Sym-H Histograms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0dd8abd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import datetime \n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "#first importing the data using pandas \n",
    "sc_compact = 'sc_compact_1995_2019_d.csv'\n",
    "dataframe = pd.read_csv(sc_compact)\n",
    "\n",
    "omni_file = 'omni_min_2012.csv'\n",
    "dataframe_omni = pd.read_csv(omni_file)\n",
    "\n",
    "#dataframe had no datetime column so I created one\n",
    "dataframe['datetime'] = dataframe.year.astype(str).str.cat(dataframe.month.astype(str),sep='-').str.cat(dataframe.day.astype(str), sep='-').str.cat(dataframe.hour.astype(str),sep=',').str.cat(dataframe.minute.astype(str),sep=':')\n",
    "\n",
    "#making column with just time without date for graph\n",
    "dataframe_omni['time'] = dataframe_omni.hour.astype(str).str.cat(dataframe_omni.minute.astype(str),sep='-')\n",
    "dataframe_omni.time\n",
    "\n",
    "#seperate dataframes for each event which took place ranging from 15 minutes before to 45 minutes after\n",
    "january22 = dataframe_omni[dataframe_omni.datetime.between('2012-01-22 05:54:00', '2012-01-22 06:56:00')]\n",
    "january24 = dataframe_omni[dataframe_omni.datetime.between('2012-01-24 14:46:00', '2012-01-24 15:48:00')]\n",
    "February26 = dataframe_omni[dataframe_omni.datetime.between('2012-02-26 21:23:00', '2012-02-26 22:25:00')]\n",
    "march7 = dataframe_omni[dataframe_omni.datetime.between('2012-03-07 04:03:00', '2012-03-07 05:05:00')]\n",
    "march8 = dataframe_omni[dataframe_omni.datetime.between('2012-03-08 10:46:00', '2012-03-08 11:48:00')]\n",
    "march12 = dataframe_omni[dataframe_omni.datetime.between('2012-03-12 08:58:00', '2012-03-12 10:00:00')]\n",
    "march15 = dataframe_omni[dataframe_omni.datetime.between('2012-03-15 12:50:00', '2012-03-15 13:52:00')]\n",
    "april23 = dataframe_omni[dataframe_omni.datetime.between('2012-04-23 03:05:00', '2012-04-23 04:07:00')]\n",
    "may21 = dataframe_omni[dataframe_omni.datetime.between('2012-05-21 19:20:00', '2012-05-21 20:22:00')]\n",
    "june161 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 9:39:00 ', '2012-06-16 10:41:00')]\n",
    "june162 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 20:03:00 ', '2012-06-16 21:05:00')]\n",
    "june163 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 20:59:00 ', '2012-06-16 22:01:00')]\n",
    "july14 = dataframe_omni[dataframe_omni.datetime.between('2012-07-14 17:55:00 ', '2012-07-14 18:57:00')]\n",
    "july20 =  dataframe_omni[dataframe_omni.datetime.between('2012-07-20 04:31:00', '2012-07-20 05:33:00')]\n",
    "july21= dataframe_omni[dataframe_omni.datetime.between('2012-07-21 15:49:00 ', '2012-07-21 16:51:00')]\n",
    "september3= dataframe_omni[dataframe_omni.datetime.between('2012-09-03 11:57:00 ', '2012-09-03 12:59:00')]\n",
    "september301 =  dataframe_omni[dataframe_omni.datetime.between('2012-09-30 11:15:00', '2012-09-30 12:17:00')]\n",
    "september302 = dataframe_omni[dataframe_omni.datetime.between('2012-09-30 22:49:00', '2012-09-30 23:51:00')]\n",
    "october31 =  dataframe_omni[dataframe_omni.datetime.between('2012-10-31 15:21:00', '2012-10-31 16:23:00')]\n",
    "november12 = dataframe_omni[dataframe_omni.datetime.between('2012-11-12 22:54:00', '2012-11-12 23:56:00')]\n",
    "november23 = dataframe_omni[dataframe_omni.datetime.between('2012-11-23 21:35:00', '2012-11-23 22:37:00')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b95c2dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making all the sym-H into lists to plot \n",
    "symh1 = january22['symh'].tolist()\n",
    "symh2 = january24['symh'].tolist()\n",
    "symh3 = February26['symh'].tolist()\n",
    "symh4 = march7['symh'].tolist()\n",
    "symh5 = march8['symh'].tolist()\n",
    "symh6 = march12['symh'].tolist()\n",
    "symh7 = march15['symh'].tolist()\n",
    "symh8 = april23['symh'].tolist()\n",
    "symh9 = may21['symh'].tolist()\n",
    "symh10 = june161['symh'].tolist()\n",
    "symh11=june162[\"symh\"].tolist()\n",
    "symh12 = june163['symh'].tolist()\n",
    "symh13 = july14['symh'].tolist()\n",
    "symh14 = july20['symh'].tolist()\n",
    "symh15 = july21['symh'].tolist()\n",
    "symh16 = september3['symh'].tolist()\n",
    "symh17 = september301['symh'].tolist()\n",
    "symh18 = september302['symh'].tolist()\n",
    "symh19 = october31['symh'].tolist()\n",
    "symh20 = november12['symh'].tolist()\n",
    "symh21 = november23['symh'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27e029a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexing out the numbers in the sym-H lists for histograms \n",
    "\n",
    "#values 15 minutes before onset \n",
    "a1 = symh1[0:15] \n",
    "a2 = symh2[0:15] \n",
    "a3 = symh3[0:15] \n",
    "a4 = symh4[0:15] \n",
    "a5= symh5[0:15] \n",
    "a6= symh6[0:15] \n",
    "a7= symh7[0:15] \n",
    "a8= symh8[0:15] \n",
    "a9= symh9[0:15] \n",
    "a10= symh10[0:15] \n",
    "a11= symh11[0:15] \n",
    "a12= symh12[0:15] \n",
    "a13= symh13[0:15] \n",
    "a14= symh14[0:15] \n",
    "a15= symh15[0:15] \n",
    "a16= symh16[0:15] \n",
    "a17= symh17[0:15] \n",
    "a18= symh18[0:15] \n",
    "a19= symh19[0:15] \n",
    "a20= symh20[0:15] \n",
    "a21= symh21[0:15] \n",
    "\n",
    "#values for 0-15 minutes after onset \n",
    "b1 = symh1[16:31]\n",
    "b2 = symh2[16:31]\n",
    "b3 = symh3[16:31]\n",
    "b4 = symh4[16:31]\n",
    "b5= symh5[16:31]\n",
    "b6= symh6[16:31]\n",
    "b7= symh7[16:31]\n",
    "b8= symh8[16:31]\n",
    "b9= symh9[16:31]\n",
    "b10= symh10[16:31]\n",
    "b11= symh11[16:31]\n",
    "b12= symh12[16:31]\n",
    "b13= symh13[16:31]\n",
    "b14= symh14[16:31]\n",
    "b15= symh15[16:31]\n",
    "b16= symh16[16:31]\n",
    "b17= symh17[16:31]\n",
    "b18= symh18[16:31]\n",
    "b19= symh19[16:31]\n",
    "b20= symh20[16:31]\n",
    "b21= symh21[16:31]\n",
    "\n",
    "#values for 15-30 minutes after onset \n",
    "c1 = symh1[31:46]\n",
    "c2 = symh2[31:46]\n",
    "c3= symh3[31:46]\n",
    "c4= symh4[31:46]\n",
    "c5= symh5[31:46]\n",
    "c6= symh6[31:46]\n",
    "c7= symh7[31:46]\n",
    "c8= symh8[31:46]\n",
    "c9= symh9[31:46]\n",
    "c10= symh10[31:46]\n",
    "c11= symh11[31:46]\n",
    "c12 = symh12[31:46]\n",
    "c13 = symh13[31:46]\n",
    "c14= symh14[31:46]\n",
    "c15= symh15[31:46]\n",
    "c16= symh16[31:46]\n",
    "c17= symh17[31:46]\n",
    "c18= symh18[31:46]\n",
    "c19= symh19[31:46]\n",
    "c20= symh20[31:46]\n",
    "c21= symh21[31:46]\n",
    "\n",
    "#values for 30-45 minutes after the onset \n",
    "d1 = symh1[46:61]\n",
    "d2= symh2[46:61]\n",
    "d3= symh3[46:61]\n",
    "d4= symh4[46:61]\n",
    "d5= symh5[46:61]\n",
    "d6= symh6[46:61]\n",
    "d7= symh7[46:61]\n",
    "d8= symh8[46:61]\n",
    "d9= symh9[46:61]\n",
    "d10= symh10[46:61]\n",
    "d11= symh11[46:61]\n",
    "d12= symh12[46:61]\n",
    "d13= symh13[46:61]\n",
    "d14= symh14[46:61]\n",
    "d15= symh15[46:61]\n",
    "d16= symh16[46:61]\n",
    "d17= symh17[46:61]\n",
    "d18= symh18[46:61]\n",
    "d19= symh19[46:61]\n",
    "d20= symh20[46:61]\n",
    "d21= symh21[46:61]\n",
    "\n",
    "\n",
    "symh = dataframe_omni['symh'].tolist()\n",
    "\n",
    "before = a1 + a2 + a3 +a4 +a5 +a6 +a7 + a8+  a9 + a10 +a11 + a12 +a13 +a14 + a15 + a16 + a17 +a18 + a19 +a20 +a21\n",
    "onset = b1 + b2 + b3 + b4 +b5 + b6 + b7 + b8 + b9 + b10 +b11 + b12 + b13 +b14+b15 + b16 + b17 + b18 +b19 +b20 +b21\n",
    "post_onset = c1 + c2 +c3 +c4 +c5 +c6 +c7 +c8 +c9+ c10+ c11 +c12 +c13 +c14 +c15 +c16 +c17 +c18 +c19 +c20 +c21\n",
    "past_onset = d1 + d2 +d3 +d4 +d5 +d6 +d7 +d8 +d9+ d10+ d11 +d12 +d13 +d14 +d15 +d16 +d17 +d18 +d19 +d20 +d21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a82076b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'D)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PLTDfn4EzCdqw5iXnaH4DzCRI5kcD/4wyz7sEBTAI2r9/F2WehISXjc8D5hMU1PJqxiH2Z7aAICkfTlDbdS5Bgr3S3adGrHsxe5sSfOHun5Q0zhR5DchrG/sjQXOMPDcQ/HBoS1AzF7cXhLCW+A6C3mXOZ9/PKM/1BO3m9wDXEhRgPyK4kRCK8X0Nrwz8jOD7uYGgxrAdwWX6rMha5/BqQV4B1okoXLv7nHA97xG0Rb403N+/xdvfGE4LY8/7YXBCONw+HH6R4Puf98NwLnCtu79IAjww0d2/LEFssQwn+NGXQfA9/leUee4h+IF9MkEzjAYedHN4AcGPqp7AhQSf66NRlt8ffyS4ytUj3HZNgh9jNxDcJ9En3P4a9t58HVN4M++McPArYGrE5GJ950n83CeSENvbNFFEIplZHXffGDH8DkGh8n53j1X7WymFtXZfAYSXbZO57sEEN8Jd7+779dCMyM8sbC+5gKBpyA3u/kzchYte9x8JCpR/cfe792ddIiJSealZiEhs95jZ0QS1I+0JCtZbSPJT09KVmR1HUDP6c4Lu65LxVMSnw0viiwhusmtFcAPZf/cjzsYEPXD0J7jZschaNRERSV9qFiIS2xyCHiwGEFxGHQt0dfdktpNMZ5kETWa2AFdE9MqyPz4luKQ/gOBJi/8h6Erxh/1YZwvg7wRNg25094JNJERERPKpWYiIiIiISJKo5lpEREREJElUuBYRERERSRIVrkVEREREkkSFaxERERGRJFHhWkREREQkSVS4FhERERFJEhWuRURERESSRIVrEREREZEkUeFaRERERCRJVLgWEREREUkSFa5FRERERJJEhWsRERERkSRR4VpEREREJElUuBYRERERSRIVrkVEREREkkSFaxERERGRJFHhWkREREQkSVS4FhERERFJEhWuRURERESSRIVrEREREZEkUeFaRERERCRJVLgWEREREUkSFa5FRERERJJEhWsRERERkSRR4VpEREREJElUuBYRERERSRIVrkVEREREkkSFaxERERGRJFHhWkREREQkSVS4FhERERFJkkpXuDazW8zMw1fLcFzPcLh9GYdXaszsWTNbbWbzC4y/x8xWmNns8NUzxvKHmtkEM/s8/HtIlHmywuN6Q8S49uG428Ph+8zsZyXch7pmdnNJlo2yrmFm9lW4z4vN7M8JLNMqnH+WmR2djDgi1l3HzF4ws6Vm9kX4vk6St5FlZqckc50ilZ2ZNY3IIXvMbI2ZvWRmtczs72b2vZnVKOs4y4qZ9TCzJeG564448403sx/M7K0C4yPPxbOj5eWKlFvC9VULvycPFBj/kJktCP9ea2ZHJGFbvc1srpktMrN5ZtZ7f9cZZRt3Jnud6abSFa6BS4A94fuLw7/jgOXAb8skorIxDOgRY9q/3L19+BobY547gInufgwwMRyOZj57jzPAZcCcvAF3v9vd/1esyPeqCyTtBAj83t3bA+2Ba8ysWRHz9wZGufuJ7v5FUSu3QKL/U88AX7p7C3c/GvgKeDrBZROVBahwLVIys4ArgPcJzms3AUOBBsDlZRhXmTGzqsAg4GzgOOAyMzsuxuwPAVfFmPb7iBw0O8Y8FSm3dAc+A35uZhYxvh/Qzt1/D1wLFKtwbWbVCgyfAPwDON/dWwO9gH+YWbv9iD0aFa73U6UqXIe/Ck8FXgFWEhS0cXcH3gIuMLPqZRdh6XH394D1+7GK84Hnw/fPExQ0o/kaqGFmDcKTSg+CHzNAfi1Fn/D9MjO718w+DX9xtwrH35NXGxEOzzezpsADwNFh7cZD4bTfm9n08Jf7veG4g8zsbTObEy57SRH7llfrtCVcvoOZTTGzmWb2jpk1DGv0bwNuMrPJ4Xy/Ddc/38xuC8c1DWtxXiBIBkdGizGSmbUAOgB/iRh9H9DRzI4Oa21yzGxUWMs+Iu+EbWYPmNnCcN3/CMfVN7P/htucbmanhsevP/Cb8Ph1KeKYiMi+1gD/A6aHwwe7++fAYsLckoYygaXu/qW77wBGEuSKQtx9IrB5P7ZVkXLLZcCjwDfAyeGyY4BawMxwuY7AiHCbNaPlnXC5HDN7xMxmAL8usJ3bgb+6+1cA4d+/Ab+PWPZBM5tmZp/lnffN7Phw3Oxw/44Jx18ZMf7fZlbVgtr3muG4EcX7yCRPtaJnqVB+TvCD4VVgLXCrmR3n7gsJTpD9CWotp5VZhOXDrWZ2NTAD+J27b4gyTwN3/y58/z1BbU0sowiO/SzgU+DHOPOudfeTLLgkdztwQ5x57wDahLXNmNmZwDEEJ3gDxphZV6A+sNLdzwnni9W84iEz+xPQAnjM3VebWQbwOEFNwJrwJDjQ3fua2RAg193/YWYdgOuATuG2PzGzKcCGMKZr3P3jWDGGP3byHAfMdvfdeSPcfbeZzQaOBzYBJ4bvVwIfAKea2SLgAqCVu7uZ1Q0Xf5TgasRUMzsKeMfdW0fGH+cYi0h0ZwKrw/crgSfC99OBC82sirvvibpk5dUI+DZieDnBObG4BprZ3YRXRd09Vs4o97nFgiZCPwN+QVAjfhnwobv3MrPciG3cBNzu7jNi5R2gb7ja6u7eMUrcxxPUXEeaAdwSMVzN3TPDCqI/h7H1Bx519xEWVDBWNbPWBD8ST3X3nWb2JHCFu99hZrfmxS0lU6lqrgkuIe0gqFn4JByX90tzZfi3aSnHVN4MBo4m+JHxHfBwUQuENf8eZ5ZXCE6AlwEvF7G618K/Myn+Z3Fm+Mo70bYiOCHOA7qHv9i7uPvGGMvnNQv5CdDNgvbILYE2wISwcPsnoHGUZU8DRrv7FnfPDfcjrzb4a3f/uIgYi2uauy8Pk/dsgmO1EdgOPGNmFwJbw3l/BjwRxj8GONjMapVgmyKy1ycEl/sHElzOvykcvxI4CDisjOKq6P6P4Lz4U+BQ4I9x5q0IueVcYLK7bwP+C/S2oPlMPEXlnf8UM/5I0Y7DR8CdZvZHoEkYazeCK6jTwxi6Ac33Y7sSodIUrs3sSILLMdWBBcDwcFJemy2Ltly6cfdV7r47LLQNJfiljpk9F14GymuDvSriMlVD9tbgRFvn98BOgkQ0sYgQ8moedrP3ysku9v0uxrpZyIC/RbTVa+Huz7j7Z8BJBCfC+8MakZjCwnEOQYHZgAUR62zr7mcWsQ8FbSkqxgLzLwTaW0T77PB9+3Aa7FtDs5ugNmIXwec1iuCEPj6cXgXoHLHNRuE+ikjJrQ3b9A4Mh/Nu/k7nXLICODJiuDGwwsw62d4bFHvFW4G7f+eBH4HnCHNQjHkrQm65DPiZmS0jKNDWA7KLiLWovLMlxnILCQrEkToQlHnyFDoO7v4SQfvsbcBYM8sOY3g+IoaW7n5PEXFLgipN4Zrg160RtD+6IHy9BbSyoLF/3o0EX5dNeOVDXoE5dAFBO2Hc/brwHywvgYwBrgnfXwO8UcSq7wb+GNnUoRiWEZzAMLOTgLwbDTcDtSPmewfom1cra2aNzOxwC9rab3X3Fwluojkp3sYsuEmkE/AFsASob2Z57eQyzOz4KIu9T1AjcaCZHURw7N6PMl/UGCNncPelBDUkf4oY/Sfg03BarLhrAXU8uAn1N8AJ4aR3gV9GzNc+fFvw+IlI4o4ws0vZexl+Wd54gsLP2rIIqoxNB44xs2Zh84JLgTHu/klEIW1MvBVEVNoYwb088+PNTznOLWZ2MMEVzKPcvam7NyVoonFZlFgit5lo3inoH8D/WdBunPDvnRRxBdrMmhPcQP8YQS5vR/BjpU9efrKgh7Am4SI7w6YrUkKVqc31JQRNF/7l7msAwn/+cwlqr+sBuQSFmkrPzF4m6C3iMDNbDvw5rEH9e1j4coITzy9irOIB4BUzu57gB8nFMeYDwN0/3I9w/wtcbWYLCC7Ffhauc52ZfWBBd4Lj3P33YTuxj4LzMrnAlQRtqB8ysz0EtRw3RdsIe9tcVyc4sbwWtl3uAzwWtqerBjzCvjUBuPunZjaMve31n3b3WXknuYj53o0RY8Ga/+uBx80srxeSj8Jx8dQG3gjb+Bl7e7/5FTDIzOaG8b9H0MbuTWCUmZ0P/NLdo/0YEJHoTiRoirCJoPD1+3B8R+CDNGxvjbvvMrNbCY5HVeBZd18QbV4ze5+geUWtMAdd7+7vENzUV5/gHDab4FwVb5vlObdcAEwq0Gb8DYI8e0CBeYcBQ8xsG8FV9iLzTkHuPjts2vFmWPjdCfzBY/e4kudi4Coz20lwD9Vf3X19mA/fDa+c7iT4YfA18BQw18w+dfcrili3RGFBc9rKLfyF/A3BP8E1Rc0vIiJSkAU9/XwO9HX358o6HhEpnypTs5B4ziZoG/avsg5EREQqrH4ENX9F3VwnImkspYVrK+JJTmZ2gJn9J5z+ScFL7GZ2lJnlWkQ/lSXh7mPd3RK4dCIiIuVIeckjAO7+B3dv6O7b93ddIlJ5paxwbYk9yel6YIO7tyCoVX6wwPR/EtFpvIiIpA/lERGpiFJZc53Ik5winwI4iqDv4bwn0fUmeCR03Ab+IiJSaSmPiEiFk8reQhJ5klP+POFdyBuBema2naBj+e4ET1qKysz6EbSB46CDDurQqlWr5EUvKTdz5kw6dCjYZadI6s2cOXOtu9cv6zikSMojUu4pl6WneHmkvHbFdw9Bl3q5YQVEVO7+FEGXMXTs2NFnzJhROtFJUpgZ+sykLJhZWvd3nybuQXlESoFyWXqKl0dSWbiO+iSnGPMsDx/sUQdYR1Az0cfM/g7UBfaY2XZ3fyKF8YqISPmiPCIiFU4qC9f5T3IiOPldClxeYJ68pwB+RNCh+iQPOt7ukjeDmd0D5OqEKCKSdpRHRKTCSVnhOtaTnMzsPmBG+IjUZ4DhZrYUWE9w4hSpMHbu3Mny5cvZvl09c5VnDRs2pG7dumUdhhST8oikgx9++IHvvvuurMOQOGrUqEHjxo3JyEjsqfCV5gmNaitX8ZgZFf3799VXX1G7dm3q1atHvHadUna2bdvGihUraNGiRf44M5vp7h3LMCwph5RHpCT2N5ctXbqURo0aUbNmzSRGJcni7qxbt47NmzfTrFmz/PHx8ki6PKFRJCW2b9+ugnU5V6NGDXbu3FnWYYiIRLVz505q1KhR1mFIDGZGvXr1inWFWoVrkf2kgnX5ps9HRMo7nafKt+J+PuW1Kz6p5Ma/Nh6AUYNHRZ1eq0EtelzYozRDEhGRCmT8a+PJXZUbc7ryiJQVFa6lTOSdELMaZ0WdnrM8p/SCERGRCid3VW7MHALKI1J21CxEpILIycmhWrVqrF69GoDp06djZixbtizucsOGDWPYsGHMnj2bZ555psTbf/XVVzn99NM55ZRT6NevHwArV66kd+/edO3alU6dOjFhwoRCy82ePTvm/jRp0oSsrCyysrIYM2ZMiWMDWLZsGZMmTdqvdYiIVFbKIfElM4eo5lqkAmnfvj1vvPEGN954I6NHj6Zjx8Q7vGjfvj3t27cv0XbXrl3Lk08+yaRJk6hatSo//PADAJdddhkPPfQQmZmZ5ObmsnDhwkLLjhw5MuZ2r7rqKu6///4SxVRQ3okxOzs7KesTEalslENiS2YOUc21SAWSnZ3NxIkTAViwYAHHH3887s5NN91EdnY255xzDhs2bGDHjh2cf/759OjRI//XfE5ODn/605+AoLbgjDPO4LTTTuPmm2/On3722Wdz3nnnceqpp5Kbu7ct48qVK9myZQvff/89AHXr1mXcuHG0a9eOzMxMAGrVqpX/HmDevHk88sgjzJgxg0ceeYQ1a9YUuX/9+vVj0aJFADz++OO88sorUfcvWqxPPfUUw4cPp1u3bvt7mEVEKiXlkNLJISpci1Qg1atXp0aNGnz88ce0bt0agLfeeoujjjqKSZMmceuttzJkyBBef/11MjMzGT9+PIcddlih9Rx22GFMmDCBqVOnsmnTJj7//PP89b/55pv07Nkz/wQM0K5dOy6++GKys7Pp0aMH69atY8qUKZx77rkxY23bti233XYbHTt25LbbbqN+/fqF5hk+fHj+Jb1p06bRp08fRo0KbnIdO3Ys55xzTtT9ixZrv379uOqqq/aJW0RE9lIOKZ0cosK1SJKY2X6/EtGzZ0/69+/PhRdeCAQPSRk5ciRZWVkMHDiQ9evX8+WXX3LiiScC0KFDh0LrWLduHX369CErK4upU6eycuVKANq0aQNAo0aN8i/b5bn99ttZsmQJzZs3Z/To0axevZp69eoVGe8DDzwQc9pVV11FTk4OOTk5ZGZmkp2dzeTJk1m9ejW1atXioIMOYtGiRYX2r6hYRUQqotLII8ohqc8hKlyLJIm77/crET179qRDhw789Kc/BeCAAw7g6quvJicnh6lTp/LXv/6VZs2aMWfOHABmzZpVaB0vvfQSvXv3Jicnh1NPPTV/25En5sh4li5dmv9+06ZNtGjRgvbt2+9z88f06dOLcbSiq1atGs2aNeOhhx7iggsuAKBly5aF9i9arBkZGezevXu/YxARKSulkUeUQ1KfQ1S4FqlgatWqxTPPPJN/YujVqxfLli0jOzub7Oxsxo0bR+/evfnwww8566yzov4iz87O5uGHH6Z3795s2bKlyG3eeeeddO7cmS5dutCqVSuysrLo168fc+bMoWvXrpx88slMmzat2PsSeUlv+PDhAFx00UUMHjyY8847L+b+RdOmTRs++OADLrnkkmLHISKSLpRDUp9DLNHasvKuY8eOPmPGjLIOQxI0avAofn7zz1kzJvoNCjnLc+hzU59Sjqr4Fi1alN9uTcqvgp+Tmc1098Rvk5e0oDxSsYwaPKrIfq5LI4+YWcJXHqNRHqkYipNHVHMtIiIiIpIkKlyLiIiIiCSJHiIjFc5rb73Gqo2rYk5vUKcBF557YSlGJCIiIhJQ4VoqnFUbV9G4Y+OY05fPWF6K0YiIiIjspWYhIiIiIiJJosK1iIiIiEiSqHAtIiIiIpIkKlyLVCCvvvoqp59+Oqeccgr9+vUDYOXKlfTu3ZuuXbvSqVMnJkyYsM8ys2fPjrm+nJwcmjRpkt8J/5gxY0oc27Jly/Z52paIiJQvyiGlQzc0ilQQa9eu5cknn2TSpElUrVo1/6lZl112GQ899BCZmZnk5uaycOHCfZYbOXIk7du3j7neq666ivvvv3+/48s7MWZnZ+/3ukREJLmUQ0pPkYVrMzsZuBLoAjQEtgHzgbeBF919Y0ojFKkgiuoisChFdSG4cuVKtmzZwvfff0+jRo2oW7cu48aNo127dmRmZgLBY23z3s+bN4+JEycyY8YMHnnkEa644grq169fZBz9+vXjN7/5Da1bt+bxxx/n8MMPJycnhyVLllCzZk1efPFF5syZw4MPPki1atVYv34977zzDk899RQffPABH330ERMnTizxcZDKRTlEJHGpzCPKIaUnbuHazMYBK4E3gIHAaqAGcCxwBvCGmf3T3Ut+HUCkkiiqi8CiFNWFYLt27bj44ovJzs6mWbNmjBgxgilTpnDuuedGnb9t27a0bduW77//nttuuy3meocPH87UqVMB+Pvf/06fPn0YNWoUd911F2PHjqV///4cddRRDB48mHHjxjFkyBBOPvlkqlevzhtvvMHAgQOZOHEi/fr1o3nz5kmpwZDKQTlEpHhSmUeUQ0pPUW2ur3L36919jLuvdPdd7p7r7p+6+8PungV8WApxighw++23s2TJEpo3b87o0aNZvXo19erVi7vMAw88EHf6VVddRU5ODjk5OWRmZpKdnc3kyZNZvXo1tWrVYsmSJYwcOZKsrCwGDhzI+vXrAWjTpg0AjRo1yr+8KFKAcohIOaIcUjriFq7dfW1RK0hkHhHZf0uXLs1/v2nTJlq0aEH79u33uQFk+vTp+72datWq0axZMx566CEuuOACWrZsydVXX01OTg5Tp07lr3/9KwBmlr+Mu5ORkcHu3bv3e/tSeSiHiJQfyiGlJ27h2sw2m9mmWK/SClJE4M4776Rz58506dKFVq1akZWVRb9+/ZgzZw5du3bl5JNPZtq0acVe7/Dhw/Pv9B4+fDgAF110EYMHD+a8886jV69eLFu2jOzsbLKzsxk3blzU9bRp04YPPviASy65ZL/2UyoP5RCR8kM5pPTEbXPt7rUBzOwvwHfAcMCAKwhuTBGRUvLKK68UGlejRg1GjBhR4nVmZWXx9ddfFxrfs2dPcnNz84cff/zxqMsCXHvttfnj3nvvvRLHIpWPcohI+aEcUnoS7Yqvl7ufEDE82MzmAHenICaRCqlBnQZF3pRY1PIilZRyiEgClEcqh0QL11vM7ApgJODAZcCWlEUlUgHF60ZPJM0ph4gkQHmkckj0CY2XAxcDq8LXz8NxIiIiRVEOEZG0kVDh2t2Xufv57n6Yu9d3997uvizFsZUpu9e48rUr84d37dlF/Yfqc+5L0fuDjCVrWBYzVs4AoOeInvyw/YdkhlnINxu/4Yznz+DEf59Iu8HtGPv52KjzNX2kKW0Ht6X9kPZ0fKpj/vj129bTfXh3jnn8GLoP786GbRtSGq+IVH7pmEOgYuWRHi/2oO4DdQvF5u4MmDiAYx8/ltaDWvPYJ49FXf752c9zzOPHcMzjx/D87Ofzx89cOZO2g9vS4rEW/Grcr3D3pMcuUt4kVLg2s2PNbKKZzQ+H25nZn1IbWtk6KOMg5q+ez7ad2wCY8MUEGtVutF/rHHvFWOrWqJuE6GK7/737ufi4i5n1i1mM7DOSm9++Oea8k6+ZzOz+s5nRb0b+uAemPkC3Zt34/Jef061ZNx6YGr9/SxGRoqRjDoGKlUd+f8rvGX7B8ELjh80exrebvmXxrYtZdMsiLm1zaaF51m9bz71T7uWTGz5h2g3TuHfKvfkVMze9fRNDzxvK57/8nM/Xf874peOTHrtIeZNos5ChwP8BOwHcfS5Q+D+skul5TE/e/vxtAF6e/zKXtbksf9qWHVvo+0ZfModmcuK/T+SNxW8AsG3nNi4ddSmtB7Xmgv9cwLZd2/KXafpIU9ZuDbp07T2yNx2e6sDxTx7PUzOfyp+n1l9rMWDiAE4YcgKdn+7MqtziPQbVMDb9GPRwtXH7Ro6ofUSxln9jyRtcc8I1AFxzwjW8vuT1Yi0vIhJFWuYQqDh5pFvzbtQ+oHah8YNnDObu0++migXFhcMPOrzQPO8sfYfuzbtzaM1DOaTmIXRv3p3xS8fz3ebv2PTjJjo37oyZcXW7q3l98esJHDWRii3RwvWB7l6w88NdyQ6mvLm0zaWMnD+S7bu2M3fVXDo17pQ/beD7A8luls20G6cx+ZrJ/H7C79myYwuDZwzmwIwDWXTLIu7NupeZK2dGXfez5z/LzH4zmXHjDB775DHWbV0HwJadW+jcuDNz+s+ha5OuDP10KABjlozh7slF31h/T9Y9vDjvRRr/szE9X+rJ42cX7v4Ggs7bzxx+Jh2e6rDPSXlV7ioa1g56yPpJrZ8Uu3Avybdx48b8PkTr1KlDVlYW11133T7zPPvss3HXkZWVxa5de/9lN2/eTLdu3ejatSvnnnsumzdvBmDSpEmcfPLJnHHGGSxfHtyxfvPNN1O/fn2efvrp/OXPO+88unTpQrdu3fLnE4kjLXMIVMw8EumLDV/wn/n/oeNTHTl7xNl8vu7zQvOs2LyCI+scmT/c+ODGrNi8ghWbV9D44MaFxkvpUx4pXYn2FrLWzI4muMsbM+tD0GdpXGbWA3gUqAo87e4PFJh+APAC0AFYB1zi7svMLBPIK/EZcI+7j04w1qRp16Ady35YxsvzXqbnMT33mfbuF+8yZskY/vHhPwDYvms732z8hve+fo9fdfpV/vLtGrSLuu7HPnmM0YuDXfp207d8vv5z6h1Yj+pVq3PusUGbtw4NOzDhywkA9GrZi14texUZ88vzX+baE67ld6f8jo++/YirRl/F/Jvn59c65Jl63VQaHdyI1VtW0314d1od1oquTbruM4+Z7fMEJYlv/GvjyV2VW/SMMdRqUIseF/YoNL5OnTrk5OQAcNppp+W/j/Tss8/St2/fhLeVkZHBiy++SMOGDRk6dCjDhg3jl7/8JX/5y1949913WbhwIX/7298YNGgQd911F5mZmfucVB977DGaNWvGhAkT+Ne//sXDDz9c7P2VtFKiHBLOqzxSynkk0o+7fqRGtRrM6DeD1xa9Rt8xfXn/uveLtQ5JnPJI5ZBo4foWgpNUKzNbAXwFXBlvATOrCgwCugPLgelmNsbdF0bMdj2wwd1bmNmlwIPAJcB8oKO77zKzhsAcM3vT3Uu9pqNXy17cPuF2cq7JYd22dfnjHee/F/+Xloe1LPY6c5bl8L8v/8dH13/EgRkHkjUsi+27tgOQUSUjv0BbtUpVdu2Jv8sDJg7Iv+Q4u/9snpn1DOOvCNq0nXzkyWzftZ21W9cWupTX6OCg3d/hBx3OBa0uYNqKaXRt0pUGtRrw3ebvaFi7Id9t/i7qJUCJLndVLlmNs0q8fM7ynITme/HFF3niiSeoWrUqTz75JF9//TXz5s0jKyuLAQMGMGPGDMaPH8/27dsZMmQIJ554YqF11KhRg4YNgysUGRkZ7Ny5k61bt1KzZk1q165Np06d+OMf/wiQP1+kZs2a5S9btWrVEu6xpJFi5xBQHoknmXkknsYHN+bC1kH3cBe0uoDr3riu0DyNajciZ1lO/vDyTcvJappFo9qNWL5p+T7j97fNeWWnPFI5JNpbyJfu/jOgPtDK3U9L4E7vTGBpuOwOgv5Nzy8wz/lA3m3Fo4BuZmbuvjXiBFiDsLajLPQ9sS9/Pv3PtG3Qdp/xZx19Fo9Pezz/zudZ380CoGuTrrw07yUA5q+ez9xVcwutc+P2jRxS8xAOzDiQxWsX8/Hyj0sc38BuA5ndfzaz+88G4Kg6RzHxq4kALFqziO27tlP/wPr7LLNlxxY2/7g5//27X7xLm8PbANDr2F48Pyf4SJ6f8zzntyz4kUlZ2r17N4899hjvv/8+I0aMYMCAAfTq1Yu2bduSk5ND9+7d+fWvf82UKVMYMWIE//jHP+KuLzc3l3//+99cfvnl/PDDDxx88MH7bKuoWO6//35+8YtfJGXfpPIqYQ4B5ZFSySPx9G7Vm8nLJgMw5espHFvv2ELznNXiLN798l02bNvAhm0bePfLdzmrxVk0rN2Qgw84mI+Xf4y788LcFzi/lXJKWVMeSb1Eewv5tZkdDGwF/mVmn5rZmUUs1gj4NmJ4eTgu6jzhSXAjUC/cZiczWwDMA/qXRW0DBL/a8y7PRbqr613s3L2TdkPacfyTx3PX5LsAuKnjTeTuyKX1oNbcPfluOhzRodCyPVr0YNeeXbQe1Jo7/ncHnRt3LjKORNvKPXzmwwz9dCgnDDmBy/57GcN6D8PMWLl5JT1HBJckV21ZxWnPncYJQ04g8+lMzjnmHHq0CC4j3XHaHUz4cgLHPH4M//vyf9xx2h1FblNKz5o1a2jSpAkZGRk0bdqUjRs3Fppn+PDhdO3alRtuuIGVK1fGXJe707dvXwYOHEjdunWpU6cOmzZtyp9eVE3C7373O66++mqOPvroku+QpIUS5hBQHim1PNLluS78/NWfM/GriTT+Z2PeWfoOEOSE/y76L20Ht+X/Jv4fT58XtJmdsXIGN4y5AYBDax7KXV3v4qdDf8pPh/6Uu7vezaE1DwXgyXOe5IYxN9Di8RYcfcjRnN3i7ASOmKSS8kjqJdospK+7P2pmZxGctK4ChgPvpiowd/8EON7MWgPPm9k4d98eOY+Z9QP6ARx11FFJ3X7unYXbPGU1zSKraRYANTNq8u/z/l1onpoZNRnZZ2TUdS67bVn++3FXjCtyu32O60Of4/oAibeVO67+cXzQ94NC44+ofQRjrwj6vG5+SHPm9J8Tdfl6B9Zj4tUTi9yOlI369evz9ddfs3PnTlasWEGdOnUA9mkb/+STTzJr1iy++OILbrzxxpjruvvuuzn11FPJzs4G4KCDDmLbtm3k5uaycOFCjjvuuJjLPvPMM8Hd/1dfnaQ9k0qu1HMIKI9A4nkkVjvqujXq8vblbxca3/GIjjzda+/NaX1P7EvfEwu31+14REfm3zw/6rqlbCiPpF6iheu8I94TeMHdF1jRd7qtAI6MGG4cjos2z3IzqwbUIbghJZ+7LzKzXKANMKPAtKcIb1jp2LGjeqaXSq9q1ar079+fLl26UKVKFQYNGgRAZmYmvXv35ne/+x2ZmZl07dqVrl27xlzPypUrefDBBznllFMYPXo0l1xyCTfddBMDBgyge/fu1KhRg+efD660Dxw4kJdeegl3Z+XKldx9993cfPPNZGZmkpWVxemnn869995bKvsvFVZJcggoj4gknfJI6iVauJ5pZu8CzYD/M7PawJ4ilpkOHGNmzQhOfpdS+HG3Y4BrgI+APsAkd/dwmW/DG1GaAK2AZQnGKlImajWolfDNJLGWL8rUqVMBuOaaa/YZH3mXdZcuXQotV/DO8COOOIIdO3YUmu9nP/sZP/vZz/YZN2DAAAYMGLDPuB9//LHIWEUilCSHgPKIpBnlkcoh0cL19UB74Et332pm9YDCtwxHCE9otwLvEHSh9GxYW3EfMMPdxwDPAMPNbCmwnr0PFTgNuMPMdhKcgG9297XF3DeRUhWt+yMRAUqQQ0B5RNKP8kjlELdwbWat3H0xwUkRoHlx+j1297HA2ALj7o54vx34eZTlhhO0xxMRkQpqf3MIKI+ISMVTVM3174AbgWg9ezuQnfSIRIB5s+bB4OjTPlv8GY07No4+sQy4ux62U47ldXMmZUI5RMpMvDwS62ErZUV5pHwrbh6JW7h29xvDv2fsR0wixbZz886YHelPmTaldIOJo0aNGqxbt4569erpxFhObd++nYyMjLIOIy0ph0hZipdH9qddc7JlZGSwfft2atasWdahSBTuzrp166hRo0bCyxTVLOTCIjb4WsJbEqmEGjduzPLly1mzZk1ZhyJxRHs6mKSecohI0Q477DCWLVtW1mFIHDVq1KBx48SvmBfVLOS8ONMc0IlR0lpGRkb+I1xFpBDlEJEi1K1bl7p165Z1GJJERTULKfJubhERkWiUQ0QkHSXaFR9mdg5wPJDf6MTd70tFUFI5vPbWa6zauCrqtOXzlpdyNCJSlpRDRCRdJFS4NrMhwIHAGcDTBB31T0thXFIJrNq4KmavHgv/t7CUoxGRsqIcIiLppEqC853i7lcDG9z9XuBk4NjUhSUiIpWIcoiIpI1Em4VsC/9uNbMjgHWAbr+XuE0/Zs2bVa76oxaRMqMcIiJpI9HC9VtmVhd4CPiU4C7vp1MVlFQc8Zp+vDftvVKORkTKKeUQEUkbCRWu3f0v4dv/mtlbQA1335i6sEREpLJQDhGRdJLoDY1VgXOApnnLmBnu/s/UhSYiIpWBcoiIpJNEm4W8CWwH5gF7UheOiIhUQsohIpI2Ei1cN3b3dimNREREKivlEBFJG4l2xTfOzM5MaSQiIlJZKYeISNpItOb6Y2C0mVUBdgIGuLsfnLLIRESkslAOEZG0kWjh+p8Enf7Pc3dPYTxSiaxbuo7pL0+POm31stWlHI2IlCHlECmR8a+NJ3dVbtRpC2YtIKtxVukGJJKARAvX3wLzdVKU4qiytQqdG3SOOm3hdj3+XCSNKIdIieSuyo1ZgP70vU9LNxiRBCVauP4SyDGzccCPeSPVjZKIiCRAOURE0kaiheuvwlf18CUiIpIo5RARSRtFFq7Dzv+PdfcrSiEeERGpRJRDRCTdFNkVn7vvBpqYmWobRESkWJRDRCTdFKfN9QdmNgbYkjdS7eVERCQByiEikjYSLVx/Eb6qALVTF46IiFRCyiEikjYSKly7+70AZlYrHI7e6aSIiEgByiEikk4Sevy5mbUxs1nAAmCBmc00s+NTG5qIiFQGyiEikk4SKlwDTwG/dfcm7t4E+B0wNHVhiYhIJaIcIiJpI9HC9UHuPjlvwN1zgINSEpGIiFQ2yiEikjYS7i3EzO4ChofDVxLc/S0iIlIU5RARSRuJ1lz3BeoDr4Wv+uE4ERGRoiiHiEjaSLS3kA3Ar1Ici4iIVELKISKSTuIWrs3sOcBjTHZ3vz75IVVMI+aO4MEPHsRxalevzeBzBnPCT04AoOkjTal9QG2qWlWqVanGjH4zCi2/cftGrhx9Jd9s/IZde3Zx+8m3c92J1wHwhwl/4O3P32aP76F78+482uNRzKxU909EpLiUQ4onXh559ONHGfrpUBznxpNu5LbOtxVaPlYemf39bG56+yY2/biJqlaVAV0GcEmbS0p570TSR1E1129FGXck8BugavLDqbiaHdKMKddO4ZCahzDu83H0e6sfn9zwSf70yddM5rADD4u5/KDpgzjusON487I3WbNlDS2faMkV7a5gxsoZfPDtB8ztPxeA0547jSlfTyGraVaqd0lEZH8phxRDrDwyf/V8hn46lGk3TqN61er0eLEH5x57Li0ObbHP8rHyyIEZB/JC7xc4pt4xrNy8kg5PdeCsFmdRt0bdstlRkUoubuHa3f+b997MmgN3Al2BB4BnUhta2brjf3fw+uLXWbdtHWu3ruWAqgew/U/bY85/ypGn5L/v3LgzyzctL9b2DGPzjs24O7k7cjm05qFUq1INw9i+azs7du/AcXbu3kmDgxqUeL9EREpLOucQSF4eWbRmEZ0adeLAjAMBOL3J6by26DX+cOof9lk+Vh45tt6x+fMcUfsIDj/ocNZsWaPCtUiKFHlDo5m1MrMXgTeBqcBx7j7Y3XekPLoy8sE3HzBu6Thm/WIW39z2De0atOPjGz4G4O7JdzNmyZi4yz8z6xnObnF2/rCZcebwM+nwVAeemvlU1GVuzbyVRWsXccQ/j6Dt4LY82uNRqlgVTj7yZM5oegYNH25Iw4cbctbRZ9G6fuvk7ayISAqlYw6B5OaRNoe34f1v3mfd1nVs3bmVsUvH8u3GbwstEyuPRJq2Yho7du/g6EOPTtKeikhBRbW5fhXoADxMcBlvN3BwXntfd19fxPI9gEcJLv897e4PFJh+APBCuI11wCXuvszMuhPUbFQHdgC/d/dJxd67Epq2Yhq9W/amZkZNAM5veT6Tv5pM+5+0574z7ou77OSvJvPMrGeYet3U/HFTr5tKo4MbsXrLaroP706rw1rRtUnXfZZ754t3aN+gPZOunsQXG76g+/DudGnShdVbVrNo7SKW/zaoweg+vDvvf/0+XZp0SfJei4gk1/7mkHAdaZ9HWtdvzR9P/SNnvngmB2UcRPsG7alapXCrmlh55OADDgbgu83fcdXoq3i+9/OFCt0ikjxFtbn+KcHNKLcTPFELIO9OOgeax1rQzKoCg4DuwHJgupmNcfeFEbNdD2xw9xZmdinwIHAJsBY4z91Xmlkb4B2gUbH2bD9Uq1KNPb4nf3iP76FalaI7Vpm7ai43vHkD464YR70D6+WPb3RwEPrhBx3OBa0uYNqKaYUK18/Nfo47Tr0DM6PFoS1oVrcZi9cuZsqyKXRu1Jla1WsBcHaLs/lo+UdpXbj+btl3TH95eszpmzdshitKMSARiaXEOQSURyLzyPUnXc/1JwX3f9458U4aH9y40LKx8khmo0w2/biJc146h4HZA+ncuHMS9rBimzdrHgyOPq1Wg1r0uLBH6QYklUrcn67u3tTdm4Wv5uErf7iIdWcCS939y/Dy30jg/ALznA88H74fBXQzM3P3We6+Mhy/AKgZ1k6UiqymWby+5HW27tzKlh1bGL14NKc3PT3uMt9s/IYL/3Mhwy8Yvk/7ti07trD5x83579/94l3aHN6m0PJHHXwUE7+aCMCq3FUsWbeE5oc056g6RzHl6yns2rOLnbt3MuXrKbQ+LM2bhWyHzg06x3zt2rSrrCMUEfY7h4DySL7VW1bnz/Paote4vO3lhZaPlUd27N7BBf+5gKtPuJo+x/VJ0h5WbDs37ySrcVbUV+6q3LIOTyq4opqFNHX3ZXGmG9DI3aPdvdcIiGwUthzoFGsed99lZhuBegQ1DnkuAj519x+jbL8f0A/gqKOOircrxdK2QVv6d+hP5tBMAH7R4Re0a9AOCNrKdTyiI71a9tpnmfum3Me6beu4+e2bAfK73Fu1ZRUX/OcCAHbt2cXlbS6nR4vgF/GQGUMA6N+xP3edfhfXvn4tbQe3xd158GcPctiBh9HnuD5M+moSbQe3xTB6tOjBeS3PS9q+ioikyn7mEFAeye+69aJXLmLd1nVkVM1gUM9B+TcjJpJHXpz7Iu99/R7rtq5j2OxhAAzrPYz2P2mftP0Vkb2Kukb1kJlVAd4AZgJrgBpAC+AMoBvwZ4ITXtKZ2fEEl/jOjDbd3Z8CngLo2LFjrL5US+SWzFu4JfOWQuNjtZV7utfTPN3r6ULjmx/SnDn950Rdpn/H/vnvj6h9BO9e9W6heapWqcq/z/t3omGLiJQnZZpDoHLkEYD3r3s/6vhE8siV7a7kynZXJhKyiCRBUV3x/dzMjiNowdoXaAhsBRYBY4GB7h6rX6EVBP2Z5mkcjos2z3IzqwbUIbghBTNrDIwGrnb3L4qzUyIiUvb2M4eA8oiIVEBF3l0R3jgyoATrng4cY2bNCE5+lwIFG4mNAa4BPgL6AJPc3c2sLvA2cIe7f1CCbYuISDmwHzkElEdEpAIq+tblEgrbvt1KcId2VeBZd19gZvcBM9x9DMFDBIab2VJgPcGJE+BWgsuGd5vZ3eG4M919dariFRGR8kV5ROL5dsW3jMsZF3d6SZZdvG0xfdCNn1JyKStcA7j7WIJLf5Hj7o54vx34eZTl7gfuT2VsUrZ++OEHgJgnt3gnRRFJH8ojEsu2nduo16Je7OkfbivRspumbdrv2CS9pbRwLZXfZ9M+47vPv4s6bcuaLTGX27l7J0DMk1u8k6KIiFQO418bH7fruwWzFpDVOKv0AhJJgoQK12F3SVcAzd39PjM7CviJu09LaXRS7u3atIvOraI/kGDKjimlHI2IlEfKIRJL7qrcuIXnT9/7tPSCEUmSRJ9/+iRwMnBZOLyZ4KlZIiIiRVEOEZG0kWizkE7ufpKZzQJw9w1mVj2FcYmISOWhHCIiaSPRmuudZlYVcAAzqw/sSVlUIiJSmSiHiEjaSLRw/RhBR/yHm9lAYCrw15RFJSIilYlyiIikjYSahbj7CDObSfCoWgN6u/uilEYmIiKVgnKIiKSTRHsL6QwscPdB4fDBZtbJ3T9JaXQiIlLhKYeISDpJtFnIYCCyI8rccJyIiEhRlENEJG0k2luIubvnDbj7HjPTA2hERCQRyiGS9l576zVWbVwVc3qDOg248NwLSzEiSZVET25fmtmv2FvTcDPwZWpCEhGRSkY5RNLeqo2raNyxcczpy2csL8VoJJUSbRbSHzgFWAEsBzoB/VIVlIiIVCrKISKSNhLtLWQ1cGmKY5EyEu9SlS5Ticj+Ug4RkXSSaG8h9YEbgaaRy7h739SEJaUp3qUqXaYSkf2lHCIi6STRNtdvAO8D/wN2py4cERGphJRDRCRtJFq4PtDd/5jSSEREpLJSDhGRtJHoDY1vmVnPlEYiIiKVlXKIiKSNRAvXvyY4OW43s01mttnMNqUyMBERqTSUQ0QkbSTaW0jtVAciIiKVk3KIiKSTRHsLMeAKoJm7/8XMjgQauvu0lEYnIiIVnnKIpIt4XdvOmjcr7kNkpPJI9IbGJ4E9QDbwFyAXGAT8NEVxiYhI5aEcImkhXte27017r5SjkbKSaOG6k7ufZGazANx9g5lVT2FcIiJSeSiHiEjaSPSGxp1mVhVwyH8gwJ6URSUiIpWJcoiIpI1EC9ePAaOBw81sIDAV+GvKohIRkcpEOURE0kaRzULMrArwFfAHoBtgQG93X5Ti2EREpIJTDhGRdFNk4drd95jZIHc/EVhcCjGJiEgloRwiIukm0WYhE83sorA7JRERkeJQDhGRtJFo4foXwKvAj3q6loiIFJNyiIikjUTbXPdw9w9KIR4REalElENEJN0UWXPt7nuAJ0ohFhERqWSUQ0Qk3ajNtYiIpJpyiIikDbW5FhGRVFMOEZG0kdDjz929dqoDERGRykk5RETSSUKFazPrGm28u7+X3HBERKSyUQ6RsvDDDz8wLmdczGkl9dpbr7Fq46p9xg0eMRiAWfNm0bhj4xKvWyqHhArXwO8j3tcAMoGZQHa8hcysB/AoUBV42t0fKDD9AOAFoAOwDrjE3ZeZWT1gFPBTYJi735pgnCIiUv6UKIeA8oiU3M7dO6nXol7MaSW1auOqQgXovOH3pun3oiTeLOS8yGEzOxJ4JN4yZlYVGAR0B5YD081sjLsvjJjtemCDu7cws0uBB4FLgO3AXUCb8CUiIhVUSXJIOJ/yiAjRa8sjNajTgAvPvbAUI5J4Eq25Lmg50LqIeTKBpe7+JYCZjQTOByJPiucD94TvRwFPmJm5+xZgqpm1KGF8IiJSfiWSQ0B5RASIXlseafmM5aUYjRQl0TbXjwMeDlYB2gOfFrFYI+DbiOHlQKdY87j7LjPbCNQD1iYYVz+gH8BRRx2VyCIiIlLKSphDQHlERCqgRGuuZ0S83wW8XB6etuXuTwFPAXTs2NGLmF3SxLcrvs2/uaQgXToTKRPlMoeA8oiIJF+ihetRwHZ33w1BOzgzO9Ddt8ZZZgVwZMRw43BctHmWm1k1oA7BDSlSTnw27TNGbRoVc/rqZauDC7flyPad22NePtOlM5EyUZIcAsojlcL418aTuyo36rQFsxaQ1TirdAMSSbFEC9cTgZ8Bef8dNYF3gVPiLDMdOMbMmhGc/C4FLi8wzxjgGuAjoA8wyd1Vc1CO7Nq0K+6Jb+T2kaUXjIhUVCXJIaA8UinkrsqNmUc+fS+R1kEiFUuihesa7p7/s9Pdc83swHgLhG3fbgXeIehC6Vl3X2Bm9wEz3H0M8Aww3MyWAusJTpwAmNky4GCgupn1Bs4scId4uRXvrl41S0g/ustbpPg5JJwvbfNIZTJr3iy2Ld0Wddq3K76NOj5yeir6qhZJpUQL11vM7CR3/xTAzDoA0f9TIrj7WGBsgXF3R7zfDvw8xrJNE4yt3Il3V6+aJaQf3eUtUrIcAumbRyqTTds2Ua9t9P6mt30Y/2uwbee2lPRVLZJKiRaubwNeNbOVgAE/IehHVEREpCi3oRwiFUS8m+Khcj2FUVdWUyPRh8hMN7NWQMtw1BJ310/GSuKzaZ/x3effRZ22etnqUo5GRCob5ZDKL95Ni+Xxxvd44t0UD5XrKYy6spoaifZzfQswwt3nh8OHmNll7v5kSqOTUrFr0y46t+ocddrC7WqeKCL7Rzmk8ot306JufJd0k2izkBvdfVDegLtvMLMbAZ0YRUSkKMohIkWYNXcWg4neHKUyNUVJB4kWrquGj5N1CPooBaqnLiyRktu0ZhPTX54eddrmDZvhilIOSESUQ6TCiJdDANYtTU036pu3b45ZgK5MTVHSQaKF6/HAf8zs3+HwL8JxIuVOxo4MOjeI3sxl3NfRu3QSkZRSDpEKI14OAZi2dVopRiMVUaKF6z8C/YCbwuEJwNMpiUhERCob5RARSRuJFq6rA1PD19KwX1EREZFEKIeISNqIW7g2s2rAX4G+wNcE/ZMeaWbPAQPUlZKIiMSiHCJSOuLdDKm+qktfUTXXDwG1gWbuvhnAzA4G/hG+fp3a8CRZ4nUUX9TjZ0VESkg5RKQUxLsZUn1Vl76iCtfnAsfm3eEN4O6bzOwmYDE6MVYY8TqK3/60rtCKSEooh4hI2imqcO2RJ8WIkbvNrNB4Kb/iPYVxy5otpRxN2Yn3WFtdOhNJOuWQSiLeExgBFsxaEPMhMiLppqjC9UIzu9rdX4gcaWZXEtQ6SAUR7ymMU3ZMKeVoyk68x9rq0plI0imHVBLxnsAI8Ol7n5ZeMCLlXFGF61uA18ysLzAzHNcRqAlckMrARESkwlMOEZG0E7dw7e4rgE5mlg0cH44e6+4TUx6ZiIhUaMohIukpXicKUPmbYSbUz7W7TwImpTgWERGphJRDRNJLvE4UoPI3w0z0ITIiIiIiSfXDDz8wLmdc3OkiFY0K1xKXTnzpKd4lvcp+OU9ESs/O3Tup16Je3OkiFY0K1xJXZTvxbVqziekvT486bfOGzXBFKQdUTsW7pFfZL+eJiMQTLY/kDa9buq4sQpJyRoVrSSsZOzLo3CB6l4Tjvo5dQ1/ZFHWzyax5s+K2lxMRSVfR8kje8LSt08oipLjiPRpd5/rUUOFaJA0VdbPJe9PeK8VoREQkVeI9Gl3n+tSoUtYBiIiIiIhUFqq5FgnFezQ66EY+ERGR8qy83IyvwnUlMf618eSuyo05ffWy1ZBZigFVQPEejQ66kU9EKrd4eWTBrAVxH38uUh6Ul5vxVbiuJHJX5cY98Y3cPrL0ghERkQonXh759L1PSzcYkQpMhesKRLUKIiKyP5RHRFJPhesKRLUKIiKyP5RHRFJPhWsRERHZL9+u+Dbm03z1JN/KKd7Ng+Wx/+x4/X1Dcm94VOFaRERE9su2ndtiPs23oj3JVxIT7+bB8th/drz+viG5Nzyqn2sRERERkSRRzXUp+2zaZ4zaNCrqtFoNatHjwh6lHJGIiFQk8W5KVB4RKXsqXJeyXZt2xbyZ5PE3H4/bV7Xu5BYRkXg3JSqPiJQ9Fa7LkZ2bd8Y96elO7tTatGYT01+eHnP65g2b4YpSDEhEpJiUR8pWvDyybum6Uo5GyooK1yKhjB0ZdG7QOeb0Z6c8y6jBatIjIiLRxcsj//v6f3ErcMpj4TteDxtl1SNIReilRIXrEijqUeOT35pMq89bRZ22etnqVIUlqbadmDVCOctzSjUUEanY4uWRz7/6nGOaHRNzWTXtqJiKqsCZtnVaKUaTmHg9bJRVjyAVoZeSlBauzawH8ChQFXja3R8oMP0A4AWgA7AOuMTdl4XT/g+4HtgN/Mrd30llrAUV9RSrW867JeayI9ePjPkPtHD7wqTEJyKSDtIxj3z63qdkdcmKud54TTvi9TedN70ky86aNyvuetWXtRRHvBrxZPY3XVZSVrg2s6rAIKA7sByYbmZj3D2ydHk9sMHdW5jZpcCDwCVmdhxwKXA8cATwPzM71t13Jyu+omqfizrxiUSaN2sesfqm/2zxZ+XiMpVIRaM8Unzx+psG2PbhthItu/XNrXHXq76s9186tdeOVyOezP6my0oqa64zgaXu/iWAmY0EzgciT4rnA/eE70cBT5iZheNHuvuPwFdmtjRc30fJCi7e3dagArQUT7ybiEa+MjJuO7vF0xdTf1P9qNPUllvSnPKIpI39aa/99ftfM/2Q9CiYVwTm7qlZsVkfoIe73xAOXwV0cvdbI+aZH86zPBz+AuhEcKL82N1fDMc/A4xz91EFttEP6BcOtgSWJCn8w4C1SVrX/lAc+1Ic5SsGqLhxNHH36L9opNxQHkkKxVG+YgDFUVBFjSNmHqnQNzS6+1PAU8ler5nNcPeOyV6v4lAclSkGxSGVgfJI+sRRHmJQHOkRRyoff74CODJiuHE4Luo8ZlYNqENwQ0oiy4qISOWmPCIiFU4qC9fTgWPMrJmZVSe4sWRMgXnGANeE7/sAkzxopzIGuNTMDjCzZsAxQPnro0ZERFJJeUREKpyUNQtx911mdivwDkEXSs+6+wIzuw+Y4e5jgGeA4eGNJusJTpyE871CcNPKLuCWZN7hnYCkXyIsIcWxL8WxV3mIARSHpJDySFIojr3KQwygOAqqdHGk7IZGEREREZF0k8pmISIiIiIiaUWFaxERERGRJFHhOoKZ/cfMZoevZWY2Oxzf1My2RUwbkuI47jGzFRHb6xkx7f/MbKmZLTGzs1IYw0NmttjM5prZaDOrG44v1WMRbrNHuL9LzeyOVG8vYrtHmtlkM1toZgvM7Nfh+JifTwpjWWZm88LtzQjHHWpmE8zs8/DvISmOoWXEPs82s01mdltpHA8ze9bMVod9GueNi7r/Fngs/L7MNbOTkh2PSCzlIY+UhxwSbkt5RHkkcvvpk0PcXa8oL+Bh4O7wfVNgfilu+x7g9ijjjwPmAAcAzYAvgKopiuFMoFr4/kHgwTI6FlXD/WwOVA/3/7hS2nZD4KTwfW3gs/AziPr5pDiWZcBhBcb9HbgjfH9H3mdUip/L90CT0jgeQFfgpMjvXqz9B3oC4wADOgOflOZnpZdeea+yyiPlIYeE21MeUR6J95lU2hyimusozMyAi4GXyzqWAvIf5+vuXwF5j/NNOnd/1913hYMfE/QRWxbyH3/s7juAvMcfp5y7f+fun4bvNwOLgEalse0EnQ88H75/HuhditvuBnzh7l+Xxsbc/T2CniAixdr/84EXPPAxUNfMGpZGnCJ5ymkeKbUcAsojoDwSR6XOISpcR9cFWOXun0eMa2Zms8xsipl1KYUYbg0vRzwbcZmmEfBtxDzLKZ1/0r4Ev+LylOaxKKt93oeZNQVOBD4JR0X7fFLJgXfNbKYFj2sGaODu34XvvwcalEIceS5l30JDaR8PiL3/5eI7I2mvrPNIecohoDyiPLKvSp1D0q5wbWb/M7P5UV6Rv2IvY98P/TvgKHc/Efgt8JKZHZzCOAYDRwPtw20/vD/bKmEMefMMIOgjdkQ4KunHorwzs1rAf4Hb3H0TpfT5FHCau58EnA3cYmZdIyd6cC2rVPrVtOBhHr2AV8NRZXE89lGa+y9SHvJIecghCcSRN4/yiPJIvnTIISl7iEx55e4/izfdgsfnXgh0iFjmR+DH8P1MM/sCOBaYkao4IuIZCrwVDib1cb4JHItrgXOBbuEXLyXHoghl+ghjM8sgOCGOcPfXANx9VcT0yM8nZdx9Rfh3tZmNJrjMucrMGrr7d+Elq9WpjiN0NvBp3nEoi+MRirX/euy1pFR5yCPlIYckEofyiPJIFJU+h6RdzXUCfgYsdvfleSPMrL6ZVQ3fNyd4jO6XqQqgQNueC4C8u1tL7XG+ZtYD+APQy923Rowv1WNBYo8/TgkzM4Knvy1y939GjI/1+aQqjoPMrHbee4KbhOaz72OfrwHeSGUcEfapkSvt4xEh1v6PAa62QGdgY8SlP5HSUKZ5pDzkkDAO5RHlkWgqfw7Z3zswK9sLGAb0LzDuImABMBv4FDgvxTEMB+YBc8MPuWHEtAEEdz0vAc5OYQxLCdoczQ5fQ8riWITb7Elwh/UXwIBS/C6cRnCZaG7EcegZ7/NJURzNCe5unxMe+wHh+HrAROBz4H/AoaVwTA4C1gF1Evm+JnG7LxNcLtxJ0P7t+lj7T3CH96Dw+zIP6Fha3xm99HIv+zxSHnJIuC3lEeWRgnGkRQ7R489FRERERJJEzUJERERERJJEhWsRERERkSRR4VpEREREJElUuBYRERERSRIVrkVEREREkkSFaykTZjbAzBZY8LjT2WbWycwGmtmDEfM0MbMvzayumeWY2Tdhn6F50183s9wY67/HzG4vMG6ZmR0WZV4zs0nxnhBmZoPCOBea2bbw/Wwz62Nm/zCz7JIdCRERKS7lECnP0u4JjVL2zOxkgid2neTuP4Ynq+rA/cBsMxvm7ouAR4G73P2H8Hz4A3AqMNXM6gINo62/BHoCczx4JG1U7n5LGHtT4C13bx+xP9OBocCkJMUjIiIxKIdIeaeaaykLDYG1HjwCF3df6+4r3X0b8BtgkJn1BGq7+4iI5UYSPFkLgkcLv5akeK4gfDKTmTU1s0VmNjSsFXnXzGrGW9jdvwbqmdlPkhSPiIjEphwi5ZoK11IW3gWONLPPzOxJMzs9b4K7jwU2AM8DNxdYbiLQNXxs7qXAf4rYzm8iLr3NBo6IMd+pwMyI4WOAQe5+PEFNx0UJ7NOn4XpERCS1lEOkXFPhWkqdu+cCHYB+wBrgP2Z2bcQsg4Dp7r6kwKK7gakEJ8Wa7r6siE39y93b572AlTHmO9TdN0cMf+Xus8P3M4GmRWwHYDWxT7wiIpIkyiFS3qlwLWXC3Xe7e467/xm4lX1/2e8JX9GMBB4DXokcGd7Ikle7UFy7zCzyf+HHiPe7SezehBrAthJsW0REikk5RMozFa6l1JlZSzM7JmJUe+DrBBd/H/gb8HLkSHcfEFG7UFxLgOYlWC7SscD8/VyHiIgUQTlEyjv1FiJloRbweHi39i5gKcHlvSK5uwP/SHI8bwNZYRzFZmYZQAtgRhJjEhGR6JRDpFyz4Hsmkr7MrCHwgrt3L+HyFxB0CXVXciMTEZHyTjlEClKzEEl77v4dMDTeAwCKUA14OIkhiYhIBaEcIgWp5lpEREREJElUcy0iIiIikiQqXIuIiIiIJIkK1yIiIiIiSaLCtYiIiIhIkqhwLSIiIiKSJCpci4iIiIgkiQrXIiIiIiJJosK1iIiIiEiSqHAtIiIiIpIkKlyLiIiIiCSJCtciIiIiIkmiwrWIiIiISJKocC0iIiIikiQqXIuIiIiIJIkK1yIiIiIiSaLCtYiIiIhIkqhwLSIiIiKSJCpci4iIiIgkiQrXIiIiIiJJosK1iIiIiEiSqHAtIiIiIpIkKlyLiIiIiCSJCtciIiIiIkmiwrWIiIiISJKocC0iIiIikiQqXIuIiIiIJIkK1yIiIiIiSaLCtYiIiIhIkqhwLSIiIiKSJCpci4iIiIgkiQrXIiIiIiJJosK1iIiIiEiSqHAtIiIiIpIkKlyLiIiIiCSJCtciIiIiIkmiwrWIiIiISJKocC0iIiIikiQqXIuIiIiIJIkK1yIiIiIiSaLCtYiIiIhIkqhwLSIiIiKSJJWucG1mtczsETNbbmbbzewzM+tvZq+Y2ayyjq+0mdmzZrbazOYXGH+Pma0ws9nhq2eM5f9iZnPDed41syPC8WZmj5nZ0nD6STGWdzN7MWK4mpmtMbO3wuFeZnbHfuzfbWZ2YEmXj7K+183s4wLjfm5mi8xsspllmdkpSdjO8WY2ycyWmNnnZnaXmdn+rrfANq7N+7xEJDFm1jQ8b3mYQ741sxFm1iycPs3MXi/jMEuFmdUI93eOmS0ws3sjpjUzs0/CHPAfM6texLrGROahRHJQxGdxf8S4w8xsp5k9EQ73N7Or92Mf7yzpsjHWN9vMRhYY96swh4wws95mdlwStnNa+NksDl/99nedUbaR1PyaTipV4TosnLwF/BpYCPwSeBX4KfAU0N7MsssuwjIxDOgRY9q/3L19+BobY56H3L2du7cnOLZ3h+PPBo4JX/2AwTGW3wK0MbOa4XB3YEXeRHcf4+4PJLozUdwGJOWf38zqAh2AOmbWPGLS9cCN7n4GkAUUq3BtZtUKDNcExgAPuHtL4IRwnTeXOPjorgVUuBYpmVlAf2AScDnwoZkdTpBLepnZ0WUZXCn5Ech29xOA9kAPM+scTnuQIIe0ADYQnCejMrMLgdwokxLJQV8B50QM/xxYkDfg7kPc/YVEdyiKpBWuzaw1UBXoYmYHRUy6Geju7lcAvYFiFa6j5JCfAC8B/d29FXAa8AszOyfa8vvhNpKUX9NNpSpcA9nA6QQF6x7uPtTdBwA3AjnAZuCSsguv9Ln7e8D6/Vh+U8TgQYCH788HXvDAx0BdM2sYYzVj2XtyvAx4OW9CWLuaVwMxLKwN/9DMvjSzPuH4rLya7nD4iXC5XxEUHieb2eRw2plm9pGZfWpmr5pZrXD8A2a2MKxl/0eMOC8E3gRGApeGy91NcOJ6xsxeJUi2vwlrJ7qYWX0z+6+ZTQ9fp4bL3WNmw83sA2B4ge1cDnzg7u+Gx3grcCtwR8Syz5pZTngcfhWOP8jM3g5rkeab2SXh+A5mNsXMZprZO2bWMDx2HYERYaw1EZHiWOnuw9z9GmAo8BOC/9M3AQMuLsvgSkN4fs8rFGeELw8rsrKBUeG05wkKjYWE5+DfAvdHm56ArcAiM+sYDl8CvBKx/nvM7PbwfY6ZPRjW6H5mZl3C8fl5Jhx+K8wrDwA1w3PkiHDaleHys83s32ZWNXwNC8+788zsNzFivYzgfP8uQY7EzIYAzYFxZjYA6AU8FK7/6PA1Pjx/v29mrcLlhpnZEDP7BPh7ge3cAgxz908B3H0t8Af25pBYubShmb0Xbnt+xPEplDej5VdJXGUrXHcI/05w9z15I919j7vvAmYDXcoisHLq1rCw+ayZHRJrJjMbaGbfAlewt+a6EfBtxGzLw3HRjAQuNbMaQDvgkzgxNSQozJ4LxK3RdvfHgJXAGe5+hpkdBvwJ+Jm7nwTMAH5rZvWAC4Dj3b0dsU/yeQX/l8P3uPt94XqucPefA0PYW9vyPvBoOPxT4CLg6Yj1HRfGclmB7RwPzCywL18Atczs4HBUK+AsIBP4s5llEFyBWOnuJ7h7G2B8OP5xoI+7dwCeBQa6+6iIuNu7+7Z4x1JE4hoX/u3o7qsIzn1pkUvCguVsYDVBbv0EqAf8EOZViH/+/wvwMEEhuaCEchB7c8iRwG6C834s1dw9k6DW9c9x5sPd7wC2hefIKyyoeb4EODW8WrubIO+1Bxq5ext3bws8F2OVl4SxRuaQ/uzNUwMJrlr+PtzmFwRXQn4Znr9vB56MWF9j4BR3/22B7RTKIQTn++MjhqPl0suBd8J9OwGYHStvFsyvMfZXYqhshWsvYvpKoGkpxFERDAaOJjhpfEdw8ovK3Qe4+5HACIKam2Jx97kEx/0yglrseF4PfwwtBBoUc1OdCQq0H4TJ4BqgCbAR2E5Q+3whUU7yZtaAoInLVHf/DNhpZm0S2ObPgCfC7Y0BDs6rLQfG7Eeh9m13/zGskVhNcCzmAd3Dmpku7r4RaAm0ASaEMfyJ4IQsIsmTdz9EXo5Jm1zi7rvDwlhjIDPB8yIAZtYeONrdR0eZnHAOAsYTNCm8FPhPEZt9Lfw7k+J/Rt0IKummh+fTbgS1zl8Czc3scTPrAWwquGBYs77W3b8BJgInmtmh8TYW5opTgFfD7f2boFCc51V3313MfcgTLZdOB64zs3uAtu6+mdh5U/ZDtaJnqVDyfsn9zMyq5NVeR7xP6g1jFVlY+wKAmQ0laE+NmT0HnEhQQ1rwBpMRBIXjPxO0mz4yYlpjItpSRzEG+AdBm+V6ceb7MeJ93ue1i31/CNaIsawR1KwUrCnGzDIJTpR9CH4gFGx7fzFwCPBVcMWTgwl+DAyIEythXJ3dfXuB7UHQ3jyahUDXAvM3B3LdfVO4bORx2E1QG/OZBTeO9gTuN7OJwGhggbufXEScIlJyZ4V/83JM2uUSd/8hbB7Qg6AgXNfMqoW1142BFWZWlb3HaAxBobmjmS0jKG8cbmY57p4VKwfF2PYOM5sJ/I6gINgrTqh5587d7C3jFCeHPO/u/1dogtkJBN+D/gT5om+BWS4DWoX7CkEOuYigSVEsVQiuALSPMT1eDukAvBExrgMRbdGJkkvd/T0z60rQTHOYmf2ToL181LwpJVfZaq4nE7StPh4Ya2Y3mNl9BJddIGg/9HUZxVau2L7toy8A5gO4+3Xh5aqe4XzHRMx3PrA4fD8GuNoCnYGN7v5dnE0+C9zr7vNKEO7XwHFmdoAFNx12i5i2Gagdvv8YONXMWoSxH2Rmx4a1A3XCG2Z+Q3A5rKDLCNrpN3X3pgQnqkujzBe5PQja1v0ybyCsqSnKCOA0M/tZuExN4DEKt6vbhwU9f2x19xeBh4CTgCVAfTM7OZwnw8zyLg0WjFVEEneEBW11nyO4b+d7YFDeNNIgl1hwT0nd8H1Ngtrjxe7uBPm2TzjrNcAbebXc4etudx/s7keE59TTgM/cPStcX9QcFMfDwB/dvST3EC0j6NCgSti0JDNi2s6weR0ENc59LLhxFTM71MyahE0nqrj7fwmuDu7TO5aZVSEocLeNyCHnEzYNKSD/vBze0/SVmf08XI+FhfiiDAKuzcs3YdPHByk6hzQBVrn7UIImjCcRI28WjFWKp1LVXLu7m9l5wECCf/psgrZx/7DgbtsTiLiZLh2Y2csEtcWHmdly4M/u/gzw9/Af0wlOPL+IsYoHzKwlsIcgmfQPx48lqEFdStDM4rp4cbj7coICZLG5+7dm9grByfcrgrv48zxF0PZ4Zdju+lrgZTM7IJz+J4ITxBthm28juLkmn5k1JbgMlt8Fn7t/ZWYbzaxTgXDeBEaZ2fkEhepfAYPMbC7B/9N77D1GsfZnW7j842Y2iODu8uHAE/GWA9oS3AizB9gJ3BTW6PQBHjOzOmEMjxDUYAwDhpjZNuBktbsWKZYTCS7TrybomeFP7r4qbELWmH3bxlZWDYHnwxrpKsAr7p5Xw/xHYKQF3eTNAp4p5roTzUEAuPsC9q2ZLY4PCHLHQmAR8GnEtKeAuWb2adju+k/Au2GBeSfBzYPbgOfCcQAFa7a7ACvcPbIt+HsElUIFb/QfCQy14IbBPgRtugeH280Ip8+JtzPu/p2ZXRmupzZBXnvE3d8s4jhkAb83s50Evbdc7e5rYuTNzyiQX4tYt0Sw4Ado5RfWEk4g6FZId76KiEixmdkNBIWOY8Ib0kRE9pFOhetXCE6GJ5Z1LCIiUjGZ2TSCe1J6l3UsIlI+pbTNtZn1sOAJdEstylP4wja0/wmnfxJeno+cfpSZ5VrYh+X+cPeLVbAWEalYylMeAXD3TBWsRSSelBWuwzZagwie5HcccJkVfuTn9cAGD57w9C+CBvmR/sne/kVFRCSNKI+ISEWUyprrTGCpu3/p7jsIGumfX2Ce8wme7ATBk566mQX9kJlZb4IbEEp6A4OIiFRsyiMiUuGksreQaE/wK9jzQv487r7LzDYC9cxsO8GdyN0JnlgUlZn1A/oBHHTQQR1atWqVvOil0ps5cyYdOnQoekapdGbOnLnW3euXdRxSJOURKXeUOwTi55Hy2hXfPQSPlM4NKyCicvenCPuw7tixo8+YMaN0opNKwczQdyY9mVml76NYlEckNZQ7BOLnkVQWrhN5gl/ePMvDfqjrAOsIaib6mNnfgbrAHjPb7u5F9QMsIiKVh/KIiFQ4qSxcTweOMbNmBCe/S4HLC8wzhuDJTh8RdKY+KXzyU5e8GczsHoLHQuuEKCKSXpRHRKTCSVnhOmz7divwDsET6J519wUWPI58hruPIXii03AzWwqsJ/rjpkXKrR9++IG1a9eyc+fOsg5F4jj00ENp0KBBWYchxaQ8Iulg1apVrF9fkqe6S2nJyMjgsMMOo27dugnNn9I21+4+luAx2ZHj7o54vx34eRHruCclwYkkwXfffUfTpk2pUaMG8dp1StnZvXs3n332mQrXFZTyiFR269ev59hjj6Vq1aplHYpE4e5s376dZcuWlY/CtUg6qFmzZlmHIHEoYYlIeafzVPllZsXO8yl9QqOIiIiISDpRzbWUifGvjSd3VW7M6bUa1KLHhT1KMSIREalIlEekvFLhWspE7qpcshpnxZyeszyn1GIREZGKR3lEyis1CxGpIHJycqhWrRqrV68GYPr06ZgZy5Yti7vcsGHDGDZsGLNnz+aZZ54p8fZfffVVTj/9dE455RT69esHwMqVK+nduzddu3alU6dOTJgwodBys2fPjrk/TZo0ISsri6ysLMaMGVPi2ACWLVvGpEmT9msdIiKVlXJIfMnMIaq5FqlA2rdvzxtvvMGNN97I6NGj6dixY7GWbd++fYm2u3btWp588kkmTZpE1apV+eGHHwC47LLLeOihh8jMzCQ3N5eFCxcWWnbkyJExt3vVVVdx//33lyimgvJOjNnZ2UlZn4hIZaMcElsyc4hqrkUqkOzsbCZOnAjAggULOP7443F3brrpJrKzsznnnHPYsGEDO3bs4Pzzz6dHjx75v+ZzcnL405/+BAS1BWeccQannXYaN998c/70s88+m/POO49TTz2V3Ny9bRlXrlzJli1b+P777wGoW7cu48aNo127dmRmZgJQq1at/PcA8+bN45FHHmHGjBk88sgjrFmzpsj969evH4sWLQLg8ccf55VXXom6f9Fifeqppxg+fDjdunXb38MsIlIpKYeUTg5R4VqkAqlevTo1atTg448/pnXr1gC89dZbHHXUUUyaNIlbb72VIUOG8Prrr5OZmcn48eM57LDDCq3nsMMOY8KECUydOpVNmzbx+eef56//zTffpGfPnvknYIB27dpx8cUXk52dTY8ePVi3bh1Tpkzh3HPPjRlr27Ztue222+jYsSO33XYb9evXLzTP8OHD8y/pTZs2jT59+jBq1CgAxo4dyznnnBN1/6LF2q9fP6666qp94hYRkb2UQ0onh6hwLZIkZrbfr0T07NmT/v37c+GFFwKwbds2Ro4cSVZWFgMHDmT9+vV8+eWXnHjiiQB06NCh0DrWrVtHnz59yMrKYurUqaxcuRKANm3aANCoUaP8y3Z5br/9dpYsWULz5s0ZPXo0q1evpl69ekXG+8ADD8ScdtVVV5GTk0NOTg6ZmZlkZ2czefJkVq9eTa1atTjooINYtGhRof0rKlYRkYqoNPKIckjqc4gK1yJJ4u77/UpEz5496dChAz/96U8BOOCAA7j66qvJyclh6tSp/PWvf6VZs2bMmTMHgFmzZhVax0svvUTv3r3Jycnh1FNPzd925Ik5Mp6lS5fmv9+0aRMtWrSgffv2+9z8MX369GIcreiqVatGs2bNeOihh7jgggsAaNmyZaH9ixZrRkYGu3fv3u8YRETKSmnkEeWQ1OcQFa5FKphatWrxzDPP5J8YevXqxbJly8jOziY7O5tx48bRu3dvPvzwQ84666yov8izs7N5+OGH6d27N1u2bClym3feeSedO3emS5cutGrViqysLPr168ecOXPo2rUrJ598MtOmTSv2vkRe0hs+fDgAF110EYMHD+a8886LuX/RtGnThg8++IBLLrmk2HGIiKQL5ZDU5xBLtLasvOvYsaPPmDGjrMOQBI0aPKrI/kn73NQnpTGYWcK1xbEsWrQov92alF8FPyczm+nuid8mL2lBeaRiKas8kozcEUl5pGIoTh5RzbWIiIiISJKocC0iIiIikiQqXIuIiIiIJIkK1yIiIiIiSaLCtYiIiIhIkqhwLSIiIiKSJCpci4iIiIgkiQrXIhXIq6++yumnn84pp5xCv379AFi5ciW9e/ema9eudOrUiQkTJuyzzOzZs2OuLycnhyZNmuR3wj9mzJgSx7Zs2bJ9nrYlIiLli3JI6ahW1gGISGLWrl3Lk08+yaRJk6hatWr+U7Muu+wyHnroITIzM8nNzWXhwoX7LDdy5Ejat28fc71XXXUV999//37Hl3dizM7O3u91iYhIcimHlJ4iC9dmdjJwJdAFaAhsA+YDbwMvuvvGlEYoUkG89tZrrNq4qsTLN6jTgAvPvTDm9JUrV7Jlyxa+//57GjVqRN26dRk3bhzt2rUjMzMTCB5rm/d+3rx5TJw4kRkzZvDII49wxRVXUL9+/SLj6NevH7/5zW9o3bo1jz/+OIcffjg5OTksWbKEmjVr8uKLLzJnzhwefPBBqlWrxvr163nnnXd46qmn+OCDD/joo4+YOHFiiY+DVC7KISKJS2UeUQ4pPXEL12Y2DlgJvAEMBFYDNYBjgTOAN8zsn+5e8usAIpXEqo2raNyxcYmXXz5jedzp7dq14+KLLyY7O5tmzZoxYsQIpkyZwrnnnht1/rZt29K2bVu+//57brvttpjrHT58OFOnTgXg73//O3369GHUqFHcddddjB07lv79+3PUUUcxePBgxo0bx5AhQzj55JOpXr06b7zxBgMHDmTixIn069eP5s2bJ6UGQyoH5RCR4kllHlEOKT1Ftbm+yt2vd/cx7r7S3Xe5e667f+ruD7t7FvBhKcQpIsDtt9/OkiVLaN68OaNHj2b16tXUq1cv7jIPPPBA3OlXXXUVOTk55OTkkJmZSXZ2NpMnT2b16tXUqlWLJUuWMHLkSLKyshg4cCDr168HoE2bNgA0atQo//KiSAHKISLliHJI6YhbuHb3tUWtIJF5RGT/LV26NP/9pk2baNGiBe3bt9/nBpDp06fv93aqVatGs2bNeOihh7jgggto2bIlV199NTk5OUydOpW//vWvAJhZ/jLuTkZGBrt3797v7UvloRwiUn4oh5SeuIVrM9tsZptivUorSBGBO++8k86dO9OlSxdatWpFVlYW/fr1Y86cOXTt2pWTTz6ZadOmFXu9w4cPz7/Te/jw4QBcdNFFDB48mPPOO49evXqxbNkysrOzyc7OZty4cVHX06ZNGz744AMuueSS/dpPqTyUQ0TKD+WQ0hO3zbW71wYws78A3wHDAQOuILgxRURCDeo0KLLddFHLx/PKK68UGlejRg1GjBhR4m1mZWXx9ddfFxrfs2dPcnNz84cff/zxqMsCXHvttfnj3nvvvRLHIpWPcohI8aQyjyiHlJ5Eu+Lr5e4nRAwPNrM5wN0piEmkQorX04dImlMOEUmA8kjlkOhDZLaY2RVmVtXMqpjZFcCWVAYmIiKVhnKIiKSNRAvXlwMXA6vC18/DcSIiIkVRDhGRtJFQ4drdl7n7+e5+mLvXd/fe7r4sxbGVKbvXuPK1K/OHd+3ZRf2H6nPuS9H7g4wla1gWM1bOAKDniJ78sP2HZIYJQI8Xe1D3gbqFYrv29Wtp9mgz2g9pT/sh7Zn9/eyoyz8/+3mOefwYjnn8GJ6f/Xz++JkrZ9J2cFtaPNaCX437Fe6e9NhFpPJLxxwClSOPXP/G9Zww5ATaDW5Hn1f6kLsjN+ryf3v/b7R4rAUtn2jJO0vfyR8/ful4Wj7RkhaPteCBqfG7dBOpLBIqXJvZsWY20czmh8PtzOxPqQ2tbB2UcRDzV89n285tAEz4YgKNajfar3WOvWIsdWvUTUJ0+/r9Kb9n+AXDo057qPtDzO4/m9n9Z9P+J+0LTV+/bT33TrmXT274hGk3TOPeKfeyYdsGAG56+yaGnjeUz3/5OZ+v/5zxS8cnPXYRqfzSMYdA5cgj/+rxL+b0n8Pcm+ZyVJ2jeGLaE4XmWbhmISMXjGTBzQsYf8V4bh57M7v37Gb3nt3cMvYWxl0xjoW3LOTl+S+zcM3CQsuLVDaJNgsZCvwfsBPA3ecCl6YqqPKi5zE9efvztwF4ef7LXNbmsvxpW3Zsoe8bfckcmsmJ/z6RNxa/AcC2ndu4dNSltB7Umgv+cwHbdm3LX6bpI01ZuzXo0rX3yN50eKoDxz95PE/NfCp/nlp/rcWAiQM4YcgJdH66M6tyi34Marfm3ah9QO0S7eM7S9+he/PuHFrzUA6peQjdm3dn/NLxfLf5Ozb9uInOjTtjZlzd7mpeX/x6ibYhImkvLXMIVPw8cvABBwNBP8Tbdm7DsELzvLH4DS49/lIOqHYAzQ5pRotDWzBtxTSmrZhGi0Nb0PyQ5lSvWp1Lj780fx9FKrNEC9cHunvBzg93JTuY8ubSNpcycv5Itu/aztxVc+nUuFP+tIHvDyS7WTbTbpzG5Gsm8/sJv2fLji0MnjGYAzMOZNEti7g3615mrpwZdd3Pnv8sM/vNZMaNM3jsk8dYt3UdAFt2bqFz487M6T+Hrk26MvTToQCMWTKGuycX/8b6AZMG0G5wO34z/jf8uOvHQtNXbF7BkXWOzB9ufHBjVmxewYrNK2h8cONC46X0bdy4Mb8P0Tp16pCVlcV11123zzzPPvts3HVkZWWxa9fef9nNmzfTrVs3unbtyrnnnsvmzZsBmDRpEieffDJnnHEGy5cH3UHdfPPN1K9fn6effjp/+fPOO48uXbrQrVu3/PlE4kjLHAKVI49c98Z1/OThn7B43WJ+2emXhaYXyiO19+aRIw8unF+k9CmPlK5Eu+Jba2ZHAw5gZn0I+iyNy8x6AI8CVYGn3f2BAtMPAF4AOgDrgEvcfZmZZQJ5P8MNuMfdRycYa9K0a9COZT8s4+V5L9PzmJ77THv3i3cZs2QM//jwHwBs37WdbzZ+w3tfv8evOv0qf/l2DdpFXfdjnzzG6MXBLn276Vs+X/859Q6sR/Wq1Tn32KDNW4eGHZjw5QQAerXsRa+WvYoV/9+6/Y2f1PoJO3bvoN9b/Xjwgwe5+3T1fJUq418bT+6q6O0RE1GrQS16XNij0Pg6deqQk5MDwGmnnZb/PtKzzz5L3759E95WRkYGL774Ig0bNmTo0KEMGzaMX/7yl/zlL3/h3XffZeHChfztb39j0KBB3HXXXWRmZu5zUn3sscdo1qwZEyZM4F//+hcPP/xwsfdX0kqJckg4r/JIGeYRgOfOf47de3bzy3G/5D/z/8N1J15X9EJSIsojlUOihetbCE5SrcxsBfAVcGW8BcysKjAI6A4sB6ab2Rh3j2xwdT2wwd1bmNmlwIPAJcB8oKO77zKzhsAcM3vT3Uu9pqNXy17cPuF2cq7JYd22dfnjHee/F/+Xloe1LPY6c5bl8L8v/8dH13/EgRkHkjUsi+27tgOQUSUj/5GgVatUZdeeku9yw9rBMxoOqHYA17W/Lv8EHqlR7UbkLMvJH16+aTlZTbNoVLsRyzct32f8/rYVrOhee+s1Vm3c9/Jqh5YdWL1uNRlVM8hdlUtW46wSrz9neU5C87344os88cQTVK1alSeffJKvv/6aefPmkZWVxYABA5gxYwbjx49n+/btDBkyhBNPPLHQOmrUqEHDhsH3IyMjg507d7J161Zq1qxJ7dq16dSpE3/84x8B8ueL1KxZs/xlq1atWsI9ljRS7BwCyiPxlFYeyVO1SlUubXMpf//g74UK141qN+Lbjd/mDy/fvDdffLspYrzySJGURyqHRHsL+dLdfwbUB1q5+2kJ3OmdCSwNl90BjATOLzDP+UBe9xSjgG5mZu6+NeIEWIOwtqMs9D2xL38+/c+0bdB2n/FnHX0Wj097PL8HjVnfzQKga5OuvDTvJQDmr57P3FVzC61z4/aNHFLzEA7MOJDFaxfz8fKPUxL7d5uDiiF35/XFr9Pm8DaF5jmrxVm8++W7bNi2gQ3bNvDul+9yVouzaFi7IQcfcDAfL/8Yd+eFuS9wfquCH196WbVxFY07Nt7nVbV6VaofVJ2du3eWSgy7d+/mscce4/3332fEiBEMGDCAXr160bZtW3JycujevTu//vWvmTJlCiNGjOAf/yj8gypSbm4u//73v7n88sv54YcfOPjgg/fZVlGx3H///fziF79Iyr5J5VXCHALKI2WaR9ydpeuX5r8fs2QMrQ5rVWi+Xi17MXLBSH7c9SNfbfiKz9d9TmajTH7a6Kd8vu5zvtrwFTt272DkgpElqjmX5FIeSb2Eaq7N7NfAc8BmYKiZnQTc4e7vxlmsEfBtxPByoFOsecLahY1APYJLiJ2AZ4EmwFVlUdsAQRuxvMtzke7qehe3jb+NdkPascf30KxuM966/C1u6ngT171xHa0Htab1Ya3pcESHQsv2aNGDITOH0HpQa1rWa0nnxp2LjGPMkjHMWDmD+864r9C0Ls91YfHaxeTuyKXxPxvzTK9nOKvFWVzx2hWs2boGd6f9T9oz5NwhAMxYOYMhM4bwdK+nObTmodzV9S5+OvSnANzd9W4OrXkoAE+e8yTXvn4t23Zt4+wWZ3N2i7OLdewk+dasWcOJJ55IRkYGTZs2ZePGjYXmGT58OCNGjKBKlSr5tVfRuDt9+/Zl4MCB1K1bl4yMDDZt2pQ/vaiahN/97ndcffXVHH300SXfIUkLJcwhoDxSpnmk+9Hdueb1a9j04ybcnRN+cgKDzxlcaF3HH348Fx93Mcc9eRzVqlRjUM9BVK0SnD+e6PkEZ714Frt9N33b9+X4w48v7uGTJFMeSb1Em4X0dfdHzewsgpPWVcBwoKgTY4m5+yfA8WbWGnjezMa5+/bIecysH9AP4Kijjkrq9nPvLNzmKatpFllNswComVGTf5/370Lz1Myoycg+I6Ouc9lty/Lfj7tiXJHb7XNcH/oc1weI31bu/evejzp+0jWToo7veERHnu6196aCvif2pe+JhdtZdTyiI/Nvnh91HVI26tevz9dff83OnTtZsWIFderUAdjn5Pfkk08ya9YsvvjiC2688caY67r77rs59dRTyc7OBuCggw5i27Zt5ObmsnDhQo477riYyz7zzDNBLzJXX52kPZNKrtRzCCiPwP7nkQ/6fhB1fMF1Deg6gAFdBxSar+cxPQu1NZeypTySeokWrvOOeE/gBXdfYPF+ygRWAEdGDDcOx0WbZ7mZVQPqENyQks/dF5lZLtAGmFFg2lOEN6x07NhRTziRMlWrQa2E27vFWr4oVatWpX///nTp0oUqVaowaNAgADIzM+nduze/+93vyMzMpGvXrnTt2jXmelauXMmDDz7IKaecwujRo7nkkku46aabGDBgAN27d6dGjRo8/3xwpX3gwIG89NJLuDsrV67k7rvv5uabbyYzM5OsrCxOP/107r333hLvt6SFkuQQUB6RNKM8UjlYIk/dM7PnCC69NQNOILhrO8fdC1+r2rtMNeAzoBvByW86cLm7L4iY5xagrbv3D29EudDdLzazZsC34SW+JsBHQDt3Xxtrex07dvQZM2bEmizlzKjBo+LetJGzPIc+N/VJaQxmVqynTg4eMZjGHRvvM+6IH4/g6GOPZseWHRxe7/BkhyhJsmjRIlq3bp0/bGYz3b1jGYaUVkqSQ8LllEckprLKI8XNHUUpeH6S8qk4eSTRmuvrgfbAl+6+1czqAXH74glPaLcC7xCcSJ8NayvuA2a4+xjgGWC4mS0F1rP3oQKnAXeY2U5gD3BzvBOiiIiUa8XOIaA8IiIVU9zCtZm1cvfFBCdFgOaJXckLuPtYYGyBcXdHvN8O/DzKcsMJ2uOJiEgFtb85BJRHRKTiKarm+nfAjUC0nr0dyE56RCLAvFnzYHD0abE6yS8rybw8KMmnz6dMKYdImaloeaS4Pzyl9BQ3j8QtXLv7jeHfM/YjJpFi27l5Z8y2dPtzs0ey7bSdbNqwiRrVa5R1KBLD9u3bycjIKOsw0pJyiJSlipJHMjIy2L59OzVr1izrUCQKd2fdunXUqJF4ni+qWciFRWzwtYS3JFIJrau2DtZBle1VWL9mfVmHIzFEezqYpJ5yiEjRDjvsMJYtW1bWYUgcNWrUoHHjxkXPGCqqWch5caY5oBOjpLU9Vfawpvoals9dzk1X3FTW4YiUN8ohIkWoW7cudevWLeswJImKahZS5N3cIiIi0SiHiEg6SrQrPszsHOB4IL/RibsXfoaqiIhIAcohIpIuqiQyk5kNAS4BfknwpK2fA01SGJeIiFQSyiEikk4SKlwDp7j71cAGd78XOBk4NnVhiYhIJaIcIiJpI9HC9bbw71YzOwLYCej2exERSYRyiIikjUTbXL9lZnWBh4BPCe7yfjpVQYmISKWiHCIiaSOhwrW7/yV8+18zewuo4e4bUxeWiIhUFsohIpJOEipcm1lV4Bygad4yZoa7/zN1oYmISGWgHCIi6STRZiFvAtuBecCe1IUjIiKVkHKIiKSNRAvXjd29XUojERGRyko5RETSRqK9hYwzszNTGomIiFRWyiEikjYSrbn+GBhtZlUIulAywN394JRFJiIilYVyiJQrs+bNYs2INVGnNajTgAvPvbCUI5LKJNHC9T8JOv2f5+6ewnikEhn/2nhyV+VGnbZg1gKyGmeVbkAiUlaUQ6REUpVHNm3bROOOjaNOWz5jeYnWKZIn0cL1t8B8nRSlOHJX5cY88X363qelG4yIlCXlECmRssgjs+bOYjCDU7JuSQ+JFq6/BHLMbBzwY95IdaMkIiIJUA6RCmPz9s0xa7VFEpFo4fqr8FU9fImIiCRKOURE0kaRheuw8/9j3f2KUohHREQqEeUQEUk3RXbF5+67gSZmptoGEREpFuUQEUk3xWlz/YGZjQG25I1UezkREUmAcoiIpI1EC9dfhK8qQO3UhSMiIpWQcoiIpI2ECtfufi+AmdUKh6N3OikiIlKAcoiIpJOEHn9uZm3MbBawAFhgZjPN7PjUhiYiIpWBcoiIpJOECtfAU8Bv3b2JuzcBfgcMTV1YIiJSiSiHiEjaSLRwfZC7T84bcPcc4KCURCQiIpWNcoiIpI2Eewsxs7uA4eHwlQR3f4uIiBRFOURE0kaiNdd9gfrAa+GrfjhORESkKMohIpI2Eu0tZAPwqxTHIiIilZByiIikk7iFazN7DvAYk93dr09+SBXTiLkjePCDB3Gc2tVrM/icwZzwkxMAePTjRxn66VAc58aTbuS2zrcVa/l/ffQvnp71NIbRtkFbnjv/OWpUq1GauyciUmzKIcXzxuI3uGvyXVSxKlSrUo1HejzCaUedxuzvZ3PT2zex6cdNVLWqDOgygEvaXFJo+fe+fo/bxt/G3FVzGdlnJH2O65M/7Q8T/sDbn7/NHt9D9+bdebTHo5hZae6eSNooqub6rSjjjgR+A1RNfjgVV7NDmjHl2ikcUvMQxn0+jn5v9eOTGz5h/ur5DP10KNNunEb1qtXp8WIPzj32XFoc2iKh5VdsWsFj0x5j4c0LqZlRk4tfvZiR80dybftry2ZHy4FZ82axZsSamNMb1GnAhedeWIoRiUgMyiHF0K15N3q17IWZMXfVXC5+9WIW37qYAzMO5IXeL3BMvWNYuXklHZ7qwFktzqJujbr7LH9UnaMY1nsY//jwH/uM//DbD/ng2w+Y238uAKc9dxpTvp5CVtOsUtozkfQSt3Dt7v/Ne29mzYE7ga7AA8AzqQ2tbN3xvzt4ffHrrNu2jrVb13JA1QPY/qftMec/5chT8t93btyZ5ZuWA7BozSI6NerEgRkHAnB6k9N5bdFr/OHUPyS0PMCuPbvYtmsbGVUz2LpzK0fUPiIp+1hRbdq2icYdG8ecvnzG8pjTRKT0pHMOgeLnkVrVa+W/37JjS37N8rH1js0ff0TtIzj8oMNZs2VNocJ107pNAahi+95OZRjbd21nx+4dOM7O3TtpcFCD/dw7EYmlyBsazayVmb0IvAlMBY5z98HuviPl0ZWRD775gHFLxzHrF7P45rZvaNegHR/f8DEAd0++mzFLxsRd/plZz3B2i7MBaHN4G97/5n3WbV3H1p1bGbt0LN9u/Dbh5Rsd3IjbT76do/51FA0fbkidGnU48+gzk7CXIiKpl445BEqeR0YvGk2rJ1pxzkvn8GyvZwtNn7ZiGjt27+DoQ49OOJaTjzyZM5qeQcOHG9Lw4YacdfRZtK7fumQ7JiJFilu4NrNXgbHAR0AWMAY42MwONbNDi1q5mfUwsyVmttTM7ogy/QAz+084/RMzaxqO7x4+wWte+De7BPtWYtNWTKN3y97UzKhJzYyanN/yfCZ/FXTRet8Z99GrZa+Yy07+ajLPzHqGB3/2IACt67fmj6f+kTNfPJMeL/agfYP2VK0S+2poweU3bNvAG0ve4Ktff8XK365ky44tvDj3xSTurYhIauxvDgnXkVZ55ILWF7D41sW8funr3DX5rn2mfbf5O64afRXPnf9codrpeJauX8qitYtY/tvlrPjtCiYtm8T7X79f8p0TkbiKanP9U4KbUW4neKIWQN4dEA40j7WgmVUFBgHdgeXAdDMb4+4LI2a7Htjg7i3M7FLgQeASYC1wnruvNLM2wDtAo2Lt2X6oVqUae3xP/vAe30O1KkV3rDJ31VxuePMGxl0xjnoH1ssff/1J13P9ScF9O3dOvJPGB0dv0hBt+f99+T+a1W1G/YPqA3Bh6wv58NsPubLdlSXev4ruu2XfMf3l6TGnb96wGa4oxYBEJJYS5xBIzzySp2uTrny54UvWbl3LYQcexqYfN3HOS+cwMHsgnRt3LlYsoxeNpnOjzvnNTs5ucTYfLf+ILk26FGs9lUm8PLJu6bpSjkYqm6LaXDfdj3VnAkvd/UsAMxsJnA9EnhTPB+4J348CnjAzc/dZEfMsAGqa2QHu/uN+xJOwrKZZXP7a5fxfl//D3Rm9eDQvX/Ry3GW+2fgNF/7n/9u7+2g56jrP4+8PSdDo8GTUGAkx0SAOri6EaxJnxMXHwRwRAnGNLouzuAbWcEbGMzvo4YjIUXdYdXd1jFFABmFZHsS4ZhAQIQuBmcEk5EbgJhAuJDE3MjcYIQ8mgSR894+qC51Ld3Xlpqv66fM6p0/qsetb3Z36/m7V7+FMrpt93X714wA2/3Ezr3/16/nt1t+yaM2iFx8N5tl/0hGTeGDTA+zcs5Oxo8dy97q76ZnQ05gTbVe7Yeb42snl9g23lxiMmdVykDkEuiyP9P+hn7cc9RYksfKplTy37znGjR3H8/ueZ/ZNsznn356zXw8geU06YhJXrrySL72QxHLvhnu5cMaFIzyzDpGRR5btXFZyMNZp6nXFNzki1mesF3B0RFRrQXY0UFm5eACYUWubiNgraSswjuSOw5CzgJXVLoiS5gHzACZNmpR1KgfkHePfwfknnc/0K6cDcN5J5/HO8e8EkrpyPW/sedkjvcvuvYwtu7bwuV98DkjuWqyYtyI5gZvPYsvOLYwZNYYFsxa82AjlByt+AMD5PefX3H/GxBnM+dM5TPvhNEYfMpoTJ5zIvJPmNexczcyKcpA5BLosj/x09U+59qFrGXPIGMaOGctNc25CEjf33czSDUvZsnML16y6BoBrzriGE95wwn7vtXzTcmbfNJtndj/DP679R75yz1fo+1wfc46fw5J1S3jHwncgxKlTT+W0405r2Lma2f7qPaP6pqRDgJ8DDwJPA68EpgLvAz4AfIXkgtdwkt5O8oivagu+iLgCuAKgp6enVl+qIzJ/+nzmT5//suWXve+yqttf9bGruOpjV1Vdd99/ql637fye83Pt/9X3fZWvvu+r9UI2M2s1Tc0h0F555KL3XMRF77noZcvPfufZNasCVr7Xu45+FwNfePlHOeqQUfzwtB/mDdvMDlK9aiEfl3Q8SQ3Wc4EJwE5gDUkjla9HRK1+hTaR9Gc6ZGK6rNo2A5JGA0cAWwAkTQR+BpwTEU8cyEmZmVnzHWQOAecRM2tDdVtXpA1HLh7Bey8HjpU0heTiNxf41LBtFgOfJmlJPgdYEhEh6UjgF8AXI+KfRnBsMzNrAQeRQ8B5xFrUwusXVl3uQcwMchSuRyqt+3YBSQvtUcDVEdEn6TJgRUQsJhlE4DpJ/cAfSC6cABeQPDa8RNIl6bIPR8TmouI1M7PW4jxirarWQGYexMygwMI1QETcRvLor3LZJRXTu4GPV9nva8DXiozNGuOORXewY3BH1XV9vX2cMvGUcgOqY9GtixjcOvjifOXdB99xMGs9ziOdLSuHQGvmEbN6Ci1cW+fbMbij5oVv5dKV5QaTw+DWwf3uOFRO+46DmVm5snIItGYeMasn1xBPSpw99GhN0iRJ04sNzczMOoFziJl1k7zjp34feDfwyXR+O8moWWZmZvU4h5hZ18hbLWRGREyT1AsQEc9IOrTAuMzMrHM4h5hZ18h753qPpFFAAEh6HfBCYVGZmVkncQ4xs66Rt3D9XZKO+F8v6evA/cA3CovKzMw6iXOImXWNXNVCIuJ6SQ+SDFUr4IyIWFNoZGZm1hGcQ8ysm+QqXEuaCfRFxIJ0/nBJMyLi14VGZ2Zmbc85xMy6Sd5qIQuByl7ed6TLzMzM6nEOMbOukbdwrYiIoZmIeAEPQGNmZvk4h5hZ18hbuH5S0l9JGpO+Pg88WWRgZmbWMZxDzKxr5C1cnw/8GbAJGABmAPOKCsrMzDqKc4iZdY28vYVsBuYWHItZQ2zctJGF11evztn7cC8TeyaWHJFZd3MOMbNukre3kNcBnwUmV+4TEecWE5bZyO3es7tmAXrpsqUlR2NmziFm1k3yNij5OXAfcBewr7hwzMysAzmHWNdbdOsiBrcO1lw//ojxnPnRM0uMyIqSt3D9qoi4qNBIzMysUzmHWNcb3DqYWS1xYMVAidFYkfI2aLxV0qxCIzEzs07lHGJmXSNv4frzJBfH3ZK2SdouaVuRgZmZWcdwDjGzrpG3t5DDig7EzMw6k3OImXWTXHeulThb0pfT+WMkTS82NDMz6wTOIWbWTfJWC/k+8G7gU+n8DmBBIRGZmVmncQ4xs66Rt7eQGRExTVIvQEQ8I+nQAuMyM7PO4RxiZl0j753rPZJGAQEvDgjwQmFRmZlZJ3EOMbOukbdw/V3gZ8DrJX0duB/4RmFRmZlZJ3EOMbOuUbdaiKRDgHXA3wIfAAScERFrCo7NzMzanHOImXWbuoXriHhB0oKIOBF4tISYzMysQziHmFm3yVst5G5JZ0lSodGYmVkncg4xs66Rt7eQ84AvAHsl7SZ5rBcRcXhhkZmZWadwDjGro/ehXhaysOq68UeM58yPnllyRDZSeetcnxoR/1RCPNZBNm7ayO333F5znZl1PucQs3y2797OxJ6JVdcNrBgoORo7GHnrXH8POLGEeKyD7Nqzi3FTx1Vf98+7So7GzJrBOcTMuo3rXJuZWdGcQ8ysa+QtXJ8H/AR4TtI2SdslbSswLjMz6xzOIWbWNXI1aIyIw4oOxMzMOpNziJl1k1yFa0nvrbY8IpY2NhwzM+s0ziFm1k3ydsX3XyumXwlMBx4E3p+1k6RTge8Ao4CrIuLvhq1/BXAtcBKwBfhERKyXNA64BXgXcE1EXJAzTjMzaz0jyiHgPGJm7SdvtZDTKuclHQP8r6x9JI0CFgAfAgaA5ZIWR8Tqis0+AzwTEVMlzQUuBz4B7Aa+DPyb9GVmZm1qJDkk3c55xGrK6u51aL1ZM+S9cz3cAPCndbaZDvRHxJMAkm4ETgcqL4qnA5em07cA35OkiPgjcL+kqSOMz8zMWleeHALOI5Yhq7tXcJev1jx561z/PRDp7CHACcDKOrsdDVT+2TgAzKi1TUTslbQVGAf8Pmdc84B5AJMmTcqzi5mZlWyEOQScR8ysDeW9c72iYnovcEMrjLYVEVcAVwD09PREnc3NCuNha80ytWQOAecRM2u8vIXrW4DdEbEPknpwkl4VETsz9tkEHFMxPzFdVm2bAUmjgSNIGqRYi7hj0R3sGNxRc31fbx+nTDylvIBalIetNcs0khwCziMdISuPOIdYJ8pbuL4b+CAw9L9jLHAn8GcZ+ywHjpU0heTiNxf41LBtFgOfBv4FmAMsiQjfOWghOwZ3ZF74Vi7N82TXzLrcSHIIOI8csEW3LmJw62DN9c14kpaVRzoth2Q9xex9uLfmTRjrLHkL16+MiBf/7IyIHZJelbVDWvftAuCXJF0oXR0RfZIuA1ZExGLgR8B1kvqBP5BcOAGQtB44HDhU0hnAh4e1EDczs/ZwwDkk3c555AANbh3MLMD5SVqxsp5iLl3mbt27Rd7C9R8lTYuIlQCSTgLqNsONiNuA24Ytu6Riejfw8Rr7Ts4Zm5mZtbYR5RBwHjGz9pO3cH0h8BNJvwMEvIGkH1EzM7N6LsQ5xMy6RN5BZJZLehtwXLrosYjYU1xYViY3NmlfWfUr3UuJtQrnkM7nPGL2krz9XM8Hro+IR9L5oyR9MiK+X2h0VopuamzSabLqV7pupbUK55DO5zxi9pK81UI+GxELhmYi4hlJnwV8YTQzs3qcQ8yaxE84y5e3cD0qHU42IOmjFDi0uLDMRm7b09tYfsPyquu29Lv7W7MmcA6xtpGVQ9qRn3CWL2/h+g7gJkk/TOfPS5eZtZwxz49h5viZVdct27ms5GjMDOcQayNZOcQsj7yF64uAecB/Sed/BVxVSERmwMZNG7n9nturrnv22WfLDcbMDpZziJl1jbyF60OB+9NXf9qvqFlhdu3Zxbip46qu27PPnQyYtRnnEDPrGpmFa0mjgW8A5wIbSPonPUbSPwAXuyslMzOrxTmkc9QbVn3g4QF3t2eWqnfn+pvAYcCUiNgOIOlw4Fvp6/PFhmdmZm3MOaRD1BtWffVdHT2qvNkBOaTO+o+SdKG0fWhBRGwjqTc3q8jAzMys7TmHmFnXqXfnOoa6Thq2cJ+kly231tVqo2c9++yzNRssDq0vW+9DvSxkYe31D/dm3rkxs5dxDukQa5et5anHn6q5fvP6zSVGY9ba6hWuV0s6JyKurVwo6Wzg0eLCskZrtdGz9uzbU7PB4tD6sm3fvT2z8Lx02dISozHrCM4hHWLvtr3MfFvt7ulW73a1ELMh9QrX84FFks4FHkyX9QBjgdlFBmZmZm3POcTMuk5m4ToiNgEzJL0feHu6+LaIuLvwyKwtuD/qzuThcq0RnEPMypF1zXaVxvLl6uc6IpYASwqOxdqQ+6PuTB4u1xrJOcSsWFnXbFdpLF/eQWTMzMzMqspqpL5x08aSozFrLheuzZrMVTDMrN1lNVLf9c+7So7GrLlcuLausu3pbSy/YfmL85XTW/q3NCMkV8EwM2szlbmjUrPyiLUWF66tq4x5fgwzx7/UnVTl9LKdy5oRkpmZtZnK3FHJecSg/giNZmZmZmaWkwvXZmZmZmYN4mohHSKrURzAwMMDpQ9xbq2r3u+lqH5Rs467rn8dU6ZOqbmvG3eadZ6ssRKG1o9UVg8m7TYOQ+9DvSxkYe317su6rnp5r5E5xoXrDnHfr+7jsKMOq7m+f00/fKTEgKylZTWihOL6Ra3XF+vJPSfX3NeNO82KdceiO9gxuKPqus3rN8P0xh8za6wEOLieRrJ6MGm3cRi2797elGt2J6mX9xqZY1y47hB7t+1l5tuqN7AAWL17dYnRmJlZu9kxuKPmE84bd9844vftpDvI3WakXcWWeZe4Fblw3UaKuquQ9VjOnf83lx8FmlkjrV22llu23VJ1XV9vXyHVBzvpDnK3GWlXsWXeJW5FLly3kaLuKmQ9lnPn/83lR4Fm1kh7t+2tmUdWLl1ZbjBmHcqF6xbiRolmZmbWDrKerDbrqWpWOarMmFy4biH1HqOsvsv1ps3MzKz5sp6sNuupar1G82VxP9dmZmZmZg3iO9cly2qUuPbRtZl3rrMaHhbV4jqrlXeRx+0kbpRoZo10MHnErJLzUzFcuC5ZZqPEm29k+Q3La+675XdbGDe73BbXWa28izxuJ3GjRDNrpIPJI5vXby4oKmtHzk/FcOG6leyGmeNr91V97/P3lhhM99n29LbsP276t5QYTXtq1siPZpaqk0c85kGxsvKIc0j3cOHaLDXm+TGZSemuDXf5ollHs0Z+NDNrBVl5JCuHgPNIJ3HhegSy6rsBPL7ucY6dcmzVdUV10m/Fy7poLtu5rORozKydZeWRrBwCziPtqt4NHOeRzlFo4VrSqcB3gFHAVRHxd8PWvwK4FjgJ2AJ8IiLWp+u+BHwG2Af8VUT8sshYh8u68PX19jH/tPk19125dCWnnHxKzXVm7awV+za1ztWNeSQrhwytN+tWrdKXdZbCCteSRgELgA8BA8BySYsjorLC12eAZyJiqqS5wOXAJyQdD8wF3g68EbhL0lsjYl+j4qt397nehc+sUjfVs2vFvk2tMzmPWDfppjxyMFqlL+ssRd65ng70R8STAJJuBE4HKi+KpwOXptO3AN+TpHT5jRHxHLBOUn/6fv/SqOCyWluDL3x2YA6mnt2G+zaw/ChfUM2qcB6xruE80jkUEcW8sTQHODUi/nM6/x+BGRFxQcU2j6TbDKTzTwAzSC6UD0TE/06X/wi4PSJuGXaMecC8dPY44LEGhf9a4PcNeq+D4Tj25zhaKwZo3zjeFBGvKyoYawznkYZwHK0VAziO4do1jpp5pK0bNEbEFcAVjX5fSSsioqfR7+s4HEcnxeA4rBM4j3RPHK0Qg+PojjiKHP58E3BMxfzEdFnVbSSNBo4gaZCSZ18zM+tsziNm1naKLFwvB46VNEXSoSQNSxYP22Yx8Ol0eg6wJJJ6KouBuZJeIWkKcCzgPmrMzLqL84iZtZ3CqoVExF5JFwC/JOlC6eqI6JN0GbAiIhYDPwKuSxua/IHkwkm63c0kjVb2AvMb2cI7h4Y/Ihwhx7E/x/GSVogBHIcVyHmkIRzHS1ohBnAcw3VcHIU1aDQzMzMz6zZFVgsxMzMzM+sqLlybmZmZmTWIC9cVJN0kaVX6Wi9pVbp8sqRdFet+UHAcl0raVHG8WRXrviSpX9Jjkv6iwBi+KelRSQ9J+pmkI9PlpX4W6TFPTc+3X9IXiz5exXGPkfT/JK2W1Cfp8+nymt9PgbGsl/RwerwV6bLXSPqVpMfTf48qOIbjKs55laRtki4s4/OQdLWkzWmfxkPLqp6/Et9Nfy8PSZrW6HjMammFPNIKOSQ9lvOI80jl8bsnh0SEX1VewLeBS9LpycAjJR77UuBvqiw/HvgN8ApgCvAEMKqgGD4MjE6nLwcub9JnMSo9zzcDh6bnf3xJx54ATEunDwPWpt9B1e+n4FjWA68dtuy/A19Mp7849B2V+L38K/CmMj4P4L3AtMrfXq3zB2YBtwMCZgK/LvO78suvoVez8kgr5JD0eM4jziNZ30nH5hDfua5CkoB/D9zQ7FiGeXE434hYBwwN59twEXFnROxNZx8g6SO2GV4c/jgingeGhj8uXEQ8FREr0+ntwBrg6DKOndPpwI/T6R8DZ5R47A8AT0TEhjIOFhFLSXqCqFTr/E8Hro3EA8CRkiaUEafZkBbNI6XlEHAeAeeRDB2dQ1y4ru5kYDAiHq9YNkVSr6R7JZ1cQgwXpI8jrq54THM0sLFimwHK+U96LslfcUPK/Cyadc77kTQZOBH4dbqo2vdTpADulPSgkuGaAcZHxFPp9L8C40uIY8hc9i80lP15QO3zb4nfjHW9ZueRVsoh4DziPLK/js4hXVe4lnSXpEeqvCr/iv0k+3/pTwGTIuJE4AvA/5F0eIFxLATeApyQHvvbB3OsEcYwtM3FJH3EXp8uavhn0eok/QnwU+DCiNhGSd/PMO+JiGnAR4D5kt5buTKSZ1ml9KupZDCPjwE/SRc14/PYT5nnb9YKeaQVckiOOIa2cR5xHnlRN+SQwgaRaVUR8cGs9UqGzz0TOKlin+eA59LpByU9AbwVWFFUHBXxXAncms42dDjfHJ/FXwIfBT6Q/vAK+SzqaOoQxpLGkFwQr4+IRQARMVixvvL7KUxEbEr/3SzpZySPOQclTYiIp9JHVpuLjiP1EWDl0OfQjM8jVev8Pey1FaoV8kgr5JA8cTiPOI9U0fE5pOvuXOfwQeDRiBgYWiDpdZJGpdNvJhlG98miAhhWt2c2MNS6tbThfCWdCvwt8LGI2FmxvNTPgnzDHxdCkkhGf1sTEf+jYnmt76eoOF4t6bChaZJGQo+w/7DPnwZ+XmQcFfa7I1f251Gh1vkvBs5RYiawteLRn1kZmppHWiGHpHE4jziPVNP5OeRgW2B22gu4Bjh/2LKzgD5gFbASOK3gGK4DHgYeSr/kCRXrLiZp9fwY8JECY+gnqXO0Kn39oBmfRXrMWSQtrJ8ALi7xt/AeksdED1V8DrOyvp+C4ngzSev236Sf/cXp8nHA3cDjwF3Aa0r4TF4NbAGOyPN7beBxbyB5XLiHpP7bZ2qdP0kL7wXp7+VhoKes34xffkU0P4+0Qg5Jj+U84jwyPI6uyCEe/tzMzMzMrEFcLcTMzMzMrEFcuDYzMzMzaxAXrs3MzMzMGsSFazMzMzOzBnHh2szMzMysQVy4tqaQdLGkPiXDna6SNEPS1yVdXrHNmyQ9KelISfdI+m3aZ+jQ+v8raUeN979U0t8MW7Ze0murbCtJS7JGCJO0II1ztaRd6fQqSXMkfUvS+0f2SZiZ2YFyDrFW1nUjNFrzSXo3yYhd0yLiufRidSjwNWCVpGsiYg3wHeDLEfFsej18Fvhz4H5JRwITqr3/CMwCfhPJkLRVRcT8NPbJwK0RcULF+SwHrgSWNCgeMzOrwTnEWp3vXFszTAB+H8kQuETE7yPidxGxC/hrYIGkWcBhEXF9xX43koysBcnQwosaFM9/IB2ZSdJkSWskXZneFblT0tisnSNiAzBO0hsaFI+ZmdXmHGItzYVra4Y7gWMkrZX0fUn/bmhFRNwGPAP8GPjcsP3uBt6bDps7F7ipznH+uuLR2yrgjTW2+3PgwYr5Y4EFEfF2kjsdZ+U4p5Xp+5iZWbGcQ6yluXBtpYuIHcBJwDzgaeAmSX9ZsckCYHlEPDZs133A/SQXxbERsb7Oof5nRJww9AJ+V2O710TE9or5dRGxKp1+EJhc5zgAm6l94TUzswZxDrFW58K1NUVE7IuIeyLiK8AF7P+X/Qvpq5obge8CN1cuTBuyDN1dOFB7JVX+X3iuYnof+domvBLYNYJjm5nZAXIOsVbmwrWVTtJxko6tWHQCsCHn7vcB/w24oXJhRFxccXfhQD0GvHkE+1V6K/DIQb6HmZnV4Rxirc69hVgz/Anw92lr7b1AP8njvboiIoBvNTieXwCnpHEcMEljgKnAigbGZGZm1TmHWEtT8jsz616SJgDXRsSHRrj/bJIuob7c2MjMzKzVOYfYcK4WYl0vIp4CrswaAKCO0cC3GxiSmZm1CecQG853rs3MzMzMGsR3rs3MzMzMGsSFazMzMzOzBnHh2szMzMysQVy4NjMzMzNrEBeuzczMzMwa5P8DzPaOzAgtw9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#beginnig to plot the histograms \n",
    "fig = plt.figure()\n",
    "#setting the gridspec as a 2x2 figure\n",
    "gs = fig.add_gridspec(2, 2, hspace=0.5, wspace=0.5)\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(12)\n",
    "(ax1, ax2),(ax3, ax4) = gs.subplots(sharex=False, sharey=False)\n",
    "fig.suptitle('Histograms Displaying Sym-H Values Over 15 Minute Intervals', fontweight=\"bold\")\n",
    "\n",
    "   \n",
    "#making the panels\n",
    "\n",
    "ax1.hist(before, bins=np.linspace(-80,100,34),alpha=0.3, color = 'green',edgecolor='black', density=True )\n",
    "ax1.hist(symh, bins = np.linspace(-80,100,34), alpha=0.3, color ='m', edgecolor='black',density=True)\n",
    "ax1.set_xlabel('SYM-H (nT)')\n",
    "ax1.set_ylabel('Occurrence (Normalised)')\n",
    "#including median line and legend \n",
    "ax1.axvline(np.median(before), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax1.text(-75,0.03, 'Median: {:.2f}'.format(np.median(before)), color='green')\n",
    "ax1.text(-75, 0.02, '\\u03C3: {:.2f}'.format(np.std(before)), color='green')\n",
    "ax1.legend(['Median $SC^+$ Event','$SC^+$ Event','Total 2012'], fontsize =8)\n",
    "\n",
    "ax2.hist(onset, bins=np.linspace(-80,100,34), alpha=0.3, color = 'green',edgecolor='black',density=True)\n",
    "ax2.hist(symh, bins = np.linspace(-80,100,34), alpha=0.3, color ='m', edgecolor='black',density=True)\n",
    "ax1.set_xlabel('SYM-H (nT)')\n",
    "ax2.set_xlabel('SYM-H (nT)')\n",
    "ax2.set_ylabel('Occurrence (Normalised)')\n",
    "ax2.axvline(np.median(onset), color='k', linestyle='-', linewidth=1)\n",
    "#ax2.set_ylim([0,90])\n",
    "ax2.text(-75,0.03, 'Median: {:.2f}'.format(np.median(onset)), color='green')\n",
    "ax2.text(-75, 0.02, '\\u03C3: {:.2f}'.format(np.std(onset)), color='green')\n",
    "ax2.legend(['Median $SC^+$ Event','$SC^+$ Event','Total 2012'], fontsize =8)\n",
    "\n",
    "ax3.hist(post_onset,bins=np.linspace(-80,100,34),alpha=0.3, color='green',edgecolor='black',density=True)\n",
    "ax3.hist(symh, bins = np.linspace(-80,100,34), alpha=0.3, color ='m', edgecolor='black',density=True)\n",
    "ax1.set_xlabel('SYM-H (nT)')\n",
    "ax3.set_xlabel('SYM-H (nT)')\n",
    "ax3.set_ylabel('Occurrence (Normalised)')\n",
    "ax3.axvline(np.median(post_onset), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax3.text(-75,0.03,'Median: {:.2f}'.format(np.median(post_onset)), color='green')\n",
    "ax3.text(-75, 0.02,'\\u03C3: {:.2f}'.format(np.std(post_onset)), color='green')\n",
    "ax3.legend(['Median $SC^+$ Event','$SC^+$ Event','Total 2012'], fontsize =8)\n",
    "\n",
    "ax4.hist(past_onset, bins=np.linspace(-80,100,34),alpha=0.3, color='green',edgecolor='black',density=True)\n",
    "ax4.hist(symh, bins = np.linspace(-80,100,34), alpha=0.3, color ='m', edgecolor='black',density=True)\n",
    "ax1.set_xlabel('SYM-H (nT)')\n",
    "ax4.set_xlabel('SYM-H (nT)')\n",
    "ax4.set_ylabel('Occurrence (Normalised)')\n",
    "ax4.axvline(np.median(past_onset), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax4.text(-75, 0.03, 'Median: {:.2f}'.format(np.median(past_onset)), color='green')\n",
    "ax4.text(-75, 0.02,'\\u03C3: {:.2f}'.format(np.std(past_onset)), color='green')\n",
    "ax4.legend(['Median $SC^+$ Event','$SC^+$ Event','Total 2012'], fontsize =8)\n",
    "\n",
    "#setting indiviual titles \n",
    "ax1.set_title('15-0 Minutes Before Onset')\n",
    "ax2.set_title('0-15 Minutes After Onset')\n",
    "ax3.set_title('15-30 Minutes After Onset')\n",
    "ax4.set_title('30-45 Minutes After Onset')\n",
    "\n",
    "#including a,b,c,d labels \n",
    "ax1.set_title('A)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "ax2.set_title('B)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "ax3.set_title('C)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "ax4.set_title('D)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ead88df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RO7zcMw8uwGbWYHmM4FbZBgzZ4OuTJ0/e/gh8XVauXMlXvvKV7U+M/uhHPypVmkVxATaz3UpNgzr16tVre1vwBRdcsMPQqb/73e+2TzenR+LdBmxmlhMXYDOznLgAm1m9+C06NavvuXEBNrOi7bXXXqxfv95FuBoRwfr16+t8C00h34Qzs6L16NGD8vLyavvdWvYLqkePHnWvmLgAm1nR2rVrR+/evfNOo8VwE4SZWU5cgM3McuICbGaWExdgM7OcuACbmeXEBdjMLCcuwGZmOXEBNjPLiQuwmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxAXYzCwnLsBmZjkpWQGWNEnSGkkLC2K/kTQ/fV6XND/Fe0naUrDslwXbDJT0kqQySRMlKcUPkDRT0tL0s3OKK61XJmmBpONL9R3NzHZFKa+A7wKGFgYi4v9GxICIGAA8CPy2YPFfKpdFxDcK4rcBXwP6pE/lPscDsyKiDzArzQOcVbDu2LS9mVmzU7ICHBF/AN6ublm6ij0PmFLbPiR1A/aLiNmRvYTqbmBkWjwCmJymJ1eJ3x2Z2UCntB8zs2YlrzbgzwBvRcTSglhvSfMk/Y+kz6RYd6C8YJ3yFAM4OCJWp+k3gYMLtllRwzY7kDRW0hxJc/yOKzNrankV4NHsePW7Gjg0Io4D/hm4T9J+xe4sXR3X+zWtEXF7RAyKiEFdu3at7+ZmZrukyV/KKaktcA4wsDIWEe8D76fpuZL+AhwBrAQKXzHaI8UA3pLULSJWpyaGNSm+EuhZwzZmZs1GHlfAfw8siYjtTQuSukpqk6YPI7uBtiw1MWyUNDi1G58PTEubTQfGpOkxVeLnp94Qg4F3C5oqzMyajVJ2Q5sCPAMcKalc0kVp0Sh2vvl2CrAgdUt7APhGRFTewLsY+BVQBvwFeDTFfwycLmkpWVH/cYrPAJal9e9I25uZNTsla4KIiNE1xC+oJvYgWbe06tafAxxVTXw9cFo18QDG1TNdM7Mm5yfhzMxy4gJsZpYTF2Azs5y4AJuZ5cQF2MwsJy7AZmY5cQE2M8uJC7CZWU5cgM3McuICbGaWExdgM7OcuACbmeXEBdjMLCcuwGZmOXEBNjPLiQuwmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxAXYzCwnLsBmZjlxATYzy4kLsJlZTkpWgCVNkrRG0sKC2LWSVkqanz7DCpZdJalM0iuSziyID02xMknjC+K9JT2b4r+R1D7F90zzZWl5r1J9RzOzXVHKK+C7gKHVxG+KiAHpMwNAUj9gFNA/bfMLSW0ktQFuBc4C+gGj07oAN6R9HQ68A1yU4hcB76T4TWk9M7Nmp2QFOCL+ALxd5OojgKkR8X5EvAaUASemT1lELIuIvwFTgRGSBJwKPJC2nwyMLNjX5DT9AHBaWt/MrFnJow34EkkLUhNF5xTrDqwoWKc8xWqKdwE2RERFlfgO+0rL303rm5k1K01dgG8D/g4YAKwGftrEx9+BpLGS5kias3bt2jxTMbNWqEkLcES8FRHbIuJD4A6yJgaAlUDPglV7pFhN8fVAJ0ltq8R32Fdavn9av7p8bo+IQRExqGvXrrv69czM6qVJC7CkbgWzXwAqe0hMB0alHgy9gT7Ac8DzQJ/U46E92Y266RERwFPAuWn7McC0gn2NSdPnAk+m9c3MmpW2da/SMJKmAEOAAyWVA9cAQyQNAAJ4Hfg6QES8LOl+YBFQAYyLiG1pP5cAjwFtgEkR8XI6xPeAqZJ+CMwD7kzxO4FfSyojuwk4qlTf0cxsV5SsAEfE6GrCd1YTq1z/euD6auIzgBnVxJfxURNGYXwr8A/1StbMLAd+Es7MLCd1XgFLOhn4EvAZoBuwhazt9hHgnoh4t6QZmpm1ULVeAUt6FPgqWRvsULIC3A/4V2AvYJqk4aVO0sysJarrCvjLEbGuSuw94IX0+amkA0uSmZlZC1frFXBl8ZW0t6Q90vQRkoZLale4jpmZ1U+xN+H+AOwlqTvwOPBlssF2zMysgYotwIqIzcA5wC8i4h/IRi4zM7MGKroAp94Q/0TW+wGyByPMzKyBii3AlwFXAQ+lp9YOI3sU2MzMGqjYJ+EOjojt3c0iYpmkP5YoJzOzVqHYK+CrioyZmVmRar0ClnQWMAzoLmliwaL9yAbNMTOzBqqrCWIVMAcYDswtiP8VuKJUSZmZtQa1FuCIeBF4UdJ9EfFBE+VkZtYqFHsT7kRJ1wIfT9sIiIg4rFSJmZm1dMUW4DvJmhzmAttKl46ZWetRbAF+NyIeLWkmZmatTLEF+ClJPwF+C7xfGYyIF0qSlZlZK1BsAT4p/RxUEAvg1MZNx8ys9SiqAEfE50qdiJlZa1NUAZZ0dXXxiLiucdMxM2s9im2C2FQwvRdwNrC48dMxM2s9im2C+GnhvKQbyd4TZ2ZmDdTQ19J3BHo0ZiJmZq1NsW3AL5H1eoBsIPaugNt/zcx2QbFXwGcD/yd9zgAOiYhbattA0iRJayQtLIj9RNISSQskPSSpU4r3krRF0vz0+WXBNgMlvSSpTNJESUrxAyTNlLQ0/eyc4krrlaXjHF+fE2Jm1lSKKsARsRzoRFaAvwD0K2Kzu4ChVWIzgaMi4hjgVXYcU/gvETEgfb5REL8N+BrQJ30q9zkemBURfYBZaR7grIJ1x6btzcyanaIKsKTLgHuBg9LnXkmX1rZNRPwBeLtK7PGIqBxHeDZ1tCNL6gbsFxGzIyKAu4GRafEIYHKanlwlfndkZgOd0n7MzJqVYpsgLgJOioirI+JqYDDZVemuuBAoHF+it6R5kv5H0mdSrDtQXrBOeYpB9pqk1Wn6TeDggm1W1LDNDiSNlTRH0py1a9fuwlcxM6u/ot+KzI6joG1LsQaRNIHsjRr3ptBq4NCIOA74Z+A+SfsVu790dRx1rrjzdrdHxKCIGNS1a9f6bm5mtkuKfRDjP4FnJT2U5keSDVFZb5IuILupd1oqnETE+6RBfiJirqS/AEcAK9mxmaJHigG8JalbRKxOTQxrUnwl0LOGbczMmo1ib8L9DPgKWZvu28BXIuLn9T2YpKHAd4HhEbG5IN5VUps0fRjZDbRlqYlho6TBqffD+cC0tNl0YEyaHlMlfn7qDTGYbCjNyqYKM7Nmo66Xcp4AHBgRj6ahJ19I8WGS9oiIubVsOwUYAhwoqRy4hqzXw57AzNSbbHbq8XAKcJ2kD4APgW9EROUNvIvJelR0IGszrmw3/jFwv6SLgOXAeSk+g+xFomXAZrJfHGZmzU5dTRA3UH0Be5msWaLG4SgjYnQ14WqbLSLiQeDBGpbNAY6qJr4eOK2aeADjasrLzKy5qKsJYt/UB3gHKXZgaVIyM2sd6irAnWtZ1rExEzEza23qKsBPSLq+8vFf2P6o73XAk6VNzcysZaurDfjbwK+AMknzU+xYYA7w1RLmZWbW4tVagCNiEzA6dQ3rn8IvR8SykmdmZtbC1dUNrVdEvJ4K7k5FNzVNdI+I8p23NjOz2tTVBPETSXuQPeQwF1hL9kqiw4HPkXUDu4Ydx2swM7Mi1NUE8Q+S+gH/RDZ4TjeyhxsWkz3wcH1EbC15lmZmLVCdY0FExCJgQhPkYmbWqhQ7GI/Zbqvsilpf3tIIxLd/P4ifDs2GJqn4cBufvONfOfZjH+eOEd+oY9uPDBkyhBtvvJFBgwYxbNgw7rvvPjp16lSinOGNN95gzJgxbNiwgW3btvHjH/+YYcOG7bTe73//ey677DK2bdvGV7/6VcaPz9598NprrzFq1CjWr1/PwIED+fWvf0379u1Llm9L1NCXcprZdnvz6rrVbK34GwB/Xr6Eg/fef5f2OGPGjJIWX4Af/vCHnHfeecybN4+pU6dy8cUX77TOtm3bGDduHI8++iiLFi1iypQpLFq0CIDvfe97XHHFFZSVldG5c2fuvLNBAyS2ai7AZo1gSO9+PPXaywD87tW5nH3kwO3LNn/wPuMfv5cvTvkJw++9gSf+sgCArRV/4/IZ/8mZk3/Ixf99B1u2bNm+Ta9evVi3bh0AI0eOZODAgfTv35/bb799+zr77LMPEyZM4Nhjj2Xw4MG89dZb9cpZEhs3bgTg3Xff5ZBDDtlpneeee47DDz+cww47jPbt2zNq1CimTZtGRPDkk09y7rnnAjBmzBgefvjheh3fin8lkSR9SdLVaf5QSSeWNjWz3cfnjxjII6+8wPsVH7Bk7SoGfKzX9mW3PfcYJ/c8ggdHf4d7vngpN/zxYTZ/8D73LfgTe7Vtz2Nj/pVvDR7G3LnVDy44adIk5s6dy5w5c5g4cSLr168HYNOmTQwePJgXX3yRU045hTvuuAOA6dOnc/XVV9eZ87XXXss999xDjx49GDZsGDfffPNO66xcuZKePT8aXrtHjx6sXLmS9evX06lTJ9q2bbtD3Oqn2DbgX5ANE3kq2evo/0o2etkJJcrLbLfyia7dWblxPf/9ylyG9N7xnbV/Wr6EWcsW8qu5swB4f1sFq/76Ds+Xl3H+cZ/dvv0xxxxT7b4nTpzIQw9l70JYsWIFS5cupUuXLrRv356zzz4bgIEDBzJz5kwAhg8fzvDhw+vMecqUKVxwwQV8+9vf5plnnuHLX/4yCxcuZI89/IdxUym2AJ8UEcdLmgcQEe9Icmu7WYFTDzuaG/74EPecexkbtmzaHg/gls9fxGEHHFzzxjV4+umneeKJJ3jmmWfo2LEjQ4YMYevWrOdnu3btqBympU2bNlRUVNS2KyZMmMAjjzwCwPz587nzzjv5/e9/D8DJJ5/M1q1bWbduHQcddND2bbp3786KFR+9YrG8vJzu3bvTpUsXNmzYQEVFBW3btt0et/op9lfdB+mNFQHZGyzIrojNLDm3/2AuOeksjjxwx7bUz3z8E/z6xf8hvYGLl9dkBe2EHofz30uyZodX161iwYIFO+3z3XffpXPnznTs2JElS5Ywe/bsBud3/fXXM3/+fObPnw/AoYceyqxZ2VX54sWL2bp1K1XfjXjCCSewdOlSXnvtNf72t78xdepUhg8fjiQ+97nP8cADDwAwefJkRowY0eDcWqtiC/BE4CHgIEnXA38C/l/JsjLbDXXbtzNjjhuyU3zcSUP5YNuHnH3Pjzjr7uv5+TPZVeg/HvNpNn/wPmdO/iE/f+YRBg4cuNO2Q4cOpaKigr59+zJ+/HgGDx5cZx7FtgH/9Kc/5Y477uDYY49l9OjR3HXXXUhi1apV27ujtW3blltuuYUzzzyTvn37ct5559G/fzYszA033MDPfvYzDj/8cNavX89FF11U5zFtR6r8rVznitInyB49FjArIhaXMrGmNmjQoJgzZ07eaVgJlLofcJ+fX8LSy3f9GIffdEkjZGPNkaS5ETGoaryoNuD0csuXI+LWNL+fpJMi4tlGztPMrNUotgniNuC9gvn3UszMzBqo2AKsKGiriIgP8WPMZma7pNgCvEzStyS1S5/LqGZ8YDMzK16xBfgbwCeBlWRj/54EjC1VUmZmrUFRzQgRsQYYVeJczMxalWJ7QXQFvgb0KtwmIi4sTVpmZi1fsU0Q04D9gSeARwo+tZI0SdIaSQsLYgdImilpafrZOcUlaaKkMkkLJB1fsM2YtP5SSWMK4gMlvZS2mZjeUVfjMczMmpNiC3DHiPheRNwfEQ9WforY7i5gaJXYeLIHOfoAs9I8wFlAn/QZS+rmJukAsvfOnQScCFxTUFBvI7syr9xuaB3HMDNrNootwL+TtPNQ+XWIiD8Ab1cJjwAmp+nJwMiC+N2RmQ10ktQNOBOYGRFvR8Q7wExgaFq2X0TMTl3k7q6yr+qOYWbWbBRbgC8jK8JbJW2U9FdJGxt4zIMjYnWafhOoHCKqO7CiYL3yFKstXl5NvLZj7EDSWElzJM1Zu3ZtA7+OmVnDFFWAI2LfiNgjIvaKiP3S/H67evB05VrcYBQlOEZE3B4RgyJiUNVRoMzMSq2+b8T4fprvuQtvxHgrNR+Qfq5J8ZVAz4L1eqRYbfEe1cRrO4aZWbNRbBPEL4CTgX9M8+8BtzbwmNOByp4MY8h6WFTGz0/FfjDwbmpGeAw4Q1LndPPtDOCxtGyjpMGp98P5VfZV3THMzJqNkr4RQ9IUYAhwoKRyst4MPwbul3QRsBw4L60+AxgGlAGbga+kY70t6d+A59N610VE5Y29i8l6WnQAHk0fajmGmVmzUWwBbtAbMSJidA2LTqtm3QDG1bCfScCkauJzgKOqia+v7hhmZs2J34hhZpaTOq+AJe0BvAZ8l4/eiDGypb0Rw8ysqdVZgCPiQ0m3RsRxwJImyMnMrFUotglilqQvVo61YGZmu67YAvx14L+A9xvhSTgzM6P4NuChEfHnJsjHzKzVqPMKOL3/rbTv9TYza4XcBmxmlhO3AZuZ5aTYd8LtW+pEzMxam2LfCXdKdfE04LqZmTVAsWNBfKdgei+yVwPNBU5t9IzMzFqJYpsg/k/hvKSewM9LkZCZWWtR7E24qsqBvo2ZiJlZa1NsG/DNfPRanz2AAcALJcrJzKxVKLYNeE7BdAUwxU/GmZntmmIL8APA1ojYBiCpjaSOEbG5dKmZmbVsxRbgWcDfk70LDrJXAD0OfLIUSZm1RmVXNP4T/4ffdEmj79MaT7E34faKiMriS5ruWJqUzMxah2IL8CZJx1fOSBoIbClNSmZmrUOxTRCXA/8laRXZK4k+BvzfUiVlZtYaFPsgxvOSPgEcmUKvRMQHpUvLzKzlK6oJQtI4YO+IWBgRC4F9JF1c2tTMzFq2YtuAvxYRGypnIuId4GslycjMrJUotgC3KRyMXVIboH1pUjIzax2KLcC/B34j6TRJpwFTUqzeJB0paX7BZ6OkyyVdK2llQXxYwTZXSSqT9IqkMwviQ1OsTNL4gnhvSc+m+G8k+ZeFmTU7xRbg7wFPAt9Mn1nAdxtywIh4JSIGRMQAYCCwGXgoLb6pcllEzACQ1A8YBfQHhgK/SE/itQFuBc4C+gGj07oAN6R9HQ68A1zUkFzNzEqp2G5o7YE/pU9ZRGxtpOOfBvwlIpbX8rq5EcDUiHgfeE1SGdl4xKRclgFImgqMkLSYbJzif0zrTAauBW5rpJzNzBpFrVfAktpK+ney4ScnA3cDKyT9u6R2jXD8UWTNGZUukbRA0iRJnVOsO7CiYJ3yFKsp3gXYEBEVVeI7kTRW0hxJc9auXbvr38bMrB7qaoL4CXAA0DsiBkbE8cDfAZ2AG3flwKlddjjZyz4hu0L9O7KhLlcDP92V/RcjIm6PiEERMahr166lPpyZ2Q7qaoI4GzgiIirHAiYiNkr6JrAEuGwXjn0W8EJEvJX2+1blAkl3AL9LsyuBngXb9UgxaoivBzpJapuuggvXNzNrNuq6Ao7C4lsQ3MZHA7Q31GgKmh8kdStY9gVgYZqeDoyStKek3kAf4DngeaBP6vHQnqw5Y3rK9yng3LT9GGDaLuZqZtbo6irAiySdXzUo6UtkV8ANImlv4HTgtwXhf5f0kqQFwOeAKwAi4mXgfmARWde3cRGxLV3dXgI8BiwG7k/rQtZr45/TDbsuwJ0NzdXMrFTqaoIYB/xW0oVkb0EGGEQ2HvAXGnrQiNhEVhgLY1+uZf3rgeuric8AZlQTX8ZHPSXMzJqlWgtwRKwETpJ0Klk/XIAZETGr5JmZmbVwxY6G9iTZgxhmZtZIGvpaejMz20UuwGZmOXEBNjPLiQuwmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxAXYzCwnLsBmZjlxATYzy4kLsJlZTlyAzcxy4gJsZpYTF2Azs5y4AJuZ5cQF2MwsJy7AZmY5cQE2M8tJUe+EM2sKZVfckncKZk3KV8BmZjlxATYzy0luBVjS65JekjRf0pwUO0DSTElL08/OKS5JEyWVSVog6fiC/YxJ6y+VNKYgPjDtvyxtq6b/lmZmNcv7CvhzETEgIgal+fHArIjoA8xK8wBnAX3SZyxwG2QFG7gGOAk4Ebimsmindb5WsN3Q0n8dM7Pi5V2AqxoBTE7Tk4GRBfG7IzMb6CSpG3AmMDMi3o6Id4CZwNC0bL+ImB0RAdxdsC8zs2YhzwIcwOOS5koam2IHR8TqNP0mcHCa7g6sKNi2PMVqi5dXE9+BpLGS5kias3bt2l39PmZm9ZJnN7RPR8RKSQcBMyUtKVwYESEpSplARNwO3A4waNCgkh7LzKyq3K6AI2Jl+rkGeIisDfet1HxA+rkmrb4S6FmweY8Uqy3eo5q4mVmzkUsBlrS3pH0rp4EzgIXAdKCyJ8MYYFqang6cn3pDDAbeTU0VjwFnSOqcbr6dATyWlm2UNDj1fji/YF9mZs1CXk0QBwMPpZ5hbYH7IuL3kp4H7pd0EbAcOC+tPwMYBpQBm4GvAETE25L+DXg+rXddRLydpi8G7gI6AI+mj5lZs5FLAY6IZcCx1cTXA6dVEw9gXA37mgRMqiY+Bzhql5M1MyuR5tYNzcys1XABNjPLiQuwmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxAXYzCwnLsBmZjlxATYzy4kLsJlZTlyAzcxy4gJsZpYTF2Azs5y4AJuZ5cQF2MwsJy7AZmY5cQE2M8uJC7CZWU5cgM3McuICbGaWExdgM7OcuACbmeXEBdjMLCcuwGZmOWnyAiypp6SnJC2S9LKky1L8WkkrJc1Pn2EF21wlqUzSK5LOLIgPTbEySeML4r0lPZviv5HUvmm/pZlZ3fK4Aq4Avh0R/YDBwDhJ/dKymyJiQPrMAEjLRgH9gaHALyS1kdQGuBU4C+gHjC7Yzw1pX4cD7wAXNdWXMzMrVpMX4IhYHREvpOm/AouB7rVsMgKYGhHvR8RrQBlwYvqURcSyiPgbMBUYIUnAqcADafvJwMiSfBkzs12QaxuwpF7AccCzKXSJpAWSJknqnGLdgRUFm5WnWE3xLsCGiKioEq/u+GMlzZE0Z+3atY3xlczMipZbAZa0D/AgcHlEbARuA/4OGACsBn5a6hwi4vaIGBQRg7p27Vrqw5mZ7aBtHgeV1I6s+N4bEb8FiIi3CpbfAfwuza4EehZs3iPFqCG+HugkqW26Ci5c3xpJ2RW35J2C2W4vj14QAu4EFkfEzwri3QpW+wKwME1PB0ZJ2lNSb6AP8BzwPNAn9XhoT3ajbnpEBPAUcG7afgwwrZTfycysIfK4Av4U8GXgJUnzU+xfyHoxDAACeB34OkBEvCzpfmARWQ+KcRGxDUDSJcBjQBtgUkS8nPb3PWCqpB8C88gKvplZs9LkBTgi/gSomkUzatnmeuD6auIzqtsuIpaR9ZIwM2u2/CScmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxAXYzCwnLsBmZjlxATYzy0kuY0GYWdMoxZgdh990SaPvs7XyFbCZWU5cgM3McuICbGaWExdgM7OcuACbmeXEvSBaAb+9wqx58hWwmVlOXIDNzHLiAmxmlhMXYDOznLgAm5nlxL0gmhn3WDBrPXwFbGaWE18Bm1m9lOqvtNY4ypqvgM3McuIr4F3g9loz2xUt9gpY0lBJr0gqkzQ+73zMzKpqkQVYUhvgVuAsoB8wWlK/fLMyM9tRS22COBEoi4hlAJKmAiOARblmZWY1ao2vT1JE5J1Do5N0LjA0Ir6a5r8MnBQRl1RZbywwNs0eCbxSz0MdCKzbxXQbk/OpW3PLyfnUrbnl1JB8Ph4RXasGW+oVcFEi4nbg9oZuL2lORAxqxJR2ifOpW3PLyfnUrbnl1Jj5tMg2YGAl0LNgvkeKmZk1Gy21AD8P9JHUW1J7YBQwPeeczMx20CKbICKiQtIlwGNAG2BSRLxcgkM1uPmiRJxP3ZpbTs6nbs0tp0bLp0XehDMz2x201CYIM7NmzwXYzCwnLsD1JGmApNmS5kuaI+nEFJekienR5wWSjm/ivC6VtETSy5L+vSB+VcrpFUlnNnFO35YUkg5M87mcI0k/SedmgaSHJHUqWJbL+WkOj8pL6inpKUmL0r+by1L8AEkzJS1NPzs3cV5tJM2T9Ls031vSs+lc/SbdWG+qXDpJeiD9+1ks6eRGPT8R4U89PsDjwFlpehjwdMH0o4CAwcCzTZjT54AngD3T/EHpZz/gRWBPoDfwF6BNE+XUk+wm6HLgwDzPEXAG0DZN3wDckOf5Ibsx/BfgMKB9yqFfU/17KcijG3B8mt4XeDWdk38Hxqf4+Mrz1YR5/TNwH/C7NH8/MCpN/xL4ZhPmMhn4appuD3RqzPPjK+D6C2C/NL0/sCpNjwDujsxsoJOkbk2U0zeBH0fE+wARsaYgp6kR8X5EvAaUkT2m3RRuAr5Ldr4q5XKOIuLxiKhIs7PJ+oVX5pPH+dn+qHxE/A2ofFS+SUXE6oh4IU3/FVgMdE+5TE6rTQZGNlVOknoAnwd+leYFnAo80NT5SNofOAW4EyAi/hYRG2jE8+MCXH+XAz+RtAK4EbgqxbsDKwrWK0+xpnAE8Jn0Z9r/SDohz5wkjQBWRsSLVRbleY4qXUh2FZ5nPs3hPOxAUi/gOOBZ4OCIWJ0WvQkc3ISp/JzsF/eHab4LsKHgF2hTnqvewFrgP1OTyK8k7U0jnp8W2Q94V0l6AvhYNYsmAKcBV0TEg5LOI/vt+Pc559QWOIDsz/oTgPslHZZjPv9C9md/k6ktn4iYltaZAFQA9zZlbs2dpH2AB4HLI2JjdtGZiYiQ1CR9VSWdDayJiLmShjTFMevQFjgeuDQinpX0H2RNDtvt6vlxAa5GRNRYUCXdDVyWZv+L9KcSJX78uY6cvgn8NrJGqeckfUg2YEjJcqopH0lHk105vJj+R+4BvJBuVjZ5PgV5XQCcDZyWzhOlzKcOzeZReUntyIrvvRHx2xR+S1K3iFidmojW1LyHRvUpYLikYcBeZE19/0HWVNU2XQU35bkqB8oj4tk0/wBZAW608+MmiPpbBXw2TZ8KLE3T04Hz053+wcC7BX+mlNrDZDfikHQE2c2CdSmnUZL2lNQb6AM8V8pEIuKliDgoInpFRC+yf8THR8Sb5HSOJA0l+7N2eERsLljU5OcnaRaPyqf21TuBxRHxs4JF04ExaXoMMK0p8omIqyKiR/p3Mwp4MiL+CXgKODeHfN4EVkg6MoVOIxvStvHOT1PdTWwpH+DTwFyyO9fPAgNTXGSDwP8FeAkY1IQ5tQfuARYCLwCnFiybkHJ6hdR7o4nP1+t81Asil3NEdnNtBTA/fX6Z9/kh6xHyajr2hKb+75Jy+DTZTdIFBedmGFm76yyyi4sngANyyG0IH/WCOIzsF2MZ2V+dezZhHgOAOekcPQx0bszz40eRzcxy4iYIM7OcuACbmeXEBdjMLCcuwGZmOXEBNjPLiQuwtRiSJqRRvRYoG63uJEnXS7qhYJ2PS1qWRrl6WtIbKnj0S9LDkt6rYf/XSrqySux1pdHeqsQl6UlJ+1VdVrDOrSnPRZK2pOn5ks6VdKOkUxt2Jmx34SfhrEWQdDLZk27HR8T7qSi2B34IzJd0V0QsJnuy6vsRsSHV3Q1kT2D9SdkwlY01ONAw4MWI2FjTChExLuXei6zP64CC7/M8cAfwZCPlY82Qr4CtpegGrIuPRoRbFxGrImILcAVwa3rEdd+IKBwLYirZU1cA5wC/pXH8E+kJKUm90liyd6Qr9Mcldaht44hYDnSRVN34FtZCuABbS/E40FPSq5J+IanycXEiYgbwDtnQgRdX2W4WcIqkNmSF+Dd1HOeKgqaC+cAhNaz3KbInJiv1AW6NiP5kV91fLOI7vZD2Yy2UC7C1CBHxHjAQGEs2hOBv0gA8lW4Fno+IV6psug34E1nx7RARr9dxqJsiYkDlh4/Gg67qgMjG2K30WkTMT9NzgV51HAeyQV5qKvDWArgAW4sREdsi4umIuAa4hB2vMj/kozFmq5oKTCR788J26QZe5ZVufVVIKvz/6/2C6W0Ud/9lL2BLA45tuwkXYGsRJB0pqU9BaADZ65CK8UfgR8CUwmBETCi40q2vV8gGkdkVR5ANsGQtlHtBWEuxD3Bz6slQQTZy1thiNoxsRKobGzmfR8hG9CpryMZpnN7DyUbishbKo6GZlUAaqPvuiDi9gdt/gaxL3fcbNzNrTtwEYVYCkQ00f0dtD2LUoS3w00ZMyZohXwGbmeXEV8BmZjlxATYzy4kLsJlZTlyAzcxy4gJsZpaT/w/8S6W0FeMsegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making histogram of sym-H for all of 2012 to compare to \n",
    "symh = dataframe_omni['symh'].tolist()\n",
    "\n",
    "fig, axs = plt.subplots(1, 1,figsize =(5, 5),tight_layout = True)\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "#plt.setp(plt.gca(),xlim=(-150,75)) \n",
    "axs.hist(symh, bins = np.linspace(-80,60,13), color ='palevioletred' )\n",
    "axs.set_xlabel('SYM-H (nT)')\n",
    "axs.set_ylabel('Occurrence (Counts)')\n",
    "axs.axvline(np.median(symh), color='b', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "axs.text(np.median(symh)*1.1, max_ylim*0.5, 'Median: {:.2f}'.format(np.median(symh)))\n",
    "axs.text(-60,200000, '\\u03C3: {:.2f}'.format(np.std(symh)))\n",
    "axs.legend(['Median','Bins'])\n",
    "axs.set_title('Sym-H Values In 2012')\n",
    "plt.plot()\n",
    "#overplot to others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf286150",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0d5de22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adec50d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a503614b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}