Truth mc monitoring

icaro/analysis/monitoring/MCTruthMonitor.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# This notebook produces meaningful plots of the MC true information to monitor MC production "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import numpy             as np\n",
+    "import tables            as tb\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas            as pd\n",
+    "\n",
+    "from invisible_cities.io.mchits_io           import load_mchits\n",
+    "from invisible_cities.io.mchits_io           import load_mcparticles\n",
+    "\n",
+    "from invisible_cities.database               import load_db\n",
+    "\n",
+    "from invisible_cities.icaro.hst_functions    import hist\n",
+    "from invisible_cities.icaro.hst_functions    import hist2d\n",
+    "from invisible_cities.icaro.hst_functions    import hist2d_profile\n",
+    "from invisible_cities.icaro.hst_functions    import labels\n",
+    "\n",
+    "from invisible_cities.core.system_of_units_c import units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "DetGeo = load_db.DetectorGeo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energies = []\n",
+    "Xorigin = []\n",
+    "Yorigin = []\n",
+    "Zorigin = []\n",
+    "partE = []\n",
+    "numpart = []\n",
+    "for number in range(0,1):\n",
+    "    for p in range(0,1):\n",
+    "\n",
+    "        PATH_IN = \"/path/to/data/dst_NEXT_v1_00_05_Tl_ACTIVE_{0}_{1}_7bar_PMP_10000.h5\".format(number, p)\n",
+    "        print('Analyzing file {}'.format(PATH_IN))\n",
+    "        try:\n",
+    "            load_mchits(PATH_IN)\n",
+    "        except ValueError:\n",
+    "            continue\n",
+    "        except OSError:\n",
+    "            continue\n",
+    "        hit_dict = load_mchits(file_name)\n",
+    "        for _, hits in hit_dict.items():\n",
+    "            e = 0.\n",
+    "            for hit in hits:\n",
+    "                # Check that only the hits of the ACTIVE volume are considered\n",
+    "                if hit.pos[2] < DetGeo.ZMAX[0] and hit.pos[0] < DetGeo.XMAX[0] and hit.pos[0] > DetGeo.XMIN[0]:\n",
+    "                    e += hit.E\n",
+    "                    Xorigin.append(hit.pos[0])  \n",
+    "                    Yorigin.append(hit.pos[1])  \n",
+    "                    Zorigin.append(hit.pos[2]) \n",
+    "            energies.append(e)\n",
+    "        particle_dict = load_mcparticles(PATH_IN)\n",
+    "        \n",
+    "        for ide, particles in particle_dict.items():\n",
+    "            numpart.append(len(particles))\n",
+    "            for _, p in particles.items():\n",
+    "                partE.append(p.E)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Total energy of events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure();\n",
+    "fig.set_figwidth(16.0)\n",
+    "fig.set_figheight(12.0)\n",
+    "plt.hist(energies, bins=1000, range=((0.,3)), label='Event energy')\n",
+    "plt.xlabel('Energy (MeV)')\n",
+    "plt.ylabel('Counts/bin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XY position of hits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure();\n",
+    "ax1 = fig.add_subplot(111);\n",
+    "fig.set_figheight(10.0)\n",
+    "fig.set_figwidth(13.0)\n",
+    "\n",
+    "plt.hist2d(Xorigin,Yorigin, bins=100)\n",
+    "plt.colorbar()\n",
+    "        \n",
+    "plt.xlabel(\"x (mm)\");\n",
+    "plt.ylabel(\"y (mm)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## XZ position of hits "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure();\n",
+    "ax1 = fig.add_subplot(111);\n",
+    "fig.set_figheight(10.0)\n",
+    "fig.set_figwidth(13.0)\n",
+    "\n",
+    "plt.hist2d(Xorigin,Zorigin, bins=100)\n",
+    "plt.colorbar()\n",
+    "        \n",
+    "plt.xlabel(\"x (mm)\");\n",
+    "plt.ylabel(\"z (mm)\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Number of particles per event"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(numpart, bins=30, label='Number of particles')\n",
+    "plt.xlabel('Number of particles in one event')\n",
+    "plt.ylabel('Counts/bin')"
+   ]
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}