2020.02.09.MLChallenge4/ML4_DataCheck.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JLab ML Challenge 4\n",
    "\n",
    "This notebook takes a peek at the input data for ML Challenge 4. The data\n",
    "is in the form of a single csv file with each line representing one event.\n",
    "The line will contain all hits from the GlueX FCAL calorimeter from the event\n",
    "with every 4 values representing the x,y,E,t values of the hit. Thus, each\n",
    "line will have `N*4` values where `N` is the number of hits.\n",
    "\n",
    "Since each line may have a different number of values, we first need to find\n",
    "the maximum number of values for all lines. The total number of lines is\n",
    "also probed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Num. rows: 207687\n",
      "Max. columns: 376  (=94 blocks hit)\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "datafile = '/work/halld2/home/davidl/2020.02.09.MLChallenge4/fcalhits_071743_002.csv'\n",
    "\n",
    "# Find the maximum number of entries on a single line\n",
    "num_rows = 0\n",
    "max_columns = 0\n",
    "with open(datafile) as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    max_columns = max([len(row) for row in reader])\n",
    "    num_rows = reader.line_num\n",
    "    print('   Num. rows: '+ str(num_rows))\n",
    "    print('Max. columns: ' + str(max_columns) + '  (=%d blocks hit)' % (max_columns/4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read into dataframe\n",
    "\n",
    "Pandas dataframes expect the data to be well formatted into tables where there are \n",
    "exactly the same number of entries per row. If you don't tell it otherwise, it will\n",
    "look at the first row and assume all subsequent rows will have the same number of \n",
    "columns. Here, we want to give it a list of column names so it will reserve space\n",
    "enough to fit the row with the maximum number of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining 376 columns and reading into dataframe\n"
     ]
    },
    {
     "data": {
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       "      <th>x0</th>\n",
       "      <th>y0</th>\n",
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       "      <th>y2</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0314</td>\n",
       "      <td>8.0314</td>\n",
       "      <td>0.015484</td>\n",
       "      <td>-1.76450</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>16.0628</td>\n",
       "      <td>0.104652</td>\n",
       "      <td>-0.20200</td>\n",
       "      <td>4.0157</td>\n",
       "      <td>16.0628</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.1256</td>\n",
       "      <td>52.2041</td>\n",
       "      <td>0.207305</td>\n",
       "      <td>14.81670</td>\n",
       "      <td>32.1256</td>\n",
       "      <td>52.2041</td>\n",
       "      <td>0.033006</td>\n",
       "      <td>7.12921</td>\n",
       "      <td>36.1413</td>\n",
       "      <td>52.2041</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>84.3297</td>\n",
       "      <td>0.291655</td>\n",
       "      <td>18.95290</td>\n",
       "      <td>4.0157</td>\n",
       "      <td>84.3297</td>\n",
       "      <td>0.027082</td>\n",
       "      <td>19.07070</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>8.0314</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0157</td>\n",
       "      <td>-44.1727</td>\n",
       "      <td>0.077216</td>\n",
       "      <td>15.45570</td>\n",
       "      <td>8.0314</td>\n",
       "      <td>-44.1727</td>\n",
       "      <td>0.200318</td>\n",
       "      <td>15.45780</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>-44.1727</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.1570</td>\n",
       "      <td>-60.2355</td>\n",
       "      <td>0.102078</td>\n",
       "      <td>9.81986</td>\n",
       "      <td>44.1727</td>\n",
       "      <td>-60.2355</td>\n",
       "      <td>0.047153</td>\n",
       "      <td>10.10440</td>\n",
       "      <td>36.1413</td>\n",
       "      <td>-56.2198</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 376 columns</p>\n",
       "</div>"
      ],
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       "        x0       y0        E0        t0       x1       y1        E1        t1  \\\n",
       "0   8.0314   8.0314  0.015484  -1.76450   0.0000  16.0628  0.104652  -0.20200   \n",
       "1  32.1256  52.2041  0.207305  14.81670  32.1256  52.2041  0.033006   7.12921   \n",
       "2   0.0000  84.3297  0.291655  18.95290   4.0157  84.3297  0.027082  19.07070   \n",
       "3   4.0157 -44.1727  0.077216  15.45570   8.0314 -44.1727  0.200318  15.45780   \n",
       "4  40.1570 -60.2355  0.102078   9.81986  44.1727 -60.2355  0.047153  10.10440   \n",
       "\n",
       "        x2       y2  ...  E91  t91  x92  y92  E92  t92  x93  y93  E93  t93  \n",
       "0   4.0157  16.0628  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1  36.1413  52.2041  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2  12.0471   8.0314  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3  12.0471 -44.1727  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4  36.1413 -56.2198  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 376 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "colnames = []\n",
    "Nhits = max_columns/4\n",
    "for i in range (0, int(Nhits)):\n",
    "    xnam = 'x%d' % i\n",
    "    ynam = 'y%d' % i\n",
    "    Enam = 'E%d' % i\n",
    "    tnam = 't%d' % i\n",
    "    colnames += [xnam, ynam, Enam, tnam]\n",
    "print('defining ' + str(len(colnames)) + ' columns and reading into dataframe')\n",
    "df = pd.read_csv(datafile, names=colnames)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some stats\n",
    "\n",
    "First, we should print a few stats from the df. Run the `describe` method first with no arguments for a genral overview ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>E91</th>\n",
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       "      <th>t93</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>207687.000000</td>\n",
       "      <td>207687.000000</td>\n",
       "      <td>207687.000000</td>\n",
       "      <td>207687.000000</td>\n",
       "      <td>207687.000000</td>\n",
       "      <td>207687.000000</td>\n",
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       "      <td>207687.000000</td>\n",
       "      <td>207645.000000</td>\n",
       "      <td>207645.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.096508</td>\n",
       "      <td>20.896503</td>\n",
       "      <td>0.075292</td>\n",
       "      <td>-4.028240</td>\n",
       "      <td>23.959667</td>\n",
       "      <td>10.537746</td>\n",
       "      <td>0.098366</td>\n",
       "      <td>3.510331</td>\n",
       "      <td>20.263482</td>\n",
       "      <td>5.901434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.900969</td>\n",
       "      <td>38.606334</td>\n",
       "      <td>0.163697</td>\n",
       "      <td>53.363032</td>\n",
       "      <td>34.430141</td>\n",
       "      <td>41.175788</td>\n",
       "      <td>0.200894</td>\n",
       "      <td>46.228755</td>\n",
       "      <td>37.233088</td>\n",
       "      <td>42.822722</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-126.985000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-128.170000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.031400</td>\n",
       "      <td>4.015700</td>\n",
       "      <td>0.018625</td>\n",
       "      <td>-33.346300</td>\n",
       "      <td>4.015700</td>\n",
       "      <td>-8.031400</td>\n",
       "      <td>0.021218</td>\n",
       "      <td>-7.514500</td>\n",
       "      <td>4.015700</td>\n",
       "      <td>-16.062800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.062800</td>\n",
       "      <td>12.047100</td>\n",
       "      <td>0.032323</td>\n",
       "      <td>11.433600</td>\n",
       "      <td>16.062800</td>\n",
       "      <td>8.031400</td>\n",
       "      <td>0.037086</td>\n",
       "      <td>13.971300</td>\n",
       "      <td>12.047100</td>\n",
       "      <td>8.031400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.204100</td>\n",
       "      <td>44.172700</td>\n",
       "      <td>0.070863</td>\n",
       "      <td>23.579500</td>\n",
       "      <td>44.172700</td>\n",
       "      <td>32.125600</td>\n",
       "      <td>0.087755</td>\n",
       "      <td>23.185550</td>\n",
       "      <td>40.157000</td>\n",
       "      <td>28.109900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>6.250620</td>\n",
       "      <td>118.006000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>6.611010</td>\n",
       "      <td>115.975000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041713</td>\n",
       "      <td>-111.15</td>\n",
       "      <td>-4.0157</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.048918</td>\n",
       "      <td>-112.289</td>\n",
       "      <td>-20.0785</td>\n",
       "      <td>12.0471</td>\n",
       "      <td>0.034244</td>\n",
       "      <td>-114.708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 376 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x0             y0             E0             t0  \\\n",
       "count  207687.000000  207687.000000  207687.000000  207687.000000   \n",
       "mean       29.096508      20.896503       0.075292      -4.028240   \n",
       "std        31.900969      38.606334       0.163697      53.363032   \n",
       "min      -116.455000    -116.455000       0.000002    -126.985000   \n",
       "25%         8.031400       4.015700       0.018625     -33.346300   \n",
       "50%        16.062800      12.047100       0.032323      11.433600   \n",
       "75%        52.204100      44.172700       0.070863      23.579500   \n",
       "max       116.455000     116.455000       6.250620     118.006000   \n",
       "\n",
       "                  x1             y1             E1             t1  \\\n",
       "count  207687.000000  207687.000000  207687.000000  207687.000000   \n",
       "mean       23.959667      10.537746       0.098366       3.510331   \n",
       "std        34.430141      41.175788       0.200894      46.228755   \n",
       "min      -116.455000    -116.455000       0.000002    -128.170000   \n",
       "25%         4.015700      -8.031400       0.021218      -7.514500   \n",
       "50%        16.062800       8.031400       0.037086      13.971300   \n",
       "75%        44.172700      32.125600       0.087755      23.185550   \n",
       "max       116.455000     116.455000       6.611010     115.975000   \n",
       "\n",
       "                  x2             y2  ...       E91     t91     x92      y92  \\\n",
       "count  207645.000000  207645.000000  ...  1.000000    1.00  1.0000   1.0000   \n",
       "mean       20.263482       5.901434  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "std        37.233088      42.822722  ...       NaN     NaN     NaN      NaN   \n",
       "min      -116.455000    -116.455000  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "25%         4.015700     -16.062800  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "50%        12.047100       8.031400  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "75%        40.157000      28.109900  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "max       116.455000     116.455000  ...  0.041713 -111.15 -4.0157  12.0471   \n",
       "\n",
       "            E92      t92      x93      y93       E93      t93  \n",
       "count  1.000000    1.000   1.0000   1.0000  1.000000    1.000  \n",
       "mean   0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "std         NaN      NaN      NaN      NaN       NaN      NaN  \n",
       "min    0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "25%    0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "50%    0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "75%    0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "max    0.048918 -112.289 -20.0785  12.0471  0.034244 -114.708  \n",
       "\n",
       "[8 rows x 376 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now run for a couple of specific coulmns more in the middle of the list. A couple of observations on the table above and below:\n",
    "\n",
    "1.) The min/max x and y values all look to be +/-116.455 which is the outer edge of the calorimeter. The beam hole is in the middle at x,y=0,0 and we would expect the x,y hits to be more or less evenly distributed azimuthally about the beam hole. There will be significantly more hits near the beam hole than at the outer edges.\n",
    "\n",
    "2.) The actual order of the hits in the input list is not purposefully set to anything so it is left up to other factors like DAQ readout or possibly even memory address ordering during the data extraction. In other words, there may be an order to which hit is first and which is second etc., but it may have nothing to do with the actual values in the hit. The x0 mean above is 29cm to beam left while the other x values below look to be at increasing larger distances to beam right. The y-values don't show the  same pattern. This does not really mean anything, other than there may be something at least correlated with the values that determine the order of hits since if the ordering were completely random, we'd expect all of the means to be near 0.\n",
    "\n",
    "3.) The standard deviations all tend to be around 35-45. Since these values are in cm and blocks are 4cm x 4cm, this equivalent to 9-11 blocks. The blocks are arranged roughly in a circular shape with a diameter of roughly 58 blocks. If we were to evenly distribute some region with hits, the standard deviation would be the width of the region over sqrt(12). Thus, the standard deviation of 9-11 blocks (lets call it 10) could be said to represent hits evenly distributed over 35 blocks. What does this mean? Not too much. Just that 35 blocks is not a lot smaller than 58 so one can assume the hits in say the 30th position aren't really crowded around a specific area of the calorimeter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x10</th>\n",
       "      <th>y10</th>\n",
       "      <th>x20</th>\n",
       "      <th>y20</th>\n",
       "      <th>x30</th>\n",
       "      <th>y30</th>\n",
       "      <th>x40</th>\n",
       "      <th>y40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179128.000000</td>\n",
       "      <td>179128.000000</td>\n",
       "      <td>97249.000000</td>\n",
       "      <td>97249.000000</td>\n",
       "      <td>40558.000000</td>\n",
       "      <td>40558.000000</td>\n",
       "      <td>12405.000000</td>\n",
       "      <td>12405.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.510865</td>\n",
       "      <td>-2.829942</td>\n",
       "      <td>-15.180949</td>\n",
       "      <td>-3.476125</td>\n",
       "      <td>-24.616486</td>\n",
       "      <td>-2.603602</td>\n",
       "      <td>-30.084895</td>\n",
       "      <td>-1.398456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44.354657</td>\n",
       "      <td>44.419713</td>\n",
       "      <td>42.560068</td>\n",
       "      <td>45.130231</td>\n",
       "      <td>37.555731</td>\n",
       "      <td>45.268536</td>\n",
       "      <td>32.868629</td>\n",
       "      <td>45.178421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "      <td>-116.455000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-28.109900</td>\n",
       "      <td>-28.109900</td>\n",
       "      <td>-44.172700</td>\n",
       "      <td>-32.125600</td>\n",
       "      <td>-48.188400</td>\n",
       "      <td>-36.141300</td>\n",
       "      <td>-52.204100</td>\n",
       "      <td>-36.141300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.015700</td>\n",
       "      <td>-4.015700</td>\n",
       "      <td>-16.062800</td>\n",
       "      <td>-4.015700</td>\n",
       "      <td>-24.094200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-28.109900</td>\n",
       "      <td>4.015700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.109900</td>\n",
       "      <td>24.094200</td>\n",
       "      <td>8.031400</td>\n",
       "      <td>28.109900</td>\n",
       "      <td>-8.031400</td>\n",
       "      <td>28.109900</td>\n",
       "      <td>-12.047100</td>\n",
       "      <td>32.125600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "      <td>116.455000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x10            y10           x20           y20           x30  \\\n",
       "count  179128.000000  179128.000000  97249.000000  97249.000000  40558.000000   \n",
       "mean       -1.510865      -2.829942    -15.180949     -3.476125    -24.616486   \n",
       "std        44.354657      44.419713     42.560068     45.130231     37.555731   \n",
       "min      -116.455000    -116.455000   -116.455000   -116.455000   -116.455000   \n",
       "25%       -28.109900     -28.109900    -44.172700    -32.125600    -48.188400   \n",
       "50%        -4.015700      -4.015700    -16.062800     -4.015700    -24.094200   \n",
       "75%        28.109900      24.094200      8.031400     28.109900     -8.031400   \n",
       "max       116.455000     116.455000    116.455000    116.455000    116.455000   \n",
       "\n",
       "                y30           x40           y40  \n",
       "count  40558.000000  12405.000000  12405.000000  \n",
       "mean      -2.603602    -30.084895     -1.398456  \n",
       "std       45.268536     32.868629     45.178421  \n",
       "min     -116.455000   -116.455000   -116.455000  \n",
       "25%      -36.141300    -52.204100    -36.141300  \n",
       "50%        0.000000    -28.109900      4.015700  \n",
       "75%       28.109900    -12.047100     32.125600  \n",
       "max      116.455000    116.455000    116.455000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['x10', 'y10','x20', 'y20','x30', 'y30','x40', 'y40']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E10</th>\n",
       "      <th>E20</th>\n",
       "      <th>E30</th>\n",
       "      <th>E40</th>\n",
       "      <th>t10</th>\n",
       "      <th>t20</th>\n",
       "      <th>t30</th>\n",
       "      <th>t40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179128.000000</td>\n",
       "      <td>9.724900e+04</td>\n",
       "      <td>40558.000000</td>\n",
       "      <td>12405.000000</td>\n",
       "      <td>179128.000000</td>\n",
       "      <td>97249.000000</td>\n",
       "      <td>40558.000000</td>\n",
       "      <td>12405.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.124490</td>\n",
       "      <td>1.191446e-01</td>\n",
       "      <td>0.106754</td>\n",
       "      <td>0.096133</td>\n",
       "      <td>9.717807</td>\n",
       "      <td>12.613265</td>\n",
       "      <td>13.353269</td>\n",
       "      <td>14.357695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.278097</td>\n",
       "      <td>2.767179e-01</td>\n",
       "      <td>0.231658</td>\n",
       "      <td>0.208030</td>\n",
       "      <td>35.826308</td>\n",
       "      <td>33.820679</td>\n",
       "      <td>33.059365</td>\n",
       "      <td>32.247286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.368990e-07</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>-127.611000</td>\n",
       "      <td>-127.137000</td>\n",
       "      <td>-125.369000</td>\n",
       "      <td>-123.485000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.023971</td>\n",
       "      <td>2.428450e-02</td>\n",
       "      <td>0.024472</td>\n",
       "      <td>0.024499</td>\n",
       "      <td>5.896360</td>\n",
       "      <td>11.191700</td>\n",
       "      <td>12.533800</td>\n",
       "      <td>13.194900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.043490</td>\n",
       "      <td>4.256940e-02</td>\n",
       "      <td>0.041417</td>\n",
       "      <td>0.040254</td>\n",
       "      <td>15.971600</td>\n",
       "      <td>17.532700</td>\n",
       "      <td>17.842400</td>\n",
       "      <td>18.002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.104728</td>\n",
       "      <td>9.936260e-02</td>\n",
       "      <td>0.091825</td>\n",
       "      <td>0.082496</td>\n",
       "      <td>22.108300</td>\n",
       "      <td>22.549500</td>\n",
       "      <td>22.376125</td>\n",
       "      <td>22.115900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.732060</td>\n",
       "      <td>7.082470e+00</td>\n",
       "      <td>4.597660</td>\n",
       "      <td>3.759470</td>\n",
       "      <td>116.130000</td>\n",
       "      <td>113.054000</td>\n",
       "      <td>111.742000</td>\n",
       "      <td>109.603000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 E10           E20           E30           E40            t10  \\\n",
       "count  179128.000000  9.724900e+04  40558.000000  12405.000000  179128.000000   \n",
       "mean        0.124490  1.191446e-01      0.106754      0.096133       9.717807   \n",
       "std         0.278097  2.767179e-01      0.231658      0.208030      35.826308   \n",
       "min         0.000002  4.368990e-07      0.000035      0.000070    -127.611000   \n",
       "25%         0.023971  2.428450e-02      0.024472      0.024499       5.896360   \n",
       "50%         0.043490  4.256940e-02      0.041417      0.040254      15.971600   \n",
       "75%         0.104728  9.936260e-02      0.091825      0.082496      22.108300   \n",
       "max         7.732060  7.082470e+00      4.597660      3.759470     116.130000   \n",
       "\n",
       "                t20           t30           t40  \n",
       "count  97249.000000  40558.000000  12405.000000  \n",
       "mean      12.613265     13.353269     14.357695  \n",
       "std       33.820679     33.059365     32.247286  \n",
       "min     -127.137000   -125.369000   -123.485000  \n",
       "25%       11.191700     12.533800     13.194900  \n",
       "50%       17.532700     17.842400     18.002000  \n",
       "75%       22.549500     22.376125     22.115900  \n",
       "max      113.054000    111.742000    109.603000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['E10','E20','E30','E40', 't10', 't20', 't30', 't40']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some values\n",
    "\n",
    "Below we plot some values to give us a visual. Note that this takes a few seconds to plot since it runs over the dat multiple times.\n",
    "\n",
    "In the top row, the reason for the varied means in x/y becomes more obvious. It is due to some regions of the detector being excluded from that row. There is still a wide enough region illuminated to keep the standard deviations high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f251b8a6240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251b8b03c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bea1940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251be4eef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251be084e0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f251be35a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bdf4080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bd9e5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bd4cbe0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bd081d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f251bd38780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bcecd30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bca9320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bc5a8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bc0ee80>]],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create subplot figure with multiple plots\n",
    "fig, axes = plt.subplots(nrows=3,ncols=5,figsize=(20,12))\n",
    "\n",
    "df.plot.scatter('x0', 'y0', ax=axes[0,0])\n",
    "df.plot.scatter('x10', 'y10', ax=axes[0,1])\n",
    "df.plot.scatter('x20', 'y20', ax=axes[0,2])\n",
    "df.plot.scatter('x30', 'y30', ax=axes[0,3])\n",
    "df.plot.scatter('x40', 'y40', ax=axes[0,4])\n",
    "df['E0'].plot.hist(ax=axes[1,0], grid=True, logy=True, bins=200, title='E0')\n",
    "df['E10'].plot.hist(ax=axes[1,1], grid=True, logy=True, bins=200, title='E10')\n",
    "df['E20'].plot.hist(ax=axes[1,2], grid=True, logy=True, bins=200, title='E20')\n",
    "df['E30'].plot.hist(ax=axes[1,3], grid=True, logy=True, bins=200, title='E30')\n",
    "df['E40'].plot.hist(ax=axes[1,4], grid=True, logy=True, bins=200, title='E40')\n",
    "df['t0'].plot.hist(ax=axes[2,0], grid=True, logy=True, bins=200, title='t0')\n",
    "df['t10'].plot.hist(ax=axes[2,1], grid=True, logy=True, bins=200, title='t10')\n",
    "df['t20'].plot.hist(ax=axes[2,2], grid=True, logy=True, bins=200, title='t20')\n",
    "df['t30'].plot.hist(ax=axes[2,3], grid=True, logy=True, bins=200, title='t30')\n",
    "df['t40'].plot.hist(ax=axes[2,4], grid=True, logy=True, bins=200, title='t40')\n",
    "display(axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some single events\n",
    "\n",
    "Plot a few events. This is a little complicated since we have to pull all of the non-NaN x values and y-values for a single row of the dataframe into separate lists and then plot them using 2-D histogram. We just plot whether a cell is hit or not here and don't use the energy in coloring. It looks like there is some variation of color, likely due to one block having more than one hit in the event, separated by time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2522071f98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2520f5d438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bbca198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f25218a80b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f251bac2c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f25209e1240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f251bee74a8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f25208f1278>]],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2,ncols=4,figsize=(20,10))\n",
    "\n",
    "# Get column names for all x and y columns\n",
    "xcols = [colname for colname in df.columns if colname.startswith('x')]\n",
    "ycols = [colname for colname in df.columns if colname.startswith('y')]\n",
    "\n",
    "# Specify some random events\n",
    "myevents = [3,27,102,214,337, 516, 814, 1024]\n",
    "\n",
    "# Loop over events, drawing them into subplots\n",
    "for ievent in range(0, len(myevents)):\n",
    "    # Slice single row (=event) from dataframe and capture all non-NaN x,y values in lists\n",
    "    mdf = df.iloc[[myevents[ievent]]]\n",
    "    xvals = [x for x in mdf[xcols].values[0] if not np.isnan(x)]\n",
    "    yvals = [y for y in mdf[ycols].values[0] if not np.isnan(y)]\n",
    "    ix = int(ievent/4)\n",
    "    iy = int(ievent%4)\n",
    "    h = axes[ix,iy].hist2d(xvals, yvals, bins=60, range=[[-120.0, 120.0], [-120.0, 120.0]])\n",
    "    \n",
    "display(axes)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}