{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jelle, updated May 2020\n",
    "\n",
    "updated Feb 2022 by Joran\n",
    "\n",
    "This notebook shows how to do basic analysis with straxen, much like `hax.minitrees`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, here are some jargon terms which we will introduce below:\n",
    "\n",
    "  * **Context**: Holds configuration on how to process\n",
    "  * **Dataframe** or **array**: table of related information produced by a plugin.\n",
    "  * **Plugin**: an algorithm that produces a dataframe\n",
    "  * **Data type**: specification of which columns are in a dataframe. \n",
    "  * **Data kind**: e.g. 'events' or 'peaks'. Dataframes of the same kind  have the same number of rows and can be merged.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# This just ensures some comments in dataframes below display nicely\n",
    "pd.options.display.max_colwidth = 100\n",
    "import straxen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cutax is not installed\n"
     ]
    },
    {
     "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>module</th>\n",
       "      <th>version</th>\n",
       "      <th>path</th>\n",
       "      <th>git</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>python</td>\n",
       "      <td>3.10.0</td>\n",
       "      <td>/home/angevaare/miniconda3/envs/dev_strax/bin/python</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>strax</td>\n",
       "      <td>1.1.7</td>\n",
       "      <td>/home/angevaare/software/dev_strax/strax/strax</td>\n",
       "      <td>branch:master | db14f80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>straxen</td>\n",
       "      <td>1.2.8</td>\n",
       "      <td>/home/angevaare/software/dev_strax/straxen/straxen</td>\n",
       "      <td>branch:master | 024602e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    module version                                                  path  \\\n",
       "0   python  3.10.0  /home/angevaare/miniconda3/envs/dev_strax/bin/python   \n",
       "1    strax   1.1.7        /home/angevaare/software/dev_strax/strax/strax   \n",
       "2  straxen   1.2.8    /home/angevaare/software/dev_strax/straxen/straxen   \n",
       "\n",
       "                       git  \n",
       "0                     None  \n",
       "1  branch:master | db14f80  \n",
       "2  branch:master | 024602e  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "straxen.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load a strax **context**, much like `hax.init()`. A strax context contains all information on *how* to process: where to read what files from, what plugins provide what data, etc. \n",
    "\n",
    "You can make a context yourselves using `strax.Context`, but straxen provides standardized contexts as well. Most future analyses will use such standardized contexts defined by analysis coordinators or straxen maintainers.\n",
    "\n",
    "Unlike `hax.init`, you can have multiple active contexts, e.g. to load analysis and MC data, or compare data processed with different settings (we will see examples of this below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = straxen.contexts.xenonnt_online()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suposse we want to make a cS1/cS2 plot. We have to figure out which type of **dataframes** to load. A specific type of dataframe is also called a **data type**. (in hax these were called minitrees)\n",
    "\n",
    "We can find this out automatically if we know (part of) the name of a field to load:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cs1 is part of corrected_areas (provided by CorrectedAreas)\n",
      "cs1 is part of event_info (provided by EventInfo)\n",
      "\n",
      "cs1 is used in CorrectedAreas.infer_dtype\n",
      "cs1 is used in CorrectedAreas.compute\n",
      "cs1 is used in EnergyEstimates.compute\n",
      "cs1 is used in EnergyEstimates.cs1_to_e\n"
     ]
    }
   ],
   "source": [
    "st.search_field(\"cs1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems we're after one of the data types called `event_info` or `corrected_areas`. In the current context, these are provided by **plugins** called EventInfo and CorrectedAreas, respectively (but this doesn't concern us yet). \n",
    "\n",
    "Additionally, we see the occurrences of `cs1` of a field in `EnergyEstimates` and `CorrectedAreas`. This means that the field is used there directly\n",
    "\n",
    "Let's see what else is in these data types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Field name</th>\n",
       "      <th>Data type</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time</td>\n",
       "      <td>int64</td>\n",
       "      <td>Start time since unix epoch [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>endtime</td>\n",
       "      <td>int64</td>\n",
       "      <td>Exclusive end time since unix epoch [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cs1</td>\n",
       "      <td>float32</td>\n",
       "      <td>Corrected area of main S1 [PE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cs2_wo_elifecorr</td>\n",
       "      <td>float32</td>\n",
       "      <td>Corrected area of main S2 before elife correction (s2 xy correction + SEG/EE correction applied)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cs2_wo_timecorr</td>\n",
       "      <td>float32</td>\n",
       "      <td>Corrected area of main S2 before SEG/EE and elife corrections(s2 xy correction applied) [PE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>z_naive</td>\n",
       "      <td>float32</td>\n",
       "      <td>Interaction z-position using mean drift velocity only (cm)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>r_naive</td>\n",
       "      <td>float32</td>\n",
       "      <td>Interaction r-position using observed S2 positions directly (cm)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>r_field_distortion_correction</td>\n",
       "      <td>float32</td>\n",
       "      <td>Correction added to r_naive for field distortion (cm)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>z_field_distortion_correction</td>\n",
       "      <td>float32</td>\n",
       "      <td>Correction added to z_naive for field distortion (cm)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>theta</td>\n",
       "      <td>float32</td>\n",
       "      <td>Interaction angular position (radians)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Field name Data type  \\\n",
       "0                             time     int64   \n",
       "1                          endtime     int64   \n",
       "2                              cs1   float32   \n",
       "3                 cs2_wo_elifecorr   float32   \n",
       "4                  cs2_wo_timecorr   float32   \n",
       "..                             ...       ...   \n",
       "106                        z_naive   float32   \n",
       "107                        r_naive   float32   \n",
       "108  r_field_distortion_correction   float32   \n",
       "109  z_field_distortion_correction   float32   \n",
       "110                          theta   float32   \n",
       "\n",
       "                                                                                                 Comment  \n",
       "0                                                                       Start time since unix epoch [ns]  \n",
       "1                                                               Exclusive end time since unix epoch [ns]  \n",
       "2                                                                         Corrected area of main S1 [PE]  \n",
       "3    Corrected area of main S2 before elife correction (s2 xy correction + SEG/EE correction applied)...  \n",
       "4           Corrected area of main S2 before SEG/EE and elife corrections(s2 xy correction applied) [PE]  \n",
       "..                                                                                                   ...  \n",
       "106                                           Interaction z-position using mean drift velocity only (cm)  \n",
       "107                                     Interaction r-position using observed S2 positions directly (cm)  \n",
       "108                                                Correction added to r_naive for field distortion (cm)  \n",
       "109                                                Correction added to z_naive for field distortion (cm)  \n",
       "110                                                               Interaction angular position (radians)  \n",
       "\n",
       "[111 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.data_info(\"event_info\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, `event_info` has a lot more information; let's load that one. You can see from the documentation (TODO link) that `event_info`'s job is to merge the info from `corrected_areas` and other things.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you'll want to select a run. The `select_runs` function will return a dataframe with all available runs; there is a separate tutorial on more advanced use of this. In this demo context, we only have high-level data for the run `180215_1029` available (and low-level data for another):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.select_runs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So lets' take 021932.\n",
    "\n",
    "To actually load data, you use `get_df` to get a pandas DataFrame, or `get_array` to get a numpy (structured) array. Let's go with pandas for now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22bcf8d7f0e74a8980a53922f9cfc3f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading event_info: |          | 0.00 % [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_id = \"021932\"\n",
    "# The seconds_range=[0,60] is an optional argument to prevent loading too much data at once\n",
    "df = st.get_df(run_id, \"event_info\", seconds_range=[0, 60])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first time you run this, it will take a moment: it has to actually process the data somewhat. We didn't ship highest-level demo data with straxen: that would mean we'd have to constantly update the test data when the algorithms change.\n",
    "\n",
    "You can also specify a list of runid's instead of one run, and get the concatenated result back. Likewise, you can specify multiple data types (of the same kind) to load, and they will be merged for you.\n",
    "\n",
    "Just like hax.minitrees.load, we got a dataframe back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>option</th>\n",
       "      <th>default</th>\n",
       "      <th>current</th>\n",
       "      <th>applies_to</th>\n",
       "      <th>help</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gain_model</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(to_pe_model, ONLINE, True)</td>\n",
       "      <td>(afterpulses,)</td>\n",
       "      <td>PMT gain model. Specify as (model_type, model_config)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n_tpc_pmts</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>494</td>\n",
       "      <td>(afterpulses,)</td>\n",
       "      <td>Number of PMTs in TPC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LED_window_left</td>\n",
       "      <td>50</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(afterpulses,)</td>\n",
       "      <td>Left boundary of sample range for LED pulse integration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LED_window_right</td>\n",
       "      <td>100</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(afterpulses,)</td>\n",
       "      <td>Right boundary of sample range for LED pulse integration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baseline_samples</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(afterpulses,)</td>\n",
       "      <td>Number of samples to use at start of WF to determine the baseline</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>269</th>\n",
       "      <td>recon_alg_included</td>\n",
       "      <td>(_mlp, _gcn, _cnn)</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(s2_recon_pos_diff,)</td>\n",
       "      <td>The list of all reconstruction algorithm considered.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>max_veto_window</td>\n",
       "      <td>7200000000000</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(veto_intervals,)</td>\n",
       "      <td>Maximum separation between veto stop and start pulses [ns]. Set to be &gt;&gt; than the max duration o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>event_window_fields</td>\n",
       "      <td>(time, endtime)</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(veto_proximity,)</td>\n",
       "      <td>Fields to determine where to look for overlaps for using this plugin in the events. The default ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>veto_proximity_window</td>\n",
       "      <td>300000000000</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(veto_proximity,)</td>\n",
       "      <td>Maximum separation between veto stop and start pulses [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>time_no_aqmon_veto_found</td>\n",
       "      <td>3600000000000</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(veto_proximity,)</td>\n",
       "      <td>If no next/previous veto is found, we will fill the fields time_to_previous_XX with this time. S...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       option             default  \\\n",
       "0                  gain_model           <OMITTED>   \n",
       "1                  n_tpc_pmts           <OMITTED>   \n",
       "2             LED_window_left                  50   \n",
       "3            LED_window_right                 100   \n",
       "4            baseline_samples                  40   \n",
       "..                        ...                 ...   \n",
       "269        recon_alg_included  (_mlp, _gcn, _cnn)   \n",
       "270           max_veto_window       7200000000000   \n",
       "271       event_window_fields     (time, endtime)   \n",
       "272     veto_proximity_window        300000000000   \n",
       "273  time_no_aqmon_veto_found       3600000000000   \n",
       "\n",
       "                         current            applies_to  \\\n",
       "0    (to_pe_model, ONLINE, True)        (afterpulses,)   \n",
       "1                            494        (afterpulses,)   \n",
       "2                      <OMITTED>        (afterpulses,)   \n",
       "3                      <OMITTED>        (afterpulses,)   \n",
       "4                      <OMITTED>        (afterpulses,)   \n",
       "..                           ...                   ...   \n",
       "269                    <OMITTED>  (s2_recon_pos_diff,)   \n",
       "270                    <OMITTED>     (veto_intervals,)   \n",
       "271                    <OMITTED>     (veto_proximity,)   \n",
       "272                    <OMITTED>     (veto_proximity,)   \n",
       "273                    <OMITTED>     (veto_proximity,)   \n",
       "\n",
       "                                                                                                    help  \n",
       "0                                                  PMT gain model. Specify as (model_type, model_config)  \n",
       "1                                                                                  Number of PMTs in TPC  \n",
       "2                                                Left boundary of sample range for LED pulse integration  \n",
       "3                                               Right boundary of sample range for LED pulse integration  \n",
       "4                                      Number of samples to use at start of WF to determine the baseline  \n",
       "..                                                                                                   ...  \n",
       "269                                                 The list of all reconstruction algorithm considered.  \n",
       "270  Maximum separation between veto stop and start pulses [ns]. Set to be >> than the max duration o...  \n",
       "271  Fields to determine where to look for overlaps for using this plugin in the events. The default ...  \n",
       "272                                           Maximum separation between veto stop and start pulses [ns]  \n",
       "273  If no next/previous veto is found, we will fill the fields time_to_previous_XX with this time. S...  \n",
       "\n",
       "[274 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.show_config(\"event_info\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>time</th>\n",
       "      <th>endtime</th>\n",
       "      <th>cs1</th>\n",
       "      <th>cs2_wo_elifecorr</th>\n",
       "      <th>cs2_wo_timecorr</th>\n",
       "      <th>cs2_area_fraction_top</th>\n",
       "      <th>cs2_bottom</th>\n",
       "      <th>cs2</th>\n",
       "      <th>alt_cs1</th>\n",
       "      <th>alt_cs2_wo_elifecorr</th>\n",
       "      <th>...</th>\n",
       "      <th>alt_s2_y_mlp</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>r</th>\n",
       "      <th>z_naive</th>\n",
       "      <th>r_naive</th>\n",
       "      <th>r_field_distortion_correction</th>\n",
       "      <th>z_field_distortion_correction</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1623144887003148092</td>\n",
       "      <td>1623144887005863030</td>\n",
       "      <td>2.659672</td>\n",
       "      <td>348.436493</td>\n",
       "      <td>370.887268</td>\n",
       "      <td>0.777278</td>\n",
       "      <td>82.604637</td>\n",
       "      <td>370.887268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.098747</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.990379</td>\n",
       "      <td>-7.353356</td>\n",
       "      <td>-15.314320</td>\n",
       "      <td>-43.339104</td>\n",
       "      <td>16.988239</td>\n",
       "      <td>-43.339104</td>\n",
       "      <td>16.190155</td>\n",
       "      <td>0.798083</td>\n",
       "      <td>0.007348</td>\n",
       "      <td>-2.018448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1623144887006147772</td>\n",
       "      <td>1623144887008854250</td>\n",
       "      <td>13155.237305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.279365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.517769</td>\n",
       "      <td>55.207436</td>\n",
       "      <td>-0.189628</td>\n",
       "      <td>71.552284</td>\n",
       "      <td>-0.189628</td>\n",
       "      <td>69.542831</td>\n",
       "      <td>2.009451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.881302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1623144887030319882</td>\n",
       "      <td>1623144887033327360</td>\n",
       "      <td>61723.960938</td>\n",
       "      <td>28599.917969</td>\n",
       "      <td>29029.152344</td>\n",
       "      <td>0.723225</td>\n",
       "      <td>8034.542969</td>\n",
       "      <td>29029.152344</td>\n",
       "      <td>19.304628</td>\n",
       "      <td>1338.243530</td>\n",
       "      <td>...</td>\n",
       "      <td>53.898460</td>\n",
       "      <td>-35.264309</td>\n",
       "      <td>55.141556</td>\n",
       "      <td>-10.200156</td>\n",
       "      <td>65.453514</td>\n",
       "      <td>-10.200156</td>\n",
       "      <td>61.628628</td>\n",
       "      <td>3.824886</td>\n",
       "      <td>0.744288</td>\n",
       "      <td>2.139771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1623144887054958322</td>\n",
       "      <td>1623144887057677700</td>\n",
       "      <td>76545.257812</td>\n",
       "      <td>1266.177612</td>\n",
       "      <td>1266.558594</td>\n",
       "      <td>0.159955</td>\n",
       "      <td>1063.966309</td>\n",
       "      <td>1266.558594</td>\n",
       "      <td>2.062342</td>\n",
       "      <td>17.207802</td>\n",
       "      <td>...</td>\n",
       "      <td>2.884162</td>\n",
       "      <td>31.293451</td>\n",
       "      <td>9.913309</td>\n",
       "      <td>-0.026945</td>\n",
       "      <td>32.826115</td>\n",
       "      <td>-0.026945</td>\n",
       "      <td>37.803139</td>\n",
       "      <td>-4.977024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.306784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1623144887127075472</td>\n",
       "      <td>1623144887129777490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5178.317383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.749424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>61.323208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.864871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time              endtime           cs1  cs2_wo_elifecorr  \\\n",
       "0  1623144887003148092  1623144887005863030      2.659672        348.436493   \n",
       "1  1623144887006147772  1623144887008854250  13155.237305               NaN   \n",
       "2  1623144887030319882  1623144887033327360  61723.960938      28599.917969   \n",
       "3  1623144887054958322  1623144887057677700  76545.257812       1266.177612   \n",
       "4  1623144887127075472  1623144887129777490           NaN       5178.317383   \n",
       "\n",
       "   cs2_wo_timecorr  cs2_area_fraction_top   cs2_bottom           cs2  \\\n",
       "0       370.887268               0.777278    82.604637    370.887268   \n",
       "1              NaN                    NaN          NaN           NaN   \n",
       "2     29029.152344               0.723225  8034.542969  29029.152344   \n",
       "3      1266.558594               0.159955  1063.966309   1266.558594   \n",
       "4              NaN               0.749424          NaN           NaN   \n",
       "\n",
       "     alt_cs1  alt_cs2_wo_elifecorr  ...  alt_s2_y_mlp          x          y  \\\n",
       "0        NaN             34.098747  ...    -23.990379  -7.353356 -15.314320   \n",
       "1   4.279365                   NaN  ...           NaN  45.517769  55.207436   \n",
       "2  19.304628           1338.243530  ...     53.898460 -35.264309  55.141556   \n",
       "3   2.062342             17.207802  ...      2.884162  31.293451   9.913309   \n",
       "4        NaN                   NaN  ...           NaN        NaN        NaN   \n",
       "\n",
       "           z          r    z_naive    r_naive  r_field_distortion_correction  \\\n",
       "0 -43.339104  16.988239 -43.339104  16.190155                       0.798083   \n",
       "1  -0.189628  71.552284  -0.189628  69.542831                       2.009451   \n",
       "2 -10.200156  65.453514 -10.200156  61.628628                       3.824886   \n",
       "3  -0.026945  32.826115  -0.026945  37.803139                      -4.977024   \n",
       "4        NaN        NaN        NaN  61.323208                            NaN   \n",
       "\n",
       "   z_field_distortion_correction     theta  \n",
       "0                       0.007348 -2.018448  \n",
       "1                       0.000000  0.881302  \n",
       "2                       0.744288  2.139771  \n",
       "3                       0.000000  0.306784  \n",
       "4                            NaN  2.864871  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a quick plot of the events we just loaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "df.plot.scatter(\"cs1\", \"cs2\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.xlim(1, None)\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since making a cS1, cS2 plot for a dataset is such a common task that straxen has a built-in method for it. There are other similar mini-analyses, such a waveform plotting, which we will see in action below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st.event_scatter(run_id, s=20, seconds_range=[0, 60])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you guess what kind of data this is?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Waveform analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *peaks* data type contains the sum waveform information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Field name</th>\n",
       "      <th>Data type</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time</td>\n",
       "      <td>int64</td>\n",
       "      <td>Start time since unix epoch [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>length</td>\n",
       "      <td>int32</td>\n",
       "      <td>Length of the interval in samples</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dt</td>\n",
       "      <td>int32</td>\n",
       "      <td>Width of one sample [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>channel</td>\n",
       "      <td>int16</td>\n",
       "      <td>Channel/PMT number</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>type</td>\n",
       "      <td>int8</td>\n",
       "      <td>Classification of the peak(let)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>area</td>\n",
       "      <td>float32</td>\n",
       "      <td>Integral across channels [PE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>area_per_channel</td>\n",
       "      <td>('&lt;f4', (494,))</td>\n",
       "      <td>Integral per channel [PE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>n_hits</td>\n",
       "      <td>int32</td>\n",
       "      <td>Number of hits contributing at least one sample to the peak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>data</td>\n",
       "      <td>('&lt;f4', (200,))</td>\n",
       "      <td>Waveform data in PE/sample (not PE/ns!)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>width</td>\n",
       "      <td>('&lt;f4', (11,))</td>\n",
       "      <td>Peak widths in range of central area fraction [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>area_decile_from_midpoint</td>\n",
       "      <td>('&lt;f4', (11,))</td>\n",
       "      <td>Peak widths: time between nth and 5th area decile [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>saturated_channel</td>\n",
       "      <td>('i1', (494,))</td>\n",
       "      <td>Does the channel reach ADC saturation?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>n_saturated_channels</td>\n",
       "      <td>int16</td>\n",
       "      <td>Total number of saturated channels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tight_coincidence</td>\n",
       "      <td>int16</td>\n",
       "      <td>Channel within tight range of mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>max_gap</td>\n",
       "      <td>int32</td>\n",
       "      <td>Largest gap between hits inside peak [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>max_goodness_of_split</td>\n",
       "      <td>float32</td>\n",
       "      <td>Maximum interior goodness of split</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Field name        Data type  \\\n",
       "0                        time            int64   \n",
       "1                      length            int32   \n",
       "2                          dt            int32   \n",
       "3                     channel            int16   \n",
       "4                        type             int8   \n",
       "5                        area          float32   \n",
       "6            area_per_channel  ('<f4', (494,))   \n",
       "7                      n_hits            int32   \n",
       "8                        data  ('<f4', (200,))   \n",
       "9                       width   ('<f4', (11,))   \n",
       "10  area_decile_from_midpoint   ('<f4', (11,))   \n",
       "11          saturated_channel   ('i1', (494,))   \n",
       "12       n_saturated_channels            int16   \n",
       "13          tight_coincidence            int16   \n",
       "14                    max_gap            int32   \n",
       "15      max_goodness_of_split          float32   \n",
       "\n",
       "                                                         Comment  \n",
       "0                               Start time since unix epoch [ns]  \n",
       "1                              Length of the interval in samples  \n",
       "2                                       Width of one sample [ns]  \n",
       "3                                             Channel/PMT number  \n",
       "4                                Classification of the peak(let)  \n",
       "5                                  Integral across channels [PE]  \n",
       "6                                      Integral per channel [PE]  \n",
       "7   Number of hits contributing at least one sample to the peak   \n",
       "8                        Waveform data in PE/sample (not PE/ns!)  \n",
       "9             Peak widths in range of central area fraction [ns]  \n",
       "10        Peak widths: time between nth and 5th area decile [ns]  \n",
       "11                        Does the channel reach ADC saturation?  \n",
       "12                            Total number of saturated channels  \n",
       "13                            Channel within tight range of mean  \n",
       "14                     Largest gap between hits inside peak [ns]  \n",
       "15                            Maximum interior goodness of split  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.data_info(\"peaks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the compound data types of the `data`, `width` and `saturated_channel` fields. Pandas does not support such types (well, it sort of does, but the resulting dataframes are quite inefficient), so we have to load this as a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9c934aa54c74cf6b8e5927d2969bf9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading peaks: |          | 0.00 % [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray,\n",
       " ('time',\n",
       "  'length',\n",
       "  'dt',\n",
       "  'channel',\n",
       "  'type',\n",
       "  'area',\n",
       "  'area_per_channel',\n",
       "  'n_hits',\n",
       "  'data',\n",
       "  'width',\n",
       "  'area_decile_from_midpoint',\n",
       "  'saturated_channel',\n",
       "  'n_saturated_channels',\n",
       "  'tight_coincidence',\n",
       "  'max_gap',\n",
       "  'max_goodness_of_split'))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks = st.get_array(run_id, \"peaks\", seconds_range=[0, 60])\n",
    "type(peaks), peaks.dtype.names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot peak waveforms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "def plot_peak(p, t0=None, **kwargs):\n",
    "    n = p[\"length\"]\n",
    "    if t0 is None:\n",
    "        t0 = p[\"time\"]\n",
    "    plt.plot(\n",
    "        (p[\"time\"] - t0) + np.arange(n) * p[\"dt\"],\n",
    "        p[\"data\"][:n] / p[\"dt\"],\n",
    "        drawstyle=\"steps-mid\",\n",
    "        **kwargs\n",
    "    )\n",
    "    plt.xlabel(\"Time (ns)\")\n",
    "    plt.ylabel(\"Sum waveform (PE / ns)\")\n",
    "\n",
    "\n",
    "plot_peak(peaks[148])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "def plot_peaks(main_i, n_before=0, n_after=0, label_threshold=0, legendloc=\"best\"):\n",
    "    for i in main_i + np.arange(-n_before, n_after + 1):\n",
    "        p = peaks[mask][i]\n",
    "        label = None\n",
    "        if p[\"area\"] > label_threshold:\n",
    "            label = \"%d PE, %d ns dt\" % (\n",
    "                p[\"area\"],\n",
    "                p[\"dt\"],\n",
    "            )\n",
    "            color = None\n",
    "        else:\n",
    "            color = \"gray\"\n",
    "        plot_peak(p, t0=peaks[main_i][\"time\"], label=label, color=color)\n",
    "    plt.ylim(0, None)\n",
    "    plt.legend(loc=legendloc)\n",
    "    plt.yscale(\"symlog\")\n",
    "\n",
    "\n",
    "# Find the largest peak close to 1e4 PE\n",
    "mask = peaks[\"area\"] < 1e4\n",
    "i_of_largest_peak = np.argmax(peaks[mask][\"area\"])\n",
    "plot_peaks(i_of_largest_peak, n_after=5, n_before=2, label_threshold=10, legendloc=(1.1, 0.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The abrupt termination of the S2 above is due to strax's data reduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have access to the raw data (at least the `records` level) you can use straxen's built-in waveform display. For example, try:\n",
    "\n",
    "```python\n",
    "st.waveform_display(run_id, seconds_range=(0, 0.15))\n",
    "```\n",
    "\n",
    "(we didn't evaluate this in the tutorial, as it creates a substantial amount of javascript, which would have made the notebook quite huge)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the above plot, we have many events high up in the TPC at low S1. Perhaps you want to get rid of them by increasing the 'S1 coincidence requirement', i.e. the number of PMTs that must see something before a peak is labeled as S1. Then, of course, you want to load the event-level data again to see if it worked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to see which configuration option we have to change. Strax plugins declare what configuration they take and what other plugins they depend on, so this is not very difficult. We just ask which options with `s1` in their name influence `event_basics`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>option</th>\n",
       "      <th>applies_to</th>\n",
       "      <th>help</th>\n",
       "      <th>current</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1_xyz_map</td>\n",
       "      <td>(corrected_areas,)</td>\n",
       "      <td></td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>itp_map://resource://cmt://format://s1_xyz_map_{algo}?version=ONLINE&amp;run_id=plugin.run_id&amp;fmt=js...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s1_risetime_area_parameters</td>\n",
       "      <td>(peaklet_classification,)</td>\n",
       "      <td>norm, const, tau in the empirical boundary in the risetime-area plot</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(50, 80, 12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s1_risetime_aft_parameters</td>\n",
       "      <td>(peaklet_classification,)</td>\n",
       "      <td>Slope and offset in exponential of emperical boundary in the rise time-AFT plot. Specified as (s...</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(-1, 2.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s1_flatten_threshold_aft</td>\n",
       "      <td>(peaklet_classification,)</td>\n",
       "      <td>Threshold for AFT, above which we use a flatted boundary for rise timeSpecified values: (AFT bou...</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(0.6, 100)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s1_max_rise_time_post100</td>\n",
       "      <td>(peaklet_classification,)</td>\n",
       "      <td>Maximum S1 rise time for &gt; 100 PE [ns]</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>s1_min_coincidence</td>\n",
       "      <td>(peaklet_classification,)</td>\n",
       "      <td>Minimum tight coincidence necessary to make an S1</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>s1_optical_map</td>\n",
       "      <td>(event_pattern_fit,)</td>\n",
       "      <td>S1 (x, y, z) optical/pattern map.</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>XENONnT_s1_xyz_patterns_LCE_corrected_qes_MCva43fa9b_wires.pkl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>s1_aft_map</td>\n",
       "      <td>(event_pattern_fit,)</td>\n",
       "      <td>Date drive S1 area fraction top map.</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>s1_aft_dd_xyz_XENONnT_Kr83m_41500eV_31Oct2021.json</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>s1_min_area_pattern_fit</td>\n",
       "      <td>(event_pattern_fit,)</td>\n",
       "      <td>Skip EventPatternFit reconstruction if S1 area (PE) is less than this</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s1_risetime_area_parameters</td>\n",
       "      <td>(peaklet_classification_he,)</td>\n",
       "      <td>norm, const, tau in the empirical boundary in the risetime-area plot</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(50, 80, 12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s1_risetime_aft_parameters</td>\n",
       "      <td>(peaklet_classification_he,)</td>\n",
       "      <td>Slope and offset in exponential of emperical boundary in the rise time-AFT plot. Specified as (s...</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(-1, 2.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s1_flatten_threshold_aft</td>\n",
       "      <td>(peaklet_classification_he,)</td>\n",
       "      <td>Threshold for AFT, above which we use a flatted boundary for rise timeSpecified values: (AFT bou...</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>(0.6, 100)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>s1_max_rise_time_post100</td>\n",
       "      <td>(peaklet_classification_he,)</td>\n",
       "      <td>Maximum S1 rise time for &gt; 100 PE [ns]</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s1_min_coincidence</td>\n",
       "      <td>(peaklet_classification_he,)</td>\n",
       "      <td>Minimum tight coincidence necessary to make an S1</td>\n",
       "      <td>&lt;OMITTED&gt;</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         option                    applies_to  \\\n",
       "0                    s1_xyz_map            (corrected_areas,)   \n",
       "1   s1_risetime_area_parameters     (peaklet_classification,)   \n",
       "2    s1_risetime_aft_parameters     (peaklet_classification,)   \n",
       "3      s1_flatten_threshold_aft     (peaklet_classification,)   \n",
       "4      s1_max_rise_time_post100     (peaklet_classification,)   \n",
       "5            s1_min_coincidence     (peaklet_classification,)   \n",
       "6                s1_optical_map          (event_pattern_fit,)   \n",
       "7                    s1_aft_map          (event_pattern_fit,)   \n",
       "8       s1_min_area_pattern_fit          (event_pattern_fit,)   \n",
       "9   s1_risetime_area_parameters  (peaklet_classification_he,)   \n",
       "10   s1_risetime_aft_parameters  (peaklet_classification_he,)   \n",
       "11     s1_flatten_threshold_aft  (peaklet_classification_he,)   \n",
       "12     s1_max_rise_time_post100  (peaklet_classification_he,)   \n",
       "13           s1_min_coincidence  (peaklet_classification_he,)   \n",
       "\n",
       "                                                                                                   help  \\\n",
       "0                                                                                                         \n",
       "1                                  norm, const, tau in the empirical boundary in the risetime-area plot   \n",
       "2   Slope and offset in exponential of emperical boundary in the rise time-AFT plot. Specified as (s...   \n",
       "3   Threshold for AFT, above which we use a flatted boundary for rise timeSpecified values: (AFT bou...   \n",
       "4                                                                Maximum S1 rise time for > 100 PE [ns]   \n",
       "5                                                     Minimum tight coincidence necessary to make an S1   \n",
       "6                                                                     S1 (x, y, z) optical/pattern map.   \n",
       "7                                                                  Date drive S1 area fraction top map.   \n",
       "8                                 Skip EventPatternFit reconstruction if S1 area (PE) is less than this   \n",
       "9                                  norm, const, tau in the empirical boundary in the risetime-area plot   \n",
       "10  Slope and offset in exponential of emperical boundary in the rise time-AFT plot. Specified as (s...   \n",
       "11  Threshold for AFT, above which we use a flatted boundary for rise timeSpecified values: (AFT bou...   \n",
       "12                                                               Maximum S1 rise time for > 100 PE [ns]   \n",
       "13                                                    Minimum tight coincidence necessary to make an S1   \n",
       "\n",
       "      current  \\\n",
       "0   <OMITTED>   \n",
       "1   <OMITTED>   \n",
       "2   <OMITTED>   \n",
       "3   <OMITTED>   \n",
       "4   <OMITTED>   \n",
       "5   <OMITTED>   \n",
       "6   <OMITTED>   \n",
       "7   <OMITTED>   \n",
       "8   <OMITTED>   \n",
       "9   <OMITTED>   \n",
       "10  <OMITTED>   \n",
       "11  <OMITTED>   \n",
       "12  <OMITTED>   \n",
       "13  <OMITTED>   \n",
       "\n",
       "                                                                                                default  \n",
       "0   itp_map://resource://cmt://format://s1_xyz_map_{algo}?version=ONLINE&run_id=plugin.run_id&fmt=js...  \n",
       "1                                                                                          (50, 80, 12)  \n",
       "2                                                                                             (-1, 2.6)  \n",
       "3                                                                                            (0.6, 100)  \n",
       "4                                                                                                   200  \n",
       "5                                                                                                     2  \n",
       "6                                        XENONnT_s1_xyz_patterns_LCE_corrected_qes_MCva43fa9b_wires.pkl  \n",
       "7                                                    s1_aft_dd_xyz_XENONnT_Kr83m_41500eV_31Oct2021.json  \n",
       "8                                                                                                     2  \n",
       "9                                                                                          (50, 80, 12)  \n",
       "10                                                                                            (-1, 2.6)  \n",
       "11                                                                                           (0.6, 100)  \n",
       "12                                                                                                  200  \n",
       "13                                                                                                    2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.show_config(\"event_basics\", \"s1*\")[[\"option\", \"applies_to\", \"help\", \"current\", \"default\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we're after the `s1_min_coincidence option`. Note this is not part of the `event_basics` data type, but of a data type called `peak_classification`. As you can see from the table, this option is not set in the current context, so the default value (3) is used.\n",
    "\n",
    "To try out a different option, just pass it to get_df:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use a short run for the following exaples\n",
    "run_id = \"038769\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c7b5d64b41b422998d39903f8f66af8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading event_info: |          | 0.00 % [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "st2 = st.new_context()\n",
    "st2.set_config(dict(s1_min_coincidence=50))\n",
    "df_2 = st2.get_df(\n",
    "    run_id,\n",
    "    \"event_info\",\n",
    "    config=dict(s1_min_coincidence=50),\n",
    ")\n",
    "st2.event_scatter(run_id, events=df_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice all the small S1 events are indeed gone now.\n",
    "\n",
    "Behind the scenes, this figured out which dataframes had to be remade: as it happens this time just `event_basics` and `peak_basics`. You will now have a new `event_basics_<somehash>` folder in `./custom_data` which contains the results, as well as a new `peak_basics_<somehash> folder`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More on configuration changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing configuration can be done in two other ways. We can change it permanently in the current context:\n",
    "```python\n",
    "st.set_config(dict(s1_min_coincidence=50))\n",
    "```\n",
    "Or we could make a new context, with this option set:\n",
    "```python\n",
    "st_2 = st.new_context(config=dict(s1_min_coincidence=50))\n",
    "```\n",
    "(feeding it to get_df just does the latter behind the scenes).\n",
    "\n",
    "If you just want to run a mini-analysis (like `event_scatter`), you can also pass a new `config` option directly to it, as in the example below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strax protects you from typos in the configuration. Suppose we typed `s1_min_n_channelz` instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Option s1_min_n_channelz not taken by any registered plugin\n",
      "Option s1_min_n_channelz not taken by any registered plugin\n"
     ]
    },
    {
     "ename": "DataNotAvailable",
     "evalue": "Time range selection assumes data is already available, but event_info for 038769 is not.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mDataNotAvailable\u001b[0m                          Traceback (most recent call last)",
      "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevent_scatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms1_min_n_channelz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseconds_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/software/dev_strax/straxen/straxen/mini_analysis.py:106\u001b[0m, in \u001b[0;36mmini_analysis.<locals>.decorator.<locals>.wrapped_f\u001b[0;34m(context, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m         kwargs[dkind] \u001b[38;5;241m=\u001b[39m strax\u001b[38;5;241m.\u001b[39mapply_selection(\n\u001b[1;32m    101\u001b[0m             kwargs[dkind],\n\u001b[1;32m    102\u001b[0m             selection_str\u001b[38;5;241m=\u001b[39mkwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mselection_str\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m    103\u001b[0m             time_range\u001b[38;5;241m=\u001b[39mkwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime_range\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m    104\u001b[0m             time_selection\u001b[38;5;241m=\u001b[39mkwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime_selection\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 106\u001b[0m         kwargs[dkind] \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    107\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    108\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdtypes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    109\u001b[0m \u001b[43m            \u001b[49m\u001b[43mselection_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mselection_str\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    110\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtime_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime_range\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    111\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtime_selection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime_selection\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    112\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# Arguments for new context, if needed\u001b[39;49;00m\n\u001b[1;32m    113\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconfig\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[43m            \u001b[49m\u001b[43mregister\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mregister\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstorage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstorage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    116\u001b[0m \u001b[43m            \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;66;03m# If user did not give time kwargs, but the function expects\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;66;03m# a time_range, try to add one based on the time range of the data\u001b[39;00m\n\u001b[1;32m    121\u001b[0m base_dkind \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(deps_by_kind\u001b[38;5;241m.\u001b[39mkeys())[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m~/software/dev_strax/strax/strax/context.py:1406\u001b[0m, in \u001b[0;36mContext.get_array\u001b[0;34m(self, run_id, targets, save, max_workers, **kwargs)\u001b[0m\n\u001b[1;32m   1399\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1400\u001b[0m     source \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_iter(\n\u001b[1;32m   1401\u001b[0m         run_ids[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m   1402\u001b[0m         targets,\n\u001b[1;32m   1403\u001b[0m         save\u001b[38;5;241m=\u001b[39msave,\n\u001b[1;32m   1404\u001b[0m         max_workers\u001b[38;5;241m=\u001b[39mmax_workers,\n\u001b[1;32m   1405\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1406\u001b[0m     results \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m source]\n\u001b[1;32m   1408\u001b[0m results \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(results)\n\u001b[1;32m   1409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
      "File \u001b[0;32m~/software/dev_strax/strax/strax/context.py:1406\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1399\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1400\u001b[0m     source \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_iter(\n\u001b[1;32m   1401\u001b[0m         run_ids[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m   1402\u001b[0m         targets,\n\u001b[1;32m   1403\u001b[0m         save\u001b[38;5;241m=\u001b[39msave,\n\u001b[1;32m   1404\u001b[0m         max_workers\u001b[38;5;241m=\u001b[39mmax_workers,\n\u001b[1;32m   1405\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1406\u001b[0m     results \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m source]\n\u001b[1;32m   1408\u001b[0m results \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(results)\n\u001b[1;32m   1409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
      "File \u001b[0;32m~/software/dev_strax/strax/strax/context.py:1234\u001b[0m, in \u001b[0;36mContext.get_iter\u001b[0;34m(self, run_id, targets, save, max_workers, time_range, seconds_range, time_within, time_selection, selection_str, keep_columns, drop_columns, allow_multiple, progress_bar, _chunk_number, **kwargs)\u001b[0m\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext_config[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m7200\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   1226\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext_config[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallow_lazy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   1227\u001b[0m             \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext_config[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallow_multiprocess\u001b[39m\u001b[38;5;124m'\u001b[39m])):\n\u001b[1;32m   1228\u001b[0m         \u001b[38;5;66;03m# For allow_multiple we don't want allow this when in lazy mode\u001b[39;00m\n\u001b[1;32m   1229\u001b[0m         \u001b[38;5;66;03m# with long timeouts (lazy-mode is disabled if multiprocessing\u001b[39;00m\n\u001b[1;32m   1230\u001b[0m         \u001b[38;5;66;03m# so if that is activated, we can also continue)\u001b[39;00m\n\u001b[1;32m   1231\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCannot allow_multiple in lazy mode or \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1232\u001b[0m                            \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwith long timeouts.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1234\u001b[0m components \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1235\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mtargets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1236\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43msave\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1237\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mtime_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1238\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mchunk_number\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_chunk_number\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1240\u001b[0m \u001b[38;5;66;03m# Cleanup the temp plugins\u001b[39;00m\n\u001b[1;32m   1241\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_plugin_class_registry\u001b[38;5;241m.\u001b[39mkeys()):\n",
      "File \u001b[0;32m~/software/dev_strax/strax/strax/context.py:979\u001b[0m, in \u001b[0;36mContext.get_components\u001b[0;34m(self, run_id, targets, save, time_range, chunk_number)\u001b[0m\n\u001b[1;32m    975\u001b[0m             savers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_saver(savers, d_to_save, target_plugin,\n\u001b[1;32m    976\u001b[0m                                      _is_superrun, loading_this_data)\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m target_i \u001b[38;5;129;01min\u001b[39;00m targets:\n\u001b[0;32m--> 979\u001b[0m     \u001b[43mcheck_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_i\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    980\u001b[0m plugins \u001b[38;5;241m=\u001b[39m to_compute\n\u001b[1;32m    982\u001b[0m intersec \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(plugins\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;241m&\u001b[39m loaders\u001b[38;5;241m.\u001b[39mkeys())\n",
      "File \u001b[0;32m~/software/dev_strax/strax/strax/context.py:894\u001b[0m, in \u001b[0;36mContext.get_components.<locals>.check_cache\u001b[0;34m(target_i)\u001b[0m\n\u001b[1;32m    891\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m target_plugin\u001b[38;5;241m.\u001b[39msave_when[target_i] \u001b[38;5;241m==\u001b[39m strax\u001b[38;5;241m.\u001b[39mSaveWhen\u001b[38;5;241m.\u001b[39mTARGET:\n\u001b[1;32m    892\u001b[0m         error_message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mFirst run st.make(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrun_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    893\u001b[0m                           \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget_i\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) to make \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget_i\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 894\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m strax\u001b[38;5;241m.\u001b[39mDataNotAvailable(error_message)\n\u001b[1;32m    895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext_config[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mforbid_creation_of\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m    896\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m strax\u001b[38;5;241m.\u001b[39mDataNotAvailable(\n\u001b[1;32m    897\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget_i\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrun_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in any storage, and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    898\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour context specifies no new data can be created.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mDataNotAvailable\u001b[0m: Time range selection assumes data is already available, but event_info for 038769 is not."
     ]
    }
   ],
   "source": [
    "st.event_scatter(run_id, config=dict(s1_min_n_channelz=10), seconds_range=[0, 60])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of get_df is just the same as if the option wasn't set (just like in pax/hax), but you also get a warning about an unknown configuration option. \n",
    "\n",
    "By the way, you can use \n",
    "```python\n",
    "import warnings\n",
    "warnings.filterwarnings(\"error\")\n",
    "```\n",
    "to ensure any warning raises an exception instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customization: new plugins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add or change processing algorithms, or to define new variables to use in cuts, you have to write new strax plugins. These are somewhat similar to hax's treemakers.\n",
    "\n",
    "Suppose you have a brilliant new idea for peak classification. Strax does this in the peaklet_classification plugin, which produces:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Field name</th>\n",
       "      <th>Data type</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time</td>\n",
       "      <td>int64</td>\n",
       "      <td>Start time since unix epoch [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>length</td>\n",
       "      <td>int32</td>\n",
       "      <td>Length of the interval in samples</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dt</td>\n",
       "      <td>int32</td>\n",
       "      <td>Width of one sample [ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>channel</td>\n",
       "      <td>int16</td>\n",
       "      <td>Channel/PMT number</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>type</td>\n",
       "      <td>int8</td>\n",
       "      <td>Classification of the peak(let)</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  Field name Data type                            Comment\n",
       "0       time     int64   Start time since unix epoch [ns]\n",
       "1     length     int32  Length of the interval in samples\n",
       "2         dt     int32           Width of one sample [ns]\n",
       "3    channel     int16                 Channel/PMT number\n",
       "4       type      int8    Classification of the peak(let)"
      ]
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.data_info(\"peaklet_classification\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * The first three fields contain time information of the peak. This is duplicated in many datatypes -- unfortunately, this is necessary for strax to be able to track the data and combine it with other data. Instead of (time, length, dt), plugins could also provide (time, endtime). See [here](https://strax.readthedocs.io/en/latest/advanced/plugin_dev.html#special-time-fields) for more information.\n",
    "  * The 'channel' field is an historical artifact.\n",
    "  * The 'type' field contains the classification: 0 for unknown, 1 for S1, 2 for S2. (note [#8](https://github.com/XENONnT/straxen/issues/8))\n",
    "  \n",
    "You can find the original plugin in [peaklet_processing.py](https://github.com/XENONnT/straxen/blob/master/straxen/plugins/peaklet_processing.py.) Here's how you would make a different classification plugin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import strax\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class AdvancedExpertClassification(strax.Plugin):\n",
    "    \"\"\"Everything is an S1!\"\"\"\n",
    "\n",
    "    # Name of the data type this plugin provides\n",
    "    provides = \"peaklet_classification\"\n",
    "\n",
    "    # Data types this plugin requires. Note we don't specify\n",
    "    # what plugins should produce them: maybe the default PeakBasics\n",
    "    # has been replaced by another AdvancedExpertBlabla plugin?\n",
    "    depends_on = (\"peaklets\",)\n",
    "\n",
    "    # Numpy datatype of the output\n",
    "    dtype = straxen.PeakletClassification.dtype\n",
    "\n",
    "    # Version of the plugin. Increment this if you change the algorithm.\n",
    "    __version__ = \"0.0.2\"\n",
    "\n",
    "    def compute(self, peaklets):\n",
    "        # Your code here.\n",
    "        # This function will be called several times with\n",
    "        # 'peaks' a numpy array of the datatype 'peaks'.\n",
    "        # Each time you'll see a small part of the run.\n",
    "\n",
    "        # You have to return a numpy array of the dtype you declared above\n",
    "        result = np.zeros(len(peaklets), self.dtype)\n",
    "\n",
    "        # Copy the basic time fields over from peaklets\n",
    "        for (_, field), _ in strax.time_dt_fields:\n",
    "            result[field] = peaklets[field]\n",
    "\n",
    "        # Store the classification results\n",
    "        # You might want to do real work here\n",
    "        result[\"type\"] = 1\n",
    "\n",
    "        return result\n",
    "\n",
    "        # Instead of an array, you are also allowed to return a dictionary\n",
    "        # we can transform into an array.\n",
    "        # That is, (dict keys -> field names, values -> field values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use it in place of PeakClassification, we only have to register it. Again, we can do so permanently using \n",
    "```python\n",
    "st.register(AdvancedExpertClassification)\n",
    "```\n",
    "or temporarily, by feeding the registration as an extra argument to `get_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7289983bcb54401aca75e151137b30f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading event_info: |          | 0.00 % [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found chunk without events?!\n",
      "Found chunk without events?!\n",
      "Found chunk without events?!\n"
     ]
    }
   ],
   "source": [
    "df = st.get_df(\n",
    "    run_id,\n",
    "    \"event_info\",\n",
    "    register=AdvancedExpertClassification,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"s2_area\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, all events are now S1-only events, as expected. Maybe this is not the best alternative classification :-)\n",
    "\n",
    "This plugin was a rather basic plugin. You'll also want to learn about `LoopPlugin`s and `OverlapWindowPlugin`s, but that's beyond the scope of this tutorial."
   ]
  }
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