05ex_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas analysis\n",
    "\n",
    "This exercise consists in analyzing a dataset containg timing information from a series of Time-to-Digital-Converters (TDC) implemented in a couple of FPGAs. Each measurement (i.e. each row of the input file) consists of a flag that specifies the type of message ('HEAD', which in this case is always 1), two addresses of the TDC providing the signal ('FPGA' and 'TDC_CHANNEL'), and the timing information ('ORBIT_CNT', 'BX_COUNTER', and 'TDC_MEAS'). Each TDC count corresponds to 25/30 ns, whereas a unit of BX_COUNTER corresponds to 25 ns, and the ORBIT_CNT is increased every 'x' BX_COUNTER. This allows to store the time in a similar way to hours, minutes and seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you didn't download it yet, please get the relevant file now!\n",
    "#!wget -O data_000637.txt https://www.dropbox.com/s/xvjzaxzz3ysphme/data_000637.txt -P ../data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1\\. Create a Pandas DataFrame reading N rows of the 'data_000637.txt' dataset. Choose N to be smaller than or equal to the maximum number of rows and larger that 10k.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HEAD</th>\n",
       "      <th>FPGA</th>\n",
       "      <th>TDC_CHANNEL</th>\n",
       "      <th>ORBIT_CNT</th>\n",
       "      <th>BX_COUNTER</th>\n",
       "      <th>TDC_MEAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2374</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3869200167</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2553</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2558</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>3869200167</td>\n",
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       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2378</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2472</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2558</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2561</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2565</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HEAD  FPGA  TDC_CHANNEL   ORBIT_CNT  BX_COUNTER  TDC_MEAS\n",
       "0         1     0          123  3869200167        2374        26\n",
       "1         1     0          124  3869200167        2374        27\n",
       "2         1     0           63  3869200167        2553        28\n",
       "3         1     0           64  3869200167        2558        19\n",
       "4         1     0           64  3869200167        2760        25\n",
       "...     ...   ...          ...         ...         ...       ...\n",
       "99995     1     0           64  3869201161        2378        29\n",
       "99996     1     0           70  3869201161        2472        26\n",
       "99997     1     0           58  3869201161        2558         0\n",
       "99998     1     0           57  3869201161        2561        23\n",
       "99999     1     0           56  3869201161        2565        12\n",
       "\n",
       "[100000 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = '../data/data_000637.txt'\n",
    "N = 100000\n",
    "df = pd.read_csv(file_name, nrows=N)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. Find out the number of BX in a ORBIT (the value 'x').\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3564\n"
     ]
    }
   ],
   "source": [
    "t = df.BX_COUNTER.max()-df.BX_COUNTER.min()+1\n",
    "print(t)\n",
    "#print(df.groupby(['BX_COUNTER']).get_group(3563))\n",
    "#print(df.groupby(['BX_COUNTER']).get_group(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "3\\. Find out how much the data taking lasted. You can either make an estimate based on the fraction of the measurements (rows) you read, or perform this check precisely by reading out the whole dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440 ms ± 21.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df_tot = pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08869365 s\n"
     ]
    }
   ],
   "source": [
    "time = (df.tail(1)['ORBIT_CNT'].values * t * 25 + df.tail(1)['BX_COUNTER'].values * 25 -\n",
    "        df.head(1)['ORBIT_CNT'].values * t * 25 + df.tail(1)['BX_COUNTER'].values * 25 )\n",
    "print((time[0] * 10**(-9)), 's')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "4\\. Create a new column with the absolute time in ns (as a combination of the other three columns with timing information).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>FPGA</th>\n",
       "      <th>TDC_CHANNEL</th>\n",
       "      <th>ORBIT_CNT</th>\n",
       "      <th>BX_COUNTER</th>\n",
       "      <th>TDC_MEAS</th>\n",
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       "      <th>99996</th>\n",
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       "      <td>2472</td>\n",
       "      <td>26</td>\n",
       "      <td>8.862726e+07</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2558</td>\n",
       "      <td>0</td>\n",
       "      <td>8.862940e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>3869201161</td>\n",
       "      <td>2561</td>\n",
       "      <td>23</td>\n",
       "      <td>8.862947e+07</td>\n",
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       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2565</td>\n",
       "      <td>12</td>\n",
       "      <td>8.862957e+07</td>\n",
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       "<p>100000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HEAD  FPGA  TDC_CHANNEL   ORBIT_CNT  BX_COUNTER  TDC_MEAS          time\n",
       "0         1     0          123  3869200167        2374        26  5.945250e+04\n",
       "1         1     0          124  3869200167        2374        27  5.945333e+04\n",
       "2         1     0           63  3869200167        2553        28  6.387750e+04\n",
       "3         1     0           64  3869200167        2558        19  6.400333e+04\n",
       "4         1     0           64  3869200167        2760        25  6.905333e+04\n",
       "...     ...   ...          ...         ...         ...       ...           ...\n",
       "99995     1     0           64  3869201161        2378        29  8.862490e+07\n",
       "99996     1     0           70  3869201161        2472        26  8.862726e+07\n",
       "99997     1     0           58  3869201161        2558         0  8.862940e+07\n",
       "99998     1     0           57  3869201161        2561        23  8.862947e+07\n",
       "99999     1     0           56  3869201161        2565        12  8.862957e+07\n",
       "\n",
       "[100000 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time'] = (df['ORBIT_CNT'] - df.loc[0, 'ORBIT_CNT']) * t * 25 + df['BX_COUNTER'] * 25 + df['TDC_CHANNEL'] * 25/30\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "5\\. Replace the values (all 1) of the HEAD column randomly with 0 or 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>2378</td>\n",
       "      <td>29</td>\n",
       "      <td>8.862490e+07</td>\n",
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       "      <th>99996</th>\n",
       "      <td>1</td>\n",
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       "      <td>2472</td>\n",
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       "      <td>8.862726e+07</td>\n",
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       "      <th>99997</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2558</td>\n",
       "      <td>0</td>\n",
       "      <td>8.862940e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2561</td>\n",
       "      <td>23</td>\n",
       "      <td>8.862947e+07</td>\n",
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       "      <td>3869201161</td>\n",
       "      <td>2565</td>\n",
       "      <td>12</td>\n",
       "      <td>8.862957e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HEAD  FPGA  TDC_CHANNEL   ORBIT_CNT  BX_COUNTER  TDC_MEAS          time\n",
       "0         0     0          123  3869200167        2374        26  5.945250e+04\n",
       "1         0     0          124  3869200167        2374        27  5.945333e+04\n",
       "2         0     0           63  3869200167        2553        28  6.387750e+04\n",
       "3         1     0           64  3869200167        2558        19  6.400333e+04\n",
       "4         0     0           64  3869200167        2760        25  6.905333e+04\n",
       "...     ...   ...          ...         ...         ...       ...           ...\n",
       "99995     1     0           64  3869201161        2378        29  8.862490e+07\n",
       "99996     1     0           70  3869201161        2472        26  8.862726e+07\n",
       "99997     0     0           58  3869201161        2558         0  8.862940e+07\n",
       "99998     0     0           57  3869201161        2561        23  8.862947e+07\n",
       "99999     0     0           56  3869201161        2565        12  8.862957e+07\n",
       "\n",
       "[100000 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HEAD'] = np.random.randint(0, 2, N)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "6\\. Create a new DataFrame that contains only the rows with HEAD=1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HEAD</th>\n",
       "      <th>FPGA</th>\n",
       "      <th>TDC_CHANNEL</th>\n",
       "      <th>ORBIT_CNT</th>\n",
       "      <th>BX_COUNTER</th>\n",
       "      <th>TDC_MEAS</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2558</td>\n",
       "      <td>19</td>\n",
       "      <td>6.400333e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2774</td>\n",
       "      <td>21</td>\n",
       "      <td>6.940167e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2785</td>\n",
       "      <td>4</td>\n",
       "      <td>6.963083e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2797</td>\n",
       "      <td>0</td>\n",
       "      <td>7.004083e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>3869200167</td>\n",
       "      <td>2790</td>\n",
       "      <td>10</td>\n",
       "      <td>6.980250e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99990</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2297</td>\n",
       "      <td>26</td>\n",
       "      <td>8.862292e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99991</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2298</td>\n",
       "      <td>7</td>\n",
       "      <td>8.862289e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99992</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2292</td>\n",
       "      <td>3</td>\n",
       "      <td>8.862279e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2378</td>\n",
       "      <td>29</td>\n",
       "      <td>8.862490e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>3869201161</td>\n",
       "      <td>2472</td>\n",
       "      <td>26</td>\n",
       "      <td>8.862726e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50006 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HEAD  FPGA  TDC_CHANNEL   ORBIT_CNT  BX_COUNTER  TDC_MEAS          time\n",
       "3         1     0           64  3869200167        2558        19  6.400333e+04\n",
       "8         1     0           62  3869200167        2774        21  6.940167e+04\n",
       "10        1     1            7  3869200167        2785         4  6.963083e+04\n",
       "15        1     1          139  3869200167        2797         0  7.004083e+04\n",
       "17        1     0           63  3869200167        2790        10  6.980250e+04\n",
       "...     ...   ...          ...         ...         ...       ...           ...\n",
       "99990     1     1          116  3869201161        2297        26  8.862292e+07\n",
       "99991     1     1           47  3869201161        2298         7  8.862289e+07\n",
       "99992     1     1          107  3869201161        2292         3  8.862279e+07\n",
       "99995     1     0           64  3869201161        2378        29  8.862490e+07\n",
       "99996     1     0           70  3869201161        2472        26  8.862726e+07\n",
       "\n",
       "[50006 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby(['HEAD']).get_group(1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "7\\. Make two occupancy plots (one for each FPGA), i.e. plot the number of counts per TDC channel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iYX6wT8c3UETMbAmBGW46HWCKiJmnuhDo1wdmm9/u88EeMXtM97/l6kJgMumDj9kq78vBmUn/1rM2BKbybXqiaSe7ul8bNSL2x2Tvqcnet+OH97aMXv5OajZT/8ZnRQjs65t0Jv6Hxcwz0Yfu/ixvGDWmMr4WM/WDv9OMCoFevrVEDEIvH7pT+SDtNjwT3+9tBtF0UMPez8BDQNIySfdL2i5p1WTTb3306Vn1Dx4zw77e6CPv1Rebaf+WNXzwdxpoCEiaA3wGeAdwLHCupGMHuQ4RMT1MtMeztw/bNo7fTbYHVptB7wksBbbbftD2z4DrgLMGvA4R+6zWD4ph29cTM/L/NTnZHlwx6T3AMtsfKK/fC7zJ9gfHTbcSWFlevh54CviHga0oHDnAeoOsNdvrzeZtG3S92bxtg643rG17je15k008t//r8wLq0vaiFLK9Bljz/2aSRm2P9HPFOg2y3mzetkHXm83bNuh6s3nbBl1vum/boLuDdgKLOl4vBB4b8DpEREQx6BC4BVgi6RhJLwWWAxsGvA4REVEMtDvI9vOSPgj8DTAH+KztbT3MumbySVo1yHqzedsGXW82b9ug683mbRt0vWm9bQM9MBwREdPLjPrFcEREtCshEBFRsYRARETFEgLRN5JeOex1iIi9m1EhIOn9w16H/SHpVZKukPQZSa+U9HFJWyWtk3R0H+ot6xh+haSrJN0l6VpJR7Vc61JJR5bhEUkPAjdL+r6kt7RZq6PGNyV9XtIiSRslPS3pFkknpd70rDWMejEJ2zPmAewYcL0bWl7ejcBFwCrgLuA/Aq8ubev7sP63dQxfCfwJ8Brgw8DXWq61tWP4m8A/LcOvA0b7sG1baC5EeC7wCPCe0n4asDn1pmetYdQry341cFgZXgy8Bzi+H7WGVG8EeDfwr4A3TGnefq3UfmzMXRM8tgLP9aHeGyd4nAw83nKt2zuGd4wbd0cftu22iZbfdj3gPmBuGf77ceO2tlmrh3/L21NvetYaUr1VwEPlPfqB8nwVsA34yEyuB7wFGAX+FvhH4OvAd4GbgEW9LGPQ1w7qxVHAmTQb1EnA/+xDvVuAb9H9ukaHtVyrs/vtmnHj5rRcC2C+pI/QbNuhkuTyzqH9rsDPAH8t6VLgRkmfAv6K5tvdHS3XAvjfks4AXgFY0tm2v1a6nnan3rStNYx676W5dP3BwMPAa22PSXoZcDPwyRlc71PAGWX5xwCftH2qpNNpgueMyRYwHUPg68Ahtu8YP0LSTX2ody/wH2w/0KXeIy3XWi/pENvP2v7Djjq/DNzfci2AvwReXobX0lxdcEzSq2j5g9n2f5N0N/A7NF1Ac8vzeppuqLZdAPxn4Oc0XxoukPQ5mmtRrdzbjPvod4D/MuR6VwOPAr89gFqz6d9yt+2fSvoZ8FOaqxJj+8dSt+9+M6reHNtjZXgHTXcvtjeWL2KTqv4Xw2oub73V9os+hPd8Q2m53huABcDNtp/taF9m+8Y2a5XlLgVs+xY1N/BZBtxn+6/brtWl9jW2z+t3nVLrzTT3q9hq+xt9WP6baP7dnpZ0EHAJTbfhNuATtp9uud6HgK/abvuLSLdaL6Xpn3/U9t9K+nfAPwfuAdbY/j99qPnLNH3YC4Hnge3AtW3/O5ZaVwMvBV4G/KTUuxF4G/By278xU+tJ+izNlZg30dyb5VHbH5F0ME138BsmXUbtIbA3kt5v+3MtLu8i4IM0ex8nAhfbXl/G3Wb7jW3VKsv8I5oDcHOBjcCbaPoK3w78je3VLdbqdiHAtwF/B2D719uqVeptsb20DP828LvA12h2f/+H7UtbrrcNOMHN9a/WAD8GvkLT3XWC7X/dcr2nS43vAV8EvtTxja9Vkr5A8x45GPghzYfXV2m2TbZXtFzvQ8C7gG8Dv0azV/qPNKHwu7ZvarneXOAcmg/LL9P8HZxL8835M7Z/3Od6S4Hf7Ec9SS+h2TM8FriT5npsu8sXlfm2vz/pQto+KDKbHrR8NhLNwe1DyvBimgM6F5fXt/dh/bfSHGs4GPgRcGhpPwi4q+VatwGfB95Kc7DqrcDjZfgtfdi22zuGbwHmleGX0Z8D0fd2buu4cXf0Y/tojtucQdO3O0bzbXIFzbfJNmvdVZ7nAk/SdDFAcyyp1fdJWe7WjhoHAzeV4Vf34+8gj70/ZtTvBPpBzXnz3R5baQ5St2mOSxeQ7YdpPijfIemTdD8wvb+et73b9k+A79n+Uan9U5r+2DaNALcC/wl42s23uZ/a/pbtb7VcC+AASYer+UGaXL4lu/mW9Xwf6t2t//87lTsljQBIeh3QencJTRfez21/w/b5wC8Cl9N05z3Ycq0DSpfQy2k+lF9R2g8EXtJyrT32HI88sNTF9o5+1FPzG5lLJd0n6QeSnpJ0b2k7rO16k6zLDS0v71WSLtd+/PZoOh4YHrRBno30hKQTXQ56235W0ruAzwL/pOVaAD+TdHAJgZP3NEp6BS2HgO2fA38m6Uvl+Un6+/56BU3oiOYMk1fZfkLSIfQnUD8A/LmkP6S5dd/mcuLAI2Vc216wDW765TcAG8qufpuuojmNcQ5NiH9JzY/9TqG5D3jbrgRukfT3wK/SHOBH0jzgB32ot46mW/Kttp8otV4FvA/4EnB6m8UkTdStK5pu4DZdDVxPswf8TeALwDtpjg/8BT3cw736YwKSrgI+Z/s7XcZda/s3W6y1kObb+RNdxp1q+7tt1SrLPND2c13ajwSOtr21zXrjarwTONX2x/pVY4K6BwNH2X6oT8t/OfBamoDbafvJPtV5ne3/1Y9lT1DvFwFsP1a+Hb+dpjt0S5/qHQf8CnC37fv6UaOj1v22Xz/VcftRbzcTn3Z+iu3WQlzS7bZPKsM7bL+6Y9wdtk+cdBm1h0BEzG6SvkHzY6q1e0JbzWVT3gecbvvtLde7G3i3Jzjt3PaiLrPta607bZ9Qhv/ELzz1fKvtSXsYqj8mEBGz3r8FXgl8qxwT+AHNWXJH0JzF07aPM/Fn60Ut11pfukDxPv72KHsCEVGttk8Dn071eq2VEIiIao3vR59N9XqtlbODImJWk3TXRKNo/zTwgdZro1ZCICJmu0FflHKQ9fa7VkIgIma7QV+UcpD19rtWjglERFQsp4hGRFQsIRARUbGEQERExRICMSOUKyTeUR5PSHq047XL8zZJd0r6iKQDOuZdKunbku4vV5K8slxjaKJa75A0Wq40eZ+kPy3tH5f0B+Omfbhci2nP63eX9XlDR9vi0nZRR9unJb2vDF9dtufA8vpISQ93zPvTjm29Q9J53WpH7IucHRQzgu2nKFdglPRx4Fnbez6cn91zoSxJ84Fraa4y+kflGjFfApbb3ixJwL+huXzxT8bXkXQ88GngnbbvU3ODkKnc8vBc4DvAcprLB+yxC7hY0n+3/bMu8+0G/j1wRZdx3+vlQmAR+yJ7AjGr2N5F86H9wfKBfyHNhcM2l/G2/eW9XP3zo8DqPVe2tP287ct7qV2u4XIqcD5NCHQao7kF4ER36foU8OESOhEDkxCIWcf2gzTv7fnA8TT3HejVZNN/uLNrhuZmL3ucDdxYLgH9gy7Xlb8U+H1Jc7osdwfNHsR7u4z7pXHdQW/udWMiJpNvHTFb9ePGMgB/tqcbCpp++Y5x59J8o4fmZizn0tx2EwDbD0naQnO/2W4+QXPjmOvHtac7KPomIRCzjqTX0vSx7wK20dxVbX2Ps++Z/s4p1nwl8DbgeEmmuUuXJX103KSfoLn5+LfHL8P29rJ38RtTqR2xP9IdFLOKmlsU/gXwaTc/h/80sELSmzqm+S01txfs5r8CH1Nz72AkHSDpIz2Ufg9wje3X2F5cbhzyEPAvOicqxxruAd41wXJWA38wwbiI1iUEYjY4aM8pojR3kPoG8McA5QDwcuBPyymi9wJvBn7UbUG27wJ+D/himfZuoJcbdp8LfHVc21fo3vWzGlg4Qf1tdHQhFeOPCXyoY9xdknaWxyd7WM+IF8i1gyIiKpY9gYiIiuXAcFRJ0vuBi8c1f9f2hcNYn4hhSXdQRETF0h0UEVGxhEBERMUSAhERFUsIRERU7P8CxNo9fX/xZNoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpga0 = df.groupby(['FPGA']).get_group(0)\n",
    "ax = fpga0.groupby(['TDC_CHANNEL']).size().plot.bar()\n",
    "\n",
    "n=10\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpga1 = df.groupby(['FPGA']).get_group(1)\n",
    "ax = fpga1.groupby(['TDC_CHANNEL']).size().plot.bar()\n",
    "\n",
    "n=10\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "8\\. Use the groupby method to find out the noisy channels, i.e. the TDC channels with most counts (say the top 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TDC_CHANNEL\n",
       "139    8320\n",
       "64     5027\n",
       "63     4917\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['TDC_CHANNEL']).size().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "9\\. Count the number of unique orbits. Count the number of unique orbits with at least one measurement from TDC_CHANNEL=139"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "995\n",
      "994\n"
     ]
    }
   ],
   "source": [
    "print(df['ORBIT_CNT'].nunique())\n",
    "print(df['ORBIT_CNT'].loc[df['TDC_CHANNEL']==139].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}