2DofArm_simulation_testing_learned_readouts-A-STP_ON.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the coefficients in a real simulation where the feedback is generated by the readout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Makes possible to show the output from matplotlib inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Makes the figures in the PNG format:\n",
    "# For more information see %config InlineBackend\n",
    "%config InlineBackend.figure_formats=set([u'png'])\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = 5, 10\n",
    "\n",
    "import numpy\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import save_load_file as slf\n",
    "\n",
    "from generates_lsm_start import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main functions to the 2 joint arm simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from simulation_2DoF_Arm_physics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of the simulation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Experiment identifier\n",
    "sim_sets = [\"set_A\", \"set_B\", \"set_C\", \"set_D\"]\n",
    "\n",
    "sim_set = sim_sets[0]\n",
    "\n",
    "base_dir = \"2DofArm_simulation_data\"\n",
    "\n",
    "trajectories = [[[0.75,0.25],[0.0,0.5]],\\\n",
    "                [[0.25,0.60],[-0.25,0.60]],\\\n",
    "                [[-0.10,0.75],[-0.10,0.25]],\\\n",
    "                [[-0.75,0.50],[-0.40,0.00]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First the arm parametres according to Joshi/Maass paper\n",
    "aparams = {\n",
    "    'l1' : 0.5, # metres\n",
    "    'l2' : 0.5,\n",
    "    'lc1' : 0.25,\n",
    "    'lc2' : 0.25,\n",
    "    'm1' : 1.0, # kg\n",
    "    'm2' : 1.0,\n",
    "    'i1' : 0.03, # kg*m*m\n",
    "    'i2' : 0.03\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Total time spent during the movement (in seconds)\n",
    "MT = 0.5\n",
    "# Simulation time step (in seconds)\n",
    "time_step = 2/1000.0\n",
    "# Number of neurons at each input layer (defines the resolution of the system)\n",
    "Ninput = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t_mov=numpy.arange(0, MT, time_step) # t starts in 0s and steps time_step(s) until reaches MT(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Reads all the experiments to check which range of torques is necessary to control the arm\n",
    "# These values are based ONLY in the 4 trajectories used!\n",
    "torques_all_1 = numpy.array([]);\n",
    "torques_all_2 = numpy.array([]);\n",
    "number_of_trajectories = len(trajectories)\n",
    "for i in range(1,number_of_trajectories+1):\n",
    "    states,torques = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(i)+\".gzpickle\")\n",
    "    torques_all_1 = numpy.concatenate((torques_all_1,torques[:,0])) # torques joint 1\n",
    "    torques_all_2 = numpy.concatenate((torques_all_2,torques[:,1])) # torques joint 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of torques for each joint (probably will be different)\n",
    "torques_values_1=numpy.linspace(torques_all_1.min(),torques_all_1.max(),num=50)\n",
    "torques_values_2=numpy.linspace(torques_all_2.min(),torques_all_2.max(),num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of angles for each joint\n",
    "teta1_values=numpy.linspace(-numpy.pi/6,numpy.pi,num=50)\n",
    "teta2_values=numpy.linspace(0,numpy.pi,num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# These are the 50 possible values that the input neurons will represent (x,y) \n",
    "x_values=numpy.linspace(-1,1,num=50)\n",
    "y_values=numpy.linspace(-1,1,num=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads the coefficients generated by the linear regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[c_tau1,r_tau1] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau1_08-05-15.pickle\")\n",
    "[c_tau2,r_tau2] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau2_08-05-15.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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2vz/pZ3vriXc8FF03v2/SfdktUb9rWsdS3uVt//RK/fc2ecdA7fu3\nqncle1vtGHKSxl8l3XuS9KY90jde2s3t1/m1Jtu6rfHaGff33ZLldsnqO6etF3WJt5+WXiDNWCyN\n/0iasjfJfur0PEpb1udlElHblVls6zTazXkTpe5sPP+60YbJ+lht3rd/IOnQhPTsY159sOcU6aOv\nlKYOeWW8VNK9kj7yaee2f6Lb5eumdu2WrMsXpJPjxU63OzSiBU2/2K+d7jm3KtWCFkhH8Zaso3Yp\nReJc/c+9ucOgNFK5Ox4W19ldN7VOnUvh+3V+F7X146eTbbf2+yT+3ZlOvmNax0Piz+3S3fr2d4S6\nPVQnP9lCcb5rvHOsuHdU87R/sjsWGh+mEF7vBs+X8VtOOvvPu19mf7nOf8GbqDn6MZeHO6R5PFfy\nsF36Yel2XZLWfuq0zZbX79XbMkedU6f32zqNdnMRl+YpP9L/vlkfq837dpOrPSmxsq+vLXnDCisT\n2l96qF/2fdGy0jt62EPBh8x3b5vKJf7brAvfjQ3QzUqpqWN+3vAPtGBJSXPem0Ljw3UQtAqb4Kt9\nA7vVZ6Uzb0GHj/ls+fjp9C/qaQTJOvvsDNLAu1S5th8+1KJznUIQLMt92at928tGWNod9jztn0Tl\nT3iMtusIhdW7zcdQyUnnvdCr7Ve71pz96bjHXB46Yll3WPK6Xfph6XZdkuZ+ylOgvajHX9TyZbGt\ni9YxT2t/dPtanodjtXkOsTc9Xl+e1z7u/b53N4N6u4/zEyzv5vHSLwGntJMJOgk4TVJf2r5BWvNx\naYUv9e+WR6Ttd3e86hEtqB/eVZL0qLTuFGk0/mc455zZwpukTeU0zY1j0rYbXXnPRrN9g3RohbQ7\n4LPdRmlVQHrvwzf7y9B63ZVtmWwbxvsuMUwee1ratCDpdgtPt1zY0bC8TqRzPORJ63Ox1fe1pbYg\ncCjl+uif7q3fujY0LU7abRr7NvDzlkh6/vpx7d81M/X6rkn7+iTW2rq8f7qu6XpQ1uYYrR0Lq9cG\nHwPB9W7zMbRpTDrxIWnh7WZX92CoQfVa8wlpzRviXGN7XbcF1+/LJH1mXFrRg3Mlmv6r87PR7bok\n3f3Uqs3WW0U9/qKXL4tt3Vx/Zz0cLIo47Znm73O1pAlJ/3m7pNjfJ8r2ycex6t+3tz4ibW8ozwuf\nlNb8nrRxufSR3dKOP+hd2bqrfbslX/JxvLQW65yrvPfY5EVyM6fX3jvjqPZf+KPAumTG5Eu18vhp\nTev/wuRF6XyD6Poy4JTNQfbjkjSzg8ZH8qBO64ZWZ8G3XFcwk0p7pTWHkgfDwjvP2X7PzoN8eRHt\nXOzu9+3qvowdcOjwu4Z93hf3/VD7N81Iu75rbgS+StItJWmF0uqwd2P/FGP8fXhHqHW923gM7bhb\nOsfSDASG8V9rkl1je1m3hdXvH7lV2rQ6Xw3Q/qnzs9T9/Rh9P7Wsg5xbla9Ae/8ef1nc1Aiqv80W\nxr5Z0/PrWKz2TFj9+tGE15zK+v51pjRVXvti74S09BSzq7fUOtPZHquN+1Z68u7m67GZ9FtrpRMf\nys853pnqsbhE0rGd45pausGW2g2Nx2L+2l45r9vinHPV9x6XdKD2+rrz52j/e2cF1iXDE/skNQec\nvNd7qi8DTp4eH2Q2WpJmvk1S7EkB0wjqhP1NOsG3jO/G7dfOwJNvv3ZK276SfLt1MROuA7kI8m0u\n/zsx/Ho7Z6RWsx04tsftGz833span4u9/r5Z3mns2nftOPgaJqhR+SdHpXuPSW+fmp8Oe4OEmUe9\n1L4jFFzvBndmrGd1Wa288a+x1bLPXXSXTt0xW5O02Zb6zsNUG6Vh9fsOX3ZW9vW9lJM6H23F2k9t\n6qA87eN+P/7S+j7x2g6N9XeC9mZK17HutHnSbj9X1nfDcmlI3oTbGpK01B+Yy8exWtu34dfjfGQw\npqbuWCxJlSBG47GYs7ZXPo6Xbmtx7k0q7Q38k7DXu6ivAk71lerVkvbMlj5UkraNd/8ge8Uk6di9\nyf++m0GdeB2Dpgj1EknaLe3XOkmr0i9fm3KMNPzC1ynxnpyQbLvlO90yByn3l0hSaVg6MKf62heH\nn4m7muALclDmzDfHJSUK2sYsUarDwuLrwr49OneZdPQZ6QPH0z2GgxqVD26VHpT0ttx02POqri71\np0Hvn3FU+1/3I+mdMjs3sNHfPnO1dgz1qi6r+z5LJJ24f7aOnV7S+L4jbneMIS4nD+/Tu0tLm36V\nYqO01TYJa4Bme3c2B3U+IujX/dSv3ytN0dsOjfV3tu3NDto83s3HZbbUttS9Pl879fy21L5PbfuU\n7pT+eab0FoUHsrp3rEa5BjRfm4Oux3nKYBwsqfQvWiY65IztnZCO5aTvGq6vAk7NlaqT9CsnJHdL\n2F+0qFzO1IieaRorOTR5hpfO1vjRLzjpkhvMVt5Q++zodw8qFVQ35uKIHeHNS4Q6Qjk6r9jTz4RL\no9MS9r26lp3TXLkukzSn6X2Jo+KNDYSgBtBXxqTf6SBoG6cs2WW2RTlmQ/fzOZMXBdY/pfPnSB+b\nJP3mN6VdqX2P4EbyEzdKsv6+Y5SSujrMlwa97vw52r/+peVGf8kbMlDe1yPfWqyR8WmysSNaoser\nGUAtG7xSGnVZ27orcA7DXdJ6DUX9DOecs6X2uILSvCvlCDz+dyzy/mTG4761qXXdF7ZNQjosGV77\nGvdpEeZ8aVTEMsfV7c5kVtuQTnIU8doOTcHsJVqgI+8uaWKZNLS1pOkHb7DT7YpuBbNrx9KrJN2a\nfCj8Ss3R5qZ6cZlGti/Q3quGNf01te+z1Lw+UKIg/fYN0i3lLKd7JqQrh4ICWV09ViNeA9qVh/Mo\nSw39izmrpBFNaMrOC+oCpy3nJrtaer66PuX6GmbPHJYO5v5GQZ8FnBovBpJ05HvemNoQYZXLl4ZL\nWlk6p2ms5OdeKt1x8oQm/fhZnTjvDLl5Q7KdEzpwtkl/71tP/IyJcueu4+yL0ImFd/3kBf30eE8P\nylaNJ81/YiiNu8lBFXvUoE9X0i1T6rQElmXeU6/QzClvkJtX6+DZngmNPTXR+NY4DdemSterlJu/\nQ0LBd4QaG2631jeAunCHodroO3H/bL34MxPe+btnQmPbYmRodC44y0uqnhthdySH796pwI56Zfvt\nenP6DZ2wTnu+7oZ723POW6VXTNeCk2dJPRii3tExWmn0T9xat69Puli69gHJC/jW9nWLBm/l507q\nsnKd+Q6t9AWaN1f/V7nDvSzOOpMLG8r5KpPeHvk6G7ZN8tq5ri9P1pmYSSQvc2B9OPKtxXrJHqep\nxx+ve3OENkI3AzfdPW6y2+95Ox/ypqMspWq78KCkb0rSSZIuShrMjtbG9R9L90n6Z3lD1b5yLFF7\n95K6n+ZIukhfPjaud/m+T0WC71Xbvv+0Vtq+S7rivLDAWN6O1byVZ6DN3/52TXt3SS9Ok2Ydk4YO\nSe+aKA/P9Ik1N1mOr7uTx54OPf4qbdQTw6fo2LzFmrrnR5pU2hvURu12hndfBZzC7sYnqgjcrGFp\ntPn1SYuknTO+Kz16ofSyB6VNy6W3frf8Rw13PfQ2s6tviNfgaX8HpW1DKjTgcfwBt6/XlWKLk3dE\nN3TtbnKsoE+BUsnnHTetfLQ+m2CzhvTC8C/awinP1zXM50122vOXr05ccW4OfHWZnW53JKl8mu8I\nlc/VOX8/UyMPTWjK441zuux0P3QXx/2clqrHRfnpkp6hOBkadToLOLS4sG2/O7AeOFR6XOvlZZmd\nGD5FLy56pdy8IY3aiW5lG4V12vPXYXcbpSuvkS6YJrdH0gOR/qqTjmlnF2Gvs1I/6WhlXyfVQV02\nogWaF5DV6HUyvM5FcJ0gKe0OfruhnHHu0ofPhRWvTMGqjbTDWqxZmlb9RUlHNawfJW+sZZOJ2Vmj\ns5MyB9SHJ53q9J7jpsYge6Qn7BSw4yAp6wzcikHIVksmJ5MQR2rj+st6qaTfkTdU7daHA8sd3J4J\nznavsICU67BheJHrkEMrpJmbyKJOT/4m8+6iES3QyoMnafNBr/3Sot3SrMf1b5w+RNB7jw0v0nhp\nWtO5Vn5//fQzE7dJpfB2e5czvPsq4ORJ62IQUr/5xkpWO2HzvnFEIxOnVjMm9KjT0PGf0/5Ji3Xg\n10/y0kIrot6RbXUHpUgNqZYn7w2drj320KMAebjb3fQ9/MNq/EGkkhYHruBXS8NS6TTVZUQcf0B7\n1jwSp+L0XZS8TIbGytrrkL7DltqWzi9U5WNj7vhyrXq07d2HKA3gXl9UO1pn5S7MxDJV9499b4pG\n59yq/Vff4M1BV0kr/ydvbqbnXHX8uXcBqQS9V3yvuw3f5k57/hqB2zdI39oq7VuuAy+X1l0kyUlD\nWw9q+sGHJYUEArOoT33n4nyt02xfxt20h5KvNUFd1nTOJ//01LZj66Gc8bILul6/VxppmxWUAfDS\noMZaq3pKzy+cqKb0B9UB3T7vOmh0tmq/tK+bA9oKQ0eOSpre9DcBT9gJnHvyyLtL+uk7pNE71fPA\nTcKbER1l0ZSlEywqUjszubjbqitZ8ZWyNB7Dh7VY6zVaDV5X7NfOwHMptKyVY+klR6UPurAb8UHt\nmUjZ7o0d+gNSOVO2/u++MHlR3fBx71NVt63na51Gdi+Q9Ns69rnQp6ENgk7as01/O6JlddnLFVGD\nCVHrsyLNfRSg5TUstK54+HTpVc91+6Zl4PlpZlqizd2ffqZzfRdwSu9iEBKs8I+VnL/97Zqxc1xT\nSz/vncjVjAmTNEPrfnGGDmyRdIU05zpp7pMRxzm3C5rl4w5YkMBG34n7Z+uFs51GrzVp34Q0dbZ0\n9WYdvX9Z3XDFROIOPQpZS+ad54bvET6sJiDtrpVtN2nuG7+kOROTZHvqs4iCjj1/58m/a4JSqcuV\nWZSLYnBF/U5JD49LYyVJw+2/S4QGcF7mH4uichfGS6+vmKw7Fszz5vdx0pyl0tzPSENbTzTVG86t\nqns873yts6W2oOlzyvuhk8ZLq4tVuwZ7p0HA+MNjX/d3Gn3rNI1eKumeY9LHrnNutMVEkZ3Xp6Fl\nDAsQ68claabXiFlqC7SytLR6/Yh1N65ZLQMtYkfKf853IiiAOrS1pKMHr1DjRJ2RGqVh18H4N5Sy\nrt9jNf6fd/9frZ5zkj4m6QpJtzyZh2t8eyH7LcJT25oa+pOP7FTjjQgpeC7BwPUflO743oRGFTgX\nTJoC9rEnRkc5eM6dW0uK/TCNNIJF6bYzu5kx1dk1Jsm26lJWfPg5srUx2zswoyGQfz8+uFV6+FHN\n331FtS3h10lQx99GDLuWDE/sk1bVZ93PveSETn3ylOr3qasbWzwNbRDMmHypVh5v7sdEyfBsPJY6\nzE6OPP9spc3fXB8uSOdGdS+EtT3C6or/uFVatVpz/n6m5u70XrY9E5pSPq67+J3LbcjI7+1GGaLq\nu4CTJ42LwbHgTvDksaerHbsRLdPK0pzWDfVLJX1NmrtTui7aOOd2QbPEd8COzl1mtnKLb03SSzae\nodOOPFv3vsNarLv0Qt0dFSlahDrwglmS7tx7RKOvnS5dVstkmVg60TLgFLtT4msUTSoda1vWspZ3\nmyvfKeB36VYgQd8jpubjcJmWbP9tvfiU06+fkKqPmC1rdQGPk4YaJcgTOvfUTx7QpIk9ihQcbNxG\nn5C0rSS9qjZZ/9lz64OYKcxB0/NUZHvmsOROkkyaO1ypN6YrsN7w1XVhQ1Q/P3eZ2dVbdM7kRYGN\nl4gNufD6pU2D3RuqFXSHNNrwzNjDY8tZTq2GDpTVzek1NuI0Sya9KLkji1sGZqOWca1e0PqAsX2H\n9p0ee3L3o+X9GLmD1qUMhc2S9mhU0lZJ3tP3fjpvsUbGl+naA7UAqnfunaQ95YzIiojnTfhQzi4+\n4jjomnNYizVdi5MNDylrDOq1vM/SWM+9RdIHS9L2TzbdZa1kwkqqy4bNsFHf2U2/hoZ+jOt4qGp9\n2tlNufgT6pfF6ig3zrnzMUmvnSL9OPRhGsGdxWWSPjMurZiZNFgU1s5M/mCbLmZMdbTt4wfWep4t\ncDig/onYlqk/H5+4sdxWaJsRUVWrE1sPrYtiUmmvtOZQ3bYe+Y/xuieWdnrjo42253GeMnS8TM7m\nNltAhmdnunBupnTzN4uhgOHXsJC6Yv6OUzXt3SVNkXTNwcpqav2tLn3nTrLTQz6jq3N09k3AqVoZ\nV+/8R7wYhFUuo26R1p02rzrBllQZK/m96HeE98kLOK1ykosWgqxqFzRLMHRw4vTZ0npfBXDvmE65\ne2dIpfBAu/lzIh2wlW007bDTy39N0s9IMunAy8sNQZ0UFCgp78+2c/g0N4refUKaOltH574ycvZU\nu4ox+C7BstBOVIILVvV7VLKRkg6rCcpE+vKJcW3WZEnSLsk3y8jrbbHt6myekdaqx8g8LWuae0oa\n0ufn/rwXW2u/r5r39cyj0nVTpbdfpDmrvKDu5ID+ibdNmuegqT/ulvnqEO8JlRWdpiLHNXnsac19\n4yLvONje8q3+hm/oXY7S+XOk9746btZfdBEb7I13QP3DMysSHId19dASSSe+PlsvPjSh/RudRt24\n9M7NZq9eJL2iNjRn7oNT9JLxFzVH07VSU6SStHnUX8a5qgT4kuznyrHln9NHqmWbmVnL69NmSbs1\nriNLplWDtAcWzJHe+erojcCEGQr+86LxtKxsH98dd7OFV0mfvVMn3Tizad4s/7mn6t/GEHQd7N6c\ne6FDSrxrwEvrfpH0/G8TzG+6FtieCdmPD2n6CS/D0T83Xy0TVvJnw2aeDZBsH9W++6K7dOqO2ZJe\n2XFRJo89La0+q4OJ9BdISuU6EG9Y4aXygk7f2tp6Ds8zT5F+/ZXNUzd85FZp0+rOHlkf1M48x5J1\nTvM5J5lzztmpU45qpu8hIpVM8NOtvi3g/1sXksnRsH7/z4kDGbM0ramNGiEwUy3bEknHdo5raukG\nSTeEZ942q8tYWd8w3G+WpumQpspLRQrPUve1m3Xe/c9Uh4+P2gnv/AzIYowhVlCiXaZlnrJvwp4K\nnfhp0WGSn5vxs7tbrCvoZsqc4VlaWWo+tuJe4/znyx6N6rCOtr6RFDyNRGAQfkQ3eHNGtfhuKR6j\nTe9LEqQN+owuB3v7JuBU3XC+9OwofxZWuQRNsFXpJERPZa201h9/XnrqpYowbKjuAqHdkoLvsre8\nixgaRNu7RHLz4mYCtZwnqTFjIuiA9TodM6Sfqnr3+44lEzp85PvlyZobG3J1Q7ba81eWT+2Q/nCB\n9s2dqXU7y19gz4SmPP5Y2Mz8LcofHO1t04lKnlK6fYNG9ozrvS0qrsM6qvXlzILKUwdm7DomaUbI\nd6UpD4cAACAASURBVJBMU6vl3ix/p3pYXicqcJ6RRBq3X6WRHvZ9SufPke1pnelWpzE9vDyJ8Nyd\n0nUPRKswaxka8h13tf25XqNaqXNCvlP3TSrt1cj3z2p5HJRVMnTKwabg4/XEY9Irfq2kSaUzUy+r\nOsi47DQYUbnr29QRLEn6qfSF0V0afVn5CXDD5Tv9l3pvmXOxdO0D01tu3yjB5VZafL+6TJXGrLxa\nUOch7XzZVG+OrkqdvSLqHGzSy7RYM++brYmZ0omTpNLJXkfqkNZJal/+SqPsLqlltmtlKN2UtmuM\nLSiTIA9zEaQqKPC9RKpmpm7WkPwBUB0f1cyVU7R/oQIfbJKhVu2X6hCzsBtM/ob+ycP79O7SUm1u\neP9ujcv0UKxsg0mlvdKGf5dmx3+Ii79RXilHY/nHdKEttQNyvmtwmNjDCt84pnm3HtE838M0ztL5\nmvHsXJ04STqxuPwdflM6sKFhrqodn5DWvEFasVwj7xvX/N21IdkH7QLNGpqiE0MTmpg5Xi3HgWN7\n3L7xc6tbJ6CdaWaJOqeJrxPqMMuhzTa30+0OzdOrfDfE/JkJ9W2Bhr+Nsv6KWIGM5nNkVtPvdkla\nr5Kkw5L8Dyk4M8LwtNgVR+CTjBvPjVaq18NSbfqRdSebTsw+UzpQW0flUti2nvAp0lQKAdrORRtn\nWyTQybnZYtsnuDj5Mq0qN1PCjq1j4fOBaX65D3xYi7Un+EEevnIH3kgKamdUM+KPlKcOKE+REym4\nFqUeqtVxXcs0apsVtd6XvV6RUnZf/wScUhLaYNo79PM6e+6EnTMiTRmeVa686zWeGNP2Sy8/XTq2\na0jT29wBqPBHLP3ZKCN6vS21d6ikozqo+8IisFKrINrCq5rTo6NM3N2Qbjlnlbynih0/2v5vQ9gz\nh7Xn+Bvd7moAL2jITcB3aDUX0OqD0guflNb8nkY3LZeu8+bNsmcOV+8IjGhB6DCezfIudr9cfaXN\ncMk2IlwEm7Iz3MHW5+Qs/aiWWVAOis7XcPnOUy3luT6bJML8SCEq2ySO2mdH2H5OGt27T+v1qI5N\nXiQ3s5aFsn/G4sYhRM3p4ZVJhDUzeP0hZVtfrVAjHXdRpZb+G/WuX1CHqNGsQ9I1R9oGr4IEfh9v\niFF9EGKJpOevH9f+XTM7umsdZ+hA5a5v2PeaWnpcerLcOdq4XPpdecOTKp2kCOIGxRobzS2V69XS\nnubMoKpt8RqBjcfDJZJ0pLy84HWkKk85bHRJw8/rtdU9FeEpkdUnwrR9p6dFQzFskt6g19qdaz1L\nxd8fsXHbir/OrBxrXz4xLpUzU5syV8eldROSFkh6qm0RA6+bI99arJfscXUPpfC+z5larwcCnrq3\nuG0nJ/yuaf05HJSJ62/oL7XHJZ1WfV/l2Ko8ESv2fCC73y/N/pXEw0Yar4P119fJuqT1tS7e0Af/\nTZVbH9G840N19dxkSb9cPadrfxYwV1XtwTb7dtdnajvpkpKk0rB0oBas/+JwNZsnKDvGltoNmq+d\nej5mvRT43WJcJ8KHLFeuF83bNei4C1t349M5a/tyVuPbe6ZVG67+pqFX9nLWaexAULfUbuxtVehw\nvJ8ZVklzqte/xuzPFvVEHiS5xgT+zXnDp2j3WYt04IJJ0lR59fTeCU3MPFqteNLYFvX7pFpW7z+9\nf/piywc9tFOZDyxg3iRN0t7yPrhYCrz+LdD0hjmEg24wLpHsdKvty2o9dFDl5AlvipxEwbUGUdry\nrTRmcElbAwNFrbKiLpHXN2pss/iutwlKVpVpwMnMLpP0l/Iqzducc38e8J6bJb1V0rikVc65hwNX\nVtl4E8Ovt3NGDmj/jKPaf+GPYj/BKuzC9uWJcb3rwOy6z2r8/PpgRbnI49J6Tde8BPd/T1PQhWVO\nJa01yp3epoBGJTJ78Efj2r872pPiGieDnfawdM3BIW3WjMCnU0QxeezpZHeoQydtu1bacLkXfDMv\nCDF358yo82ZVzY1foo40Tgro355tKpDq/n+unIEXI3AXKmwOk7vktW+DOiAJUmerbO+E9s/+oPvp\nCxuqQ3Nq+3aOdO+s5g6BL9A6X+uasivqt1vECckDtAoelBuzTcGRk7RYK8t3TIIyF6TakL3KE2gq\nDmmqhjSsGbpQk0qNww878KJTfe8+uqCJJyerUs/V3xn64r4fav/qqbGGbjTWH7N0SpppvrVO11fv\nlC6dKW2SdLl33Hl3sqNrkfFUzd6QYsx/Vm7gHXj58vAn6vWgEZj2nBXtgm5BE8dG6PjHfupOj+56\n1z0YYWR8RXVOJWmajmuZZowtUul48/nXbjsFPW687vfPlOReMly3rpqGO+AB1805Syf0nueG1DjM\n9guTd+nRK6Wzvz5NKw/4t2/oU/faqmS5JrmBUdEu+NviOO4kM6eqsW0QJbhcCXq0y/T1acwqkr+N\n1qpuCZyrqnytnFRqztIJMqm0t3qetZq8+flk9VKU+b1izS3S7qZDxG0eyNvWwW2HY5MX2UvO2KWR\n8WmhN6AjqH7XSnB3TDPKWUtDSnrNzkplO/sDGpXgf2jbNKRpErGeSO8JqwklucYEdvhLkj1Q0sz/\nkKaZdOJ8SRrSi1Nn6K7p0tCR0PZKi37tmYHzR4YEwzqbey9E0Lk3Mfx6O3nGNrdv/NyWD3rwz/ca\npDIf2Nzx5eV+XiUbuHKjttZO8w9Dr5YjYA7hoGvM2rqboJ0fZ2HX6k76UY03C+84eZZ2XrJMOroo\n3ryfZa2O6+cTlzK7gJOZDUtaI+lNkp6V9JCZ3eOce8z3nhWSFjrnzjWz10j6K0nLW674peU7NpPd\nHM15dJ7Gnpqo/i5sI36+Nhmrl9kUVOCQxp//4DkeeRyzZ49GO40Ytq0Umr5zOTJ759yZ2h/xSXGN\nT9Nq/B71d2K8ZaeOa6bG5d0hat4mrcYgt+rYSddJaz6uOeuXa+5Tqj4JafrBytO71un57d575JYH\nljfNdNSondAwYZMCBmUbBMxnFfmiELGR32p71N0583gdEP/EyE6/IEUYWlBdqe+pj5XOdcC+LT8W\n1wvUnDN5kdyL06W5V2nKoVn61YPD1f3QvN0Oq5OJLls18IMe+9t4pyMocyE8TX9U8zSn6TP9/39B\nL+rFuUe87K+VW0InSd81Rzr6Mkn7pCnPj6uSAdYYxGw7jj1AWFD2aOkUneN7jHFtXWfKG/sTvB+i\nZON9abikF2cfrpsY2btAR9i3231ZTisOSytmVeePiyus05tgYtVqA2/0w3dq9PKZXtDlo9Un6pXT\ntzfrxP2zdez0kqbuqcwrkmqGTqrZPv4bL2HnTdDEsVE6rTGeutOyfP66utLRC3jsePzt4jZqztz3\n6trnGhvKc3x1pHd8NF4z49osadqBE3Lfq50vLe+AB8zREXYOVAKCpT0ztbmhv7JL0rBv7j//PC6z\ndUytMkIas5WSaPzbyg3GA8f2uH3u3MC/KYsybCSg89a6g9GqrvYP2479neuy19vfFNwsadrBSXq5\nr55YatJ8namR3c8oTkcpwrwgnXVO28zvlXRukbrr5HA5AhQw90uUdbRrLw1P7NOcua/Utc8NNZWt\nRZuw5VOB29UJUcvWSmUdXmCr/mbXbB1TSYvt5baremPRXy+2qhP9ZffP73e63RGYgVYpx7SHpMMn\nqS6o0lhPtGi/Nx2r3Wzrd0NdnT0xXJtHsqHeXa9DCmtXhAcH2s7D2yzF+REP66icJutdjf2B0rDu\nnHp6YPu1wp45LHdK6/bZ0bnLpKPPeHMl+wS2uY8/oD1rHol0/Wvknz8tbj3kTxTwX1eCr9WdZ0lV\nPnvqiWG9fI+XoGI7Tq2Lg7QrcxeDt1lmOF0g6Qnn3E5JMrMvSbpS0mO+91wh6U5Jcs79u5nNNbOX\nOudeaFyZpIadOCppdKg8R1BrpfPnaO7e1+nUJ0+RFG2ek6BovtcBemng+xs7wpJXMadQCVbTBUce\nXOy7uyqvc6bpgX80cf4Maf1F2rdKuuOh2hxHFUkDYZXvuV7jdQ2t1heN+vfUMsWaOnbuuXJDZ+Qn\n67WqOua+lsHkf4/GvqxKsKv+Yl5Zr1eGSqaJ0wydWh7CoLr3t04Njjvsxq+Dyf+a0kRHTl+sz4+P\nasqB6ZIv5ydyxkVMlXX6s55a3ZX3l2G3xjV+2lSN7/t+pbFabcSG79tyoOa4qhepdt/LP+eVn/8C\n0LyO5k6T/z27Kru/B/OnNNYbXxzeph1/t0C63Jt7q3Sx6tLRK9Ytk576hqRX7tYS95iki4KCmJql\nyvx3jVlZ9U/185+bYQGnWZqmlaXaxMW1z3lA+/VvdROOxnnizSWS1r1+WE99fJL00d/S/O1XaEQL\nNNwwIXd9+WvfYYmkn35tgaaeUdLkvft1/PTpmuYbuloN0qk2J4bX0J4WWkb/50wMv17TZy/TUUlD\nYzMUdgo0Bjuk1kMRqw3JUrlwyZ54Uvf9vC32eltqB/xDvlJpjAcFFMIaX9r2lWRDclLgDxTXOnpe\n8LwiYuZM/QvbN8hOCm7ElodCN02860n29Kdf1VRtHo+UDRA4MfK0kOHb1YBg+YZNY0f4Et/cf5Xt\n5/07rSdDeOrqsfINRt9QsNbaZOa0e5pgq20dVL96AjI7gtUNYzu8e7Fmab9cm05RpUzv0oy6esIr\ng3fN7Mp+Sdo5XXi7dP7Z0tBms5Xl15wi3YUPu1Y31j1u1rD0ory07JiitJcmlfbqeIsOq79NGBSA\nqg1Lb76GhWkXyI/KK9tk+esc7zjxOqj+c7q+XowfvGk3fKc63FuV7JRWZa5oPZys3XtjZPQ2BaCb\nb1C07Zw3tdPDgiwZijp3cCyzqjdwmuu/Sv/T3371mzz2tI79eLEq/bfGvsORJdO0/xfmSOvmSGdF\nLFDDMOAUJquvatHPqGZAR577uWyzKjd4ShrThGZqSK48X6Bvvrbqzf4jJ71KU3SS95S8Ufm2a3gc\npLnPLXU6lUwLWQaczpD0tO/nZyS9JsJ7zlTdwPU0OGnk4fHqJJXthEXzvQMqOODUKKjzIUWb2b9x\nnpPzhk/Ri1NeqeHSkN5TV5G1OHDKbfvRddLoiu9Kj17Y2OCPlK4aVm9O+NKM/Xe9/WWrXAj82QFh\ndwP922uJpNKQ871eU5nAc5GOylpknAUHiZqzMGoBtK11HYba/BbBGVxBaneXvDKOaYZmtg1weQID\ngA3DJPZL2n/vmM57xzap1FyRfmm4pMNDE3phYkjjpaFyzssJmcYjzc8RpHE7hu27xu9zQGdqxp6Z\nmlcaqjuW52tnaKfNv77aeoJfr3zece10P2wxlt5/3NXW42VM7S7nezd2JNYtk3f+fDO0iKkI+j6H\nSjubs0PCbBqTTnxIkeZp8wluLIc3xGuvhWY3NE04Wut4R+xs+7JgRnRDUyM27MJZDVa6Ua3cNSzp\nLGmXVBkO7D8uhxXUQAjvfFaPC988KHdNbS6T1DhJfX0DLMlQxLhqQQHJPzxbeqm+MHlRuz9vNRw9\nNJOzRZamN/+cr+M/f/sVdVlxvvXHbvAeDpjXpXEfxsgWiPr5zjlniya1bMSGPgUvqFFea8SGnyNh\nWUNBndz6J4V6Q+JDbbtJNnW94g47TUtzpzB4G1S+56TSmW2zSRQzM6dyzjS2XzptjIcGYRU8B1uU\nzwsb7tdYJ4edBzXhbb3GY6rSOd0f8SEEVW6jdN2dQXOv1A0tCbvOt8uIvUSSRiMGvhveEz58vvlv\nZ4+dosq5Xv+e5nZnUDspzo3AVu8Nu0nU+Ps91Ttk0YPbQcGbyhCjxikB/HPWtS5nZ1MdhGkTePcN\nFa0XZ066xhsUkfZfQzs9LMiSpfDAYGiQsW44aNhxEDp3oWv4t8Gk0l7t3bdD6/Vc0+8OaKeef9ki\naV25DTwib5BUOw03Gw6VHm+Yz7J1W9R/fDVup8pN91bHUpwseP91/RINa7OGA9tuviw2s4VXacHU\n9drc9DRwSVrWNHdxGhnHMWUZcIrasLaEfxeu8Y7vjH+V5II7SxE6U23XH3ZwBXU+/Kn3jRMZVszQ\nSH0KZUnSo9JdMUaIVOcwaXF3uV1qdauLoJs1LI02H9SN769kBzw/46hGxtXyilF3Z9qXIegvx1xN\n1i/77tQk0fh3vqGPdZ3Rdo+jDGo0ewG18ElGGwJcLcvZOLdWZQjaodK0urHbJ4ZP0YuLXqn9vzCs\n0Tu8i/yCpRN61/9h7+7j5Kjue89/z8zoGY0kjMSTbQTICMSNgYRkZV8nCDsERzjY14BycfYayHo3\njuL44cZZO+vdm4fX7ms3FzZ744B3vbH1gO9CrJsrYgOyY+JIjhPsOMTgCGNACITBIPSANKMZaSRN\n99k/qqu7qvrUY1d3V8983q9Xv9AMPd3V1VWnTv3O7/zOU0NqjXQVr88RtU1jOjFqVL9ytFmTJtIQ\ne/uvdo3as2Gk6cjIg+scjB5XCTe3cdoCC9HX/rJOKG56YFwNoHpMRmFe0c9z71Lp+eskaaV0cLH0\n1dPSe+dobGha9w0/E8qUOzF6lY5e0Kjl8fwDOqIbQ99ra4Wit2ukPiTVOk/X987BzB3IzEEdnzlU\nl07e2Sgo3Pq9K6iTpUPt798vzTmhve9VsJhkc3r1uUoPhLeZL+lkvmyH5ipaGTIFogMV0RFXv/NX\n05ROSI0i1u6guP955p4ezTz1Iyo8YOBL7FA1V3oJTxV8n7MeU5H6CXF1XaLXq25kfhbJWI0bcW8E\nywtnRWUZDIiVkK0VfK2kG3R/dNYz3Pac9kGV4OpaCvzeD2q2H3++5ZG+VH34Hebshb+qM+a1VuH1\np+Oeq3169dhfZjrfkgJ6aYMdrhuuaLuwSVM6Q62irHHHePL71bQ8JvASbJODgTNXIM2T3M55rxnt\nt+bMfGnc+Pm1V7z3a9VeORgoiBTc/ykBhcRtDor2DcIBkvB5Fff9z9G7NVI7K+b1O5vG79spaf+o\ndEp+Xb/WtkW//w3NG9FrmvuqVdbC08ra7UxwilFQUp8r6z4KrpDlL8Lgrf7YPjDrev2kYzdaL9WX\nYcClI64auHllydQvS9JskOj7phwHsVk90z+wumCd0fx/Cv8+EDzVOadDA+6h+wez6iYtfddfaEl9\nRPP3ZvpYbYMNr9jtwf+fqQbuUqUnUMS9f9z9hs/9HefIfg5cs9tnVDVrP4f6G35W1Dnj7pfMMyCR\nQT8DTj9ROBfuTfIymJKe80bFhTL3KW7ax5XNOcnBZWtDI742nM4Z3MkHJP2qWlkPPtcoiStTJzhK\nFv5bKXgg+f/fj5ROaEoTWqTXhkdCsbBpWQ2PHZdcRcgDUznSRrLmPW11wTXB4uH5tDqM4c/RlDEu\nGKy5Nd3htdB1A+GKSsd1WiY0pehykFLxtNJXV9X1qh8MknTRI+43TwhwJU6XXKYFodpanlFt0+PB\nC7+XTfDmR70RASnX/OW8rpV3c/3C8j+T1mySftKsSZOZ66YtZ0AjZrWzfN9l7PTAhP03qePasmZE\ndvmQ5j+h0qbd1a5YIm1rXKQenJQ++4J04yU6Ov8xHa2FshO94usLgyP4t4c+1uqRJ/Vva5dLtXCb\nlpTBFB2R3KlWQH26lE/obiv3D9U09Y5hjT99uO0GsYQpLEkFrP0OSa5i/BPzFFsAPLFjeOBDOleb\nzeUm+Bz3xT3blLDmCnOJ2+/97UKFMz3Ta9kEO4Z5gwPBqYI7G1OA4trktPoJrsBFsLZXB4HU2KWq\n8xTgDOynuKBeWnuUKysqL/eN6aVao516/dhx7Z8zGjtF1NVeuKbfufs/T7gHIVKDmu4OePR9z64N\ny5wa1q+eCHYqvOm+2yS9euDa3NmE7cdaez0ub3vdN1yufbBAl6bWJYsGXfYvkOypH2qqdlagX5t8\nbYwboEkLQob7wlb+yRDNfNnpuCmVWkWjQ+fSLfIGTQK1V+KylrIESYPfS23oHVK9/e/9eoVecD7a\nN9gX+b7a2xtXUKeb/P2x5Y117Tt5h7TiPbrokDcVKy54E92mRRqKBPHbn+MazEsTN6h2ssPATaPv\nGA1aldbWxdVL9X5fnDt4vq/5b1cNXFefKklgdeqotKlaHa/W6p4N0pkzjtV167dc14bweRZ4v1BG\n1RrNl9070rg3z3Gdb00Dji1JEqwTKoX7ExnbzUIZ4Y7vOM+xnyXDWgr3J5pZUXJkRfnH6M9quHk0\nxyXBZNTPgNNjkt5ijFkp6RV5YZ1bI8/5qqSPSPoLY8xaSUdj6zct0piuDXQyW43yEu0MFOJNGyWJ\nNub3L1EoU8fnaBhTMyZ89/rRsKPhE8VVt2jronG9cGS0NX1m/T9qzddPKnjR82/8hiLz1eNSwiV5\nF+W/k+4bPmBfz9DxSgq2haeYSVZzZCaTC0e3RWDHpPvz9f9SuRqHpFHtwAkfajBao485g08ZUmnj\nGjD/Pc8xqzV/7grNmxzSraHgReYMLmex1Lj5y1NLrzRmw67GXyp0c5Urk6HE4oNpgp1K/6If7cj7\n8lwoJzSl+4Zf1qnVlzXrntSeOqG531uoU1N1ZxrxtPbppTevlnas1ZI7pM0vhAMPkr96iCuw+UZN\naCq0tLoknRpZqKMXNEb4rKTP7Zb2xk8LOXfvr2jhPlfxbu/YzZOF0cq4ax+RnK/Oa0pEbySj58NQ\n/aT07DwtP3xA53oFszMV4/eldajzFrD2A21xV86R5dKLu+QsAB6thxWoqaQ1OiKrhdoQGMkN7te8\ngZNx83Pm4mXes4usopSlQHdcRzTaTrSysNxLmMcNFHjOaP68X5L/rZwO1Noa19fbAhfS2alFeFPF\nrIi6/Cv1uGC21PjsJ4dXa17tGbWm65bbYXfZKa9Y8qnFE806fsHPHTcl1K+32NLIdrXf0sm65LpJ\nyyOm7xQoKOyLHT1NHR12vefO+GM+d7DJkXkck3mmmEHItucEVu/KVobB34atc8b1womf0hrtTM2y\nLur+BdLUz7V+NgfrmvvUuJLW8d3QDDwFeUGotlWirBIH7ndKOjAsraglF9OOfC/monlHpFNLQ/9P\n8jJZzzj9TKNNCwfn7w30efz6OlkzclyDhTVNJX84x2v4bZvfrp0aWajTb5/TGmi52Kh+3m2pF4C4\nwF3a3+QZzDOalmvAO2/gxnXcBlcwS3/+lZrQlO7Xa4EFH+L7pHH9ng7qqDb3t5+p6AsuIx/tr7T+\n5pC2BRaqkLxrpZcZGv79/gXne5nX7YMfgUzsMNc03aCka1CW/sbJOaudgxHHl73NkSUevCZKtn6F\npKW5sgOjBfajwXN/u7dFgn9Sc18EV3Y3ZpW7JIk+/lvWPtccJM9f/kF5M8JD25hJ3ECW0VmZX0NS\nbCZzsN9/oZbowsD/i9zC5tG3gJO1dtoY8xFJfy0vyvtFa+2PjDG/0fj/n7fW7jDGrDfGPCdpUtId\nsS/46nlTurcxSDf32BnO1SmyjuqEtzTPk7Opn7fYW/Kx8c0lpvbbEemrdem9Q95/9xxXMCoezNRK\nGx0L8n/OUvsgISji/42WSeEblMY0nf1DNQ3X/16ZTlZHhpYUn0mVZ5RgW8756/E3XO3z5IM3kMPN\nG6FLtWbvb+vEB2p6/X3S2FalHkvBIGmo9sxT7Suh5BaZvzxSO+l8WiiTJrxUedsc7oTOf7Bhz72p\nSYGtaMA4eLHJWE8t80pEZ+hpPVW71ssO27FWWv89Xbxnpf7d1CJFO95fmnPCPn3Ke2+z6iZpx1aN\nbVmksXDgIXF7lkUyPJujw6tukjZsldQssCy98IC03R3QW6aV2lA7RzvbbhRby68Gj9kHlD1LKe0m\n3u88z1er83y6Efw4snBK9sSolg41Oqv1mhYsPaEpScNjc53nx4q2QrhXNjr02aV2qCPFJOOmGGe5\nfgy9dEKyC5wFwIOjQ6EM28aIXtz+LLLa1WJr9atH26df9ICznQje6GXdnuj3Fh6O8vZZI3AbuobV\n9bbE1422IbGDKY6V3XTPbi0/PRTXmQzWqGpM/9wlVxt5cs7quBuITNlTfhvpT5f2l5A+unJYYzeP\nhOr4JfYvAlw3dKdr4Rs6f5pLXaPyNz56wzypuhZKMhpS0rXZdY11b2er/sSRthuYBamjro4Ab2zR\n/Bx1NrIMPHVlZaw5ky+1TS2WwvsuafpdlmyorXPG9eLO4EDn97TmKW+gM000+0m6JrRK1JI7pKUv\nSPMnrIInQNt3X6DvPaf2E7mCYknBkGCf5/Vrp3Xft3/YFoRwrYjaNmgqL8t2XPsyL5DRfsP99/aH\ndp13zV+zSfrJh619zRpjtmtsxWFt/oXlgfIJ3kBW5tVam+/RnqUnhc+TL0veLEfH4lZxmd8jtUPO\nItv+FO/wKqBvjF3B7L5T4b5pdOZIixfIv3fpmJ65zvvNsn/w7gOjWSreCsc5b8ZziJ9mmPQ37QPc\nZ0QCTaE+oGPwI5CJXbq060Zcxph961zptquj2+ZfE6USs9Z8cdvpuJ9t9e1iru+Rwcbcgx0pSrsm\nuAb8Num1Rh8mU3sQyooqI3ieop8ZTrLWfk3S1yK/+3zk549kerEjPzlb9dulpfsk+z0VWp2i2eAu\nkaaukIa+f0JDZr7a60gVt1PedLa22YMx6lctlHbJW7BvuyR7j5KKAOcZCS+SKpn1b/wL6X0mujpP\nfG2qidOntU2Ptv1+/4LztXTe8tCHSsq8cG1LdLpNUsZW0utEU6pdUwiCNZE0Lm15rK4xDcXW/emS\n0MV/jdSsmXL88In47bCB/7ozPTIVC1SBUWT/neMCW9EbfscNUuyoUlDcjY4jLTo671vD9T+S6yJb\nXzTaygy7UtKfHJfWt688lmV7fM4lzb3XC43UJAXQnJ3/xncWFzxynxutrKxg9kZcvYtgIEVW0tEF\n2nzFEul16fbd/l8Myx8p/XJgSk9Qe6d+iTZpKlSjzNfKuAyKq3sRE1jI8H35osfffmN1dPqotPF0\nbNAqGuxIEhi9b/wre2fHRq7tflBgm2qyOqn6nIUaOj2tPvcBSpElG8sXbUOix9eX5pywz51a3ZG7\n2wAAIABJREFU16gztVPTjyzWqcbKbtNPWC0au1QZ+gOpbV/CVM64P4lk3XpGaod08NBhHXnSuxFd\ncpt04a/VNFJrX23Xrz8RFO1UtrcXS+RYXtusHnlSql3uumHWIqmjzBvXOd+4gW92/s8zW7SsJmWt\nKREN8O4MXqNDo8T7ElYTLL56bx7+tSiY7VrTlKbnWNUWnamxhWfFLikerZuVJ4MgZKruLTpxwyIt\nfee0Vnx7saTLnNsqZWzTGoH9pfsWNWo3tZ9HoWtSzEreOyW9MCQtqh9vBj/87Mljmq+to+OqXzka\nCsxMjT8Wuyrt/MelC9ZJRy+Qa4q6lHFBIO91r2zrexzRobaMZj+T5dTogmitS+8J4QzxRj9kt478\nq3XS8iHpUF1a9pI0R7r4KzZ22mtUoy/cNoUwepz855GXdPDn36TN9UZ2W2AV66QAV0pdn8jgmjsA\nFAn2pd7w189b3AwY+lkqy9//Iy0P9HGX6axC04H8YJefpTuphdqmIU3KaJGsrOoyOh4q15JVcNDh\noqVXasNRV02rhkhwZMlt0pl/VdOC8VYWe2vlw3DQKmEhKkdfPvsiSLGZYTGBm26vQuscsI9/unPm\nR9kLtyTdo0hS2lTH4PEXnPkQV9csaWXAOCO1Q7HBc+9IyJaBm+Wtynqh/rNesOmObxUb0Q3u8M1X\nSi9+clL62Ad14aHNKrvWza31Ye0cz3Hzcb2kT0jasydSBNh9wycV2weNC2aoIG14dCJHAbOGRqOU\nKUq8yD7rmqdszKqbVH/zX2hzTZr+J+mMOZKOS0PTxzWhedqmsdAJGHdj6Z/cMSn+uT6X/z6umg5R\np5+t6YJrhnT62Zq26VjgojWkL8vIWynOKO9qd2nHTtvFv7G8+jYNtV1IJW8q5MgPFuiCdUZjZlpH\nX0ouJh8VnW6Q1Jh2mlbqZ7dtCO2z1MJ9TpG0/PYnBDp/IzHBXn+ZV0nejePHPidt/I0yLmBxKys1\nXzfPDXeaDOeGM3sj+J4HhuWewmWluEhgbL2sAP89zohMOUyY5hrboY58rsSVq+KK/0dtuczqyNRH\npO03ODPPCmXYFhSc9hDOAvx7HdU+Laxfr3laoLxTP1rtXf5rQZbX9yVlYoT3X3yR5f2j0tRVkqw0\n/ehp7Z9+vdkZP6YrtH/BUk2PStON/rg5WNfknh9JCteZ0lP+axpdq6VZvr9mfYi4JbD9qZxLtq3V\n0hfVXPBhwfhHzQXmc84bh1Fdqg2Ozt99h3+oIzsWSjcs0rLH6rptfLQZRI/LBvUV7VR2Mg2liGh9\nmOD0ik4WOvC03aB0MgrdUc2U4LXoxWi7u+om6U+3SjecLens5hR916BXNFvMNXAgJZ0Hj0t3L5DW\nr9Wy77dWcHbt7ywZdBOBjO/gZKy07y9uGuh+1SLZMY1poPqWnlr+Kel/bNxEtjKMQ1mQoXNhXNK3\npC1r6ho72dn12rUQkCNg6wtkMoWyoN0Z4vXPSb+0Tnqv1Cyy/tCkxr/yPW1T+wBQJ+ZOP68xO09j\nO1dI4VWsm8HYE6NXtQXL0gpYR1dhLIN5eUKygWy8e3ZrVJdpQ2BgMG6QMlAv1aVtkaDg67mykx0B\nhtjtDg46+Ody3Hb4fcBmoezvSbeeGJWc2xRuy/9iuBY7pT5+8DU9E/KI9uk+P8N2eEjTP/bCVSM/\nsLpkYrFGtNNcborXwO2CtrZ5jdSahXJgt7T3gYT2+43Ngc5gACiYvec6llyDvvcu9Y6LxCBj/HUo\n8T46/t4qPJsp8PvYAdySC9LPoIDTjklJi1Kf5tspr1jmtsgqDqdGF4RWeJqY89vafM7Pa97Bp0Od\nrKQb42hUsq4F0vCwpofqjVWhhjPffJhDden6Ienz01r+2Cta3uXbFfcF0xudKCOl0Ntv7gtjbCd2\n73YdfctjOrpjrS64Qrq1mSHhdzjmN1b3e7rtT9uXOS+HI0gRu38W14f1b/9Oaq0I5//tt/TUir3S\ngQ9pjXY6/zbp/eM7lfvS/jz2QqoxSd+Sto4e19Hx4vWXUrJ2cl2A4hpQb+SmtOh7nEYW0bCWaadi\nOxDRzLDnPyVtf0N5NaxKrIk1oSlZzVF0Bb6dkg7ouIxaS4c4jqW2Dnn0Rvb+RfI68RHmUL059ce1\nTeFRYHcmpCvD8kvx05O8pbuziN+/maeSmpcnpPEHJD3Q1dG84Dm/T6d1/9CQJucMa9F8eZ97oqah\nevyqPq0pl+nBeV+kvev4WtAKXknuwsutTIy4m1z/b6NXRP//bXljXS/uGmrcdH4wVJfBGCNd05gq\nG5g2pKfeVfgzhTTqP9X+cZHzjvrknNXSno9o2Y+36fZGwc6d8m4g5sivtxhu2/zMxOjnHam9URc2\nOs3+YgZJ/YssAbs0SQMGwWMn7/UpLvgQNyUqeh3c75Wuai7WkBbQiO6f+vA7zMXLjuroyYPOOjJZ\n5KmZkmvgxTUFJIYjc6+xGufLetX/pZX0+ANaduw9Gl29vPk78/KElp8ekr45pYMbx5v1HoPHVPT8\njdMaJDjL2yfj7dmF/usdcHyGuN7uUP2k4laQLZqx2mq/27nq0h0MrA7qyRTwacs8P9mot3ieuTF4\nrWm/hrywXbr7OenGS0LBlYOn32UPxEzfjQ6KnpyzWq8vv7StZpXTixsbgzDHpZt3GrOh8ftbpFet\npMcfk9ZcHQyWldrHzmrO5EvSjpXBLJVmJnpwcPbfOP52m57I1BeNq1fkEg0w7JS7rtCx2r7moMP8\n9pKe7fZu17KDx5sDCln5K4Zn4Zo5cr/UHOJr1U68VNI+PVv7Ka/kxMNrtfINNd16dFjewdmqD+tq\n3/YvOF/3zmvfqInxIW2rZw9+ZB1897na5p3j0tz7pbPrizXS6OO7BhQTAse5RcvqlCgxSOW+LsUH\nxL3gclThft8MCjjdvVuya5s/7lRrzv5RWW1TvZn+6F8kTmmf3Re5mYis8GSMeadU/4JUy1yPpu0G\npTUytUgXrFPsQebqnI0dWiNpuXTisbbaEWmNjt/InRxerdM1o4Oar2Oap8U6qWjqZCcjhVONaG2G\nF7CvxC7zHBvEC6c+LnMHFRNWcQi9fqfF2oqwZww5b8AlSQc+5KzH4BJtXMvK1HJp1Iko5bUyanbE\nTs5ZLbuotRLK1HlTeurtT0frmySmuScULi60NHBwRD3mDT2ttNzCNawcoq8XGYXJd9PoSrttdTT+\nKfMx5DqfpofPUs2skWvakXl5QvYsd7Zo5PzNFdBInp4UzkhrfX/hYovnap9ePXBHR99XGedM9AZB\nCne22s/5R/XUhX+mlfO26dbmSiPDylIENqH9iOlotNpKV4ak1JrakjSqGZwqeKSt3knzdeQqIp+H\neXlCS24bbZt6IDW+74y1uwpp3PQevWStNi+VdGZgu/xMKkfBzizZIv7zWpZI49Lmb9RiF4OI//sl\nuUa1G5Ju0prnVd7rU9JnT8qoCr7P1jnjev29o9rczBob14Jxq6RAQGg7G6vm3jecseZBDs0FObxg\nuLV7fj3PwEvbFJCxoWndN/xMZN+4gx5JbeTywz+nD/zd8sCzvcDnttPf0sHtf6mR2sWh14r7nuL6\nkN7z448rf/9706rbg0j3L5GmGpc4c7Au8+y45k7HBpziMoJT63MltN/hqZxq1drxP1WW1Tl9zszz\nxuqJCbzPddH/JH3lful9c9rarLT+7TKt1HD9sJYsvUyjB7x+07E3SFvOC0+Za/7NC9u9OpGLdrjr\nB33/g7HZvN3iOu5Gam/Usl8/riP7WyUMgpnoWdvUJHH1irKITGMMMmbVTVr24206Zyw8EOfqp6yR\nND0eM880QeGVcxWdiiw1yyR405ztK/659lubNDIxpvCq8k2hqXtnaL7O1hzVtbCZWXl0pTR2szcw\n9FprYKjtNVzTy7z7+fnqJLvv1tOSf90sI6Ujra1J6guXJHOt2gTOFXKNKdxHmkEBp6nF0h7vn8FO\nqvdvf7pSs9BofMfHNXe60xvH4IiLQ8xNRqtY3MZN0nN3ylW7KS5TK1AMs61YW9yNc/z6I8lqVyzR\nmHEXW3RlSBRKr/T3odz7MIMOs2rKn0KiwChWQhpk2/z/Lm1LSLemTEy0LUns8S9+G3SNN5IU6Llu\nvmKJjtx2RlJ9k5CppVfq1esUGsm9+CtWG057F8q4C4q/bdGA15GFU1q2dH5zm4J/v3+hNHW1dPRl\nK1kTHFEtO2AXer201PScN5CFtsd9MTK60B6VaxrynMmXdPLZS53bFF0d8cwF50snshU/TFpprjX9\n2Be7aph9JeP3FTxXg9MKpsYfy/Q3fodpUgv1ZUmm0Xa7ihg3JAeAYlYa8a8rJ4dPNFeHyZgmndZW\nxmdINqe2hLcxYaUu1+u3DUqEMwrcncvoeTB/fETn3C/dejo89UDyRl1fTcmEiL5epkLjnuZN79h/\n2qqxby6S/kTRTKpQwc4yjBw86Gy309oC16h2L7iPi86ubXMmX9LYhpUaa02n0prxjyrvqGw3rn/N\n4tSpKx3G1vaQdEfzmHXUG4oN1Ce1kXHTxCVJBz6kuFuw4CIG7uynJ2T1s4rNRIpwTau+VtLmKxor\nfzbPnbc3MsITspvbM1ZTyzpk+c6LrPhVqhe2S//PP0vvbWuzUoPA0SnCkrT5F6R9C0NT5kJ/Z8yH\nvH+5iis/33k2r3/8vKZTGtJ3JMUPAPuzI9qzT5Z404o3zmsO9mUZwM0jy7FRqM8Vc+2W4urlZkxV\nCphzVl37mpm+oambsRlp4YzzlDa5ca7Nnb5YMQGnthXmJHl1lk9Iek3abKWx5GzEDIMchTNvSvfq\nqrpevVK66G/kjMLPmXxJpw5dpqI1fbMkT5RZaqMkMyjg9LeXS8utOizw7QowlVeDZcdWRaf9OaZm\nhYUunOGOQetvH00aOYzd/nCnuablGebsRm+0ZLxii2MHnMUWy9Lah5NfVhdvopvv577hW9n2xLgl\nkIOrB408kXpc5gmGddy4TjmWAS5bXAryQi3L32ELdI6zCBaRlCQ9NKnhB/ZJjjn9/jLikjT32HJt\nqJ3tDHjVD9adyyVvHRnXi7tGpX+9R/rNcwplSWS4eOQarfC30bXsbvB1t6l1HpvHTmto7nEdWeil\n2y/7h0u17Ph8x2ovifPxrbXWrJj7fec05GO1fTp5eK626RVJXvHxYR3WqdWX6cjPLpG2NL6zhyb1\n+uMf1Ly9Nyrraj/xK82FtrWMjknw88fV4Ej6m07f06UtcBHMItpbu9ZxU9ozRT577s6l39kP13Rb\nqJ3x0yBiMyGCgud79Obal3jtvueT0kfXtlaajWQllBnY8IOKQdG24MToVZqrUWfGbfu0Vk/eDOCM\nmcTOgHVSfZgsQoGVSNA5PDKevThuqRrXsoSVDiXJ+f/uXXql9IsrpcePSxvHncds3HXXn8LpaCOT\nbs5z37y3jjdvmfi42nz+uRQcaHUNrk0Pn6Wx4dWSRrJuc3O7S8wwropMbVYe5lBdiq9b1Ryozlpc\nOc8sgmA7uk3fScustq/Y25ODhdv/MTHbKmWQoCPBoP1OZx+ltdJmQGjQIbh9ZXbJmzWucvShgyvo\npfSZ/HNNnYQ0Uo7DVHlnr3RrXzf5U+oPLnLOaBqpHdLpl92BxiyvXpGaWHnNoICTpPHhQ9oWDN8X\nGy3rzkXKT7G/YK22jrbSGn1J08lS68cUkFQzI0b7jVaoMGO51f0VKLzajNVcJZmHu5q1ESf1hi8a\nEPAblCNXj+rMx4+p7MLzWcRNeXr9qtXNgExtnXLPI87SuMelIAeLGWcVdzGK246xQ2skuzw0GjdS\nay21Gzz2t6w22vdD77i/4K2SdqudbdVGiZoz+ZK08U1a/thPNPq9Uc2rhafuBGuIxRUjbKyM5Ph9\nS9pohasDf1r77A9TjttmwOTlz0uf3dgsSDv6ZumDRyWvTWh9j1+KFO91OXg6dhqyMcbYw34n1jRO\n7Dc/Km1uZC62govR5X6TOz4F63Z0rMTaWp1ICFxkbpejWWZ64nnv38E2uPX/gtNbk18r498U1VGG\nTsz3F1drMCYb2fXKrZvDr2yS9u73aq90cGy2AkLx/Zq4NrHRFhiz6iatnLdNeqp9ZDXLtPQMUq+V\n0et6MLi9TF7QbM7kSzrw6iU6utwdyE28Bu15MHJD3rY9oVVze5nV5deYSVppOE7tiiXecuMJU5nG\n9bS2rFkRqpUXnMKZJ6OvtaKSq6hs8uqfC3RpbCZ9ONCQOM3Su0bMe9QrlxHY5gx9kNh2r1ulFXpW\nsiH5mtPeb1b8wGJS3aq290y/vqbeCBfYR6HP438Ox7GqNQekI9osqbUojZ9h12n5Cf/1/ED49KOn\ndXD6eKSGV/tU8JbmSpttXKuEZQndRM+7CU01VqkMDzJOnLDSxp/OuCBK6/c5dJxVdvrZmtZMh/vO\nje3IElwpFIDpZLp+qsD9ftx0xukTNqY+dFxx78oUXy9qBgWcHp7UkcUftq+/Fk0XrESaXSvFfuUm\njcV0ouKk1o8pUfQ9Xhuu6bWzL/WK8gZvGHpxg9eIEgfnjh+9NvPUvZ5yplo36mpMnPhnbXOkThbd\n5k5GkEMduEZ2Wo4AqKRWumj0JvTMh/91s6Gc0lm6f6gmnTGs6QXS9GqvwZ07fkJZU+ybG+3uFMVO\nxzGrboqOximugx9a4SRuBbVD9djaKP7I2vLTFzdWRQkH2YLfU7fS8XN04Ns1O6+fku7++Vba/BJJ\nL7U/Pa54b0DS6LIrc7SM0dPSR34zGsiR9Oh+9LMxo1lm3hRW467fkTK91ZVlmHVKbN7t9yUt1x0j\n7vtzjqanZiO7+OfXoh3OYzP8WZJv4v2gUUK/Jr1DmjCFo2ci1/XRddIHvyWFgts16UvP79fRWu4b\nW+8al3BDHh/4L4fr+DwxelVrMZq97dnqmWSYynTw9Dt18M3OYvi5M/paq762FZVNW/3TWV/xfjmL\nD7syP5qfOGabO7nxKvK3qatOdrhNeaRfcxz95vrl9bipPVmuXWVdX4vto8DnCQ6Quo/V0PvkuQfM\nsrpkKKP55fZaQ+Y8s0XDep+6FcROyEKN7w8bI23/QpYFUTJpXZvCdTCjgWm/NMWRhd6AQlIq0ej0\n6ZgSEa33KCPgklZbybdT0v6hmobrfx/624xa9/sf2Rqc3h2azmiMcQ3MJhT3dgeT8wzm5Rgw64YZ\nFHDq5ah2USWMhvciLTRo8zuGdWTX2dJDoRo6jRPquPSBmjRvsXRLYxWLMkeyHauyOOoWVNrIwYM6\nWHuXPZBve5Mblg46TLpF0sHFzSkeY//mtMb+IV8A1NWh0UOTOuuBfeGGsi4v6HaV9OIng/U0cq4s\nN1UPr5IiJR9nzmCou4MfXOHEHKrLNafavDyhY7XHtE3tWSRHtC+xzoVCHZnyMhRLEuy8hgI/pu7e\nFxmnADUyMzMuD17S6GnR9nWnWqtTBacQ7l9wvl6/4SdpF3dXW9RxxyCvHCOV7YtaGBOXZeb97Krf\nkXKtdS1VXdL1ObZjvWruq5lXEgq+Xvyx2vH5GphuIK8Ab/jYDGUNJ0zbDn3mDkalrU2Y8tqzjmfG\n1dbm1Z4pcp1PvSGP1r9rTq/OUEcmy/s7B3oii9EUyQbIMO2krR1vC+A72sisN2FF+ft3voIrhYWK\nDye/QBWySFOmyDhkWl49/Pt9mbcm8bxwnF9xGdq5pvRm/x7Kvf6l1MBN4h/b7XU52wfRMw0IJu+D\nxGl/adsY5j4XG1mojv27sj0poLFNjvYwe9/MuX1xdTBDgenAYllna3SdEs+b6NTb1r1o6z1KqJMW\nG4j026j9o9Kp+gnVz5ir44f/zh6ovbO5ieYtm+JWQ85xH9L6y0KDlTH3XlkG8/x9eu9S6fnrgtuf\nvxB9QTMo4NS9dMGydDwa3mlaqJS9OLWfOnp0peJvGOqfk37156X3BTI/Oh/Jjg2O6Cune5m9UIqC\nneaOGpbU17OS/r2kGyXd80L+G0HXDcM9u1UbWtlYcSX63ooN/ARGSxqjN15RRD9V+eTwak3Yp6Xb\nfzbrvnCNxpnzjPu495em1fq1SdPm7PMp00SSbiC6laFY0mhF+Gak8b3G7Ys8MmZ0lTZ6mqV99du/\nYI21+U9It455q1MFsyzuO/VDvR67+l3a1jTOt28vkubJO08O1aXLz4rrGHaioxH/lJvUxBvYuOvJ\nxAkr7fjZUJbhudpsLjcr255bVqp43DTeL+u4jP4p+p6hn53LJce8z8k5q/N0PFv7MfnYzLoPOt5X\nCVNee6E9uB0T6O/wPTI9MWcdmeJKCJqUMP3J1UYWyQbJ3Ld21UDLqZ9ZpK2+6FWSPlfT0QviV/yK\nir/+lbe8uoOzPS8hyz7f91Be/zX0eY6uXKQtB+ta8NSUMmTKhzOT/jS4PUu09No3aMULZwWydFIH\nGLpxLCbWsotqfl/59m/rGhTI1NtwtD2g5Tin2wao4tqJtuviVZLuqUnrvVXoNls1pyL6/H5+gezk\n0gTbqM0La3px4X8rTdwgnf5Q+In593lavzb/cRRz7+W6h8uXyd4TMyjg1L7Dqzjfsd/Bkqz7pL1G\nk+uECa6YUeZIdpnBkXi5ovw9l6NhKfR6vyTpwzVp7+/lPSbjblA1XP8jOes2PVuXFsUFfoKjJa0L\njp+ab4zxglh3P5pvX0RWm0ycgtFYCdLvlE0Pn6WTyy/VvINPq6Y36HgtfvWxfik0vSdZOGtxqMfX\nhnJGsdOO5VYH1M/q2bFWF1yr2MKOcSs7pfLPt4+u9e6lr5e8f1ye9yLvbKcmdKkWqL1eQ+G2K2k0\nLuEGNjGN3z9nG3+zTB+NK4YcXS4+//ZLOlZ7TFtH39RczMK/KZwaf6Bwe37/Eunk+eFlw8dl8wTA\nff2+9vv6eQPfUnJwu+La9nlawCZ6o3By+aU6fvifs3xnaTc6ZXzvve4f5dlm84aFe7R03vK2/3H0\n5EF7+Phbms/L1P8L9kW/Lo0Zaex6SV89Kf37nJnhvRRpsw+ezp1l75L9e+hS/3Vsx1qNrf+eLt6z\nUjrdHnA6Me/n3IMBel66e3doe5Z9/7g+UGsNlkcDonFTx87VPjnqwpUpWzZ3kf2bP1Mvs+H6YSk6\nQPdHU9JDJzW2ZZ7GHpqUPv5B51RE1xTcAjrOrBs5eFCqxUxZLrLPC2QFJk3bTc1gDTw3dyZ7982Y\ngFNVOnNFJZ0oWtOPLUqf5pLn4C/+3p0FRxL1fYnbeHn2bZZGtv317p2Wjk5IZ95jzC2bWn83dUKa\n/0x6A+04PuKWVx4+eEDa7wz8ZFkBw/uM+Y6zfDdVjYvCwdMfsgesDc6tDm5DX/UsW7ORtfj6vIXa\nvE/S65IOSyMHXtK86ee7lR2a5fsqM00/fD5EVg4NiV39LuPr17ZK31jktWEFO95x2Tde0DHcScvR\ndoX3p59N+ps1ac/xUAZEhpG6KNffxGYBBpeL72C0zb5ibzdm1YORxSyy3RS6Vve6Vo0l2cPLhoeD\nae7vtNqDGfnbtLI/T+j4iKuPN8O4soqyaF2PoiPuSfo8DW3/gvN177zwEu5zj50h1bq/4MtZp07q\nAyfasyXuG3459HPmKVR+P+d6SZ+Q15Z/7vFSBz9LnoJdpM0uU9n3BtHPEzu4OVw71B708K8rex4O\nbU+Wdse7xkanjiWL66sdGvppc/Gy9khCJBCaRfH96x/PNvP0xLZjMy4g4hyge/QJ6VFJ7+nRYi4Z\ns5DivqPjtX1x+7DIPm8Msg9rmXa29X/arp1Zg4HF6id37349uxkTcErS83oahSScKP6yvlFdnBqY\n/YJVfvHw9hNjelL66nf9oEVWPf3euxIQyLpvs6Z6Bl/vxeelz5wvDS/19s/1/pOWSA8tzjJan/mG\nsuC0wvD310hp13pJ/9dxf1+k1LrK9B1Hgx2FMgCSvv/gjVpwFC1HDbbe3aQ2shbHvrZWY0ZeHa7L\nDkivXNDtC1P665c9zTRL56uT9m3vdumeRpaTPy24yxf5XNO9ovvTSnr/tGTvcX+WvDewef6mrNE2\n9/eVei2IXVXzsJV+Evq+MnXcKjyYUUhXPk/j+DhWG46vj4ci16PSr2F5vX7ep/X6n4bb6pWX1xVa\nRLpL4uoS5apX5Gk/18+ckj5spedKbsPLvrZJfQ86ln5vEPg8x3RjTDbr30p3r44fDAhsT3Dl4hLF\nZv2uHnlSHzjaHuCKBkIzy79/m8ezmbdNmacxR47N2jrFB0SiA3TP3SnJpN5Hxi2ekVu2LKTi/enk\nfR67QmSm6YsZVrZTp8Hkfq3m7JkVAafuNOZliz9RosuD93abki9Y3RtJCe6Pz+2W9l+X/7V7971n\nacDyBsCKBf0SGtnQ6732e9Ldvys9vFb6HRXLwIgcH9GlY/3GsvCNQ/T7+7qk35a0b3NrX5TzHUf3\nbd5jrS1zy88ICBYLvzbyRyVOhytLewd7x6Q0/Zuldq6nik6hKjdNv/VZJ78sr4htwnPyt2+tv/3K\nJmnvfunGS7p+kXemtd867dWO2rArsHVS2zQDSTrxWGMVLMdnyTsNa9UXpSsukoa8gv8JqeJlBeLi\nv6+UdiLuZtS8dMxdN6f8jlv89eHx86SrXqn2gFl+zWPqlR5nkHYpW7SbA1xFzotOr2GdcbTVY4cO\na5sj4lT5wGLwszz6hPT4U5mCOK6syebvk96jnPIUxdrs8pR9bxD5PLHZrK5Viv33DmVJuRaS2alg\nXcxyi+iXGAiV4vdvejZq3pVKI8dm4srWcdfFlPvIjItnpG1pt7N40o/pHCtExr12wsp2LcWCyd27\nX89mlgScym/My9btE6X4NmW5YJW/ekU5J0bvvvdsn6tIcCRP0C/LsRN8PWOkr22Vrl8k7ZB0g/JP\nGwpkBrmWju2oxoFrauXHDkjPfzr+ORU5t+NWQgoW+a5sRzvaaWgPQGQSvLELFuk+urJRtDBfYLA7\nbeTe7To253lt0yvO7fefU3ik2P/bRTt6cpF3prW/+Lz0mZXSDY7FHSLTDBJG7osF/O+BW4/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x478a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(20,10))\n",
    "plt.plot(c_tau1,'v',label=\"Tau1\")\n",
    "plt.plot(c_tau2,'s',label=\"Tau2\")\n",
    "plt.title(\"Coefficients - \" + sim_set)\n",
    "plt.xlabel(\"Readout index\")\n",
    "plt.ylabel(\"Weight value\")\n",
    "plt.legend()\n",
    "plt.savefig(os.getcwd()+\"/\"+base_dir+\"/\"+\"coefficients_\"+sim_set+\".pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DON'T FORGET TO SET PROPERLY THE SIMULATION'S VARIABLES!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def new_experiment():\n",
    "    global run_number\n",
    "    run_number+=1\n",
    "    #####DEBUG\n",
    "    #####Reads exactly the same input spikes using during the training to check if the system is reproducing the same simulation!\n",
    "    # inputs_training = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(tji)+\"_INPUTS_LSM_1.gzpickle\")\n",
    "    # tau_temp = []\n",
    "    # teta_temp = []\n",
    "    # torque_idx = []\n",
    "    #####END_DEBUG\n",
    "\n",
    "    import numpy\n",
    "    import time\n",
    "    import save_load_file as slf\n",
    "\n",
    "    import step_by_step_brian\n",
    "    reload(sys.modules['step_by_step_brian']) # Makes sure the interpreter is going to reload the module\n",
    "    step_by_step_brian_sim = step_by_step_brian.step_by_step_brian_sim\n",
    "\n",
    "\n",
    "    #\n",
    "    # It's necessary to uncomment the import statement according to which parameters should be used\n",
    "    #\n",
    "\n",
    "    # Simulator based on Joshi/Maass 2006 parameters\n",
    "    import simulation_2DoF_Arm_md \n",
    "    reload(sys.modules['simulation_2DoF_Arm_md']) # Makes sure the interpreter is going to reload the module\n",
    "    simulation_2DoF_Arm = simulation_2DoF_Arm_md.simulation_2DoF_Arm\n",
    "\n",
    "    import membrane_lowpass_md\n",
    "    reload(sys.modules['membrane_lowpass_md']) # Makes sure the interpreter is going to reload the module\n",
    "    membrane_lowpass = membrane_lowpass_md.membrane_lowpass\n",
    "\n",
    "\n",
    "    # Calculates the initial teta1 and teta2 angles according to the start position\n",
    "    teta1_0, teta2_0 = hand_to_joints(numpy.array([[xstart,ystart]]),aparams,ang_error=0.01)[0]\n",
    "\n",
    "    step_size = 2\n",
    "    \n",
    "#     noiselevel=0\n",
    "    noiselevellist = [1.0,2.0,10.0]\n",
    "\n",
    "    ################## Simulation's variables\n",
    "    simulation_2DoF_Arm.func_globals['my_seed']=93200         # This seed is important to always repeat the liquid's structure\n",
    "    simulation_2DoF_Arm.func_globals['input_gain']=70.0    # Base value used within the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy']=1            # Controls if the liquid is going to use the noisy currents/input weights\n",
    "    simulation_2DoF_Arm.func_globals['gaussian_pop']=1         # Controls which type of input weight configuration is used (1=>gaussian distributed)\n",
    "    simulation_2DoF_Arm.func_globals['w_SD']=3.0           # The width of the gaussian used above.\n",
    "    simulation_2DoF_Arm.func_globals['stratification']=1   # Type of connections: stratified or 30% random\n",
    "    simulation_2DoF_Arm.func_globals['STP_OFF']=False      # Uses or not STP (False means it uses STP)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['i_noiseA'] = noiselevellist[noiselevel]*1.0/1     # amplitude (dimensionless) of the random currents (updated every time step).\n",
    "    simulation_2DoF_Arm.func_globals['offset_noise'] = (13.5/1,14.5/1) \n",
    "                              # Range of the uniform distribution used to generate the constant offset current.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [14.975nA,15.025nA]\n",
    "                              # Joshi/Maass 2005 does [13.5nA,14.5nA]. Maybe it is to avoid too many spikes without inputs...\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['initial_volt'] = (13.5,14.9) \n",
    "                              # Range of the uniform distribution used to generate the initial membrane voltage.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [13.5mV,15.0mV]\n",
    "                              # Joshi/Maass 2005 does [13.5mV,14.9mV], this way it's impossible to generate spikes (14.9mV is below threshold)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['random_reset'] = (13.8,14.5)    # Range of the uniform distribution used to generate the constant reset voltages.\n",
    "    simulation_2DoF_Arm.func_globals['Net_shape'] = (20,5,6)          # Defines the shape of the network (liquid state machine)\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_input_layers'] = 6    # It means I will have 6 input layers...\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_neurons_inputs'] = 50 # ...with 50 neurons each.\n",
    "    simulation_2DoF_Arm.func_globals['lbd_value'] = 1.2               # lbd controls the connection probability\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['noisy_currents'] = True # Injects noisy currents the neurons\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input_weights'] = False # Injects noise into the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input'] = True # Ignores the random seed for the input noise\n",
    "    simulation_2DoF_Arm.func_globals['input_noise_level'] = 0 # 1/SNR\n",
    "\n",
    "    # STP parameters\n",
    "    simulation_2DoF_Arm.func_globals['AII']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>2.8\n",
    "    simulation_2DoF_Arm.func_globals['AIE']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>3.0\n",
    "    simulation_2DoF_Arm.func_globals['AEI']=150.0 # Joshi/Maass2006=>150.0 / Maass2002=>1.6\n",
    "    simulation_2DoF_Arm.func_globals['AEE']=70.0  # Joshi/Maass2006=>70.0 / Maass2002=>1.2\n",
    "    \n",
    "\n",
    "    indices2kill=numpy.arange(600)\n",
    "    numpy.random.shuffle(indices2kill)\n",
    "#     decimationlevel=0\n",
    "    decimationlevellist=[0,6,60]\n",
    "    simulation_2DoF_Arm.func_globals['killed_neurons']=indices2kill[:decimationlevellist[decimationlevel]] #Sets the indices of the neurons I whant to 'kill'\n",
    "    \n",
    "    \n",
    "\n",
    "    cheat_feedback = 0 #Uses the torque values from the mechanical simulation instead of the calculated ones by the LSM\n",
    "    using_spikes = 0 #Uses only the spikes without the membrane low-pass filtering\n",
    "    membrane_time_constant = 30E-3 #time constant used with the membrane low-pass filter\n",
    "\n",
    "    delta_t = 200 # Time delay imposed to the joint angles - proprioceptive (in ms)\n",
    "    ################## END OF Simulation's variables\n",
    "\n",
    "    # Starting the simulation object!\n",
    "    # Initializing the simulation...\n",
    "    s = step_by_step_brian_sim(simulation_2DoF_Arm, step_size)\n",
    "\n",
    "    init_t = time.time()\n",
    "\n",
    "\n",
    "    # The input (one big neurongroup with 300 neurons) will be divided like this:\n",
    "    # 6 groups of 50 neurons.\n",
    "    # - Group 1: xdest => offset:0\n",
    "    # - Group 2: ydest => offset:50\n",
    "    # - Group 3: teta1 => offset:100\n",
    "    # - Group 4: teta2 => offset:150\n",
    "    # - Group 5: tau1  => offset:200\n",
    "    # - Group 6: tau2  => offset:250\n",
    "    input_spikes = [x_lsm, y_lsm] # At the first step the system only receives the X,Y destination position\n",
    "\n",
    "    delta_t_idx = int(delta_t/(1000*time_step)) # Number of steps the delay (delta_t) takes\n",
    "\n",
    "    m_v=membrane_lowpass(600,membrane_time_constant) # Initialize the membrane\n",
    "\n",
    "    teta = [] # Used to save the calculated teta1 and teta2 angles\n",
    "    torques = [] # Used to save the calculated tau1 and tau2 torques\n",
    "    velocities = [] # Used to save the calculated joint velocities\n",
    "    OutputSpks_save = []\n",
    "    OutputSpks_Filtered_save = []\n",
    "\n",
    "\n",
    "    tau1 = 0 # Initial values of the torques (because of the type of velocity curve, it's always zero at the beginning)\n",
    "    tau2 = 0\n",
    "\n",
    "    teta1 = teta1_0 # Initial values of the joint angles\n",
    "    teta2 = teta2_0\n",
    "\n",
    "    if delta_t==0:\n",
    "        teta.append([teta1_0,teta2_0]) # otherwise the generation of joint_states_idx will raise an error.\n",
    "\n",
    "    # Runs the simulation going step-by-step (250 steps = 500ms)\n",
    "    for step_i in range(250):\n",
    "\n",
    "        # Because the LSM accepts only indexes, I have to convert the real values to indexes\n",
    "        # torques_values_1 and torques_values_2 are numpy.arrays with all the possible torque values.\n",
    "        if cheat_feedback:\n",
    "            torques_normalized_idx = [abs(torques_values_1-torques_all_1[(tji-1)*250:(tji+1)*250][step_i]).argmin(), abs(torques_values_2-torques_all_2[(tji-1)*250:(tji+1)*250][step_i]).argmin()]\n",
    "        else:\n",
    "            torques_normalized_idx = [abs(torques_values_1-tau1).argmin(), abs(torques_values_2-tau2).argmin()]\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau_temp.append(torques_normalized_idx)\n",
    "    #     torque_idx.append(torques_normalized_idx)\n",
    "    #####END_DEBUG\n",
    "\n",
    "        if step_i>=delta_t_idx: # Tests if the delayed angles can be feed to the LSM\n",
    "            # teta1_values and teta2_values  are numpy.arrays with all the possible joint angles.\n",
    "            joint_states_idx = [abs(teta1_values-teta[step_i-delta_t_idx][0]).argmin(), abs(teta2_values-teta[step_i-delta_t_idx][1]).argmin()]\n",
    "\n",
    "            # Here I start to send the teta1 and teta2 delayed inputs\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, joint_states_idx[0]+100, joint_states_idx[1]+150, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "    #####DEBUG\n",
    "    #         teta_temp.append(joint_states_idx)\n",
    "    #####END_DEBUG\n",
    "        else:\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "\n",
    "    #####DEBUG\n",
    "    #####Replaces the calculated inputs by the ones used during the training!\n",
    "    #     input_spikes = inputs_training[step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "        # Here the simulator is receiving the input spikes (and returning the output ones):\n",
    "        r, current_t, received_spikes, OutputSpks, t = s.run_step(input_spikes) # Sends the spikes and reads the output\n",
    "\n",
    "\n",
    "        if OutputSpks!=[]:\n",
    "            OutputSpks_save.append([OutputSpks[0],t_mov[step_i]])\n",
    "        else:\n",
    "            OutputSpks_save.append([[],t_mov[step_i]])\n",
    "\n",
    "        if using_spikes:\n",
    "            Output = numpy.zeros(600); Output[OutputSpks]=1 # Using the output of the liquid directly\n",
    "        else:\n",
    "            m_v.process_spikes(OutputSpks,t_mov[step_i]) # Send the spikes to the membrane filter\n",
    "            OutputSpks_Filtered = m_v.check_values(t_mov[step_i]) # Reads the values of the membranes\n",
    "            OutputSpks_Filtered_save.append(OutputSpks_Filtered)        \n",
    "            Output = OutputSpks_Filtered # Using the low-pass (membrane) filtered version of the liquid's output\n",
    "\n",
    "        torques.append([tau1,tau2])\n",
    "        tau1=(Output.dot(c_tau1)+r_tau1) # Calculates the torques based on the membrane filtered output of the liquid\n",
    "        tau2=(Output.dot(c_tau2)+r_tau2) # These are real numbers, not input indexes!\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau1=torques_all_1[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #     tau2=torques_all_2[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "    #####DEBUG    \n",
    "    ##### Moving averaging the torques before sending to the simulator\n",
    "    #     ws=2 #window size\n",
    "    #     temp_tau1_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,0],[tau1[-1]]))\n",
    "    #     temp_tau2_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,1],[tau2[-1]]))\n",
    "    #     tau1[-1]=moving_average(temp_tau1_v, window=len(temp_tau1_v))[-1]\n",
    "    #     tau2[-1]=moving_average(temp_tau2_v, window=len(temp_tau2_v))[-1] \n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "        if step_i>0: # Because the simulator needs two time steps to be able to calculate the next state\n",
    "            state = odeint_arms(twojointarm, state, [t_mov[step_i-1], t_mov[step_i]], aparams, [tau1,tau2])[1]\n",
    "            teta1,teta2 = state[0],state[1] \n",
    "            teta.append([teta1,teta2])\n",
    "            velocities.append([state[2],state[3]])\n",
    "        else:\n",
    "            state = [teta1_0,teta2_0,0,0]\n",
    "            teta.append([teta1_0,teta2_0])        \n",
    "            velocities.append([0,0])\n",
    "\n",
    "    print \"Total simulation time (seconds):\",time.time()-init_t\n",
    "\n",
    "    slf.save_to_file(numpy.array(torques),\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(teta),\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_Filtered_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_Filtered_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "\n",
    "# # Here I'm going to run 5 trials for each experiment\n",
    "# for tji in [1,2,3,4]: #[1,2,3,4] are the training set trajectories\n",
    "#     x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "    \n",
    "#     for decimationlevel in range(3): #[0,6,60] neurons\n",
    "#         for noiselevel in range(3):  #[1.0,2.0,10.0] multiplication\n",
    "#             run_number=5\n",
    "#             while(run_number<25): #trials for each situation\n",
    "#                 new_experiment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f3hgLetWZ/LpIR1fnl/i8/gCWJ0KtFRH4bBFM3Qub18FV+ztW6PmFkyIDQ5ot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\nUF0P2/6hc24eL5kG134VKg5tvrH960Ww5G6rvqSeLf17eO4w2P+V3O3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11rk66vra/znl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e2bd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=25\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "torques = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "ymin = numpy.array([torques_all_1.min(),torques_all_2.min()]).min()\n",
    "ymax = numpy.array([torques_all_1.max(),torques_all_2.max()]).max()\n",
    "plt.figure(figsize =(30,10))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(torques[:,0],'v-',label=\"Tau1 Calculated\")\n",
    "plt.plot(torques_all_1[(tji-1)*250:(tji+1)*250],'s-',label=\"Tau1 Original\")\n",
    "plt.title(\"Tau1\")\n",
    "plt.xlim(0,250)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(torques[:,1],'.-',label=\"Tau2 Calculated\")\n",
    "plt.plot(torques_all_2[(tji-1)*250:(tji+1)*250],'o-',label=\"Tau2 Original\")\n",
    "plt.xlim(0,250)\n",
    "plt.title(\"Tau2\")\n",
    "# plt.ylim(ymin,ymax)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x76e8590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "teta = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "H,E = joints_to_hand(teta,aparams)\n",
    "\n",
    "plt.figure(figsize =(10,10))\n",
    "plt.plot(H[:,0],H[:,1],'.',markersize=1)\n",
    "plt.plot([xstart,xdest],[ystart,ydest],'.-')\n",
    "plt.xlim(-1,1)\n",
    "plt.ylim(-1,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3a6394dbaH738+4+IyPeJyP8pIj8vIp++hH1aRH7u8u+fF5Efaa19U2vt20Tk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\nbuP3nPWyZi43A04iavZcW47XrzxL+VS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f6e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "OutputSpks_save = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "y = numpy.array([j for i in OutputSpks_save for j in i[0]])\n",
    "x = numpy.array([i[1] for i in OutputSpks_save for j in i[0]])\n",
    "\n",
    "plt.figure(figsize =(20,10))\n",
    "plt.plot(x*1000,y,'.',markersize=1)\n",
    "# plt.xlim(-1,251)\n",
    "plt.ylim(-1,601)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}