PSF_checking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy  as np\n",
    "import pandas as pd\n",
    "import tables as tb\n",
    "import h5py\n",
    "\n",
    "from IC.invisible_cities.reco.psf_functions    import create_psf\n",
    "from IC.invisible_cities.reco.psf_functions    import hdst_psf_processing\n",
    "from IC.invisible_cities.reco.psf_functions    import add_empty_sensors_and_normalize_q\n",
    "from IC.invisible_cities.reco.psf_functions    import add_variable_weighted_mean\n",
    "\n",
    "import IC.invisible_cities.core.core_functions as     coref\n",
    "import IC.invisible_cities.io.dst_io         as     dstio\n",
    "\n",
    "from IC.invisible_cities.database              import load_db\n",
    "from IC.invisible_cities.io.kdst_io      import psf_writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"]          = 10, 10\n",
    "plt.rcParams[\"font.size\"]               = 16\n",
    "plt.rcParams[\"figure.max_open_warning\"] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = load_db.DataSiPM('next100', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input and output path\n",
    "#psf_path   = '/Users/halmamol/NEXT/'\n",
    "#psf_filename = 'next100.kr83m_psf200.psf.h5'\n",
    "psf_path   = '/Users/halmamol/NEXT/files/NFlex/'\n",
    "#psf_filename =  'FLEX.M6.06_PSF.h5'\n",
    "psf_filename =  'nflex_d15_M6_H6.psf.h5'\n",
    "out_psf  = psf_path + psf_filename\n",
    "\n",
    "psf_files = glob.glob(psf_path+'psf_*.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "File(filename=/Users/halmamol/NEXT/files/NFlex/nflex_d15_M6_H6.psf.h5, title='', mode='r', root_uep='/', filters=Filters(complevel=0, shuffle=False, bitshuffle=False, fletcher32=False, least_significant_digit=None))\n",
       "/ (RootGroup) ''\n",
       "/PSF (Group) ''\n",
       "/PSF/PSFs (Table(2440000,), shuffle, zlib(4)) 'XYZ dependent point spread functions'\n",
       "  description := {\n",
       "  \"nevt\": UInt32Col(shape=(), dflt=0, pos=0),\n",
       "  \"xr\": Float32Col(shape=(), dflt=0.0, pos=1),\n",
       "  \"yr\": Float32Col(shape=(), dflt=0.0, pos=2),\n",
       "  \"zr\": Float32Col(shape=(), dflt=0.0, pos=3),\n",
       "  \"x\": Float32Col(shape=(), dflt=0.0, pos=4),\n",
       "  \"y\": Float32Col(shape=(), dflt=0.0, pos=5),\n",
       "  \"z\": Float32Col(shape=(), dflt=0.0, pos=6),\n",
       "  \"factor\": Float32Col(shape=(), dflt=0.0, pos=7)}\n",
       "  byteorder := 'little'\n",
       "  chunkshape := (2048,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5file = tb.open_file(out_psf)\n",
    "h5file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>-99.5</td>\n",
       "      <td>-95.5</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   nevt    xr    yr   zr    x    y     z  factor\n",
       "0    31 -99.5 -99.5  0.0  0.0  0.0  10.0     0.0\n",
       "1    27 -99.5 -98.5  0.0  0.0  0.0  10.0     0.0\n",
       "2    33 -99.5 -97.5  0.0  0.0  0.0  10.0     0.0\n",
       "3    44 -99.5 -96.5  0.0  0.0  0.0  10.0     0.0\n",
       "4    43 -99.5 -95.5  0.0  0.0  0.0  10.0     0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psf = pd.read_hdf(out_psf, 'PSF/PSFs')\n",
    "psf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>zr</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>95.5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1210.0</td>\n",
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       "      <td>18</td>\n",
       "      <td>99.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1210.0</td>\n",
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       "    <tr>\n",
       "      <th>2439997</th>\n",
       "      <td>26</td>\n",
       "      <td>99.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1210.0</td>\n",
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       "<p>2440000 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "         nevt    xr    yr   zr    x    y       z  factor\n",
       "0          31 -99.5 -99.5  0.0  0.0  0.0    10.0     0.0\n",
       "1          27 -99.5 -98.5  0.0  0.0  0.0    10.0     0.0\n",
       "2          33 -99.5 -97.5  0.0  0.0  0.0    10.0     0.0\n",
       "3          44 -99.5 -96.5  0.0  0.0  0.0    10.0     0.0\n",
       "4          43 -99.5 -95.5  0.0  0.0  0.0    10.0     0.0\n",
       "...       ...   ...   ...  ...  ...  ...     ...     ...\n",
       "2439995    18  99.5  95.5  0.0  0.0  0.0  1210.0     0.0\n",
       "2439996    18  99.5  96.5  0.0  0.0  0.0  1210.0     0.0\n",
       "2439997    26  99.5  97.5  0.0  0.0  0.0  1210.0     0.0\n",
       "2439998    18  99.5  98.5  0.0  0.0  0.0  1210.0     0.0\n",
       "2439999    23  99.5  99.5  0.0  0.0  0.0  1210.0     0.0\n",
       "\n",
       "[2440000 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psf =  dstio.load_dst(out_psf, 'PSF', 'PSFs')\n",
    "plt.hist(psf.z, bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "bin_size   = 1\n",
    "Xrange     = [ -100, 100]\n",
    "Yrange     = [ -100, 100]\n",
    "ranges     = [Xrange, Yrange]\n",
    "nbinX      = int(np.diff(Xrange)/bin_size)\n",
    "nbinY      = int(np.diff(Yrange)/bin_size)\n",
    "\n",
    "### Plot PSF at center (X dimension)\n",
    "p1 = psf[(psf.z==610.) & (psf.yr==0.5)]\n",
    "x, y = sorted(set(p1.xr)), p1.groupby(['xr'])['factor'].sum().values\n",
    "axes[0].plot(x, y)\n",
    "axes[0].set_xlabel('X distance (mm)')\n",
    "axes[0].set_ylabel('Charge fraction')\n",
    "\n",
    "### PSF (2D)\n",
    "p1 = psf[(psf.z==610.)]\n",
    "x, y, e = p1.xr.values, p1.yr.values, p1.factor.values\n",
    "xb, yb  = np.linspace(Xrange[0], Xrange[1], Xrange[1]), np.linspace(Yrange[0], Yrange[1], Yrange[1])\n",
    "img = axes[1].hist2d(x, y, bins=[xb,yb], weights=e)\n",
    "axes[1].set_xlabel('X distance (mm)')\n",
    "axes[1].set_ylabel('Y distance (mm)')\n",
    "axes[1].set_xlim(-50, 50)\n",
    "axes[1].set_ylim(-50, 50)\n",
    "cb = fig.colorbar(img[3], ax=axes[1])\n",
    "\n",
    "cb.set_label('Charge fraction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}