{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logfile parsing\n",
    "(back to overview [offline](../Main.ipynb),[online](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/Main.ipynb))\n",
    "\n",
    "[read on nbviewer](https://nbviewer.jupyter.org/github/QCoDeS/Qcodes/tree/master/docs/examples/logging/logfile_parsing.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we provide an example concerning how to benefit from QCoDeS logs by simple analysis and visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import dateutil\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.logger.log_analysis import logfile_to_dataframe, time_difference, log_to_dataframe\n",
    "from zipfile import ZipFile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put the 30MB into a zip file\n",
    "filepath = os.path.join(os.getcwd(), 'static', 'pythonlog.zip')\n",
    "with ZipFile(filepath) as z:\n",
    "    with z.open('pythonlog.log', 'r') as f:\n",
    "        my_log = [line.decode() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.exists(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdata = log_to_dataframe(my_log, separator=' - ', columns=['time', 'module', 'function', 'loglevel', 'message'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `logdata` is, now, a nice and tidy `DataFrame` that one can further manipulate to extract more information, if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>time</th>\n",
       "      <th>module</th>\n",
       "      <th>function</th>\n",
       "      <th>loglevel</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-05-10 16:01:50,497</td>\n",
       "      <td>qcodes.instrument_drivers.QDev.QDac_channels</td>\n",
       "      <td>write</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Writing to instrument qdac: wav 2 0 0 0;set 2 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-05-10 16:01:50,546</td>\n",
       "      <td>qcodes.instrument.visa</td>\n",
       "      <td>ask_raw</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Querying instrument SR860_120: OUTP? 2\\r\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-05-10 16:01:50,552</td>\n",
       "      <td>qcodes.instrument.visa</td>\n",
       "      <td>ask_raw</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Got response from instrument SR860_120: 8.9832...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-10 16:01:50,553</td>\n",
       "      <td>qcodes.instrument.visa</td>\n",
       "      <td>ask_raw</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Querying instrument SR860_120: SLVL?\\r\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-05-10 16:01:50,561</td>\n",
       "      <td>qcodes.instrument.visa</td>\n",
       "      <td>ask_raw</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Got response from instrument SR860_120: 9.9999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255146</th>\n",
       "      <td>2018-05-10 17:12:03,208</td>\n",
       "      <td>qcodes.instrument.visa</td>\n",
       "      <td>ask_raw</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Querying instrument SR860_120: OUTP? 2\\r\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255147</th>\n",
       "      <td>2018-05-10 17:12:14,257</td>\n",
       "      <td>qcodes.data.data_set</td>\n",
       "      <td>finalize</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Finalising the DataSet. Writing.\\r\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255148</th>\n",
       "      <td>2018-05-10 17:12:14,258</td>\n",
       "      <td>qcodes.data.gnuplot_format</td>\n",
       "      <td>write</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Attempting to write the following group: qdac_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255149</th>\n",
       "      <td>2018-05-10 17:12:14,259</td>\n",
       "      <td>qcodes.data.gnuplot_format</td>\n",
       "      <td>write</td>\n",
       "      <td>DEBUG</td>\n",
       "      <td>Wrote to file from 40200 to 40201\\r\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255150</th>\n",
       "      <td>2018-05-10 17:12:14,378</td>\n",
       "      <td>qdev_wrappers.sweep_functions</td>\n",
       "      <td>_do_measurement</td>\n",
       "      <td>ERROR</td>\n",
       "      <td>Exception in doND\\r\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>255151 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time                                        module  \\\n",
       "0       2018-05-10 16:01:50,497  qcodes.instrument_drivers.QDev.QDac_channels   \n",
       "1       2018-05-10 16:01:50,546                        qcodes.instrument.visa   \n",
       "2       2018-05-10 16:01:50,552                        qcodes.instrument.visa   \n",
       "3       2018-05-10 16:01:50,553                        qcodes.instrument.visa   \n",
       "4       2018-05-10 16:01:50,561                        qcodes.instrument.visa   \n",
       "...                         ...                                           ...   \n",
       "255146  2018-05-10 17:12:03,208                        qcodes.instrument.visa   \n",
       "255147  2018-05-10 17:12:14,257                          qcodes.data.data_set   \n",
       "255148  2018-05-10 17:12:14,258                    qcodes.data.gnuplot_format   \n",
       "255149  2018-05-10 17:12:14,259                    qcodes.data.gnuplot_format   \n",
       "255150  2018-05-10 17:12:14,378                 qdev_wrappers.sweep_functions   \n",
       "\n",
       "               function loglevel  \\\n",
       "0                 write    DEBUG   \n",
       "1               ask_raw    DEBUG   \n",
       "2               ask_raw    DEBUG   \n",
       "3               ask_raw    DEBUG   \n",
       "4               ask_raw    DEBUG   \n",
       "...                 ...      ...   \n",
       "255146          ask_raw    DEBUG   \n",
       "255147         finalize    DEBUG   \n",
       "255148            write    DEBUG   \n",
       "255149            write    DEBUG   \n",
       "255150  _do_measurement    ERROR   \n",
       "\n",
       "                                                  message  \n",
       "0       Writing to instrument qdac: wav 2 0 0 0;set 2 ...  \n",
       "1              Querying instrument SR860_120: OUTP? 2\\r\\n  \n",
       "2       Got response from instrument SR860_120: 8.9832...  \n",
       "3                Querying instrument SR860_120: SLVL?\\r\\n  \n",
       "4       Got response from instrument SR860_120: 9.9999...  \n",
       "...                                                   ...  \n",
       "255146         Querying instrument SR860_120: OUTP? 2\\r\\n  \n",
       "255147               Finalising the DataSet. Writing.\\r\\n  \n",
       "255148  Attempting to write the following group: qdac_...  \n",
       "255149              Wrote to file from 40200 to 40201\\r\\n  \n",
       "255150                              Exception in doND\\r\\n  \n",
       "\n",
       "[255151 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = logdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the query time for the SR860\n",
    "\n",
    "We know that the log file documents an experiment quering an SR860 for R and amplitude over and over. Let us analyse and visualise query response times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "qstr_R = 'Querying instrument SR860_120: OUTP\\? 2'  # remember to escape\n",
    "queries_R = data[data.message.str.contains(qstr_R)]\n",
    "responses_R = data.loc[queries_R.index + 1]\n",
    "\n",
    "qstr_lvl = 'Querying instrument SR860_120: SLVL\\?'  # remember to escape\n",
    "queries_lvl = data[data.message.str.contains(qstr_lvl)][:-1]\n",
    "responses_lvl = data.loc[queries_lvl.index + 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the elapsed times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "elapsed_times_R = time_difference(queries_R.time, responses_R.time)\n",
    "elapsed_times_lvl =  time_difference(queries_lvl.time, responses_lvl.time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20592e91048>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(queries_R.time.str.replace(',', '.').astype(\"datetime64[ns]\"), elapsed_times_R, '.', label='R')\n",
    "ax.plot(queries_lvl.time.str.replace(',', '.').astype(\"datetime64[ns]\"), elapsed_times_lvl, '.', label='LVL')\n",
    "fig.autofmt_xdate()\n",
    "ax.set_ylabel('Response time (s)')\n",
    "plt.legend()"
   ]
  },
  {
   "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": "3.7.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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