data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from util import load_events, plot_event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function load_events in module util:\n",
      "\n",
      "load_events(path, chamber_id=None, start=0, end=-1)\n",
      "    Load events from a csv file\n",
      "    Arguments :\n",
      "    1. path (str) : input .csv file name\n",
      "    2. chamber_id (str) : which chambers to selec e.g. '1,2'\n",
      "    3. start (int) : start event index\n",
      "    4. end (int) : end event index\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(load_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stations selected are:  [1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "events = load_events('toy_dataset.csv', chamber_id=None, start=0, end=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select one event\n",
    "idx = 4\n",
    "df  = events[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     event_id  hit_id       x        z         a  chamber_id  layer_id  \\\n",
      "312         4       0  11.120  294.895  0.037690           1         1   \n",
      "313         4       1  11.151  295.770  0.037684           1         2   \n",
      "314         4       2  11.623  309.390  0.037550           1         3   \n",
      "315         4       3  11.654  310.265  0.037544           1         4   \n",
      "316         4       4  12.231  326.895  0.037398           2         5   \n",
      "\n",
      "     particle_id  \n",
      "312            0  \n",
      "313            0  \n",
      "314            0  \n",
      "315            0  \n",
      "316            0  \n"
     ]
    }
   ],
   "source": [
    "print (df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 7 particles in this event\n"
     ]
    }
   ],
   "source": [
    "print ('There are', len(np.unique(df.particle_id)), 'particles in this event')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 168 hits in this event\n"
     ]
    }
   ],
   "source": [
    "print ('There are', df.shape[0], 'hits in this event')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot_event in module util:\n",
      "\n",
      "plot_event(event, para='z,x')\n",
      "    Plot an event as a scatter plot\n",
      "    Arguments :\n",
      "    1. event (dataframe) : An event dataframe\n",
      "    2. para  (str) : Which observables to plot, seperated by comma\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plot_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_event(df,'z,x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create more variables\n",
    "df = df.assign( r = np.sqrt(df.x**2 + df.z**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_event(df, 'z,a')"
   ]
  },
  {
   "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
}