hist(zCMB, bins=10, label='true', histtype='step', density=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e538aec-c47e-43ef-95ad-84afc388fc8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /data101/bartlett/fsigma8/PV_data/Foundation_DR1/kcor_PS1_none.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      41   (0,)      \n",
      "  1  ZPoff         1 BinTableHDU     19   11R x 5C   [20A, 20A, 1E, 1E, 1E]   \n",
      "  2  SN SED        1 BinTableHDU     19   97626R x 1C   [1E]   \n",
      "  3  KCOR          1 BinTableHDU     15   0R x 3C   [1E, 1E, 1E]   \n",
      "  4  MAG+MWXTCOR    1 BinTableHDU     59   0R x 25C   [1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E]   \n",
      "  5  FilterTrans    1 BinTableHDU     33   921R x 12C   [1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1E]   \n",
      "  6  PrimarySED    1 BinTableHDU     15   921R x 3C   [1E, 1E, 1E]   \n"
     ]
    }
   ],
   "source": [
    "# Open the FITS file\n",
    "fname = '/data101/bartlett/fsigma8/PV_data/Foundation_DR1/kcor_PS1_none.fits'\n",
    "with fits.open(fname) as hdul:\n",
    "    hdul.info()  # Show summary of FITS file contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d012b67e-1600-4f5c-8724-6b926e4c32a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "borg_new",
   "language": "python",
   "name": "borg_new"
  },
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