02_SizeAndSuccess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See whether larger protests are more likely to succeed \n",
    "\n",
    "#### Trying to check if protests engaging more than X% of total population have more chances to succeed in final goal.\n",
    "\n",
    "By calculating % of total pop for each protest and adding it as a new feature to a new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pylab import rcParams\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'NAVCO'\n",
    "data_path = \"datasets/NAVCO.xls\"\n",
    "data = pd.read_excel(data_path)\n",
    "\n",
    "# 'World Population 1960-2018'\n",
    "world_pop_path = \"datasets/population.csv\"\n",
    "world_pop = pd.read_csv(world_pop_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### merging dataframes to add total population column to each campaing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_pop = world_pop.rename(columns={'Year': 'year', 'Country Name': 'location'}) # rename columns for easier merge\n",
    "# create 'prot' to keep original dataframe\n",
    "prot = pd.merge(data, world_pop) # merge the two datasets to get population per country for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>camp_name</th>\n",
       "      <th>id</th>\n",
       "      <th>year</th>\n",
       "      <th>campyearid</th>\n",
       "      <th>location</th>\n",
       "      <th>target</th>\n",
       "      <th>loc_iso</th>\n",
       "      <th>loc_cow</th>\n",
       "      <th>loc_vdem</th>\n",
       "      <th>territory</th>\n",
       "      <th>...</th>\n",
       "      <th>coding_notes</th>\n",
       "      <th>ra_initials</th>\n",
       "      <th>tccode</th>\n",
       "      <th>lccode</th>\n",
       "      <th>start_date_obsolete</th>\n",
       "      <th>end_date_obsolete</th>\n",
       "      <th>success</th>\n",
       "      <th>progress</th>\n",
       "      <th>outcomes_refs</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Active Forces</td>\n",
       "      <td>1</td>\n",
       "      <td>1991</td>\n",
       "      <td>1_1991</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Didier Radsiraka</td>\n",
       "      <td>450.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>http://www.iss.org.za/pubs/papers/89/Paper89.h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>5/1/1991</td>\n",
       "      <td>2/10/1993</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gow 1997</td>\n",
       "      <td>11942819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Active Forces</td>\n",
       "      <td>1</td>\n",
       "      <td>1992</td>\n",
       "      <td>1_1992</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Didier Radsiraka</td>\n",
       "      <td>450.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Reuters lba0000020011123dobp03sm6 ;  ;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>5/1/1991</td>\n",
       "      <td>2/10/1993</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gow 1997</td>\n",
       "      <td>12301336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Active Forces</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "      <td>1_1993</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Didier Radsiraka</td>\n",
       "      <td>450.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Reuters lba0000020011120dp6o06zif ;  ;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>5/1/1991</td>\n",
       "      <td>2/10/1993</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12675460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afar insurgency</td>\n",
       "      <td>2</td>\n",
       "      <td>1991</td>\n",
       "      <td>2_1991</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>Djibouti regime</td>\n",
       "      <td>262.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>; UCDP provides extensive coverage of the cam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>522.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>11/12/1991</td>\n",
       "      <td>26/12/1994</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FRUD was able to take over large areas of the ...</td>\n",
       "      <td>606844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afar insurgency</td>\n",
       "      <td>2</td>\n",
       "      <td>1992</td>\n",
       "      <td>2_1992</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>Djibouti regime</td>\n",
       "      <td>262.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>; UCDP provides extensive coverage of the cam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>522.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>11/12/1991</td>\n",
       "      <td>26/12/1994</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>DJIBOUTI Human Rights Practices, 1992: Departm...</td>\n",
       "      <td>615054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 143 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         camp_name  id  year campyearid    location            target  \\\n",
       "0    Active Forces   1  1991     1_1991  Madagascar  Didier Radsiraka   \n",
       "1    Active Forces   1  1992     1_1992  Madagascar  Didier Radsiraka   \n",
       "2    Active Forces   1  1993     1_1993  Madagascar  Didier Radsiraka   \n",
       "3  Afar insurgency   2  1991     2_1991    Djibouti   Djibouti regime   \n",
       "4  Afar insurgency   2  1992     2_1992    Djibouti   Djibouti regime   \n",
       "\n",
       "   loc_iso  loc_cow  loc_vdem territory  ...  \\\n",
       "0    450.0    580.0     125.0       NaN  ...   \n",
       "1    450.0    580.0     125.0       NaN  ...   \n",
       "2    450.0    580.0     125.0       NaN  ...   \n",
       "3    262.0    522.0     113.0       NaN  ...   \n",
       "4    262.0    522.0     113.0       NaN  ...   \n",
       "\n",
       "                                        coding_notes  ra_initials  tccode  \\\n",
       "0  http://www.iss.org.za/pubs/papers/89/Paper89.h...          NaN   580.0   \n",
       "1            Reuters lba0000020011123dobp03sm6 ;  ;           NaN   580.0   \n",
       "2            Reuters lba0000020011120dp6o06zif ;  ;           NaN   580.0   \n",
       "3   ; UCDP provides extensive coverage of the cam...          NaN   522.0   \n",
       "4   ; UCDP provides extensive coverage of the cam...          NaN   522.0   \n",
       "\n",
       "  lccode  start_date_obsolete  end_date_obsolete  success  progress  \\\n",
       "0  580.0             5/1/1991          2/10/1993        0         3   \n",
       "1  580.0             5/1/1991          2/10/1993        0         3   \n",
       "2  580.0             5/1/1991          2/10/1993        1         4   \n",
       "3  522.0           11/12/1991         26/12/1994        0         1   \n",
       "4  522.0           11/12/1991         26/12/1994        0         2   \n",
       "\n",
       "                                       outcomes_refs     Count  \n",
       "0                                           Gow 1997  11942819  \n",
       "1                                           Gow 1997  12301336  \n",
       "2                                                NaN  12675460  \n",
       "3  FRUD was able to take over large areas of the ...    606844  \n",
       "4  DJIBOUTI Human Rights Practices, 1992: Departm...    615054  \n",
       "\n",
       "[5 rows x 143 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prot.head() # protests df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "new dataframe is going to contain the following features:\n",
    "- `camp_name` - name of the campaign \n",
    "- `most_participants` - most participants \n",
    "- `total_population` - total population that year \n",
    "- `success` - did the campaign achieve its final goal \n",
    "- `location` - where the campaign occur \n",
    "- `perc_of_total` - participants % of total population "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  `most_participants` feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering data to calculate most participants\n",
    "prot = prot[prot.reliability != 1] # delete non reliable rows\n",
    "prot['total_part'] = prot['total_part'].replace(-99,np.nan) # replace unknowns in total_part to NaNs\n",
    "# total_part => most participants\n",
    "\n",
    "# Look for each campaigns MAX of participants + few other features\n",
    "# sorting the dataframe by total_part will help to find the largest total_part value for each campaign\n",
    "\n",
    "most_part = prot.copy() # store max participants for each campaign\n",
    "most_part = most_part.sort_values(by='total_part', ascending=False) # sort by most participants\n",
    "most_part = most_part.drop_duplicates(subset=['camp_name'], keep='first') # keeps the row with the most participants per camp\n",
    "\n",
    "most_part = most_part.dropna(subset=['total_part']) # drops rows with NaNs from total_part\n",
    "most_part = most_part.loc[:, ['camp_name', # keep camp name\n",
    "                              'total_part', # max participants\n",
    "                              'Count', # number of total population in that year\n",
    "                              'location', # where the protest happend\n",
    "                              'year']] # what year the protest occur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `success` feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter dataframe to get success column for each campaign\n",
    "# df with 'camp name' & 'success'\n",
    "succ = prot.copy()\n",
    "succ = succ.sort_values(by='success', ascending=False) # [success = 1] rows going up\n",
    "succ = succ.drop_duplicates(subset=['camp_name'], keep='first') # keep the row with [success = 1] first\n",
    "\n",
    "succ = succ.loc[:, ['camp_name','success']] # keep only name and success features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging dataframes and creating `perc_of_total` feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "camp = pd.merge(succ, most_part) # contains: camp_name, success, total_part, Count, location\n",
    "\n",
    "# calculating ( campaign size / total population ) percentage\n",
    "# to see participants as percent of total population in that year\n",
    "\n",
    "perc_of_total = [] # future column to camp df\n",
    "perc = 0\n",
    "\n",
    "# create new feature called 'perc_of_total'\n",
    "\n",
    "# (max of participants / total population that year) * 100 [to show percentage]\n",
    "for x in range(len(camp)): \n",
    "    perc = (camp.total_part.iloc[x] / camp.Count.iloc[x]) * 100 \n",
    "    perc_of_total.append(perc) # adding each value to its camp_name index\n",
    "\n",
    "camp['perc_of_total'] = perc_of_total # adding the new column\n",
    "\n",
    "camp = camp.rename(columns={'total_part': 'most_participants', 'Count': 'total_population'}) # renaming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### check for errors in new feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>camp_name</th>\n",
       "      <th>success</th>\n",
       "      <th>most_participants</th>\n",
       "      <th>total_population</th>\n",
       "      <th>location</th>\n",
       "      <th>year</th>\n",
       "      <th>perc_of_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Singing Revolution</td>\n",
       "      <td>1</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>1568131</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>1989</td>\n",
       "      <td>127.540365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             camp_name  success  most_participants  total_population location  \\\n",
       "92  Singing Revolution        1          2000000.0           1568131  Estonia   \n",
       "\n",
       "    year  perc_of_total  \n",
       "92  1989     127.540365  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camp[camp.perc_of_total>=100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks like an error, but Estonia ended up with more than its population turning up for the 'Traditional singing day'\n",
    "\n",
    "(people from other baltic republics would come to singing events in other countries)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using describe func `perc_of_total`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    241.000000\n",
       "mean       2.958548\n",
       "std       11.413629\n",
       "min        0.000032\n",
       "25%        0.061419\n",
       "50%        0.290777\n",
       "75%        1.150894\n",
       "max      127.540365\n",
       "Name: perc_of_total, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camp.perc_of_total.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "going to save mean into `perc_mean`\n",
    "### Seperate failed protests based on their % of total population \n",
    "select failed campaigns & seperate them by `perc_of_total < mean` & `perc_of_total > mean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the mean to perc_mean\n",
    "perc_mean = camp.perc_of_total.mean() # perc_mean = 3\n",
    "\n",
    "# find campaigns which ended with failure\n",
    "fail_camp = camp[camp.success == 0] # 'fail_camp' - failed campaigns\n",
    "\n",
    "# seperate between campaigns by perc_mean:\n",
    "\n",
    "# campaigns in which the particpants number was smaller than 3% of total population\n",
    "camp_low_perc = fail_camp[fail_camp.perc_of_total < perc_mean]\n",
    "\n",
    "# campaigns in which the particpants number was greater than 3% of total population\n",
    "camp_high_perc = fail_camp[fail_camp.perc_of_total > perc_mean] \n",
    "\n",
    "# save the number of campaigns in both dataframes\n",
    "high_cnt = len(camp_high_perc) # number of campaigns with 'perc_of_total > 3'\n",
    "low_cnt = len(camp_low_perc) # number of campaigns with 'perc_of_total < 3'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have 2 groups of failed protests:\n",
    "    - greater than 3% of population invovlved\n",
    "    - less than 3% of  population involved\n",
    "#### Piechart to show difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pU/N9EwV7vXc3+lwVlm7duhEZGUndunVp1KgR0dHRvPvuuwV6T/7qscceY9CgQQwfPpxevXpx4sQJpkyZUqDrydr7MyMieVNZFbGzdu3asXbtWsaNG8fgwYPJzc0lNDSUZcuW0a1bN+t61apVY/r06cyYMcN6zOCcOXMYPHgwgwYNokqVKkyZMoVhw4aRnJxMuXLlaNy4sfXakJ6enrRq1Yq5c+dy/PhxsrKyqFKlCi+//DJjxoyxbueFF15g3759vPbaayQlJWE2mzGbzdx3331ERkby7bffcvHiRcqWLcu9997LV199dcsf2C4uLoSHh7NkyRKbYhMWFoaPjw9paWl2KZBX+fn5sWnTJsaPH89nn33GhAkTCAgIoEWLFvz973+3yzaCgoLYvn07b7zxBu+++y5xcXH4+flRrVo1OnfufFszZ08++SQA77zzDj179iQkJITXXnuNDRs2WE+aAssJeMuWLWPGjBnMnTuXt956y3qr33bt2tlcQmr27NmUL1+eyMhIPvjgAxo3bsyiRYvo169fvjLZ67270eeqsMyYMQM3Nzfeeustrly5QtOmTVm8eDH/+te/bmu8AQMGEB8fzyeffMKcOXO45557mDVrFhMmTMj3GIXxmRGR65nMhfmvi4iIiIjIHdDVAERERETEYamsioiIiIjDUlkVEREREYelsioiIiIiDktlVUREREQclsqqiIiIiDgslVURERERcVgqqyIiIiLisFRWRURERMRhqayKiIiIiMNSWRURERERh6WyKiIiIiIOS2VVRERERByWyqqIiIiIOCyVVRERERFxWCqrIiIiIuKwVFZFRERExGGprIqIiIiIw1JZFRERERGHpbIqIiIiIg5LZVVEREREHJbKqoiIiIg4LJVVEREREXFYKqsiIiIi4rBUVkVERETEYamsioiIiIjDUlkVEREREYelsioiIiIiDktlVUREREQclsqqiIiIiDgslVURERERcVgqqyIiIiLisFRWRURERMRhqayKiIiIiMNSWRURERERh6WyKiIiIiIOS2VVRERERByWyqqIiIiIOCyVVRERERFxWCqrIiIiIuKwVFZFRERExGGprIqIiIiIw1JZFRERERGHpbIqIiIiIg5LZVVEREREHJbKqoiIiIg4LFejA4iIFFhuLqRdhNTzkHrO8v+U//3f5s/nIPWC5evsNDC5gIsrmEqAS4n//d/lz6+tj11d5gpepcAnEHzLgU858A383//Lg38l8KtoWV9ERAqFyWw2m40OISKSp4xkOHMATu+DM/vh9H44d9hSRM05RqezcHGFkhWhVFXwrwylKkNANQhqBIF1LSVYRERum8qqiBgvJ8tSQk/vhzP7/ldQ90PSSaOT3Rk3byh/D1RsDEGNoWITCKyjAisiUgAqqyJyd5nNkLgHjm+EU9GWUnr+KORmGZ3s7rAW2CZ/llgVWBGRG1JZFZHCd+YgHIuC41Fw/BdIu2B0Isfi5g0VGkH1dlC7s6XImkxGpxIRcQgqqyJif2mXIHYNHFkFsWvhymmjEzkX3wpQ+0FLca3eHty9jU4kImIYlVURsY+E3XDkZ0tBjdvmOCdAOTtXTwi5D2p3gjoPgX+w0YlERO4qlVURuX0XjsHO+bB7IVxy8pOhnEX5eywzrrU7Q6VmumyWiBR5KqsiUjAZV2DfEktJPfkroH9CDOMTCPf0huZDIbC20WlERAqFyqqI3JrZDMc2wM4FcOB7yEoxOpFcq2pbaDYE6j8Mrh5GpxERsRuVVRG5sQu/W2ZQd33j/Nc8LS68y0Bof8tsa5kaRqcREbljKqsiYiszBfYu+stufnFOJqh2n2W2tV53KOFmdCARkduisioiFulJsPVT2DJL10EtanwCofFAaDYYSlczOo2ISIGorIoUdynn4dePYNvnkHHZ6DRSqExQ429w3ygIaWt0GBGRfFFZFSmukhPhlw8gOlInTBVHIfdBu5cthwqIiDgwlVWR4ubSSdg0HXZ8DTkZRqcRo1VtC+3GQPVwo5OIiORJZVWkuDgfCxvfg93fQG6W0WnE0VQJg7+N1eEBIuJwVFZFirqzh2DDFMuF/HULVLmVWg/CA+Ogwj1GJxERAVRWRYqujGRY/zZs/QRys41OI87E5AINH4X2/4SAqkanEZFiTmVVpCja8x/4+V+QnGB0EnFmJdyh+ZMQ/gp4lTI6jYgUUyqrIkXJ2UOw/CU4FmV0EilKfCtAlylQP8LoJCJSDKmsihQFmSmw4R34daZOnpLCU7cbdJkKJYOMTiIixYjKqoiz27cUVv4TLscZnUSKAw9/6DAOmg8Fk8noNCJSDKisijir87GwfDTErjE6iRRHVcKg+wcQWNvoJCJSxKmsijibrDSImgqbP9RF/cVYJTwst2699x/g6m50GhEpolRWRZxJ4h5Y9BScPWh0EpE/BdaDhz+Ayi2NTiIiRZDKqogzMJthy0xYPUGzqeKYTC6Wy1x1GAcefkanEZEiRGVVxNEln4alz+jYVHEOAdWg79e6A5aI2I3KqogjO7QC/vsspJ4zOolI/rl6QffpENrP6CQiUgSorIo4opxsWDPechKViLNq/iR0flsnX4nIHVFZFXE0l+PhP0Ph5K9GJxG5c5WaQ58vwT/Y6CQi4qRUVkUcSew6y9n+2u0vRYl3Gej9BVQPNzqJiDghlVURR5CbC1FTLLdMNecanUbE/kwu0P6fluuy6s5XIlIAKqsiRstKh0VPwsEfjE4iUvjqdIEen4Cnv9FJRMRJqKyKGCn1AizoD39sMTqJyN2jy1uJSAGorIoY5dIf8HUvOHfI6CQid5+rF/T8DOo/bHQSEXFwKqsiRkjcC/N6Q3KC0UlEjGMqYblNa5NBRicREQemsipytx2LgoUDIeOy0UlEHIAJOr8FrZ8xOoiIOCiVVZG7ac9/LLdOzck0OomIY2n3CrR/1egUIuKAVFZF7pbNH8HP/wL0V04kT62HQ6c3dWkrEbGhsipS2MxmS0n99SOjk4g4viaDoPsH4FLC6CQi4iBUVkUKU3YmLP0/2LvI6CQizqN+BPT8HFzdjU4iIg5AZVWksORkwcIBcORno5OIOJ8aD1iuxerubXQSETGYi9EBRIqk3FxYMkxFVeR2xa6BuT0gPcnoJCJiMJVVkcKwfJR2/YvcqT+2QGQ3SDlvdBIRMZDKqoi9rZ4A278wOoVI0ZC4G+b1gowrRicREYOorIrY0y8fwKb3jE4hUrTE74BvBlpOWBSRYkdlVcReYr6CVWONTiFSNP2+HhY/bTkeXESKFZVVEXvYtxS+f8HoFCJF2/6l8NNoo1OIyF2msipyp2LXWmZ8zJrxESl02z6H9W8bnUJE7iKVVZE78cc2WDgIcnQsnchds/4t2DHP6BQicpeorIrcrtP7YF5vyEoxOolI8fP9C3AsyugUInIXqKyK3I4rZ+Hr3pB+yegkIsVTbhZ88xicO2J0EhEpZCqrIgWVmwP/GQLJ8UYnESne0i9Z9m6knDM6iYgUIpVVkYJa+wYc32h0ChEBuHgcFg6A7Ayjk4hIIVFZFSmIQz/BpulGpxCRv/pjK/w0xugUIlJIVFZF8uvCMVgyDDAbnURErhUdCfuWGJ1CRAqByqpIfmSlw7ePQ3qS0UlE5EaWvQAXTxidQkTsTGVVJD+WvwSJu41OISI3k5EEi56EnGyjk4iIHamsitxKzFzYMdfoFCKSH3HbYN0ko1OIiB2ZzGazDsATuZGE3TC7I2SnG51ERPLNBI8tgRrtjQ4iInagmVWRG0m7ZDlOVUVVxMmYLSdDXjlrdBARsQOVVZG8mM2w9Bm4eMzoJCJyO66cthRW7TwUcXoqqyJ52f4FHFpudAoRuROxa2DzB0anEJE7pGNWRa516Q+YGQaZyUYnEZE75eIGQ1dCcDOjk4jIbdLMqsi1fhipoipSVORmwaKhkH7Z6CQicptUVkX+asc8OLra6BQiYk8Xj8Oq141OISK3SWVV5Krk07DyNaNTiEhhiPkS4ncYnUJEboPKqshVP74I6ZeMTiEihcGcCz++pKsDiDghlVURgIPL4eAPRqcQkcJ0arvuRifihFRWRTJT4aeXjU4hInfD6vGQdtHoFCJSACqrIlFTIOmk0SlE5G5IPQ9r3jA6hYgUgMqqFG9nD8Hmj4xOISJ3U/QciN9pdAoRySeVVSnefhxluQ6jiBQf5lxYrpOtRJyFyqoUX7u/g+MbjU4hIkaI2wY75xmdQkTyQWVViqecbFg32egUImKkVeMgTZerE3F0KqtSPO2cBxePGZ1CRIyUeg7WTjI6hYjcgsqqFD/ZmRA11egUIuIItn8BCbuNTiEiN6GyKsXPjq90qSoRsTDnwPq3jE4hIjehsirFS3YGRE0zOoWIOJJDP8HpfUanEJEbUFmV4mX7HEiONzqFiDgUM2x8z+gQInIDKqtSfGSlwSb9QBKRPOxbAudjjU4hInlQWZXiY9vncOW00SlExBGZc+CX6UanEJE8qKxK8ZCZApv0g0hEbmLXQkg6ZXQKEbmGyqoUD1s/tVxTUUTkRnIyYfMHRqcQkWuorErRl5GsH0Aikj8xX0GKfrEVcSQqq1L0bfkE0i4anUJEnEFWKvz6sdEpROQvVFalaMvJhm3/NjqFiDiTbbMhPcnoFCLyPyqrUrQdWq4rAIhIwWQkwW+fGZ3ituTkmjl/JcPoGCJ2pbIqRVt0pNEJRMQZbZkFmalGp8i3g4mXmfzjfsLeWsNrS/YYHUfErlyNDiBSaC6egN/XGZ1CRJxR6nnYtQBaPGl0khs6dyWD/+6MZ1F0HPsTLluXrzt0lqS0LPy93AxMJ2I/KqtSdMV8BeZco1OIiLPaMdfhympGdg5rDpxhUXQcGw6fJTvXfN06mdm5rNibQN8WVQxIKGJ/KqtSNOVkw855RqcQEWcWvwMS90CFhkYnIebkRRZFx/HD7gSS0rJuuf5/d8arrEqRobIqRdPhFZCcYHQKEXF2MXOhyxRDNn3qUhpLYuJYHHOK38+lFOi5W34/z+nL6ZQv6VlI6UTuHpVVKZpivjQ6gYgUBXu+hQffAFePu7K5lIxslu9JYHHMKbYcO4/5+r38+ZJrhu93xfPUfdXtG1DEACqrUvQkxcHR1UanEJGiIO0iHPgeGvYutE3k5prZHHuexTFxrNiXSGpmjl3GXbX/tMqqFAkqq1L06MQqEbEXVy/LL8CFIPbsFRZFx7F0xynik9LtPv6Ok5dIycjGx0M/6sW56TqrUrTk5sCOr41OISJOzQRV28LDH8JLh+HekXYb+VJqJl/9epyIj3/hgWkbmLk+tlCKKkBmTi5bj50vlLGLk8jISEqVKmV0DMMdPHiQ1q1b4+npSePGje/qtlVWpWg58jNcPmV0ChFxRgHVIPw1eGEnDFkOTR8Hz5J3PGxWTi4/70vk/+ZG03LyGl7/7z52/XHJDoFvLerwubuynbvFZDKxdOnSQhs/JCSE6dOnF9r4BTV+/Hjq1q2Lj48PAQEBdOjQga1bt1ofz8jI4LHHHqNkyZLUqVOHtWvX2jx/ypQpPP/883bJMm7cOHx8fDh06BBr1qyxy5j5pX0DUrRoVlVECsLDHxo8AqH9oWqYXYfeE5fEopg4vt8Vz/mUTLuOnV+bjhpfVjMzM3F3dzc6ho2srCzc3Bz/pgm1a9fmo48+onr16qSlpfH+++/z4IMPcvToUQIDA/nss8+Ijo7m119/5aeffqJ///4kJiZiMpk4duwYn3/+Odu3b7dLltjYWLp27UrVqlXtMl5BaGZVio6sdIhde+v1RKR4M5WAmh2h12zLbv6HP7BbUT19OZ1PNsTy4Psb6P7RJiI3HzesqAIcPXOFhKQ0u42XnJzMwIED8fHxISgoiPfff5/w8HBGjvzzUImQkBAmTZrE4MGD8ff35+mnnwbg1KlT9O3bl4CAAMqUKUNERATHjx+3Pm/btm107NiRsmXL4u/vT7t27YiJibEZF6BHjx6YTCbr1wDff/89zZo1w9PTk+rVqzNhwgSys7Otj5tMJj755BMiIiLw8fFh0qRJ131v4eHhnDhxgn/84x+YTCZMJpPN4ytXrqRevXr4+vrSuXNnEhL+vDzirbJfzfD555/To0cPvL29qfhsFXAAACAASURBVFWrFsuWLbvp6z1gwAA6dOhA9erVadCgAe+99x6XL19m9+7dABw4cICHH36YBg0a8Oyzz3LmzBnOnbP8gvLMM8/wzjvvULLkrfcO5ObmMnHiRIKDg/Hw8KBx48asWLHCJnt0dDQTJ07EZDIxfvz4W45pTyqrUnQci4Is57mXt4jcZeUawIOT4MUDMOg/ljP83e78OqTpWTn8d+cpHpu9lTZvr+Xtnw5y+PQVOwS2j412PBTgxRdf5JdffmHZsmWsWrWKjRs3XlfKAN59913uueceoqOjGTt2LKmpqbRv3x5fX1+ioqLYtGmTtfRlZlrKfHJyMk888QQbN25ky5Yt1KpViy5dupCcnAxYCiHAnDlzSEhIsH69cuVKBg0axIgRI9i/fz+ffvopkZGRTJ482SbTuHHjiIiIYM+ePQwdOvS6zIsXLyY4OJiJEyeSkJBgU0ZTU1OZOnUqc+fOJSoqipMnT/LSSy9ZH79V9qsmTJhAnz592L17N126dGHgwIFcuHAhX699ZmYmn332Gf7+/oSGhgIQGhrKpk2bSEtLY+XKlQQFBVG2bFm+/vprPD096dGjR77GnjFjBtOmTWPq1Kns3r2bTp068fDDD3PkyBEAEhISaNCgAaNGjSIhIcHme78bTGbz7V7FTcTB/DgKtn1udAoRcSQ+gdDwUQjtB0GhdhvWbDbz27ELLIqJ46c9iSRnZN/6SQbpHlqRD/s3ueNxkpOTKVOmDPPnz6d3b8ulvJKSkqhYsSJPP/209VjPkJAQmjRpwpIlS6zP/eKLL5gyZQoHDhywzlhmZmZSqlQpli5dyoMPPnjd9nJycggICGD+/Pl069YNsMzwLVmyhEceecS63v33389DDz3Eq6++al329ddfM2bMGOLj463PGzlyJO+///5Nv8eQkBBGjhxpM1McGRnJkCFDOHr0KDVq1ABg5syZTJw4kcTExDzHuVH2f/3rX7zxxhsApKSk4Ofnx/Lly+ncufMNM/3www/069eP1NRUgoKCWLp0KS1atAAshzOMHDmS5cuXU7ZsWd5//33q169PixYtWLduHZ999hkLFy6kRo0afPHFF1SqVCnPbVSqVIlnn32W1157zbqsZcuWtGjRgo8//hiAxo0b88gjj9z1WVXQMatSlBz+2egEIuIISnhAnc4QOgBqdoAS9vtRd+J8CotiTrFkRxx/XLDf7vXC9MvRc5jN5ut2axfU77//TlZWFi1btrQu8/f3p06dOtet27x5c5uvo6OjOXr0KH5+fjbL09PTiY2NBeDMmTO8/vrrrF27ltOnT5OTk0NqaionT568aa7o6Gi2bdtmM5Oak5NDeno6qampeHt755mpILy9va1FFSAoKIgzZ85Yv85v9kaNGln/7OPjg5+fn804eWnfvj07d+7k3Llz/Pvf/6ZPnz5s3bqVcuXK4ebmZi2TVw0ePJgRI0awc+dOli5dyq5du5gyZQojRoxg0aJF141/+fJl4uPjadu2rc3ytm3bsmvXrlu/OHeByqoUDaf3Q9LN/0ETkSIuuIXlRKl7eoJXgN2GvZyexY+7E1gUHcf2ExftNu7dciElk72nLtMw2P+Oxrm6I/ba0pvXDlofHx+br3Nzc2nWrBnz5s27bt3AwEDAUrLOnj3L9OnTqVq1Kh4eHoSFhVkPE7iR3NxcJkyYQM+ePa97zNPzz8M8rs1UENeejGUymWy+7/xmz2uc3NybXxfcx8eHmjVrUrNmTVq3bk2tWrWYPXu2zUzyVWvXrmX//v3Mnj2b0aNH06VLF3x8fOjTpw8fffTRTbeT1/t6p7/g2IvKqhQNR1YanUBEjOBfGRr1tZTUsjXtNmxOrpmoI2dZFB3Hqv2nych27huNbDx69o7Lao0aNXBzc+O3336jcuXKgGVW7siRI7Rr1+6mz23atCnffPMN5cqVu+EJPxs3bmTmzJl06dIFgD/++MN6stBVbm5u5OTY3uGradOmHDp0iJo17/z9d3d3v278/MhPdnsxm81kZGRctzw9PZ1nn32W+fPnU6JECXJycqyFOisr64bfV8mSJalYsSKbNm3i/vvvty7fvHmzzSy6kVRWpWg4rLIqUmy4+0K9h6Fxfwi5D+w4+3Mw8bLlrlI74zmbfH0hcFYbD59jePidlTk/Pz+eeOIJRo8eTenSpSlXrhzjxo3DxcXlljNwAwcO5N133yUiIsJ61vnJkydZvHgxo0ePJjg4mJo1azJ37lyaN2/O5cuXGT16NF5eXjbjhISEsGbNGtq2bYuHhwcBAQG8/vrrdOvWjcqVK/Poo4/i4uLC7t272bNnT55n/d9MSEgIUVFR9OvXDw8PD8qWLZuv5+Une0GlpKQwefJkHn74YYKCgjh//jwzZ84kLi6ORx999Lr1J06cSNeuXWnSxHJ8ctu2bRk9ejRDhgzho48+um43/1+NHj2acePGUaNGDRo3bsycOXPYuXNnnjPhRtDVAMT5pV6AP34zOoWIFCaTC1RrBz0+tVxuqscsqHa/XYrquSsZfL7xd7rM2Ejn6Rv598ZjRaqoAkSfuEh6VsFnDK/13nvvERYWRrdu3ejQoQNt27alXr16Nrvb8+Lt7U1UVBRVqlShZ8+e1KtXj6FDh5KWlmadaf3iiy+4ePEiTZo04bHHHmPEiBGUK1fOZpxp06axatUqKleubC1lnTp14ocffmDVqlW0aNGC1q1b8957793W9UAnTpzI8ePHqVGjhvXwhPzIT/aCKlGiBAcPHqRXr17Url2bbt26cfbsWTZu3EiDBg1s1t27dy/fffcdEyZMsC7r3bs3Xbt25b777mP37t3MmDHjhtsaMWIEo0aNYtSoUTRs2JAVK1awbNkyatWqdUffg73oagDi/Pb8BxY9aXQKESkMZWpZZlAb9QX/YLsNm5Gdw5oDZ1gUHceGw2fJzi36PwqXPtuWxpXte9vQlJQUKlWqxLRp03jySf07LIVDhwGI8zu84tbriIjz8AqAe3pZzuYPbmbXoaNPXGRxTBw/7E4gKS3LrmM7ur2nku64rO7YsYODBw/SsmVLkpKSmDhxIgARERH2iCiSJ5VVcW65OXB0tdEpROROubhBrY6WE6VqdwZX+92e89SlNBZHx7Fkxyl+P5dit3Gdzb74y3YZZ+rUqRw6dAh3d3eaNWvGxo0b831sp8jtUFkV5/bHb5DmfJeSEZH/CWpsKagNe4OP/QpPSkY2y/cksDjmFFuOnUcHvMG++KQ7HqNJkyZER0fbIY1I/qmsinM7usroBCJSUH5BlrtKNR4A5erZbdjcXDObY8+zKCaOlfsSSc288xOKipKDiclk5+TiWkLnVotz0SdWnNvJLUYnEJH8cPWyFNRBi+Af++DBN+xWVI+eucI7Kw7S9p21DJq9lSU7Tqmo5iEzO5cjZ64YHaPQREZGUqrUnZ9AZq9xbiUkJMR6i1pHsX79ekwmE5cuXSrU7QwePNjmlrm3oplVcV65ORC/0+gUInJDJqjaBkL7Qf1HwDPvi8HfjkupmSzbFc+i6Dh2xd357u3i4mDiZeoF2e99cHYhISGMHDmSkSNHWpf17dvXenH/wrRt27Y7uqtWcaKyKs7r7EHIKr4nS4g4rIBqluNQQ/tCQIjdhs3KyWXdwTMsiolj3cGzZOYYe1ep7ORzXFofSdrv0ZizM3EtXZEyD72ARwXLxfeTti7m8m+LAfBv3ZuSLf6cScqIP8SFn2dS4fH3MLmUuGuZj5wuujOr9uLl5XXHF/TPj4Jcx7W402EA4rzithudQESu8vCHpk/AkBXwwk4If9luRXV33CXGL9tHqzfX8Pe50azcd9rwopqTfoXEr8eAiyvlHh1PxadmEtD+SVw8LDNlmWePk7RpHmUfHk3Z7i9xKeorMs8eB8Cck835lR9TutOzd7WoguWQCXu6ust86dKl1K5dG09PTzp27Mgff/xhs96sWbOoUaMG7u7u1KlTh7lz59o8bjKZmDVrFg899BBeXl5Uq1aN7777zvp4Xrund+7ciclk4vjx43lmi42NJSIigvLly+Pr60uLFi1YvfrPq8eEh4dz4sQJ/vGPf2Aymax34crrMID85P/888/p0aMH3t7e1KpVi2XLlt30tbv2MICbjZGbm0twcDCffPKJzRgxMTGYTCZ+//13AE6ePElERAS+vr6ULFmSPn36cPr0aQAOHTqEyWTi4MGDNmO89957hISEcO1l95OSkvDy8mLFCtvLQy5evBgfHx+uXLF8lk6dOkXfvn0JCAigTJkyRERE2LwnOTk5vPjii5QqVYoyZcowZsyY67Z1Kyqr4rxO6YxUEUOZSkDNjtBrtuWuUg9/AFXD7DL06cvpfLIhlgff38DDH/1C5ObjXEjJtMvY9nB5y39wLVmWsl1H4lGxDq7+5fEKaYxbQBAAWef+wC0wBK+qoZblgSFknY+zPPe3xXhWboBHUO27ntveZRUgNTWVyZMn8+WXX/LLL79w+fJl+vXrZ318yZIlvPDCC4waNYq9e/cybNgwhgwZwrp162zGGTt2LL169WLXrl0MGjSI/v37c+DAgdvOdeXKFbp06cLq1avZsWMHnTp1onv37pw8eRKwlK7g4GAmTpxIQkICCQkJeY6T3/wTJkygT58+7N69my5dujBw4EAuXLhQoMw3GsPFxYV+/fpdd/vT+fPnExYWRvXq1TGbzTzyyCNcuHCBDRs2sGrVKmJjY+nbty8AderUoVmzZnmOMWDAgOtumevv70/Xrl3zXP9qIU5NTaV9+/b4+voSFRXFpk2b8PX1pXPnzmRmWv6+Tps2jS+++ILZs2ezadMmLly4wJIlSwr0uqisivM6FWN0ApHiqVwDeHASvHgABv3Hctkpt5vfbjM/0rNy+O/OUzw2eytt3l7L2z8d5LCD7rZOO7oV9wq1OLv0Lf74cCDxc0aQvPPPGSj3wBCyL54i+/IZspPOkH3hFO5lq5J1MZ4re1ZT6r7HDMl94kIqmdn2nZXOysrio48+IiwsjGbNmvHll1+yefNmfvvNchvsqVOnMnjwYIYPH07t2rV58cUX6dmzJ1OnTrUZ59FHH+Wpp56idu3avPHGGzRv3pwPP/zwtnOFhoYybNgwGjZsSK1atZg0aRLVq1e3zlaWLl2aEiVK4OfnR4UKFahQoUKe4+Q3/+DBg+nfvz81a9bkzTffJCUlxfoa5NfNxhg4cCC//PILJ06cACyzrQsXLmTQoEEArF69mt27dzN//nyaNWtGq1atmDt3Lhs2bGDbtm3WMebPn2/d3uHDh4mOjraOca2BAweydOlSUlNTAbh8+TI//vijdf2FCxfi4uLC559/TsOGDalXrx5z5szh5MmTrF+/HoDp06fz6quv0qtXL+rVq8cnn3yCv79/gV4XlVVxTlnpcGa/0SlEig+fQGg9HIZFwfDN0OZ58Ct/x8OazWa2/H6eMf/ZRfNJq3lh4U42HjlHjoPf/jTrUiLJO5bjGlCR8n0m4tfkIS6u+Ywre9cA4Fa2MqXuf5zT34zl9LdjKdXuCdzKVubCyo8JCB9C2rEY4mcPJ37OCNL/2HvXcufkmvn9nH1/AXB1daV58+bWr+vWrUupUqWss6IHDhygbdu2Ns9p27btdbOmYWFh1319JzOrKSkpjBkzhvr161OqVCl8fX05ePCgdWY1v/Kbv1GjRtY/+/j44Ofnx5kzZwq0rZuN0aRJE+rWrcuCBQsA2LBhA2fOnKFPnz7WnJUrV6Zy5crWMa5+71ez9uvXjxMnTrBli+VKOvPmzaNx48bUr18/zzxdu3bF1dXVWvAXLVqEn58fDz74IADR0dEcPXoUPz8/fH198fX1pXTp0qSnpxMbG0tSUhIJCQk27+21n5f80AlW4pzO7AezLk0jUqhKeECdzpbbntbsACXs9yPjxPkUFkXHsWTnKf64kGa3ce8asxmPCjUJaPcEAO7la5B17iTJO5bje88DAPg16YJfkz/PKr+yZzUmdy88KtXl1L//j6DH3yMn+Tznlk2h0rDZmFzd7kr0Y2dTqFvBvlcEuHYX8rXLrn3cbDbn+ZwbjeHi4mJ93lVZWTe/Xe7o0aNZuXIlU6dOpWbNmnh5edG7d2/r7umCyE9+Nzfb989kMpGbW7BZ7FuNcXVm9JVXXmH+/Pl06tTJevewG72mf10eFBRE+/btmT9/Pq1bt2bBggUMGzbshnnc3d3p3bs38+fPp1+/fsyfP5++ffvi6mr5tyA3NzfPQwvAvieQaWZVnNPpfUYnECm6gltA1/fgpUPQ5ytLYbVDUb2cnsX8rSfpPWsz7d5dzwdrjzpnUQVK+AbgVraKzTK3MpXJuXw2z/VzUpNI+mUBpTv8Hxnxh3ErXRG30pXwrNoIc042WRdP3Y3YAJxJzrDreNnZ2Wzf/ucJr4cOHeLSpUvUrVsXgHr16rFp0yab52zevJl69Wyvs3t1tu+vX18d42rx+etxpTt33vzShRs3bmTw4MH06NGDhg0bUqFChetOxnJ3dycn5+YTH/nNfzcMGDCAPXv2EB0dzX/+8x8GDhxofax+/fqcPHnS5uS2/fv3k5SUZJN14MCBfPPNN/z666/ExsbaHF+cl4EDB7JixQr27dvHunXrbLbZtGlTjhw5Qrly5ahZs6bNf/7+/vj7+xMUFGTz3mZnZxf4LmiaWRXnpLIqYl/+laFRX8slp8rWtNuwOblmog6fZVFMHKv2nybDzsdLGsWjUn2yLsTZLMu6cArXkuXyXP/imn/j1+IRXEuWJTPxMOa/FqTcHCjgDNydOH/FvmXVzc2N559/ng8++AA3Nzeee+45WrduTcuWLQHLDGefPn1o2rQpDzzwAN9//z2LFy+2OTMf4LvvvqN58+bce++9zJs3j99++43Zs2cDULNmTSpXrsz48eOZNGkSR44cYdq0aTfNVbNmTRYvXkz37t0xmUyMHTv2upnOkJAQoqKi6NevHx4eHtZZyr/Kb/67oVq1arRp04Ynn3yS7OxsIiIirI916NCBRo0aMXDgQKZPn052djbDhw+nXbt2Nrvde/bsyTPPPMMzzzxD+/btqVSp0k232a5dO8qXL8/AgQMJCQmhdevW1scGDhzIu+++S0REBBMnTiQ4OJiTJ0+yePFiRo8eTXBwMC+88AJvv/02tWrVol69erz33nsFvumAZlbFOZ2+e8d4iRRZ7r6WXfxPfA8j98ADY+1WVA8kXGbSD/tp/dYahkRu44fdCUWmqAKUbBFBRvwhkn79lqyL8aTsX8+VXSvwbdr1unXTju0g62I8fv97zD2oNtkX4kiL3W45KculBK6lb14Y7OnsFfteVcHb25uXX36ZAQMGEBYWhpeXFwsXLrQ+/sgjjzBjxgzeffddGjRowKeffsqcOXMIDw+3GWfChAksXLiQRo0a8eWXXzJv3jzrsZRubm4sWLCAgwcPEhoayjvvvMOkSZNumuv9998nICCANm3a0L17dzp16kTTpk1t1pk4cSLHjx+nRo0aN9xtnd/8d8vAgQPZtWsXPXv2tLkerMlkYunSpQQEBHD//ffToUMHqlevzjfffGPz/JIlS9K9e3d27dplM0t6IyaTif79++e5vre3N1FRUVSpUoWePXtSr149hg4dSlpaGiVLWg41GTVqFI8//jiDBw8mLCwMPz8/evToUaDv2WQu6MWuRBzBlOqQet7oFCLOx+QCIfdB4wFQrzu42+8OOueuZLB0xykWx5xif8Jlu43rqFKP/salDV+SdTEeV//ylGzxCH6NO9usk5uVQULkCAIffhn38tWty5N3reTSxrmYSrhR+sHheNdocddyd6xfnn8/XrATXG4kMjKSkSNH3vHtOU0mE0uWLCnQLTil+NBhAOJ8LieoqIoUVJla0Li/ZVe/f7Ddhs3IzmH1fstdpaIOnyXbwc/ityfvmi3xrtnypuu4uHlQ6elPr1vuF9oJv9BOhRXtps7Z+TAAkcKmsirO50Ks0QlEnINXANzTy7KrP7iZXYeOPnGRRTFx/Lg7gaS0m5+VLY7lvJ0PAxApbDoMQJzP7u9g8VNGpxBxTC5uUKuj5USp2p3B1d1uQ8ddTGVJzCkW7zjFsXMpdhtX7i5v9xLsn9j51iuKOAjNrIrzSc77lngixVpQY0tBbdgbfK4/o/l2pWRks3xPAoti4th67AKa3nB+qZk5pGZm4+2uCiDOQZ9UcT7JiUYnEHEMfkHQ8FHLyVLl7HfNx9xcM5tjz7MoJo4VexNJy9INOIqa81cy8S6tCiDOQZ9UcT6aWZXizNUL6nWD0H5QvT24lLDb0EfPXGFRTBxLd5wiISndbuOK4zl7JYPKpb2NjiGSLyqr4nw0syrFjgmqtrEU1PqPgKf9bpV5KTWTZbviWRQdx664JLuNK47tnJ3vYiVSmFRWxfloZlWKi4BqluNQQ/tCQIjdhs3KyWXtwTMsjolj3cGzZOYUnYv1S/6c0xUBxImorIrzuXLa6AQihcfDHxo8YimpVcPsOvTuuEssjjnFsl3xXEhRWSnOLqbq/RfnobIqziXtEmSlGp1CxL5MJaDG3yy7+et2AzdPuw19+nI6S3acYnFMHIdPX7HbuOLcsjSbLk5EZVWci45XlaKkXAPLXaUa9gG/8nYbNi0zh5X7ElkUE8cvR89RjG4qJfmUow+FOBGVVXEuOl5VnJ1PoOVyU6H9ICjUbsOazWa2HrvAoug4ftqbyJWMbLuNLUVPcbotrjg/lVVxLjpeVZxRCQ+o09ly29OaHaCE/f7pPXE+hUXRcSzZeYo/LqTZbVwp2jSzKs5EZVWci2ZWxZkEt7CcKHVPT/AKsNuwl9Oz+GFXAotj4th+4qLdxpXiIztHZVWch8qqOJdkzayKg/OvDI36Wkpq2Zp2GzYn18zGI2dZFHOK1ftPk55tuauUyWS3TUgxkqv75ooTUVkV55Kt3ZzigNx9od7DlpOlQu4rlAZZwsVEeJ1yhNcpZ/exRUQcmcqqOBezLrciDsLkYimmjQdAve7g7mN0IhGRIkllVZyLyqoYrUwtywxqo77gH2x0GhGRIk9lVZyLjrMSI3gFwD29LGfzBzczOo2ISLGisirOJTfH6ARSXLi4Qa2OlhOlancGV3ejE4mIFEsqq+JcdBiAFLagxpaC2rA3+JQ1Oo2ISLGnsirORWVVCoNfkOWuUo0HQLl6RqcREZG/UFkV56KyKvZ09WSp6uFgKgHZGRC/0+hU4oxKuEH5BkanECmSTGazzlgRJ/LtE7B/qdEpRERs+QTC6KNGpxApklyMDiBSIJpZFRFH5KIdlSKFRWVVnIvKqog4IpVVkUKjsirORUetiIgjcilhdAKRIktlVZyLWddZFREHpJlVkUKjsirOxaTZCxFxQCqrIoVGZVWci3eA0QlERK7n4mZ0ApEiS2VVnItPoNEJRESup1+kRQqNyqo4F2/d/lJEHJBveaMTiBRZKqviXDSzKiKOSGVVpNCorIpz8dHMqog4IN9yRicQKbJUVsW5qKyKiCPSzKpIoVFZFeeiwwBExBHp3yaRQqOyKs5FJ1iJiCPSzKpIoSkyZTU8PJyRI0caHcNq/fr1mEwmLl26ZHQUQyUmJtKxY0d8fHwoVarUnQ/o6g4e/nc+zk0kZ5gZuSKdqtOT8Zp8mTazU9h2Ku87Zw37Pg3ThMtM35JhXZaRbeaxJWmUfOsydT66wtpj2TbPmfJLBs8vTyvU70FE7jKVVZFCU2TKqpEcrSjPmjWLRo0aUbJkSUqWLElYWBg//fSTzTovvvgipUuXpkqVKixcuNDmsW+//Zbu3bvbJcv7779PQkICO3fu5PDhw3YZs7CPW33q+zRW/Z7N3B5e7HnGlwdrlKDD3BROXc61WW/pwSy2nsqhop/JZvln0VlEx+fw65M+PN3Ujf6L0jCbzQAcu5jL5zFZTH7As1C/BxG5i0wlwLuM0SlEiiyV1SIoODiYt99+m+3bt7N9+3b+9re/ERERwb59+wD4/vvvmT9/Pj///DPvvPMOQ4YM4fz58wBcunSJf/7zn3z88cd2yRIbG0uzZs2oVasW5crZ6WzZQiyraVlmFu3PZkoHD+6v6krN0i6MD/ekWikXZm3PtK536nIuzy1PZ15PL9yu+Vt04FwOD9dxpUG5Ejzbwp0zKWbOpVrK6jM/pvFOBw9KetgWXBFxYj5lwUU/TkUKS5H925WZmcmYMWOoVKkSPj4+tGrVivXr11sfP3HiBN27dycgIAAfHx8aNGjA8uXLAbh48SIDBw4kMDAQLy8vatWqxZw5c/LczuDBg9mwYQMzZszAZDJhMpk4fvy49fHo6GiaN2+Ot7c3bdq04dChQ9bHYmNjiYiIoHz58vj6+tKiRQtWr15tM35ISAhvvvkmQ4cOxc/PjypVqvDZZ5/d9Hvv3r07Xbp0oXbt2tSuXZvJkyfj6+vLli1bADhw4ADh4eE0b96c/v37U7JkSX7//XcAxowZw/Dhw6lSpUq+XudZs2ZRo0YN3N3dqVOnDnPnzrXJvmjRIr766itMJhODBw/O15i3VIgnMmTnQo4ZPF1ty6SXm4lNJy2HAuSaLbv5R7dxp0G5EteNEVq+BJtO5pCWZWZlbDZBvibKepv4encmnq4metTTbRlFihRdtkqkUBXZsjpkyBB++eUXFi5cyO7du3n00Ufp3LkzR44cAeDZZ58lIyODqKgo9uzZwzvvvIOvry8AY8eOZf/+/fz0008cOHCAWbNmUbZs3rN5M2bMICwsjKeffpqEhAQSEhKoXLmy9fF//vOfTJs2je3bt+Pq6srQoUOtj125coUuXbqwevVqduzYQadOnejevTsnT5602ca0adNo3rw5O3bsYPjw4TzzzDMcPHgwX69DTk4OCxcuJCUlhbCwMABCQ0PZvn07Fy9eJDo6mrS0NGrWrMmmTZuIiYlhxIgR+Rp7yZIlvPDCC4waNYq9e/cybNgwhgwZwrp1ha80jQAAHcVJREFU6wDYtm0bnTt3pk+fPiQkJDBjxox8jXtLhTiz6udhIiy4BG9EZRCfnEtOrpmvd2eyNS6HhCuW2dF3NmXi6gIjWrnnOcbQJm6Elneh/swrTN6YwbePenExHcatz+CDhzz519p0an6QTKevrz+0QESckI5XFSlUrkYHKAyxsbEsWLCAuLg4KlasCMBLL73EihUrmDNnDm+++SYnT56kV69eNGzYEIDq1atbn3/y5EmaNGlC8+bNAcsM4Y34+/vj7u6Ot7c3FSpUuO7xyZMn065dOwBeeeUVunbtSnp6Op6enoSGhhIaGmpdd9KkSSxZsoRly5bx3HPPWZd36dKF4cOHA/Dyyy/z/vvvs379eurWrXvDXHv27CEsLIz09HR8fX1ZsmQJ9evXB6BTp04MGjSIFi1a4OXlxZdffomPjw/PPPMMkZGRzJo1iw8//JCyZcvy2Wef0aBBgzy3MXXqVAYPHmzN9uKLL7JlyxamTp1K+/btCQwMxMPDAy8vrzxfm9vmX/nW69yBuT28GLosjUrvXaGECZoGuTCgoRsxCTlEx+cwY2smMcN8MJny3pXvVsLEx129bJYNXprGiJbu7Ez8//buPKzKcuH3+G8xKbIAQVFMaSMgCgoOmIWUQ6k4ZI7hgBmab2Wjadpxn9POzKz9qqlX7Z1dlcMxlU6FXu18s/ItUTPTNLRSHNgO6TbZvuacA7DOHyvJpYggw/2w1vdzXetC1nA/v4Wpv+51P/dTqBW5Bdr2qF3/+fUFPbXqvD5Kq1Ol7wdAFaOsAlXKLWdWt27dKofDodjYWNnt9uJbdna28vLyJElPPfWUpk2bppSUFL3wwgvavn178evHjh2rzMxMtWnTRpMmTdKGDRtuOktiYmLxrxs1aiRJys/PlySdPXtWkyZNUnx8vOrWrSu73a7c3NxrZlavHMNmsyk8PLx4jOtp3ry5cnJytHHjRo0dO1YPPvigduzYUfz4lClTtHfvXv3www8aMGCApk+frm7dusnX11fTpk3T+vXrNWbMGI0cOfK6x9i5c6dSUlJc7ktJSdHOnTtv8FOpoLDmVTp8dKiXsjMCdGZyoH5+xq5N/2HXpSKHmoZ4ad3BAuWfdejW2WfkM/WUfKae0oGTDk34/IIi55wucbwv9xVox78L9UQHP63ZX6jezXwU4GdTWktfrdlf8i4DAGqQoMamEwBuzS1nVouKiuTt7a0tW7bI29t1TeHlj/rHjBmj1NRUrVy5Up9//rleeeUVzZo1S08++aR69eqlAwcOaOXKlVq9erXuuecePf7445o5c2a5s/j6/rE+8fJMXFGR86PfiRMn6rPPPtPMmTMVExMjf39/DR48WBcvXrzuGJfHuTzG9fj5+SkmJkaS1L59e23evFlz587VW2+9dc1zc3NztWTJEn3//feaP3++OnXqpLCwMKWlpWn06NE6deqUgoKCSjzO1bOLDofjujOOlSbs+jPKlSnAz6YAP5t+/c2hz/YW6D+719agOB91i3L9Y5P63jk9kOirUW2uXYt6vsChx//rvJYO9Je3l02FRdLvGwPoUpFUWOSojrcCoCqFtzKdAHBrbjmz2rZtWxUWFio/P18xMTEutys/jo6IiNCjjz6qrKwsTZgwQW+//XbxY2FhYcrIyNB7772nOXPmlHpSk5+fnwoLyz9Dtm7dOmVkZGjAgAFKSEhQeHi4y8lZlcnhcOjChQsl3v/www9r1qxZstvtKiws1KVLlySp+Ov1inFcXJzWr1/vct+GDRsUFxdXyemvEtJU8i55vWhl+GxvgVbtLdC+X4v0RV6Bui46q+b1vTSqja/q1fFSqwbeLjdfLyncblPz+teebDU1+4L6NPNR20bOx1Ju9VZW7iVtP1qoNzZdVMqtbvn/i4BnCU8wnQBwa275L2VsbKzS09M1cuRIzZo1S23bttWxY8f05ZdfKiEhQb1799a4cePUq1cvxcbG6tdff9WXX35ZXLL+8pe/KCkpSS1bttSFCxf0ySeflFrAIiMj9e2332r//v2y2+0KDQ0tU86YmBhlZWWpb9++stlsev755284Y1oWf/7zn9WrVy9FRETo9OnTyszM1Jo1a7Rq1aprnvv222+rQYMGuu+++yQ5P8afMmWKNm7cqE8//bR4iUJJJk6cqLS0NLVr10733HOP/vGPfygrK+uaHQ0qnbePFBot/btqlhucvODQ5P8+r0OnHAr1t2lQnI9evru2fL3LN2P8Y36hPthRoJxHAorvGxzvozX7fXTXgrNqXs9LSwexXhWo0WoFOf8HGkCVccuyKkkLFizQtGnTNGHCBB0+fFj16tVTcnKyevfuLcl5lvzjjz+uQ4cOKSgoSD179tTs2bMlOWdKJ0+erP3798vf31933XXXNRvnX+nZZ5/Vgw8+qPj4eP3222/at29fmTLOnj1bo0ePVseOHVW/fn0999xzOnXqVIXf+9GjR/XAAw/oyJEjCg4OVmJiolatWqXu3btf87zp06e7rMnt0KGDJkyYoD59+qhBgwZatGjRdY/Tv39/zZ07VzNmzNBTTz2lpk2basGCBerSpUuF38MNhTWvsrKa1tJXaS3Lvr3U/nGBJd7fqoG39jxpd7nPy2bT3/v46+9XnYAFoIZq2FKq6qVPgIezOS5fWgeoSda8Kq15xXQKAJ6uw8NS7xmmUwBuzS3XrMIDNCx5Oy0AqFasVwWqHGUVNVOj1jd+DgBUNcoqUOUoq6iZ6t4q+YeYTgHAk3n5SA3iTacA3B5lFTVXeOKNnwMAVaV+rORTy3QKwO25TVlduHDhdbdYMjHOjURGRmrOnDlVfpzyWLNmjWw2m06cOFGlx8nIyFD//v0rPhBLAQCYxBIAoFq4TVm9GSUVxiFDhmj37t1VfuzNmzfr4YcfrvLjuDXKKgCTKKtAtXDbfVZvlr+/v/z9q34PzLCwsCo/httr3M50AgCejLIKVIsKzaxe/sh8xYoVio2NVe3atdW9e3f9/PPPLs978803FR0dLT8/PzVv3lyLFy92edxms+nNN99Ur1695O/vr6ZNm+qDDz4ofrykj6dzcnJks9mue3nSvLw89evXTw0bNpTdbtdtt93mcmWlLl266MCBA3rmmWdks9mKr2df0jKAsuR/5513NGDAANWpU0fNmjXTxx9/XOrP7upZ3dLGKCoqUpMmTTRv3jyXMbZu3SqbzaZ//vOfkqSDBw+qX79+stvtCgoKUlpamo4ePSpJ2rVrl2w2m3Jzc13GeO211xQZGamrt9s9efKk/P39r7nqVVZWlgICAnTmzBlJ0uHDhzVkyBCFhISoXr166tevn8vvSWFhocaPH6+6deuqXr16mjRp0jXHummhUVLwrZUzFgCUF+vmgWpR4WUA586d08svv6xFixbp66+/1qlTpzR06NDix5cvX66nn35aEyZM0I8//qhHHnlEo0aN0ldffeUyzvPPP69BgwZp27ZtGjFihIYNG6adO2/+CkVnzpxR7969tXr1an3//fdKTU1V3759dfDgQUnO0tWkSRNNnTpVR44c0ZEjR0ocp6z5X3zxRaWlpWn79u3q3bu30tPTdfz48XJlvt4YXl5eGjp0qJYsWeLy/KVLlyo5OVlRUVFyOBzq37+/jh8/ruzsbH3xxRfKy8vTkCFDJEnNmzdXUlJSiWMMHz68uKxfFhwcrD59+pT4/MuF+Ny5c+ratavsdrvWrl2r9evXy263q2fPnrp48aIkadasWZo/f77effddrV+/XsePH9fy5cvL9XMpVXTXyhsLAMqqYYJUp2yX1gZQMRUuq5cuXdIbb7yh5ORkJSUladGiRdqwYYM2bdokSZo5c6YyMjL02GOPKTY2VuPHj9fAgQM1c+ZMl3Huv/9+jRkzRrGxsXrppZfUvn17vf766zedq3Xr1nrkkUeUkJCgZs2aadq0aYqKiiqerQwNDZW3t7cCAwMVHh6u8PDwEscpa/6MjAwNGzZMMTExmj59us6ePVv8Myir0sZIT0/X119/rQMHDkhyzrZmZmZqxIgRkqTVq1dr+/btWrp0qZKSknT77bdr8eLFys7O1ubNm4vHWLp0afHxdu/erS1bthSPcbX09HStWLFC586dkySdOnVKK1euLH5+ZmamvLy89M477yghIUFxcXFasGCBDh48qDVr1kiS5syZo8mTJ2vQoEGKi4vTvHnzFBwcXK6fS6li7qm8sQCgrJp1M50A8BgVLqs+Pj5q37598fctWrRQ3bp1i2dFd+7cqZSUFJfXpKSkXDNrmpycfM33FZlZPXv2rCZNmqT4+HjVrVtXdrtdubm5xTOrZVXW/ImJf3wcFBAQoMDAQOXn55frWKWN0bZtW7Vo0ULLli2TJGVnZys/P19paWnFOSMiIhQREVE8xuX3fjnr0KFDdeDAAW3cuFGStGTJErVp00bx8SXvE9inTx/5+PgUF/yPPvpIgYGB6tGjhyRpy5Yt2rt3rwIDA2W322W32xUaGqrz588rLy9PJ0+e1JEjR1x+b6/+76XCmnaWbN6VNx4AlEVMd9MJAI9RKbsBXP0R8tX3Xf24w+Eo8TXXG8PLy6v4dZddunSp1NdOnDhRH330kV5++WWtW7dOOTk5SkhIKP54ujzKkt/X1/ea1xQVFZXrODca48qZ0aVLlyo1NVX169e/bqar72/UqJG6du1aPMayZcuuO6sqSX5+fho8eLDLMYcMGSIfH+d5eUVFRUpKSlJOTo7Lbffu3Ro+fHi53vtN868rNanE8gsAN1IrWIq43XQKwGNUuKwWFBTou+++K/5+165dOnHihFq0aCFJiouL0/r1611es2HDBsXFxbncd3m278rvL49x+cz5K9eV5uTklJpr3bp1ysjI0IABA5SQkKDw8PBrTsby8/NTYWFhqeOUNX91GD58uH744Qdt2bJFH374odLT04sfi4+P18GDB11ObtuxY4dOnjzpkjU9PV3vv/++vvnmG+Xl5bmsLy5Jenq6Vq1apZ9++klfffWVyzHbtWunPXv2qEGDBoqJiXG5BQcHKzg4WI0aNXL5vS0oKNCWLVsq48fxh2iWAgCoRtFdJG820wGqS4XLqq+vr5588kl9++232rp1q0aNGqU77rhDHTp0kOSc4Vy4cKHmzZunPXv26LXXXlNWVpaeffZZl3E++OADzZ8/X7t379YLL7ygTZs26YknnpAkxcTEKCIiQlOmTNHu3bu1cuVKzZo1q9RcMTExysrKUk5OjrZt26bhw4dfM9MZGRmptWvX6vDhwzp27FiJ45Q1f3Vo2rSpOnbsqIceekgFBQXq169f8WPdunVTYmKi0tPTtXXrVm3atEkjR45U586dXT52HzhwoE6dOqWxY8eqa9euaty4canH7Ny5sxo2bKj09HRFRkbqjjvuKH4sPT1d9evXV79+/bRu3Trt27dP2dnZevrpp3Xo0CFJ0tNPP61XX31Vy5cvV25urh577LHKv+hA9N2VOx4AlIYlAEC1qnBZrVOnjp577jkNHz5cycnJ8vf3V2ZmZvHj/fv319y5czVjxgy1bNlSb731lhYsWKAuXbq4jPPiiy8qMzNTiYmJWrRokZYsWVK8ltLX11fLli1Tbm6uWrdurb/+9a+aNm1aqblmz56tkJAQdezYUX379lVqaqratXPdl3Pq1Knav3+/oqOjr7vvaVnzV5f09HRt27ZNAwcOdNkP1mazacWKFQoJCVGnTp3UrVs3RUVF6f3333d5fVBQkPr27att27a5zJJej81m07Bhw0p8fp06dbR27VrdeuutGjhwoOLi4jR69Gj99ttvCgoKkiRNmDBBI0eOVEZGhpKTkxUYGKgBAwZUwk/iCo3bSbWr/qpjACBJiuHkKqA62RwV2PRy4cKFGjduXIVnymw2m5YvX145l+CEZ/p/D0o7VphOAcDdNUyQxq6/8fMAVBqPvtwq3AhbWAGoDmxZBVQ7yircAydZAagOrFcFql2FlgEAlvJGB+nYLtMpALirWsHSpH+yEwBQzZhZhfvgpAcAVSmqM0UVMICyCveRMMh0AgDurBlLAAATKKtwH42TpLDqv1gDAA/g7Se1uNd0CsAjUVbhXtreeO9YACi3Zj2kOqGmUwAeibIK95I4RPJiTRmAStZmuOkEgMeirMK92Bs4Z0AAoLLUqcffK4BBlFW4nzYsBQBQiRLul7x9TacAPBZlFe4nNlWqU990CgDuovUw0wkAj0ZZhfvx9nWuXQWAimrYSrqljekUgEejrMI9tR1hOgEAd5CUYToB4PEoq3BPDeOlW9qaTgGgJvOzS62Hmk4BeDzKKtwXJ1oBqIhWg6RagaZTAB6Psgr3lTBY8q5lOgWAmqr9aNMJAIiyCnfmHyLF32c6BYCa6Ja2nFgFWARlFe4tZZwkm+kUAGqa9g+ZTgDgd5RVuLfwVlLz3qZTAKhJgiPY/g6wEMoq3F/niaYTAKhJ7nxG8vEznQLA7yircH+3tOW63gDKJjhCavuA6RQArkBZhWfoNMl0AgA1AbOqgOVQVuEZIm6TorqYTgHAyphVBSyJsgrPwewqgNIwqwpYEmUVniMyRfpTiukUAKyIWVXAsiir8Cyd2BkAQAmYVQUsi7IKzxLdVWpym+kUAKyEWVXA0iir8DysXQVwJWZVAUujrMLzxPaQGnHNbwBiVhWoASir8EypL5tOAMAKmFUFLI+yCs8UeafUcqDpFABMathKaveg6RQAboCyCs/VY5rkG2A6BQAjbNK9syVvH9NBANwAZRWeK7ixdNd40ykAmJCUIUV0MJ0CQBnYHA6Hw3QIwJiCC9Lfbpd+3Wc6CYDqEtBAemKz5F/XdBIAZcDMKjybTy2p56umUwCoTqnTKapADUJZBZr3lFrcazoFgOoQ1VVKvN90CgDlQFkFJKn3DKlWkOkUAKqST22pzyzTKQCUE2UVkKSgW6R7/mI6BYCqdOd4qV606RQAyomyClzW/iGpyW2mUwCoCvWaOS8AAKDGoawCl3l5SX3nSl6+ppMAqGz3zuZKVUANRVkFrtSwpXTnONMpAFSm1sOkpneZTgHgJrHPKnC1wgJpYW/p529NJwFQUQFh0mMbpYD6ppMAuEnMrAJX8/aRBs+X/ENMJwFQETYvaeDbFFWghqOsAiUJbiL1f9N0CgAV0WmSFN3VdAoAFURZBa6neS8p+QnTKQDcjKguUufnTKcAUAlYswqUpvCSND9VOrzFdBIAZWUPlx5dL9nDTCcBUAmYWQVK4+0rDV4g1Q42nQRAWdi8nWvOKaqA26CsAjcS8iep399MpwBQFnf/bykyxXQKAJWIsgqURVxfqcPDplMAKE2zHs5LqgJwK6xZBcqq4IL0bnfpyDbTSQBcLThCemStVCfUdBIAlYyZVaCsfGpJ9y+UagWZTgLgSl6/ry2nqAJuibIKlEdolHTf65JsppMAuKz7i1LEbaZTAKgilFWgvFr2l3pMM50CgCTF95eSHzedAkAVoqwCN6PjE1wwADDt1mRpwFumUwCoYpRV4Gb1mCYlpJlOAXim+s2lYcsk39qmkwCoYpRV4GbZbFL/v0vRd5tOAngWe7g04kPJP8R0EgDVgLIKVIS3r5S2WGrUxnQSwDP4BTqLat1bTScBUE0oq0BF1bJL6R86dwoAUHW8/aQhi6XwBNNJAFQjyipQGexh0ogsKaCB6SSAe7J5S4PelaK7mk4CoJpRVoHKEtpUSv9A8rObTgK4GZvU729S/H2mgwAwgLIKVKZb2jg/pvTyNZ0EcB+9Z0hthplOAcAQyipQ2aLvlgbMk2z88QIq7O7npQ7/YToFAIP41xSoCgmDpfsXOk8IAXBz7pogdXrWdAoAhtkcDofDdAjAbeV9Jb0/Qrp4xnQSoAaxST1ekjo+aToIAAugrAJV7dAWaclg6bfjppMA1ufl6zyZqvUQ00kAWARlFagO/94lLR4gnTpsOglgXb4BUtr/lZp1M50EgIVQVoHqcuJnZ2H9nz2mkwDW4x/q3PqtSXvTSQBYDGUVqE5nj0nvDZKO5JhOAlhHcITzohphsaaTALAgyipQ3S6clpYNk/avM50EMC8sTnogSwq6xXQSABZFWQVMKLggfThayv3EdBLAnIg7pOGZkn+I6SQALIx9VgETfGo5TyRpM8J0EsCM2F7SyBUUVQA3xMwqYNr62dJ/vyQ5Ck0nAapH2wekvnMlL2/TSQDUAJRVwAryvnIuC2AvVrgzn9pSz1el9qNMJwFQg1BWAas4cdB5tasj20wnASpfaLSUtkgKTzCdBEANQ1kFrOTSeemTZ6RtS00nASpPq0HOj/1rBZpOAqAGoqwCVvTdAmnV/5IKzptOAtw8n9pS6nTptodMJwFQg1FWAav65Ufpw1HSsd2mkwDlFxol3b9IapRoOgmAGo6yCljZxbPSymdZFoCapeUA6b7X+dgfQKWgrAI1wbZMaeUE6eIZ00mA6/OuJfWcLt02xnQSAG6EsgrUFMf2Sh8/IR38xnQS4FqhUdL9C6VGrU0nAeBmKKtATeJwSFsXSV+8IJ0/YToNIHn5SLc/KnWZLNWym04DwA1RVoGa6Ey+tGqy9OOHppPAkzXpIN07WwpvZToJADdGWQVqsr2rnWtZf91vOgk8iX+I1G2K1O5ByWYznQaAm6OsAjXdpd+k7L9KG16XigpMp4G7az1c6vGSFFDfdBIAHoKyCriLoz9J/xgnHdpkOgncUVgLqc9rUmSK6SQAPAxlFXAnDof03bvS6qnShZOm08Ad+PhLnSdKHZ+SvH1NpwHggSirgDs6/Yv0+fPOE7AcRabToKZqlir1niGF/Ml0EgAejLIKuLP8XCn7VemnFZL4o44yCk+U7v4/Umyq6SQAQFkFPMLRHc7SuuNjUVpxXWFxUtfJUtx9nOUPwDIoq4An+eVHac0rUu5KUVpRLDTaual/q0GSl5fpNADggrIKeKIj26U1r0q7VppOApPqNZPufEZqPVTy8jadBgBKRFkFPNm/cpyldfenppOgOjVqLd053vlxPzOpACyOsgpAOrzVeWGBPZ+ze4A7+1OKdNd4Kaab6SQAUGaUVQB/OHFQ2rpY+v496fS/TKdBZfANkFr2l5IypIgOptMAQLlRVgFcq6hQ2rta2rJI2vMZl3GtiZp0kNqOkFoNlGoFmk4DADeNsgqgdKd/cc60fr9Y+nW/6TQoTUAD58lSbR+QwmJNpwGASkFZBVA2Doe0L9s525r7iVR40XQiSJKXj9Ssh3MWtVmq5O1jOhEAVCrKKoDyO/s/0rZlUs5SKf8n02k8U/1YqU261HqYFNjQdBoAqDKUVQAVc3yftOtTadd/SQe/YX1rVbF5SY2TnGfyN+vu/DUAeADKKoDKc+64tOcLZ3HN+0q6cNJ0oprNHi7F3OO8RXWV6oSaTgQA1Y6yCqBqFBVKh76T8r503g5vkRyFplNZm7efFHG7c/Y0ppsU3sp0IgAwjrIKoHqcPyn9M9t5kta/vpeO/iQVnDedyrzQKOesaUw3qWknqZbddCIAsBTKKgAzigqlY7ulI9ulX7ZLR7Y5v55306UDvnWkBnFSw1ZSeILza8OWUu0g08kAwNIoqwCs5df9VxTY37+ePmI6VfkENf69lLb6o5yGRkteXqaTAUCNQ1kFYH1n/i0dz5POHJVOH5XO/HLt17PHJFXTX2f+oVLQLVJgIymokfNrYCOpXoxztpQToQCg0lBWAbiHwgLpbL7ziltn8p0F9ky+VHhJksN5UQOXr0VX3ac/vvfylmrXdZZO/1DJP+SPX9epJ/nWNvY2AcDTUFYBAABgWSygAgAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABYFmUVAAAAlkVZBQAAgGVRVgEAAGBZlFUAAABY1v8HRG7Q79ZAzGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['greater than 3% of \\n population invovlved',\n",
    "          'less than 3% of \\n population invovlved']\n",
    "\n",
    "explode = [0.2,0.1] # for distance between the objects in plot\n",
    "\n",
    "plt.pie([high_cnt,low_cnt], # the two categories of protests\n",
    "        labels=labels,\n",
    "        pctdistance=0.75, # 'X%' distance\n",
    "        explode=explode, # distance between pies\n",
    "        autopct='%.0f%%') # show percentage\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.title(\"Protests which ended with failure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The pie chart shows that most of failed protests were less than 3% of total population \n",
    "is it possible that protests which involve higher than 3% of the population are less likely to fail?\n",
    " \n",
    " \n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a look at protests with greater than 3% of population and their success "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperate all capaigns by their success\n",
    "# already have fail_camp, now create suc = successed campaigns\n",
    "suc = camp.copy()\n",
    "suc = suc[suc.success == 1] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize with plot for easier understanding\n",
    "present a timeline from 1960 to 2013 showing successful and failed protests based on their % of population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xticks = [1960,1970,1980,1990,2000,2010]\n",
    "yticks = [3,10,20,30,40,50,60,70,80,90,100,110,120,130]\n",
    "\n",
    "plt.plot(suc.year,suc.perc_of_total,# show perc_of_total for each year\n",
    "         'go', # color and shape\n",
    "         linestyle=\"\", # no lines\n",
    "         label='Success') # successed campaigns\n",
    "\n",
    "plt.plot(fail_camp.year,fail_camp.perc_of_total,'ro',linestyle=\"\",label='Fail') # same for failed campaigns\n",
    "\n",
    "plt.axhline(y=3,label='3% limit') # 3% limit line\n",
    "plt.ylim(0,camp.perc_of_total.max()+3) # Y axis limits\n",
    "plt.title('failed/successfull protests and their size as % of population')\n",
    "plt.xlabel('Calendar Year') # x lable\n",
    "plt.ylabel('% of population') # y lable\n",
    "plt.xticks(xticks) # x ticks\n",
    "plt.yticks(yticks) # y ticks\n",
    "plt.style.use('default') # set to default\n",
    "plt.legend() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most of failed protests had less than 3% of population, only few of them had more than 3%.\n",
    "\n",
    "- Most of the protests with greater than 3% of population achieved final goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So based on the data, a campaign has more chances to achieve success if the number of participants is higher than 3% of the total population.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lets compare the current protests in the US with the data we have to see main differences\n",
    "\n",
    "#### How much is 3% of US population?\n",
    "\n",
    "According to [worldometer](https://www.worldometers.info/world-population/us-population/), \n",
    "US total population is aprox. 331,000,000, therefore 3% is around 9,900,000 protestors.\n",
    "\n",
    "#### Is it even possible to compare with the rest of the protests?\n",
    "\n",
    "The rise of social media has made protests more accessable, unbiased and immediate. Movements can now spread at very high speeds. If we count the activism in social media, the number of participants may be surprisingly high:\n",
    "\n",
    "\n",
    "- BLM movement worldwide - 5,000,000\n",
    "- Pew Research found that *#BlackLivesMatter* hashtag has been used roughly **47,800,000** times on Twitter\n",
    "- The social media streaming app TikTok has recently surpassed **10,500,000,000** views on the *#BlackLivesMatter* hashtag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a row for current protests in USA\n",
    "\n",
    "usa_most_participants = 10**10 # 10 billion\n",
    "usa_total_pop = 331000000\n",
    "\n",
    "# most_participants / total_population * 100 to show in %\n",
    "usa_perc = (usa_most_participants / usa_total_pop) * 100 # perc of total\n",
    "\n",
    "usa = {'camp_name':'George Floyd and Black Lives Matter Movement Worldwide', # name\n",
    "       'success':2, # still going\n",
    "       'most_participants':usa_most_participants, # participants\n",
    "       'total_population':usa_total_pop, # total population\n",
    "       'location':'Worldwide', # location\n",
    "       'year':2020,\n",
    "       'perc_of_total': usa_perc\n",
    "      }\n",
    "\n",
    "#append row to dataframe\n",
    "camp_b = camp.copy()\n",
    "camp_b = camp_b.append(usa, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new added protest has a very high ammount of participants\n",
    "#### It easily goes to top 1 if we sort by `most_participants` or  `perc_of_total`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>camp_name</th>\n",
       "      <th>success</th>\n",
       "      <th>most_participants</th>\n",
       "      <th>total_population</th>\n",
       "      <th>location</th>\n",
       "      <th>year</th>\n",
       "      <th>perc_of_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>George Floyd and Black Lives Matter Movement W...</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>331000000</td>\n",
       "      <td>Worldwide</td>\n",
       "      <td>2020</td>\n",
       "      <td>3021.148036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Solidarity</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>35574150</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1980</td>\n",
       "      <td>28.110299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Argentina pro-democracy movement</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000e+06</td>\n",
       "      <td>29262047</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1983</td>\n",
       "      <td>30.756563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>South Africa Second Defiance Campaign</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000e+06</td>\n",
       "      <td>38672607</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>10.343239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nigeria Anti-Military</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000e+06</td>\n",
       "      <td>105293700</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1994</td>\n",
       "      <td>3.324036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             camp_name  success  \\\n",
       "241  George Floyd and Black Lives Matter Movement W...        2   \n",
       "98                                          Solidarity        1   \n",
       "30                    Argentina pro-democracy movement        1   \n",
       "94               South Africa Second Defiance Campaign        1   \n",
       "3                                Nigeria Anti-Military        1   \n",
       "\n",
       "     most_participants  total_population      location  year  perc_of_total  \n",
       "241       1.000000e+10         331000000     Worldwide  2020    3021.148036  \n",
       "98        1.000000e+07          35574150        Poland  1980      28.110299  \n",
       "30        9.000000e+06          29262047     Argentina  1983      30.756563  \n",
       "94        4.000000e+06          38672607  South Africa  1992      10.343239  \n",
       "3         3.500000e+06         105293700       Nigeria  1994       3.324036  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camp_b.sort_values(by=['most_participants'],ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot of a timeline with max number of participants in campaings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "only_usa = camp_b[camp_b.camp_name == 'George Floyd and Black Lives Matter Movement Worldwide'] # to make different color\n",
    "\n",
    "plt.bar(fail_camp.year, fail_camp.most_participants,label = 'Unsuccessful')\n",
    "plt.bar(suc.year, suc.most_participants, label='Successful')\n",
    "plt.bar(only_usa.year, only_usa.most_participants,label=\"George Floyd & BLM Movement Worldwide\")\n",
    "plt.yscale('symlog',basey=10)\n",
    "plt.title(\"max of participants in protests\")\n",
    "plt.ylabel('number of participants')\n",
    "plt.xlabel('calendar years')\n",
    "plt.legend()\n",
    "plt.ylim(0,camp_b.most_participants.max())\n",
    "plt.style.use('default') # set to default\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Y axis scale is 'symlog' with base 10, the difference is almost uncomparable. It looks like an outlier.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simple visualization of protests throughout the years to show difference in sizes of protests\n",
    "just a nice way to see the differences in protests sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 5))\n",
    "fig.set_size_inches(15,5)\n",
    "# sns.set_style('ticks')\n",
    "labels = ['size = protest size','','fail','success']\n",
    "camp['randNumCol'] = np.random.uniform(0.1, 0.16, len(camp)) # generate dummy feature with random floats to use in the plot\n",
    "\n",
    "# plot\n",
    "chart = sns.scatterplot(x = 'year', # X axis = year\n",
    "                        y = 'randNumCol', # Y axis = random float (just for the visualization)\n",
    "                        hue ='success', # color = success/failure\n",
    "                        size=\"most_participants\", # size = campaign size\n",
    "                        sizes=(10, 11000), # sizes limits\n",
    "                        data=camp)\n",
    "\n",
    "sns.set_style('ticks') # style\n",
    "plt.xlim(1959,2014) # X limits\n",
    "plt.title('sizes of protests throughout the years')\n",
    "plt.ylabel('') # hide y label\n",
    "plt.yticks([]) # hide y axis\n",
    "plt.legend(loc=3, # legend location\n",
    "           labels=labels, \n",
    "           fontsize='large',\n",
    "           markerscale=0.5, # legend marker size\n",
    "           frameon=0, # legend frame off\n",
    "           ncol=2) # legend cols = 2\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}