AIRDOS_calibration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AIRDOS Clibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import Image as ImageDisp\n",
    "from pandas import DataFrame\n",
    "import string\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Logfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/storage/experiments/2021/12_test_geodos1024/27k_Na22_LS/DATALOG.TXT'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-5542d828faec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1034\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomment\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 610\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             )\n\u001b[1;32m   1049\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1361\u001b[0m         \"\"\"\n\u001b[0;32m-> 1362\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/nix/store/rgv8i8ai1hn3g0a026liljyggviinrq0-python3-3.8.9-env/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"replace\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 642\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/storage/experiments/2021/12_test_geodos1024/27k_Na22_LS/DATALOG.TXT'"
     ]
    }
   ],
   "source": [
    "fto = '/storage/experiments/2021/12_test_geodos1024/27k_Na22_LS/DATALOG.TXT' #  File to Open\n",
    "l=[]\n",
    "l.extend(range(0,1034))\n",
    "df = pd.read_csv(fto, sep=',', header=None, names=l, comment='*')\n",
    "\n",
    "#\n",
    "''' DEBUG\n",
    "df = df.reset_index(drop=True)\n",
    "df.drop(df[df.index > 100].index, inplace=True)\n",
    "'''#'''\n",
    "\n",
    "df.drop(df[df[0]=='$GPTXT'].index, inplace=True)\n",
    "#df.drop(r[r[0]=='$GPRMC'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPVTG'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGLL'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGSA'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGSV'].index, inplace=True)\n",
    "#df.drop(df[df[0]=='$CANDY'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$HITS'].index, inplace=True)\n",
    "\n",
    "# Convert strings to numbers\n",
    "#df = df.apply(pd.to_numeric, errors='ignore')\n",
    "\n",
    "#df.iloc[:,0:13]\n",
    "\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
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       "      <td>170.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15738</td>\n",
       "      <td>13134</td>\n",
       "      <td>197.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>46.0</td>\n",
       "      <td>15868</td>\n",
       "      <td>12975</td>\n",
       "      <td>216.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15875</td>\n",
       "      <td>13069</td>\n",
       "      <td>214.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16118</td>\n",
       "      <td>12799</td>\n",
       "      <td>206.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15851</td>\n",
       "      <td>13076</td>\n",
       "      <td>200.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>16281</td>\n",
       "      <td>12755</td>\n",
       "      <td>187.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>16063</td>\n",
       "      <td>12850</td>\n",
       "      <td>207.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16353</td>\n",
       "      <td>12649</td>\n",
       "      <td>191.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 990 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   10    11     12     13     14     15     16     17     18     19   ...  \\\n",
       "0    9  50.0  15757  13096  210.0  200.0  199.0  174.0  170.0  132.0  ...   \n",
       "1    8  63.0  15738  13134  197.0  192.0  183.0  173.0  168.0  120.0  ...   \n",
       "2    8  46.0  15868  12975  216.0  208.0  190.0  164.0  175.0  126.0  ...   \n",
       "3    4  45.0  15875  13069  214.0  185.0  173.0  175.0  168.0   99.0  ...   \n",
       "4    4  50.0  16118  12799  206.0  180.0  216.0  158.0  145.0  119.0  ...   \n",
       "5    7  39.0  15851  13076  200.0  183.0  211.0  176.0  174.0  119.0  ...   \n",
       "6    7  38.0  16281  12755  187.0  215.0  172.0  172.0  138.0  123.0  ...   \n",
       "7    6  67.0  16063  12850  207.0  202.0  204.0  164.0  155.0  124.0  ...   \n",
       "8    5  50.0  16353  12649  191.0  178.0  188.0  186.0  149.0  121.0  ...   \n",
       "\n",
       "   990  991  992  993  994  995  996  997  998  999  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "7  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[9 rows x 990 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.iloc[:,10:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Energy spectrum preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Biel3O+d6FRjzPpDsnIsrQbhjgeuAXkBjAnNnbSdwS+TbwNuuQEgzuxI4g0BRVR+IAjYAKcCzB17BFdznMaAfgbm26gJ+Arcp/gg86Zwr9LZHM6tCoIi7gEDRtZNAyfaAc25ecd6fyq6S27NrOwt+/QSb9zmddqVS1fazjZosrnssVeLPpFPyUKKiY7yOKWHO+f3MnvwZUb8/TueceWymNkvaX0GPYbdQtXpRz66QI7Frx1bWPXMKrXOWMG/Ac/Q8/jyvI4mIiIiISDkVqrLrUuB1AldT5QBxwC3OuWcKjFkGzHfODT3S0OFCZdeR2bt7Bwt++ww393M67fyDapbFDqqxsPaxxPQ4g879Tyc6JtbrmBJGnN/PzJ8+oGraU3TIXUQm9VnZ+WriT7+Z2ColefaFHI4d2zazcewptMpZzoLjXqTHwBFeRxIRERERkXIoJGVX8EAPA/mPy3qXQNnlgtsGEbhS6k7n3ONHFjl8qOwKnay9u5n/23jy5oyn447fqGH72ElVFtY6mqhuZ9Dp6GEqI+Sw+fPymPHdW9SeNpq2ectZa41Y2+16ep56vQrVMrZj6yY2jT2ZFrmrWHT8y3QfcIbXkUREREREpJwJWdl1iJNEA1WAPc653JAcNAyo7Cod+7P2suCPL8ieNZ6O2ydTkz3scbHMr9mfiG7D6HT0mVSpVsPrmFIB5OZkk/HNazTIeJZW/tWs8jVjQ48b6TX0as0T56HtmzPZ+tzJNM1by5ITX6Pb0ad7HUlERERERMqRMim7pHAqu0pf9v4sFqR8TdbMT2m/bRJ12MVeF8OCGkm4LsPodMxZVKtR2+uYUs7kZO8n46sXaDL7eZq79Sz3tWJrn5H0PPkyPQW0nNi6cS07XhhM47z1LDv5TboeNcTrSCIiIiIiUk6o7PKQyq6ylZuTzYLUb9iT8SnttvxMPXawz0Uzv3oi/s6n0/GYEXqCXiW3P2svGV+MpeW8F2nCJpZEtGVnv1vpecIF+CIivI4nB9iyYQ27XjyFhnkbWTH4LbokneJ1JBERERERKQdUdnlIZZd38nJzWTDle3ZP/5g2myfSgG3sd1HMq9aX3I6n0WHAOdSqU9/rmFJG9u3ZxczPR9Nm0as0ZCsLIzuRddTt9DhuBObzeR1PDmJz5ir2vnQK9fK2sHroO3Tqd6LXkURERERExGMquzyksqt88OflsSj9J7ZP+5jWG3+kEVvIdhHMr9qH/R1Oo+OAc6lVr5HXMaUU7N65jTnjn6T9sjepxw7mRnfHHfMXuvY/TSVXBbJp3QqyXh5MHf821p72Hh0TBnkdSUREREREPKSyy0Mqu8off14ei2dMYlv6x7Tc8ANN3UZyXATzq/Qkq92ptBtwLnUbNvM6phyhndu3MPezf9N55TvUZjezYvsQedydug2uAtuwZim5rw6mhtvFhmHjaN9rgNeRRERERETEIyq7PKSyq3xzfj9LZv3O5ikf0mL99zR3meQ5Y35sPHvaDqXtgPOo37il1zGlBLZvzmT++EfpuuYDarKXjCpJVDn+Ll0JFCYyVy3G//oQqrs9bDzjQ9rFH+11JBERERER8YDKLg+p7Ko4nN/PsrlT2Jg2jmbrvqOlfy1+ZyyI6cbO1kNoM+B8GjZr7XVMKcLmzNUs/vwReqz7mGqWxfRqA6h50t20i+/vdTQJsXUrFuJ7Yyix7GPLWZ/QtnuS15FERERERKSMqezykMquisn5/axcOJ31f3xAk7XfEedfBcCCqC5sjxtM3DHn07hle49TCsDGtctZ9vnDxG8YTzQ5zKg5iPpD7iWuc6Hf8yRMrF02n8i3hhJNNtvP/oTWXRO9jiQiIiIiImWozMouM7sS6O+cuyJkB63gVHaFh5ULprMuZRwN13xH27zlACyK7MDWVoNpefQFNG3dyeOElc/6lQtZ9cXD9Nr8FT78zKhzMo2H3kOL9vFeR5MysmbJHKLfOY1Ictl17nhade7jdSQRERERESkjZVl2vQ5c4pyLCNlBKziVXeFn9ZLZrPn9A+qv+pb2eUsAWBzRjs0tT6F5//No0a67xwnD25olc1j31T/pte07HDCj3lBanHafCsdKavXimVR593QA9pz/Oa069vQ2kIiIiIiIlAmVXR5S2RXe1i1fwKrf36fOim/omLsQgKURrdnY/GSaHnW+fvAOoZULprNpwj/pteMncohkZsNhxA27l0bN23odTTy2cmEG1d8/HT8+si76UoWziIiIiEglcNhll5mV9HbEK4BklV3/pbKr8shctZgVv31AreUT6JwzD4AVvpasb3YyjZPPJa5TH8zn8zhlxbN0dio7vnuYnrsmk0U0s5qMoN3wu/WUTPkfK+anU2vcGeQQSc4lX9GsTVevI4mIiIiISCk6krLLD5Tk0i8DnMqu/1LZVTltXLuc5b9+QPVlX9N5/xx85ljla8baJifRMPEc2nRLUvF1CIumT2LPj4/Qa+8f7HZVmN38XDoNv5s6DZp4HU3KqWVz0qjz8VnsJwb/pV/r1lYRERERkTB2JGXXfmA98HoxzzUc6KGy679UdsnmzFUsnTyOaku/onPWTCLMscYas7rxSdTvdzbt4o9W8VXAgrTvyf75UXpkpbODasxreRFdhv+FWnUbeB1NKoCls/6g/qcj2EtVuPxrmrTq6HUkEREREREpBUdSds0AGjnnmhbzRJqz6wAqu6SgrRvXsuTXD4ld/BWd980gyvJYTwNWNjqB2n3PpkOv4/BFVL4/Ps7vZ+4fX2GTH6dr9ky2UpOFbS6j27BbqVGrrtfxpIJZMvM3Gn52DrutOr4rJtC4RTuvI4mIiIiISIgdSdn1GnAp0NQ5t6EYJ1LZdQCVXVKUHVs2sHDyh8Qs+pLOe9OJtjw2UI/lDY+nVp8RdEw4IeyLL+f3M3vSp0T/8QSdcuaxiTosbX8FPYaNomr1Wl7Hkwps0fRJNPn8XHb4ahF91bc0bNba60giIiIiIhJCR1J2jQKeAgY7574rxonuAk5xzg083LDhRmWXFMeObZtZ/OtHRCz4gi57phJjOWyiDsvqD6R6rxF0SjyZiMhIr2OGjPP7yfjxPapPeZr2uYvJpD4rO19D/Ok3EVulmtfxJEwsSP+J5l9eyDZfHapc/S31m7byOpKIiIiIiITIkZRdkUA1YK9zLqeU8oU1lV1SUrt3bmPBrx/jm/cFnXenUsWy2UItltQbSLWeZ9IpaTCRUdFexzws/rw8Znz3FnXSn6aNfwVrrRFru11Pz1OvJzom1ut4EoYWpH1PywkXsSmiAdWu+Zb6jVt4HUlERERERELgsMsuOXIqu+RI7N29g/mTP4V54+m8K4Wqtp9t1GBxnWOJjT+TzkedSlR0jNcxDyk3J5uMb16lQcZYWvlXs8rXjA3xN9FryFUVtriTimNeyjfEfXspGyMaUuPab6nXqLnXkURERERE5Aip7PKQyi4JlX17djH/t/H4546n047fqW772EE1FtUeQHSP4XQ66nRiYqt6HfN/ZO/PYubXL9Jk9nM0d5ks98WxNWEkPU+6NKxuy5Tyb+7vX9Pm+8vIjGhK7eu/pU6DJl5HEhERERGRI6Cyy0Mqu6Q0ZO3bw4LfvyBn9md03PErNdnLLleFhbWOJqLbcDofPZzYqtU9zTfzy7G0mvcSjdnE4oh27E68lfjjzw/7Sfel/Jrz6+e0+/FK1kY2p/4N31GrXiOvI4mIiIiIyGEqs7LLzB4DznTOtQ3ZQSs4lV1S2rL3ZzH/jy/ZP/NTOmyfTG12s8fFsqDmUVjXYXQ+5iyqVKtRJln27dnFzM+fps2i12jIVhZEdmZ//9vpcexZmM9XJhlEDmb2pE/pMPEaVke2pMGN31GrbgOvI4mIiIiIyGEoy7LrdeAS55wu3QhS2SVlKSd7PwtSJ7A341M6bP2FOuxkr4thQY1EXOdhdBowgmo1aof8vLt3bmPO+Cdpv+xN6rGDudE9cAP+QtejTlXJJeXOzIkf0nnS9ayIakPjm76lZu16XkcSEREREZESUtnlIZVd4pXcnGwWTPmOPTM+pe3midRnO1kuivnV+pHX6XQ6DDj7iH/I37FtM/PGP0bnle9Qm93Miu1D1MC76Jx4cojehUjpyPjxfbr8eiPLo9rT9OZvqFGrrteRRERERESkBA677DKzt0p4rqOA1iq7/ktll5QHebm5LEz/kZ3TPqbNpp9oyFayXSTzqiaQ0/E0Ogw4t0S3c23fnMn8zx6h25oPqGH7yKiaTNUT7qZD7+NK702IhNj0796m+x+jWBrdkeY3T6B6zTpeRxIRERERkWI6krLLDzjASnA+p7Lrv1R2SXnjz8tj0fSf2Z7+MXEbfqQxm8hxEcyv0pus9qfSfsC5RT6pbnPmapaM/xc91n9MVdvP9GoDqHXyPbTtcVQZvwuR0Jj+zev0SL2NRdFdiBs1garVa3kdSUREREREiuFIyq4dwBrghmKe627gJJVd/6WyS8oz5/ezOONXtkz9kFaZP9DUbSDX+ZgfG8/edqfSbsB51GvUnI1rl7Ns/D/puXE8UeQyo9bx1B98D3GdC/2+IlKhTPv6FXpOuYMFMd1pM2pCmT3QQUREREREDt+RlF2TgXjnXLH+q1tzdv2Zyi6pKJzfz9LZKWya8iHN131PC7eOPGcsjWpPXM4yDMeMOifT5NR7adGuu9dxRUIq/csX6ZV+F/Nj42k36mtiq1b3OpKIiIiIiBzEwcquyEPsmwH0N7O2zrmlIU8mIuWG+Xy0i+9Pu/j+OL+f5fOnsiFlHHUzf2VG/aG0OO0++sV19DqmSKlIOO1apubl0mfGfcwZM4wOo74gtko1r2OJiIiIiMhhOFTZNQk4BmgOFKfsGg+sOLJIIuI18/lo3TWR1l0TvY4iUmb6Dr+Rqf5c+s66n5ljhtNp1OfExFb1OpaIiIiIiJSQ72AbnXOfOOd6OecmFedgzrnPnXMPhSaaiIhI2ep75ijSut5P/L4pzB9zJtn7s7yOJCIiIiIiJXTQsktERKSySTz7dtK63EfPvSnMHX0mOdn7vY4kIiIiIiIloLJLRETkAInn3Elqx7votfd3Zo8eocJLRERERKQCUdklIiJSiKTz7yW1/e303jOZWc+cS25OtteRRERERESkGFR2iYiIFCHpwvtJbXcLfXb9TMYz55OXm+t1JBEREREROQSVXSIiIgeRdNFDpLS+kYSdPzL9mQtUeImIiIiIlHMqu0RERA4h+dKHSWl1HX13fMe0Zy/Gn5fndSQRERERESmCyi4REZFiSL78UVJaXEW/7RNIH3upCi8RERERkXKq2GWXmd1vZheXZhgREZHyLOnyx0hpdhn9tn7J1OeuwPn9XkcSEREREZEDlOTKrv8DupdWEBERkfLOfD6SrnyKlCYXkbhlPFOev1qFl4iIiIhIOVOSsmstULO0goiIiFQE5vORdPUzpDY6n8RNH5P24vUqvEREREREypGSlF2fASeYWZXSCiMiIlIRmM9H4rXPkdrgbJI2fEDaSzep8BIRERERKSdKUnY9AGwDxptZt1LKIyIiUiGYz0fi9S+RVm84SZnvkvrKLSq8RERERETKgcgSjJ0JRAO9gZlmlgVsBNwB45xzrm2I8omIiJRb5vPR94bXSBvrJ3ndm6S8HkXylU94HUtEREREpFIryZVdPiAHWBV8bQyutwNeJTmmiIhIheaLiKDvjW8wpfYQkle/Qsprd3odSURERESkUiv2lV3OubhSzCEiIlJh+SIiSLj5HaaOuYDkVS+S8mYkyZc+7HUsEREREZFKSVdhiYiIhIAvIoLeN79Les0TSF4+ltS37/c6koiIiIhIpXTYZZeZ1TGzFqEMIyIiUpFFREbS8+b3mVZjEElLR5P67t+8jiQiIiIiUumUqOwys+pm9oSZZQKbgeUFtiWa2QQz6x3qkCIiIhVFZFQ08SPHMb36AJIWP0Hq+7qdUURERESkLBW77DKzWkAKcCuwDphPYEL6fLOBY4DzQxlQRESkoomMiqb7yI+ZUe1okhY+Stq4R72OJCIiIiJSaZTkyq77gK7AZc653sBHBTc65/YCk4DjSxrCzFaYmSvilXnA2LiDjHVm9sFBznOpmU0xs91mtsPMfjGzUw8yvoqZPWRmC80sy8w2mtmHZta5pO9RREQql6joGLqO/ISMqskkzn+YtI+e8DqSiIiIiEilUOynMQJnAt855946yJiVQN/DzLIDeLqQ9buLGD8TGF/I+jmFDTazx4HbgTXAy0A0cB7wpZnd7Jx79oDxMcAPQH8gHRgNtADOBoaa2SDnXNrB35KIiFRm0TGxdB75KTNHDyNx7t+YGhFJ3zNHeR1LRERERCSslaTsag58cogxu4Fah5llu3PuwRKMzyjueDM7ikDRtRTo65zbFlz/GDANeNzMvnLOrSiw220Eiq6PgXOdc/7gPuMIlGyvmVn3/PUiIiKFiYmtSseR45k1+nT6zHyAqb5I+g6/0etYIiIiIiJhqyS3Me4CGh5iTGsCE9eXN9cFl//ML7oAguXWWCAGuDx/vZlZgX3uLFhoOec+B34FugDHlm5sEREJB7FVqtFh5OfMje1Jnxn3kf7FC15HEhEREREJWyUpu6YCp5pZjcI2mlkTYAjw22FmiTGzi8zsXjMbZWYDzSziIOObmtm1wfHXmlmPg4wdFFx+W8i2bw4YA9AWaAkscs4t//Muhe4jIiJSpNiq1Wk38kvmx3Sn17S7mfb1K15HEhEREREJSyW5jXE0gZJngpldU3BDcML2l4FYYMxhZmkMvH3AuuVmdrlzblIh408Mvgrm+AW41Dm3qsC6akAzYLdzbn0hx1kcXHYosK5jcLmoiKyF7SMiInJQVarVoPXIr1g4egjxU/7CdF8EvQdffugdRURERESk2Ip9ZZdz7jvgQQLzWM0B7gEws83Bj48C7nHO/XEYOV4n8BTHxkA1oDvwIhAHfGNm8QXG7gX+DvQB6gRfxwI/A8cBPwULrnz5c4jtKOLc+etrH+E+/2Fm15hZupmlb9q0qYhDiIhIZVS1ei1a3vwVi6M70T31dqZ/d+D/84iIiIiIyJEoyW2MOOf+RqCU+gLYBuQBDpgAnOCce+xwQjjnHnLOTXTObXDO7XXOzXHOXQc8CVQhULLlj93onLvfOTfdObc9+JoMnASkAe2Aqw4nRgnG2sH2cc695JxLcM4lNGjQ4DCiiIhIOKtesw7Nb/qaZVHt6fbHKDJ+fN/rSCIiIiIiYaNEZReAc+5n59wZzrkmzrlo51wD59xpzrmJpZAvfwbfAcXIlQvkT4BScHz+VVhFPSWysKu4DrVPzUL2ERERKbYaterS5KYJrIhqS5dfb2LmxA+9jiQiIiIiEhZKXHaVsY3BZbWDjvqv/HsG/zPeObcHWAtUD06if6D2wWXB+bkWBpdFzclV2D4iIiIlUrN2PRrd+A0rI+PoNOkGZv3yideRREREREQqvBKXXWYWZ2Z/NbNPzeyn4PKvZta6FPIlB5fLijk+qYjx+VednVLIPoMPGAOwFFgFdCjifRW2j4iISInVqlOfhjdMYE1kCzr8fC2zJ3/udSQRERERkQqtRGWXmd0OLCAwh9ZwYGBw+RCwwMxuK2kAM+tqZnULWd8KeDb44TsF1ieaWXQh4wcBtx44Pij/dsj7zKxOgX3igBuB/QQmyQfAOecK7PNvM/MV2GcYcAwwDyjsKZEiIiIlUqteI+peN4H1Ec1o99NVzPn9S68jiYiIiIhUWBbodYox0Ox84F0CE9OPAX4BMgk8QXEgMJLAHFcXOOfGFTuA2YPA3QSeprgc2AW0BYYCsQQmvz/DOZcdHP8L0DV4/jXBw/QABgV//Vfn3D8KOc8TwG3BfT4GooFzgXrAzc65Zw8YH0Pgyq2jgHTgJ6AlcDaQDQxyzqUd6v0lJCS49PT0Q34eREREtm5cy84XTqFh3gZWnPImXZIHH3onEREREZFKyMymOecSCt1WgrIrHWgN9HbOrSxke2tgGrDUOde3BOGOBa4DehEozqoB24EM4G3gbVcgpJldCZwBdAPqA1HABiAFeNY59+tBznUpcBPQBfAD04HHnHNfFTG+CoEi7gICRddOAiXbA865ecV5fyq7RESkJDZnrmbPS6fQIG8Tq4a8Q6fEk7yOJCIiIiJS7oSq7NoLvOmcu/4gY14ALnHOVT2spGFIZZeIiJTU5nUr2ffyKdTxb2PNae/SKeF4ryNJJZGbk01ERCTmK+/PMBIREZHK7mBlV2QJjrOLwBVXB7OdwNVPIiIicpjqN23FxqsmsP2VwTT78kIW+cbRofexXseSMJOXm8uK+elsnvcLkWvSaLZrFo3ZDEC2iyCXSHItuCSSXIskj0jy7H9ffoskzxeFs0j8vij8FonzReL3ReN8keCLwkVE4XzREBH4mIgoLCIKIqKxiCh8kdGBdZFRRERGYxHR+CKj8UUGtkVERhMRFU1EVAwRkVFERsUSERUYGxUdS2RUFFHRsURFRauoExERkRKVXd8DJwP3FLbRzAw4KThOREREjkDDZq3JvOIrdr02hMZfnM9i3zja9zzG61hSge3dvYNlMyeza9FvVNuQTut9c2lr+2gLbKQuq6v3YHndDuD8kJeN5eVg/hzw52J52ZjLxefPxfw5+Fwuvv8sc4nJ3U2Eyw28yCXyP7/OI4ocIl0eUeQSSR4+K95dBYcrx0UQSBFBrkUFfx0o5nILFnQWRZ5F4Lco/L5I/BYVLOmicL7Ar13+ryOi/lPSHVjUWf4yWMz5DijqfFGBsi4yKhpfZAyRUTFEREURGRVNVFQMEVHRREXHEBUVgy8iolQ/NyIiIpVFSW5jbAL8AaQCdxect8vMWgKPAonAUc65zFLIWiHpNkYRETkS61cuhNeHUpW9bD7zY9r2OMrrSFJBbM5czeqZE9m/7A/qbplO65ylRFkeAMt9rdhYpycRrZJp2mMQTVq2L7MrovJyc8nJziInJ5vc7P3k5WSTk5NFXk4OebnZ5OZk48/NJi9nP3m5Ofhz9+PPzcGfm40/NweXF9hOXg4uL/Ax/1nmBoo6fy74cwIlXbCgM3+goLNgURfhcvC5PCKCpV1+URfhAmVdZP41bS6PSHKJIpeIUi7qcp3vP+VcjkWSR0TgzBYRKOeICF5RFyjo8os7f7CcCxR2waIu4n+vrMMXKOaIiCxQ1EVhkYFfN+12LE1bdyrV9yciIhJKoZqzayJQh8CTD/OAVQQmhm9EYPL2CGAWgac1FuScc5V2shGVXSIicqTWLV+A782hxLCfbSM+oU23RK8jSTnj/H5WLZ7Fhjm/YKtTabIjg+ZuPQD7XRRLozuyo0EfqrY7mrieA6lVt4HHiSumvNxccnL2k5uTHSzp8n+9n9ycnGBBl01ebjb+/NIuNxuXX9rl5eCChZ3Ly8HlZoM/UNoRfFleduBqOn/Of0o68+cEiroCV9P5XA4RLi9Y2hW4mu4/RV0ekS6HSIJX1Zn/oO9tJ9XYdt5XtOrUu4w+myIiIkcmVGXXwf+GLJpzzlXaa7JVdomISCisXTaXqLdOJYpcdpz7GXGdC/17XSqJ/Vl7WT77D7YvmEzM+qnE7Z1DneC0qduowYqq3dnfpC+1Ow2gdfejiInVs4MqO39eHrm5OeRkZ5GbE1jm5WaTl5PDri3rqPfV5WRbNNHX/kT9xi29jisiInJIISm75PCo7BIRkVBZvWQ2se+chg8/u88bryswKpEdWzexIuMn9i75g1qbptE2eyExlgPAamtKZq14XIskGnU7jpbte2iSdimxxTMm02z8CNZGtqDZrROpWr2W15FEREQOSmWXh1R2iYhIKK1alEHV94YBsPeCz2nZoae3gSTknN/P+pWLWDf7Z/JWptBwWwat/YGpUnNcBMuj2rK1Xh9i2iTTIn4Q9Ru38DixhIuMnz6g++TrmF01kW63fUlkVLTXkURERIqksstDKrtERCTUVs6fRo1xw8klkuyLvqR5u25eR5IjkJuTzYp5U9g8bxJRa6fQYvcsGrIVgF2uCsurdGNPoz7U7DCANj0HUKVaDY8TSzhL+/AxEuf9g7R6w+l34+u6SlBERMotlV0eUtklIiKlYfm8qdT+8EyyiSL3kq9p1qaz15GkmPbs2s6yGZPYveQ3qm+YSpus+VSzLAAyqc+aGvHkNU+kfpdjieucQERkpMeJpbJJefFmkte/RWrbUSRd/Dev44iIiBRKZZeHVHaJiEhpWTo7lXqfnEUWVfBf9jVN4zp6HUkKsWndClZl/EzO8t+pt3UGrXOXEWl+/M5YHhnH5jq9iIhLpnmPgTRu2d7ruCL48/KY8fQI+uyayLS+T9Bn6FVeRxIREfkTlV0eUtklIiKlacnM32n42dnstmr4Lp+gssRj/rw8Vi2awYY5k/CtSaXZzpk0dRsA2OeiWRbTiZ0NE6jWtj9xvQZSs3Y9jxOLFG5/1l6WPnkS7fbPZ8ngd+mSdIrXkURERP6Hyi4PqewSEZHStnjGZBp9fi47rSZRV06gUfO2XkeqNLL27WH5rN/YvmAyVTLTab1vNrXYA8AWarGyWg+ym/albudjad0tmajoGI8TixTfji0b2DF2ILX829l+/gRadezpdSQREZH/UNnlIZVdIiJSFhZN/4Umn5/Hdl8dYq/+hgZN47yOFJa2b85kxYyJ7Fv2O3U2T6dN9iKiLReAVb5mZNbqCS2TadL9OJq36arJvaXCW7d8ATFvnsR+iyH62ol6+qeIiJQbKrs8pLJLRETKyoKpP9LiqwvZElGPqtd8S/3GLb2OVKE5v591K+azbtbPuJUpNNoxk1b+1QBkuwiWRXVge/3exLQ5ilY9B1K3YTOPE4uUjsUzJtNs/AjWRrWk2S0/UbV6La8jiYiIhKbsMrM6QBNgqXNuf4H1lwPDgT3A0865KUecOIyo7BIRkbI0P+07Wk24mE0RDal+7bfUa9Tc60gVRk72fpbPSWXrgslEr51Cyz2zqM92AHZSjeVVurK3cV9qdRxAmx5HE1u1ureBRcpQxo/v0/3X65ldLYnut32lp4SKiIjnQlV2PQ9cBDR0zu0LrrsZeBqw4LAsIME5N+9IQ4cLlV0iIlLW5v4xgTbfXUpmRBNqXfeNrjgqwq4dW1mR8Qt7Fv9GjY3ptN6/gKoW+P+8ddaQdTXiyWueSMNux9GqY298EREeJxbxVtq4R0mc/zBp9c+k3w2v6jZdERHxVKjKrlnAMufc8ALrVhIoui4AGgNvAe855/R84iCVXSIi4oU5v31Bux+uYF1EM+re8B216zf2OpLnNqxZyuqZE8lbnkL9bTOIy11OhDnynLE8sg1b6vYisvVRtIgfRMNmrb2OK1Iupbx4I8nr3yG13S0kXfSQ13FERKQSO1jZVZLrj5sBPxU4aBegBXCXc+634LqzgQFHkFVERERCoNvRpzPb/zIdfrqa1c8PwW78jlp1G3gdq8zk5eaycsE0Ns2bRMSaNJrvnEljNtEI2OtiWBbbmSlNr6J6+/607nkc7WrWoZ3XoUUqgMSrxjDt6bUkLXmaaRNa0mfIlV5HEhER+ZOSlF1VCNymmK8/4IAfC6xbCpwaglwiIiJyhLoPOINZeXl0+uVaVj43GG78llp16nsdq1Rk7d3N0ozJ7Fw0maqZ6bTOmksb9tIG2ExtVlXvwYqml1Gvy7G07ppIt6horyOLVEi+iAi63vAu8588ke5pdzKvbjO6JJ3idSwREZH/UZKyay3QqcDHJwM7gZkF1tUB9oUgl4iIiIRAj4EjmOly6TzpBlaMHYzd9C01a9fzOtYR27pxLSszfmb/0t+os2U6rXOW0NXyAFjha8H8uifga5lE0x6DaBrXkfqaW0gkZGKrVKPpdZ+ROXYgzb69glV1J9CyQ0+vY4mIiPxHScqun4FLzewmAld4nQ584pzzFxjTDlgdwnwiIiJyhOIHnceM3Fy6/T6Spc8OxTfyG6rXrON1rGJzfj9rls4mc/YvuFWpNNmRQQu3jrpAtotkaXRHpjW9kCrt+tO650Di6jUizuPMIuGuVr1G7L7oE3LfOpnI989h87U/Ub9xC69jiUgxrF4ym93jrmFHm1NJPPcePWxCwlJJJqhvDaQDtQlMSr8b6OucWxjc3hBYA7zsnLuxVNJWQJqgXkREyosZ371J9z9uYXF0Z1qOnEC1GrW9jlSo7P1ZLJv9O9sX/ErMuim02jubuuwEYDvVWVG1O1mN+1K74zHE9ehPbJVqHicWqbwWTZ9Ei89HsDoqjua3/EjV6rW8jiQiB7F6yWxi3jmdOm4HUZbH9GrH0PaqN8J2mgMJbyF5GmPwQI2BEcEPv3DOrSqwrS+BpzK+75ybcgR5w4rKLhERKU+mTXid+LTbWBjTldYjvy4XP5ju2LaZFRk/s3fJb9TcNI22+xcQazkArLHGrK8Zj2uRRKNux9GifTy+iAiPE4tIQRk/vEf3325gVrVketz2JRGRJbl5RETKypolc4h+5zSiyGXbiI/ZOPNb+iwazSZfffac/grte+lZc1KxhKzskpJT2SUiIuVN+lcv0WvqncyPjaftyK+oUq1GmZ3b+f1krl7M2pk/k7cyhYbbMmiVtxKfOXKdj+VRbdlStzfRrZNpGT+I+k1blVk2ETl8aeMeIXH+v0irfyb9bnhVt0WJlDNrl80l8q3TiCabbSM+oU23RAAWpP9E7a+upa7byvROd5B47t368ysVRkjKLjNbBjztnBtzkDE3Arc759ocVtIwpLJLRETKo/Qvnqf3tHuYG9uL9rd8VWq3Aubl5rJi3hQ2z5tE5No0mu+aRSO2ALDHxbK0Slf2NEygRoejaR0/oNzeWikih5b6wg0kZb5LartbSbroQa/jiEjQ2mXziXjrVGLYz9azPqZt96T/2b5jywaWv3IJPfelMr3aANpd/UZYPMxGwl+oyi4/8KBz7m8HGXMf8DfnnO4vCFLZJSIi5dWUz54hIeOvzKmSQMdbviAmtuoRH3Pv7h0sy5jErkW/UW1jOm32zaO6BR7UvJG6rK4RT26zROp1HkBcl75ERkUf8TlFpHzw5+WR8dQZ9N49iWn9nqbPkMu9jiRS6a1bvgDfm6cSyz62nPkRbXscVeg45/eT9t5DJCwewwZfA/YNf4128UeXcVqRkinLsutfwC3OuSqHlTQMqewSEZHybMonT9Fv9oPMrJJIp1HjS1x4bc5cxaqMiWQv+4N6W6fTOmcpkebH74yVEa3YWKcnEa2SaRY/kMYt2uvWCJEwl7VvD8ufPIE22YtZPuQ9OiWe5HUkkUpr3YqF+N4YShX2sumMj2gX3/+Q+yyY8gO1J1xHXbedGV3upN/Zf9Hf3VJuHXbZZWYtC3y4Ang6+DpQBNASeAXIds51PcysYUdll4iIlHdpHz1O4ty/M6PqUXQd9RnRMbGFjnN+P6sWZbBhzi/Y6jSa7MygucsEIMtFsSymEzsa9KFqu6OJ6zlQT3YSqaS2b85k19iB1HA72XXhBFq0j/c6kkils27FQuyNoVRlL5vO+LBEV2lt35zJylcvJn7fFKZXP5Z2V72u2xqlXDqSsssP5A+wAr8uchfgNufc04eRMyyp7BIRkYogf3Lp6dWOofuoT4iKjmF/1l6Wz/qd7QsmE5s5lVZ751CHXQBsoyYrqnZnf9O+1O40gDbd+xdZkolI5bN22XyqvHUS+6wKsddNpF6j5l5HEqk01q9ciHvjVKq7PWwsYdGVz5+Xx5T3HiJhyTNk+hqSNfxV3dYo5c6RlF1vECi4DLgEmAlkFDI0D9gC/OSc+/4I84YVlV0iIlJRpL73D5IWPca86O6Y89MmexExlgPAamvK+lo9sZZJNOp2LC3a9dBtDSJyUIum/0KLz89mdVQcLW6dWKZPfhWprDJXLcb/+lCqu91sGD6O9j2POaLjLUj7nrrfXEttt5MZXe+i34g79Pe/lBtlNmeX/JnKLhERqUhS3/0bXRY/z/rIFmyr15voNv1p1XOgrsoQkcOS8cN7dP/tBmZVS6bHbV8SERnpdSSRsJW5egn+14ZQ3e1iw7BxtO81ICTH3bZpPatevZj4rKlMqzGQDle9Ro1adUNybJEjEZKySw6Pyi4REalonN+v/7UVkZBJ++BfJC54hLT6Z9Hvhlf0/UWkFGSuXkLea0Oo6d/J+mHj6ND72JAe35+XR9o799N32XNk+hqx/4zXinyyo0hZOVjZpb9pRERE5H/oB1ERCaXE8+4htdH5JG7+hLT3/+51HJGws2HNUnJfGxosuj4IedEF4IuIIPnSf7Jo8PtEu/00/+R00j56HOf3h/xcIqFQoiu7zKwucAXQD6hD4CmMB3LOueNDE6/i05VdIiIiIlLZ+fPyyHhqOL13T2Z64tP0Hny515FEwsLGtcvJfmUwtf3bWXvae3RMGFTq59y6cS1rXruEHlnppNc4nk5Xv0r1mnVK/bwiBwrVnF2dgF+ABgQmrC+Kc84VVoJVSiq7REREREQga98elj95Am2yF7N86Pt06nei15FEKrSNa5ez/5Uh1PFvY81p79IpoeyuOQnc1vhX+i17jnW+JmSf+TptuyeV2flFIHS3MT4ONAQeBdoAUc45XyEvFV0iIiIiIvI/YqtUo8m1n7HR14BGEy5n9eKZXkcSqbA2rVvB/leGUNe/lTWnvlOmRRfk39b4MAtPeZ9Yt49mH5/KlI+f1G2NUm6UpOw6BvjaOXevc26Fcy6vtEKJiIiIiEj4qV2/Mb6LP8Zh+N47my0b1ngdSaTC2bxuJVkvD6aufyurh75Dp74neJalS/JgfNf/xqIqPeg35yGmPX02e3Zt9yyPSL6SlF0GzCutICIiIiIiEv6atenKplPfpK5/G5tfPpN9e3Z5HUmkwti8biV7XxlMPf8WVg95u1zcDlyvUXO6/eUHUuKup9eOn9jy1FEsm5PmdSyp5EpSdk0DOpZWEBERERERqRw6JgxiQf+naJ+ziAVjzyUvN9frSCLl3ubMVex5ZQj18zazcsjbdEo8yetI/+GLiCD5skdYcNK7VPXvoelHQ5nyyVO6rVE8U5Ky62/AEDM7rnSiiIiIiIhIZdHrpIuY0ulOeu39nfQXr9UPxSIHsTlzNXteGkKDvE2sGPwWnRNP9jpSobr2HwrX/cbi2G70m/0g054+R7c1iidK8jTGS4DTgGHA+wSu9Npe2Fjn3Fshylfh6WmMIiIiIiJFS33+OpI2vE9q+9tJuvB+r+OIlDubM1ez+6XBNMzbyIpT3qRL8mCvIx1SXm4uU96+j34rXmRNRDP8I96gdZe+XseSMHOwpzGWpOzyA47A3F35DtzZAKcnMv6Xyi4RERERkaL58/LIeHI4PXf/Skby0/Q+5TKvI4mUG1s2rGHXi6fQMG8jy09+g65HDfE6UonM+f1LGv9wE9XcXmb3/Ct9h92E+Upyg5lI0UJVdl1a3BM6594s7thwp7JLREREROTgsvbuZsVTJxKXvZgVQ98vF5Nui3ht68a17HzhFBrlZbLspDcCtwhWQJszV5H5+sV025/B1Fon0/Xql6lavZbXsSQMhKTsksOjsktERERE5NC2bVrP7ucGUd3tYvdF39CiXXevI4l4ZuvGtex4YTCN89az9MRX6Xb06V5HOiJ5ublMeeseEle+zOqI5riz3yCuc6EdhUixHazs0vWDIiIiIiLiuToNmuC7+GMchr07gq0b13odScQT2zatZ8cLQ2iSt46lJ7xS4YsugIjISJKveIx5J7xJNf8uGn0wmCmfPeN1LAljKrtERERERKRcaNamKxtPfYP6/i1seukM9u3Z5XUkkTK1bdN6tj1/Ck3y1rL4+FfpdswwryOFVLdjhsF1v7IspjP9Zv4fU58+j727d3gdS8JQscsuM1tWzNfS0gwsIiIiIiLhq1PC8cw76ina5yxiwXPnkZeb63UkkTKxfXMmW18YTNO8tSw+/mW6Dwivoitf/cYt6XTnRFJaXEWfbd+y8cn+rJw/zetYEmZKcmWXj8DTFg981Qbigq/oEh5TRERERETkf/Q++WKmdPoLvfb8xtSXbvA6jkip27FlA1ueH0zz3DUsHvQS3Qec4XWkUhURGUnylU8w9/g3qeHfSYMPBjN1/FivY0kYKXYx5ZyLc861LuRVF+gAfAssBTqXVlgREREREakcks6/j9RG55G0cRyp7/3d6zgipWbHlg1sem4wzXNXs3Dgi3Q/9kyvI5WZ7gOG4b9mMstjOtA3416mPH2+bl+WkAjJVVjOuSXAmUAz4IFQHFNERERERCq3ftc8x/RqA+i38AlmfPem13FEQm7H1k1sem4wLXNXsvC4F+hx3FleRypzDZrG0fEvE0lpfgUJ274h84n+rFyY4XUsqeBCdsuhcy4L+AE4P1THFBERERGRyssXEUGXG99nUVQnOv9xOwum/uh1JJGQ2bF1ExvHnkLL3JXMP/Z5egwc4XUkz0RGRZN81VPMHfQatfzbaPDeSaR/8bzXsaQCC/X8WrlA4xAfU0REREREKqnYqtVpdO1nbPbVp9HXl7F6yWyvI4kcsR3bNrPxucG0yl3B/GPHEj/oHK8jlQvdjz2TvGt+ZUVMBxKm382U0ReStXe317GkAgpZ2WVm9YEzgNWHse8KM3NFvDKLsf+rBca3K2T7ZQc5vjOz64o4bhUze8jMFppZlpltNLMPzUzzkomIiIiIlJE6DZrAhR8Dhr17Nls3rvU6kshh27FtMxvGDqZVzjLmDRhL/KDzvI5UrjRoGkeHv0wkpdnl9Nv2Fesf78+qRRlex5IKJrK4A83s/oMcowUwDKgF3HOYWXYATxey/qA1rpmdBlwRHFf9EOf4HMgoZH16IceNIXBbZv/g9tEE3ufZwFAzG+ScSzvE+UREREREJASat+vGgqGvE/fVeax86Uyq3vYTsVUP9c9/kfJl5/YtbBg7mLicpcw7Ziw9j1fRVZjIqGiSr36aWb8cQ4tfbiHm3ZNIT/g7Cadd63U0qSDMOVe8gWb+QwzZCYx2zpV4gnozWwGBJz6WcL8GwGzgFwK3Tx4LtA9OmF9w3GXA68Dlzrk3innse4CHgY+Bc51z/uD6YcB4YB7QPX99URISElx6+p+6NBEREREROQwzvnuT+D9GkVH9aOJvHU9EZLH//17EUzu3b2H9s4NpnbOEeUc/S88TL/A6UoWwYc1Str55MZ1z5jKl7mn0uOoFFd0CgJlNc84lFLatJLcxDizidSzQA6h/OEXXEXopuLwxlAc1MwPyb228s2Ch5Zz7HPgV6ELgvYuIiIiISBnpdfKlTOl4B733/MrUl0P6Y4BIqdm1Yyvrnx1Cm5wlzO0/RkVXCTRq3pb2d/5CStNL6Lf1S9Y93p/Vi2d6HUvKuWL/N4hzblJpBgFizOwioCWwB5gFTHbO5RU2OHi11nDgDOfclkA/dUg9zewWIBZYC/zsnFtTyLi2wRyLnHPLC9n+DXAMMAj4uTgnFhERERGR0Ei64P9IfW4VSRs+IPW9FiRd8H9eRxIp0q4dW1n7zBDa5ixm9lGj6X3SRV5HqnAio6JJvuYZZv48gJaTbiX6nZNI7/cPEoZe7XU0KafK0zW/jYG3D1i33MwuP7BoM7NWBObQesc5N74E5xh1wMd5ZvYKcItzLqvA+o7B5aIijrM4uOxQgnOLiIiIiEiI9L3mOaY/tY5+Cx9n+net6H3yxV5HEvmT3Tu3sfbZobTNWcSco57S79MjFD/wbDa0703mmxeRMPUO0pb9RvxVzxFbpZrX0aScKfHTGM0sycxeMbNpZrbUzKab2ctmdtQR5HgdOJ5A4VUN6A68CMQB35hZfIHz+4A3CUxIP7KYx18O3EygxKoGNAXOAVYA1wKvHTC+VnC5o4jj5a+vXdhGM7vGzNLNLH3Tpk3FjCgiIiIiIsUVERlJlxs/YHFUR7r8cSsL0n/yOpLI/9i9cxurnxlCu+wFzE5+kl4nX+p1pLDQqHlb2v3lF1KaXELilvGsfbw/q5fM9jqWlDMlKrvM7B/A7wSeftgLaA30BK4EfjWzhw8nhHPuIefcROfcBufcXufcHOfcdcCTQBXgwQLDbyUwV9bVzrltxTz+JOfcs865RcHjr3fOfURgzrFtwPkFC7ViyL9nstDZ/Z1zLznnEpxzCQ0aNCjBYUVEREREpLhiq1anwTWfstlXj0ZfXcaaJXO8jiQCwJ5d21n9zFDaZy9gVtKT9D7lMq8jhZWo6BiSr32GmQNepF7eJuq8fSLTvn7F61hSjhS77DKzs4F7gVXAVUAbAkVUm+DHq4C7zOycEOZ7IbgcEMzQHvgn8LpzbsKRHtw5txrIP86AApvyr9yqReFqHjBOREREREQ8ULdhM9yFHwMO3h3Btk3rvY4kldyeXdtZOWYo7bPnMzPxSXoPvtzrSGErftB5ZF05iTVRcfSZejtpz1xK1r49XseScqAkV3bdDGwA+jrnXnPOrXDO7Q8uXwP6ApsI7ZMRNwaX+TfgdgVigMvNzBV88d8nIy4OrhtezHPk32dY8CbfhcFlUXNytQ8ui5rTS0REREREykiLdt3ZMPQN6vs3s+GlM8nau9vrSFJJ7d29g5XPnEqH7HnM7PcYfYao6CptjVu0o+2dk0htfCGJW8az5vFjdJWnlKjsigc+ds5tLmxjcP1HBG5rDJXk4HJZcLkCeLWIV2ZwzEfBj1cU8xyJB5wDYCmBK9U6mFnrQvYZHFxOLOY5RERERESkFHXqewLzkh+nQ/Z85o09H39eoQ91Fyk1e3fvYPmYoXTcP4eMfv+mz9CrvI5UaURFx5B03XNkHP0CDfIyqf32CUyb8LrXscRDJSm7IoG9hxizlxI+4dHMuppZ3ULWtwKeDX74DoBzLsM5d1VhL/57Nda9wXUZBY51TCHHNzO7h0Chthn4Nn+bc87x31so/x2cFD9/v2HAMcA84H+eEikiIiIiIt7pfcplTOl4O733TGbKS6G84UTk4Pbt2cXyMafSaf8cZvT9NwlDr/Y6UqXU84Tz2XfFJNZGtaLPlFtIe/Zy9mcdqsaQcGSBXqcYA81mEiiyujvn/IVs9wEzCXRFPYodwOxB4G7gZwJPTdwFtAWGArEE5tQ6wzmXfYjj/ELgVsb2zrklB2xzBG45nAqsJTAXV3+gG4GC7gzn3PcH7BND4Mqto4B04CegJXA2kA0Mcs6lHer9JSQkuPT09EMNExERERGREEl97mqSNn5Iasc7STr/Pq/jSJjbt2cXy0YPpdP+WcxIeJSE0671OlKll70/i+mv3ULShvdZEtGWKhe+TbM2Xb2OJSFmZtOccwmFbSvJlV3vA52Bz4MTxRc8QVvgY6AL8F4J8/0MfEbgyY4XALcRKK1+Ay4FTj1U0VUMjxO4zXEQMAq4BIgCxhIo774/cAfn3H7gBOBvQG0CT4E8ERhPYN6yQxZdIiIiIiJS9vpe8zwzqh1NvwWPMeP7d7yOI2Fs355dLB1zaqDo6vMvFV3lRHRMLEnXv0BG/+dpmJdJzTePZ/q3b3gdS8pQSa7siga+J/DUQj+wDlgPNAaaESjOfgNOCEE5FTZ0ZZeIiIiISNnbt2cXq546nlY5y1hx2jg6JRzvdSQJM1l7d7N49Kl0zcpgWu+H6TvsBq8jSSHWrVjI7ncuokPuItIajKDnlc8QE1vV61gSAiG5sitYYJ0I3EfgdsPmBJ7A2CL48X3A8Sq6RERERETEa1Wq1aDBNZ+x2VePRl9dxtplc72OJGEkUHSdFii6ev1TRVc51jSuI3F/+ZXURueRuOljVj0+gLXL5nsdS0pZSW5jxDmX45z7l3OuPVCTQNFV0znXPrg+p1RSioiIiIiIlFDdhs1wF3wIOPxvj2DbpvVeR5IwkLV3N4vGnE7XrBlM6/UP+g7XwxDKu8BtjS8y46ixNMpdR423BjHjuze9jiWlqERlV0HOud3OubXOud2hDCQiIiIiIhIqLdrHs2HI6zT0b2LDS2eStVc/vsjhy9q3h0VjhtFt33TSe/6NvsNv8jqSlECvky5i96UTyYxsTq+UkaSOvYrs/Vlex5JSUOyyy8z6mNn9ZtaoiO2Ng9t7hiydiIiIiIjIEerU70TmJj9Oh+z5zHvufPx5eV5Hkgooa98eFo0eRo+sdKbFP0S/M0Z6HUkOQ9PWnQK3NTY8h6RNH7HisWNYt3yB17EkxEpyZdftwFXAxiK2bwCuJPA0RRERERERkXKj9ymXMaXDbfTePZkpL+m2MymZ/Vl7WTh6GD2ypjKl+0P0PXOU15HkCETHxJJ0w8tMT36WxrlrqP7mID25NcyUpOxKBn52RTy+Mbh+ItA/FMFERERERERCKfH8/yO1wdkkbXif1Pcf9jqOVBD7s/ayYPRw4rOmMqXbA/Q76xavI0mI9D75YnZdMpENkU3p9ceNpD5/rW5rDBMlKbsaA2sOMWYd0OTw44iIiIiIiJQO8/noe+0LzKjan34L/q0rOeSQ9mftZf7oM4jfl0Za1/vpN0I3MoWbZm060/KOyaQ1GEHShg8CtzWuWOh1LDlCJSm79gINDjGmAbD/8OOIiIiIiIiUnojISDrdOI4lUe3p9PutLJr+i9eRpJzK3p/F/DFn0nNfKmld/0ri2bd7HUlKSUxsVRJvfJXpSaNpkrOa6m8MJOOH97yOJUegJGVXBjDMzKoXttHMagLDguNERERERETKpSrValDv6s/Y6qtD/S8uZu2y+V5HknIme38Wc0efQc+9KaR1uY/Es+/wOpKUgd6nXMbOS39iY0Rjev5+PanPX0dOtq7nqYhKUna9RODKrR/MrEfBDWYWD3wP1A+OExERERERKbfqNWqO/4KP8OHH//aZbN+c6XUkKSfyi65ee/8grfO9JJ5zp9eRpAw1a9OVFn/5jbT6Z5G04X2W/XsA61fqtsaKpthll3NuHPAWkAjMMLN1ZjbVzNYB04F+wFvOufdLJ6qIiIiIiEjotGgfT+bg12no38T6F88ga98eryOJx3Ky9zN3zFnBouseEs+9y+tI4oGY2Kok3vQa0/o9TbOclVR9fSAZP33gdSwpgZJc2YVz7jLgOmAegQnr+wSXc4FrnHOXhzqgiIiIiIhIaemUeBJzkx6jY/Z85o09H39enteRxCM52fuZPfoseu35jdSOd5F47t1eRxKP9RlyOdsv/pFNEY3p+eu1pL5wg25rrCBKVHYBOOdecs51B6oDzYHqzrkezrlXQp5ORERERESklPUefDlT2t9C792TmPLyzV7HEQ8Eiq4R9N7zK6kd7yTp/Hu9jiTlRPN23Wh+x6+k1RtOUua7LP33sWSuXuJ1LDmEEpdd+Zxze51z65xze0MZSEREREREpKwlXnB/YI6ezHdJG/eI13GkDOVk72f2mBH03jOZ1A53kHT+fV5HknImtko1Em9+k2l9n6B5zgpiXz2WmRN1W2N5dthll4iIiIiISLgwn4+E615iRtWjSJj3CBk/vOd1JCkDuTnZzB5zNr13Tya1/e0kXfBXryNJOdZn6FVsu/gHtkQ0JH7ytaS8eKNuayyniiy7zGyemd1wuAc+0v1FRERERETKUkRkJJ1u/JClUe3p+NsoFk2f5HUkKUW5OdnMHHMOvXdPIrX9bSRdeL/XkaQCaNGuO83u+I20esNJXv8OSx47Trc1lkMHu7KrE1D/CI59pPuLiIiIiIiUqSrValDv6s/Y5qtDvS8uZu2y+V5HklIQKLrOpc+un0ltdwtJFz7gdSSpQPJva0xPeIyW2cuIefU4Zv78kdexpIDIQ2w/zswO99jucHcUERERERHxSr1GzVl1/odUfW8oee+cxfYbJlK7fmOvY0mI5OZkkzHmPBJ2TSS17SiSLnrI60hSQSWceg2rOyaS8/4lxE+6ipTFk+l7+RNERkV7Ha3SM+cK76TMzB+C4z/onPtbCI5TYSUkJLj09HSvY4iIiIiISAnNT/uONhMuZFl0B1rf9gOxVap5HUmOUF5uLjPGnEvCzh9JaTOS5Ev+7nUkCQNZe3cz85XrSdz6BfOjulLvsndp2Ky117HCnplNc84lFLrtIGXXsSE49wrn3MoQHKfCUtklIiIiIlJxTZvwOn2m3MK06sfR69ZP8UVEeB1JDlOg6DqPhJ0/kNL6JpIv/afXkSTMpH/xAl2m3c9+i2H1sU/RY+AIryOFtcMquyQ0VHaJiIiIiFRsqe88QNKSp0ltfCFJ1z3ndRw5DHm5uUx/5nz67vie1LgbSbrsYa8jSZhatSiDvA8upbV/BSnNLqPvZY/ptsZScrCy62AT1IuIiIiIiFR6iRc8QFr9M0nKfJe0cY96HUdKKFB0XUDfHd+TEne9ii4pVS079KTx7b8xpc6pJK99g0WPDWLTuhVex6p0VHaJiIiIiIgchPl8JFz3MhlVk0mY9y8yfnzf60hSTHm5uUx/9iL67viOlFbXkXzZI15HkkqgSrUa9Bv1Lum9HyFu/yIiXjqG2ZM+9TpWpaKyS0RERERE5BAiIiPpcMM4lka1p8Ovo1g0fZLXkeQQ/Hl5THv2Yvpu/4aUlteSfLmuypOylXD69Ww6/1t2+OrQdeIVpLxyK7k52V7HqhRUdomIiIiIiBRD1eq1qHvVp2z31abeFxezbvkCryNJEfx5eaQ/cxH9tk8gpcXVJF/xb68jSSXVqlNvGt/+O+l1BpO85jUW6rbGMqGyS0REREREpJjqN25B7vkfEkEeuW+fyY4tG7yOJAfw5+WR/uwlwaLrKpKvfNzrSFLJValWg363vM/Ung/TOv+2xsmfeR0rrKnsEhERERERKYGWHXqy7pRXaZy3gXUvnEHWvj1eR5Igf14e6WMvpd+2r0hpfgVJlz/mdSSR/+g7/EY2nfcNO3216PrT5aS+cht5ublexwpLKrtERERERERKqEvSKczu9yidc+Yy97kL8efleR2p0vPn5TF17GX02/olKc0uJ+mKJzCffuSV8qVV5z40vO13ptU5haQ1r7Lg3wPZvG6l17HCTrH/5JvZBcUYE2lmTx1ZJBERERERkfKvz9CrSG07ij67fibtlVu8jlOp+fPymPrcFSRu/YKUppeSdOWTKrqk3KpavRZ9b/mAKfH/oM3+BfDSAOb8+rnXscJKSf70v2Nmr5hZlcI2mlkb4A9gZEiSiYiIiIiIlHOJFz5IWv0zSV7/FmkfahJ0Lzi/P1B0bRlPStNLSLrqaRVdUiH0O+NmNpz3DXt8Nejy46WkvHqHbmsMkZJ8B5gMXAFMMbMuBTeY2TnAdCABeDpk6URERERERMox8/noc+2LZFRNJmHuw2T89IHXkSoV5/cz5bkrA0VXk4tIumq0ii6pUOI6J9Dgtt+ZVvskkle/zPzHjmdz5iqvY1V4JfkuMBD4O9AZmGpmV5pZjJm9BLwP5ACnOeduL4WcIiIiIiIi5VJkVDQdbhjHsqh2dJg8ksUzJnsdqVIIFF1Xkbj5U1IbX0jS1c+o6JIKqWr1WiSM+oAp8X+nbdY8eOEY5vz2hdexKrRifydwAQ8AJwLbgZeA1cCVwK9AvHPu69IIKSIiIiIiUp5VrV6LOld9xnZfLep+fhHrli/wOlJYc34/ac9fQ+LmT0htdD6J1zyroksqNPP56HfGSDLPncAeX3U6/3AJKa/dqdsaD1OJvxs4534GxgAG1Ac2Axc659aFOJuIiIiIiEiFUb9xC3LO+4hIcsl5+yx2bN3kdaSw5Px+0l64lqRNH5Ha6DwSr31ORZeEjdZd+lL/1t+ZUesEkle9yLzHTmBz5mqvY1U4JfqOYGbVzexd4GFgHfAB0ABIN7OTSiGfiIiIiIhIhdGqY0/WnvwqTfIyWfvCGezP2ut1pLASKLquI2njh6Q2PJfEa59X0SVhp1qN2vS55UOmdH+I9llz4IWjmfu7bqQriWJ/VzCzXgQmoT8f+A7o6Zy7ALgAqAZMMLN/m1lEqSQVERERERGpALokD2ZWv0fokj2bOWMvxJ+X53WksOD8ftJevIGkjeNIbXgOide9oKJLwpb5fPQ76xbWnf01+6wqnb6/kJTX79L3k2IqyXeGFCAOuMs5N8Q5txnAOfcB0AvIAO4Afg9xRhERERERkQolYejVpLYZSZ9dE0l79Rav41R4/ym6NrxPWoMRJF73ooouqRTadEukzq1/MKPW8SSvfIG5/z6BLRvWeB2r3CvJd4f1wNHOuccO3OCcWwokE5jLq2+IsomIiIiIiFRYiRc9RFq94SSve4u0D//0Y5QUk/P7SXvppkDRVf8s+l3/soouqVSq16xDn1s+Ykq3B+iQNRv/80cz948JXscq10ryHaKXc25KURudcznOuVuAYUecSkREREREpIIzn48+171MRpUkEub+k4yfPvA6UoXj/H5SX76ZpMx3Sat/Jv1ueEVFl1RK5vPRb8RtrBnxFVlWhU7fXUDqG/fqtsYiFPu7hHNuezHHfXXYaURERERERMJIZFQ0HW78kGWRbekweSSLZ0z2OlKF4fx+Ul8ZRfL6d0irN5x+N7yqoksqvbbdk6hz6x9k1BxI0oqxzHnsJLZuXOt1rHJH3ylERERERERKUdXqtahz9WfssJrU+fxi1q1Y6HWkci9QdN0SuAW03nD63vCaii6RoOo169D71k9I63o/HffNJPe5o5mX+q3XscqVkjyNcVkxX0tLM7CIiIiIiEhFU79xS7LP+5Bocsh56yx2bN3kdaRyy/n9pL56G8nr3iSt7un0veE1fBERXscSKVfM5yPx7NtZc9YXZFsMHb45n5Q3dVtjvpJU4z7ACnnVJvCUxjgguoTHFBERERERqRRaderNmpNfpUneeta8cAb7s/Z6HanccX4/qa/dTvLa15lS9zT63viGii6Rg2jb4yhq3fIHM2seS/Lyscx57GS2bVrvdSzPlWTOrjjnXOtCXnWBDsC3wFKgc2mFFRERERERqci6JA9mVt9/0TV7NrPHXqSrMA6Q+vpfSF7zGlPqnErCjW+q6BIphhq16tL71k9J63IfnfbNIGfsUSxI+97rWJ4KyVVYzrklwJlAM+CBUBxTREREREQkHCWceg0pbUaSsOsn0l691es45UbKa38hefUrTKk9hISb3lLRJVIC5vOReM6drDrzC7ItmnYTziX1rb9W2kI9ZLccOueygB+A80N1TBERERERkXCUdNFDpNUbHpiX6qPHvY7juZTX7iR51UuBouvmd1R0iRymdvH9qXVLCrNqHEPSsjHMfvwUtm/O9DpWmQv1/Fq5QOMQH1NERERERCSsmM9Hn+teZmaVRBLm/IOZEz/0OpJnUl6/i+RVLzK19mAVXSIhUKNWXXrdNp60zvfSee909j97FAum/OB1rDIVsrLLzOoDZwCrQ3VMERERERGRcBUZFU27Gz5keWQb2k+6icUZv3odqcylvHE3yStfYGqtk+l9k4oukVAxn4/Ec+9i5RnjybVI2n19Dqlv319pbms051zxBprdX8SmSKAFMAyoBdzjnPt3aOJVfAkJCS49Pd3rGCIiIiIiUk5tzlxFzguDiCKHnMu/p0mrjl5HKhOpb9xL0oqxTK11Er1vfp+IyEivI4mEpZ3bt7Dk5cvovWcyGVWSiLvyTWrXr/g35ZnZNOdcQqHbSlB2+Q8xZCcw2jmnCeoLUNklIiIiIiKHsnL+NOqMO41tvrrUvulnatVt4HWkUpXy5n0kL3+W9Jon0mvkByq6REqZ8/uZ8uGjdJ//FKtP+4COCYO8jnTEQlV2HVvEJj+wDVjgnMs9vIjhS2WXiIiIiIgUx9w/JtD+u4tZHNOFdrd9R0xsVa8jlYqUt/5K8rIxpNc8gV4jx6noEilD2zdnhsVVXXDwsqvY31Wcc5NCF0lEREREREQK6nrUENK3PEzCtDtJf+5i+tzyEeYL9TPFvJX69v2BoqvG8fTUrYsiZS5ciq5DKRffOc1shZm5Il6HfEammb1aYHy7g4y71MymmNluM9thZr+Y2akHGV/FzB4ys4VmlmVmG83sQzPrfLjvVUREREREpCgJp11LSuubSNj5I6mv3up1nJBKfecBkpaOZlqNQfQc+QGRUdFeRxKRMFVkjW5mLQ/3oM65VYex2w7g6ULW7z7YTmZ2GnBFcFz1g4x7HLgdWAO8DEQD5wFfmtnNzrlnDxgfA/wA9AfSgdEEJuI/GxhqZoOcc2nFemciIiIiIiLFlHTx30kbu5rktW+Q9lFLEs++3etIRyz13YdIWvI002oMJH7kOBVdIlKqipyzKzghffEm9PpfzjlXomtRzWxFcMe4Eu7XAJgN/AI0Bo4F2jvnlhww7ijgd2Ap0Nc5ty24Pg6YBlQDOjnnVhTY5x7gYeBj4FznnD+4fhgwHpgHdM9fXxTN2SUiIiIiIiWVm5PN3CeH0nVvOnOPe4n4gWd7Hemwpb77N5IWP8H06sfSY9THKrpEJCQOd86utzi8sqssvRRc3gh8cpBx1wWX/8wvugCccyvMbCzwV+By4AEAM7MC+9xZsNByzn1uZr8CxxAo134OxRsRERERERHJFxkVTbsbPmLF04No/8uNLKnbhHbxR3sdq8RS3/tHsOgaQPeRH6noEpEyUWTZ5Zy7rAxzAMSY2UVAS2APMAuY7JzLK2ywmV0GDAfOcM5tCfRTRcp/pua3hWz7hkDZNYhg2QW0DeZY5JxbXsQ+xwT3UdklIiIiIiIhV61GbWpf+Rk7Xzqe2p9dyPra39OkVUevYxVb6vv/JGnRY0yvNoDuIz8mKjrG60giUkmUiwnqgxoDbwP/JDB310RgsZkde+BAM2tFYA6td5xz4w92UDOrBjQDdjvn1hcyZHFw2aHAuvy/QRYVcdjC9hEREREREQmp+k1bsf/ccUSTzf43z2LHts1eRyqWtA/+RdLCfzO92jF0H6WiS0TK1kHLLjO7xMx6lEGO14HjCRRe1YDuwItAHPCNmcUXyOQD3iQwIf3IYhy7VnC5o4jt+etrH+E+IiIiIiIiIdeqcx9Wn/gKTfPWseb5M9iftdfrSAeVNu4REhc8woxqR9N91CcqukSkzB3qyq43CNwq+B9mdqmZTQxlCOfcQ865ic65Dc65vc65Oc6564AngSrAgwWG30pgrqyrC86/FYoYJRibf89kofuY2TVmlm5m6Zs2bTryZCIiIiIiUql17T+UWQn/omv2LGY/dzHOf9DnZHkmbdyjJM7/FzOqHkXXkSq6RMQbh3MbYxyBsqksvBBcDgAws/YEbnN83Tk3oZjHyL8Kq1YR2wu7iutQ+9QsZJ//cM695JxLcM4lNGjQoJgxRUREREREipZw2rWktL6RhJ0/kvrqbV7H+ZO0Dx8jcf7DgaJr1GdEx8R6HUlEKqnyNGdXYTYGl9WCy65ADHC5mbmCL/5bwC0OrhsO4JzbA6wFqptZk0LO0T64LDg/18Lgsqg5uQrbR0REREREpFQlXfwPptQ9jeS1rzPlk6e8jvMfaR89TuK8f5BRNVlFl4h4rsinMZYTycHlsuByBfBqEWOHEpjz6yNgZ3BsvonAxcApBOYHK2hwgTH5lgKrgA5m1rqQJzIWto+IiIiIiEipMp+P3te/xqwnhtB71t+YVbcFPQaO8DRT2kdPkDj372RUSaLzyE9VdImI5zy/ssvMuppZ3ULWtwKeDX74DoBzLsM5d1VhL/57Nda9wXUZBQ6XfzvkfWZWp8A54oAbgf0UKMGcc67APv8OToqfv88w4BhgHjDpcN+3iIiIiIjI4YiMiqbNDR+xIrI1bX+5kSUzf/csy5RPniJx7t+YWSWRzqM+Iya2qmdZRETyFafsKsnE7YfjbGCdmX1jZs+Z2aNm9jGwAGgHTAAeP5ITOOf+IDDZfVtglpk9ZWZjgXSgLnCHc27FAbs9CfwBjADSzOwRM3sP+BjYC1zhnCufs0KKiIiIiEhYq16zDrWu/JRdVoNan11I5qrFZZ5hyidP02/2g8ys0o9Oo8ar6BKRcsMCFzEVsdHMT8nLLuecK/btkWZ2LHAd0IvAbYjVgO1ABvA28LY7WMj/HucXAvN2tXfOLSlizKXATUAXwA9MBx5zzn1VxPgqwN3ABUBLArdH/gI84JybV5z3l5CQ4NLT04szVEREREREpERWzE+n7rjT2OqrT52bf6ZWnfplct6pn46mz8wHmFMlgQ6jPie2SrVD7yQiEkJmNs05l1DotmKUXSXmnPP89sjyQmWXiIiIiIiUpjm/f0mH7y9lcUw32t/+fanPmTXls2dIyPgrc6r0ocOoL1R0iYgnDlZ2HbSUcs75DudVOm9DREREREREDtSt/2nM6vMwXbNnMvO5i3H+0pttZer4Z4NFV28VXSJSbqmYEhERERERqeASTr+OlLjr6bvje9Jeu6NUzjF1/Fj6zPg/5sb2osNIFV0iUn6p7BIREREREQkDSZc8zJQ6p5K05lWmfPJ0SI+d/sXz9JlxH3Nje9J+1JfEVq0e0uOLiISSyi4REREREZEwYD4fva5/jVmxfek96yFm/fJJSI6b/sUL9Jp2D/Ni42k3UkWXiJR/KrtERERERETCRFR0DG1u+IiVkXG0/fkGls7644iOl/7li/SadjfzY+NpO/IrqlSrEaKkIiKlR2WXiIiIiIhIGKlesw41r/yMXVadmp9eQOaqxYd1nPSvXqJX+l0siOmhoktEKhSVXSIiIiIiImGmQdM4ss4dRwz7yXrjTHZs21yi/ad9/Qq9pt7JgpjutB75pYouEalQVHaJiIiIiIiEobjOCaw64UWa5q1l9Qtnkb0/q1j7TZvwKvFT/sLCmG60HvkVVavXKuWkIiKhpbJLREREREQkTHU7+nRm9v4H3fZnMPO5S3B+/0HHT5vwOvFpd7AougutblbRJSIVk8ouERERERGRMNZ32A2ktLqOvju+I/X1vxQ5bvo3rxOfdhuLozvTauTXVKtRu+xCioiEkMouERERERGRMJd06b+YUmcoyatfYeqno/+0ffq3b9Aj9TYWR3eixc0qukSkYlPZJSIiIiIiEubM56PX9a8zKzaBXjMfZNYvn/xn2/Tv3qZ7ym0sie5Ei5snUL1mHQ+TiogcuUivA4iIiIiIiEjpi4qOoc0NH7Ny9CDa/nwDS+s2YWfmMrr/MYqlUR1odtPXKrpEJCyo7BIREREREakkqtesw74rP2PXS8dT59Nzaen2sCyqPc1unkCNWnW9jiciEhK6jVFERERERKQSadA0jn3nfEAkuSyLakeTm1R0iUh40ZVdIiIiIiIilUzrLn3ZcXMGbWrUJio6xus4IiIhpbJLRERERESkEqpVr5HXEURESoVuYxQRERERERERkbChsktERERERERERMKGyi4REREREREREQkbKrtERERERERERCRsqOwSEREREREREZGwobJLRERERERERETChsouEREREREREREJGyq7REREREREREQkbKjsEhERERERERGRsKGyS0REREREREREwoY557zOENbMbBOw0uscIVIf2Ox1CPGEvvaVl772lZO+7pWXvvaVl772lZe+9pWXvvaVUzh93Vs55xoUtkFllxSbmaU75xK8ziFlT1/7yktf+8pJX/fKS1/7yktf+8pLX/vKS1/7yqmyfN11G6OIiIiIiIiIiIQNlV0iIiIiIiIiIhI2VHZJSbzkdQDxjL72lZe+9pWTvu6Vl772lZe+9pWXvvaVl772lVOl+Lprzi4REREREREREQkburJLRERERERERETChsouEREREREREREJGyq75KDMrLmZvWZm68xsv5mtMLOnzayO19mk9JjZCDN7xsx+NbOdZubM7B2vc0npMrN6ZnaVmX1mZkvMbJ+Z7TCz38zsSjPT3xlhzMweNbOfzGx18Gu/1cxmmNkDZlbP63xSdszs4uD3fWdmV3mdR0pH8N90rohXptf5pPSZ2TFm9omZrQ/+O3+9mX1vZkO8ziahZ2aXHeTPfP4rz+ucUjrMbGjwz/ea4L/zlpnZR2aW7HW20qI5u6RIZtYW+ANoCHwOLAD6AQOBhUB/59wW7xJKaTGzDCAe2A2sAToB7zrnLvIyl5QuM7sOeB5YD/wMrAIaAWcCtYBPgLOd/uIIS2aWDUwH5gEbgWpAEpAArAOSnHOrvUsoZcHMWgCzgQigOnC1c+4Vb1NJaTCzFUBt4OlCNu92zj1elnmkbJnZ/wF/BzYDXxH4u78+0Av42Tl3p4fxpBSYWU9geBGbjwEGAV87504tq0xSNszsUeBOYAswnsCf+3bA6UAkcIlzLuwubFDZJUUys++Ak4CRzrlnCqx/ErgVeNE5d51X+aT0mNlAAiXXEuBYAsWHyq4wZ2aDCBQcXzvn/AXWNwamAC2AEc65TzyKKKXIzGKdc1mFrP8ncC/wvHPuhrJPJmXFzAz4AWgNfArcgcqusBUsu3DOxXmbRMqamZ0NfAj8CJzpnNt1wPYo51yOJ+HEE2aWQuA/uIY5577wOo+ETvDf8WuBTUAP59zGAtsGAhOB5c65Nh5FLDW6JUUKZWZtCBRdK4CxB2x+ANgDXGxm1co4mpQB59zPzrnFuoKncnHOTXTOfVmw6AquzwReCH54XJkHkzJRWNEV9GFw2b6ssohnRhL4n/3LCfw9LyJhJjglwaPAXuCCA4suABVdlYuZdSNQdK0FvvY4joReKwK9T1rBogsCP/MBu4AGXgQrbZFeB5Bya1Bw+X0hP/juMrPfCZRhScBPZR1ORMpc/j98cz1NIV44Lbic5WkKKVVm1hl4BBjtnJscvNJTwl+MmV0EtCRQcM4CJjvnNG9P+DqKwNWbHwPbzGwo0A3IAqY451K8DCeeuDa4fFV/9sPSYiAb6Gdm9Z1zm/M3mNkAoAaBWxvDjsouKUrH4HJREdsXEyi7OqCySySsmVkkcEnww2+9zCKlz8zuIDBXUy0C83UdTeAH4Ee8zCWlJ/hn/G0C8/Td63EcKVuNCXztC1puZpc75yZ5EUhKXd/gcgOBeRq7F9xoZpMJTFmwqayDSdkzsyrARYAf0C3rYcg5t9XM7gKeBOaZ2XgCc3e1JTBn1w/8t/AMKyq7pCi1gssdRWzPX1+79KOIiMceIfC/vhOcc995HUZK3R0EHkyQ71vgMv3gE9buJzAp9dHOuX1eh5Ey8zrwKzCXwG0sbYCbgGuAb8ws2Tk308N8UjoaBpfXAcuBE4A0Arc6PQGcDHyEpi2oLM4h8PPc13oITfhyzj0dnKfxNeDqApuWAG8ceHtjuNCcXXK4LLjUnE4iYczMRgK3E3ga68Uex5Ey4Jxr7JwzAld8nEngB+AZZtbb22RSGsysH4GruZ7Q7UuVi3PuoeBcjRucc3udc3OCDx56EqgCPOhtQiklEcGlEbiC6yfn3G7n3FzgDAIPKDrWzJI9Syhl6Zrg8kVPU0ipMrM7Cdy6/AaBK7qqAX2AZcC7ZvZv79KVHpVdUpT8K7dqFbG95gHjRCTMmNmNwGhgHjDQObfV40hShoI/AH9G4Jb1esBbHkeSECtw++Ii4K8ex5HyI/+BJAM8TSGlZVtwuezAK/eCV3bmX8Hdr0xTSZkzsy4E5nBbA0zwOI6UEjM7jsBDKb5wzt3mnFsW/A+O6QQK7rXA7cEH1IUVlV1SlIXBZYcituc/lauoOb1EpAIzs1uAZ4E5BIquTG8TiVeccysJFJ5dzay+13kkpKoT+Hu+M5BlZi7/ReDJywAvB9c97VVIKXP5t7PoidvhKf/f+NuL2J5fhlUp/SjiMU1MXzmcGlz+fOAG59xeYAqBXqhXWYYqC5qzS4qS/4fhJDPzFXwio5nVAPoD+4BUL8KJSOkJTmL5CJABnFjwqS1SaTUNLvWP4fCyH3i1iG29CfzD9zcCPxzrFsfKI//2tWWeppDSMpnAk5Xbm1m0cy77gO3dgssVZZpKypSZxRKYnsJP0X8PSHiICS4bFLE9f/2B3wsqPF3ZJYVyzi0FvgfigBsP2PwQgf/te8s5t6eMo4lIKTKzvxIouqYBx6voqhzMrJOZNS5kvc/M/klgQuM/nHPb/ry3VFTOuX3OuasKewFfBIe9GVw3zsusElpm1tXM6hayvhWBq3oB3inbVFIWgn+vjyMwVcn9BbeZ2YkEJqjfgZ6+HO7OBuoQePiQJqYPb78Gl9eYWbOCG8xsMIGLWLKAP8o6WGnTlV1yMDcQ+E0/xsyOB+YDicBAArcv3udhNilFZjYcGB78MP8H4GQzeyP4683OuTvKOJaUMjO7FPgbgat3fgVGmtmBw1Y4594o42hS+k4BHgs+cn4pgUdSNwKOJTBBfSb/+/QeEanYzgbuNrOfCTyRbxeBSYuHArEE5u953Lt4UspuI/Bv+vvMbACB25haEZi/Jw+42jm33bt4UgbyJ6Z/ydMUUhY+Bn4k8OTV+Wb2GYF/13UmcIujAXc757Z4F7F0qOySIjnnlppZAoEffk8BhgDrgTHAQ5qsOqz1BC49YF2b4AtgJaCyK/y0Di4jgFuKGDOJwJNcJLz8SOAfvP2BeAKPId9D4D823gbG6Hu+SFj5GehI4FbVZAJX7G8ncNvq28Dbzjk9cTtMOec2mlki8H8ECq4kAoXn18C/nHOapiSMmVln4Gg0MX2l4Jzzm9kQAndrnUfgz3xVYCuBr/8Y59z3HkYsNaa/x0REREREREREJFxozi4REREREREREQkbKrtERERERERERCRsqOwSEREREREREZGwobJLRERERERERETChsouEREREREREREJGyq7REREREREREQkbKjsEhERERERERGRsKGyS0REREREREREwobKLhERERGpkMzsDTNzZhZXwv2OC+7nzOzDIsbEBbf/FpKwIiIiUmZUdomIiIhIZXa2mSV7HUJERERCR2WXiIiIiFRWS4LLxz1NISIiIiGlsktEREQqhAK3lb1hZm3N7GMz22Jmu8zsezPrFhzXwMxeMrP1ZpZlZlPNbGARx4w0sxvMLNXMdprZXjObYWY3mdmf/p1kZpeZ2SdmtszM9gX3+d3MLiri+G2CWZYEx281s9lm9oKZ1Ssw7sHgezvuYO/7gPX5t/C1MbObzWxW8By/FBhT18z+ZWbzg9t2mNlPZnZSEe/NBZcnmtmvZrbbzDaZ2etmVjs4rpeZfWVm24LbvyjqNsIjOP9AM/sl+LXdaWZfm1nnA8Y74NLgh8sL3Ja4orAsRUgDPgeOMrOziruTmcWY2d3Bz/neYMZfzeycEpxbRERESkmk1wFERERESiiOQEkxH3gj+PEZwC/B29G+BXYC44C6wHnAN2bWwTm3Kv8gZhYFfAmcDCwE3gOygIHAM0AicPEB534emAdMBtYD9YAhwNtm1tE599cCx28CTAVqAhOAT4BYoHXwuM8CW47808Fo4Bjg6+B58oLnbwX8QuDz8yuBz0s14FTgWzO71jn3ciHHOz045ivgBeAo4DKgtZndDfwUPN6rQHfgNKCtmXV3zvkLvP/DPf+pwDDgm+D5uxD4HPc1sy7Ouc3BcQ8Bw4H44Odge3D9dkrmTmAo8IiZfeGcyznYYDOLBr4DjgUWAGOBqsAIYJyZ9XTO3VvCDCIiIhJCKrtERESkojkW+D/n3D/zV5jZX4G/ESjBPgRuyC9ezOwH4C3g1uAr330Eiq5ngVucc/klUQTwEnCFmX3snPu8wD7dnHNLC4YJlh/fAHeb2QvOubXBTSMIlG23OOdGH7BPNcBPaPQGejnnlh+w/k2gFXC+c+6DAueuTaCEGhMsdzYcsN/pwPHOuUnB8T4C5c4JBMq0a5xz7xY43qvAFQRKr4Kfq8M9/3DgZOfcTwX2+Rdwd/A8/wZwzj0YvKIsHnjaObeiqE/QwTjnFpnZi8CNwPXAmEPscjuB34PfAKc753KDGR8CpgD3mNlXzrk/DiePiIiIHDndxigiIiIVzQrgkQPWvRlcxgB/KXiFEYErtnKBnvkrggXOTUAmcGt+0QUQ/PXtgAMuLHiSA4uu4LpsAlf3RALHF5J3XyH77HHO/Wn9Yfr3gUWXmcUTKGQ+KVg0Bc+9HXiAwFVmhd26935+0RUc7wfeDn44p2DRFfRWcNkzROf/oGDRFfRScNmvkPGh8BCBqwHvN7Nahxh7BYHfG7flF10AzrmNwN+DH15VKilFRESkWHRll4iIiFQ0GQXLqaB1weUi59yughucc3lmtgFoXmB1BwK3IC4G/s/MCjvPPuDAeaJaAncRKLVaAlUO2KdZgV9/ATwMjDWzkwlcHfU7MM855w76DktmSiHr8p8uWMvMHixke4PgsnMh29ILWZf/+Z1WyLb8K9kKfn5Dff7VwWWdQrYdMefcJjN7hMDX6z4Ctzb+iZnVANoBa51zCwoZMjG47FUaOUVERKR4VHaJiIhIRbPjwBXOudxgYfWnbUG5QFSBj/Mnh29P4CqjolTP/4WZtSFQLNUhMAfV98Hz5RGYl+pSAleW5WdaaWb9gAeBU4Azg5tWm9njzrlD3S5XXJmFrMt/fycGX0WpXsi6wj6HucXYVtjn93DOv/3AFQW+vhEHOdaReorAbYwjzWxsEWPyr/paX8T2/PW1Q5hLRERESki3MYqIiEhllF/afOacs4O8WhfY5zYCJc6VzrnjnHMjnXN/dc49SOCqrT9xzs13zp0b3C+BwLxTPmC0mV1ZYGj+bZeF/Udk7UO8l8KuEst/f6MO8f4uP8SxD5fX5y8x51wW8H8ECsuHixiW/74aF7G9yQHjRERExAMqu0RERKQyWkDgCqKk4FMZi6NdcPlJIduOPdiOzrlc59w059yjwPnB1cMLDNkWXLYoZPeEYuYrKDW4POYw9g2Fsjp//u2sobri621gBoGv0Z8+78FbZJcCzcysfSH7Dwwup4coj4iIiBwGlV0iIiJS6QQnFn+GwJU4Y8zswLm3MLMmZtalwKoVweVxB4w7mUImJDezfmbWqJDT56/bW2Bd/rxbl5vZf67uMrMWwP0HfTOFcM6lE7jV8kwzu6KwMWbW3cwalvTY5ez8W4LLlkd4HACCc6ndARjwryKGvRbc/ljwyZ0AmFl94K8FxuSvr2pmnYLzvYmIiEgZ0JxdIiIiUln9HYgHrgNOM7OJBCZbb0hgLq/+BCYrnxcc/xxwOfCRmX0SHNuNwHxcHwLnHnD8C4AbzWwSsITA1VttgdOA/cDT+QOdc2lmNhkYAEwJZmkUHPsdhV/xdSgXEJgw/VUzGwmkEbiarTnQI5g9Gdh4GMcuL+f/CfgL8LKZfQzsBrY755493AM65yaa2QRgSBFDHgcGA8OAmcGxVYGzCfze+bdz7rcC4/sBPwOTOKAoFRERkdKhsktEREQqJedcjpkNBy4CLgNOJTBh+iZgOYGrdN4tMH6WmQ0E/kGgCIkEZhKYeH47fy673icw/9NRQG8CT25cC3wAPOGcm3PA+GHAY8HlzQSeFHkngYnwzzmM97fGzPoEj3UWcCGB2/0yCRR4zwCzS3rc8nR+59x3ZnY7cDVwKxANrAQOu+wK+gtwMoXcHumcyzazEwnM4XYBgfeXS+D3wi3OufeP8NwiIiJyhCy0T74WERERERERERHxjubsEhERERERERGRsKGyS0REREREREREwobKLhERERERERERCRsqu0REREREREREJGyo7BIRERERERERkbChsktERERERERERMKGyi4REREREREREQkbKrtERERERERERCRsqOwSEREREREREZGwobJLRERERERERETCxv8D1atA2DDcMAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfrom = 0\n",
    "xto = 100\n",
    "CH0 = 14\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 20})\n",
    "\n",
    "rc = df.loc[df[0]=='$HIST']\n",
    "rc.reset_index(drop=True, inplace=True)\n",
    "\n",
    "rc = rc.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "rc['sum'] = rc[range(CH0,1024)].sum(axis=1)\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "plt.plot(rc['sum'])\n",
    "plt.plot(rc.loc[xfrom:xto,'sum'])\n",
    "\n",
    "#plt.title('AIRDOS (HIMAC, 2017-06)')\n",
    "plt.xlabel('measurement No.')\n",
    "plt.ylabel('Flux [counts per 11 s]')\n",
    "\n",
    "ener1 = rc.iloc[xfrom:xto,CH0:1024].sum()\n",
    " \n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(ener1, drawstyle='steps-post')\n",
    "plt.yscale('log')\n",
    "#plt.xlim([20,40])\n",
    "#plt.legend()\n",
    "#plt.title('AIRDOS (HIMAC, 2017-06)')\n",
    "plt.xlabel('channel')\n",
    "plt.ylabel('count')\n",
    "#plt.xticks(range(0,600,50))\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.015173076923076923 0.2682307692307693\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15.59303846153846"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = np.array([[30-CH0, 1],[82-CH0,1]]) # Calibration\n",
    "r = np.array([0.511, 1.3])\n",
    "coef = np.linalg.solve(l,r)\n",
    "print (coef[0], coef[1])\n",
    "(1024-CH0)  * coef[0] + coef[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Empty 'DataFrame': no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-2f63e7f646ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m#rc['sum'].plot()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m50775\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m51350\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m#plt.ylim([0,20])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   2739\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2740\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2741\u001b[0;31m                            **kwds)\n\u001b[0m\u001b[1;32m   2742\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2743\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   2000\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2001\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2002\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   2003\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1802\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1804\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1805\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1806\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    371\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m             raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 373\u001b[0;31m                             'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rc = df.loc[df[0]=='$CANDY']\n",
    "rc.reset_index(drop=True, inplace=True)\n",
    "\n",
    "rc = rc.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "#rc.to_csv('ble.txt')\n",
    "\n",
    "#rc['sum'] = rc[range(24,544)].sum(axis=1)\n",
    "rc['sum'] = rc[range(22,544)].sum(axis=1)\n",
    "#rc['sum']\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "#rc['sum'].plot()\n",
    "rc.ix[50775:51350,'sum'].plot()\n",
    "\n",
    "#plt.ylim([0,20])\n",
    "e1 = [0,10]\n",
    "e2 = [0,0]\n",
    "e3 = [0,0]\n",
    "e4 = [0,0]\n",
    "\n",
    "rc.ix[e1[0]:e1[1],'sum'].plot(c='b')\n",
    "rc.ix[e2[0]:e2[1],'sum'].plot(c='orange')\n",
    "rc.ix[e3[0]:e3[1],'sum'].plot(c='g')\n",
    "rc.ix[e4[0]:e4[1],'sum'].plot(c='r')\n",
    "\n",
    "plt.title('AIRDOS (HIMAC, He, 150 MeV, 2017-06)')\n",
    "plt.xlabel('measurement No.')\n",
    "plt.ylabel('Flux [counts per 11 s]')\n",
    "\n",
    "print e1[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Energy spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LX5g2bRorVqxg1KhRXHbZZbzyyivcdtttPP7441x//fV8/fXX3H///a18N0RE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JWb+/vy+Z5UotNL/9TYjmeWU64zUHCB8sq8ijqWHsxAlmVzPlh/P2fG9vBgW1xBm9D+E1llBLrZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f13a51eb0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 20})\n",
    "\n",
    "'''\n",
    "plt.figure(figsize=(20,5))\n",
    "rc['sum'][91:154].plot(c='blue')\n",
    "rc['sum'][155:235].plot(c='red')\n",
    "rc['sum'][236:296].plot(c='green')\n",
    "rc['sum'][300:350].plot(c='yellow')\n",
    "'''\n",
    "\n",
    "ener1 = rc.ix[e1[0]:e1[1],22:520].sum()\n",
    "ener2 = rc.ix[e2[0]:e2[1],22:520].sum()\n",
    "ener3 = rc.ix[e3[0]:e3[1],22:520].sum()\n",
    "ener4# = rc.ix[e4[0]:e4[1],22:520].sum()\n",
    " \n",
    "plt.figure(figsize=(20,5))\n",
    "#plt.yscale('log')\n",
    "\n",
    "plt.plot(ener1, label='0 mm')\n",
    "plt.plot(ener2, label='114.28 mm')\n",
    "plt.plot(ener3, label='136.88 mm')\n",
    "plt.plot(ener4, label='141.39 mm')\n",
    "plt.ylim([0,500])\n",
    "plt.legend()\n",
    "plt.title('AIRDOS (measurement at HIMAC)')\n",
    "plt.xlabel('channel')\n",
    "plt.ylabel('count')\n",
    "plt.xticks(range(0,600,50))\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectrum Interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-3bfd5589490e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mginput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import Image as ImageDisp\n",
    "from pandas import DataFrame\n",
    "import string\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fto = './data/DATALOG.TXT' #  File to Open\n",
    "l=[]\n",
    "l.extend(range(0,548))\n",
    "df = pd.read_table(fto, sep=',', header=None, names=l, comment='*')\n",
    "\n",
    "#\n",
    "''' DEBUG\n",
    "df = df.reset_index(drop=True)\n",
    "df.drop(df[df.index > 100].index, inplace=True)\n",
    "'''#'''\n",
    "\n",
    "df.drop(df[df[0]=='$GPTXT'].index, inplace=True)\n",
    "#df.drop(r[r[0]=='$GPRMC'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPVTG'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGLL'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGSA'].index, inplace=True)\n",
    "df.drop(df[df[0]=='$GPGSV'].index, inplace=True)\n",
    "#df.drop(df[df[0]=='$CANDY'].index, inplace=True)\n",
    "\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 20})\n",
    "\n",
    "rc = df.loc[df[0]=='$CANDY']\n",
    "rc.reset_index(drop=True, inplace=True)\n",
    "\n",
    "rc = rc.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "#rc.to_csv('ble.txt')\n",
    "\n",
    "#rc['sum'] = rc[range(24,544)].sum(axis=1)\n",
    "rc['sum'] = rc[range(22,544)].sum(axis=1)\n",
    "#rc['sum']\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "#rc['sum'].plot()\n",
    "rc.ix[:,'sum'].plot()\n",
    "\n",
    "plt.title('AIRDOS (HIMAC, 2017-06)')\n",
    "plt.xlabel('measurement No.')\n",
    "plt.ylabel('Flux [counts per 11 s]')\n",
    "\n",
    "points=plt.ginput(2)\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "rc.ix[points[0][0]:points[1][0],'sum'].plot()\n",
    "points=plt.ginput(2)\n",
    "\n",
    "ener = rc.ix[points[0][0]:points[1][0],22:520].sum()\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "plt.plot(ener)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "date = np.nan #pd.to_datetime('210317000000.00', format='%d%m%y%H%M%S.%f')\n",
    "altitude = np.nan\n",
    "LatDD = np.nan\n",
    "LonDD = np.nan\n",
    "for i,r in df.iterrows():\n",
    "    if (r[0] == '$GPRMC'):\n",
    "        date = pd.to_datetime(str(r[9])+str(r[1]), format='%d%m%y%H%M%S.%f',errors='coerce')\n",
    "    '''\n",
    "        Lat = float(r[3])\n",
    "        Lon = float(r[5])\n",
    "\n",
    "        if (r[4] == 'N'):\n",
    "            Lats = 1\n",
    "        else:\n",
    "            Lats = -1\n",
    "\n",
    "        if (r[6] == 'E'):\n",
    "            Lons = 1\n",
    "        else:\n",
    "            Lons = -1\n",
    "\n",
    "        LatDD = int(Lat/100)\n",
    "        LatDD = LatDD + (Lat - 100.0*LatDD)/60.0\n",
    "        LatDD = LatDD * Lats  \n",
    "\n",
    "        LonDD = int(Lon/100)\n",
    "        LonDD = LonDD + (Lon - 100.0*LonDD)/60.0\n",
    "        LonDD = LonDD * Lons  \n",
    "    '''       \n",
    "    if (r[0] == '$GPGGA'):\n",
    "        altitude = r[9]\n",
    "    #r[20] = date\n",
    "    df.set_value(i,20,date)\n",
    "    df.set_value(i,21,altitude)\n",
    "    #df.set_value(i,22,LatDD)\n",
    "    #df.set_value(i,23,LonDD)\n",
    "\n",
    "#df.to_csv('complete.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfo = df[[25,26,27]].reset_index().groupby(20).sum()\n",
    "#df.reset_index(inplace=True)\n",
    "dfo = df\n",
    "#dfo.reset_index(inplace=True)\n",
    "dfo.drop(dfo[dfo[0]=='$GPRMC'].index, inplace=True)\n",
    "dfo.drop(dfo[dfo[0]=='$GPGGA'].index, inplace=True)\n",
    "\n",
    "#dfo = dfo[range(20,544)].groupby(20).sum()\n",
    "#dfo\n",
    "\n",
    "#df.iloc[20:40,0:30]\n",
    "#dfo['sum'] = dfo[range(27,544)].sum(axis=1)\n",
    "\n",
    "dfo.to_csv('radiation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:16: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=30,center=False).mean()\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6844a03590>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MyayIiIiIiIiIiIiIiIiIVJkCRhERERERERERERERERGpMgWMIiIiIiIiIiIiIiIiIlJl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6844b73910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "matplotlib.rcParams.update({'font.size': 15})\n",
    "\n",
    "dfo = pd.read_table('radiation.csv', sep=',', header=0)\n",
    "dfo = dfo.dropna(how='any')\n",
    "dfo['20'] = pd.to_datetime(dfo['20'])\n",
    "dfo = dfo.set_index(['20'])\n",
    "NOISE_LEVEL=22\n",
    "dfo[range(NOISE_LEVEL,500)] = dfo[range(NOISE_LEVEL,500)].mul(range(1,500+1-NOISE_LEVEL),axis=1)\n",
    "dfo['rad'] = dfo[range(NOISE_LEVEL,500)].sum(axis=1)\n",
    "xx = dfo.loc['2017-05-31 09:00:00':'2017-06-01 01:00:00']\n",
    "#xx = dfo\n",
    "#xx = dfo\n",
    "#pandas.rolling_mean(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)\n",
    "WINDOW = 30\n",
    "xx['rad'] = pd.rolling_mean(xx['rad'],WINDOW)\n",
    "\n",
    "xx['rad'].plot(c='r', label='Doserate (rolling mean '+str(WINDOW)+')')\n",
    "plt.xlabel('UT')\n",
    "plt.ylabel('Effective Doserate in Silicon [arbitrary unit]', color='red')\n",
    "#, xticks=['2017-03-27 00:00','2017-03-27 12:00','2017-03-28 00:00','2017-03-28 12:00','2017-03-29 00:00'],figsize=(20,5))\n",
    "plt.legend(loc=2)\n",
    "\n",
    "ax = plt.twinx()\n",
    "xx['21'].plot(c='b',label='alt')\n",
    "plt.plot(xx['18']*100,c='tan',label='noise',zorder=10)\n",
    "#plt.plot(xx[19]*5000-xx['18']*100,c='green',label='corr')\n",
    "plt.ylabel('altitude [m a.s.l.]', color='blue')\n",
    "#plt.ylim([0,500])\n",
    "plt.grid(b=True, which='major', color='grey', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='grey', linestyle='-')\n",
    "plt.legend()\n",
    "plt.title('CANDY 2017 - Flight to Japan')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=30,center=False).mean()\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:29: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=30,center=False).mean()\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fceccef3f10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n49mtko/bzqWsQZTr0S3OKpck320sr+XLpeOOk55/PjtcSg6+3Mwxl8znP5++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\nnv73elYu2XkxaVJt+xloFqi/9ZZfdvLYYPVgzx/hEgDkl9U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io0e7Cw79+2se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\nZHxt1QkgZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcece5d1f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5/XU/zbsbW3ztyguW0nf+6otwjmuv9QNLr7FG73OX6eJ2wgnNaUuZMZYarVi6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\n1RrmnJvvnFvROTcnWneoc25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gKQzSveyyybZ6Bu6WkqqiRowYkT+wdz2oWEIt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\nCCG5goolQgghhBBCCCGEEJIr/h+6Gn4sqtf3ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcecef7c090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rc = df.loc[df[0]=='$CANDY']\n",
    "rc.reset_index(drop=True, inplace=True)\n",
    "\n",
    "rc = rc.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "#rc.to_csv('ble.txt')\n",
    "\n",
    "#rc['sum'] = rc[range(24,544)].sum(axis=1)\n",
    "#rc['sum'] = rc[range(21,544)].sum(axis=1)\n",
    "#rc['sum']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim([0,200])\n",
    "NOISE_LEVEL=21\n",
    "xx = rc.ix[99000:105000]\n",
    "xx[range(NOISE_LEVEL,500)] = xx[range(NOISE_LEVEL,500)].mul(range(1,500+1-NOISE_LEVEL),axis=1)\n",
    "xx['rad'] = xx[range(NOISE_LEVEL,500)].sum(axis=1)\n",
    "WINDOW = 30\n",
    "xx['rad'] = pd.rolling_mean(xx['rad'],WINDOW)\n",
    "xx['rad'].plot(c='b')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.ylim([0,200])\n",
    "xx = rc.ix[125000:135000]\n",
    "xx[range(NOISE_LEVEL,500)] = xx[range(NOISE_LEVEL,500)].mul(range(1,500+1-NOISE_LEVEL),axis=1)\n",
    "xx['rad'] = xx[range(NOISE_LEVEL,500)].sum(axis=1)\n",
    "WINDOW = 30\n",
    "xx['rad'] = pd.rolling_mean(xx['rad'],WINDOW)\n",
    "xx['rad'].plot(c='b')\n",
    "\n",
    "plt.title('AIRDOS')\n",
    "plt.xlabel('measurement No.')\n",
    "plt.ylabel('Flux [counts per 11 s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>539</th>\n",
       "      <th>540</th>\n",
       "      <th>541</th>\n",
       "      <th>542</th>\n",
       "      <th>543</th>\n",
       "      <th>544</th>\n",
       "      <th>545</th>\n",
       "      <th>546</th>\n",
       "      <th>547</th>\n",
       "      <th>rad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30687</td>\n",
       "      <td>985</td>\n",
       "      <td>571.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>571</td>\n",
       "      <td>437</td>\n",
       "      <td>487.0</td>\n",
       "      <td>293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30688</td>\n",
       "      <td>972</td>\n",
       "      <td>549.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>594</td>\n",
       "      <td>462</td>\n",
       "      <td>451.0</td>\n",
       "      <td>286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30689</td>\n",
       "      <td>895</td>\n",
       "      <td>574.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>613</td>\n",
       "      <td>456</td>\n",
       "      <td>469.0</td>\n",
       "      <td>288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30690</td>\n",
       "      <td>996</td>\n",
       "      <td>521.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>559</td>\n",
       "      <td>454</td>\n",
       "      <td>489.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30691</td>\n",
       "      <td>922</td>\n",
       "      <td>547.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>624</td>\n",
       "      <td>486</td>\n",
       "      <td>501.0</td>\n",
       "      <td>265</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30692</td>\n",
       "      <td>953</td>\n",
       "      <td>514.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>580</td>\n",
       "      <td>442</td>\n",
       "      <td>484.0</td>\n",
       "      <td>281</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30693</td>\n",
       "      <td>907</td>\n",
       "      <td>538.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>610</td>\n",
       "      <td>426</td>\n",
       "      <td>500.0</td>\n",
       "      <td>280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30694</td>\n",
       "      <td>976</td>\n",
       "      <td>590.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>620</td>\n",
       "      <td>426</td>\n",
       "      <td>490.0</td>\n",
       "      <td>243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30695</td>\n",
       "      <td>936</td>\n",
       "      <td>596.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>574</td>\n",
       "      <td>442</td>\n",
       "      <td>489.0</td>\n",
       "      <td>301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30696</td>\n",
       "      <td>895</td>\n",
       "      <td>539.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>596</td>\n",
       "      <td>456</td>\n",
       "      <td>496.0</td>\n",
       "      <td>293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30697</td>\n",
       "      <td>974</td>\n",
       "      <td>547.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>561</td>\n",
       "      <td>443</td>\n",
       "      <td>487.0</td>\n",
       "      <td>291</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125011</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30698</td>\n",
       "      <td>939</td>\n",
       "      <td>586.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>580</td>\n",
       "      <td>424</td>\n",
       "      <td>528.0</td>\n",
       "      <td>273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30699</td>\n",
       "      <td>980</td>\n",
       "      <td>568.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>555</td>\n",
       "      <td>471</td>\n",
       "      <td>499.0</td>\n",
       "      <td>261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30700</td>\n",
       "      <td>913</td>\n",
       "      <td>538.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>592</td>\n",
       "      <td>448</td>\n",
       "      <td>489.0</td>\n",
       "      <td>279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30701</td>\n",
       "      <td>960</td>\n",
       "      <td>564.0</td>\n",
       "      <td>758.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>609</td>\n",
       "      <td>489</td>\n",
       "      <td>503.0</td>\n",
       "      <td>255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30702</td>\n",
       "      <td>962</td>\n",
       "      <td>538.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>600</td>\n",
       "      <td>435</td>\n",
       "      <td>491.0</td>\n",
       "      <td>315</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30703</td>\n",
       "      <td>926</td>\n",
       "      <td>592.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>593</td>\n",
       "      <td>450</td>\n",
       "      <td>462.0</td>\n",
       "      <td>287</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30704</td>\n",
       "      <td>964</td>\n",
       "      <td>575.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>586</td>\n",
       "      <td>453</td>\n",
       "      <td>488.0</td>\n",
       "      <td>282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30705</td>\n",
       "      <td>956</td>\n",
       "      <td>547.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>611</td>\n",
       "      <td>451</td>\n",
       "      <td>449.0</td>\n",
       "      <td>267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30706</td>\n",
       "      <td>958</td>\n",
       "      <td>561.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>609</td>\n",
       "      <td>489</td>\n",
       "      <td>474.0</td>\n",
       "      <td>279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30707</td>\n",
       "      <td>908</td>\n",
       "      <td>554.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>604</td>\n",
       "      <td>427</td>\n",
       "      <td>459.0</td>\n",
       "      <td>283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30708</td>\n",
       "      <td>954</td>\n",
       "      <td>534.0</td>\n",
       "      <td>785.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>608</td>\n",
       "      <td>465</td>\n",
       "      <td>490.0</td>\n",
       "      <td>297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30709</td>\n",
       "      <td>953</td>\n",
       "      <td>543.0</td>\n",
       "      <td>796.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>584</td>\n",
       "      <td>454</td>\n",
       "      <td>498.0</td>\n",
       "      <td>319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30710</td>\n",
       "      <td>910</td>\n",
       "      <td>565.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>615</td>\n",
       "      <td>427</td>\n",
       "      <td>474.0</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125024</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30711</td>\n",
       "      <td>978</td>\n",
       "      <td>540.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>562</td>\n",
       "      <td>475</td>\n",
       "      <td>493.0</td>\n",
       "      <td>258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125025</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30712</td>\n",
       "      <td>958</td>\n",
       "      <td>580.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>626</td>\n",
       "      <td>446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125026</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30713</td>\n",
       "      <td>922</td>\n",
       "      <td>569.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>597</td>\n",
       "      <td>464</td>\n",
       "      <td>533.0</td>\n",
       "      <td>279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125027</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30714</td>\n",
       "      <td>927</td>\n",
       "      <td>586.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>562</td>\n",
       "      <td>440</td>\n",
       "      <td>488.0</td>\n",
       "      <td>273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125028</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30715</td>\n",
       "      <td>936</td>\n",
       "      <td>539.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>609</td>\n",
       "      <td>472</td>\n",
       "      <td>469.0</td>\n",
       "      <td>293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125029</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30716</td>\n",
       "      <td>1007</td>\n",
       "      <td>571.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>581</td>\n",
       "      <td>438</td>\n",
       "      <td>504.0</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134971</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40658</td>\n",
       "      <td>900</td>\n",
       "      <td>552.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>627</td>\n",
       "      <td>491</td>\n",
       "      <td>552.0</td>\n",
       "      <td>318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134972</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40659</td>\n",
       "      <td>926</td>\n",
       "      <td>558.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>617</td>\n",
       "      <td>472</td>\n",
       "      <td>492.0</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134973</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40660</td>\n",
       "      <td>910</td>\n",
       "      <td>569.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>594</td>\n",
       "      <td>463</td>\n",
       "      <td>524.0</td>\n",
       "      <td>308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134974</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40661</td>\n",
       "      <td>882</td>\n",
       "      <td>558.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>606</td>\n",
       "      <td>463</td>\n",
       "      <td>543.0</td>\n",
       "      <td>306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134975</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40662</td>\n",
       "      <td>891</td>\n",
       "      <td>566.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>584</td>\n",
       "      <td>451</td>\n",
       "      <td>533.0</td>\n",
       "      <td>296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134976</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40663</td>\n",
       "      <td>927</td>\n",
       "      <td>539.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>670</td>\n",
       "      <td>452</td>\n",
       "      <td>545.0</td>\n",
       "      <td>279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134977</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40664</td>\n",
       "      <td>911</td>\n",
       "      <td>545.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>580</td>\n",
       "      <td>482</td>\n",
       "      <td>532.0</td>\n",
       "      <td>328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134978</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40665</td>\n",
       "      <td>932</td>\n",
       "      <td>537.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>636</td>\n",
       "      <td>456</td>\n",
       "      <td>501.0</td>\n",
       "      <td>320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134979</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40666</td>\n",
       "      <td>939</td>\n",
       "      <td>536.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>618</td>\n",
       "      <td>454</td>\n",
       "      <td>511.0</td>\n",
       "      <td>309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40667</td>\n",
       "      <td>897</td>\n",
       "      <td>572.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>586</td>\n",
       "      <td>477</td>\n",
       "      <td>508.0</td>\n",
       "      <td>284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134981</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40668</td>\n",
       "      <td>910</td>\n",
       "      <td>548.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>577</td>\n",
       "      <td>461</td>\n",
       "      <td>488.0</td>\n",
       "      <td>290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134982</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40669</td>\n",
       "      <td>902</td>\n",
       "      <td>542.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>606</td>\n",
       "      <td>482</td>\n",
       "      <td>547.0</td>\n",
       "      <td>294</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134983</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40670</td>\n",
       "      <td>858</td>\n",
       "      <td>548.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>612</td>\n",
       "      <td>466</td>\n",
       "      <td>509.0</td>\n",
       "      <td>299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40671</td>\n",
       "      <td>881</td>\n",
       "      <td>549.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>615</td>\n",
       "      <td>454</td>\n",
       "      <td>474.0</td>\n",
       "      <td>341</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134985</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40672</td>\n",
       "      <td>866</td>\n",
       "      <td>562.0</td>\n",
       "      <td>796.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>631</td>\n",
       "      <td>466</td>\n",
       "      <td>503.0</td>\n",
       "      <td>282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134986</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40673</td>\n",
       "      <td>921</td>\n",
       "      <td>520.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>591</td>\n",
       "      <td>453</td>\n",
       "      <td>516.0</td>\n",
       "      <td>323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134987</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40674</td>\n",
       "      <td>899</td>\n",
       "      <td>547.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>600</td>\n",
       "      <td>472</td>\n",
       "      <td>496.0</td>\n",
       "      <td>307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40675</td>\n",
       "      <td>913</td>\n",
       "      <td>553.0</td>\n",
       "      <td>811.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>567</td>\n",
       "      <td>440</td>\n",
       "      <td>511.0</td>\n",
       "      <td>290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40676</td>\n",
       "      <td>886</td>\n",
       "      <td>573.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>559</td>\n",
       "      <td>461</td>\n",
       "      <td>524.0</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40677</td>\n",
       "      <td>877</td>\n",
       "      <td>591.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>646</td>\n",
       "      <td>417</td>\n",
       "      <td>495.0</td>\n",
       "      <td>314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40678</td>\n",
       "      <td>935</td>\n",
       "      <td>566.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>633</td>\n",
       "      <td>430</td>\n",
       "      <td>545.0</td>\n",
       "      <td>305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40679</td>\n",
       "      <td>879</td>\n",
       "      <td>519.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>618</td>\n",
       "      <td>484</td>\n",
       "      <td>531.0</td>\n",
       "      <td>325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40680</td>\n",
       "      <td>895</td>\n",
       "      <td>543.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>649</td>\n",
       "      <td>449</td>\n",
       "      <td>527.0</td>\n",
       "      <td>332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40681</td>\n",
       "      <td>898</td>\n",
       "      <td>527.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>600</td>\n",
       "      <td>485</td>\n",
       "      <td>480.0</td>\n",
       "      <td>314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40682</td>\n",
       "      <td>884</td>\n",
       "      <td>551.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>586</td>\n",
       "      <td>491</td>\n",
       "      <td>497.0</td>\n",
       "      <td>280</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40683</td>\n",
       "      <td>903</td>\n",
       "      <td>534.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>632</td>\n",
       "      <td>430</td>\n",
       "      <td>502.0</td>\n",
       "      <td>287</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134997</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40684</td>\n",
       "      <td>892</td>\n",
       "      <td>509.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>599</td>\n",
       "      <td>479</td>\n",
       "      <td>523.0</td>\n",
       "      <td>336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40685</td>\n",
       "      <td>905</td>\n",
       "      <td>552.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>571</td>\n",
       "      <td>460</td>\n",
       "      <td>547.0</td>\n",
       "      <td>336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40686</td>\n",
       "      <td>930</td>\n",
       "      <td>546.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>585</td>\n",
       "      <td>485</td>\n",
       "      <td>535.0</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40687</td>\n",
       "      <td>891</td>\n",
       "      <td>520.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>614</td>\n",
       "      <td>483</td>\n",
       "      <td>492.0</td>\n",
       "      <td>310</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001 rows × 549 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0      1     2      3      4      5    6    7      8    9    ...     \\\n",
       "125000 NaN  30687   985  571.0  822.0  638.0  571  437  487.0  293    ...      \n",
       "125001 NaN  30688   972  549.0  783.0  661.0  594  462  451.0  286    ...      \n",
       "125002 NaN  30689   895  574.0  772.0  692.0  613  456  469.0  288    ...      \n",
       "125003 NaN  30690   996  521.0  783.0  629.0  559  454  489.0  300    ...      \n",
       "125004 NaN  30691   922  547.0  765.0  631.0  624  486  501.0  265    ...      \n",
       "125005 NaN  30692   953  514.0  799.0  664.0  580  442  484.0  281    ...      \n",
       "125006 NaN  30693   907  538.0  791.0  670.0  610  426  500.0  280    ...      \n",
       "125007 NaN  30694   976  590.0  802.0  676.0  620  426  490.0  243    ...      \n",
       "125008 NaN  30695   936  596.0  816.0  650.0  574  442  489.0  301    ...      \n",
       "125009 NaN  30696   895  539.0  753.0  684.0  596  456  496.0  293    ...      \n",
       "125010 NaN  30697   974  547.0  776.0  643.0  561  443  487.0  291    ...      \n",
       "125011 NaN  30698   939  586.0  797.0  669.0  580  424  528.0  273    ...      \n",
       "125012 NaN  30699   980  568.0  723.0  669.0  555  471  499.0  261    ...      \n",
       "125013 NaN  30700   913  538.0  790.0  660.0  592  448  489.0  279    ...      \n",
       "125014 NaN  30701   960  564.0  758.0  610.0  609  489  503.0  255    ...      \n",
       "125015 NaN  30702   962  538.0  802.0  691.0  600  435  491.0  315    ...      \n",
       "125016 NaN  30703   926  592.0  781.0  689.0  593  450  462.0  287    ...      \n",
       "125017 NaN  30704   964  575.0  821.0  644.0  586  453  488.0  282    ...      \n",
       "125018 NaN  30705   956  547.0  816.0  662.0  611  451  449.0  267    ...      \n",
       "125019 NaN  30706   958  561.0  784.0  652.0  609  489  474.0  279    ...      \n",
       "125020 NaN  30707   908  554.0  790.0  696.0  604  427  459.0  283    ...      \n",
       "125021 NaN  30708   954  534.0  785.0  654.0  608  465  490.0  297    ...      \n",
       "125022 NaN  30709   953  543.0  796.0  674.0  584  454  498.0  319    ...      \n",
       "125023 NaN  30710   910  565.0  797.0  693.0  615  427  474.0  250    ...      \n",
       "125024 NaN  30711   978  540.0  768.0  672.0  562  475  493.0  258    ...      \n",
       "125025 NaN  30712   958  580.0  759.0  652.0  626  446  496.0  280    ...      \n",
       "125026 NaN  30713   922  569.0  787.0  690.0  597  464  533.0  279    ...      \n",
       "125027 NaN  30714   927  586.0  820.0  680.0  562  440  488.0  273    ...      \n",
       "125028 NaN  30715   936  539.0  782.0  644.0  609  472  469.0  293    ...      \n",
       "125029 NaN  30716  1007  571.0  765.0  656.0  581  438  504.0  276    ...      \n",
       "...     ..    ...   ...    ...    ...    ...  ...  ...    ...  ...    ...      \n",
       "134971 NaN  40658   900  552.0  738.0  662.0  627  491  552.0  318    ...      \n",
       "134972 NaN  40659   926  558.0  801.0  649.0  617  472  492.0  292    ...      \n",
       "134973 NaN  40660   910  569.0  762.0  655.0  594  463  524.0  308    ...      \n",
       "134974 NaN  40661   882  558.0  781.0  683.0  606  463  543.0  306    ...      \n",
       "134975 NaN  40662   891  566.0  772.0  690.0  584  451  533.0  296    ...      \n",
       "134976 NaN  40663   927  539.0  804.0  648.0  670  452  545.0  279    ...      \n",
       "134977 NaN  40664   911  545.0  783.0  667.0  580  482  532.0  328    ...      \n",
       "134978 NaN  40665   932  537.0  776.0  666.0  636  456  501.0  320    ...      \n",
       "134979 NaN  40666   939  536.0  802.0  651.0  618  454  511.0  309    ...      \n",
       "134980 NaN  40667   897  572.0  784.0  668.0  586  477  508.0  284    ...      \n",
       "134981 NaN  40668   910  548.0  786.0  711.0  577  461  488.0  290    ...      \n",
       "134982 NaN  40669   902  542.0  808.0  662.0  606  482  547.0  294    ...      \n",
       "134983 NaN  40670   858  548.0  824.0  674.0  612  466  509.0  299    ...      \n",
       "134984 NaN  40671   881  549.0  813.0  692.0  615  454  474.0  341    ...      \n",
       "134985 NaN  40672   866  562.0  796.0  692.0  631  466  503.0  282    ...      \n",
       "134986 NaN  40673   921  520.0  784.0  660.0  591  453  516.0  323    ...      \n",
       "134987 NaN  40674   899  547.0  791.0  699.0  600  472  496.0  307    ...      \n",
       "134988 NaN  40675   913  553.0  811.0  682.0  567  440  511.0  290    ...      \n",
       "134989 NaN  40676   886  573.0  795.0  657.0  559  461  524.0  313    ...      \n",
       "134990 NaN  40677   877  591.0  786.0  688.0  646  417  495.0  314    ...      \n",
       "134991 NaN  40678   935  566.0  829.0  650.0  633  430  545.0  305    ...      \n",
       "134992 NaN  40679   879  519.0  807.0  647.0  618  484  531.0  325    ...      \n",
       "134993 NaN  40680   895  543.0  757.0  687.0  649  449  527.0  332    ...      \n",
       "134994 NaN  40681   898  527.0  754.0  661.0  600  485  480.0  314    ...      \n",
       "134995 NaN  40682   884  551.0  795.0  680.0  586  491  497.0  280    ...      \n",
       "134996 NaN  40683   903  534.0  827.0  701.0  632  430  502.0  287    ...      \n",
       "134997 NaN  40684   892  509.0  792.0  681.0  599  479  523.0  336    ...      \n",
       "134998 NaN  40685   905  552.0  802.0  685.0  571  460  547.0  336    ...      \n",
       "134999 NaN  40686   930  546.0  752.0  682.0  585  485  535.0  300    ...      \n",
       "135000 NaN  40687   891  520.0  791.0  680.0  614  483  492.0  310    ...      \n",
       "\n",
       "        539  540  541  542  543   544  545  546  547       rad  \n",
       "125000  0.0  0.0  0.0  0.0  0.0  16.0  1.0  7.0  0.0       NaN  \n",
       "125001  0.0  0.0  0.0  0.0  0.0  19.0  0.0  1.0  0.0       NaN  \n",
       "125002  0.0  0.0  0.0  0.0  0.0  19.0  0.0  2.0  0.0       NaN  \n",
       "125003  0.0  0.0  0.0  0.0  0.0  18.0  0.0  1.0  0.0       NaN  \n",
       "125004  0.0  0.0  0.0  0.0  0.0  18.0  0.0  1.0  0.0       NaN  \n",
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       "\n",
       "[10001 rows x 549 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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