Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018)

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Accession:234992
We present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units.
Reference:
1 . Zerlaut Y, Chemla S, Chavane F, Destexhe A (2018) Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. J Comput Neurosci 44:45-61 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2; Python;
Model Concept(s): Vision;
Implementer(s):
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "This notebook describes the implementation of the following paper:\n",
    "\n",
    "##### Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons\n",
    "\n",
    "Yann Zerlaut, Sandrine Chemla, Frederic Chavane and Alain Destexhe\n",
    "\n",
    "paper's link: https://link.springer.com/article/10.1007%2Fs10827-017-0668-2\n",
    "\n",
    "If you use this code, please cite:\n",
    "#### Zerlaut et al. *J Comput Neurosci* (2017). https://doi.org/10.1007/s10827-017-0668-2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Abstract\n",
    "\n",
    "Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## DEPENDENCIES : numpy, matplotlib, scipy and Brian2 \n",
    "\n",
    "You'll need a full features Scientific Python distribution\n",
    "you can get one at :\n",
    "https://www.continuum.io/downloads\n",
    "\n",
    "get Brian2 for numerical simulations of network dynamics\n",
    "http://brian2.readthedocs.io/en/stable/#\n",
    "you can now get it through \"conda\" with:\n",
    "conda install -c brian-team brian2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------> Tested under the following settings:\n",
      "SciPy version : 0.18.1\n",
      "NumPy version : 1.12.1\n",
      "Matplotlib version : 2.0.0\n",
      "Brian2 version : 2.0.1\n"
     ]
    }
   ],
   "source": [
    "print('-------> Tested under the following settings:')\n",
    "import scipy\n",
    "print('SciPy version :', scipy.__version__)\n",
    "import numpy\n",
    "print('NumPy version :', numpy.__version__)\n",
    "import matplotlib\n",
    "print('Matplotlib version :', matplotlib.__version__)\n",
    "import brian2\n",
    "print('Brian2 version :', brian2.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# displaying figures inline:\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Transfer Functions of Single Cell Models (within a given network setting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Here, we build up the description of the single cell computation within the mean-field formalism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Excitatory cells: RS cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters --NOT-- in SI units\n",
      "{'N': 1, 'Vthre': -50.0, 'name': 'RS-cell', 'El': -65.0, 'Gl': 10.0, 'Cm': 200.0, 'tauw': 500.0, 'Vreset': -65.0, 'a': 4.0, 'b': 20.0, 'delta_v': 2.0, 'Trefrac': 5.0}\n"
     ]
    }
   ],
   "source": [
    "# the parameters are written within a library of neuron parameters\n",
    "from modeling_mesoscopic_dynamics.single_cell_models.cell_library import get_neuron_params\n",
    "RS_params = get_neuron_params('RS-cell', name='RS-cell', SI_units=False)\n",
    "print(RS_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters in SI units\n"
     ]
    },
    {
     "data": {
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1rehTfO3saRMR55Nm3o1Udi5Ns/xGes+IeIC5pwwBtu8oyN5rjK/NrjSKsiye790mZkOk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\nyQnKzMyK5ARlZmZF+v+fxXMH7IY0SQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e1fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the responses to current steps\n",
    "from modeling_mesoscopic_dynamics.single_cell_models.step_response import make_model_figure\n",
    "make_model_figure('RS-cell', I0=200e-12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters in SI units\n",
      "synaptic network parameters in SI units\n",
      "numerical TF data saved in : data/RS-cell_CONFIG1.npy\n"
     ]
    }
   ],
   "source": [
    "# Now making the numerical simulations at various levels of both excitatory and inhibitory inputs\n",
    "# note the rescaling of the excitatory input to insure that the response remains in the <30 range: it focuses on low range when inhibition is low and on a larger range when inhibition is larger\n",
    "%run  modeling_mesoscopic_dynamics/transfer_functions/tf_simulation.py RS-cell CONFIG1 -s --SEED 4 --tstop 10 --discret_Fe 10  --discret_Fi 10 --max_Fe 30\n",
    "# paper's value -> 5-10 minutes sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 7.33631085882e-08\n",
      "            Iterations: 9\n",
      "            Function evaluations: 120\n",
      "            Gradient evaluations: 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.041754\n",
      "         Iterations: 1087\n",
      "         Function evaluations: 1541\n",
      "==================================================\n",
      "[-54.7150805    7.64453782  -4.62722888   1.71283147  -0.10710902\n",
      "   0.72054481  -2.00463685   0.11756368   5.61325855  -0.99436164\n",
      "  -2.87604362] mV\n",
      "coefficients saved in  data/RS-cell_CONFIG1_fit.npy\n"
     ]
    }
   ],
   "source": [
    "from modeling_mesoscopic_dynamics.transfer_functions.theoretical_tools import make_fit_from_data\n",
    "P_RS = make_fit_from_data('data/RS-cell_CONFIG1.npy', with_square_terms=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i0qfe7IThuK4A7CoX9qGRnTp1wvTp0zFmzBgpO3aHk5iYiAceeAAdO3bEyy+/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\nWiJ0m7O1b98+2y8Q6enpCUURFhUVsW/fvqj9WVlZ9O3b19UYhpaPqv6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B4Itwi6O8vJz5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105ff5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now displaying the numerical values and the analytical function\n",
    "from modeling_mesoscopic_dynamics.transfer_functions.plots import make_exc_inh_fig\n",
    "make_exc_inh_fig('data/RS-cell_CONFIG1.npy', P=P_RS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Inhibitory cells: FS-cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters --NOT-- in SI units\n",
      "{'Cm': 200.0, 'delta_v': 0.5, 'Trefrac': 5.0, 'Gl': 10.0, 'N': 1, 'Vthre': -50.0, 'tauw': 1000000000.0, 'Vreset': -65.0, 'a': 0.0, 'name': 'FS-cell', 'b': 0.0, 'El': -65.0}\n"
     ]
    }
   ],
   "source": [
    "# the parameters are written within a library of neuron parameters\n",
    "from modeling_mesoscopic_dynamics.single_cell_models.cell_library import get_neuron_params\n",
    "FS_params = get_neuron_params('FS-cell', name='FS-cell', SI_units=False)\n",
    "print(FS_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters in SI units\n"
     ]
    },
    {
     "data": {
      "image/png": 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PhtC7ie1cjgK+VlL1xVKKHFRDlG6bXYCLx9gqYTw6tWT/EdiQ2MStXzrp7VKe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\npEQOSgghRErkoIQQQqTk/wPu8Bgq+m4oLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101ac6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the responses to current steps\n",
    "from modeling_mesoscopic_dynamics.single_cell_models.step_response import make_model_figure\n",
    "make_model_figure('FS-cell', I0=200e-12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters in SI units\n",
      "synaptic network parameters in SI units\n",
      "numerical TF data saved in : data/FS-cell_CONFIG1.npy\n"
     ]
    }
   ],
   "source": [
    "# Now making the numerical simulations at various levels of both excitatory and inhibitory inputs\n",
    "%run modeling_mesoscopic_dynamics/transfer_functions/tf_simulation.py FS-cell CONFIG1 -s --SEED 4 --tstop 10 --discret_Fe 10  --discret_Fi 10 --max_Fe 30\n",
    "# paper's value -> 5-10 minutes sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 8.31599656436e-08\n",
      "            Iterations: 7\n",
      "            Function evaluations: 92\n",
      "            Gradient evaluations: 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.236405\n",
      "         Iterations: 468\n",
      "         Function evaluations: 679\n",
      "==================================================\n",
      "[ -5.39174436e+01   4.07642701e+00   1.69747036e+00   9.11869386e-01\n",
      "  -3.43496494e-02   5.00307136e-01   6.52314112e-02   2.42498031e-02\n",
      "  -2.34327870e+00  -1.15712989e-01   1.48402587e-01] mV\n",
      "coefficients saved in  data/FS-cell_CONFIG1_fit.npy\n"
     ]
    }
   ],
   "source": [
    "from modeling_mesoscopic_dynamics.transfer_functions.theoretical_tools import make_fit_from_data\n",
    "P_FS = make_fit_from_data('data/FS-cell_CONFIG1.npy', with_square_terms=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IT0/XlQV54oknTDmvgoICmjRpQp8+ffycV1RUVMjThKHsDSsoKPDb46qoqAhY1VlTHA5H\n2CLFcxWl1Hql1GNKqXR02pOUUnm+BzAM+Fop5a/EeYpewE9KqeeVUrlKqY+A+cD1YflH1JDT7nEp\npSb4vnbvOY0DZiildpldwGlUOxcC6VXObUTb8/qn2a9d27nzzju5+uqrK4nkrVmzhsGDBxu2efLk\nSb9zZhwXaKOYbr/9dtasWYPD4cBms/H0008bdlqgOdiq+xdxcXEBo9CaEugp3Wx66bnnngu4n1NW\nVsbcuXNZtmyZIdueNXucl2evy6zTAv3CjFA5ri+++IKff/7ZO6IrJyeHgQMHhqT1Qm//MyYmxnSr\nRC0mwb394WGRUmqRUWMiUhcYibYVUx3bgGdE5DbgHaCR+3PrjX7tUGBkjytkelbu3GoKWrXgURFp\n6j7qAiilDrkrDr0HWlVhnlLqq1CtozbicDgoLS31zmKLj49n9OjR3HzzzYZtOp1OioqK/M6bdVyF\nhYW0aNGC22+/HYDbb7/d9M0pXAUD4do3S0lJCZjKi46O5qGHHjJs29e5eJwXYNppKaXC8n3euHEj\nDz30EJs2baK0tJQdO3awY8cOLrnkkpDNPjxXJ32YIN+zBeI+DDstN6OBKLQUYECUUtvRHNUbQDlw\nGE2zbLzJr2+Ks12cURPVzgsSXwfTunVrJk+eTIcOHUz11uilVuLi4gLecGuCUor169fz/PPPk56u\nBcfp6elcf/31IdnfOnDgAMuXL+fAgQMhcTDhijCaNm1KUlKS3/cyOjqamTNnBkz7ng6Xy+W9Se/d\nu5f09HTvMOSZM2d6J/sbQa/B22azmW48vuKKK/zkRiIiImjYsGFI2hnA2t8KAfcBa5VSh6t7k4hc\nA7yEpnR/HXAz0BT4R9hXWA1ndS6KUiro/IxSqlUYllLr0IuM6tSpY8qmnuO66KKLTNksLS2lR48e\nlQbURkZG8uCDD5pKvx0/fpwDBw6wfv16HA4H69evp3379qbWCuF7Ui8oKPA6L0+Bht1uN+W0AF5/\n/XXeeecdv/N33XUXd999t5kl6zrx+vXrm3o42r9/P4sXL/abxOFyudi9ezfdu3c3XZRRXl6u+/tx\nnkdcIUNEOqPVKTxWg7c/CmQrpf7mfr1HRIqAD0XkMaXUWak1MBNxhSxlaOGP3l5UOByX2TRhoOnt\nZveMXn31VdatW+eNCBwOB7Nnz+axx2ryu6ZPoJl8oSqxh1ORF2hDhs04LYAbb7yRxMREbyQXGRnJ\nsGHDTDst0HdcjRo1MmUzLS0toCqx0+kkLS3NlH0IXAlpdv/zAuJ+IBd4vwbvjQOq9mF4Xp+1jF1N\nyuEPi8ghz4GWygPY7Hve57pFCNB7ojSbwgmH4wrHdIvs7OyA0yA2bdpEdraxrgu91FhUVJTpBwKH\nw1GpArJpU63FxUxxiodPP/20UuOx0+lk/fr1IZGfCcfEjJEjRwZUJbbZbKZ7t+CC3N+qESJSV0Q6\nuyOqCOAy9+vLfN4TB4wBXlVVq5+068tExLeK6G1gmIj8XkQud5fHvwjsUkr9L7z/osDUJFW4ACu6\nOuOcqVRhKAozqmLWcU2fPj1ghV5paSnTp0/nP//5T9B2PTdqXw2uLl26mI4Ojx07hsvlYs+ePZWm\nt3uKVcaMGWNo7FNOTg6rVq3SLbE3Oy2jtLTUO0rMF7MOoE2bNowbN47U1NRKZfARERGkpKSYThOG\nq6DkPKEblcV8n3QfS9H6YUGr4K4DBGoErSTCppRa4la3fwCYCxxzf41HQrZqA9SkHH7GGViHRRXO\nVKrQbBQXjojroYceYtq0abrOKyYmxvAQ3Ndff531609V8W7YsIENGzaQl5fHvfcGLW7gxeMQO3Xq\n5NXzatGihWkhw4ULFwZ04J5pGUYnZujd/C+66KKA0VIwfPHFF7rzCX/44QfTOlklJSWUlZVVOici\nISv6OJdRSmWgVfxV955UAjstlFJJOudeQivQqDWc1nGJyFjgzWCUjkWkDdBMKfWhmcVdyJypVKHZ\n4gy9iMuszVatWnH55ZdX0svyMHDgQMNKxU2bNiUyMrJSBBMVFUWXLl0MrxXCN6R2zJgxzJ8/X3fU\nk5lpGevWrePtt9/2O9+jR4+QqAa3a9eOevXqUVBQQE5ODh06dKBr1660bdvWtO29e/fy0Ucf0bZt\nW+/3+KKLLrL0ty4wavK/PRV4SkSWA+lKKd0RACLSCK1UciTQD2MyJxZuzpWqwnBEXMeOHWPAgAG0\na9fOW1UYFRXFK6+8Ythp7dy5k2XLlukOqX344Yf529/+Znq4ri+hKLFv3LhxpYZjD2Ybj4cOHUqD\nBg345ptv2L59O7169SIhIYGuXbuaXjOc+n40bNjQqx1m9vuRkZHB1q2nBjzs2bMHgJYtW4bE2VqE\nDxHpiCZJ1RSIAQqAr4EspZT/L08NOG1xhlKqC1o+sx+a2nGhiHwsIu+KyGoR+UBEcoFDaENyvwXa\nKqWMNZdYAFqqMDc3l7///e/k5uYC5hyXy+XSTT+aieJcLldY9rg8lX/Nmzdn8ODB1K1bl2nTphl2\nWgBPP/10tftmRtOPTqczbI5Lb9ST3W433XjscDjYv3+/t8glOzub/Pz8kKw50N6Z2crNli1b6vaG\nxcfHW/tbtRB3IcffRORntMHprwCT0QZOPIVW9HFYRDaLyCgRCapCsUbxtVLqLeAtEbkCuAnoiuY9\n6wC/AJloo0EylFIXjF5WOPnss8+8o55WrFjBqFGjDDuZ2bNnM2fOHL/zSUlJpkqIT5w44beXERsb\na3pAq2/JevPmzRk7dqzpWYJ//OMf+ctf/qKbdjOjwfXqq6+yevVqv/NKKcaNG2fIpgdPCtLjvDIz\nM/n9739vevrEzp07+fjjjytVKmZnZ9OrVy+uvvpqU7bDsXeWm5vr/V3wxeVykZOTw7XXXmvtcdUi\nRGQxWuXif4GZQBaw13e7SUQSgO7AIOCvwAwRuUcp9d+afI1g9bi+RYuoLMLI5s2bSU1N9f6iepzX\n2LFjDelmPfroo4wfP56ZM2d6nWDLli1JSDCn+nL8+HF+/PFHNm7cyKBBg2jRooXp1KPL5QqLDleL\nFi0YOHCgnzxITEyMqTThgAEDiI2NJS8vj82bNzNgwAC6dOliWN/MQ2lpKSdPnmTv3r18+eWX3vMv\nvvgiL774IsOHD2fEiBFB2923bx9Llizxc+BOp5P58+czadIkUwUUR44c8TtnNiJau3ZtwN4wl8vF\nunXrmDx5sqmvcTqcTmdIJGouEEqAdqfRb8wHNgAbRGQqkAw0r+kXsHY0axlbtmzh5ptv9ktrVVRU\nMHjwYMMCklu2bGHVqlUArFq1iuTkZFPy6fPmzeP555/3vvaMe7r11lu9CsBG0Ou1io6ONi0OWFBQ\nwMGDB/1u2KWlpezevduw4yooKPDT4GrVqpWptcKpyKV9+/beiSHx8fEMGjTIlN3U1NRqKxVTU1N5\n9tlnDdsPR9p02LBhuhEXaL1hw4YNM2XfIrQopSZ5/i4iicCPSqncqu9zl9l3UUplAm8F8zXO9qxC\niyqkpKRUqxprREAyMzOTKVOmVJpCsWrVKv73P+P9gz179vRLM9psNjp37mzYJgQegmu21+ro0aN0\n7dqV3/zmN9xyyy3UqVOHP//5z2RlZZkqhf/ss8/8NLiWL19uWoNLL3IJxR7UhAkTqtX1MipQClqp\nejh6w1q3bs2IESP8RlFFRERw11130bp1a1P2LcJKBpAjIr/WuXYNlfvOaozluGoZqampAaUZjAhI\nZmZmMmLECL/eF4fDwV//+lfvwNZgyMrKYsKECZV0rTw2n3vuObKysoK26SFczaW+dps1a8bdd9/t\nnbJulN27d/Pvf/9bt1IxFBpcVTE7jgngkksuoUePHrq6XmbThHprjo+PD0lv2Ndff63bG3bw4MEA\nn7CoRbwLLBWRF0XE+ERvHyzHVcvo168f8+fP9/tlj4qK4p133gk6TThx4kRKSvTEUDWdKCO9QFOn\nTq3W5tSpU4O26SGQHLsZysvL/VoBRMS03blz5+oWe8ApDS4jKKXCFnEdOXKEhISESs7LZrOZdloQ\nvn42gCuvvJK+ffvSqVMnoqOj6dSpEyNHjjQsqGpxRnkOTbRyLLBFRJqYNWg5rlpI+/btGTVqlNd5\n2e12ZsyYYeiXdOHChQH3h2JiYgxNXpg3b161Ns3KmfhKmYD5wgy9KK5evXqmm1bHjBkTFg2uEydO\n+O3n2Gw204UvcMq5eJwXaJPmzTotpVTYokQ4te4GDRrQs2dPGjRoYJXBn0Mopd5BU01OQGur6mXG\nnuW4aiEnT56sJCA5atQoevUy9v+cmJjIypUr/SI4m83G4sWLvQ2iwdC7d28WLVrkd+O32WykpqYa\nHi67YMECHn/8cdatW8fJkydZt24dL7/8sm65eTCES4OrcePGYdHgChRtmZEbAX/n4qkqNTs5BLT9\nrdLS0krnRCQkk/dLS0t1I3zLcVVGRBJFZJ2IHBARJSITqlxf4j7ve3xUA7tRIjJTRHJFpExE/ici\nDwa7PqXU12iNyB+j7W0Z3lwOWVWhiLQA5GxODD5f8EzN8AhIgrlG4RtvvJERI0bw1ltv4XA4sNls\nJCcnM3DgQMM2P/zwQ78iEofDQXZ2tuGeq2uvvRabzVbJrs1mM70XFS7HpafBFRUVZVqDS89xhSJy\nKS4uprS0lK+++qrSOK1p06YBcNtttzF06FBDtvXWHB8fbyqqrTotw0PLli3p2LFjSPbOzjPqAjnA\nMvehx/toKTsP5QHe50sacCmaHMo3wMWAoTJfpdRJ4E4R+Qsww4gNCG05/HdoEVxINt8uZEI97qmo\nqIgWLVqQnJzMihUrSE5O5qqrrjLVKDxy5Ejq1KlTqY/rhhtu4K677jJkLzs7mz/+8Y+6znDSpEks\nXLjQ8OSMcJRou1wur0P0OK/Nmzczbdo00xpc4Uq5eZxL27ZtvXMDGzZsaLq5G8LzcJCUlERSUhI5\nOTn861//om/fviGzfT6ilFoPrActugrwtjKlVF5NbYrIQGAAcIW79wrg+yCW1ZpTUli+a31KRD4A\nDMkFhNJx3cNpJhNb1IxQO65nnnmGBQsWeF+vWLEC0AoIHn30UUM2PUUULVq08JaTm5leMH36dL/K\nRw9mpEwC7b2YvfEVFhZSUVHhJ2Uyc+ZMwLiUSUVFhW4DdqgKM6oSCocYzmISl8vFd999B2gPIJ70\noOW4DHODWzfxGLAVmK6Uqk5H8XbgE2CqiIxDay7eADzmjp6q5TRNyNvQJi4FTcgcl1IqUGhqESSh\nljQZP368n+7W5ZdfbkpFN9TDdWfNmsXvfvc7vxJ7MCdlcvLkST+bdrvdtA6ZnpRJ06ZN+dWvfmXa\nblV9v7p16wZskQiGcDkuvWKSyMjIkOxv7d2719tW4Jk037hx45AUqpxjJIjIDp/Xi5RSi4K08R6w\nGk39uBXwNPCBiFynlNJ/aoTLgRuAMuAuoD6axMklwPAgv37ICMpxubuga4y7I9oiCJRSIZc0OVPK\nx2ZuVD169GDChAm89tprldKFUVFRptKEixYt4l//+pff+YqKipBocPkSynReqO0WFxeHpTkY9Ndc\nv379gBWXNSU3N5e1a9d6+7c8swlvuOEG0w3p5yD5SqluZgwopdJ8Xn4uIjuBH4Bb0RyaHhFoQsKj\nlVLHAUTkAWCjiFyslPql6gdE5BOCEB9WSgX9yx1sxJVB5QWJzmvverD2u4KmvLzcb5/HZrOZGoYb\nDs2scMiZfPHFF37/9vLycj755BPDjqt///7Ex8dz8OBBNmzYwC233EK/fv1M7+uEKzWWn5/vdy4U\njkvP0davXz8kBQ7hcOKewbpV++RcLhdZWVm0atXKmphhEqXUzyLyE3BlNW87CBzwOC03nuGZl6EN\nWa/KXvz9wjjgHcD/F8cAwTqugcBrnAo5DwFN0ELIQWgaXFYruwkC7W+ZecLUc1xmIi6n0xlyOROH\nw0GnTp3o2LEjBw4c4IMPPqB///7MmDHDlNP2zCjctGkTAJs2bfLO/jOKb2GGL2Zv1oH248wOQ4bw\nOVqXy6Vb/GL2e1HdYF2Hw8HatWvDPlj3fEdEGqMNtq3unr0NSBaRuj57Wle5/9Tdv1JKTajydWxo\njmuGUmrxky3qAAAgAElEQVSXqUW7CbYxZBKwTCl1v1LqPaXULvef96GVX05WSu31HKFY4IVGqPe3\nIPSpwsLCQr/xO3FxcaaqFI8dO+bd2/FImVx11VWmnBZoswR9J8I7nU5eeukldu7cadjm8ePH/SLD\nqKiokChUV92PC1XjcbhSkMeOHePw4cNkZmZ6nW4o9hCHDRsWsJTebrdbg3V1EJG6ItJZRDqj3dsv\nc7++zH3tORHpJSKtRCQJWIcWfPzbx8YyEfGtV3gTLUpKFZH2ItIHTXcx/TRFHb7UOG1YU4J1XAPQ\nKlH02AokmVqNRcj3tyD0ysfhUj2uitn9l48//pg1a9b4pZvKysp4+OGHDTuvQE7A7L6LXpowFI3H\nHokUPdtm2LhxI7NmzWLHjh2UlpayY8cONm3axI8//mj6e9G6dWt69+7t92+PjIxk1KhRVppQn27A\nbvcRCzzp/vtMwAl0BNaiqQ8vBb4CeimlfG8Ql7kPwNt3dRMQj1ZduBLtXn9WFe6D/Y0oQJs5pccd\n7usWJvA4Ll/1Y7PKx6F2XIEmuJshHCXrzzzzTFhUjz2OKy8vjzVr1pCXlxe2woxwpQkvuugi04Kf\nV1xxhV8BRkREBFdeWd2WSc1wuVzYbDY6dOjgdV4REREMHTrUcloBUEplKKVE55iglCpRSg1SSjVR\nSkUppVq6z/9YxUaSUiqpyrmvlFIDlVJxSqnmSqk/VHF2Z5xg97jmAPNFpBWnwswmaM7sFuCBUC7u\nQqSoqKiS4uuKFSvo2LGjKXtV03rR0dGmUnBnKuIy6wwnTJjAvHnzQq56fOTIEa8Gl9PpJCMjw3TT\nscfuoUOH2LFjB926daNJkyamHOK6det4++23/c5fddVV3HLLLWaWyv79+1m8eLFu8cS6deto0qQJ\nbdoY6i0FtHS00+mkQYMGdOjQgT179nDttdeaVn+2OKuELGUYVMSllFqIFlk1BhagFWgscL++033d\nwgRPPfUUy5Ytq6R+/PDDDzN+/HhD9kJdmAHnTsTVpEkTBg4c6BcVmFE9djqdfPnll34aXAsWLDAl\nY1JaWsr+/fvZtm0bxcXFbNu2jUOHDplyXEOHDuWf//wn/fv3B6BXr17cdttttG3b1nSEmJaWFrB4\noqKigrS0NN1rNcU3SvSkjK+44grTaVOL8CMih0XkkOfgVPHHZt/zPteDJugGZKXUWmCtW1clAa2/\nQF/bwSIotmzZwocffqh7LT09nQkTJgQ9IV4vTVjboiOXy6UbxZnd4yooKKBZs2YMHDjQW6ARFRVl\n2GkBbNu2jffff9+vSbisrIzHHnuMZ555xlD09eKLL7Jjx6n+UqfT6dVKe+AB44mMPXv2eOf9ZWdn\n06NHDxISEkw7rpEjR7Jo0SLdaNZutzNy5EhT9o8cOcL333/PDz+cKlxLT08nPT2dvn37kpSUZMq+\nRVhZQBgKMnwxPDnD7az0avgtDJKSkqI7OQK0BtKUlBS+//77oGyGuodLKRXyVGFhYaHfXlRsbGxA\n6ZSa4Fuy7nFeGzZs4KmnnjLstABefvllP6flwel0MnfuXJYtC26ITE5ODp9++qnutY8++oikpCRD\nKbJ9+/bxyiuvVIoMs7OzSUpKMl2tecUVV3D99dfz0UcfVUpF22w27r33XlNpQo+ScqtWrWjVqpX3\nfO/evU2v2yL8KKVmhPtrGIq7ReQaERkrIo+JSFP3uTYiYi4HdYGTmpoasCHUiPoxhD5VWFRU5Odk\n7HY7cXFxhm0GEiA0U5nmGUO0a9cuXnvtNTZs2ADAI488Qu/evVm8eLEhu0OGDAm4rsjISEMaXAsX\nLgxYRFJeXm5IM23fvn289NJLfuk8p9PJ1q1b2bdvX9A2fSkpKSEvL89v/9ThcPDtt9+asq3381Cv\nXj3LaVl4CXbkU120BuS7AIf78+8BecAzwP+AP4V4jRcM/fr1o0OHDuzevdvv2vDhw4NKE86ePZs5\nc+b4ne/Tpw9DhgwxvMZAaUIzTkavgdVsmtBTWt61a1e6du0KaJHXrbfeaspuvXr16N+/f6U9LjCn\nwfW73/2O2bNn66bdoqKiDKlUp6amBozeHQ4HqampPPvss0Hb9ZCfn0+bNm1o06YNBQUF5OTk0LNn\nT2677TbDNj2Eq+fMDE6nU/dn38IfERkLvBnMFpKItAGaKaX090qqEGzENQ/ojVbXX4/KI57WAzcH\nac/CB6fTydChQxk3blwl9eP333+fpUuXBmXr0Ucf5fjx47zwwguAprk0bdo0brzxRlMRl94vr9k9\ns3AUZoRjskV5eTnHjx/3yph4ij7MCkc2a9aM5s2b617r2bOnoTRhSkpKwOg9KiqKlJSUoG364ttz\n1rBhQxITE7n66qtN2YTADsKaBn9OMRX4VkSeEpGAvxQi0khExojI28CnQLOafoFgHdedwCNKqS1o\nDW2+/AC0DNKehQ++ApIe9eOUlBQGDBhg2Kan0s13k9tsD9ePP/7I4sWL+fFHrQXEbHQUjogrHCOO\nfG16nFfdunVNC0fm5+fTo0cPEhMTvc7QZrPxxBNPGC7MaNeuHSNHjvSrqIyMjGTSpEm0a9fO8HrD\nNfIKtJ+FqunHUEzisDhzKKW6AI8A/YDdIlIoIh+LyLsislpEPhCRXLR2qheAb4G2SqmVNf0awRZn\nxBJ4SGI9/J2ZRRD4Ts3wqB83adLEsL2MjAxvocCqVatITk6mZcuWhh3XvHnzeP75572v09PTARg9\nejQ33nijIZuBZt2ZcTKB9KHM3lirTrZo2rQpkydPNt3DdfjwYUAr3+/Tpw87duxg1KhRpnuW6tat\nS48ePcjOzsbpdBIZGckdd9xhymmB5lyqpjVtNpvpyBsCDxm+AKfBn9Mopd4C3hKRK9AydF2BpkAd\ntKK+TLQ5iBlKKf2+imoI1nF9gjYs8T2da8OBrGAXYHGKUApIZmZmMnLkSO+mv8PhYNWqVYwdO9aw\nnHrPnj2x2+2VNvxtNhu9evUyZA+0IoqqhQnR0dGmij2Ki4spKSmpdC4U+lB6N9XGjRubsulyuSo5\n2SZNmjB48GDD0/A9KKXIz88nISGBHj16sH37dnr06EGXLl1M2QX970NCQoLpHqtADxyhmB5icXZQ\nSn2LFlGFlGDvYH8B/iMi7wOr0Gr1B4vIFDTHFZRel0Vl9ObJGZlTmJmZyYgRI/xu3g6Hg9dff507\n77yTxMTg/quysrKYMGGCX5Waw+Hg4YcfpkmTJvTu3TvotQZK6Zl5wtaz2aBBA1P6UB5HUBWzUdzx\n48f9vqd2u9109FJUVMRnn33G119/7T23fft2tm/fzm233cbQoUMN2z6TgpQREREh0QyzOL8IynEp\npT4UkQG4Rz+hFWc8CXwE3KSU+iT0S7xwCFXENXHiRD+n5aGiooKJEyeSk5MTlM2pU6cGtFlaWsrU\nqVP56KOPgl5rqNOEEJ4b68mTJyktLa10LjIyMmTVj740atTIdPSSn59P27Ztadu2rfdcw4YNTeuQ\nlZaW6ja1hyIq0vtemH3gsDg/qfFvh4jY3SPtc5VSNwIXAZcC9ZRSfZRS28K1yAuFUEmaLFy4MGDz\nbnR0tKG+oHnz5gWUj4+NjWXevHlB24TwVBSeif0t0NZp9qbq2d/yxWz6MZDdcDmXevXqBfzZCIba\nWAZ/LiEiiSKyTkQOiIgSkQlVrj8lIvtEpEhEjorIZhGpNk0iIn1FJEtEjohIifvzZ73lKZiIywl8\ngDZM92elVAmg/whuYYhQRVyJiYmsXLmSu+66q1Ivj81m4+mnnw46TQja1IInn3yS6dOnV9qTioqK\nYsmSJYbShBC4+dgM7777Lh9//HGlc6+99hq/+c1vuPfeew3ZDMfeS6D0o1m7LpcrLPtxEHh/yywl\nJSW6P/+W4wqKukAOmjai3viWr4A/ALlohXZTgPdE5EqlVKApSCeBF4HPgWKgD/APESk+m7Npaxxx\nKaVcwDdolSEWYSCUWlyJiYlMmjTJW4hhs9lITk425LQ8ZGdn+xVSlJeXG0oRgnaD1evZMXOzKisr\no3379txyyy2VSsuff/55w04LwhPBnDhxQjf9aDbi1Ns3C0Vas2ohiYdwRnJmpVcuJJRS65VSjyml\n0gGXzvXXlVKblVLfuYV+p6JVg3euxuZOpVSaWxw4Vyn1OrARMFZGHCKCTaRPB54QEeM6GxYBCWVV\nIWiNrcnJyQCmS+EBbrrpJqZMmcLw4cOpV68ew4cPZ/369UydOtWQPT0lYbMzCvPz8zl48GAl1WOH\nw8Gjjz5qWDjS6XTqRoZmIxg9Z9ioUaOwpB9DUfV37Ngxv/8vm81muloTwhfJnUckiMgOn+N+M8ZE\nJAq4HyhEa/6t6ee6oA2hCCQoHMwaWojIZad/pz/B/iQ/DjQCPhWR/4nIJyKS7XsEu4DT5WXd77nK\n3bh2TESKRWSXiJhv069lhGqPy0NhYSEtW2o94Z4/zTguTyFFixYtuPfee2nRooWpm1ag/S0zFYVZ\nWVmVnJaH0tJSw6rHBQUFfk2xcXFxpkr24czuQ4XCbiBHa9YheiaSVMVyXJXIV0p18zkWGTEiIkNE\n5CRQipYq/FU1aULfz/0kImXADmChUuoVI1+/Ct+hpS2DJthy+Bz3EUqqzcuKSGu0RrVlQH/gGNAO\nLfd63qCUori42O+80VRhaWmp36w6m81m2BEqpXTTembST+GYbpGamqo78w9OqR6vXr06KJuBHIwZ\nBxtof8tsFOdwOMISHUL4Ckn0fg5iY2NNPxhY6LIFLTWYANwHrBSRXkqpg9V/jBvR7tU9gWdFJFcp\ntdzkWu6h8tjAGnNaxyUirwFPKaVygVRgdyhlm5VS69HmHCIiS3TeMgvYpJTyHbv9Xai+fm2huLgY\nl8tFbm4ua9euZdiwYbRr185ws/DTTz/NSy+95H3tGbjrSZsFS1FRkW6/kZmIMBwVhQMGDGDNmjUh\nVT0Oh4MpKirye1CJiIgwXYygFx3GxMSYHplUXFysm8oOVyRn9sHAQh+lVBGw3318JCLfAPcCT53m\nc57I6HMRuRiYAZhyXEqp4PR/fKjJXXE88ApaSLcF6AUEnRI0gohEALcBc0TkPeA64HvgOfdIEb3P\n3I+WuwXtqeKcYMaMGTz33HPe155RTWVlZcyYMSNoexMmTPBzKi1btmT06NGG1hdonqCZm0uo592V\nlZURHx9fSTjSg1HVY6UUhw75i7SGY3+rYcOGhh9UPOitNRRO4PDhw94p8B06dKBhw4bEx8ebLp5w\nOBy6P1tWmvCMEQEE+59o5DMhpSa/JQeBJBH5Ai2sixGRgDG8Uso/32WcJmjh6WNoUzumoaUL3xCR\nk0qpd3W+/iJgEYCI7Kh6vbbSpUsXv3FKUVFR9O3b15C948eP88MPP/Duu+9y6623mi7MCOUg3AUL\nFvDyyy/7ne/WrZupyj9Pyqmq6rFRpwVaZFS18ToiIsJ0ZBgOZwj6DtHMvEsPe/fuZdeuXbhcLnbt\n2kXXrl254oorTNstKCjwE+aMiooy9bN6oeKWnfIoeEYAl4lIZ6AAbYvlz8DbaPf0xmil8ZcCK31s\nLANQSo1zv56EFrR85X5LIpp0VY1K4UUkqDJmpVRmTd5XE8e1CG1Sxmy0EU9bTvP+ULa5e3Z91yql\nPB2un4pIN+ABwM9xnYts2bKFe+65xy8VV15ezpAhQ3jnnXdMaXGtWLECgFGjRhnW4tJzXEYLM/7w\nhz9w5ZVX8uc//xmHw4HNZmPw4MG0a9fO1BO8J6W3a9euSorCpaWlTJo0yVAfV6DIyGjl38qVK73D\niX25+uqrTbUqgJbOC8dUizfeeINdu3Z5X7tcLnbs2EF5ebkppWMIXEhipQkN0Y3K9+cn3cdSYCLQ\nHvgNWoHdEbTZs4lKqT0+n6la5RcJPAu0QtNg/BYtgKhpcUYGmt/wIDqvPShq6D9O67iUUjNF5F3g\narQCiacJw9DEAOSjfbO+qHL+S2DkGVpD2ElJSfHr5/FQXFxMSkoK33//fY3t9enTh6ioKL/m4+uv\nv97wGj/++GOWLFnCoEGDaNGiBWA84srOzmbatGmVBgCvX7+eiy++2PD6QF880qielYdQFySMGDGC\nW265haVLl5KZmUliYiJNmjQhIiLCtIPRW+tFF11kaqrF/v372bNnj+61L7/8kv379xt2Xk6n0xqq\nG0KUUhlUX+xwRw1sJFV5/Xfg7yaWNRBNfPg9YDWalEkTNDHiQWiO9HSFIX7UqI7V3YT2OprnTlVK\nLQ10BLuA03zdcrSngrZVLl2Fpv91XpCamhow0oiLiyM1NbXGtjwDdqtWFDocDqZNm0ZmZo0i8Upk\nZWXxyiuvcOLECdasWWNKhys7O5uJEyfqrm/p0qVkZxvfPg1HeXk4Kum2b9/Otm3ahLRt27Zx6NCh\nkOxvhSNNmJaW5te75aGiooK0tDTDtgPJo4SiL8yi1jAJWKaUul8p9Z5Sapf7z/vQAqHJ7ubmve6m\n6BoRVAOGUirFp7okJIhIXRHp7M7FevOyPo1pfwXuFpH7RaSNiNyHFm0tCOU6zib9+vXj8ccf91Os\njYmJCTpNWN2A3dLS0qBl4KdMmcLdd9/tTWM6HA7S09N57733DN1gpk+fHjC6LC8vN1T1B/rDX0XE\nVLFHeXm5borUjDPIyclhyZIl3hu20+lk27ZtuhV7weByucLiZEeMGBEwLWq32xk50njiI1yN0ha1\nigEEblbeCiQZMVobfkK6AbvdRyxaTnY3MBNAKbUGrUrwT2jzsiYB4/QKM85lPKrHHudlt9t5+eWX\ng3JaUP2A3djY2KAG7GZlZfHuu/rf5m+++Ybdu3cHtTaAWbNmBUxdRUdHM2vWrKBtgv5eSf369QPK\n19eExYsX8/rrr/PGG29UOvT2qGpCTk4Oc+bM8YtgnE4naWlpQU/s9+XYsWO6Y57Mltc3btw4oKO+\n9tprDacJwzlP0aJWUQAMC3DtDvf1oDGXmwgBNcjLopRaAiw5A8s5a6SlpfHOO+94X1dUVHj3toIp\nh09MTOTVV19l3LhxlW6QdrudlStXBlUAUJ2UicPhMCRl0qNHD+bNm8eDDz5YaX02m4358+cbFlAM\nR5qwT58+FBUVkZGR4VUQHj16NGPGjDFkb+HChX4pUg8VFRUsXLjQ0OR+CFwGbzZ6OXToEB07dqR5\n8+beqsLIyEjuv/9+U4UZBQUFfmnCUMxTDBcOh0O3Ad/itMwB5otIK2Adp/a4hqENbH/AiNGz7rgs\nNPr16+dXrv3ggw8aKru+5pprSE5OZtWqVd6qvd/+9rdBV63NmzePCRMm6DqvqKgow1ImrVq1YvDg\nwaxfv967vlGjRplSUg5HmmzHjh1epwWnIqMOHTpw7bXXBm1v4sSJzJ492y8yAu37GWwa1xc9xxWK\nMniP3YYNG9K1a1dycnIYMmSI6WpCK014YaCUWigiB9BamhagVQ060bJqd7ozakFjOa5agFIqpJPh\n//73v3tL4EF7Wpw/fz5169YNampG7969WbJkCWPHjvWrUJw+fbphKZMjR47QvHlzBg8ezLp16xg8\neLChHisP4RiftGvXLlavXu0XFVRUVPDEE08wc+bMoJ1Xhw4dGDJkCOvWratk1263M23aNMPVj6Wl\npbrRgNkqzZMnT1aa7tGwYUMSExO9FZvBkpGRwdat/tsdLVu2pFWrVlaa8DxFKbUWWCsikWhDIfKV\nUvpz2WqI5bhqAaWlpX43SLvdTlRUlCF7d911F02bVlaf6dmzZ9D7ZQAfffSRbgXgd98Zn7r1+uuv\ns3HjRu/rdevWsW7dOn7/+9/zhz/8IWh7euOTzMqDzJ07N+DMw7KyMubOneudblJTXC4XMTEx9OnT\nh23btnnTjw888ICpkv1Vq1YFrBYdOnSoYbt6UVz9+vUN99olJSWRlJREfn4+CxYsqNRcHwo5F4va\ni4hcgzb5qAVaeXyeiLQBfjEyQjBkjktEWgCilPpfqGxeKIQy2gJ0J20bnUQwdepU4uLi+Oqrr9i4\ncaO3j2vChAmG19e9e3cuv/zySuduu+02LrvMkMJByJuEQaumW7Roka7zio6O5qGHHtL5VPUUFBRQ\nUVFBkyZN6NOnD5mZmSQlJZnqrwOteTk+Pp78/Hy2b99Or169uP7662nfvr0pu7/84j80PBTpR08R\nytGjR717Wlaa8PzEPc3jNWA4UIHmc94D8oBngP+hFd4FRSh/UgyPqL/QCbWciZ7jMtobU1JSQnFx\ncSUpk4iICOLj4w3ZC8dsunCMT7roootISkryc37R0dGG0oRQeZ0eB9ChQwdTN2xPGXx+fr63By47\nO1t3gkYwlJSUUFhY6HfebPrx22+/5cMPPwQ0B+b5WQiFQ7SolcxD0+8agCZa6VuItx642YjRUDqu\ne9C6oC2CRM9x1ZaIS2/vpH79+oajmWPHjvlNL69Tp44pCYtQN956Bus2bdq0kvOKiooy7LRAi2D2\n7t1Lenq6t6T+hRdeYMSIEaxcufI0n9anoKCAgwcPkp2dXamIZPny5ezbt8+QTdB/GKhXr56p/6fc\n3FzS0tK8//8ul4ucnBwKCwtrbTWhhWnuBB5RSm1BK8rw5QegpRGjIUsVmhlRf6ETylSh0+nUfdo2\nGiHpTXA3c5MJtchhOPqBjh07Rnl5OXv27OHzzz/3ni8vL+eRRx5hzJgxjB07NiibFRUVHDlyhPbt\n21dK4d18882mBsru2LGjktPyXetLL73EpEmTaNeuXdB2Q50mzM3NZcWKFX49bC6Xiz179tCxY0da\nt25t2L6Fd6Dtn9D2ki4BUtytRJ7rdwK/BbqiFUn0c7cjVWezGTDX/ZkrgeVKqQlBLCsWbS6iHvXw\nd2Y1IijHJSJPAIuVUj/rXGsG3KeUmmlkIRcyoUwVFhYW+k3bjouLM1zoEWrNLD0nY6ZJ9ujRo343\nw+joaFPOwHPT7tSpE506dQK0ifM33XSTYZuHDx/WVVE2q5O1YcOGgEUk5eXlpKam8uyzzwZls6ys\nTDedayZNuHbtWt02ANAettauXcvkyZMN27cATiPKC9QBsoDXA1zXIxptZuwcTslFBcMnwDi0fa2q\nDHevJ2iCTRX+H9oYfD0ucV+3CJJQpgr10oRGoy3QV6etTRFXoP4lM9PF9Wya3dvJy8vzO9e0aVNT\n6zx58iTXXnttwLRtVFQUKSkpQdvV+/fHxsaaSl8PGzYs4CxGu93OsGGBhitY1BSl1Hql1GNKqXTA\npXN9uVLqSWBDEDa/V0o96I7cjEy5+Atwp4i8jyZYqYDBIrIcSMagzwg2VVh1JL0vlwL+j2kWp0Uv\nVWg04gq149J78jYaIQXqtzLruA4ePOidtN6sWTNTaUKllG6azKzjCofNvLw8EhIS6NGjh1+6MCoq\nKug04caNG9m0aZPf+csvv5ybbrrJlJNt3bo1PXr04KOPPqoUeUZGRjJq1CgrTXieopT6UEQG4J6g\ngeZDngQ+Am5SSn1ixO5pHZeIjEdTQQbNab0sIlXLjWKAjoD/T73FadGLuIymkPSKKcxUAOrZMxpx\nFRYWUlZWVumc3W43NQ18586dXsHITZs2MXDgQFN7McePH/cbAmyz2UylM0+ePKk7ADhUzrCq8zLi\ntAAGDRpEUlISGRkZ3u+lh6p9gcFSVlaG3W6nQ4cO5OTk4HK5iIiI4I477rCcVs1IqCKMu8gtmltr\nERE70APIVUrdKCKxQAPgmFnB4ZqkCovRNteOoHnL4z6vPUcu2hR3IznQC55Q7nGFshT+2LFjfvtl\nderUMazvFGh/y+iT/Pbt2/n3v/9dqZpu06ZN/PTTT4bsgX5k1LhxY1M9YXo2GzRoYHjfETRH4Nl/\n/Oqrr9i+fbv3+1BeXs7cuXNZt25d0HYPHTrktev5MzY21rQised70KBBA2+z9XXXXcc111xjyu4F\nRL5SqpvPUaudlhsn8AHQDkApVaKU+tms04KaCUmuAlYBiEgqMDPU0iYXMr7jnnJzc1m7di3Dhg0L\nej+hquqxhz59+jBixAhDa9Pb3wp1YYbRtN7OnTv585//7FeY4HQ6efjhh/n73/9uaIxUOFJ6Bw/6\n6+SZjWAOHTrkfaho27Ytbdu2JS4ujgEDBphK6e3Zs8erdrxr1y6vKKdZRWLf76snYr/mmmsspePz\nGKWUS0S+Acz9sOsQ7B7X34BY9/gOXZRSVdWKLarBM+7JUy5cUVHBihUrmDBhQlAjmh599FEeffRR\npkyZwmuvvcaoUaNo2VJrkTAacWVmZrJ48eJKqsdGU2YLFizg5Zdf9js/fPhwQ5L1s2bNClhN53Q6\nmTVrFqtXrw7KZqD9LTOpR6fT6S12OHToEDt27KBbt27079/fsE0IT7HHF198QUZGRqU+q127dnH1\n1VcbtglaRqGoqIjvv/+eH344pf+6dKmmO9u3b1+SkpJMfQ2LWst04FkR+Vwp9flp311DgnVcOQQu\nzvBgPKdyAXLy5En+/e9/V5JHr6iooH///owbN877y10TPvjgA+/8vFWrVpGcnEyrVq0M7XFlZWUx\nZ84cysvLWbNmDbfffjstWrQwHHFNnDiRw4cPs2bNGu9E+MGDBxuaTQiaIOWUKVN01XljYmIMCVIe\nPXpUd3/LTPFIfn4+n332GV9++aX3XGZmJpmZmQwfPtxQNOxwOHQr/8xEcfv372fp0qV+Jfsul4u0\ntDTq1q1reCK852GgVatWtGrVCtAa4o0O67XQxz1eyfOf5BXlBQqUUv8TkYbAZYDnSbaNiBwD8pRS\neW4bywCUUuN87HZ2//UiwOV+XV7DIOVxoBHwqXtK/C9U8SFKqaC1jIIth+8H9K9y3AUsQuuCtmpa\ng+Q///lPpZuaL+np6WzZsqVGdjIzMxk1apT3Ru5wOFi1ahWHDh0KWhLeo3rsGa7rq3psNOLKzMz0\nOi2PzfXr17N//35D9jp37hxwU19PIqYm6EUxZve38vLydG1ERUUZ3t85fPiwX7QZFRVlKo2blpam\n+wte+PgAACAASURBVBAA2oNUWlqaIbvhqtK00KVaUV5gqPu156byT/fr3/nYuMx9+OKxeSNwm/vv\n62u4phzgHbS+sc3u13urHEETlONSSm3VOdYopX4PvAkY20y5gPnTn/4UsDGzuLi4Rn04mZmZjBgx\nwi9acDgcLFu2LODkcD1Op3r8zTff1NiWh+zsbKZOnep3Y3Q4HEyaNMk7Yy8YCgoKuPHGG7nlllu8\nTsFms/Hiiy/yl7/8JWh7oL+/1axZM0O2POzatcs7Cd6X8vJy5syZY0j1WM/BXnzxxaZmHt5xxx0B\nP2+32xk5cqQhuwUFBX7qAiJiSZiEAaVUhlJKdI4J7utLAlyf4WMjSSmVVMWu3mdaBVqHiLwmIp6n\nylTgQaVUSqDDyL81lLMKt2BFXEHz2GOPBZSXj4uLIzU19bQ2Jk6cGFCpuKKiIiiBwtOpHj/22GM1\ntuVh+vTpfmXwHkpLSw2l9TxOplmzZgwcOJA6deowZswYunXrFrQt0FJieo7LTPqtqKiIrVu3VjvZ\nIljFY5fLFXB/ywxxcXF07drVz3nZ7Xbuvfdew2lCvbU2atTIVEWlRa1nPOB5MtkCmNsk1SGUjutW\nwNK2DpK2bdsG3PwePnx4jQo0Fi5cSGxsrO616OjooG6O8+bNC2jLbrcbUj2eNWtWQOccExPDrFmz\ngrbpu8fTrFkz7r77bnr27Bm0HQ9Hjhzxi3yjoqJMTQk5ePAg3bp1q3ayRbCqx/n5+X7rjIyMNB3B\n5OXleVWOPc7LZrMZcloZGRk8+eSTPPnkk6Snp7N161a2bt3K999/D5h3sha1noNAknvPTYAYEYkL\ndBj5AkE5LhFZqXOsEZF9wB+B4B4fLThx4gR33HEH48aN897cY2Ji+OCDD2pcmJGYmMjKlSv9nmJt\nNht/+9vfgqra86ge69maOnWqIdXj7t27c/vtt/vttXmcao8ewe3NKqV0n+TNVP/9/LPf+E3T6bef\nf/7Zq72lt8dlRPX44MGD5Ofn8/7773vbCy6++OKg9zF9KSoq8kqYeJwXwK9//WtDkVZSUhL/93//\nx+DBgwFt5mPfvn1p1aoVdrvdEow8/1mENinjOFohxhbgRDVH0AT7W9lY54gGPgRuU0oFN83Twtt8\n3Lp1a0aNGkV8fDz/+Mc/glYrTkxM5J577vHewGw2G8nJyYbKrgOpHv/vf8Y0QouKikhISGDw4MGV\n1jd//vygnRZozl5P8dhM1KHnCM3sb/lW/ukVU5SXl/PFF8F1jngmqWdnZ1NSUkJ2djb5+fmm9+Gq\n9pl5HEvHjh0N28zNzfWqXPvqbjVt2tQSjDzPcQ9a746WMhRgFprkVaAjaIJ6TFNKBa/9blEtvlMz\nWrduzeTJkw1NIVdKkZCQQHJyMitWrCA5OZmWLVsa6uGaOnUqDRs25LPPPqukepycnBy0LTiV1mve\nvDmDBw9m3bp1jB49ml69ehmyp+dkEhISDEcd5eXlus3RZhzCL7/84i0t90iZHDt2jE8//ZSJEycG\nHWkBvPLKK+zevdv72ul0sn37dlwuF/fee6+hdSqlvNHm/v37+e6777zXPCrPAwcOZNCgQTW2mZub\ny5tvvul11h7drQ4dOhjegzzbOJ1O3fFnFvoopXYCO91zClNDPbQiZHpcFsYI1ZzCmTNnVtp/WrFi\nBaDdmIItqPDMKPSoHnsw2s90+PBhPvnkE3bsODVqbdmyZSxbtozf//73Qfdyhbo4wdfJeKhbt64p\nyRG91GPXrl25/35jU9H27dtXSRvMl927d7Nv3z5DultHjx71FuO0adPGmxrs27evodFe1elu7d27\nl86dO5uaMm9xbmG0avB0BO24RCQKmIA2PLEZ2kbcx8BSpVR5NR+1qILT6fRLeYmIoTmF99xzj9++\nVEJCAvfdd1/Qto4ePaqrUhyoaON0/PLLL3Tv3p3u3bt7z/Xr189wH1OoHVegkUxGp1C4XC5dx2Um\ngktNTQ3YZ2VUdwv0/+2NGjUyPI/S0t2yOBMEW5xxNfANsADogDZEsYP79f7qRkFZ+KMXbcXFxRna\nAwjlFPdQSo8opUKqb+VbSOBBREJemGHGyRw5csSv/D8yMtJU0+0dd9wR0JFGRkYa0t1yOp26DwGX\nXHJJ0LY8VKe7ZbPZLN0ti5AQ7B1yEVqlyBVKqZ5KqaFKqZ5oY0aOAa+EeoHnM6EUkNTTzTI6o1Bv\nuK7RiRnHjx/XlTIx6lT1brQNGzYkOjrakL0TJ054JUfy8vJYs2YNv/zyiynHFahC0UzlX0xMDD17\n9tStTpw8ebKhNOHhw4f9orjIyEhTDwGtW7eme/fufg9fkZGRjB492pIwsQgJwTqubsATSqlK5WXu\n1/+HVkliUUOqajSBcR0uPcdl1DkcPnzY75zRiCvQ0FqjlWWhrv77+eef2bNnD2+88QabN2+mqKiI\n999/n2HDhrF8+fKg7SmlOHDggN/55s2bG16jy+Xi4MGDXt0tj/Oy2+2GdLc86K3TrIM9efIkUVFR\ndOjQwft/bOluWYSaYO8e36OJRuoRAxirl75A0XNcRiMuvVSh0YgrlKnCUE9bD7VEiKfXqmokEx0d\nTadOnYK2V1hY6BdJi4gp53ro0CHvvpHHecXGxvKHP/zBsNMqKSnR/X82kyaEU87QV3ere/fulu6W\nRUgJ1nFNA54Wket9T4pIT+Ap4JFQLexCIJTKx6GKuEpLS3XVeo2mCkM5YLWoqMjPQZtxCk6nk927\nd5ORkeHXZ1VWVsYTTzzBZ599FpRNvSjGTLGDns2EhATGjx9P+/btDdvUS2fGxsaaag52OBz88ssv\nfP/992zdutWrePDxxx8zc+ZMMjIyDNu2qB4RiRSRp0QkV0RK3X8+LSLVhs8iMkJEPhWRYhH5QUQe\nPoNrbiEiVQf61ohgcwKPo422zxKRQ8AhoIn7OAI8JiLe2msj4+ovJEKVKiwtLfWrToyIiDAUcek9\nhTdo0CDgyKbqqKio0LVn1HGFen/rl19+0R2A66GsrIy5c+d6pWJqwk8//VRJd6tJkyZceumlhtYH\n2vcw1AUU1aUzzeh55eXl4XK5KsmXREZG0rt3b1MT9i1qxCPAH9Cafj8HOgFLgDK0oMIPEbkFbTj6\ng8B7aDMF/ykiJUqp+Wdgzd+hBU9B/3AY0eMKfpy1hS6eiMuM8jHoR1vx8fGG9pFCub+Vn5+v2x9l\nNB0a6uq/AwcO0LNnT92IC7R0oacJtyYsX76ct99+2/vaM5W/vLycq666ytAa8/Ly/NZmt9tNpVt9\ne7d8CYczbNq0qeW0zgy9gbeVUp4fwO9F5G3g+mo+M9b9Gc+ovu9EZDbwiIgsUB6J7fBxD9pkjaAJ\n1nHlAouVUn53EBFpBtznHvdhUQNOnDjhp3w8ePDgoPcD9ByX0ZRPKB1XIPkNo4TScXlutE2bNiUp\nKcnPeUVHRzNz5kyuvfbaGtts1KgRkZGRlezYbDY6d+5czaeq56effvI7d8kll5hyBno2GzVqZLhP\nDwI7QzNFKRaVSBCRHT6vFymlFvm8/i8wUUTaKaX2uVuT+gOzq7EZDZRWOVcCXAq0RKtpCBtKqZqn\nMqoQ7CP5/6H9o/S4xH3doob885//ZNmyZd6N94qKCkaOHMn48eODslNQUOB3LpQVhUaf7kPpuAoL\nC3X33owWZvjaO3TokO4el68q9enIycnh9ddf97PjcDgM626Vlpbq/n+YST2WlZXp7juadTB60VaD\nBg2IizM0/NvCn3ylVDefY1GV688Cy4EvRKQCTaBxqU80pcdGYJiIDBSRCBG5CvCkGMwNwKyCiFwl\nIv1FZHDVw4i9YCMuoYrssg+XAv6P/ha6bN68OeCNMT09nQkTJtR40G6oIi6Xy8WuXbvYsGGDdz4h\nGIu4Ak1wN+po9KoJGzdubHh/yzfq6NSpE506daKsrIzMzEweeuihoCItgAULFlQ72WLhwoVBa2/p\nRUZxcXGmCih+/vlnv/RtVFSUqdRjcXGxbu+fFW2dUe4GxgGj0ZxWZ+AFEclVSr0a4DP/BK4A1gJ2\noBB4AZgBuAJ8JijckV8a0B79tKAiHHtcIjIebcPP80VeFpHCKm+LAToCm4JdwIVKSkrKaZWPPfpF\np0Mv4jJSmLFx40ZWr16Nw+FgzZo13H777Vx55ZWGCkYKCwtDOsFd74nezJ6MnlPo2bOn4WG1Q4YM\n0Y24wJjullKKH3/80e/8pZdeariAQikV0tRjRkYGW7du9TvfsmVL2rVrZ7gS1cIQfwOeU0qluV9/\nLiItgUcBXcfl3sN6xF1Q1xQ4DAxwX/5O7zMG+AdaSvJO4AsgJGMBa5IqLEarGDyC5jGP+7z2HLnA\nXwFjE0QvQJ577jnTysce9BxXsE/lU6ZM4f777/dGDQ6Hg/T0dP7zn/8YulHqRVuNGzc21NzqO8Hc\nF6OOKxwpOLvdHjDC6NmzZ9DT4I8ePapbdeqJgo1QUFDg9zBhxmZSUhKjR4+u1GjcqVMnWrVqZcrB\nWhgiDm0Eny9OanCPV0o5lVIH3LNmRwHblVL+vyDG6AI8pJRaq5T6Rin1Q9XDiNHT3kWUUquAVQAi\nkgo8pZQKlTe+YOnUqRNXX321brqwpsrHoEVnpaWV91cjIyOJj4+v8VqysrJ49913da/t2bOHrKys\noAUkQ9konJ+fT2lpKQcPHiQzM5P/b+/M46Mq7/3//k4WQkJYQ0jYAoGASFAQiFEBBdwKtnKta1sU\nqdoWW69Xf71XpVrbSm2rou0V7KVF63bBpa29IioiIpvIjgFRtrCGkEBCCBBIZub5/XFmxknmmeSc\nk42Q5/16nVeYM+c888wkPJ/5fp/vMmbMGHr16uXavXXw4EFqBkwlJyc7+szCOX78OMeOHSMnJ4c+\nffqEQuzdNosE2L9/P0eOHGHTpk0MHTqUlJQUunTp4qoAcxBdP7UuXbq43ofKz8/njTfeCLkeg+1L\nLrzwQtPluOl5F3hIRPKxXIXDgAeAUABEIGIwRyk1PvA4BbgJWIplFd0ZeHx5A85rF9GLVrjGaT+u\nRilR3xoJdj4eOnRoKKqwTZs2vP/++46aSJaUlLB3717ee+89Jk6cSEZGBp06dXIUCv/AAw9oI8LA\n2p954IEHWL16ta2xZs2axQsvvBBxfsSIEXzrW9+yPadw/ud//oeFCxeGHr///vuAZRW6ce01tAsu\nXBCCHY83bNjAAw884Eq0vF4vmzZtYs2aNfh8PtasWUNOTk69ohMrKiq0xY7dWpnBaNiartFgs8sh\nQ4aYEk9Ny8+w8rVmY+XVHsLawwqP8k7H2tMK53YsN6MAnwFXKKXWNOC8HgT+ICIbGtLgcSRcdqq/\nK6WctXVtpQTdQMHOx//617949NFHHXc+/uijj3jrrbfwer289dZb3HTTTWRlZTkaY+bMmUyZMkUr\nXgkJCdX6fNXFvffeywUXXMDPfvYzvF4vsbGxTJgwgR49erj+Ft6pU6eIMPP4+HiGDRvmaJxXX32V\n119/PeL8kCFDuOqqq1zNTSkVYcmkpqby0EMPuc7deuGFF6pZ4g3RMFJnbSUkJLi2Wk37krMLpVQ5\ncH/giHbNlBqPjwDuurnWgoispXoQXw/gKxHZg1WMvea8HBeqcBoOvwUrK7u2w2CD8NYcwc7HY8aM\ncTTGj3/8Y+6///5q+1Lz5s3jtddeczTOpZdeGnXhnjhxoiM34Zo1ayLmtHDhQsrKyly5pD7//HPe\neOONiG/2lZWV/PznP2f9+vW2x5o8eTIPPPBAKBAhJiaG8ePHk5OT43oBP3r0qLY2odt9o6+++oqt\nW7dqnws2jHSKz+fTBrf06tXLdbHj73znO1HvjYuLM+1LWjdbaxwLgdeBlZrn9H/sdeD0r3YsVlJb\n+PFdrHYne4EG/2t1W4PrbEe38d6+fXvb9y9btox33nlH+9yqVatCVRvsct9993HjjTeGgidiY2P5\n0Y9+xHPPPWd7jDVr1jBt2rSINiZBa3DNGuceiBkzZkQNMz99+jQzZsywPdbmzZv505/+FBJBn8/H\n0qVLqaqqcr2A6yyZ1NRU18m8L774YtQSVMGGkU45dOhQhHXk8XjqFa7erl27ahXgg8TGxnLbbbcZ\nN2ErRik1RSl1p93DzWs4+t+qlPpUc7yjlPoJVs2rm91Mog6CNbjuA84D/h2YhhXm2WKp2QwRnNUp\nnDZtWtR9qTNnzjgOvz58+DC9evVi0qRJJCcnM2nSJC677DJHY0yfPj0iUCRIVVUV06dPdzQewA03\n3BA1VDshIcH2mJs3b+axxx6LEEGfz8f8+fMdF9MFaz9HJ1wZGRmOxwoyduzYqO83Pj7eccNIpRRr\n165l2bJl1aJP09LSXOfABd2jZWVlETlhXq+XvXtdBYoZDLZpSKvlE+AfDTheEDc1uM566mtxzZ49\nm5tvvlkrXm3btnWc7BqsptCrV6/QPorTKhczZsxg2rRpWvFKSEhwZB2BtUAmJCRw9dVXs2jRomqW\nSEJCAk899RTDhw+3NdYzzzwTYQkGqaysdFxMF6xk3srK6mkpsbGxri2ZoKimp6dr862GDx/uqI3J\nhx9+yKJF36RWrltnVQzKzMwkNzfX1RzBCgg6ceJEqJhuaWkpX3/9NRMmTHDVCsZw7hOoytETTYSh\nUmph5B2105DCNRHNxlsD4KYG11lNZWVlxOLu8XgchTqPGTOGv/zlL0yZMqWaFREXF8ebb77paL9M\nKdUg7UdycnJ47LHHIiyb2NhYZs+eTU6Osz3Y0tJSTpw4waFDhyLcZ6dPn2bjxo22hevBBx9k+vTp\nWrej02K6QXQJ4t27d3dVSR8sIayqqmLYsGH06tUrFFUYHx/vqmFkv379IoJaPB4PvXv3dh36r5SK\nsKg6derEVVddxZAhQ1yN2RLw+XzanneG2mm2yhk1JvGm5nQ8lgsvC3hE83x9+T2QjFWDy4c15xnR\nanCJyD18kwjtrjpsIxPNTeg0HHvp0qURC3FVVRUrV650JFylpaURlkNcXJyreoerVq2KmJPX62Xt\n2rWOhSvohrvooou46KKLOHToEKtWreKJJ56wLVhBsrOzGT9+PIsXL46ITnRaTBcs4dQlWQfbeThF\nKcXu3d9ECwcbRubl5fGjH/3IsWjt3LmTv/71r9pw9RUrVjBo0CD69+/veJ6lpaXav9+MjAyTcGzQ\n0SiVM5xaXLp6PaeB5cADbkw+GziqwRUoPjkHoEY15bMG3X98J27CIDfccAMpKSnV8rgmTZrEtdde\n62gc3QKcmprqKmBh+PDh9OrVi4MHD7JkyRLGjRvH7bff7qpTb818q/T0dP7whz+4+mZfUFBASkpK\ntUrwMTExPP74445FCyxrq+b+TmJiouvoxJKSkoi/i5SUFH75y1+6Kt81f/78qOHqXq+X+fPn84tf\n/MLRmEoprZXZrl07U97JEI1hwK1KqQUNOajTBGRnSUYNg+MaXGc7DSVcwSaNGRkZoWAMNwVxdcLl\npl1IeCmlHj16MHnyZMBdkuupU6e0CbO9e7tqmEp+fj5ffPEFeXnfZGz4fD4eeeQRvv/974fmaodo\nC3ifPn1cRyfqxuvUqZMr0QK4/vrreeWVVyLEFSxr+tZbb3U8ZjRrq0+fPsbaMkSj+StnBBGR7liJ\na52xahWu1vXoaiBc1+A6W2lo4QrHzTffhqririul1LFjR1eNI/fv3x8xVocOHVztzVRWVnLgwIFQ\nFfgg48aNcxVIUVxcHNVd5oaKigptLcb6hJSLCBdddBEbNmyoJl5xcXHcddddjt2ESiny8/Mjzhtr\ny1AHZ0XljBjgv4G7qb6h5hOROcDPlFINUg4/jDprcLU0ysrKIs45XZD9fr+2uK5Ti6uqqkpr2bgR\nLl0knNsIO11ItVth2L9/f8ReT0JCgutKHuF7UUFSU1NdVdEHyxqsKdJt2rRxXUS4oqKCQ4cOUVJS\nEmFxVVVVsWvXLsfCVVxcrI2ENXtbhjp4kkaonOHU4voVMBUrCOMN4DDQDWsf6tdY1tdjTidRB3Zq\ncLUoGsLiOnbsWEQQREJCgmPrpqioKGJxa9eunSsLUFcD0E0FiaqqKm2lB7fCpbMUMjIyXLXyOHPm\njFagMzMzXc2tqqqqUdyOfr+f/v37079/f0pKSti6dSuTJ09m4MCBjseLZm0lJye77o5taHgCXeh/\nB0zACmjbDfxEKRXZe8a6/nGiN//tppSK/EbrnC2Bo0FxKly3A79QSj0ddm4f8JSIKKwk4QYVLjs1\nuFoaOovLqVDo2nJ07drV8bdfXRV3N/tbZWVlEe9LRFxZXDoLyW3gw8mTJ7WuULduuPz8/Aihb9Om\njWvLct++fRQWFlarAu/xeFxHJ54+fTpCWDt37syUKVNcRRGC5UrW5Qv27dvXWFtnCSLSEauk0gqs\n1KRiIBOoTXyeBv5c49x8rFZdDSFajVaY3alwpQLR+pl/EXjeUAc6i8upq1AnXG6+/er2VtwIl87a\nSk1NJSHB+b6szgLp3bu3q0VS54Zzayn4/X527doVcb5Pnz6urDe/38/q1asjqsAPHz7cdVWL3bt3\nRwhrTEyM66AWn8+ntbY6duzoKl3C0Gj8J3BIKXV72LnIX1wYSqkTQKjQpoj0AkYD9iOVmgmnvojt\nQLRwpFuBr+s3nXOfM2fOaPtnOXXxRbO4nNCQDRp1pY/cuAm9Xi+rV6/mjTfeqGYNurFAlFJaocnM\nzHQlgocOHeLkyZPVzokI/frV7BRhjxUrVrB8+fJqtRODIuaGiooKrYu1d+/exMfHuxpz3759ETl+\n4P4zNDQak4DPReQNESkSkU0i8lNx9kv6IVAK/L0+ExGRNcFOIiKyNvA46uHmNZxaXE8A80WkN/A2\n1h5XKlbzsbFEFzVDgGhuQqeLQEMI17FjxyI64sbExDh2yfl8Pu2+j5tv+R988AHvv/8+Pp+PRYsW\ncfXVV9OnTx9XVmC06D+3+1E7d+6MOJeenu4qanLbtm289tprEdagz+djzpw53H///Y5z33bt2hVh\nbcXGxrp2i54+fVprSXft2tXVHqihXqTUyEudE8hZDZKJVcP1Wax9rqFYgXQAz9c1eCDwbirwqlJK\nXxvNPluBirB/q1qudYXTPK43ReQYVpDGH4E4oApYD1yrlPqooSd4rtEQEYVVVVXaiEKnwqWztrp1\n6xaqEG8XXfXxhIQExyWj1q9fz1NPPVXNAlm0aBFTp0515YrbsWNHxLm0tDRXQnPs2DFtWSy3+0Zz\n586NEK0gPp+Pl156id///ve2xysvL48a0OK2BJXO7SgiroXfUC+OKKVG1PK8B1inlAoWH98oIllY\nBcrrFC7gWqAXVuBbvQjf16rZA6yhcBy2pJRapJS6BGgLpAFtlVKXGtGyh67emZv9rZqLXvv27R23\n0mio8HVd6HrPnj0dRcWtX7+e+++/P0IAfT4fL774oqO+W2C5ZHXzcis027dvjziXnJzsWJzBcmGO\nGDGiQavA60Q6Li7OdSRmWVlZ1I7Jblu2GBqVQ1gllcLZBth1e9wDrGopjYBdJ/EqpfxKqaJGyNs6\np9FZXE6rI+gWFDcRd7pv6G6qXOiCKZwumDNmzIi6t+Pz+RxXls/Pz48Yr02bNq7clxUVFdo9vKys\nLFf7PAUFBSQmJkZ1fzqtAl9SUqJ1Hfft29eVteX3+7VCXR8hNDQ6K4GauQ4DsPok1kqgoMREGsDa\naipabPWJlkpDWFwNIVzl5eURcxERx3tJpaWl2nGcLnDTp0+Pusg66bsFlkWjW3gzMzNduxx1IfBu\nAkb8fj9ff23FMA0bNoxLLrkkNKf4+HgefPBBpk6dans8pVRovHASEhJcRxIePHgwIggFLCF06kY2\nNBnPArkiMl1E+ovITVjpSbOCF4jIkyLysebeqcBJQFdE/azECFcToxMupxZXQ7QgWbBgAX/961+r\nbb537drVcfi6LlS6W7dujt1J5513XlSxGzt2rKNq8IWFhVrL1o2bsLKyUhuZ2K9fP1eL+MGDBzlx\nIhSBHKoC37FjR1etSw4ePKgNQMnKynKdYK2zoJOTk10FyBiaBqXUWqzIwpuxEn5nAI9iFW4Ikg5U\nC4ENRB3+EHhdKVU9Uussxnx9amLqK1zRemfZtbhmzpzJs88+G3r89ttvA5Cbm8tPf/pT2/MIoit/\n5CaKbdeuXYwZM4asrKxQ08jY2FhmzpzJiBG17UlHorO2unXr5qpg7c6dOyP23WJiYlyJoM/n01pH\nAwcOdGRlBamqqtLubbkVmaClqnPZDhgwwIS/n+Uopd4D3qvl+SmacwpwXxSzmTAWVxNSVVVV7ds2\nWG41J67CkpKSiIU0Pj7edjJobm5uhDUUGxtLz549HeddnTx5UiuiTqPOwl176enpXH311SQlJfGT\nn/zEsWiVl5drQ7jdlDqqqqpi+/btFBUVsXDhwpCLtm/fvq4Sq/Pz8yPSDwBXLV/AEntdjtV5553n\nSmSKioo4evRoxPnu3bu7rsNoMAQJ1LNFRG4OdER2jRGuJiTc2srPz+e5557j8OHDjlw6uhJNaWlp\nthaqVatWMWXKlIjyPV6vl3feeUcbgFAbOhda586dXQWbhH826enp3HLLLUycONHROABff/11RMRl\nYmKiq2TonTt3cuDAAVauXMmpU6dYuXIlR44ccSWClZWVWusoNTXVVXX148ePa39faWlpdO7c2dX8\ndHlq8fHx9apSbzCE8Uzg51jgZREpFJFVIjI70ADYNka4mpDS0lLAEq158+ZRVlbG3Llz+eSTT2yP\nUZ8WJA888IC25hxY4vXQQw/ZngfohctNjo/OfZaenu44yTXa4jtw4EDHBWsrKyt56aWXWLZsWbW8\nsqVLl/LSSy85Ggss96WuseP555/veCy/38+XX34ZIdAxMTEMGOD8i2zQ4tXNLysry3UemMEQjlLq\n68DPnwRSqtKBO4FPceiuNMLVhJSWlvLPf/6TV155JbRIVFZWMm7cOO644w5bY9SnKO7MmTOjYWKW\n0gAAIABJREFUBk3Ex8czc+ZMW+OA5SbUzcVp+aMzZ85o98ncLMC6xTcuLo6srCzHY3344YdalyPA\n6tWr2bLFfsHr48ePa4NYevfu7aoCxYEDB7TBJ3379nWVY1VYWKjt7da1a1fHSe0GQzgi8kSg7FNh\noMTT/xORBLD215RSXyul3ghLnLaFEa4mZOnSpWzbtk373Ntvv12n5eX3++tlcV166aXMnTs3Ihou\nNjaWZ599lksvvdTWOGCFiOuaRjp1e+3cuTOiPYsb95TX69V+tpmZmY4L1p4+fZq///3vUfPKKisr\nmT17tva5miil2LJli9Y6crO3VVFRoQ0+SUpKcuXSq6io0FqpbgXfYKjBdcBCrDZUq4D/AjaIyOD6\nDGqiCpuQp59+WuuOAatV/Z133qkNRQ5SVFSk7cHlpEp3nz59mDRpEu+88w5er5fY2Fhuuukmrrvu\nOttjgD5yz2lCrlJKKzZZWVmOQ8137twZUbzY4/EwaNAgR+MAbN26lREjRrBy5UqteMXHxzNt2jRb\nYxUUFGitmaysLMcBHkop8vLytHMaPHiwY3eo3+9n27Zt2vGysrJcF+Y9F/H5fNqIYEPtKKWGhj8W\nkV9h5ZYtF5FvK6VWuhnXWFxNyPe+972o+wWJiYl17p3oKl306NHDkVg8/fTTvP322yEB9Hq9zJs3\nj+eee872GNEqNTj9hn7w4EHtYuDUEvF6vWzdujXifEZGhuNouKBbLzU1NWr5q9zcXLKzs+scq7Ky\nUutSTEpKclVRft++faF90nB69uzpqsXI7t27tTlgaWlpriqxGAx1oZQqVUp9D6sz8ociMsbNOMbi\naiL8fj8pKSkMGjSIL76IbGl24403Mnbs2FrH0AmX03ydYcOGRST6Xn311bYW4iBfffVVxLmuXbs6\nXjzz8vIiznXv3t3xONu3b9eGmTt5T2BZNBs3bgxVycjJyaFPnz4hyys+Pp6HHnrI9rhffvklZ85E\nFtoePHiw4+Tg8vJyrZWbkJDgaj+wuLhYW6syISHBdT1HgyEaIpIIdA87fMB+4F3AWekgjHA1GceP\nH8fn8/Fv//ZvDB06lHnz5lFVVUViYiILFiyoU7QgusVll9LSUm1VeSd7I9Hq2DkNES8pKdG+n8GD\nnbm+Kysro1pbTsPyDx48GJGXlpqayuTJk1mwYAHTpk2zLVpFRUXacPX09HTbe5JBfD4fX3zxRUTZ\nKbDE2WnU36lTp7RfPkSEQYMGmbJOhgZDRLZhRQ8GXR9B95AfqztzZI6IDcxfaBMRntjZt29fbrvt\nNhYsWMDbb79tS7Si1RZ0IlzR+kklJSXZHmPv3r0Rdew8Ho9jN6HO6mzfvr3j+nrbtm1jz549rF69\nmtzc3FBO2wUXXOBoHK/Xy+bNmyPOJyUlcfXVVzNhwgTbY1VVVbFp06aI87GxsY6tQLDSBWomroPV\nqNNpMExVVVXUfbLMzEzHdTMNhjpYjlW5viDsOAQU1qdAe72FS0QmKKUW1necc52aG/R9+/blr3/9\nqy3RAqI29HMSMacTLqd7LV9+Gdn1ICMjg8TERNtjHD9+XBsCn52d7Wi/rqKigiVLlrB06dJQjtUV\nV1zBJZdc4tja2rJli7aw7NChQx1bIHl5eRGBIgCDBg1yHK5eUFCg/d0nJSU5dhH6/X62bt2qzeVL\nSUlx1RnAYKgNpZSjxGK7NITF9QRWuGMIEfmBUuq1Bhj7nEFXSsfut+Unn3yS3/3udxHnb775Zn74\nwx/aGuP48ePavCsn+xnHjx/X9rhy6t7bvHlzhNurbdu2jhfif/7znyxevDgiQXjUqFGOxikpKYna\ndLJ79+6Oxtq/f7927yglJcVxNfmysjKtG9Tj8XDhhRc6ElSlFDt27NAGw7Rt29Z1mSiDoTlwHVUo\nIveIyOtAFxG5VkTCHffOSjC0AnRReCkpKbbuffjhh/nRj34UWqhiY2O57bbbePDBB22/vm5fqkuX\nLo7KA+Xl5WkbWDopp3T8+HGtSJx//vmOFuJPP/2UV199NcLl5fP5eOKJJ7RuPx0+n481a9Zo86yG\nDRvmaDEvLy/XukBjY2MZOnSoo7HOnDnDpk2btPtaAwcOdBwtuWfPHu0Xl9jYWIYMGWL2tc4xRORh\nEVEiYqf7MSKSJSLlIhLpkz4LqU84/BvAa0ACgbIdIlIgIuuAyJjdVk59hOuDDz5g7ty51ULY33rr\nraiVHXToNuOdWDhVVVXanKvzzz/fUf7Qhg0btL2tnJQ+8vv9PP/88xFiE8Tn8/HMM89on6vJli1b\ntCHhgwcPdiQOXq+XdevWafeOsrOzHblSvV4vGzZs0Lob09PTHdddPHDggNZSBuv352RuhrMfEcnF\n6mgc+S1Kf308MB9Y1pjzakjq0wG5TCn1PjBBKXWLUmogkAXcAVzVUBM8F6ioqIjYXPd4PLZchcuW\nLeP222+PSDz2er1MnjyZZcvq/ls7cuSItvmkk3wpXWh3bGysI8E5cuSIdp8tOzvb0V7dV199RU5O\nTlQLJiYmxpY1WlhYqLVEO3bs6EjUg2H05eXlEc85rbrv9/vJy8vTimlycjKDBw92ZLkVFBRoP3Ow\n8u7cFOQ1nL2ISAfgdazmkHYNiN9jidxbjTWvhqYh/AOhr/1KqZNApFO+laMTjc6dO9vK5Zk2bZo2\nFwgsQZw2bVqddfN0+yTdunWzlS81a9YsXnjhhYjzI0aM4I477rAdbKCUYs2aNRHnExISHO2RlZeX\ns3nzZtLS0hg3blwoMCNImzZt+PWvf82FF15Y6zgVFRVaF6HH4yEnJ8eRFbl9+3atGy4pKYkhQ4bY\nFhqlFFu3btX+vcTFxTFs2DBH+V8FBQVaYQYroMZJRKqh2UkJeLOCzFFKzdFcNwd4Wyn1iYj8sq5B\nRWQiVlmmYcCNDTPVxqchKmesFJGDIvK+iPxORG4VEed1ds5hdAuR3coEs2bNipqn07Zt2zpr5vl8\nvqguPjvce++9PProo9X2177zne8wcuRIhg4dWsfd37Bnzx4KCgoizg8bNsx2aSG/38/KlStD1mda\nWhpXXHFFaDG3K1p+v5/Vq1drXXHZ2dmOIhL37dunrW4fExPDyJEjbedYKaX46quvtJ+Rx+Nh2LBh\njiIS9+/fH1W00tPTHQeKGJqdI0qpEWFHhGiJyN1Af+AXdgYUke7AX4AfKKVaxN5WkHoLl1IqCzgP\nq1V0ceDnm/Ud91xCVxi3W7dutu7NzMzkxhtvjNg8b9u2LW+++SZjxtReMWXnzp0RVSU8Ho9tN+Fn\nn33Gk08+WW1/beHChfh8PtsVLiorK1m9enXE+fbt2ztyV27ZsiVirzAtLY3bb7+d1NRUW6IFsHHj\nRu2eY7du3Ry5CAsLC6MGgQwdOtR25fdgzcZo/dCGDBli+7NWSrF7925tyxmwvjCZbsbnHiIyEPgt\n8D2llL4gaiSvAi8opT5vvJk1Dg1Sq1ApVa6UWqGUegYYCXzQEOOeK+i6BNsVrh07drBv376IPa6K\nigpWrqy7PuXGjRsjzmVlZdn69r5mzRruvfde7f7ayy+/rHX96Vi7dq02Ryo3N9e266uwsFAbsZec\nnMwNN9zAK6+8Yku0tm/frl3UExISuPjii227CIuLi1m3bp02QCQrK8u2Gy6YWxUt0GbQoEG2K20E\nretoAti1a1cGDRpkROvc5BIgBdgqIl4R8QKXA9MCj3WbyOOAX4ZdPxdICjxulPyrhqIhEpAvBTYH\n9rdQSpWIiPN2s+co0VqR2KkxGGzwN3r0aEaPHs3evXt57733+NWvfsXUqVPrvL+wsFDrerJbVeKR\nRx7RtoYHK1x7+vTpfPTRR7WOcfDgQZYsWcKyZcsYM2ZM6H336tXLdpWMkydPsnz58giREBEuvfRS\n2+64/fv3ay0kj8dDbm6u7WrtxcXFrFmzRhuq3qNHD9tWpNfr5YsvvuDrr79my5YtZGdnVwuWyMrK\nsv0ZnTlzhi1btmgDRMCytIxondO8A6yrce4lrJJKvwV0/5GH1Hh8PTAdyAEi67GdRTREcMajwIWB\n+P+NWJEszktfn6MUFxdHWCyJiYm23EiFhYXVqoFnZGRw7733csstt9h67bVr10acc1Ih4ZZbbmHW\nrFkR8wfLQpkxY0at9//5z3/mlVdeCT1+//33Abjoootsv4eqqiqWLl0adT/K7l5hQUEBn3/+udZC\nGjp0qO1xDh06xPr167WilZKSYjtf6/Tp07z++uvVKpGsW2etO5mZmVxzzTW2u0mXlJSwbdu2qC1z\nunfv7rjljKFloZQ6BlTLLheRk0CJUmpL4PGTQI5Sanzgni01rh8B+GuePxupt3Appb4FICLpwFCg\nK1BnNEtrIVpFdzuLiC4asGfPnrZqC0YLPbebVHv48GGqqqqYMGECCxcurCZeCQkJzJ49m5ycnKj3\nK6UQEWJiYqpF/cXExHDFFVfYypHy+XwsX75cWxi4W7duti3HgoICVq1apRWb/v372yp7pZQiPz8/\nagRnp06dGDlypC3XZ0lJCZs3byYxMRGPx1NtXh6PhyFDhtiqaOL3+9mzZ09U1yBYX3b69OljRMsA\nVrHbc8KoqPcel4iMEJE1wGfANOCgUipyU6eVotu7sLP/4fP5tHUB7UYDfvbZZxHWRVJSkq3Gil6v\nlyVLlqCUoqCgIMLiOn36tNaaC2fevHm89tpr2soWs2fPZv369bXe7/f7+eyzz7TCn5iYyKhRo2zt\nR+3ZsyeqaPXo0cOWhRSszh5NtDp27Ehubm6dLku/38+uXbtYt24dhYWF2mRsv9/PwoULo+ZeBTlx\n4gTr16+PKlrBJpp9+/Y1otVKUUpdoZT6adjjKUqpPrVc/zelVLsmmVw9aQhX4f8A9wNbgAuB34pI\nmlLq9QYYu8WjW1jsJKTu3LkzIqDBbrv3goICbVmlkSNH2irts3r16pCVM3LkSEaOHMnBgwdZuXIl\nTz31VK2WFlgBEC+99JK2igRYwjdjxgz+8Y9/aJ/3+Xx89tln5OfnRzwXExPD5ZdfXme1h2BOlE78\nwdrzsROMcerUKdatWxe1+23nzp25+OKL6xStEydOsHXr1tA4W7Zs0YopWO7R+fPn84tfREY1e71e\n9u7dy4EDB6JWDomPjyc7O9t2VKPB0NJoCOHyKKWWB/69VES+hVXKvtULV3l5eURxXRGxtceks0j6\n9+9f54Lt9/tZsmRJxPlgMmxd7Nq1SxvAMHToUJ544ok6F/o9e/awYsUKxowZw6JFi7TilZCQwPTp\n07X3V1VVsXz5cq2lJSKMGjWqzlJZlZWVrF27VjsGWNF1o0aNqlXElVLs37+fLVu2aPf4wBK/ESNG\n1DqOz+djz5497N69u5pQZWdnay0usJKNb7311oj5FBYWkp+fHzVgBiyX5aBBg2znxhkMLZGGEK4D\nIjJWKfUJWKWgxPgmALQWQ7du3eoMRT906JC2tpydhN/169drE57tRN8VFxezePHiiPMxMTGMGzeu\nTtHKz89n6dKl+P3+UJKrLvR87NixDB8+POJ8eXk5n376qbY9PVgdieuKsisqKooafg/W53/ZZZfV\nKjYnTpwgLy9Pm+sVpE+fPmRnZ0f9TJRSHD58mO3bt2vbiHTu3Jlu3bppK25ceOGFoT0upRTFxcXs\n2bNH2+U5iIiQkZFBRkaGcQ0aznkaQrh+AvyfiOzGqnd1PhDZk70VotunsNNtWJef1aVLlzrvPXz4\nsPbe1NTUOssqlZaW8u6772qti9zc3FqtnKBbrmbU3uWXX86AAQNYvHgxVVVVJCQk8NRTT2lFa8+e\nPXz++edRrYmRI0fWmhxcWVlJXl5e1MRbsFy0OTk5UQMoKisr2bFjB/n5+VHdeB6Ph8GDB0cNeFBK\nUVRUxK5du6KGpge5/vrrUUrx4osvUlVVRVxcHHfddRf9+/fH5/Nx+PBh9u/frxW+cJKSkjjvvPMc\nV4w3GFoqDRFVeEBEhgPfwsoLWIBVabhV4/P5tPtMdUWw7d27V3tfbUVlwdqLeffddyMWXBFh/Pjx\ntVpLxcXFvPvuu9oFMjMzs9bE3srKSlatWhU1mGDEiBFcc801/O53v2P69OkRonXy5EnWr18ftXq5\niHDxxRdH7bDs8/nYtWsX27Zti1rTEayglmgFas+cOcPu3bvZs2dP1JBysKqVDB8+XFuY1uv1hizl\naNZe+Djh1TDuuusu5s+fz6233kpaWho7d+6ksLAwqosyiMfjISMjg169ejmqrWhwjs/ni7rPaWh6\nHAmXiDwBXAP0AvZhlXZ6Xil1GquZpOmEHGD37t0RQhAfH19rjTiv18uiRYsizrdv377Wlu+nT5/m\n6aefZv78+VxzzTXVgj+GDRtWa7Lzzp07WbJkiXbB7tKlC+PHj48qmPv372fVqlVRLYv09HSuvPJK\n4uLiIgIxKisr2bZtW635R7GxsVx22WVa92BlZSX5+fns2LGDPXv2sG7dOkaMGBGRjxUfH8+IESMi\n9hWVUpSWloZqKEazsIL07NmTIUOGVHO3+v1+SktLOXToEIWFhVGDUYKICH369KFfv34hq08pFbJG\nd+7cqW1CqSMlJYX+/fvbTpo2GM4lnFpc1wH/Ag4DA4D/AqaKyE1KKVMVPowNGzZEnMvKyoq6t6KU\nYvHixRw5ciRUIWPixIlkZGQwevToqPeVlZXx7LPP8vLLL+P1ennnnXeYNGkSvXr1CgUh6Dh16hQr\nV66MWoi1ffv2XHfdddpN/qKiItavXx81+AGgd+/ejB07NmJfLdhIcseOHbVaN+3atWPMmDHVWr/4\n/f7Q57N//368Xi9FRUWsXLkSn8/HypUrueyyy0LilZKSQk5ODu3aWRG+SimOHTsWqihSl2UE31hH\nwbJLVVVVHD16lCNHjlBcXFxroEQ4KSkpDBgwgOTkZCorKykpKaG0tJQdO3aEgjTWrl1LdnZ2rXUJ\nO3ToQGZmJh06dLD1ugbDuYhEC6m1dbNIJ2AWcC3wbaVU3cXzmhARWaeUGlHjtPs3bJOjR4+GGh3m\n5+fzr3/9i+uvv55HHnmEgQMHRlyvlGLFihWsWLGCvXv38tZbb+H1eomNjeXHP/4xTzzxRITVE9xX\neuWVV0LXB4mNjeXmm2/m4Ycfjqh0fvLkSfLy8njvvff46KOPGDduXEReWXJyMtdff321xfH06dPs\n2bOH7du3a4M/wjn//PPJzc3F4/GglGLVqlU8//zzjB071laNxN69e5Obm0ubNm04deoUxcXFHD58\nmEOHDlVzBwZFC6zcrmDwwpgxYxg/fjyZmZmcPHmSkpISSkpKHAlNTEwMffr0oXv37lRUVHDs2DGO\nHTtW575VOEop2rVrR3p6OvHx8ZSXl1NeXh6yxEtLSyPC4j0ej1a8OnXqRO/evenYsaMJvmhctB+u\niDT6ulEH6zVrWaulXsIVGkTk51jVMiYopRqli6aITAN+jpX9vRW4PywMP9o9DS5c99xzD2lpaRQW\nFjJnzjedBT755BPuvPNOXnzxxVDzvvz8fObNm4ff76dt27b85je/4f7776823rFjx/j444/Zvn17\nNdFq27YtnTp14sSJE7z++uuhKvAnTpxgx44dbN68mc2bN/OPf/xD6+aKjY3l9ddfJzc3l9LSUgoK\nCsjPz+fgwYPs378/VA0j6I7r0qULZWVlXHLJJUyYMAGPx0NJSQlFRUUUFhZSVFRUpzst2O4+JSWF\nY8eOhSpEfPDBB/h8vlDVjJpFY5VS+Hw+PB4PWVlZJCcnU1ZWxrFjx6JG0hUVFbF8+XI8Hg8pKSl0\n7tyZkpISjhw5glKKb3/723To0KHWOSul8Pv9+Hw+vF4vPp8Pn89Hhw4d6NChA1VVVXXe7/P5QmME\nD6/XS5s2bWjXrh3x8fFaodGJVpCgeHXp0oXU1FR69OhhAi+aDiNcLQBXwiUiiUD3GsfdQHelVIP7\nMETkFuA1rMocKwI/7wTOV0pFrXfTkML1yCOPcPDgwVAlgqA11b179wgrady4cQB8/PHHgGUNBO8Z\nNWoUw4cPp6qqioqKitC378OHD7Ns2bKQyHXp0iV0T2lpKVdeeSVdu3atZjF8+OGHtUacJSUlMWnS\npNDei1KKo0ePVqu1N2zYMPr16xd6rSNHjtC1a9fQ80qpUKRg8N/Bw+/3h474+Hjat28fWqSD89ZZ\nFAMHDqR9+/ahBV8pRVJSEsnJySErTXf4/f7Qz7y8PPx+PykpKdU+q6AbLy4ujtGjR2vnGy5YQUSE\n5ORk2rVrF3UONe+vKToej4fk5GTat29fZ0fn1atX1xpM0q5dO+677z7bBYQNDYYRrhaAI+ESkW1Y\nFk/w61/wl+wHioCCxvhwReRz4Aul1N1h53Zgdfp8uJb7Gky4br/9djIzM6t9e1ZKsWvXLubPn1/N\nVefxeELXBUUr/B5dE8b58+dz4sSJaqIVfs+JEye48cbqDUqDVocuKCAmJobRo0dXa59Sm2iFv9bh\nw4cdfcMPf79BysvL2bVrl7a6g4jQr1+/kFA56eobpKysDL/fr/2sjh49Gvoc6yIuLo42bdpEtYzq\nQkRITEykXbt2JCUl2Y7uKy0tJS8vT/v5eDwefvCDH9hKnTA0OC1SuETkYeAGYCBwBlgNPFxbwVwR\nuQL4D6xq8B2AncBzSqkXG3DejYLTGNrlwB+xLJ5JWL23egLxSqnujSRa8cBwoGa43SLg0oZ+PR33\n3HNPhGgF5ka/fv347ne/W+18UBhqilbwnrVr10a0GxkzZgxJSUkRC3Hwnnbt2kXkKKWmpjJ69OiI\nhV8nWkDISgG9aAVfq1u3brb2coKio1vw9+7dG7UkkVKKffv2ERcX51i0goV7gaifVZcuXWrdy4qL\niyMpKYlOnTqFrCMnohUXF0eHDh1IT0+nb9++pKenh0TYLj179mTs2LERQTdxcXFGtAxuuAKYjbUm\njgO8wGIRiczd+IZLsXJubwSygReAOSLyvcadav1xFFWolGqO5mIpQAxWJGM4h4Era14caIAWnGeD\nxAqnpaVFXdhERJsY27Zt21rvWbZsWbWyPt27d6dTp0613rNjx46IPLBu3boxevTokOUVTbTA6qQb\ntLhqK74qInTt2lXbSkREQkdtZGRkRLW4PB6P7ZYdIoLH4wkdQaGsLUhBREKBJSJCbGwssbGxxMXF\nERsb60ikYmJiQhZZQkICCQkJtuo9hs8lKSkpdCQnJ5OcnBwao2fPnsybNy+UgHzbbbcZ0TI4Ril1\nTfhjEZkMlAGXAe9Guee3NU69ICJjge8C/9sY82woGqJyxlmFUmoOMKfOCx1QWFhI9+7do1ZK0IWU\nnzlzhoSEBO09sbGxoWCLcLKystixY0fU1wlPwg2/Jihea9asIScnRytaIkJKSgojRoxg3bp15Ofn\nay2u4GsVFxeTnJxcTaTsLvhB4cjKymLnzp0Re1wDBgyIKAAbLohB12NQrHSvGxMTg9/vjzr/xMRE\nOnXqVKcVFBTDoLgFBS4uLo74+Pg6LcLgdcEjISGBNm3akJCQQGJiYp3WXN++fbnttttCkadGtAwN\nRDKWR01fPy067QF7yYTNSINEFTYmAVfhKeA2pdRbYednAdlKqcsdDunqDU+ePFm7H6Tb44qNjWXi\nxImUlpaycePGavcE6/4Frbiai1p+fj67d++OeJ1+/fqF3JW1LYThIlPz2uC/jx49ytq1a7ngggu0\n76m4uDjCDVdTvKIJTU2hKykpYdOmTfj9fjweD8OHDyclJaWaKAWtoJr36saPiYkJ3XvkyBEKCwsj\n5h/srhz+GjExMaEjXKCCFpxOMMOtvOB9NUXOhKafc5yte1x7gSNhj+cEvqRrEZE3gSxghFKq9sz4\nb+65DvgncJlSak19JtvYnPUWl1KqUkTWA1cBb4U9dRXw96aax6uvvso999wTsryCvapeffVVpk6d\nynXXXcepU6dITExkwYIFjB07FoA//elPPProo4DlPnzzzTe11lY4L774Is8880zodR588EGmTp3a\noO9nzZo1TJ8+nR49eoQsw2Dzx1mzZjXoa23evJlnnnmGBx98sNbyUW5YsWIFixcvDs3/yiuvjJp0\nbTC0YI7YjSEQkZnAKGCUA9G6DMs9eN/ZLlpAZIjz2XgAtwCVwF3AIKwAkRNAhovx6sXdd9+tHn30\nUXX33XdXO79kyRKVkZGhlixZEnHPp59+qgYPHqw+/fRT268zd+5clZ2drebOnVvfKdfJ448/rh59\n9FH1+OOPN/prNQbLly9Xjz/+uFq+fHlzT8XQ8om2BqlmPtZFm1uNeT4LHALOs3N94J5RwHGs3Nhm\nX+/tHGe9qzCIWAnI/4kVjr8F+A/lLtm5Zbxhg8HQHJytrsI687hE5I9YX/LHKqW22RlURMYA7wG/\nVErNrP80m4YWI1wNSKt7wwaDwTYtUrgCe/6TsdKUwtt+n1BKnQhc8ySQo5QaH3h8BZZozQaeCbvH\np5SK3ozuLMD0QjAYDIaWzzSsSMKPsVyFweP/hV2TDoTn00wBEgPXhN+ztvGnWz+MxWUwGAzf0CIt\nrtaGsbgMBoPB0KIwwmUwGAyGFoURLoPBYDC0KIxwGQwGg6FFcdZXzmgETI0eg8HglA+xCn43F0fq\nvqT10BqjCg0Gg8HQgjGuQoPBYDC0KIxwGQwGg6FFYYTLYDAYDC2K1hicUW9EZAsQ2R649ZBC694s\nNu//7Hz/R5RS1zb3JAyNjxEud5xuzeVXRGSdef/m/Tf3PAytF+MqNBgMBkOLwgiXwWAwGFoURrjc\nMae5J9DMmPffumnt79/QzJgEZIPBYDC0KIzFZTAYDIYWhREug8FgMLQojHAZDAaDoUVhhMsBIjJN\nRPJF5LSIrBeR0c09p+ZARB4WESUizzf3XJoCEYkRkd+E/e7zReQJETkn8yBFZIyI/J+IHAz8nqeE\nPRcnIr8XkS9E5KSIHBKR/xWR3s04ZUMrwwiXTUTkFuCPwG+BYcAq4P3W9h9WRHKBe4AAle2eAAAI\nqUlEQVQvmnsuTch/AfcC9wHnAf8OTAMebs5JNSLtgC1Y77OixnOJwEXAjMDP64FewAfnqpAbzj5M\nVKFNRORz4Aul1N1h53YAbyulztUFrBoi0gHYANwF/BLYopT6afPOqvERkQXAUaXUHWHnXga6KKWu\na76ZNT4icgL4qVLqb7Vccz6wFbhAKZXXVHMztF6MxWUDEYkHhgOLajy1CLi06WfUbMzBEupPmnsi\nTcwKYKyInAehhXocsLBZZ3X20D7ws7RZZ2FoNRjT3h4pQAxwuMb5w8CVTT+dpkdE7gb6Az9o7rk0\nA78HkoEvRcSH9f9mhlJqdvNOq/kJfKl7BnhXKXWguedjaB0Y4TLUiYgMxNrbG6WUqmru+TQDtwC3\nA9/DcokNBf4oIvlKqbnNOrNmJLCn9RrQEfhOM0/H0IowwmWPI4AP6FbjfDegsOmn0+RcgmV1bhWR\n4LkYYIyI/BhIUkqdaa7JNQFPAU8rpeYHHueJSAZWcEarFK6AaM0DhgBXKKWONvOUDK0Is8dlA6VU\nJbAeuKrGU1dhRRee67yDtUANDTvWAfMD/65svqk1CYlYX1zC8dFK//+ISBzwBnABMFYp1Rq+vBnO\nIozFZZ+ZwKsisgZYCfwY6A78uVln1QQopY4Bx8LPichJoEQptaV5ZtWkvAs8JCL5WK7CYcADwCvN\nOqtGQkTaYe1ngiXOvUVkKFACFABvASOBbwNKRNIC15YppWqGzxsMDY4Jh3eAiEwD/hNIx8pz+Q+l\n1LLmnVXzICJLaT3h8MnAb4B/A1KBQ1jW5q+VUudcJ2wRuQLQRY6+DDwO5Ee59c7awuYNhobCCJfB\nYDAYWhSt0kdvMBgMhpaLES6DwWAwtCiMcBkMBoOhRWGEy2AwGAwtCiNcBoPBYGhRGOEyGAwGQ4vC\nCJfBYDAYWhRGuAwGg8HQojDCZWgViMhjgVb0fhH5W3PPpyZisUlEwptVPi4iR6Jc/zcRWedg/OdF\npFUWBDace5hahYZzHhEZAfwKeARYChQ164T03Ax0Bv63kcZ/GvhKRJ5USu1spNcwGJoEY3EZWgPn\nBX7OUkp9ppTapbtIRGICjRGbg/uAVxur35lSag9WJ+efNMb4BkNTYoTLAICI/EVEvqpxbrOI/LYe\nY44WkU9F5JSIHA28RnLguY4ickBEXqlxz/+JyHYRSQw7N0ZEPhGREyJSJiJLRWSYzTn8DXg18LBM\nRFSgiGzI3SYik0RkK3AauLiuuYeNPU1E9ovISRF5V0SuCh/fwefUH7gUeNvJfWH39wm8ru4In8vf\nge+LiPl/b2jRmD9gQ5BDWG1awvkXcI2bwUTkMmAxVqPNG4H7gQnASxBqlfJDYLKIXB+4505gInCH\nUupU4NwVwMdAFXAHVjfi5UAPm1P5DfBE4N/jsJpibgh7vg/wB+BJ4FtAfl1zD8zremAWsAC4AcgD\nXrQ5p5qMB04Cm3VPikhszQOQsEsOBd5X+PE2lhDvD7tuFVbz0yEu52kwnBWYPS5DkAIgWUTaKaVO\nBM4dBnq5HO93wCql1C3BEyJyEPhYRLKVUluUUh+KyBxgjojsA57F6jT8Wdg4T2It6Neob1oZfGB3\nEkqpXSISdA2uDXtvQboAVyqlNoXNc15dcwemAx8opYKutw9FpCtwl925hTEc2KaU8mue64Il2jrW\nAwS6T68Om+t1wHex2oyEu0W3YjXAzCGKSBoMLQFjcRmCFAR+hltdA4B9TgcKuPkuAd6sYSWswFqE\nh4dd/iCWtfEZcAB4LGycJCzX3cuq8frvHKwhWnXOPfD4IiyLNJx/uJxDGqCNHgTKsJo21jwW6C4W\nkQHAa8ALSqmXw59TSnmxGoKm6e41GFoKRrgMQaoJl4h0Ar6P1e3WKZ2AGGA21mIfPM4AcYRZcQEL\naAHQBpgbsB7CxxEsV1hjcbjGYztzTwlcUzM60W20YkJgfB1epdS6mgdwtOaFgT24d7Asq/ujjHcm\n8HoGQ4vFuAoNQYLiELS4ZmFZAf8NEGjPPhPIBNoDjyql/h5lrGOAwuqWu1DzfFAkEZGRWJFuG4Ff\niMg8pVRh4OlSwI/VcbqxqGnJ2Zn7ESyXW2qN52o+tksJ9bSCRESwOhR3AsbXEp3YMfB6BkOLxQiX\nIUghlkh0F5GfYwUjjFFKnRKRGCz308+VUhtFJBUrwEErXEqpkyKyGhiolPp1tBcUkQSsxfZDrDym\nzcAc4Dth43wO3C4izzeiu9DN3DcC1wN/Djt9g8uX/RrLPVkffgFcB4xTSmkt1MAeXCKwvZ6vZTA0\nK0a4DAAopXwiUoQVXNAbuFYp9UXg6QnAhcBL1hd7AE7VMeR/YgUz+LEi3MoD404EpiultmNF+6Vh\nWQinRGQKsExEpiil/hYY5yGsCL/3A4EcJ7EW+XVKqQUicjtWNF8/pdTeen0Izub+W+AfIvIC8E/g\ncuDamgPZnN9K4DER6aqUKnY6WREZhZVg/RLgFZHcsKe/VEodD/x7BJY1ucrpaxgMZxNmj8sQTgHQ\nE5iolFoWdv4C4A9KqaFhx4DaBlJKrQDGAF2x8qjexRKE/cDhQMj5fwA/DVoISqmVWO7I50SkZ+Dc\nMuAqLEvhNeANLJE4EHgpD9Z+U3h4eL2oa+6Ba/4J/Az4Nta+0jCs8P6a2JnfUiz3XYTw2aR/YPyp\nWEEu4cdFYdddC3yqlIrYHzMYWhLSBN4XQwsnkF/1AywrrEpE0gG/UqpmYEOrRkSysfK5xiqlljq8\n949Af6XUxEaaWwywF3hIKfVaY7yGwdBUGIvLYIfXsSycbSKyCcvyMTQsTwFjA+HsjcFNQAUwv5HG\nNxiaDLPHZagTpVQlVtUKQyOhlDogIlOxIigbI3hCgB8GcrkMhhaNcRUaDAaDoUVhXIUGg8FgaFEY\n4TIYDAZDi8IIl8FgMBhaFEa4DAaDwdCiMMJlMBgMhhaFES6DwWAwtCiMcBkMBoOhRfH/ASm9S++h\nnqo/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc4d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now displaying the numerical values and the analytical function\n",
    "from modeling_mesoscopic_dynamics.transfer_functions.plots import make_exc_inh_fig\n",
    "make_exc_inh_fig('data/FS-cell_CONFIG1.npy', P=P_FS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "synaptic network parameters --NOT-- in SI units\n",
      "[[ {'gei': 0.2, 'Tsyn': 5.0, 'Ntot': 10000, 'p_conn': 0.05, 'Erev': 0.0, 'ext_drive': 4.0, 'afferent_exc_fraction': 1.0, 'name': 'ee', 'Q': 1.0}\n",
      "  {'Tsyn': 5.0, 'name': 'ei', 'Q': 1.0, 'p_conn': 0.05, 'Erev': 0.0}]\n",
      " [{'Tsyn': 5.0, 'name': 'ie', 'Q': 5.0, 'p_conn': 0.05, 'Erev': -80.0}\n",
      "  {'Tsyn': 5.0, 'name': 'ii', 'Q': 5.0, 'p_conn': 0.05, 'Erev': -80.0}]]\n"
     ]
    }
   ],
   "source": [
    "# Note that we also used the network parameters because it requires the synaptic properties (number of synapses)\n",
    "# they are stored in the following module\n",
    "from modeling_mesoscopic_dynamics.synapses_and_connectivity.syn_and_connec_library import get_connectivity_and_synapses_matrix\n",
    "print(get_connectivity_and_synapses_matrix('CONFIG1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### Network dynamics: numerical simulations vs mean-field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Stationary dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "performing the numerical simulation with the Brian2 package !\n",
    "see http://brian2.readthedocs.io/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "synaptic network parameters --NOT-- in SI units\n",
      "cell parameters --NOT-- in SI units\n",
      "\n",
      "        dV/dt = (10.000000*nS*(-65.000000*mV - V) + 10.000000*nS*2.000000*mV*exp(-(-50.000000*mV-V)/(2.000000*mV)) + I - w_adapt)/(200.000000*pF) : volt (unless refractory) \n",
      "        dw_adapt/dt = ( -4.000000*nS*( -65.000000*mV - V) - w_adapt )/(500.000000*ms) : amp  \n",
      "        I = I0 +Gee*(0.000000*mV - V)+Gie*(-80.000000*mV - V) : amp\n",
      "        dGee/dt = -Gee*(1./(5.000000*ms)) : siemens\n",
      "        dGie/dt = -Gie*(1./(5.000000*ms)) : siemens\n",
      "        I0 : amp \n",
      "cell parameters --NOT-- in SI units\n",
      "\n",
      "        dV/dt = (10.000000*nS*(-65.000000*mV - V) + 10.000000*nS*0.500000*mV*exp(-(-50.000000*mV-V)/(0.500000*mV)) + I - w_adapt)/(200.000000*pF) : volt (unless refractory) \n",
      "        dw_adapt/dt = ( -0.000000*nS*( -65.000000*mV - V) - w_adapt )/(1000000000.000000*ms) : amp  \n",
      "        I = I0 +Gei*(0.000000*mV - V)+Gii*(-80.000000*mV - V) : amp\n",
      "        dGei/dt = -Gei*(1./(5.000000*ms)) : siemens\n",
      "        dGii/dt = -Gii*(1./(5.000000*ms)) : siemens\n",
      "        I0 : amp \n"
     ]
    }
   ],
   "source": [
    "%run modeling_mesoscopic_dynamics/network_simulations/ntwk_sim_demo.py --CONFIG RS-cell--FS-cell--CONFIG1 -f data/config1.npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lxTcFbH2qFX17+zB+fBwrLlqB5upmJOIJjI6NomtnV04eRsdGkRnPIF2Xzomz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\nTgpEZBYhQoSTAhGZRYgQ4aTA/wcBG0UlD/mEtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11da72da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GLtvpLBlp/+mRT7X4rLYebTUN6Xj6cWoMNeSV5VFWVUZUdhTOds6Euofy2pjX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\nP4tbX2DIfUfDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3d0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ArHvnHXQrKcHsqCggJATDY2Lwqdtx8VC3bkgF0BvgSpqQoBqBVCfkMPj+/Xwe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ddef3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eCAgIwIcffoigoCB0794dqcWmL/Xy8gIAPHokJ8bLVBFClOmyFV0sEgBOnz6N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\nAHNOz8H4J+Ph2tQViZmJcLFywdHxR0t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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e38d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing the ouput of the simulations\n",
    "from modeling_mesoscopic_dynamics.network_simulations.plot_single_sim import plot_ntwk_sim_output\n",
    "DATA = np.load('data/config1.npy')\n",
    "AX, FIG = plot_ntwk_sim_output(*DATA,\\\n",
    "                               zoom_conditions=[0,1000], bar_ms=100,\\\n",
    "                               raster_number=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell parameters in SI units\n",
      "synaptic network parameters in SI units\n",
      "starting fixed point\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "first order prediction:  [ 2.45719675  9.64063894]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "Make sure that those two values are similar !!\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "first order prediction:  0.252840089744\n",
      "end fixed point\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e09dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b5a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2y5RX3oHKG134i8Blqnpnyvr0P64d+A+s2TwJ/D/ckPZNuFnoCgqAqtooIs/j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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111bc49e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now comparing the simulation with the mean-field\n",
    "from modeling_mesoscopic_dynamics.network_simulations.compare_with_mean_field import plot_ntwk_sim_output\n",
    "FIG = plot_ntwk_sim_output(*DATA, min_time=500) # we discard the initial 500ms to evaluate the dynamics\n",
    "# the fixed point of the mean field is found by launching a trajectory\n",
    "# of the mean-field dynamical system and taking the point of convergence\n",
    "# see modeling_mesoscopic_dynamics/mean_field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Time-varying dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "synaptic network parameters --NOT-- in SI units\n",
      "synaptic network parameters --NOT-- in SI units\n",
      "cell parameters --NOT-- in SI units\n",
      "\n",
      "        dV/dt = (10.000000*nS*(-65.000000*mV - V) + 10.000000*nS*2.000000*mV*exp(-(-50.000000*mV-V)/(2.000000*mV)) + I - w_adapt)/(200.000000*pF) : volt (unless refractory) \n",
      "        dw_adapt/dt = ( -4.000000*nS*( -65.000000*mV - V) - w_adapt )/(500.000000*ms) : amp  \n",
      "        I = I0 +Gee*(0.000000*mV - V)+Gie*(-80.000000*mV - V) : amp\n",
      "        dGee/dt = -Gee*(1./(5.000000*ms)) : siemens\n",
      "        dGie/dt = -Gie*(1./(5.000000*ms)) : siemens\n",
      "        I0 : amp \n",
      "cell parameters --NOT-- in SI units\n",
      "\n",
      "        dV/dt = (10.000000*nS*(-65.000000*mV - V) + 10.000000*nS*0.500000*mV*exp(-(-50.000000*mV-V)/(0.500000*mV)) + I - w_adapt)/(200.000000*pF) : volt (unless refractory) \n",
      "        dw_adapt/dt = ( -0.000000*nS*( -65.000000*mV - V) - w_adapt )/(1000000000.000000*ms) : amp  \n",
      "        I = I0 +Gei*(0.000000*mV - V)+Gii*(-80.000000*mV - V) : amp\n",
      "        dGei/dt = -Gei*(1./(5.000000*ms)) : siemens\n",
      "        dGii/dt = -Gii*(1./(5.000000*ms)) : siemens\n",
      "        I0 : amp \n"
     ]
    }
   ],
   "source": [
    "# running simulation\n",
    "%run modeling_mesoscopic_dynamics/network_simulations/waveform_input.py --CONFIG RS-cell--FS-cell--CONFIG1 -f data/waveform.npy --sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "synaptic network parameters in SI units\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "first order prediction:  [ 2.45719675  9.64063894]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "Make sure that those two values are similar !!\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "[ 2.45947019  9.66354488  0.09778797  0.11800296  0.25284009]\n",
      "first order prediction:  0.252840089744\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "synaptic network parameters in SI units\n"
     ]
    },
    {
     "data": {
      "image/png": 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jjz+mr33tazR58mSaNWsW9fb2EhHR2nnz6GZJ85s4ejR9tGkT0ciRtJYxutk0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f632748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PFZ644cy97Izsi5mSrxsWwqjz15HvS52lGEgPoNxdDkCXKrCZbMgq2TOeJ5FNnDW+D+jP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\nKGtoaGh0IDRR1tDQ0OhA/D/6l9n7B/cSiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108b65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ImVVF3/8+yvjyl5NHeqYJQriDQTQaHPvN/q9B0DsmSzvFxYm75b79NiSpTZvw\nfdo0EDaf19yMwcU+rTyoeEmfn4/oISbR/HwM1MJCkOb27XBFam/HNRMmKKOUThRhlo82dHejXrZk\n3eyBwNInS52sK2aJJx7Hc0ajIDb21c3KwoDr6sI5fqGdGzYkhpLedhueMR4HaT35JMrs6QFBNDYq\nyUonQ5ayTePTj39sv3dPj1d65TYw8ypwcEhzM5b2jY1ot/p6TMK1tYlqgNmz/dvdhljMu3JgiTU7\nG++Hf+MVRn4+7l1e7r1u+3b0PzYM2jKcsQqrpEQZERmlpeijfX2YIDhJkdn3V6/Gb+wdov9eUIDv\nvMqsrcW4uPJKSOWc4IivGzUKeuOmJtW/pk+HdJ4uX+IQEMIdDHhQ+MEvCIBTMRIlLnfy8xNDNpmE\n+Dz9egZLUBy5xlICX9fYCGLNzVWkN28eQjdvuQUO/nfeSXTttVhqEdn1bgUFGHhBfpM2xGJewmXS\nY3LlJSiDVQfRKKS7TZsgJfOEY0IP5GBJKT9fGaKI1KcuQelLysZGEDnj0UeVVJeVhba77TYV6LBx\noyJIIu8WR2bbhGmraBSkvGwZjKxf+AImyGgUhJOVhTbs6VHqCdbH9vR4iTgIHC5tA79XVm81N+P7\nnDl43kMPVecm22GEKDF4xgbHQXsWFtr9eVn/zioKHXV1CAWvq0PbcTtPm6ZcEDk5Eqfd1AOEbJ4M\nGYAQ7mDQ1qY6fkcH/jeJwzbQ9Ixh7e2QTPk8m0HruecgtZ1yCr6zsUAH6wiZvLi8nh5IFitXEl10\nkZJientBuK+/rnwZ6+sxyII2mYxEwvtq6qTMybiZAPV24d0RolEs/SorMVBZCtR15CUlaD+Wqqqr\nMXAaG5GPYvJkLOmvuorom9+EpX/LFtz3uOOwjM3Lw/doFCG+TU0guEmToP97/XUM0tJSnNfaijpG\no16Heg5MePJJpQfdtAlkVluLtk8Whq2jo0Ppc885B++yoAD1mzYNpKITruMoyXnsWCzv99sv+X14\nMjLBRMv1LS8HAUYi6KNswGS0tSUPba+v9ydlU0Xj1+84BWdzc2JIcnc36rZqFSTV/Hx8z8rC++vu\nxn3Y95snRNfFimY4dikOASHcwYCTNXd3Q/fT3Q1fymuvDb6O3bKys0G8xx2HgczLLzNGPjcXOkL2\nteScpTq6ulQWLn0w6UlVCgtxTzZQzJiBYAsO0123DoSlS+0mWfT1hdv1l0gRBktf+fmoZ2MjLOgH\nHgiyeusDVWb1AAAgAElEQVQtuytcQQGuW7AAJLxhA/SYo0eraLJYDGX292PQ8bYwFRWQDuNxtGlf\nn5KQeInb0IDByR4PkyYpQ0pHB9FHPwqJ7oknVNDBkUfCZaqhAe3Q3g7Ju7sb/7NPKOPqq/G+WD3A\nFvVt2xLJysxkxf+zRF1b6920MjtbSWgHHOC14idLqGQDX8OTY1kZjE+cp9hccusrOM6CZxIi5y3W\nd2221Y/zN/ghGsV70q/bvBmT3h//iIlu/ny8w4MPxu8nnIB2PvpoXF9bq+r71lveVVaGIYQ7GDDh\nRiIqocbWrSp81e9FFhVh0I0ahTyoBxygfHlZAmX85z/orNOno8NkZaFzHXSQt8xoFFIOEy/fm6Wh\nAw7A95wcEAQnzC4ogBX86adR96Ki4I6vbzFt08WakmturiLc1lbcIxbDHxv0GCUlSic3dizOPeEE\ntPHmzSg7GsWgYhXKxRfDX7SzExIMG8qKipS3R26uV9JkwxKrLTgZDe/0y6sUXc/Kkv0hh0Cqzs+H\nf2xeHtr2kUdQzvz5+J1zOvA9WlqQdIbx5pv4zM+HF8G4cSBxTjJUWKjc2Lq6lE55/nxFfOyXTKS2\nhWfwxLtqlbc/BUl0/LxvvIG8HyUlMELtvz+MsGYWrs5OtaoaMwb154RJjMZGtJsZlsuJ6RmsUvBD\nby/K1suor0fZRUV4d5wjQif99euxY8mWLSDngw7C+6ysxOSt6+gzCCHcwaCzEwPu05+G9fXhhxHK\nOXMm0be+5U+4hYUg3KKiRElVJ6zXXyf6+99hFOHk4TNnwime9awMVinEYl7PAVYfcBgrS8d6Jqv5\n87Hx4o4dGPhhE5SbiMVUmW1t6OBbt+LTJsGOGYMlcGen0hk2NSlDo+N4ddoLFmBZGYmoZDCRCM5p\nasJKIR5XQQY8INlYxbrwSEQZyqJR1JUNR6yqMXWFtqQpLO07jhroRx2F98W+rF/7Gnbh7eiAHrSu\nDj6x8TgIKhbDeevXJ7ZPYaFKNtTfjzaMRlV7lJUpks3O9rrc8aT/1FNeQ5Xf9jrcJ9gOcOSRaIOP\nfxzvx5by0HXhXdDYiP5pI1w2grW1eYMftmzxqqb0LYr8YBpsObAjKwttvno13AQZ772HepeWor88\n9hj6UGsr6rTffhhjbW0ZVy0I4Q4GLOFmZcGqz5JHbq5KkmFDRQUGXVmZ16hmEnQshg7c0YFzN28G\nmZaWJuYC5QHGZNPXh/o0NqIzV1SoiKveXpXsOy8PZY0aBX9GjtIhsvsu6v/rblVVVYi6++ADSOVm\n/RwHz9Dfj84/cyYihGbMgJ61sdHeVuyov3MnCOtjHwMp/PvfiRMDG426u0HeL76IiYmfR/e3ZcKN\nxVTmMSYtMxE2HzMNUuZmk/qqQt89Ij8f348/HseKi6E7zskh+sc/ICHv3AkJuLMTf7xjcVcXJhPG\n4sUokw2Xc+aASNg1bPt2tekmEVQmr70GoSASwfPqhiNGV5dSaR1+uHrWggIQFE9a9fWYKNnXlveh\nGz8efVpfefX3q00fi4u9kuRtt8EHmNvIdZMb/Myw92gU5Z5zDoyb5eXeMk47Tb0fNobm5qKd8vLU\nKuidd7Ai+epXg++fRgjhDgZMuByS+9RT0IO2tOAlmst+xsyZ6CDHHedPbnwsEkEH4Wgmx4HEYi7R\nmGyYcHt7lQGqpQWDmg0TXN+aGuiGH34Y+kQmIXbR0tHfj3t2d+PzP/8Byb7+up0s2Z1s7lyUt3Mn\nJGkiZQAhUhZkvzBb9vtl/0jeYcP0dDjgALXPGrfBPvsoyzT7S/PAZtXI6tXQ+S1fnqhS0J/dlAod\nxxuSrE9ANl2mbfKKRNBG+++Pv/Z2dV55OaRejorr7EQ92trwx21uJjJavhyfrOYqKgIp/eUvWL1w\ncEZ1Nd4l7wDS2anaSnd1LCpSainXhW9rYSHeJScPYrcuU2D4xS/Qr1hnzhNHSws2NNW3YfdbDfb1\nQXjhOpkrwvx8kGZPD+6lu97NnKkCiw4/XJVRW4v3U1SEfpMs8f8wQAh3MOCoIpYu2Z/vmmuQJf/i\ni+3XTZwISSQnx9u5zU73wQeqA02bhk7185+j3Joau661p0dZ1zduVM75HCbLUV9cPkueFRVqc8Z/\n/xvnbd2KZ3zhBZCBXj/dAT87G5191ChIOsccA+n+r3+FnnPzZq/EG4+rqDPeGlz3jeW24KW6LQ5f\ndwNzHDzzpk3e/cXYiT4SUeoCPaAiOxuTxje/Cad6Dh0m8hKu3y6+uhuWvn+ZjXAZv/yl2klZh/nu\nCwpQxpgxqk0WLkS/qarCRPnUUzBoNTZiAlu3DvWur1fRhazHNhOE/+Y3+LzqKtXORGpC/t3vUIdo\nFMt/3j+uvBx9vLcXaofWVvSj9euVGonVMyUlOP/VV72qo61bQYZ6O/oZ8jh3BuuCzb6Qm6u2qTrs\nMG/uCM77zEn32cDY1IR2KirCWHz6aaJPftJ+/2GCEG4KcIzO4S5eDB1WURH0RvE4Iriuusp74XXX\nwQh02mlKmmGXmRdfhDU8GkUHWb4cUiH7lP7wh8oN7bHHVDDASy/hfj09kDqjUTh8EynCefpp/Olg\nwnziCXzqEUBmHlY8tEo0U1iIIIxoFCQwdiw6/Ntvg3RYcmFiNcuJx5XkFI0qQtRd4zgZiqmbZOiE\nyKqS5mbcr7tb6T0Z7IepG/0iEbV7BB/TCZcj0mzPwedmZytdNYc0s0VdBxPq9u0qQ5YOVu+wWop9\niPk+PDEzcYwbh0nyuONUGU88gdVPdTU8OubPx8SzciWu37QJE2J9PfpRfb1aFTEZ8efq1d76meHm\nzc3Q+zN+/Wt8moZQTpb/n//g89VX8ckqCZ4A43Gir38d73LZMqXWYaOj66KPRiJYPTqOUoO88Qba\n6IYbEiMCOztxbxY0VqzA2Ovvxzvr68P5d90FQcHUQQ8ThHCHgg0boJPLzcUAfughuKOYg+6997CE\nYd1hf7/S4XZ0eFPsMeGwEYeX4GZQhO6qwwPUFjETiag4eNbpsq6TQzw5hHXmTKWWYFItLcVAfv55\nnHPUUZCodD11V5e3w7KXAkN3qs/NxbOxxMISpOuC0HfuVFt525abuoWbByQvl2trlYscG0R6elRi\ncJ6suFz21eT21lUKsRjaxxbyyTrctjZI8Dx58rZA+nMz5s61O9tzblh+d7zdEJFXei4sDM7Mdddd\nSK3puijvwAOV98a770IwaG/HXm/33AOS6+8HUT78MCbLceOgZuGt3h96COewJ86sWWjLkhJMIJyJ\njXW9BQVoR87UxuTp5y3B/3Nf8AuV5/bwC1bQdd1m29r+15NONTYG78uWZgjhDgVNTeikfX3QTx17\nLFytvv5173nsq5iVhW1JduxA5i8ifB52GJZn27fDmPTUU7AS/+tfGAjd3URnnAFCef55SCtnnYXO\n/OyzuOfPfoYtWEaPRoRWLAZf0KwsGLVycyE9L1oEH9gjj4TEzNvW5OZiUmAPik2blPWYc+H6gT0l\nKitVikWTcFma4ZR9HF3Fhpy+PhXhxCTz6quQCvv70dacBKWqCmW2tqItLrkEv02bhqX28uVwv/r4\nx1VC8G3bQJ6sq/7Qh3CveBxEtH076uS6ShJcvx7XmJI263B5RweePM0NPtkXmAhLbNvmh5xWsrER\nddXz97KOlPuQHyHxuyksxP8tLd4E33pWLV0K5El4+nRkB3v3Xeg8Ga2tUA3dey/e2WWXQW32rW9h\n5XT44eiTy5ah3XfuxFZQa9dCxz9nDlZ2DzwAw9QTT8DQ9cwz6OfLlqFO55yD8s48U020b7yBsrdt\nwxj74AMc/8530OeLiqA6mzQJ9zHDgp97Ds+6YAHuc+qpuK6+Hu5iPT0wVs6Zg8koQxDCHQp4aZmV\nBUl1wgSVyk7fzry4GCQxbhw6jb4temmpN/NYNIrvEydi+d7ejkHHCWBGj8ZA5q258/OVTpf9a6+8\nErlxeSDyUpv1XK6LgZ6bq3SqvMRko4uOri570AWDiWblSi/h6slpsrPxyYnDmfjYqOU4GDRr1mCQ\nNDSg3iwNT54MCby5megb38DzL1ig/GnffRf65MMPRzvNno3zs7LQ7ps2YRne0YHf778fxPTgg1hS\nnnEGzonHMXEy6bPL3datICD2Hmhuxp/rKsJlaZWfW1cJFBTY80mwHYBz1bI3SW6u2qI9Gdgtr6AA\n7d/QoFYOBx+M42vXglxMtLTgfc+c6Y2m473aiouVa1tWFtHnP6+2qy8pwaTkOBASZszAvbnf82qC\nJV32+IjH0beffhr1LShQQSKM2loYn3fuxH0mTsR5paV4f7Nmgdx501Ezd8n06XifEybgGXgrq7w8\ntTpbvRrjMGxATxogCcjTAV0nxdFkY8aoePziYmzv/fnPq8gbM1PX2rXoUNu343zeSVU/jzslO8/r\nmah0acqEnnyZ0dSEjsxEyNFLtm3YeYtqP7C+k5eSrPvUVQmsk+UlM+tJOY0fJ6jhvcjmz0dqwTPO\ngJT06U/DvWrMGAyS3FxILbqvaUeHkoIXL1Zt1t2tlsaMeFxJqixZ5udjQM+YAdXKYYeBpE86Cbkn\njj4av82ZAzKIRJTkWl2tUiNyykvWMfOE9sEHKs0mg0ma/aN5wuJoNv1d+BmYFi6EJJqdjet0SXrm\nTLQb65fNMjg03URXl/J0aG8nuvxyHN9nH7XlDZMxEZIIHXAASJulTXYJM9VDyTLCEcHQO3kynofT\njdbUQJhYvly9S78k55Mnq3DnsWMxMbz77ogEO+gQwk03Jk9GZ/nUp7B0J0LH239/oi9+ER1f1yfx\nAPj3vzFA6+qUnyC7hPGA4AHFuixTovJzsbLtRsBL95wcJW3yUt+c8ZublUTuN0i2bVOGEZZw+/oU\n0WZn41rO7coO/byNTEUFlrW9vfC5PfNMhNhWVHhJwo90HAdLzBdewPKZHeGZcNkljkiFGnPYL5GS\n3jmT1A9/CHUL349XEiUlqNPChQj//eIXkebv8svxjo86SsXvt7SAkHg5nJ+vwnC5HXn3Xt7xggk6\nEkmc/Pzant36cnLw7th1jM8vLPTuVKGX09ZmN+ZxgElODsicwd45vDrhSTUnB2qF995TdR49WuUH\nMXcv5nfmB86jcMghRBdeiHaeMQMTbGmpepfsUWFi1iy8CyK8l1//GhOBafvIcHivEO5QYHtZnEh8\n1iwVtMA+oLNmYWBUVhJ94hPqmvffxycvnyMRJeHW1KgBwTvVcjlMuLrxhygxNt/Ms0CE8nn3W84x\nq0u4+rO1tnqNQTbEYnCz4vsz4TKhcy6H9nblPzlpEkjrj3+EHvbQQ2HUYenYfA5be+tGsLffhkva\nl76k/KRZco/HlUqloACTiE64uq8uEfxF/Zbz+saY7Nc5ZQqWtaeeikn3hz+E4WrhQqgsDjwQ5zQ2\nQhpubcUk1dCg/IDz89UENW0a+k6y5W5PD0i8vV1F333rW/Y2srmsdXSgP5nQCdJcrjc2qvSf3Max\nGAhx331hhMvJQf/nxOO1tV4BwswBrIPfZ1ubWs2Ul0M19JnPeL18WOUThNJSjLcJE1QQCj+XvtVV\nBiCEm26UlSkJ49hj4RLW3q70Uzk5MCjoSv4VKzAbd3crSYsThFx6KToxX8u+oay7LCpC59WNWm1t\n3sHFBKGjvR0kyhIgk6y+LQ+jqyt4yyAirzTGu+n29KDMtjYVinryyVjmHnUUpNDycq/0feyx3nrr\nz8ESsx8cB/fm3LQcRdbTA8LlQVpaCp25Tris5tADGrgMM5sW18ucEPTzGdOn4/kuvBDPPXEidNCH\nHAJ1SUkJymtpAQlzYEJpKe6RrN3feAO+taefDvLs7wfZb9zorYefyonzIZvQCZeDUBj77aeCezhR\nEadEPPtsGKzy80Gw7e0gSnYd5I1N2aPEBo6Ks6myqqpg+NVD1sOkWlywIHH7qnHj7LusDCOEcNON\nyZMhmZSUYFadMUMt01m319XlzZ/KiVA6OnCOLh2WlioSYEmId7Vlwu3s9A58MxepqTNmX8ayMpTV\n3q7CI9kSrp/f2+s/OBiclL2nB+SxYQPKLSsD0Zx/PtrirLOUkUyHTXo1k3f7RXLpKpXGRkVSvNQ1\nJVxW2bAOl13L9OTnNh9cluZsk5LtWTgdIod78zOPGYP2Pv54SL7f/jaW45/5DMhk9GhIdpz1qroa\nhqr2dqgqWOe9cyeS51x3nfJEiERAvm+/7a2TGY7M9eN96EzoWwbxfnuM445T/ZfzPrS3g+hZf5yd\njet4B+Fnn8VzbdyItmhq8l9BtLUp/2o9BN5xEDk3bZpqyyC1hA7bKq+sLHib92GAeCmkC/ziJ02C\ntZ4ltQ8+wGdpqdod9cgjvQTD7lKtrUpisHkE8D14CceE29jolXBbWoJ3pOBUkExAZWWod0+PChHV\n4ZfRqa9P6WF7emCc4D3Hzj8fRDFuHOrK+RRs9TIHDZOoKeHquQqCoE8+nIVNl3ALC9VOGX4Srp5/\nguvDErY5gfk9C5ehe6xs3py4KSKfO3s2yKSsDHrs9nboLltaUN9t2/B7PI7/778fqo/eXpz77rtQ\ny3AQhI7iYtyX9ZpEUG3pkXtE6tnYaEakCNe2iwmrfzZuVEEF116LurBHAr+zUaPQv049Fbpev2RJ\nLDA89pjan4wI6plzzglPsjo4S5k+7nhCyCBEwh0KTF9TPnbPPSpGu7ISOq2CAiynDjmE6M9/TrzO\ncZQqgsMsbWApi5OcFBRgsOnSS2url5jMDsrRTByKOXs2pC5OtWeCs2txPoXWViztGhowCGfPhoR+\n/fWYTLKz1S4P7AyvW+vDIoyEy2hpgT/xsmXe434SbkuLV49oSrgs/epSGG/hHvRudOheGXyf2lqQ\njflO2HWPVRfNzSDewkJMhvvvjxXCRRdBwrz+eqgpfvADfJ5yilqmmyqKqiq171djo7IVNDUl+q8y\n9DSKZgpIHVlZINulS6EuqqxUxkUi3JOX/OzrHYmoFI/sc8ybnxJ5k+bYJuPBwEwLyfXx820eJoiE\nOxT4LbNzc5W70vLlapfX2loERehW4dZWtQRualIuQUHg7UPYvYb3aWJ0dATnauDE3Bz11t2Ngcjh\nr+xzWl2Na3t61BLx6KMhrV1zDb47jtdNiJfbvNtvcTGWukyUPGBM3adeRz7HlHB5A0cdLH3W1iLQ\ngXfHYLCEq7uFcZpMvZ1sEi4/E0cy2ZLZBIFVCvqzXXqpXcJlvS1PhDt3JiZW4eARfq6iIm9ugn/8\nA3U99lgYInt7ISF3dGDnkKlTcT63Cb9flobb2tT/vBsv68BratBHOBybPzmxDudYuPtuqBa+9jWU\nk5WFsmbNwoTIbcFeIzt24HtVFcbG3Xfj2XNzoV5JF3RjnQ6/RFPDBCHcocCWRMZ10eE5eqWnR6Wj\nq631GoWIVCx+XR062kUXKV9ec1lIhOPRKCQCPocNQPpgIUInJlLk2dKiEqV3dyu9VmEhBkRpKZZt\nJSWQmr/6Vfz/8ssYQCtXwmCi60nZx5elk/p6pdPr7lY+pvoEwElFGKZPMn+axr4glUJRUeLykEN1\n2eOB78mJcXTC1SVcfVPL4mIV4MASrk0q0h38GSzZ6/WZNw/vzrbrMbvmFRdDB87J4/VnZD9Sm8R2\n9tkwoFVUQE9aXa3yPJSUQEo+8UREfl19NUg0Px/RW+yh8rnPod0bGzGB8UqqshJ9pKcH53KoNPs4\nn3QSztu5U21EWlWFFdDWrfBpfustPDd7afA2QmvX4rnnzlUrJ97Sftu25K5bfmNFR1UV3jtPJAze\nsihDEMIdCmwSrpnoubMTEmFzsz1X7ubNCIiYMQOd69pr0QE6O7GZoA7HQefq7ETn3LED/p9NTfjt\nK1/BJ7uWXXWVkkK++1103P/7P/jLvv66ch9qbMSyVEd5uX9eXx3svlZbi++XX46y29tBaBway8TD\n/pWlpV6C1FUrDFOFEGQ0u/NOJDExoRO4KZ2ycUZPVsOkx7pa/R2zDtdGuLo0bos042i94mI8P2/q\naapYenqgSnjnHeg6dXAQAhHevc1/lv2399tPbfvDRsrLLkM7cf04kTf/lZZCAo5EQJS6/+1rryX2\nESL09zvvRJaxH/8YZV9/vdqB4777kFTp3nuh/igvR/BCLAaj4F//Cre5jRuhXz7vPEza2dm4/+mn\ne9vUDKAgwns0x4qJBx4A6b/+OtFnP6uOO47/xprDACHcoYAlJpZO2M9UJwV2r2luVmGcRCoXKSeS\n4R0dGOXlIGETHL6bm4sBMmUK7l1ergYgx9GXlaldDnhfM06aHoSNG1XmMFMfbIIHOBPuYYepKKme\nHpW9y2yTkhIMFJsBys9oZlMpMPLyUpdUOOcAkVfC1b0XiotVuewry8tSve5cNz8Jl7fR4eQ6OTlo\n53328daJd+lYvTpRCh47ViVe2bzZ7j9LhDrsu68KF+cw7ooKr1XeFuQSVj/N6OpCfyopIfrTn1Se\n46wsPOOsWWjn0lJs5Pm1r+H5Cgvx7BzqPns2JHDW2zP5J9uskkhthR70/mMxEPi6dV41TIYhRrOh\ngMmTAwc4rNRGuLm5Xn/BAw/EEj1VuC7ChHXp0DZwgjB3LlyQ/DBnDnyDH300cUsUXdJYtkxJuDp4\nnyndjYoJkSVc3iLFLFdHKkYzovCEy/VobVWEq0u4nFWsv9+bf4JVCrb72dQd+i659fVq37hoFEv0\n1avV9uO6j29FBQjVtOLPm6cSa2/ZYidcLmfSJEWu+q7A+flKD2z6Vwe1H6fX9PvtuOMgQZ92mve3\nOXOUn+8zzyCnMxtPKypQBzb2TZsG7wY2Gpt+s36YP98/ypKh6+dHEEK4QwEPRnax6uxUCTIY7Lo1\nbZrSqRLBovuDH9iJMohYKiq8W4739mL2TiVsksi+RQzjs5/FcvQ73/ESrl5mWxv0vU1NynjG0AmX\nk+PobcLeAbwvmAk/o5mN1HQ/XBvMOH5GURH05nouCtbBssqAE8owTMLVYZO+OV+DvmFkSQkI5tZb\nkfltxgxkd+P30dOD82w6ydmzIRUTQdINWgqbuWG5bhMn4h6LFiEXc7LACsbUqd40oiY+9CGUe+aZ\n3uMFBdALE6G+ubkqN3B5uVotRCKQUt9+W20jpPvgBuGcc+A+5ifA2HyQGXrCngxACHco4M7a2amy\nzOt6QSIl4c6cqaz5RGrZP2OGfwYpGz7xCVhWOztVKj4iL+Ga+i4bqfmRVH+/WnJdcgmkWPYh1a/b\nvh3PtmxZ4qDlhDSmhMvo61OqlJwc//qlKuGaCJp0Jk2CFMntrO8+wD7Q7Dlg+uHawJOBaQCsqsK9\nNmxQhEsE74GaGvSXyy5TXi28bbge+s3Iz1eug8mCL3ToEu7kyZiglywBOZrvzq9f7L9/YsiwiaDJ\nU8fbbyOLGYd9M1h3XV7uzRmdDKWlRLfcgv3SbGAXOyLv8/X0IHAkgxDCHQqY5DhyrKMDkh3v+cS/\nFRVh9ta3pyFCKkd9t1FGEOFOmoSlZU0NBktjIzqRTri6tTxMcg59UDQ2wh/3pJOQ6vD22+15FE44\nAXVfvx7ka1rMWbLk3AC6Qem730WbcGixXz1TcQvzg1/+hcmTYXjiyVHf6p0J9yc/8epRTV20Dr1u\n+j0XL4YBitUVY8YgHPfFFyGtNjVh0mV9PU8yS5bY71Naan+moIgp3RtjyhRM0vfcg76kT6ZBE9Qx\nx6DfDQZmfd99V203pLvaZWVBkOAdRsISLhHqxh4ZJurr8R7N53vzTa9hMAMQwh0K+AWyLox1uKNH\ne913CgsxwNlqzPjGN+zE2t4enA6xqAhLyjFjMGBdVw0oc4PDVCQhItU5Tz4Z8ecrV9rreOaZGLyb\nNyMnAFuwOT8Bb3ujqxS4vThYo6jILuEyidok3DCRZmHAEi63FbuBsbHOdZHQnXc7IPJmPTNhk3A5\ntJj1mo4D9Qv7J9fUwD/27rvVNba8Fzo4AYwO14VfNN/DREeHIrVZs/CO99kHfdXcotxv8jJd3sLC\ndo2ed6OtDUTJ9+UcChzyng7o+af153vxRfTzDEIIdyjQ1QYVFZAO29sV+fJvrKv08w5IRaVAhI7I\nkmhLizfPqZn71pYLl8h/8DDhEmHg64nL9bp2d+OZt2zBH0to7KXB0qOpUuDr2UMgSMLVQ2mJ/FUK\ng5FwJ0zAxpxMRKZKgffe0svRJy+zXJv0zYY2k6TPOw/v+AtfUDpLRjK1ydixiTldW1shLeuTLT8n\nEd4pG6A47++hhyqXRfP84cQVV3i/79yp/I05pNe0gwwVfhKuX8j6MEIINx3o6oK0t307ZufycjUo\nOITRD34SSZCEm5+vtgY3E9XwpoQ6AZudN2i3VNO/Uyd//ZpoFDrJk0/2uq+xDrumBp8cCmsSQVub\nl3CJVKSbLlHq14XZhtwG3t7IvK6szCvhcqAEqxRMy7ce2msLykjmQcF1vfpq6G1/9CN4HZSVeck+\nSMKdNi0xw1V1NVYUNo8RIhhB+R05Dghu1CjU2Ra847dbMft0pwp9srnjDq+3Tn8/9MOOg77HtpBU\nVmUM0xOIoUu4Iwwh3HSgqwsk1dAAgi0rU4SrJy7xgyktsUO6H/Lz1Q6upp8sEy6Xa8uSxFuy2O5f\nXZ24i4JNwo1GsTydP99bdlsb7nfPPXhuPyMT70SsqxRMdQgTrunrGha6yseW+UsndJvRjH9jFQdL\nq+wdYlN3mCoFrrd+bk4O2m3MGOjAx4zxuoUFPeO8ecpTgbFqFfTpunGIn5EI3iS2IAmbhOfXZ4gS\ng1VMidoGc0K86CJvyO6XvqT6YlMTJgBdn54Kxo5VRkUd9fXJE+hnCEK4Q4FOPoWFynm+pMQr4QaR\n52Ak3Lw8SC1TpyriYrAKwU/F4HeMwW5sel1s9e/vV5nAdLAfMm9vYiM6xwFBRSLeJbqZq8Am4abq\nFkYEy7curZoStM1oxtKuWXeuj064TFKm0YyfVVfT6KqJrCwVOKI/Y5B0N24cNj/UrfsbNqC9Gxrs\nOv2jlbQAACAASURBVNmKCu89dHK3hVj7SeucXJ/R0pJ4Pxv09zN7tnfTRr2Nm5pUvxwM4Y4ebU+y\nwxOIWeYIkK8QbjrARgB+oYWFQ9s7KZmE6zgYEA0NMLqsXq1+0yVcIrtKwdy6Jahz8xYwNpSVJRpw\nWKVApPwfuXxTJ8qkZUq4pkTJSNUtjLFoUeKSkklWl3D5OIfC8r25fno2MbPuNgm3pwceCjt34lMn\ndP259GuSqRSI4PNqnlNWBslX9yjJzQUB9fUpwjXv5Sfh2trZzBrW3Jx89abfz5b/QQfnyB0sEfoR\nrtnvRhBCuEMBvzxTYuSos1TLYSQjXD7/yiuxhNITfASpFHSJPMxusESJEq9e17IyfwmXyG4w0v83\npUSTYPUQW362wRjNeMdZhul1QJQo4bI+WS9Hl7iDJFwGbzvjJ+ESqR1wGWEmlWOOIfryl1U9cnNB\ntC+/rFYWROgH1dWos+5ipUu4tn7g5w0yahR0zzzBh1GX6ferq0vMqau/u8ZGSPBhdnCwwY9w/ZAJ\nI6EBIdx0gAmsrAzSTCoSrp/bTDLraXc33FrKyxGjzrCpFMJIuH5hmzrhmnX1I1xdwrVJa2xAMpfP\nuoRLlJrRbDCSi6nDJVJGs56exElPl4jDSLh9fSBC00qukzX7joY1mhGhXiyxb90KH2/2GNGJNS8P\ngReO491hhGEzZvKz+KkUduxAhBwRCDeMSoFhI1wdbW1wn7RtChkGZWUqlaYN+rPbtkfKAIRw0wEm\nMN6oTt/XLAxMstCTotjgOAiimDhRJa7WDTS6hKtL33yOSbhshLPpD4Mk3NGj7SoFvl9QSGU8rqQz\nm8RpM5oFRXr5ERWrJ2z6O51wdQmXdxXmSY/L0AnKbCu/0N78/ESdvM0LIKzRzERNDTxkbMnjS0uh\nvyby6nCD+hb7P9vqsN9+CNPlXSJSVSnYCNecAMaPT211qGPatMTgIhO6W2LYsOY0Qgh3KDD9Q6dM\nwf5SFRXo/CtXqqxbfhjssuaMMxB6axK76Rbm56WgH+NNAm0bCtr0brzcZid+HXoYqd++YDZpjpfa\nusRl2yHCL0giSDI01QN+RjMGG81sBMpEaaZj1Jfh3C94uyS9vZMFoyQzmplgNz72A9dRVqaW/7ZJ\n05zok3mD5OerFIxEqasUGhuD3bNcFyG/f/zj4PxweU89P+h9J6zBL80Qwk0XTHegLVuQgzMoK9dQ\n8PnPE11wQaKEaQt8SKZS4ATitbXKfUgflObSiyVNWz5XnVBNCddsI3NZrrtd2XS6QWoDG4kFPYNp\nNNPrNxijmS09I0u45iRomxxSUSno4EmyokKlbmQUFcHPV7+3zW1Nhzl5BKG5ObmUqN/DdFuzobAQ\nSfpTWSH63c8G7hNh6j4MkHy4Q0EQAfCLP+GEoZUTdP7EiSBe/ViQSkE3mulRb0wKtbX+e1zp9+Yl\nsa6vtcGmUuClOZHXSs6Sn81oxvAbTHqd/OphIxq/LXNY2tWJjwmaiduUzm3LcJNww9Q1VU8MzjXr\nOMj9oIO36jFXOH4Sbqp1CLvlEN8nmTGYCM/zuc/Zk52nE2HIfxggEu5w4ZOfDJfv1kYiYdUMNgdx\n0+fQdBMjSpRwCwuhCtAjcoLqxcvpMIRrk9a6u3FPm4QbZDSzQZdK/aRGPwOJqWowrzElXJsfrmlo\nskm4LGEH1ZWRqoSrR7DZsnmZ25Ensw3oz5IMgxEUkhmqmpthYEwlcY2OggJ/g3Wq0vYwQAg3HbB1\nvAULsI31YK5P1pG546xdq3RoNq8EU/LyM5qxQ3sYQwJLzbzvWtDA9JOAeE8zk1x0Cdc0aiVDkEHN\nRqwsafsRkCmd8zvxW/rbSJR1uCZspDpYo1kY+E1EfiqFVCP6gpCqjWKoRJgsb6+uUhDC3c0QRIwF\nBd4QWT8MxRdw61bsoqsjjL7Q1PMy4dqWfLZnZMLUPRL8YCM6Tso9GKOZCd1o5ke4JrEmizhiMraV\n52fw00mdr+cyTD/kZCqFweQR8MOmTYnH/Pqt6eKWLpiTlQ3cJ4ZqzJo6Fb7Hn/lMoqRrGs3ES2E3\nxQg4UBMRdK6mm43plWAjXJuEyw7jNi8AE7yzLKeiZDchE+ZAM1USQUYz/XsYBC3TbRIuI4yEazvH\nrLtZnm2jyTDqjzD5CVLBlCnJDWUmwqgUwuY7CDs2YjF4vQzVmDV1KnyPly/3Jvw3kUrwTxohhDsU\npCtMUC8nKJTWhC1AIkiloF/nR7hh6sqDo70dRraaGvuz+F3f24s6Bkm4Nok3qMxkKgWbVBtEBn4e\nEiy5mnU3wSkebXVNpqcdqQmcKNgPl5GfDykyKIjBLDMZYjH0w6Eu9SdPRn+cORP5js13r9dFIs32\nQpgvnSOHwsDM+kWUaLyxDW6TTMwY+WR1jUZxTSwGA8ctt6hybdKUOeBYwuXls5+EG8bIoj9nkErB\n5odr/s/fWUXA7WYOWI5GC5Jw2Zc31bqmgsFIw37ko/+WTIc7ahQCKvx2WPC7XxC6u1FuslzQycAp\nGmfNgoSrCyVBz54hCOHuCtBffmWlfXt0G6LR4OTifpKf2dny8sLHr7su7sthvWecoUi/oSFxa28b\nbBIuUaKEmwpSVSno7mm2e5k6XD2YISsruZSqE65JOkE+w6mgudm+/dFQEMYPd9QobJMThnDDgiVc\noqFLnps3Ex15JPqjuQocIaJlCOEOBel4eWbnWrWK/reJYzIEDfpU9XZ+uRRs6O6GdwRvFVNcjLrs\n2JEYqWaCJwHW4erRWqnobBkskabipRCmPYJ8dHUJl+uug9UGfiqFoLqmQjYNDYMj3DBGszASbtiV\nWFiVQtht0ZNhzhyiU07B/7zjCtHIqmoGIIS7q2Hx4nBSImMwnch2DTvQh0Espgg3NxcbTl5zDdGD\nD9rrbjOa6RKuaTAazDOl4qVgq5sJP6MZe04MRaVgq89gnrm+PnirdBuS+SYTJSfc0aNhmArTX1Ix\nmqXLa+D665X0rRPuYOqVZgjhDgXJInZSLWfVKqJLLx1aWbZywyA3N9w2JCyJFhYqfeaCBZBO8vMR\nlhlUF90oE5Se0W+Z71f2YFQKQYPOzw/XplLwM5rZdNjpXNLu3Jna5Kyju9s+QYUxmrFhKizCPDPr\ncNMJx0nMBZKuMTtISGjvSEMf9Lffjr90lxsGN9wQPqrLde1uTUF+szrp8aaS6ZBw9Wv9SMIvtNf8\nX//uJ+GaKoUgCdcvii0IqRBBfX240HFbHfz2eSMKVnkQwdWsujq1+yV7Lls6zHRA3wFbVAp7AJJJ\nSmHLIIIPYTp9A3XDkO1+OvLzkxtyuLx4PLVIJN3Fyy/6jSi5hBvUzql4KejPQpSov+ZntEm4pkrB\nLIu/21QKQRiMxDVYHS6R2uJoMPXIySF65pnU7xdkiONxlE5SzM/HpKSnxkwWYTjMEMIdClwXM+hg\n476Jhu/Fp6Ncv2W6TcL129qaB3BfX6KEa543WKf/VFUKYY2ItvJYpZAsIsyPcJMRWirvbTBGRkaQ\nhBsGZ5wR/lzX9delDifKyuBmaUq4NnfKDEFUCkNFutK8DXan0mSwlZmKIUN3O9OXh6aEe8kl9sHP\nxhmdTNloZpZr+uGGQZhlcLJIM9v9goxmNpWCzTCYimEs0xKXnw433eDnSka4w/H85eVE77+fuCHr\nSJD/AIRwhwLXTU8iY9dFLoN0x3b7qRTCwra7rxkUwDj44MTziFTOXF2lYJNkbZFmYREml0Ik4l1K\nDrfRTF+yD5fD/VBIqqMjeEWSTrDvdpBUORz3LS9P3HaIpe0RknBFpTAEjPvIR+iAc8/9H1E2NTXR\n6aefTnPmzKHTTz+dmpubkxcyMGjuv+ceWrRkieenhoYGGjt2LHUPZlO9dEgMqUi4tnvr/qg6mbJU\naeZaGKqEG6RS0I16YdyzgoxmPHHoKRfN61OVcBmZsp53ddm3nE83wkq4jHQ+/9y5RCtWKGNcqnUZ\nBgjhDgHP3HwzBvEA4d500020YMEC2rBhAy1YsIBuuummcAW5Lp13+OH03PvvU5eW4eihhx6ihQsX\nUp5N32kpw3rMJl2F6dSum0i4utEsWUYrXcJlwmUp01aHIAk3WX2DIuq4vpGI1/dUL5ONZny+n9GM\nXddMovULfPBzCxuMbtd2bph28Tve2WmPUhwuCdeP5IZzgpk6FSq/sHXJAIRwh4Dy0lIMzAGVwuOP\nP06XXHIJERFdcskl9NhjjxER0X333UfnnnsunX766TRjxgy6/fbb6eabb6ZDDz2UjrniCmpqa6PS\n9nY66Ygj6Mknn/xf+UuWLKFFixYNrZLpVilwmWG9FJhow+ySmswIFPQsYVQKLJ2GNZoFRZolAxPu\nCPl7/g9+rnp+hDtcGInsXI5j31MwzK7YwwQh3KFCk3Dr6upo4sCeYBMmTKC6urr/nbZmzRp65JFH\naPny5fT973+fCgsLadWqVXTs/vvTn599lmjnTlp0wQW0ZECtsH37dlq/fj2deuqpifcMYwgLOicM\n4ThOsEohTLYrx0nU4Qad7+eHG8bY5Jfy0HESVQq2c8z/bSoFLivZtj/83GYIcLqMZmHPdRz7jh8c\nEGAS7kioFLifZWcPz9bl+iaXolLYzcFLT4tbmOM45Ggd+JRTTqGSkhIaO3YsjRo1ihYuXEhERAfO\nnEmVO3YQNTXR2RdcQK+++iq1tbXR3//+dzr//PMpK8hNKsyS0hxEqXhD+BnNbH6tfnWzGc1sdQsT\naeZXbz+Vgv67TaUQ1A79/Yl7mvFxm8HPBEu4JuGlyy0srOTsFzXmZzjKtEqBSG0DZNthOtN1GWYI\n4Q4VGvmMHz+eduzYQUREO3bsoHFanLuuh41EIv/7HolEqK+/n6iriwrKy+mss86iRx99dOjqBL8I\nHz81gQ1+Em7YexP5G81s5zNpDkbaS7aJpE2lMFijmc2n12xnG+Gm02jW1RUub7JNwiWyZ5obTgk3\nSKXQ1AQCTKVvDqUu4qWwm2LnTk+U0kc/+lG6f2Afs/vvv58+9rGPhSunqws6NcehRYsW0c0330x1\ndXV0rF9egr4+OHTr6O/HMX3AVlVh4DO6u4k2bAjX2dracL0+AOJxog8+UPe37WrAqKzE/aqqcF51\nNciupgZ1qqnxbvHe1ISoICbl7dvV73w/PzJqbMS1tki56mo8S28vyuDya2owwIng2ldXh+drasJf\nV5eXwKuqUJ/6erjwdXZ6f9PbORqF/2c8jlSBehtu26bua9Z161a8x2SIxVB+soCbWAz3NyXc9na8\nR5uUV1UFqTidaGrCc9vu19aGukQiRBs3Dr+E29yMPiES7u6HY7/3PVrX0kJTpkyhe++9l6699lp6\n7rnnaM6cOfT888/Ttddem7yQadNAKCefTEREp59+Om3fvp0uvPBCj0rCg/POI1q2zLuV9Mc/jmOf\n/CS+L1xI9NZbROeco845+2zsJDygzgjE8cdjEMyfr4598YvYI+uii3Cv886zX3vBBUQvvUS0aBE6\n98UXg0A/+1n4RV58MQbg6afj/MJCov32I5o9GyTyoQ8h1eNpp+H388/3PpuOigrE9s+fnyjxXXgh\npLtjj0VdVq1CG5x4IojljDPw/5w5cI7/+teRmGXCBKKPfUwR7tlnE61eTfSJTyA71/jxyJBGRHTu\nuURvvEH00Y+q+553Hgj8S18ieu89orPOwvEFC0AqZ56Z+BwXXhjcpjrOOotozRqij3wk+LwPfxgb\njZpRYaedBjI+91zv8YUL0T8+/OHkdQiLvDyigw6Cx4AtOdLxx8Mn+OKLvW01HIhEiI44Au8wWRrR\nYYLjjrQVNbMY0sOaBLiXtZ1AIPBHKH2MRJqlACFYgUAwFIhKQSAQCDIEIVyBQCDIEIRwBQKBIEMQ\nwhUIBIIMQQhXIBAIMgQhXIFAIMgQhHAFAoEgQxDCFQgEggxhbwt8GJmtOgUCgYBEwhUIBIKMQQhX\nIBAIMgQhXIFAIMgQhHAFAoEgQxDCFQgEggxBCFcgEAgyBCFcgUAgyBCEcAUCgSBDEMIVCASCDEEI\nVyAQCDIEIVyBQCDIEIRwBQKBIEMQwhUIBIIMQQhXIBAIMgQhXIFAIMgQ9rZ8uO5IV0AgEOyRCJVr\nWyRcgUAgyBCEcAUCgSBDEMIVCASCDEEIV7DnwxXVvWDXwB5LuI7jPDPSdRDsIohEiHbsGOlaCAR7\nLuESUcVIV0CwC2HdupGugUCwRxOuQKDQ2zvSNRAIhHAFewl6eka6BgLByBGu4zhZjuP8xHGcLY7j\nxAY+f+o4TrZ2juM4zvWO42x3HCfqOM4LjuPsP1J1FuzG6O4e6RoIBCMq4X6HiL5KRFcS0Xwi+joR\nfYWIvqud820iupqIvkZERxLRTiJ6znGcksxWVbDbo719pGsgEIxoaO9xRPSk67pPDnyvdBznSSI6\nmgjSLRF9g4hucl334YFjlxBI9yIiujvzVRbsdmhuxmdHx8jWQyCgkZVwXyGiUxzHmU9E5DjOfkR0\nKhH9c+D3mUQ0gYj+xRe4rhslopcIZC0QJMfYsfj8+c9Hth4CAY2shPtzIiohovcdx+kfqMsNruve\nOfD7hIHPOuO6OiKabCvQcZzLiOiyga/iFiYg6u/HZ3X1yNZDIKCRlXAvJKLPENQDhw38/xXHcb4w\n2AJd1/2967pHuK57BBE1pKeagt0CNTXBv4uEK9gFMJIS7i+J6Feu6y4Z+P6u4zjTCUaze4moduD4\neCKq0q4br/0mEABTpgSH8IpbmGAXwEhKuIVE1G8c6ydVpy0EYj2df3QcJ5+ITiCi1zJRQcEehL4+\n7/frriOKxRLPE28GwTBiJAn3SSK61nGcsx3HmeE4znlE9E0iepSIyHVdl4huIaLvOI7zccdxDiCi\n+4iog4geGKE6C3YXRKNE8bj6bpLrj39M1NWVeN3Pfkb0m98Mb90Eey0cN8VMSo7jHEhERxGMWvlE\n1ERE64noNdd1m1Mop4SIfkJE5xHROCLaQURLiOjHruvGBs5xiOg6IrqciMqI6A0i+qrrumtClL9i\nQJerQ9JG7alwHBjIIgMyxIUXEl10EdG556pzuK/H40RZWUSLFhE9YMzdV1xBdMcdkmFMkCpC7fgQ\nSofrOM4sIvoyEV1M0KHGiaiFiLqJaDRBPRB3HOdFIrqHiB50XTfuUxwREbmu207ws/1GwDkuEV0/\n8CcQBGPiRKK6AaeWvj4VXXbXXURf+pI674UX8Pm3vyUSbmvrsFdTsPciqUrBcZx7iOg9IjqEiH5M\nRIcSUb7rumNd153ium4xQUJdSETvEtEviOgDx3GOH75qCwQW7Nyp/n/kEeUSVlbmPW/BAv8y2trS\nXy+BYABhdLhRIprvuu7pruve5bruO67reoxdrus2uK77tOu63yCi6UT0I/LxlRUI0g59+f+pT6n/\nmXDHj/e/trLS+72+Pm3VEghMJCVc13W/5rru1rAFuq4bd133Qdd1Hxxa1QSCAGzdSjRqFP5fu1Yd\nX7xY/W96JthgqhBef33odRMIfCDpGQW7J2bMUMv/xkbvb5s24ZNz4Eaj/uXoWcTeftv72/GiFROk\nF4MOfHAcp5CIvkDI9FVLRH9ORRIWCNKGp5/2fv/Rj/DJBBqNEl1/PVQMWVnec/3SNvb3E736alqr\nKRCEMZr92nGc9caxEiJaSfCTvZCgs33bcZy5qdzccZyJjuPc7zhO/UBO3PcdxzlJ+13y4e6JMPWm\nd989eDcs1yXKNuSGpUvxeccd+OzqIioo8PriTpuGTz0CTS/HFhQhEAwRYVQKpxDRX41j1xDRXCL6\nouu6FUQ0iYgqieiHYW/sOM5oInqV4L92NhHtS8h7q5maJR/uHonf/c77/RvfSI3gtm9X/3d2JkaH\nmSqEf/yD6LXXQMDXXYdjY8bg89ZblXEtog0HlpqfeCJ8vQSCJAijUphBRG8Zx84novdd1/0jEZHr\nuvWO4/yaiP4vhXt/m4h2uK77Ge3YFv5H8uHuwTAJsrAQxFlQoI5VV0M3e8ghiddPHnCAcRyin/6U\n6I9/9P5u7l+WmwtVQns7zidS+t8nn4S6YdIkL+Gy8Wwom0++8gqe6fDDB1+GYI9CGAk3m4j+J344\njlNOkEaXGedVkkqpGAbnEtEbjuM86DjOTsdxVjuOc8UA0RJJPtw9FzbCNcNsn3uO6Le/9R7r6SFa\nuVJ9d114KiQLVujvh7pg1Sp1jPPkEoHAu7rwO5PuzTfjc+NGu9Gtqopo/frE4zqeeoro2WeDzxHs\nVQhDuOuJ6GTt+zkDn2ZPGkcI8w2LWYQtdTYT0ZlEdCsR3UTYdocoOB+uldgdx7nMcZwVjuOsIMmH\nu2vin//E8l5HUVEi4ebmEv3pTyDVvj6oAK65huihh7znfe97+Jw1y3v8K19R//f24u+pp9Qx3WUs\nFkPQxNatRDk53nJefJHohhsgRa9cSdTSguPHHUd0553ec009dE6O7BYs8CCMSuF2IvqD4zijCGR3\nJWHp/y/jvDOIKGmOAw0RIlrhui7vYbbKcZw5BMK9PYVy/gfXdX9PRL8nQi6FwZQhGGY88QTR5s3q\ne3U1lu02wiWC1Pnoo0RNTUQvveQlUvP8/HylC2ZJdc4c5E4wDWsrBrrHhz9MtGMHiHzzZpShY906\npH6sr0f0muvie01NojvafvsRffCB+p6TA4+J004j+tCHcKy5OTHyTbDXIEzgw30EL4SPE3LVriOi\n81zX/d/U7TjOWCL6GBE9nsK9dxDR+8axD4howHzsyYerQ/Lh7s5gImV0dno/Gew98OlPq9/0AAcT\n27d7DW9hvR4iEZTbNLA4i8WIFi5MPKesDJ4N06ap/dHefJPosMOI1qzBdWvXeu/LLmgbNqhj5eXh\n6iXYIxHKD9d13RuJ6MaA3+spNf0tETwU5hnH5hIR+/Lq+XCXE3ny4X4rxXsJdhWYS/bbBxYzLOH+\nv/9HdPnlyjugtZWotBT/9/QQvWXabwfQr0Wbn3gi0f4D3oPxOIxrER/ZwnEgObOqgCg4UMJxVITb\njh3QR//tbyoD2fr1kKojEaLvfz95eYK9CoMKfBjwt51CSM/ogeu6/0y8worfENFrjuN8n4geJCTF\nuZKIvjdQjus4zi1E9D3HcdYSdMk/IMmHu3uDJVzXBXndcw++d3URnXUWjEwPalHhup+s6xL9/vf2\ncsvKlCR8yCEgvwsugEqirg5qABuWLlUuYoznn/d+j8fthN3ejglk+nRI2PE4fIrHj1cqBCIQ7j/+\ngfoI9mqkRLgDO+suIaL9yZ7/0SWiLMvxxBNdd7njOOcS0c8I/rtVA5+6JeIXRFRARHeQyod7xkBq\nR8HuCCbcBx8kOvpoFem1YoXdol9frwIZgjB6NNG2bfg/GoXEe+qpIOF//pNo+XL/a9kxZswYXGvq\nk++4g2jePGQZ27wZEiyjtxdS+MaNahLJzyd65hl1TnU10dVXE/0wtJu6YA9FqrkU7iaiPII+dx7B\ndUv/m+V/aSJc133Kdd2DXdfNd113ruu6v3W1jOgucL3ruhMHzjkpTPJxQQbx8supnc+Eu2gR0Te/\nqY7r7l4m/NQIc7XAxuJi9f/GjUTXXhusStDB53R1JUq7RNDRPvwwpO1f/cq7kwQR0erVKmnO0qXw\nveVINiKiW27B509+gk+OgBPsdUiVcA8loqtd133cdd0NrutuNf+Go5KCXRSdndCXpgLdaPbYY+r/\nkiTBg1/7mvf77NleP9h331X/v/EGjFh+G0cywc6ciU/OoxuNEh10kH8dOHG57uo1dao3PwMb1Do7\nifbZx17O179O9N57/nkcBHssUiXcTWTR2wr2UgxmdwQzeQwRJD6dHA8+GJ+HHqqO6VFoRF6Jlsjr\n5dDVhfIeewykPNlIzVxejnvOtaT+CErpWDWweXSd5hpeUJBIuHV1kMo5a5mJ/n6iP/yB6MYb4Rtc\nUyNb+uwlSJVwryYYsVJSHQh2cdx66+CuY9/WIHWAjp4euE6ZBJiV5d1pga36LC0SefMnEIXLvbBs\nGQi3psZ7vKEBe5e9+GLiNc8+S1ThEzPDPraPPqqOxePQHzO6u+F7uyKJG/ittyLabflyBFDccgsk\nc9lxYo9GqoR7I2Enh7WO46x3HOdN828Y6igYbnzDd1u5YPxrIPbly19Ofu6WLUS33YZAgjPO8P4W\njXq3x2FVgb50/+9/vdcE+eQyTjoJ+5zNnm3/3Y+0W1qgWthvP+9xlsL1iWDjRq9UzGoCP1cwXc2Q\nk4McDtOnQ8J94gmin/+c6N57id5/XzKW7YFI1S1sDaUWTSbY1WEm3U4Fn/40PhsavMc7OqCjPPpo\nkNdjj4FAxo6FlKi7aJ1zDiz/trwEehrHjRu9vxUVobyqKkiZeXmJOtH58yHFnniiuv7IIyFV3nEH\n8jXo0iqjr4/onXfUd8cBIbKvr5kLwow4I/K2ybx5UHls2+avZiguxt/ll2MFcMYZ0A8fcADc3KZP\nTwwaEex2SIlwXdf93HBVxHGc7xJcxO5wXfeKgWO8Tfpl5N0m/b3hqsdeB12yZFRWEv3730Rf+EK4\nMnbs8H7ftIno0ktBas8/Dwv/ccdB0mxv9/rZxmIgklRyDuTlweUrHsc26LfcglwKelgtkVJD6DpX\nJtysLP+or+nTQaI5OdBTm14JZl1NlQWR95qLL0ZGsrCoqYFf8YQJkORXr0ZdDjwQf9Onow0Eux12\niS12HMc5hkCq7xg/7Vn5cN83I5l3ARQW4vMXv1DHtm1LTHkYBD2/QU8PCLemBiqHcePgFdDTA6KK\nxbxRYc8/ryS3eWbgoQWOA4+GSZPw/7774jhHf+l48kmE3uoeEJwlrKEhMeqNUVaG8qZPh7RZUeF1\nL9NVCkSJRE+k1AFHHgm/XJO0iYL3T+vqwqQwfjzqMW4c+s/99yPF5F//Cs8M2dZ9t0KYHR8+7ThO\nqGAG7ZrZjuOcEPLcUUS0mIg+T0TN2nFPPtwB/9tLiKiEkA9314WfEWr//VXKwRtuSL3ceNy+tqsl\n+QAAIABJREFUfB0KWC2gbydTVJSY28APP/oRzo/FkFvgV7/CUt1x4Iuamws/1u3bvUSrJ5PhnXJt\nUu6RR3q/uy6I8rDDvMf9JL5Jk7xGLQ5yaGmxu22NH4/yp06F21hXF77bkuZwubadfvk+Bx+s6vaV\nrxCdfLI657777HUm8rYVEdqLyXfSJKhSlizBRHnrrViRVFaKq9kujjAS7jeJaJPjOD9xHOdgv5Mc\nxxnjOM7FjuM8SUSriWhiyDr8nogecl33P8bx3Tcfrs0IxYaVfw5EPv/gB6mXu2YNBuz996vIqe5u\nu/QUFlsGcr7rpJGdHW7HWyIQ0qZNRDfdRPT44yDfceMU4RBBin7+eW+Z+v+s79SziDH0qC7GEUfg\nMxJBOQsXQorUpNCn9MvOPVf9P0Bk27vqVI4GHXV1UHvMmUN01FGY4HbswFLexE03YTNLG9jN6/33\n1f+FhXZJXNfrFhXZyyNCQnMiSL4VFSDf6dPx/l96CWHSP/0pwp9ffhnv1oya25ug+2bvIgiTLexQ\nIvoOYaudVY7jtDmO84bjOE85jvOI4zjLHMfZQlju30rw1Z3nuu7fk5XtOM4XiWg2IUeCid03H25u\nrtfCvGoV0gASJRpcUsHBB4N077oLf0Rwon9gCKkleDmvG2T6+2H08nNRamtDqkIiSFbV1ViqT58O\nv9QHH/RK4kyQfs9u28Zm3Dh8TrTM25ynID8fZLh1K/7n1IpnnknvTh/w2126lNxYjPpOOQlENfBe\nrqr7s70uRNCb+uVP0OE4XsMek6UeZfbaawjrJUJ+38cHEurpnhO6OoclYJuXw1/+Yq9HURFUH9Om\n4bO1Feqce+/FSuo3v8F9334bqp7Bej88/PDgrhsp3HADjJC7EMJmC3uQiB50HGcfIjqNiA4jkF4R\ngQBfImT/ekFP2xgEx3HmEYxkx4e9JkQ9d418uKNHY8k6YWBeyM1Vbky25WeqWLtWbYqYnY0cq4NF\nYSGMY3rgAUufnKmrt5eothbE9s47GLScRLy4GNLVxRer602i4iW130BnCb2iQkm7/f3QvbouSEQz\nTL3VU0mHUxmenbfMYV0uEdH++9OtzrN07UAOmi1Vb9N3Dt5I/9gwkeiXv6SaDx9PvZFXyHVd6s4i\nqj1oFv146mb6o877rhsuLFhHZycmnUWLIP0S4d1z2+qT0IwZKveDDl4Z+LmV/fnPRJ/6lH/dIhFI\n0aNGoT7f/CYI9513oPJhj4vRo9GuU6ei3UePxrsOkrAXLcKkubsY7Orqdrmdl1P1UthEkGDTgWMJ\nUuh7alcdyiKiEx3H+RIhQQ4R8t9Wadft2vlwr78elv/mZkW4eXnK0KJLeRs22JfMydDSAh3e9deH\n22ImCJ/9LNEXvwj98j33YAlaO9C8zzwD4uNIKE5NOG2acuOyRUjpGcH++1/odIkw8KdMsRMNkVeH\ny+QUjeL+xcX/a8Pzmu6iLeN+R1m6d0B+Puqel0c0bRotaNiPiN6nzZOLqKmrid7PraF40VyKENHt\nPa/Q5HaiLreH4jkO9Z+xgJb2b6Y/H5FNn1kxMNn099O17Y/SlXMn0qT1hhfG/2/vvOOjKrP//74z\nmZRJ7yRA6KEICNJFASkiKtjbrijugrq7rGV3UfH7A2IXdV0LlhXXhugqYMUFERQUUJAiRXooIaSR\nnsmUTHl+f5y53JlJgghS3J3P63VfM7lzy5N7n+c85znnc84B492GIrBqRHp68xPs0qXyPHV7f02N\nYW546SWxjWsajBol+5xOEabr1kkSndDgkb175f/v3t3Yp0/E33wDo0cHH+90Sh/avt14t7p9PCVF\n2p6WJt9jY2VLTJR30bbtz5+MTge83uC0m2cAjis94y+Ej4BQLfR1YDei+e7i15gPV48wCnzRXq9h\nSyspMdgKubk/P6QzJUVI+cuXy7Xatm3cqXw+2bxe0VY9HqPMTEODCH2lpE01NUabH3hAjtEF4nff\niebYsmXjAaZr7Lpd0e02BI0+AbhcwY4hl8u4dnS0tD0wgKGpiUOv8OvXEn2PP8ZB51TKfTYydadV\nv34G+yA3F2Ji6JjUnln9tjFibz3v7/+MhwvBGRuFFXCb4PlFUBH/BfZIxYr67RxOg6lDPNyk90iH\ngw8bNtOhg5VJu2hM/dInpdat5fPgQeM3/VnddZeREzcQOmdYKaMv3HOPYYrYtEkE9fz5cN558qym\nTpV3qhQ8+KCkgQxEfr60IVDg6hPfBx80Fri6CSaUGuf1ijDev1/ejdttVMxQSswfqamyMoqNlYlQ\n/261ysrLapXjLRbZIiLEnKN/6pvJJFvgd5Pp2JMO/RTc7saMktOM0yZwlVLVQJCk0DStHqjUM4Kd\n0flw3W6xn+p1rfLyYMoUo25WoBD0+aRj6lUFNoew3w4ckHypgZ77QEGslGwXXCCdUee9Ll0qgqBP\nHxGWupDVk25D40+AJ58Uu6KmiZaj5whIT5c26Et/q7VpxxKILTkry2iL3S4a0Jw5MHCgDNh582Qw\nBXrczWa48kq5h1KGwE1JMZ4PoJKT0KqqUfjzgPoFbuuGmQDMtn/D3XHRxALuC4Zga5eNFg3z0gqY\nBMxzrOeZcihINaPh5bKdkHvXNj4shGI/S8z1+X+wWeCdmpWQBpWB6RpsNkqVjZy0HjC0a9PVe5WC\nK66Ar74Sjdw/qR70VtJarxTcFM45RyhhF1xgsDtMJuM9BOL++0XbjYw0ckSATKKhTI81a+B3vzP2\nzZ7d9P2PBrPZ0Gh1fPCBvLOEBHnHL74oppy6Olm2R0UZjla9/4GhNf+U8NT7dyh0wRsohAM/wbi2\nphkbyPvSJzO9IGjgGAg85+abg5ksJxGnU8M9Fpy5+XC9XlmCv/CCvLQHHgiO9w+0q+rOp4cekpyo\nocEGtbWiMenaEjTdOSIj5VPnfQYKdd3JdCzagcsltkYQT25qqmgiuoDQ63b9FAWtY0cRuGefLfbC\n+nrReK+6Stqha79g2DI7d5aghTfeCHYuPfBAUEawg95q6lNhXne461uIbxDBW+ST/3ma7RPeToId\nQIr579hKgfsAVcMkn49t3mJK02PwuhyUWsE8A8BLTz+7a+4HkF3jZXl3cPpHgdMCWh5ctwX+veBL\nvAPgWe+3jHINxKspGrF2n3tOoti2bMGTkUaEX+BuLN5I69CJ6oorxO5dWmoIzfXrRYh6vTJ5v/lm\n42dut0ufCX2n1dXBOR/0vBC6eQCCBXjg/p8Lvf/qxT6rq6W/6OaeuXONlUgoli6VCTg02RDIxP+H\nPzT9GxiCWxfGgYI5VEArJfdKThbzSkWF0e7mJj6Q9tvtp0zgnlGGGKXUMD3KzP/3mZcPd9Kk4Bd/\n553Gb4HhnPp3pWRQVlYaCagDaUD6daKjZTnW3BYdLVpEoFajc2VDl2nNobxctGkwBr0+EMeONfY9\n/bSE3L77bvMmj4gIIxfupk2ydHvgAfl72TLjPL0906cbbbXbYc0aJliXBF8vZOD9bTTMuAA63gFv\n9ILBvwv6mZ3psCNVTARHoEFFvnSR9zs4OJgAb4ZQdod4W/PW2bC0Hfz2aljVJvh3XQP2alAdDabv\nvuNaP+lmdj8TKVP9N9y27YiDcafFMIdUrlspDqZA5senn8qKoH17mfD69xeBoDsobbbmQ3fd7sb8\n2nffFWdlcXHwO2rO2XYieRl0U4/VGszP9vmk3UejJTY0NL+s37PHMM00hUCTg26S0M0UkZHBW1SU\nUCXffFP6pR7QA0cfV6fYAXhGCdxfBV59VTqBThvavdvo5IFx+XfeKR2tQ4fgDmk2G8EP+nFr1wbT\ni5pDcxrKT2m0DQ1ij929WzJogVGqJtBhoju3wKBYNTdQL7ww+L46TQxEA9LP1x1BemLvysojz2NH\npEE7q6PhiMBZ0gHePws2+v1Sh+NgZxqsDlCIdVTGwMjEXmRHGdre1WdJ5PennWHaiMbnDOl2Mf/u\nDqNulr+ztWBubJW/6T7NMDPcuxJsFrj1Eh9VUT7e6Q5/vJgjwm7XEIOn27YaVmmFjXnHsbGifdXX\nNxagNTWNQ6R1WK2y+gmckLZuFc0yL0/Kx+vYsiVY6Orv6FgDWUJRUyMTS3GxtEP3RXi9sv2UjbSi\nwjimqcn759R7e+SRowv3wIrL+vejOaVPQ0rMY4k06+Tn3d4Ysv9qP03sfxN1dTKI1q4VvmNTmDdP\nyOeBZHddu9Vn4Oefl848d+5Pp0kMXBaed96xtXPZMunU77wjM7o+8IqKZDBUVMg1Fy8WLUyHru0G\nsirKymTp6vOJDTIwUksPoNChc0t104qmSVRbaSk89xxV0VAQb3T4hNI7jwyS0eNhYS4UJ8CovfL/\nzjwPYn2NLWAVVqhKtVLkMlYXyw8sb/JRnOVndC+sXsOigHF4Tb8JTDl3CibNRPfkLlT5hazXBLX+\n7wMPBdeU+u3V8F0rKPeJMBnf4QfGPCTUtGEHYO/utUz9MeR96u/c4TBWGjqqqpqfUO12MfEMH27s\nGzBAbPcJCcFC77XXxFmnC+Fu3UQjPN6cu7rA3rxZHLU6E8Rmk377U2anlSuN9gX2Lx3Hko5ywwbR\n5gsKGgvotWuFa/vyy8E5k3XNtamcxyDPozkzyEnEsWi4dwAZwBHWsz/U931gl6Zp1ZqmLdc0beZJ\nauOZiawsmeErKyVIoCnoFQ0CO6W+PLvqKmPfqlUy2AKjkpqD3qkimjG/h2ocixeLxulyyXe9w1ZU\niFb67bei7YbeV3d0rV4tAraoSJZqH38s5Wuqqozy35oWbE8cNarxUs3lEg3PbAaXi5T7RKB+1BlW\ndZRjnaVFePw9coW/GMMX7Y121ZtEY+zfsv+RfWWxsPrg6iYfRXqDhTkLoGcJfPd+IgNdwmT4ofSH\noOOy4rPomNKRly55iT8Pvptqv3Lk1eBwwBiODWGLb8yG9DaSiKfe7ONrbz6rBmQDsNxyiNKqQ8HP\nRRe4LldjZonDcfR3Hxtr9KPERLnWb38rScxDERFhTJRbt4pJ4sUXZTX1yCOiIOh13vLzpS2hmuMX\nXxisCDBWMD6fODhtNun7ei7koyUfKi6W+5aXNz4uNKKxKUefzWasAEPZGfqEvm1bcK4SfZyYzU0H\n1mzffmKZ8o4Tx+I0uxCY5Q+rDcU9gBXoA0zWNG2lUqqJaey/CIHkfB2BQQP68hwapxQEgz5UUyNR\nRcuXiyA0meQ8nagOoo3OnSvHTpki146NlaV34GzudIrQbttWKEMvvGDQeNxuI5w4UAu1WAwn3/r1\nMoicTiOMtbpa7rFokZghmorRj46Wc3r3Dk5C3lQ4qc0mbXe5grSUK26AifnRgIvCRHg7gNXU2ZPE\nzohqfrsZ5gZUvumX3Y+1hyT18v1NmAx0HI50M94/rw28tmmucrukdny661OSY5JJiU4hOSaZ9Ix2\nOCP2oUzgAb5oD6OaiDoOggZX17ZmaXQ+gwGLF17+DLh2uExSgwcbjkqnU+h2gUlvdAej1Qq33mrU\nQdNRXGxM7GlpcvyKFUZfCcRzzzXO/WCxyLMPZKUEIiJCHE4pKfK5bp28s9D37vEYttxAYTl5sgjV\nwAlGHwclJXLPqChhx+gmtQsvbKyxrlsn3HCdYqiUjAHdKbxzpwhKvSqIXmA0tJ1lZXDHHXLsihUw\nblzw76FMoVOEYxG4OUAzVfz4Sim1AUDTtH8B1wH/3QJ3yBARfIFhjvn54m196SWxoelcyNDsYFlZ\nhvmhujqYlaBrGDo31uUSLUMvoFhWZnimdaGrw2qVCKQRfumzfbtBx3I4JL1fKLzeplkVehrBwDh0\nl8uITPrxRxHs+/cbIcyhWsqqVXDTTeJ4W7hQaFA1NQbVyK99RRGBCw+vdqjh2UVQEgf/GGRcZmeE\naIEfdAm+/AvfG0UYy/xmTQ2NCFMEbp9oUDERMaRFJWM9UMTONEiNSMCpebG77SgMTXJf9T72VYeY\nQwBrAH32iXNhdyp08LPWWtbAoUSIc4EtCnr+ATqXwVsZ+fifnghb4NHS+RweDk9u2w39+8uAU6qx\nbVwXTnZ7Yy0OgvMF5+YapqlAgatTswI5wToSE4XLW1JibGVlhpbn8Qi1LDBQI7D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3B/CcydK5/r1zd/zJQpSt1///Hf40DzGjB5qLWFa4//2mGcdDjcDkUeyul2Ko/XE/yj16tUZaVS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\nYYQRxilCWOCGEUYYYZwi/H+NSAGpWsVTrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a34e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PhoeBsiv8gvxoHtac61tfz8ydM8krzqNBYAMO5xxmVeIqCksK8fGUOgdKKX7f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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c38860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing with the time-varying mean-field\n",
    "# again the dynamical system is implemented in modeling_mesoscopic_dynamics/mean_field\n",
    "%run modeling_mesoscopic_dynamics/network_simulations/waveform_input.py --CONFIG RS-cell--FS-cell--CONFIG1 -f data/waveform.npy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Spatial model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### baseline configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation [...]\n",
      "----- loading parameters [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we look for the fixed point [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we load the transfer functions [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## ring initialisation [...]\n",
      "----- ## stimulation initialisation [...]\n",
      "----- ## model initialisation [...]\n",
      "----- starting the temporal loop [...]\n",
      "----- temporal loop over !\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e27ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e4ac048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FY2c7EFjkMLgTU9y2saMpbBi8avlQjN4i8r9YIrwIqBMR37PaJlVtCnNuOjAR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a44438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run modeling_mesoscopic_dynamics/ring_model/single_trial.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Varying parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation [...]\n",
      "----- loading parameters [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we look for the fixed point [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we load the transfer functions [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## ring initialisation [...]\n",
      "----- ## stimulation initialisation [...]\n",
      "----- ## model initialisation [...]\n",
      "----- starting the temporal loop [...]\n",
      "----- temporal loop over !\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e79fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114f7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j/Ub1aKnC+yJQL7Z4iOQ9HcVtTfs/T56fq+5GrFlDI4HpWPPP9wG3dXiTB1Ev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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115f77f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run modeling_mesoscopic_dynamics/ring_model/single_trial.py --conduction_velocity_mm_s 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation [...]\n",
      "----- loading parameters [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we look for the fixed point [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we load the transfer functions [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## ring initialisation [...]\n",
      "----- ## stimulation initialisation [...]\n",
      "----- ## model initialisation [...]\n",
      "----- starting the temporal loop [...]\n",
      "----- temporal loop over !\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e542b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab15ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LR32SEfhGvPWwByW1B68hqltaCUbdih3HYg6iPfXkJgoL8xMqx5ETnAa86F+l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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127d38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run modeling_mesoscopic_dynamics/ring_model/single_trial.py --Tau1 10e-3 --Tau2 50e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation [...]\n",
      "----- loading parameters [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we look for the fixed point [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## we load the transfer functions [...]\n",
      "synaptic network parameters in SI units\n",
      "cell parameters in SI units\n",
      "cell parameters in SI units\n",
      "----- ## ring initialisation [...]\n",
      "----- ## stimulation initialisation [...]\n",
      "----- ## model initialisation [...]\n",
      "----- starting the temporal loop [...]\n",
      "----- temporal loop over !\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127d0518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fb7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z7hq+i0NsPOwd/joH+I6q/iQs/XcicgiwDBOu6Uo3Bue9wCG+yDe+uY1PfPq9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c6c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run modeling_mesoscopic_dynamics/ring_model/single_trial.py --exc_connect_extent 1 --sX 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Visually evoked responses recorded through VSDi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "see the details of those experimental data in the paper\n",
    "\n",
    "we illustrate the procedure for one recording stored in \"data/VSD_data_session_example.mat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "plotting the VSD data and evidence the propagation\n",
    "\"\"\"\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "from graphs.my_graph import set_plot\n",
    "from scipy.signal import convolve2d\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "def load_data(filename, Nsmooth=1, tshift=0):\n",
    "    f = loadmat(filename)\n",
    "    data = 1e3*f['matNL'][0]['stim1'][0]\n",
    "    time = f['matNL'][0]['time'][0].flatten()+tshift\n",
    "    space = f['matNL'][0]['space'][0].flatten()\n",
    "    if Nsmooth>0:\n",
    "        smoothing = np.ones((Nsmooth, Nsmooth))/Nsmooth**2\n",
    "        smooth_data = convolve2d(data, smoothing, mode='same')\n",
    "    else:\n",
    "        smooth_data = data\n",
    "    return time, space, smooth_data\n",
    "    \n",
    "def plot_response(filename,\n",
    "                  tshift=0, Nsmooth=2,\n",
    "                  t0=-np.inf,t1=np.inf,\n",
    "                  Nlevels=10,\n",
    "                  vsd_ticks=[-0.5, 0, 0.5, 1.]):\n",
    "    \n",
    "    fig, ax = plt.subplots(1, figsize=(4.7,3))\n",
    "    plt.subplots_adjust(bottom=.23, top=.9, right=.84, left=.25)\n",
    "\n",
    "    time, space, smooth_data = load_data(filename, Nsmooth=Nsmooth, tshift=tshift)\n",
    "    cond = (time>t0) & (time<t1)\n",
    "    c = ax.contourf(time[cond], space, smooth_data[:,cond],\\\n",
    "             np.linspace(smooth_data.min(), smooth_data.max(), Nlevels), cmap=cm.viridis)\n",
    "    plt.colorbar(c, label='VSD signal ($\\perthousand$)',\n",
    "                 ticks=vsd_ticks)\n",
    "\n",
    "    set_plot(ax, xlabel='time (ms)', ylabel='space (mm)')\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p6Axd/Ahuz/H1sXrGR/08QNLPoqQ88yR9PLp+dJSM5xlJJ0kaFrt3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\neoFAYKD4/2kBifoXOZSRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108f4b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full response\n",
    "fig, _ = plot_response('data/VSD_data_session_example.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SSGmi2DTy1hfbWbdhPSN678zWXXvUeFT1o5TVVeieX4X0FGAYLkg1zKeBGWZ2\nA0CQC/+nuFldbKIK2BnAnriSSIOBc0P3Ki6JlAWWrV3NU+1z6aqum0XlN/OHNje1THt+r3yaPXhV\n0laSfiGpXdIKSX8JdtSUes14SVbg2Kw+bB7dcPGiuXa2A0YAD+Y99xouKURsIglYtUoipY1iVtjU\njVH5yfvBPPWhiUXscpwV9AVcNpnewB2SupZ53UpcRpqNh5m9V/olvAR8OHT+X8HXe/Oe60+FdTVi\nxYGZ2dsFrj1fScdZ4sGFL3POPkey/3Yj6UIXNrApKj9nfWTZL9RsNKsfTFIf4CvAl3OJSCV9CVc0\n+gg2F5YwZmZxk+NdCVwX9NuGcz/NAf6W99yRuCpKsakopXSz8fryxby6tJ2e3VoY0Xvngs9kbSrl\nyRR9JU0LHRMqbGd/YAtCAmJm84CZwAfKvLaHpNclvSHpjiBRYUnM7EbcKuOngHNwkQyfNLO1uWcC\nJ/7RwO0xfxagk5H4WaXljS6s3DHe3sYHF77Ezr37Mmq7vZm+9JXiz6W8AEiOZhXbjFpf7WZ2QALt\ntLIpK2qYNkr7oF4ETgCewyUiPA14TNI+ZlZ0i4qkLcxsIjCx2DOBE78i/xd4C2wzivnBctkpDthu\nz7JtZEEcxvSdlQmhTZKMildZJF1cxMkePj5caftm9oSZ3WRmz5rZI7iFu1dwAe6laJN0naQxQV78\nxEm9gEk6J/gFXJlkuy1vFP/RC4nYv96ex5I1qxjc0kpr975l28+CiEHz+O4aVbwCLset7pU6ngQW\nAV1x2WXCDAjuRSIIoXoa2K3Mo7fgHPf3AwskXS7poKj9RCHVAibp/cAE4N/1Hst6Mx5eNBuAAxts\nNbIZRKyRVx3NrN3MZpU5VuJEZy2uQhCwMSnpCDalxipLYE3tg6tMVmpcpwA74Jz0d+ICVR+X9Kqk\nH0nq9AcptQIWrFz8H27u/U6dhwNsmkaO7Pm+SM9nxQrzNAdmtgS4Hpgk6YjAEX8zzkDYWPxB0gOS\nJobOfyDpPyXtHCRyuB7YG7gmQp8bzOx+M/sqztI7Ghc3egrwrKQXJH1PUuHVsTKk2Yk/GbjNzB6S\n9INqdFDKmb9uQQvdBq3scO2RRa+wbsMGDug7hK1n92DF+nRvtPUC2vBTx0r4FrAOmAL0AB4A/ju0\nswZgF2Be6Hwb3OexFZdh9RngMDN7Mk7HZrYOl8XmDklb4aoSHQtcGByx9SiVAibpRFyO7C9GeHYC\nbpoJm88g80nPAAAKyklEQVTtO0W+iC1Z+x5Pvz2Xg/oNo7/ezxweSrK7RPCitQkvXpsTVAM6lRIO\neDMblnf+beDbCQ9lP+AwXPhGF1w0fmxSN4WUtAeuiMix4XiRYpjZZDM7IFhmrnp1pIcWuGnk6IEV\nbZ6vOs24uliMRvZ7ZRFJ+0m6RNIc3DTyGOD3wAfMrKIpZOoEDDgYZ0lNl7QuSEf7IeDk4HyrJDsr\ntRpZiJwf7LDWXeiSyrfP4UUsW2j1WrrNnl/wyDKShkv6oaRZwDTgRNy09UhgBzM7zcz+UWn7aZxC\n/hn3g4b5Fa6gyI9xlXzrxpzlb/PasrcZ1mt7hvfeiRlLZ5d83m818jQrkv6NSwKxCvgrcBZwd5SZ\nVVRSJ2BBjv0OefYlrQAWm1lF+6VKUS4iv5Az/6GFL/PlXtuz01YHMYPSApbDC5mnCXkNF4V/exDG\nkTjpnQPViLhTSNg0jRzdWi6Ob3Nq5WT3znxPvTGzj5vZb6slXpBCC6wQZvbhevafb4VNa5/L0jXv\nsVuffgzYanvaVm+WpKMk1dwv2QzClVtdLOek96uQjU8mBKzaxN3cvc428EjbbD46eE9Gbb83dyyY\nGrvPakwpG1288gXJC5Sn6aeQlbIxKn/raFH5xUhCdBo9lc/MRQO8WHkK4gWM8o78Qjy86BXW2wZG\n9RvK3PbBneq/M+LTyMIF3srylMZPISvk3TWr+Ff7PEb1G8qhA3bh9fXzyr+oBKWEqNA0s9GFC7x4\necrjLTAqW4mETdPIMYN2r9qHrdnCLnLTRS9enih4AYtIoRxhHaPyVZUPXTOVMPOi5YmLn0J2gtnL\n2pm7fDFDem7HPtvvwDNvv8HMRQMS34PXqIKVz4jWNi9inlh4C6yTbJxGpnRzd9Zo9rqNnnh4AYtB\nqWnkhwfGj8r3eDydwwtYQKWO/Kfeep3la1ezR5/+7NDSB/C+nM7gHfieOHgfWIhKyq2tDaLyP7Lj\nSEYP3I3fzM5PpOHJp9EEqlglK0/1kZnVewyJIekeMzuq3uPweOIi6R6KZxRu93/XhWkoAfN4PM2F\n94F5PJ7M4gXM4/FkFi9gHo8nszTtKmQJp2lfalDdqBOkfXyQ/jFWY3ze0V4HvBM/D0nTghJtqSTt\n44P0jzHt4/NEx08hPR5PZvEC5vF4MosXsM2ZXO8BlCHt44P0jzHt4/NExPvAPB5PZvEWmMfjySxe\nwDweT2bxAhZCjrslmaTP5N3bVtLNkpYEx82Stqnx+E6WNEfSe5KelnRoLfsvhqRzgvfsytA1SbpA\n0gJJqyRNlbRnjcbTVdJFofdqjqSLJXULPVO38XmSwwtYR84AiuXTuQV4H3BUcLwPuLlG40LSMcAV\nwI+B/YDHgbslDanVGIqM6/3ABODfebfOwr2fpwKjgDeB+yT1qsGwzga+AXwTGA6cBpwMnJOS8XmS\nwsz84RYyRgHzgP6AAZ8J3RsRXPtg6NohwbU9ajS+fwLX5V17GZhYx/esDzAbGA1MBa4MrgtYCJwb\nerYHsAz4Wg3GdQdwU961m4A70jA+fyR3eAsMCP7r3gJMMLM3CzxyMLAcZ/XkeAxYAXygBuPbEtgf\n+Fverb/Vov8STAZuM7OH8q7vBLQSGq+ZrQIepjbjfRQYLWk4gKSRwBjgrpSMz5MQTbsXMo9rgHvM\n7O4i91uBtyz4Vw1gZibpzeBetekLdAXyq120AUfUoP/NkHQisCvwxQK3c+9JofHuUM1xBVwC9AJm\nSFqP+zv/kZldnZLxeRKiYQVM0sXAuWUeGw0MBvYB/N64iEjaA+eLO8TM1tZ7PAU4Bvhv4FhgOrAv\ncIWkOWZ2fV1H5kmUhhUw4HLgN2WemQuMB0YCyyWF702R9ISZHQIsAvpJUs4Kk3u4f3Cv2rQD64H8\nZPIDatR/PgfjrMLpofesK3CYpK8DudW8Abj3mNB5Lcb7E+BSM7s1OH9e0lCcE//60BjqNT5PQjSs\ngJlZOxFSpkg6F7g07/LzwJnA7cH5E0BP3Ac35wc7GNiajn6xqmBmayQ9DYwFfh+6NRb4Q7X7L8Cf\ngfzqJb/CLSr8GHgJJwRjgacAJHUHDgW+U4PxteAEP8x6Nq26z6nz+DwJ0bACFhUzmw/MD18LrIp5\nZvZq8MzMIH/YtZImBI9di1vVerFGQ70MuFnSk7gFhK8Dg3D+u5piZu8C74avSVoBLDazF4Lzy4Hv\nSZqFE7TzcAsht9RgiH8FvitpDm4KuR9wOvDrYPxW5/F5EqLpBSwGxwK/AO4Nzv8CnFKrzs1siqTt\ncR+0gcALwDgze71WY4jJJFxowlXAtrgwkCPNbFkN+j4VuAi4GjfNXwhcB1yYkvF5EsJv5vZ4PJnF\nx4F5PJ7M4gXM4/FkFi9gHo8ns3gB83g8mcULmMfjySxewDweT2bxAubxeDKLF7CUIelzksYXuD5V\n0m11GFL+OHaQtEzSLjXo60pJfvO1pyg+kDVlBCLV18w+nHd9JLDWzF6uy8A2jeN/gW3M7As16GsY\nMAvYy8xeqXZ/nuzhLbCMYGYzUiBevYHjgRtq0Z+ZvYZLTnhSLfrzZA8vYClC0o3Ap4EPBUUyTNIF\nwb0OU8igIEW7pIMkTQsKUzwqaSdJ/SX9WdJySTMljSnQ11clTZe0WtLrks6KMMTPAauAB0PtDAvG\n+XlJv5K0VNIbkr4Y3D8rKJzxlqRLJHUJvXZHSb+T9GYw/tmSLsrr8w/AceHXeTw5/GbudHERMATY\nBleEAuCNEs+34NI6T8Klt/45rtDIauBu3Gbms4DfSxpsZisBJH0Hl/ZmEi6X/f7ARZJWmtmV+Z2E\nOBx40szyU9WAy4L6fzgBPgG4SdJ+wNDgfH/gYuAZIJen69e4DdUTcNktdsYV4QjzOC5P197AcyXG\n5mlG6p2U3x8dD+A2YGqB61Nx+edz5xfgiop8KHTt5ODa+aFrI4NrHwnOe+PSxvwgr/0LcTmyupYY\n20vAT/KuDQva/1XoWm9gLS4/WNfQ9SeBKaHz5cDHyrwf3YB1wIn1/t34I32HN8uzzRrgkdB5ztH9\nYIFruVzvuUSMv5fULXcErxkA7Fiiv1aKJ4l8IPeNmS0F3gL+bh2ttVfomHP+WWCipPHFysOZ2Tqc\ndVaL2gOejOEFLNssM7NwHcs1wdeNyQbNLHete/C1b/B1Os5Kyh25ykKDS/TXHTc9LcS7eedrilzr\nHjo/BpfZ9WfA65KelXR4gbZX573O4wG8D6wZWRx8/S82r8oDUCrD7GKcfy4RzGXDHR846A/ETYv/\nImmImb0denQbNo3b49mIF7D0kW+lJM0TuJXEQWZ2Z8zXvoirqZgogRX5D0k/xDnthwJvA0jqh1us\neCnpfj3ZxwtY+pgFHC3pE7gVyAVmtiCpxs3s3SA044qgUs/DOFfC7sBoM/tkiZc/Bnw8iXFI6oNL\nz/1rnDhtBZyBW0iYGXr0ANwiQdWLp3iyh/eBpY+rcRWjb8BVzJlQ+vH4mNmkoN2P4Cov/RY4jo4L\nAoX4IzCymMM9Ju/hqj+dhqsvcBOwEpeXflXouaNwiwFvb96Ep9nxW4k8sZD0HPAbM/tJDfrqCrwO\nfNfMytX49DQh3gLzxOVi4BtB6EW1+SzOX3druQc9zYn3gXnichsuYn4HnHVUTQR8JYgF83g2w08h\nPR5PZvFTSI/Hk1m8gHk8nsziBczj8WQWL2AejyezeAHzeDyZ5f8BAmzmgYAa5p8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108226ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoomed response\n",
    "fig, ax = plot_response('data/VSD_data_session_example.mat',\n",
    "                        t0=-50, t1=100, Nsmooth=0)\n",
    "time, space, data = load_data('data/VSD_data_session_example.mat', Nsmooth=0, tshift=0)\n",
    "\n",
    "\"\"\"\n",
    "amp_criteria is the threshold of the crossing of maximum amplitude\n",
    "signal_criteria is a minimum amplitude criteria, beyond this level of the maximum observed signal we consider that there is no real evoked reponse, it does not goes beyond noise level\n",
    "\"\"\"\n",
    "signal_criteria, amp_criteria = 0.4, 0.6\n",
    "XX, TT = [], []\n",
    "for i in range(data.T.shape[1]):\n",
    "    imax = np.argmax(data.T[:,i])\n",
    "    if data.T[imax,i]>=signal_criteria:\n",
    "        ii = np.argmin(np.abs(data.T[:imax,i]-amp_criteria*data.T[imax,i]))\n",
    "        XX.append(space[i])\n",
    "        TT.append(time[ii])\n",
    "# ax.plot(np.array(TT), np.array(XX), 'wo', ms=1)        \n",
    "tt, xx = np.array(TT), np.array(XX)\n",
    "# for intervals in [[0,2.3], [2.5,5.7], [5.9,8.5]]:\n",
    "for intervals in [[3, 5.5], [6,8], [8.2, 12]]:\n",
    "    cond = (xx>intervals[0]) & (xx<intervals[1]) & (tt<20)\n",
    "    pol = np.polyfit(xx[cond], tt[cond], 1)\n",
    "    xxx = np.linspace(xx[cond][0], xx[cond][-1])\n",
    "    plt.plot(np.polyval(pol, xxx), xxx, 'w-', lw=2)\n",
    "set_plot(ax, xlabel='time (ms)', ylabel='space (mm)', ylim=[3,11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Optimizing network parameters\n",
    "\n",
    "#### N.B. shown for a minimal grid discretization (N=2), turn it to N=5 to have the paper's results (but the simulation takes a few hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# we build up the scan run many configurations over a grid\n",
    "# for testing\n",
    "%run modeling_mesoscopic_dynamics/ring_model/parameter_scan.py --N 2 \n",
    "# UNCOMMENT for paper value\n",
    "# %run modeling_mesoscopic_dynamics/ring_model/parameter_scan.py --N 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "execute the following script\n",
      "modeling_mesoscopic_dynamics/ring_model/bash_parameter_scan.sh\r\n"
     ]
    }
   ],
   "source": [
    "# this writes a bash file in the 'modeling_mesoscopic_dynamics/ring_model' folder\n",
    "# launch it with \"bash modeling_mesoscopic_dynamics/ring_model/bash_parameter_scan.sh\"\n",
    "# (but ideally on a server, this is a bit long ~5h of simulations)\n",
    "print('execute the following script')\n",
    "!(ls modeling_mesoscopic_dynamics/ring_model/bash_parameter_scan.sh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual with this configuration =  18.2543083783\n"
     ]
    },
    {
     "data": {
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I2CetvAvO6qQVfPVNfpVgI+ArZjY77Ybj/u+vBbTKfzCS4GqlSiQk1wKvBSYCr5E0JjoG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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10822ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we align the data and the model to compute a residual:\n",
    "from modeling_mesoscopic_dynamics.ring_model.compare_to_model import get_data, get_residual\n",
    "new_time, space, new_data = get_data('data/VSD_data_session_example.mat', t0 = -50, t1=200)\n",
    "print('Residual with this configuration = ', get_residual(new_time, space, new_data,\n",
    "                                                          fn='data/example_data.npy',\n",
    "                                                          with_plot=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# then we compute the residual with respect to that datafile for all the scanned configuration\n",
    "%run modeling_mesoscopic_dynamics/ring_model/parameter_scan.py --analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JNq6eWN4TBJyCGzW0h88QQ32GeRU3r8tRRd58GvEBDgB+CHzOZ8qjQ9snA3/D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\nozRspo5FathIPTtFxyrnfzH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10834ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2BAaZ2UZmNtLMBgMbAx8BHgAuAGZL2j1/s7OnU3NM4sJIZcWnPN1Po5HJUmAb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10949f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and we just take the minimum among all configurations, here the red point\n",
    "# (note that here it is only for N=2, run N=5 to have it identical to the paper)\n",
    "%run modeling_mesoscopic_dynamics/ring_model/parameter_scan.py --plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this procedure is repeated for all session's data to yield the results of Fig. 9\n",
    "# (N.B. with the N=5 grid discretization)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "name": "Modeling_Mesoscopic_Dynamics_JCNS_2017.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}