{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Minimal example for working with measurement wheel data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the worklab module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import worklab as wl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data with `com.load()` or the device specific load function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "Initializing loading for C:\\Users\\rick_\\Development\\worklab2\\example_data\\Optipush_example.data ...\n",
      "File identified as Optipush datafile. Attempting to load ...\n",
      "Data loaded!\n",
      "================================================================================\n",
      "\n",
      "Measurement wheel data is stored in a:  <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "filename = os.getcwd()\n",
    "filename = os.path.join(os.path.split(filename)[0], 'example_data', 'Optipush_example.data')\n",
    "mw_data = wl.com.load(filename)\n",
    "print(\"Measurement wheel data is stored in a: \", type(mw_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame contains all the information from the file, but nothing extra:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time', 'fx', 'fy', 'fz', 'mx', 'my', 'torque', 'angle'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before getting more infos it would be a good idea to apply some filtering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mw_data = wl.kin.filter_mw(mw_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get more infos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>time</th>\n",
       "      <th>fx</th>\n",
       "      <th>fy</th>\n",
       "      <th>fz</th>\n",
       "      <th>mx</th>\n",
       "      <th>my</th>\n",
       "      <th>torque</th>\n",
       "      <th>angle</th>\n",
       "      <th>aspeed</th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "      <th>acc</th>\n",
       "      <th>ftot</th>\n",
       "      <th>uforce</th>\n",
       "      <th>force</th>\n",
       "      <th>power</th>\n",
       "      <th>work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>2.838031</td>\n",
       "      <td>1.753805</td>\n",
       "      <td>-4.058034</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.005135</td>\n",
       "      <td>-0.054472</td>\n",
       "      <td>3.673093</td>\n",
       "      <td>3.352298</td>\n",
       "      <td>1.039212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.535617</td>\n",
       "      <td>5.253370</td>\n",
       "      <td>-0.198079</td>\n",
       "      <td>-0.175715</td>\n",
       "      <td>-0.182605</td>\n",
       "      <td>-0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005</td>\n",
       "      <td>2.401395</td>\n",
       "      <td>1.666210</td>\n",
       "      <td>-4.385999</td>\n",
       "      <td>-0.002135</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>-0.058072</td>\n",
       "      <td>3.689854</td>\n",
       "      <td>3.393195</td>\n",
       "      <td>1.051890</td>\n",
       "      <td>0.005228</td>\n",
       "      <td>3.594796</td>\n",
       "      <td>5.270667</td>\n",
       "      <td>-0.211171</td>\n",
       "      <td>-0.187329</td>\n",
       "      <td>-0.197050</td>\n",
       "      <td>-0.000985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010</td>\n",
       "      <td>1.963322</td>\n",
       "      <td>1.620943</td>\n",
       "      <td>-4.642096</td>\n",
       "      <td>-0.006774</td>\n",
       "      <td>0.002821</td>\n",
       "      <td>-0.060339</td>\n",
       "      <td>3.707025</td>\n",
       "      <td>3.468259</td>\n",
       "      <td>1.075160</td>\n",
       "      <td>0.010545</td>\n",
       "      <td>4.264677</td>\n",
       "      <td>5.294445</td>\n",
       "      <td>-0.219414</td>\n",
       "      <td>-0.194642</td>\n",
       "      <td>-0.209271</td>\n",
       "      <td>-0.001046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015</td>\n",
       "      <td>1.527743</td>\n",
       "      <td>1.645536</td>\n",
       "      <td>-4.771329</td>\n",
       "      <td>-0.007243</td>\n",
       "      <td>-0.001333</td>\n",
       "      <td>-0.060383</td>\n",
       "      <td>3.724537</td>\n",
       "      <td>3.530765</td>\n",
       "      <td>1.094537</td>\n",
       "      <td>0.015970</td>\n",
       "      <td>3.537893</td>\n",
       "      <td>5.273269</td>\n",
       "      <td>-0.219573</td>\n",
       "      <td>-0.194782</td>\n",
       "      <td>-0.213197</td>\n",
       "      <td>-0.001066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.020</td>\n",
       "      <td>1.104789</td>\n",
       "      <td>1.748478</td>\n",
       "      <td>-4.743367</td>\n",
       "      <td>-0.002454</td>\n",
       "      <td>-0.007348</td>\n",
       "      <td>-0.058016</td>\n",
       "      <td>3.742333</td>\n",
       "      <td>3.582384</td>\n",
       "      <td>1.110539</td>\n",
       "      <td>0.021482</td>\n",
       "      <td>2.901078</td>\n",
       "      <td>5.174675</td>\n",
       "      <td>-0.210967</td>\n",
       "      <td>-0.187148</td>\n",
       "      <td>-0.207835</td>\n",
       "      <td>-0.001039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time        fx        fy        fz        mx        my    torque  \\\n",
       "0  0.000  2.838031  1.753805 -4.058034  0.004851  0.005135 -0.054472   \n",
       "1  0.005  2.401395  1.666210 -4.385999 -0.002135  0.004741 -0.058072   \n",
       "2  0.010  1.963322  1.620943 -4.642096 -0.006774  0.002821 -0.060339   \n",
       "3  0.015  1.527743  1.645536 -4.771329 -0.007243 -0.001333 -0.060383   \n",
       "4  0.020  1.104789  1.748478 -4.743367 -0.002454 -0.007348 -0.058016   \n",
       "\n",
       "      angle    aspeed     speed      dist       acc      ftot    uforce  \\\n",
       "0  3.673093  3.352298  1.039212  0.000000  2.535617  5.253370 -0.198079   \n",
       "1  3.689854  3.393195  1.051890  0.005228  3.594796  5.270667 -0.211171   \n",
       "2  3.707025  3.468259  1.075160  0.010545  4.264677  5.294445 -0.219414   \n",
       "3  3.724537  3.530765  1.094537  0.015970  3.537893  5.273269 -0.219573   \n",
       "4  3.742333  3.582384  1.110539  0.021482  2.901078  5.174675 -0.210967   \n",
       "\n",
       "      force     power      work  \n",
       "0 -0.175715 -0.182605 -0.000913  \n",
       "1 -0.187329 -0.197050 -0.000985  \n",
       "2 -0.194642 -0.209271 -0.001046  \n",
       "3 -0.194782 -0.213197 -0.001066  \n",
       "4 -0.187148 -0.207835 -0.001039  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_data = wl.kin.process_mw(mw_data)\n",
    "mw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's more like it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WjtiOyx0HLwzxBtz+TV8vyV0pwOXA01DgZe8yddz9yjq0zH0MeSeFLYkBTK2+enJmUqdmmOyWrhw1Y4ET27E4cCFEC4CTAbwGYIyUcjPgKXkAhxS55kohxAIhxILt27fHhdRy4MxVM6BQqBRNZZA2v+ekwYHXoBK54U8rAQCdOafKkigwlVLtDbkW5ICCtC3wGjtSrZwUCgBACDEEwAMArpVSkr05UsrbpZQzpJQzRo8e7f+Oeq26N7F9wXUE2fyf0uXAw2iadKoRprHzrsXdfFBdsspyKHCVUhoLcbURZiHT4sDT0rC1liRVVgtcCNEMT3n/Rkr5oP/rrUKIsf7fxwLYRheO5mzkxoFzqYS0OfZe92Fcw1ks0ohnr0U+lls7J22EkQ+09mqeZEX+coAe0pt2sluq3ZcdZePAhbd03glghZTyR5E/PQzgCv/nKwA8VG7huLyYSpjher7JrQ0nQfq1UMrfZy0qkTB1nR5G+syqbamFTHL7zeqiWcr4kC9N+avXGiVIlZdigZ8G4DMAZgshFvn/LgJwA4BzhRBrAJzr/58oHA3BhOZSKFQ5uFEohpMgDCNkUCgcJ2YKGjyryiQJapvuEMfj/15Zh7//3/l47K3NqcijpCDvIDNqJpYmFs0IC+ZbSkMQdp/NMS4E9fXTHuggpXwRxcf/bLpIffVN+zs9zE/GPnVgUyjGYYTqPulmmZWz94zqkkSo4aVyyRt2dQIANrenkx7PdLEY5zGkjYpY4Kp95r59tpHtYlYpc+CVmixph0il8eLXYiamGl9qVcrwTNSUKBT/k16MLRUxSkYpYmWEQanJHSUFVS5mlTyo7Dht5i6Mm+JeSQ6ccy8Tiqac988KQgqNyB8Ks1R3sjzECAyF7FIoZbDAian0ac07mXL/5UbZnJjpgCZd+Ox51QjZTtIMViPkII1ayrVosbBF9scrtQNGmNQWtxxypVCKPNwDydP66rVGzZTTiVl2UFdbvgVuFlWSthMz9SPVUuDYM2oMJoKtAJk7sOAydi0fWr/5jA56KQo8MF509zC/BU2OdH2kZUemLXBqlAB3Cxp+aWr/MOyfhzCe3ex6ff/l59hrzWIBEDx2Lu+a+ikwxCezvzufjiAa6BakUuYCtSCdTHn3Qd6VuxK/eOEd7KvSs1CgDkNVz8TUW+DeJ3cLSgV3YprOrbAWSlphhN6n5cB5UJwzd9zSyiQOrcT0B5+XKFbKfXjtUnMoEwV5bs12fPexFfjeY8tTkYOKsqfSlxNUZ43xC8CcNFRUtpwsHTYT00PaYaRseZi1UCoZ+RO9lT6ctxILSmX6191H7YJ2d+bSFUiDjFvg3mfZo1CYq3elqhGmXqw+oGisBQ4wFnBmvfnC+2jbMSm6StZC4dyrHBa4/kCHlCmUGqMEs82Bky0k3hY3SKUnysF9pKWHEXIoFPrNHGuBAzDZUXmfaZ8CQwW/FIQ5os9Xe7tKRKGUfqvk/itIT5UFWVbgVIeFaSYmXQ6/f3oYihEqlchjLXDu8zdb+MjzjDmGlVw0o8lOqToxGc7DNMHeNVW58GymwwjpUSj+D+RMzILrtP0rxZruwzJxMnKmcxrFsrKiwPOOi3U79pPasp+//8kdNurQhBmyzPleAVSMQvE/6bVQ0hkE9uJY5aqzmaZQqBOCa7mmHVVSahx4WpMiDY49KxTKDX9aiTNvehab2jv1jZnb5MB3kFIcIZtCSTmZpa97Ue5XWi0UnkM5dQpFt+tP5d78XjPtxKQipAao7b1P7gucdjErE/3NuZfa0TSWURFlRYG/8s5OAEDb/h5tW2MnNlMm7g6PKlZlKZSIAtfcthwWuFaelL87OfkqjZwKg6+W6TBCKtIO8+Nb+GYwczIytrjME3+klJi/ri1xvLKWFEh5tNxdeNAstYN0C+6jQSXHnHOvcnDg+hN5aH4xYzmY7cuZ1WwSXVQTFrjue3EtS9M4cI7iM0Hap9KrCdJI7P/hxZvwNz9/BX9ctDGhVcY0OAFchWnKbKXFaafNA0cR9UPpvk9p4tAupvrFjKVIofstu7vws7+s0eoFk++WaQ6cijBOm6dgyS9whTIxTRJGTCgUKoOydrvnFFy3o6Nom3RO+eF3GpyyQxh9fv/pLqzBgpJBCoUzVqWIRf/uqn06YxA+i+T+ObTttfe9iZueXI012/YltjNS4FmOQlGgJvJwOXDy/ZkWWKlx4GkpimCciAPVnfdObe/fXPzxp6FMTBYFTgQPVwmEffNkIg8Nl9Ij1jHv+1oZPFcKott6PQdeAoXif2qLWaXswOXOCcputr3Dy9bMO8l9GxUpqwcL3FGDSXzDuKnUkjq7CvrnwiTGlXOFwwxTzOW93vs1Fn/8aRhCJorAhH7iOs7SPqu0EiVVr773TRz/r/PI7XkcuDmoj7xSFAp5sWKVvdAYoim+/9nmwJkvrynnmPaJKWkfqcalUCgp5GlY4GbeeO+zgTFT+XHgaVEoTAu8hCF/bAnvXE+OUiklyYYdRpiaE5Nn1FF0DvV9NrHA64IDZ59ownz63GE1VWom4WqcW3GtVIqRkRULPIjgSUHJmlrgXCd5FjnwWBx4ircNFknNGKdPocTlKQY1LtSAgGjfxVDHHHgy+JYl8/4pLxDhfeDfh3EN49t05Tzus18T7XFSTvBJ46U26dPM+uOZ4GkfNk2ndOLXpYlKRaGQKZTU48Djn8UQ1O4nvErUWZPmd8u0BR6EETIVLDuMkCiP6XNI48zKKLrzHgk+sF8jqT2F0knHiWnOgXMWND6FwgO3QBO53zKMOZeyoLUtgUIhjkJYyCsdZUeWg2HUUSV1NE7OPvsuF4UihPilEGKbEGJp5HfXCyE2CiEW+f8uYksIekgP3UJm3j/ggmntS6VQWPXAGbcKdyrUs0P18qTCgRtck6ZVGuxEUjJjuE71clhqJnSN7ppSpKLmWqS96Qh3Q7rdBv1dTfO5Uq+gTN1fAbigj9//WEo5zf/3OFkyBrhHkUmmtcYdV/MwQvVTWlt13gLhBIcsF78gjRfKZFHI+8JyLuVSFvwzMVnNyVB0USlWKHWMObVQSlnMqf6frFQjDHUOJWyVtuN3DOJDy5ZKL6V8HkAbWwIC6Bx4WlEo3if1BTa2wINU9+R2kmEVReEE/VPrpvvUVILGT2MrKw3inDft7vKuTZNC4ToxmXJw25cCqh7kONZKkYv6DqeeSk/snxMQQA0TzWoUypeEEEt8iuXgYo2EEFcKIRYIIRZs376ddYPAI0xOpedy4FwKhdau2HW625jGwnJ3KpTU+1QyMSuUns+Nu662HArloK2ofUSbaeuBl8KBE+emia/DRA4dHKKxBYQy696VNGPcTRX4rQCOATANwGYAPyzWUEp5u5RyhpRyxujRo1k34QwmYMKBM9uXGkao+R7RlZozkfknF+lXlDS2tJU4HLcSfbIdhWRemtW8T3AVFalPQ1mi99EXsyrhJgRQHwUnDjyMbEnuNXMKXEq5VUrpSCldAHcAmGnWT/LfOXwUp9+wHddCYjVn36fHCTkGMwqF2J6wt+d+1QcWtuL//XphYptKxTlnJe7atDpmKeAeVwjQqUwThGUeaO1S8y8wx4XjxNT1nCaF0sTuGYAQYqyUUqV+XQZgaVJ7U7gufTBNQPWQB/IYW+Dx+xVDLm9WDIO70FEmFPe7fvX3i7VtKhFPzGlrqjTInHbwyVeqaYMThVIWBU7lwI3vpJPD/0Eb+eZ98mrvaNqlmMijVeBCiHsAnAlglBCiFcC/AzhTCDEN3nivA/AFtoS+mEkI4qeZGpz7gtFT6c2ml5PiSg0YhFuqaIcEidIM2zO6NoVX2wkOwU5JbRBfcIVyhExmLQ6c6v8ppZAXBfRxoeuccFyS+66qBS6lnNPHr+9kymMEbhQKF9xUatN5TLX0ekwtcOZORY1rkjxpKPBKHVhAVcjb93Wb9Z8SRVMOC5zaQ8yqTtECz04Uiv+pbcehUOKfxZBmiGSmi1lx45sLryv1/oUoNZWes1KzOHBCVEkUlO1qOok8vD6Xb9oTXpsChbJ4Qzu7bw5MDxipBOIcuI5WMKcyqUZS2lEo1AXCKA5c087IAie2y3gqvfeZFQvcOIyQeGHOKY0DJzsxCRZ4FuqBP7l8S/BzuaVxDBdL7wJaM+MF3+gq/1qqbIypVsp7SOXADac+Ww5qOwodSU/kMaFQaNdku5gVk9ul9luI1BN5iJZYlEJhhRESFHIUlNIMpkok8ZxN5kTu3xTWdil3zLYqAGYCbqZvBQ1ro8WFqoCMFDhR+VMTbUwRPgvdbsP7pOw2qAeImFUjpCHTFrgxd8TcsnItVy5MtlqcicxdWFRqb9JkNh37cr6AzY3hg+F0S5EhqsDTUrCVjCrhInYij6ZtKQdfUA/0Tj+kk9qOLkcWwggzzYFTvbym4NbpNqU4qEWNTPunRrkE7QkWuzmdU7xT7gLYHDkxiMWBE0YiHnPPk4sfpsjt33y+p5GFyj1cvK/76HbRaZ/IQ+fA6TsB6s46c4k8lYJpdba0ilmZPgiqgs3FKBQ62M7YIHwuQRaDEphAMs+dZ8aKmSgMKrpzZmPNQTUoFDKNFnkUWgqAyGMn3Uf3KMOIjnSNNX07ep/U4nlmeqwWOHBdUD2T22Xfn9led3hpMVAnRc50gWBWsQszMYu35ypbhaQXhbuVHNAcrW/O2drq23QbhmxyJKlU2KQJOJRFUIythPuQo1BSGjM197R0EbEdQC+VYFaNkNYu2xa44TtG/fKKJmhKONw3CtNEG6qDJmaBG2xxy9leHXxcTnAXwMH9ok5M+nWUprET3NMyEBhKqVyWJ3lxidXdSQa3VENf1zZqcunTPpGHmmPBeZeovi0TPUaVouIKnHWYasqrsnqoSaezR5E35YUJlAVgbvVyzxN0CFZGzpeliUljJFl2XF6dkWvCRrdhxA/AyDNg9ckSIaEfPlVA5YW5GdFAxEjSXFuOQl5JyBN385TdqQK1qYkFTkXFFbiJ95sL6lVKgTcTz5L87mMrjOShOrN6HPrYRMG1XkhOzLzanXAVePG/cXcwsbliwE0mIb7bYYnFoFCIe2yYz3VTcOZMKXHgVAdo2mGEVOOB47egJh/VVRQKZ4sSWoqEF8BgkFQkAtUC39jeyb4HwODADZUKN+OPsqCoSdfMPG8ssU8mhRI/4ILBgRPapL1lB3hz0nSxKgT10tjYppiJqeYR1QJPC+rd0jocU2AI0ixmVXEFHv2y5QzpMYmh7vZjgZuZViYXVOVj7Dg05MATKRRf2ZbTAu9hUyimPgdK35H2KfQfbUfjwJlClNgPpxYKt1haFMry1VngHGMN8MostMx9DEta22lyEDkah6FzqLSP0k36SBy+DqsqhaIDR5+ZKL/utPN3fVAXovhWy8ASILanPIPwxSufBd7e0cPqK/pIy22guQYLPhd0AiX9OOhCcBYwh6iAkq5NUv4mz+LS/3kJAPCnpVs0LT3QKRTGTo9IjVJpJJPyDlWlUKjxk5TvknPo/SooDpz7AvcncuYKSjTtVtU4jJDbXr+g5AMFzus7qc/93bz0dU66d4EUvL65Tkxie1aoXgny7IxUVSTLZkBlUoulRaHeyyQlZ0Jnqd0ctSRCSKEkg0XbEi1waikCE64821EojLYmESLcWOAp44cD4CeYcCuhUdpGQbUEFGhRKPStZBTljEKJ0W1ljgPnWrwmYX4mWX0m+Mmf14T/IfbDWRyVEm40oBop72Xe0HkPMEKGiQMc1IgnPTOaBs+bWOBZ5cDd+FuZCA4fZbL96CE6NhTMOVmqAje0wJnXhQlSCcq2xNOB+gK33rnDmCulHI/H9bFwa+2k5YRX2N+TD+9JvCYeDZZ8Vadv5Q5oakxs1xcoVmWM/iQ+x5aRgwAAM1qKnqce6VIGxkM5D3CmPl/XwALPLoXC2lbS+zXJYty824sqoTt+wGqvQKXnTR1r7EQewhegZq4VIukFKMWJSd2mUtoChVEYesxf18Zq792D2BCl0EVmB4FwFgxFU8QzY2lQyjnpO5nw/0eMHAwAaCL4aBxX0t9xpj8JoPu2dBt3k0W8ChRK+LNOXMrRXwrRrRp1GF56eyexZVweLqjxoiZeaG5bgMaZ9zhm/oGk5krRUGCjsh4AACAASURBVJODWAu4wxs77q6ls4dffrZSFMrJR4RWqEkUiu4aFa3FjUgCaKGjOQMKJXxX6MaId11yW7Pa3eXpsyYscA5NwBlM0zR3gL/t5DqZqHHaxhQKk692CFaRcdZpGS1wzoLGrS4YD/jRt49uf8lhhAWfyfKUoMFj96T1E9Wruis6S6idnicYYSb0JydEkzPvOP0G12j+TrXqTRaP6kahaLcexBx0FKz0xNEfN3wAp3npHLi2/97XUMCvB65/qcLxNFus+gI1EiDoi+HUyTF3YJxaIIDZARDmUSg8mJT+jaZ36+ZaV85sNwaEhkCisWAQAswJneVU+eTGowP68aNb4PzyDlW1wMuZgmoUBx48WNp9jMP8iN7q0g+MIH4PgpVhTKGUlQOP9pvcll9nhWf1dcYOgKANSmjN6duXEgfeY5DByxkutXiZSEjxTeUNQoBNk/x0MAlp1F2Rd2jvf55JAwIZt8Ap4W599kuUpZsZB86JiomCOn+4jjVu/wqUcTUtnZs0NtRIAAWOVcqd/FyF2ZUzo5SoKIVBeWvj7rAf4jVxCzy5bVcJpXcdR//OmHC/YQy2/gLOAueGHZNBpUZ1XXbl+adEVcECp7flWFUmPBrXajctKkYt/G5qhLE5cFIUCo/uUEiOA6cvyIV96QuB8bafsQhFwnjEjmBj70r0bUo5ZPmp5Vt5F8DMAjdZZfKE4/tKOfSXIlKOMTfMaFIdk0AzXKJGQtkONRZC/FIIsU0IsTTyuxFCiKeEEGv8T30wpg+T0DCaU4o/8AyKPS4P9z7EyWYaSmbMgSdSKPSxjyKpOZeC4oRVxhZ7wm24nHNnymdolqu4FvtQDxAs8FIoFFIUCj+CjMhKAuBSKDw5orIU7ZO4WJr4WSgW+K8AXFDwu7kAnpZSTgDwtP9/EuKWhm7loivM+BaaN4lNtvQcUBeKuMKi34tbN53mxDSsTZ50X6acnKSv6AEUJMqNuVh2p3yKfdkOdCDPAfrzNS054d1H/8xNdh+c80ZNKJRyVr90iLtZk4O2tQpcSvk8gLaCX38EwF3+z3cBuJR4P2NeUwcTC4ZbBIqaOlv0PhoZTV/icIEon7VsmshTzkON4/qbQaGQLHCWKHELvIy7EoX4BoLX/5F+ViIHnPtxneRRUGhQkxDgtKhY04qgyX3SDJc4hULr25QDHyOl3OzdSG4GcEixhkKIK4UQC4QQC7Zv387ikZVzhs0hEvqWMszO4lquXKRhcUaRRm1r0/NIEykUBqcN8CwzDs8JmIQR8l5sblRMKVEoJjtDjhOT62OJgmII5JnPLtof5btzFojAGGJ8V7JVr/lusWP+shJGKKW8XUo5Q0o5Y/To0az6FpxmpZwLSb3SJEYUoFv6ptVtOcqWykObLwrls8A5iinHtsB5iwnXicltX0oij0EpEdZcC3jhEhR40uTn1CYP29HnPKeuj4kFXq5w6EqWk90qhBgLAP7nNuqFnIpb6qSccltrgGG8p+E7RuV+zcMI6a3jIVvFrzPhAr32xf/mMBWNaRx4Ggs+NxuR6/QspRaKlNK4Qiagl4975moUpGqEJVAo3Hddm3SjX296gRqcoGtXyXKyDwO4wv/5CgAPUS/kpEenlXYPFFot6Vql1NBS82qEtP4B+kLHCYeNWvVJj4Eb+cFZ0HryvLHjDjWXQonWTkk7kceVYaEk6u4wz8jELEW2fBA6WryPeAYiDRz/Fe8YR77DVtc0TwyfNWEFKGGE9wB4BcDxQohWIcRnAdwA4FwhxBoA5/r/J8HozDlGW2p7E2VsygUGL4jmQm6J0179E0B9WViTPjb2CVY9k7bgtM+xa6HwLF6uk5RrsZdSzMqRMqjVYkKhUBWLiZOdUo3QJIIstMCZz1rTlkotcc5rdYjWkElCU5OugZRyTpE/nU27RRyc8K3g+xC+DPvE8xJCl7igXmd6Kgt1gsTaguhwLGOffA48/Fl3JZtCYY41N4Y9ZoET2pfCgRtRKAaGlAko76VJ/xxah8X3ExKPvHZmBk5iO6YRAlT7UOOEdqYH9Xr30F/LOZU7kIlBK8SuI8aBm74nnKHiOlTKS8vwdkmcsxLznPJ6Bf1Rxp1baChmgTMXQS5cGR53Ru2FY0iVFIVCoA9Mdp5BuzIaGAA95M+kRC3H2Ukd6irXQiFutwlfp5QolDSvASqQicmIjqE6kTkUU7QtNQ6cH5mRfAE3lb4UnwkF3Ky6kqJQpERDg6JQiMaISx9bTt2RQlDSyE0qMXJi0zm6hGo45onvERBZxIiKnoPMnsjDXZVLOZWGpEyYFn4UkvgCpE3RAHTLIaTty0cvcJWgcRQKc3GgSJ9nxE0DQEcPr3qhiVM9vJZPobDSy8tggSfBxC5Sz49ybRrnCjiM4mlUnVcbBzoQtwlca5Sr/Lixs6Uky1DDCJnlPNj9AwUTL1GW8i8KbN6ZoWTZCpwddqpvE0U3M6uulPklZTQKhQaeL0pZu3wEFSgT2picRKWasRdr3XclzvucSSKUps8aOdQ4/JliAVLBXb1MlUljg+Bz4MQvY55Kb0Z3JA0UhwOnHrzApVA4PDX3WK64g1R/hcMMdSvlgAkuXIMoFE4mqmlOAECz3k2c99RdbVQGCnLE78oJ+aNG8ZgcnF1VDjxJyrSjBLgWmHp/GzUnS/eFkI7Q3IMRmhS/zr+G1JbWr2nBfGocOEcGQD8H4gWLeNY9BQ6T4uAesl1KIo8rwaZQWHX5S7HAKVEoBhnInHKyHGWbJ+wYAHpCXPT+ekXPj4fP7JmY3AnNcSoA/KB5NYkbG4TBC0Z7gCYZxQAztt6AwuLcP2kyl1TuQGuBM2uPMOcXp3ofUHCMV5mVTCGiFjj1at7zZQoUAUUhmixeHKOFZYETuTLOIepUXr2SmZjGoD4syZw0aafSq/65lg4QtWaT71nymZuE66mKywlCH3nWbPKLGv5M4y71bRS4lIVkWq1cBcsNOywtlR5oaOBdyzF4jKtwujKSy1H8YpNpH+Y+lG/OA/SjBDl0BzW7k+MYVahuGGFCO3ZmJXP1iik7EoXiNTLQ3+QtYuyhMb4OZzGKOW8JWZPs3U8ShcLcJXFS6XMGR6o1MkLvHFdCGbmU/qmWnAKXk4/CkRJNvganXpnW0YZRUC1Ks2qKdJnUvNOxn1LKGBWXBM45niYWeIadmEQLkLvFZSv88GeuE5OL1A+MYBhI5PE3pWWSKBTmMzU9Uo0yEq6ULH9G3pVoYjx7blRMKUeqeRQK/xpyW0MnJrVsQ3xnRrsHK/LKv4H3/IpfsLg1eraoTinzo1C8tsUbc0NVgapY4OHP1LoZFHAnvSnlYkah0O5jEkYEMKNQqDsghoVDzSAr5Ug1rQwGYYShBc5rTxkVdhRKCfSZlDCohcKwwA2dmGbhpTRw6rMoY1nnv1rS2h7KwXpXk0E9mCaVYlblBj2kLvY/fb8lZPnRtmHeJ2fbrRC+AMnXGL7DLLqDaoGnUguFO+YMiz1vEEbIiZ3OuxLNiqYgXMA5TQowd2IqWYJ5SbyaYyxwj+xToI5BSeGJhLZKN+h2XEeMCE820vXLoeyoljV3zgDVTuShKBBNu7A9Tw7TzE2zMELaC2DqyDKp8BiVq88+OVtsap/MbFYOZ26SB8CywKVEYyO93kgPs/h5/FAD/tg3MOclxwI3PbKPetIO1/gCwloz3N1zUvvo7rqcu2Xq4sx9P4Aqp9KTvwyhX3btFAa/CkScmIyXPpRNyaVrZ6bAOUEC1GxPzktLjgNnP1OQHYdc+olbwc/jwM0scP78pUN9bW4UCsfY4x5GrUB2bkd2EdRFIjhomdCWGkFGzVEBeJSdmROThipnYhYXkztZTOpHBNdS2pfEgdNkM03GS8Va5tAy1ExM7jOKOBop21Qepx3hjYl5A00MmiLnuGF7ipVocCwaEJmXJWRi6mBaC4VKCRR+Bw44xldjQwPZ78ZRyjoJHGK9c5Oj5apKoSS241rIzB2oat9ETMwpXMU5c5nqcDE9Uo0TE2tCYZHvDyQKzlVSUkZ2PAQZWHHdUjIVrERTI719zpFobqS/Xqa14NVlDUzDglwC2NSqAL1eiPqbENR5wZMppFA07RjzM88oppQjRuNwcweAqqfSFwd3MeKnRocKmbqdB/hcI0CP6IhSBhyw+PJI42KPQkU2ALwtqq597BmRFtmoBa7Z0roSzYzFNVaCldA+ZoFTKBTXRXMjTXYlj4KJBc7ZTQDe9wnkK+OuqfAeCpR5QX0X2WWJ3XCMEp3sHJ8LgyJL68ATIMtx4CmGnEXlaGqgLfulbPMoJ3MDvhVJVFix6xTdQWhLSRjhOlOo9cBNaqFQdzyehUw/BNt1JWsx5lr4OcdFv6ZGTx5i/yYodK7TOXDa9zdxrClQyxsEHDjxebDLZvjzSNc7ZxHlHhJBmTu1UU6WuFXklpjkOqXUA6BuPXtRKAYUg94Cl+ytsLqO3JYwSZRSoIpCjvflvnhuxOege0mc0KKkwJXRvmlKIMx21LfPORL9GPIYhxH6OrKB+Sa7xB2FqXMVCK1Uj6bUL+xUCoWfJEU7sYjzDHLEhc11vd0sJcwztivKqgKnFsYxoVDC95FuNdIplLgCp0JKSfZCS8mv6xyjO4i0hLaN/4yaGhpYcbaALrKFR0NEF2VKqBYnSsSRXmq8EISF1X8JORx43nEjOwJ9+1IpFHY1QuLuxjC6EUC4sDdpFjIZ+w76m5hE+DQ0AEJj4fMscCqv7f21mTDv8w6/JEJmKRQTJyZnS6yUDtWJ6QaWDs+JSY268drSoy766p8CypY4xkmSLBzas6Iqjagc1Gead91QwRLDCBuEt62mbIEBsFLp81GnJ6E9t7SDQi8OnHhp3gkpp2S5eLum+D28L9Ws2R5Ewwgp4BymANAXd1Z9E2IiT5DGTxzrJobfBMhwIg+fA+cVtY87MSkLhN+eyXBED7dNg0LhvmDRhbHYEHPLBtAjGniWohvdkegsIokg6oO6IDcKobXKPDniLyHVom5i0G2hIgaLq+Aqv+j9KHSgSZKNQmB9NjWQjDWhcTIG/TIULeDNz3Bnm2xgeHKA5K8KZSgOFVlCWfw9xzdPJTexWhdACLEOwF4ADoC8lHKG7hpqIg93skR5JgpCJ2by5FIoVGpU+Xbt74nJmCwTSqrrTFMs4c/FJjO38iL18A1HSvRvYDgaWU5MZty1jFQX1FlbBRY4XWnwOXAqbaUgA8XPj0JpIoytSZ0Shei4Jcdfe5/EeAKy9augHNC6xxHbletkoO46e/nOkvvkhimXpMB9nCWl3EFtbBYHTmsfvpCU9t4n1flT+KJQoVKqKZY+1xEL8MeJYi1za59zwqQoSiPaF3VXRaUEwr7DyAQ936kscLO4cQpCpzr5EgC9w1vp1JtEPwKtUEoceEChaJ6L61vI+jgRDznm4RpR6i4xTyFGHSZ/b2rggHquwe4wQYC8I9GPscsDqp1KT/BMU+EylV/M4iG9kN4nt2hQLpjEtJAtRmBEcI0CbeHStw+35TRrmaLAo9lwSe0K+6Xy2tzFQVnIFCemUyA7BVynqjJABPTKo/A6TzbyJRH5CHMyQu1wd8Wq4FNzYzI1Et9p6W+SJ2Y2Bv1HnrWuHUDbBXE5cIpuii/6tMEuVYFLAE8KIRYKIa7sq4EQ4kohxAIhxILt27eTo0u4mWlR+oHj9U87CiUfTGL9QsF1xKprFPgJI0UolIBeoo1nVIEXW3ip2XCFclCdulwHkCs9hzTF6ovXkyYuEJJ/4k+jWlAYirJ3Ig8N8dhkPQXgLUY8DR514CW/6x7/nV4YIY2aiPnFGHOewqtT3qV8hAakolQFfpqUcjqACwF8UQhxRmEDKeXtUsoZUsoZo0ePjm/JElflaB96QVRUgdet/oLoS8kqZsXcqioKpR9BgcfD5mjgbnEp6cIlUShF2vSq2UGKFGGk0kueBR4PO9X0XajAifOFY1U6vgOb6cMMZG9gyKbko+wQwv5NnJg0B56UikKh4cnlW8NrCe2VYaRbrJVR3dzI5cAT2qlYeEJEEnfXBpSowKWUm/zPbQD+AGCm7hpyKj3T++1FOJC6jslBVVKm3n71APs16YXj7iK8a7jjpG9fyMdSlKeuz6CeutoiEndJQXYegZNsYDhBgr4FgZ6R9JdQIXqCD/W5qKgYjqIM5jG3nKykRTyZOleBkELp19SgpUs5O4ibn14T/EzdbYfVGhPkiFngmjlBjAOP72A0Dk8DA85YgQshBgshhqqfAZwHYKnuOkoURGE7CrhhhFxKhJudqJCLOHJIceAGCRkK/AOIdRQKnwMvNvVM6qlz6pVEQxRJ4+Aqq0zfueMUjoe2e8/CDRxXeqisU88Cp0/+wnlM37nRKCGOY6/XtRELXEehNBgsXgB9t91I4MDzjMWKWviqcPFPguvyHOVAaVEoYwD8wY+jbQLwWynlPN1FRok8TA6cAvUAdAVuCuXhctRRJ2beSW4bi3smvopR2Ul8YOyFLC4HwAgjjHHgyW04isbjkb2fWfQTcRxUJialLRClAmiUCCdnQFE6XCvXNAollviU9B5G6CNuYatcQB/oEnmkURE3gGOBCzhu8lsVWwy1tBqtciBn3sd3IrSxNlbgUsp3AEzlXmdUoYzwXWR0m0SRI/JS0iw2nsWukIs4MTuQrMElcVsbBTsKJfJClkvZ8qJQ6IrGU8rEXQBzGy4l3UIOLEmGdeRx4LyIm8YGgbwrWVZomIbOu8aVNE4/uuDn81wLXM39ZIUoJViKa8Tgfmjz8ytIc96fGy4hkgmg6QQuB045jo9bMA2ochhhEgLOmbg0c9KugejEN6wHTrV0fAu8vyYbTd2jFA6cVOdEjWvCJA0XT5oConjkwwWTXhDKjVixWgpF0qNmgEgYISFsL+8WjIe+e6+8LSO1n1oxrxCFvhlO7XxONUKqsz8KKiWhdh+UDEgAOO3YUThypH9+JcmhTKvWGC1wR3tX9bL2omoTWQd+VcmKK3BqlcFYhTJCvzEKhfDto8kZ1P4BAws8EshPOTyW64yK5TQwqKDGhIENFyuaDDQnJn8HE+O1CRQKZ/fiSD+MkBC6FrMkqf27PHnCWGWzOHDO7qNXYlKiXN6nSZVMtXPRjZtHoeg5aoW846JfYwOZcgrpMh0X77WjxLznXVr5YmpBL9UPh0UAqnSgA4XnjW71qYqJymkqOQD/mCWS1RJRfKBzVDn/7L5+BAtcSpNiWUwKRVlfCY6lXha4busZSWrQx4HzHM3UXRXXf+BFfdBC10x2X/FaKLR7qHRvjp3bO8SRJhtA22Fxq3ZGkY+MW3IUCm8ByjlezRAhBG3X6dJ2N4GzE3oKxXElqcKgUxBKqYsZpwYOKFSlnCzFmx9XNPovI5mTgFucKrB0uGGEbhiFQrlHIyHcKQru4cyqjceBF6FQVMgfNU6as+MxTaUn9M9yYhKTO1RbgBcHbnLEG7U6YhSqrVLGFIXGqa7IcewVwok4MZMuDePAabuPnF/7nTpW1MJdQSw+cVdGSw7yPimLpcvctQFVolAoGcncML+ARyPKER1YzraTy1H1RDMxNW2jHBgVsdhVSvvIQlTsO3CrEVJiy4NFgaMEiS8eoF5SOketrHtK6FphSVDqfDFJ/PG2+XwKhVunBaCFXXIKPBWCulAEIZ3EqR9a4PRoJiH0ykHF4gP6fj0fB6FELaMaoff+a5vFUBUnJmWQosWjuNttzjaSStGoPtkWuMrEbNJ/D8UFchB19tK+d6R9kTbcJCfKEVfcRQHgxcXL6ORnWMie30y/XQaYTtLIgkKBSr3nOzHD98STjbZYABGlkkgBhHQLPw6cVjAslhVL6NcrXKYc0Pr2ii7T9e+qZ0Ba1Gn158MdEi2MkBskUR0OnJDDHC0eRbOoVD0F2ivgMC3XwEPNfMNykVR63fSU0uBklYiDjVXXPIGX7J3Io5GB4MQ04ZGj40FZ/FiHFKv2hOHulU1HTB7h8tIMH3zsOk82BodcQOslUgCR/k0scMquInh3QfvuPb4FTsmi9foP6akkOK4qKAboRtLLtKUfiEGicGK0DG20q3IiD2XicA8RllIGD4ijyBqI4VGmGW85VjEr82qETY0NoFTZjFoExW4R1H0xoFCKxZYXKhpSv5Ke2OBEt79kq4zWvlc2HbV/xlwJU+l5VHNhdBSJAy+oz5EsV9g/0wAParTr7iIRRolQoA4+EN7FBDmiRmOCHDLiSNa9q66kJUIx9AbHYFGouAL3zn2jO0+8kB6KgvVqd9A58DBkiIKSMzEJtVC4hwAAUScpT9kmWeBh2rv3f15SQ5E+C2OpiVYpPRPTjF+njFq+wIqiIFoLhYLQ+uIpykI6hHJtUJ+HwOFyE95i9yE6ljnOaiDUIdTFzqPX9LtzFQ1D6Zd6+EJAvVJi7qN+nKwqcOrWMso50x6SDKqNkSaxK8PsKEL/JgkTgDfZGhsEKWbVlTTHUhTxuua0hQ5IPgHcZXjOvfYRBa6Rk1VO1qXveDin96i+qXHXvZQkpX8pg8JdVN9EGAtNV5TRaC11Xx1yhZmlBCemWRy4/65rLNpoAAInwocateLIaHx1AoUh6dEwsXrqCW1jR+UlN40lrlFReQucGAAfzwDT9yuBMJuLAFU/msq7cR17CjlHbSP1k4JbzwOIJwqQFjqE1FRRvjrY9vFk8G/QJ3rx6kSai5wNynB4AkoJ0OZLryPViPOFcwSb2m1Q52P0uphshGt6nRJDoQAMCk15VmqDtoxrEAdOfXclPdwPiGbdJiM46JpsgesjYXoZfkVaSymN8kCqY4FTMsAiHDUFrkTwNlK3YZywrd5nD9KQ849JokwKbjkAIP4yUi09wI/uKSIRl++npPObJsNQaJxg8jM4cE7pzkB2wrFYSh5vN0V/vdyI8uCAk6qtkCMedQbwM5Zj1zLoUkYlYL+WOb12OtUwiip6EgdOiLDpZfgVNXC8z9AvQxvtKljgbkBdJEHGFI0eyolJDcTKRxwsPAuc1H2AnOOSLf1YNiHTAqecuen1G9adKOpwDCJuaGPJOtCB5cSMHlRQvF0hz0hVAtSXNVgkiYuPUdihywuDVTBxYnKSqkpJ5Mm5bsQpmLwAU0v7Ar71yyg/60QWx+Tdhv/+EXQONcJGEuc9p7xBFFWxwCmrYbxmB3E1Yig/tQXS8XMKTvCi8JwMgcecoAxN6oG7bAtcn8DCzTyMFW4q2qf3yeGppaTVhik8F5LlxOQUdKJWL2TuYIDQCUtJ447CxIkZjYzyril+kXpuJk7MKE2ZhKg/ggKVsehZ4AR6Kqj9rqNyIv0SOHBajLv3qTMuegVJEIe6Khx4M2GVCQ5QIFMoMsi2IsXpKk820WKX0QWFgZ68JL0o3t/DUCrqq5IPFHjx8rBRBJ72hLv0roWi6ZMQv5pmOrpTIC8FMYuXnciT3D4oAMUsrqZ2RiYcOCuVnnPQdmQxMuPAaVZ+qLeIi69618lGi75/6uHHgGeYUWrX9E60Sm6X+XKyXhaVXqGFW0O6BeNRKEQ5iMpBoReFwrLA6Vwr+2isWKouwRqRYdEvvbVMlEGG9Y51ceCsAx1iu7WkbWpcXtJLTbSglBwAfXsbTZ+mPs5osglHT6rxNnFiUnYUnJPae93H8SK9KNQFJ5HHcb2j+ahvSrRQWBJUFBh1ToTBCUl9FuiNhHsDvLroQLWiUCiOjUjcMIcaAECaxSoVVsfPhf17n9xwKo8DbyDVOqYW3YkiH3kZKZcoZ1+Sb6HQotVPZjdy1mWyBc7lwCnUgEmlyJBCoVMi1LjuwhKslCfJqc1SeF3sXoSLoyWOdQLGT2rnqfCAJyak8vAiyNwIB04zWii+lIcXb8K7O/aTAhsC40Kz4+/1fKiBA1mlUKI1BJLQk48chEByzgFgTIIc05Ntnsgj/awxvWNESn4YUTyVXg/XjToxi0ymAspA13HsNO1ibQwmqONKEo1TuLjSLHD1LPXPs/BAB+14BAsKPSzQJAcAME3kiVMoutho1T/bAifWCwnCCMGZF/REnmj/FPAscCQKEVb21FAoBrkGQGlnYhqBWsWrJ3KSTUePvl+JaD1oPTwOXL+9C9qrl5i5xfEqp9G20iaB/PnIgbvkjFXNdpVrLTuuXoH0WhQoVjLBORqVl0UjSHqWZ++XS2+dAWFhJAqCOhzM5987CkV/TfSkHB1kgQXJQT6qaDU7qCCBhkQD6udwXA43jCBKaHfi2GEYMbgfuvOOtt88cRGhUocmRghQtUxMfTytssCp0RUqE5MeRkjd3nkwTeSJlg6glETleqHj5/jpETiMkigUpsJyZRjbr48DZ5wTKfXOUa8vvpKJn1SeLEzhqSpUzpx6IIbXZ8QnY0ChcJKGenH6iYuj92mSyBNYqZrHomqVUBevfEB/0hS+46j+k28gAQxobiQf6EDJBlV/0u0ACg1EKqrixKR4v5Xl2kC0kL0IDvUzZRK7rC1roSOEOpl7FAeO5PdSJX+wDzWW4ctIXeh0oVIBBUBUWJRdFdWZE5PDpZVd6OUAIlrU4TjQ2lPpn2gmK9kxJ+lZgDHZChYvigXOiVMvnAscUKNQco5LOvRXIQwLpI8t5bvmHBf9mgQpukVVI9TtLoJoOk2CTqERklknJjUOvCfv+sV2aBPHo1A4q7gMqA3aNszs0Fl1fh/FEw/wLCnVP6B2KvTtZ9LCyLUGKJX3ep1MQugTALr9nVhS+8Ki+VrrKeL05KTSU6poAvFEKDJPG4lVNjkTk3V4RMG2Plkufv8K4W472bhQ9b0BHv1FHVuH+O4qnwvFLxaLYkpqFzGwgIR3rtdz1Ajgo0px4PoXocdxgwp+LGoAtC/fkw+jQ2iTwI1FIVCncbhQ6Goim3GNKikjeuZm3nExb+nmPl82V/oZq4RUeiqnTDl9hjtB17d1AACeXbUdugvykTGg9B09fZzW3lsgqBFCUYqCuuRHT2ZnWeC9TjrSP5HHrQAAFqpJREFUX9P7RB59W5Ms0ShPnIScK/13UT9WUspIuK2IFVJLkoPie1FcOSViTPWpixqiUq+FvgzqMJekwIUQFwghVgkh3hZCzKVcE1WEUgJrt+/r9ULs7shhX1c+YrkmcUwSTyzbgq6cN5M7ehz84sV30ZVzil63eutevPZuW2DhS+kpvR37urGxvbPPa6LpuwBw23NrIaXEjn3duOf19diyuwu7O3PIOy7e3rYvuE4tFAACGV9/tw23P78Wbft74LgSXTkHm/z7Kqv3O4+uwD/+ZiFeXrsDXTknmKiuK7Frfw/um78eHT15bN3TBcArDdrjuHhy2Rbc8cK7uOrXb+Dxt7bAdSV68i6klHhgYSva9vcE2XHrdnRg3tIt6Mo5+OTtr+DVd3Z639VRk86Te9GGXZHnJ/FW6260d/SgJ+8i57jeFtW3MPZ15/HYks3o6MkH37+9o6cXDbF04+7g++zc141te73vocauK+8AAK468+iYUos+Uyklco6Lnfs8L7cyDJ5fvR0nfWseZv/wWbTu6kDeCb//y2t3YNp/PBnIkne8Z+j6z2HZpt1Yuz18fm37e/DuDm8xUfP21ufW4r756zFv6WYAwIJ1bVj4XjhG+YjSy7su9nblgmcopcQ3HlyCJ5ZtAQBs3dOFfd35IIT0vZ0deGjRJriuDMbQmzsOVmzeg/nr2hBFoTW9q6MnqHWyuzOHb/7hLby5flfsmmKJSXe/sg5/WbkVjiux8L22WFu1OG7b2437F7Zix75urNm6Fwvf24XP3bUAb2/bG3yf1Vv3YndnrsDokVixeQ+27+1GT96F60rs785j6cbdyDuhkzHnPyvXlXh0ySZ8+hevoaMnj+68g/nr2vDrV98DAPRvbsSOfd24d/4GAEBnj4M9XbngWgDYtqcLrbs60N6RQ0ODwO7OHBZt8Obd3q4c/rJyK6SUwS424Mp9Dnznvm7s7szhqeVb0Z130NGTR95xcfPTa7BlT1dAwe3q6MHa7fswf10bZt/0LDa0dWDBujbs685j825vXg9sbvTH2pvzG9s7sbsjh6Ubd+O259YG779aKF9duxMb2jqwasteJEFwYzuDC4VoBLAawLkAWgHMBzBHSrm82DX9x06QY6/4CebMPAL3vL4+9rfB/Rqxv8fpdc2kw4Zh6cY9ffZ3xIhBaN3VEaxeHzpuNJ5bvb2ozBdPHov1bR14y1ceR40ajOlHHIwH3mjts/2so0fg1XfiL8yXz56Am59eU/QefeGiyYfitXfasHO/Ppzm7z7Qgl+9vI7VP9C3rMVw2EED0Zlz0NaHPJMPGx6MzyVTx+HhxZtIfR4xYlBgNSfhny84AT+Yt5LUJwD83z/MxN/+8nVS2//62BRcd/8Sct//ctFEfO/xFeT2j179QfzVT18kt//Z5SfjS799k9z+4slj8dhbm8nto/jt59+Py+94jXXNHX87A5//vwWkth+fMR6/W9D3e5KE804cg668i+cT3ksAeF/LwXh72z7s6siR+r3+wyfi+keKqppemDPzcNzz+gZtu7OOH41nViXLqsAZk//4yEn41kPLtO2Kzcn3fvBXC6WUMwp/X0oY4UwAb0sp3wEAIcS9AD4CQDuq7X3EBfalvAEUVd4AeikM3Q6s8OXo7HGKKm8AfSrE1ZoVsS/s2p/DoP6N2Llf37azyDjosKGt751DXyi2ywAQKG8g5JYpoChvwLNWOBh30EBWew64dBUlTT0KbkTBpt30Z1jqvQDe93np7Z3s/gFv56RT3gCweMPuIHSYgkH9earrhTU7SO0WrNulb+SjbT9tsQGA9Ttp7wf3MZaiwA8DEF3SWgG8v7dA4koAVwJAv0OPBRAqkFOPHomPv288vnLfYu3NfvKJadjblcO/Jaxiz0ZWzte+eTbueP4dvLR2J1Zs7nsR2LKnCz/46GT88wNvAQBOGjcMR48egkcSrM593d629l8vnojGBoGLp4zFn5dvQ1ODwPBBzfjta+uxZutefHjaOGzf040H39yIjp48nrz2Q/jRU6twxMjBWLKhHf/wwaOwY183Nrd3YdjAZjy6ZBMeXbIZ2/d1B/f6709Ow2EHDcQzq7Zh574efPnsCWhqFLj9uXewets+TDv8oGA38IUPHY3/mrcKL31jNp5YugUHD+qHFZv3IO9KnHzEQRgzbABeWbsT//Got75ePftY/PQvb2Petadj4Xu78Nyq7Xhy+VZMOGQIAODIkYOwr9tbTObMPAJHjxqM9W0duOIDR2JDWyda2ztxyND++Mmf1/Qa3598YhoWbWjHk8u2YNPuLpw+YVTwAm3cFSqp0yeMwiVTx+G/n16D1l2d+PSsI7B6yz68vq4N154zAXNmHoExwwbgo9PH44E3WnHznJNx/klj8JcV29DemUN7Rw6PLN6E5f79d+wLF4cH//EDWLVlL1Zs3oMF63Zh+eY9+K+PTcH+7nxguW2KLGQ3fnQKmho9CmNxa3swlz496wj8+lVvt9gesQ6/eu5xOOuEQ9C6qwMPvrERU8YPx4a2Tty3IHwlFK0xqF8jvnDGMXhfy8Hoyjt4e9s+tHfk8NvX16O9I4ePTBuHhxZtwrY93Tjh0KFYuWUvrp59LE6fMBr3zl+POTOPwMtv78SfV2zFyi17Ar/HcWOGYPVWj+6J1mQff/BAXDx5LIYNbMb0Iw7GqCH98Mo7O/Gjp1bHvsPerpCiuezkw3D6hFFobBD4wDGj8MAbrXj/USNw2S0v+/17yvWSqeNw2fTDcNDAZtzxwjvo19iAE8cNwxPLtmLs8AFoahA4pWUEHl+yGa+8sxNb93RhZssIvL6uDedMHIPzThrj9edI3Prc27j6rAn4+gPxXdP/XD4d4w4aAFdKjBk2AH98cyMeXbIZ151/PD57l7djGNwvVF0tIwfhGxdNRM5xccKhQ/HtR5Zj6IAmtHfkMLh/E55avhUHDWpGqz/3fvyJqdjdkcPEscPw3OrtuOXZtegLJxw6FBdMOhQzW0bgrlfWYc3WfXj/0SPRtr8bTyzbih37ujF2+ABs3t2FWz41HRMOGYI7X3wXf1m5DbOOHhnbvW72qc6mBoHZJxyCi6eMxd2vvIerz56Ax5ZswgNvbITjSvT3qapB/Rrx6jfPxs+fXYt5y7bgvT4lLI1C+RsA50spP+f//zMAZkopry52zaSpJ8t9F34XEw4Zgj9+8TQM6tdY1HHRMvex2P/X3XBxrzauK/Gb19dj2IAmXHPvIm17ha6cgxP+bR6OGDEIz3/9rKLt+sLHbn0ZC97bhd994VTMPGpEYttnVm3D3//vfBw/Ziie+MoZiW0fWbwJV9/zJk4aNwzLNu3BYQcNxEtzZ2vlUeO07oaLgyptxeC4Esd88/GgvQ5/fctLeGN9O+6/6lTMaOn7u25o68DpNz4DAPj6BcfjwkljcdSowUXlPGfiGPx5xVZ85yMn4TOntmhloODJZVtw5d0L8bPLT8aujhzOmDAKR47sLYPCdx5djjtffBeXThuHPy7ahEmHDcOjV59etL2S/ZEvfRAf/tmLmHvhCbjqQ8do2995xQycPXGMVv7lm/bgoptfwJD+TVj0rXPJCUCvvrMTn7z9VQDA/H85B8+u2oYLJh2KoQOai14z6d+fCIyQ2z5zCr5w90J8efax+Kfzjk/8Ln+65nQ8sngTrjv/eJJsr6zdiTl3vIpDhw3AM187E/u68xg9tH+vdlv3dOH9338aAPDiP5+FBiESd12n3/gXbGjrxM1zTsaX7/Hoqbe/d2HgZyrEis17cOF/vxBb7ArnvtIHCrOOHoEtu7vw7HV964a3Wnfjwz97EYePGIg/XXMG+jc19FlbfX93Hif9+xMAgBe+fhZ+/NRqfP+vJ2OAz4dH8Y0Hl+Ce1zfg+5dNxqUnj8PA5rhuFEKUnUJpBXB45P/jASQSpiqq5KOnjMdgwhZoYHMjfn/Vqdjd2fdWpaFB4DOzjoSUMlDgU8cPx4enjkvsd0BzI16aOxtDmNswAOjMOX4fev/vUL9/5ZBLgpJFrcBXnVlcQRSDjhLgJiFdMnUc3ljf3qdCVhgxuF/w8z+eeay2z78/rQWLNuzC+ScdypIlCeeddCjmXXs6jh8zlKRcph1+EAAv0mn5f5yvzUg8ZvRgrN2+H5PHD8cT156B48YMSWx/9KjBeGfHfjJFc9AgT+F25pyiiqgvROdvgwD+ZsbhCa09/P6qU/FPv1uMFZv34MzjR+Oez8/SGiIAMHHsMEwcO4ws2/iDPSW8dW8XBvZrxMB+vZUWAAyLLDbjDx6k7ffzpx+Nbz20DIcOGxD8LmnMlGLNuxKvffPsPt+B/k0NOO3YkXjp7Z249pwJuPac4xJlGDLAf69zbqIOGdy/CQ9/6TTs3NeDw0cMwo8+Ma1o20H+jmJ/dz74mYJSFPh8ABOEEEcB2AjgkwAuT7qgUQis/M4FgZJKwpLrz0ODECQlG31pH/rSB7XtAc+RZ4J/Ovc4fOm3b+KY0ckvMRA+aIcQ6tTfXxAG92/Cu/95ETkF+/6rTg0ohHLjig+04NOzjkx8QQYVeTGLYeLYYVjwr+eWKlovnHAoXbmoiIqevCS9LI9efXrAzx5/6FBt+yNHDsI7O/aTY6aHMuZJFCeNC78zdXGeOHYY/nRNuNs49ZiRie3//E8fwuqtfL/P2OGegh05uLfVHYUyhFpG6pU3AHxm1pGYdfRIHDdmKK6efSyOPST5PVRRMI7r0TF9QQiB33xuFun+UZnzBM5+yviDSH0O9t+j/ZHIIwqMFbiUMi+E+BKAJwA0AvillFLrZu1r+9AXhiVsA6uJsyeOwYrvXEBqGw0d0mFmywh8ZtaRuOrMY8jKGwBmtIwoSm/0he9eOolcVU8IoS08RpW1sUEYlctNAwcN9OaWshJ1GNivEQNBX6hU+KUKx9RB8bkXTebtSoQQ6NfUgJ68y5ozHBx7yBCtkuwLTY0NuPVT03HiuOSFVQiBB/7fqYmUV2H748Z4i+hXi9A+USh9czjBuqein2/QcBfcJAz05wA3iKGkYlZSyscBPF5KH/WMQ4Z6K/4150zQtm1qbMB3Lp2Utkj49Kwjy97nf/715ESaJYr+BOopbbz/6JG45VPTMfuEQ1LpXy2QeeIL3tAgsOBfz8HwgXyjRa3F1EW5krhw8lhSu1OOpBsgXBw6fABu/dR0fOCYUWXrU1HBI4ck7y44OO+kMfjBvJW4eAptzBQqXo0wLVz/4RNxNIHWqCQG9mskOQtrHXNmHqFt8+XZE/DjP68OrJdq4yKicjGBqhtCVeAAMMpQGfzmc+/H7+a3sqmsAwnUhYSKYQOa8d1LJ+HM40eXrc9jRg8x0hV1o8D/7rSjqi2CRQKuOWcCaSdSD9AVLionTjlyRKoWrEXfSGMna4JsmEMWFnWEQT7vmhYvbWGhUDcWuIVFVvDNiybioMHNuHBS+UIlLSz6glXgFhZlxvBBzfjGhROrLYbFAQBLoVhYWFjUKKwCt7CwsKhRWAVuYWFhUaOwCtzCwsKiRmEVuIWFhUWNwipwCwsLixqFVeAWFhYWNQqrwC0sLCxqFMYn8hjdTIi9AFZV7IZ0jAJAOzSv8siqbFYuHrIqF5Bd2axcIY6UUvaqnlXpTMxVfR0LVG0IIRZkUS4gu7JZuXjIqlxAdmWzculhKRQLCwuLGoVV4BYWFhY1ikor8NsrfD8qsioXkF3ZrFw8ZFUuILuyWbk0qKgT08LCwsKifLAUioWFhUWNwipwCwsLixpFRRS4EOICIcQqIcTbQoi5lbhngiy/FEJsE0IsjfxuhBDiKSHEGv/z4CrIdbgQ4hkhxAohxDIhxDVZkE0IMUAI8boQYrEv17f93x8lhHjNl+s+IUS/SsoVka9RCPGmEOLRjMm1TgjxlhBikRBigf+7LMyzg4QQ9wshVvpz7dRqyyWEON4fJ/VvjxDi2mrLFZHvK/7cXyqEuMd/JzIxz1JX4EKIRgD/A+BCACcCmCOEODHt+ybgVwAuKPjdXABPSyknAHja/3+lkQfwVSnlRACzAHzRH6dqy9YNYLaUciqAaQAuEELMAvADAD/25doF4LMVlkvhGgArIv/PilwAcJaUclokZrjazxIA/hvAPCnlCQCmwhu7qsolpVzlj9M0AKcA6ADwh2rLBQBCiMMAfBnADCnlJACNAD6JrMwzKWWq/wCcCuCJyP+/AeAbad9XI1MLgKWR/68CMNb/eSy8hKOqyefL8RCAc7MkG4BBAN4A8H54mWhNfT3jCsozHt6LPRvAowBEFuTy770OwKiC31X1WQIYBuBd+MELWZGrQJbzALyUFbkAHAZgA4AR8BIfHwVwflbmWSUoFDUACq3+77KEMVLKzQDgfx5STWGEEC0ATgbwGjIgm09TLAKwDcBTANYCaJdS5v0m1XqmPwHwdQCu//+RGZELACSAJ4UQC4UQV/q/q/azPBrAdgD/69NOvxBCDM6AXFF8EsA9/s9Vl0tKuRHATQDWA9gMYDeAhcjIPKuEAhd9/M7GLhaBEGIIgAcAXCul3FNteQBASulIb3s7HsBMAH2d2FvRZyqE+CsA26SUC6O/7qNptebaaVLK6fCowy8KIc6okhxRNAGYDuBWKeXJAPajOjROn/B55EsA/L7asij4vPtHABwFYByAwfCeaSGqMs8qocBbARwe+f94AJsqcF8OtgohxgKA/7mtGkIIIZrhKe/fSCkfzJJsACClbAfwLDyO/iAhhKqlU41nehqAS4QQ6wDcC49G+UkG5AIASCk3+Z/b4PG5M1H9Z9kKoFVK+Zr///vhKfRqy6VwIYA3pJRb/f9nQa5zALwrpdwupcwBeBDAB5CReVYJBT4fwATfa9sP3hbp4Qrcl4OHAVzh/3wFPP65ohBCCAB3AlghpfxRVmQTQowWQhzk/zwQ3oReAeAZAB+rllxSym9IKcdLKVvgzam/SCk/VW25AEAIMVgIMVT9DI/XXYoqP0sp5RYAG4QQx/u/OhvA8mrLFcEchPQJkA251gOYJYQY5L+jasyqPs8ApO/E9En+iwCshsed/ks1yP6ILPfA47Jy8CySz8LjTp8GsMb/HFEFuT4Ibxu2BMAi/99F1ZYNwBQAb/pyLQXwLf/3RwN4HcDb8La8/av4TM8E8GhW5PJlWOz/W6bmfLWfpS/DNAAL/Of5RwAHZ0SuQQB2Ahge+V3V5fLl+DaAlf78vxtA/yzMMymlTaW3sLCwqFXYTEwLCwuLGoVV4BYWFhY1CqvALSwsLGoUVoFbWFhY1CisArewsLCoUVgFblG38Cvv/aP/8zghxP3VlsnCopywYYQWdQu/psyj0qsiZ2FRd2jSN7GwqFncAOAYvxDXGgATpZSThBB/B+BSeKVBJwH4IYB+AD4Dr3zuRVLKNiHEMfBKIY+GV+L081LKlZX/GhYWfcNSKBb1jLkA1kqvENd1BX+bBOByeDVKvgegQ3oFnl4B8Ld+m9sBXC2lPAXA1wDcUhGpLSyIsBa4xYGKZ6SUewHsFULsBvCI//u3AEzxq0J+AMDvvRIYALwUaguLzMAqcIsDFd2Rn93I/11470UDvJrP0yotmIUFFZZCsahn7AUw1ORC6dVif1cI8TeAVy1SCDG1nMJZWJQKq8At6hZSyp0AXhLeAdb/ZdDFpwB8Vgihqgp+pJzyWViUChtGaGFhYVGjsBa4hYWFRY3CKnALCwuLGoVV4BYWFhY1CqvALSwsLGoUVoFbWFhY1CisArewsLCoUVgFbmFhYVGj+P8LmHTRNH9sngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mw_data.plot(\"time\", \"torque\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can find some pushes in these data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 29 complete pushes in the data\n"
     ]
    },
    {
     "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>stop</th>\n",
       "      <th>start</th>\n",
       "      <th>peak</th>\n",
       "      <th>tstart</th>\n",
       "      <th>tstop</th>\n",
       "      <th>tpeak</th>\n",
       "      <th>cangle</th>\n",
       "      <th>ptime</th>\n",
       "      <th>meanpower</th>\n",
       "      <th>maxpower</th>\n",
       "      <th>meantorque</th>\n",
       "      <th>maxtorque</th>\n",
       "      <th>meanforce</th>\n",
       "      <th>maxforce</th>\n",
       "      <th>work</th>\n",
       "      <th>feff</th>\n",
       "      <th>slope</th>\n",
       "      <th>ctime</th>\n",
       "      <th>reltime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195</td>\n",
       "      <td>97</td>\n",
       "      <td>176</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.676823</td>\n",
       "      <td>0.490</td>\n",
       "      <td>36.376315</td>\n",
       "      <td>94.426828</td>\n",
       "      <td>10.559981</td>\n",
       "      <td>24.357933</td>\n",
       "      <td>38.399932</td>\n",
       "      <td>88.574302</td>\n",
       "      <td>18.006276</td>\n",
       "      <td>55.197071</td>\n",
       "      <td>61.665653</td>\n",
       "      <td>2.695</td>\n",
       "      <td>18.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>735</td>\n",
       "      <td>636</td>\n",
       "      <td>718</td>\n",
       "      <td>3.180</td>\n",
       "      <td>3.675</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.630329</td>\n",
       "      <td>0.495</td>\n",
       "      <td>36.170961</td>\n",
       "      <td>86.937413</td>\n",
       "      <td>10.449778</td>\n",
       "      <td>22.283936</td>\n",
       "      <td>37.999192</td>\n",
       "      <td>81.032495</td>\n",
       "      <td>18.085480</td>\n",
       "      <td>52.638096</td>\n",
       "      <td>54.351063</td>\n",
       "      <td>3.095</td>\n",
       "      <td>15.993538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1349</td>\n",
       "      <td>1255</td>\n",
       "      <td>1330</td>\n",
       "      <td>6.275</td>\n",
       "      <td>6.745</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1.601879</td>\n",
       "      <td>0.470</td>\n",
       "      <td>45.783597</td>\n",
       "      <td>102.833058</td>\n",
       "      <td>12.917944</td>\n",
       "      <td>25.623400</td>\n",
       "      <td>46.974343</td>\n",
       "      <td>93.175999</td>\n",
       "      <td>21.747209</td>\n",
       "      <td>63.410359</td>\n",
       "      <td>68.329066</td>\n",
       "      <td>3.130</td>\n",
       "      <td>15.015974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994</td>\n",
       "      <td>1881</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.405</td>\n",
       "      <td>9.970</td>\n",
       "      <td>9.86</td>\n",
       "      <td>1.781313</td>\n",
       "      <td>0.565</td>\n",
       "      <td>33.413155</td>\n",
       "      <td>86.508159</td>\n",
       "      <td>10.060917</td>\n",
       "      <td>22.747502</td>\n",
       "      <td>36.585153</td>\n",
       "      <td>82.718191</td>\n",
       "      <td>19.045498</td>\n",
       "      <td>56.930325</td>\n",
       "      <td>49.994511</td>\n",
       "      <td>3.190</td>\n",
       "      <td>17.711599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2629</td>\n",
       "      <td>2519</td>\n",
       "      <td>2612</td>\n",
       "      <td>12.595</td>\n",
       "      <td>13.145</td>\n",
       "      <td>13.06</td>\n",
       "      <td>1.897524</td>\n",
       "      <td>0.550</td>\n",
       "      <td>43.382667</td>\n",
       "      <td>118.642142</td>\n",
       "      <td>12.244082</td>\n",
       "      <td>29.976050</td>\n",
       "      <td>44.523935</td>\n",
       "      <td>109.003817</td>\n",
       "      <td>24.077380</td>\n",
       "      <td>61.715078</td>\n",
       "      <td>64.464623</td>\n",
       "      <td>3.590</td>\n",
       "      <td>15.320334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stop  start  peak  tstart   tstop  tpeak    cangle  ptime  meanpower  \\\n",
       "0   195     97   176   0.485   0.975   0.88  1.676823  0.490  36.376315   \n",
       "1   735    636   718   3.180   3.675   3.59  1.630329  0.495  36.170961   \n",
       "2  1349   1255  1330   6.275   6.745   6.65  1.601879  0.470  45.783597   \n",
       "3  1994   1881  1972   9.405   9.970   9.86  1.781313  0.565  33.413155   \n",
       "4  2629   2519  2612  12.595  13.145  13.06  1.897524  0.550  43.382667   \n",
       "\n",
       "     maxpower  meantorque  maxtorque  meanforce    maxforce       work  \\\n",
       "0   94.426828   10.559981  24.357933  38.399932   88.574302  18.006276   \n",
       "1   86.937413   10.449778  22.283936  37.999192   81.032495  18.085480   \n",
       "2  102.833058   12.917944  25.623400  46.974343   93.175999  21.747209   \n",
       "3   86.508159   10.060917  22.747502  36.585153   82.718191  19.045498   \n",
       "4  118.642142   12.244082  29.976050  44.523935  109.003817  24.077380   \n",
       "\n",
       "        feff      slope  ctime    reltime  \n",
       "0  55.197071  61.665653  2.695  18.181818  \n",
       "1  52.638096  54.351063  3.095  15.993538  \n",
       "2  63.410359  68.329066  3.130  15.015974  \n",
       "3  56.930325  49.994511  3.190  17.711599  \n",
       "4  61.715078  64.464623  3.590  15.320334  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pushes = wl.kin.push_by_push_mw(mw_data)\n",
    "print(f\"There are {len(pushes)} complete pushes in the data\")\n",
    "pushes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can achieve the exact same thing using the `auto_process` function. Which chains the above operations into one monolithic function and returns the data and the pushes, as such:\n",
    "```python\n",
    "mw_data, pushes = wl.kin.auto_process(mw_data, *args, **kwargs)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make a christmas tree!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ITe07FQVqsc1VvNaG02kzV4p9ZshZM47b/YnTQb20qTcFF7WK7hX4rjaE+958BP78A+ioom6mTDrko9c2c23A8vkkCRWekMOavdax3OlZVJu9154oU/tORdBfYPpNpjVnYlZy0XEjUfPFTORvn9fkYgllI7mkDefSvTQlclfYN3y6pURQO6jXNvWK7hX4wUk/R7S+LaBc1JoN2WJGTIWmfwdNm9w14xB6FsfcQtXs0+GJMrC0HPNXtEb/bsdi6aRzpekzoUlp7aVm7O6QS9pwLgxcTZGc1ey1/mRlxeGaW6jeOOnwRNHuKatfmayuXNMjF4R9LtxDUyZnhb1Me+CaW6jeEGlxYWPWKRNEJYMtBxV6Ice04Ry6laZEzptxfBaGBK65harZp+P816ag2bslnJxsIstFsnhcJ8NRToQQrjlXKGLksGbfgEV/4ZpbODtovT7VKj6Q8dKnk6jsARBpCpvI0kEOyHqdXLqXVEj3jDV3hb1EwnHNLekIl0DVfHRPIxc1H7c7XtSlLce5HHqB0+bZbLKjwrmDHLhdWzIRLDB3hX3Dp5U4ZO8YzMLOR9bsJZXP5iiVuRp6gdvmWdj9+OTETbhDJmasuSvsCR2LY27Jltg4gPtajxcdL+rSbslcjXPOdxBIR63SQy7di1MyMWPNXWEPd7dmp8eMQ03HuzdZvl50PLeaK1fjnHPbvNmZcTyrRXqQmegyMWPNWWGv+9m7tK0nW0IcA0YTlTv35kXHc3NTldcL3pmA2+ZZ1P1SJqfcSJNAMdFlYsaas8I+k4HQHJfBvYCq2Uv+7snJXo6vbNpQF125bZ5N7el0MZ/retlUoZjoMjFjzX0/+yZkxskUWge7782HcTjyLVrndcBd596W4slezS/CIWdPAHc/RrYIPzf2PWTJrXgG1USX7pO5clizd7dHZVMH5fvZyytf0b0Cl5c8gcNbH8JVHZ9MWcPIpvZKF9xFV455KluaM5XF/OZis89WD7LcFfZMgSgju/zsvdGa9XxdyCubvJfShZceFtkyeLpxj1lyK54xte9U5Pt4YcjTgWfCfteuXZg4cSJGjhyJiooKLFq0CABw4MABTJ48GeXl5Zg8eTIOHjzoSflubzzKKuHFrAs1uZumr2xfhPNi96KXGl22tGcq98gZsLJlcHNCRfcKjOt6aywMObLHg8wzYR8IBDBz5kysXbsWzz33HJ555hl8/vnnWLhwIQYOHIh169Zh4MCBWLhwoSflu23jTIfNlCyUGz7d8sZplK8L0j4abcgrC4Mce7WJzEsPi2wRfm4cOZkt6w9e0rfdENR8MROD8hZljQeZZ8K+pKQEPXv2BAAUFRWhe/fuqKqqwoYNGzBu3DgAwLhx47B+/XpPynffjONSRi7A1cDp/vvO6pM0L49MTW7g1e7Fiu4VuPucezzxsMgW+VjRvQI/7zvb0eE5nNlJtsxkUiWb7iMt3jg7duzAxx9/jDPOOAN79+5FSUkJgNiAsG/fvnRUIWVy2RtHw00B7fZg6yZe2tZHfK8CU76I6VBuelhkk9AYdtxIzHw6H8e2zMe6e8rZ16f7VcrETEKb0WaT2PB8gbampgZTpkzBnXfeiaKiIq+L0xFxW4crZNUOWq5QZtqH3DC9xGcf2SfuvbSte9VP0rLPw+NAfJmy2WdyVp5N3d9TYR8KhTBlyhSMHj0a5eUxDaBdu3aorq4GAFRXV6O4uNiTst30LAGya4SWxep3nG/DpxsdNJtnQlzbOi86patVjefrTbYmqELR6awtUz0iE5p9fAd59kh7z4S9EAKzZ89G9+7dMXnyZP33IUOGYMWKFQCAFStWYOjQoR6V701+fg+fHXeqTrbZc/NlpU6OW4HQvICze5F/fKVXmr33Aotb92x8tsnIxCDjpuLkFp7Z7N99912sXLkSPXr0wNixYwEA06dPxw033IBbb70VS5cuRceOHbFggTchPd13vdSEV+afnled102Bko2d3Qh196LdYm6ywaEpm3GodY8rD8yHmyHVPhOzTJFF8kLDM2Hfv39/fPLJJ0n/pvnce0mkYU4acEkV1zqMl5o9FW5H4nrjuLNAm33TWCd4dXxlNpKufkIpx1XPsAw8k3RYArjk8A5annCWabX6AdoeCq9Mm7nd9N93OzZRpuBHp/TmIUbSMIpQy3B6i5nyKMrMsZzurhm6Qc4Ke7fOQNXQX+IseHrcvpuJHbRRF5srk5tw+MdXelOPcBqEPdXckaogS7fQz8Qgk43eaDkb9VLTUuiavb2Qc9mTMyWER1qDmy+FmzbLTM54uNEpvbIPp0OzJ3vjNHxyHy3L9dLVvuhaVmSy0UEhZ4W91tiu2eyj2fPw+DtoedNzV8IluKnZu5BHKnBC0Xol7MNa/AkPofYT/V1w+HTTLXwpz2T1ttVY8N4C7K7ZjbJWZbYDOoW4zT4LBEYDOS/sqY0t6w7RLHp4bgrSZLgjoN2rZFOKpeJVVbPJZq/B1uw5ad1coJX83Y0Y/Y3LlFsW0t2vc95m75b265XpxFwGNR2vk9Bt9u7NXtxc4mhKHi5evb/paANqGanb7NOL7Jl4ESuJ4tCR7n6ds8I+brN3N8RxNmj2bu8haJSvG3m5aBLKprgwMpqyzZ5r7vMqf8DdAUFWrhexknQzjo2EVZq9S7BdLyV/j7qpqlrWgfqy8TQrvv+0GzZ792ZCTciK49nCXHp20FLTpbb4nm4hJyvOi1hJ8UFfafae475mnz1+s1EXhbIRV6Nepp5FPK8mJOy90gnSIRgizEBoXHg2e/duWDbb8uIcAorHUrp39ubwAm3s062Y716ZTpzAdislzxhin9kWG4fzUrjtVcHFq7AaaXG9JG+qcvZsMzVoy4rlutfSCqUs0DrP3gk5K+zjZhx3N1V5GgiNOY12e/1AL94V3/jU3PNMeRHTeeFVwYXjKcWyYadBMnDNfY7LcSkNuTxCZhz3WgqUBdp0r0XlrhmH7Xpp3/D6MXtZoNnrwp769Lg2e36VrPNKo+ulVydQceDMaDjKenq8cXhmnCx4FUhkwnWXohwqm71LcM048vzSoNkT00U8G3hogooS393NdQXqS+HlCVRUODMajhCi2tNTgVpGqrHa0x4Izb2syFD6f7pt9rkr7JkLtLJ2dzvWTipwF4upXUqbvdi1GTW+u6v7Eog34OUJVFT0fuK6Zt/0XS8zhZv1pR5kQzJVeb8p2kTuCnuuqUOCmxuOUoU7kFGhnO5FNZW4ObOiCjovvCq4cExhrAO4s8j1MnUTHUW1d5p3Y7gDpVVbcw6yoawZKpu9S7jteslQ2JyXQZ5Gxz45Qd44+dolp5tK3OvI1Jw4J1B5Bcdmz5FBEQdaIOc4RSB9US/TDbcnWg16nDUhyoCYbps9yRvnm2++wf/+9z+ce+65qK2tRTgcTuvh4U5w2xfdKw8YJ3AXn6lQXvayVmXYVbMr6e/mvGKf6Xa9dNurggsrsqMDMw51gHfimZQO906AaLN3UVmgupTK4KwJUWLjZJ3NfsmSJZgyZQruueceAMDu3btx8803e16xVOG+HFSbvZeynm5b57nNcP3s7dqCaiqJmzOadohjLnFTmPy+OS87N2S0E8+kTOy0NuebHQ/aqh6cNaEmuUC7ePFiPPvss7om/73vfQ/79u3zvGKpooc4dsn10m2//VTwapZB0eyophI3N1U1pdg4HHs2564i+uI5Lb0Tz6T0HV5ila/DDGXlMfO1Ss5ZEyL1g2wz4wSDQQSDQf3/4XDY0wq5hVeul15CLUI/X9flM2i1e5QJV4qpxM01jixR+EhwFmg5fYq7M5dqbjOXQauLV4/DqGy4+czdenc5O20p7qlZZ7MfMGAAnnjiCdTW1uLNN9/EM888gyFDhqSjbikRP2zEJddLTbPKgiVtNzcsGdGFvQudULhYySYl7FkeNvR8uWbJqX2nmmz2gNwziX4GLc+MSMUrhYqbq101uGtC2RQbRyq6fv7zn6O4uBg9evTAc889h8GDB+PWW29NR91Swu1NUKluJHGTCFPLo3apI/URhzVKUqaL8iDdL0UqcOzZHBs113SXaG4rayn3TOJ6gzk241gUY3zObj7x7N1Bm956STV7v9+PCRMmYMKECayMZ82ahVdffRXt2rXDqlWrAACPPfYYlixZguLiYgDA9OnTMXjwYAfVlsN1vZQ1u26W8NT3kpbMq928oQbDMKcLCiGSCjZ3bfZNB449m6XZEza8JWLUQpfNKUfrwnz7Msj18eaJeGXW4ObrxhoR5fyLrAuENmTIkKQv84YNG2yvu/TSS3HttddixowZpt8nTZqE66+/nllNPlpDunUGbTpOqqLC3x1M61XhCMEdJ7EuAggkqYarmn0TOqqKU1NHNntmffy+2DOimGjIsXFSfBxWwtSk2bsqCdPffyhrLFkn7F944QX9e319PdauXYuDBw9KMx4wYAB27NiRWu1SgG3qkLR8Ok6qomoUbodv1gg7EKpRIRBIIoK8CvWb7eht7bI3jtOZks/nA4SgCXuqzd6Yt4t4NaizNXsXqkExdWWdzf7YY4/V/5WWlmLSpEl4++23HRe4ePFijB49GrNmzSINGk7xymafDao9xUTlRDNyYsax6rDU5qIFVcsezd7Ns4p5mn3s08/s0Fp9KCWRT6qK0u8xeZ3k5btrs3cxM2ah2bRAK9Xs//vf/+rfo9EoPvzwQ9TU1Dgq7KqrrsJPf/pT+Hw+LFiwAA899BAefPBBR3nJYJs6ZPkxNHuvF4Qo+TvZDRmK8K+xqgrl1CvqLk/erlSv297+nlilO/LG4YlYPQQGoSxuiGO38c4bJ/3SXrMs2LlHp7tWUmH/0EMPxRPn5aFz58747W9/66iw9u3b69/Hjx+Pm266yVE+FLjxY2RwNDanfZbuD2+uUzKcmGTCmmbvQAg1/j32aee9ZLfL0yTs6dVxdN9uwvHGYUW9ZJ5OlghF4HFt9k6tOJaavVd+9mmOLgkYNsHZPLB0ewlJhf3f/vY31wqrrq5GSUkJAGD9+vU46aSTXMs7EU2zdcvPntPBvZ6eUfIPGSJncW32vBOUeL8boe7y5LSn1/Fd5DNARtsxhjGvzh02l0EV9pri43ZsJlez0+Fq9u7Y7CmDa+rlcJAK+6eeesr275MnT076+/Tp0/Hvf/8b+/fvx6BBg/Czn/0M//73v7F161YAQOfOnTF37lwHVabBttlLGp4zjXb6DKnXUaJTOjHJhB2oQFal6MHabFaFqLs8OS+f15p9TNDJPSzcP6kqNTs5pXNRH3+qLUzyxnEzamoGJntuej+5hVTYf/jhh9iyZYu+a/aVV15B//790bFjR9vr5s2b1+i38ePHO6wmH60d3fKe4byYXj9DfdHNppywUbOnevlEtfT8uliVn2cj7am7PFknOjkY5NyEciaAntbBjMVpf6Yt0KbHjGOFV7My7vvoxkBDuZesc73cv38/li1bpgdCu+WWWzB16lTcf//9nlcuFdw+g5YTCM3rBSFKRwql8OKwFkQtftfj99hMraixRjh3su9IPSM1jZq6eDwoaV0Y5hbewnPs07HNnrRAS8srVY2UZPpz8RXKRLjkeOgR6/RZp9nv3LnTFAgtGAzim2++8bRSbuDmDk5ufs4XaKkdqSG9TScOO7DZO+l8Vpdo5pQ8iXSixBrhaHy3/r/N5LRUXngvvl9E1kScUAJOFsKzwWavmfvyku2mS0P5/Hx56e2qQT2nt0lq9mPHjsXll1+OYcOGwefz4R//+AcuueSSdNQtJbw6g5bmekkqMpavw41MsnKc2OzjUS8ZWAn7hsGG6xeeDI6w/+ZArTwRE+MdSGeArEVXhhmHsAZih5veOFrfsjPROcEoSN2Ug256vVD7IuUdzTph/5Of/ASDBg3CO++8AwB48MEHcdppp3lesVRx2/WSpdkzuqqTDk7ZKGNcbKXnS0xovMYid02zp4ZhtoMj7AMeRCUN5tEz5dizeWsjsU/HNnsXzThhXdg3jcNLuLnapSfH/Cesf2WVGScajWLMmDFYtWoVevbsma46uYLrgdA8mp47WZSiXBJ2pNnHPt1wvdTb3wXhS506A95EJTVqsPIZoJZAXg/Os0911yoFcgylBknmVtwpDdMOWtuJREMAACAASURBVBfloJsLv9S8KH02q8Il+P1+nHzyydi5c2e66uMabsfc5sRn5zxEp/FoYnWyTlNvstnTynCiWVldEffZZ2fZCM7L6nYkUMDcljJ42jp/BujUZk8piWvGyXc4jZIpCG4TYp7WbruoSswqSuj/Wednv2fPHlRUVKB3795o0aKF/vsTTzzhacVSRR9Z7RrbtGPPvuU5ZiHOMzS6CvIXUu0WaN0T3LbXSFwv3YAjBKizAI6gPcqJ888w4/D87On5JoMWYoOWlyY83dfsvZF+kYT3PJVFbvICLSldFplxgJirZVOE8iJxzAMRxjRaMOSck41Muj3QpvpmP3tivoQZQyLp0Ow5i9jj+3XF4698Lk3HqVc9w7OJtV7jwIzjVD7QbPZEM05De+Q79MaxaiNj8W66S3IVH7vUZDOO1v9tcss6zf6ss87Ct99+iy1btgAAevfujXbt2nlesVTRp1E2aUwjviw/xjSaM+0314G4+EN4KZ342ccnQ/bXGrXEdEzJOaaulgUBUjonseQBedtoA7FXZ9B6CXW2o5lx3Nbs02HGESI1d2zuoey2adIs7aVGtzVr1mD8+PF46aWXsHbtWv17tkOLTcG3rVNeCI6wP3A0RE6rQYlk6KWfvTkUbfJrNIHgym5EFxaME3EShIxUfsOn2/sxUlTsWWXIiPvZu2uzD5vMLfZ5UEJjJ8s3lfoBDjT7pmSzf+KJJ7B06VJdm9+3bx8mTZqEESNGeF65VKDIW85p9hytIxSmC/uZL1SS02rEXS+t62T2s6fasc2fVkTM0t4iDT+CpmV5jGk4/QAOjlZtuE5mxnGw6EqrQ2oNSbmc2sdDHrleUsunhsbWcDNeEl3Yxz7tUqc79LJ0aBZCmMw2bdu2TXtoTidQtitzTChhXVOVw9Hst31rOBuAqYHbavZOgpoRN1VRzF9uvmCce6EWa7IPSxfnOea+2CfFBZRlxnEQkZQL9/hKp2Yc6z5DW2eyC42dNF/u+pWtNk4V9vI+m3Wbqs4//3xcf/31qKiIjZhr1qzBoEGDPK9YqlAeCsvLgyFw6hmavRO4fvbUTkVtDdNLaTUlZwyOMo40eMNQhEviOaZWayzmdJIThVgDl7a2Q0jp0SwgaVmkHbS0vDQbuNtHdFLfR2pobA0nnmlW0P3sG74Qlc10INXsfT4frrjiCnzyySfYunUrrrjiinTUK2UoW/9NL5Ck3TneJRzNvuuxLalV0IkQ7s3JtJ/qjWMc96yEiJsdWRP2hYSdrJTFY8CZ22Ni/nZpKWKQ451K8CQmXW8H2c8+RROdpbsu0RU6MQS27PeQSTlJnq+p39h60Lin2WedsN+0aRPKy8sxa9Ys3HnnnRg2bBg2btyYjrqlBKUdOZYOzoPhaPaDe3SgV6IBSohjglnd9ho7SJo9USBQtOa6hvYsyJd72lDv2/xyy/Kkp9XDRBDcEr0+lOXDb3hnPFPLcFNTNpVPzHdq36koDBSafksWGluDUl9636cKe7lClu5T1SzNOM888wyeffZZbN++HaNHj9Z/r6mpQd++fdNSuVSgCBFOXJow4eFpcHbsOZmex+NupK6BGKEs/AK0dou/YPS8rKgLxzT7AoJmn2jGsdKxEzfa2OnivP0Y8jj+Gk5cLzmP9et9R/TvlMuoeevHV7q8wEj1xqGGxk6ar0WeiWY9K7jeOKmmcRNLYT969GgMGjQI8+bNw2233ab/3qpVK7Rt2zYtlUsFysvBscVy0nI0eyfnblKEBNWcYb6Glr6xoGwMVwOyoy6kbeKhCFBSseaNUpK05sVc+7SbPt8LgLi+wJhZOhm8jXWgrA/QzTip2ZSsLuMIP0pobA3Kbm5yNEviM6O4XmaNsG/dujVat26d9MSppoDbrpe68CK8EO9vPyAvvAFntvWGLy7ZpLl1oSz+Ujo7tUxNMHMXPe1y5oSANg30ksuefzcW+z5IGpg4ddCKp19jrAPlKvICrUcOCKkuQltBmTFQl++48eztnpdX92uFBwFhswNaGGBvbKaP/VO+XT+eb/w79UWm3JuT8zypt8g98NwOjmZPwZibXTWNAks6k2G8lCN7xRYJ27cukKZlmYecCAamswz98BK6STMZ1gqCN4MIpS+aTJO2HjTEfk3yBvTWay+RnBX23BCjUj97bcGRUPZ13z8eAFBUIPVsdaTZ6944NtemMkOUTfkpmhJlgQqgTYs1mz3Jo4S4d8K0hV4aHsLwXZK2RcMiMsVswnG9dGKz5wxoAN1UqbWd2z7/TtyFKVD209C9bGhl6q7HNtmmul+BS84Ke1IgNI80e86zS8mMY4MTmz0VystDttkzZglu+opz3GOpi3cAUBumR8hk2ewdjN7mZ+Be26XujZP8eu9i4xDunRgni7rBj+L6raVx44AfCrkr7Ak7Dp2ES6AITqfmIfpCKqEjpfDi8BZo7dPItD+a14I0iQ5VMNcztF7OgFzLMDnxwiXEPlmaPTPMdGNPpuRwZrkcwsRZGTtfQjuQTZhUzZ4gL7RByOXTHS3JXWHPNuPY40SA0zwgyNkarpF3JE48l1gahwOURZq4Nm4Px7PIzfvgLNCa11Xs4ZicnJhxOCxY/5mhLHl6qmZdTzBRJJLuSKlGOGZHuzSxvIiaPeFeakMNmwUJ+0fcwDNhP2vWLAwcOBCjRo3Sfztw4AAmT56M8vJyTJ48GQcP8jZ9cHDbzzU+LeMNItK0xOmj+Rp5eq5w4GzCoux0dNUfmSHsqYNcYthbOwRR4wXimj3H5EQ76IQvCI1xlyhXhwib5QBnB9NQnjMn6iUHygzHzdDFgHER2zq9Juwp+0fcwLNSLr30UvzpT38y/bZw4UIMHDgQ69atw8CBA7Fw4UKviieFhKVOWwGenZJir0tWB27+dnAWFRPzZJlxJGk4eaWSRoO66O7UZi+jjmGz55yTnA7HDWofpy6+m64hzKIjxEBogPOZKCWNXb+hthHlsBlNMaBswHMDz0oZMGAAjjnmGNNvGzZswLhx4wAA48aNw/r1670qnuge6ECAuzgdBpy51GmX2Hvj8LQkp1v3rS4LMRey7OA0EVmzD9O9cThmHF2zJ9RZn8YTNDuKB1YiZx1frH8nrTURN5pRIso2uobQHaixcQBmyHHCe07eVOWie+rREOO4SxdIq81+7969KCkpAQCUlJRg3759npWlPxSb1t518Kj+XfYMOTZ7zmKaSQOnun+RVvpJWcXTM/z9zXZLCzMOMeql25q9E5s9z4xjn5aj2WvCvkVQbrOlhp82ckyL/Pj1hCtDRGHrxLQeIbQh59wCJyEs7Mo2CXE7E5aLg0JtLgv7dEIREDv2HZWm0aCcFp+YloKTRSnKQMa32dMFmmlgsEjr5kvB0Wqpi+71kQgpXeM87VNzNPujus1WLuyd9BOu+y3VFu/IjMO12UvScro3ZQxxc40JoJ1spz3/dJ0PklZh365dO1RXVwMAqqurUVxcLLnCOZT2a1ck3+WowfLGYSzmUjSeRLxwWuAd0Sif7tNdL+XlUc4T1utDnCmFwpznSU6qa2uUZ3+0Pnbzhfn0mD9OTVoUqMJWEJSNRnXh2s0lyR0d1g7r50JVEuibr+TKoeb+mx5Rn2ZhP2TIEKxYsQIAsGLFCgwdOtSzsminu/M1cMoVnJfMyahOiU7JDbDG8cYh+dkzXwo7eNEh49/tNXt5jPOk5UvNOPSVVE2zoxxin+pCPk2zJz4zB3WhhCPgbAJzOzw0d2cstUzKwJEmxd47YT99+nRceeWV+PLLLzFo0CA8//zzuOGGG/Dmm2+ivLwcb775Jm644Qaviuf72XugSbi9mKvnT+gkfJu9s5fHUlNy1YzTUBYhS6rpIsSKekk3L8RDCUgSgmez5bj+anA9sijnFABxwcg6x5cwa+CYndyOGEqdVXA1+1TTuIk8eItDrKJlLlq0yKsiTbgd4tip3Vielpw0fo32shE7JXtvAMMTwnrBi5QVTbMnzNLi5dK0cNaZA8SHJIRgaWlH6+k225TWdoiYYtPYNB5nk5sGxVzJ0tZZ75h7ZbMPL3F50EyFnF2gpTwT85SfLuBksEw+Hr3EXPOQ801VVvm5ZxLwYvDkhUuIf7d/eekzAMCwQEdI68Rmzxi/G8pwT5BZXQPY2c1hSGOP22YcqnJEfV8p63YR4mlubpGzwp7SIb06Fs6phkJfoJV3JC/DJVBeDMqaCcBbuOMKLPtNVXTJQj2fNMyRVjD4WVPuKw2aPXlQc7TORCmfY8bhvGOGelilYXrZ2AU7jEbjMzz7drSvk9vkrLCnLGJyNB+esNcyZaRlQNHyuC+6MbXsUkooWurik9sLtNRnyglxTH1G3DZ3ZrOnw60P3VzV8MmoTYQwEHLWGDgDDttllzB7s9v1TK1bKsEKnZCzwp4ST4UTCM2rcMjU+OumawgvPlPJZNn4aYvfhEJBNePEPvn3YQ3ntCXq4h11y71GrQMzDgfu7M5LMw7FXdertS7KMZrUJRxd2Nuo9vRZgjLjuAItWBg9PyemBLeDpmm4bZaJ1YOTVi78yMcSMo6P5JsCrC/gBEIj+2Az49eE9OiR9H7Cs9nzBh83PU0SqSOskXhmxqFs6CJ6Z2nKiZ0Zhxr2Id5f0iPtc1fYs6du7k0bvVoLSMyfPGuhtAXDL1+2sM0KUuXhAq3dVfUmzxN7qMVTXRc1qGGgAad+9vHvlMupMxgnoRtCBE8fjlmVs8lREPpEiJifFtLB7sARatgHTjRXN8h5YU/ptKT8GO5/HE2U6zEBGF98G60hhQ4ku1SmgXHMGRQNjXK/8frQbrzesRnHOn9KZEcjlKPr4nXg5BwjtTDX8vvkZE9pb8+84wgVDRM32cU1e2thTzFZxdLxB81UyGFhr32zeXCMaIacjs0yDznR2Nh+9oQ8HQx8VnnzglRRBs+GslwcPEMOd9DatjnbjMM4SD1VzZ6SnuxDzq4KydWVtanKYR+zuoz6LDjnL9iVB6gFWteg7GLlmQfo2gwnlgvXN9tYFzu4soEz5TenTWbGoedFMuM4dHulHzhuD7UtKSEBTHXQj/dzd7aYrA5ueqTE86JXhtLemXSFpkZBdTM2PudEOzfIXWFPEogcTYJetttuYY3rIh9M0hbP3sHfTeUyzDi0GYrhP2TN3j5PcpwfxjoAEA/G5vbzMV4TaFhJJCkezEGNZcYhzKScetjIoLTdB9sP0Mol9EXyAScO1j5SIWeFvdsx350IHNI2+BSm59TDS2i2bmPq1Mwabm979ybEscPZgp3Wx3yWHNc7jvupfk00vpDImZHKynHiVBAmDIQsbxyWcmL4j8Vlf3rjS1peBA8q+nm2/EEzFXJW2Mft2u4IRJbNnuVnbyyDdh3FG4L7PgqztLdF1m6Es010OLFxKFBNSPVhYzx72eBGK5sTohdwdlIah6gQiJ94xzXjWKfX/sSpEeWgbo7C4XQvS6qQNHvioBlVZhx34G48IufH0C5pswDnmr0d3IMreG1hLKfx3912U3W6qcoOyo5O/c/EdQCuxuskGBtHMAjB0+y5B3hw6hIixCzgvAucUNIRwvO7vF+XeBq72RtBGydvwkuXSt9A7gt7m/ZMx+q/DK7XjPEaNz1DOPWQpeXsxo0QZjZeHDjudEc0+SUn5BtmbKpyok3HNHu6zZ46uXOioFDCU3CcBLSIoRQo9Q0SzgEGaDGfyDtoiUd3ukXOCnvSCUgsAUdLZ8zXTW3KlD/B24c7iJjNHxKzhkT4sTZoGdJaNQXLE4ooMBhx0MgujJww0UIIfdHSuxmgYYGW0heJZThZWAwTFsQ5fbaeMSui9Edq2BKuZm93J1EnI3gK5Kywd9Ou7W3oAVbWAAxanp1dlZmn412qSa5z6rlk7aVB14Cpi4ycAYl8ELxH8ZOM6TldUYj45h83d1E7qQvFtZHjKmoOxieZPVKcALgmLLu6kQ+BUZq9K1A8OOin0xj+Q9EumWYHLc4G9eVx+xSuxDSy9FLXS4aGRjF9cDbxUBcZObt8qTtotYU5n09+31S/bg1HSgEMwp6Qnnrwi5O6UNYnTM4KkrSc0BSUfKkbofS+QNTs6TMA78lZYU8RuFSNjR0qlrlAGbCLqpT0Gnka7qyfd9ydfWd2vAlKMr2nCaz4d/sB3GEdCenyCM/TvKWePnhzzx0INLzhbplxqDOnRCgeKpw+GGLsaaA8a7Jmr/dF+aBPzitNC7W5K+yZ2hL1YHKuhiS3fxvjbHA7iTt1iKWPf5e/PMa0jVOzAswRNGyObdPJAC4345Cy1O8l4PfxZkekvsoXCFEh4t44lAHFpAEnT8/dJaxBCU/BWaClxrIBnOx6tSmX641Dsf9La+cOOSzseQLRDu57xj0MmavZUw5m4c4QOcLPpAEmSeu2p4ujg2M45crSEk1tmtab5/e76iduTM+5Kho1xF13aUBxMugAtE1VZqFtXw7Vlx2gKR9UBYXiHEE2D6tNVe5AcZFyMuV3M8aIltYugl4iQgjSNJHvjeOeWcPtQ2E4sYbIQctMz14y+zINyPLpO2Xw5kbIdCIQhGCGSyB5rRi+O9bsLfJ2rNnbp6WMq9TBn7LHgD1LSJNun7PCXp/527QjfcrPK5urifoYC7RH6iOu3ls8veG7bEepzPWS8dJSFsb0NTG2OYr2QkrzjNqHtNXzNNjsWYuGHGHEeKxRwQyXYD9hi9WDufCvYd4tLNeuZZg1e/ksSjbBiRCfB9ckZIfTWZJT8tJaWgNDhgxBq1at4Pf7EQgEsGzZMtfL4Nu1iekIZbP896M8M87hunA8b9spIkjp9PQMAS0TqE63slt1fqeB5dx49lpair+6di92R9ZpGAcGSq9yHi6B3re88PLSoIRL4KwbcbyZtHcsahOegho6hR0ugTBwpEvmZ0TYA8CiRYtQXFzsWf6cbfgyBHs3KietcRFNTl2I5sXBP7iCnl423eeZceLfrdI6NuPYpYuKmIskaSCkPSOtXWiafdzkw1mg5TzVqIBBo6VppFqbWNq2GRq1Eb6fvX1+YU6oiYbBOhQRpHWhVAU0ZRe3EIK1UdMNctKMI0T8odK9bIjpOFNuUOzBNE1QI0Rc/aWaM+LpHWrPyfJiDI4U7w63D0PR8rQ7Ws4I9Rnpwj7gIy8aUmd1zjbfCcOmKkoZ8jbhehFpcOPZy94b6jGCWr6y+6IqCaTzbAlTFE5sJrfImLC//vrrcemll+K5555zPW/ywqtHtjVe1Ethe3hxImHi9JUTxZKcpgHZ4qbZW0cy2EkWs0wDN6GOQlI3vY5RkEMJxISmvA7afVMGEZObpjS1s+Bj3Hj20ag8lo5TGUUKl8DYVBVhbqqK35eFQkEcxCiHiVPe/3QHQQMyZMZ59tlnUVpair1792Ly5Mno3r07BgwY4Fr+1NVwujeOIR1TS5Y9d5OJgPD8qedb8s048e+c7edJNXuOJi6ZNaXS9rL2CUgEgF7HqECgIVYwZdE3ZpqRzegaZgGEtE433USFcUCjtJ18QZe6yzaRkOmdtBCSjPv8orpGmp9GRMjXxai+8YnOCcnGdYpbqPYa+308c1gqZESzLy0tBQC0a9cOw4YNQ2Vlpav5UzsN3c+e9zAoIVXjdeCZccLECF5cDYxjZ5fNGuL+5gRzhqSiXH90s4CzS0cPEhbLk1K2dt/yxJqiG/D7mZvY6BjdekmzBxeFYiLhSFQ6g2VMCPHSf3eT00aiBjMOwaxC1uwt0pj6v8Q0mef3566f/ZEjR3D48GH9+5tvvomTTjrJ1TKo2ge1kTmeKtz05hdSnjnFq0HLl4PjBdokddb+7OfuJE2aF+0lNKYPEHYkRxheUKZnZCcIInHNXkZ8FiAv32kMFWEY+EibqqLy2Y5jb5yIQH7DzdrNGqizLSN2KTVhK1OoyIeEM9aY7Ex0kYg8jduk3Yyzd+9e3HzzzQCASCSCUaNGYdCgQa6WQZ36O3G9JJXfYIenvKPcHbTmmCDWBXC0pFhe9PSywUwXZAzfdKu8tLanBBfT8vD7AUTk6zUFeZppxp7Y86TNFoDYAm1diGrG8aNW4u7lVMCywyUQ+iLHrm4kIgTy/D7U2VwnEDNrRJiZUzY4SRdoiTMWyk7gsEGzt6yXsa+E0yPu0y7su3btihdffNHTMujRLPnmHsolESGQ7/ejPhIlCU7OAq12b/kBe62ZO0BRT2NKzDtZMVpeMa1FkpfEz54zcGh5UAcZqj07IgTyAhRtPfZJ2oDlwL7PJeZnH/tOWYSO9UWJuYOg2Sa9LiqQF4iNwnYhC2LlM/uuXbmGvmiX1plmb5GG4FZrfP7pImddLylQZwCcd03zHtEeouxFpZoINDQXtvyAva3XFDqZotVx4vlI7Ja6GYfQj+WLvQ15+WmqPdVObTyMW54nSBqy5iGSRxjkTLttCeUD2uyG3hmp6xfGv8vMStwNhhoxM47M/ZEXklmvh03i+H3J1iJoZVF27oZ1hcz6HY0ynr9b5KSwpy5ocSMkUoSXVnaeRJPQyjfZVQlo00hZGF2TlkaAt6M0/j354SUN7UWy2RvzSlKWabGXYoowuNlJNDTqkX0mW7ZNYpOfvawNObttmbMbDUEc+IDGbqOWQsqhnz1ljcQUy0eSd1FBHghLM400aDttPI/Sbwj3HyXM2jgRUt0iJ4U9dVMT1/WSs/DmJ5gI4how3/UyP+C3zZu7oUZYfE8GNRBawEcXerG8kphxEoSQDMoio5Yv5eXW0tKCm8U+Of0kjzBjEYzB0wjH9bJxv7XOU68XvSoN7S1foKXORsPRKPIp7rBERS1iGvyt86MEISTZ7PU06RPBOSnsZTs8NbgBi3w+nv+0rHzuLkqANkUEDAuVRDj2V9PAkOQy7TdOjJhGGTfAmSVo6WVanDajitfPftAUxFlSXKOTu1NSPDb0tAxXVlN9BD0UB/XglQhVQ0ogHI2ve1h6+jA2uhnNQhRNnOJlRBn8SVFatWdrs5M68d1PxwEmOSnsHS222OSna1YkM058Kg/ANq4OxzykoZlx8vPkC7S8I+ni3ymLeYb/Nfq70exAMZFY5xRfS+CEFZBvDIp9Uk6U0tMSBAtFo9PzjcbrQAmpAfDMOIkDmtycRheKifWiEIlGpcKUGu5bC/OdF5B7U0UMA7B9nnITljE/7Rq7NHl+v2VmnL7iFrkp7Im9kDqYaul8oAsHyoKeni/DPU5foJV0XqPQo8Dx+JBF1OTMWGQDM0cD1sqWug/qgyxdk6OYhoz3Td2FTLkv4+yGqtqLhH4ou5A6qFLjSSUSiUJqxgGM74JdXtrslm4qlR3PaFrDscmPsmHSHArDahaTqNlbFukaOSnsqb7A1MBL2l8o75r+oAmaoBP3K7oZRxji5Mt7kjmJTNM05J3077HPWARFidCTbaoi7EY0IoThfADCyxhLZ41x/UEGx9yS+LLbpnVg7mtsJrBPHx987AWy04XFSDQqdV+NDdRoKN86c+M7ALjz/KhxhCiavfbu2ZVIdQl1k5wU9tRAXHQzTuzTx7Dbapq3ncac2BFJC7QNmn1ewCe5N2fCgVIPAXutWDCEk8wskKiFU/KT2+xhqh/F1EcxhyQuctrh5LxaThwVk8sqgUiCWcGqFPPshZQ1gAabvTRvnimFtveB9kyMB9RQbfZ29yHLK/EelM3eIRFysDBaftoLRol/3kg4EMrnLKRSNXuuzZ7T14Sw35mZ6GVkh8xzihsdkhSmlzGAUIWgltavaXRETZpyXq1gtKdG4nqQdKaR0G+t08U+KesxRoyul1aCLaYRy+9RX7cimIWoz08YZhV2rUXx9NO8iuzkhW42Y7rTpkKOCntaOvKpRpydkQleDRRPAc5LrHd0ygYVhmZvFDjyAQ22OzONgkautRrqYJMX5UCQeN3kC4EA1Q4PU1o7NBdNzgyQoiE70aYTbfbkBVqJ6c98QAtd3Md30NoJXIPiY5O15n4s8+4B6DNDysI+EB/Q7aoYFTErgN0anzLjuAQ9XAItP60zUabRiQtd1AU9KiGTn711OmMMdkpPMp8YJblHYe/Sx7kv2cKwHoKA2EayWQfAWxxLHJBtFwOjDjYxcfZuONHsCf3QWEaeZAut3nYEE0pi/rqCYjMIUxwb4p4smsZhXy4gN5dEhIivs1lnZx60LBKazj+grhulQdrnvrAnCFstpRUcm308wJF8iskxd2joO2gD9tN/bhCsCCNegll7bpy30exAFTLG68xlaRon3fZM3RjECdRGNePk+WP6HHVhmuaNA3La+DXmdpMJk7h5kN52XJu9zP2RMisz1ZUQyI6q2ZuUBLvBQ8TXHuwGLb/PZ2vGSVzXSkdM+9wU9kQzAveUKJJZQrcnyjtOov8+aYE2Gs9fNpCw4uQzzArG3bnJy6ZrojJXvsYakGzWIY/vwtGqqLZsINaGfr8v6YEWVvlSdtA688aJfVJDQugL/xKBbHYZpQsoUzgCi+sEaLu+IwkhQ+zNLiCllSkw2u8Rwn2YFmgt6qX9ztnlniq5KewN0zyZQKS88PG/UV54uj2RM5XX84/ENqfIBAo3wFriQpYdQtJuxoVnqq3YOi96G+nakuxFY5g4EhfSZAMD1QYbMZj7qFFGY8oGTSrENUfz/61I1Owt62Ky2ZOqoucvFbjE4HSNZ1s27xjRxZWy2zi+wU4e9sHns7cEOFmvS5WcFPZUAUHesWey2dsT3/DBN+PQDi8RpN2cRoFMIWTQluRmBfsDV5z4psfySlZW7JMW3CwxrbXmFasfpHnqA7IuBO0FS8CnmXGs8wR4fvZO9mNwd90mxmqRLW77GSYlLX+ZX7xpb4htXvF1K7u6Gusrj9Mvd9kNG6Ka2tVRCLnGrv2cRlmfm8I+TBRclOmWlg6gjcKJGz5s/ewdvMShiBYASnZvxk1V8nz1FyhPfkyaefNLkr833JePEi7BkIF9PHtJRmg8yFiVzZlR6c+IOHBRvXFM5hBJI5nXQGhw3DYrOQAAGtNJREFU9gcA8Z3ZMt91p944YcOmKjsBSFkIjzRygpDXVx5xU650RRNMQlborpc2teNuenODnBT2pkBENq1o8lixwWhbp5olKPZE40tMJRKNeQ3IdqdSD/HQaOThYIPMTU2f7hIEAtWMo2uctvWiCTjOFJrzUpp8yaneLwQN2Zlmnzjw0eoT177lQoojn4xBzizTEM1gjd8xm4FBr6/9LOC7ujC+PVwHu8I1zT4g8eyhLNBqZXBm9amSk8Kean8ORaLxFX07odzwSdrwkeApYFtPpvYFxMwteX6/dPXAtCNQnq1p0Uva8YR9DBPOombElIG1Zu8n2J61P8niwTQKVicR4ABRi9Recps0iXUI+O1DVZvTOvCzJ2v2NCXFtO+AIZ/CxkBodgubDHddjuus3YTlcF0YAFD9XV1D/azyin3KFA/dz96mzEYmXKXZO4OqCYUiAkHCic/ag4g9F5mGpAUqi5VNCZfA8QgKR6LSRTSgYdbiwBsnFobBPq05JEHjxKaZkKRc2WEQTnz25RokGurHyJO4UYoygADGPkpoI709+Zo9N+qlzM/eGEWSKp+0Iw9Z/uk2mSeaSin1tXsXNEXnxJIi27Ib2ewp92FRpnEdMF3ktLCXeeOEIlHDIo9d1439jfKuGf3gY/lap20ca5uQf4NAkYVuiK1HoKEO8pzj9mZ5HWTTbdauU9kCrW6fpZhxYp+ytQpeJMvYJ9X/O9AQAYtu7qOZzYz1pZC4QCvrAfFDcWQeKYYZIFEdpYcsMA5oNqYZhussxXNH26h4Wsc2lmmMeUnDQDfMqn2wbqPGzhnek5vC3mRXtG7GUCSKAsLGDNMCmeSpNNqcQkjLiWkdc2HzxzqSxK2TG02TOugYB5JkiU2aKGGWoJGsbTnhJxr5X1uka+SiSTDjUOzDWphcWihsummGs0iceA0lbC/Ac1HU0kWJEqrxjtfkF1LcM411oLheUkI3N95la59On1FYpDPFxrEoM+7+a/6/l+SmsDc8PLs2HBp+Dc/W/AjbCq5GnxfOAyqXJE2nZUGZRmteDUHCjIF6sIIRo5+9/eKvwOC6V/FGcAqGPX8KML+X5f2Z8iUIKvPZpo0rYdRaZF1YGuKYYcI4EorZXlsV5DXU06LMRhqavH7UWcpFodcw+9PxeL32Uus2r1yCq96swLaCqzGl8hKM8b9hn68DwZC4fmF7ReUSnPv3C7Ct4Grc9e55+LLganT564CkdTduUqIuKibGn7ciHIkiSFC+whGBMf43cPfnE7Ct4Gp0tqgr0Lj/JGu6Ru+s5D5k/Ua32VvdQOUSnL1yMLYVXI37vrwKY/xvpEWzz0tDGWmHdEBz5RLMEU+iBeoBH1BQsxP4+5TY33pPMCWNuxLKX5qzX7oX2wp2Qmz2Y05BFOFFnYHyOY3yNNaTojVqhCJCaldF5RL8IzwNbb87DJ+W9OB2y/sDYlrVaP8bmHb0/6H042+B+V2AofckTSsg2VRFFZCVS/C37+5EScEe7BTt4fv0HqDsh6a/n6O155d++AuiwGNdgKH3Jq1XTcNCW+tCTdgnb8+iT5bjjeBcdP7vXkwNtkP487uBrpOSpm3z2XK8G5yN4vcO494CIPzcsUDFr5OW3//gekw8+jgK0eDVkazNK5cAf5+C1qGjgA84pn43Hsr/E0RlH/iS5Bm7j9insc1lY1+LrS/gjeAcdH7jW9xc4EdgVRR4vWvjZ9pQnxYN9QGigA/IP/xN0rqPXH8PLi3YBVHlhx9RYH6SPBPwbXkebwTvQud/78XEYDts2zkdwM2N0oUMmr0dxdtW4KH8P6FlKPbu+r9LUtcGIiI2MNz+0Qu4r6AK0UWdgWFzTOkKPm5oqy17cUuwHQ5+OQvo8aNG5WrpOu3Zi53Bdij4aC5wzjWN0gkhMCzyGn66bzE6fJvwLiW0d7twFR7K/xPw395Av6uk954KPpGO+UMCGzduxP33349oNIrx48fjhhtu0P+2Y8cODB06FBs2bECXLl34mVcuwZG196DwyC4IX6xD+o5J0iF/dTxwdF/j64/pCkz70PTTZ+v/guKNd6PYfzgm6VoWwzfyV0lfGoSONs4zvwUw+tFGHXHbP5/CMa/epecbKTwWeRaCRCvj25WzURyphkD83o4Omo38M6+IDQJ29bC4P1QuweEVt6FV9JBJG6lFAV49+S6MuGqKKW3VijvRIboHUeFHwBerw76BMxE+7XKUfPUiDq2+G0W1u+3bP0k9BXzw9b8OGDWP156VS4ANcyEO7sC+aCu0yA+gMHwQoaJOKBj+i0blRl+cAn/YXO63p1yLgnHz0aYwX/89+sESRJf/BHkIm8sPBIGxv2uUb2TZjQggSYwhY5vP7xUbBBIQvgB8lzwB9J6Az6sPo6RNQawulUtQ+9K9CNbsjLWniAJtu8JnJ2CT3GNi20V7jY+ZeCzqo9frmC7wTfsv+XnUhSMAgIK8gF4X8eIU+EztDfhaFAPGd6hyCb55YRY6+b5FxNCvMPQefFo6Eid0KIoNdpVLEF1+E/wi0rgeiX27cgnq/3478usPmAdHY/9J2h+S1y9Zv/H1vw5vnTobZx9fHGvPyiX4bvltKBLmd0kvc8Nc6fN3ikx2pl3YRyIRDB8+HE899RRKS0tx+eWXY968eTjxxBNJFbbFpkOG/IWoHTEfS0MD0ebT5bj0q18k1Y4EfFh44bvo1q4lhvcsQ+XaP+KUf81Egc/cuepFHlZ3vwtvtxqKoaeWYNi6i+CzeWn2+Evwl7P+jmkX9cCWbw4g+sHz6Lv5TgSEWZCEffmYXvdjvBg9DwDw68t74/3tBzA8shHnfTwXgUhto7yPiCBmhn6Ez0svxqJD16FDpNqyHgI+3HPmGzgaiuDg0RBabH0hpiX56pOm3xFtj0Vn/x1/fP1L/KXvlzjnv79ImvaICOL5yCD8oMWb8CVpf5HXAm/3uhdXvX0cAODtFlNRJvY0ShcVwM4hj6LjO79G4Lsdlvex29cBt3dZjIvF6xi341exGVoSjqIAv289BUdPvhT5eX7c+N4YtK2vSlruraGf4sXoeXhyYj/c+Ld38UZwCrr4v7Vsl/PqH8W8CWegw1cvYkDlnLhGn3jv8GFc+1X4YMdBfFl4jeV2uDpfAeYX3oIn9vdD+6Iglp33DUpfuwMFonG+R0UQn5x1P57c3w9rP9yt/75wYj+cv/pCtDiyM2kZxrpPGXoSpr15lu32vKjw4Y5er2Hm1svR3qZfaXlq/HXyAGz89Ftc95/Rlm0Y8hfikYKbEYkKTKt93LJfzQz9CK8XXojFZ2/H9zbNtOyr0YZ2LmtTiAdO3IqiddMtn8mOaHtcUvAkXgncgqLaXUnTHBFB3Bn5MXZ1HY3HqiaiJNq4vwoAU+tj/WaM/w38OvhnyzIPFZShqHa3pQfOERHEii4zcOX1t7E86fR7yjZhv3nzZjz++OP485//DAB48sknAQA33ngjgBSFvURL0Tok5SXWoKbdVnC1rRtVVPjQvW4xO19KeuM1snpw7i+x3rK0YeFHns86eia1vXZE26OT71tSe8rqxC3X2DZ2aTntYszXq7RGOH3RrX6V2L+pddkRbQ8ApDq42XZafanvC6XfUMrcKdpJ7+GOrovxzI/Psa6U1bUS2Zn2BdqqqiqUlZXp/y8tLUVVVWNNywnioLUmCACdfHsbPpM3thDAr8PmaZRVWmN+ALBTtLcte6do5yhfSnrjNXb1OCKCrPuL5RevtyxtUhNGkjrG8rWuZyffXnJ7yurELddchnVaarsktvmvwxNwRARJdaA+88Z1o/dFu/oY687t39S6dPLtJd8np52p/ZpSP1k6Sv20MinPX1tzcpu0L9Amm0hQdqZS8B3TxVazD7fuhPU3D0LdXzslneb6WhZjwR0P4Ce7v8M/t1bD5wN2/rM9ulg8RF/bzlh/zWC0Kgjg200z0fGdu5KaWaJ5LdB5zINY1vZcnFLWGqGwQPQPnWMLS0nwt+2Cr6ZV6P8/Wh+BeLwLcMh6MNOvqayxtEG2HPkrPNp7Aoy6oJjfBbAaJPNboMvoB/FV74a6zO9q277wB4BkttRk91VZA7Hsx0k9Fnxtu6DswrsRXTU1qd05mtcCxSN+iQ96lsP3hKROCeXuf3s/xEs3Jy3X37YLtk29GIdqQwj4fWj96YOoe+GmRiY8BIIoG/cAtvW+GPWRKKKPJn+WwhdAy0t/h0d7T8Bvo9omtwrsev1UBNdPTToLMtY1/EgX+G1MWVrao/URfFcbwvb9R7C/JgTf3ntwZP3Pk5o7RMIzjUQvxvx5LXHlob+ik+9bfV3gaMuOaDlybry/VNbY2uxN/cRIZQ3qlt+S1BQFxN6hUEQgeDj5u2BqE4v+FxZ+tLzsd3pdhRDAb236RUN9vzz9Yry3egeK/3OXpWnI19AnsKUGYrl1f33/xmHAE9bvqMhvgU6jHsQjvS5H/n/7ILzsRuQlUY78bbvgyYn9rZoiJdKu2ZeVlWH37riNsaqqCiUlJe5kPvSe2EJIMvJbIFg+ByeWtEaLEb9AJFBo+nMk0AIY+Sv4fD6c2rENbr7wRPz0ghPR5fIHUScCjfMLBOEbei9OLClCx2Na4PSRP0Zg7GOoa9UZArEXHQBwTFf4xzwKX+8J6HvcsWgZzMMxLfORN2wO4M9Pmi+G3mP6qUUwgMBF9yKaZ31v+jW9J+DgRQ9jR7Q9osKHHdH2eLbL3cCML5Mu/viG3pu8zVoUN15UHnqPbR18/SbZtr/pvnpPwIHTftDYVzu/BXxD70HemVfAP+ZR7EJ7RAUQRmyDXKRNF/jHPIqW/a/CMS3yYwuVVmUmKffYc67F0d6TGlupG9L5/T60bRlE68J8oPcELC6dgb3RotgzBRBtUQyM/V2sfn4fCvMDyBs2ByKhDvX+QtOCm9EGe8zZV2N66KbGGl5CXfOGWTybhLQtggGUtClEv27FuOi0UnQ+/4eYGfoRdkTbx+7T0Bd9Cc804Peh0/k/xHn1j6J73TP4x+UfYUjrlaj/WaX52feeAIx+tKFfxfq3aMgzmfOB8bqVXeJtmHgPvqH3Ilg+p9H7mLRNkjzrIyKId/o8YCrf5/Ml7RcCgDD0a5/Ph0jP8ZgZ+pFN/WJ9wn/GBLzRdpxlf23bMgj/RY2flwCAFsXwjX4U/jMmxHz0e0/AL/OmSJ+/26Rdsz/99NPx1VdfYfv27SgtLcXq1avxyCOPuJN5wwMPrZuDvMPfwOdr0DQTvUF6T4CICuxffTfahqrhO6YLAlbeDb0n4G+vb8MlVY/FvGaQZKXekLaAupqupVs7A+LoPkAA9cG2KBj9G8t6HDhSjyNr7o3Zs/0W9wbAf8YVOG/FsSjI8+P24SfjqrOOk9Zj17JZKBV74W9r7XKJ3hMQjkRRvXy2dR2OO0f3OBAN7Z/UGwdAaORvcOv7Rbgjfwm6+PYCx3Rp9JzGrSpG1aE6rJ5yHo7URzDge8XJ23HD3NgMpcWxsf8f3d84vwZaXroAOPH78Wss0gFAZXE55n7dC/OvOAOX9OmS3He69wT4ABxecw9a1e6G75guCNp4y7QM5sUW4EPAox3+bl0H/dnciVKxBz5/AD6LZ57Ii9Hz8GL9eZgx4hT85IITLNMBwFVndcXQU0vw1hd7MaJXR4zo1TF5wt4T8HzVmViw4TNse+Bi8iLizm6j0e+zU/C70z9HRfUfk97vJ7sOoc2bD6Kz/1vb9xYAal+6FwVHduFwQRlmf3cJJvW9MmldAWD38jtREv0WkdadkJ/EBfqs44sRvv42rK6+EVe3+BfyXrnP8nms6nIbnq/uhAfbLEer2t1J+ysAU7+y8pra1GoIZu6px8PFKxE8vNO2D7qGyACvvvqqKC8vF0OHDhW///3vTX/bvn276NGjh9i+fXsmqpaUn/7fu6LbjFXixfe/8ST/cCQq1m7ZJaLRqG26Q0frRbcZq0S3Gats09WHI6LbjFVi2nObyXU4Wh8WR+vDpLTdZqwSE57YRM7birpQRHo/5z64QXSbsUp8vbcm5fKcMO25zaLbjFViyX++djVfynPUGDbvVdFtxirx1beHyfn/5qWtotuMVWLB+k+dVtE1Pq/+TnSbsUq8+799lmkO1NSLfvetE//5ci8530gkKrbuOmSbZugjsbb7dLd9OgqzllWKbjNWiaff+irlvMY8/oboNmOVeOcr6zbhIpOdGdlUNXjwYAwePDgTRTvD42BFAb8PI3qVSdO1CsYeV2mbAtt0+QE//jP7IrRtmcRMZEFhfhJTlQVvzLgQxa2sF5moaLslx5zRyTJNOg93SMa153TDsve+wfdPtF/I85LfX9MPz/3na3Q9tiX5Gm3rf9h4knyGOKFDEb56KIk938AxLfPxzl3DWPn6/T6cXNbaNs3PhpyIqf/vfXRqa2PqI+JmV/xeu5b4YPsBPVxLOsjJHbRuc+4J7bC6che6d2iV0Xr4/T787uq+OL3zMdK0HVrbDwip0IUhdGRsmVOOFjYDTUnrAuzYf1QfGNJN3+OOlQoqrzmxpAizK05jXXNKWSyo18kNn82VsWd2xtgzO7uSV3nPMiz+19fo3+3YlPN64JLTMeSUEvQivMtuoYQ9gavPOg4XnVqK0jYWi0hppKK3hT21idK60H728cTEfnjtkz1Z0fZNiRG9yvDyrYOkmq+CzuAeHfDlgxe74j3YqiDPtUGISk4GQnMbn8+nhE2GKGldiPH9u2a6Gk0SJejdxy038UyghL1CkQG02OkKRbpQZhyFIgOsvOX7pvDOCoXXKGGvUGSA/IAfDAcohSJllBlHoVAomgFK2CsUCkUzQAl7hUKhaAYoYa9QKBTNACXsFQqFohmghL1CoVA0A7LO9TISiR0UYYx5r1AoFAp7NJmpydBEsk7Y79kTO9T3mmuuyXBNFAqFoumxZ88edOvWrdHvaT9wXEZtbS0+/PBDdOjQAYGA2nWiUCgUFCKRCPbs2YNevXqhsLBxLK+sE/YKhUKhcB+1QKtQKBTNgJwS9hs3bsTw4cMxbNgwLFy4MNPVyRi7du3CxIkTMXLkSFRUVGDRokUAgAMHDmDy5MkoLy/H5MmTcfDgwQzXNP1EIhGMGzcON954IwBg+/btGD9+PMrLy3Hrrbeivr4+wzVMP4cOHcKUKVMwYsQIjBw5Eps3b1Z9BcBf//pXVFRUYNSoUZg+fTrq6uqadH/JGWEfiUQwd+5c/OlPf8Lq1auxatUqfP7555muVkYIBAKYOXMm1q5di+eeew7PPPMMPv/8cyxcuBADBw7EunXrMHDgwGY5ID799NM44YT4AdwPP/wwJk2ahHXr1qFNmzZYunRpBmuXGe6//36cf/75eOmll7By5UqccMIJzb6vVFVV4emnn8YLL7yAVatWIRKJYPXq1U26v+SMsK+srES3bt3QtWtXBINBVFRUYMOGDZmuVkYoKSlBz549AQBFRUXo3r07qqqqsGHDBowbNw4AMG7cOKxfvz6T1Uw7u3fvxquvvorLL78cACCEwNtvv43hw4cDAC655JJm12cOHz6M//znP3qbBINBtGnTptn3FSCmQNbW1iIcDqO2thYdOnRo0v0lZ4R9VVUVysrih3aXlpaiqqoqgzXKDnbs2IGPP/4YZ5xxBvbu3YuSkhIAsQFh3759Ga5dennggQdw++23w++Pdfv9+/ejTZs2yMuLeSCXlZU1uz6zfft2FBcXY9asWRg3bhxmz56NI0eONPu+Ulpaiuuuuw4XXnghzjvvPBQVFaFnz55Nur/kjLBP5lTUlI8Qc4OamhpMmTIFd955J4qKijJdnYzyyiuvoLi4GL169bJN19z6TDgcxkcffYSrrroKK1asQIsWLZqdySYZBw8exIYNG7Bhwwa8/vrrOHr0KDZu3NgoXVPqL1m3qcopZWVlpl23VVVVumbSHAmFQpgyZQpGjx6N8vJyAEC7du1QXV2NkpISVFdXo7i4OMO1TB/vvfce/vnPf2Ljxo2oq6vD4cOHcf/99+PQoUMIh8PIy8vD7t27m12fKSsrQ1lZGc444wwAwIgRI7Bw4cJm3VcAYNOmTejSpYt+3+Xl5di8eXOT7i85o9mffvrp+Oqrr7B9+3bU19dj9erVGDJkSKarlRGEEJg9eza6d++OyZMn678PGTIEK1asAACsWLECQ4cOzVQV085tt92GjRs34p///CfmzZuHc845B4888gjOPvtsvPzyywCA5cuXN7s+06FDB5SVlWHbtm0AgLfeegsnnHBCs+4rANCpUyd88MEHOHr0KIQQeOutt3DiiSc26f6SU5uqXnvtNTzwwAOIRCK47LLL8JOf/CTTVcoI77zzDq655hr06NFDt09Pnz4dvXv3xq233opdu3ahY8eOWLBgAdq2bZvh2qaff/3rX/jLX/6CJ598Etu3b8e0adNw8OBBnHrqqXj44YcRDAYzXcW08vHHH2P27NkIhULo2rUrHnzwQUSj0WbfVx599FGsWbMGeXl5OPXUU3H//fejqqqqyfaXnBL2CoVCoUhOzphxFAqFQmGNEvYKhULRDFDCXqFQKJoBStgrFApFM0AJe4VCoWgGKGGvUCAW+XHx4sUAYhvypkyZkuEaKRTuolwvFQrEYgjddNNNWLVqVaarolB4ghL2CgWAadOmYcOGDTj++OPRrVs3bNu2DatWrcKyZcuwfv16RKNRfPrpp7juuusQCoWwcuVKBINBLFy4EG3btsXXX3+NX/ziF9i/fz8KCwtx3333mUIpKxSZRplxFArEwikcd9xxWLlyJe644w7T3z777DM8/PDDWLp0KebPn4/CwkKsWLECZ555ph5S4O6778bdd9+NZcuWYcaMGfjFL36RidtQKCzJmUBoCoVXnH322XrU0NatW+vxUHr06IFPPvkENTU12Lx5M6ZOnapf05ROMFI0D5SwVygkGGOf+P1+5Ofn698jkQiEEGjTpg1WrlyZqSoqFFKUGUehANCqVSvU1NQ4uraoqAhdunTB2rVrAcSijm7dutXN6ikUKaM0e4UCwLHHHou+ffti1KhR6N69O/v63/zmN5gzZw7+8Ic/IBwO4+KLL8Ypp5ziQU0VCmcobxyFQqFoBigzjkKhUDQDlLBXKBSKZoAS9gqFQtEMUMJeoVAomgFK2CsUCkUzQAl7hUKhaAYoYa9QKBTNACXsFQqFohnw/wHvtPXar+0uIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wl.plots.plot_pushes(mw_data, pushes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Magnificent! 😍"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
