docs/quickstart/dynamics_intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamics Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@[Chaoming Wang](mailto:adaduo@outlook.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> What I cannot create, I do not understand.   --- Richard Feynman"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brain is a complex dynamical system. In order to simulate it, we provide [brainpy.DynamicalSystem](../apis/simulation/generated/brainpy.simulation.brainobjects.DynamicalSystem.rst). ``brainpy.DynamicalSystem`` can be used to define any brain objects which have dynamics. Various children classes are implemented to model these brain elements, such like: [brainpy.Channel](../apis/simulation/generated/brainpy.simulation.brainobjects.Channel.rst) for neuron channels, [brainpy.NeuGroup](../apis/simulation/generated/brainpy.simulation.brainobjects.NeuGroup.rst) for neuron groups, [brainpy.TwoEndConn](../apis/simulation/generated/brainpy.simulation.brainobjects.TwoEndConn.rst) for synaptic connections, [brainpy.Network](../apis/simulation/generated/brainpy.simulation.brainobjects.Network.rst) for networks, etc. Arbitrary composition of these objects is also an instance of ``brainpy.DynamicalSystem``. Therefore, ``brainpy.DynamicalSystem`` is the universal language to define dynamical models in BrainPy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:02:48.939126Z",
     "start_time": "2021-03-25T03:02:47.073698Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``brainpy.DynamicalSystem``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, let's try to understand the mechanism and the function of ``brainpy.DynamicalSystem``. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is DynamicalSystem?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, *what can be defined as DynamicalSystem?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intuitively, a `dynamical system` is a system which has the time-dependent state. \n",
    "\n",
    "Mathematically, it can be expressed as\n",
    "\n",
    "$$\n",
    "\\dot{X} = f(X, t)\n",
    "$$\n",
    "\n",
    "where $X$ is the state of the system, $t$ is the time, and $f$ is a function describes the time dependence of the system state. \n",
    "\n",
    "Alternatively, the evolution of the system along the time can be given by\n",
    "\n",
    "$$\n",
    "X(t+dt) = F\\left(X(t), t, dt\\right)\n",
    "$$\n",
    "\n",
    "where $dt$ is the time step, and $F$ is the evolution rule to update the system's state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accordingly, in BrainPy, any subclass of ``brainpy.DynamicalSystem`` must implement this updating rule in the *update* function (``def update(self, _t, _dt)``). One dynamical system may have multiple updating rules, therefore, users can define multiple *update* functions. All updating functions are wrapped into an inner data structure **self.steps** (a Python dictionary specifies the *name* and the *function* of updating rules). Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FitzHughNagumoModel(bp.DynamicalSystem):\n",
    "    def __init__(self, a=0.8, b=0.7, tau=12.5, **kwargs):\n",
    "        super(FitzHughNagumoModel, self).__init__(**kwargs)\n",
    "        \n",
    "        # parameters\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.tau = tau\n",
    "        \n",
    "        # variables\n",
    "        self.v = bm.Variable([0.])\n",
    "        self.w = bm.Variable([0.])\n",
    "        self.I = bm.Variable([0.])\n",
    "        \n",
    "    def update(self, _t, _dt):\n",
    "        # _t : the current time, the system keyword \n",
    "        # _dt : the time step, the system keyword \n",
    "        \n",
    "        self.w += (self.v + self.a - self.b * self.w) / self.tau * _dt\n",
    "        self.v += (self.v - self.v ** 3 / 3 - self.w + self.I) * _dt\n",
    "        self.I[:] = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have defined a dynamical system called [FitzHugh–Nagumo neuron model](https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model), whose dynamics is given by: \n",
    "\n",
    "$$\n",
    "{\\dot {v}}=v-{\\frac {v^{3}}{3}}-w+I, \\\\\n",
    "\\tau {\\dot {w}}=v+a-bw.\n",
    "$$\n",
    "\n",
    "By using the [Euler method](../apis/integrators/generated/brainpy.integrators.ode.explicit_rk.Euler.rst), this system can be updated by the following rule:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "v(t+dt) &= v(t) + [v(t)-{v(t)^{3}/3}-w(t)+RI] * dt, \\\\\n",
    "w(t + dt) &= w(t) + [v(t) + a - b w(t)] * dt.\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can inspect all update functions in the model by ``xxx.steps``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'update': <bound method FitzHughNagumoModel.update of <__main__.FitzHughNagumoModel object at 0x7f7f4c7ceaf0>>}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnh = FitzHughNagumoModel()\n",
    "\n",
    "fnh.steps  # all update functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why to use DynamicalSystem?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, *why should I define my dynamical system as brainpy.DynamicalSystem?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several benefits. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``brainpy.DynamicalSystem`` has a systematic naming system. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, every instance of ``DynamicalSystem`` has its unique name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FitzHughNagumoModel0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnh.name  # name for \"fnh\" instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FitzHughNagumoModel1\n",
      "FitzHughNagumoModel2\n",
      "FitzHughNagumoModel3\n",
      "FitzHughNagumoModel4\n",
      "FitzHughNagumoModel5\n"
     ]
    }
   ],
   "source": [
    "# every instance has its unique name\n",
    "\n",
    "for _ in range(5):\n",
    "    print(FitzHughNagumoModel().name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the model name can be specified by yourself\n",
    "\n",
    "fnh2 = FitzHughNagumoModel(name='X')\n",
    "\n",
    "fnh2.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In BrainPy, each object should have a unique name. However, we detect that <__main__.FitzHughNagumoModel object at 0x7f7f4c7f3850> has a used name \"X\".\n"
     ]
    }
   ],
   "source": [
    "# same name will cause error\n",
    "\n",
    "try:\n",
    "    FitzHughNagumoModel(name='X')\n",
    "except bp.errors.UniqueNameError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, variables, children nodes, etc. inside an instance can be easily accessed by the *absolute* or *relative* path. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X.I': Variable([0.]), 'X.v': Variable([0.]), 'X.w': Variable([0.])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All variables can be acessed by \n",
    "# 1). the absolute path\n",
    "\n",
    "fnh2.vars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I': Variable([0.]), 'v': Variable([0.]), 'w': Variable([0.])}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2). or, the relative path\n",
    "\n",
    "fnh2.vars(method='relative')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wrap many instances into a container: `brainpy.Network`, variables and nodes can also be accessed by absolute or relative path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnh_net = bp.Network(f1=fnh, f2=fnh2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FitzHughNagumoModel0.I': Variable([0.]),\n",
       " 'FitzHughNagumoModel0.v': Variable([0.]),\n",
       " 'FitzHughNagumoModel0.w': Variable([0.]),\n",
       " 'X.I': Variable([0.]),\n",
       " 'X.v': Variable([0.]),\n",
       " 'X.w': Variable([0.])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absolute access of variables\n",
    "\n",
    "fnh_net.vars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'f1.I': Variable([0.]),\n",
       " 'f1.v': Variable([0.]),\n",
       " 'f1.w': Variable([0.]),\n",
       " 'f2.I': Variable([0.]),\n",
       " 'f2.v': Variable([0.]),\n",
       " 'f2.w': Variable([0.])}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relative access of variables\n",
    "\n",
    "fnh_net.vars(method='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'FitzHughNagumoModel0': <__main__.FitzHughNagumoModel at 0x7f7f4c7ceaf0>,\n",
       " 'X': <__main__.FitzHughNagumoModel at 0x7f7f4c7f3700>,\n",
       " 'Network0': <brainpy.simulation.brainobjects.network.Network at 0x7f7f4c7f3340>}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absolute access of nodes\n",
    "\n",
    "fnh_net.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': <brainpy.simulation.brainobjects.network.Network at 0x7f7f4c7f3340>,\n",
       " 'f1': <__main__.FitzHughNagumoModel at 0x7f7f4c7ceaf0>,\n",
       " 'f2': <__main__.FitzHughNagumoModel at 0x7f7f4c7f3700>}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relative access of nodes\n",
    "\n",
    "fnh_net.nodes(method='relative')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Automatic monitors. Any instance of ``brainpy.DynamicalSystem`` can call ``.run(duration)``. During running, a [brainpy.Monitor](../apis/simulation/generated/brainpy.simulation.monitor.Monitor.rst) inside the dynamical system (``xxx.mon``) can be used to automatically monitor the history values of the interested variables. Details please see the tutorial of [Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in \"fnh3\" instance, we try to monitor \"v\", \"w\", and \"I\" variables\n",
    "fnh3 = FitzHughNagumoModel(monitors=['v', 'w', 'I'])\n",
    "\n",
    "# in \"fnh4\" instance, we only monitor \"v\" variable\n",
    "fnh4 = FitzHughNagumoModel(monitors=['v'], name='Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convenient input operations. During the model running, users can specify the inputs for each model component, with the format of ``(target, value, [type, operation])`` (the details please see the tutorial of [Monitors and Inputs]).(../tutorial_simulation/monitors_and_inputs.ipynb).\n",
    "    - The ``target`` is the variable accessed by the *absolute* or *relative* path. *Absolute* path access will be very useful in a huge network model.\n",
    "    - The default input ``type`` is \"fix\", means the ``value`` must be a constant scalar or array over time. \"iter\" type of input is also allowed, which means the ``value`` can be an iterable objects (arrays, or iterable functions, etc.). \n",
    "    - The default ``operation`` is ``+``, which means the input ``value`` will be added to the ``target``. Allowed operations include ``+``, ``-``, ``*``, ``/``, and ``=``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f4c761e80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm.set_dt(dt=0.01)\n",
    "\n",
    "fnh3.run(duration=100, \n",
    "         # relative path to access variable 'I'\n",
    "         inputs=('I', 1.5))\n",
    "\n",
    "plt.plot(fnh3.mon.ts, fnh3.mon.v, label='v')\n",
    "plt.plot(fnh3.mon.ts, fnh3.mon.w, label='w')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f4c7f3b80>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inputs = bm.linspace(1., 2., 10000)\n",
    "\n",
    "fnh4.run(duration=100, \n",
    "         inputs=('Y.I',     #  specify 'target' by the absolute path access\n",
    "                 inputs,    #  specify 'value' with an iterable array\n",
    "                 'iter'))   #  \"iter\" input 'type' must be explicitly specified\n",
    "\n",
    "plt.plot(fnh4.mon.ts, fnh4.mon.v, label='v')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f4c082d60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def inputs():\n",
    "    for i in range(10000): \n",
    "        yield 1.5\n",
    "\n",
    "fnh4.run(duration=100, \n",
    "         inputs=('Y.I',     # specify 'target' by the absolute path access\n",
    "                 inputs(),    # specify 'value' with an iterable function\n",
    "                 'iter'))   # \"iter\" input 'type' must be explicitly specified\n",
    "\n",
    "plt.plot(fnh4.mon.ts, fnh4.mon.v, label='v')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``brainpy.DynamicalSystem`` is a subclass of [brainpy.Base](../apis/generated/brainpy.base.Base.rst), therefore, any instance of ``brainpy.DynamicalSystem`` can be [just-in-time compiled](./jit_compilation.ipynb) into efficient machine codes targeting on CPUs, GPUs, or TPUs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f2c595b50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fnh3_jit = bm.jit(fnh3)\n",
    "\n",
    "fnh3_jit.run(duration=100, inputs=('I', 1.5))\n",
    "\n",
    "plt.plot(fnh3_jit.mon.ts, fnh3_jit.mon.v, label='v')\n",
    "plt.plot(fnh3_jit.mon.ts, fnh3_jit.mon.w, label='w')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``brainpy.DynamicalSystem`` can be combined arbitrarily. Any composed system can also benefit from the above convenient interfaces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f2c2c6ee0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compose two FitzHughNagumoModel instances into a Network\n",
    "net2 = bp.Network(f1=fnh3, f2=fnh4, monitors=['f1.v', 'Y.v'])\n",
    "\n",
    "net2.run(100, inputs=[\n",
    "    ('f1.I', 1.5), # relative access variable \"I\" in 'fnh3'\n",
    "    ('Y.I', 1.0), # absolute access variable \"I\" in 'fnh4'\n",
    "])\n",
    "\n",
    "plt.plot(net2.mon.ts, net2.mon['f1.v'], label='v1')\n",
    "plt.plot(net2.mon.ts, net2.mon['Y.v'], label='v2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next sections, we will illustrate how to define common brain objects (specifically, the neuron, the synapse and the network) by subclasses of ``brainpy.DynamicalSystem``. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``brainpy.NeuGroup``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`brainpy.NeuGroup` is used for neuron group modeling. User-defined neuron group models should inherit from the `brainpy.NeuGroup`. Let's take the [leaky integrate-and-fire](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.LIF.html) (LIF) model as the illustrated example. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIF neuron model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formal equations of a LIF model is given by:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\tau_m \\frac{dV}{dt} = - (V(t) - V_{rest}) + I(t)  \\quad\\quad (1) \\\\\n",
    "\\text{after} \\, V(t) \\gt V_{th}, V(t) =V_{rest} \\,\n",
    "\\text{last} \\, \\tau_{ref} \\,  \\text{ms}        \\quad\\quad (2)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "where $V$ is the membrane potential, $V_{rest}$ is the rest membrane potential, $V_{th}$ is the spike threshold, $\\tau_m$ is the time constant, $\\tau_{ref}$ is the refractory time period, and $I$ is the time-variant synaptic inputs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above two equations mean that: when the membrane potential $V$ is below $V_{th}$, the model integrates $V$ with the equation (1); once $V > V_{th}$, according to equation (2), we will reset the membrane potential to $V_{rest}$, and the model enters into the refractory period which lasts $\\tau_{ref}$ ms. In the refractory period, the membrane potential $V$ will no longer change.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start to code this LIF neuron model. First, we will define the following items to store the neuron state:\n",
    "\n",
    "- ``V``: The membrane potential.\n",
    "- ``input``: The synaptic input.\n",
    "- ``spike``: Whether produce a spike.\n",
    "- ``refractory``: Whether the neuron is in the refractory state.\n",
    "- ``t_last_spike``: To record the last spike time.\n",
    "\n",
    "Based on these states, the updating logic of LIF model from the current time $t$ to the next time $t+dt$ will be coded as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:02:48.995265Z",
     "start_time": "2021-03-25T03:02:48.969008Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class LIF(bp.NeuGroup):\n",
    "  def __init__(self, size, t_refractory=1., V_rest=0., V_reset=-5.,\n",
    "               V_th=20., R=1., tau=10., **kwargs):\n",
    "    super(LIF, self).__init__(size=size, **kwargs)\n",
    "\n",
    "    # parameters\n",
    "    self.V_rest = V_rest\n",
    "    self.V_reset = V_reset\n",
    "    self.V_th = V_th\n",
    "    self.R = R\n",
    "    self.tau = tau\n",
    "    self.t_refractory = t_refractory\n",
    "\n",
    "    # variables\n",
    "    self.V = bm.Variable(bm.random.randn(self.num) * 5. + V_reset)\n",
    "    self.input = bm.Variable(bm.zeros(self.num))\n",
    "    self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7)\n",
    "    self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
    "    self.spike = bm.Variable(bm.zeros(self.num, dtype=bool))\n",
    "    \n",
    "    # functions\n",
    "    self.integral = bp.odeint(f=self.derivative, method='exponential_euler')\n",
    "\n",
    "  def derivative(self, V, t, Iext):\n",
    "    dvdt = (- (V - self.V_rest) + self.R * Iext) / self.tau\n",
    "    return dvdt\n",
    "\n",
    "  def update(self, _t, _dt):\n",
    "    for i in range(self.num):\n",
    "      if _t - self.t_last_spike[i] <= self.t_refractory:\n",
    "        self.refractory[i] = True\n",
    "        self.spike[i] = False\n",
    "      else:\n",
    "        V = self.integral(self.V[i], _t, self.input[i])\n",
    "        if V >= self.V_th:\n",
    "          self.V[i] = self.V_reset\n",
    "          self.t_last_spike[i] = _t\n",
    "          self.spike[i] = True\n",
    "          self.refractory[i] = True\n",
    "        else:\n",
    "          self.V[i] = V\n",
    "          self.spike[i] = False\n",
    "          self.refractory[i] = False\n",
    "      self.input[i] = 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all, we have coded a LIF neuron model. Note, here we define equation (1) by [brainpy.odeint](../apis/integrators/generated/brainpy.integrators.odeint.rst) as an [ODEIntegrator](../apis/integrators/generated/brainpy.integrators.ODEIntegrator.rst). We will illustrate how to define ODE numerical integration in the [Numerical Solvers for ODEs](../tutorial_intg/ode_numerical_solvers.ipynb) tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each NeuGroup has a powerful function: ``.run()``. In this function, it receives the following arguments:\n",
    "\n",
    "- ``duration``: Specify the simulation duration. It can be a *tuple* with ``(start time, end time)``, or a *int* to specify the duration ``length`` (then the default start time is ``0``).\n",
    "- ``inputs``: Specify the inputs for each model component. With the format of ``(target, value, [type, operation])``. Details please see the tutorial of [Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb).\n",
    "- ``report``: a *float* to specify the progress percent to report. \"0\" (default) means doesn't report running progress. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run the defined model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:02:49.010946Z",
     "start_time": "2021-03-25T03:02:49.000504Z"
    }
   },
   "outputs": [],
   "source": [
    "group = LIF(100, monitors=['V'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:02:49.941507Z",
     "start_time": "2021-03-25T03:02:49.014011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation used 0.0004 s.\n",
      "Start running ...\n",
      "Run 50.0% used 3.319 s.\n",
      "Run 100.0% used 6.479 s.\n",
      "Simulation is done in 6.480 s.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group.run(duration=200., inputs=('input', 26.), report=0.5)\n",
    "bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:02:50.811867Z",
     "start_time": "2021-03-25T03:02:49.944594Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation used 0.0003 s.\n",
      "Start running ...\n",
      "Run 20.0% used 1.364 s.\n",
      "Run 40.0% used 2.711 s.\n",
      "Run 60.0% used 4.024 s.\n",
      "Run 80.0% used 5.299 s.\n",
      "Run 100.0% used 6.589 s.\n",
      "Simulation is done in 6.590 s.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group.run(duration=(200, 400.), report=0.2)\n",
    "bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the model definition, BrainPy endows you with the fully data/logic flow control. You can define models with any data you need and any logic you want. There are little limitations/constrains on your customization. \n",
    "\n",
    "1. you should \"super()\" initialize the `brainpy.NeuGroup` with the keyword of the group `size`. \n",
    "2. you should define the `update` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``brainpy.TwoEndConn``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For synaptic computations, BrainPy provides [brainpy.TwoEndConn](../apis/simulation/generated/brainpy.simulation.brainobjects.TwoEndConn.rst) to help you construct the connections between pre-synaptic and post-synaptic neuron groups, and provides [brainpy.connect.TwoEndConnector](../apis/simulation/generated/brainpy.simulation.connect.TwoEndConnector.rst) for synaptic projections between pre- and post-synaptic groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``brainpy.TwoEndConn`` can help to construct **automatic delay** in synaptic computations. The modeling of synapses usually includes a delay time (typically 0.3–0.5 ms) required for a neurotransmitter to be released from a presynaptic membrane, diffuse across the synaptic cleft, and bind to a receptor site on the post-synaptic membrane. BrainPy provides [register_constant_dely()](../apis/simulation/brainobjects.html#brainpy.simulation.brainobjects.DynamicalSystem.register_constant_delay) for automatic state delay. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``brainpy.connect.TwoEndConnector`` provides convenient interface for **connectivity structure construction**. Various synaptic structures, like *pre_ids*, *post_ids*, *conn_mat*, *pre2post*, *post2pre*, *pre2syn*, *post2syn*, *pre_slice*, and *post_slice* can be constructed. Users just need to **require** such data structures by calling `connector.require('pre_ids', 'post_ids', ...)`. We will detail this function in [Efficient Synaptic Connections](../tutorial_simulation/efficient_synaptic_computation.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, let's illustrate how to use `brainpy.TwoEndConn` with the [Exponential synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.ExpCOBA.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential synapse model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exponential synapse model assumes that once a pre-synaptic neuron generates a spike, the synaptic state arises instantaneously, then decays with a certain time constant $\\tau_{decay}$. Its dynamics is given by:\n",
    "\n",
    "$$\n",
    "\\frac{d s}{d t} = -\\frac{s}{\\tau_{decay}}+\\sum_{k} \\delta(t-D-t^{k})\n",
    "$$\n",
    "\n",
    "where $s$ is the synaptic state, $t^{k}$ is the spike time of the pre-synaptic neuron, and $D$ is the synaptic delay. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterward, the current output onto the post-synaptic neuron is given in the conductance-based form\n",
    "\n",
    "$$\n",
    "I_{syn}(t) = g_{max} s \\left( V(t)-E \\right)\n",
    "$$\n",
    "\n",
    "where $E$ is the reversal potential of the synapse, $V$ is the post-synaptic membrane potential, $g_{max}$ is the maximum synaptic conductance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let's try to implement this synapse model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:03:12.401462Z",
     "start_time": "2021-03-25T03:03:12.369072Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class Exponential(bp.TwoEndConn):\n",
    "  def __init__(self, pre, post, conn, g_max=1., delay=0., tau=8.0, E=0., **kwargs):\n",
    "    super(Exponential, self).__init__(pre=pre, post=post, conn=conn, **kwargs)\n",
    "\n",
    "    # parameters\n",
    "    self.g_max = g_max\n",
    "    self.E = E\n",
    "    self.tau = tau\n",
    "    self.delay = delay\n",
    "\n",
    "    # connections\n",
    "    self.pre_ids, self.post_ids = self.conn.requires('pre_ids', 'post_ids')\n",
    "    self.num = len(self.pre_ids)\n",
    "    \n",
    "    # variables\n",
    "    self.s = bm.Variable(bm.zeros(self.num))\n",
    "    self.pre_spike = self.register_constant_delay('ps', size=self.pre.num, delay=delay)\n",
    "    \n",
    "    # functions\n",
    "    self.integral = bp.odeint(self.derivative, method='exponential_euler')\n",
    "\n",
    "  def derivative(self, s, t):\n",
    "    dsdt = - s / self.tau\n",
    "    return dsdt\n",
    "\n",
    "  def update(self, _t, _dt):\n",
    "    # P1: push the pre-synaptic spikes into the delay\n",
    "    self.pre_spike.push(self.pre.spike)\n",
    "    \n",
    "    # P2: pull the delayed pre-synaptic spikes\n",
    "    delayed_pre_spike = self.pre_spike.pull()\n",
    "    \n",
    "    # P3: update the synatic state\n",
    "    self.s[:] = self.integral(self.s, _t)\n",
    "    \n",
    "    for syn_i in range(self.num):\n",
    "      pre_i, post_i = self.pre_ids[syn_i], self.post_ids[syn_i]\n",
    "    \n",
    "      # P4: whether pre-synaptic neuron generates a spike\n",
    "      if delayed_pre_spike[pre_i]:\n",
    "        self.s[syn_i] += 1.\n",
    "      \n",
    "      # P5: output the synapse current onto the post-synaptic neuron\n",
    "      self.post.input[post_i] += self.g_max * self.s[syn_i] * (self.E - self.post.V[post_i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we create a synaptic model by using the synaptic structures of *pre_ids* and *post_ids* , looks like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre_ids-post_ids.png\" width=\"700 px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pre-synaptic neuron index (``pre ids``) is shown in the green color. The post-synaptic neuron index (``post ids``) is shown in the red color. Each pair of *(pre id,  post id)* denotes a synapse between two neuron groups. Each synapse connection also has a unique index, called the *synapse index*, which is shown in the third row (``syn ids``). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``brainpy.Network``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above, we have illustrated how to define **neurons** by [brainpy.NeuGroup](../apis/simulation/generated/brainpy.simulation.brainobjects.NeuGroup.rst) and **synapses** by [brainpy.TwoEndConn](../apis/simulation/generated/brainpy.simulation.brainobjects.TwoEndConn.rst). In the next, we talk about how to create a network by using [brainpy.Network](../apis/simulation/generated/brainpy.simulation.brainobjects.Network.rst). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E/I balanced network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we try to create a [E/I balanced network](https://brainmodels.readthedocs.io/en/latest/examples/EI_nets/Brette_2007_COBA.html) according to the reference [1]. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This EI network has 4000 leaky integrate-and-fire neurons. Each integrate-and-fire neuron is characterized by a time constant, $\\tau$ = 20 ms, and a resting membrane potential, $V_{rest}$ = -60 mV. Whenever the membrane potential crosses a spiking threshold of -50 mV, an action potential is generated and the membrane potential is reset to the resting potential, where it remains clamped for a 5 ms refractory period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_exc = 3200\n",
    "num_inh = 800\n",
    "\n",
    "E = LIF(num_exc, tau=20, V_th=-50, V_rest=-60, V_reset=-60, t_refractory=5., monitors=['spike'])\n",
    "I = LIF(num_inh, tau=20, V_th=-50, V_rest=-60, V_reset=-60, t_refractory=5.)\n",
    "E.V[:] = bm.random.randn(num_exc) * 5. - 55.\n",
    "I.V[:] = bm.random.randn(num_inh) * 5. - 55."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ratio of the excitatory and inhibitory neurons are 4:1. The neurons connect to each other randomly with a connection probability of 2%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kinetics of the synapse is governed by the exponential synapse model shown above. Specifically, synaptic time constants $\\tau_e$ = 5 ms for excitatory synapses and $\\tau_i$ = 10 ms for inhibitory synapse. The maximum  synaptic conductance is $0.6$ for the excitatory synapse and $6.7$ for the inhibitory synapse. Reversal potentials are $E_e$ = 0 mV and $E_i$ = -80 mV. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "E2E = Exponential(E, E, bp.connect.FixedProb(prob=0.02), E=0., g_max=0.6, tau=5)\n",
    "E2I = Exponential(E, I, bp.connect.FixedProb(prob=0.02), E=0., g_max=0.6, tau=5)\n",
    "I2E = Exponential(I, E, bp.connect.FixedProb(prob=0.02), E=-80., g_max=6.7, tau=10)\n",
    "I2I = Exponential(I, I, bp.connect.FixedProb(prob=0.02), E=-80., g_max=6.7, tau=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this, we can create a network to wrap these object together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "net = bp.Network(E2E, E2I, I2I, I2E, E=E, I=I)\n",
    "net = bm.jit(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compilation used 4.8391 s.\n",
      "Start running ...\n",
      "Run 10.0% used 1.485 s.\n",
      "Run 20.0% used 2.806 s.\n",
      "Run 30.0% used 4.120 s.\n",
      "Run 40.0% used 5.506 s.\n",
      "Run 50.0% used 6.886 s.\n",
      "Run 60.0% used 8.214 s.\n",
      "Run 70.0% used 9.513 s.\n",
      "Run 80.0% used 10.834 s.\n",
      "Run 90.0% used 12.197 s.\n",
      "Run 100.0% used 13.507 s.\n",
      "Simulation is done in 13.507 s.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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uvJuOX2o/mvOTE/EprV/k4IEh7/91Fx1oYoHFFWhlSL73e78XP//zP49rrrkG4+Pj2L9/fyN9s7OzAICZmRncc889g/9nZ2exuro6dE+JPu2Z48ePD/09ffo0ZmZmAAAnTpzAqVOnsHPnTpw6dQonT57E448/joWFBZw7dw6bN2/G7OwsxsbGBs/PzMxg69atg/TGxsYwNzeHlZUVbNmyBbOzs3jHO96BxcVFvPDCC5iensapU6cwPT2NhYUFnDx5cvDcY489hoWFBayurmJlZQWzs7Po9Xp49tlnh/L46KOPAgCmp6dxxx134NZbb8V73/te/Nqv/RoA4PDhw/jqV78KAHjkkUdw9913q/WxsLCAxcVFTExM4C1vecvg95WVFZw4cQIzMzPYtWsXvva1r6GqKtx1110YGxtbp+fEiRNYXFzE1q1b8da3vnXod9I1Ozs7+PvjP/7jOHr0KABgbm4OmzdvBgAcOXIEnU4HExMT+IVf+AXMzMzgjjvuGOihdqBneDpUh0ePHsXi4iKmp6cxOzs7VBZe1zMzMzh9+jT279+PY8eOYWZmZtDXAODRRx/F3NwcNm3ahP379w/ye/jwYYyNjQ3V58zMzJCObrdr3rewsDCUBv9d5peXS0uPfqM+F9HF22V1dRVzc3ND/Y300ziUadO1lZUVAECv1wMAdLtdvPGNb8SmTZuG0isZvysrK3jwwQexuLiITqeDL3/5y3jkkUcwOTk5GEvUBrzdT5w4gSNHjgDAoB6A8+N5enoad999N+6///515Rq5aAhyKXzqeha7d+9OANLVV189CCPUie9GwgWl90djtFrISIbDSk+ztISHX3KbvySz1rwfvvoKBrvSnpNlkQxNXqeFCzz8ZoWg+HuYeWhPq2Puhci6l0xQhgdlnct8eSELrV4526RjP/juYH6P1d6WZ8zzJ71ULUyptZtW19HVWp4u+k0uxc6J5sVQGtaYiI5zXo85D1PTX7KRtKmg9Sxi8kd/9EcAgD//8z/HDTfcgOeff37AkoiBRcRiBABw9uxZPPjggzh+/PiAXXr3R3SurKwMWP773/9+PP/885iengaAdc/Mzc3h2LFj664TWzt37hwA4Ny5cyrr5PLkk09ieXkZTz75JAAMvIy777578Azlu9vtYn5+fojlPfHEE+j1elhZWcHY2Bg2b96MU6dO4a677kK328Xq6ioWFhYGbTA3N7fOuyL9q6ur2LJly4D9EUufmZkZlIPK9sUvfnFdvdLzkp39jb/xN3DrrbfirrvuwpEjR3D69Gns2bMHi4uLOHjw4KCOT58+PfBmtm/fjnPnzuHRRx9Fr9fD5z73OfzIj/wIXnjhBVRVhUceeWTQXtROvC1PnDiB1dXVdQydmD0vyx133IE3vOEN6+pl//79+MIXvoDFxcVB2wDAV77yFfziL/7ioK5eeOEFnDp1Ck899dSgHUjIS3nkkUeGPGNeb6dPn8apU6cAAHv37sVTTz2FhYWFIT3E9rvdrsnEiS3LetDYsuWJ0G/UFjt37sTi4iIWFhbUtpXeIY0J6TmQ13Tu3Lmh9HJ9R+aXl528Hio778/Ss+LjlXuylJZXVyMTDUEuhU9TzwKMfSLIsrl4yK8tD2zqWUg2RXmOTgByHTJ2iwwb1Ng2fyY34cfvlZOMMJiqNoEv2bIWX6ZJSb4iSDJ83tZyzkaLeWseHXB+UlR7eyHllbwyPlnKy80/sh0k27U8sMnJydTpdAanzko9ck7JmyfgZaV88e/Wm+W8OQlNjzVHIcXTq3lS3tyGN4FunfVkec2aWPVqfc89O6qly1LQTnDH5L777ksA0uWXX57e//73D3XeUUnTg78inVrmuRSMaBDnlllKN7kkfW1FiQYCmh5tcHouuwUQ/J7IMdfe/5QOTWCSHt7eNKinp6dNw8PrMrcCTQtT0eo2CV6WQZS6uDGi479pj44XlpJlteqei0dItPrI1QEXudybg5LXx7hohpzqw+tPOV3WWNPO4JIiwT1XD6XSgkVQbrzxxkHn3bp1a7HBtaSE4efEYxGWPslCR5UG6fXe/uXp88of8X4ks48MfC1uzD0zfjaPJ3LQcmPigZIFALKNNH2eyBVppLv0eAieR+6p8uWe0b0a0qsj/Vr/sUBBA6jIeDpz5szQ+ymkHu3gP60u+HszOFGxQDhHjLQ0eT1FliLXOfwwKi1YBOXee+8d6gjcuDZx9SxjXUcn74zW0c2ccWhs19LnXdPyTOWikIsWyvH0eSCiLQf0AEQzJpJFasdByN+syVzpBclwgMb4rPbwWLY0qlEvihsxvk4/B/yWJ8h3PO/du3eo3mQbe4sbOJHQ/udtQelp9aCVVe5/oOudTmfdBDfXI5dfa+OCJu75PhxeZxwwPSIi+12uf8k+y9PiukfpTXBpwSIo+/btS8D5I8p5J2rSOBZYNG1wOf8hBxY3ILmjJqKA5bGoUg8mByIlRzPwQWaFS3h6miG3WBtnfp7rr3kSGksu8QpLAFIafg+crHLxdDWjqrWxdbyIVeeaB9XtdtetqrKEeyS8/PT/1q1bB56R57V65IZ7VQRMWpmojqV3rR1dw79bXohWTl4nWv8dJXC0YBEUYlCeca0jF4oFWIyOD0DJoEu8CKsclhtcWk7tfn6WlTWYS/JpGcvScufeOBepE894l+SHH13htaf0fjQ98uA+rcyUjlWfkfc2kE7Jynl+qb2tXfmyjBpBICM/OTmZ5ubmwmegceFkpdPprMsrZ/iWl8PHoEWurHFKQrr5KQuyLqXH0VRasAjKoUOHEoC0Z88e19Bc7GIZy5zxyAk9L98UVkcXZ648P/KaZ0AtJq8ZEWsg5Ri9d5S1VifSG5Xs34pJa8bFyp/XjpJxejF6r244q6XfLeNV6p3yOuOhl7rkgOqp2z3/ciA6Diay612KdWYT96roUxp61dKRJ0PLvmcBnuwzo5jLaMEiKHfeeeeAUXA3dBSIvREi3X7uLtfZrETfaSA28cJo4MnlnBbb9YwmPWuFZ+qEhqQuacR5vrrd8+diafMFHPis+tJYq5W/5eXldceEW/UkASMXSpPtw8sjwyJW+CsnPE+yjpsQNN7faRNlHQNq9SstDGT1i5J0ZLmt9HNtXQramrRgERT5Du6JiYlGnkWdgaTpsNzYCPOWrmrXmGSz9GkdkDNM75yhiDHSTsuUafZ6ZUtbvZCQ1BP1LLwwBpXFes9DyfLPSBtL0Il6YJHVP1Zfo99lGEnrH9F+Kd9dHfEEc+UbxcuBIm2Q6zelRKw0/RJ9JdKCRVA2b948BBaHDh2qpSel9eypLtrzwcMNvvyNp5vrgNZ3zUhL4+DpkiLzy58tiSVLD6nuoJB6mko0DOAZwCbGcZSMclR5iejxfq9jKK1xIIlanX7jPaP9pvX5nK46YDAKYNCkBYug0JwFfaamphq7mNF18pbwTiE7otZ5KEQUfbuf5gprKzk8T8QyktrA8QaTVweem15XT+7eXNtHB6y3EfNCMNGIeGw5Grqpmx+t39YBWis9i6hFwznR9D2vW+vflq6oreDPS+9yVNKCRVCWl5fTLbfckgCkbdu2DfYnyLBCk4HdRCKdmuYAJicn1Ul6i7nxWLlkzHInLwmPa0fj53Rv3Ze1lBj8nB4v/TpGypIIQNZh0E362Ci8nVF4NNzoaQazThml8SUgsl5mVuLp5H4rJQbUn70j9zXdHBCbepRcWrAICl+V8LrXvc6csIw0jucy1nE7I+l1u92hM4C0deseu+KehcbkrQnPqOEnPTt37gy9C1m7LoGsrlHRPCqNZY/CePO3mUV0WYBo1QVdswyRBqxe+XJLakcVCun1hpfnemHbEk9PeimlpEaKBgDWM1b/1PLa7Q6/MMwjQRo48PKOYq4mpRYswsL3WdAx5Vq8PTI4OGPirJ3/NmrmJjsqX7WUM8oyPWmINOApNRy8w8uJ5pyLLgdI3TrleeF6IgPc0pEjE6WeBQfnqGejpR3VI8V7LgdQJSLT8UAq0r5WPUtSUzLRn5K+6ZCPbw3QeV40EJcv35J5tvqWt1xa5rGOtGARFLlrM/J6Q0v48sa6rnXOGOc8Fs6ocgeoSRaq6baOR+CdPxdDtdgfT0+62xGPo5RZa/dIACppCysP3nWvjrrKwoLc/ZonUidkpz3H86QdP1FHyLPInQ6bUuwATvLgaCOd1ZdzHqEkXVr4SmP6smyS1Mhr09PTqdPpqB6Bl0et77eexYsMFnTMxyjAghqbjh7wWGVK9WLoJWEIGkgEXHV1aQaMnrXW/peKd3wEF6/OJHvLhR5yhlUyRamzxDB7QBcFLy1vkjmXvPRKyyMvD/fAcntPvDLLvOQ8O0pbHu2h6eJ9PKeT17cV3rHqXyM8ljHXvD+eJwsUvGXWTUHakxYsgsI9C+3s/xKRxioHFjk3O+dZWM/TdT6AtDmGnC7uBstYfx1jJMvGDVP0GHePffFyawZcPqvp0owl/S/LKUMqWr1aaWtAJI2KB4ycqXLQlpvx+HvYc20k80FpTE1NDXZJ54738PLIDyrkR2p4+dA2N/Ly8mNiot6PDO9YHq72jEW2rL6kEa3cYaBaunzs1SWzlrzoYAHglQA+CuDzAD4H4If6148C+AMAv9H/HGTPPAzgeQBnAXwfu/56AM/1f/tJAFUu/VGARafTqWX4uDRhmxHd/H7v5E8y5LnXVtK9ltttMUKN7VlApIlmaLVnrGMYtPBCLk0ZH7d0RfRFvC4PQCQQaX3GMyK87rXjPSTzjpCgXm/9CbGy3Uvi45r3w0HNy4esD628EiC9fqTVndaHrDrPEbdcX+LiER4vrzlyWVc2Aiy2AfjO/vfrAPwWgNf0weLdyv2vAfAZAFcBmADwOwAu7//2SQB7AVQAlgC8KZf+KHZwR/cpXAiJAEeOGVt6NUMkDUFkSR7XJY2Ux7o8XdzYcgNKwg1DbjDlfvMGcKSuNEOeC3lY+bGMD08/Sia8+wggPSbv6aI8yTh7znha92gHENYlTU29Wy390rx4OnMHUTbtz6OSFx0s1iUE/BKAv+6AxcMAHmb/f7gPENsALLPr9wP4mVx6o9jB7bG8qNRtUI/RyNCPNxj5de1APAkSZOxKV4twVz6ytyNX7unp6cEeF9ovkpJ/JpIly8vLaWpqKu3YscMNM0V10et2tSOrtbq12iN3aJ4FZjkj7NW1DN9E2bd83mLHPDySC/9Y+iymPYpD8upKUzuQUr09KRphutCyoWAB4HYAvwfg+j5Y/C6A3wTwQQA39u9ZAPB29swHALwFwG4Ap9j1/QB+xUjnXQCeAfDM9u3ba1XUO9/5zgSsbcjjqzPqunvRDhId8Dk27OVBm5T0GLGXx5zrXDIwJJPj8zy0OIBePOPVjaWT6yOPxGKPuXbgunievDqScyZ0PfeuDsuzkEZYGhSPaMgjx0vbi3uA2jHjfLxE+pQ8al0jGhbA5fI5SmDh425ubi40nybzY4GNl1cNLC40eG4YWAC4FsCnANzb//9mAJcDuAzAjwL4YP/6+xSwuA/AHgUsfjmXbl3PYmlpaWhFFA2MukvSmjI2TV8pw6E80DwENxQlg4rn0VsPz9PMGfRuV39pDJUxd3SJlQ4fZORZHDhwQJ1E5MZIegbSQGm6vPLJ1ViyzCVHNVjALg2KZUw8opFrLw0ELa8n0j8luOTeUlhqHHn7lpTNyy+Vi7cdF29Rhje+PeKl6awDniWyIWAB4EqshZMeNH6/HcBn+98vijAUn+C+5pprhmLxpY1SYohzq3/qMCWLsecmuS093IiXTJRaOiVL18IWOUNBA2fnzp1DdZdz32XdaO/n4IMyUu+aoZTXop6cVu5cWEte57vGS+cqZJnkstVOp5MmJiZSp9Mp1sVDVFQPRGSWlpbUBQyReuDCPRbNo/a8ZC3flMfp6el06NChdeSx1+u5k/VeObzd5taY0Ly7UcmLDhZYm4z+dwB+Qlzfxr7/MIAP9b/vwvAE9xdwfoL7aQDfg/MT3Adz6TfxLGiS+21ve1uxweBSEorJ3Vsn3mmxZ25EIoxN08OXYNaZWNRCMR5gWsbfGqSl5/Pw8AlneB5LtoDAArycwdd01Z1b6PV6Q0e+yH0rpUuT5RiwFhp4+eO6pOG2PAvZ3+i53FiQ9bdz585Bv+f9OOIlc0JiESSLtEixgIADR26PiFa+UXoXGwEWf7Vf4N8EWyYL4N9jbRnsbwI4KcDjPVhbBXUWbMUT1uYtPtv/bQEXcOksZ8zUefgO0xLAiA7IlMrCANbvMlymGSnuHcgQgBVO4GDAr0kvQ5674+VZxqotJk/XvbeRae1jMXgttCTLm7tX/pYzOl65rBBRjnXmvEJpvGRfzK0q8+qF2o+MJ69jz5BbujggWEfh8/aPho2o/qKehdV3pWeROy/Lu85JiVZv0cMU63pcEXnRwWKjP3XB4ju+4zsGjUnvAa77TgrqGHVPV/XE2shDHS2XJ+qIZOytAayxPPk/MdiSV21qv0mGqQ10Kw6f24GrbebKDTTPq+QGjtelZtA00JHl99qCS+RAQl5+rWy93loYiRYPRPu0pkcLUZV4mRxAZGgq6rFY+Yx6vBqI8fbR0o7mR5IWIlkaKHrtb40Xayw0sTUtWARl06ZNAyMZdVMt4S5nZFCWDAgZVuj1eoOlppRPTZdkphZTzXkqKQ2zy/HxcddgWMxIY2I8xMU7vzS6mvej6ZPPSDbX7XaH6jOXR+s3MuSHDh2q9ca8lNYbBU4KeN3kPAtKkxtzCZilhoXXF/UNKrPU4xEFLhrA0jXLUHvllQQh6ulK4WOe50eWL3oOmvSMtXGnEQg+fixAs8ZHXWnBIih8JdSePXsaHcpVynCiAywlfScz70weCETDGrkBwUMdmjGqM7h4nqRHJkMrpQNWMkPJaJuea0UGgfZzaCGeXBvLvHIQK2WOHBR42TTDExFpjAnQI+EXSzSw4OOkZExIFi9PISipP1532lLZUsPMiaM1Pi1PwfJGtT1HJYBoSQsWQZHv4G6C0LnGz91fkoZkP1Gw0DwIfm/kVE0JhtZxH6W7wiNeguU15HRpYYa68V/JHjudzpA3EPGoNH29Xm+I4Zauq+dtK1cZlQqvo253/Q7uOiI99kioJZJHjXlTvku8fEkmOIP3QkZaXnhf5Xq1fSUy/YhnMard6ym1YBEW2p0LrK2GalLpnlvZVCRIEJvKGSWNXcmQlkwnykKjYOh1aEuH9Sz3sCwmpunTNoOVALuW506nk3bu3JmWlpZqMWMSybabhBak95cD6ly7lC679kQLpY5qfJA+ucqKxkmJUZVtIPuVlm8P+Hj78vlQPoZzXoWsL+65tGGoFwksrr322kHjHThwoJYOkpLVUKW6pMtdwjblgM/lM5KW56FY+jxvhdiUjI3LZzQPwfIsNGAhI1rizlvp0EQ/zd9Q2iUMNKXhMJw2l1Ii/HgSWrBhSaRd5LLrJoZd63dWyLC0j3PPTK6642WNjh/el6Q3EPWIpHdG43BycnLd3EipR8r7TOtZvEhgsW3btgFYTE1N1dJBUodVWhJlYZHBVtqxaKB4rMVj8Zq+SN65ziaxcbpHTvTy5aMyzODp1NqVrm3ZsmWgS7I9LT4tw4f0G69vDSgiXhonBpQPr34sXXKS3cq3pjMKkCQyfOp5VjnvmXsBlmcpSUjEu4rmQ/6m5UvOJXn9rm6dlkoLFkF5wxvekACkK6+8Mi0tLTVqhFE2YHRFlmfI+ICQxj9qIC2Gy70AyeKiIvNZcrRGRK+cqLSMZA7kLUCWXgAZJPmucZ6GxWxlfVtA6oWoONjSuydyxlUTvvafM2PuLVn58/LFyy49F6orr99ZdSi9Uy2Maj2rjRWZx5J+bXm+su9FwmKeFz1KacEiKPTebT44mngHo2zUSF68DqkNFhqEktHVKYdmwKLl1wZ1HSYXzbtnBEpDAFIn1bc1n8TLa4WocsZXa1sO2OQF5MIlXnlJN60q4stkc4QksoOe6od7YRJAeP6tfHpeDveueD14S621es+FqyxQyvU9rf6sPFpeyqilBYug0LJHAOmBBx5o7FlEJgRLjVE0LxYr0zp2dLNXJH/a0ke5Cc7Kq/R+NENB91pvwIvkkRsYKrsV0y8BLd7mueNUOPPP9Q/NEFn5tEJO0f4jw2f0jLeyqsSz4M9YO5Z5PqSXYbF0r+14GiUEJAJIMp3c/JJWNzx9rf5lniL9oa60YBGUl73sZYNG37FjRy0dJLKjWg1blyXkOjjfAe2xwJTsk0tzHkpO+Fpwb5NUzthwhsdDaCX1xo0TD0mVvK+Zi8Y6NQDwwIYDi2UAov2j1+ulqampIeCrQ3aoL/A5LX4MTmTFTS7dXB9NaT2BsZi1lxYZVr7Rs04dR8ojQ7uldUPXqdzyRIRoXptKCxZBeeCBBwaD4nWve10j9LY6UN0whxSr0/DrWjgg8nYyrsNjOpzl5MJdkVUkVn60gVR66ibPCz/Pis78ob0D0YEo80T5kPWRm4Tmdax5GSX9Q3qzdQyLVve00kuCSF3hfcqqH0lgrDzy+zxDHNkzVDomc8+VtB0nHyV5HbW0YBGUt73tbYNB4bHNiFiM3BsEUZ0aa/TCQLzTefsqtHQ0pkki3W/phnsgVKfTy6PRS+pSKxOvG27A6jBxCRa50IXMW+mSZq1cMj+lR9VobUL6brzxxtTpdIrOfbIMpwTHnIccYeK8zi2AyYVvImEny8PRJBdy4qKtOsvls6736EkLFkHh+yz27duXut38C3gssToSdUT+utCmOrlebuw0gxMxQrLjWoOM/0YeCwcD+VwdA6gZGWKkdTaJkR7pldSZt5FllGGokkEsB30E1FPyJ5d5fiKgpRlbzbBH9EldlpeZM8wWyyZ9PLSktWHUuPO0vLaThEcbGx7QWYSpxAvk9446LNWCRVC0d3Dz9wKUiOcSy3cw1HV7uWhsmQ9Qi5lpuqShioQE6B7twDrJ3i3jrjE7a3DK9CLl8upE5i3C7iRYacajhCVy4eFCry9RenIVlmfMLNHah5eTADYS/tPqUzP8ZOS1DZ25/iv18TS1vuS1BaWVeyeFRRB4meQ1Dh7as7xPl3oTrWexQWBxzTXXJADpsssuG3rLVq4DRYR3GDk4mrADqyOmtH6C2WI4UuTOVx7jl52SMzz5XmZpyHI7gLXBY4XxrElhq1y8nmTITDOquTbh4MzLIwcvL1OuzFbIzsqLdk+dd0RrdaSFkDhA5/prCXBaXlQ0FMX1SzJBnoc8WFCKdjCflRbpprkz2QetExesfkcLEyRI87Jr5EiCRgkxsKQFi6Bcd911AwNw4403DoU86qI3Naj1kiB+T52QhTQ2PK/Uwfgb6Uo6FzfMFivsdod3G3tGjdK3XmbknfUk61HLE+VHO+iO65OGQWPUueMoeN3y8sh1/dJ4SVCQm/e0JbeWl+F5FrnlyrI8Wnt5GxkjhskKuci0c3UdJVPc2+h2u0Pfvdee8rxaS1/573zuLAfist4kYHAPnPc/CTCyPLKtOXlpEpJqwSIotIObfyxDGRXOvD1WXUcnH0wa6+PGXrrkUo8m3MBphsUDKi7yWc04W+WyGLrGtOgeOp+JGwbN+Mjls9pg1eqYDKVceaR5GxygtCMmOEu1yAQ3MDJfsm96y5WtfiQBjMpL70jRQrCWQeRtb9V5BACsvhAJJcmd71THXoQgR9gkEHWdcJynK6JHIwEcVDk54cvB67x3R0oLFkHhnsXVV189eMG9xzg8oUafm5sbybHOXK/lfsvORctC5W+aHnkt5+Za3ok3kW2t0OF1Rfo8I2GxUR46PHPmTDZ/1L4yxKZ5ERMTE0OHvpGRpRAHLcPVAJazdV5+0kX9g9pK5lc7qpx0khHUyh/xLCSJoOvW4o5eb/08g2X8NVatkQErb7IecqEibRzkjHeOvPEy5PR46dL/1F+0o2B4XVqhY+7xad79hoAFgNco1+7JPbfRn6ZzFtwtjHQmSyRrbOoiklis23KHS9KO6OK/afd7K3ms36TbPT/vr/TIGSfp4lt5trwcLtxj4MyY6l/uRPbYpgwXWNetsvBycHDgoQjtFF0rXzIdXj/0jCQI2qZIa5x43qKXB004EFr3af3Ik4h3nPOCtHQ565deIO8vWt/TPEZeRxLcOWloamOagMVnAfwjABWAawD8FIAzuec2+jMKz4LYXl2gSOm8keHufBPUJ4mEAOg6370aSdvTpa1asRig51lov1Fcnr8Poq5x4V6KFiqIhjWinqEVYrLYJjfA/B5pmDWjrXmHMpYt06PfI31G5lsaLQ7AfJex128idR0Bbdl3vL5nvavEKiv3MHk+ckCr1RnXJ8NufC4p4qlIryTXh5rYq5SagcUWAAsAzvSB42EAl+We2+hPXbC49957E7C2GgpA2r17t+qGR0W6nXXjpdb9kTmC6N6GXFoWu62rS0p0X0FEl+VRRHV5bD+nhw9ij/FZLFHTxe/RjGqkLqampobALmKcef65gdbyJRm6lxctVCLzExkTWhlkH432WUrPOvk2ArKaPt6nIqBjiVUuuQqxqTQBi00AjgH4DQDPA3hr7pmL4VMXLPbu3TvkWVx//fWDBqkjOYNAUmqELb3a9Tqd3QII3uEjLMZiW1pePFDTBp4W/6Z0aO4gOtlnDcSpqanB8sgcg9PqnudTnkvEB7sc6DI2z8sf2Two63V6enrg3dL16CGXXjhEi897fd3SR79pQOuNiQhwWvM9kbrT/pegSB5hxGDTs97yZu04Hitf3GNsGn4iaQIWnwHwTwFcCeDbAPwSgJ8LPPdKAB8F8HkAnwPwQ/3rNwH4dQC/3f97I3vm4T4gnQXwfez66wE81//tJwFUufTrgsWuXbuGPAtiVXXf0UCdObcCKtdBpVgsTruec489/dJQ8d/ImEWZpHZvjt1aBkgyVJlO6YvscwNRS1fWDy+L56XwPFmG04vNl3gEnCzw+uIhl1KGzPOg9evSuubXrcUBlp4IU5fl94AsMsZlyNEy2Jo+uiYno/l9/O2NJXVmHTxYKk3AYrdy7W8HntsG4Dv7368D8FsAXgNgHsBD/esPAfix/vfX9IHpKgATAH4HwOX93z4JYC/W5k2WALwpl35dsLjhhhsSgHTttdcOrUyx9gXkxBvYkdCBxRSsjugNHg4kuUHj6eFGjk+qeszTYnQ5w2eBogWu2jr2Udanxqh5HnP1FjWsXmy+1DPUylG3X5JwsBkVo+VGPTLOIp4HeWLkHXqeRUQfvy/nWXiES9tPRP1V8yz4PZENk00AowlYVADeDuCf9P/fDuC7cs8pen4JwF/vew3b0nlAOZvOexUPs/s/3AeIbQCW2fX7AfxMLr2mcxZ33nnnEAPTdlhGxDMQOXc96snk9HEDR0bDm+uIrCLJufbRgecxpqhrH9GpGXku0fxqafDvUU8p6kFG+kpE5HLlpkSl0+mkycnJocUITYS3d/Sd5bm+w0lenb7I9SwtLWWPXvH0yX7BwUIbc1pe+EIVDgoaUWoC3k3A4l8DeB+Az/f/vxHA07nnhI7bAfwegOsBfF389rX+3wUAb2fXPwDgLQB2AzjFru8H8CtGOu8C8AyAZ7Zv316rovbt2zfoXAcOHGj0gnrLQNFgrDMxpXVUi33L32WoRNOtsXJNtNBGLp+eWMcj1On43tyHZQhzrF+2peVpyvqVeuXvklXyZ/jg12LcJXXMl27n2tYDVj6XwnU1AYyIp0risXV5D4Chc90i+dMYP3B+kycPDUXKpYFFr5c/G07rp9TnOp2Ourw859lGpQlYfLr/91l27TO559i91wL4FIB7+/9bYPE+BSzuA7BHAYtfzqVb17N49atfPRR/tAxwRLghkKxBrreOGkStE0XZqdZptQ4acWNJV2kYwmKGciWUFjqJGiXOwLz0LdGWwcq29NbHe3WqgYm1NJXXiQZOJd4QTW7fdNNN4Wc0/VTu7du3Dx2D04TN0vNyDsxbREB/LeDnfdML3ch+IMlSE8/CAja6TsfE5DwoEjl2ZZSgqfdJ0gQsPgHgcgYa4xw4Ms9eibVw0oPs2kUdhuKb8rZv3+6+ICgnHkPLhUVyOksNqPa8HGx1dJemzzs8T1+65U1CRaVHjcv0OCBIYOM7taOhiNyuZQmMWt/QPEIvDKN5BCX5tvRQ3e7YscPd61Aism5zO8K9/Fm/S9C2vPHIMnMvX543ysdVKdGiZ3gkIjd+60oTsHgbgJMAfh/Aj/aN/d8MPFcB+HcAfkJcP4bhCe75/vddGJ7g/gLOT3A/DeB7cH6C+2Au/bpg8eijjw55FuS+jhK5RylRA5oLs5TqrtsxqQ7rvEbUS9PyWCIiGaB1XLaXN08i+WlqGJvmMSpaCKVOHjXxjN8o9gp5K9hKJoW9cmogpLVH1IvX8ukRj1FIbbBYexY7AXQAHAbw6uAzf7VvcH8Ta3s0fgPAQQBbAXwEa0tnPwLgJvbMe7C2Cuos2IonrM1bfLb/2wIu4NLZ2267LQFIVVUNjJrFQqKSM0B1RLKW3OY8bzWX1em0FTk8LlrHIGkHoUVBzBscHIRKvSGNgVptXXeASu8hlycv39EdyU3r1BLLcJeAlNYGXv1YGzZL8m/Vi7Wsu45o460OUSOR/XoUO7RzUgwWWNsPYX6s5y6WT12w2L59+8CrmJycrMVUpdAg0jp7XZEDU7JjHtohD4l7Sp4ueV267tR5S5ieZEg0MDnolHg68l7Sw137nHHQPBcPDD2QyQmvO6kzOinJ20PLf52Jfe16XeNWUh8yrJYDmihA1RmrVuSgybgvCRuW6rnQUgcsvtgPBX0RwP8AsALgq/3vX7Seu1g+dcHiQx/60GBD3qFDh0aC4hfSs/DYMWdj3rI6j3VKt7euZ2Exdi3fkVCMZSS0/Q9R998DI/m79n+ufTRQsEDEK792hlRK/nEpJZ6FV2+lnqQl0giWgKaX/1wbes9aunifjRp8r175ktdcmZsS1TpSOwwF4N+AzREAeBOA/zP33EZ/RnHcB23QG8UAuVCN7rFA6bJG4r5R9hwNiZCUpF16JIOVRy+MppUnFyaSv1tlynkvFoiUhBg0411SXk+sPtWtue9FE7nvwypTqXiExvJGLdauecNN5gxkv+BEgQDpxQgz5aQJWHxKuaYqu5g+dcHi5ptvHprgplBUU/EYTxMg0Ri2tWM1Mhg1xuflNXc/XcstEba8BgtAcvdr5Y16Fbk8yWekPu26FerQ8hBdupzrM5TWKAgPb8NR6OOGkgCuDrB5uq2zuCjvUbDInSLr1YU3fjiwjfItmk2lCVh8GMA/xtrGutuwNgn94dxzG/0Z1fssDh06VEsPF8nwSoxYRLdmvOnD386XY87z8/aJm8R8+ACL3M/LGzmLShoOPkCtEJAHHLzM8nA+rx69NPgz2h4aL9zkzfVEQNUTady896aXimWA6wrVU+51vNpzEa8zd/hht6u/epeLRsQ84kT/8yXAUW+J+rlcoNHrxQ98HBWgNAGLmwD8KwDP9j//CpfwBDc3tKPyLORA01jyKLbpp3R+Hfz111+f9u7dO3gTV25Nt2bkU1r/fmjrTXEeg454B9KQ8/zI8If0nnj7eMcoSNDKhbK8ASgNj0UELHYqAciqo+j+CF5W7fW9ubJaOjUWXKLD0+nVo6Xf6qde+1hv9JMhIKkr1yfk76SXLy22yiHbW1vVKL05bd9QxGMtldpg8VL91AWL22+/PQFIV111VdqxY0dtxs8l4sKPihlIzyIaC7XOeuJAx8MG0YHkhcH4/5ZnQfmissi6tEJDPL5sgUP0MEStzrR0NA9EGjerPjj4ch2kO+cR0fP8KHLORLW69gyL5oVZOnIG0fLWpEgCEiUjUjRPk+qGfucEJKqL51/bVa95FhE9uTqmI0t4e8p6yIFniTTxLP4KgPcDeALAf6RP7rmN/tQFi1tvvTUBSC972ctGdjBXSvUnG+umUzoRyQcVLzNntnNzc2nv3r3pwIED6k5rzRBoRkVjUxwccpOANPC9l9zz8FWEBfLB5hkky1OwCIXnaXGjohlKWf8e06bwFvcko4xYE2qDrVu3Drw3a5e9BQCal6WNAdKh7U2QbWB5YFq6lBYtWiFm7nk3OXDjAG4Zcc1D0sqhjVOeN6rvpaWldX1AtvuoVlmm1AwsPgPg7wH4Lqy9V+L1AF6fe26jP3XB4s477xxiIqNYnXAhgKJOWCGaR23OgPRzF1sbSJorbQ1A0s9DZJxx0fN0wmnuXB7Orq3DGyV4SBbIjQGvAwsQtfxoq6Q042p5JJbx8rw5XnaN7eb6hrxPHhgoWaz3rHXd8has6yU6+P2y78p2zOUjAm485Kf1D+l1auQm15dkP9FInLe5ta40AYtP5e65GD9NPQvOsr3BmhPesUbhoUidMiRg3S8ZfA5ktAHF2ThnlznDbOWHBjO/VzO0PBTj1b2mz6ozi/HxvEsvJ8eM6Zq334Hu11il1CXb1TNmuZh3rt9RPch5Dp7Pkne6eCAaeU+5p1cSDC6WV6a1o9RhGWvPs9RCURKsOMGKeLtyHwbVvxUelqSuqTQBi6MA/nesHeh3ye/gpndub9myZTBnYQ3WiMhGHQXyWyzZ0i0ZigUyOW/FSkfqOXPmzOBIZ6+uNOPLddHv5FlY+iQYakZAGmcNDHu94c2Tsp45e9c8JdJDk+/04ixJNjQQ0gBCtocEet6e3nJgCe6eV8bf20L31zmmXxsrHiAdOnQobd26NTS+ZLlkX6D+QiE5aaS5Do1UUD1rcw+yXJ63JYGcXmrk9WOq/wMHDqiEgd/bZM+HJ03A4ovK5wu55zb6UxcsHnjggZF7FqNqxLo6udHV3iftMalI+haznpycLAZI7llIlz7nBeUYYg4YuWeiDUD5u1Yf3JDJe7WwggcQXLflAU1PT5ssmxsUHhLxAFeGNSgNCSCex2iRDc2w8joF1t4boYGjTMdbTkrEYmJiYmCkvbCg1n683LzPyJVVVj61seQBFKXPF5LkQOBC2JaUGoDFS/VTFyy+7du+bdBYk5OTjSeORt2gdfVJg+mFCXIsNGecrXBDpGwyxhuZqLcYuDWBLsvFvQw58LnuM2fODBird3AjB0sOzFoeNAPERWPjWltZOihd+XY1TeSuau4JaR6RFM1bs9pJlm/37t1pYmIidTqdIS/OAgSL0aeUBh4FHdfDgVbLgwRrbekxv88CAYtAUHmsvsyf533MaqcLBRIkxWAB4Hv7f+/VPtZzF8unLlhcd911CcDgfCitM5aIxSaa6it1PSUD0gY2z6tksqSDG4+cR5LzQKyySVZewq74gNY2zOU2a3mGhJ6dmprKhhOkwdHqM6X8LutI6Cslf56E6/DmBXJzLbx/aHosL8gSq09KL08bg54XI/Oh9XHZ/jwfVn/TjiiRefHqLefZWvmz9JXagKjUAYtH+39PKJ8PWs9dLJ+6YEEu7I033jgSsOAGahSAEe1YOUMtGScZS20pKYmVXnTjWG4gcKOtGZHIMR0Wg+f3WpPpOYZI99M+Bj7hLgFHHt9gsW0yaJoXq7WR1b6WIeNp0LyZ1Qa5duR16+mxQjNSpHegsfHcLuuUbI9GIwF1jTQfx1ZbWqLVRxOCpeV7VGQ0pRpg8VL/1AWLl73sZQlYe5/FoUOHsh3eEt5BchNbng5pqPig0a6RSPbhrQSSsXPJHOk3K/arLe3TypFbUmsxfMsjkHUk613qltflKqvcwKT/O53OUMiE1zfppPXvOcNL4SOtbNwIemGmnMHTPB2vjJ7htAyz1BXxLrxQEknO24mkaZWnhKHTvXKOqC6z97xZLrk6ssZ2E+BowSIo991338BljKzosYS7nnU9Cy0EwgdN1LOQ7MMa8Nrk8vz8+vdP8/QkwEiDbBl6Le9eOtHBpbn8VnpnzpwZrMCxBqN8juvn1yUrzxlLridneCUgW/nzwM5b9qp5Z1YdR41qhH1H0tEIivWspc8jEFHDKgGJVmLlPJKcztzS5gigWuO2rrRgEZTXvva1CVibs3jb296WjWNa4oVDrPu9zp/b6OX9H9ncldIwe5JnMVmG2mKlcp+CtwJEXrMMBNcvjSPPo2YYyLvj71SXBlvLm+W5yMl3zeB6BsrKpyVy4jNSV7yMmqcj81K66z/iMUbHjdTFwVarS8+L0uqV9+3ImPaAh/dpqx0j9UB54gshtHzk+gkH6JI+ZUkLFkG59tprVebYFLGjjKwJI5AspFQnHwh1YrNSxyg8Kk2/9Ji492OVVWPmOcOSq7ccq/e8p9K6lR6T5YVZRoqHHLU6szwyXldW+SIrpKJ1qc0LaHnLlVPzTHP7jDQ9Vl3kNsVGxqKWp1xaWntE8lwijcACwD4ADwD4O/SJPLeRn7pgsWXLlgSszVnwTVWlnoWU3KApcaut32QHrZNnbkAluyt12UfhUUXSyXk/KcVevtQk/dKylQ5sCWyWZ+ExbunRSI/AY6UaMGq7xuvWkacrwq6pPr2j+K065IATncTOeZ9yH4jV/7inyvOsgby2g9sCkCZSGywA/HsA/wnATwP4qf7nJ3PPbfSnLli84Q1vGCD9KCaySDy31tLrAUzdzlJyT65zSn11DL/U2wQ8uD65u90LoXl7JiJ5yRkzzZiU3G/9prHOXHvwOiKDmAtlpRSLi9cZI7K9onrkPRwsogAmy03zOrR7Wit7SZko7MmNvnWSg+WJaB7WqMJNljQBi88DqHL3XWyfumBx/fXXD8CC3t3gGcuclBpL79mIIfDy0WXLMD3mlAMibmSlPmm4cruGZacnHXxQyYHk1atMk+ujv1papJ/r1epBM250jedbA1pt1ZlX/9zYyLR4W1ovN+L553M2krXSQg5vxRHXJSfzrWNWIiLZfKfTCS1bt0iGXLKsHQnC65eXm56ZmpoaqnNez6Vl4mWTu7O5RKICmrdRl1R50gQs/l8A23L3XWyfpju4q6oa6kh1GyIHMhEXO6or8ix1em9gRwGOx1tpWScvS9QoSlCS8yba0Q5RkJVGxDMwHPA4e8sdzieBmNcNPceX1PJ3Tlj5J0Mmj8GWBsMDC14PpI+WCXOjbwGTpUu2P28v6dHl+jTv/7xMGnhb+eHCPQztJUS8jy4tLQ0terBOHuBnY1llkN6Mds3yXLzyWOnxdsuFIEulCVh8FMDXsPZ61ZP0CTz3QQBfAfBZdu0ogD8A8Bv9z0H228MAngdwFsD3seuvB/Bc/7efjHo5o1g6e8stt5irFKISCSlQB881cukAlCua5KCMTjxL48uNqGSUvMN6npAGktqAttz3Ei+L0rI2ePF0yZhzgyFZtbX5jde7TIvSoIPipqamXJJAhouOrpCGk/KRAx26f2lpKY2Pj6elpaUhY1/yClDZPvw0VFkWzRvUdHFPYHl5ed0eFu4FWGEgrf2Wl5eHPCBtLxDPo6xbHnby+irPY+4MLa9OtDHmHXfDCUBTQiulCVgc0D6B56YAfKcCFu9W7n0N1t6bcRWACQC/A+Dy/m+fBLAXQAVgCcCbcmmnBmCxbdu2AVjQBr2mSG3JhYpBci9Cy39pp+JGJBqikrrlc16IR9PtAWrOyEkmTB6ElUd5Dy9TJCTB9cg8yRNcNWDlZdc8Nd4OWlm0OpNpacZTa0ftXKhu1z+UMKVhw6ixbMoPgacHeDmjKI211a/kJDHPI/dIIvuCtLbTPFRrnFh9RnqNXt2UzqVEpTZYrD2LmwF8f//z8sgz/eduD4LFwwAeZv9/uA8Q2wAss+v3A/iZSNpNV0MBa++xHpUB10RjJ3WAyWLtHispASfPiESf596I9bwXctDyS/fnJmglI7YO1VteXk5TU1ODNwFqwo29Z+C9ute8NGm4tJ3dUW+KG2Ji2ZpHyNvDAk4evqI6o7xG2D43yvyYefqdPCctNMPbhXtzcqxIXfv27TP7mlWH3IPlfUPzdjXvI9o+1j38O/Wxffv2Zfe+1BmPOWniWfwtAF8C8BiAf4e1I8rfknsu2WDxuwB+E2thqhv71xcAvJ3d9wEAbwGwG8Apdn0/gF9x0nsXgGcAPLN9+/ZaFcVPnaXOM0qw0Ax708aWLDO3CY8zlwg4aXn2joi28md5Jl56XlrSY4iUhZ6Ru/M52/Z0WeEHjf3zZyxwlt4lZ8caoOWEjA0ZZo0NR+qL1wfpotAZ9zIidc77mzTC0Rcq5bwwng7lV9vnY/U/axxyEOf153m6Vn1Kb4/r1spRMrZK2iMnTcDiM9ybADAO4DO555IOFjcDuBzAZQB+FP0DCQG8TwGL+wDsUcDilyNp1/Us3vzmN4dcwFKxwim8seuCktQtV8nIAcLvi6Qp88jDLFaH9hhUzqvRwIWnJT2K3AGGMl/aDlxeZyUel5yM52WTBtEzUrxeaad2Ha+W15fcGVxKTGT+aX6EA6Omw/J0eXkon5F3SEc9Ya1tZZitxKNOaX04jQNoyXymJHS9Xm/gCfH5mZzXSOWk3/m8TGnZLGkCFs+J/y+T15xnh8DC+g0XURiK7+C+7rrr0vbt27PvfvZEGnI5OHgHrMPytd+kcWrqzcg8EnvVJnBzDC73G+miUIw2yVfKpqzyS+BrOuFvEYHcElde5jrLTy+EHqmzrheQ05lj6E3Hh9bWJXWi6ZNzKHX0pJTWEZQIEKdke+p1yqdJE7A41jfef7f/WQLwY7nnku5ZbGPffxjAh/rfd2F4gvsLOD/B/TSA78H5Ce6DkbRHddwHNWbdhpCMQhscJUAUHYgay5DXS8pkhbLIcEhdJcxT+42HLiTgRcHOM5xcR5S1SqNDbSpj49ZfqZ/r5GSilJRo9VFKbiLMNjehKj2+3MSrx/Rl21lAHmHhnHjwtEo9XnqGiJJ2zlZJu3GvwNuv5JW5xCOJSi2w6BvoV2LthUfHAfw4gP/Fe4Y9+ziAPwTwlwB+H8A7sbYb/DmszVmcFODxHqytgjoLtuIJa/MWn+3/toALvHT227/92wdGijYHWZOZEeGdsPTwOEtfrnNYxpSzaM3jKEmfL3kcRUeVIMHfo1wCbHwwcw+rbqxcu1+rA82j0PJqgU7JHJAU0lPnGHypw4vH59qAnot6NVpoRiMLnsfH86R5ATxMKPOksXIt35Z3UofVa+OE1310LJVGB0qkiWfxqdw9F+OnLlhs3rw5AUhXXnnlAPGb7rWIhk1KDa7VQWV6nAE3ASs++Lrd2DudPcanARnlVTJKL9/aYCajJZ+j32nJJm20svJL92vGnAMbnzvxjtXWlqpyI0ukIsIWucGQsfpSQyLblp7j3kTOQEW8I00fByde354n4LW9nFubmppS3z2fOxtLy1+E1XskLncoYFRKgblEmoDF+wDsyd13sX3qggVnITQB1bQx5PEIEZYU0eltDuODIRcXjkoJA8qVRf4udUXOIpK6aLB7b1eTXkHutaeegdRCJR4pkPXHddCHG4Dcm+00Vi2Bo6TdtfakvExOTq4DXI+BW9d53rTfLYPK0+VgJtPxwDji+ZWkl3tOekTW9YjIum0StsxJE7D4zwC+0Q8D/Sb6YaTccxv9qQsWFHoC1s6G4qGWOuJ1fu3eqPvJDVQkDxozL2U0Ug89r3lgJZ6F/J6bPLd0eYZVC1HwZ0oNAgk3ACVzFPJ5Dhg8fOblKxfS4Pmou4HLOyal1GvkYJZb/ST7hrarP+rR5vqX9Wx0fGjPWUfCeF59hHzxKMGoQYKkCVjcpn1yz230py5Y0Pp7AOmBBx5ovEOyhI2X6NOWHHoDhTPpyKDLpc8Ztbamv0SkwSZdJatNUoq/ZpZLXQNhPc+vRU4+1bw/blgiIalICISMFy3WsO7V6qOUaPD2zN2b+y230zynxwKuUs8lYsi5PitdD/w9j0cjRE1tiSVNwGK79sk9t9GfUayGIs+Cx6lLRRqF6MRqTl/OBSehDkiGndbK5zquZXhlp6VwnRYSixjNlNavZZ+bm0t79+5NBw4cWLd+vHTQUj6IsZMBtlheZMBKxiuvUXksj1JrKy2WT3+pbiyDKfOs5ZPmUSi0yhcA8L7o6eL5985BkmCh1Z9se817t7yknEdVl+FLYqXVixZCihhtjbSVAJJ2T6St6kgTsKDVS88B+O1+SOpzuec2+lMXLGhFCT/2wzuKICrSaI9iZzjvHNags8Aql74WW+Y6ufG19PAB5rF6DUz4qZqcCVosToKBxuq0U1JlnqLeiVY2L1SgDXJrZzH/rgGQNBQWi9Xqi4M89UVuuDxdJFwH9wZzRkvTFd2w6Omh9uebBvl90Y2WHlhYwOMZ5xw4Wt6IdgJDxLvziE6J1AaLdQ+sHQ4Y2hi3kZ+mBwmOj48PhVeaeBcp6StXmjYq7xxRNhdlH6UhHS80YDF5r3PT+xeWlpayngXpsQykHIRafjQ3X0rUs8h5PV5dWXpK2tHTxQ1PZAFExBuI9qmoZ1FqhCUR4+Gvbje+QVGCfc6DyIGkN040gqQREA5SuTJsuGehPgR8us5zL+anLliMjY0lAIMltPJ9AqOQUTVqzsCkNDq2YaUbTScKMHXyLZlXnfrNGYqmMqo2vxBSmrdRlqVOX5LPegsLNC/OktJFAKXgUaccpd7RKMQCiyuQkaqqHmT/Xtb3LHq5516qcu7cucHfAwcO4F/8i3+BJ598ErOzs7V1rqysYGFhAefOncPmzZtx+PBhzM3NDf1+4sQJzM7OYmxsrFYaY2NjQzpJ7+rqKrrd7iD/Z8+exYMPPojjx4/jjjvuMPNr5YeXZWFhAYcPHwaAQTozMzM4duzYumcpfV6PY2NjmJmZwTve8Y51+dHut/I3NjaGw4cPY2FhYZAnWReWkK6ZmRmsrq4O2r+OUN0AwP3334+TJ08O5TGSJ66D6rZu3/DywyWXt6ieSH6ork+ePImZmRk8/vjj6+7z2p7r6fV6OHbsGFZXV3H06NFBf6c+MDY2NqRLyy/vSydPnsTi4iLuueeeQX14Y4HXmzbWSL761a+uG/9c14kTJ3DkyBGsrq5iy5YtZl3QM7KP1LUZxaIhCP8A6LLPewC8DcDVuec2+tN0Ux59mhzwl9JwrBOGK1nKpKIxSu13PheQC+toennIB1jb8MRP+fRCGppQfqamptadmVXidfB8RSZWtbAT11FnIpM/nzumwtIpQ2qUPzrxtY63RPmgv6XLLrVyeZ62Vd9yzoiPi5LjPKywk6w7KZrnkAsPet4C/+71SW38a3pyCyO09hhlxIAETcNQALZE770YPnXB4uabbx40xK233tq4QahhvZNEo+5qxM3NhaaWl5fVdxNY4SEr5s3ftwAgbd261XWXrTLSAN67d+9Q7Nrb6WptkKMJTvn6S63e+FJSHr7ix3uXgnqv1xvsDN+3b18oVq7ljU/W8wng0r5IGzc7nc5Qe5UezKflxwMLrZ743IScO6J2479phEhubrUmgq2d35QOf8e2DGFa5affOVBxsNLCoLKcPF8y7MnvkedYWWNQK+MowlS1wQJrp7/+ZwC/1///tQB+OvfcRn/qggUtl5Wdqq5Yhq+urlx+ooCiHe2giff+YW4c6ciLnEdi5Ut7g5y1A9aqRzJIclmixd61s5R42tEDAbU8aEZEE6tNuTGZm5sbOq4iKpLZaseJRHXQ/bnyWGWieuHGkn7ndeatOMrt/tbyzP+nvipfRWv1N6sOuKewc+fOoVfwRse75U3w1ZIl3npubJRIE7D4BNYOE3yWXVOPHb+YPnXB4vLLLx90BHmWTF3QGCXq59KwmJl8q1k0T/I1otKVz3kh1m9ygPLfrTQip8zyAagNHPIe6NRQafy0Y0Zy71aW+kmnt/Itoocb6LqhH87II0bfa8NoSCaXP6mHH13jAbMXRpIMnZeNr6yzPBJv05/8n/LLPVnZZ7zwo9bXqS9KnRFCRzojQB6RRmDR//ssuxZ6+dFGfpoe91FV1aChRuUZeNIEULywTbfbHZpTKBXp+nPwKPVyKE/SgMnBIAdcKWvy6pKz2Pn59ctsJYucn6//bmXLcJR6mxGwyBkprd414yiNrwY+BA7aHpMIKFrEgT9rldky5JKhc13eniFNb66/yTri5CQHNLK82neNQEXsw6i8iyZg8XMA9gH4NIBNAN6N/nsoLuZPXbB4xSteMRhIN954Y3iiskQirnOJ0LPy7V10PfI2slx+acCRLtoJXMp2NWMsPQEZcoiGTSLsVm6KjAzu3KAtMZT8/mifihiKKABxb0nGz702sdLKHWMT6deax2CBhVfXcl7A029JhKFH+oxXDqs/lerJ5auuNAGLMQD/AcCXAXwFwM8C2Jp7bqM/TXdwAxhMuo4SKFIq33OQEz54rTDBKPLLQ1my80bTyoVLIiGcXD6tuQKPueXy7BmQURkjCpecOXMmVF5Pt7VZMML0ZZksjyJXj15ZeZtrK6yaGNQmJ7HmvOEcGZG/l44T63euZ1Tj2pLaYPFS/dQFi+uvvz4BSJdffvlI3msbHZzRZ7XrnFXJVRTR2LSXh7m5ubR9+/bBQORpSnZqGRD6X55Qy+8jHZ7B5wOXnx7K64Cv0uLhFJkn/m7pXs8+mVXmi4tcXZYzntbS016vNzjEcnx8vMiY8/vkRCkPnWnhMMsg8xAhB20yWuSZ8fJLkmIdm8H1cHLW6XSyhpLrkf2D7uOr2SwAleIBAvfGuMGW6Wv1LNsu54VaHp02TpqGmywpBgsA/8T5PGI9d7F86oLF7t27B52XDG/O/dOEOpO1fDJirGVs1Dqegd+nxZGpo/Ozk6KsndLieuRvpJMbDdmhuXfCPSAZApEGX+aPD0jSc/DgwXXX+YuPrHJyUJmfn18X286x35TOx8PJ0Gtll3n3wit0Jhl/VtOnxeF5Hcg3+2ntzfVq3/lKHDKAcjzIfTtcr3zBFP+N91Uy7hLgJAhwj4HXJQcp6fXIMWCxc88Aa+CXS98aVzlCIkmSRTabklhPLLDwdnCvKte2YO31qFsBvNd59iUr7373u/HAAw/gm9/8Jj7xiU9g06ZNAICnnnoKKysr4d2SJ06cwKOPPgoAOHjw4LqdzbRrE4C5e5Z2b66uruLIkSM4ffo0FhcX0e12MT8/j9nZ2aGdowCwuLiI6elprK6u4v7778fq6io++tGP4tSpUzh16hQA4Omnn8bi4iIOHjyY3SVLO5p3796Nr3/963jve9+77jcAePzxx7G8vIzp6ekhnXI37v79+/HII4/grrvuWnff2NgYjh49iqNHj+LUqVPrdHE9MzMz2LVrF5599lkcP34cW7duHezA3rNnz2Bn68rKyqAOZfvRTl/Ky8zMDADg+PHjWFlZwTve8Q4sLi4Oyke7Zrme48ePAwAeeeSRdTv9Zd5nZmbwxBNPDHaqcz28XLRD2qpDSveFF17Arl27cPbs2cGOaH7f+Pj4oF4fe+yxwe5p6oc8zccff3ywA5++0w7yLVu2DPTQNco/lf/48eOD+u71epiensYdd9yBj3/849i8ebNZFrmj++TJk4P+Tjua77//fgBrY5D68OzsLFZXV4d0yXKeOHEC+/fvx/T0NO666y4AGDwvx5+3a5x2gi8sLAzVi5e+Na5olzgAPPbYY0N1QLoob7SbW9qJsbExbNmyZXBPZLf5SERDEPkBcB2AfwzgiwB+DMDLI89t5KeuZ/Hyl788AWurod75zncOseESt88K09SZT4i4yJxxSfefmBntAo5MGJNemtDmOrVVTN5+jNLyakwyoiPH6GXdR44qt06GtfJjhd9kqK60P6W0npVq4ZGc8Ge0vhkJcWj1wBkxlBAglxwz1rxyK5RG90dDR1ZI0tJDEo0wyLGqLdPVQoS8rrx8WmHYkjzmBHXmLADcBOCf9UHiKIAbvfsvps8oXqu6e/fuQVikqctXYiyb6pWdpgkwEQjQpjv6Xw7aXJraoMjlgevk/1uru7y5Ag24ZRgqp8sy1vxZWQ8amFvv6LbSp+/y7YiUn8jKNF4GGQrU0rJ2yWu/yfBaLkSihXBkmSU4eH1YawcKHfKTAbwy5eYCeLt6YCf7lhZO5f1YgqwXBpN5lKHIHGGLSjFYADiGtVep/iMA11r3XayfumDx5je/edCRt27d2niPBY/D885VFzy8wRy9J8J+qbzcK+GgIQ2dXMljsXBtUEhjRYNHztEQaHivlNWel+Wi/8nIWu8rkW0n864ZWenVaV6MVwfavJT08mhCX/PwtPS8U0ytPuJ5INpEOd/oGPG2tLzxdrYm8CPsm+cfOD+fJBciyH7qjUnevymfGkDLfqB5x1R2fo/1qlcvj7yOlpeXR3ZCdh2w+CaAPwPwpwD+hH3+FMCfWM9dLJ+6YLG8vDzYkEe7uZu8qIh3Wj74PCYR0Wcdi2AZSE0HXdc6I+nR3rCm6bQmhy2gsvIpWaoGenKjIB+IcuLbMwSc4WkGjuuShlOWKbfbViuXlhdtktczhpaHx9/FwvMmiYvVR2T56RlrEtrql1p/42nI85W4bk+P9rtGOkg/AYW1cssjXbwu+KS/Z5gtvTzfss1zBMKzQXyxgOfVRaQYLJp+AHwQa/syPsuu3QTg17H2xr1fBwtrAXgYwPMAzgL4Pnb99Vh7S9/zAH4SQBVJvy5YvO1tbxs0HHWsJm4dZ6fRV5rm9MkOJDuzZRikDs/LkYPf05dS+R4Biy1x5pUDVG5cuM7oGUgeC+S6vNVZsq4s4+6FLnhZKA911vRzPVo9SCDhgGEZS+qz/C10vM74EloNwGVeNS+W9Ft7Qyw9vD7lGJB9O3K+l2TtHFjkvhOvLT0PSOZZeoEa8YkASJ2QpCUbARZTWHv3BQeLeQAP9b8/BODH+t9fA+AzAK4CMIG18Nfl/d8+ibXDDCsASwDeFEm/LlhcffXVCUC64oorwq8gjQpnEqPQRyINGze0VhjNG9wpne+UPL5usTnO4KKeUs7zsAwGFwkWERD09PAyeINcKwv/3WLSXj14p616z2n1Z+VVegs5sOB1I70vDkociHJGSpIEyaqjbSZ10fec5+Dp5/q0JcFRXZYH5HlYss0sz9Hy4EiHfLVsHXnRwWItTdwuwOIsgG3979sAnE3nvYqH2X0f7gPENgDL7Pr9CL7StS5Y3HbbbQOwWFpaGtm5UDmDU0efNKocIOTxyDJdGqTWcdyUhgzlkC7JVklX6cmoUjxALQUDGlg5L457f5pHYg1cLS0NaCJ55Lr5wXpWHeS8Ll4uyVg1YIucKSXrnuvJgYXUQySE5gAkycm1GdfJ65nrlSCWqzfpWfA5E43pW/3L8yw8cPGIj9cekgTIQ0NL5WIBi6+L37/W/7sA4O3s+gcAvAXAbgCn2PX9AH7FSe9dAJ4B8Mz27dtrVdSVV145ZPxKmA4X+VwJ24yIxfK1OL02APkqpxzT4uAjWbiMZUfK6NUpBx+LVUng8kCAh0w8Q6bFpDlryy1PtfJjgYiWNqXHWa0FXJHzvrgRiqTvGWje7rlVRVobyDqVc1zSg9GMemQsSuYt5wg9w+6JBqoecFt1bdVRpH/lxunk5OS649fryMUOFu9TwOI+AHsUsPjlSNp1PYsrrrhiMMD27NlTmyVLY15yflBELH38umQ+vFNH8sPvkYNZ7hDm7DA68DTjJI9/1gagPDk1x0Jl3iUTlZOfmj6LOfNBzO/RGKBFGGRb8Hi5NCI5z4KnK9vEC4PkWLcE8ahBstpazgFY5bSA1xJ+HHlugUNOp+ZJe96XlldebplWiQfKwVoDmwjgR+ViAYuLPgx11VVXDQYFMe86Rj6yJr+ks0Q9FX59aWkpjY+Pp6WlpYEO6vyeYdB0SWNJ3snU1NSQYY8wNc04SS/AOwyO8mWtLJN50DwMeobCcBqLLwkhcO+KBmyUGWveBxl7WrLsgZSmy1tWG+1j/Jpcipvz6nL55OIZwlJd0iuLep9ePyvp21Kv1setY3u8vHGwtghUU5AguVjA4hiGJ7jn+993YXiC+ws4P8H9NIDvwfkJ7oORtEfhWQBIO3bsGHTkEpEGIBeiyLEmj5Vw4YyZzhnauXPn4LcSBmIZSyoP1Q0ZJ4+9evq5p0DG2wMLCwzof8v4cMCj7xYZ0MJDXr2QPrkiKjKILRDnhr90zwzF7iVAaoYzYjBluTioR/Va/UkjIlFQk/fKgx29fS8WMPL88rJp3qWlz0uH9EhPV3oxEmjkXoxeL+aRlcqLDhYAHgfwhwD+EsDv4/yZUh/B2tLZjwC4id3/HqytgjoLtuIJa/MWn+3/toALvHT2mmuuGRjAG264IR06dGjQsCWiGcKI0anDgLTfqBNt3rx53Ua5OhNgPA3OuIDhTW2l5eBGOfdu8GidRVhYjiVLI8lFA+5ud/2pv9q9lnAPhT7aSjQJ8tHr/F3eljeqEQgJmnQvvSSMLwe39NJ1ixzxCX1vvGh6LQ+OPDJasELGVHpe/DlLF7UHLUvVDka0Jr81cqeBBR9TFqGTOnk/l+WLkDVLXnSw2OhPXbDgm5moQZvMNURd7BKRRlLrINrWfznwS1iI5inJ1UORvQHWoJLs2cobN2weCHtslz+XAxYLnLl+aTA0Jp6rY21+SFvKK0E0d10e2zI5OWkek+IBNAdXflyI5llE2LZWf7wOvZVnEcLAF1x4+y48zyKl4VCU994NL4TJ9VhtqXkWOU9Nsy0vSc9ioz91wYJCN8RKmq4u0FzXlMq8Cikea6H/I6EBjz3L+z1A8NiaVhdNOjcZVTrk0TqqwxIJTtqb8zR33/KGeF2SESzZc6IZDsk8IyDaBFxzOqQemqvqdDpqH8uxW/m79CyaGjups4muiBGP6omAU1Odo5AWLIKyadOmIc8iZ0xzkhvElitdR6emO6cnx+S8UILMT46tWcBZIpQ+fwdCieTAUrJcrw5yexNKPDYecoh4jtEy8u9NVuSRHm2RgEeEcvnj5WsyzriMcuXhKAzyqEBhlJPYnrRgEZR9+/YNjMUtt9zSmJlYIpcOjrJDlgxWyZK1e7mXUpfZcgMdKWcuncjJrVFdEtwinsWoBm+k3ep6nt4+lVxerOscIEYB/qR/VJtfU9JfDMXTauLF19FVQt68MRSpozYMtYGeRdPdkCQlBnoU+iyD5t1rGSyN/dJzfEI2kn6uHJL1l4KM1MnzX0eXps+qx1Jp6kFoeqhNZCiMrlt7Ybx+IWPr9FeeB+bVbe63CCFJKe81cLD3Dprk/dbLrwwB8mciYCmfsdLgbSZBnredfG2yrKdRAHgLFkGhk2YBpOuuu65WqIOLNnjJKHinmOYGlvdiGD4ovAllyZSlkSahASrfZcFDNtahdblwFU9PGnUaIJrh8wy+LIO2UbEk5KHVCV3jZKIJa+VGpdRj0epQLszg80UaIGn1Yumicsqj4j2wi/wWOZHZ8ho0YJNp8jLSfVo6FhHg/Z2n5RnmSLn5wgjrmHHNu9B0t2DxIoLFTTfdtG7OotPp1NKV0voYOGcE1qCIdDBNnxzk2vp8jclbzJHu52xMOyuKGw3eWS0PxVqRJQ2mtrooVz+UZ87mpFEu8QgsI67piXoGJaAZkUib8uNZNCCyvCetn+ZApI5nwVcvaX2HxJq4pjzxZ7W+LPua5mlZhCLnAWnt4AE/P8yT9PLDS7WFDdZih0h6UWnBIijXX399ApA2bdo0eMtWkyPKI4fCee6pNoBzLntOb5Q5ciPmLQXlyynlC3q0weR1aAkoW7duTZ1OJ7QiSNNjAaA0FJZOboS88B3VQ8R45gxbzjBEhN9Pxp5eq1tqSKJGtq7OlMqWmGttG9386BEYzeiXiNdXtDxY5eDnWkW9hFKC4UkLFvGKGnxGcdyvZhhKn/U6QInbKfVJ5qjtwo4ADx9wfLVMRI8Uum9paWmwjPngwYO12bY1Ec49NI/Na96TVRcWA+c6Odh74ZaUhtvW0hWpU9JDG+l4X/HYsSaUD7nkuCQ/XA8nIdEwnEdm5O9aCFLWYW6BRw6wZV/TXj2s9RGtLxAQT09Pm++mKCGIdaQFi6DccsstQ4ChbWLKidbxc4ZBE+6mWuzJAwvphWiTg9IYWobdGxA8Xzw/UQ9F+0752bp169D6++hmKhItxs3ZMd9YWBKW0MrG56AsBs4NltUeVl3ytuS6NCIi+yDfSMf7ijSgWlk1ndqO8BygSyMu3z0hvUqPYPH+aOmZn58fYulW+3reCdfF65uvmJP51jbyyraSZ3/Jd9x75bc8Etk/6koLFkG5+eabh8CijmvHWSsNzDrILzuhxoC8sADPB71rQj4f9XzoPn4aKnX0Q4cOZZcBSwanDUL+vdPppJ07dw7YlWeQPCOlGeRc2hEGLwest5DACl1pg95invJ+rksjIrIsVjhU8ywspi3zK41T1Cvhde3trvaOltfGBunhba4tzpD93atHjcjQfTxdPgbpdy+0xduHe2oEQtROOS9C1kMdeyWlBYugbN68eVDp1157rfkSdU96veEDvuq6iHLwWO65Z+TIoFOHrOu+0qBbWloadGYK023dunUde/fKwweBZER03Xvhvcyvt5wyN9i40Ttz5kx4169mZGVbWSCu6dCYpwYeFhDnPIuS/uel04S9Wt6kla7mNUfGhAZq8mgaLVwa8WTIK6LTlqnP5ciDrL/l5eXBTnjuOUvA8ohQSvq4bD2LFwks+EGCdAhZHaTOxUqjEjHm3iCmzjcxMVE0yS5FY4XkbtO7A+rsR9EMgnfdy5s22CODjR9gaIVgSstDE/PRdpftQM8eOnQoDBBSKERTkg8vX1roqK4u7bo0lJ5HFzHsckUdB1z5Pha5EVMKT1cDArmyKefp8/JKMOSAYo1rWWeR0F1UWrAICk2qVlWVDh06NED+ksqXzCLCoizRDgTMpa0xXs3FTslfWWJtSCphqqRTm2iWHphXDi7WqqOI+y+Fz41QHiPG0KpnHnOemJiozehpvf3k5KR6rweCpId0bN26dZ3nFvWceL+huuHvpdbqwiqTlWfLq/L0aW1t1RGfS7LSkntGrHSpfckuSL3Wcm/SwUGJyiC9Z6lLAwD5rJxLbBKKasEiKK973esSgHVzFyWVzxvb6shWR5HCV51I0QxWxP21wjp8cAHrV4Pl2L7HBMfHx9cNRq+ePBbKPQH5W+muamLduVCPFGn4uBGiJdcPPPBAkcfFdfLd0bmJcPk/6el0OoMQIRm1aP1oDF4uuJDGTdOpkZFceCviiUfKonncMg/SS861FY0Bfjq1JFrdrn2YJKVHbSsBWRurGtGja7QarbQcnrRgEZT77rsvAUivfvWrB52hdH16xOAQA8idauvF4y2DJY8o0UJIEkx4R6c8ybe0WW6zxxy59yBXlkUMnjXYxsfH05kzZ1QdluuuCZVp79697nlQUuTvNIHc6XQGjJPqL3oCgJWmRxhkvVDZqfy8zTSmXZoXnhb1s6hn4f3G2zt3pId83lshxze5RfOXAzVqj4mJiaF5i0i5l5eXh4iTBZQ575iDJQF461m8yGDBN+V1Op3BpFGdBvA6YuT1oRH9vKNZYR2eD/5drhThYELlLjmLxhrkOY+E8uiF7rhhsFZVaaGTSJ3Nz88PAJKMSmTtvbYDmz58AQAdo54zTPI75VN76Y4Uqhd+LycA1D4e69XypAl/C2NuUYMnFkBI1lyiR16bmJgYtIk8icEqp0VUaJzQMtc6UQcqG80hyr4a9ZjkWJBLhOvYE5IWLILC91nw5Wx1XDuvE3P3Phr6iKTBDXNOL+9gMi+ckVlxU5meNcgi7E0yKut3CSQaO7XyygeizKcEb2/gcWMmwwfcmyD27QGbtYmRg49VHi1PlD73CjW9VohUM1ayLej38fHxojER8SQ5mEUNcY5500fO/5T015TSOg+5xIMlkX1H62M571jrR9p+k7rSgkVQ3v/+96fLLrtsKPzkMVVPPCNpnQtUKhYbjej1jiKhMpPx1BiQLEcJ6Mm8SX1WeqWhE80gWQNUloPngf8u60Yb6HKpJvfkOHvm80LS6/OWQsqykj5ivdzD0Dwpqx41MJFM/8yZM2lycjJ1Op0iQ+n1R6o3z4ha7WTloddbm1DevXt32rFjx+B0XJlm1ODLlXN1yJ3mFRCgR22CVm66fxSnZLdgEZRt27YlAIPTZ+UO5VGJxZRHpTOl2MCKsiv5v/Xa1mg5NP3cWIxiok6y5FyZ5LPc6FvAoenR6lTmxQMcS4f2u/TEpKfnbWyT5ZUGi4R0kjHy3uddUqdWmSMrnLrd7jpj67Fw6x45DjXwkONIO+okIlK/XNqcW0EphRMKGZ5tIi1YBIW/z+KGG25YZxBHIaWMpolOaYRLBreVzvz8cIyfpImHxMMuxCojcVsv37lwEv2uHaQoY8Ea+9PKLT0CWf/SuFtExJr/4QZXa1PLsGlgEdkLxNPjbye0jL5HWPjvJXXB9ch5l4MHD7p9hZ6zQFNj+lbomPKmhclK+iM9R3qmpqZqeek8vzT2R2FTWrAIyq5du4binMDaSplRMH8tvDEK4caNMyh+vdR1loOTQire+TeRsIBnDOSud4/lRerQulcaJ+u9DJpXqTFVbnTlWVQ542jND1jl4/rk0RCy7LlNhnzOStuUJr0hCWAawMh7uKdDOrmx5x6CBXoyLRkWi/Q7zxvg5cyFKC0CYoGktVep1+u5e6hyoEt1TW03SpvSgkVQDh06lACkq666Kt15551DceWmDZFziZvqnZ6eVl+KNDU1VXRsicas+Xe+RtwrQyQ8492vhWrq1KEcpFIv3zCY8/q0s4b4QNU2DPJjHXgZZPyb17sVhuP54+EhiyRYCwa0skg9PJ/akmJNp2xjXte8XiUoSkDTjCU9n/MSpKHn/VbrO5Hwl9WXLK+I68x5Pd7SXzkWNA/RKntdscDiCmyAVFX1uwD+FMD/APCNlNLuqqpuAvB/A7gdwO8C+Fsppa/1738YwDv79/+DlNKHL1TennvuOQDAX/zFX+DP//zP0e12sbKygiuvvBIzMzONdM/OzgIA9u/fj/e+972YmZnB2NhY4zyT3i996Ut43/vehx07dmBmZgZbt27F6uoqnnrqKZw6dQonT57E3NxcVt+JEyewuLiIyclJTExMoNPp4OzZs/j7f//v47d+67ewvLyMhx56CB/72MewurqKo0ePmnqOHDkCAJibm8PMzAxOnz6NmZkZrKys4MSJE4O6OH78OO64446h/J07dw4A8MlPfhIf+9jHhso6OzuLsbExrKys4NixY5iZmcHJkycH17mMjY3h3LlzOHbsGM6dO4fNmzcDAJ599lmcOnUK8/PzeOyxx3DixAn0ej0cO3YM3W5XbZuTJ09icXERe/bswfT0NBYXF7Fr1y7Mz89j//79ePDBBwdlobTHx8fx8Y9/HB//+McxPj6O2dlZnD59GouLizh48CCOHz+OEydOYHV1FYuLi9i5cyfOnTs30E3PjI2NYWxsbFDfKysrQ3mjugaAxcVFTE9PY2JiAvfffz/uuusuHDt2DKurq9iyZQtmZ2exdetW3HPPPZiZmRnUCdfD8/n4448DwKAv8bbgMjs7i9XVVayurmJlZQWHDx/Gli1bsLq6ikcffRQA8PTTT+Oxxx7D7OwsFhYW0O12cf/99+PEiRODcvK+QvW4ZcsWHDlyBN1uF/Pz85iZmcGxY8cGzywsLODRRx9Fr9cb1NnCwsKgLhYWFnDy5Emsrq6q/fL48eODPsXzQnW9sLAwKOejjz6KLVu2rKsveo7q4dy5c9izZw9mZ2fX6R0bG8Pc3ByOHTu2Tge3NSsrKzh9+jSA82PC0nVBRUOQC/3BGhiMiWvzAB7qf38IwI/1v78GwGcAXAVgAsDvALg8l0Zdz4LO/AeQPvShD62LM45CtCWroxAtllrCmki08BX/i35oDpn5HI8daexaPmu93U0LC+Qm93h8mIfVZPw/98IZzgZpeSp5DPxEXhl6kPF9rQw8PMP3aHj50fIly0Tl5qukeBt44R8ZNuJ9yWK7lt5utztUHn6ffCaSN8uLobkV7rnwPErvIJK2DP9J70oLKVp6qezasTpaf5ZpS93RTYxRwcUUhjLA4iyAbf3v2wCc7X9/GMDD7L4PA9ibS2MULz/i66mbvBlMupuj3G1Jus+cOZOmpqbSvn37hmLYdd1TPqB4p86FAjzhA5fnW+voHsjxgeK58ly0DYhcF6UVXfnGn5Pgp21y9NqYt5EMkZXWszQ2FILU9s3kQm5Sp4zTW2Dh9TmtD2ghrUi/1Z6RcxOWHm64ZR1Y4SxODmQ+tDkPqYfmGPjiEFl/2pEqWtqchBA4yjO76srFBhZfBPBpAJ8C8K7+ta+Le77W/7sA4O3s+gcAvMXQ+y4AzwB4Zvv27bUq6pWvfOUQYJCxigx6S+QApnh0JO4f1c1XqpDUYRzW4JID2zMyER30v6Un91sEGHicXQKfx/q9dDjIWe8d0L5rLFJLQy6HjBhzLc9LS0uDuSV+3YqtW/1aa4coQGv12IS8RJ6zAMzKB90fGdeWl8I9b0+fJCYaEbDaQ5sM5/roCJFRRD8uNrB4Rf/vy7EWYppywOJ9Cljcl0uj6T4LAIPd3JIFRcUyFLkTLj1dlkHTjoSQK3M8PSRWZ9VCBlZoKwqsvLPTvdw4lQxkLpEVSfy6Vg5uBHj6lCfvNateWeWqNdluMq/R8st25ZO6Jezcyjf34qgO6pS9LjHyQICXwwMwrV964yricciy5fTljs+xnpcepyRto9wLdlGBxVAGgKMA3o2LJAzFT5ulc2VKQi1cLKNZh/FHGKDsLFo6OT3e+n7eSSOrPCKMUy7Z1MI7paxahrZoUMl9CV45OCjw9PmgjZ7ho3kYpEd6hJoBsEJ1XGS78t3GvLx1CQ/3rufm5syVRZaOCPB5+cuFvCLhukj5ZaioSZ65eOMuosPyhOp6ap5cNGABYAuA69j3/wTgjQCOYXiCe77/fReGJ7i/gAs4wU3uHL3PoiQEwIUziSbzHVxf1HBE8mWxkWin9sJEJSLTa6I3l3dNb47JWcbJCz1F8yPb08qLXAbreZiybLzMmpdQAsTS4xsfHw+/WEmmVxoSsvJLZeKLIUraWQpfxMH3METaO1p++WzUE9fKQc+WEitPLiaw2NE3/p8B8DkA7+lf3wrgIwB+u//3JvbMe7C2CuosgDdF0qkLFtdee+3QnAUfICUNIcMOo2IDWmhJ0+mlIQcYN4j8PQpSj+zU0qBK9mzF6b188+scbCOg6+ni9ba8vJx9M5pVn9YmK27otHtJNM/NmnzXAIXaQJ4BZIGhBAi6x2Li2nyPNOS93vmVX5OTk2qb5ECf50uGWCIhFV7HPLyjHUUS8Wx4XdNR87xd5RyDF1rzxp7sizngydkK6QlFACcnFw1YvFifumBx0003DTrbgQMHBp2ltCH4qgY5aEqYRErDHcYKE0mdEVarGQwJRhwQcrtzJUBaR2ZEQJKHo+SnlEWRLh7y4eyxJIzAyyJZqGTwmiHRwJ7ys3fv3uwOaW6ouQ5eX9rSTtn3rCXCElStcJMW5tLqywJQDghWP/L6LgcvDXh4+FBrjxy754yd6oQMMi1qsPq1l38O9to9pV4Hr+cmc0JcLLDYkE15F7O88MILg+/f9V3fhXPnzmHv3r3YtGlT0aY82rx1zz33DG2iOnbsGPbv34+DBw+G9dHmttOnT+Oxxx7Dr/7qr667h29W43/5xiUAWFhYGGxMe+c73znYvESbfB555BG88MIL2LVr17pNX1QmAHjsscdw//334+mnn8Yb3/jGwcY4SpM2e/G/cjOUtvmJl4c2dn3605/GN77xDVx55ZW4++67ASCsh9fFl770JTz//PPYvn079u7diy9/+cvYvn07jhw5MrSRy9NBZen1elhcXMT27dtxxRVX4NSpU7j77rsHG6qeeOIJLC4uYmFhYWjT4vHjxwd/aaPX6uoqAODs2bP44z/+Y+zZswdbtmzBzMwMVldX0ev1cPToURw+fBhjY2P4xV/8RRw+fBi7du3C2bNncfLkyUGap06dwi/8wi/g+eefH9qAR5vNVldXB5vgHn/8cZw7d25IN8/fgw8+iOXlZezcuROHDx8e9B8AOHz4MJ588kksLCysyx9vP/rO+/Di4uLgt4WFBbzxjW/EU089hXPnzmFlZWVdH+ZCerr9jXmzs7P46le/iieeeAK9Xg/33nsvnnrqKdx1113r2o63rdwwOjs7i16vhyeeeAL79+8f1BPljTa8fexjH8PmzZsxNjY22Mgp+7U29nj9k64f/MEfXFdGLV+U97Nnzw5t+lxZWUGv18PU1BS++7u/G0eOHBnJJl9TNAS5FD51PQt6DeU111wzxEBR6Fl4MWzLJY6EUUrdTI110UdjX5KpSXdfO04kt5HNKl/Ew5IehgynaOExSzjrJH1y81u0frW65PUQqRNeNporGx8fV/dpyLxpfanXG95nIOcAJMPWdHterHZ/TgeJ9Cx4/ZUczOeNK14fpCuyYINEHgsyPz+/LmTkhUy9EKDVZlK8NKRHafWNpoI2DBUTelMeHVEuX8zeRLxYdkrNV0zk0qIByt8epg1O7Vnulsv76q5EiawK43MXVigvVzdaCETG7kvfWEj5km9T5Gvtc2EBbmSWlpYGgCE3dmmGyOpLVtiQ9GgvbOK6ZUiIC+1a568S1XREwFubP7HIS6QtaA5Khn2tOT5NtA1x0ZWLVsiQt01u/Ef0y3O6tL7RVFqwCMoDDzwwxBhzbHmUUgoInljegTSWVgzX0qExHuvE0pwXETUqGkuNDhLplVkDOFLnXh2Rga+zkY7r4ayWlzO6SIB/17xFucJHigcWksla6XqehZX/SH+M1J801rmFDBGJlKdu+aXnbrVLDmhGaTtasAgKLQXcvXv3gDWO6swVKaNsYE03dT4yAN4LhTzPwWPs1isw5eDVBhcfHFZaMnSi6dbyJQ0mlbvJRjepi4c5pFGOrHTh6XBmLPPOw2SeEc8ZcDoywgu5eUaPe3l0ki7vM55OL/QqPZ5IW0iRXtT8vL4UN+qF8jJ7mzO99uB91rpfkgRen97Sdum1auO3rrRgEZR9+/YlAGnfvn1FLmwdKXG1m+inDuTtHK8zwLgxk5vXckxI5k1Ll/TIwZpjoppRl9cjLFHmieeF50mufNPmdbyQFK9HKhPp5QcgWsZJK7v2XgYCiX379oUMCicZGmhyIJPMmJdBxv21NLx2iYA8H0tavkmXDJl6HjAnQ3ROHAmRyk6nsy4vcqUY18Pbt9frDd1LbUb511ZbWavQoqAbkRYsgnLbbbclAOnWW29d58I29QQso1Q3dOGloR2AGJ0jsN6/YLFCL98W45EG2frdYrjSeMhBrhkXS6dkfR5L00CQAwL34DTwsOqcGxPNU4juXeFll2nK84W8Ok5JP8WVtzsBGOVb87D4pKyWZ0pDeymRrFcPcDWA1eZA5G+eByxBkV/X3hSp5VsjKTxNjUzINuekxSMfEWISkRYs4hU19OFMrqknkHNFmzICjflqA80DvV6vNzAOudCGfE67bm04057TBqpm7DnzssqUAzJupHJsMxoCoRAS160xc6vtInUsj1LX+otV9ogRkvXNVwhpmwApJKW9NlZbSSTT8AiMBZRe35MgJUFAeik5UtLtL2LgC12oTa23FFobG+k+LWSmlU37zcovb4+mpLMFi6BQGEqCRYRF58QCi1HoTmk4/kmsVhvIkrnytDlwHThwYN3xIrIM9Iy2ooXnibN0a4BojFrT5R0DrdWHZlB5+IMbPG8+xwJyi51yECoFIO0emefI81q/8sI0nqfCAUNjxzL0ZZVZ88os0LPAjf/Or9M163ywubk5dS6Sl1Pmhdc79WMvvGaNc5kOHxNaPXhej9SjhUfrSgsWQaFXqVpubKQRStl3XX3afbzTcIbLGSlnH9KrkQaG9NF7EeilRzQQyCDSdS/0Q+KxP86O5DHbWrzWAxj+v8b2KIyyZ8+ewf4abUd3jv1RffFjy2VoUfYfDqKWFyCNkRayiXgh0qB5xszqV1TvdIotrxNehsgY4elHwN7aKe2BngQQHprT+ilPy1qOzN+Roe3J0MrnlckjE1TvVh/ieii96OuOc9KCRVCuuuqqBKwdJOgtBfUkx5BLpUSfxTj4dxpgKQ2/P8EzsvwUTjI8y8vLgzcL7t69W3XLrUFgsTKaOATOb1TbuXNnSkmPb3P90iDzsmneFT9HiNLTwmU0aGXoh4PXwYMH1xlnDQilR8RZK73Fj5dFTpRGmTknATQPx71BDsqRvnnmzBn1nQkS9CI6tXczaGLl0/PcuKGWIEZ9QcsjT0s7nVgz2tY8jLZfQ6ZDaeVA2wJWrqd0lV9OWrAIyq5duwaDbN++feHBxEUzYNFJJ8u4luizGHWvd34+YmpqSnXtU9K9KA1UOPjI5Zgaw8uVM6WUduzYkQCkm266aZ1nkTMyUic33nzZMOWJwhGHDh0ahCW0fHGDzuudyr9ly5aBvsnJydTpdNzJXG4cpAcgDZ23fJKHQ7Qdyt5KKulR5tqGg5dGoqhsEaPlTbRL8cIz3NPgbUqG2vJILOF1woGADteMEjbpEUmPnYOXZV80QPR+k4DWRFqwCMqhQ4fWuauWmxgRzqIjqJ8LdzXVJyectYEULSsHEGv+ITpQScizOHToUMjYekJlpFfN8kEFrF/lYwkf3Lyc3LOQr1OVwsNeEuyoHgm0PD2kS5IHbog05pkLcfDr1us+qb60+tfCUlab8/q0mLo3qS37J88z9yCpjbVJaE3kDnXKF5V7YmIiZIx5XfCJcG5PuGetTWB7JwtQvmQ75WxHVFqwCMqrX/3qBCBdccUVg8bVztkpkYjx1UIMTe/lrrXlykc7Pn8uwmC0sIv3nCyTVt/Rpb/SaHHPgH6Ta9utPFH9ccJghaIsYJTegzS2komSnqihlOEsel7z9HLeKNejzdlZBkwrj1cfvKxdFuLTJmqjTJ7H7HloppRYSYJIBKbk/DB6Vp7VxceDVlYLCGQISusfdQmtlBYsgnLZZZcNOszu3buHds42bQirMUu9BRLNIPAOKY1QSThIS4PnM6KLP+uFPbR0NGDh5bEMqDbYvHrxls1K46ENUJkn+T4Qfg+xVC1kxEHJA0zPk7A2fml9RQqlRXnw4vZ0rxYK1e6zPC1eRrk3ha9YKvG2eZ7OnDkzCAtGSFG32x3aPyEJUnSOh+dnampqaM8SF21uwwKCUXkNEWnBIiivetWrhkJQpQfMeWKxPW/weWIZBM6gqTNqx0nwZ6y0eRqUTz4Rq5VPM6xRz4KnIxm4ZRQ0A+qtNOEeikxLAy1t0pPyI8vEXwokyy433uWODNGOZ5FhRN7W1rp/K69cZD14Xpy1i1irP88DtgCIezeaZ6ERHAmWKdkvCpN54GOwK0JE0gvwgE/2R774wRv3Ee/PKvOobBOXFiyCsmfPniGwaHLqbK6T59x1T5d2XTIg3hnpu7d5KsdaLCNO+ZDx2bosiJ4/cOCA64XxMsjBx42dBGnNoEkWyRm7tXJLY3vkWfDXjUqjSfnkoTDudXgkQgNGuSqLyiH7C/e6ZH+2vC/NkHkEQwMZfr+1kovXsyybVQ6Zvpw41za/yTJrk+ASDHOESv5ueYq5ca9dl+P7xfA2WrAICl8NdfXVVw8BRynzL1294wk31NrA1UIVnU5naIMeZ6v8mehcgMdMuVeTO9k0J3IykOdBY+Lyd1kvuR21VlmkoZCDPrdE0mojbqQ4sMg+pgG5BwLj4+OD8JfWL7gR9LwCbqw1w+TVn5auLG+Jx6AZasuz8ciMN07kJk+LjFj9RYK1NS55fXielna2mPR+KN3cuK0jLVgEZfPmzUMAIdfpl6A4n9xqKryj5AYuXdP2FpQyFMtl1u7jHb3Jum8ehrA2RuYGMP/dMyK5Zz1Gzf/P1Y3Mq5wYl0bCe1bTXwIq1qS+19ZRYuMZQstDy+mTZdOYPPfcNPDxxokXgooI5adkeW1OlwZUPDqQA6Qm0oJFUPgEN18SynfnRjuANtmpSaRjRVxqzSDRM5wFS9bo6bZcZulqc/dbc70jxp0/qxnPXs9/P4GVhufhecbNC1twgxBZLipFY8w5782rw1z783siq+gi/SviGVh6te/e3Ab/3QrryD0gljG18k3GmHtokfLTng7pYWrtWVJH8nqn0xmcNGBtCByFtGARlFe84hVDbrpkjyVIHkX+yH25ezTWzQcX3w8gy8GflXp4Z9dYtlZXWh6j+ecegDaoZWw+VweWZ5HzmDRjbuXJ02MZmVy9aqw+x/hlWnKCl9LVQDgHmJI0yFCg5gF49cBZMv/ueYBWmywvLw92l9NvkshoejQCZPUvrQ9xT03LN+/T2ljLCR/H3P5oO+lHKS1YBGXTpk0JWHutqmQ9pewx6opG7ithJBLgeLxY8ywsgyMHpzTG8rhnz3B5v9Hv2qDT8iMZZK4ONK/QMhhcV2StP+nRVi5ZBlSmzUHAmyCmduNt5wG99MK48YoscOD65CosuaksF76RRpKHGqkeaO5GeoCa0dTqkzwCjXho+jTwtbyx6E5qmU6JF2/VGV+Gq706NmpnovKSBwsAbwRwFsDzAB7K3V8XLK655poErE1ue65vVEbVkCUuf85tj+ZTTiR6jFjqsZh2SrYHIPVpcf0oWHMjqL3NLwde8p6cp0Hl1VYuSXDzdtFr6XjG0iIzlh4LbLSwBi8/5bXT6azrU1bdaHXJ+6TMixUutPoL1y33mGjvtLDy5I0h6QF590b6JenjC008wmKNX17nuQMJS+UlDRYALgfwOwB2ANgE4DMAXuM9UxcsbrjhhgRgcKDgxMTEkMEpdf0ko6rbmFKP5s428WS0Ac8HisZkvXzycIU0yvya5t6T0GDiB/xF9oPIa/z4dG/gcWOkGU7P09AYOr+mAQIxxYg3wvOpHQXu7Q2wjJGsN89L8hZrRMBWq2spWl/QAE4T+axXj5H8837cZLGGlU++EMQjVl7dSjJE7dw0ry91sNgL4MPs/4cBPOw9M4oJbv4p3ZxnGZ06jakNmNKByHVY7rdcjlg6eD3mOj+vz2lobJLKxg9wyw3aXNmlQeQG3WLkHhjn9MhneJ3zga7luUQXL7v1wh/N69GE7tdev1tnsjxKaCzvKNeuWt65jmg0QEvDCxeVED7rWY1EldahFjloPQvgLQD+Lfv/bwNYUO57F4BnADyzffv2WhV17733DgYxHSDmubKWWJ28TmOWDhiv01lMU/MsLH1WfnLGQdOnAZLG9HMDoa5XZXl+pSuGcm2kGQ2PMUfBL5LnEpadUmz9vgeqVj5zujRCUTpeeD+PErJS4lVC+Joy/dK8jUJe6mDxNxWw+CnvmbqeBR/ETZamjQLhR6krZ5yasKUm+fSY5ijqrk76G6nnpaZvFMs3o4QgqisStirJU8lvTe4dRd5GIRZYVGu/XdxSVdVeAEdTSt/X//9hAEgp/XPrmd27d6dnnnnmRcphK6200sqlIVVVfSqltFtev2wjMlNDngbwqqqqJqqq2gTgrQBObnCeWmmllVa+ZeSKjc5ARFJK36iq6jCAD2NtZdQHU0qf2+BstdJKK618y8hLAiwAIKW0CGBxo/PRSiuttPKtKC+VMFQrrbTSSisbKC1YtNJKK620kpUWLFpppZVWWslKCxattNJKK61k5SWxz6KOVFXVA/Clmo+PAVgZYXZeCtKW+VtDvtXK/K1WXqB5mW9LKY3Li5csWDSRqqqe0TalXMrSlvlbQ77VyvytVl7gwpW5DUO10korrbSSlRYsWmmllVZayUoLFrq8f6MzsAHSlvlbQ77VyvytVl7gApW5nbNopZVWWmklK61n0UorrbTSSlZasGillVZaaSUrLVgwqarqjVVVna2q6vmqqh7a6PxcCKmq6pVVVX20qqrPV1X1uaqqfqh//aaqqn69qqrf7v+9caPzOmqpquryqqqerarqV/r/X9Jlrqrqhqqqfq6qquV+e+/9FijzD/f79Werqnq8qqqrL7UyV1X1waqqvlJV1WfZNbOMVVU93LdpZ6uq+r666bZg0Zeqqi4H8D4AbwLwGgD3V1X1mo3N1QWRbwD4kZTSqwF8D4BOv5wPAfhISulVAD7S//9Skx8C8Hn2/6Ve5n8F4NdSSjsBvBZrZb9ky1xV1S0A/gGA3SmlO7H2OoO34tIr8/8F4I3imlrG/th+K4Bd/Wd+um/riqUFi/PyXQCeTyl9IaX0AoAPAfiBDc7TyCWl9IcppU/3v/8p1gzILVgr62P92x4D8D9vSAYvkFRVdSuANwP4t+zyJVvmqqquBzAF4AMAkFJ6IaX0dVzCZe7LFQCuqarqCgCbAfxXXGJlTil9HMAfi8tWGX8AwIdSSn+RUvoigOexZuuKpQWL83ILgP/C/v/9/rVLVqqquh3AXQA+AeDmlNIfAmuAAuDlG5i1CyE/AeAIgG+ya5dymXcA6AE40Q+9/duqqrbgEi5zSukPAPxLAL8H4A8B/LeU0hO4hMvMxCrjyOxaCxbnpVKuXbLriququhbA/wfgH6aU/mSj83Mhpaqq7wfwlZTSpzY6Ly+iXAHgOwH865TSXQBW8dIPv7jSj9P/AIAJAK8AsKWqqrdvbK42XEZm11qwOC+/D+CV7P9bsebCXnJSVdWVWAOK/5BS+vn+5S9XVbWt//s2AF/ZqPxdALkbwExVVb+LtfDi91ZV9bO4tMv8+wB+P6X0if7/P4c18LiUyzwN4IsppV5K6S8B/DyAfbi0y0xilXFkdq0Fi/PyNIBXVVU1UVXVJqxNCp3c4DyNXKqqqrAWx/58Suk4++kkgHf0v78DwC+92Hm7UJJSejildGtK6Xastet/TCm9HZd2mf8IwH+pquqO/qW/BuA/4xIuM9bCT99TVdXmfj//a1ibk7uUy0xilfEkgLdWVXVVVVUTAF4F4JN1Emh3cDOpquog1mLblwP4YErpRzc2R6OXqqr+KoAnATyH8/H7/wNr8xb/D4DtWBt0fzOlJCfRXvJSVdU9AN6dUvr+qqq24hIuc1VVr8PahP4mAF8AMIs1gngpl/lRAD+ItVV/zwL4XwFci0uozFVVPQ7gHqwdRf5lAF0AvwijjFVVvQfAIazVyT9MKS3VSrcFi1ZaaaWVVnLShqFaaaWVVlrJSgsWrbTSSiutZKUFi1ZaaaWVVrLSgkUrrbTSSitZacGilVZaaaWVrLRg0UorTKqq2lpV1W/0P39UVdUf9L//96qqfvoCpfkPq6r6OyPQ86Gqql41ijy10oqUdulsK60YUlXVUQD/PaX0Ly9gGlcA+DSA70wpfaOhrgMA3p5S+t9GkrlWWmHSehattBKQqqruYe/BOFpV1WNVVT1RVdXvVlV1b1VV81VVPVdV1a/1j1NBVVWvr6rqY1VVfaqqqg/TcQxCvhfApwkoqqo6XVXVj1dV9fH+Oyj2VFX18/33FPyz/j1bqqr61aqqPtN/b8MP9nU9CWC6D0CttDJSacGilVbqybdj7cjzHwDwswA+mlL6DgB/BuDNfcD4KQBvSSm9HsAHAWgnAtwNQB5w+EJKaQrAv8HasQ0dAHcC+Lv9XedvBPBfU0qv7b+34dcAIKX0TawdQf3akZa0lVbQgkUrrdSVpf5hdc9h7XiYX+tffw7A7QDuwJqB//Wqqn4DwD/G2iFuUrZh7ShxLnQm2XMAPtd/B8lfYO3Ijlf2r09XVfVjVVXtTyn9N/bsV7B24morrYxUWne1lVbqyV8Aa2y+qqq/TOcn/76JtXFVYc3Q783o+TMAV2u6+7r+gl3/JoArUkq/VVXV6wEcBPDPq6p6IqX0T/v3XN3X2UorI5XWs2illQsjZwGMV1W1F1g7Fr6qql3KfZ8HMFmiuKqqVwA4l1L6Way97Oc72c9/BcDn6mW5lVZsaT2LVlq5AJJSeqGqqrcA+Mmqql6GtbH2E1hvyJcA/PtC9d8B4FhVVd8E8JcA/h4AVFV1M4A/ozemtdLKKKVdOttKKxssVVX9AoAjKaXfbqjnhwH8SUrpA6PJWSutnJc2DNVKKxsvD2FtorupfB3AYyPQ00or66T1LFpppZVWWslK61m00korrbSSlRYsWmmllVZayUoLFq200korrWSlBYtWWmmllVay0oJFK6200korWfn/AeSE4OKZflCVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.run(100., inputs=[('E.input', 20.), ('I.input', 20.)], report=0.1)\n",
    "bp.visualize.raster_plot(E.mon.ts, E.mon.spike, show=True)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "**References**: \n",
    "\n",
    "[1] Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007), Simulation of networks of spiking neurons: a review of tools and strategies., J. Comput. Neurosci., 23, 3, 349–98\n"
   ]
  }
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