{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Orientation maps and training\n",
    "\n",
    "This notebook builds a LISSOM model (see *Lissom_modules* ipynb), trains it with orientated gaussian inputs, and plots the prefered orientation of the V1 neurons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**REMARK**: In this ipynb, as oppose to the other notebooks, we use a **pylissom** helper for instantiating layers, that reads from a config file. Another tool used is *Pipeline*, that has training and testing evaluation logic given a dataset. For more information regarding these helpful tools, please read the documentation page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook as tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylissom.utils.training import Pipeline\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from pylissom.datasets.datasets import OrientatedGaussians, ThreeDotFaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pylissom.models.models import get_lissom \n",
    "from pylissom.utils.plotting import *\n",
    "from pylissom.utils.helpers import debug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### If this takes more than 5 seconds,  it's a problem with the pytorch and cuda versions, only happens first time cuda is called\n",
    "###### If this takes more than 30 seconds, maybe your cuda is broken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "foo = torch.Tensor(2)\n",
    "%time foo.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training variables\n",
    "\n",
    "The input consists in gaussian oriented images (2 oriented gaussians per picture, in random positions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "size = 24\n",
    "count = 10000\n",
    "epochs = 1\n",
    "cuda = False\n",
    "gaussians_per_image = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Init and train Lissom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use one of the helpers that returns a model according to a config file. This simplifies a lot the parameter tracking, a huge problem in LISSOM training. For more information, please check the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lissom (\n",
       "  (v1): ReducedLissom (\n",
       "    (inhibitory_module): Cortex (9216 -> 9216, sigma=47.84, radius=23.0)\n",
       "    (excitatory_module): Cortex (9216 -> 9216, sigma=7.4879999999999995, radius=9.6)\n",
       "    (afferent_module): Cortex (576 -> 9216, sigma=5.0, radius=6.5)\n",
       "    (piecewise_sigmoid): PiecewiseSigmoid (min_theta=0.083, max_theta=0.633)\n",
       "  , 576 -> 9216, settling_steps=9, afferent_strength=1.0, excitatory_strength=0.9, inhibitory_strength=0.9)\n",
       "  (off): LGN (\n",
       "    (diff_of_gaussians): DifferenceOfGaussiansLinear (576 -> 576, sigma_surround=2.0, sigma_center=0.5, radius=9.4, on=False)\n",
       "    (strength): *2.33\n",
       "    (piecewise_sigmoid): PiecewiseSigmoid (min_theta=0.0, max_theta=1.0)\n",
       "  )\n",
       "  (on): LGN (\n",
       "    (diff_of_gaussians): DifferenceOfGaussiansLinear (576 -> 576, sigma_surround=2.0, sigma_center=0.5, radius=9.4, on=True)\n",
       "    (strength): *2.33\n",
       "    (piecewise_sigmoid): PiecewiseSigmoid (min_theta=0.0, max_theta=1.0)\n",
       "  )\n",
       ", 576 -> 9216)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lissom, optimizer, _ = get_lissom(retinal_density=size, lgn_params='lgn', rlissom_params='rlissom', optim_params='optim')\n",
    "display(lissom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pylissom** implements two of the most common LISSOM stimuli, *OrientatedGaussians* and *ThreeDotFaces*. They extend the *torch.Datasets* interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "gaussians_inputs = OrientatedGaussians(size=size, length=count, gaussians=gaussians_per_image)\n",
    "# gaussians_inputs.cuda()\n",
    "gaussians_inputs = ThreeDotFaces(size=size, length=count, faces=1)\n",
    "train_loader = DataLoader(gaussians_inputs, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample, test_sample = iter(train_loader).next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_tensor(sample, shape=(size, size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are some samples of such datasets:\n",
    "\n",
    "ThreeDotFaces:\n",
    "![ThreeDotFaces.png](imgs/three_dot_faces_dataset.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OrientatedGaussians:\n",
    "![image.png](imgs/two_gaussians_dataset.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the *Pipeline* object, we can train or evaluate a model for a given dataset. It simplifies the pytorch boilerplate for training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe = Pipeline(lissom, optimizer, cuda=cuda, log_interval=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inpect activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gauss, _ = iter(train_loader).next()\n",
    "plot_tensor(gauss, (size, size), vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pylissom.nn.modules.lissom import *\n",
    "from pylissom.nn.modules import register_recursive_forward_hook, input_output_hook\n",
    "\n",
    "register_recursive_forward_hook(lissom, input_output_hook)\n",
    "inp = Pipeline.process_input(torch.autograd.Variable(gauss))\n",
    "out = lissom(inp)\n",
    "plot_layer_activation(lissom, 'lissom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we do the actual training, passing the stimuli as argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe.train(train_loader, epoch=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Orientations Maps\n",
    "In the next cells we calculate the prefered orientation for each neuron in V1, and plot it with a color encoding. For example, a red color mark a neuron that activates more strongly with horizontal oriented gaussians."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylissom.utils.orientation_maps import *\n",
    "orientations = 180\n",
    "\n",
    "keys_arrays = get_oriented_lines(size, orientations=orientations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keys_arrays = {k: map(lambda l: torch.autograd.Variable(l.cuda()), lines) for k, lines in keys_arrays.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "om = OrientationMap(model=lissom, inputs=keys_arrays)\n",
    "\n",
    "orientation_map = om.get_orientation_map()\n",
    "orientation_hist = om.get_orientation_hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0IV8GAFifTgQALOEzk7q3RNQcJT/kkt0rGL/21Q5Lg47pC28XpJahMbSkgeIk\nM98nyXNt2Z3mcAYACb6HQwH8hpl7AcwloufhXvbS5LlRsR8REZFFfZFdNyYi6y1+BTNfUcfVvgZA\nY1FtDvdRUzQmee5AoZ5MU1XaPmFufUdlZf7sEojsmjfutPRNO9gZteC7D1nQcmVJ+brHhV2NhbIL\njbBqXZMcu9hPVdjGbIHaX5K9OW6zlWbVM4rpFxyLuFTYxKWJ8WoaqVYjTFwiiw+XmvtY27gHAcDS\n5PqXmDaOS5czML0fZYK1XZNCJhP++aEFgvmifOe6o8+6399nYIM8RT8AzJNrDJLjpeMD3fl53WwZ\nvLqQFYiSxXzADde+v5EzgHqZWF3JczOXITobwIcAbujL+Yqm+YhFREQ0CQYgKCIRfQXAwQD24TQe\n2IpJnttILEexbd4OBeUInFP2fHfU5emyJewQRLfgOzxnTRuySjV6oNPtfPLlpMzGgHfHZW5HjjlM\nw+Skbmyis/H1menUmzKWAPOY7yLB0uaO7fEjUrehMQDdXVjEg2JacWOX9Gv1dy7G13dEp5WNp+/a\nJ7owMfFg3jfXJrnmltLPmWlHNFEZqapI8sauKfPKU4MqDIyStIXOIOfL+ESuTfpsVe+YN79YwlML\nx1GEQUYwWvqpkoZyuWkPumdzxIHyN20VyGoFvo13bN/z2gJCNaxgEwsiOgDOduifmdmGdehT8tzI\nxCIiIrJooMW+JM/thNOdzYeLrX4WnJPNXUQEADOY+d+Y+Ski0uS5H6Ji8tyBzXaE/Bznf/CrZI7x\n24aQ0Ym9XdwuD/fhJw4pHbJt8MnQJFFbv6OMIWVk6VPh7dQ9yO+neGq01+Iq96oMbJlcSwj7GRld\nVxcAYIrou6YEuhkkq5uDKizG8SnCwE4w7kgXuvP5xbIQOj7bqs8gN2W0t3j9W92a/zvaZ+10g4NE\nJ1YPbGTXXe9zBw/vJQ/LrirKi5wwMMVzyEM/LiH17FwAvXUPM4/Grk5+KVB8VUn7yYARTSogMrGI\niIgsou9kRERES6PF3I4G9CP2EZwlw4TzUto8/fzsmrCKkbsH2vgi5u5fSts8+GvXhk6UpfSritea\naYhvSFoV+suqAjyvCPbToIUiTPh4ECa+12wnbt3qRWSgZZ1pmzXU0NL1/XUjBi7myZlrqajKH5jo\nD2rF81W3mfgt98yusMr4+137k8Rs4FIbfeIWZfueP6O9v59IOrlfyG/0i7yJQyJifisf2M1/VuEF\nkrBiPyPNokz4AAAgAElEQVReewawS2gvqbFWob6Rq1V69AAAlnqmFUuNsWtSVmLekLwFIT2JiJbT\np7sOJuh7HVqcmu8dv+q1aYSxawyKGBER0dJoMSY2oCnbRncQ/6S7nImVwZ5XCJlUF01P+x2+lTtv\n0QuubDjqYWIhBb/PwGzU1CfcztXCfL5WklhW3I9ouT0/rKSezflIp6GQ7MPhjF0XwZla6IQaylkw\nX17UkVJ3q3lxDxG7Vzo//6wuFeanGecuChiQlpk99AUhJX61+PtuHN0y5l1krNZ4eLKw3bMDzzj8\n+wPYKm/sWvqH7xPzCuYQ9/06fYf3+pL37qvOylh+T/014WcdwLxurv5HFQCN6WD8tmLKtu1jyraI\niIhmQ4sxsQH9iD0/fxdMOKMb06bkJ4pKLKsMwsDmCQMbbparlYFVjSCVRd7xWntKdTI2Zr2wghIG\ndp3M+EcLA5iWiSfvzveZ164mpvsS0U89KyvR22WYZdZtbb2fufPmnZo+81HHhp/149ekbW4936vc\nK9W7naQxy/wY+22dyX7CBVQ3dlpxXLNyY9VitlUtg5P22SX96XNNxxpmYAr9/fW3XzOzqQy/fWj1\nz6PWGfZVkBHp4ovT32zSSQy83ABSFFcnIyIiWhoD4HbUSMSPWERERBarqjgpwcm6AbzCzAcT0VC4\nEBrtcOvQX2TmsjBgzmT/A+CISSlNvlLpsJcY91ojch6j7X1lpqG8KkaOEovoefel5/ti5DwRw0bV\npeC3cCYWwSQUn/dEk5vzYuXRlC07PJPYwolWj8ux3uLDRler4mMqRuZDIgyTPr8upSNPzg/169e6\n7S9FPNzRjGOs3Nt0eVY//0sqTqoBg/pVqmL/6wFrcbpcfrvTzslXCsqT3tafqLcMaQJjKy+ryUwo\n9Z3WlSgjQlEnasHGaffil+rfxPFWdpz0VrZO/iYmmb+lhplYtNhHrJ5bngTgGXP8XQD3MPNoAPfI\ncURERKtD44k1JrLrCkclJkZEIwEcBOfT9C0pPhSpdvQaOM3pmf65IVxplJHHTwormY8JlfuBxMzx\nKG0vS9fKyCyUndXHwNIldv4vSVr7VT/6q8Gmsv3PEtMKgSr4bdS3Z2W7nReB1Cr6Hxd2pIlSRv0u\nvdd9JfrsfGFuvxTF/i8vyT+PQ7SNKPrpGFO5eycA4M4H3aLF3WyZlKMeF13uzp94ghjL3m8WbMQI\nkz8vz6GCqUXISNWv66/JRrpQYGOnOVZ2hrC0KUG/WY+alP0BB8wf/LorH8//DVxr/i7cgIxg418v\ncP1rpxDOrf3aVcPqyxvU0YpHVXHyZ3DGUOuZsmHMvFD2FwEYFjqRiCYCmAgAWO9joSYRERFNBdH7\ntAhqGrsS0cEAPsvMJxFRJ4DTRSf2NjPbLCVvMfOGhR0BaO8g/r5nQ+dHokhk/DJ7iHqiYwKYd6mb\n4dQ4c68+68J85AeZRKGYXTIlbrt3zTYaE/5TVHtqpXM7k/1jf+C21/xUxnOyu/e/meezbqIXcuxm\nkTC7YUHdnKvb35h43CEMToMsqE7vCcMWVb+mbPMOKb8BtQ177fX7z7zGSz8zvPJUn5maepwmJfZl\n2jtQVoADRW+oYfxqBlZ2uPZ29zyPOaP4b1F1xNpGj580bS46g4HrOsCLuvtn7NoxjtH954qNN2oJ\nY9c9AEwgos/CRd5dn4iuB7CYiEYw80IiGoGsF1dERETLorWYWM2PGDOfhTSlUiccEzuKiKYAOBbA\nj2VboihyCEV2nXSJx7w0GWgolpKijKVJ3X2XppPR3GxVQUdhKscnGkNWmZTpi8omJFmrZQvbuvZ9\nZRLKEO6R4z8HDDE/7bGzecZ1a+QPXN3lp4qRqejE3j01PzkrA7tfjvc019JVPNzs2tiYq2OFyY4Q\nV55vSLl1xNlRtsrArpf+rs+NIoyi5xZ27vYdwAP3IVjLc6x37bO/Z/Zu+2AirQwsFCNfMP329PeY\n4McR8wPNIv1z+I4ysv4ah5ciFPmvbyCiq+HCUL/KzGOkrNCygYjOAnAcXLyIbzLzHYFuM+jPguyP\nAexHRHPgwhn8uB99RURENA2UiVX5VxO/Qj7vZNCyYUDyTjJzF8R/g5nfALBPWfuIiIhWROOYWCjv\nJIotG5o/7+S8xbtg0s8KvON9Zb01m91NtsKpVVTMefYDid2B/QnUXHFQTqEfUtZ6yUAuq70IkBFx\ntsv2Q/xPaR09mjlPRUUrHqbK7gODbUPXnWfLbul0O6IA5qldAIC/nZq28X03/y7Hw43SHYkpgmuz\njzG2/ZWUzZCy8XK8mVH+H/1NV3fDdq7sevVdPKAzvcbtWR/MTJ0mxLjYtTkFGietSuyxwGLIAe4e\nl2Gcu1aixLe4UrbHm7KDAu1cTynkPdJlrlCMdS2T0yZMMO+unH6OGGxfIOLlObfnVQBqWXFOIPrL\nBQcy8H7BcOtC4z5iBSiybGjtvJMRERHNgrK8ZDn0K3kuMzMR9UvB1zwfMf/Dv1ugjczOe4myerqJ\nt6TRMKcLS9vHsLSk3ZdkCfzXxS4w9flb5GcrelZSpSXs4NFcGzWt+HQdC+GWrc0X5rRIjq1phEZO\n7VXmNtXVaTQLi8OESd0q79Aphm39h/T5cWH9LxsWe7Rsx3uMZ8Fck46tXdoLoaR/cf3gfGMPfaBb\nCiCJknqQiebxB2EjTKpqdde6yTDSwxNTkWyki6D5xO3dcvyutFnPtNGybDKRcpj3ZCN5r3Q9QBjY\nPeb91E/ChJD0IFAiNzXAwCphf2RtLvqMuphYX5LnFlk29CnvZCM8rSIiIlYpNFSxH8J0OIsGIGvZ\nMB3AkUTURkRboBnzTm497BH85FQKxw47X5LU/swlrbU6MY3+OkHZRMDNXNmWPtZpZhbULGbvStl6\n8JfUQ+iLV296Xj7WftpXFbMLGtrp2r7Zlasb6TGgxYad6Gze5rVZakwsJsh2vZO8uPcBXdJLV7vz\nrrNjk7j90yQ3wDeEyS1st/pDbyZXBnZgaoihiXRJWNdNt6fmLG1/cmO6SdIOHi7DP8IwQn2LfAaW\njf4qv8NkdWDvku2Nps2dct5P5fgPqAe8SO5juBvRg2+4491DrEuUWlOvybMtXy18ujn/4l9n2z8l\nW5t0euqphJ9VtWEpRUNNLEJ5J38M4EYiOg7ASwC+CAAtkXcyIiKiFbAcjTJ2Lcg7CRRYNjR93kmN\n7BrEGcLApjhGdi02SKqSfLAeA7OM7mJhaxsGmn5NtuvBjy4amm3W9LZlyPs/+a401xmWdIywiOtK\nI4lqP9U9eYcF2pYxOTWc5Mu6MuOxy0CqiDgmGO7HQXVSd4ou7QobMfYaWXkURgVlS8ZiiC6U9le7\nzSBr9Knt1JE/EE3WZ14JIzuxM2mjaj7Vu9HZyLR18N2O6sMM75XZXRjug4Y9aRmyTQFIRFYAF4s+\nV+ss+/KZ1w5euZb1y98owXLkYmM1MSITi4iI8LCKuR1FRET8o2GF24k1FM33EROx8hgUL0WXQcXI\nSbun5+/9oJJsX/wLObf5P15o2f0w7/y0H2v4CmTFwqOTPScShcTKoxNDUlfnmzFYhETG3oBvIJBV\n9PfIearkVpHRimrfSPpRf8TUbOFQSRaL/xExUs+/Nh0HXSNbMb7lT7i2S43MqmOir3UiB89M6UFR\nk1iLopBJBQDwava53lvaNlxWZOAaRmLTKoEfHt/e3etYG5XCi1k/ydb9XMo8sXLSSfnzL/aS7Fxh\n+pk4hRpj6xo/YhEREa2N1hInBzR5Lg3vYBzdnZllk1lHZ5SAy4bOPtrGP7Z4XOosD9F5eWxOsV/b\n7Qi40+zrDK3L84chD9fntcIcfmhqnvOMM5PrP53Gr+cdulwbccFJFOMlONgwutv0GtfL+Ue583tN\nmzYv1pca4vzCuMJcOz07459r9ud68ciC+J3c03Fy/EbexOIgMepUw9YkKi6AB6/JujQp6FOdyT7/\npav4+jWQZV9Zhn4SpWO8lMsMo7PQ2Gtjn5b7OS6tu/bB7PMMxQ4rihlW1D50/rkdwNz+Js/tGMXo\nPrV2QwCg01sinlhERMQ/FOpyO1rpGNCP2MiRj+DUKYTTTwrMKvLMpnrLzKE2hccAxkrf95h4Yq/J\n9nExzkwZWRW5/zOBMqWJeROLNWU2Vt3WMQG2MlXG8W1p8/PMxCk6nAoMTHFbyCn6qOz5VieWxt/S\nMTpWcophXzeJ8kkfcQ+fac73mKTidymjxOeUwbgy7nH33GsMWgctzDMwxe5iWvGgPJvdAzkTfF3W\nJvJcXzOsRR3g8ygO9nUp31dwThjKwDQU3ruiE7ulLPbClKXp/hmDsnXruM0xVsOl7QvaAsLW5jeC\nFEWdWEREREujccauA4H4EYuIiPAQmVhNTDWi3rJAmS0PnZfUWXFSJLwkioURWVW07PE73MgEVH6j\nKNvcnYEyfwk+FU2WibnBNoki3V9MSMVI8mJ2NQKqwPd9JzE3VeynqmoxqdjdPYdfGPHnUNnezU6M\n2QOpGNgpouZkETFT8wWjBP+jES0BUHs2BR0AYIHbqGkGHdOZVKnSnkSMTI4zIqSahmikEO27C7WQ\ntfzXOAh3ettqUDFyPVHoTxdx0kh6OEIXTT4tBdalZJ3MBtMCscKOUO8UFSvPc2KljSt2wXncoJAO\n8SMWERHR0ogmFsUX6xjB6D4O+Fk6S0+V6Aqnex7/5xi/sQv8aAASCeAcEwlAo2FqAoZvBq4vNoWY\nINEGsKhKrt+QiYWizGg2j7IUZQMJVexroo/jAivyFwiT/cJl7vi3HHpW7r6/IP3cGIonpoxM/ib4\nsK6kjZ+WrjR5blenK+9Mzw9FrfBRlLCl1DykAt6t4KMc4tdJJBbzykw/2YvSojh1Sbov0V3Okb+X\nCwLx4fAWgF92gBf0N2XbRozuiu8q3RBNLCIiIpoNrSVODjATG87oPhYhA4pzvJntAqtRONApv5Rl\nqXFmKNm8wsYwbxfTgR4teyBwQk4nVqYXcYws5JKTLPMHXHkU+UikxSnG6k39dpT0db3fj9E34Rp3\nryPEqHOB6pAuT8fxDZezHfNE//X715BirttME1ei/5E2vy01DBXD4N/tkpQoK1OWlYHkgbvncdFx\n/q/oxgwT8xFyKUqfcRXmpeY09enEFPfIO5xEcX3e/G39xG1UZzvB6GyTnBEBlpbAJ/1F35j/1wGe\n118mtiGju3akFdf4lsjEIiIimg2tpRMb0I/YCCzGcWaVCwAukNU7TW5zjMYmPdAs30hWHI0rpgws\nl3QUaUr46cZwU/ve9lqJwKk6ttvLdGK1Z+V0dk+nzteS+FvqsJyfMvM6nPwKZoIJfkRSc/1tO93O\nFmmZMjmX5xh4Qx/RQ+bEYx0D+w/Vhf1U+puYj8yq/Pj3G6fPatHG7jccdp47PkLOP5ZSXafGI7s7\niQP2bu4+NpD4/aXsShltoE2IeRW3qRIfTn9ry/HD0ZEHmXtdKo9xn1dc2X0jpOAn5gQvIRIufSOp\n2ss3/t4BeZTFLrBoSECxxoqTRHQaXAopBvAEgK/CLcYGE+jWixhjPyIiwsNHcEJxlX/lIKLN4dbZ\nOiQD+OpwCXKDCXT7gvgRi4iI8KBMrMq/SlgDwNpEtAYcA1sAZ4ooAZtwDYDP9XW0AypOqjPDRSmp\nxrUiXqoEfqUIjWsF0lYd44mPV5o2F3ht2k1djyRwVdMMniFiZZKEGEhFCE2Z115+M7CK/ZBpQF7p\nX2wKUCzq8K1drp9DOnNlSSbVuYET93ebjaTunGfT53GVKOKP0Jht16ioaMJs/1TE/svd9hDT9XC8\nDgDY9wcbubaaGPfhdIxJDGwxsaCA0n+exhdREVgT/yJV+pctaBQ9z2DKtrpMKgbXbLHU3M+zInS/\n6rW579L8O7zXpMBCmi8q6rPbxrQ5+WXZ8VM6mgBtl5xYPOC6UJdOrDTvJDO/QkRTAbwMl9r3Tma+\nk4iKEujWjajYj4iI8FCXTqw07yQRbQjHuraAU2v/loiOylytnwl0B/QjthjDcBGOxZVGua9RRa+U\n2ex4STN/JWblOxBWcaWwirNCF1HmYYo0Kf3Fsly/pxzvYZTMDyRMTGwLEiVvcfTX/hpMKh43SmJi\nl+qEKRtRNmFfyMcaox0707rTZX+KPIEDXd8XWBabsAhPS3yGGccUZOq+bsYIMXy9m12d8o19KR2H\nKvSTyBbvSzSLddL72ED48zvyO1hD2GQcvjmKMccoWhAIpmyrC9Y8pUiTnv6RK2/bAsUYJZE15l3s\nntaoACObd6lX95ytzbLD9O/lpbTsZMIP/6tkEJXRUMX+vgDmMvNrAEBENwP4JIoT6NaNqBOLiIjw\noOJkQ5LnvgxgPBGtQ0QEl6rtGRQn0K0bK0WcPN6YFOiMkqRlE9OG4yfNzJ03VRiYrom8tl06m90n\ndXvpNUzdHlI3absSxvqsJnsb7VXYGVCvnE2Ca2f7MuPUMWKI+mTOATxtowzM7yfDKMZ2ujLDwJLz\nJX4W3Zx1wLb6lZyRrjCwRSaS6HD9jXb3+kEal21Hz13o7oxrkjCXHrf5utguZ/VVUmZ0YUmdsLL1\nxSCXuqS8xByjEj6U+1mjzDDXmliELKOB+pMqZ6GMDEiZ16iy6K2TnP4RFztJ4fhJ+d/leDAwrxF2\np41L2cbMDxHRNACPwiXEnQmn2FsXgQS6fUHUiUVERHhorLErM58Hl/nbohcFCXTrxYC6HY3qID61\nIHfu6TLzT/WMYUPQefLhQJnCSvQ6Z+61lefWYRu9oCxCew3ZwCgr2wNA+epkFedkhT1/ieie1k9Y\njQx2bH7mTVAlkrBd6RKVz069bvuYkILHl6fswM9H8Lj5XXpk+3PRxd6lq5MB/VPZ6qI+o0XSt00C\nnGRJCujJ/PPxuoRU2jhkvNwXxrRz4PwiRpa2mVdf4uoEoyTMznQJqzNBGZnxvLtW6nRdfwL+PTyO\njovB3fP66Xa0OqN77YqN34tuRxEREc2G6HYUERHR0mitKBZN8xHzxUgr/Rwj22tle7hsrzJt9JHv\n44uMSBX7VSSLfxXR6Aao3Ht6YdsyJX4oWesOco9PehEmsud3Ba/FT6TlIYV+TRiD2EXi7Dd8kHtW\nj8vx2EGpamGEiJo3i/Z9R6PRGHu++DMmYqS7x6+bVGe/FHHY913M3qsrG34DZ/oDgAc/5647Q4ak\nvphHhPxMN/YjzNrFg8DCSE3YFyUsRi4KqD3KAk1o2XARHe8JRG+doHVTsgtYtg7nTwUATMcZALJ2\nzlsA+FZwtPWBUP3D0Ayfuqb5iEVERDQHVkMVnwWHt2s3WeGo9BEjoiEArgQwBo5rfg3AbNTphT4f\n6+B07ACckY8MMFVmn21yNcDbEmd9gpcp7YPp+dls+guiHF0tsGAhxoNKOJaa0wcJi1gHwiZYlwrs\nz7lHprsqMb6yiv2+RHYVw9oQ+woo9PnFLtd+S2n/Qt6UYLgYGCeMTCNvLE3bLrhIonBo3Z1mYeF8\nZVnKROW+Pkgf6C+TaB46V0+Uc/JjXvSv4gY22KRaS8w3NDa/Yz72dOLTZO8zmfH4v1N1qELf8gvt\n6wY31iRxch4+0Q8R/xADU9x3fjaHxBBT55+3T4DRPQm35NdfEFKPtlZAVWPXiwH8iZm3A7ATnLFa\nw7zQIyIimgcE9wGu8q8ZUNPEgog2ADALwJZsGhPRbACdxm2gi5m3Le1rWAfjyG5MN4Z+Ezz3i3Ok\nblzg/CN2d22nPVh7BfmovrtiAQCOlu0vTdTVM4QdTMk5M6d6E96qy+28mI+wno9/FYrCLtfTTExv\nhIwyZa7e6sJAncMiYaTDt8o/h0Evuu1SL27+g0bPM17pwC+ylwQAnCnjnilj3TkfIz83VmFLTKsn\nNdpek8+OTdib0WX9tNMVnHpmboy7e9FzQ7HD+hZb/xWzf7Bsb5OtY+aLcEnurGGefejidfJ1M9ZB\nDsr1yyxlfH1bKMrZYAA/7AB6ugNJE+pAWwfxZgWmUD56CC1hYrEFXBLt/yKinQA8AmASgEpe6EQ0\nESpLrPex/o43IiJiBWNVFCfXAPBPAC5j5p3hJoyM6CgMLUh9mPkKZu5g5g6svUl/xxsREbGCoYr9\nKv+aAVWY2HwA85lZAxxPg/uI9dkLvcyM7oK98t/Cafdl2bGKlSFcGxA1rxB2PbEOEfOXfE+ubArf\nV9DaKJJf9L0rQlChIJSMQer8xCUbBURH1fzaJaI33BgTMVIV+1vlwynzRSKaiXhIbflr8LuidF/P\nRsFwz4ZzPqD5hQvij2TPCUuL+dCkTs9fzHlL9ydEjE8Sp5ymfZtryHVVRFwiizOWReTjmGmUkr+a\nMvXTVcW+FdZuQxbuTzcjOha80MNM+RND7NnlCLVRUfOpkjaNgurEWgU1mRgzLwIwj4hU37UPgKfR\nQC/0iIiI5kGrKfar2ol9A8ANRLQWgBfhAv2vhj56oR9xrJnVZYa69uJs6nc70fn+9Mq2jilhZEvH\np/s+A9OZ+oNAWXJ+MvfZmby6aR/x8wAApq0DtRqrTOdT6/kp10gU+2dmtzBGnUM6i/t+4Q9uq8p/\nYxXJ3AUAWPtM91zeVyPTpYb9DZLz1nJMaIS5wkIcIXvZmGfZhQv1L3VJX2nZBADAsDXzixnDZRnn\nceN7uGMudV3W1MLtu+ewqbC29ZOfeVmuzQsSxXfrJHFJqvBPX4/LZXuCubLPxIT2rgCvnDJ2FXpT\nfIRTmtSP1dBaOrFKHzFmngUgtALREC/0iIiI5kGriZMDa7H/EfImvnJ8jKatCq0zq8pCpqOEgVnT\nWpk6jhlbzM50dhkmOrLFAR3ZEp7uldgB6XyouizHeqaR1SV1Zc4OL+n7ujDDOjaS+TRnWpHOszRU\nrif6rhEm6upC7Xur7Pyi7AtI07nZMsDXaXXJ+PMxzzAr+yOeJPd/qdE/vUET5HwtWyLH6X0sTcbt\nkhGPTdLkhVaJinnKPfI77ihjXovt83X3tLWnGys3B7GQpL/COvltFz6ehkxMWvArVwSHOHsIGobn\nvGM1Crfxj9dCYzK2tdrqZHQ7ioiIyGA1ZKIANT0G9iO2DG4NM8RVvbXNKwPZjo73I7O+ZvaVMLXn\nu17a5rZriU+GMrBQTPS5wiDyjCw11ExX3Bxmm33iZ2SvR84JrdidL3XnybG5xptd0kgMOId2ZssB\nkGekutAcjxBWtJA1OavTyRFONvch/bwmfW+S9u0jlC0oWTEUY9dLx6mRaspOhmrcfLmPd5PMUhOS\nNm1emzLos7JI3Z7cserGllVaY/qq2ffzOfzC7LfL1nGTlIGVmAsNzmwyvbyGYqhgoTGGbb5j3x1P\nmZn/sWlEvPlGM7FGuS0WIcbYj4iIyGAFrE6uULfF+BGLiIjIQJlYlX81+3Jui3tBImcx8wfM/DZa\nNXluAqsXVs6t+t4d3OZ4a/T6FLJQ0TGk6+0JlMly+BIRO7aWrh+zEuvyy2THJCP1sF8iEqkhrJuL\nsg6j7gZ+SZrItCvXT0g0UqRmCqrslwgNgbap6JiKk+nsKCLS+3mGnirb1VhUa6zI+WLJGFUcdmPk\npPwS00b7ceLoeoHnkFeu500j8s8jxRAxWCVW31X1pVzfXEPjj30GWaS5zVLfS+3HcoyeTBk/dZc7\ntO9wu2y9v2orcLbJrW0SoC9FdbsF+lJxdNPASHcGgkGr60Wdq5OlyXPRT7fFKoiK/YiIiAxWR10e\nAaXJc5G6LX5DMh9djIDbYsskz8VyOBZlmdUOXpt6LPZC5hgVjBCfF1pjApliaYXL3Z0o+7OmFkcY\ndySm5bLXBSAUuaIc6UJAdkEgq1h3dZvlzkmV/lr2cSl/6TjD5a4SVvNvbvx0mSZgTSfQ3cQV6GGo\nkakdjzKX7AJFWVKUcBTcZTXPU6iRa8qsgLe8lHHhCLsXZdqEx3a+V2JNLdqzbXeYCB/89yuyBXJ6\nm+1mzUBZDVgm5ztGad3DXptGJFprsJ1Yw90WfUSdWERERAaN1IkNhNviwDKxpXBrw5Z96Vqxz8is\ndV+fv9EOgyoR1U1rN0mQVcql7Au4SfRmhyc6oHxsqzLmoMjHxso7nCwU3RZZXdKJyjy0RIw1jRsW\n8RK5vjCyy7Ljc23keh+q0erL5j5elG1tZ/cq9+q3te2rsDQ1Ot5U2ONrgbhkfYu1D+R0Yn9wOjE6\nyDAyZf9lf9VlAfgFbUVtkfn5MtjVtHkOjTOxaLDFfkPdFn1EnVhEREQGjbYTW9FuiyvnI+b7UNiy\nwCyUkKQSRlaNbWWR4V7kcii9zDcFBuAjq/a0xq9MXV7b/JSbjzZqs/McmNmmbazeJsuAsjqxbFnC\nyJZZx+l09S57jY9ybUhDEr2/j2mnbYpXMFOos3uIiWWfcZYJir5ttS53vFx1YfnwRYlT+Jai43sh\nf6W8brJOd+nfyX10Ot2iuh9lUKaP1a9CmU7Mrwu1lUf2t8DruQUMm+sHou9kRERES6OebEfNgPgR\ni4iIyCAysTJoYuHQE/Ko86DF/bvUSLPv611DrF/91V7OaV5Dg/WtbUebuqyIlRX1skpueqBTjgOX\ngEZA1UWb1L8v109GWS1i0raS4myX/G3QMrnuml1SckWmP9fnllKm99Nl6u6W7USvTQjenL5bKg4m\nYwuKmrKwwCo6B0xVtnV9bfisO3wriUGWPg/1PcVJsr1UDYND9jkh/tHuNp8TX0v9GWyECjV8VbuH\nUDd9iT9mFVP++SvwK7NKxhOLiIj4x0FkYmVQY9fATDXoTbddOjS7tXXKruYje1xUptjQOy6X9+tJ\nzK6z+ZykpFcWXNqC7kZZxsF75NukyJrN8Fapn4uvuM70u5tERxWmQDPEFWe8iWO/hrIRXUQIGYkW\ns6u0fW3FfqkZhveXElrg0AS5TD8NdODavCVMdkM/Lj+AhEGKGQldqgp5y54VIX+2HtfnA12B9h5e\n87ZV0R4u7jUhKtqUicl2zUCsnLZlQChndL2IH7GIiIiWRgyKWAUBdYTG/Cqtk3hgIbZVmrXX61rn\n2atDKPEAABaJSURBVNBs0yGmFt2JqUUZtKc01a8yMI2fFXJ87hNMMt4kHljApYnVEuLP92baWr1T\nTgeneqqHQsl886C/d7p+1ukqbhOIiV/rGmHDVt9xuxhvUZXxKwObEygr4ei+irSqSq0KesLFGXMJ\nNQYXl7023zi8gYirkxERES2NVhMnyeW9HaCLrdbBWMvLj16wdLh1ybD6O0uEfiA/lbwe/28gwqti\nKU0orAvpxBS9wtLK2viwjs85dtMg6Iqku0YVQ9bWA7HGzLc6seI3iv/QlS3QVcmAc3cOZQkk+/sS\n+6FeZRwd44HuR8Lr3VWxfQfx9d212wHALoRHakSxWOGITCwiIiKDqBOLiIhoabSaODnwxq4f2CSn\nKTvfW5T2mo61kSJjmdmqD/+6XzAi429LREsfvUkU2DzqESMVK0KEpDmdru/RXXKNZhYhQwax9Uak\nAJjcG5YVz8/zyioo+MvKQsp/v8tQXWixQKGBxEJ+xzB1VQLj1UBU7EdERLQ0IhMrwWoA1i1Q2Ks3\nh0aWGBduBiAfch9IGd3b3nGorMyc1Xc6+q0xtfiCmF/ciA8BJBYfdcNX7IdYW8rWDs3VVYsf5zOX\nPGtRBlYG+kOna3tQ7bZVYO81ucd185EpElbCVcwm3L1eJVE4ekzNDyUax6YSQ+3VJIJHGnnrBDGS\nzcdsDaAsmrBaVYdeMP88G5FFX3r/xd7D7CsD0+Abobhkm6Ahf9Gt5nYUI7tGRERk0OiUbUS0OhHN\nJKLb5HgoEd1FRHNk6zvV1DfegTSxaKMOHo7uoF+rzsUPeOVAupqs3hzK0l7p4zj0iZVl6gw91d9e\n77Z81IeVr9VrpsY2fBisG3QicuDLumQvxMQUZUqUMtSvS2oYLOv6WxnL2lO2g2QbUhwVGcak6MQ7\nAIAubAAA+L4ysorDVVyuyZHKYtspK9IX1r5gZa5I2r5M35UP7Otgw6LtAXR8Eeh+qn8mFrt0ED9Y\n0cSirYKJBRF9Cy4o4vrMfDARXQTgTWb+MRF9F8CGzF5G6DoQmVhEREQGjWRiRDQSwEFwGcAVDcs5\nCUTFfkREhI/lAFVPm1Qr7+TPAHwHwHqmrGE5J4EB/ohtCpcx4D9M2dGyVaW7Jgy1ouJ8r06Pi9Pc\nhkVNbR/6feoRyukCeWwivfBpefGSznVt+AeBulvkfIl4zJelbehE/UmcGElHOnGIf7OB6UEV+2Vm\nB1JHEnKaVw+0XQkoFSEt7i8otyKjEyOvE4X+0QEh6hCRrL4iMbW3DKYhro0Tvpo9TsRLIK/ILxML\nQ/Dbq1/knX7DAKyYuQxpJuP+QOP+VUNh3kkiOhjAq8z8CBF1Bi/Vz5yTQGRiERERPsSeswHYA8AE\nIvosnHJzfSK6Hg3MOQkMsGJ/EHXwKHTjX0zZZNHnnU0XAsibSgD5WGHz0T8oE7N6V1/Zf/f1JR30\neMfmB+cfdGWq6JbO/Hn+ikJI6Vu26qDX+k1XzTZloNM7XT9T+9fPQOI6ThcGjk7MJZSZ+tFwgf8n\n7cuWQOpZILo8kB+kEDb7rf8bzzX7yqb8rIFlZhjK1m7LntLxK6B7Yf8U+x07EXdXzPlMm1fznRQm\ndroo9qcAeMMo9ocyG+vjOhGZWERERBb1iZN9wY/RoJyTwEBHsRjdwbi4G/hsupo6TRiYTjo6Y76N\nPHwGdojZn1nQJgSdea1O7Zoy5qXQ2VQHWUK5lZHRkZ1pmcec6NzObL9AwsC0bXJ+gErwra7NbMNO\ntpWYWnRi9jy+NnvtVoMysKNNzDAt03fn21L3c/M8/L/FECPri6lOj9m/Q9lZ6MXysUmgTM0k/CQQ\nRWYVoXMA4DNAx3FA97P9ZGI7EnffXK0tbROjWERERDQbPkLfTRBXAlbOR+xXFya7R/gMSCjYtFPy\np+2cL+oTdKKsxL4sqvywonBLmFCAGiYMTGFnZ2FilsEVgbaVNmNTNs3iypMay0rbT6X98V+8uvGu\njmdky1cmbuWAKxKyOjGFMrA/B+p875yQyay+D1UYWfAV8E7cX/yX7gjpz0L6T2Vcyq62CLSRuqN+\n5LYbSlg0qzpdBuDFdUMDrBMrXpxsKCoZuxLRaUT0FBE9SUS/JqJBjXYdiIiIaBLo6mSVf02Amh8x\nItocwDcBdDDzGACrAzgSwHcB3MPMowHcI8cRERGtDmViVf41AaqKk2sAWJuIlgFYB8ACAGcBSUiC\na+ByY5X7P30Ex38tJ1ebCqXZwv+PuMC08b74037gtjORRyiJiGKyOmaGrFL6owOw1rM61oBPBh3S\n6XZ04PoSjOnHte01AdCWncEmVoRU8dEH7ZiW8xNdmbonjKi2Y4WEHIul/TBpq8f2vR8pdfOlzv6e\nh0idipWaocw+6nbZniOv4afl2Epj1pKhCPUo9lUcvd5mopPnv7+IenecFTjRV9rbQfZky04QcdRG\nYoGIj/6agf3TeA/ooymvhxYTJ2t+xJj5FSKaCuBlAO8DuJOZ7ySiSq4DRDQRGuVko481ZNAREREr\nEJoftkVQ8yMmuq5D4eaJtwH8loiOsm3KXAfEj+oKAKCPdXDOdkKPlbkoS7KGf2rgJwrwI06XY/Og\nz76s1p0Y+EaFQLUpu122VdxKymYyv84mQi2y6M1721S7lmCxVXr7GVZ3QyGUgd1hft4dNQ7Ye54i\nPcA+Fxco6IHUNETNQp4zbX3FvhKZkMvYpz2LglBUUt+Iur+RS/c/P933mVeQkfn6I/O+/Vza688Y\nej2LMMQ7bkhEh8ZZ7A8IqoiT+wKYy8yvAQAR3Qzgk2iw60BERESTYFUTJ+HEyPFEtA6cOLkPgG44\nPnAsnPXtsagWbjQPnRr9L7/9JOq0WeJZP3m82549Q44fKG6boAr7sqjXsbci+JWAJkPumd4TBlQW\nr93CtykQhc/wQQGiLAxskDwzu7yc6Mc2cuc9btmO/GZPrJMd2njz+6huYfGa2eFY6G3MLmFrPmwq\nXf05QkFOFap6muu1tXqwKiYWfsQyaxWjzEtxxyTZMQx7trRRKwobudh3eVN2FfI68mEFmyFwq279\nxqr2EWPmh4hoGoBHAXwIp3+9AsC6aKDrQERERJNgOVY5cRLMfB6A87ziXjhWVh0fwU0dVR5QWSRM\nXcYK6GAmb+u2Z5v45JN1+vNnl5BRoc/O7Dj8qJ4hZqjKmyosT6Z3esWwJLk3PthjZwGdGM9wbWh8\nieuY0Ix3X0j7W091Yg9lh7zgrbQN7VXcJ20l7OwN13584Dks9n6bEFsaLXq2PwjL2wPF0POeLKkL\noYi0WmajP2co+IDfTxV99/4Xu+1VgbpQ7ghN4zvHKw+xLx2rr0oG3PgbkuBjVWNiERER/2BY1VYn\nIyIi/sEQmVgJdOnWapBVO1uW80HLVJbQHBIPmTZe/K3JNoHCkbK9TrYhM44i8c9qcENyRhHqkD94\ncyPGzXQiFt0m4lzgZeLHs2IkP2rO39ETA0VbvZ5vVgFAdf0q3W+2YdrmXRHx1tUFkp78OHb1rGre\nf6/Y1DL0NzFHrhEK7FAFRaKTvVbR4w8pzfVVDKX7qwK9jx7Z2vFVMelQUVPH7IuXQDrW0L037Luz\nCppYRERE/CMhMrESLIdThoeU9v6U+VCgjT7YO2RbNlscafb9vnU6C7GvsnTz/rQc8oWpZwZTM4rn\nTBQKUegnTKwE/J/S1mdfIQTy5C0V4rR+wIpjG49lLZibsix+xO2vvYVrE2Jge8r59wvbOliObzOm\nGodJmTKgVyuEwbIeWiElv48iBmTdd/y/1zL2VbbepHV/CtQVJ5VLy+aXtPHNQIqiLTTkD7rFmFhM\n2RYREZFFAx3AiWgUEd1LRE9LJJxJUt6wKDgDz8SKlBQ6/SgDsw+oivJD8V6+bsSbbruprOE/dkvg\nPDW38EPM2qlXZyffpCA0a4XusyjMqLGytKysEA9nD/naAIPx/FFol75F8F0wM9C3/B4+A9tgcHoN\nvbwysnsDLOuvatIhv31vn0ZYDT67KWNbZWpMX+8FpEyyLN6dXj9kGqHwjV5HB9r48FMxfFThnJpo\n7OrkhwC+zcyPEtF6AB4horsAfAUuCo7G2f8uagWQKEBkYhEREVk0kIkx80JmflT23wXwDJx03LAE\nulGxHxERkUV9Fvu1kucmIKJ2OML6EBqYQHflfMQstw8p8IEs3/a5f4jqemKkipBAqsd/TMwFdioT\nK33Y9f8i57qA0rwuE+8AeLH7aWhoPvmuyh0b7O1EtXfuD4h88hzG7SwiXpvp+01ZEBhaLGIukD5H\ny/lz5ptreDPw2iJGWqWGPpI5Io6q0vpgs2AwW7aqrL8/IHJWSfRR5jup8J0tQuLcEG8LFP/k1tuz\nXbbX93iNzMl/2yPbd9lrUWaO4Zth2PFtCGDtknMro0HJcy2IaF0ANwE4lZmXEJmFon4m0I1MLCIi\nIosGm1gQ0ZpwH7AbmFnzKDUsCs7AfsQ+gJuSLbMqYixlSvuiYyDRdG4aaLZ1mXOeoke27SXX8GGp\nt/p1+oGeLPw+7XQq59EwYWAlL9M79xabJCQM6sms8hzIM7Al0oT/Yvrz6MDokZyrUialCv7NjGJf\nH8nHd86zNIW4uSYK/pBi32dM9cYBUyJdxZU1lGlNfxpd9/mhxD47wUTemOU3LutQUHYfoUUHVeD7\n59lLzEGDLCMaqNgnR7muAvAMM//EVE1HI6LgIDKxiIgIH421E9sDwNEAniAi/d5/Dw1MoDuwHzGN\nYmHRKAZWwlj82UtnZz6sKymjBzrdTrsUhOKRaZ1GmtWObXB4HYeaZpT51KiphJlOR7zotgs9NSdf\nGWBde0v0qPvNwrqMSXVROFzKZ+RP51vzLE2xmTCoxMTCMkuhR/uNzDK6mUantbOoOLRrZRIvmUgZ\n48TN6ROy/asx2ei10W6RPtZ1rXmL10ZhH7m+KsrMQwTZz9UQYo3a5/eFgdnX40nNOXC/FxfNMLN1\nZb9XpIGQBZFvhlFrTED2T2E0gEEF7epCA8VJZr4fxaH/64uCU4DIxCIiIrKIbkclWA1uugkxKbUY\nDMX4qiKfe1Z/ZatPOtPRTzvzJ+iUrfozO+U+hSxCfi/+UlmPqSvSkRiFTcLAvJeIjjeuSdNlYrs/\nYNooz2r0nlmdmNWD8c5yvt6j3NcGe6Zt3vFWJy1ZU1ZylzCn/UQXZh+PPgaVSpRJjNswvwiVMLCS\nYFj1xMkKMRkds47RvlI+y7GvUlFe2ydDGZ/0wiG65/32ZfezeaBNj2zLpLzBiDH2IyIiIiITi4iI\naHF8hBgUsRAhxb7Cz+gQgs4OyveNHDBCpBSVBkNWHKprV4PBbmtyoX0rh1cxMvRjlq2PqyzQE2ir\nil4VnZWyG2PZEYuzw3h5aOAaOtaQYr8AO1kp7h632U9Eu7tEdLRGs5vtmRcjFUWRHHYw+3fNzIqj\noSgOs97KipG9ARmrrafgYkCy0DJenseMNfPX8HIyY5tAN/4lrHipr9r1Ij5+P5TU5GYpKzOrEbTJ\nwoS/cBGCJUO+JqLeHDd1ITKxiIiIlkbUiVWAnXHLFPpFkKnSuhb5jMEe6+X8ifLQXdP930lC2MTU\nQlnanYGOygJZleX98uv81PbIrwtsLff4vGVkfuiCABIj1zFb5eoSk4bbRDEvrOsuE7FigcekZpl4\nYuMkjth+g/NKeoUaufqwv4sq+fWe/xqImNHrZdZoCyQlUQamDMr+/alCX5lgyEWpSsq2nwsD+2FI\noe9TUx1Iietcm7HGVlZWpuwv+jPZOdCm34hMLCIioqURmVgJCG66sV953a8wjSjzWijOzHZW1/3Q\nTOubVujMq+wrA1lL59NcHd3SmdYpK6vicdxH+Ko5xdaGdeLz1XVh2PYFAMCsH9WOOZbR/0ndHGFg\no7fIux2V2fP696HkZJZxJB8nxrLa9hOGvakzeJtvWGwYzHjv+c+Wi9Xrj1/GwL4ZYl4+TpA294pu\nLKCzzcF4CrZ5gfNVN2hvr4qg8h6cx1C/EZlYREREa4OA5VVt/99foSOpgpWvE1PoslEJI1uoeqEK\nOqHQJXbwjieiM9m/wmNldLrUhdyP1HO5TDfWR7amp61V1ujeYgb2cdFvvaQ6MQn4Ps4wqVk3S52w\ngbuG5lna6DFZnVZolbLMo0p1aqob059snHFVSlYnewIdqJedMMLedrcN6cT8Vc2qgXYV0nVwGNeJ\nLuzoKozMfy/tuHQAZRl6hZElUZPM+W3yHum9KjPzXaYaAxWZquAf9SMWERHRxKjnI7byET9iERER\nHlZDDVmgqbByPmK+DyKQFyMN/9/JCzRVFj2tSDEO5GND+SIkkCr0FYlYCaThSatAB2LlGF1R8O/V\nvC/LegurclhbRMf3jZHqS/g/2UZy/USEtBDF835violFINiAmmqMG5M3mfCf9V1mHPuJGFmmJVAT\ni1mhZCQKGX9bT6BOnqeKWirlzzY/vi9aht6PgISaO/8qESuPKxMr/ZUOq0rwA4KVGcYGBumLzKH3\ne2cUBvaoE5GJRUREtDQ0UkNrgJj7HNq6/osRvQY3t74+YBdtDDZG640ZaM1xxzH3Dx9n5rI1l5og\noj/B3VMVvM7MB/Tnev3FgH7EAICIuqskFmgmtOKYgdYcdxxzRL2IeScjIiJaGvEjFhER0dJYGR+x\nYGLNJkcrjhlozXHHMUfUhQHXiUVEREQ0ElGcjIiIaGnEj1hERERLY0A/YkR0ABHNJqLniei7A3nt\nqiCiUUR0LxE9TURPEdEkKR9KRHcR0RzZlgVaWSkgotWJaCYR3SbHTT1mIhpCRNOI6FkieoaIdm/2\nMQMAEZ0m78aTRPRrIhrUCuNeVTFgHzEiWh3AJQAOBLA9gC8R0fYDdf068CGAbzPz9gDGAzhZxvld\nAPcw82i4KPXN+BGeBOAZc9zsY74YwJ+YeTsAO8GNvanHTESbA/gmgA5mHgNgdQBHosnHvSpjIJnY\nrgCeZ+YXmfkDAL8BcOgAXr8SmHkhMz8q++/C/WFtDjfWa6TZNQA+t3JGGAYRjQRwEIArTXHTjpmI\nNgCwF4CrAICZP2Dmt9HEYzZYA8DaRLQGnLviArTGuFdJDORHbHMA88zxfBSnk20KEFE7nF/tQwCG\nMfNCqVoEYFjBaSsLPwPwHWSDezbzmLeAc5n+LxGBrySiwWjuMYOZXwEwFcDLABYCeIeZ70STj3tV\nRlTsF4CI1gVwE4BTmXmJrWNnl9I0tilEdDCAV5n5kaI2zTZmODbzTwAuY+ad4XxqMyJYE44Zous6\nFO4jvBmAwUR0lG3TjONelTGQH7FXAIwyxyNRHt58pYGI1oT7gN3AzDdL8WIiGiH1I1AeEWigsQeA\nCUTUAyemf5qIrkdzj3k+gPnM/JAcT4P7qDXzmAFgXwBzmfk1Zl4G4GYAn0Tzj3uVxUB+xP4KYDQR\nbUFEa8EpQ6cP4PUrgYgITk/zDDP/xFRNB3Cs7B8L4PcDPbYiMPNZzDySmdvhnuufmfkoNPeYFwGY\nR0QaBmwfAE+jiccseBnAeCJaR96VfeD0ps0+7lUWAx2K57NwupvVAVzNzJMH7OIVQUR7AvgLgCeQ\n6pe+B6cXuxHAxwC8BOCLzPxmsJOVCCLqBHA6Mx9MRBuhicdMROPgFiLWAvAigK/CTaxNO2YAIKLz\nAfwL3Er2TADHA1gXTT7uVRXR7SgiIqKlERX7ERERLY34EYuIiGhpxI9YRERESyN+xCIiIloa8SMW\nERHR0ogfsYiIiJZG/IhFRES0NP5/VKhP1jN2I3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd324ab8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_orientation_map(orientation_map)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}