{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opening data to do analyses\n",
    "\n",
    "06-01-2020\n",
    "\n",
    "updated 25-02-2022\n",
    "\n",
    "j.angevaare@nikhef.nl\n",
    "\n",
    "\n",
    "To this end we will play with one of our most recent 1T datasets (run), load it, make some selections and plots. We will only be looking at two parameters but the data is rich and can be explored in many more ways.\n",
    "\n",
    "## Goal & setup\n",
    "We want to use straxen to open some data, to this end we show how to make a basic selection and plot some waveforms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cutax is not installed\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "\n",
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       "<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/...</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/str...</td>\n",
       "      <td>branch:master | 024602e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    module version                                               path  \\\n",
       "0   python  3.10.0  /home/angevaare/miniconda3/envs/dev_strax/bin/...   \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/str...   \n",
       "\n",
       "                       git  \n",
       "0                     None  \n",
       "1  branch:master | db14f80  \n",
       "2  branch:master | 024602e  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import socket\n",
    "import strax\n",
    "import straxen\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "import datetime\n",
    "\n",
    "straxen.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup straxen\n",
    "We just have to use the `xenonnt_online` context for nT or the one below for 1T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = straxen.contexts.xenonnt_online()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-availability\n",
    "## Using the context to check what is available.\n",
    "This loads the RunsDB, looks at the runs that are considered in the context and checks if they are available.\n",
    "\n",
    "NB: I'm going to slightly alter the config here to check for multiple datatypes. This takes considereably longer so you can sit back for a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "# This config change will take time\n",
    "st.set_context_config({\"check_available\": (\"raw_records\", \"records\", \"peaklets\", \"peak_basics\")})\n",
    "st.select_runs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `select_runs`\n",
    "This means we can make selections on what kind of runs are available.\n",
    "E.g. I want good runs where we have peaklets available:\n",
    "The second time we run this, it will be much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = st.select_runs(available=(\"raw_records\", \"peak_basics\"), exclude_tags=(\"bad\", \"messy\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For nT one can also select a run from the website:\n",
    "https://xenon1t-daq.lngs.infn.it/runsui\n",
    "\n",
    "we're just going to take the shortest run available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                            019942\n",
       "start       2021-05-21 08:23:38.908000\n",
       "livetime        0 days 00:07:47.569000\n",
       "Name: 19680, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[np.argmin(df[\"livetime\"])][[\"name\", \"start\", \"livetime\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The name of the run\n",
    "run_id = \"021932\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative method.\n",
    "If you know what to look for you can use this command to double check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.is_stored(run_id, \"peak_basics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets load the peaks of this run and have a look. We are going to look at peak_basics \n",
    "(loading e.g. peaks) is much more data, making everything slow. You can check the size before loading using the command below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: raw_records \t is 333275 MB\n",
      "Target: peaklets \t is 85069 MB\n",
      "Target: peak_basics \t is 1087 MB\n"
     ]
    }
   ],
   "source": [
    "for p in (\"raw_records\", \"records\", \"peaklets\", \"peak_basics\", \"pulse_counts\", \"event_basics\"):\n",
    "    if st.is_stored(run_id, p):\n",
    "        print(f\"Target: {p} \\t is {st.size_mb(run_id, p):.0f} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "### NB: Don't crash your notebook!\n",
    "The prints above are why you only want to load small portions of data. Especially for 'low-level' data. If you try to load all of records or even peaklets, your notebook crashes.\n",
    "\n",
    "We are going to load only 100 seconds of data as that will be enough to see some populations. Later we can load more if we want.\n",
    "\n",
    "My favorite reduction method id just loading one or a few seconds, like below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "peaks = st.get_array(\n",
    "    run_id,\n",
    "    targets=\"peak_basics\",\n",
    "    seconds_range=(0, 10),  # Only first 10 seconds\n",
    "    progress_bar=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We loaded 7.0 MB of peaks-data\n"
     ]
    }
   ],
   "source": [
    "# Let's see how much we have loaded:\n",
    "print(f\"We loaded {peaks.nbytes/1e6:.1f} MB of peaks-data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>4.566041</td>\n",
       "      <td>0</td>\n",
       "      <td>283.604370</td>\n",
       "      <td>726.881470</td>\n",
       "      <td>0.790140</td>\n",
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       "      <td>10</td>\n",
       "      <td>307.613495</td>\n",
       "      <td>3</td>\n",
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       "      <td>434.371857</td>\n",
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       "      <td>196.397079</td>\n",
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       "      <td>10</td>\n",
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       "      <td>521.367737</td>\n",
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       "      <td>64</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
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      "text/plain": [
       "                  time              endtime          center_time       area  \\\n",
       "0  1623144887001319160  1623144887001319840  1623144887001319412   2.608931   \n",
       "1  1623144887001344050  1623144887001345250  1623144887001344516  35.741817   \n",
       "2  1623144887001440090  1623144887001440590  1623144887001440313   2.029903   \n",
       "3  1623144887001592260  1623144887001593500  1623144887001592788  26.209696   \n",
       "4  1623144887001621500  1623144887001622460  1623144887001621945   2.298434   \n",
       "5  1623144887001672590  1623144887001673270  1623144887001672850   2.681920   \n",
       "6  1623144887001717510  1623144887001717810  1623144887001717601   1.903725   \n",
       "7  1623144887001733790  1623144887001734220  1623144887001733923   1.387927   \n",
       "8  1623144887004867870  1623144887004868510  1623144887004868140   2.541787   \n",
       "9  1623144887004916780  1623144887004917610  1623144887004917085   2.374870   \n",
       "\n",
       "   n_channels  max_pmt  max_pmt_area  n_saturated_channels  range_50p_area  \\\n",
       "0           2      271      1.452120                     0      422.341400   \n",
       "1          16      119      9.631803                     0      278.976562   \n",
       "2           2      369      1.308512                     0      236.908005   \n",
       "3          19      152      4.566041                     0      283.604370   \n",
       "4           2      332      1.203942                     0      702.893799   \n",
       "5           2      314      1.480481                     0      434.371857   \n",
       "6           2       16      1.474823                     0       34.871223   \n",
       "7           2      357      0.910686                     0      187.775513   \n",
       "8           2      231      1.311875                     0      390.297974   \n",
       "9           2      465      1.452399                     0      554.643555   \n",
       "\n",
       "   range_90p_area  area_fraction_top  length  dt   rise_time  \\\n",
       "0      560.615479           0.443404      68  10  137.940628   \n",
       "1      685.755554           0.815509     120  10  276.803223   \n",
       "2      440.395020           0.000000      50  10  233.996887   \n",
       "3      726.881470           0.790140     124  10  307.613495   \n",
       "4      884.098877           0.476190      96  10  695.808716   \n",
       "5      598.363342           0.447977      68  10   66.415497   \n",
       "6      196.397079           0.774704      30  10   49.543037   \n",
       "7      321.371155           0.000000      43  10   57.672348   \n",
       "8      521.367737           0.516123      64  10  387.107025   \n",
       "9      670.871643           0.388430      83  10  138.770844   \n",
       "\n",
       "   tight_coincidence  tight_coincidence_channel  type  \n",
       "0                  1                          0     0  \n",
       "1                  6                          0     2  \n",
       "2                  1                          0     0  \n",
       "3                  3                          0     2  \n",
       "4                  1                          0     0  \n",
       "5                  1                          0     0  \n",
       "6                  2                          0     1  \n",
       "7                  1                          0     0  \n",
       "8                  1                          0     0  \n",
       "9                  1                          0     0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also use get_df that will give you a pandas.DataFrame. For example:\n",
    "st.get_df(\n",
    "    run_id,\n",
    "    targets=\"peak_basics\",\n",
    "    seconds_range=(0, 0.01),  # Only first 10 ms because I'm going to use the array from above\n",
    "    progress_bar=False,\n",
    ").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make plots of this one run\n",
    "\n",
    "These are my helper functions:\n",
    " - I want to make an area vs width plot to check which population to select\n",
    " - I want to be able to check some waveforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My function to make a 2D histogram\n",
    "def plots_area_vs_width(data, log=None, **kwargs):\n",
    "    \"\"\"basic wrapper to plot area vs width\"\"\"\n",
    "    # Things to put on the axes\n",
    "    x, y = data[\"area\"], data[\"range_50p_area\"]\n",
    "\n",
    "    # Make a log-plot manually\n",
    "    if log:\n",
    "        x, y = np.log10(x), np.log10(y)\n",
    "    plt.hist2d(x, y, norm=LogNorm(), **kwargs)\n",
    "    plt.ylabel(f\"Width [ns]\")\n",
    "    plt.xlabel(f\"Area [PE]\")\n",
    "\n",
    "    # Change the ticks slightly for nicer formats\n",
    "    if log:\n",
    "        xt = np.arange(int(plt.xticks()[0].min()), int(plt.xticks()[0].max()))\n",
    "        plt.xticks(xt, 10**xt)\n",
    "        yt = np.arange(int(plt.yticks()[0].min()), int(plt.yticks()[0].max()))\n",
    "        plt.yticks(yt, 10**yt)\n",
    "    plt.colorbar(label=\"counts/bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My function to plot some waveforms\n",
    "def plot_some_peaks(peaks, max_plots=3, randomize=False):\n",
    "    \"\"\"Plot the first peaks in the peaks collection (max number of 5 by default)\"\"\"\n",
    "    if randomize:\n",
    "        # This randomly takes max_plots in the range (0, len(peaks)). Plot these indices\n",
    "        indices = np.random.randint(0, len(peaks), max_plots)\n",
    "    else:\n",
    "        # Just take the first max_plots peaks in the data\n",
    "        indices = range(max_plots)\n",
    "    for i in indices:\n",
    "        p = peaks[i]\n",
    "        start, stop = p[\"time\"], p[\"endtime\"]\n",
    "        st.plot_peaks(run_id, time_range=(start, stop))\n",
    "        plt.title(\n",
    "            f'S{p[\"type\"]} of ~{int(p[\"area\"])} PE (width {int(p[\"range_50p_area\"])} ns)\\n'\n",
    "            f'detected by {p[\"n_channels\"]} PMTs at:\\n'\n",
    "            f\"{datetime.datetime.fromtimestamp(start/1e9).isoformat()}\"\n",
    "        )  # should in s not ns hence /1e9\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting waveforms\n",
    "This may help you if you want to look at specific waveforms. Below I'm just going to show that it works. As you can see, there are a lot of junk waveforms! We are going to do some cuts to select 'nice' ones later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oe5DNzMzMzHJcIJuZmZmZ5bhANrNSkTRI0rmSHpP0YloRbe/c/vdKul/Sy5L+3DWncNr3P5IeSufdL+nwmrbPkvSApNckHdlELNtJmpbea5qk7Wr27yDpRkkvSXpG0nEN2pKk0yQ9lx6n5RczkbSHpNslzZf0iKSj0/avpvZfkrRQ0pLc6+npmNHps3g5XfeedWL4k6RQtrR1vTj/LGlOiuOu3DzNtcedl9ratOGHmB27WYr9oty2/HW9JGlBykvhinc9XaOk/5T0dIr7PEmDcvu+JenvkhZLOqnmvPekffNSXq6RNLLg/VdPn0vh1HSSTkyfR+Fnb2bV4gLZzMpmINlcw+8GhgFfBy5PBdKaZHMaf4Ns1bipwC9z5/4T2CeddwRwuqTdcvvvAj4D3N5TEGkhiV+TTZM2gmxO4V93LTCRYvkd8DOyFe82BX7foMmjgf2AtwLbpjj/LbW1MtmCHD9LsR8E/J+kt0bEdyJiSEQMAY4Bbul6HRFbp7YvBe5IcXwNuFLSWjXXcxhQuDRzjeOAdSNiaIr5Iknr1rT1DmCTJtrqcgbZoiX/kr+udG2nAZMbzK9c9xolfYBsfub3ki3nvTFwcu7cGcCXgN8WtHsv8IG04t56wEPATwuOO41s/uVuJG0CHEg2f7WZdQAXyGZWKhHxz4g4KSJmRsRrEfEb4FGyhTDGAdMj4oqIWAicBLxV0hbp3G9GxP3pvNuAvwC75to+IyL+BCxsIpTdyYr1H0TEooj4IdlyxXuk/Z8Hro+Ii9P+FyOisIBKjgD+NyJmpaWl/5fXFyhZHRhKtqR1RMQUsmJsq56ClLQ5sAPwzYhYEBFXAX8H9s8dM4xsQZEv9dReRNwdEYu7XpIV1fklrQcCPwI+21Nb6fiDyWbU+FODY0S2bPT5dfb3dI1HAOdGxPSIeJ5sVcEjc9d0fkRcB7xYcL3PRMSTuU1LyH7Zyb//bmSr8f28ziWcAZwAvFLvGs2sWlwgm1mpSVob2Jxs9bOtyXqBgayYBh5O22vPWxV4WzqvL7YG7q6ZQ/ju3HvtAsyVdLOkf0i6VtIGPbR3V+71XV1tRcQzZD2kn1C2nPOuZD2hzaw0tzXwSETki79/tZ18h6xX9OnakyX9RtKXC7YtBG4DJpP11Hf5T+DGiLi7oK2fSPpJ7vVQsiWzP9/DNbwTeBNwVZ39PV1j0We7tqQ1enjfrjg3kDSPbAXB44Hv5vYNIFt18ViyXxhqzz0QWBQRE5t5LzOrhrrj0MzM2i0NPbgYOD8i7pc0hGy557wXgNUKTj+TrFC6vo9vPyS1Xe+9RpH1ar6PrDfzu2RF7tubbO8FYIgkpSL8UuAcsuWoAf49LXPd1zhHAkgak2I6LsW8lIj4UNG29NnvCWwZEa+lttYnGxayY1EgEfGZmk3fIuvZnZUbbl3kCODKiHipzv6G11iwv+v5asBzjd44xf04MFzS6mTLkN+f2/054LaImCbpLfnzJK1G9svH+3p6DzOrFvcgm1kpSVoJuJDsa+tj0+aXyIYi5A2l5qtzSd8j+0r8o82uIldzw9gGTbzXAuCaiJiShnucDOwmaVjNDWhn1ol9KPBSREQaInIZ2TCDVch6RL8k6YNNhF43zvQZ/gQ4LjdsoikR8WoalvB+SR9Om38AnBIRtcVqN8puaNwT+H4Px72BbPxu4fCKpKdcFH22UDCkopGImMvrY80HSlqPrED+Wp1TTiIbFjOzN+9jZuXnAtnMSieNST0XWBvYPyJeTbumk93k1nXcG8luFpue23YysDfw/oiY3+x75m8YSz2K04FttXTX57a597qbpb9y/9fzmhvQjimKPT3vamsb4MGIuD6Nn36A7IayvenZdGDj1JtZ2/ZQYAzwS0lP8/qNcrMkvbOJtiH7prHrhrz3At9Ls0V0Dde4RdKhBeftDowGHk/HHg/sL6n2BsmPAHPJhnLU0+gau/bXfrbPRESPvccFBpIN9xgK7ES2GuG96RpOB3ZK1z+A7PP4XO7zWJ/shtIT+vC+ZlYiLpDNrIx+CmwJ7BMRC3LbrwG2kbS/pMHAiWTjhO8HkPQV4FBgz6LiSNIq6TwBK0sanHpZi0wmu2Hrc8qmnuvqxZ6U/vw58BFlU8GtTDazxk0NelcvAD4vaWTqmfwC8Iu07w5gM2VTvSnNivAhsiK8oYh4ELgT+Ga6no+QFfJXkQ01WA/YLj3GptN2JBtfvBRJW0jaW9KqklaW9DHgXcAN6ZDNyYrPrvYgm43jmoLQziIrrLuOPZOs6P9AzXFHABc06unv4Roh+2yPkrSVpOFkM5/8InddK6e8rwQMTG0MSPvGSXqzpJXSrBj/B9yRepOvIyvyu67hRLJcbRcRS8gK5G1y+58kG4JyRr1rMbOKiAg//PDDj9I8yG5OC7KZJl7KPQ5L+/ckGyO6gKyIHZ07N4BFNed9Nbd/cjom/9i9QSzbA9PSe90ObF+z/9+B2cDzwLXA+g3aEtk45bnp8V1Auf0fBe4hGxYwi2xasZVq2jiSrAivbXt0urYFwANkvyAUxTA6XfPA3Lbruj4jsl9KbksxzCPrcf5Ig2sKYNPc6zOBM+scexJwUc22kcDifBv12urpGsluBHwGmE/2y8ug3L5fFOT9yLTvs2SzpPyT7CbGy4AN61xD4eef2z+z3mfvhx9+VOuhiKaG55mZmZmZ9QseYmFmZmZmluMC2czMzMwsxwWymZmZmVmOC2QzMzMzs5x+sZLemmuuGaNHj253GGa2AixcuJDBgwe3OwwzM6uAadOmPRsRa9Vu7xcF8ujRo5k6dWq7wzAzMzOzEpH0WNF2D7Ews44ybdq0dodgZmYV5wLZzDqKC2QzM1tWLpDNzMzMzHJcIJuZmZmZ5bhANrOOMm7cuHaHYGZmFecC2czMzMwsp19M8/bCCy9w7bXXtjsMM2uRg648iAWvLshezAA2hTeu/EYuPeDStsZlZmbV1C8K5GHDhrHPPvu0Owwza5EFty9g6qezuc6vvuhqxn1sHGPOHuO/92Zm1iceYmFmZmZmluMC2cw6ypbbbtnuEMzMrOJcIJtZR3GBbGZmy8oFspl1lIlXTWx3CGZmVnEukM2soyxcsLDdIZiZWcW5QDYzMzMzy3GBbGYdZfjqw9sdgpmZVZwLZDPrKHuM3aPdIZiZWcW5QDazjnL7rbe3OwQzM6u4lhbIkvaS9ICkGZK+XLD/XZJul7RY0gE1+5ZIujM9JuS2byTpttTmLyWt0sprMLNqmTljZrtDMDOzimtZgSxpAHAGsDewFXCIpK1qDnscOBK4pKCJBRGxXXp8OLf9NOD7EbEp8Dxw1HIP3szMzMz6rVb2IO8EzIiIRyLiFeAyYN/8ARExMyLuBl5rpkFJAvYArkybzgf2W24Rm5mZmVm/18oCeSTwRO71rLStWYMlTZV0q6T90rY1gHkRsbinNiUdnc6fOmfOnF6GbmZVNXb/se0OwczMKq7MN+ltGBFjgEOBH0japDcnR8RZETEmIsastdZarYnQzErn+eeeb3cIZmZWca0skGcD6+dej0rbmhIRs9OfjwCTge2B54Dhkgb2pU0z63y3TL6l3SGYmVnFtbJAngJslmadWAU4GJjQwzkASBohaVB6vibwduDeiAjgz0DXjBdHAL9e7pGbmZmZWb/VsgI5jRM+FrgeuA+4PCKmSzpF0ocBJL1N0izgQOBnkqan07cEpkq6i6wgPjUi7k37TgA+L2kG2Zjkc1t1DWZmZmbW/wzs+ZC+i4iJwMSabSfmnk8hGyZRe97NwFvqtPkI2QwZZmbdbL/z9gAMHTQUnayl9o0YPIK5J8xtR1hmZlYhLS2QzcxWtI022wiASYdP6rZvzNljVnQ4ZmZWQWWexcLMrNeuvujqdodgZmYV5wLZzMzMzCzHBbKZmZmZWY4LZDPrKOuMXKfdIZiZWcW5QDazjrLbe3ZrdwhmZlZxLpDNrKPc/Oeb2x2CmZlVnAtkM+soT89+ut0hmJlZxblANjMzMzPLcYFsZmZmZpbjAtnMOsq4j41rdwhmZlZxLpDNrKM8+tCj7Q7BzMwqzgWymXWUO267o90hmJlZxblANjMzMzPLaWmBLGkvSQ9ImiHpywX73yXpdkmLJR2Q276dpFskTZd0t6SDcvt+IelRSXemx3atvAYzMzMz618GtqphSQOAM4D3AbOAKZImRMS9ucMeB44Ejq85/WXg8Ih4SNJ6wDRJ10fEvLT/ixFxZatiN7Pq2nX3XdsdgpmZVVzLCmRgJ2BGRDwCIOkyYF/gXwVyRMxM+17LnxgRD+aePynpH8BawLwWxmtmHWDEGiPaHYKZmVVcK4dYjASeyL2elbb1iqSdgFWAh3Obv52GXnxf0qA65x0taaqkqXPmzOnt25pZRU28amK7QzAzs4or9U16ktYFLgQ+ERFdvcxfAbYA3gasDpxQdG5EnBURYyJizFprrbVC4jUzMzOz6qs7xELS/B7OFfBURGxeZ/9sYP3c61FpW1MkDQV+C3wtIm7t2h4RT6WniyT9nO7jl82sQoadOoz5i7r/czNi8AjmnjC3DRGZmVl/12gM8sMRsX2jkyU1mnB0CrCZpI3ICuODgUObCUrSKsA1wAW1N+NJWjcinpIkYD/gnmbaNLNymr9oPlM/PbXb9jFnj+lTe6M3HV1339BBQ9HJ6rbdxbiZmeU1KpD3b+L8usdExGJJxwLXAwOA8yJiuqRTgKkRMUHS28gK4RHAPpJOjoitgY8C7wLWkHRkavLIiLgTuFjSWmQ92HcCxzQRp5lVTFEx20whu8MuO9TdN+nwSYXb+1qMm5lZZ6pbIOdmn3gjsCAiXpO0Odn43+si4tWuYxq0MRGYWLPtxNzzKWRDL2rPuwi4qE6bezR6TzPrDEXFbDOF7KSJk9hjrP+ZMDOzvmvmJr0bgcGSRgK/Bz4O/KKVQZmZ9dW8ufPaHYKZmVVcMwWyIuJlYBzwk4g4ENi6tWGZmZmZmbVHUwWypF2Bw8hmlYBsTLGZWekMXnVwu0MwM7OKa6ZAPo5s7uFr0k12GwN/bm1YZmZ9M3b/se0OwczMKq7HpaYj4kaycchdrx8BPtfKoMzM+uq+u+9jy223bHcYZmZWYT0WyGnmiuOB0fnjPZuEmZWRC2QzM1tWPRbIwBXAmcA5wJLWhmNmZmZm1l7NFMiLI+KnLY/ErCS89LGZmVn/1kyBfK2kz5CteLeoa2NEuFKwjrS8lz62FcuLhJiZ2bJqpkA+Iv35xdy2ADZe/uGYmZmZmbVX3WneJK0HEBEbFTxcHJtZKU2a2H2JajMzs95o1IN8jqTVgcnA74CbImLxConKzMzMzKxN6hbIETFW0mBgd+AjwP9IepysWP5dRDy+YkI0M8sMHTQUnazC7WXT3272LLreTr1WM+t8DccgR8RCUkEMIGkjYG/gx5LWiYidWh+imVlm0uE9D58oyxzI/e1mz6Lr7dRrNbPO12gM8ha554MAIuLRiPgJ8N/AO3pqXNJekh6QNEPSlwv2v0vS7ZIWSzqgZt8Rkh5KjyNy23eU9PfU5g8lde9OMrN+qywFspmZVVfdAhm4JPf8lpp9Z0TEK40aljQAOIOsx3kr4BBJW9Uc9jhwZM17kcY+fxPYGdgJ+KakEWn3T4FPA5ulx16N4jCz/mXiVRPbHYKZmVVcowJZdZ4XvS6yEzAjIh5JxfRlwL75AyJiZkTcDbxWc+4HgD9ExNyIeB74A7CXpHWBoRFxa0QEcAGwXxOxmFk/sXDBwnaHYGZmFdeoQI46z4teFxkJPJF7PStta0a9c0em5z22KeloSVMlTZ0zZ06Tb2tmZmZm/V2jm/RGSfohWW9x13PS62YL3baJiLOAswDGjBnTTEFvZh1g+OrD2x2CmZlVXKMCOb9yXu2t2N1vze5uNrB+7vWotK0Zs8mml8ufOzltH9XHNs2sH/BS02ZmtqwazYN8vqS1gA3JxhLP62XbU4DN0tRws4GDgUObPPd64Du5G/PeD3wlIuZKmi9pF+A24HDgR72My8w62O233s4Ou+zQ7jDMzKzCGk3z9ilgOlkBer+kD/em4bTq3rFkxe59wOURMV3SKV1tSXqbpFnAgcDPJE1P584FvkVWZE8BTknbAD4DnAPMAB4GrutNXGbW2WbOmNnuEMzMrOIaDbEYD2wdEXMkbQxcDEzoTeMRMRGYWLPtxNzzKSw9ZCJ/3HnAeQXbpwLb9CYOMzNb8eqtfOgV9sys7BoVyK9ExByAiHika7EQMzOzZtRb+dAr7JlZ2TUzi0Xh64j4XOvCMjPrm7H7j213CGZmVnHNzmIBMK2VgZiZLQ/PP/c8645ad4W+57BThzF/0fyltg0dNHSFxmBmZstPw1ksVmQgZva6ooLL4zabc8vkWxj3sXEr9D3nL5rP1E83M/ulmZlVQd0CWdJJEXFSo5ObOcbMeq+o4PK4TTMzsxWj0RCLT0ma32C/yOY2Pmm5RmRmZmZm1kaNCuSzgdV6OP/s5RiLlYS/3rcq237n7dsdgpmZVVyjMcgnr8hArDz89b5V2UabbdTuEMzMrOLqrqRnZlZFV190dbtDMDOzinOBbGZmZmaW02OBLGmNFRGImZmZmVkZNNODfKukKySNlaSWR2RmtgzWGblOu0MwM7OKa6ZA3hw4C/g48JCk70javLVhmZn1zW7v2a3dIZiZWcX1WCBH5g8RcQjwaeAI4G+SbpC0a8sjNDPrhZv/fHO7QzAzs4pragyypOMkTQWOBz4LrAl8Abikh3P3kvSApBmSvlywf5CkX6b9t0kanbYfJunO3OM1SdulfZNTm1373tTrqzazjvX07KfbHYKZmVVco4VCutwCXAjsFxGzctunSjqz3kmSBgBnAO8DZgFTJE2IiHtzhx0FPB8Rm0o6GDgNOCgiLgYuTu28BfhVRNyZO++wiFh6ol4zMzMzs+WgmTHIX4+Ib+WLY0kHAkTEaQ3O2wmYERGPRMQrwGXAvjXH7Aucn55fCby34EbAQ9K5ZmZmZmYt10yB3G1oBPCVJs4bCTyRez0rbSs8JiIWAy8AtdPKHQRcWrPt52l4xTfqzawh6WhJUyVNnTNnThPhmlknGPexce0OwczMKq7uEAtJewNjgZGSfpjbNRRY3OrAUgw7Ay9HxD25zYdFxGxJqwFXkc2ucUHtuRFxFtnsG4wZMyZWRLxm1n6PPvSol5s2M7Nl0qgH+UlgKrAQmJZ7TAA+0ETbs4H1c69HpW2Fx0gaCAwDnsvtP5ia3uOImJ3+fJHsJsGdmojFzPqJO267o90hmJlZxdXtQY6Iu4C7JF2chj/01hRgM0kbkRXCBwOH1hwzgWzauFuAA4BJEREAklYCPgq8s+vgVEQPj4hnJa0MfAj4Yx9iMzMzMzMr1GiIxeUR8VHgDkn5IQoimx5520YNR8RiSccC1wMDgPMiYrqkU4CpETEBOBe4UNIMYC5ZEd3lXcATEfFIbtsg4PpUHA8gK47PbvZizcz6u2GnDmP+ovndto8YPIK5J8xtQ0RmZuXTaJq349KfH+pr4xExEZhYs+3E3POFwIF1zp0M7FKz7Z/Ajn2Nx8w63667e/2iRuYvms/UT3efJXPM2WPaEI2ZWTnVHYMcEU+lp8+S9eQ+RtaD+1ay8clmZqUzYo0R7Q7BzMwqrpmFQm4E3ilpBPB7srHFBwGHtTIwM7O+mHjVxFJP9TZ00FB08tKzU3p4QzEPBzGzdmmmQFZEvCzpKOAnEfFdSXe2OC4z60BFBc/QQUPbFE17TDp8UrdtHt5QzMNBzKxdmiqQJe1K1mN8VNo2oHUhmVmnqlfwmJmZlUkzBfJxZCvnXZNmodgY+HNrwzIz65vRm45udwgtVdQL7yEHZmbLV48FckTcSDYOuev1I8DnWhmUmVlf7bDLDu0OoaWKeuE95MDMbPnqsUCWtDlwPDA6f3xE7NG6sMzKxzdXVcOkiZPYY6z/eTIzs75rZojFFcCZwDnAktaGY1ZevrmqGubNndfuEMzMrOKaKZAXR8RPWx6JmZn1SdG3G+BvOMzM+qqZAvlaSZ8BrgEWdW2MCP+ray3hm5BsWQxedXC7Q1jhir7dAH/DYWbWV80UyEekP7+Y2xbAxss/HCurFTn+1jch2bIYu//YdodgZmYV18wsFhutiECs3Dz+1qrivrvvY8ttt2x3GGZmVmHNzGLxBuDzwAYRcbSkzYA3R8RvWh6dWYt5ZbfO0+oC2T8zZmadr5khFj8HpgG7pdezyWa26LFAlrQXcDrZynvnRMSpNfsHARcAOwLPAQdFxExJo4H7gAfSobdGxDHpnB2BXwCrAhOB4yIimrgOs268spv1ln9mzMw630pNHLNJRHwXeBUgIl4Gut8uXUPSAOAMYG9gK+AQSVvVHHYU8HxEbAp8Hzgtt+/hiNguPY7Jbf8p8Glgs/TYq4lrMDMzMzNrSjM9yK9IWpXsxjwkbUJuNosGdgJmpJX3kHQZsC9wb+6YfYGT0vMrgR9Lqlt8S1oXGBoRt6bXFwD7Adc1EY+Z9QNeJOR1RTfXejiImVnPmimQTwJ+B6wv6WLg7cAnmjhvJPBE7vUsYOd6x0TEYkkvAGukfRtJugOYD3w9Iv6Sjp9V0+bIJmIxM6urU1dJrDf9m5mZNdbMLBa/lzQN2IVsaMVxEfFsi+N6iuymwOfSmONfSdq6Nw1IOho4GmCDDTZoQYhmVkaTJk5i3MfG9e4cz9JiZmY5zcxi8aeIeC/w24JtjcwG1s+9HpW2FR0zS9JAYBjwXLrpbhFAREyT9DCweTp+VA9tks47CzgLYMyYMb6Jz8ysgU7tRTcz64u6BbKkwcAbgDUljeD1G/OG0tywhinAZpI2IitiDwYOrTlmAtlCJLcABwCTIiIkrQXMjYglkjYmuxnvkYiYK2m+pF2A24DDgR81ea1mZlaHe9HNzF7XqAf534DxwHpk07x1FcjzgR/31HAaU3wscD3ZNG/nRcR0SacAUyNiAnAucKGkGcBcsiIa4F3AKZJeBV4Djsktbf0ZXp/m7Tp8g56Z5XiREDMzW1Z1C+SIOB04XdJnI6JPvbQRMZFsruL8thNzzxcCBxacdxVwVZ02pwLb9CUeM+t8LpDNzGxZNXOT3o8k7QaMzh8fERe0MC4zW0GKVoar8tjTiVdNZOz+Y9sdhpmZVVgzN+ldCGwC3AksSZuDbAU8M1tBim6iguaL2aJCuKvd2pXhqjz2dOGChe0OwczMKq6ZeZDHAFt5OWez9qo3p22zxayXSDYzM2tOMwXyPcA6ZHMTm5VevZ7SKg8bsOYNX314u0MwM7OKa6ZAXhO4V9LfyC0xHREfbllUJdNpYzTLpOizXdalcOv1lFZ52IA1z0tNW3/g/5fMWqvZpab7taKCy8XW8uGv/W15u/3W29lhlx3aHYZZS/n/pRXLv5D0P83MYnHDigjErD9qdOOc9c3MGTNdIFdQK75NMlte/AtJ/9NoJb0XyWar6LYLiIjwv1xmy8g96NYfFc3IUjSbiplZuzRaKGS1FRmImVl/taxT+FVNvRlZzMzKopkxyGZmlVHFRUKWdQo/MzNbvlwgm1lHef6551l31LrL3E69Xt3+NC626DPo1F5tM7M8F8hm1lFumXwL4z42bpnb8TCA4s/Avdpm1h+4QO4nPEWNmVln629j2c1ayQVyP9GpU9R4aiiz/sdDP4p5LLvZ8tPSAlnSXsDpwADgnIg4tWb/IOACYEfgOeCgiJgp6X3AqcAqwCvAFyNiUjpnMrAusCA18/6I+Ecrr8PKy9OkWa3td96+3SFYi3noh5m1WssKZEkDgDOA9wGzgCmSJkTEvbnDjgKej4hNJR0MnAYcBDwL7BMRT0raBrgeGJk777CIcFXUZv46z8poo802ancIZmZWca3sQd4JmBERjwBIugzYF8gXyPvy+lLWVwI/lqSIuCN3zHRgVUmDImJRC+O1XvLXeeXgr5uXdvVFVy+Xm/TMzKz/amWBPBJ4Ivd6FrBzvWMiYrGkF4A1yHqQu+wP3F5THP9c0hLgKuC/IqLbin+SjgaOBthggw2W8VKs3co65VYZetH9dbOZmdnyVeqb9CRtTTbs4v25zYdFxGxJq5EVyB8nG8e8lIg4CzgLYMyYMUVLZluFlHXKrU7tRS9D4W9mVmb+9q6ztbJAng2sn3s9Km0rOmaWpIHAMLKb9ZA0CrgGODwiHu46ISJmpz9flHQJ2VCObgVyf1U0qwO0v6fVqqXKhf86I9dpdwjLTdF/wP67bFYO/vaus7WyQJ4CbCZpI7JC+GDg0JpjJgBHALcABwCTIiIkDQd+C3w5Iv7adXAqoodHxLOSVgY+BPyxhddQOZ7VoRzcs9A+u71nt3aHsNyU9ZsTM7NO17ICOY0pPpZsBooBwHkRMV3SKcDUiJgAnAtcKGkGMJesiAY4FtgUOFHSiWnb+4F/Aten4ngAWXF8dquuwayv3LPQPjf/+eaOKpLNzGzFa+kY5IiYCEys2XZi7vlC4MCC8/4L+K86ze64PGM0s87y9Oyn2x2CmZlV3ErtDsDMzMzMrExKPYuF2fLkG57M+p+iG5d9P4CZ9cQFsvUb/emGp/78y4AXCbG8ohuXfT+AmfXEBbLZCrIii9b+9MtArUcfetTLTbdQWRftqRr3bJuVmwvkPvJCCtZb/bloXZHuuO0OF8gtVNaf46oV7kU923tcsIenhzQrCRfIfVTlhRTMzDpNWQv33vD0kGbl4QK5H2tVj4sXyTAzM7Mqc4Hcj7Wqx8W9INZOu+6+a7tDsA7SCWOF3Wlh1nsukM2saVX4j3bEGiPaHYJ1kHbPgrE8vulzp8WK4/uTOocLZDNrWhX+o5141URP9WYdo1Xf9NUr5Ip0anHXim8HfH9S53CBbGZm1gud0EvYm8K7U4u7dn87YOXmAnk5q8JX0O1QtSmYrHn+mbeqWdafWfcSmnU+F8g1ir5y8VivZdcJUzBZsaLcFs3nCivmF6LRm45u+XtYta3If6eL/k8Bdw60SifcVFl19X7mq5YHF8g1ir5yWVad8HWcWW+08xeiHXbZoW3vbVarFf+ndLJlLXCbXYAF2v9LSqcUkrXq/cxXrbOwpQWypL2A04EBwDkRcWrN/kHABcCOwHPAQRExM+37CnAUsAT4XERc30ybZVSvWKj3l7ZWvb8sy9rbbdaJJk2cxB5j92h3GFYxHgZW37IOSelN0dubccHN/h/Ym1/YV+TPQRkKSfe419eyAlnSAOAM4H3ALGCKpAkRcW/usKOA5yNiU0kHA6cBB0naCjgY2BpYD/ijpM3TOT21WRnN/qWt95fFPRNm3c2bO6/dIVgFLY9vPYqKq04osJd1SMqy9uo2KlqX9/+B/W044LLeqLisHXUrshe93nvV08oe5J2AGRHxCICky4B9gXwxuy9wUnp+JfBjSUrbL4uIRcCjkmak9miizW4WLlzIgw8+2HTgjz38WNPHrghDBg4p/MdhyMAhpYvVrN2em/Oc/15YW/z87T8v3N6pP4/L8v9qvc9qWY8ts6LPq178tcfueMWOvLT4pT6/97BVhvG3/f9WuK8ohmZzO3/RfK7a46qm2ixqt975+0/av6nPoN51FR07ZOCQ4vc6af/CWFtZII8Ensi9ngXsXO+YiFgs6QVgjbT91ppzR6bnPbUJgKSjgaMBWBfefOmbmwp62CrD2GbtbZo6dkWZduC0dodgVhkPv+nh0v0dNutE9zxzT7dtn7zpk7zwygtLbSvj/6sr2rBVhnWrQ+p9LvWOfeCQB/r8/m++9M2F+QK6xVD0/vX0Jret+AzqXddLi19aps8LOvgmvYg4CzgLYMyYMTH1mx6KYNYffPGLX2x3CGb9woabbNht2wuTXiC+GW2IptzmfWVeS47tjaJ8MQk233zzpTa16v1b9Rk0e1291coCeTawfu71qLSt6JhZkgYCw8hu1mt0bk9tmlk/Nm3aNHbcccd2h2FmZi02YvCIwjHTIwaPWOa2W1kgTwE2k7QRWRF7MHBozTETgCOAW4ADgEkREZImAJdI+j+ym/Q2A/4GqIk2zawfc4Fs1nqtLExs+evUfLVyto2WFchpTPGxwPVkU7KdFxHTJZ0CTI2ICcC5wIXpJry5ZAUv6bjLyW6+Wwz8R0QsAShqs1XXYGZmZt15GrBqcb56r6VjkCNiIjCxZtuJuecLgQPrnPtt4NvNtGlmZmZmtrys1O4AzMyWp3HjxrU7BDMzqzgXyGZmZmZmOS6QzayjXH311e0OwczMKs4FspmZmZlZjgtkMzMzM7McRXT+ijeS5gDVWbC9nNYEnm13ENY056tanK9qcb6qxfmqnhWZsw0jYq3ajf2iQLZlJ2lqRHSfZdxKyfmqFuerWpyvanG+qqcMOfMQCzMzMzOzHBfIZmZmZmY5LpCtWWe1OwDrFeerWpyvanG+qsX5qp6258xjkM3MzMzMctyDbGZmZmaW4wLZzMzMzCzHBbKZmZmZWY4LZDMzMzOznIHtDsDKSdIWwL7AyLRpNjAhIu5rX1RWj/NVLc5XtThf1eJ8VU8Zc+YeZOtG0gnAZYCAv6WHgEslfbmdsVl3zle1OF/V4nxVi/NVPWXNmad5s24kPQhsHRGv1mxfBZgeEZu1JzIr4nxVi/NVLc5XtThf1VPWnLkH2Yq8BqxXsH3dtM/KxfmqFuerWpyvanG+qqeUOfMYZCsyHviTpIeAJ9K2DYBNgWPbFZTVNR7nq0rG43xVyXicryoZj/NVNeMpYc48xMIKSVoJ2ImlB8xPiYgl7YvK6nG+qsX5qhbnq1qcr+opY87cg2z1bA68m6V/WOcB97crIGvI+aoW56tanK9qcb6qp3Q58xhk66bBHaWX+S7g8nG+qsX5qhbnq1qcr+opa848xMK6KesdpVbM+aoW56tanK9qcb6qp6w5cw+yFSnlHaVWl/NVLc5XtThf1eJ8VU8pc+YxyFZkPCW8o9TqGo/zVSXjcb6qZDzOV5WMx/mqmvGUMGceYmGFynhHqdXnfFWL81Utzle1OF/VU8acuUA2MzMzM8vxGGQrJGkLSe+VNKRm+17tismaJ+mCdsdgxSTtLGloer6qpJMlXSvpNEnD2h2fLU3SKpIOl7Rnen2opB9L+g9JK7c7Pusu/f91gqQfpscJkrZsd1xWXxlrDvcgWzeSPgf8B3AfsB1wXET8Ou27PSJ2aGN4VkPShNpNwHuASQAR8eEVHpTVJWk68NaIWCzpLOBl4ErgvWn7uLYGaEuRdDHZ/TpvIJuXdQhwNVm+FBFHtC86q5WmDDuEbNqwWWnzKOBg4LKIOLVdsVmxstYcvknPinwa2DEiXpI0GrhS0uiIOJ2s+LJyGQXcC5wDBFmOxgD/286grK6VImJxej4m94//TZLubFNMVt9bImJbSQPJxkWuFxFLJF0E3NXm2Ky7oyieMuz/gOmAC+TyKWXN4SEWVmSliHgJICJmArsDe6d/YFwgl88YYBrwNeCFiJgMLIiIGyLihrZGZkXukfSJ9PwuSWMAJG0OvFr/NGuTldJ8rKuR9SJ3DYMZBHiIRfmUcsowa6iUNYd7kK3IM5K2i4g7AdJvdR8CzgPe0tbIrJuIeA34vqQr0p/P4L/bZfYp4HRJXweeBW6R9ATZ9EafamtkVuRcsuVuB5D9EnqFpEeAXci+xrdyGU8JpwyzhkpZc3gMsnUjaRSwOCKeLtj39oj4axvCsiZJ+iDw9oj4artjsfrSjXobkf0yMysinmlzSFaHpPUAIuJJScOBPYHHI+JvbQ3MCpVxyjCrr6w1hwtkMzMzM7Mcj0E2MzMzM8txgWxmZmZmluMC2XokaU1JZ0navt2xWM+cr2pxvqrF+aoWSRtKep+kddodizWnLDnzne7WjaQf1mwaBnwMGClpRkQc14awrA7nq1qcr2pxvqpF0u3ACRHxB0mfBM4km4FkgaT9IuKP7Y3QapU1Z75Jz7qR9BqvLzhRKyJiwAoOyRpwvqrF+aoW56taUr4OjojL05SXrwC/BA4HHo2IndsaoHVT1px5iIUVeYhs+dvjyZYs/nja/k1gj3YFZXU5X9XifFWL81U9IWkIsBZZz+TxwH8BW7c3LGugdDlzgWxFtgV+RPbDOZbXl1Od7pXZSsn5qhbnq1qcr+r5DPBDspXzFqZti8l+0bFyKl3OXCBbNxGxKC0ysQvZko9/JPuK0UrI+aoW56tanK/KeRwYTdbb/wTw5rR9L8ALu5RTKXPmMcjWkCQBnwRGAb+MiPvbHJI14HxVi/NVLc5XdUl6CzCnaLU2K6d258w9yNZQZL9B/Zpsyc5V2xyO9cD5qhbnq1qcr+rpmpYPGOjiuBrKkjNP82bdeFqjanG+qsX5qhbnq1qcr+opa848xMK68bRG1eJ8VYvzVS3OV7U4X9VT1px5iIUV8bRG1eJ8VYvzVS3OV7U4X9VTypy5QLYintaoWpyvanG+qsX5qhbnq3pKmTMXyNaNpzWqFuerWpyvanG+qsX5qp6y5sxjkK0hT2tULc5XtThf1eJ8VYvzVT1lypkLZGuKpNUjYm6747DmOF/V4nxVi/NVLc5X9ZQhZx5iYd1Ieqek+yVNl7SbpD8CcyTNlrRju+OzpTlf1eJ8VYvzVS3OV/WUNWfuQbZuJE0BtgMWpceqZMs97gbcGBF7ti86q+V8VYvzVS3OV7U4X9VT1py5B9mKbAV8nWx6lRHAFyNid+A7wA5tjMuKOV/V4nxVi/NVLc5X9ZQyZy6QrcgS4Cng4fS6a5D848CgtkRkjThf1eJ8VYvzVS3OV/WUMmcukK3IfcBbI+I5st/mJqftu/H6D7CVh/NVLc5XtThf1eJ8VU8pc+YxyNY0Se8H5kbE1HbHYj1zvqrF+aoW56tanK/qaXfOXCCbmZmZmeV4iIWZmZmZWY4LZDMzMzOzHBfI1iuSPtHuGKx5zle1OF/V4nxVi/NVPe3MmccgW69IejwiNmh3HNYc56tanK9qcb6qxfmqnnbmbGA73tTKTdLd9XYBa6/IWKxnzle1OF/V4nxVi/NVPWXNmQtkK7I28AHg+ZrtAm5e8eFYD5yvanG+qsX5qhbnq3pKmTMXyFbkN8CQiLizdoekySs8GuuJ81Utzle1OF/V4nxVTylz5jHIZmZmZmY5nsXCzMzMzCzHQyyskKQtgH2BkWnTbGBCRNzXvqisHuerWpyvanG+qsX5qp4y5sw9yNaNpBOAy8gGyP8tPQRcKunL7YzNunO+qsX5qhbnq1qcr+opa848Btm6kfQgsHVEvFqzfRVgekRs1p7IrIjzVS3OV7U4X9XifFVPWXPmHmQr8hqwXsH2ddM+Kxfnq1qcr2pxvqrF+aqeUubMY5CtyHjgT5IeAp5I2zYANgWObVdQVtd4nK8qGY/zVSXjcb6qZDzOV9WMp4Q58xALKyRpJWAnlh4wPyUilrQvKqvH+aoW56tanK9qcb6qp4w5cw+y1bM58G6W/mGdB9zfroCsIeerWpyvanG+qsX5qp7S5cxjkK2bBneUXua7gMvH+aoW56tanK9qcb6qp6w58xAL66asd5RaMeerWpyvanG+qsX5qp6y5sw9yFaklHeUWl3OV7U4X9XifFWL81U9pcyZxyBbkfGU8I5Sq2s8zleVjMf5qpLxOF9VMh7nq2rGU8KceYiFFSrjHaVWn/NVLc5XtThf1eJ8VU8Zc+YC2XpF0joR8XS747DmOF/V4nxVi/NVLc5X9bQzZx6DbL11brsDsF5xvqrF+aoW56tanK/qaVvO3INsZmZmZpbjm/SskKQtgH1ZejzQhIi4r31RWT3OV7U4X9XifFWL81U9ZcyZh1hYNw0m7b7UE62Xj/NVLc5XtThf1eJ8VU9Zc+YhFtZNWSfttmLOV7U4X9XifFWL81U9Zc2Ze5CtSCkn7ba6nK9qcb6qxfmqFuerekqZM49BtiLjKeGk3VbXeJyvKhmP81Ul43G+qmQ8zlfVjKeEOfMQCytUxkm7rT7nq1qcr2pxvqrF+aqeMubMBbKZmZmZWY7HIJuZmZmZ5bhANjMzMzPLcYFsZmZmZpbjAtnMzMzMLMcFspn1K5LWkHRnejwtaXZ6/pKkn6ygGMZI+uGKeK9WkTRc0mf6eO5XG+xbkvJRNC9qX95r1dTeK5LWXB5tmlnn8ywWZtZvSToJeCki/qfdsbSKpIERsbgF7Y4GfhMR2/TiHJEtITs/IobUOealevuWhaSZwJiIeHZ5t21mncc9yGZmgKTdJf0mPT9J0vmS/iLpMUnjJH1X0t8l/U7Syum4HSXdIGmapOslrVvQ7oGS7pF0l6Qb67zXeZImS3pE0udy5x4u6e507oVp21qSrpI0JT3eXvCeR0qaIGkS2QT8/3q/tP/Hko5Mz2dKOlnS7en6tihob2tJf0s9sXdL2gw4FdgkbfuepCGS/pRrZ9907mhJD0i6ALgHOBfo6tW9uIecDJD0i/T5/V3Sf6btm6Q8TEs52iJtX1vSNenzukvSbo3aNzOrxyvpmZkV2wR4D7AVcAuwf0R8SdI1wAcl/Rb4EbBvRMyRdBDwbeCTNe2cCHwgImZLGl7nvbZI77Ua8ICknwKbA18HdouIZyWtno49Hfh+RNwkaQPgemDLgjZ3ALaNiLmSdu/hWp+NiB3SkInjgU/V7D8GOD0iLpa0CjAA+DKwTURsB1lPNfCRiJifhjLcKmlCOn8z4IiIuDUde2DXeT3YDhjZ1Uud+/zOAo6JiIck7Qz8BNgD+CFwQ0R8RNIAYLn3RJtZ/+AC2cys2HUR8aqkv5MVhL9L2/8OjAbeDGwD/CEbOcAA4KmCdv4K/ELS5cDVdd7rtxGxCFgk6R/A2mQF3xVdQwIiYm46dk9gq/SeAEMlDYmIl2ra/EPunJ50xTUNGFew/xbga5JGAVenwrT2GAHfkfQu4DWyFbHWTvse6yqOe+kRYGNJPwJ+C/xe0hBgN+CKXAyD0p97AIcDpBW4XujDe5qZuUA2M6tjEUBEvCbp1Xj9ho3XyP7tFDA9InZt1EhEHJN6OT8ITJO0Y733SpbQ+N/mlYBdImJhD/H/M/d8MUsPqRtc5/0L3zsiLpF0G9k1TJT0b2TFa95hwFrAjukXi5m59/knfRARz0t6K/ABsl7sjwLjgXlN9kCbmfWJxyCbmfXNA8BaknYFkLSypK1rD5K0SUTcFhEnAnOA9ZtsfxJwoKQ1UjtdQyx+D3w21/52TbT1GFmv86A0TOG9TcbQ9R4bA49ExA+BXwPbAi+SDQnpMgz4RyqO3wNs2KDJV7vGcffwvmsCK0XEVWTDTXaIiPnAo5IOTMcoFdEAfwL+PW0fIGlYb67TzKyLC2Qzsz6IiFeAA4DTJN0F3En21X+t76UbzO4BbgbuarL96WRjmm9I7f9f2vU5YEy6We5esp7Vntp6Aric7Ca5y4E7mokh56PAPZLuJBtWckFEPAf8Nd1A9z3g4hTX38mGOdzfoL2zgLt7ukmPbJjG5PS+FwFfSdsPA45Kn8t0YN+0/TjgPSmGaWTjx83Mes3TvJmZWWnI07yZWQm4B9nMzMpkvlqwUAiwMtn4cTOzHrkH2czMzMwsxz3IZmZmZmY5LpDNzMzMzHJcIJuZmZmZ5bhANjMzMzPL+X+CHw0ZVn1G+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_some_peaks(peaks[peaks[\"area\"] > 25], max_plots=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display populations\n",
    "### Area vs width plot \n",
    "You see these plots a lot on Slac. We'll make a 2D histogram of two propperties of the peaks. Herefrom we try to isolatea population to look at the waveforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/17887550/ipykernel_46864/1171974014.py:9: RuntimeWarning: invalid value encountered in log10\n",
      "  x, y = np.log10(x), np.log10(y)\n",
      "/tmp/jobs/17887550/ipykernel_46864/1171974014.py:9: RuntimeWarning: divide by zero encountered in log10\n",
      "  x, y = np.log10(x), np.log10(y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in log-log space of all populations\\n10 s of data')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All peaks\n",
    "plots_area_vs_width(peaks, log=True, bins=100, range=[[0, 4], [1, 5]])\n",
    "plt.title(\"Area vs width in log-log space of all populations\\n10 s of data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to zoom in on the population on the lower right. Therefore I reduce the range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/jobs/17887550/ipykernel_46864/1171974014.py:9: RuntimeWarning: invalid value encountered in log10\n",
      "  x, y = np.log10(x), np.log10(y)\n",
      "/tmp/jobs/17887550/ipykernel_46864/1171974014.py:9: RuntimeWarning: divide by zero encountered in log10\n",
      "  x, y = np.log10(x), np.log10(y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in log-log space zoomed on large S1s\\n10 s of data')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_area_vs_width(\n",
    "    peaks, log=True, bins=100, range=[[2, 4], [1.5, 2.5]]\n",
    ")  # zoomed wrt to previous plot!\n",
    "plt.title(\"Area vs width in log-log space zoomed on large S1s\\n10 s of data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's select this blob of peaks. \n",
    "For this I switch to lin-lin space rather than log-log as I'm too lazy to convert the selection I'm going to make from lin to log and vise versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in lin-lin space zoomed on large S1s\\n10 s of data')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look in linear-space\n",
    "plots_area_vs_width(peaks, log=False, bins=100, range=[[10**2, 10**4], [10**1.5, 10**2.5]])\n",
    "plt.title(\"Area vs width in lin-lin space zoomed on large S1s\\n10 s of data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data selection\n",
    "### Let's make a very rudimentary selection in this data\n",
    "\n",
    "Below I'm going to draw a box around the blob as above and load some peaks therefrom. I suspect this are nice fat S1s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters used for plot below\n",
    "low_area = 2000\n",
    "high_area = 8000\n",
    "low_width = 80\n",
    "high_width = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in lin-lin space zoomed on large S1s\\nwith selection\\n10 s of data')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select data in box\n",
    "plots_area_vs_width(peaks, log=False, bins=100, range=[[10**2, 10**4], [10**1.5, 10**2.5]])\n",
    "\n",
    "ax = plt.gca()\n",
    "x = np.arange(low_area, high_area, 2)\n",
    "y = np.full(len(x), low_width)\n",
    "y2 = np.full(len(x), high_width)\n",
    "ax.fill_between(x, y, y2, alpha=0.5, color=\"red\", label=\"selection\")\n",
    "plt.legend()\n",
    "plt.title(\"Area vs width in lin-lin space zoomed on large S1s\\nwith selection\\n10 s of data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load more data from the selection.\n",
    "For this I will use a 'selection string' this will reduce memory usage, see straxferno 0 and 1: https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:xenon1t:aalbers:straxferno\n",
    "\n",
    "I'm also going to load all the of data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear peaks from our namespace, this reduces our RAM usage (what usually crashes a notebook).\n",
    "del peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7105599e1d84554bdd55ac5ae838cfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading peak_basics: |          | 0.00 % [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.87 s, sys: 1.46 s, total: 3.32 s\n",
      "Wall time: 9.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# use selection string to load a lot of data\n",
    "selected_peaks = st.get_array(\n",
    "    run_id,\n",
    "    targets=\"peak_basics\",\n",
    "    selection_str=(\n",
    "        f\"({high_area}>area) & (area>{low_area}) \"\n",
    "        f\"& ({high_width}>range_50p_area) & (range_50p_area>{low_width})\"\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative method yielding the same (if all data were loaded)\n",
    "The cell above does return the same results as this selection (only now we are taking the entire run instead of 10s):\n",
    "```\n",
    "mask = ((high_area>peaks['area'] ) & (peaks['area']>{low_area}) &\n",
    "        high_width > peaks['range_50p_area'] & peaks['range_50p_area'] > low_area )\n",
    "\n",
    "selected_peaks \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in log-log space with selected S1s\\nall data')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HBTOzilzRbGZmW+nt8ofXHBTMzCoKxObo7p/N7v50ZmYjyBXNZmZWF8jFR2ZmVnBFs5mZARCBm6SamVmSKprdzYWZmWWuaDYzMyDlFLr9JTsOCmZmTXBOwczMgNzNhSuazcwsqb9qs2s5KJiZVRTg1kdmZpZEyMVHZmZW8MNrZmYG1N6n4DoFMzMD/OY1MzOrS01SnVMwMzPc95GZmfXTqq6zJb0e+F/AFOAbEXH9aBynuwvHzMxGUOo6W5WGKiRdLGmppHv7zT9B0m8kPSzpnHTs+F5E/BnwF8CbR/zDZQ4KZmZN6AtVGipaAJxQniFpDPAl4ETgIOA0SQeVVvlYXj4qXHxkZlZR6iW18r30LEl3lKYvioiLttpfxM2S5vfb7kjg4Yh4BEDSFcApkh4ALgS+HxG/HNYHqMBBwcysotTNReWg8ExEHD6Mw+wFLCxNLwKOAt4LHAdMlbRfRHx1GPtuyEHBzKyy1nVzERFfAL4w2sdxUDAza8IOeKJ5MbB3aXpunrdDOCiYmVVUa300ym4H9pe0LykYnAq8ZbQPWuPWR2ZmTeiLnkoDqez/IkknD7YvSZcDtwIHSlok6cyI2AK8B7gOeAC4MiLu2xGfDZxTMDOrrMl3NK+KiLOG3F/EaYPMvxa4tsnkjQgHBTOzigLY4g7xzMysxi/ZMTOzpLmnlTtSd4c8M7MRVHvJTpWBChXN7cg5BTOzJoxkRXM7clAwM6vIL9kxM7O6QGzp6+5SdwcFM7Mm7IBuLlqqu0OemdlIiqbep+CKZjOzbtZknYIrms3Mup0rms3MDEgVzb2uaDYzs5pur2h2UDAzqyjCxUdmZlYSXR4UurtwzMxsRFVrjuomqWZmO4kmcgpukmpm1s0ioLevu4uPHBTMzJrg1kdmZgakJ5q7vaLZQcHMrLLuf/Oag4KZWRMiWp2C0eWgYGbWhG4vPvJzCmZmFaXWRz2VBvycgplZ92ui+MjPKZiZdbtuLz5yUDAzqyiQg4KZmRW6vPGRg4KZWWUB4W4uzMysZqcuPpK0usH2ApZExAEjlyQzs/bVKQ+vSToamE/pdz4iLm20XaOcwm8j4oUNDvyrKgk0M+t0ndL3kaTLgN8D7gJ68+wAtjso/O8Kx6+yjplZ5wugA4ICcDhwUETz+Zohn2iOiEcAJE2S1JPHD5D0OknjyuuYme0MIqoNtPaJ5nuBPYezYdWK5puBl0qaDlwP3A68GTh9OAc1M+tMaqb1USufaJ4F3C/pNmBjbWZEvK7RhlWDgiJivaQzgS9HxN9LumtYSTUz62SdUdF8/nA3rBwUJP0hKWdwZp43ZrgHNTPrSNEZFc0RcdNwt60aFN4PnAt8NyLuk/Rc4EfDPaiZWcdq45yCpJ9ExDGS1rB1SgVERExptI9KQSEibibVK9SmHwHe12R6zcy6QPvmFCLimPx38nD3USkoSDoAOJttH4R45XAPbGbWkfpanYBqJL0IOIaUY/hJRFR6pqxq8dFVwFeBr1M8CGFmtnPpkOcUJH0ceCPwH3nWAklXRcTfNdq2alDYEhFfGW4Czcy6RYd0c3E6cGhEbACQdCHp6eaGQaHq6zivkfRuSbMlzagNw06umVmniopDaz0BjC9N7wosrrJh1ZzCGfnvh0vzAnhuxe3NzLpDGxcfSfoi6bd5FXCfpBvy9KuB26rso2rro32Hm0gz29qJ+3ygPr7slfvUxzdPTD82M+97tj5v9b7Fzd7Ep7YAsH6P4mu7bnbxAzX+mfR3l3VFTWjvLsXy3nFpfFOpXUrPADWEkxcWM1ccWDyONOXxtN/1uxcFDBOfLo61aVLPNsef8kDR0fLq56XWkGv3LLafsLy4pa59vsFsmVBs95P/OHvIdUeTWp8LGMod+e+dwHdL839cdQeNus5+UUT8cnvXMTPrCiGo3s3FVEkXAddExDWjmKqylwDfB34YEWuGs4NGOYVLJB3L0A1zvwEM2b22mVnXqJ5TaEXfR98ATgT+StImUl91P4iIX1fdQaOgMJWUDRkqKDxd9WBmBvefN6c+PnZVMb9vXCp2Wfm8Xevzoqf0CzSu9jUsind6NhRFKs/OHuhoxfba49ltlvaU9r/52XEArDqg9LMweVNp7V0AWLvf5vqctZOKIp/IpUZ9K4r0L3lZqaxqYtpu7JJi+dp9i+PH2G3bvWhKcSwtK4qyXt3zRgBu6Ltqm21GXRsXH0XEL4BfAOdLmgm8BviQpEOAX5ICxJVD7WPIoBAR80corWZm3aGNg0JZRCwDLs8Dkg4DTmi0nd/RbLaDve6FRU7+2b5x9fGe/GuzOYo75pWbJtbHdxubekDeUlreVxqfuevatN6YjcXyUqvz3cZsAGBiT3H3vzmKu+/a/Klj1tfnLe+dVKx7eFp3c1/xszG+p7iTX74lrbvHuKJyeVXvhPp4b05r3wuKgodxpZruJzemiuhp44oczYyx6+rjs8etqI+fxxtpic55eO39wCXAGuBrwIuAcyPik422rfqcwnASdbGkpZLuLc2bIekGSQ/lv9PzfEn6gqSHJd2dH882M2s7impDi/1JRKwmFR/NBN4GfLrKhqOZU1gA/DNbvxP0HODGiLhQ0jl5+qOkipH983AU8JX816zrnDi9yCnM7CnuhMcrlc/3lqrw9hxTlNlvyo/SPt1XlMnv3lPkCib3pDv5jVE0CZ2oIiewvG/b/U/r2faud0Np+97Sj1tt7oObZw70sViTcwVzxhZ39E9smV4fX9a7W/pMY1fW5z2+afcirRNSTmWcBu5JZ8nmYl9jV4/a/Wxjrf/Br6J2Yl8LXJp7t66UxakcFCTtBcxj6w7xbh5s/Yi4WdL8frNPAY7N498ktZ39aJ5/aX6f6M8lTZM0OyKWVE2fmdmO0Aa5gCrulHQ9sC9wrqTJVOzKr2ovqZ8hvX7zfoqmD0GpO+2K9ij90D8J7JHH9wIWltZblOdtExQknQWcBbDPPvv0X2zW9taV7vSn9RTl9xti26/jpijK7Mfk+7z5Y4t5G0o/UONzrmB6T1EPsbZvQ328dm+9rlQP8dDmXbY55uLS3f0upbv2Dbn+Y01f8UDdPevm1scnjEnp+hXz6vNWbynWXbclfe61W4rP31O67d7Qmz7/+i1FPcu6TcW60ycU/6s5Py3+BztcB9QpkF6G9gLgkfzWzJnAO6tsWDWn8HrgwIjY2GjFqiIipOZjbkRcBFwEcPjhh3dGzDaz7tAe/RpVcUNEvKo2ERHLJF0JvGqIbYDqQeERYBylF0AP01O1YiFJs4Glef5iYO/SenOp2HmTWae5dvkhA85fn++gJ44tvmaH7Lbt16DcOmjm2LX18XG5TmKSitZFc8YWdRILt6TWPSt7i5xEuUz/dxtTH5cbS62L1pXu6ot0bpu7AFi+MdUp9JXupDf1Fvvq7Us5lGc3F/PGjilKNGq5huWrixZP06YUn/V3K4oczJTpLXwbcBsHBUnjgYnArNyQp3YyppBKXxpq1M1FrXOl9cBdkm6kFBgiotm3r11N6lzvwvz3P0vz3yPpClIF8yrXJ5hZO1J7v2Tnz4EPAHPY+sHj1aSGPw01yimUO1e6ut+yIeOlpMtJlcqzJC0CziMFgyslnQk8Drwpr34tqZb8YVIAqlT2ZdaJfrV07oDza2XmB04tWiQ99Oxz6uN77pLa/88aWzwHMFHbZt4f3Fg82rwultXHa3UCD2wobhif3Di1Pl5rlVTOHZTL/6fk5wdqOQKA8aXWURNzXcfiNcU+y/UANRPGlZ6T6Cvu+DdtST9Hz5ledNnTUyphnjCuqEd4dlJxjB2ujXMKEfF54POS3hsRXxzOPho90fxNSA9C5IPV5Ycjhtr2tEEWbVOmlVsd/eXQSTUza602eQahoYj4oqSj2fYVypcOulFWtbHvGQPMe0fFbc3Mukeo2tBCki4DPkt6R/MReTi8yraN6hROA94C7CupXHw0GVg+rNSa7eTGlyp/p44vunR4zvhUaVxr2glbd0lx4PhUzTZ/3DP1edNKD6+Ny7ewM8cUxU+Lt0yrjz9RevirptylxpSxqfnqkmeLoplNveUK3VRstLFUefzY0uJBtt2npfSXGxWO7SkK4FdtSNvvMakoHlq4eto2667bWFRkz5hYarJbaqo6ZnMLb9c7IKdACgAH5VKYpjSqU/gZ6VmBWcA/luavAe5u9mBmZp2uE4qPgHuBPRngWa9GGtUpPE6qEP7D4aXLzPp73own6+PlDu2eWJ+ajPaVuqEoP9y1vi/dQd87pqionjWuuOuu7WtiKffw1Obirn/RhpRTWLy+mFe+k7/p6f0A2GNKsc+VG4pK5ZXLUlPRSVOLB+J6NxY5iSWL0v7HTChyQsuemlIfV+4a/MlHitxFz+bis46ZvW3X3o8tKXW9vbn4X81v8Ja2URNNtT5qxUt2amYB90u6ja1bjL6u0YaNio/WMERmKSKmDLbMzKwrtfdLdmrOH+6GjXIKkwEkfYKUDbmM1O71dGDAV3qY2dAuOeKSVieh87WyrWIHFB9FxE3D3bbqE82vi4hDS9NfkfRr4OPDPbCZWSfqhDqFfqU8u5B6pFhXpXSnalBYJ+l04Ip8oNOAdUNvYmZmrVAr5YH0vhpST9QvrrJt1ecU3kJ6+vipPLwxzzMz27lExaFNRPI94Pgq61fKKUTEY6RIY2a282qu9VHLSHpDabKH9NzChkFW30qj1kcfiYi/L3WMt5VhdIhnZtbZ2igXMISTS+NbgMeoeGPfKKfwQP57x5BrmZntBERnVDRHxLA7FW0UFH5P0pHAv0ZEi54WMTNrIx0QFCTNBb4IvCTPugV4f0QsarRto4rmucD/BZZKuknSpySdJGnG9iTYzKwjRdFTaqOhxS4hve5gTh6uyfMaGjIoRMTZEXE0qQ+Nc0md4L0TuFfS/duTYjOzjtRXcWit3SPikojYkocFwO6NNoLqTVInkF7nNjUPTwC/GE5Kzcw6WYfkFJZJequkMXl4K7Cs4VY0bn10EfB8Uq+ovyD1mvq5iFixvSk2M+tIrf/Br+JPSHUK/0RK8c+o+A6cRhXN+wC7Ag8Bi4FFwMphJtLMrLO12YNpQ7gAOKN2A5/rgT9LChZDatQh3gn5EennA0cDHwIOlrQcuDUiztvelJuZdZI2KBqq4pByiU5ELJf0wiobNnyiOb+5515JK4FVeTgJOBJwUDCznUtnBIUeSdP75RQq9WDRqE7hfaQcwtHAZlK51M+Ai4F7tifFZmadqBO6uSC9KfNWSVfl6TcCn6yyYaPIMR+4CvhgRDT9Wjczs67SIXUKEXGppDuAV+ZZb4iISo8RNKpT+KvtTZyZWbdQHjpBDgJNP09W9X0KZmYGHZFT2B4OCmZmTeiQ1kfD5qBgZtYMBwUzMwM65iU728NBwcysGc4pmJlZjesUzMys0OVBoWrX2WZmRuu6zpb0XEnfkPSdkd97wUHBzKyqYERfsiPpYklLJd3bb/4Jkn4j6WFJ5wBExCMRceZIfZTBOCiYmVUkRjynsAA4YatjSGOALwEnAgcBp0k6aOQ+xdAcFMzMmhEVB5gl6Y7ScNY2u4q4mfSa47IjgYdzzmATcAVwyih9mm24otnMrAmKytmAZyLi8GEcYi9gYWl6EXCUpJmknk5fKOnciPj0MPbdkIOCmVlVLewlNSKWAX8x2sdxUDAza8IOeE5hMbB3aXpunrdDuE7BzKwJ6qs2bIfbgf0l7StpF+BU4OqRSHsVDgpmZs2oXtE8VdJFkk4ebFeSLgduBQ6UtEjSmRGxBXgPcB3wAHBlRNw3eh9oay4+MjOrqrnmpqsiYpsWR1vtLuK0QeZfC1zbXOJGhoOCmVkzurybCwcFM7OKag+vdTMHBTOzJqivu6OCK5rNzKqqWslcsaK5HTmnYGbWhCaamzasaG5HDgpmZs3o7tIjBwUzs2a4otnMzJIAqneI15EcFMzMmrCdXVi0Pbc+MjOrqMmX7Lj1kZlZV4topvjIrY/MzLqdK5rNzKzgoGBmZjXOKZiZWRJAb3dHBbc+MjNrglsfmZlZwa2PzMysxnUKZmaWFN1idy0HBTOzigSoyyuaHRTMzJogd4hnZmaAi4/MzKysqb6POpKfUzAza4KfUzAzs4KfUzAzMwDCrY/MzKysu2OCg4KZWTPcJNXMzAoOCmZmBqSio75WJ2J0OSiYmVUkwsVHZmZW0tfdWQUHBTOzqnaC4iM/0Wxm1gRFVBrwE81mZjsBP9FsZmZJ93eI56BgZlZVAO7mwszMatwk1czMCg4KZmYG5CapDgpmZga4otnMzLbmoGBmZkBufdTdjzQ7KJiZVRYQDgpmZlbT5cVH7vvIzKyqWuujKoP7PjIz2wm47yMzM6vr8uIjBwUzs6oioLe31akYVQ4KZmbNcE7BzMzqHBTMzCwJ931kZmZZQPjhNTMzq3M3F2ZmBqT6hD4HBTMzq3FFs5mZ1YRzCmZmlvglO2ZmVuPXcZqZWU0A0eXdXLSk62xJF0taKune0rwZkm6Q9FD+O70VaTMzG1Tkl+xUGTpUq96nsAA4od+8c4AbI2J/4MY8bWbWVqIvKg2dqiVBISJuBpb3m30K8M08/k3g9TsyTWZmlXR5TkHRopp0SfOB/4qIg/P0yoiYlscFrKhN99vuLKD24oqDgXv7r2M7vVnAM61OhLWdeRGx+/bsQNIPSNdXpdWBO4FrIuKa7TnujtSWQSFPr4iIIesVJN0REYePakKt4/i6MBu+dnpH81OSZgPkv0tbnB4zs51OOwWFq4Ez8vgZwH+2MC1mZjulVjVJvRy4FThQ0iJJZwIXAq+W9BBwXJ5u5KJRTKZ1Ll8XZsPUsjoFMzNrP+1UfGRmZi3moGBmZnVtHRSa6Q5DyRckPSzpbkkval3KbaSN1LUg6Yy8/kOSzhjoWGY7s7YOCjTXHcaJwP55OAv4yg5Ko+0YC9jOa0HSDOA84CjgSOA897FltrW2DgpNdodxCnBpJD8HptWee7DON0LXwvHADRGxPCJWADewbaAx26m1dVAYxB4RsSSPPwnskcf3AhaW1luU51n3avZa8DVi1kAnBoW6SO1p3abWfC2YjZBODAqDdYexGNi7tN7cPM+6V7PXgq8RswY6MSgM1h3G1cDbc8uTFwOrSkUL1p2avRauA14jaXquYH5NnmdmWVu/jjN3h3EsMEvSIlLLkQuBK3PXGI8Db8qrXwu8FngYWA+8c4cn2EbNSFwLEbFc0ieA2/N6F0RE/8prs52au7kwM7O6Tiw+MjOzUeKgYGZmdQ4KZmZW56BgZmZ1DgpmZlbnoGBmZnUOCtZSkl4vKST9/igeo1fSXZLm5OnHJN2Tu9W+XtKe/ebflYcv5Pn/IOlJSWePVhrN2oWfU7CWkvRtYA7w3xFx3gDLx0bElu08xtqI2K00/RhweEQ8I+lTwG4R8b7y/AH2cT6wNiI+uz1pMWt3zilYy0jaDTgGOBM4tTT/WEm3SLoauF/SmHy3fnu+u//z2vaSbpT0y3yHf8owknEzsN9IfB6zbtDW3VxY1zsF+EFE/I+kZZIOi4g787IXAQdHxKOSziL1X3SEpF2Bn0q6ntQN9h9FxGpJs4CfS7o6msv+ngTcU5r+kaTePP7NiPin7fuIZp3FQcFa6TTg83n8ijxdCwq3RcSjefw1wCGS/jhPTyW9VW0R8ClJLwP6SO9G2IP0boVGaj/+dwMfK81/xUDFR2Y7CwcFa4n8asxXAn8gKYAxQEj6cF5lXXl14L0RcV2/fbwD2B04LCI25zqB8RWT4B9/swG4TsFa5Y+ByyJiXkTMj4i9gUeBlw6w7nXAuySNA5B0gKRJpBzD0hwQXgHM21GJN+tWzilYq5wGfKbfvH/P87/db/7XgfnALyUJeJr0PuZ/Ba6RdA9wB/DgCKSrXKdwd0S8fQT2adYx3CTVul7/JqnD3Mf5uEmq7QRcfGQ7g9Xlh9eaJekfgLeydT2HWVdyTsHMzOqcUzAzszoHBTMzq3NQMDOzOgcFMzOr+//6M0BKG+PT8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's double check that we have the data we want in the area vs width space\n",
    "plots_area_vs_width(selected_peaks, log=True, bins=100, range=[[2, 4], [1, 3]])\n",
    "plt.title(\"Area vs width in log-log space with selected S1s\\nall data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area vs width in lin-lin space with selected S1s\\nall data')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select data in box\n",
    "plots_area_vs_width(selected_peaks, log=False, bins=100, range=[[10**2, 10**4], [10**1, 10**2.5]])\n",
    "plt.title(\"Area vs width in lin-lin space with selected S1s\\nall data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting waveforms\n",
    "Let's check that these are indeed big S1s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lnwGfz+9ze5a0VPQ4BfhH+m+vEKkX+uH8/o4CDqr08G4InA88CdxEKjz3zzE/FxEP9jxIfc8v5K+XS0Rc1rJ6RjuHAHfltonDSRcw9uYLwHakWH9H5bORLfX9B5B0i6S+xjMzQ0u39ZmZ2Yoqz2bPADYO//A2M+sYzyCbmQ0BuRf3KODHLo7NzDrLBbKZ2QpO0hbA46QL6P630WDMzIYBt1iYmZmZmVV4BtnMzMzMrMIFspmZmZlZhQtkMytG0uqSTpR0t6QnJV0vaa/K/rdImifpaUl/7FlfOO/7pqS/5NfNk/SBlrGnS/qzpJckHVYjlm0kzcnnmiNpm5b920maJWmRpIckHdXPWJL0NUmP5sfXVLkbhqTdJV0raaGkOyRNzduPyeMvkvSspMWV57fkYybkXDyd3/db+4jhIkmhdJvrvuL8o6T5OY4bKms2tx73kzzWq/tNYjp2sxz7jMq26vtaJOmZ/H3p9Q53ko6TdJOkFyUd28v+9+XPzFOSfi1pTGXfFpJmSnpC0l8l7d/y2jUlnSDpkXzMrF7GX03SXEn3Vbbt0vIeFuWcHNAuJ2Y29LlANrOSViGtO/wmYBTwWeCMXASOJa1h+5+kO8jNBn5Ree1TwL75dYcC35a0c2X/DcBHgWtpQ+lGHL8hLZk2miW3W14t7x9LWiv4h6S7370auLCfIacC7wS2Bl6X4/xIHmtV0u2Vf5hjPwj4H0lbR8SXI2JERIwgrfV7Zc/ziNgqj3066WYd65JutPErpRt8VN/P+4FV271v0ioY60fEyBzzDKW7B1bHeiN933WvN98j3cDk76rvK7+3rwEXR8QjfYzxV+DTpHWMlyJpK1LuDiHdwvtp4IS8bxXS9/G3pM9Mz3vavDLE9Lxvi/zvx3s5/6dIa0ZX38OlLe9hH9INSc7vKxFm1kUiwg8//PCjsQdwI3AAqbi5orJ9LeAZYFIfrzsH+EQv2y8DDmtzzrcB95MvVM7b7gH2zF9/mXQ76rrv4QpgauX5h0h3wYNU1AWwZmX/NcB7W8Y4DLisZdvmwHPA2pVtlwKHV56PAm4DdsznWaVmzDsAzwI7VLatQirGX5fHenWbMd5DugX0scCMPo4RcAdwaI2YZgDHtmz7MvCzyvOJwPOkO+i9llS0Vr+PFwLH5a8nke7CN7Kfc24CzAX2Au7r57iTgJOa+G/EDz/8KP/wDLKZNUbSeqQi8BZgK9IsMAAR8RRwe97e+rpXAP+UXzcQWwE3RkR1GZ8bK+faEVgg6QpJD0s6V9L4NuPdUHl+Q89YEfEQaRb4n5Vubb0TsDGpkK8T5x0R8WRvY2dfBr4PvOxud5J+K+noXrY9C1wFXEyaqe/xcWBWRNzYy1gnSDqh8nwk6fbZ/7fNe9gF+AfgzDbH9aX1c3E7qUDevI/jRSqcIf0ScDfwhdxicVMvLRLfJd1VsddbdwNIWgt4F+kvDWY2DLhANrNG5NaD04BTIt3GeQTpdsFVT5BmClv9gFQ0XTDA07c716tIbRxHAeOBO0lFbt3xngBGVPqQTwc+R5oNvhT4j0i3vF6uOCVNBt5AKvJeJiL2iYivtm7Lr98buDAiXspjbURqC/lcH2N9NCI+Wtl0HHBiRLS7ffahwK8iYlGb4/rSXw7+TLql9qckrSrpbaT2nTXzca8iFctPABsARwCnKK0rTe5XXjkizm4TwxTgEeCSAb4HMxtiXCCbWXGSVgJOJc0EHpE3LwJGthw6EqjOniLpG6Si590tM8D9na96odX4Gud6Bjg7Iq6JiGeBLwA7SxrVcgHaD/qIfSSwKCJC0iTg58AHgNVIM6KflvT2GqH3GWfO4QnAURHxYo2x/i4iXoiI3wNvk/SOvPl/gf+KiNZi9GWULmh8K/CtNsetCRzI8s289pmDiHiB1Pv9dtIM+idILR89RfszwAvAFyPi+Yi4BPgj6X2vBXwdOLJGDIcCP637eTOzoc8FspkVlWdVTyT15h6QixxI7RJbV45bi9Rvektl2xdIvaJvi4iFdc8ZlYutIuKePObrKjO8kPpue851I6kH9+9DVMaqXoB2eG+x5697xnotcFtEXBARL0XEn0kXo+1Fe7cAm0qqzqL3jD0SmAz8QtKDLLlQ7j5Ju9QYG1LPcc8FeW8BviHpwTwewJWS3tfL63YDJgD35GM/CRwgqfUCyf2BBaRWjoFq/VxsCqxO6rsmIm6MiDdFxLoRsQewKXB1PvxlrSIs+V5ult/Dpfk9nAWsn9//hMr5NiK9358ux3swsyHGBbKZlfZ90ooC+0ZEte/zbOC1kg6QtAbpT/035vYLJH0GeB/w1oh4tHXQvFTXGqQe1FUlrZFnWXtzMbAYOFJp6bmeWeyZ+d+TgP2VloJblbSyxmX9zK7+FPi/kjaUtAFpJvPkvO86YDOlpd4kaSJpRYTeirelRMRtwPXA5/P72Z9UyJ/JkraBbfJj7/yy7Un9xUuRNEnSXpJekdsRDgZ2ZUnbwOakQrRnPEircfTWfjCdVFj3HPsDUtG/R8txtWZeczxrkP6ftEp+ryvn3acB++Zl19Yi9T2f1dOXLel1+fg1JX2SdDvuk/NrZ5EuvvyMpFUkvQF4M6k152Zgo8p7+DDwUP662v5yCOni0dv7ew9m1mWavkrQDz/8GD4P0sVpQVo9YVHl8f68/63APNKfxi8GJlReG6Qe3urrjqnsvzgfU33s1k8s2wJz8rmuBbZt2f+vpJUuHgPOBTbqZyyR/ly/ID++ztIrK7ybVJA9Sfrz/9eAlVrGOIyWVSzy9gn5vT1D6rl9ax8xTKBlFQvg9z05Iv1SclWO4XHSjPP+/bynpVaxIBXBP+jj2GNpWcUC2BB4kV5Wwmgdi1TQtn7vDqvsfx+p0H2KtKzbmMq+b+Tv0aL8fl/dcq6tgCvza2/t6z2TZolftopF/jx+qOn/dvzww4+yD0W4pcrMzMzMrIdbLMzMzMzMKlwgm5mZmZlVuEA2MzMzM6twgWxmZmZmVrFK0wHUMXbs2JgwYULTYSyXZ599ljXWWKPpMMzMzMwsmzNnziMRMa51+5AokCdMmMDs2bObDsPMzMzMuoiku3vb3rEWC0kbSfqjpFsl3SLpqLz9WEn3S7o+P/ZuN1Y3mDNnTtMhmJmZmVkNnexBfhH4RERsCewI/JukLfO+b0XENvlxXgdjWGG4QDYzMzMbGjrWYhERDwAP5K+flDSXdGclMzMzM7MVVpFVLCRNIN3W9aq86QhJN0r6iaTRJWIwMzMzM6uj4wWypBHAmcC0iFgIfB+YCGxDmmH+7z5eN1XSbEmz58+f3+kwO27KlClNh2BmZmZmNXS0QJa0Kqk4Pi0izgKIiIciYnFEvAT8CNiht9dGxPSImBwRk8eNe9nqG2ZmZmZmHdGxHmRJAk4E5kbE/1S2r5/7kwH2B25uN9YTTzzBueee25lAKw46CJ55Jn291lpw+umDN/b555/PnnvuOXgDmpmZmVlHdHId5DcAhwA3Sbo+bzsGeK+kbYAA7gI+0m6gUaNGse+++3YmyopnnoGe5ZYnT4bBPOUDDzxQ5D2YmZmZ2fLp5CoWlwHqZdewWNbNzMzMzIamIqtYGGy//fZNh2BmZmZmNbhALsQFspmZmdnQ4AK5kBkzZjQdgpmZmZnV4AK5kKeffrrpEMzMzMysBhfIZmZmZmYVLpALGTt2bNMhmJmZmVkNLpAL8a2mzczMzIYGF8iFzJo1q+kQzMzMzKwGF8iFzJs3r+kQzMzMzKwGF8hmZmZmZhUukPswciRISx5jxjQdkZmZmZmVsErTAayoZs5c+vnkycs33sEHH7x8A5iZmZlZEcN6BnnUqKVniUeO7Ny55s+f37nBzczMzGzQDOsZ5IULYfbsMue64IILmDp1apmTmZmZmdmADesZZDMzMzOzVi6QzczMzMwqXCAXsssuuzQdgpmZmZnV4AK5kC222KLpEMzMzMysBhfIhUyfPr3pEMzMzMysBhfIZmZmZmYVLpDNzMzMzCpcIBcyfvz4pkMwMzMzsxpcIBey5557Nh2CmZmZmdXQsQJZ0kaS/ijpVkm3SDoqbx8j6Q+S/pL/Hd2pGFYk559/ftMhmJmZmVkNnZxBfhH4RERsCewI/JukLYGjgYsiYjPgovy8691zzz1Nh2BmZmZmNXSsQI6IByLi2vz1k8BcYENgP+CUfNgpwDs7FcNgGjkSpCWPMWOajsjMzMzMOmGVEieRNAHYFrgKWC8iHsi7HgTW6+M1U4GpsGJc4DZz5tLPJ09uJg4zMzMz66yOX6QnaQRwJjAtIhZW90VEANHb6yJiekRMjojJ48aN63SYHTd16tSmQzAzMzOzGjpaIEtalVQcnxYRZ+XND0laP+9fH3i4kzGsKObOndt0CGZmZmZWQydXsRBwIjA3Iv6nsusc4ND89aHAbzoVw4rk0ksvbToEMzMzM6uhkz3IbwAOAW6SdH3edgzwVeAMSR8C7gbe3cEYzMzMzMyWSccK5Ii4DFAfu9/SqfOamZmZmS0P30mvkD322KPpEMzMzMysBhfIhXTDShxmZmZmw4EL5EJmzJjRdAhmZmZmVoMLZDMzMzOzij4v0pO0sK99PYcAD0TE5oMbkpmZmZlZc/pbxeL2iNi2vxdLum6Q4+lakyZNajoEMzMzM6uhvxaLA2q8vs4xXWnkSJDSY8yY9sfvuuuunQ/KzMzMzJZbnwVyRNwBIGktSSvlrzeX9I58C+m/HzMczZwJs2enx2OPtT/+rLPOan+QmZmZmTWuzkV6s4A1JG0IXEi6O97JnQyqGz3yyCNNh2BmZmZmNdQpkBURTwNTgBMi4kBgq86GZWZmZmbWjFoFsqSdgPcDv8vbVu5cSN1pzTXXbDoEMzMzM6uhToF8FPAZ4OyIuEXSpsAfOxtW9zn44IObDsHMzMzMamhbIEfErIh4R0R8LT+/IyKO7Hxo3WXOnDlNh2BmZmZmNfS3DjKQVq4APglMqB4fEbt3LqzuM2fOHLbffvumwzAzMzOzNtoWyMAvgR8APwYWdzYcMzMzM7Nm1SmQX4yI73c8EjMzMzOzFUCdi/TOlfRRSetLGtPz6HhkXWbKlClNh2BmZmZmNdSZQT40//upyrYANh38cMzMzMzMmtXnDLKkDQAiYpNeHi6Ol5FvNW1mZmY2NPQ3g/zj3EpxMXA+cFlEvFgkKjMzMzOzhvRZIEfE3pLWAHYD9ge+KekeUrF8fkTcUyZEMzMzM7Ny+u1BjohnyQUxgKRNgL2A4yW9MiJ26HyIg2fUKFi4cMnzkSPLndtrIJuZmZkNDX0WyJImRcS8/PXqEfFcRNwJnCDpOqDfW8NJ+gmwD/BwRLw2bzsW+Bdgfj7smIg4b/nfRj0LF8Ls2aXOtjQXyGZmZmZDQ3/LvP2s8vWVLfu+FxHPtxn7ZGDPXrZ/KyK2yY9ixXHTZsyY0XQIZmZmZlZDfwWy+vi6t+cvExGzgAUDCaobPf30002HYGZmZmY19FcgRx9f9/Z8WRwh6UZJP5E0uq+DJE2VNFvS7Pnz5/d12Aph5EiQljzG+DYqZmZmZkNWfxfpvUrSd0izxT1fk59vOMDzfR84jlRgHwf8N/DB3g6MiOnAdIDJkycvT0HecTNnLv188uSXHzN27NgywZiZmZnZcumvQK7eOa/10rYBXeoWEQ/1fC3pR8BvBzLOUORbTZuZmZkNDf2tg3yKpHHAxsBfI+Lx5T2ZpPUj4oH8dH/g5uUdc6iYNWsWu+66a9NhmJmZmVkb/d1q+sPALcB3gXmS3rEsA0s6nbT6xWsk3SfpQ8DXJd0k6UbgzcDHBx760DJv3rymQzAzMzOzGvprsZgGbBUR8yVtCpwGnFN34Ih4by+bT1y28MzMzMzMyupvFYvnI2I+QETcAaxeJiQzMzMzs+bUWcWi1+cRcWTnwuo+Bx98cNMhmJmZmVkNdVexgDa3lrb+zZ8/n4033rjpMMzMzMysjX5XsSgZSLe74IILmDp1atNhmJmZmVkb/a1icWy7F9c5xszMzMxsKOmvxeLDkhb2s1/Ae4BjBzUiMzMzM7MG9Vcg/whYu83rfzSIsXSNkSNBWvJ89Gi4/PJdmgvIzMzMzGrrrwf5CyUD6SYzZy79fPJk2GKLLZoJxszMzMyWSX/rINsgmj59etMhmJmZmVkNLpDNzMzMzCraFsiS1i0RiJmZmZnZiqDODPKfJP1S0t5S9dIzWxbjx49vOgQzMzMzq6FOgbw5MB04BPiLpC9L2ryzYXWfPffcs+kQzMzMzKyGtgVyJH+IiPcC/wIcClwt6RJJO3U8wi5x/vnnNx2CmZmZmdXQ3zrIwN97kA8mzSA/BHwMOAfYBvglsEkH4+sa99xzT9MhmJmZmVkNbQtk4ErgVOCdEXFfZftsST/oTFhmZmZmZs2o04P82Yg4rlocSzoQICK+1rHIzMzMzMwaUKdAPrqXbZ8Z7EC63dSpU5sOwczMzMxq6LPFQtJewN7AhpK+U9k1Enix04F1k5EjQZoLbMHo0bBgQdMRmZmZmVlf+ptB/hswG3gWmFN5nAPs0fnQusfMmXDMMZcyezY89ljT0ZiZmZlZf/qcQY6IG4AbJJ0WEZ4xNjMzM7Nhob8WizMi4t3AdZKiuou0PPLrOh6dmZmZmVlh/S3zdlT+d5+BDCzpJ/m1D0fEa/O2McAvgAnAXcC7I2JYNB3stJO7UszMzMyGgj57kCPigfzlI8C9EXE3sDqwNak/uZ2Tgdb7Kx8NXBQRmwEX0fsKGV1p9OhxTYdgZmZmZjXUWeZtFrCGpA2BC0l31Du53YsiYhbQul7DfsAp+etTgHfWDXSoO++8GU2HYGZmZmY11CmQFRFPA1OAEyLiQGCrAZ5vvcrM9IPAen2eVJoqabak2fPnzx/g6czMzMzMlk2tAlnSTsD7gd/lbSsv74kjIoDoZ//0iJgcEZPHjeue9oS0JvKSx5gxTUdkZmZmZlX9XaTX4yjSnfPOjohbJG0K/HGA53tI0voR8YCk9YGHBzjOkDNhwiQgrYlcNXlyA8GYmZmZWZ/aFsi5l3hW5fkdwJEDPN85wKHAV/O/vxngOEPOdtvt2nQIZmZmZlZD2xYLSZtLmi7pQkkzex41Xnc6cCXwGkn3SfoQqTD+P5L+Arw1Px8WZs48q+kQzMzMzKyGOi0WvwR+APwYWFx34Ih4bx+73lJ3jG7y+OOPNB2CmZmZmdVQp0B+MSK+3/FIhqmei/Z6jB4NC1oXxzMzMzOzYuoUyOdK+ihwNvBcz8aIcBm3DNZYY81et/uiPTMzM7MVS50C+dD876cq2wLYdPDD6V57731w0yGYmZmZWQ11VrHYpEQg3W7u3DlsscX2TYdhZmZmZm3UWcViTUmflTQ9P99M0j6dD627zJ07p+kQzMzMzKyGOnfSOwl4Htg5P78f+GLHIjIzMzMza1CdAnliRHwdeAEgIp4G1P9LzMzMzMyGpjoF8vOSXkG6MA9JE6msZmH17L77lKZDMDMzM7Ma6hTIxwLnAxtJOg24CPj3TgY1nPWsiyzBmDFNR2NmZmY2/NRZxeJCSXOAHUmtFUdFhG8Lt4xmzjyLKVOm1jhuyddeE9nMzMysvDqrWFwUEY9GxO8i4rcR8Yiki0oEZ2ZmZmZWWp8zyJLWANYExkoazZIL80YCGxaIbVCMGgULF6avR45sNhYzMzMzW/H112LxEWAasAEwhyUF8kLg+M6GNXgWLoTZs5uOggHdJKSnH7nH6NGwwDf4NjMzM+uoPgvkiPg28G1JH4uI7xaMqSsNpECu9iODe5LNzMzMSqhzkd53Je0MTKgeHxE/7WBcXee882aw994HNx2GmZmZmbXRtkCWdCowEbgeWJw3B+ACeRk8++zTTYdgZmZmZjW0LZCBycCWERGdDsb6555kMzMzs86rUyDfDLwSeKDDsXS1ddYZu9xjuCfZzMzMrPPqFMhjgVslXU3lFtMR8Y6ORdWFfKtpMzMzs6GhToF8bKeDGA6uvXYW2223a9NhmJmZmVkbdVaxuKREIN3urrvmuUA2MzMzGwL6u5Pek6TVKl62C4iI8H3pzMzMzKzr9HejkLU7dVJJdwFPkpaNezEifLnZAFRXtfCKFmZmZmaDo04Pcqe8OSIeafD8RXXiJiHVVS28ooWZmZnZ4Fip6QCGi8cem990CGZmZmZWQ1MFcgAXSpojaWpvB0iaKmm2pNnz5w/94vLKKy9oOgQzMzMzq6GpAvmNEbEdsBfwb5JetrxDREyPiMkRMXncuHHlIzQzMzOzYamRAjki7s//PgycDezQRBzdpOeCvZ7HmDFNR2RmZmY2NBW/SE/SWsBKEfFk/vptwH+VjqO0bbfdpaPj+zbUZmZmZoOjiVUs1gPOVlqfbBXgZxFxfgNxFLXJJls0HYKZmZmZ1VC8xSIi7oiIrfNjq4j4UukYmnDWWdOLns8tF2ZmZmYD0+Q6yNZBbrkwMzMzGxivg2xmZmZmVuECuZBXvnJ8o+evtly43cLMzMysb26xKGTnnfds9PzVlovdd0+Fco/Ro2HBgvIxmZmZma2IPINcyBVXrDgLdcycCbNnL3ksXuwL+szMzMx6eAa5kAcfvKfpEPrkC/rMzMzMlvAMspmZmZlZhQtkexlf0GdmZmbDmVssCpkyZWrTIdRWbblwu4WZmZkNN55BLuTOO+c2HYKZmZmZ1eACuZDrrru06RAGxLesNjMzs+HGLRbWL69wYWZmZsONZ5BtmXhG2czMzLqdZ5AL2WmnPZoOYVB4RtnMzMy6nWeQCxk9elzTIXSEl4QzMzOzbuMCuZDzzpvRdAgdUb1t9WOPNR2NmZmZ2fJzgWyDxv3JZmZm1g3cg2yDxv3JZmZm1g08g1zIhAmTmg6hOM8om5mZ2VDkGeRCtttu16ZDKK51Rnn33VOhDDB6NCxYUD4mMzMzs3Y8g1zIzJlnNR1C46oX9C1e7NllMzMzWzF13QzyqFGwcOGS5yNHNhdL1eOPP9J0CCsU9yubmZnZiqrrCuSFC9MMpQ0tPf3KvXE7hpmZmZXUSIEsaU/g28DKwI8j4qtNxFHSGmus2XQIK7TWGeWqau8yuGC2pbX+1aiVPy/DW+vnw58HM6ujeIEsaWXge8D/Ae4DrpF0TkTcWjqWkvbe++CmQxiy+rvYrx3/z7CzVoTio91fjdy+M/xUP5cjRy79+fDnwczqaGIGeQfgrxFxB4CknwP7AX0WyM8++yy33XZbr/u2334iixat/PfnI0Ys5u67bx/UgAfD7bffzMSJr206jK5w0kn1jz300Imk38mW3ahRi7n66hXvs9QJ1f+O2r3v6rEjRizmzDOXHLs8+R6odv/NjxhRPqblMZw+d52ycOHmnHnmkv9n3H33kn2d+jz4+2bWXRQRZU8ovQvYMyI+nJ8fArw+Io5oOW4qMDU923576H2KaKj8UPrFL37BQQcd1HQYtgx22GEiTzwxdAqr5VH976jd+x4q/80NVcPpc9cpTXxG/X0zG6o0JyJe9relFfYivYiYDkwHmDx5cvT9J9SVgc1LhTVg6623HptvvuLHaUs8/njTEZS05L+j9u97aPw3N1QNr89dp5T/jPr7ZjY09dWy2cQ6yPcDG1WevypvMzMzMzNrXBMF8jXAZpI2kbQa8B7gnAbiKGrKlClNh2BmZmZmNRRvsYiIFyUdAVxA+jvYTyLiltJxmJmZmZn1ppFbTUfEeRGxeURMjIgvNRFDaWed5VtNm5mZmQ0FjRTIZmZmZmYrKhfIZmZmZmYVxddBHghJ84G72x5Y1ljgkaaDGCac67Kc77Kc77Kc77Kc73Kc64HZOCLGtW4cEgXyikjS7N4WlrbB51yX5XyX5XyX5XyX5XyX41wPLrdYmJmZmZlVuEA2MzMzM6twgTxw05sOYBhxrstyvstyvstyvstyvstxrgeRe5DNzMzMzCo8g2xmZmZmVuEC2czMzMyswgWymZmZmVmFC2QzMzMzs4pVmg5gqJA0CdgP2DBvuh84JyLmNhdVd3Kuy3K+y3K+y3K+y3K+y3GuO8szyDVI+nfg54CAq/NDwOmSjm4ytm7jXJflfJflfJflfJflfJfjXHeel3mrQdJtwFYR8ULL9tWAWyJis2Yi6z7OdVnOd1nOd1nOd1nOdznOded5Brmel4ANetm+ft5ng8e5Lsv5Lsv5Lsv5Lsv5Lse57jD3INczDbhI0l+Ae/O28cCrgSOaCqpLTcO5LmkazndJ03C+S5qG813SNJzvUqbhXHeUWyxqkrQSsANLN8NfExGLm4uqOznXZTnfZTnfZTnfZTnf5TjXneUZ5Po2B97E0h/Ex4F5TQXUrSLiJeBPAJLeCOwMrA38ocm4upg/2wX5812cP98F+fNdlD/bHeQe5Br6uVr0575adHBJurry9b8Ax5N+uB7rXA8+f7bL8ue7LH++y/Lnuxx/tjvPLRY1+GrRciRdFxHb5q+vAfaOiPmS1gL+FBH/2GyE3cWf7bL8+S7Ln++y/Pkux5/tzvMMcj2+WrSclSSNlrQu6Re4+QAR8RTwYrOhdSV/tsvy57ssf77L8ue7HH+2O8w9yPVMw1eLljIKmEP6U1FIWj8iHpA0Im+zwTUNf7ZL8ue7rGn4812SP9/lTMOf7Y5yi0VNvlq0WZLWBNaLiDubjqXb+LPdPH++O8ef7+b5890Z/mx3lgtkMzMzM7MKt1jUJGkSsB9L/6Z2TkTMbS6q7pRzvSFwVUQsqmzfMyLOby6y7peXZdoBuDkiLmw6nm4j6Ujg7Ii4t+3BNij8s7ss//xuhn92Dz5fpFdDP8upnO7lVAZXLiB+A3wMuFnSfpXdX24mqu7Vz7JMn/dnuyOOA66SdKmkj0oa13RA3cw/u8vyz+9y/LO789xiUYOXUylH0k3AThGxSNIE4FfAqRHx7eoSQjY4vCxTWZKuA7YH3gocBLyDdFHT6cBZEfFkg+F1Hf/sLss/v8vxz+7Oc4tFPT3Lqdzdst3LqQy+lXr+LBcRd0naDfiVpI3xVdCdsJKk0aS/Ji21LJMkL8s0+CLfaexC4EJJqwJ7Ae8Fvgl4Rnlw+Wd3Wf75XY5/dneYC+R6puHlVEp5SNI2EXE9QJ6J2Af4CeDfiAefl2Uqa6mc5pnNc4Bz8pX+Nrim4Z/dJfnndzn+2d1hbrGoycuplCHpVcCLEfFgL/veEBGXNxDWsONlmTpD0uYRcVvTcQwn/tldjn9+N88/uwePC2QzMzMzswqvYmFmZmZmVuEC2czMzMyswgVyDZLGS1q56TiGA+e6LOe7LOe7PEkbSlo3f72BpAMk+YKxDnG+y3GuO8sFcj13AfdIOk7S+KaD6XJ34VyXdBfOd0l34XwXI+kI4B5Szg8GbgXOAK6XdHijwXUh57sc57rzfJFeDZJeAp4HVgMWA78Hvh8Rv280sC7kXJflfJflfJcl6Q5gVeBBYBvgRuAs4P3AahHx6uai6z7OdznOdee5QK4h/0/tYOA54HBg97zrHmB6RHylqdi6jXNdlvNdlvNdlqRngY8DFwHzgP0i4lxJHwSOjwivPT2InO9ynOvOc4FcQ/6f2nsi4oz8fFPS/9wOA9aNCPcUDhLnuiznuyznuyxJd5Jm6u8H3ghcR5plOxhYPSImNhhe13G+y3GuO889yAMQEXdExKdJC8+/r+l4uplzXZbzXZbz3XHfBjYlFRCfAdYDvghMAr7bYFzdyvkux7nuMM8g1yDpUOCSiLir6Vi6nXNdlvNdlvNdnqRXAytFxG2SxgK7AXdGxJxmI+tOznc5znVneQa5hog4pfo/NEljJU2XtG2DYXUl57os57ss57u8iPhry+293wa81FQ83c75Lse57qxVmg5gKJD0nZZNo0h9PhtK+mtEHNVAWF3JuS7L+S7L+S7L+S7L+S7Hue48t1jUkC+sCUC97A5fWDN4nOuynO+ynO+ynO+ynO9ynOvOc4tFPX8BngY+CbwZOCRv/zxLlmmyweFcl+V8l+V8l+V8l+V8l+Ncd5gL5HpeR7oq9IvA3sANefstEXFJY1F1J+e6LOe7LOe7LOe7LOe7HOe6w1wg1xARz0XEMcCOpKtE/x/pTxs2yJzrspzvspzvspzvspzvcpzrznMP8jKSJOCfgY2AX0TEvIZD6lrOdVnOd1nOd1nOd1nOdznOdWd4FYtlFBEh6S5gZTwD31HOdVnOd1nOd1nOd1nOdznOdWc4kTVIuljSj/PXXwX+APwAuF7SQY0G12Wc67Kc77Kc77Kc77Kc73Kc685zi0UNkp4EjgJOBp4CLgfOAD4IrBMRk5qLrrs412U532U532U532U53+U4153nFot6XiTd33zl/Dg+In6d1yFsXazblo9zXZbzXZbzXZbzXZbzXY5z3WEukOs5F/g48ErgDuA7kg4E3gJc12RgXci5Lsv5Lsv5Lsv5Lsv5Lse57jC3WNQgaRRwEvDOll03Ae+JiLnFg+pSznVZzndZzndZzndZznc5znXnuUBeBpI2AbYEVgfujAj/ltYhznVZzndZzndZzndZznc5znXnuEA2MzMzM6vwMm9mZmZmZhUukM3MzMzMKryKRU2SNgWmkG7luBi4DfhZRCxsNLAu5FyX5XyX5XyX5XyX5XyX41x3lmeQa5B0JOkONWsA/0Rqht8I+JOk3ZqLrPs412U532U532U532U53+U4153ni/RqkHQTsE1ELJa0JnBeROwmaTzwm4jYtuEQu4ZzXZbzXZbzXZbzXZbzXY5z3XmeQa6vpx1ldWAEQETcA6zaWETdy7kuy/kuy/kuy/kuy/kux7nuIPcg1/Nj4BpJVwG7AF8DkDQOWNBkYF3IuS7L+S7L+S7L+S7L+S7Hue4wt1jUJGkrYAvg5oiY13Q83cy5Lsv5Lsv5Lsv5Lsv5Lse57iwXyGZmZmZmFW6xqEnSJGA/YMO86X7gHN/vfPA512U532U532U532U53+U4153li/RqkPTvwM8BAVfnh4DTJR3dZGzdxrkuy/kuy/kuy/kuy/kux7nuPLdY1CDpNmCriHihZftqwC0RsVkzkXUf57os57ss57ss57ss57sc57rzPINcz0vABr1sXz/vs8HjXJflfJflfJflfJflfJfjXHeYe5DrmQZcJOkvwL1523jg1cDHmgqqS03DuS5pGs53SdNwvkuahvNd0jSc71Km4Vx3lFssapK0ErADSzfDXxMRi5uLqjs512U532U532U532U53+U4153lAnk5SfrniDip6TiGA+e6LOe7LOe7LOe7LOe7HOd6cLhAXk6S7omI8U3HMRw412U532U532U532U53+U414PDPcg1SLqxr13AeiVj6XbOdVnOd1nOd1nOd1nOdznOdee5QK5nPWAP4LGW7QKuKB9OV3Ouy3K+y3K+y3K+y3K+y3GuO8wFcj2/BUZExPWtOyRdXDya7uZcl+V8l+V8l+V8l+V8l+Ncd5h7kM3MzMzMKnyjkOUk6ZVNxzBcONdlOd9lOd9lOd9lOd/lONeDwwXy8jux6QCGEee6LOe7LOe7LOe7LOe7HOd6ELjFwszMzMyswhfp1SRpErAfS9+x5pyImNtcVN3JuS7L+S7L+S7L+S7L+S7Hue4st1jUIOnfgZ+Tlk+5Oj8EnC7p6CZj6zbOdVnOd1nOd1nOd1nOdznOdee5xaIGSbcBW0XECy3bVwNuiYjNmoms+zjXZTnfZTnfZTnfZTnf5TjXnecZ5HpeAjboZfv6eZ8NHue6LOe7LOe7LOe7LOe7HOe6w9yDXM804CJJfwHuzdvGA68GjmgqqC41Dee6pGk43yVNw/kuaRrOd0nTcL5LmYZz3VFusahJ0krADizdDH9NRCxuLqru5FyX5XyX5XyX5XyX5XyX41x3lgtkMzMzM7MK9yCbmZmZmVW4QDYzMzMzq3CBbGZmZmZW4QLZzMzMzKzCBbKZdRVJ60q6Pj8elHR//nqRpBMKxTBZ0ndKnKtTJK0j6aMDfO0x/exbnL8fva3hOpBzvSKP97yksYMxppmZV7Ews64l6VhgUUR8s+lYOkXSKhHxYgfGnQD8NiJeuwyvEel2twsjYkQfxyzqa9/ykHQXMDkiHhnssc1s+PEMspkNC5J2k/Tb/PWxkk6RdKmkuyVNkfR1STdJOl/Sqvm47SVdImmOpAskrd/LuAdKulnSDZJm9XGun0i6WNIdko6svPYDkm7Mrz01bxsn6UxJ1+THG3o552GSzpE0k3SzgL+fL+8/XtJh+eu7JH1B0rX5/U3qZbytJF2dZ2JvlLQZ8FVgYt72DUkjJF1UGWe//NoJkv4s6afAzcCJQM+s7mltvicrSzo55+8mSR/P2yfm78Oc/D2alLevJ+nsnK8bJO3c3/hmZgPlO+mZ2XA1EXgzsCVwJXBARHxa0tnA2yX9DvgusF9EzJd0EPAl4IMt43wO2CMi7pe0Th/nmpTPtTbwZ0nfBzYHPgvsHBGPSBqTj/028K2IuEzSeOACYItextwOeF1ELJC0W5v3+khEbJdbJj4JfLhl/+HAtyPiNEmrASsDRwOvjYhtIM1UA/tHxMLcyvAnSefk128GHBoRf8rHHtjzuja2ATbsmaWu5G86cHhE/EXS64ETgN2B7wCXRMT+klYGBn0m2swMXCCb2fD1+4h4QdJNpILw/Lz9JmAC8BrgtcAfUucAKwMP9DLO5cDJks4AzurjXL+LiOeA5yQ9DKxHKvh+2dMSEBEL8rFvBbbM5wQYKWlERCxqGfMPlde00xPXHGBKL/uvBP5D0quAs3Jh2nqMgC9L2hV4iXT3rvXyvrt7iuNldAewqaTvAr8DLpQ0AtgZ+GUlhtXzv7sDHwDIdwt7YgDnNDNrywWymQ1XzwFExEuSXoglF2S8RPrZKOCWiNipv0Ei4vA8y/l2YI6k7fs6V7aY/n/2rgTsGBHPton/qcrXL7J0y9wafZy/13NHxM8kXUV6D+dJ+gipeK16PzAO2D7/YnFX5TxPMQAR8ZikrYE9SLPY7wamAY/XnIE2M+sI9yCbmfXuz8A4STsBSFpV0latB0maGBFXRcTngPnARjXHnwkcKGndPE5Pi8WFwMcq429TY6y7SbPOq+c2hbfUjKHnHJsCd0TEd4DfAK8DniS1hPQYBTyci+M3Axv3M+QLPX3cbc47FlgpIs4ktZtsFxELgTslHZiPUS6iAS4C/jVvX1nSqGV5n2ZmdblANjPrRUQ8D7wL+JqkG4DrSX/6b/WNfIHZzcAVwA01x7+F1NN8SR7/f/KuI4HJ+WK5W0kzq+3Guhc4g3SR3BnAdXViqHg3cLOk60ltJT+NiEeBy/MFdN8ATstx3URqc5jXz3jTgRvbXaRHatO4OJ93BvCZvP39wIdyXm4B9svbjwLenGOYQ+ofNzMbdF7mzczMipGXeTOzIcAzyGZmVtJCdeBGIcCqpP5xM7Pl5hlkMzMzM7MKzyCbmZmZmVW4QDYzMzMzq3CBbGZmZmZW4QLZzMzMzKzi/wOmCSDkMrqEeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rMzOzXpF6oacDW4T/sTYz6wj3/JqZ9aA0W8KRwMlu+JqZdY4bv2ZmPUbSeIpZF0YB3y+1GDOzVYyHPZiZmZlZZbjn18zMzMwqw41fMzMzM6sMN37NrGskrS3pFEl3SXpc0o2S9q/Zv4+k+ZKeTKt2bVGz7zuS/pbeN1/S++rOPU3SbZJekHREG7XsIGlOutYcSTvU7d9J0qw0LdZDko5scS5JOl7SP9Pj+NpFOdKctddLWiLpDklT0vb/SedfKulpSctqXs9Nx4xJWTyZPve+TWq4TFKoWFq5WZ19K6EtkXRTzZzE9cf9PJ3rZS1DLI59eap9es222s+1VNJT6evScOU1SV+VdIuk5yUdU7dvlKQLJN2fahpTt39TSedLWizpXkkfqdvf8vtC0laSfpu+rxZJ+lbNvqV1j2WSfthfJmY2uLjxa2bdtAbFvLqvA4YDXwDOTg28ERRzzn6RYmWz2RSLI/R5AnhLet/hwAmS9qjZfxPFnK/X91eEikUkzqeYNmwDivlzz0/bSbVcDPyUYlW2lwGXtjjlFOCtwPbAdqnOD6dzrQmcl841nGKe4u9K2j4ivhERQyNiKMWyvFf3vY6IbdK5zwJuSHV8HviNpJF1n+dQYM3+PjfFbBGjImJYqnm6pFF159oT2LqNc/X5McXiG/9S+7nSZzsemBkRi5qc4+/AZ4DfNdj3AsXX4uAm750O3EmxpPSbgG9I2rtmf9Pvi/T1/gNwObAxsFk6X9/nqP0MG1PMmXxOkzrMbJBy49fMuiYinoiIYyJiQUS8EBG/pWi47EyxkMXciDgnIp4GjgG2V7EKGRHx5YiYn953DfAnYPeac/84Ii6jWBGsPxMpGuLfj4hnIuIHFEviTkr7/xu4JCLOTPsfj4h5Lc53OPC/EXFvWtr4f3lxoY0NgWEUSypHRFwHzANe2V+RksYCOwFfTgtFnAvcQk1DUMWKc1+maDy2FBE3R8TzfS8pGsy1SyqvAfwQ+Hh/50rHv4tiForLWhwj4H0UP2A0q+u0iPg98HiDfQ9FxInUNbDTuYdSfC2/HhHPRcRNFCvrfaDm/a2+L44A7o+I76bvzacj4uYmZR5MsejJn5p9DjMbnNz4NbMBo2KZ3bHAXGAbil46oGgoA/9I2+vf9xLg1el9ObYBbq6bL/fmmmvtBiyWdJWkhyVdqNYrgi1Xe3q+TfocD1H03r5fxXLKuwNbAH9us847IqK2UfivcyffoFiV7cH6N6df5x/dYNvTwDXATIoe9j6fBGY1agBKOlHSiTWvh1Es2fzf/XyG11Ks0HZuP8flUN2ffc+3bfP9uwELJP0+DXmYKelVTY49HDjdcyybrXqajhUzM+ukNBzgTOC0iJifevEW1h32GMWSu/VOomgEXpJ5+aHp3M2utRlFj+vrKXpav0XRgH1Nm+d7DBgqSamxdBZwMsVyyAD/mZZZzq1zUwBJE1JNR6aalxMRb260LWW/LzA+Il5I59qcYqjGzo0KiYiP1m36KnBKRNxbM7y5kcOB30TE0lYH5YiIxyVdCXxR0qcpetMP5t+/j5rZDNgbOJCi9/pIiuEv49Ky2gCoGHv+OuCDnazfzHqDe37NrOskrQacATwLfCxtXkoxPKDWMOp+FS7p2xQ9e+9otxeu7qal0W1c6yngvIi4Lg3BOBbYQ9Lwupu5TmpS+zBgaUREGrbxK4pf/a9F0Wv7GUlvaqP0pnWmDE8EjqwZytCWNETg98AbJB2YNn8f+EpE1De2/42KmwP3Bb7Xz3FDgENoMeShAw4FtqQYS/4TijG797b53qeAP0fE71Nj9zsUY6vH1x333nTcnZ0p2cx6iRu/ZtZVaQzoKRQ3KB0cEc+lXXMpbhjrO25dihuv5tZsOxbYH3hDRCxp95q1Ny5FxN3pnNtp+S7L7WqudTPFmNh/naLmXLU3c/XNLLBc7el537m2BW6PiEvSeOXbKG7s2p/+zQW2klTb+9137mHABODXkh7kxTGx90p6bRvnhuK3fX03t+0DfFvSg+l8AFdLek+D900ExgB3p2OPAg6WVH9T2duAxRTDK7oiIu6KiDdHxMiI2BUYAVzb5tvrv87NtByzbGaDmxu/ZtZtP6HoWXtLRDxVs/08YFtJB0taB/gSxbjc+QCSPge8B9g3Iv5Zf1JJa6X3CVhT0jqpd7SRmcAy4BMqpl/r632+PP35C+BtKqZDW5NiBoo/t+gVPR347zTt1ibAp4BT074bgJermO5MkrYG3kzR8GopIm4HbgS+nD7P2yga6edSDH/YBNghPQ5Ib9uZYjzvciSNk7S/pJdIWlPSYcBewBXpkLEUDeu+80Exa8V5DUqbRtFo7jv2JIoG/X51x7U1TjbVsw7F/0FrpM+6es3+dYC108u10+u+feMlrZe+/ocBbwC+W7O/1ffFdGA3Sfum600FFlHckNj3/j0ohpl4lgezVVVE+OGHH3505UFxo1dQ3Hm/tOZxaNq/LzCf4tfRM4ExNe8N4Jm69/1Pzf6Z6Zjax8QWtewIzEnXuh7YsW7/fwL3AY8AFwKbtziXKMYFL06Pb5GWi0/73wHcSjGs4l6Kqb9WqzvHERQN7Ppzj0mf7SngNorGf6MaxqTPvEbNtt/3ZUTxA8c1qYZHKXqK39biMwXwsprXJwEnNTn2GGB63bZNgedrz9HsXBQ/KNR/7Y6oq2W5R82+qRRjfJ+guIlwQt21Wn5fUMwy8ndgSTp2m7r3/5Ripo7S//744Ycf3XkowjeympmZmVk1eNiDmZmZmVWGG79mZmZmVhlda/xK2lzFuvJ/lTRX0pFp+4aS/iDpb+nPDbpVg5mZmZlZra6N+VWxfvyoiLg+TdszB3grxU0eiyPiuLQS0QYR8dmuFGFmZmZmVmPAbniTdD7wo/SYGBEPpAbyzIh4Rav3jhgxIsaMGTMAVVoZnnzySYYMGVJ2GWZmZraKmDNnzqKIGNlo34AsbyxpDMU0Q9cAG0XEA2nXgxQT3zd6zxRgCsDo0aOZPXt2o8NsFTBt2jSmTJlSdhlmZma2ipB0V7N9Xb/hTdJQignap0bdCk1RdDs37HqOiGkRMSEiJowc2bDhbmZmZma2Qrra+E0rJZ0LnBkRM9Lmh9Jwh75xwQ93swbrfZMnTy67BDMzM6uIbs72IOAUYF5EfLdm1wUUS2CS/jy/WzWYmZmZmdXqZs/va4D3ApMk3ZgeBwDHAa+X9DeKpU2P62INNgjMmDGj/4PMzMzMOqBrN7xFxJ8BNdm9T7eua2ZmZmbWjFd4MzMzM7PKGJCpzlbWY489xoUXXlh2GYPOO98JTz3VfP+668JZZw1cPc088cQT/vqamZnZgBiwRS5WxoQJE8Lz/K44CVrFNmECDIIvv5mZmdkKkTQnIiY02udhD1a66dOnl12CmZmZVYQbv1a6J598suwSzMzMrCLc+DUzMzOzynDj10o3YsSIskswMzOzinDj10rn5Y3NzMxsoLjxO4gNH17M6NDsMWxY6/cPG9b6/RtuODCfY9asWQNzITMzM6u8QTHPrzW2ZEnrqcz6c/nlrfdPaDhBSOfNnz+fvfbaa2AuZmZmZpXmnl8zMzMzqww3fs3MzMysMtz4tdIddthhZZdgZmZmFeHGr5Vu4cKFZZdgZmZmFeHGr5XukksuKbsEMzMzqwg3fs3MzMysMtz4NTMzM7PKcOPXSvfa17627BLMzMysItz4taZarQDXydXfxo8f37mTmZmZmbXgFd6sqVYrwHVy9bdp06YxZcqUzp3QzMzMrAn3/JqZmZlZZbjxa1laDYno9LAIMzMzs07xsAfL0mpIBKzYsIjRo0evXDFmZmZmbXLPr5XujW98Y9klmJmZWUW48dvDhg9vPbRg2LCyK+yMiy++uOwSzMzMrCI87KGHLVkCs2eXXUX33X333WWXYGZmZhXhnl8zMzMzqww3fs3MzMysMtz4tdJ5gQszMzMbKG78WunmzZtXdglmZmZWEV1r/Er6uaSHJd1as+0YSfdJujE9DujW9W3w+NOf/lR2CWZmZlYR3ez5PRVoNIHr9yJih/S4qIvXNzMzMzNbTtcavxExC1jcrfNbb/Pyx2ZmZtaLypjn92OS3gfMBj4VEY+UUIN12Yosf7zffvt1txgzMzOzZKBvePsJsDWwA/AA8L/NDpQ0RdJsSbMXLlw4QOVZGUaOHFl2CWZmZlYRA9r4jYiHImJZRLwA/AzYpcWx0yJiQkRMcONo1TZ9+vSySzAzM7OKGNDGr6RRNS/fBtza7FgzMzMzs07r2phfSWcBE4ERku4FvgxMlLQDEMAC4MPduv5gMXw4LFnSeN+wYQNbi5mZmdmqrmnjV1KTJtmLhwAPRMTYRjsj4t0NNp+yArVVwpIlMHt22VWUa9y4cWWXYGZmZhXRquf3HxGxY6s3S7qhw/VYBe21115ll2BmZmYV0WrM78FtvL+dY8xamjFjRtklmJmZWUU0bfxGxB0AktaVtFp6PlbSgZLWrD3GbGUsWrSo7BLMzMysItqZ7WEWsI6kTYFLgfdSLF1sZmZmZjaotNP4VUQ8CUwGToyIQ4BtuluWrepqlz/+8IeHePljMzMzGxDtTHUmSbsDhwIfTNtW715JVgXLL3982L/tr13+2MzMzKxT2un5PRL4HHBeRMyVtBXwx+6WZVUyb96cskswMzOziui35zciZlGM++17fQfwiW4WZdUyb94cxo/fuewyzMzMrAL6bfxKGgscBYypPT4iJnWvLDMzMzOzzmtnzO85wEnAycCy7pZjZmZmZtY97TR+n4+In3S9EqusSZMml12CmZmZVUQ7N7xdKOmjkkZJ2rDv0fXKzMzMzMw6rJ3G7+HAp4GrgDnpMbubRVm1XH65lzc2MzOzgdF02IOkTSLi/ojYciALMjMzMzPrllZjfk9OwxtmAhcDf46I5wekKjMzMzOzLmg67CEiDgAmUjR+3wb8RdIMSVMkjR6Y8qwKGs3xW7v8caOHlz82MzOzHC1ne4iIpyl6fS8GkLQlsD/wI0kbR8Qu3S/RVnWNGr/LL3/877z8sZmZmeVo2vMraVzN87UBIuLOiDgR+CawZ/fLsyq46KLpZZdgZmZmFdFqtodf1jy/um7fjyPi2S7UYxX09NNPll2CmZmZVUSrxq+aPG/02szMzMys57Vq/EaT541em2Vbf/0RZZdgZmZmFdHqhrfNJP2Aope37znp9aZdr8wqw8sbm5mZ2UBp1fj9dM3z+hXdvMKbdcz1189ip532KrsMMzMzq4Cmjd+IOE3SSGAL4O8R8eiAVWWVsmDBfDd+zczMbEC0mursQ8Bc4IfAfEkHDlhVZv1otQiGF8AwMzOzZloNe5gKbBMRCyVtBZwJXDAgVZn1o9UiGF4Aw8zMzJppNdvDsxGxECAi7gDWHpiSrGoOOOCwskswMzOzimhntoeGryPiE90ry6rkkUcWMmrUFmWXYWZmZhXQ7mwPAHO6WYhV19VXX8LkyVPKLsPMzMwqoOVsDwNZyKpq+HBYsqT5/mHDBq4WMzMzs6pr2viVdExEHNPqze0cU3VLlsBsz4psZmZm1hNaDXv4kKQWfZYIeBdwTMOd0s+BNwMPR8S2aduGwK+BMcAC4B0R8cgKV22rlB13fG3ZJZiZmVlFtJrt4WfAei0eQ9MxzZwKvLFu29HAZRHxcuCy9Noqbsstx3f0fK3mAPY8wGZmZtXWaszvsStz4oiYJWlM3eaDgInp+WnATOCzK3MdG/xmzJjW0RveWs0BDJ4H2MzMrMpa9fx2w0YR8UB6/iCwUbMDJU2RNFvS7IULFw5MdWZmZma2Shvoxu+/REQA0WL/tIiYEBETRo4cOYCVmZmZmdmqqt/Gr6SXdvB6D0kalc47Cni4g+e2QWrjjUeXXYKZmZlVRDs9v3+RdI6kAyRpJa93AXB4en44cP5Kns9WAXvsUX9fpJmZmVl3tNP4HQtMA94L/E3SNySN7e9Nks4CrgZeIeleSR8EjgNeL+lvwL7ptVXcVVddXHYJZmZmVhGt5vkF/jU29w/AHyTtDUwHPirpJuDoiLi6yfve3eSU++QWa6umBx+8u+wSzMzMrCL6bfymMb+HUfT8PgR8nGL4wg7AOcCWXazPzMzMzKxj+m38UgxdOAN4a0TcW7N9tqSTulOWmZmZmVnntTPm9wsR8dXahq+kQwAi4viuVWaV0ckFLtrhFeDMzMyqq52e36OBs+u2fY5iyIPZSrvzznkdX+K4Fa8AZ2ZmVl1NG7+S9gcOADaV9IOaXcOA57tdmFXHDTf8aUAbv2ZmZlZdrXp+7wdmAwcCc2q2Pw58sptFmZmZmZl1Q9PGb0TcBNwk6cyIcE+vmZmZmQ16rYY9nB0R7wBukBS1uyim/92u69VZJey++35ll2BmZmYV0WrYw5HpzzcPRCFWXRtsMLLsEszMzKwimk51FhEPpKeLgHsi4i5gbWB7ivHAZh1x0UXTyy7BzMzMKqKdeX5nAetI2hS4lGKlt1O7WZSZmZmZWTe00/hVRDwJTAZOjIhDgG26W5aZmZmZWee11fiVtDtwKPC7tG317pVkVTNmzLiySzAzM7OKaKfxeyTFim7nRcRcSVsBf+xuWVYlO+20V9klmJmZWUX02/iNiFkRcWBEHJ9e3xERn+h+aVYVl18+o+wSzMzMrCJaTXUGgKSxwFHAmNrjI2JS98qyKnn00UVll7CcYcNAar5/gw1g8eKBq8fMzMw6p9/GL3AOcBJwMrCsu+WYle/yy1vvnzBhYOowMzOzzmun8ft8RPyk65VYZa2zzpCySzAzM7OKaOeGtwslfVTSKEkb9j26XplVxgEHHFZ2CWZmZlYR7fT8Hp7+/HTNtgC26nw5VkXz5s1h/Pidyy7DzMzMKqDfxm9EbDkQhVh1ufFrZmZmA6XfYQ+Shkj6gqRp6fXLJb25+6WZmZmZmXVWO2N+fwE8C+yRXt8HfK1rFZn1uL6p0Jo9NvSIeDMzs57VzpjfrSPinZLeDRART0qtZkE1WzGTJk0uu4QV0t9UaJMmNZ8n2HMEm5mZlaudxu+zkl5CcZMbkrYGnulqVWaDWKvGsecINjMzK1c7wx6OAS4GNpd0JnAZ8NluFmXV4uWNzczMbKC0M9vDpZLmALsBAo6MiN5aj9bMzMzMrA3tzPZwWUT8MyJ+FxG/jYhFki4biOLMzMzMzDqpac+vpHWAIcAISRtQ9PoCDAM2HYDarCI8x6+ZmZkNlFbDHj4MTAU2AebwYuN3CfCj7pY1eAwfDkuWNN8/bNjA1TJYVanx2zdNWjOeDcLMzKy7mjZ+I+IE4ARJH4+IH3byopIWAI8Dy4DnI2LQ3gO/ZAnMnl12FYPbRRdN54ADDiu7jAHR3zRpng3CzMysu9q54e2HkvYAxtQeHxGnr+S19/aNcwbw9NNPll2CmZmZVUS/jV9JZwBbAzdS9NRCMefvyjZ+zczMzMwGVDuLXEwAXhkR0cHrBnCppAB+GhHT6g+QNAWYAjB69OgOXtp6zfrrjyi7BDMzM6uIdha5uBXYuMPX3TMidgL2B/5L0l71B0TEtIiYEBETRo4c2eHLWy8ZbMsbd1PfDXHNHhtuWHaFZmZmg1s7Pb8jgL9KupaaZY0j4sDci0bEfenPhyWdB+wCzMo9nw1u118/i512+reffyrJN8SZmZl1VzuN32M6eUFJ6wKrRcTj6fkbgK908ho2uCxYMN+NXzMzMxsQ7cz2cEWHr7kRcJ6KyU7XAH4ZERd3+BpmqyTPE2xmZrZyWq3w9jjFjWn/tguIiMhaviEi7gC2z3mvWdV5WISZmdnKabXIxXoDWYhVV1UWuDAzM7PytTPbg1lXPfLIwrJLWGW0mi3CM0WYmZm1d8ObWVddffUlTJ48pewyVgmthkV4SISZmZl7fs0qw3MIm5mZuefXrDJ8s5yZmZl7fq0H7Ljja8suwczMzCrCjV8r3ZZbji+7BDMzM6sIN36tdDNmTCu7BMNjgs3MrBo85tfMAI8JNjOzanDPr5mZmZlVhhu/VrqNNx5ddgnWBg+LMDOzVYGHPVjp9tjjjWWXYG3wsAgzM1sVuOfXSnfVVReXXYKZmZlVhBu/VroHH7y77BLMzMysItz4NTMzM7PKcOPXzDrCN8SZmdlg4BverHSTJ08puwTrgP5uiJs0qWgEN7LBBrB4cedrMjMzq+fGr5XuzjvneYnjCmjVOPZMEWZmNlA87MFKd8MNfyq7BCuZh0yYmdlAcc+vmZXOcwibmdlAcc+vmfU89wybmVmnuOfXSrf77vuVXYL1OPcMm5lZp7jn10q3wQYjyy7BVnHDh7vn2MzMCu75tdJddNF0T3dmK6VvWESr/bNnN9/vnmMzs+pw49fMBr3+hkX0p1Xj2XMQm5mtWtz4NbPK8xzEZmbV4TG/VroxY8aVXYJZU55pwsxs1eKe334MHw5LljTfP2zYwNWyqtppp73KLsGsKc80YWa2anHjtx9LlrS+UcZW3uWXz2DSpMlll2GWpb+b7Txm2Myst7jxa6V79NFFZZdgls09w2Zmg4vH/JqZdZHHDJuZ9ZZSen4lvRE4AVgdODkijiujDusN66wzpOwSzLqmv57hSZNaD5toxUMqzMxW3IA3fiWtDvwYeD1wL3CdpAsi4q8DXYv1hgMOOKzsEsxKszJzFK9MwxnceDazaiqj53cX4O8RcQeApF8BBwFNG79PP/00t99++wCVV28sd91V1rWr4R//uJWtt9627DLMBp1f/GLl3n/44VtT9EfkGT58Gdde+4+VK8JsFbDzzluzdGnjv0v9/T1p9d523m8rrozG76bAPTWv7wV2rT9I0hQgrXm7M694xdiBqO3fDB++jG3dLuuquXPnsq1DNhtwc+as3H+ou+yydWn/Npv1kuHDl3HbbY07yvr7e9Lqve2831Zcz872EBHTgGkAEyZMiPKmG1sd8DddN2200UaMHeuMzQabRx8tuwKzXtG8rdD/35PW7Qz/PcvTakhYGbM93AdsXvN6s7TNzMzMzKyrymj8Xge8XNKWktYC3gVcUEId1iMmT/YCF2ZmZjYwBnzYQ0Q8L+ljwCUUff0/j4i5A12HmZmZmVVPKYtcRMRFETE2IraOiK+XUYP1jhkzZpRdgpmZmVWEV3gzMzMzs8pw49fMzMzMKkMRUXYN/ZK0ELirS6cfASzq0rlXZc4tj3PL49zyOLc8zi2Pc8vj3PL0l9sWETGy0Y5B0fjtJkmzI2JC2XUMNs4tj3PL49zyOLc8zi2Pc8vj3PKsTG4e9mBmZmZmleHGr5mZmZlVhhu/aQllW2HOLY9zy+Pc8ji3PM4tj3PL49zyZOdW+TG/ZmZmZlYd7vk1MzMzs8pw49fMzMzMKsONXzMzMzOrDDd+zczMzKwy1ii7gIEmaRxwELBp2nQfcEFEzCuvqt7n3PI4tzzOLY9zy+Pc8ji3PM4tTydzq1TPr6TPAr8CBFybHgLOknR0mbX1MueWx7nlcW55nFse55bHueVxbnk6nVulpjqTdDuwTUQ8V7d9LWBuRLy8nMp6m3PL49zyOLc8zi2Pc8vj3PI4tzydzq1SPb/AC8AmDbaPSvusMeeWx7nlcW55nFse55bHueVxbnk6mlvVxvxOBS6T9DfgnrRtNPAy4GNlFTUITMW55ZiKc8sxFeeWYyrOLcdUnFuOqTi3HFNxbjmm0sHcKjXsAUDSasAuLD9g+rqIWFZeVb3PueVxbnmcWx7nlse55XFueZxbnk7mVrWeX4CxwOtYPrxHgfllFTRIOLc8zi2Pc8vj3PI4tzzOLY9zy9Ox3Co15rfF3YK/8l2WzTm3PM4tj3PL49zyOLc8zi2Pc8vT6dwqNezBd1nmcW55nFse55bHueVxbnmcWx7nlsezPawc32WZx7nlcW55nFse55bHueVxbnmcWx7P9rASpuK7LHNMxbnlmIpzyzEV55ZjKs4tx1ScW46pOLccU3FuOabi2R7y+S7LPM4tj3PL49zyOLc8zi2Pc8vj3PJ0MrfKNX7NzMzMrLqqNua3KUnvL7uGXiZpnKR9JA2t2/7GsmoaDCTtIunV6fkrJf23pAPKrmuwkXR62TUMNpL2TN9vbyi7ll4maVdJw9Lzl0g6VtKFko6XNLzs+nqVpE9I2rzsOgYbSWtJep+kfdPr90j6kaT/krRm2fX1MklbSTpK0gmSvivpI31/d1f4XO75LUi6OyJGl11HL5L0CeC/gHnADsCREXF+2nd9ROxUYnk9S9KXgf0pxtb/AdgV+CPweuCSiPh6ieX1LEkX1G8C9gYuB4iIAwe8qEFA0rURsUt6/v8o/s6eB7wBuDAijiuzvl4laS6wfUQ8L2ka8CTwG2CftH1yqQX2KEmPAU8A/wDOAs6JiIXlVtX7JJ1J8X/CEIo5aocCMyi+3xQRh5dXXe9K7ZA3A7OAA4AbKPJ7G/DRiJi5QuerUuNX0s3NdgFjI2LtgaxnsJB0C7B7RCyVNIbiP4YzIuIESTdExI7lVtibUm47AGsDDwKbRcQSSS8BromI7cqsr1dJuh74K3AyEBR/P88C3gUQEVeUV13vqv27KOk64ICIWChpXeAvEfGqcivsTZLmRcT49Hy5H+Yl3RgRO5RWXA+TdAOwM7Av8E7gQGAOxd/VGRHxeInl9SxJN0fEdpLWoBizuklELJMk4Cb/v9BY3/+nKashwEURMVHSaOD8FW2HVG22h42A/YBH6rYLuGrgyxk0VouIpQARsUDSROA3kragyM4aez4NxH9S0j8iYglARDwlyVPaNDcBOBL4PPDpiLhR0lNu9PZrNUkbUAxnU18vXEQ8Ien5ckvrabdKen9E/AK4SdKEiJgtaSzwXH9vrrCIiBeAS4FL06/s9wfeDXwHGFlmcT1stTQ37boUvb/DgcUUnSQe9tDaGsAyiqyGAkTE3TnDRarW+P0tMDQibqzfIWnmgFczeDwkaYe+3FIP8JuBnwPuTWruWUlDIuJJih4SANI4Qjd+m0j/oX5P0jnpz4eo3r9VOYZT9LwJCEmjIuKBNE7fP6Q29yHgBElfABYBV0u6h2I6pQ+VWllvW+57Ki0+cAFwQeqZs8ZOoViOd3WKH/DPkXQHsBvFCmbW2MnAdZKuAV4LHA8gaSTFDw8rpFLDHiyPpM0oejEfbLDvNRFxZQll9TxJa0fEMw22jwBGRcQtJZQ16Eh6E/CaiPifsmsZjFJDZKOIuLPsWnpZunFmS4oftO6NiIdKLqmnSRobEbeXXcdgJGkTgIi4X9L6FENH7o6Ia0strMdJ2gYYD9waEfNX6lxu/JqZmZlZVXiqMzMzMzOrDDd+zczMzKwyKtX4lTRa0upl1zHYOLc8zi2Pc8vj3PI4tzzOLY9zy9Pp3CrV+AUWAHdL+mqaG87aswDnlmMBzi3HApxbjgU4txwLcG45FuDccizAueVYQAdzq9QNb2lu1WeBtSjmivs98JOI+H2phfU455bHueVxbnmcWx7nlse55XFueTqdWxUbv4cBzwAfASalXXcD0yLim2XV1sucWx7nlse55XFueZxbHueWx7nl6XRuVWz8visizk6vt6II8QjgpRHhcTgNOLc8zi2Pc8vj3PI4tzzOLY9zy9Pp3Ko25nc5EXFHRHwG2BR4T9n1DBbOLY9zy+Pc8ji3PM4tj3PL49zyrGxuVev5PRy4IiIWlF3LYOLc8ji3PM4tj3PL49zyOLc8zi1Pp3OrVOO3nqRJwNbAlRHx17LrGSycWx7nlse55XFueZxbHueWx7nlWencIqIyD2AmcHJ6fhzFHYN9dxC+s+z6evXh3Jybc+v9h3Nzbs6t9x/OrTdyq1TPr6THgSOBU4EngCuBs4EPAOtHxLjyqutdzi2Pc8vj3PI4tzzOLY9zy+Pc8nQ6tzU6XWCPex4YB6yeHj+KiP9LdxH+oNTKeptzy+Pc8ji3PM4tj3PL49zyOLc8Hc2tao3fC4FPAhsDdwA/kHQIsA9wQ5mF9Tjnlse55XFueZxbHueWx7nlcW55Oppb1YY9DAd+Aby1btctwLvDg80bcm55nFse55bHueVxbnmcWx7nlqfTuVWq8dtH0pbAK4G1gTsjwj9ttcG55XFueZxbHueWx7nlcW55nFueTuVWycZvPUlbA6+JiNPLrmUwcW55nFse55bHueVxbnmcWx7nlic3t0qv8FZjIkV3uq2YiTi3HBNxbjkm4txyTMS55ZiIc8sxEeeWYyLOLcdEMnKr1A1vki5osmvzAS1kkHFueZxbHueWx7nlcW55nFse55an07lVathDmhKjmYiI1QesmEHEueVxbnmcWx7nlse55XFueZxbnk7nVrVhDw8DJwBb1j0+V2ZRg4Bzy+Pc8ji3PM4tj3PL49zyOLc8Hc2tUsMegF9T9HbfVbtR0i3ArHJKGhScWx7nlse55XFueZxbHueWx7nl6WhulRr2YGZmZmbVVrVhD2ZmZmZWYW78mpmZmVlluPFrZmZmZpVRtRvekLQVMJlibrhlwO3ALyNiSamF9Tjnlse55XFueZxbHueWx7nlcW55OplbpXp+JX0COAlYB3g1xdrQmwN/kTSxvMp6m3PL49zyOLc8zi2Pc8vj3PI4tzwdzy0iKvMAbgFWT8+HADPT89HADWXX16sP5+bcnFvvP5ybc3Nuvf9wbr2RW6V6fpO+oR5rA0MBIuJuYM3SKhocnFse55bHueVxbnmcWx7nlse55elYblUb83sycJ2ka4DXAscDSBoJLC6zsB7n3PI4tzzOLY9zy+Pc8ji3PM4tT0dzq9wiF5K2AcYDt0bE/LLrGSycWx7nlse55XFueZxbHueWx7nl6WRulWv8mpmZmVl1VXHMb0OS3l92DYORc8vj3PI4tzzOLY9zy+Pc8ji3PDm5uec3kXR3RIwuu47BxrnlcW55nFse55bHueVxbnmcW56c3Cp1w5ukm5vtAjYayFoGE+eWx7nlcW55nFse55bHueVxbnk6nVulGr8UAe0HPFK3XcBVA1/OoOHc8ji3PM4tj3PL49zyOLc8zi1PR3OrWuP3t8DQiLixfoekmQNezeDh3PI4tzzOLY9zy+Pc8ji3PM4tT0dz85hfMzMzM6sMz/aQ+C7LPM4tj3PL49zyOLc8zi2Pc8vj3PJ4toeV4Lss8zi3PM4tj3PL49zyOLc8zi2Pc8vj2R764bss8zi3PM4tj3PL49zyOLc8zi2Pc8vj2R5Wju+yzOPc8ji3PM4tj3PL49zyOLc8zi2PZ3tYCb7LMo9zy+Pc8ji3PM4tj3PL49zyOLc8nu3BzMzMzCyHZ3tIJG1cdg2DkXPL49zyOLc8zi2Pc8vj3PI4tzw5ubnx+6JTyi5gkHJueZxbHueWx7nlcW55nFse55ZnhXPzsAczMzMzq4yq3fCGpHHAQcCmadN9wAURMa+8qnqfc8vj3PI4tzzOLY9zy+Pc8ji3PJ3MrVLDHiR9FvgVxdQY16aHgLMkHV1mbb3MueVxbnmcWx7nlse55XFueZxbnk7nVqlhD5JuB7aJiOfqtq8FzI2Il5dTWW9zbnmcWx7nlse55XFueZxbHueWp9O5VarnF3gB2KTB9lFpnzXm3PI4tzzOLY9zy+Pc8ji3PM4tT0dzq9qY36nAZZL+BtyTto0GXgZ8rKyiBoGpOLccU3FuOabi3HJMxbnlmIpzyzEV55ZjKs4tx1Q6mFulhj0ASFoN2IXlB0xfFxHLyquq9zm3PM4tj3PL49zyOLc8zi2Pc8vTydwq1/g1MzMzs+qq2phfMzMzM6swN37NzMzMrDLc+DUzMzOzynDj18zMzMwqw41fMxu0JL1U0o3p8aCk+9LzpZJOHKAaJkj6wUBcq1skrS/po5nv/Z8W+5alr0ej+TlzrvWSdL5nJY3oxDnNrHo824OZrRIkHQMsjYjvlF1Lt0haIyKe78J5xwC/jYhtV+A9olhedElEDG1yzNJm+1aGpAXAhIhY1Olzm9mqzz2/ZrbKkTRR0m/T82MknSbpT5LukjRZ0rck3SLpYklrpuN2lnSFpDmSLpE0qsF5D5F0q6SbJM1qcq2fS5op6Q5Jn6h57/sk3Zzee0baNlLSuZKuS4/XNLjmEZIukHQ5xSTv/7pe2v8jSUek5wskHSvp+vT5xjU43zaSrk09qDdLejlwHLB12vZtSUMlXVZznoPSe8dIuk3S6cCtwClAX2/smf18TVaXdGrK7xZJn0zbt05fhznpazQubd9I0nkpr5sk7dHq/GZm7araCm9mVk1bA3sDrwSuBg6OiM9IOg94k6TfAT8EDoqIhZLeCXwd+EDdeb4E7BcR90lav8m1xqVrrQfcJuknwFjgC8AeEbFI0obp2BOA70XEnyWNBi4Bxjc4507AdhGxWNLEfj7roojYKQ1jOAr4UN3+jwAnRMSZktYCVgeOBraNiB2g6GEG3hYRS9Lwgr9IuiC9/+XA4RHxl3TsIX3v68cOwKZ9vcs1+U0DPhIRf5O0K3AiMAn4AXBFRLxN0upAx3uQzaya3Pg1syr4fUQ8J+kWisbexWn7LcAY4BXAtsAfit/mszrwQIPzXAmcKulsYEaTa/0uIp4BnpH0MLARRWPunL5f00fE4nTsvsAr0zUBhkkaGhFL6875h5r39KevrjnA5Ab7rwY+L2kzYEZqdNYfI+AbkvYCXqBYUWmjtO+uvobvCroD2ErSD4HfAZdKGgrsAZxTU8Pa6c9JwPsA0gpOj2Vc08zs37jxa2ZV8AxARLwg6bl48WaHFyj+HRQwNyJ2b3WSiPhI6p18EzBH0s7NrpUso/W/s6sBu0XE0/3U/0TN8+dZfsjaOk2u3/DaEfFLSddQfIaLJH2YomFa61BgJLBz+qFhQc11niBDRDwiaXtgP4re53cAU4FH2+w5NjPrCI/5NTOD24CRknYHkLSmpG3qD5K0dURcExFfAhYCm7d5/suBQyS9NJ2nb9jDpcDHa86/Qxvnuouit3jtNHRgnzZr6LvGVsAdEfED4HxgO+BximEafYYDD6eG797AFi1O+VzfuOl+rjsCWC0izqUYArJTRCwB7pR0SDpGqYEMcBnwn2n76pKGr8jnNDNrxo1fM6u8iHgWeDtwvKSbgBspfh1f79vpZq1bgauAm9o8/1yKMcRXpPN/N+36BDAh3Xj2V4oe0f7OdQ9wNsUNZ2cDN7RTQ413ALdKupFiqMfpEfFP4Mp0M9q3gTNTXbdQDD2Y3+J804Cb+7vhjWLoxMx03enA59L2Q4EPplzmAgel7UcCe6ca5lCM1zYzW2me6szMzLpCnurMzHqQe37NzKxblqgLi1wAa1KM1zYzW2Hu+TUzMzOzynDPr5mZmZlVhhu/ZmZmZlYZbvyamZmZWWW48WtmZmZmlfH/Aye5t6CzD9iQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_some_peaks(selected_peaks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice, we made a simple cut and checked that some waveforms are of the kind we expected. Of course we can do more elaborate cuts and check the other dimensions of the parameter space. Very interesting times to be an analyst! "
   ]
  }
 ],
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