SummaryStats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "import fastparquet\n",
    "import snappy\n",
    "\n",
    "%matplotlib inline\n",
    "import curbdata as cd\n",
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldays_timestamped = pd.read_parquet('TimestampToSep11new.parquet')\n",
    "alldays_timestamped.sort_index(level='Timestamp', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "format_dict = {'unrestricted spaces': ['Space 3'],\n",
    "              'key spaces': ['Space 1', 'Space 2', 'Space 3', 'SB bike lane'],\n",
    "              'location column': 'Vehicle Location',\n",
    "              'violator column': 'Violator',\n",
    "              'enf start/end': ('18:00', '22:00'),\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(cd);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_curb = cd.CurbData(alldays_timestamped, format_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on CurbData in module curbdata object:\n",
      "\n",
      "class CurbData(builtins.object)\n",
      " |  CurbData(timestamped_df, format_dict)\n",
      " |  \n",
      " |  Stores curb utilization data and associated functions\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, timestamped_df, format_dict)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __time_filter__(self)\n",
      " |      filters df to enforcement interval provided in format (as 24-hr time hh:mm)\n",
      " |  \n",
      " |  aggregate_activities(self, percent=False, lz_only=True)\n",
      " |      return counts and total time for each type of activity in subset interval\n",
      " |      also percent blocking, percent viol presence\n",
      " |  \n",
      " |  blk_viol_times(self, condition=None)\n",
      " |      returns dict of total times any blocker or violator present in interval\n",
      " |  \n",
      " |  blockers_exceed_viol(self)\n",
      " |      Returns a value (in minutes) of the amount of time that the number of blockers\n",
      " |      exceeds the number of violators. A relatively high number may suggest the zone is\n",
      " |      too small\n",
      " |  \n",
      " |  conditional(self, condition)\n",
      " |      prints and returns observed conditional of either 'Blocking' or 'Violator\n",
      " |  \n",
      " |  download_selection(self, download_all=False)\n",
      " |      Generates downloadable CSV from input df\n",
      " |      Total copy/paste job from \n",
      " |      https://stackoverflow.com/questions/31893930/download-csv-from-an-ipython-notebook\n",
      " |      Javascript allows for client-side CSV generation, avoids creating server-side CSV\n",
      " |      for each request\n",
      " |      Tends to generate two duplicate downloads on Firefox, but one on Safari. Have yet \n",
      " |      to test with Chrome. Likely ipython/Jupyter/browser quirk.\n",
      " |  \n",
      " |  subset(self, weekday=None, timestamps=None, verbose=False)\n",
      " |      returns new CurbData object containing specified subset. Can specify as timestamps\n",
      " |      = ('yyyy-mm-dd hh:mm:ss', 'yyyy-mm-dd hh:mm:ss'), or weekday = any day of week, \n",
      " |      Weekdays, Weekends, All Data.\n",
      " |  \n",
      " |  violator_timestamp_table(self)\n",
      " |      Generates time-indexed df counting number and type of violators present.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sm_curb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = sm_curb.subset(timestamps=('2019-09-01 18:00:01', '2019-09-01 19:00:01'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity Id</th>\n",
       "      <th>Begin Date</th>\n",
       "      <th>Begin Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Vehicle Location</th>\n",
       "      <th>Vehicle Type</th>\n",
       "      <th>Vehicle Characteristics</th>\n",
       "      <th>Transaction Characteristics</th>\n",
       "      <th>Bikeway Users Displaced</th>\n",
       "      <th>LZ Space Avail</th>\n",
       "      <th>Occupied while idle?</th>\n",
       "      <th>CNS?</th>\n",
       "      <th>TNC?</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Violator</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>1338</td>\n",
       "      <td>09/01/2019</td>\n",
       "      <td>5:56:56 PM</td>\n",
       "      <td>6:08:49 PM</td>\n",
       "      <td>Space 2</td>\n",
       "      <td>Car</td>\n",
       "      <td>None</td>\n",
       "      <td>Interaction with parking meter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>looked at the meter but still decided to park</td>\n",
       "      <td>0:11:53</td>\n",
       "      <td>Likely Non-Violator, CNS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>1340</td>\n",
       "      <td>09/01/2019</td>\n",
       "      <td>5:59:19 PM</td>\n",
       "      <td>6:00:11 PM</td>\n",
       "      <td>SB bike lane</td>\n",
       "      <td>Car</td>\n",
       "      <td>Blinkers on</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>0:00:52</td>\n",
       "      <td>Bike Lane Blocking, CNS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>1322</td>\n",
       "      <td>09/01/2019</td>\n",
       "      <td>5:00:00 PM</td>\n",
       "      <td>6:32:42 PM</td>\n",
       "      <td>Space 3</td>\n",
       "      <td>Car</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>1:32:42</td>\n",
       "      <td>Likely Non-Violator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>1321</td>\n",
       "      <td>09/01/2019</td>\n",
       "      <td>5:00:00 PM</td>\n",
       "      <td>6:08:26 PM</td>\n",
       "      <td>Space 1</td>\n",
       "      <td>Car</td>\n",
       "      <td>None</td>\n",
       "      <td>Interaction with parking meter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>1:08:26</td>\n",
       "      <td>Likely Non-Violator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>1339</td>\n",
       "      <td>09/01/2019</td>\n",
       "      <td>5:58:32 PM</td>\n",
       "      <td>6:01:04 PM</td>\n",
       "      <td>NB right turn pocket</td>\n",
       "      <td>Car</td>\n",
       "      <td>Blinkers on</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>0:02:32</td>\n",
       "      <td>Likely Non-Violator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Activity Id  Begin Date  Begin Time    End Time  \\\n",
       "Timestamp                                                              \n",
       "2019-09-01 18:00:02         1338  09/01/2019  5:56:56 PM  6:08:49 PM   \n",
       "2019-09-01 18:00:02         1340  09/01/2019  5:59:19 PM  6:00:11 PM   \n",
       "2019-09-01 18:00:02         1322  09/01/2019  5:00:00 PM  6:32:42 PM   \n",
       "2019-09-01 18:00:02         1321  09/01/2019  5:00:00 PM  6:08:26 PM   \n",
       "2019-09-01 18:00:02         1339  09/01/2019  5:58:32 PM  6:01:04 PM   \n",
       "\n",
       "                         Vehicle Location Vehicle Type  \\\n",
       "Timestamp                                                \n",
       "2019-09-01 18:00:02               Space 2          Car   \n",
       "2019-09-01 18:00:02          SB bike lane          Car   \n",
       "2019-09-01 18:00:02               Space 3          Car   \n",
       "2019-09-01 18:00:02               Space 1          Car   \n",
       "2019-09-01 18:00:02  NB right turn pocket          Car   \n",
       "\n",
       "                    Vehicle Characteristics     Transaction Characteristics  \\\n",
       "Timestamp                                                                     \n",
       "2019-09-01 18:00:02                    None  Interaction with parking meter   \n",
       "2019-09-01 18:00:02             Blinkers on                            None   \n",
       "2019-09-01 18:00:02                    None                            None   \n",
       "2019-09-01 18:00:02                    None  Interaction with parking meter   \n",
       "2019-09-01 18:00:02             Blinkers on                            None   \n",
       "\n",
       "                     Bikeway Users Displaced  LZ Space Avail  \\\n",
       "Timestamp                                                      \n",
       "2019-09-01 18:00:02                      NaN             NaN   \n",
       "2019-09-01 18:00:02                      0.0             0.0   \n",
       "2019-09-01 18:00:02                      NaN             NaN   \n",
       "2019-09-01 18:00:02                      NaN             NaN   \n",
       "2019-09-01 18:00:02                      NaN             0.0   \n",
       "\n",
       "                     Occupied while idle?  CNS?   TNC?  \\\n",
       "Timestamp                                                \n",
       "2019-09-01 18:00:02                 False   1.0  False   \n",
       "2019-09-01 18:00:02                 False   1.0  False   \n",
       "2019-09-01 18:00:02                 False   0.0  False   \n",
       "2019-09-01 18:00:02                 False   0.0  False   \n",
       "2019-09-01 18:00:02                  True   0.0  False   \n",
       "\n",
       "                                                             Notes Duration  \\\n",
       "Timestamp                                                                     \n",
       "2019-09-01 18:00:02  looked at the meter but still decided to park  0:11:53   \n",
       "2019-09-01 18:00:02                                           None  0:00:52   \n",
       "2019-09-01 18:00:02                                           None  1:32:42   \n",
       "2019-09-01 18:00:02                                           None  1:08:26   \n",
       "2019-09-01 18:00:02                                           None  0:02:32   \n",
       "\n",
       "                                     Violator  \n",
       "Timestamp                                      \n",
       "2019-09-01 18:00:02  Likely Non-Violator, CNS  \n",
       "2019-09-01 18:00:02   Bike Lane Blocking, CNS  \n",
       "2019-09-01 18:00:02       Likely Non-Violator  \n",
       "2019-09-01 18:00:02       Likely Non-Violator  \n",
       "2019-09-01 18:00:02       Likely Non-Violator  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.df_subset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Likely Non-Violator, CNS</th>\n",
       "      <th>Bike Lane Blocking, CNS</th>\n",
       "      <th>Likely Non-Violator</th>\n",
       "      <th>Bike Lane Blocking, TNC</th>\n",
       "      <th>Bike Lane Blocking</th>\n",
       "      <th>Likely Non-Violator, TNC</th>\n",
       "      <th>Violator</th>\n",
       "      <th>Total Violators</th>\n",
       "      <th>Total Blockers</th>\n",
       "      <th>Blockers Less Violators</th>\n",
       "      <th>Any Violator</th>\n",
       "      <th>Any Blocking</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
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       "      <th>2019-09-01 18:00:02</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-01 18:00:03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-01 18:00:04</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-01 18:00:05</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-01 18:00:06</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "                     Likely Non-Violator, CNS  Bike Lane Blocking, CNS  \\\n",
       "Timestamp                                                                \n",
       "2019-09-01 18:00:02                       2.0                      1.0   \n",
       "2019-09-01 18:00:03                       2.0                      1.0   \n",
       "2019-09-01 18:00:04                       2.0                      1.0   \n",
       "2019-09-01 18:00:05                       2.0                      1.0   \n",
       "2019-09-01 18:00:06                       2.0                      1.0   \n",
       "\n",
       "                     Likely Non-Violator  Bike Lane Blocking, TNC  \\\n",
       "Timestamp                                                           \n",
       "2019-09-01 18:00:02                  2.0                      NaN   \n",
       "2019-09-01 18:00:03                  2.0                      NaN   \n",
       "2019-09-01 18:00:04                  2.0                      NaN   \n",
       "2019-09-01 18:00:05                  2.0                      NaN   \n",
       "2019-09-01 18:00:06                  2.0                      NaN   \n",
       "\n",
       "                     Bike Lane Blocking  Likely Non-Violator, TNC  Violator  \\\n",
       "Timestamp                                                                     \n",
       "2019-09-01 18:00:02                 NaN                       NaN       NaN   \n",
       "2019-09-01 18:00:03                 NaN                       NaN       NaN   \n",
       "2019-09-01 18:00:04                 NaN                       NaN       NaN   \n",
       "2019-09-01 18:00:05                 NaN                       NaN       NaN   \n",
       "2019-09-01 18:00:06                 NaN                       NaN       NaN   \n",
       "\n",
       "                     Total Violators  Total Blockers  Blockers Less Violators  \\\n",
       "Timestamp                                                                       \n",
       "2019-09-01 18:00:02              0.0             1.0                      1.0   \n",
       "2019-09-01 18:00:03              0.0             1.0                      1.0   \n",
       "2019-09-01 18:00:04              0.0             1.0                      1.0   \n",
       "2019-09-01 18:00:05              0.0             1.0                      1.0   \n",
       "2019-09-01 18:00:06              0.0             1.0                      1.0   \n",
       "\n",
       "                     Any Violator  Any Blocking  \n",
       "Timestamp                                        \n",
       "2019-09-01 18:00:02             0             1  \n",
       "2019-09-01 18:00:03             0             1  \n",
       "2019-09-01 18:00:04             0             1  \n",
       "2019-09-01 18:00:05             0             1  \n",
       "2019-09-01 18:00:06             0             1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.violator_timestamp_table().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'blockers_exceed': 7.833333333333333, 'total_minutes': 59.96666666666667}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.blockers_exceed_viol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 43 minutes that at least one violator was parked in the loading zone, the bike lane was blocked for 13 minutes (30 % of the time!)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'block_sec': 804.0, 'block_min': 13, 'viol_sec': 2634.0, 'viol_min': 43}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.conditional('Violator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 21 minutes the bike lane was blocked in the study period, at least one violator was parked in the loading zone for 13 minutes (61 % of the time!)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'block_sec': 1274.0, 'block_min': 21, 'viol_sec': 804.0, 'viol_min': 13}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.conditional('Blocking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mondays = sm_curb.subset(weekday='Monday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out of the 555 minutes that at least one violator was parked in the loading zone, the bike lane was blocked for 66 minutes (11 % of the time!)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'block_sec': 3965.0, 'block_min': 66, 'viol_sec': 33343.0, 'viol_min': 555}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mondays.conditional('Violator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'blockers_exceed': 24.866666666666667, 'total_minutes': 239.96666666666667}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mondays.blockers_exceed_viol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'blockers_exceed': 7.833333333333333, 'total_minutes': 59.96666666666667}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.blockers_exceed_viol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Total Time</th>\n",
       "      <th>Activity Count</th>\n",
       "      <th>Bikeway Users Displaced</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator Classification</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking</th>\n",
       "      <td>00:09:50</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, CNS</th>\n",
       "      <td>01:16:39</td>\n",
       "      <td>20</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, TNC</th>\n",
       "      <td>00:12:41</td>\n",
       "      <td>23</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator</th>\n",
       "      <td>01:33:13</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, CNS</th>\n",
       "      <td>04:22:41</td>\n",
       "      <td>41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, TNC</th>\n",
       "      <td>00:24:07</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator</th>\n",
       "      <td>14:42:04</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator, CNS</th>\n",
       "      <td>00:15:04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Total Time  Activity Count  Bikeway Users Displaced\n",
       "Violator Classification                                                     \n",
       "Bike Lane Blocking         00:09:50              10                      4.0\n",
       "Bike Lane Blocking, CNS    01:16:39              20                     17.0\n",
       "Bike Lane Blocking, TNC    00:12:41              23                      7.0\n",
       "Likely Non-Violator        01:33:13              33                      0.0\n",
       "Likely Non-Violator, CNS   04:22:41              41                      0.0\n",
       "Likely Non-Violator, TNC   00:24:07              16                      0.0\n",
       "Violator                   14:42:04              12                      0.0\n",
       "Violator, CNS              00:15:04               1                      0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mondays.aggregate_activities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Total Time</th>\n",
       "      <th>Activity Count</th>\n",
       "      <th>Bikeway Users Displaced</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator Classification</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking</th>\n",
       "      <td>00:04:41</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, CNS</th>\n",
       "      <td>00:18:35</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, TNC</th>\n",
       "      <td>00:01:28</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator</th>\n",
       "      <td>00:12:19</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, CNS</th>\n",
       "      <td>00:32:19</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, TNC</th>\n",
       "      <td>00:00:37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator</th>\n",
       "      <td>01:20:12</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Total Time  Activity Count  Bikeway Users Displaced\n",
       "Violator Classification                                                     \n",
       "Bike Lane Blocking         00:04:41               3                      6.0\n",
       "Bike Lane Blocking, CNS    00:18:35               6                      9.0\n",
       "Bike Lane Blocking, TNC    00:01:28               5                      1.0\n",
       "Likely Non-Violator        00:12:19               3                      0.0\n",
       "Likely Non-Violator, CNS   00:32:19               7                      0.0\n",
       "Likely Non-Violator, TNC   00:00:37               2                      0.0\n",
       "Violator                   01:20:12               2                      0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.aggregate_activities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Percent Time</th>\n",
       "      <th>Percent Activities</th>\n",
       "      <th>Percent Bikeway Users Displaced</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator Classification</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking</th>\n",
       "      <td>0.031184</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, CNS</th>\n",
       "      <td>0.123738</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.5625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Lane Blocking, TNC</th>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>0.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator</th>\n",
       "      <td>0.082011</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, CNS</th>\n",
       "      <td>0.215181</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likely Non-Violator, TNC</th>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Violator</th>\n",
       "      <td>0.534014</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Percent Time  Percent Activities  \\\n",
       "Violator Classification                                      \n",
       "Bike Lane Blocking            0.031184            0.107143   \n",
       "Bike Lane Blocking, CNS       0.123738            0.214286   \n",
       "Bike Lane Blocking, TNC       0.009766            0.178571   \n",
       "Likely Non-Violator           0.082011            0.107143   \n",
       "Likely Non-Violator, CNS      0.215181            0.250000   \n",
       "Likely Non-Violator, TNC      0.004106            0.071429   \n",
       "Violator                      0.534014            0.071429   \n",
       "\n",
       "                          Percent Bikeway Users Displaced  \n",
       "Violator Classification                                    \n",
       "Bike Lane Blocking                                 0.3750  \n",
       "Bike Lane Blocking, CNS                            0.5625  \n",
       "Bike Lane Blocking, TNC                            0.0625  \n",
       "Likely Non-Violator                                0.0000  \n",
       "Likely Non-Violator, CNS                           0.0000  \n",
       "Likely Non-Violator, TNC                           0.0000  \n",
       "Violator                                           0.0000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.aggregate_activities(percent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "smc_plot = cd.CurbDataPlotter(pd.read_parquet('TimestampToSep11new.parquet'), format_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on CurbDataPlotter in module curbdata object:\n",
      "\n",
      "class CurbDataPlotter(CurbData)\n",
      " |  CurbDataPlotter(timestamped_df, format_dict)\n",
      " |  \n",
      " |  Stores curb utilization data and associated functions\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      CurbDataPlotter\n",
      " |      CurbData\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, timestamped_df, format_dict)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __make_palette__(self)\n",
      " |      #DONE more color-blind safe colors to break out TNC/CNS blocking?\n",
      " |      #Also further abstraction concurrent with timestamper improvements.\n",
      " |  \n",
      " |  __make_widgets__(self)\n",
      " |  \n",
      " |  interactive_plot(self)\n",
      " |      Using ipython widgets, generates interactive plot enabling plot type and \n",
      " |      subset specification.\n",
      " |  \n",
      " |  time_activity_plot(self, percent=True)\n",
      " |      Generate plot aggregating the total number of activities and amount of time\n",
      " |      for each user/violator type in subset interval. Can specify percent=false for\n",
      " |      absolute counts instead of stacked percentage.\n",
      " |  \n",
      " |  time_occ_plot(self, save=False)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from CurbData:\n",
      " |  \n",
      " |  __time_filter__(self)\n",
      " |      filters df to enforcement interval provided in format (as 24-hr time hh:mm)\n",
      " |  \n",
      " |  aggregate_activities(self, percent=False, lz_only=True)\n",
      " |      return counts and total time for each type of activity in subset interval\n",
      " |      also percent blocking, percent viol presence\n",
      " |  \n",
      " |  blk_viol_times(self, condition=None)\n",
      " |      returns dict of total times any blocker or violator present in interval\n",
      " |  \n",
      " |  blockers_exceed_viol(self)\n",
      " |      Returns a value (in minutes) of the amount of time that the number of blockers\n",
      " |      exceeds the number of violators. A relatively high number may suggest the zone is\n",
      " |      too small\n",
      " |  \n",
      " |  conditional(self, condition)\n",
      " |      prints and returns observed conditional of either 'Blocking' or 'Violator\n",
      " |  \n",
      " |  download_selection(self, download_all=False)\n",
      " |      Generates downloadable CSV from input df\n",
      " |      Total copy/paste job from \n",
      " |      https://stackoverflow.com/questions/31893930/download-csv-from-an-ipython-notebook\n",
      " |      Javascript allows for client-side CSV generation, avoids creating server-side CSV\n",
      " |      for each request\n",
      " |      Tends to generate two duplicate downloads on Firefox, but one on Safari. Have yet \n",
      " |      to test with Chrome. Likely ipython/Jupyter/browser quirk.\n",
      " |  \n",
      " |  subset(self, weekday=None, timestamps=None, verbose=False)\n",
      " |      returns new CurbData object containing specified subset. Can specify as timestamps\n",
      " |      = ('yyyy-mm-dd hh:mm:ss', 'yyyy-mm-dd hh:mm:ss'), or weekday = any day of week, \n",
      " |      Weekdays, Weekends, All Data.\n",
      " |  \n",
      " |  violator_timestamp_table(self)\n",
      " |      Generates time-indexed df counting number and type of violators present.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from CurbData:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from CurbData:\n",
      " |  \n",
      " |  __slotnames__ = []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(smc_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_sub = smc_plot.subset(timestamps=('2019-09-01 18:00:01', '2019-09-01 19:30:01'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub.time_occ_plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub.time_activity_plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YMNf/LwA7AVxmjKWYyyhOfyUDeJuErcS9IUxTmtt1aG3cmZ7vkxCmcZeI/fksgJH4xVr4IPgQgvne3BolOwBvimPgWQiK9b9ExfA9AO8Skad4fnyJaBggrIEgYbvuQAt1JgBYSEQ+ROQLYAEkO5uIKEQsww3CwuhLjLH/iNGm52o3gNFE1J+EtWgrAPyf0TIlWtUcIDz87MRzJJeUdURSrxuEsfMVY+wvFuR+RRyTCgjr+KRyy8W6ZABkYl0yMc6RiALFvuoKweq4if2yPshcXX8loieJSAth7H8A1FsvfgDwF/HcBELYFNBoytWa3OJ05AEAK4nIiYgGif3SrG9HiRbOcAhLC2zNY2vdUyGc0wrTMqQQ0Suidc44Zb4UwH/EujIgKDdviOdlPIR702eWyrMg80UAz6LhLjfObxVm4+pm2L5LSy6JvwbgWcnxbgB/kRxPgbCYrhTCm/77Fup2hnChlUGwGESJdfUQ4z+AZKcAhAF+TXI8AsJ2xCZ3aUFY05APoJ1JuAOE+fAI8XgMhLe2MggLpIeL4c+I9dzBL7sN6uUUj/0gKDP/ttSX4vEKCIrPXQhrQ6ah4S6tnhBumLchPJC/gqCctYewduOO2K+nAQy00K/TIOyA+ofYN9kAhpmkMcr7Nyvjww7AnyG8qZdCeBOeIYmfCOENsQKCorAFv+yacoOwe+maKEcqxB1GZtpN4jnqaEaGZ8X+XmoSrkTDXVq+EJTGYgjTk7MsjGlr404FYYfLXYi71yAsqvxabEcGgDGSsj+AhR0tZtpyAhZ2EopxuSZhpru0LgOYZDKG34GwdsN4fl4V4/xFeZ9s4txuEMdUsVgOSeL/T8xfAmFNmqckXwEAd5PypkCwCFZAeIB1kMTNFPtY+ntPjNsPYZ2GMe0MNNzNaPz5SNIshrAupBTCy0p7SdxbZur6ixingHBvqoAw3up3DlroI0cIUzhlEKbX55nEd4GgnJWLY066A+9ZAHdN0jclt/FTABXieX7JdBxYkLEHxJkeM3EjYGWXlrW6xXgnUeb/MZN3KiQ7+iAo77fEsn4GsAaAvSS+G4RrqRLC5hHpLrgGu7TMXQsW4szu/uO/38aPxEHA4ZhFnCK5BeApxpjVaYdHIE8YhC3/YS0tS0tBRAkQrChvSsKGQFAMlPdR3jQIn3H464OSUSy3P4D1jLH+D6Cs9hCm55SMMatrcDgcDscUufUknN84sQBOtwZlR8LylhagpSDBTckfAPR6UGUyxj54UGWZUAfhUxEPAgWEb7pwZYfD4dwXXOHhWISI8iBMIY1uYVHqYYydsp6qbUJEbwOYDWFa7Iq19C0NExa9P6iybkJYR8PhcDj3BZ/S4nA4HA6H0+ZpyV1aHA6Hw+FwOI8ErvBwOBwOh8Np83CFh8PhcDgcTpuHKzwcDofD4XDaPFzh4XA4HA6H0+bhCg+Hw+FwOJw2D1d4OBwOh8PhtHm4wsPhcDgcDqfNwxUeDofD4XA4bR6u8HA4HA6Hw2nzcIWHw+FwOBxOm4crPBwOh8PhcNo8XOHhcDgcDofT5uEKD4fD4XA4nDaPvKUFaA14eHgwpVLZ0mJw2ihnzpwpZIx5tkTdfGxzHibWxvaZM2c6yuXy9wAEgb9gcx4udQDSDQbDzD59+twyl4ArPACUSiVSUlJaWgxOG4WILrdU3Xxscx4m1sa2XC5/r1OnTgGenp537Ozs2KOSi/Pbo66ujm7fvq29efPmewBGmUvDNW4Oh8PhPCyCPD09S7myw3nY2NnZMU9PzxII1kTzaR6hPBwOh8P5bWHHlR3Oo0Icaxb1Gq7wcDgcDofDafPwNTwcDofDeSRMmjSpz4Ms7+OPPz5jLY1MJuujUqkqGWOQyWRs8+bNV4YOHVqRl5fXbtasWV2SkpIubdmyxT0lJeWJhISEK82V4fDhwy4bNmzwOn78+MX7a0XzsNSerKys9hEREaqcnJzzzS2zc+fOvVJSUi54e3sbpOEhISGa1NTUzAcnfcvCFR4Oh8PhtFns7e3rMjMzMwBg3759rsuWLfMdOnRollKprElKSrrU0vI1F0vteRh1tSVlB+BTWhxOq4OI3ieiW0SULglTENExIsoR/3ZoSRk5nMeRkpISmZubmwEAsrKy2qtUqkDTNHv37nULDg7W5Ofny2/cuCEfPnx496CgoICgoKCAo0ePPmFrXYsWLfIOCgoKUKlUgRMnTuxaV1cHAAgLC+sZGxvbuVevXgFKpTIoKSnJGQAMBgNiYmJ8g4KCAtRqtXbdunUezWmPFL1eT+PHj1eq1WptQECA9tChQy7GOqKjo33VarVWrVZrV61a1VGar7y8nAYNGqTasGGDBwA4OTmFAIIVKywsrOeIESO6+fv7B44aNcrf2J7ExEQ3f3//wD59+vScNm1al/Dw8B629tGjptVbeIjofQARAG4xxoJM4hYBWAfAkzFWKIa9BmAGgFoAcxljXzxikTmcX8sHAP4BIEES9mcA/2GMvUNEfxaPl7aAbBzOY0V1dbWdRqPRVldXU2FhYbvPP/8821LahISEJzdv3ux17NixHE9Pz9qRI0f6x8XFFQwfPrw8Jyen/fDhw1WXLl2yacpo8eLFt9avX58PAKNHj/bfu3ev26RJk0oAwGAw0Llz5y4kJia6rVixwmfEiBHZ7777roebm1ttenr6hcrKSurbt69m5MiRpRqN5l5z27NmzZqOAJCdnZ2Rmprq8MILL6hyc3PT//73v3tcvnzZ/vz58xnt2rVDQUGBzJintLTUbty4cd0mTZpUNHv27CLTMi9cuOD4008/XVIqlTV9+vTRHDt2zHnQoEEV8+bN63rixIlMjUZzb+TIkf629E1L0eoVHpi/+YOIugAYCuCKJEwLYAKAQAA+AL4kIjVjrPaRScvh/EoYY98QkdIk+A8AnhX/3wXgBLjCw+FYRToF9OWXXz4xffp0/+zs7EZKS3JysktaWprT8ePHsxUKRR0AfP/99645OTmOxjTl5eWyO3fu2HXo0KHOWr1Hjhxx2bhxY6eqqiq7u3fvyrVabSWAEgCIjIy8AwD9+/evWLx4cXtRNtfMzEyngwcPdgCAsrIyWUZGhoOpwmNLe5KTk53nzJlzCwBCQkKqfHx87p07d87hq6++cp01a9btdu3aAQC8vLzqn42jRo3qMX/+/JuxsbHF5trTq1eviu7du9cAQGBgoD43N7e9i4tLbZcuXaqNMk6YMKH4vffea5GPrNpCq1d4LNz8AWATgCUADkjC/gBgL2OsGsDPRHQRQBiAHx62nBzOQ8aLMZYPAIyxfCLqaC0Dh8NpyJAhQyru3Lkjz8/Pb/Ts8/Pzq75y5Yp9enq6w+DBg/UAwBhDSkrKBWdn52Ztrdfr9bRw4cKuJ0+ezOjRo0dNXFycT1VVVf0SEgcHBwYAcrkctbW1JNZFGzZsuDJu3LjSX9sexsyLyxgDEZmN7Nu3b3lSUpJbTExMsZ1d49Uu9vb29flkMhkMBgNZqqe10uoVHnMQ0SgA1xljaUQkjeoM4EfJ8TUxzFwZ0QCiAcDPz+8hScr5tZwcR9YTifTb93hdfA+Ltja2165da3Pabt262ZRu/Pjx9ysO5zEmNTXVoa6uDl5eXoby8vL20jhfX997mzZtujp27NgeiYmJuaGhoVUDBw4sXbNmTceVK1cWAEBycrJj//79K63Vo9fr7QCgU6dOhpKSErtDhw51GDly5J2m8gwdOrRk69atnhEREWX29vbs7Nmz9kqlssbV1dWiNclSewYOHFi+e/duxahRo8rOnj1rn5+f3753795VQ4YMKd22bZvn73//+zLjlJbRyrNu3boby5Yt854yZYrfRx99ZNNuNZ1OV3X16lX7rKys9j179ryXmJiosCVfS/HYKTxE5ATgdQDDzEWbCTP7FGSMbQewHQBCQ0P5k5LT2ikgIm/RuuMNwKyvGICPbU7rxZZt5A8a45oXQLBwbN26NU8uN//o0+l01QkJCZdeeuml7gcPHry4ffv2qzNnzvRTq9Xa2tpa6tevX1n//v0bKQM//PCDq5eXV2/j8UcffZQ7efLk21qtNtDX1/eeTqersCbnggULCvPy8ux79eoVwBgjhUJR8/nnn+feT3uWLFlya8qUKV3VarVWJpMhPj4+z9HRkS1YsOB2dna2vUajCZTL5Wzq1Km3ly1bdtuYb8eOHVdffPFF5axZs3y3bdt2zZrMzs7ObOPGjZdHjBihUigUhpCQEKvtbEkeC5OUOKV1mDEWRES9APwHgF6M9gVwA8LU1XQAYIy9Leb7AsCbjLEmp7RCQ0MZ9zfUOmkLFh4iOsMYC21mHiXEMS8erwNQJFm0rGCMLbFWTlsY29zC03qxNrbT0tLydDpd4aOUifNoKSkpsXNzc6urq6tDVFSUn0qlqlq+fLnFF7KHTVpamodOp1Oai3vstqUzxs4xxjoyxpSMMSWEaaunGGM3ARwEMIGI7InIH4AKwKkWFJfDaTZEtAfCurOeRHSNiGYAeAfAUCLKgbBY/52WlJHD4XAA4N133/XQaDRalUoVWFpaKouLi2u1Cm6rn9ISb/7PAvAgomsAljPGdphLyxg7T0SfAMgAYADwKt+hxXncYIxNtBD1u0cqCIfD4Vhh+fLlt1rSotMcWr3C08TN3xivNDleBWDVw5SJw+FwOBzO48VjN6XF4XA4HA6H01y4wsPhcDgcDqfNwxUeDofD4XA4bZ5Wv4aHw+FwOG2DtWvX9nmQ5S1ZssTqd31kMlkflUpVyRiDTCZjmzdvvjJ06NCKvLy8drNmzeqSlJR0acuWLe4pKSlPJCQk2PTBPSmHDx922bBhg9fx48cv3l8rmoel9mRlZbWPiIhQ5eTk2OTrS0rnzp17paSkXPD29m7giDQkJETzMDymnz171n7OnDldfv75Zwe5XM40Gk1lfHz8lbS0NMeRI0eqP/roo4tGv2Ph4eE9Fi5cWBAREVG2Z88etxUrVnSuq6uDwWCgWbNmFSxevNjmXWFc4eFwOBxOm0Xqe2rfvn2uy5Yt8x06dGiWUqmsSUpKutTS8jUXS+15GHU9DGVHr9fTyJEjVW+//fZVo1Jz6NAhl5s3b8oBwMvLq2bNmjXexjgj1dXVNG/evK4//PDDhe7du9dUVlZSdnZ2e3N1WIJPaXE4HA7nN0FJSYnMzc3NAABZWVntVSpVoGmavXv3ugUHB2vy8/PlN27ckA8fPrx7UFBQQFBQUMDRo0efsLWuRYsWeQcFBQWoVKrAiRMndq2rEzxEhIWF9YyNje3cq1evAKVSGZSUlOQMAAaDATExMb5BQUEBarVau27dOo/mtEeKXq+n8ePHK9VqtTYgIEB76NAhF2Md0dHRvmq1WqtWq7WrVq1q4JOvvLycBg0apNqwYYMHADg5OYUAghUrLCys54gRI7r5+/sHjho1yt/YnsTERDd/f//APn369Jw2bVqX8PDwHk3JvH37dsVTTz1VLlVoRo4cWda3b98qAAgICNC7uLjUfvbZZ67SfHfv3rUzGAzk5eVlAABHR0em0+mqrfWRFG7h4XA4HE6bxeiKobq6mgoLC9t9/vnn2ZbSJiQkPLl582avY8eO5Xh6etaOHDnSPy4urmD48OHlOTk57YcPH666dOmSTVNGixcvvrV+/fp8ABg9erT/3r173YwPeYPBQOfOnbuQmJjotmLFCp8RI0Zkv/vuux5ubm616enpFyorK6lv376akSNHlpp6S7elPWvWrOkIANnZ2RmpqakOL7zwgio3Nzf973//u8fly5ftz58/n2H0pWXMU1paajdu3LhukyZNKpo9e3aRaZkXLlxw/Omnny4plcqaPn36aI4dO+Y8aNCginnz5nU9ceJEpkajuTdy5Eh/a/2Snp7u+NRTT+mbSvOXv/wl/69//WvnMWPG1DtS9fLyqh06dOhdPz+/3gMGDCh94YUXSqKjo4tlMllTRTWAKzwcDofDabNIp4C+/PLLJ6ZPn+6fnZ3dSGlJTk52SUtLczp+/Hi2QqGoA4Dvv//eNScnx9GYpry8XHbnzh27Dh06WHToaeTIkSMuGzdu7FRVVWV39+5duVarrQRQAgCRkZF3AKB///4Vixcvbi/K5pqZmel08ODBDgBQVmIoxbMAACAASURBVFYmy8jIcDBVeGxpT3JysvOcOXNuAUBISEiVj4/PvXPnzjl89dVXrrNmzbrdrl07AIISYcwzatSoHvPnz78ZGxtbbK49vXr1qujevXsNAAQGBupzc3Pbu7i41Hbp0qXaKOOECROK33vvPU9rfWONESNGlP/1r3+F0fplJDEx8fKpU6duHTlyxGXLli2dvvzyS9d9+/bl2VouV3g4HA6H85tgyJAhFXfu3JHn5+c3evb5+flVX7lyxT49Pd1h8ODBekBwzpmSknLB2dm5WY769Ho9LVy4sOvJkyczevToURMXF+dTVVVVv4TEwcGBAYBcLkdtbS2JddGGDRuujBs3rtRSuba2x5KPTMYYiMhsZN++fcuTkpLcYmJiiu3sGq92sbe3r88nk8lgMBjuyxdnYGBg1TfffONsLd1rr72Wv2rVKm+5XN6gkrCwsMqwsLDK6Ojo4h49evQCkGdr3XwND4fD4XB+E6SmpjrU1dXBuA5Eiq+v7719+/ZdnD59un9KSooDAAwcOLDUOD0EAMnJyY6m+cyh1+vtAKBTp06GkpISu0OHDnWwlmfo0KElW7du9ayuriZA2MlUWlra5DPaUnsGDhxYvnv3boWxnPz8/Pa9e/euGjJkSOm2bds8a2pqAKDBlNa6detuKBQKw5QpU/xsaSMA6HS6qqtXr9pnZWW1B4DExESFMe748eNOY8aMUZrmeeWVV4rOnDnjvHfvXjdj2L/+9S/XU6dONejbsWPHlpaUlMguXLjgBAhOSg8fPuxijD958qSjj49PA+uXNbiFh8PhcDiPBFu2kT9ojGteAMHCsXXr1jy53PyjT6fTVSckJFx66aWXuh88ePDi9u3br86cOdNPrVZra2trqV+/fmX9+/dvtHX9hx9+cPXy8uptPP7oo49yJ0+efFur1Qb6+vre0+l0FdbkXLBgQWFeXp59r169AhhjpFAoaj7//PPc+2nPkiVLbk2ZMqWrWq3WymQyxMfH5zk6OrIFCxbczs7OttdoNIFyuZxNnTr19rJly24b8+3YsePqiy++qJw1a5bvtm3brlmT2dnZmW3cuPHyiBEjVAqFwhASElLfzry8PHtHR8dGJiBnZ2d24MCBi3Pnzu2ydOnSLnK5nAUEBFRu3br1yq1btxo0ZOnSpfkvv/xyDwCoq6vDunXrvGbPnt3VwcGhzsnJqW7Hjh0/W5NRyn2ZpNoaoaGhLCUlpaXF4Jjh5DiyOW2/fa1zLBPRGcZYaEvU3RbG9tq1a21O261bN5vSjR8//n7F4UiwNrbT0tLydDpdq/Wezfn1lJSU2Lm5udXV1dUhKirKT6VSVS1fvvxWTEyM7x//+Meifv36VT5KedLS0jx0Op3SXBy38HA4HA6Hw7kv3n33XY89e/Z41NTUUGBgoD4uLq4QAOLj461aiB41XOHhcDgcDodzXyxfvvzW8uXLb7W0HLbAFy1zOBwOh8Np83CFh8PhcDgcTpuHKzwcDofD4XDaPFzh4XA4HA6H0+bhi5Y5HA6H80j417/+1edBljd+/Hir3/WRyWR9VCpVJWMMMpmMbd68+crQoUMr8vLy2s2aNatLUlLSpS1btrinpKQ8kZCQ0OgbO9Y4fPiwy4YNG7yOHz9+8f5a0TwstScrK6t9RESEKicnxyZfX1I6d+7cKyUl5YK3t3eDDxiGhIRoHqTH9FOnTjlGRUX5A0B+fn57Z2fnWhcXl1qFQmHYuXNnnkaj6fXWW29dff31128BQFRUlF9oaGjF3LlziwDgjTfe8Prwww895HI57Ozs2Jw5cwrM+f2yBLfwcDgcDqfNYvQ9lZWVlbFy5crry5Yt8wUApVJZk5SUdKml5WsultrzMHiQyg4guIXIzMzMyMzMzBgyZMjdt95661pmZmZGcnJyNgAoFApDfHx8x6qqqkYfYFu7dq3nV1995XrmzJkLOTk555OTk7Oa+x3BVq/wENH7RHSLiNIlYeuIKJOIzhLRZ0T0pCTuNSK6SERZRDS8ZaTmcDgcTmujpKRE5ubmZgCArKys9iqVKtA0zd69e92Cg4M1+fn58hs3bsiHDx/ePSgoKCAoKCjg6NGjT9ha16JFi7yDgoICVCpV4MSJE7vW1Qn+RsPCwnrGxsZ27tWrV4BSqQwyOsg0GAyIiYnxDQoKClCr1dp169Z5NKc9UvR6PY0fP16pVqu1AQEB2kOHDrkY64iOjvZVq9VatVqtXbVqVUdpvvLycho0aJBqw4YNHgDg5OQUAghWrLCwsJ4jRozo5u/vHzhq1Ch/Y3sSExPd/P39A/v06dNz2rRpXcLDw3vY2kemKBQKw8CBA8v+93//1900btOmTZ3i4+OvGB27uru7186ZM8dm6w7weExpfQDgHwASJGHHALzGGDMQ0RoArwFYSkRaABMABALwAfAlEakZY7XgcDgczm8OoyuG6upqKiwsbPf5559nW0qbkJDw5ObNm72OHTuW4+npWTty5Ej/uLi4guHDh5fn5OS0Hz58uOrSpUs2TRktXrz41vr16/MBYPTo0f579+51mzRpUgkAGAwGOnfu3IXExES3FStW+IwYMSL73Xff9XBzc6tNT0+/UFlZSX379tWMHDmy1NRbui3tMfr/ys7OzkhNTXV44YUXVLm5uel///vfPS5fvmx//vz5jHbt2jXwpVVaWmo3bty4bpMmTSoyN0104cIFx59++umSUqms6dOnj+bYsWPOgwYNqpg3b17XEydOZGo0mnsjR470t6VvmuKNN97If/7551Xz5s2r/0L3nTt37CoqKmSBgYHVv6bsVq/wMMa+ISKlSdhRyeGPAIzfif8DgL2MsWoAPxPRRQBhAH54BKJyOBwOp5VhnAICgC+//PKJ6dOn+2dnZzdSWpKTk13S0tKcjh8/nm20Inz//feuOTk59U4ty8vLZXfu3LHr0KFDnbV6jxw54rJx48ZOVVVVdnfv3pVrtdpKACUAEBkZeQcA+vfvX7F48eL2omyumZmZTgcPHuwAAGVlZbKMjAwHU4XHlvYkJyc7z5kz5xYAhISEVPn4+Nw7d+6cw1dffeU6a9as2+3atQMAeHl51RsDRo0a1WP+/Pk3Y2Nji821p1evXhXdu3evAYDAwEB9bm5uexcXl9ouXbpUG2WcMGFC8XvvvedprW+aQqPR3AsODq6Ij4+vd0Qqenn/NcUCeAwUHhv4I4BE8f/OEBQgI9fEsEYQUTSAaADw87PZOSyHw+FwHlOGDBlScefOHXl+fn6jZ5+fn1/1lStX7NPT0x0GDx6sB4QHbUpKygVnZ+dmLRbR6/W0cOHCridPnszo0aNHTVxcnE9VVVX9EhIHBwcGAHK5HLW1tSTWRRs2bLgybty40l/bHktrW0TFwWxk3759y5OSktxiYmKK7ewar3axt7evzyeTyWAwGB6aL8433njj5osvvti9X79+ZQCgUCjqHB0d6zIyMtprtdpmeUiX0urX8DQFEb0OwADgI2OQmWRmzwhjbDtjLJQxFurp+asUUg6Hw+E8BqSmpjrU1dXBy8ur0boXX1/fe/v27bs4ffp0/5SUFAcAGDhwYKlxeggAkpOTHU3zmUOv19sBQKdOnQwlJSV2hw4d6mAtz9ChQ0u2bt3qWV1dTQBw9uxZ+9LS0iaf0ZbaM3DgwPLdu3crjOXk5+e37927d9WQIUNKt23b5llTUwMADaa01q1bd0OhUBimTJliswVAp9NVXb161T4rK6s9ACQmJtZbZY4fP+40ZswYpa1lSQkJCalSqVSV//nPf9yMYfPnz8+fNWtW1+LiYjsAKC4utlu/fr3VdU5SHlsLDxFNBRAB4HfsFzXzGoAukmS+AG48atk4HA6H0xhbtpE/aIxrXgDBwrF169Y8udz8o0+n01UnJCRceumll7ofPHjw4vbt26/OnDnTT61Wa2tra6lfv35l/fv3b7R1/YcffnD18vLqbTz+6KOPcidPnnxbq9UG+vr63tPpdBXW5FywYEFhXl6efa9evQIYY6RQKGo+//zz3Ptpz5IlS25NmTKlq1qt1spkMsTHx+c5OjqyBQsW3M7OzrbXaDSBcrmcTZ069fayZctuG/Pt2LHj6osvvqicNWuW77Zt26w6/3R2dmYbN268PGLECJVCoTCEhITUtzMvL8/e0dHxvk1Af/3rX/MHDBiglbTpdnl5ud1TTz2lbdeuHZPL5WzOnDk3m1PmQzNJPUjENTyHGWNB4vEIABsB/A9j7LYkXSCAjyGs2/EB8B8AKmuLlkNDQ1lKSsrDEZ7zqzg5zvZ52377WudYJqIzjLHQlqi7LYzttWvX2py2W7duNqUbP3689UQcq1gb22lpaXk6na7QUjzn8aekpMTOzc2trq6uDlFRUX4qlapq+fLlt2JiYnz/+Mc/FvXr16/yUcqTlpbmodPplObiWr2Fh4j2AHgWgAcRXQOwHMKuLHsAx8SFTD8yxmYxxs4T0ScAMiBMdb3Kd2hxOBwOh/NwePfddz327NnjUVNTQ4GBgfq4uLhCAIiPj7dqIXrUtHqFhzE20UzwjibSrwKw6uFJxOFwOBwOBwCWL19+a/ny5bdaWg5beKwXLXM4HA6Hw+HYAld4OBwOh8PhtHm4wsPhcDgcDqfNwxUeDucxgogWENF5Ikonoj1E5NDSMnE4HM7jQKtftMzhcASIqDOAuQC0jLFKcUfiBAj+5jicVs+HJ4v6PMjypvRzt/pdHycnpxC9Xp8qDVu7dq2nk5NT3ezZs4vCwsJ6rl+//qrx68rWaG76zp079woKCtJ/8cUXuQCwc+fODocPH3bbt29fni35LZGVldV+8ODBmhs3bpyVyeq/HwiNRqPdunVr3unTp58wttFSGePGjVNGRESUTJ8+/Y6lNFu2bHEfNWpUqVKprPk18rYGuMLD4TxeyAE4ElENACfwD2tyOM1myZIlt62nenCcO3fOKSUlxSE0NLTqQZXZs2fPe97e3veSkpKcf//735cDwpeXKyoq7MLDw/Xh4eE2KWTW2L17t0dwcHBlcxQeg8EASx93bEn4lBaH85jAGLsOYD2AKwDyAZSYONIFIPiJI6IUIkq5ffuR3tc5nMeCuLg4nzfeeMNLGlZbW4uxY8cq586d6wMAn376qWtwcLBGq9UGPP/8891KSkoaPC83bdrkMWPGjPov+2/YsMFj5syZvubqe/XVVwtWrFjhbRpeUFAgGzJkSHe1Wq3V6XSakydPOhrli4yMVIaFhfX09fXt9dZbb3VsXCowfvz44o8//rjencOHH36oGDNmTLFpG5OTkx11Op1GrVZrhw4d2v327dsy07IWLVrkHRQUFKBSqQInTpzYta6uDjt37uyQnp7uFBUV1U2j0WjLy8vpwIEDLgEBAVq1Wq2NjIxUVlZWEiBYshYtWuTdp0+fnu+//75VVxotAVd4OJzHBCLqAOAPAPwhfEn8CSJ62TQd9xPH4TSPmpoaGj16tL9KparasmXLjfz8fPnq1au9v/nmm+yMjIwLTz31lH7lypUNFKQZM2YUHz161M3o+2r37t0e0dHRZqePoqKiitPT053S09PtpeFLlizx0el0+uzs7IyVK1denzp1qr8x7uLFiw5ff/119unTpy+sX7/ex1iPablHjx590ugba//+/R2mTJnSyNv5tGnT/FevXn0tOzs7IzAwsHLp0qU+pmkWL158Kz09/UJOTs75yspKu71797pNnz79TlBQkD4hIeFSZmZmhp2dHWJiYvwTExNzs7OzMwwGA9atW1d/k3FwcKg7c+ZMVnR0tMUpspaEKzwczuPDEAA/M8ZuM8ZqAHwKoH8Ly8ThPPb86U9/6qrVaivXrFlzEwBOnDjxRG5urkNYWJhGo9Fo9+7d637lypX20jyurq51AwYMKEtMTHRLTU11qKmpobCwMLNuFORyOebOnXtzxYoVnaThp06dcpkxY0YRAIwaNars7t278qKiIhkADBs27K6joyPz9vY2KBSKmmvXrpnz8G5QqVRVBw8edE1OTnaUy+Wsb9++DabNioqKZGVlZTLjtNcrr7xS9OOPPzqblnXkyBGX3r17a9RqtTY5OdklPT29kaPUtLQ0B19f3+revXtXA8C0adOKvvvuOxdjfFRUVKtUdIy0vkk2DodjiSsAniYiJwCVAH4H4PF2lMXhtAJCQ0PLv/32W1e9Xl/g5OTEGGMYOHBg6aFDh35uKl90dHThqlWrOqnV6qqXX365SZ9hsbGxxZs2bfLWarX1Cok5X5ZExADA3t6+PlImk8FgMNDbb7/tuWvXLk8ASEpKylEqlTWRkZHFe/bsUXTs2LFm3Lhxjaw7tqDX62nhwoVdT548mdGjR4+auLg4n6qqqkYGEWu+N11cXOrup/5HBbfwcDiPCYyxkwD+BeC/AM5BuH63t6hQHE4bICYmpnDYsGElERER3WtqavDss89WpKSkOBunoMrKyuzOnj1rb5rvueeeq8jPz2//2Wefuc+YMaNJZcPe3p7FxsYWxMfH16/Hefrpp8t27tzpDgCHDx926dChg0GhUFhUGl577bXbmZmZGZmZmRnGRcRTpky5c/z4cbcDBw4opk6d2kgGd3f3WldX19qkpCRnANixY4f7M888Uy5No9fr7QCgU6dOhpKSErtDhw7Vr8FxdnauLSkpkQFAcHBw1fXr19sb+yUhIcF90KBBZU21uzXBLTwczmMEY2w5BAe6HM5jhy3byB80VVVVdl5eXr2Nx7GxsQXm0r355psFCxYskI0dO9Z///79P8fHx+dNmDCh27179wgAli9fft04lSNl9OjRd86ePevk6elp1VH1vHnzCjdu3Fi/eHnNmjU3Jk2apFSr1VpHR8e6Dz74oEmLkjk8PDxqg4ODywsLC9tpNJp75tLs3Lnz59jY2K5z58618/Pzq96zZ0+eaRmTJ0++rdVqA319fe/pdLoKY1xUVFThnDlzui5evLguJSXlwrZt2/IiIyO719bWQqfT6RctWvTY7Iwgayaq3wKhoaEsJYXPDLRGTo5rtE7PIv32tc6xTERnGGOhLVF3Wxjba9eutTltt27dbEo3fvz4+xWHI8Ha2E5LS8vT6XRNTvU87oSHh/eYP39+wR/+8IfHxtLRlklLS/PQ6XRKc3F8SovD4XA4nGZSWFgoUyqVQQ4ODnVc2Xk84FNaHA6Hw+E0Ew8Pj9q8vLz0lpaDYzvcwsPhcDgcDqfNwxUeDofD4XA4bR6u8HA4HA6Hw2nzcIWHw+FwOBxOm4cvWuZwOBzOI2HazrN9HmR5H0zvbfW7Pk5OTiF6vT5VGrZ27VpPJyenutmzZxeFhYX1XL9+/dXBgwfb5F28uek7d+7cKygoSP/FF1/kAsDOnTs7HD582G3fvn15tuS3RFZWVvvBgwdrbty4cVYm+8UXqEaj0W7dujXv9OnTTxjbaKmMcePGKSMiIkqmT59u0SXEli1b3EeNGlXaHG/p1vjkk09cV6xY0Vmv19sxxjB06NCS7du3X4uLi/PZunWr18WLF8917tzZADQ8f0uXLu20b98+dzs7O2ZnZ4d//vOfl5977rmKpmv7Ba7wcDgcDuc3xZIlSx7px/LOnTvnlJKS4hAaGlplPbVt9OzZ8563t/e9pKQkZ6OfrNTUVIeKigq78PBwfXh4uE0KmTV2797tERwcXNkchcdgMEAuN69enD592mHhwoV+Bw8evBgSElJVU1ODDRs21DsgffLJJw1vvfWW19atW69L83355ZdPfPHFF0+eO3cuw9HRkeXn58vNOVRtilY/pUVE7xPRLSJKl4QpiOgYEeWIfztI4l4jootElEVEw1tGag6Hw+G0VuLi4nzeeOONBt7Pa2trMXbsWOXcuXN9AODTTz91DQ4O1mi12oDnn3++W0lJSYPn5aZNmzxmzJjRxXi8YcMGj5kzZ/qaq+/VV18tWLFihbdpeEFBgWzIkCHd1Wq1VqfTaU6ePOlolC8yMlIZFhbW09fXt9dbb73VsXGpwPjx44s//vhjhfH4ww8/VIwZM6bYtI3JycmOOp1Oo1artUOHDu1++/ZtmWlZixYt8g4KCgpQqVSBEydO7FpXV4edO3d2SE9Pd4qKiuqm0Wi05eXldODAAZeAgACtWq3WRkZGKisrKwkQLFmLFi3y7tOnT8/333+/g2n5RlavXt1p4cKF+SEhIVUA0K5dO/z5z3+uV0AnTpxYdPDgQUVBQUEDGa9fv95OoVAYHB0dGQB4e3sbmmt1avUKD4APAIwwCfszgP8wxlQA/iMeg4i0ACYACBTz/JOIGp1YDofD4XCM1NTU0OjRo/1VKlXVli1bbuTn58tXr17t/c0332RnZGRceOqpp/QrV65soCDNmDGj+OjRo25GK8Pu3bs9oqOjzU4fRUVFFaenpzsZfVAZWbJkiY9Op9NnZ2dnrFy58vrUqVP9jXEXL150+Prrr7NPnz59Yf369T7mrBlRUVHFR48efbKmRnju79+/v8OUKVMa+dOaNm2a/+rVq69lZ2dnBAYGVi5dutTHNM3ixYtvpaenX8jJyTlfWVlpt3fvXrfp06ffCQoK0ickJFzKzMzMsLOzQ0xMjH9iYmJudnZ2hsFgwLp16+qtMw4ODnVnzpzJio6OtjhFlpWV5divXz+L1idnZ+faiRMnFr7zzjsN+nv06NGlN27caK9UKoNefvllv3//+9+NPL5bo9UrPIyxbwCYnsA/ANgl/r8LwGhJ+F7GWDVj7GcAFwGEPRJBORwOh/NY8qc//amrVqutXLNmzU0AOHHixBO5ubkOYWFhGo1Go927d6/7lStX2kvzuLq61g0YMKAsMTHRLTU11aGmpobCwsIqzZUvl8sxd+7cmytWrOgkDT916pTLjBkzigBg1KhRZXfv3pUXFRXJAGDYsGF3HR0dmbe3t0GhUNRcu3at0RyRn5+fQaVSVR08eNA1OTnZUS6Xs759+zaYNisqKpKVlZXJjNNer7zyStGPP/7YSFk4cuSIS+/evTVqtVqbnJzskp6e7miaJi0tzcHX17fa6FNs2rRpRd99952LMT4qKsqiotMc/vznP9/65JNP3IuLi+t1FDc3t7r09PSMf/zjH5c9PT0NU6dO7b5lyxb35pT7uK7h8WKM5QMAYyyfiIzmvs4AfpSkuyaGNYKIogFEA4Cfn99DFJXD4XA4rZnQ0NDyb7/91lWv1xc4OTkxxhgGDhxYeujQoSadeUZHRxeuWrWqk1qtrnr55Zeb9BkWGxtbvGnTJm+tVluvkJjzZUlEDBC8qxvDZDIZDAYDvf322567du3yBICkpKQcpVJZExkZWbxnzx5Fx44da8aNG9ekx3ZL6PV6WrhwYdeTJ09m9OjRoyYuLs6nqqqqkUHEmu9NFxcXi57ejajV6qqTJ086PfPMM2aVQ0D4ivWYMWOK169f32AqTy6XIyIioiwiIqKsd+/elR9++KH73LlzLS7KNqXVW3iaibkFTGbPEGNsO2MslDEW6unpaS4Jh8PhcH4DxMTEFA4bNqwkIiKie01NDZ599tmKlJQUZ+MUVFlZmd3Zs2ftTfM999xzFfn5+e0/++wz9xkzZjSpbNjb27PY2NiC+Pj4+of4008/XbZz5053ADh8+LBLhw4dDAqFwqLS8Nprr93OzMzMyMzMzDCuX5kyZcqd48ePux04cEAxderURjK4u7vXurq61iYlJTkDwI4dO9yfeeaZcmkavV5vBwCdOnUylJSU2B06dKh+DY6zs3NtSUmJDACCg4Orrl+/3t7YLwkJCe6DBg0y60ds9erVnqtXr270cH3ttddubty40dvYn7W1tXjzzTe9TNO9/vrrBbt27fKsra0lAEhLS7M/d+5c/TlITU119PX1Nesd3hKPq4WngIi8ReuON4BbYvg1AF0k6XwB3Hjk0nE4HA6nEbZsI3/QVFVV2Xl5efU2HsfGxhaYS/fmm28WLFiwQDZ27Fj//fv3/xwfH583YcKEbvfu3SMAWL58+XXjVI6U0aNH3zl79qyTp6dnrTVZ5s2bV7hx48b6xctr1qy5MWnSJKVardY6OjrWffDBB01alMzh4eFRGxwcXF5YWNhOo9GYVQB27tz5c2xsbNe5c+fa+fn5Ve/ZsyfPtIzJkyff1mq1gb6+vvd0Ol39Vu+oqKjCOXPmdF28eHFdSkrKhW3btuVFRkZ2r62thU6n0y9atMjsjrfMzEzHAQMGlJuG9+vXr3LNmjVXJ06c2K2ystKOiDBkyJAS03Te3t6G559//s6OHTu8AKC0tFQ2d+5cv9LSUplMJmNKpbJ6165dl5vTV2TNRNUaICIlgMOMsSDxeB2AIsbYO0T0ZwAKxtgSIgoE8DGEdTs+EBY0qxhjTQ7E0NBQlpKS8jCbwLlPTo6zfddhv32tcywT0RnGWGhL1N0WxvbatWttTtutWzeb0o0fP97mMqftPGtz2g+m97aeqA1hbWynpaXl6XS6Jqd6HnfCw8N7zJ8/v4B7TG9IeHh4jyNHjuQ6ODg80htzWlqah06nU5qLa/VTWkS0B8APAHoS0TUimgHgHQBDiSgHwFDxGIyx8wA+AZABIAnAq9aUHQ6Hw+FwmkthYaFMqVQGOTg41HFlpzHHjx+/+KiVHWu0+iktxthEC1G/s5B+FYBVD08iDofD4fzW8fDwqM3Ly0u3npLTWmj1Fh4Oh8PhcDicXwtXeDgcDofD4bR5uMLD4XA4HA6nzcMVHg6Hw+FwOG2eVr9omcPhcDhtA5r9UZ8HWR77x2Sr3/VxcnIK0ev1qdKwtWvXejo5OdXNnj27KCwsrOf69euvDh482Cbv4s1N37lz515BQUH6L774IhcAdu7c2eHw4cNu+/bty7MlvyWysrLaDx48WHPjxo2zMtkvLiM1Go1269ateadPn37C2EZLZYwbN04ZERFRMn36dIsuIbZs2eI+atSo0uY66jTH5s2b3bdu3eoFALm5uQ7+/v5VdnZ2lZorQwAAIABJREFUeO6550o0Gk3V/PnzlT/88ENGv379KgFApVIFHj58OKdnz573SkpK7GJjY7t8++23Lvb29uzJJ580rF279tpzzz1X0XStv8AVHg6Hw+H8pliyZInZj+U9LM6dO+eUkpLiEBoaWmU9tW307Nnznre3972kpCRno5+s1NRUh4qKCrvw8HB9eHi4TQqZNXbv3u0RHBxc2RyFx2AwQC5vrF7MmzevaN68eUWAoAh+/fXX2d7e3gZAUKy8vLzurVixwvvf//73JdO8kydPVnbt2rU6Ly8vXSaTISMjo/3Zs2cb+ftqCj6lxeFwOJzfFHFxcT5vvPFGA3cGtbW1GDt2rHLu3Lk+APDpp5+6BgcHa7RabcDzzz/fraSkpMHzctOmTR4zZsyo/7L/hg0bPGbOnOlrrr5XX321YMWKFd6m4QUFBbIhQ4Z0V6vVWp1Opzl58qSjUb7IyEhlWFhYT19f315vvfVWx8alAuPHjy/++OOPFcbjDz/8UDFmzJhi0zYmJyc76nQ6jVqt1g4dOrT77du3ZaZlLVq0yDsoKChApVIFTpw4sWtdXR127tzZIT093SkqKqqbRqPRlpeX04EDB1wCAgK0arVaGxkZqaysrCRAUGAWLVrk3adPn57vv/9+B9PybeF3v/tdSXZ2tmNaWloDNx7nz5+3T01NfWLz5s3XjdYsrVZ7b8KECY2+0NwUXOHhcDgczm+ampoaGj16tL9KparasmXLjfz8fPnq1au9v/nmm+yMjIwLTz31lH7lypUNFKQZM2YUHz161K26upoAwRISHR1tdvooKiqqOD093cnog8rIkiVLfHQ6nT47Oztj5cqV16dOnepvjLt48aLD119/nX369OkL69ev9zHWY1ru0aNHn6ypEYwv+/fv7zBlypRG/rSmTZvmv3r16mvZ2dkZgYGBlUuXLvUxTbN48eJb6enpF3Jycs5XVlba7d2712369Ol3goKC9AkJCZcyMzMz7OzsEBMT45+YmJibnZ2dYTAYsG7dunp/WQ4ODnVnzpzJio6Ovi+v6XZ2dpg3b97Nv/3tbw2Uw59++slBq9XqzVmNmlX+r8rN4XA4HM5jzp/+9KeuWq22cs2aNTcB4MSJE0/k5uY6hIWFaTQajXbv3r3uV65caS/N4+rqWjdgwICyxMREt9TUVIeamhoKCwsz6wFcLpdj7ty5N1esWNFJGn7q1CmXGTNmFAHAqFGjyu7evSsvKiqSAcCwYcPuOjo6Mm9vb4NCoai5du1ao6e9n5+fQaVSVR08eNA1OTnZUS6Xs759+zaYNisqKpKVlZXJjNNer7zyStGPP/7obFrWkSNHXHr37q1Rq9Xa5ORkl/T09EbTRWlpaQ6+vr7VRp9i06ZNK/ruu+9cjPFRUVH3pehIiYmJKfrvf//rnJmZ2d566ubB1/BwOBwO5zdNaGho+bfffuuq1+sLnJycGGMMAwcOLD106FCTzjyjo6MLV61a1UmtVle9/PLLTfoMi42NLd60aZO3VqutV0jM+bIkIgYI3tWNYTKZDAaDgd5++23PXbt2eQJAUlJSjlKprImMjCzes2ePomPHjjXjxo1r0mO7JfR6PS1cuLDryZMnM3r06FETFxfnU1VV1cggYs33pouLi0VP77bSrl07zJ49u4FyGBwcXHXhwgWn2tpaSBdoNxdu4eFwOBzOb5qYmJjCYcOGlURERHSvqanBs88+W5GSkuJsnIIqKyuzO3v2rL1pvueee64iPz+//WeffeY+Y8aMJpUNe3t7FhsbWxAfH1+/Hufpp58u27lzpzsAHD582KVDhw4GhUJhUWl47bXXbmdmZmZkZmZmGBcRT5ky5c7x48fdDhw4oJg6dWojGdzd3WtdXV1rk5KSnAFgx44d7s8880wDL+Z6vd4OADp16mQoKSmxO3ToUP0aHGdn59qSkhIZICge169fb2/sl4SEBPdBgwaZ9SO2evVqz9WrV3uai7PG7Nmzi7777jvX4uJiOQAEBgZW9+7duyIuLs6nrk7onnPnztnv3r37yeaUyy08HA6Hw3kk2LKN/EFTVVVl5+XlVe/GPjY2tsBcujfffLNgwYIFsrFjx/rv37//5/j4+LwJEyZ0u3fvHgHA8uXLrxuncqSMHj36ztmzZ508PT2tOqqeN29e4caNG+vXp6xZs+bGpEmTlGq1Wuvo6Fj3wQcfNGlRMoeHh0dtcHBweWFhYTuNRnPPXJqdO3f+HBsb23Xu3Ll2fn5+1Xv27MkzLWPy5Mm3tVptoK+v7z2dTle/1TsqKqpwzpw5XRcvXlyXkpJyYdu2bXmRkZHda2trodPp9IsWLTK74y0zM9NxwIAB5ebirOHw/9u792ir6nr94+9HwEAQBEHFC4IJApobkpQUNdRO3iKP4l1Ckzid1PTk74B2fmXaRcU8wzx20TQlMbTEVDQ1JazUhLYpeBdQEo4gW9DECyryOX9858LFZiMbZa+51tzPawzHZs31XYvPHs7Betb32r59jB07dsm3v/3t1ZPCJ02aNP/rX//6DjvuuONuHTp0WLXFFlu8f8kllyzYkPfV+rqoWoMhQ4ZEfX193mVYE2YctdY8vXXaa0p13suSHomIIXn83UW4tydMmNDstjvttFOz2o0cObLZ73nytbOb3fa6U3Zff6MCWd+9PWvWrPl1dXUfOtRT64YPH77zWWed9bJPTF/T8OHDd77rrrvmVfrE9FmzZnWvq6vr3dRzHtIyMzPbQK+88kqb3r1779a+fftVDjtrmz59+txKh5318ZCWWQ2RtAVwNbAbEMBXIuKv+VZl1vp07979/fnz5z+Rdx3WfA48ZrXlx8DdETFS0qbAZnkXZGZWCxx4zGqEpM7AfsDJABHxLtDkJEUzM1uT5/CY1Y6dgAbgWkmPSrpaUsfGjSSNlVQvqb6hoaJHBpmZVS0HHrPa0Rb4NPCziBgMvAmc07hRRFwVEUMiYkiPHh9pGwwzs8LxkJZZ7VgILIyIGdnjm2ki8JhVqxlHaY+N+X57TYkP3ddnzz333GX8+PGLjjrqqNdL1y644IKtHn/88c2WL1++yd13373Wqdwld9xxx+aXXnrp1tOnT5+7rjYPPfRQhwULFmx67LHHbtAhlpaPmu7hkfQfkp6U9ISkyZLaS+om6V5Jc7KfH+nUVrNqExGLgQWSdskuHQg8lWNJZlXt6KOPXjp58uRu5demTJnS7dRTT33lw8JOc9XX12925513dtmQ15QO+rTKq9nAI2k74BvAkIjYDWgDHEf6xjstIvoC0/A3YCuWM4AbJM0GBgE/zLkes6o1atSoV6dNm9bl7bffFsCzzz676ZIlS9r17t373b59++4K6RypkSNH9u7Xr9/AAQMGDJw6dermjd9n+vTpmw0ePLj/gAEDBg4ePLj/rFmzPrFixQpdeOGF206dOrVr//79B/7iF7/o+vLLL7c56KCDPtmvX7+BdXV1/WfMmNEB4Jvf/Oa2xx9//I777LNP3yOPPLJP4/e3yqj1Ia22QAdJ75GW574EnAt8Lnt+InA/MD6P4sw2toh4DMhl12azWrPNNtu8X1dX9+aUKVO6nHTSSa9NnDix24gRI16VPtjB/eKLL94K4Lnnnnvq0UcfbX/ooYf2nTdv3hr769TV1a2YOXPmM+3atePWW2/dfNy4cdvfc889884999yX6uvrO/7qV796EWD06NE71NXVvXXffffNu/322zcfPXp0n2eeeeYpgNmzZ282Y8aMZzp16lRVm/G1JjXbwxMR/wv8CHgRWAT8MyL+AGwdEYuyNouArZp6vVeymJkV3zHHHLPspptu6gpwyy23dBs1atQaB2w+9NBDnb785S8vBRg8ePCKbbfd9t3HH3+8fXmbZcuWtTn00EM/2bdv313HjRu3w3PPPbfG8yUzZ87c/NRTT10KMGLEiOWvvfZa26VLl7YBOPjgg19z2MlXzQaebG7Ol4A+wLZAR0knNff1XsliZlZ8J5544msPPvhg5wceeGCzFStWbDJs2LC3yp9vznmS48eP327//fdfPmfOnCenTp069913323ys7Op95IUAB07dlznKehWGTUbeICDgBcioiEi3gNuAfYGXpbUEyD7uSTHGs3MLEddunRZNXTo0OVjxozpfeSRRy5r/PywYcPemDRpUjeA2bNnf2LRokWb7r777ivK27z++utttt9++3cBrrzyyu6l6507d37/jTfeWP05OnTo0OXXXnvtlpBWeXXt2nVlt27dHHSqRC3P4XkRGCppM+Bt0oqVetLeJKOBi7Kft+VWoZmZrba+ZeQt5bjjjls2evToT06ePHmtlVnjxo1bMmrUqB379es3sE2bNlx55ZXzO3TosEZXzfjx4xePGTOmz+WXX77Nvvvuu3qJ+yGHHLL8Rz/6Uc/+/fsPPPvssxddfPHFL51wwgm9+/XrN7BDhw6rrrvuuhcq8ftZ86g53XnVStL5wLHASuBRYAzQCfgN0IsUio6OiLVSfbkhQ4ZEfX19C1drH8WMo7T+Rpm9plTnvSzpkYjIZaJxEe7tCRMmNLvtTjvt1Kx2I0eObPZ7nnzt7Ga3ve6U3ZvdtgjWd2/PmjVrfl1d3SuVrMlat1mzZnWvq6vr3dRztdzDQ0ScB5zX6PI7pN4eMzMzM6C25/CYmZmZNYsDj5mZtZRVq1atav64tNnHkN1r65wk7sBjZmYt5YmGhoYuDj3W0latWqWGhoYuwBPralPTc3jMzKx6rVy5cszixYuvXrx48W74C7a1rFXAEytXrhyzrgYOPGZm1iL22GOPJcCIvOswAyduMzMzawUceMzMzKzwHHjMzMys8Bx4zMzMrPA8adnMKu6EE05odttBgwa1YCVm1lq4h8fMzMwKz4HHzMzMCs+Bx8zMzArPgcfMzMwKz4HHzMzMCs+Bx8zMzArPgcfMzMwKz4HHzMzMCs+Bx8zMzArPgcfMzMwKz4HHzMzMCq+mA4+kLSTdLOkZSU9L+qykbpLulTQn+9k17zrNzMwsXzUdeIAfA3dHRH+gDngaOAeYFhF9gWnZYzMzM2vFajbwSOoM7AdcAxAR70bEa8CXgIlZs4nAEflUaGZmZtWiZgMPsBPQAFwr6VFJV0vqCGwdEYsAsp9bNfViSWMl1Uuqb2hoqFzVZmZmVnG1HHjaAp8GfhYRg4E32YDhq4i4KiKGRMSQHj16tFSNZmZmVgVqOfAsBBZGxIzs8c2kAPSypJ4A2c8lOdVnZmZmVaJmA09ELAYWSNolu3Qg8BRwOzA6uzYauC2H8sxajKQ22TDuHXnXYmZWK9rmXcDHdAZwg6RNgeeBU0gh7jeSTgVeBI7OsT6zlnAmaUVi57wLMTOrFTUdeCLiMWBIE08dWOlazCpB0vbAYcAPgG/mXI6ZWc2o6cBj1gpdBowDNl9XA0ljgbEAvXr1qlBZteX6GUvzLsHMKqxm5/CYtTaSDgeWRMQjH9bOKxDNzNbmwGNWO/YBRkiaD9wIHCBpUr4lmZnVBgcesxoREedGxPYR0Rs4DvhjRJyUc1lmZjXBgcfMzMwKz5OWzWpQRNwP3J9zGWZmNcM9PGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeDUfeCS1kfSopDuyx90k3StpTvaza941mpmZWb7a5l3ARnAm8DTQOXt8DjAtIi6SdE72eHxexZlZ66HTb2h227jixBasxMwaq+keHknbA4cBV5dd/hIwMfvzROCIStdlZmZm1aWmAw9wGTAOWFV2beuIWASQ/dwqj8LMzMysetRs4JF0OLAkIh75iK8fK6leUn1DQ8NGrs7MzMyqSc0GHmAfYISk+cCNwAGSJgEvS+oJkP1c0tSLI+KqiBgSEUN69OhRqZrNzMwsBzUbeCLi3IjYPiJ6A8cBf4yIk4DbgdFZs9HAbTmVaGZmZlWiZgPPh7gI+LykOcDns8dmZmbWihVhWToRcT9wf/bnpcCBedZjZmZm1aWIPTxmZmZma3DgMTMzs8Jz4DEzM7PCc+AxMzOzwnPgMTMzs8Jz4DEzM7PCK8SydKsOJ187u9ltrztl9xasxMzMbE3u4TEzM7PCc+AxMzOzwvOQVkFMmDCh2W3HjRvXgpVYS5G0A/ArYBtgFXBVRPw436rMzGqDA49Z7VgJnB0Rf5e0OfCIpHsj4qm8CzMzq3Ye0jKrERGxKCL+nv15OfA0sF2+VZmZ1Qb38JjVIEm9gcHAjCaeGwuMBejVq1dF67Lmm3GUmt12rynRgpWYtQ7u4TGrMZI6AVOAsyLi9cbPR8RVETEkIob06NGj8gWamVUhBx6zGiKpHSns3BARt+Rdj5lZrXDgMasRkgRcAzwdEf+ddz1mZrXEgcesduwDjAIOkPRY9t+heRdlZlYLPGnZrEZExANA82e6mpnZau7hMTMzs8Jz4DEzM7PCc+AxMzOzwqvZwCNpB0nTJT0t6UlJZ2bXu0m6V9Kc7GfXvGs1MzOzfNVs4OGDc4UGAEOB0yQNBM4BpkVEX2Ba9tjMzMxasZoNPB9yrtCXgIlZs4nAEflUaGZmZtWiZgNPuUbnCm0dEYsghSJgq3W8Zqykekn1DQ0NlSrVzMzMclDzgWd95wqti88bMjMzaz1qOvCs41yhlyX1zJ7vCSzJqz4zMzOrDjW70/KHnCt0OzAauCj7eVsO5RXG9TOW5l2CmZnZx1azgYcPzhV6XNJj2bVvkYLObySdCrwIHJ1TfWZmZlYlajbwrOdcoQMrWYuZmZlVt5qew2NmZmbWHA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVnht8y7A1u2EE05odttBgwa1YCVmZma1zT08ZmZmVngOPGZmZlZ4DjxmZmZWeA48ZmZmVnietNwK3Xzzzc1vvMPwlivEzMysQgrZwyPpYEnPSpor6Zy86zHbWHxvm5l9NIXr4ZHUBvgJ8HlgIfA3SbdHxFP5VmbldPoNzWr3cAvXUUt8b5uZfXRF7OHZE5gbEc9HxLvAjcCXcq7JbGPwvW1m9hEVMfBsBywoe7wwu2ZW63xvm5l9RIUb0gLUxLVYq5E0FhibPXxD0rMtWlULmzx5ciX/uu7AK5X4i4ZuSGM19b++Kuy4kd7H93bL8729YTbWvW3W4ooYeBYCO5Q93h54qXGjiLgKuKpSRRWJpPqIGJJ3Ha2Q7+0W5nvbrLiKOKT1N6CvpD6SNgWOA27PuSazjcH3tpnZR1S4Hp6IWCnpdOAeoA3wy4h4MueyzD4239tmZh+dItaaAmD2oSSNzYZNzArF97ZZcTnwmJmZWeEVcQ6PmZmZ2RoceMzMzKzwHHisSZI65l2D2cYkaRNJffKuw8zy4cBja5H0KeAmSftKapd3PWYbSVfgdElDJI2T9Lm8CzKzynHgsdWywymJiMeBK4AjgO9nz1XtVq9m66JkE4CIWAp0Bv4ADAdeyLM2M6ssBx5b/aEQEe9nj9tExN3AhcBRkg4IL+ezGpPd0xERqyRtkV3+Eyno/HdE/CPH8syswhx4jLIPhQGSbgUOk9Q+Il4BLgLOktQ15zLNNkh2T7eVNAGYLukU4NfAecBpknbKt0IzqyQHnlaq1M1f9vgk4CbgDmBqRKwAiIirgU7AiKZeZ1atJB0E3AgsAf4L+BxwWkTcAawAvpC12yuvGs2scvzh1cqU5uJExKrs8SeypzoAV5OOLegvaR9JPbPnLgO+Vv46s2pVthJra+BIYFJE/B6YCuwiqQ64GNhf0mzgAkkd8qnWzCrFOy23UpK+AHwDeBT4JTAYGA20AxYD/YC/AN8GPgFcAFwWES/mUrBZM0jainTI6rER8bCku4GHI+K72XNjgM2B7wFdgF4RMSO/is2sUtzDU3CSNm2894ikY0lh5yygIynwzAZOiYhDIuIU4KfAWxHxHrAV0B5oqGjxZuvQeNWgpP0k9YiIJcCvSPc3wHdIE+93yJ6bRerN3DEiFpXCTmmFopkVlwNP8fUBxkrqI+lYSTsA7wOTgb1J8xpuiYg5wD8l9ZR0PvD/gBcBIuJ50nCAWa6yzQNVvmow20/nq6R7GeB8oK+koyJiJmkZ+o+z5+4Bzo2Ip8vft7RC0cyKy4GnoMomFzcAQ4GHgT2A14DdSENVw4B/iYjLJXUDNgUOIm3Q9oWIuK70fhFxV0S8XbnfwGxtEbEqIkLSXpLOlfSZiLgf+DswSFK/iFgJXAtcmO0YfjnwpqTNgVUR8bb3lTJrfRx4CipbkrtrRCwDlgHPAtdGxHLSHIe5wJUR0SDpM6TVWYOByRHxjYhY4hVZljdJPSR9T9K/lu5HSeeSNsZsC1wh6UzgVtLw7PDspX8CegEjI+IfETEqIpaXJt17Xymz1seTlgtM0jzg5xFxiaTTgT2y+TlIOovUw7MZsD1wSURcX/Za+UPB8iRpHPAVUhifCdwXEcsk/QT4bUTcL2l34Hek4dnPAicCQZpofwtwfdmKxE28ytCs9WqbdwH28TT+R1zSfsCCiHgBOB6Ymn1A3AXsK2lERNwOTAeuBPYC/lQKN6Wg47BjeZK0M7AncHhEzC273oM0L+2fktpGxGxJfwXOjohxkuYAxwK/jIj52WtK97TDjlkr5iGLGpUdB6FGYeeTpA3W9sh2Sp5J6tr/aUTMI008Pk/SI8B+EfF2RNyfzYkonTfkoGPVYARARMzNdktWduRJA/A8qedn06ztw6QhWyLiyYj4TkTMb2qCs5m1Xh7SqnHZJmr/BtwQEQ9KOhn4DPCziHhCUm/gaeAz2eORwMsR8Ze8ajZbn2zrhN2AH5R2/S71ZmbHnFxPOhOrA7APMDoL+JS3zaF0M6tS7uGpEZLaSfqapC2zx52ycHMO6ZvuVyWdBkwizcsZlr30HWAB8EOAiLjZYcdqgEiTjvvA6lWHkW0euBUp5N8JPEeamzaz/MUOO2bWmHt4aoCknhGxSNLh2TlASBoL/Jz0j/2jkr4IjCQd9tkVOJW0tX4n4BLShM938vkNzD5cNmdnQekezTYCnERabn5TaYdvSceTJif/Z/n9nA13eS8dM1sn9/BUMUkdJT0IXCNpO+AeSVdLGhgRV5HmMuydNX8MeJK0W/JDpI0D7wO+FhF3RsQ7XmZu1aDxrsaStiHNL+uRPW6XhZefANsAt0oaKekW0n19S6OwI4cdM1sffwBWt/bAG0AbYGh2zENb4JTs+bOAbwFExAJgBtBb0mER8WpEXBYRz5RNSHY3v+VG0taQdjWW1EbSllnPzGLSvfu57Pn3sp8PRMTZpF2S+wMPRsQe2UaDq3lSspk1hwNPFcnm6Xwn21uEiFhKOt6hMzBYUi/g/wOflbR/Nrz1mKRLs7eYBVwQEXeWv6+DjuVNUn/g35TOdvs0cB1p+PX4rMkCYGnWVuU/I2JiRHw/Ii7NrvvcKzPbYA481eUA4LvA/2QTkgF+wwe7xh4eEQuB20hzdADOBs6Q1CUiXouIJ2DtwxXNKi1bSn6bpJ2Af5D21ZlHunfPIx1a+01J+wNb8sEuycDaPTdlWzF4+MrMNpgnLVeBbEfZh0lzcP4dGAD0BcaRlpg/D7xO2nvkIuAp4M/A1RFxjaRtI+KlPGo3a0o2D+c9pcNqS/fmdNIk+qOyjTGRdBBwIGki8iLgK+Ez28ysBbiHJ2eSOpCW2T6bDWHNIm2i9legJ/CvwBkRcS/pW/JR2RyHC/hgs7WXPCHZqknZPJwFwBPAyRGxH/BrUpAvtbsPmEBaXt4l0sGevpfNbKPzPyw5y77N9uSDicgzSCearwKmkQ76XKp00vPvgJ0l9Yl0evkDZe/jeTqWq/JhVEmdJV0kaQ/gTNJQLaQjTrbJhrGQ1DubYD8W2CXrrfS9bGYbnQNPjsq+yV4FDJHUOSKWAPVAO9IOsv9B+kb8ZkTMAMaUhgPM8iZpF0l7QZpzI6lT9lQb0qG0bSLiD8BcSRMi4kngHuBCSb8Hjsmm5gwkTdBfmcOvYWatgANPBZTOAWp8veyb7BJgIXBk9vjvpJ6dEcDOETGv1DYi3nKXv1WRAcDb2T1+GHCppJ0i4lXgFWBs1u5U4FRJ20fEz4EbgMkRMSGbnDwMuDEL/GZmG50/OCsgO6n5fUndJe0saTMASaXT6l8ghZwDJO2anR00G5hL6ulp/H7u8rfcSOpV6smJiFuBLsCJpDlli4CfZ5sJTgbektQ9Ip4nHQVxXfa6n0TE9aX3jIirIuLKyv4mZtaaeJVWhUg6GxhDmoezZ0QclF1XNhTQgzSP53MRcWiOpZqtUzZP51ukicgdsstLgf8iDbfOlXQJaWhqG9Iw1fdLk5gl9SodE2FmVknu4dnIsq79TRpd60eaz7AnaQv9A7LluKv3GomIhoiYACyT9C1Je5fer6K/gFkTSkOy2f36ImlI6mxgWbaC8DnStgmQVhDeAxxCCkLblt4nIl70kKyZ5aHt+pvYhsg+ECLbbG1JRLxBCjptgCuA3sDB2XJcYHWo2STbUO0M0qqtbqXen0r/DmaNZUOyHYHDgWXAX4Dp2YRkgEtJQ1l7Aw9HxP2STgc6RcQ/yu9lD8maWR48pLURSNqk9I949u31P0nffm8lLcOdSdo88OsRcU3Wrg7YNiLuyqdqs+aT9GXSsSaTSUvMdwauzK7NjIiVks4DdgVOi4iGvGo1M2uKu5Y/hrKzfkph5wjgaOBd0gfC74HRwArSZM19JQ2V9O/A9cB2OZRttk6Shkvq0+jaVqQNML8YEedlk/DnkPaMOi41UX9SL8/zpPvdzKyquIfnI5C0H2n06i/Z40+T9svpQTrz6uGI+IqajXfVAAAFMElEQVSkLUlDVO1IZwedBQwiDW99P9uTxKwqSOpGOt5kDjCxrDeyNymwnxIRL0hqHxErsvb/Q9opfBdgr4hYlEftZmbr4zk8G0DSLqQ5OC8D75Vd+ynw14gYJeloYHTpfCtJtwHfAw6NiB9J6pTN61ljKMysCrxP2h7hDtKeOe9HxHWkXb+fJU28fyELO1uTejLHAoMi4sHSm/i+NrNq5CGtZpDUTtIPgN8Cu0XEY8AgSeMi4lnSQZ6DACLit8BbwEnZy58lnW5e+gB4M3tP+UPBqkV2P/6TNCG5E/ANYJikc7Jl5E8BX5U0RtLxwE3A8Ih4sxR2SvtK+b42s2rkIa31kHQycA4pHJ4YEX/Lrg8jfRPeFuhOOsX89xExKdtq/xekAxP/7tVWVu3K9oM6EugfET+UdAbwA+Dy7L9PAceQ9tf5YXbUiZlZTXDg+RCSvgd8lhRedgOeIX2zfT/7cLgBeDsixkgaDRwGjI2I1yR9A5gSEf+bV/1mG0rSKOCLpB7JTwGXAEeQDrT9HmlIqzRJvzRp3/+ImFnVc+BpgqQ22b4j3SJiWXZtLNCfdP5PqZenG2nDtf2B+cC1wJ8j4op8Kjf7eCR1Ja20mhQRZ2TX+gK9ImJaWTvP0zGzmuI5PHzwTVXSmbB6kzVFxLKynY6nkrbLH5ZtwEYWhi4HfhMRbwLfKg873iXZatBrpBVZt8Pq8D+nPOyA5+mYWe1x4GGNLvmvSfp+9udNyp/Lltv+lbRKa5+y114A3CfpExExF9zVbzXvk0D7LPS/n3cxZmYbQ6se0irrgdkk69X5FDCNNGlzWanbvmxCZwfSLsqbAj+PiIV51W7WUsqHcs3MiqLV9vBkXfVRdvZVm4h4nHQUxM/K22ZhRxHxNvAAaf+ido3ez8NXVghl89Z8T5tZYbS6Hp7GS8QlfZu0O/KsiLhCUifgBeDwiJhRmsD8Ye9hZmZm1a3V9PBI2lrSVqWgIqm7pAdJe+icD5wnaXS2C/IlpC3zaWoOQ6nHp4Llm5mZ2cfQanp4JB0EHEnaSO0Y4GqgD7AIuAA4gLT3yN4R8aqkF4GLIuKnOZVsZmZmG0mhe3gktSl7+BQp8DwMzI+I5aSNBC8DFkTELqRt9S/M2n8VeLuC5ZqZmVkLKfThoY2Go7YE7gf6RMTvsmudSYeA/iV7/BxwsKRtIuKeihVqZmZmLapQPTyShkt6VNIXs8cHS7pD0k+AdhFxHPCkpPOzl7wDLAROkzSDtFvy8IhYnEf9ZmZm1jIKMYdH0o7ABKAOODsi7pS0N+lAz/NIGwUOIJ12vhC4BhhKCjx7AN2A9hFxW/Z+3jbfzMysQGp+SEvSpcBw4E3gRuCP2VMDgSURMV3S/cBYUiD6A3APMAN4FxhfPnyVLTl32DEzMyuQmh7SkrQJsATYGxgNbA4clz39ArBI0s7ZUvSngUOAdyJiHPBd4F+aOCOo9ru8zMzMbA01HXgiYlVEXBwRKyLieVLI2VXSzqT5OO+QghDACtIp0J2z195SOj4ih9LNzMysggrxYV+2/PxuQMDBETEPuB74lKSpwA3AryPi1fLXevjKzMys+AoxabmcpOOBPYHfRcSfJXUE+kXEozmXZmZmZjkpRA8PrHHQ4d2kHZM/L2nziHizFHY8fGVmZtY6FaqHp3Sop6ShpBVaz+ddk5mZmeWvUIHHzMzMrCke4jEzM7PCc+AxMzOzwnPgMTMzs8Jz4DEzM7PCc+AxMzOzwnPgMTMzs8Jz4DEzM7PCc+AxMzOzwvs/gTZ8kBFDaLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub.time_activity_plot(percent=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mondays = smc_plot.subset(weekday='Monday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mondays.time_activity_plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mondays.time_activity_plot(percent=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}